Reading Samples... Reading Samples... 1 Network has 7 input(s) and 7 output(s). There is 1 internal layer. Layer 0: Layer has 2 block(s). Each block has 2 cell(s). Input Gate Weight Array: [ -0.0372 0.0217 -0.06342 0.03064 0.01177 0.009109 -0.04356 -0.04184 -0.02628 -0.01965 -0.09947 -0.05746 -0.02033 0.04398 0.04947 -0.04809 0.01702 0.03485 ] Forget Gate Weight Array: [ 0.03728 -0.01267 0.07995 0.02929 -0.02378 -0.008515 -0.005988 0.01252 -0.08257 -0.07965 -0.05027 0.01797 0.07316 -0.08793 -0.05378 -0.02914 0.09038 -0.03694 ] Output Gate Weight Array: [ -0.04525 -0.07323 -0.09223 -0.06693 -0.01911 0.07376 0.08924 -0.09052 -0.04898 0.06047 0.03834 -0.03365 0.08226 -0.01772 0.0215 0.04383 -0.07569 -0.09021 ] Cell 0: [ -0.005323 0.02892 -0.07373 -0.05428 -0.07164 -0.09322 -0.002368 0.006155 0.08084 0.04532 -0.04922 -0.01499 0.08019 0.06192 -0.0276 0.03759 -0.0973 -0.005529 ] CEC: [ 0 ] Cell 1: [ -0.07696 -0.09418 -0.0653 0.01034 -0.03507 0.06028 0.01095 -0.07021 0.02825 -0.03839 -0.09926 0.08548 0.06526 0.03486 -0.06898 0.05106 0.08749 0.08243 ] CEC: [ 0 ] Input Gate Weight Array: [ -0.01239 0.09759 0.02394 0.01706 -0.04937 -0.02916 0.02245 -0.03848 -0.02131 -0.03671 -0.08083 -0.05875 0.08325 0.004275 0.08096 0.07009 0.004119 0.06716 ] Forget Gate Weight Array: [ -0.03704 -0.04403 0.09462 -0.02523 0.00503 -0.00766 0.06951 -0.06279 -0.06479 -0.02943 0.01707 0.07698 -0.07828 0.08696 -0.0923 -0.07089 0.04794 -0.08247 ] Output Gate Weight Array: [ 0.0723 -0.02461 0.04786 0.01742 -0.01246 0.01767 0.07674 -0.0376 0.02029 -0.03977 0.0868 0.01184 0.02004 0.09318 -0.07549 0.01377 -0.01578 0.05981 ] Cell 0: [ -0.08779 0.03403 0.07318 0.07861 0.06649 -0.06268 -0.03091 -0.05513 -0.004434 0.04624 0.02154 -0.00371 -0.09887 -0.0419 -0.02869 0.005079 0.08098 -0.06277 ] CEC: [ 0 ] Cell 1: [ 0.0323 -0.0475 0.09571 -0.03201 -0.09716 0.007745 0.03657 -0.04163 0.04113 -0.01984 0.09815 0.04183 -0.07896 0.07414 0.095 0.07071 -0.07531 0.08698 ] CEC: [ 0 ] Output Layer: Neuron 0: [ 0.009807 -0.07101 0.05608 -0.08207 -0.07597 ] Neuron 1: [ -0.03023 0.0213 0.02888 0.07016 -0.02396 ] Neuron 2: [ 0.07454 -0.09014 0.08895 -0.03799 -0.03402 ] Neuron 3: [ -0.04124 -0.01033 0.09442 -0.01884 -0.03723 ] Neuron 4: [ -0.09553 0.009249 -0.01005 0.02005 -0.0698 ] Neuron 5: [ 0.06465 0.07078 0.07791 -0.0314 -0.08759 ] Neuron 6: [ 0.07215 -0.09168 0.07765 0.08854 0.01266 ] Epoch 0 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009839 -0.03181 0.07391 -0.04184 -0.09653 0.06431 0.06971 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005681 -0.005311 -0.01856 0.01042 0.5057 0.5006 0.4659 0.5597 0.4562 0.5238 ] Network output: [ 0.007581 0.07056 0.1666 -0.03838 -0.08888 0.05517 0.06372 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 -0.001759 -0.003569 -0.02049 0.03749 0.4836 0.5229 0.4616 0.5425 0.4894 0.543 ] Network output: [ 0.1076 0.06342 0.1497 -0.03428 -0.07933 0.05104 0.05597 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004935 -0.005133 -0.02625 0.02766 0.5054 0.4999 0.4644 0.5598 0.4567 0.5254 ] Network output: [ 0.09577 0.1571 0.2349 -0.03131 -0.07263 0.04387 0.05185 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 -0.002075 -0.002709 -0.02135 0.04577 0.4835 0.5231 0.4614 0.5423 0.4896 0.5427 ] Network output: [ 0.08696 0.1424 0.3125 -0.02684 -0.06449 0.1414 0.04794 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 -0.001093 0.00836 -0.0139 0.03382 0.5075 0.5104 0.4659 0.5396 0.4614 0.5356 ] Network output: [ 0.07829 0.1289 0.3814 -0.02353 -0.05754 0.2284 0.04365 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.002804 0.01416 -0.008503 0.02786 0.5071 0.5101 0.4675 0.5398 0.46 0.5352 ] Network output: [ 0.07025 0.1153 0.4431 -0.02099 -0.05254 0.3065 0.03755 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 1.409 Epoch 1 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06216 0.2052 0.3962 -0.02027 -0.04482 0.3761 0.03309 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01903 -0.00256 -0.002163 0.009491 0.5058 0.5007 0.466 0.5592 0.4557 0.5233 ] Network output: [ 0.0538 0.2847 0.4561 -0.01904 -0.04209 0.3363 0.03092 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.01736 0.0001302 0.003233 0.03602 0.4832 0.5224 0.4618 0.5416 0.4887 0.5428 ] Network output: [ 0.149 0.2562 0.41 -0.01691 -0.03723 0.3041 0.02636 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02621 -0.001191 -0.001785 0.02661 0.5047 0.4991 0.4645 0.5588 0.456 0.5255 ] Network output: [ 0.1329 0.3306 0.4692 -0.01569 -0.03471 0.2717 0.0252 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.01936 0.001091 0.003517 0.04454 0.4828 0.5223 0.4614 0.5412 0.4889 0.5428 ] Network output: [ 0.1206 0.2983 0.5234 -0.01275 -0.03044 0.3464 0.02376 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.02018 0.01206 0.008453 0.03277 0.5068 0.5096 0.466 0.5387 0.4608 0.5359 ] Network output: [ 0.1088 0.269 0.5713 -0.01083 -0.02696 0.4128 0.02175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0231 0.01761 0.01159 0.02693 0.5065 0.5094 0.4676 0.539 0.4595 0.5356 ] Network output: [ 0.09778 0.2414 0.6141 -0.009578 -0.02506 0.4726 0.0177 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 1.003 Epoch 2 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.08862 0.3175 0.5514 -0.009825 -0.02043 0.5246 0.0153 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02645 -0.001179 0.005545 0.008951 0.5059 0.5007 0.4661 0.5591 0.4556 0.5232 ] Network output: [ 0.07718 0.3861 0.5955 -0.009672 -0.01999 0.4704 0.01494 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.02809 0.002019 0.01442 0.03502 0.4831 0.5222 0.4619 0.5412 0.4885 0.5428 ] Network output: [ 0.1699 0.3475 0.5353 -0.008495 -0.01735 0.4247 0.01191 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03808 0.0008109 0.009669 0.02591 0.5044 0.4988 0.4645 0.5584 0.4558 0.5257 ] Network output: [ 0.1518 0.4128 0.582 -0.008113 -0.01679 0.3804 0.01218 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.03143 0.003039 0.01515 0.04361 0.4825 0.5219 0.4614 0.5407 0.4886 0.5429 ] Network output: [ 0.1376 0.3722 0.6249 -0.005925 -0.01435 0.4442 0.01194 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.03244 0.01403 0.01909 0.03197 0.5066 0.5093 0.4661 0.5383 0.4606 0.5361 ] Network output: [ 0.1241 0.3354 0.6628 -0.004677 -0.01251 0.5009 0.01107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.03502 0.01948 0.02132 0.02623 0.5063 0.5091 0.4677 0.5387 0.4593 0.5358 ] Network output: [ 0.1116 0.3011 0.6964 -0.004062 -0.01208 0.5519 0.007999 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.9168 Epoch 3 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.102 0.3707 0.6262 -0.004782 -0.008939 0.5954 0.00673 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03095 -0.0004675 0.00934 0.008685 0.506 0.5008 0.4661 0.5591 0.4556 0.5232 ] Network output: [ 0.08895 0.4342 0.6627 -0.00514 -0.00956 0.5343 0.007202 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.03466 0.003022 0.01992 0.03435 0.483 0.5222 0.462 0.5411 0.4884 0.5429 ] Network output: [ 0.1805 0.3908 0.5956 -0.004429 -0.007974 0.4822 0.00489 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04533 0.001862 0.01523 0.02548 0.5043 0.4986 0.4646 0.5582 0.4556 0.5258 ] Network output: [ 0.1613 0.4518 0.6363 -0.004447 -0.008328 0.4322 0.005846 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.03888 0.004082 0.02074 0.04293 0.4824 0.5217 0.4615 0.5405 0.4884 0.543 ] Network output: [ 0.1462 0.4073 0.6737 -0.002619 -0.006741 0.4909 0.006189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0402 0.01515 0.02432 0.03136 0.5065 0.5092 0.4662 0.5381 0.4604 0.5362 ] Network output: [ 0.1318 0.3669 0.7068 -0.001697 -0.005675 0.5429 0.005863 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.04273 0.0206 0.02621 0.0257 0.5062 0.509 0.4678 0.5385 0.4591 0.5359 ] Network output: [ 0.1185 0.3294 0.736 -0.001403 -0.00595 0.5898 0.003259 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.9004 Epoch 4 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1087 0.3959 0.6622 -0.002351 -0.003539 0.629 0.002644 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03397 -8.26e-05 0.01124 0.008549 0.506 0.5009 0.4662 0.5591 0.4556 0.5233 ] Network output: [ 0.09488 0.4569 0.6949 -0.002949 -0.00464 0.5648 0.003474 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.03912 0.003593 0.02269 0.03384 0.4831 0.5222 0.462 0.541 0.4883 0.5429 ] Network output: [ 0.1858 0.4113 0.6246 -0.002468 -0.003557 0.5096 0.001493 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05026 0.002449 0.01797 0.02518 0.5042 0.4986 0.4646 0.5581 0.4556 0.5259 ] Network output: [ 0.1661 0.4702 0.6624 -0.002671 -0.004333 0.4569 0.002766 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.04401 0.004686 0.02346 0.04235 0.4824 0.5217 0.4615 0.5404 0.4883 0.543 ] Network output: [ 0.1504 0.4238 0.6972 -0.001016 -0.003149 0.5131 0.003388 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0457 0.01586 0.02698 0.03086 0.5066 0.5092 0.4663 0.5381 0.4604 0.5363 ] Network output: [ 0.1357 0.3818 0.7279 -0.0002536 -0.002447 0.563 0.003331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.04833 0.02135 0.0288 0.02524 0.5063 0.5089 0.4678 0.5384 0.459 0.536 ] Network output: [ 0.1219 0.3427 0.755 -0.0001243 -0.003062 0.608 0.000933 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8981 Epoch 5 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1121 0.4078 0.6796 -0.001183 -0.001015 0.6448 0.0007319 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03626 0.0001443 0.01225 0.008473 0.5061 0.501 0.4662 0.5591 0.4556 0.5233 ] Network output: [ 0.09787 0.4676 0.7105 -0.00189 -0.002321 0.5792 0.001696 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.04253 0.003958 0.02416 0.0334 0.4831 0.5222 0.4621 0.5409 0.4883 0.5428 ] Network output: [ 0.1885 0.421 0.6385 -0.001525 -0.001481 0.5226 -0.0001464 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05406 0.002813 0.01939 0.02494 0.5042 0.4985 0.4646 0.5581 0.4555 0.5259 ] Network output: [ 0.1685 0.4789 0.6749 -0.001811 -0.002445 0.4686 0.001269 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.04801 0.005081 0.02484 0.04184 0.4824 0.5217 0.4615 0.5403 0.4882 0.543 ] Network output: [ 0.1525 0.4317 0.7085 -0.0002382 -0.001449 0.5238 0.002022 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05012 0.01638 0.02846 0.0304 0.5067 0.5092 0.4663 0.538 0.4603 0.5363 ] Network output: [ 0.1375 0.3888 0.738 0.0004475 -0.0009181 0.5727 0.002097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.05292 0.02193 0.03035 0.02483 0.5064 0.509 0.4679 0.5384 0.459 0.536 ] Network output: [ 0.1235 0.3491 0.764 0.000486 -0.001701 0.6167 -0.0002209 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8982 Epoch 6 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1138 0.4134 0.688 -0.000625 0.0001554 0.6523 -0.0001292 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0382 0.0002957 0.01287 0.008426 0.5061 0.5011 0.4663 0.5591 0.4556 0.5233 ] Network output: [ 0.09938 0.4727 0.718 -0.001379 -0.001229 0.5861 0.000861 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.04545 0.004227 0.02507 0.033 0.4832 0.5223 0.4621 0.5408 0.4882 0.5428 ] Network output: [ 0.1898 0.4256 0.6452 -0.001075 -0.000508 0.5288 -0.0009342 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05733 0.003071 0.02023 0.02474 0.5042 0.4985 0.4646 0.558 0.4554 0.5259 ] Network output: [ 0.1697 0.483 0.6809 -0.001394 -0.00155 0.4742 0.0005381 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.05147 0.005378 0.02564 0.04137 0.4824 0.5217 0.4615 0.5402 0.4881 0.5429 ] Network output: [ 0.1536 0.4354 0.7138 0.0001412 -0.0006413 0.5289 0.001351 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05403 0.01683 0.02945 0.02998 0.5068 0.5093 0.4664 0.538 0.4602 0.5363 ] Network output: [ 0.1384 0.3922 0.7429 0.0007895 -0.0001897 0.5773 0.001492 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.05705 0.02245 0.03147 0.02446 0.5065 0.509 0.468 0.5384 0.4589 0.536 ] Network output: [ 0.1243 0.3521 0.7683 0.0007733 -0.001059 0.6209 -0.0008066 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8985 Epoch 7 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1147 0.416 0.6921 -0.0003614 0.000688 0.6557 -0.0004844 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03999 0.0004114 0.01332 0.008393 0.5062 0.5012 0.4663 0.559 0.4556 0.5233 ] Network output: [ 0.1001 0.4751 0.7216 -0.001132 -0.0007128 0.5894 0.0004823 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.04816 0.004453 0.02576 0.03263 0.4832 0.5224 0.4621 0.5408 0.4881 0.5427 ] Network output: [ 0.1905 0.4277 0.6483 -0.0008627 -5.392e-05 0.5317 -0.001311 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06036 0.003281 0.02085 0.02455 0.5042 0.4986 0.4646 0.558 0.4554 0.526 ] Network output: [ 0.1704 0.485 0.6837 -0.001191 -0.001123 0.4769 0.000179 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.05471 0.005634 0.02623 0.04092 0.4824 0.5217 0.4615 0.5401 0.488 0.5429 ] Network output: [ 0.1541 0.4372 0.7164 0.0003278 -0.0002527 0.5314 0.001018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05772 0.01724 0.03026 0.02959 0.5069 0.5094 0.4665 0.5379 0.4602 0.5364 ] Network output: [ 0.1388 0.3938 0.7452 0.0009578 0.0001621 0.5796 0.001192 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06098 0.02294 0.03246 0.02411 0.5066 0.5091 0.4681 0.5383 0.4588 0.5361 ] Network output: [ 0.1246 0.3535 0.7703 0.0009044 -0.0007541 0.6231 -0.001117 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8986 Epoch 8 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1152 0.4173 0.6942 -0.0002398 0.0009208 0.6572 -0.0005974 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04173 0.0005109 0.01373 0.008365 0.5063 0.5014 0.4664 0.559 0.4556 0.5233 ] Network output: [ 0.1005 0.4762 0.7233 -0.001013 -0.0004684 0.5909 0.0003236 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.05078 0.004663 0.0264 0.03228 0.4833 0.5225 0.4622 0.5407 0.4881 0.5427 ] Network output: [ 0.1908 0.4288 0.6499 -0.000766 0.0001563 0.5331 -0.001489 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0633 0.00347 0.02142 0.02438 0.5042 0.4986 0.4646 0.5579 0.4553 0.526 ] Network output: [ 0.1707 0.4859 0.6851 -0.001092 -0.0009147 0.4782 -5.223e-08 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.05786 0.005873 0.02679 0.04049 0.4825 0.5217 0.4615 0.54 0.4879 0.5428 ] Network output: [ 0.1543 0.438 0.7176 0.0004211 -6.145e-05 0.5327 0.000848 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06134 0.01764 0.03107 0.02922 0.507 0.5095 0.4666 0.5379 0.4601 0.5364 ] Network output: [ 0.139 0.3945 0.7462 0.001042 0.0003368 0.5808 0.00104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06486 0.02343 0.03346 0.02378 0.5067 0.5092 0.4682 0.5383 0.4588 0.5361 ] Network output: [ 0.1246 0.3542 0.7712 0.0009601 -0.000608 0.6242 -0.001294 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8986 Epoch 9 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1155 0.4179 0.6952 -0.0001862 0.001013 0.6578 -0.0005956 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04346 0.0006035 0.01415 0.008341 0.5063 0.5015 0.4664 0.559 0.4556 0.5233 ] Network output: [ 0.1007 0.4768 0.7242 -0.0009562 -0.0003515 0.5916 0.0002705 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.0534 0.004869 0.02707 0.03194 0.4834 0.5226 0.4622 0.5406 0.488 0.5427 ] Network output: [ 0.191 0.4292 0.6506 -0.0007251 0.0002522 0.5337 -0.001571 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06623 0.003651 0.02202 0.02422 0.5043 0.4987 0.4646 0.5579 0.4553 0.526 ] Network output: [ 0.1708 0.4863 0.6857 -0.001043 -0.0008102 0.4789 -9.177e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.06099 0.006109 0.0274 0.04008 0.4825 0.5218 0.4615 0.5399 0.4879 0.5428 ] Network output: [ 0.1543 0.4385 0.7181 0.0004693 3.709e-05 0.5333 0.000758 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06496 0.01805 0.03194 0.02887 0.5072 0.5096 0.4668 0.5379 0.4601 0.5364 ] Network output: [ 0.139 0.3949 0.7467 0.001086 0.0004282 0.5814 0.0009604 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06874 0.02393 0.03454 0.02348 0.5069 0.5093 0.4683 0.5383 0.4587 0.5362 ] Network output: [ 0.1246 0.3546 0.7716 0.0009794 -0.0005361 0.6249 -0.001405 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8985 Epoch 10 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1157 0.4181 0.6958 -0.0001651 0.001038 0.6579 -0.0005406 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0452 0.0006936 0.01462 0.008317 0.5064 0.5016 0.4665 0.559 0.4556 0.5233 ] Network output: [ 0.1008 0.477 0.7246 -0.0009292 -0.0002945 0.592 0.0002678 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.05603 0.005078 0.02781 0.03162 0.4835 0.5227 0.4623 0.5406 0.488 0.5426 ] Network output: [ 0.1911 0.4295 0.6509 -0.0007114 0.0002946 0.534 -0.001608 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06917 0.003832 0.02271 0.02406 0.5043 0.4987 0.4646 0.5578 0.4553 0.526 ] Network output: [ 0.1709 0.4866 0.686 -0.001018 -0.000754 0.4792 -0.000141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.06415 0.006349 0.0281 0.03969 0.4826 0.5219 0.4615 0.5398 0.4878 0.5428 ] Network output: [ 0.1543 0.4387 0.7183 0.0004957 9.203e-05 0.5337 0.0007069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06861 0.01847 0.03291 0.02854 0.5074 0.5098 0.4669 0.5379 0.4601 0.5364 ] Network output: [ 0.1389 0.3951 0.7469 0.001111 0.0004805 0.5819 0.0009163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07267 0.02445 0.03573 0.0232 0.507 0.5095 0.4685 0.5383 0.4587 0.5362 ] Network output: [ 0.1245 0.3547 0.7717 0.0009811 -0.0004987 0.6253 -0.001485 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8984 Epoch 11 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1159 0.4182 0.6961 -0.0001593 0.001032 0.6579 -0.0004622 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04696 0.0007836 0.01514 0.008292 0.5065 0.5018 0.4665 0.559 0.4557 0.5233 ] Network output: [ 0.1009 0.4771 0.7248 -0.0009166 -0.0002658 0.5921 0.0002889 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.05871 0.005292 0.02865 0.03131 0.4836 0.5228 0.4623 0.5405 0.488 0.5426 ] Network output: [ 0.1911 0.4296 0.651 -0.0007108 0.000312 0.5342 -0.001624 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07216 0.004015 0.02349 0.02391 0.5043 0.4988 0.4647 0.5578 0.4552 0.5261 ] Network output: [ 0.1709 0.4867 0.6861 -0.001006 -0.0007206 0.4793 -0.0001694 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.06735 0.006595 0.02891 0.03931 0.4827 0.5219 0.4616 0.5398 0.4877 0.5427 ] Network output: [ 0.1543 0.4388 0.7184 0.0005115 0.0001265 0.534 0.0006751 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07232 0.0189 0.03401 0.02824 0.5075 0.5099 0.467 0.5379 0.46 0.5365 ] Network output: [ 0.1388 0.3952 0.7469 0.001126 0.0005143 0.5822 0.00089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07666 0.02499 0.03705 0.02295 0.5072 0.5096 0.4686 0.5383 0.4587 0.5363 ] Network output: [ 0.1243 0.3548 0.7717 0.0009744 -0.0004773 0.6257 -0.001548 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8983 Epoch 12 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.116 0.4183 0.6964 -0.0001604 0.001011 0.6577 -0.0003744 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04876 0.0008745 0.01573 0.008267 0.5066 0.5019 0.4666 0.559 0.4557 0.5233 ] Network output: [ 0.101 0.4772 0.7249 -0.0009111 -0.0002503 0.5922 0.000321 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.06143 0.005513 0.02959 0.03101 0.4837 0.5229 0.4624 0.5405 0.4879 0.5426 ] Network output: [ 0.1912 0.4297 0.651 -0.0007168 0.000318 0.5342 -0.001629 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0752 0.004202 0.02437 0.02377 0.5043 0.4988 0.4647 0.5578 0.4552 0.5261 ] Network output: [ 0.171 0.4867 0.6861 -0.001 -0.0006978 0.4794 -0.0001875 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.07061 0.006849 0.02985 0.03894 0.4827 0.522 0.4616 0.5397 0.4877 0.5427 ] Network output: [ 0.1542 0.4389 0.7184 0.0005221 0.0001513 0.5342 0.0006531 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0761 0.01936 0.03524 0.02795 0.5077 0.5101 0.4672 0.5379 0.46 0.5366 ] Network output: [ 0.1387 0.3953 0.7469 0.001136 0.0005395 0.5824 0.0008727 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08073 0.02555 0.03851 0.02272 0.5074 0.5098 0.4688 0.5383 0.4586 0.5363 ] Network output: [ 0.1241 0.3549 0.7716 0.0009637 -0.0004631 0.6261 -0.001604 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8981 Epoch 13 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1162 0.4182 0.6966 -0.0001644 0.0009836 0.6575 -0.0002841 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0506 0.0009668 0.01638 0.008241 0.5067 0.5021 0.4666 0.559 0.4557 0.5233 ] Network output: [ 0.101 0.4772 0.725 -0.000909 -0.0002411 0.5922 0.000358 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.06421 0.005742 0.03063 0.03071 0.4838 0.5231 0.4625 0.5404 0.4879 0.5426 ] Network output: [ 0.1912 0.4297 0.651 -0.000726 0.0003189 0.5343 -0.001631 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0783 0.004395 0.02537 0.02363 0.5044 0.4989 0.4647 0.5577 0.4552 0.5261 ] Network output: [ 0.171 0.4868 0.6861 -0.0009976 -0.00068 0.4795 -0.0002002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.07392 0.007113 0.0309 0.03859 0.4828 0.5221 0.4616 0.5396 0.4876 0.5427 ] Network output: [ 0.1541 0.4389 0.7183 0.0005302 0.0001715 0.5344 0.0006363 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07995 0.01983 0.0366 0.02768 0.5079 0.5103 0.4674 0.5379 0.46 0.5366 ] Network output: [ 0.1386 0.3953 0.7469 0.001145 0.0005604 0.5827 0.0008605 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08488 0.02614 0.04011 0.02251 0.5076 0.51 0.469 0.5383 0.4586 0.5364 ] Network output: [ 0.1239 0.3549 0.7715 0.0009511 -0.0004519 0.6264 -0.001655 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8979 Epoch 14 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1163 0.4182 0.6968 -0.0001695 0.0009524 0.6572 -0.0001945 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05247 0.001061 0.0171 0.008213 0.5068 0.5023 0.4667 0.559 0.4557 0.5233 ] Network output: [ 0.101 0.4772 0.725 -0.0009087 -0.000235 0.5922 0.0003968 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.06705 0.00598 0.03179 0.03043 0.4839 0.5232 0.4626 0.5404 0.4879 0.5425 ] Network output: [ 0.1912 0.4297 0.651 -0.000737 0.0003176 0.5343 -0.00163 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08145 0.004592 0.02648 0.02349 0.5044 0.499 0.4648 0.5577 0.4551 0.5262 ] Network output: [ 0.171 0.4868 0.6861 -0.0009967 -0.0006644 0.4796 -0.0002101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.0773 0.007386 0.03209 0.03826 0.4829 0.5222 0.4617 0.5396 0.4876 0.5427 ] Network output: [ 0.1541 0.4389 0.7183 0.0005369 0.0001895 0.5345 0.0006224 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08388 0.02032 0.03811 0.02743 0.5082 0.5105 0.4675 0.5379 0.46 0.5367 ] Network output: [ 0.1384 0.3953 0.7468 0.001152 0.0005794 0.583 0.0008512 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08911 0.02675 0.04185 0.02232 0.5078 0.5102 0.4692 0.5383 0.4586 0.5365 ] Network output: [ 0.1237 0.3549 0.7713 0.0009376 -0.000442 0.6268 -0.001703 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8978 Epoch 15 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1165 0.4182 0.697 -0.0001747 0.0009194 0.6569 -0.000107 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05439 0.001157 0.01788 0.008184 0.5069 0.5025 0.4668 0.559 0.4558 0.5233 ] Network output: [ 0.101 0.4772 0.7251 -0.0009093 -0.0002303 0.5922 0.0004362 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.06995 0.006226 0.03305 0.03017 0.4841 0.5234 0.4627 0.5404 0.4879 0.5425 ] Network output: [ 0.1912 0.4297 0.6509 -0.000749 0.0003155 0.5343 -0.001629 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08467 0.004796 0.02771 0.02337 0.5044 0.4991 0.4648 0.5577 0.4551 0.5262 ] Network output: [ 0.171 0.4868 0.686 -0.0009969 -0.0006499 0.4796 -0.0002183 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.08076 0.00767 0.03341 0.03794 0.483 0.5223 0.4617 0.5395 0.4875 0.5427 ] Network output: [ 0.154 0.439 0.7182 0.000543 0.0002064 0.5347 0.0006104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0879 0.02083 0.03975 0.02721 0.5084 0.5108 0.4677 0.5379 0.46 0.5368 ] Network output: [ 0.1383 0.3953 0.7467 0.001159 0.0005973 0.5833 0.0008441 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09344 0.02738 0.04375 0.02216 0.5081 0.5104 0.4694 0.5383 0.4586 0.5366 ] Network output: [ 0.1235 0.3549 0.7712 0.0009237 -0.0004323 0.6272 -0.001749 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8976 Epoch 16 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1166 0.4182 0.6972 -0.0001795 0.0008854 0.6566 -2.233e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05634 0.001255 0.01874 0.008152 0.507 0.5027 0.4669 0.559 0.4558 0.5233 ] Network output: [ 0.1011 0.4772 0.7251 -0.0009103 -0.0002263 0.5921 0.0004756 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.07292 0.006483 0.03443 0.02991 0.4842 0.5236 0.4628 0.5403 0.4879 0.5426 ] Network output: [ 0.1912 0.4297 0.6509 -0.0007616 0.0003132 0.5343 -0.001629 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08796 0.005006 0.02905 0.02324 0.5045 0.4992 0.4648 0.5576 0.4551 0.5263 ] Network output: [ 0.171 0.4869 0.686 -0.0009979 -0.0006358 0.4796 -0.0002253 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.08428 0.007964 0.03486 0.03763 0.4831 0.5225 0.4618 0.5394 0.4875 0.5427 ] Network output: [ 0.1539 0.439 0.7181 0.0005487 0.0002227 0.5349 0.0005998 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.092 0.02136 0.04154 0.027 0.5087 0.511 0.468 0.5379 0.46 0.5369 ] Network output: [ 0.1381 0.3953 0.7466 0.001166 0.0006145 0.5836 0.0008386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09787 0.02804 0.0458 0.02202 0.5083 0.5107 0.4696 0.5383 0.4586 0.5368 ] Network output: [ 0.1232 0.3548 0.7711 0.0009097 -0.0004226 0.6276 -0.001794 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8974 Epoch 17 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1168 0.4181 0.6974 -0.0001838 0.0008508 0.6563 5.925e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05834 0.001355 0.01967 0.008119 0.5071 0.5029 0.467 0.559 0.4559 0.5233 ] Network output: [ 0.1011 0.4772 0.7251 -0.0009117 -0.0002227 0.5921 0.0005146 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.07595 0.006749 0.03593 0.02966 0.4844 0.5238 0.4629 0.5403 0.4879 0.5426 ] Network output: [ 0.1912 0.4297 0.6508 -0.0007747 0.0003112 0.5343 -0.001628 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09131 0.005223 0.03052 0.02312 0.5045 0.4994 0.4649 0.5576 0.4551 0.5263 ] Network output: [ 0.171 0.4869 0.6859 -0.0009996 -0.0006219 0.4797 -0.0002313 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.08788 0.00827 0.03645 0.03734 0.4832 0.5226 0.4619 0.5394 0.4875 0.5427 ] Network output: [ 0.1538 0.439 0.718 0.0005541 0.0002387 0.5352 0.0005903 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09619 0.02191 0.04347 0.02682 0.5089 0.5113 0.4682 0.5379 0.46 0.537 ] Network output: [ 0.1379 0.3953 0.7465 0.001173 0.0006313 0.5839 0.0008345 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1024 0.02873 0.04799 0.02191 0.5086 0.5109 0.4699 0.5383 0.4586 0.5369 ] Network output: [ 0.123 0.3548 0.7709 0.0008957 -0.0004125 0.628 -0.001837 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8972 Epoch 18 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.117 0.4181 0.6976 -0.0001873 0.0008158 0.6559 0.0001377 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06039 0.001457 0.02067 0.008083 0.5072 0.5032 0.4671 0.5591 0.4559 0.5234 ] Network output: [ 0.1011 0.4772 0.7252 -0.0009133 -0.0002194 0.5921 0.0005533 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.07906 0.007025 0.03754 0.02942 0.4846 0.5241 0.463 0.5403 0.4879 0.5426 ] Network output: [ 0.1913 0.4297 0.6507 -0.0007881 0.0003095 0.5343 -0.001628 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09473 0.005446 0.0321 0.02301 0.5046 0.4995 0.4649 0.5576 0.4551 0.5264 ] Network output: [ 0.171 0.4869 0.6858 -0.001002 -0.0006081 0.4797 -0.0002366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.09156 0.008588 0.03818 0.03706 0.4833 0.5228 0.4619 0.5393 0.4875 0.5428 ] Network output: [ 0.1536 0.439 0.7179 0.0005594 0.0002544 0.5354 0.0005818 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1005 0.02248 0.04555 0.02665 0.5092 0.5116 0.4685 0.5379 0.4601 0.5372 ] Network output: [ 0.1377 0.3953 0.7464 0.00118 0.0006478 0.5843 0.0008319 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.107 0.02945 0.05034 0.02182 0.5089 0.5112 0.4702 0.5384 0.4587 0.5371 ] Network output: [ 0.1227 0.3548 0.7707 0.0008818 -0.0004022 0.6285 -0.001878 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.897 Epoch 19 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1172 0.418 0.6978 -0.0001902 0.0007805 0.6555 0.0002128 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06247 0.001561 0.02174 0.008044 0.5074 0.5035 0.4672 0.5591 0.456 0.5234 ] Network output: [ 0.1011 0.4772 0.7252 -0.0009152 -0.0002163 0.5921 0.0005915 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.08223 0.007312 0.03927 0.0292 0.4848 0.5243 0.4632 0.5403 0.4879 0.5426 ] Network output: [ 0.1913 0.4297 0.6506 -0.0008018 0.0003082 0.5343 -0.001628 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09823 0.005677 0.03381 0.0229 0.5046 0.4997 0.465 0.5575 0.4551 0.5265 ] Network output: [ 0.171 0.487 0.6857 -0.001005 -0.0005942 0.4798 -0.000241 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.09531 0.008918 0.04004 0.0368 0.4834 0.523 0.462 0.5393 0.4875 0.5428 ] Network output: [ 0.1535 0.4391 0.7178 0.0005646 0.0002698 0.5357 0.0005743 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1049 0.02308 0.04778 0.02651 0.5095 0.512 0.4688 0.538 0.4601 0.5373 ] Network output: [ 0.1375 0.3953 0.7463 0.001187 0.0006638 0.5847 0.0008307 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1117 0.0302 0.05285 0.02176 0.5092 0.5116 0.4705 0.5384 0.4587 0.5372 ] Network output: [ 0.1225 0.3547 0.7705 0.0008681 -0.0003915 0.6291 -0.001918 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8967 Epoch 20 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1174 0.418 0.698 -0.0001924 0.0007449 0.6551 0.0002848 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0646 0.001668 0.02289 0.008003 0.5075 0.5038 0.4673 0.5591 0.456 0.5234 ] Network output: [ 0.1012 0.4772 0.7252 -0.0009172 -0.0002132 0.592 0.0006294 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.08547 0.00761 0.04112 0.02899 0.485 0.5246 0.4633 0.5403 0.488 0.5427 ] Network output: [ 0.1913 0.4297 0.6506 -0.0008159 0.0003075 0.5343 -0.001628 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1018 0.005915 0.03565 0.02279 0.5047 0.4998 0.4651 0.5575 0.4551 0.5265 ] Network output: [ 0.171 0.487 0.6857 -0.001008 -0.0005803 0.4798 -0.0002447 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.09915 0.00926 0.04204 0.03655 0.4835 0.5232 0.4621 0.5393 0.4875 0.5429 ] Network output: [ 0.1534 0.4391 0.7176 0.0005697 0.000285 0.536 0.0005678 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.0237 0.05016 0.0264 0.5099 0.5124 0.4691 0.538 0.4602 0.5375 ] Network output: [ 0.1373 0.3952 0.7461 0.001195 0.0006794 0.5851 0.000831 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1166 0.03098 0.05552 0.02173 0.5095 0.5119 0.4708 0.5384 0.4587 0.5374 ] Network output: [ 0.1222 0.3546 0.7704 0.0008546 -0.0003806 0.6296 -0.001956 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8965 Epoch 21 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1176 0.418 0.6983 -0.0001938 0.0007092 0.6546 0.0003536 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06678 0.001777 0.02411 0.007958 0.5077 0.5041 0.4674 0.5592 0.4561 0.5235 ] Network output: [ 0.1012 0.4772 0.7253 -0.0009194 -0.0002104 0.592 0.0006668 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.08878 0.007919 0.04309 0.02879 0.4852 0.5249 0.4635 0.5403 0.488 0.5427 ] Network output: [ 0.1913 0.4297 0.6505 -0.0008302 0.0003072 0.5343 -0.001628 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1054 0.006162 0.03761 0.02269 0.5048 0.5 0.4651 0.5575 0.4551 0.5266 ] Network output: [ 0.171 0.487 0.6856 -0.001012 -0.0005663 0.4799 -0.0002475 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1031 0.009616 0.04419 0.03632 0.4837 0.5234 0.4622 0.5392 0.4875 0.5429 ] Network output: [ 0.1532 0.4391 0.7175 0.0005747 0.0002999 0.5363 0.0005624 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1139 0.02435 0.0527 0.02631 0.5103 0.5128 0.4695 0.5381 0.4603 0.5377 ] Network output: [ 0.1371 0.3952 0.746 0.001202 0.0006946 0.5856 0.0008329 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1215 0.0318 0.05835 0.02173 0.5099 0.5123 0.4712 0.5385 0.4588 0.5376 ] Network output: [ 0.1219 0.3546 0.7702 0.0008414 -0.0003693 0.6302 -0.001993 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8962 Epoch 22 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1179 0.418 0.6985 -0.0001945 0.0006732 0.6542 0.0004192 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.069 0.001889 0.02541 0.00791 0.5079 0.5044 0.4675 0.5592 0.4562 0.5235 ] Network output: [ 0.1012 0.4772 0.7253 -0.0009219 -0.0002076 0.5919 0.000704 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.09217 0.00824 0.04518 0.0286 0.4854 0.5252 0.4637 0.5403 0.4881 0.5428 ] Network output: [ 0.1914 0.4297 0.6504 -0.0008448 0.0003075 0.5343 -0.001628 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1091 0.006417 0.0397 0.02259 0.5048 0.5002 0.4652 0.5575 0.4551 0.5267 ] Network output: [ 0.1709 0.4871 0.6855 -0.001017 -0.0005523 0.4799 -0.0002495 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1071 0.009987 0.04647 0.0361 0.4838 0.5236 0.4624 0.5392 0.4875 0.543 ] Network output: [ 0.1531 0.4391 0.7173 0.0005797 0.0003145 0.5366 0.0005579 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1186 0.02503 0.05538 0.02624 0.5107 0.5132 0.4698 0.5381 0.4603 0.5379 ] Network output: [ 0.1369 0.3951 0.7459 0.00121 0.0007093 0.5861 0.0008364 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1266 0.03265 0.06133 0.02176 0.5103 0.5127 0.4716 0.5386 0.4589 0.5379 ] Network output: [ 0.1215 0.3545 0.77 0.0008285 -0.0003578 0.6309 -0.002027 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8959 Epoch 23 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1182 0.4179 0.6988 -0.0001945 0.0006372 0.6536 0.0004814 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07126 0.002003 0.02679 0.007859 0.5081 0.5048 0.4677 0.5593 0.4563 0.5235 ] Network output: [ 0.1012 0.4772 0.7253 -0.0009245 -0.0002049 0.5919 0.0007408 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.09563 0.008574 0.0474 0.02842 0.4857 0.5256 0.4639 0.5403 0.4882 0.5429 ] Network output: [ 0.1914 0.4297 0.6503 -0.0008598 0.0003083 0.5343 -0.001628 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1129 0.006681 0.04192 0.02249 0.5049 0.5004 0.4653 0.5575 0.4552 0.5268 ] Network output: [ 0.1709 0.4871 0.6854 -0.001022 -0.0005382 0.48 -0.0002506 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1111 0.01037 0.04891 0.0359 0.484 0.5239 0.4625 0.5392 0.4875 0.5431 ] Network output: [ 0.1529 0.4391 0.7172 0.0005847 0.0003288 0.537 0.0005546 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1234 0.02574 0.05823 0.0262 0.5111 0.5137 0.4703 0.5382 0.4604 0.5381 ] Network output: [ 0.1367 0.395 0.7457 0.001219 0.0007234 0.5867 0.0008417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1317 0.03353 0.06449 0.02183 0.5108 0.5132 0.472 0.5387 0.459 0.5381 ] Network output: [ 0.1212 0.3543 0.7698 0.000816 -0.0003462 0.6316 -0.002059 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8956 Epoch 24 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1184 0.4179 0.6991 -0.0001939 0.000601 0.653 0.0005404 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07356 0.00212 0.02824 0.007804 0.5083 0.5051 0.4678 0.5593 0.4564 0.5236 ] Network output: [ 0.1013 0.4772 0.7254 -0.0009274 -0.0002023 0.5918 0.0007773 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.09915 0.00892 0.04975 0.02826 0.4859 0.5259 0.4641 0.5404 0.4882 0.543 ] Network output: [ 0.1915 0.4297 0.6502 -0.000875 0.0003098 0.5343 -0.001628 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1167 0.006955 0.04427 0.0224 0.505 0.5007 0.4654 0.5575 0.4552 0.5269 ] Network output: [ 0.1709 0.4872 0.6853 -0.001028 -0.000524 0.48 -0.0002507 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1153 0.01077 0.05149 0.03571 0.4842 0.5242 0.4627 0.5392 0.4876 0.5432 ] Network output: [ 0.1528 0.4391 0.717 0.0005898 0.0003426 0.5374 0.0005524 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1283 0.02647 0.06123 0.02619 0.5115 0.5142 0.4707 0.5383 0.4606 0.5383 ] Network output: [ 0.1364 0.395 0.7455 0.001228 0.0007368 0.5873 0.0008488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.137 0.03446 0.06781 0.02192 0.5112 0.5137 0.4725 0.5388 0.4591 0.5384 ] Network output: [ 0.1208 0.3542 0.7695 0.0008039 -0.0003345 0.6324 -0.002089 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8952 Epoch 25 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1187 0.4179 0.6994 -0.0001926 0.0005649 0.6524 0.0005959 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0759 0.00224 0.02978 0.007745 0.5085 0.5056 0.468 0.5594 0.4565 0.5236 ] Network output: [ 0.1013 0.4772 0.7254 -0.0009306 -0.0001999 0.5918 0.0008135 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1027 0.009281 0.05222 0.02811 0.4862 0.5264 0.4644 0.5404 0.4883 0.5431 ] Network output: [ 0.1915 0.4296 0.6501 -0.0008905 0.0003118 0.5343 -0.001628 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1206 0.007239 0.04675 0.02232 0.5051 0.5009 0.4655 0.5575 0.4553 0.527 ] Network output: [ 0.1709 0.4872 0.6851 -0.001034 -0.0005098 0.4801 -0.00025 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1195 0.01119 0.05421 0.03555 0.4844 0.5245 0.4628 0.5391 0.4876 0.5433 ] Network output: [ 0.1526 0.439 0.7168 0.0005949 0.000356 0.5378 0.0005513 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1333 0.02724 0.06439 0.02621 0.512 0.5148 0.4712 0.5384 0.4607 0.5386 ] Network output: [ 0.1361 0.3949 0.7454 0.001237 0.0007494 0.5879 0.0008579 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1424 0.03543 0.0713 0.02206 0.5117 0.5143 0.4731 0.5389 0.4592 0.5387 ] Network output: [ 0.1205 0.354 0.7693 0.0007924 -0.0003228 0.6333 -0.002117 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8948 Epoch 26 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1191 0.4179 0.6997 -0.0001907 0.0005287 0.6517 0.0006479 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07828 0.002364 0.03139 0.007682 0.5087 0.506 0.4681 0.5595 0.4567 0.5237 ] Network output: [ 0.1013 0.4772 0.7255 -0.000934 -0.0001975 0.5917 0.0008494 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1064 0.009656 0.05482 0.02797 0.4865 0.5268 0.4647 0.5405 0.4885 0.5432 ] Network output: [ 0.1916 0.4296 0.65 -0.0009063 0.0003145 0.5343 -0.001628 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1246 0.007533 0.04936 0.02223 0.5052 0.5012 0.4656 0.5575 0.4553 0.5271 ] Network output: [ 0.1709 0.4872 0.685 -0.001041 -0.0004956 0.4801 -0.0002482 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1238 0.01162 0.05709 0.0354 0.4846 0.5248 0.463 0.5391 0.4877 0.5434 ] Network output: [ 0.1524 0.439 0.7166 0.0006001 0.0003689 0.5382 0.0005515 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1384 0.02804 0.06772 0.02626 0.5126 0.5154 0.4718 0.5385 0.4608 0.5389 ] Network output: [ 0.1358 0.3947 0.7452 0.001247 0.0007613 0.5886 0.0008691 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1479 0.03644 0.07496 0.02223 0.5123 0.5149 0.4736 0.539 0.4594 0.539 ] Network output: [ 0.1201 0.3539 0.7691 0.0007815 -0.0003111 0.6342 -0.002142 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8944 Epoch 27 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1194 0.4179 0.7001 -0.0001883 0.0004927 0.651 0.0006964 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0807 0.00249 0.03308 0.007614 0.5089 0.5065 0.4683 0.5596 0.4568 0.5237 ] Network output: [ 0.1014 0.4772 0.7255 -0.0009378 -0.0001951 0.5916 0.0008849 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1101 0.01005 0.05755 0.02785 0.4869 0.5273 0.465 0.5406 0.4886 0.5433 ] Network output: [ 0.1916 0.4296 0.6498 -0.0009223 0.0003178 0.5343 -0.001627 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1286 0.00784 0.05212 0.02216 0.5053 0.5016 0.4657 0.5575 0.4554 0.5273 ] Network output: [ 0.1709 0.4873 0.6849 -0.001049 -0.0004812 0.4802 -0.0002454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1282 0.01208 0.06012 0.03527 0.4848 0.5252 0.4632 0.5392 0.4878 0.5435 ] Network output: [ 0.1522 0.439 0.7164 0.0006054 0.0003813 0.5387 0.0005528 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1436 0.02888 0.07121 0.02634 0.5131 0.516 0.4723 0.5386 0.461 0.5392 ] Network output: [ 0.1355 0.3946 0.745 0.001257 0.0007722 0.5894 0.0008824 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1535 0.0375 0.07879 0.02244 0.5128 0.5155 0.4743 0.5392 0.4595 0.5394 ] Network output: [ 0.1197 0.3537 0.7688 0.0007714 -0.0002995 0.6352 -0.002164 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.894 Epoch 28 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1198 0.418 0.7004 -0.0001854 0.0004569 0.6502 0.000741 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08316 0.00262 0.03486 0.007542 0.5092 0.507 0.4685 0.5596 0.457 0.5238 ] Network output: [ 0.1014 0.4773 0.7256 -0.0009419 -0.0001928 0.5916 0.0009201 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1139 0.01045 0.06042 0.02774 0.4872 0.5278 0.4653 0.5407 0.4887 0.5435 ] Network output: [ 0.1917 0.4296 0.6497 -0.0009387 0.0003218 0.5343 -0.001626 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1327 0.008158 0.055 0.02208 0.5054 0.5019 0.4658 0.5575 0.4555 0.5274 ] Network output: [ 0.1709 0.4873 0.6848 -0.001057 -0.0004669 0.4803 -0.0002416 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1327 0.01255 0.0633 0.03516 0.4851 0.5256 0.4635 0.5392 0.4879 0.5437 ] Network output: [ 0.152 0.4389 0.7162 0.000611 0.000393 0.5393 0.0005555 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1489 0.02975 0.07486 0.02645 0.5137 0.5167 0.473 0.5388 0.4612 0.5395 ] Network output: [ 0.1352 0.3944 0.7447 0.001269 0.0007821 0.5902 0.000898 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1592 0.03861 0.08279 0.02269 0.5135 0.5162 0.4749 0.5393 0.4597 0.5397 ] Network output: [ 0.1192 0.3534 0.7686 0.0007621 -0.0002882 0.6362 -0.002183 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8935 Epoch 29 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1202 0.418 0.7008 -0.000182 0.0004213 0.6494 0.0007818 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08564 0.002753 0.03671 0.007464 0.5094 0.5075 0.4687 0.5597 0.4571 0.5239 ] Network output: [ 0.1014 0.4773 0.7256 -0.0009463 -0.0001906 0.5915 0.0009548 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1178 0.01088 0.06341 0.02765 0.4876 0.5284 0.4656 0.5408 0.4889 0.5436 ] Network output: [ 0.1918 0.4296 0.6496 -0.0009553 0.0003265 0.5342 -0.001624 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1368 0.00849 0.05803 0.02201 0.5055 0.5023 0.466 0.5575 0.4556 0.5275 ] Network output: [ 0.1708 0.4874 0.6846 -0.001065 -0.0004525 0.4803 -0.0002368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1372 0.01304 0.06664 0.03506 0.4854 0.5261 0.4637 0.5392 0.488 0.5438 ] Network output: [ 0.1518 0.4389 0.716 0.0006168 0.0004041 0.5399 0.0005594 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1543 0.03067 0.07868 0.0266 0.5144 0.5175 0.4737 0.539 0.4615 0.5399 ] Network output: [ 0.1349 0.3943 0.7445 0.001281 0.0007908 0.5911 0.0009159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.165 0.03977 0.08697 0.02298 0.5141 0.517 0.4757 0.5395 0.4599 0.5401 ] Network output: [ 0.1188 0.3532 0.7683 0.0007538 -0.0002772 0.6374 -0.002199 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.893 Epoch 30 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1206 0.4181 0.7012 -0.0001784 0.0003861 0.6484 0.0008184 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08816 0.002891 0.03865 0.007381 0.5097 0.508 0.4689 0.5599 0.4573 0.524 ] Network output: [ 0.1015 0.4773 0.7257 -0.0009512 -0.0001884 0.5914 0.0009889 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1217 0.01132 0.06653 0.02757 0.488 0.529 0.466 0.5409 0.4891 0.5438 ] Network output: [ 0.1919 0.4296 0.6495 -0.0009723 0.000332 0.5342 -0.001622 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.141 0.008836 0.06118 0.02195 0.5057 0.5027 0.4661 0.5575 0.4557 0.5277 ] Network output: [ 0.1708 0.4874 0.6845 -0.001075 -0.0004381 0.4804 -0.0002309 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1418 0.01356 0.07013 0.03499 0.4857 0.5266 0.464 0.5393 0.4881 0.544 ] Network output: [ 0.1516 0.4388 0.7157 0.000623 0.0004144 0.5405 0.0005647 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1598 0.03163 0.08267 0.02679 0.5151 0.5183 0.4744 0.5392 0.4617 0.5403 ] Network output: [ 0.1345 0.3941 0.7443 0.001294 0.0007983 0.592 0.0009364 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1709 0.04099 0.09132 0.02332 0.5148 0.5178 0.4765 0.5397 0.4602 0.5406 ] Network output: [ 0.1183 0.3529 0.768 0.0007467 -0.0002667 0.6386 -0.002212 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8924 Epoch 31 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.121 0.4181 0.7017 -0.0001744 0.0003513 0.6474 0.0008508 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09071 0.003032 0.04066 0.007293 0.51 0.5087 0.4691 0.56 0.4575 0.524 ] Network output: [ 0.1015 0.4774 0.7257 -0.0009565 -0.0001862 0.5913 0.001022 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1257 0.01178 0.06979 0.02751 0.4885 0.5296 0.4665 0.541 0.4893 0.544 ] Network output: [ 0.192 0.4295 0.6493 -0.0009896 0.0003383 0.5342 -0.001619 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1452 0.009198 0.06448 0.02189 0.5058 0.5031 0.4663 0.5576 0.4558 0.5278 ] Network output: [ 0.1708 0.4875 0.6843 -0.001084 -0.0004237 0.4805 -0.000224 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1465 0.0141 0.07377 0.03494 0.486 0.5271 0.4644 0.5393 0.4883 0.5442 ] Network output: [ 0.1513 0.4387 0.7155 0.0006295 0.0004238 0.5412 0.0005713 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1654 0.03263 0.08682 0.02701 0.5158 0.5192 0.4752 0.5394 0.462 0.5407 ] Network output: [ 0.1342 0.3938 0.744 0.001309 0.0008045 0.5931 0.0009594 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1769 0.04227 0.09585 0.02371 0.5156 0.5187 0.4773 0.54 0.4605 0.541 ] Network output: [ 0.1178 0.3526 0.7677 0.0007408 -0.0002566 0.6399 -0.002221 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8918 Epoch 32 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1215 0.4182 0.7022 -0.0001703 0.000317 0.6463 0.0008788 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09328 0.003178 0.04275 0.007199 0.5103 0.5093 0.4693 0.5601 0.4578 0.5241 ] Network output: [ 0.1016 0.4774 0.7258 -0.0009624 -0.000184 0.5911 0.001055 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1297 0.01226 0.07317 0.02747 0.489 0.5303 0.4669 0.5412 0.4896 0.5442 ] Network output: [ 0.1921 0.4295 0.6492 -0.001007 0.0003453 0.5341 -0.001616 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1495 0.009576 0.06791 0.02183 0.506 0.5036 0.4664 0.5576 0.456 0.528 ] Network output: [ 0.1708 0.4876 0.6842 -0.001095 -0.0004092 0.4806 -0.0002161 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1513 0.01466 0.07757 0.03492 0.4863 0.5277 0.4647 0.5394 0.4885 0.5444 ] Network output: [ 0.1511 0.4386 0.7152 0.0006365 0.0004324 0.5419 0.0005793 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1711 0.03368 0.09115 0.02727 0.5166 0.5202 0.4761 0.5396 0.4623 0.5412 ] Network output: [ 0.1338 0.3936 0.7437 0.001324 0.0008091 0.5942 0.0009851 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1831 0.04362 0.1005 0.02415 0.5164 0.5196 0.4783 0.5403 0.4608 0.5415 ] Network output: [ 0.1173 0.3522 0.7674 0.0007364 -0.0002473 0.6413 -0.002227 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8912 Epoch 33 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.122 0.4184 0.7027 -0.0001662 0.0002835 0.6451 0.0009021 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09587 0.003329 0.04493 0.007099 0.5106 0.51 0.4696 0.5603 0.458 0.5242 ] Network output: [ 0.1016 0.4775 0.7259 -0.0009688 -0.0001818 0.591 0.001087 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1338 0.01276 0.07669 0.02745 0.4895 0.5311 0.4674 0.5413 0.4898 0.5445 ] Network output: [ 0.1922 0.4295 0.6491 -0.001025 0.0003532 0.534 -0.001611 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1538 0.009971 0.07148 0.02178 0.5061 0.5041 0.4666 0.5577 0.4561 0.5282 ] Network output: [ 0.1708 0.4876 0.684 -0.001106 -0.0003948 0.4807 -0.0002072 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1561 0.01526 0.08151 0.03491 0.4867 0.5283 0.4651 0.5395 0.4887 0.5447 ] Network output: [ 0.1508 0.4385 0.7149 0.000644 0.00044 0.5427 0.0005887 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1768 0.03479 0.09563 0.02758 0.5175 0.5212 0.4771 0.5399 0.4626 0.5417 ] Network output: [ 0.1334 0.3933 0.7434 0.001342 0.000812 0.5954 0.001013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1893 0.04504 0.1054 0.02464 0.5173 0.5207 0.4793 0.5405 0.4611 0.5421 ] Network output: [ 0.1167 0.3518 0.7671 0.0007336 -0.0002388 0.6428 -0.002229 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8905 Epoch 34 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1225 0.4185 0.7032 -0.0001622 0.0002508 0.6438 0.0009206 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09849 0.003485 0.04718 0.006992 0.5109 0.5107 0.4698 0.5604 0.4583 0.5243 ] Network output: [ 0.1016 0.4775 0.7259 -0.0009758 -0.0001795 0.5908 0.001118 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1379 0.01328 0.08033 0.02744 0.49 0.5319 0.468 0.5415 0.4901 0.5448 ] Network output: [ 0.1923 0.4295 0.6489 -0.001043 0.000362 0.5339 -0.001607 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1582 0.01039 0.07518 0.02174 0.5063 0.5047 0.4668 0.5577 0.4563 0.5284 ] Network output: [ 0.1708 0.4877 0.6838 -0.001118 -0.0003804 0.4808 -0.0001973 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1609 0.01588 0.08562 0.03493 0.4871 0.529 0.4655 0.5396 0.4889 0.5449 ] Network output: [ 0.1505 0.4384 0.7145 0.0006522 0.0004465 0.5436 0.0005994 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1827 0.03595 0.1003 0.02793 0.5184 0.5223 0.4781 0.5402 0.463 0.5422 ] Network output: [ 0.1329 0.393 0.7431 0.001361 0.0008132 0.5967 0.001045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1956 0.04653 0.1105 0.02519 0.5183 0.5218 0.4804 0.5409 0.4615 0.5427 ] Network output: [ 0.1162 0.3514 0.7667 0.0007326 -0.0002311 0.6444 -0.002227 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8897 Epoch 35 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.123 0.4187 0.7038 -0.0001584 0.000219 0.6424 0.000934 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1011 0.003646 0.0495 0.006879 0.5113 0.5114 0.4701 0.5606 0.4585 0.5245 ] Network output: [ 0.1017 0.4776 0.726 -0.0009835 -0.0001771 0.5907 0.001148 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1421 0.01383 0.0841 0.02745 0.4906 0.5328 0.4686 0.5417 0.4904 0.545 ] Network output: [ 0.1925 0.4295 0.6488 -0.001062 0.0003717 0.5338 -0.001601 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1626 0.01082 0.07902 0.0217 0.5065 0.5052 0.467 0.5578 0.4565 0.5286 ] Network output: [ 0.1708 0.4877 0.6836 -0.00113 -0.000366 0.4809 -0.0001865 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1658 0.01653 0.08987 0.03498 0.4875 0.5297 0.466 0.5397 0.4892 0.5452 ] Network output: [ 0.1502 0.4383 0.7142 0.0006612 0.0004518 0.5445 0.0006114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1887 0.03717 0.1051 0.02833 0.5193 0.5235 0.4792 0.5405 0.4634 0.5428 ] Network output: [ 0.1325 0.3926 0.7428 0.001381 0.0008125 0.5981 0.001079 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.202 0.0481 0.1157 0.0258 0.5193 0.523 0.4816 0.5412 0.4619 0.5433 ] Network output: [ 0.1156 0.3509 0.7664 0.0007337 -0.0002246 0.6462 -0.002222 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8889 Epoch 36 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1236 0.4189 0.7044 -0.000155 0.0001883 0.6409 0.0009421 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1038 0.003813 0.0519 0.006759 0.5116 0.5122 0.4704 0.5608 0.4589 0.5246 ] Network output: [ 0.1018 0.4777 0.7261 -0.000992 -0.0001746 0.5905 0.001177 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1464 0.01441 0.088 0.02749 0.4912 0.5337 0.4692 0.542 0.4908 0.5454 ] Network output: [ 0.1926 0.4295 0.6486 -0.001081 0.0003824 0.5337 -0.001595 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.167 0.01128 0.08298 0.02167 0.5067 0.5059 0.4673 0.5579 0.4568 0.5288 ] Network output: [ 0.1707 0.4878 0.6834 -0.001143 -0.0003516 0.481 -0.0001748 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1708 0.01722 0.09427 0.03505 0.488 0.5305 0.4665 0.5399 0.4895 0.5455 ] Network output: [ 0.1499 0.4381 0.7138 0.0006709 0.0004559 0.5455 0.0006248 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1947 0.03846 0.1101 0.02878 0.5204 0.5247 0.4804 0.5409 0.4639 0.5434 ] Network output: [ 0.132 0.3923 0.7424 0.001404 0.0008096 0.5996 0.001116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2085 0.04976 0.1211 0.02648 0.5203 0.5242 0.4829 0.5416 0.4623 0.544 ] Network output: [ 0.115 0.3504 0.766 0.0007371 -0.0002193 0.648 -0.002213 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.888 Epoch 37 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1242 0.4191 0.705 -0.0001522 0.0001589 0.6392 0.0009448 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1064 0.003987 0.05438 0.006631 0.512 0.5131 0.4707 0.561 0.4592 0.5247 ] Network output: [ 0.1018 0.4777 0.7262 -0.001001 -0.000172 0.5903 0.001204 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1506 0.01501 0.09202 0.02754 0.4919 0.5347 0.47 0.5423 0.4912 0.5457 ] Network output: [ 0.1928 0.4294 0.6485 -0.001101 0.000394 0.5336 -0.001588 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1714 0.01176 0.08707 0.02164 0.5069 0.5066 0.4675 0.558 0.457 0.529 ] Network output: [ 0.1707 0.4879 0.6832 -0.001157 -0.0003372 0.4811 -0.0001623 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1758 0.01794 0.09881 0.03515 0.4885 0.5313 0.4671 0.54 0.4898 0.5458 ] Network output: [ 0.1496 0.4379 0.7134 0.0006817 0.0004587 0.5466 0.0006394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2009 0.03982 0.1152 0.02928 0.5215 0.5261 0.4817 0.5413 0.4644 0.5441 ] Network output: [ 0.1315 0.3919 0.742 0.001429 0.0008045 0.6012 0.001156 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2151 0.05151 0.1267 0.02722 0.5214 0.5256 0.4843 0.5421 0.4628 0.5447 ] Network output: [ 0.1143 0.3498 0.7656 0.0007431 -0.0002154 0.65 -0.0022 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8871 Epoch 38 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1249 0.4194 0.7057 -0.0001502 0.000131 0.6374 0.0009419 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1091 0.004167 0.05692 0.006497 0.5124 0.514 0.471 0.5612 0.4595 0.5249 ] Network output: [ 0.1019 0.4778 0.7263 -0.001012 -0.0001691 0.59 0.001229 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1549 0.01564 0.09615 0.02762 0.4925 0.5357 0.4707 0.5426 0.4916 0.5461 ] Network output: [ 0.193 0.4294 0.6483 -0.001121 0.0004068 0.5334 -0.001581 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1758 0.01227 0.09128 0.02162 0.5071 0.5073 0.4678 0.5582 0.4573 0.5293 ] Network output: [ 0.1707 0.4879 0.683 -0.001171 -0.000323 0.4812 -0.0001492 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1809 0.0187 0.1035 0.03528 0.489 0.5322 0.4677 0.5402 0.4902 0.5462 ] Network output: [ 0.1492 0.4377 0.713 0.0006935 0.00046 0.5478 0.0006553 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2071 0.04125 0.1205 0.02983 0.5226 0.5275 0.4831 0.5417 0.4649 0.5448 ] Network output: [ 0.131 0.3914 0.7416 0.001457 0.0007969 0.6029 0.001198 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2218 0.05336 0.1324 0.02803 0.5226 0.5271 0.4858 0.5426 0.4634 0.5455 ] Network output: [ 0.1136 0.3492 0.7652 0.0007519 -0.000213 0.6521 -0.002183 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8861 Epoch 39 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1255 0.4198 0.7064 -0.0001493 0.0001047 0.6355 0.0009331 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1117 0.004355 0.05953 0.006354 0.5128 0.5149 0.4714 0.5615 0.4599 0.525 ] Network output: [ 0.102 0.478 0.7264 -0.001023 -0.0001659 0.5897 0.001252 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1593 0.01631 0.1004 0.02772 0.4933 0.5369 0.4715 0.5429 0.4921 0.5465 ] Network output: [ 0.1932 0.4294 0.6482 -0.001142 0.0004206 0.5332 -0.001574 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1803 0.01281 0.09561 0.02161 0.5074 0.5081 0.4681 0.5583 0.4576 0.5295 ] Network output: [ 0.1707 0.488 0.6827 -0.001186 -0.0003087 0.4813 -0.0001354 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.186 0.01951 0.1083 0.03544 0.4896 0.5332 0.4684 0.5405 0.4906 0.5466 ] Network output: [ 0.1488 0.4375 0.7125 0.0007066 0.0004597 0.549 0.0006724 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2134 0.04277 0.126 0.03044 0.5238 0.5291 0.4846 0.5422 0.4655 0.5456 ] Network output: [ 0.1304 0.3909 0.7411 0.001488 0.0007868 0.6047 0.001244 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2286 0.05532 0.1383 0.02891 0.5239 0.5286 0.4874 0.5431 0.464 0.5463 ] Network output: [ 0.1129 0.3486 0.7647 0.0007639 -0.0002123 0.6544 -0.002162 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.885 Epoch 40 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1263 0.4201 0.7072 -0.0001496 8.017e-05 0.6334 0.0009184 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1144 0.004551 0.0622 0.006204 0.5132 0.5159 0.4717 0.5617 0.4603 0.5252 ] Network output: [ 0.102 0.4781 0.7265 -0.001036 -0.0001624 0.5894 0.001274 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1636 0.01701 0.1048 0.02785 0.4941 0.5381 0.4724 0.5432 0.4926 0.5469 ] Network output: [ 0.1934 0.4294 0.6481 -0.001163 0.0004356 0.5329 -0.001566 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1847 0.01338 0.1001 0.02161 0.5076 0.5089 0.4684 0.5585 0.4579 0.5298 ] Network output: [ 0.1707 0.488 0.6825 -0.001202 -0.0002946 0.4814 -0.0001212 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.02036 0.1133 0.03564 0.4902 0.5343 0.4691 0.5407 0.491 0.547 ] Network output: [ 0.1485 0.4373 0.712 0.000721 0.0004578 0.5503 0.0006906 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2198 0.04437 0.1316 0.03112 0.5251 0.5307 0.4863 0.5427 0.4662 0.5464 ] Network output: [ 0.1299 0.3904 0.7407 0.001521 0.0007738 0.6067 0.001293 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2355 0.05739 0.1443 0.02988 0.5253 0.5303 0.4891 0.5437 0.4646 0.5472 ] Network output: [ 0.1122 0.3478 0.7642 0.0007793 -0.0002136 0.6568 -0.002137 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8838 Epoch 41 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.127 0.4205 0.708 -0.0001514 5.772e-05 0.6311 0.0008975 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.117 0.004755 0.06493 0.006045 0.5136 0.517 0.4721 0.562 0.4608 0.5253 ] Network output: [ 0.1021 0.4782 0.7266 -0.00105 -0.0001585 0.5891 0.001293 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1681 0.01774 0.1092 0.028 0.4949 0.5393 0.4734 0.5436 0.4931 0.5474 ] Network output: [ 0.1936 0.4294 0.6479 -0.001185 0.0004518 0.5326 -0.001557 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1892 0.01398 0.1046 0.02162 0.5079 0.5098 0.4687 0.5587 0.4583 0.5301 ] Network output: [ 0.1707 0.4881 0.6822 -0.001219 -0.0002805 0.4816 -0.0001066 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1963 0.02126 0.1184 0.03587 0.4908 0.5354 0.4699 0.541 0.4915 0.5474 ] Network output: [ 0.1481 0.437 0.7115 0.000737 0.0004542 0.5518 0.0007099 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2263 0.04606 0.1374 0.03186 0.5265 0.5324 0.488 0.5433 0.4669 0.5472 ] Network output: [ 0.1293 0.3898 0.7402 0.001558 0.0007579 0.6088 0.001345 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2425 0.05959 0.1505 0.03092 0.5267 0.532 0.491 0.5443 0.4653 0.5481 ] Network output: [ 0.1114 0.3471 0.7638 0.0007985 -0.0002169 0.6594 -0.002107 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8825 Epoch 42 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1278 0.421 0.7089 -0.0001551 3.754e-05 0.6287 0.0008703 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1197 0.004968 0.06771 0.005878 0.514 0.5181 0.4725 0.5623 0.4613 0.5255 ] Network output: [ 0.1022 0.4784 0.7268 -0.001065 -0.0001542 0.5887 0.001309 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1725 0.01852 0.1138 0.02817 0.4957 0.5407 0.4744 0.5441 0.4937 0.5479 ] Network output: [ 0.1939 0.4294 0.6478 -0.001208 0.0004693 0.5323 -0.001549 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1936 0.01462 0.1093 0.02163 0.5082 0.5108 0.4691 0.5589 0.4587 0.5304 ] Network output: [ 0.1707 0.4882 0.682 -0.001236 -0.0002665 0.4817 -9.181e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2015 0.02222 0.1236 0.03613 0.4915 0.5365 0.4708 0.5413 0.492 0.5479 ] Network output: [ 0.1476 0.4367 0.711 0.0007546 0.0004486 0.5533 0.0007302 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2329 0.04786 0.1433 0.03267 0.5279 0.5343 0.4898 0.5439 0.4676 0.5482 ] Network output: [ 0.1286 0.3892 0.7396 0.001599 0.0007388 0.6111 0.001401 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2496 0.06193 0.1569 0.03206 0.5282 0.5339 0.493 0.545 0.4661 0.5491 ] Network output: [ 0.1106 0.3462 0.7632 0.000822 -0.0002225 0.6621 -0.002074 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8811 Epoch 43 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1287 0.4215 0.7098 -0.000161 1.984e-05 0.6261 0.0008367 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1223 0.005191 0.07055 0.005702 0.5145 0.5192 0.4729 0.5626 0.4618 0.5257 ] Network output: [ 0.1023 0.4785 0.7269 -0.001082 -0.0001493 0.5883 0.001322 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.177 0.01934 0.1185 0.02838 0.4966 0.5421 0.4755 0.5445 0.4944 0.5484 ] Network output: [ 0.1942 0.4295 0.6477 -0.001231 0.0004882 0.5319 -0.00154 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1981 0.01529 0.114 0.02166 0.5085 0.5118 0.4695 0.5591 0.4592 0.5307 ] Network output: [ 0.1707 0.4882 0.6817 -0.001255 -0.0002526 0.4818 -7.695e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2067 0.02323 0.1289 0.03644 0.4922 0.5378 0.4717 0.5417 0.4926 0.5484 ] Network output: [ 0.1472 0.4364 0.7104 0.0007741 0.0004411 0.555 0.0007516 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2395 0.04976 0.1493 0.03355 0.5294 0.5362 0.4918 0.5446 0.4685 0.5491 ] Network output: [ 0.1279 0.3885 0.739 0.001644 0.0007163 0.6136 0.001459 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2568 0.06441 0.1634 0.03329 0.5298 0.5359 0.4951 0.5457 0.4669 0.5502 ] Network output: [ 0.1097 0.3453 0.7627 0.00085 -0.0002307 0.665 -0.002037 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8796 Epoch 44 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1295 0.4221 0.7108 -0.0001693 4.892e-06 0.6232 0.0007965 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1249 0.005425 0.07343 0.005517 0.5149 0.5204 0.4733 0.5629 0.4623 0.5259 ] Network output: [ 0.1024 0.4787 0.7271 -0.001101 -0.0001438 0.5878 0.001333 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 0.0202 0.1232 0.02861 0.4976 0.5436 0.4767 0.545 0.4951 0.549 ] Network output: [ 0.1945 0.4295 0.6476 -0.001256 0.0005084 0.5314 -0.001532 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2025 0.01601 0.1189 0.0217 0.5088 0.5128 0.4699 0.5594 0.4596 0.5311 ] Network output: [ 0.1707 0.4883 0.6814 -0.001274 -0.0002388 0.482 -6.22e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.212 0.02431 0.1344 0.03679 0.493 0.5391 0.4728 0.542 0.4932 0.549 ] Network output: [ 0.1467 0.436 0.7097 0.0007956 0.0004315 0.5568 0.000774 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2463 0.05179 0.1555 0.0345 0.531 0.5383 0.4939 0.5453 0.4693 0.5502 ] Network output: [ 0.1272 0.3878 0.7384 0.001693 0.0006902 0.6162 0.001521 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2641 0.06705 0.17 0.03462 0.5314 0.538 0.4974 0.5465 0.4678 0.5513 ] Network output: [ 0.1088 0.3444 0.7621 0.000883 -0.0002415 0.6681 -0.001996 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8781 Epoch 45 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1305 0.4227 0.7119 -0.0001806 -7.063e-06 0.6202 0.0007497 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1275 0.005671 0.07634 0.005324 0.5153 0.5217 0.4738 0.5633 0.4629 0.5262 ] Network output: [ 0.1025 0.4789 0.7272 -0.001122 -0.0001375 0.5873 0.00134 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.186 0.02111 0.128 0.02888 0.4986 0.5452 0.4779 0.5456 0.4958 0.5496 ] Network output: [ 0.1948 0.4295 0.6475 -0.001282 0.0005302 0.5309 -0.001524 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2069 0.01678 0.1238 0.02176 0.5091 0.514 0.4704 0.5596 0.4602 0.5314 ] Network output: [ 0.1707 0.4884 0.6811 -0.001294 -0.0002251 0.4821 -4.773e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2174 0.02545 0.1399 0.03718 0.4938 0.5406 0.4739 0.5425 0.4939 0.5496 ] Network output: [ 0.1462 0.4356 0.7091 0.0008194 0.0004197 0.5587 0.0007973 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2532 0.05393 0.1618 0.03554 0.5327 0.5405 0.4962 0.5461 0.4703 0.5513 ] Network output: [ 0.1265 0.387 0.7377 0.001746 0.0006603 0.619 0.001586 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2716 0.06986 0.1768 0.03605 0.5332 0.5402 0.4998 0.5474 0.4687 0.5525 ] Network output: [ 0.1079 0.3433 0.7615 0.0009216 -0.0002553 0.6714 -0.001951 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8764 Epoch 46 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1315 0.4235 0.713 -0.0001951 -1.575e-05 0.6169 0.0006961 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1301 0.005929 0.07929 0.005121 0.5158 0.523 0.4742 0.5637 0.4635 0.5264 ] Network output: [ 0.1027 0.4791 0.7274 -0.001146 -0.0001304 0.5867 0.001343 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1905 0.02207 0.1329 0.02918 0.4996 0.5469 0.4793 0.5462 0.4966 0.5503 ] Network output: [ 0.1952 0.4296 0.6474 -0.001309 0.0005535 0.5303 -0.001516 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2114 0.01759 0.1288 0.02183 0.5094 0.5152 0.4709 0.56 0.4607 0.5318 ] Network output: [ 0.1707 0.4884 0.6808 -0.001315 -0.0002115 0.4823 -3.372e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2227 0.02667 0.1456 0.03761 0.4947 0.5421 0.475 0.5429 0.4946 0.5502 ] Network output: [ 0.1457 0.4352 0.7083 0.0008455 0.0004055 0.5607 0.0008216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2601 0.05622 0.1682 0.03666 0.5344 0.5428 0.4986 0.547 0.4713 0.5525 ] Network output: [ 0.1257 0.3861 0.737 0.001805 0.0006264 0.622 0.001655 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2791 0.07286 0.1836 0.0376 0.535 0.5426 0.5024 0.5483 0.4698 0.5538 ] Network output: [ 0.1069 0.3422 0.7608 0.000966 -0.0002722 0.6749 -0.001901 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8745 Epoch 47 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1325 0.4243 0.7142 -0.0002133 -2.09e-05 0.6133 0.0006356 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1326 0.0062 0.08227 0.004908 0.5162 0.5243 0.4747 0.5641 0.4642 0.5267 ] Network output: [ 0.1028 0.4794 0.7276 -0.001172 -0.0001223 0.5861 0.001342 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1951 0.02309 0.1379 0.02951 0.5007 0.5486 0.4807 0.5468 0.4975 0.551 ] Network output: [ 0.1956 0.4296 0.6473 -0.001337 0.0005785 0.5296 -0.001509 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2158 0.01846 0.1338 0.02191 0.5098 0.5164 0.4714 0.5603 0.4613 0.5322 ] Network output: [ 0.1707 0.4885 0.6805 -0.001337 -0.000198 0.4824 -2.036e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2281 0.02796 0.1513 0.0381 0.4955 0.5437 0.4763 0.5434 0.4954 0.5509 ] Network output: [ 0.1451 0.4347 0.7076 0.0008743 0.0003889 0.5629 0.0008469 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2672 0.05865 0.1748 0.03787 0.5362 0.5452 0.5012 0.5479 0.4724 0.5538 ] Network output: [ 0.1249 0.3852 0.7363 0.00187 0.0005882 0.6252 0.001728 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2867 0.07605 0.1907 0.03926 0.5369 0.5451 0.5051 0.5493 0.4709 0.5552 ] Network output: [ 0.1059 0.341 0.7601 0.001017 -0.0002925 0.6787 -0.001847 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8726 Epoch 48 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1336 0.4251 0.7155 -0.0002356 -2.223e-05 0.6095 0.0005682 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1351 0.006484 0.08526 0.004686 0.5167 0.5257 0.4752 0.5645 0.4649 0.5269 ] Network output: [ 0.103 0.4796 0.7278 -0.0012 -0.0001132 0.5854 0.001338 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1997 0.02416 0.1429 0.02988 0.5018 0.5505 0.4823 0.5475 0.4984 0.5518 ] Network output: [ 0.196 0.4297 0.6472 -0.001368 0.0006053 0.5289 -0.001503 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2202 0.01938 0.1389 0.02201 0.5101 0.5178 0.472 0.5607 0.462 0.5326 ] Network output: [ 0.1708 0.4886 0.6801 -0.00136 -0.0001846 0.4826 -7.875e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2335 0.02934 0.1571 0.03863 0.4965 0.5453 0.4776 0.544 0.4962 0.5517 ] Network output: [ 0.1445 0.4342 0.7067 0.0009059 0.0003698 0.5653 0.0008733 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2743 0.06125 0.1814 0.03918 0.5381 0.5477 0.5039 0.5489 0.4736 0.5551 ] Network output: [ 0.124 0.3842 0.7354 0.001941 0.0005456 0.6286 0.001805 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2945 0.07946 0.1978 0.04105 0.5389 0.5477 0.5081 0.5504 0.4721 0.5567 ] Network output: [ 0.1049 0.3397 0.7594 0.001075 -0.0003165 0.6826 -0.001788 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8705 Epoch 49 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1348 0.4261 0.7168 -0.0002626 -1.943e-05 0.6054 0.0004936 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1376 0.006784 0.08826 0.004454 0.5171 0.5272 0.4757 0.5649 0.4656 0.5272 ] Network output: [ 0.1031 0.4799 0.728 -0.001232 -0.0001028 0.5846 0.001328 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2043 0.0253 0.148 0.03028 0.503 0.5525 0.4839 0.5482 0.4994 0.5526 ] Network output: [ 0.1964 0.4298 0.6472 -0.0014 0.0006339 0.528 -0.001498 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2245 0.02037 0.1441 0.02213 0.5105 0.5192 0.4726 0.5611 0.4627 0.5331 ] Network output: [ 0.1708 0.4887 0.6798 -0.001385 -0.0001713 0.4827 3.532e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.239 0.03082 0.163 0.03922 0.4975 0.5471 0.4791 0.5446 0.4971 0.5524 ] Network output: [ 0.1439 0.4336 0.7059 0.0009405 0.0003479 0.5678 0.0009009 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2816 0.06401 0.1881 0.04059 0.5401 0.5504 0.5068 0.5499 0.4748 0.5566 ] Network output: [ 0.1231 0.3832 0.7346 0.002018 0.0004982 0.6323 0.001886 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3024 0.08309 0.205 0.04297 0.541 0.5505 0.5112 0.5515 0.4734 0.5582 ] Network output: [ 0.1038 0.3384 0.7587 0.00114 -0.0003442 0.6868 -0.001724 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8682 Epoch 50 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.136 0.4271 0.7183 -0.0002947 -1.222e-05 0.601 0.0004118 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1401 0.007099 0.09127 0.004212 0.5176 0.5287 0.4763 0.5654 0.4664 0.5275 ] Network output: [ 0.1033 0.4803 0.7283 -0.001268 -9.103e-05 0.5837 0.001314 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2089 0.02651 0.1531 0.03072 0.5042 0.5545 0.4856 0.549 0.5005 0.5534 ] Network output: [ 0.1969 0.43 0.6472 -0.001433 0.0006644 0.5271 -0.001494 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2289 0.02142 0.1493 0.02228 0.5109 0.5207 0.4732 0.5615 0.4634 0.5336 ] Network output: [ 0.1708 0.4887 0.6794 -0.001411 -0.000158 0.4828 1.363e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2445 0.03239 0.1689 0.03987 0.4985 0.549 0.4807 0.5452 0.4981 0.5533 ] Network output: [ 0.1432 0.433 0.7049 0.0009784 0.0003234 0.5705 0.0009297 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.289 0.06695 0.1949 0.0421 0.5421 0.5532 0.5098 0.551 0.4762 0.5581 ] Network output: [ 0.1221 0.382 0.7336 0.002101 0.0004459 0.6362 0.001973 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3104 0.08697 0.2123 0.04503 0.5432 0.5534 0.5144 0.5528 0.4747 0.5599 ] Network output: [ 0.1026 0.3369 0.7578 0.001214 -0.0003761 0.6913 -0.001655 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8658 Epoch 51 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1373 0.4283 0.7198 -0.0003325 -2.866e-07 0.5962 0.0003226 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1425 0.007431 0.09427 0.003959 0.518 0.5303 0.4768 0.5659 0.4672 0.5278 ] Network output: [ 0.1035 0.4806 0.7286 -0.001307 -7.769e-05 0.5827 0.001294 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2135 0.02778 0.1582 0.03121 0.5055 0.5566 0.4874 0.5498 0.5016 0.5544 ] Network output: [ 0.1974 0.4301 0.6472 -0.00147 0.000697 0.526 -0.001492 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2332 0.02254 0.1545 0.02244 0.5113 0.5223 0.4739 0.562 0.4642 0.5341 ] Network output: [ 0.1708 0.4888 0.679 -0.001438 -0.0001448 0.483 2.218e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.25 0.03406 0.1749 0.04058 0.4995 0.551 0.4823 0.5459 0.4991 0.5542 ] Network output: [ 0.1425 0.4324 0.7039 0.00102 0.0002959 0.5734 0.00096 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2964 0.07009 0.2018 0.04372 0.5443 0.5562 0.5131 0.5522 0.4776 0.5597 ] Network output: [ 0.1211 0.3808 0.7326 0.002192 0.0003884 0.6404 0.002064 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3186 0.09111 0.2197 0.04723 0.5455 0.5564 0.5179 0.5541 0.4762 0.5616 ] Network output: [ 0.1014 0.3354 0.757 0.001296 -0.0004123 0.6961 -0.00158 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8633 Epoch 52 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1386 0.4295 0.7215 -0.0003764 1.667e-05 0.5911 0.0002258 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1449 0.00778 0.09725 0.003697 0.5184 0.5319 0.4774 0.5664 0.4681 0.5282 ] Network output: [ 0.1037 0.481 0.7289 -0.00135 -6.262e-05 0.5817 0.001269 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2182 0.02914 0.1634 0.03173 0.5068 0.5589 0.4894 0.5507 0.5028 0.5553 ] Network output: [ 0.198 0.4303 0.6472 -0.001508 0.0007318 0.5248 -0.001492 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2375 0.02374 0.1597 0.02263 0.5117 0.5239 0.4746 0.5626 0.4651 0.5346 ] Network output: [ 0.1709 0.4889 0.6786 -0.001467 -0.0001316 0.4831 2.893e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2556 0.03585 0.1809 0.04135 0.5006 0.553 0.4841 0.5467 0.5003 0.5551 ] Network output: [ 0.1418 0.4317 0.7029 0.001065 0.0002654 0.5765 0.0009918 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.304 0.07343 0.2088 0.04546 0.5465 0.5592 0.5165 0.5535 0.4791 0.5614 ] Network output: [ 0.12 0.3795 0.7315 0.002291 0.0003255 0.6449 0.002162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3269 0.09553 0.2272 0.04959 0.5478 0.5596 0.5216 0.5555 0.4777 0.5635 ] Network output: [ 0.1002 0.3337 0.7561 0.001387 -0.000453 0.7011 -0.0015 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8606 Epoch 53 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1401 0.4309 0.7232 -0.000427 3.896e-05 0.5856 0.0001213 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1472 0.008148 0.1002 0.003424 0.5188 0.5336 0.4779 0.567 0.469 0.5285 ] Network output: [ 0.104 0.4815 0.7292 -0.001398 -4.562e-05 0.5805 0.001237 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2229 0.03057 0.1685 0.0323 0.5081 0.5612 0.4914 0.5516 0.5041 0.5564 ] Network output: [ 0.1986 0.4305 0.6473 -0.00155 0.0007688 0.5234 -0.001494 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2418 0.02502 0.1649 0.02284 0.5121 0.5257 0.4754 0.5631 0.466 0.5352 ] Network output: [ 0.1709 0.489 0.6782 -0.001497 -0.0001184 0.4832 3.363e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2611 0.03776 0.1869 0.04219 0.5018 0.5552 0.4859 0.5475 0.5014 0.5561 ] Network output: [ 0.141 0.4309 0.7017 0.001113 0.0002319 0.5798 0.001026 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3117 0.077 0.2159 0.04732 0.5488 0.5625 0.5201 0.5549 0.4808 0.5632 ] Network output: [ 0.1188 0.3781 0.7304 0.002398 0.000257 0.6497 0.002265 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3353 0.1003 0.2348 0.05212 0.5502 0.563 0.5255 0.557 0.4794 0.5654 ] Network output: [ 0.09884 0.3319 0.7551 0.001488 -0.0004985 0.7064 -0.001413 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8576 Epoch 54 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1416 0.4324 0.7251 -0.0004848 6.686e-05 0.5798 8.772e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1495 0.008533 0.1031 0.00314 0.5192 0.5353 0.4785 0.5676 0.47 0.5289 ] Network output: [ 0.1042 0.482 0.7296 -0.001451 -2.649e-05 0.5791 0.001199 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2276 0.03208 0.1736 0.03291 0.5095 0.5636 0.4936 0.5526 0.5054 0.5575 ] Network output: [ 0.1992 0.4308 0.6474 -0.001594 0.0008082 0.5219 -0.0015 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.246 0.02638 0.1701 0.02308 0.5125 0.5275 0.4762 0.5637 0.467 0.5358 ] Network output: [ 0.171 0.4891 0.6778 -0.001529 -0.0001051 0.4834 3.598e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2668 0.0398 0.1929 0.0431 0.503 0.5574 0.4879 0.5483 0.5027 0.5572 ] Network output: [ 0.1402 0.4301 0.7005 0.001166 0.0001953 0.5834 0.001061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3196 0.0808 0.2229 0.0493 0.5511 0.5658 0.5238 0.5563 0.4825 0.5651 ] Network output: [ 0.1176 0.3766 0.7291 0.002513 0.0001828 0.6549 0.002375 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3439 0.1053 0.2425 0.05481 0.5528 0.5664 0.5295 0.5585 0.4812 0.5674 ] Network output: [ 0.09747 0.3301 0.7541 0.001599 -0.0005489 0.7121 -0.001319 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8545 Epoch 55 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1432 0.434 0.7271 -0.0005505 0.0001007 0.5734 -0.000112 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1517 0.008938 0.106 0.002845 0.5196 0.5371 0.4791 0.5682 0.471 0.5293 ] Network output: [ 0.1045 0.4825 0.73 -0.00151 -5.019e-06 0.5777 0.001154 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2323 0.03368 0.1788 0.03357 0.5109 0.5661 0.4958 0.5537 0.5069 0.5587 ] Network output: [ 0.1999 0.4311 0.6476 -0.001642 0.0008502 0.5202 -0.001508 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2503 0.02783 0.1752 0.02335 0.513 0.5294 0.477 0.5644 0.468 0.5364 ] Network output: [ 0.1711 0.4892 0.6774 -0.001563 -9.173e-05 0.4835 3.57e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2724 0.04198 0.199 0.04408 0.5042 0.5598 0.49 0.5493 0.504 0.5583 ] Network output: [ 0.1393 0.4293 0.6993 0.001222 0.0001555 0.5871 0.001099 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3275 0.08485 0.2301 0.05142 0.5535 0.5693 0.5278 0.5578 0.4843 0.5671 ] Network output: [ 0.1164 0.375 0.7278 0.002637 0.0001025 0.6603 0.002493 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3526 0.1107 0.2502 0.05768 0.5553 0.5701 0.5338 0.5602 0.4831 0.5696 ] Network output: [ 0.09605 0.3281 0.753 0.001721 -0.0006045 0.718 -0.001217 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8513 Epoch 56 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1449 0.4357 0.7292 -0.0006243 0.0001406 0.5667 -0.0002413 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1539 0.009362 0.1089 0.002539 0.5199 0.5389 0.4796 0.5688 0.472 0.5297 ] Network output: [ 0.1048 0.483 0.7304 -0.001574 1.901e-05 0.5761 0.001102 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.237 0.03537 0.1839 0.03427 0.5123 0.5687 0.4981 0.5548 0.5084 0.5599 ] Network output: [ 0.2006 0.4314 0.6478 -0.001694 0.0008948 0.5183 -0.00152 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2544 0.02937 0.1803 0.02365 0.5134 0.5313 0.4779 0.5651 0.4691 0.5371 ] Network output: [ 0.1711 0.4893 0.677 -0.0016 -7.821e-05 0.4835 3.247e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2781 0.04429 0.205 0.04514 0.5055 0.5623 0.4922 0.5502 0.5055 0.5595 ] Network output: [ 0.1384 0.4283 0.6979 0.001283 0.0001126 0.5912 0.00114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3355 0.08917 0.2372 0.05368 0.556 0.5729 0.5319 0.5594 0.4862 0.5691 ] Network output: [ 0.115 0.3733 0.7264 0.00277 1.618e-05 0.6662 0.002619 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3615 0.1164 0.2579 0.06074 0.558 0.5738 0.5382 0.562 0.4851 0.5719 ] Network output: [ 0.09456 0.326 0.7519 0.001854 -0.0006653 0.7244 -0.001107 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8478 Epoch 57 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1466 0.4376 0.7315 -0.0007069 0.000187 0.5594 -0.0003795 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.156 0.009805 0.1116 0.002221 0.5202 0.5407 0.4802 0.5694 0.4732 0.5301 ] Network output: [ 0.1052 0.4837 0.7309 -0.001644 4.584e-05 0.5743 0.001042 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2417 0.03715 0.1889 0.03502 0.5138 0.5714 0.5006 0.556 0.51 0.5612 ] Network output: [ 0.2014 0.4318 0.648 -0.001749 0.0009423 0.5162 -0.001535 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2585 0.031 0.1854 0.02398 0.5138 0.5334 0.4788 0.5658 0.4703 0.5378 ] Network output: [ 0.1712 0.4894 0.6765 -0.001638 -6.446e-05 0.4836 2.597e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2838 0.04676 0.211 0.04627 0.5067 0.5648 0.4945 0.5513 0.507 0.5608 ] Network output: [ 0.1375 0.4273 0.6965 0.001348 6.64e-05 0.5954 0.001183 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3437 0.09375 0.2444 0.05607 0.5586 0.5766 0.5362 0.5611 0.4883 0.5713 ] Network output: [ 0.1136 0.3715 0.7249 0.002912 -7.637e-05 0.6724 0.002753 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3705 0.1225 0.2657 0.06399 0.5607 0.5778 0.5429 0.5638 0.4872 0.5743 ] Network output: [ 0.09301 0.3238 0.7507 0.001998 -0.0007314 0.731 -0.0009882 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8441 Epoch 58 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1485 0.4396 0.7338 -0.0007986 0.0002399 0.5517 -0.0005268 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.158 0.01027 0.1143 0.001891 0.5205 0.5426 0.4807 0.5701 0.4743 0.5305 ] Network output: [ 0.1055 0.4843 0.7314 -0.001722 7.567e-05 0.5723 0.0009734 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2464 0.03902 0.1939 0.03581 0.5153 0.5742 0.5031 0.5572 0.5116 0.5625 ] Network output: [ 0.2022 0.4323 0.6483 -0.001809 0.0009926 0.5139 -0.001556 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2626 0.03274 0.1904 0.02435 0.5142 0.5355 0.4798 0.5666 0.4716 0.5385 ] Network output: [ 0.1713 0.4895 0.6761 -0.001679 -5.043e-05 0.4836 1.585e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2895 0.04938 0.217 0.04749 0.5081 0.5675 0.4969 0.5524 0.5086 0.5621 ] Network output: [ 0.1365 0.4262 0.6949 0.001417 1.708e-05 0.6 0.00123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3519 0.09862 0.2516 0.05862 0.5612 0.5805 0.5406 0.5629 0.4904 0.5736 ] Network output: [ 0.1121 0.3695 0.7233 0.003063 -0.0001752 0.679 0.002897 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3796 0.129 0.2735 0.06744 0.5635 0.5818 0.5478 0.5657 0.4894 0.5768 ] Network output: [ 0.0914 0.3214 0.7495 0.002154 -0.000803 0.7381 -0.0008603 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8402 Epoch 59 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1504 0.4418 0.7364 -0.0008996 0.0002996 0.5434 -0.0006834 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1599 0.01075 0.1169 0.001549 0.5207 0.5445 0.4812 0.5708 0.4755 0.531 ] Network output: [ 0.106 0.4851 0.7319 -0.001806 0.0001087 0.5702 0.000896 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2511 0.04099 0.1988 0.03665 0.5168 0.5771 0.5058 0.5585 0.5134 0.564 ] Network output: [ 0.2031 0.4328 0.6487 -0.001873 0.001046 0.5114 -0.001581 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2666 0.03457 0.1952 0.02474 0.5146 0.5377 0.4807 0.5674 0.4729 0.5392 ] Network output: [ 0.1714 0.4897 0.6756 -0.001722 -3.602e-05 0.4836 1.762e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2953 0.05217 0.2229 0.04879 0.5094 0.5702 0.4994 0.5535 0.5102 0.5635 ] Network output: [ 0.1354 0.4251 0.6933 0.001489 -3.532e-05 0.6048 0.00128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3603 0.1038 0.2588 0.06131 0.5638 0.5845 0.5453 0.5647 0.4927 0.576 ] Network output: [ 0.1106 0.3675 0.7216 0.003223 -0.0002802 0.686 0.00305 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3889 0.1358 0.2814 0.07109 0.5664 0.586 0.5528 0.5678 0.4917 0.5794 ] Network output: [ 0.08973 0.319 0.7482 0.002322 -0.0008799 0.7455 -0.0007225 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8361 Epoch 60 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1525 0.4442 0.7391 -0.00101 0.0003662 0.5345 -0.0008498 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1618 0.01125 0.1194 0.001194 0.5209 0.5464 0.4817 0.5715 0.4768 0.5314 ] Network output: [ 0.1064 0.4859 0.7325 -0.001898 0.0001452 0.5678 0.000809 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2558 0.04306 0.2036 0.03754 0.5183 0.58 0.5085 0.5598 0.5152 0.5655 ] Network output: [ 0.2041 0.4333 0.6491 -0.001943 0.001102 0.5086 -0.001612 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2706 0.0365 0.2 0.02517 0.5151 0.54 0.4818 0.5683 0.4743 0.54 ] Network output: [ 0.1715 0.4898 0.6752 -0.001768 -2.114e-05 0.4836 -1.668e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.301 0.05512 0.2287 0.05017 0.5108 0.573 0.5021 0.5548 0.512 0.565 ] Network output: [ 0.1343 0.4239 0.6916 0.001565 -9.066e-05 0.6099 0.001333 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3687 0.1092 0.266 0.06415 0.5665 0.5885 0.5501 0.5667 0.495 0.5785 ] Network output: [ 0.1089 0.3652 0.7197 0.003391 -0.0003914 0.6935 0.003214 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3982 0.1431 0.2892 0.07495 0.5693 0.5903 0.558 0.5699 0.4942 0.5821 ] Network output: [ 0.08799 0.3164 0.7468 0.002501 -0.0009621 0.7532 -0.0005745 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8317 Epoch 61 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1547 0.4467 0.742 -0.00113 0.0004396 0.5251 -0.001026 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1635 0.01176 0.1218 0.0008252 0.521 0.5484 0.4822 0.5722 0.478 0.5319 ] Network output: [ 0.1069 0.4868 0.7332 -0.001998 0.0001854 0.5652 0.0007121 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2604 0.04522 0.2084 0.03848 0.5198 0.583 0.5113 0.5612 0.5171 0.567 ] Network output: [ 0.2051 0.434 0.6497 -0.002017 0.001162 0.5055 -0.001649 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2745 0.03854 0.2047 0.02563 0.5154 0.5424 0.4828 0.5692 0.4757 0.5408 ] Network output: [ 0.1716 0.49 0.6747 -0.001817 -5.693e-06 0.4836 -3.985e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3068 0.05824 0.2345 0.05163 0.5122 0.5759 0.5048 0.556 0.5138 0.5665 ] Network output: [ 0.1331 0.4226 0.6898 0.001644 -0.0001487 0.6153 0.00139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3771 0.115 0.2731 0.06715 0.5692 0.5927 0.555 0.5687 0.4975 0.5811 ] Network output: [ 0.1072 0.3629 0.7178 0.003568 -0.0005086 0.7014 0.003387 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4076 0.1508 0.2971 0.07903 0.5722 0.5948 0.5634 0.5721 0.4968 0.585 ] Network output: [ 0.08618 0.3136 0.7453 0.002691 -0.001049 0.7614 -0.0004162 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8272 Epoch 62 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.157 0.4494 0.745 -0.00126 0.0005197 0.5151 -0.001212 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1652 0.01229 0.124 0.0004422 0.5211 0.5503 0.4826 0.573 0.4794 0.5323 ] Network output: [ 0.1075 0.4877 0.734 -0.002107 0.0002293 0.5623 0.0006048 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.265 0.04748 0.213 0.03945 0.5213 0.586 0.5142 0.5626 0.5191 0.5686 ] Network output: [ 0.2061 0.4347 0.6502 -0.002096 0.001225 0.5022 -0.001692 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2782 0.04067 0.2092 0.02612 0.5158 0.5448 0.4838 0.5702 0.4773 0.5417 ] Network output: [ 0.1718 0.4902 0.6743 -0.001869 1.042e-05 0.4835 -6.814e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3125 0.06153 0.2401 0.05318 0.5135 0.5789 0.5076 0.5574 0.5158 0.5681 ] Network output: [ 0.1319 0.4212 0.6879 0.001726 -0.0002093 0.621 0.00145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3856 0.1211 0.2802 0.0703 0.572 0.597 0.5601 0.5708 0.5001 0.5839 ] Network output: [ 0.1054 0.3604 0.7158 0.003751 -0.0006314 0.7097 0.003572 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4172 0.1589 0.3049 0.08332 0.5752 0.5994 0.5689 0.5744 0.4995 0.5879 ] Network output: [ 0.08431 0.3107 0.7438 0.002891 -0.001141 0.7699 -0.0002474 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8225 Epoch 63 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1594 0.4523 0.7482 -0.001398 0.0006065 0.5044 -0.001408 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1667 0.01282 0.1262 4.422e-05 0.5212 0.5523 0.483 0.5737 0.4807 0.5328 ] Network output: [ 0.1081 0.4888 0.7348 -0.002223 0.0002772 0.5592 0.0004866 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2695 0.04982 0.2175 0.04047 0.5228 0.5891 0.5171 0.5641 0.5212 0.5703 ] Network output: [ 0.2073 0.4356 0.6509 -0.00218 0.001291 0.4985 -0.001742 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2819 0.0429 0.2136 0.02665 0.5162 0.5473 0.4849 0.5712 0.4789 0.5426 ] Network output: [ 0.1719 0.4904 0.6738 -0.001925 2.73e-05 0.4833 -0.0001019 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3182 0.06498 0.2456 0.05482 0.5149 0.582 0.5105 0.5588 0.5178 0.5698 ] Network output: [ 0.1307 0.4197 0.6859 0.001809 -0.0002721 0.627 0.001514 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3942 0.1275 0.2872 0.0736 0.5747 0.6014 0.5652 0.5729 0.5028 0.5866 ] Network output: [ 0.1035 0.3578 0.7136 0.00394 -0.0007595 0.7185 0.003766 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4267 0.1674 0.3127 0.08782 0.5782 0.604 0.5745 0.5768 0.5023 0.591 ] Network output: [ 0.08238 0.3077 0.7423 0.003101 -0.001238 0.7788 -6.841e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8175 Epoch 64 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1619 0.4554 0.7516 -0.001545 0.0006995 0.4931 -0.001614 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1681 0.01337 0.1281 -0.0003695 0.5211 0.5542 0.4833 0.5745 0.4821 0.5333 ] Network output: [ 0.1087 0.4899 0.7356 -0.002348 0.000329 0.5558 0.0003574 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.274 0.05225 0.2218 0.04153 0.5243 0.5923 0.5201 0.5656 0.5233 0.572 ] Network output: [ 0.2084 0.4365 0.6517 -0.002269 0.00136 0.4945 -0.0018 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2855 0.04521 0.2178 0.0272 0.5165 0.5498 0.4859 0.5722 0.4805 0.5435 ] Network output: [ 0.1721 0.4906 0.6733 -0.001984 4.506e-05 0.4831 -0.0001415 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3239 0.0686 0.251 0.05653 0.5163 0.5851 0.5135 0.5602 0.5199 0.5715 ] Network output: [ 0.1294 0.4182 0.6838 0.001893 -0.0003367 0.6332 0.001581 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4027 0.1341 0.2942 0.07704 0.5775 0.6059 0.5705 0.5752 0.5055 0.5895 ] Network output: [ 0.1016 0.3551 0.7113 0.004133 -0.0008923 0.7277 0.00397 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4363 0.1763 0.3204 0.09253 0.5813 0.6088 0.5803 0.5792 0.5053 0.5941 ] Network output: [ 0.08039 0.3046 0.7406 0.003319 -0.001338 0.7881 0.0001203 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8124 Epoch 65 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1645 0.4587 0.7551 -0.001699 0.0007984 0.4812 -0.001829 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1694 0.01392 0.13 -0.0007997 0.521 0.5562 0.4835 0.5753 0.4836 0.5337 ] Network output: [ 0.1094 0.4911 0.7366 -0.00248 0.0003849 0.5521 0.0002169 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2783 0.05475 0.226 0.04262 0.5258 0.5955 0.5232 0.5671 0.5255 0.5738 ] Network output: [ 0.2097 0.4375 0.6526 -0.002364 0.001433 0.4902 -0.001864 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2889 0.04761 0.2219 0.02777 0.5168 0.5524 0.4869 0.5733 0.4823 0.5444 ] Network output: [ 0.1723 0.4908 0.6729 -0.002046 6.379e-05 0.4828 -0.0001873 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3295 0.07238 0.2563 0.05832 0.5177 0.5882 0.5166 0.5617 0.522 0.5733 ] Network output: [ 0.128 0.4166 0.6817 0.001978 -0.0004026 0.6397 0.001651 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4112 0.1411 0.3011 0.08063 0.5803 0.6104 0.5758 0.5775 0.5084 0.5925 ] Network output: [ 0.09953 0.3522 0.7089 0.004329 -0.001029 0.7373 0.004182 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4458 0.1856 0.3281 0.09744 0.5843 0.6136 0.5862 0.5817 0.5083 0.5974 ] Network output: [ 0.07835 0.3014 0.7389 0.003543 -0.001441 0.7977 0.0003178 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8071 Epoch 66 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1672 0.4622 0.7589 -0.001859 0.0009027 0.4686 -0.002052 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1705 0.01447 0.1316 -0.001247 0.5209 0.5581 0.4836 0.576 0.485 0.5342 ] Network output: [ 0.1102 0.4924 0.7376 -0.002621 0.0004446 0.548 6.523e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2826 0.05731 0.23 0.04374 0.5272 0.5987 0.5262 0.5687 0.5278 0.5756 ] Network output: [ 0.211 0.4386 0.6535 -0.002462 0.001508 0.4855 -0.001937 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2922 0.05007 0.2257 0.02836 0.5171 0.555 0.4879 0.5744 0.484 0.5453 ] Network output: [ 0.1724 0.4911 0.6724 -0.002111 8.357e-05 0.4825 -0.0002395 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.335 0.07631 0.2614 0.06019 0.5191 0.5915 0.5197 0.5633 0.5243 0.5752 ] Network output: [ 0.1266 0.4149 0.6794 0.002061 -0.0004693 0.6465 0.001723 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4196 0.1483 0.3078 0.08435 0.583 0.6149 0.5812 0.5798 0.5114 0.5955 ] Network output: [ 0.09741 0.3492 0.7064 0.004525 -0.001169 0.7474 0.004403 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4553 0.1952 0.3358 0.1025 0.5873 0.6185 0.5921 0.5843 0.5115 0.6007 ] Network output: [ 0.07625 0.298 0.7372 0.003772 -0.001547 0.8076 0.000523 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.8017 Epoch 67 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1701 0.4659 0.7628 -0.002024 0.001011 0.4554 -0.002283 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1715 0.01502 0.1331 -0.001713 0.5206 0.56 0.4837 0.5768 0.4865 0.5346 ] Network output: [ 0.111 0.4938 0.7387 -0.002768 0.0005081 0.5436 -9.749e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2867 0.05993 0.2338 0.04488 0.5286 0.6019 0.5293 0.5703 0.5301 0.5775 ] Network output: [ 0.2124 0.4398 0.6546 -0.002565 0.001586 0.4805 -0.002016 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2953 0.0526 0.2293 0.02898 0.5173 0.5577 0.4888 0.5756 0.4859 0.5463 ] Network output: [ 0.1727 0.4914 0.672 -0.002179 0.0001045 0.4821 -0.0002983 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3404 0.08037 0.2663 0.06212 0.5205 0.5947 0.5228 0.5649 0.5266 0.5771 ] Network output: [ 0.1252 0.4131 0.6771 0.002141 -0.0005361 0.6535 0.001796 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.428 0.1557 0.3144 0.0882 0.5858 0.6195 0.5867 0.5822 0.5144 0.5986 ] Network output: [ 0.09522 0.346 0.7038 0.00472 -0.001312 0.7578 0.00463 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4648 0.2051 0.3433 0.1078 0.5903 0.6234 0.598 0.5869 0.5147 0.6041 ] Network output: [ 0.0741 0.2945 0.7354 0.004003 -0.001654 0.8178 0.0007344 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.7961 Epoch 68 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.173 0.4698 0.7669 -0.002191 0.001124 0.4415 -0.002519 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1724 0.01556 0.1344 -0.002197 0.5203 0.5619 0.4836 0.5776 0.488 0.535 ] Network output: [ 0.1119 0.4953 0.7399 -0.002922 0.000575 0.5389 -0.0002708 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2907 0.0626 0.2374 0.04605 0.53 0.6051 0.5323 0.572 0.5325 0.5794 ] Network output: [ 0.2139 0.4411 0.6558 -0.002671 0.001667 0.4751 -0.002102 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2983 0.05516 0.2327 0.0296 0.5175 0.5603 0.4897 0.5767 0.4878 0.5472 ] Network output: [ 0.1729 0.4917 0.6716 -0.002251 0.0001265 0.4816 -0.0003638 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3458 0.08455 0.271 0.06411 0.5218 0.598 0.526 0.5665 0.529 0.579 ] Network output: [ 0.1237 0.4113 0.6746 0.002218 -0.0006024 0.6608 0.00187 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4362 0.1634 0.3209 0.09215 0.5885 0.6241 0.5921 0.5847 0.5175 0.6018 ] Network output: [ 0.09297 0.3427 0.7011 0.004911 -0.001455 0.7687 0.004861 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4741 0.2153 0.3507 0.1133 0.5933 0.6284 0.604 0.5896 0.5181 0.6076 ] Network output: [ 0.07192 0.2909 0.7335 0.004235 -0.001761 0.8283 0.00095 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.7904 Epoch 69 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1761 0.4739 0.7711 -0.002358 0.001239 0.427 -0.002761 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1731 0.01609 0.1355 -0.0027 0.5199 0.5637 0.4834 0.5783 0.4895 0.5354 ] Network output: [ 0.1129 0.4969 0.7412 -0.003081 0.000645 0.5339 -0.0004539 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2945 0.06528 0.2408 0.04723 0.5313 0.6082 0.5353 0.5736 0.5349 0.5813 ] Network output: [ 0.2154 0.4426 0.6571 -0.002778 0.00175 0.4693 -0.002195 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.301 0.05776 0.2359 0.03023 0.5177 0.563 0.4905 0.5779 0.4897 0.5482 ] Network output: [ 0.1731 0.492 0.6712 -0.002326 0.0001497 0.4811 -0.0004359 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.351 0.08884 0.2755 0.06616 0.5232 0.6012 0.5292 0.5681 0.5314 0.581 ] Network output: [ 0.1222 0.4094 0.6722 0.002289 -0.0006676 0.6683 0.001944 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4443 0.1712 0.3272 0.0962 0.5912 0.6286 0.5975 0.5871 0.5207 0.605 ] Network output: [ 0.09066 0.3394 0.6983 0.005094 -0.001599 0.7798 0.005095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4833 0.2257 0.3579 0.1189 0.5962 0.6333 0.6099 0.5923 0.5215 0.6111 ] Network output: [ 0.0697 0.2873 0.7316 0.004463 -0.001868 0.839 0.001168 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.7846 Epoch 70 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1793 0.4781 0.7756 -0.002524 0.001355 0.412 -0.003004 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1736 0.0166 0.1364 -0.003222 0.5194 0.5655 0.4831 0.5791 0.491 0.5358 ] Network output: [ 0.1139 0.4986 0.7426 -0.003244 0.0007175 0.5285 -0.0006458 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2982 0.06798 0.2439 0.04841 0.5325 0.6114 0.5383 0.5753 0.5373 0.5832 ] Network output: [ 0.217 0.4441 0.6585 -0.002887 0.001834 0.4631 -0.002293 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.3036 0.06036 0.2388 0.03085 0.5178 0.5656 0.4912 0.5791 0.4917 0.5491 ] Network output: [ 0.1734 0.4923 0.6708 -0.002402 0.000174 0.4804 -0.0005145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.356 0.0932 0.2798 0.06825 0.5245 0.6045 0.5324 0.5698 0.5339 0.583 ] Network output: [ 0.1207 0.4074 0.6696 0.002354 -0.0007307 0.6759 0.002016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4522 0.1791 0.3334 0.1003 0.5938 0.6332 0.6028 0.5896 0.5239 0.6082 ] Network output: [ 0.0883 0.3359 0.6954 0.005268 -0.001741 0.7912 0.00533 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4922 0.2363 0.3651 0.1246 0.5991 0.6382 0.6158 0.5951 0.525 0.6146 ] Network output: [ 0.06745 0.2836 0.7296 0.004687 -0.001971 0.8499 0.001385 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.7788 Epoch 71 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1825 0.4826 0.7801 -0.002684 0.001472 0.3964 -0.003247 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.174 0.01709 0.1371 -0.003764 0.5189 0.5672 0.4826 0.5798 0.4925 0.5361 ] Network output: [ 0.115 0.5004 0.744 -0.003409 0.0007919 0.5228 -0.0008452 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3017 0.07068 0.2469 0.0496 0.5337 0.6145 0.5412 0.5769 0.5398 0.5851 ] Network output: [ 0.2187 0.4458 0.6601 -0.002996 0.001919 0.4566 -0.002396 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.3059 0.06296 0.2414 0.03147 0.5178 0.5683 0.4919 0.5803 0.4937 0.55 ] Network output: [ 0.1736 0.4927 0.6704 -0.002481 0.0001992 0.4798 -0.0005992 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3609 0.09762 0.2839 0.07038 0.5257 0.6078 0.5355 0.5715 0.5364 0.5851 ] Network output: [ 0.1191 0.4054 0.667 0.002411 -0.0007912 0.6837 0.002086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4599 0.1872 0.3393 0.1045 0.5963 0.6377 0.6081 0.5921 0.5272 0.6114 ] Network output: [ 0.0859 0.3323 0.6924 0.00543 -0.001881 0.8029 0.005562 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.501 0.2469 0.372 0.1304 0.6019 0.6431 0.6216 0.5978 0.5285 0.6182 ] Network output: [ 0.06519 0.2798 0.7277 0.004902 -0.002072 0.8608 0.001599 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.773 Epoch 72 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1859 0.4872 0.7848 -0.002836 0.001587 0.3803 -0.003488 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1741 0.01755 0.1375 -0.004324 0.5183 0.5689 0.482 0.5806 0.494 0.5364 ] Network output: [ 0.1162 0.5022 0.7456 -0.003574 0.0008674 0.5168 -0.001051 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3049 0.07336 0.2496 0.05079 0.5348 0.6175 0.5441 0.5786 0.5422 0.5871 ] Network output: [ 0.2204 0.4475 0.6618 -0.003103 0.002005 0.4498 -0.002502 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.308 0.06554 0.2438 0.03208 0.5178 0.5709 0.4924 0.5815 0.4957 0.5509 ] Network output: [ 0.1739 0.4931 0.67 -0.002561 0.0002253 0.479 -0.0006896 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3656 0.1021 0.2877 0.07253 0.5269 0.611 0.5387 0.5732 0.5389 0.5871 ] Network output: [ 0.1176 0.4034 0.6644 0.002458 -0.0008482 0.6915 0.002152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4673 0.1952 0.345 0.1088 0.5988 0.6421 0.6132 0.5946 0.5305 0.6147 ] Network output: [ 0.08347 0.3287 0.6894 0.005577 -0.002016 0.8147 0.005789 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5094 0.2576 0.3787 0.1362 0.6047 0.6479 0.6272 0.6005 0.532 0.6217 ] Network output: [ 0.06293 0.2761 0.7257 0.005106 -0.002167 0.8719 0.001807 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.7672 Epoch 73 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1893 0.4919 0.7897 -0.002978 0.0017 0.3639 -0.003723 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1742 0.01798 0.1378 -0.004903 0.5176 0.5704 0.4813 0.5813 0.4955 0.5366 ] Network output: [ 0.1174 0.5042 0.7472 -0.003738 0.0009431 0.5105 -0.00126 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.308 0.076 0.252 0.05197 0.5359 0.6205 0.5469 0.5802 0.5447 0.589 ] Network output: [ 0.2221 0.4494 0.6636 -0.003207 0.00209 0.4426 -0.00261 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.3099 0.06807 0.2459 0.03267 0.5178 0.5734 0.4928 0.5827 0.4977 0.5518 ] Network output: [ 0.1742 0.4935 0.6697 -0.002642 0.000252 0.4782 -0.0007851 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3701 0.1065 0.2913 0.0747 0.5281 0.6142 0.5417 0.5749 0.5415 0.5892 ] Network output: [ 0.116 0.4013 0.6617 0.002496 -0.000901 0.6994 0.002214 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4745 0.2033 0.3505 0.113 0.6012 0.6465 0.6182 0.5971 0.5338 0.6179 ] Network output: [ 0.08103 0.325 0.6863 0.005707 -0.002146 0.8267 0.006009 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5176 0.2683 0.3852 0.1421 0.6073 0.6527 0.6327 0.6032 0.5356 0.6252 ] Network output: [ 0.06067 0.2723 0.7237 0.005298 -0.002256 0.8829 0.002007 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.7615 Epoch 74 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1927 0.4968 0.7946 -0.003106 0.001808 0.3471 -0.00395 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.174 0.01839 0.1379 -0.005499 0.5169 0.5719 0.4804 0.582 0.4969 0.5368 ] Network output: [ 0.1186 0.5062 0.7489 -0.003899 0.001018 0.504 -0.001472 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3109 0.0786 0.2542 0.05313 0.5369 0.6234 0.5495 0.5818 0.5471 0.5909 ] Network output: [ 0.2239 0.4514 0.6655 -0.003306 0.002174 0.4351 -0.002717 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.3115 0.07054 0.2477 0.03323 0.5177 0.5759 0.493 0.5839 0.4997 0.5526 ] Network output: [ 0.1744 0.4939 0.6694 -0.002722 0.0002791 0.4774 -0.0008847 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3745 0.111 0.2946 0.07688 0.5292 0.6173 0.5448 0.5766 0.544 0.5913 ] Network output: [ 0.1144 0.3992 0.6591 0.002522 -0.0009491 0.7074 0.00227 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4814 0.2113 0.3557 0.1173 0.6035 0.6507 0.623 0.5995 0.537 0.621 ] Network output: [ 0.07857 0.3213 0.6831 0.005818 -0.002268 0.8387 0.00622 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5255 0.2788 0.3915 0.148 0.6099 0.6573 0.638 0.6059 0.5391 0.6287 ] Network output: [ 0.05843 0.2685 0.7216 0.005474 -0.002337 0.8939 0.002196 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.7558 Epoch 75 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1962 0.5018 0.7996 -0.003218 0.00191 0.33 -0.004165 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1738 0.01875 0.1379 -0.006111 0.516 0.5734 0.4794 0.5826 0.4983 0.537 ] Network output: [ 0.1199 0.5082 0.7506 -0.004055 0.001091 0.4972 -0.001683 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3136 0.08113 0.2562 0.05428 0.5378 0.6262 0.5521 0.5834 0.5496 0.5928 ] Network output: [ 0.2258 0.4534 0.6675 -0.003399 0.002255 0.4274 -0.002822 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.3129 0.07294 0.2493 0.03376 0.5176 0.5784 0.4931 0.5851 0.5017 0.5534 ] Network output: [ 0.1747 0.4943 0.6691 -0.002802 0.0003063 0.4765 -0.0009876 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3786 0.1154 0.2977 0.07905 0.5303 0.6204 0.5477 0.5784 0.5466 0.5933 ] Network output: [ 0.1129 0.3971 0.6564 0.002537 -0.0009919 0.7153 0.00232 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4879 0.2192 0.3607 0.1215 0.6057 0.6549 0.6277 0.602 0.5402 0.6242 ] Network output: [ 0.07613 0.3176 0.68 0.005909 -0.002383 0.8507 0.006418 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.533 0.2892 0.3976 0.1539 0.6124 0.6618 0.6432 0.6086 0.5426 0.6321 ] Network output: [ 0.05622 0.2648 0.7196 0.005634 -0.002409 0.9048 0.002371 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.7503 Epoch 76 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1997 0.5068 0.8046 -0.003312 0.002006 0.3128 -0.004366 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1733 0.01909 0.1376 -0.006738 0.5152 0.5747 0.4782 0.5832 0.4997 0.5371 ] Network output: [ 0.1213 0.5103 0.7524 -0.004203 0.001162 0.4903 -0.001893 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.316 0.0836 0.2579 0.05541 0.5386 0.6289 0.5546 0.585 0.552 0.5946 ] Network output: [ 0.2276 0.4555 0.6696 -0.003483 0.002334 0.4194 -0.002924 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.314 0.07524 0.2506 0.03425 0.5174 0.5807 0.493 0.5863 0.5036 0.5541 ] Network output: [ 0.175 0.4947 0.6688 -0.00288 0.0003331 0.4757 -0.001093 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3825 0.1198 0.3006 0.08121 0.5313 0.6234 0.5506 0.58 0.5491 0.5953 ] Network output: [ 0.1113 0.395 0.6537 0.00254 -0.001029 0.7232 0.002363 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4941 0.2269 0.3655 0.1257 0.6079 0.6589 0.6321 0.6043 0.5434 0.6272 ] Network output: [ 0.07371 0.3138 0.6769 0.005978 -0.002489 0.8626 0.006602 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5401 0.2993 0.4034 0.1597 0.6147 0.6662 0.648 0.6112 0.5461 0.6354 ] Network output: [ 0.05405 0.2611 0.7177 0.005775 -0.002471 0.9155 0.00253 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.745 Epoch 77 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2032 0.5119 0.8097 -0.003386 0.002092 0.2954 -0.00455 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1728 0.01938 0.1372 -0.007377 0.5143 0.576 0.4769 0.5839 0.501 0.5371 ] Network output: [ 0.1226 0.5125 0.7543 -0.004343 0.001229 0.4833 -0.002098 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3183 0.08598 0.2594 0.05651 0.5394 0.6316 0.557 0.5865 0.5543 0.5964 ] Network output: [ 0.2295 0.4577 0.6717 -0.003558 0.002408 0.4112 -0.003019 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.3149 0.07745 0.2517 0.03471 0.5172 0.583 0.4928 0.5874 0.5056 0.5548 ] Network output: [ 0.1753 0.4951 0.6685 -0.002956 0.0003593 0.4748 -0.001199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3862 0.124 0.3032 0.08335 0.5323 0.6263 0.5534 0.5817 0.5516 0.5974 ] Network output: [ 0.1098 0.3928 0.6511 0.002531 -0.00106 0.731 0.002399 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5 0.2345 0.37 0.1298 0.6099 0.6627 0.6363 0.6067 0.5465 0.6302 ] Network output: [ 0.07132 0.3101 0.6737 0.006025 -0.002584 0.8744 0.006771 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5468 0.3091 0.4089 0.1654 0.6169 0.6704 0.6527 0.6137 0.5495 0.6387 ] Network output: [ 0.05194 0.2575 0.7157 0.005899 -0.002522 0.9259 0.002672 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.7398 Epoch 78 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2067 0.5171 0.8148 -0.003437 0.002169 0.2781 -0.004715 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1721 0.01965 0.1366 -0.008027 0.5134 0.5772 0.4754 0.5844 0.5023 0.5371 ] Network output: [ 0.124 0.5146 0.7561 -0.004472 0.001291 0.4762 -0.002296 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3203 0.08828 0.2607 0.05759 0.5401 0.6341 0.5592 0.588 0.5566 0.5982 ] Network output: [ 0.2314 0.46 0.674 -0.003623 0.002478 0.4029 -0.003107 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.3155 0.07954 0.2526 0.03513 0.5169 0.5852 0.4925 0.5885 0.5075 0.5554 ] Network output: [ 0.1756 0.4954 0.6682 -0.003028 0.0003844 0.474 -0.001306 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3897 0.1282 0.3056 0.08546 0.5332 0.6291 0.556 0.5834 0.5541 0.5993 ] Network output: [ 0.1083 0.3907 0.6485 0.002511 -0.001085 0.7387 0.002426 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5055 0.2418 0.3742 0.1338 0.6118 0.6664 0.6403 0.6089 0.5496 0.6331 ] Network output: [ 0.06898 0.3065 0.6707 0.006051 -0.002669 0.886 0.006922 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5532 0.3186 0.4141 0.171 0.619 0.6745 0.6571 0.6161 0.5528 0.6418 ] Network output: [ 0.04988 0.254 0.7138 0.006003 -0.002562 0.9361 0.002794 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.7349 Epoch 79 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2101 0.5222 0.8199 -0.003466 0.002236 0.2609 -0.004858 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1713 0.01987 0.1359 -0.008684 0.5124 0.5783 0.4738 0.585 0.5036 0.5371 ] Network output: [ 0.1254 0.5167 0.758 -0.004591 0.001348 0.4691 -0.002486 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3222 0.09048 0.2618 0.05865 0.5408 0.6365 0.5614 0.5895 0.5589 0.5999 ] Network output: [ 0.2333 0.4623 0.6763 -0.003676 0.002543 0.3945 -0.003184 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.3159 0.08152 0.2532 0.0355 0.5166 0.5873 0.4919 0.5896 0.5093 0.5559 ] Network output: [ 0.1758 0.4958 0.6679 -0.003097 0.000408 0.4732 -0.001412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.393 0.1323 0.3078 0.08753 0.5341 0.6319 0.5586 0.585 0.5565 0.6012 ] Network output: [ 0.1069 0.3886 0.646 0.002479 -0.001103 0.7463 0.002446 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5107 0.2488 0.3782 0.1377 0.6136 0.67 0.644 0.6111 0.5525 0.6359 ] Network output: [ 0.06671 0.3029 0.6676 0.006055 -0.002742 0.8973 0.007056 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5591 0.3277 0.4191 0.1764 0.621 0.6783 0.6611 0.6185 0.556 0.6449 ] Network output: [ 0.04791 0.2507 0.7119 0.006089 -0.00259 0.9459 0.002897 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.7302 Epoch 80 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2135 0.5273 0.8249 -0.003472 0.002291 0.2439 -0.004977 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1704 0.02007 0.1351 -0.009348 0.5114 0.5793 0.4721 0.5855 0.5047 0.537 ] Network output: [ 0.1267 0.5189 0.7598 -0.004697 0.001398 0.4621 -0.002666 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3239 0.09259 0.2628 0.05968 0.5414 0.6388 0.5634 0.5909 0.561 0.6016 ] Network output: [ 0.2352 0.4646 0.6787 -0.003717 0.002601 0.386 -0.003251 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.3162 0.08338 0.2537 0.03583 0.5163 0.5894 0.4912 0.5907 0.511 0.5564 ] Network output: [ 0.1761 0.4961 0.6677 -0.003163 0.0004298 0.4725 -0.001515 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3961 0.1362 0.3099 0.08957 0.5349 0.6345 0.5611 0.5866 0.5589 0.6031 ] Network output: [ 0.1055 0.3865 0.6435 0.002436 -0.001116 0.7536 0.002458 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5155 0.2556 0.3819 0.1415 0.6153 0.6734 0.6475 0.6132 0.5553 0.6385 ] Network output: [ 0.0645 0.2993 0.6647 0.006038 -0.002803 0.9084 0.00717 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5646 0.3365 0.4238 0.1816 0.6229 0.682 0.665 0.6208 0.559 0.6478 ] Network output: [ 0.04602 0.2474 0.7101 0.006157 -0.002605 0.9554 0.002979 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.7257 Epoch 81 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2168 0.5324 0.8298 -0.003455 0.002334 0.2271 -0.005073 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1694 0.02024 0.1342 -0.01001 0.5104 0.5803 0.4703 0.586 0.5059 0.5368 ] Network output: [ 0.1281 0.5209 0.7617 -0.00479 0.001441 0.4551 -0.002835 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3255 0.09461 0.2636 0.06068 0.542 0.641 0.5653 0.5922 0.5631 0.6032 ] Network output: [ 0.2371 0.4669 0.6811 -0.003746 0.002652 0.3776 -0.003305 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.3162 0.08512 0.254 0.03611 0.516 0.5913 0.4904 0.5917 0.5127 0.5568 ] Network output: [ 0.1763 0.4964 0.6674 -0.003224 0.0004494 0.4719 -0.001616 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.399 0.14 0.3117 0.09156 0.5357 0.6371 0.5635 0.5881 0.5612 0.6049 ] Network output: [ 0.1041 0.3845 0.6411 0.002384 -0.001122 0.7608 0.002463 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5199 0.2621 0.3854 0.1451 0.6169 0.6766 0.6507 0.6152 0.558 0.6411 ] Network output: [ 0.06238 0.2959 0.6618 0.006002 -0.002853 0.919 0.007266 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5697 0.3447 0.4282 0.1866 0.6246 0.6855 0.6685 0.623 0.562 0.6505 ] Network output: [ 0.04422 0.2443 0.7084 0.006209 -0.002607 0.9644 0.00304 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.7215 Epoch 82 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.22 0.5375 0.8346 -0.003415 0.002365 0.2107 -0.005144 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1683 0.02038 0.1333 -0.01068 0.5094 0.5811 0.4683 0.5865 0.5069 0.5366 ] Network output: [ 0.1294 0.523 0.7635 -0.004871 0.001477 0.4484 -0.00299 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3269 0.09654 0.2642 0.06166 0.5425 0.6431 0.5671 0.5936 0.5652 0.6047 ] Network output: [ 0.2389 0.4692 0.6835 -0.003762 0.002697 0.3692 -0.003346 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.316 0.08674 0.2541 0.03635 0.5156 0.5931 0.4893 0.5927 0.5143 0.5571 ] Network output: [ 0.1765 0.4966 0.6671 -0.003281 0.0004665 0.4713 -0.001714 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4018 0.1437 0.3134 0.0935 0.5365 0.6395 0.5657 0.5896 0.5634 0.6067 ] Network output: [ 0.1028 0.3824 0.6387 0.002323 -0.001123 0.7677 0.00246 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.524 0.2683 0.3887 0.1486 0.6183 0.6796 0.6537 0.6172 0.5606 0.6435 ] Network output: [ 0.06036 0.2925 0.659 0.005948 -0.00289 0.9293 0.007343 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5744 0.3526 0.4323 0.1914 0.6262 0.6888 0.6717 0.625 0.5648 0.6531 ] Network output: [ 0.04252 0.2413 0.7067 0.006245 -0.002597 0.973 0.003081 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.7175 Epoch 83 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2231 0.5424 0.8393 -0.003354 0.002384 0.1946 -0.005189 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1672 0.02049 0.1322 -0.01135 0.5085 0.582 0.4663 0.587 0.5079 0.5363 ] Network output: [ 0.1307 0.5249 0.7652 -0.00494 0.001504 0.4418 -0.003133 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3282 0.09837 0.2647 0.06263 0.5429 0.6451 0.5688 0.5949 0.5671 0.6062 ] Network output: [ 0.2407 0.4715 0.6859 -0.003766 0.002734 0.3608 -0.003372 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.3157 0.08824 0.2541 0.03655 0.5152 0.5949 0.4882 0.5936 0.5159 0.5573 ] Network output: [ 0.1766 0.4968 0.6668 -0.003333 0.0004809 0.4709 -0.001807 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4044 0.1473 0.315 0.09539 0.5372 0.6419 0.5678 0.5911 0.5655 0.6084 ] Network output: [ 0.1016 0.3804 0.6365 0.002253 -0.001118 0.7743 0.002451 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5278 0.2742 0.3917 0.152 0.6197 0.6825 0.6564 0.619 0.5631 0.6458 ] Network output: [ 0.05844 0.2892 0.6563 0.005878 -0.002917 0.9391 0.007402 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5787 0.3599 0.4361 0.1959 0.6277 0.6919 0.6746 0.627 0.5674 0.6556 ] Network output: [ 0.04092 0.2385 0.7051 0.006268 -0.002575 0.9812 0.003101 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.7138 Epoch 84 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2261 0.5473 0.8439 -0.003272 0.002391 0.1791 -0.00521 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1661 0.02058 0.1312 -0.01201 0.5075 0.5827 0.4641 0.5874 0.5089 0.536 ] Network output: [ 0.1319 0.5268 0.7669 -0.004996 0.001524 0.4355 -0.003262 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3294 0.1001 0.2652 0.06357 0.5434 0.647 0.5704 0.5961 0.569 0.6076 ] Network output: [ 0.2425 0.4738 0.6883 -0.003757 0.002763 0.3526 -0.003383 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.3152 0.08962 0.254 0.03671 0.5149 0.5965 0.4869 0.5945 0.5173 0.5575 ] Network output: [ 0.1768 0.4969 0.6664 -0.003381 0.0004922 0.4707 -0.001895 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4068 0.1507 0.3164 0.09724 0.5379 0.6441 0.5699 0.5925 0.5676 0.61 ] Network output: [ 0.1005 0.3785 0.6344 0.002177 -0.001109 0.7807 0.002435 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5313 0.2797 0.3945 0.1552 0.621 0.6852 0.6588 0.6208 0.5654 0.6479 ] Network output: [ 0.05662 0.286 0.6538 0.005793 -0.002932 0.9485 0.007443 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5827 0.3668 0.4397 0.2002 0.6291 0.6948 0.6772 0.6288 0.5699 0.6578 ] Network output: [ 0.03943 0.2359 0.7037 0.006278 -0.002542 0.9888 0.003103 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.7103 Epoch 85 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.229 0.5521 0.8483 -0.003172 0.002386 0.164 -0.005206 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1649 0.02066 0.1301 -0.01267 0.5065 0.5834 0.4619 0.5878 0.5098 0.5356 ] Network output: [ 0.133 0.5286 0.7685 -0.005041 0.001535 0.4294 -0.003377 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3305 0.1018 0.2656 0.0645 0.5438 0.6488 0.572 0.5973 0.5708 0.609 ] Network output: [ 0.2442 0.476 0.6906 -0.003737 0.002785 0.3445 -0.003378 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.3146 0.09089 0.2538 0.03683 0.5145 0.5981 0.4854 0.5954 0.5187 0.5575 ] Network output: [ 0.1769 0.4969 0.666 -0.003425 0.0005004 0.4706 -0.001979 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4091 0.154 0.3177 0.09903 0.5385 0.6463 0.5718 0.5938 0.5695 0.6116 ] Network output: [ 0.09936 0.3765 0.6323 0.002095 -0.001095 0.7869 0.002414 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5344 0.2849 0.3972 0.1582 0.6222 0.6877 0.6609 0.6224 0.5675 0.6499 ] Network output: [ 0.05492 0.283 0.6514 0.005696 -0.002937 0.9573 0.007467 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5862 0.3732 0.4431 0.2042 0.6303 0.6975 0.6796 0.6306 0.5723 0.66 ] Network output: [ 0.03806 0.2334 0.7023 0.006278 -0.002497 0.996 0.003086 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.707 Epoch 86 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2318 0.5568 0.8526 -0.003055 0.00237 0.1495 -0.005179 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1637 0.02072 0.129 -0.01332 0.5056 0.5841 0.4596 0.5882 0.5106 0.5352 ] Network output: [ 0.1341 0.5304 0.77 -0.005076 0.001539 0.4237 -0.003478 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3316 0.1034 0.2659 0.06541 0.5442 0.6505 0.5734 0.5984 0.5725 0.6103 ] Network output: [ 0.2459 0.4782 0.693 -0.003707 0.0028 0.3367 -0.003359 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.3138 0.09205 0.2535 0.03691 0.5141 0.5996 0.4838 0.5962 0.5199 0.5576 ] Network output: [ 0.177 0.4969 0.6656 -0.003465 0.0005054 0.4707 -0.002057 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4113 0.1571 0.319 0.1008 0.5392 0.6483 0.5736 0.5952 0.5714 0.6131 ] Network output: [ 0.09833 0.3746 0.6304 0.002008 -0.001078 0.7927 0.002389 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5373 0.2898 0.3996 0.161 0.6232 0.6901 0.6628 0.624 0.5695 0.6518 ] Network output: [ 0.05334 0.28 0.6491 0.005588 -0.002932 0.9657 0.007475 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5894 0.3792 0.4462 0.2079 0.6315 0.7001 0.6816 0.6322 0.5744 0.6619 ] Network output: [ 0.03679 0.2311 0.701 0.00627 -0.002441 1.003 0.003052 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.7039 Epoch 87 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2344 0.5613 0.8567 -0.002922 0.002344 0.1355 -0.005129 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1625 0.02076 0.1279 -0.01396 0.5047 0.5847 0.4572 0.5886 0.5113 0.5348 ] Network output: [ 0.1351 0.532 0.7714 -0.005102 0.001534 0.4183 -0.003567 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3325 0.1049 0.2662 0.06631 0.5445 0.6522 0.5747 0.5995 0.5741 0.6115 ] Network output: [ 0.2475 0.4804 0.6952 -0.003667 0.002806 0.329 -0.003325 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.313 0.09311 0.2532 0.03696 0.5137 0.601 0.4821 0.5969 0.5211 0.5575 ] Network output: [ 0.177 0.4968 0.6652 -0.003502 0.000507 0.4709 -0.00213 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4134 0.1601 0.3202 0.1025 0.5398 0.6503 0.5753 0.5964 0.5732 0.6145 ] Network output: [ 0.09738 0.3727 0.6285 0.001917 -0.001057 0.7983 0.002358 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5399 0.2944 0.4019 0.1637 0.6242 0.6923 0.6645 0.6254 0.5714 0.6535 ] Network output: [ 0.05188 0.2772 0.647 0.005471 -0.002918 0.9736 0.007469 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5923 0.3847 0.449 0.2113 0.6325 0.7025 0.6834 0.6337 0.5764 0.6637 ] Network output: [ 0.03564 0.2289 0.6998 0.006255 -0.002376 1.009 0.003003 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.7011 Epoch 88 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2368 0.5658 0.8606 -0.002777 0.002308 0.1221 -0.00506 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1613 0.0208 0.1269 -0.01459 0.5038 0.5853 0.4548 0.5889 0.512 0.5343 ] Network output: [ 0.1361 0.5335 0.7727 -0.005119 0.001522 0.4133 -0.003642 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3334 0.1063 0.2665 0.0672 0.5449 0.6538 0.576 0.6006 0.5756 0.6127 ] Network output: [ 0.2491 0.4825 0.6975 -0.003618 0.002806 0.3215 -0.003276 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.3121 0.09408 0.2528 0.03698 0.5133 0.6023 0.4803 0.5977 0.5222 0.5573 ] Network output: [ 0.177 0.4966 0.6647 -0.003536 0.0005053 0.4714 -0.002199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4154 0.163 0.3213 0.1041 0.5404 0.6522 0.577 0.5976 0.5749 0.6158 ] Network output: [ 0.0965 0.3708 0.6268 0.001822 -0.001033 0.8036 0.002325 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5422 0.2987 0.404 0.1661 0.6251 0.6944 0.6659 0.6268 0.5731 0.6551 ] Network output: [ 0.05055 0.2744 0.645 0.005348 -0.002896 0.981 0.00745 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5948 0.3898 0.4516 0.2145 0.6335 0.7047 0.6849 0.6351 0.5782 0.6654 ] Network output: [ 0.03461 0.2269 0.6987 0.006235 -0.002301 1.015 0.002939 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6984 Epoch 89 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2391 0.5701 0.8643 -0.00262 0.002263 0.1094 -0.004971 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1601 0.02082 0.1259 -0.01521 0.5029 0.5859 0.4523 0.5892 0.5126 0.5337 ] Network output: [ 0.1369 0.5349 0.7739 -0.00513 0.001503 0.4087 -0.003706 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3343 0.1077 0.2667 0.06809 0.5452 0.6553 0.5772 0.6016 0.5771 0.6139 ] Network output: [ 0.2506 0.4846 0.6996 -0.003561 0.002799 0.3143 -0.003214 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.311 0.09496 0.2524 0.03698 0.513 0.6036 0.4783 0.5984 0.5232 0.5571 ] Network output: [ 0.177 0.4963 0.6642 -0.003568 0.0005003 0.472 -0.002263 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4172 0.1658 0.3224 0.1057 0.5409 0.654 0.5785 0.5988 0.5765 0.6171 ] Network output: [ 0.09569 0.369 0.6252 0.001726 -0.001008 0.8087 0.002288 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5443 0.3026 0.406 0.1685 0.626 0.6963 0.6671 0.628 0.5747 0.6566 ] Network output: [ 0.04933 0.2718 0.6431 0.005219 -0.002867 0.9879 0.007419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5971 0.3945 0.4541 0.2174 0.6344 0.7067 0.6861 0.6364 0.5799 0.6669 ] Network output: [ 0.03368 0.225 0.6977 0.006211 -0.002218 1.02 0.002863 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6959 Epoch 90 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2413 0.5744 0.8678 -0.002454 0.00221 0.09721 -0.004865 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1589 0.02084 0.1249 -0.01582 0.5021 0.5864 0.4498 0.5896 0.5132 0.5332 ] Network output: [ 0.1377 0.5362 0.775 -0.005134 0.001476 0.4045 -0.003759 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3352 0.109 0.267 0.06897 0.5456 0.6568 0.5784 0.6026 0.5785 0.615 ] Network output: [ 0.252 0.4866 0.7018 -0.003497 0.002786 0.3074 -0.003139 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.31 0.09576 0.252 0.03695 0.5126 0.6048 0.4763 0.599 0.5241 0.5569 ] Network output: [ 0.177 0.496 0.6636 -0.003597 0.000492 0.4728 -0.002322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4191 0.1684 0.3235 0.1073 0.5415 0.6557 0.5799 0.5999 0.578 0.6183 ] Network output: [ 0.09496 0.3672 0.6236 0.001629 -0.0009808 0.8134 0.002249 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5461 0.3064 0.4078 0.1706 0.6267 0.6981 0.6681 0.6292 0.5761 0.6579 ] Network output: [ 0.04824 0.2693 0.6414 0.005087 -0.002831 0.9943 0.007377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.599 0.3988 0.4563 0.22 0.6351 0.7086 0.6871 0.6376 0.5814 0.6682 ] Network output: [ 0.03286 0.2233 0.6968 0.006186 -0.002127 1.025 0.002775 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6936 Epoch 91 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2433 0.5785 0.8712 -0.002281 0.002151 0.08563 -0.004744 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1577 0.02084 0.1239 -0.01642 0.5014 0.5869 0.4472 0.5898 0.5137 0.5326 ] Network output: [ 0.1383 0.5374 0.776 -0.005134 0.001444 0.4006 -0.003803 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.336 0.1103 0.2672 0.06986 0.5459 0.6582 0.5795 0.6036 0.5798 0.616 ] Network output: [ 0.2534 0.4885 0.7038 -0.003427 0.002767 0.3007 -0.003052 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.3088 0.09648 0.2516 0.03689 0.5123 0.6059 0.4741 0.5996 0.5249 0.5565 ] Network output: [ 0.1769 0.4956 0.663 -0.003625 0.0004806 0.4738 -0.002378 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4208 0.1709 0.3245 0.1088 0.542 0.6574 0.5813 0.601 0.5794 0.6195 ] Network output: [ 0.0943 0.3654 0.6222 0.00153 -0.0009525 0.8179 0.002209 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5478 0.3098 0.4095 0.1726 0.6274 0.6998 0.6689 0.6303 0.5774 0.6591 ] Network output: [ 0.04727 0.2669 0.6398 0.004952 -0.00279 1 0.007327 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6007 0.4027 0.4583 0.2223 0.6358 0.7103 0.6879 0.6387 0.5827 0.6695 ] Network output: [ 0.03215 0.2217 0.6959 0.006159 -0.00203 1.029 0.002678 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6914 Epoch 92 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2452 0.5825 0.8744 -0.002102 0.002085 0.07463 -0.00461 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1565 0.02085 0.123 -0.01701 0.5006 0.5874 0.4446 0.5901 0.5142 0.532 ] Network output: [ 0.1389 0.5384 0.7769 -0.005131 0.001405 0.3972 -0.003838 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3368 0.1115 0.2675 0.07074 0.5462 0.6595 0.5805 0.6045 0.581 0.617 ] Network output: [ 0.2547 0.4904 0.7058 -0.003353 0.002743 0.2942 -0.002954 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.3076 0.09712 0.2512 0.03682 0.5119 0.6071 0.4719 0.6002 0.5257 0.5561 ] Network output: [ 0.1768 0.4951 0.6624 -0.003652 0.0004662 0.475 -0.00243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4225 0.1734 0.3255 0.1102 0.5426 0.659 0.5825 0.6021 0.5808 0.6206 ] Network output: [ 0.0937 0.3636 0.6209 0.001432 -0.0009236 0.8221 0.002167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5492 0.313 0.4111 0.1744 0.6281 0.7014 0.6695 0.6313 0.5785 0.6602 ] Network output: [ 0.04642 0.2646 0.6384 0.004816 -0.002743 1.006 0.007268 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6021 0.4063 0.4602 0.2244 0.6365 0.712 0.6885 0.6397 0.5839 0.6705 ] Network output: [ 0.03153 0.2203 0.6952 0.006133 -0.001927 1.033 0.002573 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6893 Epoch 93 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2469 0.5864 0.8774 -0.001918 0.002014 0.06421 -0.004464 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1553 0.02085 0.1221 -0.01758 0.4999 0.5878 0.4419 0.5904 0.5146 0.5313 ] Network output: [ 0.1394 0.5394 0.7777 -0.005125 0.001361 0.3941 -0.003865 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3376 0.1127 0.2678 0.07162 0.5465 0.6608 0.5815 0.6054 0.5822 0.6179 ] Network output: [ 0.2559 0.4922 0.7077 -0.003274 0.002714 0.288 -0.002846 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.3064 0.09771 0.2508 0.03672 0.5116 0.6081 0.4696 0.6007 0.5263 0.5557 ] Network output: [ 0.1766 0.4945 0.6617 -0.003678 0.0004488 0.4763 -0.002479 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4241 0.1757 0.3265 0.1117 0.5431 0.6606 0.5837 0.6031 0.5821 0.6216 ] Network output: [ 0.09318 0.3619 0.6196 0.001334 -0.0008944 0.8261 0.002125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5504 0.3159 0.4126 0.1761 0.6287 0.7028 0.6699 0.6322 0.5796 0.6612 ] Network output: [ 0.04568 0.2623 0.6371 0.00468 -0.002693 1.011 0.007204 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6033 0.4095 0.4619 0.2263 0.637 0.7135 0.6888 0.6406 0.585 0.6715 ] Network output: [ 0.03101 0.2189 0.6946 0.006108 -0.001818 1.036 0.00246 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6873 Epoch 94 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2485 0.5902 0.8802 -0.001732 0.001938 0.05432 -0.004309 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1542 0.02085 0.1213 -0.01815 0.4993 0.5883 0.4392 0.5906 0.515 0.5306 ] Network output: [ 0.1398 0.5403 0.7783 -0.005117 0.001312 0.3914 -0.003887 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3384 0.1138 0.2681 0.0725 0.5469 0.6621 0.5824 0.6062 0.5833 0.6188 ] Network output: [ 0.2571 0.494 0.7095 -0.003193 0.00268 0.2821 -0.002729 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.3051 0.09823 0.2504 0.03661 0.5114 0.6091 0.4672 0.6013 0.5269 0.5552 ] Network output: [ 0.1764 0.4939 0.661 -0.003703 0.0004286 0.4778 -0.002526 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4256 0.178 0.3275 0.1131 0.5436 0.6621 0.5848 0.604 0.5833 0.6226 ] Network output: [ 0.09273 0.3602 0.6185 0.001237 -0.0008653 0.8298 0.002083 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5515 0.3186 0.4139 0.1777 0.6292 0.7042 0.6701 0.6331 0.5805 0.6621 ] Network output: [ 0.04505 0.2602 0.636 0.004545 -0.002639 1.015 0.007134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6043 0.4124 0.4634 0.2279 0.6375 0.7149 0.689 0.6414 0.5859 0.6723 ] Network output: [ 0.03058 0.2177 0.694 0.006086 -0.001704 1.039 0.002342 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6854 Epoch 95 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2499 0.594 0.8828 -0.001545 0.001859 0.04497 -0.004145 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.153 0.02084 0.1206 -0.0187 0.4987 0.5887 0.4365 0.5909 0.5154 0.53 ] Network output: [ 0.1402 0.541 0.7789 -0.005108 0.001259 0.389 -0.003903 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3392 0.1149 0.2685 0.07339 0.5472 0.6633 0.5833 0.6071 0.5844 0.6197 ] Network output: [ 0.2582 0.4958 0.7113 -0.003109 0.002643 0.2764 -0.002605 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.3037 0.09869 0.25 0.03648 0.5111 0.6101 0.4647 0.6018 0.5275 0.5547 ] Network output: [ 0.1762 0.4932 0.6602 -0.003728 0.0004057 0.4795 -0.002571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4271 0.1801 0.3284 0.1145 0.5441 0.6635 0.5859 0.605 0.5844 0.6235 ] Network output: [ 0.09234 0.3585 0.6175 0.001141 -0.0008366 0.8333 0.002041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5524 0.3212 0.4152 0.1791 0.6297 0.7055 0.6701 0.6339 0.5813 0.6629 ] Network output: [ 0.04452 0.2581 0.6349 0.004411 -0.002582 1.019 0.007061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.605 0.415 0.4648 0.2293 0.638 0.7162 0.6889 0.6422 0.5867 0.6731 ] Network output: [ 0.03024 0.2166 0.6935 0.006066 -0.001586 1.042 0.00222 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6836 Epoch 96 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2512 0.5977 0.8853 -0.001357 0.001778 0.03611 -0.003976 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1519 0.02083 0.1198 -0.01924 0.4981 0.5891 0.4338 0.5911 0.5157 0.5293 ] Network output: [ 0.1405 0.5417 0.7793 -0.005099 0.001202 0.387 -0.003915 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.34 0.116 0.2688 0.07428 0.5475 0.6645 0.5841 0.6079 0.5854 0.6205 ] Network output: [ 0.2592 0.4975 0.713 -0.003023 0.002602 0.2709 -0.002474 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.3024 0.0991 0.2497 0.03634 0.5109 0.6111 0.4621 0.6022 0.528 0.5542 ] Network output: [ 0.176 0.4924 0.6594 -0.003754 0.0003804 0.4813 -0.002614 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4286 0.1822 0.3294 0.1158 0.5446 0.6649 0.5868 0.6059 0.5855 0.6244 ] Network output: [ 0.09201 0.3568 0.6165 0.001046 -0.0008086 0.8365 0.002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5531 0.3235 0.4164 0.1804 0.6301 0.7067 0.67 0.6347 0.582 0.6636 ] Network output: [ 0.04409 0.2561 0.634 0.00428 -0.002523 1.023 0.006985 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6056 0.4174 0.466 0.2305 0.6384 0.7174 0.6887 0.6429 0.5874 0.6737 ] Network output: [ 0.02997 0.2157 0.6931 0.00605 -0.001465 1.045 0.002095 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6819 Epoch 97 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2524 0.6013 0.8877 -0.001169 0.001695 0.02773 -0.003801 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1508 0.02082 0.1192 -0.01977 0.4975 0.5896 0.431 0.5914 0.5159 0.5286 ] Network output: [ 0.1407 0.5423 0.7797 -0.005091 0.001142 0.3853 -0.003923 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3408 0.117 0.2692 0.07517 0.5479 0.6657 0.5849 0.6087 0.5863 0.6213 ] Network output: [ 0.2602 0.4991 0.7146 -0.002936 0.002558 0.2657 -0.002337 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.301 0.09946 0.2493 0.03619 0.5107 0.612 0.4595 0.6027 0.5284 0.5536 ] Network output: [ 0.1758 0.4915 0.6585 -0.003779 0.0003526 0.4833 -0.002657 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.43 0.1842 0.3303 0.1171 0.5451 0.6663 0.5878 0.6067 0.5865 0.6253 ] Network output: [ 0.09174 0.3551 0.6156 0.0009536 -0.0007814 0.8396 0.00196 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5537 0.3256 0.4175 0.1815 0.6305 0.7079 0.6697 0.6353 0.5826 0.6642 ] Network output: [ 0.04376 0.2542 0.6332 0.004152 -0.002463 1.027 0.006907 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.606 0.4194 0.4672 0.2315 0.6387 0.7185 0.6883 0.6435 0.5879 0.6742 ] Network output: [ 0.02977 0.2148 0.6927 0.006038 -0.001341 1.047 0.001968 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6802 Epoch 98 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2534 0.6048 0.8899 -0.0009828 0.001611 0.0198 -0.003623 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1498 0.0208 0.1185 -0.02029 0.497 0.59 0.4282 0.5916 0.5162 0.5278 ] Network output: [ 0.1408 0.5427 0.78 -0.005084 0.001078 0.3839 -0.00393 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3416 0.1179 0.2696 0.07607 0.5482 0.6668 0.5857 0.6095 0.5872 0.6221 ] Network output: [ 0.2611 0.5008 0.7162 -0.002849 0.002512 0.2606 -0.002195 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2996 0.09977 0.249 0.03602 0.5105 0.613 0.4569 0.6031 0.5287 0.553 ] Network output: [ 0.1755 0.4906 0.6576 -0.003806 0.0003226 0.4854 -0.0027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4314 0.1862 0.3312 0.1184 0.5456 0.6676 0.5886 0.6076 0.5875 0.6261 ] Network output: [ 0.09153 0.3535 0.6148 0.000863 -0.0007554 0.8424 0.001921 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5542 0.3275 0.4185 0.1826 0.6309 0.709 0.6693 0.636 0.5831 0.6648 ] Network output: [ 0.04351 0.2524 0.6325 0.004027 -0.002401 1.03 0.006829 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6062 0.4213 0.4682 0.2323 0.639 0.7195 0.6878 0.6441 0.5884 0.6747 ] Network output: [ 0.02964 0.214 0.6924 0.00603 -0.001213 1.049 0.001839 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6785 Epoch 99 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2543 0.6083 0.892 -0.0007987 0.001526 0.0123 -0.003443 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1487 0.02079 0.1179 -0.0208 0.4966 0.5904 0.4254 0.5918 0.5164 0.5271 ] Network output: [ 0.1409 0.5431 0.7801 -0.005079 0.001011 0.3829 -0.003936 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3423 0.1189 0.27 0.07697 0.5486 0.668 0.5864 0.6102 0.5881 0.6229 ] Network output: [ 0.262 0.5024 0.7177 -0.002761 0.002464 0.2558 -0.002049 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2981 0.1 0.2487 0.03584 0.5103 0.6139 0.4541 0.6035 0.5291 0.5524 ] Network output: [ 0.1752 0.4896 0.6567 -0.003833 0.0002905 0.4876 -0.002743 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4328 0.188 0.3321 0.1196 0.546 0.6689 0.5894 0.6084 0.5884 0.6269 ] Network output: [ 0.09138 0.3518 0.6141 0.0007744 -0.0007307 0.8451 0.001884 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5545 0.3293 0.4195 0.1835 0.6313 0.71 0.6688 0.6366 0.5835 0.6653 ] Network output: [ 0.04335 0.2506 0.6319 0.003905 -0.002339 1.032 0.00675 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6063 0.423 0.4691 0.2329 0.6393 0.7205 0.6871 0.6446 0.5888 0.6751 ] Network output: [ 0.02958 0.2133 0.6921 0.006027 -0.001084 1.051 0.001711 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6769 Epoch 100 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2552 0.6118 0.8939 -0.0006173 0.001441 0.005184 -0.003262 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1476 0.02077 0.1174 -0.0213 0.4961 0.5909 0.4226 0.592 0.5165 0.5264 ] Network output: [ 0.1409 0.5435 0.7802 -0.005076 0.0009424 0.382 -0.003941 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3431 0.1198 0.2704 0.07788 0.5489 0.6691 0.5871 0.611 0.5889 0.6236 ] Network output: [ 0.2628 0.504 0.7192 -0.002673 0.002415 0.2511 -0.0019 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2967 0.1003 0.2484 0.03564 0.5102 0.6148 0.4514 0.6039 0.5294 0.5517 ] Network output: [ 0.1749 0.4886 0.6558 -0.003861 0.0002563 0.49 -0.002786 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4341 0.1898 0.333 0.1209 0.5465 0.6702 0.5902 0.6092 0.5892 0.6276 ] Network output: [ 0.09128 0.3502 0.6134 0.0006881 -0.0007073 0.8475 0.001849 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5547 0.331 0.4203 0.1843 0.6316 0.711 0.6681 0.6371 0.5839 0.6657 ] Network output: [ 0.04327 0.2488 0.6314 0.003787 -0.002276 1.035 0.006673 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6062 0.4244 0.4699 0.2334 0.6396 0.7215 0.6862 0.6451 0.5891 0.6754 ] Network output: [ 0.02956 0.2126 0.6919 0.006028 -0.0009517 1.052 0.001583 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6753 Epoch 101 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2559 0.6152 0.8957 -0.0004389 0.001356 -0.001558 -0.003081 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1466 0.02075 0.1168 -0.0218 0.4958 0.5913 0.4198 0.5923 0.5167 0.5257 ] Network output: [ 0.1408 0.5437 0.7803 -0.005075 0.0008713 0.3815 -0.003946 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3439 0.1207 0.2708 0.0788 0.5493 0.6702 0.5877 0.6117 0.5897 0.6243 ] Network output: [ 0.2636 0.5055 0.7206 -0.002586 0.002364 0.2466 -0.001749 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2952 0.1005 0.2481 0.03544 0.5101 0.6157 0.4486 0.6043 0.5296 0.5511 ] Network output: [ 0.1746 0.4875 0.6548 -0.003889 0.0002201 0.4925 -0.002831 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4354 0.1916 0.3338 0.1221 0.547 0.6715 0.5909 0.61 0.5901 0.6283 ] Network output: [ 0.09122 0.3485 0.6128 0.0006041 -0.0006854 0.8498 0.001816 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5548 0.3325 0.4211 0.1851 0.6319 0.7119 0.6674 0.6376 0.5842 0.6661 ] Network output: [ 0.04326 0.2472 0.6309 0.003673 -0.002213 1.037 0.006597 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.606 0.4257 0.4706 0.2337 0.6398 0.7224 0.6853 0.6455 0.5893 0.6757 ] Network output: [ 0.0296 0.2121 0.6917 0.006034 -0.0008181 1.053 0.001457 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6737 Epoch 102 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2565 0.6186 0.8975 -0.0002641 0.001273 -0.007956 -0.002902 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1456 0.02073 0.1163 -0.02229 0.4954 0.5918 0.417 0.5925 0.5168 0.525 ] Network output: [ 0.1407 0.5439 0.7802 -0.005076 0.0007983 0.3811 -0.003953 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3447 0.1215 0.2712 0.07972 0.5497 0.6713 0.5883 0.6125 0.5904 0.625 ] Network output: [ 0.2643 0.5071 0.722 -0.0025 0.002312 0.2423 -0.001597 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2937 0.1006 0.2478 0.03522 0.51 0.6166 0.4457 0.6047 0.5298 0.5504 ] Network output: [ 0.1743 0.4863 0.6538 -0.003919 0.0001821 0.495 -0.002877 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4367 0.1933 0.3346 0.1233 0.5475 0.6727 0.5915 0.6108 0.5909 0.629 ] Network output: [ 0.09122 0.3469 0.6122 0.0005224 -0.000665 0.852 0.001786 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5548 0.3338 0.4219 0.1857 0.6322 0.7129 0.6665 0.6381 0.5844 0.6664 ] Network output: [ 0.04332 0.2455 0.6306 0.003563 -0.00215 1.039 0.006524 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6057 0.4268 0.4712 0.2339 0.64 0.7232 0.6842 0.6459 0.5895 0.6759 ] Network output: [ 0.02968 0.2116 0.6915 0.006045 -0.0006828 1.054 0.001332 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6722 Epoch 103 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.257 0.622 0.8991 -9.308e-05 0.001191 -0.01403 -0.002723 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1446 0.02071 0.1159 -0.02277 0.4951 0.5922 0.4141 0.5927 0.517 0.5243 ] Network output: [ 0.1406 0.544 0.7801 -0.005081 0.0007237 0.381 -0.00396 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3454 0.1223 0.2716 0.08065 0.55 0.6724 0.5889 0.6132 0.5912 0.6257 ] Network output: [ 0.2649 0.5086 0.7233 -0.002415 0.00226 0.2381 -0.001443 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2921 0.1007 0.2475 0.035 0.5099 0.6175 0.4428 0.6051 0.53 0.5498 ] Network output: [ 0.174 0.4851 0.6528 -0.00395 0.0001423 0.4977 -0.002924 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4379 0.1949 0.3354 0.1245 0.548 0.674 0.5921 0.6116 0.5916 0.6297 ] Network output: [ 0.09126 0.3453 0.6118 0.000443 -0.0006462 0.854 0.001758 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5547 0.3351 0.4225 0.1863 0.6325 0.7137 0.6655 0.6386 0.5846 0.6667 ] Network output: [ 0.04345 0.2439 0.6303 0.003457 -0.002087 1.041 0.006453 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6052 0.4277 0.4718 0.234 0.6402 0.724 0.6831 0.6463 0.5896 0.6761 ] Network output: [ 0.02981 0.2112 0.6914 0.006061 -0.000546 1.055 0.00121 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6707 Epoch 104 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2574 0.6254 0.9006 7.394e-05 0.00111 -0.01981 -0.002548 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1436 0.02068 0.1154 -0.02325 0.4948 0.5927 0.4113 0.593 0.5171 0.5236 ] Network output: [ 0.1404 0.544 0.78 -0.005088 0.0006475 0.3811 -0.00397 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3462 0.1231 0.272 0.08158 0.5504 0.6735 0.5895 0.6139 0.5919 0.6263 ] Network output: [ 0.2655 0.5102 0.7246 -0.002331 0.002207 0.234 -0.00129 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2906 0.1008 0.2472 0.03476 0.5099 0.6184 0.4399 0.6055 0.5302 0.5491 ] Network output: [ 0.1736 0.4839 0.6518 -0.003983 0.0001007 0.5004 -0.002974 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4391 0.1965 0.3362 0.1256 0.5485 0.6752 0.5927 0.6123 0.5923 0.6303 ] Network output: [ 0.09134 0.3437 0.6113 0.000366 -0.000629 0.8558 0.001732 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5545 0.3362 0.4231 0.1867 0.6327 0.7146 0.6644 0.6391 0.5848 0.667 ] Network output: [ 0.04363 0.2423 0.6301 0.003355 -0.002024 1.042 0.006385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6046 0.4286 0.4722 0.2339 0.6404 0.7248 0.6818 0.6467 0.5897 0.6763 ] Network output: [ 0.02996 0.2108 0.6913 0.006082 -0.0004079 1.056 0.001091 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6691 Epoch 105 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2577 0.6287 0.9021 0.0002368 0.001032 -0.02532 -0.002375 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1426 0.02065 0.1149 -0.02372 0.4945 0.5932 0.4085 0.5932 0.5172 0.523 ] Network output: [ 0.1402 0.544 0.7798 -0.005098 0.00057 0.3813 -0.003983 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3469 0.1239 0.2724 0.08251 0.5508 0.6745 0.59 0.6146 0.5926 0.627 ] Network output: [ 0.2661 0.5117 0.7258 -0.002249 0.002154 0.2301 -0.001136 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.289 0.1009 0.2469 0.03451 0.5099 0.6193 0.437 0.6059 0.5303 0.5485 ] Network output: [ 0.1733 0.4826 0.6507 -0.004016 5.746e-05 0.5032 -0.003026 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4403 0.198 0.3369 0.1267 0.549 0.6764 0.5932 0.6131 0.593 0.631 ] Network output: [ 0.09146 0.3421 0.6109 0.0002913 -0.0006134 0.8575 0.00171 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5542 0.3373 0.4236 0.1872 0.633 0.7155 0.6632 0.6395 0.5849 0.6672 ] Network output: [ 0.04386 0.2408 0.6299 0.003258 -0.001963 1.043 0.006322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.604 0.4292 0.4726 0.2337 0.6406 0.7255 0.6804 0.647 0.5897 0.6764 ] Network output: [ 0.03015 0.2105 0.6912 0.006108 -0.0002684 1.057 0.0009757 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6676 Epoch 106 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2579 0.6321 0.9034 0.0003953 0.0009552 -0.03057 -0.002206 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1416 0.02062 0.1145 -0.0242 0.4943 0.5937 0.4056 0.5934 0.5173 0.5223 ] Network output: [ 0.14 0.544 0.7795 -0.005111 0.0004912 0.3817 -0.003998 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3477 0.1246 0.2728 0.08345 0.5512 0.6756 0.5906 0.6154 0.5932 0.6277 ] Network output: [ 0.2666 0.5133 0.7271 -0.002167 0.002101 0.2263 -0.000983 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2875 0.1009 0.2465 0.03424 0.5099 0.6202 0.434 0.6062 0.5304 0.5479 ] Network output: [ 0.173 0.4812 0.6496 -0.00405 1.249e-05 0.5061 -0.003081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4414 0.1995 0.3376 0.1279 0.5495 0.6776 0.5937 0.6139 0.5937 0.6316 ] Network output: [ 0.09162 0.3405 0.6106 0.0002189 -0.0005995 0.8591 0.00169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5539 0.3382 0.4241 0.1875 0.6333 0.7163 0.662 0.6399 0.5849 0.6674 ] Network output: [ 0.04415 0.2393 0.6298 0.003164 -0.001902 1.044 0.006262 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6032 0.4298 0.473 0.2334 0.6408 0.7263 0.6789 0.6473 0.5897 0.6765 ] Network output: [ 0.03036 0.2103 0.6911 0.006139 -0.0001278 1.057 0.0008641 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6661 Epoch 107 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2581 0.6354 0.9047 0.0005496 0.0008811 -0.03559 -0.002042 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1406 0.02059 0.1141 -0.02467 0.4941 0.5942 0.4028 0.5937 0.5173 0.5217 ] Network output: [ 0.1397 0.5438 0.7792 -0.005127 0.0004112 0.3823 -0.004017 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3484 0.1253 0.2731 0.08439 0.5516 0.6767 0.5911 0.6161 0.5939 0.6283 ] Network output: [ 0.2671 0.5148 0.7283 -0.002088 0.002049 0.2226 -0.0008311 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2859 0.1009 0.2462 0.03397 0.51 0.6211 0.431 0.6066 0.5305 0.5473 ] Network output: [ 0.1726 0.4798 0.6485 -0.004086 -3.415e-05 0.5091 -0.003139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4426 0.2009 0.3382 0.129 0.55 0.6788 0.5942 0.6146 0.5943 0.6323 ] Network output: [ 0.09182 0.3389 0.6103 0.0001487 -0.0005871 0.8605 0.001673 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5534 0.3391 0.4245 0.1878 0.6335 0.7171 0.6606 0.6403 0.585 0.6677 ] Network output: [ 0.04448 0.2378 0.6297 0.003074 -0.001841 1.045 0.006206 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6024 0.4303 0.4732 0.233 0.641 0.727 0.6774 0.6476 0.5896 0.6766 ] Network output: [ 0.0306 0.2101 0.691 0.006174 1.408e-05 1.058 0.0007567 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6646 Epoch 108 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2582 0.6388 0.9059 0.0006994 0.0008094 -0.04039 -0.001881 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1397 0.02055 0.1136 -0.02514 0.4939 0.5948 0.3999 0.594 0.5174 0.5212 ] Network output: [ 0.1394 0.5437 0.7788 -0.005146 0.0003301 0.383 -0.00404 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3491 0.126 0.2735 0.08534 0.552 0.6778 0.5915 0.6168 0.5945 0.629 ] Network output: [ 0.2675 0.5164 0.7295 -0.002009 0.001997 0.2189 -0.0006807 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2843 0.1009 0.2458 0.03368 0.51 0.6221 0.428 0.6071 0.5306 0.5468 ] Network output: [ 0.1723 0.4784 0.6474 -0.004122 -8.247e-05 0.512 -0.003199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4437 0.2023 0.3387 0.1301 0.5505 0.68 0.5946 0.6154 0.595 0.6329 ] Network output: [ 0.09205 0.3373 0.61 8.062e-05 -0.0005763 0.8619 0.001659 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5529 0.3398 0.4249 0.188 0.6338 0.7179 0.6592 0.6407 0.585 0.6679 ] Network output: [ 0.04485 0.2364 0.6297 0.002987 -0.001782 1.046 0.006155 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6015 0.4306 0.4734 0.2325 0.6411 0.7277 0.6758 0.648 0.5895 0.6767 ] Network output: [ 0.03085 0.2099 0.6909 0.006213 0.0001572 1.058 0.0006538 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.663 Epoch 109 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2582 0.6421 0.9071 0.0008449 0.0007404 -0.04498 -0.001725 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1387 0.02051 0.1132 -0.02561 0.4938 0.5953 0.3971 0.5942 0.5175 0.5206 ] Network output: [ 0.1391 0.5435 0.7785 -0.005169 0.0002479 0.3838 -0.004066 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3498 0.1267 0.2737 0.08629 0.5525 0.6788 0.592 0.6176 0.5951 0.6296 ] Network output: [ 0.2679 0.518 0.7306 -0.001933 0.001945 0.2154 -0.000532 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2826 0.1008 0.2455 0.03338 0.5101 0.623 0.4249 0.6075 0.5307 0.5462 ] Network output: [ 0.1719 0.4769 0.6463 -0.00416 -0.0001325 0.5151 -0.003263 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4447 0.2037 0.3392 0.1311 0.551 0.6812 0.595 0.6161 0.5956 0.6335 ] Network output: [ 0.09231 0.3358 0.6098 1.468e-05 -0.0005671 0.8631 0.001648 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5523 0.3405 0.4252 0.1881 0.6341 0.7187 0.6577 0.6412 0.585 0.6681 ] Network output: [ 0.04526 0.235 0.6297 0.002905 -0.001723 1.047 0.006109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6005 0.4309 0.4736 0.2319 0.6413 0.7284 0.6741 0.6483 0.5894 0.6768 ] Network output: [ 0.03112 0.2098 0.6908 0.006257 0.0003015 1.058 0.0005556 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6615 Epoch 110 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2582 0.6455 0.9082 0.0009862 0.000674 -0.04939 -0.001574 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1377 0.02046 0.1127 -0.02608 0.4937 0.5959 0.3942 0.5945 0.5176 0.5201 ] Network output: [ 0.1388 0.5432 0.778 -0.005194 0.0001646 0.3847 -0.004097 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3505 0.1273 0.274 0.08724 0.5529 0.6799 0.5924 0.6183 0.5958 0.6303 ] Network output: [ 0.2683 0.5196 0.7317 -0.001858 0.001895 0.212 -0.0003854 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.281 0.1007 0.2451 0.03306 0.5103 0.624 0.4219 0.6079 0.5308 0.5458 ] Network output: [ 0.1716 0.4754 0.6452 -0.004198 -0.0001842 0.5182 -0.003331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4458 0.205 0.3397 0.1322 0.5516 0.6824 0.5954 0.6169 0.5962 0.6342 ] Network output: [ 0.0926 0.3342 0.6096 -4.923e-05 -0.0005594 0.8642 0.00164 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5517 0.3411 0.4254 0.1882 0.6344 0.7195 0.6562 0.6416 0.5849 0.6683 ] Network output: [ 0.04571 0.2336 0.6298 0.002825 -0.001665 1.047 0.006068 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5994 0.4311 0.4737 0.2312 0.6415 0.7291 0.6723 0.6486 0.5893 0.6769 ] Network output: [ 0.0314 0.2098 0.6907 0.006305 0.0004472 1.058 0.0004625 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6599 Epoch 111 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.258 0.6488 0.9093 0.001123 0.0006104 -0.05364 -0.001428 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1368 0.02041 0.1123 -0.02656 0.4936 0.5965 0.3914 0.5948 0.5177 0.5196 ] Network output: [ 0.1384 0.5429 0.7776 -0.005222 8.017e-05 0.3857 -0.004132 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3512 0.1279 0.2742 0.0882 0.5534 0.681 0.5928 0.6191 0.5964 0.6309 ] Network output: [ 0.2686 0.5212 0.7329 -0.001784 0.001845 0.2086 -0.0002412 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2794 0.1006 0.2446 0.03273 0.5104 0.625 0.4188 0.6084 0.5309 0.5453 ] Network output: [ 0.1713 0.4738 0.6441 -0.004238 -0.0002377 0.5213 -0.003402 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4468 0.2063 0.3401 0.1332 0.5521 0.6836 0.5958 0.6177 0.5968 0.6348 ] Network output: [ 0.09292 0.3326 0.6094 -0.0001112 -0.0005532 0.8653 0.001635 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.551 0.3417 0.4256 0.1882 0.6347 0.7203 0.6546 0.642 0.5849 0.6685 ] Network output: [ 0.04619 0.2322 0.6299 0.00275 -0.001608 1.048 0.006032 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5983 0.4312 0.4738 0.2305 0.6417 0.7298 0.6705 0.6489 0.5892 0.677 ] Network output: [ 0.03168 0.2098 0.6906 0.006358 0.0005942 1.059 0.0003745 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6584 Epoch 112 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2579 0.6522 0.9103 0.001256 0.0005495 -0.05772 -0.001288 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1358 0.02036 0.1118 -0.02703 0.4935 0.5971 0.3886 0.5952 0.5178 0.5192 ] Network output: [ 0.1381 0.5426 0.7771 -0.005253 -5.371e-06 0.3869 -0.004171 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3519 0.1285 0.2743 0.08915 0.5538 0.6821 0.5932 0.6198 0.597 0.6316 ] Network output: [ 0.2689 0.5228 0.734 -0.001712 0.001796 0.2052 -9.952e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2777 0.1005 0.2442 0.03238 0.5106 0.626 0.4157 0.6088 0.531 0.5449 ] Network output: [ 0.171 0.4722 0.6429 -0.004278 -0.000293 0.5244 -0.003477 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4478 0.2075 0.3404 0.1342 0.5526 0.6848 0.5961 0.6185 0.5974 0.6355 ] Network output: [ 0.09327 0.3311 0.6093 -0.0001712 -0.0005485 0.8662 0.001633 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5502 0.3422 0.4257 0.1882 0.6349 0.7212 0.6529 0.6424 0.5849 0.6687 ] Network output: [ 0.04669 0.2308 0.63 0.002677 -0.001552 1.048 0.006002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5971 0.4312 0.4738 0.2297 0.6419 0.7305 0.6686 0.6493 0.589 0.6771 ] Network output: [ 0.03197 0.2098 0.6905 0.006414 0.0007427 1.059 0.000292 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6568 Epoch 113 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2576 0.6556 0.9112 0.001385 0.0004915 -0.06165 -0.001152 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1348 0.0203 0.1113 -0.02752 0.4935 0.5977 0.3858 0.5955 0.5179 0.5188 ] Network output: [ 0.1377 0.5422 0.7766 -0.005286 -9.21e-05 0.388 -0.004215 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3525 0.129 0.2744 0.09011 0.5543 0.6832 0.5936 0.6206 0.5976 0.6323 ] Network output: [ 0.2692 0.5244 0.7351 -0.001642 0.001748 0.202 3.936e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.276 0.1004 0.2437 0.03202 0.5108 0.627 0.4127 0.6093 0.5311 0.5445 ] Network output: [ 0.1707 0.4706 0.6418 -0.00432 -0.0003501 0.5276 -0.003557 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4488 0.2087 0.3406 0.1353 0.5532 0.686 0.5964 0.6193 0.598 0.6362 ] Network output: [ 0.09364 0.3295 0.6092 -0.0002294 -0.0005451 0.8671 0.001634 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5494 0.3426 0.4258 0.1881 0.6353 0.722 0.6512 0.6429 0.5848 0.6689 ] Network output: [ 0.04722 0.2295 0.6301 0.002607 -0.001496 1.048 0.005976 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5958 0.4312 0.4738 0.2288 0.6421 0.7313 0.6667 0.6496 0.5889 0.6772 ] Network output: [ 0.03227 0.2099 0.6904 0.006474 0.0008928 1.059 0.0002149 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6553 Epoch 114 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2574 0.659 0.9122 0.001509 0.0004362 -0.06545 -0.001022 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1339 0.02024 0.1108 -0.02801 0.4935 0.5983 0.383 0.5958 0.518 0.5185 ] Network output: [ 0.1374 0.5417 0.7761 -0.005322 -0.0001801 0.3893 -0.004264 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3532 0.1296 0.2745 0.09106 0.5547 0.6843 0.594 0.6214 0.5982 0.633 ] Network output: [ 0.2694 0.5261 0.7362 -0.001573 0.001701 0.1988 0.0001753 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2743 0.1002 0.2431 0.03163 0.5111 0.6281 0.4096 0.6098 0.5312 0.5442 ] Network output: [ 0.1704 0.4689 0.6406 -0.004362 -0.0004091 0.5308 -0.00364 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4498 0.2099 0.3408 0.1362 0.5538 0.6872 0.5967 0.6201 0.5986 0.6369 ] Network output: [ 0.09403 0.328 0.6092 -0.0002859 -0.000543 0.8678 0.001638 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5485 0.3429 0.4258 0.188 0.6356 0.7228 0.6494 0.6433 0.5848 0.6692 ] Network output: [ 0.04777 0.2281 0.6303 0.002541 -0.001441 1.048 0.005957 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5946 0.4311 0.4737 0.2278 0.6423 0.732 0.6647 0.65 0.5888 0.6774 ] Network output: [ 0.03256 0.21 0.6902 0.006538 0.001045 1.059 0.0001436 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6537 Epoch 115 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.257 0.6624 0.9131 0.00163 0.0003838 -0.06913 -0.0008971 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1329 0.02017 0.1103 -0.0285 0.4935 0.599 0.3802 0.5962 0.5181 0.5182 ] Network output: [ 0.137 0.5413 0.7756 -0.005361 -0.0002694 0.3906 -0.004318 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3538 0.1301 0.2744 0.09202 0.5552 0.6854 0.5944 0.6222 0.5988 0.6337 ] Network output: [ 0.2696 0.5278 0.7372 -0.001505 0.001656 0.1956 0.0003081 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2727 0.1 0.2425 0.03123 0.5114 0.6292 0.4065 0.6104 0.5313 0.544 ] Network output: [ 0.1701 0.4672 0.6395 -0.004404 -0.0004702 0.534 -0.003728 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4507 0.2111 0.3409 0.1372 0.5543 0.6884 0.597 0.621 0.5992 0.6376 ] Network output: [ 0.09445 0.3265 0.6091 -0.0003408 -0.0005423 0.8685 0.001644 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5476 0.3432 0.4257 0.1878 0.6359 0.7237 0.6476 0.6438 0.5847 0.6695 ] Network output: [ 0.04835 0.2268 0.6305 0.002477 -0.001387 1.048 0.005942 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5932 0.431 0.4736 0.2268 0.6425 0.7327 0.6627 0.6504 0.5887 0.6776 ] Network output: [ 0.03284 0.2102 0.6901 0.006605 0.001198 1.059 7.813e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6521 Epoch 116 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2566 0.6659 0.9139 0.001748 0.000334 -0.07268 -0.0007775 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.132 0.0201 0.1097 -0.029 0.4936 0.5997 0.3774 0.5966 0.5183 0.5179 ] Network output: [ 0.1367 0.5408 0.7751 -0.005403 -0.0003601 0.3919 -0.004377 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3544 0.1305 0.2743 0.09297 0.5557 0.6865 0.5947 0.6231 0.5994 0.6345 ] Network output: [ 0.2698 0.5295 0.7383 -0.001439 0.001611 0.1925 0.0004376 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.271 0.09981 0.2419 0.03081 0.5117 0.6303 0.4034 0.6109 0.5315 0.5438 ] Network output: [ 0.1698 0.4655 0.6383 -0.004448 -0.0005333 0.5372 -0.00382 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4517 0.2122 0.3409 0.1382 0.5549 0.6897 0.5973 0.6218 0.5998 0.6383 ] Network output: [ 0.09488 0.325 0.6091 -0.000394 -0.0005428 0.8691 0.001654 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5466 0.3435 0.4256 0.1875 0.6363 0.7245 0.6458 0.6443 0.5847 0.6698 ] Network output: [ 0.04894 0.2255 0.6307 0.002416 -0.001333 1.048 0.005934 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5918 0.4308 0.4735 0.2257 0.6428 0.7335 0.6607 0.6508 0.5885 0.6778 ] Network output: [ 0.03312 0.2104 0.6899 0.006676 0.001354 1.059 1.863e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6505 Epoch 117 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2562 0.6693 0.9148 0.001861 0.000287 -0.07613 -0.0006631 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.131 0.02002 0.1091 -0.02951 0.4937 0.6004 0.3746 0.597 0.5184 0.5177 ] Network output: [ 0.1363 0.5403 0.7745 -0.005446 -0.0004524 0.3933 -0.004441 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.355 0.131 0.2742 0.09393 0.5562 0.6876 0.5951 0.6239 0.6 0.6352 ] Network output: [ 0.27 0.5313 0.7394 -0.001374 0.001568 0.1894 0.0005637 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2693 0.09958 0.2413 0.03036 0.512 0.6314 0.4004 0.6115 0.5316 0.5436 ] Network output: [ 0.1696 0.4638 0.6371 -0.004491 -0.0005986 0.5404 -0.003918 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4526 0.2133 0.3408 0.1391 0.5555 0.6909 0.5975 0.6227 0.6004 0.6391 ] Network output: [ 0.09534 0.3234 0.6091 -0.0004457 -0.0005446 0.8697 0.001666 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5456 0.3437 0.4255 0.1873 0.6366 0.7254 0.6439 0.6449 0.5846 0.6702 ] Network output: [ 0.04954 0.2242 0.6309 0.002357 -0.00128 1.048 0.00593 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5904 0.4306 0.4734 0.2245 0.6431 0.7343 0.6586 0.6512 0.5884 0.6781 ] Network output: [ 0.03339 0.2106 0.6897 0.00675 0.001512 1.059 -3.477e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6488 Epoch 118 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2558 0.6728 0.9156 0.001971 0.0002427 -0.07948 -0.0005537 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1301 0.01994 0.1085 -0.03004 0.4938 0.6011 0.3719 0.5975 0.5186 0.5176 ] Network output: [ 0.136 0.5397 0.774 -0.005492 -0.0005463 0.3947 -0.004511 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3555 0.1314 0.2739 0.09488 0.5567 0.6888 0.5954 0.6248 0.6006 0.636 ] Network output: [ 0.2701 0.533 0.7405 -0.001311 0.001526 0.1863 0.0006862 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2676 0.09934 0.2405 0.0299 0.5124 0.6326 0.3973 0.6122 0.5318 0.5436 ] Network output: [ 0.1693 0.462 0.6359 -0.004536 -0.0006662 0.5436 -0.00402 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4535 0.2143 0.3406 0.1401 0.5561 0.6922 0.5978 0.6236 0.601 0.6398 ] Network output: [ 0.09581 0.322 0.6091 -0.0004959 -0.0005475 0.8701 0.001681 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5446 0.3439 0.4252 0.1869 0.637 0.7263 0.642 0.6455 0.5846 0.6706 ] Network output: [ 0.05016 0.223 0.6311 0.0023 -0.001227 1.048 0.005933 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.589 0.4304 0.4732 0.2233 0.6434 0.735 0.6565 0.6517 0.5884 0.6784 ] Network output: [ 0.03365 0.2109 0.6895 0.006826 0.001672 1.059 -8.193e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6472 Epoch 119 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2552 0.6762 0.9164 0.002078 0.0002009 -0.08273 -0.0004494 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1291 0.01985 0.1078 -0.03057 0.4939 0.6018 0.3692 0.598 0.5188 0.5175 ] Network output: [ 0.1356 0.5391 0.7734 -0.00554 -0.0006419 0.3961 -0.004586 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3561 0.1318 0.2736 0.09583 0.5573 0.6899 0.5957 0.6257 0.6012 0.6368 ] Network output: [ 0.2702 0.5348 0.7415 -0.001248 0.001485 0.1833 0.0008051 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2659 0.09907 0.2397 0.02941 0.5128 0.6338 0.3942 0.6128 0.532 0.5436 ] Network output: [ 0.1691 0.4602 0.6348 -0.00458 -0.0007362 0.5468 -0.004127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4544 0.2153 0.3403 0.141 0.5567 0.6934 0.598 0.6246 0.6017 0.6407 ] Network output: [ 0.0963 0.3205 0.6092 -0.0005448 -0.0005515 0.8705 0.001699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5435 0.344 0.425 0.1866 0.6374 0.7272 0.64 0.6461 0.5846 0.671 ] Network output: [ 0.05079 0.2217 0.6314 0.002246 -0.001174 1.048 0.005941 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5875 0.4301 0.4729 0.222 0.6437 0.7358 0.6543 0.6522 0.5883 0.6788 ] Network output: [ 0.03389 0.2113 0.6892 0.006906 0.001835 1.059 -0.0001227 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6455 Epoch 120 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2547 0.6797 0.9172 0.002181 0.0001617 -0.0859 -0.00035 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1282 0.01976 0.1071 -0.03111 0.4941 0.6026 0.3665 0.5985 0.519 0.5175 ] Network output: [ 0.1353 0.5384 0.7728 -0.00559 -0.0007394 0.3975 -0.004666 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3566 0.1322 0.2732 0.09677 0.5578 0.6911 0.596 0.6266 0.6019 0.6376 ] Network output: [ 0.2703 0.5366 0.7426 -0.001187 0.001446 0.1803 0.0009202 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2641 0.09879 0.2389 0.02889 0.5132 0.635 0.3912 0.6136 0.5322 0.5437 ] Network output: [ 0.1689 0.4584 0.6336 -0.004625 -0.0008088 0.55 -0.00424 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4552 0.2163 0.3399 0.1419 0.5574 0.6947 0.5982 0.6255 0.6023 0.6415 ] Network output: [ 0.0968 0.319 0.6093 -0.0005923 -0.0005566 0.8709 0.001719 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5424 0.3441 0.4246 0.1861 0.6379 0.7282 0.638 0.6467 0.5847 0.6715 ] Network output: [ 0.05142 0.2205 0.6316 0.002194 -0.001122 1.047 0.005955 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.586 0.4298 0.4727 0.2207 0.644 0.7367 0.6521 0.6528 0.5883 0.6792 ] Network output: [ 0.03412 0.2117 0.6889 0.006989 0.002 1.059 -0.000157 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6439 Epoch 121 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2541 0.6832 0.9179 0.002281 0.000125 -0.08897 -0.0002553 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1273 0.01966 0.1064 -0.03167 0.4943 0.6034 0.3638 0.599 0.5192 0.5176 ] Network output: [ 0.135 0.5378 0.7723 -0.005643 -0.000839 0.399 -0.004751 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3571 0.1325 0.2727 0.09771 0.5584 0.6923 0.5963 0.6276 0.6025 0.6385 ] Network output: [ 0.2703 0.5385 0.7437 -0.001126 0.001408 0.1773 0.001031 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2624 0.09849 0.238 0.02835 0.5137 0.6362 0.3881 0.6143 0.5325 0.5438 ] Network output: [ 0.1687 0.4565 0.6324 -0.004671 -0.000884 0.5532 -0.004358 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4561 0.2173 0.3394 0.1427 0.558 0.696 0.5985 0.6266 0.603 0.6424 ] Network output: [ 0.09732 0.3175 0.6093 -0.0006386 -0.0005628 0.8712 0.001742 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5412 0.3441 0.4242 0.1857 0.6383 0.7291 0.636 0.6474 0.5847 0.672 ] Network output: [ 0.05207 0.2193 0.6319 0.002144 -0.001069 1.047 0.005975 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5844 0.4294 0.4724 0.2193 0.6444 0.7375 0.6499 0.6533 0.5882 0.6797 ] Network output: [ 0.03432 0.2121 0.6886 0.007075 0.002169 1.059 -0.0001847 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6422 Epoch 122 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2535 0.6867 0.9187 0.002378 9.07e-05 -0.09197 -0.0001653 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1263 0.01955 0.1056 -0.03224 0.4945 0.6042 0.3611 0.5996 0.5195 0.5177 ] Network output: [ 0.1347 0.537 0.7717 -0.005697 -0.0009407 0.4005 -0.004842 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3576 0.1328 0.2721 0.09865 0.5589 0.6935 0.5967 0.6285 0.6032 0.6394 ] Network output: [ 0.2703 0.5404 0.7447 -0.001066 0.001371 0.1744 0.001139 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2607 0.09817 0.237 0.02778 0.5142 0.6375 0.3851 0.6151 0.5328 0.5441 ] Network output: [ 0.1685 0.4546 0.6312 -0.004716 -0.000962 0.5563 -0.004482 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4569 0.2182 0.3388 0.1436 0.5587 0.6974 0.5987 0.6276 0.6036 0.6433 ] Network output: [ 0.09785 0.3161 0.6094 -0.0006836 -0.0005699 0.8714 0.001767 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.54 0.3441 0.4237 0.1851 0.6388 0.7301 0.634 0.6481 0.5848 0.6726 ] Network output: [ 0.05271 0.218 0.6322 0.002095 -0.001017 1.047 0.006 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5829 0.429 0.4721 0.2178 0.6447 0.7384 0.6477 0.654 0.5883 0.6802 ] Network output: [ 0.03451 0.2126 0.6882 0.007163 0.00234 1.059 -0.0002057 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6405 Epoch 123 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2529 0.6902 0.9194 0.002472 5.876e-05 -0.0949 -7.957e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1254 0.01944 0.1047 -0.03282 0.4947 0.605 0.3585 0.6002 0.5198 0.5179 ] Network output: [ 0.1344 0.5363 0.7711 -0.005752 -0.001045 0.4019 -0.004938 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3581 0.1331 0.2714 0.09958 0.5595 0.6947 0.597 0.6296 0.6039 0.6403 ] Network output: [ 0.2703 0.5423 0.7458 -0.001008 0.001335 0.1715 0.001242 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.259 0.09784 0.236 0.02719 0.5147 0.6388 0.3821 0.6159 0.5331 0.5444 ] Network output: [ 0.1684 0.4527 0.6301 -0.004762 -0.001043 0.5595 -0.004612 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4578 0.2191 0.338 0.1444 0.5594 0.6987 0.5989 0.6287 0.6043 0.6443 ] Network output: [ 0.0984 0.3147 0.6096 -0.0007275 -0.0005781 0.8715 0.001794 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5388 0.3441 0.4231 0.1846 0.6393 0.7311 0.6319 0.6488 0.5849 0.6732 ] Network output: [ 0.05336 0.2169 0.6325 0.002048 -0.0009649 1.046 0.006031 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5813 0.4286 0.4717 0.2163 0.6451 0.7393 0.6455 0.6546 0.5883 0.6808 ] Network output: [ 0.03467 0.2132 0.6879 0.007253 0.002515 1.06 -0.0002198 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6387 Epoch 124 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2522 0.6937 0.9201 0.002564 2.913e-05 -0.09775 1.913e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1244 0.01932 0.1039 -0.03342 0.495 0.6059 0.3559 0.6008 0.5201 0.5182 ] Network output: [ 0.1342 0.5355 0.7705 -0.00581 -0.001151 0.4034 -0.005041 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3586 0.1333 0.2706 0.1005 0.5601 0.6959 0.5973 0.6306 0.6046 0.6413 ] Network output: [ 0.2703 0.5442 0.7469 -0.0009497 0.0013 0.1686 0.001341 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2573 0.09749 0.2349 0.02656 0.5153 0.6401 0.3791 0.6168 0.5334 0.5448 ] Network output: [ 0.1683 0.4508 0.6289 -0.004807 -0.001127 0.5626 -0.004748 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4586 0.22 0.3372 0.1453 0.5601 0.7 0.5991 0.6298 0.6051 0.6453 ] Network output: [ 0.09895 0.3132 0.6097 -0.0007704 -0.0005872 0.8716 0.001824 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5376 0.3441 0.4225 0.1839 0.6398 0.7321 0.6298 0.6496 0.5851 0.6739 ] Network output: [ 0.05402 0.2157 0.6328 0.002003 -0.0009125 1.046 0.006068 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5797 0.4282 0.4714 0.2147 0.6456 0.7402 0.6432 0.6554 0.5884 0.6815 ] Network output: [ 0.0348 0.2138 0.6874 0.007346 0.002694 1.06 -0.0002268 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.637 Epoch 125 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2515 0.6972 0.9208 0.002652 1.725e-06 -0.1005 7.941e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1235 0.0192 0.1029 -0.03404 0.4953 0.6068 0.3533 0.6014 0.5204 0.5185 ] Network output: [ 0.134 0.5347 0.77 -0.005869 -0.001261 0.4048 -0.005148 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.359 0.1336 0.2697 0.1014 0.5607 0.6972 0.5976 0.6317 0.6053 0.6423 ] Network output: [ 0.2702 0.5462 0.748 -0.0008923 0.001267 0.1657 0.001436 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2556 0.09713 0.2337 0.0259 0.5159 0.6415 0.3762 0.6178 0.5338 0.5453 ] Network output: [ 0.1682 0.4489 0.6277 -0.004853 -0.001215 0.5657 -0.00489 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4594 0.2208 0.3361 0.1461 0.5608 0.7014 0.5994 0.6309 0.6058 0.6463 ] Network output: [ 0.09952 0.3119 0.6099 -0.0008122 -0.0005973 0.8716 0.001855 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5363 0.344 0.4218 0.1833 0.6404 0.7332 0.6277 0.6505 0.5852 0.6747 ] Network output: [ 0.05467 0.2145 0.633 0.001959 -0.0008597 1.045 0.006111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5781 0.4277 0.471 0.213 0.646 0.7411 0.6409 0.6561 0.5886 0.6822 ] Network output: [ 0.03491 0.2144 0.687 0.007441 0.002877 1.06 -0.0002267 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6352 Epoch 126 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2508 0.7007 0.9215 0.002738 -2.35e-05 -0.1033 0.0001532 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1226 0.01907 0.1019 -0.03468 0.4957 0.6077 0.3507 0.6021 0.5208 0.5189 ] Network output: [ 0.1338 0.5338 0.7694 -0.00593 -0.001372 0.4062 -0.005262 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3595 0.1338 0.2687 0.1024 0.5614 0.6984 0.5979 0.6328 0.6061 0.6433 ] Network output: [ 0.2702 0.5482 0.749 -0.0008354 0.001234 0.1628 0.001527 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2539 0.09674 0.2324 0.02521 0.5165 0.6429 0.3732 0.6187 0.5343 0.5459 ] Network output: [ 0.1681 0.447 0.6265 -0.004898 -0.001306 0.5688 -0.00504 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4602 0.2216 0.335 0.1468 0.5615 0.7028 0.5996 0.6321 0.6066 0.6474 ] Network output: [ 0.1001 0.3105 0.6101 -0.000853 -0.0006083 0.8716 0.001889 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.535 0.3438 0.421 0.1825 0.6409 0.7342 0.6256 0.6514 0.5855 0.6755 ] Network output: [ 0.05531 0.2134 0.6333 0.001917 -0.0008065 1.045 0.006159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5764 0.4273 0.4705 0.2113 0.6465 0.7421 0.6387 0.657 0.5887 0.683 ] Network output: [ 0.03498 0.2151 0.6865 0.007538 0.003064 1.06 -0.0002193 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6334 Epoch 127 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.25 0.7043 0.9222 0.002821 -4.659e-05 -0.1059 0.0002234 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1216 0.01893 0.1008 -0.03534 0.496 0.6086 0.3482 0.6029 0.5212 0.5195 ] Network output: [ 0.1336 0.5329 0.7688 -0.005993 -0.001487 0.4076 -0.005381 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3599 0.1339 0.2675 0.1033 0.562 0.6997 0.5982 0.634 0.6068 0.6444 ] Network output: [ 0.2701 0.5502 0.7501 -0.0007791 0.001203 0.1599 0.001613 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2522 0.09635 0.231 0.02449 0.5172 0.6443 0.3703 0.6198 0.5348 0.5467 ] Network output: [ 0.168 0.445 0.6254 -0.004944 -0.0014 0.5718 -0.005196 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.461 0.2224 0.3337 0.1476 0.5623 0.7042 0.5998 0.6334 0.6074 0.6486 ] Network output: [ 0.1007 0.3091 0.6103 -0.0008928 -0.0006202 0.8715 0.001925 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5337 0.3437 0.4202 0.1818 0.6415 0.7353 0.6235 0.6524 0.5857 0.6765 ] Network output: [ 0.05596 0.2123 0.6336 0.001876 -0.0007529 1.044 0.006213 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5748 0.4268 0.47 0.2095 0.647 0.7431 0.6364 0.6579 0.589 0.6839 ] Network output: [ 0.03502 0.2159 0.6859 0.007638 0.003255 1.061 -0.0002044 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6316 Epoch 128 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2492 0.7078 0.9228 0.002902 -6.76e-05 -0.1085 0.0002905 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1207 0.01878 0.09971 -0.03601 0.4964 0.6096 0.3457 0.6037 0.5217 0.52 ] Network output: [ 0.1335 0.5319 0.7683 -0.006057 -0.001606 0.409 -0.005506 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3603 0.1341 0.2663 0.1042 0.5627 0.701 0.5985 0.6352 0.6076 0.6455 ] Network output: [ 0.27 0.5523 0.7512 -0.0007231 0.001173 0.1571 0.001694 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2505 0.09594 0.2295 0.02373 0.5179 0.6458 0.3674 0.6209 0.5353 0.5475 ] Network output: [ 0.168 0.443 0.6242 -0.004989 -0.001499 0.5748 -0.00536 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4618 0.2232 0.3322 0.1484 0.5631 0.7057 0.6001 0.6347 0.6082 0.6498 ] Network output: [ 0.1013 0.3078 0.6105 -0.0009318 -0.0006331 0.8714 0.001962 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5324 0.3435 0.4192 0.1809 0.6422 0.7365 0.6213 0.6534 0.5861 0.6774 ] Network output: [ 0.0566 0.2112 0.634 0.001835 -0.0006987 1.044 0.006273 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5731 0.4263 0.4695 0.2077 0.6475 0.7441 0.6341 0.6588 0.5893 0.6848 ] Network output: [ 0.03503 0.2168 0.6854 0.007739 0.003451 1.061 -0.0001819 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6298 Epoch 129 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2484 0.7113 0.9235 0.00298 -8.656e-05 -0.111 0.0003546 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1198 0.01863 0.09851 -0.03671 0.4968 0.6106 0.3432 0.6045 0.5222 0.5207 ] Network output: [ 0.1333 0.5309 0.7677 -0.006122 -0.001727 0.4104 -0.005636 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3607 0.1342 0.2649 0.1051 0.5634 0.7023 0.5988 0.6364 0.6085 0.6467 ] Network output: [ 0.2698 0.5544 0.7523 -0.0006674 0.001144 0.1543 0.001771 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2488 0.09551 0.228 0.02294 0.5187 0.6473 0.3645 0.6221 0.5359 0.5484 ] Network output: [ 0.168 0.441 0.623 -0.005034 -0.001602 0.5778 -0.005531 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4625 0.2239 0.3306 0.1491 0.5639 0.7071 0.6003 0.636 0.6091 0.651 ] Network output: [ 0.1019 0.3065 0.6107 -0.0009699 -0.0006468 0.8712 0.002002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.531 0.3433 0.4182 0.18 0.6429 0.7376 0.6192 0.6545 0.5864 0.6785 ] Network output: [ 0.05723 0.2101 0.6343 0.001796 -0.0006438 1.043 0.006339 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5715 0.4258 0.469 0.2057 0.6481 0.7452 0.6318 0.6598 0.5896 0.6859 ] Network output: [ 0.035 0.2177 0.6847 0.007842 0.003652 1.062 -0.0001516 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6279 Epoch 130 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2476 0.7147 0.9242 0.003056 -0.0001035 -0.1135 0.0004161 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1188 0.01847 0.09725 -0.03742 0.4973 0.6116 0.3408 0.6054 0.5227 0.5215 ] Network output: [ 0.1333 0.5299 0.7672 -0.006189 -0.001852 0.4117 -0.005772 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.361 0.1342 0.2633 0.106 0.5641 0.7037 0.5991 0.6377 0.6093 0.6479 ] Network output: [ 0.2697 0.5565 0.7534 -0.0006118 0.001116 0.1514 0.001844 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2471 0.09507 0.2263 0.02211 0.5195 0.6488 0.3616 0.6233 0.5365 0.5495 ] Network output: [ 0.1681 0.439 0.6218 -0.005078 -0.001709 0.5807 -0.00571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4633 0.2246 0.3288 0.1498 0.5647 0.7086 0.6005 0.6374 0.61 0.6523 ] Network output: [ 0.1025 0.3052 0.6109 -0.001007 -0.0006616 0.8709 0.002042 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5297 0.343 0.4171 0.1791 0.6436 0.7388 0.617 0.6556 0.5868 0.6796 ] Network output: [ 0.05785 0.209 0.6346 0.001758 -0.0005882 1.043 0.00641 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5698 0.4253 0.4684 0.2037 0.6487 0.7463 0.6295 0.6609 0.59 0.687 ] Network output: [ 0.03492 0.2187 0.6841 0.007946 0.003858 1.062 -0.0001134 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.626 Epoch 131 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2468 0.7182 0.9248 0.003129 -0.0001185 -0.1159 0.0004753 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1179 0.01831 0.09592 -0.03816 0.4978 0.6126 0.3383 0.6063 0.5233 0.5224 ] Network output: [ 0.1332 0.5288 0.7667 -0.006257 -0.00198 0.413 -0.005914 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3614 0.1343 0.2616 0.1069 0.5648 0.705 0.5995 0.6391 0.6102 0.6492 ] Network output: [ 0.2695 0.5587 0.7545 -0.0005564 0.001088 0.1486 0.001911 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2454 0.09461 0.2245 0.02124 0.5203 0.6504 0.3588 0.6246 0.5372 0.5506 ] Network output: [ 0.1681 0.437 0.6207 -0.005122 -0.001821 0.5836 -0.005897 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4641 0.2253 0.3268 0.1505 0.5656 0.7101 0.6008 0.6389 0.611 0.6537 ] Network output: [ 0.1031 0.3039 0.6112 -0.001044 -0.0006773 0.8706 0.002084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5283 0.3428 0.4158 0.1781 0.6443 0.74 0.6148 0.6569 0.5873 0.6808 ] Network output: [ 0.05846 0.208 0.6349 0.00172 -0.0005317 1.042 0.006488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5681 0.4247 0.4677 0.2016 0.6494 0.7474 0.6272 0.6621 0.5905 0.6882 ] Network output: [ 0.0348 0.2198 0.6834 0.008053 0.00407 1.063 -6.71e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6241 Epoch 132 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2459 0.7217 0.9255 0.003201 -0.0001314 -0.1183 0.0005326 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.117 0.01813 0.09452 -0.03893 0.4983 0.6137 0.336 0.6072 0.5239 0.5233 ] Network output: [ 0.1332 0.5276 0.7661 -0.006326 -0.002113 0.4142 -0.006062 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3617 0.1343 0.2598 0.1078 0.5656 0.7064 0.5998 0.6405 0.6112 0.6506 ] Network output: [ 0.2693 0.5609 0.7556 -0.000501 0.001062 0.1458 0.001974 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2437 0.09414 0.2226 0.02034 0.5212 0.652 0.356 0.626 0.538 0.5519 ] Network output: [ 0.1682 0.4349 0.6195 -0.005166 -0.001937 0.5865 -0.006093 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4648 0.2259 0.3246 0.1512 0.5664 0.7116 0.6011 0.6404 0.612 0.6552 ] Network output: [ 0.1037 0.3027 0.6115 -0.001079 -0.000694 0.8703 0.002127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5269 0.3425 0.4145 0.177 0.6451 0.7413 0.6126 0.6582 0.5879 0.6821 ] Network output: [ 0.05906 0.207 0.6352 0.001684 -0.0004742 1.042 0.006571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5664 0.4242 0.4671 0.1995 0.65 0.7486 0.6249 0.6633 0.591 0.6895 ] Network output: [ 0.03463 0.2209 0.6826 0.00816 0.004288 1.063 -1.255e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6221 Epoch 133 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.245 0.7251 0.9261 0.003269 -0.0001424 -0.1206 0.0005884 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1161 0.01795 0.09304 -0.03971 0.4989 0.6147 0.3336 0.6082 0.5246 0.5244 ] Network output: [ 0.1333 0.5264 0.7656 -0.006397 -0.002249 0.4155 -0.006216 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.362 0.1342 0.2578 0.1087 0.5664 0.7078 0.6001 0.6419 0.6122 0.652 ] Network output: [ 0.2691 0.5631 0.7567 -0.0004455 0.001036 0.143 0.002032 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2421 0.09366 0.2206 0.0194 0.5221 0.6536 0.3533 0.6274 0.5388 0.5533 ] Network output: [ 0.1684 0.4328 0.6183 -0.005209 -0.002059 0.5892 -0.006298 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4656 0.2265 0.3222 0.1518 0.5673 0.7132 0.6013 0.642 0.613 0.6567 ] Network output: [ 0.1043 0.3015 0.6118 -0.001115 -0.0007117 0.8699 0.002172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5255 0.3421 0.413 0.1759 0.6459 0.7426 0.6104 0.6595 0.5885 0.6835 ] Network output: [ 0.05964 0.206 0.6355 0.001647 -0.0004158 1.041 0.00666 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5647 0.4236 0.4663 0.1973 0.6508 0.7497 0.6226 0.6646 0.5916 0.6909 ] Network output: [ 0.03441 0.2222 0.6818 0.00827 0.004511 1.064 5.04e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6202 Epoch 134 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2441 0.7285 0.9268 0.003336 -0.0001513 -0.1228 0.000643 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1152 0.01776 0.09149 -0.04052 0.4995 0.6159 0.3313 0.6093 0.5254 0.5255 ] Network output: [ 0.1334 0.5251 0.7651 -0.006469 -0.002389 0.4167 -0.006375 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3623 0.1342 0.2557 0.1096 0.5672 0.7092 0.6005 0.6434 0.6132 0.6534 ] Network output: [ 0.2688 0.5654 0.7578 -0.0003899 0.001011 0.1402 0.002085 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2404 0.09316 0.2184 0.01842 0.5231 0.6553 0.3505 0.6289 0.5398 0.5548 ] Network output: [ 0.1685 0.4307 0.6172 -0.005251 -0.002186 0.592 -0.006512 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4664 0.2271 0.3196 0.1525 0.5683 0.7148 0.6016 0.6437 0.6141 0.6583 ] Network output: [ 0.1049 0.3003 0.6121 -0.001149 -0.0007306 0.8694 0.002217 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.524 0.3418 0.4115 0.1748 0.6467 0.7439 0.6082 0.661 0.5892 0.685 ] Network output: [ 0.06021 0.205 0.6358 0.001611 -0.0003563 1.041 0.006754 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.563 0.423 0.4656 0.195 0.6515 0.751 0.6203 0.666 0.5923 0.6924 ] Network output: [ 0.03414 0.2236 0.6809 0.00838 0.004741 1.065 0.0001219 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6181 Epoch 135 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2432 0.7319 0.9274 0.0034 -0.0001582 -0.125 0.000697 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1142 0.01756 0.08987 -0.04135 0.5001 0.617 0.329 0.6104 0.5262 0.5268 ] Network output: [ 0.1335 0.5237 0.7647 -0.006542 -0.002533 0.4178 -0.006539 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3626 0.1341 0.2534 0.1105 0.568 0.7107 0.6009 0.645 0.6143 0.6549 ] Network output: [ 0.2686 0.5677 0.7589 -0.000334 0.0009869 0.1374 0.002132 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2388 0.09264 0.2161 0.0174 0.5241 0.657 0.3478 0.6305 0.5407 0.5565 ] Network output: [ 0.1688 0.4286 0.616 -0.005293 -0.002318 0.5947 -0.006736 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4671 0.2276 0.3168 0.1531 0.5693 0.7164 0.6019 0.6454 0.6153 0.6599 ] Network output: [ 0.1056 0.2992 0.6124 -0.001183 -0.0007506 0.8689 0.002262 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5226 0.3414 0.4098 0.1735 0.6476 0.7452 0.606 0.6625 0.5899 0.6866 ] Network output: [ 0.06076 0.2041 0.636 0.001576 -0.0002957 1.04 0.006854 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5614 0.4225 0.4647 0.1926 0.6523 0.7522 0.618 0.6674 0.593 0.694 ] Network output: [ 0.0338 0.225 0.68 0.008491 0.004978 1.065 0.000202 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6161 Epoch 136 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2423 0.7352 0.9281 0.003463 -0.000163 -0.1271 0.0007508 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1133 0.01736 0.08817 -0.04221 0.5008 0.6182 0.3268 0.6116 0.527 0.5281 ] Network output: [ 0.1337 0.5223 0.7642 -0.006617 -0.002682 0.4189 -0.006709 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3629 0.1339 0.2509 0.1114 0.5689 0.7121 0.6012 0.6466 0.6154 0.6565 ] Network output: [ 0.2683 0.57 0.76 -0.0002778 0.0009631 0.1346 0.002175 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2372 0.09211 0.2137 0.01635 0.5252 0.6588 0.3452 0.6322 0.5418 0.5582 ] Network output: [ 0.169 0.4265 0.6149 -0.005334 -0.002456 0.5973 -0.006969 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4679 0.2281 0.3138 0.1538 0.5703 0.718 0.6022 0.6472 0.6165 0.6616 ] Network output: [ 0.1062 0.2981 0.6127 -0.001216 -0.0007719 0.8683 0.002309 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5211 0.341 0.408 0.1723 0.6485 0.7466 0.6038 0.6641 0.5908 0.6882 ] Network output: [ 0.0613 0.2032 0.6363 0.00154 -0.0002339 1.04 0.00696 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5597 0.4219 0.4639 0.1901 0.6531 0.7535 0.6157 0.669 0.5939 0.6957 ] Network output: [ 0.03341 0.2266 0.679 0.008604 0.005221 1.066 0.000291 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.614 Epoch 137 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2414 0.7385 0.9287 0.003523 -0.0001657 -0.1291 0.0008049 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1124 0.01714 0.0864 -0.04309 0.5015 0.6193 0.3246 0.6128 0.528 0.5295 ] Network output: [ 0.1339 0.5207 0.7638 -0.006693 -0.002835 0.42 -0.006884 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3632 0.1338 0.2483 0.1123 0.5698 0.7136 0.6016 0.6483 0.6166 0.6581 ] Network output: [ 0.268 0.5724 0.7612 -0.0002213 0.0009398 0.1318 0.002213 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2356 0.09157 0.2111 0.01526 0.5263 0.6606 0.3425 0.634 0.543 0.5602 ] Network output: [ 0.1693 0.4243 0.6137 -0.005375 -0.002601 0.5998 -0.007212 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4687 0.2286 0.3105 0.1544 0.5713 0.7197 0.6026 0.649 0.6178 0.6634 ] Network output: [ 0.1068 0.297 0.613 -0.001248 -0.0007945 0.8677 0.002355 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5197 0.3406 0.406 0.1709 0.6494 0.748 0.6016 0.6658 0.5917 0.69 ] Network output: [ 0.06181 0.2024 0.6366 0.001505 -0.000171 1.039 0.007072 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.558 0.4213 0.4629 0.1876 0.654 0.7548 0.6134 0.6706 0.5948 0.6974 ] Network output: [ 0.03295 0.2284 0.678 0.008717 0.005472 1.067 0.0003887 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6118 Epoch 138 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2405 0.7417 0.9294 0.003581 -0.000166 -0.1311 0.0008598 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1115 0.01692 0.08455 -0.044 0.5022 0.6206 0.3224 0.6141 0.5289 0.5311 ] Network output: [ 0.1342 0.5191 0.7634 -0.00677 -0.002992 0.421 -0.007064 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3635 0.1336 0.2455 0.1132 0.5707 0.7152 0.6021 0.6501 0.6179 0.6599 ] Network output: [ 0.2677 0.5748 0.7623 -0.0001643 0.0009169 0.129 0.002245 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.234 0.09101 0.2084 0.01414 0.5275 0.6624 0.3399 0.6359 0.5442 0.5622 ] Network output: [ 0.1697 0.4221 0.6126 -0.005415 -0.002751 0.6023 -0.007466 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4694 0.229 0.3071 0.155 0.5724 0.7214 0.6029 0.651 0.6191 0.6653 ] Network output: [ 0.1075 0.296 0.6134 -0.001281 -0.0008185 0.867 0.002402 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5182 0.3401 0.404 0.1696 0.6504 0.7494 0.5994 0.6676 0.5927 0.6918 ] Network output: [ 0.0623 0.2015 0.6369 0.001469 -0.000107 1.039 0.007189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5563 0.4207 0.462 0.185 0.6549 0.7561 0.6111 0.6723 0.5958 0.6993 ] Network output: [ 0.03242 0.2302 0.6769 0.008831 0.005729 1.068 0.0004954 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6096 Epoch 139 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2395 0.7449 0.93 0.003636 -0.000164 -0.133 0.0009161 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1106 0.01668 0.08263 -0.04492 0.503 0.6218 0.3203 0.6155 0.53 0.5327 ] Network output: [ 0.1346 0.5174 0.763 -0.006849 -0.003154 0.422 -0.007249 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3637 0.1333 0.2426 0.1141 0.5716 0.7167 0.6025 0.6519 0.6192 0.6616 ] Network output: [ 0.2674 0.5772 0.7634 -0.0001069 0.0008944 0.1263 0.002273 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2324 0.09044 0.2055 0.01298 0.5287 0.6643 0.3374 0.6378 0.5456 0.5644 ] Network output: [ 0.1701 0.4198 0.6114 -0.005453 -0.002908 0.6048 -0.007731 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4702 0.2294 0.3033 0.1557 0.5735 0.7231 0.6033 0.653 0.6205 0.6673 ] Network output: [ 0.1081 0.295 0.6138 -0.001312 -0.0008442 0.8663 0.002448 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5167 0.3396 0.4018 0.1681 0.6515 0.7509 0.5972 0.6695 0.5938 0.6938 ] Network output: [ 0.06277 0.2008 0.6371 0.001434 -4.185e-05 1.038 0.007311 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5547 0.4201 0.4609 0.1823 0.6558 0.7575 0.6089 0.6742 0.597 0.7013 ] Network output: [ 0.03182 0.2323 0.6757 0.008945 0.005994 1.069 0.000611 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6074 Epoch 140 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2385 0.7481 0.9307 0.00369 -0.0001595 -0.1348 0.0009743 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1097 0.01644 0.08064 -0.04587 0.5038 0.6231 0.3182 0.6169 0.5311 0.5345 ] Network output: [ 0.135 0.5155 0.7626 -0.006928 -0.00332 0.423 -0.007438 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3639 0.133 0.2395 0.1151 0.5726 0.7183 0.6029 0.6538 0.6205 0.6635 ] Network output: [ 0.2671 0.5797 0.7645 -4.897e-05 0.0008722 0.1235 0.002296 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2308 0.08985 0.2025 0.0118 0.5299 0.6662 0.3349 0.6398 0.547 0.5667 ] Network output: [ 0.1706 0.4175 0.6103 -0.005492 -0.003071 0.6071 -0.008006 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.471 0.2298 0.2994 0.1563 0.5747 0.7249 0.6037 0.6551 0.622 0.6693 ] Network output: [ 0.1087 0.294 0.6141 -0.001343 -0.0008715 0.8655 0.002494 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5153 0.3391 0.3994 0.1666 0.6525 0.7524 0.595 0.6714 0.5951 0.6958 ] Network output: [ 0.06321 0.2 0.6374 0.001398 2.433e-05 1.038 0.007439 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.553 0.4195 0.4598 0.1795 0.6568 0.7589 0.6066 0.6761 0.5982 0.7034 ] Network output: [ 0.03114 0.2344 0.6745 0.009059 0.006265 1.07 0.0007356 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6051 Epoch 141 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2376 0.7511 0.9314 0.003741 -0.0001524 -0.1366 0.001035 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1089 0.01619 0.07858 -0.04684 0.5046 0.6244 0.3162 0.6184 0.5323 0.5364 ] Network output: [ 0.1355 0.5135 0.7623 -0.00701 -0.00349 0.4239 -0.007632 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3642 0.1327 0.2362 0.116 0.5737 0.7199 0.6034 0.6558 0.622 0.6654 ] Network output: [ 0.2667 0.5822 0.7657 9.504e-06 0.0008503 0.1207 0.002314 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2293 0.08924 0.1994 0.01058 0.5313 0.6682 0.3324 0.642 0.5485 0.5691 ] Network output: [ 0.1711 0.4152 0.6091 -0.005529 -0.003241 0.6094 -0.008292 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4718 0.2301 0.2951 0.157 0.5759 0.7267 0.6041 0.6573 0.6236 0.6714 ] Network output: [ 0.1094 0.2931 0.6145 -0.001374 -0.0009007 0.8647 0.002539 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5138 0.3385 0.3969 0.1651 0.6537 0.7539 0.5928 0.6735 0.5964 0.6979 ] Network output: [ 0.06362 0.1994 0.6376 0.001361 9.145e-05 1.037 0.007572 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5514 0.4189 0.4586 0.1767 0.6578 0.7604 0.6044 0.6781 0.5995 0.7056 ] Network output: [ 0.03037 0.2368 0.6732 0.009173 0.006545 1.071 0.000869 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6027 Epoch 142 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2366 0.7541 0.9321 0.00379 -0.0001424 -0.1383 0.001099 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.108 0.01593 0.07645 -0.04783 0.5055 0.6257 0.3141 0.6199 0.5336 0.5383 ] Network output: [ 0.136 0.5114 0.762 -0.007092 -0.003665 0.4248 -0.007829 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3644 0.1323 0.2327 0.117 0.5747 0.7215 0.6039 0.6578 0.6235 0.6674 ] Network output: [ 0.2664 0.5847 0.7668 6.851e-05 0.0008286 0.118 0.002327 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2277 0.08862 0.1961 0.009347 0.5326 0.6702 0.3299 0.6442 0.5502 0.5717 ] Network output: [ 0.1717 0.4127 0.608 -0.005565 -0.003418 0.6117 -0.008589 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4726 0.2304 0.2907 0.1576 0.5771 0.7285 0.6045 0.6596 0.6252 0.6736 ] Network output: [ 0.11 0.2922 0.6149 -0.001405 -0.000932 0.8638 0.002584 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5123 0.338 0.3943 0.1636 0.6548 0.7555 0.5906 0.6756 0.5978 0.7002 ] Network output: [ 0.06401 0.1987 0.6378 0.001325 0.0001594 1.037 0.00771 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5497 0.4182 0.4574 0.1738 0.6589 0.7619 0.6022 0.6802 0.601 0.7078 ] Network output: [ 0.02952 0.2393 0.6718 0.009287 0.006831 1.073 0.001011 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6003 Epoch 143 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2356 0.7571 0.9327 0.003838 -0.0001295 -0.1399 0.001166 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1071 0.01566 0.07427 -0.04884 0.5065 0.6271 0.3122 0.6215 0.535 0.5404 ] Network output: [ 0.1366 0.5091 0.7617 -0.007176 -0.003843 0.4257 -0.008029 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3646 0.1319 0.2291 0.118 0.5758 0.7232 0.6044 0.66 0.6251 0.6695 ] Network output: [ 0.266 0.5872 0.7679 0.000128 0.0008071 0.1152 0.002335 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2262 0.08798 0.1926 0.008092 0.534 0.6722 0.3275 0.6465 0.552 0.5744 ] Network output: [ 0.1723 0.4102 0.6069 -0.005601 -0.003602 0.6138 -0.008897 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4734 0.2306 0.2859 0.1583 0.5784 0.7303 0.605 0.6619 0.627 0.6759 ] Network output: [ 0.1107 0.2913 0.6153 -0.001435 -0.0009654 0.8628 0.002628 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5108 0.3374 0.3915 0.162 0.656 0.7571 0.5884 0.6779 0.5993 0.7025 ] Network output: [ 0.06436 0.1982 0.638 0.001287 0.0002279 1.036 0.007852 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5481 0.4176 0.4561 0.1708 0.66 0.7634 0.6 0.6824 0.6026 0.7102 ] Network output: [ 0.02857 0.2421 0.6703 0.009401 0.007124 1.074 0.001162 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5978 Epoch 144 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2346 0.7599 0.9334 0.003883 -0.0001133 -0.1414 0.001238 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1062 0.01538 0.07202 -0.04987 0.5074 0.6284 0.3102 0.6232 0.5364 0.5426 ] Network output: [ 0.1373 0.5067 0.7615 -0.007261 -0.004025 0.4266 -0.008233 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3648 0.1314 0.2253 0.1191 0.577 0.7249 0.6049 0.6621 0.6268 0.6716 ] Network output: [ 0.2656 0.5898 0.769 0.0001881 0.0007857 0.1125 0.00234 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2247 0.08732 0.189 0.006824 0.5355 0.6743 0.3251 0.6489 0.5538 0.5773 ] Network output: [ 0.1731 0.4076 0.6058 -0.005636 -0.003792 0.6159 -0.009216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4742 0.2308 0.281 0.159 0.5797 0.7322 0.6054 0.6644 0.6288 0.6783 ] Network output: [ 0.1113 0.2905 0.6157 -0.001465 -0.001001 0.8618 0.00267 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5094 0.3367 0.3885 0.1604 0.6573 0.7587 0.5862 0.6802 0.601 0.7049 ] Network output: [ 0.06469 0.1977 0.6382 0.001249 0.0002969 1.036 0.007999 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5465 0.417 0.4547 0.1678 0.6612 0.7649 0.5978 0.6847 0.6042 0.7127 ] Network output: [ 0.02753 0.2451 0.6688 0.009513 0.007425 1.075 0.001321 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5953 Epoch 145 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2336 0.7627 0.9342 0.003926 -9.36e-05 -0.1429 0.001315 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1054 0.01509 0.06973 -0.05091 0.5085 0.6298 0.3083 0.6249 0.538 0.5448 ] Network output: [ 0.1381 0.5041 0.7613 -0.007347 -0.004211 0.4274 -0.008438 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.365 0.1309 0.2214 0.1201 0.5782 0.7266 0.6055 0.6644 0.6285 0.6739 ] Network output: [ 0.2652 0.5924 0.7702 0.0002486 0.0007645 0.1098 0.00234 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2232 0.08665 0.1852 0.005547 0.5371 0.6765 0.3228 0.6514 0.5558 0.5803 ] Network output: [ 0.1739 0.405 0.6048 -0.00567 -0.003989 0.618 -0.009546 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.475 0.2309 0.2757 0.1598 0.5811 0.7341 0.6059 0.6669 0.6307 0.6807 ] Network output: [ 0.112 0.2897 0.6162 -0.001494 -0.00104 0.8608 0.002711 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5079 0.336 0.3854 0.1588 0.6585 0.7603 0.5841 0.6827 0.6027 0.7074 ] Network output: [ 0.06498 0.1972 0.6384 0.001209 0.0003661 1.036 0.00815 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5449 0.4163 0.4533 0.1647 0.6624 0.7665 0.5956 0.6871 0.606 0.7153 ] Network output: [ 0.02638 0.2483 0.6671 0.009624 0.007731 1.076 0.001487 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5926 Epoch 146 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2325 0.7654 0.9349 0.003967 -7.023e-05 -0.1442 0.001398 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1045 0.01478 0.0674 -0.05196 0.5095 0.6313 0.3065 0.6267 0.5396 0.5472 ] Network output: [ 0.1389 0.5013 0.7612 -0.007435 -0.0044 0.4282 -0.008645 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3652 0.1303 0.2173 0.1213 0.5794 0.7284 0.6061 0.6668 0.6304 0.6762 ] Network output: [ 0.2648 0.5949 0.7713 0.0003095 0.0007435 0.1071 0.002337 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2218 0.08595 0.1813 0.004268 0.5387 0.6786 0.3205 0.654 0.558 0.5834 ] Network output: [ 0.1748 0.4021 0.6037 -0.005703 -0.004193 0.6199 -0.009886 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4759 0.231 0.2703 0.1606 0.5825 0.7361 0.6065 0.6695 0.6327 0.6832 ] Network output: [ 0.1127 0.2889 0.6166 -0.001524 -0.001081 0.8597 0.00275 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5064 0.3353 0.3822 0.1571 0.6599 0.762 0.5819 0.6852 0.6046 0.71 ] Network output: [ 0.06523 0.1968 0.6385 0.001169 0.0004351 1.035 0.008305 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5433 0.4156 0.4518 0.1616 0.6637 0.7682 0.5935 0.6895 0.608 0.7179 ] Network output: [ 0.02513 0.2517 0.6654 0.009734 0.008044 1.078 0.001662 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5899 Epoch 147 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2315 0.768 0.9356 0.004006 -4.289e-05 -0.1455 0.001486 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1036 0.01447 0.06504 -0.05302 0.5106 0.6328 0.3046 0.6286 0.5413 0.5496 ] Network output: [ 0.1399 0.4983 0.7611 -0.007523 -0.004591 0.4289 -0.008853 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3654 0.1297 0.2131 0.1224 0.5807 0.7302 0.6067 0.6692 0.6323 0.6785 ] Network output: [ 0.2644 0.5975 0.7724 0.0003707 0.0007226 0.1044 0.002331 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2203 0.08523 0.1773 0.002995 0.5403 0.6809 0.3182 0.6567 0.5602 0.5866 ] Network output: [ 0.1757 0.3992 0.6027 -0.005735 -0.004404 0.6219 -0.01024 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4767 0.2311 0.2646 0.1615 0.584 0.7381 0.607 0.6722 0.6348 0.6859 ] Network output: [ 0.1133 0.2882 0.617 -0.001554 -0.001125 0.8585 0.002787 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5049 0.3346 0.3788 0.1555 0.6613 0.7638 0.5797 0.6879 0.6066 0.7127 ] Network output: [ 0.06546 0.1965 0.6386 0.001127 0.0005037 1.035 0.008464 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5417 0.4149 0.4503 0.1584 0.665 0.7698 0.5914 0.6921 0.61 0.7206 ] Network output: [ 0.02376 0.2554 0.6636 0.009841 0.008362 1.079 0.001843 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.587 Epoch 148 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2304 0.7706 0.9364 0.004044 -1.129e-05 -0.1467 0.001582 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1028 0.01415 0.06264 -0.05408 0.5118 0.6342 0.3028 0.6305 0.5431 0.5522 ] Network output: [ 0.1409 0.495 0.7611 -0.007613 -0.004784 0.4297 -0.009061 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3656 0.1291 0.2087 0.1236 0.582 0.732 0.6073 0.6717 0.6343 0.681 ] Network output: [ 0.264 0.6001 0.7736 0.0004322 0.000702 0.1017 0.002323 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2189 0.0845 0.1732 0.001735 0.542 0.6831 0.316 0.6595 0.5626 0.59 ] Network output: [ 0.1768 0.3961 0.6017 -0.005767 -0.00462 0.6237 -0.01059 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4776 0.231 0.2586 0.1624 0.5855 0.7401 0.6076 0.675 0.637 0.6885 ] Network output: [ 0.114 0.2874 0.6175 -0.001584 -0.001172 0.8573 0.002823 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5034 0.3338 0.3753 0.1539 0.6627 0.7655 0.5776 0.6906 0.6088 0.7155 ] Network output: [ 0.06564 0.1963 0.6387 0.001083 0.0005714 1.034 0.008625 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5402 0.4142 0.4487 0.1552 0.6663 0.7715 0.5893 0.6948 0.6122 0.7235 ] Network output: [ 0.02228 0.2595 0.6617 0.009946 0.008686 1.081 0.00203 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5841 Epoch 149 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2294 0.773 0.9372 0.00408 2.484e-05 -0.1477 0.001684 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1019 0.01381 0.06024 -0.05515 0.513 0.6358 0.3011 0.6325 0.5449 0.5548 ] Network output: [ 0.142 0.4916 0.7611 -0.007703 -0.004979 0.4304 -0.009269 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3658 0.1283 0.2043 0.1249 0.5833 0.7339 0.608 0.6742 0.6364 0.6835 ] Network output: [ 0.2636 0.6026 0.7747 0.0004937 0.0006817 0.09906 0.002312 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2175 0.08374 0.1689 0.0004965 0.5438 0.6854 0.3138 0.6624 0.5651 0.5935 ] Network output: [ 0.1779 0.3929 0.6007 -0.005798 -0.004842 0.6255 -0.01096 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4785 0.2309 0.2525 0.1634 0.587 0.7422 0.6082 0.6778 0.6393 0.6913 ] Network output: [ 0.1147 0.2867 0.618 -0.001614 -0.001223 0.8561 0.002857 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.502 0.333 0.3717 0.1523 0.6641 0.7673 0.5754 0.6934 0.611 0.7183 ] Network output: [ 0.06579 0.1961 0.6388 0.001038 0.0006378 1.034 0.008789 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5386 0.4135 0.4471 0.1521 0.6677 0.7732 0.5872 0.6976 0.6145 0.7264 ] Network output: [ 0.02067 0.2638 0.6596 0.01005 0.009013 1.082 0.002223 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5811 Epoch 150 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2283 0.7754 0.938 0.004114 6.579e-05 -0.1487 0.001794 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1011 0.01347 0.05782 -0.05622 0.5142 0.6373 0.2994 0.6345 0.5469 0.5575 ] Network output: [ 0.1431 0.4879 0.7612 -0.007794 -0.005175 0.4312 -0.009476 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.366 0.1276 0.1997 0.1262 0.5847 0.7357 0.6087 0.6769 0.6386 0.6861 ] Network output: [ 0.2632 0.6052 0.7758 0.0005553 0.0006618 0.09646 0.0023 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2161 0.08295 0.1646 -0.0007109 0.5456 0.6878 0.3116 0.6654 0.5677 0.5971 ] Network output: [ 0.1792 0.3894 0.5998 -0.005829 -0.005069 0.6273 -0.01134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4794 0.2308 0.2462 0.1645 0.5886 0.7443 0.6088 0.6808 0.6417 0.6941 ] Network output: [ 0.1154 0.286 0.6185 -0.001645 -0.001278 0.8548 0.002888 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5005 0.3321 0.3679 0.1507 0.6657 0.7691 0.5733 0.6963 0.6134 0.7213 ] Network output: [ 0.06591 0.196 0.6388 0.0009912 0.0007024 1.034 0.008956 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5371 0.4128 0.4455 0.1489 0.6691 0.775 0.5851 0.7004 0.617 0.7294 ] Network output: [ 0.01894 0.2684 0.6575 0.01015 0.009343 1.084 0.002421 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5779 Epoch 151 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2272 0.7776 0.9388 0.004148 0.0001119 -0.1496 0.001912 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.1002 0.01311 0.05541 -0.05728 0.5155 0.6389 0.2977 0.6366 0.5489 0.5603 ] Network output: [ 0.1444 0.4839 0.7614 -0.007885 -0.00537 0.4319 -0.00968 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3662 0.1267 0.1951 0.1276 0.5862 0.7376 0.6094 0.6796 0.6409 0.6887 ] Network output: [ 0.2627 0.6077 0.7769 0.0006168 0.0006423 0.09389 0.002288 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2147 0.08214 0.1601 -0.001878 0.5475 0.6901 0.3094 0.6684 0.5704 0.6008 ] Network output: [ 0.1806 0.3858 0.5989 -0.005859 -0.0053 0.629 -0.01172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4803 0.2305 0.2397 0.1657 0.5903 0.7464 0.6094 0.6838 0.6442 0.697 ] Network output: [ 0.1161 0.2853 0.619 -0.001676 -0.001336 0.8535 0.002918 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.499 0.3312 0.3641 0.1492 0.6672 0.771 0.5711 0.6993 0.6159 0.7243 ] Network output: [ 0.06598 0.196 0.6388 0.0009423 0.0007646 1.033 0.009123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5356 0.412 0.4438 0.1457 0.6706 0.7767 0.583 0.7033 0.6196 0.7324 ] Network output: [ 0.01708 0.2734 0.6552 0.01024 0.009675 1.085 0.002623 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5746 Epoch 152 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.226 0.7798 0.9396 0.004181 0.0001633 -0.1504 0.002038 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09938 0.01274 0.05301 -0.05833 0.5168 0.6405 0.296 0.6387 0.5511 0.5631 ] Network output: [ 0.1458 0.4796 0.7617 -0.007976 -0.005565 0.4327 -0.009882 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3664 0.1258 0.1904 0.129 0.5877 0.7396 0.6101 0.6823 0.6433 0.6914 ] Network output: [ 0.2623 0.6101 0.7781 0.000678 0.0006235 0.09136 0.002275 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2133 0.08131 0.1556 -0.002994 0.5494 0.6926 0.3073 0.6715 0.5733 0.6046 ] Network output: [ 0.182 0.3819 0.5981 -0.005888 -0.005535 0.6306 -0.01211 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4813 0.2302 0.233 0.1669 0.592 0.7486 0.6101 0.6869 0.6468 0.7 ] Network output: [ 0.1168 0.2846 0.6195 -0.001707 -0.001398 0.8521 0.002945 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4976 0.3302 0.3602 0.1478 0.6688 0.7729 0.569 0.7023 0.6186 0.7273 ] Network output: [ 0.06602 0.1961 0.6388 0.0008913 0.000824 1.033 0.009292 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5341 0.4112 0.4421 0.1425 0.6721 0.7785 0.581 0.7063 0.6223 0.7355 ] Network output: [ 0.01509 0.2788 0.6528 0.01033 0.01001 1.087 0.002827 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5713 Epoch 153 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2249 0.782 0.9405 0.004213 0.0002204 -0.1511 0.002172 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09854 0.01236 0.05064 -0.05937 0.5182 0.6421 0.2944 0.6409 0.5533 0.566 ] Network output: [ 0.1473 0.4751 0.7621 -0.008066 -0.005757 0.4334 -0.01008 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3665 0.1249 0.1856 0.1306 0.5892 0.7416 0.6109 0.6852 0.6457 0.6942 ] Network output: [ 0.2619 0.6126 0.7792 0.0007387 0.0006054 0.08889 0.002263 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.212 0.08045 0.1511 -0.004049 0.5514 0.695 0.3053 0.6747 0.5763 0.6085 ] Network output: [ 0.1836 0.3778 0.5974 -0.005917 -0.005773 0.6323 -0.0125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4822 0.2298 0.2262 0.1683 0.5938 0.7508 0.6108 0.69 0.6495 0.703 ] Network output: [ 0.1176 0.2838 0.62 -0.00174 -0.001464 0.8507 0.002971 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4961 0.3292 0.3561 0.1464 0.6704 0.7748 0.5669 0.7054 0.6214 0.7304 ] Network output: [ 0.06603 0.1963 0.6387 0.0008379 0.0008799 1.033 0.009462 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5326 0.4104 0.4404 0.1395 0.6737 0.7804 0.579 0.7094 0.6251 0.7387 ] Network output: [ 0.01296 0.2845 0.6502 0.01041 0.01034 1.089 0.003033 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5677 Epoch 154 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2237 0.784 0.9413 0.004245 0.0002835 -0.1516 0.002314 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0977 0.01196 0.04831 -0.06039 0.5196 0.6437 0.2928 0.6432 0.5556 0.569 ] Network output: [ 0.1488 0.4703 0.7626 -0.008156 -0.005947 0.4342 -0.01027 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3667 0.1238 0.1808 0.1322 0.5908 0.7436 0.6117 0.688 0.6483 0.697 ] Network output: [ 0.2614 0.6149 0.7804 0.0007989 0.0005883 0.08646 0.002253 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2106 0.07956 0.1466 -0.005033 0.5535 0.6975 0.3032 0.6779 0.5795 0.6124 ] Network output: [ 0.1853 0.3733 0.5967 -0.005945 -0.006013 0.6339 -0.0129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4832 0.2294 0.2194 0.1698 0.5956 0.753 0.6115 0.6932 0.6523 0.7061 ] Network output: [ 0.1183 0.2831 0.6206 -0.001773 -0.001534 0.8492 0.002995 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4946 0.3281 0.3521 0.1452 0.6721 0.7767 0.5648 0.7086 0.6243 0.7336 ] Network output: [ 0.06599 0.1965 0.6387 0.000782 0.0009317 1.032 0.009631 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5311 0.4095 0.4387 0.1364 0.6753 0.7822 0.5769 0.7125 0.6281 0.7419 ] Network output: [ 0.01069 0.2906 0.6475 0.01049 0.01067 1.09 0.00324 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5641 Epoch 155 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2225 0.7861 0.9422 0.004277 0.0003526 -0.1521 0.002465 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09687 0.01156 0.04602 -0.06139 0.521 0.6454 0.2912 0.6454 0.558 0.572 ] Network output: [ 0.1505 0.4652 0.7631 -0.008243 -0.006132 0.4349 -0.01046 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3669 0.1227 0.176 0.1339 0.5924 0.7456 0.6125 0.691 0.6509 0.6999 ] Network output: [ 0.261 0.6172 0.7815 0.0008584 0.0005722 0.0841 0.002245 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2093 0.07864 0.142 -0.005935 0.5556 0.7 0.3012 0.6812 0.5827 0.6165 ] Network output: [ 0.1871 0.3687 0.5961 -0.005973 -0.006253 0.6354 -0.0133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4842 0.2288 0.2124 0.1715 0.5974 0.7552 0.6123 0.6964 0.6552 0.7092 ] Network output: [ 0.1191 0.2822 0.6211 -0.001807 -0.001609 0.8478 0.003018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4932 0.3269 0.348 0.1441 0.6738 0.7786 0.5627 0.7118 0.6273 0.7368 ] Network output: [ 0.06593 0.1968 0.6385 0.0007234 0.0009788 1.032 0.0098 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5297 0.4086 0.437 0.1335 0.6769 0.7841 0.5749 0.7157 0.6312 0.7451 ] Network output: [ 0.008291 0.2971 0.6447 0.01056 0.011 1.092 0.003445 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5603 Epoch 156 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2213 0.788 0.9431 0.004309 0.000428 -0.1525 0.002623 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09604 0.01114 0.0438 -0.06236 0.5225 0.647 0.2896 0.6477 0.5604 0.5751 ] Network output: [ 0.1522 0.4599 0.7638 -0.008329 -0.006313 0.4357 -0.01064 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3671 0.1216 0.1712 0.1356 0.5941 0.7476 0.6134 0.6939 0.6536 0.7028 ] Network output: [ 0.2606 0.6195 0.7826 0.0009171 0.0005575 0.08181 0.00224 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.208 0.07769 0.1375 -0.006746 0.5577 0.7026 0.2992 0.6846 0.5861 0.6206 ] Network output: [ 0.1891 0.3636 0.5956 -0.006001 -0.006493 0.637 -0.0137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4852 0.2282 0.2054 0.1732 0.5993 0.7575 0.613 0.6997 0.6582 0.7124 ] Network output: [ 0.1199 0.2814 0.6217 -0.001842 -0.001687 0.8463 0.00304 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4917 0.3257 0.3439 0.1431 0.6756 0.7806 0.5606 0.7151 0.6304 0.74 ] Network output: [ 0.06583 0.1972 0.6384 0.0006622 0.001021 1.032 0.009967 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5282 0.4077 0.4354 0.1306 0.6786 0.786 0.573 0.7189 0.6344 0.7484 ] Network output: [ 0.005751 0.304 0.6417 0.01063 0.01132 1.093 0.003649 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5564 Epoch 157 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.22 0.79 0.944 0.004343 0.0005098 -0.1528 0.002789 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09521 0.01072 0.04165 -0.06331 0.524 0.6487 0.2881 0.65 0.5629 0.5781 ] Network output: [ 0.1541 0.4542 0.7646 -0.008412 -0.006487 0.4365 -0.01082 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3673 0.1203 0.1664 0.1375 0.5958 0.7497 0.6142 0.6969 0.6564 0.7057 ] Network output: [ 0.2601 0.6216 0.7838 0.0009748 0.0005444 0.07958 0.002239 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2067 0.07671 0.133 -0.007456 0.5599 0.7051 0.2972 0.688 0.5895 0.6247 ] Network output: [ 0.1911 0.3583 0.5951 -0.006028 -0.006731 0.6386 -0.0141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4862 0.2274 0.1984 0.1751 0.6013 0.7598 0.6138 0.703 0.6613 0.7156 ] Network output: [ 0.1207 0.2805 0.6224 -0.001879 -0.00177 0.8447 0.003061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4902 0.3245 0.3398 0.1422 0.6774 0.7826 0.5585 0.7184 0.6336 0.7433 ] Network output: [ 0.0657 0.1977 0.6382 0.000598 0.001057 1.031 0.01013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5268 0.4067 0.4338 0.1279 0.6803 0.7879 0.571 0.7221 0.6377 0.7517 ] Network output: [ 0.003074 0.3113 0.6385 0.01068 0.01164 1.095 0.003848 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5524 Epoch 158 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2187 0.792 0.9449 0.004378 0.0005981 -0.153 0.002961 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09439 0.01028 0.03957 -0.06422 0.5256 0.6504 0.2866 0.6524 0.5655 0.5812 ] Network output: [ 0.156 0.4482 0.7655 -0.008492 -0.006653 0.4373 -0.01099 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3674 0.119 0.1617 0.1394 0.5975 0.7518 0.6151 0.6999 0.6593 0.7087 ] Network output: [ 0.2597 0.6237 0.785 0.001031 0.0005329 0.07744 0.002242 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2054 0.0757 0.1286 -0.008058 0.5622 0.7078 0.2952 0.6914 0.5931 0.6289 ] Network output: [ 0.1933 0.3526 0.5948 -0.006054 -0.006966 0.6401 -0.01449 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4872 0.2266 0.1914 0.1772 0.6033 0.7621 0.6146 0.7064 0.6644 0.7188 ] Network output: [ 0.1216 0.2795 0.623 -0.001916 -0.001857 0.8432 0.003081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4888 0.3231 0.3357 0.1415 0.6792 0.7846 0.5564 0.7217 0.6369 0.7465 ] Network output: [ 0.06554 0.1983 0.638 0.0005309 0.001086 1.031 0.0103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5253 0.4057 0.4323 0.1254 0.682 0.7898 0.569 0.7254 0.6411 0.755 ] Network output: [ 0.0002635 0.3191 0.6352 0.01073 0.01194 1.096 0.004043 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5482 Epoch 159 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2174 0.7939 0.9458 0.004415 0.0006929 -0.1532 0.00314 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09357 0.00984 0.03759 -0.06509 0.5272 0.6521 0.2851 0.6547 0.5681 0.5843 ] Network output: [ 0.158 0.442 0.7665 -0.008567 -0.00681 0.4381 -0.01115 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3676 0.1176 0.157 0.1414 0.5993 0.7538 0.616 0.703 0.6622 0.7116 ] Network output: [ 0.2592 0.6257 0.7861 0.001087 0.0005236 0.07538 0.002252 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2041 0.07466 0.1243 -0.008543 0.5645 0.7104 0.2933 0.6948 0.5968 0.6331 ] Network output: [ 0.1956 0.3466 0.5946 -0.006079 -0.007196 0.6417 -0.01488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4883 0.2256 0.1845 0.1794 0.6053 0.7645 0.6154 0.7097 0.6676 0.722 ] Network output: [ 0.1224 0.2784 0.6237 -0.001955 -0.001947 0.8417 0.003102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4873 0.3217 0.3317 0.1409 0.6811 0.7866 0.5543 0.7251 0.6404 0.7498 ] Network output: [ 0.06536 0.1989 0.6377 0.0004607 0.001108 1.031 0.01046 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5239 0.4047 0.4309 0.1229 0.6838 0.7918 0.567 0.7287 0.6446 0.7583 ] Network output: [ -0.002678 0.3273 0.6317 0.01077 0.01224 1.098 0.004231 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5439 Epoch 160 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.216 0.7959 0.9468 0.004455 0.0007941 -0.1532 0.003325 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09275 0.009387 0.03571 -0.06593 0.5288 0.6539 0.2836 0.6571 0.5708 0.5874 ] Network output: [ 0.1601 0.4355 0.7676 -0.008637 -0.006957 0.4389 -0.0113 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3678 0.1162 0.1524 0.1435 0.6011 0.756 0.6169 0.706 0.6652 0.7146 ] Network output: [ 0.2588 0.6276 0.7873 0.001141 0.0005164 0.0734 0.002267 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2028 0.07359 0.1201 -0.008906 0.5668 0.713 0.2913 0.6983 0.6005 0.6373 ] Network output: [ 0.198 0.3402 0.5945 -0.006103 -0.00742 0.6433 -0.01527 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4893 0.2246 0.1777 0.1817 0.6074 0.7668 0.6162 0.7131 0.6709 0.7252 ] Network output: [ 0.1233 0.2772 0.6245 -0.001996 -0.002041 0.8401 0.003124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4858 0.3203 0.3278 0.1406 0.6829 0.7887 0.5521 0.7284 0.6439 0.7531 ] Network output: [ 0.06515 0.1996 0.6374 0.0003874 0.001123 1.03 0.01061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5224 0.4036 0.4296 0.1207 0.6855 0.7937 0.5651 0.732 0.6481 0.7615 ] Network output: [ -0.005745 0.3359 0.628 0.0108 0.01253 1.099 0.004412 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5394 Epoch 161 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2146 0.798 0.9476 0.004497 0.0009016 -0.1532 0.003514 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09193 0.008927 0.03394 -0.06673 0.5304 0.6556 0.2821 0.6595 0.5735 0.5905 ] Network output: [ 0.1623 0.4287 0.7689 -0.008702 -0.007093 0.4397 -0.01145 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.368 0.1147 0.148 0.1457 0.603 0.7581 0.6178 0.7091 0.6682 0.7176 ] Network output: [ 0.2583 0.6294 0.7885 0.001193 0.0005117 0.07152 0.002289 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2015 0.07249 0.1161 -0.009143 0.5692 0.7157 0.2894 0.7017 0.6043 0.6415 ] Network output: [ 0.2006 0.3334 0.5945 -0.006126 -0.007637 0.6449 -0.01564 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4904 0.2234 0.171 0.1842 0.6095 0.7692 0.617 0.7165 0.6742 0.7285 ] Network output: [ 0.1242 0.2759 0.6253 -0.002037 -0.002138 0.8386 0.003148 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4844 0.3187 0.3241 0.1404 0.6848 0.7907 0.55 0.7318 0.6474 0.7564 ] Network output: [ 0.06492 0.2003 0.6371 0.000311 0.00113 1.03 0.01077 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.521 0.4024 0.4284 0.1186 0.6874 0.7957 0.5631 0.7353 0.6518 0.7648 ] Network output: [ -0.008932 0.3449 0.6242 0.01082 0.0128 1.101 0.004583 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5349 Epoch 162 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2131 0.8001 0.9485 0.004542 0.001015 -0.1531 0.003706 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09112 0.00846 0.03229 -0.06749 0.5321 0.6573 0.2806 0.6618 0.5763 0.5936 ] Network output: [ 0.1646 0.4217 0.7703 -0.00876 -0.007216 0.4406 -0.01159 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3681 0.1131 0.1436 0.1479 0.6049 0.7602 0.6187 0.7121 0.6713 0.7205 ] Network output: [ 0.2579 0.6311 0.7897 0.001245 0.0005098 0.06974 0.002319 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.2003 0.07136 0.1122 -0.00925 0.5716 0.7183 0.2874 0.7052 0.6082 0.6457 ] Network output: [ 0.2033 0.3262 0.5946 -0.006148 -0.007844 0.6465 -0.01601 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4915 0.2222 0.1644 0.1868 0.6116 0.7715 0.6178 0.7198 0.6776 0.7317 ] Network output: [ 0.1252 0.2745 0.6261 -0.00208 -0.002238 0.8371 0.003174 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4829 0.3171 0.3204 0.1404 0.6868 0.7927 0.5479 0.7351 0.6511 0.7596 ] Network output: [ 0.06467 0.2011 0.6368 0.0002314 0.001129 1.03 0.01092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5195 0.4012 0.4274 0.1167 0.6892 0.7976 0.5611 0.7385 0.6555 0.7681 ] Network output: [ -0.01223 0.3543 0.6202 0.01083 0.01305 1.102 0.004743 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5302 Epoch 163 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2115 0.8023 0.9494 0.004591 0.001134 -0.153 0.003901 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09031 0.007988 0.03077 -0.0682 0.5338 0.6591 0.2792 0.6641 0.579 0.5967 ] Network output: [ 0.1669 0.4145 0.7717 -0.00881 -0.007326 0.4414 -0.01172 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3683 0.1114 0.1394 0.1502 0.6068 0.7623 0.6196 0.7151 0.6744 0.7235 ] Network output: [ 0.2574 0.6327 0.7908 0.001294 0.0005108 0.06806 0.002357 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.199 0.07021 0.1086 -0.009226 0.5741 0.721 0.2855 0.7086 0.6121 0.6499 ] Network output: [ 0.206 0.3186 0.5949 -0.006167 -0.00804 0.6482 -0.01636 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4926 0.2208 0.1581 0.1896 0.6138 0.7739 0.6186 0.7232 0.681 0.7349 ] Network output: [ 0.1262 0.2729 0.627 -0.002125 -0.00234 0.8355 0.003203 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4814 0.3155 0.317 0.1407 0.6887 0.7948 0.5458 0.7384 0.6547 0.7629 ] Network output: [ 0.06441 0.202 0.6365 0.0001488 0.001119 1.029 0.01106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5181 0.3999 0.4265 0.1151 0.691 0.7996 0.5591 0.7418 0.6592 0.7713 ] Network output: [ -0.01564 0.3642 0.616 0.01082 0.01329 1.103 0.004891 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5254 Epoch 164 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.21 0.8046 0.9502 0.004643 0.001259 -0.1528 0.004096 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08951 0.007513 0.02938 -0.06887 0.5355 0.6608 0.2777 0.6665 0.5818 0.5997 ] Network output: [ 0.1692 0.4071 0.7733 -0.008853 -0.007421 0.4423 -0.01183 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3685 0.1097 0.1353 0.1526 0.6088 0.7645 0.6205 0.7181 0.6775 0.7264 ] Network output: [ 0.2569 0.6342 0.792 0.001342 0.000515 0.06648 0.002404 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1978 0.06902 0.1052 -0.009073 0.5766 0.7237 0.2836 0.712 0.6161 0.6541 ] Network output: [ 0.2089 0.3107 0.5953 -0.006185 -0.008224 0.65 -0.01671 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4937 0.2194 0.1521 0.1925 0.616 0.7763 0.6194 0.7265 0.6844 0.7381 ] Network output: [ 0.1272 0.2712 0.6279 -0.00217 -0.002444 0.834 0.003236 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4799 0.3137 0.3138 0.1411 0.6907 0.7968 0.5436 0.7417 0.6584 0.7661 ] Network output: [ 0.06414 0.2028 0.6361 6.314e-05 0.001101 1.029 0.0112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5166 0.3986 0.4258 0.1136 0.6929 0.8015 0.5571 0.745 0.663 0.7745 ] Network output: [ -0.01914 0.3744 0.6116 0.01081 0.01351 1.105 0.005026 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5205 Epoch 165 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2083 0.8071 0.951 0.004699 0.001388 -0.1526 0.004292 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08871 0.007035 0.02813 -0.0695 0.5373 0.6626 0.2762 0.6688 0.5846 0.6027 ] Network output: [ 0.1716 0.3995 0.775 -0.008886 -0.007501 0.4432 -0.01195 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3687 0.108 0.1314 0.155 0.6107 0.7666 0.6214 0.7211 0.6807 0.7293 ] Network output: [ 0.2564 0.6356 0.7932 0.001388 0.0005225 0.065 0.002459 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1965 0.06782 0.102 -0.008793 0.5791 0.7263 0.2817 0.7153 0.62 0.6582 ] Network output: [ 0.2119 0.3024 0.5958 -0.006201 -0.008394 0.6518 -0.01703 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4948 0.2178 0.1463 0.1955 0.6182 0.7786 0.6203 0.7297 0.6879 0.7412 ] Network output: [ 0.1282 0.2694 0.6289 -0.002217 -0.002549 0.8326 0.003273 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4784 0.3119 0.3108 0.1417 0.6927 0.7988 0.5414 0.745 0.6622 0.7692 ] Network output: [ 0.06386 0.2037 0.6358 -2.546e-05 0.001074 1.029 0.01134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5151 0.3972 0.4254 0.1123 0.6948 0.8034 0.5551 0.7481 0.6668 0.7776 ] Network output: [ -0.02273 0.385 0.607 0.01078 0.01372 1.106 0.005147 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5156 Epoch 166 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2066 0.8097 0.9517 0.00476 0.001521 -0.1523 0.004486 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08791 0.006557 0.02702 -0.07009 0.539 0.6643 0.2747 0.671 0.5874 0.6056 ] Network output: [ 0.1741 0.3918 0.7768 -0.00891 -0.007565 0.4441 -0.01205 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3689 0.1061 0.1277 0.1574 0.6127 0.7687 0.6223 0.724 0.6838 0.7322 ] Network output: [ 0.2559 0.6369 0.7944 0.001433 0.0005335 0.06363 0.002524 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1953 0.06659 0.09909 -0.008392 0.5816 0.729 0.2797 0.7186 0.624 0.6623 ] Network output: [ 0.2149 0.2938 0.5965 -0.006214 -0.00855 0.6536 -0.01734 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.496 0.2161 0.1408 0.1986 0.6205 0.781 0.621 0.7329 0.6914 0.7443 ] Network output: [ 0.1293 0.2674 0.63 -0.002265 -0.002655 0.8311 0.003316 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4769 0.31 0.3081 0.1425 0.6946 0.8008 0.5392 0.7481 0.6659 0.7723 ] Network output: [ 0.06357 0.2046 0.6354 -0.0001169 0.001038 1.028 0.01147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5136 0.3957 0.4252 0.1113 0.6966 0.8054 0.5531 0.7512 0.6706 0.7807 ] Network output: [ -0.02639 0.3959 0.6023 0.01075 0.0139 1.107 0.005253 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5106 Epoch 167 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2049 0.8125 0.9524 0.004825 0.001657 -0.1521 0.004676 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08712 0.00608 0.02607 -0.07065 0.5408 0.666 0.2732 0.6732 0.5902 0.6085 ] Network output: [ 0.1766 0.3839 0.7787 -0.008924 -0.007613 0.445 -0.01214 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3691 0.1043 0.1242 0.1599 0.6147 0.7708 0.6232 0.7268 0.6869 0.735 ] Network output: [ 0.2554 0.6382 0.7956 0.001475 0.0005481 0.06237 0.002599 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.194 0.06535 0.09649 -0.007876 0.5842 0.7316 0.2778 0.7219 0.628 0.6662 ] Network output: [ 0.218 0.2848 0.5973 -0.006225 -0.008689 0.6555 -0.01764 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4971 0.2144 0.1357 0.2019 0.6227 0.7833 0.6218 0.7361 0.6948 0.7474 ] Network output: [ 0.1303 0.2652 0.6312 -0.002315 -0.002761 0.8297 0.003366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4754 0.3081 0.3057 0.1435 0.6966 0.8029 0.537 0.7513 0.6697 0.7754 ] Network output: [ 0.06329 0.2055 0.6351 -0.0002109 0.0009933 1.028 0.0116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5121 0.3942 0.4252 0.1105 0.6985 0.8073 0.551 0.7543 0.6744 0.7837 ] Network output: [ -0.03012 0.4071 0.5974 0.0107 0.01406 1.108 0.005342 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5055 Epoch 168 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2031 0.8154 0.953 0.004894 0.001797 -0.1518 0.004862 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08634 0.005605 0.02528 -0.07116 0.5426 0.6677 0.2717 0.6754 0.593 0.6114 ] Network output: [ 0.1791 0.376 0.7807 -0.008928 -0.007644 0.4459 -0.01223 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3694 0.1024 0.1209 0.1624 0.6168 0.7729 0.624 0.7297 0.6901 0.7377 ] Network output: [ 0.2549 0.6394 0.7968 0.001515 0.0005664 0.06122 0.002684 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1928 0.0641 0.09421 -0.007253 0.5867 0.7342 0.2758 0.7251 0.632 0.6702 ] Network output: [ 0.2212 0.2755 0.5982 -0.006232 -0.008811 0.6575 -0.01791 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4982 0.2126 0.1309 0.2052 0.625 0.7857 0.6226 0.7392 0.6983 0.7503 ] Network output: [ 0.1314 0.2629 0.6324 -0.002365 -0.002866 0.8283 0.003422 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4738 0.3061 0.3036 0.1447 0.6986 0.8048 0.5348 0.7543 0.6734 0.7783 ] Network output: [ 0.063 0.2064 0.6347 -0.0003075 0.0009401 1.027 0.01172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5106 0.3927 0.4255 0.1099 0.7004 0.8092 0.549 0.7572 0.6782 0.7867 ] Network output: [ -0.0339 0.4187 0.5924 0.01063 0.01419 1.108 0.005414 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5004 Epoch 169 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2012 0.8186 0.9535 0.004968 0.001938 -0.1515 0.005041 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08556 0.005136 0.02464 -0.07164 0.5444 0.6694 0.2702 0.6776 0.5958 0.6141 ] Network output: [ 0.1816 0.3681 0.7827 -0.008921 -0.007657 0.4468 -0.01231 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3696 0.1004 0.1179 0.1648 0.6188 0.775 0.6249 0.7324 0.6932 0.7404 ] Network output: [ 0.2543 0.6405 0.798 0.001553 0.0005883 0.06017 0.002778 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1915 0.06283 0.09229 -0.006533 0.5893 0.7368 0.2738 0.7282 0.636 0.674 ] Network output: [ 0.2245 0.2659 0.5993 -0.006237 -0.008915 0.6596 -0.01817 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4994 0.2107 0.1266 0.2085 0.6273 0.788 0.6233 0.7422 0.7017 0.7533 ] Network output: [ 0.1326 0.2605 0.6337 -0.002416 -0.00297 0.8269 0.003487 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4723 0.304 0.3019 0.146 0.7006 0.8068 0.5325 0.7573 0.6771 0.7813 ] Network output: [ 0.06272 0.2073 0.6344 -0.0004063 0.0008786 1.027 0.01184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.509 0.391 0.4262 0.1094 0.7023 0.811 0.5469 0.7601 0.6819 0.7896 ] Network output: [ -0.03772 0.4305 0.5872 0.01056 0.01431 1.109 0.005469 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4952 Epoch 170 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1993 0.822 0.954 0.005046 0.002079 -0.1513 0.005212 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0848 0.004673 0.02417 -0.07209 0.5462 0.6711 0.2687 0.6796 0.5985 0.6168 ] Network output: [ 0.1841 0.3601 0.7848 -0.008902 -0.007654 0.4478 -0.01238 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3698 0.09846 0.1152 0.1673 0.6209 0.777 0.6257 0.7351 0.6963 0.7431 ] Network output: [ 0.2537 0.6415 0.7991 0.001589 0.0006139 0.05924 0.002883 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1903 0.06156 0.09073 -0.005727 0.5919 0.7394 0.2719 0.7312 0.6399 0.6778 ] Network output: [ 0.2278 0.256 0.6004 -0.006238 -0.009001 0.6617 -0.0184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5005 0.2087 0.1228 0.2119 0.6296 0.7903 0.624 0.7451 0.7051 0.7561 ] Network output: [ 0.1337 0.2579 0.6351 -0.002467 -0.003072 0.8256 0.003559 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4707 0.3019 0.3006 0.1475 0.7026 0.8087 0.5302 0.7602 0.6808 0.7841 ] Network output: [ 0.06244 0.2081 0.6342 -0.0005071 0.0008091 1.026 0.01195 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5075 0.3894 0.4271 0.1092 0.7042 0.8129 0.5447 0.7629 0.6857 0.7924 ] Network output: [ -0.04157 0.4425 0.5819 0.01048 0.0144 1.11 0.005505 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4901 Epoch 171 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1974 0.8256 0.9543 0.005128 0.002221 -0.151 0.005375 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08403 0.004218 0.02387 -0.07251 0.548 0.6728 0.2671 0.6817 0.6012 0.6194 ] Network output: [ 0.1866 0.3521 0.787 -0.008872 -0.007634 0.4487 -0.01244 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3701 0.09648 0.1127 0.1697 0.6229 0.779 0.6265 0.7377 0.6993 0.7456 ] Network output: [ 0.2531 0.6425 0.8002 0.001621 0.000643 0.0584 0.002997 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.189 0.06029 0.08957 -0.004847 0.5945 0.7419 0.2698 0.7342 0.6438 0.6814 ] Network output: [ 0.2311 0.2459 0.6017 -0.006235 -0.009067 0.664 -0.01861 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5017 0.2067 0.1194 0.2153 0.6319 0.7925 0.6247 0.748 0.7084 0.7589 ] Network output: [ 0.1348 0.2551 0.6365 -0.00252 -0.003171 0.8244 0.003641 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4691 0.2998 0.2998 0.1492 0.7046 0.8107 0.5279 0.7631 0.6844 0.7869 ] Network output: [ 0.06217 0.2089 0.6339 -0.0006097 0.000732 1.026 0.01206 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5058 0.3877 0.4284 0.1092 0.706 0.8147 0.5426 0.7657 0.6893 0.7951 ] Network output: [ -0.04543 0.4547 0.5764 0.01038 0.01446 1.11 0.005522 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.485 Epoch 172 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1954 0.8294 0.9546 0.005214 0.002362 -0.1508 0.005526 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08328 0.003774 0.02375 -0.07291 0.5497 0.6744 0.2655 0.6836 0.6039 0.6219 ] Network output: [ 0.1891 0.3442 0.7892 -0.008831 -0.007597 0.4496 -0.0125 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3704 0.09448 0.1105 0.1722 0.6249 0.781 0.6272 0.7402 0.7023 0.7481 ] Network output: [ 0.2525 0.6434 0.8014 0.001651 0.0006755 0.05767 0.003122 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1877 0.05902 0.08881 -0.003906 0.5971 0.7444 0.2678 0.7371 0.6476 0.685 ] Network output: [ 0.2345 0.2356 0.603 -0.006229 -0.009114 0.6663 -0.0188 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5028 0.2046 0.1166 0.2188 0.6342 0.7947 0.6253 0.7508 0.7117 0.7617 ] Network output: [ 0.1359 0.2522 0.6381 -0.002573 -0.003268 0.8232 0.003733 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4674 0.2977 0.2994 0.1509 0.7066 0.8126 0.5255 0.7658 0.688 0.7896 ] Network output: [ 0.0619 0.2097 0.6337 -0.0007137 0.0006477 1.025 0.01217 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5042 0.3859 0.4301 0.1093 0.7079 0.8164 0.5403 0.7683 0.693 0.7978 ] Network output: [ -0.0493 0.467 0.5709 0.01027 0.01451 1.111 0.005519 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4799 Epoch 173 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1933 0.8335 0.9548 0.005304 0.002501 -0.1506 0.005665 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08253 0.003341 0.02379 -0.07329 0.5515 0.6761 0.2639 0.6855 0.6065 0.6244 ] Network output: [ 0.1916 0.3363 0.7914 -0.008779 -0.007545 0.4506 -0.01255 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3707 0.09248 0.1086 0.1745 0.627 0.783 0.6279 0.7427 0.7052 0.7505 ] Network output: [ 0.2518 0.6444 0.8025 0.001677 0.0007113 0.05705 0.003256 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1865 0.05776 0.08849 -0.002917 0.5996 0.7469 0.2658 0.7399 0.6514 0.6885 ] Network output: [ 0.2378 0.2251 0.6045 -0.006219 -0.009141 0.6687 -0.01896 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.504 0.2025 0.1143 0.2222 0.6365 0.7969 0.6259 0.7534 0.715 0.7643 ] Network output: [ 0.1371 0.2492 0.6397 -0.002627 -0.003361 0.822 0.003835 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4658 0.2955 0.2995 0.1527 0.7085 0.8144 0.5231 0.7685 0.6916 0.7923 ] Network output: [ 0.06165 0.2104 0.6336 -0.0008189 0.0005567 1.025 0.01226 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5025 0.3841 0.4322 0.1096 0.7097 0.8182 0.5381 0.7709 0.6965 0.8003 ] Network output: [ -0.05315 0.4795 0.5653 0.01015 0.01452 1.111 0.005497 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4748 Epoch 174 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1913 0.8378 0.9549 0.005398 0.002637 -0.1504 0.005791 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0818 0.002921 0.02402 -0.07364 0.5533 0.6776 0.2623 0.6874 0.609 0.6268 ] Network output: [ 0.194 0.3285 0.7937 -0.008715 -0.007477 0.4515 -0.0126 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3709 0.09049 0.107 0.1769 0.629 0.7849 0.6286 0.745 0.7081 0.7529 ] Network output: [ 0.2511 0.6453 0.8035 0.001699 0.00075 0.05652 0.0034 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1852 0.05651 0.08863 -0.001893 0.6022 0.7493 0.2637 0.7426 0.6551 0.6919 ] Network output: [ 0.2412 0.2145 0.606 -0.006206 -0.009149 0.6712 -0.0191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5051 0.2004 0.1126 0.2256 0.6388 0.7991 0.6265 0.756 0.7182 0.7669 ] Network output: [ 0.1382 0.2461 0.6414 -0.002681 -0.003451 0.8209 0.003947 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4641 0.2932 0.3001 0.1547 0.7105 0.8162 0.5206 0.771 0.695 0.7948 ] Network output: [ 0.0614 0.211 0.6335 -0.0009248 0.0004596 1.024 0.01236 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5008 0.3823 0.4346 0.11 0.7115 0.8199 0.5358 0.7733 0.7 0.8028 ] Network output: [ -0.05698 0.4921 0.5595 0.01003 0.01452 1.112 0.005454 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4698 Epoch 175 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1892 0.8423 0.9549 0.005494 0.002769 -0.1503 0.005902 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08107 0.002516 0.02442 -0.07399 0.5551 0.6792 0.2606 0.6892 0.6115 0.6291 ] Network output: [ 0.1965 0.3209 0.7959 -0.008641 -0.007395 0.4525 -0.01264 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3712 0.08851 0.1058 0.1791 0.6311 0.7868 0.6292 0.7473 0.7109 0.7551 ] Network output: [ 0.2504 0.6461 0.8045 0.001717 0.0007914 0.05609 0.003553 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1839 0.05528 0.08923 -0.0008455 0.6047 0.7516 0.2616 0.7452 0.6588 0.6951 ] Network output: [ 0.2445 0.2038 0.6075 -0.006189 -0.009138 0.6739 -0.01921 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5062 0.1983 0.1115 0.2289 0.6411 0.8012 0.627 0.7585 0.7213 0.7694 ] Network output: [ 0.1393 0.2428 0.6431 -0.002736 -0.003536 0.8199 0.004069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4624 0.291 0.3012 0.1567 0.7124 0.818 0.5182 0.7735 0.6984 0.7973 ] Network output: [ 0.06117 0.2116 0.6335 -0.001031 0.0003567 1.023 0.01245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.499 0.3805 0.4375 0.1106 0.7133 0.8216 0.5335 0.7757 0.7035 0.8052 ] Network output: [ -0.06078 0.5047 0.5538 0.00989 0.01449 1.112 0.005391 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4649 Epoch 176 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.187 0.847 0.9548 0.005594 0.002897 -0.1503 0.005998 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08034 0.002127 0.025 -0.07432 0.5569 0.6808 0.2589 0.6909 0.614 0.6313 ] Network output: [ 0.1988 0.3133 0.7982 -0.008555 -0.007299 0.4534 -0.01268 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3715 0.08655 0.1049 0.1813 0.6331 0.7887 0.6297 0.7495 0.7137 0.7573 ] Network output: [ 0.2497 0.647 0.8055 0.00173 0.0008351 0.05575 0.003714 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1826 0.05407 0.09032 0.0002118 0.6073 0.7539 0.2594 0.7477 0.6623 0.6983 ] Network output: [ 0.2478 0.193 0.6091 -0.006169 -0.009108 0.6766 -0.0193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5073 0.1962 0.1111 0.2322 0.6434 0.8033 0.6275 0.7609 0.7244 0.7718 ] Network output: [ 0.1404 0.2394 0.645 -0.002792 -0.003617 0.8189 0.004203 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4606 0.2888 0.3029 0.1587 0.7143 0.8198 0.5156 0.7759 0.7018 0.7997 ] Network output: [ 0.06094 0.2121 0.6336 -0.001138 0.0002487 1.023 0.01253 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4972 0.3786 0.4409 0.1112 0.7151 0.8232 0.5311 0.778 0.7068 0.8076 ] Network output: [ -0.06453 0.5174 0.5479 0.009746 0.01444 1.112 0.005308 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.46 Epoch 177 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1849 0.852 0.9545 0.005696 0.00302 -0.1503 0.006076 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07963 0.001755 0.02577 -0.07464 0.5586 0.6823 0.2572 0.6926 0.6164 0.6334 ] Network output: [ 0.2012 0.306 0.8004 -0.00846 -0.007191 0.4544 -0.01271 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3718 0.08462 0.1044 0.1834 0.6351 0.7905 0.6302 0.7516 0.7164 0.7594 ] Network output: [ 0.2489 0.6479 0.8064 0.001738 0.0008809 0.0555 0.003884 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1813 0.05288 0.0919 0.001268 0.6098 0.7562 0.2573 0.7501 0.6658 0.7014 ] Network output: [ 0.2511 0.1822 0.6108 -0.006146 -0.009061 0.6794 -0.01936 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5084 0.1941 0.1112 0.2354 0.6456 0.8053 0.6279 0.7632 0.7273 0.7741 ] Network output: [ 0.1415 0.236 0.6469 -0.002848 -0.003693 0.8179 0.004346 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4588 0.2866 0.3052 0.1608 0.7162 0.8215 0.5131 0.7782 0.705 0.802 ] Network output: [ 0.06073 0.2126 0.6338 -0.001244 0.0001362 1.022 0.01261 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4954 0.3767 0.4446 0.112 0.7168 0.8248 0.5287 0.7802 0.7101 0.8098 ] Network output: [ -0.06823 0.53 0.5421 0.009596 0.01437 1.112 0.005203 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4553 Epoch 178 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1828 0.8571 0.9542 0.0058 0.003137 -0.1504 0.006138 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07892 0.001401 0.02672 -0.07495 0.5604 0.6837 0.2555 0.6942 0.6187 0.6354 ] Network output: [ 0.2035 0.2988 0.8026 -0.008354 -0.00707 0.4553 -0.01274 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3721 0.08273 0.1042 0.1855 0.6371 0.7923 0.6306 0.7536 0.719 0.7614 ] Network output: [ 0.248 0.6488 0.8073 0.001741 0.0009282 0.05534 0.004061 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.18 0.05173 0.09401 0.002312 0.6123 0.7584 0.2551 0.7525 0.6693 0.7043 ] Network output: [ 0.2543 0.1713 0.6124 -0.006121 -0.008996 0.6823 -0.0194 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5095 0.192 0.1121 0.2385 0.6478 0.8073 0.6282 0.7655 0.7303 0.7763 ] Network output: [ 0.1425 0.2325 0.6489 -0.002905 -0.003764 0.8171 0.004501 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.457 0.2844 0.308 0.1628 0.7181 0.8231 0.5104 0.7804 0.7082 0.8043 ] Network output: [ 0.06052 0.2129 0.6341 -0.001349 1.966e-05 1.021 0.01268 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4935 0.3748 0.4489 0.1129 0.7186 0.8264 0.5262 0.7822 0.7133 0.812 ] Network output: [ -0.07187 0.5425 0.5362 0.00944 0.01428 1.112 0.005078 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4506 Epoch 179 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1807 0.8624 0.9538 0.005906 0.003248 -0.1506 0.006181 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07821 0.001065 0.02785 -0.07526 0.5621 0.6852 0.2537 0.6957 0.621 0.6374 ] Network output: [ 0.2057 0.2917 0.8047 -0.008239 -0.00694 0.4563 -0.01276 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3724 0.08087 0.1044 0.1874 0.639 0.794 0.631 0.7556 0.7215 0.7634 ] Network output: [ 0.2472 0.6497 0.8082 0.001737 0.0009768 0.05527 0.004246 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1787 0.0506 0.09663 0.003334 0.6147 0.7606 0.2529 0.7547 0.6726 0.7072 ] Network output: [ 0.2575 0.1605 0.6141 -0.006092 -0.008916 0.6853 -0.01941 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5105 0.19 0.1137 0.2415 0.65 0.8092 0.6285 0.7676 0.7331 0.7785 ] Network output: [ 0.1435 0.2289 0.651 -0.002962 -0.003831 0.8162 0.004666 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4551 0.2822 0.3115 0.1649 0.7199 0.8248 0.5078 0.7825 0.7113 0.8065 ] Network output: [ 0.06033 0.2132 0.6345 -0.001453 -0.0001003 1.02 0.01275 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4915 0.3729 0.4536 0.1138 0.7202 0.8279 0.5237 0.7843 0.7164 0.8141 ] Network output: [ -0.07544 0.555 0.5304 0.009279 0.01417 1.112 0.004933 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4461 Epoch 180 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1786 0.8679 0.9532 0.006014 0.003352 -0.1508 0.006206 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07752 0.0007492 0.02916 -0.07557 0.5638 0.6866 0.2519 0.6972 0.6232 0.6393 ] Network output: [ 0.2079 0.2849 0.8069 -0.008116 -0.006799 0.4572 -0.01279 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3727 0.07907 0.105 0.1893 0.641 0.7957 0.6313 0.7574 0.724 0.7652 ] Network output: [ 0.2462 0.6506 0.8089 0.001727 0.001026 0.05528 0.004437 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1774 0.04951 0.09979 0.004326 0.6171 0.7627 0.2506 0.7569 0.6759 0.7099 ] Network output: [ 0.2606 0.1497 0.6157 -0.006062 -0.00882 0.6884 -0.0194 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5115 0.188 0.1159 0.2444 0.6522 0.8111 0.6288 0.7696 0.7359 0.7806 ] Network output: [ 0.1445 0.2252 0.6531 -0.00302 -0.003892 0.8155 0.00484 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4532 0.2801 0.3156 0.167 0.7217 0.8263 0.5051 0.7845 0.7144 0.8086 ] Network output: [ 0.06013 0.2134 0.635 -0.001555 -0.000223 1.02 0.01281 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4895 0.371 0.4587 0.1148 0.7219 0.8293 0.5211 0.7862 0.7195 0.8162 ] Network output: [ -0.07893 0.5674 0.5246 0.009115 0.01404 1.111 0.004767 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4416 Epoch 181 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1765 0.8736 0.9526 0.006124 0.003449 -0.1512 0.006212 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07682 0.0004526 0.03065 -0.07588 0.5655 0.6879 0.2501 0.6986 0.6254 0.6411 ] Network output: [ 0.2101 0.2783 0.8089 -0.007984 -0.006651 0.4581 -0.01281 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.373 0.07732 0.106 0.191 0.6429 0.7973 0.6316 0.7592 0.7264 0.767 ] Network output: [ 0.2453 0.6515 0.8096 0.00171 0.001076 0.05537 0.004635 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.176 0.04846 0.1035 0.005281 0.6195 0.7647 0.2484 0.7589 0.6791 0.7126 ] Network output: [ 0.2636 0.1391 0.6173 -0.00603 -0.008711 0.6917 -0.01936 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5124 0.1861 0.1189 0.2472 0.6543 0.813 0.6289 0.7716 0.7386 0.7826 ] Network output: [ 0.1454 0.2215 0.6553 -0.003079 -0.003949 0.8148 0.005024 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4513 0.2779 0.3203 0.1691 0.7235 0.8279 0.5024 0.7865 0.7174 0.8106 ] Network output: [ 0.05995 0.2134 0.6356 -0.001656 -0.0003481 1.019 0.01287 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4875 0.3691 0.4643 0.1159 0.7235 0.8308 0.5185 0.788 0.7224 0.8181 ] Network output: [ -0.08234 0.5796 0.5188 0.008949 0.01389 1.111 0.00458 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4373 Epoch 182 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1744 0.8794 0.9518 0.006234 0.003539 -0.1516 0.006199 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07614 0.0001758 0.03231 -0.07618 0.5671 0.6893 0.2482 0.7 0.6275 0.6429 ] Network output: [ 0.2122 0.2719 0.8109 -0.007845 -0.006495 0.459 -0.01282 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3733 0.07562 0.1073 0.1927 0.6448 0.7989 0.6318 0.7609 0.7288 0.7687 ] Network output: [ 0.2443 0.6525 0.8103 0.001685 0.001125 0.05554 0.004837 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1747 0.04745 0.1077 0.006193 0.6219 0.7667 0.2461 0.7609 0.6822 0.7152 ] Network output: [ 0.2665 0.1285 0.6189 -0.005997 -0.008588 0.695 -0.0193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5134 0.1843 0.1226 0.2498 0.6565 0.8148 0.6291 0.7734 0.7412 0.7846 ] Network output: [ 0.1463 0.2178 0.6576 -0.003139 -0.004001 0.8142 0.005216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4493 0.2759 0.3257 0.1711 0.7252 0.8294 0.4996 0.7884 0.7203 0.8126 ] Network output: [ 0.05977 0.2134 0.6364 -0.001753 -0.0004749 1.018 0.01292 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4854 0.3672 0.4704 0.1171 0.7251 0.8322 0.5159 0.7898 0.7254 0.82 ] Network output: [ -0.08566 0.5916 0.5131 0.008783 0.01373 1.11 0.004373 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4331 Epoch 183 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1724 0.8853 0.951 0.006346 0.003621 -0.1521 0.006168 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07545 -8.124e-05 0.03415 -0.07649 0.5688 0.6905 0.2464 0.7013 0.6296 0.6445 ] Network output: [ 0.2142 0.2657 0.8128 -0.007698 -0.006332 0.4598 -0.01284 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3735 0.07399 0.1091 0.1943 0.6466 0.8005 0.6319 0.7625 0.7311 0.7703 ] Network output: [ 0.2433 0.6535 0.8109 0.001653 0.001175 0.05578 0.005043 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1733 0.04648 0.1125 0.007057 0.6242 0.7686 0.2438 0.7629 0.6852 0.7177 ] Network output: [ 0.2694 0.118 0.6204 -0.005963 -0.008454 0.6984 -0.01922 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5142 0.1825 0.127 0.2523 0.6585 0.8165 0.6291 0.7752 0.7438 0.7864 ] Network output: [ 0.1471 0.214 0.66 -0.003199 -0.004048 0.8136 0.005417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4472 0.2738 0.3316 0.1731 0.7269 0.8308 0.4968 0.7901 0.7231 0.8146 ] Network output: [ 0.05959 0.2132 0.6373 -0.001847 -0.000603 1.017 0.01296 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4832 0.3653 0.4769 0.1182 0.7267 0.8335 0.5132 0.7915 0.7282 0.8219 ] Network output: [ -0.08889 0.6035 0.5075 0.008617 0.01355 1.11 0.004147 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.429 Epoch 184 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1704 0.8913 0.95 0.006458 0.003696 -0.1527 0.006118 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07478 -0.0003187 0.03616 -0.0768 0.5704 0.6918 0.2445 0.7026 0.6316 0.6461 ] Network output: [ 0.2162 0.2598 0.8147 -0.007545 -0.006165 0.4607 -0.01285 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3737 0.07242 0.1112 0.1958 0.6484 0.802 0.6319 0.7641 0.7333 0.7719 ] Network output: [ 0.2423 0.6545 0.8114 0.001613 0.001223 0.05609 0.005254 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1719 0.04555 0.1178 0.007871 0.6265 0.7705 0.2415 0.7647 0.6882 0.7201 ] Network output: [ 0.2722 0.1077 0.6218 -0.005929 -0.00831 0.7018 -0.01912 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.515 0.1809 0.1322 0.2547 0.6606 0.8182 0.6291 0.7769 0.7463 0.7882 ] Network output: [ 0.1479 0.2102 0.6624 -0.00326 -0.004091 0.8131 0.005625 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4452 0.2718 0.3382 0.1751 0.7286 0.8323 0.4939 0.7919 0.7259 0.8164 ] Network output: [ 0.05941 0.213 0.6384 -0.001937 -0.0007319 1.016 0.013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.481 0.3635 0.4839 0.1195 0.7282 0.8348 0.5104 0.7931 0.731 0.8237 ] Network output: [ -0.09203 0.6152 0.502 0.008454 0.01336 1.109 0.003901 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4251 Epoch 185 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1685 0.8975 0.949 0.00657 0.003764 -0.1534 0.00605 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0741 -0.0005369 0.03833 -0.07711 0.572 0.693 0.2425 0.7038 0.6336 0.6477 ] Network output: [ 0.2182 0.2541 0.8164 -0.007386 -0.005992 0.4614 -0.01286 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3739 0.07092 0.1137 0.1972 0.6502 0.8035 0.6319 0.7655 0.7355 0.7734 ] Network output: [ 0.2412 0.6555 0.8119 0.001564 0.00127 0.05647 0.005466 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1705 0.04467 0.1236 0.008634 0.6288 0.7723 0.2391 0.7665 0.6911 0.7224 ] Network output: [ 0.2748 0.09762 0.6232 -0.005894 -0.008157 0.7054 -0.01899 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5158 0.1793 0.138 0.257 0.6626 0.8199 0.629 0.7785 0.7487 0.79 ] Network output: [ 0.1486 0.2064 0.6649 -0.003321 -0.00413 0.8127 0.005839 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.443 0.2699 0.3454 0.177 0.7303 0.8336 0.4911 0.7935 0.7286 0.8183 ] Network output: [ 0.05923 0.2126 0.6396 -0.002024 -0.0008611 1.015 0.01303 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4788 0.3616 0.4913 0.1208 0.7298 0.8361 0.5077 0.7946 0.7337 0.8254 ] Network output: [ -0.09507 0.6266 0.4966 0.008294 0.01316 1.109 0.003637 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4212 Epoch 186 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1667 0.9037 0.9478 0.006682 0.003824 -0.1543 0.005964 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07343 -0.0007363 0.04065 -0.07742 0.5736 0.6942 0.2406 0.705 0.6356 0.6492 ] Network output: [ 0.2202 0.2486 0.8181 -0.007222 -0.005816 0.4622 -0.01288 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3741 0.06949 0.1166 0.1985 0.652 0.8049 0.6318 0.7669 0.7376 0.7748 ] Network output: [ 0.2401 0.6565 0.8123 0.001507 0.001316 0.05691 0.005681 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1691 0.04383 0.1299 0.009344 0.631 0.774 0.2368 0.7682 0.694 0.7247 ] Network output: [ 0.2774 0.08768 0.6245 -0.00586 -0.007996 0.709 -0.01884 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5165 0.1779 0.1446 0.2591 0.6646 0.8215 0.6289 0.7801 0.7511 0.7916 ] Network output: [ 0.1492 0.2026 0.6675 -0.003382 -0.004165 0.8123 0.006058 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4409 0.268 0.3531 0.1789 0.7319 0.835 0.4881 0.7951 0.7313 0.8201 ] Network output: [ 0.05905 0.2121 0.641 -0.002105 -0.0009902 1.014 0.01305 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4765 0.3598 0.4991 0.1221 0.7312 0.8373 0.5048 0.7961 0.7364 0.8271 ] Network output: [ -0.09801 0.6379 0.4913 0.008139 0.01295 1.108 0.003355 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4175 Epoch 187 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1649 0.91 0.9466 0.006794 0.003877 -0.1552 0.005862 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07276 -0.0009174 0.04313 -0.07773 0.5752 0.6953 0.2387 0.7061 0.6375 0.6506 ] Network output: [ 0.2221 0.2434 0.8196 -0.007053 -0.005638 0.4629 -0.01289 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3743 0.06813 0.1198 0.1997 0.6538 0.8063 0.6317 0.7683 0.7397 0.7762 ] Network output: [ 0.239 0.6576 0.8126 0.001441 0.001359 0.05741 0.005896 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1676 0.04304 0.1368 0.01 0.6332 0.7757 0.2344 0.7698 0.6968 0.7269 ] Network output: [ 0.2799 0.07796 0.6257 -0.005825 -0.007829 0.7127 -0.01868 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5172 0.1765 0.1518 0.2611 0.6666 0.8231 0.6287 0.7815 0.7535 0.7932 ] Network output: [ 0.1497 0.1987 0.6701 -0.003444 -0.004196 0.812 0.006282 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4387 0.2662 0.3614 0.1807 0.7335 0.8363 0.4852 0.7967 0.734 0.8218 ] Network output: [ 0.05886 0.2114 0.6426 -0.002181 -0.001119 1.013 0.01307 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4741 0.358 0.5072 0.1235 0.7327 0.8385 0.502 0.7976 0.739 0.8287 ] Network output: [ -0.1008 0.6488 0.4862 0.007991 0.01274 1.107 0.003056 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4139 Epoch 188 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1631 0.9163 0.9453 0.006905 0.003924 -0.1562 0.005744 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0721 -0.001081 0.04573 -0.07805 0.5767 0.6964 0.2367 0.7072 0.6394 0.652 ] Network output: [ 0.2239 0.2384 0.8211 -0.00688 -0.005457 0.4635 -0.0129 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3744 0.06683 0.1233 0.2009 0.6555 0.8076 0.6315 0.7696 0.7417 0.7775 ] Network output: [ 0.2378 0.6586 0.8129 0.001367 0.001401 0.05796 0.006111 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1662 0.04228 0.1441 0.01062 0.6354 0.7774 0.232 0.7714 0.6996 0.729 ] Network output: [ 0.2823 0.06846 0.6268 -0.005791 -0.007656 0.7165 -0.0185 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5178 0.1753 0.1596 0.2629 0.6686 0.8246 0.6284 0.783 0.7558 0.7948 ] Network output: [ 0.1502 0.1949 0.6728 -0.003505 -0.004224 0.8118 0.006508 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4365 0.2645 0.3703 0.1825 0.7351 0.8376 0.4822 0.7982 0.7366 0.8235 ] Network output: [ 0.05868 0.2106 0.6443 -0.002251 -0.001247 1.012 0.01308 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4718 0.3562 0.5157 0.1249 0.7341 0.8397 0.4991 0.799 0.7416 0.8303 ] Network output: [ -0.1036 0.6595 0.4812 0.007849 0.01251 1.106 0.002742 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4104 Epoch 189 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1615 0.9227 0.9438 0.007015 0.003964 -0.1573 0.005611 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07144 -0.001227 0.04847 -0.07836 0.5782 0.6975 0.2347 0.7083 0.6413 0.6534 ] Network output: [ 0.2258 0.2337 0.8225 -0.006704 -0.005274 0.4641 -0.01291 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3745 0.06561 0.1272 0.2019 0.6572 0.8089 0.6312 0.7708 0.7437 0.7787 ] Network output: [ 0.2367 0.6597 0.8131 0.001285 0.00144 0.05856 0.006325 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1648 0.04158 0.1518 0.01118 0.6375 0.779 0.2296 0.773 0.7023 0.7311 ] Network output: [ 0.2846 0.05919 0.6278 -0.005758 -0.007479 0.7203 -0.01831 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5184 0.1741 0.1681 0.2647 0.6705 0.8261 0.6281 0.7843 0.758 0.7963 ] Network output: [ 0.1506 0.1911 0.6756 -0.003565 -0.004249 0.8116 0.006737 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4343 0.2628 0.3795 0.1843 0.7366 0.8388 0.4793 0.7996 0.7392 0.8251 ] Network output: [ 0.05848 0.2097 0.6463 -0.002315 -0.001374 1.011 0.01308 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4693 0.3545 0.5244 0.1265 0.7356 0.8409 0.4962 0.8003 0.7442 0.8319 ] Network output: [ -0.1062 0.67 0.4763 0.007715 0.01228 1.105 0.002413 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.407 Epoch 190 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1599 0.9291 0.9423 0.007123 0.003998 -0.1586 0.005465 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07079 -0.001358 0.05131 -0.07867 0.5797 0.6985 0.2327 0.7093 0.6431 0.6547 ] Network output: [ 0.2276 0.2292 0.8237 -0.006524 -0.005091 0.4645 -0.01292 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3746 0.06446 0.1314 0.203 0.6589 0.8102 0.6308 0.772 0.7457 0.7799 ] Network output: [ 0.2355 0.6608 0.8133 0.001196 0.001477 0.05921 0.006536 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1633 0.04091 0.1599 0.01171 0.6396 0.7805 0.2273 0.7744 0.7051 0.7332 ] Network output: [ 0.2869 0.05018 0.6287 -0.005724 -0.007298 0.7241 -0.01811 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5189 0.1731 0.1771 0.2663 0.6724 0.8276 0.6278 0.7857 0.7603 0.7978 ] Network output: [ 0.151 0.1873 0.6785 -0.003624 -0.004271 0.8115 0.006966 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.432 0.2612 0.3893 0.186 0.7382 0.84 0.4763 0.801 0.7417 0.8268 ] Network output: [ 0.05828 0.2086 0.6484 -0.002372 -0.001499 1.01 0.01308 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4669 0.3528 0.5334 0.1281 0.737 0.842 0.4933 0.8016 0.7468 0.8334 ] Network output: [ -0.1088 0.6801 0.4716 0.007589 0.01204 1.104 0.002071 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4038 Epoch 191 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1585 0.9355 0.9407 0.00723 0.004027 -0.1599 0.005307 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07014 -0.001473 0.05425 -0.07897 0.5812 0.6995 0.2308 0.7104 0.645 0.6559 ] Network output: [ 0.2294 0.225 0.8248 -0.006342 -0.004907 0.465 -0.01293 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3747 0.06338 0.1358 0.2039 0.6606 0.8114 0.6304 0.7731 0.7476 0.7811 ] Network output: [ 0.2343 0.6619 0.8134 0.001099 0.001511 0.05989 0.006743 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1619 0.04029 0.1684 0.0122 0.6417 0.782 0.2249 0.7759 0.7078 0.7351 ] Network output: [ 0.289 0.04142 0.6295 -0.00569 -0.007115 0.728 -0.01789 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5194 0.1722 0.1866 0.2679 0.6743 0.829 0.6274 0.7869 0.7625 0.7992 ] Network output: [ 0.1513 0.1836 0.6815 -0.003682 -0.00429 0.8114 0.007195 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4297 0.2597 0.3993 0.1878 0.7397 0.8412 0.4732 0.8024 0.7443 0.8284 ] Network output: [ 0.05808 0.2074 0.6507 -0.002423 -0.001622 1.01 0.01308 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4644 0.3511 0.5426 0.1298 0.7383 0.843 0.4903 0.8029 0.7493 0.8349 ] Network output: [ -0.1112 0.69 0.4671 0.007473 0.0118 1.103 0.001718 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4006 Epoch 192 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1571 0.942 0.9389 0.007333 0.00405 -0.1613 0.005137 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0695 -0.001574 0.05728 -0.07927 0.5827 0.7005 0.2288 0.7114 0.6468 0.6572 ] Network output: [ 0.2312 0.2209 0.8258 -0.006158 -0.004723 0.4653 -0.01295 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3748 0.06237 0.1404 0.2048 0.6622 0.8126 0.63 0.7742 0.7496 0.7822 ] Network output: [ 0.233 0.663 0.8135 0.0009949 0.001543 0.06061 0.006947 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1604 0.03971 0.1771 0.01267 0.6438 0.7835 0.2225 0.7773 0.7105 0.7371 ] Network output: [ 0.291 0.03294 0.6302 -0.005655 -0.00693 0.7319 -0.01767 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5199 0.1715 0.1966 0.2693 0.6762 0.8304 0.6269 0.7882 0.7647 0.8006 ] Network output: [ 0.1515 0.1798 0.6846 -0.003737 -0.004307 0.8113 0.007422 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4274 0.2583 0.4097 0.1895 0.7412 0.8423 0.4702 0.8037 0.7468 0.8299 ] Network output: [ 0.05787 0.206 0.6532 -0.002466 -0.001744 1.009 0.01306 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.462 0.3495 0.552 0.1316 0.7397 0.8441 0.4873 0.8041 0.7518 0.8364 ] Network output: [ -0.1135 0.6996 0.4627 0.007366 0.01156 1.103 0.001356 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3975 Epoch 193 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1558 0.9485 0.9371 0.007434 0.004069 -0.1628 0.004959 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06887 -0.001661 0.06036 -0.07957 0.5842 0.7015 0.2268 0.7123 0.6487 0.6584 ] Network output: [ 0.233 0.2172 0.8267 -0.005973 -0.004539 0.4656 -0.01297 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3748 0.06142 0.1452 0.2056 0.6638 0.8138 0.6295 0.7753 0.7515 0.7832 ] Network output: [ 0.2318 0.6642 0.8135 0.0008852 0.001572 0.06134 0.007144 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.159 0.03917 0.1862 0.01311 0.6458 0.7849 0.2201 0.7787 0.7131 0.739 ] Network output: [ 0.293 0.02474 0.6308 -0.005619 -0.006744 0.7358 -0.01745 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5203 0.1708 0.2069 0.2707 0.678 0.8317 0.6264 0.7893 0.7669 0.8019 ] Network output: [ 0.1516 0.1761 0.6877 -0.003789 -0.004322 0.8113 0.007647 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4251 0.2569 0.4203 0.1913 0.7427 0.8435 0.4672 0.805 0.7494 0.8315 ] Network output: [ 0.05765 0.2045 0.656 -0.002503 -0.001864 1.008 0.01305 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4594 0.348 0.5615 0.1335 0.7411 0.8451 0.4844 0.8053 0.7543 0.8379 ] Network output: [ -0.1158 0.7088 0.4584 0.007269 0.01132 1.102 0.0009866 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3945 Epoch 194 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1546 0.9549 0.9352 0.007531 0.004083 -0.1645 0.004773 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06826 -0.001735 0.06349 -0.07985 0.5856 0.7024 0.2248 0.7133 0.6505 0.6595 ] Network output: [ 0.2347 0.2137 0.8274 -0.005786 -0.004356 0.4658 -0.01298 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3749 0.06055 0.1502 0.2064 0.6654 0.8149 0.6289 0.7763 0.7534 0.7843 ] Network output: [ 0.2305 0.6653 0.8136 0.0007702 0.001598 0.06209 0.007335 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1576 0.03868 0.1954 0.01354 0.6478 0.7863 0.2178 0.7801 0.7158 0.7408 ] Network output: [ 0.2948 0.01684 0.6313 -0.005581 -0.006558 0.7396 -0.01722 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5207 0.1702 0.2175 0.2719 0.6799 0.833 0.6258 0.7905 0.769 0.8032 ] Network output: [ 0.1517 0.1724 0.691 -0.003838 -0.004334 0.8114 0.007868 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4228 0.2556 0.4311 0.1931 0.7442 0.8446 0.4642 0.8063 0.7519 0.833 ] Network output: [ 0.05743 0.2027 0.6589 -0.002532 -0.001981 1.007 0.01302 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4569 0.3464 0.571 0.1355 0.7424 0.8461 0.4814 0.8065 0.7568 0.8393 ] Network output: [ -0.1179 0.7178 0.4543 0.007181 0.01107 1.101 0.0006122 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3915 Epoch 195 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1534 0.9614 0.9331 0.007623 0.004093 -0.1662 0.004581 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06765 -0.001798 0.06664 -0.08013 0.5871 0.7033 0.2228 0.7142 0.6523 0.6607 ] Network output: [ 0.2365 0.2104 0.828 -0.005599 -0.004173 0.4659 -0.01301 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3749 0.05973 0.1552 0.2072 0.6671 0.816 0.6283 0.7774 0.7552 0.7853 ] Network output: [ 0.2293 0.6665 0.8136 0.0006507 0.001621 0.06284 0.007518 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1562 0.03821 0.2047 0.01395 0.6499 0.7877 0.2155 0.7814 0.7185 0.7426 ] Network output: [ 0.2966 0.009243 0.6316 -0.00554 -0.006373 0.7435 -0.01699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5211 0.1698 0.2283 0.2731 0.6817 0.8344 0.6252 0.7916 0.7712 0.8045 ] Network output: [ 0.1517 0.1687 0.6943 -0.003883 -0.004345 0.8115 0.008085 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4205 0.2543 0.442 0.1949 0.7457 0.8456 0.4612 0.8076 0.7544 0.8345 ] Network output: [ 0.05721 0.2008 0.662 -0.002555 -0.002095 1.006 0.013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4544 0.345 0.5805 0.1377 0.7437 0.8471 0.4784 0.8077 0.7594 0.8407 ] Network output: [ -0.12 0.7265 0.4503 0.007101 0.01083 1.1 0.0002351 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3886 Epoch 196 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1524 0.9678 0.931 0.00771 0.0041 -0.1679 0.004384 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06706 -0.00185 0.0698 -0.08039 0.5885 0.7042 0.2209 0.7151 0.6542 0.6618 ] Network output: [ 0.2383 0.2074 0.8284 -0.005412 -0.003992 0.4659 -0.01303 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.375 0.05898 0.1604 0.2079 0.6687 0.8171 0.6277 0.7783 0.7571 0.7862 ] Network output: [ 0.228 0.6677 0.8135 0.0005275 0.001641 0.06358 0.007693 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1548 0.03779 0.2142 0.01437 0.6519 0.789 0.2131 0.7827 0.7212 0.7444 ] Network output: [ 0.2984 0.001957 0.6319 -0.005496 -0.006188 0.7473 -0.01676 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5215 0.1695 0.2393 0.2743 0.6835 0.8356 0.6246 0.7927 0.7733 0.8057 ] Network output: [ 0.1516 0.165 0.6978 -0.003924 -0.004355 0.8115 0.008296 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4182 0.2532 0.453 0.1968 0.7471 0.8467 0.4581 0.8088 0.7569 0.836 ] Network output: [ 0.05698 0.1987 0.6653 -0.002571 -0.002207 1.005 0.01297 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4519 0.3435 0.5899 0.14 0.7451 0.8481 0.4754 0.8088 0.7619 0.8421 ] Network output: [ -0.1219 0.7349 0.4465 0.00703 0.01059 1.099 -0.0001424 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3858 Epoch 197 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1515 0.9742 0.9287 0.007791 0.004103 -0.1698 0.004184 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06649 -0.001891 0.07295 -0.08063 0.59 0.705 0.2189 0.7161 0.6561 0.6629 ] Network output: [ 0.24 0.2046 0.8286 -0.005226 -0.003811 0.4659 -0.01306 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3751 0.05829 0.1655 0.2086 0.6703 0.8182 0.6271 0.7793 0.759 0.7872 ] Network output: [ 0.2267 0.669 0.8135 0.0004015 0.001659 0.06432 0.007859 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1534 0.0374 0.2236 0.01479 0.6539 0.7903 0.2109 0.784 0.7239 0.7462 ] Network output: [ 0.3 -0.005008 0.632 -0.005448 -0.006005 0.7511 -0.01653 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5218 0.1693 0.2503 0.2754 0.6854 0.8369 0.624 0.7938 0.7755 0.8069 ] Network output: [ 0.1515 0.1614 0.7013 -0.00396 -0.004362 0.8116 0.008502 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4159 0.2521 0.4639 0.1987 0.7486 0.8477 0.4551 0.8101 0.7595 0.8374 ] Network output: [ 0.05675 0.1965 0.6688 -0.002582 -0.002316 1.004 0.01295 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4494 0.3422 0.5993 0.1425 0.7464 0.8491 0.4724 0.81 0.7644 0.8434 ] Network output: [ -0.1238 0.7429 0.4427 0.006966 0.01035 1.098 -0.0005178 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3831 Epoch 198 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1506 0.9805 0.9263 0.007865 0.004103 -0.1717 0.003982 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06593 -0.001924 0.07606 -0.08086 0.5915 0.7059 0.217 0.717 0.6579 0.664 ] Network output: [ 0.2418 0.202 0.8287 -0.00504 -0.003631 0.4657 -0.0131 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3752 0.05766 0.1706 0.2092 0.6719 0.8193 0.6264 0.7802 0.7609 0.7881 ] Network output: [ 0.2254 0.6702 0.8135 0.0002736 0.001674 0.06503 0.008014 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1521 0.03704 0.233 0.01523 0.6559 0.7916 0.2086 0.7853 0.7266 0.7479 ] Network output: [ 0.3016 -0.01165 0.632 -0.005395 -0.005823 0.7548 -0.01631 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5222 0.1692 0.2613 0.2765 0.6872 0.8381 0.6233 0.7948 0.7776 0.8081 ] Network output: [ 0.1513 0.1579 0.7049 -0.003992 -0.004368 0.8118 0.008702 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4136 0.2511 0.4747 0.2006 0.7501 0.8487 0.4522 0.8113 0.762 0.8389 ] Network output: [ 0.05651 0.194 0.6724 -0.002586 -0.002421 1.003 0.01292 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4469 0.3409 0.6084 0.1451 0.7478 0.85 0.4695 0.8111 0.7669 0.8448 ] Network output: [ -0.1256 0.7507 0.4391 0.006908 0.01012 1.097 -0.0008889 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3803 Epoch 199 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1498 0.9869 0.9239 0.007933 0.004099 -0.1737 0.003781 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0654 -0.001947 0.07911 -0.08107 0.5929 0.7067 0.2151 0.7179 0.6598 0.6651 ] Network output: [ 0.2436 0.1997 0.8287 -0.004856 -0.003453 0.4655 -0.01313 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3753 0.05708 0.1756 0.2098 0.6735 0.8203 0.6256 0.7811 0.7628 0.7889 ] Network output: [ 0.2241 0.6715 0.8134 0.0001444 0.001687 0.06571 0.00816 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1507 0.03672 0.2423 0.01568 0.658 0.7928 0.2064 0.7865 0.7293 0.7496 ] Network output: [ 0.3031 -0.01796 0.6319 -0.005337 -0.005643 0.7584 -0.0161 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5226 0.1692 0.2722 0.2776 0.689 0.8393 0.6226 0.7959 0.7798 0.8092 ] Network output: [ 0.151 0.1544 0.7087 -0.004018 -0.004373 0.8119 0.008895 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4114 0.2502 0.4854 0.2027 0.7516 0.8498 0.4492 0.8125 0.7646 0.8403 ] Network output: [ 0.05627 0.1914 0.6763 -0.002586 -0.002523 1.003 0.01289 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4444 0.3396 0.6174 0.1479 0.7491 0.8509 0.4665 0.8122 0.7694 0.8461 ] Network output: [ -0.1273 0.7582 0.4355 0.006856 0.009883 1.096 -0.001254 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3777 Epoch 200 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1492 0.9931 0.9213 0.007993 0.004093 -0.1757 0.00358 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06489 -0.001964 0.0821 -0.08126 0.5944 0.7075 0.2132 0.7187 0.6617 0.6662 ] Network output: [ 0.2453 0.1975 0.8284 -0.004674 -0.003277 0.4652 -0.01318 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3755 0.05656 0.1806 0.2105 0.6751 0.8213 0.6249 0.782 0.7646 0.7898 ] Network output: [ 0.2228 0.6729 0.8134 1.472e-05 0.001697 0.06636 0.008295 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1495 0.03643 0.2515 0.01615 0.66 0.7941 0.2042 0.7878 0.7321 0.7513 ] Network output: [ 0.3046 -0.02394 0.6317 -0.005273 -0.005465 0.7619 -0.01589 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.523 0.1693 0.2829 0.2786 0.6909 0.8405 0.6219 0.7969 0.7819 0.8103 ] Network output: [ 0.1507 0.1509 0.7125 -0.004039 -0.004376 0.812 0.009082 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4092 0.2493 0.4958 0.2048 0.7531 0.8507 0.4463 0.8137 0.7671 0.8417 ] Network output: [ 0.05602 0.1886 0.6803 -0.002582 -0.002622 1.002 0.01287 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.442 0.3384 0.6262 0.1508 0.7505 0.8519 0.4636 0.8133 0.772 0.8474 ] Network output: [ -0.1289 0.7654 0.432 0.006809 0.009655 1.096 -0.00161 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.375 Epoch 201 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1486 0.9993 0.9187 0.008046 0.004084 -0.1777 0.003383 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0644 -0.001973 0.085 -0.08143 0.5959 0.7084 0.2114 0.7196 0.6636 0.6672 ] Network output: [ 0.2471 0.1956 0.828 -0.004494 -0.003102 0.4648 -0.01323 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3757 0.05609 0.1854 0.2111 0.6768 0.8224 0.6242 0.7829 0.7665 0.7906 ] Network output: [ 0.2215 0.6742 0.8134 -0.0001148 0.001705 0.06696 0.008419 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1482 0.03617 0.2604 0.01665 0.662 0.7953 0.202 0.789 0.7348 0.753 ] Network output: [ 0.306 -0.02959 0.6313 -0.005203 -0.00529 0.7654 -0.0157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5235 0.1696 0.2934 0.2796 0.6928 0.8417 0.6212 0.7979 0.7841 0.8114 ] Network output: [ 0.1503 0.1475 0.7164 -0.004056 -0.004377 0.8121 0.009262 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.407 0.2485 0.506 0.2069 0.7546 0.8517 0.4433 0.8148 0.7697 0.8431 ] Network output: [ 0.05575 0.1857 0.6845 -0.002574 -0.002717 1.001 0.01285 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4396 0.3373 0.6347 0.1539 0.7519 0.8528 0.4607 0.8144 0.7745 0.8487 ] Network output: [ -0.1304 0.7723 0.4286 0.006765 0.009432 1.095 -0.001956 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3724 Epoch 202 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1481 1.005 0.9159 0.00809 0.004072 -0.1799 0.003189 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06394 -0.001977 0.0878 -0.08157 0.5974 0.7092 0.2095 0.7205 0.6656 0.6683 ] Network output: [ 0.249 0.1939 0.8273 -0.004316 -0.002928 0.4644 -0.01328 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.376 0.05566 0.19 0.2117 0.6784 0.8234 0.6234 0.7838 0.7684 0.7914 ] Network output: [ 0.2202 0.6757 0.8134 -0.0002436 0.001711 0.06752 0.008532 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.147 0.03593 0.2691 0.01719 0.6641 0.7965 0.1998 0.7903 0.7376 0.7546 ] Network output: [ 0.3074 -0.03491 0.6309 -0.005126 -0.005116 0.7687 -0.01552 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.524 0.1699 0.3036 0.2806 0.6947 0.8429 0.6204 0.7989 0.7862 0.8124 ] Network output: [ 0.1498 0.1442 0.7204 -0.004067 -0.004377 0.8122 0.009436 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4049 0.2478 0.5159 0.2092 0.7561 0.8527 0.4405 0.816 0.7723 0.8445 ] Network output: [ 0.05547 0.1826 0.6888 -0.002563 -0.002808 1.001 0.01283 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4372 0.3362 0.643 0.1572 0.7533 0.8537 0.4578 0.8155 0.7771 0.85 ] Network output: [ -0.1319 0.779 0.4252 0.006723 0.009213 1.095 -0.00229 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3698 Epoch 203 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1476 1.011 0.9131 0.008125 0.004057 -0.182 0.002999 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06351 -0.001975 0.09049 -0.08168 0.599 0.71 0.2077 0.7213 0.6675 0.6693 ] Network output: [ 0.2508 0.1924 0.8265 -0.004142 -0.002757 0.4639 -0.01334 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3764 0.05528 0.1944 0.2122 0.6801 0.8244 0.6226 0.7846 0.7703 0.7922 ] Network output: [ 0.2189 0.6771 0.8135 -0.0003712 0.001715 0.06803 0.008635 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1459 0.03572 0.2775 0.01775 0.6662 0.7977 0.1977 0.7915 0.7403 0.7563 ] Network output: [ 0.3088 -0.0399 0.6303 -0.005043 -0.004945 0.772 -0.01534 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5245 0.1703 0.3135 0.2816 0.6966 0.8441 0.6197 0.7999 0.7884 0.8135 ] Network output: [ 0.1493 0.1409 0.7245 -0.004074 -0.004375 0.8123 0.009603 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4028 0.2471 0.5255 0.2115 0.7577 0.8537 0.4376 0.8172 0.7748 0.8458 ] Network output: [ 0.05518 0.1793 0.6932 -0.00255 -0.002894 1 0.01281 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4348 0.3351 0.6509 0.1606 0.7547 0.8546 0.455 0.8166 0.7796 0.8512 ] Network output: [ -0.1333 0.7854 0.4219 0.006683 0.008999 1.094 -0.002611 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3673 Epoch 204 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1473 1.017 0.9102 0.008152 0.004039 -0.1841 0.002816 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06311 -0.00197 0.09306 -0.08177 0.6006 0.7108 0.2059 0.7222 0.6694 0.6703 ] Network output: [ 0.2527 0.191 0.8255 -0.003971 -0.002588 0.4634 -0.01341 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3769 0.05493 0.1985 0.2128 0.6818 0.8254 0.6219 0.7854 0.7722 0.7929 ] Network output: [ 0.2176 0.6786 0.8136 -0.0004972 0.001716 0.06849 0.008727 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1448 0.03553 0.2857 0.01836 0.6683 0.7989 0.1957 0.7927 0.7431 0.7579 ] Network output: [ 0.3101 -0.04459 0.6296 -0.004953 -0.004777 0.7751 -0.01518 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5251 0.1707 0.3229 0.2826 0.6985 0.8453 0.6189 0.8008 0.7905 0.8145 ] Network output: [ 0.1488 0.1378 0.7287 -0.004077 -0.004371 0.8123 0.009765 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4008 0.2465 0.5347 0.2139 0.7592 0.8546 0.4348 0.8184 0.7774 0.8472 ] Network output: [ 0.05486 0.176 0.6978 -0.002535 -0.002977 0.9994 0.01281 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4326 0.3341 0.6586 0.1641 0.7562 0.8555 0.4522 0.8177 0.7822 0.8525 ] Network output: [ -0.1346 0.7915 0.4185 0.006643 0.008791 1.094 -0.002918 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3647 Epoch 205 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.147 1.023 0.9073 0.00817 0.004017 -0.1863 0.002638 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06274 -0.00196 0.09549 -0.08182 0.6022 0.7117 0.2042 0.723 0.6714 0.6713 ] Network output: [ 0.2546 0.1898 0.8244 -0.003805 -0.002421 0.4628 -0.01349 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3774 0.05462 0.2025 0.2134 0.6835 0.8264 0.6211 0.7863 0.7741 0.7936 ] Network output: [ 0.2163 0.6801 0.8137 -0.0006213 0.001715 0.06891 0.00881 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1437 0.03537 0.2935 0.01901 0.6705 0.8001 0.1936 0.7939 0.7459 0.7595 ] Network output: [ 0.3114 -0.04896 0.6288 -0.004857 -0.004612 0.7781 -0.01503 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5258 0.1713 0.332 0.2836 0.7005 0.8464 0.6182 0.8018 0.7927 0.8155 ] Network output: [ 0.1482 0.1346 0.733 -0.004076 -0.004365 0.8124 0.00992 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3988 0.2459 0.5435 0.2163 0.7608 0.8556 0.432 0.8195 0.78 0.8485 ] Network output: [ 0.05452 0.1725 0.7025 -0.002519 -0.003055 0.9989 0.0128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4303 0.3332 0.6659 0.1678 0.7577 0.8564 0.4494 0.8187 0.7847 0.8537 ] Network output: [ -0.1359 0.7975 0.4152 0.006604 0.008588 1.094 -0.003211 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3622 Epoch 206 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1468 1.029 0.9043 0.008179 0.003993 -0.1884 0.002468 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0624 -0.001948 0.09779 -0.08184 0.6038 0.7125 0.2025 0.7238 0.6733 0.6723 ] Network output: [ 0.2566 0.1887 0.823 -0.003642 -0.002256 0.4621 -0.01357 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.378 0.05435 0.2061 0.2139 0.6853 0.8274 0.6203 0.7871 0.776 0.7944 ] Network output: [ 0.215 0.6816 0.8139 -0.0007433 0.001712 0.06927 0.008883 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1427 0.03522 0.3009 0.0197 0.6727 0.8013 0.1917 0.7951 0.7486 0.761 ] Network output: [ 0.3127 -0.05305 0.6279 -0.004755 -0.004449 0.781 -0.01489 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5265 0.1719 0.3406 0.2846 0.7025 0.8476 0.6175 0.8027 0.7948 0.8164 ] Network output: [ 0.1475 0.1316 0.7373 -0.004071 -0.004358 0.8124 0.01007 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3969 0.2454 0.552 0.2188 0.7625 0.8566 0.4293 0.8207 0.7825 0.8498 ] Network output: [ 0.05414 0.169 0.7073 -0.002502 -0.003129 0.9985 0.0128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4281 0.3323 0.6729 0.1716 0.7592 0.8573 0.4467 0.8198 0.7873 0.8549 ] Network output: [ -0.1372 0.8032 0.4119 0.006564 0.008391 1.094 -0.003488 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3597 Epoch 207 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1466 1.034 0.9012 0.00818 0.003966 -0.1906 0.002306 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06209 -0.001933 0.09995 -0.08183 0.6054 0.7133 0.2008 0.7247 0.6753 0.6733 ] Network output: [ 0.2586 0.1877 0.8214 -0.003483 -0.002094 0.4614 -0.01365 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3788 0.0541 0.2095 0.2145 0.6871 0.8285 0.6196 0.7879 0.7779 0.7951 ] Network output: [ 0.2136 0.6831 0.8141 -0.000863 0.001707 0.06959 0.008948 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1417 0.03509 0.308 0.02044 0.6749 0.8024 0.1897 0.7963 0.7514 0.7626 ] Network output: [ 0.314 -0.05686 0.6269 -0.004647 -0.004289 0.7838 -0.01477 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5272 0.1726 0.3488 0.2857 0.7045 0.8487 0.6167 0.8037 0.797 0.8174 ] Network output: [ 0.1468 0.1287 0.7417 -0.004064 -0.004349 0.8124 0.01022 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.395 0.245 0.5601 0.2214 0.7641 0.8575 0.4266 0.8218 0.7851 0.8511 ] Network output: [ 0.05373 0.1654 0.7122 -0.002485 -0.003198 0.998 0.01281 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4259 0.3315 0.6796 0.1755 0.7607 0.8582 0.444 0.8209 0.7898 0.8562 ] Network output: [ -0.1383 0.8088 0.4086 0.006523 0.0082 1.094 -0.003751 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3572 Epoch 208 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1465 1.04 0.8981 0.008172 0.003935 -0.1927 0.002152 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06182 -0.001917 0.102 -0.08178 0.6071 0.7142 0.1992 0.7255 0.6773 0.6743 ] Network output: [ 0.2607 0.1869 0.8196 -0.003329 -0.001934 0.4607 -0.01374 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3796 0.05388 0.2127 0.2151 0.6889 0.8295 0.6188 0.7887 0.7797 0.7957 ] Network output: [ 0.2123 0.6846 0.8144 -0.0009804 0.001699 0.06987 0.009005 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1408 0.03498 0.3148 0.02123 0.6772 0.8036 0.1878 0.7975 0.7542 0.7641 ] Network output: [ 0.3153 -0.06041 0.6257 -0.004534 -0.004132 0.7865 -0.01465 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5281 0.1734 0.3566 0.2867 0.7066 0.8499 0.616 0.8046 0.7991 0.8183 ] Network output: [ 0.146 0.1258 0.7461 -0.004053 -0.004338 0.8124 0.01036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3932 0.2446 0.5678 0.224 0.7658 0.8585 0.4239 0.8229 0.7876 0.8524 ] Network output: [ 0.05328 0.1617 0.7172 -0.002468 -0.003262 0.9977 0.01282 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4238 0.3307 0.6861 0.1794 0.7623 0.8591 0.4414 0.822 0.7923 0.8574 ] Network output: [ -0.1395 0.8142 0.4053 0.006481 0.008015 1.094 -0.003999 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3547 Epoch 209 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1465 1.045 0.895 0.008157 0.003901 -0.1948 0.002006 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06157 -0.001901 0.1039 -0.0817 0.6089 0.7151 0.1976 0.7263 0.6792 0.6752 ] Network output: [ 0.2628 0.1861 0.8177 -0.00318 -0.001778 0.46 -0.01384 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3805 0.05368 0.2155 0.2157 0.6908 0.8305 0.6181 0.7895 0.7816 0.7964 ] Network output: [ 0.211 0.686 0.8148 -0.001095 0.00169 0.07012 0.009054 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1399 0.03488 0.3212 0.02208 0.6795 0.8048 0.186 0.7987 0.7569 0.7657 ] Network output: [ 0.3166 -0.06372 0.6244 -0.004417 -0.003978 0.789 -0.01455 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.529 0.1742 0.3639 0.2877 0.7087 0.8511 0.6153 0.8055 0.8012 0.8192 ] Network output: [ 0.1452 0.1231 0.7506 -0.00404 -0.004326 0.8123 0.01049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3914 0.2442 0.5751 0.2266 0.7675 0.8594 0.4213 0.8241 0.7902 0.8537 ] Network output: [ 0.05278 0.158 0.7223 -0.002451 -0.003322 0.9973 0.01284 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4218 0.3299 0.6922 0.1835 0.7639 0.86 0.4388 0.823 0.7949 0.8586 ] Network output: [ -0.1406 0.8195 0.4019 0.006438 0.007835 1.094 -0.004232 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3522 Epoch 210 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1466 1.05 0.8919 0.008134 0.003863 -0.1969 0.001869 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06136 -0.001884 0.1056 -0.08158 0.6106 0.7159 0.196 0.727 0.6812 0.6762 ] Network output: [ 0.265 0.1853 0.8156 -0.003036 -0.001624 0.4592 -0.01394 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3816 0.05349 0.2181 0.2163 0.6927 0.8315 0.6173 0.7902 0.7835 0.7971 ] Network output: [ 0.2097 0.6875 0.8153 -0.001208 0.001678 0.07033 0.009097 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1391 0.03479 0.3272 0.02297 0.6819 0.806 0.1841 0.7999 0.7597 0.7672 ] Network output: [ 0.3179 -0.0668 0.6231 -0.004296 -0.003827 0.7915 -0.01445 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.53 0.1751 0.3707 0.2888 0.7108 0.8522 0.6146 0.8064 0.8033 0.82 ] Network output: [ 0.1442 0.1204 0.7552 -0.004025 -0.004312 0.8123 0.01062 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3897 0.2439 0.5821 0.2293 0.7692 0.8604 0.4188 0.8252 0.7927 0.8549 ] Network output: [ 0.05222 0.1543 0.7274 -0.002434 -0.003377 0.997 0.01285 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4198 0.3292 0.698 0.1875 0.7655 0.8609 0.4363 0.8241 0.7974 0.8597 ] Network output: [ -0.1417 0.8246 0.3985 0.006393 0.007661 1.094 -0.004451 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3497 Epoch 211 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1467 1.055 0.8888 0.008104 0.003822 -0.199 0.001742 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06119 -0.001867 0.1072 -0.08141 0.6124 0.7168 0.1945 0.7278 0.6831 0.6771 ] Network output: [ 0.2673 0.1846 0.8133 -0.002897 -0.001474 0.4584 -0.01404 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3827 0.05332 0.2204 0.2169 0.6947 0.8326 0.6166 0.791 0.7853 0.7977 ] Network output: [ 0.2084 0.6889 0.8158 -0.001318 0.001663 0.07053 0.009134 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1383 0.03472 0.3329 0.02393 0.6842 0.8073 0.1823 0.8011 0.7624 0.7687 ] Network output: [ 0.3192 -0.06968 0.6216 -0.004171 -0.003679 0.7938 -0.01436 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.531 0.176 0.3772 0.2899 0.713 0.8534 0.614 0.8073 0.8054 0.8209 ] Network output: [ 0.1433 0.1179 0.7598 -0.004008 -0.004297 0.8122 0.01075 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3881 0.2436 0.5887 0.232 0.771 0.8614 0.4162 0.8263 0.7952 0.8562 ] Network output: [ 0.05161 0.1507 0.7326 -0.002417 -0.003428 0.9968 0.01288 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4179 0.3285 0.7036 0.1917 0.7672 0.8619 0.4338 0.8251 0.7999 0.8609 ] Network output: [ -0.1427 0.8296 0.3951 0.006347 0.007493 1.094 -0.004657 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3472 Epoch 212 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1469 1.059 0.8858 0.008068 0.003777 -0.201 0.001623 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06104 -0.001851 0.1087 -0.08121 0.6143 0.7178 0.193 0.7286 0.6851 0.678 ] Network output: [ 0.2696 0.1839 0.8108 -0.002763 -0.001327 0.4576 -0.01415 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3839 0.05316 0.2225 0.2175 0.6967 0.8337 0.6159 0.7918 0.7872 0.7983 ] Network output: [ 0.207 0.6902 0.8164 -0.001427 0.001647 0.0707 0.009166 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1376 0.03465 0.3383 0.02494 0.6867 0.8085 0.1806 0.8022 0.7651 0.7701 ] Network output: [ 0.3205 -0.07237 0.62 -0.004044 -0.003535 0.796 -0.01427 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5321 0.1769 0.3832 0.291 0.7152 0.8545 0.6133 0.8082 0.8075 0.8218 ] Network output: [ 0.1422 0.1154 0.7645 -0.00399 -0.004279 0.8121 0.01087 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3865 0.2434 0.5951 0.2348 0.7728 0.8623 0.4138 0.8275 0.7977 0.8574 ] Network output: [ 0.05094 0.1471 0.7379 -0.002401 -0.003474 0.9965 0.0129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.416 0.3279 0.7089 0.1958 0.7689 0.8628 0.4313 0.8262 0.8023 0.8621 ] Network output: [ -0.1437 0.8346 0.3917 0.0063 0.00733 1.094 -0.004851 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3447 Epoch 213 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1471 1.064 0.8827 0.008025 0.003729 -0.203 0.001513 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06093 -0.001836 0.11 -0.08096 0.6162 0.7187 0.1915 0.7293 0.687 0.679 ] Network output: [ 0.272 0.1832 0.8082 -0.002634 -0.001183 0.4568 -0.01426 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3853 0.053 0.2243 0.2181 0.6987 0.8347 0.6153 0.7925 0.789 0.7989 ] Network output: [ 0.2057 0.6914 0.8172 -0.001533 0.001628 0.07087 0.009193 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1369 0.0346 0.3434 0.02602 0.6892 0.8097 0.1789 0.8034 0.7678 0.7716 ] Network output: [ 0.3219 -0.07489 0.6183 -0.003915 -0.003394 0.7982 -0.0142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5333 0.1779 0.3889 0.2921 0.7174 0.8557 0.6127 0.8091 0.8096 0.8226 ] Network output: [ 0.1411 0.1131 0.7692 -0.003971 -0.004261 0.812 0.01098 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3849 0.2432 0.6011 0.2375 0.7747 0.8633 0.4114 0.8286 0.8002 0.8587 ] Network output: [ 0.0502 0.1435 0.7432 -0.002385 -0.003515 0.9963 0.01293 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4141 0.3273 0.714 0.1999 0.7707 0.8637 0.4289 0.8272 0.8048 0.8632 ] Network output: [ -0.1447 0.8395 0.3882 0.006251 0.007174 1.095 -0.005033 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3422 Epoch 214 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1474 1.068 0.8797 0.007977 0.003677 -0.205 0.001413 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06084 -0.001822 0.1113 -0.08067 0.6181 0.7196 0.19 0.7301 0.6889 0.6799 ] Network output: [ 0.2745 0.1825 0.8055 -0.00251 -0.001043 0.4559 -0.01437 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3867 0.05285 0.2259 0.2187 0.7008 0.8358 0.6146 0.7933 0.7909 0.7995 ] Network output: [ 0.2044 0.6926 0.818 -0.001637 0.001607 0.07104 0.009217 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1362 0.03455 0.3483 0.02716 0.6917 0.811 0.1772 0.8045 0.7705 0.7731 ] Network output: [ 0.3233 -0.07727 0.6165 -0.003784 -0.003257 0.8002 -0.01412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5345 0.1789 0.3942 0.2933 0.7197 0.8569 0.6121 0.81 0.8117 0.8234 ] Network output: [ 0.1399 0.1108 0.7739 -0.00395 -0.004241 0.812 0.0111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3834 0.243 0.6068 0.2403 0.7766 0.8643 0.409 0.8297 0.8027 0.8599 ] Network output: [ 0.04939 0.14 0.7485 -0.002369 -0.003551 0.9961 0.01296 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4124 0.3267 0.7189 0.2041 0.7724 0.8647 0.4266 0.8283 0.8072 0.8644 ] Network output: [ -0.1457 0.8444 0.3846 0.006202 0.007022 1.095 -0.005204 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3397 Epoch 215 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1477 1.072 0.8767 0.007923 0.003622 -0.2069 0.001322 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06079 -0.001811 0.1124 -0.08033 0.6201 0.7206 0.1886 0.7308 0.6908 0.6808 ] Network output: [ 0.2771 0.1817 0.8025 -0.002392 -0.0009061 0.4551 -0.01449 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3883 0.0527 0.2273 0.2194 0.7029 0.8369 0.614 0.794 0.7927 0.8001 ] Network output: [ 0.2031 0.6936 0.8189 -0.001739 0.001583 0.07121 0.009238 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1356 0.0345 0.3528 0.02837 0.6943 0.8122 0.1756 0.8057 0.7732 0.7745 ] Network output: [ 0.3247 -0.07952 0.6147 -0.003653 -0.003123 0.8022 -0.01405 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5358 0.1799 0.3992 0.2944 0.7221 0.8581 0.6115 0.8109 0.8137 0.8242 ] Network output: [ 0.1387 0.1087 0.7787 -0.003929 -0.004219 0.8119 0.01121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.382 0.2428 0.6123 0.243 0.7785 0.8653 0.4067 0.8308 0.8051 0.8611 ] Network output: [ 0.04851 0.1367 0.7538 -0.002353 -0.003583 0.9959 0.01299 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4106 0.3262 0.7236 0.2082 0.7743 0.8656 0.4244 0.8294 0.8096 0.8655 ] Network output: [ -0.1466 0.8492 0.381 0.006152 0.006876 1.096 -0.005366 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3372 Epoch 216 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1481 1.076 0.8738 0.007865 0.003564 -0.2087 0.001239 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06076 -0.001801 0.1134 -0.07995 0.6221 0.7216 0.1873 0.7316 0.6927 0.6816 ] Network output: [ 0.2798 0.1809 0.7995 -0.002279 -0.000773 0.4542 -0.0146 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3899 0.05256 0.2284 0.22 0.7051 0.838 0.6134 0.7947 0.7945 0.8007 ] Network output: [ 0.2018 0.6946 0.8199 -0.001839 0.001558 0.0714 0.009257 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1351 0.03447 0.3571 0.02965 0.6969 0.8135 0.1741 0.8068 0.7758 0.7759 ] Network output: [ 0.3261 -0.08165 0.6127 -0.003521 -0.002993 0.804 -0.01399 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5371 0.181 0.4039 0.2957 0.7244 0.8593 0.611 0.8118 0.8158 0.825 ] Network output: [ 0.1374 0.1066 0.7836 -0.003907 -0.004196 0.8118 0.01131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3806 0.2427 0.6175 0.2458 0.7804 0.8663 0.4045 0.8319 0.8075 0.8623 ] Network output: [ 0.04756 0.1334 0.7592 -0.002337 -0.003611 0.9958 0.01301 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.409 0.3257 0.7281 0.2122 0.7761 0.8666 0.4221 0.8304 0.812 0.8666 ] Network output: [ -0.1475 0.8541 0.3773 0.006102 0.006736 1.096 -0.005519 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3348 Epoch 217 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1485 1.079 0.8709 0.007803 0.003503 -0.2105 0.001166 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06076 -0.001794 0.1144 -0.07952 0.6241 0.7226 0.1859 0.7323 0.6946 0.6825 ] Network output: [ 0.2825 0.1801 0.7963 -0.002171 -0.0006434 0.4533 -0.01471 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3917 0.05241 0.2294 0.2207 0.7073 0.8392 0.6128 0.7955 0.7963 0.8012 ] Network output: [ 0.2005 0.6954 0.8211 -0.001938 0.00153 0.07161 0.009273 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1345 0.03443 0.3612 0.031 0.6996 0.8148 0.1725 0.8079 0.7785 0.7773 ] Network output: [ 0.3275 -0.08369 0.6107 -0.00339 -0.002867 0.8058 -0.01392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5385 0.182 0.4083 0.2969 0.7269 0.8605 0.6104 0.8127 0.8178 0.8257 ] Network output: [ 0.136 0.1047 0.7884 -0.003884 -0.004172 0.8117 0.01141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3793 0.2426 0.6226 0.2486 0.7824 0.8673 0.4023 0.833 0.8099 0.8635 ] Network output: [ 0.04652 0.1303 0.7646 -0.00232 -0.003634 0.9957 0.01304 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4074 0.3253 0.7324 0.2163 0.778 0.8676 0.42 0.8314 0.8144 0.8677 ] Network output: [ -0.1484 0.8589 0.3736 0.006052 0.006601 1.096 -0.005664 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3323 Epoch 218 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.149 1.083 0.8681 0.007737 0.003439 -0.2123 0.0011 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06078 -0.001789 0.1152 -0.07904 0.6262 0.7237 0.1846 0.733 0.6965 0.6834 ] Network output: [ 0.2854 0.1792 0.793 -0.002068 -0.0005175 0.4524 -0.01483 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3935 0.05226 0.2302 0.2215 0.7095 0.8403 0.6123 0.7962 0.7981 0.8018 ] Network output: [ 0.1991 0.696 0.8224 -0.002035 0.0015 0.07184 0.009288 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.134 0.03441 0.3651 0.03242 0.7023 0.8161 0.171 0.8091 0.7811 0.7787 ] Network output: [ 0.329 -0.08566 0.6087 -0.00326 -0.002745 0.8075 -0.01385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.54 0.1831 0.4125 0.2981 0.7293 0.8617 0.6099 0.8136 0.8198 0.8265 ] Network output: [ 0.1345 0.1028 0.7933 -0.003861 -0.004147 0.8117 0.0115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.378 0.2426 0.6274 0.2513 0.7844 0.8684 0.4001 0.8341 0.8123 0.8646 ] Network output: [ 0.04541 0.1273 0.77 -0.002303 -0.003652 0.9956 0.01307 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4058 0.3249 0.7366 0.2203 0.7799 0.8686 0.4179 0.8325 0.8167 0.8689 ] Network output: [ -0.1493 0.8638 0.3698 0.006002 0.00647 1.097 -0.005802 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3298 Epoch 219 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1495 1.086 0.8653 0.007667 0.003373 -0.214 0.001044 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06084 -0.001786 0.116 -0.07852 0.6284 0.7247 0.1834 0.7337 0.6984 0.6842 ] Network output: [ 0.2883 0.1782 0.7895 -0.001971 -0.0003952 0.4515 -0.01494 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3954 0.0521 0.2309 0.2222 0.7118 0.8414 0.6117 0.7969 0.7998 0.8023 ] Network output: [ 0.1978 0.6965 0.8238 -0.002131 0.001469 0.07211 0.009302 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1336 0.03438 0.3688 0.03392 0.705 0.8175 0.1696 0.8102 0.7837 0.7801 ] Network output: [ 0.3305 -0.08755 0.6066 -0.003131 -0.002626 0.8091 -0.01379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5414 0.1842 0.4165 0.2994 0.7318 0.8629 0.6095 0.8144 0.8218 0.8272 ] Network output: [ 0.133 0.1011 0.7982 -0.003838 -0.004121 0.8117 0.01159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3768 0.2425 0.6321 0.2541 0.7864 0.8694 0.3981 0.8352 0.8146 0.8658 ] Network output: [ 0.04422 0.1245 0.7754 -0.002285 -0.003667 0.9955 0.01309 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4043 0.3245 0.7406 0.2242 0.7819 0.8696 0.4159 0.8335 0.819 0.8699 ] Network output: [ -0.1502 0.8687 0.3659 0.005953 0.006345 1.097 -0.005934 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3273 Epoch 220 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1501 1.089 0.8627 0.007595 0.003303 -0.2157 0.000995 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06091 -0.001785 0.1167 -0.07795 0.6305 0.7258 0.1821 0.7344 0.7002 0.685 ] Network output: [ 0.2913 0.1771 0.786 -0.001879 -0.0002765 0.4506 -0.01505 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3974 0.05194 0.2314 0.223 0.7141 0.8426 0.6112 0.7976 0.8016 0.8028 ] Network output: [ 0.1965 0.6969 0.8253 -0.002226 0.001435 0.07241 0.009316 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1331 0.03436 0.3724 0.03549 0.7078 0.8188 0.1681 0.8113 0.7862 0.7814 ] Network output: [ 0.332 -0.0894 0.6044 -0.003003 -0.002512 0.8107 -0.01372 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5429 0.1853 0.4202 0.3008 0.7343 0.8642 0.609 0.8153 0.8238 0.828 ] Network output: [ 0.1314 0.09946 0.8031 -0.003814 -0.004093 0.8117 0.01167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3757 0.2425 0.6366 0.2568 0.7885 0.8704 0.3961 0.8363 0.8169 0.867 ] Network output: [ 0.04295 0.1218 0.7807 -0.002266 -0.003678 0.9954 0.0131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4029 0.3242 0.7446 0.228 0.7838 0.8706 0.414 0.8346 0.8213 0.871 ] Network output: [ -0.151 0.8736 0.3619 0.005904 0.006225 1.098 -0.006061 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3248 Epoch 221 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1506 1.091 0.8601 0.007521 0.003232 -0.2173 0.0009541 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.061 -0.001787 0.1173 -0.07733 0.6327 0.7269 0.1809 0.7351 0.702 0.6859 ] Network output: [ 0.2944 0.176 0.7823 -0.001791 -0.0001614 0.4497 -0.01516 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3995 0.05177 0.2318 0.2238 0.7165 0.8438 0.6107 0.7983 0.8033 0.8033 ] Network output: [ 0.1952 0.6971 0.827 -0.00232 0.0014 0.07276 0.009329 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1327 0.03435 0.3758 0.03714 0.7106 0.8202 0.1667 0.8124 0.7887 0.7828 ] Network output: [ 0.3336 -0.09121 0.6023 -0.002877 -0.002401 0.8122 -0.01364 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5444 0.1865 0.4239 0.3021 0.7369 0.8654 0.6086 0.8162 0.8257 0.8287 ] Network output: [ 0.1297 0.09791 0.8081 -0.00379 -0.004065 0.8117 0.01175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3745 0.2426 0.641 0.2595 0.7905 0.8715 0.3942 0.8374 0.8192 0.8681 ] Network output: [ 0.0416 0.1193 0.786 -0.002246 -0.003684 0.9953 0.01312 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4015 0.3239 0.7484 0.2318 0.7859 0.8716 0.4121 0.8356 0.8235 0.8721 ] Network output: [ -0.1518 0.8787 0.3578 0.005856 0.006109 1.098 -0.006183 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3223 Epoch 222 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1513 1.094 0.8576 0.007444 0.003158 -0.2189 0.0009207 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06112 -0.001792 0.1179 -0.07666 0.635 0.7281 0.1797 0.7358 0.7039 0.6867 ] Network output: [ 0.2976 0.1748 0.7786 -0.001709 -4.994e-05 0.4487 -0.01526 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4016 0.0516 0.2321 0.2246 0.7189 0.845 0.6103 0.7991 0.8051 0.8038 ] Network output: [ 0.1939 0.6971 0.8288 -0.002412 0.001363 0.07316 0.009342 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1323 0.03433 0.3792 0.03887 0.7135 0.8215 0.1654 0.8135 0.7912 0.7841 ] Network output: [ 0.3351 -0.093 0.6 -0.002754 -0.002295 0.8136 -0.01357 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.546 0.1877 0.4274 0.3035 0.7394 0.8667 0.6082 0.8171 0.8276 0.8294 ] Network output: [ 0.128 0.09645 0.8131 -0.003765 -0.004036 0.8118 0.01182 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3735 0.2427 0.6453 0.2622 0.7927 0.8726 0.3923 0.8385 0.8215 0.8692 ] Network output: [ 0.04018 0.117 0.7913 -0.002225 -0.003687 0.9953 0.01312 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4002 0.3236 0.7521 0.2355 0.7879 0.8726 0.4103 0.8367 0.8258 0.8732 ] Network output: [ -0.1526 0.8837 0.3536 0.005809 0.005997 1.099 -0.006301 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3198 Epoch 223 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1519 1.096 0.8552 0.007366 0.003082 -0.2205 0.0008944 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06126 -0.001798 0.1184 -0.07595 0.6373 0.7292 0.1786 0.7364 0.7057 0.6874 ] Network output: [ 0.3009 0.1735 0.7748 -0.001631 5.796e-05 0.4477 -0.01536 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4039 0.05143 0.2323 0.2254 0.7213 0.8462 0.6099 0.7998 0.8068 0.8043 ] Network output: [ 0.1925 0.6969 0.8308 -0.002504 0.001325 0.07361 0.009355 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.132 0.03433 0.3824 0.04068 0.7164 0.8229 0.1641 0.8146 0.7937 0.7854 ] Network output: [ 0.3367 -0.09477 0.5978 -0.002633 -0.002193 0.815 -0.01349 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5476 0.1888 0.4307 0.305 0.7421 0.8679 0.6079 0.8179 0.8296 0.8301 ] Network output: [ 0.1262 0.09509 0.8181 -0.003739 -0.004005 0.8119 0.01189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3725 0.2428 0.6494 0.2649 0.7948 0.8736 0.3905 0.8396 0.8237 0.8704 ] Network output: [ 0.03867 0.1149 0.7966 -0.002202 -0.003686 0.9952 0.01312 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.399 0.3234 0.7557 0.2391 0.79 0.8737 0.4085 0.8377 0.8279 0.8742 ] Network output: [ -0.1533 0.8889 0.3494 0.005764 0.00589 1.099 -0.006416 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3173 Epoch 224 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1526 1.098 0.8528 0.007287 0.003005 -0.222 0.000875 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06141 -0.001806 0.1188 -0.07519 0.6396 0.7304 0.1774 0.7371 0.7074 0.6882 ] Network output: [ 0.3042 0.1721 0.7709 -0.001558 0.0001623 0.4467 -0.01545 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4061 0.05125 0.2324 0.2263 0.7238 0.8474 0.6095 0.8005 0.8085 0.8048 ] Network output: [ 0.1912 0.6965 0.8329 -0.002595 0.001285 0.07412 0.009369 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1317 0.03432 0.3855 0.04257 0.7193 0.8243 0.1628 0.8157 0.7962 0.7867 ] Network output: [ 0.3383 -0.09653 0.5956 -0.002514 -0.002095 0.8163 -0.0134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5492 0.19 0.434 0.3064 0.7447 0.8692 0.6075 0.8188 0.8314 0.8308 ] Network output: [ 0.1243 0.09381 0.8231 -0.003714 -0.003974 0.812 0.01195 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3715 0.2429 0.6535 0.2676 0.797 0.8747 0.3888 0.8407 0.8259 0.8715 ] Network output: [ 0.03709 0.1131 0.8018 -0.002177 -0.003681 0.9952 0.01312 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3978 0.3233 0.7592 0.2426 0.792 0.8747 0.4069 0.8387 0.8301 0.8753 ] Network output: [ -0.1541 0.8941 0.345 0.005719 0.005787 1.099 -0.006528 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3148 Epoch 225 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1534 1.1 0.8506 0.007206 0.002926 -0.2235 0.0008621 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06159 -0.001816 0.1193 -0.07439 0.6419 0.7316 0.1763 0.7378 0.7092 0.689 ] Network output: [ 0.3077 0.1706 0.7669 -0.00149 0.0002632 0.4457 -0.01554 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4085 0.05107 0.2324 0.2272 0.7263 0.8487 0.6091 0.8012 0.8102 0.8053 ] Network output: [ 0.1898 0.696 0.8352 -0.002685 0.001244 0.07469 0.009382 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1314 0.03433 0.3886 0.04453 0.7223 0.8257 0.1616 0.8168 0.7986 0.788 ] Network output: [ 0.3399 -0.09829 0.5934 -0.002399 -0.002001 0.8176 -0.01331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5507 0.1913 0.4372 0.3079 0.7474 0.8704 0.6072 0.8197 0.8333 0.8315 ] Network output: [ 0.1223 0.09262 0.8281 -0.003687 -0.003942 0.8122 0.012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3706 0.2431 0.6575 0.2702 0.7991 0.8758 0.3871 0.8418 0.8281 0.8726 ] Network output: [ 0.03544 0.1114 0.807 -0.002151 -0.003673 0.9951 0.01311 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3966 0.3232 0.7626 0.246 0.7942 0.8758 0.4053 0.8398 0.8322 0.8763 ] Network output: [ -0.1547 0.8994 0.3405 0.005676 0.005688 1.1 -0.006638 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3123 Epoch 226 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1541 1.102 0.8484 0.007125 0.002846 -0.2249 0.0008553 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06178 -0.001827 0.1196 -0.07354 0.6443 0.7328 0.1753 0.7384 0.711 0.6897 ] Network output: [ 0.3112 0.169 0.7628 -0.001426 0.0003605 0.4446 -0.01562 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4108 0.05089 0.2324 0.2281 0.7288 0.8499 0.6087 0.8019 0.8118 0.8057 ] Network output: [ 0.1884 0.6952 0.8376 -0.002774 0.001203 0.07533 0.009397 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1311 0.03433 0.3916 0.04658 0.7253 0.8272 0.1604 0.8179 0.801 0.7893 ] Network output: [ 0.3416 -0.1001 0.5912 -0.002286 -0.001911 0.8188 -0.01322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5524 0.1925 0.4404 0.3095 0.7501 0.8717 0.607 0.8205 0.8352 0.8322 ] Network output: [ 0.1203 0.09151 0.8331 -0.00366 -0.003909 0.8125 0.01205 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3698 0.2433 0.6615 0.2728 0.8013 0.8769 0.3856 0.8429 0.8303 0.8737 ] Network output: [ 0.03372 0.1099 0.8121 -0.002124 -0.003662 0.9951 0.01309 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3955 0.3231 0.766 0.2493 0.7963 0.8768 0.4038 0.8408 0.8343 0.8773 ] Network output: [ -0.1554 0.9048 0.336 0.005633 0.005593 1.1 -0.006745 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3098 Epoch 227 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1549 1.103 0.8463 0.007043 0.002764 -0.2263 0.0008544 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06198 -0.00184 0.12 -0.07265 0.6467 0.7341 0.1742 0.7391 0.7127 0.6904 ] Network output: [ 0.3149 0.1673 0.7587 -0.001366 0.0004544 0.4435 -0.0157 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4133 0.0507 0.2324 0.229 0.7314 0.8512 0.6084 0.8026 0.8135 0.8062 ] Network output: [ 0.1871 0.6943 0.8402 -0.002863 0.00116 0.07603 0.009411 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1308 0.03435 0.3945 0.0487 0.7283 0.8286 0.1592 0.819 0.8034 0.7905 ] Network output: [ 0.3432 -0.1019 0.589 -0.002175 -0.001826 0.82 -0.01311 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.554 0.1938 0.4436 0.311 0.7528 0.873 0.6067 0.8214 0.837 0.8328 ] Network output: [ 0.1182 0.09049 0.8381 -0.003633 -0.003876 0.8128 0.01209 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.369 0.2436 0.6654 0.2754 0.8035 0.878 0.3841 0.844 0.8324 0.8747 ] Network output: [ 0.03193 0.1086 0.8171 -0.002094 -0.003647 0.995 0.01306 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3945 0.3231 0.7693 0.2525 0.7985 0.8779 0.4023 0.8418 0.8364 0.8783 ] Network output: [ -0.156 0.9103 0.3313 0.005592 0.005501 1.1 -0.00685 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3073 Epoch 228 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1557 1.104 0.8443 0.006961 0.002682 -0.2277 0.0008589 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0622 -0.001854 0.1204 -0.07172 0.6492 0.7353 0.1732 0.7397 0.7144 0.6912 ] Network output: [ 0.3185 0.1655 0.7545 -0.00131 0.0005449 0.4424 -0.01577 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4157 0.05052 0.2323 0.23 0.734 0.8525 0.6081 0.8033 0.8151 0.8066 ] Network output: [ 0.1857 0.6931 0.8429 -0.002951 0.001117 0.0768 0.009426 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1305 0.03437 0.3975 0.0509 0.7313 0.8301 0.1581 0.8201 0.8057 0.7918 ] Network output: [ 0.3448 -0.1037 0.5869 -0.002068 -0.001744 0.8212 -0.013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5556 0.1951 0.4467 0.3127 0.7555 0.8743 0.6065 0.8223 0.8389 0.8335 ] Network output: [ 0.1161 0.08954 0.8432 -0.003604 -0.003842 0.8132 0.01212 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3683 0.2439 0.6693 0.2779 0.8058 0.8791 0.3827 0.845 0.8345 0.8758 ] Network output: [ 0.03008 0.1076 0.822 -0.002062 -0.003629 0.995 0.01302 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3936 0.3232 0.7726 0.2555 0.8006 0.879 0.401 0.8429 0.8385 0.8793 ] Network output: [ -0.1565 0.9159 0.3265 0.005552 0.005413 1.1 -0.006954 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3049 Epoch 229 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1565 1.106 0.8424 0.006878 0.002599 -0.229 0.0008685 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06243 -0.001868 0.1207 -0.07074 0.6517 0.7366 0.1722 0.7404 0.7161 0.6919 ] Network output: [ 0.3223 0.1637 0.7503 -0.001259 0.0006321 0.4412 -0.01583 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4182 0.05034 0.2321 0.231 0.7366 0.8538 0.6079 0.804 0.8168 0.807 ] Network output: [ 0.1842 0.6918 0.8458 -0.003038 0.001073 0.07764 0.009441 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1303 0.03439 0.4004 0.05317 0.7344 0.8316 0.157 0.8212 0.808 0.793 ] Network output: [ 0.3464 -0.1055 0.5848 -0.001963 -0.001667 0.8223 -0.01289 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5572 0.1964 0.4498 0.3143 0.7583 0.8756 0.6063 0.8231 0.8407 0.8341 ] Network output: [ 0.1139 0.08867 0.8482 -0.003575 -0.003806 0.8136 0.01214 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3676 0.2443 0.6731 0.2804 0.808 0.8803 0.3813 0.8461 0.8366 0.8768 ] Network output: [ 0.02817 0.1068 0.8269 -0.002029 -0.003608 0.995 0.01298 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3927 0.3233 0.7758 0.2584 0.8028 0.8801 0.3997 0.8439 0.8405 0.8803 ] Network output: [ -0.157 0.9215 0.3216 0.005513 0.005328 1.101 -0.007056 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3024 Epoch 230 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1574 1.107 0.8406 0.006796 0.002516 -0.2304 0.0008829 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06267 -0.001883 0.121 -0.06973 0.6542 0.7379 0.1712 0.741 0.7178 0.6925 ] Network output: [ 0.3262 0.1617 0.746 -0.001211 0.0007159 0.44 -0.01589 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4208 0.05017 0.232 0.232 0.7392 0.8551 0.6076 0.8047 0.8184 0.8074 ] Network output: [ 0.1828 0.6903 0.8488 -0.003125 0.001029 0.07855 0.009456 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1301 0.03443 0.4033 0.05552 0.7375 0.8331 0.1559 0.8223 0.8103 0.7942 ] Network output: [ 0.3481 -0.1073 0.5828 -0.001861 -0.001595 0.8235 -0.01276 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5588 0.1978 0.4529 0.316 0.7611 0.8769 0.6062 0.824 0.8424 0.8347 ] Network output: [ 0.1116 0.08788 0.8532 -0.003545 -0.003771 0.8141 0.01216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.367 0.2447 0.6769 0.2829 0.8103 0.8814 0.3801 0.8472 0.8386 0.8779 ] Network output: [ 0.0262 0.1062 0.8317 -0.001994 -0.003585 0.9949 0.01293 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3919 0.3235 0.7789 0.2612 0.805 0.8812 0.3985 0.8449 0.8425 0.8812 ] Network output: [ -0.1575 0.9272 0.3166 0.005474 0.005246 1.101 -0.007157 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2999 Epoch 231 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1582 1.108 0.8388 0.006714 0.002432 -0.2317 0.0009017 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06293 -0.001898 0.1213 -0.06868 0.6567 0.7392 0.1703 0.7416 0.7195 0.6932 ] Network output: [ 0.3301 0.1597 0.7417 -0.001167 0.0007964 0.4387 -0.01593 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4233 0.05 0.2318 0.233 0.7419 0.8564 0.6074 0.8054 0.82 0.8078 ] Network output: [ 0.1814 0.6886 0.8521 -0.003211 0.0009848 0.07954 0.009471 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1299 0.03447 0.4062 0.05794 0.7406 0.8346 0.1549 0.8234 0.8126 0.7954 ] Network output: [ 0.3497 -0.1092 0.5808 -0.001762 -0.001526 0.8246 -0.01263 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5603 0.1992 0.456 0.3177 0.7639 0.8782 0.606 0.8248 0.8442 0.8354 ] Network output: [ 0.1093 0.08715 0.8582 -0.003514 -0.003734 0.8146 0.01217 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3665 0.2452 0.6806 0.2853 0.8125 0.8825 0.3789 0.8483 0.8406 0.8789 ] Network output: [ 0.02418 0.1058 0.8363 -0.001957 -0.003559 0.9949 0.01287 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3911 0.3237 0.782 0.2639 0.8073 0.8823 0.3974 0.846 0.8444 0.8822 ] Network output: [ -0.1579 0.933 0.3115 0.005436 0.005167 1.1 -0.007255 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2975 Epoch 232 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1591 1.108 0.8371 0.006632 0.002348 -0.233 0.0009246 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06319 -0.001913 0.1215 -0.06759 0.6592 0.7406 0.1694 0.7423 0.7211 0.6939 ] Network output: [ 0.334 0.1577 0.7374 -0.001126 0.0008737 0.4374 -0.01597 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4259 0.04985 0.2317 0.2341 0.7446 0.8577 0.6072 0.8061 0.8216 0.8082 ] Network output: [ 0.1799 0.6867 0.8554 -0.003296 0.0009405 0.08059 0.009486 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1297 0.03452 0.4091 0.06043 0.7437 0.8361 0.1539 0.8244 0.8148 0.7965 ] Network output: [ 0.3513 -0.1111 0.5789 -0.001664 -0.001462 0.8256 -0.01249 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5619 0.2007 0.4592 0.3194 0.7667 0.8796 0.6059 0.8257 0.846 0.836 ] Network output: [ 0.1069 0.08648 0.8631 -0.003482 -0.003698 0.8152 0.01217 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.366 0.2458 0.6844 0.2877 0.8148 0.8837 0.3778 0.8493 0.8426 0.8799 ] Network output: [ 0.02211 0.1056 0.8409 -0.001918 -0.00353 0.9949 0.0128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3904 0.324 0.785 0.2664 0.8095 0.8834 0.3964 0.847 0.8463 0.8831 ] Network output: [ -0.1582 0.9388 0.3063 0.005399 0.005091 1.1 -0.007352 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.295 Epoch 233 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.16 1.109 0.8355 0.00655 0.002264 -0.2342 0.0009512 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06347 -0.001927 0.1218 -0.06646 0.6618 0.7419 0.1685 0.7429 0.7228 0.6945 ] Network output: [ 0.3381 0.1555 0.733 -0.001088 0.0009479 0.4361 -0.016 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4285 0.0497 0.2315 0.2352 0.7473 0.859 0.607 0.8068 0.8231 0.8086 ] Network output: [ 0.1784 0.6847 0.859 -0.00338 0.0008964 0.08172 0.009501 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1295 0.03458 0.412 0.06299 0.7468 0.8376 0.1529 0.8255 0.817 0.7977 ] Network output: [ 0.3528 -0.1131 0.577 -0.001569 -0.001402 0.8267 -0.01235 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5635 0.2022 0.4623 0.3211 0.7695 0.8809 0.6058 0.8266 0.8477 0.8366 ] Network output: [ 0.1045 0.08588 0.8681 -0.003448 -0.00366 0.8159 0.01216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3656 0.2464 0.6881 0.2901 0.8171 0.8848 0.3768 0.8504 0.8446 0.8809 ] Network output: [ 0.02001 0.1056 0.8453 -0.001878 -0.003499 0.9948 0.01272 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3898 0.3243 0.788 0.2688 0.8118 0.8846 0.3954 0.848 0.8482 0.884 ] Network output: [ -0.1585 0.9447 0.301 0.005361 0.005017 1.1 -0.007447 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2926 Epoch 234 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1609 1.11 0.834 0.006469 0.002181 -0.2355 0.0009811 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06375 -0.00194 0.1221 -0.06531 0.6644 0.7433 0.1676 0.7435 0.7244 0.6951 ] Network output: [ 0.3421 0.1533 0.7286 -0.001054 0.001019 0.4347 -0.01602 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4311 0.04958 0.2314 0.2363 0.75 0.8603 0.6068 0.8075 0.8247 0.809 ] Network output: [ 0.1768 0.6824 0.8627 -0.003464 0.0008525 0.08291 0.009515 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1293 0.03465 0.415 0.06561 0.7499 0.8392 0.1519 0.8266 0.8192 0.7988 ] Network output: [ 0.3544 -0.1151 0.5752 -0.001475 -0.001346 0.8277 -0.01219 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.565 0.2037 0.4655 0.3229 0.7723 0.8822 0.6058 0.8274 0.8494 0.8372 ] Network output: [ 0.102 0.08534 0.873 -0.003414 -0.003622 0.8167 0.01215 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3652 0.247 0.6918 0.2923 0.8193 0.886 0.3759 0.8514 0.8465 0.8818 ] Network output: [ 0.01786 0.1058 0.8497 -0.001836 -0.003466 0.9948 0.01264 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3893 0.3247 0.791 0.2711 0.814 0.8857 0.3946 0.849 0.8501 0.8849 ] Network output: [ -0.1587 0.9506 0.2956 0.005324 0.004946 1.1 -0.00754 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2902 Epoch 235 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1618 1.11 0.8325 0.006389 0.002097 -0.2367 0.001014 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06404 -0.001952 0.1223 -0.06412 0.667 0.7447 0.1667 0.7441 0.726 0.6957 ] Network output: [ 0.3463 0.1511 0.7242 -0.001022 0.001087 0.4332 -0.01604 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4337 0.04946 0.2312 0.2374 0.7527 0.8617 0.6067 0.8081 0.8262 0.8094 ] Network output: [ 0.1753 0.68 0.8665 -0.003546 0.0008091 0.08418 0.009528 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1291 0.03474 0.4179 0.06829 0.7531 0.8407 0.151 0.8277 0.8214 0.7999 ] Network output: [ 0.3559 -0.1171 0.5735 -0.001383 -0.001294 0.8288 -0.01203 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5665 0.2053 0.4687 0.3247 0.7752 0.8835 0.6057 0.8283 0.8511 0.8377 ] Network output: [ 0.09948 0.08484 0.8779 -0.003378 -0.003583 0.8175 0.01213 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3649 0.2478 0.6954 0.2946 0.8216 0.8871 0.3751 0.8525 0.8484 0.8828 ] Network output: [ 0.01569 0.1062 0.8539 -0.001793 -0.003431 0.9948 0.01254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3888 0.3252 0.7939 0.2732 0.8163 0.8868 0.3938 0.8501 0.8519 0.8858 ] Network output: [ -0.1588 0.9565 0.2901 0.005286 0.004877 1.1 -0.00763 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2878 Epoch 236 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1628 1.111 0.8311 0.006309 0.002015 -0.238 0.00105 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06434 -0.001962 0.1226 -0.0629 0.6696 0.7461 0.1659 0.7448 0.7276 0.6963 ] Network output: [ 0.3505 0.1488 0.7198 -0.0009932 0.001152 0.4317 -0.01604 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4364 0.04937 0.2311 0.2385 0.7554 0.863 0.6066 0.8088 0.8277 0.8097 ] Network output: [ 0.1737 0.6775 0.8706 -0.003628 0.0007663 0.08551 0.00954 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.129 0.03483 0.4208 0.07104 0.7562 0.8423 0.1501 0.8287 0.8235 0.801 ] Network output: [ 0.3574 -0.1191 0.5719 -0.001292 -0.001246 0.8298 -0.01186 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.568 0.207 0.4719 0.3265 0.778 0.8849 0.6057 0.8291 0.8528 0.8383 ] Network output: [ 0.09693 0.0844 0.8827 -0.003341 -0.003544 0.8184 0.0121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3647 0.2486 0.6991 0.2968 0.8239 0.8883 0.3744 0.8535 0.8503 0.8837 ] Network output: [ 0.01348 0.1068 0.858 -0.001749 -0.003393 0.9947 0.01244 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3884 0.3258 0.7967 0.2752 0.8185 0.888 0.3931 0.8511 0.8537 0.8866 ] Network output: [ -0.1589 0.9625 0.2845 0.005247 0.00481 1.099 -0.007718 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2855 Epoch 237 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1637 1.111 0.8297 0.00623 0.001933 -0.2392 0.001087 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06464 -0.00197 0.1229 -0.06165 0.6722 0.7476 0.1651 0.7454 0.7292 0.6969 ] Network output: [ 0.3547 0.1465 0.7153 -0.0009667 0.001214 0.4302 -0.01604 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.439 0.0493 0.2309 0.2397 0.7582 0.8644 0.6065 0.8095 0.8292 0.81 ] Network output: [ 0.1721 0.6747 0.8747 -0.003708 0.0007241 0.0869 0.00955 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1288 0.03494 0.4238 0.07384 0.7594 0.8438 0.1493 0.8298 0.8256 0.8021 ] Network output: [ 0.3589 -0.1212 0.5704 -0.001202 -0.001203 0.8308 -0.01169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5695 0.2087 0.4752 0.3284 0.7808 0.8862 0.6057 0.83 0.8544 0.8389 ] Network output: [ 0.09435 0.08401 0.8875 -0.003303 -0.003505 0.8194 0.01206 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3645 0.2494 0.7027 0.2989 0.8262 0.8895 0.3737 0.8546 0.8521 0.8846 ] Network output: [ 0.01126 0.1075 0.862 -0.001703 -0.003354 0.9947 0.01233 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3881 0.3264 0.7995 0.2771 0.8208 0.8891 0.3925 0.8521 0.8555 0.8875 ] Network output: [ -0.1589 0.9684 0.2788 0.005207 0.004744 1.099 -0.007803 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2831 Epoch 238 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1646 1.111 0.8284 0.006151 0.001852 -0.2404 0.001127 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06495 -0.001976 0.1231 -0.06038 0.6749 0.749 0.1643 0.746 0.7307 0.6974 ] Network output: [ 0.3589 0.1442 0.7109 -0.0009423 0.001273 0.4286 -0.01603 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4416 0.04926 0.2308 0.2408 0.7609 0.8658 0.6064 0.8102 0.8307 0.8104 ] Network output: [ 0.1704 0.6719 0.8791 -0.003788 0.0006828 0.08835 0.009559 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1287 0.03506 0.4267 0.0767 0.7625 0.8454 0.1484 0.8308 0.8277 0.8032 ] Network output: [ 0.3603 -0.1233 0.569 -0.001112 -0.001163 0.8318 -0.01151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5709 0.2105 0.4785 0.3302 0.7837 0.8875 0.6057 0.8308 0.856 0.8394 ] Network output: [ 0.09175 0.08365 0.8923 -0.003263 -0.003465 0.8204 0.01202 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3644 0.2504 0.7063 0.301 0.8285 0.8906 0.3732 0.8556 0.854 0.8855 ] Network output: [ 0.009012 0.1085 0.8659 -0.001657 -0.003313 0.9947 0.01221 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3878 0.3271 0.8022 0.2788 0.8231 0.8902 0.392 0.8531 0.8572 0.8883 ] Network output: [ -0.1588 0.9744 0.2731 0.005166 0.004681 1.098 -0.007884 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2808 Epoch 239 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1656 1.111 0.8272 0.006074 0.001772 -0.2416 0.001169 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06526 -0.00198 0.1234 -0.05908 0.6775 0.7505 0.1635 0.7466 0.7323 0.698 ] Network output: [ 0.3632 0.1419 0.7064 -0.00092 0.001329 0.4269 -0.01601 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4442 0.04924 0.2307 0.242 0.7637 0.8671 0.6064 0.8109 0.8322 0.8107 ] Network output: [ 0.1687 0.6689 0.8836 -0.003866 0.0006425 0.08986 0.009566 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1285 0.03519 0.4297 0.0796 0.7657 0.847 0.1476 0.8319 0.8297 0.8042 ] Network output: [ 0.3617 -0.1254 0.5677 -0.001023 -0.001127 0.8328 -0.01132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5723 0.2123 0.4817 0.3321 0.7865 0.8889 0.6058 0.8317 0.8577 0.8399 ] Network output: [ 0.08912 0.08334 0.897 -0.003222 -0.003425 0.8215 0.01197 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3644 0.2514 0.7098 0.303 0.8307 0.8918 0.3727 0.8566 0.8557 0.8864 ] Network output: [ 0.006754 0.1096 0.8696 -0.00161 -0.003271 0.9946 0.01209 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3876 0.3279 0.8049 0.2804 0.8253 0.8914 0.3916 0.8541 0.859 0.8891 ] Network output: [ -0.1586 0.9803 0.2672 0.005123 0.004619 1.097 -0.007962 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2786 Epoch 240 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1665 1.111 0.826 0.005997 0.001693 -0.2428 0.001212 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06558 -0.00198 0.1236 -0.05777 0.6802 0.7519 0.1628 0.7472 0.7338 0.6985 ] Network output: [ 0.3675 0.1396 0.7019 -0.0008994 0.001383 0.4252 -0.01598 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4469 0.04925 0.2306 0.2431 0.7665 0.8685 0.6063 0.8115 0.8337 0.811 ] Network output: [ 0.167 0.6658 0.8882 -0.003942 0.0006032 0.09142 0.00957 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1284 0.03534 0.4327 0.08255 0.7688 0.8485 0.1469 0.8329 0.8318 0.8052 ] Network output: [ 0.3631 -0.1275 0.5664 -0.0009339 -0.001095 0.8337 -0.01113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5737 0.2143 0.485 0.334 0.7893 0.8902 0.6059 0.8325 0.8592 0.8404 ] Network output: [ 0.08647 0.08305 0.9016 -0.003179 -0.003385 0.8226 0.01191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3644 0.2525 0.7133 0.305 0.833 0.893 0.3724 0.8577 0.8575 0.8873 ] Network output: [ 0.004487 0.1108 0.8732 -0.001562 -0.003227 0.9946 0.01196 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3875 0.3287 0.8075 0.2819 0.8276 0.8925 0.3912 0.8551 0.8606 0.8899 ] Network output: [ -0.1583 0.9861 0.2614 0.005078 0.004559 1.097 -0.008035 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2763 Epoch 241 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1674 1.111 0.8248 0.005921 0.001615 -0.244 0.001256 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0659 -0.001978 0.1239 -0.05643 0.6829 0.7534 0.1621 0.7478 0.7353 0.699 ] Network output: [ 0.3719 0.1373 0.6974 -0.0008804 0.001434 0.4234 -0.01594 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4495 0.0493 0.2304 0.2443 0.7692 0.8699 0.6063 0.8122 0.8351 0.8113 ] Network output: [ 0.1653 0.6626 0.8929 -0.004017 0.0005651 0.09303 0.009572 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1282 0.0355 0.4356 0.08555 0.7719 0.8501 0.1461 0.834 0.8337 0.8062 ] Network output: [ 0.3644 -0.1296 0.5653 -0.0008443 -0.001067 0.8347 -0.01093 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5751 0.2163 0.4883 0.3359 0.7921 0.8915 0.606 0.8333 0.8608 0.8409 ] Network output: [ 0.08381 0.08279 0.9062 -0.003135 -0.003344 0.8239 0.01184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3645 0.2536 0.7167 0.307 0.8352 0.8941 0.3721 0.8587 0.8592 0.8881 ] Network output: [ 0.002214 0.1122 0.8767 -0.001515 -0.003182 0.9946 0.01182 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3875 0.3296 0.8101 0.2832 0.8299 0.8937 0.391 0.8561 0.8623 0.8907 ] Network output: [ -0.158 0.9919 0.2555 0.00503 0.004499 1.096 -0.008105 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2741 Epoch 242 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1683 1.112 0.8237 0.005845 0.001539 -0.2452 0.0013 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06622 -0.001972 0.1241 -0.05508 0.6855 0.7549 0.1614 0.7484 0.7368 0.6995 ] Network output: [ 0.3762 0.135 0.6929 -0.0008629 0.001483 0.4215 -0.0159 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4521 0.04938 0.2303 0.2455 0.772 0.8713 0.6063 0.8129 0.8365 0.8116 ] Network output: [ 0.1635 0.6592 0.8978 -0.00409 0.0005283 0.09468 0.00957 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1281 0.03568 0.4385 0.08858 0.7751 0.8517 0.1454 0.835 0.8357 0.8072 ] Network output: [ 0.3657 -0.1318 0.5643 -0.0007541 -0.001043 0.8357 -0.01073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5764 0.2183 0.4915 0.3378 0.7949 0.8928 0.6061 0.8342 0.8624 0.8414 ] Network output: [ 0.08114 0.08256 0.9107 -0.00309 -0.003303 0.8252 0.01176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3647 0.2549 0.7201 0.3089 0.8375 0.8953 0.3719 0.8597 0.861 0.8889 ] Network output: [ -5.953e-05 0.1137 0.88 -0.001467 -0.003136 0.9946 0.01168 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3876 0.3306 0.8126 0.2845 0.8321 0.8948 0.3908 0.8571 0.8639 0.8915 ] Network output: [ -0.1576 0.9976 0.2495 0.00498 0.004441 1.095 -0.008169 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.272 Epoch 243 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1693 1.112 0.8226 0.005771 0.001464 -0.2464 0.001345 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06655 -0.001963 0.1243 -0.05371 0.6882 0.7564 0.1607 0.749 0.7383 0.7 ] Network output: [ 0.3806 0.1328 0.6884 -0.0008465 0.001529 0.4196 -0.01585 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4546 0.04949 0.2302 0.2467 0.7747 0.8726 0.6064 0.8135 0.838 0.8118 ] Network output: [ 0.1616 0.6558 0.9029 -0.004161 0.0004929 0.09637 0.009566 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.128 0.03587 0.4415 0.09165 0.7782 0.8533 0.1447 0.836 0.8377 0.8081 ] Network output: [ 0.3669 -0.1339 0.5634 -0.0006628 -0.001022 0.8366 -0.01052 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5778 0.2205 0.4947 0.3397 0.7977 0.8942 0.6062 0.835 0.8639 0.8419 ] Network output: [ 0.07847 0.08235 0.9152 -0.003043 -0.003262 0.8265 0.01168 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.365 0.2562 0.7234 0.3107 0.8397 0.8965 0.3718 0.8607 0.8626 0.8897 ] Network output: [ -0.002332 0.1153 0.8832 -0.00142 -0.003089 0.9946 0.01153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3877 0.3317 0.8151 0.2856 0.8344 0.896 0.3908 0.8581 0.8655 0.8922 ] Network output: [ -0.1571 1.003 0.2436 0.004927 0.004385 1.094 -0.008229 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2698 Epoch 244 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1702 1.112 0.8215 0.005697 0.001391 -0.2476 0.001391 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06688 -0.00195 0.1245 -0.05233 0.6909 0.758 0.16 0.7496 0.7398 0.7004 ] Network output: [ 0.385 0.1306 0.6839 -0.0008312 0.001572 0.4176 -0.01579 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4572 0.04964 0.2301 0.2479 0.7775 0.874 0.6064 0.8142 0.8394 0.8121 ] Network output: [ 0.1598 0.6523 0.908 -0.00423 0.000459 0.09809 0.009558 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1279 0.03608 0.4444 0.09474 0.7813 0.8549 0.144 0.837 0.8396 0.809 ] Network output: [ 0.3681 -0.136 0.5625 -0.0005704 -0.001005 0.8375 -0.01031 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.579 0.2227 0.4979 0.3416 0.8005 0.8955 0.6064 0.8358 0.8654 0.8424 ] Network output: [ 0.0758 0.08215 0.9195 -0.002994 -0.00322 0.828 0.0116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3653 0.2576 0.7266 0.3125 0.842 0.8976 0.3718 0.8617 0.8643 0.8905 ] Network output: [ -0.004599 0.117 0.8863 -0.001373 -0.003041 0.9946 0.01138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3879 0.3328 0.8174 0.2866 0.8366 0.8971 0.3908 0.859 0.8671 0.8929 ] Network output: [ -0.1565 1.009 0.2376 0.004871 0.004329 1.093 -0.008283 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2677 Epoch 245 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1711 1.112 0.8205 0.005624 0.001319 -0.2488 0.001436 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0672 -0.001933 0.1248 -0.05094 0.6936 0.7595 0.1594 0.7502 0.7412 0.7008 ] Network output: [ 0.3894 0.1284 0.6794 -0.0008166 0.001613 0.4156 -0.01573 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4597 0.04983 0.2299 0.2491 0.7802 0.8754 0.6065 0.8148 0.8407 0.8123 ] Network output: [ 0.1579 0.6488 0.9132 -0.004296 0.0004266 0.09983 0.009546 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1278 0.03631 0.4472 0.09787 0.7844 0.8565 0.1434 0.838 0.8415 0.8099 ] Network output: [ 0.3693 -0.1382 0.5618 -0.0004765 -0.0009909 0.8385 -0.0101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5803 0.225 0.501 0.3435 0.8033 0.8968 0.6065 0.8366 0.8669 0.8428 ] Network output: [ 0.07314 0.08197 0.9238 -0.002944 -0.003179 0.8295 0.0115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3658 0.2591 0.7298 0.3143 0.8442 0.8988 0.3719 0.8626 0.8659 0.8913 ] Network output: [ -0.006858 0.1188 0.8893 -0.001327 -0.002992 0.9946 0.01122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3882 0.334 0.8197 0.2874 0.8389 0.8983 0.3909 0.86 0.8687 0.8936 ] Network output: [ -0.1558 1.014 0.2316 0.004812 0.004274 1.092 -0.008331 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2657 Epoch 246 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1719 1.112 0.8194 0.005552 0.001249 -0.25 0.001481 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06753 -0.001912 0.125 -0.04955 0.6962 0.761 0.1587 0.7508 0.7427 0.7013 ] Network output: [ 0.3938 0.1264 0.6748 -0.0008028 0.001653 0.4135 -0.01566 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4622 0.05006 0.2297 0.2502 0.7829 0.8768 0.6066 0.8155 0.8421 0.8126 ] Network output: [ 0.156 0.6452 0.9186 -0.004361 0.0003957 0.1016 0.009531 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1277 0.03655 0.45 0.101 0.7874 0.8581 0.1428 0.839 0.8433 0.8108 ] Network output: [ 0.3704 -0.1403 0.5611 -0.000381 -0.0009804 0.8393 -0.009877 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5816 0.2274 0.504 0.3454 0.806 0.8981 0.6067 0.8374 0.8684 0.8432 ] Network output: [ 0.07048 0.08179 0.928 -0.002893 -0.003138 0.831 0.0114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3663 0.2607 0.7328 0.316 0.8464 0.9 0.3721 0.8636 0.8675 0.892 ] Network output: [ -0.009107 0.1207 0.8921 -0.001282 -0.002942 0.9947 0.01106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3886 0.3353 0.822 0.2882 0.8411 0.8994 0.3911 0.861 0.8702 0.8943 ] Network output: [ -0.155 1.019 0.2256 0.004749 0.00422 1.091 -0.008374 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2637 Epoch 247 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1728 1.112 0.8184 0.005481 0.001181 -0.2512 0.001526 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06786 -0.001888 0.1251 -0.04815 0.6989 0.7626 0.1581 0.7514 0.7441 0.7017 ] Network output: [ 0.3982 0.1244 0.6703 -0.0007894 0.001689 0.4113 -0.01558 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4647 0.05034 0.2295 0.2514 0.7857 0.8782 0.6067 0.8161 0.8435 0.8128 ] Network output: [ 0.154 0.6416 0.924 -0.004422 0.0003666 0.1034 0.009511 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1276 0.03681 0.4528 0.1042 0.7905 0.8597 0.1422 0.84 0.8452 0.8117 ] Network output: [ 0.3715 -0.1424 0.5606 -0.0002836 -0.0009731 0.8402 -0.009656 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5828 0.2299 0.507 0.3473 0.8088 0.8994 0.607 0.8382 0.8698 0.8437 ] Network output: [ 0.06784 0.08162 0.9321 -0.002841 -0.003096 0.8327 0.0113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3669 0.2623 0.7358 0.3176 0.8485 0.9011 0.3724 0.8645 0.8691 0.8927 ] Network output: [ -0.01134 0.1227 0.8948 -0.001238 -0.002892 0.9947 0.0109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.389 0.3367 0.8241 0.2889 0.8433 0.9006 0.3914 0.8619 0.8717 0.8949 ] Network output: [ -0.1542 1.024 0.2197 0.004682 0.004166 1.09 -0.008411 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2617 Epoch 248 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1736 1.112 0.8174 0.00541 0.001115 -0.2524 0.00157 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06819 -0.001858 0.1253 -0.04674 0.7016 0.7641 0.1576 0.752 0.7455 0.7021 ] Network output: [ 0.4025 0.1225 0.6657 -0.0007764 0.001724 0.409 -0.0155 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4672 0.05065 0.2292 0.2525 0.7884 0.8795 0.6069 0.8167 0.8448 0.813 ] Network output: [ 0.1521 0.638 0.9296 -0.004482 0.0003391 0.1052 0.009487 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1275 0.03708 0.4554 0.1073 0.7935 0.8613 0.1416 0.841 0.847 0.8125 ] Network output: [ 0.3726 -0.1445 0.5601 -0.0001843 -0.0009689 0.841 -0.009433 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.584 0.2324 0.5099 0.3491 0.8115 0.9007 0.6072 0.8389 0.8712 0.8441 ] Network output: [ 0.06521 0.08146 0.9362 -0.002787 -0.003054 0.8344 0.01118 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3675 0.2641 0.7387 0.3192 0.8507 0.9023 0.3728 0.8655 0.8706 0.8934 ] Network output: [ -0.01357 0.1247 0.8974 -0.001195 -0.002841 0.9948 0.01073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3895 0.3382 0.8262 0.2894 0.8455 0.9017 0.3918 0.8628 0.8732 0.8956 ] Network output: [ -0.1533 1.029 0.2138 0.004612 0.004114 1.089 -0.008441 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2598 Epoch 249 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1745 1.112 0.8164 0.00534 0.001051 -0.2536 0.001613 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06852 -0.001825 0.1254 -0.04534 0.7042 0.7657 0.157 0.7526 0.7469 0.7024 ] Network output: [ 0.4069 0.1206 0.6612 -0.0007637 0.001757 0.4067 -0.01541 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4697 0.05101 0.2289 0.2537 0.7911 0.8809 0.6071 0.8173 0.8461 0.8132 ] Network output: [ 0.15 0.6344 0.9352 -0.004538 0.0003133 0.1069 0.009458 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1274 0.03737 0.4581 0.1105 0.7965 0.8629 0.1411 0.842 0.8488 0.8133 ] Network output: [ 0.3736 -0.1466 0.5597 -8.287e-05 -0.0009677 0.8419 -0.009207 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5852 0.235 0.5126 0.351 0.8142 0.902 0.6075 0.8397 0.8726 0.8444 ] Network output: [ 0.06261 0.0813 0.9401 -0.002732 -0.003013 0.8361 0.01107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3683 0.2659 0.7415 0.3207 0.8528 0.9034 0.3733 0.8664 0.8722 0.8941 ] Network output: [ -0.01577 0.1268 0.8999 -0.001154 -0.002789 0.9949 0.01056 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3902 0.3397 0.8282 0.2899 0.8477 0.9029 0.3923 0.8638 0.8746 0.8962 ] Network output: [ -0.1523 1.034 0.2079 0.004537 0.004062 1.088 -0.008465 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2579 Epoch 250 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1753 1.113 0.8155 0.005271 0.0009883 -0.2548 0.001656 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06885 -0.001787 0.1256 -0.04394 0.7069 0.7673 0.1564 0.7531 0.7483 0.7028 ] Network output: [ 0.4113 0.1189 0.6566 -0.0007511 0.001788 0.4043 -0.01532 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4721 0.05141 0.2286 0.2548 0.7937 0.8823 0.6073 0.8179 0.8474 0.8133 ] Network output: [ 0.148 0.6308 0.9409 -0.004591 0.0002893 0.1087 0.009425 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1274 0.03768 0.4606 0.1137 0.7995 0.8645 0.1406 0.843 0.8505 0.8141 ] Network output: [ 0.3745 -0.1486 0.5594 2.073e-05 -0.0009694 0.8426 -0.00898 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5864 0.2377 0.5153 0.3528 0.8169 0.9033 0.6078 0.8404 0.874 0.8448 ] Network output: [ 0.06002 0.08114 0.9439 -0.002677 -0.002971 0.8379 0.01095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3691 0.2678 0.7442 0.3222 0.855 0.9046 0.3739 0.8673 0.8736 0.8947 ] Network output: [ -0.01797 0.129 0.9022 -0.001115 -0.002737 0.9951 0.01039 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3908 0.3413 0.8302 0.2903 0.8499 0.904 0.3929 0.8647 0.8761 0.8968 ] Network output: [ -0.1513 1.038 0.2021 0.004458 0.00401 1.087 -0.008483 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.256 Epoch 251 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.176 1.113 0.8145 0.005203 0.0009276 -0.256 0.001697 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06918 -0.001745 0.1257 -0.04254 0.7095 0.7689 0.1559 0.7537 0.7496 0.7031 ] Network output: [ 0.4157 0.1173 0.652 -0.0007385 0.001817 0.4018 -0.01522 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4745 0.05186 0.2282 0.2559 0.7964 0.8836 0.6075 0.8185 0.8487 0.8135 ] Network output: [ 0.1459 0.6272 0.9466 -0.004642 0.000267 0.1105 0.009387 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1273 0.03801 0.463 0.1169 0.8025 0.866 0.1401 0.8439 0.8522 0.8148 ] Network output: [ 0.3755 -0.1506 0.5592 0.0001265 -0.0009738 0.8434 -0.008752 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5875 0.2405 0.5178 0.3547 0.8195 0.9045 0.6081 0.8412 0.8754 0.8451 ] Network output: [ 0.05747 0.08097 0.9476 -0.00262 -0.00293 0.8398 0.01082 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.37 0.2698 0.7468 0.3237 0.8571 0.9057 0.3746 0.8682 0.8751 0.8953 ] Network output: [ -0.02014 0.1312 0.9045 -0.001078 -0.002684 0.9953 0.01021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3916 0.343 0.832 0.2905 0.852 0.9052 0.3936 0.8656 0.8775 0.8974 ] Network output: [ -0.1501 1.043 0.1963 0.004376 0.00396 1.086 -0.008495 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2542 Epoch 252 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1768 1.113 0.8135 0.005135 0.0008689 -0.2572 0.001738 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06951 -0.001699 0.1257 -0.04115 0.7122 0.7705 0.1554 0.7543 0.7509 0.7034 ] Network output: [ 0.42 0.1158 0.6473 -0.0007259 0.001844 0.3993 -0.01512 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4769 0.05235 0.2277 0.257 0.799 0.885 0.6078 0.8191 0.8499 0.8137 ] Network output: [ 0.1438 0.6236 0.9524 -0.004689 0.0002465 0.1122 0.009345 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1273 0.03835 0.4654 0.12 0.8054 0.8676 0.1396 0.8449 0.8539 0.8156 ] Network output: [ 0.3764 -0.1526 0.559 0.0002346 -0.0009808 0.8441 -0.008523 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5887 0.2433 0.5202 0.3565 0.8221 0.9058 0.6084 0.8419 0.8767 0.8455 ] Network output: [ 0.05494 0.0808 0.9512 -0.002562 -0.002888 0.8417 0.01069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.371 0.2719 0.7492 0.3251 0.8591 0.9068 0.3754 0.8691 0.8765 0.8959 ] Network output: [ -0.0223 0.1334 0.9066 -0.001043 -0.002631 0.9955 0.01003 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3925 0.3447 0.8338 0.2907 0.8542 0.9063 0.3943 0.8665 0.8788 0.8979 ] Network output: [ -0.149 1.047 0.1907 0.004289 0.00391 1.084 -0.0085 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2524 Epoch 253 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1775 1.114 0.8126 0.005068 0.000812 -0.2584 0.001777 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06984 -0.001648 0.1257 -0.03977 0.7148 0.7721 0.1549 0.7548 0.7523 0.7037 ] Network output: [ 0.4244 0.1144 0.6427 -0.0007132 0.001869 0.3967 -0.01501 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4793 0.05288 0.2272 0.258 0.8016 0.8864 0.6081 0.8197 0.8512 0.8138 ] Network output: [ 0.1417 0.6201 0.9582 -0.004734 0.0002278 0.1139 0.009297 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1272 0.03871 0.4676 0.1231 0.8083 0.8692 0.1392 0.8458 0.8556 0.8163 ] Network output: [ 0.3773 -0.1545 0.559 0.0003448 -0.0009903 0.8448 -0.008293 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5898 0.2462 0.5225 0.3582 0.8247 0.9071 0.6088 0.8426 0.878 0.8458 ] Network output: [ 0.05244 0.08063 0.9548 -0.002504 -0.002847 0.8437 0.01056 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3721 0.274 0.7516 0.3264 0.8612 0.9079 0.3763 0.87 0.8779 0.8965 ] Network output: [ -0.02444 0.1356 0.9086 -0.00101 -0.002578 0.9957 0.009856 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3934 0.3466 0.8355 0.2908 0.8563 0.9074 0.3952 0.8674 0.8802 0.8985 ] Network output: [ -0.1477 1.05 0.1851 0.004198 0.00386 1.083 -0.008499 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2506 Epoch 254 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1782 1.114 0.8116 0.005001 0.0007569 -0.2596 0.001815 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07017 -0.001594 0.1257 -0.0384 0.7174 0.7737 0.1545 0.7554 0.7536 0.704 ] Network output: [ 0.4287 0.1131 0.6381 -0.0007004 0.001893 0.394 -0.0149 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4816 0.05346 0.2265 0.2591 0.8042 0.8877 0.6084 0.8202 0.8524 0.8139 ] Network output: [ 0.1396 0.6167 0.9641 -0.004775 0.0002107 0.1156 0.009245 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1272 0.03908 0.4698 0.1263 0.8112 0.8708 0.1388 0.8467 0.8572 0.8169 ] Network output: [ 0.3781 -0.1564 0.559 0.0004572 -0.001002 0.8454 -0.008063 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5909 0.2492 0.5246 0.36 0.8273 0.9083 0.6092 0.8433 0.8793 0.8461 ] Network output: [ 0.04997 0.08045 0.9582 -0.002445 -0.002805 0.8458 0.01042 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3733 0.2763 0.7538 0.3277 0.8632 0.9091 0.3773 0.8708 0.8793 0.8971 ] Network output: [ -0.02655 0.1379 0.9105 -0.0009796 -0.002525 0.996 0.009676 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3945 0.3485 0.8371 0.2907 0.8584 0.9085 0.3961 0.8683 0.8815 0.899 ] Network output: [ -0.1464 1.054 0.1796 0.004103 0.003811 1.082 -0.008492 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2489 Epoch 255 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1789 1.114 0.8107 0.004936 0.0007036 -0.2608 0.001852 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0705 -0.001534 0.1257 -0.03705 0.72 0.7753 0.154 0.7559 0.7549 0.7043 ] Network output: [ 0.433 0.1119 0.6334 -0.0006874 0.001914 0.3913 -0.01479 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4839 0.05408 0.2258 0.2601 0.8068 0.8891 0.6087 0.8208 0.8536 0.814 ] Network output: [ 0.1374 0.6134 0.97 -0.004814 0.0001955 0.1173 0.009188 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1272 0.03947 0.4718 0.1293 0.814 0.8723 0.1384 0.8476 0.8588 0.8176 ] Network output: [ 0.3789 -0.1583 0.559 0.0005716 -0.001016 0.846 -0.007832 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.592 0.2523 0.5266 0.3617 0.8298 0.9096 0.6096 0.8439 0.8805 0.8464 ] Network output: [ 0.04754 0.08026 0.9614 -0.002385 -0.002764 0.8479 0.01028 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3746 0.2786 0.7559 0.3289 0.8653 0.9102 0.3784 0.8717 0.8807 0.8976 ] Network output: [ -0.02866 0.1402 0.9123 -0.0009515 -0.002471 0.9963 0.009496 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3956 0.3505 0.8387 0.2906 0.8605 0.9097 0.3971 0.8691 0.8828 0.8995 ] Network output: [ -0.145 1.058 0.1742 0.004004 0.003763 1.081 -0.008479 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2472 Epoch 256 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1795 1.115 0.8097 0.00487 0.0006521 -0.262 0.001888 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07082 -0.001471 0.1256 -0.03571 0.7225 0.7769 0.1536 0.7564 0.7561 0.7045 ] Network output: [ 0.4373 0.1108 0.6287 -0.0006743 0.001935 0.3885 -0.01468 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4862 0.05475 0.2251 0.2611 0.8094 0.8904 0.6091 0.8213 0.8548 0.8141 ] Network output: [ 0.1352 0.6101 0.9759 -0.004849 0.0001819 0.1189 0.009127 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1271 0.03987 0.4737 0.1324 0.8168 0.8739 0.1381 0.8485 0.8604 0.8182 ] Network output: [ 0.3797 -0.1601 0.5592 0.0006881 -0.001033 0.8465 -0.007602 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5931 0.2554 0.5284 0.3633 0.8323 0.9108 0.61 0.8446 0.8818 0.8466 ] Network output: [ 0.04515 0.08007 0.9646 -0.002325 -0.002723 0.85 0.01013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3759 0.281 0.7579 0.33 0.8672 0.9113 0.3795 0.8725 0.882 0.8981 ] Network output: [ -0.03074 0.1426 0.914 -0.0009259 -0.002417 0.9966 0.009315 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3968 0.3525 0.8402 0.2904 0.8625 0.9108 0.3982 0.87 0.8841 0.9 ] Network output: [ -0.1436 1.061 0.1689 0.003901 0.003716 1.08 -0.00846 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2456 Epoch 257 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1801 1.116 0.8088 0.004806 0.0006023 -0.2632 0.001922 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07115 -0.001404 0.1255 -0.03439 0.7251 0.7785 0.1532 0.757 0.7574 0.7047 ] Network output: [ 0.4416 0.1098 0.624 -0.000661 0.001953 0.3856 -0.01456 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4885 0.05546 0.2242 0.262 0.8119 0.8917 0.6095 0.8218 0.856 0.8142 ] Network output: [ 0.133 0.6069 0.9818 -0.004881 0.0001699 0.1205 0.009061 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1271 0.04029 0.4755 0.1354 0.8196 0.8754 0.1377 0.8494 0.862 0.8188 ] Network output: [ 0.3805 -0.1619 0.5594 0.0008064 -0.001051 0.847 -0.007372 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5942 0.2585 0.53 0.3649 0.8348 0.912 0.6105 0.8453 0.883 0.8469 ] Network output: [ 0.04279 0.07986 0.9677 -0.002265 -0.002682 0.8522 0.009986 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3774 0.2835 0.7598 0.3311 0.8692 0.9123 0.3808 0.8733 0.8833 0.8986 ] Network output: [ -0.0328 0.1449 0.9156 -0.0009028 -0.002363 0.997 0.009134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.398 0.3546 0.8416 0.2901 0.8646 0.9119 0.3994 0.8708 0.8854 0.9005 ] Network output: [ -0.1422 1.064 0.1637 0.003795 0.003668 1.078 -0.008435 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2439 Epoch 258 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1807 1.116 0.8078 0.004742 0.0005542 -0.2643 0.001955 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07147 -0.001333 0.1254 -0.03308 0.7276 0.7801 0.1529 0.7575 0.7586 0.7049 ] Network output: [ 0.4459 0.109 0.6193 -0.0006475 0.00197 0.3827 -0.01443 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4907 0.05621 0.2232 0.2629 0.8144 0.893 0.6099 0.8223 0.8571 0.8143 ] Network output: [ 0.1307 0.6038 0.9877 -0.00491 0.0001597 0.1221 0.00899 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1272 0.04073 0.4772 0.1384 0.8223 0.8769 0.1374 0.8502 0.8635 0.8194 ] Network output: [ 0.3812 -0.1636 0.5597 0.0009265 -0.001071 0.8475 -0.007143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5953 0.2618 0.5315 0.3665 0.8373 0.9132 0.611 0.8459 0.8842 0.8471 ] Network output: [ 0.04047 0.07965 0.9706 -0.002204 -0.002642 0.8545 0.009837 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3789 0.2861 0.7616 0.3321 0.8711 0.9134 0.3822 0.8741 0.8846 0.899 ] Network output: [ -0.03484 0.1472 0.9171 -0.0008823 -0.002309 0.9974 0.008953 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3994 0.3568 0.8429 0.2896 0.8666 0.913 0.4007 0.8717 0.8866 0.9009 ] Network output: [ -0.1407 1.066 0.1586 0.003685 0.003622 1.077 -0.008405 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2423 Epoch 259 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1812 1.117 0.8069 0.004678 0.0005078 -0.2655 0.001987 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07179 -0.001257 0.1252 -0.0318 0.7302 0.7817 0.1525 0.758 0.7598 0.7051 ] Network output: [ 0.4501 0.1082 0.6146 -0.000634 0.001985 0.3797 -0.01431 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.493 0.057 0.2222 0.2638 0.8169 0.8943 0.6104 0.8228 0.8583 0.8144 ] Network output: [ 0.1284 0.6009 0.9936 -0.004937 0.000151 0.1236 0.008915 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1272 0.04118 0.4788 0.1413 0.825 0.8785 0.1372 0.8511 0.865 0.8199 ] Network output: [ 0.382 -0.1653 0.56 0.001048 -0.001094 0.8479 -0.006914 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5964 0.2651 0.5328 0.368 0.8397 0.9144 0.6115 0.8465 0.8854 0.8473 ] Network output: [ 0.0382 0.07943 0.9734 -0.002142 -0.002602 0.8568 0.009686 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3805 0.2888 0.7632 0.333 0.873 0.9145 0.3837 0.8749 0.8858 0.8995 ] Network output: [ -0.03686 0.1496 0.9185 -0.0008644 -0.002254 0.9979 0.008772 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4008 0.3591 0.8441 0.2891 0.8686 0.9141 0.4021 0.8725 0.8878 0.9013 ] Network output: [ -0.1391 1.069 0.1537 0.003573 0.003576 1.076 -0.008369 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2407 Epoch 260 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1818 1.118 0.8059 0.004616 0.000463 -0.2666 0.002018 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07211 -0.001178 0.125 -0.03053 0.7327 0.7833 0.1522 0.7585 0.761 0.7053 ] Network output: [ 0.4543 0.1075 0.6099 -0.0006204 0.001999 0.3767 -0.01418 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4952 0.05783 0.221 0.2646 0.8193 0.8956 0.6109 0.8233 0.8594 0.8144 ] Network output: [ 0.1262 0.598 0.9994 -0.00496 0.0001438 0.1251 0.008836 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1272 0.04165 0.4802 0.1441 0.8277 0.88 0.1369 0.8519 0.8664 0.8204 ] Network output: [ 0.3826 -0.167 0.5604 0.001171 -0.001117 0.8483 -0.006686 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5975 0.2684 0.534 0.3695 0.8421 0.9155 0.612 0.8471 0.8865 0.8475 ] Network output: [ 0.03596 0.07919 0.9761 -0.002081 -0.002562 0.8591 0.009534 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3822 0.2915 0.7647 0.3339 0.8749 0.9156 0.3853 0.8757 0.887 0.8999 ] Network output: [ -0.03886 0.1519 0.9198 -0.000849 -0.0022 0.9984 0.008591 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4024 0.3615 0.8453 0.2885 0.8705 0.9151 0.4035 0.8733 0.889 0.9017 ] Network output: [ -0.1376 1.071 0.1489 0.003457 0.003531 1.075 -0.008329 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2391 Epoch 261 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1822 1.118 0.805 0.004553 0.0004197 -0.2677 0.002047 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07243 -0.001095 0.1247 -0.02929 0.7351 0.7849 0.1518 0.759 0.7622 0.7054 ] Network output: [ 0.4585 0.107 0.6051 -0.0006068 0.002012 0.3736 -0.01405 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4973 0.0587 0.2198 0.2654 0.8217 0.8969 0.6115 0.8238 0.8605 0.8145 ] Network output: [ 0.1239 0.5953 1.005 -0.004981 0.0001382 0.1265 0.008753 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1273 0.04212 0.4815 0.1469 0.8303 0.8815 0.1367 0.8527 0.8679 0.8209 ] Network output: [ 0.3833 -0.1687 0.5609 0.001296 -0.001143 0.8486 -0.006459 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5986 0.2718 0.5349 0.3709 0.8444 0.9167 0.6126 0.8477 0.8876 0.8477 ] Network output: [ 0.03378 0.07895 0.9787 -0.002019 -0.002522 0.8615 0.00938 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.384 0.2943 0.7661 0.3346 0.8768 0.9166 0.3869 0.8764 0.8882 0.9003 ] Network output: [ -0.04084 0.1543 0.921 -0.0008363 -0.002146 0.9989 0.00841 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.404 0.3639 0.8465 0.2877 0.8725 0.9162 0.4051 0.8741 0.8901 0.9021 ] Network output: [ -0.1359 1.073 0.1442 0.003339 0.003486 1.074 -0.008283 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2376 Epoch 262 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1827 1.119 0.8041 0.004492 0.0003779 -0.2688 0.002075 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07275 -0.001009 0.1245 -0.02808 0.7376 0.7864 0.1515 0.7595 0.7634 0.7056 ] Network output: [ 0.4627 0.1065 0.6004 -0.0005932 0.002023 0.3705 -0.01392 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4995 0.05961 0.2185 0.2662 0.8241 0.8982 0.612 0.8242 0.8616 0.8145 ] Network output: [ 0.1215 0.5927 1.011 -0.004998 0.000134 0.1278 0.008665 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1273 0.04262 0.4827 0.1497 0.8329 0.883 0.1365 0.8535 0.8693 0.8213 ] Network output: [ 0.384 -0.1703 0.5614 0.001421 -0.00117 0.8489 -0.006233 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5997 0.2752 0.5358 0.3722 0.8467 0.9179 0.6132 0.8482 0.8887 0.8479 ] Network output: [ 0.03163 0.07869 0.9811 -0.001958 -0.002482 0.864 0.009225 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3859 0.2972 0.7674 0.3353 0.8786 0.9177 0.3887 0.8771 0.8894 0.9006 ] Network output: [ -0.0428 0.1567 0.9221 -0.000826 -0.002091 0.9994 0.00823 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4056 0.3663 0.8475 0.2869 0.8744 0.9173 0.4067 0.8749 0.8913 0.9025 ] Network output: [ -0.1343 1.075 0.1397 0.003219 0.003442 1.072 -0.008233 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.236 Epoch 263 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1831 1.12 0.8031 0.004431 0.0003376 -0.2698 0.002102 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07306 -0.0009183 0.1241 -0.02689 0.74 0.788 0.1513 0.7599 0.7645 0.7057 ] Network output: [ 0.4668 0.1061 0.5956 -0.0005798 0.002033 0.3674 -0.01378 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5016 0.06056 0.2171 0.2669 0.8264 0.8995 0.6126 0.8247 0.8626 0.8145 ] Network output: [ 0.1192 0.5902 1.017 -0.005014 0.0001313 0.1291 0.008574 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1274 0.04312 0.4838 0.1524 0.8355 0.8844 0.1363 0.8543 0.8706 0.8218 ] Network output: [ 0.3846 -0.1718 0.562 0.001547 -0.001198 0.8491 -0.006008 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6007 0.2787 0.5364 0.3735 0.849 0.919 0.6138 0.8488 0.8898 0.848 ] Network output: [ 0.02954 0.07842 0.9835 -0.001896 -0.002443 0.8664 0.009069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3878 0.3002 0.7686 0.3359 0.8804 0.9187 0.3905 0.8779 0.8905 0.901 ] Network output: [ -0.04473 0.159 0.9232 -0.0008182 -0.002037 1 0.008051 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4074 0.3689 0.8485 0.2859 0.8763 0.9183 0.4084 0.8756 0.8924 0.9029 ] Network output: [ -0.1326 1.077 0.1353 0.003096 0.003398 1.071 -0.008179 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2345 Epoch 264 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1834 1.121 0.8022 0.00437 0.0002987 -0.2709 0.002128 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07338 -0.0008246 0.1238 -0.02573 0.7424 0.7896 0.151 0.7604 0.7656 0.7058 ] Network output: [ 0.471 0.1058 0.5909 -0.0005666 0.002042 0.3642 -0.01365 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5038 0.06154 0.2156 0.2675 0.8287 0.9007 0.6132 0.8251 0.8637 0.8145 ] Network output: [ 0.1168 0.5879 1.023 -0.005026 0.00013 0.1304 0.008479 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1275 0.04364 0.4847 0.155 0.838 0.8859 0.1362 0.8551 0.872 0.8222 ] Network output: [ 0.3852 -0.1734 0.5626 0.001675 -0.001227 0.8493 -0.005784 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6018 0.2822 0.5369 0.3747 0.8512 0.9201 0.6144 0.8493 0.8909 0.8482 ] Network output: [ 0.02749 0.07813 0.9857 -0.001834 -0.002405 0.8689 0.008912 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3899 0.3033 0.7697 0.3365 0.8822 0.9197 0.3925 0.8786 0.8916 0.9013 ] Network output: [ -0.04665 0.1614 0.9242 -0.0008128 -0.001982 1.001 0.007873 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4092 0.3715 0.8494 0.2849 0.8781 0.9194 0.4102 0.8764 0.8935 0.9032 ] Network output: [ -0.1309 1.078 0.1311 0.002972 0.003355 1.07 -0.008121 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.233 Epoch 265 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1838 1.122 0.8013 0.00431 0.0002612 -0.2718 0.002152 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07369 -0.0007276 0.1234 -0.02459 0.7448 0.7912 0.1507 0.7609 0.7668 0.7059 ] Network output: [ 0.4751 0.1056 0.5861 -0.0005537 0.002049 0.361 -0.01351 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5059 0.06256 0.214 0.2682 0.831 0.902 0.6139 0.8255 0.8647 0.8145 ] Network output: [ 0.1145 0.5857 1.028 -0.005037 0.0001299 0.1316 0.008381 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1276 0.04418 0.4856 0.1575 0.8405 0.8874 0.136 0.8559 0.8733 0.8225 ] Network output: [ 0.3858 -0.1749 0.5633 0.001802 -0.001258 0.8494 -0.005562 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6029 0.2858 0.5373 0.3759 0.8534 0.9212 0.6151 0.8498 0.8919 0.8483 ] Network output: [ 0.02548 0.07784 0.9878 -0.001773 -0.002366 0.8715 0.008755 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.392 0.3064 0.7707 0.3369 0.8839 0.9207 0.3945 0.8793 0.8927 0.9016 ] Network output: [ -0.04854 0.1637 0.925 -0.0008097 -0.001928 1.001 0.007695 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4111 0.3741 0.8503 0.2837 0.8799 0.9204 0.412 0.8771 0.8946 0.9036 ] Network output: [ -0.1292 1.079 0.127 0.002846 0.003313 1.069 -0.008059 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2315 Epoch 266 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.184 1.123 0.8004 0.004251 0.000225 -0.2728 0.002175 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.074 -0.0006273 0.1229 -0.02348 0.7472 0.7928 0.1505 0.7613 0.7678 0.7059 ] Network output: [ 0.4791 0.1054 0.5814 -0.0005412 0.002055 0.3578 -0.01337 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.508 0.06362 0.2123 0.2688 0.8333 0.9032 0.6146 0.8259 0.8657 0.8145 ] Network output: [ 0.1121 0.5836 1.034 -0.005045 0.0001312 0.1328 0.00828 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1277 0.04472 0.4863 0.16 0.8429 0.8888 0.1359 0.8566 0.8746 0.8229 ] Network output: [ 0.3863 -0.1764 0.5641 0.00193 -0.001289 0.8495 -0.005341 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.604 0.2893 0.5375 0.377 0.8556 0.9223 0.6158 0.8503 0.8929 0.8484 ] Network output: [ 0.02353 0.07752 0.9898 -0.001711 -0.002328 0.8741 0.008598 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3942 0.3096 0.7715 0.3372 0.8856 0.9217 0.3966 0.8799 0.8938 0.9019 ] Network output: [ -0.0504 0.1661 0.9258 -0.0008088 -0.001874 1.002 0.007518 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4131 0.3769 0.8511 0.2824 0.8818 0.9214 0.414 0.8779 0.8956 0.9039 ] Network output: [ -0.1274 1.08 0.123 0.002719 0.003271 1.068 -0.007994 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2301 Epoch 267 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1843 1.124 0.7996 0.004192 0.0001902 -0.2737 0.002197 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07431 -0.0005239 0.1225 -0.0224 0.7495 0.7943 0.1503 0.7618 0.7689 0.7059 ] Network output: [ 0.4832 0.1053 0.5766 -0.0005292 0.00206 0.3545 -0.01323 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.51 0.06471 0.2105 0.2693 0.8355 0.9044 0.6153 0.8263 0.8667 0.8145 ] Network output: [ 0.1097 0.5818 1.04 -0.005051 0.0001337 0.1339 0.008175 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1278 0.04528 0.4869 0.1623 0.8453 0.8902 0.1359 0.8574 0.8759 0.8232 ] Network output: [ 0.3869 -0.1778 0.5649 0.002058 -0.001322 0.8495 -0.005122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6051 0.293 0.5375 0.378 0.8577 0.9234 0.6165 0.8508 0.8939 0.8485 ] Network output: [ 0.02163 0.07719 0.9917 -0.00165 -0.002291 0.8767 0.008441 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3965 0.3128 0.7723 0.3375 0.8873 0.9227 0.3988 0.8806 0.8948 0.9022 ] Network output: [ -0.05224 0.1684 0.9266 -0.0008101 -0.00182 1.003 0.007343 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4152 0.3797 0.8519 0.281 0.8835 0.9225 0.416 0.8786 0.8967 0.9042 ] Network output: [ -0.1256 1.081 0.1192 0.002592 0.003229 1.067 -0.007925 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2286 Epoch 268 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1845 1.125 0.7987 0.004133 0.0001565 -0.2746 0.002218 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07462 -0.0004174 0.122 -0.02135 0.7518 0.7959 0.1501 0.7622 0.77 0.706 ] Network output: [ 0.4872 0.1053 0.5719 -0.0005178 0.002065 0.3513 -0.01309 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5121 0.06583 0.2087 0.2698 0.8377 0.9056 0.616 0.8266 0.8677 0.8145 ] Network output: [ 0.1073 0.58 1.045 -0.005055 0.0001374 0.1349 0.008067 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.128 0.04585 0.4874 0.1646 0.8477 0.8916 0.1358 0.8581 0.8771 0.8235 ] Network output: [ 0.3874 -0.1793 0.5658 0.002186 -0.001355 0.8495 -0.004905 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6062 0.2966 0.5374 0.379 0.8598 0.9244 0.6172 0.8513 0.8949 0.8486 ] Network output: [ 0.01977 0.07685 0.9934 -0.001589 -0.002254 0.8793 0.008285 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3989 0.3162 0.773 0.3377 0.889 0.9237 0.4011 0.8812 0.8958 0.9024 ] Network output: [ -0.05406 0.1708 0.9272 -0.0008133 -0.001767 1.003 0.007169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4173 0.3825 0.8526 0.2795 0.8853 0.9235 0.4181 0.8793 0.8977 0.9045 ] Network output: [ -0.1238 1.082 0.1155 0.002464 0.003188 1.066 -0.007853 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2272 Epoch 269 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1847 1.126 0.7978 0.004075 0.0001241 -0.2755 0.002238 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07492 -0.0003081 0.1215 -0.02034 0.7541 0.7974 0.1499 0.7626 0.771 0.706 ] Network output: [ 0.4911 0.1054 0.5672 -0.0005071 0.002068 0.348 -0.01295 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5141 0.06698 0.2068 0.2703 0.8398 0.9068 0.6168 0.827 0.8687 0.8145 ] Network output: [ 0.1048 0.5784 1.051 -0.005057 0.0001422 0.1359 0.007957 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1281 0.04643 0.4878 0.1668 0.85 0.893 0.1358 0.8588 0.8783 0.8237 ] Network output: [ 0.3879 -0.1807 0.5668 0.002313 -0.001389 0.8495 -0.004689 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6073 0.3003 0.5372 0.3799 0.8619 0.9255 0.618 0.8517 0.8958 0.8487 ] Network output: [ 0.01797 0.07649 0.9951 -0.001528 -0.002218 0.882 0.008129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4013 0.3195 0.7736 0.3378 0.8906 0.9246 0.4035 0.8818 0.8968 0.9026 ] Network output: [ -0.05585 0.1731 0.9278 -0.0008185 -0.001713 1.004 0.006996 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4195 0.3854 0.8533 0.2779 0.887 0.9245 0.4202 0.88 0.8987 0.9047 ] Network output: [ -0.122 1.082 0.112 0.002335 0.003147 1.065 -0.007779 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2257 Epoch 270 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1848 1.127 0.797 0.004018 9.293e-05 -0.2763 0.002256 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07523 -0.0001961 0.1209 -0.01935 0.7563 0.799 0.1498 0.7631 0.7721 0.7059 ] Network output: [ 0.4951 0.1054 0.5625 -0.0004973 0.00207 0.3448 -0.0128 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5161 0.06816 0.2048 0.2707 0.8419 0.9079 0.6176 0.8273 0.8696 0.8144 ] Network output: [ 0.1024 0.577 1.056 -0.005057 0.0001481 0.1368 0.007844 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1283 0.04702 0.4882 0.169 0.8523 0.8944 0.1358 0.8595 0.8795 0.824 ] Network output: [ 0.3884 -0.182 0.5678 0.00244 -0.001424 0.8494 -0.004475 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6084 0.304 0.5369 0.3808 0.8639 0.9265 0.6187 0.8522 0.8968 0.8488 ] Network output: [ 0.01622 0.07611 0.9966 -0.001467 -0.002182 0.8846 0.007974 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4038 0.323 0.7741 0.3377 0.8922 0.9256 0.4059 0.8825 0.8978 0.9029 ] Network output: [ -0.05761 0.1754 0.9283 -0.0008255 -0.00166 1.005 0.006825 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4217 0.3883 0.8539 0.2762 0.8887 0.9254 0.4225 0.8807 0.8997 0.905 ] Network output: [ -0.1201 1.082 0.1087 0.002207 0.003107 1.063 -0.007702 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2243 Epoch 271 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1849 1.128 0.7962 0.003961 6.286e-05 -0.277 0.002273 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07553 -8.156e-05 0.1203 -0.01839 0.7585 0.8005 0.1496 0.7635 0.7731 0.7059 ] Network output: [ 0.499 0.1056 0.5578 -0.0004884 0.002071 0.3415 -0.01266 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5181 0.06937 0.2028 0.2711 0.844 0.9091 0.6184 0.8277 0.8705 0.8144 ] Network output: [ 0.09997 0.5757 1.061 -0.005056 0.0001551 0.1377 0.007728 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1285 0.04762 0.4884 0.171 0.8545 0.8957 0.1358 0.8602 0.8807 0.8242 ] Network output: [ 0.3888 -0.1834 0.5689 0.002567 -0.001459 0.8492 -0.004263 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6095 0.3077 0.5364 0.3815 0.8658 0.9275 0.6195 0.8526 0.8977 0.8489 ] Network output: [ 0.01452 0.07571 0.998 -0.001406 -0.002146 0.8874 0.007819 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4064 0.3265 0.7745 0.3376 0.8938 0.9265 0.4084 0.8831 0.8988 0.903 ] Network output: [ -0.05935 0.1777 0.9287 -0.0008342 -0.001607 1.006 0.006656 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4241 0.3914 0.8545 0.2744 0.8904 0.9264 0.4248 0.8814 0.9006 0.9052 ] Network output: [ -0.1183 1.082 0.1055 0.00208 0.003067 1.062 -0.007622 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2229 Epoch 272 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.185 1.129 0.7953 0.003905 3.389e-05 -0.2778 0.002289 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07583 3.542e-05 0.1197 -0.01746 0.7607 0.802 0.1495 0.7639 0.7741 0.7058 ] Network output: [ 0.5028 0.1058 0.5531 -0.0004806 0.002072 0.3383 -0.01251 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5201 0.0706 0.2007 0.2714 0.846 0.9102 0.6192 0.828 0.8715 0.8144 ] Network output: [ 0.09751 0.5746 1.067 -0.005053 0.0001631 0.1385 0.007611 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1287 0.04823 0.4885 0.173 0.8567 0.8971 0.1358 0.8608 0.8818 0.8244 ] Network output: [ 0.3892 -0.1847 0.57 0.002692 -0.001494 0.8491 -0.004054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6106 0.3114 0.5358 0.3822 0.8678 0.9285 0.6203 0.853 0.8986 0.8489 ] Network output: [ 0.01287 0.0753 0.9994 -0.001346 -0.002112 0.8901 0.007666 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4091 0.3301 0.7748 0.3374 0.8953 0.9274 0.411 0.8836 0.8997 0.9032 ] Network output: [ -0.06105 0.18 0.9291 -0.0008443 -0.001555 1.006 0.006488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4265 0.3944 0.855 0.2724 0.892 0.9274 0.4271 0.882 0.9016 0.9054 ] Network output: [ -0.1164 1.082 0.1024 0.001953 0.003027 1.061 -0.007541 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2214 Epoch 273 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.185 1.13 0.7945 0.003849 5.99e-06 -0.2784 0.002304 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07613 0.0001546 0.1191 -0.01656 0.7629 0.8035 0.1494 0.7643 0.775 0.7058 ] Network output: [ 0.5067 0.106 0.5484 -0.000474 0.002072 0.335 -0.01237 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5221 0.07186 0.1985 0.2717 0.8481 0.9113 0.6201 0.8283 0.8724 0.8143 ] Network output: [ 0.09505 0.5737 1.072 -0.005049 0.000172 0.1393 0.007491 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1289 0.04885 0.4886 0.1749 0.8589 0.8984 0.1359 0.8615 0.883 0.8245 ] Network output: [ 0.3896 -0.186 0.5712 0.002816 -0.00153 0.8488 -0.003847 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6117 0.3151 0.5351 0.3828 0.8697 0.9295 0.6211 0.8534 0.8995 0.849 ] Network output: [ 0.01128 0.07486 1.001 -0.001286 -0.002078 0.8928 0.007515 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4118 0.3337 0.7751 0.3372 0.8968 0.9283 0.4137 0.8842 0.9006 0.9034 ] Network output: [ -0.06273 0.1823 0.9294 -0.0008559 -0.001503 1.007 0.006321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4289 0.3975 0.8555 0.2704 0.8936 0.9283 0.4296 0.8827 0.9025 0.9057 ] Network output: [ -0.1145 1.081 0.09954 0.001827 0.002988 1.06 -0.007457 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.22 Epoch 274 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.185 1.132 0.7937 0.003794 -2.088e-05 -0.2791 0.002317 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07642 0.0002759 0.1184 -0.0157 0.765 0.805 0.1493 0.7647 0.776 0.7057 ] Network output: [ 0.5105 0.1063 0.5438 -0.0004686 0.002071 0.3318 -0.01222 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.524 0.07314 0.1963 0.272 0.85 0.9124 0.621 0.8286 0.8733 0.8143 ] Network output: [ 0.09257 0.5729 1.077 -0.005044 0.0001819 0.14 0.007369 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1291 0.04948 0.4885 0.1766 0.861 0.8997 0.1359 0.8621 0.8841 0.8246 ] Network output: [ 0.39 -0.1873 0.5725 0.002938 -0.001566 0.8486 -0.003641 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6128 0.3189 0.5342 0.3834 0.8716 0.9305 0.622 0.8538 0.9003 0.849 ] Network output: [ 0.009733 0.07441 1.002 -0.001226 -0.002044 0.8956 0.007365 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4147 0.3374 0.7753 0.3368 0.8983 0.9292 0.4165 0.8848 0.9015 0.9035 ] Network output: [ -0.06438 0.1846 0.9296 -0.0008687 -0.001452 1.008 0.006157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4314 0.4007 0.8559 0.2682 0.8952 0.9292 0.4321 0.8833 0.9034 0.9059 ] Network output: [ -0.1126 1.081 0.0968 0.001703 0.002949 1.059 -0.007371 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2186 Epoch 275 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.185 1.133 0.7929 0.00374 -4.677e-05 -0.2797 0.00233 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07672 0.0003991 0.1178 -0.01486 0.7671 0.8065 0.1492 0.765 0.7769 0.7056 ] Network output: [ 0.5142 0.1066 0.5392 -0.0004647 0.00207 0.3286 -0.01208 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.526 0.07444 0.194 0.2722 0.852 0.9135 0.6219 0.8289 0.8741 0.8142 ] Network output: [ 0.09009 0.5722 1.082 -0.005037 0.0001926 0.1407 0.007246 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1293 0.05011 0.4884 0.1783 0.8631 0.901 0.136 0.8627 0.8851 0.8247 ] Network output: [ 0.3903 -0.1886 0.5738 0.003059 -0.001601 0.8483 -0.003439 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6139 0.3226 0.5333 0.3839 0.8734 0.9314 0.6228 0.8541 0.9012 0.8491 ] Network output: [ 0.00824 0.07394 1.003 -0.001167 -0.002011 0.8983 0.007216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4175 0.3412 0.7754 0.3363 0.8997 0.9301 0.4193 0.8853 0.9024 0.9036 ] Network output: [ -0.06599 0.1869 0.9298 -0.0008826 -0.001401 1.009 0.005994 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.434 0.4039 0.8563 0.266 0.8968 0.9302 0.4346 0.884 0.9043 0.9061 ] Network output: [ -0.1107 1.08 0.0942 0.00158 0.00291 1.058 -0.007284 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2172 Epoch 276 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1849 1.134 0.7922 0.003686 -7.17e-05 -0.2802 0.002341 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07701 0.000524 0.1171 -0.01406 0.7692 0.808 0.1491 0.7654 0.7779 0.7054 ] Network output: [ 0.5179 0.1069 0.5346 -0.0004623 0.002069 0.3254 -0.01193 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5279 0.07576 0.1917 0.2724 0.8539 0.9146 0.6228 0.8291 0.875 0.8142 ] Network output: [ 0.0876 0.5717 1.087 -0.00503 0.0002041 0.1413 0.007121 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1296 0.05076 0.4883 0.18 0.8651 0.9022 0.1361 0.8633 0.8862 0.8248 ] Network output: [ 0.3907 -0.1898 0.5752 0.003178 -0.001637 0.848 -0.003239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6151 0.3263 0.5323 0.3843 0.8752 0.9323 0.6237 0.8545 0.902 0.8491 ] Network output: [ 0.006799 0.07344 1.004 -0.001108 -0.001979 0.9011 0.007069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4205 0.345 0.7755 0.3358 0.9012 0.931 0.4222 0.8858 0.9032 0.9037 ] Network output: [ -0.06758 0.1891 0.9299 -0.0008975 -0.00135 1.01 0.005834 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4367 0.4071 0.8567 0.2636 0.8983 0.9311 0.4372 0.8846 0.9052 0.9062 ] Network output: [ -0.1087 1.079 0.09175 0.00146 0.002872 1.057 -0.007196 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2158 Epoch 277 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1848 1.135 0.7914 0.003633 -9.572e-05 -0.2807 0.002351 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0773 0.0006504 0.1164 -0.01328 0.7712 0.8094 0.1491 0.7658 0.7788 0.7053 ] Network output: [ 0.5216 0.1073 0.53 -0.0004615 0.002067 0.3223 -0.01179 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5298 0.07709 0.1894 0.2725 0.8557 0.9157 0.6238 0.8294 0.8758 0.8141 ] Network output: [ 0.0851 0.5714 1.092 -0.005021 0.0002164 0.1419 0.006994 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1298 0.05141 0.488 0.1815 0.8671 0.9035 0.1363 0.8639 0.8872 0.8249 ] Network output: [ 0.391 -0.191 0.5766 0.003295 -0.001672 0.8476 -0.003041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6162 0.3301 0.5312 0.3847 0.877 0.9333 0.6246 0.8548 0.9028 0.8491 ] Network output: [ 0.005409 0.07293 1.005 -0.001049 -0.001947 0.9038 0.006925 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4235 0.3488 0.7755 0.3351 0.9026 0.9319 0.4251 0.8864 0.9041 0.9038 ] Network output: [ -0.06913 0.1913 0.9299 -0.0009131 -0.001301 1.01 0.005675 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4393 0.4104 0.8571 0.2611 0.8998 0.932 0.4399 0.8852 0.9061 0.9064 ] Network output: [ -0.1068 1.078 0.08944 0.001341 0.002833 1.056 -0.007106 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2145 Epoch 278 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1847 1.137 0.7906 0.00358 -0.0001189 -0.2812 0.00236 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07758 0.000778 0.1156 -0.01254 0.7733 0.8109 0.149 0.7661 0.7797 0.7051 ] Network output: [ 0.5252 0.1076 0.5255 -0.0004624 0.002065 0.3191 -0.01165 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5317 0.07844 0.187 0.2726 0.8576 0.9167 0.6247 0.8296 0.8767 0.8141 ] Network output: [ 0.08259 0.5712 1.096 -0.005012 0.0002294 0.1424 0.006867 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1301 0.05206 0.4877 0.1829 0.8691 0.9047 0.1364 0.8645 0.8882 0.8249 ] Network output: [ 0.3912 -0.1922 0.5782 0.003409 -0.001707 0.8472 -0.002847 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6173 0.3338 0.53 0.385 0.8787 0.9342 0.6255 0.8552 0.9036 0.8491 ] Network output: [ 0.004069 0.0724 1.005 -0.0009909 -0.001916 0.9066 0.006782 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4265 0.3527 0.7755 0.3344 0.9039 0.9327 0.4281 0.8869 0.9049 0.9039 ] Network output: [ -0.07064 0.1936 0.9299 -0.0009295 -0.001252 1.011 0.005519 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4421 0.4137 0.8574 0.2586 0.9013 0.9329 0.4426 0.8858 0.9069 0.9065 ] Network output: [ -0.1048 1.076 0.08728 0.001225 0.002795 1.055 -0.007015 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2131 Epoch 279 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1845 1.138 0.7899 0.003528 -0.0001412 -0.2815 0.002368 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07787 0.0009068 0.1149 -0.01183 0.7752 0.8123 0.149 0.7665 0.7806 0.7049 ] Network output: [ 0.5288 0.108 0.521 -0.0004652 0.002062 0.316 -0.0115 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5336 0.07981 0.1846 0.2727 0.8594 0.9177 0.6257 0.8299 0.8775 0.814 ] Network output: [ 0.08008 0.5712 1.101 -0.005002 0.0002432 0.1429 0.006738 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1304 0.05272 0.4874 0.1843 0.871 0.906 0.1366 0.8651 0.8892 0.8249 ] Network output: [ 0.3915 -0.1934 0.5797 0.003521 -0.001742 0.8467 -0.002655 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6185 0.3375 0.5288 0.3853 0.8804 0.9351 0.6265 0.8555 0.9044 0.8492 ] Network output: [ 0.002779 0.07185 1.006 -0.000933 -0.001886 0.9094 0.006641 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4296 0.3566 0.7754 0.3336 0.9053 0.9335 0.4312 0.8874 0.9057 0.9039 ] Network output: [ -0.07213 0.1958 0.9298 -0.0009463 -0.001203 1.012 0.005365 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4449 0.4171 0.8577 0.2559 0.9027 0.9337 0.4454 0.8864 0.9077 0.9067 ] Network output: [ -0.1029 1.075 0.08526 0.001111 0.002757 1.054 -0.006923 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2117 Epoch 280 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1843 1.139 0.7891 0.003478 -0.0001627 -0.2819 0.002375 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07815 0.001036 0.1142 -0.01114 0.7772 0.8137 0.1489 0.7668 0.7815 0.7047 ] Network output: [ 0.5323 0.1084 0.5165 -0.0004698 0.002059 0.313 -0.01136 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5354 0.08118 0.1821 0.2727 0.8612 0.9187 0.6267 0.8301 0.8783 0.814 ] Network output: [ 0.07757 0.5713 1.106 -0.004992 0.0002575 0.1434 0.006608 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1307 0.05339 0.4869 0.1855 0.8729 0.9072 0.1367 0.8657 0.8902 0.8249 ] Network output: [ 0.3917 -0.1946 0.5813 0.003631 -0.001776 0.8463 -0.002466 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6196 0.3412 0.5275 0.3855 0.8821 0.9359 0.6274 0.8558 0.9052 0.8492 ] Network output: [ 0.001537 0.07127 1.007 -0.0008756 -0.001857 0.9122 0.006503 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4328 0.3606 0.7753 0.3327 0.9066 0.9344 0.4343 0.8878 0.9065 0.904 ] Network output: [ -0.07358 0.1979 0.9297 -0.0009635 -0.001156 1.013 0.005212 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4477 0.4205 0.858 0.2532 0.9042 0.9346 0.4482 0.887 0.9086 0.9068 ] Network output: [ -0.1009 1.073 0.08337 0.001 0.002719 1.053 -0.006831 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2103 Epoch 281 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.184 1.14 0.7884 0.003427 -0.0001835 -0.2822 0.002381 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07842 0.001167 0.1134 -0.01049 0.7791 0.8151 0.1489 0.7672 0.7823 0.7045 ] Network output: [ 0.5358 0.1089 0.512 -0.0004764 0.002057 0.3099 -0.01122 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5373 0.08256 0.1796 0.2727 0.8629 0.9197 0.6278 0.8303 0.8791 0.8139 ] Network output: [ 0.07505 0.5715 1.11 -0.004981 0.0002725 0.1438 0.006478 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.131 0.05406 0.4865 0.1867 0.8747 0.9083 0.1369 0.8662 0.8912 0.8248 ] Network output: [ 0.3919 -0.1957 0.583 0.003737 -0.00181 0.8458 -0.002281 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6208 0.3449 0.5261 0.3856 0.8837 0.9368 0.6283 0.8561 0.9059 0.8492 ] Network output: [ 0.0003435 0.07068 1.007 -0.0008186 -0.001828 0.9149 0.006367 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.436 0.3646 0.7751 0.3317 0.9079 0.9352 0.4375 0.8883 0.9073 0.904 ] Network output: [ -0.07499 0.2001 0.9295 -0.000981 -0.001109 1.014 0.005063 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4506 0.4239 0.8582 0.2503 0.9056 0.9354 0.4511 0.8875 0.9094 0.9069 ] Network output: [ -0.09892 1.071 0.08162 0.0008925 0.002681 1.052 -0.006737 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.209 Epoch 282 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1838 1.142 0.7877 0.003378 -0.0002036 -0.2824 0.002386 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0787 0.001297 0.1126 -0.009868 0.781 0.8165 0.1489 0.7675 0.7832 0.7043 ] Network output: [ 0.5392 0.1093 0.5076 -0.0004851 0.002054 0.3069 -0.01108 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5391 0.08394 0.1772 0.2727 0.8646 0.9207 0.6288 0.8305 0.8799 0.8139 ] Network output: [ 0.07252 0.5719 1.115 -0.00497 0.000288 0.1441 0.006347 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1313 0.05473 0.486 0.1878 0.8765 0.9095 0.1371 0.8668 0.8921 0.8248 ] Network output: [ 0.392 -0.1968 0.5848 0.00384 -0.001843 0.8452 -0.002098 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6219 0.3486 0.5247 0.3856 0.8853 0.9376 0.6293 0.8564 0.9067 0.8492 ] Network output: [ -0.0008032 0.07006 1.008 -0.000762 -0.0018 0.9177 0.006234 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4393 0.3686 0.7749 0.3306 0.9091 0.936 0.4407 0.8888 0.908 0.904 ] Network output: [ -0.07637 0.2022 0.9293 -0.0009985 -0.001063 1.015 0.004915 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4536 0.4274 0.8584 0.2474 0.9069 0.9363 0.454 0.8881 0.9102 0.9071 ] Network output: [ -0.09694 1.07 0.08 0.0007879 0.002644 1.051 -0.006643 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2076 Epoch 283 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1835 1.143 0.787 0.00333 -0.000223 -0.2826 0.00239 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07897 0.001428 0.1119 -0.009274 0.7829 0.8179 0.1489 0.7678 0.784 0.704 ] Network output: [ 0.5426 0.1097 0.5033 -0.0004958 0.002051 0.304 -0.01094 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.541 0.08533 0.1747 0.2726 0.8663 0.9217 0.6299 0.8307 0.8807 0.8138 ] Network output: [ 0.06999 0.5725 1.119 -0.004958 0.000304 0.1445 0.006215 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1316 0.05541 0.4855 0.1888 0.8783 0.9107 0.1373 0.8673 0.893 0.8247 ] Network output: [ 0.3921 -0.1979 0.5866 0.00394 -0.001875 0.8447 -0.001919 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6231 0.3522 0.5233 0.3857 0.8868 0.9385 0.6303 0.8566 0.9074 0.8492 ] Network output: [ -0.001904 0.06942 1.008 -0.0007059 -0.001773 0.9204 0.006103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4426 0.3727 0.7746 0.3295 0.9104 0.9368 0.444 0.8892 0.9088 0.904 ] Network output: [ -0.07771 0.2044 0.929 -0.001016 -0.001018 1.015 0.004771 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4566 0.4309 0.8587 0.2444 0.9083 0.9371 0.457 0.8887 0.9109 0.9072 ] Network output: [ -0.09495 1.068 0.07852 0.0006867 0.002606 1.05 -0.006548 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2063 Epoch 284 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1832 1.144 0.7862 0.003282 -0.0002418 -0.2828 0.002393 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07924 0.001559 0.1111 -0.008709 0.7848 0.8193 0.1489 0.7682 0.7849 0.7037 ] Network output: [ 0.546 0.1102 0.4989 -0.0005088 0.002048 0.301 -0.0108 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5428 0.08671 0.1722 0.2725 0.8679 0.9226 0.6309 0.8309 0.8814 0.8137 ] Network output: [ 0.06747 0.5732 1.123 -0.004946 0.0003205 0.1448 0.006083 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1319 0.05609 0.4849 0.1897 0.8801 0.9118 0.1376 0.8678 0.8939 0.8246 ] Network output: [ 0.3922 -0.199 0.5885 0.004036 -0.001906 0.8441 -0.001744 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6242 0.3558 0.5218 0.3856 0.8884 0.9393 0.6313 0.8569 0.9082 0.8492 ] Network output: [ -0.002961 0.06877 1.008 -0.0006503 -0.001746 0.9232 0.005975 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.446 0.3768 0.7743 0.3283 0.9116 0.9375 0.4473 0.8897 0.9095 0.904 ] Network output: [ -0.07902 0.2065 0.9287 -0.001033 -0.0009736 1.016 0.004628 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4596 0.4344 0.8589 0.2413 0.9096 0.9379 0.46 0.8892 0.9117 0.9072 ] Network output: [ -0.09297 1.065 0.07715 0.0005889 0.002568 1.049 -0.006453 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2049 Epoch 285 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1828 1.145 0.7855 0.003236 -0.00026 -0.2829 0.002395 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07951 0.00169 0.1103 -0.008172 0.7866 0.8206 0.1489 0.7685 0.7857 0.7034 ] Network output: [ 0.5493 0.1106 0.4946 -0.0005239 0.002046 0.2982 -0.01066 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5446 0.0881 0.1697 0.2724 0.8695 0.9236 0.632 0.8311 0.8822 0.8137 ] Network output: [ 0.06494 0.574 1.127 -0.004934 0.0003374 0.145 0.00595 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1322 0.05677 0.4843 0.1906 0.8818 0.9129 0.1378 0.8684 0.8948 0.8244 ] Network output: [ 0.3922 -0.2001 0.5904 0.004129 -0.001936 0.8435 -0.001572 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6254 0.3594 0.5203 0.3855 0.8899 0.9401 0.6323 0.8572 0.9089 0.8492 ] Network output: [ -0.003974 0.06809 1.009 -0.0005953 -0.00172 0.9259 0.00585 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4494 0.3809 0.774 0.327 0.9128 0.9383 0.4507 0.8901 0.9102 0.904 ] Network output: [ -0.08029 0.2086 0.9283 -0.00105 -0.0009303 1.017 0.004488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4627 0.4379 0.859 0.2381 0.9109 0.9387 0.4631 0.8898 0.9125 0.9073 ] Network output: [ -0.09098 1.063 0.07591 0.0004947 0.00253 1.048 -0.006358 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2036 Epoch 286 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1824 1.147 0.7848 0.003191 -0.0002777 -0.283 0.002396 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07977 0.00182 0.1095 -0.007662 0.7884 0.822 0.1489 0.7688 0.7865 0.7031 ] Network output: [ 0.5526 0.111 0.4903 -0.0005413 0.002043 0.2953 -0.01052 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5463 0.08948 0.1672 0.2722 0.8711 0.9245 0.6331 0.8313 0.8829 0.8136 ] Network output: [ 0.0624 0.5749 1.131 -0.004921 0.0003547 0.1453 0.005818 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1326 0.05744 0.4837 0.1913 0.8834 0.914 0.1381 0.8689 0.8957 0.8243 ] Network output: [ 0.3923 -0.2011 0.5924 0.004218 -0.001965 0.8428 -0.001404 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6266 0.3629 0.5187 0.3854 0.8913 0.9409 0.6333 0.8574 0.9096 0.8492 ] Network output: [ -0.004946 0.06739 1.009 -0.0005407 -0.001695 0.9286 0.005727 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4528 0.385 0.7737 0.3257 0.914 0.939 0.4541 0.8905 0.9109 0.9039 ] Network output: [ -0.08152 0.2107 0.9278 -0.001067 -0.000888 1.018 0.004351 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4658 0.4415 0.8592 0.2349 0.9122 0.9395 0.4662 0.8903 0.9132 0.9074 ] Network output: [ -0.089 1.061 0.07479 0.0004042 0.002493 1.047 -0.006262 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2022 Epoch 287 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.182 1.148 0.7841 0.003146 -0.0002949 -0.283 0.002396 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08003 0.00195 0.1087 -0.00718 0.7901 0.8233 0.149 0.7691 0.7873 0.7028 ] Network output: [ 0.5558 0.1115 0.4861 -0.0005609 0.002041 0.2925 -0.01039 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5481 0.09086 0.1646 0.272 0.8727 0.9254 0.6342 0.8314 0.8837 0.8136 ] Network output: [ 0.05987 0.5759 1.135 -0.004909 0.0003723 0.1455 0.005685 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1329 0.05812 0.4831 0.192 0.8851 0.9151 0.1383 0.8694 0.8966 0.8241 ] Network output: [ 0.3922 -0.2021 0.5944 0.004303 -0.001993 0.8422 -0.001239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6277 0.3664 0.5172 0.3852 0.8928 0.9416 0.6343 0.8576 0.9103 0.8492 ] Network output: [ -0.005878 0.06668 1.009 -0.0004867 -0.001671 0.9313 0.005608 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4563 0.3892 0.7734 0.3243 0.9151 0.9398 0.4576 0.8909 0.9116 0.9039 ] Network output: [ -0.08272 0.2127 0.9274 -0.001083 -0.0008467 1.019 0.004217 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4689 0.4451 0.8593 0.2315 0.9134 0.9403 0.4693 0.8908 0.914 0.9075 ] Network output: [ -0.08701 1.059 0.07378 0.0003174 0.002455 1.046 -0.006166 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2009 Epoch 288 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1816 1.149 0.7834 0.003104 -0.0003116 -0.2829 0.002396 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08029 0.002078 0.1079 -0.006725 0.7919 0.8246 0.149 0.7694 0.7881 0.7024 ] Network output: [ 0.559 0.1119 0.4819 -0.0005827 0.002039 0.2898 -0.01026 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5499 0.09222 0.1621 0.2718 0.8742 0.9263 0.6354 0.8316 0.8844 0.8135 ] Network output: [ 0.05735 0.5771 1.139 -0.004896 0.0003902 0.1457 0.005553 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1333 0.0588 0.4824 0.1926 0.8867 0.9161 0.1386 0.8699 0.8974 0.8239 ] Network output: [ 0.3922 -0.2031 0.5965 0.004385 -0.00202 0.8415 -0.001078 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6289 0.3699 0.5156 0.3849 0.8942 0.9424 0.6353 0.8579 0.9109 0.8492 ] Network output: [ -0.006771 0.06594 1.009 -0.0004333 -0.001647 0.934 0.005491 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4598 0.3933 0.773 0.3228 0.9162 0.9405 0.461 0.8914 0.9123 0.9038 ] Network output: [ -0.08388 0.2148 0.9268 -0.001099 -0.0008064 1.02 0.004085 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4721 0.4487 0.8595 0.2281 0.9146 0.9411 0.4725 0.8914 0.9147 0.9075 ] Network output: [ -0.08503 1.056 0.07288 0.0002345 0.002417 1.045 -0.00607 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1995 Epoch 289 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1812 1.151 0.7827 0.003062 -0.000328 -0.2829 0.002395 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08054 0.002206 0.1072 -0.006297 0.7936 0.8259 0.1491 0.7697 0.7888 0.7021 ] Network output: [ 0.5622 0.1123 0.4778 -0.0006069 0.002038 0.2871 -0.01013 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5516 0.09358 0.1597 0.2716 0.8757 0.9271 0.6365 0.8318 0.8851 0.8135 ] Network output: [ 0.05482 0.5784 1.143 -0.004884 0.0004084 0.1458 0.00542 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1337 0.05948 0.4817 0.1932 0.8883 0.9172 0.1389 0.8704 0.8982 0.8237 ] Network output: [ 0.3921 -0.2041 0.5987 0.004462 -0.002045 0.8408 -0.0009212 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6301 0.3733 0.5141 0.3847 0.8956 0.9431 0.6364 0.8581 0.9116 0.8493 ] Network output: [ -0.007627 0.06519 1.009 -0.0003805 -0.001624 0.9367 0.005377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4633 0.3975 0.7727 0.3213 0.9173 0.9412 0.4645 0.8918 0.913 0.9038 ] Network output: [ -0.085 0.2168 0.9262 -0.001113 -0.0007672 1.02 0.003956 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4753 0.4523 0.8596 0.2247 0.9158 0.9418 0.4757 0.8919 0.9154 0.9075 ] Network output: [ -0.08305 1.053 0.07209 0.0001554 0.00238 1.044 -0.005974 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1982 Epoch 290 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1808 1.152 0.7819 0.003022 -0.0003441 -0.2827 0.002393 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0808 0.002332 0.1064 -0.005894 0.7953 0.8272 0.1491 0.77 0.7896 0.7017 ] Network output: [ 0.5653 0.1127 0.4736 -0.0006332 0.002036 0.2844 -0.01 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5533 0.09492 0.1572 0.2713 0.8771 0.928 0.6376 0.8319 0.8858 0.8134 ] Network output: [ 0.0523 0.5798 1.146 -0.004871 0.0004267 0.1459 0.005288 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.134 0.06015 0.4811 0.1936 0.8898 0.9182 0.1392 0.8708 0.8991 0.8234 ] Network output: [ 0.392 -0.2051 0.6009 0.004534 -0.002069 0.8401 -0.0007683 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6313 0.3767 0.5125 0.3843 0.8969 0.9438 0.6374 0.8583 0.9123 0.8493 ] Network output: [ -0.008449 0.06442 1.009 -0.0003283 -0.001601 0.9393 0.005265 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4669 0.4017 0.7723 0.3197 0.9184 0.9419 0.4681 0.8922 0.9137 0.9037 ] Network output: [ -0.08608 0.2188 0.9256 -0.001127 -0.0007291 1.021 0.003829 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4785 0.456 0.8597 0.2212 0.917 0.9425 0.4789 0.8924 0.9161 0.9076 ] Network output: [ -0.08108 1.051 0.0714 8.017e-05 0.002342 1.043 -0.005878 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1969 Epoch 291 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1803 1.153 0.7812 0.002983 -0.0003598 -0.2826 0.002391 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08104 0.002457 0.1056 -0.005516 0.7969 0.8284 0.1492 0.7703 0.7904 0.7013 ] Network output: [ 0.5683 0.1131 0.4695 -0.0006619 0.002036 0.2818 -0.009874 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5551 0.09624 0.1547 0.271 0.8786 0.9288 0.6388 0.832 0.8865 0.8134 ] Network output: [ 0.04978 0.5813 1.15 -0.004858 0.0004453 0.146 0.005157 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1344 0.06082 0.4804 0.194 0.8913 0.9192 0.1395 0.8713 0.8999 0.8232 ] Network output: [ 0.3918 -0.206 0.6031 0.004603 -0.002092 0.8394 -0.0006194 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6324 0.3801 0.5109 0.384 0.8983 0.9446 0.6385 0.8585 0.9129 0.8493 ] Network output: [ -0.009237 0.06363 1.009 -0.0002767 -0.00158 0.942 0.005157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4705 0.4059 0.7719 0.3181 0.9195 0.9426 0.4716 0.8925 0.9143 0.9036 ] Network output: [ -0.08713 0.2208 0.925 -0.00114 -0.000692 1.022 0.003705 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4818 0.4596 0.8599 0.2176 0.9182 0.9433 0.4821 0.8929 0.9168 0.9076 ] Network output: [ -0.07911 1.048 0.07081 8.828e-06 0.002304 1.042 -0.005783 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1956 Epoch 292 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1798 1.155 0.7805 0.002945 -0.0003753 -0.2823 0.002388 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08129 0.00258 0.1048 -0.005164 0.7986 0.8297 0.1492 0.7706 0.7911 0.7009 ] Network output: [ 0.5714 0.1135 0.4655 -0.0006926 0.002035 0.2792 -0.00975 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5568 0.09755 0.1523 0.2707 0.88 0.9297 0.64 0.8322 0.8872 0.8133 ] Network output: [ 0.04727 0.5829 1.153 -0.004845 0.000464 0.1461 0.005026 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1348 0.06149 0.4797 0.1944 0.8928 0.9202 0.1398 0.8718 0.9007 0.8229 ] Network output: [ 0.3916 -0.207 0.6054 0.004667 -0.002114 0.8387 -0.0004746 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6336 0.3833 0.5094 0.3836 0.8996 0.9453 0.6396 0.8587 0.9136 0.8493 ] Network output: [ -0.009994 0.06283 1.009 -0.0002258 -0.001559 0.9446 0.005051 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4741 0.4101 0.7715 0.3164 0.9205 0.9433 0.4752 0.8929 0.915 0.9035 ] Network output: [ -0.08814 0.2227 0.9242 -0.001152 -0.000656 1.023 0.003585 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4851 0.4633 0.86 0.214 0.9193 0.944 0.4854 0.8934 0.9175 0.9076 ] Network output: [ -0.07715 1.045 0.07031 -5.865e-05 0.002267 1.041 -0.005687 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1943 Epoch 293 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1793 1.156 0.7797 0.002909 -0.0003905 -0.2821 0.002385 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08153 0.002701 0.104 -0.004835 0.8002 0.8309 0.1493 0.7709 0.7919 0.7004 ] Network output: [ 0.5744 0.1139 0.4615 -0.0007256 0.002035 0.2766 -0.009628 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5585 0.09884 0.1498 0.2704 0.8814 0.9305 0.6411 0.8323 0.8879 0.8133 ] Network output: [ 0.04476 0.5846 1.156 -0.004832 0.0004828 0.1462 0.004896 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1352 0.06215 0.479 0.1946 0.8943 0.9212 0.1402 0.8722 0.9015 0.8226 ] Network output: [ 0.3913 -0.2079 0.6078 0.004727 -0.002134 0.8379 -0.000334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6348 0.3866 0.5079 0.3831 0.9008 0.946 0.6406 0.8589 0.9142 0.8493 ] Network output: [ -0.01072 0.06201 1.009 -0.0001755 -0.001539 0.9472 0.004949 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4777 0.4143 0.7711 0.3146 0.9215 0.944 0.4788 0.8933 0.9156 0.9033 ] Network output: [ -0.08911 0.2247 0.9235 -0.001163 -0.0006212 1.023 0.003467 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4884 0.467 0.8601 0.2103 0.9204 0.9447 0.4887 0.8939 0.9182 0.9076 ] Network output: [ -0.0752 1.042 0.06991 -0.0001223 0.002229 1.04 -0.005592 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.193 Epoch 294 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1788 1.157 0.779 0.002875 -0.0004056 -0.2818 0.002381 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08177 0.00282 0.1032 -0.004531 0.8018 0.8321 0.1494 0.7712 0.7926 0.7 ] Network output: [ 0.5773 0.1143 0.4575 -0.0007606 0.002035 0.2742 -0.009509 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5601 0.1001 0.1474 0.27 0.8827 0.9313 0.6423 0.8324 0.8886 0.8133 ] Network output: [ 0.04226 0.5864 1.16 -0.004819 0.0005016 0.1462 0.004766 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1356 0.06281 0.4783 0.1948 0.8957 0.9222 0.1405 0.8727 0.9022 0.8223 ] Network output: [ 0.3911 -0.2088 0.6102 0.004783 -0.002152 0.8371 -0.0001975 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.636 0.3898 0.5064 0.3826 0.9021 0.9466 0.6417 0.8591 0.9148 0.8493 ] Network output: [ -0.01142 0.06117 1.009 -0.000126 -0.001519 0.9497 0.004849 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4813 0.4185 0.7707 0.3128 0.9225 0.9446 0.4824 0.8937 0.9162 0.9032 ] Network output: [ -0.09005 0.2266 0.9227 -0.001173 -0.0005875 1.024 0.003352 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4917 0.4707 0.8602 0.2066 0.9215 0.9454 0.4921 0.8944 0.9189 0.9076 ] Network output: [ -0.07326 1.039 0.06959 -0.0001822 0.002192 1.039 -0.005498 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1917 Epoch 295 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1783 1.158 0.7783 0.002842 -0.0004206 -0.2814 0.002378 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.082 0.002937 0.1024 -0.00425 0.8033 0.8333 0.1494 0.7714 0.7933 0.6995 ] Network output: [ 0.5803 0.1146 0.4535 -0.0007977 0.002036 0.2717 -0.009392 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5618 0.1014 0.145 0.2696 0.884 0.9321 0.6435 0.8325 0.8893 0.8132 ] Network output: [ 0.03977 0.5883 1.163 -0.004807 0.0005205 0.1463 0.004637 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.136 0.06346 0.4776 0.1949 0.8971 0.9231 0.1408 0.8731 0.903 0.8219 ] Network output: [ 0.3907 -0.2096 0.6126 0.004834 -0.002169 0.8364 -6.524e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6372 0.3929 0.5049 0.3821 0.9033 0.9473 0.6428 0.8593 0.9155 0.8493 ] Network output: [ -0.0121 0.06032 1.009 -7.715e-05 -0.0015 0.9523 0.004752 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.485 0.4227 0.7703 0.311 0.9235 0.9453 0.4861 0.894 0.9169 0.9031 ] Network output: [ -0.09095 0.2285 0.9219 -0.001182 -0.0005549 1.025 0.003239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4951 0.4744 0.8603 0.2028 0.9225 0.9461 0.4954 0.8948 0.9195 0.9076 ] Network output: [ -0.07133 1.036 0.06936 -0.0002383 0.002154 1.038 -0.005403 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1904 Epoch 296 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1778 1.16 0.7775 0.002811 -0.0004354 -0.2811 0.002373 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08223 0.003051 0.1017 -0.003991 0.8048 0.8345 0.1495 0.7717 0.7941 0.699 ] Network output: [ 0.5832 0.1149 0.4496 -0.0008367 0.002037 0.2693 -0.009277 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5635 0.1026 0.1427 0.2692 0.8853 0.9328 0.6447 0.8327 0.8899 0.8132 ] Network output: [ 0.03729 0.5903 1.166 -0.004794 0.0005394 0.1463 0.004509 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1364 0.06411 0.4769 0.195 0.8984 0.9241 0.1412 0.8736 0.9037 0.8215 ] Network output: [ 0.3904 -0.2105 0.6151 0.00488 -0.002185 0.8356 6.282e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6384 0.396 0.5035 0.3816 0.9045 0.948 0.6439 0.8595 0.9161 0.8493 ] Network output: [ -0.01275 0.05946 1.009 -2.906e-05 -0.001481 0.9548 0.004658 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4887 0.4268 0.77 0.3091 0.9244 0.9459 0.4897 0.8944 0.9175 0.9029 ] Network output: [ -0.09181 0.2304 0.921 -0.00119 -0.0005234 1.026 0.00313 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4985 0.4781 0.8604 0.199 0.9236 0.9467 0.4988 0.8953 0.9202 0.9076 ] Network output: [ -0.06941 1.034 0.0692 -0.0002908 0.002117 1.037 -0.00531 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1891 Epoch 297 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1773 1.161 0.7767 0.002781 -0.0004502 -0.2806 0.002369 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08246 0.003163 0.1009 -0.003755 0.8064 0.8357 0.1496 0.772 0.7948 0.6985 ] Network output: [ 0.5861 0.1152 0.4457 -0.0008776 0.002039 0.2669 -0.009165 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5651 0.1038 0.1403 0.2688 0.8866 0.9336 0.6459 0.8328 0.8906 0.8132 ] Network output: [ 0.03482 0.5923 1.169 -0.004781 0.0005582 0.1463 0.004382 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1368 0.06475 0.4762 0.195 0.8998 0.925 0.1415 0.874 0.9045 0.8211 ] Network output: [ 0.39 -0.2113 0.6177 0.004923 -0.002199 0.8348 0.0001866 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6396 0.399 0.5021 0.381 0.9057 0.9486 0.645 0.8597 0.9167 0.8493 ] Network output: [ -0.01337 0.05859 1.009 1.826e-05 -0.001464 0.9572 0.004566 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4923 0.431 0.7696 0.3072 0.9254 0.9465 0.4934 0.8948 0.9181 0.9028 ] Network output: [ -0.09264 0.2323 0.9201 -0.001196 -0.0004931 1.026 0.003023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5019 0.4818 0.8604 0.1951 0.9246 0.9474 0.5022 0.8958 0.9208 0.9076 ] Network output: [ -0.0675 1.03 0.06912 -0.0003397 0.00208 1.036 -0.005216 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1878 Epoch 298 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1768 1.162 0.7759 0.002753 -0.000465 -0.2802 0.002365 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08268 0.003273 0.1001 -0.00354 0.8078 0.8369 0.1497 0.7723 0.7955 0.698 ] Network output: [ 0.5889 0.1155 0.4418 -0.0009203 0.002041 0.2646 -0.009055 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5668 0.1049 0.138 0.2684 0.8879 0.9344 0.6472 0.8329 0.8912 0.8131 ] Network output: [ 0.03237 0.5944 1.172 -0.004768 0.0005769 0.1463 0.004256 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1372 0.06539 0.4755 0.195 0.9011 0.9259 0.1419 0.8744 0.9052 0.8207 ] Network output: [ 0.3896 -0.2122 0.6203 0.004961 -0.002212 0.8341 0.0003062 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6408 0.402 0.5007 0.3804 0.9068 0.9492 0.6461 0.8598 0.9173 0.8493 ] Network output: [ -0.01398 0.0577 1.009 6.481e-05 -0.001446 0.9597 0.004478 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.496 0.4352 0.7692 0.3053 0.9263 0.9471 0.4971 0.8951 0.9187 0.9026 ] Network output: [ -0.09344 0.2341 0.9192 -0.001201 -0.0004639 1.027 0.00292 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5053 0.4855 0.8605 0.1912 0.9256 0.948 0.5056 0.8963 0.9215 0.9075 ] Network output: [ -0.06561 1.027 0.06912 -0.0003851 0.002042 1.035 -0.005124 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1866 Epoch 299 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1763 1.163 0.7752 0.002727 -0.0004798 -0.2797 0.002361 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08291 0.003379 0.09935 -0.003346 0.8093 0.838 0.1498 0.7725 0.7962 0.6974 ] Network output: [ 0.5917 0.1158 0.4379 -0.0009646 0.002043 0.2623 -0.008947 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5684 0.1061 0.1357 0.2679 0.8891 0.9351 0.6484 0.833 0.8919 0.8131 ] Network output: [ 0.02992 0.5966 1.175 -0.004755 0.0005956 0.1462 0.004131 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1376 0.06602 0.4748 0.1949 0.9024 0.9268 0.1423 0.8748 0.9059 0.8203 ] Network output: [ 0.3891 -0.213 0.6229 0.004994 -0.002223 0.8333 0.0004216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.642 0.4049 0.4994 0.3797 0.9079 0.9498 0.6473 0.86 0.9179 0.8493 ] Network output: [ -0.01457 0.0568 1.009 0.0001106 -0.00143 0.9621 0.004392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4997 0.4393 0.7689 0.3033 0.9272 0.9477 0.5007 0.8955 0.9193 0.9024 ] Network output: [ -0.0942 0.2359 0.9182 -0.001206 -0.0004358 1.028 0.002819 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5087 0.4892 0.8606 0.1873 0.9266 0.9487 0.509 0.8967 0.9221 0.9075 ] Network output: [ -0.06374 1.024 0.06918 -0.0004272 0.002005 1.035 -0.005032 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1853 Epoch 300 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1758 1.164 0.7744 0.002703 -0.0004947 -0.2792 0.002356 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08312 0.003484 0.09859 -0.003173 0.8107 0.8391 0.1499 0.7728 0.7969 0.6968 ] Network output: [ 0.5945 0.116 0.4341 -0.001011 0.002046 0.26 -0.008842 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.57 0.1072 0.1335 0.2674 0.8903 0.9358 0.6496 0.8331 0.8925 0.813 ] Network output: [ 0.0275 0.5989 1.177 -0.004742 0.0006141 0.1462 0.004007 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.138 0.06664 0.4742 0.1947 0.9036 0.9276 0.1426 0.8753 0.9066 0.8198 ] Network output: [ 0.3886 -0.2137 0.6256 0.005024 -0.002232 0.8325 0.0005328 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6432 0.4078 0.4981 0.3791 0.909 0.9504 0.6484 0.8602 0.9185 0.8493 ] Network output: [ -0.01514 0.0559 1.009 0.0001555 -0.001414 0.9645 0.004308 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5034 0.4435 0.7685 0.3013 0.9281 0.9483 0.5044 0.8958 0.9199 0.9022 ] Network output: [ -0.09493 0.2378 0.9172 -0.001208 -0.0004088 1.028 0.002721 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5121 0.4929 0.8607 0.1834 0.9276 0.9493 0.5124 0.8972 0.9227 0.9074 ] Network output: [ -0.06188 1.021 0.0693 -0.000466 0.001969 1.034 -0.004941 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1841 Epoch 301 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1753 1.166 0.7736 0.002681 -0.0005096 -0.2786 0.002352 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08334 0.003585 0.09783 -0.00302 0.8122 0.8403 0.15 0.7731 0.7976 0.6963 ] Network output: [ 0.5973 0.1163 0.4303 -0.001058 0.002049 0.2578 -0.00874 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5716 0.1083 0.1312 0.267 0.8915 0.9365 0.6509 0.8331 0.8932 0.813 ] Network output: [ 0.02508 0.6012 1.18 -0.004729 0.0006324 0.1461 0.003884 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1385 0.06726 0.4735 0.1945 0.9049 0.9285 0.143 0.8757 0.9074 0.8193 ] Network output: [ 0.388 -0.2145 0.6283 0.005049 -0.00224 0.8317 0.0006398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6445 0.4106 0.4968 0.3784 0.9101 0.951 0.6495 0.8603 0.919 0.8494 ] Network output: [ -0.0157 0.05498 1.008 0.0001996 -0.001398 0.9669 0.004228 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5071 0.4476 0.7682 0.2993 0.9289 0.9489 0.5081 0.8961 0.9205 0.902 ] Network output: [ -0.09562 0.2395 0.9161 -0.00121 -0.0003829 1.029 0.002626 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5155 0.4967 0.8608 0.1794 0.9285 0.9499 0.5158 0.8976 0.9234 0.9073 ] Network output: [ -0.06004 1.018 0.06948 -0.0005017 0.001932 1.033 -0.004851 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1828 Epoch 302 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1748 1.167 0.7728 0.00266 -0.0005247 -0.278 0.002348 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08355 0.003683 0.09708 -0.002887 0.8136 0.8414 0.1501 0.7733 0.7982 0.6957 ] Network output: [ 0.6001 0.1165 0.4265 -0.001107 0.002053 0.2556 -0.008639 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5732 0.1093 0.129 0.2664 0.8926 0.9372 0.6521 0.8332 0.8938 0.813 ] Network output: [ 0.02269 0.6036 1.183 -0.004715 0.0006506 0.1461 0.003763 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1389 0.06787 0.4728 0.1942 0.9061 0.9293 0.1434 0.8761 0.908 0.8188 ] Network output: [ 0.3874 -0.2152 0.6311 0.005071 -0.002247 0.8309 0.0007427 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6457 0.4134 0.4956 0.3777 0.9111 0.9516 0.6507 0.8605 0.9196 0.8494 ] Network output: [ -0.01624 0.05406 1.008 0.0002428 -0.001383 0.9692 0.004149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5108 0.4517 0.7679 0.2972 0.9298 0.9495 0.5118 0.8965 0.921 0.9018 ] Network output: [ -0.09629 0.2413 0.9151 -0.001211 -0.0003581 1.03 0.002533 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.519 0.5004 0.8609 0.1754 0.9294 0.9505 0.5193 0.8981 0.924 0.9073 ] Network output: [ -0.05822 1.015 0.06973 -0.0005343 0.001896 1.032 -0.004761 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1816 Epoch 303 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1743 1.168 0.7719 0.002642 -0.0005398 -0.2774 0.002344 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08376 0.003778 0.09633 -0.002772 0.8149 0.8425 0.1502 0.7736 0.7989 0.695 ] Network output: [ 0.6028 0.1167 0.4227 -0.001157 0.002057 0.2534 -0.008541 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5748 0.1103 0.1268 0.2659 0.8938 0.9379 0.6534 0.8333 0.8944 0.8129 ] Network output: [ 0.02031 0.6061 1.185 -0.004702 0.0006685 0.146 0.003643 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1393 0.06847 0.4722 0.1939 0.9073 0.9302 0.1438 0.8765 0.9087 0.8183 ] Network output: [ 0.3868 -0.216 0.6339 0.005088 -0.002252 0.8302 0.0008415 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6469 0.4161 0.4945 0.3769 0.9122 0.9522 0.6518 0.8606 0.9202 0.8494 ] Network output: [ -0.01677 0.05313 1.008 0.0002852 -0.001368 0.9716 0.004074 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5145 0.4558 0.7676 0.2952 0.9306 0.9501 0.5154 0.8968 0.9216 0.9016 ] Network output: [ -0.09692 0.2431 0.914 -0.00121 -0.0003344 1.03 0.002444 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5225 0.5041 0.8609 0.1714 0.9303 0.9511 0.5227 0.8985 0.9246 0.9072 ] Network output: [ -0.05641 1.012 0.07002 -0.0005642 0.001859 1.031 -0.004673 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1803 Epoch 304 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1738 1.169 0.7711 0.002625 -0.0005552 -0.2767 0.002341 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08396 0.003871 0.09559 -0.002676 0.8163 0.8435 0.1503 0.7738 0.7996 0.6944 ] Network output: [ 0.6056 0.1168 0.419 -0.001208 0.002061 0.2513 -0.008445 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5764 0.1113 0.1247 0.2654 0.8949 0.9386 0.6546 0.8334 0.895 0.8129 ] Network output: [ 0.01795 0.6086 1.188 -0.004688 0.0006862 0.1459 0.003524 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1397 0.06906 0.4715 0.1935 0.9084 0.931 0.1442 0.8769 0.9094 0.8177 ] Network output: [ 0.3862 -0.2167 0.6367 0.005102 -0.002256 0.8294 0.0009362 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6481 0.4187 0.4933 0.3762 0.9132 0.9527 0.653 0.8608 0.9208 0.8494 ] Network output: [ -0.01729 0.0522 1.008 0.0003267 -0.001354 0.9738 0.004 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5182 0.4599 0.7673 0.2931 0.9314 0.9506 0.5191 0.8971 0.9222 0.9014 ] Network output: [ -0.09752 0.2448 0.9129 -0.001208 -0.0003117 1.031 0.002356 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5259 0.5078 0.861 0.1674 0.9312 0.9517 0.5262 0.899 0.9252 0.9071 ] Network output: [ -0.05463 1.009 0.07037 -0.0005913 0.001824 1.03 -0.004585 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1791 Epoch 305 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1733 1.17 0.7703 0.00261 -0.0005707 -0.276 0.002338 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08416 0.00396 0.09485 -0.002598 0.8176 0.8446 0.1504 0.7741 0.8003 0.6937 ] Network output: [ 0.6083 0.117 0.4153 -0.00126 0.002065 0.2491 -0.008352 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.578 0.1122 0.1226 0.2648 0.896 0.9392 0.6559 0.8335 0.8956 0.8129 ] Network output: [ 0.01561 0.6111 1.19 -0.004675 0.0007037 0.1458 0.003407 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1402 0.06965 0.4709 0.1931 0.9095 0.9318 0.1446 0.8773 0.9101 0.8171 ] Network output: [ 0.3855 -0.2174 0.6395 0.005111 -0.002259 0.8286 0.001027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6494 0.4213 0.4923 0.3754 0.9142 0.9533 0.6541 0.8609 0.9213 0.8494 ] Network output: [ -0.0178 0.05127 1.008 0.0003673 -0.00134 0.9761 0.003929 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5218 0.4639 0.767 0.2909 0.9322 0.9512 0.5228 0.8975 0.9227 0.9011 ] Network output: [ -0.09809 0.2465 0.9117 -0.001205 -0.00029 1.032 0.002272 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5294 0.5115 0.8611 0.1634 0.9321 0.9523 0.5296 0.8994 0.9258 0.907 ] Network output: [ -0.05287 1.006 0.07077 -0.0006158 0.001788 1.029 -0.004498 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1779 Epoch 306 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1728 1.171 0.7694 0.002597 -0.0005863 -0.2753 0.002335 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08436 0.004046 0.09411 -0.002536 0.8189 0.8456 0.1505 0.7744 0.8009 0.693 ] Network output: [ 0.611 0.1171 0.4116 -0.001313 0.00207 0.2471 -0.008261 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5796 0.1132 0.1205 0.2642 0.897 0.9399 0.6571 0.8335 0.8962 0.8128 ] Network output: [ 0.01329 0.6137 1.192 -0.004661 0.0007209 0.1457 0.003292 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1406 0.07022 0.4703 0.1926 0.9107 0.9326 0.145 0.8777 0.9107 0.8165 ] Network output: [ 0.3847 -0.218 0.6424 0.005117 -0.00226 0.8279 0.001114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6506 0.4238 0.4913 0.3746 0.9151 0.9538 0.6553 0.8611 0.9219 0.8494 ] Network output: [ -0.0183 0.05033 1.008 0.000407 -0.001327 0.9783 0.00386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5255 0.468 0.7668 0.2888 0.933 0.9517 0.5264 0.8978 0.9233 0.9009 ] Network output: [ -0.09863 0.2482 0.9105 -0.001201 -0.0002693 1.032 0.00219 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5329 0.5152 0.8611 0.1593 0.933 0.9529 0.5331 0.8998 0.9264 0.9068 ] Network output: [ -0.05112 1.003 0.07121 -0.0006378 0.001753 1.028 -0.004413 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1766 Epoch 307 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1723 1.172 0.7686 0.002586 -0.0006022 -0.2746 0.002332 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08456 0.00413 0.09339 -0.002492 0.8202 0.8467 0.1506 0.7746 0.8016 0.6923 ] Network output: [ 0.6137 0.1172 0.4079 -0.001367 0.002075 0.245 -0.008171 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5811 0.1141 0.1184 0.2637 0.8981 0.9405 0.6584 0.8336 0.8968 0.8128 ] Network output: [ 0.01099 0.6164 1.194 -0.004647 0.0007378 0.1456 0.003177 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1411 0.0708 0.4696 0.1921 0.9118 0.9333 0.1454 0.8781 0.9114 0.8159 ] Network output: [ 0.384 -0.2187 0.6454 0.00512 -0.00226 0.8271 0.001197 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6518 0.4263 0.4903 0.3738 0.9161 0.9544 0.6565 0.8612 0.9224 0.8494 ] Network output: [ -0.0188 0.04939 1.008 0.0004459 -0.001314 0.9805 0.003794 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5292 0.472 0.7665 0.2866 0.9338 0.9522 0.5301 0.8981 0.9238 0.9006 ] Network output: [ -0.09914 0.2499 0.9093 -0.001196 -0.0002496 1.033 0.002111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5363 0.5189 0.8612 0.1553 0.9338 0.9534 0.5366 0.9003 0.927 0.9067 ] Network output: [ -0.04941 0.9998 0.07169 -0.0006576 0.001718 1.027 -0.004329 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1754 Epoch 308 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1718 1.173 0.7678 0.002577 -0.0006183 -0.2738 0.002331 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08475 0.00421 0.09266 -0.002464 0.8215 0.8477 0.1507 0.7749 0.8022 0.6916 ] Network output: [ 0.6164 0.1172 0.4042 -0.001421 0.00208 0.243 -0.008084 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5827 0.1149 0.1164 0.2631 0.8991 0.9411 0.6597 0.8337 0.8974 0.8128 ] Network output: [ 0.00872 0.619 1.196 -0.004633 0.0007544 0.1454 0.003065 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1415 0.07136 0.469 0.1915 0.9128 0.9341 0.1458 0.8785 0.912 0.8152 ] Network output: [ 0.3832 -0.2193 0.6483 0.005119 -0.002259 0.8264 0.001276 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.653 0.4288 0.4894 0.3729 0.917 0.9549 0.6576 0.8614 0.9229 0.8494 ] Network output: [ -0.01929 0.04844 1.008 0.0004837 -0.001301 0.9827 0.003729 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5328 0.476 0.7663 0.2845 0.9345 0.9528 0.5337 0.8984 0.9244 0.9004 ] Network output: [ -0.09962 0.2515 0.9081 -0.00119 -0.0002308 1.033 0.002035 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5398 0.5226 0.8613 0.1512 0.9346 0.954 0.54 0.9007 0.9275 0.9066 ] Network output: [ -0.04771 0.9968 0.07221 -0.0006751 0.001684 1.026 -0.004245 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1742 Epoch 309 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1714 1.174 0.7669 0.00257 -0.0006346 -0.273 0.002329 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08494 0.004288 0.09194 -0.002451 0.8227 0.8487 0.1509 0.7751 0.8029 0.6908 ] Network output: [ 0.619 0.1173 0.4005 -0.001476 0.002086 0.241 -0.007999 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5842 0.1157 0.1143 0.2624 0.9001 0.9417 0.661 0.8338 0.898 0.8127 ] Network output: [ 0.006471 0.6218 1.199 -0.004618 0.0007706 0.1453 0.002954 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1419 0.07192 0.4684 0.1909 0.9139 0.9349 0.1462 0.8788 0.9127 0.8145 ] Network output: [ 0.3823 -0.2199 0.6513 0.005115 -0.002256 0.8257 0.001352 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6543 0.4312 0.4886 0.3721 0.9179 0.9554 0.6588 0.8615 0.9235 0.8494 ] Network output: [ -0.01977 0.0475 1.008 0.0005207 -0.001289 0.9848 0.003667 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5365 0.4799 0.7661 0.2823 0.9353 0.9533 0.5374 0.8987 0.9249 0.9001 ] Network output: [ -0.1001 0.2531 0.9068 -0.001183 -0.0002129 1.034 0.00196 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5433 0.5263 0.8613 0.1472 0.9354 0.9545 0.5435 0.9011 0.9281 0.9064 ] Network output: [ -0.04604 0.9938 0.07277 -0.0006907 0.00165 1.024 -0.004163 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.173 Epoch 310 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1709 1.175 0.766 0.002564 -0.0006511 -0.2721 0.002328 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08513 0.004362 0.09123 -0.002454 0.824 0.8497 0.151 0.7754 0.8035 0.6901 ] Network output: [ 0.6217 0.1173 0.3968 -0.001532 0.002091 0.239 -0.007916 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5857 0.1165 0.1124 0.2618 0.9011 0.9423 0.6623 0.8338 0.8986 0.8127 ] Network output: [ 0.004246 0.6245 1.2 -0.004603 0.0007866 0.1452 0.002844 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1424 0.07246 0.4678 0.1902 0.9149 0.9356 0.1466 0.8792 0.9133 0.8138 ] Network output: [ 0.3814 -0.2205 0.6543 0.005108 -0.002253 0.825 0.001423 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6555 0.4335 0.4878 0.3712 0.9188 0.9559 0.66 0.8617 0.924 0.8494 ] Network output: [ -0.02025 0.04656 1.008 0.0005568 -0.001277 0.9869 0.003606 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5401 0.4839 0.7659 0.2801 0.936 0.9538 0.541 0.8991 0.9254 0.8998 ] Network output: [ -0.1005 0.2547 0.9056 -0.001175 -0.0001959 1.035 0.001889 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5467 0.5299 0.8614 0.1431 0.9362 0.955 0.547 0.9015 0.9287 0.9062 ] Network output: [ -0.04439 0.9908 0.07336 -0.0007044 0.001617 1.023 -0.004083 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1718 Epoch 311 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1705 1.176 0.7652 0.00256 -0.0006679 -0.2713 0.002328 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08531 0.004434 0.09052 -0.002471 0.8252 0.8507 0.1511 0.7756 0.8042 0.6893 ] Network output: [ 0.6244 0.1173 0.3932 -0.001587 0.002097 0.2371 -0.007835 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5872 0.1173 0.1104 0.2612 0.9021 0.9429 0.6636 0.8339 0.8992 0.8126 ] Network output: [ 0.002047 0.6273 1.202 -0.004589 0.0008021 0.145 0.002737 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1428 0.07301 0.4672 0.1895 0.9159 0.9363 0.147 0.8796 0.9139 0.8131 ] Network output: [ 0.3805 -0.2211 0.6573 0.005098 -0.002248 0.8243 0.001492 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6568 0.4358 0.487 0.3703 0.9197 0.9564 0.6612 0.8618 0.9245 0.8494 ] Network output: [ -0.02073 0.04563 1.008 0.0005919 -0.001266 0.989 0.003548 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5438 0.4878 0.7657 0.2779 0.9367 0.9543 0.5446 0.8994 0.926 0.8995 ] Network output: [ -0.1009 0.2563 0.9043 -0.001166 -0.0001798 1.035 0.001819 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5502 0.5336 0.8615 0.1391 0.937 0.9555 0.5504 0.9019 0.9292 0.9061 ] Network output: [ -0.04276 0.9878 0.07399 -0.0007163 0.001584 1.022 -0.004003 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1706 Epoch 312 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.17 1.177 0.7643 0.002558 -0.0006849 -0.2704 0.002328 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08549 0.004503 0.08981 -0.002502 0.8264 0.8517 0.1512 0.7759 0.8048 0.6885 ] Network output: [ 0.627 0.1173 0.3896 -0.001643 0.002102 0.2352 -0.007756 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5888 0.118 0.1085 0.2605 0.903 0.9435 0.6649 0.834 0.8998 0.8126 ] Network output: [ -0.0001249 0.6301 1.204 -0.004573 0.0008174 0.1448 0.00263 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1433 0.07354 0.4666 0.1888 0.9169 0.937 0.1475 0.88 0.9146 0.8123 ] Network output: [ 0.3796 -0.2217 0.6604 0.005086 -0.002242 0.8236 0.001557 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.658 0.438 0.4863 0.3694 0.9205 0.9569 0.6624 0.8619 0.9251 0.8494 ] Network output: [ -0.02121 0.0447 1.008 0.0006261 -0.001255 0.991 0.003491 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5474 0.4916 0.7655 0.2757 0.9374 0.9548 0.5482 0.8997 0.9265 0.8991 ] Network output: [ -0.1013 0.2579 0.903 -0.001157 -0.0001646 1.036 0.001752 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5537 0.5372 0.8615 0.135 0.9377 0.956 0.5539 0.9023 0.9298 0.9059 ] Network output: [ -0.04116 0.9849 0.07465 -0.0007266 0.001551 1.021 -0.003925 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1694 Epoch 313 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1696 1.177 0.7635 0.002558 -0.0007021 -0.2695 0.002329 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08567 0.004569 0.08911 -0.002547 0.8275 0.8526 0.1514 0.7762 0.8054 0.6876 ] Network output: [ 0.6297 0.1172 0.3859 -0.001699 0.002108 0.2333 -0.007678 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5903 0.1187 0.1065 0.2598 0.9039 0.9441 0.6662 0.834 0.9003 0.8125 ] Network output: [ -0.00227 0.6329 1.206 -0.004558 0.0008322 0.1447 0.002526 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1438 0.07407 0.466 0.188 0.9179 0.9377 0.1479 0.8804 0.9152 0.8115 ] Network output: [ 0.3786 -0.2222 0.6634 0.00507 -0.002235 0.8229 0.001618 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6592 0.4402 0.4856 0.3685 0.9214 0.9573 0.6636 0.8621 0.9256 0.8494 ] Network output: [ -0.02168 0.04377 1.008 0.0006593 -0.001244 0.9931 0.003436 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.551 0.4955 0.7653 0.2735 0.9381 0.9552 0.5518 0.9 0.927 0.8988 ] Network output: [ -0.1016 0.2594 0.9017 -0.001146 -0.0001501 1.036 0.001688 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5571 0.5409 0.8615 0.131 0.9385 0.9565 0.5574 0.9028 0.9303 0.9057 ] Network output: [ -0.03959 0.982 0.07533 -0.0007355 0.001519 1.02 -0.003848 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1683 Epoch 314 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1692 1.178 0.7626 0.002559 -0.0007195 -0.2686 0.002331 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08584 0.004633 0.08842 -0.002604 0.8287 0.8536 0.1515 0.7764 0.806 0.6868 ] Network output: [ 0.6323 0.1172 0.3823 -0.001755 0.002113 0.2314 -0.007602 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5918 0.1193 0.1046 0.2591 0.9049 0.9446 0.6674 0.8341 0.9009 0.8125 ] Network output: [ -0.004386 0.6358 1.208 -0.004542 0.0008467 0.1445 0.002423 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1442 0.07459 0.4654 0.1872 0.9188 0.9384 0.1483 0.8807 0.9158 0.8107 ] Network output: [ 0.3776 -0.2227 0.6665 0.005052 -0.002228 0.8222 0.001677 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6605 0.4424 0.485 0.3675 0.9222 0.9578 0.6648 0.8622 0.9261 0.8493 ] Network output: [ -0.02216 0.04285 1.008 0.0006917 -0.001233 0.995 0.003383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5545 0.4993 0.7652 0.2713 0.9388 0.9557 0.5554 0.9003 0.9275 0.8985 ] Network output: [ -0.102 0.2609 0.9003 -0.001135 -0.0001364 1.037 0.001625 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5606 0.5445 0.8616 0.1269 0.9392 0.957 0.5608 0.9032 0.9309 0.9055 ] Network output: [ -0.03803 0.9791 0.07604 -0.000743 0.001488 1.019 -0.003772 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1671 Epoch 315 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1688 1.179 0.7618 0.002562 -0.0007372 -0.2676 0.002333 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08602 0.004694 0.08773 -0.002674 0.8298 0.8545 0.1516 0.7767 0.8067 0.6859 ] Network output: [ 0.6349 0.1171 0.3787 -0.00181 0.002119 0.2295 -0.007528 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5933 0.12 0.1028 0.2584 0.9057 0.9452 0.6688 0.8342 0.9015 0.8124 ] Network output: [ -0.006474 0.6387 1.209 -0.004526 0.0008608 0.1443 0.002322 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1447 0.07511 0.4648 0.1864 0.9197 0.9391 0.1488 0.8811 0.9164 0.8098 ] Network output: [ 0.3766 -0.2232 0.6696 0.005032 -0.002219 0.8216 0.001732 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6617 0.4445 0.4845 0.3666 0.923 0.9582 0.666 0.8623 0.9266 0.8493 ] Network output: [ -0.02263 0.04193 1.008 0.0007231 -0.001223 0.997 0.003331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5581 0.5031 0.765 0.269 0.9394 0.9562 0.5589 0.9006 0.928 0.8981 ] Network output: [ -0.1023 0.2624 0.899 -0.001123 -0.0001234 1.038 0.001565 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5641 0.5481 0.8616 0.1229 0.9399 0.9575 0.5643 0.9036 0.9314 0.9052 ] Network output: [ -0.03651 0.9762 0.07677 -0.0007492 0.001457 1.018 -0.003698 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1659 Epoch 316 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1684 1.179 0.761 0.002567 -0.000755 -0.2667 0.002336 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08619 0.004752 0.08704 -0.002755 0.831 0.8554 0.1518 0.7769 0.8073 0.685 ] Network output: [ 0.6376 0.117 0.3751 -0.001865 0.002124 0.2277 -0.007455 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5947 0.1206 0.1009 0.2577 0.9066 0.9457 0.6701 0.8342 0.902 0.8123 ] Network output: [ -0.008532 0.6416 1.211 -0.00451 0.0008746 0.1441 0.002223 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1451 0.07562 0.4642 0.1855 0.9206 0.9397 0.1492 0.8815 0.917 0.809 ] Network output: [ 0.3755 -0.2237 0.6728 0.005009 -0.00221 0.8209 0.001784 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.663 0.4466 0.4839 0.3656 0.9238 0.9587 0.6672 0.8625 0.9271 0.8493 ] Network output: [ -0.0231 0.04102 1.008 0.0007536 -0.001213 0.9989 0.003281 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5617 0.5069 0.7649 0.2668 0.9401 0.9566 0.5625 0.9009 0.9285 0.8977 ] Network output: [ -0.1025 0.2639 0.8976 -0.001111 -0.0001111 1.038 0.001506 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5675 0.5517 0.8616 0.1189 0.9406 0.958 0.5677 0.904 0.9319 0.905 ] Network output: [ -0.03501 0.9734 0.07753 -0.0007543 0.001427 1.017 -0.003625 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1647 Epoch 317 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.168 1.18 0.7601 0.002573 -0.0007731 -0.2657 0.00234 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08635 0.004808 0.08636 -0.002848 0.8321 0.8563 0.1519 0.7772 0.8079 0.6841 ] Network output: [ 0.6402 0.1168 0.3716 -0.00192 0.00213 0.2259 -0.007384 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5962 0.1211 0.09911 0.257 0.9075 0.9462 0.6714 0.8343 0.9026 0.8123 ] Network output: [ -0.01056 0.6445 1.212 -0.004493 0.0008879 0.1439 0.002125 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1456 0.07612 0.4636 0.1845 0.9215 0.9404 0.1496 0.8819 0.9175 0.808 ] Network output: [ 0.3744 -0.2242 0.6759 0.004984 -0.002199 0.8203 0.001833 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6642 0.4486 0.4835 0.3646 0.9246 0.9591 0.6684 0.8626 0.9276 0.8492 ] Network output: [ -0.02358 0.04012 1.008 0.0007832 -0.001203 1.001 0.003233 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5652 0.5107 0.7648 0.2646 0.9407 0.9571 0.566 0.9012 0.929 0.8974 ] Network output: [ -0.1028 0.2653 0.8962 -0.001098 -9.957e-05 1.039 0.00145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5709 0.5553 0.8616 0.1149 0.9413 0.9584 0.5711 0.9044 0.9325 0.9047 ] Network output: [ -0.03353 0.9706 0.0783 -0.0007584 0.001397 1.016 -0.003554 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1636 Epoch 318 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1676 1.181 0.7593 0.00258 -0.0007913 -0.2647 0.002344 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08652 0.004862 0.08569 -0.002951 0.8331 0.8572 0.152 0.7774 0.8085 0.6831 ] Network output: [ 0.6428 0.1167 0.368 -0.001975 0.002135 0.2241 -0.007314 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5977 0.1217 0.09731 0.2563 0.9083 0.9468 0.6727 0.8343 0.9031 0.8122 ] Network output: [ -0.01256 0.6474 1.214 -0.004476 0.0009009 0.1437 0.002029 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1461 0.07662 0.463 0.1836 0.9224 0.941 0.1501 0.8822 0.9181 0.8071 ] Network output: [ 0.3733 -0.2246 0.6791 0.004958 -0.002188 0.8197 0.001879 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6655 0.4506 0.4831 0.3636 0.9253 0.9595 0.6696 0.8627 0.9281 0.8492 ] Network output: [ -0.02405 0.03924 1.008 0.0008118 -0.001193 1.003 0.003186 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5687 0.5144 0.7647 0.2624 0.9413 0.9575 0.5695 0.9015 0.9295 0.897 ] Network output: [ -0.103 0.2667 0.8948 -0.001084 -8.867e-05 1.039 0.001396 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5744 0.5589 0.8617 0.1109 0.942 0.9589 0.5746 0.9048 0.933 0.9045 ] Network output: [ -0.03208 0.9679 0.0791 -0.0007615 0.001369 1.015 -0.003484 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1624 Epoch 319 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1672 1.181 0.7585 0.002589 -0.0008097 -0.2636 0.002349 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08668 0.004913 0.08502 -0.003064 0.8342 0.8581 0.1522 0.7777 0.8091 0.6822 ] Network output: [ 0.6454 0.1165 0.3644 -0.002029 0.00214 0.2223 -0.007245 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5991 0.1222 0.09553 0.2555 0.9091 0.9473 0.674 0.8344 0.9037 0.8121 ] Network output: [ -0.01452 0.6504 1.215 -0.004459 0.0009134 0.1435 0.001935 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1465 0.07711 0.4624 0.1826 0.9233 0.9416 0.1505 0.8826 0.9187 0.8061 ] Network output: [ 0.3722 -0.2251 0.6822 0.004929 -0.002177 0.8191 0.001922 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6667 0.4526 0.4827 0.3626 0.9261 0.96 0.6708 0.8629 0.9285 0.8492 ] Network output: [ -0.02453 0.03836 1.008 0.0008396 -0.001184 1.005 0.003141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5722 0.5181 0.7646 0.2601 0.9419 0.9579 0.573 0.9018 0.93 0.8966 ] Network output: [ -0.1032 0.2681 0.8934 -0.00107 -7.839e-05 1.04 0.001344 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5778 0.5625 0.8617 0.107 0.9426 0.9593 0.578 0.9052 0.9335 0.9042 ] Network output: [ -0.03066 0.9652 0.07991 -0.0007639 0.00134 1.015 -0.003416 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1612 Epoch 320 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1668 1.182 0.7577 0.002599 -0.0008282 -0.2626 0.002354 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08684 0.004962 0.08435 -0.003187 0.8352 0.8589 0.1523 0.7779 0.8097 0.6812 ] Network output: [ 0.6481 0.1163 0.3609 -0.002082 0.002145 0.2206 -0.007178 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6006 0.1227 0.09377 0.2548 0.91 0.9478 0.6753 0.8345 0.9042 0.812 ] Network output: [ -0.01645 0.6533 1.216 -0.004442 0.0009256 0.1432 0.001843 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.147 0.0776 0.4618 0.1816 0.9241 0.9423 0.151 0.883 0.9193 0.8051 ] Network output: [ 0.371 -0.2255 0.6854 0.004899 -0.002164 0.8185 0.001963 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.668 0.4545 0.4823 0.3616 0.9268 0.9604 0.672 0.863 0.929 0.8491 ] Network output: [ -0.02501 0.03749 1.009 0.0008666 -0.001174 1.006 0.003096 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5757 0.5217 0.7645 0.2579 0.9425 0.9584 0.5765 0.9022 0.9305 0.8961 ] Network output: [ -0.1034 0.2695 0.892 -0.001056 -6.872e-05 1.04 0.001293 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5812 0.566 0.8616 0.103 0.9433 0.9598 0.5814 0.9056 0.934 0.9039 ] Network output: [ -0.02926 0.9625 0.08074 -0.0007655 0.001312 1.014 -0.003348 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1601 Epoch 321 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1665 1.182 0.7569 0.002611 -0.000847 -0.2615 0.00236 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08699 0.005009 0.08369 -0.003319 0.8363 0.8598 0.1525 0.7782 0.8103 0.6801 ] Network output: [ 0.6507 0.1161 0.3573 -0.002134 0.002149 0.2188 -0.007112 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.602 0.1231 0.09203 0.254 0.9108 0.9483 0.6766 0.8345 0.9047 0.8119 ] Network output: [ -0.01836 0.6563 1.217 -0.004424 0.0009374 0.143 0.001752 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1475 0.07808 0.4612 0.1805 0.9249 0.9429 0.1514 0.8833 0.9198 0.8041 ] Network output: [ 0.3698 -0.2258 0.6886 0.004867 -0.002151 0.818 0.002001 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6693 0.4564 0.482 0.3606 0.9275 0.9608 0.6732 0.8631 0.9295 0.849 ] Network output: [ -0.02549 0.03663 1.009 0.0008926 -0.001165 1.008 0.003054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5791 0.5254 0.7644 0.2557 0.9431 0.9588 0.5799 0.9025 0.931 0.8957 ] Network output: [ -0.1036 0.2708 0.8906 -0.001041 -5.964e-05 1.041 0.001244 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5846 0.5696 0.8616 0.09914 0.9439 0.9602 0.5848 0.906 0.9345 0.9036 ] Network output: [ -0.02788 0.9598 0.08158 -0.0007665 0.001285 1.013 -0.003283 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.159 Epoch 322 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1661 1.183 0.7562 0.002624 -0.0008658 -0.2604 0.002367 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08714 0.005054 0.08303 -0.00346 0.8373 0.8606 0.1526 0.7785 0.8109 0.6791 ] Network output: [ 0.6533 0.1159 0.3538 -0.002186 0.002154 0.2171 -0.007047 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6034 0.1236 0.09031 0.2532 0.9115 0.9487 0.6779 0.8346 0.9053 0.8118 ] Network output: [ -0.02022 0.6592 1.218 -0.004406 0.0009488 0.1428 0.001663 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1479 0.07856 0.4606 0.1794 0.9257 0.9435 0.1519 0.8837 0.9204 0.803 ] Network output: [ 0.3686 -0.2262 0.6918 0.004833 -0.002138 0.8174 0.002037 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6705 0.4583 0.4818 0.3596 0.9282 0.9612 0.6745 0.8633 0.9299 0.849 ] Network output: [ -0.02597 0.03579 1.009 0.0009178 -0.001157 1.01 0.003012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5825 0.529 0.7643 0.2535 0.9437 0.9592 0.5833 0.9028 0.9314 0.8952 ] Network output: [ -0.1037 0.2721 0.8892 -0.001026 -5.11e-05 1.041 0.001197 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.588 0.5731 0.8616 0.09526 0.9445 0.9606 0.5882 0.9063 0.935 0.9032 ] Network output: [ -0.02653 0.9572 0.08243 -0.0007669 0.001259 1.012 -0.003218 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1578 Epoch 323 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1658 1.183 0.7554 0.002638 -0.0008847 -0.2593 0.002374 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08729 0.005098 0.08239 -0.003608 0.8383 0.8615 0.1527 0.7787 0.8115 0.678 ] Network output: [ 0.6559 0.1156 0.3503 -0.002237 0.002158 0.2154 -0.006983 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6048 0.124 0.08861 0.2524 0.9123 0.9492 0.6792 0.8347 0.9058 0.8117 ] Network output: [ -0.02206 0.6622 1.219 -0.004388 0.0009598 0.1425 0.001576 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1484 0.07903 0.46 0.1783 0.9265 0.944 0.1524 0.884 0.9209 0.8019 ] Network output: [ 0.3673 -0.2265 0.695 0.004798 -0.002124 0.8169 0.00207 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6718 0.4601 0.4815 0.3585 0.9289 0.9615 0.6757 0.8634 0.9304 0.8489 ] Network output: [ -0.02645 0.03496 1.009 0.0009421 -0.001148 1.012 0.002972 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.586 0.5326 0.7642 0.2513 0.9442 0.9596 0.5867 0.9031 0.9319 0.8948 ] Network output: [ -0.1039 0.2734 0.8878 -0.001011 -4.31e-05 1.042 0.001152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5914 0.5766 0.8615 0.0914 0.9451 0.961 0.5916 0.9067 0.9355 0.9029 ] Network output: [ -0.02521 0.9547 0.08329 -0.0007668 0.001233 1.011 -0.003156 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1567 Epoch 324 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1654 1.183 0.7547 0.002653 -0.0009037 -0.2582 0.002382 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08744 0.005139 0.08174 -0.003764 0.8393 0.8623 0.1529 0.779 0.812 0.6769 ] Network output: [ 0.6585 0.1153 0.3468 -0.002287 0.002162 0.2137 -0.00692 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6063 0.1244 0.08692 0.2516 0.913 0.9497 0.6805 0.8347 0.9063 0.8116 ] Network output: [ -0.02386 0.6652 1.22 -0.004369 0.0009705 0.1423 0.00149 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1489 0.0795 0.4593 0.1772 0.9273 0.9446 0.1528 0.8844 0.9214 0.8008 ] Network output: [ 0.3661 -0.2269 0.6982 0.004762 -0.002109 0.8164 0.002101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.673 0.4619 0.4814 0.3575 0.9295 0.9619 0.6769 0.8636 0.9309 0.8488 ] Network output: [ -0.02694 0.03414 1.009 0.0009657 -0.001139 1.013 0.002932 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5893 0.5361 0.7641 0.2491 0.9448 0.96 0.5901 0.9034 0.9324 0.8943 ] Network output: [ -0.104 0.2746 0.8864 -0.0009948 -3.561e-05 1.042 0.001109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5947 0.5801 0.8615 0.08758 0.9457 0.9614 0.5949 0.9071 0.936 0.9025 ] Network output: [ -0.0239 0.9521 0.08416 -0.0007663 0.001208 1.01 -0.003094 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1555 Epoch 325 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1651 1.184 0.754 0.002669 -0.0009228 -0.2571 0.002391 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08759 0.005179 0.0811 -0.003927 0.8402 0.8631 0.153 0.7793 0.8126 0.6758 ] Network output: [ 0.6611 0.1151 0.3433 -0.002337 0.002166 0.212 -0.006859 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6077 0.1248 0.08526 0.2508 0.9138 0.9501 0.6818 0.8348 0.9068 0.8115 ] Network output: [ -0.02562 0.6682 1.221 -0.004351 0.0009808 0.142 0.001406 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1494 0.07997 0.4587 0.176 0.928 0.9452 0.1533 0.8848 0.922 0.7996 ] Network output: [ 0.3648 -0.2272 0.7014 0.004725 -0.002094 0.8159 0.002129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6743 0.4637 0.4812 0.3564 0.9302 0.9623 0.6781 0.8637 0.9313 0.8487 ] Network output: [ -0.02743 0.03334 1.01 0.0009884 -0.001131 1.015 0.002894 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5927 0.5396 0.7641 0.2469 0.9453 0.9604 0.5935 0.9037 0.9328 0.8938 ] Network output: [ -0.1041 0.2758 0.8849 -0.0009788 -2.861e-05 1.043 0.001067 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5981 0.5836 0.8614 0.08379 0.9463 0.9618 0.5983 0.9075 0.9365 0.9021 ] Network output: [ -0.02263 0.9496 0.08504 -0.0007654 0.001183 1.009 -0.003034 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1544 Epoch 326 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1648 1.184 0.7533 0.002686 -0.0009419 -0.2559 0.0024 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08773 0.005217 0.08047 -0.004095 0.8412 0.8639 0.1532 0.7795 0.8132 0.6747 ] Network output: [ 0.6637 0.1147 0.3398 -0.002385 0.002169 0.2104 -0.006798 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.609 0.1251 0.08361 0.25 0.9145 0.9506 0.6831 0.8349 0.9073 0.8114 ] Network output: [ -0.02736 0.6712 1.222 -0.004332 0.0009907 0.1417 0.001324 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1499 0.08043 0.4581 0.1748 0.9288 0.9457 0.1538 0.8851 0.9225 0.7984 ] Network output: [ 0.3635 -0.2275 0.7047 0.004687 -0.002078 0.8154 0.002155 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6755 0.4655 0.4811 0.3553 0.9308 0.9626 0.6794 0.8638 0.9318 0.8486 ] Network output: [ -0.02791 0.03255 1.01 0.00101 -0.001123 1.016 0.002857 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5961 0.5431 0.764 0.2447 0.9458 0.9608 0.5968 0.904 0.9333 0.8933 ] Network output: [ -0.1042 0.277 0.8835 -0.0009626 -2.208e-05 1.043 0.001027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6014 0.587 0.8613 0.08003 0.9469 0.9622 0.6016 0.9079 0.937 0.9018 ] Network output: [ -0.02138 0.9472 0.08593 -0.0007643 0.001159 1.008 -0.002976 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1533 Epoch 327 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1644 1.184 0.7527 0.002704 -0.0009611 -0.2548 0.002409 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08787 0.005254 0.07984 -0.00427 0.8421 0.8647 0.1533 0.7798 0.8138 0.6735 ] Network output: [ 0.6663 0.1144 0.3363 -0.002432 0.002172 0.2088 -0.006738 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6104 0.1254 0.08198 0.2492 0.9152 0.951 0.6844 0.8349 0.9078 0.8113 ] Network output: [ -0.02905 0.6741 1.223 -0.004312 0.001 0.1414 0.001244 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1503 0.08089 0.4574 0.1736 0.9295 0.9463 0.1542 0.8855 0.923 0.7971 ] Network output: [ 0.3621 -0.2277 0.7079 0.004647 -0.002062 0.8149 0.002179 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6768 0.4672 0.481 0.3542 0.9314 0.963 0.6806 0.864 0.9322 0.8485 ] Network output: [ -0.02841 0.03178 1.01 0.001031 -0.001115 1.018 0.002821 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5994 0.5466 0.7639 0.2425 0.9464 0.9611 0.6001 0.9043 0.9338 0.8927 ] Network output: [ -0.1042 0.2782 0.8821 -0.0009462 -1.599e-05 1.044 0.0009877 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6048 0.5905 0.8612 0.07631 0.9474 0.9626 0.6049 0.9083 0.9374 0.9013 ] Network output: [ -0.02015 0.9447 0.08683 -0.0007628 0.001136 1.007 -0.002919 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1522 Epoch 328 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1641 1.184 0.752 0.002722 -0.0009803 -0.2536 0.002419 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08801 0.005289 0.07922 -0.00445 0.8431 0.8655 0.1534 0.7801 0.8143 0.6723 ] Network output: [ 0.6689 0.1141 0.3329 -0.002478 0.002175 0.2072 -0.006679 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6118 0.1258 0.08037 0.2484 0.9159 0.9514 0.6857 0.835 0.9083 0.8111 ] Network output: [ -0.03072 0.6771 1.224 -0.004293 0.001009 0.1412 0.001165 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1508 0.08135 0.4567 0.1724 0.9302 0.9468 0.1547 0.8859 0.9235 0.7958 ] Network output: [ 0.3608 -0.228 0.7111 0.004607 -0.002046 0.8144 0.002201 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6781 0.4689 0.481 0.3532 0.9321 0.9633 0.6818 0.8641 0.9326 0.8484 ] Network output: [ -0.0289 0.03103 1.01 0.001052 -0.001107 1.02 0.002786 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6027 0.55 0.7638 0.2403 0.9469 0.9615 0.6034 0.9046 0.9342 0.8922 ] Network output: [ -0.1043 0.2793 0.8806 -0.0009296 -1.033e-05 1.044 0.0009504 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6081 0.5939 0.8611 0.07262 0.948 0.963 0.6083 0.9086 0.9379 0.9009 ] Network output: [ -0.01894 0.9424 0.08773 -0.0007612 0.001114 1.006 -0.002863 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.151 Epoch 329 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1638 1.185 0.7514 0.002742 -0.0009994 -0.2524 0.00243 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08814 0.005323 0.07861 -0.004635 0.844 0.8662 0.1536 0.7803 0.8149 0.671 ] Network output: [ 0.6715 0.1137 0.3294 -0.002523 0.002177 0.2056 -0.00662 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6132 0.126 0.07877 0.2475 0.9166 0.9519 0.687 0.8351 0.9088 0.811 ] Network output: [ -0.03234 0.6801 1.225 -0.004273 0.001018 0.1409 0.001088 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1513 0.0818 0.4561 0.1711 0.9309 0.9473 0.1552 0.8862 0.9241 0.7945 ] Network output: [ 0.3594 -0.2282 0.7143 0.004566 -0.00203 0.814 0.002222 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6793 0.4706 0.481 0.3521 0.9327 0.9637 0.6831 0.8643 0.9331 0.8483 ] Network output: [ -0.02939 0.03029 1.011 0.001072 -0.001099 1.021 0.002752 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.606 0.5535 0.7637 0.2382 0.9474 0.9619 0.6067 0.9049 0.9347 0.8916 ] Network output: [ -0.1043 0.2804 0.8792 -0.0009129 -5.091e-06 1.045 0.0009145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6114 0.5973 0.861 0.06898 0.9485 0.9634 0.6115 0.909 0.9383 0.9004 ] Network output: [ -0.01776 0.94 0.08863 -0.0007594 0.001092 1.005 -0.002808 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1499 Epoch 330 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1635 1.185 0.7508 0.002762 -0.001019 -0.2512 0.00244 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08827 0.005355 0.078 -0.004824 0.8449 0.867 0.1537 0.7806 0.8155 0.6698 ] Network output: [ 0.6741 0.1133 0.326 -0.002567 0.00218 0.204 -0.006563 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6145 0.1263 0.07719 0.2467 0.9172 0.9523 0.6883 0.8352 0.9093 0.8108 ] Network output: [ -0.03393 0.683 1.225 -0.004253 0.001027 0.1406 0.001013 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1518 0.08225 0.4554 0.1699 0.9316 0.9478 0.1556 0.8866 0.9246 0.7931 ] Network output: [ 0.358 -0.2284 0.7176 0.004524 -0.002013 0.8136 0.00224 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6806 0.4722 0.481 0.351 0.9332 0.964 0.6843 0.8644 0.9335 0.8481 ] Network output: [ -0.02989 0.02957 1.011 0.001091 -0.001092 1.023 0.002719 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6092 0.5568 0.7637 0.236 0.9479 0.9622 0.6099 0.9052 0.9351 0.891 ] Network output: [ -0.1043 0.2814 0.8778 -0.0008961 -2.451e-07 1.045 0.00088 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6146 0.6006 0.8608 0.06538 0.949 0.9637 0.6148 0.9094 0.9388 0.9 ] Network output: [ -0.0166 0.9377 0.08953 -0.0007575 0.00107 1.004 -0.002755 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1488 Epoch 331 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1631 1.185 0.7503 0.002783 -0.001038 -0.25 0.002452 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0884 0.005386 0.0774 -0.005016 0.8457 0.8677 0.1539 0.7809 0.816 0.6685 ] Network output: [ 0.6767 0.1129 0.3226 -0.00261 0.002181 0.2024 -0.006506 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6158 0.1266 0.07563 0.2458 0.9179 0.9527 0.6896 0.8353 0.9098 0.8106 ] Network output: [ -0.03549 0.686 1.226 -0.004233 0.001035 0.1403 0.0009388 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1523 0.0827 0.4547 0.1686 0.9323 0.9483 0.1561 0.8869 0.9251 0.7917 ] Network output: [ 0.3566 -0.2286 0.7208 0.004481 -0.001995 0.8132 0.002256 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6818 0.4739 0.481 0.3499 0.9338 0.9643 0.6855 0.8646 0.9339 0.848 ] Network output: [ -0.03039 0.02887 1.011 0.001109 -0.001084 1.024 0.002687 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6124 0.5602 0.7636 0.2339 0.9483 0.9626 0.6132 0.9055 0.9355 0.8904 ] Network output: [ -0.1043 0.2825 0.8764 -0.0008792 4.22e-06 1.045 0.0008468 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6179 0.604 0.8606 0.06182 0.9496 0.9641 0.6181 0.9098 0.9393 0.8995 ] Network output: [ -0.01547 0.9355 0.09044 -0.0007556 0.001049 1.003 -0.002704 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1477 Epoch 332 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1628 1.185 0.7497 0.002804 -0.001057 -0.2487 0.002463 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08853 0.005417 0.0768 -0.005212 0.8466 0.8684 0.154 0.7812 0.8166 0.6672 ] Network output: [ 0.6793 0.1125 0.3192 -0.002651 0.002183 0.2009 -0.00645 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6172 0.1268 0.07409 0.245 0.9185 0.9531 0.6909 0.8354 0.9103 0.8105 ] Network output: [ -0.03701 0.6889 1.226 -0.004213 0.001042 0.14 0.0008666 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1528 0.08314 0.454 0.1673 0.933 0.9488 0.1566 0.8873 0.9255 0.7903 ] Network output: [ 0.3552 -0.2287 0.724 0.004438 -0.001978 0.8128 0.002271 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6831 0.4755 0.4811 0.3488 0.9344 0.9647 0.6867 0.8647 0.9343 0.8478 ] Network output: [ -0.03089 0.02818 1.012 0.001126 -0.001077 1.026 0.002655 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6156 0.5635 0.7635 0.2318 0.9488 0.9629 0.6164 0.9058 0.936 0.8898 ] Network output: [ -0.1043 0.2835 0.875 -0.0008622 8.318e-06 1.046 0.000815 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6211 0.6073 0.8605 0.05831 0.9501 0.9644 0.6213 0.9101 0.9397 0.899 ] Network output: [ -0.01436 0.9332 0.09135 -0.0007535 0.001029 1.002 -0.002653 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1466 Epoch 333 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1625 1.185 0.7492 0.002826 -0.001075 -0.2475 0.002475 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08865 0.005446 0.07621 -0.005411 0.8475 0.8692 0.1542 0.7815 0.8171 0.6659 ] Network output: [ 0.6818 0.1121 0.3158 -0.002692 0.002184 0.1993 -0.006395 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6185 0.127 0.07256 0.2441 0.9192 0.9535 0.6921 0.8354 0.9107 0.8103 ] Network output: [ -0.0385 0.6919 1.227 -0.004192 0.00105 0.1397 0.000796 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1533 0.08358 0.4532 0.1659 0.9336 0.9493 0.1571 0.8876 0.926 0.7888 ] Network output: [ 0.3537 -0.2289 0.7272 0.004395 -0.00196 0.8124 0.002284 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6843 0.4771 0.4812 0.3477 0.9349 0.965 0.688 0.8649 0.9347 0.8477 ] Network output: [ -0.03139 0.02752 1.012 0.001143 -0.00107 1.027 0.002625 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6188 0.5668 0.7634 0.2297 0.9493 0.9632 0.6195 0.9061 0.9364 0.8891 ] Network output: [ -0.1042 0.2845 0.8736 -0.0008452 1.206e-05 1.046 0.0007844 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6244 0.6107 0.8602 0.05485 0.9506 0.9648 0.6245 0.9105 0.9401 0.8984 ] Network output: [ -0.01327 0.931 0.09225 -0.0007514 0.00101 1.001 -0.002604 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1455 Epoch 334 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1622 1.185 0.7487 0.002848 -0.001094 -0.2462 0.002487 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08877 0.005474 0.07563 -0.005612 0.8483 0.8699 0.1543 0.7817 0.8177 0.6645 ] Network output: [ 0.6844 0.1117 0.3124 -0.002731 0.002185 0.1978 -0.00634 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6198 0.1272 0.07104 0.2433 0.9198 0.9538 0.6934 0.8355 0.9112 0.8101 ] Network output: [ -0.03994 0.6948 1.227 -0.004171 0.001057 0.1393 0.0007271 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1538 0.08402 0.4525 0.1646 0.9342 0.9498 0.1576 0.888 0.9265 0.7873 ] Network output: [ 0.3523 -0.229 0.7305 0.00435 -0.001942 0.812 0.002295 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6856 0.4786 0.4813 0.3466 0.9355 0.9653 0.6892 0.865 0.9351 0.8475 ] Network output: [ -0.03189 0.02687 1.012 0.00116 -0.001063 1.028 0.002595 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.622 0.5701 0.7633 0.2276 0.9497 0.9636 0.6227 0.9064 0.9368 0.8884 ] Network output: [ -0.1042 0.2854 0.8722 -0.0008281 1.547e-05 1.047 0.0007551 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6276 0.614 0.86 0.05144 0.951 0.9651 0.6277 0.9109 0.9406 0.8979 ] Network output: [ -0.0122 0.9289 0.09316 -0.0007493 0.0009906 1 -0.002556 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1444 Epoch 335 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1619 1.185 0.7483 0.002871 -0.001113 -0.245 0.002499 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08889 0.005501 0.07505 -0.005815 0.8491 0.8706 0.1544 0.782 0.8182 0.6631 ] Network output: [ 0.6869 0.1112 0.3091 -0.002769 0.002186 0.1963 -0.006286 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6211 0.1274 0.06955 0.2424 0.9204 0.9542 0.6947 0.8356 0.9117 0.8099 ] Network output: [ -0.04136 0.6977 1.227 -0.00415 0.001064 0.139 0.0006597 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1543 0.08446 0.4517 0.1632 0.9349 0.9502 0.1581 0.8884 0.927 0.7857 ] Network output: [ 0.3508 -0.2291 0.7337 0.004306 -0.001924 0.8116 0.002304 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6868 0.4802 0.4814 0.3454 0.936 0.9656 0.6904 0.8652 0.9355 0.8473 ] Network output: [ -0.03239 0.02624 1.013 0.001176 -0.001056 1.03 0.002566 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6251 0.5733 0.7631 0.2256 0.9502 0.9639 0.6258 0.9067 0.9372 0.8877 ] Network output: [ -0.1041 0.2863 0.8708 -0.000811 1.854e-05 1.047 0.000727 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6308 0.6172 0.8598 0.04808 0.9515 0.9654 0.6309 0.9113 0.941 0.8973 ] Network output: [ -0.01115 0.9268 0.09406 -0.0007473 0.0009722 0.9993 -0.00251 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1433 Epoch 336 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1615 1.185 0.7479 0.002893 -0.001131 -0.2437 0.002512 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08901 0.005527 0.07449 -0.006018 0.8499 0.8712 0.1546 0.7823 0.8188 0.6617 ] Network output: [ 0.6895 0.1108 0.3058 -0.002806 0.002186 0.1949 -0.006232 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6224 0.1276 0.06807 0.2415 0.921 0.9546 0.696 0.8357 0.9121 0.8097 ] Network output: [ -0.04273 0.7006 1.228 -0.004129 0.00107 0.1387 0.0005939 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1548 0.0849 0.4509 0.1618 0.9355 0.9507 0.1585 0.8887 0.9275 0.7841 ] Network output: [ 0.3493 -0.2292 0.7369 0.004261 -0.001906 0.8113 0.002313 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6881 0.4817 0.4816 0.3443 0.9365 0.9659 0.6917 0.8653 0.9359 0.8471 ] Network output: [ -0.03289 0.02563 1.013 0.001191 -0.001049 1.031 0.002537 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6282 0.5765 0.763 0.2236 0.9506 0.9642 0.6289 0.907 0.9376 0.887 ] Network output: [ -0.1041 0.2872 0.8694 -0.0007939 2.13e-05 1.048 0.0007001 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6339 0.6205 0.8595 0.04477 0.952 0.9658 0.6341 0.9116 0.9414 0.8967 ] Network output: [ -0.01012 0.9247 0.09496 -0.0007452 0.0009543 0.9984 -0.002464 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1422 Epoch 337 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1612 1.184 0.7475 0.002917 -0.00115 -0.2424 0.002525 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08912 0.005553 0.07392 -0.006223 0.8507 0.8719 0.1547 0.7826 0.8193 0.6602 ] Network output: [ 0.692 0.1103 0.3024 -0.002842 0.002186 0.1934 -0.006179 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6237 0.1278 0.06661 0.2407 0.9215 0.9549 0.6972 0.8358 0.9126 0.8095 ] Network output: [ -0.04407 0.7035 1.228 -0.004108 0.001076 0.1384 0.0005296 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1553 0.08534 0.4501 0.1605 0.9361 0.9511 0.159 0.8891 0.9279 0.7825 ] Network output: [ 0.3478 -0.2293 0.7401 0.004215 -0.001887 0.811 0.002319 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6893 0.4832 0.4818 0.3432 0.937 0.9662 0.6929 0.8655 0.9363 0.8469 ] Network output: [ -0.03339 0.02504 1.014 0.001205 -0.001042 1.032 0.00251 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6313 0.5797 0.7629 0.2215 0.951 0.9645 0.632 0.9073 0.9381 0.8863 ] Network output: [ -0.104 0.288 0.868 -0.0007767 2.375e-05 1.048 0.0006743 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6371 0.6237 0.8592 0.04151 0.9524 0.9661 0.6372 0.912 0.9418 0.8961 ] Network output: [ -0.009116 0.9227 0.09585 -0.0007432 0.000937 0.9976 -0.00242 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1412 Epoch 338 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1609 1.184 0.7472 0.00294 -0.001168 -0.2411 0.002538 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08923 0.005577 0.07337 -0.006428 0.8515 0.8726 0.1549 0.7829 0.8199 0.6587 ] Network output: [ 0.6945 0.1098 0.2991 -0.002877 0.002185 0.1919 -0.006127 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.625 0.1279 0.06516 0.2398 0.9221 0.9553 0.6985 0.8359 0.9131 0.8092 ] Network output: [ -0.04538 0.7064 1.228 -0.004087 0.001082 0.138 0.0004668 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1557 0.08577 0.4493 0.1591 0.9366 0.9516 0.1595 0.8894 0.9284 0.7808 ] Network output: [ 0.3463 -0.2293 0.7433 0.00417 -0.001869 0.8106 0.002324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6906 0.4847 0.482 0.3421 0.9375 0.9664 0.6941 0.8656 0.9367 0.8466 ] Network output: [ -0.03389 0.02447 1.014 0.00122 -0.001036 1.034 0.002483 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6343 0.5829 0.7627 0.2196 0.9515 0.9648 0.635 0.9076 0.9385 0.8855 ] Network output: [ -0.1039 0.2888 0.8667 -0.0007595 2.59e-05 1.049 0.0006497 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6402 0.6269 0.8589 0.03832 0.9529 0.9664 0.6404 0.9124 0.9423 0.8955 ] Network output: [ -0.008131 0.9207 0.09674 -0.0007413 0.0009202 0.9967 -0.002377 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1401 Epoch 339 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1605 1.184 0.7468 0.002963 -0.001186 -0.2399 0.002551 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08934 0.005601 0.07282 -0.006633 0.8523 0.8732 0.155 0.7832 0.8204 0.6572 ] Network output: [ 0.6971 0.1093 0.2959 -0.00291 0.002184 0.1905 -0.006075 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6262 0.1281 0.06373 0.2389 0.9227 0.9556 0.6997 0.836 0.9135 0.809 ] Network output: [ -0.04664 0.7093 1.228 -0.004065 0.001088 0.1377 0.0004056 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1562 0.0862 0.4485 0.1577 0.9372 0.952 0.16 0.8898 0.9288 0.779 ] Network output: [ 0.3448 -0.2294 0.7465 0.004124 -0.00185 0.8103 0.002328 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6918 0.4862 0.4822 0.341 0.938 0.9667 0.6953 0.8658 0.9371 0.8464 ] Network output: [ -0.03439 0.02392 1.014 0.001233 -0.001029 1.035 0.002457 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6373 0.586 0.7626 0.2176 0.9519 0.9651 0.638 0.908 0.9389 0.8848 ] Network output: [ -0.1037 0.2896 0.8653 -0.0007423 2.776e-05 1.049 0.0006261 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6433 0.6301 0.8586 0.03518 0.9533 0.9667 0.6435 0.9127 0.9427 0.8948 ] Network output: [ -0.007166 0.9187 0.09762 -0.0007394 0.0009039 0.9958 -0.002335 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.139 Epoch 340 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1602 1.184 0.7466 0.002987 -0.001203 -0.2386 0.002564 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08944 0.005625 0.07229 -0.006838 0.8531 0.8739 0.1551 0.7835 0.8209 0.6557 ] Network output: [ 0.6996 0.1088 0.2926 -0.002942 0.002183 0.1891 -0.006024 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6275 0.1282 0.06232 0.238 0.9232 0.956 0.701 0.8362 0.914 0.8087 ] Network output: [ -0.04788 0.7121 1.228 -0.004043 0.001093 0.1373 0.0003459 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1567 0.08663 0.4476 0.1562 0.9378 0.9524 0.1605 0.8902 0.9293 0.7772 ] Network output: [ 0.3433 -0.2294 0.7497 0.004077 -0.001831 0.81 0.002331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.693 0.4877 0.4825 0.3399 0.9385 0.967 0.6966 0.866 0.9374 0.8461 ] Network output: [ -0.03489 0.02339 1.015 0.001246 -0.001023 1.036 0.002431 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6403 0.5891 0.7624 0.2156 0.9523 0.9654 0.641 0.9083 0.9393 0.8839 ] Network output: [ -0.1036 0.2903 0.864 -0.0007251 2.933e-05 1.049 0.0006036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6464 0.6333 0.8582 0.0321 0.9537 0.967 0.6465 0.9131 0.9431 0.8941 ] Network output: [ -0.00622 0.9168 0.0985 -0.0007376 0.0008882 0.995 -0.002294 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1379 Epoch 341 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1599 1.183 0.7463 0.00301 -0.001221 -0.2372 0.002577 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08954 0.005647 0.07176 -0.007041 0.8538 0.8745 0.1553 0.7838 0.8214 0.6541 ] Network output: [ 0.7021 0.1083 0.2894 -0.002974 0.002182 0.1877 -0.005974 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6287 0.1283 0.06093 0.2372 0.9237 0.9563 0.7022 0.8363 0.9144 0.8085 ] Network output: [ -0.04907 0.7149 1.228 -0.004022 0.001098 0.137 0.0002877 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1572 0.08706 0.4468 0.1548 0.9383 0.9529 0.161 0.8905 0.9297 0.7754 ] Network output: [ 0.3417 -0.2294 0.7528 0.004031 -0.001812 0.8098 0.002332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6943 0.4892 0.4827 0.3388 0.9389 0.9673 0.6978 0.8661 0.9378 0.8459 ] Network output: [ -0.03539 0.02288 1.015 0.001259 -0.001017 1.037 0.002406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6433 0.5921 0.7622 0.2137 0.9527 0.9657 0.644 0.9086 0.9397 0.8831 ] Network output: [ -0.1035 0.291 0.8627 -0.0007079 3.063e-05 1.05 0.0005821 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6494 0.6364 0.8578 0.02909 0.9542 0.9673 0.6496 0.9134 0.9435 0.8934 ] Network output: [ -0.005294 0.9149 0.09937 -0.0007359 0.0008729 0.9941 -0.002254 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1369 Epoch 342 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1595 1.183 0.7461 0.003034 -0.001238 -0.2359 0.002591 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08964 0.005669 0.07123 -0.007243 0.8545 0.8752 0.1554 0.7841 0.822 0.6525 ] Network output: [ 0.7045 0.1077 0.2862 -0.003004 0.002181 0.1863 -0.005924 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6299 0.1285 0.05955 0.2363 0.9243 0.9566 0.7034 0.8364 0.9148 0.8082 ] Network output: [ -0.05023 0.7177 1.228 -0.004 0.001102 0.1366 0.0002309 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1577 0.08749 0.4459 0.1534 0.9389 0.9533 0.1615 0.8909 0.9302 0.7735 ] Network output: [ 0.3401 -0.2294 0.756 0.003984 -0.001793 0.8095 0.002332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6955 0.4906 0.483 0.3377 0.9394 0.9675 0.699 0.8663 0.9382 0.8456 ] Network output: [ -0.03588 0.02239 1.015 0.001271 -0.001011 1.038 0.002382 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6462 0.5952 0.762 0.2118 0.9531 0.966 0.6469 0.9089 0.9401 0.8823 ] Network output: [ -0.1033 0.2917 0.8614 -0.0006907 3.166e-05 1.05 0.0005616 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6525 0.6395 0.8574 0.02614 0.9546 0.9676 0.6526 0.9138 0.9439 0.8926 ] Network output: [ -0.004387 0.9131 0.1002 -0.0007343 0.0008581 0.9933 -0.002216 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1358 Epoch 343 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1591 1.183 0.7459 0.003057 -0.001255 -0.2346 0.002604 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08974 0.005691 0.07072 -0.007443 0.8553 0.8758 0.1555 0.7844 0.8225 0.6509 ] Network output: [ 0.707 0.1072 0.283 -0.003033 0.002179 0.1849 -0.005874 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6311 0.1286 0.05819 0.2354 0.9248 0.957 0.7047 0.8365 0.9153 0.8079 ] Network output: [ -0.05136 0.7205 1.228 -0.003977 0.001106 0.1363 0.0001756 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1583 0.08792 0.4449 0.152 0.9394 0.9537 0.162 0.8913 0.9306 0.7716 ] Network output: [ 0.3386 -0.2293 0.7592 0.003938 -0.001774 0.8092 0.002331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6967 0.4921 0.4833 0.3366 0.9398 0.9678 0.7002 0.8665 0.9385 0.8453 ] Network output: [ -0.03637 0.02193 1.016 0.001283 -0.001005 1.04 0.002358 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6491 0.5982 0.7618 0.21 0.9534 0.9663 0.6498 0.9092 0.9404 0.8814 ] Network output: [ -0.1032 0.2923 0.8601 -0.0006735 3.243e-05 1.051 0.0005422 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6555 0.6426 0.857 0.02325 0.955 0.9679 0.6556 0.9142 0.9442 0.8919 ] Network output: [ -0.003499 0.9113 0.1011 -0.0007328 0.0008438 0.9924 -0.002178 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1347 Epoch 344 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1588 1.182 0.7458 0.00308 -0.001272 -0.2333 0.002617 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08983 0.005712 0.07021 -0.007641 0.856 0.8764 0.1557 0.7848 0.823 0.6492 ] Network output: [ 0.7095 0.1067 0.2798 -0.003061 0.002176 0.1836 -0.005825 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6324 0.1286 0.05685 0.2346 0.9253 0.9573 0.7059 0.8367 0.9157 0.8076 ] Network output: [ -0.05245 0.7233 1.228 -0.003955 0.00111 0.1359 0.0001217 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1588 0.08835 0.444 0.1505 0.9399 0.9541 0.1625 0.8916 0.931 0.7696 ] Network output: [ 0.337 -0.2293 0.7623 0.003891 -0.001755 0.809 0.002328 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.698 0.4935 0.4836 0.3355 0.9403 0.968 0.7014 0.8667 0.9389 0.845 ] Network output: [ -0.03687 0.02148 1.016 0.001294 -0.0009992 1.041 0.002335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.652 0.6011 0.7615 0.2081 0.9538 0.9666 0.6527 0.9095 0.9408 0.8805 ] Network output: [ -0.103 0.293 0.8588 -0.0006563 3.294e-05 1.051 0.0005237 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6585 0.6457 0.8565 0.02043 0.9554 0.9681 0.6586 0.9145 0.9446 0.8911 ] Network output: [ -0.002629 0.9095 0.1019 -0.0007313 0.0008299 0.9916 -0.002142 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1337 Epoch 345 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1584 1.182 0.7457 0.003103 -0.001288 -0.232 0.00263 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08993 0.005733 0.06971 -0.007836 0.8567 0.877 0.1558 0.7851 0.8235 0.6475 ] Network output: [ 0.7119 0.1061 0.2767 -0.003088 0.002174 0.1822 -0.005777 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6335 0.1287 0.05553 0.2337 0.9258 0.9576 0.7071 0.8368 0.9161 0.8073 ] Network output: [ -0.0535 0.726 1.228 -0.003933 0.001114 0.1356 6.926e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1593 0.08877 0.4431 0.1491 0.9404 0.9544 0.163 0.892 0.9315 0.7676 ] Network output: [ 0.3354 -0.2292 0.7654 0.003844 -0.001735 0.8088 0.002324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6992 0.4949 0.484 0.3345 0.9407 0.9683 0.7026 0.8668 0.9392 0.8446 ] Network output: [ -0.03735 0.02105 1.017 0.001306 -0.0009936 1.042 0.002313 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6549 0.6041 0.7613 0.2063 0.9542 0.9668 0.6556 0.9098 0.9412 0.8795 ] Network output: [ -0.1028 0.2935 0.8575 -0.000639 3.32e-05 1.052 0.0005061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6614 0.6487 0.856 0.01768 0.9558 0.9684 0.6616 0.9149 0.945 0.8902 ] Network output: [ -0.001778 0.9077 0.1028 -0.00073 0.0008165 0.9908 -0.002106 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1326 Epoch 346 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.158 1.182 0.7456 0.003126 -0.001304 -0.2306 0.002643 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09001 0.005753 0.06922 -0.008029 0.8574 0.8776 0.1559 0.7854 0.824 0.6457 ] Network output: [ 0.7144 0.1055 0.2736 -0.003114 0.002171 0.1809 -0.005729 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6347 0.1288 0.05422 0.2328 0.9263 0.9579 0.7083 0.8369 0.9165 0.8069 ] Network output: [ -0.05452 0.7288 1.228 -0.00391 0.001118 0.1352 1.823e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1598 0.0892 0.4421 0.1477 0.9409 0.9548 0.1635 0.8924 0.9319 0.7655 ] Network output: [ 0.3338 -0.2291 0.7686 0.003796 -0.001716 0.8085 0.00232 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7004 0.4963 0.4843 0.3334 0.9411 0.9685 0.7039 0.867 0.9396 0.8443 ] Network output: [ -0.03784 0.02064 1.017 0.001316 -0.0009881 1.043 0.002291 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6577 0.607 0.761 0.2046 0.9546 0.9671 0.6584 0.9101 0.9416 0.8786 ] Network output: [ -0.1026 0.2941 0.8563 -0.0006218 3.321e-05 1.052 0.0004895 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6643 0.6517 0.8555 0.015 0.9561 0.9687 0.6645 0.9152 0.9454 0.8894 ] Network output: [ -0.0009443 0.906 0.1036 -0.0007288 0.0008035 0.99 -0.002071 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1316 Epoch 347 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1577 1.181 0.7456 0.003149 -0.00132 -0.2293 0.002656 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0901 0.005772 0.06874 -0.008217 0.8581 0.8782 0.156 0.7857 0.8246 0.644 ] Network output: [ 0.7168 0.105 0.2705 -0.003138 0.002169 0.1796 -0.005681 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6359 0.1289 0.05294 0.232 0.9267 0.9582 0.7095 0.8371 0.917 0.8066 ] Network output: [ -0.0555 0.7315 1.228 -0.003888 0.001121 0.1349 -3.14e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1603 0.08962 0.4411 0.1462 0.9414 0.9552 0.164 0.8927 0.9323 0.7633 ] Network output: [ 0.3322 -0.229 0.7717 0.003749 -0.001696 0.8083 0.002314 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7016 0.4977 0.4847 0.3323 0.9415 0.9687 0.7051 0.8672 0.9399 0.8439 ] Network output: [ -0.03832 0.02026 1.017 0.001327 -0.0009828 1.044 0.00227 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6605 0.6099 0.7607 0.2028 0.9549 0.9673 0.6612 0.9105 0.9419 0.8776 ] Network output: [ -0.1024 0.2946 0.8551 -0.0006045 3.297e-05 1.052 0.0004738 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6673 0.6547 0.8549 0.01239 0.9565 0.9689 0.6674 0.9156 0.9458 0.8885 ] Network output: [ -0.0001282 0.9044 0.1044 -0.0007277 0.0007909 0.9892 -0.002038 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1306 Epoch 348 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1573 1.181 0.7456 0.003171 -0.001335 -0.228 0.002669 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09018 0.005792 0.06827 -0.008402 0.8587 0.8787 0.1562 0.786 0.8251 0.6422 ] Network output: [ 0.7192 0.1044 0.2674 -0.003162 0.002165 0.1783 -0.005634 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6371 0.1289 0.05167 0.2311 0.9272 0.9585 0.7107 0.8372 0.9174 0.8062 ] Network output: [ -0.05645 0.7341 1.227 -0.003865 0.001124 0.1345 -7.964e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1608 0.09004 0.44 0.1448 0.9419 0.9556 0.1645 0.8931 0.9327 0.7611 ] Network output: [ 0.3306 -0.2289 0.7748 0.003702 -0.001676 0.8082 0.002307 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7029 0.499 0.485 0.3313 0.9419 0.969 0.7063 0.8674 0.9402 0.8435 ] Network output: [ -0.0388 0.01989 1.018 0.001337 -0.0009776 1.045 0.002249 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6633 0.6128 0.7604 0.2011 0.9553 0.9676 0.664 0.9108 0.9423 0.8766 ] Network output: [ -0.1022 0.295 0.8539 -0.0005872 3.249e-05 1.053 0.0004589 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6701 0.6576 0.8543 0.009853 0.9569 0.9692 0.6703 0.9159 0.9461 0.8876 ] Network output: [ 0.0006705 0.9028 0.1052 -0.0007267 0.0007787 0.9884 -0.002005 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1295 Epoch 349 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1569 1.18 0.7456 0.003194 -0.00135 -0.2266 0.002682 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09026 0.005811 0.0678 -0.008582 0.8594 0.8793 0.1563 0.7864 0.8256 0.6403 ] Network output: [ 0.7216 0.1038 0.2644 -0.003185 0.002162 0.177 -0.005588 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6382 0.129 0.05042 0.2302 0.9276 0.9588 0.7119 0.8374 0.9178 0.8059 ] Network output: [ -0.05736 0.7368 1.227 -0.003842 0.001126 0.1341 -0.0001265 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1613 0.09046 0.439 0.1434 0.9424 0.9559 0.165 0.8935 0.9331 0.7589 ] Network output: [ 0.329 -0.2288 0.7779 0.003654 -0.001657 0.808 0.002299 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7041 0.5004 0.4854 0.3303 0.9423 0.9692 0.7075 0.8676 0.9406 0.8431 ] Network output: [ -0.03927 0.01955 1.018 0.001347 -0.0009726 1.046 0.002229 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.666 0.6156 0.76 0.1994 0.9556 0.9679 0.6667 0.9111 0.9427 0.8755 ] Network output: [ -0.102 0.2955 0.8527 -0.0005699 3.177e-05 1.053 0.0004449 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.673 0.6606 0.8537 0.007391 0.9572 0.9694 0.6731 0.9163 0.9465 0.8866 ] Network output: [ 0.001452 0.9012 0.106 -0.0007257 0.0007669 0.9877 -0.001973 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1285 Epoch 350 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1565 1.18 0.7457 0.003215 -0.001365 -0.2253 0.002694 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09034 0.005829 0.06735 -0.008758 0.86 0.8799 0.1564 0.7867 0.8261 0.6385 ] Network output: [ 0.724 0.1032 0.2614 -0.003207 0.002159 0.1758 -0.005542 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6394 0.129 0.04919 0.2294 0.9281 0.959 0.713 0.8375 0.9182 0.8055 ] Network output: [ -0.05824 0.7394 1.227 -0.003819 0.001128 0.1338 -0.000172 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1618 0.09089 0.4379 0.142 0.9429 0.9563 0.1655 0.8938 0.9335 0.7566 ] Network output: [ 0.3274 -0.2287 0.7809 0.003607 -0.001637 0.8078 0.00229 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7053 0.5017 0.4858 0.3292 0.9427 0.9694 0.7087 0.8678 0.9409 0.8427 ] Network output: [ -0.03974 0.01923 1.018 0.001356 -0.0009677 1.047 0.002209 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6687 0.6184 0.7597 0.1978 0.9559 0.9681 0.6694 0.9114 0.943 0.8744 ] Network output: [ -0.1018 0.2959 0.8515 -0.0005525 3.081e-05 1.054 0.0004318 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6758 0.6634 0.853 0.005005 0.9576 0.9697 0.676 0.9167 0.9468 0.8856 ] Network output: [ 0.002217 0.8996 0.1068 -0.0007249 0.0007555 0.9869 -0.001942 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1275 Epoch 351 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1561 1.179 0.7458 0.003237 -0.001379 -0.224 0.002707 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09042 0.005848 0.0669 -0.008929 0.8607 0.8804 0.1565 0.787 0.8266 0.6366 ] Network output: [ 0.7263 0.1026 0.2584 -0.003229 0.002155 0.1745 -0.005497 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 0.1291 0.04798 0.2285 0.9285 0.9593 0.7142 0.8377 0.9186 0.8051 ] Network output: [ -0.05908 0.742 1.226 -0.003796 0.00113 0.1334 -0.0002162 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1623 0.0913 0.4368 0.1405 0.9433 0.9566 0.166 0.8942 0.9339 0.7542 ] Network output: [ 0.3257 -0.2285 0.784 0.003559 -0.001617 0.8076 0.00228 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7065 0.5031 0.4862 0.3282 0.9431 0.9696 0.7099 0.868 0.9412 0.8423 ] Network output: [ -0.04021 0.01893 1.019 0.001366 -0.0009629 1.048 0.00219 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6714 0.6211 0.7593 0.1962 0.9563 0.9683 0.6721 0.9117 0.9434 0.8733 ] Network output: [ -0.1015 0.2962 0.8504 -0.0005351 2.962e-05 1.054 0.0004196 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6786 0.6663 0.8524 0.002696 0.9579 0.9699 0.6788 0.917 0.9472 0.8846 ] Network output: [ 0.002966 0.8981 0.1075 -0.0007242 0.0007445 0.9862 -0.001911 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1264 Epoch 352 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1556 1.179 0.746 0.003258 -0.001393 -0.2226 0.002719 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09049 0.005866 0.06646 -0.009095 0.8613 0.881 0.1566 0.7874 0.8271 0.6346 ] Network output: [ 0.7287 0.102 0.2554 -0.003249 0.002151 0.1733 -0.005452 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6416 0.1291 0.0468 0.2277 0.929 0.9596 0.7154 0.8379 0.919 0.8047 ] Network output: [ -0.05989 0.7446 1.226 -0.003772 0.001132 0.133 -0.000259 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1628 0.09172 0.4357 0.1391 0.9438 0.957 0.1665 0.8946 0.9343 0.7518 ] Network output: [ 0.3241 -0.2283 0.787 0.003512 -0.001597 0.8075 0.00227 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7077 0.5044 0.4866 0.3272 0.9434 0.9698 0.711 0.8681 0.9415 0.8418 ] Network output: [ -0.04067 0.01865 1.019 0.001375 -0.0009584 1.049 0.002171 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.674 0.6239 0.7588 0.1946 0.9566 0.9686 0.6747 0.9121 0.9437 0.8721 ] Network output: [ -0.1013 0.2966 0.8492 -0.0005176 2.819e-05 1.054 0.0004081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6814 0.6692 0.8516 0.0004652 0.9582 0.9701 0.6815 0.9174 0.9475 0.8836 ] Network output: [ 0.003698 0.8966 0.1083 -0.0007236 0.0007338 0.9854 -0.001882 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1254 Epoch 353 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1552 1.178 0.7461 0.003278 -0.001407 -0.2213 0.002731 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09056 0.005883 0.06604 -0.009254 0.8619 0.8815 0.1567 0.7877 0.8276 0.6327 ] Network output: [ 0.731 0.1014 0.2525 -0.003268 0.002147 0.172 -0.005408 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6427 0.1291 0.04563 0.2269 0.9294 0.9598 0.7165 0.838 0.9194 0.8043 ] Network output: [ -0.06067 0.7472 1.225 -0.003749 0.001133 0.1326 -0.0003005 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1633 0.09214 0.4345 0.1377 0.9442 0.9573 0.167 0.895 0.9347 0.7493 ] Network output: [ 0.3225 -0.2282 0.7901 0.003464 -0.001577 0.8074 0.002258 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7089 0.5057 0.487 0.3263 0.9438 0.97 0.7122 0.8683 0.9418 0.8413 ] Network output: [ -0.04112 0.01839 1.02 0.001384 -0.000954 1.049 0.002153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6767 0.6266 0.7584 0.1931 0.9569 0.9688 0.6773 0.9124 0.9441 0.871 ] Network output: [ -0.101 0.2969 0.8481 -0.0005 2.653e-05 1.055 0.0003975 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6841 0.672 0.8509 -0.001686 0.9586 0.9704 0.6843 0.9177 0.9479 0.8825 ] Network output: [ 0.004414 0.8951 0.109 -0.0007231 0.0007234 0.9847 -0.001853 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1244 Epoch 354 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1548 1.177 0.7464 0.003298 -0.00142 -0.2199 0.002742 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09063 0.005901 0.06562 -0.009408 0.8625 0.882 0.1569 0.7881 0.8281 0.6307 ] Network output: [ 0.7333 0.1008 0.2496 -0.003287 0.002143 0.1708 -0.005364 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6438 0.1292 0.04448 0.226 0.9298 0.9601 0.7177 0.8382 0.9198 0.8038 ] Network output: [ -0.06141 0.7497 1.225 -0.003725 0.001135 0.1323 -0.0003407 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1638 0.09256 0.4334 0.1363 0.9446 0.9576 0.1675 0.8953 0.9351 0.7468 ] Network output: [ 0.3208 -0.228 0.7931 0.003416 -0.001557 0.8072 0.002245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7101 0.507 0.4875 0.3253 0.9441 0.9702 0.7134 0.8685 0.9422 0.8408 ] Network output: [ -0.04157 0.01815 1.02 0.001393 -0.0009497 1.05 0.002136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6793 0.6293 0.7579 0.1916 0.9572 0.969 0.6799 0.9127 0.9444 0.8697 ] Network output: [ -0.1008 0.2971 0.847 -0.0004824 2.464e-05 1.055 0.0003876 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6869 0.6747 0.8501 -0.003755 0.9589 0.9706 0.687 0.918 0.9482 0.8814 ] Network output: [ 0.005114 0.8937 0.1098 -0.0007227 0.0007134 0.984 -0.001825 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1234 Epoch 355 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1543 1.177 0.7466 0.003318 -0.001433 -0.2186 0.002753 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09069 0.005918 0.0652 -0.009555 0.8631 0.8825 0.157 0.7884 0.8286 0.6286 ] Network output: [ 0.7356 0.1002 0.2467 -0.003305 0.002139 0.1696 -0.005321 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6449 0.1292 0.04336 0.2252 0.9302 0.9604 0.7188 0.8384 0.9201 0.8034 ] Network output: [ -0.06212 0.7522 1.224 -0.003702 0.001135 0.1319 -0.0003796 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1643 0.09297 0.4322 0.135 0.945 0.958 0.168 0.8957 0.9355 0.7442 ] Network output: [ 0.3192 -0.2278 0.7961 0.003368 -0.001537 0.8071 0.002232 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7113 0.5083 0.4879 0.3243 0.9445 0.9704 0.7146 0.8687 0.9425 0.8403 ] Network output: [ -0.04202 0.01793 1.02 0.001402 -0.0009456 1.051 0.002119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6818 0.6319 0.7574 0.1902 0.9575 0.9693 0.6825 0.913 0.9448 0.8685 ] Network output: [ -0.1005 0.2974 0.846 -0.0004646 2.252e-05 1.055 0.0003786 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6895 0.6775 0.8492 -0.005743 0.9592 0.9708 0.6897 0.9184 0.9485 0.8802 ] Network output: [ 0.005798 0.8923 0.1105 -0.0007223 0.0007038 0.9833 -0.001798 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1224 Epoch 356 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1538 1.176 0.7469 0.003337 -0.001445 -0.2173 0.002764 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09075 0.005934 0.0648 -0.009696 0.8637 0.883 0.1571 0.7888 0.829 0.6265 ] Network output: [ 0.7379 0.09959 0.2439 -0.003322 0.002134 0.1684 -0.005279 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.646 0.1292 0.04226 0.2244 0.9306 0.9606 0.72 0.8386 0.9205 0.8029 ] Network output: [ -0.06279 0.7547 1.223 -0.003678 0.001136 0.1315 -0.0004172 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1648 0.09339 0.4309 0.1336 0.9455 0.9583 0.1685 0.8961 0.9358 0.7415 ] Network output: [ 0.3175 -0.2276 0.7991 0.00332 -0.001517 0.807 0.002217 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7125 0.5096 0.4883 0.3234 0.9448 0.9706 0.7158 0.8689 0.9427 0.8398 ] Network output: [ -0.04245 0.01774 1.021 0.00141 -0.0009417 1.052 0.002102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6843 0.6345 0.7569 0.1888 0.9578 0.9695 0.685 0.9134 0.9451 0.8672 ] Network output: [ -0.1002 0.2976 0.8449 -0.0004468 2.017e-05 1.056 0.0003703 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6922 0.6802 0.8484 -0.007647 0.9595 0.971 0.6924 0.9187 0.9489 0.879 ] Network output: [ 0.006467 0.8909 0.1112 -0.0007221 0.0006944 0.9826 -0.001772 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1214 Epoch 357 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1534 1.176 0.7472 0.003355 -0.001457 -0.2159 0.002775 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09081 0.005951 0.06441 -0.009829 0.8643 0.8836 0.1572 0.7891 0.8295 0.6244 ] Network output: [ 0.7401 0.09898 0.2411 -0.003339 0.00213 0.1673 -0.005237 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.647 0.1292 0.04118 0.2236 0.931 0.9608 0.7211 0.8388 0.9209 0.8024 ] Network output: [ -0.06343 0.7571 1.223 -0.003653 0.001136 0.1311 -0.0004535 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1652 0.0938 0.4297 0.1322 0.9459 0.9586 0.169 0.8965 0.9362 0.7388 ] Network output: [ 0.3159 -0.2274 0.8021 0.003272 -0.001497 0.8069 0.002202 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7136 0.5109 0.4888 0.3225 0.9451 0.9708 0.7169 0.8692 0.943 0.8393 ] Network output: [ -0.04288 0.01757 1.021 0.001419 -0.0009379 1.053 0.002086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6868 0.6371 0.7564 0.1874 0.9581 0.9697 0.6875 0.9137 0.9455 0.8659 ] Network output: [ -0.09992 0.2977 0.8439 -0.0004288 1.758e-05 1.056 0.0003628 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6948 0.6829 0.8475 -0.009466 0.9598 0.9712 0.695 0.9191 0.9492 0.8778 ] Network output: [ 0.007121 0.8896 0.1119 -0.0007219 0.0006853 0.9819 -0.001746 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1204 Epoch 358 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1529 1.175 0.7476 0.003373 -0.001469 -0.2146 0.002785 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09087 0.005967 0.06403 -0.009955 0.8649 0.884 0.1573 0.7895 0.83 0.6223 ] Network output: [ 0.7424 0.09836 0.2383 -0.003355 0.002125 0.1661 -0.005195 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6481 0.1292 0.04012 0.2228 0.9314 0.9611 0.7222 0.839 0.9213 0.8019 ] Network output: [ -0.06404 0.7595 1.222 -0.003629 0.001136 0.1308 -0.0004886 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1657 0.09421 0.4284 0.1309 0.9463 0.9589 0.1695 0.8968 0.9366 0.7361 ] Network output: [ 0.3142 -0.2271 0.8051 0.003224 -0.001476 0.8069 0.002186 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7148 0.5122 0.4892 0.3216 0.9454 0.971 0.7181 0.8694 0.9433 0.8387 ] Network output: [ -0.0433 0.01741 1.021 0.001427 -0.0009343 1.053 0.00207 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6893 0.6396 0.7558 0.1861 0.9584 0.9699 0.69 0.914 0.9458 0.8645 ] Network output: [ -0.09962 0.2979 0.8429 -0.0004108 1.477e-05 1.057 0.0003561 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6974 0.6856 0.8465 -0.0112 0.9601 0.9715 0.6976 0.9194 0.9495 0.8765 ] Network output: [ 0.007759 0.8883 0.1125 -0.0007219 0.0006765 0.9813 -0.001721 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1194 Epoch 359 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1524 1.174 0.748 0.003391 -0.00148 -0.2133 0.002795 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09092 0.005983 0.06365 -0.01007 0.8655 0.8845 0.1574 0.7899 0.8305 0.6201 ] Network output: [ 0.7446 0.09773 0.2356 -0.00337 0.00212 0.165 -0.005154 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6491 0.1292 0.03908 0.222 0.9317 0.9613 0.7233 0.8392 0.9216 0.8014 ] Network output: [ -0.06462 0.7619 1.221 -0.003605 0.001136 0.1304 -0.0005224 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1662 0.09462 0.4271 0.1296 0.9466 0.9592 0.17 0.8972 0.937 0.7332 ] Network output: [ 0.3125 -0.2269 0.808 0.003176 -0.001456 0.8068 0.002169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.716 0.5134 0.4897 0.3208 0.9457 0.9711 0.7193 0.8696 0.9436 0.8381 ] Network output: [ -0.04372 0.01728 1.021 0.001436 -0.0009309 1.054 0.002055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6917 0.6422 0.7551 0.1848 0.9587 0.9701 0.6924 0.9143 0.9461 0.8631 ] Network output: [ -0.09932 0.298 0.8419 -0.0003925 1.172e-05 1.057 0.0003501 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7 0.6882 0.8455 -0.01284 0.9604 0.9717 0.7002 0.9198 0.9498 0.8752 ] Network output: [ 0.008383 0.887 0.1132 -0.0007219 0.000668 0.9806 -0.001696 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1184 Epoch 360 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1519 1.174 0.7484 0.003407 -0.001491 -0.212 0.002805 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09097 0.005999 0.06328 -0.01018 0.866 0.885 0.1575 0.7902 0.831 0.6179 ] Network output: [ 0.7468 0.09711 0.2328 -0.003385 0.002115 0.1638 -0.005114 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6502 0.1292 0.03807 0.2213 0.9321 0.9616 0.7244 0.8394 0.922 0.8008 ] Network output: [ -0.06516 0.7643 1.221 -0.00358 0.001135 0.13 -0.0005549 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1667 0.09503 0.4257 0.1283 0.947 0.9595 0.1705 0.8976 0.9373 0.7303 ] Network output: [ 0.3109 -0.2266 0.8109 0.003128 -0.001435 0.8067 0.002151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7171 0.5147 0.4902 0.3199 0.946 0.9713 0.7204 0.8698 0.9439 0.8375 ] Network output: [ -0.04412 0.01718 1.022 0.001444 -0.0009277 1.055 0.002041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6942 0.6447 0.7545 0.1836 0.959 0.9703 0.6948 0.9147 0.9464 0.8617 ] Network output: [ -0.09901 0.298 0.841 -0.0003742 8.436e-06 1.057 0.0003448 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7026 0.6908 0.8445 -0.0144 0.9607 0.9719 0.7027 0.9201 0.9501 0.8738 ] Network output: [ 0.008991 0.8858 0.1139 -0.000722 0.0006598 0.98 -0.001672 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1174 Epoch 361 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1514 1.173 0.7488 0.003424 -0.001502 -0.2106 0.002814 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09102 0.006014 0.06293 -0.01029 0.8666 0.8855 0.1576 0.7906 0.8314 0.6156 ] Network output: [ 0.749 0.09649 0.2301 -0.003399 0.002111 0.1627 -0.005074 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6512 0.1291 0.03709 0.2205 0.9325 0.9618 0.7255 0.8396 0.9224 0.8003 ] Network output: [ -0.06567 0.7666 1.22 -0.003555 0.001134 0.1296 -0.0005862 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1672 0.09544 0.4244 0.127 0.9474 0.9598 0.171 0.898 0.9377 0.7273 ] Network output: [ 0.3092 -0.2264 0.8139 0.003079 -0.001415 0.8067 0.002133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7183 0.5159 0.4906 0.3191 0.9463 0.9715 0.7216 0.87 0.9442 0.8368 ] Network output: [ -0.04452 0.01709 1.022 0.001453 -0.0009247 1.055 0.002027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6965 0.6471 0.7538 0.1824 0.9592 0.9705 0.6972 0.915 0.9468 0.8602 ] Network output: [ -0.0987 0.298 0.84 -0.0003556 4.917e-06 1.058 0.0003403 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7051 0.6934 0.8434 -0.01587 0.9609 0.9721 0.7052 0.9204 0.9504 0.8724 ] Network output: [ 0.009585 0.8846 0.1145 -0.0007223 0.0006519 0.9793 -0.001649 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1164 Epoch 362 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1508 1.172 0.7493 0.003439 -0.001512 -0.2093 0.002823 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09107 0.006029 0.06258 -0.01038 0.8671 0.886 0.1577 0.791 0.8319 0.6133 ] Network output: [ 0.7511 0.09586 0.2275 -0.003412 0.002106 0.1616 -0.005035 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6522 0.1291 0.03612 0.2198 0.9328 0.962 0.7266 0.8398 0.9227 0.7997 ] Network output: [ -0.06615 0.7689 1.219 -0.00353 0.001133 0.1293 -0.0006162 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1677 0.09584 0.4229 0.1257 0.9478 0.9601 0.1714 0.8984 0.938 0.7243 ] Network output: [ 0.3075 -0.2261 0.8168 0.003031 -0.001394 0.8066 0.002114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7195 0.5172 0.4911 0.3183 0.9466 0.9716 0.7228 0.8702 0.9444 0.8362 ] Network output: [ -0.04491 0.01702 1.022 0.001461 -0.0009218 1.056 0.002013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6989 0.6495 0.7531 0.1813 0.9595 0.9707 0.6995 0.9153 0.9471 0.8587 ] Network output: [ -0.09838 0.298 0.8391 -0.0003369 1.161e-06 1.058 0.0003364 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7075 0.6959 0.8423 -0.01725 0.9612 0.9722 0.7077 0.9208 0.9507 0.871 ] Network output: [ 0.01016 0.8834 0.1151 -0.0007226 0.0006442 0.9787 -0.001627 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1155 Epoch 363 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1503 1.172 0.7498 0.003454 -0.001521 -0.208 0.002831 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09111 0.006044 0.06224 -0.01046 0.8676 0.8864 0.1577 0.7913 0.8324 0.611 ] Network output: [ 0.7532 0.09524 0.2249 -0.003425 0.002101 0.1605 -0.004997 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6532 0.1291 0.03519 0.219 0.9331 0.9622 0.7277 0.84 0.9231 0.7991 ] Network output: [ -0.0666 0.7712 1.218 -0.003504 0.001132 0.1289 -0.000645 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1682 0.09625 0.4215 0.1245 0.9481 0.9603 0.1719 0.8987 0.9384 0.7212 ] Network output: [ 0.3059 -0.2258 0.8197 0.002982 -0.001373 0.8066 0.002094 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7206 0.5184 0.4916 0.3175 0.9469 0.9718 0.7239 0.8704 0.9447 0.8355 ] Network output: [ -0.04529 0.01698 1.022 0.00147 -0.0009192 1.057 0.002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7012 0.6519 0.7523 0.1802 0.9598 0.9709 0.7019 0.9156 0.9474 0.8571 ] Network output: [ -0.09805 0.298 0.8382 -0.000318 -2.832e-06 1.058 0.0003333 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.71 0.6984 0.8411 -0.01853 0.9615 0.9724 0.7101 0.9211 0.951 0.8695 ] Network output: [ 0.01073 0.8822 0.1157 -0.0007229 0.0006368 0.9781 -0.001605 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1145 Epoch 364 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1498 1.171 0.7504 0.003468 -0.00153 -0.2067 0.002839 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09115 0.006059 0.06191 -0.01054 0.8682 0.8869 0.1578 0.7917 0.8329 0.6086 ] Network output: [ 0.7554 0.09461 0.2223 -0.003438 0.002096 0.1594 -0.004959 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6542 0.1291 0.03427 0.2183 0.9335 0.9624 0.7287 0.8402 0.9234 0.7985 ] Network output: [ -0.06702 0.7735 1.217 -0.003479 0.00113 0.1285 -0.0006726 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1686 0.09665 0.4201 0.1232 0.9485 0.9606 0.1724 0.8991 0.9387 0.718 ] Network output: [ 0.3042 -0.2256 0.8226 0.002933 -0.001352 0.8066 0.002073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7218 0.5196 0.4921 0.3168 0.9472 0.972 0.7251 0.8706 0.945 0.8348 ] Network output: [ -0.04566 0.01696 1.023 0.001478 -0.0009167 1.057 0.001988 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7035 0.6543 0.7515 0.1792 0.96 0.9711 0.7041 0.916 0.9477 0.8555 ] Network output: [ -0.09771 0.2979 0.8374 -0.0002988 -7.068e-06 1.059 0.000331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7124 0.7009 0.8399 -0.01973 0.9617 0.9726 0.7126 0.9214 0.9513 0.8679 ] Network output: [ 0.01128 0.8811 0.1163 -0.0007234 0.0006296 0.9776 -0.001583 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1135 Epoch 365 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1492 1.17 0.751 0.003482 -0.001539 -0.2054 0.002846 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09119 0.006073 0.06159 -0.0106 0.8687 0.8873 0.1579 0.7921 0.8333 0.6062 ] Network output: [ 0.7575 0.09399 0.2197 -0.00345 0.002091 0.1583 -0.004922 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6552 0.129 0.03339 0.2176 0.9338 0.9626 0.7298 0.8405 0.9238 0.7979 ] Network output: [ -0.0674 0.7757 1.216 -0.003453 0.001128 0.1282 -0.000699 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1691 0.09705 0.4186 0.122 0.9489 0.9609 0.1729 0.8995 0.9391 0.7148 ] Network output: [ 0.3025 -0.2253 0.8254 0.002884 -0.001331 0.8066 0.002051 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7229 0.5208 0.4925 0.3161 0.9474 0.9721 0.7262 0.8709 0.9452 0.8341 ] Network output: [ -0.04602 0.01696 1.023 0.001487 -0.0009144 1.058 0.001976 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7057 0.6566 0.7507 0.1782 0.9603 0.9713 0.7064 0.9163 0.948 0.8538 ] Network output: [ -0.09737 0.2978 0.8365 -0.0002794 -1.155e-05 1.059 0.0003293 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7148 0.7033 0.8386 -0.02083 0.962 0.9728 0.7149 0.9218 0.9516 0.8663 ] Network output: [ 0.01182 0.88 0.1169 -0.000724 0.0006227 0.977 -0.001562 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1126 Epoch 366 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1486 1.169 0.7516 0.003495 -0.001548 -0.2041 0.002853 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09122 0.006087 0.06128 -0.01066 0.8692 0.8878 0.158 0.7925 0.8338 0.6038 ] Network output: [ 0.7595 0.09337 0.2172 -0.003462 0.002086 0.1573 -0.004885 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6562 0.129 0.03252 0.2169 0.9341 0.9628 0.7308 0.8407 0.9241 0.7973 ] Network output: [ -0.06776 0.7779 1.215 -0.003427 0.001125 0.1278 -0.0007241 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1696 0.09745 0.417 0.1209 0.9492 0.9611 0.1734 0.8999 0.9394 0.7115 ] Network output: [ 0.3008 -0.225 0.8283 0.002835 -0.00131 0.8066 0.002029 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7241 0.522 0.493 0.3154 0.9477 0.9723 0.7273 0.8711 0.9455 0.8333 ] Network output: [ -0.04637 0.01699 1.023 0.001495 -0.0009124 1.058 0.001964 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7079 0.6589 0.7498 0.1772 0.9605 0.9715 0.7086 0.9166 0.9483 0.8521 ] Network output: [ -0.09702 0.2977 0.8357 -0.0002598 -1.627e-05 1.059 0.0003283 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7172 0.7057 0.8373 -0.02183 0.9622 0.973 0.7173 0.9221 0.9519 0.8647 ] Network output: [ 0.01234 0.8789 0.1175 -0.0007246 0.000616 0.9764 -0.001542 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1116 Epoch 367 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1481 1.169 0.7522 0.003507 -0.001556 -0.2028 0.00286 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09126 0.006101 0.06098 -0.01071 0.8697 0.8882 0.1581 0.7929 0.8343 0.6013 ] Network output: [ 0.7616 0.09275 0.2147 -0.003473 0.002081 0.1562 -0.004849 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6571 0.129 0.03169 0.2162 0.9344 0.963 0.7319 0.8409 0.9245 0.7966 ] Network output: [ -0.06809 0.78 1.214 -0.0034 0.001123 0.1274 -0.0007481 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.17 0.09784 0.4155 0.1197 0.9495 0.9614 0.1738 0.9003 0.9398 0.7081 ] Network output: [ 0.2991 -0.2247 0.8311 0.002786 -0.001289 0.8066 0.002006 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7252 0.5232 0.4935 0.3148 0.948 0.9724 0.7285 0.8713 0.9457 0.8326 ] Network output: [ -0.04671 0.01704 1.023 0.001504 -0.0009105 1.059 0.001953 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7101 0.6612 0.7488 0.1764 0.9608 0.9717 0.7108 0.917 0.9486 0.8504 ] Network output: [ -0.09666 0.2975 0.8349 -0.00024 -2.125e-05 1.06 0.000328 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7195 0.7081 0.8359 -0.02274 0.9625 0.9731 0.7196 0.9224 0.9522 0.863 ] Network output: [ 0.01284 0.8779 0.1181 -0.0007254 0.0006095 0.9759 -0.001522 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1107 Epoch 368 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1475 1.168 0.7529 0.003519 -0.001563 -0.2015 0.002866 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09129 0.006114 0.06068 -0.01074 0.8702 0.8886 0.1581 0.7933 0.8347 0.5988 ] Network output: [ 0.7636 0.09212 0.2122 -0.003484 0.002076 0.1552 -0.004814 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6581 0.1289 0.03088 0.2156 0.9348 0.9632 0.7329 0.8412 0.9248 0.796 ] Network output: [ -0.06838 0.7821 1.213 -0.003374 0.00112 0.127 -0.0007709 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1705 0.09824 0.4139 0.1186 0.9499 0.9617 0.1743 0.9007 0.9401 0.7047 ] Network output: [ 0.2974 -0.2244 0.834 0.002736 -0.001267 0.8067 0.001982 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7263 0.5243 0.494 0.3142 0.9482 0.9726 0.7296 0.8715 0.946 0.8318 ] Network output: [ -0.04704 0.01711 1.023 0.001513 -0.0009089 1.059 0.001942 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7123 0.6634 0.7479 0.1755 0.961 0.9719 0.713 0.9173 0.9489 0.8486 ] Network output: [ -0.09629 0.2973 0.8342 -0.0002198 -2.648e-05 1.06 0.0003285 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7218 0.7105 0.8345 -0.02356 0.9627 0.9733 0.7219 0.9228 0.9524 0.8613 ] Network output: [ 0.01334 0.8768 0.1186 -0.0007262 0.0006032 0.9754 -0.001503 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1097 Epoch 369 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1469 1.167 0.7536 0.00353 -0.00157 -0.2003 0.002872 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09132 0.006127 0.06039 -0.01077 0.8707 0.8891 0.1582 0.7937 0.8352 0.5963 ] Network output: [ 0.7656 0.0915 0.2098 -0.003495 0.002072 0.1542 -0.004779 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6591 0.1289 0.03009 0.2149 0.9351 0.9634 0.734 0.8414 0.9251 0.7953 ] Network output: [ -0.06865 0.7842 1.212 -0.003346 0.001116 0.1267 -0.0007924 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.171 0.09863 0.4123 0.1175 0.9502 0.9619 0.1747 0.9011 0.9404 0.7011 ] Network output: [ 0.2958 -0.2241 0.8368 0.002686 -0.001246 0.8067 0.001958 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7275 0.5255 0.4944 0.3136 0.9485 0.9727 0.7307 0.8717 0.9462 0.831 ] Network output: [ -0.04736 0.01719 1.024 0.001522 -0.0009074 1.06 0.001932 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7144 0.6656 0.7469 0.1748 0.9613 0.972 0.7151 0.9176 0.9492 0.8467 ] Network output: [ -0.09593 0.2971 0.8334 -0.0001994 -3.196e-05 1.06 0.0003296 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7241 0.7128 0.833 -0.02426 0.9629 0.9735 0.7242 0.9231 0.9527 0.8595 ] Network output: [ 0.01382 0.8758 0.1192 -0.0007272 0.0005972 0.9749 -0.001484 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1088 Epoch 370 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1463 1.167 0.7543 0.00354 -0.001577 -0.199 0.002877 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09134 0.00614 0.06012 -0.01078 0.8712 0.8895 0.1583 0.7941 0.8357 0.5937 ] Network output: [ 0.7676 0.0909 0.2074 -0.003506 0.002067 0.1531 -0.004745 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.66 0.1288 0.02933 0.2143 0.9354 0.9636 0.735 0.8417 0.9255 0.7946 ] Network output: [ -0.06888 0.7863 1.211 -0.003319 0.001113 0.1263 -0.0008129 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1714 0.09901 0.4106 0.1164 0.9505 0.9622 0.1752 0.9015 0.9408 0.6976 ] Network output: [ 0.2941 -0.2237 0.8396 0.002636 -0.001224 0.8067 0.001933 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7286 0.5266 0.4949 0.313 0.9487 0.9728 0.7318 0.872 0.9464 0.8301 ] Network output: [ -0.04766 0.01733 1.024 0.001531 -0.0009062 1.06 0.001923 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7165 0.6678 0.7458 0.174 0.9615 0.9722 0.7172 0.918 0.9495 0.8448 ] Network output: [ -0.09555 0.2969 0.8327 -0.0001787 -3.769e-05 1.061 0.0003313 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7263 0.7151 0.8314 -0.0249 0.9632 0.9736 0.7264 0.9234 0.953 0.8576 ] Network output: [ 0.01428 0.8748 0.1197 -0.0007282 0.0005914 0.9744 -0.001466 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1078 Epoch 371 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1456 1.166 0.7551 0.00355 -0.001584 -0.1977 0.002882 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09136 0.006152 0.05986 -0.01078 0.8716 0.8899 0.1583 0.7945 0.8361 0.5911 ] Network output: [ 0.7696 0.09025 0.2051 -0.003516 0.002062 0.1521 -0.004712 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6609 0.1288 0.02863 0.2137 0.9357 0.9638 0.736 0.8419 0.9258 0.7939 ] Network output: [ -0.0691 0.7883 1.21 -0.003292 0.001109 0.1259 -0.0008322 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1719 0.0994 0.4089 0.1154 0.9508 0.9624 0.1757 0.9019 0.9411 0.6939 ] Network output: [ 0.2924 -0.2234 0.8424 0.002586 -0.001202 0.8068 0.001907 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7297 0.5278 0.4954 0.3125 0.9489 0.973 0.7329 0.8722 0.9467 0.8293 ] Network output: [ -0.04795 0.01747 1.024 0.001541 -0.0009052 1.061 0.001914 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7186 0.6699 0.7447 0.1734 0.9617 0.9724 0.7192 0.9183 0.9498 0.8429 ] Network output: [ -0.09516 0.2966 0.832 -0.0001575 -4.373e-05 1.061 0.0003339 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7285 0.7173 0.8298 -0.02543 0.9634 0.9738 0.7286 0.9237 0.9532 0.8557 ] Network output: [ 0.01474 0.8739 0.1202 -0.0007292 0.0005857 0.9739 -0.001448 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1069 Epoch 372 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.145 1.165 0.7559 0.003559 -0.00159 -0.1965 0.002886 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09138 0.006163 0.05959 -0.01078 0.8721 0.8903 0.1584 0.7949 0.8366 0.5884 ] Network output: [ 0.7715 0.08963 0.2027 -0.003526 0.002058 0.1511 -0.004679 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6618 0.1287 0.0279 0.2131 0.9359 0.964 0.737 0.8422 0.9261 0.7932 ] Network output: [ -0.06927 0.7903 1.209 -0.003263 0.001104 0.1255 -0.00085 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1723 0.09978 0.4072 0.1143 0.9511 0.9626 0.1761 0.9023 0.9414 0.6902 ] Network output: [ 0.2907 -0.2231 0.8452 0.002536 -0.00118 0.8069 0.00188 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7308 0.5289 0.4958 0.3121 0.9491 0.9731 0.7341 0.8724 0.9469 0.8284 ] Network output: [ -0.04824 0.01758 1.024 0.001551 -0.0009046 1.061 0.001905 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7206 0.6721 0.7435 0.1728 0.9619 0.9725 0.7213 0.9186 0.9501 0.8409 ] Network output: [ -0.09478 0.2962 0.8313 -0.0001361 -5.001e-05 1.061 0.0003371 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7307 0.7195 0.8282 -0.0258 0.9636 0.9739 0.7308 0.924 0.9535 0.8538 ] Network output: [ 0.01517 0.873 0.1207 -0.0007305 0.0005804 0.9734 -0.001431 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.106 Epoch 373 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1444 1.164 0.7566 0.003567 -0.001595 -0.1953 0.00289 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0914 0.006174 0.05932 -0.01077 0.8726 0.8907 0.1585 0.7953 0.837 0.5857 ] Network output: [ 0.7735 0.0891 0.2003 -0.003535 0.002053 0.1501 -0.004647 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6628 0.1286 0.02719 0.2125 0.9362 0.9642 0.738 0.8424 0.9265 0.7924 ] Network output: [ -0.0694 0.7924 1.207 -0.003234 0.001099 0.1251 -0.0008669 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1728 0.1002 0.4054 0.1133 0.9514 0.9629 0.1766 0.9027 0.9418 0.6864 ] Network output: [ 0.289 -0.2227 0.8478 0.002485 -0.001158 0.8069 0.001853 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7319 0.53 0.4963 0.3116 0.9494 0.9732 0.7352 0.8726 0.9471 0.8275 ] Network output: [ -0.04851 0.01782 1.024 0.00156 -0.0009038 1.061 0.001897 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7226 0.6741 0.7423 0.1722 0.9622 0.9727 0.7233 0.919 0.9504 0.8388 ] Network output: [ -0.09439 0.2959 0.8307 -0.0001145 -5.646e-05 1.062 0.0003408 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7328 0.7217 0.8265 -0.02615 0.9638 0.9741 0.7329 0.9244 0.9538 0.8518 ] Network output: [ 0.01558 0.872 0.1213 -0.000732 0.0005752 0.973 -0.001414 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.105 Epoch 374 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1437 1.164 0.7575 0.003574 -0.0016 -0.194 0.002893 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09141 0.006187 0.05911 -0.01073 0.873 0.8911 0.1585 0.7957 0.8375 0.583 ] Network output: [ 0.7753 0.08842 0.1982 -0.003546 0.002049 0.1492 -0.004616 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6637 0.1286 0.02664 0.212 0.9365 0.9644 0.739 0.8427 0.9268 0.7916 ] Network output: [ -0.06958 0.7943 1.206 -0.003207 0.001095 0.1248 -0.0008834 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1732 0.1005 0.4037 0.1124 0.9517 0.9631 0.177 0.903 0.9421 0.6825 ] Network output: [ 0.2873 -0.2224 0.8506 0.002433 -0.001135 0.8071 0.001825 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.733 0.5312 0.4968 0.3112 0.9496 0.9733 0.7363 0.8729 0.9473 0.8265 ] Network output: [ -0.04875 0.01811 1.024 0.00157 -0.0009036 1.062 0.00189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7246 0.6762 0.7411 0.1716 0.9624 0.9729 0.7252 0.9193 0.9507 0.8367 ] Network output: [ -0.09395 0.2956 0.8299 -9.208e-05 -6.335e-05 1.062 0.0003454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7349 0.7239 0.8247 -0.02645 0.964 0.9742 0.7351 0.9247 0.954 0.8497 ] Network output: [ 0.01601 0.8711 0.1217 -0.0007329 0.00057 0.9725 -0.001398 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1042 Epoch 375 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.143 1.163 0.7584 0.003581 -0.001605 -0.1928 0.002896 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09143 0.006196 0.05887 -0.01067 0.8735 0.8915 0.1586 0.7961 0.8379 0.5803 ] Network output: [ 0.7772 0.0877 0.196 -0.003556 0.002045 0.1482 -0.004586 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6645 0.1285 0.026 0.2115 0.9368 0.9645 0.74 0.843 0.9271 0.7909 ] Network output: [ -0.06967 0.7961 1.205 -0.003177 0.001089 0.1245 -0.0008972 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1736 0.1009 0.4018 0.1116 0.952 0.9633 0.1774 0.9034 0.9424 0.6786 ] Network output: [ 0.2856 -0.2223 0.8536 0.002383 -0.001113 0.8072 0.001797 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7341 0.5322 0.4973 0.311 0.9498 0.9735 0.7374 0.8731 0.9476 0.8256 ] Network output: [ -0.04901 0.01814 1.024 0.001581 -0.000904 1.062 0.001884 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7265 0.6782 0.7398 0.1714 0.9626 0.973 0.7272 0.9197 0.9509 0.8346 ] Network output: [ -0.09355 0.295 0.8294 -6.931e-05 -7.06e-05 1.062 0.000351 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.737 0.726 0.8229 -0.02642 0.9642 0.9744 0.7371 0.925 0.9543 0.8476 ] Network output: [ 0.01641 0.8703 0.1222 -0.0007346 0.0005653 0.9721 -0.001382 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1032 Epoch 376 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1424 1.162 0.7591 0.003589 -0.00161 -0.1916 0.002899 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09144 0.006202 0.05856 -0.01066 0.8739 0.8919 0.1586 0.7965 0.8384 0.5775 ] Network output: [ 0.7793 0.08739 0.1933 -0.003563 0.00204 0.1471 -0.004555 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6654 0.1284 0.02519 0.2109 0.937 0.9647 0.741 0.8432 0.9274 0.79 ] Network output: [ -0.06963 0.7982 1.204 -0.003145 0.001083 0.124 -0.0009097 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.174 0.1013 0.3998 0.1105 0.9523 0.9636 0.1778 0.9038 0.9427 0.6745 ] Network output: [ 0.284 -0.2217 0.8559 0.00233 -0.00109 0.8072 0.001766 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7352 0.5333 0.4976 0.3105 0.95 0.9736 0.7384 0.8733 0.9478 0.8246 ] Network output: [ -0.04927 0.01846 1.024 0.001591 -0.0009036 1.062 0.001876 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7284 0.6801 0.7384 0.1709 0.9628 0.9732 0.7291 0.92 0.9512 0.8323 ] Network output: [ -0.09321 0.2945 0.829 -4.736e-05 -7.763e-05 1.063 0.0003565 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.739 0.7281 0.821 -0.0264 0.9644 0.9745 0.7392 0.9253 0.9545 0.8454 ] Network output: [ 0.01672 0.8694 0.1228 -0.0007372 0.000561 0.9717 -0.001367 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1023 Epoch 377 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1416 1.161 0.7601 0.003591 -0.001613 -0.1903 0.0029 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09144 0.00622 0.05844 -0.0106 0.8744 0.8923 0.1587 0.797 0.8388 0.5746 ] Network output: [ 0.7808 0.08674 0.1916 -0.003575 0.002037 0.1464 -0.004527 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6663 0.1284 0.02491 0.2104 0.9373 0.9649 0.742 0.8435 0.9277 0.7892 ] Network output: [ -0.06981 0.8002 1.202 -0.00312 0.001079 0.1237 -0.000925 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1745 0.1017 0.3981 0.1096 0.9526 0.9638 0.1783 0.9042 0.943 0.6705 ] Network output: [ 0.2821 -0.2211 0.8586 0.002276 -0.001067 0.8075 0.001735 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7363 0.5344 0.4982 0.3101 0.9502 0.9737 0.7395 0.8735 0.948 0.8236 ] Network output: [ -0.04942 0.01918 1.023 0.001601 -0.0009036 1.063 0.001869 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7303 0.6821 0.7371 0.1703 0.963 0.9733 0.731 0.9203 0.9515 0.8301 ] Network output: [ -0.09266 0.2944 0.828 -2.34e-05 -8.533e-05 1.063 0.0003627 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7411 0.7302 0.819 -0.0267 0.9646 0.9747 0.7412 0.9256 0.9547 0.8431 ] Network output: [ 0.01716 0.8684 0.1232 -0.0007375 0.000556 0.9713 -0.001351 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1014 Epoch 378 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1409 1.16 0.7614 0.003595 -0.001616 -0.1891 0.002901 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09145 0.006228 0.05829 -0.01042 0.8748 0.8927 0.1587 0.7974 0.8393 0.5718 ] Network output: [ 0.7824 0.08549 0.1902 -0.003588 0.002034 0.1456 -0.0045 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6672 0.1283 0.02458 0.2102 0.9375 0.965 0.7429 0.8438 0.928 0.7884 ] Network output: [ -0.06995 0.8012 1.202 -0.00309 0.001072 0.1235 -0.0009352 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1749 0.102 0.3963 0.1092 0.9529 0.964 0.1787 0.9047 0.9433 0.6664 ] Network output: [ 0.2803 -0.2217 0.8622 0.002227 -0.001045 0.8078 0.001708 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7374 0.5355 0.4988 0.3104 0.9504 0.9738 0.7406 0.8738 0.9482 0.8226 ] Network output: [ -0.04962 0.01887 1.024 0.001616 -0.0009061 1.063 0.001867 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7321 0.684 0.7356 0.1706 0.9632 0.9735 0.7328 0.9207 0.9518 0.8277 ] Network output: [ -0.09218 0.2937 0.8275 2.247e-06 -9.412e-05 1.063 0.0003714 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.743 0.7322 0.817 -0.02616 0.9648 0.9748 0.7432 0.9259 0.955 0.8408 ] Network output: [ 0.01758 0.8678 0.1235 -0.0007387 0.0005515 0.9709 -0.001337 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1006 Epoch 379 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1405 1.16 0.7616 0.003607 -0.001622 -0.1881 0.002906 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09145 0.006216 0.05778 -0.01044 0.8752 0.893 0.1588 0.7978 0.8397 0.5689 ] Network output: [ 0.7852 0.08574 0.1862 -0.003587 0.002027 0.1438 -0.004469 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6681 0.1279 0.02313 0.2095 0.9378 0.9652 0.7439 0.8441 0.9283 0.7875 ] Network output: [ -0.06947 0.8037 1.2 -0.003047 0.001061 0.1227 -0.0009393 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1753 0.1023 0.3937 0.1081 0.9532 0.9642 0.1791 0.905 0.9436 0.662 ] Network output: [ 0.2791 -0.2208 0.8639 0.002174 -0.001022 0.8074 0.001675 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7384 0.5363 0.4985 0.31 0.9506 0.9739 0.7417 0.874 0.9484 0.8215 ] Network output: [ -0.04999 0.01889 1.024 0.001625 -0.000906 1.063 0.001861 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7339 0.6858 0.7339 0.1705 0.9634 0.9736 0.7346 0.921 0.952 0.8253 ] Network output: [ -0.0921 0.2925 0.8281 2.306e-05 -0.0001013 1.064 0.0003787 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7449 0.7341 0.8149 -0.02535 0.965 0.9749 0.745 0.9262 0.9552 0.8384 ] Network output: [ 0.01766 0.867 0.1244 -0.000745 0.0005491 0.9707 -0.001325 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0995 Epoch 380 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1395 1.16 0.7626 0.003604 -0.001622 -0.1868 0.002902 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09145 0.00625 0.05776 -0.01045 0.8756 0.8934 0.1588 0.7982 0.8402 0.5659 ] Network output: [ 0.7862 0.08574 0.1845 -0.003601 0.002025 0.1434 -0.004443 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6689 0.1281 0.02322 0.2087 0.938 0.9653 0.7448 0.8444 0.9286 0.7867 ] Network output: [ -0.06968 0.8065 1.198 -0.003028 0.001059 0.1224 -0.0009573 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1757 0.1027 0.3921 0.1066 0.9534 0.9644 0.1795 0.9054 0.9439 0.6578 ] Network output: [ 0.2771 -0.219 0.8657 0.002113 -0.0009943 0.8077 0.001636 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7396 0.5376 0.4995 0.3088 0.9507 0.974 0.7428 0.8742 0.9486 0.8204 ] Network output: [ -0.04995 0.02115 1.022 0.001631 -0.0009041 1.063 0.00185 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7358 0.6878 0.7326 0.1688 0.9636 0.9738 0.7364 0.9213 0.9523 0.8229 ] Network output: [ -0.09136 0.2931 0.8261 4.73e-05 -0.000109 1.064 0.0003847 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.747 0.7362 0.8128 -0.02641 0.9652 0.9751 0.7471 0.9265 0.9554 0.836 ] Network output: [ 0.01811 0.8657 0.125 -0.0007439 0.0005438 0.9704 -0.001309 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09868 Epoch 381 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1384 1.158 0.7653 0.003595 -0.001621 -0.1852 0.002897 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09145 0.006277 0.05809 -0.009942 0.876 0.8938 0.1588 0.7987 0.8406 0.5631 ] Network output: [ 0.7864 0.08245 0.1867 -0.003629 0.002028 0.1441 -0.004424 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6697 0.1284 0.02438 0.2095 0.9383 0.9655 0.7458 0.8447 0.929 0.7859 ] Network output: [ -0.07048 0.8054 1.2 -0.00301 0.001056 0.123 -0.0009695 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.176 0.1032 0.3911 0.1075 0.9537 0.9647 0.1799 0.9059 0.9443 0.6537 ] Network output: [ 0.2746 -0.2216 0.8718 0.002068 -0.0009763 0.809 0.001616 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7406 0.539 0.5009 0.3106 0.9509 0.9741 0.7438 0.8745 0.9488 0.8194 ] Network output: [ -0.04992 0.02011 1.023 0.001656 -0.0009116 1.063 0.001857 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7375 0.6897 0.7311 0.1701 0.9638 0.9739 0.7381 0.9217 0.9526 0.8204 ] Network output: [ -0.09038 0.2928 0.8247 8.159e-05 -0.0001215 1.063 0.000399 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7489 0.7381 0.8105 -0.02548 0.9653 0.9752 0.749 0.9268 0.9557 0.8334 ] Network output: [ 0.01889 0.8656 0.1243 -0.0007397 0.0005378 0.9697 -0.001293 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09825 Epoch 382 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1388 1.158 0.7641 0.003625 -0.001634 -0.1849 0.002912 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09146 0.006197 0.05686 -0.01 0.8765 0.8941 0.1589 0.7991 0.841 0.56 ] Network output: [ 0.7917 0.08357 0.179 -0.003606 0.002013 0.14 -0.004387 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6706 0.1271 0.02071 0.2086 0.9385 0.9657 0.7468 0.8449 0.9292 0.7848 ] Network output: [ -0.06883 0.808 1.196 -0.002934 0.001031 0.1215 -0.0009498 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1764 0.1032 0.3869 0.1067 0.954 0.9648 0.1803 0.9062 0.9445 0.6489 ] Network output: [ 0.2746 -0.2214 0.8727 0.002022 -0.0009547 0.8076 0.001583 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7416 0.539 0.4988 0.311 0.9511 0.9742 0.7448 0.8746 0.949 0.8181 ] Network output: [ -0.05079 0.0177 1.026 0.001667 -0.0009137 1.064 0.001857 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.739 0.691 0.7287 0.1723 0.964 0.9741 0.7397 0.922 0.9528 0.8178 ] Network output: [ -0.09125 0.289 0.8289 9.832e-05 -0.0001285 1.065 0.0004097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7504 0.7396 0.8081 -0.0219 0.9655 0.9753 0.7505 0.9271 0.9559 0.8308 ] Network output: [ 0.01833 0.8651 0.1258 -0.0007569 0.0005401 0.9697 -0.001288 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0967 Epoch 383 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1378 1.159 0.7636 0.00362 -0.001632 -0.1838 0.002906 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09142 0.006255 0.05664 -0.0106 0.8769 0.8945 0.1588 0.7995 0.8415 0.5569 ] Network output: [ 0.7931 0.08747 0.1734 -0.003608 0.002008 0.139 -0.00436 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6714 0.1274 0.02022 0.206 0.9387 0.9658 0.7477 0.8452 0.9295 0.7839 ] Network output: [ -0.06848 0.8155 1.189 -0.00292 0.001032 0.1201 -0.0009754 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1769 0.1037 0.3847 0.1024 0.9542 0.965 0.1807 0.9066 0.9448 0.6443 ] Network output: [ 0.2728 -0.2138 0.869 0.001936 -0.0009144 0.807 0.001519 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7428 0.5404 0.4998 0.306 0.9512 0.9743 0.7461 0.8749 0.9491 0.817 ] Network output: [ -0.05052 0.02492 1.019 0.001652 -0.000901 1.063 0.001826 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.741 0.6932 0.7273 0.1665 0.9642 0.9742 0.7417 0.9224 0.953 0.8152 ] Network output: [ -0.09049 0.2913 0.8251 0.0001131 -0.0001317 1.065 0.0004076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7526 0.7419 0.806 -0.02574 0.9657 0.9754 0.7528 0.9274 0.9561 0.8283 ] Network output: [ 0.01847 0.8625 0.1278 -0.0007579 0.0005357 0.97 -0.001273 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0956 Epoch 384 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1346 1.154 0.7716 0.003562 -0.001611 -0.1805 0.002872 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09142 0.006408 0.05898 -0.009153 0.8773 0.8948 0.1588 0.8 0.842 0.5542 ] Network output: [ 0.7864 0.0776 0.1894 -0.003704 0.002035 0.1458 -0.004367 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6721 0.1297 0.02771 0.2096 0.939 0.966 0.7484 0.8457 0.9299 0.7835 ] Network output: [ -0.07236 0.8084 1.2 -0.002969 0.001055 0.1236 -0.00102 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1771 0.1046 0.3878 0.1065 0.9545 0.9653 0.181 0.9072 0.9453 0.6409 ] Network output: [ 0.267 -0.2217 0.8834 0.001899 -0.0009031 0.812 0.001517 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7438 0.5435 0.5053 0.3109 0.9515 0.9744 0.747 0.8752 0.9494 0.8161 ] Network output: [ -0.04934 0.02416 1.018 0.001704 -0.0009199 1.063 0.00185 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7427 0.6955 0.7268 0.1682 0.9644 0.9744 0.7434 0.9228 0.9534 0.8127 ] Network output: [ -0.08715 0.2947 0.8174 0.0001752 -0.0001542 1.063 0.0004334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7547 0.7442 0.8036 -0.02666 0.9659 0.9756 0.7549 0.9277 0.9563 0.8254 ] Network output: [ 0.02095 0.8632 0.124 -0.0007273 0.0005197 0.9683 -0.001245 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09668 Epoch 385 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1373 1.154 0.7681 0.003644 -0.001645 -0.1818 0.00292 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09146 0.006111 0.05594 -0.008781 0.8777 0.8952 0.159 0.8004 0.8424 0.5508 ] Network output: [ 0.7987 0.07728 0.1751 -0.003632 0.002003 0.1362 -0.004313 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6732 0.1257 0.01831 0.21 0.9392 0.9661 0.7498 0.8458 0.9301 0.7821 ] Network output: [ -0.0681 0.8066 1.197 -0.002797 0.0009868 0.1211 -0.0009305 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1775 0.104 0.3796 0.1089 0.9547 0.9654 0.1813 0.9074 0.9454 0.6351 ] Network output: [ 0.2704 -0.2289 0.8874 0.001896 -0.0009005 0.8085 0.001513 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7446 0.5411 0.4984 0.3172 0.9516 0.9745 0.7478 0.8753 0.9495 0.8145 ] Network output: [ -0.05195 0.0101 1.034 0.001739 -0.0009374 1.066 0.001885 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7435 0.6956 0.7225 0.1803 0.9645 0.9745 0.7442 0.923 0.9536 0.8096 ] Network output: [ -0.09074 0.2821 0.8336 0.0001903 -0.000165 1.066 0.00046 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7551 0.7445 0.8004 -0.01282 0.966 0.9757 0.7553 0.9279 0.9566 0.8224 ] Network output: [ 0.01892 0.8648 0.1261 -0.0007709 0.0005334 0.9684 -0.001257 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09418 Epoch 386 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1383 1.163 0.7582 0.003684 -0.001661 -0.1829 0.002935 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09136 0.006116 0.05324 -0.01189 0.878 0.8955 0.1589 0.8007 0.8427 0.5473 ] Network output: [ 0.8076 0.09844 0.1452 -0.003535 0.001963 0.1273 -0.004251 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.674 0.1245 0.01007 0.1995 0.9394 0.9662 0.7507 0.8459 0.9302 0.7804 ] Network output: [ -0.06321 0.8327 1.168 -0.00273 0.0009729 0.1143 -0.0009453 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1781 0.1041 0.3722 0.09387 0.9549 0.9656 0.1819 0.9076 0.9454 0.6289 ] Network output: [ 0.2724 -0.1998 0.8602 0.001736 -0.000819 0.8018 0.001363 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7461 0.541 0.4951 0.2985 0.9517 0.9745 0.7493 0.8753 0.9495 0.8131 ] Network output: [ -0.05207 0.03101 1.016 0.001638 -0.0008834 1.064 0.001779 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7459 0.6976 0.7204 0.1624 0.9647 0.9746 0.7466 0.9233 0.9537 0.8068 ] Network output: [ -0.09224 0.2858 0.8307 0.0001489 -0.0001443 1.068 0.000421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7577 0.747 0.7986 -0.023 0.9662 0.9758 0.7578 0.9283 0.9567 0.8202 ] Network output: [ 0.01655 0.8578 0.135 -0.0008045 0.0005413 0.9712 -0.001257 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09128 Epoch 387 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1272 1.15 0.7831 0.003438 -0.001562 -0.1735 0.00279 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0913 0.006848 0.06289 -0.008304 0.8784 0.8959 0.1586 0.8015 0.8433 0.5453 ] Network output: [ 0.7748 0.07165 0.2068 -0.003872 0.002078 0.1568 -0.004356 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6742 0.1349 0.04093 0.2098 0.9397 0.9664 0.7506 0.8469 0.9309 0.7815 ] Network output: [ -0.07823 0.8137 1.203 -0.003059 0.001109 0.1264 -0.001152 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1781 0.1069 0.3901 0.1034 0.9553 0.966 0.182 0.9088 0.9464 0.6288 ] Network output: [ 0.2543 -0.2156 0.8948 0.001679 -0.0008089 0.819 0.001384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7472 0.5513 0.517 0.3064 0.952 0.9748 0.7504 0.8762 0.9501 0.8131 ] Network output: [ -0.04589 0.0419 0.9975 0.001743 -0.0009205 1.059 0.001821 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7485 0.7025 0.7242 0.1561 0.9649 0.9748 0.7491 0.9239 0.9543 0.8046 ] Network output: [ -0.07959 0.3074 0.7943 0.0002919 -0.0001924 1.059 0.0004664 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7616 0.7514 0.7966 -0.03884 0.9664 0.9759 0.7617 0.9286 0.9569 0.8169 ] Network output: [ 0.02527 0.8586 0.122 -0.0006741 0.0004858 0.9665 -0.001174 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09754 Epoch 388 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1344 1.141 0.7817 0.003625 -0.001643 -0.177 0.002912 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09154 0.005988 0.05714 -0.004449 0.8788 0.8962 0.1593 0.8017 0.8437 0.5418 ] Network output: [ 0.7998 0.05122 0.1958 -0.003753 0.002028 0.1387 -0.004284 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6758 0.1248 0.02263 0.2212 0.9399 0.9665 0.7527 0.8469 0.9311 0.7797 ] Network output: [ -0.07105 0.7827 1.222 -0.002673 0.0009397 0.1261 -0.0008847 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1782 0.1047 0.3762 0.1247 0.9555 0.9661 0.1821 0.9087 0.9465 0.6221 ] Network output: [ 0.2631 -0.2631 0.9291 0.001857 -0.0008938 0.8152 0.001537 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7471 0.5436 0.5006 0.3414 0.952 0.9748 0.7503 0.876 0.9502 0.8108 ] Network output: [ -0.05321 -0.01792 1.062 0.001926 -0.001019 1.07 0.002019 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.747 0.6993 0.7158 0.207 0.965 0.9749 0.7477 0.9239 0.9545 0.8006 ] Network output: [ -0.08885 0.2668 0.845 0.0003662 -0.0002408 1.067 0.000579 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7587 0.7482 0.7909 0.01068 0.9664 0.976 0.7589 0.9285 0.9572 0.8125 ] Network output: [ 0.02148 0.8687 0.1208 -0.0007504 0.0005141 0.9648 -0.00121 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09508 Epoch 389 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1482 1.18 0.7296 0.003964 -0.001776 -0.1898 0.003089 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09131 0.00529 0.04089 -0.01649 0.8792 0.8965 0.1591 0.8016 0.8437 0.5363 ] Network output: [ 0.8546 0.1364 0.05512 -0.003174 0.001818 0.08703 -0.004035 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6771 0.1115 -0.02856 0.182 0.9399 0.9665 0.7545 0.8457 0.9305 0.7746 ] Network output: [ -0.04249 0.8715 1.108 -0.002185 0.0007678 0.09593 -0.0007022 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1795 0.1021 0.3399 0.07496 0.9554 0.9658 0.1834 0.9076 0.9453 0.6076 ] Network output: [ 0.2888 -0.1652 0.8148 0.00152 -0.0007018 0.7789 0.001135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7492 0.5324 0.4699 0.2816 0.9518 0.9745 0.7525 0.8749 0.9492 0.8072 ] Network output: [ -0.05853 0.03211 1.024 0.001539 -0.0008335 1.067 0.001689 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7495 0.6986 0.707 0.1626 0.9651 0.9748 0.7502 0.9238 0.9539 0.7967 ] Network output: [ -0.1053 0.2593 0.8701 8.839e-05 -0.0001263 1.081 0.000411 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7604 0.7493 0.7896 -0.00407 0.9665 0.976 0.7606 0.9291 0.9572 0.8114 ] Network output: [ 0.00617 0.8494 0.1573 -0.0009685 0.0005938 0.9772 -0.001305 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08662 Epoch 390 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.11 1.151 0.8016 0.003075 -0.00141 -0.1604 0.002549 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09089 0.008305 0.07427 -0.01003 0.8796 0.8968 0.1577 0.8031 0.8447 0.536 ] Network output: [ 0.7317 0.08111 0.2471 -0.00426 0.002206 0.1915 -0.004456 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6751 0.1525 0.07929 0.2014 0.9403 0.9669 0.7512 0.8484 0.932 0.7798 ] Network output: [ -0.09221 0.8473 1.192 -0.003512 0.001325 0.1306 -0.001551 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1791 0.1119 0.4059 0.08201 0.9562 0.9667 0.1829 0.9107 0.9478 0.618 ] Network output: [ 0.2296 -0.1637 0.8776 0.001239 -0.0006124 0.8319 0.001076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7515 0.569 0.5478 0.2698 0.9526 0.9751 0.7547 0.8773 0.9507 0.8101 ] Network output: [ -0.03265 0.119 0.9097 0.001674 -0.0008541 1.043 0.001641 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7568 0.7144 0.7271 0.09588 0.9656 0.9753 0.7574 0.9253 0.955 0.7962 ] Network output: [ -0.06012 0.3529 0.7234 0.0004335 -0.0002273 1.046 0.0004625 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7724 0.763 0.7908 -0.09297 0.967 0.9762 0.7726 0.9296 0.9572 0.808 ] Network output: [ 0.03446 0.837 0.1263 -0.0005072 0.0004023 0.966 -0.001025 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1088 Epoch 391 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1191 1.093 0.8433 0.003308 -0.001522 -0.1607 0.002742 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09176 0.006439 0.07232 0.01147 0.88 0.8972 0.1597 0.8031 0.8451 0.5337 ] Network output: [ 0.7538 -0.05163 0.3413 -0.004435 0.002261 0.1851 -0.004503 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6777 0.1351 0.0725 0.2674 0.9406 0.9671 0.7548 0.8489 0.9326 0.7798 ] Network output: [ -0.09573 0.6797 1.345 -0.002841 0.0009901 0.155 -0.0009285 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1779 0.1076 0.3992 0.1863 0.9564 0.967 0.1817 0.9108 0.9486 0.6145 ] Network output: [ 0.2343 -0.3823 1.071 0.002043 -0.001019 0.8507 0.001847 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7484 0.5553 0.5288 0.4206 0.9527 0.9753 0.7516 0.8769 0.9516 0.8061 ] Network output: [ -0.04922 -0.1067 1.137 0.002586 -0.001322 1.078 0.002542 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7488 0.7039 0.7119 0.2886 0.9655 0.9754 0.7494 0.9244 0.9557 0.7883 ] Network output: [ -0.07399 0.2181 0.8714 0.001008 -0.0005252 1.063 0.001043 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7605 0.7505 0.7755 0.08165 0.9667 0.9763 0.7606 0.9282 0.9577 0.7959 ] Network output: [ 0.03595 0.8697 0.1045 -0.0004384 0.000371 0.9525 -0.000971 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1306 Epoch 392 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1901 1.232 0.6303 0.004931 -0.002172 -0.2221 0.003652 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09137 0.001831 0.0009065 -0.02843 0.8803 0.8975 0.1603 0.8006 0.8432 0.5183 ] Network output: [ 1.007 0.2446 -0.2189 -0.002265 0.0015 -0.04709 -0.003706 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.682 0.06073 -0.1518 0.1379 0.94 0.9665 0.7614 0.8419 0.9288 0.7586 ] Network output: [ 0.03401 0.9588 0.9365 -0.0002408 -2.018e-05 0.03539 0.0004169 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1804 0.09014 0.2389 0.03795 0.9552 0.9654 0.1844 0.9034 0.9421 0.5626 ] Network output: [ 0.3785 -0.0819 0.6462 0.001524 -0.000632 0.685 0.0008663 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7505 0.4855 0.3635 0.2518 0.9509 0.9739 0.7539 0.8693 0.9463 0.7892 ] Network output: [ -0.07364 0.00232 1.076 0.001452 -0.0008016 1.075 0.001657 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7466 0.6842 0.6633 0.1991 0.9648 0.9744 0.7473 0.9212 0.9516 0.7743 ] Network output: [ -0.1554 0.1402 1.044 -9.015e-05 -9.807e-05 1.126 0.0004768 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7526 0.7395 0.7695 0.09987 0.9663 0.9759 0.7528 0.9279 0.9564 0.7949 ] Network output: [ -0.03385 0.8114 0.2505 -0.001387 0.0007347 1 -0.001442 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1352 Epoch 393 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06172 1.191 0.8232 0.00197 -0.0009357 -0.1299 0.001795 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08911 0.01355 0.1072 -0.02291 0.8805 0.8977 0.1534 0.8027 0.8441 0.5188 ] Network output: [ 0.5895 0.1859 0.3173 -0.005188 0.002546 0.2971 -0.004825 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6714 0.2131 0.1924 0.1519 0.9406 0.9671 0.7456 0.8476 0.9317 0.7723 ] Network output: [ -0.1163 1.011 1.078 -0.004799 0.001934 0.1238 -0.002638 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1791 0.1256 0.4483 -0.01421 0.9569 0.9673 0.1828 0.9118 0.9483 0.5998 ] Network output: [ 0.1839 0.09875 0.6911 0.0002135 -0.0001201 0.8433 0.0002108 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7581 0.6178 0.6241 0.1004 0.9531 0.9753 0.7612 0.876 0.9498 0.8 ] Network output: [ 0.01846 0.3924 0.592 0.001321 -0.0005992 0.984 0.001016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7729 0.7409 0.7367 -0.1237 0.9663 0.9755 0.7735 0.9256 0.954 0.7796 ] Network output: [ -0.00543 0.4594 0.5434 0.0007395 -0.0003022 1.011 0.0004499 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7934 0.7862 0.7796 -0.2575 0.9676 0.9763 0.7935 0.9287 0.9551 0.7899 ] Network output: [ 0.05475 0.7266 0.1905 4.728e-05 0.0001365 0.9739 -0.0005562 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2192 Epoch 394 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05768 0.9447 1.052 0.001901 -0.0009605 -0.1047 0.00194 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09181 0.01043 0.1338 0.0564 0.8811 0.8982 0.1589 0.8017 0.8439 0.5209 ] Network output: [ 0.5378 -0.3321 0.8494 -0.006849 0.00314 0.3794 -0.00551 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.676 0.1962 0.2768 0.386 0.9412 0.9677 0.7517 0.8492 0.9335 0.7788 ] Network output: [ -0.1575 0.425 1.651 -0.003461 0.001185 0.224 -0.001086 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1735 0.1187 0.4811 0.335 0.9576 0.9686 0.1771 0.9108 0.9507 0.603 ] Network output: [ 0.1558 -0.6227 1.379 0.00253 -0.001334 0.9417 0.002595 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7459 0.6001 0.6381 0.5827 0.9532 0.976 0.749 0.8717 0.9513 0.7774 ] Network output: [ -0.006854 -0.3413 1.293 0.004842 -0.002348 1.082 0.00428 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7467 0.7137 0.7052 0.5007 0.9655 0.9759 0.7473 0.92 0.9547 0.7414 ] Network output: [ -0.01674 -0.09128 1.075 0.003882 -0.001838 1.065 0.003271 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7544 0.7471 0.7214 0.3885 0.9661 0.9763 0.7546 0.9214 0.9551 0.7313 ] Network output: [ 0.06968 0.5671 0.3388 0.001892 -0.000729 0.9627 0.0009656 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4478 Epoch 395 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.2487 1.363 0.4431 0.005478 -0.002357 -0.2807 0.003816 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.09038 -0.004783 -0.03838 -0.04168 0.8812 0.8983 0.1606 0.7886 0.8334 0.4579 ] Network output: [ 1.194 0.4735 -0.6253 -0.004136 0.002457 -0.253 -0.005566 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6842 -0.03205 -0.2597 0.0713 0.9383 0.9654 0.7677 0.8232 0.9189 0.6929 ] Network output: [ 0.1616 1.149 0.6187 0.002322 -0.001046 -0.08135 0.001848 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1748 0.06204 0.09699 -0.0148 0.9532 0.9635 0.1789 0.8858 0.9296 0.4436 ] Network output: [ 0.6139 0.2194 0.1293 0.0006392 -5.371e-05 0.4265 -0.0005009 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7465 0.3648 0.161 0.1633 0.9471 0.9714 0.7501 0.8442 0.9332 0.7072 ] Network output: [ 0.002425 0.2284 0.8102 0.002438 -0.001152 0.9664 0.002049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7284 0.6364 0.5255 0.1663 0.9624 0.9722 0.7291 0.9044 0.9375 0.6732 ] Network output: [ -0.1736 0.1343 1.084 0.0007086 -0.0004631 1.131 0.001107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7326 0.7139 0.6811 0.1981 0.9639 0.9739 0.7328 0.9142 0.9454 0.7153 ] Network output: [ -0.09025 0.649 0.4756 -0.0007907 0.0003922 1.053 -0.000709 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5048 Epoch 396 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.05778 1.179 0.9735 -0.0002812 7.483e-06 -0.0379 0.0003613 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08384 0.02669 0.187 0.0135 0.8811 0.8982 0.1413 0.7902 0.8323 0.4572 ] Network output: [ 0.2107 0.1649 0.7719 -0.006475 0.002906 0.6155 -0.004904 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6501 0.3595 0.4573 0.2504 0.9388 0.9663 0.7185 0.8326 0.924 0.7207 ] Network output: [ -0.1353 0.9433 1.166 -0.004129 0.001617 0.1438 -0.002083 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.16 0.1372 0.4568 0.0972 0.9561 0.9672 0.1632 0.9014 0.943 0.5017 ] Network output: [ 0.1137 0.01957 0.8305 0.002003 -0.000978 0.9306 0.001781 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.739 0.6867 0.6703 0.2757 0.9504 0.9741 0.7419 0.8523 0.94 0.7068 ] Network output: [ 0.09095 0.3241 0.5701 0.002864 -0.001269 0.9355 0.002082 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7507 0.7399 0.666 0.05738 0.9642 0.9743 0.7513 0.9118 0.9455 0.6743 ] Network output: [ 0.07976 0.4545 0.4562 0.001738 -0.0007134 0.937 0.00105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7685 0.7662 0.6816 -0.1012 0.9651 0.9747 0.7686 0.9129 0.945 0.6837 ] Network output: [ 0.115 0.7623 0.09009 0.0004292 -1.106e-05 0.9198 -0.0003679 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3826 Epoch 397 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02723 1.022 1.016 0.0004479 -0.0003019 -0.09137 0.0008184 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08703 0.01866 0.1661 0.04929 0.8813 0.8984 0.1488 0.7825 0.8285 0.444 ] Network output: [ 0.3933 -0.1346 0.8384 -0.007939 0.003591 0.4773 -0.006146 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6631 0.2819 0.3991 0.3461 0.9383 0.966 0.736 0.8241 0.9214 0.7012 ] Network output: [ -0.08363 0.6311 1.382 -0.002468 0.0008433 0.1435 -0.0007465 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1618 0.1273 0.4266 0.2637 0.9557 0.967 0.1652 0.8939 0.9408 0.4803 ] Network output: [ 0.179 -0.3929 1.132 0.002752 -0.001365 0.9141 0.002518 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.741 0.6523 0.634 0.5137 0.9496 0.974 0.7441 0.8436 0.9387 0.6806 ] Network output: [ 0.07837 -0.2044 1.07 0.004694 -0.002204 0.9961 0.003876 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7377 0.7191 0.6424 0.4593 0.9634 0.9741 0.7383 0.9051 0.9447 0.6532 ] Network output: [ 0.0467 -0.09185 1.005 0.003717 -0.001731 1.009 0.003019 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7421 0.7381 0.652 0.4166 0.9642 0.9748 0.7422 0.9067 0.9455 0.6547 ] Network output: [ 0.08731 0.3638 0.4993 0.002056 -0.0008432 0.9708 0.001236 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3677 Epoch 398 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.126 1.29 0.6565 0.001821 -0.000803 -0.1912 0.001403 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08683 0.008126 0.08639 0.003512 0.8816 0.8987 0.1511 0.7719 0.8204 0.406 ] Network output: [ 0.73 0.3394 0.03133 -0.008117 0.003941 0.1367 -0.007342 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6711 0.1461 0.1434 0.2008 0.9366 0.9648 0.7495 0.806 0.9116 0.644 ] Network output: [ 0.007235 1.019 0.9259 -0.001554 0.0005751 0.03427 -0.0006065 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1653 0.1044 0.3083 0.1077 0.9535 0.9646 0.169 0.8815 0.9299 0.4194 ] Network output: [ 0.2838 0.0798 0.6024 -0.0008562 0.0004044 0.7467 -0.0007658 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7492 0.5625 0.5222 0.3374 0.9477 0.9724 0.7528 0.8332 0.9311 0.6643 ] Network output: [ 0.03872 0.211 0.7619 0.002318 -0.00108 0.9591 0.00189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7359 0.6946 0.6202 0.2599 0.963 0.9734 0.7366 0.9021 0.9399 0.6555 ] Network output: [ -0.01786 0.2395 0.7876 0.001715 -0.0008038 1.016 0.001425 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7457 0.7368 0.6666 0.2014 0.9645 0.9746 0.7458 0.9068 0.9432 0.6756 ] Network output: [ 0.03973 0.6977 0.2566 0.0003122 -2.334e-05 0.9677 -0.0002001 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1637 Epoch 399 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07208 1.166 0.8289 0.001456 -0.0006917 -0.1328 0.001327 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08611 0.01086 0.1204 0.02818 0.8823 0.8993 0.1491 0.7756 0.8235 0.4266 ] Network output: [ 0.5755 0.1265 0.3957 -0.008545 0.004008 0.2922 -0.00717 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6683 0.1897 0.2545 0.2819 0.938 0.9657 0.7447 0.8134 0.916 0.6767 ] Network output: [ -0.06042 0.8299 1.176 -0.002099 0.0007456 0.1065 -0.0007324 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1612 0.1108 0.3702 0.1984 0.9552 0.9663 0.1647 0.8872 0.9352 0.4568 ] Network output: [ 0.1988 -0.1515 0.8959 0.0002267 -0.0001693 0.8588 0.0003889 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7474 0.5973 0.5849 0.4546 0.9493 0.9736 0.7508 0.8382 0.9351 0.6832 ] Network output: [ 0.01505 0.008195 0.9641 0.003105 -0.001481 1.01 0.002663 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.734 0.7015 0.6447 0.3917 0.9639 0.9743 0.7347 0.9041 0.9428 0.6688 ] Network output: [ -0.01587 0.09246 0.9128 0.002547 -0.001201 1.037 0.002137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7428 0.7358 0.6705 0.3291 0.965 0.9752 0.743 0.9071 0.9448 0.6767 ] Network output: [ 0.04791 0.5976 0.3315 0.001067 -0.0003773 0.9795 0.0004201 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.14 Epoch 400 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1264 1.223 0.7084 0.002599 -0.001161 -0.1739 0.002008 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08728 0.005877 0.08027 0.01071 0.8829 0.8998 0.1524 0.7725 0.8211 0.4188 ] Network output: [ 0.7639 0.2324 0.07677 -0.007562 0.003683 0.1328 -0.0069 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6768 0.1229 0.1156 0.2263 0.9379 0.9656 0.7561 0.8077 0.9127 0.6633 ] Network output: [ -0.01036 0.9393 1.015 -0.001234 0.0004136 0.06132 -0.0003049 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1647 0.09918 0.3101 0.1386 0.9548 0.9656 0.1684 0.8829 0.931 0.4383 ] Network output: [ 0.2726 -0.01924 0.6988 -0.0008645 0.000385 0.7718 -0.0006829 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7526 0.5455 0.5141 0.3859 0.9489 0.9732 0.7561 0.8353 0.9326 0.6847 ] Network output: [ -0.006246 0.1453 0.8757 0.0022 -0.001061 1 0.001936 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7332 0.6873 0.6317 0.3127 0.964 0.9742 0.7339 0.9037 0.9413 0.6747 ] Network output: [ -0.055 0.2134 0.8547 0.001652 -0.0007971 1.049 0.001466 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7437 0.7338 0.6793 0.2403 0.9653 0.9753 0.7438 0.9082 0.9444 0.6902 ] Network output: [ 0.01789 0.7264 0.2525 0.0002692 -1.294e-05 0.9866 -0.0001959 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1125 Epoch 401 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.09964 1.167 0.7892 0.002554 -0.001165 -0.1452 0.002066 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08696 0.008213 0.09597 0.01774 0.8835 0.9003 0.1512 0.7751 0.823 0.4291 ] Network output: [ 0.6836 0.1468 0.2404 -0.007154 0.003441 0.2169 -0.006358 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6765 0.1557 0.1679 0.2512 0.9388 0.9662 0.7546 0.812 0.915 0.6809 ] Network output: [ -0.04467 0.8728 1.115 -0.001546 0.0005243 0.09476 -0.0004273 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1639 0.1056 0.3418 0.161 0.9558 0.9666 0.1675 0.8867 0.9338 0.4575 ] Network output: [ 0.2238 -0.09682 0.8166 -0.0001087 8.844e-06 0.8321 3.228e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7538 0.5746 0.548 0.4122 0.95 0.9739 0.7572 0.8388 0.9347 0.6973 ] Network output: [ -0.01234 0.1027 0.9167 0.002409 -0.001163 1.015 0.002126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7359 0.6968 0.6445 0.3242 0.9646 0.9748 0.7366 0.9056 0.9428 0.6819 ] Network output: [ -0.04619 0.2109 0.8436 0.001742 -0.0008319 1.045 0.001511 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7473 0.7388 0.6833 0.2305 0.9658 0.9757 0.7474 0.9091 0.9451 0.6928 ] Network output: [ 0.02795 0.7452 0.2204 0.0002623 -2.915e-06 0.9797 -0.0002292 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09961 Epoch 402 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.12 1.152 0.7778 0.003084 -0.001394 -0.1572 0.002424 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08753 0.00623 0.08162 0.01575 0.884 0.9008 0.1527 0.7751 0.8231 0.4301 ] Network output: [ 0.7561 0.1232 0.1752 -0.006552 0.0032 0.1633 -0.006025 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6807 0.1307 0.1164 0.2451 0.9392 0.9664 0.7598 0.8116 0.9149 0.6826 ] Network output: [ -0.02933 0.8641 1.104 -0.001136 0.000351 0.08519 -0.0001655 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1652 0.1013 0.3216 0.156 0.9561 0.9667 0.1688 0.8864 0.9333 0.456 ] Network output: [ 0.2506 -0.1152 0.805 -0.0002081 6.439e-05 0.8081 -8.682e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7561 0.5562 0.5202 0.4101 0.9502 0.9739 0.7596 0.8391 0.9347 0.7028 ] Network output: [ -0.02467 0.09608 0.9418 0.002198 -0.001075 1.02 0.001994 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7356 0.6918 0.6404 0.3232 0.9649 0.9749 0.7363 0.9063 0.9431 0.6865 ] Network output: [ -0.06424 0.2119 0.8666 0.001487 -0.0007259 1.056 0.001354 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7473 0.7379 0.6868 0.2272 0.966 0.9759 0.7474 0.9101 0.9456 0.6985 ] Network output: [ 0.01478 0.7625 0.2212 1.473e-05 0.000102 0.987 -0.0003904 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08879 Epoch 403 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1201 1.145 0.783 0.003299 -0.001491 -0.1549 0.002584 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08764 0.006431 0.0784 0.01356 0.8845 0.9012 0.1528 0.7762 0.8239 0.4327 ] Network output: [ 0.7644 0.1163 0.1675 -0.005949 0.00293 0.1636 -0.005571 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6827 0.133 0.1045 0.2392 0.9396 0.9667 0.7619 0.8128 0.9154 0.6881 ] Network output: [ -0.03106 0.8697 1.101 -0.00107 0.0003248 0.08684 -0.0001313 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1661 0.1024 0.3186 0.1458 0.9565 0.967 0.1698 0.8876 0.9337 0.4587 ] Network output: [ 0.2487 -0.108 0.7988 2.048e-06 -2.775e-05 0.8118 6.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7583 0.5598 0.5142 0.3962 0.9506 0.9742 0.7617 0.8405 0.9351 0.7088 ] Network output: [ -0.02921 0.1249 0.9212 0.002015 -0.0009884 1.02 0.001842 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.738 0.6946 0.6402 0.2951 0.9653 0.9752 0.7386 0.9074 0.9435 0.6892 ] Network output: [ -0.06768 0.2564 0.8304 0.001177 -0.00058 1.053 0.001097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7506 0.7413 0.6893 0.1841 0.9664 0.9761 0.7508 0.9112 0.9459 0.7017 ] Network output: [ 0.01224 0.8036 0.1859 -0.0003011 0.0002475 0.985 -0.0006416 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08772 Epoch 404 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1164 1.119 0.8114 0.00335 -0.00152 -0.1496 0.002646 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08771 0.006676 0.08024 0.01621 0.885 0.9016 0.153 0.7775 0.8249 0.4359 ] Network output: [ 0.7563 0.0723 0.2152 -0.005732 0.002816 0.1769 -0.005346 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6841 0.1377 0.1104 0.2484 0.9401 0.967 0.7632 0.8147 0.9165 0.6949 ] Network output: [ -0.03895 0.8388 1.139 -0.001119 0.0003399 0.09527 -0.0001434 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1664 0.1037 0.3244 0.1545 0.9571 0.9675 0.17 0.8893 0.9349 0.4641 ] Network output: [ 0.239 -0.151 0.8481 0.0001982 -0.0001272 0.8257 0.0002577 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7598 0.5656 0.5197 0.4082 0.9511 0.9745 0.7632 0.8422 0.9361 0.7147 ] Network output: [ -0.03267 0.1 0.9478 0.002055 -0.001011 1.026 0.001889 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7396 0.6974 0.6426 0.3016 0.9657 0.9755 0.7402 0.9086 0.9443 0.6916 ] Network output: [ -0.06725 0.2509 0.8345 0.001165 -0.0005745 1.054 0.001086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7528 0.7437 0.6902 0.1816 0.9667 0.9764 0.7529 0.912 0.9464 0.7027 ] Network output: [ 0.01378 0.8095 0.1778 -0.0003632 0.0002748 0.9839 -0.0006872 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08541 Epoch 405 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1248 1.116 0.8042 0.003598 -0.001628 -0.1551 0.002814 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08796 0.005908 0.07256 0.01462 0.8854 0.902 0.1536 0.7781 0.8255 0.4361 ] Network output: [ 0.7914 0.06514 0.1794 -0.005272 0.002622 0.1517 -0.005048 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6865 0.1278 0.08323 0.2437 0.9405 0.9672 0.7662 0.8151 0.9167 0.6969 ] Network output: [ -0.03159 0.8408 1.128 -0.0009071 0.0002523 0.09012 -1.626e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1673 0.1023 0.3124 0.1488 0.9573 0.9676 0.171 0.8897 0.9349 0.4622 ] Network output: [ 0.2514 -0.1553 0.8387 0.0002717 -0.0001537 0.8148 0.0002885 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7616 0.5593 0.5034 0.403 0.9513 0.9746 0.765 0.8429 0.9363 0.7181 ] Network output: [ -0.03819 0.1054 0.9513 0.001926 -0.0009532 1.027 0.001794 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7407 0.6968 0.6386 0.2929 0.9659 0.9757 0.7414 0.9093 0.9446 0.6926 ] Network output: [ -0.07564 0.2626 0.8348 0.0009561 -0.000482 1.058 0.0009357 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7543 0.7449 0.6908 0.1685 0.9669 0.9765 0.7545 0.9129 0.9468 0.7045 ] Network output: [ 0.007382 0.8239 0.1724 -0.0005761 0.0003672 0.9868 -0.0008352 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08215 Epoch 406 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1212 1.11 0.8145 0.003607 -0.001635 -0.152 0.002829 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08797 0.006259 0.07294 0.01454 0.8859 0.9024 0.1536 0.7793 0.8263 0.4374 ] Network output: [ 0.7857 0.05372 0.1949 -0.005037 0.002508 0.1599 -0.004835 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6876 0.1327 0.08477 0.2441 0.9409 0.9674 0.7673 0.8165 0.9175 0.7013 ] Network output: [ -0.03646 0.8379 1.137 -0.00097 0.0002803 0.09376 -6.425e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.168 0.1038 0.3143 0.1465 0.9578 0.968 0.1716 0.891 0.9357 0.464 ] Network output: [ 0.2442 -0.1619 0.8523 0.000388 -0.0002093 0.8227 0.000391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7633 0.5651 0.5057 0.3996 0.9517 0.9748 0.7667 0.8442 0.9369 0.722 ] Network output: [ -0.03921 0.1139 0.9441 0.001873 -0.0009263 1.028 0.001744 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7432 0.7002 0.639 0.2798 0.9663 0.9759 0.7438 0.9104 0.9452 0.6931 ] Network output: [ -0.07413 0.2838 0.8129 0.0008369 -0.0004233 1.055 0.0008261 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7575 0.7483 0.691 0.1453 0.9672 0.9767 0.7576 0.9137 0.9472 0.7048 ] Network output: [ 0.008654 0.8395 0.1555 -0.0007087 0.0004277 0.985 -0.0009397 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08339 Epoch 407 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1212 1.099 0.8252 0.003646 -0.001654 -0.1514 0.002864 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08806 0.006105 0.07188 0.01592 0.8863 0.9028 0.1539 0.7802 0.8272 0.4382 ] Network output: [ 0.7909 0.03094 0.2094 -0.004888 0.002435 0.1584 -0.004699 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.689 0.1317 0.08127 0.2489 0.9412 0.9677 0.7689 0.8177 0.9182 0.7049 ] Network output: [ -0.03853 0.8229 1.154 -0.0009542 0.0002724 0.09632 -5.13e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1684 0.104 0.3136 0.1511 0.9581 0.9683 0.1721 0.8921 0.9365 0.4648 ] Network output: [ 0.2424 -0.188 0.8787 0.0004725 -0.0002516 0.8263 0.0004726 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7647 0.566 0.5046 0.4074 0.952 0.975 0.7681 0.8454 0.9376 0.7252 ] Network output: [ -0.04182 0.09733 0.9626 0.001904 -0.0009424 1.031 0.001776 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7446 0.7017 0.6383 0.2852 0.9665 0.9761 0.7453 0.9112 0.9458 0.6933 ] Network output: [ -0.07541 0.2774 0.8202 0.0008357 -0.0004234 1.057 0.0008276 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7593 0.7501 0.6902 0.1466 0.9674 0.9769 0.7594 0.9145 0.9477 0.7042 ] Network output: [ 0.008448 0.84 0.1549 -0.0007492 0.0004436 0.9853 -0.0009623 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08257 Epoch 408 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1249 1.102 0.8182 0.003754 -0.0017 -0.1545 0.002934 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08816 0.005705 0.06708 0.01465 0.8867 0.9031 0.1542 0.781 0.8278 0.4376 ] Network output: [ 0.811 0.03251 0.1839 -0.004597 0.002309 0.1433 -0.0045 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6906 0.1264 0.06476 0.2451 0.9415 0.9678 0.7709 0.8183 0.9186 0.7063 ] Network output: [ -0.03456 0.8296 1.143 -0.0008456 0.0002298 0.09298 4.59e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1692 0.1035 0.3054 0.1459 0.9584 0.9684 0.1729 0.8927 0.9367 0.4622 ] Network output: [ 0.2486 -0.186 0.8707 0.0005195 -0.0002685 0.8202 0.0004928 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7662 0.5634 0.4947 0.4022 0.9522 0.9751 0.7696 0.8461 0.9378 0.7273 ] Network output: [ -0.04467 0.1055 0.9593 0.001824 -0.0009051 1.032 0.001711 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7462 0.7024 0.6348 0.2761 0.9667 0.9763 0.7469 0.912 0.9461 0.6926 ] Network output: [ -0.07967 0.2892 0.8145 0.0006983 -0.0003609 1.058 0.0007216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7612 0.7519 0.6895 0.1339 0.9676 0.977 0.7614 0.9152 0.948 0.7041 ] Network output: [ 0.0051 0.8486 0.1508 -0.0008894 0.0005041 0.9869 -0.001059 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08118 Epoch 409 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1205 1.098 0.8271 0.003691 -0.001675 -0.1516 0.002895 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08813 0.005997 0.06829 0.01523 0.8871 0.9035 0.1542 0.782 0.8286 0.4378 ] Network output: [ 0.8025 0.02327 0.2021 -0.00453 0.002269 0.1517 -0.00441 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6915 0.1307 0.06971 0.2474 0.9419 0.968 0.7717 0.8196 0.9193 0.7096 ] Network output: [ -0.0397 0.8259 1.153 -0.0009382 0.0002708 0.09661 -6.438e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1697 0.1048 0.308 0.1463 0.9588 0.9687 0.1734 0.894 0.9375 0.4627 ] Network output: [ 0.2406 -0.1951 0.8882 0.0005584 -0.0002901 0.8279 0.0005401 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7676 0.5685 0.4991 0.4028 0.9525 0.9753 0.771 0.8473 0.9384 0.7299 ] Network output: [ -0.04453 0.1068 0.9572 0.001833 -0.0009067 1.032 0.001709 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7485 0.7056 0.6348 0.2699 0.967 0.9765 0.7491 0.9129 0.9467 0.6918 ] Network output: [ -0.07682 0.2995 0.8007 0.0006797 -0.0003486 1.056 0.000692 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.764 0.7549 0.6884 0.121 0.9678 0.9772 0.7642 0.9159 0.9484 0.7028 ] Network output: [ 0.007507 0.854 0.1421 -0.0009292 0.0005218 0.9853 -0.00109 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08281 Epoch 410 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1215 1.095 0.8296 0.003717 -0.001687 -0.1525 0.002914 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0882 0.005699 0.0662 0.01599 0.8874 0.9038 0.1544 0.7829 0.8294 0.4373 ] Network output: [ 0.8121 0.01196 0.2015 -0.004421 0.002218 0.1448 -0.004318 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6927 0.1275 0.0629 0.2501 0.9422 0.9682 0.7732 0.8204 0.9199 0.7115 ] Network output: [ -0.03947 0.8191 1.159 -0.0008946 0.000253 0.09686 -4.03e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1702 0.1046 0.3045 0.1489 0.9591 0.969 0.1739 0.8948 0.938 0.4612 ] Network output: [ 0.2415 -0.2115 0.9033 0.0005901 -0.0003057 0.8276 0.0005705 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7688 0.5675 0.4956 0.4084 0.9528 0.9754 0.7722 0.8481 0.9389 0.7317 ] Network output: [ -0.04674 0.09469 0.9712 0.001865 -0.0009224 1.035 0.001738 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7498 0.7065 0.6326 0.2748 0.9672 0.9766 0.7505 0.9136 0.9472 0.6907 ] Network output: [ -0.07869 0.2923 0.8093 0.0006947 -0.0003563 1.059 0.0007068 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7656 0.7563 0.6866 0.1245 0.968 0.9773 0.7657 0.9166 0.9488 0.7013 ] Network output: [ 0.006712 0.8518 0.1449 -0.0009491 0.000528 0.9862 -0.001095 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08174 Epoch 411 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1226 1.101 0.8231 0.00375 -0.0017 -0.1541 0.002932 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08821 0.005477 0.06304 0.01492 0.8878 0.9041 0.1546 0.7836 0.83 0.4361 ] Network output: [ 0.824 0.01801 0.1818 -0.004242 0.002139 0.1352 -0.004189 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6939 0.1245 0.05241 0.2467 0.9424 0.9684 0.7746 0.821 0.9203 0.7125 ] Network output: [ -0.03735 0.8279 1.148 -0.000844 0.0002355 0.09462 -2.394e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1708 0.1045 0.2985 0.1441 0.9593 0.9691 0.1745 0.8955 0.9384 0.458 ] Network output: [ 0.244 -0.2061 0.8963 0.0005946 -0.0003048 0.8241 0.000564 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7701 0.5665 0.4895 0.4032 0.9529 0.9755 0.7736 0.8488 0.9392 0.7329 ] Network output: [ -0.04812 0.1042 0.9642 0.001815 -0.0008972 1.035 0.001691 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7516 0.7078 0.6294 0.2658 0.9674 0.9768 0.7522 0.9143 0.9475 0.6891 ] Network output: [ -0.08061 0.3035 0.801 0.000608 -0.0003156 1.059 0.0006348 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7677 0.7584 0.685 0.1126 0.9682 0.9774 0.7678 0.9172 0.9491 0.7 ] Network output: [ 0.005107 0.8567 0.142 -0.001039 0.0005663 0.987 -0.001156 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.081 Epoch 412 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1181 1.099 0.831 0.003658 -0.001661 -0.1512 0.002871 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08816 0.005711 0.06455 0.01583 0.8881 0.9045 0.1545 0.7846 0.8308 0.4357 ] Network output: [ 0.8146 0.01001 0.2005 -0.004263 0.002139 0.1433 -0.004165 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6945 0.1282 0.0587 0.25 0.9427 0.9686 0.7753 0.8222 0.921 0.715 ] Network output: [ -0.04232 0.8238 1.159 -0.0009401 0.0002781 0.09813 -9.554e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1712 0.1056 0.3011 0.1459 0.9596 0.9694 0.1749 0.8966 0.9391 0.4576 ] Network output: [ 0.236 -0.2162 0.9153 0.0005868 -0.0003055 0.8312 0.0005762 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7714 0.5708 0.4947 0.4062 0.9532 0.9757 0.7748 0.8498 0.9397 0.7346 ] Network output: [ -0.04748 0.1014 0.9652 0.00186 -0.0009156 1.036 0.001718 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7536 0.7106 0.629 0.2639 0.9677 0.977 0.7542 0.9151 0.9481 0.6875 ] Network output: [ -0.07728 0.307 0.7926 0.0006495 -0.0003312 1.058 0.0006539 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7702 0.761 0.683 0.1062 0.9683 0.9775 0.7703 0.9178 0.9495 0.6977 ] Network output: [ 0.007968 0.857 0.1375 -0.001028 0.0005607 0.9856 -0.001146 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08248 Epoch 413 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1197 1.099 0.8289 0.003683 -0.001672 -0.1528 0.002885 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0882 0.005329 0.06187 0.01617 0.8885 0.9048 0.1548 0.7853 0.8314 0.4344 ] Network output: [ 0.8269 0.005636 0.1906 -0.004169 0.002097 0.1334 -0.004093 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6957 0.1236 0.04982 0.2512 0.9429 0.9687 0.7767 0.8229 0.9214 0.7159 ] Network output: [ -0.04063 0.8218 1.159 -0.000875 0.000252 0.09706 -6.042e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1716 0.1051 0.2959 0.147 0.9599 0.9696 0.1753 0.8972 0.9395 0.4545 ] Network output: [ 0.2385 -0.2264 0.9231 0.0005859 -0.0003048 0.8286 0.0005754 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7724 0.5686 0.4896 0.4102 0.9534 0.9758 0.7759 0.8504 0.9401 0.7355 ] Network output: [ -0.04959 0.0925 0.9763 0.001889 -0.000929 1.038 0.001742 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7548 0.711 0.6259 0.2682 0.9678 0.9771 0.7554 0.9157 0.9485 0.6856 ] Network output: [ -0.07965 0.2996 0.8021 0.0006702 -0.0003416 1.06 0.0006737 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7715 0.7622 0.6806 0.1108 0.9685 0.9776 0.7716 0.9184 0.9499 0.6956 ] Network output: [ 0.00672 0.8535 0.1423 -0.001039 0.0005628 0.9867 -0.001144 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08089 Epoch 414 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1192 1.106 0.8235 0.00367 -0.001665 -0.1533 0.002871 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08816 0.005236 0.05991 0.01524 0.8888 0.9051 0.1548 0.786 0.832 0.4328 ] Network output: [ 0.8331 0.01376 0.1759 -0.004064 0.002049 0.1279 -0.00401 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6965 0.1223 0.04366 0.2482 0.9432 0.9689 0.7778 0.8235 0.9218 0.7166 ] Network output: [ -0.03981 0.8314 1.149 -0.0008626 0.0002509 0.09569 -6.951e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1721 0.1053 0.2916 0.1424 0.9601 0.9698 0.1759 0.8979 0.9399 0.4511 ] Network output: [ 0.2385 -0.2187 0.917 0.0005557 -0.0002893 0.827 0.0005465 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7736 0.5688 0.4863 0.4049 0.9535 0.9759 0.7771 0.851 0.9403 0.7363 ] Network output: [ -0.04991 0.1032 0.9666 0.001855 -0.000911 1.037 0.001705 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7565 0.7127 0.6231 0.259 0.968 0.9772 0.7572 0.9163 0.9488 0.6833 ] Network output: [ -0.07989 0.3108 0.7914 0.0006159 -0.0003148 1.06 0.0006234 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7737 0.7644 0.6785 0.09912 0.9686 0.9777 0.7738 0.919 0.9501 0.6936 ] Network output: [ 0.006303 0.8567 0.1395 -0.001099 0.000588 0.9869 -0.001184 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08061 Epoch 415 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1148 1.104 0.8314 0.003569 -0.001622 -0.1504 0.002803 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0881 0.005421 0.06155 0.01641 0.8892 0.9053 0.1547 0.7869 0.8328 0.4319 ] Network output: [ 0.8236 0.005544 0.1952 -0.004134 0.002071 0.1357 -0.004024 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6971 0.1254 0.05058 0.2522 0.9434 0.969 0.7784 0.8245 0.9225 0.7186 ] Network output: [ -0.04452 0.8263 1.159 -0.0009517 0.0002904 0.09915 -0.0001357 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1724 0.1063 0.294 0.1453 0.9604 0.97 0.1761 0.899 0.9406 0.4501 ] Network output: [ 0.2306 -0.2302 0.9373 0.0005214 -0.000278 0.8338 0.0005385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7747 0.5725 0.4917 0.4097 0.9538 0.976 0.7782 0.8519 0.9409 0.7374 ] Network output: [ -0.04908 0.09716 0.9704 0.001925 -0.0009412 1.038 0.001753 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7583 0.7151 0.6223 0.2604 0.9682 0.9774 0.759 0.9171 0.9494 0.6811 ] Network output: [ -0.07646 0.3093 0.7874 0.0006973 -0.0003491 1.059 0.0006756 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7758 0.7666 0.6759 0.09768 0.9688 0.9779 0.776 0.9195 0.9505 0.6906 ] Network output: [ 0.009319 0.854 0.1378 -0.001058 0.0005685 0.9855 -0.00115 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08185 Epoch 416 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.117 1.107 0.8261 0.003603 -0.001636 -0.1525 0.002822 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08812 0.004971 0.05834 0.01637 0.8895 0.9056 0.1549 0.7876 0.8334 0.43 ] Network output: [ 0.8386 0.006073 0.1776 -0.004035 0.002029 0.1232 -0.003955 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6981 0.1197 0.03968 0.252 0.9436 0.9692 0.7797 0.825 0.9228 0.7187 ] Network output: [ -0.04158 0.8279 1.154 -0.0008644 0.0002553 0.09704 -8.738e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1728 0.1055 0.2874 0.1451 0.9606 0.9701 0.1765 0.8994 0.9409 0.4459 ] Network output: [ 0.2345 -0.2353 0.939 0.0004994 -0.0002666 0.8293 0.0005166 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7757 0.5692 0.4852 0.4121 0.9539 0.9761 0.7791 0.8524 0.9411 0.7377 ] Network output: [ -0.05128 0.09127 0.9789 0.001943 -0.0009491 1.04 0.001767 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7593 0.7152 0.6185 0.2638 0.9684 0.9775 0.76 0.9176 0.9497 0.6787 ] Network output: [ -0.07944 0.3024 0.7973 0.0007117 -0.0003569 1.062 0.0006912 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7769 0.7676 0.6731 0.1026 0.9689 0.9779 0.777 0.92 0.9508 0.6881 ] Network output: [ 0.007522 0.8503 0.1437 -0.001072 0.0005719 0.9868 -0.00115 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07977 Epoch 417 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1152 1.113 0.8225 0.003557 -0.001615 -0.1518 0.002785 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08804 0.005006 0.05746 0.01558 0.8898 0.9059 0.1549 0.7883 0.834 0.4282 ] Network output: [ 0.8395 0.01471 0.1686 -0.003985 0.002003 0.1219 -0.003905 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6988 0.12 0.0375 0.2494 0.9438 0.9693 0.7805 0.8257 0.9232 0.7193 ] Network output: [ -0.04197 0.8374 1.146 -0.0008842 0.0002677 0.09656 -0.0001173 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1732 0.106 0.2848 0.1407 0.9608 0.9703 0.177 0.9001 0.9413 0.4425 ] Network output: [ 0.2321 -0.2263 0.9343 0.0004435 -0.0002403 0.8296 0.0004717 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7768 0.5707 0.4846 0.4067 0.954 0.9761 0.7802 0.853 0.9414 0.7381 ] Network output: [ -0.05065 0.1033 0.9667 0.001922 -0.0009362 1.039 0.001738 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7612 0.7172 0.6161 0.254 0.9686 0.9776 0.7618 0.9182 0.95 0.676 ] Network output: [ -0.07817 0.3143 0.7839 0.0006796 -0.0003395 1.061 0.0006553 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7791 0.7698 0.6707 0.09037 0.9691 0.978 0.7793 0.9205 0.9511 0.6856 ] Network output: [ 0.008114 0.8527 0.1403 -0.001113 0.0005893 0.9865 -0.001177 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08002 Epoch 418 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.111 1.11 0.8308 0.003459 -0.001573 -0.1491 0.002719 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08797 0.005135 0.05911 0.017 0.8901 0.9062 0.1548 0.7892 0.8347 0.4271 ] Network output: [ 0.8305 0.005126 0.1887 -0.004081 0.002037 0.1291 -0.00394 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6993 0.1225 0.04447 0.2543 0.9441 0.9694 0.781 0.8267 0.9238 0.7209 ] Network output: [ -0.04631 0.8306 1.158 -0.0009591 0.0003008 0.09996 -0.0001727 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1734 0.1067 0.2869 0.1446 0.9611 0.9706 0.1772 0.9011 0.942 0.4411 ] Network output: [ 0.2246 -0.24 0.9563 0.0003961 -0.0002231 0.836 0.0004533 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7778 0.5737 0.4898 0.4134 0.9542 0.9763 0.7812 0.8538 0.9419 0.7388 ] Network output: [ -0.04991 0.09376 0.9739 0.002012 -0.0009758 1.04 0.001803 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7627 0.7192 0.615 0.2586 0.9687 0.9778 0.7633 0.9188 0.9505 0.6735 ] Network output: [ -0.07497 0.3083 0.7845 0.0007905 -0.0003879 1.06 0.000733 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7809 0.7718 0.6676 0.09352 0.9692 0.9781 0.7811 0.9209 0.9514 0.6822 ] Network output: [ 0.01106 0.8479 0.1407 -0.001052 0.0005606 0.9851 -0.001128 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08096 Epoch 419 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1141 1.115 0.8226 0.003509 -0.001594 -0.1516 0.002747 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08798 0.004618 0.05531 0.01654 0.8904 0.9065 0.155 0.7897 0.8352 0.4247 ] Network output: [ 0.8486 0.009952 0.1632 -0.00396 0.001987 0.1138 -0.003862 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7003 0.1157 0.03126 0.2525 0.9442 0.9696 0.7824 0.827 0.9241 0.7205 ] Network output: [ -0.04209 0.8355 1.148 -0.0008481 0.0002559 0.09682 -0.0001094 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1737 0.1057 0.279 0.1429 0.9612 0.9707 0.1775 0.9014 0.9422 0.436 ] Network output: [ 0.2301 -0.2401 0.9517 0.00036 -0.0002041 0.8297 0.000416 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7787 0.5695 0.4817 0.4139 0.9543 0.9763 0.7821 0.8541 0.942 0.7387 ] Network output: [ -0.05231 0.09138 0.9795 0.00201 -0.0009748 1.042 0.001801 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7635 0.7189 0.6106 0.2605 0.9689 0.9779 0.7642 0.9193 0.9508 0.6707 ] Network output: [ -0.0787 0.3029 0.7939 0.0007869 -0.0003876 1.064 0.0007353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7819 0.7725 0.6646 0.09775 0.9693 0.9782 0.782 0.9214 0.9517 0.6795 ] Network output: [ 0.008578 0.8447 0.1472 -0.001079 0.0005698 0.9867 -0.001137 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07847 Epoch 420 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1108 1.12 0.8217 0.003432 -0.00156 -0.1497 0.00269 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08786 0.004803 0.05562 0.01596 0.8907 0.9067 0.1548 0.7904 0.8358 0.4229 ] Network output: [ 0.8437 0.01781 0.1618 -0.00396 0.001982 0.1173 -0.003841 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7007 0.118 0.03345 0.2506 0.9444 0.9697 0.7829 0.8277 0.9245 0.7211 ] Network output: [ -0.0439 0.8441 1.142 -0.000902 0.0002827 0.09744 -0.0001611 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1741 0.1065 0.2782 0.1389 0.9615 0.9708 0.1779 0.9022 0.9426 0.4329 ] Network output: [ 0.225 -0.2307 0.9494 0.0002852 -0.0001704 0.8324 0.0003607 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7798 0.5724 0.4839 0.4085 0.9544 0.9764 0.7832 0.8547 0.9423 0.7389 ] Network output: [ -0.05067 0.1045 0.9648 0.002002 -0.0009674 1.04 0.00178 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7654 0.7213 0.6086 0.2501 0.969 0.978 0.7661 0.9199 0.9511 0.6678 ] Network output: [ -0.0758 0.3161 0.7772 0.0007735 -0.000378 1.061 0.0007111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7842 0.7749 0.662 0.08453 0.9694 0.9783 0.7843 0.9219 0.9519 0.6767 ] Network output: [ 0.01024 0.8469 0.1426 -0.001107 0.0005815 0.9857 -0.001156 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07938 Epoch 421 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1073 1.116 0.8303 0.003347 -0.001523 -0.1473 0.002633 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08781 0.004848 0.05705 0.01763 0.891 0.907 0.1548 0.7912 0.8365 0.4216 ] Network output: [ 0.8363 0.006089 0.1819 -0.004065 0.00202 0.1231 -0.003886 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7013 0.1195 0.03966 0.2563 0.9447 0.9698 0.7835 0.8286 0.9251 0.7225 ] Network output: [ -0.04767 0.8352 1.155 -0.0009546 0.0003058 0.1006 -0.0001997 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1742 0.107 0.2798 0.1439 0.9617 0.9711 0.178 0.9031 0.9433 0.4312 ] Network output: [ 0.2184 -0.2474 0.9734 0.0002344 -0.0001515 0.8381 0.0003389 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7807 0.5744 0.4885 0.4173 0.9546 0.9765 0.7841 0.8554 0.9428 0.7394 ] Network output: [ -0.05034 0.0909 0.9764 0.002108 -0.001015 1.042 0.00186 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7666 0.7228 0.6072 0.2582 0.9692 0.9781 0.7673 0.9205 0.9516 0.6651 ] Network output: [ -0.07325 0.3051 0.7834 0.0009087 -0.0004383 1.062 0.0008108 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7856 0.7764 0.6585 0.09262 0.9695 0.9784 0.7857 0.9223 0.9522 0.6729 ] Network output: [ 0.01286 0.8404 0.1453 -0.00103 0.0005457 0.9845 -0.001094 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07991 Epoch 422 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1113 1.123 0.819 0.003417 -0.001552 -0.1504 0.002674 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08781 0.004267 0.0526 0.01664 0.8913 0.9073 0.155 0.7917 0.8369 0.4189 ] Network output: [ 0.8582 0.01555 0.1477 -0.003909 0.001955 0.1049 -0.003788 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7023 0.1115 0.02378 0.2527 0.9448 0.9699 0.7848 0.8288 0.9253 0.7216 ] Network output: [ -0.04209 0.8437 1.141 -0.0008199 0.0002509 0.09639 -0.0001214 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1745 0.1058 0.2704 0.1404 0.9618 0.9711 0.1783 0.9033 0.9433 0.4253 ] Network output: [ 0.2256 -0.2417 0.9614 0.000188 -0.0001267 0.8298 0.0002891 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7814 0.5694 0.4787 0.4152 0.9547 0.9765 0.7849 0.8556 0.9429 0.7389 ] Network output: [ -0.05297 0.09312 0.9783 0.002079 -0.001001 1.043 0.001836 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7673 0.7223 0.6024 0.2577 0.9693 0.9782 0.768 0.9208 0.9518 0.6621 ] Network output: [ -0.0778 0.3026 0.7913 0.0008745 -0.0004242 1.065 0.0007899 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7863 0.7769 0.6555 0.09492 0.9696 0.9784 0.7865 0.9227 0.9525 0.6702 ] Network output: [ 0.009584 0.8384 0.1517 -0.00108 0.000565 0.9865 -0.00112 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07709 Epoch 423 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1062 1.126 0.8219 0.003304 -0.001503 -0.1471 0.002594 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08766 0.004642 0.05434 0.01639 0.8916 0.9075 0.1547 0.7925 0.8376 0.4171 ] Network output: [ 0.8458 0.02123 0.157 -0.003965 0.001974 0.1145 -0.0038 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7025 0.1165 0.03139 0.2517 0.945 0.97 0.7851 0.8296 0.9258 0.7224 ] Network output: [ -0.04575 0.8506 1.139 -0.0009152 0.0002952 0.09845 -0.0001997 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1748 0.1071 0.272 0.1371 0.9621 0.9713 0.1786 0.9042 0.9439 0.4227 ] Network output: [ 0.2172 -0.2331 0.9635 9.922e-05 -8.797e-05 0.8356 0.000228 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7825 0.5742 0.4843 0.4103 0.9548 0.9766 0.786 0.8563 0.9432 0.7392 ] Network output: [ -0.05012 0.1067 0.9613 0.002089 -0.001002 1.041 0.001828 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7693 0.7251 0.601 0.2468 0.9695 0.9783 0.7699 0.9214 0.9522 0.659 ] Network output: [ -0.07291 0.317 0.7708 0.0008833 -0.0004239 1.062 0.0007798 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7888 0.7795 0.6529 0.08051 0.9698 0.9785 0.7889 0.9231 0.9526 0.6671 ] Network output: [ 0.01257 0.8407 0.1453 -0.001093 0.0005707 0.9845 -0.00113 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07883 Epoch 424 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1039 1.12 0.8303 0.003245 -0.001477 -0.1453 0.002554 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08763 0.004551 0.05522 0.01829 0.8919 0.9078 0.1547 0.7932 0.8382 0.4157 ] Network output: [ 0.8421 0.007015 0.1751 -0.004061 0.00201 0.1174 -0.003843 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7031 0.1162 0.03549 0.2583 0.9452 0.9702 0.7858 0.8304 0.9263 0.7236 ] Network output: [ -0.04854 0.8394 1.152 -0.0009342 0.0003034 0.1012 -0.0002135 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1748 0.1072 0.2726 0.1432 0.9623 0.9715 0.1786 0.905 0.9445 0.4208 ] Network output: [ 0.2123 -0.2537 0.9892 5.261e-05 -7.06e-05 0.84 0.0002079 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7834 0.5748 0.4873 0.4215 0.955 0.9767 0.7868 0.8569 0.9436 0.7395 ] Network output: [ -0.05065 0.0882 0.9788 0.002208 -0.001056 1.043 0.001921 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7702 0.726 0.5992 0.2591 0.9696 0.9785 0.7708 0.922 0.9527 0.6562 ] Network output: [ -0.07163 0.3001 0.7842 0.001039 -0.0004945 1.063 0.0008995 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7898 0.7805 0.649 0.09438 0.9699 0.9786 0.7899 0.9235 0.953 0.6631 ] Network output: [ 0.01451 0.8323 0.1509 -0.001003 0.0005288 0.9839 -0.001057 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07875 Epoch 425 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1088 1.129 0.8156 0.003334 -0.001514 -0.149 0.002607 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08761 0.003919 0.05008 0.01665 0.8922 0.908 0.1548 0.7936 0.8386 0.4127 ] Network output: [ 0.8677 0.02215 0.1309 -0.003859 0.001926 0.09626 -0.003717 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7041 0.1073 0.01676 0.2523 0.9453 0.9702 0.7871 0.8304 0.9264 0.7221 ] Network output: [ -0.04157 0.8524 1.132 -0.0007785 0.0002398 0.09573 -0.0001223 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1751 0.1057 0.2618 0.1374 0.9624 0.9716 0.1789 0.905 0.9444 0.414 ] Network output: [ 0.2212 -0.2406 0.9684 -2.862e-06 -4.047e-05 0.8297 0.000146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.784 0.569 0.4757 0.416 0.955 0.9767 0.7875 0.8569 0.9436 0.7387 ] Network output: [ -0.0534 0.09679 0.9752 0.002142 -0.001025 1.043 0.001867 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7707 0.7252 0.5939 0.2548 0.9697 0.9785 0.7714 0.9223 0.9528 0.653 ] Network output: [ -0.07695 0.3025 0.7887 0.0009605 -0.0004602 1.067 0.0008442 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7904 0.7809 0.6462 0.09299 0.9699 0.9787 0.7906 0.9239 0.9532 0.6607 ] Network output: [ 0.01039 0.8323 0.1565 -0.001085 0.0005626 0.9862 -0.001107 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07574 Epoch 426 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1013 1.13 0.8238 0.003176 -0.001446 -0.1439 0.002498 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08743 0.004535 0.05363 0.01691 0.8924 0.9082 0.1544 0.7944 0.8392 0.4112 ] Network output: [ 0.8456 0.02382 0.1553 -0.00399 0.001974 0.1138 -0.003772 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7042 0.1157 0.03135 0.2532 0.9455 0.9704 0.7871 0.8314 0.9269 0.7234 ] Network output: [ -0.0477 0.8561 1.136 -0.0009258 0.000306 0.09971 -0.0002341 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1753 0.1076 0.2665 0.1355 0.9627 0.9718 0.1791 0.9061 0.9451 0.4125 ] Network output: [ 0.2086 -0.2347 0.9775 -0.0001019 1.274e-06 0.8395 8.366e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7852 0.5763 0.4857 0.4122 0.9552 0.9769 0.7886 0.8577 0.944 0.739 ] Network output: [ -0.04907 0.1097 0.9566 0.002182 -0.001039 1.041 0.00188 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7729 0.7287 0.5934 0.2439 0.9699 0.9786 0.7735 0.9229 0.9532 0.6499 ] Network output: [ -0.06955 0.3177 0.7641 0.001002 -0.0004739 1.061 0.0008558 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7931 0.7838 0.6434 0.07768 0.9701 0.9787 0.7932 0.9243 0.9533 0.6572 ] Network output: [ 0.01511 0.8346 0.1478 -0.001077 0.0005594 0.9831 -0.001104 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07851 Epoch 427 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.101 1.123 0.8309 0.003158 -0.001438 -0.1433 0.002486 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08743 0.004225 0.05345 0.01899 0.8927 0.9085 0.1546 0.7951 0.8398 0.4097 ] Network output: [ 0.8487 0.007251 0.1678 -0.004053 0.001997 0.1114 -0.0038 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.705 0.1126 0.03128 0.2603 0.9457 0.9705 0.788 0.8321 0.9274 0.7243 ] Network output: [ -0.04885 0.8428 1.15 -0.0008953 0.0002923 0.1015 -0.000212 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1753 0.1071 0.2652 0.1427 0.9629 0.972 0.1791 0.9067 0.9456 0.4101 ] Network output: [ 0.2068 -0.2595 1.004 -0.0001379 1.452e-05 0.8415 6.866e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7859 0.5746 0.4859 0.4261 0.9553 0.9769 0.7893 0.8583 0.9444 0.7392 ] Network output: [ -0.05107 0.0853 0.9818 0.002307 -0.001097 1.044 0.001983 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7733 0.7287 0.591 0.2612 0.97 0.9788 0.7739 0.9234 0.9536 0.6472 ] Network output: [ -0.07042 0.2936 0.7871 0.001173 -0.0005529 1.065 0.0009931 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7934 0.7841 0.6393 0.09853 0.9701 0.9788 0.7936 0.9246 0.9537 0.6531 ] Network output: [ 0.01581 0.8238 0.1574 -0.0009762 0.0005119 0.9833 -0.00102 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07747 Epoch 428 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1065 1.135 0.8123 0.003261 -0.001481 -0.1475 0.002549 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08739 0.003579 0.04771 0.01653 0.893 0.9087 0.1547 0.7954 0.8401 0.4063 ] Network output: [ 0.8775 0.02959 0.1127 -0.0038 0.001892 0.08768 -0.003642 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7059 0.1031 0.009971 0.2513 0.9458 0.9706 0.7893 0.832 0.9274 0.7222 ] Network output: [ -0.04058 0.8616 1.122 -0.0007258 0.0002234 0.09482 -0.0001134 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1756 0.1055 0.2532 0.1339 0.9629 0.972 0.1794 0.9066 0.9454 0.4026 ] Network output: [ 0.2172 -0.2368 0.9724 -0.0002036 5.057e-05 0.8292 -6.255e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7865 0.5684 0.4728 0.4158 0.9553 0.9769 0.7899 0.8582 0.9443 0.7381 ] Network output: [ -0.05365 0.1028 0.9698 0.002196 -0.001045 1.044 0.001891 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7738 0.7278 0.5853 0.2513 0.9701 0.9788 0.7745 0.9236 0.9536 0.6438 ] Network output: [ -0.07621 0.3036 0.7853 0.001035 -0.0004914 1.068 0.0008906 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7941 0.7845 0.6369 0.09104 0.9702 0.9788 0.7943 0.925 0.9538 0.6511 ] Network output: [ 0.01094 0.8271 0.1609 -0.001102 0.0005657 0.9858 -0.001104 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07453 Epoch 429 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.09616 1.132 0.8277 0.003047 -0.001389 -0.1402 0.002401 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08717 0.004495 0.05351 0.01758 0.8933 0.9089 0.1541 0.7963 0.8408 0.4052 ] Network output: [ 0.8428 0.02476 0.1581 -0.004031 0.001981 0.1155 -0.003756 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7058 0.1158 0.03355 0.2551 0.946 0.9707 0.7889 0.8331 0.9281 0.7243 ] Network output: [ -0.04993 0.8603 1.134 -0.0009373 0.0003167 0.1013 -0.0002666 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1757 0.1083 0.2618 0.1342 0.9632 0.9722 0.1795 0.9079 0.9463 0.4025 ] Network output: [ 0.1992 -0.2364 0.9923 -0.0003092 9.319e-05 0.8444 -6.524e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7877 0.5788 0.4883 0.4145 0.9556 0.9771 0.7911 0.8591 0.9448 0.7386 ] Network output: [ -0.04752 0.1129 0.951 0.002281 -0.001079 1.04 0.001938 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7762 0.7322 0.586 0.2415 0.9703 0.9789 0.7768 0.9243 0.9541 0.6407 ] Network output: [ -0.06567 0.3181 0.7571 0.001127 -0.0005272 1.061 0.0009377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.797 0.7878 0.6339 0.07578 0.9703 0.9789 0.7972 0.9253 0.954 0.6472 ] Network output: [ 0.01791 0.8288 0.1498 -0.00106 0.0005482 0.9814 -0.001079 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07851 Epoch 430 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.09887 1.125 0.8317 0.003091 -0.001408 -0.1415 0.002432 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08723 0.003848 0.05155 0.01969 0.8935 0.9092 0.1544 0.7968 0.8414 0.4036 ] Network output: [ 0.8571 0.006674 0.1588 -0.004027 0.001978 0.1043 -0.003747 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7067 0.1082 0.02631 0.2623 0.9462 0.9708 0.7902 0.8336 0.9285 0.7246 ] Network output: [ -0.04847 0.8455 1.146 -0.0008356 0.0002718 0.1015 -0.0001939 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1756 0.1069 0.2575 0.1422 0.9634 0.9724 0.1794 0.9083 0.9466 0.3993 ] Network output: [ 0.2021 -0.2653 1.017 -0.0003292 0.0001004 0.8423 -7.3e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7883 0.5737 0.4838 0.4312 0.9556 0.9771 0.7917 0.8595 0.9451 0.7387 ] Network output: [ -0.05184 0.08194 0.9858 0.002401 -0.001137 1.046 0.002043 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.776 0.7308 0.5826 0.2645 0.9704 0.9791 0.7766 0.9247 0.9545 0.6381 ] Network output: [ -0.06992 0.2856 0.7925 0.001305 -0.0006109 1.067 0.001087 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7967 0.7872 0.6296 0.1049 0.9704 0.979 0.7968 0.9256 0.9544 0.6431 ] Network output: [ 0.01662 0.815 0.165 -0.000951 0.0004961 0.983 -0.0009848 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07605 Epoch 431 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1045 1.141 0.8091 0.003198 -0.001452 -0.146 0.002496 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08715 0.003261 0.04547 0.01624 0.8938 0.9094 0.1545 0.7971 0.8416 0.3998 ] Network output: [ 0.8873 0.03802 0.09315 -0.003726 0.001852 0.07934 -0.003557 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7076 0.09895 0.003424 0.2496 0.9463 0.9708 0.7914 0.8334 0.9284 0.7221 ] Network output: [ -0.03922 0.8715 1.11 -0.0006659 0.0002034 0.09368 -9.784e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1759 0.1053 0.2447 0.1297 0.9634 0.9724 0.1798 0.9081 0.9463 0.391 ] Network output: [ 0.2133 -0.2301 0.9732 -0.0004086 0.0001439 0.8285 -0.0001634 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7888 0.5679 0.47 0.4144 0.9556 0.9771 0.7923 0.8593 0.9448 0.7373 ] Network output: [ -0.05363 0.1114 0.9617 0.002236 -0.001059 1.043 0.001904 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7766 0.7301 0.5767 0.2466 0.9705 0.9791 0.7773 0.9249 0.9545 0.6345 ] Network output: [ -0.0755 0.3066 0.7803 0.001092 -0.0005146 1.068 0.0009239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7975 0.7878 0.6276 0.08818 0.9705 0.979 0.7977 0.926 0.9544 0.6416 ] Network output: [ 0.01128 0.8231 0.1645 -0.001133 0.0005759 0.9854 -0.001114 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0736 Epoch 432 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.09055 1.132 0.8341 0.002914 -0.00133 -0.1359 0.002303 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0869 0.004531 0.05403 0.01847 0.894 0.9096 0.1537 0.7981 0.8424 0.3993 ] Network output: [ 0.8368 0.02336 0.1667 -0.004092 0.001997 0.1199 -0.003753 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7073 0.1169 0.03822 0.2577 0.9465 0.971 0.7906 0.8348 0.9292 0.7252 ] Network output: [ -0.05261 0.8628 1.135 -0.0009523 0.0003283 0.1035 -0.0002991 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.176 0.1091 0.258 0.1335 0.9638 0.9727 0.1798 0.9096 0.9475 0.3929 ] Network output: [ 0.1887 -0.2392 1.009 -0.000516 0.0001847 0.8505 -0.0002132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7902 0.5819 0.4921 0.4175 0.9559 0.9773 0.7936 0.8604 0.9455 0.7382 ] Network output: [ -0.04546 0.1155 0.9454 0.002391 -0.001124 1.04 0.002005 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7792 0.7356 0.5788 0.24 0.9707 0.9792 0.7799 0.9257 0.955 0.6314 ] Network output: [ -0.06122 0.318 0.75 0.001262 -0.0005847 1.06 0.001027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8007 0.7916 0.6244 0.07497 0.9706 0.9791 0.8008 0.9263 0.9546 0.6372 ] Network output: [ 0.02107 0.8231 0.1513 -0.00104 0.0005357 0.9793 -0.001052 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07899 Epoch 433 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.09767 1.125 0.8321 0.003047 -0.001388 -0.1402 0.002395 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08702 0.003395 0.04933 0.02035 0.8943 0.9099 0.1543 0.7984 0.8428 0.3974 ] Network output: [ 0.8684 0.005592 0.1467 -0.003972 0.001947 0.0951 -0.003678 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7085 0.1027 0.01979 0.2642 0.9466 0.9711 0.7924 0.835 0.9294 0.7247 ] Network output: [ -0.04723 0.8478 1.143 -0.000753 0.0002408 0.1009 -0.0001578 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1758 0.1063 0.2492 0.1418 0.9639 0.9728 0.1796 0.9097 0.9476 0.3884 ] Network output: [ 0.1988 -0.2711 1.029 -0.0005164 0.0001849 0.842 -0.0002136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7905 0.5718 0.4807 0.4366 0.9559 0.9773 0.7939 0.8606 0.9457 0.738 ] Network output: [ -0.05316 0.07812 0.9913 0.002486 -0.001172 1.047 0.002097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7783 0.7323 0.5742 0.269 0.9708 0.9793 0.7789 0.9259 0.9553 0.6292 ] Network output: [ -0.07043 0.2761 0.8008 0.001429 -0.000666 1.07 0.001178 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7994 0.7899 0.62 0.1134 0.9706 0.9792 0.7996 0.9265 0.955 0.6333 ] Network output: [ 0.01677 0.8059 0.1738 -0.0009286 0.0004817 0.9831 -0.000952 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07445 Epoch 434 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1025 1.146 0.806 0.003137 -0.001424 -0.1444 0.002446 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08688 0.002988 0.04347 0.01578 0.8945 0.9101 0.1542 0.7987 0.843 0.3932 ] Network output: [ 0.8965 0.04768 0.073 -0.00364 0.001807 0.07179 -0.003464 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7092 0.09533 -0.002485 0.2471 0.9467 0.9711 0.7934 0.8347 0.9292 0.7217 ] Network output: [ -0.0377 0.8821 1.098 -0.0006054 0.0001828 0.09241 -8.036e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1762 0.1052 0.2364 0.1249 0.9639 0.9727 0.18 0.9095 0.9472 0.3795 ] Network output: [ 0.2093 -0.2201 0.9709 -0.000615 0.0002381 0.828 -0.0003229 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.791 0.5676 0.4674 0.4114 0.9558 0.9772 0.7945 0.8603 0.9454 0.7363 ] Network output: [ -0.05318 0.1232 0.9502 0.002263 -0.001066 1.042 0.001908 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7792 0.7323 0.5683 0.2402 0.9708 0.9793 0.7799 0.9261 0.9552 0.6253 ] Network output: [ -0.07458 0.3123 0.7727 0.001129 -0.0005285 1.069 0.0009412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8007 0.791 0.6186 0.08363 0.9707 0.9792 0.8008 0.9269 0.955 0.6323 ] Network output: [ 0.01156 0.8206 0.1669 -0.001181 0.0005939 0.9848 -0.001138 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07317 Epoch 435 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.08444 1.13 0.8431 0.002776 -0.001269 -0.1311 0.002201 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0866 0.004641 0.05519 0.01963 0.8948 0.9103 0.1533 0.7999 0.8438 0.3937 ] Network output: [ 0.8274 0.01894 0.1822 -0.004178 0.002024 0.1273 -0.003767 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7086 0.1192 0.04549 0.2615 0.9469 0.9713 0.7921 0.8365 0.9302 0.7261 ] Network output: [ -0.0558 0.8629 1.139 -0.0009711 0.0003409 0.1061 -0.0003318 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1761 0.1099 0.2552 0.1336 0.9643 0.9731 0.1799 0.9114 0.9486 0.3839 ] Network output: [ 0.1773 -0.2442 1.029 -0.0007166 0.000273 0.8578 -0.0003555 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7925 0.5856 0.497 0.4218 0.9562 0.9775 0.796 0.8617 0.9462 0.7376 ] Network output: [ -0.0429 0.1165 0.9405 0.002518 -0.001177 1.039 0.002086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.782 0.7388 0.5718 0.2398 0.9711 0.9795 0.7827 0.927 0.9559 0.6223 ] Network output: [ -0.05621 0.3164 0.7436 0.001411 -0.0006489 1.058 0.001128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8041 0.7951 0.6149 0.07586 0.9708 0.9793 0.8042 0.9272 0.9552 0.6271 ] Network output: [ 0.02464 0.8172 0.1526 -0.00101 0.0005196 0.977 -0.001019 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0801 Epoch 436 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.09765 1.125 0.8314 0.003028 -0.001379 -0.1397 0.002375 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0868 0.002839 0.04662 0.02087 0.895 0.9105 0.1541 0.8 0.8441 0.3912 ] Network output: [ 0.8837 0.004769 0.1293 -0.003879 0.0019 0.08293 -0.003586 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7103 0.09569 0.01091 0.2656 0.9471 0.9714 0.7946 0.8362 0.9302 0.7245 ] Network output: [ -0.04488 0.8503 1.137 -0.0006451 0.0001984 0.09946 -0.0001023 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1758 0.1053 0.2401 0.141 0.9643 0.9731 0.1796 0.911 0.9484 0.3773 ] Network output: [ 0.1974 -0.2763 1.038 -0.0006966 0.0002668 0.8401 -0.0003516 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7925 0.5687 0.4761 0.4421 0.9561 0.9774 0.796 0.8615 0.9462 0.7372 ] Network output: [ -0.05518 0.07427 0.9979 0.002555 -0.001201 1.048 0.00214 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7801 0.7332 0.5656 0.2743 0.9711 0.9796 0.7808 0.927 0.9561 0.6205 ] Network output: [ -0.07221 0.2654 0.812 0.001539 -0.0007153 1.073 0.00126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8017 0.792 0.6106 0.1237 0.9708 0.9793 0.8019 0.9274 0.9556 0.6238 ] Network output: [ 0.01607 0.7963 0.1842 -0.0009102 0.0004691 0.9838 -0.0009223 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07273 Epoch 437 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1001 1.151 0.8034 0.00307 -0.001394 -0.1424 0.002392 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08658 0.002804 0.04191 0.01518 0.8952 0.9107 0.1538 0.8002 0.8443 0.3867 ] Network output: [ 0.9035 0.05857 0.05404 -0.003551 0.001761 0.06618 -0.003369 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7108 0.09279 -0.006841 0.2441 0.9471 0.9714 0.7952 0.8359 0.93 0.7212 ] Network output: [ -0.03635 0.8936 1.085 -0.0005535 0.0001657 0.09124 -6.751e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1763 0.1051 0.2289 0.1195 0.9643 0.973 0.1801 0.9108 0.948 0.3682 ] Network output: [ 0.2048 -0.2069 0.9659 -0.0008225 0.0003328 0.828 -0.0004837 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7931 0.568 0.4658 0.4069 0.956 0.9773 0.7966 0.8612 0.9458 0.7352 ] Network output: [ -0.05197 0.1385 0.9345 0.00228 -0.001069 1.04 0.001903 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7817 0.7346 0.56 0.2318 0.9711 0.9795 0.7823 0.9272 0.9559 0.616 ] Network output: [ -0.07303 0.3212 0.7614 0.001145 -0.0005333 1.068 0.0009427 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8037 0.7939 0.6099 0.07674 0.9709 0.9793 0.8038 0.9278 0.9555 0.6232 ] Network output: [ 0.01205 0.8195 0.1675 -0.001244 0.000619 0.9839 -0.001174 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0735 Epoch 438 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07793 1.126 0.8548 0.002633 -0.001206 -0.1259 0.002096 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08628 0.004803 0.05687 0.02116 0.8955 0.9109 0.1528 0.8015 0.8453 0.3882 ] Network output: [ 0.815 0.01088 0.2047 -0.004291 0.002062 0.1373 -0.003801 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7099 0.1223 0.05505 0.2667 0.9474 0.9716 0.7935 0.8381 0.9312 0.7272 ] Network output: [ -0.05942 0.8604 1.145 -0.0009897 0.0003529 0.1091 -0.0003618 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.176 0.1109 0.2532 0.1349 0.9648 0.9735 0.1798 0.9131 0.9497 0.3754 ] Network output: [ 0.1652 -0.2526 1.052 -0.0009054 0.0003559 0.8662 -0.0004882 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7949 0.5897 0.5028 0.4279 0.9565 0.9777 0.7983 0.8628 0.9469 0.737 ] Network output: [ -0.03999 0.1146 0.938 0.002666 -0.001241 1.038 0.002185 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7846 0.7419 0.565 0.242 0.9714 0.9798 0.7852 0.9282 0.9567 0.6131 ] Network output: [ -0.05088 0.3118 0.7396 0.001582 -0.0007235 1.057 0.001248 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8071 0.7982 0.6053 0.07967 0.9711 0.9795 0.8073 0.928 0.9557 0.617 ] Network output: [ 0.02857 0.8102 0.1544 -0.0009649 0.0004964 0.9745 -0.0009752 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.082 Epoch 439 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.09897 1.126 0.8285 0.003036 -0.001381 -0.1401 0.002373 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08657 0.002162 0.04324 0.02115 0.8957 0.9111 0.154 0.8013 0.8453 0.3848 ] Network output: [ 0.9042 0.005514 0.1043 -0.00374 0.001834 0.06683 -0.003466 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.712 0.08687 -0.001022 0.266 0.9474 0.9716 0.7968 0.8373 0.931 0.7237 ] Network output: [ -0.04116 0.8541 1.129 -0.0005102 0.000144 0.09696 -2.701e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1757 0.1039 0.2298 0.1396 0.9647 0.9734 0.1795 0.912 0.949 0.3658 ] Network output: [ 0.1986 -0.2794 1.043 -0.0008692 0.0003461 0.836 -0.0004873 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7944 0.5642 0.4694 0.4469 0.9563 0.9775 0.7979 0.8622 0.9466 0.7361 ] Network output: [ -0.05793 0.07157 1.005 0.0026 -0.001219 1.05 0.002167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7815 0.7333 0.5568 0.2797 0.9714 0.9798 0.7822 0.928 0.9568 0.6119 ] Network output: [ -0.07546 0.2545 0.8255 0.001626 -0.0007545 1.078 0.001326 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8035 0.7935 0.6015 0.1351 0.971 0.9795 0.8037 0.9282 0.9561 0.6146 ] Network output: [ 0.01437 0.7864 0.1961 -0.0008994 0.00046 0.9852 -0.0008983 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.071 Epoch 440 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.09668 1.156 0.8023 0.002983 -0.001355 -0.1396 0.002324 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08623 0.002776 0.04116 0.01457 0.8959 0.9113 0.1533 0.8017 0.8454 0.3802 ] Network output: [ 0.9058 0.0702 0.03985 -0.003481 0.001722 0.06452 -0.003285 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7121 0.0923 -0.008014 0.2408 0.9474 0.9716 0.7967 0.8371 0.9308 0.7206 ] Network output: [ -0.03573 0.9055 1.073 -0.0005226 0.0001574 0.09055 -6.803e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1763 0.1055 0.2226 0.1137 0.9647 0.9734 0.1802 0.9121 0.9487 0.3573 ] Network output: [ 0.1986 -0.1906 0.9596 -0.001032 0.0004284 0.8296 -0.0006456 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7952 0.5699 0.4659 0.4008 0.9563 0.9774 0.7986 0.862 0.9463 0.7338 ] Network output: [ -0.04952 0.1572 0.9138 0.002294 -0.001071 1.037 0.001894 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7841 0.7371 0.5522 0.2211 0.9714 0.9798 0.7847 0.9283 0.9565 0.6066 ] Network output: [ -0.07017 0.3337 0.7452 0.001149 -0.0005317 1.066 0.0009326 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8066 0.7969 0.6014 0.06707 0.9711 0.9795 0.8068 0.9286 0.9559 0.6143 ] Network output: [ 0.0132 0.8198 0.1661 -0.001316 0.0006488 0.9824 -0.001219 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07488 Epoch 441 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07143 1.119 0.8688 0.002493 -0.001144 -0.1206 0.001994 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08594 0.004962 0.05881 0.0231 0.8961 0.9115 0.1522 0.8031 0.8466 0.3829 ] Network output: [ 0.8011 -0.001343 0.2329 -0.004424 0.00211 0.1485 -0.003851 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7111 0.1256 0.06583 0.2735 0.9478 0.9719 0.7948 0.8395 0.9322 0.7283 ] Network output: [ -0.06309 0.8548 1.155 -0.0009977 0.0003597 0.1123 -0.0003819 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1758 0.1117 0.2515 0.1376 0.9653 0.9739 0.1796 0.9146 0.9508 0.3672 ] Network output: [ 0.1532 -0.2657 1.08 -0.001077 0.0004307 0.8751 -0.0006074 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7971 0.5935 0.5087 0.4367 0.9568 0.9778 0.8005 0.8638 0.9475 0.7361 ] Network output: [ -0.03713 0.1081 0.9399 0.00284 -0.001316 1.038 0.002305 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7867 0.7446 0.5582 0.2478 0.9717 0.9801 0.7874 0.9292 0.9575 0.604 ] Network output: [ -0.04578 0.3022 0.7407 0.001783 -0.0008121 1.056 0.001393 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8098 0.8009 0.5956 0.08826 0.9713 0.9796 0.8099 0.9286 0.9562 0.6067 ] Network output: [ 0.03256 0.801 0.1582 -0.0008938 0.0004616 0.9722 -0.0009115 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08471 Epoch 442 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1017 1.128 0.8224 0.003067 -0.001393 -0.1415 0.002388 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08632 0.001354 0.0391 0.02101 0.8964 0.9117 0.1538 0.8024 0.8463 0.3782 ] Network output: [ 0.9306 0.00976 0.06882 -0.003553 0.001749 0.04609 -0.003317 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7138 0.07603 -0.01649 0.2648 0.9478 0.9718 0.7991 0.838 0.9315 0.7223 ] Network output: [ -0.03579 0.8605 1.116 -0.0003486 7.786e-05 0.09304 6.76e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1754 0.102 0.2181 0.1369 0.965 0.9736 0.1792 0.9128 0.9495 0.3537 ] Network output: [ 0.2028 -0.2782 1.039 -0.001036 0.0004235 0.8291 -0.0006225 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.796 0.558 0.4603 0.4501 0.9565 0.9776 0.7995 0.8626 0.9469 0.7345 ] Network output: [ -0.06123 0.07215 1.01 0.002612 -0.001223 1.051 0.002169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7824 0.7327 0.5476 0.284 0.9716 0.98 0.7831 0.9288 0.9573 0.6034 ] Network output: [ -0.08022 0.2453 0.8396 0.001675 -0.000777 1.082 0.001365 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8048 0.7946 0.5926 0.146 0.9712 0.9796 0.805 0.9288 0.9566 0.6058 ] Network output: [ 0.01153 0.7771 0.209 -0.0009048 0.0004583 0.9873 -0.0008866 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06955 Epoch 443 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.09139 1.16 0.8045 0.002858 -0.001299 -0.1356 0.002229 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08583 0.002996 0.04178 0.01415 0.8966 0.9118 0.1526 0.803 0.8465 0.3737 ] Network output: [ 0.8993 0.08126 0.03638 -0.003462 0.001704 0.06985 -0.003233 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7132 0.09526 -0.003533 0.2381 0.9478 0.9718 0.798 0.8381 0.9315 0.72 ] Network output: [ -0.03663 0.9169 1.063 -0.0005278 0.0001645 0.09095 -9.238e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1763 0.1064 0.2184 0.1082 0.9651 0.9737 0.1801 0.9134 0.9495 0.3471 ] Network output: [ 0.1892 -0.1724 0.9546 -0.001247 0.0005254 0.8343 -0.0008086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7972 0.5742 0.4693 0.3939 0.9565 0.9776 0.8007 0.8627 0.9466 0.7323 ] Network output: [ -0.04523 0.1788 0.8878 0.002318 -0.001077 1.033 0.001892 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7865 0.7402 0.5448 0.2084 0.9717 0.98 0.7872 0.9293 0.9571 0.5972 ] Network output: [ -0.06505 0.3496 0.7229 0.001152 -0.0005293 1.062 0.0009199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8096 0.8001 0.593 0.05455 0.9713 0.9796 0.8097 0.9293 0.9562 0.6054 ] Network output: [ 0.01566 0.821 0.1621 -0.001387 0.0006788 0.9801 -0.001266 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07757 Epoch 444 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06577 1.11 0.8839 0.002371 -0.00109 -0.1157 0.001904 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0856 0.005019 0.06048 0.02545 0.8968 0.912 0.1517 0.8044 0.8477 0.3778 ] Network output: [ 0.7892 -0.01795 0.2629 -0.004554 0.002157 0.1582 -0.003903 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7123 0.1276 0.0756 0.2819 0.9482 0.9721 0.7961 0.8409 0.933 0.7292 ] Network output: [ -0.06617 0.8461 1.167 -0.0009788 0.0003541 0.1152 -0.0003807 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1754 0.1121 0.2493 0.1418 0.9657 0.9743 0.1792 0.916 0.9518 0.3593 ] Network output: [ 0.1427 -0.2845 1.111 -0.001226 0.0004955 0.8833 -0.0007101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7991 0.5961 0.5134 0.4488 0.9571 0.978 0.8026 0.8647 0.9481 0.7351 ] Network output: [ -0.03511 0.09513 0.9491 0.003036 -0.001403 1.038 0.002447 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7884 0.7465 0.5511 0.2586 0.972 0.9803 0.7891 0.9302 0.9582 0.595 ] Network output: [ -0.04193 0.2847 0.7507 0.002018 -0.0009175 1.057 0.001568 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8118 0.803 0.5855 0.104 0.9714 0.9797 0.8119 0.9292 0.9566 0.5962 ] Network output: [ 0.03601 0.7878 0.1664 -0.0007862 0.0004102 0.9706 -0.000819 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08801 Epoch 445 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1058 1.133 0.812 0.003116 -0.001413 -0.144 0.002414 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08605 0.000429 0.03417 0.02026 0.8971 0.9123 0.1536 0.8033 0.8472 0.3711 ] Network output: [ 0.9629 0.0199 0.02061 -0.00332 0.001644 0.02048 -0.00314 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7155 0.0632 -0.03558 0.2611 0.9481 0.972 0.8014 0.8384 0.9319 0.7199 ] Network output: [ -0.0286 0.8714 1.098 -0.0001627 1.239e-06 0.08744 0.000179 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1749 0.09955 0.2048 0.1322 0.9653 0.9738 0.1787 0.9133 0.9498 0.3406 ] Network output: [ 0.2105 -0.2694 1.025 -0.001195 0.0004992 0.8187 -0.0007577 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7974 0.5502 0.4484 0.4501 0.9565 0.9776 0.8009 0.8627 0.947 0.7323 ] Network output: [ -0.06466 0.07901 1.009 0.002584 -0.001208 1.051 0.002139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7829 0.7314 0.5377 0.2854 0.9718 0.9801 0.7836 0.9294 0.9577 0.5945 ] Network output: [ -0.0863 0.2407 0.8515 0.00167 -0.0007749 1.087 0.001362 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8057 0.7951 0.584 0.1543 0.9713 0.9797 0.8059 0.9294 0.9569 0.5973 ] Network output: [ 0.007517 0.7698 0.2214 -0.000941 0.0004706 0.99 -0.000899 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06898 Epoch 446 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.08319 1.162 0.8121 0.002676 -0.001218 -0.1294 0.002094 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08536 0.003578 0.0444 0.01424 0.8971 0.9124 0.1517 0.8042 0.8475 0.3675 ] Network output: [ 0.8792 0.08963 0.05165 -0.00354 0.001728 0.08607 -0.003243 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.714 0.1034 0.009678 0.2374 0.9481 0.972 0.7987 0.8392 0.9322 0.7196 ] Network output: [ -0.0399 0.9262 1.058 -0.000583 0.000193 0.09312 -0.0001499 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1761 0.1082 0.2173 0.1036 0.9656 0.974 0.1799 0.9147 0.9504 0.338 ] Network output: [ 0.1751 -0.1547 0.9548 -0.001464 0.0006224 0.8436 -0.0009691 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7993 0.5821 0.4772 0.3872 0.9567 0.9777 0.8028 0.8634 0.947 0.7306 ] Network output: [ -0.03838 0.2018 0.8567 0.002371 -0.001095 1.028 0.001911 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7891 0.7442 0.5382 0.1943 0.972 0.9802 0.7897 0.9302 0.9576 0.5877 ] Network output: [ -0.05662 0.3681 0.6938 0.001172 -0.000534 1.056 0.0009176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8127 0.8034 0.5845 0.03967 0.9715 0.9797 0.8128 0.9298 0.9564 0.5962 ] Network output: [ 0.02021 0.8222 0.1552 -0.001444 0.0007026 0.9764 -0.001303 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08199 Epoch 447 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06208 1.099 0.8985 0.002289 -0.001054 -0.1122 0.001843 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08526 0.004848 0.06117 0.02813 0.8974 0.9126 0.1512 0.8056 0.8487 0.3727 ] Network output: [ 0.7843 -0.03858 0.2886 -0.004635 0.002185 0.1626 -0.003927 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7136 0.1268 0.08116 0.2915 0.9485 0.9723 0.7976 0.8419 0.9337 0.7297 ] Network output: [ -0.06779 0.8346 1.18 -0.0009147 0.0003283 0.1172 -0.0003454 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1748 0.1118 0.2455 0.1474 0.9662 0.9747 0.1785 0.9171 0.9526 0.3511 ] Network output: [ 0.1354 -0.3092 1.144 -0.001347 0.0005483 0.8892 -0.000794 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.801 0.5963 0.5153 0.4644 0.9573 0.9782 0.8044 0.8652 0.9485 0.7338 ] Network output: [ -0.03492 0.07489 0.9679 0.00324 -0.001494 1.04 0.002599 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7894 0.7473 0.5436 0.2753 0.9723 0.9805 0.7901 0.9309 0.9588 0.5862 ] Network output: [ -0.04068 0.2573 0.7735 0.002285 -0.001038 1.06 0.001773 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8131 0.8043 0.5752 0.1287 0.9716 0.9799 0.8132 0.9296 0.957 0.5856 ] Network output: [ 0.03805 0.7679 0.1827 -0.0006307 0.0003367 0.9708 -0.0006879 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09157 Epoch 448 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.111 1.142 0.7968 0.003174 -0.001436 -0.1475 0.002446 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08572 -0.0005678 0.02856 0.01876 0.8977 0.9128 0.1534 0.804 0.8478 0.3634 ] Network output: [ 1 0.03803 -0.04123 -0.003046 0.001522 -0.009339 -0.002938 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7171 0.04892 -0.05779 0.2542 0.9483 0.9722 0.8036 0.8385 0.9321 0.7165 ] Network output: [ -0.01959 0.888 1.071 4.155e-05 -8.322e-05 0.0801 0.0003027 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1743 0.09676 0.1899 0.1251 0.9655 0.974 0.1781 0.9134 0.9498 0.3263 ] Network output: [ 0.222 -0.2499 0.9955 -0.001339 0.0005689 0.8048 -0.0008863 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7985 0.5413 0.4335 0.4456 0.9565 0.9776 0.802 0.8624 0.9469 0.7291 ] Network output: [ -0.06743 0.0952 0.9999 0.002514 -0.001173 1.05 0.002075 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.783 0.7294 0.5266 0.282 0.972 0.9802 0.7837 0.9298 0.9579 0.5848 ] Network output: [ -0.09324 0.2436 0.8578 0.001594 -0.0007408 1.092 0.001305 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8062 0.7952 0.5753 0.1577 0.9714 0.9798 0.8063 0.9298 0.9571 0.5889 ] Network output: [ 0.002437 0.7665 0.2317 -0.001025 0.0005048 0.9929 -0.0009487 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07057 Epoch 449 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07119 1.161 0.8273 0.002419 -0.001105 -0.1206 0.001908 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08478 0.00462 0.0495 0.01515 0.8977 0.9128 0.1505 0.8053 0.8484 0.3614 ] Network output: [ 0.8409 0.09299 0.09338 -0.003757 0.00181 0.1167 -0.003344 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7145 0.1181 0.03424 0.2399 0.9484 0.9722 0.7989 0.8401 0.9328 0.7195 ] Network output: [ -0.04601 0.932 1.06 -0.0006902 0.0002437 0.09746 -0.0002418 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1757 0.1111 0.2196 0.1008 0.966 0.9744 0.1795 0.9161 0.9513 0.3302 ] Network output: [ 0.1554 -0.1402 0.9637 -0.001669 0.000713 0.8589 -0.001116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8015 0.5942 0.4904 0.3823 0.957 0.9779 0.8049 0.8639 0.9474 0.7284 ] Network output: [ -0.02829 0.2242 0.8216 0.002474 -0.001136 1.021 0.001966 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7917 0.7493 0.5322 0.1802 0.9723 0.9804 0.7924 0.9311 0.9581 0.5776 ] Network output: [ -0.04411 0.3873 0.6587 0.001228 -0.0005546 1.047 0.0009406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8159 0.8071 0.5757 0.02389 0.9717 0.9798 0.816 0.9301 0.9565 0.5865 ] Network output: [ 0.02748 0.8225 0.1457 -0.00147 0.0007135 0.971 -0.00132 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08925 Epoch 450 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06131 1.086 0.9106 0.002263 -0.001042 -0.1106 0.001821 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08493 0.00437 0.06025 0.03092 0.898 0.9131 0.1508 0.8063 0.8495 0.3673 ] Network output: [ 0.7906 -0.06173 0.3033 -0.004615 0.002169 0.1586 -0.003886 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7149 0.1218 0.07955 0.3015 0.9488 0.9725 0.7993 0.8424 0.9343 0.7294 ] Network output: [ -0.06708 0.8217 1.192 -0.0007934 0.0002771 0.1175 -0.0002682 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.174 0.1106 0.2393 0.1536 0.9665 0.975 0.1777 0.9179 0.9532 0.3422 ] Network output: [ 0.1329 -0.338 1.175 -0.001435 0.0005872 0.8912 -0.0008567 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8026 0.5934 0.5126 0.4825 0.9575 0.9783 0.8061 0.8654 0.9488 0.7321 ] Network output: [ -0.0372 0.04877 0.996 0.003422 -0.001576 1.043 0.002738 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7896 0.7467 0.5354 0.2971 0.9725 0.9807 0.7903 0.9315 0.9593 0.5776 ] Network output: [ -0.04301 0.2198 0.8105 0.002561 -0.001164 1.066 0.00199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8135 0.8045 0.5647 0.1623 0.9717 0.98 0.8136 0.9298 0.9573 0.575 ] Network output: [ 0.03781 0.7384 0.2108 -0.0004207 0.0002378 0.9736 -0.0005119 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09552 Epoch 451 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1163 1.154 0.7777 0.003221 -0.001454 -0.1513 0.002469 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08534 -0.001529 0.02271 0.01664 0.8982 0.9133 0.153 0.8044 0.8481 0.3551 ] Network output: [ 1.039 0.06419 -0.1128 -0.002745 0.001389 -0.04058 -0.002719 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7185 0.03465 -0.08122 0.2445 0.9485 0.9723 0.8057 0.8381 0.9321 0.7118 ] Network output: [ -0.009354 0.9104 1.038 0.0002515 -0.0001701 0.07148 0.0004301 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1734 0.09387 0.1741 0.1157 0.9657 0.9741 0.1772 0.9132 0.9495 0.3109 ] Network output: [ 0.2368 -0.2185 0.9511 -0.001437 0.0006196 0.788 -0.000986 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7993 0.5322 0.4157 0.4362 0.9564 0.9776 0.8028 0.8616 0.9465 0.7244 ] Network output: [ -0.06839 0.122 0.9784 0.002418 -0.001126 1.046 0.001987 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7827 0.7272 0.5137 0.2732 0.9721 0.9803 0.7834 0.93 0.9578 0.5735 ] Network output: [ -0.1001 0.2554 0.8561 0.001448 -0.0006744 1.094 0.001192 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8063 0.795 0.5661 0.1555 0.9715 0.9798 0.8064 0.9299 0.957 0.5799 ] Network output: [ -0.003261 0.7672 0.239 -0.001161 0.0005628 0.9957 -0.001039 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07627 Epoch 452 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05549 1.156 0.8507 0.00209 -0.0009594 -0.1091 0.00167 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08408 0.006103 0.05678 0.01709 0.8982 0.9133 0.149 0.8061 0.849 0.3555 ] Network output: [ 0.7843 0.09012 0.1631 -0.004128 0.001959 0.1616 -0.003549 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7144 0.1394 0.06969 0.2467 0.9487 0.9724 0.7984 0.8409 0.9334 0.7195 ] Network output: [ -0.05418 0.9335 1.068 -0.000824 0.0003053 0.1034 -0.0003498 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.175 0.1148 0.2242 0.1003 0.9664 0.9748 0.1787 0.9175 0.9522 0.3229 ] Network output: [ 0.1312 -0.1316 0.9823 -0.001838 0.0007861 0.8792 -0.001231 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8036 0.6099 0.5071 0.3811 0.9572 0.978 0.807 0.8641 0.9476 0.7253 ] Network output: [ -0.01491 0.2428 0.7856 0.002646 -0.001207 1.012 0.002073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7942 0.7549 0.5256 0.1688 0.9726 0.9806 0.7949 0.9317 0.9585 0.5663 ] Network output: [ -0.028 0.4035 0.6219 0.001336 -0.000598 1.036 0.001002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8188 0.8107 0.5656 0.01084 0.9718 0.9799 0.819 0.9302 0.9564 0.5755 ] Network output: [ 0.03733 0.8202 0.1353 -0.001452 0.0007052 0.9641 -0.001306 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1011 Epoch 453 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06339 1.075 0.9186 0.002284 -0.001051 -0.111 0.001832 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08455 0.003648 0.05763 0.03349 0.8986 0.9136 0.1504 0.8066 0.8498 0.3612 ] Network output: [ 0.8083 -0.08335 0.3026 -0.004467 0.002099 0.1461 -0.003759 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7162 0.1132 0.07017 0.3105 0.9491 0.9727 0.801 0.8423 0.9344 0.7276 ] Network output: [ -0.0635 0.8108 1.198 -0.0006201 0.0002032 0.1154 -0.0001543 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1728 0.1086 0.2301 0.1588 0.9668 0.9752 0.1765 0.9181 0.9534 0.3319 ] Network output: [ 0.136 -0.365 1.198 -0.001488 0.0006119 0.8886 -0.000899 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8039 0.5879 0.5048 0.5003 0.9576 0.9784 0.8074 0.8651 0.9488 0.7296 ] Network output: [ -0.04127 0.02285 1.027 0.003543 -0.001631 1.047 0.002833 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7891 0.7448 0.5263 0.32 0.9727 0.9809 0.7897 0.9317 0.9596 0.569 ] Network output: [ -0.04856 0.1783 0.8558 0.002794 -0.001272 1.074 0.002179 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8131 0.8039 0.5541 0.1999 0.9718 0.9801 0.8133 0.9298 0.9574 0.5644 ] Network output: [ 0.03505 0.699 0.2511 -0.0001752 0.0001222 0.9792 -0.0003057 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1003 Epoch 454 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1201 1.168 0.7592 0.003224 -0.001453 -0.1542 0.00246 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08485 -0.002239 0.01776 0.01461 0.8988 0.9138 0.1524 0.8044 0.8481 0.346 ] Network output: [ 1.071 0.09349 -0.1798 -0.002459 0.001261 -0.06636 -0.002505 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7197 0.02361 -0.1011 0.2344 0.9486 0.9724 0.8074 0.8374 0.9318 0.706 ] Network output: [ 0.0001984 0.9347 1.003 0.0004356 -0.0002459 0.06317 0.0005403 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1724 0.09151 0.1592 0.1059 0.9658 0.9741 0.1762 0.9128 0.949 0.2949 ] Network output: [ 0.2515 -0.1802 0.8991 -0.00146 0.0006365 0.772 -0.001029 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7999 0.5254 0.3974 0.4242 0.9563 0.9775 0.8035 0.8603 0.9459 0.7182 ] Network output: [ -0.06634 0.1562 0.946 0.00233 -0.001082 1.04 0.001902 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7823 0.7254 0.499 0.2608 0.9722 0.9803 0.7829 0.9298 0.9574 0.5602 ] Network output: [ -0.1048 0.2735 0.8463 0.001264 -0.0005906 1.095 0.001047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8061 0.7946 0.5555 0.1497 0.9714 0.9798 0.8062 0.9297 0.9567 0.5696 ] Network output: [ -0.008244 0.7697 0.2439 -0.001322 0.0006324 0.9975 -0.00115 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0865 Epoch 455 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03877 1.148 0.8779 0.001732 -0.0008016 -0.09694 0.001411 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08327 0.007708 0.06441 0.0199 0.8987 0.9137 0.1472 0.8066 0.8494 0.3493 ] Network output: [ 0.7209 0.08213 0.246 -0.004578 0.002143 0.2116 -0.003813 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.714 0.1627 0.1081 0.2569 0.9489 0.9726 0.7974 0.8412 0.9337 0.7189 ] Network output: [ -0.06174 0.9309 1.08 -0.0009246 0.0003519 0.1089 -0.000432 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1737 0.1183 0.228 0.1024 0.9668 0.9751 0.1774 0.9184 0.953 0.315 ] Network output: [ 0.1075 -0.1306 1.007 -0.001958 0.0008365 0.9004 -0.001307 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8053 0.6261 0.5226 0.3856 0.9574 0.9782 0.8087 0.8636 0.9476 0.7205 ] Network output: [ -0.0006436 0.2529 0.7566 0.002869 -0.001303 1.003 0.002223 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7959 0.7599 0.5172 0.1648 0.9728 0.9808 0.7965 0.932 0.9587 0.5533 ] Network output: [ -0.0117 0.4105 0.5937 0.001488 -0.0006628 1.025 0.001101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8208 0.8133 0.5538 0.007118 0.9719 0.9799 0.821 0.9298 0.9561 0.5626 ] Network output: [ 0.04788 0.8142 0.1276 -0.00139 0.0006766 0.957 -0.001257 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1165 Epoch 456 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06687 1.067 0.9212 0.002309 -0.00106 -0.1127 0.001843 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08406 0.002908 0.05398 0.03522 0.8991 0.9141 0.1497 0.8062 0.8497 0.354 ] Network output: [ 0.8317 -0.0962 0.2866 -0.004231 0.001992 0.1292 -0.003575 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7172 0.1039 0.05611 0.3161 0.9492 0.9728 0.8026 0.8413 0.9342 0.7238 ] Network output: [ -0.05738 0.8072 1.195 -0.0004288 0.0001221 0.1107 -3.007e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1713 0.1063 0.2185 0.1606 0.9669 0.9753 0.175 0.9177 0.9533 0.3197 ] Network output: [ 0.1428 -0.3801 1.206 -0.001527 0.0006313 0.8823 -0.0009359 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8049 0.5818 0.4933 0.5127 0.9576 0.9784 0.8083 0.8639 0.9485 0.7257 ] Network output: [ -0.04498 0.008017 1.048 0.003562 -0.00164 1.048 0.002848 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7879 0.7423 0.5161 0.3366 0.9728 0.981 0.7886 0.9316 0.9596 0.5599 ] Network output: [ -0.05515 0.1459 0.8946 0.002902 -0.001323 1.082 0.00227 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8122 0.8027 0.5438 0.2309 0.9718 0.9801 0.8124 0.9296 0.9573 0.5543 ] Network output: [ 0.03082 0.6591 0.2936 2.573e-05 2.667e-05 0.9858 -0.0001334 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1041 Epoch 457 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1198 1.178 0.7497 0.00314 -0.001414 -0.154 0.002391 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08425 -0.002426 0.01546 0.01377 0.8993 0.9142 0.1514 0.8041 0.8478 0.3371 ] Network output: [ 1.085 0.115 -0.2176 -0.002257 0.001167 -0.0764 -0.002337 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7204 0.02024 -0.11 0.2288 0.9487 0.9724 0.8084 0.8362 0.9314 0.7 ] Network output: [ 0.005646 0.9536 0.9791 0.0005383 -0.0002869 0.0581 0.0005964 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1711 0.09061 0.1487 0.09897 0.9659 0.9742 0.1749 0.9122 0.9486 0.2805 ] Network output: [ 0.2587 -0.1477 0.8601 -0.001435 0.0006295 0.7645 -0.001026 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8006 0.5245 0.3849 0.4158 0.9562 0.9774 0.8042 0.8587 0.9452 0.7115 ] Network output: [ -0.06158 0.1882 0.9116 0.00228 -0.001055 1.033 0.001846 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.782 0.7251 0.485 0.2501 0.9722 0.9803 0.7827 0.9295 0.957 0.5464 ] Network output: [ -0.1053 0.2919 0.8304 0.001106 -0.0005172 1.093 0.0009191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8061 0.7946 0.5444 0.144 0.9714 0.9797 0.8062 0.9292 0.9561 0.5585 ] Network output: [ -0.01009 0.7714 0.2455 -0.001453 0.0006893 0.9975 -0.001242 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09573 Epoch 458 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02689 1.14 0.9 0.001453 -0.0006779 -0.08813 0.001206 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08247 0.008726 0.069 0.0228 0.8992 0.9142 0.1456 0.8066 0.8494 0.3427 ] Network output: [ 0.6771 0.07192 0.3078 -0.004911 0.002279 0.2462 -0.004009 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7137 0.1782 0.1335 0.2675 0.949 0.9727 0.7968 0.8409 0.9337 0.7169 ] Network output: [ -0.06538 0.9261 1.089 -0.0009344 0.0003583 0.1114 -0.0004483 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.172 0.12 0.2269 0.106 0.9671 0.9754 0.1756 0.9188 0.9533 0.3055 ] Network output: [ 0.09126 -0.1377 1.031 -0.002045 0.0008739 0.9153 -0.001364 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8065 0.6365 0.53 0.3963 0.9575 0.9783 0.8099 0.8625 0.9473 0.7148 ] Network output: [ 0.008593 0.2512 0.7462 0.003059 -0.001386 0.9978 0.002356 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7961 0.7623 0.5072 0.1718 0.973 0.9809 0.7968 0.9318 0.9586 0.5403 ] Network output: [ -0.001864 0.4041 0.5868 0.00163 -0.0007254 1.019 0.001203 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8213 0.8142 0.5415 0.01781 0.9719 0.9799 0.8214 0.9292 0.9556 0.5497 ] Network output: [ 0.05521 0.8055 0.1267 -0.001312 0.0006401 0.9522 -0.001193 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1271 Epoch 459 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07007 1.066 0.9179 0.002305 -0.001057 -0.1145 0.001832 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08344 0.002321 0.05013 0.03556 0.8996 0.9145 0.1489 0.8054 0.8492 0.3461 ] Network output: [ 0.8546 -0.09465 0.2571 -0.003971 0.001874 0.1123 -0.003376 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.718 0.09617 0.04121 0.3165 0.9493 0.9729 0.8038 0.8398 0.9337 0.7188 ] Network output: [ -0.05038 0.814 1.181 -0.0002677 5.508e-05 0.1047 6.912e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1697 0.1042 0.2064 0.1575 0.9671 0.9754 0.1733 0.917 0.9528 0.3068 ] Network output: [ 0.1501 -0.3762 1.195 -0.001584 0.0006603 0.875 -0.0009921 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8056 0.5774 0.4809 0.5164 0.9575 0.9783 0.809 0.8623 0.9479 0.721 ] Network output: [ -0.04734 0.01233 1.05 0.003462 -0.001593 1.047 0.002768 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7867 0.7402 0.5059 0.3416 0.9729 0.981 0.7873 0.9312 0.9593 0.5507 ] Network output: [ -0.06091 0.1359 0.9121 0.002824 -0.001289 1.085 0.002216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8113 0.8017 0.5351 0.2452 0.9719 0.9802 0.8114 0.9291 0.957 0.5459 ] Network output: [ 0.02704 0.6377 0.3185 7.191e-05 2.299e-06 0.9901 -8.429e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.102 Epoch 460 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1146 1.178 0.7549 0.002978 -0.001342 -0.1499 0.002269 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08357 -0.00202 0.01662 0.01458 0.8998 0.9147 0.1502 0.8036 0.8475 0.3298 ] Network output: [ 1.075 0.1199 -0.2115 -0.002172 0.00112 -0.06614 -0.002236 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7207 0.02606 -0.1043 0.2303 0.9488 0.9725 0.8088 0.8351 0.9311 0.6961 ] Network output: [ 0.004696 0.9608 0.9736 0.0005258 -0.0002786 0.05828 0.0005755 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1698 0.09153 0.1452 0.09675 0.9661 0.9744 0.1736 0.9121 0.9485 0.2705 ] Network output: [ 0.2515 -0.133 0.8531 -0.001455 0.0006386 0.771 -0.001041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8018 0.5311 0.3845 0.4151 0.9563 0.9775 0.8054 0.8575 0.9448 0.7067 ] Network output: [ -0.05664 0.2085 0.8867 0.002257 -0.001041 1.027 0.001814 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7823 0.7269 0.4759 0.2444 0.9724 0.9804 0.783 0.9293 0.9568 0.5356 ] Network output: [ -0.101 0.3063 0.8118 0.00101 -0.0004717 1.088 0.0008365 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8066 0.7955 0.5351 0.1399 0.9715 0.9798 0.8068 0.9288 0.9557 0.5489 ] Network output: [ -0.007313 0.7728 0.2408 -0.001521 0.0007189 0.9949 -0.00129 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0963 Epoch 461 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0242 1.132 0.9106 0.001349 -0.0006307 -0.08553 0.001125 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08184 0.008639 0.06861 0.02492 0.8997 0.9146 0.1446 0.8062 0.8491 0.3368 ] Network output: [ 0.6726 0.06035 0.3238 -0.004951 0.002291 0.2506 -0.004016 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7142 0.1788 0.1354 0.2751 0.9492 0.9729 0.7976 0.84 0.9335 0.7146 ] Network output: [ -0.06447 0.9199 1.095 -0.0008584 0.000327 0.1107 -0.000403 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1703 0.1192 0.2206 0.1097 0.9674 0.9756 0.1739 0.9186 0.9533 0.2963 ] Network output: [ 0.08657 -0.1516 1.05 -0.002119 0.000907 0.92 -0.001419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8075 0.6375 0.527 0.4102 0.9576 0.9783 0.8109 0.8612 0.9469 0.7108 ] Network output: [ 0.008247 0.2402 0.7582 0.003121 -0.001414 0.9978 0.002404 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7952 0.7616 0.4984 0.187 0.9731 0.981 0.7959 0.9316 0.9585 0.531 ] Network output: [ -0.002696 0.3881 0.603 0.001691 -0.0007538 1.021 0.001254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8206 0.8136 0.5321 0.03868 0.9719 0.98 0.8207 0.9287 0.9553 0.5402 ] Network output: [ 0.05664 0.7972 0.1329 -0.001258 0.0006134 0.9516 -0.001143 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1246 Epoch 462 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07285 1.069 0.9102 0.002304 -0.001054 -0.1161 0.001822 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08282 0.001813 0.04626 0.03457 0.9001 0.9149 0.148 0.8048 0.8488 0.3392 ] Network output: [ 0.8775 -0.08255 0.2172 -0.003676 0.001743 0.09557 -0.003154 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7188 0.08918 0.02599 0.3121 0.9494 0.973 0.805 0.8384 0.9332 0.7146 ] Network output: [ -0.04433 0.8274 1.161 -0.0001521 8.479e-06 0.09905 0.0001343 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1682 0.1024 0.1954 0.151 0.9672 0.9755 0.1719 0.9165 0.9524 0.2954 ] Network output: [ 0.1557 -0.3587 1.172 -0.001656 0.0006969 0.8685 -0.001062 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8063 0.5741 0.4698 0.5128 0.9575 0.9783 0.8098 0.8608 0.9473 0.7176 ] Network output: [ -0.04994 0.0317 1.037 0.003277 -0.001508 1.044 0.002619 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7857 0.7386 0.4977 0.3363 0.9731 0.9811 0.7864 0.931 0.9591 0.5435 ] Network output: [ -0.06619 0.1481 0.9086 0.0026 -0.001188 1.086 0.002044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8108 0.801 0.5294 0.2424 0.9719 0.9802 0.8109 0.9289 0.9568 0.5403 ] Network output: [ 0.02448 0.6427 0.3174 -5.159e-05 5.623e-05 0.9909 -0.0001715 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09361 Epoch 463 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.1066 1.17 0.7716 0.002798 -0.001262 -0.1433 0.002139 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08291 -0.001307 0.01992 0.0162 0.9003 0.9151 0.1489 0.8034 0.8474 0.3252 ] Network output: [ 1.05 0.1104 -0.1748 -0.002157 0.001101 -0.04346 -0.002176 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7211 0.03707 -0.0893 0.2361 0.9491 0.9727 0.8091 0.8347 0.9311 0.6957 ] Network output: [ -0.001068 0.9577 0.9835 0.0004329 -0.000236 0.06257 0.0005011 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1688 0.0935 0.147 0.09754 0.9665 0.9747 0.1725 0.9126 0.9488 0.2653 ] Network output: [ 0.2333 -0.1348 0.8742 -0.001552 0.0006795 0.7876 -0.001103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8034 0.5416 0.3942 0.4191 0.9565 0.9776 0.807 0.8571 0.9447 0.7054 ] Network output: [ -0.05353 0.217 0.8742 0.002243 -0.001032 1.025 0.001793 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7832 0.73 0.4726 0.2418 0.9726 0.9806 0.7839 0.9296 0.957 0.5295 ] Network output: [ -0.09431 0.3172 0.7927 0.0009664 -0.0004492 1.083 0.0007922 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8079 0.7972 0.5294 0.1345 0.9717 0.9799 0.8081 0.9287 0.9556 0.5426 ] Network output: [ -0.001391 0.7764 0.2295 -0.001545 0.0007294 0.9907 -0.001308 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08972 Epoch 464 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02834 1.123 0.9135 0.001398 -0.0006509 -0.08739 0.001154 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08142 0.007731 0.06493 0.02616 0.9003 0.9151 0.1442 0.806 0.849 0.3324 ] Network output: [ 0.6981 0.0457 0.3067 -0.004729 0.002191 0.2324 -0.003847 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7156 0.1682 0.1208 0.2796 0.9494 0.973 0.7995 0.8393 0.9334 0.7135 ] Network output: [ -0.06138 0.9123 1.099 -0.0007454 0.0002793 0.1085 -0.0003307 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1691 0.1167 0.2125 0.1127 0.9676 0.9758 0.1726 0.9185 0.9532 0.289 ] Network output: [ 0.08992 -0.1709 1.065 -0.00217 0.0009305 0.9176 -0.00146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8085 0.6315 0.5183 0.4237 0.9577 0.9784 0.812 0.8605 0.9468 0.7102 ] Network output: [ 0.0007057 0.2251 0.7838 0.003079 -0.001397 1.002 0.00238 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.794 0.7591 0.4928 0.2034 0.9733 0.9812 0.7947 0.9317 0.9586 0.5266 ] Network output: [ -0.0108 0.3704 0.6302 0.001679 -0.0007512 1.028 0.001256 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8197 0.8124 0.5269 0.06015 0.972 0.9801 0.8198 0.9286 0.9554 0.5353 ] Network output: [ 0.05404 0.7912 0.1419 -0.001227 0.0005969 0.9539 -0.00111 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1135 Epoch 465 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07549 1.074 0.9015 0.002331 -0.001064 -0.1174 0.001833 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08231 0.001291 0.04251 0.03292 0.9006 0.9154 0.1473 0.8047 0.8487 0.334 ] Network output: [ 0.9011 -0.06838 0.1741 -0.003339 0.001591 0.07861 -0.0029 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7199 0.08196 0.01084 0.3056 0.9496 0.9731 0.8066 0.8377 0.933 0.7127 ] Network output: [ -0.03991 0.8413 1.143 -6.85e-05 -2.44e-05 0.09488 0.0001779 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1672 0.1008 0.1863 0.1437 0.9674 0.9756 0.1708 0.9163 0.9521 0.2867 ] Network output: [ 0.1596 -0.3388 1.149 -0.001719 0.0007288 0.8636 -0.001124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8072 0.571 0.4609 0.506 0.9575 0.9783 0.8107 0.8601 0.947 0.7166 ] Network output: [ -0.05352 0.05557 1.021 0.00308 -0.001417 1.043 0.002461 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7853 0.7376 0.4921 0.326 0.9732 0.9812 0.786 0.9312 0.959 0.5389 ] Network output: [ -0.0712 0.1714 0.894 0.002333 -0.001066 1.086 0.001837 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8108 0.801 0.5263 0.2294 0.9721 0.9803 0.811 0.929 0.9567 0.5375 ] Network output: [ 0.02296 0.6638 0.2998 -0.0002535 0.0001466 0.9895 -0.0003228 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.084 Epoch 466 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.09827 1.158 0.7921 0.002643 -0.001194 -0.1361 0.002028 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08233 -0.000621 0.0236 0.01777 0.9008 0.9155 0.1478 0.8038 0.8477 0.3227 ] Network output: [ 1.023 0.09499 -0.1295 -0.002163 0.001092 -0.01918 -0.002133 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7216 0.04814 -0.07224 0.2424 0.9494 0.9729 0.8094 0.835 0.9314 0.6981 ] Network output: [ -0.008489 0.95 0.9999 0.0003161 -0.0001832 0.06831 0.0004112 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.168 0.09552 0.151 0.09903 0.9669 0.975 0.1717 0.9136 0.9495 0.2632 ] Network output: [ 0.2122 -0.1453 0.9072 -0.001688 0.0007368 0.8067 -0.00119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8053 0.552 0.4079 0.424 0.9568 0.9778 0.8089 0.8574 0.9451 0.707 ] Network output: [ -0.05204 0.2191 0.8695 0.002245 -0.001031 1.025 0.001788 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7845 0.7334 0.4729 0.2392 0.9729 0.9809 0.7852 0.9303 0.9574 0.5269 ] Network output: [ -0.0876 0.3268 0.7745 0.000957 -0.0004425 1.078 0.0007748 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8097 0.7994 0.5265 0.1266 0.9719 0.98 0.8099 0.9289 0.9557 0.5391 ] Network output: [ 0.005358 0.7821 0.2148 -0.001546 0.00073 0.9862 -0.00131 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08229 Epoch 467 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03454 1.113 0.9142 0.001511 -0.0006998 -0.09058 0.001231 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08114 0.00656 0.0604 0.02687 0.9008 0.9156 0.144 0.8061 0.8492 0.3294 ] Network output: [ 0.7335 0.0291 0.2794 -0.004391 0.00204 0.2068 -0.003597 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7173 0.1537 0.1014 0.2824 0.9497 0.9732 0.8018 0.839 0.9335 0.714 ] Network output: [ -0.05823 0.9041 1.103 -0.0006321 0.0002312 0.1064 -0.0002573 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1681 0.1139 0.2048 0.1148 0.9679 0.976 0.1717 0.9186 0.9532 0.2837 ] Network output: [ 0.09574 -0.1927 1.079 -0.002194 0.0009425 0.9129 -0.001483 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8097 0.6231 0.5091 0.4353 0.9579 0.9785 0.8131 0.8604 0.9469 0.7119 ] Network output: [ -0.009027 0.2097 0.8124 0.003015 -0.00137 1.008 0.002339 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7932 0.7563 0.4896 0.2172 0.9735 0.9813 0.7938 0.9321 0.9588 0.5252 ] Network output: [ -0.02069 0.3549 0.6574 0.001652 -0.0007414 1.036 0.001245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8191 0.8115 0.5244 0.07714 0.9722 0.9803 0.8193 0.929 0.9557 0.5332 ] Network output: [ 0.05052 0.787 0.15 -0.001199 0.0005817 0.9571 -0.001078 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.102 Epoch 468 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0776 1.079 0.8939 0.002367 -0.001078 -0.1183 0.001853 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0819 0.0007771 0.0392 0.0312 0.9011 0.9159 0.1467 0.805 0.8489 0.3301 ] Network output: [ 0.9233 -0.05618 0.1347 -0.003003 0.00144 0.06278 -0.002645 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7212 0.07488 -0.002617 0.2991 0.9499 0.9733 0.8082 0.8375 0.9331 0.7126 ] Network output: [ -0.03696 0.853 1.129 -8.241e-06 -4.746e-05 0.09209 0.0002066 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1665 0.09949 0.1791 0.1369 0.9677 0.9758 0.1701 0.9166 0.9521 0.2799 ] Network output: [ 0.1616 -0.3224 1.132 -0.001774 0.0007563 0.8603 -0.001176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8083 0.5682 0.4545 0.4989 0.9577 0.9784 0.8118 0.86 0.9469 0.7172 ] Network output: [ -0.05712 0.07726 1.007 0.002923 -0.001345 1.042 0.002334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7855 0.7371 0.4883 0.3145 0.9734 0.9814 0.7861 0.9316 0.9591 0.5359 ] Network output: [ -0.07528 0.1961 0.8765 0.0021 -0.0009605 1.086 0.001655 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8114 0.8016 0.5246 0.2132 0.9722 0.9804 0.8116 0.9294 0.9568 0.536 ] Network output: [ 0.02232 0.6877 0.2783 -0.0004479 0.0002339 0.9876 -0.0004697 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07673 Epoch 469 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.09067 1.147 0.8114 0.002513 -0.001137 -0.1297 0.001935 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08183 -0.0001047 0.02681 0.01904 0.9013 0.916 0.1469 0.8045 0.8483 0.3211 ] Network output: [ 0.9993 0.07951 -0.08817 -0.002178 0.001089 0.001361 -0.002101 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7223 0.05689 -0.0569 0.2479 0.9497 0.9731 0.81 0.8357 0.9319 0.7017 ] Network output: [ -0.01557 0.9418 1.016 0.0002061 -0.0001334 0.07379 0.0003264 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1674 0.09711 0.1549 0.1003 0.9673 0.9754 0.171 0.9149 0.9503 0.262 ] Network output: [ 0.1929 -0.1583 0.9413 -0.00183 0.0007975 0.8238 -0.001283 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8071 0.5603 0.4211 0.4282 0.9572 0.9781 0.8107 0.8581 0.9455 0.7098 ] Network output: [ -0.05119 0.2183 0.8682 0.002273 -0.001043 1.025 0.001804 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.786 0.7364 0.474 0.2363 0.9733 0.9811 0.7866 0.9312 0.958 0.5255 ] Network output: [ -0.08168 0.3348 0.7584 0.0009762 -0.0004489 1.074 0.0007803 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8117 0.8017 0.5247 0.1178 0.9721 0.9802 0.8119 0.9294 0.956 0.5368 ] Network output: [ 0.0117 0.7875 0.2009 -0.001529 0.0007225 0.9821 -0.001297 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07704 Epoch 470 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04016 1.105 0.9145 0.001622 -0.0007482 -0.09351 0.001308 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0809 0.005435 0.05621 0.02729 0.9013 0.916 0.1439 0.8065 0.8496 0.327 ] Network output: [ 0.7677 0.01383 0.2525 -0.004043 0.001885 0.1819 -0.003337 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.719 0.1395 0.08303 0.2842 0.95 0.9734 0.8042 0.8392 0.9338 0.7154 ] Network output: [ -0.05566 0.8971 1.107 -0.0005334 0.0001895 0.1047 -0.000194 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1674 0.1113 0.1981 0.1161 0.9681 0.9762 0.171 0.919 0.9534 0.2793 ] Network output: [ 0.1011 -0.2136 1.094 -0.002209 0.0009505 0.9084 -0.001499 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8109 0.6149 0.5014 0.4446 0.9581 0.9786 0.8143 0.8608 0.9471 0.7145 ] Network output: [ -0.01807 0.1958 0.8384 0.002971 -0.001352 1.014 0.002312 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7927 0.754 0.4874 0.2277 0.9737 0.9815 0.7934 0.9327 0.9592 0.5247 ] Network output: [ -0.02958 0.3421 0.6805 0.00164 -0.0007378 1.043 0.001243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8191 0.8111 0.5228 0.08954 0.9724 0.9804 0.8192 0.9295 0.9561 0.5319 ] Network output: [ 0.04761 0.7834 0.1568 -0.001163 0.0005631 0.9599 -0.001042 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09303 Epoch 471 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07879 1.083 0.8879 0.002385 -0.001085 -0.1187 0.001861 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08154 0.0003071 0.03652 0.02967 0.9016 0.9163 0.1463 0.8056 0.8494 0.3269 ] Network output: [ 0.9422 -0.04582 0.1014 -0.002711 0.001308 0.04913 -0.00242 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7224 0.06842 -0.01352 0.2934 0.9501 0.9734 0.8098 0.8379 0.9333 0.7134 ] Network output: [ -0.0351 0.8629 1.117 3.099e-05 -6.163e-05 0.09023 0.0002218 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.166 0.09835 0.1733 0.131 0.9679 0.976 0.1696 0.9171 0.9523 0.2741 ] Network output: [ 0.162 -0.31 1.12 -0.001835 0.0007861 0.8584 -0.001232 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8094 0.5657 0.4505 0.4926 0.9578 0.9785 0.8129 0.8602 0.947 0.7186 ] Network output: [ -0.06003 0.09507 0.9943 0.002818 -0.001295 1.042 0.002247 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7859 0.737 0.4852 0.3038 0.9736 0.9815 0.7866 0.9322 0.9594 0.5333 ] Network output: [ -0.07813 0.2179 0.8599 0.001927 -0.0008809 1.086 0.001517 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8124 0.8024 0.5231 0.1975 0.9724 0.9806 0.8125 0.9299 0.957 0.5345 ] Network output: [ 0.02237 0.7079 0.2593 -0.0006002 0.0003022 0.9857 -0.0005844 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07215 Epoch 472 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.08395 1.138 0.8278 0.002394 -0.001085 -0.1243 0.001848 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08138 0.0002399 0.02939 0.02003 0.9017 0.9164 0.1461 0.8055 0.849 0.3195 ] Network output: [ 0.9807 0.06668 -0.05421 -0.002204 0.001091 0.01734 -0.002084 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.723 0.0631 -0.04413 0.2524 0.95 0.9733 0.8106 0.8367 0.9326 0.7054 ] Network output: [ -0.0216 0.9352 1.03 0.0001119 -9.046e-05 0.07837 0.0002523 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1669 0.09822 0.1578 0.101 0.9677 0.9757 0.1705 0.9161 0.9511 0.2606 ] Network output: [ 0.1766 -0.171 0.9719 -0.001969 0.0008575 0.8378 -0.001378 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8089 0.5663 0.4324 0.4317 0.9575 0.9783 0.8124 0.859 0.9461 0.7128 ] Network output: [ -0.0504 0.2165 0.8679 0.002323 -0.001064 1.026 0.001838 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7874 0.7389 0.4747 0.2335 0.9736 0.9814 0.7881 0.9321 0.9586 0.5241 ] Network output: [ -0.07664 0.3408 0.7453 0.001017 -0.0004653 1.071 0.0008033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8137 0.8039 0.523 0.1098 0.9724 0.9804 0.8138 0.93 0.9563 0.5347 ] Network output: [ 0.01731 0.7909 0.1897 -0.001498 0.0007083 0.9787 -0.001273 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07408 Epoch 473 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0444 1.099 0.9145 0.001705 -0.0007838 -0.09573 0.001364 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08065 0.004446 0.05262 0.02753 0.9018 0.9165 0.1438 0.8071 0.8502 0.3247 ] Network output: [ 0.7974 0.001857 0.2283 -0.003733 0.001746 0.16 -0.003103 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7206 0.1268 0.0674 0.2856 0.9503 0.9736 0.8063 0.8397 0.9341 0.7171 ] Network output: [ -0.05365 0.8922 1.11 -0.0004524 0.0001556 0.1033 -0.0001436 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1668 0.109 0.1922 0.1166 0.9684 0.9764 0.1704 0.9195 0.9537 0.275 ] Network output: [ 0.1052 -0.2316 1.107 -0.002232 0.0009617 0.9046 -0.00152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.812 0.6075 0.4952 0.4519 0.9582 0.9787 0.8155 0.8613 0.9474 0.7171 ] Network output: [ -0.02557 0.1841 0.86 0.002952 -0.001344 1.019 0.002301 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7925 0.7521 0.4854 0.2354 0.9739 0.9817 0.7932 0.9333 0.9597 0.5239 ] Network output: [ -0.03683 0.3315 0.6994 0.001647 -0.0007425 1.049 0.001254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8193 0.811 0.5211 0.09873 0.9726 0.9806 0.8194 0.9302 0.9565 0.5305 ] Network output: [ 0.0456 0.7794 0.1627 -0.001118 0.0005409 0.9622 -0.001 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0867 Epoch 474 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07896 1.087 0.8834 0.002377 -0.001081 -0.1186 0.00185 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08119 -0.0001056 0.03443 0.02841 0.9021 0.9167 0.1458 0.8064 0.85 0.3237 ] Network output: [ 0.9574 -0.03639 0.07386 -0.002476 0.0012 0.03787 -0.002235 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7235 0.06272 -0.02192 0.2887 0.9504 0.9736 0.8112 0.8384 0.9337 0.7144 ] Network output: [ -0.03397 0.8716 1.108 5.281e-05 -6.84e-05 0.08896 0.0002256 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1656 0.09737 0.1683 0.1259 0.9682 0.9762 0.1691 0.9177 0.9526 0.2687 ] Network output: [ 0.1612 -0.3003 1.113 -0.001911 0.0008222 0.8574 -0.001296 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8105 0.5634 0.448 0.4874 0.958 0.9786 0.814 0.8606 0.9472 0.7199 ] Network output: [ -0.06204 0.1094 0.9837 0.002754 -0.001265 1.042 0.002191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7866 0.7371 0.4823 0.2943 0.9739 0.9817 0.7873 0.9329 0.9597 0.5305 ] Network output: [ -0.07992 0.2357 0.8453 0.001806 -0.0008251 1.086 0.00142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8135 0.8034 0.5213 0.1838 0.9726 0.9807 0.8136 0.9305 0.9572 0.5327 ] Network output: [ 0.02296 0.7228 0.2445 -0.0007087 0.0003506 0.984 -0.000665 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06949 Epoch 475 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07794 1.132 0.8411 0.002277 -0.001033 -0.1196 0.001763 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08095 0.0004549 0.03141 0.02082 0.9021 0.9168 0.1453 0.8065 0.8498 0.3177 ] Network output: [ 0.9659 0.057 -0.02731 -0.002238 0.001099 0.02959 -0.002077 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7236 0.06728 -0.03368 0.2562 0.9503 0.9735 0.8113 0.8378 0.9332 0.7086 ] Network output: [ -0.02651 0.9305 1.041 3.42e-05 -5.464e-05 0.08204 0.0001896 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1664 0.09893 0.1596 0.1013 0.9681 0.976 0.17 0.9173 0.9519 0.2586 ] Network output: [ 0.1632 -0.1821 0.9981 -0.002103 0.0009158 0.8489 -0.001472 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8104 0.5704 0.4416 0.4347 0.9578 0.9784 0.8139 0.86 0.9466 0.7152 ] Network output: [ -0.04946 0.2145 0.8678 0.002389 -0.001092 1.026 0.001882 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7887 0.7409 0.4746 0.2309 0.9738 0.9816 0.7894 0.9329 0.9592 0.5223 ] Network output: [ -0.07239 0.3448 0.7351 0.00107 -0.0004876 1.069 0.0008373 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8155 0.8058 0.5209 0.1033 0.9726 0.9806 0.8157 0.9306 0.9567 0.5322 ] Network output: [ 0.02222 0.7922 0.1816 -0.001457 0.0006896 0.9759 -0.001241 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07264 Epoch 476 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04729 1.096 0.9142 0.001754 -0.0008048 -0.09723 0.001396 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08039 0.003594 0.04962 0.02765 0.9022 0.9168 0.1436 0.8079 0.8509 0.3222 ] Network output: [ 0.8223 -0.006523 0.2066 -0.00347 0.001627 0.1413 -0.002901 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7218 0.1157 0.05441 0.2865 0.9506 0.9737 0.8081 0.8402 0.9345 0.7186 ] Network output: [ -0.05202 0.8893 1.111 -0.0003867 0.0001286 0.1021 -0.0001043 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1663 0.1069 0.1866 0.1165 0.9687 0.9766 0.1698 0.9201 0.9541 0.2705 ] Network output: [ 0.1082 -0.2463 1.119 -0.002268 0.0009788 0.9014 -0.001551 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8131 0.6009 0.4902 0.4578 0.9584 0.9788 0.8165 0.8618 0.9478 0.7192 ] Network output: [ -0.03157 0.1747 0.8773 0.002952 -0.001344 1.023 0.002303 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7926 0.7506 0.483 0.2411 0.9741 0.9819 0.7932 0.934 0.9601 0.5226 ] Network output: [ -0.04262 0.3224 0.7149 0.001668 -0.0007528 1.055 0.001274 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8197 0.8111 0.5189 0.1059 0.9728 0.9807 0.8199 0.9308 0.9569 0.5285 ] Network output: [ 0.04436 0.775 0.1682 -0.001068 0.0005165 0.9639 -0.000955 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08226 Epoch 477 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07829 1.091 0.88 0.002345 -0.001065 -0.118 0.001821 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08083 -0.0004612 0.03284 0.02737 0.9025 0.917 0.1454 0.8073 0.8507 0.3205 ] Network output: [ 0.9691 -0.02742 0.05115 -0.002293 0.001116 0.02881 -0.002087 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7244 0.05774 -0.0282 0.2849 0.9506 0.9737 0.8124 0.839 0.9341 0.7153 ] Network output: [ -0.0333 0.8795 1.099 6.236e-05 -6.996e-05 0.08805 0.0002214 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1651 0.0965 0.164 0.1214 0.9684 0.9763 0.1687 0.9184 0.953 0.2634 ] Network output: [ 0.1596 -0.2923 1.108 -0.002002 0.0008644 0.857 -0.00137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8115 0.5614 0.4465 0.483 0.9581 0.9787 0.8151 0.8611 0.9475 0.721 ] Network output: [ -0.06327 0.1212 0.9743 0.00272 -0.001247 1.042 0.002157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7873 0.7373 0.4792 0.2861 0.9741 0.9818 0.788 0.9336 0.9601 0.5274 ] Network output: [ -0.08092 0.2498 0.833 0.001724 -0.000787 1.086 0.001353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8146 0.8044 0.519 0.1724 0.9727 0.9808 0.8148 0.9311 0.9575 0.5303 ] Network output: [ 0.02393 0.7331 0.2335 -0.0007821 0.000383 0.9825 -0.0007182 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06803 Epoch 478 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07248 1.127 0.8521 0.002163 -0.0009821 -0.1154 0.001678 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08053 0.0005806 0.033 0.02146 0.9025 0.9171 0.1446 0.8075 0.8506 0.3155 ] Network output: [ 0.9539 0.05005 -0.006084 -0.002276 0.001109 0.03913 -0.002078 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7241 0.06999 -0.02509 0.2593 0.9506 0.9737 0.8119 0.8388 0.9338 0.7112 ] Network output: [ -0.03044 0.9274 1.048 -2.783e-05 -2.562e-05 0.08492 0.0001378 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1659 0.09932 0.1603 0.1013 0.9684 0.9763 0.1695 0.9185 0.9527 0.256 ] Network output: [ 0.1522 -0.1914 1.02 -0.00223 0.0009721 0.8577 -0.001563 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8118 0.5731 0.4489 0.4373 0.958 0.9786 0.8153 0.8608 0.9471 0.7172 ] Network output: [ -0.04839 0.2124 0.8676 0.00246 -0.001123 1.027 0.001932 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7899 0.7425 0.4736 0.2289 0.9741 0.9818 0.7906 0.9338 0.9597 0.5198 ] Network output: [ -0.06882 0.347 0.7274 0.001128 -0.0005127 1.068 0.0008764 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8172 0.8075 0.5184 0.09845 0.9727 0.9807 0.8173 0.9312 0.957 0.5293 ] Network output: [ 0.02654 0.7917 0.1761 -0.001412 0.0006684 0.9735 -0.001204 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07209 Epoch 479 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04912 1.093 0.9139 0.001777 -0.000814 -0.09812 0.001408 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0801 0.00286 0.04711 0.02769 0.9026 0.9172 0.1434 0.8087 0.8516 0.3194 ] Network output: [ 0.8431 -0.01184 0.187 -0.003246 0.001525 0.1255 -0.002728 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7229 0.106 0.04358 0.2871 0.9508 0.9739 0.8096 0.8408 0.935 0.7198 ] Network output: [ -0.05062 0.8882 1.111 -0.0003323 0.0001065 0.101 -7.31e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1657 0.1051 0.1815 0.116 0.9689 0.9768 0.1692 0.9207 0.9544 0.2658 ] Network output: [ 0.1103 -0.2577 1.129 -0.002316 0.001002 0.8987 -0.001591 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.814 0.595 0.4861 0.4625 0.9585 0.9789 0.8175 0.8624 0.9481 0.7209 ] Network output: [ -0.03634 0.1673 0.891 0.002961 -0.001349 1.026 0.00231 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7927 0.7493 0.4804 0.2455 0.9743 0.982 0.7933 0.9346 0.9606 0.5206 ] Network output: [ -0.04731 0.3145 0.7277 0.001695 -0.0007658 1.059 0.001297 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8202 0.8113 0.5164 0.1118 0.9729 0.9808 0.8203 0.9313 0.9573 0.5261 ] Network output: [ 0.04371 0.77 0.1734 -0.001015 0.0004911 0.9651 -0.0009086 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07906 Epoch 480 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07696 1.095 0.8776 0.002294 -0.001041 -0.1171 0.001779 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08046 -0.0007644 0.03167 0.02653 0.9029 0.9174 0.1449 0.8081 0.8513 0.3172 ] Network output: [ 0.9781 -0.01884 0.03244 -0.002151 0.001049 0.02168 -0.001968 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7252 0.05341 -0.03276 0.2817 0.9508 0.9739 0.8135 0.8397 0.9345 0.7161 ] Network output: [ -0.03292 0.8867 1.092 6.438e-05 -6.838e-05 0.08738 0.0002126 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1646 0.0957 0.16 0.1174 0.9686 0.9765 0.1682 0.9191 0.9533 0.2581 ] Network output: [ 0.1574 -0.2853 1.105 -0.002103 0.0009109 0.857 -0.001451 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8124 0.5595 0.4458 0.4794 0.9582 0.9787 0.816 0.8616 0.9477 0.7218 ] Network output: [ -0.06387 0.1311 0.9658 0.002704 -0.001239 1.042 0.002139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.788 0.7375 0.476 0.2791 0.9742 0.9819 0.7887 0.9342 0.9604 0.5239 ] Network output: [ -0.08138 0.2609 0.8227 0.001669 -0.0007609 1.086 0.001306 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8157 0.8054 0.5163 0.1631 0.9729 0.9809 0.8159 0.9316 0.9577 0.5275 ] Network output: [ 0.02519 0.7398 0.2255 -0.0008302 0.0004039 0.981 -0.0007518 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06727 Epoch 481 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06752 1.124 0.8613 0.002052 -0.0009325 -0.1117 0.001595 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08011 0.0006441 0.03428 0.022 0.9029 0.9174 0.144 0.8085 0.8514 0.313 ] Network output: [ 0.9441 0.04513 0.01079 -0.002312 0.001119 0.04668 -0.00208 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7246 0.07164 -0.01796 0.2619 0.9508 0.9739 0.8124 0.8397 0.9344 0.7133 ] Network output: [ -0.03353 0.9255 1.054 -7.544e-05 -2.928e-06 0.08716 9.625e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1654 0.09948 0.1603 0.101 0.9687 0.9765 0.169 0.9195 0.9533 0.2528 ] Network output: [ 0.143 -0.1991 1.039 -0.002351 0.001026 0.8648 -0.001652 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8129 0.5748 0.4547 0.4396 0.9582 0.9787 0.8165 0.8616 0.9475 0.7186 ] Network output: [ -0.04727 0.2105 0.8673 0.002533 -0.001155 1.027 0.001982 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.791 0.7437 0.472 0.2273 0.9743 0.9819 0.7917 0.9345 0.9602 0.5169 ] Network output: [ -0.06587 0.3478 0.7219 0.001186 -0.0005378 1.067 0.0009163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8186 0.809 0.5155 0.09512 0.9729 0.9808 0.8187 0.9317 0.9574 0.5261 ] Network output: [ 0.03038 0.7898 0.1726 -0.001364 0.0006462 0.9713 -0.001165 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07199 Epoch 482 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05017 1.092 0.9135 0.00178 -0.0008145 -0.09853 0.001406 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07978 0.002221 0.04499 0.02767 0.903 0.9175 0.143 0.8094 0.8522 0.3164 ] Network output: [ 0.8607 -0.0148 0.1691 -0.003052 0.001436 0.1119 -0.002575 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7238 0.09732 0.03446 0.2874 0.951 0.974 0.8108 0.8415 0.9354 0.7206 ] Network output: [ -0.04933 0.8882 1.109 -0.0002856 8.779e-05 0.1 -4.737e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1651 0.1035 0.1765 0.115 0.9691 0.9769 0.1686 0.9213 0.9548 0.2609 ] Network output: [ 0.1118 -0.2665 1.137 -0.002373 0.001028 0.8964 -0.001639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8148 0.5896 0.4826 0.4662 0.9586 0.979 0.8183 0.8629 0.9484 0.7221 ] Network output: [ -0.04019 0.1616 0.9017 0.002975 -0.001355 1.029 0.00232 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7928 0.7482 0.4774 0.2488 0.9745 0.9821 0.7935 0.9352 0.9609 0.5181 ] Network output: [ -0.05121 0.3076 0.7385 0.001723 -0.0007788 1.063 0.00132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8207 0.8116 0.5136 0.1167 0.973 0.9809 0.8208 0.9319 0.9577 0.5233 ] Network output: [ 0.04348 0.7649 0.1784 -0.0009631 0.0004662 0.9659 -0.0008629 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07669 Epoch 483 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07518 1.098 0.8762 0.00223 -0.001013 -0.1158 0.001729 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.08007 -0.00102 0.03082 0.02584 0.9032 0.9177 0.1443 0.8089 0.852 0.3137 ] Network output: [ 0.9846 -0.01078 0.01716 -0.002039 0.0009959 0.01625 -0.00187 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7257 0.04967 -0.03592 0.2791 0.951 0.974 0.8143 0.8404 0.9349 0.7166 ] Network output: [ -0.0327 0.8932 1.085 6.233e-05 -6.517e-05 0.08688 0.0002015 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1641 0.09495 0.1564 0.1139 0.9689 0.9767 0.1677 0.9198 0.9537 0.2528 ] Network output: [ 0.1549 -0.279 1.103 -0.002208 0.0009596 0.8573 -0.001536 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8132 0.5577 0.4455 0.4764 0.9584 0.9788 0.8168 0.8621 0.948 0.7222 ] Network output: [ -0.06402 0.1395 0.9579 0.002701 -0.001235 1.042 0.002129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7887 0.7377 0.4728 0.273 0.9744 0.9821 0.7893 0.9348 0.9608 0.5202 ] Network output: [ -0.08147 0.2698 0.8139 0.00163 -0.0007424 1.086 0.001272 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8167 0.8064 0.5133 0.1554 0.973 0.9809 0.8169 0.9321 0.958 0.5244 ] Network output: [ 0.02666 0.7441 0.2196 -0.0008608 0.0004168 0.9796 -0.0007717 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06692 Epoch 484 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06303 1.121 0.8692 0.001947 -0.0008854 -0.1082 0.001516 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07969 0.0006613 0.03528 0.02245 0.9032 0.9177 0.1433 0.8094 0.8522 0.3103 ] Network output: [ 0.936 0.04161 0.02424 -0.00234 0.001127 0.05269 -0.002079 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.725 0.07246 -0.01204 0.2642 0.951 0.974 0.8129 0.8406 0.935 0.7149 ] Network output: [ -0.03591 0.9243 1.058 -0.0001099 1.396e-05 0.08889 6.428e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1649 0.09944 0.1596 0.1005 0.9689 0.9767 0.1684 0.9204 0.9539 0.2492 ] Network output: [ 0.1353 -0.2056 1.054 -0.002465 0.001076 0.8705 -0.001736 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.814 0.5756 0.4592 0.4418 0.9584 0.9788 0.8175 0.8623 0.9479 0.7195 ] Network output: [ -0.0462 0.2088 0.867 0.002601 -0.001185 1.027 0.00203 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7919 0.7446 0.4698 0.2262 0.9745 0.9821 0.7925 0.9352 0.9607 0.5137 ] Network output: [ -0.06349 0.3476 0.7181 0.00124 -0.0005614 1.066 0.000954 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8198 0.8102 0.5124 0.09305 0.973 0.9809 0.82 0.9322 0.9577 0.5228 ] Network output: [ 0.03382 0.7871 0.1706 -0.001317 0.0006238 0.9693 -0.001125 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0721 Epoch 485 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05069 1.091 0.9132 0.001771 -0.0008092 -0.09858 0.001395 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07945 0.001661 0.04318 0.02759 0.9034 0.9178 0.1426 0.8102 0.8529 0.3132 ] Network output: [ 0.8759 -0.01603 0.1526 -0.002879 0.001357 0.1001 -0.002438 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7245 0.08959 0.02668 0.2875 0.9512 0.9741 0.8119 0.842 0.9358 0.7212 ] Network output: [ -0.0481 0.8891 1.107 -0.0002442 7.135e-05 0.099 -2.52e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1644 0.1019 0.1718 0.1138 0.9693 0.9771 0.168 0.9218 0.9551 0.2559 ] Network output: [ 0.1129 -0.273 1.143 -0.002436 0.001058 0.8944 -0.001691 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8155 0.5846 0.4794 0.4692 0.9587 0.979 0.819 0.8634 0.9486 0.7229 ] Network output: [ -0.04333 0.1575 0.9099 0.002988 -0.00136 1.031 0.002328 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7929 0.7472 0.4743 0.2512 0.9746 0.9823 0.7936 0.9357 0.9613 0.5154 ] Network output: [ -0.05454 0.3018 0.7477 0.001748 -0.0007899 1.067 0.001339 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8212 0.8118 0.5105 0.1209 0.9731 0.981 0.8213 0.9324 0.958 0.5204 ] Network output: [ 0.04355 0.7597 0.1831 -0.0009138 0.0004425 0.9664 -0.0008197 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07487 Epoch 486 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07309 1.101 0.8757 0.00216 -0.0009804 -0.1142 0.001674 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07968 -0.001234 0.03025 0.02528 0.9035 0.918 0.1438 0.8098 0.8527 0.3102 ] Network output: [ 0.9892 -0.003417 0.004914 -0.001947 0.0009517 0.01231 -0.001788 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7262 0.04648 -0.03792 0.2771 0.9512 0.9741 0.815 0.841 0.9353 0.7169 ] Network output: [ -0.03261 0.899 1.08 5.835e-05 -6.128e-05 0.08651 0.0001897 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1635 0.09426 0.1531 0.1107 0.969 0.9768 0.1671 0.9204 0.9541 0.2476 ] Network output: [ 0.1522 -0.2733 1.101 -0.002315 0.001009 0.858 -0.00162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8139 0.556 0.4455 0.4738 0.9584 0.9789 0.8175 0.8626 0.9482 0.7224 ] Network output: [ -0.06385 0.1469 0.9506 0.002704 -0.001235 1.041 0.002126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7893 0.7378 0.4694 0.2677 0.9745 0.9822 0.7899 0.9354 0.9611 0.5164 ] Network output: [ -0.08131 0.277 0.8064 0.001602 -0.0007288 1.086 0.001247 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8177 0.8072 0.5101 0.1491 0.9731 0.981 0.8178 0.9325 0.9582 0.5211 ] Network output: [ 0.02831 0.7466 0.2152 -0.000879 0.0004241 0.9781 -0.0007822 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06681 Epoch 487 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05902 1.119 0.8761 0.001849 -0.0008417 -0.1051 0.001443 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07927 0.0006405 0.03607 0.02284 0.9036 0.918 0.1426 0.8103 0.8529 0.3074 ] Network output: [ 0.9296 0.03902 0.03485 -0.002357 0.00113 0.05741 -0.002072 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7253 0.0726 -0.007211 0.2662 0.9512 0.9741 0.8134 0.8414 0.9354 0.7161 ] Network output: [ -0.03769 0.9235 1.061 -0.0001327 2.565e-05 0.09019 4.095e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1642 0.09924 0.1584 0.09992 0.9692 0.9769 0.1678 0.9213 0.9545 0.2454 ] Network output: [ 0.1288 -0.211 1.068 -0.002569 0.001123 0.8752 -0.001815 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8149 0.5758 0.4626 0.4439 0.9585 0.9789 0.8184 0.8629 0.9482 0.7202 ] Network output: [ -0.04528 0.2071 0.867 0.002664 -0.001212 1.027 0.002073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7926 0.7452 0.4674 0.2255 0.9746 0.9822 0.7933 0.9358 0.9611 0.5103 ] Network output: [ -0.06167 0.3467 0.7159 0.001288 -0.0005823 1.066 0.0009877 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8209 0.8112 0.5091 0.09204 0.9731 0.981 0.821 0.9326 0.9579 0.5193 ] Network output: [ 0.03692 0.7837 0.1698 -0.00127 0.0006019 0.9676 -0.001086 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07226 Epoch 488 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05083 1.091 0.913 0.001752 -0.0008 -0.09834 0.001378 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0791 0.001166 0.04163 0.02748 0.9037 0.9181 0.1422 0.8109 0.8535 0.31 ] Network output: [ 0.889 -0.01599 0.1372 -0.00272 0.001284 0.08989 -0.00231 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7251 0.08262 0.01999 0.2874 0.9514 0.9742 0.8128 0.8426 0.9361 0.7215 ] Network output: [ -0.04689 0.8906 1.104 -0.0002066 5.656e-05 0.09799 -5.561e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1637 0.1005 0.1673 0.1123 0.9694 0.9772 0.1673 0.9224 0.9554 0.2508 ] Network output: [ 0.1137 -0.2775 1.147 -0.0025 0.001088 0.8926 -0.001744 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8161 0.58 0.4766 0.4715 0.9588 0.9791 0.8196 0.8638 0.9489 0.7234 ] Network output: [ -0.04592 0.1547 0.9162 0.002999 -0.001364 1.033 0.002334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.793 0.7462 0.4711 0.2529 0.9748 0.9824 0.7937 0.9363 0.9617 0.5124 ] Network output: [ -0.05744 0.2971 0.7553 0.001765 -0.0007979 1.07 0.001353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8216 0.812 0.5074 0.1244 0.9732 0.9811 0.8217 0.9328 0.9583 0.5173 ] Network output: [ 0.04385 0.7549 0.1874 -0.0008686 0.0004208 0.9666 -0.00078 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07345 Epoch 489 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07081 1.103 0.876 0.002086 -0.0009466 -0.1124 0.001616 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07927 -0.001409 0.02991 0.02484 0.9038 0.9182 0.1431 0.8105 0.8533 0.3067 ] Network output: [ 0.9921 0.003126 -0.004561 -0.001871 0.000914 0.009691 -0.001717 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7266 0.04378 -0.03894 0.2754 0.9513 0.9742 0.8155 0.8416 0.9357 0.7171 ] Network output: [ -0.03259 0.904 1.075 5.363e-05 -5.722e-05 0.08623 0.000178 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1629 0.09361 0.15 0.1078 0.9692 0.977 0.1665 0.9211 0.9544 0.2425 ] Network output: [ 0.1492 -0.2683 1.101 -0.002419 0.001056 0.8589 -0.001703 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8146 0.5545 0.4458 0.4717 0.9585 0.9789 0.8181 0.863 0.9484 0.7225 ] Network output: [ -0.06344 0.1533 0.9439 0.002712 -0.001238 1.041 0.002126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7898 0.7379 0.4661 0.2631 0.9747 0.9823 0.7904 0.9359 0.9614 0.5125 ] Network output: [ -0.08099 0.283 0.7999 0.00158 -0.0007179 1.086 0.001227 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8185 0.8079 0.5069 0.1439 0.9732 0.9811 0.8186 0.933 0.9585 0.5177 ] Network output: [ 0.0301 0.7478 0.2118 -0.0008883 0.0004274 0.9766 -0.0007857 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06683 Epoch 490 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05548 1.116 0.8822 0.001761 -0.0008018 -0.1023 0.001375 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07885 0.0005856 0.03665 0.02316 0.9038 0.9183 0.142 0.8111 0.8536 0.3044 ] Network output: [ 0.9248 0.03703 0.04296 -0.00236 0.001127 0.06093 -0.002057 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7256 0.07213 -0.003396 0.2679 0.9513 0.9742 0.8137 0.8422 0.9359 0.717 ] Network output: [ -0.03894 0.9231 1.063 -0.0001454 3.281e-05 0.09111 2.522e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1636 0.09889 0.1568 0.09919 0.9694 0.9771 0.1671 0.922 0.955 0.2414 ] Network output: [ 0.1235 -0.2158 1.079 -0.002664 0.001166 0.879 -0.001887 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8157 0.5753 0.4652 0.4458 0.9587 0.979 0.8192 0.8634 0.9485 0.7205 ] Network output: [ -0.04457 0.2055 0.8672 0.002719 -0.001236 1.027 0.002112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7932 0.7456 0.4648 0.2251 0.9748 0.9823 0.7938 0.9364 0.9615 0.5069 ] Network output: [ -0.06038 0.3451 0.715 0.001328 -0.0006 1.066 0.001016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8218 0.8121 0.5058 0.09191 0.9732 0.9811 0.8219 0.9331 0.9582 0.5158 ] Network output: [ 0.0397 0.78 0.1697 -0.001225 0.0005805 0.9659 -0.001047 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0724 Epoch 491 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0507 1.09 0.913 0.001726 -0.000788 -0.0979 0.001356 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07873 0.0007277 0.04029 0.02732 0.904 0.9184 0.1417 0.8116 0.8541 0.3066 ] Network output: [ 0.9003 -0.01502 0.123 -0.002572 0.001216 0.081 -0.002191 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7256 0.07632 0.01423 0.2871 0.9515 0.9743 0.8136 0.8431 0.9365 0.7217 ] Network output: [ -0.04571 0.8924 1.101 -0.0001723 4.314e-05 0.09698 1.202e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.163 0.09914 0.1631 0.1107 0.9696 0.9773 0.1665 0.9229 0.9557 0.2457 ] Network output: [ 0.1142 -0.2805 1.151 -0.002565 0.001118 0.891 -0.001798 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8166 0.5757 0.4741 0.4732 0.9589 0.9791 0.8202 0.8642 0.9491 0.7236 ] Network output: [ -0.04808 0.1531 0.9207 0.003005 -0.001367 1.035 0.002337 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7931 0.7453 0.4679 0.2539 0.9749 0.9825 0.7937 0.9367 0.962 0.5093 ] Network output: [ -0.06 0.2934 0.7615 0.001774 -0.0008022 1.072 0.00136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8219 0.8121 0.5043 0.1271 0.9733 0.9812 0.8221 0.9332 0.9586 0.5141 ] Network output: [ 0.04434 0.7504 0.191 -0.0008285 0.0004015 0.9666 -0.0007445 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07233 Epoch 492 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06839 1.105 0.8772 0.00201 -0.0009121 -0.1105 0.001557 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07886 -0.00155 0.02976 0.0245 0.9041 0.9185 0.1425 0.8113 0.8539 0.3033 ] Network output: [ 0.9936 0.008763 -0.01151 -0.001805 0.0008812 0.008233 -0.001654 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7269 0.04153 -0.03912 0.2742 0.9515 0.9743 0.816 0.8422 0.936 0.7173 ] Network output: [ -0.03263 0.9083 1.071 4.878e-05 -5.325e-05 0.08604 0.0001668 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1622 0.09299 0.1472 0.1052 0.9694 0.9771 0.1658 0.9217 0.9548 0.2376 ] Network output: [ 0.1461 -0.2639 1.101 -0.002519 0.001102 0.8602 -0.001781 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8151 0.553 0.4464 0.47 0.9586 0.979 0.8187 0.8635 0.9486 0.7223 ] Network output: [ -0.06289 0.1589 0.9378 0.002722 -0.001241 1.04 0.002129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7902 0.738 0.4628 0.2591 0.9748 0.9824 0.7909 0.9364 0.9617 0.5087 ] Network output: [ -0.08056 0.2878 0.7943 0.001561 -0.0007089 1.085 0.00121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8192 0.8086 0.5037 0.1397 0.9732 0.9811 0.8194 0.9333 0.9587 0.5144 ] Network output: [ 0.03202 0.7482 0.2092 -0.0008906 0.0004275 0.975 -0.0007839 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06693 Epoch 493 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05243 1.114 0.8877 0.001681 -0.0007658 -0.0998 0.001314 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07844 0.0004984 0.03704 0.02344 0.9041 0.9185 0.1413 0.8119 0.8542 0.3013 ] Network output: [ 0.9215 0.03544 0.04879 -0.002346 0.001117 0.0633 -0.00203 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7258 0.07109 -0.0005521 0.2695 0.9515 0.9743 0.8141 0.8429 0.9363 0.7176 ] Network output: [ -0.03975 0.9228 1.064 -0.0001495 3.613e-05 0.0917 1.594e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1629 0.0984 0.1549 0.09837 0.9696 0.9773 0.1664 0.9227 0.9554 0.2372 ] Network output: [ 0.119 -0.2199 1.089 -0.002749 0.001205 0.882 -0.001953 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8163 0.5744 0.467 0.4478 0.9588 0.9791 0.8199 0.8639 0.9488 0.7207 ] Network output: [ -0.04412 0.204 0.8678 0.002766 -0.001256 1.028 0.002144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7936 0.7457 0.462 0.2251 0.9749 0.9824 0.7942 0.9369 0.9618 0.5035 ] Network output: [ -0.05963 0.3431 0.7153 0.001361 -0.0006142 1.066 0.00104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8225 0.8127 0.5026 0.09248 0.9733 0.9811 0.8226 0.9334 0.9585 0.5124 ] Network output: [ 0.04219 0.7762 0.1703 -0.001181 0.0005597 0.9644 -0.00101 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07246 Epoch 494 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05036 1.09 0.9132 0.001696 -0.0007739 -0.09729 0.00133 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07835 0.0003399 0.03915 0.02714 0.9043 0.9186 0.1412 0.8123 0.8547 0.3033 ] Network output: [ 0.9103 -0.01333 0.1097 -0.002433 0.001151 0.07327 -0.002077 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.726 0.07062 0.009267 0.2867 0.9516 0.9744 0.8143 0.8437 0.9368 0.7217 ] Network output: [ -0.04455 0.8946 1.098 -0.0001412 3.104e-05 0.09597 2.765e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1622 0.09785 0.159 0.1089 0.9697 0.9774 0.1657 0.9234 0.956 0.2406 ] Network output: [ 0.1145 -0.2821 1.153 -0.002629 0.001148 0.8896 -0.00185 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8171 0.5716 0.4718 0.4744 0.9589 0.9792 0.8206 0.8646 0.9493 0.7237 ] Network output: [ -0.04986 0.1526 0.9237 0.003008 -0.001367 1.036 0.002336 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7931 0.7444 0.4647 0.2544 0.975 0.9825 0.7938 0.9372 0.9623 0.5061 ] Network output: [ -0.06226 0.2908 0.7664 0.001775 -0.0008025 1.074 0.001361 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8222 0.8122 0.5012 0.1291 0.9734 0.9812 0.8224 0.9336 0.9588 0.511 ] Network output: [ 0.04499 0.7464 0.1941 -0.0007939 0.0003847 0.9663 -0.0007134 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07143 Epoch 495 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0659 1.105 0.8791 0.001933 -0.0008774 -0.1085 0.001498 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07844 -0.001663 0.02976 0.02425 0.9044 0.9187 0.1419 0.812 0.8545 0.2998 ] Network output: [ 0.994 0.01347 -0.01618 -0.001747 0.0008518 0.007778 -0.001596 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7271 0.03967 -0.03861 0.2733 0.9516 0.9744 0.8163 0.8428 0.9364 0.7173 ] Network output: [ -0.03272 0.9118 1.068 4.409e-05 -4.948e-05 0.08592 0.0001564 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1615 0.09241 0.1446 0.1028 0.9695 0.9772 0.165 0.9223 0.9552 0.2328 ] Network output: [ 0.1429 -0.2603 1.102 -0.002613 0.001145 0.8616 -0.001855 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8156 0.5517 0.447 0.4687 0.9587 0.979 0.8192 0.8639 0.9489 0.7221 ] Network output: [ -0.06226 0.1637 0.9323 0.002734 -0.001245 1.04 0.002132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7906 0.738 0.4597 0.2557 0.9749 0.9825 0.7912 0.9369 0.962 0.5049 ] Network output: [ -0.08007 0.2918 0.7894 0.001545 -0.0007009 1.085 0.001195 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8199 0.8092 0.5005 0.1361 0.9733 0.9812 0.82 0.9337 0.9589 0.511 ] Network output: [ 0.03405 0.7479 0.207 -0.0008871 0.0004251 0.9734 -0.0007778 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06705 Epoch 496 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04983 1.112 0.8925 0.00161 -0.0007336 -0.09757 0.001259 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07803 0.0003807 0.03725 0.02366 0.9044 0.9187 0.1407 0.8126 0.8548 0.2982 ] Network output: [ 0.9197 0.03413 0.05249 -0.002316 0.0011 0.06459 -0.001993 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.726 0.06951 0.001369 0.2708 0.9516 0.9744 0.8144 0.8435 0.9367 0.7181 ] Network output: [ -0.04019 0.9227 1.065 -0.0001468 3.634e-05 0.09199 1.195e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1621 0.09779 0.1527 0.09749 0.9697 0.9774 0.1656 0.9234 0.9558 0.2331 ] Network output: [ 0.1154 -0.2237 1.097 -0.002825 0.00124 0.8844 -0.002012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8169 0.573 0.4681 0.4497 0.9588 0.9791 0.8204 0.8644 0.949 0.7208 ] Network output: [ -0.04396 0.2025 0.8689 0.002804 -0.001273 1.028 0.00217 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7939 0.7456 0.4592 0.2255 0.975 0.9825 0.7945 0.9374 0.9622 0.5001 ] Network output: [ -0.05937 0.3407 0.7166 0.001386 -0.0006252 1.067 0.001057 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.823 0.8132 0.4994 0.09362 0.9734 0.9812 0.8231 0.9338 0.9587 0.5091 ] Network output: [ 0.04442 0.7722 0.1713 -0.001138 0.0005392 0.9631 -0.0009732 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07243 Epoch 497 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04985 1.09 0.9135 0.001662 -0.000758 -0.09655 0.001302 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07797 -2.889e-06 0.03817 0.02692 0.9045 0.9189 0.1406 0.813 0.8552 0.2999 ] Network output: [ 0.9189 -0.0111 0.09747 -0.002301 0.00109 0.06661 -0.001969 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7263 0.06548 0.005032 0.2861 0.9518 0.9745 0.8148 0.8441 0.9371 0.7216 ] Network output: [ -0.04342 0.8969 1.094 -0.0001132 2.027e-05 0.09497 4.13e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1614 0.09664 0.1551 0.1071 0.9699 0.9775 0.1649 0.9238 0.9563 0.2357 ] Network output: [ 0.1146 -0.2826 1.154 -0.002691 0.001177 0.8884 -0.001902 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8174 0.5679 0.4697 0.4751 0.959 0.9792 0.821 0.8649 0.9494 0.7236 ] Network output: [ -0.05133 0.1531 0.9253 0.003006 -0.001366 1.036 0.002332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7931 0.7435 0.4614 0.2543 0.9751 0.9826 0.7937 0.9376 0.9625 0.5029 ] Network output: [ -0.06426 0.2892 0.7701 0.001767 -0.0007989 1.076 0.001355 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8225 0.8123 0.4981 0.1305 0.9735 0.9813 0.8226 0.9339 0.959 0.508 ] Network output: [ 0.04579 0.743 0.1965 -0.0007646 0.0003704 0.9659 -0.0006867 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07072 Epoch 498 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06338 1.106 0.8816 0.001857 -0.000843 -0.1064 0.001439 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07802 -0.001752 0.02988 0.02407 0.9046 0.9189 0.1412 0.8127 0.855 0.2965 ] Network output: [ 0.9933 0.01727 -0.01885 -0.001694 0.000825 0.008166 -0.001543 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7272 0.03814 -0.03753 0.2727 0.9517 0.9745 0.8166 0.8434 0.9367 0.7173 ] Network output: [ -0.03284 0.9147 1.065 3.971e-05 -4.597e-05 0.08585 0.0001466 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1608 0.09185 0.1422 0.1007 0.9697 0.9773 0.1643 0.9228 0.9555 0.2283 ] Network output: [ 0.1396 -0.2575 1.104 -0.002699 0.001185 0.8633 -0.001924 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8161 0.5505 0.4479 0.4677 0.9587 0.9791 0.8197 0.8642 0.9491 0.7218 ] Network output: [ -0.06159 0.1678 0.9273 0.002746 -0.001249 1.039 0.002137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7908 0.7379 0.4566 0.2527 0.975 0.9825 0.7915 0.9374 0.9623 0.5012 ] Network output: [ -0.07956 0.2951 0.7851 0.00153 -0.0006937 1.085 0.001181 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8204 0.8097 0.4973 0.1332 0.9734 0.9812 0.8205 0.934 0.9591 0.5078 ] Network output: [ 0.03616 0.7472 0.2053 -0.0008785 0.0004204 0.9717 -0.0007679 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06719 Epoch 499 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04765 1.11 0.8968 0.001547 -0.000705 -0.09561 0.001211 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07762 0.000235 0.03731 0.02384 0.9046 0.919 0.14 0.8133 0.8554 0.2952 ] Network output: [ 0.9194 0.03303 0.05424 -0.00227 0.001076 0.06485 -0.001946 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7262 0.06744 0.002438 0.2719 0.9517 0.9745 0.8147 0.8441 0.9371 0.7184 ] Network output: [ -0.0403 0.9226 1.065 -0.0001387 3.414e-05 0.09203 1.211e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1613 0.09706 0.1504 0.09656 0.9699 0.9775 0.1648 0.9239 0.9562 0.2289 ] Network output: [ 0.1126 -0.2271 1.104 -0.002892 0.00127 0.8861 -0.002065 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8174 0.5711 0.4686 0.4515 0.9589 0.9792 0.8209 0.8648 0.9493 0.7207 ] Network output: [ -0.04409 0.201 0.8703 0.002834 -0.001285 1.028 0.00219 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.794 0.7452 0.4563 0.226 0.9751 0.9826 0.7947 0.9378 0.9625 0.4969 ] Network output: [ -0.05956 0.3382 0.7187 0.001403 -0.0006329 1.068 0.00107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8234 0.8135 0.4962 0.0952 0.9735 0.9812 0.8235 0.9341 0.9589 0.5058 ] Network output: [ 0.04642 0.7682 0.1728 -0.001095 0.0005191 0.9618 -0.0009369 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0723 Epoch 500 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04919 1.09 0.914 0.001625 -0.0007405 -0.0957 0.001271 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07758 -0.0003053 0.03734 0.02668 0.9048 0.9191 0.1401 0.8136 0.8557 0.2966 ] Network output: [ 0.9263 -0.008447 0.08622 -0.002176 0.001032 0.06091 -0.001867 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7265 0.06084 0.001457 0.2854 0.9519 0.9746 0.8153 0.8446 0.9374 0.7214 ] Network output: [ -0.04233 0.8993 1.091 -8.848e-05 1.085e-05 0.09398 5.294e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1606 0.09549 0.1515 0.1051 0.97 0.9776 0.1641 0.9243 0.9566 0.2308 ] Network output: [ 0.1145 -0.2821 1.154 -0.00275 0.001205 0.8875 -0.001951 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8177 0.5644 0.4678 0.4754 0.959 0.9792 0.8213 0.8652 0.9496 0.7234 ] Network output: [ -0.05251 0.1543 0.9258 0.003001 -0.001363 1.037 0.002325 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.793 0.7426 0.4583 0.2538 0.9752 0.9827 0.7937 0.938 0.9628 0.4996 ] Network output: [ -0.06603 0.2885 0.7726 0.001752 -0.0007917 1.078 0.001342 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8227 0.8124 0.4952 0.1312 0.9735 0.9813 0.8228 0.9343 0.9592 0.5049 ] Network output: [ 0.04673 0.7402 0.1982 -0.0007401 0.0003583 0.9652 -0.000664 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07015 Epoch 501 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06085 1.105 0.8846 0.001782 -0.000809 -0.1043 0.001381 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0776 -0.001822 0.0301 0.02395 0.9049 0.9192 0.1405 0.8134 0.8556 0.2933 ] Network output: [ 0.9919 0.02024 -0.01983 -0.001646 0.0008001 0.009232 -0.001493 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7273 0.03687 -0.03601 0.2725 0.9518 0.9746 0.8168 0.8439 0.9371 0.7173 ] Network output: [ -0.03299 0.917 1.063 3.576e-05 -4.277e-05 0.0858 0.0001377 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.16 0.09131 0.14 0.09883 0.9698 0.9775 0.1635 0.9234 0.9559 0.2239 ] Network output: [ 0.1363 -0.2553 1.106 -0.002779 0.001221 0.8652 -0.001986 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8165 0.5493 0.4488 0.4671 0.9588 0.9791 0.8201 0.8646 0.9492 0.7215 ] Network output: [ -0.06092 0.1713 0.923 0.002758 -0.001254 1.039 0.002142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7911 0.7378 0.4536 0.2502 0.9751 0.9826 0.7917 0.9378 0.9626 0.4977 ] Network output: [ -0.07905 0.2978 0.7815 0.001516 -0.0006868 1.085 0.001168 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8209 0.8101 0.4943 0.1308 0.9735 0.9813 0.821 0.9344 0.9592 0.5046 ] Network output: [ 0.03833 0.746 0.2038 -0.0008654 0.0004136 0.97 -0.0007546 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06733 Epoch 502 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04584 1.108 0.9007 0.00149 -0.0006794 -0.0939 0.001167 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07721 6.448e-05 0.03724 0.02397 0.9049 0.9192 0.1394 0.8139 0.8559 0.2921 ] Network output: [ 0.9203 0.03211 0.05425 -0.002209 0.001046 0.06419 -0.001888 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7263 0.06492 0.002751 0.2728 0.9519 0.9746 0.815 0.8446 0.9374 0.7185 ] Network output: [ -0.04016 0.9225 1.065 -0.0001268 3.016e-05 0.09185 1.536e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1605 0.09624 0.1479 0.09558 0.97 0.9776 0.1639 0.9245 0.9565 0.2247 ] Network output: [ 0.1104 -0.2303 1.11 -0.002952 0.001298 0.8874 -0.002113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8177 0.5688 0.4686 0.4533 0.959 0.9792 0.8213 0.8651 0.9495 0.7206 ] Network output: [ -0.04449 0.1995 0.8722 0.002856 -0.001295 1.029 0.002204 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.794 0.7447 0.4535 0.2267 0.9752 0.9827 0.7947 0.9382 0.9627 0.4937 ] Network output: [ -0.06015 0.3355 0.7215 0.001414 -0.0006377 1.069 0.001078 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8237 0.8136 0.4932 0.09709 0.9735 0.9813 0.8238 0.9344 0.9591 0.5027 ] Network output: [ 0.04821 0.7641 0.1745 -0.001053 0.000499 0.9607 -0.0009008 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07209 Epoch 503 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0484 1.09 0.9146 0.001584 -0.0007217 -0.09477 0.001238 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07718 -0.000572 0.03664 0.02643 0.905 0.9193 0.1395 0.8142 0.8562 0.2933 ] Network output: [ 0.9326 -0.005491 0.07595 -0.002059 0.0009772 0.05608 -0.00177 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7267 0.05665 -0.001519 0.2846 0.952 0.9747 0.8156 0.845 0.9377 0.7211 ] Network output: [ -0.04129 0.9018 1.087 -6.694e-05 2.764e-06 0.09301 6.257e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1598 0.09441 0.148 0.1031 0.9701 0.9777 0.1632 0.9247 0.9568 0.2261 ] Network output: [ 0.1142 -0.2809 1.154 -0.002807 0.001231 0.8868 -0.001998 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.818 0.5611 0.4662 0.4754 0.9591 0.9793 0.8216 0.8655 0.9498 0.7231 ] Network output: [ -0.05344 0.1563 0.9254 0.002993 -0.001359 1.037 0.002316 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7929 0.7418 0.4552 0.2528 0.9753 0.9828 0.7935 0.9384 0.963 0.4964 ] Network output: [ -0.06758 0.2887 0.774 0.001729 -0.0007814 1.079 0.001324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8228 0.8124 0.4923 0.1313 0.9736 0.9814 0.823 0.9346 0.9594 0.502 ] Network output: [ 0.04779 0.7378 0.1993 -0.0007198 0.0003482 0.9644 -0.0006446 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06971 Epoch 504 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05836 1.104 0.8881 0.001708 -0.0007758 -0.1022 0.001325 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07718 -0.001879 0.03038 0.02388 0.9051 0.9194 0.1398 0.814 0.8561 0.2901 ] Network output: [ 0.9899 0.02247 -0.01943 -0.0016 0.0007765 0.01081 -0.001446 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7274 0.03579 -0.03417 0.2725 0.9519 0.9746 0.817 0.8445 0.9374 0.7173 ] Network output: [ -0.03314 0.9187 1.062 3.236e-05 -3.99e-05 0.08577 0.0001295 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1592 0.09077 0.138 0.0971 0.97 0.9776 0.1627 0.9239 0.9562 0.2197 ] Network output: [ 0.133 -0.2539 1.109 -0.002851 0.001254 0.8672 -0.002043 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8169 0.548 0.4497 0.4667 0.9589 0.9791 0.8205 0.8649 0.9494 0.7212 ] Network output: [ -0.06027 0.1741 0.9193 0.00277 -0.001258 1.038 0.002148 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7912 0.7377 0.4508 0.248 0.9752 0.9827 0.7919 0.9382 0.9628 0.4942 ] Network output: [ -0.07858 0.2999 0.7784 0.001502 -0.0006801 1.085 0.001155 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8213 0.8104 0.4913 0.1289 0.9735 0.9813 0.8214 0.9346 0.9594 0.5015 ] Network output: [ 0.04055 0.7446 0.2026 -0.0008482 0.0004051 0.9683 -0.0007383 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06747 Epoch 505 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04436 1.105 0.9041 0.00144 -0.0006563 -0.09241 0.001127 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07681 -0.0001263 0.03705 0.02407 0.9051 0.9194 0.1387 0.8146 0.8564 0.2891 ] Network output: [ 0.9223 0.03135 0.05278 -0.002136 0.00101 0.06276 -0.001823 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7264 0.06203 0.002426 0.2736 0.952 0.9747 0.8152 0.8451 0.9377 0.7186 ] Network output: [ -0.03981 0.9226 1.065 -0.0001123 2.499e-05 0.09148 2.075e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1596 0.09533 0.1453 0.09456 0.9701 0.9777 0.163 0.9249 0.9568 0.2205 ] Network output: [ 0.1088 -0.2332 1.115 -0.003003 0.001322 0.8882 -0.002155 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8181 0.5662 0.4682 0.455 0.959 0.9792 0.8216 0.8654 0.9497 0.7204 ] Network output: [ -0.04512 0.1981 0.8743 0.002872 -0.001301 1.029 0.002213 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7939 0.744 0.4507 0.2275 0.9753 0.9828 0.7946 0.9386 0.963 0.4906 ] Network output: [ -0.06108 0.3328 0.7247 0.001419 -0.0006399 1.07 0.001081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8239 0.8137 0.4902 0.09919 0.9736 0.9813 0.824 0.9347 0.9593 0.4997 ] Network output: [ 0.04984 0.7601 0.1764 -0.00101 0.0004789 0.9597 -0.0008646 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07182 Epoch 506 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04751 1.09 0.9154 0.00154 -0.0007015 -0.09376 0.001203 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07678 -0.0008074 0.03606 0.02618 0.9052 0.9195 0.1389 0.8148 0.8567 0.2901 ] Network output: [ 0.9379 -0.002324 0.06662 -0.001949 0.0009257 0.05203 -0.001679 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7269 0.05286 -0.003957 0.2838 0.9521 0.9747 0.8159 0.8455 0.9379 0.7208 ] Network output: [ -0.04031 0.9043 1.084 -4.846e-05 -4.037e-06 0.09206 7.027e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1589 0.09337 0.1447 0.1011 0.9702 0.9778 0.1624 0.9251 0.9571 0.2215 ] Network output: [ 0.1138 -0.2792 1.154 -0.002861 0.001257 0.8863 -0.002044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8182 0.558 0.4648 0.4751 0.9591 0.9793 0.8218 0.8658 0.9499 0.7227 ] Network output: [ -0.05416 0.1587 0.9241 0.002983 -0.001353 1.038 0.002306 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7927 0.741 0.4521 0.2516 0.9754 0.9828 0.7934 0.9387 0.9632 0.4932 ] Network output: [ -0.06893 0.2896 0.7746 0.001701 -0.0007684 1.081 0.001302 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8229 0.8123 0.4894 0.131 0.9736 0.9814 0.8231 0.9349 0.9596 0.4991 ] Network output: [ 0.04896 0.7358 0.2 -0.0007026 0.0003395 0.9635 -0.0006279 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06939 Epoch 507 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05592 1.103 0.8918 0.001637 -0.0007434 -0.1001 0.00127 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07675 -0.001926 0.03069 0.02385 0.9053 0.9195 0.1391 0.8147 0.8566 0.287 ] Network output: [ 0.9875 0.0241 -0.018 -0.001556 0.0007537 0.01276 -0.0014 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7274 0.03483 -0.03212 0.2726 0.952 0.9747 0.817 0.845 0.9377 0.7173 ] Network output: [ -0.03328 0.9201 1.061 2.958e-05 -3.74e-05 0.08573 0.0001222 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1584 0.09023 0.136 0.09552 0.9701 0.9777 0.1618 0.9245 0.9565 0.2157 ] Network output: [ 0.1299 -0.253 1.112 -0.002915 0.001284 0.8692 -0.002095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8172 0.5468 0.4506 0.4666 0.9589 0.9792 0.8208 0.8653 0.9496 0.7209 ] Network output: [ -0.05967 0.1765 0.9161 0.002782 -0.001263 1.038 0.002153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7913 0.7374 0.448 0.2461 0.9753 0.9828 0.792 0.9386 0.9631 0.4909 ] Network output: [ -0.07817 0.3017 0.7758 0.001488 -0.0006734 1.085 0.001143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8216 0.8107 0.4885 0.1273 0.9736 0.9814 0.8218 0.9349 0.9596 0.4985 ] Network output: [ 0.04279 0.7429 0.2016 -0.0008273 0.0003949 0.9666 -0.0007193 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06761 Epoch 508 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04312 1.103 0.9071 0.001393 -0.0006352 -0.09109 0.00109 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07641 -0.0003323 0.03678 0.02413 0.9053 0.9196 0.1381 0.8152 0.8569 0.2861 ] Network output: [ 0.9251 0.03074 0.05011 -0.002053 0.0009707 0.06071 -0.00175 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7265 0.05885 0.001594 0.2742 0.9521 0.9747 0.8155 0.8456 0.938 0.7185 ] Network output: [ -0.03931 0.9227 1.065 -9.641e-05 1.911e-05 0.09097 2.745e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1587 0.09436 0.1426 0.09351 0.9703 0.9778 0.1621 0.9254 0.9571 0.2165 ] Network output: [ 0.1077 -0.236 1.119 -0.003049 0.001343 0.8887 -0.002192 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8183 0.5633 0.4675 0.4567 0.9591 0.9793 0.8219 0.8657 0.9498 0.7202 ] Network output: [ -0.04593 0.1969 0.8766 0.002881 -0.001305 1.03 0.002218 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7938 0.7432 0.4479 0.2283 0.9754 0.9828 0.7944 0.9389 0.9632 0.4876 ] Network output: [ -0.06229 0.3301 0.7284 0.001419 -0.0006398 1.072 0.001081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8239 0.8136 0.4874 0.1014 0.9736 0.9814 0.8241 0.935 0.9595 0.4968 ] Network output: [ 0.05134 0.7562 0.1785 -0.0009675 0.0004586 0.9588 -0.0008282 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07152 Epoch 509 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04651 1.089 0.9164 0.001494 -0.0006803 -0.09269 0.001166 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07638 -0.001016 0.03558 0.02592 0.9054 0.9197 0.1382 0.8153 0.8572 0.2869 ] Network output: [ 0.9424 0.0009662 0.05821 -0.001846 0.0008775 0.04868 -0.001593 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7269 0.04942 -0.00592 0.283 0.9522 0.9748 0.8162 0.8459 0.9382 0.7204 ] Network output: [ -0.03938 0.9068 1.081 -3.282e-05 -9.651e-06 0.09114 7.621e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.158 0.09238 0.1416 0.09911 0.9703 0.9779 0.1615 0.9255 0.9573 0.2171 ] Network output: [ 0.1133 -0.277 1.153 -0.002913 0.001281 0.8859 -0.002087 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8184 0.5551 0.4635 0.4746 0.9591 0.9793 0.822 0.866 0.95 0.7222 ] Network output: [ -0.05469 0.1616 0.9222 0.002971 -0.001347 1.038 0.002293 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7926 0.7402 0.4491 0.25 0.9754 0.9829 0.7932 0.9391 0.9635 0.49 ] Network output: [ -0.07013 0.291 0.7745 0.001668 -0.0007532 1.082 0.001276 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.823 0.8123 0.4867 0.1302 0.9737 0.9814 0.8231 0.9351 0.9598 0.4963 ] Network output: [ 0.05022 0.7342 0.2002 -0.0006875 0.0003318 0.9624 -0.0006129 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06917 Epoch 510 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05354 1.102 0.8957 0.001567 -0.0007118 -0.09802 0.001216 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07633 -0.001968 0.03103 0.02385 0.9055 0.9197 0.1384 0.8153 0.8571 0.284 ] Network output: [ 0.9848 0.02524 -0.01585 -0.001513 0.0007314 0.01493 -0.001356 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7274 0.03394 -0.02997 0.2729 0.9521 0.9748 0.8171 0.8455 0.938 0.7173 ] Network output: [ -0.03339 0.9211 1.06 2.75e-05 -3.528e-05 0.08566 0.0001157 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1575 0.08967 0.1341 0.09405 0.9702 0.9778 0.161 0.925 0.9568 0.2118 ] Network output: [ 0.1268 -0.2526 1.115 -0.002973 0.00131 0.8713 -0.002141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8175 0.5455 0.4515 0.4666 0.959 0.9792 0.8211 0.8656 0.9498 0.7206 ] Network output: [ -0.05912 0.1785 0.9134 0.002793 -0.001267 1.038 0.002158 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7913 0.7371 0.4454 0.2445 0.9754 0.9828 0.792 0.939 0.9633 0.4877 ] Network output: [ -0.07784 0.303 0.7737 0.001474 -0.0006664 1.085 0.001131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8219 0.8108 0.4857 0.126 0.9736 0.9814 0.822 0.9352 0.9597 0.4956 ] Network output: [ 0.04503 0.741 0.2008 -0.0008032 0.0003832 0.9649 -0.000698 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06778 Epoch 511 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04209 1.101 0.9098 0.00135 -0.0006155 -0.08992 0.001056 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07601 -0.0005482 0.03646 0.02416 0.9055 0.9197 0.1375 0.8157 0.8574 0.2832 ] Network output: [ 0.9285 0.03027 0.04654 -0.001962 0.0009277 0.05821 -0.001673 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7266 0.05545 0.0003896 0.2747 0.9521 0.9748 0.8157 0.846 0.9383 0.7184 ] Network output: [ -0.0387 0.9229 1.064 -7.994e-05 1.293e-05 0.09033 3.48e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1578 0.09333 0.1399 0.09243 0.9704 0.9779 0.1612 0.9258 0.9574 0.2124 ] Network output: [ 0.1069 -0.2385 1.123 -0.003089 0.001362 0.8888 -0.002225 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8185 0.5602 0.4665 0.4583 0.9591 0.9793 0.822 0.866 0.95 0.7199 ] Network output: [ -0.04688 0.1957 0.8791 0.002885 -0.001306 1.031 0.00222 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7935 0.7423 0.4452 0.2292 0.9755 0.9829 0.7942 0.9393 0.9635 0.4847 ] Network output: [ -0.0637 0.3275 0.7322 0.001414 -0.0006376 1.073 0.001077 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8239 0.8135 0.4846 0.1036 0.9737 0.9814 0.8241 0.9352 0.9597 0.494 ] Network output: [ 0.05275 0.7523 0.1806 -0.000924 0.0004381 0.9579 -0.0007916 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07121 Epoch 512 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04545 1.089 0.9174 0.001446 -0.0006582 -0.09157 0.001128 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07597 -0.001202 0.03519 0.02566 0.9056 0.9198 0.1376 0.8159 0.8576 0.2837 ] Network output: [ 0.946 0.0043 0.05066 -0.00175 0.0008322 0.04593 -0.001512 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.727 0.04627 -0.007477 0.2822 0.9522 0.9749 0.8163 0.8463 0.9384 0.72 ] Network output: [ -0.0385 0.9092 1.077 -1.973e-05 -1.421e-05 0.09023 8.057e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1571 0.09142 0.1387 0.09714 0.9704 0.978 0.1606 0.9259 0.9575 0.2128 ] Network output: [ 0.1126 -0.2747 1.152 -0.002962 0.001304 0.8858 -0.002128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8185 0.5522 0.4623 0.474 0.9591 0.9793 0.8221 0.8662 0.9501 0.7218 ] Network output: [ -0.05507 0.1647 0.9199 0.002957 -0.00134 1.038 0.00228 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7923 0.7393 0.4461 0.2483 0.9755 0.9829 0.793 0.9394 0.9637 0.4868 ] Network output: [ -0.07119 0.2928 0.7738 0.001631 -0.0007364 1.082 0.001247 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.823 0.8122 0.484 0.1292 0.9737 0.9815 0.8232 0.9354 0.9599 0.4936 ] Network output: [ 0.05157 0.7327 0.2002 -0.0006733 0.0003246 0.9613 -0.0005988 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06904 Epoch 513 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05126 1.1 0.8996 0.001499 -0.0006812 -0.09601 0.001164 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07591 -0.002008 0.03137 0.02386 0.9057 0.9199 0.1377 0.8159 0.8576 0.2811 ] Network output: [ 0.9821 0.02603 -0.01326 -0.001469 0.0007092 0.0172 -0.001312 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7273 0.03306 -0.02781 0.2734 0.9522 0.9748 0.8171 0.846 0.9383 0.7173 ] Network output: [ -0.03346 0.9219 1.06 2.613e-05 -3.355e-05 0.08556 0.00011 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1566 0.0891 0.1322 0.09267 0.9703 0.9779 0.1601 0.9254 0.9571 0.2081 ] Network output: [ 0.124 -0.2526 1.119 -0.003024 0.001334 0.8733 -0.002182 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8177 0.5441 0.4522 0.4668 0.959 0.9793 0.8213 0.8659 0.9499 0.7202 ] Network output: [ -0.05863 0.1802 0.911 0.002802 -0.00127 1.037 0.002162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7913 0.7367 0.4427 0.2431 0.9755 0.9829 0.7919 0.9393 0.9635 0.4846 ] Network output: [ -0.07761 0.3042 0.7719 0.001458 -0.000659 1.085 0.001117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8221 0.811 0.4829 0.1249 0.9737 0.9814 0.8222 0.9354 0.9599 0.4927 ] Network output: [ 0.04726 0.7389 0.2002 -0.0007763 0.0003704 0.9633 -0.0006746 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06796 Epoch 514 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04119 1.099 0.9124 0.00131 -0.0005968 -0.08885 0.001024 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07561 -0.0007686 0.0361 0.02417 0.9057 0.9199 0.1369 0.8163 0.8578 0.2803 ] Network output: [ 0.9324 0.02993 0.04236 -0.001866 0.0008827 0.05544 -0.001592 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7266 0.05193 -0.001059 0.2751 0.9522 0.9749 0.8158 0.8465 0.9385 0.7183 ] Network output: [ -0.03802 0.9232 1.063 -6.366e-05 6.783e-06 0.08961 4.223e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1568 0.09227 0.1372 0.09133 0.9705 0.978 0.1602 0.9262 0.9576 0.2085 ] Network output: [ 0.1065 -0.2409 1.126 -0.003123 0.001378 0.8888 -0.002254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8186 0.5569 0.4653 0.4597 0.9591 0.9793 0.8222 0.8663 0.9501 0.7196 ] Network output: [ -0.04791 0.1947 0.8815 0.002885 -0.001306 1.031 0.002218 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7932 0.7412 0.4424 0.23 0.9756 0.983 0.7938 0.9396 0.9637 0.4819 ] Network output: [ -0.06525 0.3251 0.7361 0.001405 -0.0006337 1.075 0.001071 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8239 0.8132 0.4819 0.1058 0.9737 0.9814 0.824 0.9355 0.9598 0.4912 ] Network output: [ 0.05409 0.7484 0.1828 -0.0008798 0.0004174 0.9571 -0.0007546 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07093 Epoch 515 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04432 1.089 0.9186 0.001396 -0.0006354 -0.09042 0.001089 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07556 -0.00137 0.03486 0.02542 0.9058 0.92 0.1369 0.8164 0.858 0.2806 ] Network output: [ 0.9491 0.007606 0.04392 -0.001659 0.0007896 0.04369 -0.001435 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.727 0.04337 -0.008696 0.2814 0.9523 0.9749 0.8164 0.8467 0.9387 0.7196 ] Network output: [ -0.03767 0.9115 1.074 -8.864e-06 -1.784e-05 0.08935 8.359e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1562 0.09049 0.1359 0.09522 0.9705 0.9781 0.1596 0.9262 0.9577 0.2086 ] Network output: [ 0.1119 -0.2722 1.15 -0.003008 0.001326 0.8857 -0.002166 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8186 0.5495 0.4613 0.4734 0.9592 0.9794 0.8222 0.8665 0.9503 0.7212 ] Network output: [ -0.05534 0.1679 0.9173 0.002943 -0.001333 1.037 0.002267 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7921 0.7385 0.4432 0.2466 0.9756 0.983 0.7928 0.9397 0.9638 0.4837 ] Network output: [ -0.07215 0.2949 0.7728 0.001591 -0.0007184 1.083 0.001216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.823 0.8121 0.4813 0.1281 0.9737 0.9815 0.8232 0.9356 0.96 0.4908 ] Network output: [ 0.05298 0.7313 0.2 -0.000659 0.0003174 0.9601 -0.0005848 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06901 Epoch 516 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04907 1.098 0.9036 0.001434 -0.0006516 -0.09407 0.001114 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07549 -0.002049 0.03169 0.02388 0.9059 0.9201 0.137 0.8164 0.858 0.2783 ] Network output: [ 0.9794 0.02659 -0.01049 -0.001425 0.0006866 0.01948 -0.001268 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7272 0.03216 -0.02571 0.2738 0.9523 0.9749 0.8171 0.8464 0.9385 0.7172 ] Network output: [ -0.03348 0.9225 1.059 2.546e-05 -3.219e-05 0.0854 0.0001051 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1558 0.0885 0.1304 0.09136 0.9704 0.978 0.1592 0.9259 0.9574 0.2044 ] Network output: [ 0.1213 -0.2528 1.122 -0.003068 0.001354 0.8752 -0.002218 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8179 0.5425 0.4529 0.467 0.9591 0.9793 0.8215 0.8662 0.9501 0.7198 ] Network output: [ -0.05822 0.1816 0.909 0.00281 -0.001273 1.037 0.002165 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7912 0.7363 0.4402 0.2418 0.9755 0.983 0.7918 0.9396 0.9637 0.4816 ] Network output: [ -0.07748 0.3051 0.7704 0.001441 -0.0006509 1.085 0.001103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8222 0.811 0.4802 0.124 0.9737 0.9815 0.8224 0.9357 0.96 0.4899 ] Network output: [ 0.04946 0.7367 0.1997 -0.0007471 0.0003565 0.9616 -0.0006495 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06816 Epoch 517 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04038 1.098 0.9147 0.00127 -0.0005786 -0.08786 0.0009926 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07521 -0.0009888 0.03572 0.02416 0.9059 0.9201 0.1362 0.8168 0.8583 0.2774 ] Network output: [ 0.9364 0.02973 0.03783 -0.001767 0.0008365 0.05257 -0.00151 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7266 0.04836 -0.002637 0.2755 0.9523 0.9749 0.816 0.8469 0.9388 0.7181 ] Network output: [ -0.03729 0.9235 1.062 -4.813e-05 9.146e-07 0.08882 4.934e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1559 0.09119 0.1345 0.09022 0.9706 0.9781 0.1593 0.9265 0.9579 0.2046 ] Network output: [ 0.1062 -0.2431 1.129 -0.003153 0.001392 0.8887 -0.00228 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8187 0.5535 0.464 0.4611 0.9592 0.9793 0.8222 0.8665 0.9502 0.7193 ] Network output: [ -0.04898 0.1939 0.8838 0.002881 -0.001304 1.032 0.002214 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7928 0.7401 0.4397 0.2307 0.9756 0.983 0.7934 0.9399 0.9639 0.4791 ] Network output: [ -0.06689 0.3229 0.7399 0.001393 -0.0006281 1.077 0.001061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8237 0.8129 0.4792 0.1078 0.9737 0.9815 0.8239 0.9357 0.96 0.4885 ] Network output: [ 0.05539 0.7446 0.1849 -0.0008351 0.0003964 0.9563 -0.0007173 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0707 Epoch 518 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04315 1.089 0.9199 0.001345 -0.0006121 -0.08925 0.001049 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07516 -0.001523 0.03459 0.02518 0.906 0.9202 0.1363 0.817 0.8584 0.2776 ] Network output: [ 0.9515 0.01082 0.0379 -0.001574 0.0007494 0.04188 -0.001363 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7269 0.04065 -0.009644 0.2807 0.9524 0.975 0.8165 0.8471 0.9389 0.7192 ] Network output: [ -0.03689 0.9137 1.072 9.734e-08 -2.069e-05 0.08848 8.549e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1553 0.08958 0.1332 0.09336 0.9706 0.9781 0.1587 0.9266 0.9579 0.2045 ] Network output: [ 0.1112 -0.2698 1.149 -0.003051 0.001346 0.8857 -0.002202 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8186 0.5468 0.4603 0.4727 0.9592 0.9794 0.8222 0.8667 0.9504 0.7207 ] Network output: [ -0.05553 0.1711 0.9145 0.002927 -0.001325 1.037 0.002252 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7918 0.7376 0.4403 0.2447 0.9757 0.9831 0.7925 0.94 0.964 0.4806 ] Network output: [ -0.07305 0.2971 0.7716 0.001549 -0.0006994 1.084 0.001184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.823 0.8119 0.4787 0.1268 0.9738 0.9815 0.8231 0.9359 0.9602 0.4881 ] Network output: [ 0.05444 0.7299 0.1998 -0.0006438 0.0003098 0.9588 -0.0005702 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06904 Epoch 519 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04698 1.096 0.9074 0.00137 -0.0006229 -0.0922 0.001065 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07507 -0.002092 0.03198 0.0239 0.906 0.9202 0.1362 0.817 0.8585 0.2755 ] Network output: [ 0.9768 0.02701 -0.007755 -0.001379 0.0006636 0.02168 -0.001223 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7271 0.03119 -0.02373 0.2743 0.9524 0.9749 0.817 0.8469 0.9388 0.7171 ] Network output: [ -0.03344 0.9231 1.059 2.54e-05 -3.115e-05 0.08518 0.0001008 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1549 0.08788 0.1286 0.09008 0.9705 0.9781 0.1582 0.9263 0.9577 0.2009 ] Network output: [ 0.1189 -0.2532 1.126 -0.003107 0.001372 0.8771 -0.00225 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.818 0.5409 0.4534 0.4673 0.9591 0.9793 0.8216 0.8665 0.9502 0.7194 ] Network output: [ -0.05786 0.1829 0.9072 0.002815 -0.001275 1.037 0.002166 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.791 0.7357 0.4376 0.2406 0.9756 0.983 0.7917 0.9399 0.9639 0.4786 ] Network output: [ -0.07747 0.3059 0.7693 0.001421 -0.0006418 1.086 0.001087 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8223 0.811 0.4776 0.1233 0.9737 0.9815 0.8224 0.9359 0.9602 0.4872 ] Network output: [ 0.05162 0.7344 0.1994 -0.000716 0.0003418 0.96 -0.0006231 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0684 Epoch 520 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03962 1.096 0.9169 0.001231 -0.0005607 -0.08691 0.0009616 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07482 -0.001205 0.03535 0.02414 0.9061 0.9203 0.1356 0.8173 0.8587 0.2746 ] Network output: [ 0.9404 0.02966 0.0332 -0.001668 0.00079 0.04971 -0.001427 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7266 0.0448 -0.004251 0.2758 0.9524 0.975 0.8161 0.8473 0.939 0.7178 ] Network output: [ -0.03655 0.9238 1.061 -3.375e-05 -4.49e-06 0.088 5.581e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1549 0.0901 0.1319 0.08909 0.9706 0.9782 0.1583 0.9269 0.9581 0.2009 ] Network output: [ 0.1061 -0.2451 1.131 -0.003179 0.001404 0.8885 -0.002303 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8187 0.5501 0.4626 0.4624 0.9592 0.9794 0.8223 0.8667 0.9504 0.7189 ] Network output: [ -0.05006 0.1932 0.8859 0.002875 -0.0013 1.033 0.002207 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7924 0.7389 0.4371 0.2313 0.9757 0.9831 0.793 0.9402 0.9641 0.4763 ] Network output: [ -0.06857 0.3209 0.7436 0.001377 -0.0006209 1.078 0.001049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8236 0.8126 0.4766 0.1097 0.9738 0.9815 0.8237 0.9359 0.9601 0.4859 ] Network output: [ 0.05669 0.7409 0.1871 -0.00079 0.0003753 0.9555 -0.0006799 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07053 Epoch 521 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04196 1.088 0.9213 0.001293 -0.0005885 -0.08807 0.001008 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07475 -0.001665 0.03436 0.02497 0.9062 0.9203 0.1356 0.8175 0.8589 0.2746 ] Network output: [ 0.9536 0.0139 0.03253 -0.001492 0.000711 0.04041 -0.001294 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7268 0.03808 -0.01039 0.2801 0.9524 0.975 0.8165 0.8474 0.9391 0.7187 ] Network output: [ -0.03614 0.9159 1.069 7.445e-06 -2.289e-05 0.08762 8.646e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1544 0.08868 0.1307 0.09157 0.9707 0.9782 0.1577 0.9269 0.9581 0.2006 ] Network output: [ 0.1105 -0.2675 1.148 -0.00309 0.001364 0.8858 -0.002235 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8186 0.5442 0.4594 0.472 0.9592 0.9794 0.8222 0.8669 0.9505 0.7201 ] Network output: [ -0.05567 0.1743 0.9118 0.002911 -0.001317 1.037 0.002238 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7915 0.7368 0.4374 0.2429 0.9757 0.9831 0.7922 0.9403 0.9642 0.4775 ] Network output: [ -0.07391 0.2993 0.7703 0.001506 -0.0006797 1.084 0.00115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8229 0.8117 0.476 0.1255 0.9738 0.9815 0.8231 0.9361 0.9603 0.4855 ] Network output: [ 0.05595 0.7285 0.1995 -0.000627 0.0003015 0.9576 -0.0005545 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06915 Epoch 522 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04501 1.095 0.9111 0.001309 -0.0005951 -0.09041 0.001018 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07465 -0.00214 0.03224 0.02392 0.9062 0.9204 0.1355 0.8175 0.8589 0.2727 ] Network output: [ 0.9744 0.02737 -0.005213 -0.001331 0.0006397 0.02374 -0.001177 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.727 0.03014 -0.02192 0.2748 0.9524 0.975 0.8169 0.8473 0.939 0.717 ] Network output: [ -0.03334 0.9236 1.058 2.584e-05 -3.04e-05 0.0849 9.72e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1539 0.08723 0.1268 0.08884 0.9706 0.9782 0.1573 0.9267 0.958 0.1974 ] Network output: [ 0.1167 -0.2538 1.129 -0.00314 0.001388 0.8787 -0.002278 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8181 0.5391 0.4536 0.4677 0.9591 0.9793 0.8217 0.8667 0.9504 0.719 ] Network output: [ -0.05758 0.1842 0.9056 0.002817 -0.001275 1.037 0.002166 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7908 0.7351 0.435 0.2395 0.9757 0.9831 0.7915 0.9402 0.9641 0.4757 ] Network output: [ -0.07757 0.3067 0.7683 0.001399 -0.0006316 1.086 0.001069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8223 0.8109 0.475 0.1226 0.9738 0.9815 0.8225 0.9361 0.9603 0.4845 ] Network output: [ 0.05372 0.732 0.1993 -0.0006834 0.0003265 0.9585 -0.0005957 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06867 Epoch 523 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03888 1.094 0.919 0.001191 -0.0005427 -0.08598 0.0009306 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07442 -0.001412 0.03499 0.02411 0.9062 0.9204 0.135 0.8178 0.8591 0.2719 ] Network output: [ 0.9442 0.0297 0.02863 -0.001568 0.0007437 0.047 -0.001345 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7265 0.04131 -0.005825 0.276 0.9524 0.975 0.8161 0.8476 0.9392 0.7175 ] Network output: [ -0.0358 0.9243 1.06 -2.081e-05 -9.306e-06 0.08715 6.142e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1539 0.08901 0.1293 0.08795 0.9707 0.9782 0.1573 0.9272 0.9583 0.1972 ] Network output: [ 0.1061 -0.247 1.133 -0.003201 0.001415 0.8882 -0.002322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8186 0.5466 0.4612 0.4636 0.9592 0.9794 0.8222 0.8669 0.9505 0.7186 ] Network output: [ -0.0511 0.1928 0.8879 0.002866 -0.001296 1.033 0.002199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7919 0.7377 0.4344 0.2318 0.9757 0.9831 0.7925 0.9404 0.9642 0.4736 ] Network output: [ -0.07025 0.3192 0.747 0.001358 -0.0006122 1.08 0.001034 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8233 0.8122 0.474 0.1114 0.9738 0.9815 0.8235 0.9361 0.9602 0.4833 ] Network output: [ 0.058 0.7373 0.1891 -0.0007446 0.0003541 0.9546 -0.0006425 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07043 Epoch 524 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04077 1.088 0.9227 0.001241 -0.0005649 -0.08688 0.0009675 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07434 -0.001798 0.03417 0.02477 0.9063 0.9205 0.1349 0.8179 0.8592 0.2717 ] Network output: [ 0.9553 0.01679 0.02775 -0.001414 0.0006741 0.03922 -0.001228 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7267 0.03562 -0.01098 0.2795 0.9525 0.975 0.8164 0.8478 0.9393 0.7182 ] Network output: [ -0.03542 0.9178 1.066 1.343e-05 -2.455e-05 0.08678 8.668e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1534 0.08779 0.1282 0.08986 0.9708 0.9783 0.1568 0.9273 0.9583 0.1967 ] Network output: [ 0.1098 -0.2654 1.147 -0.003125 0.001381 0.886 -0.002265 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8186 0.5415 0.4584 0.4715 0.9592 0.9794 0.8222 0.8671 0.9506 0.7195 ] Network output: [ -0.05578 0.1774 0.9091 0.002894 -0.001309 1.037 0.002222 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7911 0.7358 0.4346 0.2412 0.9758 0.9831 0.7918 0.9405 0.9643 0.4744 ] Network output: [ -0.07475 0.3015 0.769 0.001462 -0.0006596 1.085 0.001116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8228 0.8114 0.4735 0.1242 0.9738 0.9816 0.8229 0.9363 0.9604 0.4828 ] Network output: [ 0.05748 0.727 0.1993 -0.000608 0.0002923 0.9563 -0.0005373 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06932 Epoch 525 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04315 1.093 0.9147 0.00125 -0.0005683 -0.0887 0.000972 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07424 -0.002192 0.03246 0.02393 0.9064 0.9205 0.1348 0.818 0.8593 0.27 ] Network output: [ 0.9722 0.02775 -0.002987 -0.00128 0.0006148 0.02562 -0.00113 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7268 0.02899 -0.02033 0.2753 0.9525 0.975 0.8168 0.8477 0.9393 0.7168 ] Network output: [ -0.03318 0.9241 1.058 2.664e-05 -2.984e-05 0.08454 9.4e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.153 0.08655 0.125 0.08762 0.9707 0.9782 0.1563 0.9271 0.9582 0.194 ] Network output: [ 0.1148 -0.2543 1.132 -0.003169 0.001401 0.8802 -0.002301 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8182 0.5372 0.4538 0.468 0.9592 0.9794 0.8218 0.8669 0.9505 0.7186 ] Network output: [ -0.05737 0.1854 0.9041 0.002817 -0.001275 1.037 0.002163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7905 0.7344 0.4325 0.2385 0.9757 0.9831 0.7912 0.9405 0.9643 0.4728 ] Network output: [ -0.0778 0.3074 0.7675 0.001374 -0.0006201 1.086 0.001049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8223 0.8108 0.4724 0.122 0.9738 0.9815 0.8224 0.9363 0.9604 0.4819 ] Network output: [ 0.05578 0.7296 0.1993 -0.0006498 0.0003107 0.957 -0.0005676 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06897 Epoch 526 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03813 1.092 0.9211 0.001152 -0.0005246 -0.08505 0.0008993 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07402 -0.00161 0.03466 0.02408 0.9064 0.9206 0.1343 0.8183 0.8595 0.2692 ] Network output: [ 0.9477 0.02984 0.02429 -0.001471 0.0006981 0.0445 -0.001265 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7264 0.03794 -0.007309 0.2763 0.9525 0.9751 0.8161 0.848 0.9394 0.7172 ] Network output: [ -0.03507 0.9248 1.059 -9.466e-06 -1.345e-05 0.08628 6.605e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1529 0.08794 0.1268 0.08681 0.9708 0.9783 0.1562 0.9275 0.9585 0.1936 ] Network output: [ 0.1061 -0.2486 1.135 -0.003219 0.001424 0.888 -0.002339 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8186 0.5431 0.4598 0.4647 0.9592 0.9794 0.8222 0.8671 0.9506 0.7181 ] Network output: [ -0.05208 0.1926 0.8895 0.002855 -0.001291 1.034 0.002189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7914 0.7364 0.4318 0.2321 0.9758 0.9832 0.792 0.9407 0.9644 0.4709 ] Network output: [ -0.07191 0.3177 0.7501 0.001335 -0.000602 1.081 0.001017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8231 0.8118 0.4715 0.1128 0.9738 0.9816 0.8232 0.9363 0.9604 0.4807 ] Network output: [ 0.05933 0.7337 0.191 -0.0006993 0.000333 0.9538 -0.0006052 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0704 Epoch 527 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03957 1.087 0.9243 0.001189 -0.0005413 -0.0857 0.0009272 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07393 -0.001925 0.03399 0.02459 0.9065 0.9206 0.1342 0.8184 0.8596 0.2689 ] Network output: [ 0.9567 0.01947 0.02348 -0.001338 0.0006383 0.03825 -0.001163 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7265 0.03324 -0.01148 0.2791 0.9526 0.9751 0.8164 0.8481 0.9395 0.7178 ] Network output: [ -0.03473 0.9196 1.064 1.824e-05 -2.574e-05 0.08594 8.628e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1525 0.0869 0.1258 0.08822 0.9708 0.9783 0.1558 0.9276 0.9585 0.193 ] Network output: [ 0.1091 -0.2635 1.146 -0.003156 0.001396 0.8862 -0.002291 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8185 0.5388 0.4574 0.471 0.9592 0.9794 0.8221 0.8673 0.9507 0.7188 ] Network output: [ -0.0559 0.1803 0.9065 0.002876 -0.001301 1.037 0.002206 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7907 0.7349 0.4317 0.2395 0.9758 0.9832 0.7914 0.9408 0.9645 0.4714 ] Network output: [ -0.07561 0.3036 0.7678 0.001416 -0.000639 1.086 0.001081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8226 0.8112 0.4709 0.123 0.9738 0.9816 0.8228 0.9364 0.9605 0.4802 ] Network output: [ 0.05903 0.7253 0.1992 -0.0005866 0.000282 0.955 -0.0005184 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06955 Epoch 528 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0414 1.091 0.918 0.001192 -0.0005423 -0.08708 0.0009278 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07382 -0.00225 0.03263 0.02393 0.9065 0.9207 0.1341 0.8185 0.8597 0.2674 ] Network output: [ 0.9704 0.02817 -0.001159 -0.001227 0.0005889 0.02729 -0.001082 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7266 0.02774 -0.01896 0.2758 0.9526 0.9751 0.8166 0.8481 0.9395 0.7166 ] Network output: [ -0.03295 0.9246 1.057 2.763e-05 -2.941e-05 0.08412 9.112e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.152 0.08584 0.1231 0.08641 0.9708 0.9783 0.1554 0.9275 0.9584 0.1906 ] Network output: [ 0.1131 -0.2549 1.134 -0.003192 0.001412 0.8816 -0.002322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8182 0.5351 0.4537 0.4683 0.9592 0.9794 0.8218 0.8672 0.9506 0.7181 ] Network output: [ -0.05722 0.1866 0.9027 0.002814 -0.001273 1.037 0.002159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7902 0.7336 0.4299 0.2374 0.9758 0.9832 0.7909 0.9408 0.9645 0.4699 ] Network output: [ -0.07814 0.3082 0.7669 0.001345 -0.0006071 1.087 0.001027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8222 0.8106 0.4699 0.1214 0.9738 0.9816 0.8223 0.9365 0.9605 0.4793 ] Network output: [ 0.05778 0.7271 0.1994 -0.0006153 0.0002946 0.9555 -0.0005388 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06929 Epoch 529 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03736 1.091 0.9232 0.001112 -0.0005062 -0.08411 0.0008677 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07363 -0.001796 0.03435 0.02403 0.9066 0.9207 0.1337 0.8187 0.8598 0.2665 ] Network output: [ 0.9509 0.03008 0.02026 -0.001375 0.0006536 0.04229 -0.001186 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7263 0.03471 -0.008669 0.2765 0.9526 0.9751 0.8161 0.8483 0.9396 0.7169 ] Network output: [ -0.03436 0.9253 1.058 1.781e-07 -1.689e-05 0.08542 6.961e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1519 0.08688 0.1244 0.08566 0.9709 0.9784 0.1552 0.9278 0.9586 0.1901 ] Network output: [ 0.1062 -0.2502 1.137 -0.003233 0.001431 0.8877 -0.002352 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8185 0.5396 0.4583 0.4656 0.9592 0.9794 0.8221 0.8673 0.9507 0.7177 ] Network output: [ -0.053 0.1926 0.8909 0.002841 -0.001284 1.034 0.002177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7908 0.7352 0.4291 0.2323 0.9758 0.9832 0.7915 0.9409 0.9645 0.4682 ] Network output: [ -0.07352 0.3166 0.7529 0.001309 -0.0005902 1.083 0.0009972 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8228 0.8114 0.4689 0.114 0.9738 0.9816 0.823 0.9365 0.9605 0.4782 ] Network output: [ 0.06069 0.7302 0.1929 -0.0006542 0.000312 0.9529 -0.0005682 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07046 Epoch 530 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0384 1.086 0.9259 0.001138 -0.000518 -0.08453 0.0008872 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07353 -0.002047 0.03383 0.02443 0.9066 0.9207 0.1336 0.8189 0.86 0.2661 ] Network output: [ 0.9579 0.02192 0.01967 -0.001263 0.000603 0.03744 -0.0011 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7264 0.03091 -0.01192 0.2787 0.9526 0.9751 0.8163 0.8485 0.9397 0.7173 ] Network output: [ -0.03407 0.9213 1.062 2.203e-05 -2.652e-05 0.0851 8.535e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1515 0.08602 0.1235 0.08667 0.9709 0.9784 0.1548 0.9279 0.9587 0.1894 ] Network output: [ 0.1085 -0.2619 1.146 -0.003182 0.001408 0.8864 -0.002314 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8184 0.5361 0.4564 0.4706 0.9592 0.9794 0.822 0.8674 0.9508 0.7182 ] Network output: [ -0.05603 0.183 0.9042 0.002857 -0.001292 1.036 0.00219 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7903 0.7338 0.4289 0.238 0.9759 0.9832 0.791 0.941 0.9646 0.4684 ] Network output: [ -0.0765 0.3056 0.7668 0.00137 -0.0006181 1.086 0.001045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8224 0.8108 0.4683 0.1219 0.9739 0.9816 0.8226 0.9366 0.9606 0.4776 ] Network output: [ 0.0606 0.7235 0.1993 -0.0005626 0.0002705 0.9538 -0.0004976 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06982 Epoch 531 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03976 1.089 0.9212 0.001137 -0.0005171 -0.08554 0.000885 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07341 -0.002314 0.03276 0.02392 0.9067 0.9208 0.1334 0.819 0.86 0.2648 ] Network output: [ 0.9688 0.02867 0.0002281 -0.001171 0.0005617 0.02874 -0.001032 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7264 0.02638 -0.01784 0.2762 0.9526 0.9751 0.8164 0.8485 0.9397 0.7164 ] Network output: [ -0.03267 0.9252 1.057 2.862e-05 -2.902e-05 0.08364 8.842e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1511 0.08511 0.1213 0.08521 0.9709 0.9784 0.1544 0.9278 0.9586 0.1873 ] Network output: [ 0.1116 -0.2554 1.136 -0.003211 0.001421 0.8828 -0.002338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8181 0.5329 0.4534 0.4687 0.9592 0.9794 0.8217 0.8674 0.9507 0.7176 ] Network output: [ -0.05714 0.1879 0.9014 0.002807 -0.001269 1.036 0.002152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7898 0.7328 0.4273 0.2364 0.9759 0.9832 0.7905 0.941 0.9646 0.467 ] Network output: [ -0.0786 0.309 0.7663 0.001313 -0.0005925 1.087 0.001002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8221 0.8103 0.4674 0.1208 0.9739 0.9816 0.8222 0.9366 0.9606 0.4767 ] Network output: [ 0.05973 0.7245 0.1996 -0.0005801 0.0002781 0.9541 -0.0005097 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06965 Epoch 532 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03656 1.089 0.9252 0.001071 -0.0004876 -0.08316 0.0008356 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07323 -0.001969 0.03406 0.02399 0.9067 0.9208 0.133 0.8192 0.8602 0.2639 ] Network output: [ 0.9537 0.03042 0.01659 -0.001282 0.0006102 0.04039 -0.00111 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7261 0.03165 -0.009893 0.2767 0.9526 0.9751 0.816 0.8487 0.9398 0.7165 ] Network output: [ -0.03368 0.9258 1.057 8.107e-06 -1.96e-05 0.08456 7.207e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1509 0.08585 0.122 0.08452 0.9709 0.9784 0.1542 0.9281 0.9588 0.1866 ] Network output: [ 0.1063 -0.2516 1.138 -0.003244 0.001436 0.8876 -0.002363 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8183 0.5362 0.4569 0.4665 0.9592 0.9794 0.8219 0.8675 0.9508 0.7172 ] Network output: [ -0.05385 0.1928 0.8919 0.002826 -0.001277 1.034 0.002164 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7903 0.7339 0.4265 0.2322 0.9759 0.9832 0.7909 0.9411 0.9647 0.4655 ] Network output: [ -0.07507 0.3158 0.7553 0.001279 -0.0005767 1.084 0.0009745 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8225 0.8109 0.4664 0.1149 0.9739 0.9816 0.8227 0.9367 0.9606 0.4756 ] Network output: [ 0.06209 0.7269 0.1946 -0.0006094 0.0002912 0.9519 -0.0005316 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07058 Epoch 533 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03724 1.086 0.9275 0.001088 -0.000495 -0.08337 0.0008479 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07312 -0.002165 0.03368 0.02429 0.9068 0.9209 0.1329 0.8193 0.8603 0.2634 ] Network output: [ 0.959 0.02414 0.01625 -0.001189 0.0005681 0.03678 -0.001038 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7261 0.02863 -0.01234 0.2784 0.9527 0.9752 0.8161 0.8488 0.9399 0.7168 ] Network output: [ -0.03343 0.9228 1.06 2.489e-05 -2.695e-05 0.08428 8.394e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1505 0.08512 0.1212 0.08518 0.971 0.9785 0.1538 0.9282 0.9589 0.1859 ] Network output: [ 0.108 -0.2607 1.145 -0.003203 0.001418 0.8866 -0.002333 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8183 0.5333 0.4553 0.4703 0.9592 0.9794 0.8219 0.8676 0.9508 0.7176 ] Network output: [ -0.05619 0.1855 0.9021 0.002837 -0.001282 1.036 0.002173 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7898 0.7328 0.4262 0.2365 0.9759 0.9833 0.7905 0.9412 0.9648 0.4655 ] Network output: [ -0.07742 0.3074 0.7659 0.001323 -0.0005968 1.087 0.001009 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8222 0.8105 0.4658 0.1209 0.9739 0.9816 0.8223 0.9368 0.9607 0.4751 ] Network output: [ 0.06218 0.7215 0.1995 -0.000536 0.0002579 0.9525 -0.0004749 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07014 Epoch 534 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03823 1.088 0.9241 0.001083 -0.0004928 -0.08407 0.0008435 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07301 -0.002383 0.03285 0.0239 0.9068 0.9209 0.1327 0.8194 0.8604 0.2622 ] Network output: [ 0.9676 0.02926 0.001163 -0.001111 0.0005334 0.02996 -0.0009797 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7261 0.0249 -0.01696 0.2766 0.9527 0.9752 0.8162 0.8488 0.9399 0.7161 ] Network output: [ -0.03233 0.9258 1.056 2.945e-05 -2.86e-05 0.0831 8.575e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1501 0.08434 0.1194 0.08402 0.971 0.9784 0.1534 0.9282 0.9588 0.1841 ] Network output: [ 0.1104 -0.2559 1.138 -0.003225 0.001428 0.8838 -0.002351 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.818 0.5305 0.4529 0.469 0.9592 0.9794 0.8216 0.8676 0.9508 0.7171 ] Network output: [ -0.05713 0.1892 0.9002 0.002797 -0.001264 1.036 0.002142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7894 0.7318 0.4247 0.2354 0.9759 0.9833 0.79 0.9412 0.9648 0.4642 ] Network output: [ -0.07918 0.3099 0.7659 0.001277 -0.0005763 1.088 0.0009744 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8219 0.81 0.4649 0.1202 0.9739 0.9816 0.822 0.9368 0.9607 0.4742 ] Network output: [ 0.06162 0.7219 0.2 -0.0005444 0.0002614 0.9527 -0.0004802 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07004 Epoch 535 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03572 1.088 0.9272 0.001029 -0.0004686 -0.08218 0.000803 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07284 -0.00213 0.03381 0.02394 0.9069 0.921 0.1324 0.8196 0.8605 0.2613 ] Network output: [ 0.9561 0.03084 0.01331 -0.001191 0.0005681 0.0388 -0.001035 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7259 0.02875 -0.01098 0.2769 0.9527 0.9752 0.8158 0.849 0.94 0.7161 ] Network output: [ -0.03303 0.9264 1.056 1.435e-05 -2.16e-05 0.08372 7.346e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1499 0.08483 0.1198 0.08337 0.971 0.9785 0.1532 0.9284 0.959 0.1833 ] Network output: [ 0.1063 -0.2528 1.139 -0.003251 0.00144 0.8875 -0.002372 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8182 0.5329 0.4554 0.4673 0.9592 0.9794 0.8218 0.8677 0.9509 0.7167 ] Network output: [ -0.05462 0.1932 0.8927 0.002809 -0.001269 1.035 0.002149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7897 0.7326 0.4238 0.2321 0.9759 0.9833 0.7903 0.9414 0.9648 0.4628 ] Network output: [ -0.07656 0.3154 0.7573 0.001245 -0.0005616 1.086 0.0009489 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8222 0.8104 0.464 0.1156 0.9739 0.9816 0.8223 0.9368 0.9607 0.4731 ] Network output: [ 0.06354 0.7236 0.1961 -0.0005652 0.0002707 0.951 -0.0004956 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07079 Epoch 536 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03612 1.085 0.9292 0.001038 -0.0004725 -0.08223 0.0008093 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07272 -0.002281 0.03352 0.02416 0.9069 0.921 0.1322 0.8197 0.8606 0.2608 ] Network output: [ 0.96 0.02613 0.01318 -0.001115 0.0005333 0.03621 -0.0009757 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7259 0.02638 -0.01277 0.2783 0.9527 0.9752 0.8159 0.8491 0.94 0.7164 ] Network output: [ -0.03281 0.9242 1.058 2.688e-05 -2.703e-05 0.08345 8.209e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1495 0.08423 0.119 0.08376 0.971 0.9785 0.1528 0.9285 0.959 0.1825 ] Network output: [ 0.1075 -0.2598 1.145 -0.003218 0.001426 0.8868 -0.002348 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8181 0.5304 0.4542 0.4701 0.9592 0.9794 0.8217 0.8677 0.9509 0.717 ] Network output: [ -0.05639 0.1879 0.9003 0.002815 -0.001272 1.036 0.002155 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7893 0.7317 0.4234 0.2351 0.976 0.9833 0.79 0.9414 0.9649 0.4625 ] Network output: [ -0.07839 0.3092 0.7652 0.001275 -0.000575 1.088 0.0009719 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8219 0.8101 0.4633 0.12 0.9739 0.9816 0.8221 0.9369 0.9607 0.4725 ] Network output: [ 0.06376 0.7193 0.1999 -0.0005069 0.0002442 0.9513 -0.0004504 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0705 Epoch 537 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03679 1.086 0.9268 0.001031 -0.0004692 -0.08268 0.0008033 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0726 -0.002458 0.03289 0.02387 0.907 0.921 0.132 0.8198 0.8607 0.2596 ] Network output: [ 0.9666 0.02992 0.001661 -0.001049 0.0005039 0.03097 -0.000926 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7259 0.02333 -0.01633 0.2769 0.9527 0.9752 0.816 0.8491 0.9401 0.7158 ] Network output: [ -0.03196 0.9264 1.055 2.997e-05 -2.808e-05 0.08251 8.301e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1491 0.08356 0.1175 0.08283 0.971 0.9785 0.1524 0.9285 0.959 0.1809 ] Network output: [ 0.1094 -0.2564 1.14 -0.003234 0.001433 0.8847 -0.002361 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8179 0.5281 0.4522 0.4693 0.9592 0.9794 0.8215 0.8677 0.9509 0.7165 ] Network output: [ -0.05718 0.1906 0.899 0.002784 -0.001258 1.036 0.00213 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7889 0.7308 0.4221 0.2343 0.976 0.9833 0.7896 0.9415 0.9649 0.4614 ] Network output: [ -0.07985 0.3109 0.7654 0.001238 -0.0005584 1.088 0.000944 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8216 0.8097 0.4624 0.1195 0.9739 0.9816 0.8218 0.9369 0.9607 0.4716 ] Network output: [ 0.06347 0.7193 0.2004 -0.0005081 0.0002446 0.9513 -0.0004504 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07045 Epoch 538 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03484 1.086 0.9292 0.0009869 -0.0004494 -0.08119 0.0007701 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07244 -0.002279 0.03358 0.02389 0.907 0.9211 0.1317 0.82 0.8609 0.2588 ] Network output: [ 0.9582 0.03134 0.0104 -0.001103 0.0005271 0.03751 -0.0009628 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7256 0.026 -0.01195 0.2772 0.9527 0.9752 0.8157 0.8493 0.9401 0.7158 ] Network output: [ -0.0324 0.927 1.055 1.898e-05 -2.292e-05 0.08288 7.38e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1489 0.08384 0.1176 0.08223 0.9711 0.9785 0.1521 0.9286 0.9591 0.1801 ] Network output: [ 0.1063 -0.2539 1.141 -0.003255 0.001443 0.8874 -0.002377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.818 0.5295 0.4539 0.468 0.9592 0.9794 0.8216 0.8678 0.9509 0.7162 ] Network output: [ -0.05532 0.1938 0.8932 0.002789 -0.00126 1.035 0.002133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7891 0.7313 0.4212 0.2317 0.976 0.9833 0.7897 0.9416 0.9649 0.4601 ] Network output: [ -0.07799 0.3152 0.7589 0.001208 -0.0005448 1.087 0.0009205 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8218 0.8099 0.4615 0.116 0.9739 0.9816 0.822 0.937 0.9607 0.4707 ] Network output: [ 0.06502 0.7203 0.1976 -0.0005216 0.0002505 0.9499 -0.0004601 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07106 Epoch 539 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03502 1.084 0.9309 0.0009893 -0.0004504 -0.0811 0.0007716 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07232 -0.002394 0.03337 0.02404 0.907 0.9211 0.1315 0.8201 0.861 0.2582 ] Network output: [ 0.9609 0.02791 0.01041 -0.00104 0.0004985 0.03574 -0.0009137 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7256 0.02415 -0.01322 0.2782 0.9527 0.9752 0.8157 0.8494 0.9402 0.7159 ] Network output: [ -0.03222 0.9255 1.056 2.803e-05 -2.679e-05 0.08264 7.98e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1485 0.08333 0.1169 0.08241 0.9711 0.9786 0.1517 0.9287 0.9592 0.1792 ] Network output: [ 0.1072 -0.2591 1.145 -0.003229 0.001431 0.887 -0.002358 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8179 0.5275 0.4529 0.4701 0.9592 0.9794 0.8215 0.8679 0.951 0.7163 ] Network output: [ -0.05664 0.19 0.8986 0.002792 -0.001261 1.036 0.002136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7887 0.7305 0.4207 0.2338 0.976 0.9833 0.7894 0.9416 0.965 0.4597 ] Network output: [ -0.07941 0.3108 0.7647 0.001225 -0.0005527 1.088 0.0009341 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8216 0.8096 0.4608 0.119 0.9739 0.9816 0.8218 0.937 0.9608 0.47 ] Network output: [ 0.06536 0.717 0.2004 -0.0004754 0.0002295 0.95 -0.0004242 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0709 Epoch 540 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03544 1.085 0.9294 0.0009806 -0.0004463 -0.08135 0.0007644 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0722 -0.002537 0.03288 0.02384 0.9071 0.9212 0.1314 0.8202 0.8611 0.2572 ] Network output: [ 0.9659 0.03067 0.001757 -0.0009845 0.0004732 0.03177 -0.0008708 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7256 0.02165 -0.01594 0.2772 0.9528 0.9752 0.8157 0.8495 0.9402 0.7154 ] Network output: [ -0.03156 0.9271 1.054 3.003e-05 -2.739e-05 0.08188 8.007e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1481 0.08275 0.1156 0.08165 0.9711 0.9786 0.1514 0.9287 0.9592 0.1778 ] Network output: [ 0.1086 -0.2569 1.141 -0.00324 0.001436 0.8854 -0.002368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8177 0.5255 0.4513 0.4695 0.9592 0.9794 0.8213 0.8679 0.951 0.716 ] Network output: [ -0.0573 0.192 0.8978 0.002767 -0.00125 1.036 0.002116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7884 0.7298 0.4195 0.2332 0.976 0.9833 0.7891 0.9417 0.965 0.4586 ] Network output: [ -0.08063 0.312 0.7651 0.001195 -0.000539 1.089 0.0009111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8214 0.8093 0.4599 0.1188 0.9739 0.9816 0.8215 0.9371 0.9608 0.4692 ] Network output: [ 0.06527 0.7166 0.2009 -0.0004714 0.0002275 0.95 -0.0004203 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0709 Epoch 541 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03394 1.085 0.9312 0.0009443 -0.00043 -0.08018 0.0007369 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07205 -0.002417 0.03336 0.02383 0.9071 0.9212 0.1311 0.8204 0.8612 0.2563 ] Network output: [ 0.9598 0.03191 0.007832 -0.001018 0.0004873 0.0365 -0.0008925 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7254 0.02341 -0.01282 0.2774 0.9528 0.9753 0.8154 0.8496 0.9403 0.7154 ] Network output: [ -0.03181 0.9277 1.054 2.207e-05 -2.36e-05 0.08206 7.316e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1479 0.08287 0.1154 0.08109 0.9711 0.9786 0.1511 0.9289 0.9593 0.1769 ] Network output: [ 0.1063 -0.255 1.142 -0.003255 0.001443 0.8874 -0.00238 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8177 0.5263 0.4525 0.4685 0.9592 0.9794 0.8213 0.868 0.951 0.7157 ] Network output: [ -0.05597 0.1946 0.8934 0.002768 -0.00125 1.035 0.002116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7885 0.73 0.4186 0.2312 0.976 0.9834 0.7891 0.9417 0.9651 0.4574 ] Network output: [ -0.07937 0.3154 0.7602 0.001167 -0.0005263 1.088 0.0008893 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8215 0.8094 0.4591 0.1161 0.9739 0.9816 0.8216 0.9371 0.9608 0.4682 ] Network output: [ 0.06654 0.7172 0.199 -0.0004786 0.0002306 0.9489 -0.0004252 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07139 Epoch 542 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03395 1.083 0.9327 0.0009417 -0.0004288 -0.07999 0.0007347 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07192 -0.002505 0.03322 0.02394 0.9072 0.9212 0.1309 0.8205 0.8613 0.2557 ] Network output: [ 0.9617 0.0295 0.007911 -0.0009656 0.0004635 0.03535 -0.0008516 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7253 0.02194 -0.0137 0.2781 0.9528 0.9753 0.8154 0.8497 0.9404 0.7154 ] Network output: [ -0.03164 0.9266 1.055 2.836e-05 -2.622e-05 0.08183 7.708e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1475 0.08243 0.1148 0.08111 0.9712 0.9786 0.1507 0.929 0.9593 0.176 ] Network output: [ 0.1069 -0.2588 1.145 -0.003235 0.001435 0.8872 -0.002365 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8176 0.5246 0.4516 0.4701 0.9592 0.9795 0.8213 0.868 0.9511 0.7157 ] Network output: [ -0.05693 0.192 0.8972 0.002768 -0.00125 1.036 0.002116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7882 0.7293 0.418 0.2326 0.976 0.9834 0.7888 0.9418 0.9651 0.4569 ] Network output: [ -0.08047 0.3124 0.7643 0.001174 -0.0005297 1.089 0.0008952 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8213 0.8092 0.4583 0.1182 0.9739 0.9817 0.8214 0.9372 0.9609 0.4675 ] Network output: [ 0.06696 0.7146 0.201 -0.0004419 0.0002139 0.9488 -0.0003966 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07133 Epoch 543 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03418 1.084 0.9318 0.0009316 -0.0004241 -0.08009 0.0007265 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0718 -0.00262 0.03285 0.02379 0.9072 0.9213 0.1307 0.8207 0.8614 0.2547 ] Network output: [ 0.9655 0.03148 0.001502 -0.0009172 0.0004415 0.0324 -0.0008141 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7252 0.0199 -0.01575 0.2775 0.9528 0.9753 0.8154 0.8498 0.9404 0.7151 ] Network output: [ -0.03113 0.9278 1.053 2.953e-05 -2.648e-05 0.08121 7.686e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1471 0.08192 0.1137 0.08048 0.9712 0.9786 0.1503 0.929 0.9594 0.1747 ] Network output: [ 0.108 -0.2574 1.142 -0.003241 0.001438 0.886 -0.002371 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8175 0.5228 0.4503 0.4697 0.9592 0.9794 0.8211 0.868 0.9511 0.7154 ] Network output: [ -0.05749 0.1934 0.8968 0.002747 -0.00124 1.036 0.0021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7878 0.7287 0.4169 0.2321 0.976 0.9834 0.7885 0.9419 0.9651 0.4559 ] Network output: [ -0.08149 0.3132 0.7647 0.001148 -0.000518 1.09 0.0008755 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8211 0.8089 0.4575 0.118 0.9739 0.9817 0.8212 0.9372 0.9609 0.4667 ] Network output: [ 0.06704 0.714 0.2016 -0.000434 0.0002101 0.9487 -0.0003898 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07138 Epoch 544 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03302 1.084 0.9332 0.0009016 -0.0004106 -0.07916 0.0007037 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07165 -0.002546 0.03317 0.02377 0.9073 0.9213 0.1304 0.8208 0.8615 0.2539 ] Network output: [ 0.9612 0.03256 0.005555 -0.0009339 0.0004484 0.03573 -0.000824 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7251 0.02094 -0.01362 0.2776 0.9528 0.9753 0.8152 0.8499 0.9405 0.715 ] Network output: [ -0.03125 0.9283 1.053 2.375e-05 -2.367e-05 0.08126 7.162e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1468 0.08192 0.1134 0.07996 0.9712 0.9786 0.15 0.9291 0.9594 0.1738 ] Network output: [ 0.1064 -0.256 1.143 -0.003251 0.001442 0.8874 -0.002379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8174 0.523 0.451 0.469 0.9592 0.9795 0.8211 0.8681 0.9511 0.7152 ] Network output: [ -0.05657 0.1955 0.8934 0.002744 -0.001239 1.035 0.002097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7878 0.7287 0.416 0.2305 0.9761 0.9834 0.7885 0.9419 0.9652 0.4547 ] Network output: [ -0.08071 0.3158 0.7612 0.001122 -0.000506 1.089 0.0008551 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8211 0.8089 0.4567 0.1159 0.9739 0.9817 0.8213 0.9372 0.9609 0.4658 ] Network output: [ 0.06809 0.7141 0.2002 -0.000436 0.0002109 0.9477 -0.0003907 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07179 Epoch 545 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03291 1.082 0.9345 0.0008952 -0.0004077 -0.0789 0.0006986 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07153 -0.002614 0.03306 0.02384 0.9073 0.9214 0.1302 0.8209 0.8616 0.2532 ] Network output: [ 0.9624 0.03093 0.005639 -0.0008903 0.0004284 0.03502 -0.0007893 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.725 0.01976 -0.01423 0.2781 0.9528 0.9753 0.8151 0.85 0.9405 0.715 ] Network output: [ -0.03109 0.9276 1.054 2.788e-05 -2.533e-05 0.08103 7.392e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1465 0.08153 0.1127 0.07986 0.9712 0.9787 0.1497 0.9292 0.9595 0.1729 ] Network output: [ 0.1067 -0.2587 1.145 -0.003235 0.001435 0.8873 -0.002368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8174 0.5216 0.4503 0.4701 0.9592 0.9795 0.821 0.8681 0.9511 0.7151 ] Network output: [ -0.05727 0.1938 0.896 0.002742 -0.001238 1.036 0.002095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7876 0.7281 0.4153 0.2314 0.9761 0.9834 0.7882 0.942 0.9652 0.4541 ] Network output: [ -0.08157 0.3139 0.764 0.001122 -0.0005059 1.09 0.000855 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8209 0.8087 0.4559 0.1172 0.9739 0.9817 0.8211 0.9373 0.9609 0.4651 ] Network output: [ 0.06856 0.712 0.2017 -0.0004065 0.0001974 0.9475 -0.0003676 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07181 Epoch 546 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03298 1.082 0.934 0.0008841 -0.0004026 -0.07887 0.0006898 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0714 -0.002707 0.03278 0.02373 0.9073 0.9214 0.13 0.821 0.8617 0.2523 ] Network output: [ 0.9652 0.03234 0.000951 -0.0008478 0.000409 0.03287 -0.0007561 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7249 0.01807 -0.01577 0.2777 0.9528 0.9753 0.8151 0.8501 0.9406 0.7147 ] Network output: [ -0.03068 0.9285 1.052 2.84e-05 -2.532e-05 0.08052 7.332e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1461 0.08108 0.1118 0.07932 0.9712 0.9787 0.1493 0.9293 0.9595 0.1718 ] Network output: [ 0.1075 -0.2579 1.143 -0.003238 0.001437 0.8865 -0.002371 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8172 0.5199 0.4492 0.47 0.9592 0.9795 0.8208 0.8682 0.9511 0.7149 ] Network output: [ -0.05774 0.1949 0.8957 0.002724 -0.00123 1.036 0.002081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7873 0.7275 0.4143 0.231 0.9761 0.9834 0.7879 0.942 0.9652 0.4532 ] Network output: [ -0.08244 0.3145 0.7644 0.001099 -0.0004955 1.09 0.0008374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8207 0.8084 0.4551 0.1171 0.9739 0.9817 0.8209 0.9373 0.9609 0.4643 ] Network output: [ 0.06878 0.7112 0.2023 -0.0003961 0.0001925 0.9474 -0.000359 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0719 Epoch 547 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03209 1.082 0.9352 0.0008588 -0.0003912 -0.07812 0.0006705 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07126 -0.002666 0.03298 0.02371 0.9074 0.9214 0.1297 0.8212 0.8618 0.2515 ] Network output: [ 0.9623 0.03326 0.00352 -0.000852 0.0004104 0.03516 -0.000757 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7247 0.01857 -0.01437 0.2778 0.9528 0.9753 0.8149 0.8502 0.9406 0.7146 ] Network output: [ -0.03071 0.929 1.052 2.412e-05 -2.32e-05 0.08046 6.924e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1458 0.08098 0.1114 0.07884 0.9712 0.9787 0.149 0.9294 0.9596 0.1708 ] Network output: [ 0.1064 -0.2569 1.143 -0.003244 0.00144 0.8875 -0.002377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8171 0.5198 0.4494 0.4694 0.9592 0.9795 0.8208 0.8682 0.9512 0.7146 ] Network output: [ -0.05713 0.1965 0.8933 0.002719 -0.001227 1.035 0.002076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7872 0.7274 0.4134 0.2297 0.9761 0.9834 0.7878 0.9421 0.9653 0.4521 ] Network output: [ -0.08201 0.3166 0.7618 0.001073 -0.0004841 1.09 0.0008181 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8207 0.8084 0.4543 0.1155 0.9739 0.9817 0.8209 0.9373 0.9609 0.4635 ] Network output: [ 0.06967 0.7111 0.2014 -0.0003939 0.0001914 0.9466 -0.0003567 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07224 Epoch 548 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03189 1.081 0.9363 0.0008498 -0.0003871 -0.07782 0.0006634 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07113 -0.002721 0.03289 0.02375 0.9074 0.9215 0.1295 0.8213 0.8619 0.2508 ] Network output: [ 0.9631 0.03223 0.003561 -0.0008145 0.0003931 0.03475 -0.000727 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7246 0.01759 -0.0148 0.2782 0.9529 0.9753 0.8148 0.8503 0.9407 0.7146 ] Network output: [ -0.03056 0.9286 1.052 2.659e-05 -2.411e-05 0.08023 7.031e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1455 0.08063 0.1107 0.07865 0.9713 0.9787 0.1487 0.9294 0.9596 0.1699 ] Network output: [ 0.1066 -0.2588 1.145 -0.003231 0.001434 0.8875 -0.002368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.817 0.5185 0.4488 0.4702 0.9592 0.9795 0.8207 0.8683 0.9512 0.7146 ] Network output: [ -0.05764 0.1956 0.8949 0.002714 -0.001225 1.036 0.002072 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7869 0.7268 0.4127 0.2302 0.9761 0.9834 0.7876 0.9422 0.9653 0.4514 ] Network output: [ -0.08271 0.3154 0.7637 0.001067 -0.0004812 1.091 0.0008133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8206 0.8081 0.4535 0.1163 0.9739 0.9817 0.8207 0.9374 0.961 0.4627 ] Network output: [ 0.07018 0.7093 0.2025 -0.0003696 0.0001803 0.9463 -0.0003376 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07232 Epoch 549 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03185 1.081 0.9362 0.000838 -0.0003817 -0.0777 0.0006542 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.071 -0.002797 0.03268 0.02367 0.9075 0.9215 0.1293 0.8214 0.862 0.25 ] Network output: [ 0.9651 0.03324 0.000162 -0.0007766 0.0003758 0.03322 -0.0006972 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7245 0.01619 -0.01596 0.2779 0.9529 0.9753 0.8147 0.8503 0.9407 0.7144 ] Network output: [ -0.03023 0.9292 1.052 2.66e-05 -2.39e-05 0.07981 6.941e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1451 0.08022 0.1099 0.07817 0.9713 0.9787 0.1483 0.9295 0.9597 0.1688 ] Network output: [ 0.1072 -0.2584 1.144 -0.003231 0.001434 0.8869 -0.002368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8169 0.517 0.4479 0.4701 0.9592 0.9795 0.8205 0.8683 0.9512 0.7144 ] Network output: [ -0.05804 0.1963 0.8948 0.002699 -0.001218 1.036 0.00206 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7867 0.7263 0.4117 0.2298 0.9761 0.9834 0.7873 0.9422 0.9653 0.4505 ] Network output: [ -0.08345 0.316 0.764 0.001045 -0.0004715 1.091 0.0007969 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8204 0.8079 0.4528 0.1161 0.9739 0.9817 0.8205 0.9374 0.961 0.4619 ] Network output: [ 0.07049 0.7085 0.2031 -0.0003575 0.0001747 0.9461 -0.0003277 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07245 Epoch 550 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03115 1.081 0.9371 0.0008163 -0.0003718 -0.07708 0.0006375 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07087 -0.00278 0.03279 0.02364 0.9075 0.9215 0.1291 0.8215 0.8621 0.2492 ] Network output: [ 0.9632 0.03403 0.001676 -0.0007716 0.000373 0.03475 -0.0006913 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7243 0.0163 -0.0151 0.278 0.9529 0.9754 0.8146 0.8504 0.9408 0.7142 ] Network output: [ -0.03019 0.9296 1.051 2.331e-05 -2.223e-05 0.07968 6.611e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1448 0.08006 0.1094 0.07772 0.9713 0.9787 0.148 0.9296 0.9597 0.1679 ] Network output: [ 0.1064 -0.2578 1.144 -0.003234 0.001436 0.8876 -0.002371 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8168 0.5166 0.4478 0.4698 0.9592 0.9795 0.8204 0.8683 0.9512 0.7141 ] Network output: [ -0.05768 0.1977 0.893 0.002691 -0.001214 1.036 0.002054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7865 0.7261 0.4108 0.2288 0.9761 0.9835 0.7872 0.9423 0.9654 0.4495 ] Network output: [ -0.08329 0.3176 0.7622 0.001021 -0.0004606 1.091 0.0007784 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8203 0.8078 0.4519 0.1149 0.9739 0.9817 0.8205 0.9374 0.961 0.4611 ] Network output: [ 0.07127 0.7081 0.2026 -0.0003519 0.000172 0.9454 -0.0003228 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07275 Epoch 551 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0309 1.08 0.938 0.0008055 -0.000367 -0.07676 0.0006291 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07074 -0.002825 0.03273 0.02366 0.9075 0.9216 0.1289 0.8217 0.8622 0.2485 ] Network output: [ 0.9637 0.03343 0.001648 -0.0007384 0.0003577 0.03454 -0.0006647 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7242 0.01544 -0.01542 0.2783 0.9529 0.9754 0.8144 0.8505 0.9408 0.7142 ] Network output: [ -0.03004 0.9294 1.051 2.452e-05 -2.258e-05 0.07945 6.626e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1445 0.07973 0.1088 0.07747 0.9713 0.9788 0.1476 0.9297 0.9598 0.167 ] Network output: [ 0.1066 -0.2592 1.145 -0.003223 0.001431 0.8876 -0.002364 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8167 0.5154 0.4473 0.4703 0.9592 0.9795 0.8203 0.8684 0.9512 0.714 ] Network output: [ -0.05806 0.1972 0.894 0.002684 -0.001211 1.036 0.002049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7863 0.7256 0.4101 0.229 0.9761 0.9835 0.787 0.9423 0.9654 0.4488 ] Network output: [ -0.08387 0.317 0.7634 0.00101 -0.0004556 1.091 0.0007699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8202 0.8076 0.4512 0.1152 0.9739 0.9817 0.8203 0.9375 0.961 0.4604 ] Network output: [ 0.07181 0.7066 0.2034 -0.0003312 0.0001626 0.9451 -0.0003066 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07288 Epoch 552 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03078 1.08 0.9382 0.0007933 -0.0003614 -0.07657 0.0006195 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07061 -0.002889 0.03256 0.02359 0.9076 0.9216 0.1287 0.8218 0.8622 0.2477 ] Network output: [ 0.9652 0.03418 -0.0008111 -0.0007041 0.000342 0.03348 -0.0006374 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7241 0.01425 -0.0163 0.2781 0.9529 0.9754 0.8143 0.8506 0.9409 0.714 ] Network output: [ -0.02978 0.9299 1.051 2.409e-05 -2.219e-05 0.07909 6.51e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1441 0.07935 0.108 0.07704 0.9713 0.9788 0.1473 0.9297 0.9598 0.166 ] Network output: [ 0.107 -0.2591 1.145 -0.00322 0.00143 0.8872 -0.002362 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8165 0.514 0.4464 0.4703 0.9592 0.9795 0.8201 0.8684 0.9512 0.7138 ] Network output: [ -0.05841 0.1978 0.894 0.00267 -0.001205 1.036 0.002038 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.786 0.725 0.4092 0.2286 0.9762 0.9835 0.7867 0.9424 0.9654 0.4479 ] Network output: [ -0.08453 0.3175 0.7637 0.000989 -0.0004461 1.092 0.000754 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.82 0.8074 0.4504 0.115 0.9739 0.9817 0.8201 0.9375 0.961 0.4596 ] Network output: [ 0.07219 0.7057 0.2039 -0.0003181 0.0001565 0.9448 -0.0002959 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07303 Epoch 553 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03021 1.08 0.939 0.0007742 -0.0003527 -0.07605 0.0006049 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07048 -0.002889 0.03261 0.02357 0.9076 0.9217 0.1284 0.8219 0.8623 0.2469 ] Network output: [ 0.964 0.03484 -2.332e-05 -0.0006923 0.0003363 0.03447 -0.0006268 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7239 0.01409 -0.01583 0.2781 0.9529 0.9754 0.8142 0.8507 0.9409 0.7139 ] Network output: [ -0.02969 0.9303 1.05 2.144e-05 -2.081e-05 0.07892 6.232e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1438 0.07915 0.1075 0.07661 0.9714 0.9788 0.1469 0.9298 0.9598 0.1651 ] Network output: [ 0.1065 -0.2587 1.145 -0.00322 0.00143 0.8877 -0.002363 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8164 0.5134 0.4462 0.4701 0.9592 0.9795 0.82 0.8685 0.9513 0.7136 ] Network output: [ -0.05822 0.1989 0.8927 0.002661 -0.001201 1.036 0.002031 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7859 0.7247 0.4083 0.2278 0.9762 0.9835 0.7865 0.9424 0.9654 0.447 ] Network output: [ -0.08456 0.3188 0.7624 0.0009655 -0.0004355 1.092 0.0007361 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8199 0.8072 0.4497 0.114 0.9739 0.9817 0.82 0.9375 0.961 0.4589 ] Network output: [ 0.07289 0.7051 0.2037 -0.0003099 0.0001527 0.9442 -0.0002891 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07332 Epoch 554 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02993 1.079 0.9398 0.0007624 -0.0003474 -0.07571 0.0005957 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07035 -0.002928 0.03255 0.02357 0.9077 0.9217 0.1282 0.822 0.8624 0.2462 ] Network output: [ 0.9643 0.03456 -0.0001315 -0.0006622 0.0003224 0.03438 -0.0006025 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7238 0.01332 -0.0161 0.2783 0.9529 0.9754 0.8141 0.8508 0.9409 0.7138 ] Network output: [ -0.02955 0.9302 1.05 2.171e-05 -2.075e-05 0.07869 6.178e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1434 0.07883 0.1069 0.07632 0.9714 0.9788 0.1466 0.9299 0.9599 0.1642 ] Network output: [ 0.1066 -0.2597 1.146 -0.00321 0.001426 0.8877 -0.002356 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8163 0.5122 0.4457 0.4704 0.9592 0.9795 0.8199 0.8685 0.9513 0.7135 ] Network output: [ -0.05853 0.1987 0.8932 0.002653 -0.001197 1.036 0.002024 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7856 0.7243 0.4075 0.2277 0.9762 0.9835 0.7863 0.9425 0.9655 0.4462 ] Network output: [ -0.08507 0.3187 0.763 0.0009506 -0.0004288 1.092 0.0007248 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8198 0.807 0.4489 0.114 0.9739 0.9817 0.8199 0.9376 0.961 0.4581 ] Network output: [ 0.07344 0.7038 0.2044 -0.0002917 0.0001443 0.9438 -0.0002747 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07348 Epoch 555 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02975 1.079 0.9401 0.0007499 -0.0003417 -0.07547 0.000586 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07022 -0.002982 0.03242 0.02352 0.9077 0.9217 0.128 0.8221 0.8625 0.2454 ] Network output: [ 0.9653 0.03513 -0.001921 -0.0006305 0.0003078 0.03366 -0.0005772 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7237 0.01228 -0.01679 0.2783 0.9529 0.9754 0.8139 0.8509 0.941 0.7137 ] Network output: [ -0.02933 0.9306 1.05 2.091e-05 -2.02e-05 0.07836 6.04e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1431 0.07848 0.1062 0.07592 0.9714 0.9788 0.1462 0.9299 0.9599 0.1632 ] Network output: [ 0.1069 -0.2598 1.145 -0.003205 0.001424 0.8875 -0.002353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8161 0.5109 0.4449 0.4704 0.9592 0.9795 0.8198 0.8685 0.9513 0.7133 ] Network output: [ -0.05883 0.1992 0.8932 0.00264 -0.001191 1.036 0.002014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7854 0.7237 0.4066 0.2274 0.9762 0.9835 0.786 0.9425 0.9655 0.4454 ] Network output: [ -0.08565 0.3192 0.7632 0.0009296 -0.0004193 1.093 0.0007089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8196 0.8068 0.4482 0.1137 0.9739 0.9817 0.8197 0.9376 0.9611 0.4574 ] Network output: [ 0.07388 0.7028 0.2048 -0.0002779 0.000138 0.9435 -0.0002636 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07366 Epoch 556 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02928 1.079 0.9408 0.0007327 -0.0003339 -0.07501 0.0005728 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.07009 -0.002994 0.03243 0.02349 0.9077 0.9218 0.1278 0.8222 0.8626 0.2447 ] Network output: [ 0.9646 0.03571 -0.001616 -0.0006141 0.0003 0.03429 -0.0005632 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7235 0.01193 -0.01658 0.2783 0.9529 0.9754 0.8138 0.8509 0.941 0.7135 ] Network output: [ -0.02922 0.9309 1.049 1.861e-05 -1.899e-05 0.07816 5.794e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1428 0.07824 0.1056 0.07552 0.9714 0.9788 0.1459 0.93 0.9599 0.1623 ] Network output: [ 0.1066 -0.2596 1.145 -0.003202 0.001423 0.8878 -0.002352 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.816 0.5101 0.4446 0.4703 0.9592 0.9795 0.8196 0.8686 0.9513 0.7131 ] Network output: [ -0.05877 0.2001 0.8923 0.00263 -0.001186 1.036 0.002006 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7852 0.7234 0.4058 0.2266 0.9762 0.9835 0.7859 0.9426 0.9655 0.4445 ] Network output: [ -0.08582 0.3203 0.7623 0.0009063 -0.0004088 1.093 0.0006911 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8195 0.8067 0.4474 0.1129 0.974 0.9817 0.8196 0.9376 0.9611 0.4566 ] Network output: [ 0.07453 0.7021 0.2048 -0.0002678 0.0001333 0.9429 -0.0002554 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07393 Epoch 557 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02898 1.078 0.9416 0.0007204 -0.0003283 -0.07466 0.0005632 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06996 -0.003029 0.03237 0.02348 0.9078 0.9218 0.1275 0.8224 0.8627 0.2439 ] Network output: [ 0.9648 0.03564 -0.001804 -0.0005859 0.000287 0.03427 -0.0005405 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7234 0.0112 -0.01683 0.2784 0.953 0.9754 0.8136 0.851 0.9411 0.7135 ] Network output: [ -0.02908 0.9309 1.049 1.818e-05 -1.863e-05 0.07793 5.69e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1424 0.07794 0.1051 0.0752 0.9714 0.9788 0.1456 0.9301 0.96 0.1615 ] Network output: [ 0.1067 -0.2603 1.146 -0.003193 0.001419 0.8878 -0.002345 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8159 0.509 0.444 0.4705 0.9592 0.9795 0.8195 0.8686 0.9513 0.713 ] Network output: [ -0.05903 0.2001 0.8926 0.00262 -0.001182 1.036 0.001998 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.785 0.7229 0.405 0.2264 0.9762 0.9835 0.7856 0.9426 0.9656 0.4437 ] Network output: [ -0.08629 0.3204 0.7626 0.0008886 -0.0004009 1.093 0.0006777 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8193 0.8065 0.4467 0.1127 0.974 0.9817 0.8195 0.9377 0.9611 0.4559 ] Network output: [ 0.07509 0.7009 0.2054 -0.0002511 0.0001256 0.9425 -0.0002421 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07413 Epoch 558 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02876 1.078 0.942 0.0007077 -0.0003226 -0.07439 0.0005534 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06983 -0.003076 0.03227 0.02343 0.9078 0.9218 0.1273 0.8225 0.8628 0.2432 ] Network output: [ 0.9655 0.03611 -0.003129 -0.0005563 0.0002734 0.03379 -0.0005167 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7232 0.01028 -0.01739 0.2784 0.953 0.9754 0.8135 0.8511 0.9411 0.7133 ] Network output: [ -0.02888 0.9312 1.049 1.706e-05 -1.796e-05 0.07764 5.534e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1421 0.0776 0.1044 0.07482 0.9714 0.9789 0.1452 0.9301 0.96 0.1605 ] Network output: [ 0.107 -0.2605 1.146 -0.003186 0.001416 0.8876 -0.002341 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8157 0.5078 0.4433 0.4705 0.9592 0.9795 0.8193 0.8686 0.9514 0.7129 ] Network output: [ -0.0593 0.2006 0.8925 0.002607 -0.001176 1.036 0.001988 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7847 0.7224 0.4042 0.226 0.9762 0.9835 0.7854 0.9426 0.9656 0.4429 ] Network output: [ -0.08682 0.321 0.7627 0.0008671 -0.0003912 1.093 0.0006614 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8192 0.8062 0.446 0.1123 0.974 0.9817 0.8193 0.9377 0.9611 0.4552 ] Network output: [ 0.07557 0.6999 0.2058 -0.000237 0.0001191 0.9422 -0.0002308 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07434 Epoch 559 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02835 1.077 0.9426 0.0006921 -0.0003155 -0.07398 0.0005414 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0697 -0.003095 0.03225 0.0234 0.9078 0.9219 0.1271 0.8226 0.8628 0.2425 ] Network output: [ 0.9651 0.03662 -0.003135 -0.0005367 0.0002642 0.03418 -0.0005005 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.723 0.009801 -0.01737 0.2784 0.953 0.9754 0.8133 0.8512 0.9412 0.7132 ] Network output: [ -0.02876 0.9315 1.049 1.495e-05 -1.684e-05 0.07742 5.305e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1418 0.07734 0.1038 0.07443 0.9714 0.9789 0.1449 0.9302 0.9601 0.1597 ] Network output: [ 0.1068 -0.2605 1.146 -0.003181 0.001414 0.8878 -0.002338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8156 0.5068 0.4428 0.4704 0.9592 0.9795 0.8192 0.8687 0.9514 0.7127 ] Network output: [ -0.05934 0.2013 0.8919 0.002596 -0.001171 1.036 0.001979 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7845 0.722 0.4033 0.2254 0.9762 0.9835 0.7852 0.9427 0.9656 0.4421 ] Network output: [ -0.08708 0.3219 0.7621 0.0008437 -0.0003806 1.094 0.0006436 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8191 0.8061 0.4452 0.1115 0.974 0.9817 0.8192 0.9377 0.9611 0.4545 ] Network output: [ 0.07618 0.6991 0.206 -0.0002253 0.0001137 0.9417 -0.0002214 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0746 Epoch 560 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02805 1.077 0.9433 0.0006795 -0.0003098 -0.07363 0.0005316 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06958 -0.003129 0.03219 0.02338 0.9079 0.9219 0.1269 0.8227 0.8629 0.2418 ] Network output: [ 0.9652 0.0367 -0.003393 -0.0005099 0.0002518 0.03419 -0.0004788 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7229 0.009101 -0.01762 0.2785 0.953 0.9754 0.8132 0.8513 0.9412 0.7131 ] Network output: [ -0.02862 0.9316 1.048 1.4e-05 -1.625e-05 0.07719 5.165e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1414 0.07704 0.1033 0.0741 0.9715 0.9789 0.1445 0.9303 0.9601 0.1588 ] Network output: [ 0.1069 -0.2611 1.147 -0.003172 0.00141 0.8878 -0.002332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8154 0.5057 0.4423 0.4705 0.9592 0.9795 0.819 0.8687 0.9514 0.7126 ] Network output: [ -0.05957 0.2015 0.892 0.002585 -0.001166 1.036 0.001971 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7843 0.7216 0.4026 0.2251 0.9762 0.9835 0.785 0.9427 0.9656 0.4413 ] Network output: [ -0.08752 0.3223 0.7622 0.0008238 -0.0003717 1.094 0.0006285 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8189 0.8059 0.4445 0.1111 0.974 0.9817 0.819 0.9378 0.9611 0.4538 ] Network output: [ 0.07674 0.698 0.2064 -0.0002096 0.0001065 0.9412 -0.0002089 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07483 Epoch 561 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02781 1.077 0.9438 0.0006669 -0.0003041 -0.07333 0.0005219 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06945 -0.00317 0.0321 0.02334 0.9079 0.9219 0.1267 0.8228 0.863 0.2411 ] Network output: [ 0.9657 0.03711 -0.004404 -0.0004817 0.0002389 0.03388 -0.0004563 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7227 0.008261 -0.01809 0.2785 0.953 0.9755 0.813 0.8514 0.9412 0.713 ] Network output: [ -0.02845 0.9319 1.048 1.262e-05 -1.546e-05 0.07692 4.993e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1411 0.07672 0.1026 0.07373 0.9715 0.9789 0.1442 0.9303 0.9601 0.1579 ] Network output: [ 0.1071 -0.2614 1.147 -0.003164 0.001406 0.8877 -0.002327 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8152 0.5045 0.4416 0.4706 0.9592 0.9795 0.8189 0.8688 0.9514 0.7124 ] Network output: [ -0.05982 0.202 0.8919 0.002572 -0.00116 1.036 0.001961 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.784 0.7211 0.4017 0.2246 0.9762 0.9835 0.7847 0.9428 0.9656 0.4405 ] Network output: [ -0.08802 0.3229 0.7621 0.0008016 -0.0003617 1.094 0.0006117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8187 0.8056 0.4438 0.1107 0.974 0.9817 0.8189 0.9378 0.9611 0.4531 ] Network output: [ 0.07725 0.697 0.2069 -0.0001951 9.983e-05 0.9408 -0.0001974 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07505 Epoch 562 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02745 1.076 0.9444 0.0006525 -0.0002975 -0.07295 0.0005107 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06932 -0.003195 0.03206 0.02331 0.9079 0.922 0.1264 0.8229 0.8631 0.2404 ] Network output: [ 0.9655 0.03757 -0.004605 -0.0004601 0.0002288 0.03412 -0.0004386 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7225 0.007697 -0.0182 0.2785 0.953 0.9755 0.8128 0.8514 0.9413 0.7129 ] Network output: [ -0.02832 0.9321 1.048 1.058e-05 -1.439e-05 0.0767 4.773e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1408 0.07644 0.1021 0.07335 0.9715 0.9789 0.1439 0.9304 0.9602 0.1571 ] Network output: [ 0.107 -0.2615 1.147 -0.003157 0.001403 0.8878 -0.002322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8151 0.5035 0.4411 0.4705 0.9592 0.9795 0.8187 0.8688 0.9514 0.7123 ] Network output: [ -0.05993 0.2026 0.8915 0.00256 -0.001155 1.036 0.001951 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7838 0.7207 0.4009 0.2241 0.9762 0.9836 0.7845 0.9428 0.9657 0.4397 ] Network output: [ -0.08834 0.3238 0.7616 0.0007778 -0.000351 1.094 0.0005936 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8186 0.8055 0.4431 0.1099 0.974 0.9817 0.8188 0.9378 0.9611 0.4524 ] Network output: [ 0.07784 0.6961 0.2071 -0.0001823 9.395e-05 0.9404 -0.0001871 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07532 Epoch 563 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02715 1.076 0.945 0.0006399 -0.0002918 -0.07261 0.000501 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06919 -0.003227 0.032 0.02328 0.908 0.922 0.1262 0.823 0.8632 0.2397 ] Network output: [ 0.9656 0.03776 -0.004922 -0.0004341 0.0002168 0.03415 -0.0004176 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7224 0.007009 -0.01847 0.2786 0.953 0.9755 0.8127 0.8515 0.9413 0.7129 ] Network output: [ -0.02819 0.9322 1.048 9.243e-06 -1.363e-05 0.07647 4.608e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1404 0.07615 0.1015 0.07301 0.9715 0.9789 0.1435 0.9304 0.9602 0.1563 ] Network output: [ 0.1071 -0.262 1.147 -0.003147 0.001399 0.8878 -0.002315 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8149 0.5024 0.4405 0.4706 0.9592 0.9795 0.8185 0.8688 0.9514 0.7122 ] Network output: [ -0.06015 0.2028 0.8915 0.002548 -0.001149 1.036 0.001942 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7836 0.7202 0.4002 0.2236 0.9763 0.9836 0.7843 0.9429 0.9657 0.439 ] Network output: [ -0.08878 0.3243 0.7616 0.0007561 -0.0003412 1.095 0.0005772 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8185 0.8053 0.4425 0.1094 0.974 0.9817 0.8186 0.9378 0.9611 0.4517 ] Network output: [ 0.07841 0.6951 0.2075 -0.000167 8.692e-05 0.9399 -0.000175 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07557 Epoch 564 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02689 1.076 0.9455 0.0006275 -0.0002862 -0.0723 0.0004914 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06907 -0.003265 0.03192 0.02325 0.908 0.922 0.126 0.8231 0.8633 0.239 ] Network output: [ 0.966 0.03814 -0.005726 -0.0004071 0.0002044 0.03395 -0.000396 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7222 0.006222 -0.01888 0.2786 0.953 0.9755 0.8125 0.8516 0.9414 0.7128 ] Network output: [ -0.02803 0.9325 1.047 7.641e-06 -1.276e-05 0.07621 4.423e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1401 0.07584 0.1009 0.07265 0.9715 0.9789 0.1432 0.9305 0.9602 0.1554 ] Network output: [ 0.1073 -0.2623 1.147 -0.003138 0.001395 0.8878 -0.002309 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8147 0.5012 0.4398 0.4706 0.9592 0.9795 0.8183 0.8689 0.9514 0.712 ] Network output: [ -0.0604 0.2032 0.8915 0.002535 -0.001143 1.036 0.001932 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7834 0.7197 0.3994 0.2232 0.9763 0.9836 0.784 0.9429 0.9657 0.4382 ] Network output: [ -0.08925 0.3249 0.7615 0.000733 -0.0003308 1.095 0.0005597 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8183 0.805 0.4418 0.1089 0.974 0.9817 0.8184 0.9379 0.9611 0.4511 ] Network output: [ 0.07894 0.6941 0.208 -0.0001523 8.016e-05 0.9395 -0.0001633 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07582 Epoch 565 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02656 1.075 0.9461 0.0006139 -0.00028 -0.07193 0.0004809 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06894 -0.003293 0.03187 0.02321 0.908 0.922 0.1258 0.8232 0.8633 0.2383 ] Network output: [ 0.9659 0.03857 -0.006044 -0.0003843 0.0001938 0.0341 -0.0003774 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.722 0.005606 -0.01908 0.2786 0.953 0.9755 0.8123 0.8517 0.9414 0.7127 ] Network output: [ -0.0279 0.9327 1.047 5.606e-06 -1.169e-05 0.07599 4.207e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1398 0.07555 0.1004 0.07228 0.9715 0.9789 0.1428 0.9305 0.9603 0.1546 ] Network output: [ 0.1073 -0.2625 1.147 -0.00313 0.001392 0.8878 -0.002304 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8145 0.5001 0.4393 0.4705 0.9592 0.9795 0.8182 0.8689 0.9515 0.7119 ] Network output: [ -0.06055 0.2038 0.8911 0.002523 -0.001138 1.036 0.001922 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7831 0.7193 0.3986 0.2226 0.9763 0.9836 0.7838 0.9429 0.9657 0.4374 ] Network output: [ -0.08962 0.3258 0.761 0.0007087 -0.0003199 1.095 0.0005412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8182 0.8048 0.4411 0.1081 0.974 0.9817 0.8183 0.9379 0.9612 0.4504 ] Network output: [ 0.07952 0.6931 0.2083 -0.0001386 7.39e-05 0.939 -0.0001524 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07609 Epoch 566 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02626 1.075 0.9467 0.0006015 -0.0002744 -0.07159 0.0004713 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06881 -0.003325 0.03181 0.02318 0.9081 0.9221 0.1256 0.8233 0.8634 0.2376 ] Network output: [ 0.966 0.03883 -0.006407 -0.000359 0.0001821 0.03413 -0.000357 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7218 0.004921 -0.01936 0.2787 0.953 0.9755 0.8121 0.8517 0.9414 0.7126 ] Network output: [ -0.02777 0.9328 1.047 3.988e-06 -1.082e-05 0.07576 4.024e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1394 0.07526 0.0998 0.07194 0.9715 0.979 0.1425 0.9306 0.9603 0.1538 ] Network output: [ 0.1074 -0.2629 1.148 -0.003119 0.001387 0.8878 -0.002297 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8144 0.499 0.4387 0.4705 0.9592 0.9795 0.818 0.8689 0.9515 0.7118 ] Network output: [ -0.06078 0.2041 0.8911 0.00251 -0.001132 1.037 0.001912 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7829 0.7188 0.3979 0.2222 0.9763 0.9836 0.7836 0.943 0.9657 0.4367 ] Network output: [ -0.09004 0.3265 0.7608 0.0006855 -0.0003094 1.096 0.0005236 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.818 0.8046 0.4405 0.1075 0.974 0.9817 0.8182 0.9379 0.9612 0.4498 ] Network output: [ 0.08009 0.6921 0.2087 -0.0001234 6.692e-05 0.9386 -0.0001403 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07636 Epoch 567 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.026 1.075 0.9472 0.0005894 -0.0002689 -0.07127 0.0004619 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06869 -0.00336 0.03173 0.02314 0.9081 0.9221 0.1254 0.8234 0.8635 0.237 ] Network output: [ 0.9663 0.0392 -0.007079 -0.0003328 0.0001701 0.03401 -0.0003361 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7216 0.004169 -0.01975 0.2787 0.9531 0.9755 0.812 0.8518 0.9415 0.7125 ] Network output: [ -0.02763 0.933 1.047 2.208e-06 -9.872e-06 0.07552 3.83e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1391 0.07495 0.09923 0.07159 0.9716 0.979 0.1422 0.9307 0.9603 0.153 ] Network output: [ 0.1076 -0.2633 1.148 -0.003109 0.001383 0.8877 -0.00229 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8142 0.4978 0.438 0.4705 0.9592 0.9795 0.8178 0.869 0.9515 0.7117 ] Network output: [ -0.06102 0.2045 0.8911 0.002497 -0.001126 1.037 0.001902 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7827 0.7183 0.3971 0.2217 0.9763 0.9836 0.7834 0.943 0.9658 0.436 ] Network output: [ -0.0905 0.3272 0.7606 0.0006614 -0.0002986 1.096 0.0005054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8179 0.8044 0.4398 0.1068 0.974 0.9817 0.818 0.9379 0.9612 0.4491 ] Network output: [ 0.08064 0.691 0.2091 -0.0001084 6.004e-05 0.9382 -0.0001284 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07663 Epoch 568 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02569 1.074 0.9477 0.0005766 -0.0002631 -0.07092 0.0004521 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06856 -0.003389 0.03167 0.02311 0.9081 0.9221 0.1252 0.8235 0.8636 0.2363 ] Network output: [ 0.9663 0.03962 -0.007466 -0.0003094 0.0001593 0.0341 -0.0003172 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7214 0.00352 -0.02001 0.2787 0.9531 0.9755 0.8118 0.8519 0.9415 0.7125 ] Network output: [ -0.0275 0.9332 1.046 1.48e-07 -8.804e-06 0.07529 3.615e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1388 0.07466 0.09868 0.07123 0.9716 0.979 0.1418 0.9307 0.9604 0.1522 ] Network output: [ 0.1077 -0.2635 1.148 -0.0031 0.001379 0.8877 -0.002283 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.814 0.4967 0.4374 0.4704 0.9592 0.9795 0.8176 0.869 0.9515 0.7116 ] Network output: [ -0.06121 0.2049 0.8909 0.002483 -0.00112 1.037 0.001892 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7825 0.7179 0.3964 0.2211 0.9763 0.9836 0.7831 0.9431 0.9658 0.4353 ] Network output: [ -0.09089 0.328 0.7602 0.0006364 -0.0002873 1.096 0.0004864 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8177 0.8042 0.4392 0.1061 0.974 0.9817 0.8179 0.938 0.9612 0.4485 ] Network output: [ 0.08121 0.6901 0.2095 -9.405e-05 5.346e-05 0.9377 -0.0001171 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07692 Epoch 569 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0254 1.074 0.9483 0.0005646 -0.0002576 -0.07058 0.0004428 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06844 -0.003421 0.03161 0.02307 0.9082 0.9222 0.1249 0.8236 0.8636 0.2356 ] Network output: [ 0.9663 0.03993 -0.007864 -0.0002845 0.0001478 0.03413 -0.0002972 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7212 0.002834 -0.02031 0.2787 0.9531 0.9755 0.8116 0.852 0.9415 0.7124 ] Network output: [ -0.02737 0.9334 1.046 -1.671e-06 -7.847e-06 0.07507 3.42e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1384 0.07436 0.09814 0.07088 0.9716 0.979 0.1415 0.9308 0.9604 0.1514 ] Network output: [ 0.1078 -0.2639 1.148 -0.003089 0.001374 0.8877 -0.002275 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8138 0.4955 0.4368 0.4704 0.9592 0.9795 0.8174 0.869 0.9515 0.7115 ] Network output: [ -0.06145 0.2053 0.8908 0.00247 -0.001114 1.037 0.001881 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7822 0.7174 0.3957 0.2206 0.9763 0.9836 0.7829 0.9431 0.9658 0.4346 ] Network output: [ -0.09132 0.3288 0.76 0.0006119 -0.0002763 1.096 0.0004678 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8176 0.804 0.4385 0.1053 0.974 0.9817 0.8177 0.938 0.9612 0.4479 ] Network output: [ 0.08178 0.689 0.2099 -7.871e-05 4.643e-05 0.9372 -0.0001049 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0772 Epoch 570 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02513 1.073 0.9488 0.0005527 -0.0002523 -0.07026 0.0004336 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06831 -0.003455 0.03154 0.02303 0.9082 0.9222 0.1247 0.8237 0.8637 0.235 ] Network output: [ 0.9665 0.04031 -0.008454 -0.0002591 0.0001361 0.03407 -0.0002769 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.721 0.002103 -0.02069 0.2787 0.9531 0.9755 0.8114 0.8521 0.9416 0.7124 ] Network output: [ -0.02724 0.9335 1.046 -3.586e-06 -6.849e-06 0.07484 3.219e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1381 0.07406 0.09759 0.07053 0.9716 0.979 0.1412 0.9308 0.9604 0.1506 ] Network output: [ 0.108 -0.2643 1.148 -0.003077 0.001369 0.8877 -0.002267 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8136 0.4943 0.4362 0.4704 0.9592 0.9795 0.8172 0.869 0.9515 0.7114 ] Network output: [ -0.0617 0.2056 0.8908 0.002456 -0.001107 1.037 0.001871 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.782 0.7169 0.3949 0.22 0.9763 0.9836 0.7827 0.9431 0.9658 0.4339 ] Network output: [ -0.09177 0.3296 0.7597 0.0005868 -0.000265 1.097 0.0004488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8174 0.8038 0.4379 0.1046 0.974 0.9817 0.8176 0.938 0.9612 0.4473 ] Network output: [ 0.08234 0.688 0.2103 -6.337e-05 3.941e-05 0.9368 -9.282e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07749 Epoch 571 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02484 1.073 0.9493 0.0005406 -0.0002468 -0.06991 0.0004243 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06819 -0.003486 0.03148 0.02299 0.9082 0.9222 0.1245 0.8238 0.8638 0.2343 ] Network output: [ 0.9666 0.04072 -0.008881 -0.0002354 0.0001252 0.03413 -0.0002578 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7208 0.001432 -0.02099 0.2787 0.9531 0.9755 0.8112 0.8521 0.9416 0.7123 ] Network output: [ -0.02711 0.9337 1.046 -5.679e-06 -5.774e-06 0.07462 3.005e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1378 0.07377 0.09706 0.07018 0.9716 0.979 0.1408 0.9309 0.9604 0.1498 ] Network output: [ 0.1081 -0.2646 1.148 -0.003067 0.001364 0.8876 -0.00226 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8134 0.4932 0.4356 0.4703 0.9592 0.9795 0.817 0.8691 0.9515 0.7113 ] Network output: [ -0.06192 0.206 0.8907 0.002443 -0.001101 1.037 0.00186 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7818 0.7165 0.3942 0.2195 0.9763 0.9836 0.7825 0.9432 0.9658 0.4333 ] Network output: [ -0.09218 0.3304 0.7593 0.0005609 -0.0002534 1.097 0.0004292 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8173 0.8036 0.4373 0.1037 0.974 0.9817 0.8174 0.9381 0.9612 0.4467 ] Network output: [ 0.08292 0.687 0.2107 -4.838e-05 3.254e-05 0.9363 -8.097e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07779 Epoch 572 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02456 1.073 0.9499 0.000529 -0.0002415 -0.06958 0.0004153 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06806 -0.003517 0.03141 0.02295 0.9083 0.9223 0.1243 0.8239 0.8639 0.2337 ] Network output: [ 0.9666 0.04107 -0.009305 -0.000211 0.0001139 0.03416 -0.0002382 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7206 0.0007425 -0.02131 0.2787 0.9531 0.9755 0.811 0.8522 0.9417 0.7123 ] Network output: [ -0.02699 0.9339 1.046 -7.633e-06 -4.763e-06 0.0744 2.803e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1375 0.07347 0.09654 0.06984 0.9716 0.979 0.1405 0.9309 0.9605 0.1491 ] Network output: [ 0.1082 -0.265 1.149 -0.003055 0.001359 0.8876 -0.002252 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8132 0.492 0.4349 0.4703 0.9592 0.9795 0.8168 0.8691 0.9515 0.7112 ] Network output: [ -0.06217 0.2063 0.8907 0.002429 -0.001095 1.037 0.001849 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7816 0.716 0.3935 0.2189 0.9763 0.9836 0.7822 0.9432 0.9659 0.4326 ] Network output: [ -0.09261 0.3313 0.7589 0.0005352 -0.0002418 1.097 0.0004098 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8171 0.8034 0.4367 0.1029 0.974 0.9817 0.8173 0.9381 0.9612 0.4461 ] Network output: [ 0.08349 0.6859 0.2111 -3.275e-05 2.539e-05 0.9359 -6.865e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0781 Epoch 573 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02429 1.072 0.9504 0.0005176 -0.0002363 -0.06925 0.0004065 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06794 -0.00355 0.03134 0.02291 0.9083 0.9223 0.1241 0.824 0.8639 0.2331 ] Network output: [ 0.9668 0.04146 -0.009847 -0.0001862 0.0001026 0.03413 -0.0002184 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7204 2.33e-05 -0.02168 0.2787 0.9531 0.9755 0.8107 0.8523 0.9417 0.7122 ] Network output: [ -0.02686 0.934 1.045 -9.64e-06 -3.731e-06 0.07417 2.597e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1371 0.07317 0.09601 0.06949 0.9716 0.979 0.1402 0.931 0.9605 0.1483 ] Network output: [ 0.1084 -0.2654 1.149 -0.003043 0.001354 0.8875 -0.002243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8129 0.4908 0.4343 0.4702 0.9592 0.9795 0.8166 0.8691 0.9516 0.7111 ] Network output: [ -0.06243 0.2067 0.8907 0.002415 -0.001088 1.037 0.001838 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7813 0.7155 0.3929 0.2183 0.9763 0.9836 0.782 0.9432 0.9659 0.432 ] Network output: [ -0.09305 0.3321 0.7586 0.000509 -0.0002301 1.097 0.00039 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.817 0.8032 0.4362 0.1021 0.974 0.9817 0.8171 0.9381 0.9612 0.4455 ] Network output: [ 0.08406 0.6848 0.2116 -1.701e-05 1.82e-05 0.9354 -5.625e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07841 Epoch 574 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02401 1.072 0.9509 0.0005061 -0.0002311 -0.06891 0.0003976 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06781 -0.003581 0.03128 0.02287 0.9083 0.9223 0.1239 0.8241 0.864 0.2325 ] Network output: [ 0.9668 0.04187 -0.0103 -0.0001626 9.165e-05 0.03417 -0.0001995 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7202 -0.000662 -0.02201 0.2787 0.9531 0.9756 0.8105 0.8523 0.9417 0.7122 ] Network output: [ -0.02674 0.9342 1.045 -1.176e-05 -2.653e-06 0.07396 2.385e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1368 0.07288 0.09549 0.06915 0.9716 0.979 0.1398 0.931 0.9605 0.1476 ] Network output: [ 0.1086 -0.2658 1.149 -0.003031 0.001349 0.8875 -0.002235 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8127 0.4896 0.4337 0.4701 0.9592 0.9795 0.8163 0.8692 0.9516 0.711 ] Network output: [ -0.06268 0.207 0.8906 0.0024 -0.001082 1.037 0.001827 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7811 0.715 0.3922 0.2177 0.9763 0.9836 0.7818 0.9433 0.9659 0.4313 ] Network output: [ -0.09347 0.333 0.7581 0.0004822 -0.000218 1.098 0.0003697 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8168 0.803 0.4356 0.1012 0.974 0.9817 0.817 0.9381 0.9612 0.445 ] Network output: [ 0.08464 0.6838 0.212 -1.405e-06 1.106e-05 0.9349 -4.395e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07873 Epoch 575 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02374 1.072 0.9514 0.0004949 -0.000226 -0.06858 0.0003889 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06769 -0.003613 0.03121 0.02282 0.9084 0.9223 0.1237 0.8242 0.8641 0.2318 ] Network output: [ 0.9669 0.04226 -0.01074 -0.0001386 8.062e-05 0.0342 -0.0001803 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.72 -0.001357 -0.02235 0.2786 0.9531 0.9756 0.8103 0.8524 0.9418 0.7121 ] Network output: [ -0.02663 0.9343 1.045 -1.379e-05 -1.614e-06 0.07375 2.179e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1365 0.07258 0.09498 0.0688 0.9717 0.9791 0.1395 0.9311 0.9606 0.1469 ] Network output: [ 0.1087 -0.2662 1.149 -0.003018 0.001343 0.8874 -0.002226 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8125 0.4884 0.433 0.47 0.9592 0.9795 0.8161 0.8692 0.9516 0.711 ] Network output: [ -0.06294 0.2073 0.8906 0.002386 -0.001075 1.038 0.001816 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7809 0.7146 0.3915 0.2172 0.9764 0.9837 0.7816 0.9433 0.9659 0.4307 ] Network output: [ -0.0939 0.3339 0.7577 0.0004554 -0.0002059 1.098 0.0003494 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8167 0.8028 0.435 0.1003 0.974 0.9817 0.8168 0.9382 0.9612 0.4445 ] Network output: [ 0.08522 0.6827 0.2124 1.464e-05 3.728e-06 0.9345 -3.133e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07905 Epoch 576 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02347 1.071 0.9519 0.000484 -0.0002211 -0.06825 0.0003805 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06757 -0.003646 0.03114 0.02278 0.9084 0.9224 0.1234 0.8243 0.8641 0.2312 ] Network output: [ 0.967 0.04267 -0.01126 -0.0001145 6.953e-05 0.03419 -0.000161 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7198 -0.002071 -0.02272 0.2786 0.9531 0.9756 0.8101 0.8525 0.9418 0.7121 ] Network output: [ -0.02651 0.9345 1.045 -1.585e-05 -5.661e-07 0.07353 1.973e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1362 0.07228 0.09447 0.06846 0.9717 0.9791 0.1392 0.9311 0.9606 0.1462 ] Network output: [ 0.1089 -0.2666 1.149 -0.003006 0.001338 0.8873 -0.002217 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8123 0.4872 0.4324 0.4699 0.9592 0.9795 0.8159 0.8692 0.9516 0.7109 ] Network output: [ -0.06321 0.2076 0.8906 0.002371 -0.001069 1.038 0.001805 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7807 0.7141 0.3909 0.2166 0.9764 0.9837 0.7813 0.9433 0.9659 0.4301 ] Network output: [ -0.09434 0.3349 0.7573 0.0004282 -0.0001937 1.098 0.0003288 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8165 0.8026 0.4345 0.09932 0.974 0.9817 0.8167 0.9382 0.9612 0.4439 ] Network output: [ 0.08579 0.6817 0.2129 3.087e-05 -3.685e-06 0.934 -1.858e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07938 Epoch 577 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0232 1.071 0.9524 0.000473 -0.0002161 -0.06792 0.000372 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06744 -0.003677 0.03108 0.02273 0.9084 0.9224 0.1232 0.8244 0.8642 0.2306 ] Network output: [ 0.967 0.0431 -0.01172 -9.107e-05 5.875e-05 0.03422 -0.0001423 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7195 -0.002767 -0.02307 0.2786 0.9531 0.9756 0.8098 0.8526 0.9418 0.7121 ] Network output: [ -0.02639 0.9346 1.045 -1.797e-05 5.066e-07 0.07332 1.763e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1358 0.07199 0.09397 0.06812 0.9717 0.9791 0.1388 0.9312 0.9606 0.1454 ] Network output: [ 0.1091 -0.267 1.149 -0.002993 0.001332 0.8872 -0.002208 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.812 0.4859 0.4317 0.4698 0.9592 0.9795 0.8157 0.8693 0.9516 0.7109 ] Network output: [ -0.06348 0.208 0.8906 0.002356 -0.001062 1.038 0.001793 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7804 0.7136 0.3903 0.2159 0.9764 0.9837 0.7811 0.9434 0.9659 0.4296 ] Network output: [ -0.09476 0.3358 0.7568 0.0004005 -0.0001812 1.098 0.0003078 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8164 0.8023 0.434 0.09833 0.974 0.9817 0.8165 0.9382 0.9612 0.4434 ] Network output: [ 0.08637 0.6806 0.2134 4.712e-05 -1.11e-05 0.9335 -5.828e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07972 Epoch 578 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02294 1.071 0.9529 0.0004624 -0.0002113 -0.06759 0.0003638 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06732 -0.003709 0.03101 0.02269 0.9085 0.9224 0.123 0.8245 0.8643 0.23 ] Network output: [ 0.9671 0.04351 -0.01218 -6.759e-05 4.795e-05 0.03425 -0.0001235 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7193 -0.003468 -0.02343 0.2786 0.9531 0.9756 0.8096 0.8526 0.9419 0.7121 ] Network output: [ -0.02628 0.9347 1.045 -2.003e-05 1.553e-06 0.07311 1.558e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1355 0.07169 0.09348 0.06778 0.9717 0.9791 0.1385 0.9312 0.9606 0.1447 ] Network output: [ 0.1093 -0.2674 1.15 -0.00298 0.001326 0.8871 -0.002199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8118 0.4847 0.4311 0.4697 0.9592 0.9795 0.8154 0.8693 0.9516 0.7108 ] Network output: [ -0.06376 0.2082 0.8907 0.002341 -0.001055 1.038 0.001782 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7802 0.7131 0.3896 0.2153 0.9764 0.9837 0.7809 0.9434 0.9659 0.429 ] Network output: [ -0.09519 0.3368 0.7564 0.0003726 -0.0001687 1.099 0.0002866 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8162 0.8021 0.4335 0.09732 0.974 0.9817 0.8164 0.9382 0.9612 0.4429 ] Network output: [ 0.08696 0.6795 0.2138 6.371e-05 -1.867e-05 0.933 7.183e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08006 Epoch 579 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02268 1.07 0.9534 0.0004519 -0.0002065 -0.06726 0.0003557 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0672 -0.003741 0.03095 0.02264 0.9085 0.9224 0.1228 0.8246 0.8643 0.2294 ] Network output: [ 0.9672 0.04394 -0.01268 -4.41e-05 3.716e-05 0.03426 -0.0001048 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7191 -0.004182 -0.0238 0.2785 0.9531 0.9756 0.8094 0.8527 0.9419 0.7121 ] Network output: [ -0.02616 0.9349 1.044 -2.21e-05 2.598e-06 0.07291 1.354e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1352 0.07139 0.09299 0.06744 0.9717 0.9791 0.1382 0.9313 0.9606 0.144 ] Network output: [ 0.1095 -0.2678 1.15 -0.002966 0.00132 0.887 -0.002189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8115 0.4834 0.4305 0.4696 0.9592 0.9795 0.8152 0.8693 0.9516 0.7108 ] Network output: [ -0.06405 0.2085 0.8907 0.002326 -0.001049 1.038 0.00177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.78 0.7126 0.389 0.2147 0.9764 0.9837 0.7807 0.9434 0.9659 0.4285 ] Network output: [ -0.09563 0.3378 0.7559 0.0003443 -0.000156 1.099 0.0002653 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8161 0.8019 0.433 0.09629 0.974 0.9817 0.8162 0.9383 0.9612 0.4425 ] Network output: [ 0.08754 0.6784 0.2143 8.053e-05 -2.635e-05 0.9326 2.036e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08041 Epoch 580 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02242 1.07 0.9539 0.0004416 -0.0002019 -0.06693 0.0003477 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06708 -0.003773 0.03088 0.02259 0.9085 0.9225 0.1226 0.8247 0.8644 0.2288 ] Network output: [ 0.9672 0.04439 -0.01316 -2.111e-05 2.659e-05 0.03428 -8.643e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7188 -0.004887 -0.02417 0.2785 0.9531 0.9756 0.8091 0.8528 0.9419 0.7121 ] Network output: [ -0.02605 0.935 1.044 -2.419e-05 3.653e-06 0.0727 1.148e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1349 0.07109 0.09251 0.06711 0.9717 0.9791 0.1378 0.9313 0.9607 0.1434 ] Network output: [ 0.1097 -0.2682 1.15 -0.002953 0.001315 0.8869 -0.00218 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8113 0.4822 0.4298 0.4694 0.9592 0.9795 0.8149 0.8694 0.9516 0.7108 ] Network output: [ -0.06434 0.2088 0.8908 0.002311 -0.001042 1.039 0.001759 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7798 0.7121 0.3885 0.214 0.9764 0.9837 0.7805 0.9435 0.966 0.4279 ] Network output: [ -0.09606 0.3388 0.7554 0.0003156 -0.0001431 1.099 0.0002435 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8159 0.8017 0.4325 0.09521 0.974 0.9817 0.8161 0.9383 0.9612 0.442 ] Network output: [ 0.08813 0.6773 0.2148 9.746e-05 -3.407e-05 0.9321 3.362e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08076 Epoch 581 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02216 1.07 0.9543 0.0004315 -0.0001973 -0.06661 0.0003399 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06695 -0.003805 0.03082 0.02254 0.9085 0.9225 0.1224 0.8248 0.8645 0.2283 ] Network output: [ 0.9673 0.04484 -0.01363 1.8e-06 1.606e-05 0.03431 -6.815e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7186 -0.005596 -0.02453 0.2784 0.9532 0.9756 0.8089 0.8528 0.9419 0.7121 ] Network output: [ -0.02594 0.9351 1.044 -2.624e-05 4.686e-06 0.0725 9.472e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1345 0.07079 0.09203 0.06677 0.9717 0.9791 0.1375 0.9314 0.9607 0.1427 ] Network output: [ 0.1099 -0.2687 1.15 -0.002939 0.001308 0.8868 -0.00217 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.811 0.4809 0.4292 0.4693 0.9592 0.9795 0.8147 0.8694 0.9516 0.7107 ] Network output: [ -0.06464 0.209 0.8909 0.002296 -0.001035 1.039 0.001747 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7796 0.7117 0.3879 0.2134 0.9764 0.9837 0.7802 0.9435 0.966 0.4274 ] Network output: [ -0.09649 0.3398 0.7548 0.0002866 -0.00013 1.099 0.0002216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8158 0.8015 0.432 0.09411 0.974 0.9817 0.8159 0.9383 0.9613 0.4415 ] Network output: [ 0.08872 0.6761 0.2153 0.0001147 -4.194e-05 0.9316 4.711e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08113 Epoch 582 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02191 1.069 0.9548 0.0004215 -0.0001928 -0.06628 0.0003322 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06683 -0.003837 0.03075 0.02249 0.9086 0.9225 0.1222 0.8249 0.8645 0.2277 ] Network output: [ 0.9673 0.0453 -0.01412 2.462e-05 5.571e-06 0.03433 -4.995e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7183 -0.006313 -0.02491 0.2784 0.9532 0.9756 0.8086 0.8529 0.942 0.7121 ] Network output: [ -0.02583 0.9352 1.044 -2.827e-05 5.711e-06 0.0723 7.478e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1342 0.07049 0.09156 0.06643 0.9717 0.9791 0.1372 0.9314 0.9607 0.142 ] Network output: [ 0.1102 -0.2691 1.15 -0.002925 0.001302 0.8867 -0.00216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8108 0.4796 0.4286 0.4691 0.9592 0.9795 0.8144 0.8694 0.9517 0.7107 ] Network output: [ -0.06495 0.2092 0.891 0.00228 -0.001028 1.039 0.001735 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7794 0.7112 0.3873 0.2127 0.9764 0.9837 0.78 0.9435 0.966 0.4269 ] Network output: [ -0.09692 0.3409 0.7543 0.0002573 -0.0001169 1.1 0.0001995 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8156 0.8013 0.4316 0.09298 0.974 0.9817 0.8158 0.9383 0.9613 0.4411 ] Network output: [ 0.08931 0.675 0.2158 0.0001322 -4.992e-05 0.9311 6.079e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0815 Epoch 583 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02166 1.069 0.9553 0.0004118 -0.0001883 -0.06595 0.0003247 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06671 -0.003869 0.03068 0.02244 0.9086 0.9225 0.122 0.825 0.8646 0.2271 ] Network output: [ 0.9673 0.04577 -0.01461 4.704e-05 -4.733e-06 0.03436 -3.208e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7181 -0.007026 -0.02528 0.2783 0.9532 0.9756 0.8084 0.853 0.942 0.7121 ] Network output: [ -0.02572 0.9353 1.044 -3.031e-05 6.734e-06 0.07211 5.493e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1339 0.0702 0.09109 0.0661 0.9717 0.9791 0.1369 0.9314 0.9607 0.1414 ] Network output: [ 0.1104 -0.2695 1.15 -0.002911 0.001296 0.8866 -0.00215 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8105 0.4783 0.4279 0.469 0.9592 0.9795 0.8141 0.8695 0.9517 0.7107 ] Network output: [ -0.06526 0.2095 0.8911 0.002265 -0.001021 1.039 0.001723 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7791 0.7107 0.3868 0.212 0.9764 0.9837 0.7798 0.9435 0.966 0.4265 ] Network output: [ -0.09735 0.342 0.7537 0.0002276 -0.0001035 1.1 0.000177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8155 0.8011 0.4311 0.09181 0.974 0.9818 0.8156 0.9384 0.9613 0.4407 ] Network output: [ 0.08991 0.6739 0.2164 0.0001499 -5.799e-05 0.9306 7.462e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08187 Epoch 584 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02141 1.069 0.9557 0.0004022 -0.000184 -0.06562 0.0003173 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06659 -0.003902 0.03062 0.02238 0.9086 0.9226 0.1218 0.825 0.8646 0.2266 ] Network output: [ 0.9674 0.04625 -0.01509 6.929e-05 -1.496e-05 0.0344 -1.434e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7178 -0.007743 -0.02565 0.2782 0.9532 0.9756 0.8081 0.8531 0.942 0.7122 ] Network output: [ -0.02562 0.9354 1.044 -3.23e-05 7.736e-06 0.07191 3.547e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1336 0.0699 0.09064 0.06577 0.9717 0.9791 0.1365 0.9315 0.9608 0.1407 ] Network output: [ 0.1106 -0.27 1.15 -0.002897 0.00129 0.8865 -0.002139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8102 0.477 0.4273 0.4688 0.9592 0.9795 0.8138 0.8695 0.9517 0.7107 ] Network output: [ -0.06558 0.2097 0.8912 0.002249 -0.001014 1.039 0.001711 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7789 0.7102 0.3863 0.2113 0.9764 0.9837 0.7796 0.9436 0.966 0.426 ] Network output: [ -0.09778 0.3431 0.7531 0.0001976 -9.002e-05 1.1 0.0001543 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8153 0.8009 0.4307 0.09062 0.974 0.9818 0.8155 0.9384 0.9613 0.4403 ] Network output: [ 0.09051 0.6727 0.2169 0.000168 -6.62e-05 0.9301 8.868e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08226 Epoch 585 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02117 1.068 0.9562 0.0003928 -0.0001797 -0.0653 0.00031 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06647 -0.003934 0.03056 0.02233 0.9086 0.9226 0.1215 0.8251 0.8647 0.226 ] Network output: [ 0.9674 0.04673 -0.01559 9.138e-05 -2.51e-05 0.03442 3.253e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7176 -0.008465 -0.02603 0.2782 0.9532 0.9756 0.8078 0.8531 0.9421 0.7122 ] Network output: [ -0.02551 0.9355 1.044 -3.427e-05 8.724e-06 0.07172 1.63e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1333 0.0696 0.09018 0.06544 0.9718 0.9792 0.1362 0.9315 0.9608 0.1401 ] Network output: [ 0.1108 -0.2704 1.151 -0.002882 0.001284 0.8863 -0.002129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8099 0.4757 0.4267 0.4686 0.9592 0.9795 0.8136 0.8695 0.9517 0.7107 ] Network output: [ -0.06591 0.2099 0.8914 0.002233 -0.001006 1.04 0.001699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7787 0.7097 0.3858 0.2107 0.9764 0.9837 0.7794 0.9436 0.966 0.4256 ] Network output: [ -0.09821 0.3442 0.7525 0.0001673 -7.64e-05 1.1 0.0001314 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8152 0.8006 0.4303 0.08939 0.974 0.9818 0.8153 0.9384 0.9613 0.4399 ] Network output: [ 0.09111 0.6715 0.2174 0.0001863 -7.454e-05 0.9296 0.000103 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08265 Epoch 586 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02092 1.068 0.9566 0.0003836 -0.0001756 -0.06497 0.0003029 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06635 -0.003966 0.03049 0.02227 0.9087 0.9226 0.1213 0.8252 0.8648 0.2255 ] Network output: [ 0.9674 0.04724 -0.01608 0.0001131 -3.509e-05 0.03445 2.057e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7173 -0.009188 -0.02641 0.2781 0.9532 0.9756 0.8076 0.8532 0.9421 0.7122 ] Network output: [ -0.02541 0.9356 1.044 -3.621e-05 9.7e-06 0.07153 -2.63e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1329 0.0693 0.08974 0.06511 0.9718 0.9792 0.1359 0.9316 0.9608 0.1395 ] Network output: [ 0.1111 -0.2708 1.151 -0.002868 0.001277 0.8862 -0.002119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8097 0.4744 0.4261 0.4684 0.9592 0.9795 0.8133 0.8696 0.9517 0.7108 ] Network output: [ -0.06624 0.21 0.8915 0.002217 -0.0009993 1.04 0.001687 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7785 0.7092 0.3853 0.2099 0.9764 0.9837 0.7792 0.9436 0.966 0.4252 ] Network output: [ -0.09864 0.3453 0.7518 0.0001366 -6.26e-05 1.101 0.0001082 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.815 0.8004 0.4299 0.08813 0.974 0.9818 0.8152 0.9385 0.9613 0.4396 ] Network output: [ 0.09171 0.6704 0.218 0.0002049 -8.3e-05 0.9291 0.0001174 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08305 Epoch 587 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02068 1.068 0.9571 0.0003747 -0.0001715 -0.06465 0.0002959 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06623 -0.003999 0.03043 0.02222 0.9087 0.9226 0.1211 0.8253 0.8648 0.2249 ] Network output: [ 0.9675 0.04775 -0.01657 0.0001346 -4.496e-05 0.03449 3.769e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.717 -0.009913 -0.02679 0.278 0.9532 0.9756 0.8073 0.8533 0.9421 0.7123 ] Network output: [ -0.02531 0.9357 1.043 -3.811e-05 1.065e-05 0.07134 -2.114e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1326 0.069 0.0893 0.06478 0.9718 0.9792 0.1356 0.9316 0.9608 0.1389 ] Network output: [ 0.1113 -0.2713 1.151 -0.002853 0.001271 0.886 -0.002108 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8094 0.4731 0.4255 0.4682 0.9592 0.9795 0.813 0.8696 0.9517 0.7108 ] Network output: [ -0.06657 0.2102 0.8917 0.002201 -0.0009921 1.04 0.001674 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7783 0.7087 0.3849 0.2092 0.9764 0.9837 0.779 0.9437 0.966 0.4248 ] Network output: [ -0.09907 0.3465 0.7512 0.0001057 -4.868e-05 1.101 8.475e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8149 0.8002 0.4296 0.08684 0.974 0.9818 0.815 0.9385 0.9613 0.4392 ] Network output: [ 0.09232 0.6692 0.2186 0.0002239 -9.162e-05 0.9285 0.0001322 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08345 Epoch 588 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02045 1.067 0.9575 0.0003659 -0.0001675 -0.06432 0.0002891 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06611 -0.004031 0.03037 0.02216 0.9087 0.9227 0.1209 0.8254 0.8649 0.2244 ] Network output: [ 0.9675 0.04827 -0.01707 0.0001559 -5.473e-05 0.03452 5.463e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7168 -0.01064 -0.02716 0.2779 0.9532 0.9756 0.807 0.8533 0.9421 0.7123 ] Network output: [ -0.0252 0.9358 1.043 -3.996e-05 1.159e-05 0.07116 -3.927e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1323 0.0687 0.08887 0.06445 0.9718 0.9792 0.1352 0.9317 0.9608 0.1383 ] Network output: [ 0.1116 -0.2717 1.151 -0.002838 0.001264 0.8858 -0.002098 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8091 0.4717 0.4248 0.468 0.9592 0.9795 0.8127 0.8696 0.9517 0.7108 ] Network output: [ -0.06692 0.2104 0.8919 0.002185 -0.0009848 1.04 0.001662 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7781 0.7082 0.3844 0.2085 0.9764 0.9837 0.7788 0.9437 0.966 0.4244 ] Network output: [ -0.0995 0.3477 0.7505 7.439e-05 -3.461e-05 1.101 6.109e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8147 0.8 0.4292 0.08552 0.974 0.9818 0.8149 0.9385 0.9613 0.4389 ] Network output: [ 0.09293 0.668 0.2192 0.0002431 -0.0001004 0.928 0.0001472 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08386 Epoch 589 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02021 1.067 0.9579 0.0003572 -0.0001636 -0.064 0.0002825 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06599 -0.004064 0.03031 0.0221 0.9087 0.9227 0.1207 0.8255 0.8649 0.2239 ] Network output: [ 0.9675 0.04881 -0.01757 0.0001768 -6.434e-05 0.03456 7.129e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7165 -0.01137 -0.02754 0.2778 0.9532 0.9756 0.8067 0.8534 0.9422 0.7124 ] Network output: [ -0.0251 0.9359 1.043 -4.178e-05 1.25e-05 0.07097 -5.705e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.132 0.06839 0.08844 0.06412 0.9718 0.9792 0.1349 0.9317 0.9609 0.1377 ] Network output: [ 0.1119 -0.2721 1.151 -0.002824 0.001258 0.8857 -0.002087 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8088 0.4703 0.4242 0.4677 0.9592 0.9795 0.8124 0.8696 0.9517 0.7109 ] Network output: [ -0.06727 0.2105 0.8921 0.002169 -0.0009775 1.041 0.00165 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7779 0.7077 0.384 0.2078 0.9764 0.9837 0.7786 0.9437 0.9661 0.4241 ] Network output: [ -0.09992 0.3489 0.7498 4.276e-05 -2.04e-05 1.101 3.717e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8146 0.7998 0.4289 0.08416 0.974 0.9818 0.8147 0.9385 0.9613 0.4386 ] Network output: [ 0.09354 0.6667 0.2198 0.0002627 -0.0001093 0.9275 0.0001624 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08428 Epoch 590 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01998 1.067 0.9583 0.0003488 -0.0001598 -0.06368 0.0002759 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06587 -0.004097 0.03025 0.02204 0.9088 0.9227 0.1205 0.8256 0.865 0.2234 ] Network output: [ 0.9675 0.04935 -0.01807 0.0001975 -7.382e-05 0.03461 8.773e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7162 -0.01211 -0.02791 0.2777 0.9532 0.9757 0.8064 0.8535 0.9422 0.7125 ] Network output: [ -0.025 0.936 1.043 -4.355e-05 1.339e-05 0.07079 -7.435e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1317 0.06809 0.08802 0.0638 0.9718 0.9792 0.1346 0.9317 0.9609 0.1371 ] Network output: [ 0.1121 -0.2726 1.151 -0.002809 0.001251 0.8855 -0.002076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8085 0.469 0.4236 0.4675 0.9592 0.9795 0.8121 0.8697 0.9517 0.7109 ] Network output: [ -0.06763 0.2106 0.8924 0.002153 -0.0009701 1.041 0.001637 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7777 0.7072 0.3836 0.207 0.9764 0.9837 0.7784 0.9437 0.9661 0.4238 ] Network output: [ -0.1003 0.3501 0.749 1.084e-05 -6.047e-06 1.102 1.304e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8145 0.7996 0.4286 0.08278 0.974 0.9818 0.8146 0.9386 0.9613 0.4383 ] Network output: [ 0.09416 0.6655 0.2204 0.0002827 -0.0001184 0.927 0.0001779 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08471 Epoch 591 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01975 1.067 0.9587 0.0003406 -0.000156 -0.06335 0.0002696 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06575 -0.00413 0.03019 0.02198 0.9088 0.9227 0.1203 0.8257 0.8651 0.2229 ] Network output: [ 0.9675 0.04991 -0.01857 0.0002178 -8.317e-05 0.03465 0.0001039 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7159 -0.01285 -0.02828 0.2776 0.9532 0.9757 0.8061 0.8535 0.9422 0.7126 ] Network output: [ -0.02491 0.936 1.043 -4.526e-05 1.426e-05 0.07061 -9.12e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1314 0.06779 0.08761 0.06348 0.9718 0.9792 0.1343 0.9318 0.9609 0.1365 ] Network output: [ 0.1124 -0.273 1.152 -0.002794 0.001244 0.8853 -0.002065 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8081 0.4676 0.4231 0.4673 0.9592 0.9795 0.8118 0.8697 0.9517 0.711 ] Network output: [ -0.06799 0.2107 0.8926 0.002137 -0.0009627 1.041 0.001625 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7775 0.7067 0.3832 0.2063 0.9764 0.9838 0.7782 0.9438 0.9661 0.4235 ] Network output: [ -0.1008 0.3513 0.7483 -2.138e-05 8.436e-06 1.102 -1.132e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8143 0.7994 0.4283 0.08137 0.974 0.9818 0.8145 0.9386 0.9613 0.4381 ] Network output: [ 0.09478 0.6642 0.221 0.0003031 -0.0001276 0.9264 0.0001936 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08514 Epoch 592 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01953 1.066 0.9591 0.0003326 -0.0001524 -0.06303 0.0002633 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06563 -0.004163 0.03013 0.02192 0.9088 0.9228 0.1201 0.8257 0.8651 0.2223 ] Network output: [ 0.9674 0.05049 -0.01908 0.0002378 -9.236e-05 0.03469 0.0001199 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7156 -0.01359 -0.02865 0.2775 0.9532 0.9757 0.8058 0.8536 0.9423 0.7127 ] Network output: [ -0.02481 0.9361 1.043 -4.692e-05 1.51e-05 0.07043 -1.076e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.131 0.06748 0.0872 0.06316 0.9718 0.9792 0.1339 0.9318 0.9609 0.1359 ] Network output: [ 0.1127 -0.2734 1.152 -0.002779 0.001238 0.8851 -0.002055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8078 0.4662 0.4225 0.467 0.9592 0.9795 0.8115 0.8697 0.9517 0.7111 ] Network output: [ -0.06836 0.2108 0.8929 0.00212 -0.0009553 1.042 0.001612 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7773 0.7062 0.3828 0.2055 0.9765 0.9838 0.778 0.9438 0.9661 0.4232 ] Network output: [ -0.1012 0.3526 0.7475 -5.39e-05 2.306e-05 1.102 -3.592e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8142 0.7991 0.428 0.07992 0.974 0.9818 0.8143 0.9386 0.9613 0.4378 ] Network output: [ 0.0954 0.6629 0.2217 0.0003238 -0.000137 0.9259 0.0002097 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08559 Epoch 593 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0193 1.066 0.9595 0.0003247 -0.0001488 -0.06271 0.0002573 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06551 -0.004196 0.03008 0.02185 0.9088 0.9228 0.1199 0.8258 0.8652 0.2218 ] Network output: [ 0.9674 0.05107 -0.01959 0.0002575 -0.0001014 0.03474 0.0001355 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7153 -0.01433 -0.02902 0.2774 0.9532 0.9757 0.8055 0.8537 0.9423 0.7128 ] Network output: [ -0.02471 0.9362 1.043 -4.853e-05 1.591e-05 0.07026 -1.235e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1307 0.06718 0.0868 0.06284 0.9718 0.9792 0.1336 0.9319 0.9609 0.1354 ] Network output: [ 0.113 -0.2739 1.152 -0.002763 0.001231 0.8849 -0.002044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8075 0.4648 0.4219 0.4667 0.9592 0.9795 0.8111 0.8698 0.9517 0.7112 ] Network output: [ -0.06874 0.2108 0.8932 0.002104 -0.0009478 1.042 0.0016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7771 0.7057 0.3825 0.2048 0.9765 0.9838 0.7778 0.9438 0.9661 0.4229 ] Network output: [ -0.1016 0.3538 0.7468 -8.672e-05 3.781e-05 1.102 -6.072e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.814 0.7989 0.4278 0.07845 0.974 0.9818 0.8142 0.9387 0.9613 0.4376 ] Network output: [ 0.09603 0.6617 0.2223 0.0003449 -0.0001466 0.9254 0.0002261 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08603 Epoch 594 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01909 1.066 0.9599 0.0003171 -0.0001454 -0.06239 0.0002513 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06539 -0.004229 0.03002 0.02179 0.9089 0.9228 0.1197 0.8259 0.8652 0.2214 ] Network output: [ 0.9674 0.05167 -0.0201 0.0002769 -0.0001103 0.03479 0.000151 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.715 -0.01508 -0.02938 0.2772 0.9532 0.9757 0.8052 0.8537 0.9423 0.7129 ] Network output: [ -0.02462 0.9362 1.043 -5.007e-05 1.669e-05 0.07008 -1.388e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1304 0.06688 0.08641 0.06252 0.9718 0.9792 0.1333 0.9319 0.961 0.1348 ] Network output: [ 0.1132 -0.2743 1.152 -0.002748 0.001224 0.8847 -0.002033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8072 0.4633 0.4213 0.4665 0.9592 0.9795 0.8108 0.8698 0.9518 0.7113 ] Network output: [ -0.06913 0.2109 0.8935 0.002087 -0.0009403 1.042 0.001587 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7769 0.7052 0.3822 0.204 0.9765 0.9838 0.7776 0.9438 0.9661 0.4227 ] Network output: [ -0.102 0.3551 0.746 -0.0001198 5.268e-05 1.103 -8.573e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8139 0.7987 0.4275 0.07695 0.974 0.9818 0.814 0.9387 0.9613 0.4374 ] Network output: [ 0.09666 0.6603 0.223 0.0003665 -0.0001564 0.9248 0.0002427 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08649 Epoch 595 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01887 1.065 0.9603 0.0003096 -0.000142 -0.06206 0.0002455 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06527 -0.004262 0.02997 0.02173 0.9089 0.9228 0.1195 0.826 0.8653 0.2209 ] Network output: [ 0.9674 0.05229 -0.02061 0.0002959 -0.000119 0.03485 0.0001661 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7147 -0.01583 -0.02974 0.2771 0.9532 0.9757 0.8048 0.8538 0.9423 0.713 ] Network output: [ -0.02452 0.9363 1.043 -5.155e-05 1.744e-05 0.06991 -1.536e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1301 0.06657 0.08602 0.0622 0.9718 0.9792 0.133 0.9319 0.961 0.1343 ] Network output: [ 0.1135 -0.2747 1.152 -0.002733 0.001218 0.8845 -0.002022 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8068 0.4619 0.4208 0.4662 0.9592 0.9795 0.8105 0.8698 0.9518 0.7114 ] Network output: [ -0.06952 0.2109 0.8938 0.00207 -0.0009328 1.043 0.001574 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7767 0.7047 0.3819 0.2032 0.9765 0.9838 0.7774 0.9439 0.9661 0.4225 ] Network output: [ -0.1025 0.3564 0.7451 -0.0001532 6.768e-05 1.103 -0.000111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8138 0.7985 0.4273 0.07542 0.974 0.9818 0.8139 0.9387 0.9613 0.4372 ] Network output: [ 0.0973 0.659 0.2237 0.0003885 -0.0001664 0.9243 0.0002597 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08696 Epoch 596 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01866 1.065 0.9606 0.0003024 -0.0001387 -0.06174 0.0002399 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06515 -0.004296 0.02992 0.02166 0.9089 0.9229 0.1193 0.8261 0.8653 0.2204 ] Network output: [ 0.9673 0.05292 -0.02113 0.0003145 -0.0001276 0.03491 0.0001809 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7144 -0.01659 -0.0301 0.277 0.9532 0.9757 0.8045 0.8539 0.9424 0.7131 ] Network output: [ -0.02443 0.9364 1.043 -5.296e-05 1.816e-05 0.06974 -1.678e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1298 0.06626 0.08564 0.06188 0.9719 0.9793 0.1326 0.932 0.961 0.1338 ] Network output: [ 0.1138 -0.2752 1.152 -0.002718 0.001211 0.8842 -0.002011 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8065 0.4604 0.4202 0.4659 0.9592 0.9795 0.8101 0.8699 0.9518 0.7115 ] Network output: [ -0.06991 0.2109 0.8941 0.002054 -0.0009253 1.043 0.001561 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7765 0.7042 0.3816 0.2024 0.9765 0.9838 0.7772 0.9439 0.9661 0.4223 ] Network output: [ -0.1029 0.3577 0.7443 -0.0001868 8.28e-05 1.103 -0.0001364 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8136 0.7983 0.4271 0.07387 0.9741 0.9818 0.8138 0.9388 0.9613 0.437 ] Network output: [ 0.09794 0.6577 0.2244 0.0004109 -0.0001765 0.9237 0.000277 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08743 Epoch 597 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01845 1.065 0.961 0.0002953 -0.0001354 -0.06142 0.0002344 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06503 -0.004329 0.02986 0.02159 0.9089 0.9229 0.1191 0.8261 0.8654 0.2199 ] Network output: [ 0.9672 0.05356 -0.02164 0.0003327 -0.000136 0.03497 0.0001955 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7141 -0.01735 -0.03045 0.2768 0.9532 0.9757 0.8042 0.8539 0.9424 0.7132 ] Network output: [ -0.02433 0.9364 1.042 -5.43e-05 1.885e-05 0.06957 -1.815e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1295 0.06595 0.08527 0.06157 0.9719 0.9793 0.1323 0.932 0.961 0.1333 ] Network output: [ 0.1141 -0.2756 1.152 -0.002703 0.001205 0.884 -0.002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8061 0.459 0.4197 0.4656 0.9592 0.9795 0.8098 0.8699 0.9518 0.7116 ] Network output: [ -0.07031 0.2109 0.8945 0.002037 -0.0009177 1.043 0.001549 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7763 0.7037 0.3813 0.2016 0.9765 0.9838 0.777 0.9439 0.9661 0.4221 ] Network output: [ -0.1033 0.3591 0.7434 -0.0002207 9.802e-05 1.103 -0.000162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8135 0.7981 0.427 0.07228 0.9741 0.9818 0.8136 0.9388 0.9613 0.4369 ] Network output: [ 0.09858 0.6563 0.2252 0.0004338 -0.0001869 0.9231 0.0002947 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08791 Epoch 598 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01824 1.064 0.9613 0.0002884 -0.0001323 -0.0611 0.000229 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06491 -0.004363 0.02981 0.02153 0.909 0.9229 0.1188 0.8262 0.8654 0.2194 ] Network output: [ 0.9672 0.05422 -0.02216 0.0003506 -0.0001442 0.03504 0.0002097 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7138 -0.01811 -0.03079 0.2767 0.9532 0.9757 0.8038 0.854 0.9424 0.7134 ] Network output: [ -0.02424 0.9365 1.042 -5.557e-05 1.951e-05 0.0694 -1.945e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1291 0.06565 0.0849 0.06126 0.9719 0.9793 0.132 0.932 0.961 0.1328 ] Network output: [ 0.1144 -0.276 1.152 -0.002688 0.001198 0.8838 -0.001989 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8058 0.4575 0.4192 0.4653 0.9592 0.9795 0.8094 0.8699 0.9518 0.7117 ] Network output: [ -0.07072 0.2109 0.8949 0.00202 -0.0009102 1.044 0.001536 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7762 0.7031 0.3811 0.2008 0.9765 0.9838 0.7768 0.9439 0.9661 0.422 ] Network output: [ -0.1037 0.3604 0.7426 -0.0002548 0.0001134 1.103 -0.0001878 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8133 0.7978 0.4268 0.07068 0.9741 0.9818 0.8135 0.9388 0.9613 0.4368 ] Network output: [ 0.09923 0.6549 0.2259 0.0004572 -0.0001975 0.9226 0.0003127 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08839 Epoch 599 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01804 1.064 0.9617 0.0002817 -0.0001293 -0.06078 0.0002238 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06479 -0.004397 0.02977 0.02146 0.909 0.9229 0.1186 0.8263 0.8655 0.219 ] Network output: [ 0.9671 0.05489 -0.02268 0.000368 -0.0001522 0.03511 0.0002236 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7134 -0.01888 -0.03113 0.2766 0.9533 0.9757 0.8035 0.8541 0.9424 0.7135 ] Network output: [ -0.02414 0.9365 1.042 -5.676e-05 2.012e-05 0.06924 -2.07e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1288 0.06534 0.08454 0.06095 0.9719 0.9793 0.1317 0.9321 0.961 0.1322 ] Network output: [ 0.1148 -0.2764 1.153 -0.002673 0.001191 0.8835 -0.001978 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8054 0.456 0.4187 0.465 0.9592 0.9795 0.809 0.87 0.9518 0.7119 ] Network output: [ -0.07114 0.2109 0.8953 0.002003 -0.0009026 1.044 0.001523 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.776 0.7026 0.3809 0.2 0.9765 0.9838 0.7766 0.944 0.9661 0.4219 ] Network output: [ -0.1041 0.3618 0.7417 -0.0002892 0.0001288 1.104 -0.0002137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8132 0.7976 0.4267 0.06905 0.9741 0.9818 0.8133 0.9389 0.9614 0.4367 ] Network output: [ 0.09989 0.6535 0.2267 0.0004811 -0.0002083 0.922 0.0003311 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08889 Epoch 600 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01784 1.064 0.962 0.0002751 -0.0001263 -0.06047 0.0002188 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06467 -0.004431 0.02972 0.02139 0.909 0.9229 0.1184 0.8264 0.8655 0.2185 ] Network output: [ 0.967 0.05558 -0.02321 0.0003851 -0.00016 0.03518 0.0002372 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7131 -0.01965 -0.03146 0.2764 0.9533 0.9757 0.8031 0.8541 0.9425 0.7137 ] Network output: [ -0.02405 0.9366 1.042 -5.788e-05 2.071e-05 0.06907 -2.188e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1285 0.06502 0.08419 0.06064 0.9719 0.9793 0.1313 0.9321 0.961 0.1318 ] Network output: [ 0.1151 -0.2768 1.153 -0.002658 0.001185 0.8832 -0.001968 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.805 0.4545 0.4182 0.4646 0.9592 0.9795 0.8087 0.87 0.9518 0.712 ] Network output: [ -0.07156 0.2108 0.8957 0.001986 -0.000895 1.045 0.00151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7758 0.7021 0.3807 0.1992 0.9765 0.9838 0.7765 0.944 0.9661 0.4218 ] Network output: [ -0.1045 0.3631 0.7408 -0.0003237 0.0001443 1.104 -0.0002399 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.813 0.7974 0.4266 0.06739 0.9741 0.9818 0.8132 0.9389 0.9614 0.4366 ] Network output: [ 0.1005 0.6521 0.2275 0.0005054 -0.0002193 0.9214 0.0003498 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08939 Epoch 601 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01764 1.064 0.9623 0.0002688 -0.0001234 -0.06015 0.0002138 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06455 -0.004465 0.02968 0.02132 0.909 0.923 0.1182 0.8265 0.8656 0.2181 ] Network output: [ 0.9669 0.05628 -0.02373 0.0004017 -0.0001677 0.03526 0.0002505 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7127 -0.02042 -0.03178 0.2762 0.9533 0.9757 0.8027 0.8542 0.9425 0.7138 ] Network output: [ -0.02396 0.9366 1.042 -5.892e-05 2.126e-05 0.06891 -2.3e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1282 0.06471 0.08384 0.06034 0.9719 0.9793 0.131 0.9321 0.9611 0.1313 ] Network output: [ 0.1154 -0.2772 1.153 -0.002643 0.001178 0.883 -0.001957 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8047 0.4529 0.4177 0.4643 0.9592 0.9795 0.8083 0.87 0.9518 0.7122 ] Network output: [ -0.07198 0.2107 0.8961 0.00197 -0.0008873 1.045 0.001497 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7756 0.7016 0.3805 0.1984 0.9765 0.9838 0.7763 0.944 0.9661 0.4217 ] Network output: [ -0.1049 0.3645 0.7399 -0.0003585 0.00016 1.104 -0.0002661 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8129 0.7972 0.4265 0.06572 0.9741 0.9818 0.813 0.9389 0.9614 0.4365 ] Network output: [ 0.1012 0.6506 0.2283 0.0005302 -0.0002306 0.9208 0.0003689 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0899 Epoch 602 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01745 1.063 0.9626 0.0002626 -0.0001206 -0.05983 0.000209 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06443 -0.004499 0.02963 0.02125 0.909 0.923 0.118 0.8265 0.8657 0.2176 ] Network output: [ 0.9668 0.057 -0.02426 0.0004179 -0.0001751 0.03535 0.0002634 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7124 -0.0212 -0.0321 0.2761 0.9533 0.9757 0.8024 0.8543 0.9425 0.714 ] Network output: [ -0.02386 0.9367 1.042 -5.989e-05 2.177e-05 0.06874 -2.405e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1279 0.0644 0.08351 0.06003 0.9719 0.9793 0.1307 0.9322 0.9611 0.1308 ] Network output: [ 0.1157 -0.2776 1.153 -0.002629 0.001172 0.8827 -0.001946 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8043 0.4514 0.4172 0.464 0.9591 0.9795 0.8079 0.8701 0.9518 0.7123 ] Network output: [ -0.07241 0.2106 0.8965 0.001953 -0.0008797 1.046 0.001484 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7754 0.701 0.3804 0.1976 0.9765 0.9838 0.7761 0.944 0.9661 0.4217 ] Network output: [ -0.1053 0.3659 0.7389 -0.0003935 0.0001757 1.104 -0.0002926 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8128 0.797 0.4264 0.06402 0.9741 0.9818 0.8129 0.939 0.9614 0.4365 ] Network output: [ 0.1019 0.6492 0.2291 0.0005556 -0.0002421 0.9202 0.0003884 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09041 Epoch 603 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01726 1.063 0.9629 0.0002566 -0.0001179 -0.05951 0.0002043 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06431 -0.004533 0.02959 0.02118 0.9091 0.923 0.1178 0.8266 0.8657 0.2172 ] Network output: [ 0.9667 0.05774 -0.02479 0.0004337 -0.0001824 0.03544 0.000276 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7121 -0.02198 -0.0324 0.2759 0.9533 0.9757 0.802 0.8543 0.9425 0.7141 ] Network output: [ -0.02377 0.9367 1.042 -6.077e-05 2.224e-05 0.06858 -2.503e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1275 0.06408 0.08317 0.05973 0.9719 0.9793 0.1304 0.9322 0.9611 0.1303 ] Network output: [ 0.116 -0.278 1.153 -0.002614 0.001165 0.8824 -0.001936 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8039 0.4498 0.4167 0.4636 0.9591 0.9795 0.8075 0.8701 0.9518 0.7125 ] Network output: [ -0.07285 0.2105 0.897 0.001936 -0.0008721 1.046 0.001472 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7752 0.7005 0.3803 0.1968 0.9765 0.9838 0.7759 0.9441 0.9661 0.4217 ] Network output: [ -0.1057 0.3673 0.738 -0.0004286 0.0001915 1.104 -0.0003191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8126 0.7967 0.4264 0.0623 0.9741 0.9818 0.8128 0.939 0.9614 0.4365 ] Network output: [ 0.1026 0.6477 0.23 0.0005815 -0.0002538 0.9196 0.0004083 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09093 Epoch 604 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01707 1.063 0.9632 0.0002508 -0.0001152 -0.05919 0.0001998 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06419 -0.004568 0.02955 0.0211 0.9091 0.923 0.1176 0.8267 0.8658 0.2168 ] Network output: [ 0.9666 0.05849 -0.02532 0.000449 -0.0001894 0.03553 0.0002883 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7117 -0.02276 -0.0327 0.2757 0.9533 0.9757 0.8016 0.8544 0.9426 0.7143 ] Network output: [ -0.02368 0.9368 1.042 -6.157e-05 2.268e-05 0.06842 -2.596e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1272 0.06377 0.08285 0.05943 0.9719 0.9793 0.13 0.9322 0.9611 0.1299 ] Network output: [ 0.1164 -0.2784 1.153 -0.0026 0.001159 0.8821 -0.001925 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8035 0.4482 0.4163 0.4633 0.9591 0.9795 0.8071 0.8701 0.9518 0.7127 ] Network output: [ -0.07329 0.2104 0.8975 0.001919 -0.0008645 1.046 0.001459 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.775 0.7 0.3802 0.196 0.9765 0.9838 0.7757 0.9441 0.9661 0.4217 ] Network output: [ -0.1061 0.3687 0.737 -0.0004639 0.0002073 1.105 -0.0003458 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8125 0.7965 0.4264 0.06057 0.9741 0.9818 0.8126 0.9391 0.9614 0.4365 ] Network output: [ 0.1032 0.6462 0.2308 0.0006079 -0.0002657 0.919 0.0004286 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09146 Epoch 605 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01689 1.063 0.9634 0.0002451 -0.0001126 -0.05888 0.0001953 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06407 -0.004602 0.02952 0.02103 0.9091 0.923 0.1174 0.8268 0.8658 0.2163 ] Network output: [ 0.9664 0.05926 -0.02585 0.0004639 -0.0001963 0.03563 0.0003002 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7113 -0.02355 -0.03299 0.2756 0.9533 0.9757 0.8012 0.8544 0.9426 0.7145 ] Network output: [ -0.02358 0.9368 1.042 -6.229e-05 2.308e-05 0.06826 -2.681e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1269 0.06345 0.08253 0.05914 0.9719 0.9793 0.1297 0.9323 0.9611 0.1294 ] Network output: [ 0.1167 -0.2788 1.153 -0.002585 0.001152 0.8818 -0.001915 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8031 0.4467 0.4158 0.4629 0.9591 0.9795 0.8067 0.8701 0.9518 0.7129 ] Network output: [ -0.07374 0.2103 0.898 0.001902 -0.0008569 1.047 0.001446 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7749 0.6994 0.3801 0.1951 0.9765 0.9838 0.7755 0.9441 0.9662 0.4217 ] Network output: [ -0.1065 0.3701 0.7361 -0.0004994 0.0002233 1.105 -0.0003726 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8123 0.7963 0.4264 0.05882 0.9741 0.9819 0.8125 0.9391 0.9614 0.4365 ] Network output: [ 0.1039 0.6446 0.2317 0.0006348 -0.0002779 0.9184 0.0004492 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.092 Epoch 606 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01671 1.062 0.9637 0.0002396 -0.0001101 -0.05856 0.000191 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06395 -0.004637 0.02948 0.02096 0.9091 0.9231 0.1172 0.8268 0.8658 0.2159 ] Network output: [ 0.9663 0.06005 -0.02638 0.0004783 -0.0002029 0.03574 0.0003117 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.711 -0.02434 -0.03327 0.2754 0.9533 0.9757 0.8008 0.8545 0.9426 0.7147 ] Network output: [ -0.02349 0.9369 1.042 -6.293e-05 2.344e-05 0.06809 -2.76e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1266 0.06313 0.08221 0.05884 0.9719 0.9793 0.1294 0.9323 0.9611 0.129 ] Network output: [ 0.117 -0.2792 1.153 -0.002571 0.001146 0.8815 -0.001904 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8027 0.4451 0.4154 0.4626 0.9591 0.9795 0.8063 0.8702 0.9518 0.7131 ] Network output: [ -0.07419 0.2101 0.8985 0.001885 -0.0008492 1.047 0.001433 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7747 0.6989 0.3801 0.1943 0.9765 0.9838 0.7753 0.9441 0.9662 0.4217 ] Network output: [ -0.1069 0.3715 0.7351 -0.0005349 0.0002393 1.105 -0.0003994 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8122 0.796 0.4264 0.05706 0.9741 0.9819 0.8123 0.9391 0.9614 0.4365 ] Network output: [ 0.1046 0.6431 0.2326 0.0006623 -0.0002903 0.9178 0.0004703 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09254 Epoch 607 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01654 1.062 0.9639 0.0002342 -0.0001077 -0.05825 0.0001869 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06383 -0.004671 0.02945 0.02089 0.9091 0.9231 0.1169 0.8269 0.8659 0.2155 ] Network output: [ 0.9661 0.06085 -0.02691 0.0004922 -0.0002093 0.03586 0.0003229 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7106 -0.02514 -0.03354 0.2752 0.9533 0.9757 0.8004 0.8546 0.9426 0.7149 ] Network output: [ -0.02339 0.9369 1.042 -6.349e-05 2.376e-05 0.06793 -2.832e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1262 0.06281 0.08191 0.05855 0.9719 0.9793 0.129 0.9323 0.9611 0.1286 ] Network output: [ 0.1174 -0.2795 1.153 -0.002557 0.00114 0.8812 -0.001894 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8022 0.4434 0.415 0.4622 0.9591 0.9795 0.8058 0.8702 0.9519 0.7133 ] Network output: [ -0.07464 0.2099 0.899 0.001868 -0.0008416 1.048 0.00142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7745 0.6983 0.38 0.1935 0.9765 0.9838 0.7752 0.9441 0.9662 0.4218 ] Network output: [ -0.1073 0.3729 0.7341 -0.0005706 0.0002553 1.105 -0.0004264 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.812 0.7958 0.4264 0.05528 0.9741 0.9819 0.8122 0.9392 0.9614 0.4366 ] Network output: [ 0.1053 0.6415 0.2336 0.0006902 -0.000303 0.9171 0.0004917 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09309 Epoch 608 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01637 1.062 0.9642 0.000229 -0.0001053 -0.05793 0.0001828 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06371 -0.004706 0.02942 0.02081 0.9092 0.9231 0.1167 0.827 0.8659 0.2151 ] Network output: [ 0.9659 0.06167 -0.02745 0.0005056 -0.0002155 0.03598 0.0003337 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7102 -0.02594 -0.03379 0.2751 0.9533 0.9757 0.8 0.8546 0.9426 0.7151 ] Network output: [ -0.0233 0.937 1.042 -6.397e-05 2.405e-05 0.06777 -2.898e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1259 0.06249 0.08161 0.05827 0.9719 0.9793 0.1287 0.9324 0.9612 0.1281 ] Network output: [ 0.1177 -0.2799 1.153 -0.002543 0.001134 0.8809 -0.001884 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8018 0.4418 0.4146 0.4618 0.9591 0.9795 0.8054 0.8702 0.9519 0.7135 ] Network output: [ -0.0751 0.2097 0.8996 0.001851 -0.000834 1.048 0.001407 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7743 0.6978 0.38 0.1927 0.9765 0.9838 0.775 0.9442 0.9662 0.4219 ] Network output: [ -0.1077 0.3744 0.7331 -0.0006064 0.0002714 1.105 -0.0004534 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8119 0.7956 0.4265 0.0535 0.9741 0.9819 0.812 0.9392 0.9614 0.4367 ] Network output: [ 0.106 0.6399 0.2345 0.0007188 -0.0003158 0.9165 0.0005136 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09364 Epoch 609 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0162 1.062 0.9644 0.0002239 -0.000103 -0.05762 0.0001788 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06359 -0.00474 0.02939 0.02074 0.9092 0.9231 0.1165 0.8271 0.866 0.2147 ] Network output: [ 0.9657 0.06251 -0.02798 0.0005186 -0.0002215 0.03611 0.0003442 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7098 -0.02674 -0.03404 0.2749 0.9533 0.9757 0.7996 0.8547 0.9427 0.7153 ] Network output: [ -0.0232 0.937 1.041 -6.437e-05 2.43e-05 0.06761 -2.957e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1256 0.06217 0.08132 0.05798 0.9719 0.9793 0.1284 0.9324 0.9612 0.1277 ] Network output: [ 0.118 -0.2803 1.153 -0.002529 0.001128 0.8805 -0.001874 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8014 0.4401 0.4143 0.4615 0.9591 0.9795 0.805 0.8703 0.9519 0.7137 ] Network output: [ -0.07556 0.2095 0.9001 0.001834 -0.0008264 1.049 0.001394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7741 0.6972 0.38 0.1919 0.9765 0.9838 0.7748 0.9442 0.9662 0.422 ] Network output: [ -0.1081 0.3758 0.7321 -0.0006423 0.0002875 1.106 -0.0004805 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8117 0.7953 0.4266 0.0517 0.9741 0.9819 0.8119 0.9392 0.9615 0.4368 ] Network output: [ 0.1067 0.6383 0.2355 0.0007478 -0.000329 0.9158 0.0005358 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0942 Epoch 610 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01604 1.062 0.9646 0.000219 -0.0001007 -0.0573 0.000175 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06346 -0.004775 0.02936 0.02067 0.9092 0.9231 0.1163 0.8271 0.866 0.2143 ] Network output: [ 0.9655 0.06336 -0.02852 0.0005311 -0.0002273 0.03625 0.0003542 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7094 -0.02755 -0.03427 0.2747 0.9533 0.9757 0.7992 0.8548 0.9427 0.7155 ] Network output: [ -0.02311 0.9371 1.041 -6.469e-05 2.451e-05 0.06745 -3.01e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1253 0.06184 0.08103 0.0577 0.9719 0.9794 0.128 0.9324 0.9612 0.1273 ] Network output: [ 0.1184 -0.2806 1.153 -0.002515 0.001122 0.8802 -0.001864 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8009 0.4385 0.4139 0.4611 0.9591 0.9795 0.8045 0.8703 0.9519 0.7139 ] Network output: [ -0.07602 0.2093 0.9007 0.001818 -0.0008189 1.05 0.001382 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7739 0.6966 0.3801 0.1911 0.9765 0.9839 0.7746 0.9442 0.9662 0.4222 ] Network output: [ -0.1084 0.3772 0.7311 -0.0006782 0.0003036 1.106 -0.0005076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8116 0.7951 0.4267 0.0499 0.9742 0.9819 0.8117 0.9393 0.9615 0.4369 ] Network output: [ 0.1074 0.6367 0.2364 0.0007774 -0.0003423 0.9152 0.0005584 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09476 Epoch 611 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01588 1.061 0.9648 0.0002142 -9.854e-05 -0.05699 0.0001712 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06334 -0.00481 0.02934 0.02059 0.9092 0.9231 0.1161 0.8272 0.8661 0.2139 ] Network output: [ 0.9653 0.06423 -0.02905 0.0005431 -0.0002328 0.03639 0.0003639 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.709 -0.02836 -0.03449 0.2745 0.9533 0.9758 0.7987 0.8548 0.9427 0.7158 ] Network output: [ -0.02301 0.9372 1.041 -6.493e-05 2.469e-05 0.06729 -3.056e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1249 0.06152 0.08075 0.05742 0.972 0.9794 0.1277 0.9325 0.9612 0.1269 ] Network output: [ 0.1187 -0.2809 1.153 -0.002502 0.001116 0.8799 -0.001855 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8005 0.4368 0.4136 0.4607 0.9591 0.9795 0.8041 0.8703 0.9519 0.7141 ] Network output: [ -0.07649 0.209 0.9013 0.001801 -0.0008113 1.05 0.001369 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7738 0.6961 0.3801 0.1903 0.9765 0.9839 0.7744 0.9442 0.9662 0.4224 ] Network output: [ -0.1088 0.3786 0.7301 -0.0007141 0.0003198 1.106 -0.0005348 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8114 0.7948 0.4268 0.04809 0.9742 0.9819 0.8116 0.9393 0.9615 0.4371 ] Network output: [ 0.1081 0.6351 0.2374 0.0008075 -0.0003559 0.9145 0.0005815 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09533 Epoch 612 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01572 1.061 0.9649 0.0002096 -9.642e-05 -0.05668 0.0001676 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06322 -0.004845 0.02931 0.02052 0.9092 0.9232 0.1159 0.8273 0.8661 0.2135 ] Network output: [ 0.9651 0.06511 -0.02958 0.0005547 -0.0002381 0.03655 0.0003733 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7086 -0.02917 -0.0347 0.2743 0.9533 0.9758 0.7983 0.8549 0.9427 0.716 ] Network output: [ -0.02291 0.9372 1.041 -6.509e-05 2.483e-05 0.06712 -3.096e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1246 0.06119 0.08047 0.05714 0.972 0.9794 0.1274 0.9325 0.9612 0.1265 ] Network output: [ 0.1191 -0.2813 1.153 -0.002489 0.00111 0.8795 -0.001845 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.8 0.4351 0.4133 0.4604 0.9591 0.9795 0.8036 0.8704 0.9519 0.7144 ] Network output: [ -0.07696 0.2087 0.9019 0.001784 -0.0008038 1.051 0.001356 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7736 0.6955 0.3802 0.1895 0.9765 0.9839 0.7742 0.9442 0.9662 0.4225 ] Network output: [ -0.1092 0.38 0.7291 -0.00075 0.0003359 1.106 -0.0005619 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8113 0.7946 0.4269 0.04628 0.9742 0.9819 0.8114 0.9394 0.9615 0.4372 ] Network output: [ 0.1089 0.6334 0.2385 0.000838 -0.0003697 0.9138 0.0006048 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09591 Epoch 613 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01557 1.061 0.9651 0.000205 -9.434e-05 -0.05637 0.000164 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0631 -0.00488 0.02929 0.02045 0.9092 0.9232 0.1156 0.8274 0.8662 0.2131 ] Network output: [ 0.9649 0.06602 -0.03011 0.0005657 -0.0002433 0.03671 0.0003822 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7082 -0.02998 -0.0349 0.2742 0.9533 0.9758 0.7978 0.8549 0.9428 0.7162 ] Network output: [ -0.02282 0.9373 1.041 -6.518e-05 2.494e-05 0.06696 -3.13e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1242 0.06086 0.0802 0.05687 0.972 0.9794 0.127 0.9325 0.9612 0.1261 ] Network output: [ 0.1194 -0.2816 1.154 -0.002475 0.001104 0.8791 -0.001835 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7995 0.4334 0.4129 0.46 0.9591 0.9795 0.8031 0.8704 0.9519 0.7146 ] Network output: [ -0.07743 0.2085 0.9025 0.001768 -0.0007963 1.051 0.001344 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7734 0.6949 0.3803 0.1887 0.9765 0.9839 0.774 0.9443 0.9662 0.4227 ] Network output: [ -0.1096 0.3814 0.7281 -0.000786 0.0003521 1.106 -0.0005891 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8111 0.7943 0.4271 0.04447 0.9742 0.9819 0.8112 0.9394 0.9615 0.4374 ] Network output: [ 0.1096 0.6317 0.2395 0.0008691 -0.0003838 0.9131 0.0006286 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09648 Epoch 614 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01542 1.061 0.9652 0.0002006 -9.232e-05 -0.05606 0.0001605 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06297 -0.004914 0.02928 0.02037 0.9093 0.9232 0.1154 0.8274 0.8662 0.2127 ] Network output: [ 0.9646 0.06694 -0.03064 0.0005763 -0.0002481 0.03688 0.0003908 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7078 -0.0308 -0.03508 0.274 0.9533 0.9758 0.7974 0.855 0.9428 0.7165 ] Network output: [ -0.02272 0.9373 1.041 -6.52e-05 2.501e-05 0.0668 -3.159e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1239 0.06053 0.07994 0.0566 0.972 0.9794 0.1267 0.9325 0.9612 0.1257 ] Network output: [ 0.1198 -0.2819 1.154 -0.002463 0.001098 0.8788 -0.001826 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.799 0.4316 0.4127 0.4597 0.9591 0.9795 0.8027 0.8704 0.9519 0.7149 ] Network output: [ -0.07791 0.2081 0.9031 0.001751 -0.0007889 1.052 0.001331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7732 0.6943 0.3805 0.1879 0.9765 0.9839 0.7738 0.9443 0.9662 0.423 ] Network output: [ -0.1099 0.3828 0.7271 -0.0008219 0.0003682 1.107 -0.0006162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8109 0.7941 0.4273 0.04265 0.9742 0.9819 0.8111 0.9394 0.9615 0.4376 ] Network output: [ 0.1103 0.6301 0.2405 0.0009007 -0.000398 0.9124 0.0006527 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09706 Epoch 615 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01528 1.061 0.9654 0.0001963 -9.035e-05 -0.05575 0.0001572 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06285 -0.004949 0.02926 0.0203 0.9093 0.9232 0.1152 0.8275 0.8663 0.2123 ] Network output: [ 0.9643 0.06787 -0.03117 0.0005863 -0.0002528 0.03706 0.000399 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7074 -0.03162 -0.03525 0.2738 0.9533 0.9758 0.7969 0.855 0.9428 0.7167 ] Network output: [ -0.02262 0.9374 1.041 -6.515e-05 2.505e-05 0.06663 -3.181e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1236 0.06019 0.07969 0.05633 0.972 0.9794 0.1263 0.9326 0.9613 0.1254 ] Network output: [ 0.1201 -0.2822 1.154 -0.00245 0.001093 0.8784 -0.001817 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7986 0.4299 0.4124 0.4593 0.9591 0.9795 0.8022 0.8704 0.9519 0.7151 ] Network output: [ -0.07839 0.2078 0.9037 0.001735 -0.0007814 1.052 0.001318 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.773 0.6937 0.3806 0.1871 0.9765 0.9839 0.7737 0.9443 0.9662 0.4232 ] Network output: [ -0.1103 0.3842 0.7261 -0.0008578 0.0003843 1.107 -0.0006433 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8107 0.7938 0.4275 0.04085 0.9742 0.9819 0.8109 0.9395 0.9615 0.4378 ] Network output: [ 0.1111 0.6284 0.2416 0.0009327 -0.0004125 0.9117 0.0006772 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09765 Epoch 616 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01514 1.06 0.9655 0.000192 -8.842e-05 -0.05544 0.0001538 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06272 -0.004984 0.02925 0.02023 0.9093 0.9232 0.115 0.8276 0.8663 0.2119 ] Network output: [ 0.964 0.06882 -0.0317 0.0005959 -0.0002573 0.03726 0.0004068 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7069 -0.03244 -0.0354 0.2736 0.9533 0.9758 0.7964 0.8551 0.9428 0.7169 ] Network output: [ -0.02251 0.9375 1.041 -6.503e-05 2.506e-05 0.06646 -3.199e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1232 0.05986 0.07943 0.05607 0.972 0.9794 0.126 0.9326 0.9613 0.125 ] Network output: [ 0.1205 -0.2825 1.154 -0.002437 0.001087 0.878 -0.001808 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7981 0.4281 0.4121 0.4589 0.9591 0.9795 0.8017 0.8705 0.9519 0.7154 ] Network output: [ -0.07886 0.2075 0.9044 0.001718 -0.0007741 1.053 0.001306 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7728 0.6931 0.3808 0.1863 0.9765 0.9839 0.7735 0.9443 0.9662 0.4235 ] Network output: [ -0.1107 0.3855 0.7251 -0.0008936 0.0004004 1.107 -0.0006703 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8106 0.7935 0.4277 0.03904 0.9742 0.9819 0.8107 0.9395 0.9615 0.4381 ] Network output: [ 0.1118 0.6267 0.2427 0.0009652 -0.0004271 0.911 0.000702 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09824 Epoch 617 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.015 1.06 0.9656 0.0001879 -8.654e-05 -0.05513 0.0001506 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0626 -0.005018 0.02923 0.02016 0.9093 0.9232 0.1148 0.8276 0.8664 0.2116 ] Network output: [ 0.9637 0.06979 -0.03223 0.000605 -0.0002615 0.03746 0.0004142 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7065 -0.03326 -0.03554 0.2735 0.9533 0.9758 0.7959 0.8552 0.9428 0.7172 ] Network output: [ -0.02241 0.9376 1.041 -6.486e-05 2.505e-05 0.0663 -3.211e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1229 0.05952 0.07919 0.05581 0.972 0.9794 0.1256 0.9326 0.9613 0.1246 ] Network output: [ 0.1208 -0.2828 1.154 -0.002425 0.001082 0.8777 -0.001799 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7976 0.4263 0.4119 0.4586 0.9591 0.9795 0.8012 0.8705 0.9519 0.7157 ] Network output: [ -0.07934 0.2071 0.905 0.001702 -0.0007667 1.053 0.001294 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7726 0.6925 0.381 0.1856 0.9766 0.9839 0.7732 0.9443 0.9662 0.4238 ] Network output: [ -0.111 0.3869 0.7241 -0.0009293 0.0004164 1.107 -0.0006973 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8104 0.7932 0.4279 0.03725 0.9742 0.9819 0.8105 0.9396 0.9616 0.4383 ] Network output: [ 0.1126 0.6249 0.2438 0.0009981 -0.000442 0.9103 0.0007271 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09883 Epoch 618 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01487 1.06 0.9656 0.0001839 -8.47e-05 -0.05482 0.0001474 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06247 -0.005053 0.02923 0.02009 0.9093 0.9233 0.1145 0.8277 0.8664 0.2112 ] Network output: [ 0.9634 0.07077 -0.03275 0.0006136 -0.0002655 0.03767 0.0004213 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7061 -0.03408 -0.03567 0.2733 0.9533 0.9758 0.7955 0.8552 0.9429 0.7175 ] Network output: [ -0.02231 0.9377 1.041 -6.462e-05 2.5e-05 0.06613 -3.218e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1225 0.05918 0.07895 0.05555 0.972 0.9794 0.1252 0.9327 0.9613 0.1243 ] Network output: [ 0.1212 -0.283 1.154 -0.002413 0.001076 0.8773 -0.00179 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.797 0.4246 0.4117 0.4582 0.9591 0.9795 0.8007 0.8705 0.9519 0.716 ] Network output: [ -0.07982 0.2068 0.9057 0.001686 -0.0007594 1.054 0.001281 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7724 0.6919 0.3812 0.1848 0.9766 0.9839 0.773 0.9443 0.9662 0.4241 ] Network output: [ -0.1114 0.3882 0.7232 -0.0009649 0.0004324 1.107 -0.0007242 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8102 0.793 0.4281 0.03547 0.9742 0.982 0.8103 0.9396 0.9616 0.4386 ] Network output: [ 0.1133 0.6232 0.2449 0.001031 -0.000457 0.9095 0.0007525 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09942 Epoch 619 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01475 1.06 0.9657 0.0001799 -8.289e-05 -0.05451 0.0001443 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06234 -0.005087 0.02922 0.02001 0.9094 0.9233 0.1143 0.8278 0.8664 0.2108 ] Network output: [ 0.9631 0.07177 -0.03328 0.0006218 -0.0002693 0.03789 0.0004281 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7056 -0.03491 -0.03578 0.2731 0.9533 0.9758 0.795 0.8553 0.9429 0.7177 ] Network output: [ -0.0222 0.9377 1.04 -6.433e-05 2.493e-05 0.06596 -3.221e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1222 0.05884 0.07871 0.0553 0.972 0.9794 0.1249 0.9327 0.9613 0.1239 ] Network output: [ 0.1215 -0.2833 1.154 -0.002401 0.001071 0.8769 -0.001781 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7965 0.4227 0.4115 0.4579 0.9591 0.9795 0.8001 0.8705 0.9519 0.7162 ] Network output: [ -0.0803 0.2064 0.9063 0.00167 -0.0007522 1.055 0.001269 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7722 0.6913 0.3814 0.1841 0.9766 0.9839 0.7728 0.9444 0.9662 0.4244 ] Network output: [ -0.1117 0.3895 0.7222 -0.001 0.0004484 1.108 -0.0007509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.81 0.7927 0.4284 0.03369 0.9742 0.982 0.8102 0.9397 0.9616 0.4389 ] Network output: [ 0.1141 0.6215 0.246 0.001065 -0.0004722 0.9088 0.0007781 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1 Epoch 620 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01462 1.06 0.9657 0.000176 -8.111e-05 -0.0542 0.0001413 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06222 -0.005121 0.02921 0.01994 0.9094 0.9233 0.1141 0.8278 0.8665 0.2104 ] Network output: [ 0.9627 0.07278 -0.03379 0.0006295 -0.0002729 0.03812 0.0004344 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7052 -0.03574 -0.03588 0.2729 0.9533 0.9758 0.7944 0.8553 0.9429 0.718 ] Network output: [ -0.0221 0.9378 1.04 -6.398e-05 2.484e-05 0.06578 -3.22e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1218 0.0585 0.07848 0.05505 0.972 0.9794 0.1245 0.9327 0.9613 0.1236 ] Network output: [ 0.1219 -0.2836 1.154 -0.002389 0.001066 0.8765 -0.001773 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.796 0.4209 0.4113 0.4576 0.9591 0.9795 0.7996 0.8706 0.9519 0.7165 ] Network output: [ -0.08077 0.206 0.907 0.001654 -0.000745 1.055 0.001257 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.772 0.6906 0.3816 0.1834 0.9766 0.9839 0.7726 0.9444 0.9662 0.4248 ] Network output: [ -0.1121 0.3908 0.7213 -0.001036 0.0004642 1.108 -0.0007776 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8098 0.7924 0.4287 0.03194 0.9742 0.982 0.81 0.9397 0.9616 0.4391 ] Network output: [ 0.1148 0.6197 0.2471 0.001099 -0.0004875 0.908 0.0008041 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1006 Epoch 621 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01451 1.06 0.9658 0.0001722 -7.937e-05 -0.0539 0.0001383 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06209 -0.005155 0.02921 0.01987 0.9094 0.9233 0.1138 0.8279 0.8665 0.2101 ] Network output: [ 0.9624 0.07381 -0.03431 0.0006367 -0.0002763 0.03837 0.0004404 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7047 -0.03656 -0.03596 0.2728 0.9533 0.9758 0.7939 0.8554 0.9429 0.7182 ] Network output: [ -0.02199 0.9379 1.04 -6.36e-05 2.472e-05 0.06561 -3.215e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1214 0.05816 0.07826 0.05481 0.972 0.9794 0.1241 0.9327 0.9613 0.1232 ] Network output: [ 0.1223 -0.2838 1.153 -0.002377 0.00106 0.8761 -0.001764 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7955 0.4191 0.4111 0.4572 0.9591 0.9795 0.7991 0.8706 0.9519 0.7168 ] Network output: [ -0.08125 0.2056 0.9077 0.001638 -0.0007379 1.056 0.001245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7717 0.69 0.3819 0.1827 0.9766 0.9839 0.7724 0.9444 0.9662 0.4251 ] Network output: [ -0.1124 0.3921 0.7203 -0.001071 0.00048 1.108 -0.0008041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8096 0.7921 0.4289 0.03019 0.9743 0.982 0.8098 0.9397 0.9616 0.4395 ] Network output: [ 0.1156 0.618 0.2483 0.001133 -0.000503 0.9072 0.0008302 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1012 Epoch 622 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01439 1.06 0.9658 0.0001684 -7.764e-05 -0.05359 0.0001353 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06196 -0.005189 0.02921 0.01981 0.9094 0.9233 0.1136 0.828 0.8666 0.2097 ] Network output: [ 0.962 0.07484 -0.03482 0.0006435 -0.0002795 0.03862 0.0004461 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7042 -0.03739 -0.03603 0.2726 0.9533 0.9758 0.7934 0.8554 0.9429 0.7185 ] Network output: [ -0.02188 0.938 1.04 -6.317e-05 2.459e-05 0.06543 -3.207e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1211 0.05782 0.07804 0.05456 0.972 0.9794 0.1238 0.9328 0.9613 0.1229 ] Network output: [ 0.1226 -0.284 1.153 -0.002365 0.001055 0.8757 -0.001756 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7949 0.4172 0.411 0.4569 0.9591 0.9795 0.7985 0.8706 0.9519 0.7171 ] Network output: [ -0.08173 0.2052 0.9084 0.001622 -0.0007308 1.056 0.001233 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7715 0.6893 0.3822 0.182 0.9766 0.9839 0.7722 0.9444 0.9662 0.4255 ] Network output: [ -0.1128 0.3933 0.7194 -0.001106 0.0004957 1.108 -0.0008304 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8094 0.7918 0.4292 0.02847 0.9743 0.982 0.8096 0.9398 0.9616 0.4398 ] Network output: [ 0.1164 0.6163 0.2494 0.001168 -0.0005186 0.9064 0.0008566 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1018 Epoch 623 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01428 1.06 0.9658 0.0001647 -7.594e-05 -0.05329 0.0001324 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06183 -0.005223 0.02921 0.01974 0.9094 0.9233 0.1134 0.828 0.8666 0.2094 ] Network output: [ 0.9616 0.0759 -0.03533 0.0006499 -0.0002825 0.03889 0.0004515 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7037 -0.03822 -0.03608 0.2725 0.9533 0.9758 0.7929 0.8555 0.943 0.7188 ] Network output: [ -0.02177 0.9382 1.04 -6.271e-05 2.444e-05 0.06525 -3.195e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1207 0.05747 0.07782 0.05433 0.972 0.9794 0.1234 0.9328 0.9614 0.1226 ] Network output: [ 0.123 -0.2843 1.153 -0.002354 0.00105 0.8753 -0.001747 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7944 0.4154 0.4108 0.4566 0.9591 0.9795 0.798 0.8706 0.952 0.7174 ] Network output: [ -0.0822 0.2047 0.9091 0.001607 -0.0007238 1.057 0.001221 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7713 0.6887 0.3824 0.1813 0.9766 0.9839 0.7719 0.9444 0.9662 0.4259 ] Network output: [ -0.1131 0.3946 0.7185 -0.00114 0.0005113 1.108 -0.0008566 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8092 0.7914 0.4296 0.02677 0.9743 0.982 0.8093 0.9398 0.9616 0.4401 ] Network output: [ 0.1171 0.6145 0.2505 0.001203 -0.0005343 0.9056 0.0008831 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1024 Epoch 624 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01418 1.06 0.9657 0.000161 -7.426e-05 -0.05298 0.0001295 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0617 -0.005256 0.02921 0.01967 0.9094 0.9234 0.1131 0.8281 0.8667 0.209 ] Network output: [ 0.9612 0.07696 -0.03584 0.0006558 -0.0002853 0.03917 0.0004565 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7033 -0.03905 -0.03612 0.2723 0.9533 0.9758 0.7923 0.8555 0.943 0.719 ] Network output: [ -0.02166 0.9383 1.04 -6.221e-05 2.427e-05 0.06507 -3.181e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1203 0.05712 0.07761 0.05409 0.972 0.9794 0.123 0.9328 0.9614 0.1222 ] Network output: [ 0.1233 -0.2845 1.153 -0.002342 0.001045 0.8749 -0.001739 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7938 0.4135 0.4107 0.4563 0.9591 0.9795 0.7974 0.8707 0.952 0.7177 ] Network output: [ -0.08267 0.2043 0.9098 0.001591 -0.0007169 1.058 0.00121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.771 0.688 0.3827 0.1806 0.9766 0.9839 0.7717 0.9444 0.9662 0.4263 ] Network output: [ -0.1134 0.3958 0.7176 -0.001175 0.0005267 1.109 -0.0008826 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.809 0.7911 0.4299 0.02509 0.9743 0.982 0.8091 0.9399 0.9617 0.4405 ] Network output: [ 0.1179 0.6128 0.2517 0.001238 -0.0005501 0.9047 0.0009098 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.103 Epoch 625 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01407 1.059 0.9657 0.0001574 -7.259e-05 -0.05268 0.0001266 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06156 -0.005289 0.02922 0.0196 0.9094 0.9234 0.1129 0.8282 0.8667 0.2086 ] Network output: [ 0.9608 0.07804 -0.03634 0.0006614 -0.0002879 0.03946 0.0004612 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7028 -0.03988 -0.03615 0.2722 0.9533 0.9758 0.7918 0.8556 0.943 0.7193 ] Network output: [ -0.02155 0.9384 1.04 -6.17e-05 2.41e-05 0.06489 -3.165e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.12 0.05677 0.0774 0.05387 0.972 0.9795 0.1226 0.9328 0.9614 0.1219 ] Network output: [ 0.1237 -0.2847 1.153 -0.002331 0.00104 0.8745 -0.001731 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7932 0.4116 0.4106 0.456 0.9591 0.9795 0.7968 0.8707 0.952 0.718 ] Network output: [ -0.08314 0.2038 0.9105 0.001576 -0.0007101 1.058 0.001198 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7708 0.6873 0.3831 0.18 0.9766 0.9839 0.7715 0.9444 0.9662 0.4267 ] Network output: [ -0.1138 0.3969 0.7167 -0.001209 0.0005421 1.109 -0.0009084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8087 0.7908 0.4302 0.02344 0.9743 0.982 0.8089 0.9399 0.9617 0.4408 ] Network output: [ 0.1187 0.611 0.2529 0.001273 -0.0005659 0.9039 0.0009366 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1036 Epoch 626 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01398 1.059 0.9657 0.0001537 -7.093e-05 -0.05238 0.0001237 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06143 -0.005322 0.02922 0.01954 0.9095 0.9234 0.1126 0.8282 0.8667 0.2083 ] Network output: [ 0.9603 0.07913 -0.03684 0.0006665 -0.0002903 0.03976 0.0004656 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7023 -0.0407 -0.03616 0.2721 0.9533 0.9758 0.7912 0.8556 0.943 0.7196 ] Network output: [ -0.02144 0.9385 1.039 -6.117e-05 2.391e-05 0.0647 -3.148e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1196 0.05642 0.0772 0.05364 0.972 0.9795 0.1222 0.9328 0.9614 0.1216 ] Network output: [ 0.1241 -0.2849 1.153 -0.00232 0.001035 0.8741 -0.001723 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7926 0.4097 0.4105 0.4557 0.9591 0.9795 0.7962 0.8707 0.952 0.7183 ] Network output: [ -0.0836 0.2033 0.9112 0.001561 -0.0007033 1.059 0.001187 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7706 0.6866 0.3834 0.1794 0.9766 0.9839 0.7712 0.9445 0.9662 0.4271 ] Network output: [ -0.1141 0.3981 0.7159 -0.001243 0.0005573 1.109 -0.000934 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8085 0.7904 0.4306 0.02182 0.9743 0.982 0.8087 0.94 0.9617 0.4412 ] Network output: [ 0.1195 0.6093 0.254 0.001308 -0.0005819 0.903 0.0009635 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1041 Epoch 627 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01388 1.059 0.9656 0.0001501 -6.927e-05 -0.05208 0.0001209 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0613 -0.005355 0.02923 0.01947 0.9095 0.9234 0.1124 0.8283 0.8668 0.2079 ] Network output: [ 0.9599 0.08023 -0.03733 0.0006713 -0.0002926 0.04007 0.0004697 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7018 -0.04153 -0.03616 0.2719 0.9533 0.9758 0.7907 0.8557 0.943 0.7199 ] Network output: [ -0.02132 0.9387 1.039 -6.062e-05 2.372e-05 0.06451 -3.129e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1192 0.05607 0.077 0.05342 0.972 0.9795 0.1218 0.9329 0.9614 0.1213 ] Network output: [ 0.1244 -0.2851 1.153 -0.002309 0.00103 0.8737 -0.001715 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.792 0.4078 0.4104 0.4555 0.9591 0.9795 0.7956 0.8707 0.952 0.7186 ] Network output: [ -0.08406 0.2029 0.9119 0.001546 -0.0006966 1.06 0.001175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7703 0.6859 0.3837 0.1787 0.9766 0.9839 0.771 0.9445 0.9662 0.4275 ] Network output: [ -0.1144 0.3992 0.7151 -0.001276 0.0005724 1.109 -0.0009593 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8083 0.7901 0.4309 0.02022 0.9743 0.982 0.8084 0.94 0.9617 0.4415 ] Network output: [ 0.1203 0.6076 0.2552 0.001344 -0.0005978 0.9022 0.0009904 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1047 Epoch 628 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0138 1.059 0.9655 0.0001465 -6.762e-05 -0.05178 0.0001181 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06116 -0.005387 0.02924 0.01941 0.9095 0.9234 0.1121 0.8284 0.8668 0.2076 ] Network output: [ 0.9594 0.08134 -0.03782 0.0006756 -0.0002946 0.0404 0.0004735 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7013 -0.04235 -0.03614 0.2718 0.9533 0.9758 0.7901 0.8557 0.943 0.7201 ] Network output: [ -0.02121 0.9388 1.039 -6.008e-05 2.353e-05 0.06432 -3.109e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1188 0.05571 0.07681 0.0532 0.972 0.9795 0.1215 0.9329 0.9614 0.121 ] Network output: [ 0.1248 -0.2853 1.153 -0.002298 0.001026 0.8733 -0.001707 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7914 0.4059 0.4104 0.4552 0.9591 0.9795 0.795 0.8707 0.952 0.7189 ] Network output: [ -0.08451 0.2024 0.9126 0.001532 -0.00069 1.06 0.001164 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.77 0.6852 0.3841 0.1782 0.9766 0.9839 0.7707 0.9445 0.9662 0.428 ] Network output: [ -0.1147 0.4002 0.7143 -0.00131 0.0005873 1.11 -0.0009844 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.808 0.7897 0.4313 0.01866 0.9743 0.982 0.8082 0.94 0.9617 0.4419 ] Network output: [ 0.1211 0.6059 0.2564 0.001379 -0.0006138 0.9013 0.001017 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1053 Epoch 629 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01371 1.059 0.9654 0.0001429 -6.597e-05 -0.05148 0.0001152 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06102 -0.005419 0.02925 0.01935 0.9095 0.9234 0.1119 0.8284 0.8669 0.2072 ] Network output: [ 0.9589 0.08246 -0.0383 0.0006797 -0.0002966 0.04074 0.000477 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7007 -0.04317 -0.03611 0.2717 0.9533 0.9758 0.7895 0.8558 0.9431 0.7204 ] Network output: [ -0.02109 0.939 1.039 -5.953e-05 2.334e-05 0.06413 -3.09e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1184 0.05536 0.07662 0.05299 0.972 0.9795 0.121 0.9329 0.9614 0.1207 ] Network output: [ 0.1252 -0.2855 1.153 -0.002287 0.001021 0.8729 -0.001699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7908 0.404 0.4103 0.455 0.9591 0.9795 0.7944 0.8708 0.952 0.7192 ] Network output: [ -0.08496 0.2018 0.9133 0.001517 -0.0006835 1.061 0.001153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7698 0.6845 0.3844 0.1776 0.9766 0.9839 0.7704 0.9445 0.9662 0.4284 ] Network output: [ -0.1151 0.4013 0.7135 -0.001342 0.0006021 1.11 -0.001009 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8078 0.7894 0.4316 0.01713 0.9743 0.9821 0.8079 0.9401 0.9617 0.4423 ] Network output: [ 0.1218 0.6042 0.2575 0.001414 -0.0006297 0.9004 0.001044 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1059 Epoch 630 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01363 1.059 0.9653 0.0001392 -6.431e-05 -0.05118 0.0001124 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06089 -0.00545 0.02927 0.01929 0.9095 0.9235 0.1116 0.8285 0.8669 0.2069 ] Network output: [ 0.9585 0.08359 -0.03878 0.0006833 -0.0002983 0.04109 0.0004803 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.7002 -0.04399 -0.03607 0.2716 0.9533 0.9758 0.7889 0.8558 0.9431 0.7207 ] Network output: [ -0.02097 0.9391 1.039 -5.9e-05 2.315e-05 0.06393 -3.07e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.118 0.055 0.07643 0.05278 0.972 0.9795 0.1206 0.9329 0.9614 0.1203 ] Network output: [ 0.1255 -0.2857 1.153 -0.002276 0.001016 0.8725 -0.001691 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7902 0.402 0.4103 0.4548 0.9591 0.9795 0.7938 0.8708 0.952 0.7195 ] Network output: [ -0.08541 0.2013 0.914 0.001503 -0.0006771 1.062 0.001142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7695 0.6838 0.3848 0.177 0.9766 0.9839 0.7702 0.9445 0.9663 0.4289 ] Network output: [ -0.1154 0.4023 0.7128 -0.001375 0.0006167 1.11 -0.001034 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8075 0.789 0.432 0.01564 0.9743 0.9821 0.8076 0.9401 0.9618 0.4427 ] Network output: [ 0.1226 0.6025 0.2587 0.00145 -0.0006456 0.8994 0.001071 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1064 Epoch 631 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01355 1.059 0.9651 0.0001356 -6.263e-05 -0.05088 0.0001095 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06075 -0.005481 0.02928 0.01923 0.9095 0.9235 0.1114 0.8285 0.8669 0.2065 ] Network output: [ 0.9579 0.08472 -0.03925 0.0006867 -0.0003 0.04145 0.0004833 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6997 -0.04481 -0.03601 0.2715 0.9533 0.9758 0.7883 0.8559 0.9431 0.721 ] Network output: [ -0.02085 0.9393 1.038 -5.848e-05 2.297e-05 0.06373 -3.052e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1176 0.05464 0.07624 0.05258 0.972 0.9795 0.1202 0.933 0.9614 0.12 ] Network output: [ 0.1259 -0.2859 1.153 -0.002265 0.001011 0.8721 -0.001683 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7896 0.4001 0.4103 0.4546 0.9591 0.9795 0.7932 0.8708 0.952 0.7199 ] Network output: [ -0.08585 0.2008 0.9147 0.001489 -0.0006708 1.062 0.001132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7692 0.683 0.3852 0.1765 0.9766 0.984 0.7699 0.9445 0.9663 0.4293 ] Network output: [ -0.1157 0.4032 0.7121 -0.001407 0.0006311 1.11 -0.001058 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8072 0.7886 0.4324 0.01419 0.9744 0.9821 0.8073 0.9402 0.9618 0.4431 ] Network output: [ 0.1234 0.6009 0.2598 0.001485 -0.0006615 0.8985 0.001098 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.107 Epoch 632 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01348 1.059 0.965 0.0001319 -6.095e-05 -0.05059 0.0001066 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06061 -0.005512 0.0293 0.01917 0.9096 0.9235 0.1111 0.8286 0.867 0.2062 ] Network output: [ 0.9574 0.08587 -0.03972 0.0006897 -0.0003015 0.04182 0.0004861 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6991 -0.04562 -0.03595 0.2714 0.9533 0.9758 0.7877 0.8559 0.9431 0.7212 ] Network output: [ -0.02073 0.9395 1.038 -5.799e-05 2.28e-05 0.06353 -3.036e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1172 0.05428 0.07606 0.05238 0.972 0.9795 0.1198 0.933 0.9615 0.1197 ] Network output: [ 0.1263 -0.286 1.153 -0.002254 0.001006 0.8716 -0.001675 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7889 0.3981 0.4103 0.4544 0.959 0.9795 0.7925 0.8708 0.952 0.7202 ] Network output: [ -0.08628 0.2002 0.9154 0.001475 -0.0006647 1.063 0.001121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7689 0.6823 0.3855 0.176 0.9766 0.984 0.7696 0.9445 0.9663 0.4298 ] Network output: [ -0.116 0.4041 0.7114 -0.001439 0.0006453 1.111 -0.001082 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8069 0.7882 0.4328 0.01277 0.9744 0.9821 0.8071 0.9402 0.9618 0.4435 ] Network output: [ 0.1242 0.5992 0.261 0.00152 -0.0006773 0.8975 0.001125 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1075 Epoch 633 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01341 1.059 0.9648 0.0001281 -5.924e-05 -0.05029 0.0001037 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06047 -0.005542 0.02931 0.01912 0.9096 0.9235 0.1108 0.8287 0.867 0.2059 ] Network output: [ 0.9569 0.08701 -0.04018 0.0006925 -0.0003028 0.04221 0.0004886 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6986 -0.04643 -0.03587 0.2713 0.9533 0.9758 0.7871 0.856 0.9431 0.7215 ] Network output: [ -0.02061 0.9397 1.038 -5.754e-05 2.264e-05 0.06333 -3.021e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1168 0.05392 0.07588 0.05219 0.972 0.9795 0.1194 0.933 0.9615 0.1194 ] Network output: [ 0.1266 -0.2862 1.153 -0.002243 0.001001 0.8712 -0.001667 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7883 0.3961 0.4102 0.4542 0.959 0.9795 0.7919 0.8708 0.952 0.7205 ] Network output: [ -0.08671 0.1997 0.9161 0.001462 -0.0006586 1.064 0.001111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7686 0.6815 0.3859 0.1756 0.9766 0.984 0.7693 0.9445 0.9663 0.4303 ] Network output: [ -0.1163 0.405 0.7107 -0.00147 0.0006593 1.111 -0.001105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8066 0.7878 0.4332 0.0114 0.9744 0.9821 0.8068 0.9402 0.9618 0.4439 ] Network output: [ 0.125 0.5976 0.2621 0.001555 -0.000693 0.8966 0.001151 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1081 Epoch 634 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01335 1.059 0.9646 0.0001243 -5.751e-05 -0.04999 0.0001007 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06032 -0.005571 0.02933 0.01906 0.9096 0.9235 0.1106 0.8287 0.8671 0.2055 ] Network output: [ 0.9564 0.08817 -0.04064 0.000695 -0.000304 0.04261 0.0004909 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.698 -0.04724 -0.03578 0.2712 0.9533 0.9758 0.7864 0.856 0.9432 0.7218 ] Network output: [ -0.02049 0.9399 1.038 -5.712e-05 2.25e-05 0.06312 -3.01e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1164 0.05356 0.0757 0.052 0.9721 0.9795 0.119 0.933 0.9615 0.1192 ] Network output: [ 0.127 -0.2864 1.152 -0.002232 0.0009963 0.8708 -0.001659 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7876 0.3942 0.4103 0.4541 0.959 0.9795 0.7912 0.8708 0.952 0.7208 ] Network output: [ -0.08712 0.1991 0.9168 0.001449 -0.0006526 1.064 0.001101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7683 0.6808 0.3863 0.1751 0.9766 0.984 0.769 0.9445 0.9663 0.4307 ] Network output: [ -0.1167 0.4058 0.7101 -0.001501 0.0006731 1.111 -0.001129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8063 0.7874 0.4336 0.01007 0.9744 0.9821 0.8064 0.9403 0.9618 0.4443 ] Network output: [ 0.1258 0.596 0.2633 0.001589 -0.0007086 0.8956 0.001178 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1086 Epoch 635 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01329 1.059 0.9644 0.0001205 -5.575e-05 -0.0497 9.768e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06018 -0.0056 0.02935 0.01901 0.9096 0.9235 0.1103 0.8288 0.8671 0.2052 ] Network output: [ 0.9558 0.08933 -0.04109 0.0006972 -0.0003052 0.04302 0.0004931 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6975 -0.04804 -0.03568 0.2712 0.9533 0.9758 0.7858 0.8561 0.9432 0.7221 ] Network output: [ -0.02036 0.9401 1.037 -5.675e-05 2.238e-05 0.06291 -3.001e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.116 0.0532 0.07553 0.05181 0.9721 0.9795 0.1185 0.933 0.9615 0.1189 ] Network output: [ 0.1274 -0.2865 1.152 -0.002221 0.0009914 0.8704 -0.001651 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.787 0.3922 0.4103 0.4539 0.959 0.9795 0.7905 0.8709 0.952 0.7211 ] Network output: [ -0.08753 0.1985 0.9175 0.001436 -0.0006468 1.065 0.001091 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.768 0.68 0.3867 0.1747 0.9766 0.984 0.7687 0.9446 0.9663 0.4312 ] Network output: [ -0.117 0.4066 0.7095 -0.001531 0.0006867 1.112 -0.001151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.806 0.787 0.434 0.008782 0.9744 0.9821 0.8061 0.9403 0.9619 0.4447 ] Network output: [ 0.1266 0.5945 0.2644 0.001624 -0.000724 0.8946 0.001204 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1091 Epoch 636 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01323 1.059 0.9642 0.0001165 -5.397e-05 -0.04941 9.462e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.06003 -0.005629 0.02937 0.01896 0.9096 0.9235 0.11 0.8288 0.8671 0.2048 ] Network output: [ 0.9552 0.09049 -0.04153 0.0006992 -0.0003062 0.04344 0.000495 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6969 -0.04884 -0.03557 0.2711 0.9533 0.9758 0.7852 0.8561 0.9432 0.7223 ] Network output: [ -0.02024 0.9403 1.037 -5.644e-05 2.229e-05 0.0627 -2.997e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1156 0.05284 0.07535 0.05163 0.9721 0.9795 0.1181 0.9331 0.9615 0.1186 ] Network output: [ 0.1277 -0.2867 1.152 -0.002209 0.0009864 0.87 -0.001643 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7863 0.3902 0.4103 0.4538 0.959 0.9795 0.7899 0.8709 0.952 0.7214 ] Network output: [ -0.08794 0.1979 0.9182 0.001423 -0.0006411 1.065 0.001082 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7677 0.6792 0.3871 0.1743 0.9766 0.984 0.7683 0.9446 0.9663 0.4317 ] Network output: [ -0.1173 0.4074 0.709 -0.001561 0.0007001 1.112 -0.001174 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8057 0.7865 0.4344 0.007544 0.9744 0.9821 0.8058 0.9403 0.9619 0.4452 ] Network output: [ 0.1274 0.593 0.2655 0.001657 -0.0007393 0.8936 0.001229 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1097 Epoch 637 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01318 1.059 0.9639 0.0001125 -5.214e-05 -0.04912 9.15e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05989 -0.005657 0.02939 0.01891 0.9096 0.9236 0.1098 0.8289 0.8672 0.2045 ] Network output: [ 0.9547 0.09166 -0.04197 0.000701 -0.0003071 0.04387 0.0004968 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6964 -0.04963 -0.03546 0.2711 0.9533 0.9758 0.7845 0.8562 0.9432 0.7226 ] Network output: [ -0.02011 0.9405 1.037 -5.619e-05 2.222e-05 0.06248 -2.997e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1151 0.05247 0.07518 0.05145 0.9721 0.9795 0.1177 0.9331 0.9615 0.1183 ] Network output: [ 0.1281 -0.2869 1.152 -0.002198 0.0009814 0.8696 -0.001635 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7856 0.3882 0.4103 0.4537 0.959 0.9795 0.7892 0.8709 0.952 0.7217 ] Network output: [ -0.08833 0.1973 0.9189 0.001411 -0.0006356 1.066 0.001072 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7673 0.6784 0.3875 0.1739 0.9766 0.984 0.768 0.9446 0.9663 0.4322 ] Network output: [ -0.1176 0.4081 0.7085 -0.00159 0.0007132 1.112 -0.001196 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8053 0.7861 0.4348 0.006353 0.9744 0.9821 0.8055 0.9404 0.9619 0.4456 ] Network output: [ 0.1281 0.5915 0.2666 0.001691 -0.0007544 0.8925 0.001255 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1102 Epoch 638 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01313 1.059 0.9637 0.0001084 -5.028e-05 -0.04882 8.832e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05974 -0.005684 0.02941 0.01886 0.9097 0.9236 0.1095 0.8289 0.8672 0.2041 ] Network output: [ 0.9541 0.09283 -0.0424 0.0007025 -0.0003079 0.04431 0.0004984 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6958 -0.05042 -0.03533 0.271 0.9534 0.9758 0.7839 0.8562 0.9432 0.7229 ] Network output: [ -0.01999 0.9408 1.037 -5.601e-05 2.219e-05 0.06226 -3.002e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1147 0.05211 0.07501 0.05128 0.9721 0.9795 0.1172 0.9331 0.9615 0.118 ] Network output: [ 0.1285 -0.287 1.152 -0.002186 0.0009763 0.8692 -0.001626 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7849 0.3862 0.4104 0.4536 0.959 0.9795 0.7885 0.8709 0.952 0.722 ] Network output: [ -0.08871 0.1967 0.9196 0.001399 -0.0006301 1.067 0.001063 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.767 0.6776 0.3879 0.1736 0.9766 0.984 0.7676 0.9446 0.9663 0.4326 ] Network output: [ -0.1179 0.4087 0.708 -0.001619 0.0007261 1.112 -0.001218 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.805 0.7856 0.4351 0.005211 0.9744 0.9821 0.8051 0.9404 0.9619 0.446 ] Network output: [ 0.1289 0.59 0.2677 0.001724 -0.0007692 0.8915 0.00128 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1107 Epoch 639 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01308 1.059 0.9634 0.0001042 -4.837e-05 -0.04853 8.506e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05959 -0.005711 0.02943 0.01881 0.9097 0.9236 0.1092 0.829 0.8673 0.2038 ] Network output: [ 0.9535 0.094 -0.04283 0.0007039 -0.0003086 0.04476 0.0004999 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6952 -0.0512 -0.03519 0.271 0.9534 0.9758 0.7832 0.8562 0.9432 0.7231 ] Network output: [ -0.01986 0.941 1.036 -5.591e-05 2.219e-05 0.06204 -3.013e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1143 0.05175 0.07484 0.05111 0.9721 0.9795 0.1168 0.9331 0.9615 0.1177 ] Network output: [ 0.1288 -0.2872 1.152 -0.002175 0.0009712 0.8688 -0.001618 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7843 0.3842 0.4104 0.4536 0.959 0.9795 0.7878 0.8709 0.952 0.7224 ] Network output: [ -0.08909 0.1961 0.9202 0.001387 -0.0006248 1.067 0.001054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7666 0.6768 0.3883 0.1732 0.9766 0.984 0.7673 0.9446 0.9663 0.4331 ] Network output: [ -0.1182 0.4093 0.7076 -0.001647 0.0007388 1.113 -0.001239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8046 0.7851 0.4355 0.004121 0.9744 0.9821 0.8048 0.9405 0.9619 0.4464 ] Network output: [ 0.1297 0.5886 0.2688 0.001756 -0.0007838 0.8904 0.001305 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1112 Epoch 640 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01303 1.059 0.9631 9.994e-05 -4.642e-05 -0.04825 8.172e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05944 -0.005737 0.02946 0.01877 0.9097 0.9236 0.1089 0.8291 0.8673 0.2034 ] Network output: [ 0.9529 0.09517 -0.04325 0.0007051 -0.0003092 0.04523 0.0005012 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6946 -0.05198 -0.03505 0.271 0.9534 0.9758 0.7825 0.8563 0.9433 0.7234 ] Network output: [ -0.01974 0.9413 1.036 -5.59e-05 2.222e-05 0.06182 -3.03e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1138 0.05138 0.07468 0.05095 0.9721 0.9795 0.1163 0.9331 0.9616 0.1174 ] Network output: [ 0.1292 -0.2874 1.152 -0.002163 0.000966 0.8684 -0.001609 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7836 0.3822 0.4105 0.4535 0.959 0.9795 0.7871 0.8709 0.952 0.7227 ] Network output: [ -0.08945 0.1955 0.9209 0.001376 -0.0006197 1.068 0.001045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7662 0.676 0.3887 0.173 0.9766 0.984 0.7669 0.9446 0.9663 0.4336 ] Network output: [ -0.1185 0.4099 0.7072 -0.001674 0.0007512 1.113 -0.00126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8042 0.7847 0.4359 0.003082 0.9744 0.9822 0.8044 0.9405 0.9619 0.4468 ] Network output: [ 0.1304 0.5872 0.2699 0.001788 -0.0007982 0.8893 0.001329 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1116 Epoch 641 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01299 1.06 0.9628 9.552e-05 -4.442e-05 -0.04796 7.83e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05929 -0.005763 0.02948 0.01873 0.9097 0.9236 0.1087 0.8291 0.8673 0.2031 ] Network output: [ 0.9522 0.09635 -0.04366 0.0007061 -0.0003098 0.0457 0.0005024 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.694 -0.05274 -0.0349 0.271 0.9534 0.9758 0.7818 0.8563 0.9433 0.7237 ] Network output: [ -0.01961 0.9415 1.036 -5.598e-05 2.23e-05 0.06159 -3.054e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1134 0.05102 0.07451 0.05079 0.9721 0.9795 0.1159 0.9331 0.9616 0.1171 ] Network output: [ 0.1296 -0.2875 1.152 -0.002151 0.0009606 0.868 -0.0016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7828 0.3802 0.4105 0.4535 0.959 0.9795 0.7864 0.8709 0.952 0.723 ] Network output: [ -0.0898 0.1949 0.9215 0.001365 -0.0006147 1.069 0.001037 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7659 0.6751 0.389 0.1727 0.9766 0.984 0.7665 0.9446 0.9663 0.434 ] Network output: [ -0.1188 0.4104 0.7068 -0.001701 0.0007633 1.113 -0.00128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8039 0.7842 0.4363 0.002096 0.9745 0.9822 0.804 0.9405 0.962 0.4472 ] Network output: [ 0.1312 0.5859 0.2709 0.001819 -0.0008123 0.8883 0.001353 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1121 Epoch 642 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01296 1.06 0.9625 9.099e-05 -4.236e-05 -0.04767 7.479e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05914 -0.005788 0.0295 0.01869 0.9097 0.9236 0.1084 0.8292 0.8674 0.2028 ] Network output: [ 0.9516 0.09752 -0.04406 0.000707 -0.0003103 0.04618 0.0005035 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6934 -0.05351 -0.03475 0.271 0.9534 0.9759 0.7811 0.8563 0.9433 0.7239 ] Network output: [ -0.01948 0.9418 1.036 -5.617e-05 2.243e-05 0.06136 -3.085e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1129 0.05065 0.07435 0.05064 0.9721 0.9795 0.1154 0.9331 0.9616 0.1168 ] Network output: [ 0.13 -0.2877 1.151 -0.002139 0.0009552 0.8676 -0.001591 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7821 0.3782 0.4106 0.4535 0.959 0.9795 0.7857 0.8709 0.952 0.7233 ] Network output: [ -0.09014 0.1943 0.9222 0.001354 -0.0006099 1.069 0.001029 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7655 0.6743 0.3894 0.1725 0.9766 0.984 0.7661 0.9446 0.9663 0.4345 ] Network output: [ -0.1191 0.4108 0.7065 -0.001728 0.0007751 1.114 -0.0013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8035 0.7837 0.4367 0.001164 0.9745 0.9822 0.8036 0.9405 0.962 0.4476 ] Network output: [ 0.132 0.5846 0.2719 0.00185 -0.0008261 0.8871 0.001376 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1125 Epoch 643 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01292 1.06 0.9622 8.633e-05 -4.024e-05 -0.04739 7.119e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05899 -0.005812 0.02952 0.01865 0.9097 0.9237 0.1081 0.8292 0.8674 0.2024 ] Network output: [ 0.951 0.09869 -0.04446 0.0007078 -0.0003107 0.04668 0.0005045 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6928 -0.05426 -0.03459 0.271 0.9534 0.9759 0.7805 0.8564 0.9433 0.7242 ] Network output: [ -0.01935 0.9421 1.035 -5.646e-05 2.26e-05 0.06113 -3.125e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1125 0.05029 0.07418 0.05049 0.9721 0.9795 0.115 0.9332 0.9616 0.1166 ] Network output: [ 0.1303 -0.2879 1.151 -0.002126 0.0009497 0.8672 -0.001582 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7814 0.3762 0.4106 0.4536 0.959 0.9795 0.7849 0.8709 0.952 0.7236 ] Network output: [ -0.09047 0.1936 0.9228 0.001343 -0.0006052 1.07 0.001021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7651 0.6734 0.3898 0.1722 0.9766 0.984 0.7657 0.9446 0.9663 0.4349 ] Network output: [ -0.1194 0.4112 0.7062 -0.001753 0.0007867 1.114 -0.001319 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8031 0.7832 0.4371 0.0002877 0.9745 0.9822 0.8032 0.9406 0.962 0.448 ] Network output: [ 0.1327 0.5833 0.2729 0.00188 -0.0008395 0.886 0.001399 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.113 Epoch 644 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01289 1.06 0.9619 8.153e-05 -3.807e-05 -0.0471 6.748e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05884 -0.005835 0.02955 0.01861 0.9098 0.9237 0.1078 0.8293 0.8675 0.2021 ] Network output: [ 0.9504 0.09985 -0.04486 0.0007084 -0.0003111 0.04718 0.0005054 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6922 -0.05501 -0.03443 0.271 0.9534 0.9759 0.7798 0.8564 0.9433 0.7244 ] Network output: [ -0.01923 0.9424 1.035 -5.687e-05 2.283e-05 0.0609 -3.172e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.112 0.04993 0.07402 0.05034 0.9721 0.9796 0.1145 0.9332 0.9616 0.1163 ] Network output: [ 0.1307 -0.288 1.151 -0.002114 0.0009441 0.8668 -0.001573 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7807 0.3742 0.4107 0.4536 0.959 0.9795 0.7842 0.8709 0.9521 0.7239 ] Network output: [ -0.09079 0.193 0.9234 0.001333 -0.0006007 1.071 0.001013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7647 0.6725 0.3902 0.1721 0.9766 0.984 0.7653 0.9446 0.9663 0.4353 ] Network output: [ -0.1197 0.4115 0.706 -0.001779 0.000798 1.115 -0.001338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8027 0.7826 0.4375 -0.0005332 0.9745 0.9822 0.8028 0.9406 0.962 0.4484 ] Network output: [ 0.1334 0.5821 0.2739 0.001909 -0.0008526 0.8849 0.001421 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1134 Epoch 645 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01286 1.06 0.9615 7.658e-05 -3.583e-05 -0.04682 6.367e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05868 -0.005858 0.02957 0.01858 0.9098 0.9237 0.1075 0.8293 0.8675 0.2017 ] Network output: [ 0.9497 0.101 -0.04525 0.000709 -0.0003115 0.04769 0.0005062 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6916 -0.05575 -0.03426 0.2711 0.9534 0.9759 0.779 0.8564 0.9433 0.7247 ] Network output: [ -0.0191 0.9427 1.035 -5.74e-05 2.311e-05 0.06066 -3.229e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1116 0.04957 0.07386 0.0502 0.9721 0.9796 0.114 0.9332 0.9616 0.116 ] Network output: [ 0.1311 -0.2882 1.151 -0.002101 0.0009384 0.8664 -0.001564 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7799 0.3722 0.4108 0.4537 0.959 0.9795 0.7835 0.8709 0.9521 0.7242 ] Network output: [ -0.09109 0.1924 0.924 0.001324 -0.0005963 1.071 0.001006 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7643 0.6717 0.3905 0.1719 0.9766 0.984 0.7649 0.9446 0.9663 0.4358 ] Network output: [ -0.1199 0.4118 0.7058 -0.001803 0.000809 1.115 -0.001357 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8022 0.7821 0.4378 -0.001298 0.9745 0.9822 0.8024 0.9406 0.962 0.4488 ] Network output: [ 0.1342 0.5809 0.2749 0.001937 -0.0008653 0.8837 0.001442 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1138 Epoch 646 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01283 1.06 0.9611 7.149e-05 -3.352e-05 -0.04654 5.974e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05853 -0.00588 0.02959 0.01854 0.9098 0.9237 0.1072 0.8294 0.8675 0.2014 ] Network output: [ 0.9491 0.1022 -0.04563 0.0007095 -0.0003118 0.0482 0.000507 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.691 -0.05648 -0.03409 0.2711 0.9534 0.9759 0.7783 0.8565 0.9434 0.725 ] Network output: [ -0.01897 0.943 1.034 -5.806e-05 2.344e-05 0.06043 -3.295e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1111 0.0492 0.07369 0.05007 0.9721 0.9796 0.1136 0.9332 0.9616 0.1157 ] Network output: [ 0.1315 -0.2884 1.151 -0.002088 0.0009325 0.866 -0.001554 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7792 0.3703 0.4108 0.4538 0.959 0.9795 0.7827 0.8709 0.9521 0.7245 ] Network output: [ -0.09138 0.1917 0.9246 0.001315 -0.0005921 1.072 0.0009988 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7638 0.6708 0.3909 0.1718 0.9766 0.984 0.7645 0.9446 0.9663 0.4362 ] Network output: [ -0.1202 0.4121 0.7056 -0.001827 0.0008196 1.115 -0.001375 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8018 0.7815 0.4382 -0.002005 0.9745 0.9822 0.8019 0.9407 0.9621 0.4491 ] Network output: [ 0.1349 0.5798 0.2758 0.001965 -0.0008777 0.8826 0.001463 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1142 Epoch 647 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01281 1.06 0.9608 6.624e-05 -3.114e-05 -0.04626 5.57e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05837 -0.005902 0.02961 0.01851 0.9098 0.9237 0.1069 0.8294 0.8676 0.2011 ] Network output: [ 0.9484 0.1033 -0.046 0.0007099 -0.000312 0.04873 0.0005076 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6903 -0.05721 -0.03392 0.2712 0.9534 0.9759 0.7776 0.8565 0.9434 0.7252 ] Network output: [ -0.01884 0.9433 1.034 -5.886e-05 2.384e-05 0.06019 -3.371e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1107 0.04884 0.07353 0.04993 0.9721 0.9796 0.1131 0.9332 0.9616 0.1154 ] Network output: [ 0.1319 -0.2886 1.151 -0.002074 0.0009265 0.8656 -0.001544 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7784 0.3683 0.4109 0.4539 0.959 0.9795 0.782 0.8709 0.9521 0.7248 ] Network output: [ -0.09166 0.1911 0.9251 0.001306 -0.0005881 1.072 0.000992 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7634 0.6699 0.3912 0.1717 0.9766 0.984 0.764 0.9446 0.9663 0.4366 ] Network output: [ -0.1205 0.4123 0.7055 -0.00185 0.00083 1.116 -0.001392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8013 0.781 0.4385 -0.002655 0.9745 0.9822 0.8015 0.9407 0.9621 0.4495 ] Network output: [ 0.1356 0.5787 0.2767 0.001991 -0.0008897 0.8814 0.001483 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1146 Epoch 648 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01278 1.06 0.9604 6.083e-05 -2.869e-05 -0.04598 5.154e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05821 -0.005922 0.02963 0.01848 0.9098 0.9237 0.1066 0.8295 0.8676 0.2007 ] Network output: [ 0.9478 0.1045 -0.04637 0.0007102 -0.0003123 0.04926 0.0005083 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6897 -0.05792 -0.03375 0.2712 0.9534 0.9759 0.7769 0.8565 0.9434 0.7254 ] Network output: [ -0.01872 0.9436 1.034 -5.979e-05 2.43e-05 0.05995 -3.457e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1102 0.04848 0.07337 0.04981 0.9721 0.9796 0.1126 0.9332 0.9617 0.1151 ] Network output: [ 0.1322 -0.2887 1.151 -0.00206 0.0009204 0.8653 -0.001534 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7777 0.3663 0.411 0.4541 0.959 0.9795 0.7812 0.8709 0.9521 0.7251 ] Network output: [ -0.09193 0.1904 0.9257 0.001297 -0.0005842 1.073 0.0009855 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7629 0.669 0.3915 0.1716 0.9766 0.984 0.7636 0.9446 0.9663 0.437 ] Network output: [ -0.1208 0.4124 0.7055 -0.001872 0.0008401 1.116 -0.001409 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8009 0.7804 0.4389 -0.003247 0.9745 0.9822 0.801 0.9407 0.9621 0.4498 ] Network output: [ 0.1363 0.5777 0.2776 0.002017 -0.0009012 0.8802 0.001503 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1149 Epoch 649 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01276 1.06 0.96 5.525e-05 -2.617e-05 -0.0457 4.725e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05805 -0.005942 0.02965 0.01846 0.9098 0.9238 0.1063 0.8295 0.8676 0.2004 ] Network output: [ 0.9471 0.1056 -0.04673 0.0007105 -0.0003125 0.0498 0.0005089 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6891 -0.05863 -0.03357 0.2713 0.9534 0.9759 0.7762 0.8566 0.9434 0.7257 ] Network output: [ -0.01859 0.944 1.033 -6.087e-05 2.482e-05 0.0597 -3.554e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1097 0.04812 0.0732 0.04968 0.9721 0.9796 0.1122 0.9332 0.9617 0.1148 ] Network output: [ 0.1326 -0.2889 1.15 -0.002046 0.0009141 0.8649 -0.001524 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7769 0.3643 0.411 0.4542 0.959 0.9795 0.7804 0.8709 0.9521 0.7254 ] Network output: [ -0.09218 0.1898 0.9262 0.001289 -0.0005805 1.074 0.0009792 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7625 0.6681 0.3918 0.1716 0.9766 0.984 0.7631 0.9446 0.9663 0.4373 ] Network output: [ -0.1211 0.4125 0.7054 -0.001894 0.0008499 1.117 -0.001426 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.8004 0.7798 0.4392 -0.00378 0.9745 0.9822 0.8006 0.9407 0.9621 0.4502 ] Network output: [ 0.137 0.5768 0.2785 0.002041 -0.0009123 0.879 0.001522 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1153 Epoch 650 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01274 1.061 0.9596 4.951e-05 -2.357e-05 -0.04543 4.284e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0579 -0.005961 0.02967 0.01843 0.9099 0.9238 0.106 0.8296 0.8677 0.2 ] Network output: [ 0.9464 0.1068 -0.04709 0.0007108 -0.0003127 0.05034 0.0005094 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6884 -0.05933 -0.0334 0.2714 0.9534 0.9759 0.7754 0.8566 0.9434 0.7259 ] Network output: [ -0.01846 0.9443 1.033 -6.21e-05 2.541e-05 0.05946 -3.662e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1093 0.04777 0.07304 0.04956 0.9721 0.9796 0.1117 0.9332 0.9617 0.1146 ] Network output: [ 0.133 -0.2891 1.15 -0.002032 0.0009077 0.8645 -0.001513 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7762 0.3623 0.4111 0.4544 0.959 0.9795 0.7797 0.8709 0.9521 0.7257 ] Network output: [ -0.09241 0.1891 0.9267 0.001281 -0.000577 1.074 0.0009733 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.762 0.6672 0.3921 0.1716 0.9766 0.984 0.7627 0.9446 0.9663 0.4377 ] Network output: [ -0.1214 0.4125 0.7055 -0.001915 0.0008593 1.117 -0.001441 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7999 0.7793 0.4395 -0.004255 0.9745 0.9822 0.8001 0.9408 0.9621 0.4505 ] Network output: [ 0.1377 0.5758 0.2793 0.002065 -0.000923 0.8778 0.00154 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1156 Epoch 651 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01272 1.061 0.9592 4.358e-05 -2.089e-05 -0.04515 3.83e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05774 -0.00598 0.02969 0.01841 0.9099 0.9238 0.1057 0.8296 0.8677 0.1997 ] Network output: [ 0.9458 0.1079 -0.04744 0.000711 -0.0003129 0.05089 0.00051 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6878 -0.06002 -0.03323 0.2715 0.9534 0.9759 0.7747 0.8566 0.9434 0.7262 ] Network output: [ -0.01834 0.9447 1.033 -6.349e-05 2.607e-05 0.05921 -3.781e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1088 0.04741 0.07288 0.04945 0.9721 0.9796 0.1112 0.9333 0.9617 0.1143 ] Network output: [ 0.1334 -0.2893 1.15 -0.002017 0.0009012 0.8642 -0.001502 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7754 0.3604 0.4111 0.4546 0.959 0.9795 0.7789 0.8709 0.9521 0.7259 ] Network output: [ -0.09264 0.1885 0.9272 0.001274 -0.0005737 1.075 0.0009676 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7615 0.6663 0.3924 0.1716 0.9766 0.984 0.7622 0.9446 0.9663 0.4381 ] Network output: [ -0.1217 0.4125 0.7055 -0.001936 0.0008685 1.117 -0.001457 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7995 0.7787 0.4398 -0.004672 0.9745 0.9822 0.7996 0.9408 0.9621 0.4508 ] Network output: [ 0.1384 0.575 0.2802 0.002087 -0.0009332 0.8766 0.001557 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1159 Epoch 652 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01271 1.061 0.9588 3.749e-05 -1.813e-05 -0.04488 3.363e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05758 -0.005997 0.02971 0.01839 0.9099 0.9238 0.1054 0.8297 0.8678 0.1993 ] Network output: [ 0.9451 0.109 -0.04779 0.0007112 -0.0003131 0.05145 0.0005105 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6872 -0.0607 -0.03306 0.2716 0.9534 0.9759 0.7739 0.8567 0.9435 0.7264 ] Network output: [ -0.01821 0.945 1.032 -6.503e-05 2.679e-05 0.05897 -3.912e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1083 0.04706 0.07271 0.04934 0.9721 0.9796 0.1107 0.9333 0.9617 0.114 ] Network output: [ 0.1337 -0.2895 1.15 -0.002002 0.0008945 0.8638 -0.001491 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7746 0.3584 0.4112 0.4549 0.959 0.9795 0.7781 0.8709 0.9521 0.7262 ] Network output: [ -0.09284 0.1878 0.9277 0.001267 -0.0005705 1.075 0.0009622 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.761 0.6653 0.3927 0.1717 0.9766 0.984 0.7617 0.9446 0.9664 0.4384 ] Network output: [ -0.122 0.4124 0.7056 -0.001955 0.0008773 1.118 -0.001472 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.799 0.7781 0.4401 -0.005031 0.9745 0.9822 0.7991 0.9408 0.9621 0.4512 ] Network output: [ 0.1391 0.5742 0.281 0.002109 -0.0009429 0.8754 0.001573 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1162 Epoch 653 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01269 1.061 0.9584 3.121e-05 -1.53e-05 -0.04461 2.882e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05742 -0.006014 0.02973 0.01837 0.9099 0.9238 0.1051 0.8297 0.8678 0.199 ] Network output: [ 0.9445 0.1101 -0.04813 0.0007114 -0.0003133 0.052 0.000511 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6865 -0.06137 -0.03289 0.2718 0.9534 0.9759 0.7732 0.8567 0.9435 0.7266 ] Network output: [ -0.01809 0.9454 1.032 -6.673e-05 2.759e-05 0.05872 -4.055e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1078 0.04671 0.07255 0.04923 0.9721 0.9796 0.1102 0.9333 0.9617 0.1137 ] Network output: [ 0.1341 -0.2897 1.15 -0.001987 0.0008876 0.8635 -0.00148 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7738 0.3565 0.4112 0.4552 0.959 0.9795 0.7773 0.8709 0.9521 0.7265 ] Network output: [ -0.09304 0.1872 0.9281 0.00126 -0.0005675 1.076 0.0009572 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7605 0.6644 0.3929 0.1717 0.9766 0.984 0.7612 0.9446 0.9664 0.4387 ] Network output: [ -0.1222 0.4123 0.7058 -0.001974 0.0008858 1.118 -0.001486 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7985 0.7775 0.4404 -0.005331 0.9745 0.9823 0.7986 0.9408 0.9622 0.4515 ] Network output: [ 0.1397 0.5734 0.2817 0.002129 -0.0009521 0.8741 0.001589 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1165 Epoch 654 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01267 1.061 0.9579 2.475e-05 -1.238e-05 -0.04434 2.387e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05726 -0.006031 0.02974 0.01835 0.9099 0.9238 0.1048 0.8298 0.8679 0.1986 ] Network output: [ 0.9438 0.1112 -0.04847 0.0007117 -0.0003135 0.05257 0.0005115 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6859 -0.06203 -0.03273 0.2719 0.9534 0.9759 0.7725 0.8567 0.9435 0.7268 ] Network output: [ -0.01797 0.9457 1.031 -6.859e-05 2.846e-05 0.05847 -4.21e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1074 0.04636 0.07238 0.04913 0.9721 0.9796 0.1097 0.9333 0.9617 0.1134 ] Network output: [ 0.1345 -0.29 1.15 -0.001971 0.0008806 0.8631 -0.001468 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7731 0.3546 0.4112 0.4554 0.959 0.9795 0.7765 0.8709 0.9521 0.7268 ] Network output: [ -0.09321 0.1866 0.9286 0.001254 -0.0005647 1.076 0.0009524 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.76 0.6635 0.3932 0.1718 0.9766 0.984 0.7607 0.9446 0.9664 0.439 ] Network output: [ -0.1225 0.4121 0.706 -0.001992 0.0008939 1.119 -0.0015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7979 0.7768 0.4407 -0.005574 0.9745 0.9823 0.7981 0.9408 0.9622 0.4517 ] Network output: [ 0.1404 0.5727 0.2825 0.002149 -0.0009609 0.8729 0.001604 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1168 Epoch 655 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01266 1.061 0.9575 1.81e-05 -9.378e-06 -0.04407 1.879e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05709 -0.006046 0.02976 0.01834 0.91 0.9239 0.1045 0.8298 0.8679 0.1983 ] Network output: [ 0.9431 0.1123 -0.0488 0.0007119 -0.0003137 0.05313 0.0005121 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6852 -0.06267 -0.03257 0.2721 0.9534 0.9759 0.7717 0.8567 0.9435 0.7271 ] Network output: [ -0.01785 0.9461 1.031 -7.062e-05 2.941e-05 0.05822 -4.377e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1069 0.04601 0.07221 0.04903 0.9721 0.9796 0.1093 0.9333 0.9617 0.1131 ] Network output: [ 0.1349 -0.2902 1.15 -0.001955 0.0008734 0.8628 -0.001456 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7723 0.3527 0.4113 0.4557 0.959 0.9795 0.7758 0.8709 0.9521 0.7271 ] Network output: [ -0.09338 0.1859 0.929 0.001248 -0.0005621 1.077 0.0009479 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7595 0.6625 0.3934 0.1719 0.9766 0.984 0.7602 0.9446 0.9664 0.4393 ] Network output: [ -0.1228 0.4119 0.7062 -0.00201 0.0009018 1.119 -0.001513 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7974 0.7762 0.441 -0.00576 0.9745 0.9823 0.7975 0.9409 0.9622 0.452 ] Network output: [ 0.141 0.5721 0.2832 0.002167 -0.0009691 0.8716 0.001618 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.117 Epoch 656 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01265 1.062 0.957 1.128e-05 -6.296e-06 -0.04381 1.358e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05693 -0.006061 0.02977 0.01833 0.91 0.9239 0.1042 0.8299 0.8679 0.198 ] Network output: [ 0.9425 0.1134 -0.04913 0.0007121 -0.0003138 0.0537 0.0005126 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6846 -0.06331 -0.03241 0.2722 0.9534 0.9759 0.771 0.8568 0.9435 0.7273 ] Network output: [ -0.01773 0.9465 1.031 -7.282e-05 3.043e-05 0.05797 -4.556e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1064 0.04567 0.07204 0.04893 0.9721 0.9796 0.1088 0.9333 0.9618 0.1128 ] Network output: [ 0.1352 -0.2904 1.15 -0.001938 0.0008661 0.8625 -0.001444 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7715 0.3508 0.4113 0.4561 0.959 0.9795 0.775 0.8709 0.9521 0.7273 ] Network output: [ -0.09352 0.1853 0.9294 0.001243 -0.0005596 1.077 0.0009437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.759 0.6616 0.3936 0.1721 0.9766 0.984 0.7596 0.9446 0.9664 0.4395 ] Network output: [ -0.1231 0.4117 0.7064 -0.002027 0.0009093 1.12 -0.001525 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7969 0.7756 0.4412 -0.005888 0.9745 0.9823 0.797 0.9409 0.9622 0.4523 ] Network output: [ 0.1416 0.5715 0.2838 0.002184 -0.0009768 0.8704 0.001631 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1173 Epoch 657 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01263 1.062 0.9566 4.271e-06 -3.133e-06 -0.04354 8.227e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05677 -0.006075 0.02978 0.01832 0.91 0.9239 0.1039 0.8299 0.868 0.1976 ] Network output: [ 0.9418 0.1144 -0.04945 0.0007124 -0.0003141 0.05427 0.0005131 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6839 -0.06394 -0.03226 0.2724 0.9534 0.9759 0.7702 0.8568 0.9436 0.7275 ] Network output: [ -0.01761 0.9469 1.03 -7.519e-05 3.152e-05 0.05772 -4.748e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.106 0.04533 0.07188 0.04884 0.9721 0.9796 0.1083 0.9333 0.9618 0.1126 ] Network output: [ 0.1356 -0.2907 1.15 -0.001921 0.0008586 0.8621 -0.001431 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7707 0.3489 0.4113 0.4564 0.959 0.9795 0.7742 0.8709 0.9521 0.7276 ] Network output: [ -0.09366 0.1847 0.9297 0.001237 -0.0005573 1.078 0.0009397 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7585 0.6606 0.3938 0.1723 0.9766 0.984 0.7591 0.9446 0.9664 0.4398 ] Network output: [ -0.1234 0.4113 0.7067 -0.002043 0.0009165 1.12 -0.001538 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7963 0.7749 0.4414 -0.005961 0.9746 0.9823 0.7965 0.9409 0.9622 0.4525 ] Network output: [ 0.1422 0.5709 0.2845 0.0022 -0.0009841 0.8691 0.001643 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1175 Epoch 658 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01262 1.062 0.9562 -2.918e-06 1.113e-07 -0.04328 2.74e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05661 -0.006088 0.02979 0.01831 0.91 0.9239 0.1036 0.83 0.868 0.1973 ] Network output: [ 0.9412 0.1155 -0.04977 0.0007127 -0.0003143 0.05484 0.0005137 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6833 -0.06456 -0.03212 0.2726 0.9534 0.9759 0.7695 0.8568 0.9436 0.7277 ] Network output: [ -0.01749 0.9473 1.03 -7.772e-05 3.269e-05 0.05747 -4.952e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1055 0.04499 0.0717 0.04875 0.9721 0.9796 0.1078 0.9333 0.9618 0.1123 ] Network output: [ 0.136 -0.2909 1.149 -0.001904 0.0008509 0.8618 -0.001419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7699 0.347 0.4113 0.4568 0.9589 0.9795 0.7734 0.8709 0.9521 0.7279 ] Network output: [ -0.09377 0.1841 0.9301 0.001233 -0.0005551 1.078 0.0009361 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7579 0.6597 0.394 0.1725 0.9766 0.9841 0.7586 0.9446 0.9664 0.44 ] Network output: [ -0.1236 0.411 0.7071 -0.002058 0.0009234 1.121 -0.001549 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7958 0.7743 0.4417 -0.005979 0.9746 0.9823 0.7959 0.9409 0.9622 0.4528 ] Network output: [ 0.1428 0.5704 0.2851 0.002215 -0.0009908 0.8679 0.001654 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1177 Epoch 659 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01261 1.062 0.9557 -1.029e-05 3.436e-06 -0.04302 -2.881e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05645 -0.0061 0.0298 0.0183 0.91 0.9239 0.1033 0.83 0.8681 0.1969 ] Network output: [ 0.9405 0.1165 -0.05009 0.0007131 -0.0003145 0.05541 0.0005143 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6826 -0.06516 -0.03198 0.2728 0.9534 0.9759 0.7687 0.8568 0.9436 0.7279 ] Network output: [ -0.01737 0.9477 1.03 -8.042e-05 3.394e-05 0.05722 -5.168e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.105 0.04465 0.07153 0.04867 0.9721 0.9796 0.1073 0.9333 0.9618 0.112 ] Network output: [ 0.1363 -0.2912 1.149 -0.001887 0.0008431 0.8615 -0.001406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7691 0.3452 0.4113 0.4572 0.9589 0.9795 0.7726 0.8709 0.9521 0.7281 ] Network output: [ -0.09387 0.1834 0.9304 0.001228 -0.0005531 1.079 0.0009327 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7574 0.6587 0.3941 0.1727 0.9766 0.9841 0.758 0.9446 0.9664 0.4402 ] Network output: [ -0.1239 0.4106 0.7075 -0.002073 0.00093 1.121 -0.00156 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7952 0.7736 0.4419 -0.005942 0.9746 0.9823 0.7954 0.9409 0.9622 0.453 ] Network output: [ 0.1434 0.57 0.2857 0.002229 -0.000997 0.8666 0.001665 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1179 Epoch 660 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01259 1.062 0.9552 -1.783e-05 6.839e-06 -0.04276 -8.633e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05629 -0.006112 0.02981 0.0183 0.9101 0.924 0.103 0.8301 0.8681 0.1966 ] Network output: [ 0.9399 0.1176 -0.0504 0.0007134 -0.0003148 0.05599 0.0005149 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.682 -0.06575 -0.03186 0.273 0.9534 0.9759 0.7679 0.8569 0.9436 0.7281 ] Network output: [ -0.01726 0.9481 1.029 -8.329e-05 3.526e-05 0.05696 -5.397e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1045 0.04432 0.07136 0.04858 0.9721 0.9796 0.1068 0.9333 0.9618 0.1117 ] Network output: [ 0.1367 -0.2914 1.149 -0.001869 0.000835 0.8612 -0.001392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7684 0.3433 0.4113 0.4576 0.9589 0.9795 0.7718 0.8709 0.9521 0.7284 ] Network output: [ -0.09396 0.1828 0.9307 0.001224 -0.0005513 1.079 0.0009295 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7568 0.6578 0.3942 0.1729 0.9766 0.9841 0.7575 0.9446 0.9664 0.4404 ] Network output: [ -0.1242 0.4101 0.7079 -0.002087 0.0009363 1.122 -0.001571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7947 0.773 0.4421 -0.005851 0.9746 0.9823 0.7948 0.9409 0.9623 0.4532 ] Network output: [ 0.144 0.5696 0.2863 0.002241 -0.001003 0.8653 0.001674 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1181 Epoch 661 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01258 1.062 0.9548 -2.555e-05 1.032e-05 -0.04251 -1.451e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05613 -0.006123 0.02982 0.01829 0.9101 0.924 0.1027 0.8301 0.8681 0.1963 ] Network output: [ 0.9392 0.1186 -0.05071 0.0007139 -0.000315 0.05656 0.0005155 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6813 -0.06634 -0.03174 0.2732 0.9534 0.9759 0.7672 0.8569 0.9436 0.7283 ] Network output: [ -0.01715 0.9485 1.029 -8.633e-05 3.665e-05 0.05671 -5.639e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1041 0.04399 0.07119 0.04851 0.9721 0.9796 0.1064 0.9334 0.9618 0.1114 ] Network output: [ 0.137 -0.2917 1.149 -0.00185 0.0008269 0.8609 -0.001379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7676 0.3415 0.4113 0.4581 0.9589 0.9795 0.771 0.8709 0.9521 0.7286 ] Network output: [ -0.09403 0.1823 0.9309 0.001221 -0.0005496 1.08 0.0009267 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7563 0.6568 0.3943 0.1732 0.9766 0.9841 0.7569 0.9446 0.9664 0.4405 ] Network output: [ -0.1245 0.4096 0.7083 -0.0021 0.0009423 1.122 -0.001581 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7941 0.7723 0.4422 -0.005709 0.9746 0.9823 0.7942 0.9409 0.9623 0.4534 ] Network output: [ 0.1445 0.5693 0.2868 0.002253 -0.001008 0.8641 0.001683 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1182 Epoch 662 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01257 1.063 0.9543 -3.343e-05 1.387e-05 -0.04226 -2.052e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05596 -0.006133 0.02982 0.01829 0.9101 0.924 0.1024 0.8302 0.8682 0.1959 ] Network output: [ 0.9386 0.1196 -0.05102 0.0007144 -0.0003153 0.05713 0.0005162 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6807 -0.06691 -0.03162 0.2735 0.9534 0.9759 0.7664 0.8569 0.9436 0.7285 ] Network output: [ -0.01704 0.9489 1.028 -8.952e-05 3.811e-05 0.05646 -5.892e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1036 0.04367 0.07101 0.04843 0.9721 0.9796 0.1059 0.9334 0.9618 0.1111 ] Network output: [ 0.1374 -0.2919 1.149 -0.001832 0.0008185 0.8606 -0.001365 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7668 0.3397 0.4113 0.4585 0.9589 0.9795 0.7702 0.8709 0.9521 0.7289 ] Network output: [ -0.09408 0.1817 0.9311 0.001217 -0.0005481 1.08 0.0009241 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7557 0.6559 0.3944 0.1735 0.9766 0.9841 0.7563 0.9446 0.9664 0.4407 ] Network output: [ -0.1248 0.4091 0.7088 -0.002113 0.000948 1.123 -0.00159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7935 0.7716 0.4424 -0.005515 0.9746 0.9823 0.7937 0.9409 0.9623 0.4535 ] Network output: [ 0.1451 0.569 0.2873 0.002263 -0.001012 0.8628 0.001691 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1184 Epoch 663 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01255 1.063 0.9539 -4.148e-05 1.75e-05 -0.04201 -2.665e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0558 -0.006142 0.02982 0.0183 0.9101 0.924 0.102 0.8302 0.8682 0.1956 ] Network output: [ 0.938 0.1206 -0.05132 0.0007149 -0.0003156 0.0577 0.0005169 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6801 -0.06747 -0.03152 0.2737 0.9534 0.9759 0.7657 0.8569 0.9437 0.7287 ] Network output: [ -0.01693 0.9493 1.028 -9.288e-05 3.965e-05 0.05621 -6.157e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1031 0.04335 0.07083 0.04836 0.9721 0.9796 0.1054 0.9334 0.9619 0.1108 ] Network output: [ 0.1377 -0.2922 1.149 -0.001813 0.00081 0.8603 -0.001351 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.766 0.3379 0.4112 0.459 0.9589 0.9795 0.7694 0.8709 0.9521 0.7291 ] Network output: [ -0.09412 0.1811 0.9313 0.001214 -0.0005467 1.081 0.0009217 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7551 0.6549 0.3945 0.1738 0.9766 0.9841 0.7558 0.9446 0.9664 0.4408 ] Network output: [ -0.125 0.4085 0.7093 -0.002124 0.0009533 1.124 -0.001599 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7929 0.7709 0.4425 -0.005272 0.9746 0.9823 0.7931 0.9409 0.9623 0.4537 ] Network output: [ 0.1456 0.5687 0.2878 0.002272 -0.001017 0.8616 0.001698 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1185 Epoch 664 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01254 1.063 0.9534 -4.969e-05 2.12e-05 -0.04176 -3.29e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05564 -0.006151 0.02983 0.0183 0.9101 0.924 0.1017 0.8303 0.8683 0.1952 ] Network output: [ 0.9374 0.1215 -0.05162 0.0007155 -0.000316 0.05827 0.0005176 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6794 -0.06801 -0.03143 0.274 0.9534 0.9759 0.7649 0.857 0.9437 0.7289 ] Network output: [ -0.01682 0.9498 1.028 -9.639e-05 4.126e-05 0.05596 -6.433e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1027 0.04303 0.07065 0.04829 0.9721 0.9796 0.1049 0.9334 0.9619 0.1105 ] Network output: [ 0.1381 -0.2925 1.149 -0.001793 0.0008014 0.8601 -0.001336 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7652 0.3362 0.4112 0.4595 0.9589 0.9795 0.7687 0.8709 0.9522 0.7294 ] Network output: [ -0.09415 0.1806 0.9315 0.001212 -0.0005455 1.081 0.0009195 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7546 0.6539 0.3945 0.1741 0.9766 0.9841 0.7552 0.9446 0.9664 0.4409 ] Network output: [ -0.1253 0.4079 0.7099 -0.002136 0.0009584 1.124 -0.001608 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7924 0.7703 0.4427 -0.00498 0.9746 0.9823 0.7925 0.941 0.9623 0.4538 ] Network output: [ 0.1461 0.5685 0.2882 0.00228 -0.00102 0.8603 0.001704 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1186 Epoch 665 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01252 1.063 0.953 -5.805e-05 2.497e-05 -0.04151 -3.926e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05548 -0.006159 0.02982 0.0183 0.9102 0.9241 0.1014 0.8303 0.8683 0.1949 ] Network output: [ 0.9368 0.1225 -0.05192 0.0007161 -0.0003163 0.05883 0.0005184 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6788 -0.06855 -0.03135 0.2742 0.9534 0.9759 0.7642 0.857 0.9437 0.7291 ] Network output: [ -0.01672 0.9502 1.027 -0.0001001 4.293e-05 0.05571 -6.721e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1022 0.04272 0.07048 0.04822 0.9721 0.9797 0.1045 0.9334 0.9619 0.1102 ] Network output: [ 0.1384 -0.2928 1.149 -0.001773 0.0007926 0.8598 -0.001322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7644 0.3345 0.4111 0.46 0.9589 0.9795 0.7679 0.8708 0.9522 0.7296 ] Network output: [ -0.09416 0.18 0.9317 0.001209 -0.0005444 1.082 0.0009176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.754 0.653 0.3946 0.1745 0.9766 0.9841 0.7546 0.9446 0.9664 0.4409 ] Network output: [ -0.1256 0.4073 0.7105 -0.002147 0.0009632 1.125 -0.001616 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7918 0.7696 0.4428 -0.004642 0.9746 0.9823 0.7919 0.941 0.9623 0.454 ] Network output: [ 0.1466 0.5684 0.2886 0.002287 -0.001023 0.8591 0.00171 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1187 Epoch 666 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01251 1.063 0.9525 -6.656e-05 2.88e-05 -0.04127 -4.573e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05532 -0.006166 0.02982 0.01831 0.9102 0.9241 0.1011 0.8304 0.8684 0.1946 ] Network output: [ 0.9362 0.1234 -0.05222 0.0007168 -0.0003167 0.05939 0.0005192 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6781 -0.06907 -0.03128 0.2745 0.9534 0.9759 0.7635 0.857 0.9437 0.7293 ] Network output: [ -0.01662 0.9506 1.027 -0.0001039 4.467e-05 0.05546 -7.02e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1017 0.04241 0.07029 0.04816 0.9721 0.9797 0.104 0.9334 0.9619 0.1099 ] Network output: [ 0.1387 -0.2931 1.149 -0.001753 0.0007836 0.8595 -0.001307 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7637 0.3328 0.411 0.4606 0.9589 0.9795 0.7671 0.8708 0.9522 0.7299 ] Network output: [ -0.09416 0.1795 0.9318 0.001207 -0.0005434 1.082 0.0009159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7534 0.652 0.3946 0.1748 0.9766 0.9841 0.754 0.9446 0.9664 0.441 ] Network output: [ -0.1259 0.4066 0.7111 -0.002157 0.0009678 1.125 -0.001624 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7912 0.7689 0.4429 -0.004257 0.9746 0.9823 0.7913 0.941 0.9623 0.4541 ] Network output: [ 0.1471 0.5683 0.289 0.002292 -0.001026 0.8578 0.001714 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1188 Epoch 667 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01249 1.064 0.9521 -7.52e-05 3.27e-05 -0.04103 -5.23e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05517 -0.006173 0.02982 0.01832 0.9102 0.9241 0.1008 0.8304 0.8684 0.1942 ] Network output: [ 0.9356 0.1244 -0.05252 0.0007176 -0.0003171 0.05995 0.0005201 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6775 -0.06958 -0.03122 0.2748 0.9534 0.9759 0.7627 0.857 0.9437 0.7295 ] Network output: [ -0.01652 0.951 1.026 -0.0001078 4.647e-05 0.05521 -7.33e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1013 0.04211 0.07011 0.0481 0.9721 0.9797 0.1035 0.9334 0.9619 0.1096 ] Network output: [ 0.1391 -0.2934 1.149 -0.001733 0.0007745 0.8593 -0.001292 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7629 0.3311 0.4109 0.4611 0.9589 0.9795 0.7663 0.8708 0.9522 0.7301 ] Network output: [ -0.09414 0.179 0.9319 0.001205 -0.0005425 1.082 0.0009144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7528 0.6511 0.3945 0.1752 0.9766 0.9841 0.7534 0.9446 0.9664 0.441 ] Network output: [ -0.1261 0.4059 0.7117 -0.002166 0.0009721 1.126 -0.001631 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7906 0.7682 0.443 -0.003829 0.9746 0.9823 0.7907 0.941 0.9623 0.4542 ] Network output: [ 0.1476 0.5682 0.2894 0.002297 -0.001028 0.8566 0.001718 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1189 Epoch 668 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01247 1.064 0.9516 -8.397e-05 3.665e-05 -0.04079 -5.896e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05501 -0.006179 0.02981 0.01833 0.9102 0.9241 0.1005 0.8305 0.8684 0.1939 ] Network output: [ 0.935 0.1253 -0.05281 0.0007184 -0.0003176 0.06051 0.000521 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6769 -0.07008 -0.03117 0.2751 0.9534 0.9759 0.762 0.8571 0.9438 0.7297 ] Network output: [ -0.01642 0.9515 1.026 -0.0001119 4.834e-05 0.05497 -7.649e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1008 0.04181 0.06993 0.04804 0.9721 0.9797 0.1031 0.9334 0.9619 0.1093 ] Network output: [ 0.1394 -0.2937 1.149 -0.001712 0.0007652 0.859 -0.001276 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7621 0.3294 0.4108 0.4617 0.9589 0.9795 0.7655 0.8708 0.9522 0.7303 ] Network output: [ -0.09411 0.1785 0.932 0.001203 -0.0005417 1.083 0.0009131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7522 0.6502 0.3945 0.1756 0.9766 0.9841 0.7528 0.9446 0.9665 0.441 ] Network output: [ -0.1264 0.4051 0.7124 -0.002175 0.0009761 1.126 -0.001638 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.79 0.7675 0.443 -0.003359 0.9746 0.9823 0.7901 0.941 0.9624 0.4543 ] Network output: [ 0.1481 0.5682 0.2897 0.002301 -0.00103 0.8554 0.001721 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.119 Epoch 669 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01245 1.064 0.9512 -9.286e-05 4.065e-05 -0.04056 -6.572e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05485 -0.006184 0.0298 0.01834 0.9103 0.9241 0.1002 0.8305 0.8685 0.1936 ] Network output: [ 0.9344 0.1262 -0.05311 0.0007193 -0.000318 0.06105 0.0005219 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6763 -0.07056 -0.03113 0.2754 0.9534 0.9759 0.7613 0.8571 0.9438 0.7298 ] Network output: [ -0.01632 0.9519 1.026 -0.0001161 5.026e-05 0.05472 -7.979e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.1004 0.04151 0.06974 0.04799 0.9721 0.9797 0.1026 0.9334 0.9619 0.109 ] Network output: [ 0.1397 -0.294 1.149 -0.001691 0.0007558 0.8588 -0.00126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7614 0.3278 0.4107 0.4623 0.9589 0.9795 0.7648 0.8708 0.9522 0.7306 ] Network output: [ -0.09406 0.178 0.932 0.001202 -0.0005411 1.083 0.0009119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7516 0.6492 0.3944 0.176 0.9766 0.9841 0.7523 0.9446 0.9665 0.441 ] Network output: [ -0.1267 0.4043 0.7131 -0.002183 0.0009798 1.127 -0.001644 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7894 0.7668 0.4431 -0.002848 0.9746 0.9823 0.7895 0.941 0.9624 0.4544 ] Network output: [ 0.1485 0.5682 0.29 0.002303 -0.001031 0.8541 0.001723 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1191 Epoch 670 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01243 1.064 0.9508 -0.0001019 4.471e-05 -0.04033 -7.256e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05469 -0.006189 0.02979 0.01835 0.9103 0.9241 0.09994 0.8306 0.8685 0.1932 ] Network output: [ 0.9338 0.127 -0.0534 0.0007202 -0.0003185 0.0616 0.0005229 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6756 -0.07104 -0.03111 0.2757 0.9535 0.9759 0.7605 0.8571 0.9438 0.73 ] Network output: [ -0.01623 0.9523 1.025 -0.0001205 5.224e-05 0.05448 -8.317e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09994 0.04122 0.06956 0.04793 0.9721 0.9797 0.1021 0.9334 0.962 0.1088 ] Network output: [ 0.14 -0.2943 1.149 -0.00167 0.0007462 0.8586 -0.001245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7606 0.3262 0.4106 0.4629 0.9589 0.9795 0.764 0.8708 0.9522 0.7308 ] Network output: [ -0.094 0.1776 0.932 0.001201 -0.0005405 1.083 0.000911 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.751 0.6483 0.3944 0.1764 0.9766 0.9841 0.7517 0.9446 0.9665 0.441 ] Network output: [ -0.127 0.4035 0.7138 -0.002191 0.0009834 1.128 -0.00165 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7888 0.7661 0.4432 -0.002299 0.9746 0.9823 0.7889 0.941 0.9624 0.4544 ] Network output: [ 0.149 0.5682 0.2903 0.002305 -0.001032 0.8529 0.001724 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1191 Epoch 671 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01241 1.064 0.9503 -0.000111 4.881e-05 -0.0401 -7.947e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05454 -0.006193 0.02978 0.01837 0.9103 0.9242 0.09964 0.8306 0.8686 0.1929 ] Network output: [ 0.9333 0.1279 -0.0537 0.0007211 -0.000319 0.06214 0.0005239 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.675 -0.0715 -0.0311 0.276 0.9535 0.976 0.7598 0.8571 0.9438 0.7302 ] Network output: [ -0.01614 0.9527 1.025 -0.000125 5.427e-05 0.05423 -8.664e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0995 0.04094 0.06937 0.04788 0.9721 0.9797 0.1017 0.9335 0.962 0.1085 ] Network output: [ 0.1403 -0.2947 1.149 -0.001648 0.0007365 0.8584 -0.001228 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7599 0.3246 0.4105 0.4635 0.9589 0.9795 0.7633 0.8708 0.9522 0.731 ] Network output: [ -0.09393 0.1772 0.932 0.0012 -0.0005401 1.084 0.0009101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7504 0.6474 0.3943 0.1768 0.9766 0.9841 0.7511 0.9446 0.9665 0.4409 ] Network output: [ -0.1272 0.4027 0.7146 -0.002199 0.0009867 1.128 -0.001655 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7882 0.7654 0.4432 -0.001713 0.9746 0.9823 0.7883 0.941 0.9624 0.4545 ] Network output: [ 0.1494 0.5683 0.2906 0.002305 -0.001032 0.8517 0.001725 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1191 Epoch 672 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01239 1.065 0.9499 -0.0001202 5.295e-05 -0.03988 -8.645e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05439 -0.006196 0.02977 0.01838 0.9103 0.9242 0.09935 0.8307 0.8686 0.1926 ] Network output: [ 0.9328 0.1287 -0.05399 0.0007222 -0.0003195 0.06267 0.0005249 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6744 -0.07194 -0.0311 0.2763 0.9535 0.976 0.7591 0.8572 0.9438 0.7304 ] Network output: [ -0.01606 0.9532 1.024 -0.0001295 5.635e-05 0.05399 -9.02e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09906 0.04066 0.06918 0.04783 0.9721 0.9797 0.1012 0.9335 0.962 0.1082 ] Network output: [ 0.1406 -0.295 1.149 -0.001626 0.0007267 0.8582 -0.001212 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7591 0.3231 0.4103 0.4642 0.9589 0.9795 0.7625 0.8708 0.9522 0.7312 ] Network output: [ -0.09385 0.1767 0.9319 0.001199 -0.0005397 1.084 0.0009095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7498 0.6464 0.3941 0.1773 0.9766 0.9841 0.7505 0.9446 0.9665 0.4409 ] Network output: [ -0.1275 0.4019 0.7153 -0.002206 0.0009898 1.129 -0.001661 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7876 0.7648 0.4432 -0.001091 0.9746 0.9823 0.7877 0.941 0.9624 0.4545 ] Network output: [ 0.1498 0.5684 0.2908 0.002305 -0.001032 0.8506 0.001725 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1192 Epoch 673 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01236 1.065 0.9495 -0.0001294 5.713e-05 -0.03966 -9.349e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05423 -0.006199 0.02975 0.0184 0.9103 0.9242 0.09906 0.8307 0.8687 0.1922 ] Network output: [ 0.9322 0.1296 -0.05429 0.0007232 -0.0003201 0.0632 0.000526 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6738 -0.07238 -0.03111 0.2766 0.9535 0.976 0.7584 0.8572 0.9439 0.7305 ] Network output: [ -0.01597 0.9536 1.024 -0.0001342 5.848e-05 0.05375 -9.382e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09862 0.04038 0.06899 0.04779 0.9722 0.9797 0.1008 0.9335 0.962 0.1079 ] Network output: [ 0.1409 -0.2953 1.149 -0.001604 0.0007167 0.858 -0.001195 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7584 0.3216 0.4102 0.4648 0.9589 0.9795 0.7618 0.8708 0.9522 0.7314 ] Network output: [ -0.09375 0.1763 0.9319 0.001198 -0.0005394 1.084 0.0009089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7492 0.6455 0.394 0.1777 0.9766 0.9841 0.7498 0.9446 0.9665 0.4408 ] Network output: [ -0.1278 0.401 0.7161 -0.002212 0.0009926 1.129 -0.001665 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.787 0.7641 0.4432 -0.0004361 0.9746 0.9823 0.7871 0.941 0.9624 0.4545 ] Network output: [ 0.1502 0.5685 0.291 0.002304 -0.001031 0.8494 0.001724 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1192 Epoch 674 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01234 1.065 0.9491 -0.0001388 6.133e-05 -0.03944 -0.0001006 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05408 -0.006201 0.02973 0.01842 0.9104 0.9242 0.09877 0.8308 0.8687 0.1919 ] Network output: [ 0.9317 0.1304 -0.05458 0.0007243 -0.0003206 0.06372 0.0005271 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6732 -0.0728 -0.03114 0.277 0.9535 0.976 0.7577 0.8572 0.9439 0.7307 ] Network output: [ -0.01589 0.954 1.024 -0.000139 6.064e-05 0.05351 -9.752e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09819 0.04011 0.0688 0.04774 0.9722 0.9797 0.1003 0.9335 0.962 0.1076 ] Network output: [ 0.1411 -0.2957 1.149 -0.001581 0.0007067 0.8578 -0.001179 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7577 0.3201 0.41 0.4655 0.9589 0.9795 0.761 0.8708 0.9522 0.7317 ] Network output: [ -0.09365 0.176 0.9318 0.001198 -0.0005392 1.084 0.0009085 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7486 0.6446 0.3938 0.1782 0.9766 0.9841 0.7492 0.9446 0.9665 0.4407 ] Network output: [ -0.128 0.4001 0.7169 -0.002218 0.0009953 1.13 -0.00167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7863 0.7634 0.4432 0.0002502 0.9746 0.9823 0.7865 0.941 0.9624 0.4545 ] Network output: [ 0.1506 0.5687 0.2912 0.002301 -0.00103 0.8482 0.001722 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1192 Epoch 675 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01231 1.065 0.9487 -0.0001482 6.557e-05 -0.03922 -0.0001077 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05393 -0.006203 0.02971 0.01844 0.9104 0.9242 0.09849 0.8308 0.8687 0.1916 ] Network output: [ 0.9312 0.1312 -0.05488 0.0007255 -0.0003212 0.06423 0.0005282 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6726 -0.07321 -0.03118 0.2773 0.9535 0.976 0.757 0.8572 0.9439 0.7309 ] Network output: [ -0.01581 0.9544 1.023 -0.0001439 6.285e-05 0.05328 -0.0001013 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09777 0.03985 0.06861 0.0477 0.9722 0.9797 0.09991 0.9335 0.962 0.1073 ] Network output: [ 0.1414 -0.296 1.149 -0.001558 0.0006965 0.8576 -0.001162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7569 0.3186 0.4098 0.4661 0.9589 0.9795 0.7603 0.8708 0.9522 0.7319 ] Network output: [ -0.09353 0.1756 0.9316 0.001198 -0.000539 1.085 0.0009082 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.748 0.6437 0.3937 0.1786 0.9766 0.9841 0.7486 0.9445 0.9665 0.4405 ] Network output: [ -0.1283 0.3992 0.7177 -0.002223 0.0009978 1.131 -0.001674 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7857 0.7627 0.4432 0.0009662 0.9746 0.9823 0.7859 0.941 0.9624 0.4545 ] Network output: [ 0.151 0.5689 0.2913 0.002298 -0.001029 0.8471 0.00172 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1192 Epoch 676 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01228 1.065 0.9483 -0.0001576 6.982e-05 -0.03901 -0.0001149 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05378 -0.006203 0.02969 0.01846 0.9104 0.9243 0.0982 0.8309 0.8688 0.1913 ] Network output: [ 0.9307 0.1319 -0.05518 0.0007267 -0.0003218 0.06473 0.0005293 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.672 -0.07361 -0.03123 0.2777 0.9535 0.976 0.7563 0.8572 0.9439 0.731 ] Network output: [ -0.01574 0.9549 1.023 -0.0001488 6.509e-05 0.05304 -0.0001051 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09735 0.03959 0.06841 0.04766 0.9722 0.9797 0.09949 0.9335 0.9621 0.107 ] Network output: [ 0.1417 -0.2964 1.149 -0.001535 0.0006862 0.8574 -0.001144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7562 0.3172 0.4096 0.4668 0.9589 0.9795 0.7596 0.8708 0.9522 0.7321 ] Network output: [ -0.0934 0.1753 0.9315 0.001198 -0.0005389 1.085 0.000908 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7474 0.6428 0.3935 0.1791 0.9766 0.9841 0.748 0.9445 0.9665 0.4404 ] Network output: [ -0.1286 0.3983 0.7186 -0.002229 0.001 1.131 -0.001678 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7851 0.762 0.4431 0.00171 0.9746 0.9823 0.7853 0.941 0.9624 0.4545 ] Network output: [ 0.1514 0.5691 0.2914 0.002294 -0.001027 0.846 0.001717 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1192 Epoch 677 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01225 1.066 0.948 -0.0001671 7.409e-05 -0.03881 -0.0001221 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05364 -0.006204 0.02967 0.01848 0.9104 0.9243 0.09792 0.8309 0.8688 0.191 ] Network output: [ 0.9303 0.1327 -0.05548 0.0007279 -0.0003224 0.06523 0.0005305 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6715 -0.07399 -0.0313 0.278 0.9535 0.976 0.7557 0.8573 0.944 0.7312 ] Network output: [ -0.01567 0.9553 1.023 -0.0001538 6.735e-05 0.05281 -0.000109 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09694 0.03933 0.06822 0.04762 0.9722 0.9797 0.09906 0.9335 0.9621 0.1067 ] Network output: [ 0.1419 -0.2967 1.149 -0.001512 0.0006758 0.8573 -0.001127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7555 0.3158 0.4094 0.4675 0.9589 0.9795 0.7589 0.8708 0.9523 0.7323 ] Network output: [ -0.09326 0.175 0.9313 0.001197 -0.0005389 1.085 0.0009078 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7468 0.6419 0.3932 0.1796 0.9766 0.9841 0.7474 0.9445 0.9665 0.4402 ] Network output: [ -0.1289 0.3973 0.7194 -0.002233 0.001002 1.132 -0.001682 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7845 0.7613 0.4431 0.002479 0.9746 0.9823 0.7847 0.941 0.9624 0.4545 ] Network output: [ 0.1518 0.5694 0.2915 0.00229 -0.001025 0.8449 0.001714 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1192 Epoch 678 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01222 1.066 0.9476 -0.0001766 7.837e-05 -0.0386 -0.0001293 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05349 -0.006204 0.02964 0.01851 0.9105 0.9243 0.09765 0.831 0.8689 0.1906 ] Network output: [ 0.9298 0.1334 -0.05578 0.0007291 -0.000323 0.06572 0.0005316 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6709 -0.07436 -0.03138 0.2784 0.9535 0.976 0.755 0.8573 0.944 0.7314 ] Network output: [ -0.0156 0.9557 1.022 -0.0001588 6.965e-05 0.05258 -0.0001129 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09653 0.03908 0.06802 0.04758 0.9722 0.9797 0.09864 0.9335 0.9621 0.1064 ] Network output: [ 0.1421 -0.2971 1.15 -0.001489 0.0006653 0.8571 -0.00111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7548 0.3145 0.4092 0.4682 0.9589 0.9795 0.7582 0.8708 0.9523 0.7325 ] Network output: [ -0.09311 0.1747 0.9311 0.001197 -0.0005389 1.085 0.0009078 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7462 0.6411 0.393 0.18 0.9766 0.9841 0.7468 0.9445 0.9665 0.44 ] Network output: [ -0.1291 0.3964 0.7203 -0.002238 0.001004 1.132 -0.001685 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7839 0.7607 0.443 0.003273 0.9746 0.9823 0.7841 0.941 0.9624 0.4544 ] Network output: [ 0.1521 0.5696 0.2916 0.002284 -0.001023 0.8438 0.00171 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1192 Epoch 679 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01218 1.066 0.9473 -0.0001861 8.265e-05 -0.0384 -0.0001365 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05334 -0.006203 0.02962 0.01853 0.9105 0.9243 0.09737 0.831 0.8689 0.1903 ] Network output: [ 0.9293 0.1342 -0.05609 0.0007304 -0.0003236 0.0662 0.0005328 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6703 -0.07472 -0.03148 0.2787 0.9535 0.976 0.7543 0.8573 0.944 0.7315 ] Network output: [ -0.01553 0.9561 1.022 -0.0001639 7.196e-05 0.05235 -0.0001168 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09612 0.03884 0.06783 0.04754 0.9722 0.9797 0.09823 0.9335 0.9621 0.1061 ] Network output: [ 0.1424 -0.2974 1.15 -0.001465 0.0006547 0.857 -0.001092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7541 0.3131 0.4089 0.4689 0.9589 0.9795 0.7575 0.8708 0.9523 0.7327 ] Network output: [ -0.09296 0.1745 0.9309 0.001198 -0.0005389 1.085 0.0009077 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7456 0.6402 0.3928 0.1805 0.9766 0.9841 0.7462 0.9445 0.9666 0.4398 ] Network output: [ -0.1294 0.3954 0.7212 -0.002242 0.001006 1.133 -0.001688 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7833 0.76 0.443 0.004088 0.9746 0.9823 0.7835 0.941 0.9624 0.4544 ] Network output: [ 0.1525 0.5699 0.2917 0.002278 -0.00102 0.8427 0.001705 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1191 Epoch 680 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01215 1.066 0.9469 -0.0001957 8.694e-05 -0.03821 -0.0001437 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0532 -0.006202 0.02959 0.01856 0.9105 0.9243 0.0971 0.8311 0.869 0.19 ] Network output: [ 0.9289 0.1349 -0.05639 0.0007316 -0.0003242 0.06667 0.000534 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6698 -0.07507 -0.03159 0.2791 0.9535 0.976 0.7537 0.8573 0.944 0.7317 ] Network output: [ -0.01547 0.9565 1.022 -0.0001691 7.429e-05 0.05212 -0.0001208 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09572 0.0386 0.06763 0.04751 0.9722 0.9797 0.09782 0.9336 0.9621 0.1058 ] Network output: [ 0.1426 -0.2978 1.15 -0.001441 0.000644 0.8568 -0.001074 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7535 0.3119 0.4087 0.4696 0.9589 0.9795 0.7568 0.8708 0.9523 0.7329 ] Network output: [ -0.09279 0.1742 0.9306 0.001198 -0.000539 1.086 0.0009078 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.745 0.6393 0.3925 0.181 0.9766 0.9841 0.7456 0.9445 0.9666 0.4396 ] Network output: [ -0.1297 0.3944 0.7221 -0.002246 0.001008 1.134 -0.001691 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7827 0.7593 0.4429 0.004924 0.9746 0.9823 0.7829 0.941 0.9625 0.4543 ] Network output: [ 0.1528 0.5702 0.2917 0.002271 -0.001017 0.8417 0.0017 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1191 Epoch 681 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01211 1.066 0.9466 -0.0002052 9.121e-05 -0.03801 -0.0001509 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05306 -0.0062 0.02956 0.01858 0.9105 0.9244 0.09683 0.8311 0.869 0.1897 ] Network output: [ 0.9285 0.1356 -0.0567 0.0007329 -0.0003248 0.06713 0.0005352 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6692 -0.0754 -0.03171 0.2795 0.9535 0.976 0.7531 0.8574 0.944 0.7318 ] Network output: [ -0.0154 0.9569 1.021 -0.0001743 7.663e-05 0.0519 -0.0001247 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09533 0.03837 0.06744 0.04747 0.9722 0.9797 0.09742 0.9336 0.9621 0.1055 ] Network output: [ 0.1428 -0.2981 1.15 -0.001417 0.0006333 0.8567 -0.001056 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7528 0.3106 0.4084 0.4703 0.9589 0.9795 0.7561 0.8708 0.9523 0.7331 ] Network output: [ -0.09262 0.174 0.9303 0.001198 -0.000539 1.086 0.0009079 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7444 0.6385 0.3922 0.1815 0.9766 0.9841 0.745 0.9445 0.9666 0.4393 ] Network output: [ -0.13 0.3935 0.723 -0.002249 0.001009 1.134 -0.001694 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7821 0.7587 0.4428 0.005779 0.9746 0.9823 0.7823 0.941 0.9625 0.4542 ] Network output: [ 0.1532 0.5706 0.2917 0.002264 -0.001014 0.8407 0.001695 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1191 Epoch 682 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01207 1.067 0.9463 -0.0002147 9.548e-05 -0.03782 -0.0001581 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05292 -0.006197 0.02953 0.01861 0.9106 0.9244 0.09657 0.8312 0.8691 0.1894 ] Network output: [ 0.9281 0.1362 -0.05701 0.0007341 -0.0003255 0.06759 0.0005363 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6687 -0.07572 -0.03185 0.2798 0.9535 0.976 0.7524 0.8574 0.9441 0.732 ] Network output: [ -0.01535 0.9573 1.021 -0.0001795 7.898e-05 0.05167 -0.0001287 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09494 0.03814 0.06724 0.04744 0.9722 0.9797 0.09702 0.9336 0.9622 0.1052 ] Network output: [ 0.143 -0.2985 1.15 -0.001393 0.0006224 0.8566 -0.001038 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7521 0.3094 0.4082 0.471 0.9589 0.9795 0.7555 0.8708 0.9523 0.7333 ] Network output: [ -0.09244 0.1738 0.93 0.001198 -0.0005391 1.086 0.000908 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7438 0.6377 0.3919 0.182 0.9766 0.9841 0.7444 0.9445 0.9666 0.4391 ] Network output: [ -0.1302 0.3925 0.7239 -0.002253 0.001011 1.135 -0.001696 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7815 0.758 0.4427 0.00665 0.9745 0.9823 0.7817 0.941 0.9625 0.4542 ] Network output: [ 0.1535 0.5709 0.2917 0.002255 -0.00101 0.8397 0.001689 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.119 Epoch 683 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01203 1.067 0.946 -0.0002241 9.973e-05 -0.03764 -0.0001653 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05278 -0.006195 0.02949 0.01864 0.9106 0.9244 0.0963 0.8312 0.8691 0.1891 ] Network output: [ 0.9277 0.1369 -0.05733 0.0007353 -0.0003261 0.06803 0.0005375 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6682 -0.07603 -0.03199 0.2802 0.9535 0.976 0.7518 0.8574 0.9441 0.7321 ] Network output: [ -0.01529 0.9577 1.021 -0.0001847 8.134e-05 0.05145 -0.0001327 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09456 0.03792 0.06704 0.04741 0.9722 0.9797 0.09663 0.9336 0.9622 0.105 ] Network output: [ 0.1432 -0.2988 1.15 -0.001368 0.0006116 0.8565 -0.00102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7515 0.3082 0.4079 0.4718 0.9589 0.9795 0.7548 0.8708 0.9523 0.7335 ] Network output: [ -0.09226 0.1737 0.9297 0.001199 -0.0005392 1.086 0.0009081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7432 0.6369 0.3916 0.1825 0.9766 0.9841 0.7438 0.9446 0.9666 0.4388 ] Network output: [ -0.1305 0.3915 0.7248 -0.002256 0.001012 1.135 -0.001698 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7809 0.7574 0.4426 0.007537 0.9745 0.9823 0.7811 0.9409 0.9625 0.4541 ] Network output: [ 0.1538 0.5712 0.2916 0.002247 -0.001006 0.8387 0.001682 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.119 Epoch 684 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01199 1.067 0.9457 -0.0002335 0.000104 -0.03746 -0.0001724 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05265 -0.006191 0.02946 0.01867 0.9106 0.9244 0.09605 0.8313 0.8692 0.1888 ] Network output: [ 0.9273 0.1375 -0.05764 0.0007366 -0.0003267 0.06847 0.0005386 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6676 -0.07633 -0.03216 0.2806 0.9535 0.976 0.7512 0.8575 0.9441 0.7323 ] Network output: [ -0.01524 0.9581 1.02 -0.0001899 8.369e-05 0.05123 -0.0001367 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09419 0.0377 0.06685 0.04738 0.9722 0.9797 0.09624 0.9336 0.9622 0.1047 ] Network output: [ 0.1434 -0.2992 1.151 -0.001344 0.0006006 0.8564 -0.001002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7508 0.307 0.4076 0.4725 0.9589 0.9795 0.7542 0.8708 0.9523 0.7337 ] Network output: [ -0.09206 0.1735 0.9294 0.001199 -0.0005394 1.086 0.0009083 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7426 0.636 0.3913 0.183 0.9766 0.9841 0.7432 0.9446 0.9666 0.4385 ] Network output: [ -0.1308 0.3905 0.7258 -0.002258 0.001013 1.136 -0.0017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7803 0.7567 0.4425 0.008437 0.9745 0.9823 0.7805 0.9409 0.9625 0.454 ] Network output: [ 0.1541 0.5716 0.2916 0.002237 -0.001002 0.8377 0.001676 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1189 Epoch 685 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01195 1.067 0.9454 -0.0002429 0.0001082 -0.03728 -0.0001795 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05251 -0.006188 0.02942 0.0187 0.9106 0.9244 0.09579 0.8313 0.8692 0.1885 ] Network output: [ 0.927 0.1382 -0.05796 0.0007377 -0.0003273 0.06889 0.0005397 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6671 -0.07661 -0.03233 0.281 0.9536 0.976 0.7506 0.8575 0.9441 0.7324 ] Network output: [ -0.01519 0.9584 1.02 -0.000195 8.604e-05 0.05102 -0.0001407 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09382 0.03749 0.06665 0.04735 0.9722 0.9798 0.09587 0.9336 0.9622 0.1044 ] Network output: [ 0.1436 -0.2996 1.151 -0.001319 0.0005896 0.8563 -0.0009832 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7502 0.3059 0.4073 0.4732 0.9589 0.9795 0.7535 0.8708 0.9523 0.7339 ] Network output: [ -0.09186 0.1734 0.929 0.001199 -0.0005395 1.086 0.0009084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.742 0.6352 0.3909 0.1834 0.9766 0.9841 0.7426 0.9446 0.9666 0.4382 ] Network output: [ -0.1311 0.3895 0.7267 -0.002261 0.001015 1.137 -0.001702 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7797 0.7561 0.4424 0.009348 0.9745 0.9823 0.7799 0.9409 0.9625 0.4539 ] Network output: [ 0.1544 0.572 0.2915 0.002228 -0.0009976 0.8368 0.001668 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1189 Epoch 686 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01191 1.067 0.9452 -0.0002521 0.0001123 -0.03711 -0.0001865 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05238 -0.006183 0.02938 0.01873 0.9107 0.9245 0.09554 0.8314 0.8693 0.1882 ] Network output: [ 0.9266 0.1388 -0.05829 0.0007389 -0.0003278 0.06931 0.0005408 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6666 -0.07689 -0.03252 0.2814 0.9536 0.976 0.75 0.8575 0.9442 0.7326 ] Network output: [ -0.01514 0.9588 1.02 -0.0002002 8.839e-05 0.0508 -0.0001447 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09345 0.03728 0.06645 0.04732 0.9722 0.9798 0.09549 0.9336 0.9622 0.1041 ] Network output: [ 0.1437 -0.2999 1.151 -0.001295 0.0005786 0.8562 -0.0009648 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7496 0.3048 0.407 0.474 0.9589 0.9795 0.7529 0.8708 0.9524 0.7341 ] Network output: [ -0.09166 0.1733 0.9286 0.001199 -0.0005396 1.086 0.0009086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7414 0.6345 0.3905 0.1839 0.9766 0.9841 0.742 0.9446 0.9666 0.4379 ] Network output: [ -0.1313 0.3886 0.7276 -0.002263 0.001016 1.137 -0.001704 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7792 0.7554 0.4422 0.01027 0.9745 0.9823 0.7793 0.9409 0.9625 0.4538 ] Network output: [ 0.1547 0.5723 0.2914 0.002218 -0.000993 0.8359 0.001661 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1188 Epoch 687 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01186 1.067 0.9449 -0.0002613 0.0001165 -0.03694 -0.0001935 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05225 -0.006179 0.02934 0.01876 0.9107 0.9245 0.09529 0.8315 0.8693 0.188 ] Network output: [ 0.9263 0.1394 -0.05861 0.00074 -0.0003284 0.06971 0.0005419 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6661 -0.07715 -0.03272 0.2818 0.9536 0.976 0.7494 0.8575 0.9442 0.7327 ] Network output: [ -0.0151 0.9592 1.02 -0.0002054 9.071e-05 0.05059 -0.0001487 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09309 0.03708 0.06625 0.04729 0.9722 0.9798 0.09513 0.9337 0.9623 0.1038 ] Network output: [ 0.1439 -0.3003 1.151 -0.00127 0.0005675 0.8561 -0.0009463 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.749 0.3037 0.4066 0.4747 0.9589 0.9795 0.7523 0.8708 0.9524 0.7342 ] Network output: [ -0.09145 0.1733 0.9282 0.0012 -0.0005397 1.086 0.0009087 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7408 0.6337 0.3902 0.1844 0.9766 0.9841 0.7414 0.9446 0.9667 0.4376 ] Network output: [ -0.1316 0.3876 0.7286 -0.002266 0.001017 1.138 -0.001706 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7786 0.7548 0.4421 0.0112 0.9745 0.9823 0.7787 0.9409 0.9625 0.4536 ] Network output: [ 0.155 0.5727 0.2913 0.002207 -0.0009883 0.835 0.001653 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1188 Epoch 688 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01182 1.067 0.9447 -0.0002704 0.0001206 -0.03677 -0.0002004 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05212 -0.006174 0.0293 0.01879 0.9107 0.9245 0.09505 0.8315 0.8694 0.1877 ] Network output: [ 0.926 0.1399 -0.05894 0.0007411 -0.0003289 0.07011 0.0005429 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6657 -0.0774 -0.03293 0.2821 0.9536 0.976 0.7489 0.8576 0.9442 0.7329 ] Network output: [ -0.01506 0.9595 1.019 -0.0002105 9.303e-05 0.05038 -0.0001526 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09274 0.03688 0.06606 0.04726 0.9722 0.9798 0.09476 0.9337 0.9623 0.1035 ] Network output: [ 0.1441 -0.3007 1.151 -0.001245 0.0005564 0.8561 -0.0009277 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7484 0.3027 0.4063 0.4755 0.9589 0.9795 0.7517 0.8708 0.9524 0.7344 ] Network output: [ -0.09124 0.1732 0.9278 0.0012 -0.0005398 1.086 0.0009089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7402 0.6329 0.3898 0.1849 0.9766 0.9842 0.7408 0.9446 0.9667 0.4373 ] Network output: [ -0.1319 0.3866 0.7295 -0.002268 0.001018 1.138 -0.001707 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.778 0.7542 0.4419 0.01214 0.9745 0.9823 0.7781 0.9409 0.9625 0.4535 ] Network output: [ 0.1553 0.5731 0.2911 0.002196 -0.0009834 0.8341 0.001645 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1187 Epoch 689 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01177 1.067 0.9445 -0.0002794 0.0001246 -0.03661 -0.0002072 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05199 -0.006168 0.02926 0.01883 0.9107 0.9245 0.0948 0.8316 0.8694 0.1874 ] Network output: [ 0.9256 0.1405 -0.05927 0.000742 -0.0003294 0.0705 0.0005438 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6652 -0.07764 -0.03315 0.2825 0.9536 0.976 0.7483 0.8576 0.9442 0.733 ] Network output: [ -0.01502 0.9599 1.019 -0.0002155 9.532e-05 0.05018 -0.0001565 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09239 0.03669 0.06586 0.04723 0.9722 0.9798 0.09441 0.9337 0.9623 0.1033 ] Network output: [ 0.1442 -0.301 1.152 -0.00122 0.0005453 0.856 -0.0009092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7478 0.3017 0.406 0.4762 0.9589 0.9795 0.7511 0.8708 0.9524 0.7346 ] Network output: [ -0.09103 0.1732 0.9274 0.0012 -0.0005399 1.086 0.000909 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7396 0.6322 0.3894 0.1854 0.9766 0.9842 0.7403 0.9446 0.9667 0.4369 ] Network output: [ -0.1321 0.3856 0.7305 -0.00227 0.001019 1.139 -0.001709 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7774 0.7535 0.4418 0.01308 0.9745 0.9823 0.7776 0.9409 0.9625 0.4534 ] Network output: [ 0.1556 0.5735 0.291 0.002184 -0.0009783 0.8333 0.001636 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1186 Epoch 690 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01172 1.068 0.9443 -0.0002883 0.0001286 -0.03645 -0.000214 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05186 -0.006162 0.02922 0.01886 0.9107 0.9245 0.09456 0.8316 0.8695 0.1871 ] Network output: [ 0.9254 0.1411 -0.05961 0.000743 -0.0003299 0.07088 0.0005447 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6647 -0.07786 -0.03339 0.2829 0.9536 0.976 0.7478 0.8576 0.9443 0.7332 ] Network output: [ -0.01498 0.9602 1.019 -0.0002205 9.758e-05 0.04997 -0.0001603 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09205 0.0365 0.06566 0.04721 0.9722 0.9798 0.09406 0.9337 0.9623 0.103 ] Network output: [ 0.1443 -0.3014 1.152 -0.001195 0.0005341 0.856 -0.0008905 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7472 0.3008 0.4056 0.477 0.9589 0.9796 0.7505 0.8708 0.9524 0.7348 ] Network output: [ -0.09081 0.1732 0.9269 0.0012 -0.00054 1.086 0.000909 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7391 0.6314 0.389 0.1858 0.9766 0.9842 0.7397 0.9446 0.9667 0.4365 ] Network output: [ -0.1324 0.3847 0.7314 -0.002272 0.001019 1.139 -0.00171 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7769 0.7529 0.4416 0.01403 0.9745 0.9823 0.777 0.9409 0.9625 0.4533 ] Network output: [ 0.1558 0.5739 0.2908 0.002173 -0.000973 0.8325 0.001628 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1186 Epoch 691 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01167 1.068 0.9441 -0.0002971 0.0001326 -0.0363 -0.0002206 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05174 -0.006156 0.02917 0.01889 0.9108 0.9246 0.09433 0.8317 0.8695 0.1868 ] Network output: [ 0.9251 0.1416 -0.05995 0.0007438 -0.0003303 0.07125 0.0005456 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6643 -0.07808 -0.03364 0.2833 0.9536 0.9761 0.7472 0.8576 0.9443 0.7333 ] Network output: [ -0.01495 0.9605 1.019 -0.0002255 9.982e-05 0.04977 -0.0001641 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09172 0.03632 0.06547 0.04718 0.9722 0.9798 0.09372 0.9337 0.9623 0.1027 ] Network output: [ 0.1444 -0.3017 1.152 -0.00117 0.000523 0.8559 -0.0008719 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7467 0.2998 0.4052 0.4777 0.9589 0.9796 0.75 0.8708 0.9524 0.735 ] Network output: [ -0.09059 0.1733 0.9265 0.001201 -0.00054 1.086 0.0009091 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7385 0.6307 0.3886 0.1863 0.9766 0.9842 0.7391 0.9446 0.9667 0.4362 ] Network output: [ -0.1327 0.3837 0.7324 -0.002273 0.00102 1.14 -0.001712 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7763 0.7523 0.4415 0.01498 0.9745 0.9823 0.7764 0.9409 0.9625 0.4531 ] Network output: [ 0.1561 0.5743 0.2906 0.002161 -0.0009677 0.8317 0.001619 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1185 Epoch 692 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01163 1.068 0.9439 -0.0003058 0.0001365 -0.03615 -0.0002272 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05161 -0.006149 0.02913 0.01893 0.9108 0.9246 0.0941 0.8317 0.8696 0.1866 ] Network output: [ 0.9248 0.1421 -0.06029 0.0007446 -0.0003307 0.07161 0.0005463 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6638 -0.07828 -0.03389 0.2837 0.9536 0.9761 0.7467 0.8577 0.9443 0.7334 ] Network output: [ -0.01491 0.9608 1.018 -0.0002304 0.000102 0.04957 -0.0001679 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09139 0.03614 0.06527 0.04716 0.9722 0.9798 0.09338 0.9338 0.9624 0.1024 ] Network output: [ 0.1446 -0.3021 1.152 -0.001145 0.0005118 0.8559 -0.0008532 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7461 0.2989 0.4049 0.4784 0.9589 0.9796 0.7494 0.8708 0.9524 0.7352 ] Network output: [ -0.09037 0.1733 0.926 0.001201 -0.00054 1.086 0.0009091 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7379 0.63 0.3882 0.1868 0.9766 0.9842 0.7385 0.9446 0.9667 0.4358 ] Network output: [ -0.133 0.3828 0.7334 -0.002275 0.001021 1.141 -0.001713 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7757 0.7517 0.4413 0.01594 0.9745 0.9823 0.7759 0.9409 0.9625 0.453 ] Network output: [ 0.1564 0.5747 0.2904 0.002148 -0.0009622 0.8309 0.00161 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1184 Epoch 693 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01158 1.068 0.9437 -0.0003143 0.0001403 -0.036 -0.0002336 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05149 -0.006142 0.02908 0.01896 0.9108 0.9246 0.09387 0.8318 0.8696 0.1863 ] Network output: [ 0.9245 0.1426 -0.06063 0.0007453 -0.000331 0.07196 0.000547 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6634 -0.07847 -0.03416 0.2841 0.9536 0.9761 0.7462 0.8577 0.9443 0.7336 ] Network output: [ -0.01488 0.9612 1.018 -0.0002352 0.0001042 0.04937 -0.0001716 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09106 0.03597 0.06508 0.04713 0.9722 0.9798 0.09305 0.9338 0.9624 0.1022 ] Network output: [ 0.1447 -0.3025 1.153 -0.001121 0.0005006 0.8559 -0.0008346 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7456 0.2981 0.4045 0.4792 0.9589 0.9796 0.7489 0.8708 0.9525 0.7353 ] Network output: [ -0.09014 0.1734 0.9255 0.001201 -0.00054 1.086 0.000909 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7373 0.6293 0.3877 0.1872 0.9766 0.9842 0.7379 0.9446 0.9667 0.4354 ] Network output: [ -0.1332 0.3818 0.7343 -0.002277 0.001022 1.141 -0.001714 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7752 0.7511 0.4411 0.01689 0.9745 0.9823 0.7753 0.9409 0.9625 0.4528 ] Network output: [ 0.1566 0.5751 0.2902 0.002136 -0.0009566 0.8302 0.0016 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1184 Epoch 694 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01152 1.068 0.9436 -0.0003227 0.0001441 -0.03586 -0.00024 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05137 -0.006135 0.02904 0.01899 0.9108 0.9246 0.09365 0.8318 0.8696 0.186 ] Network output: [ 0.9243 0.1431 -0.06098 0.0007458 -0.0003313 0.0723 0.0005477 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6629 -0.07865 -0.03444 0.2845 0.9536 0.9761 0.7457 0.8577 0.9444 0.7337 ] Network output: [ -0.01486 0.9615 1.018 -0.0002399 0.0001063 0.04917 -0.0001752 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09074 0.0358 0.06488 0.04711 0.9723 0.9798 0.09272 0.9338 0.9624 0.1019 ] Network output: [ 0.1447 -0.3028 1.153 -0.001096 0.0004895 0.8559 -0.0008159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.745 0.2973 0.4041 0.4799 0.9589 0.9796 0.7483 0.8708 0.9525 0.7355 ] Network output: [ -0.08991 0.1735 0.925 0.001201 -0.00054 1.086 0.0009089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7367 0.6286 0.3873 0.1877 0.9766 0.9842 0.7374 0.9446 0.9667 0.435 ] Network output: [ -0.1335 0.3809 0.7353 -0.002278 0.001022 1.142 -0.001715 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7746 0.7505 0.441 0.01785 0.9745 0.9823 0.7748 0.9409 0.9625 0.4527 ] Network output: [ 0.1569 0.5755 0.2899 0.002123 -0.0009509 0.8295 0.001591 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1183 Epoch 695 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01147 1.068 0.9435 -0.0003309 0.0001478 -0.03572 -0.0002462 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05126 -0.006127 0.02899 0.01903 0.9109 0.9246 0.09342 0.8319 0.8697 0.1858 ] Network output: [ 0.9241 0.1436 -0.06133 0.0007463 -0.0003316 0.07263 0.0005482 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6625 -0.07883 -0.03473 0.2848 0.9536 0.9761 0.7452 0.8578 0.9444 0.7338 ] Network output: [ -0.01483 0.9618 1.018 -0.0002445 0.0001084 0.04898 -0.0001787 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09043 0.03564 0.06469 0.04708 0.9723 0.9798 0.0924 0.9338 0.9624 0.1016 ] Network output: [ 0.1448 -0.3032 1.153 -0.001071 0.0004783 0.8559 -0.0007973 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7445 0.2965 0.4037 0.4807 0.959 0.9796 0.7478 0.8709 0.9525 0.7357 ] Network output: [ -0.08969 0.1736 0.9245 0.001201 -0.00054 1.086 0.0009088 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7362 0.6279 0.3868 0.1881 0.9766 0.9842 0.7368 0.9446 0.9668 0.4346 ] Network output: [ -0.1338 0.38 0.7362 -0.00228 0.001023 1.142 -0.001716 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7741 0.7499 0.4408 0.0188 0.9745 0.9823 0.7742 0.9409 0.9625 0.4525 ] Network output: [ 0.1571 0.5758 0.2897 0.00211 -0.0009452 0.8288 0.001581 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1182 Epoch 696 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01142 1.068 0.9433 -0.000339 0.0001514 -0.03559 -0.0002523 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05114 -0.006119 0.02894 0.01906 0.9109 0.9246 0.09321 0.8319 0.8697 0.1855 ] Network output: [ 0.9239 0.1441 -0.06168 0.0007467 -0.0003318 0.07295 0.0005487 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6621 -0.07899 -0.03502 0.2852 0.9536 0.9761 0.7447 0.8578 0.9444 0.734 ] Network output: [ -0.01481 0.962 1.018 -0.000249 0.0001105 0.04879 -0.0001822 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.09012 0.03548 0.06449 0.04706 0.9723 0.9798 0.09208 0.9338 0.9624 0.1014 ] Network output: [ 0.1449 -0.3035 1.154 -0.001046 0.0004672 0.8559 -0.0007787 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.744 0.2957 0.4033 0.4814 0.959 0.9796 0.7473 0.8709 0.9525 0.7358 ] Network output: [ -0.08946 0.1738 0.924 0.0012 -0.0005399 1.086 0.0009086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7356 0.6272 0.3864 0.1885 0.9766 0.9842 0.7362 0.9446 0.9668 0.4342 ] Network output: [ -0.1341 0.379 0.7372 -0.002281 0.001024 1.143 -0.001718 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7735 0.7494 0.4406 0.01975 0.9745 0.9823 0.7737 0.9409 0.9625 0.4524 ] Network output: [ 0.1574 0.5762 0.2894 0.002097 -0.0009393 0.8281 0.001571 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1181 Epoch 697 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01137 1.068 0.9432 -0.0003469 0.000155 -0.03546 -0.0002583 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05103 -0.006111 0.02889 0.0191 0.9109 0.9247 0.09299 0.832 0.8698 0.1853 ] Network output: [ 0.9236 0.1445 -0.06203 0.0007469 -0.0003319 0.07327 0.000549 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6617 -0.07914 -0.03533 0.2856 0.9537 0.9761 0.7443 0.8578 0.9445 0.7341 ] Network output: [ -0.01478 0.9623 1.018 -0.0002534 0.0001125 0.0486 -0.0001856 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08982 0.03533 0.0643 0.04703 0.9723 0.9798 0.09178 0.9338 0.9624 0.1011 ] Network output: [ 0.145 -0.3039 1.154 -0.001021 0.0004561 0.8559 -0.0007601 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7435 0.2949 0.4029 0.4821 0.959 0.9796 0.7468 0.8709 0.9525 0.736 ] Network output: [ -0.08923 0.174 0.9235 0.0012 -0.0005397 1.086 0.0009084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7351 0.6266 0.3859 0.189 0.9766 0.9842 0.7357 0.9446 0.9668 0.4338 ] Network output: [ -0.1343 0.3781 0.7381 -0.002282 0.001024 1.143 -0.001719 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.773 0.7488 0.4404 0.0207 0.9745 0.9823 0.7731 0.9408 0.9625 0.4522 ] Network output: [ 0.1576 0.5766 0.2891 0.002084 -0.0009334 0.8275 0.001562 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.118 Epoch 698 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01132 1.068 0.9432 -0.0003547 0.0001585 -0.03533 -0.0002642 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05091 -0.006102 0.02884 0.01914 0.9109 0.9247 0.09278 0.832 0.8698 0.185 ] Network output: [ 0.9234 0.145 -0.06239 0.000747 -0.000332 0.07357 0.0005493 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6613 -0.07928 -0.03564 0.286 0.9537 0.9761 0.7438 0.8579 0.9445 0.7343 ] Network output: [ -0.01476 0.9626 1.017 -0.0002577 0.0001144 0.04841 -0.0001889 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08952 0.03518 0.06411 0.04701 0.9723 0.9798 0.09147 0.9339 0.9625 0.1009 ] Network output: [ 0.145 -0.3042 1.154 -0.0009963 0.000445 0.8559 -0.0007416 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.743 0.2942 0.4025 0.4828 0.959 0.9796 0.7463 0.8709 0.9525 0.7362 ] Network output: [ -0.08901 0.1741 0.9229 0.0012 -0.0005396 1.086 0.0009081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7345 0.6259 0.3854 0.1894 0.9766 0.9842 0.7351 0.9446 0.9668 0.4334 ] Network output: [ -0.1346 0.3773 0.7391 -0.002284 0.001025 1.144 -0.00172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7724 0.7482 0.4403 0.02165 0.9745 0.9823 0.7726 0.9408 0.9625 0.4521 ] Network output: [ 0.1579 0.577 0.2888 0.002071 -0.0009275 0.8269 0.001552 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.118 Epoch 699 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01127 1.068 0.9431 -0.0003623 0.0001619 -0.03521 -0.00027 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0508 -0.006093 0.02879 0.01917 0.911 0.9247 0.09257 0.8321 0.8699 0.1848 ] Network output: [ 0.9233 0.1454 -0.06275 0.000747 -0.0003321 0.07387 0.0005494 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6609 -0.0794 -0.03597 0.2864 0.9537 0.9761 0.7433 0.8579 0.9445 0.7344 ] Network output: [ -0.01474 0.9628 1.017 -0.0002619 0.0001163 0.04822 -0.0001922 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08923 0.03503 0.06392 0.04699 0.9723 0.9798 0.09117 0.9339 0.9625 0.1006 ] Network output: [ 0.1451 -0.3046 1.154 -0.0009716 0.000434 0.8559 -0.0007231 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7425 0.2936 0.402 0.4836 0.959 0.9796 0.7458 0.8709 0.9526 0.7364 ] Network output: [ -0.08878 0.1743 0.9224 0.001199 -0.0005394 1.086 0.0009077 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7339 0.6253 0.385 0.1898 0.9766 0.9842 0.7346 0.9446 0.9668 0.433 ] Network output: [ -0.1349 0.3764 0.74 -0.002285 0.001025 1.144 -0.001721 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7719 0.7477 0.4401 0.02259 0.9745 0.9823 0.772 0.9408 0.9625 0.4519 ] Network output: [ 0.1581 0.5774 0.2885 0.002057 -0.0009215 0.8263 0.001542 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1179 Epoch 700 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01122 1.068 0.943 -0.0003697 0.0001652 -0.03509 -0.0002756 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05069 -0.006083 0.02874 0.01921 0.911 0.9247 0.09237 0.8321 0.8699 0.1845 ] Network output: [ 0.9231 0.1458 -0.06311 0.0007468 -0.000332 0.07416 0.0005495 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6605 -0.07952 -0.0363 0.2867 0.9537 0.9761 0.7429 0.8579 0.9445 0.7345 ] Network output: [ -0.01472 0.9631 1.017 -0.000266 0.0001182 0.04804 -0.0001953 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08894 0.03489 0.06373 0.04696 0.9723 0.9798 0.09088 0.9339 0.9625 0.1003 ] Network output: [ 0.1451 -0.3049 1.155 -0.000947 0.000423 0.856 -0.0007047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7421 0.2929 0.4016 0.4843 0.959 0.9796 0.7453 0.8709 0.9526 0.7365 ] Network output: [ -0.08855 0.1746 0.9219 0.001199 -0.0005392 1.086 0.0009073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7334 0.6246 0.3845 0.1902 0.9766 0.9842 0.734 0.9447 0.9668 0.4325 ] Network output: [ -0.1352 0.3755 0.7409 -0.002286 0.001026 1.145 -0.001722 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7714 0.7471 0.4399 0.02352 0.9745 0.9823 0.7715 0.9408 0.9626 0.4518 ] Network output: [ 0.1583 0.5777 0.2882 0.002044 -0.0009155 0.8257 0.001532 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1178 Epoch 701 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01116 1.068 0.943 -0.0003769 0.0001685 -0.03498 -0.0002811 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05058 -0.006074 0.02868 0.01924 0.911 0.9247 0.09217 0.8322 0.87 0.1843 ] Network output: [ 0.9229 0.1462 -0.06348 0.0007465 -0.0003319 0.07444 0.0005494 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6602 -0.07963 -0.03664 0.2871 0.9537 0.9761 0.7425 0.858 0.9446 0.7346 ] Network output: [ -0.01471 0.9633 1.017 -0.00027 0.00012 0.04786 -0.0001984 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08866 0.03475 0.06354 0.04694 0.9723 0.9798 0.09059 0.9339 0.9625 0.1001 ] Network output: [ 0.1452 -0.3053 1.155 -0.0009225 0.000412 0.856 -0.0006864 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7416 0.2923 0.4012 0.485 0.959 0.9796 0.7449 0.8709 0.9526 0.7367 ] Network output: [ -0.08833 0.1748 0.9213 0.001198 -0.0005389 1.085 0.0009069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7328 0.624 0.384 0.1906 0.9766 0.9842 0.7334 0.9447 0.9668 0.4321 ] Network output: [ -0.1354 0.3747 0.7419 -0.002288 0.001027 1.145 -0.001723 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7708 0.7466 0.4397 0.02445 0.9745 0.9823 0.771 0.9408 0.9626 0.4516 ] Network output: [ 0.1586 0.5781 0.2878 0.00203 -0.0009094 0.8252 0.001522 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1177 Epoch 702 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01111 1.068 0.9429 -0.000384 0.0001717 -0.03486 -0.0002864 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05048 -0.006064 0.02863 0.01928 0.911 0.9248 0.09197 0.8322 0.87 0.1841 ] Network output: [ 0.9228 0.1466 -0.06384 0.000746 -0.0003318 0.07471 0.0005492 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6598 -0.07973 -0.03698 0.2875 0.9537 0.9761 0.7421 0.858 0.9446 0.7348 ] Network output: [ -0.01469 0.9636 1.017 -0.0002738 0.0001217 0.04768 -0.0002013 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08839 0.03462 0.06336 0.04692 0.9723 0.9798 0.09031 0.934 0.9626 0.09985 ] Network output: [ 0.1452 -0.3056 1.155 -0.0008981 0.0004011 0.8561 -0.0006681 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7412 0.2917 0.4007 0.4857 0.959 0.9796 0.7444 0.8709 0.9526 0.7368 ] Network output: [ -0.08811 0.1751 0.9208 0.001198 -0.0005386 1.085 0.0009064 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7323 0.6234 0.3835 0.191 0.9766 0.9842 0.7329 0.9447 0.9669 0.4317 ] Network output: [ -0.1357 0.3738 0.7428 -0.002289 0.001027 1.145 -0.001724 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7703 0.746 0.4395 0.02538 0.9745 0.9823 0.7705 0.9408 0.9626 0.4514 ] Network output: [ 0.1588 0.5785 0.2875 0.002017 -0.0009034 0.8247 0.001512 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1177 Epoch 703 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01106 1.068 0.9429 -0.0003908 0.0001747 -0.03476 -0.0002916 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05037 -0.006054 0.02858 0.01932 0.911 0.9248 0.09177 0.8323 0.8701 0.1838 ] Network output: [ 0.9226 0.147 -0.06421 0.0007454 -0.0003315 0.07497 0.0005489 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6594 -0.07982 -0.03734 0.2878 0.9537 0.9761 0.7416 0.858 0.9446 0.7349 ] Network output: [ -0.01468 0.9638 1.017 -0.0002775 0.0001234 0.0475 -0.0002041 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08812 0.03449 0.06317 0.04689 0.9723 0.9799 0.09003 0.934 0.9626 0.0996 ] Network output: [ 0.1452 -0.3059 1.156 -0.0008738 0.0003902 0.8561 -0.0006499 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7407 0.2911 0.4003 0.4864 0.959 0.9796 0.744 0.871 0.9526 0.737 ] Network output: [ -0.08789 0.1754 0.9202 0.001197 -0.0005383 1.085 0.0009058 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7317 0.6228 0.383 0.1914 0.9766 0.9842 0.7323 0.9447 0.9669 0.4312 ] Network output: [ -0.136 0.373 0.7437 -0.00229 0.001028 1.146 -0.001725 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7698 0.7455 0.4393 0.02629 0.9744 0.9823 0.7699 0.9408 0.9626 0.4513 ] Network output: [ 0.159 0.5788 0.2871 0.002003 -0.0008973 0.8242 0.001501 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1176 Epoch 704 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01101 1.068 0.9429 -0.0003975 0.0001778 -0.03465 -0.0002967 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05027 -0.006043 0.02852 0.01935 0.9111 0.9248 0.09158 0.8324 0.8701 0.1836 ] Network output: [ 0.9225 0.1474 -0.06458 0.0007446 -0.0003312 0.07523 0.0005485 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6591 -0.0799 -0.0377 0.2882 0.9537 0.9761 0.7412 0.8581 0.9446 0.735 ] Network output: [ -0.01467 0.964 1.017 -0.0002811 0.000125 0.04732 -0.0002069 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08785 0.03437 0.06298 0.04687 0.9723 0.9799 0.08976 0.934 0.9626 0.09936 ] Network output: [ 0.1453 -0.3063 1.156 -0.0008496 0.0003794 0.8562 -0.0006318 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7403 0.2906 0.3998 0.4871 0.959 0.9796 0.7436 0.871 0.9526 0.7372 ] Network output: [ -0.08767 0.1757 0.9196 0.001196 -0.000538 1.085 0.0009051 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7312 0.6222 0.3825 0.1917 0.9766 0.9842 0.7318 0.9447 0.9669 0.4308 ] Network output: [ -0.1362 0.3722 0.7446 -0.002291 0.001028 1.146 -0.001726 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7693 0.7449 0.4391 0.0272 0.9744 0.9823 0.7694 0.9408 0.9626 0.4511 ] Network output: [ 0.1592 0.5792 0.2867 0.00199 -0.0008913 0.8237 0.001491 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1175 Epoch 705 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01096 1.068 0.9429 -0.000404 0.0001807 -0.03455 -0.0003016 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05016 -0.006032 0.02847 0.01939 0.9111 0.9248 0.09139 0.8324 0.8702 0.1834 ] Network output: [ 0.9224 0.1478 -0.06495 0.0007437 -0.0003308 0.07548 0.000548 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6588 -0.07997 -0.03806 0.2886 0.9537 0.9761 0.7409 0.8581 0.9447 0.7351 ] Network output: [ -0.01465 0.9642 1.017 -0.0002845 0.0001265 0.04715 -0.0002095 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08759 0.03425 0.0628 0.04685 0.9723 0.9799 0.08949 0.934 0.9626 0.09911 ] Network output: [ 0.1453 -0.3066 1.156 -0.0008255 0.0003686 0.8562 -0.0006138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7399 0.2901 0.3994 0.4877 0.959 0.9796 0.7431 0.871 0.9527 0.7373 ] Network output: [ -0.08745 0.176 0.9191 0.001196 -0.0005376 1.085 0.0009045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7306 0.6217 0.382 0.1921 0.9766 0.9842 0.7313 0.9447 0.9669 0.4303 ] Network output: [ -0.1365 0.3714 0.7455 -0.002293 0.001029 1.147 -0.001727 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7688 0.7444 0.439 0.0281 0.9744 0.9823 0.7689 0.9408 0.9626 0.4509 ] Network output: [ 0.1595 0.5795 0.2863 0.001976 -0.0008852 0.8233 0.001481 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1174 Epoch 706 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01091 1.068 0.9429 -0.0004103 0.0001835 -0.03446 -0.0003064 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.05006 -0.006021 0.02841 0.01942 0.9111 0.9248 0.0912 0.8325 0.8702 0.1831 ] Network output: [ 0.9222 0.1481 -0.06532 0.0007426 -0.0003304 0.07572 0.0005473 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6584 -0.08004 -0.03843 0.2889 0.9537 0.9761 0.7405 0.8581 0.9447 0.7353 ] Network output: [ -0.01464 0.9644 1.017 -0.0002878 0.000128 0.04697 -0.0002121 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08733 0.03413 0.06262 0.04683 0.9723 0.9799 0.08923 0.9341 0.9626 0.09888 ] Network output: [ 0.1453 -0.3069 1.157 -0.0008016 0.0003579 0.8563 -0.0005959 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7395 0.2896 0.3989 0.4884 0.959 0.9796 0.7427 0.871 0.9527 0.7375 ] Network output: [ -0.08723 0.1763 0.9185 0.001195 -0.0005372 1.085 0.0009037 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7301 0.6211 0.3815 0.1925 0.9766 0.9842 0.7307 0.9447 0.9669 0.4299 ] Network output: [ -0.1368 0.3706 0.7464 -0.002294 0.00103 1.147 -0.001728 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7683 0.7439 0.4388 0.029 0.9744 0.9823 0.7684 0.9408 0.9626 0.4508 ] Network output: [ 0.1597 0.5798 0.2859 0.001963 -0.0008792 0.8229 0.001471 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1173 Epoch 707 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01086 1.068 0.9429 -0.0004164 0.0001863 -0.03436 -0.000311 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04996 -0.00601 0.02836 0.01946 0.9111 0.9248 0.09102 0.8325 0.8703 0.1829 ] Network output: [ 0.9221 0.1485 -0.06569 0.0007413 -0.0003298 0.07596 0.0005465 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6581 -0.08009 -0.03881 0.2893 0.9537 0.9761 0.7401 0.8582 0.9447 0.7354 ] Network output: [ -0.01463 0.9646 1.017 -0.0002909 0.0001295 0.0468 -0.0002145 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08708 0.03401 0.06244 0.0468 0.9723 0.9799 0.08897 0.9341 0.9627 0.09864 ] Network output: [ 0.1453 -0.3073 1.157 -0.0007778 0.0003472 0.8564 -0.0005781 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7391 0.2892 0.3984 0.4891 0.959 0.9796 0.7423 0.871 0.9527 0.7376 ] Network output: [ -0.08702 0.1766 0.9179 0.001194 -0.0005367 1.084 0.0009029 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7296 0.6206 0.381 0.1928 0.9766 0.9842 0.7302 0.9447 0.9669 0.4294 ] Network output: [ -0.137 0.3699 0.7473 -0.002295 0.00103 1.148 -0.001728 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7678 0.7434 0.4386 0.02988 0.9744 0.9823 0.7679 0.9408 0.9626 0.4506 ] Network output: [ 0.1599 0.5802 0.2855 0.001949 -0.0008732 0.8225 0.001461 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1173 Epoch 708 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01082 1.068 0.9429 -0.0004223 0.0001889 -0.03428 -0.0003155 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04987 -0.005999 0.0283 0.01949 0.9111 0.9249 0.09084 0.8326 0.8703 0.1827 ] Network output: [ 0.922 0.1488 -0.06606 0.0007398 -0.0003292 0.07619 0.0005456 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6578 -0.08013 -0.03919 0.2896 0.9538 0.9762 0.7397 0.8582 0.9447 0.7355 ] Network output: [ -0.01462 0.9648 1.017 -0.000294 0.0001308 0.04663 -0.0002169 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08683 0.0339 0.06226 0.04678 0.9723 0.9799 0.08872 0.9341 0.9627 0.09841 ] Network output: [ 0.1453 -0.3076 1.157 -0.0007541 0.0003366 0.8565 -0.0005603 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7387 0.2888 0.3979 0.4898 0.959 0.9796 0.7419 0.871 0.9527 0.7378 ] Network output: [ -0.08681 0.177 0.9174 0.001193 -0.0005362 1.084 0.0009021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.729 0.62 0.3805 0.1931 0.9766 0.9842 0.7296 0.9447 0.9669 0.429 ] Network output: [ -0.1373 0.3691 0.7482 -0.002297 0.001031 1.148 -0.001729 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7673 0.7429 0.4384 0.03076 0.9744 0.9823 0.7674 0.9408 0.9626 0.4505 ] Network output: [ 0.1601 0.5805 0.2851 0.001936 -0.0008672 0.8221 0.001451 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1172 Epoch 709 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01077 1.068 0.943 -0.0004281 0.0001915 -0.03419 -0.0003199 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04977 -0.005987 0.02825 0.01953 0.9112 0.9249 0.09066 0.8326 0.8704 0.1825 ] Network output: [ 0.9219 0.1492 -0.06643 0.0007382 -0.0003285 0.07641 0.0005445 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6575 -0.08017 -0.03958 0.29 0.9538 0.9762 0.7394 0.8582 0.9448 0.7356 ] Network output: [ -0.01461 0.9649 1.017 -0.0002968 0.0001321 0.04646 -0.0002191 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08658 0.0338 0.06208 0.04676 0.9724 0.9799 0.08847 0.9341 0.9627 0.09818 ] Network output: [ 0.1453 -0.3079 1.158 -0.0007306 0.0003261 0.8566 -0.0005427 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7383 0.2883 0.3975 0.4904 0.959 0.9796 0.7415 0.8711 0.9527 0.7379 ] Network output: [ -0.08661 0.1774 0.9168 0.001192 -0.0005357 1.084 0.0009012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7285 0.6195 0.38 0.1935 0.9766 0.9842 0.7291 0.9448 0.967 0.4285 ] Network output: [ -0.1376 0.3684 0.7491 -0.002298 0.001031 1.148 -0.001731 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7668 0.7424 0.4382 0.03162 0.9744 0.9823 0.7669 0.9408 0.9626 0.4503 ] Network output: [ 0.1603 0.5808 0.2847 0.001923 -0.0008612 0.8217 0.001441 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1171 Epoch 710 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01072 1.068 0.943 -0.0004336 0.000194 -0.03411 -0.0003241 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04967 -0.005975 0.02819 0.01957 0.9112 0.9249 0.09048 0.8327 0.8704 0.1823 ] Network output: [ 0.9218 0.1495 -0.0668 0.0007364 -0.0003278 0.07663 0.0005433 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6572 -0.08019 -0.03997 0.2903 0.9538 0.9762 0.739 0.8583 0.9448 0.7357 ] Network output: [ -0.0146 0.9651 1.017 -0.0002996 0.0001334 0.0463 -0.0002212 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08635 0.03369 0.06191 0.04673 0.9724 0.9799 0.08822 0.9341 0.9627 0.09795 ] Network output: [ 0.1453 -0.3082 1.158 -0.0007073 0.0003156 0.8567 -0.0005252 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7379 0.288 0.397 0.4911 0.959 0.9796 0.7412 0.8711 0.9527 0.738 ] Network output: [ -0.0864 0.1778 0.9162 0.00119 -0.0005352 1.084 0.0009002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.728 0.6189 0.3795 0.1938 0.9767 0.9842 0.7286 0.9448 0.967 0.4281 ] Network output: [ -0.1378 0.3677 0.75 -0.002299 0.001032 1.149 -0.001732 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7663 0.7419 0.438 0.03248 0.9744 0.9823 0.7664 0.9407 0.9626 0.4502 ] Network output: [ 0.1605 0.5811 0.2842 0.001909 -0.0008553 0.8214 0.001431 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.117 Epoch 711 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01068 1.068 0.9431 -0.000439 0.0001964 -0.03403 -0.0003281 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04958 -0.005963 0.02814 0.0196 0.9112 0.9249 0.09031 0.8327 0.8705 0.1821 ] Network output: [ 0.9218 0.1498 -0.06717 0.0007345 -0.0003269 0.07683 0.000542 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6569 -0.08021 -0.04036 0.2907 0.9538 0.9762 0.7387 0.8583 0.9448 0.7358 ] Network output: [ -0.01459 0.9652 1.017 -0.0003022 0.0001346 0.04613 -0.0002232 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08611 0.03359 0.06173 0.04671 0.9724 0.9799 0.08798 0.9342 0.9627 0.09772 ] Network output: [ 0.1452 -0.3085 1.158 -0.000684 0.0003052 0.8568 -0.0005078 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7375 0.2876 0.3965 0.4917 0.959 0.9796 0.7408 0.8711 0.9528 0.7382 ] Network output: [ -0.0862 0.1782 0.9157 0.001189 -0.0005346 1.083 0.0008992 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7274 0.6184 0.379 0.1941 0.9767 0.9842 0.728 0.9448 0.967 0.4276 ] Network output: [ -0.1381 0.367 0.7509 -0.002301 0.001033 1.149 -0.001733 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7658 0.7414 0.4378 0.03333 0.9744 0.9823 0.7659 0.9407 0.9626 0.45 ] Network output: [ 0.1607 0.5814 0.2838 0.001896 -0.0008494 0.8211 0.001421 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.117 Epoch 712 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01063 1.068 0.9432 -0.0004441 0.0001987 -0.03396 -0.000332 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04949 -0.00595 0.02808 0.01964 0.9112 0.9249 0.09014 0.8328 0.8705 0.1819 ] Network output: [ 0.9217 0.1501 -0.06755 0.0007323 -0.000326 0.07704 0.0005405 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6566 -0.08022 -0.04076 0.291 0.9538 0.9762 0.7384 0.8583 0.9449 0.736 ] Network output: [ -0.01458 0.9654 1.017 -0.0003046 0.0001357 0.04596 -0.0002251 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08588 0.0335 0.06156 0.04669 0.9724 0.9799 0.08774 0.9342 0.9628 0.0975 ] Network output: [ 0.1452 -0.3088 1.159 -0.000661 0.0002949 0.8569 -0.0004905 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7372 0.2873 0.396 0.4923 0.959 0.9796 0.7404 0.8711 0.9528 0.7383 ] Network output: [ -0.086 0.1786 0.9151 0.001188 -0.000534 1.083 0.0008982 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7269 0.6179 0.3785 0.1944 0.9767 0.9842 0.7275 0.9448 0.967 0.4272 ] Network output: [ -0.1384 0.3663 0.7518 -0.002302 0.001033 1.149 -0.001734 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7653 0.7409 0.4377 0.03417 0.9744 0.9823 0.7654 0.9407 0.9626 0.4498 ] Network output: [ 0.1609 0.5817 0.2833 0.001883 -0.0008435 0.8208 0.001412 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1169 Epoch 713 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01059 1.068 0.9433 -0.0004491 0.000201 -0.03388 -0.0003358 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04939 -0.005938 0.02803 0.01967 0.9112 0.9249 0.08997 0.8328 0.8706 0.1817 ] Network output: [ 0.9216 0.1505 -0.06792 0.00073 -0.000325 0.07724 0.0005389 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6563 -0.08022 -0.04116 0.2913 0.9538 0.9762 0.738 0.8584 0.9449 0.7361 ] Network output: [ -0.01457 0.9655 1.017 -0.0003069 0.0001367 0.0458 -0.0002269 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08565 0.0334 0.06138 0.04666 0.9724 0.9799 0.08751 0.9342 0.9628 0.09728 ] Network output: [ 0.1452 -0.3091 1.159 -0.0006381 0.0002846 0.857 -0.0004734 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7368 0.287 0.3955 0.4929 0.959 0.9796 0.7401 0.8711 0.9528 0.7385 ] Network output: [ -0.08581 0.179 0.9145 0.001186 -0.0005334 1.083 0.0008971 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7264 0.6174 0.378 0.1947 0.9767 0.9842 0.727 0.9448 0.967 0.4267 ] Network output: [ -0.1386 0.3656 0.7526 -0.002303 0.001034 1.15 -0.001735 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7648 0.7404 0.4375 0.035 0.9744 0.9823 0.7649 0.9407 0.9626 0.4497 ] Network output: [ 0.1612 0.5819 0.2828 0.00187 -0.0008377 0.8205 0.001402 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1168 Epoch 714 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01055 1.068 0.9433 -0.0004539 0.0002031 -0.03382 -0.0003395 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0493 -0.005925 0.02797 0.01971 0.9113 0.925 0.08981 0.8329 0.8706 0.1815 ] Network output: [ 0.9215 0.1508 -0.06829 0.0007275 -0.0003239 0.07743 0.0005372 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.656 -0.08021 -0.04156 0.2916 0.9538 0.9762 0.7377 0.8584 0.9449 0.7362 ] Network output: [ -0.01456 0.9656 1.017 -0.0003091 0.0001377 0.04564 -0.0002286 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08543 0.03331 0.06121 0.04664 0.9724 0.9799 0.08728 0.9343 0.9628 0.09706 ] Network output: [ 0.1452 -0.3095 1.159 -0.0006153 0.0002744 0.8572 -0.0004563 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7365 0.2867 0.395 0.4936 0.959 0.9796 0.7397 0.8712 0.9528 0.7386 ] Network output: [ -0.08562 0.1794 0.914 0.001185 -0.0005327 1.083 0.000896 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7259 0.6169 0.3775 0.195 0.9767 0.9842 0.7265 0.9448 0.967 0.4263 ] Network output: [ -0.1389 0.3649 0.7535 -0.002305 0.001034 1.15 -0.001736 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7643 0.7399 0.4373 0.03582 0.9744 0.9823 0.7644 0.9407 0.9626 0.4495 ] Network output: [ 0.1614 0.5822 0.2823 0.001857 -0.000832 0.8203 0.001392 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1167 Epoch 715 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01051 1.067 0.9434 -0.0004585 0.0002052 -0.03375 -0.000343 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04921 -0.005912 0.02791 0.01974 0.9113 0.925 0.08965 0.8329 0.8707 0.1813 ] Network output: [ 0.9215 0.151 -0.06866 0.0007248 -0.0003227 0.07762 0.0005353 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6557 -0.0802 -0.04197 0.292 0.9538 0.9762 0.7374 0.8585 0.9449 0.7363 ] Network output: [ -0.01455 0.9658 1.017 -0.0003111 0.0001386 0.04548 -0.0002302 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08521 0.03323 0.06104 0.04662 0.9724 0.9799 0.08706 0.9343 0.9628 0.09685 ] Network output: [ 0.1451 -0.3098 1.16 -0.0005927 0.0002643 0.8573 -0.0004394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7362 0.2865 0.3945 0.4942 0.959 0.9796 0.7394 0.8712 0.9528 0.7387 ] Network output: [ -0.08543 0.1799 0.9134 0.001184 -0.000532 1.082 0.0008948 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7253 0.6164 0.377 0.1953 0.9767 0.9843 0.7259 0.9448 0.967 0.4258 ] Network output: [ -0.1392 0.3643 0.7544 -0.002306 0.001035 1.15 -0.001737 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7638 0.7394 0.4371 0.03663 0.9744 0.9823 0.7639 0.9407 0.9626 0.4494 ] Network output: [ 0.1616 0.5825 0.2819 0.001844 -0.0008262 0.82 0.001383 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1166 Epoch 716 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01047 1.067 0.9436 -0.0004629 0.0002072 -0.03369 -0.0003463 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04913 -0.005898 0.02786 0.01978 0.9113 0.925 0.08948 0.833 0.8707 0.1811 ] Network output: [ 0.9214 0.1513 -0.06902 0.000722 -0.0003215 0.0778 0.0005333 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6555 -0.08017 -0.04238 0.2923 0.9538 0.9762 0.7371 0.8585 0.945 0.7364 ] Network output: [ -0.01454 0.9659 1.017 -0.000313 0.0001395 0.04532 -0.0002316 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08499 0.03314 0.06088 0.0466 0.9724 0.9799 0.08683 0.9343 0.9628 0.09663 ] Network output: [ 0.1451 -0.3101 1.16 -0.0005703 0.0002543 0.8574 -0.0004226 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7358 0.2862 0.394 0.4948 0.959 0.9796 0.7391 0.8712 0.9529 0.7389 ] Network output: [ -0.08524 0.1803 0.9129 0.001182 -0.0005313 1.082 0.0008936 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7248 0.616 0.3765 0.1956 0.9767 0.9843 0.7254 0.9449 0.9671 0.4254 ] Network output: [ -0.1394 0.3636 0.7552 -0.002308 0.001036 1.151 -0.001738 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7633 0.7389 0.4369 0.03743 0.9744 0.9823 0.7634 0.9407 0.9626 0.4492 ] Network output: [ 0.1618 0.5827 0.2814 0.001832 -0.0008206 0.8198 0.001373 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1166 Epoch 717 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01044 1.067 0.9437 -0.0004671 0.0002091 -0.03363 -0.0003495 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04904 -0.005885 0.0278 0.01981 0.9113 0.925 0.08933 0.8331 0.8708 0.1809 ] Network output: [ 0.9214 0.1516 -0.06939 0.0007189 -0.0003202 0.07797 0.0005311 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6552 -0.08014 -0.0428 0.2926 0.9538 0.9762 0.7368 0.8585 0.945 0.7365 ] Network output: [ -0.01453 0.966 1.017 -0.0003147 0.0001403 0.04516 -0.000233 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08478 0.03306 0.06071 0.04657 0.9724 0.9799 0.08662 0.9343 0.9629 0.09642 ] Network output: [ 0.145 -0.3103 1.16 -0.0005481 0.0002443 0.8576 -0.0004059 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7355 0.286 0.3935 0.4954 0.959 0.9796 0.7388 0.8712 0.9529 0.739 ] Network output: [ -0.08506 0.1808 0.9123 0.00118 -0.0005306 1.082 0.0008924 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7243 0.6155 0.376 0.1959 0.9767 0.9843 0.7249 0.9449 0.9671 0.4249 ] Network output: [ -0.1397 0.363 0.7561 -0.002309 0.001036 1.151 -0.001739 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7628 0.7384 0.4368 0.03822 0.9744 0.9823 0.763 0.9407 0.9626 0.4491 ] Network output: [ 0.162 0.583 0.2809 0.001819 -0.0008149 0.8196 0.001364 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1165 Epoch 718 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0104 1.067 0.9438 -0.0004712 0.0002109 -0.03358 -0.0003526 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04895 -0.005871 0.02775 0.01984 0.9113 0.925 0.08917 0.8331 0.8708 0.1807 ] Network output: [ 0.9213 0.1519 -0.06976 0.0007158 -0.0003188 0.07815 0.0005289 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.655 -0.0801 -0.04321 0.2929 0.9538 0.9762 0.7365 0.8586 0.945 0.7366 ] Network output: [ -0.01452 0.9661 1.017 -0.0003164 0.000141 0.045 -0.0002343 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08457 0.03298 0.06055 0.04655 0.9724 0.9799 0.0864 0.9344 0.9629 0.09621 ] Network output: [ 0.145 -0.3106 1.161 -0.000526 0.0002344 0.8577 -0.0003894 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7352 0.2858 0.393 0.4959 0.959 0.9796 0.7384 0.8712 0.9529 0.7391 ] Network output: [ -0.08488 0.1813 0.9118 0.001179 -0.0005299 1.082 0.0008911 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7238 0.615 0.3755 0.1961 0.9767 0.9843 0.7244 0.9449 0.9671 0.4245 ] Network output: [ -0.1399 0.3624 0.7569 -0.00231 0.001037 1.151 -0.00174 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7624 0.738 0.4366 0.039 0.9744 0.9823 0.7625 0.9407 0.9626 0.4489 ] Network output: [ 0.1621 0.5832 0.2804 0.001806 -0.0008093 0.8195 0.001355 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1164 Epoch 719 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01037 1.067 0.9439 -0.0004751 0.0002127 -0.03353 -0.0003556 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04887 -0.005857 0.02769 0.01988 0.9114 0.925 0.08902 0.8332 0.8708 0.1805 ] Network output: [ 0.9213 0.1522 -0.07012 0.0007124 -0.0003173 0.07831 0.0005265 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6547 -0.08005 -0.04363 0.2932 0.9538 0.9762 0.7362 0.8586 0.945 0.7367 ] Network output: [ -0.01451 0.9661 1.017 -0.0003178 0.0001417 0.04484 -0.0002355 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08436 0.0329 0.06038 0.04653 0.9724 0.9799 0.08619 0.9344 0.9629 0.09601 ] Network output: [ 0.1449 -0.3109 1.161 -0.000504 0.0002246 0.8579 -0.000373 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7349 0.2856 0.3925 0.4965 0.959 0.9796 0.7381 0.8713 0.9529 0.7392 ] Network output: [ -0.08471 0.1817 0.9112 0.001177 -0.0005291 1.081 0.0008898 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7232 0.6146 0.375 0.1964 0.9767 0.9843 0.7239 0.9449 0.9671 0.424 ] Network output: [ -0.1402 0.3618 0.7577 -0.002312 0.001038 1.151 -0.001741 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7619 0.7375 0.4364 0.03978 0.9743 0.9823 0.762 0.9407 0.9626 0.4488 ] Network output: [ 0.1623 0.5835 0.2798 0.001794 -0.0008038 0.8193 0.001345 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1163 Epoch 720 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01033 1.067 0.9441 -0.0004788 0.0002144 -0.03348 -0.0003584 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04878 -0.005843 0.02763 0.01991 0.9114 0.9251 0.08887 0.8332 0.8709 0.1803 ] Network output: [ 0.9212 0.1524 -0.07048 0.0007089 -0.0003157 0.07848 0.0005239 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6545 -0.08 -0.04405 0.2935 0.9539 0.9762 0.736 0.8586 0.9451 0.7368 ] Network output: [ -0.01449 0.9662 1.017 -0.0003192 0.0001423 0.04468 -0.0002365 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08416 0.03283 0.06022 0.0465 0.9724 0.9799 0.08598 0.9344 0.9629 0.09581 ] Network output: [ 0.1449 -0.3112 1.161 -0.0004822 0.0002148 0.858 -0.0003566 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7346 0.2855 0.392 0.4971 0.959 0.9796 0.7378 0.8713 0.9529 0.7394 ] Network output: [ -0.08454 0.1822 0.9107 0.001175 -0.0005283 1.081 0.0008885 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7227 0.6141 0.3745 0.1966 0.9767 0.9843 0.7233 0.9449 0.9671 0.4236 ] Network output: [ -0.1404 0.3612 0.7586 -0.002313 0.001038 1.152 -0.001742 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7614 0.737 0.4363 0.04054 0.9743 0.9823 0.7615 0.9407 0.9626 0.4487 ] Network output: [ 0.1625 0.5837 0.2793 0.001782 -0.0007983 0.8191 0.001336 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1163 Epoch 721 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0103 1.067 0.9442 -0.0004824 0.000216 -0.03344 -0.0003611 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0487 -0.005829 0.02758 0.01995 0.9114 0.9251 0.08872 0.8333 0.8709 0.1802 ] Network output: [ 0.9212 0.1527 -0.07084 0.0007052 -0.0003141 0.07863 0.0005213 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6542 -0.07993 -0.04447 0.2938 0.9539 0.9762 0.7357 0.8587 0.9451 0.7369 ] Network output: [ -0.01448 0.9663 1.017 -0.0003204 0.0001429 0.04453 -0.0002375 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08396 0.03276 0.06006 0.04648 0.9724 0.9799 0.08578 0.9344 0.9629 0.09561 ] Network output: [ 0.1448 -0.3115 1.162 -0.0004606 0.0002052 0.8582 -0.0003404 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7343 0.2854 0.3915 0.4976 0.959 0.9796 0.7375 0.8713 0.9529 0.7395 ] Network output: [ -0.08437 0.1827 0.9102 0.001174 -0.0005275 1.081 0.0008871 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7222 0.6137 0.374 0.1969 0.9767 0.9843 0.7228 0.9449 0.9671 0.4231 ] Network output: [ -0.1407 0.3606 0.7594 -0.002315 0.001039 1.152 -0.001743 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7609 0.7366 0.4361 0.04129 0.9743 0.9823 0.761 0.9407 0.9626 0.4485 ] Network output: [ 0.1627 0.584 0.2788 0.00177 -0.0007929 0.819 0.001327 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1162 Epoch 722 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01028 1.067 0.9444 -0.0004858 0.0002175 -0.0334 -0.0003637 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04862 -0.005814 0.02752 0.01998 0.9114 0.9251 0.08857 0.8333 0.871 0.18 ] Network output: [ 0.9212 0.153 -0.0712 0.0007013 -0.0003124 0.07879 0.0005185 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 -0.07986 -0.04489 0.2941 0.9539 0.9762 0.7354 0.8587 0.9451 0.737 ] Network output: [ -0.01447 0.9664 1.017 -0.0003215 0.0001434 0.04437 -0.0002384 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08376 0.03269 0.0599 0.04646 0.9724 0.9799 0.08558 0.9345 0.963 0.09541 ] Network output: [ 0.1448 -0.3118 1.162 -0.0004392 0.0001956 0.8584 -0.0003244 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.734 0.2853 0.3909 0.4982 0.959 0.9796 0.7372 0.8713 0.953 0.7396 ] Network output: [ -0.08421 0.1832 0.9096 0.001172 -0.0005267 1.08 0.0008857 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7217 0.6133 0.3735 0.1971 0.9767 0.9843 0.7223 0.945 0.9672 0.4227 ] Network output: [ -0.1409 0.3601 0.7602 -0.002316 0.001039 1.152 -0.001744 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7604 0.7361 0.4359 0.04204 0.9743 0.9823 0.7606 0.9407 0.9626 0.4484 ] Network output: [ 0.1629 0.5842 0.2783 0.001758 -0.0007875 0.8189 0.001318 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1161 Epoch 723 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01025 1.066 0.9445 -0.000489 0.000219 -0.03336 -0.0003662 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04853 -0.005799 0.02747 0.02001 0.9114 0.9251 0.08842 0.8334 0.871 0.1798 ] Network output: [ 0.9211 0.1532 -0.07155 0.0006973 -0.0003106 0.07894 0.0005156 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6537 -0.07978 -0.04532 0.2944 0.9539 0.9762 0.7352 0.8588 0.9451 0.7371 ] Network output: [ -0.01445 0.9664 1.017 -0.0003225 0.0001438 0.04421 -0.0002392 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08357 0.03263 0.05974 0.04643 0.9724 0.98 0.08538 0.9345 0.963 0.09521 ] Network output: [ 0.1447 -0.3121 1.162 -0.0004179 0.000186 0.8585 -0.0003084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7337 0.2852 0.3904 0.4987 0.9591 0.9796 0.737 0.8714 0.953 0.7397 ] Network output: [ -0.08405 0.1837 0.9091 0.00117 -0.0005259 1.08 0.0008843 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7212 0.6128 0.373 0.1973 0.9767 0.9843 0.7218 0.945 0.9672 0.4223 ] Network output: [ -0.1412 0.3595 0.761 -0.002317 0.00104 1.152 -0.001745 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.76 0.7356 0.4358 0.04278 0.9743 0.9823 0.7601 0.9407 0.9626 0.4483 ] Network output: [ 0.1631 0.5844 0.2777 0.001746 -0.0007822 0.8188 0.001309 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.116 Epoch 724 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01022 1.066 0.9447 -0.0004921 0.0002203 -0.03333 -0.0003685 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04845 -0.005784 0.02741 0.02005 0.9114 0.9251 0.08828 0.8334 0.8711 0.1796 ] Network output: [ 0.9211 0.1535 -0.0719 0.0006931 -0.0003088 0.07909 0.0005126 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6535 -0.0797 -0.04574 0.2947 0.9539 0.9762 0.7349 0.8588 0.9452 0.7371 ] Network output: [ -0.01444 0.9665 1.017 -0.0003234 0.0001442 0.04406 -0.0002398 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08338 0.03256 0.05959 0.04641 0.9724 0.98 0.08519 0.9345 0.963 0.09502 ] Network output: [ 0.1447 -0.3123 1.163 -0.0003968 0.0001766 0.8587 -0.0002926 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7335 0.2851 0.3899 0.4992 0.9591 0.9796 0.7367 0.8714 0.953 0.7398 ] Network output: [ -0.08389 0.1843 0.9086 0.001168 -0.000525 1.08 0.0008829 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7207 0.6124 0.3725 0.1976 0.9767 0.9843 0.7213 0.945 0.9672 0.4218 ] Network output: [ -0.1414 0.359 0.7618 -0.002319 0.001041 1.153 -0.001746 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7595 0.7352 0.4356 0.0435 0.9743 0.9822 0.7596 0.9406 0.9626 0.4481 ] Network output: [ 0.1633 0.5846 0.2772 0.001734 -0.0007769 0.8187 0.0013 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.116 Epoch 725 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0102 1.066 0.9448 -0.000495 0.0002217 -0.0333 -0.0003707 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04837 -0.005769 0.02735 0.02008 0.9115 0.9251 0.08814 0.8335 0.8711 0.1795 ] Network output: [ 0.9211 0.1537 -0.07225 0.0006888 -0.0003069 0.07923 0.0005094 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6533 -0.0796 -0.04617 0.295 0.9539 0.9762 0.7347 0.8588 0.9452 0.7372 ] Network output: [ -0.01442 0.9665 1.017 -0.0003241 0.0001446 0.04391 -0.0002404 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08319 0.0325 0.05943 0.04638 0.9725 0.98 0.085 0.9345 0.963 0.09483 ] Network output: [ 0.1446 -0.3126 1.163 -0.0003758 0.0001672 0.8589 -0.0002769 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7332 0.285 0.3894 0.4998 0.9591 0.9796 0.7364 0.8714 0.953 0.7399 ] Network output: [ -0.08374 0.1848 0.9081 0.001166 -0.0005242 1.079 0.0008814 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7202 0.612 0.372 0.1978 0.9767 0.9843 0.7208 0.945 0.9672 0.4214 ] Network output: [ -0.1417 0.3584 0.7626 -0.00232 0.001041 1.153 -0.001747 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.759 0.7347 0.4354 0.04422 0.9743 0.9822 0.7592 0.9406 0.9626 0.448 ] Network output: [ 0.1635 0.5848 0.2766 0.001722 -0.0007717 0.8187 0.001292 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1159 Epoch 726 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01018 1.066 0.945 -0.0004978 0.0002229 -0.03327 -0.0003729 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0483 -0.005754 0.0273 0.02011 0.9115 0.9251 0.088 0.8335 0.8712 0.1793 ] Network output: [ 0.921 0.1539 -0.0726 0.0006843 -0.0003049 0.07937 0.0005062 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6531 -0.0795 -0.04659 0.2953 0.9539 0.9762 0.7344 0.8589 0.9452 0.7373 ] Network output: [ -0.0144 0.9665 1.017 -0.0003247 0.0001449 0.04375 -0.000241 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08301 0.03244 0.05928 0.04636 0.9725 0.98 0.08481 0.9346 0.963 0.09464 ] Network output: [ 0.1445 -0.3129 1.163 -0.000355 0.0001578 0.8591 -0.0002613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7329 0.285 0.3888 0.5003 0.9591 0.9796 0.7362 0.8714 0.953 0.74 ] Network output: [ -0.08359 0.1853 0.9076 0.001164 -0.0005233 1.079 0.0008799 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7196 0.6116 0.3715 0.198 0.9767 0.9843 0.7203 0.945 0.9672 0.4209 ] Network output: [ -0.1419 0.3579 0.7635 -0.002322 0.001042 1.153 -0.001748 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7586 0.7343 0.4353 0.04493 0.9743 0.9822 0.7587 0.9406 0.9626 0.4479 ] Network output: [ 0.1636 0.585 0.2761 0.001711 -0.0007665 0.8186 0.001283 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1158 Epoch 727 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01016 1.066 0.9452 -0.0005004 0.0002241 -0.03325 -0.0003749 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04822 -0.005738 0.02724 0.02014 0.9115 0.9251 0.08786 0.8336 0.8712 0.1791 ] Network output: [ 0.921 0.1542 -0.07294 0.0006796 -0.0003028 0.0795 0.0005028 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6529 -0.07939 -0.04702 0.2955 0.9539 0.9763 0.7342 0.8589 0.9452 0.7374 ] Network output: [ -0.01438 0.9666 1.017 -0.0003252 0.0001451 0.0436 -0.0002414 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08282 0.03239 0.05913 0.04634 0.9725 0.98 0.08462 0.9346 0.9631 0.09445 ] Network output: [ 0.1445 -0.3131 1.164 -0.0003343 0.0001486 0.8593 -0.0002458 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7327 0.285 0.3883 0.5008 0.9591 0.9796 0.7359 0.8715 0.9531 0.7401 ] Network output: [ -0.08344 0.1858 0.9071 0.001162 -0.0005224 1.079 0.0008784 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7191 0.6112 0.371 0.1982 0.9767 0.9843 0.7197 0.945 0.9672 0.4205 ] Network output: [ -0.1422 0.3574 0.7642 -0.002323 0.001043 1.153 -0.001749 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7581 0.7338 0.4351 0.04564 0.9743 0.9822 0.7582 0.9406 0.9626 0.4477 ] Network output: [ 0.1638 0.5852 0.2755 0.001699 -0.0007614 0.8186 0.001274 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1157 Epoch 728 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01014 1.066 0.9454 -0.0005029 0.0002252 -0.03323 -0.0003768 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04814 -0.005723 0.02719 0.02018 0.9115 0.9252 0.08772 0.8336 0.8713 0.179 ] Network output: [ 0.921 0.1544 -0.07328 0.0006749 -0.0003007 0.07963 0.0004993 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6526 -0.07928 -0.04745 0.2958 0.9539 0.9763 0.734 0.8589 0.9453 0.7375 ] Network output: [ -0.01436 0.9666 1.017 -0.0003256 0.0001453 0.04345 -0.0002417 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08264 0.03233 0.05898 0.04631 0.9725 0.98 0.08444 0.9346 0.9631 0.09427 ] Network output: [ 0.1444 -0.3134 1.164 -0.0003138 0.0001394 0.8595 -0.0002304 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7324 0.285 0.3878 0.5013 0.9591 0.9796 0.7356 0.8715 0.9531 0.7402 ] Network output: [ -0.0833 0.1864 0.9066 0.00116 -0.0005215 1.078 0.0008769 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7186 0.6108 0.3706 0.1984 0.9767 0.9843 0.7192 0.9451 0.9672 0.4201 ] Network output: [ -0.1424 0.3569 0.765 -0.002324 0.001043 1.153 -0.001751 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7576 0.7334 0.435 0.04633 0.9743 0.9822 0.7577 0.9406 0.9626 0.4476 ] Network output: [ 0.164 0.5854 0.2749 0.001688 -0.0007563 0.8185 0.001266 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1157 Epoch 729 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01013 1.065 0.9456 -0.0005052 0.0002263 -0.03321 -0.0003785 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04806 -0.005707 0.02713 0.02021 0.9115 0.9252 0.08759 0.8337 0.8713 0.1788 ] Network output: [ 0.921 0.1546 -0.07362 0.00067 -0.0002985 0.07976 0.0004958 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6524 -0.07916 -0.04788 0.2961 0.9539 0.9763 0.7337 0.859 0.9453 0.7375 ] Network output: [ -0.01434 0.9666 1.017 -0.0003259 0.0001454 0.04329 -0.000242 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08247 0.03228 0.05883 0.04629 0.9725 0.98 0.08426 0.9347 0.9631 0.09408 ] Network output: [ 0.1444 -0.3137 1.164 -0.0002934 0.0001303 0.8597 -0.0002151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7322 0.285 0.3872 0.5018 0.9591 0.9796 0.7354 0.8715 0.9531 0.7403 ] Network output: [ -0.08316 0.1869 0.9061 0.001158 -0.0005206 1.078 0.0008753 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7181 0.6104 0.3701 0.1986 0.9767 0.9843 0.7187 0.9451 0.9673 0.4196 ] Network output: [ -0.1427 0.3564 0.7658 -0.002326 0.001044 1.154 -0.001752 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7572 0.7329 0.4348 0.04702 0.9743 0.9822 0.7573 0.9406 0.9626 0.4475 ] Network output: [ 0.1642 0.5856 0.2744 0.001677 -0.0007512 0.8185 0.001258 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1156 Epoch 730 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01011 1.065 0.9458 -0.0005074 0.0002273 -0.03319 -0.0003802 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04799 -0.005691 0.02708 0.02024 0.9115 0.9252 0.08746 0.8337 0.8713 0.1786 ] Network output: [ 0.921 0.1549 -0.07395 0.0006649 -0.0002963 0.07989 0.0004921 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6522 -0.07902 -0.04831 0.2963 0.9539 0.9763 0.7335 0.859 0.9453 0.7376 ] Network output: [ -0.01432 0.9666 1.018 -0.0003261 0.0001455 0.04314 -0.0002422 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08229 0.03223 0.05868 0.04627 0.9725 0.98 0.08408 0.9347 0.9631 0.0939 ] Network output: [ 0.1443 -0.3139 1.164 -0.0002732 0.0001212 0.8599 -0.0002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7319 0.2851 0.3867 0.5023 0.9591 0.9797 0.7352 0.8715 0.9531 0.7404 ] Network output: [ -0.08302 0.1875 0.9056 0.001156 -0.0005197 1.078 0.0008737 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7176 0.6101 0.3696 0.1988 0.9767 0.9843 0.7182 0.9451 0.9673 0.4192 ] Network output: [ -0.1429 0.3559 0.7666 -0.002327 0.001044 1.154 -0.001753 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7567 0.7325 0.4346 0.0477 0.9743 0.9822 0.7568 0.9406 0.9626 0.4473 ] Network output: [ 0.1643 0.5858 0.2738 0.001665 -0.0007462 0.8185 0.001249 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1155 Epoch 731 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0101 1.065 0.946 -0.0005095 0.0002282 -0.03318 -0.0003818 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04791 -0.005674 0.02702 0.02027 0.9116 0.9252 0.08732 0.8338 0.8714 0.1785 ] Network output: [ 0.9209 0.1551 -0.07428 0.0006597 -0.000294 0.08001 0.0004883 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.652 -0.07889 -0.04874 0.2966 0.9539 0.9763 0.7333 0.8591 0.9454 0.7377 ] Network output: [ -0.0143 0.9666 1.018 -0.0003262 0.0001456 0.04299 -0.0002423 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08212 0.03218 0.05854 0.04624 0.9725 0.98 0.0839 0.9347 0.9631 0.09373 ] Network output: [ 0.1442 -0.3142 1.165 -0.0002531 0.0001122 0.8601 -0.0001849 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7317 0.2851 0.3862 0.5027 0.9591 0.9797 0.7349 0.8716 0.9531 0.7405 ] Network output: [ -0.08289 0.188 0.9051 0.001154 -0.0005188 1.077 0.0008722 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7171 0.6097 0.3691 0.199 0.9767 0.9843 0.7177 0.9451 0.9673 0.4188 ] Network output: [ -0.1431 0.3554 0.7674 -0.002328 0.001045 1.154 -0.001754 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7562 0.7321 0.4345 0.04837 0.9743 0.9822 0.7563 0.9406 0.9626 0.4472 ] Network output: [ 0.1645 0.586 0.2732 0.001654 -0.0007413 0.8185 0.001241 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1154 Epoch 732 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01009 1.065 0.9462 -0.0005115 0.0002291 -0.03318 -0.0003833 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04784 -0.005658 0.02697 0.0203 0.9116 0.9252 0.0872 0.8338 0.8714 0.1783 ] Network output: [ 0.9209 0.1553 -0.0746 0.0006544 -0.0002917 0.08013 0.0004844 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6518 -0.07874 -0.04917 0.2968 0.9539 0.9763 0.7331 0.8591 0.9454 0.7377 ] Network output: [ -0.01427 0.9667 1.018 -0.0003262 0.0001456 0.04284 -0.0002424 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08195 0.03213 0.05839 0.04622 0.9725 0.98 0.08373 0.9347 0.9632 0.09355 ] Network output: [ 0.1441 -0.3145 1.165 -0.0002332 0.0001033 0.8603 -0.00017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7315 0.2852 0.3856 0.5032 0.9591 0.9797 0.7347 0.8716 0.9531 0.7406 ] Network output: [ -0.08276 0.1886 0.9046 0.001152 -0.0005178 1.077 0.0008706 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7166 0.6093 0.3686 0.1991 0.9767 0.9843 0.7172 0.9451 0.9673 0.4184 ] Network output: [ -0.1434 0.3549 0.7682 -0.00233 0.001046 1.154 -0.001755 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7558 0.7316 0.4343 0.04903 0.9743 0.9822 0.7559 0.9406 0.9626 0.4471 ] Network output: [ 0.1647 0.5862 0.2726 0.001643 -0.0007364 0.8186 0.001233 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1154 Epoch 733 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01009 1.065 0.9464 -0.0005133 0.0002299 -0.03317 -0.0003847 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04776 -0.005641 0.02691 0.02033 0.9116 0.9252 0.08707 0.8339 0.8715 0.1782 ] Network output: [ 0.9209 0.1555 -0.07492 0.000649 -0.0002893 0.08024 0.0004805 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6516 -0.07859 -0.0496 0.2971 0.954 0.9763 0.7329 0.8591 0.9454 0.7378 ] Network output: [ -0.01425 0.9667 1.018 -0.0003261 0.0001456 0.04269 -0.0002423 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08178 0.03209 0.05825 0.0462 0.9725 0.98 0.08356 0.9348 0.9632 0.09338 ] Network output: [ 0.1441 -0.3147 1.165 -0.0002134 9.443e-05 0.8605 -0.0001551 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7312 0.2853 0.3851 0.5037 0.9591 0.9797 0.7345 0.8716 0.9532 0.7407 ] Network output: [ -0.08263 0.1891 0.9042 0.00115 -0.0005169 1.077 0.000869 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7161 0.6089 0.3681 0.1993 0.9767 0.9843 0.7167 0.9451 0.9673 0.4179 ] Network output: [ -0.1436 0.3545 0.7689 -0.002331 0.001046 1.154 -0.001756 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7553 0.7312 0.4342 0.04969 0.9743 0.9822 0.7554 0.9406 0.9626 0.447 ] Network output: [ 0.1648 0.5863 0.272 0.001632 -0.0007315 0.8186 0.001225 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1153 Epoch 734 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01008 1.064 0.9466 -0.000515 0.0002307 -0.03317 -0.000386 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04769 -0.005625 0.02686 0.02036 0.9116 0.9252 0.08694 0.8339 0.8715 0.178 ] Network output: [ 0.9209 0.1557 -0.07524 0.0006435 -0.0002868 0.08036 0.0004764 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6514 -0.07843 -0.05004 0.2973 0.954 0.9763 0.7327 0.8592 0.9454 0.7379 ] Network output: [ -0.01422 0.9666 1.018 -0.0003259 0.0001455 0.04254 -0.0002422 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08162 0.03205 0.05811 0.04617 0.9725 0.98 0.08339 0.9348 0.9632 0.0932 ] Network output: [ 0.144 -0.315 1.165 -0.0001938 8.563e-05 0.8608 -0.0001404 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.731 0.2854 0.3845 0.5041 0.9591 0.9797 0.7342 0.8717 0.9532 0.7407 ] Network output: [ -0.0825 0.1897 0.9037 0.001148 -0.0005159 1.076 0.0008673 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7156 0.6086 0.3677 0.1995 0.9767 0.9843 0.7162 0.9452 0.9673 0.4175 ] Network output: [ -0.1438 0.354 0.7697 -0.002332 0.001047 1.154 -0.001757 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7548 0.7308 0.434 0.05034 0.9742 0.9822 0.755 0.9406 0.9626 0.4469 ] Network output: [ 0.165 0.5865 0.2714 0.001622 -0.0007267 0.8187 0.001217 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1152 Epoch 735 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01008 1.064 0.9468 -0.0005166 0.0002314 -0.03317 -0.0003872 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04762 -0.005608 0.0268 0.02039 0.9116 0.9253 0.08682 0.834 0.8716 0.1778 ] Network output: [ 0.9209 0.1559 -0.07555 0.0006378 -0.0002843 0.08047 0.0004722 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6513 -0.07826 -0.05047 0.2976 0.954 0.9763 0.7325 0.8592 0.9455 0.7379 ] Network output: [ -0.01419 0.9666 1.018 -0.0003256 0.0001454 0.04239 -0.0002421 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08146 0.03201 0.05797 0.04615 0.9725 0.98 0.08323 0.9348 0.9632 0.09303 ] Network output: [ 0.1439 -0.3152 1.166 -0.0001742 7.688e-05 0.861 -0.0001258 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7308 0.2855 0.384 0.5046 0.9591 0.9797 0.734 0.8717 0.9532 0.7408 ] Network output: [ -0.08238 0.1903 0.9032 0.001146 -0.000515 1.076 0.0008657 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7151 0.6082 0.3672 0.1996 0.9767 0.9843 0.7157 0.9452 0.9673 0.4171 ] Network output: [ -0.1441 0.3536 0.7705 -0.002334 0.001047 1.155 -0.001758 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7544 0.7303 0.4339 0.05099 0.9742 0.9822 0.7545 0.9406 0.9626 0.4467 ] Network output: [ 0.1651 0.5867 0.2708 0.001611 -0.0007219 0.8187 0.001209 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1151 Epoch 736 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01008 1.064 0.947 -0.000518 0.0002321 -0.03317 -0.0003884 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04755 -0.00559 0.02675 0.02043 0.9116 0.9253 0.08669 0.834 0.8716 0.1777 ] Network output: [ 0.9209 0.1561 -0.07586 0.0006321 -0.0002817 0.08058 0.000468 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6511 -0.07809 -0.0509 0.2978 0.954 0.9763 0.7323 0.8593 0.9455 0.738 ] Network output: [ -0.01416 0.9666 1.018 -0.0003253 0.0001452 0.04224 -0.0002418 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0813 0.03197 0.05783 0.04613 0.9725 0.98 0.08306 0.9349 0.9632 0.09287 ] Network output: [ 0.1439 -0.3155 1.166 -0.0001549 6.819e-05 0.8612 -0.0001112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7306 0.2857 0.3834 0.505 0.9591 0.9797 0.7338 0.8717 0.9532 0.7409 ] Network output: [ -0.08226 0.1908 0.9028 0.001144 -0.000514 1.076 0.0008641 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7146 0.6079 0.3667 0.1998 0.9767 0.9843 0.7152 0.9452 0.9674 0.4167 ] Network output: [ -0.1443 0.3531 0.7712 -0.002335 0.001048 1.155 -0.001758 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7539 0.7299 0.4337 0.05163 0.9742 0.9822 0.754 0.9406 0.9626 0.4466 ] Network output: [ 0.1653 0.5869 0.2702 0.0016 -0.0007171 0.8188 0.001201 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1151 Epoch 737 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01008 1.064 0.9472 -0.0005194 0.0002327 -0.03318 -0.0003894 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04748 -0.005573 0.02669 0.02046 0.9117 0.9253 0.08657 0.8341 0.8716 0.1775 ] Network output: [ 0.9208 0.1563 -0.07616 0.0006262 -0.0002791 0.08068 0.0004637 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6509 -0.07791 -0.05133 0.2981 0.954 0.9763 0.7321 0.8593 0.9455 0.738 ] Network output: [ -0.01413 0.9666 1.018 -0.0003248 0.000145 0.04209 -0.0002415 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08114 0.03193 0.05769 0.0461 0.9725 0.98 0.0829 0.9349 0.9632 0.0927 ] Network output: [ 0.1438 -0.3157 1.166 -0.0001356 5.956e-05 0.8615 -9.679e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7304 0.2858 0.3829 0.5054 0.9591 0.9797 0.7336 0.8717 0.9532 0.7409 ] Network output: [ -0.08215 0.1914 0.9023 0.001142 -0.000513 1.075 0.0008624 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7141 0.6075 0.3662 0.2 0.9768 0.9843 0.7147 0.9452 0.9674 0.4163 ] Network output: [ -0.1445 0.3527 0.772 -0.002336 0.001048 1.155 -0.001759 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7534 0.7295 0.4336 0.05226 0.9742 0.9822 0.7536 0.9406 0.9626 0.4465 ] Network output: [ 0.1655 0.587 0.2696 0.00159 -0.0007124 0.8189 0.001193 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.115 Epoch 738 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01008 1.063 0.9474 -0.0005207 0.0002333 -0.03319 -0.0003904 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0474 -0.005556 0.02664 0.02049 0.9117 0.9253 0.08645 0.8341 0.8717 0.1774 ] Network output: [ 0.9208 0.1565 -0.07646 0.0006203 -0.0002765 0.08078 0.0004593 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6507 -0.07772 -0.05176 0.2983 0.954 0.9763 0.7319 0.8593 0.9455 0.7381 ] Network output: [ -0.0141 0.9666 1.018 -0.0003243 0.0001448 0.04194 -0.0002412 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08098 0.0319 0.05755 0.04608 0.9725 0.98 0.08274 0.9349 0.9633 0.09254 ] Network output: [ 0.1437 -0.316 1.166 -0.0001165 5.099e-05 0.8617 -8.244e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7302 0.286 0.3823 0.5059 0.9591 0.9797 0.7334 0.8718 0.9533 0.741 ] Network output: [ -0.08203 0.192 0.9019 0.001139 -0.000512 1.075 0.0008608 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7135 0.6072 0.3658 0.2001 0.9768 0.9843 0.7141 0.9452 0.9674 0.4159 ] Network output: [ -0.1448 0.3523 0.7728 -0.002337 0.001049 1.155 -0.00176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.753 0.729 0.4335 0.05288 0.9742 0.9822 0.7531 0.9406 0.9626 0.4464 ] Network output: [ 0.1656 0.5872 0.269 0.001579 -0.0007077 0.819 0.001185 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1149 Epoch 739 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01009 1.063 0.9476 -0.0005218 0.0002338 -0.03321 -0.0003913 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04733 -0.005538 0.02659 0.02052 0.9117 0.9253 0.08633 0.8342 0.8717 0.1773 ] Network output: [ 0.9208 0.1567 -0.07675 0.0006142 -0.0002738 0.08088 0.0004549 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6505 -0.07753 -0.0522 0.2985 0.954 0.9763 0.7317 0.8594 0.9456 0.7381 ] Network output: [ -0.01407 0.9665 1.018 -0.0003237 0.0001446 0.04178 -0.0002408 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08083 0.03187 0.05741 0.04606 0.9725 0.98 0.08259 0.9349 0.9633 0.09238 ] Network output: [ 0.1436 -0.3162 1.167 -9.745e-05 4.247e-05 0.862 -6.819e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.73 0.2862 0.3818 0.5063 0.9591 0.9797 0.7332 0.8718 0.9533 0.7411 ] Network output: [ -0.08192 0.1926 0.9015 0.001137 -0.000511 1.074 0.0008591 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.713 0.6069 0.3653 0.2003 0.9768 0.9843 0.7136 0.9453 0.9674 0.4155 ] Network output: [ -0.145 0.3518 0.7735 -0.002338 0.00105 1.155 -0.001761 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7525 0.7286 0.4333 0.0535 0.9742 0.9822 0.7526 0.9405 0.9627 0.4463 ] Network output: [ 0.1658 0.5874 0.2684 0.001569 -0.000703 0.8191 0.001177 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1148 Epoch 740 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0101 1.063 0.9479 -0.0005229 0.0002343 -0.03322 -0.0003921 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04727 -0.00552 0.02653 0.02055 0.9117 0.9253 0.08621 0.8343 0.8718 0.1771 ] Network output: [ 0.9208 0.1569 -0.07704 0.0006081 -0.0002711 0.08098 0.0004503 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6504 -0.07733 -0.05263 0.2988 0.954 0.9763 0.7315 0.8594 0.9456 0.7382 ] Network output: [ -0.01403 0.9665 1.019 -0.0003231 0.0001443 0.04163 -0.0002403 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08068 0.03184 0.05728 0.04603 0.9725 0.98 0.08243 0.935 0.9633 0.09222 ] Network output: [ 0.1436 -0.3165 1.167 -7.855e-05 3.401e-05 0.8622 -5.402e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7298 0.2864 0.3812 0.5067 0.9591 0.9797 0.733 0.8718 0.9533 0.7411 ] Network output: [ -0.08182 0.1932 0.901 0.001135 -0.0005101 1.074 0.0008574 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7125 0.6065 0.3648 0.2004 0.9768 0.9843 0.7131 0.9453 0.9674 0.415 ] Network output: [ -0.1452 0.3514 0.7743 -0.00234 0.00105 1.155 -0.001762 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.752 0.7282 0.4332 0.05412 0.9742 0.9822 0.7522 0.9405 0.9627 0.4462 ] Network output: [ 0.1659 0.5876 0.2678 0.001559 -0.0006984 0.8192 0.001169 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1147 Epoch 741 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01011 1.063 0.9481 -0.0005238 0.0002347 -0.03324 -0.0003928 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0472 -0.005502 0.02648 0.02058 0.9117 0.9253 0.08609 0.8343 0.8718 0.177 ] Network output: [ 0.9208 0.1571 -0.07733 0.0006018 -0.0002683 0.08107 0.0004457 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6502 -0.07712 -0.05306 0.299 0.954 0.9763 0.7314 0.8595 0.9456 0.7382 ] Network output: [ -0.014 0.9665 1.019 -0.0003223 0.000144 0.04148 -0.0002398 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08053 0.03181 0.05715 0.04601 0.9725 0.98 0.08228 0.935 0.9633 0.09206 ] Network output: [ 0.1435 -0.3167 1.167 -5.978e-05 2.56e-05 0.8625 -3.994e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7296 0.2866 0.3807 0.5071 0.9591 0.9797 0.7328 0.8719 0.9533 0.7412 ] Network output: [ -0.08171 0.1937 0.9006 0.001133 -0.0005091 1.074 0.0008557 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.712 0.6062 0.3644 0.2005 0.9768 0.9843 0.7126 0.9453 0.9674 0.4146 ] Network output: [ -0.1454 0.351 0.775 -0.002341 0.001051 1.155 -0.001763 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7516 0.7278 0.433 0.05473 0.9742 0.9822 0.7517 0.9405 0.9627 0.4461 ] Network output: [ 0.166 0.5877 0.2672 0.001548 -0.0006938 0.8193 0.001162 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1147 Epoch 742 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01012 1.063 0.9483 -0.0005247 0.0002351 -0.03326 -0.0003935 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04713 -0.005484 0.02642 0.02061 0.9117 0.9253 0.08598 0.8344 0.8719 0.1768 ] Network output: [ 0.9208 0.1573 -0.07761 0.0005955 -0.0002655 0.08117 0.0004411 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.65 -0.07691 -0.05349 0.2992 0.954 0.9763 0.7312 0.8595 0.9456 0.7383 ] Network output: [ -0.01396 0.9664 1.019 -0.0003215 0.0001436 0.04133 -0.0002393 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08038 0.03178 0.05702 0.04599 0.9726 0.98 0.08213 0.935 0.9633 0.0919 ] Network output: [ 0.1434 -0.3169 1.167 -4.113e-05 1.724e-05 0.8627 -2.595e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7294 0.2869 0.3801 0.5075 0.9591 0.9797 0.7326 0.8719 0.9533 0.7413 ] Network output: [ -0.08161 0.1943 0.9002 0.001131 -0.0005081 1.073 0.000854 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7115 0.6059 0.3639 0.2007 0.9768 0.9843 0.7121 0.9453 0.9674 0.4142 ] Network output: [ -0.1456 0.3506 0.7758 -0.002342 0.001051 1.155 -0.001764 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7511 0.7273 0.4329 0.05534 0.9742 0.9822 0.7513 0.9405 0.9627 0.446 ] Network output: [ 0.1662 0.5879 0.2666 0.001538 -0.0006893 0.8194 0.001154 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1146 Epoch 743 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01013 1.062 0.9485 -0.0005255 0.0002354 -0.03329 -0.0003941 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04706 -0.005465 0.02637 0.02064 0.9117 0.9253 0.08586 0.8344 0.8719 0.1767 ] Network output: [ 0.9208 0.1575 -0.07788 0.0005891 -0.0002626 0.08126 0.0004364 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6498 -0.07669 -0.05393 0.2994 0.954 0.9763 0.731 0.8595 0.9457 0.7383 ] Network output: [ -0.01392 0.9664 1.019 -0.0003207 0.0001432 0.04118 -0.0002386 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08023 0.03175 0.05688 0.04597 0.9726 0.98 0.08198 0.935 0.9634 0.09175 ] Network output: [ 0.1434 -0.3172 1.167 -2.259e-05 8.933e-06 0.863 -1.205e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7292 0.2871 0.3795 0.5079 0.9591 0.9797 0.7324 0.8719 0.9533 0.7413 ] Network output: [ -0.08151 0.1949 0.8997 0.001128 -0.0005071 1.073 0.0008523 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.711 0.6056 0.3635 0.2008 0.9768 0.9843 0.7116 0.9453 0.9675 0.4138 ] Network output: [ -0.1459 0.3502 0.7765 -0.002343 0.001052 1.155 -0.001765 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7507 0.7269 0.4328 0.05594 0.9742 0.9822 0.7508 0.9405 0.9627 0.4459 ] Network output: [ 0.1663 0.5881 0.266 0.001528 -0.0006847 0.8196 0.001146 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1145 Epoch 744 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01015 1.062 0.9488 -0.0005262 0.0002358 -0.03332 -0.0003947 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04699 -0.005447 0.02632 0.02067 0.9118 0.9254 0.08575 0.8345 0.8719 0.1765 ] Network output: [ 0.9207 0.1577 -0.07815 0.0005826 -0.0002597 0.08134 0.0004316 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6497 -0.07646 -0.05436 0.2997 0.954 0.9763 0.7309 0.8596 0.9457 0.7383 ] Network output: [ -0.01388 0.9663 1.019 -0.0003197 0.0001428 0.04104 -0.000238 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.08009 0.03173 0.05675 0.04594 0.9726 0.98 0.08183 0.9351 0.9634 0.09159 ] Network output: [ 0.1433 -0.3174 1.168 -4.164e-06 6.771e-07 0.8633 1.772e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.729 0.2874 0.379 0.5083 0.9591 0.9797 0.7322 0.872 0.9534 0.7413 ] Network output: [ -0.08141 0.1955 0.8993 0.001126 -0.0005061 1.073 0.0008506 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7105 0.6053 0.363 0.201 0.9768 0.9843 0.7111 0.9454 0.9675 0.4134 ] Network output: [ -0.1461 0.3498 0.7772 -0.002344 0.001052 1.156 -0.001765 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7502 0.7265 0.4326 0.05653 0.9742 0.9822 0.7503 0.9405 0.9627 0.4458 ] Network output: [ 0.1664 0.5882 0.2653 0.001518 -0.0006802 0.8197 0.001139 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1144 Epoch 745 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01017 1.062 0.949 -0.0005268 0.000236 -0.03335 -0.0003951 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04693 -0.005428 0.02626 0.0207 0.9118 0.9254 0.08564 0.8345 0.872 0.1764 ] Network output: [ 0.9207 0.1579 -0.07842 0.0005761 -0.0002568 0.08143 0.0004268 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6495 -0.07622 -0.05479 0.2999 0.954 0.9763 0.7307 0.8596 0.9457 0.7384 ] Network output: [ -0.01384 0.9663 1.019 -0.0003188 0.0001424 0.04089 -0.0002373 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07995 0.03171 0.05663 0.04592 0.9726 0.98 0.08169 0.9351 0.9634 0.09144 ] Network output: [ 0.1432 -0.3177 1.168 1.415e-05 -7.529e-06 0.8635 1.551e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7288 0.2877 0.3784 0.5087 0.9591 0.9797 0.732 0.872 0.9534 0.7414 ] Network output: [ -0.08131 0.1961 0.8989 0.001124 -0.000505 1.072 0.0008489 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.71 0.605 0.3625 0.2011 0.9768 0.9843 0.7106 0.9454 0.9675 0.413 ] Network output: [ -0.1463 0.3494 0.778 -0.002345 0.001052 1.156 -0.001766 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7497 0.7261 0.4325 0.05712 0.9742 0.9822 0.7499 0.9405 0.9627 0.4456 ] Network output: [ 0.1666 0.5884 0.2647 0.001508 -0.0006757 0.8199 0.001131 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1143 Epoch 746 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01019 1.062 0.9492 -0.0005273 0.0002363 -0.03338 -0.0003956 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04686 -0.005409 0.02621 0.02072 0.9118 0.9254 0.08553 0.8346 0.872 0.1763 ] Network output: [ 0.9207 0.158 -0.07868 0.0005695 -0.0002539 0.08151 0.0004219 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6493 -0.07598 -0.05522 0.3001 0.954 0.9763 0.7305 0.8597 0.9457 0.7384 ] Network output: [ -0.0138 0.9662 1.019 -0.0003177 0.0001419 0.04074 -0.0002365 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0798 0.03169 0.0565 0.0459 0.9726 0.98 0.08154 0.9351 0.9634 0.0913 ] Network output: [ 0.1431 -0.3179 1.168 3.236e-05 -1.569e-05 0.8638 2.916e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7286 0.288 0.3778 0.509 0.9591 0.9797 0.7319 0.872 0.9534 0.7414 ] Network output: [ -0.08122 0.1967 0.8985 0.001122 -0.000504 1.072 0.0008472 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7095 0.6047 0.3621 0.2012 0.9768 0.9843 0.7101 0.9454 0.9675 0.4126 ] Network output: [ -0.1465 0.3491 0.7787 -0.002346 0.001053 1.156 -0.001767 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7493 0.7257 0.4323 0.05771 0.9742 0.9822 0.7494 0.9405 0.9627 0.4455 ] Network output: [ 0.1667 0.5886 0.2641 0.001498 -0.0006712 0.82 0.001124 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1143 Epoch 747 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01021 1.061 0.9494 -0.0005277 0.0002365 -0.03342 -0.0003959 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04679 -0.00539 0.02616 0.02075 0.9118 0.9254 0.08542 0.8346 0.8721 0.1761 ] Network output: [ 0.9207 0.1582 -0.07893 0.0005628 -0.0002509 0.08159 0.0004169 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6492 -0.07573 -0.05566 0.3003 0.9541 0.9763 0.7304 0.8597 0.9458 0.7384 ] Network output: [ -0.01375 0.9661 1.019 -0.0003166 0.0001415 0.04059 -0.0002358 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07967 0.03167 0.05637 0.04588 0.9726 0.98 0.0814 0.9352 0.9634 0.09115 ] Network output: [ 0.1431 -0.3181 1.168 5.045e-05 -2.38e-05 0.8641 4.274e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7285 0.2883 0.3772 0.5094 0.9591 0.9797 0.7317 0.872 0.9534 0.7415 ] Network output: [ -0.08113 0.1973 0.8981 0.001119 -0.000503 1.071 0.0008454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.709 0.6044 0.3616 0.2013 0.9768 0.9843 0.7096 0.9454 0.9675 0.4122 ] Network output: [ -0.1467 0.3487 0.7794 -0.002347 0.001053 1.156 -0.001768 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7488 0.7252 0.4322 0.05829 0.9742 0.9822 0.7489 0.9405 0.9627 0.4454 ] Network output: [ 0.1668 0.5887 0.2635 0.001488 -0.0006668 0.8202 0.001116 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1142 Epoch 748 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01023 1.061 0.9497 -0.0005281 0.0002366 -0.03346 -0.0003962 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04673 -0.005371 0.0261 0.02078 0.9118 0.9254 0.08531 0.8347 0.8721 0.176 ] Network output: [ 0.9207 0.1584 -0.07919 0.0005561 -0.0002479 0.08167 0.0004119 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.649 -0.07548 -0.05609 0.3005 0.9541 0.9763 0.7302 0.8597 0.9458 0.7384 ] Network output: [ -0.01371 0.9661 1.02 -0.0003155 0.0001409 0.04044 -0.0002349 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07953 0.03165 0.05625 0.04586 0.9726 0.9801 0.08126 0.9352 0.9634 0.091 ] Network output: [ 0.143 -0.3184 1.168 6.844e-05 -3.186e-05 0.8644 5.623e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7283 0.2886 0.3767 0.5098 0.9591 0.9797 0.7315 0.8721 0.9534 0.7415 ] Network output: [ -0.08104 0.1979 0.8977 0.001117 -0.000502 1.071 0.0008437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7085 0.6041 0.3612 0.2014 0.9768 0.9843 0.7091 0.9454 0.9675 0.4118 ] Network output: [ -0.1469 0.3483 0.7802 -0.002348 0.001054 1.156 -0.001768 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7484 0.7248 0.4321 0.05887 0.9741 0.9822 0.7485 0.9405 0.9627 0.4453 ] Network output: [ 0.1669 0.5889 0.2628 0.001478 -0.0006623 0.8204 0.001109 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1141 Epoch 749 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01026 1.061 0.9499 -0.0005284 0.0002368 -0.0335 -0.0003964 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04666 -0.005352 0.02605 0.02081 0.9118 0.9254 0.0852 0.8347 0.8722 0.1759 ] Network output: [ 0.9207 0.1586 -0.07943 0.0005493 -0.0002449 0.08174 0.0004069 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6489 -0.07522 -0.05652 0.3007 0.9541 0.9764 0.7301 0.8598 0.9458 0.7385 ] Network output: [ -0.01366 0.966 1.02 -0.0003143 0.0001404 0.04029 -0.0002341 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07939 0.03163 0.05612 0.04584 0.9726 0.9801 0.08112 0.9352 0.9635 0.09086 ] Network output: [ 0.1429 -0.3186 1.168 8.633e-05 -3.987e-05 0.8646 6.965e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7281 0.289 0.3761 0.5101 0.9591 0.9797 0.7314 0.8721 0.9535 0.7415 ] Network output: [ -0.08095 0.1985 0.8973 0.001115 -0.000501 1.071 0.000842 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.708 0.6038 0.3607 0.2016 0.9768 0.9843 0.7086 0.9455 0.9675 0.4115 ] Network output: [ -0.1471 0.3479 0.7809 -0.002349 0.001054 1.156 -0.001769 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7479 0.7244 0.4319 0.05945 0.9741 0.9821 0.748 0.9405 0.9627 0.4452 ] Network output: [ 0.1671 0.5891 0.2622 0.001468 -0.0006579 0.8206 0.001102 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.114 Epoch 750 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01029 1.061 0.9502 -0.0005286 0.0002369 -0.03354 -0.0003966 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0466 -0.005332 0.026 0.02084 0.9118 0.9254 0.0851 0.8348 0.8722 0.1758 ] Network output: [ 0.9207 0.1587 -0.07967 0.0005424 -0.0002418 0.08181 0.0004018 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6487 -0.07495 -0.05695 0.3009 0.9541 0.9764 0.7299 0.8598 0.9458 0.7385 ] Network output: [ -0.01361 0.9659 1.02 -0.0003131 0.0001399 0.04014 -0.0002332 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07926 0.03162 0.056 0.04582 0.9726 0.9801 0.08099 0.9353 0.9635 0.09072 ] Network output: [ 0.1429 -0.3188 1.169 0.0001041 -4.784e-05 0.8649 8.299e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.728 0.2893 0.3755 0.5105 0.9591 0.9797 0.7312 0.8721 0.9535 0.7416 ] Network output: [ -0.08087 0.1991 0.8969 0.001113 -0.0004999 1.07 0.0008402 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7075 0.6035 0.3603 0.2017 0.9768 0.9843 0.7081 0.9455 0.9676 0.4111 ] Network output: [ -0.1473 0.3476 0.7816 -0.002349 0.001054 1.156 -0.00177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7474 0.724 0.4318 0.06002 0.9741 0.9821 0.7476 0.9405 0.9627 0.4451 ] Network output: [ 0.1672 0.5892 0.2616 0.001458 -0.0006535 0.8208 0.001094 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1139 Epoch 751 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01032 1.06 0.9504 -0.0005287 0.0002369 -0.03359 -0.0003967 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04654 -0.005312 0.02594 0.02087 0.9118 0.9254 0.08499 0.8348 0.8722 0.1756 ] Network output: [ 0.9206 0.1589 -0.07991 0.0005355 -0.0002388 0.08189 0.0003967 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6486 -0.07468 -0.05739 0.3011 0.9541 0.9764 0.7298 0.8599 0.9459 0.7385 ] Network output: [ -0.01356 0.9659 1.02 -0.0003118 0.0001393 0.03999 -0.0002322 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07913 0.03161 0.05588 0.0458 0.9726 0.9801 0.08085 0.9353 0.9635 0.09058 ] Network output: [ 0.1428 -0.3191 1.169 0.0001218 -5.576e-05 0.8652 9.625e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7278 0.2897 0.3749 0.5108 0.9591 0.9797 0.731 0.8722 0.9535 0.7416 ] Network output: [ -0.08078 0.1996 0.8966 0.00111 -0.0004989 1.07 0.0008385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.707 0.6033 0.3598 0.2018 0.9768 0.9843 0.7076 0.9455 0.9676 0.4107 ] Network output: [ -0.1475 0.3472 0.7824 -0.00235 0.001055 1.156 -0.00177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.747 0.7236 0.4317 0.06059 0.9741 0.9821 0.7471 0.9405 0.9627 0.445 ] Network output: [ 0.1673 0.5894 0.2609 0.001448 -0.0006491 0.821 0.001087 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1138 Epoch 752 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01035 1.06 0.9506 -0.0005288 0.000237 -0.03364 -0.0003968 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04647 -0.005293 0.02589 0.0209 0.9119 0.9254 0.08489 0.8349 0.8723 0.1755 ] Network output: [ 0.9206 0.1591 -0.08014 0.0005286 -0.0002357 0.08195 0.0003916 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6484 -0.0744 -0.05782 0.3013 0.9541 0.9764 0.7296 0.8599 0.9459 0.7385 ] Network output: [ -0.01351 0.9658 1.02 -0.0003105 0.0001387 0.03984 -0.0002313 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.079 0.03159 0.05576 0.04578 0.9726 0.9801 0.08072 0.9353 0.9635 0.09044 ] Network output: [ 0.1427 -0.3193 1.169 0.0001394 -6.363e-05 0.8655 0.0001094 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7276 0.2901 0.3743 0.5111 0.9592 0.9797 0.7309 0.8722 0.9535 0.7416 ] Network output: [ -0.0807 0.2002 0.8962 0.001108 -0.0004979 1.069 0.0008368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7065 0.603 0.3594 0.2019 0.9768 0.9844 0.7071 0.9455 0.9676 0.4103 ] Network output: [ -0.1477 0.3468 0.7831 -0.002351 0.001055 1.156 -0.001771 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7465 0.7232 0.4315 0.06115 0.9741 0.9821 0.7466 0.9405 0.9627 0.4449 ] Network output: [ 0.1674 0.5896 0.2603 0.001439 -0.0006447 0.8212 0.00108 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1137 Epoch 753 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01039 1.06 0.9509 -0.0005288 0.000237 -0.03369 -0.0003969 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04641 -0.005273 0.02584 0.02093 0.9119 0.9254 0.08478 0.8349 0.8723 0.1754 ] Network output: [ 0.9206 0.1592 -0.08036 0.0005216 -0.0002325 0.08202 0.0003864 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6483 -0.07411 -0.05825 0.3015 0.9541 0.9764 0.7295 0.8599 0.9459 0.7385 ] Network output: [ -0.01346 0.9657 1.02 -0.0003091 0.0001381 0.03969 -0.0002303 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07887 0.03158 0.05564 0.04575 0.9726 0.9801 0.08059 0.9353 0.9635 0.0903 ] Network output: [ 0.1427 -0.3195 1.169 0.0001568 -7.146e-05 0.8658 0.0001225 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7275 0.2905 0.3738 0.5115 0.9592 0.9797 0.7307 0.8722 0.9535 0.7416 ] Network output: [ -0.08062 0.2008 0.8958 0.001106 -0.0004968 1.069 0.000835 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.706 0.6027 0.3589 0.202 0.9768 0.9844 0.7066 0.9455 0.9676 0.4099 ] Network output: [ -0.1479 0.3465 0.7838 -0.002352 0.001055 1.156 -0.001771 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.746 0.7228 0.4314 0.06172 0.9741 0.9821 0.7462 0.9405 0.9627 0.4448 ] Network output: [ 0.1675 0.5898 0.2596 0.001429 -0.0006404 0.8214 0.001072 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1137 Epoch 754 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01042 1.06 0.9511 -0.0005287 0.000237 -0.03375 -0.0003968 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04635 -0.005253 0.02578 0.02096 0.9119 0.9255 0.08468 0.835 0.8724 0.1752 ] Network output: [ 0.9206 0.1594 -0.08058 0.0005145 -0.0002294 0.08208 0.0003812 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6481 -0.07382 -0.05868 0.3017 0.9541 0.9764 0.7293 0.86 0.9459 0.7385 ] Network output: [ -0.01341 0.9656 1.02 -0.0003077 0.0001375 0.03954 -0.0002293 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07874 0.03157 0.05552 0.04574 0.9726 0.9801 0.08046 0.9354 0.9636 0.09017 ] Network output: [ 0.1426 -0.3197 1.169 0.0001742 -7.925e-05 0.8661 0.0001356 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7273 0.2909 0.3732 0.5118 0.9592 0.9797 0.7306 0.8723 0.9536 0.7417 ] Network output: [ -0.08054 0.2014 0.8954 0.001104 -0.0004958 1.069 0.0008333 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7055 0.6025 0.3585 0.2021 0.9768 0.9844 0.7061 0.9456 0.9676 0.4095 ] Network output: [ -0.1481 0.3461 0.7845 -0.002352 0.001056 1.156 -0.001772 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7456 0.7224 0.4313 0.06228 0.9741 0.9821 0.7457 0.9405 0.9627 0.4447 ] Network output: [ 0.1676 0.5899 0.259 0.001419 -0.000636 0.8217 0.001065 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1136 Epoch 755 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01046 1.059 0.9513 -0.0005286 0.0002369 -0.0338 -0.0003968 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04628 -0.005232 0.02573 0.02099 0.9119 0.9255 0.08458 0.835 0.8724 0.1751 ] Network output: [ 0.9206 0.1595 -0.0808 0.0005075 -0.0002262 0.08214 0.0003759 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.648 -0.07352 -0.05911 0.3019 0.9541 0.9764 0.7292 0.86 0.9459 0.7385 ] Network output: [ -0.01335 0.9655 1.021 -0.0003062 0.0001368 0.03939 -0.0002282 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07861 0.03157 0.0554 0.04572 0.9726 0.9801 0.08033 0.9354 0.9636 0.09003 ] Network output: [ 0.1425 -0.32 1.169 0.0001915 -8.699e-05 0.8664 0.0001485 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7272 0.2913 0.3726 0.5121 0.9592 0.9797 0.7304 0.8723 0.9536 0.7417 ] Network output: [ -0.08047 0.202 0.8951 0.001101 -0.0004948 1.068 0.0008315 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.705 0.6022 0.3581 0.2022 0.9768 0.9844 0.7056 0.9456 0.9676 0.4091 ] Network output: [ -0.1483 0.3458 0.7852 -0.002353 0.001056 1.156 -0.001772 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7451 0.722 0.4311 0.06283 0.9741 0.9821 0.7453 0.9405 0.9627 0.4446 ] Network output: [ 0.1677 0.5901 0.2584 0.001409 -0.0006317 0.8219 0.001058 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1135 Epoch 756 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0105 1.059 0.9516 -0.0005284 0.0002368 -0.03386 -0.0003966 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04622 -0.005212 0.02568 0.02101 0.9119 0.9255 0.08448 0.8351 0.8724 0.175 ] Network output: [ 0.9206 0.1597 -0.08101 0.0005003 -0.0002231 0.0822 0.0003707 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6478 -0.07322 -0.05954 0.3021 0.9541 0.9764 0.7291 0.8601 0.946 0.7385 ] Network output: [ -0.0133 0.9654 1.021 -0.0003047 0.0001362 0.03924 -0.0002271 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07849 0.03156 0.05529 0.0457 0.9726 0.9801 0.0802 0.9354 0.9636 0.0899 ] Network output: [ 0.1425 -0.3202 1.169 0.0002086 -9.468e-05 0.8667 0.0001614 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.727 0.2917 0.372 0.5124 0.9592 0.9797 0.7303 0.8723 0.9536 0.7417 ] Network output: [ -0.08039 0.2026 0.8947 0.001099 -0.0004937 1.068 0.0008298 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7045 0.602 0.3576 0.2023 0.9768 0.9844 0.7051 0.9456 0.9676 0.4088 ] Network output: [ -0.1485 0.3454 0.7859 -0.002353 0.001056 1.156 -0.001773 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7447 0.7215 0.431 0.06339 0.9741 0.9821 0.7448 0.9405 0.9627 0.4445 ] Network output: [ 0.1678 0.5903 0.2577 0.0014 -0.0006273 0.8221 0.001051 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1134 Epoch 757 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01055 1.059 0.9518 -0.0005282 0.0002367 -0.03392 -0.0003965 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04616 -0.005191 0.02562 0.02104 0.9119 0.9255 0.08438 0.8351 0.8725 0.1749 ] Network output: [ 0.9206 0.1599 -0.08121 0.0004932 -0.0002199 0.08226 0.0003654 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6477 -0.07291 -0.05997 0.3022 0.9541 0.9764 0.7289 0.8601 0.946 0.7385 ] Network output: [ -0.01324 0.9653 1.021 -0.0003032 0.0001355 0.0391 -0.000226 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07836 0.03155 0.05517 0.04568 0.9726 0.9801 0.08008 0.9355 0.9636 0.08977 ] Network output: [ 0.1424 -0.3204 1.17 0.0002257 -0.0001023 0.867 0.0001742 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7269 0.2922 0.3714 0.5128 0.9592 0.9797 0.7301 0.8724 0.9536 0.7417 ] Network output: [ -0.08032 0.2032 0.8943 0.001097 -0.0004927 1.068 0.000828 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.704 0.6017 0.3572 0.2024 0.9768 0.9844 0.7046 0.9456 0.9677 0.4084 ] Network output: [ -0.1487 0.3451 0.7867 -0.002354 0.001057 1.156 -0.001773 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7442 0.7211 0.4309 0.06394 0.9741 0.9821 0.7443 0.9404 0.9627 0.4444 ] Network output: [ 0.1679 0.5905 0.2571 0.00139 -0.000623 0.8224 0.001043 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1133 Epoch 758 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01059 1.059 0.952 -0.0005279 0.0002366 -0.03399 -0.0003963 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0461 -0.005171 0.02557 0.02107 0.9119 0.9255 0.08428 0.8352 0.8725 0.1748 ] Network output: [ 0.9205 0.16 -0.08141 0.000486 -0.0002167 0.08231 0.00036 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6475 -0.07259 -0.0604 0.3024 0.9541 0.9764 0.7288 0.8601 0.946 0.7385 ] Network output: [ -0.01318 0.9652 1.021 -0.0003017 0.0001348 0.03895 -0.0002249 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07824 0.03155 0.05506 0.04566 0.9726 0.9801 0.07995 0.9355 0.9636 0.08964 ] Network output: [ 0.1423 -0.3206 1.17 0.0002427 -0.0001099 0.8673 0.0001869 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7267 0.2926 0.3708 0.5131 0.9592 0.9797 0.73 0.8724 0.9536 0.7417 ] Network output: [ -0.08025 0.2038 0.894 0.001094 -0.0004917 1.067 0.0008262 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7035 0.6015 0.3568 0.2025 0.9768 0.9844 0.7041 0.9456 0.9677 0.408 ] Network output: [ -0.1489 0.3448 0.7874 -0.002355 0.001057 1.156 -0.001774 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7437 0.7207 0.4307 0.06449 0.9741 0.9821 0.7439 0.9404 0.9627 0.4443 ] Network output: [ 0.1679 0.5907 0.2564 0.00138 -0.0006187 0.8226 0.001036 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1132 Epoch 759 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01064 1.058 0.9523 -0.0005275 0.0002365 -0.03406 -0.000396 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04604 -0.00515 0.02552 0.0211 0.9119 0.9255 0.08419 0.8352 0.8726 0.1747 ] Network output: [ 0.9205 0.1602 -0.0816 0.0004788 -0.0002134 0.08237 0.0003547 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6474 -0.07227 -0.06083 0.3026 0.9541 0.9764 0.7287 0.8602 0.946 0.7385 ] Network output: [ -0.01312 0.9651 1.021 -0.0003001 0.0001341 0.0388 -0.0002237 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07812 0.03155 0.05494 0.04564 0.9726 0.9801 0.07983 0.9355 0.9636 0.08952 ] Network output: [ 0.1423 -0.3209 1.17 0.0002595 -0.0001175 0.8677 0.0001996 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7266 0.2931 0.3702 0.5134 0.9592 0.9797 0.7298 0.8724 0.9536 0.7417 ] Network output: [ -0.08018 0.2044 0.8936 0.001092 -0.0004906 1.067 0.0008245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.703 0.6012 0.3563 0.2026 0.9768 0.9844 0.7036 0.9457 0.9677 0.4076 ] Network output: [ -0.1491 0.3444 0.7881 -0.002355 0.001057 1.156 -0.001774 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7433 0.7203 0.4306 0.06504 0.9741 0.9821 0.7434 0.9404 0.9627 0.4442 ] Network output: [ 0.168 0.5909 0.2558 0.001371 -0.0006144 0.8229 0.001029 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1131 Epoch 760 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01069 1.058 0.9525 -0.0005271 0.0002363 -0.03412 -0.0003957 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04598 -0.005129 0.02547 0.02113 0.9119 0.9255 0.08409 0.8353 0.8726 0.1745 ] Network output: [ 0.9205 0.1603 -0.08179 0.0004715 -0.0002102 0.08242 0.0003493 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6473 -0.07194 -0.06126 0.3028 0.9541 0.9764 0.7285 0.8602 0.9461 0.7385 ] Network output: [ -0.01306 0.965 1.021 -0.0002985 0.0001334 0.03865 -0.0002225 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.078 0.03155 0.05483 0.04562 0.9726 0.9801 0.07971 0.9355 0.9637 0.08939 ] Network output: [ 0.1422 -0.3211 1.17 0.0002763 -0.000125 0.868 0.0002122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7264 0.2936 0.3696 0.5137 0.9592 0.9797 0.7297 0.8725 0.9537 0.7417 ] Network output: [ -0.08011 0.205 0.8933 0.00109 -0.0004896 1.066 0.0008227 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7025 0.601 0.3559 0.2027 0.9768 0.9844 0.7031 0.9457 0.9677 0.4073 ] Network output: [ -0.1493 0.3441 0.7888 -0.002355 0.001057 1.156 -0.001774 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7428 0.7199 0.4305 0.06558 0.9741 0.9821 0.743 0.9404 0.9627 0.4441 ] Network output: [ 0.1681 0.591 0.2551 0.001361 -0.0006101 0.8232 0.001022 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.113 Epoch 761 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01074 1.058 0.9528 -0.0005267 0.0002361 -0.0342 -0.0003954 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04592 -0.005108 0.02541 0.02116 0.912 0.9255 0.08399 0.8353 0.8727 0.1744 ] Network output: [ 0.9205 0.1604 -0.08198 0.0004642 -0.000207 0.08246 0.0003439 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6471 -0.0716 -0.06169 0.303 0.9541 0.9764 0.7284 0.8603 0.9461 0.7385 ] Network output: [ -0.013 0.9649 1.021 -0.0002968 0.0001327 0.0385 -0.0002213 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07789 0.03155 0.05472 0.0456 0.9726 0.9801 0.07959 0.9356 0.9637 0.08927 ] Network output: [ 0.1421 -0.3213 1.17 0.0002929 -0.0001325 0.8683 0.0002247 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7263 0.2941 0.369 0.514 0.9592 0.9797 0.7296 0.8725 0.9537 0.7417 ] Network output: [ -0.08005 0.2056 0.8929 0.001087 -0.0004885 1.066 0.000821 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7021 0.6008 0.3555 0.2028 0.9768 0.9844 0.7027 0.9457 0.9677 0.4069 ] Network output: [ -0.1494 0.3438 0.7895 -0.002356 0.001057 1.156 -0.001774 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7424 0.7195 0.4303 0.06613 0.9741 0.9821 0.7425 0.9404 0.9627 0.444 ] Network output: [ 0.1682 0.5912 0.2545 0.001352 -0.0006058 0.8235 0.001015 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1129 Epoch 762 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01079 1.058 0.953 -0.0005261 0.0002358 -0.03427 -0.000395 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04586 -0.005087 0.02536 0.02119 0.912 0.9255 0.0839 0.8354 0.8727 0.1743 ] Network output: [ 0.9205 0.1606 -0.08215 0.0004569 -0.0002037 0.08251 0.0003384 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.647 -0.07126 -0.06212 0.3031 0.9541 0.9764 0.7283 0.8603 0.9461 0.7385 ] Network output: [ -0.01294 0.9648 1.022 -0.0002951 0.0001319 0.03836 -0.0002201 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07777 0.03155 0.05461 0.04559 0.9726 0.9801 0.07947 0.9356 0.9637 0.08915 ] Network output: [ 0.1421 -0.3215 1.17 0.0003095 -0.0001399 0.8686 0.0002371 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7262 0.2946 0.3684 0.5143 0.9592 0.9797 0.7294 0.8726 0.9537 0.7417 ] Network output: [ -0.07998 0.2062 0.8926 0.001085 -0.0004875 1.066 0.0008192 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7016 0.6005 0.355 0.2029 0.9768 0.9844 0.7022 0.9457 0.9677 0.4065 ] Network output: [ -0.1496 0.3434 0.7902 -0.002356 0.001058 1.156 -0.001775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7419 0.7191 0.4302 0.06667 0.974 0.9821 0.742 0.9404 0.9627 0.4439 ] Network output: [ 0.1683 0.5914 0.2538 0.001342 -0.0006015 0.8237 0.001007 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1128 Epoch 763 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01085 1.057 0.9532 -0.0005256 0.0002356 -0.03434 -0.0003946 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0458 -0.005065 0.02531 0.02121 0.912 0.9255 0.08381 0.8355 0.8727 0.1742 ] Network output: [ 0.9204 0.1607 -0.08233 0.0004496 -0.0002004 0.08255 0.000333 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6468 -0.07092 -0.06254 0.3033 0.9541 0.9764 0.7282 0.8604 0.9461 0.7385 ] Network output: [ -0.01287 0.9647 1.022 -0.0002934 0.0001312 0.03821 -0.0002188 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07765 0.03155 0.0545 0.04557 0.9727 0.9801 0.07936 0.9356 0.9637 0.08903 ] Network output: [ 0.142 -0.3217 1.17 0.0003259 -0.0001472 0.869 0.0002494 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.726 0.2951 0.3678 0.5145 0.9592 0.9797 0.7293 0.8726 0.9537 0.7417 ] Network output: [ -0.07992 0.2068 0.8923 0.001083 -0.0004865 1.065 0.0008174 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7011 0.6003 0.3546 0.203 0.9768 0.9844 0.7017 0.9457 0.9677 0.4062 ] Network output: [ -0.1498 0.3431 0.7909 -0.002356 0.001058 1.156 -0.001775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7414 0.7187 0.4301 0.06721 0.974 0.9821 0.7416 0.9404 0.9627 0.4438 ] Network output: [ 0.1683 0.5916 0.2531 0.001332 -0.0005972 0.824 0.001 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1127 Epoch 764 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0109 1.057 0.9535 -0.0005249 0.0002353 -0.03442 -0.0003941 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04574 -0.005044 0.02525 0.02124 0.912 0.9256 0.08371 0.8355 0.8728 0.1741 ] Network output: [ 0.9204 0.1609 -0.0825 0.0004423 -0.0001971 0.08259 0.0003275 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6467 -0.07057 -0.06297 0.3035 0.9542 0.9764 0.7281 0.8604 0.9462 0.7385 ] Network output: [ -0.01281 0.9646 1.022 -0.0002917 0.0001304 0.03806 -0.0002175 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07754 0.03156 0.05439 0.04555 0.9727 0.9801 0.07924 0.9357 0.9637 0.08891 ] Network output: [ 0.1419 -0.322 1.17 0.0003422 -0.0001546 0.8693 0.0002617 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7259 0.2957 0.3672 0.5148 0.9592 0.9797 0.7292 0.8726 0.9537 0.7417 ] Network output: [ -0.07985 0.2074 0.8919 0.001081 -0.0004854 1.065 0.0008157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7006 0.6001 0.3542 0.2031 0.9768 0.9844 0.7012 0.9458 0.9677 0.4058 ] Network output: [ -0.15 0.3428 0.7916 -0.002357 0.001058 1.156 -0.001775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.741 0.7183 0.43 0.06775 0.974 0.9821 0.7411 0.9404 0.9627 0.4438 ] Network output: [ 0.1684 0.5918 0.2525 0.001323 -0.000593 0.8243 0.0009931 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1126 Epoch 765 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01096 1.057 0.9537 -0.0005242 0.000235 -0.0345 -0.0003936 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04568 -0.005022 0.0252 0.02127 0.912 0.9256 0.08362 0.8356 0.8728 0.174 ] Network output: [ 0.9204 0.161 -0.08266 0.0004349 -0.0001938 0.08263 0.000322 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6466 -0.07021 -0.0634 0.3037 0.9542 0.9764 0.7279 0.8605 0.9462 0.7385 ] Network output: [ -0.01274 0.9645 1.022 -0.0002899 0.0001296 0.03791 -0.0002162 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07743 0.03156 0.05429 0.04554 0.9727 0.9801 0.07913 0.9357 0.9637 0.08879 ] Network output: [ 0.1419 -0.3222 1.17 0.0003585 -0.0001618 0.8696 0.0002739 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7258 0.2962 0.3666 0.5151 0.9592 0.9797 0.729 0.8727 0.9538 0.7417 ] Network output: [ -0.07979 0.208 0.8916 0.001078 -0.0004844 1.064 0.0008139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.7001 0.5999 0.3538 0.2032 0.9768 0.9844 0.7007 0.9458 0.9678 0.4054 ] Network output: [ -0.1501 0.3425 0.7923 -0.002357 0.001058 1.156 -0.001775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7405 0.7179 0.4298 0.06829 0.974 0.9821 0.7406 0.9404 0.9627 0.4437 ] Network output: [ 0.1684 0.592 0.2518 0.001313 -0.0005887 0.8246 0.000986 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1125 Epoch 766 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01102 1.056 0.954 -0.0005235 0.0002347 -0.03458 -0.0003931 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04562 -0.005001 0.02515 0.0213 0.912 0.9256 0.08353 0.8356 0.8729 0.1739 ] Network output: [ 0.9204 0.1611 -0.08282 0.0004275 -0.0001905 0.08266 0.0003165 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6464 -0.06985 -0.06382 0.3038 0.9542 0.9764 0.7278 0.8605 0.9462 0.7384 ] Network output: [ -0.01268 0.9644 1.022 -0.0002881 0.0001288 0.03777 -0.0002149 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07732 0.03157 0.05418 0.04552 0.9727 0.9801 0.07902 0.9357 0.9638 0.08868 ] Network output: [ 0.1418 -0.3224 1.17 0.0003746 -0.000169 0.87 0.000286 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7257 0.2968 0.366 0.5154 0.9592 0.9797 0.7289 0.8727 0.9538 0.7417 ] Network output: [ -0.07973 0.2086 0.8913 0.001076 -0.0004833 1.064 0.0008122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6996 0.5996 0.3533 0.2033 0.9768 0.9844 0.7002 0.9458 0.9678 0.4051 ] Network output: [ -0.1503 0.3421 0.793 -0.002357 0.001058 1.156 -0.001775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7401 0.7175 0.4297 0.06882 0.974 0.9821 0.7402 0.9404 0.9627 0.4436 ] Network output: [ 0.1685 0.5922 0.2512 0.001304 -0.0005844 0.8249 0.0009789 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1124 Epoch 767 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01108 1.056 0.9542 -0.0005227 0.0002343 -0.03467 -0.0003925 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04556 -0.004979 0.0251 0.02133 0.912 0.9256 0.08344 0.8357 0.8729 0.1738 ] Network output: [ 0.9204 0.1613 -0.08297 0.00042 -0.0001872 0.08269 0.000311 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6463 -0.06948 -0.06425 0.304 0.9542 0.9764 0.7277 0.8605 0.9462 0.7384 ] Network output: [ -0.01261 0.9642 1.022 -0.0002863 0.000128 0.03762 -0.0002136 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07721 0.03157 0.05408 0.04551 0.9727 0.9801 0.0789 0.9358 0.9638 0.08856 ] Network output: [ 0.1417 -0.3226 1.17 0.0003906 -0.0001762 0.8703 0.000298 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7255 0.2973 0.3654 0.5156 0.9592 0.9797 0.7288 0.8727 0.9538 0.7417 ] Network output: [ -0.07967 0.2092 0.891 0.001074 -0.0004823 1.064 0.0008104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6991 0.5994 0.3529 0.2033 0.9768 0.9844 0.6997 0.9458 0.9678 0.4047 ] Network output: [ -0.1505 0.3418 0.7937 -0.002357 0.001058 1.156 -0.001775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7396 0.7171 0.4296 0.06936 0.974 0.9821 0.7397 0.9404 0.9627 0.4435 ] Network output: [ 0.1686 0.5924 0.2505 0.001294 -0.0005802 0.8252 0.0009717 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1123 Epoch 768 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01115 1.056 0.9544 -0.0005219 0.000234 -0.03475 -0.0003919 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04551 -0.004957 0.02504 0.02136 0.912 0.9256 0.08335 0.8357 0.8729 0.1737 ] Network output: [ 0.9203 0.1614 -0.08312 0.0004126 -0.0001839 0.08272 0.0003055 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6462 -0.06911 -0.06467 0.3041 0.9542 0.9764 0.7276 0.8606 0.9463 0.7384 ] Network output: [ -0.01254 0.9641 1.022 -0.0002845 0.0001272 0.03748 -0.0002122 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0771 0.03158 0.05397 0.04549 0.9727 0.9801 0.07879 0.9358 0.9638 0.08845 ] Network output: [ 0.1417 -0.3228 1.17 0.0004065 -0.0001833 0.8707 0.0003099 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7254 0.2979 0.3648 0.5159 0.9592 0.9797 0.7287 0.8728 0.9538 0.7416 ] Network output: [ -0.07961 0.2098 0.8906 0.001071 -0.0004813 1.063 0.0008087 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6986 0.5992 0.3525 0.2034 0.9769 0.9844 0.6992 0.9458 0.9678 0.4044 ] Network output: [ -0.1507 0.3415 0.7944 -0.002357 0.001058 1.156 -0.001775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7391 0.7167 0.4294 0.06989 0.974 0.9821 0.7393 0.9404 0.9627 0.4434 ] Network output: [ 0.1686 0.5926 0.2498 0.001285 -0.0005759 0.8255 0.0009646 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1122 Epoch 769 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01121 1.056 0.9547 -0.000521 0.0002336 -0.03484 -0.0003913 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04545 -0.004935 0.02499 0.02139 0.9121 0.9256 0.08326 0.8358 0.873 0.1736 ] Network output: [ 0.9203 0.1615 -0.08327 0.0004051 -0.0001806 0.08275 0.0002999 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6461 -0.06873 -0.0651 0.3043 0.9542 0.9764 0.7275 0.8606 0.9463 0.7384 ] Network output: [ -0.01247 0.964 1.022 -0.0002826 0.0001263 0.03733 -0.0002108 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07699 0.03159 0.05387 0.04548 0.9727 0.9801 0.07869 0.9358 0.9638 0.08834 ] Network output: [ 0.1416 -0.323 1.171 0.0004222 -0.0001904 0.871 0.0003217 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7253 0.2985 0.3642 0.5162 0.9592 0.9797 0.7286 0.8728 0.9538 0.7416 ] Network output: [ -0.07956 0.2104 0.8903 0.001069 -0.0004802 1.063 0.0008069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6981 0.599 0.3521 0.2035 0.9769 0.9844 0.6987 0.9459 0.9678 0.404 ] Network output: [ -0.1508 0.3412 0.7951 -0.002357 0.001058 1.156 -0.001775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7387 0.7163 0.4293 0.07043 0.974 0.9821 0.7388 0.9404 0.9627 0.4433 ] Network output: [ 0.1686 0.5929 0.2492 0.001275 -0.0005716 0.8259 0.0009575 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1121 Epoch 770 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01128 1.055 0.9549 -0.0005201 0.0002331 -0.03493 -0.0003906 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04539 -0.004913 0.02494 0.02142 0.9121 0.9256 0.08317 0.8358 0.873 0.1735 ] Network output: [ 0.9203 0.1616 -0.08341 0.0003976 -0.0001772 0.08278 0.0002943 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6459 -0.06834 -0.06552 0.3045 0.9542 0.9764 0.7274 0.8607 0.9463 0.7383 ] Network output: [ -0.0124 0.9639 1.023 -0.0002807 0.0001255 0.03719 -0.0002094 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07689 0.0316 0.05377 0.04546 0.9727 0.9801 0.07858 0.9359 0.9638 0.08823 ] Network output: [ 0.1415 -0.3232 1.171 0.0004379 -0.0001974 0.8714 0.0003335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7252 0.2991 0.3636 0.5164 0.9592 0.9797 0.7284 0.8729 0.9539 0.7416 ] Network output: [ -0.0795 0.211 0.89 0.001067 -0.0004792 1.062 0.0008052 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6976 0.5988 0.3517 0.2036 0.9769 0.9844 0.6982 0.9459 0.9678 0.4037 ] Network output: [ -0.151 0.3409 0.7958 -0.002357 0.001058 1.156 -0.001775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7382 0.716 0.4292 0.07096 0.974 0.9821 0.7383 0.9404 0.9627 0.4432 ] Network output: [ 0.1687 0.5931 0.2485 0.001266 -0.0005674 0.8262 0.0009504 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.112 Epoch 771 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01135 1.055 0.9551 -0.0005191 0.0002327 -0.03502 -0.0003898 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04534 -0.004891 0.02489 0.02144 0.9121 0.9256 0.08309 0.8359 0.8731 0.1734 ] Network output: [ 0.9203 0.1618 -0.08354 0.0003901 -0.0001739 0.0828 0.0002888 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6458 -0.06796 -0.06594 0.3046 0.9542 0.9764 0.7273 0.8607 0.9463 0.7383 ] Network output: [ -0.01233 0.9637 1.023 -0.0002788 0.0001246 0.03704 -0.000208 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07678 0.03161 0.05367 0.04545 0.9727 0.9801 0.07847 0.9359 0.9638 0.08812 ] Network output: [ 0.1415 -0.3235 1.171 0.0004534 -0.0002044 0.8717 0.0003451 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7251 0.2997 0.363 0.5167 0.9592 0.9797 0.7283 0.8729 0.9539 0.7416 ] Network output: [ -0.07944 0.2116 0.8897 0.001064 -0.0004782 1.062 0.0008034 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6972 0.5986 0.3513 0.2037 0.9769 0.9844 0.6978 0.9459 0.9678 0.4033 ] Network output: [ -0.1512 0.3406 0.7965 -0.002357 0.001058 1.156 -0.001775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7378 0.7156 0.4291 0.07149 0.974 0.9821 0.7379 0.9404 0.9627 0.4431 ] Network output: [ 0.1687 0.5933 0.2478 0.001256 -0.0005631 0.8265 0.0009433 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1119 Epoch 772 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01142 1.055 0.9554 -0.000518 0.0002322 -0.03511 -0.0003891 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04528 -0.004869 0.02483 0.02147 0.9121 0.9256 0.083 0.8359 0.8731 0.1733 ] Network output: [ 0.9203 0.1619 -0.08367 0.0003826 -0.0001705 0.08282 0.0002832 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6457 -0.06756 -0.06636 0.3048 0.9542 0.9764 0.7272 0.8608 0.9464 0.7383 ] Network output: [ -0.01225 0.9636 1.023 -0.0002768 0.0001238 0.0369 -0.0002065 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07668 0.03163 0.05357 0.04544 0.9727 0.9801 0.07837 0.9359 0.9639 0.08802 ] Network output: [ 0.1414 -0.3237 1.171 0.0004688 -0.0002113 0.8721 0.0003567 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.725 0.3004 0.3624 0.5169 0.9592 0.9798 0.7282 0.8729 0.9539 0.7415 ] Network output: [ -0.07939 0.2122 0.8894 0.001062 -0.0004771 1.062 0.0008017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6967 0.5984 0.3508 0.2038 0.9769 0.9844 0.6973 0.9459 0.9679 0.403 ] Network output: [ -0.1513 0.3402 0.7972 -0.002356 0.001058 1.156 -0.001775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7373 0.7152 0.4289 0.07202 0.974 0.982 0.7374 0.9404 0.9627 0.443 ] Network output: [ 0.1688 0.5935 0.2472 0.001247 -0.0005589 0.8269 0.0009362 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1118 Epoch 773 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01149 1.055 0.9556 -0.0005169 0.0002318 -0.03521 -0.0003883 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04522 -0.004846 0.02478 0.0215 0.9121 0.9256 0.08292 0.836 0.8731 0.1732 ] Network output: [ 0.9202 0.162 -0.08379 0.0003751 -0.0001671 0.08284 0.0002775 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6456 -0.06716 -0.06678 0.3049 0.9542 0.9765 0.7271 0.8608 0.9464 0.7382 ] Network output: [ -0.01218 0.9635 1.023 -0.0002748 0.0001229 0.03675 -0.0002051 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07658 0.03164 0.05347 0.04542 0.9727 0.9801 0.07826 0.936 0.9639 0.08791 ] Network output: [ 0.1414 -0.3239 1.171 0.000484 -0.0002181 0.8724 0.0003681 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7249 0.301 0.3618 0.5172 0.9592 0.9798 0.7281 0.873 0.9539 0.7415 ] Network output: [ -0.07934 0.2128 0.8891 0.00106 -0.0004761 1.061 0.0008 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6962 0.5982 0.3504 0.2039 0.9769 0.9844 0.6968 0.946 0.9679 0.4026 ] Network output: [ -0.1515 0.3399 0.7978 -0.002356 0.001058 1.156 -0.001775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7368 0.7148 0.4288 0.07255 0.974 0.982 0.737 0.9404 0.9627 0.4429 ] Network output: [ 0.1688 0.5937 0.2465 0.001237 -0.0005546 0.8272 0.000929 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1117 Epoch 774 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01157 1.054 0.9558 -0.0005158 0.0002313 -0.03531 -0.0003874 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04517 -0.004824 0.02473 0.02153 0.9121 0.9256 0.08283 0.836 0.8732 0.1731 ] Network output: [ 0.9202 0.1621 -0.08391 0.0003675 -0.0001638 0.08285 0.0002719 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6454 -0.06676 -0.06719 0.3051 0.9542 0.9765 0.727 0.8609 0.9464 0.7382 ] Network output: [ -0.0121 0.9633 1.023 -0.0002728 0.000122 0.03661 -0.0002036 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07647 0.03166 0.05337 0.04541 0.9727 0.9801 0.07816 0.936 0.9639 0.08781 ] Network output: [ 0.1413 -0.3241 1.171 0.0004992 -0.0002249 0.8728 0.0003795 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7247 0.3016 0.3612 0.5174 0.9592 0.9798 0.728 0.873 0.9539 0.7415 ] Network output: [ -0.07928 0.2134 0.8888 0.001058 -0.0004751 1.061 0.0007982 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6957 0.5981 0.35 0.2039 0.9769 0.9844 0.6963 0.946 0.9679 0.4023 ] Network output: [ -0.1516 0.3396 0.7985 -0.002356 0.001057 1.156 -0.001775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7364 0.7144 0.4287 0.07308 0.974 0.982 0.7365 0.9404 0.9627 0.4428 ] Network output: [ 0.1688 0.594 0.2458 0.001228 -0.0005504 0.8276 0.0009219 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1116 Epoch 775 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01164 1.054 0.9561 -0.0005146 0.0002307 -0.0354 -0.0003866 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04511 -0.004802 0.02468 0.02156 0.9121 0.9257 0.08275 0.8361 0.8732 0.173 ] Network output: [ 0.9202 0.1622 -0.08403 0.0003599 -0.0001604 0.08287 0.0002663 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6453 -0.06635 -0.06761 0.3053 0.9542 0.9765 0.7269 0.8609 0.9464 0.7382 ] Network output: [ -0.01203 0.9632 1.023 -0.0002708 0.0001211 0.03647 -0.0002021 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07637 0.03167 0.05328 0.0454 0.9727 0.9801 0.07806 0.936 0.9639 0.08771 ] Network output: [ 0.1412 -0.3243 1.171 0.0005142 -0.0002316 0.8731 0.0003908 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7246 0.3023 0.3606 0.5176 0.9592 0.9798 0.7279 0.8731 0.954 0.7414 ] Network output: [ -0.07923 0.214 0.8885 0.001055 -0.0004741 1.06 0.0007965 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6952 0.5979 0.3496 0.204 0.9769 0.9844 0.6958 0.946 0.9679 0.4019 ] Network output: [ -0.1518 0.3393 0.7992 -0.002355 0.001057 1.155 -0.001774 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7359 0.714 0.4285 0.07361 0.974 0.982 0.7361 0.9404 0.9627 0.4427 ] Network output: [ 0.1688 0.5942 0.2452 0.001218 -0.0005462 0.8279 0.0009148 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1115 Epoch 776 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01172 1.054 0.9563 -0.0005134 0.0002302 -0.0355 -0.0003856 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04506 -0.004779 0.02462 0.02159 0.9121 0.9257 0.08266 0.8362 0.8733 0.1729 ] Network output: [ 0.9202 0.1624 -0.08414 0.0003523 -0.000157 0.08287 0.0002606 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6452 -0.06594 -0.06802 0.3054 0.9542 0.9765 0.7268 0.861 0.9465 0.7381 ] Network output: [ -0.01195 0.963 1.023 -0.0002687 0.0001202 0.03633 -0.0002006 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07628 0.03169 0.05318 0.04539 0.9727 0.9801 0.07796 0.9361 0.9639 0.08761 ] Network output: [ 0.1412 -0.3245 1.171 0.0005291 -0.0002383 0.8735 0.0004019 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7245 0.3029 0.36 0.5179 0.9592 0.9798 0.7278 0.8731 0.954 0.7414 ] Network output: [ -0.07918 0.2146 0.8882 0.001053 -0.0004731 1.06 0.0007948 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6947 0.5977 0.3492 0.2041 0.9769 0.9844 0.6953 0.946 0.9679 0.4016 ] Network output: [ -0.1519 0.339 0.7999 -0.002355 0.001057 1.155 -0.001774 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7355 0.7136 0.4284 0.07413 0.9739 0.982 0.7356 0.9404 0.9627 0.4426 ] Network output: [ 0.1689 0.5944 0.2445 0.001209 -0.0005419 0.8283 0.0009078 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1114 Epoch 777 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0118 1.053 0.9565 -0.0005121 0.0002296 -0.03561 -0.0003847 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.045 -0.004756 0.02457 0.02162 0.9121 0.9257 0.08258 0.8362 0.8733 0.1728 ] Network output: [ 0.9202 0.1625 -0.08425 0.0003447 -0.0001536 0.08288 0.0002549 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6451 -0.06553 -0.06844 0.3056 0.9542 0.9765 0.7267 0.861 0.9465 0.7381 ] Network output: [ -0.01188 0.9629 1.024 -0.0002666 0.0001192 0.03619 -0.000199 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07618 0.03171 0.05309 0.04538 0.9727 0.9801 0.07786 0.9361 0.964 0.08751 ] Network output: [ 0.1411 -0.3247 1.171 0.0005438 -0.0002449 0.8739 0.000413 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7244 0.3036 0.3594 0.5181 0.9592 0.9798 0.7277 0.8731 0.954 0.7414 ] Network output: [ -0.07913 0.2152 0.8879 0.001051 -0.000472 1.06 0.0007931 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6943 0.5975 0.3488 0.2042 0.9769 0.9844 0.6948 0.9461 0.9679 0.4012 ] Network output: [ -0.1521 0.3387 0.8006 -0.002354 0.001057 1.155 -0.001774 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.735 0.7132 0.4283 0.07466 0.9739 0.982 0.7351 0.9404 0.9627 0.4425 ] Network output: [ 0.1689 0.5947 0.2438 0.001199 -0.0005377 0.8286 0.0009007 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1112 Epoch 778 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01188 1.053 0.9568 -0.0005108 0.000229 -0.03571 -0.0003837 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04495 -0.004734 0.02452 0.02165 0.9122 0.9257 0.0825 0.8363 0.8733 0.1727 ] Network output: [ 0.9201 0.1626 -0.08435 0.0003371 -0.0001502 0.08289 0.0002492 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.645 -0.0651 -0.06885 0.3057 0.9542 0.9765 0.7266 0.861 0.9465 0.738 ] Network output: [ -0.0118 0.9628 1.024 -0.0002645 0.0001183 0.03605 -0.0001974 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07608 0.03172 0.053 0.04537 0.9727 0.9801 0.07776 0.9361 0.964 0.08741 ] Network output: [ 0.141 -0.3249 1.171 0.0005584 -0.0002514 0.8742 0.0004239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7243 0.3043 0.3588 0.5183 0.9592 0.9798 0.7276 0.8732 0.954 0.7413 ] Network output: [ -0.07908 0.2158 0.8876 0.001049 -0.000471 1.059 0.0007914 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6938 0.5973 0.3484 0.2043 0.9769 0.9844 0.6944 0.9461 0.9679 0.4009 ] Network output: [ -0.1522 0.3384 0.8013 -0.002354 0.001056 1.155 -0.001773 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7346 0.7128 0.4282 0.07519 0.9739 0.982 0.7347 0.9404 0.9627 0.4424 ] Network output: [ 0.1689 0.5949 0.2431 0.00119 -0.0005334 0.829 0.0008936 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1111 Epoch 779 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01197 1.053 0.957 -0.0005094 0.0002284 -0.03581 -0.0003827 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0449 -0.004711 0.02447 0.02168 0.9122 0.9257 0.08242 0.8363 0.8734 0.1726 ] Network output: [ 0.9201 0.1627 -0.08445 0.0003294 -0.0001467 0.08289 0.0002435 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6448 -0.06468 -0.06926 0.3058 0.9542 0.9765 0.7265 0.8611 0.9465 0.738 ] Network output: [ -0.01172 0.9626 1.024 -0.0002624 0.0001173 0.03591 -0.0001959 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07599 0.03174 0.0529 0.04536 0.9727 0.9801 0.07767 0.9362 0.964 0.08732 ] Network output: [ 0.141 -0.3251 1.171 0.0005728 -0.0002579 0.8746 0.0004348 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7242 0.305 0.3582 0.5186 0.9592 0.9798 0.7275 0.8732 0.954 0.7413 ] Network output: [ -0.07904 0.2163 0.8873 0.001046 -0.00047 1.059 0.0007897 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6933 0.5972 0.348 0.2043 0.9769 0.9844 0.6939 0.9461 0.968 0.4006 ] Network output: [ -0.1524 0.3381 0.8019 -0.002353 0.001056 1.155 -0.001773 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7341 0.7125 0.428 0.07571 0.9739 0.982 0.7342 0.9404 0.9627 0.4423 ] Network output: [ 0.1689 0.5952 0.2425 0.001181 -0.0005292 0.8294 0.0008865 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.111 Epoch 780 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01205 1.053 0.9572 -0.0005079 0.0002277 -0.03592 -0.0003816 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04484 -0.004688 0.02442 0.02171 0.9122 0.9257 0.08234 0.8364 0.8734 0.1725 ] Network output: [ 0.9201 0.1628 -0.08454 0.0003218 -0.0001433 0.08289 0.0002378 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6447 -0.06425 -0.06966 0.306 0.9543 0.9765 0.7264 0.8611 0.9466 0.7379 ] Network output: [ -0.01164 0.9625 1.024 -0.0002602 0.0001164 0.03577 -0.0001942 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07589 0.03176 0.05281 0.04535 0.9727 0.9801 0.07757 0.9362 0.964 0.08722 ] Network output: [ 0.1409 -0.3253 1.171 0.0005871 -0.0002643 0.875 0.0004455 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7241 0.3057 0.3576 0.5188 0.9592 0.9798 0.7274 0.8733 0.9541 0.7412 ] Network output: [ -0.07899 0.2169 0.887 0.001044 -0.0004691 1.058 0.0007881 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6928 0.597 0.3476 0.2044 0.9769 0.9844 0.6934 0.9461 0.968 0.4002 ] Network output: [ -0.1525 0.3378 0.8026 -0.002352 0.001056 1.155 -0.001772 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7336 0.7121 0.4279 0.07624 0.9739 0.982 0.7338 0.9404 0.9627 0.4422 ] Network output: [ 0.1689 0.5954 0.2418 0.001171 -0.000525 0.8298 0.0008794 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1109 Epoch 781 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01214 1.052 0.9575 -0.0005065 0.0002271 -0.03603 -0.0003805 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04479 -0.004666 0.02437 0.02173 0.9122 0.9257 0.08226 0.8364 0.8735 0.1724 ] Network output: [ 0.9201 0.1629 -0.08463 0.0003141 -0.0001399 0.08288 0.0002321 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6446 -0.06382 -0.07007 0.3061 0.9543 0.9765 0.7264 0.8612 0.9466 0.7379 ] Network output: [ -0.01156 0.9623 1.024 -0.000258 0.0001154 0.03563 -0.0001926 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0758 0.03179 0.05272 0.04534 0.9727 0.9801 0.07748 0.9362 0.964 0.08713 ] Network output: [ 0.1409 -0.3255 1.171 0.0006012 -0.0002706 0.8753 0.0004561 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7241 0.3064 0.357 0.519 0.9593 0.9798 0.7273 0.8733 0.9541 0.7412 ] Network output: [ -0.07894 0.2175 0.8867 0.001042 -0.0004681 1.058 0.0007864 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6923 0.5968 0.3473 0.2045 0.9769 0.9844 0.6929 0.9461 0.968 0.3999 ] Network output: [ -0.1526 0.3375 0.8033 -0.002352 0.001056 1.155 -0.001771 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7332 0.7117 0.4278 0.07676 0.9739 0.982 0.7333 0.9404 0.9627 0.4421 ] Network output: [ 0.1689 0.5957 0.2411 0.001162 -0.0005208 0.8301 0.0008724 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1108 Epoch 782 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01222 1.052 0.9577 -0.0005049 0.0002264 -0.03613 -0.0003794 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04473 -0.004643 0.02431 0.02176 0.9122 0.9257 0.08218 0.8365 0.8735 0.1723 ] Network output: [ 0.92 0.163 -0.08471 0.0003064 -0.0001364 0.08288 0.0002263 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6445 -0.06338 -0.07047 0.3063 0.9543 0.9765 0.7263 0.8612 0.9466 0.7378 ] Network output: [ -0.01148 0.9622 1.024 -0.0002558 0.0001144 0.03549 -0.000191 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0757 0.03181 0.05264 0.04533 0.9727 0.9802 0.07738 0.9363 0.964 0.08704 ] Network output: [ 0.1408 -0.3257 1.171 0.0006152 -0.0002769 0.8757 0.0004666 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.724 0.3071 0.3564 0.5192 0.9593 0.9798 0.7272 0.8733 0.9541 0.7411 ] Network output: [ -0.0789 0.2181 0.8864 0.00104 -0.0004671 1.057 0.0007847 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6919 0.5967 0.3469 0.2046 0.9769 0.9844 0.6925 0.9462 0.968 0.3996 ] Network output: [ -0.1528 0.3372 0.804 -0.002351 0.001055 1.155 -0.001771 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7327 0.7113 0.4277 0.07729 0.9739 0.982 0.7329 0.9404 0.9627 0.442 ] Network output: [ 0.1689 0.5959 0.2404 0.001152 -0.0005165 0.8305 0.0008653 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1107 Epoch 783 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01231 1.052 0.9579 -0.0005033 0.0002257 -0.03624 -0.0003782 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04468 -0.00462 0.02426 0.02179 0.9122 0.9257 0.0821 0.8365 0.8736 0.1722 ] Network output: [ 0.92 0.1631 -0.08479 0.0002986 -0.000133 0.08287 0.0002206 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6444 -0.06294 -0.07087 0.3064 0.9543 0.9765 0.7262 0.8613 0.9466 0.7378 ] Network output: [ -0.0114 0.962 1.024 -0.0002535 0.0001134 0.03535 -0.0001893 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07561 0.03183 0.05255 0.04532 0.9727 0.9802 0.07729 0.9363 0.9641 0.08695 ] Network output: [ 0.1407 -0.3259 1.171 0.000629 -0.0002831 0.8761 0.000477 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7239 0.3078 0.3559 0.5194 0.9593 0.9798 0.7272 0.8734 0.9541 0.7411 ] Network output: [ -0.07885 0.2187 0.8862 0.001038 -0.0004661 1.057 0.0007831 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6914 0.5965 0.3465 0.2047 0.9769 0.9844 0.692 0.9462 0.968 0.3992 ] Network output: [ -0.1529 0.3369 0.8046 -0.00235 0.001055 1.155 -0.00177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7323 0.7109 0.4275 0.07781 0.9739 0.982 0.7324 0.9404 0.9628 0.4419 ] Network output: [ 0.1689 0.5962 0.2397 0.001143 -0.0005123 0.8309 0.0008582 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1106 Epoch 784 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0124 1.051 0.9581 -0.0005017 0.000225 -0.03635 -0.000377 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04463 -0.004597 0.02421 0.02182 0.9122 0.9257 0.08203 0.8366 0.8736 0.1722 ] Network output: [ 0.92 0.1632 -0.08487 0.0002909 -0.0001295 0.08286 0.0002148 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6443 -0.0625 -0.07127 0.3066 0.9543 0.9765 0.7261 0.8613 0.9467 0.7377 ] Network output: [ -0.01132 0.9618 1.025 -0.0002513 0.0001124 0.03522 -0.0001876 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07552 0.03185 0.05246 0.04532 0.9727 0.9802 0.0772 0.9363 0.9641 0.08687 ] Network output: [ 0.1407 -0.3261 1.171 0.0006426 -0.0002892 0.8765 0.0004872 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7238 0.3086 0.3553 0.5196 0.9593 0.9798 0.7271 0.8734 0.9541 0.741 ] Network output: [ -0.0788 0.2193 0.8859 0.001036 -0.0004652 1.057 0.0007815 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6909 0.5964 0.3461 0.2047 0.9769 0.9844 0.6915 0.9462 0.968 0.3989 ] Network output: [ -0.1531 0.3366 0.8053 -0.002349 0.001054 1.155 -0.001769 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7318 0.7105 0.4274 0.07833 0.9739 0.982 0.7319 0.9404 0.9628 0.4418 ] Network output: [ 0.1689 0.5965 0.2391 0.001133 -0.0005081 0.8313 0.0008512 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1104 Epoch 785 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0125 1.051 0.9584 -0.0005 0.0002242 -0.03647 -0.0003757 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04458 -0.004574 0.02416 0.02185 0.9122 0.9257 0.08195 0.8367 0.8736 0.1721 ] Network output: [ 0.92 0.1633 -0.08494 0.0002831 -0.000126 0.08284 0.000209 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6442 -0.06205 -0.07167 0.3067 0.9543 0.9765 0.726 0.8614 0.9467 0.7377 ] Network output: [ -0.01123 0.9617 1.025 -0.0002489 0.0001113 0.03508 -0.0001859 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07543 0.03188 0.05238 0.04531 0.9728 0.9802 0.07711 0.9364 0.9641 0.08678 ] Network output: [ 0.1406 -0.3263 1.171 0.0006561 -0.0002953 0.8769 0.0004973 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7237 0.3093 0.3547 0.5198 0.9593 0.9798 0.727 0.8735 0.9542 0.741 ] Network output: [ -0.07876 0.2199 0.8856 0.001033 -0.0004642 1.056 0.0007799 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6904 0.5962 0.3457 0.2048 0.9769 0.9844 0.691 0.9462 0.968 0.3986 ] Network output: [ -0.1532 0.3363 0.806 -0.002348 0.001054 1.155 -0.001769 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7314 0.7102 0.4273 0.07886 0.9739 0.982 0.7315 0.9404 0.9628 0.4417 ] Network output: [ 0.1689 0.5967 0.2384 0.001124 -0.0005039 0.8317 0.0008441 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1103 Epoch 786 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01259 1.051 0.9586 -0.0004982 0.0002234 -0.03658 -0.0003744 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04452 -0.004551 0.02411 0.02188 0.9122 0.9258 0.08188 0.8367 0.8737 0.172 ] Network output: [ 0.92 0.1634 -0.08501 0.0002753 -0.0001226 0.08282 0.0002032 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6441 -0.0616 -0.07206 0.3068 0.9543 0.9765 0.726 0.8614 0.9467 0.7376 ] Network output: [ -0.01115 0.9615 1.025 -0.0002466 0.0001103 0.03495 -0.0001841 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07535 0.0319 0.05229 0.0453 0.9728 0.9802 0.07702 0.9364 0.9641 0.0867 ] Network output: [ 0.1405 -0.3265 1.171 0.0006694 -0.0003012 0.8772 0.0005073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7236 0.3101 0.3541 0.52 0.9593 0.9798 0.7269 0.8735 0.9542 0.7409 ] Network output: [ -0.07872 0.2205 0.8853 0.001031 -0.0004633 1.056 0.0007783 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.69 0.596 0.3453 0.2049 0.9769 0.9844 0.6906 0.9463 0.9681 0.3983 ] Network output: [ -0.1533 0.336 0.8066 -0.002346 0.001053 1.154 -0.001768 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7309 0.7098 0.4271 0.07938 0.9739 0.982 0.731 0.9404 0.9628 0.4417 ] Network output: [ 0.1689 0.597 0.2377 0.001115 -0.0004997 0.8321 0.0008371 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1102 Epoch 787 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01269 1.05 0.9588 -0.0004964 0.0002226 -0.03669 -0.0003731 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04447 -0.004528 0.02406 0.02191 0.9123 0.9258 0.0818 0.8368 0.8737 0.1719 ] Network output: [ 0.9199 0.1635 -0.08507 0.0002675 -0.0001191 0.0828 0.0001974 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.644 -0.06114 -0.07245 0.307 0.9543 0.9765 0.7259 0.8615 0.9467 0.7375 ] Network output: [ -0.01107 0.9614 1.025 -0.0002442 0.0001092 0.03481 -0.0001823 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07526 0.03193 0.05221 0.0453 0.9728 0.9802 0.07693 0.9364 0.9641 0.08661 ] Network output: [ 0.1405 -0.3267 1.171 0.0006825 -0.0003071 0.8776 0.0005172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7235 0.3108 0.3535 0.5202 0.9593 0.9798 0.7268 0.8736 0.9542 0.7408 ] Network output: [ -0.07867 0.221 0.8851 0.001029 -0.0004623 1.055 0.0007767 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6895 0.5959 0.345 0.205 0.9769 0.9844 0.6901 0.9463 0.9681 0.398 ] Network output: [ -0.1534 0.3357 0.8073 -0.002345 0.001053 1.154 -0.001767 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7305 0.7094 0.427 0.0799 0.9739 0.982 0.7306 0.9404 0.9628 0.4416 ] Network output: [ 0.1688 0.5973 0.237 0.001105 -0.0004955 0.8325 0.0008301 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1101 Epoch 788 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01278 1.05 0.959 -0.0004946 0.0002218 -0.03681 -0.0003717 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04442 -0.004505 0.02401 0.02194 0.9123 0.9258 0.08173 0.8368 0.8738 0.1718 ] Network output: [ 0.9199 0.1636 -0.08513 0.0002597 -0.0001156 0.08278 0.0001915 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6439 -0.06069 -0.07284 0.3071 0.9543 0.9765 0.7258 0.8615 0.9468 0.7375 ] Network output: [ -0.01098 0.9612 1.025 -0.0002418 0.0001081 0.03468 -0.0001806 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07517 0.03196 0.05213 0.04529 0.9728 0.9802 0.07684 0.9365 0.9641 0.08653 ] Network output: [ 0.1404 -0.3269 1.171 0.0006955 -0.0003129 0.878 0.0005269 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7235 0.3116 0.353 0.5204 0.9593 0.9798 0.7268 0.8736 0.9542 0.7408 ] Network output: [ -0.07863 0.2216 0.8848 0.001027 -0.0004614 1.055 0.0007751 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.689 0.5957 0.3446 0.205 0.9769 0.9844 0.6896 0.9463 0.9681 0.3976 ] Network output: [ -0.1536 0.3354 0.8079 -0.002344 0.001052 1.154 -0.001766 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.73 0.709 0.4269 0.08043 0.9739 0.982 0.7301 0.9404 0.9628 0.4415 ] Network output: [ 0.1688 0.5975 0.2363 0.001096 -0.0004913 0.833 0.000823 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.11 Epoch 789 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01288 1.05 0.9593 -0.0004927 0.0002209 -0.03693 -0.0003702 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04437 -0.004482 0.02396 0.02197 0.9123 0.9258 0.08165 0.8369 0.8738 0.1717 ] Network output: [ 0.9199 0.1636 -0.08519 0.0002518 -0.0001121 0.08275 0.0001857 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6438 -0.06023 -0.07322 0.3072 0.9543 0.9765 0.7258 0.8616 0.9468 0.7374 ] Network output: [ -0.0109 0.961 1.025 -0.0002394 0.0001071 0.03455 -0.0001787 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07509 0.03198 0.05205 0.04529 0.9728 0.9802 0.07676 0.9365 0.9642 0.08645 ] Network output: [ 0.1403 -0.3271 1.171 0.0007083 -0.0003186 0.8784 0.0005365 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7234 0.3124 0.3524 0.5206 0.9593 0.9798 0.7267 0.8737 0.9542 0.7407 ] Network output: [ -0.07859 0.2222 0.8846 0.001025 -0.0004605 1.055 0.0007736 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6885 0.5956 0.3442 0.2051 0.9769 0.9844 0.6891 0.9464 0.9681 0.3973 ] Network output: [ -0.1537 0.3351 0.8086 -0.002342 0.001051 1.154 -0.001765 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7295 0.7086 0.4268 0.08095 0.9739 0.982 0.7297 0.9404 0.9628 0.4414 ] Network output: [ 0.1688 0.5978 0.2357 0.001087 -0.0004871 0.8334 0.000816 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1098 Epoch 790 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01298 1.05 0.9595 -0.0004907 0.0002201 -0.03704 -0.0003688 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04432 -0.00446 0.02391 0.022 0.9123 0.9258 0.08158 0.837 0.8738 0.1717 ] Network output: [ 0.9199 0.1637 -0.08524 0.0002439 -0.0001085 0.08272 0.0001798 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6437 -0.05976 -0.0736 0.3073 0.9543 0.9765 0.7257 0.8616 0.9468 0.7373 ] Network output: [ -0.01081 0.9608 1.025 -0.0002369 0.0001059 0.03442 -0.0001769 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.075 0.03201 0.05197 0.04528 0.9728 0.9802 0.07667 0.9365 0.9642 0.08637 ] Network output: [ 0.1403 -0.3272 1.171 0.0007208 -0.0003243 0.8788 0.0005459 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7233 0.3131 0.3518 0.5208 0.9593 0.9798 0.7266 0.8737 0.9543 0.7406 ] Network output: [ -0.07855 0.2228 0.8843 0.001023 -0.0004596 1.054 0.0007721 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6881 0.5955 0.3439 0.2052 0.9769 0.9844 0.6887 0.9464 0.9681 0.397 ] Network output: [ -0.1538 0.3348 0.8093 -0.002341 0.001051 1.154 -0.001764 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7291 0.7083 0.4267 0.08147 0.9739 0.982 0.7292 0.9404 0.9628 0.4413 ] Network output: [ 0.1687 0.5981 0.235 0.001077 -0.0004829 0.8338 0.000809 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1097 Epoch 791 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01308 1.049 0.9597 -0.0004887 0.0002192 -0.03716 -0.0003673 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04427 -0.004437 0.02386 0.02203 0.9123 0.9258 0.08151 0.837 0.8739 0.1716 ] Network output: [ 0.9199 0.1638 -0.08529 0.000236 -0.000105 0.08269 0.0001739 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6436 -0.0593 -0.07398 0.3075 0.9543 0.9765 0.7256 0.8617 0.9468 0.7373 ] Network output: [ -0.01072 0.9607 1.026 -0.0002344 0.0001048 0.03429 -0.000175 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07492 0.03204 0.05189 0.04528 0.9728 0.9802 0.07659 0.9366 0.9642 0.0863 ] Network output: [ 0.1402 -0.3274 1.171 0.0007332 -0.0003298 0.8792 0.0005552 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7233 0.3139 0.3513 0.521 0.9593 0.9798 0.7265 0.8738 0.9543 0.7406 ] Network output: [ -0.07851 0.2234 0.884 0.001021 -0.0004587 1.054 0.0007706 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6876 0.5953 0.3435 0.2053 0.9769 0.9844 0.6882 0.9464 0.9681 0.3967 ] Network output: [ -0.1539 0.3345 0.8099 -0.002339 0.00105 1.154 -0.001762 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7286 0.7079 0.4265 0.08199 0.9738 0.982 0.7288 0.9404 0.9628 0.4412 ] Network output: [ 0.1687 0.5984 0.2343 0.001068 -0.0004787 0.8342 0.000802 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1096 Epoch 792 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01319 1.049 0.9599 -0.0004866 0.0002182 -0.03728 -0.0003657 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04422 -0.004414 0.02381 0.02206 0.9123 0.9258 0.08144 0.8371 0.8739 0.1715 ] Network output: [ 0.9199 0.1639 -0.08534 0.0002281 -0.0001014 0.08265 0.0001679 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6435 -0.05883 -0.07435 0.3076 0.9543 0.9765 0.7256 0.8617 0.9469 0.7372 ] Network output: [ -0.01064 0.9605 1.026 -0.0002318 0.0001037 0.03416 -0.0001731 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07484 0.03207 0.05182 0.04527 0.9728 0.9802 0.07651 0.9366 0.9642 0.08622 ] Network output: [ 0.1401 -0.3276 1.171 0.0007454 -0.0003353 0.8796 0.0005644 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7232 0.3147 0.3507 0.5212 0.9593 0.9798 0.7265 0.8738 0.9543 0.7405 ] Network output: [ -0.07847 0.2239 0.8838 0.001019 -0.0004578 1.053 0.0007691 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6871 0.5952 0.3431 0.2054 0.9769 0.9844 0.6877 0.9464 0.9681 0.3964 ] Network output: [ -0.154 0.3343 0.8106 -0.002338 0.001049 1.154 -0.001761 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7282 0.7075 0.4264 0.08252 0.9738 0.982 0.7283 0.9404 0.9628 0.4411 ] Network output: [ 0.1687 0.5987 0.2336 0.001059 -0.0004746 0.8347 0.000795 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1095 Epoch 793 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01329 1.049 0.9601 -0.0004845 0.0002173 -0.0374 -0.0003641 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04417 -0.004391 0.02376 0.02209 0.9123 0.9258 0.08137 0.8371 0.874 0.1715 ] Network output: [ 0.9198 0.164 -0.08538 0.0002202 -9.788e-05 0.08262 0.000162 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6434 -0.05836 -0.07472 0.3077 0.9543 0.9765 0.7255 0.8618 0.9469 0.7371 ] Network output: [ -0.01055 0.9603 1.026 -0.0002293 0.0001025 0.03403 -0.0001712 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07475 0.0321 0.05174 0.04527 0.9728 0.9802 0.07642 0.9366 0.9642 0.08615 ] Network output: [ 0.1401 -0.3278 1.171 0.0007574 -0.0003406 0.88 0.0005734 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7231 0.3155 0.3502 0.5213 0.9593 0.9798 0.7264 0.8739 0.9543 0.7404 ] Network output: [ -0.07843 0.2245 0.8835 0.001017 -0.0004569 1.053 0.0007676 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6867 0.595 0.3428 0.2054 0.9769 0.9844 0.6873 0.9465 0.9682 0.3961 ] Network output: [ -0.1541 0.334 0.8112 -0.002336 0.001049 1.154 -0.00176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7277 0.7071 0.4263 0.08304 0.9738 0.982 0.7279 0.9404 0.9628 0.441 ] Network output: [ 0.1686 0.599 0.2329 0.001049 -0.0004704 0.8351 0.000788 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1093 Epoch 794 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01339 1.048 0.9604 -0.0004823 0.0002163 -0.03752 -0.0003625 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04412 -0.004368 0.02371 0.02212 0.9123 0.9258 0.0813 0.8372 0.874 0.1714 ] Network output: [ 0.9198 0.1641 -0.08541 0.0002122 -9.432e-05 0.08258 0.000156 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6433 -0.05788 -0.07509 0.3078 0.9543 0.9766 0.7254 0.8619 0.9469 0.7371 ] Network output: [ -0.01046 0.9601 1.026 -0.0002266 0.0001014 0.0339 -0.0001692 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07467 0.03213 0.05167 0.04527 0.9728 0.9802 0.07634 0.9367 0.9643 0.08608 ] Network output: [ 0.14 -0.328 1.171 0.0007692 -0.0003459 0.8804 0.0005822 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7231 0.3163 0.3496 0.5215 0.9593 0.9798 0.7264 0.8739 0.9543 0.7403 ] Network output: [ -0.07839 0.2251 0.8833 0.001015 -0.0004561 1.053 0.0007662 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6862 0.5949 0.3424 0.2055 0.9769 0.9844 0.6868 0.9465 0.9682 0.3958 ] Network output: [ -0.1543 0.3337 0.8119 -0.002334 0.001048 1.153 -0.001758 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7273 0.7068 0.4262 0.08356 0.9738 0.982 0.7274 0.9404 0.9628 0.4409 ] Network output: [ 0.1686 0.5993 0.2322 0.00104 -0.0004662 0.8356 0.0007811 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1092 Epoch 795 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0135 1.048 0.9606 -0.0004801 0.0002153 -0.03764 -0.0003608 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04407 -0.004345 0.02366 0.02215 0.9123 0.9258 0.08123 0.8373 0.8741 0.1713 ] Network output: [ 0.9198 0.1641 -0.08545 0.0002042 -9.074e-05 0.08253 0.0001501 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6432 -0.05741 -0.07545 0.308 0.9544 0.9766 0.7254 0.8619 0.9469 0.737 ] Network output: [ -0.01037 0.96 1.026 -0.000224 0.0001002 0.03378 -0.0001673 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07459 0.03216 0.05159 0.04527 0.9728 0.9802 0.07626 0.9367 0.9643 0.08601 ] Network output: [ 0.1399 -0.3282 1.171 0.0007807 -0.0003511 0.8808 0.0005909 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.723 0.3171 0.3491 0.5217 0.9593 0.9798 0.7263 0.8739 0.9544 0.7403 ] Network output: [ -0.07835 0.2257 0.883 0.001014 -0.0004552 1.052 0.0007647 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6857 0.5948 0.3421 0.2056 0.9769 0.9844 0.6863 0.9465 0.9682 0.3955 ] Network output: [ -0.1544 0.3334 0.8125 -0.002332 0.001047 1.153 -0.001757 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7268 0.7064 0.426 0.08408 0.9738 0.982 0.727 0.9404 0.9628 0.4408 ] Network output: [ 0.1685 0.5996 0.2315 0.001031 -0.0004621 0.836 0.0007741 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1091 Epoch 796 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01361 1.048 0.9608 -0.0004778 0.0002143 -0.03776 -0.0003591 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04402 -0.004322 0.02362 0.02218 0.9124 0.9259 0.08116 0.8373 0.8741 0.1712 ] Network output: [ 0.9198 0.1642 -0.08548 0.0001961 -8.714e-05 0.08248 0.0001441 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6431 -0.05693 -0.07581 0.3081 0.9544 0.9766 0.7253 0.862 0.947 0.7369 ] Network output: [ -0.01029 0.9598 1.026 -0.0002213 9.898e-05 0.03365 -0.0001653 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07452 0.03219 0.05152 0.04526 0.9728 0.9802 0.07618 0.9368 0.9643 0.08594 ] Network output: [ 0.1398 -0.3283 1.171 0.0007921 -0.0003562 0.8812 0.0005994 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7229 0.3179 0.3486 0.5218 0.9593 0.9798 0.7262 0.874 0.9544 0.7402 ] Network output: [ -0.07831 0.2262 0.8828 0.001012 -0.0004544 1.052 0.0007633 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6853 0.5946 0.3417 0.2057 0.977 0.9844 0.6859 0.9465 0.9682 0.3952 ] Network output: [ -0.1545 0.3331 0.8131 -0.00233 0.001046 1.153 -0.001756 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7264 0.706 0.4259 0.0846 0.9738 0.982 0.7265 0.9404 0.9628 0.4407 ] Network output: [ 0.1685 0.5999 0.2309 0.001021 -0.0004579 0.8364 0.0007672 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1089 Epoch 797 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01372 1.048 0.961 -0.0004755 0.0002132 -0.03789 -0.0003574 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04397 -0.0043 0.02357 0.02221 0.9124 0.9259 0.08109 0.8374 0.8741 0.1712 ] Network output: [ 0.9198 0.1643 -0.08551 0.0001881 -8.354e-05 0.08243 0.000138 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.643 -0.05645 -0.07616 0.3082 0.9544 0.9766 0.7253 0.862 0.947 0.7368 ] Network output: [ -0.0102 0.9596 1.026 -0.0002186 9.776e-05 0.03353 -0.0001632 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07444 0.03223 0.05145 0.04526 0.9728 0.9802 0.0761 0.9368 0.9643 0.08587 ] Network output: [ 0.1398 -0.3285 1.171 0.0008033 -0.0003612 0.8816 0.0006078 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7229 0.3187 0.348 0.522 0.9593 0.9798 0.7262 0.874 0.9544 0.7401 ] Network output: [ -0.07828 0.2268 0.8826 0.00101 -0.0004536 1.051 0.000762 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6848 0.5945 0.3414 0.2058 0.977 0.9844 0.6854 0.9466 0.9682 0.3949 ] Network output: [ -0.1546 0.3328 0.8138 -0.002328 0.001045 1.153 -0.001754 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7259 0.7057 0.4258 0.08512 0.9738 0.982 0.7261 0.9404 0.9628 0.4406 ] Network output: [ 0.1684 0.6002 0.2302 0.001012 -0.0004538 0.8369 0.0007603 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1088 Epoch 798 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01383 1.047 0.9612 -0.000473 0.0002121 -0.03801 -0.0003556 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04392 -0.004277 0.02352 0.02224 0.9124 0.9259 0.08102 0.8374 0.8742 0.1711 ] Network output: [ 0.9198 0.1643 -0.08553 0.00018 -7.993e-05 0.08238 0.000132 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6429 -0.05597 -0.07651 0.3083 0.9544 0.9766 0.7252 0.8621 0.947 0.7367 ] Network output: [ -0.01011 0.9594 1.027 -0.0002158 9.653e-05 0.03341 -0.0001612 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07436 0.03226 0.05138 0.04526 0.9728 0.9802 0.07603 0.9368 0.9643 0.0858 ] Network output: [ 0.1397 -0.3287 1.171 0.0008142 -0.0003661 0.882 0.000616 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7228 0.3196 0.3475 0.5222 0.9593 0.9798 0.7261 0.8741 0.9544 0.74 ] Network output: [ -0.07824 0.2274 0.8823 0.001008 -0.0004528 1.051 0.0007607 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6844 0.5944 0.3411 0.2058 0.977 0.9844 0.685 0.9466 0.9682 0.3946 ] Network output: [ -0.1547 0.3325 0.8144 -0.002326 0.001044 1.153 -0.001752 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7255 0.7053 0.4257 0.08564 0.9738 0.982 0.7256 0.9404 0.9628 0.4405 ] Network output: [ 0.1684 0.6005 0.2295 0.001003 -0.0004497 0.8374 0.0007533 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1087 Epoch 799 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01394 1.047 0.9614 -0.0004706 0.000211 -0.03813 -0.0003537 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04387 -0.004254 0.02347 0.02227 0.9124 0.9259 0.08096 0.8375 0.8742 0.171 ] Network output: [ 0.9198 0.1644 -0.08555 0.0001719 -7.629e-05 0.08232 0.0001259 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6428 -0.05548 -0.07685 0.3084 0.9544 0.9766 0.7252 0.8621 0.9471 0.7367 ] Network output: [ -0.01002 0.9592 1.027 -0.000213 9.528e-05 0.03329 -0.0001591 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07428 0.03229 0.05132 0.04526 0.9728 0.9802 0.07595 0.9369 0.9644 0.08574 ] Network output: [ 0.1396 -0.3289 1.171 0.0008249 -0.0003709 0.8824 0.000624 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7228 0.3204 0.347 0.5223 0.9593 0.9798 0.7261 0.8741 0.9545 0.7399 ] Network output: [ -0.0782 0.2279 0.8821 0.001006 -0.000452 1.05 0.0007593 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6839 0.5942 0.3407 0.2059 0.977 0.9844 0.6845 0.9466 0.9682 0.3943 ] Network output: [ -0.1548 0.3322 0.8151 -0.002324 0.001043 1.153 -0.001751 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.725 0.7049 0.4256 0.08616 0.9738 0.9819 0.7252 0.9404 0.9628 0.4404 ] Network output: [ 0.1683 0.6008 0.2288 0.0009938 -0.0004455 0.8378 0.0007464 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1085 Epoch 800 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01406 1.047 0.9616 -0.000468 0.0002099 -0.03826 -0.0003518 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04382 -0.004232 0.02343 0.0223 0.9124 0.9259 0.08089 0.8376 0.8743 0.171 ] Network output: [ 0.9197 0.1645 -0.08557 0.0001638 -7.266e-05 0.08227 0.0001199 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6427 -0.055 -0.07719 0.3085 0.9544 0.9766 0.7251 0.8622 0.9471 0.7366 ] Network output: [ -0.009928 0.959 1.027 -0.0002102 9.4e-05 0.03317 -0.000157 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07421 0.03233 0.05125 0.04526 0.9728 0.9802 0.07587 0.9369 0.9644 0.08568 ] Network output: [ 0.1395 -0.329 1.171 0.0008354 -0.0003756 0.8828 0.0006319 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7227 0.3212 0.3465 0.5225 0.9594 0.9798 0.726 0.8742 0.9545 0.7398 ] Network output: [ -0.07817 0.2285 0.8819 0.001005 -0.0004513 1.05 0.0007581 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6834 0.5941 0.3404 0.206 0.977 0.9844 0.684 0.9467 0.9683 0.394 ] Network output: [ -0.1549 0.332 0.8157 -0.002321 0.001042 1.153 -0.001749 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7246 0.7045 0.4254 0.08668 0.9738 0.9819 0.7247 0.9404 0.9628 0.4404 ] Network output: [ 0.1682 0.6011 0.2281 0.0009846 -0.0004414 0.8383 0.0007395 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1084 Epoch 801 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01417 1.046 0.9619 -0.0004655 0.0002087 -0.03838 -0.0003499 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04378 -0.004209 0.02338 0.02233 0.9124 0.9259 0.08082 0.8376 0.8743 0.1709 ] Network output: [ 0.9197 0.1645 -0.08559 0.0001556 -6.901e-05 0.0822 0.0001138 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6426 -0.05451 -0.07753 0.3086 0.9544 0.9766 0.7251 0.8622 0.9471 0.7365 ] Network output: [ -0.009838 0.9589 1.027 -0.0002073 9.272e-05 0.03305 -0.0001548 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07413 0.03236 0.05119 0.04527 0.9728 0.9802 0.0758 0.937 0.9644 0.08561 ] Network output: [ 0.1395 -0.3292 1.171 0.0008456 -0.0003802 0.8832 0.0006396 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7227 0.3221 0.346 0.5226 0.9594 0.9798 0.726 0.8742 0.9545 0.7398 ] Network output: [ -0.07813 0.2291 0.8816 0.001003 -0.0004505 1.05 0.0007568 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.683 0.594 0.3401 0.2061 0.977 0.9844 0.6836 0.9467 0.9683 0.3937 ] Network output: [ -0.155 0.3317 0.8163 -0.002319 0.001041 1.152 -0.001747 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7241 0.7042 0.4253 0.08721 0.9738 0.9819 0.7243 0.9404 0.9628 0.4403 ] Network output: [ 0.1682 0.6015 0.2274 0.0009754 -0.0004373 0.8388 0.0007327 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1083 Epoch 802 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01429 1.046 0.9621 -0.0004628 0.0002076 -0.03851 -0.0003479 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04373 -0.004187 0.02333 0.02236 0.9124 0.9259 0.08076 0.8377 0.8743 0.1709 ] Network output: [ 0.9197 0.1646 -0.08559 0.0001474 -6.533e-05 0.08214 0.0001076 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6425 -0.05403 -0.07786 0.3088 0.9544 0.9766 0.7251 0.8623 0.9471 0.7364 ] Network output: [ -0.009747 0.9587 1.027 -0.0002044 9.141e-05 0.03293 -0.0001526 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07406 0.0324 0.05112 0.04527 0.9728 0.9802 0.07572 0.937 0.9644 0.08555 ] Network output: [ 0.1394 -0.3294 1.171 0.0008556 -0.0003847 0.8836 0.0006471 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7226 0.3229 0.3455 0.5228 0.9594 0.9798 0.7259 0.8743 0.9545 0.7397 ] Network output: [ -0.0781 0.2296 0.8814 0.001002 -0.0004498 1.049 0.0007556 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6825 0.5939 0.3397 0.2062 0.977 0.9844 0.6831 0.9467 0.9683 0.3934 ] Network output: [ -0.155 0.3314 0.8169 -0.002316 0.00104 1.152 -0.001745 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7237 0.7038 0.4252 0.08772 0.9738 0.9819 0.7238 0.9404 0.9629 0.4402 ] Network output: [ 0.1681 0.6018 0.2267 0.0009663 -0.0004332 0.8392 0.0007258 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1081 Epoch 803 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0144 1.046 0.9623 -0.0004601 0.0002064 -0.03863 -0.0003459 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04368 -0.004164 0.02329 0.02239 0.9124 0.9259 0.0807 0.8377 0.8744 0.1708 ] Network output: [ 0.9197 0.1647 -0.0856 0.0001392 -6.166e-05 0.08207 0.0001015 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6425 -0.05354 -0.07818 0.3089 0.9544 0.9766 0.725 0.8623 0.9472 0.7363 ] Network output: [ -0.009656 0.9585 1.027 -0.0002014 9.008e-05 0.03282 -0.0001504 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07399 0.03243 0.05106 0.04527 0.9728 0.9802 0.07565 0.937 0.9644 0.0855 ] Network output: [ 0.1393 -0.3295 1.17 0.0008654 -0.0003891 0.884 0.0006545 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7226 0.3237 0.345 0.5229 0.9594 0.9799 0.7259 0.8744 0.9545 0.7396 ] Network output: [ -0.07806 0.2302 0.8812 0.001 -0.0004491 1.049 0.0007544 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6821 0.5937 0.3394 0.2062 0.977 0.9844 0.6827 0.9467 0.9683 0.3932 ] Network output: [ -0.1551 0.3311 0.8176 -0.002314 0.001039 1.152 -0.001743 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7233 0.7034 0.4251 0.08824 0.9738 0.9819 0.7234 0.9404 0.9629 0.4401 ] Network output: [ 0.168 0.6021 0.226 0.0009571 -0.0004291 0.8397 0.000719 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.108 Epoch 804 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01452 1.045 0.9625 -0.0004574 0.0002051 -0.03876 -0.0003438 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04363 -0.004142 0.02324 0.02242 0.9125 0.9259 0.08063 0.8378 0.8744 0.1707 ] Network output: [ 0.9197 0.1647 -0.08561 0.000131 -5.797e-05 0.08199 9.532e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6424 -0.05305 -0.0785 0.309 0.9544 0.9766 0.725 0.8624 0.9472 0.7362 ] Network output: [ -0.009564 0.9583 1.027 -0.0001984 8.874e-05 0.0327 -0.0001482 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07392 0.03247 0.051 0.04527 0.9729 0.9802 0.07558 0.9371 0.9644 0.08544 ] Network output: [ 0.1392 -0.3297 1.17 0.0008749 -0.0003933 0.8844 0.0006616 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7225 0.3246 0.3446 0.5231 0.9594 0.9799 0.7259 0.8744 0.9546 0.7395 ] Network output: [ -0.07803 0.2308 0.881 0.0009984 -0.0004484 1.048 0.0007532 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6816 0.5936 0.3391 0.2063 0.977 0.9844 0.6822 0.9468 0.9683 0.3929 ] Network output: [ -0.1552 0.3308 0.8182 -0.002311 0.001037 1.152 -0.001741 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7228 0.7031 0.425 0.08877 0.9738 0.9819 0.7229 0.9404 0.9629 0.44 ] Network output: [ 0.1679 0.6025 0.2254 0.000948 -0.000425 0.8402 0.0007121 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1079 Epoch 805 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01464 1.045 0.9627 -0.0004545 0.0002039 -0.03888 -0.0003417 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04359 -0.00412 0.0232 0.02245 0.9125 0.9259 0.08057 0.8379 0.8745 0.1707 ] Network output: [ 0.9197 0.1648 -0.08561 0.0001227 -5.426e-05 0.08192 8.91e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6423 -0.05256 -0.07882 0.3091 0.9544 0.9766 0.725 0.8625 0.9472 0.7361 ] Network output: [ -0.009473 0.9581 1.027 -0.0001954 8.738e-05 0.03259 -0.0001459 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07384 0.0325 0.05094 0.04527 0.9729 0.9802 0.07551 0.9371 0.9645 0.08538 ] Network output: [ 0.1391 -0.3299 1.17 0.0008842 -0.0003975 0.8848 0.0006686 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7225 0.3254 0.3441 0.5232 0.9594 0.9799 0.7258 0.8745 0.9546 0.7394 ] Network output: [ -0.07799 0.2313 0.8807 0.000997 -0.0004478 1.048 0.0007521 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6812 0.5935 0.3388 0.2064 0.977 0.9844 0.6818 0.9468 0.9683 0.3926 ] Network output: [ -0.1553 0.3306 0.8188 -0.002308 0.001036 1.152 -0.001739 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7224 0.7027 0.4249 0.08928 0.9738 0.9819 0.7225 0.9405 0.9629 0.4399 ] Network output: [ 0.1678 0.6028 0.2247 0.000939 -0.000421 0.8407 0.0007053 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1077 Epoch 806 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01476 1.045 0.9629 -0.0004517 0.0002026 -0.03901 -0.0003395 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04354 -0.004098 0.02315 0.02248 0.9125 0.926 0.08051 0.8379 0.8745 0.1706 ] Network output: [ 0.9197 0.1648 -0.08561 0.0001144 -5.055e-05 0.08184 8.291e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6422 -0.05206 -0.07912 0.3092 0.9544 0.9766 0.7249 0.8625 0.9472 0.736 ] Network output: [ -0.009381 0.9579 1.028 -0.0001923 8.6e-05 0.03248 -0.0001436 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07377 0.03254 0.05088 0.04528 0.9729 0.9802 0.07544 0.9372 0.9645 0.08533 ] Network output: [ 0.1391 -0.33 1.17 0.0008933 -0.0004016 0.8852 0.0006754 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7225 0.3263 0.3436 0.5233 0.9594 0.9799 0.7258 0.8745 0.9546 0.7393 ] Network output: [ -0.07796 0.2319 0.8805 0.0009956 -0.0004471 1.048 0.0007511 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6807 0.5934 0.3385 0.2065 0.977 0.9844 0.6813 0.9468 0.9684 0.3923 ] Network output: [ -0.1554 0.3303 0.8194 -0.002305 0.001035 1.152 -0.001737 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7219 0.7023 0.4248 0.0898 0.9738 0.9819 0.722 0.9405 0.9629 0.4398 ] Network output: [ 0.1678 0.6031 0.224 0.0009299 -0.0004169 0.8412 0.0006985 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1076 Epoch 807 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01488 1.044 0.9631 -0.0004487 0.0002012 -0.03914 -0.0003373 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04349 -0.004076 0.02311 0.02251 0.9125 0.926 0.08045 0.838 0.8746 0.1706 ] Network output: [ 0.9197 0.1649 -0.08561 0.0001061 -4.683e-05 0.08176 7.668e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6422 -0.05157 -0.07943 0.3093 0.9545 0.9766 0.7249 0.8626 0.9473 0.7359 ] Network output: [ -0.009288 0.9577 1.028 -0.0001892 8.461e-05 0.03236 -0.0001413 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0737 0.03258 0.05083 0.04528 0.9729 0.9802 0.07537 0.9372 0.9645 0.08528 ] Network output: [ 0.139 -0.3302 1.17 0.0009021 -0.0004055 0.8856 0.000682 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7224 0.3272 0.3432 0.5235 0.9594 0.9799 0.7258 0.8746 0.9546 0.7392 ] Network output: [ -0.07793 0.2324 0.8803 0.0009942 -0.0004465 1.047 0.00075 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6803 0.5932 0.3382 0.2066 0.977 0.9844 0.6809 0.9469 0.9684 0.3921 ] Network output: [ -0.1554 0.33 0.82 -0.002302 0.001033 1.151 -0.001734 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7215 0.702 0.4246 0.09032 0.9738 0.9819 0.7216 0.9405 0.9629 0.4397 ] Network output: [ 0.1677 0.6035 0.2233 0.0009209 -0.0004129 0.8417 0.0006917 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1075 Epoch 808 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01501 1.044 0.9633 -0.0004457 0.0001999 -0.03926 -0.0003351 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04345 -0.004054 0.02307 0.02254 0.9125 0.926 0.08038 0.8381 0.8746 0.1705 ] Network output: [ 0.9197 0.1649 -0.0856 9.768e-05 -4.307e-05 0.08168 7.04e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6421 -0.05108 -0.07972 0.3094 0.9545 0.9766 0.7249 0.8626 0.9473 0.7358 ] Network output: [ -0.009196 0.9575 1.028 -0.000186 8.319e-05 0.03226 -0.0001389 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07364 0.03261 0.05077 0.04529 0.9729 0.9802 0.0753 0.9372 0.9645 0.08522 ] Network output: [ 0.1389 -0.3303 1.17 0.0009106 -0.0004093 0.886 0.0006884 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7224 0.328 0.3427 0.5236 0.9594 0.9799 0.7257 0.8746 0.9547 0.7391 ] Network output: [ -0.07789 0.233 0.8801 0.0009928 -0.0004459 1.047 0.000749 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6798 0.5931 0.3379 0.2066 0.977 0.9845 0.6804 0.9469 0.9684 0.3918 ] Network output: [ -0.1555 0.3297 0.8206 -0.002299 0.001032 1.151 -0.001732 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.721 0.7016 0.4245 0.09084 0.9738 0.9819 0.7212 0.9405 0.9629 0.4396 ] Network output: [ 0.1676 0.6038 0.2226 0.0009118 -0.0004088 0.8421 0.000685 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1073 Epoch 809 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01513 1.044 0.9635 -0.0004427 0.0001985 -0.03939 -0.0003328 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0434 -0.004032 0.02302 0.02257 0.9125 0.926 0.08032 0.8381 0.8746 0.1705 ] Network output: [ 0.9197 0.165 -0.08559 8.933e-05 -3.933e-05 0.08159 6.415e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.642 -0.05059 -0.08001 0.3095 0.9545 0.9766 0.7248 0.8627 0.9473 0.7358 ] Network output: [ -0.009103 0.9573 1.028 -0.0001828 8.175e-05 0.03215 -0.0001365 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07357 0.03265 0.05072 0.04529 0.9729 0.9802 0.07523 0.9373 0.9645 0.08518 ] Network output: [ 0.1388 -0.3305 1.17 0.000919 -0.0004131 0.8865 0.0006947 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7224 0.3289 0.3423 0.5237 0.9594 0.9799 0.7257 0.8747 0.9547 0.739 ] Network output: [ -0.07786 0.2335 0.8799 0.0009916 -0.0004453 1.046 0.000748 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6794 0.593 0.3376 0.2067 0.977 0.9845 0.68 0.9469 0.9684 0.3915 ] Network output: [ -0.1556 0.3294 0.8212 -0.002295 0.00103 1.151 -0.001729 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7206 0.7013 0.4244 0.09136 0.9738 0.9819 0.7207 0.9405 0.9629 0.4395 ] Network output: [ 0.1675 0.6042 0.2219 0.0009029 -0.0004048 0.8426 0.0006782 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1072 Epoch 810 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01525 1.044 0.9637 -0.0004395 0.0001971 -0.03951 -0.0003305 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04336 -0.00401 0.02298 0.0226 0.9125 0.926 0.08026 0.8382 0.8747 0.1704 ] Network output: [ 0.9197 0.165 -0.08558 8.092e-05 -3.557e-05 0.0815 5.786e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6419 -0.05009 -0.0803 0.3095 0.9545 0.9766 0.7248 0.8627 0.9474 0.7357 ] Network output: [ -0.009011 0.9571 1.028 -0.0001795 8.03e-05 0.03204 -0.0001341 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0735 0.03269 0.05067 0.0453 0.9729 0.9802 0.07516 0.9373 0.9646 0.08513 ] Network output: [ 0.1387 -0.3306 1.17 0.000927 -0.0004167 0.8869 0.0007007 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7223 0.3298 0.3419 0.5238 0.9594 0.9799 0.7257 0.8747 0.9547 0.7389 ] Network output: [ -0.07783 0.2341 0.8797 0.0009903 -0.0004448 1.046 0.0007471 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6789 0.5929 0.3373 0.2068 0.977 0.9845 0.6795 0.947 0.9684 0.3913 ] Network output: [ -0.1557 0.3292 0.8218 -0.002292 0.001029 1.151 -0.001727 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7201 0.7009 0.4243 0.09188 0.9738 0.9819 0.7203 0.9405 0.9629 0.4395 ] Network output: [ 0.1674 0.6045 0.2212 0.0008939 -0.0004008 0.8432 0.0006715 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.107 Epoch 811 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01538 1.043 0.9639 -0.0004364 0.0001957 -0.03964 -0.0003281 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04331 -0.003989 0.02294 0.02263 0.9126 0.926 0.08021 0.8382 0.8747 0.1704 ] Network output: [ 0.9197 0.1651 -0.08556 7.245e-05 -3.178e-05 0.08141 5.152e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6419 -0.0496 -0.08057 0.3096 0.9545 0.9766 0.7248 0.8628 0.9474 0.7355 ] Network output: [ -0.008918 0.9568 1.028 -0.0001763 7.883e-05 0.03194 -0.0001316 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07343 0.03273 0.05062 0.0453 0.9729 0.9803 0.07509 0.9374 0.9646 0.08508 ] Network output: [ 0.1386 -0.3308 1.17 0.0009348 -0.0004201 0.8873 0.0007065 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7223 0.3306 0.3415 0.5239 0.9594 0.9799 0.7257 0.8748 0.9547 0.7388 ] Network output: [ -0.0778 0.2346 0.8795 0.0009891 -0.0004442 1.045 0.0007462 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6785 0.5928 0.337 0.2069 0.977 0.9845 0.6791 0.947 0.9684 0.391 ] Network output: [ -0.1557 0.3289 0.8224 -0.002289 0.001027 1.151 -0.001724 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7197 0.7005 0.4242 0.09239 0.9737 0.9819 0.7198 0.9405 0.9629 0.4394 ] Network output: [ 0.1673 0.6049 0.2205 0.0008849 -0.0003968 0.8437 0.0006648 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1069 Epoch 812 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01551 1.043 0.9641 -0.0004331 0.0001943 -0.03977 -0.0003256 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04326 -0.003967 0.0229 0.02266 0.9126 0.926 0.08015 0.8383 0.8748 0.1703 ] Network output: [ 0.9197 0.1651 -0.08554 6.403e-05 -2.801e-05 0.08131 4.521e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6418 -0.0491 -0.08085 0.3097 0.9545 0.9767 0.7248 0.8629 0.9474 0.7354 ] Network output: [ -0.008825 0.9566 1.028 -0.0001729 7.733e-05 0.03183 -0.0001291 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07337 0.03276 0.05057 0.04531 0.9729 0.9803 0.07503 0.9374 0.9646 0.08504 ] Network output: [ 0.1385 -0.3309 1.17 0.0009423 -0.0004235 0.8877 0.0007122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7223 0.3315 0.3411 0.5241 0.9594 0.9799 0.7256 0.8748 0.9548 0.7387 ] Network output: [ -0.07777 0.2352 0.8793 0.0009881 -0.0004437 1.045 0.0007453 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.678 0.5926 0.3367 0.207 0.977 0.9845 0.6786 0.947 0.9685 0.3907 ] Network output: [ -0.1558 0.3286 0.823 -0.002285 0.001026 1.151 -0.001721 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7193 0.7002 0.4241 0.09291 0.9737 0.9819 0.7194 0.9405 0.9629 0.4393 ] Network output: [ 0.1672 0.6052 0.2198 0.000876 -0.0003928 0.8442 0.0006581 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1067 Epoch 813 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01564 1.043 0.9643 -0.0004298 0.0001928 -0.03989 -0.0003232 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04322 -0.003946 0.02286 0.02269 0.9126 0.926 0.08009 0.8384 0.8748 0.1703 ] Network output: [ 0.9197 0.1651 -0.08553 5.554e-05 -2.421e-05 0.08121 3.886e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6417 -0.04861 -0.08111 0.3098 0.9545 0.9767 0.7248 0.8629 0.9474 0.7353 ] Network output: [ -0.008731 0.9564 1.029 -0.0001695 7.582e-05 0.03173 -0.0001266 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0733 0.0328 0.05052 0.04532 0.9729 0.9803 0.07496 0.9374 0.9646 0.08499 ] Network output: [ 0.1384 -0.3311 1.17 0.0009496 -0.0004268 0.8881 0.0007176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7223 0.3324 0.3407 0.5242 0.9594 0.9799 0.7256 0.8749 0.9548 0.7386 ] Network output: [ -0.07774 0.2357 0.8791 0.000987 -0.0004433 1.045 0.0007445 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6776 0.5925 0.3365 0.2071 0.977 0.9845 0.6782 0.947 0.9685 0.3905 ] Network output: [ -0.1558 0.3283 0.8236 -0.002281 0.001024 1.15 -0.001719 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7188 0.6998 0.424 0.09343 0.9737 0.9819 0.719 0.9405 0.9629 0.4392 ] Network output: [ 0.167 0.6056 0.2192 0.0008671 -0.0003888 0.8447 0.0006514 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1066 Epoch 814 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01576 1.042 0.9645 -0.0004265 0.0001913 -0.04002 -0.0003207 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04317 -0.003925 0.02282 0.02272 0.9126 0.926 0.08003 0.8384 0.8749 0.1702 ] Network output: [ 0.9197 0.1652 -0.0855 4.699e-05 -2.038e-05 0.08111 3.245e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6417 -0.04812 -0.08137 0.3099 0.9545 0.9767 0.7248 0.863 0.9475 0.7352 ] Network output: [ -0.008638 0.9562 1.029 -0.0001661 7.429e-05 0.03163 -0.000124 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07324 0.03284 0.05048 0.04532 0.9729 0.9803 0.0749 0.9375 0.9646 0.08495 ] Network output: [ 0.1383 -0.3312 1.17 0.0009566 -0.0004299 0.8885 0.0007229 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7223 0.3332 0.3403 0.5243 0.9594 0.9799 0.7256 0.8749 0.9548 0.7384 ] Network output: [ -0.07771 0.2363 0.8789 0.000986 -0.0004428 1.044 0.0007437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6771 0.5924 0.3362 0.2071 0.977 0.9845 0.6777 0.9471 0.9685 0.3902 ] Network output: [ -0.1559 0.3281 0.8242 -0.002277 0.001022 1.15 -0.001716 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7184 0.6994 0.4239 0.09395 0.9737 0.9819 0.7185 0.9405 0.9629 0.4391 ] Network output: [ 0.1669 0.606 0.2185 0.0008582 -0.0003848 0.8452 0.0006447 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1065 Epoch 815 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01589 1.042 0.9647 -0.0004231 0.0001897 -0.04015 -0.0003181 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04313 -0.003904 0.02278 0.02275 0.9126 0.9261 0.07997 0.8385 0.8749 0.1702 ] Network output: [ 0.9197 0.1652 -0.08548 3.851e-05 -1.658e-05 0.081 2.61e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6416 -0.04762 -0.08162 0.31 0.9545 0.9767 0.7247 0.863 0.9475 0.7351 ] Network output: [ -0.008545 0.956 1.029 -0.0001627 7.274e-05 0.03153 -0.0001214 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07317 0.03288 0.05043 0.04533 0.9729 0.9803 0.07483 0.9375 0.9647 0.08491 ] Network output: [ 0.1382 -0.3314 1.17 0.0009634 -0.000433 0.8889 0.000728 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.3341 0.3399 0.5244 0.9595 0.9799 0.7256 0.875 0.9548 0.7383 ] Network output: [ -0.07768 0.2368 0.8788 0.000985 -0.0004424 1.044 0.000743 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6767 0.5923 0.3359 0.2072 0.977 0.9845 0.6773 0.9471 0.9685 0.39 ] Network output: [ -0.156 0.3278 0.8248 -0.002273 0.001021 1.15 -0.001713 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.718 0.6991 0.4238 0.09447 0.9737 0.9819 0.7181 0.9405 0.963 0.439 ] Network output: [ 0.1668 0.6063 0.2178 0.0008494 -0.0003808 0.8457 0.0006381 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1063 Epoch 816 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01602 1.042 0.9649 -0.0004196 0.0001882 -0.04027 -0.0003155 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04309 -0.003883 0.02274 0.02278 0.9126 0.9261 0.07992 0.8386 0.875 0.1701 ] Network output: [ 0.9197 0.1652 -0.08546 2.994e-05 -1.274e-05 0.08088 1.968e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6415 -0.04713 -0.08187 0.31 0.9545 0.9767 0.7247 0.8631 0.9475 0.735 ] Network output: [ -0.008451 0.9558 1.029 -0.0001592 7.118e-05 0.03143 -0.0001188 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07311 0.03292 0.05039 0.04534 0.9729 0.9803 0.07477 0.9376 0.9647 0.08487 ] Network output: [ 0.1382 -0.3315 1.17 0.0009698 -0.0004359 0.8894 0.0007328 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.335 0.3395 0.5245 0.9595 0.9799 0.7256 0.8751 0.9548 0.7382 ] Network output: [ -0.07765 0.2373 0.8786 0.0009841 -0.000442 1.043 0.0007423 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6762 0.5922 0.3357 0.2073 0.977 0.9845 0.6768 0.9471 0.9685 0.3897 ] Network output: [ -0.156 0.3275 0.8254 -0.002269 0.001019 1.15 -0.00171 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7175 0.6987 0.4237 0.09498 0.9737 0.9819 0.7176 0.9405 0.963 0.4389 ] Network output: [ 0.1667 0.6067 0.2171 0.0008405 -0.0003768 0.8462 0.0006314 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1062 Epoch 817 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01615 1.041 0.9651 -0.0004161 0.0001866 -0.0404 -0.0003129 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04304 -0.003862 0.0227 0.02281 0.9126 0.9261 0.07986 0.8386 0.875 0.1701 ] Network output: [ 0.9197 0.1653 -0.08542 2.132e-05 -8.879e-06 0.08078 1.322e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6415 -0.04664 -0.0821 0.3101 0.9545 0.9767 0.7247 0.8632 0.9475 0.7349 ] Network output: [ -0.008358 0.9556 1.029 -0.0001556 6.959e-05 0.03134 -0.0001161 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07305 0.03296 0.05035 0.04535 0.9729 0.9803 0.07471 0.9376 0.9647 0.08483 ] Network output: [ 0.1381 -0.3317 1.17 0.000976 -0.0004386 0.8898 0.0007374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.3359 0.3392 0.5246 0.9595 0.9799 0.7256 0.8751 0.9549 0.7381 ] Network output: [ -0.07762 0.2379 0.8784 0.0009833 -0.0004416 1.043 0.0007417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6758 0.5921 0.3354 0.2074 0.9771 0.9845 0.6764 0.9472 0.9685 0.3895 ] Network output: [ -0.1561 0.3273 0.8259 -0.002265 0.001017 1.15 -0.001707 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7171 0.6984 0.4236 0.0955 0.9737 0.9819 0.7172 0.9405 0.963 0.4388 ] Network output: [ 0.1666 0.6071 0.2164 0.0008317 -0.0003729 0.8468 0.0006248 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.106 Epoch 818 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01629 1.041 0.9652 -0.0004125 0.000185 -0.04053 -0.0003101 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.043 -0.003841 0.02266 0.02284 0.9126 0.9261 0.07981 0.8387 0.875 0.17 ] Network output: [ 0.9197 0.1653 -0.08539 1.279e-05 -5.058e-06 0.08066 6.829e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6414 -0.04614 -0.08233 0.3102 0.9546 0.9767 0.7247 0.8632 0.9476 0.7348 ] Network output: [ -0.008264 0.9554 1.029 -0.000152 6.798e-05 0.03124 -0.0001135 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07299 0.033 0.05031 0.04536 0.9729 0.9803 0.07465 0.9377 0.9647 0.08479 ] Network output: [ 0.138 -0.3318 1.17 0.000982 -0.0004413 0.8902 0.000742 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.3368 0.3388 0.5247 0.9595 0.9799 0.7256 0.8752 0.9549 0.738 ] Network output: [ -0.07759 0.2384 0.8782 0.0009826 -0.0004413 1.043 0.0007411 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6753 0.5919 0.3352 0.2075 0.9771 0.9845 0.6759 0.9472 0.9686 0.3893 ] Network output: [ -0.1561 0.327 0.8265 -0.002261 0.001015 1.149 -0.001703 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7166 0.698 0.4235 0.09601 0.9737 0.9819 0.7168 0.9406 0.963 0.4388 ] Network output: [ 0.1664 0.6075 0.2157 0.0008229 -0.000369 0.8473 0.0006182 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1059 Epoch 819 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01642 1.041 0.9654 -0.0004089 0.0001834 -0.04065 -0.0003074 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04295 -0.003821 0.02262 0.02286 0.9127 0.9261 0.07975 0.8388 0.8751 0.17 ] Network output: [ 0.9198 0.1653 -0.08537 4.145e-06 -1.189e-06 0.08054 3.552e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6414 -0.04565 -0.08255 0.3103 0.9546 0.9767 0.7247 0.8633 0.9476 0.7347 ] Network output: [ -0.00817 0.9551 1.029 -0.0001484 6.636e-05 0.03115 -0.0001107 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07292 0.03304 0.05027 0.04537 0.9729 0.9803 0.07459 0.9377 0.9648 0.08476 ] Network output: [ 0.1379 -0.3319 1.17 0.0009877 -0.0004438 0.8906 0.0007462 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.3376 0.3385 0.5247 0.9595 0.9799 0.7256 0.8752 0.9549 0.7379 ] Network output: [ -0.07756 0.2389 0.878 0.0009818 -0.0004409 1.042 0.0007406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6749 0.5918 0.3349 0.2076 0.9771 0.9845 0.6755 0.9472 0.9686 0.389 ] Network output: [ -0.1561 0.3267 0.8271 -0.002257 0.001013 1.149 -0.0017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7162 0.6976 0.4234 0.09653 0.9737 0.9819 0.7163 0.9406 0.963 0.4387 ] Network output: [ 0.1663 0.6078 0.215 0.0008141 -0.000365 0.8478 0.0006116 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1057 Epoch 820 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01655 1.04 0.9656 -0.0004052 0.0001817 -0.04078 -0.0003047 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04291 -0.0038 0.02259 0.02289 0.9127 0.9261 0.0797 0.8388 0.8751 0.17 ] Network output: [ 0.9198 0.1654 -0.08532 -4.541e-06 2.702e-06 0.08042 -6.154e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6413 -0.04516 -0.08276 0.3103 0.9546 0.9767 0.7247 0.8633 0.9476 0.7346 ] Network output: [ -0.008077 0.9549 1.03 -0.0001448 6.472e-05 0.03106 -0.000108 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07286 0.03308 0.05023 0.04538 0.973 0.9803 0.07453 0.9377 0.9648 0.08473 ] Network output: [ 0.1378 -0.3321 1.17 0.0009931 -0.0004463 0.891 0.0007502 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.3385 0.3382 0.5248 0.9595 0.9799 0.7256 0.8753 0.9549 0.7377 ] Network output: [ -0.07753 0.2395 0.8779 0.0009812 -0.0004406 1.042 0.0007401 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6745 0.5917 0.3347 0.2077 0.9771 0.9845 0.6751 0.9473 0.9686 0.3888 ] Network output: [ -0.1562 0.3265 0.8277 -0.002252 0.001011 1.149 -0.001697 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7158 0.6973 0.4233 0.09704 0.9737 0.9819 0.7159 0.9406 0.963 0.4386 ] Network output: [ 0.1662 0.6082 0.2143 0.0008053 -0.0003611 0.8484 0.000605 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1056 Epoch 821 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01669 1.04 0.9658 -0.0004014 0.00018 -0.0409 -0.0003018 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04287 -0.00378 0.02255 0.02292 0.9127 0.9261 0.07965 0.8389 0.8752 0.1699 ] Network output: [ 0.9198 0.1654 -0.08528 -1.311e-05 6.541e-06 0.08029 -1.258e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6413 -0.04467 -0.08297 0.3104 0.9546 0.9767 0.7247 0.8634 0.9477 0.7345 ] Network output: [ -0.007983 0.9547 1.03 -0.000141 6.305e-05 0.03097 -0.0001052 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0728 0.03312 0.0502 0.04539 0.973 0.9803 0.07447 0.9378 0.9648 0.08469 ] Network output: [ 0.1377 -0.3322 1.17 0.0009983 -0.0004486 0.8914 0.0007541 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.3394 0.3379 0.5249 0.9595 0.9799 0.7256 0.8753 0.955 0.7376 ] Network output: [ -0.07751 0.24 0.8777 0.0009806 -0.0004404 1.041 0.0007397 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.674 0.5916 0.3344 0.2078 0.9771 0.9845 0.6746 0.9473 0.9686 0.3886 ] Network output: [ -0.1562 0.3262 0.8282 -0.002247 0.001009 1.149 -0.001693 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7153 0.6969 0.4232 0.09756 0.9737 0.9819 0.7155 0.9406 0.963 0.4385 ] Network output: [ 0.166 0.6086 0.2137 0.0007966 -0.0003572 0.8489 0.0005985 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1054 Epoch 822 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01683 1.04 0.966 -0.0003976 0.0001783 -0.04103 -0.000299 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04282 -0.00376 0.02252 0.02295 0.9127 0.9261 0.07959 0.839 0.8752 0.1699 ] Network output: [ 0.9198 0.1654 -0.08525 -2.182e-05 1.044e-05 0.08016 -1.91e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6412 -0.04418 -0.08317 0.3105 0.9546 0.9767 0.7247 0.8635 0.9477 0.7343 ] Network output: [ -0.007889 0.9545 1.03 -0.0001373 6.139e-05 0.03088 -0.0001024 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07274 0.03316 0.05016 0.0454 0.973 0.9803 0.07441 0.9378 0.9648 0.08466 ] Network output: [ 0.1375 -0.3323 1.169 0.001003 -0.0004508 0.8919 0.0007577 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.3403 0.3376 0.525 0.9595 0.9799 0.7256 0.8754 0.955 0.7375 ] Network output: [ -0.07748 0.2405 0.8776 0.0009801 -0.0004401 1.041 0.0007392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6736 0.5915 0.3342 0.2078 0.9771 0.9845 0.6742 0.9473 0.9686 0.3883 ] Network output: [ -0.1563 0.3259 0.8288 -0.002243 0.001007 1.149 -0.00169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7149 0.6966 0.4231 0.09807 0.9737 0.9819 0.715 0.9406 0.963 0.4384 ] Network output: [ 0.1659 0.609 0.213 0.0007879 -0.0003533 0.8494 0.0005919 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1053 Epoch 823 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01696 1.039 0.9662 -0.0003937 0.0001766 -0.04115 -0.0002961 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04278 -0.00374 0.02248 0.02298 0.9127 0.9261 0.07954 0.839 0.8753 0.1698 ] Network output: [ 0.9198 0.1654 -0.0852 -3.055e-05 1.435e-05 0.08004 -2.565e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6412 -0.04369 -0.08336 0.3105 0.9546 0.9767 0.7247 0.8635 0.9477 0.7342 ] Network output: [ -0.007796 0.9542 1.03 -0.0001335 5.969e-05 0.03079 -9.958e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07269 0.0332 0.05013 0.04541 0.973 0.9803 0.07435 0.9379 0.9648 0.08463 ] Network output: [ 0.1374 -0.3324 1.169 0.001008 -0.0004528 0.8923 0.0007612 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.3412 0.3373 0.525 0.9595 0.9799 0.7256 0.8755 0.955 0.7374 ] Network output: [ -0.07745 0.241 0.8774 0.0009796 -0.0004399 1.04 0.0007389 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6731 0.5914 0.334 0.2079 0.9771 0.9845 0.6737 0.9474 0.9687 0.3881 ] Network output: [ -0.1563 0.3257 0.8293 -0.002238 0.001005 1.148 -0.001686 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7145 0.6962 0.423 0.09858 0.9737 0.9819 0.7146 0.9406 0.963 0.4383 ] Network output: [ 0.1658 0.6094 0.2123 0.0007792 -0.0003494 0.85 0.0005854 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1051 Epoch 824 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0171 1.039 0.9664 -0.0003898 0.0001748 -0.04127 -0.0002931 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04274 -0.00372 0.02245 0.02301 0.9127 0.9262 0.07949 0.8391 0.8753 0.1698 ] Network output: [ 0.9198 0.1654 -0.08516 -3.915e-05 1.82e-05 0.0799 -3.209e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6411 -0.04321 -0.08355 0.3106 0.9546 0.9767 0.7248 0.8636 0.9477 0.7341 ] Network output: [ -0.007702 0.954 1.03 -0.0001297 5.797e-05 0.03071 -9.67e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07263 0.03324 0.0501 0.04543 0.973 0.9803 0.07429 0.9379 0.9649 0.0846 ] Network output: [ 0.1373 -0.3326 1.169 0.001012 -0.0004548 0.8927 0.0007645 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.3421 0.337 0.5251 0.9595 0.98 0.7256 0.8755 0.955 0.7372 ] Network output: [ -0.07743 0.2416 0.8772 0.0009793 -0.0004398 1.04 0.0007386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6727 0.5912 0.3337 0.208 0.9771 0.9845 0.6733 0.9474 0.9687 0.3879 ] Network output: [ -0.1563 0.3254 0.8299 -0.002233 0.001002 1.148 -0.001682 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.714 0.6959 0.4229 0.0991 0.9737 0.9819 0.7142 0.9406 0.963 0.4383 ] Network output: [ 0.1656 0.6098 0.2116 0.0007705 -0.0003455 0.8505 0.0005789 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.105 Epoch 825 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01724 1.039 0.9665 -0.0003859 0.0001731 -0.0414 -0.0002901 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0427 -0.003701 0.02242 0.02304 0.9127 0.9262 0.07944 0.8392 0.8753 0.1698 ] Network output: [ 0.9199 0.1654 -0.08512 -4.792e-05 2.213e-05 0.07975 -3.867e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6411 -0.04272 -0.08372 0.3106 0.9546 0.9767 0.7248 0.8636 0.9478 0.734 ] Network output: [ -0.007609 0.9538 1.03 -0.0001258 5.625e-05 0.03062 -9.382e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07257 0.03328 0.05007 0.04544 0.973 0.9803 0.07423 0.938 0.9649 0.08458 ] Network output: [ 0.1372 -0.3327 1.169 0.001016 -0.0004566 0.8931 0.0007675 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.3429 0.3368 0.5252 0.9595 0.98 0.7256 0.8756 0.9551 0.7371 ] Network output: [ -0.0774 0.2421 0.8771 0.0009789 -0.0004396 1.04 0.0007383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6723 0.5911 0.3335 0.2081 0.9771 0.9845 0.6729 0.9475 0.9687 0.3877 ] Network output: [ -0.1563 0.3252 0.8304 -0.002228 0.001 1.148 -0.001679 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7136 0.6955 0.4228 0.09961 0.9737 0.9819 0.7137 0.9406 0.963 0.4382 ] Network output: [ 0.1655 0.6102 0.2109 0.0007618 -0.0003416 0.8511 0.0005723 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1048 Epoch 826 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01737 1.038 0.9667 -0.0003818 0.0001713 -0.04152 -0.0002871 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04265 -0.003681 0.02238 0.02307 0.9128 0.9262 0.07939 0.8392 0.8754 0.1697 ] Network output: [ 0.9199 0.1654 -0.08505 -5.668e-05 2.606e-05 0.07962 -4.524e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.641 -0.04224 -0.08388 0.3107 0.9546 0.9767 0.7248 0.8637 0.9478 0.7338 ] Network output: [ -0.007515 0.9536 1.03 -0.0001219 5.45e-05 0.03054 -9.089e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07251 0.03332 0.05004 0.04545 0.973 0.9803 0.07418 0.938 0.9649 0.08455 ] Network output: [ 0.1371 -0.3328 1.169 0.00102 -0.0004583 0.8936 0.0007703 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.3438 0.3365 0.5252 0.9595 0.98 0.7256 0.8756 0.9551 0.737 ] Network output: [ -0.07737 0.2426 0.8769 0.0009787 -0.0004395 1.039 0.0007382 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6718 0.591 0.3333 0.2082 0.9771 0.9845 0.6724 0.9475 0.9687 0.3875 ] Network output: [ -0.1564 0.3249 0.8309 -0.002223 0.0009978 1.148 -0.001675 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7132 0.6952 0.4227 0.1001 0.9737 0.9819 0.7133 0.9406 0.9631 0.4381 ] Network output: [ 0.1653 0.6106 0.2103 0.0007532 -0.0003377 0.8516 0.0005658 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1047 Epoch 827 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01751 1.038 0.9669 -0.0003777 0.0001694 -0.04164 -0.000284 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04261 -0.003662 0.02235 0.0231 0.9128 0.9262 0.07934 0.8393 0.8754 0.1697 ] Network output: [ 0.9199 0.1654 -0.08502 -6.529e-05 2.991e-05 0.07947 -5.169e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.641 -0.04176 -0.08405 0.3107 0.9546 0.9767 0.7248 0.8638 0.9478 0.7337 ] Network output: [ -0.007422 0.9533 1.031 -0.000118 5.273e-05 0.03046 -8.793e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07246 0.03336 0.05002 0.04546 0.973 0.9803 0.07412 0.9381 0.9649 0.08453 ] Network output: [ 0.137 -0.3329 1.169 0.001024 -0.0004599 0.894 0.000773 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.3447 0.3363 0.5253 0.9595 0.98 0.7257 0.8757 0.9551 0.7368 ] Network output: [ -0.07735 0.2431 0.8768 0.0009786 -0.0004394 1.039 0.000738 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6714 0.5909 0.3331 0.2083 0.9771 0.9845 0.672 0.9475 0.9687 0.3872 ] Network output: [ -0.1564 0.3246 0.8315 -0.002217 0.0009954 1.148 -0.001671 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7127 0.6948 0.4226 0.1006 0.9737 0.9819 0.7129 0.9406 0.9631 0.438 ] Network output: [ 0.1651 0.611 0.2096 0.0007446 -0.0003338 0.8522 0.0005594 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1045 Epoch 828 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01765 1.038 0.9671 -0.0003736 0.0001676 -0.04176 -0.000281 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04257 -0.003643 0.02232 0.02313 0.9128 0.9262 0.07929 0.8394 0.8755 0.1697 ] Network output: [ 0.9199 0.1655 -0.08497 -7.41e-05 3.386e-05 0.07932 -5.83e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6409 -0.04127 -0.0842 0.3108 0.9547 0.9768 0.7248 0.8638 0.9479 0.7336 ] Network output: [ -0.007329 0.9531 1.031 -0.0001141 5.096e-05 0.03038 -8.497e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0724 0.0334 0.04999 0.04548 0.973 0.9803 0.07407 0.9381 0.9649 0.0845 ] Network output: [ 0.1369 -0.3331 1.169 0.001027 -0.0004613 0.8944 0.0007754 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7223 0.3456 0.3361 0.5254 0.9596 0.98 0.7257 0.8758 0.9551 0.7367 ] Network output: [ -0.07732 0.2436 0.8767 0.0009784 -0.0004394 1.038 0.0007379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.671 0.5908 0.3329 0.2084 0.9771 0.9845 0.6716 0.9476 0.9687 0.387 ] Network output: [ -0.1564 0.3244 0.832 -0.002212 0.000993 1.147 -0.001667 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7123 0.6944 0.4226 0.1011 0.9737 0.9819 0.7125 0.9407 0.9631 0.4379 ] Network output: [ 0.165 0.6114 0.2089 0.0007359 -0.00033 0.8527 0.0005529 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1044 Epoch 829 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01779 1.038 0.9672 -0.0003695 0.0001657 -0.04188 -0.0002778 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04253 -0.003624 0.02229 0.02316 0.9128 0.9262 0.07924 0.8394 0.8755 0.1696 ] Network output: [ 0.92 0.1654 -0.08489 -8.288e-05 3.78e-05 0.07918 -6.488e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6409 -0.04079 -0.08433 0.3108 0.9547 0.9768 0.7249 0.8639 0.9479 0.7335 ] Network output: [ -0.007236 0.9529 1.031 -0.00011 4.916e-05 0.0303 -8.196e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07235 0.03344 0.04997 0.04549 0.973 0.9803 0.07401 0.9382 0.965 0.08448 ] Network output: [ 0.1368 -0.3332 1.169 0.00103 -0.0004627 0.8948 0.0007777 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7223 0.3465 0.3359 0.5254 0.9596 0.98 0.7257 0.8758 0.9552 0.7365 ] Network output: [ -0.0773 0.2441 0.8765 0.0009784 -0.0004394 1.038 0.0007379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6705 0.5906 0.3327 0.2085 0.9771 0.9845 0.6711 0.9476 0.9688 0.3868 ] Network output: [ -0.1564 0.3241 0.8325 -0.002207 0.0009905 1.147 -0.001663 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7119 0.6941 0.4225 0.1016 0.9737 0.9819 0.712 0.9407 0.9631 0.4379 ] Network output: [ 0.1648 0.6118 0.2082 0.0007273 -0.0003261 0.8533 0.0005464 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1042 Epoch 830 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01793 1.037 0.9674 -0.0003652 0.0001638 -0.04201 -0.0002746 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04249 -0.003605 0.02226 0.02318 0.9128 0.9262 0.07919 0.8395 0.8756 0.1696 ] Network output: [ 0.92 0.1654 -0.08486 -9.148e-05 4.165e-05 0.07902 -7.133e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6409 -0.04032 -0.08447 0.3109 0.9547 0.9768 0.7249 0.864 0.9479 0.7333 ] Network output: [ -0.007143 0.9526 1.031 -0.000106 4.735e-05 0.03023 -7.892e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07229 0.03348 0.04995 0.04551 0.973 0.9803 0.07396 0.9382 0.965 0.08446 ] Network output: [ 0.1367 -0.3333 1.169 0.001033 -0.000464 0.8952 0.0007798 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7223 0.3474 0.3357 0.5254 0.9596 0.98 0.7257 0.8759 0.9552 0.7364 ] Network output: [ -0.07728 0.2446 0.8764 0.0009784 -0.0004394 1.038 0.0007379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6701 0.5905 0.3325 0.2086 0.9771 0.9845 0.6707 0.9476 0.9688 0.3866 ] Network output: [ -0.1564 0.3239 0.8331 -0.002201 0.0009879 1.147 -0.001658 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7115 0.6937 0.4224 0.1022 0.9737 0.9819 0.7116 0.9407 0.9631 0.4378 ] Network output: [ 0.1646 0.6122 0.2075 0.0007188 -0.0003223 0.8538 0.00054 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1041 Epoch 831 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01807 1.037 0.9676 -0.000361 0.0001619 -0.04213 -0.0002715 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04245 -0.003587 0.02223 0.02321 0.9128 0.9262 0.07914 0.8396 0.8756 0.1696 ] Network output: [ 0.92 0.1654 -0.08481 -0.0001003 4.562e-05 0.07886 -7.798e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6408 -0.03984 -0.0846 0.3109 0.9547 0.9768 0.7249 0.864 0.9479 0.7332 ] Network output: [ -0.00705 0.9524 1.031 -0.0001019 4.554e-05 0.03016 -7.589e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07224 0.03352 0.04993 0.04552 0.973 0.9803 0.0739 0.9383 0.965 0.08444 ] Network output: [ 0.1365 -0.3334 1.169 0.001035 -0.000465 0.8957 0.0007816 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7223 0.3483 0.3355 0.5255 0.9596 0.98 0.7257 0.8759 0.9552 0.7363 ] Network output: [ -0.07725 0.2451 0.8763 0.0009784 -0.0004394 1.037 0.0007379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6697 0.5904 0.3323 0.2087 0.9771 0.9845 0.6703 0.9477 0.9688 0.3864 ] Network output: [ -0.1564 0.3236 0.8336 -0.002195 0.0009854 1.147 -0.001654 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.711 0.6934 0.4223 0.1027 0.9737 0.9819 0.7112 0.9407 0.9631 0.4377 ] Network output: [ 0.1645 0.6126 0.2069 0.0007101 -0.0003184 0.8544 0.0005335 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1039 Epoch 832 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01821 1.037 0.9678 -0.0003567 0.00016 -0.04225 -0.0002682 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0424 -0.003568 0.02221 0.02324 0.9129 0.9263 0.07909 0.8396 0.8757 0.1695 ] Network output: [ 0.9201 0.1654 -0.08472 -0.0001091 4.954e-05 0.07872 -8.455e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6408 -0.03936 -0.0847 0.311 0.9547 0.9768 0.7249 0.8641 0.948 0.7331 ] Network output: [ -0.006958 0.9522 1.031 -9.782e-05 4.369e-05 0.03008 -7.279e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07219 0.03356 0.04991 0.04554 0.973 0.9803 0.07385 0.9383 0.965 0.08443 ] Network output: [ 0.1364 -0.3335 1.169 0.001037 -0.000466 0.8961 0.0007832 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7224 0.3491 0.3353 0.5255 0.9596 0.98 0.7258 0.876 0.9552 0.7361 ] Network output: [ -0.07723 0.2456 0.8761 0.0009787 -0.0004395 1.037 0.0007381 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6692 0.5903 0.3322 0.2088 0.9771 0.9845 0.6698 0.9477 0.9688 0.3862 ] Network output: [ -0.1564 0.3234 0.8341 -0.002189 0.0009828 1.147 -0.00165 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7106 0.693 0.4222 0.1032 0.9737 0.9819 0.7107 0.9407 0.9631 0.4376 ] Network output: [ 0.1643 0.6131 0.2062 0.0007016 -0.0003146 0.855 0.0005271 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1038 Epoch 833 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01836 1.036 0.9679 -0.0003523 0.000158 -0.04237 -0.0002649 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04236 -0.00355 0.02218 0.02327 0.9129 0.9263 0.07904 0.8397 0.8757 0.1695 ] Network output: [ 0.9201 0.1654 -0.08468 -0.0001177 5.339e-05 0.07854 -9.099e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6408 -0.03889 -0.08482 0.311 0.9547 0.9768 0.725 0.8641 0.948 0.7329 ] Network output: [ -0.006865 0.9519 1.031 -9.368e-05 4.183e-05 0.03001 -6.968e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07213 0.0336 0.04989 0.04555 0.9731 0.9804 0.0738 0.9383 0.9651 0.08441 ] Network output: [ 0.1363 -0.3336 1.169 0.001039 -0.000467 0.8965 0.0007848 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7224 0.35 0.3352 0.5256 0.9596 0.98 0.7258 0.8761 0.9553 0.736 ] Network output: [ -0.07721 0.2461 0.876 0.000979 -0.0004396 1.036 0.0007383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6688 0.5902 0.332 0.2089 0.9771 0.9845 0.6694 0.9477 0.9688 0.386 ] Network output: [ -0.1565 0.3231 0.8346 -0.002183 0.00098 1.146 -0.001645 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7102 0.6927 0.4222 0.1037 0.9737 0.9819 0.7103 0.9407 0.9631 0.4376 ] Network output: [ 0.1641 0.6135 0.2055 0.000693 -0.0003107 0.8555 0.0005206 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1036 Epoch 834 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0185 1.036 0.9681 -0.0003479 0.0001561 -0.04248 -0.0002617 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04232 -0.003532 0.02215 0.0233 0.9129 0.9263 0.079 0.8398 0.8758 0.1695 ] Network output: [ 0.9202 0.1654 -0.08462 -0.0001266 5.737e-05 0.07838 -9.765e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6407 -0.03842 -0.08492 0.311 0.9547 0.9768 0.725 0.8642 0.948 0.7328 ] Network output: [ -0.006773 0.9517 1.032 -8.955e-05 3.998e-05 0.02994 -6.659e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07208 0.03364 0.04988 0.04557 0.9731 0.9804 0.07374 0.9384 0.9651 0.0844 ] Network output: [ 0.1362 -0.3337 1.169 0.001041 -0.0004676 0.8969 0.0007859 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7224 0.3509 0.335 0.5256 0.9596 0.98 0.7258 0.8761 0.9553 0.7358 ] Network output: [ -0.07718 0.2466 0.8759 0.0009792 -0.0004397 1.036 0.0007385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6684 0.59 0.3318 0.2089 0.9772 0.9845 0.669 0.9478 0.9689 0.3859 ] Network output: [ -0.1565 0.3229 0.8351 -0.002177 0.0009774 1.146 -0.001641 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7098 0.6923 0.4221 0.1042 0.9737 0.9819 0.7099 0.9407 0.9631 0.4375 ] Network output: [ 0.1639 0.6139 0.2049 0.0006844 -0.0003069 0.8561 0.0005142 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1034 Epoch 835 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01864 1.036 0.9683 -0.0003435 0.0001541 -0.0426 -0.0002583 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04228 -0.003514 0.02213 0.02333 0.9129 0.9263 0.07895 0.8399 0.8758 0.1695 ] Network output: [ 0.9202 0.1654 -0.08453 -0.0001353 6.128e-05 0.07822 -0.0001042 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6407 -0.03794 -0.085 0.3111 0.9547 0.9768 0.725 0.8643 0.9481 0.7326 ] Network output: [ -0.006681 0.9515 1.032 -8.533e-05 3.809e-05 0.02988 -6.341e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07203 0.03368 0.04987 0.04558 0.9731 0.9804 0.07369 0.9384 0.9651 0.08438 ] Network output: [ 0.1361 -0.3338 1.169 0.001042 -0.0004683 0.8974 0.0007871 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7224 0.3518 0.3349 0.5256 0.9596 0.98 0.7259 0.8762 0.9553 0.7357 ] Network output: [ -0.07716 0.2471 0.8758 0.0009796 -0.0004399 1.035 0.0007388 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6679 0.5899 0.3317 0.209 0.9772 0.9845 0.6685 0.9478 0.9689 0.3857 ] Network output: [ -0.1564 0.3226 0.8356 -0.002171 0.0009746 1.146 -0.001636 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7093 0.692 0.422 0.1047 0.9737 0.9819 0.7095 0.9407 0.9632 0.4374 ] Network output: [ 0.1638 0.6143 0.2042 0.0006759 -0.000303 0.8567 0.0005078 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1033 Epoch 836 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01879 1.035 0.9684 -0.000339 0.000152 -0.04272 -0.0002549 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04224 -0.003496 0.0221 0.02335 0.9129 0.9263 0.07891 0.8399 0.8758 0.1694 ] Network output: [ 0.9202 0.1654 -0.0845 -0.0001438 6.511e-05 0.07803 -0.0001106 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6407 -0.03748 -0.08509 0.3111 0.9547 0.9768 0.7251 0.8643 0.9481 0.7325 ] Network output: [ -0.00659 0.9512 1.032 -8.11e-05 3.62e-05 0.02981 -6.024e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07198 0.03372 0.04985 0.0456 0.9731 0.9804 0.07364 0.9385 0.9651 0.08437 ] Network output: [ 0.1359 -0.3339 1.169 0.001044 -0.0004689 0.8978 0.0007881 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7225 0.3526 0.3348 0.5256 0.9596 0.98 0.7259 0.8762 0.9553 0.7355 ] Network output: [ -0.07714 0.2476 0.8757 0.0009801 -0.0004401 1.035 0.0007392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6675 0.5898 0.3315 0.2091 0.9772 0.9845 0.6681 0.9479 0.9689 0.3855 ] Network output: [ -0.1565 0.3224 0.8361 -0.002165 0.0009717 1.146 -0.001631 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7089 0.6916 0.4219 0.1052 0.9737 0.9819 0.709 0.9408 0.9632 0.4373 ] Network output: [ 0.1636 0.6148 0.2035 0.0006673 -0.0002992 0.8573 0.0005013 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1031 Epoch 837 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01893 1.035 0.9686 -0.0003345 0.00015 -0.04284 -0.0002516 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0422 -0.003479 0.02208 0.02338 0.9129 0.9263 0.07886 0.84 0.8759 0.1694 ] Network output: [ 0.9203 0.1654 -0.08442 -0.0001527 6.91e-05 0.07787 -0.0001173 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6407 -0.03701 -0.08516 0.3111 0.9548 0.9768 0.7251 0.8644 0.9481 0.7324 ] Network output: [ -0.006498 0.951 1.032 -7.69e-05 3.431e-05 0.02975 -5.709e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07193 0.03376 0.04984 0.04562 0.9731 0.9804 0.07359 0.9385 0.9651 0.08436 ] Network output: [ 0.1358 -0.334 1.168 0.001044 -0.0004693 0.8982 0.0007886 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7225 0.3535 0.3347 0.5256 0.9596 0.98 0.726 0.8763 0.9554 0.7354 ] Network output: [ -0.07711 0.2481 0.8755 0.0009805 -0.0004403 1.035 0.0007395 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6671 0.5897 0.3314 0.2092 0.9772 0.9845 0.6677 0.9479 0.9689 0.3853 ] Network output: [ -0.1564 0.3221 0.8366 -0.002159 0.0009689 1.145 -0.001626 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7085 0.6913 0.4219 0.1057 0.9737 0.9819 0.7086 0.9408 0.9632 0.4373 ] Network output: [ 0.1634 0.6152 0.2029 0.0006587 -0.0002953 0.8578 0.0004949 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.103 Epoch 838 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01907 1.035 0.9688 -0.0003299 0.000148 -0.04295 -0.0002481 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04216 -0.003461 0.02206 0.02341 0.9129 0.9263 0.07881 0.8401 0.8759 0.1694 ] Network output: [ 0.9203 0.1653 -0.08432 -0.0001614 7.297e-05 0.07771 -0.0001238 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6406 -0.03655 -0.08521 0.3111 0.9548 0.9768 0.7252 0.8645 0.9482 0.7322 ] Network output: [ -0.006407 0.9507 1.032 -7.259e-05 3.238e-05 0.02968 -5.385e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07188 0.03381 0.04984 0.04564 0.9731 0.9804 0.07354 0.9386 0.9652 0.08435 ] Network output: [ 0.1357 -0.3341 1.168 0.001045 -0.0004696 0.8986 0.0007892 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7226 0.3544 0.3346 0.5257 0.9597 0.98 0.726 0.8764 0.9554 0.7352 ] Network output: [ -0.07709 0.2486 0.8754 0.0009813 -0.0004406 1.034 0.00074 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6667 0.5895 0.3312 0.2093 0.9772 0.9845 0.6673 0.9479 0.969 0.3851 ] Network output: [ -0.1564 0.3219 0.8371 -0.002152 0.0009659 1.145 -0.001621 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7081 0.6909 0.4218 0.1062 0.9737 0.9819 0.7082 0.9408 0.9632 0.4372 ] Network output: [ 0.1632 0.6156 0.2022 0.0006502 -0.0002915 0.8584 0.0004885 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1028 Epoch 839 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01922 1.034 0.9689 -0.0003253 0.0001459 -0.04307 -0.0002446 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04212 -0.003444 0.02203 0.02344 0.913 0.9263 0.07877 0.8401 0.876 0.1694 ] Network output: [ 0.9204 0.1653 -0.08429 -0.0001699 7.678e-05 0.0775 -0.0001301 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6406 -0.03609 -0.08528 0.3111 0.9548 0.9768 0.7252 0.8645 0.9482 0.7321 ] Network output: [ -0.006316 0.9505 1.032 -6.829e-05 3.045e-05 0.02962 -5.062e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07183 0.03384 0.04983 0.04565 0.9731 0.9804 0.07349 0.9386 0.9652 0.08434 ] Network output: [ 0.1356 -0.3342 1.168 0.001046 -0.0004699 0.899 0.0007897 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7226 0.3553 0.3345 0.5257 0.9597 0.98 0.7261 0.8764 0.9554 0.7351 ] Network output: [ -0.07708 0.2491 0.8754 0.000982 -0.000441 1.034 0.0007405 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6662 0.5894 0.3311 0.2094 0.9772 0.9845 0.6668 0.948 0.969 0.385 ] Network output: [ -0.1564 0.3216 0.8376 -0.002145 0.0009629 1.145 -0.001616 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7076 0.6906 0.4217 0.1067 0.9737 0.9819 0.7078 0.9408 0.9632 0.4371 ] Network output: [ 0.163 0.6161 0.2015 0.0006417 -0.0002877 0.859 0.0004821 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1027 Epoch 840 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01937 1.034 0.9691 -0.0003207 0.0001439 -0.04318 -0.0002412 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04208 -0.003427 0.02201 0.02346 0.913 0.9264 0.07873 0.8402 0.876 0.1694 ] Network output: [ 0.9204 0.1653 -0.08421 -0.0001788 8.077e-05 0.07733 -0.0001368 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6406 -0.03563 -0.08532 0.3112 0.9548 0.9768 0.7253 0.8646 0.9482 0.7319 ] Network output: [ -0.006226 0.9503 1.032 -6.402e-05 2.854e-05 0.02956 -4.741e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07178 0.03389 0.04982 0.04567 0.9731 0.9804 0.07344 0.9387 0.9652 0.08434 ] Network output: [ 0.1354 -0.3343 1.168 0.001046 -0.0004699 0.8995 0.0007896 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7226 0.3562 0.3345 0.5257 0.9597 0.98 0.7261 0.8765 0.9554 0.7349 ] Network output: [ -0.07705 0.2495 0.8752 0.0009826 -0.0004412 1.033 0.000741 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6658 0.5893 0.3309 0.2095 0.9772 0.9846 0.6664 0.948 0.969 0.3848 ] Network output: [ -0.1564 0.3214 0.838 -0.002139 0.00096 1.145 -0.001611 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7072 0.6902 0.4217 0.1072 0.9737 0.9819 0.7074 0.9408 0.9632 0.4371 ] Network output: [ 0.1628 0.6165 0.2009 0.000633 -0.0002838 0.8596 0.0004756 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1025 Epoch 841 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01951 1.034 0.9692 -0.000316 0.0001417 -0.04329 -0.0002376 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04204 -0.00341 0.02199 0.02349 0.913 0.9264 0.07868 0.8403 0.8761 0.1693 ] Network output: [ 0.9205 0.1652 -0.0841 -0.0001873 8.459e-05 0.07716 -0.0001432 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6406 -0.03517 -0.08535 0.3112 0.9548 0.9769 0.7253 0.8647 0.9482 0.7318 ] Network output: [ -0.006135 0.95 1.033 -5.963e-05 2.657e-05 0.02951 -4.411e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07173 0.03393 0.04982 0.04569 0.9731 0.9804 0.07339 0.9387 0.9652 0.08433 ] Network output: [ 0.1353 -0.3344 1.168 0.001046 -0.00047 0.8999 0.0007897 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7227 0.357 0.3344 0.5257 0.9597 0.9801 0.7262 0.8765 0.9555 0.7348 ] Network output: [ -0.07703 0.25 0.8752 0.0009836 -0.0004417 1.033 0.0007417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6654 0.5892 0.3308 0.2096 0.9772 0.9846 0.666 0.948 0.969 0.3846 ] Network output: [ -0.1564 0.3212 0.8385 -0.002132 0.0009569 1.144 -0.001606 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7068 0.6899 0.4216 0.1077 0.9737 0.9819 0.7069 0.9408 0.9632 0.437 ] Network output: [ 0.1626 0.617 0.2002 0.0006246 -0.00028 0.8602 0.0004692 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1023 Epoch 842 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01966 1.033 0.9694 -0.0003113 0.0001396 -0.04341 -0.0002341 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.042 -0.003394 0.02197 0.02352 0.913 0.9264 0.07864 0.8403 0.8761 0.1693 ] Network output: [ 0.9206 0.1652 -0.08408 -0.0001958 8.838e-05 0.07695 -0.0001496 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6406 -0.03472 -0.08539 0.3112 0.9548 0.9769 0.7254 0.8647 0.9483 0.7316 ] Network output: [ -0.006046 0.9498 1.033 -5.528e-05 2.462e-05 0.02945 -4.085e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07168 0.03397 0.04982 0.04571 0.9731 0.9804 0.07335 0.9388 0.9653 0.08433 ] Network output: [ 0.1352 -0.3345 1.168 0.001046 -0.0004699 0.9003 0.0007896 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7227 0.3579 0.3344 0.5257 0.9597 0.9801 0.7262 0.8766 0.9555 0.7346 ] Network output: [ -0.07702 0.2505 0.8751 0.0009845 -0.0004421 1.032 0.0007424 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6649 0.589 0.3307 0.2097 0.9772 0.9846 0.6656 0.9481 0.969 0.3845 ] Network output: [ -0.1564 0.3209 0.839 -0.002125 0.0009537 1.144 -0.001601 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7064 0.6895 0.4215 0.1082 0.9737 0.9819 0.7065 0.9408 0.9632 0.4369 ] Network output: [ 0.1624 0.6174 0.1996 0.000616 -0.0002762 0.8607 0.0004628 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1022 Epoch 843 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0198 1.033 0.9696 -0.0003067 0.0001376 -0.04352 -0.0002306 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04196 -0.003377 0.02195 0.02354 0.913 0.9264 0.0786 0.8404 0.8762 0.1693 ] Network output: [ 0.9206 0.1652 -0.08397 -0.0002046 9.236e-05 0.07678 -0.0001562 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6406 -0.03426 -0.0854 0.3112 0.9548 0.9769 0.7254 0.8648 0.9483 0.7315 ] Network output: [ -0.005956 0.9495 1.033 -5.095e-05 2.268e-05 0.0294 -3.76e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07163 0.03401 0.04982 0.04573 0.9731 0.9804 0.0733 0.9388 0.9653 0.08432 ] Network output: [ 0.135 -0.3346 1.168 0.001045 -0.0004695 0.9007 0.000789 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7228 0.3588 0.3344 0.5257 0.9597 0.9801 0.7263 0.8767 0.9555 0.7344 ] Network output: [ -0.07699 0.251 0.875 0.0009853 -0.0004424 1.032 0.000743 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6645 0.5889 0.3306 0.2098 0.9772 0.9846 0.6651 0.9481 0.9691 0.3843 ] Network output: [ -0.1563 0.3207 0.8394 -0.002118 0.0009507 1.144 -0.001596 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.706 0.6892 0.4215 0.1087 0.9737 0.9819 0.7061 0.9409 0.9633 0.4369 ] Network output: [ 0.1622 0.6179 0.1989 0.0006073 -0.0002723 0.8613 0.0004562 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.102 Epoch 844 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01995 1.033 0.9697 -0.0003018 0.0001354 -0.04363 -0.000227 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04193 -0.003361 0.02194 0.02357 0.913 0.9264 0.07855 0.8405 0.8762 0.1693 ] Network output: [ 0.9206 0.1651 -0.08386 -0.000213 9.612e-05 0.07659 -0.0001625 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 -0.03381 -0.0854 0.3112 0.9548 0.9769 0.7255 0.8649 0.9483 0.7313 ] Network output: [ -0.005867 0.9493 1.033 -4.649e-05 2.068e-05 0.02934 -3.424e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07158 0.03405 0.04982 0.04575 0.9731 0.9804 0.07325 0.9389 0.9653 0.08432 ] Network output: [ 0.1349 -0.3347 1.168 0.001045 -0.0004693 0.9012 0.0007887 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7228 0.3596 0.3344 0.5256 0.9597 0.9801 0.7263 0.8767 0.9556 0.7343 ] Network output: [ -0.07697 0.2514 0.8749 0.0009865 -0.000443 1.032 0.000744 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6641 0.5888 0.3305 0.2099 0.9772 0.9846 0.6647 0.9482 0.9691 0.3842 ] Network output: [ -0.1563 0.3204 0.8398 -0.00211 0.0009474 1.144 -0.00159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7056 0.6888 0.4214 0.1092 0.9737 0.9819 0.7057 0.9409 0.9633 0.4368 ] Network output: [ 0.162 0.6183 0.1982 0.0005989 -0.0002685 0.8619 0.0004499 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1019 Epoch 845 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0201 1.032 0.9699 -0.000297 0.0001332 -0.04374 -0.0002234 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04189 -0.003345 0.02192 0.0236 0.9131 0.9264 0.07851 0.8406 0.8762 0.1693 ] Network output: [ 0.9207 0.1651 -0.08385 -0.0002214 9.988e-05 0.07637 -0.0001688 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 -0.03337 -0.08542 0.3112 0.9549 0.9769 0.7255 0.8649 0.9484 0.7312 ] Network output: [ -0.005778 0.949 1.033 -4.21e-05 1.871e-05 0.02929 -3.095e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07154 0.03409 0.04982 0.04577 0.9732 0.9804 0.07321 0.9389 0.9653 0.08432 ] Network output: [ 0.1348 -0.3348 1.168 0.001044 -0.0004689 0.9016 0.000788 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7229 0.3605 0.3344 0.5256 0.9597 0.9801 0.7264 0.8768 0.9556 0.7341 ] Network output: [ -0.07696 0.2519 0.8748 0.0009876 -0.0004435 1.031 0.0007448 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6637 0.5886 0.3304 0.21 0.9772 0.9846 0.6643 0.9482 0.9691 0.384 ] Network output: [ -0.1563 0.3202 0.8403 -0.002103 0.0009441 1.143 -0.001585 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7051 0.6885 0.4214 0.1097 0.9737 0.9819 0.7053 0.9409 0.9633 0.4367 ] Network output: [ 0.1618 0.6188 0.1976 0.0005902 -0.0002646 0.8625 0.0004434 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1017 Epoch 846 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02024 1.032 0.97 -0.0002923 0.0001311 -0.04385 -0.0002198 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04185 -0.003329 0.0219 0.02362 0.9131 0.9264 0.07847 0.8406 0.8763 0.1693 ] Network output: [ 0.9208 0.1651 -0.08372 -0.0002303 0.0001038 0.0762 -0.0001755 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 -0.03291 -0.08539 0.3112 0.9549 0.9769 0.7256 0.865 0.9484 0.731 ] Network output: [ -0.005689 0.9488 1.033 -3.772e-05 1.675e-05 0.02924 -2.766e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07149 0.03413 0.04983 0.04579 0.9732 0.9804 0.07316 0.939 0.9654 0.08432 ] Network output: [ 0.1346 -0.3349 1.168 0.001042 -0.0004683 0.902 0.0007869 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.723 0.3614 0.3344 0.5256 0.9597 0.9801 0.7264 0.8769 0.9556 0.7339 ] Network output: [ -0.07693 0.2524 0.8747 0.0009887 -0.000444 1.031 0.0007455 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6633 0.5885 0.3303 0.2101 0.9772 0.9846 0.6639 0.9482 0.9691 0.3839 ] Network output: [ -0.1562 0.32 0.8407 -0.002096 0.000941 1.143 -0.001579 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7047 0.6881 0.4213 0.1102 0.9737 0.9819 0.7049 0.9409 0.9633 0.4367 ] Network output: [ 0.1615 0.6193 0.1969 0.0005815 -0.0002607 0.8631 0.0004369 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1015 Epoch 847 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02039 1.032 0.9702 -0.0002874 0.0001289 -0.04396 -0.0002161 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04181 -0.003313 0.02189 0.02365 0.9131 0.9265 0.07843 0.8407 0.8763 0.1692 ] Network output: [ 0.9208 0.165 -0.08362 -0.0002384 0.0001075 0.076 -0.0001816 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 -0.03247 -0.08537 0.3112 0.9549 0.9769 0.7257 0.8651 0.9484 0.7309 ] Network output: [ -0.005601 0.9485 1.033 -3.32e-05 1.472e-05 0.0292 -2.426e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07144 0.03417 0.04983 0.04581 0.9732 0.9804 0.07311 0.939 0.9654 0.08433 ] Network output: [ 0.1345 -0.335 1.168 0.001041 -0.0004678 0.9024 0.0007861 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.723 0.3622 0.3344 0.5256 0.9597 0.9801 0.7265 0.8769 0.9556 0.7338 ] Network output: [ -0.07692 0.2528 0.8747 0.0009902 -0.0004446 1.03 0.0007467 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6628 0.5884 0.3302 0.2102 0.9772 0.9846 0.6635 0.9483 0.9691 0.3837 ] Network output: [ -0.1562 0.3198 0.8412 -0.002088 0.0009374 1.143 -0.001573 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7043 0.6878 0.4212 0.1107 0.9737 0.9819 0.7044 0.9409 0.9633 0.4366 ] Network output: [ 0.1613 0.6197 0.1963 0.0005731 -0.0002569 0.8637 0.0004305 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1014 Epoch 848 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02054 1.032 0.9703 -0.0002825 0.0001267 -0.04407 -0.0002125 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04177 -0.003298 0.02187 0.02367 0.9131 0.9265 0.07839 0.8408 0.8764 0.1692 ] Network output: [ 0.9209 0.165 -0.08361 -0.0002468 0.0001113 0.07576 -0.0001879 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 -0.03204 -0.08537 0.3112 0.9549 0.9769 0.7257 0.8651 0.9485 0.7307 ] Network output: [ -0.005513 0.9483 1.033 -2.88e-05 1.275e-05 0.02915 -2.096e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0714 0.0342 0.04984 0.04583 0.9732 0.9804 0.07307 0.9391 0.9654 0.08433 ] Network output: [ 0.1344 -0.335 1.168 0.00104 -0.0004671 0.9029 0.0007849 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7231 0.3631 0.3345 0.5256 0.9598 0.9801 0.7266 0.877 0.9557 0.7336 ] Network output: [ -0.07691 0.2533 0.8746 0.0009914 -0.0004452 1.03 0.0007476 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6624 0.5883 0.3301 0.2103 0.9772 0.9846 0.663 0.9483 0.9692 0.3836 ] Network output: [ -0.1562 0.3195 0.8416 -0.002081 0.0009341 1.143 -0.001568 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7039 0.6875 0.4212 0.1112 0.9737 0.9819 0.704 0.9409 0.9633 0.4365 ] Network output: [ 0.1611 0.6202 0.1956 0.0005644 -0.000253 0.8643 0.000424 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1012 Epoch 849 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02068 1.031 0.9705 -0.0002778 0.0001246 -0.04417 -0.0002089 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04173 -0.003282 0.02186 0.0237 0.9131 0.9265 0.07835 0.8408 0.8764 0.1692 ] Network output: [ 0.921 0.1649 -0.08346 -0.0002556 0.0001152 0.0756 -0.0001945 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 -0.03158 -0.0853 0.3111 0.9549 0.9769 0.7258 0.8652 0.9485 0.7306 ] Network output: [ -0.005426 0.948 1.034 -2.438e-05 1.076e-05 0.02911 -1.763e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07135 0.03425 0.04985 0.04585 0.9732 0.9804 0.07302 0.9391 0.9654 0.08433 ] Network output: [ 0.1342 -0.3351 1.168 0.001038 -0.0004662 0.9033 0.0007834 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7231 0.364 0.3346 0.5255 0.9598 0.9801 0.7266 0.8771 0.9557 0.7334 ] Network output: [ -0.07687 0.2537 0.8745 0.0009927 -0.0004458 1.03 0.0007486 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.662 0.5881 0.33 0.2104 0.9773 0.9846 0.6626 0.9484 0.9692 0.3834 ] Network output: [ -0.1561 0.3193 0.842 -0.002074 0.0009308 1.142 -0.001562 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7035 0.6871 0.4211 0.1117 0.9737 0.9819 0.7036 0.941 0.9633 0.4365 ] Network output: [ 0.1609 0.6206 0.195 0.0005556 -0.0002491 0.8649 0.0004174 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1011 Epoch 850 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02083 1.031 0.9706 -0.0002727 0.0001223 -0.04428 -0.0002051 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0417 -0.003267 0.02185 0.02373 0.9131 0.9265 0.07831 0.8409 0.8765 0.1692 ] Network output: [ 0.921 0.1648 -0.08336 -0.0002635 0.0001187 0.07538 -0.0002004 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 -0.03114 -0.08526 0.3111 0.9549 0.9769 0.7259 0.8653 0.9485 0.7304 ] Network output: [ -0.005339 0.9478 1.034 -1.981e-05 8.717e-06 0.02906 -1.42e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07131 0.03428 0.04986 0.04587 0.9732 0.9805 0.07298 0.9392 0.9655 0.08434 ] Network output: [ 0.1341 -0.3352 1.168 0.001036 -0.0004655 0.9037 0.0007822 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7232 0.3648 0.3346 0.5255 0.9598 0.9801 0.7267 0.8771 0.9557 0.7333 ] Network output: [ -0.07687 0.2541 0.8745 0.0009945 -0.0004466 1.029 0.0007499 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6616 0.588 0.3299 0.2105 0.9773 0.9846 0.6622 0.9484 0.9692 0.3833 ] Network output: [ -0.1561 0.3191 0.8425 -0.002065 0.0009271 1.142 -0.001556 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7031 0.6868 0.4211 0.1122 0.9737 0.9819 0.7032 0.941 0.9634 0.4364 ] Network output: [ 0.1606 0.6211 0.1944 0.0005471 -0.0002453 0.8655 0.000411 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1009 Epoch 851 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02098 1.031 0.9707 -0.0002678 0.0001201 -0.04439 -0.0002014 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04166 -0.003253 0.02183 0.02375 0.9132 0.9265 0.07827 0.841 0.8765 0.1692 ] Network output: [ 0.9212 0.1648 -0.08335 -0.0002718 0.0001225 0.07514 -0.0002067 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 -0.03072 -0.08523 0.3111 0.9549 0.9769 0.726 0.8653 0.9485 0.7302 ] Network output: [ -0.005252 0.9476 1.034 -1.541e-05 6.744e-06 0.02902 -1.09e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07126 0.03432 0.04987 0.04589 0.9732 0.9805 0.07294 0.9392 0.9655 0.08435 ] Network output: [ 0.1339 -0.3353 1.167 0.001034 -0.0004645 0.9041 0.0007804 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7233 0.3656 0.3347 0.5254 0.9598 0.9801 0.7268 0.8772 0.9557 0.7331 ] Network output: [ -0.07685 0.2546 0.8745 0.0009959 -0.0004472 1.029 0.0007509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6612 0.5879 0.3299 0.2106 0.9773 0.9846 0.6618 0.9484 0.9692 0.3832 ] Network output: [ -0.156 0.3189 0.8429 -0.002058 0.0009237 1.142 -0.00155 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7027 0.6864 0.4211 0.1127 0.9737 0.9819 0.7028 0.941 0.9634 0.4364 ] Network output: [ 0.1604 0.6216 0.1937 0.0005383 -0.0002414 0.8661 0.0004044 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1007 Epoch 852 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02112 1.03 0.9709 -0.000263 0.000118 -0.04449 -0.0001978 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04162 -0.003237 0.02183 0.02378 0.9132 0.9265 0.07823 0.8411 0.8766 0.1692 ] Network output: [ 0.9212 0.1647 -0.08317 -0.0002805 0.0001263 0.07498 -0.0002132 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 -0.03026 -0.08513 0.3111 0.9549 0.9769 0.726 0.8654 0.9486 0.7301 ] Network output: [ -0.005166 0.9473 1.034 -1.095e-05 4.743e-06 0.02898 -7.542e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07122 0.03436 0.04989 0.04591 0.9732 0.9805 0.07289 0.9393 0.9655 0.08436 ] Network output: [ 0.1338 -0.3353 1.167 0.001031 -0.0004633 0.9046 0.0007784 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7233 0.3665 0.3348 0.5254 0.9598 0.9801 0.7269 0.8772 0.9558 0.7329 ] Network output: [ -0.07682 0.2551 0.8744 0.0009974 -0.0004479 1.028 0.0007521 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6608 0.5877 0.3298 0.2107 0.9773 0.9846 0.6614 0.9485 0.9693 0.3831 ] Network output: [ -0.156 0.3187 0.8432 -0.00205 0.0009203 1.142 -0.001545 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7022 0.6861 0.421 0.1131 0.9737 0.9819 0.7024 0.941 0.9634 0.4363 ] Network output: [ 0.1602 0.6221 0.1931 0.0005296 -0.0002374 0.8667 0.0003978 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1006 Epoch 853 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02127 1.03 0.971 -0.0002579 0.0001157 -0.04459 -0.0001939 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04158 -0.003223 0.02182 0.0238 0.9132 0.9265 0.07819 0.8411 0.8766 0.1692 ] Network output: [ 0.9213 0.1646 -0.08309 -0.0002882 0.0001298 0.07474 -0.0002189 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 -0.02985 -0.08508 0.3111 0.955 0.9769 0.7261 0.8655 0.9486 0.7299 ] Network output: [ -0.005081 0.947 1.034 -6.361e-06 2.686e-06 0.02894 -4.093e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07117 0.0344 0.0499 0.04594 0.9732 0.9805 0.07285 0.9394 0.9655 0.08437 ] Network output: [ 0.1337 -0.3354 1.167 0.001029 -0.0004624 0.905 0.0007769 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7234 0.3674 0.3349 0.5253 0.9598 0.9801 0.7269 0.8773 0.9558 0.7327 ] Network output: [ -0.07682 0.2555 0.8744 0.0009994 -0.0004488 1.028 0.0007536 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6603 0.5876 0.3298 0.2108 0.9773 0.9846 0.661 0.9485 0.9693 0.3829 ] Network output: [ -0.1559 0.3184 0.8437 -0.002041 0.0009164 1.141 -0.001538 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7018 0.6857 0.421 0.1136 0.9737 0.9819 0.702 0.941 0.9634 0.4363 ] Network output: [ 0.1599 0.6225 0.1924 0.000521 -0.0002336 0.8673 0.0003914 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1004 Epoch 854 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02143 1.03 0.9712 -0.000253 0.0001135 -0.0447 -0.0001902 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04155 -0.003209 0.02181 0.02383 0.9132 0.9266 0.07815 0.8412 0.8767 0.1692 ] Network output: [ 0.9214 0.1646 -0.08308 -0.0002964 0.0001335 0.0745 -0.0002251 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 -0.02943 -0.08501 0.311 0.955 0.977 0.7262 0.8655 0.9486 0.7297 ] Network output: [ -0.004996 0.9468 1.034 -1.978e-06 7.202e-07 0.0289 -7.995e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07113 0.03444 0.04992 0.04596 0.9732 0.9805 0.07281 0.9394 0.9656 0.08438 ] Network output: [ 0.1335 -0.3355 1.167 0.001026 -0.000461 0.9054 0.0007746 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7235 0.3682 0.3351 0.5253 0.9598 0.9801 0.727 0.8774 0.9558 0.7326 ] Network output: [ -0.0768 0.2559 0.8743 0.001001 -0.0004495 1.027 0.0007548 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6599 0.5875 0.3297 0.2109 0.9773 0.9846 0.6605 0.9486 0.9693 0.3828 ] Network output: [ -0.1559 0.3182 0.8441 -0.002034 0.0009129 1.141 -0.001532 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7014 0.6854 0.4209 0.1141 0.9737 0.9819 0.7016 0.941 0.9634 0.4362 ] Network output: [ 0.1597 0.623 0.1918 0.0005121 -0.0002296 0.8679 0.0003847 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1002 Epoch 855 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02156 1.029 0.9713 -0.0002481 0.0001113 -0.0448 -0.0001866 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04151 -0.003193 0.0218 0.02385 0.9132 0.9266 0.07812 0.8413 0.8767 0.1692 ] Network output: [ 0.9214 0.1645 -0.08287 -0.0003049 0.0001373 0.07434 -0.0002315 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 -0.02897 -0.08488 0.311 0.955 0.977 0.7263 0.8656 0.9487 0.7296 ] Network output: [ -0.004911 0.9466 1.034 2.515e-06 -1.295e-06 0.02887 2.577e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07109 0.03448 0.04994 0.04598 0.9732 0.9805 0.07276 0.9395 0.9656 0.08439 ] Network output: [ 0.1334 -0.3355 1.167 0.001023 -0.0004596 0.9059 0.0007722 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7236 0.3691 0.3352 0.5252 0.9598 0.9801 0.7271 0.8774 0.9558 0.7324 ] Network output: [ -0.07677 0.2564 0.8742 0.001003 -0.0004503 1.027 0.0007561 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6595 0.5873 0.3297 0.211 0.9773 0.9846 0.6601 0.9486 0.9693 0.3827 ] Network output: [ -0.1558 0.318 0.8444 -0.002026 0.0009094 1.141 -0.001526 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.701 0.685 0.4209 0.1146 0.9737 0.9819 0.7012 0.9411 0.9634 0.4361 ] Network output: [ 0.1594 0.6235 0.1912 0.0005033 -0.0002256 0.8685 0.000378 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1001 Epoch 856 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02172 1.029 0.9715 -0.0002429 0.000109 -0.0449 -0.0001827 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04147 -0.00318 0.02179 0.02388 0.9132 0.9266 0.07808 0.8413 0.8768 0.1692 ] Network output: [ 0.9215 0.1644 -0.08281 -0.0003123 0.0001406 0.07408 -0.0002371 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 -0.02857 -0.08481 0.311 0.955 0.977 0.7264 0.8657 0.9487 0.7294 ] Network output: [ -0.004827 0.9463 1.035 7.11e-06 -3.355e-06 0.02884 6.031e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07105 0.03452 0.04996 0.046 0.9733 0.9805 0.07272 0.9395 0.9656 0.0844 ] Network output: [ 0.1332 -0.3357 1.167 0.001021 -0.0004585 0.9063 0.0007703 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7236 0.3699 0.3354 0.5252 0.9598 0.9802 0.7272 0.8775 0.9559 0.7322 ] Network output: [ -0.07677 0.2567 0.8743 0.001005 -0.0004513 1.027 0.0007578 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6591 0.5872 0.3296 0.2112 0.9773 0.9846 0.6597 0.9487 0.9693 0.3826 ] Network output: [ -0.1558 0.3177 0.8449 -0.002017 0.0009053 1.141 -0.00152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7006 0.6847 0.4209 0.1151 0.9737 0.9819 0.7007 0.9411 0.9634 0.4361 ] Network output: [ 0.1592 0.624 0.1906 0.0004947 -0.0002218 0.8691 0.0003715 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0999 Epoch 857 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02187 1.029 0.9716 -0.0002381 0.0001068 -0.04501 -0.000179 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04144 -0.003166 0.02178 0.0239 0.9133 0.9266 0.07804 0.8414 0.8768 0.1691 ] Network output: [ 0.9216 0.1644 -0.08279 -0.0003206 0.0001443 0.07384 -0.0002433 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 -0.02815 -0.08471 0.3109 0.955 0.977 0.7264 0.8657 0.9487 0.7292 ] Network output: [ -0.004743 0.9461 1.035 1.146e-05 -5.306e-06 0.0288 9.3e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.071 0.03456 0.04998 0.04602 0.9733 0.9805 0.07268 0.9396 0.9656 0.08442 ] Network output: [ 0.1331 -0.3357 1.167 0.001017 -0.0004568 0.9067 0.0007676 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7237 0.3707 0.3355 0.5251 0.9599 0.9802 0.7273 0.8776 0.9559 0.732 ] Network output: [ -0.07675 0.2572 0.8742 0.001007 -0.000452 1.026 0.0007591 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6587 0.587 0.3296 0.2113 0.9773 0.9846 0.6593 0.9487 0.9694 0.3825 ] Network output: [ -0.1557 0.3176 0.8453 -0.002009 0.0009018 1.14 -0.001514 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.7002 0.6844 0.4208 0.1156 0.9737 0.9819 0.7003 0.9411 0.9635 0.436 ] Network output: [ 0.1589 0.6245 0.1899 0.0004856 -0.0002177 0.8697 0.0003647 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09974 Epoch 858 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02201 1.028 0.9717 -0.0002331 0.0001046 -0.0451 -0.0001753 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0414 -0.003151 0.02178 0.02392 0.9133 0.9266 0.078 0.8415 0.8768 0.1691 ] Network output: [ 0.9216 0.1643 -0.08256 -0.0003288 0.000148 0.07368 -0.0002495 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 -0.0277 -0.08455 0.3109 0.955 0.977 0.7265 0.8658 0.9488 0.7291 ] Network output: [ -0.00466 0.9458 1.035 1.598e-05 -7.332e-06 0.02877 1.27e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07096 0.0346 0.05001 0.04605 0.9733 0.9805 0.07264 0.9396 0.9657 0.08443 ] Network output: [ 0.1329 -0.3358 1.167 0.001013 -0.0004552 0.9071 0.0007648 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7238 0.3716 0.3358 0.525 0.9599 0.9802 0.7273 0.8776 0.9559 0.7318 ] Network output: [ -0.07672 0.2576 0.8741 0.001009 -0.000453 1.026 0.0007607 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6583 0.5869 0.3296 0.2114 0.9773 0.9846 0.6589 0.9487 0.9694 0.3824 ] Network output: [ -0.1556 0.3174 0.8456 -0.002001 0.0008981 1.14 -0.001507 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6998 0.684 0.4208 0.116 0.9737 0.9819 0.6999 0.9411 0.9635 0.436 ] Network output: [ 0.1587 0.625 0.1893 0.0004767 -0.0002137 0.8703 0.000358 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09958 Epoch 859 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02216 1.028 0.9719 -0.0002279 0.0001022 -0.0452 -0.0001713 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04136 -0.003138 0.02177 0.02395 0.9133 0.9266 0.07797 0.8416 0.8769 0.1691 ] Network output: [ 0.9218 0.1642 -0.08253 -0.000336 0.0001512 0.0734 -0.0002548 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 -0.02731 -0.08446 0.3109 0.955 0.977 0.7266 0.8659 0.9488 0.7289 ] Network output: [ -0.004578 0.9455 1.035 2.056e-05 -9.386e-06 0.02874 1.614e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07092 0.03463 0.05003 0.04607 0.9733 0.9805 0.0726 0.9397 0.9657 0.08445 ] Network output: [ 0.1328 -0.3359 1.167 0.00101 -0.0004539 0.9075 0.0007626 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7239 0.3724 0.3359 0.5249 0.9599 0.9802 0.7274 0.8777 0.956 0.7317 ] Network output: [ -0.07673 0.258 0.8742 0.001011 -0.0004541 1.025 0.0007625 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6579 0.5868 0.3295 0.2115 0.9773 0.9846 0.6585 0.9488 0.9694 0.3823 ] Network output: [ -0.1556 0.3171 0.846 -0.001991 0.0008939 1.14 -0.0015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6994 0.6837 0.4208 0.1165 0.9737 0.9819 0.6995 0.9411 0.9635 0.436 ] Network output: [ 0.1584 0.6255 0.1887 0.000468 -0.0002098 0.8709 0.0003515 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09941 Epoch 860 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02231 1.028 0.972 -0.0002231 0.0001001 -0.0453 -0.0001677 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04133 -0.003125 0.02177 0.02397 0.9133 0.9266 0.07793 0.8416 0.8769 0.1691 ] Network output: [ 0.9219 0.1641 -0.08249 -0.0003442 0.0001549 0.07316 -0.000261 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6406 -0.02689 -0.08433 0.3108 0.955 0.977 0.7267 0.8659 0.9488 0.7287 ] Network output: [ -0.004495 0.9453 1.035 2.486e-05 -1.132e-05 0.02871 1.937e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07088 0.03467 0.05005 0.04609 0.9733 0.9805 0.07256 0.9397 0.9657 0.08447 ] Network output: [ 0.1326 -0.3359 1.167 0.001006 -0.0004519 0.908 0.0007593 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.724 0.3733 0.3361 0.5248 0.9599 0.9802 0.7275 0.8778 0.956 0.7315 ] Network output: [ -0.07671 0.2585 0.8741 0.001013 -0.0004549 1.025 0.0007638 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6575 0.5866 0.3295 0.2116 0.9773 0.9846 0.6581 0.9488 0.9694 0.3822 ] Network output: [ -0.1555 0.317 0.8464 -0.001983 0.0008904 1.14 -0.001494 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.699 0.6833 0.4208 0.117 0.9737 0.9819 0.6991 0.9412 0.9635 0.4359 ] Network output: [ 0.1582 0.626 0.1881 0.0004588 -0.0002057 0.8715 0.0003446 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09924 Epoch 861 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02245 1.027 0.9721 -0.0002181 9.781e-05 -0.04539 -0.000164 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04129 -0.00311 0.02177 0.02399 0.9133 0.9267 0.07789 0.8417 0.877 0.1691 ] Network output: [ 0.9219 0.164 -0.08223 -0.0003522 0.0001585 0.07301 -0.000267 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6406 -0.02645 -0.08414 0.3108 0.9551 0.977 0.7268 0.866 0.9488 0.7286 ] Network output: [ -0.004414 0.945 1.035 2.94e-05 -1.335e-05 0.02869 2.279e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07084 0.03471 0.05009 0.04612 0.9733 0.9805 0.07252 0.9398 0.9657 0.08449 ] Network output: [ 0.1325 -0.336 1.167 0.001002 -0.0004502 0.9084 0.0007564 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7241 0.3741 0.3364 0.5248 0.9599 0.9802 0.7276 0.8778 0.956 0.7313 ] Network output: [ -0.07668 0.2589 0.8741 0.001015 -0.000456 1.025 0.0007657 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6571 0.5865 0.3295 0.2117 0.9773 0.9846 0.6577 0.9489 0.9695 0.3821 ] Network output: [ -0.1554 0.3168 0.8467 -0.001975 0.0008865 1.139 -0.001488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6986 0.683 0.4207 0.1174 0.9737 0.9819 0.6987 0.9412 0.9635 0.4359 ] Network output: [ 0.1579 0.6265 0.1874 0.0004499 -0.0002017 0.8721 0.0003379 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09908 Epoch 862 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02261 1.027 0.9723 -0.0002128 9.545e-05 -0.0455 -0.00016 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04125 -0.003099 0.02177 0.02402 0.9133 0.9267 0.07786 0.8418 0.877 0.1691 ] Network output: [ 0.9221 0.1639 -0.08224 -0.000359 0.0001615 0.0727 -0.0002721 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6406 -0.02607 -0.08404 0.3107 0.9551 0.977 0.7269 0.8661 0.9489 0.7284 ] Network output: [ -0.004333 0.9448 1.035 3.394e-05 -1.539e-05 0.02866 2.62e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0708 0.03475 0.05011 0.04615 0.9733 0.9805 0.07248 0.9398 0.9658 0.08451 ] Network output: [ 0.1324 -0.3361 1.167 0.0009988 -0.0004487 0.9088 0.0007538 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7241 0.3749 0.3366 0.5247 0.9599 0.9802 0.7277 0.8779 0.956 0.7311 ] Network output: [ -0.07669 0.2592 0.8742 0.001018 -0.0004572 1.024 0.0007676 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6567 0.5864 0.3295 0.2118 0.9774 0.9847 0.6573 0.9489 0.9695 0.382 ] Network output: [ -0.1553 0.3165 0.8471 -0.001965 0.0008822 1.139 -0.001481 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6982 0.6827 0.4207 0.118 0.9737 0.9819 0.6983 0.9412 0.9635 0.4358 ] Network output: [ 0.1576 0.627 0.1868 0.0004411 -0.0001977 0.8727 0.0003312 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09891 Epoch 863 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02275 1.027 0.9724 -0.0002081 9.333e-05 -0.04559 -0.0001564 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04122 -0.003085 0.02176 0.02404 0.9134 0.9267 0.07782 0.8419 0.8771 0.1691 ] Network output: [ 0.9222 0.1639 -0.08217 -0.0003672 0.0001652 0.07247 -0.0002783 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6406 -0.02565 -0.08387 0.3106 0.9551 0.977 0.727 0.8661 0.9489 0.7282 ] Network output: [ -0.004252 0.9445 1.035 3.818e-05 -1.729e-05 0.02864 2.939e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07076 0.03479 0.05014 0.04617 0.9733 0.9805 0.07244 0.9399 0.9658 0.08453 ] Network output: [ 0.1322 -0.336 1.166 0.0009939 -0.0004464 0.9093 0.0007501 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7242 0.3758 0.3369 0.5246 0.9599 0.9802 0.7278 0.878 0.9561 0.7309 ] Network output: [ -0.07666 0.2597 0.8741 0.00102 -0.000458 1.024 0.0007691 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6563 0.5862 0.3295 0.2119 0.9774 0.9847 0.6569 0.949 0.9695 0.382 ] Network output: [ -0.1552 0.3164 0.8474 -0.001957 0.0008786 1.139 -0.001475 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6978 0.6823 0.4207 0.1184 0.9737 0.9819 0.6979 0.9412 0.9636 0.4358 ] Network output: [ 0.1574 0.6275 0.1862 0.0004316 -0.0001935 0.8733 0.0003241 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09875 Epoch 864 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02289 1.027 0.9725 -0.000203 9.105e-05 -0.04568 -0.0001526 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04118 -0.003071 0.02177 0.02406 0.9134 0.9267 0.07779 0.8419 0.8771 0.1691 ] Network output: [ 0.9222 0.1637 -0.08188 -0.0003748 0.0001686 0.07231 -0.000284 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6406 -0.02521 -0.08365 0.3106 0.9551 0.977 0.7271 0.8662 0.9489 0.728 ] Network output: [ -0.004173 0.9443 1.036 4.274e-05 -1.933e-05 0.02862 3.282e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07072 0.03483 0.05018 0.04619 0.9733 0.9806 0.0724 0.9399 0.9658 0.08456 ] Network output: [ 0.132 -0.3361 1.166 0.0009897 -0.0004446 0.9097 0.0007469 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7243 0.3766 0.3371 0.5245 0.9599 0.9802 0.7279 0.878 0.9561 0.7307 ] Network output: [ -0.07663 0.2601 0.8741 0.001023 -0.0004593 1.023 0.0007712 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6559 0.5861 0.3295 0.212 0.9774 0.9847 0.6565 0.949 0.9695 0.3819 ] Network output: [ -0.1551 0.3162 0.8477 -0.001948 0.0008745 1.138 -0.001468 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6974 0.682 0.4207 0.1189 0.9737 0.9819 0.6975 0.9412 0.9636 0.4357 ] Network output: [ 0.1571 0.628 0.1856 0.0004227 -0.0001895 0.8739 0.0003174 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09858 Epoch 865 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02305 1.026 0.9726 -0.0001978 8.87e-05 -0.04578 -0.0001487 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04115 -0.00306 0.02176 0.02409 0.9134 0.9267 0.07776 0.842 0.8772 0.1691 ] Network output: [ 0.9224 0.1636 -0.08194 -0.0003814 0.0001716 0.07198 -0.0002889 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6406 -0.02485 -0.08354 0.3105 0.9551 0.9771 0.7272 0.8663 0.949 0.7278 ] Network output: [ -0.004093 0.944 1.036 4.721e-05 -2.134e-05 0.0286 3.617e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07068 0.03486 0.05021 0.04622 0.9733 0.9806 0.07236 0.94 0.9658 0.08458 ] Network output: [ 0.1319 -0.3362 1.166 0.0009859 -0.0004429 0.9101 0.0007441 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7244 0.3774 0.3374 0.5244 0.96 0.9802 0.728 0.8781 0.9561 0.7305 ] Network output: [ -0.07665 0.2604 0.8741 0.001026 -0.0004605 1.023 0.0007732 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6554 0.5859 0.3295 0.2121 0.9774 0.9847 0.6561 0.949 0.9696 0.3818 ] Network output: [ -0.1551 0.3159 0.8482 -0.001938 0.0008702 1.138 -0.001461 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.697 0.6816 0.4207 0.1194 0.9737 0.9819 0.6971 0.9413 0.9636 0.4357 ] Network output: [ 0.1568 0.6285 0.185 0.0004137 -0.0001855 0.8745 0.0003107 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0984 Epoch 866 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02319 1.026 0.9728 -0.0001931 8.661e-05 -0.04587 -0.0001452 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04111 -0.003046 0.02177 0.0241 0.9134 0.9267 0.07772 0.8421 0.8772 0.1691 ] Network output: [ 0.9224 0.1636 -0.08182 -0.0003897 0.0001753 0.07177 -0.0002951 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6407 -0.02442 -0.08333 0.3104 0.9551 0.9771 0.7273 0.8663 0.949 0.7277 ] Network output: [ -0.004014 0.9438 1.036 5.139e-05 -2.321e-05 0.02858 3.932e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07064 0.0349 0.05025 0.04624 0.9734 0.9806 0.07233 0.94 0.9659 0.0846 ] Network output: [ 0.1317 -0.3362 1.166 0.0009803 -0.0004404 0.9105 0.0007398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7245 0.3783 0.3377 0.5242 0.96 0.9802 0.7281 0.8782 0.9562 0.7303 ] Network output: [ -0.07661 0.2609 0.8741 0.001028 -0.0004614 1.022 0.0007747 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6551 0.5858 0.3296 0.2122 0.9774 0.9847 0.6557 0.9491 0.9696 0.3817 ] Network output: [ -0.155 0.3158 0.8484 -0.001931 0.0008666 1.138 -0.001455 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6966 0.6813 0.4207 0.1198 0.9737 0.9819 0.6967 0.9413 0.9636 0.4357 ] Network output: [ 0.1565 0.629 0.1844 0.0004041 -0.0001811 0.8751 0.0003034 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09825 Epoch 867 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02333 1.026 0.9729 -0.000188 8.429e-05 -0.04596 -0.0001413 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04107 -0.003033 0.02178 0.02413 0.9134 0.9267 0.07769 0.8422 0.8773 0.1691 ] Network output: [ 0.9225 0.1634 -0.08153 -0.0003968 0.0001785 0.0716 -0.0003005 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6407 -0.024 -0.08309 0.3104 0.9551 0.9771 0.7274 0.8664 0.949 0.7275 ] Network output: [ -0.003936 0.9435 1.036 5.596e-05 -2.526e-05 0.02856 4.276e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07061 0.03494 0.05029 0.04627 0.9734 0.9806 0.07229 0.9401 0.9659 0.08463 ] Network output: [ 0.1316 -0.3363 1.166 0.000976 -0.0004384 0.911 0.0007366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7246 0.3791 0.338 0.5242 0.96 0.9802 0.7282 0.8782 0.9562 0.7301 ] Network output: [ -0.0766 0.2612 0.8741 0.001031 -0.0004628 1.022 0.0007771 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6547 0.5857 0.3296 0.2123 0.9774 0.9847 0.6553 0.9491 0.9696 0.3817 ] Network output: [ -0.1549 0.3156 0.8487 -0.001921 0.0008623 1.138 -0.001447 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6962 0.681 0.4207 0.1203 0.9737 0.9819 0.6963 0.9413 0.9636 0.4356 ] Network output: [ 0.1563 0.6295 0.1838 0.0003951 -0.0001771 0.8757 0.0002967 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09807 Epoch 868 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02349 1.025 0.973 -0.0001828 8.197e-05 -0.04606 -0.0001374 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04104 -0.003023 0.02177 0.02415 0.9135 0.9268 0.07765 0.8422 0.8773 0.1691 ] Network output: [ 0.9227 0.1633 -0.08163 -0.0004031 0.0001813 0.07124 -0.0003052 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6407 -0.02365 -0.08297 0.3103 0.9552 0.9771 0.7275 0.8665 0.9491 0.7273 ] Network output: [ -0.003859 0.9433 1.036 6.033e-05 -2.722e-05 0.02854 4.604e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07057 0.03497 0.05032 0.0463 0.9734 0.9806 0.07225 0.9401 0.9659 0.08466 ] Network output: [ 0.1315 -0.3364 1.166 0.0009718 -0.0004365 0.9114 0.0007334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7247 0.3799 0.3383 0.5241 0.96 0.9802 0.7283 0.8783 0.9562 0.73 ] Network output: [ -0.07662 0.2616 0.8742 0.001034 -0.0004641 1.022 0.0007792 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6542 0.5855 0.3296 0.2125 0.9774 0.9847 0.6549 0.9492 0.9696 0.3816 ] Network output: [ -0.1548 0.3153 0.8492 -0.001911 0.0008579 1.137 -0.00144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6958 0.6806 0.4207 0.1208 0.9737 0.9819 0.6959 0.9413 0.9636 0.4356 ] Network output: [ 0.156 0.6301 0.1832 0.0003859 -0.000173 0.8764 0.0002898 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0979 Epoch 869 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02363 1.025 0.9731 -0.0001782 7.992e-05 -0.04615 -0.000134 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.041 -0.003009 0.02178 0.02417 0.9135 0.9268 0.07762 0.8423 0.8774 0.1691 ] Network output: [ 0.9227 0.1632 -0.08146 -0.0004114 0.000185 0.07106 -0.0003114 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6407 -0.02321 -0.08271 0.3102 0.9552 0.9771 0.7276 0.8665 0.9491 0.7271 ] Network output: [ -0.003781 0.943 1.036 6.445e-05 -2.907e-05 0.02853 4.914e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07053 0.03502 0.05036 0.04632 0.9734 0.9806 0.07222 0.9402 0.9659 0.08469 ] Network output: [ 0.1313 -0.3364 1.166 0.0009657 -0.0004338 0.9118 0.0007287 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7248 0.3807 0.3386 0.5239 0.96 0.9803 0.7284 0.8784 0.9562 0.7298 ] Network output: [ -0.07657 0.262 0.8741 0.001036 -0.000465 1.021 0.0007808 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6539 0.5854 0.3297 0.2125 0.9774 0.9847 0.6545 0.9492 0.9697 0.3816 ] Network output: [ -0.1547 0.3152 0.8494 -0.001903 0.0008543 1.137 -0.001434 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6954 0.6803 0.4207 0.1212 0.9737 0.9819 0.6955 0.9413 0.9637 0.4356 ] Network output: [ 0.1557 0.6306 0.1826 0.0003761 -0.0001686 0.877 0.0002824 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09774 Epoch 870 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02377 1.025 0.9733 -0.000173 7.756e-05 -0.04623 -0.00013 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04097 -0.002997 0.02179 0.0242 0.9135 0.9268 0.07759 0.8424 0.8774 0.1691 ] Network output: [ 0.9228 0.163 -0.08117 -0.0004179 0.000188 0.07087 -0.0003164 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6407 -0.0228 -0.08245 0.3102 0.9552 0.9771 0.7277 0.8666 0.9491 0.727 ] Network output: [ -0.003706 0.9428 1.036 6.902e-05 -3.112e-05 0.02852 5.257e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07049 0.03505 0.05041 0.04635 0.9734 0.9806 0.07218 0.9403 0.966 0.08472 ] Network output: [ 0.1311 -0.3365 1.166 0.0009613 -0.0004318 0.9123 0.0007255 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7249 0.3816 0.339 0.5238 0.96 0.9803 0.7285 0.8784 0.9563 0.7296 ] Network output: [ -0.07656 0.2623 0.8741 0.001039 -0.0004666 1.021 0.0007835 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6535 0.5852 0.3297 0.2127 0.9774 0.9847 0.6541 0.9493 0.9697 0.3815 ] Network output: [ -0.1546 0.315 0.8497 -0.001893 0.0008497 1.137 -0.001426 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.695 0.68 0.4207 0.1217 0.9737 0.9819 0.6952 0.9414 0.9637 0.4355 ] Network output: [ 0.1554 0.6311 0.182 0.0003671 -0.0001646 0.8776 0.0002756 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09757 Epoch 871 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02394 1.024 0.9734 -0.0001679 7.529e-05 -0.04634 -0.0001262 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04093 -0.002987 0.02178 0.02421 0.9135 0.9268 0.07755 0.8424 0.8774 0.1692 ] Network output: [ 0.9231 0.163 -0.08132 -0.0004241 0.0001907 0.07049 -0.000321 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6408 -0.02246 -0.08232 0.3101 0.9552 0.9771 0.7278 0.8667 0.9492 0.7268 ] Network output: [ -0.003629 0.9425 1.037 7.326e-05 -3.302e-05 0.0285 5.576e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07045 0.03509 0.05044 0.04637 0.9734 0.9806 0.07214 0.9403 0.966 0.08475 ] Network output: [ 0.131 -0.3365 1.166 0.0009565 -0.0004297 0.9126 0.0007219 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.725 0.3823 0.3393 0.5237 0.96 0.9803 0.7286 0.8785 0.9563 0.7294 ] Network output: [ -0.07658 0.2627 0.8742 0.001042 -0.0004679 1.02 0.0007856 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6531 0.5851 0.3298 0.2128 0.9774 0.9847 0.6537 0.9493 0.9697 0.3815 ] Network output: [ -0.1545 0.3148 0.8501 -0.001883 0.0008453 1.136 -0.001419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6946 0.6796 0.4207 0.1222 0.9737 0.9819 0.6947 0.9414 0.9637 0.4355 ] Network output: [ 0.1551 0.6316 0.1815 0.0003577 -0.0001603 0.8782 0.0002685 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09739 Epoch 872 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02406 1.024 0.9735 -0.0001634 7.327e-05 -0.04641 -0.0001228 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0409 -0.002973 0.0218 0.02423 0.9135 0.9268 0.07752 0.8425 0.8775 0.1692 ] Network output: [ 0.923 0.1629 -0.08107 -0.0004323 0.0001944 0.07034 -0.0003272 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6408 -0.02202 -0.08201 0.31 0.9552 0.9771 0.7279 0.8667 0.9492 0.7266 ] Network output: [ -0.003553 0.9423 1.037 7.732e-05 -3.484e-05 0.02849 5.881e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07042 0.03513 0.05049 0.0464 0.9734 0.9806 0.07211 0.9404 0.966 0.08478 ] Network output: [ 0.1308 -0.3365 1.166 0.00095 -0.0004267 0.9131 0.0007169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7251 0.3832 0.3397 0.5235 0.96 0.9803 0.7287 0.8786 0.9563 0.7292 ] Network output: [ -0.07652 0.2631 0.8741 0.001044 -0.0004689 1.02 0.0007874 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6527 0.585 0.3298 0.2129 0.9774 0.9847 0.6533 0.9493 0.9697 0.3814 ] Network output: [ -0.1544 0.3147 0.8502 -0.001875 0.0008417 1.136 -0.001413 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6942 0.6793 0.4207 0.1225 0.9737 0.9819 0.6944 0.9414 0.9637 0.4355 ] Network output: [ 0.1548 0.6322 0.1808 0.0003477 -0.0001558 0.8788 0.000261 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09724 Epoch 873 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02421 1.024 0.9736 -0.0001581 7.088e-05 -0.0465 -0.0001188 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04086 -0.002961 0.02181 0.02426 0.9135 0.9268 0.07749 0.8426 0.8775 0.1692 ] Network output: [ 0.9231 0.1627 -0.08081 -0.0004383 0.0001971 0.07012 -0.0003316 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6408 -0.02162 -0.08175 0.3099 0.9552 0.9771 0.728 0.8668 0.9492 0.7264 ] Network output: [ -0.00348 0.942 1.037 8.188e-05 -3.689e-05 0.02848 6.224e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07038 0.03517 0.05054 0.04643 0.9734 0.9806 0.07207 0.9404 0.966 0.08482 ] Network output: [ 0.1307 -0.3366 1.166 0.0009457 -0.0004248 0.9135 0.0007137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7252 0.384 0.34 0.5234 0.9601 0.9803 0.7288 0.8786 0.9563 0.729 ] Network output: [ -0.07653 0.2634 0.8742 0.001048 -0.0004707 1.02 0.0007903 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6523 0.5848 0.3299 0.213 0.9774 0.9847 0.6529 0.9494 0.9698 0.3814 ] Network output: [ -0.1543 0.3144 0.8507 -0.001864 0.0008369 1.136 -0.001405 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6938 0.679 0.4207 0.123 0.9737 0.9819 0.694 0.9414 0.9637 0.4355 ] Network output: [ 0.1545 0.6327 0.1803 0.0003387 -0.0001518 0.8794 0.0002542 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09706 Epoch 874 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02437 1.024 0.9737 -0.0001532 6.868e-05 -0.0466 -0.0001151 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04083 -0.002952 0.02181 0.02428 0.9136 0.9268 0.07746 0.8427 0.8776 0.1692 ] Network output: [ 0.9234 0.1626 -0.08099 -0.0004444 0.0001998 0.06972 -0.0003363 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6409 -0.02129 -0.0816 0.3098 0.9552 0.9771 0.7282 0.8669 0.9492 0.7262 ] Network output: [ -0.003405 0.9418 1.037 8.595e-05 -3.872e-05 0.02847 6.531e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07035 0.0352 0.05058 0.04645 0.9734 0.9806 0.07204 0.9405 0.9661 0.08485 ] Network output: [ 0.1306 -0.3366 1.165 0.0009403 -0.0004224 0.9139 0.0007096 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7253 0.3848 0.3404 0.5233 0.9601 0.9803 0.7289 0.8787 0.9564 0.7288 ] Network output: [ -0.07655 0.2638 0.8743 0.001051 -0.0004719 1.019 0.0007924 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6519 0.5847 0.3299 0.2131 0.9775 0.9847 0.6525 0.9494 0.9698 0.3813 ] Network output: [ -0.1542 0.3142 0.851 -0.001855 0.0008326 1.136 -0.001398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6934 0.6786 0.4207 0.1235 0.9737 0.9819 0.6936 0.9414 0.9637 0.4355 ] Network output: [ 0.1542 0.6332 0.1797 0.000329 -0.0001474 0.88 0.0002469 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09688 Epoch 875 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0245 1.023 0.9738 -0.0001487 6.668e-05 -0.04667 -0.0001118 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0408 -0.002938 0.02183 0.02429 0.9136 0.9269 0.07743 0.8427 0.8776 0.1692 ] Network output: [ 0.9234 0.1625 -0.08065 -0.0004525 0.0002035 0.06962 -0.0003423 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6409 -0.02083 -0.08124 0.3097 0.9553 0.9771 0.7283 0.8669 0.9493 0.726 ] Network output: [ -0.003331 0.9415 1.037 8.997e-05 -4.052e-05 0.02847 6.833e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07031 0.03524 0.05064 0.04648 0.9734 0.9806 0.072 0.9405 0.9661 0.08489 ] Network output: [ 0.1303 -0.3366 1.165 0.0009334 -0.0004193 0.9144 0.0007044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7254 0.3857 0.3408 0.5231 0.9601 0.9803 0.7291 0.8788 0.9564 0.7286 ] Network output: [ -0.07648 0.2642 0.8742 0.001054 -0.0004731 1.019 0.0007944 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6515 0.5845 0.33 0.2132 0.9775 0.9847 0.6521 0.9495 0.9698 0.3813 ] Network output: [ -0.154 0.3142 0.8511 -0.001847 0.0008289 1.135 -0.001391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6931 0.6783 0.4207 0.1239 0.9737 0.9819 0.6932 0.9415 0.9638 0.4354 ] Network output: [ 0.1539 0.6338 0.1791 0.0003188 -0.0001429 0.8806 0.0002393 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09673 Epoch 876 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02464 1.023 0.974 -0.0001433 6.426e-05 -0.04676 -0.0001077 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04076 -0.002928 0.02183 0.02432 0.9136 0.9269 0.07739 0.8428 0.8777 0.1692 ] Network output: [ 0.9235 0.1623 -0.08045 -0.0004577 0.0002058 0.06935 -0.0003463 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6409 -0.02046 -0.08099 0.3097 0.9553 0.9772 0.7284 0.867 0.9493 0.7259 ] Network output: [ -0.003261 0.9412 1.037 9.45e-05 -4.255e-05 0.02846 7.173e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07028 0.03528 0.05069 0.04651 0.9735 0.9806 0.07197 0.9406 0.9661 0.08493 ] Network output: [ 0.1302 -0.3368 1.165 0.0009292 -0.0004174 0.9148 0.0007012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7255 0.3864 0.3412 0.523 0.9601 0.9803 0.7292 0.8788 0.9564 0.7284 ] Network output: [ -0.0765 0.2645 0.8744 0.001058 -0.000475 1.018 0.0007976 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6511 0.5844 0.3301 0.2134 0.9775 0.9847 0.6517 0.9495 0.9698 0.3813 ] Network output: [ -0.1539 0.3139 0.8515 -0.001835 0.0008238 1.135 -0.001383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6927 0.678 0.4207 0.1244 0.9737 0.9819 0.6928 0.9415 0.9638 0.4354 ] Network output: [ 0.1536 0.6343 0.1785 0.0003098 -0.0001388 0.8812 0.0002325 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09654 Epoch 877 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02481 1.023 0.974 -0.0001386 6.214e-05 -0.04686 -0.0001041 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04073 -0.002918 0.02183 0.02433 0.9136 0.9269 0.07736 0.8429 0.8777 0.1692 ] Network output: [ 0.9238 0.1622 -0.08065 -0.0004639 0.0002086 0.06895 -0.0003509 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.641 -0.02013 -0.08081 0.3095 0.9553 0.9772 0.7285 0.8671 0.9493 0.7257 ] Network output: [ -0.003187 0.941 1.037 9.839e-05 -4.429e-05 0.02845 7.466e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07024 0.03531 0.05073 0.04653 0.9735 0.9806 0.07193 0.9406 0.9661 0.08496 ] Network output: [ 0.1301 -0.3367 1.165 0.0009232 -0.0004147 0.9152 0.0006967 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7256 0.3872 0.3416 0.5228 0.9601 0.9803 0.7293 0.8789 0.9565 0.7282 ] Network output: [ -0.07651 0.2648 0.8744 0.001061 -0.0004762 1.018 0.0007996 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6507 0.5842 0.3302 0.2135 0.9775 0.9847 0.6514 0.9496 0.9699 0.3813 ] Network output: [ -0.1539 0.3137 0.8519 -0.001826 0.0008197 1.135 -0.001376 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6923 0.6777 0.4207 0.1249 0.9737 0.9819 0.6924 0.9415 0.9638 0.4354 ] Network output: [ 0.1533 0.6349 0.178 0.0002997 -0.0001343 0.8818 0.0002249 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09637 Epoch 878 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02492 1.022 0.9742 -0.0001342 6.016e-05 -0.04692 -0.0001008 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04069 -0.002904 0.02186 0.02435 0.9136 0.9269 0.07733 0.843 0.8778 0.1692 ] Network output: [ 0.9237 0.1621 -0.08021 -0.0004718 0.0002121 0.06889 -0.0003568 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.641 -0.01966 -0.0804 0.3094 0.9553 0.9772 0.7286 0.8672 0.9494 0.7255 ] Network output: [ -0.003115 0.9408 1.037 0.0001024 -4.608e-05 0.02845 7.766e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07021 0.03535 0.0508 0.04656 0.9735 0.9807 0.0719 0.9407 0.9662 0.08501 ] Network output: [ 0.1299 -0.3367 1.165 0.0009161 -0.0004115 0.9157 0.0006913 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7258 0.3881 0.3421 0.5226 0.9601 0.9803 0.7294 0.879 0.9565 0.728 ] Network output: [ -0.07644 0.2652 0.8743 0.001064 -0.0004775 1.018 0.0008018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6504 0.5841 0.3303 0.2135 0.9775 0.9847 0.651 0.9496 0.9699 0.3812 ] Network output: [ -0.1537 0.3137 0.8519 -0.001817 0.0008159 1.134 -0.001369 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6919 0.6773 0.4207 0.1252 0.9737 0.982 0.692 0.9415 0.9638 0.4354 ] Network output: [ 0.153 0.6354 0.1774 0.0002894 -0.0001297 0.8824 0.0002172 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09622 Epoch 879 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02508 1.022 0.9743 -0.0001287 5.771e-05 -0.04701 -9.67e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04066 -0.002895 0.02187 0.02438 0.9137 0.9269 0.0773 0.843 0.8778 0.1692 ] Network output: [ 0.9239 0.1618 -0.08009 -0.0004763 0.0002141 0.06857 -0.0003602 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.641 -0.01932 -0.08016 0.3093 0.9553 0.9772 0.7287 0.8672 0.9494 0.7253 ] Network output: [ -0.003047 0.9405 1.038 0.0001068 -4.809e-05 0.02845 8.102e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07017 0.03539 0.05085 0.0466 0.9735 0.9807 0.07187 0.9408 0.9662 0.08505 ] Network output: [ 0.1298 -0.3369 1.165 0.0009121 -0.0004097 0.9161 0.0006882 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7259 0.3889 0.3424 0.5225 0.9601 0.9803 0.7295 0.879 0.9565 0.7278 ] Network output: [ -0.07647 0.2655 0.8746 0.001068 -0.0004795 1.017 0.0008052 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.65 0.584 0.3304 0.2137 0.9775 0.9847 0.6506 0.9497 0.9699 0.3812 ] Network output: [ -0.1536 0.3133 0.8524 -0.001805 0.0008105 1.134 -0.00136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6915 0.677 0.4208 0.1258 0.9737 0.982 0.6917 0.9416 0.9638 0.4354 ] Network output: [ 0.1527 0.6359 0.1768 0.0002804 -0.0001256 0.8831 0.0002104 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09603 Epoch 880 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02524 1.022 0.9743 -0.0001243 5.571e-05 -0.04711 -9.335e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04063 -0.002886 0.02187 0.02439 0.9137 0.9269 0.07727 0.8431 0.8779 0.1692 ] Network output: [ 0.9242 0.1618 -0.08029 -0.0004827 0.000217 0.06818 -0.000365 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6411 -0.01898 -0.07995 0.3092 0.9553 0.9772 0.7289 0.8673 0.9494 0.7251 ] Network output: [ -0.002975 0.9403 1.038 0.0001105 -4.974e-05 0.02844 8.378e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07014 0.03542 0.0509 0.04662 0.9735 0.9807 0.07184 0.9408 0.9662 0.08509 ] Network output: [ 0.1296 -0.3368 1.165 0.0009053 -0.0004066 0.9164 0.0006831 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.726 0.3896 0.3429 0.5223 0.9601 0.9803 0.7296 0.8791 0.9565 0.7276 ] Network output: [ -0.07647 0.2658 0.8746 0.001071 -0.0004807 1.017 0.0008071 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6496 0.5838 0.3305 0.2138 0.9775 0.9848 0.6502 0.9497 0.9699 0.3812 ] Network output: [ -0.1535 0.3132 0.8526 -0.001797 0.0008065 1.134 -0.001354 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6911 0.6767 0.4208 0.1262 0.9737 0.982 0.6913 0.9416 0.9639 0.4354 ] Network output: [ 0.1523 0.6365 0.1763 0.0002698 -0.0001209 0.8837 0.0002024 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09586 Epoch 881 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02535 1.021 0.9745 -0.0001198 5.372e-05 -0.04716 -9.001e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04059 -0.002871 0.0219 0.02441 0.9137 0.927 0.07724 0.8432 0.8779 0.1693 ] Network output: [ 0.924 0.1616 -0.07975 -0.0004902 0.0002204 0.06815 -0.0003706 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6411 -0.01851 -0.0795 0.3091 0.9553 0.9772 0.729 0.8674 0.9495 0.7249 ] Network output: [ -0.002906 0.94 1.038 0.0001145 -5.153e-05 0.02845 8.678e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07011 0.03547 0.05097 0.04664 0.9735 0.9807 0.07181 0.9409 0.9662 0.08514 ] Network output: [ 0.1294 -0.3368 1.165 0.0008982 -0.0004035 0.917 0.0006778 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7261 0.3905 0.3434 0.5221 0.9602 0.9803 0.7298 0.8792 0.9566 0.7274 ] Network output: [ -0.0764 0.2662 0.8745 0.001074 -0.0004822 1.016 0.0008096 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6492 0.5837 0.3306 0.2139 0.9775 0.9848 0.6498 0.9497 0.97 0.3812 ] Network output: [ -0.1533 0.3132 0.8527 -0.001788 0.0008025 1.133 -0.001347 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6908 0.6764 0.4208 0.1265 0.9737 0.982 0.6909 0.9416 0.9639 0.4354 ] Network output: [ 0.152 0.6371 0.1757 0.0002595 -0.0001163 0.8843 0.0001947 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0957 Epoch 882 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02551 1.021 0.9746 -0.0001143 5.126e-05 -0.04726 -8.588e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04056 -0.002863 0.02191 0.02443 0.9137 0.927 0.07721 0.8433 0.878 0.1693 ] Network output: [ 0.9243 0.1614 -0.07973 -0.0004939 0.000222 0.06776 -0.0003735 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6411 -0.0182 -0.07928 0.309 0.9554 0.9772 0.7291 0.8674 0.9495 0.7247 ] Network output: [ -0.002839 0.9397 1.038 0.0001189 -5.348e-05 0.02845 9.007e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07007 0.0355 0.05102 0.04668 0.9735 0.9807 0.07177 0.9409 0.9663 0.08518 ] Network output: [ 0.1293 -0.337 1.165 0.0008943 -0.0004017 0.9173 0.0006748 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7262 0.3913 0.3438 0.522 0.9602 0.9804 0.7299 0.8792 0.9566 0.7272 ] Network output: [ -0.07645 0.2664 0.8748 0.001079 -0.0004843 1.016 0.0008132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6488 0.5836 0.3307 0.2141 0.9775 0.9848 0.6495 0.9498 0.97 0.3812 ] Network output: [ -0.1532 0.3128 0.8532 -0.001775 0.000797 1.133 -0.001338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6904 0.676 0.4209 0.1271 0.9737 0.982 0.6905 0.9416 0.9639 0.4354 ] Network output: [ 0.1517 0.6376 0.1752 0.0002504 -0.0001122 0.8849 0.0001878 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09551 Epoch 883 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02567 1.021 0.9747 -0.0001102 4.939e-05 -0.04736 -8.274e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04053 -0.002853 0.02192 0.02444 0.9137 0.927 0.07718 0.8433 0.878 0.1693 ] Network output: [ 0.9245 0.1614 -0.0799 -0.0005006 0.000225 0.0674 -0.0003785 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6412 -0.01784 -0.07902 0.3088 0.9554 0.9772 0.7292 0.8675 0.9495 0.7245 ] Network output: [ -0.002768 0.9395 1.038 0.0001223 -5.503e-05 0.02845 9.266e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07004 0.03553 0.05107 0.0467 0.9735 0.9807 0.07174 0.941 0.9663 0.08522 ] Network output: [ 0.1292 -0.3369 1.164 0.0008867 -0.0003983 0.9177 0.0006691 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7263 0.3921 0.3443 0.5217 0.9602 0.9804 0.73 0.8793 0.9566 0.727 ] Network output: [ -0.07643 0.2668 0.8747 0.001081 -0.0004853 1.016 0.0008149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6484 0.5834 0.3308 0.2141 0.9775 0.9848 0.6491 0.9498 0.97 0.3812 ] Network output: [ -0.1531 0.3128 0.8533 -0.001767 0.0007933 1.133 -0.001332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.69 0.6757 0.4209 0.1274 0.9737 0.982 0.6901 0.9417 0.9639 0.4354 ] Network output: [ 0.1513 0.6382 0.1746 0.0002394 -0.0001073 0.8855 0.0001795 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09535 Epoch 884 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02577 1.021 0.9748 -0.0001057 4.737e-05 -0.0474 -7.936e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04049 -0.002839 0.02195 0.02447 0.9137 0.927 0.07715 0.8434 0.8781 0.1693 ] Network output: [ 0.9243 0.1611 -0.07926 -0.0005076 0.0002282 0.0674 -0.0003837 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6412 -0.01736 -0.07853 0.3087 0.9554 0.9772 0.7294 0.8676 0.9496 0.7244 ] Network output: [ -0.002702 0.9393 1.038 0.0001263 -5.683e-05 0.02846 9.568e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07001 0.03558 0.05115 0.04673 0.9735 0.9807 0.07171 0.941 0.9663 0.08528 ] Network output: [ 0.1289 -0.337 1.164 0.0008799 -0.0003952 0.9182 0.0006639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7265 0.3929 0.3448 0.5215 0.9602 0.9804 0.7301 0.8794 0.9567 0.7268 ] Network output: [ -0.07636 0.2672 0.8747 0.001085 -0.0004871 1.015 0.0008179 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6481 0.5833 0.3309 0.2142 0.9775 0.9848 0.6487 0.9499 0.97 0.3812 ] Network output: [ -0.1529 0.3127 0.8534 -0.001758 0.000789 1.133 -0.001324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6896 0.6754 0.4209 0.1278 0.9737 0.982 0.6898 0.9417 0.9639 0.4354 ] Network output: [ 0.151 0.6387 0.174 0.0002291 -0.0001027 0.8861 0.0001718 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09518 Epoch 885 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02595 1.02 0.9749 -0.0001002 4.493e-05 -0.04751 -7.525e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04046 -0.002833 0.02195 0.02449 0.9138 0.927 0.07713 0.8435 0.8781 0.1693 ] Network output: [ 0.9247 0.1609 -0.07939 -0.0005107 0.0002295 0.06693 -0.000386 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6412 -0.01709 -0.07834 0.3086 0.9554 0.9772 0.7295 0.8676 0.9496 0.7242 ] Network output: [ -0.002637 0.939 1.038 0.0001306 -5.873e-05 0.02846 9.885e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06998 0.0356 0.0512 0.04677 0.9736 0.9807 0.07168 0.9411 0.9664 0.08532 ] Network output: [ 0.1289 -0.3371 1.164 0.0008759 -0.0003934 0.9186 0.0006609 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7266 0.3936 0.3452 0.5214 0.9602 0.9804 0.7302 0.8794 0.9567 0.7266 ] Network output: [ -0.07642 0.2673 0.875 0.00109 -0.0004892 1.015 0.0008215 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6477 0.5831 0.3311 0.2144 0.9776 0.9848 0.6483 0.9499 0.9701 0.3813 ] Network output: [ -0.1528 0.3123 0.854 -0.001745 0.0007833 1.132 -0.001315 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6892 0.6751 0.421 0.1284 0.9737 0.982 0.6894 0.9417 0.964 0.4354 ] Network output: [ 0.1507 0.6393 0.1735 0.0002198 -9.848e-05 0.8867 0.0001648 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09499 Epoch 886 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02609 1.02 0.975 -9.637e-05 4.32e-05 -0.04759 -7.235e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04043 -0.002822 0.02197 0.0245 0.9138 0.927 0.0771 0.8436 0.8781 0.1693 ] Network output: [ 0.9249 0.1609 -0.07948 -0.0005178 0.0002327 0.06663 -0.0003914 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6413 -0.0167 -0.07802 0.3084 0.9554 0.9772 0.7296 0.8677 0.9496 0.724 ] Network output: [ -0.002568 0.9388 1.038 0.0001338 -6.018e-05 0.02846 0.0001013 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06995 0.03564 0.05126 0.04678 0.9736 0.9807 0.07165 0.9411 0.9664 0.08537 ] Network output: [ 0.1287 -0.337 1.164 0.0008675 -0.0003897 0.919 0.0006546 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7267 0.3945 0.3457 0.5212 0.9602 0.9804 0.7304 0.8795 0.9567 0.7264 ] Network output: [ -0.07638 0.2678 0.8749 0.001092 -0.0004902 1.015 0.0008231 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6473 0.583 0.3312 0.2145 0.9776 0.9848 0.648 0.95 0.9701 0.3813 ] Network output: [ -0.1527 0.3124 0.854 -0.001737 0.0007799 1.132 -0.001309 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6889 0.6748 0.421 0.1287 0.9737 0.982 0.689 0.9417 0.964 0.4354 ] Network output: [ 0.1503 0.6399 0.173 0.0002084 -9.333e-05 0.8873 0.0001562 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09483 Epoch 887 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02619 1.02 0.9752 -9.174e-05 4.112e-05 -0.04763 -6.887e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04039 -0.002808 0.02201 0.02452 0.9138 0.927 0.07707 0.8436 0.8782 0.1694 ] Network output: [ 0.9247 0.1606 -0.07877 -0.000524 0.0002355 0.06664 -0.000396 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6413 -0.01623 -0.0775 0.3084 0.9554 0.9773 0.7297 0.8678 0.9497 0.7238 ] Network output: [ -0.002505 0.9385 1.039 0.0001379 -6.2e-05 0.02848 0.0001043 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06992 0.03569 0.05135 0.04682 0.9736 0.9807 0.07162 0.9412 0.9664 0.08543 ] Network output: [ 0.1285 -0.3371 1.164 0.0008611 -0.0003868 0.9195 0.0006498 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7268 0.3953 0.3463 0.521 0.9602 0.9804 0.7305 0.8796 0.9567 0.7262 ] Network output: [ -0.07632 0.268 0.8749 0.001096 -0.0004923 1.014 0.0008265 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.647 0.5829 0.3313 0.2146 0.9776 0.9848 0.6476 0.95 0.9701 0.3813 ] Network output: [ -0.1525 0.3122 0.8541 -0.001727 0.0007752 1.132 -0.001301 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6885 0.6745 0.4211 0.1291 0.9737 0.982 0.6887 0.9418 0.964 0.4354 ] Network output: [ 0.15 0.6404 0.1724 0.0001982 -8.879e-05 0.8879 0.0001485 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09466 Epoch 888 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02638 1.019 0.9752 -8.64e-05 3.872e-05 -0.04775 -6.485e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04036 -0.002803 0.02201 0.02454 0.9138 0.9271 0.07704 0.8437 0.8782 0.1694 ] Network output: [ 0.9252 0.1604 -0.07906 -0.0005265 0.0002367 0.06609 -0.0003979 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6414 -0.01599 -0.07735 0.3082 0.9554 0.9773 0.7299 0.8678 0.9497 0.7236 ] Network output: [ -0.002442 0.9382 1.039 0.0001419 -6.38e-05 0.02848 0.0001074 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06988 0.03571 0.0514 0.04685 0.9736 0.9807 0.07159 0.9413 0.9664 0.08548 ] Network output: [ 0.1284 -0.3372 1.164 0.000857 -0.0003849 0.9198 0.0006467 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7269 0.396 0.3467 0.5209 0.9603 0.9804 0.7306 0.8796 0.9568 0.726 ] Network output: [ -0.0764 0.2682 0.8753 0.001101 -0.0004944 1.014 0.00083 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6466 0.5827 0.3315 0.2148 0.9776 0.9848 0.6472 0.9501 0.9701 0.3813 ] Network output: [ -0.1524 0.3119 0.8547 -0.001714 0.0007695 1.131 -0.001292 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6881 0.6741 0.4211 0.1297 0.9737 0.982 0.6883 0.9418 0.964 0.4354 ] Network output: [ 0.1497 0.641 0.1719 0.0001887 -8.45e-05 0.8885 0.0001414 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09446 Epoch 889 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02651 1.019 0.9752 -8.286e-05 3.714e-05 -0.04782 -6.218e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04033 -0.002791 0.02203 0.02454 0.9138 0.9271 0.07701 0.8438 0.8783 0.1694 ] Network output: [ 0.9253 0.1604 -0.07902 -0.0005341 0.00024 0.06587 -0.0004036 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6414 -0.01557 -0.07696 0.308 0.9555 0.9773 0.73 0.8679 0.9497 0.7234 ] Network output: [ -0.002374 0.938 1.039 0.0001449 -6.516e-05 0.02849 0.0001096 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06985 0.03576 0.05146 0.04687 0.9736 0.9807 0.07156 0.9413 0.9665 0.08552 ] Network output: [ 0.1282 -0.337 1.164 0.0008479 -0.0003808 0.9203 0.0006398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.727 0.3969 0.3473 0.5205 0.9603 0.9804 0.7307 0.8797 0.9568 0.7258 ] Network output: [ -0.07633 0.2687 0.8751 0.001103 -0.0004953 1.013 0.0008316 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6462 0.5826 0.3316 0.2148 0.9776 0.9848 0.6469 0.9501 0.9702 0.3813 ] Network output: [ -0.1522 0.312 0.8546 -0.001707 0.0007664 1.131 -0.001286 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6878 0.6738 0.4212 0.1299 0.9737 0.982 0.6879 0.9418 0.964 0.4354 ] Network output: [ 0.1493 0.6416 0.1713 0.0001767 -7.913e-05 0.8891 0.0001323 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09431 Epoch 890 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02661 1.019 0.9755 -7.805e-05 3.497e-05 -0.04786 -5.856e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04029 -0.002778 0.02207 0.02457 0.9139 0.9271 0.07698 0.8438 0.8783 0.1694 ] Network output: [ 0.9251 0.16 -0.07828 -0.0005393 0.0002424 0.06586 -0.0004075 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6414 -0.01512 -0.07643 0.308 0.9555 0.9773 0.7301 0.868 0.9498 0.7233 ] Network output: [ -0.002315 0.9377 1.039 0.000149 -6.7e-05 0.02851 0.0001127 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06983 0.0358 0.05155 0.04691 0.9736 0.9808 0.07153 0.9414 0.9665 0.08559 ] Network output: [ 0.128 -0.3372 1.164 0.0008422 -0.0003783 0.9208 0.0006354 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7272 0.3977 0.3479 0.5204 0.9603 0.9804 0.7309 0.8798 0.9568 0.7256 ] Network output: [ -0.07628 0.2689 0.8752 0.001108 -0.0004976 1.013 0.0008355 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6459 0.5825 0.3318 0.2149 0.9776 0.9848 0.6465 0.9502 0.9702 0.3814 ] Network output: [ -0.152 0.3118 0.8547 -0.001696 0.0007612 1.131 -0.001278 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6874 0.6735 0.4212 0.1304 0.9737 0.982 0.6875 0.9418 0.9641 0.4354 ] Network output: [ 0.149 0.6422 0.1708 0.0001668 -7.468e-05 0.8897 0.0001249 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09414 Epoch 891 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0268 1.019 0.9755 -7.291e-05 3.267e-05 -0.04799 -5.469e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04026 -0.002774 0.02206 0.02459 0.9139 0.9271 0.07696 0.8439 0.8784 0.1694 ] Network output: [ 0.9257 0.1599 -0.07873 -0.0005415 0.0002434 0.06524 -0.0004092 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6415 -0.0149 -0.0763 0.3078 0.9555 0.9773 0.7303 0.8681 0.9498 0.7231 ] Network output: [ -0.002252 0.9375 1.039 0.0001528 -6.868e-05 0.02851 0.0001155 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06979 0.03582 0.0516 0.04694 0.9736 0.9808 0.0715 0.9414 0.9665 0.08564 ] Network output: [ 0.128 -0.3373 1.164 0.0008377 -0.0003763 0.921 0.0006321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7273 0.3984 0.3483 0.5202 0.9603 0.9804 0.731 0.8798 0.9569 0.7254 ] Network output: [ -0.07637 0.269 0.8756 0.001113 -0.0004996 1.013 0.0008389 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6455 0.5823 0.3319 0.2151 0.9776 0.9848 0.6461 0.9502 0.9702 0.3814 ] Network output: [ -0.152 0.3114 0.8553 -0.001683 0.0007556 1.13 -0.001268 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.687 0.6732 0.4213 0.1309 0.9737 0.982 0.6872 0.9419 0.9641 0.4354 ] Network output: [ 0.1486 0.6427 0.1703 0.0001569 -7.023e-05 0.8903 0.0001174 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09394 Epoch 892 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02692 1.018 0.9755 -6.966e-05 3.121e-05 -0.04804 -5.224e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04023 -0.002761 0.02209 0.02459 0.9139 0.9271 0.07693 0.844 0.8784 0.1695 ] Network output: [ 0.9256 0.1599 -0.07852 -0.0005495 0.000247 0.06512 -0.0004152 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6415 -0.01444 -0.07582 0.3076 0.9555 0.9773 0.7304 0.8681 0.9498 0.7229 ] Network output: [ -0.002187 0.9373 1.039 0.0001556 -6.996e-05 0.02852 0.0001177 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06977 0.03587 0.05168 0.04695 0.9736 0.9808 0.07147 0.9415 0.9665 0.08569 ] Network output: [ 0.1278 -0.337 1.163 0.0008279 -0.0003719 0.9215 0.0006247 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7274 0.3992 0.349 0.5199 0.9603 0.9804 0.7311 0.8799 0.9569 0.7252 ] Network output: [ -0.07627 0.2695 0.8753 0.001115 -0.0005006 1.012 0.0008405 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6452 0.5822 0.3321 0.2151 0.9776 0.9848 0.6458 0.9503 0.9703 0.3815 ] Network output: [ -0.1517 0.3116 0.8551 -0.001677 0.0007527 1.13 -0.001263 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6867 0.6729 0.4213 0.1311 0.9737 0.982 0.6868 0.9419 0.9641 0.4354 ] Network output: [ 0.1483 0.6434 0.1697 0.0001445 -6.467e-05 0.8909 0.0001081 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0938 Epoch 893 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02702 1.018 0.9758 -6.461e-05 2.895e-05 -0.04809 -4.844e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0402 -0.002749 0.02213 0.02462 0.9139 0.9271 0.0769 0.8441 0.8785 0.1695 ] Network output: [ 0.9255 0.1595 -0.07779 -0.0005535 0.0002487 0.06507 -0.0004182 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6416 -0.01402 -0.07531 0.3075 0.9555 0.9773 0.7305 0.8682 0.9498 0.7227 ] Network output: [ -0.002131 0.937 1.039 0.0001598 -7.184e-05 0.02855 0.0001208 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06974 0.03591 0.05177 0.047 0.9736 0.9808 0.07145 0.9415 0.9666 0.08576 ] Network output: [ 0.1276 -0.3373 1.163 0.000823 -0.0003697 0.922 0.000621 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7275 0.4001 0.3496 0.5197 0.9603 0.9804 0.7313 0.88 0.9569 0.725 ] Network output: [ -0.07625 0.2696 0.8755 0.001121 -0.0005032 1.012 0.0008449 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6448 0.5821 0.3323 0.2153 0.9776 0.9848 0.6455 0.9503 0.9703 0.3815 ] Network output: [ -0.1516 0.3113 0.8554 -0.001664 0.000747 1.13 -0.001254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6863 0.6726 0.4214 0.1316 0.9738 0.982 0.6865 0.9419 0.9641 0.4355 ] Network output: [ 0.148 0.6439 0.1692 0.0001349 -6.035e-05 0.8915 0.0001008 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09361 Epoch 894 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02723 1.018 0.9757 -5.978e-05 2.678e-05 -0.04822 -4.48e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04017 -0.002746 0.02213 0.02463 0.9139 0.9272 0.07688 0.8441 0.8785 0.1695 ] Network output: [ 0.9262 0.1594 -0.07841 -0.0005557 0.0002497 0.06438 -0.0004198 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6416 -0.01382 -0.0752 0.3073 0.9555 0.9773 0.7307 0.8683 0.9499 0.7225 ] Network output: [ -0.002069 0.9368 1.039 0.0001632 -7.337e-05 0.02855 0.0001234 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0697 0.03593 0.05182 0.04703 0.9736 0.9808 0.07142 0.9416 0.9666 0.08581 ] Network output: [ 0.1276 -0.3373 1.163 0.000818 -0.0003674 0.9223 0.0006172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7276 0.4007 0.35 0.5196 0.9603 0.9805 0.7314 0.8801 0.9569 0.7248 ] Network output: [ -0.07634 0.2698 0.8759 0.001125 -0.000505 1.012 0.0008479 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6444 0.5819 0.3325 0.2155 0.9776 0.9848 0.6451 0.9503 0.9703 0.3816 ] Network output: [ -0.1516 0.311 0.8559 -0.001652 0.0007417 1.129 -0.001245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6859 0.6723 0.4215 0.1322 0.9738 0.982 0.6861 0.9419 0.9641 0.4355 ] Network output: [ 0.1476 0.6445 0.1688 0.0001244 -5.565e-05 0.8921 9.296e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09341 Epoch 895 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02732 1.018 0.9758 -5.677e-05 2.543e-05 -0.04825 -4.254e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04014 -0.002731 0.02217 0.02464 0.914 0.9272 0.07685 0.8442 0.8786 0.1695 ] Network output: [ 0.926 0.1594 -0.07797 -0.000564 0.0002535 0.06439 -0.0004261 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6417 -0.01332 -0.07463 0.3071 0.9555 0.9773 0.7308 0.8683 0.9499 0.7223 ] Network output: [ -0.002006 0.9366 1.04 0.000166 -7.46e-05 0.02857 0.0001255 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06968 0.03598 0.05191 0.04704 0.9737 0.9808 0.07139 0.9416 0.9666 0.08587 ] Network output: [ 0.1273 -0.3371 1.163 0.0008077 -0.0003628 0.9228 0.0006094 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7278 0.4016 0.3507 0.5192 0.9603 0.9805 0.7315 0.8801 0.957 0.7246 ] Network output: [ -0.07621 0.2703 0.8756 0.001127 -0.0005061 1.011 0.0008497 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6441 0.5818 0.3326 0.2154 0.9776 0.9848 0.6447 0.9504 0.9703 0.3816 ] Network output: [ -0.1512 0.3112 0.8556 -0.001646 0.0007389 1.129 -0.00124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6856 0.672 0.4215 0.1323 0.9738 0.982 0.6857 0.942 0.9642 0.4355 ] Network output: [ 0.1472 0.6451 0.1682 0.0001117 -4.995e-05 0.8927 8.339e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09327 Epoch 896 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02744 1.017 0.976 -5.146e-05 2.304e-05 -0.04831 -3.854e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0401 -0.002721 0.02221 0.02467 0.914 0.9272 0.07682 0.8443 0.8786 0.1696 ] Network output: [ 0.926 0.1589 -0.07732 -0.0005666 0.0002546 0.06424 -0.000428 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6417 -0.01293 -0.07415 0.3071 0.9556 0.9773 0.7309 0.8684 0.9499 0.7222 ] Network output: [ -0.001954 0.9362 1.04 0.0001702 -7.65e-05 0.02859 0.0001286 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06965 0.03602 0.052 0.04709 0.9737 0.9808 0.07137 0.9417 0.9667 0.08594 ] Network output: [ 0.1271 -0.3374 1.163 0.0008038 -0.000361 0.9233 0.0006065 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7279 0.4024 0.3513 0.5191 0.9604 0.9805 0.7316 0.8802 0.957 0.7244 ] Network output: [ -0.07623 0.2703 0.8759 0.001134 -0.000509 1.011 0.0008546 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6438 0.5817 0.3328 0.2157 0.9777 0.9848 0.6444 0.9504 0.9704 0.3817 ] Network output: [ -0.1511 0.3108 0.856 -0.001632 0.0007326 1.129 -0.00123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6852 0.6717 0.4216 0.1329 0.9738 0.982 0.6854 0.942 0.9642 0.4356 ] Network output: [ 0.1469 0.6457 0.1677 0.0001024 -4.578e-05 0.8933 7.641e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09308 Epoch 897 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02764 1.017 0.976 -4.705e-05 2.106e-05 -0.04844 -3.522e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04007 -0.002718 0.0222 0.02467 0.914 0.9272 0.0768 0.8444 0.8787 0.1696 ] Network output: [ 0.9267 0.1589 -0.07807 -0.0005691 0.0002557 0.06353 -0.0004299 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6418 -0.01274 -0.07405 0.3069 0.9556 0.9774 0.7311 0.8685 0.95 0.722 ] Network output: [ -0.001892 0.936 1.04 0.0001732 -7.785e-05 0.02859 0.0001309 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06962 0.03604 0.05205 0.04711 0.9737 0.9808 0.07134 0.9418 0.9667 0.08599 ] Network output: [ 0.1271 -0.3373 1.163 0.0007978 -0.0003583 0.9235 0.000602 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.728 0.4031 0.3517 0.5188 0.9604 0.9805 0.7317 0.8803 0.957 0.7242 ] Network output: [ -0.07631 0.2706 0.8762 0.001137 -0.0005106 1.01 0.0008572 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6434 0.5815 0.333 0.2158 0.9777 0.9849 0.644 0.9505 0.9704 0.3818 ] Network output: [ -0.1511 0.3106 0.8565 -0.001621 0.0007277 1.128 -0.001222 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6849 0.6713 0.4217 0.1333 0.9738 0.982 0.685 0.942 0.9642 0.4356 ] Network output: [ 0.1465 0.6463 0.1672 9.123e-05 -4.077e-05 0.8939 6.8e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09289 Epoch 898 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02771 1.017 0.9761 -4.419e-05 1.978e-05 -0.04846 -3.307e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04004 -0.002701 0.02225 0.02468 0.914 0.9272 0.07677 0.8444 0.8787 0.1696 ] Network output: [ 0.9263 0.1588 -0.07736 -0.0005776 0.0002595 0.06366 -0.0004363 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6418 -0.01219 -0.07338 0.3067 0.9556 0.9774 0.7312 0.8685 0.95 0.7218 ] Network output: [ -0.001833 0.9358 1.04 0.0001759 -7.906e-05 0.02863 0.0001329 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0696 0.0361 0.05215 0.04713 0.9737 0.9808 0.07132 0.9418 0.9667 0.08606 ] Network output: [ 0.1268 -0.3371 1.163 0.0007874 -0.0003536 0.9241 0.0005941 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7282 0.404 0.3525 0.5185 0.9604 0.9805 0.7319 0.8803 0.9571 0.724 ] Network output: [ -0.07615 0.271 0.8759 0.00114 -0.0005118 1.01 0.0008593 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6431 0.5815 0.3332 0.2158 0.9777 0.9849 0.6437 0.9505 0.9704 0.3818 ] Network output: [ -0.1507 0.3109 0.856 -0.001615 0.0007249 1.128 -0.001217 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6845 0.6711 0.4217 0.1335 0.9738 0.982 0.6847 0.9421 0.9642 0.4356 ] Network output: [ 0.1461 0.6469 0.1666 7.834e-05 -3.499e-05 0.8945 5.83e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09275 Epoch 899 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02785 1.016 0.9763 -3.861e-05 1.728e-05 -0.04853 -2.887e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.04001 -0.002694 0.02228 0.02471 0.914 0.9272 0.07674 0.8445 0.8788 0.1697 ] Network output: [ 0.9264 0.1583 -0.07688 -0.0005786 0.00026 0.0634 -0.000437 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6418 -0.01186 -0.07296 0.3066 0.9556 0.9774 0.7313 0.8686 0.95 0.7216 ] Network output: [ -0.001783 0.9355 1.04 0.0001801 -8.096e-05 0.02865 0.0001361 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06957 0.03613 0.05224 0.04719 0.9737 0.9808 0.07129 0.9419 0.9667 0.08614 ] Network output: [ 0.1267 -0.3375 1.163 0.0007845 -0.0003524 0.9245 0.0005919 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7283 0.4047 0.3531 0.5184 0.9604 0.9805 0.732 0.8804 0.9571 0.7238 ] Network output: [ -0.07621 0.271 0.8763 0.001147 -0.000515 1.01 0.0008646 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6427 0.5813 0.3334 0.216 0.9777 0.9849 0.6434 0.9506 0.9704 0.3819 ] Network output: [ -0.1506 0.3104 0.8566 -0.001599 0.000718 1.128 -0.001205 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6842 0.6708 0.4218 0.1341 0.9738 0.982 0.6843 0.9421 0.9643 0.4357 ] Network output: [ 0.1458 0.6475 0.1662 6.94e-05 -3.098e-05 0.8951 5.157e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09254 Epoch 900 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02805 1.016 0.9762 -3.474e-05 1.554e-05 -0.04866 -2.595e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03998 -0.002691 0.02227 0.02471 0.914 0.9273 0.07672 0.8446 0.8788 0.1697 ] Network output: [ 0.9271 0.1584 -0.07772 -0.0005817 0.0002614 0.06268 -0.0004394 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6419 -0.01166 -0.07285 0.3063 0.9556 0.9774 0.7315 0.8687 0.9501 0.7214 ] Network output: [ -0.001722 0.9353 1.04 0.0001827 -8.212e-05 0.02865 0.0001381 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06954 0.03615 0.05229 0.0472 0.9737 0.9808 0.07126 0.9419 0.9668 0.08618 ] Network output: [ 0.1267 -0.3373 1.162 0.0007774 -0.0003492 0.9247 0.0005865 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7284 0.4054 0.3535 0.5181 0.9604 0.9805 0.7321 0.8805 0.9571 0.7236 ] Network output: [ -0.07626 0.2713 0.8765 0.00115 -0.0005162 1.009 0.0008667 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6423 0.5812 0.3336 0.2161 0.9777 0.9849 0.643 0.9506 0.9705 0.382 ] Network output: [ -0.1506 0.3103 0.8569 -0.00159 0.0007138 1.127 -0.001198 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6838 0.6704 0.4219 0.1345 0.9738 0.982 0.6839 0.9421 0.9643 0.4357 ] Network output: [ 0.1453 0.6481 0.1657 5.737e-05 -2.558e-05 0.8957 4.251e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09236 Epoch 901 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0281 1.016 0.9764 -3.191e-05 1.427e-05 -0.04865 -2.382e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03995 -0.002672 0.02234 0.02472 0.9141 0.9273 0.07669 0.8447 0.8788 0.1697 ] Network output: [ 0.9266 0.1581 -0.07672 -0.0005901 0.0002652 0.06294 -0.0004457 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6419 -0.01107 -0.07207 0.3062 0.9556 0.9774 0.7316 0.8687 0.9501 0.7212 ] Network output: [ -0.001667 0.9351 1.04 0.0001854 -8.334e-05 0.02869 0.0001401 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06952 0.03621 0.0524 0.04722 0.9737 0.9808 0.07124 0.942 0.9668 0.08626 ] Network output: [ 0.1263 -0.3371 1.162 0.0007671 -0.0003445 0.9254 0.0005787 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7285 0.4064 0.3544 0.5177 0.9604 0.9805 0.7323 0.8805 0.9571 0.7234 ] Network output: [ -0.07608 0.2717 0.8762 0.001153 -0.0005177 1.009 0.0008692 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6421 0.5811 0.3338 0.2161 0.9777 0.9849 0.6427 0.9507 0.9705 0.3821 ] Network output: [ -0.1502 0.3105 0.8564 -0.001583 0.0007107 1.127 -0.001193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6835 0.6702 0.422 0.1346 0.9738 0.982 0.6836 0.9421 0.9643 0.4357 ] Network output: [ 0.145 0.6488 0.1651 4.451e-05 -1.981e-05 0.8963 3.283e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09222 Epoch 902 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02826 1.016 0.9765 -2.611e-05 1.167e-05 -0.04874 -1.945e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03991 -0.002667 0.02236 0.02475 0.9141 0.9273 0.07667 0.8447 0.8789 0.1698 ] Network output: [ 0.927 0.1576 -0.07648 -0.0005896 0.0002649 0.06252 -0.0004452 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.642 -0.01081 -0.07175 0.3061 0.9557 0.9774 0.7317 0.8688 0.9501 0.7211 ] Network output: [ -0.001619 0.9348 1.041 0.0001896 -8.522e-05 0.02871 0.0001433 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06949 0.03623 0.05249 0.04728 0.9737 0.9809 0.07121 0.942 0.9668 0.08634 ] Network output: [ 0.1263 -0.3375 1.162 0.0007652 -0.0003437 0.9257 0.0005773 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7287 0.407 0.3549 0.5177 0.9604 0.9805 0.7324 0.8806 0.9572 0.7233 ] Network output: [ -0.07619 0.2716 0.8768 0.001161 -0.0005211 1.009 0.0008748 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6417 0.581 0.3341 0.2164 0.9777 0.9849 0.6423 0.9507 0.9705 0.3822 ] Network output: [ -0.1502 0.3099 0.8572 -0.001567 0.0007033 1.127 -0.001181 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6831 0.6699 0.4221 0.1353 0.9738 0.9821 0.6833 0.9422 0.9643 0.4358 ] Network output: [ 0.1447 0.6493 0.1647 3.585e-05 -1.592e-05 0.8969 2.631e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.092 Epoch 903 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02846 1.015 0.9764 -2.288e-05 1.022e-05 -0.04886 -1.702e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03989 -0.002663 0.02236 0.02474 0.9141 0.9273 0.07665 0.8448 0.8789 0.1698 ] Network output: [ 0.9276 0.1578 -0.07732 -0.0005936 0.0002667 0.06185 -0.0004483 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.642 -0.01058 -0.07158 0.3058 0.9557 0.9774 0.7319 0.8689 0.9502 0.7209 ] Network output: [ -0.001558 0.9346 1.041 0.0001917 -8.616e-05 0.02872 0.0001449 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06947 0.03626 0.05254 0.04728 0.9738 0.9809 0.07119 0.9421 0.9668 0.08639 ] Network output: [ 0.1263 -0.3372 1.162 0.0007566 -0.0003398 0.9259 0.0005708 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7288 0.4077 0.3554 0.5173 0.9605 0.9805 0.7325 0.8807 0.9572 0.723 ] Network output: [ -0.07621 0.272 0.8769 0.001162 -0.0005219 1.008 0.0008763 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6413 0.5808 0.3343 0.2165 0.9777 0.9849 0.642 0.9508 0.9706 0.3823 ] Network output: [ -0.15 0.31 0.8573 -0.001559 0.0006998 1.126 -0.001175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6828 0.6696 0.4222 0.1356 0.9738 0.9821 0.6829 0.9422 0.9644 0.4358 ] Network output: [ 0.1442 0.6499 0.1642 2.283e-05 -1.008e-05 0.8975 1.651e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09183 Epoch 904 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02848 1.015 0.9766 -1.992e-05 8.892e-06 -0.04885 -1.48e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03985 -0.002643 0.02244 0.02476 0.9141 0.9273 0.07662 0.8449 0.879 0.1698 ] Network output: [ 0.927 0.1575 -0.07604 -0.0006016 0.0002703 0.06222 -0.0004543 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6421 -0.00995 -0.07072 0.3056 0.9557 0.9774 0.732 0.869 0.9502 0.7207 ] Network output: [ -0.001508 0.9344 1.041 0.0001946 -8.745e-05 0.02877 0.000147 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06945 0.03632 0.05267 0.04731 0.9738 0.9809 0.07117 0.9422 0.9669 0.08647 ] Network output: [ 0.1259 -0.3372 1.162 0.0007469 -0.0003355 0.9266 0.0005635 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7289 0.4087 0.3563 0.5169 0.9605 0.9805 0.7327 0.8807 0.9572 0.7228 ] Network output: [ -0.07602 0.2723 0.8765 0.001167 -0.0005238 1.008 0.0008795 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6411 0.5808 0.3345 0.2164 0.9777 0.9849 0.6417 0.9508 0.9706 0.3823 ] Network output: [ -0.1496 0.3102 0.8568 -0.001551 0.0006963 1.126 -0.001169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6824 0.6693 0.4223 0.1357 0.9738 0.9821 0.6826 0.9422 0.9644 0.4359 ] Network output: [ 0.1439 0.6506 0.1636 1.025e-05 -4.436e-06 0.898 7.038e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09169 Epoch 905 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02867 1.015 0.9768 -1.398e-05 6.223e-06 -0.04896 -1.032e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03982 -0.002642 0.02244 0.02479 0.9141 0.9273 0.07659 0.845 0.879 0.1699 ] Network output: [ 0.9275 0.157 -0.07613 -0.0005995 0.0002693 0.06161 -0.0004527 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6421 -0.009773 -0.07051 0.3056 0.9557 0.9774 0.7321 0.869 0.9502 0.7206 ] Network output: [ -0.001462 0.934 1.041 0.0001986 -8.926e-05 0.02878 0.0001501 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06942 0.03634 0.05274 0.04737 0.9738 0.9809 0.07114 0.9422 0.9669 0.08655 ] Network output: [ 0.1259 -0.3376 1.162 0.0007458 -0.000335 0.9269 0.0005627 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.729 0.4093 0.3568 0.5169 0.9605 0.9806 0.7328 0.8808 0.9573 0.7227 ] Network output: [ -0.07617 0.2721 0.8773 0.001174 -0.0005273 1.008 0.0008852 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6407 0.5806 0.3347 0.2168 0.9777 0.9849 0.6413 0.9509 0.9706 0.3825 ] Network output: [ -0.1497 0.3095 0.8577 -0.001534 0.0006886 1.126 -0.001156 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6821 0.669 0.4224 0.1365 0.9738 0.9821 0.6822 0.9423 0.9644 0.436 ] Network output: [ 0.1435 0.6511 0.1632 1.731e-06 -6.121e-07 0.8987 6.248e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09146 Epoch 906 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02885 1.015 0.9766 -1.147e-05 5.098e-06 -0.04906 -8.433e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03979 -0.002636 0.02245 0.02478 0.9142 0.9274 0.07657 0.845 0.8791 0.1699 ] Network output: [ 0.9281 0.1573 -0.07688 -0.0006049 0.0002718 0.06105 -0.0004568 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6422 -0.009483 -0.07026 0.3052 0.9557 0.9774 0.7323 0.8691 0.9503 0.7203 ] Network output: [ -0.001402 0.9339 1.041 0.0002003 -8.999e-05 0.02879 0.0001513 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06939 0.03637 0.0528 0.04737 0.9738 0.9809 0.07112 0.9423 0.9669 0.0866 ] Network output: [ 0.1258 -0.3372 1.161 0.0007356 -0.0003304 0.9272 0.000555 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7292 0.4101 0.3574 0.5164 0.9605 0.9806 0.7329 0.8809 0.9573 0.7225 ] Network output: [ -0.07614 0.2726 0.8772 0.001176 -0.0005278 1.007 0.0008861 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6404 0.5805 0.335 0.2168 0.9777 0.9849 0.641 0.9509 0.9706 0.3826 ] Network output: [ -0.1495 0.3097 0.8576 -0.001528 0.0006859 1.125 -0.001151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6817 0.6687 0.4225 0.1367 0.9738 0.9821 0.6819 0.9423 0.9644 0.436 ] Network output: [ 0.1431 0.6518 0.1628 -1.238e-05 5.722e-06 0.8992 -1e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0913 Epoch 907 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02886 1.014 0.9769 -8.216e-06 3.638e-06 -0.04903 -5.984e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03976 -0.002615 0.02254 0.0248 0.9142 0.9274 0.07654 0.8451 0.8791 0.1699 ] Network output: [ 0.9273 0.1567 -0.07533 -0.0006119 0.0002749 0.0615 -0.000462 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6422 -0.008841 -0.06933 0.3051 0.9557 0.9775 0.7324 0.8692 0.9503 0.7202 ] Network output: [ -0.001356 0.9336 1.041 0.0002033 -9.137e-05 0.02885 0.0001536 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06938 0.03644 0.05294 0.04741 0.9738 0.9809 0.0711 0.9423 0.967 0.0867 ] Network output: [ 0.1254 -0.3372 1.161 0.000727 -0.0003265 0.9279 0.0005485 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7293 0.4111 0.3582 0.5161 0.9605 0.9806 0.7331 0.8809 0.9573 0.7223 ] Network output: [ -0.07596 0.2729 0.877 0.001181 -0.0005302 1.007 0.0008902 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6401 0.5805 0.3352 0.2168 0.9778 0.9849 0.6407 0.951 0.9707 0.3827 ] Network output: [ -0.1491 0.3098 0.8572 -0.001519 0.0006817 1.125 -0.001144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6814 0.6684 0.4226 0.1368 0.9738 0.9821 0.6816 0.9423 0.9644 0.4361 ] Network output: [ 0.1428 0.6525 0.1621 -2.439e-05 1.111e-05 0.8998 -1.905e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09115 Epoch 908 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02908 1.014 0.977 -2.264e-06 9.664e-07 -0.04917 -1.501e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03973 -0.002618 0.02253 0.02482 0.9142 0.9274 0.07652 0.8452 0.8792 0.17 ] Network output: [ 0.9281 0.1564 -0.07583 -0.0006085 0.0002734 0.06068 -0.0004594 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6423 -0.008748 -0.06925 0.305 0.9557 0.9775 0.7326 0.8692 0.9503 0.72 ] Network output: [ -0.001311 0.9333 1.041 0.0002071 -9.307e-05 0.02886 0.0001564 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06934 0.03645 0.053 0.04747 0.9738 0.9809 0.07107 0.9424 0.967 0.08677 ] Network output: [ 0.1255 -0.3376 1.161 0.0007264 -0.0003262 0.928 0.000548 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7294 0.4116 0.3587 0.5161 0.9605 0.9806 0.7332 0.881 0.9574 0.7221 ] Network output: [ -0.07615 0.2727 0.8779 0.001188 -0.0005335 1.007 0.0008958 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6397 0.5802 0.3355 0.2172 0.9778 0.9849 0.6404 0.951 0.9707 0.3828 ] Network output: [ -0.1491 0.3091 0.8583 -0.001501 0.0006738 1.125 -0.001131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6811 0.6681 0.4227 0.1376 0.9738 0.9821 0.6812 0.9424 0.9645 0.4361 ] Network output: [ 0.1423 0.653 0.1618 -3.299e-05 1.497e-05 0.9004 -2.552e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09091 Epoch 909 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02923 1.014 0.9768 -5.07e-07 1.786e-07 -0.04925 -1.801e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0397 -0.002608 0.02255 0.02481 0.9142 0.9274 0.0765 0.8453 0.8792 0.17 ] Network output: [ 0.9284 0.1567 -0.07636 -0.0006155 0.0002765 0.06029 -0.0004647 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6423 -0.008378 -0.06887 0.3046 0.9558 0.9775 0.7327 0.8693 0.9504 0.7198 ] Network output: [ -0.001252 0.9332 1.041 0.0002083 -9.36e-05 0.02888 0.0001573 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06933 0.03649 0.05308 0.04745 0.9738 0.9809 0.07105 0.9424 0.967 0.08683 ] Network output: [ 0.1253 -0.3371 1.161 0.0007144 -0.0003209 0.9284 0.000539 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7295 0.4124 0.3594 0.5156 0.9605 0.9806 0.7333 0.8811 0.9574 0.7219 ] Network output: [ -0.07605 0.2732 0.8775 0.001189 -0.0005338 1.006 0.0008962 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6394 0.5802 0.3357 0.2171 0.9778 0.9849 0.6401 0.951 0.9707 0.3829 ] Network output: [ -0.1489 0.3095 0.8578 -0.001497 0.000672 1.124 -0.001128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6807 0.6678 0.4228 0.1377 0.9738 0.9821 0.6809 0.9424 0.9645 0.4362 ] Network output: [ 0.1419 0.6537 0.1613 -4.822e-05 2.18e-05 0.901 -3.699e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09078 Epoch 910 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02924 1.014 0.9772 3.222e-06 -1.495e-06 -0.04922 2.628e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03967 -0.002588 0.02264 0.02484 0.9142 0.9274 0.07647 0.8453 0.8793 0.17 ] Network output: [ 0.9277 0.156 -0.07462 -0.0006209 0.000279 0.06076 -0.0004688 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6423 -0.007745 -0.06792 0.3046 0.9558 0.9775 0.7328 0.8694 0.9504 0.7197 ] Network output: [ -0.001212 0.9329 1.041 0.0002117 -9.511e-05 0.02894 0.0001599 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06931 0.03655 0.05323 0.04751 0.9738 0.9809 0.07104 0.9425 0.967 0.08693 ] Network output: [ 0.1249 -0.3373 1.161 0.0007075 -0.0003178 0.9291 0.0005338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7297 0.4134 0.3603 0.5153 0.9606 0.9806 0.7335 0.8812 0.9574 0.7217 ] Network output: [ -0.0759 0.2734 0.8774 0.001196 -0.0005368 1.006 0.0009012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6392 0.5801 0.336 0.2171 0.9778 0.9849 0.6398 0.9511 0.9707 0.383 ] Network output: [ -0.1485 0.3094 0.8576 -0.001485 0.0006668 1.124 -0.001119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6804 0.6676 0.4229 0.1379 0.9738 0.9821 0.6806 0.9424 0.9645 0.4362 ] Network output: [ 0.1416 0.6543 0.1607 -5.937e-05 2.681e-05 0.9015 -4.538e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09061 Epoch 911 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02949 1.013 0.9771 8.991e-06 -4.084e-06 -0.04939 6.973e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03964 -0.002594 0.02262 0.02485 0.9143 0.9274 0.07645 0.8454 0.8793 0.1701 ] Network output: [ 0.9288 0.1558 -0.07558 -0.0006166 0.000277 0.05972 -0.0004656 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6424 -0.00773 -0.06797 0.3044 0.9558 0.9775 0.733 0.8694 0.9504 0.7195 ] Network output: [ -0.001167 0.9326 1.042 0.0002151 -9.664e-05 0.02894 0.0001624 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06928 0.03656 0.05328 0.04755 0.9738 0.9809 0.07101 0.9426 0.9671 0.087 ] Network output: [ 0.1252 -0.3376 1.161 0.0007068 -0.0003174 0.9292 0.0005332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7298 0.4139 0.3607 0.5152 0.9606 0.9806 0.7336 0.8812 0.9574 0.7216 ] Network output: [ -0.07613 0.2731 0.8784 0.001202 -0.0005399 1.006 0.0009064 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6388 0.5799 0.3362 0.2175 0.9778 0.9849 0.6394 0.9511 0.9708 0.3832 ] Network output: [ -0.1486 0.3087 0.8587 -0.001468 0.0006591 1.124 -0.001106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6801 0.6673 0.423 0.1388 0.9739 0.9821 0.6802 0.9425 0.9646 0.4363 ] Network output: [ 0.1411 0.6548 0.1605 -6.835e-05 3.084e-05 0.9022 -5.214e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09037 Epoch 912 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0296 1.013 0.977 1.002e-05 -4.546e-06 -0.04943 7.747e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03961 -0.00258 0.02266 0.02484 0.9143 0.9275 0.07643 0.8455 0.8794 0.1701 ] Network output: [ 0.9288 0.1561 -0.07576 -0.0006255 0.000281 0.05957 -0.0004722 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6425 -0.007257 -0.06742 0.304 0.9558 0.9775 0.7331 0.8695 0.9504 0.7193 ] Network output: [ -0.001109 0.9325 1.042 0.0002159 -9.7e-05 0.02897 0.000163 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06926 0.03661 0.05337 0.04753 0.9739 0.9809 0.07099 0.9426 0.9671 0.08706 ] Network output: [ 0.1249 -0.337 1.16 0.0006931 -0.0003113 0.9297 0.0005229 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7299 0.4148 0.3614 0.5146 0.9606 0.9806 0.7337 0.8813 0.9575 0.7214 ] Network output: [ -0.07595 0.2738 0.8778 0.001203 -0.0005399 1.005 0.0009064 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6385 0.5799 0.3365 0.2173 0.9778 0.985 0.6391 0.9512 0.9708 0.3833 ] Network output: [ -0.1482 0.3093 0.858 -0.001466 0.0006581 1.123 -0.001105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6797 0.667 0.4231 0.1386 0.9739 0.9821 0.6799 0.9425 0.9646 0.4364 ] Network output: [ 0.1407 0.6557 0.1599 -8.462e-05 3.814e-05 0.9027 -6.439e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09025 Epoch 913 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02961 1.013 0.9774 1.439e-05 -6.509e-06 -0.0494 1.104e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03958 -0.002562 0.02275 0.02487 0.9143 0.9275 0.0764 0.8456 0.8794 0.1702 ] Network output: [ 0.9281 0.1552 -0.07394 -0.0006287 0.0002825 0.05999 -0.0004746 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6425 -0.006665 -0.06649 0.304 0.9558 0.9775 0.7332 0.8696 0.9505 0.7192 ] Network output: [ -0.001076 0.9322 1.042 0.0002196 -9.867e-05 0.02904 0.0001658 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06924 0.03667 0.05352 0.0476 0.9739 0.9809 0.07097 0.9427 0.9671 0.08717 ] Network output: [ 0.1245 -0.3374 1.161 0.0006884 -0.0003092 0.9303 0.0005194 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7301 0.4157 0.3623 0.5145 0.9606 0.9806 0.7339 0.8814 0.9575 0.7212 ] Network output: [ -0.07585 0.2737 0.878 0.001211 -0.0005436 1.005 0.0009126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6382 0.5798 0.3368 0.2175 0.9778 0.985 0.6389 0.9512 0.9708 0.3834 ] Network output: [ -0.1479 0.309 0.858 -0.001452 0.0006517 1.123 -0.001094 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6795 0.6668 0.4233 0.139 0.9739 0.9821 0.6796 0.9425 0.9646 0.4365 ] Network output: [ 0.1404 0.6562 0.1593 -9.463e-05 4.263e-05 0.9033 -7.193e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09006 Epoch 914 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02989 1.013 0.9773 1.974e-05 -8.91e-06 -0.04959 1.507e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03955 -0.00257 0.02271 0.02488 0.9143 0.9275 0.07639 0.8456 0.8795 0.1702 ] Network output: [ 0.9295 0.1552 -0.07537 -0.0006241 0.0002804 0.05876 -0.0004712 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6426 -0.006715 -0.06666 0.3037 0.9558 0.9775 0.7334 0.8697 0.9505 0.719 ] Network output: [ -0.001029 0.9319 1.042 0.0002225 -9.995e-05 0.02903 0.000168 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06921 0.03667 0.05356 0.04764 0.9739 0.981 0.07094 0.9427 0.9671 0.08724 ] Network output: [ 0.1248 -0.3376 1.16 0.0006869 -0.0003085 0.9303 0.0005183 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7302 0.4161 0.3627 0.5143 0.9606 0.9806 0.734 0.8814 0.9575 0.721 ] Network output: [ -0.07609 0.2736 0.8789 0.001217 -0.0005462 1.005 0.000917 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6379 0.5796 0.337 0.2179 0.9778 0.985 0.6385 0.9513 0.9709 0.3836 ] Network output: [ -0.1481 0.3083 0.8591 -0.001436 0.0006445 1.123 -0.001082 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6791 0.6664 0.4234 0.1398 0.9739 0.9821 0.6792 0.9425 0.9646 0.4366 ] Network output: [ 0.1399 0.6568 0.1591 -0.0001044 4.701e-05 0.9039 -7.928e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08982 Epoch 915 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02995 1.013 0.9773 2.014e-05 -9.089e-06 -0.0496 1.537e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03953 -0.002551 0.02277 0.02486 0.9143 0.9275 0.07636 0.8457 0.8795 0.1702 ] Network output: [ 0.9291 0.1554 -0.07506 -0.0006347 0.0002851 0.05889 -0.0004791 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6426 -0.006122 -0.06591 0.3033 0.9559 0.9775 0.7335 0.8697 0.9505 0.7188 ] Network output: [ -0.0009748 0.9319 1.042 0.000223 -0.0001002 0.02908 0.0001684 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0692 0.03673 0.05367 0.04762 0.9739 0.981 0.07094 0.9428 0.9672 0.08731 ] Network output: [ 0.1244 -0.3368 1.16 0.0006719 -0.0003018 0.9309 0.0005069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7303 0.4171 0.3635 0.5137 0.9606 0.9806 0.7341 0.8815 0.9576 0.7208 ] Network output: [ -0.07584 0.2743 0.8782 0.001217 -0.0005462 1.004 0.000917 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6376 0.5796 0.3373 0.2176 0.9778 0.985 0.6383 0.9513 0.9709 0.3837 ] Network output: [ -0.1476 0.3091 0.8581 -0.001435 0.0006441 1.122 -0.001081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6788 0.6662 0.4235 0.1396 0.9739 0.9821 0.6789 0.9426 0.9646 0.4366 ] Network output: [ 0.1395 0.6576 0.1584 -0.0001215 5.469e-05 0.9044 -9.216e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08971 Epoch 916 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02999 1.012 0.9777 2.53e-05 -1.14e-05 -0.04959 1.925e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03949 -0.002537 0.02286 0.02491 0.9144 0.9275 0.07633 0.8458 0.8796 0.1703 ] Network output: [ 0.9286 0.1544 -0.0733 -0.0006352 0.0002854 0.05918 -0.0004795 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6426 -0.005605 -0.06506 0.3034 0.9559 0.9775 0.7336 0.8698 0.9506 0.7187 ] Network output: [ -0.0009472 0.9314 1.042 0.0002271 -0.000102 0.02914 0.0001715 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06918 0.03678 0.05382 0.0477 0.9739 0.981 0.07092 0.9428 0.9672 0.08743 ] Network output: [ 0.1241 -0.3374 1.16 0.0006697 -0.0003008 0.9315 0.0005053 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7305 0.4179 0.3644 0.5136 0.9607 0.9807 0.7343 0.8816 0.9576 0.7207 ] Network output: [ -0.0758 0.2741 0.8786 0.001226 -0.0005506 1.004 0.0009243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6373 0.5795 0.3376 0.2179 0.9778 0.985 0.638 0.9514 0.9709 0.3839 ] Network output: [ -0.1473 0.3086 0.8584 -0.001418 0.0006364 1.122 -0.001068 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6785 0.666 0.4237 0.1401 0.9739 0.9821 0.6787 0.9426 0.9647 0.4367 ] Network output: [ 0.1392 0.6581 0.1579 -0.0001302 5.857e-05 0.905 -9.868e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0895 Epoch 917 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03029 1.012 0.9774 2.995e-05 -1.349e-05 -0.0498 2.276e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03947 -0.002546 0.02281 0.0249 0.9144 0.9275 0.07632 0.8458 0.8796 0.1703 ] Network output: [ 0.9301 0.1546 -0.07518 -0.0006311 0.0002835 0.0578 -0.0004764 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6427 -0.005696 -0.06532 0.3031 0.9559 0.9776 0.7338 0.8699 0.9506 0.7185 ] Network output: [ -0.000898 0.9313 1.042 0.0002293 -0.000103 0.02913 0.0001731 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06915 0.03678 0.05384 0.04772 0.9739 0.981 0.07089 0.9429 0.9672 0.08748 ] Network output: [ 0.1244 -0.3375 1.16 0.0006668 -0.0002995 0.9314 0.0005031 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7305 0.4184 0.3647 0.5134 0.9607 0.9807 0.7344 0.8816 0.9576 0.7205 ] Network output: [ -0.07605 0.274 0.8795 0.001231 -0.0005525 1.004 0.0009277 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.637 0.5793 0.3379 0.2182 0.9779 0.985 0.6376 0.9514 0.9709 0.3841 ] Network output: [ -0.1475 0.308 0.8594 -0.001404 0.0006301 1.122 -0.001058 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6781 0.6656 0.4238 0.1408 0.9739 0.9821 0.6783 0.9426 0.9647 0.4368 ] Network output: [ 0.1387 0.6587 0.1578 -0.0001411 6.35e-05 0.9056 -0.0001069 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08927 Epoch 918 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03029 1.012 0.9775 2.99e-05 -1.347e-05 -0.04976 2.271e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03944 -0.002522 0.0229 0.02489 0.9144 0.9276 0.0763 0.8459 0.8796 0.1704 ] Network output: [ 0.9293 0.1547 -0.07425 -0.0006431 0.0002889 0.05826 -0.0004854 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6427 -0.004971 -0.06434 0.3027 0.9559 0.9776 0.7339 0.8699 0.9506 0.7183 ] Network output: [ -0.0008481 0.9312 1.042 0.0002297 -0.0001032 0.02919 0.0001734 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06914 0.03685 0.05398 0.04771 0.9739 0.981 0.07088 0.943 0.9673 0.08756 ] Network output: [ 0.1239 -0.3367 1.159 0.0006509 -0.0002923 0.9322 0.0004911 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7307 0.4195 0.3657 0.5127 0.9607 0.9807 0.7345 0.8817 0.9576 0.7203 ] Network output: [ -0.07572 0.2747 0.8785 0.001231 -0.0005527 1.003 0.0009279 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6368 0.5794 0.3382 0.2179 0.9779 0.985 0.6374 0.9515 0.971 0.3842 ] Network output: [ -0.1469 0.3089 0.8581 -0.001403 0.0006299 1.121 -0.001057 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6779 0.6654 0.4239 0.1405 0.9739 0.9821 0.678 0.9427 0.9647 0.4369 ] Network output: [ 0.1383 0.6596 0.157 -0.0001587 7.14e-05 0.9061 -0.0001202 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08918 Epoch 919 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03036 1.011 0.9779 3.591e-05 -1.617e-05 -0.04978 2.724e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03941 -0.002513 0.02296 0.02494 0.9144 0.9276 0.07627 0.846 0.8797 0.1704 ] Network output: [ 0.9291 0.1536 -0.07273 -0.0006405 0.0002877 0.05832 -0.0004834 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6428 -0.004569 -0.06364 0.3028 0.9559 0.9776 0.734 0.87 0.9507 0.7182 ] Network output: [ -0.0008255 0.9307 1.043 0.0002342 -0.0001052 0.02925 0.0001768 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06912 0.03689 0.05412 0.0478 0.9739 0.981 0.07086 0.943 0.9673 0.08769 ] Network output: [ 0.1237 -0.3375 1.16 0.0006515 -0.0002926 0.9326 0.0004915 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7309 0.4202 0.3665 0.5127 0.9607 0.9807 0.7347 0.8818 0.9577 0.7202 ] Network output: [ -0.07577 0.2743 0.8793 0.001242 -0.0005577 1.003 0.0009363 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6365 0.5793 0.3385 0.2183 0.9779 0.985 0.6371 0.9515 0.971 0.3844 ] Network output: [ -0.1468 0.3081 0.8589 -0.001383 0.0006208 1.121 -0.001042 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6776 0.6652 0.4241 0.1413 0.9739 0.9822 0.6777 0.9427 0.9648 0.437 ] Network output: [ 0.138 0.6601 0.1566 -0.0001659 7.462e-05 0.9067 -0.0001256 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08894 Epoch 920 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03068 1.011 0.9776 3.959e-05 -1.782e-05 -0.05 3.001e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03938 -0.002522 0.02291 0.02491 0.9144 0.9276 0.07626 0.8461 0.8797 0.1705 ] Network output: [ 0.9308 0.154 -0.07498 -0.0006376 0.0002864 0.05686 -0.0004812 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6429 -0.004666 -0.06394 0.3023 0.9559 0.9776 0.7342 0.8701 0.9507 0.718 ] Network output: [ -0.0007731 0.9306 1.043 0.0002355 -0.0001058 0.02924 0.0001778 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06909 0.03689 0.05414 0.0478 0.9739 0.981 0.07084 0.9431 0.9673 0.08774 ] Network output: [ 0.124 -0.3373 1.159 0.0006464 -0.0002903 0.9325 0.0004876 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7309 0.4206 0.3668 0.5124 0.9607 0.9807 0.7348 0.8818 0.9577 0.72 ] Network output: [ -0.07598 0.2743 0.88 0.001245 -0.0005588 1.003 0.0009382 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6361 0.579 0.3388 0.2185 0.9779 0.985 0.6367 0.9516 0.971 0.3846 ] Network output: [ -0.1469 0.3078 0.8596 -0.001372 0.0006159 1.121 -0.001034 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6772 0.6648 0.4242 0.1418 0.9739 0.9822 0.6773 0.9428 0.9648 0.4371 ] Network output: [ 0.1374 0.6607 0.1564 -0.0001786 8.031e-05 0.9073 -0.0001351 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08873 Epoch 921 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03062 1.011 0.9777 3.934e-05 -1.771e-05 -0.04991 2.982e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03935 -0.002493 0.02303 0.02491 0.9145 0.9276 0.07623 0.8461 0.8798 0.1705 ] Network output: [ 0.9296 0.1539 -0.07333 -0.0006507 0.0002923 0.05767 -0.0004911 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6429 -0.00381 -0.06273 0.302 0.956 0.9776 0.7343 0.8701 0.9507 0.7179 ] Network output: [ -0.0007298 0.9305 1.043 0.0002361 -0.000106 0.02931 0.0001782 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06909 0.03698 0.05431 0.04779 0.974 0.981 0.07083 0.9431 0.9673 0.08783 ] Network output: [ 0.1234 -0.3367 1.159 0.0006302 -0.0002831 0.9334 0.0004755 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7311 0.4218 0.3679 0.5117 0.9607 0.9807 0.7349 0.8819 0.9577 0.7198 ] Network output: [ -0.07558 0.2751 0.8789 0.001246 -0.0005594 1.002 0.0009391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6359 0.5791 0.3391 0.2182 0.9779 0.985 0.6366 0.9516 0.9711 0.3847 ] Network output: [ -0.1462 0.3087 0.8581 -0.001371 0.0006156 1.12 -0.001033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6769 0.6646 0.4243 0.1414 0.9739 0.9822 0.6771 0.9428 0.9648 0.4372 ] Network output: [ 0.1371 0.6616 0.1556 -0.0001962 8.821e-05 0.9078 -0.0001484 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08864 Epoch 922 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03074 1.011 0.9781 4.621e-05 -2.079e-05 -0.04997 3.5e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03932 -0.00249 0.02307 0.02496 0.9145 0.9276 0.0762 0.8462 0.8798 0.1706 ] Network output: [ 0.9297 0.1528 -0.07227 -0.0006444 0.0002895 0.0574 -0.0004864 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6429 -0.003559 -0.06223 0.3021 0.956 0.9776 0.7344 0.8702 0.9508 0.7178 ] Network output: [ -0.0007109 0.93 1.043 0.0002407 -0.0001081 0.02936 0.0001817 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06906 0.037 0.05443 0.0479 0.974 0.981 0.07081 0.9432 0.9674 0.08796 ] Network output: [ 0.1234 -0.3376 1.16 0.0006337 -0.0002846 0.9337 0.0004781 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7312 0.4224 0.3686 0.5118 0.9607 0.9807 0.7351 0.882 0.9578 0.7197 ] Network output: [ -0.07575 0.2744 0.8801 0.001258 -0.0005649 1.002 0.0009484 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6356 0.579 0.3394 0.2187 0.9779 0.985 0.6363 0.9517 0.9711 0.3849 ] Network output: [ -0.1462 0.3076 0.8593 -0.001348 0.0006052 1.12 -0.001016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6767 0.6644 0.4245 0.1424 0.9739 0.9822 0.6768 0.9428 0.9648 0.4373 ] Network output: [ 0.1367 0.662 0.1553 -0.0002019 9.079e-05 0.9084 -0.0001527 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08838 Epoch 923 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03105 1.011 0.9777 4.862e-05 -2.187e-05 -0.05018 3.681e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0393 -0.002498 0.02302 0.02493 0.9145 0.9276 0.0762 0.8463 0.8799 0.1706 ] Network output: [ 0.9314 0.1535 -0.07475 -0.0006438 0.0002892 0.05596 -0.0004859 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.643 -0.003617 -0.06251 0.3016 0.956 0.9776 0.7346 0.8703 0.9508 0.7176 ] Network output: [ -0.0006549 0.93 1.043 0.0002411 -0.0001083 0.02935 0.000182 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06904 0.037 0.05445 0.04788 0.974 0.981 0.07079 0.9432 0.9674 0.088 ] Network output: [ 0.1237 -0.3371 1.159 0.0006255 -0.0002809 0.9336 0.0004719 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7313 0.4229 0.369 0.5113 0.9608 0.9807 0.7351 0.8821 0.9578 0.7195 ] Network output: [ -0.07589 0.2747 0.8804 0.001259 -0.0005651 1.002 0.0009488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6353 0.5788 0.3397 0.2188 0.9779 0.985 0.6359 0.9517 0.9711 0.3851 ] Network output: [ -0.1462 0.3076 0.8597 -0.001341 0.0006019 1.12 -0.00101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6763 0.6641 0.4247 0.1427 0.9739 0.9822 0.6764 0.9429 0.9649 0.4374 ] Network output: [ 0.1361 0.6627 0.1551 -0.0002167 9.744e-05 0.909 -0.0001639 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08819 Epoch 924 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03094 1.01 0.978 4.853e-05 -2.183e-05 -0.05005 3.674e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03927 -0.002464 0.02316 0.02493 0.9145 0.9277 0.07616 0.8464 0.8799 0.1706 ] Network output: [ 0.9297 0.1531 -0.07232 -0.0006572 0.0002952 0.05712 -0.000496 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.643 -0.002643 -0.06107 0.3014 0.956 0.9776 0.7347 0.8704 0.9508 0.7174 ] Network output: [ -0.00062 0.9298 1.043 0.000242 -0.0001087 0.02944 0.0001827 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06904 0.0371 0.05464 0.04789 0.974 0.981 0.07079 0.9433 0.9674 0.08812 ] Network output: [ 0.1229 -0.3366 1.159 0.0006101 -0.000274 0.9347 0.0004603 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7315 0.4241 0.3702 0.5107 0.9608 0.9807 0.7353 0.8821 0.9578 0.7193 ] Network output: [ -0.07545 0.2754 0.8793 0.001261 -0.0005663 1.001 0.0009508 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6351 0.5789 0.34 0.2185 0.9779 0.985 0.6358 0.9518 0.9711 0.3852 ] Network output: [ -0.1455 0.3085 0.8582 -0.001339 0.000601 1.119 -0.001009 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6761 0.6639 0.4248 0.1423 0.9739 0.9822 0.6762 0.9429 0.9649 0.4375 ] Network output: [ 0.1359 0.6636 0.1542 -0.0002338 0.0001051 0.9094 -0.0001767 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0881 Epoch 925 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03113 1.01 0.9783 5.616e-05 -2.525e-05 -0.05017 4.248e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03924 -0.002468 0.02318 0.02498 0.9145 0.9277 0.07614 0.8464 0.88 0.1707 ] Network output: [ 0.9304 0.152 -0.07194 -0.0006473 0.0002908 0.05641 -0.0004885 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6431 -0.002577 -0.06084 0.3015 0.956 0.9776 0.7349 0.8704 0.9509 0.7173 ] Network output: [ -0.0006029 0.9293 1.043 0.0002468 -0.0001108 0.02948 0.0001862 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06901 0.03711 0.05475 0.048 0.974 0.9811 0.07076 0.9434 0.9675 0.08824 ] Network output: [ 0.1231 -0.3377 1.159 0.0006161 -0.0002767 0.9348 0.0004648 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7316 0.4246 0.3707 0.5109 0.9608 0.9807 0.7355 0.8822 0.9579 0.7192 ] Network output: [ -0.07573 0.2745 0.8809 0.001274 -0.0005721 1.001 0.0009606 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6348 0.5787 0.3404 0.2192 0.9779 0.985 0.6354 0.9518 0.9712 0.3855 ] Network output: [ -0.1456 0.3071 0.8598 -0.001313 0.0005895 1.119 -0.0009894 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6758 0.6636 0.425 0.1434 0.974 0.9822 0.6759 0.9429 0.9649 0.4376 ] Network output: [ 0.1354 0.6639 0.1541 -0.0002382 0.0001071 0.9101 -0.0001801 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08781 Epoch 926 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0314 1.01 0.9778 5.705e-05 -2.565e-05 -0.05036 4.316e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03922 -0.002472 0.02314 0.02493 0.9146 0.9277 0.07613 0.8465 0.88 0.1707 ] Network output: [ 0.9319 0.1529 -0.07445 -0.0006499 0.0002919 0.05512 -0.0004905 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6432 -0.002539 -0.06102 0.3008 0.956 0.9776 0.7351 0.8705 0.9509 0.7171 ] Network output: [ -0.0005436 0.9294 1.043 0.0002461 -0.0001106 0.02947 0.0001858 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06899 0.03712 0.05477 0.04795 0.974 0.9811 0.07074 0.9434 0.9675 0.08827 ] Network output: [ 0.1232 -0.3368 1.158 0.0006042 -0.0002713 0.9348 0.0004558 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7316 0.4252 0.3712 0.5102 0.9608 0.9807 0.7355 0.8823 0.9579 0.719 ] Network output: [ -0.07578 0.2751 0.8808 0.001273 -0.0005714 1.001 0.0009592 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6345 0.5786 0.3406 0.2191 0.9779 0.985 0.6351 0.9518 0.9712 0.3857 ] Network output: [ -0.1455 0.3075 0.8597 -0.00131 0.0005882 1.119 -0.0009873 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6754 0.6633 0.4251 0.1435 0.974 0.9822 0.6755 0.943 0.9649 0.4377 ] Network output: [ 0.1348 0.6648 0.1538 -0.0002556 0.0001149 0.9107 -0.0001932 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08765 Epoch 927 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03124 1.01 0.9783 5.751e-05 -2.586e-05 -0.05019 4.35e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03918 -0.002435 0.0233 0.02496 0.9146 0.9277 0.0761 0.8466 0.8801 0.1708 ] Network output: [ 0.9299 0.1522 -0.07123 -0.0006625 0.0002976 0.05657 -0.0004999 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6431 -0.001477 -0.05938 0.3007 0.9561 0.9777 0.7351 0.8706 0.9509 0.717 ] Network output: [ -0.000519 0.9291 1.043 0.0002476 -0.0001112 0.02958 0.0001869 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06899 0.03723 0.05499 0.04798 0.974 0.9811 0.07074 0.9435 0.9675 0.08841 ] Network output: [ 0.1224 -0.3366 1.158 0.0005907 -0.0002653 0.9359 0.0004457 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7319 0.4265 0.3725 0.5097 0.9608 0.9808 0.7357 0.8823 0.9579 0.7188 ] Network output: [ -0.07531 0.2756 0.8798 0.001278 -0.0005736 1 0.000963 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6344 0.5787 0.341 0.2188 0.978 0.9851 0.635 0.9519 0.9712 0.3858 ] Network output: [ -0.1448 0.3082 0.8582 -0.001306 0.0005861 1.118 -0.0009837 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6752 0.6632 0.4253 0.1431 0.974 0.9822 0.6753 0.943 0.965 0.4378 ] Network output: [ 0.1346 0.6657 0.1529 -0.0002713 0.0001219 0.9111 -0.0002049 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08755 Epoch 928 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03152 1.009 0.9784 6.568e-05 -2.952e-05 -0.05037 4.965e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03916 -0.002447 0.02328 0.025 0.9146 0.9277 0.07608 0.8466 0.8801 0.1709 ] Network output: [ 0.9313 0.1513 -0.07176 -0.0006492 0.0002916 0.05536 -0.0004899 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6432 -0.001619 -0.05949 0.3007 0.9561 0.9777 0.7353 0.8706 0.951 0.7169 ] Network output: [ -0.0005013 0.9286 1.044 0.0002522 -0.0001133 0.02959 0.0001904 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06896 0.03722 0.05506 0.04809 0.974 0.9811 0.07071 0.9435 0.9675 0.08852 ] Network output: [ 0.1228 -0.3377 1.159 0.0005986 -0.0002689 0.9358 0.0004516 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7319 0.4267 0.3728 0.5099 0.9608 0.9808 0.7358 0.8824 0.9579 0.7188 ] Network output: [ -0.07572 0.2746 0.8817 0.00129 -0.0005794 1 0.0009727 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.634 0.5785 0.3414 0.2196 0.978 0.9851 0.6346 0.9519 0.9713 0.3862 ] Network output: [ -0.145 0.3066 0.8603 -0.001278 0.0005738 1.118 -0.0009631 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6749 0.6629 0.4255 0.1445 0.974 0.9822 0.675 0.943 0.965 0.438 ] Network output: [ 0.1341 0.6659 0.1529 -0.0002749 0.0001235 0.9118 -0.0002077 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08723 Epoch 929 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03174 1.009 0.9779 6.489e-05 -2.917e-05 -0.05052 4.906e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03913 -0.002445 0.02326 0.02494 0.9146 0.9277 0.07607 0.8467 0.8802 0.1709 ] Network output: [ 0.9323 0.1523 -0.07402 -0.0006559 0.0002946 0.05436 -0.000495 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6433 -0.001426 -0.05947 0.3 0.9561 0.9777 0.7355 0.8707 0.951 0.7167 ] Network output: [ -0.0004397 0.9288 1.044 0.0002506 -0.0001126 0.0296 0.0001891 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06895 0.03725 0.0551 0.04802 0.9741 0.9811 0.0707 0.9436 0.9676 0.08855 ] Network output: [ 0.1228 -0.3365 1.157 0.0005824 -0.0002616 0.9359 0.0004394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.732 0.4275 0.3734 0.5091 0.9609 0.9808 0.7359 0.8825 0.958 0.7185 ] Network output: [ -0.07563 0.2754 0.8811 0.001287 -0.0005776 1 0.0009697 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6337 0.5784 0.3416 0.2193 0.978 0.9851 0.6344 0.952 0.9713 0.3863 ] Network output: [ -0.1448 0.3074 0.8596 -0.00128 0.0005747 1.117 -0.0009647 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6745 0.6626 0.4256 0.1442 0.974 0.9822 0.6747 0.9431 0.965 0.4381 ] Network output: [ 0.1336 0.6669 0.1525 -0.0002951 0.0001326 0.9123 -0.0002229 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08711 Epoch 930 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03155 1.009 0.9786 6.635e-05 -2.982e-05 -0.05032 5.016e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0391 -0.002407 0.02345 0.02498 0.9146 0.9278 0.07603 0.8468 0.8802 0.171 ] Network output: [ 0.9301 0.1512 -0.0701 -0.0006664 0.0002993 0.05601 -0.0005029 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6433 -0.0003232 -0.05768 0.3 0.9561 0.9777 0.7355 0.8708 0.951 0.7166 ] Network output: [ -0.0004268 0.9284 1.044 0.0002529 -0.0001136 0.02972 0.0001909 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06895 0.03735 0.05534 0.04808 0.9741 0.9811 0.0707 0.9436 0.9676 0.08871 ] Network output: [ 0.122 -0.3366 1.158 0.0005722 -0.000257 0.9371 0.0004317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7322 0.4288 0.3748 0.5086 0.9609 0.9808 0.7361 0.8825 0.958 0.7184 ] Network output: [ -0.07518 0.2756 0.8804 0.001294 -0.0005811 0.9996 0.0009756 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6336 0.5786 0.342 0.2191 0.978 0.9851 0.6343 0.952 0.9713 0.3865 ] Network output: [ -0.144 0.3079 0.8583 -0.001271 0.0005707 1.117 -0.000958 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6744 0.6625 0.4258 0.144 0.974 0.9822 0.6745 0.9431 0.9651 0.4382 ] Network output: [ 0.1334 0.6677 0.1516 -0.0003086 0.0001386 0.9127 -0.000233 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08699 Epoch 931 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03191 1.009 0.9785 7.471e-05 -3.357e-05 -0.05057 5.645e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03907 -0.002428 0.02339 0.02501 0.9147 0.9278 0.07602 0.8469 0.8803 0.1711 ] Network output: [ 0.9321 0.1506 -0.07174 -0.0006503 0.0002921 0.05424 -0.0004908 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6434 -0.000682 -0.05816 0.3 0.9561 0.9777 0.7357 0.8708 0.9511 0.7165 ] Network output: [ -0.0004057 0.928 1.044 0.0002571 -0.0001155 0.02972 0.000194 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06891 0.03732 0.05538 0.04818 0.9741 0.9811 0.07067 0.9437 0.9676 0.08881 ] Network output: [ 0.1226 -0.3377 1.158 0.000581 -0.000261 0.9367 0.0004383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7323 0.4289 0.3749 0.5089 0.9609 0.9808 0.7362 0.8826 0.958 0.7183 ] Network output: [ -0.0757 0.2746 0.8826 0.001306 -0.0005865 0.9996 0.0009847 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6332 0.5783 0.3424 0.22 0.978 0.9851 0.6339 0.9521 0.9713 0.3868 ] Network output: [ -0.1445 0.3062 0.8607 -0.001244 0.0005583 1.117 -0.0009371 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.674 0.6622 0.426 0.1455 0.974 0.9822 0.6742 0.9431 0.9651 0.4384 ] Network output: [ 0.1328 0.6679 0.1518 -0.000312 0.0001402 0.9135 -0.0002357 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08666 Epoch 932 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03205 1.009 0.978 7.216e-05 -3.243e-05 -0.05066 5.453e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03905 -0.002416 0.0234 0.02494 0.9147 0.9278 0.07602 0.8469 0.8803 0.171 ] Network output: [ 0.9326 0.1517 -0.07342 -0.0006618 0.0002973 0.0537 -0.0004994 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6434 -0.0002704 -0.05783 0.2991 0.9561 0.9777 0.7358 0.8709 0.9511 0.7162 ] Network output: [ -0.0003437 0.9282 1.044 0.0002545 -0.0001143 0.02974 0.0001921 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06891 0.03738 0.05544 0.04808 0.9741 0.9811 0.07067 0.9437 0.9676 0.08884 ] Network output: [ 0.1223 -0.3361 1.157 0.0005604 -0.0002517 0.9371 0.0004228 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7324 0.4298 0.3757 0.5079 0.9609 0.9808 0.7363 0.8827 0.9581 0.7181 ] Network output: [ -0.07544 0.2757 0.8814 0.001301 -0.0005839 0.9991 0.0009803 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.633 0.5783 0.3426 0.2194 0.978 0.9851 0.6337 0.9521 0.9714 0.3869 ] Network output: [ -0.144 0.3075 0.8593 -0.00125 0.0005614 1.116 -0.0009423 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6737 0.6619 0.4261 0.1449 0.974 0.9822 0.6738 0.9432 0.9651 0.4385 ] Network output: [ 0.1323 0.6691 0.1511 -0.0003351 0.0001505 0.9139 -0.000253 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08658 Epoch 933 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03185 1.008 0.9788 7.509e-05 -3.375e-05 -0.05045 5.674e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03902 -0.002379 0.02359 0.02501 0.9147 0.9278 0.07597 0.847 0.8804 0.1711 ] Network output: [ 0.9303 0.1502 -0.06898 -0.0006689 0.0003004 0.05542 -0.0005047 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6434 0.0008077 -0.056 0.2994 0.9562 0.9777 0.7359 0.871 0.9511 0.7162 ] Network output: [ -0.0003433 0.9277 1.044 0.0002579 -0.0001159 0.02987 0.0001946 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06891 0.03748 0.0557 0.04818 0.9741 0.9811 0.07067 0.9438 0.9677 0.08902 ] Network output: [ 0.1216 -0.3368 1.158 0.0005546 -0.0002491 0.9382 0.0004184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7326 0.431 0.3771 0.5076 0.9609 0.9808 0.7365 0.8828 0.9581 0.718 ] Network output: [ -0.07507 0.2756 0.8811 0.001312 -0.000589 0.9988 0.0009888 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6329 0.5784 0.3431 0.2195 0.978 0.9851 0.6336 0.9522 0.9714 0.3871 ] Network output: [ -0.1433 0.3075 0.8585 -0.001236 0.0005549 1.116 -0.0009314 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6735 0.6618 0.4263 0.145 0.974 0.9823 0.6737 0.9432 0.9651 0.4386 ] Network output: [ 0.1321 0.6697 0.1503 -0.0003455 0.0001552 0.9143 -0.0002609 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08642 Epoch 934 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0323 1.008 0.9785 8.316e-05 -3.737e-05 -0.05078 6.281e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.039 -0.002409 0.02349 0.02501 0.9147 0.9278 0.07597 0.8471 0.8804 0.1712 ] Network output: [ 0.9331 0.1499 -0.07187 -0.000651 0.0002924 0.05307 -0.0004912 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6435 0.0002421 -0.05685 0.2991 0.9562 0.9777 0.7361 0.871 0.9512 0.7161 ] Network output: [ -0.0003157 0.9274 1.044 0.0002613 -0.0001174 0.02984 0.0001972 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06887 0.03743 0.0557 0.04826 0.9741 0.9811 0.07063 0.9438 0.9677 0.0891 ] Network output: [ 0.1223 -0.3376 1.158 0.0005631 -0.0002529 0.9377 0.0004248 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7326 0.431 0.3771 0.5078 0.9609 0.9808 0.7365 0.8828 0.9581 0.7179 ] Network output: [ -0.07568 0.2746 0.8834 0.001322 -0.0005935 0.9988 0.0009964 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6325 0.5781 0.3434 0.2203 0.978 0.9851 0.6331 0.9522 0.9714 0.3875 ] Network output: [ -0.1439 0.3057 0.8611 -0.00121 0.0005431 1.116 -0.0009116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6732 0.6614 0.4265 0.1465 0.974 0.9823 0.6733 0.9433 0.9652 0.4388 ] Network output: [ 0.1314 0.67 0.1507 -0.0003497 0.0001571 0.9151 -0.000264 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08609 Epoch 935 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03234 1.008 0.9781 7.892e-05 -3.547e-05 -0.05079 5.962e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03897 -0.002386 0.02355 0.02494 0.9147 0.9278 0.07596 0.8471 0.8804 0.1712 ] Network output: [ 0.9328 0.1511 -0.07262 -0.0006677 0.0002999 0.05317 -0.0005038 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6436 0.0009322 -0.0561 0.2983 0.9562 0.9777 0.7362 0.8711 0.9512 0.7158 ] Network output: [ -0.0002562 0.9276 1.044 0.000258 -0.0001159 0.02989 0.0001947 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06888 0.03751 0.0558 0.04814 0.9741 0.9811 0.07064 0.9439 0.9677 0.08914 ] Network output: [ 0.1218 -0.3358 1.156 0.0005382 -0.0002417 0.9383 0.000406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7328 0.4322 0.3781 0.5066 0.9609 0.9808 0.7367 0.8829 0.9581 0.7176 ] Network output: [ -0.07522 0.276 0.8816 0.001315 -0.0005903 0.9982 0.000991 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6323 0.5782 0.3437 0.2196 0.978 0.9851 0.633 0.9523 0.9714 0.3876 ] Network output: [ -0.1432 0.3075 0.8589 -0.001221 0.0005481 1.115 -0.00092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6729 0.6612 0.4267 0.1455 0.974 0.9823 0.673 0.9433 0.9652 0.4389 ] Network output: [ 0.131 0.6713 0.1498 -0.0003754 0.0001687 0.9154 -0.0002834 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08604 Epoch 936 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03216 1.008 0.9791 8.378e-05 -3.765e-05 -0.05059 6.328e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03894 -0.002353 0.02374 0.02503 0.9147 0.9279 0.07591 0.8472 0.8805 0.1713 ] Network output: [ 0.9307 0.1491 -0.06793 -0.0006697 0.0003008 0.05476 -0.0005053 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6435 0.001903 -0.05435 0.2987 0.9562 0.9778 0.7362 0.8712 0.9512 0.7159 ] Network output: [ -0.0002684 0.9269 1.045 0.0002626 -0.000118 0.03002 0.0001982 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06887 0.0376 0.05607 0.04829 0.9741 0.9812 0.07063 0.944 0.9678 0.08934 ] Network output: [ 0.1212 -0.3369 1.157 0.0005381 -0.0002417 0.9393 0.000406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.733 0.4332 0.3793 0.5066 0.961 0.9808 0.7369 0.883 0.9582 0.7176 ] Network output: [ -0.07498 0.2754 0.8819 0.00133 -0.0005971 0.998 0.001002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6322 0.5783 0.3442 0.2199 0.978 0.9851 0.6329 0.9523 0.9715 0.3878 ] Network output: [ -0.1426 0.307 0.8588 -0.0012 0.0005386 1.115 -0.0009041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6728 0.6611 0.4268 0.1459 0.9741 0.9823 0.6729 0.9433 0.9652 0.439 ] Network output: [ 0.1308 0.6718 0.1491 -0.000382 0.0001716 0.9159 -0.0002883 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08584 Epoch 937 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03269 1.007 0.9785 9.094e-05 -4.086e-05 -0.05099 6.867e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03892 -0.00239 0.02359 0.02501 0.9148 0.9279 0.07592 0.8473 0.8805 0.1714 ] Network output: [ 0.9341 0.1494 -0.07214 -0.0006514 0.0002926 0.05187 -0.0004915 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6437 0.001164 -0.05556 0.2983 0.9562 0.9778 0.7365 0.8713 0.9512 0.7157 ] Network output: [ -0.000231 0.9268 1.045 0.0002648 -0.0001189 0.02997 0.0001998 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06884 0.03754 0.05602 0.04832 0.9742 0.9812 0.0706 0.944 0.9678 0.08939 ] Network output: [ 0.1221 -0.3374 1.157 0.0005445 -0.0002445 0.9386 0.0004107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.733 0.4331 0.3792 0.5067 0.961 0.9809 0.7369 0.883 0.9582 0.7175 ] Network output: [ -0.07563 0.2746 0.8841 0.001337 -0.0006003 0.998 0.001008 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6318 0.5779 0.3445 0.2207 0.9781 0.9851 0.6324 0.9524 0.9715 0.3882 ] Network output: [ -0.1433 0.3054 0.8614 -0.001177 0.0005283 1.115 -0.0008868 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6723 0.6608 0.4271 0.1473 0.9741 0.9823 0.6725 0.9434 0.9653 0.4392 ] Network output: [ 0.13 0.6721 0.1495 -0.0003881 0.0001744 0.9167 -0.000293 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08551 Epoch 938 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0326 1.008 0.9783 8.526e-05 -3.831e-05 -0.05089 6.439e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0389 -0.002353 0.0237 0.02494 0.9148 0.9279 0.0759 0.8474 0.8806 0.1714 ] Network output: [ 0.9329 0.1503 -0.07156 -0.0006734 0.0003024 0.05276 -0.0005081 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6437 0.002183 -0.05428 0.2975 0.9562 0.9778 0.7366 0.8713 0.9513 0.7155 ] Network output: [ -0.0001779 0.927 1.044 0.0002612 -0.0001173 0.03005 0.000197 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06885 0.03765 0.05617 0.04821 0.9742 0.9812 0.07062 0.9441 0.9678 0.08946 ] Network output: [ 0.1213 -0.3354 1.155 0.000516 -0.0002318 0.9395 0.0003893 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7331 0.4345 0.3805 0.5053 0.961 0.9809 0.7371 0.8831 0.9582 0.7172 ] Network output: [ -0.07497 0.2762 0.8817 0.001329 -0.0005969 0.9974 0.001002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6317 0.5781 0.3448 0.2197 0.9781 0.9851 0.6324 0.9524 0.9715 0.3883 ] Network output: [ -0.1423 0.3076 0.8584 -0.001191 0.0005348 1.114 -0.0008977 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6721 0.6606 0.4272 0.146 0.9741 0.9823 0.6723 0.9434 0.9653 0.4393 ] Network output: [ 0.1297 0.6735 0.1485 -0.0004159 0.0001868 0.917 -0.0003139 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08551 Epoch 939 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03247 1.007 0.9794 9.242e-05 -4.152e-05 -0.05074 6.979e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03886 -0.00233 0.02387 0.02505 0.9148 0.9279 0.07585 0.8474 0.8806 0.1715 ] Network output: [ 0.9312 0.148 -0.067 -0.0006687 0.0003004 0.05399 -0.0005046 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6437 0.002951 -0.05276 0.298 0.9563 0.9778 0.7366 0.8714 0.9513 0.7155 ] Network output: [ -0.0002015 0.9262 1.045 0.000267 -0.0001199 0.03017 0.0002015 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06884 0.03772 0.05643 0.0484 0.9742 0.9812 0.0706 0.9442 0.9679 0.08967 ] Network output: [ 0.1208 -0.3371 1.157 0.0005227 -0.0002348 0.9404 0.0003943 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7333 0.4354 0.3816 0.5056 0.961 0.9809 0.7373 0.8832 0.9583 0.7172 ] Network output: [ -0.07491 0.2751 0.8829 0.001348 -0.0006054 0.9973 0.001016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6315 0.5781 0.3453 0.2203 0.9781 0.9851 0.6322 0.9525 0.9716 0.3886 ] Network output: [ -0.142 0.3064 0.8592 -0.001162 0.0005218 1.114 -0.0008759 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.672 0.6605 0.4274 0.147 0.9741 0.9823 0.6721 0.9435 0.9653 0.4394 ] Network output: [ 0.1295 0.6738 0.148 -0.0004179 0.0001877 0.9175 -0.0003154 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08525 Epoch 940 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03307 1.007 0.9785 9.797e-05 -4.401e-05 -0.0512 7.396e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03884 -0.00237 0.02369 0.025 0.9148 0.9279 0.07587 0.8475 0.8807 0.1716 ] Network output: [ 0.9351 0.1489 -0.07251 -0.0006519 0.0002928 0.05067 -0.0004919 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6438 0.002098 -0.05425 0.2973 0.9563 0.9778 0.7369 0.8715 0.9513 0.7153 ] Network output: [ -0.0001515 0.9262 1.045 0.0002676 -0.0001202 0.03011 0.0002019 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0688 0.03765 0.05634 0.04838 0.9742 0.9812 0.07057 0.9442 0.9679 0.08969 ] Network output: [ 0.1218 -0.3371 1.156 0.0005249 -0.0002358 0.9394 0.000396 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7333 0.4353 0.3814 0.5055 0.961 0.9809 0.7372 0.8832 0.9583 0.7171 ] Network output: [ -0.07556 0.2746 0.8848 0.001351 -0.0006067 0.9972 0.001019 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6311 0.5777 0.3456 0.2209 0.9781 0.9851 0.6318 0.9525 0.9716 0.389 ] Network output: [ -0.1426 0.3052 0.8616 -0.001145 0.0005141 1.114 -0.0008629 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6716 0.6601 0.4276 0.1481 0.9741 0.9823 0.6717 0.9435 0.9654 0.4396 ] Network output: [ 0.1286 0.6742 0.1484 -0.0004274 0.000192 0.9183 -0.0003225 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08495 Epoch 941 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03283 1.007 0.9786 9.128e-05 -4.101e-05 -0.05097 6.893e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03882 -0.002319 0.02388 0.02494 0.9148 0.9279 0.07584 0.8476 0.8807 0.1716 ] Network output: [ 0.9327 0.1495 -0.07021 -0.0006787 0.0003048 0.05249 -0.000512 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6438 0.00348 -0.05237 0.2966 0.9563 0.9778 0.7369 0.8715 0.9514 0.7151 ] Network output: [ -0.0001095 0.9264 1.045 0.0002639 -0.0001186 0.03022 0.0001991 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06883 0.03779 0.05656 0.04828 0.9742 0.9812 0.07059 0.9442 0.9679 0.08979 ] Network output: [ 0.1207 -0.3351 1.155 0.0004942 -0.000222 0.9407 0.0003728 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7335 0.4369 0.3829 0.504 0.961 0.9809 0.7374 0.8833 0.9583 0.7168 ] Network output: [ -0.0747 0.2763 0.8819 0.001345 -0.0006038 0.9966 0.001014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6311 0.5781 0.3459 0.2198 0.9781 0.9852 0.6318 0.9526 0.9716 0.389 ] Network output: [ -0.1414 0.3077 0.8579 -0.001161 0.0005213 1.113 -0.0008751 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6714 0.66 0.4278 0.1465 0.9741 0.9823 0.6715 0.9435 0.9654 0.4397 ] Network output: [ 0.1284 0.6757 0.1471 -0.0004563 0.0002049 0.9185 -0.0003443 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08497 Epoch 942 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03281 1.006 0.9796 0.000101 -4.537e-05 -0.05091 7.625e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03878 -0.002308 0.024 0.02506 0.9149 0.928 0.0758 0.8477 0.8808 0.1717 ] Network output: [ 0.9318 0.1469 -0.0663 -0.000666 0.0002991 0.05309 -0.0005025 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6438 0.003939 -0.05126 0.2973 0.9563 0.9778 0.737 0.8716 0.9514 0.7152 ] Network output: [ -0.000142 0.9255 1.046 0.000271 -0.0001217 0.03032 0.0002045 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0688 0.03783 0.05679 0.0485 0.9742 0.9812 0.07057 0.9443 0.968 0.09 ] Network output: [ 0.1206 -0.3374 1.157 0.0005083 -0.0002283 0.9413 0.0003835 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7337 0.4375 0.3838 0.5047 0.9611 0.9809 0.7376 0.8834 0.9584 0.7169 ] Network output: [ -0.07487 0.2747 0.884 0.001367 -0.0006139 0.9966 0.001031 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6309 0.578 0.3464 0.2208 0.9781 0.9852 0.6316 0.9526 0.9717 0.3894 ] Network output: [ -0.1414 0.3057 0.8598 -0.001124 0.0005047 1.113 -0.0008471 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6712 0.6599 0.428 0.148 0.9741 0.9823 0.6714 0.9436 0.9654 0.4399 ] Network output: [ 0.1282 0.6758 0.1469 -0.0004533 0.0002036 0.9191 -0.0003421 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08465 Epoch 943 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03343 1.007 0.9784 0.0001042 -4.679e-05 -0.0514 7.862e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03877 -0.002349 0.0238 0.02498 0.9149 0.928 0.07582 0.8477 0.8808 0.1717 ] Network output: [ 0.9361 0.1485 -0.07291 -0.0006529 0.0002933 0.04952 -0.0004926 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.644 0.003061 -0.05292 0.2963 0.9563 0.9778 0.7373 0.8717 0.9514 0.7149 ] Network output: [ -7.741e-05 0.9257 1.045 0.0002696 -0.0001211 0.03024 0.0002034 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06878 0.03777 0.05667 0.04843 0.9742 0.9812 0.07055 0.9443 0.968 0.08999 ] Network output: [ 0.1216 -0.3366 1.155 0.0005042 -0.0002265 0.9403 0.0003804 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7336 0.4374 0.3836 0.5042 0.9611 0.9809 0.7376 0.8834 0.9584 0.7167 ] Network output: [ -0.07545 0.2746 0.8854 0.001365 -0.0006129 0.9965 0.001029 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6305 0.5776 0.3467 0.2211 0.9781 0.9852 0.6311 0.9526 0.9717 0.3897 ] Network output: [ -0.1419 0.305 0.8615 -0.001115 0.0005005 1.113 -0.0008402 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6708 0.6595 0.4282 0.1488 0.9741 0.9823 0.6709 0.9436 0.9654 0.4401 ] Network output: [ 0.1272 0.6764 0.1473 -0.0004675 0.00021 0.9199 -0.0003528 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08439 Epoch 944 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03303 1.007 0.9788 9.712e-05 -4.363e-05 -0.05103 7.332e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03874 -0.002284 0.02406 0.02495 0.9149 0.928 0.07578 0.8478 0.8809 0.1717 ] Network output: [ 0.9325 0.1485 -0.06858 -0.0006834 0.0003069 0.05233 -0.0005156 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6439 0.004815 -0.05038 0.2958 0.9563 0.9778 0.7373 0.8717 0.9515 0.7148 ] Network output: [ -5.177e-05 0.9258 1.045 0.0002665 -0.0001197 0.0304 0.0002011 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06881 0.03794 0.05696 0.04835 0.9742 0.9812 0.07058 0.9444 0.968 0.09013 ] Network output: [ 0.1201 -0.3348 1.154 0.000473 -0.0002124 0.942 0.0003569 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7339 0.4393 0.3854 0.5027 0.9611 0.9809 0.7378 0.8835 0.9584 0.7165 ] Network output: [ -0.0744 0.2764 0.8821 0.001361 -0.000611 0.9958 0.001026 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6306 0.5781 0.3471 0.22 0.9781 0.9852 0.6312 0.9527 0.9717 0.3898 ] Network output: [ -0.1405 0.3077 0.8573 -0.00113 0.0005074 1.111 -0.0008517 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6707 0.6594 0.4284 0.147 0.9741 0.9823 0.6708 0.9437 0.9655 0.4402 ] Network output: [ 0.1271 0.678 0.1457 -0.0004962 0.0002228 0.9199 -0.0003743 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08443 Epoch 945 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03315 1.005 0.9798 0.0001095 -4.917e-05 -0.05109 8.262e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03871 -0.00229 0.02412 0.02508 0.9149 0.928 0.07575 0.8479 0.8809 0.1719 ] Network output: [ 0.9327 0.1458 -0.06588 -0.0006615 0.0002971 0.05202 -0.000499 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6439 0.004858 -0.04986 0.2965 0.9564 0.9778 0.7374 0.8718 0.9515 0.7149 ] Network output: [ -8.905e-05 0.9248 1.046 0.0002746 -0.0001233 0.03047 0.0002072 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06877 0.03794 0.05715 0.04861 0.9743 0.9812 0.07054 0.9445 0.968 0.09034 ] Network output: [ 0.1204 -0.3377 1.157 0.0004947 -0.0002222 0.9422 0.0003732 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.734 0.4395 0.386 0.5037 0.9611 0.9809 0.738 0.8836 0.9585 0.7165 ] Network output: [ -0.07487 0.2741 0.8852 0.001386 -0.0006225 0.996 0.001045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6303 0.5779 0.3476 0.2213 0.9781 0.9852 0.6309 0.9528 0.9717 0.3902 ] Network output: [ -0.1408 0.3048 0.8605 -0.001085 0.0004872 1.112 -0.0008178 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6705 0.6593 0.4286 0.1491 0.9741 0.9824 0.6706 0.9437 0.9655 0.4404 ] Network output: [ 0.1268 0.6778 0.146 -0.0004882 0.0002193 0.9207 -0.0003684 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08404 Epoch 946 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03377 1.006 0.9783 0.0001094 -4.916e-05 -0.05158 8.26e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0387 -0.002326 0.02391 0.02495 0.915 0.928 0.07577 0.8479 0.881 0.1719 ] Network output: [ 0.937 0.1482 -0.07328 -0.0006548 0.0002941 0.04846 -0.000494 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6441 0.004073 -0.05152 0.2952 0.9564 0.9779 0.7377 0.8719 0.9515 0.7146 ] Network output: [ -8.92e-06 0.9253 1.045 0.0002709 -0.0001217 0.03039 0.0002044 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06875 0.03789 0.05701 0.04846 0.9743 0.9812 0.07053 0.9445 0.968 0.09029 ] Network output: [ 0.1213 -0.336 1.154 0.0004821 -0.0002165 0.9412 0.0003637 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7339 0.4396 0.3858 0.5028 0.9611 0.9809 0.7379 0.8836 0.9585 0.7164 ] Network output: [ -0.07529 0.2747 0.8857 0.001378 -0.0006187 0.9957 0.001039 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6299 0.5776 0.3479 0.2213 0.9781 0.9852 0.6306 0.9528 0.9718 0.3905 ] Network output: [ -0.1412 0.3051 0.8613 -0.001087 0.0004877 1.112 -0.0008187 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.67 0.6588 0.4289 0.1493 0.9742 0.9824 0.6702 0.9437 0.9655 0.4406 ] Network output: [ 0.1258 0.6787 0.1462 -0.0005087 0.0002285 0.9214 -0.0003838 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08384 Epoch 947 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03321 1.006 0.9792 0.0001029 -4.623e-05 -0.05108 7.769e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03867 -0.002247 0.02425 0.02496 0.915 0.928 0.07572 0.848 0.881 0.1719 ] Network output: [ 0.9321 0.1474 -0.06666 -0.0006871 0.0003086 0.05228 -0.0005183 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.644 0.006175 -0.04832 0.2951 0.9564 0.9779 0.7376 0.8719 0.9516 0.7145 ] Network output: [ -5.206e-06 0.9251 1.045 0.000269 -0.0001208 0.03058 0.0002029 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06879 0.03809 0.05738 0.04843 0.9743 0.9813 0.07056 0.9446 0.9681 0.09048 ] Network output: [ 0.1195 -0.3347 1.154 0.0004529 -0.0002034 0.9433 0.0003417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7342 0.4416 0.3879 0.5014 0.9611 0.981 0.7382 0.8837 0.9585 0.7161 ] Network output: [ -0.07409 0.2763 0.8824 0.001378 -0.0006187 0.9951 0.001039 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6301 0.5781 0.3483 0.2201 0.9782 0.9852 0.6307 0.9528 0.9718 0.3906 ] Network output: [ -0.1396 0.3077 0.8568 -0.001098 0.0004929 1.11 -0.0008274 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.67 0.6589 0.429 0.1475 0.9742 0.9824 0.6702 0.9438 0.9656 0.4406 ] Network output: [ 0.1259 0.6802 0.1444 -0.0005353 0.0002404 0.9214 -0.0004038 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08388 Epoch 948 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03353 1.005 0.9799 0.0001177 -5.288e-05 -0.0513 8.884e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03863 -0.002274 0.02423 0.02508 0.915 0.9281 0.0757 0.8481 0.8811 0.1721 ] Network output: [ 0.9338 0.1448 -0.06583 -0.0006553 0.0002943 0.05076 -0.0004944 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6441 0.005702 -0.04859 0.2957 0.9564 0.9779 0.7377 0.872 0.9516 0.7146 ] Network output: [ -4.154e-05 0.9242 1.046 0.0002777 -0.0001247 0.03062 0.0002095 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06874 0.03804 0.05749 0.0487 0.9743 0.9813 0.07052 0.9446 0.9681 0.09067 ] Network output: [ 0.1203 -0.3379 1.156 0.0004816 -0.0002163 0.9429 0.0003633 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7343 0.4415 0.3881 0.5027 0.9612 0.981 0.7383 0.8838 0.9585 0.7162 ] Network output: [ -0.0749 0.2735 0.8866 0.001405 -0.000631 0.9954 0.001059 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6297 0.5778 0.3488 0.2219 0.9782 0.9852 0.6303 0.9529 0.9718 0.3911 ] Network output: [ -0.1402 0.304 0.8613 -0.001046 0.0004697 1.111 -0.0007884 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6698 0.6587 0.4293 0.1502 0.9742 0.9824 0.6699 0.9438 0.9656 0.4409 ] Network output: [ 0.1254 0.6797 0.1451 -0.0005228 0.0002348 0.9223 -0.0003944 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08342 Epoch 949 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03408 1.006 0.9782 0.0001138 -5.111e-05 -0.05173 8.586e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03863 -0.002301 0.02404 0.02491 0.915 0.9281 0.07573 0.8481 0.8811 0.1721 ] Network output: [ 0.9377 0.1479 -0.0735 -0.0006578 0.0002954 0.04754 -0.0004962 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6443 0.005157 -0.05003 0.2941 0.9564 0.9779 0.7381 0.8721 0.9516 0.7142 ] Network output: [ 5.328e-05 0.9249 1.046 0.0002714 -0.0001219 0.03054 0.0002047 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06874 0.03802 0.05735 0.04848 0.9743 0.9813 0.07052 0.9447 0.9681 0.0906 ] Network output: [ 0.1209 -0.3352 1.153 0.0004584 -0.0002059 0.9421 0.0003458 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7342 0.4418 0.3881 0.5012 0.9612 0.981 0.7382 0.8838 0.9586 0.716 ] Network output: [ -0.07506 0.2749 0.8859 0.00139 -0.0006242 0.995 0.001048 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6294 0.5776 0.349 0.2213 0.9782 0.9852 0.63 0.9529 0.9718 0.3913 ] Network output: [ -0.1404 0.3053 0.8608 -0.00106 0.0004757 1.11 -0.0007985 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6693 0.6583 0.4295 0.1497 0.9742 0.9824 0.6695 0.9439 0.9656 0.4411 ] Network output: [ 0.1244 0.6811 0.145 -0.0005509 0.0002474 0.9229 -0.0004156 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0833 Epoch 950 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03338 1.005 0.9796 0.0001089 -4.89e-05 -0.05112 8.215e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03859 -0.002211 0.02445 0.02497 0.915 0.9281 0.07566 0.8482 0.8812 0.1721 ] Network output: [ 0.9316 0.1462 -0.06449 -0.0006894 0.0003096 0.05229 -0.0005201 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6441 0.007542 -0.04622 0.2944 0.9564 0.9779 0.7379 0.8722 0.9517 0.7143 ] Network output: [ 2.975e-05 0.9244 1.046 0.0002714 -0.0001219 0.03078 0.0002047 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06877 0.03824 0.05781 0.04852 0.9743 0.9813 0.07055 0.9448 0.9682 0.09085 ] Network output: [ 0.1189 -0.3347 1.154 0.0004343 -0.0001951 0.9445 0.0003277 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7346 0.444 0.3905 0.5002 0.9612 0.981 0.7386 0.8839 0.9586 0.7158 ] Network output: [ -0.07379 0.276 0.8828 0.001397 -0.000627 0.9944 0.001053 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6296 0.5782 0.3495 0.2203 0.9782 0.9852 0.6302 0.953 0.9719 0.3914 ] Network output: [ -0.1386 0.3075 0.8565 -0.001064 0.0004776 1.109 -0.0008017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6694 0.6584 0.4296 0.1481 0.9742 0.9824 0.6695 0.9439 0.9656 0.4411 ] Network output: [ 0.1246 0.6825 0.1431 -0.0005731 0.0002574 0.9228 -0.0004323 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08332 Epoch 951 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03392 1.004 0.9799 0.0001257 -5.644e-05 -0.05153 9.481e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03856 -0.002262 0.02432 0.02508 0.915 0.9281 0.07565 0.8483 0.8812 0.1723 ] Network output: [ 0.9352 0.1439 -0.06623 -0.0006479 0.000291 0.04929 -0.0004888 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6442 0.006468 -0.04746 0.2949 0.9565 0.9779 0.7381 0.8722 0.9517 0.7143 ] Network output: [ 1.769e-06 0.9236 1.047 0.0002802 -0.0001259 0.03077 0.0002114 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06872 0.03814 0.05783 0.04879 0.9743 0.9813 0.0705 0.9448 0.9682 0.091 ] Network output: [ 0.1202 -0.3381 1.156 0.0004685 -0.0002104 0.9436 0.0003534 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7346 0.4434 0.3902 0.5016 0.9612 0.981 0.7386 0.884 0.9586 0.716 ] Network output: [ -0.07495 0.2728 0.888 0.001424 -0.0006392 0.9948 0.001073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6291 0.5777 0.35 0.2224 0.9782 0.9852 0.6298 0.953 0.9719 0.392 ] Network output: [ -0.1397 0.3031 0.8622 -0.001007 0.0004522 1.11 -0.0007591 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6691 0.6581 0.4299 0.1513 0.9742 0.9824 0.6692 0.9439 0.9657 0.4414 ] Network output: [ 0.1239 0.6817 0.1443 -0.0005573 0.0002503 0.9239 -0.0004204 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0828 Epoch 952 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03434 1.006 0.9781 0.0001172 -5.262e-05 -0.05186 8.841e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03856 -0.002271 0.02418 0.02487 0.9151 0.9281 0.07568 0.8483 0.8812 0.1722 ] Network output: [ 0.9381 0.1477 -0.07344 -0.0006621 0.0002974 0.04685 -0.0004995 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6444 0.006336 -0.04841 0.293 0.9565 0.9779 0.7384 0.8723 0.9517 0.7139 ] Network output: [ 0.0001082 0.9245 1.046 0.0002712 -0.0001218 0.03071 0.0002046 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06873 0.03816 0.05772 0.04848 0.9743 0.9813 0.07051 0.9448 0.9682 0.09091 ] Network output: [ 0.1205 -0.3343 1.152 0.0004332 -0.0001946 0.9431 0.0003268 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7346 0.4441 0.3904 0.4996 0.9612 0.981 0.7385 0.8841 0.9587 0.7157 ] Network output: [ -0.07475 0.2751 0.8858 0.001402 -0.0006295 0.9942 0.001057 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6289 0.5776 0.3502 0.2212 0.9782 0.9852 0.6296 0.9531 0.9719 0.3921 ] Network output: [ -0.1395 0.3057 0.86 -0.001035 0.0004644 1.109 -0.0007796 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6687 0.6577 0.4301 0.1499 0.9742 0.9824 0.6688 0.944 0.9657 0.4416 ] Network output: [ 0.123 0.6835 0.1437 -0.0005941 0.0002668 0.9243 -0.0004481 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08277 Epoch 953 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03353 1.004 0.9801 0.0001151 -5.17e-05 -0.05115 8.685e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03852 -0.002175 0.02465 0.02499 0.9151 0.9281 0.0756 0.8484 0.8813 0.1724 ] Network output: [ 0.9312 0.1447 -0.06214 -0.0006898 0.0003098 0.0523 -0.0005204 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6442 0.00889 -0.04412 0.2937 0.9565 0.9779 0.7382 0.8724 0.9518 0.7141 ] Network output: [ 5.292e-05 0.9237 1.046 0.0002738 -0.000123 0.03098 0.0002066 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06876 0.03839 0.05825 0.04863 0.9744 0.9813 0.07054 0.9449 0.9683 0.09122 ] Network output: [ 0.1184 -0.3349 1.154 0.0004177 -0.0001876 0.9457 0.0003152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.735 0.4463 0.393 0.499 0.9612 0.981 0.739 0.8841 0.9587 0.7155 ] Network output: [ -0.07351 0.2755 0.8834 0.001416 -0.0006359 0.9938 0.001068 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6291 0.5783 0.3507 0.2206 0.9782 0.9852 0.6298 0.9531 0.972 0.3923 ] Network output: [ -0.1377 0.3071 0.8563 -0.001028 0.0004613 1.108 -0.0007743 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6688 0.6579 0.4303 0.1488 0.9742 0.9824 0.6689 0.944 0.9657 0.4416 ] Network output: [ 0.1234 0.6846 0.1419 -0.0006094 0.0002737 0.9243 -0.0004596 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08275 Epoch 954 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03434 1.004 0.9798 0.0001331 -5.976e-05 -0.05178 0.0001004 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0385 -0.002252 0.0244 0.02506 0.9151 0.9282 0.07561 0.8485 0.8813 0.1725 ] Network output: [ 0.9368 0.1432 -0.06714 -0.0006395 0.0002872 0.0476 -0.0004824 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6444 0.007162 -0.04647 0.2939 0.9565 0.9779 0.7385 0.8724 0.9518 0.7141 ] Network output: [ 4.226e-05 0.923 1.047 0.000282 -0.0001266 0.0309 0.0002127 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06869 0.03823 0.05814 0.04886 0.9744 0.9813 0.07048 0.945 0.9683 0.09132 ] Network output: [ 0.1202 -0.3382 1.156 0.0004549 -0.0002043 0.9441 0.0003432 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7349 0.4453 0.3922 0.5005 0.9613 0.981 0.7389 0.8842 0.9587 0.7157 ] Network output: [ -0.07502 0.2722 0.8895 0.001441 -0.0006471 0.9943 0.001086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6285 0.5776 0.3513 0.2229 0.9782 0.9853 0.6292 0.9532 0.972 0.393 ] Network output: [ -0.1392 0.3022 0.8631 -0.0009696 0.0004353 1.109 -0.0007306 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6684 0.6575 0.4306 0.1524 0.9742 0.9824 0.6685 0.9441 0.9658 0.442 ] Network output: [ 0.1224 0.6838 0.1435 -0.000592 0.0002659 0.9255 -0.0004465 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08218 Epoch 955 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03455 1.005 0.9781 0.0001196 -5.373e-05 -0.05194 9.027e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03849 -0.002236 0.02434 0.02483 0.9151 0.9282 0.07563 0.8485 0.8814 0.1724 ] Network output: [ 0.9382 0.1474 -0.07297 -0.0006681 0.0003001 0.04643 -0.000504 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6445 0.007634 -0.04661 0.2918 0.9565 0.978 0.7388 0.8725 0.9518 0.7136 ] Network output: [ 0.0001545 0.9241 1.046 0.0002705 -0.0001215 0.03088 0.000204 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06873 0.03831 0.0581 0.04848 0.9744 0.9813 0.07051 0.945 0.9683 0.09124 ] Network output: [ 0.1199 -0.3333 1.151 0.0004064 -0.0001825 0.9442 0.0003066 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7349 0.4465 0.3929 0.4979 0.9613 0.981 0.7389 0.8843 0.9587 0.7153 ] Network output: [ -0.07436 0.2754 0.8856 0.001414 -0.0006346 0.9935 0.001065 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6285 0.5778 0.3514 0.221 0.9782 0.9853 0.6291 0.9532 0.972 0.3929 ] Network output: [ -0.1385 0.3062 0.8588 -0.001011 0.0004538 1.108 -0.0007617 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6681 0.6572 0.4308 0.1499 0.9743 0.9824 0.6682 0.9441 0.9658 0.4421 ] Network output: [ 0.1217 0.6861 0.1424 -0.0006382 0.0002866 0.9256 -0.0004813 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08225 Epoch 956 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03369 1.004 0.9806 0.0001218 -5.471e-05 -0.05119 9.191e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03844 -0.002141 0.02485 0.02502 0.9151 0.9282 0.07554 0.8487 0.8814 0.1726 ] Network output: [ 0.9308 0.143 -0.05972 -0.0006877 0.0003089 0.05224 -0.0005188 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6443 0.01019 -0.04207 0.2931 0.9565 0.978 0.7385 0.8726 0.9519 0.7139 ] Network output: [ 6.448e-05 0.923 1.047 0.0002764 -0.0001241 0.03118 0.0002085 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06875 0.03853 0.05869 0.04875 0.9744 0.9814 0.07053 0.9451 0.9684 0.09161 ] Network output: [ 0.1179 -0.3353 1.154 0.0004036 -0.0001813 0.9469 0.0003045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7353 0.4485 0.3954 0.498 0.9613 0.981 0.7393 0.8844 0.9588 0.7153 ] Network output: [ -0.07328 0.2748 0.8843 0.001438 -0.0006456 0.9932 0.001084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6287 0.5783 0.352 0.221 0.9783 0.9853 0.6294 0.9533 0.972 0.3932 ] Network output: [ -0.1368 0.3064 0.8564 -0.0009882 0.0004436 1.107 -0.0007447 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6682 0.6575 0.4309 0.1496 0.9743 0.9825 0.6684 0.9442 0.9658 0.4422 ] Network output: [ 0.1221 0.6867 0.1408 -0.0006436 0.000289 0.9257 -0.0004854 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08216 Epoch 957 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03479 1.003 0.9796 0.0001397 -6.275e-05 -0.05206 0.0001054 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03843 -0.002245 0.02446 0.02503 0.9152 0.9282 0.07557 0.8487 0.8815 0.1727 ] Network output: [ 0.9388 0.1428 -0.06859 -0.0006307 0.0002832 0.04572 -0.0004758 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6445 0.007793 -0.04563 0.2928 0.9566 0.978 0.7389 0.8726 0.9519 0.7138 ] Network output: [ 8.134e-05 0.9225 1.047 0.0002828 -0.000127 0.03104 0.0002133 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06867 0.03832 0.05845 0.04891 0.9744 0.9814 0.07046 0.9451 0.9684 0.09164 ] Network output: [ 0.1203 -0.338 1.155 0.0004401 -0.0001977 0.9445 0.000332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7351 0.4471 0.3941 0.4993 0.9613 0.981 0.7391 0.8844 0.9588 0.7155 ] Network output: [ -0.0751 0.2715 0.8909 0.001457 -0.0006543 0.9938 0.001098 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.628 0.5775 0.3525 0.2234 0.9783 0.9853 0.6287 0.9533 0.9721 0.3939 ] Network output: [ -0.1388 0.3014 0.864 -0.0009335 0.0004191 1.108 -0.0007034 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6677 0.6569 0.4313 0.1534 0.9743 0.9825 0.6679 0.9442 0.9659 0.4426 ] Network output: [ 0.1209 0.6858 0.1428 -0.0006275 0.0002818 0.9271 -0.0004732 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08156 Epoch 958 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0347 1.005 0.9781 0.0001213 -5.447e-05 -0.05198 9.15e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03843 -0.002196 0.02453 0.02479 0.9152 0.9282 0.07558 0.8488 0.8815 0.1726 ] Network output: [ 0.9379 0.147 -0.0719 -0.0006759 0.0003035 0.04636 -0.0005098 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6446 0.009072 -0.04459 0.2907 0.9566 0.978 0.7391 0.8727 0.9519 0.7133 ] Network output: [ 0.0001903 0.9237 1.046 0.0002692 -0.0001209 0.03107 0.0002031 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06873 0.03848 0.05851 0.04848 0.9744 0.9814 0.07052 0.9452 0.9684 0.09158 ] Network output: [ 0.1193 -0.3322 1.15 0.0003784 -0.00017 0.9454 0.0002855 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7352 0.4489 0.3954 0.4961 0.9613 0.9811 0.7392 0.8845 0.9588 0.715 ] Network output: [ -0.07387 0.2757 0.885 0.001425 -0.0006398 0.9928 0.001074 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6281 0.578 0.3526 0.2208 0.9783 0.9853 0.6288 0.9533 0.9721 0.3938 ] Network output: [ -0.1374 0.3069 0.8574 -0.0009882 0.0004436 1.106 -0.0007446 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6675 0.6568 0.4315 0.1498 0.9743 0.9825 0.6676 0.9442 0.9659 0.4426 ] Network output: [ 0.1203 0.6887 0.1409 -0.0006827 0.0003066 0.9269 -0.0005149 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08174 Epoch 959 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03387 1.003 0.9811 0.0001292 -5.803e-05 -0.05124 9.747e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03837 -0.002111 0.02504 0.02505 0.9152 0.9282 0.07549 0.8489 0.8816 0.1728 ] Network output: [ 0.9307 0.1412 -0.05738 -0.0006825 0.0003065 0.05203 -0.0005148 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6444 0.01139 -0.04012 0.2925 0.9566 0.978 0.7388 0.8728 0.952 0.7137 ] Network output: [ 6.506e-05 0.9222 1.047 0.0002791 -0.0001254 0.03139 0.0002105 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06874 0.03867 0.05913 0.04888 0.9744 0.9814 0.07053 0.9453 0.9685 0.09201 ] Network output: [ 0.1175 -0.336 1.155 0.0003922 -0.0001762 0.9479 0.0002959 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7356 0.4506 0.3978 0.497 0.9614 0.9811 0.7397 0.8846 0.9589 0.7151 ] Network output: [ -0.07311 0.2738 0.8856 0.001461 -0.0006559 0.9927 0.001101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6283 0.5784 0.3533 0.2215 0.9783 0.9853 0.629 0.9534 0.9721 0.3942 ] Network output: [ -0.136 0.3055 0.8569 -0.0009458 0.0004246 1.106 -0.0007127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6677 0.657 0.4316 0.1506 0.9743 0.9825 0.6678 0.9443 0.9659 0.4428 ] Network output: [ 0.1208 0.6887 0.1397 -0.0006753 0.0003032 0.9272 -0.0005093 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08154 Epoch 960 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03525 1.003 0.9792 0.0001453 -6.527e-05 -0.05236 0.0001096 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03837 -0.002239 0.02451 0.02498 0.9152 0.9283 0.07554 0.8489 0.8816 0.1729 ] Network output: [ 0.9409 0.1426 -0.07061 -0.0006222 0.0002795 0.04366 -0.0004694 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6447 0.008382 -0.04491 0.2916 0.9566 0.978 0.7393 0.8728 0.952 0.7135 ] Network output: [ 0.0001204 0.9221 1.048 0.0002827 -0.0001269 0.03116 0.0002132 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06866 0.03841 0.05874 0.04893 0.9744 0.9814 0.07045 0.9453 0.9684 0.09194 ] Network output: [ 0.1204 -0.3376 1.154 0.0004233 -0.0001901 0.9448 0.0003193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7353 0.4489 0.396 0.498 0.9614 0.9811 0.7394 0.8846 0.9589 0.7152 ] Network output: [ -0.07515 0.271 0.8921 0.001471 -0.0006606 0.9932 0.001109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6275 0.5774 0.3537 0.2237 0.9783 0.9853 0.6282 0.9534 0.9721 0.3949 ] Network output: [ -0.1383 0.3007 0.8647 -0.0009001 0.0004041 1.108 -0.0006783 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.667 0.6564 0.432 0.1542 0.9743 0.9825 0.6672 0.9443 0.966 0.4432 ] Network output: [ 0.1193 0.688 0.1421 -0.0006642 0.0002983 0.9286 -0.0005009 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08095 Epoch 961 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03477 1.005 0.9783 0.0001222 -5.49e-05 -0.05196 9.222e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03836 -0.002149 0.02475 0.02475 0.9152 0.9283 0.07553 0.849 0.8816 0.1728 ] Network output: [ 0.937 0.1465 -0.07008 -0.0006853 0.0003078 0.04671 -0.0005169 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6447 0.01067 -0.04231 0.2896 0.9566 0.978 0.7394 0.8729 0.952 0.7131 ] Network output: [ 0.0002138 0.9233 1.046 0.0002677 -0.0001202 0.03128 0.0002019 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06874 0.03866 0.05895 0.04848 0.9745 0.9814 0.07054 0.9453 0.9685 0.09193 ] Network output: [ 0.1185 -0.3311 1.149 0.0003496 -0.000157 0.9467 0.0002638 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7356 0.4514 0.3981 0.4943 0.9614 0.9811 0.7396 0.8847 0.9589 0.7147 ] Network output: [ -0.07329 0.2761 0.8843 0.001437 -0.0006452 0.992 0.001083 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6278 0.5782 0.3539 0.2204 0.9783 0.9853 0.6285 0.9535 0.9722 0.3946 ] Network output: [ -0.1362 0.3078 0.8557 -0.000966 0.0004336 1.105 -0.0007279 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.667 0.6564 0.4321 0.1495 0.9743 0.9825 0.6671 0.9444 0.966 0.4432 ] Network output: [ 0.1191 0.6913 0.1394 -0.0007274 0.0003266 0.9281 -0.0005485 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08125 Epoch 962 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03408 1.002 0.9817 0.0001374 -6.169e-05 -0.05133 0.0001036 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0383 -0.002087 0.02522 0.02508 0.9153 0.9283 0.07543 0.8491 0.8817 0.1731 ] Network output: [ 0.9309 0.1393 -0.0553 -0.0006735 0.0003025 0.05155 -0.000508 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6444 0.01247 -0.03836 0.2919 0.9567 0.978 0.7391 0.873 0.9521 0.7136 ] Network output: [ 5.581e-05 0.9213 1.048 0.000282 -0.0001267 0.03159 0.0002127 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06872 0.03879 0.05956 0.04903 0.9745 0.9814 0.07052 0.9454 0.9685 0.09241 ] Network output: [ 0.1172 -0.3369 1.155 0.000384 -0.0001725 0.9488 0.0002897 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.736 0.4526 0.4001 0.4961 0.9614 0.9811 0.74 0.8848 0.959 0.715 ] Network output: [ -0.07303 0.2725 0.8872 0.001486 -0.000667 0.9923 0.00112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6279 0.5785 0.3547 0.2222 0.9783 0.9853 0.6286 0.9536 0.9722 0.3952 ] Network output: [ -0.1353 0.3041 0.8578 -0.0008999 0.000404 1.105 -0.0006781 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6671 0.6566 0.4323 0.1518 0.9744 0.9825 0.6673 0.9444 0.966 0.4433 ] Network output: [ 0.1195 0.6906 0.1389 -0.0007042 0.0003162 0.9287 -0.000531 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08091 Epoch 963 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03571 1.003 0.9787 0.0001496 -6.72e-05 -0.05267 0.0001129 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03831 -0.002233 0.02454 0.02491 0.9153 0.9283 0.07551 0.8491 0.8817 0.1731 ] Network output: [ 0.9432 0.1427 -0.07315 -0.000615 0.0002762 0.04149 -0.000464 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6449 0.00896 -0.04428 0.2903 0.9567 0.978 0.7397 0.873 0.9521 0.7132 ] Network output: [ 0.0001605 0.9219 1.048 0.0002812 -0.0001263 0.03128 0.0002121 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06865 0.0385 0.05901 0.04891 0.9745 0.9814 0.07045 0.9454 0.9685 0.09223 ] Network output: [ 0.1205 -0.3368 1.152 0.0004035 -0.0001812 0.9451 0.0003044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7355 0.4507 0.3979 0.4965 0.9614 0.9811 0.7396 0.8848 0.959 0.715 ] Network output: [ -0.07517 0.2706 0.8931 0.001483 -0.0006659 0.9927 0.001118 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6271 0.5774 0.355 0.2239 0.9783 0.9853 0.6277 0.9536 0.9722 0.3958 ] Network output: [ -0.1378 0.3004 0.8651 -0.0008703 0.0003907 1.107 -0.0006558 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6664 0.6559 0.4327 0.1548 0.9744 0.9825 0.6666 0.9445 0.9661 0.4438 ] Network output: [ 0.1177 0.6902 0.1414 -0.0007028 0.0003156 0.9301 -0.00053 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08036 Epoch 964 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03476 1.004 0.9787 0.0001227 -5.512e-05 -0.05187 9.258e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03829 -0.002096 0.025 0.02472 0.9153 0.9283 0.07547 0.8492 0.8818 0.1729 ] Network output: [ 0.9356 0.1457 -0.06733 -0.0006961 0.0003126 0.04752 -0.0005251 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6448 0.01244 -0.03974 0.2886 0.9567 0.978 0.7396 0.8731 0.9521 0.7129 ] Network output: [ 0.0002227 0.9229 1.046 0.000266 -0.0001194 0.03152 0.0002006 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06876 0.03885 0.05942 0.04848 0.9745 0.9814 0.07056 0.9455 0.9685 0.09231 ] Network output: [ 0.1176 -0.3301 1.148 0.0003206 -0.000144 0.9481 0.0002419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7359 0.454 0.4009 0.4924 0.9614 0.9811 0.7399 0.8849 0.959 0.7144 ] Network output: [ -0.07261 0.2764 0.8834 0.00145 -0.0006511 0.9913 0.001093 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6276 0.5785 0.3552 0.22 0.9783 0.9853 0.6283 0.9536 0.9722 0.3955 ] Network output: [ -0.1348 0.3086 0.8538 -0.0009433 0.0004235 1.103 -0.0007108 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6665 0.6561 0.4328 0.1491 0.9744 0.9825 0.6666 0.9445 0.9661 0.4437 ] Network output: [ 0.1179 0.6941 0.1378 -0.0007716 0.0003465 0.9293 -0.0005818 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08076 Epoch 965 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03434 1.001 0.9821 0.0001464 -6.575e-05 -0.05147 0.0001104 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03823 -0.002069 0.02536 0.02512 0.9153 0.9284 0.07538 0.8493 0.8819 0.1733 ] Network output: [ 0.9315 0.1373 -0.05371 -0.0006601 0.0002964 0.05068 -0.0004979 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6445 0.01336 -0.03686 0.2914 0.9567 0.9781 0.7394 0.8732 0.9522 0.7135 ] Network output: [ 3.858e-05 0.9205 1.049 0.0002851 -0.000128 0.03178 0.000215 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06871 0.0389 0.05997 0.04919 0.9745 0.9815 0.07051 0.9456 0.9686 0.09281 ] Network output: [ 0.1171 -0.3381 1.156 0.0003791 -0.0001703 0.9495 0.000286 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7362 0.4545 0.4023 0.4954 0.9615 0.9811 0.7403 0.885 0.9591 0.7148 ] Network output: [ -0.07306 0.2709 0.8894 0.001512 -0.0006787 0.992 0.001139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6275 0.5785 0.356 0.223 0.9784 0.9853 0.6282 0.9537 0.9723 0.3963 ] Network output: [ -0.1348 0.3024 0.8593 -0.0008506 0.0003818 1.104 -0.0006409 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6666 0.6562 0.4331 0.1532 0.9744 0.9826 0.6667 0.9446 0.9661 0.444 ] Network output: [ 0.1181 0.6924 0.1383 -0.00073 0.0003278 0.9302 -0.0005504 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08024 Epoch 966 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03617 1.003 0.9781 0.0001523 -6.838e-05 -0.05298 0.0001148 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03825 -0.002225 0.02457 0.02482 0.9154 0.9284 0.07549 0.8493 0.8819 0.1732 ] Network output: [ 0.9455 0.1433 -0.07612 -0.0006101 0.000274 0.0393 -0.0004602 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6451 0.009568 -0.04368 0.2888 0.9567 0.9781 0.7401 0.8732 0.9522 0.7129 ] Network output: [ 0.0002024 0.9217 1.048 0.0002785 -0.0001251 0.0314 0.00021 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06865 0.0386 0.05927 0.04886 0.9745 0.9814 0.07045 0.9455 0.9686 0.0925 ] Network output: [ 0.1206 -0.3356 1.151 0.0003798 -0.0001706 0.9454 0.0002865 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7357 0.4526 0.3997 0.4947 0.9614 0.9811 0.7398 0.885 0.9591 0.7147 ] Network output: [ -0.0751 0.2705 0.8936 0.001492 -0.0006699 0.9922 0.001125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6267 0.5774 0.3562 0.2239 0.9784 0.9853 0.6273 0.9537 0.9723 0.3968 ] Network output: [ -0.1373 0.3003 0.8652 -0.0008451 0.0003794 1.106 -0.0006368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6658 0.6554 0.4335 0.1552 0.9744 0.9826 0.6659 0.9446 0.9662 0.4444 ] Network output: [ 0.1161 0.6927 0.1406 -0.000744 0.0003341 0.9315 -0.000561 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0798 Epoch 967 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03466 1.004 0.9792 0.000123 -5.526e-05 -0.05172 9.282e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03822 -0.002036 0.02529 0.0247 0.9154 0.9284 0.07541 0.8494 0.8819 0.1732 ] Network output: [ 0.9336 0.1445 -0.0635 -0.0007081 0.000318 0.04883 -0.000534 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6448 0.01437 -0.03685 0.2877 0.9567 0.9781 0.7398 0.8733 0.9522 0.7128 ] Network output: [ 0.0002145 0.9224 1.046 0.0002644 -0.0001188 0.03177 0.0001995 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06878 0.03906 0.05994 0.04851 0.9745 0.9815 0.07058 0.9457 0.9686 0.09271 ] Network output: [ 0.1165 -0.3293 1.148 0.0002924 -0.0001314 0.9497 0.0002207 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7363 0.4567 0.4038 0.4906 0.9615 0.9811 0.7403 0.8851 0.9591 0.7142 ] Network output: [ -0.07185 0.2765 0.8825 0.001465 -0.0006578 0.9907 0.001104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6275 0.5789 0.3565 0.2197 0.9784 0.9853 0.6281 0.9538 0.9723 0.3964 ] Network output: [ -0.1334 0.3095 0.8518 -0.0009191 0.0004126 1.102 -0.0006926 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6661 0.6558 0.4335 0.1488 0.9744 0.9826 0.6662 0.9446 0.9662 0.4443 ] Network output: [ 0.1167 0.6968 0.1361 -0.0008145 0.0003657 0.9304 -0.0006141 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08027 Epoch 968 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03468 1 0.9825 0.0001563 -7.02e-05 -0.05167 0.0001179 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03816 -0.002061 0.02547 0.02515 0.9154 0.9284 0.07533 0.8495 0.882 0.1736 ] Network output: [ 0.9329 0.1353 -0.0529 -0.0006418 0.0002882 0.04929 -0.0004841 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6446 0.01404 -0.03571 0.2909 0.9568 0.9781 0.7397 0.8734 0.9523 0.7134 ] Network output: [ 1.605e-05 0.9196 1.05 0.0002882 -0.0001294 0.03195 0.0002173 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06869 0.03899 0.06035 0.04935 0.9746 0.9815 0.07049 0.9458 0.9687 0.09321 ] Network output: [ 0.1172 -0.3395 1.157 0.0003774 -0.0001695 0.95 0.0002847 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7365 0.4561 0.4043 0.4948 0.9615 0.9812 0.7405 0.8852 0.9592 0.7148 ] Network output: [ -0.07323 0.269 0.892 0.001539 -0.0006908 0.9918 0.00116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6271 0.5785 0.3574 0.2241 0.9784 0.9854 0.6278 0.9538 0.9724 0.3974 ] Network output: [ -0.1344 0.3003 0.8613 -0.0007981 0.0003583 1.104 -0.0006014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6661 0.6558 0.4338 0.1549 0.9744 0.9826 0.6662 0.9447 0.9663 0.4446 ] Network output: [ 0.1167 0.6939 0.1379 -0.0007527 0.000338 0.9318 -0.0005675 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07955 Epoch 969 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0366 1.003 0.9773 0.0001529 -6.865e-05 -0.05327 0.0001153 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0382 -0.002214 0.02461 0.0247 0.9154 0.9284 0.07547 0.8495 0.882 0.1734 ] Network output: [ 0.9477 0.1442 -0.07937 -0.0006086 0.0002733 0.03722 -0.0004591 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6452 0.01026 -0.04303 0.2871 0.9568 0.9781 0.7405 0.8734 0.9522 0.7126 ] Network output: [ 0.0002463 0.9217 1.047 0.0002742 -0.0001231 0.03152 0.0002068 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06866 0.03871 0.05952 0.04876 0.9746 0.9815 0.07047 0.9457 0.9686 0.09276 ] Network output: [ 0.1206 -0.3338 1.148 0.0003512 -0.0001577 0.9456 0.000265 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7359 0.4545 0.4016 0.4927 0.9615 0.9812 0.74 0.8852 0.9592 0.7145 ] Network output: [ -0.07493 0.2707 0.8937 0.001498 -0.0006726 0.9916 0.001129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6263 0.5775 0.3574 0.2237 0.9784 0.9854 0.627 0.9538 0.9724 0.3977 ] Network output: [ -0.1366 0.3008 0.8647 -0.0008252 0.0003705 1.104 -0.0006219 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6652 0.6549 0.4342 0.1552 0.9744 0.9826 0.6654 0.9447 0.9663 0.445 ] Network output: [ 0.1144 0.6953 0.1397 -0.0007883 0.000354 0.9329 -0.0005944 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07926 Epoch 970 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03448 1.003 0.98 0.0001236 -5.55e-05 -0.05149 9.322e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03815 -0.00197 0.02562 0.02471 0.9154 0.9284 0.07535 0.8496 0.8821 0.1734 ] Network output: [ 0.931 0.1428 -0.05847 -0.0007202 0.0003234 0.05064 -0.0005432 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6448 0.01647 -0.03365 0.2869 0.9568 0.9781 0.74 0.8735 0.9523 0.7127 ] Network output: [ 0.0001871 0.9218 1.047 0.0002633 -0.0001183 0.03204 0.0001986 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0688 0.03928 0.0605 0.04856 0.9746 0.9815 0.07061 0.9459 0.9687 0.09314 ] Network output: [ 0.1154 -0.3287 1.148 0.0002663 -0.0001196 0.9515 0.000201 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7367 0.4594 0.4067 0.4889 0.9615 0.9812 0.7407 0.8853 0.9592 0.714 ] Network output: [ -0.07102 0.2765 0.8815 0.001483 -0.0006657 0.99 0.001118 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6274 0.5794 0.3578 0.2193 0.9784 0.9854 0.6281 0.9539 0.9724 0.3973 ] Network output: [ -0.1319 0.3102 0.8497 -0.0008918 0.0004004 1.1 -0.000672 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6657 0.6556 0.4342 0.1484 0.9745 0.9826 0.6659 0.9448 0.9663 0.4448 ] Network output: [ 0.1156 0.6994 0.1343 -0.000855 0.0003839 0.9315 -0.0006446 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0798 Epoch 971 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03512 0.9997 0.9827 0.000167 -7.498e-05 -0.05196 0.0001259 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0381 -0.002063 0.02554 0.02517 0.9154 0.9285 0.07529 0.8497 0.8821 0.1739 ] Network output: [ 0.935 0.1334 -0.05318 -0.0006185 0.0002777 0.04725 -0.0004665 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6448 0.01444 -0.035 0.2903 0.9568 0.9781 0.74 0.8736 0.9524 0.7134 ] Network output: [ -8.068e-06 0.9188 1.05 0.0002911 -0.0001307 0.0321 0.0002195 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06867 0.03906 0.06068 0.04951 0.9746 0.9815 0.07048 0.9459 0.9688 0.09358 ] Network output: [ 0.1175 -0.341 1.157 0.0003783 -0.0001699 0.9501 0.0002854 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7367 0.4576 0.406 0.4943 0.9616 0.9812 0.7408 0.8854 0.9593 0.7147 ] Network output: [ -0.07355 0.2668 0.8951 0.001566 -0.000703 0.9916 0.00118 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6267 0.5784 0.3588 0.2252 0.9784 0.9854 0.6274 0.954 0.9725 0.3986 ] Network output: [ -0.1342 0.2978 0.864 -0.0007433 0.0003337 1.104 -0.0005601 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6656 0.6553 0.4346 0.1568 0.9745 0.9826 0.6657 0.9448 0.9664 0.4453 ] Network output: [ 0.1152 0.6954 0.1378 -0.0007725 0.0003469 0.9334 -0.0005825 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07883 Epoch 972 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03697 1.004 0.9765 0.0001511 -6.787e-05 -0.05352 0.000114 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03815 -0.002196 0.02466 0.02456 0.9155 0.9285 0.07545 0.8497 0.8821 0.1735 ] Network output: [ 0.9496 0.1455 -0.08262 -0.0006118 0.0002748 0.03542 -0.0004615 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6454 0.01112 -0.04223 0.2852 0.9568 0.9781 0.7409 0.8735 0.9523 0.7123 ] Network output: [ 0.0002911 0.9219 1.047 0.0002682 -0.0001205 0.03164 0.0002023 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06868 0.03884 0.05978 0.04861 0.9746 0.9815 0.07049 0.9458 0.9687 0.093 ] Network output: [ 0.1205 -0.3315 1.146 0.0003169 -0.0001423 0.946 0.0002391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7361 0.4565 0.4036 0.4904 0.9615 0.9812 0.7402 0.8854 0.9592 0.7142 ] Network output: [ -0.07459 0.2713 0.893 0.001501 -0.0006737 0.991 0.001131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6261 0.5777 0.3586 0.2231 0.9784 0.9854 0.6267 0.9539 0.9724 0.3986 ] Network output: [ -0.1358 0.3018 0.8634 -0.0008116 0.0003643 1.103 -0.0006115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6647 0.6545 0.4349 0.1548 0.9745 0.9826 0.6648 0.9449 0.9664 0.4456 ] Network output: [ 0.1128 0.6982 0.1386 -0.0008365 0.0003756 0.9341 -0.0006307 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07876 Epoch 973 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03422 1.002 0.9811 0.0001248 -5.606e-05 -0.05121 9.415e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03808 -0.001899 0.02598 0.02474 0.9155 0.9285 0.07527 0.8498 0.8822 0.1736 ] Network output: [ 0.9279 0.1406 -0.0522 -0.0007315 0.0003285 0.05288 -0.0005517 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6448 0.01868 -0.03018 0.2864 0.9568 0.9781 0.7401 0.8737 0.9524 0.7127 ] Network output: [ 0.0001386 0.921 1.047 0.000263 -0.0001181 0.03233 0.0001983 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06883 0.03951 0.0611 0.04866 0.9746 0.9815 0.07064 0.946 0.9689 0.0936 ] Network output: [ 0.1142 -0.3287 1.148 0.0002438 -0.0001095 0.9533 0.000184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7371 0.4622 0.4098 0.4873 0.9616 0.9812 0.7412 0.8855 0.9593 0.7138 ] Network output: [ -0.07015 0.2761 0.8809 0.001504 -0.0006753 0.9895 0.001134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6273 0.5799 0.3592 0.2191 0.9784 0.9854 0.628 0.9541 0.9725 0.3982 ] Network output: [ -0.1303 0.3105 0.8478 -0.0008598 0.000386 1.099 -0.0006479 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6654 0.6554 0.4349 0.1482 0.9745 0.9826 0.6656 0.9449 0.9664 0.4454 ] Network output: [ 0.1146 0.702 0.1326 -0.0008918 0.0004004 0.9325 -0.0006724 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07932 Epoch 974 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03567 0.999 0.9827 0.0001781 -7.997e-05 -0.05235 0.0001343 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03804 -0.002078 0.02554 0.02518 0.9155 0.9285 0.07526 0.8499 0.8823 0.1741 ] Network output: [ 0.9381 0.1318 -0.05493 -0.00059 0.000265 0.04442 -0.000445 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6449 0.01453 -0.03484 0.2895 0.9569 0.9782 0.7404 0.8738 0.9525 0.7133 ] Network output: [ -2.885e-05 0.9181 1.051 0.0002935 -0.0001318 0.03222 0.0002214 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06865 0.0391 0.06096 0.04965 0.9746 0.9815 0.07046 0.9461 0.9689 0.09393 ] Network output: [ 0.1181 -0.3425 1.158 0.000381 -0.0001711 0.95 0.0002874 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7369 0.4589 0.4075 0.4938 0.9616 0.9812 0.7409 0.8856 0.9593 0.7147 ] Network output: [ -0.07402 0.2644 0.8986 0.001592 -0.0007147 0.9915 0.0012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6263 0.5783 0.3602 0.2265 0.9785 0.9854 0.6269 0.9541 0.9726 0.3997 ] Network output: [ -0.1342 0.2949 0.8672 -0.0006874 0.0003086 1.103 -0.0005179 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.665 0.6549 0.4353 0.159 0.9745 0.9827 0.6652 0.945 0.9665 0.4459 ] Network output: [ 0.1135 0.6967 0.1379 -0.0007902 0.0003548 0.9351 -0.0005958 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0781 Epoch 975 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03724 1.004 0.9756 0.0001467 -6.589e-05 -0.0537 0.0001107 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0381 -0.002167 0.02474 0.02441 0.9155 0.9285 0.07543 0.8499 0.8823 0.1736 ] Network output: [ 0.9509 0.1471 -0.08545 -0.0006211 0.0002789 0.03414 -0.0004685 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6456 0.01223 -0.04113 0.2832 0.9569 0.9782 0.7413 0.8737 0.9524 0.7119 ] Network output: [ 0.0003346 0.9222 1.046 0.0002607 -0.0001171 0.03178 0.0001966 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06871 0.039 0.06006 0.04842 0.9746 0.9815 0.07053 0.946 0.9688 0.09324 ] Network output: [ 0.1201 -0.3285 1.143 0.000276 -0.000124 0.9465 0.0002083 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7363 0.4588 0.4058 0.4878 0.9616 0.9812 0.7404 0.8856 0.9593 0.7139 ] Network output: [ -0.07404 0.2724 0.8916 0.0015 -0.0006735 0.9902 0.001131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6259 0.578 0.3598 0.2222 0.9784 0.9854 0.6266 0.9541 0.9725 0.3994 ] Network output: [ -0.1348 0.3035 0.8613 -0.0008045 0.0003611 1.102 -0.0006062 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6642 0.6541 0.4357 0.1539 0.9745 0.9826 0.6643 0.945 0.9665 0.4462 ] Network output: [ 0.1113 0.7013 0.1373 -0.0008888 0.0003991 0.9352 -0.0006701 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07831 Epoch 976 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03391 1.001 0.9825 0.0001274 -5.721e-05 -0.05089 9.609e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03801 -0.001827 0.02637 0.0248 0.9155 0.9285 0.07519 0.85 0.8824 0.1739 ] Network output: [ 0.9243 0.1376 -0.04474 -0.0007402 0.0003324 0.05544 -0.0005582 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6448 0.02095 -0.02649 0.2862 0.9569 0.9782 0.7402 0.8739 0.9525 0.7127 ] Network output: [ 6.739e-05 0.9201 1.048 0.0002637 -0.0001184 0.03265 0.0001989 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06885 0.03974 0.06173 0.04881 0.9747 0.9816 0.07067 0.9462 0.969 0.0941 ] Network output: [ 0.1129 -0.3294 1.149 0.0002269 -0.0001019 0.9552 0.0001713 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7375 0.4649 0.4129 0.4861 0.9616 0.9812 0.7416 0.8857 0.9594 0.7137 ] Network output: [ -0.06931 0.2752 0.8806 0.001531 -0.0006871 0.989 0.001154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6273 0.5804 0.3606 0.2192 0.9785 0.9854 0.628 0.9542 0.9726 0.3992 ] Network output: [ -0.1288 0.3104 0.8464 -0.0008206 0.0003684 1.097 -0.0006184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6652 0.6553 0.4356 0.1484 0.9745 0.9827 0.6653 0.9451 0.9665 0.446 ] Network output: [ 0.1137 0.7044 0.131 -0.0009233 0.0004146 0.9335 -0.0006961 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07884 Epoch 977 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03636 0.9986 0.9823 0.0001891 -8.493e-05 -0.05286 0.0001426 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03799 -0.002106 0.02547 0.02516 0.9156 0.9286 0.07524 0.8501 0.8824 0.1744 ] Network output: [ 0.9424 0.1308 -0.05854 -0.0005571 0.0002502 0.04067 -0.0004202 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6451 0.01428 -0.0353 0.2886 0.9569 0.9782 0.7408 0.874 0.9525 0.7132 ] Network output: [ -3.992e-05 0.9175 1.051 0.000295 -0.0001325 0.03231 0.0002224 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06863 0.03911 0.06116 0.04976 0.9747 0.9816 0.07044 0.9462 0.9689 0.09424 ] Network output: [ 0.119 -0.3438 1.158 0.0003838 -0.0001724 0.9494 0.0002895 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.737 0.4599 0.4087 0.4933 0.9617 0.9813 0.741 0.8858 0.9594 0.7147 ] Network output: [ -0.07465 0.2619 0.9025 0.001616 -0.0007253 0.9915 0.001218 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6258 0.5781 0.3615 0.2278 0.9785 0.9854 0.6265 0.9542 0.9726 0.401 ] Network output: [ -0.1344 0.2919 0.8709 -0.0006325 0.0002839 1.103 -0.0004766 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6645 0.6544 0.4361 0.1613 0.9745 0.9827 0.6646 0.9451 0.9666 0.4466 ] Network output: [ 0.1118 0.6979 0.1384 -0.0008068 0.0003622 0.9369 -0.0006083 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07737 Epoch 978 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03737 1.005 0.9748 0.0001394 -6.261e-05 -0.05377 0.0001052 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03806 -0.002124 0.02487 0.02424 0.9156 0.9286 0.0754 0.85 0.8824 0.1737 ] Network output: [ 0.9511 0.1488 -0.08732 -0.0006381 0.0002866 0.03368 -0.0004813 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6457 0.01371 -0.03953 0.2811 0.9569 0.9782 0.7416 0.8739 0.9525 0.7116 ] Network output: [ 0.0003728 0.9226 1.046 0.0002515 -0.000113 0.03194 0.0001897 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06876 0.03919 0.06038 0.04818 0.9747 0.9815 0.07058 0.9461 0.9688 0.09349 ] Network output: [ 0.1194 -0.3249 1.14 0.0002282 -0.0001025 0.9474 0.0001722 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7366 0.4613 0.4082 0.4848 0.9616 0.9812 0.7406 0.8857 0.9594 0.7136 ] Network output: [ -0.07321 0.274 0.8891 0.001497 -0.0006719 0.9895 0.001128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6258 0.5785 0.361 0.221 0.9785 0.9854 0.6265 0.9542 0.9726 0.4002 ] Network output: [ -0.1335 0.3059 0.8581 -0.0008041 0.000361 1.1 -0.0006059 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6637 0.6538 0.4364 0.1524 0.9745 0.9827 0.6639 0.9451 0.9665 0.4468 ] Network output: [ 0.1098 0.7048 0.1356 -0.0009455 0.0004245 0.9362 -0.0007128 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07791 Epoch 979 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03357 0.9998 0.9841 0.0001321 -5.932e-05 -0.05055 9.963e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03794 -0.001756 0.02678 0.02491 0.9156 0.9286 0.07511 0.8502 0.8825 0.1742 ] Network output: [ 0.9207 0.1338 -0.03626 -0.0007439 0.0003341 0.05813 -0.000561 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6447 0.02318 -0.02273 0.2862 0.9569 0.9782 0.7402 0.8741 0.9526 0.7129 ] Network output: [ -2.713e-05 0.919 1.049 0.0002661 -0.0001195 0.03297 0.0002007 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06887 0.03996 0.06239 0.04903 0.9747 0.9816 0.07069 0.9464 0.9691 0.09462 ] Network output: [ 0.1117 -0.3309 1.151 0.0002181 -9.799e-05 0.9571 0.0001646 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7379 0.4675 0.416 0.4852 0.9617 0.9813 0.742 0.8859 0.9595 0.7137 ] Network output: [ -0.06855 0.2737 0.8812 0.001563 -0.0007017 0.9886 0.001178 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6273 0.5809 0.362 0.2196 0.9785 0.9854 0.628 0.9543 0.9727 0.4003 ] Network output: [ -0.1273 0.3094 0.8458 -0.0007717 0.0003464 1.096 -0.0005815 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.665 0.6552 0.4363 0.149 0.9746 0.9827 0.6651 0.9452 0.9666 0.4465 ] Network output: [ 0.1128 0.7064 0.1295 -0.0009474 0.0004254 0.9346 -0.0007143 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07834 Epoch 980 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03719 0.9984 0.9815 0.0001993 -8.951e-05 -0.0535 0.0001503 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03794 -0.002147 0.02532 0.0251 0.9156 0.9286 0.07524 0.8502 0.8825 0.1746 ] Network output: [ 0.948 0.1305 -0.06449 -0.0005209 0.000234 0.0359 -0.0003929 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6453 0.01367 -0.03646 0.2873 0.957 0.9782 0.7413 0.8741 0.9526 0.713 ] Network output: [ -3.329e-05 0.9172 1.052 0.0002948 -0.0001324 0.03235 0.0002223 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06861 0.0391 0.06127 0.0498 0.9747 0.9816 0.07042 0.9463 0.969 0.09449 ] Network output: [ 0.1201 -0.3446 1.157 0.0003846 -0.0001727 0.9484 0.0002901 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.737 0.4607 0.4096 0.4927 0.9617 0.9813 0.7411 0.886 0.9595 0.7146 ] Network output: [ -0.07542 0.2596 0.9064 0.001635 -0.0007339 0.9915 0.001232 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6253 0.5779 0.3628 0.2291 0.9785 0.9855 0.626 0.9543 0.9727 0.4022 ] Network output: [ -0.1348 0.289 0.8749 -0.0005815 0.000261 1.103 -0.0004381 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6639 0.6539 0.4369 0.1635 0.9746 0.9827 0.664 0.9452 0.9667 0.4474 ] Network output: [ 0.1099 0.6991 0.1391 -0.0008242 0.0003701 0.9387 -0.0006214 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07666 Epoch 981 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0373 1.005 0.9742 0.000129 -5.795e-05 -0.05367 9.732e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03801 -0.002059 0.02508 0.02406 0.9157 0.9286 0.07537 0.8502 0.8825 0.1738 ] Network output: [ 0.9499 0.1505 -0.08744 -0.0006644 0.0002984 0.03442 -0.0005011 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6458 0.0157 -0.03718 0.279 0.957 0.9782 0.7419 0.8741 0.9526 0.7113 ] Network output: [ 0.0003998 0.923 1.045 0.000241 -0.0001082 0.03214 0.0001818 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06882 0.03942 0.06077 0.04792 0.9747 0.9816 0.07064 0.9463 0.9689 0.09375 ] Network output: [ 0.1184 -0.3207 1.136 0.0001733 -7.785e-05 0.9486 0.0001308 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7368 0.4641 0.4109 0.4814 0.9617 0.9813 0.7409 0.8859 0.9595 0.7132 ] Network output: [ -0.07204 0.2761 0.8855 0.001491 -0.0006695 0.9886 0.001124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6259 0.5792 0.3621 0.2193 0.9785 0.9854 0.6266 0.9543 0.9727 0.401 ] Network output: [ -0.1319 0.3091 0.8536 -0.0008102 0.0003637 1.098 -0.0006106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6634 0.6536 0.4371 0.1503 0.9746 0.9827 0.6635 0.9453 0.9666 0.4473 ] Network output: [ 0.1084 0.7086 0.1335 -0.001006 0.0004519 0.9369 -0.0007587 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07757 Epoch 982 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03325 0.9982 0.9861 0.0001399 -6.281e-05 -0.05023 0.0001055 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03786 -0.001694 0.02718 0.02506 0.9157 0.9286 0.07502 0.8505 0.8827 0.1745 ] Network output: [ 0.9172 0.1291 -0.0271 -0.0007395 0.0003321 0.06064 -0.0005576 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6446 0.02522 -0.01909 0.2866 0.957 0.9782 0.7403 0.8744 0.9527 0.7131 ] Network output: [ -0.0001447 0.9176 1.05 0.0002705 -0.0001215 0.03329 0.000204 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06888 0.04017 0.06306 0.04934 0.9748 0.9816 0.0707 0.9466 0.9692 0.09517 ] Network output: [ 0.1108 -0.3337 1.154 0.0002203 -9.896e-05 0.9589 0.0001663 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7383 0.4699 0.4189 0.4849 0.9618 0.9813 0.7424 0.8861 0.9596 0.7137 ] Network output: [ -0.06797 0.2712 0.8828 0.001603 -0.0007196 0.9885 0.001208 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6273 0.5814 0.3635 0.2205 0.9786 0.9855 0.628 0.9545 0.9728 0.4014 ] Network output: [ -0.126 0.3072 0.8465 -0.0007099 0.0003187 1.096 -0.0005349 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6648 0.6551 0.437 0.1504 0.9746 0.9827 0.6649 0.9454 0.9667 0.4471 ] Network output: [ 0.1119 0.7079 0.1285 -0.0009614 0.0004317 0.9358 -0.0007248 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07781 Epoch 983 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03819 0.9987 0.98 0.0002076 -9.322e-05 -0.0543 0.0001565 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03791 -0.002202 0.02508 0.02498 0.9157 0.9287 0.07525 0.8504 0.8826 0.1747 ] Network output: [ 0.9549 0.1314 -0.07325 -0.0004834 0.0002171 0.03001 -0.0003646 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6456 0.01271 -0.03839 0.2856 0.957 0.9783 0.7418 0.8743 0.9527 0.7127 ] Network output: [ 8.488e-07 0.9172 1.052 0.0002922 -0.0001312 0.03233 0.0002204 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06859 0.03907 0.06127 0.04976 0.9747 0.9816 0.07041 0.9464 0.969 0.09466 ] Network output: [ 0.1216 -0.3445 1.156 0.0003803 -0.0001708 0.947 0.0002869 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.737 0.4613 0.4101 0.4917 0.9617 0.9813 0.7411 0.8861 0.9596 0.7146 ] Network output: [ -0.07627 0.2577 0.9102 0.001647 -0.0007394 0.9914 0.001241 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6248 0.5776 0.364 0.2302 0.9785 0.9855 0.6255 0.9544 0.9728 0.4033 ] Network output: [ -0.1354 0.2863 0.8788 -0.000538 0.0002415 1.103 -0.0004054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6632 0.6533 0.4377 0.1656 0.9746 0.9827 0.6634 0.9454 0.9668 0.4481 ] Network output: [ 0.1078 0.7005 0.14 -0.0008451 0.0003795 0.9405 -0.0006371 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.076 Epoch 984 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03695 1.006 0.974 0.0001155 -5.187e-05 -0.05335 8.711e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03796 -0.001967 0.02541 0.0239 0.9157 0.9287 0.07532 0.8504 0.8826 0.1738 ] Network output: [ 0.9466 0.1519 -0.08486 -0.0007018 0.0003152 0.03684 -0.0005293 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6459 0.01838 -0.0338 0.277 0.957 0.9783 0.742 0.8742 0.9527 0.711 ] Network output: [ 0.0004076 0.9234 1.044 0.0002293 -0.000103 0.0324 0.000173 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0689 0.03972 0.06126 0.04765 0.9747 0.9816 0.07072 0.9464 0.969 0.09405 ] Network output: [ 0.1168 -0.3161 1.133 0.0001116 -5.016e-05 0.9505 8.433e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7372 0.4674 0.414 0.4778 0.9617 0.9813 0.7413 0.8861 0.9596 0.7128 ] Network output: [ -0.07045 0.2787 0.8807 0.001485 -0.0006667 0.9876 0.001119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6261 0.58 0.3633 0.2172 0.9785 0.9855 0.6268 0.9544 0.9727 0.4016 ] Network output: [ -0.1297 0.3132 0.8478 -0.000822 0.000369 1.095 -0.0006195 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6631 0.6535 0.4377 0.1476 0.9746 0.9827 0.6633 0.9454 0.9667 0.4478 ] Network output: [ 0.1073 0.7127 0.1309 -0.001071 0.0004808 0.9374 -0.0008073 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07731 Epoch 985 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03303 0.9963 0.9882 0.0001519 -6.82e-05 -0.05 0.0001145 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03778 -0.001647 0.02755 0.02525 0.9157 0.9287 0.07493 0.8507 0.8828 0.1749 ] Network output: [ 0.9144 0.1234 -0.01777 -0.0007227 0.0003245 0.06256 -0.000545 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6445 0.02691 -0.01583 0.2875 0.9571 0.9783 0.7403 0.8746 0.9528 0.7133 ] Network output: [ -0.0002838 0.916 1.052 0.0002774 -0.0001246 0.0336 0.0002092 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06888 0.04033 0.06371 0.04975 0.9748 0.9817 0.0707 0.9468 0.9693 0.09575 ] Network output: [ 0.1101 -0.3379 1.158 0.0002367 -0.0001063 0.9603 0.0001786 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7387 0.4719 0.4215 0.4853 0.9618 0.9814 0.7428 0.8863 0.9597 0.7138 ] Network output: [ -0.06771 0.2675 0.8861 0.001652 -0.0007415 0.9885 0.001245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6273 0.5817 0.365 0.2222 0.9786 0.9855 0.628 0.9546 0.9729 0.4026 ] Network output: [ -0.1251 0.3035 0.849 -0.0006318 0.0002836 1.095 -0.0004761 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6646 0.6551 0.4376 0.1528 0.9747 0.9828 0.6648 0.9455 0.9668 0.4476 ] Network output: [ 0.111 0.7088 0.1281 -0.0009624 0.0004321 0.9371 -0.0007255 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07724 Epoch 986 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03935 0.9996 0.9778 0.0002125 -9.541e-05 -0.05524 0.0001602 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03788 -0.002269 0.02473 0.02478 0.9158 0.9287 0.07528 0.8505 0.8827 0.1748 ] Network output: [ 0.9633 0.1339 -0.08531 -0.0004473 0.0002009 0.02297 -0.0003375 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.646 0.01141 -0.04113 0.2833 0.9571 0.9783 0.7424 0.8744 0.9528 0.7123 ] Network output: [ 7.426e-05 0.9176 1.051 0.0002862 -0.0001285 0.03225 0.0002158 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06858 0.03901 0.06114 0.04958 0.9748 0.9816 0.0704 0.9465 0.969 0.09473 ] Network output: [ 0.1234 -0.343 1.153 0.0003671 -0.0001649 0.9451 0.0002769 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7369 0.4618 0.4103 0.4901 0.9618 0.9813 0.741 0.8863 0.9597 0.7144 ] Network output: [ -0.07715 0.2564 0.9133 0.00165 -0.0007407 0.9913 0.001243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6243 0.5773 0.3652 0.2309 0.9786 0.9855 0.625 0.9545 0.9728 0.4044 ] Network output: [ -0.1361 0.2843 0.8825 -0.0005064 0.0002273 1.103 -0.0003816 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6626 0.6527 0.4385 0.1673 0.9747 0.9828 0.6627 0.9455 0.9669 0.4488 ] Network output: [ 0.1055 0.7021 0.1411 -0.0008728 0.0003919 0.9423 -0.000658 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07544 Epoch 987 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03624 1.006 0.9744 9.876e-05 -4.435e-05 -0.05275 7.45e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0379 -0.00184 0.0259 0.02378 0.9158 0.9287 0.07526 0.8506 0.8828 0.1739 ] Network output: [ 0.9407 0.1525 -0.07838 -0.0007522 0.0003378 0.04146 -0.0005672 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6458 0.02193 -0.02902 0.2753 0.9571 0.9783 0.742 0.8744 0.9527 0.7108 ] Network output: [ 0.0003873 0.9238 1.044 0.000217 -9.747e-05 0.03272 0.0001637 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.069 0.04009 0.0619 0.04739 0.9748 0.9816 0.07082 0.9466 0.9691 0.09441 ] Network output: [ 0.1145 -0.3113 1.129 4.42e-05 -1.989e-05 0.9531 3.353e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7376 0.4712 0.4177 0.4738 0.9618 0.9813 0.7417 0.8863 0.9596 0.7124 ] Network output: [ -0.06834 0.2818 0.8744 0.001479 -0.0006642 0.9864 0.001115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6265 0.5811 0.3645 0.2146 0.9786 0.9855 0.6272 0.9546 0.9728 0.4022 ] Network output: [ -0.127 0.318 0.8403 -0.0008381 0.0003762 1.092 -0.0006315 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.663 0.6535 0.4383 0.1442 0.9747 0.9828 0.6631 0.9455 0.9668 0.4483 ] Network output: [ 0.1064 0.7171 0.1277 -0.001138 0.0005109 0.9377 -0.0008578 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07715 Epoch 988 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03299 0.9941 0.9905 0.0001693 -7.602e-05 -0.04991 0.0001277 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03771 -0.001624 0.02784 0.02551 0.9158 0.9287 0.07485 0.8509 0.883 0.1753 ] Network output: [ 0.9131 0.1167 -0.008995 -0.0006888 0.0003093 0.06338 -0.0005194 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6444 0.02801 -0.01331 0.2887 0.9571 0.9783 0.7403 0.8748 0.9529 0.7137 ] Network output: [ -0.0004402 0.914 1.054 0.0002874 -0.0001291 0.03389 0.0002167 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06886 0.04044 0.06431 0.05028 0.9749 0.9817 0.07069 0.947 0.9694 0.09632 ] Network output: [ 0.11 -0.3438 1.164 0.0002706 -0.0001215 0.9613 0.0002042 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7391 0.4734 0.4237 0.4866 0.9619 0.9814 0.7432 0.8865 0.9598 0.714 ] Network output: [ -0.06788 0.2623 0.8916 0.00171 -0.0007678 0.9889 0.001289 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6272 0.5819 0.3665 0.2248 0.9786 0.9855 0.6278 0.9548 0.973 0.4039 ] Network output: [ -0.1246 0.2977 0.8539 -0.0005338 0.0002396 1.095 -0.0004022 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6645 0.655 0.4383 0.1567 0.9747 0.9828 0.6646 0.9456 0.9669 0.4482 ] Network output: [ 0.11 0.7087 0.1286 -0.0009465 0.000425 0.9388 -0.0007135 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07659 Epoch 989 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04066 1.001 0.9747 0.0002122 -9.527e-05 -0.05633 0.00016 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03786 -0.002347 0.02429 0.02449 0.9158 0.9288 0.07533 0.8507 0.8828 0.1748 ] Network output: [ 0.9731 0.1384 -0.1011 -0.0004164 0.000187 0.0148 -0.0003141 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6464 0.009836 -0.04466 0.2802 0.9571 0.9783 0.7431 0.8745 0.9528 0.7117 ] Network output: [ 0.0002007 0.9187 1.05 0.0002754 -0.0001237 0.03209 0.0002077 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06858 0.03895 0.06087 0.04922 0.9748 0.9816 0.07041 0.9465 0.969 0.09468 ] Network output: [ 0.1254 -0.3396 1.148 0.0003408 -0.000153 0.9427 0.000257 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7367 0.4623 0.4102 0.4879 0.9618 0.9813 0.7408 0.8864 0.9597 0.7141 ] Network output: [ -0.07795 0.2564 0.9152 0.00164 -0.0007364 0.991 0.001236 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6238 0.577 0.3661 0.231 0.9786 0.9855 0.6245 0.9546 0.9729 0.4054 ] Network output: [ -0.1369 0.2835 0.8853 -0.0004914 0.0002206 1.103 -0.0003702 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6618 0.652 0.4392 0.1683 0.9747 0.9828 0.662 0.9456 0.967 0.4495 ] Network output: [ 0.103 0.7043 0.1421 -0.0009108 0.000409 0.944 -0.0006867 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07505 Epoch 990 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03508 1.006 0.9757 7.882e-05 -3.54e-05 -0.05178 5.947e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03784 -0.001672 0.02658 0.02371 0.9158 0.9288 0.07516 0.8508 0.8829 0.174 ] Network output: [ 0.9313 0.1519 -0.06671 -0.0008176 0.0003671 0.04885 -0.0006164 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6457 0.02653 -0.02249 0.2741 0.9571 0.9783 0.7419 0.8746 0.9528 0.7107 ] Network output: [ 0.0003306 0.9239 1.043 0.0002046 -9.188e-05 0.03313 0.0001543 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06911 0.04054 0.06272 0.04717 0.9748 0.9817 0.07094 0.9468 0.9692 0.09485 ] Network output: [ 0.1115 -0.3065 1.127 -2.686e-05 1.201e-05 0.9566 -2.003e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7382 0.4757 0.4221 0.4698 0.9618 0.9814 0.7423 0.8865 0.9597 0.712 ] Network output: [ -0.06564 0.2853 0.8668 0.001477 -0.0006632 0.9851 0.001113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6272 0.5826 0.3656 0.2116 0.9786 0.9855 0.6279 0.9547 0.9729 0.4027 ] Network output: [ -0.1237 0.3237 0.8311 -0.0008558 0.0003842 1.089 -0.0006449 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.663 0.6537 0.4389 0.1401 0.9747 0.9828 0.6632 0.9457 0.9669 0.4486 ] Network output: [ 0.1059 0.7217 0.1239 -0.001206 0.0005413 0.9377 -0.0009087 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07719 Epoch 991 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03323 0.9916 0.9928 0.0001931 -8.67e-05 -0.05006 0.0001456 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03763 -0.001634 0.02802 0.02581 0.9158 0.9288 0.07477 0.851 0.8831 0.1757 ] Network output: [ 0.9138 0.109 -0.001677 -0.0006323 0.0002839 0.06251 -0.0004768 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6444 0.02829 -0.01193 0.2903 0.9572 0.9784 0.7404 0.875 0.953 0.714 ] Network output: [ -0.0006054 0.9118 1.057 0.0003005 -0.0001349 0.03411 0.0002266 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06882 0.04047 0.06481 0.05094 0.9749 0.9818 0.07064 0.9471 0.9695 0.09688 ] Network output: [ 0.1106 -0.3517 1.17 0.0003252 -0.000146 0.9616 0.0002453 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7393 0.4742 0.4252 0.489 0.9619 0.9814 0.7434 0.8867 0.9599 0.7142 ] Network output: [ -0.06866 0.2554 0.8996 0.001779 -0.0007985 0.9895 0.001341 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6269 0.5818 0.368 0.2286 0.9787 0.9856 0.6276 0.9549 0.9731 0.4052 ] Network output: [ -0.1248 0.2895 0.862 -0.0004128 0.0001853 1.096 -0.0003111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6643 0.6548 0.4388 0.1622 0.9748 0.9829 0.6645 0.9458 0.967 0.4487 ] Network output: [ 0.1089 0.7073 0.1304 -0.0009095 0.0004084 0.9408 -0.0006857 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0759 Epoch 992 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04209 1.004 0.9705 0.0002044 -9.179e-05 -0.05755 0.0001541 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03786 -0.002429 0.02374 0.02407 0.9159 0.9289 0.07541 0.8508 0.8829 0.1747 ] Network output: [ 0.9841 0.1453 -0.1208 -0.0003952 0.0001775 0.005674 -0.0002981 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6469 0.008094 -0.04889 0.2763 0.9572 0.9784 0.7439 0.8746 0.9528 0.7109 ] Network output: [ 0.0003954 0.9206 1.048 0.0002588 -0.0001162 0.03186 0.0001951 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0686 0.03888 0.06045 0.04866 0.9748 0.9816 0.07043 0.9465 0.969 0.09447 ] Network output: [ 0.1275 -0.3336 1.14 0.0002969 -0.0001333 0.94 0.000224 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7364 0.4627 0.4097 0.4846 0.9618 0.9814 0.7405 0.8865 0.9598 0.7137 ] Network output: [ -0.07855 0.2579 0.9153 0.001616 -0.0007255 0.9905 0.001218 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6233 0.5768 0.3668 0.2303 0.9786 0.9855 0.624 0.9546 0.9729 0.4061 ] Network output: [ -0.1377 0.2842 0.8868 -0.0004968 0.000223 1.102 -0.0003743 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.661 0.6513 0.4399 0.1683 0.9747 0.9828 0.6612 0.9457 0.967 0.4501 ] Network output: [ 0.1003 0.7071 0.1429 -0.0009627 0.0004322 0.9455 -0.0007257 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07492 Epoch 993 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03339 1.006 0.9782 5.562e-05 -2.498e-05 -0.05039 4.198e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03776 -0.001456 0.0275 0.02373 0.9159 0.9288 0.07503 0.851 0.883 0.1741 ] Network output: [ 0.9179 0.1496 -0.04853 -0.0008995 0.0004039 0.05952 -0.0006782 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6454 0.03236 -0.01386 0.2736 0.9571 0.9783 0.7416 0.8748 0.9529 0.7107 ] Network output: [ 0.0002335 0.9237 1.043 0.0001925 -8.645e-05 0.03364 0.0001452 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06924 0.04109 0.06375 0.04705 0.9749 0.9817 0.07107 0.9471 0.9693 0.0954 ] Network output: [ 0.1077 -0.3021 1.125 -9.795e-05 4.392e-05 0.9612 -7.361e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7389 0.4808 0.4271 0.4657 0.9619 0.9814 0.743 0.8867 0.9598 0.7115 ] Network output: [ -0.06225 0.2891 0.8578 0.001482 -0.0006652 0.9837 0.001117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.628 0.5843 0.3668 0.2084 0.9787 0.9855 0.6287 0.9549 0.973 0.4031 ] Network output: [ -0.1196 0.3299 0.8204 -0.0008711 0.0003911 1.085 -0.0006564 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6632 0.6541 0.4393 0.1356 0.9748 0.9828 0.6634 0.9458 0.9669 0.4489 ] Network output: [ 0.1058 0.7264 0.1195 -0.001271 0.0005705 0.9373 -0.0009578 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07755 Epoch 994 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03383 0.9888 0.9949 0.0002235 -0.0001004 -0.05052 0.0001685 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03756 -0.001687 0.02803 0.02617 0.9159 0.9289 0.07471 0.8512 0.8832 0.1761 ] Network output: [ 0.9174 0.1006 0.003076 -0.0005487 0.0002464 0.05934 -0.0004138 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6444 0.02753 -0.01213 0.2924 0.9572 0.9784 0.7406 0.8752 0.9531 0.7144 ] Network output: [ -0.0007633 0.9093 1.059 0.0003164 -0.0001421 0.03425 0.0002386 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06875 0.0404 0.06516 0.05172 0.975 0.9818 0.07057 0.9472 0.9696 0.09739 ] Network output: [ 0.1122 -0.3615 1.178 0.0004021 -0.0001806 0.9609 0.0003033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7395 0.4743 0.4258 0.4924 0.962 0.9815 0.7436 0.8869 0.96 0.7146 ] Network output: [ -0.07014 0.2466 0.9107 0.001856 -0.0008333 0.9905 0.001399 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6264 0.5814 0.3694 0.2336 0.9787 0.9856 0.6271 0.955 0.9732 0.4067 ] Network output: [ -0.1258 0.2784 0.8737 -0.0002678 0.0001202 1.098 -0.0002017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.664 0.6546 0.4394 0.1697 0.9748 0.9829 0.6642 0.9459 0.9671 0.4492 ] Network output: [ 0.1075 0.7043 0.1339 -0.0008476 0.0003806 0.9433 -0.000639 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07523 Epoch 995 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04356 1.007 0.9652 0.0001867 -8.385e-05 -0.05885 0.0001408 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03787 -0.002509 0.02312 0.02354 0.916 0.9289 0.07551 0.8509 0.883 0.1744 ] Network output: [ 0.9958 0.1549 -0.144 -0.0003889 0.0001747 -0.003971 -0.0002934 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6475 0.006392 -0.05357 0.2713 0.9572 0.9784 0.7447 0.8746 0.9529 0.7097 ] Network output: [ 0.0006726 0.9234 1.045 0.0002348 -0.0001054 0.03153 0.000177 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06865 0.03884 0.05987 0.04785 0.9748 0.9816 0.07048 0.9465 0.969 0.09407 ] Network output: [ 0.1298 -0.3246 1.129 0.0002321 -0.0001042 0.9369 0.0001751 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7361 0.4635 0.4089 0.4802 0.9618 0.9814 0.7402 0.8866 0.9598 0.7129 ] Network output: [ -0.07872 0.2614 0.9129 0.001576 -0.0007076 0.9896 0.001188 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.623 0.5766 0.3672 0.2286 0.9786 0.9855 0.6236 0.9547 0.9729 0.4064 ] Network output: [ -0.1383 0.2867 0.8865 -0.000525 0.0002357 1.101 -0.0003956 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6601 0.6505 0.4403 0.1673 0.9747 0.9828 0.6603 0.9458 0.9671 0.4505 ] Network output: [ 0.09736 0.7106 0.1435 -0.00103 0.0004626 0.9469 -0.0007767 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0752 Epoch 996 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03114 1.004 0.9819 2.906e-05 -1.306e-05 -0.04856 2.197e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03767 -0.001191 0.02866 0.02386 0.9159 0.9289 0.07486 0.8512 0.8832 0.1742 ] Network output: [ 0.9001 0.145 -0.02292 -0.000998 0.0004481 0.07371 -0.0007524 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6449 0.03945 -0.002983 0.2741 0.9572 0.9784 0.741 0.875 0.953 0.7108 ] Network output: [ 0.0001003 0.9231 1.043 0.0001812 -8.139e-05 0.03423 0.0001367 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06937 0.04174 0.06501 0.04706 0.9749 0.9818 0.07121 0.9473 0.9695 0.09603 ] Network output: [ 0.1031 -0.2986 1.125 -0.0001632 7.321e-05 0.9669 -0.0001228 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7398 0.4866 0.4329 0.4621 0.962 0.9814 0.7439 0.8868 0.9599 0.7109 ] Network output: [ -0.05814 0.2927 0.8476 0.001497 -0.0006722 0.9821 0.001128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6291 0.5863 0.3678 0.2052 0.9787 0.9856 0.6298 0.955 0.973 0.4033 ] Network output: [ -0.1147 0.336 0.8086 -0.0008771 0.0003938 1.081 -0.000661 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6636 0.6547 0.4395 0.1311 0.9748 0.9829 0.6638 0.9459 0.967 0.4489 ] Network output: [ 0.1062 0.7308 0.1146 -0.001328 0.0005963 0.9368 -0.001001 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07842 Epoch 997 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0349 0.9861 0.9966 0.0002591 -0.0001164 -0.05138 0.0001954 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0375 -0.001789 0.02782 0.02655 0.916 0.9289 0.07466 0.8513 0.8833 0.1765 ] Network output: [ 0.9246 0.09183 0.003941 -0.0004361 0.0001958 0.0533 -0.0003289 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6445 0.02555 -0.01434 0.2946 0.9573 0.9784 0.7409 0.8753 0.9532 0.7146 ] Network output: [ -0.0008858 0.9068 1.062 0.0003336 -0.0001498 0.03425 0.0002515 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06864 0.04023 0.06527 0.05258 0.975 0.9818 0.07047 0.9473 0.9696 0.09779 ] Network output: [ 0.115 -0.3728 1.186 0.0004999 -0.0002245 0.9589 0.000377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7395 0.4735 0.4253 0.4969 0.9621 0.9815 0.7436 0.887 0.96 0.7149 ] Network output: [ -0.07237 0.2363 0.9245 0.001938 -0.00087 0.9918 0.001461 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6257 0.5806 0.3707 0.2398 0.9787 0.9856 0.6264 0.9551 0.9732 0.4082 ] Network output: [ -0.1277 0.2646 0.8893 -0.0001019 4.573e-05 1.101 -7.673e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6637 0.6542 0.4397 0.1793 0.9749 0.9829 0.6638 0.946 0.9673 0.4496 ] Network output: [ 0.1057 0.6993 0.1397 -0.0007589 0.0003407 0.9465 -0.0005721 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0747 Epoch 998 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04493 1.012 0.9589 0.0001565 -7.025e-05 -0.06012 0.000118 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03789 -0.002572 0.02249 0.02291 0.916 0.929 0.07563 0.8509 0.883 0.1739 ] Network output: [ 1.007 0.1669 -0.1693 -0.0004032 0.0001811 -0.01322 -0.0003042 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6481 0.005106 -0.05817 0.2656 0.9573 0.9784 0.7457 0.8746 0.9529 0.7082 ] Network output: [ 0.001038 0.9272 1.04 0.0002025 -9.092e-05 0.03114 0.0001527 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06872 0.03885 0.05917 0.04681 0.9748 0.9816 0.07056 0.9465 0.9689 0.09349 ] Network output: [ 0.1319 -0.3122 1.115 0.000145 -6.516e-05 0.934 0.0001095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7358 0.4646 0.408 0.4745 0.9619 0.9814 0.7399 0.8866 0.9598 0.7117 ] Network output: [ -0.07821 0.2672 0.9072 0.001522 -0.0006833 0.9883 0.001147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6227 0.5768 0.3671 0.2258 0.9786 0.9855 0.6234 0.9547 0.9729 0.4062 ] Network output: [ -0.1386 0.2914 0.8839 -0.0005754 0.0002583 1.1 -0.0004336 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6592 0.6497 0.4405 0.1651 0.9747 0.9828 0.6594 0.9459 0.9671 0.4506 ] Network output: [ 0.09434 0.7149 0.1439 -0.001113 0.0004998 0.948 -0.0008392 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07599 Epoch 999 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02838 1.003 0.987 -4.695e-07 1.945e-07 -0.04635 -2.869e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03757 -0.0008857 0.03003 0.02415 0.9159 0.9289 0.07465 0.8514 0.8833 0.1743 ] Network output: [ 0.8783 0.1378 0.01003 -0.001109 0.0004979 0.09106 -0.000836 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6443 0.0476 0.00989 0.2759 0.9572 0.9784 0.7403 0.8752 0.9531 0.7109 ] Network output: [ -5.424e-05 0.9221 1.044 0.0001713 -7.693e-05 0.03487 0.0001292 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06951 0.04245 0.06644 0.04727 0.975 0.9818 0.07135 0.9476 0.9697 0.09673 ] Network output: [ 0.09796 -0.2968 1.126 -0.0002136 9.583e-05 0.9735 -0.0001608 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7408 0.493 0.439 0.4593 0.9621 0.9815 0.7449 0.8869 0.9599 0.7101 ] Network output: [ -0.05342 0.2957 0.837 0.001529 -0.0006866 0.9803 0.001153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6303 0.5886 0.3686 0.2025 0.9788 0.9856 0.631 0.9552 0.9731 0.4031 ] Network output: [ -0.1093 0.3412 0.7968 -0.0008635 0.0003877 1.077 -0.0006507 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6641 0.6555 0.4394 0.1274 0.9749 0.9829 0.6643 0.946 0.967 0.4485 ] Network output: [ 0.1071 0.7345 0.1098 -0.001371 0.0006153 0.936 -0.001033 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07998 Epoch 1000 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03647 0.9837 0.9973 0.000296 -0.0001329 -0.05272 0.0002231 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03744 -0.001941 0.02734 0.02693 0.9161 0.929 0.07463 0.8514 0.8834 0.1768 ] Network output: [ 0.9358 0.08383 -0.0006632 -0.0002982 0.0001339 0.04403 -0.000225 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6447 0.02232 -0.01889 0.2968 0.9573 0.9785 0.7413 0.8754 0.9533 0.7145 ] Network output: [ -0.0009311 0.9046 1.065 0.0003486 -0.0001565 0.03406 0.0002628 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06851 0.03995 0.06507 0.05342 0.975 0.9818 0.07033 0.9473 0.9697 0.09798 ] Network output: [ 0.119 -0.3844 1.193 0.0006106 -0.0002742 0.9555 0.0004604 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7393 0.4718 0.4234 0.5021 0.9621 0.9815 0.7434 0.8871 0.9601 0.715 ] Network output: [ -0.07524 0.2253 0.9403 0.002015 -0.0009045 0.9931 0.001518 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6248 0.5795 0.3716 0.2469 0.9788 0.9856 0.6255 0.9551 0.9733 0.4095 ] Network output: [ -0.1306 0.2487 0.9082 7.257e-05 -3.26e-05 1.105 5.476e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6631 0.6536 0.4399 0.1905 0.9749 0.983 0.6633 0.9461 0.9674 0.4498 ] Network output: [ 0.1035 0.6922 0.148 -0.0006477 0.0002908 0.9502 -0.0004883 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07447 Epoch 1001 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04595 1.017 0.9523 0.0001119 -5.023e-05 -0.06114 8.435e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03792 -0.002593 0.02194 0.02224 0.9161 0.929 0.07575 0.851 0.883 0.1732 ] Network output: [ 1.016 0.1804 -0.1933 -0.0004431 0.000199 -0.02042 -0.0003342 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6488 0.004864 -0.06171 0.2596 0.9573 0.9784 0.7465 0.8745 0.9528 0.7063 ] Network output: [ 0.001475 0.9317 1.035 0.000162 -7.274e-05 0.03074 0.0001222 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06885 0.03896 0.05845 0.0456 0.9748 0.9816 0.07069 0.9465 0.9688 0.09274 ] Network output: [ 0.1335 -0.2968 1.098 3.798e-05 -1.709e-05 0.9316 2.879e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7356 0.4667 0.4073 0.4677 0.9619 0.9814 0.7397 0.8865 0.9598 0.71 ] Network output: [ -0.07669 0.2754 0.8975 0.001458 -0.0006547 0.9864 0.001099 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6227 0.5773 0.3665 0.2219 0.9786 0.9855 0.6234 0.9547 0.9729 0.4054 ] Network output: [ -0.1382 0.2981 0.8786 -0.0006437 0.000289 1.097 -0.0004851 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6584 0.649 0.4403 0.1619 0.9747 0.9828 0.6585 0.9459 0.9671 0.4502 ] Network output: [ 0.09142 0.7197 0.1437 -0.001208 0.0005424 0.9488 -0.0009106 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07726 Epoch 1002 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02534 0.9999 0.9932 -3.077e-05 1.38e-05 -0.04399 -2.312e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03746 -0.0005678 0.0315 0.02461 0.916 0.9289 0.07441 0.8516 0.8834 0.1744 ] Network output: [ 0.8545 0.1277 0.04834 -0.00122 0.0005478 0.11 -0.0009197 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6436 0.05616 0.02374 0.279 0.9573 0.9784 0.7394 0.8754 0.9532 0.711 ] Network output: [ -0.000211 0.9206 1.045 0.0001636 -7.347e-05 0.03547 0.0001234 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06962 0.04318 0.06791 0.04774 0.9751 0.9819 0.07146 0.9478 0.9698 0.09742 ] Network output: [ 0.0928 -0.2977 1.131 -0.000237 0.0001064 0.9806 -0.0001785 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7419 0.4994 0.4449 0.4582 0.9621 0.9815 0.746 0.887 0.96 0.709 ] Network output: [ -0.04848 0.2972 0.8276 0.001584 -0.000711 0.9786 0.001194 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6315 0.5908 0.369 0.2011 0.9788 0.9856 0.6322 0.9553 0.9732 0.4026 ] Network output: [ -0.1039 0.3438 0.7873 -0.0008166 0.0003666 1.073 -0.0006154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6647 0.6563 0.4388 0.1257 0.9749 0.9829 0.6649 0.9461 0.967 0.4477 ] Network output: [ 0.1082 0.7369 0.1057 -0.001389 0.0006234 0.9354 -0.001047 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08222 Epoch 1003 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03853 0.9824 0.9965 0.0003269 -0.0001468 -0.05455 0.0002464 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03739 -0.002136 0.02656 0.02721 0.9161 0.9291 0.07464 0.8514 0.8834 0.1769 ] Network output: [ 0.9511 0.07827 -0.01259 -0.0001479 6.644e-05 0.03143 -0.0001117 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.645 0.01801 -0.02587 0.2982 0.9574 0.9785 0.742 0.8753 0.9533 0.7139 ] Network output: [ -0.0008493 0.9034 1.066 0.0003561 -0.0001599 0.0336 0.0002685 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06835 0.03958 0.06448 0.05407 0.975 0.9819 0.07018 0.9473 0.9696 0.09788 ] Network output: [ 0.1244 -0.3941 1.198 0.0007171 -0.000322 0.9506 0.0005406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7389 0.4696 0.42 0.5069 0.9621 0.9815 0.7431 0.8871 0.9601 0.7148 ] Network output: [ -0.07847 0.2151 0.9561 0.00207 -0.0009294 0.9941 0.00156 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6236 0.5781 0.3721 0.254 0.9788 0.9857 0.6243 0.9551 0.9733 0.4105 ] Network output: [ -0.1342 0.2327 0.9284 0.0002303 -0.0001034 1.108 0.0001736 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6623 0.6528 0.4398 0.2022 0.9749 0.983 0.6625 0.9461 0.9674 0.4498 ] Network output: [ 0.1006 0.684 0.1583 -0.0005312 0.0002385 0.9543 -0.0004005 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07458 Epoch 1004 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04624 1.023 0.9465 5.39e-05 -2.421e-05 -0.06161 4.067e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03795 -0.002538 0.02167 0.02163 0.9162 0.9291 0.07584 0.8509 0.8829 0.1724 ] Network output: [ 1.019 0.1934 -0.2106 -0.0005115 0.0002297 -0.02305 -0.0003858 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6493 0.006579 -0.06268 0.2542 0.9573 0.9784 0.7472 0.8744 0.9528 0.7042 ] Network output: [ 0.001923 0.9366 1.03 0.0001155 -5.187e-05 0.03042 8.713e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06902 0.03926 0.05791 0.04439 0.9748 0.9816 0.07087 0.9464 0.9688 0.09197 ] Network output: [ 0.1338 -0.2796 1.081 -8.334e-05 3.738e-05 0.9309 -6.265e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7356 0.4701 0.4073 0.4604 0.9619 0.9814 0.7398 0.8864 0.9597 0.7079 ] Network output: [ -0.07387 0.2855 0.8838 0.001392 -0.000625 0.9841 0.001049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6231 0.5783 0.3654 0.217 0.9786 0.9855 0.6238 0.9547 0.9728 0.4039 ] Network output: [ -0.1365 0.3068 0.8698 -0.0007238 0.0003249 1.093 -0.0005455 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6577 0.6486 0.4395 0.1575 0.9748 0.9828 0.6579 0.9459 0.9671 0.4494 ] Network output: [ 0.089 0.725 0.1426 -0.00131 0.0005883 0.9491 -0.0009877 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07869 Epoch 1005 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02251 0.9967 1 -5.562e-05 2.495e-05 -0.04186 -4.185e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03733 -0.0002871 0.03287 0.02525 0.916 0.929 0.07416 0.8517 0.8834 0.1745 ] Network output: [ 0.8322 0.1151 0.08753 -0.001306 0.0005864 0.1277 -0.0009845 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6428 0.06389 0.03661 0.2835 0.9573 0.9785 0.7385 0.8755 0.9533 0.711 ] Network output: [ -0.0003643 0.9185 1.047 0.0001602 -7.196e-05 0.03596 0.0001209 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06968 0.04381 0.06923 0.04854 0.9751 0.9819 0.07153 0.948 0.97 0.09802 ] Network output: [ 0.08842 -0.3026 1.138 -0.0002197 9.859e-05 0.987 -0.0001654 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7429 0.5051 0.4499 0.4596 0.9622 0.9816 0.7471 0.887 0.96 0.7077 ] Network output: [ -0.04416 0.2956 0.8223 0.001664 -0.0007469 0.9772 0.001254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6326 0.5927 0.369 0.2021 0.9788 0.9856 0.6332 0.9553 0.9732 0.4018 ] Network output: [ -0.0992 0.3418 0.783 -0.0007224 0.0003243 1.071 -0.0005444 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6653 0.657 0.4378 0.1277 0.975 0.9829 0.6654 0.9461 0.967 0.4465 ] Network output: [ 0.1093 0.7373 0.1033 -0.001372 0.0006159 0.9353 -0.001034 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08463 Epoch 1006 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04102 0.9828 0.9934 0.0003439 -0.0001544 -0.05683 0.0002592 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03737 -0.002359 0.02548 0.02728 0.9162 0.9291 0.0747 0.8513 0.8833 0.1767 ] Network output: [ 0.9703 0.07749 -0.03382 -5.859e-06 2.693e-06 0.0158 -4.673e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6456 0.01295 -0.03511 0.298 0.9574 0.9785 0.7428 0.8752 0.9532 0.7126 ] Network output: [ -0.0006017 0.9038 1.066 0.0003505 -0.0001574 0.03287 0.0002643 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06821 0.03916 0.06345 0.05431 0.975 0.9818 0.07004 0.9471 0.9695 0.09741 ] Network output: [ 0.1307 -0.3989 1.197 0.0007946 -0.0003568 0.9442 0.0005991 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7383 0.4673 0.4153 0.5102 0.9621 0.9815 0.7425 0.887 0.9601 0.7141 ] Network output: [ -0.08164 0.2082 0.9691 0.002084 -0.0009358 0.9945 0.001571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6223 0.5765 0.3721 0.2597 0.9788 0.9857 0.623 0.955 0.9733 0.4111 ] Network output: [ -0.1381 0.2197 0.9469 0.0003345 -0.0001502 1.111 0.0002521 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6612 0.6517 0.4397 0.2124 0.9749 0.983 0.6614 0.9461 0.9675 0.4497 ] Network output: [ 0.09706 0.6765 0.1691 -0.0004428 0.0001989 0.9585 -0.0003339 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07486 Epoch 1007 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04538 1.027 0.9429 -1.21e-05 5.423e-06 -0.06112 -9.076e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03795 -0.002367 0.0219 0.02122 0.9162 0.9291 0.07585 0.8509 0.8829 0.1715 ] Network output: [ 1.014 0.203 -0.2143 -0.0006083 0.0002732 -0.0182 -0.0004588 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6495 0.01129 -0.05927 0.2502 0.9573 0.9784 0.7475 0.8743 0.9527 0.7021 ] Network output: [ 0.002269 0.9409 1.024 6.821e-05 -3.064e-05 0.03036 5.15e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06925 0.0398 0.05783 0.04336 0.9748 0.9816 0.07111 0.9464 0.9687 0.0914 ] Network output: [ 0.1318 -0.2626 1.065 -0.0002121 9.52e-05 0.9331 -0.0001597 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7361 0.4754 0.4089 0.4531 0.9619 0.9814 0.7402 0.8863 0.9597 0.7057 ] Network output: [ -0.06963 0.2968 0.8665 0.00133 -0.0005972 0.9814 0.001003 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.624 0.5802 0.3643 0.2114 0.9786 0.9855 0.6247 0.9547 0.9728 0.4021 ] Network output: [ -0.1332 0.3175 0.8567 -0.000812 0.0003645 1.089 -0.0006119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6575 0.6485 0.4385 0.1519 0.9748 0.9828 0.6576 0.9459 0.967 0.4482 ] Network output: [ 0.08756 0.7309 0.1395 -0.001418 0.0006364 0.9487 -0.001068 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07969 Epoch 1008 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02052 0.9926 1.007 -6.344e-05 2.847e-05 -0.04047 -4.775e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03722 -0.0001081 0.03387 0.02603 0.9161 0.929 0.07394 0.8517 0.8835 0.1746 ] Network output: [ 0.8163 0.1003 0.1215 -0.001332 0.000598 0.1401 -0.001004 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6422 0.0692 0.04594 0.2891 0.9573 0.9785 0.7378 0.8755 0.9534 0.711 ] Network output: [ -0.0005393 0.9157 1.05 0.0001654 -7.43e-05 0.03627 0.0001248 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06968 0.04422 0.07023 0.0497 0.9752 0.982 0.07152 0.9481 0.9701 0.09852 ] Network output: [ 0.08584 -0.313 1.149 -0.0001487 6.671e-05 0.9917 -0.0001119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7438 0.5091 0.453 0.4642 0.9623 0.9816 0.7479 0.8869 0.96 0.7066 ] Network output: [ -0.04168 0.2894 0.8245 0.001768 -0.0007936 0.9767 0.001332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6331 0.5939 0.369 0.2065 0.9789 0.9857 0.6338 0.9554 0.9732 0.4011 ] Network output: [ -0.09651 0.3332 0.7875 -0.0005723 0.0002569 1.07 -0.0004313 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6657 0.6575 0.4366 0.1346 0.975 0.983 0.6658 0.946 0.967 0.4452 ] Network output: [ 0.11 0.7348 0.1037 -0.001311 0.0005885 0.9362 -0.0009881 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08618 Epoch 1009 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04387 0.9857 0.9874 0.0003419 -0.0001535 -0.05948 0.0002577 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03739 -0.002593 0.02413 0.02701 0.9163 0.9292 0.07482 0.8511 0.8832 0.1762 ] Network output: [ 0.9926 0.08373 -0.06605 0.0001071 -4.802e-05 -0.002382 8.046e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6464 0.007557 -0.04622 0.2952 0.9575 0.9785 0.744 0.8749 0.9531 0.7107 ] Network output: [ -0.0001799 0.9063 1.064 0.0003286 -0.0001476 0.03191 0.0002478 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06811 0.03876 0.06203 0.05391 0.975 0.9818 0.06993 0.9469 0.9693 0.09656 ] Network output: [ 0.1373 -0.3959 1.188 0.00082 -0.0003682 0.9369 0.0006181 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7376 0.4652 0.4098 0.5105 0.9621 0.9815 0.7417 0.8868 0.96 0.7129 ] Network output: [ -0.0844 0.2067 0.9764 0.002042 -0.0009167 0.994 0.001539 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.621 0.5751 0.3715 0.2627 0.9788 0.9856 0.6217 0.9549 0.9732 0.4112 ] Network output: [ -0.1421 0.213 0.9601 0.0003512 -0.0001577 1.113 0.0002648 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6599 0.6503 0.4396 0.219 0.9749 0.983 0.66 0.946 0.9675 0.4497 ] Network output: [ 0.09295 0.6727 0.1776 -0.0004224 0.0001897 0.962 -0.0003185 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07499 Epoch 1010 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04305 1.03 0.9432 -7.751e-05 3.478e-05 -0.05937 -5.837e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03792 -0.00205 0.02281 0.02107 0.9163 0.9292 0.07575 0.8508 0.8828 0.1709 ] Network output: [ 0.9963 0.2069 -0.1987 -0.0007328 0.000329 -0.003643 -0.0005525 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6494 0.01974 -0.04998 0.2485 0.9573 0.9785 0.7472 0.8741 0.9527 0.7005 ] Network output: [ 0.002391 0.9436 1.021 2.701e-05 -1.215e-05 0.0307 2.044e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06952 0.04065 0.0585 0.04267 0.9749 0.9816 0.07138 0.9465 0.9688 0.0913 ] Network output: [ 0.1268 -0.2478 1.054 -0.0003438 0.0001543 0.939 -0.000259 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.737 0.4829 0.4132 0.4461 0.962 0.9814 0.7411 0.8862 0.9596 0.7036 ] Network output: [ -0.06405 0.3083 0.8465 0.001277 -0.0005731 0.9785 0.0009621 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6255 0.5829 0.3637 0.2051 0.9786 0.9855 0.6261 0.9547 0.9727 0.4006 ] Network output: [ -0.1277 0.3305 0.8385 -0.0009087 0.000408 1.083 -0.0006848 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6577 0.649 0.4377 0.1444 0.9748 0.9829 0.6579 0.9459 0.967 0.4471 ] Network output: [ 0.08755 0.7378 0.1334 -0.00153 0.000687 0.9474 -0.001153 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07987 Epoch 1011 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01995 0.9875 1.013 -4.04e-05 1.812e-05 -0.04019 -3.038e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03712 -8.331e-05 0.03431 0.02692 0.9162 0.9291 0.07377 0.8517 0.8835 0.1749 ] Network output: [ 0.8111 0.08335 0.1453 -0.001264 0.0005674 0.144 -0.0009527 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6419 0.07079 0.04973 0.2954 0.9574 0.9785 0.7375 0.8755 0.9534 0.7112 ] Network output: [ -0.0007772 0.9116 1.054 0.0001848 -8.3e-05 0.03641 0.0001394 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06959 0.04431 0.07083 0.05126 0.9752 0.9821 0.07143 0.9481 0.9702 0.09901 ] Network output: [ 0.08592 -0.33 1.165 -1.495e-05 6.67e-06 0.9936 -1.109e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7443 0.5104 0.4537 0.4724 0.9624 0.9817 0.7485 0.8868 0.96 0.7061 ] Network output: [ -0.04209 0.2772 0.8371 0.001891 -0.0008487 0.9776 0.001425 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6329 0.594 0.3692 0.2149 0.9789 0.9857 0.6336 0.9554 0.9733 0.4013 ] Network output: [ -0.0967 0.3167 0.8029 -0.0003651 0.0001639 1.072 -0.0002751 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6658 0.6578 0.4358 0.147 0.975 0.983 0.666 0.946 0.9671 0.4443 ] Network output: [ 0.1098 0.7289 0.1078 -0.001197 0.0005375 0.9388 -0.0009024 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08608 Epoch 1012 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04702 0.9914 0.9782 0.0003217 -0.0001444 -0.0624 0.0002425 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03745 -0.00283 0.02255 0.02631 0.9164 0.9293 0.07503 0.8509 0.883 0.1754 ] Network output: [ 1.018 0.09818 -0.11 0.0001809 -8.113e-05 -0.02272 0.000136 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6475 0.002063 -0.05881 0.2892 0.9575 0.9786 0.7454 0.8745 0.953 0.7083 ] Network output: [ 0.0004063 0.9111 1.058 0.0002921 -0.0001311 0.03082 0.0002202 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06808 0.03841 0.06028 0.05277 0.975 0.9818 0.06991 0.9466 0.9691 0.09538 ] Network output: [ 0.1439 -0.3839 1.17 0.0007841 -0.000352 0.9291 0.0005911 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7367 0.4638 0.4038 0.507 0.9621 0.9815 0.7409 0.8865 0.9599 0.7113 ] Network output: [ -0.0865 0.2114 0.9768 0.001941 -0.0008716 0.9927 0.001463 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6198 0.574 0.3707 0.2623 0.9787 0.9856 0.6205 0.9548 0.9731 0.4109 ] Network output: [ -0.1458 0.2143 0.966 0.0002693 -0.0001209 1.112 0.000203 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6583 0.6488 0.4397 0.2207 0.9749 0.983 0.6585 0.9459 0.9674 0.4499 ] Network output: [ 0.08838 0.6746 0.1821 -0.0004922 0.000221 0.9646 -0.0003711 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07507 Epoch 1013 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03914 1.029 0.948 -0.000136 6.106e-05 -0.05624 -0.0001025 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03784 -0.001582 0.02449 0.02122 0.9163 0.9292 0.07552 0.8508 0.8827 0.1706 ] Network output: [ 0.9663 0.2039 -0.1614 -0.000888 0.0003987 0.02127 -0.0006695 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6488 0.03209 -0.03414 0.2491 0.9574 0.9785 0.7463 0.8741 0.9527 0.6998 ] Network output: [ 0.00223 0.9442 1.02 -3.186e-06 1.409e-06 0.03151 -2.315e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06981 0.04179 0.06006 0.04238 0.9749 0.9817 0.07168 0.9467 0.9689 0.09184 ] Network output: [ 0.1186 -0.2363 1.049 -0.0004748 0.0002131 0.9487 -0.0003577 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7384 0.4926 0.4205 0.4395 0.962 0.9815 0.7426 0.8861 0.9596 0.7021 ] Network output: [ -0.05724 0.3194 0.8246 0.001234 -0.0005542 0.9755 0.0009303 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6275 0.5865 0.3641 0.1981 0.9787 0.9855 0.6282 0.9548 0.9727 0.3997 ] Network output: [ -0.1201 0.3458 0.8148 -0.001013 0.0004547 1.076 -0.0007632 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6585 0.6502 0.4372 0.1346 0.9749 0.9829 0.6587 0.9459 0.9669 0.4464 ] Network output: [ 0.08911 0.746 0.1238 -0.001646 0.0007392 0.9453 -0.001241 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07977 Epoch 1014 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02099 0.9812 1.018 2.098e-05 -9.435e-06 -0.04111 1.588e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03703 -0.0002261 0.03414 0.02791 0.9163 0.9292 0.07365 0.8517 0.8834 0.1755 ] Network output: [ 0.8176 0.06409 0.1577 -0.001086 0.0004878 0.1386 -0.0008189 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6419 0.06829 0.04738 0.3022 0.9575 0.9786 0.7376 0.8755 0.9534 0.7116 ] Network output: [ -0.001083 0.9062 1.06 0.0002218 -9.958e-05 0.03643 0.0001672 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06941 0.04405 0.07104 0.05319 0.9753 0.9821 0.07125 0.9481 0.9702 0.09954 ] Network output: [ 0.08909 -0.3537 1.184 0.0001846 -8.293e-05 0.9921 0.0001393 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7445 0.5089 0.4519 0.4837 0.9624 0.9817 0.7487 0.8868 0.9601 0.7064 ] Network output: [ -0.04568 0.2588 0.8609 0.002029 -0.0009108 0.9799 0.001529 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6321 0.5928 0.3701 0.2271 0.9789 0.9857 0.6327 0.9554 0.9733 0.4026 ] Network output: [ -0.09991 0.2921 0.8298 -0.0001024 4.595e-05 1.077 -7.712e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6658 0.6576 0.4355 0.1647 0.9751 0.9831 0.6659 0.946 0.9671 0.4442 ] Network output: [ 0.1086 0.7186 0.1169 -0.001019 0.0004576 0.9432 -0.0007683 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08458 Epoch 1015 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05042 0.9999 0.9659 0.0002855 -0.0001282 -0.06549 0.0002152 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03756 -0.003067 0.02078 0.02522 0.9165 0.9293 0.07533 0.8507 0.8828 0.1744 ] Network output: [ 1.045 0.1205 -0.1649 0.0002148 -9.636e-05 -0.04474 0.0001616 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6489 -0.003443 -0.07252 0.2801 0.9575 0.9786 0.7473 0.8742 0.9528 0.7055 ] Network output: [ 0.001167 0.9182 1.051 0.0002439 -0.0001095 0.02967 0.0001839 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06814 0.03812 0.05823 0.05093 0.975 0.9817 0.06998 0.9463 0.9688 0.09387 ] Network output: [ 0.1504 -0.3633 1.144 0.0006955 -0.0003123 0.9208 0.0005243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7358 0.4631 0.3974 0.4998 0.9621 0.9815 0.74 0.8862 0.9598 0.7092 ] Network output: [ -0.08773 0.2221 0.97 0.001798 -0.0008073 0.9907 0.001355 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6189 0.5732 0.3693 0.2586 0.9787 0.9856 0.6196 0.9546 0.973 0.4101 ] Network output: [ -0.1491 0.2228 0.9651 0.0001075 -4.828e-05 1.111 8.11e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6566 0.6472 0.4399 0.2178 0.9749 0.983 0.6568 0.9459 0.9674 0.4502 ] Network output: [ 0.08341 0.6818 0.1826 -0.0006407 0.0002877 0.9662 -0.000483 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07596 Epoch 1016 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03375 1.026 0.9572 -0.0001901 8.533e-05 -0.05183 -0.0001432 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03771 -0.0009815 0.02687 0.02166 0.9163 0.9292 0.07516 0.8508 0.8827 0.1706 ] Network output: [ 0.9245 0.1944 -0.1035 -0.001083 0.0004861 0.05572 -0.0008161 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6477 0.04788 -0.01203 0.2522 0.9574 0.9785 0.7448 0.8741 0.9527 0.7 ] Network output: [ 0.001842 0.9429 1.021 -2.31e-05 1.035e-05 0.0327 -1.732e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0701 0.04316 0.06242 0.04248 0.975 0.9818 0.07197 0.947 0.9691 0.09293 ] Network output: [ 0.1077 -0.2281 1.049 -0.0005963 0.0002677 0.9617 -0.0004493 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7402 0.5041 0.4304 0.4335 0.9621 0.9815 0.7443 0.8861 0.9596 0.7009 ] Network output: [ -0.04909 0.33 0.801 0.001212 -0.0005442 0.9721 0.0009135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.63 0.5908 0.365 0.1905 0.9788 0.9856 0.6307 0.9549 0.9728 0.3992 ] Network output: [ -0.1105 0.3627 0.7866 -0.001111 0.0004987 1.067 -0.0008372 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6598 0.6518 0.437 0.123 0.975 0.9829 0.6599 0.9459 0.9669 0.4458 ] Network output: [ 0.0921 0.7545 0.1118 -0.001753 0.0007871 0.9423 -0.001321 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08086 Epoch 1017 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02344 0.974 1.023 0.0001133 -5.088e-05 -0.04307 8.545e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03695 -0.0005097 0.03341 0.02898 0.9164 0.9293 0.07359 0.8516 0.8834 0.1762 ] Network output: [ 0.8344 0.04305 0.1598 -0.0008096 0.0003635 0.1251 -0.0006103 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6421 0.06231 0.03952 0.3095 0.9576 0.9786 0.738 0.8754 0.9534 0.7122 ] Network output: [ -0.001384 0.8999 1.068 0.0002718 -0.000122 0.03628 0.0002049 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06915 0.04348 0.07083 0.05539 0.9753 0.9821 0.07098 0.948 0.9702 0.1 ] Network output: [ 0.09531 -0.3826 1.206 0.0004419 -0.0001984 0.9875 0.0003332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7444 0.5049 0.4477 0.4974 0.9625 0.9817 0.7485 0.8867 0.9601 0.7073 ] Network output: [ -0.05178 0.2354 0.8937 0.002176 -0.000977 0.9833 0.00164 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6305 0.5907 0.3714 0.2422 0.979 0.9858 0.6312 0.9553 0.9734 0.4047 ] Network output: [ -0.1056 0.2604 0.8666 0.0002088 -9.373e-05 1.085 0.0001574 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6654 0.6572 0.4355 0.1869 0.9751 0.9831 0.6656 0.946 0.9672 0.4444 ] Network output: [ 0.1064 0.7023 0.1322 -0.0007647 0.0003433 0.9495 -0.0005765 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08311 Epoch 1018 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05381 1.011 0.9511 0.0002296 -0.0001031 -0.06856 0.0001731 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03771 -0.003289 0.01892 0.02388 0.9166 0.9294 0.0757 0.8504 0.8826 0.1731 ] Network output: [ 1.073 0.1488 -0.2263 0.0002073 -9.3e-05 -0.06687 0.000156 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6505 -0.008532 -0.08639 0.2689 0.9576 0.9786 0.7493 0.8737 0.9526 0.702 ] Network output: [ 0.00213 0.9273 1.041 0.0001842 -8.272e-05 0.02847 0.0001389 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0683 0.03792 0.05593 0.04859 0.9749 0.9817 0.07015 0.9459 0.9685 0.09198 ] Network output: [ 0.1571 -0.3355 1.111 0.0005747 -0.000258 0.9125 0.0004333 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7349 0.4631 0.3906 0.4898 0.962 0.9815 0.739 0.8858 0.9596 0.7064 ] Network output: [ -0.08753 0.2379 0.956 0.001637 -0.0007351 0.9879 0.001234 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6184 0.5729 0.367 0.2523 0.9787 0.9855 0.619 0.9544 0.9728 0.4083 ] Network output: [ -0.1518 0.236 0.9592 -9.486e-05 4.257e-05 1.108 -7.142e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6549 0.6456 0.4395 0.2122 0.9749 0.983 0.655 0.9458 0.9672 0.4499 ] Network output: [ 0.07814 0.6916 0.1816 -0.0008284 0.0003719 0.9672 -0.0006245 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07884 Epoch 1019 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02717 1.022 0.9694 -0.00025 0.0001122 -0.04654 -0.0001884 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03754 -0.0002903 0.02974 0.02239 0.9164 0.9293 0.07471 0.8509 0.8827 0.1708 ] Network output: [ 0.8736 0.1796 -0.02935 -0.001319 0.000592 0.0972 -0.0009939 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6463 0.06603 0.01478 0.2577 0.9574 0.9785 0.7428 0.8741 0.9528 0.7005 ] Network output: [ 0.001383 0.9405 1.023 -3.887e-05 1.743e-05 0.034 -2.921e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07037 0.04466 0.06519 0.04296 0.9751 0.9819 0.07224 0.9473 0.9694 0.09421 ] Network output: [ 0.09564 -0.2233 1.053 -0.0006893 0.0003094 0.9765 -0.0005193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7422 0.5166 0.4414 0.429 0.9623 0.9816 0.7463 0.886 0.9596 0.6994 ] Network output: [ -0.03949 0.3396 0.776 0.001225 -0.0005498 0.9684 0.0009229 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6327 0.5954 0.3656 0.1835 0.9788 0.9856 0.6334 0.955 0.9728 0.3981 ] Network output: [ -0.09934 0.3787 0.7572 -0.001173 0.0005266 1.058 -0.000884 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6613 0.6537 0.4362 0.1122 0.975 0.983 0.6614 0.9459 0.9668 0.4447 ] Network output: [ 0.09617 0.7617 0.09963 -0.001827 0.0008201 0.9389 -0.001377 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08483 Epoch 1020 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02684 0.9672 1.026 0.0002117 -9.504e-05 -0.04585 0.0001596 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03688 -0.0008811 0.03217 0.03004 0.9165 0.9294 0.07355 0.8513 0.8832 0.1767 ] Network output: [ 0.8588 0.02286 0.1522 -0.0004722 0.000212 0.1056 -0.000356 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6424 0.05412 0.02719 0.3166 0.9576 0.9787 0.7386 0.8751 0.9533 0.7121 ] Network output: [ -0.00153 0.894 1.075 0.0003192 -0.0001433 0.03578 0.0002406 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06882 0.04272 0.07003 0.05753 0.9753 0.9821 0.07065 0.9478 0.9701 0.1002 ] Network output: [ 0.1042 -0.4119 1.226 0.0007248 -0.0003254 0.98 0.0005464 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7439 0.4994 0.4408 0.5117 0.9625 0.9818 0.748 0.8866 0.9601 0.708 ] Network output: [ -0.05902 0.2108 0.93 0.00231 -0.001037 0.9867 0.001741 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6285 0.5879 0.3722 0.2584 0.979 0.9858 0.6292 0.9553 0.9734 0.4067 ] Network output: [ -0.1129 0.225 0.9093 0.0005322 -0.000239 1.094 0.0004012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6647 0.6563 0.4352 0.2118 0.9752 0.9832 0.6648 0.946 0.9673 0.4444 ] Network output: [ 0.1032 0.6796 0.1547 -0.0004435 0.0001991 0.9574 -0.0003344 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08314 Epoch 1021 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05645 1.023 0.9358 0.0001469 -6.597e-05 -0.07109 0.0001108 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03788 -0.003439 0.01728 0.02258 0.9167 0.9295 0.07608 0.8501 0.8823 0.1714 ] Network output: [ 1.095 0.1785 -0.2832 0.0001541 -6.912e-05 -0.08465 0.0001159 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6521 -0.01173 -0.09792 0.258 0.9576 0.9786 0.7513 0.8732 0.9524 0.6977 ] Network output: [ 0.003238 0.9379 1.029 0.0001115 -5.008e-05 0.02728 8.411e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06855 0.03791 0.0536 0.04612 0.9749 0.9816 0.0704 0.9455 0.9682 0.08976 ] Network output: [ 0.1633 -0.3038 1.074 0.0004484 -0.0002013 0.9054 0.000338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7343 0.4646 0.3836 0.4789 0.962 0.9815 0.7384 0.8853 0.9594 0.7024 ] Network output: [ -0.0851 0.2571 0.9349 0.001489 -0.0006683 0.9843 0.001122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6183 0.5733 0.3632 0.2448 0.9786 0.9855 0.619 0.9542 0.9725 0.4046 ] Network output: [ -0.1531 0.2511 0.9498 -0.0002931 0.0001316 1.104 -0.0002208 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6532 0.6441 0.4378 0.2061 0.9748 0.9829 0.6533 0.9456 0.967 0.4482 ] Network output: [ 0.07309 0.7005 0.1814 -0.001009 0.0004529 0.9678 -0.0007603 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08378 Epoch 1022 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02031 1.017 0.9823 -0.0003217 0.0001444 -0.04124 -0.0002424 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03735 0.0003912 0.03259 0.0234 0.9164 0.9293 0.07422 0.8508 0.8827 0.1707 ] Network output: [ 0.8212 0.1617 0.05004 -0.001563 0.0007016 0.1394 -0.001178 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6446 0.08404 0.04193 0.2656 0.9574 0.9786 0.7405 0.874 0.9528 0.7006 ] Network output: [ 0.001017 0.9381 1.025 -5.687e-05 2.551e-05 0.03505 -4.278e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07056 0.0461 0.06771 0.04386 0.9752 0.982 0.07243 0.9476 0.9696 0.09516 ] Network output: [ 0.08431 -0.2231 1.06 -0.000726 0.0003259 0.9911 -0.000547 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7441 0.5287 0.4509 0.4281 0.9624 0.9817 0.7482 0.8858 0.9596 0.6969 ] Network output: [ -0.02948 0.3463 0.7536 0.001285 -0.0005768 0.9643 0.0009683 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6352 0.5998 0.3648 0.1796 0.9789 0.9856 0.6359 0.955 0.9728 0.3957 ] Network output: [ -0.08836 0.3888 0.7334 -0.001161 0.0005211 1.05 -0.0008748 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6627 0.6555 0.4339 0.1063 0.9751 0.983 0.6628 0.9458 0.9667 0.4421 ] Network output: [ 0.1006 0.7654 0.09011 -0.001842 0.0008269 0.9359 -0.001388 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09168 Epoch 1023 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03061 0.9632 1.026 0.0002799 -0.0001257 -0.04918 0.000211 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03682 -0.001269 0.0305 0.03086 0.9166 0.9294 0.07354 0.851 0.883 0.1766 ] Network output: [ 0.8871 0.009193 0.1332 -0.0001438 6.459e-05 0.0828 -0.0001085 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6429 0.04541 0.0118 0.3219 0.9577 0.9787 0.7394 0.8746 0.9532 0.7108 ] Network output: [ -0.001378 0.8908 1.079 0.0003399 -0.0001526 0.03476 0.0002562 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06847 0.04192 0.0685 0.05902 0.9753 0.9821 0.07029 0.9475 0.9699 0.09965 ] Network output: [ 0.1149 -0.4332 1.237 0.0009671 -0.0004342 0.9703 0.000729 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.743 0.4939 0.4315 0.5235 0.9625 0.9818 0.7471 0.8862 0.96 0.7075 ] Network output: [ -0.06571 0.1917 0.9608 0.002377 -0.001067 0.9886 0.001791 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6261 0.5848 0.3716 0.2723 0.979 0.9858 0.6268 0.9551 0.9733 0.4076 ] Network output: [ -0.1205 0.1938 0.9493 0.0007767 -0.0003487 1.101 0.0005854 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6634 0.6548 0.4343 0.2346 0.9752 0.9832 0.6636 0.9458 0.9673 0.4438 ] Network output: [ 0.09889 0.6546 0.1814 -0.0001322 5.94e-05 0.9656 -9.981e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08439 Epoch 1024 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05724 1.034 0.924 4.182e-05 -1.878e-05 -0.07216 3.155e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03801 -0.003416 0.01638 0.02171 0.9167 0.9296 0.07634 0.8497 0.8819 0.1694 ] Network output: [ 1.103 0.2017 -0.3177 6.243e-05 -2.796e-05 -0.09071 4.677e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6532 -0.0104 -0.103 0.2506 0.9576 0.9786 0.7526 0.8726 0.9521 0.6929 ] Network output: [ 0.004219 0.9478 1.017 3.166e-05 -1.423e-05 0.02646 2.393e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06888 0.03829 0.05188 0.04414 0.9748 0.9816 0.07074 0.9452 0.9679 0.08767 ] Network output: [ 0.1667 -0.2741 1.04 0.0003381 -0.0001518 0.9026 0.0002549 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7343 0.4689 0.3783 0.4695 0.962 0.9815 0.7385 0.8847 0.9591 0.6976 ] Network output: [ -0.08012 0.2769 0.9088 0.001373 -0.0006165 0.9801 0.001035 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.619 0.5748 0.3584 0.2376 0.9786 0.9854 0.6196 0.9539 0.9723 0.3996 ] Network output: [ -0.1518 0.2659 0.9368 -0.000459 0.000206 1.099 -0.0003459 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6521 0.6432 0.4348 0.2007 0.9748 0.9829 0.6522 0.9453 0.9668 0.4451 ] Network output: [ 0.06953 0.7074 0.1808 -0.001156 0.0005189 0.968 -0.0008712 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08817 Epoch 1025 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01496 1.013 0.9933 -0.0003879 0.0001741 -0.03744 -0.0002922 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03718 0.0008847 0.03461 0.02456 0.9165 0.9294 0.0738 0.8507 0.8825 0.1703 ] Network output: [ 0.7815 0.1433 0.1154 -0.001729 0.0007761 0.1712 -0.001303 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6433 0.0975 0.06189 0.2746 0.9575 0.9786 0.7387 0.8738 0.9527 0.6999 ] Network output: [ 0.0007533 0.9359 1.027 -7.545e-05 3.385e-05 0.03555 -5.679e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07063 0.04711 0.06927 0.04518 0.9753 0.982 0.07249 0.9476 0.9697 0.09551 ] Network output: [ 0.07634 -0.2299 1.072 -0.0006849 0.0003074 1.002 -0.000516 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7456 0.5379 0.4564 0.4327 0.9624 0.9817 0.7497 0.8853 0.9594 0.6937 ] Network output: [ -0.02208 0.3465 0.742 0.001381 -0.00062 0.9613 0.001041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6368 0.6028 0.3628 0.1815 0.9789 0.9857 0.6375 0.9549 0.9727 0.3924 ] Network output: [ -0.08073 0.3881 0.7243 -0.001061 0.0004765 1.045 -0.0007998 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6635 0.6566 0.4306 0.1093 0.9751 0.983 0.6637 0.9455 0.9665 0.4385 ] Network output: [ 0.1039 0.7646 0.08571 -0.001793 0.0008048 0.9346 -0.001351 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09776 Epoch 1026 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03421 0.9638 1.022 0.0002946 -0.0001323 -0.05265 0.0002221 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0368 -0.0016 0.02858 0.0311 0.9167 0.9295 0.07358 0.8504 0.8826 0.1759 ] Network output: [ 0.9158 0.008188 0.1008 0.0001009 -4.525e-05 0.05979 7.587e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6435 0.03798 -0.004635 0.3229 0.9577 0.9787 0.7403 0.8739 0.9529 0.708 ] Network output: [ -0.0009592 0.892 1.078 0.0003192 -0.0001433 0.03334 0.0002406 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0682 0.04128 0.06639 0.05927 0.9753 0.9821 0.07002 0.947 0.9696 0.0984 ] Network output: [ 0.125 -0.4385 1.233 0.001096 -0.000492 0.9601 0.0008261 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7419 0.4902 0.4212 0.5293 0.9625 0.9818 0.746 0.8856 0.9598 0.7058 ] Network output: [ -0.07062 0.1846 0.978 0.002327 -0.001045 0.9882 0.001754 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.624 0.5823 0.37 0.2802 0.979 0.9858 0.6247 0.9548 0.9731 0.4072 ] Network output: [ -0.1271 0.1768 0.976 0.0008354 -0.000375 1.105 0.0006296 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6617 0.6531 0.4333 0.249 0.9751 0.9832 0.6619 0.9456 0.9672 0.443 ] Network output: [ 0.09394 0.6378 0.2022 2.976e-05 -1.333e-05 0.9722 2.229e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08422 Epoch 1027 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05549 1.04 0.9199 -5.789e-05 2.598e-05 -0.07103 -4.359e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03805 -0.003146 0.01665 0.02148 0.9168 0.9296 0.07637 0.8493 0.8815 0.1678 ] Network output: [ 1.092 0.212 -0.3162 -4.167e-05 1.877e-05 -0.07997 -3.168e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6536 -0.00269 -0.09848 0.2483 0.9576 0.9786 0.7529 0.872 0.9519 0.689 ] Network output: [ 0.004654 0.954 1.01 -3.695e-05 1.657e-05 0.02643 -2.778e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06924 0.03916 0.0515 0.04311 0.9748 0.9816 0.07112 0.945 0.9677 0.0865 ] Network output: [ 0.1639 -0.253 1.019 0.0002385 -0.0001071 0.9071 0.0001799 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7352 0.4768 0.3778 0.463 0.962 0.9815 0.7394 0.8841 0.9589 0.6934 ] Network output: [ -0.07376 0.2935 0.8829 0.001286 -0.0005775 0.9763 0.0009695 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6205 0.5777 0.3548 0.2313 0.9786 0.9854 0.6212 0.9537 0.9721 0.3954 ] Network output: [ -0.1473 0.2808 0.9188 -0.0006026 0.0002705 1.093 -0.0004541 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.652 0.6434 0.4319 0.1948 0.9748 0.9829 0.6521 0.945 0.9665 0.4421 ] Network output: [ 0.06876 0.7146 0.1756 -0.001286 0.0005775 0.967 -0.0009696 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08834 Epoch 1028 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01285 1.008 1.001 -0.0004069 0.0001827 -0.0363 -0.0003066 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03706 0.001035 0.03529 0.02566 0.9165 0.9294 0.07354 0.8504 0.8822 0.1702 ] Network output: [ 0.7667 0.1257 0.1511 -0.001726 0.0007749 0.1828 -0.001301 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6426 0.1026 0.06879 0.2829 0.9575 0.9786 0.7379 0.8734 0.9526 0.6991 ] Network output: [ 0.000421 0.9327 1.031 -7.985e-05 3.583e-05 0.0356 -6.01e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07054 0.04745 0.06975 0.04682 0.9753 0.9821 0.0724 0.9475 0.9696 0.09558 ] Network output: [ 0.07387 -0.2452 1.088 -0.0005653 0.0002538 1.007 -0.0004259 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7463 0.5417 0.4567 0.4422 0.9625 0.9818 0.7504 0.8848 0.9593 0.6916 ] Network output: [ -0.02061 0.3381 0.7486 0.001474 -0.0006618 0.9606 0.001111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6369 0.6035 0.3612 0.1896 0.9789 0.9857 0.6376 0.9548 0.9726 0.3906 ] Network output: [ -0.07926 0.3756 0.7343 -0.0009117 0.0004093 1.045 -0.0006871 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6637 0.657 0.4283 0.1209 0.9751 0.983 0.6639 0.9453 0.9663 0.4361 ] Network output: [ 0.1049 0.7602 0.08682 -0.0017 0.0007633 0.9362 -0.001281 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09826 Epoch 1029 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0375 0.9687 1.013 0.0002744 -0.0001232 -0.05584 0.0002068 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03685 -0.001849 0.02665 0.03062 0.9168 0.9296 0.07373 0.8499 0.8821 0.1749 ] Network output: [ 0.9428 0.02025 0.05662 0.0002472 -0.0001109 0.03846 0.0001861 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6446 0.03255 -0.0203 0.3188 0.9577 0.9787 0.7417 0.8732 0.9526 0.7048 ] Network output: [ -0.0004947 0.8966 1.073 0.0002728 -0.0001225 0.03204 0.0002057 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06811 0.0409 0.06421 0.0583 0.9752 0.982 0.06993 0.9465 0.9692 0.09696 ] Network output: [ 0.1325 -0.4279 1.216 0.001099 -0.0004936 0.9515 0.0008286 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7409 0.4889 0.4122 0.5279 0.9625 0.9818 0.745 0.885 0.9595 0.7038 ] Network output: [ -0.07396 0.1895 0.9807 0.002171 -0.0009748 0.9865 0.001636 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6224 0.5808 0.3684 0.281 0.9789 0.9858 0.6231 0.9545 0.9729 0.4067 ] Network output: [ -0.1322 0.1775 0.9849 0.0006942 -0.0003117 1.105 0.0005232 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6599 0.6514 0.4331 0.2522 0.9751 0.9832 0.6601 0.9453 0.967 0.4431 ] Network output: [ 0.08931 0.6375 0.2082 -3.149e-05 1.417e-05 0.9755 -2.386e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08089 Epoch 1030 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05178 1.04 0.9243 -0.0001185 5.318e-05 -0.06803 -8.926e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03799 -0.002684 0.01797 0.02172 0.9169 0.9297 0.07615 0.849 0.8813 0.1673 ] Network output: [ 1.064 0.2096 -0.283 -0.0001357 6.096e-05 -0.05588 -0.0001025 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6532 0.01 -0.08585 0.2497 0.9576 0.9786 0.7521 0.8716 0.9518 0.6877 ] Network output: [ 0.004365 0.9548 1.009 -7.455e-05 3.345e-05 0.0273 -5.612e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06958 0.04036 0.05264 0.04294 0.9749 0.9816 0.07145 0.945 0.9677 0.08673 ] Network output: [ 0.1548 -0.242 1.015 0.0001308 -5.877e-05 0.918 9.871e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7367 0.4872 0.3833 0.4585 0.9621 0.9815 0.7408 0.8838 0.9588 0.6915 ] Network output: [ -0.06791 0.3051 0.862 0.001212 -0.000544 0.9736 0.0009133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6225 0.5814 0.3544 0.2256 0.9786 0.9855 0.6232 0.9537 0.972 0.3939 ] Network output: [ -0.1406 0.2967 0.8959 -0.000747 0.0003353 1.085 -0.0005629 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6528 0.6445 0.4309 0.1861 0.9748 0.9829 0.6529 0.945 0.9664 0.4409 ] Network output: [ 0.07069 0.7251 0.1629 -0.001427 0.0006408 0.9649 -0.001076 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08428 Epoch 1031 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01404 1.002 1.006 -0.0003509 0.0001575 -0.03747 -0.0002644 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03698 0.0008648 0.03486 0.0266 0.9167 0.9295 0.07342 0.8501 0.882 0.1707 ] Network output: [ 0.7752 0.108 0.1596 -0.001547 0.0006943 0.1757 -0.001166 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6427 0.09991 0.06422 0.2895 0.9576 0.9786 0.7381 0.8731 0.9525 0.6994 ] Network output: [ -7.4e-05 0.9274 1.037 -5.321e-05 2.387e-05 0.03557 -4.003e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07035 0.04717 0.06971 0.04864 0.9754 0.9821 0.07221 0.9474 0.9695 0.09597 ] Network output: [ 0.07647 -0.2678 1.108 -0.0003853 0.000173 1.005 -0.0002903 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7463 0.5404 0.4536 0.4541 0.9626 0.9818 0.7504 0.8846 0.9592 0.692 ] Network output: [ -0.02514 0.3221 0.7718 0.001543 -0.0006928 0.9627 0.001163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6359 0.6024 0.362 0.2012 0.979 0.9857 0.6366 0.9547 0.9726 0.392 ] Network output: [ -0.08328 0.3561 0.7581 -0.0007568 0.0003398 1.049 -0.0005703 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6636 0.6568 0.4285 0.1364 0.9752 0.9831 0.6637 0.9452 0.9663 0.4365 ] Network output: [ 0.1038 0.7542 0.09128 -0.001587 0.0007127 0.9404 -0.001196 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09327 Epoch 1032 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04066 0.9753 1.003 0.0002589 -0.0001162 -0.05853 0.0001951 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03696 -0.002047 0.02487 0.02967 0.9169 0.9297 0.07398 0.8495 0.8818 0.1742 ] Network output: [ 0.9681 0.03951 0.006646 0.0003381 -0.0001517 0.01897 0.0002546 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.646 0.02844 -0.03434 0.3109 0.9578 0.9787 0.7434 0.8726 0.9524 0.7025 ] Network output: [ -0.0001229 0.9022 1.068 0.0002317 -0.0001041 0.03127 0.0001747 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06819 0.04073 0.06238 0.05666 0.9752 0.982 0.07001 0.9462 0.9689 0.09582 ] Network output: [ 0.1373 -0.4092 1.194 0.00103 -0.0004623 0.9452 0.0007762 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7401 0.4893 0.4056 0.5217 0.9625 0.9818 0.7443 0.8846 0.9594 0.7026 ] Network output: [ -0.07659 0.2007 0.9755 0.001975 -0.0008865 0.9851 0.001488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6217 0.5804 0.368 0.277 0.9789 0.9857 0.6223 0.9543 0.9727 0.4071 ] Network output: [ -0.1359 0.1905 0.9805 0.0004451 -0.0001998 1.103 0.0003355 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6585 0.65 0.4344 0.2468 0.9751 0.9832 0.6586 0.9453 0.9669 0.4444 ] Network output: [ 0.08545 0.6511 0.2007 -0.0002464 0.0001106 0.9763 -0.0001858 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07614 Epoch 1033 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04734 1.035 0.9342 -0.0001326 5.953e-05 -0.06415 -9.991e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03788 -0.002163 0.01987 0.02215 0.9169 0.9297 0.07581 0.8489 0.8811 0.1678 ] Network output: [ 1.03 0.1994 -0.2335 -0.0002227 0.0001 -0.02658 -0.000168 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6523 0.02422 -0.06895 0.2526 0.9576 0.9786 0.7508 0.8714 0.9517 0.6889 ] Network output: [ 0.003562 0.9514 1.012 -7.785e-05 3.493e-05 0.02879 -5.86e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06985 0.04161 0.05484 0.04329 0.975 0.9817 0.07172 0.9453 0.9678 0.08809 ] Network output: [ 0.1424 -0.2389 1.023 1.617e-05 -7.286e-06 0.9315 1.229e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7382 0.4979 0.3929 0.4547 0.9622 0.9816 0.7424 0.8838 0.9588 0.6919 ] Network output: [ -0.06329 0.3121 0.8471 0.001153 -0.0005176 0.972 0.000869 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6246 0.5852 0.3569 0.2199 0.9787 0.9855 0.6253 0.9538 0.972 0.3952 ] Network output: [ -0.133 0.3134 0.8706 -0.0008837 0.0003967 1.078 -0.0006659 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6541 0.6462 0.4321 0.1748 0.975 0.983 0.6543 0.945 0.9664 0.4418 ] Network output: [ 0.07405 0.7383 0.1451 -0.001569 0.0007042 0.9621 -0.001182 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07921 Epoch 1034 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01715 0.9939 1.011 -0.0002362 0.000106 -0.03975 -0.000178 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03693 0.0005202 0.03389 0.02738 0.9168 0.9297 0.0734 0.85 0.8819 0.1718 ] Network output: [ 0.7966 0.08963 0.1536 -0.001264 0.0005673 0.1584 -0.0009525 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6432 0.09285 0.05398 0.2945 0.9577 0.9787 0.7387 0.873 0.9525 0.7009 ] Network output: [ -0.0006516 0.9204 1.045 3.206e-06 -1.457e-06 0.03569 2.489e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07012 0.04656 0.06961 0.05048 0.9754 0.9822 0.07197 0.9473 0.9695 0.0968 ] Network output: [ 0.08181 -0.2946 1.13 -0.0001717 7.705e-05 1.001 -0.0001293 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7461 0.5363 0.4495 0.4658 0.9627 0.9819 0.7502 0.8845 0.9593 0.6943 ] Network output: [ -0.03292 0.302 0.8039 0.001605 -0.0007206 0.9664 0.00121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6344 0.6003 0.3648 0.2136 0.979 0.9858 0.6351 0.9547 0.9726 0.3959 ] Network output: [ -0.08985 0.3341 0.7872 -0.000601 0.0002698 1.056 -0.0004529 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6633 0.6564 0.4307 0.1518 0.9752 0.9832 0.6635 0.9452 0.9664 0.439 ] Network output: [ 0.1016 0.7479 0.09697 -0.001459 0.000655 0.9459 -0.0011 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0866 Epoch 1035 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04373 0.9821 0.9923 0.0002596 -0.0001166 -0.0608 0.0001957 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0371 -0.002224 0.0233 0.02851 0.917 0.9298 0.07429 0.8493 0.8816 0.1738 ] Network output: [ 0.9917 0.06075 -0.04355 0.0004008 -0.0001799 0.0009687 0.0003018 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6475 0.02486 -0.04657 0.3013 0.9578 0.9788 0.7452 0.8723 0.9523 0.7012 ] Network output: [ 0.0001748 0.9076 1.062 0.0002066 -9.278e-05 0.03096 0.0001558 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06837 0.04066 0.06096 0.05483 0.9753 0.982 0.0702 0.9459 0.9687 0.09506 ] Network output: [ 0.1402 -0.3887 1.171 0.0009322 -0.0004185 0.9406 0.0007027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7396 0.4902 0.4015 0.5132 0.9625 0.9818 0.7437 0.8844 0.9593 0.7024 ] Network output: [ -0.07875 0.2132 0.9674 0.001792 -0.0008045 0.9842 0.001351 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6214 0.5805 0.3687 0.2709 0.9789 0.9857 0.6221 0.9542 0.9726 0.4084 ] Network output: [ -0.1384 0.2082 0.9696 0.0001833 -8.231e-05 1.1 0.0001382 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6574 0.649 0.4364 0.2371 0.9752 0.9832 0.6575 0.9453 0.9669 0.4466 ] Network output: [ 0.08215 0.6705 0.1872 -0.0005044 0.0002265 0.976 -0.0003803 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07228 Epoch 1036 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04286 1.028 0.9459 -0.0001212 5.441e-05 -0.0601 -9.131e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03774 -0.001669 0.02197 0.02265 0.917 0.9298 0.07544 0.849 0.8812 0.1688 ] Network output: [ 0.9945 0.1858 -0.1789 -0.0003229 0.000145 0.002772 -0.0002436 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6513 0.03776 -0.05051 0.2558 0.9577 0.9787 0.7493 0.8715 0.9518 0.6914 ] Network output: [ 0.002547 0.9462 1.018 -6.086e-05 2.731e-05 0.03053 -4.58e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07006 0.04276 0.05753 0.04388 0.9751 0.9818 0.07193 0.9456 0.9681 0.09001 ] Network output: [ 0.1296 -0.2403 1.036 -9.354e-05 4.197e-05 0.9448 -7.039e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7398 0.5075 0.4042 0.4513 0.9624 0.9817 0.7439 0.8839 0.9589 0.6936 ] Network output: [ -0.05934 0.3161 0.8361 0.001124 -0.0005047 0.9711 0.0008472 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6267 0.5888 0.3609 0.2145 0.9788 0.9856 0.6274 0.9541 0.9721 0.398 ] Network output: [ -0.1253 0.3292 0.8455 -0.0009873 0.0004432 1.072 -0.000744 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6557 0.6481 0.4343 0.1626 0.9751 0.9831 0.6558 0.9452 0.9664 0.4438 ] Network output: [ 0.07772 0.7515 0.1268 -0.001683 0.0007555 0.9594 -0.001268 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0755 Epoch 1037 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02093 0.9861 1.014 -0.000101 4.532e-05 -0.04232 -7.605e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0369 0.0001198 0.03276 0.02806 0.9169 0.9298 0.07342 0.8499 0.8819 0.1732 ] Network output: [ 0.8222 0.07184 0.1421 -0.0009556 0.000429 0.1378 -0.0007203 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6438 0.08426 0.04219 0.2986 0.9578 0.9788 0.7396 0.8729 0.9525 0.7029 ] Network output: [ -0.001215 0.9131 1.054 7.21e-05 -3.239e-05 0.03593 5.441e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06989 0.04584 0.06951 0.05224 0.9755 0.9822 0.07173 0.9472 0.9695 0.09782 ] Network output: [ 0.08808 -0.3222 1.151 4.818e-05 -2.166e-05 0.995 3.644e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7457 0.5312 0.4456 0.4767 0.9627 0.9819 0.7498 0.8846 0.9593 0.6974 ] Network output: [ -0.04141 0.2805 0.8383 0.001678 -0.0007533 0.9708 0.001265 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6328 0.5979 0.3684 0.2255 0.9791 0.9858 0.6335 0.9548 0.9727 0.4007 ] Network output: [ -0.09676 0.3116 0.817 -0.0004306 0.0001933 1.063 -0.0003245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6631 0.656 0.4336 0.1663 0.9753 0.9832 0.6632 0.9454 0.9666 0.4422 ] Network output: [ 0.09922 0.7408 0.1038 -0.001309 0.0005876 0.9516 -0.0009864 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08089 Epoch 1038 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04649 0.989 0.9817 0.0002615 -0.0001174 -0.06271 0.0001971 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03724 -0.002384 0.02193 0.02732 0.9172 0.9299 0.07462 0.8492 0.8814 0.1736 ] Network output: [ 1.013 0.08235 -0.09127 0.0004306 -0.0001933 -0.01547 0.0003243 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.649 0.02162 -0.05696 0.2914 0.9579 0.9788 0.747 0.8721 0.9522 0.7004 ] Network output: [ 0.0004394 0.9131 1.056 0.0001878 -8.435e-05 0.03089 0.0001416 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06859 0.04064 0.05978 0.05296 0.9753 0.982 0.07043 0.9458 0.9685 0.09444 ] Network output: [ 0.1423 -0.3683 1.15 0.0008188 -0.0003676 0.9368 0.0006172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7391 0.4912 0.399 0.5039 0.9625 0.9818 0.7432 0.8843 0.9592 0.7025 ] Network output: [ -0.08016 0.2257 0.9578 0.001642 -0.000737 0.9835 0.001237 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6214 0.5808 0.3697 0.2639 0.979 0.9858 0.6221 0.9542 0.9725 0.4098 ] Network output: [ -0.14 0.2267 0.9563 -4.733e-05 2.124e-05 1.097 -3.562e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6565 0.6483 0.4385 0.226 0.9752 0.9833 0.6567 0.9454 0.9669 0.4487 ] Network output: [ 0.07911 0.6899 0.1735 -0.0007436 0.0003338 0.9754 -0.0005605 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07014 Epoch 1039 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03842 1.021 0.9576 -0.000109 4.891e-05 -0.05611 -8.208e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0376 -0.001217 0.02413 0.02318 0.9171 0.9299 0.07507 0.8492 0.8813 0.17 ] Network output: [ 0.9596 0.1713 -0.1233 -0.0004526 0.0002032 0.03097 -0.0003413 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6502 0.05018 -0.03149 0.2594 0.9578 0.9788 0.7478 0.8718 0.952 0.6941 ] Network output: [ 0.001495 0.9408 1.024 -3.994e-05 1.792e-05 0.03229 -3.004e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07024 0.04378 0.06032 0.04458 0.9753 0.9819 0.0721 0.9461 0.9684 0.09202 ] Network output: [ 0.1179 -0.244 1.05 -0.0001914 8.591e-05 0.9571 -0.0001442 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7412 0.516 0.4157 0.4485 0.9625 0.9818 0.7453 0.8841 0.959 0.6954 ] Network output: [ -0.05532 0.3183 0.8266 0.001131 -0.0005076 0.9704 0.0008521 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6287 0.592 0.365 0.2096 0.9789 0.9857 0.6294 0.9543 0.9723 0.4009 ] Network output: [ -0.1177 0.3428 0.8223 -0.001043 0.0004682 1.066 -0.000786 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6572 0.6499 0.4366 0.1513 0.9752 0.9832 0.6574 0.9454 0.9665 0.4458 ] Network output: [ 0.08126 0.7628 0.1106 -0.001755 0.000788 0.957 -0.001323 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07379 Epoch 1040 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02472 0.9794 1.016 2.58e-05 -1.159e-05 -0.04488 1.949e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03687 -0.0002849 0.03158 0.02864 0.9171 0.9299 0.07347 0.8499 0.8819 0.1744 ] Network output: [ 0.8485 0.05603 0.1278 -0.0006648 0.0002985 0.1165 -0.0005011 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6444 0.07538 0.03024 0.302 0.9579 0.9788 0.7405 0.873 0.9526 0.7048 ] Network output: [ -0.001711 0.9067 1.061 0.0001368 -6.144e-05 0.03617 0.0001032 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06966 0.04509 0.0693 0.05382 0.9756 0.9823 0.0715 0.9472 0.9694 0.09871 ] Network output: [ 0.09466 -0.3483 1.171 0.0002547 -0.0001144 0.9887 0.0001921 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7453 0.5258 0.4418 0.4866 0.9628 0.982 0.7494 0.8847 0.9594 0.7003 ] Network output: [ -0.04943 0.2594 0.8716 0.001761 -0.0007906 0.9751 0.001327 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6313 0.5956 0.3719 0.2369 0.9792 0.9859 0.6319 0.9548 0.9728 0.4054 ] Network output: [ -0.1032 0.2889 0.8464 -0.000243 0.0001091 1.07 -0.0001831 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6628 0.6556 0.4362 0.1805 0.9754 0.9833 0.663 0.9456 0.9668 0.445 ] Network output: [ 0.09674 0.7321 0.1124 -0.001135 0.0005095 0.9573 -0.0008554 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07684 Epoch 1041 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04872 0.9966 0.9713 0.0002492 -0.0001119 -0.06425 0.0001878 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03737 -0.002517 0.02078 0.02614 0.9173 0.93 0.07492 0.8492 0.8814 0.1733 ] Network output: [ 1.031 0.1042 -0.1351 0.0004183 -0.0001877 -0.02969 0.000315 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6503 0.01899 -0.0654 0.2817 0.958 0.9788 0.7486 0.872 0.9521 0.6994 ] Network output: [ 0.000689 0.9189 1.049 0.0001646 -7.393e-05 0.03089 0.0001241 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06883 0.04065 0.05872 0.05108 0.9753 0.982 0.07067 0.9457 0.9683 0.09377 ] Network output: [ 0.1437 -0.3475 1.129 0.0006889 -0.0003093 0.9336 0.0005193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7387 0.4923 0.3976 0.4942 0.9626 0.9818 0.7428 0.8842 0.9592 0.7022 ] Network output: [ -0.08047 0.2384 0.9461 0.001521 -0.0006826 0.9827 0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6217 0.5814 0.3704 0.2565 0.979 0.9858 0.6224 0.9543 0.9725 0.4106 ] Network output: [ -0.1407 0.2444 0.9423 -0.0002389 0.0001072 1.094 -0.00018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6558 0.6477 0.4401 0.215 0.9753 0.9833 0.656 0.9455 0.9669 0.4503 ] Network output: [ 0.0762 0.7071 0.162 -0.0009462 0.0004248 0.9747 -0.0007132 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06963 Epoch 1042 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03396 1.015 0.9688 -0.0001086 4.876e-05 -0.05222 -8.183e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03745 -0.0008006 0.02628 0.02377 0.9172 0.93 0.07471 0.8494 0.8814 0.171 ] Network output: [ 0.9254 0.1564 -0.06777 -0.0006125 0.000275 0.05804 -0.0004618 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6491 0.06161 -0.01236 0.2636 0.9579 0.9788 0.7462 0.872 0.9521 0.6966 ] Network output: [ 0.0004897 0.936 1.029 -2.382e-05 1.068e-05 0.03393 -1.789e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07037 0.04469 0.06302 0.04536 0.9754 0.9821 0.07224 0.9465 0.9687 0.09388 ] Network output: [ 0.1076 -0.2492 1.065 -0.000272 0.0001221 0.9681 -0.0002049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7426 0.5234 0.4265 0.4467 0.9627 0.9818 0.7467 0.8843 0.9591 0.6967 ] Network output: [ -0.0509 0.3191 0.8178 0.001171 -0.0005256 0.9696 0.0008824 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6306 0.595 0.3684 0.2057 0.979 0.9858 0.6313 0.9546 0.9724 0.4031 ] Network output: [ -0.1103 0.3531 0.8022 -0.001047 0.0004702 1.061 -0.0007892 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6587 0.6516 0.4382 0.1422 0.9754 0.9832 0.6589 0.9456 0.9666 0.4471 ] Network output: [ 0.08458 0.7712 0.09756 -0.001783 0.0008004 0.9549 -0.001344 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07391 Epoch 1043 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02836 0.9745 1.017 0.0001303 -5.849e-05 -0.04742 9.821e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03685 -0.0006777 0.03034 0.02911 0.9172 0.93 0.07353 0.8499 0.8819 0.1753 ] Network output: [ 0.8746 0.04338 0.1106 -0.0004059 0.0001822 0.09512 -0.000306 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.645 0.06659 0.01822 0.3047 0.958 0.9789 0.7413 0.873 0.9526 0.7061 ] Network output: [ -0.002098 0.9019 1.067 0.000188 -8.44e-05 0.03628 0.0001417 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06943 0.04436 0.06885 0.05514 0.9756 0.9823 0.07127 0.9472 0.9694 0.09929 ] Network output: [ 0.1015 -0.3711 1.188 0.0004354 -0.0001955 0.982 0.0003282 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7447 0.5205 0.4378 0.4954 0.9629 0.982 0.7488 0.8848 0.9595 0.7027 ] Network output: [ -0.05663 0.2399 0.902 0.001842 -0.0008269 0.9788 0.001388 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6298 0.5933 0.3746 0.2474 0.9792 0.9859 0.6304 0.9549 0.9729 0.4092 ] Network output: [ -0.1092 0.2665 0.8749 -5.192e-05 2.33e-05 1.077 -3.909e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6625 0.6551 0.4382 0.1944 0.9755 0.9834 0.6626 0.9457 0.9669 0.4471 ] Network output: [ 0.0941 0.7216 0.1235 -0.0009454 0.0004244 0.9628 -0.0007126 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07433 Epoch 1044 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05019 1.005 0.9613 0.0002169 -9.736e-05 -0.0653 0.0001635 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03749 -0.0026 0.01988 0.02506 0.9174 0.9301 0.07518 0.8492 0.8813 0.1728 ] Network output: [ 1.045 0.1255 -0.1727 0.0003622 -0.0001626 -0.04043 0.0002728 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6515 0.01751 -0.07142 0.2727 0.958 0.9789 0.7499 0.8718 0.9521 0.6981 ] Network output: [ 0.0009192 0.9253 1.042 0.000133 -5.971e-05 0.03093 0.0001003 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06906 0.04075 0.05779 0.04925 0.9753 0.982 0.07091 0.9457 0.9682 0.09298 ] Network output: [ 0.1445 -0.3262 1.108 0.000544 -0.0002443 0.9311 0.0004101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7384 0.4939 0.3969 0.4845 0.9626 0.9818 0.7425 0.8841 0.9592 0.7014 ] Network output: [ -0.07949 0.2515 0.9319 0.001424 -0.0006393 0.9814 0.001073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6221 0.5822 0.3704 0.2489 0.979 0.9858 0.6228 0.9543 0.9724 0.4105 ] Network output: [ -0.1403 0.2609 0.9279 -0.0003945 0.0001771 1.09 -0.0002973 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6552 0.6472 0.4409 0.2048 0.9753 0.9833 0.6554 0.9456 0.9669 0.451 ] Network output: [ 0.07354 0.7214 0.153 -0.001112 0.0004993 0.974 -0.0008382 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07034 Epoch 1045 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02963 1.01 0.9792 -0.0001207 5.42e-05 -0.04858 -9.096e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0373 -0.0004286 0.0283 0.02443 0.9172 0.93 0.07435 0.8496 0.8815 0.1717 ] Network output: [ 0.893 0.1412 -0.01373 -0.0007864 0.0003531 0.08327 -0.0005928 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.648 0.07187 0.006012 0.2684 0.9579 0.9788 0.7447 0.8723 0.9522 0.6985 ] Network output: [ -0.0004144 0.9317 1.034 -1.374e-05 6.155e-06 0.03537 -1.03e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07047 0.04548 0.06544 0.04627 0.9755 0.9822 0.07233 0.9468 0.9689 0.09545 ] Network output: [ 0.09899 -0.2559 1.079 -0.0003276 0.000147 0.9778 -0.0002468 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7438 0.5299 0.436 0.4463 0.9628 0.9819 0.7479 0.8845 0.9592 0.6973 ] Network output: [ -0.04632 0.3184 0.8106 0.001241 -0.000557 0.9687 0.000935 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6323 0.5976 0.3708 0.2034 0.9791 0.9858 0.6329 0.9548 0.9725 0.4044 ] Network output: [ -0.1034 0.3591 0.7867 -0.001 0.0004491 1.057 -0.000754 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6601 0.6532 0.439 0.1364 0.9755 0.9833 0.6602 0.9457 0.9666 0.4476 ] Network output: [ 0.0876 0.7762 0.08818 -0.001768 0.0007936 0.9532 -0.001332 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07539 Epoch 1046 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03179 0.9718 1.015 0.0002071 -9.298e-05 -0.04995 0.0001561 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03684 -0.00105 0.02902 0.02943 0.9173 0.9301 0.0736 0.8499 0.8819 0.1758 ] Network output: [ 0.9005 0.0351 0.08925 -0.0001851 8.313e-05 0.07389 -0.0001396 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6456 0.05813 0.006024 0.3063 0.9581 0.9789 0.7423 0.8729 0.9526 0.7065 ] Network output: [ -0.002337 0.899 1.07 0.0002212 -9.933e-05 0.03618 0.0001668 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06921 0.04366 0.06806 0.05608 0.9756 0.9823 0.07105 0.9471 0.9694 0.09945 ] Network output: [ 0.1085 -0.3889 1.199 0.0005785 -0.0002597 0.9749 0.0004361 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.744 0.5155 0.4332 0.5025 0.9629 0.982 0.7481 0.8849 0.9596 0.7041 ] Network output: [ -0.06285 0.2236 0.928 0.001903 -0.0008544 0.9818 0.001434 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6283 0.5911 0.3765 0.2566 0.9792 0.986 0.6289 0.9549 0.9729 0.412 ] Network output: [ -0.1148 0.2462 0.9012 0.000117 -5.253e-05 1.083 8.821e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6619 0.6544 0.4394 0.2073 0.9755 0.9834 0.6621 0.9458 0.9671 0.4485 ] Network output: [ 0.09119 0.7103 0.1362 -0.0007615 0.0003419 0.9681 -0.000574 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07294 Epoch 1047 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05068 1.012 0.9528 0.0001671 -7.505e-05 -0.06566 0.000126 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03757 -0.002609 0.01936 0.02414 0.9175 0.9302 0.07536 0.8491 0.8813 0.1721 ] Network output: [ 1.051 0.1447 -0.2003 0.0002674 -0.00012 -0.04597 0.0002014 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6523 0.01788 -0.07415 0.265 0.9581 0.9789 0.7509 0.8717 0.952 0.6965 ] Network output: [ 0.00109 0.9316 1.036 9.469e-05 -4.253e-05 0.03106 7.142e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0693 0.04098 0.0571 0.0476 0.9753 0.982 0.07116 0.9456 0.9681 0.09217 ] Network output: [ 0.1441 -0.3053 1.089 0.0003894 -0.0001748 0.9302 0.0002936 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7383 0.4963 0.3972 0.4752 0.9626 0.9818 0.7425 0.8841 0.9591 0.6999 ] Network output: [ -0.07719 0.2646 0.9155 0.00135 -0.0006059 0.9798 0.001017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6228 0.5834 0.3699 0.2416 0.979 0.9858 0.6235 0.9543 0.9724 0.4096 ] Network output: [ -0.1388 0.2762 0.9128 -0.0005182 0.0002326 1.087 -0.0003905 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6548 0.6469 0.4409 0.1954 0.9754 0.9833 0.655 0.9456 0.9668 0.4508 ] Network output: [ 0.07143 0.7333 0.1457 -0.001245 0.0005591 0.9731 -0.0009386 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07158 Epoch 1048 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02582 1.005 0.9885 -0.0001374 6.166e-05 -0.04548 -0.0001035 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03715 -0.0001303 0.03004 0.02516 0.9173 0.9301 0.07403 0.8497 0.8816 0.1722 ] Network output: [ 0.8651 0.1259 0.03536 -0.0009451 0.0004243 0.1046 -0.0007124 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6469 0.08027 0.02201 0.2737 0.958 0.9789 0.7434 0.8724 0.9524 0.6999 ] Network output: [ -0.001184 0.928 1.038 -6.535e-06 2.919e-06 0.03654 -4.865e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0705 0.04611 0.06744 0.04731 0.9756 0.9823 0.07236 0.9471 0.9691 0.09666 ] Network output: [ 0.09233 -0.2649 1.093 -0.0003497 0.000157 0.9856 -0.0002635 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7449 0.5352 0.4436 0.4479 0.9629 0.982 0.749 0.8845 0.9593 0.6973 ] Network output: [ -0.04226 0.315 0.8068 0.001333 -0.0005986 0.9681 0.001005 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6336 0.5997 0.3724 0.2034 0.9792 0.9859 0.6343 0.9549 0.9726 0.4049 ] Network output: [ -0.09763 0.3597 0.7779 -0.0009058 0.0004067 1.054 -0.0006827 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6612 0.6544 0.4389 0.1349 0.9755 0.9834 0.6614 0.9458 0.9667 0.4474 ] Network output: [ 0.09013 0.7778 0.0826 -0.001712 0.0007687 0.9523 -0.00129 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07739 Epoch 1049 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03499 0.9714 1.012 0.0002551 -0.0001145 -0.05246 0.0001923 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03684 -0.001392 0.02762 0.02951 0.9174 0.9302 0.07368 0.8498 0.8818 0.176 ] Network output: [ 0.9258 0.0325 0.06283 -9.714e-06 4.394e-06 0.05307 -7.458e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6463 0.05028 -0.006211 0.3063 0.9582 0.979 0.7432 0.8728 0.9526 0.7061 ] Network output: [ -0.002407 0.8984 1.072 0.0002353 -0.0001057 0.03587 0.0001774 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06903 0.04302 0.06697 0.05649 0.9756 0.9823 0.07086 0.9469 0.9693 0.09917 ] Network output: [ 0.1154 -0.3995 1.204 0.000671 -0.0003013 0.9674 0.0005058 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7432 0.5111 0.4282 0.5072 0.963 0.982 0.7473 0.8848 0.9596 0.7047 ] Network output: [ -0.06797 0.2122 0.9478 0.001929 -0.000866 0.9838 0.001454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6269 0.5891 0.3775 0.2635 0.9793 0.986 0.6276 0.9549 0.9729 0.4139 ] Network output: [ -0.1197 0.2305 0.9228 0.0002338 -0.000105 1.087 0.0001762 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6611 0.6535 0.44 0.2178 0.9756 0.9835 0.6613 0.9459 0.9671 0.4493 ] Network output: [ 0.08799 0.7005 0.1484 -0.0006151 0.0002762 0.9726 -0.0004637 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07207 Epoch 1050 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05002 1.018 0.9471 0.0001085 -4.87e-05 -0.06514 8.176e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03761 -0.00252 0.01934 0.02348 0.9175 0.9303 0.07545 0.8491 0.8812 0.1713 ] Network output: [ 1.05 0.1596 -0.2135 0.0001428 -6.406e-05 -0.04466 0.0001074 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6528 0.02069 -0.07268 0.2593 0.9581 0.9789 0.7514 0.8716 0.952 0.6948 ] Network output: [ 0.001128 0.937 1.03 5.546e-05 -2.491e-05 0.03138 4.185e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06954 0.04137 0.05687 0.04627 0.9754 0.982 0.0714 0.9456 0.968 0.09155 ] Network output: [ 0.142 -0.2866 1.072 0.0002312 -0.0001038 0.9315 0.0001743 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7386 0.5 0.3991 0.4669 0.9626 0.9818 0.7427 0.8839 0.9591 0.6982 ] Network output: [ -0.07378 0.2769 0.898 0.001296 -0.0005817 0.978 0.0009765 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6239 0.585 0.3692 0.2346 0.979 0.9858 0.6245 0.9543 0.9723 0.4083 ] Network output: [ -0.136 0.2904 0.8966 -0.000614 0.0002756 1.082 -0.0004627 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6547 0.647 0.4404 0.1866 0.9754 0.9833 0.6548 0.9456 0.9668 0.4502 ] Network output: [ 0.07024 0.7432 0.1388 -0.001351 0.0006066 0.9719 -0.001018 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07252 Epoch 1051 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02306 1 0.9964 -0.0001458 6.543e-05 -0.04329 -0.0001098 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03702 5.392e-05 0.0313 0.02591 0.9174 0.9301 0.07375 0.8498 0.8817 0.1726 ] Network output: [ 0.8453 0.1108 0.07495 -0.001054 0.0004734 0.1194 -0.0007948 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6461 0.08582 0.03376 0.2793 0.9581 0.9789 0.7423 0.8725 0.9524 0.7008 ] Network output: [ -0.001821 0.9244 1.042 3.046e-06 -1.382e-06 0.03741 2.354e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07048 0.04649 0.06889 0.04848 0.9757 0.9823 0.07233 0.9473 0.9693 0.09755 ] Network output: [ 0.0881 -0.2768 1.108 -0.0003338 0.0001498 0.9911 -0.0002514 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7457 0.5386 0.4488 0.4517 0.963 0.982 0.7497 0.8845 0.9593 0.697 ] Network output: [ -0.03969 0.3084 0.8088 0.001438 -0.0006457 0.968 0.001084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6344 0.6009 0.3734 0.2063 0.9793 0.9859 0.6351 0.955 0.9727 0.4052 ] Network output: [ -0.09381 0.3542 0.7774 -0.0007721 0.0003466 1.053 -0.0005818 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.662 0.6553 0.4385 0.1379 0.9756 0.9834 0.6622 0.9458 0.9667 0.4468 ] Network output: [ 0.09189 0.7763 0.08087 -0.001622 0.000728 0.9524 -0.001222 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07882 Epoch 1052 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03794 0.9735 1.007 0.0002774 -0.0001246 -0.05487 0.0002091 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03686 -0.001694 0.0262 0.02931 0.9175 0.9303 0.0738 0.8496 0.8817 0.1757 ] Network output: [ 0.9499 0.03632 0.03111 0.0001149 -5.157e-05 0.03318 8.648e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.647 0.04333 -0.01807 0.3043 0.9582 0.979 0.7443 0.8726 0.9525 0.705 ] Network output: [ -0.002325 0.8999 1.07 0.0002326 -0.0001045 0.03542 0.0001754 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0689 0.04249 0.06566 0.05631 0.9756 0.9823 0.07073 0.9467 0.9691 0.09855 ] Network output: [ 0.1217 -0.4019 1.201 0.0007039 -0.000316 0.9602 0.0005306 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7424 0.5076 0.4233 0.5087 0.963 0.982 0.7465 0.8847 0.9595 0.7045 ] Network output: [ -0.07193 0.2068 0.96 0.001911 -0.000858 0.9848 0.00144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6257 0.5875 0.3778 0.2674 0.9793 0.986 0.6264 0.9548 0.9729 0.415 ] Network output: [ -0.124 0.2216 0.9377 0.0002776 -0.0001246 1.09 0.0002092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6602 0.6525 0.4405 0.2242 0.9756 0.9835 0.6604 0.9459 0.9672 0.4499 ] Network output: [ 0.08461 0.6949 0.1575 -0.0005369 0.0002411 0.9762 -0.0004047 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07106 Epoch 1053 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0482 1.022 0.9451 5.178e-05 -2.325e-05 -0.06366 3.905e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03761 -0.002328 0.01989 0.02311 0.9176 0.9303 0.07541 0.8491 0.8812 0.1707 ] Network output: [ 1.039 0.1684 -0.2099 -1.488e-06 7.12e-07 -0.03593 -1.302e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6528 0.02613 -0.06657 0.256 0.9581 0.9789 0.7514 0.8714 0.9519 0.6936 ] Network output: [ 0.0009615 0.9405 1.026 2.216e-05 -9.962e-06 0.03199 1.676e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06978 0.04195 0.05726 0.04539 0.9754 0.982 0.07164 0.9457 0.9681 0.09135 ] Network output: [ 0.1376 -0.2717 1.061 7.458e-05 -3.35e-05 0.9354 5.629e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7391 0.5048 0.4031 0.4598 0.9627 0.9819 0.7432 0.8839 0.959 0.6968 ] Network output: [ -0.06969 0.2875 0.8809 0.00126 -0.0005659 0.9762 0.0009499 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6251 0.5871 0.3691 0.2282 0.979 0.9858 0.6258 0.9543 0.9723 0.4073 ] Network output: [ -0.1318 0.3038 0.8792 -0.000687 0.0003084 1.078 -0.0005177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6549 0.6474 0.44 0.1782 0.9754 0.9833 0.6551 0.9456 0.9668 0.4495 ] Network output: [ 0.0702 0.7523 0.1311 -0.001437 0.0006452 0.9703 -0.001083 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07255 Epoch 1054 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02173 0.9956 1.003 -0.0001335 5.995e-05 -0.04222 -0.0001006 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03691 9.718e-05 0.03198 0.02663 0.9175 0.9302 0.07354 0.8499 0.8817 0.1729 ] Network output: [ 0.836 0.09593 0.1018 -0.001089 0.0004889 0.1258 -0.0008208 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6456 0.08786 0.04004 0.2845 0.9581 0.979 0.7417 0.8726 0.9525 0.7015 ] Network output: [ -0.002352 0.9206 1.046 2.027e-05 -9.112e-06 0.03802 1.533e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07038 0.04658 0.06981 0.04976 0.9757 0.9824 0.07224 0.9474 0.9694 0.09822 ] Network output: [ 0.08651 -0.292 1.124 -0.0002803 0.0001258 0.9936 -0.0002111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7461 0.5398 0.4516 0.4575 0.9631 0.9821 0.7502 0.8845 0.9594 0.697 ] Network output: [ -0.03934 0.298 0.8181 0.001544 -0.000693 0.9688 0.001163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6346 0.6013 0.3743 0.2118 0.9793 0.986 0.6353 0.955 0.9727 0.4059 ] Network output: [ -0.09243 0.3431 0.7853 -0.0006127 0.000275 1.054 -0.0004617 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6626 0.6559 0.4383 0.1449 0.9756 0.9835 0.6627 0.9458 0.9667 0.4464 ] Network output: [ 0.09266 0.7721 0.08267 -0.001503 0.000675 0.9538 -0.001133 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07883 Epoch 1055 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04063 0.9777 0.9993 0.0002818 -0.0001265 -0.05708 0.0002124 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03691 -0.001951 0.02483 0.02881 0.9176 0.9303 0.07396 0.8495 0.8816 0.1752 ] Network output: [ 0.9725 0.04621 -0.005101 0.0001917 -8.602e-05 0.01465 0.0001443 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.648 0.03742 -0.02914 0.2999 0.9583 0.979 0.7455 0.8723 0.9524 0.7035 ] Network output: [ -0.002138 0.9033 1.067 0.0002189 -9.828e-05 0.03496 0.000165 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06884 0.04208 0.06427 0.05554 0.9756 0.9823 0.07067 0.9465 0.9689 0.09773 ] Network output: [ 0.127 -0.3964 1.191 0.0006795 -0.0003051 0.9536 0.0005122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7417 0.5052 0.4189 0.507 0.963 0.982 0.7458 0.8845 0.9595 0.7039 ] Network output: [ -0.0748 0.2072 0.9649 0.001854 -0.0008325 0.985 0.001398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6248 0.5864 0.3779 0.2681 0.9793 0.986 0.6255 0.9547 0.9728 0.4156 ] Network output: [ -0.1274 0.2202 0.9447 0.0002479 -0.0001113 1.091 0.0001869 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6592 0.6515 0.441 0.2261 0.9756 0.9835 0.6594 0.9459 0.9672 0.4505 ] Network output: [ 0.08123 0.6953 0.1616 -0.000539 0.000242 0.9784 -0.0004063 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06967 Epoch 1056 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04542 1.024 0.9469 5.873e-06 -2.643e-06 -0.06133 4.452e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03756 -0.002049 0.02096 0.02302 0.9177 0.9304 0.07526 0.8491 0.8811 0.1704 ] Network output: [ 1.02 0.1708 -0.1903 -0.0001572 7.061e-05 -0.02069 -0.0001186 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6525 0.03378 -0.05619 0.2549 0.9582 0.979 0.7508 0.8714 0.9519 0.6931 ] Network output: [ 0.0005685 0.9418 1.024 -1.638e-07 6.09e-08 0.0329 -7.139e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06998 0.04265 0.05831 0.04496 0.9755 0.9821 0.07185 0.9458 0.9681 0.09166 ] Network output: [ 0.1311 -0.2616 1.057 -7.527e-05 3.377e-05 0.9417 -5.664e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7399 0.5106 0.4093 0.454 0.9628 0.9819 0.7441 0.8839 0.9591 0.6958 ] Network output: [ -0.06532 0.2957 0.8654 0.001242 -0.0005575 0.9746 0.0009359 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6267 0.5895 0.3698 0.2225 0.9791 0.9858 0.6273 0.9544 0.9723 0.407 ] Network output: [ -0.1265 0.3163 0.8607 -0.0007417 0.000333 1.073 -0.0005589 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6556 0.6482 0.4399 0.1696 0.9755 0.9834 0.6557 0.9457 0.9668 0.4492 ] Network output: [ 0.07125 0.7612 0.1218 -0.001508 0.000677 0.9683 -0.001137 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07171 Epoch 1057 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02188 0.9908 1.007 -9.498e-05 4.263e-05 -0.04225 -7.155e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03682 4.387e-06 0.0321 0.02729 0.9175 0.9303 0.0734 0.8499 0.8818 0.1734 ] Network output: [ 0.8373 0.08154 0.1158 -0.001043 0.0004684 0.1238 -0.0007863 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6454 0.0865 0.04096 0.2891 0.9582 0.979 0.7415 0.8726 0.9526 0.7022 ] Network output: [ -0.002807 0.9164 1.051 4.788e-05 -2.151e-05 0.03843 3.614e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07024 0.04641 0.07028 0.05109 0.9758 0.9824 0.07209 0.9474 0.9695 0.09879 ] Network output: [ 0.08738 -0.3099 1.141 -0.000195 8.753e-05 0.9935 -0.0001469 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7463 0.5389 0.4523 0.4646 0.9631 0.9821 0.7504 0.8845 0.9594 0.6975 ] Network output: [ -0.04129 0.2843 0.8343 0.001642 -0.0007371 0.9707 0.001237 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6343 0.6009 0.3757 0.2194 0.9793 0.986 0.635 0.955 0.9728 0.4073 ] Network output: [ -0.09336 0.3276 0.8004 -0.0004403 0.0001976 1.057 -0.0003318 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6628 0.6561 0.4385 0.1548 0.9757 0.9835 0.663 0.9459 0.9668 0.4467 ] Network output: [ 0.09248 0.7658 0.08742 -0.001365 0.0006127 0.9563 -0.001029 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0774 Epoch 1058 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04308 0.9833 0.9907 0.0002765 -0.0001241 -0.05905 0.0002084 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03698 -0.002166 0.02354 0.02807 0.9177 0.9304 0.07415 0.8494 0.8815 0.1747 ] Network output: [ 0.9933 0.06083 -0.04416 0.0002303 -0.0001034 -0.002282 0.0001734 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.649 0.03251 -0.03912 0.2937 0.9583 0.9791 0.7468 0.8721 0.9524 0.7019 ] Network output: [ -0.001888 0.9078 1.062 0.0002003 -8.991e-05 0.03456 0.000151 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06885 0.04179 0.06293 0.05433 0.9756 0.9823 0.07069 0.9463 0.9688 0.09682 ] Network output: [ 0.1311 -0.3847 1.177 0.00061 -0.0002739 0.9479 0.0004598 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.741 0.5038 0.4154 0.5025 0.963 0.9821 0.7451 0.8844 0.9594 0.7031 ] Network output: [ -0.07674 0.2122 0.9637 0.001773 -0.0007959 0.9848 0.001336 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6243 0.5857 0.378 0.266 0.9793 0.986 0.6249 0.9547 0.9728 0.4161 ] Network output: [ -0.13 0.2252 0.9448 0.0001631 -7.323e-05 1.091 0.000123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6583 0.6505 0.4417 0.2239 0.9756 0.9835 0.6584 0.9459 0.9672 0.4513 ] Network output: [ 0.07798 0.7013 0.1607 -0.0006093 0.0002736 0.9795 -0.0004593 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06816 Epoch 1059 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04197 1.022 0.952 -2.679e-05 1.202e-05 -0.05839 -2.017e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03747 -0.001715 0.02244 0.02315 0.9177 0.9304 0.07503 0.8491 0.8812 0.1704 ] Network output: [ 0.995 0.1677 -0.1582 -0.0003205 0.0001439 -0.0008383 -0.0002417 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6519 0.04286 -0.04252 0.2554 0.9582 0.979 0.7499 0.8714 0.952 0.6935 ] Network output: [ -8.222e-06 0.941 1.025 -1.063e-05 4.761e-06 0.03403 -7.963e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07016 0.04343 0.05992 0.04491 0.9755 0.9821 0.07203 0.9461 0.9683 0.09246 ] Network output: [ 0.1233 -0.2561 1.059 -0.0002125 9.536e-05 0.9497 -0.00016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.741 0.5169 0.4171 0.4493 0.9629 0.982 0.7451 0.8839 0.9591 0.6955 ] Network output: [ -0.06091 0.3015 0.852 0.00124 -0.0005567 0.9734 0.0009346 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6283 0.5921 0.3714 0.2173 0.9792 0.9858 0.629 0.9545 0.9724 0.4075 ] Network output: [ -0.1205 0.328 0.8418 -0.000778 0.0003493 1.068 -0.0005863 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6565 0.6493 0.4403 0.161 0.9756 0.9834 0.6566 0.9458 0.9668 0.4493 ] Network output: [ 0.07314 0.7698 0.1115 -0.001565 0.0007024 0.9661 -0.001179 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07059 Epoch 1060 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02324 0.9859 1.011 -3.381e-05 1.517e-05 -0.04313 -2.544e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03676 -0.0001934 0.03178 0.02787 0.9176 0.9304 0.07332 0.8499 0.8818 0.1739 ] Network output: [ 0.847 0.06778 0.1191 -0.0009309 0.000418 0.1153 -0.0007017 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6455 0.08248 0.03762 0.2929 0.9583 0.9791 0.7417 0.8726 0.9526 0.7029 ] Network output: [ -0.003197 0.9119 1.056 8.439e-05 -3.79e-05 0.03872 6.365e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07006 0.04603 0.07042 0.0524 0.9758 0.9825 0.0719 0.9474 0.9695 0.09931 ] Network output: [ 0.09018 -0.3295 1.158 -8.72e-05 3.912e-05 0.9912 -6.562e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7462 0.5364 0.4515 0.4723 0.9632 0.9822 0.7503 0.8845 0.9595 0.6985 ] Network output: [ -0.04506 0.2683 0.8557 0.001732 -0.0007774 0.9732 0.001305 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6336 0.5998 0.3775 0.2282 0.9794 0.986 0.6343 0.9551 0.9728 0.4095 ] Network output: [ -0.09601 0.3093 0.8205 -0.0002627 0.0001179 1.061 -0.0001979 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6629 0.6561 0.4392 0.1664 0.9758 0.9836 0.663 0.946 0.9669 0.4475 ] Network output: [ 0.09154 0.7578 0.09459 -0.00121 0.0005433 0.9596 -0.0009122 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07518 Epoch 1061 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04525 0.9898 0.9815 0.0002652 -0.0001191 -0.06074 0.0001999 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03706 -0.002342 0.02238 0.02716 0.9178 0.9305 0.07436 0.8493 0.8814 0.1741 ] Network output: [ 1.012 0.07853 -0.08405 0.0002399 -0.0001077 -0.01743 0.0001806 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6501 0.02853 -0.04787 0.2862 0.9583 0.9791 0.7481 0.8719 0.9523 0.7003 ] Network output: [ -0.001603 0.9132 1.057 0.0001799 -8.078e-05 0.03427 0.0001356 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06893 0.04161 0.06167 0.05283 0.9757 0.9823 0.07077 0.9462 0.9686 0.0959 ] Network output: [ 0.1342 -0.3688 1.159 0.0005101 -0.000229 0.9431 0.0003846 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7404 0.5031 0.4128 0.4958 0.963 0.9821 0.7446 0.8842 0.9594 0.7022 ] Network output: [ -0.07781 0.2203 0.9579 0.001682 -0.000755 0.9842 0.001267 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.624 0.5855 0.3781 0.2619 0.9793 0.986 0.6247 0.9546 0.9727 0.4164 ] Network output: [ -0.1318 0.2346 0.9398 4.79e-05 -2.151e-05 1.089 3.614e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6574 0.6497 0.4425 0.2186 0.9757 0.9836 0.6576 0.9459 0.9672 0.4521 ] Network output: [ 0.0749 0.7111 0.1565 -0.0007219 0.0003241 0.9797 -0.0005441 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06702 Epoch 1062 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03814 1.019 0.9592 -4.981e-05 2.235e-05 -0.05509 -3.751e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03736 -0.001355 0.02418 0.02344 0.9178 0.9305 0.07474 0.8493 0.8813 0.1707 ] Network output: [ 0.9667 0.1604 -0.1176 -0.0004907 0.0002203 0.02172 -0.00037 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6511 0.05261 -0.02669 0.2573 0.9583 0.979 0.7488 0.8716 0.9521 0.6944 ] Network output: [ -0.0006905 0.9389 1.027 -1.214e-05 5.437e-06 0.03529 -9.098e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07031 0.04422 0.06189 0.04515 0.9756 0.9822 0.07217 0.9464 0.9685 0.09357 ] Network output: [ 0.115 -0.2542 1.064 -0.0003309 0.0001485 0.9585 -0.0002493 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7421 0.5233 0.4258 0.4457 0.963 0.982 0.7462 0.884 0.9592 0.6956 ] Network output: [ -0.05645 0.3052 0.8405 0.001257 -0.0005645 0.9723 0.0009476 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.63 0.5947 0.3735 0.2128 0.9792 0.9859 0.6307 0.9547 0.9724 0.4084 ] Network output: [ -0.1141 0.3383 0.8234 -0.0007922 0.0003556 1.063 -0.000597 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6576 0.6506 0.441 0.1527 0.9757 0.9835 0.6577 0.9459 0.9668 0.4497 ] Network output: [ 0.07553 0.7775 0.1011 -0.001603 0.0007195 0.9638 -0.001208 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06986 Epoch 1063 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0254 0.9813 1.013 3.962e-05 -1.779e-05 -0.0446 2.989e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03671 -0.000458 0.03114 0.02837 0.9177 0.9305 0.07329 0.8499 0.8818 0.1745 ] Network output: [ 0.8626 0.05517 0.1143 -0.0007751 0.000348 0.1022 -0.0005842 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6458 0.07672 0.0313 0.2961 0.9583 0.9791 0.7421 0.8726 0.9526 0.7037 ] Network output: [ -0.003513 0.9075 1.061 0.0001247 -5.6e-05 0.03888 9.403e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06985 0.04552 0.07029 0.05363 0.9759 0.9825 0.07169 0.9474 0.9695 0.09974 ] Network output: [ 0.09435 -0.3495 1.174 3.194e-05 -1.436e-05 0.9873 2.417e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7459 0.5328 0.4497 0.48 0.9632 0.9822 0.75 0.8846 0.9596 0.6997 ] Network output: [ -0.04988 0.2511 0.8798 0.001812 -0.0008136 0.9762 0.001366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6327 0.5983 0.3795 0.2374 0.9794 0.9861 0.6333 0.9551 0.9729 0.412 ] Network output: [ -0.09971 0.2894 0.8435 -8.527e-05 3.827e-05 1.066 -6.424e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6628 0.6559 0.4402 0.1788 0.9758 0.9836 0.663 0.9461 0.9671 0.4485 ] Network output: [ 0.09005 0.7483 0.1038 -0.001044 0.0004687 0.9635 -0.0007868 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07292 Epoch 1064 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04705 0.9969 0.9721 0.0002471 -0.0001109 -0.06211 0.0001862 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03716 -0.002478 0.02137 0.02617 0.9179 0.9306 0.07458 0.8493 0.8814 0.1734 ] Network output: [ 1.028 0.09784 -0.1225 0.0002243 -0.0001006 -0.03037 0.0001689 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6511 0.02553 -0.05513 0.2781 0.9584 0.9791 0.7494 0.8717 0.9522 0.6986 ] Network output: [ -0.001299 0.919 1.05 0.0001576 -7.078e-05 0.03407 0.0001189 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06906 0.04152 0.06053 0.05115 0.9757 0.9823 0.0709 0.9461 0.9685 0.09497 ] Network output: [ 0.1363 -0.3503 1.14 0.0003911 -0.0001756 0.9392 0.0002948 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.74 0.5032 0.4111 0.4878 0.963 0.9821 0.7441 0.8841 0.9593 0.7011 ] Network output: [ -0.07797 0.2305 0.9485 0.001592 -0.0007148 0.9834 0.0012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6241 0.5857 0.3781 0.2563 0.9793 0.986 0.6247 0.9546 0.9727 0.4164 ] Network output: [ -0.1328 0.2466 0.9313 -7.675e-05 3.445e-05 1.087 -5.78e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6566 0.649 0.4432 0.2116 0.9757 0.9836 0.6568 0.946 0.9672 0.4527 ] Network output: [ 0.07202 0.7224 0.1507 -0.0008516 0.0003824 0.9794 -0.0006419 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06661 Epoch 1065 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03413 1.015 0.9677 -6.881e-05 3.089e-05 -0.05168 -5.183e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03723 -0.0009926 0.02603 0.02385 0.9178 0.9305 0.07442 0.8494 0.8814 0.1711 ] Network output: [ 0.9369 0.1503 -0.07209 -0.0006659 0.000299 0.04532 -0.000502 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6501 0.0624 -0.0098 0.2603 0.9583 0.9791 0.7475 0.8717 0.9522 0.6955 ] Network output: [ -0.001398 0.9359 1.03 -8.838e-06 3.956e-06 0.03656 -6.613e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07042 0.04499 0.06401 0.04562 0.9757 0.9823 0.07228 0.9467 0.9687 0.09481 ] Network output: [ 0.1069 -0.2554 1.073 -0.0004251 0.0001908 0.9672 -0.0003203 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7432 0.5295 0.4347 0.4432 0.9631 0.9821 0.7473 0.8842 0.9593 0.6957 ] Network output: [ -0.05192 0.307 0.8308 0.001295 -0.0005816 0.9713 0.0009763 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6317 0.5972 0.3757 0.2092 0.9793 0.9859 0.6324 0.9548 0.9725 0.4094 ] Network output: [ -0.1076 0.3463 0.8068 -0.000779 0.0003497 1.059 -0.0005871 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6588 0.652 0.4416 0.1457 0.9757 0.9835 0.6589 0.946 0.9669 0.4501 ] Network output: [ 0.07815 0.7838 0.09174 -0.001617 0.0007261 0.9616 -0.001219 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06998 Epoch 1066 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02801 0.9775 1.013 0.0001133 -5.088e-05 -0.04644 8.544e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03668 -0.0007567 0.03026 0.02877 0.9179 0.9305 0.07329 0.8499 0.8818 0.175 ] Network output: [ 0.8817 0.04457 0.1032 -0.0005987 0.0002688 0.0864 -0.0004513 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6462 0.06999 0.02301 0.2984 0.9584 0.9791 0.7427 0.8726 0.9527 0.7042 ] Network output: [ -0.003734 0.9038 1.065 0.0001624 -7.292e-05 0.0389 0.0001224 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06964 0.04494 0.06988 0.05468 0.9759 0.9825 0.07148 0.9474 0.9695 0.09999 ] Network output: [ 0.09945 -0.368 1.187 0.0001497 -6.724e-05 0.9824 0.0001129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7455 0.5287 0.4469 0.4872 0.9633 0.9822 0.7496 0.8847 0.9596 0.7009 ] Network output: [ -0.05507 0.2344 0.9042 0.001881 -0.0008443 0.9792 0.001417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6316 0.5966 0.3814 0.2465 0.9795 0.9861 0.6322 0.9552 0.973 0.4146 ] Network output: [ -0.1039 0.2693 0.8675 8.347e-05 -3.748e-05 1.071 6.293e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6626 0.6556 0.4412 0.1912 0.9759 0.9837 0.6627 0.9462 0.9672 0.4496 ] Network output: [ 0.08814 0.7376 0.1149 -0.0008726 0.0003918 0.9677 -0.0006577 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07115 Epoch 1067 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04831 1.004 0.9631 0.0002196 -9.86e-05 -0.06308 0.0001655 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03724 -0.002564 0.02058 0.0252 0.918 0.9307 0.07477 0.8492 0.8813 0.1727 ] Network output: [ 1.04 0.1173 -0.1566 0.0001837 -8.244e-05 -0.04025 0.0001383 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6521 0.02375 -0.06047 0.27 0.9584 0.9791 0.7505 0.8716 0.9522 0.6969 ] Network output: [ -0.001002 0.925 1.044 0.0001322 -5.936e-05 0.03393 9.968e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06921 0.04153 0.05954 0.04945 0.9757 0.9822 0.07106 0.946 0.9684 0.09402 ] Network output: [ 0.1376 -0.3303 1.12 0.0002607 -0.000117 0.9364 0.0001965 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7396 0.5039 0.4101 0.4791 0.963 0.9821 0.7437 0.884 0.9593 0.6998 ] Network output: [ -0.07715 0.2419 0.9363 0.001512 -0.0006786 0.9823 0.001139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6244 0.5862 0.3777 0.25 0.9793 0.9859 0.625 0.9546 0.9726 0.4159 ] Network output: [ -0.1329 0.2595 0.9206 -0.0001965 8.822e-05 1.085 -0.0001481 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.656 0.6484 0.4435 0.2038 0.9757 0.9836 0.6562 0.946 0.9672 0.453 ] Network output: [ 0.06945 0.7337 0.1448 -0.0009803 0.0004401 0.9787 -0.0007389 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06698 Epoch 1068 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0302 1.011 0.9766 -8.72e-05 3.914e-05 -0.04838 -6.569e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0371 -0.0006533 0.02786 0.02437 0.9179 0.9306 0.07409 0.8496 0.8815 0.1714 ] Network output: [ 0.9075 0.1383 -0.02512 -0.0008381 0.0003763 0.06829 -0.0006317 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6491 0.07163 0.006916 0.2641 0.9584 0.9791 0.7461 0.8719 0.9523 0.6967 ] Network output: [ -0.002066 0.9326 1.034 -3.729e-06 1.663e-06 0.03773 -2.764e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0705 0.04569 0.06606 0.04628 0.9758 0.9824 0.07236 0.947 0.969 0.09599 ] Network output: [ 0.09967 -0.2593 1.083 -0.0004906 0.0002202 0.9754 -0.0003697 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7443 0.5351 0.4428 0.4422 0.9632 0.9822 0.7484 0.8843 0.9593 0.6957 ] Network output: [ -0.04748 0.3068 0.8232 0.001354 -0.0006079 0.9705 0.00102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6333 0.5996 0.3774 0.2069 0.9794 0.986 0.634 0.955 0.9726 0.4101 ] Network output: [ -0.1015 0.3512 0.7934 -0.0007343 0.0003296 1.055 -0.0005534 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.66 0.6533 0.4419 0.1409 0.9758 0.9836 0.6601 0.9461 0.9669 0.4501 ] Network output: [ 0.08074 0.7879 0.08438 -0.001604 0.0007203 0.9597 -0.001209 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07097 Epoch 1069 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03081 0.9749 1.013 0.0001772 -7.955e-05 -0.04851 0.0001336 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03666 -0.001065 0.02922 0.02903 0.918 0.9306 0.07331 0.8499 0.8818 0.1752 ] Network output: [ 0.9027 0.03708 0.08664 -0.0004222 0.0001896 0.06907 -0.0003183 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6467 0.06292 0.01345 0.2998 0.9585 0.9792 0.7434 0.8726 0.9527 0.7043 ] Network output: [ -0.00384 0.9013 1.068 0.0001918 -8.61e-05 0.03874 0.0001446 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06944 0.04435 0.06919 0.05545 0.9759 0.9825 0.07127 0.9473 0.9694 0.09998 ] Network output: [ 0.1051 -0.3833 1.198 0.0002523 -0.0001133 0.9766 0.0001902 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7449 0.5246 0.4434 0.4933 0.9633 0.9822 0.749 0.8847 0.9597 0.7017 ] Network output: [ -0.06011 0.2197 0.9266 0.00193 -0.0008663 0.9818 0.001454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6304 0.5948 0.3828 0.2547 0.9795 0.9861 0.6311 0.9552 0.973 0.4168 ] Network output: [ -0.1083 0.2507 0.8904 0.0002291 -0.0001029 1.076 0.0001727 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6622 0.655 0.4419 0.2029 0.9759 0.9837 0.6623 0.9462 0.9673 0.4505 ] Network output: [ 0.08586 0.7265 0.127 -0.0007102 0.0003189 0.9718 -0.0005353 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06996 Epoch 1070 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04884 1.011 0.9554 0.0001823 -8.183e-05 -0.0635 0.0001374 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03731 -0.002586 0.02009 0.02434 0.9181 0.9307 0.07492 0.8492 0.8813 0.1719 ] Network output: [ 1.047 0.1353 -0.1831 0.0001182 -5.303e-05 -0.04587 8.894e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6528 0.02359 -0.06322 0.2628 0.9585 0.9791 0.7514 0.8714 0.9521 0.6951 ] Network output: [ -0.0007529 0.9309 1.037 0.0001036 -4.654e-05 0.0339 7.815e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06939 0.04167 0.05879 0.04785 0.9757 0.9822 0.07124 0.9459 0.9683 0.09313 ] Network output: [ 0.1378 -0.3103 1.1 0.0001249 -5.607e-05 0.9349 9.418e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7395 0.5056 0.41 0.4704 0.963 0.9821 0.7436 0.8839 0.9592 0.6982 ] Network output: [ -0.07531 0.2537 0.9218 0.001444 -0.0006483 0.981 0.001088 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6249 0.5871 0.3771 0.2435 0.9793 0.9859 0.6256 0.9546 0.9725 0.415 ] Network output: [ -0.132 0.2726 0.9083 -0.0003026 0.0001358 1.082 -0.000228 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6556 0.6481 0.4434 0.1959 0.9757 0.9836 0.6557 0.946 0.9671 0.4528 ] Network output: [ 0.06736 0.744 0.1391 -0.001097 0.0004925 0.9777 -0.0008268 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06783 Epoch 1071 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02668 1.007 0.985 -0.0001037 4.656e-05 -0.0455 -7.814e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03696 -0.0003675 0.02949 0.02496 0.9179 0.9306 0.07377 0.8497 0.8816 0.1717 ] Network output: [ 0.8814 0.1253 0.01935 -0.0009908 0.0004448 0.08853 -0.0007468 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6482 0.07952 0.02197 0.2685 0.9584 0.9791 0.7448 0.8721 0.9524 0.6976 ] Network output: [ -0.002652 0.9292 1.037 2.511e-06 -1.138e-06 0.03871 1.938e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07054 0.04626 0.06785 0.0471 0.9759 0.9825 0.07239 0.9472 0.9692 0.097 ] Network output: [ 0.09379 -0.2658 1.094 -0.000524 0.0002352 0.9825 -0.0003948 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7453 0.5398 0.4496 0.4429 0.9633 0.9822 0.7493 0.8843 0.9594 0.6954 ] Network output: [ -0.04355 0.3042 0.8188 0.00143 -0.0006418 0.9699 0.001077 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6346 0.6016 0.3787 0.2066 0.9794 0.986 0.6353 0.9551 0.9727 0.4104 ] Network output: [ -0.09619 0.3521 0.785 -0.0006571 0.000295 1.053 -0.0004952 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.661 0.6545 0.4418 0.1391 0.9759 0.9836 0.6612 0.9462 0.967 0.4498 ] Network output: [ 0.08308 0.7894 0.07965 -0.001562 0.0007014 0.9584 -0.001177 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07245 Epoch 1072 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03361 0.9742 1.01 0.0002246 -0.0001008 -0.05069 0.0001693 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03665 -0.001362 0.02806 0.02911 0.918 0.9307 0.07337 0.8498 0.8818 0.1752 ] Network output: [ 0.9245 0.03389 0.06485 -0.0002628 0.000118 0.05123 -0.0001981 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6472 0.05602 0.003261 0.2999 0.9585 0.9792 0.7442 0.8725 0.9527 0.7037 ] Network output: [ -0.00382 0.9005 1.07 0.0002091 -9.388e-05 0.03841 0.0001576 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06926 0.04378 0.06823 0.05581 0.9759 0.9825 0.07108 0.9472 0.9694 0.09967 ] Network output: [ 0.1109 -0.3933 1.202 0.0003255 -0.0001462 0.9704 0.0002454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7443 0.5206 0.4394 0.4976 0.9634 0.9823 0.7483 0.8846 0.9597 0.702 ] Network output: [ -0.06459 0.2085 0.9448 0.001951 -0.0008758 0.9838 0.00147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6293 0.5932 0.3838 0.2612 0.9795 0.9861 0.63 0.9552 0.973 0.4184 ] Network output: [ -0.1125 0.2356 0.9103 0.0003337 -0.0001498 1.08 0.0002515 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6616 0.6544 0.4423 0.2127 0.9759 0.9837 0.6617 0.9463 0.9674 0.451 ] Network output: [ 0.08327 0.7168 0.1387 -0.0005776 0.0002593 0.9756 -0.0004353 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06912 Epoch 1073 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04847 1.017 0.9498 0.0001381 -6.2e-05 -0.06324 0.0001041 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03736 -0.002528 0.01999 0.02367 0.9181 0.9308 0.07499 0.8492 0.8812 0.171 ] Network output: [ 1.047 0.1498 -0.1982 2.967e-05 -1.329e-05 -0.04601 2.222e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6533 0.02544 -0.06263 0.2571 0.9585 0.9792 0.7519 0.8713 0.9521 0.6934 ] Network output: [ -0.0006104 0.9359 1.032 7.422e-05 -3.333e-05 0.03403 5.598e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06958 0.04196 0.05841 0.04652 0.9757 0.9822 0.07143 0.9459 0.9682 0.09241 ] Network output: [ 0.1366 -0.2919 1.083 -1.137e-05 5.089e-06 0.9352 -8.5e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7396 0.5083 0.4111 0.4624 0.963 0.9821 0.7436 0.8838 0.9592 0.6964 ] Network output: [ -0.07255 0.265 0.9063 0.001392 -0.0006251 0.9795 0.001049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6258 0.5884 0.3765 0.2371 0.9793 0.9859 0.6265 0.9546 0.9725 0.4139 ] Network output: [ -0.13 0.2852 0.8949 -0.0003905 0.0001753 1.078 -0.0002943 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6554 0.648 0.443 0.1882 0.9758 0.9836 0.6555 0.9461 0.9671 0.4522 ] Network output: [ 0.06602 0.7533 0.1334 -0.001197 0.0005374 0.9764 -0.0009022 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06861 Epoch 1074 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02396 1.002 0.9926 -0.0001126 5.057e-05 -0.04332 -8.487e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03684 -0.0001695 0.03076 0.02559 0.918 0.9307 0.0735 0.8498 0.8817 0.1719 ] Network output: [ 0.8614 0.1118 0.05712 -0.001102 0.0004948 0.1038 -0.0008307 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6474 0.08523 0.03379 0.2732 0.9585 0.9792 0.7438 0.8722 0.9525 0.6983 ] Network output: [ -0.003139 0.9259 1.041 1.138e-05 -5.12e-06 0.03945 8.621e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07052 0.04665 0.06924 0.04806 0.9759 0.9825 0.07237 0.9474 0.9693 0.09778 ] Network output: [ 0.08971 -0.2752 1.106 -0.0005239 0.0002352 0.9877 -0.0003948 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.746 0.5431 0.4547 0.4454 0.9633 0.9823 0.7501 0.8843 0.9595 0.6949 ] Network output: [ -0.04078 0.2988 0.8191 0.001515 -0.0006801 0.9698 0.001142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6356 0.603 0.3797 0.2085 0.9795 0.9861 0.6362 0.9552 0.9728 0.4106 ] Network output: [ -0.09231 0.3482 0.7829 -0.0005515 0.0002476 1.051 -0.0004156 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6619 0.6554 0.4414 0.1409 0.9759 0.9837 0.662 0.9462 0.967 0.4492 ] Network output: [ 0.08487 0.7883 0.07788 -0.001492 0.0006699 0.958 -0.001125 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07372 Epoch 1075 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03626 0.9753 1.006 0.0002536 -0.0001139 -0.05284 0.0001912 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03666 -0.001633 0.02685 0.02897 0.9181 0.9308 0.07345 0.8497 0.8817 0.1749 ] Network output: [ 0.9459 0.03579 0.03823 -0.0001329 5.969e-05 0.03373 -0.0001003 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6479 0.04969 -0.006973 0.2984 0.9586 0.9792 0.7451 0.8723 0.9526 0.7027 ] Network output: [ -0.003688 0.9013 1.069 0.0002139 -9.602e-05 0.03795 0.0001612 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06912 0.04328 0.06706 0.0557 0.976 0.9825 0.07094 0.947 0.9692 0.09907 ] Network output: [ 0.1163 -0.3968 1.201 0.000359 -0.0001612 0.9641 0.0002706 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7435 0.5174 0.4352 0.4994 0.9634 0.9823 0.7476 0.8845 0.9596 0.7018 ] Network output: [ -0.06828 0.2021 0.9572 0.001939 -0.0008704 0.9851 0.001461 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6283 0.5918 0.3842 0.2654 0.9795 0.9861 0.629 0.9551 0.973 0.4194 ] Network output: [ -0.1162 0.2259 0.9249 0.0003825 -0.0001717 1.083 0.0002883 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6609 0.6536 0.4427 0.2194 0.976 0.9838 0.661 0.9463 0.9674 0.4515 ] Network output: [ 0.08046 0.7104 0.148 -0.0004965 0.0002229 0.9787 -0.0003742 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06824 Epoch 1076 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04716 1.021 0.9472 9.32e-05 -4.185e-05 -0.0622 7.026e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03736 -0.002387 0.02034 0.02325 0.9182 0.9308 0.07497 0.8491 0.8812 0.1703 ] Network output: [ 1.04 0.1594 -0.1997 -7.756e-05 3.485e-05 -0.04008 -5.859e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6535 0.02947 -0.05829 0.2534 0.9585 0.9792 0.752 0.8712 0.9521 0.692 ] Network output: [ -0.0006319 0.9396 1.028 4.781e-05 -2.148e-05 0.03439 3.608e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06977 0.04239 0.05854 0.04555 0.9757 0.9823 0.07162 0.9459 0.9682 0.09203 ] Network output: [ 0.1336 -0.2767 1.071 -0.0001441 6.468e-05 0.9376 -0.0001085 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7399 0.5119 0.4139 0.4554 0.9631 0.9821 0.744 0.8837 0.9592 0.6949 ] Network output: [ -0.06916 0.2749 0.8909 0.001357 -0.0006091 0.978 0.001023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6269 0.5901 0.3762 0.2312 0.9793 0.986 0.6276 0.9546 0.9725 0.4129 ] Network output: [ -0.1269 0.297 0.8803 -0.0004596 0.0002063 1.075 -0.0003463 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6555 0.6483 0.4426 0.1807 0.9758 0.9836 0.6557 0.9461 0.9671 0.4516 ] Network output: [ 0.06561 0.7617 0.127 -0.001281 0.000575 0.9748 -0.0009652 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0688 Epoch 1077 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02238 0.998 0.9988 -0.0001066 4.784e-05 -0.04206 -8.029e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03673 -8.452e-05 0.03155 0.02621 0.9181 0.9307 0.07327 0.8499 0.8817 0.1721 ] Network output: [ 0.8499 0.09843 0.08493 -0.001153 0.0005178 0.1122 -0.0008692 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6469 0.08813 0.04121 0.2777 0.9585 0.9792 0.7431 0.8722 0.9526 0.6988 ] Network output: [ -0.00354 0.9224 1.045 2.543e-05 -1.143e-05 0.03997 1.921e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07045 0.04682 0.07017 0.04911 0.976 0.9826 0.0723 0.9475 0.9694 0.09836 ] Network output: [ 0.08767 -0.2874 1.119 -0.0004926 0.0002211 0.9908 -0.0003711 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7465 0.5448 0.4578 0.4497 0.9634 0.9823 0.7506 0.8843 0.9595 0.6947 ] Network output: [ -0.03973 0.2903 0.8253 0.001601 -0.0007187 0.9704 0.001206 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6361 0.6037 0.3805 0.2126 0.9795 0.9861 0.6367 0.9552 0.9728 0.4111 ] Network output: [ -0.0903 0.3396 0.7876 -0.0004258 0.0001912 1.052 -0.0003209 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6625 0.6561 0.441 0.146 0.976 0.9837 0.6626 0.9463 0.967 0.4487 ] Network output: [ 0.08593 0.7849 0.07901 -0.001399 0.0006279 0.9585 -0.001054 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0741 Epoch 1078 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03871 0.9783 1 0.0002672 -0.00012 -0.05487 0.0002014 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0367 -0.00187 0.02566 0.02859 0.9182 0.9309 0.07356 0.8496 0.8816 0.1745 ] Network output: [ 0.9661 0.04276 0.007644 -3.707e-05 1.667e-05 0.0172 -2.804e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6487 0.04417 -0.01673 0.295 0.9586 0.9793 0.7461 0.872 0.9526 0.7013 ] Network output: [ -0.003477 0.9038 1.067 0.0002087 -9.37e-05 0.03745 0.0001573 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06903 0.04288 0.06581 0.05512 0.9759 0.9825 0.07086 0.9468 0.9691 0.09829 ] Network output: [ 0.1211 -0.3936 1.195 0.0003499 -0.0001571 0.9582 0.0002637 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7428 0.5149 0.4313 0.4985 0.9634 0.9823 0.7469 0.8844 0.9596 0.7012 ] Network output: [ -0.07111 0.2007 0.9635 0.001896 -0.0008511 0.9857 0.001429 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6276 0.5908 0.3844 0.2669 0.9795 0.9861 0.6283 0.955 0.973 0.4201 ] Network output: [ -0.1195 0.2224 0.9333 0.0003729 -0.0001674 1.085 0.000281 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6601 0.6528 0.443 0.2224 0.976 0.9838 0.6603 0.9463 0.9675 0.4519 ] Network output: [ 0.07757 0.7089 0.1532 -0.0004789 0.000215 0.9807 -0.000361 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06707 Epoch 1079 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04502 1.023 0.9478 5.386e-05 -2.418e-05 -0.06044 4.061e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03733 -0.00217 0.02114 0.02306 0.9183 0.9309 0.07486 0.8491 0.8812 0.1699 ] Network output: [ 1.026 0.1635 -0.1874 -0.0001982 8.902e-05 -0.02851 -0.0001495 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6533 0.03544 -0.05032 0.2516 0.9585 0.9792 0.7516 0.8711 0.9521 0.6912 ] Network output: [ -0.0008433 0.9413 1.025 2.808e-05 -1.261e-05 0.03499 2.12e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06994 0.04295 0.05922 0.04498 0.9758 0.9823 0.0718 0.9461 0.9683 0.09208 ] Network output: [ 0.129 -0.2657 1.065 -0.0002699 0.0001212 0.9421 -0.0002033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7405 0.5165 0.4184 0.4496 0.9631 0.9821 0.7446 0.8837 0.9592 0.6937 ] Network output: [ -0.06544 0.2829 0.8767 0.001336 -0.0005999 0.9767 0.001007 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6282 0.5921 0.3765 0.2259 0.9793 0.986 0.6289 0.9546 0.9725 0.4124 ] Network output: [ -0.1229 0.3081 0.8648 -0.0005111 0.0002294 1.071 -0.0003851 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.656 0.6489 0.4424 0.1734 0.9758 0.9836 0.6561 0.9461 0.9671 0.4512 ] Network output: [ 0.06613 0.7698 0.1194 -0.00135 0.000606 0.973 -0.001017 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06828 Epoch 1080 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02205 0.9937 1.004 -8.077e-05 3.626e-05 -0.04176 -6.085e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03665 -0.0001161 0.03187 0.02678 0.9181 0.9308 0.07311 0.8499 0.8817 0.1724 ] Network output: [ 0.8473 0.08545 0.1017 -0.001137 0.0005104 0.1135 -0.0008569 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6466 0.08812 0.04402 0.2818 0.9586 0.9792 0.7428 0.8722 0.9526 0.6993 ] Network output: [ -0.003876 0.9187 1.049 4.648e-05 -2.088e-05 0.04031 3.507e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07034 0.04676 0.07071 0.0502 0.9761 0.9826 0.07218 0.9476 0.9695 0.09882 ] Network output: [ 0.08763 -0.3021 1.134 -0.0004347 0.0001951 0.9915 -0.0003275 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7467 0.5447 0.4591 0.4553 0.9635 0.9823 0.7508 0.8843 0.9595 0.6948 ] Network output: [ -0.04059 0.279 0.8372 0.001681 -0.0007545 0.9718 0.001267 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6361 0.6037 0.3816 0.2185 0.9795 0.9861 0.6368 0.9553 0.9729 0.4121 ] Network output: [ -0.09021 0.3274 0.7984 -0.0002897 0.00013 1.053 -0.0002183 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6629 0.6564 0.441 0.1536 0.976 0.9838 0.663 0.9463 0.9671 0.4487 ] Network output: [ 0.08618 0.7796 0.08267 -0.001287 0.000578 0.9601 -0.0009704 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07337 Epoch 1081 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04092 0.9826 0.9933 0.0002705 -0.0001215 -0.05669 0.0002039 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03675 -0.00207 0.02453 0.028 0.9183 0.9309 0.0737 0.8495 0.8815 0.1739 ] Network output: [ 0.9848 0.05397 -0.02556 2.763e-05 -1.238e-05 0.002024 2.072e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6496 0.03952 -0.02565 0.29 0.9587 0.9793 0.7472 0.8718 0.9525 0.6998 ] Network output: [ -0.003223 0.9075 1.063 0.0001976 -8.874e-05 0.03699 0.000149 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06901 0.04258 0.06458 0.05413 0.9759 0.9825 0.07084 0.9467 0.969 0.09743 ] Network output: [ 0.1249 -0.3849 1.183 0.0003039 -0.0001365 0.953 0.0002291 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7422 0.5133 0.4281 0.4952 0.9634 0.9823 0.7463 0.8842 0.9596 0.7004 ] Network output: [ -0.07311 0.2036 0.9643 0.001831 -0.0008219 0.9858 0.00138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6271 0.5902 0.3844 0.2659 0.9795 0.9861 0.6278 0.955 0.9729 0.4206 ] Network output: [ -0.122 0.2246 0.9357 0.000315 -0.0001414 1.085 0.0002374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6593 0.652 0.4435 0.2218 0.976 0.9838 0.6595 0.9463 0.9675 0.4524 ] Network output: [ 0.07473 0.7124 0.1541 -0.0005203 0.0002336 0.9818 -0.0003922 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06569 Epoch 1082 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04225 1.022 0.9514 2.332e-05 -1.048e-05 -0.05813 1.76e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03726 -0.0019 0.02229 0.02309 0.9183 0.9309 0.07467 0.8492 0.8812 0.1697 ] Network output: [ 1.006 0.1625 -0.1636 -0.0003281 0.0001473 -0.01264 -0.0002474 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6529 0.04278 -0.03939 0.2514 0.9586 0.9792 0.7509 0.8711 0.9521 0.6911 ] Network output: [ -0.001225 0.9413 1.025 1.656e-05 -7.443e-06 0.0358 1.252e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0701 0.04358 0.0604 0.04477 0.9758 0.9823 0.07195 0.9462 0.9684 0.09256 ] Network output: [ 0.123 -0.2589 1.063 -0.000385 0.0001728 0.9482 -0.0002901 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7413 0.5215 0.4243 0.4449 0.9632 0.9822 0.7454 0.8837 0.9592 0.6931 ] Network output: [ -0.06163 0.2886 0.8644 0.00133 -0.0005973 0.9756 0.001003 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6297 0.5943 0.3775 0.2211 0.9794 0.986 0.6304 0.9547 0.9725 0.4125 ] Network output: [ -0.1181 0.3182 0.849 -0.0005455 0.0002449 1.067 -0.0004111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6567 0.6498 0.4425 0.166 0.9759 0.9837 0.6569 0.9462 0.9671 0.4511 ] Network output: [ 0.06743 0.7776 0.1109 -0.001405 0.0006309 0.9709 -0.001059 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06732 Epoch 1083 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02282 0.9893 1.007 -3.637e-05 1.632e-05 -0.04226 -2.738e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03659 -0.0002457 0.03176 0.02728 0.9182 0.9309 0.07301 0.8499 0.8818 0.1727 ] Network output: [ 0.8527 0.07309 0.1086 -0.00106 0.000476 0.1087 -0.0007991 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6466 0.08564 0.04283 0.2852 0.9586 0.9793 0.7428 0.8723 0.9526 0.6998 ] Network output: [ -0.00416 0.9149 1.053 7.411e-05 -3.328e-05 0.04051 5.589e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0702 0.04651 0.07091 0.05128 0.9761 0.9827 0.07203 0.9476 0.9695 0.09922 ] Network output: [ 0.08927 -0.3184 1.148 -0.0003573 0.0001604 0.9904 -0.0002692 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7467 0.5431 0.459 0.4615 0.9635 0.9824 0.7508 0.8843 0.9596 0.6953 ] Network output: [ -0.0431 0.2655 0.8539 0.001752 -0.0007864 0.9739 0.00132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6358 0.6031 0.383 0.2256 0.9796 0.9862 0.6364 0.9553 0.9729 0.4137 ] Network output: [ -0.0917 0.3124 0.8137 -0.0001499 6.728e-05 1.057 -0.0001129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6631 0.6566 0.4414 0.1629 0.9761 0.9838 0.6632 0.9464 0.9672 0.4491 ] Network output: [ 0.08574 0.773 0.08838 -0.001164 0.0005227 0.9624 -0.0008774 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07181 Epoch 1084 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04286 0.9879 0.9857 0.0002672 -0.00012 -0.05827 0.0002014 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03682 -0.002235 0.02351 0.02725 0.9184 0.931 0.07386 0.8494 0.8815 0.1733 ] Network output: [ 1.002 0.06814 -0.05971 6.665e-05 -2.99e-05 -0.01157 5.012e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6505 0.03573 -0.03352 0.2838 0.9587 0.9793 0.7483 0.8716 0.9524 0.6983 ] Network output: [ -0.002956 0.9119 1.058 0.0001837 -8.249e-05 0.03663 0.0001385 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06905 0.04237 0.06341 0.05286 0.9759 0.9825 0.07088 0.9465 0.9688 0.09655 ] Network output: [ 0.1277 -0.372 1.169 0.0002309 -0.0001037 0.9486 0.0001741 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7417 0.5123 0.4256 0.4899 0.9634 0.9823 0.7457 0.8841 0.9595 0.6996 ] Network output: [ -0.07436 0.2096 0.9607 0.001755 -0.0007877 0.9856 0.001322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6269 0.5899 0.3845 0.2629 0.9795 0.9861 0.6276 0.9549 0.9729 0.4209 ] Network output: [ -0.1239 0.2312 0.9332 0.0002263 -0.0001016 1.084 0.0001706 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6586 0.6513 0.4442 0.2182 0.976 0.9838 0.6588 0.9464 0.9675 0.4531 ] Network output: [ 0.07201 0.7198 0.1516 -0.000604 0.0002712 0.9821 -0.0004553 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06442 Epoch 1085 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0391 1.02 0.957 7.478e-07 -3.404e-07 -0.05546 5.829e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03717 -0.001601 0.0237 0.02327 0.9184 0.931 0.07442 0.8493 0.8813 0.1698 ] Network output: [ 0.9831 0.1575 -0.1315 -0.0004648 0.0002087 0.005923 -0.0003504 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6522 0.05088 -0.02644 0.2525 0.9586 0.9793 0.75 0.8712 0.9522 0.6916 ] Network output: [ -0.001726 0.9399 1.027 1.223e-05 -5.498e-06 0.03675 9.251e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07023 0.04424 0.06193 0.04485 0.9759 0.9824 0.07208 0.9465 0.9685 0.09335 ] Network output: [ 0.1164 -0.2558 1.066 -0.0004856 0.000218 0.9551 -0.0003659 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7422 0.5268 0.4312 0.4413 0.9633 0.9822 0.7463 0.8838 0.9593 0.6929 ] Network output: [ -0.05782 0.2923 0.854 0.00134 -0.0006016 0.9748 0.00101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6313 0.5966 0.3791 0.217 0.9794 0.986 0.6319 0.9549 0.9726 0.4131 ] Network output: [ -0.1129 0.3271 0.8336 -0.0005615 0.0002521 1.063 -0.0004232 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6577 0.6509 0.443 0.159 0.976 0.9837 0.6578 0.9463 0.9671 0.4513 ] Network output: [ 0.06924 0.7848 0.1021 -0.001446 0.0006491 0.9688 -0.00109 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06643 Epoch 1086 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02439 0.9852 1.009 2.035e-05 -9.14e-06 -0.04335 1.536e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03655 -0.000444 0.03134 0.0277 0.9183 0.931 0.07296 0.8499 0.8818 0.1731 ] Network output: [ 0.8639 0.0617 0.1075 -0.0009396 0.0004218 0.09923 -0.0007082 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6468 0.08138 0.03868 0.2879 0.9587 0.9793 0.743 0.8723 0.9527 0.7004 ] Network output: [ -0.004396 0.9111 1.057 0.0001054 -4.735e-05 0.04061 7.951e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07003 0.04613 0.07086 0.05229 0.9761 0.9827 0.07187 0.9476 0.9696 0.09954 ] Network output: [ 0.09216 -0.3354 1.162 -0.0002686 0.0001206 0.9878 -0.0002024 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7465 0.5405 0.4579 0.4679 0.9636 0.9824 0.7506 0.8844 0.9596 0.6962 ] Network output: [ -0.04673 0.251 0.8734 0.001814 -0.0008144 0.9764 0.001367 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6352 0.6021 0.3847 0.2333 0.9796 0.9862 0.6359 0.9553 0.973 0.4157 ] Network output: [ -0.09427 0.296 0.8319 -1.088e-05 4.881e-06 1.061 -8.185e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6632 0.6566 0.4421 0.1729 0.9762 0.9839 0.6633 0.9465 0.9673 0.4499 ] Network output: [ 0.08478 0.7652 0.09573 -0.001033 0.0004636 0.9653 -0.0007783 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06996 Epoch 1087 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04449 0.9937 0.9779 0.0002581 -0.0001159 -0.05957 0.0001945 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03689 -0.002365 0.02261 0.02642 0.9185 0.9311 0.07402 0.8493 0.8814 0.1727 ] Network output: [ 1.016 0.08401 -0.09305 8.362e-05 -3.751e-05 -0.02332 6.291e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6514 0.03277 -0.04018 0.277 0.9588 0.9793 0.7493 0.8715 0.9524 0.6968 ] Network output: [ -0.002693 0.9168 1.053 0.0001679 -7.537e-05 0.03635 0.0001265 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06913 0.04225 0.06236 0.05142 0.976 0.9825 0.07096 0.9464 0.9687 0.09568 ] Network output: [ 0.1297 -0.3566 1.153 0.0001399 -6.281e-05 0.945 0.0001055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7412 0.512 0.4239 0.4832 0.9634 0.9823 0.7452 0.884 0.9595 0.6987 ] Network output: [ -0.07488 0.2176 0.9538 0.001677 -0.0007531 0.9852 0.001264 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6269 0.59 0.3846 0.2585 0.9795 0.9861 0.6276 0.9549 0.9728 0.421 ] Network output: [ -0.1252 0.2405 0.9272 0.000124 -5.569e-05 1.083 9.35e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.658 0.6507 0.4448 0.2128 0.9761 0.9838 0.6582 0.9464 0.9675 0.4537 ] Network output: [ 0.06946 0.7293 0.147 -0.0007102 0.0003189 0.9819 -0.0005353 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06358 Epoch 1088 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03576 1.017 0.9639 -1.715e-05 7.696e-06 -0.05263 -1.291e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03707 -0.001295 0.02523 0.02356 0.9184 0.931 0.07415 0.8494 0.8813 0.17 ] Network output: [ 0.9583 0.1498 -0.09449 -0.0006063 0.0002722 0.02573 -0.000457 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6515 0.05916 -0.01239 0.2546 0.9587 0.9793 0.7489 0.8713 0.9523 0.6924 ] Network output: [ -0.002284 0.9375 1.029 1.253e-05 -5.635e-06 0.03776 9.482e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07034 0.04489 0.06365 0.04514 0.976 0.9825 0.07219 0.9467 0.9687 0.09432 ] Network output: [ 0.1097 -0.2557 1.072 -0.0005683 0.0002551 0.9623 -0.0004283 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7431 0.5319 0.4385 0.4387 0.9634 0.9823 0.7472 0.8839 0.9594 0.693 ] Network output: [ -0.05401 0.2942 0.8453 0.001366 -0.0006131 0.9741 0.001029 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6328 0.5989 0.3809 0.2137 0.9795 0.9861 0.6335 0.955 0.9726 0.414 ] Network output: [ -0.1076 0.3344 0.8193 -0.000557 0.0002501 1.059 -0.0004198 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6587 0.6521 0.4435 0.1528 0.9761 0.9838 0.6589 0.9464 0.9672 0.4517 ] Network output: [ 0.07132 0.7909 0.09376 -0.001469 0.0006593 0.9668 -0.001107 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06599 Epoch 1089 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02645 0.9816 1.011 8.09e-05 -3.633e-05 -0.04482 6.1e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03652 -0.0006824 0.03069 0.02804 0.9184 0.931 0.07294 0.85 0.8818 0.1735 ] Network output: [ 0.8789 0.05178 0.1004 -0.0007938 0.0003564 0.08685 -0.0005983 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6471 0.07604 0.03252 0.29 0.9588 0.9794 0.7435 0.8723 0.9527 0.7009 ] Network output: [ -0.004576 0.9078 1.061 0.0001364 -6.122e-05 0.04061 0.0001028 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06986 0.04566 0.07058 0.05317 0.9762 0.9827 0.07169 0.9476 0.9696 0.09974 ] Network output: [ 0.0959 -0.3517 1.175 -0.0001777 7.974e-05 0.9841 -0.0001338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7462 0.5373 0.4561 0.4739 0.9636 0.9824 0.7503 0.8844 0.9597 0.6972 ] Network output: [ -0.05091 0.2363 0.894 0.001867 -0.0008383 0.9791 0.001407 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6345 0.6009 0.3864 0.241 0.9797 0.9862 0.6351 0.9554 0.973 0.4179 ] Network output: [ -0.09743 0.2792 0.8514 0.0001227 -5.509e-05 1.065 9.248e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6631 0.6565 0.4429 0.1831 0.9762 0.9839 0.6633 0.9466 0.9674 0.4507 ] Network output: [ 0.08342 0.7566 0.1044 -0.0008974 0.0004029 0.9685 -0.0006764 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06827 Epoch 1090 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04572 0.9998 0.9703 0.000242 -0.0001087 -0.06055 0.0001824 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03697 -0.002457 0.02187 0.02558 0.9186 0.9311 0.07418 0.8493 0.8814 0.172 ] Network output: [ 1.028 0.1004 -0.1237 7.963e-05 -3.572e-05 -0.03282 5.99e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6522 0.03075 -0.04539 0.27 0.9588 0.9794 0.7503 0.8714 0.9523 0.6954 ] Network output: [ -0.002451 0.9219 1.047 0.0001496 -6.718e-05 0.03616 0.0001128 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06923 0.04222 0.06142 0.04993 0.976 0.9825 0.07107 0.9464 0.9686 0.09482 ] Network output: [ 0.131 -0.3396 1.136 3.77e-05 -1.694e-05 0.9421 2.848e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7408 0.5123 0.4228 0.4758 0.9634 0.9823 0.7448 0.8839 0.9595 0.6976 ] Network output: [ -0.07465 0.2269 0.9444 0.001606 -0.0007208 0.9845 0.00121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6272 0.5903 0.3844 0.2532 0.9795 0.9861 0.6278 0.9549 0.9728 0.4208 ] Network output: [ -0.1257 0.2513 0.9189 2.094e-05 -9.408e-06 1.081 1.581e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6575 0.6503 0.4452 0.2063 0.9761 0.9839 0.6577 0.9465 0.9675 0.454 ] Network output: [ 0.06711 0.7395 0.1417 -0.0008227 0.0003693 0.9812 -0.00062 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0633 Epoch 1091 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03241 1.013 0.9714 -3.339e-05 1.499e-05 -0.04983 -2.514e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03696 -0.001001 0.02679 0.02394 0.9185 0.9311 0.07386 0.8496 0.8815 0.1702 ] Network output: [ 0.9333 0.1402 -0.05525 -0.000748 0.0003358 0.04548 -0.0005638 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6507 0.06715 0.001858 0.2574 0.9587 0.9793 0.7477 0.8715 0.9524 0.6934 ] Network output: [ -0.002844 0.9347 1.032 1.505e-05 -6.764e-06 0.03873 1.138e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07042 0.0455 0.06539 0.04559 0.976 0.9826 0.07227 0.947 0.9689 0.09532 ] Network output: [ 0.1035 -0.258 1.079 -0.0006308 0.0002832 0.9693 -0.0004753 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7441 0.5367 0.4455 0.4371 0.9635 0.9823 0.7481 0.884 0.9594 0.693 ] Network output: [ -0.05026 0.2944 0.8383 0.001407 -0.0006317 0.9736 0.00106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6343 0.601 0.3826 0.2112 0.9796 0.9861 0.635 0.9551 0.9727 0.4147 ] Network output: [ -0.1024 0.3395 0.8071 -0.0005302 0.000238 1.056 -0.0003996 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6598 0.6533 0.4439 0.1479 0.9761 0.9838 0.66 0.9465 0.9672 0.4518 ] Network output: [ 0.07348 0.7954 0.08671 -0.001471 0.0006606 0.9649 -0.001109 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06617 Epoch 1092 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02875 0.9788 1.011 0.0001375 -6.174e-05 -0.04652 0.0001037 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0365 -0.0009381 0.0299 0.02827 0.9185 0.9311 0.07294 0.85 0.8819 0.1737 ] Network output: [ 0.896 0.04398 0.08861 -0.0006395 0.0002871 0.07277 -0.0004821 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6475 0.07018 0.02509 0.2913 0.9588 0.9794 0.744 0.8723 0.9528 0.7011 ] Network output: [ -0.00469 0.9052 1.064 0.0001628 -7.311e-05 0.04049 0.0001228 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06969 0.04517 0.07008 0.05386 0.9762 0.9827 0.07151 0.9476 0.9695 0.09978 ] Network output: [ 0.1002 -0.3661 1.186 -9.342e-05 4.192e-05 0.9797 -7.033e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7458 0.5339 0.4537 0.4793 0.9637 0.9825 0.7498 0.8845 0.9597 0.6981 ] Network output: [ -0.05518 0.2227 0.9138 0.001909 -0.0008568 0.9817 0.001438 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6337 0.5996 0.3879 0.2481 0.9797 0.9863 0.6343 0.9554 0.9731 0.4199 ] Network output: [ -0.1009 0.263 0.8707 0.0002438 -0.0001095 1.069 0.0001838 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.663 0.6562 0.4436 0.193 0.9763 0.9839 0.6631 0.9467 0.9675 0.4515 ] Network output: [ 0.08176 0.7476 0.114 -0.0007657 0.0003438 0.9717 -0.0005771 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06695 Epoch 1093 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04644 1.006 0.9634 0.0002182 -9.797e-05 -0.06115 0.0001645 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03703 -0.002505 0.02133 0.02479 0.9186 0.9312 0.0743 0.8493 0.8814 0.1713 ] Network output: [ 1.036 0.1163 -0.1496 5.475e-05 -2.455e-05 -0.03945 4.116e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6529 0.02982 -0.04882 0.2633 0.9588 0.9794 0.7511 0.8713 0.9523 0.6939 ] Network output: [ -0.002246 0.927 1.042 0.0001287 -5.78e-05 0.03605 9.705e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06936 0.04226 0.06065 0.04847 0.976 0.9825 0.07119 0.9463 0.9685 0.09399 ] Network output: [ 0.1315 -0.3221 1.119 -7.05e-05 3.164e-05 0.9401 -5.307e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7405 0.5132 0.4224 0.4681 0.9634 0.9823 0.7446 0.8839 0.9594 0.6964 ] Network output: [ -0.07365 0.2367 0.9331 0.001543 -0.0006927 0.9837 0.001163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6276 0.591 0.3841 0.2476 0.9795 0.9861 0.6283 0.9549 0.9727 0.4203 ] Network output: [ -0.1256 0.2625 0.9092 -7.456e-05 3.347e-05 1.079 -5.617e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6571 0.6499 0.4454 0.1994 0.9761 0.9839 0.6573 0.9465 0.9675 0.4541 ] Network output: [ 0.06508 0.7493 0.1364 -0.0009302 0.0004176 0.9804 -0.0007011 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06352 Epoch 1094 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02926 1.01 0.9789 -4.874e-05 2.188e-05 -0.04723 -3.671e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03684 -0.0007366 0.02825 0.02438 0.9185 0.9311 0.07357 0.8497 0.8816 0.1704 ] Network output: [ 0.9098 0.1294 -0.01655 -0.0008812 0.0003956 0.06384 -0.0006642 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6498 0.07438 0.01535 0.2607 0.9588 0.9794 0.7466 0.8717 0.9525 0.6943 ] Network output: [ -0.003365 0.9318 1.035 1.85e-05 -8.314e-06 0.03961 1.398e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07047 0.04603 0.06701 0.04618 0.9761 0.9826 0.07231 0.9473 0.9691 0.09623 ] Network output: [ 0.09807 -0.2624 1.088 -0.0006711 0.0003013 0.9755 -0.0005057 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7449 0.5409 0.4517 0.4367 0.9636 0.9824 0.7489 0.8841 0.9595 0.6929 ] Network output: [ -0.04675 0.2928 0.8334 0.001462 -0.0006562 0.9732 0.001102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6357 0.603 0.384 0.21 0.9796 0.9862 0.6363 0.9553 0.9728 0.4153 ] Network output: [ -0.09766 0.3419 0.7979 -0.0004807 0.0002158 1.054 -0.0003622 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6608 0.6544 0.444 0.1449 0.9762 0.9839 0.661 0.9466 0.9673 0.4518 ] Network output: [ 0.07553 0.7981 0.08148 -0.001453 0.0006522 0.9634 -0.001095 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06688 Epoch 1095 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0311 0.9773 1.01 0.0001845 -8.284e-05 -0.04832 0.0001391 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03649 -0.001194 0.02899 0.02839 0.9186 0.9312 0.07296 0.8499 0.8819 0.1738 ] Network output: [ 0.9142 0.039 0.07272 -0.0004903 0.0002201 0.05792 -0.0003696 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.648 0.06424 0.01694 0.2918 0.9589 0.9794 0.7446 0.8722 0.9528 0.7009 ] Network output: [ -0.004728 0.9037 1.066 0.000182 -8.17e-05 0.04026 0.0001372 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06952 0.04468 0.06938 0.0543 0.9762 0.9828 0.07134 0.9475 0.9695 0.0996 ] Network output: [ 0.1047 -0.3773 1.193 -2.488e-05 1.115e-05 0.9748 -1.868e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7452 0.5305 0.4508 0.4836 0.9637 0.9825 0.7493 0.8845 0.9598 0.6986 ] Network output: [ -0.05919 0.2111 0.9312 0.001934 -0.0008682 0.9839 0.001457 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6329 0.5984 0.389 0.2544 0.9797 0.9863 0.6336 0.9554 0.9731 0.4216 ] Network output: [ -0.1043 0.2487 0.8885 0.0003428 -0.0001539 1.073 0.0002584 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6627 0.6559 0.4441 0.2018 0.9763 0.984 0.6628 0.9467 0.9676 0.4521 ] Network output: [ 0.07985 0.739 0.1238 -0.000648 0.0002909 0.9749 -0.0004884 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06599 Epoch 1096 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04655 1.011 0.9577 0.0001875 -8.419e-05 -0.06128 0.0001413 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03707 -0.002501 0.02105 0.02411 0.9187 0.9313 0.07438 0.8493 0.8814 0.1706 ] Network output: [ 1.04 0.1303 -0.1684 9.555e-06 -4.264e-06 -0.0425 7.096e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6534 0.03022 -0.05005 0.2576 0.9589 0.9794 0.7516 0.8712 0.9523 0.6924 ] Network output: [ -0.002106 0.9318 1.037 0.000106 -4.758e-05 0.03604 7.99e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06949 0.04241 0.06011 0.04715 0.976 0.9825 0.07133 0.9463 0.9685 0.09326 ] Network output: [ 0.1312 -0.3053 1.103 -0.0001805 8.102e-05 0.9393 -0.000136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7404 0.5147 0.4227 0.4606 0.9634 0.9823 0.7444 0.8838 0.9594 0.695 ] Network output: [ -0.07193 0.2466 0.9207 0.001492 -0.0006698 0.9827 0.001124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6283 0.5919 0.3837 0.2419 0.9795 0.9861 0.6289 0.9549 0.9727 0.4196 ] Network output: [ -0.1246 0.2735 0.8985 -0.0001575 7.069e-05 1.077 -0.0001187 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6569 0.6498 0.4453 0.1926 0.9761 0.9839 0.657 0.9466 0.9675 0.4539 ] Network output: [ 0.0635 0.7583 0.1312 -0.001027 0.0004609 0.9793 -0.0007738 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06397 Epoch 1097 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02654 1.006 0.9859 -6.121e-05 2.747e-05 -0.04502 -4.611e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03673 -0.0005233 0.02952 0.02486 0.9186 0.9312 0.07331 0.8498 0.8817 0.1706 ] Network output: [ 0.8898 0.1182 0.01881 -0.0009935 0.0004461 0.0794 -0.0007488 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6491 0.08033 0.02707 0.2643 0.9588 0.9794 0.7455 0.8718 0.9525 0.6951 ] Network output: [ -0.003821 0.9289 1.038 2.303e-05 -1.035e-05 0.04034 1.739e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07048 0.04645 0.06839 0.04686 0.9762 0.9827 0.07232 0.9475 0.9693 0.097 ] Network output: [ 0.09381 -0.2689 1.098 -0.0006887 0.0003092 0.9807 -0.0005189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7456 0.5443 0.4569 0.4375 0.9637 0.9824 0.7496 0.8842 0.9596 0.6927 ] Network output: [ -0.0438 0.2894 0.8314 0.001526 -0.000685 0.9731 0.00115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6368 0.6045 0.3851 0.2102 0.9797 0.9862 0.6375 0.9553 0.9729 0.4156 ] Network output: [ -0.09365 0.3411 0.7927 -0.0004103 0.0001842 1.052 -0.0003092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6618 0.6554 0.4439 0.1442 0.9763 0.9839 0.6619 0.9467 0.9673 0.4514 ] Network output: [ 0.07731 0.7988 0.07839 -0.001413 0.0006342 0.9624 -0.001065 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06779 Epoch 1098 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03337 0.9771 1.007 0.000219 -9.831e-05 -0.05014 0.000165 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03649 -0.001436 0.02803 0.02837 0.9187 0.9313 0.073 0.8499 0.8818 0.1736 ] Network output: [ 0.9324 0.03744 0.05324 -0.0003564 0.00016 0.04305 -0.0002687 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6485 0.05856 0.008537 0.2912 0.9589 0.9795 0.7453 0.8721 0.9528 0.7004 ] Network output: [ -0.004691 0.9035 1.067 0.0001924 -8.64e-05 0.03993 0.0001451 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06938 0.04422 0.06851 0.05442 0.9762 0.9828 0.0712 0.9474 0.9694 0.09921 ] Network output: [ 0.1091 -0.3841 1.196 2.024e-05 -9.1e-06 0.9697 1.531e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7446 0.5273 0.4477 0.4863 0.9637 0.9825 0.7487 0.8844 0.9598 0.6987 ] Network output: [ -0.06271 0.2026 0.9451 0.001939 -0.0008706 0.9856 0.001462 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6321 0.5972 0.3897 0.2591 0.9797 0.9863 0.6328 0.9554 0.9731 0.4229 ] Network output: [ -0.1075 0.2375 0.9034 0.0004098 -0.000184 1.076 0.0003089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6623 0.6554 0.4444 0.2089 0.9763 0.984 0.6624 0.9468 0.9677 0.4526 ] Network output: [ 0.07774 0.7319 0.1327 -0.0005573 0.0002502 0.9776 -0.00042 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06521 Epoch 1099 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04599 1.016 0.9538 0.0001528 -6.859e-05 -0.06089 0.0001152 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0371 -0.002439 0.02106 0.02359 0.9188 0.9313 0.0744 0.8493 0.8813 0.1699 ] Network output: [ 1.039 0.1414 -0.1782 -5.405e-05 2.429e-05 -0.0414 -4.084e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6537 0.0321 -0.04875 0.2531 0.9589 0.9794 0.7519 0.8711 0.9522 0.6911 ] Network output: [ -0.002059 0.9358 1.033 8.324e-05 -3.738e-05 0.03616 6.276e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06963 0.04265 0.05988 0.04606 0.976 0.9825 0.07147 0.9463 0.9684 0.0927 ] Network output: [ 0.1299 -0.2902 1.09 -0.0002887 0.0001296 0.9398 -0.0002175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7404 0.5169 0.424 0.4538 0.9635 0.9823 0.7445 0.8837 0.9594 0.6936 ] Network output: [ -0.0696 0.2557 0.9078 0.001454 -0.0006527 0.9816 0.001096 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6291 0.5931 0.3833 0.2365 0.9795 0.9861 0.6298 0.9549 0.9727 0.4187 ] Network output: [ -0.1229 0.2839 0.8871 -0.0002254 0.0001012 1.074 -0.0001699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6568 0.6498 0.445 0.186 0.9762 0.9839 0.657 0.9466 0.9674 0.4535 ] Network output: [ 0.0625 0.7664 0.126 -0.001109 0.000498 0.978 -0.0008361 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06432 Epoch 1100 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02449 1.002 0.9921 -6.682e-05 2.999e-05 -0.04339 -5.033e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03663 -0.0003804 0.03048 0.02535 0.9186 0.9312 0.07307 0.8499 0.8817 0.1707 ] Network output: [ 0.8749 0.1068 0.04829 -0.001071 0.0004809 0.09079 -0.0008074 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6485 0.08451 0.03609 0.2679 0.9589 0.9794 0.7447 0.8719 0.9526 0.6956 ] Network output: [ -0.004205 0.926 1.042 2.976e-05 -1.337e-05 0.04089 2.246e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07046 0.04672 0.06944 0.04762 0.9762 0.9827 0.0723 0.9476 0.9694 0.09759 ] Network output: [ 0.09089 -0.2773 1.108 -0.000684 0.0003071 0.9845 -0.0005154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7462 0.5466 0.4607 0.4397 0.9637 0.9825 0.7502 0.8842 0.9596 0.6924 ] Network output: [ -0.04182 0.2839 0.8329 0.001594 -0.0007154 0.9733 0.001201 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6376 0.6057 0.3859 0.212 0.9797 0.9862 0.6383 0.9554 0.9729 0.416 ] Network output: [ -0.09075 0.3369 0.7922 -0.0003234 0.0001452 1.051 -0.0002437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6625 0.6563 0.4436 0.146 0.9763 0.9839 0.6627 0.9468 0.9674 0.451 ] Network output: [ 0.07866 0.7975 0.07752 -0.001354 0.0006077 0.9621 -0.00102 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06848 Epoch 1101 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03549 0.9784 1.004 0.0002408 -0.0001081 -0.05188 0.0001815 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0365 -0.001655 0.02706 0.02819 0.9188 0.9313 0.07306 0.8498 0.8818 0.1733 ] Network output: [ 0.95 0.03958 0.0307 -0.0002445 0.0001098 0.02872 -0.0001843 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6491 0.0534 0.0002991 0.2894 0.959 0.9795 0.746 0.872 0.9527 0.6995 ] Network output: [ -0.004589 0.9045 1.066 0.0001947 -8.741e-05 0.03953 0.0001468 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06927 0.04382 0.06752 0.05419 0.9762 0.9828 0.07108 0.9473 0.9693 0.09864 ] Network output: [ 0.1132 -0.3861 1.195 3.721e-05 -1.672e-05 0.9648 2.811e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.744 0.5247 0.4445 0.4871 0.9638 0.9825 0.748 0.8844 0.9598 0.6985 ] Network output: [ -0.06561 0.1977 0.9545 0.001923 -0.0008635 0.9868 0.00145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6315 0.5962 0.3901 0.262 0.9797 0.9863 0.6322 0.9554 0.9731 0.4238 ] Network output: [ -0.1104 0.2304 0.9143 0.0004386 -0.0001969 1.078 0.0003305 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6618 0.6548 0.4447 0.2136 0.9763 0.984 0.662 0.9468 0.9677 0.4529 ] Network output: [ 0.0755 0.7277 0.1395 -0.0005045 0.0002265 0.9797 -0.0003803 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06438 Epoch 1102 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04478 1.019 0.9521 0.000118 -5.297e-05 -0.05996 8.893e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03709 -0.002319 0.0214 0.02324 0.9188 0.9314 0.07435 0.8493 0.8813 0.1693 ] Network output: [ 1.032 0.1487 -0.178 -0.0001329 5.967e-05 -0.03604 -0.0001002 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6538 0.03548 -0.04476 0.25 0.9589 0.9794 0.7518 0.871 0.9522 0.69 ] Network output: [ -0.002127 0.9386 1.029 6.296e-05 -2.827e-05 0.03645 4.748e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06977 0.04299 0.06002 0.04525 0.976 0.9825 0.0716 0.9463 0.9685 0.0924 ] Network output: [ 0.1273 -0.2779 1.08 -0.0003922 0.0001761 0.9417 -0.0002956 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7407 0.5197 0.4263 0.4478 0.9635 0.9824 0.7447 0.8837 0.9594 0.6924 ] Network output: [ -0.06686 0.2635 0.8954 0.001428 -0.0006411 0.9806 0.001076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6301 0.5946 0.3832 0.2315 0.9795 0.9861 0.6308 0.9549 0.9727 0.4181 ] Network output: [ -0.1204 0.2935 0.8752 -0.000278 0.0001248 1.071 -0.0002095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.657 0.6501 0.4447 0.1797 0.9762 0.9839 0.6572 0.9466 0.9674 0.4531 ] Network output: [ 0.06217 0.7738 0.1205 -0.001178 0.000529 0.9766 -0.000888 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06431 Epoch 1103 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02329 0.9985 0.9971 -6.15e-05 2.761e-05 -0.04243 -4.633e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03655 -0.0003196 0.0311 0.02581 0.9187 0.9313 0.07288 0.85 0.8818 0.1708 ] Network output: [ 0.8662 0.09576 0.07015 -0.001104 0.0004957 0.09717 -0.0008323 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6481 0.08664 0.04186 0.2714 0.9589 0.9794 0.7441 0.872 0.9527 0.6961 ] Network output: [ -0.004522 0.9231 1.045 4.007e-05 -1.8e-05 0.04129 3.023e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07041 0.04684 0.07017 0.04842 0.9763 0.9828 0.07224 0.9477 0.9695 0.09803 ] Network output: [ 0.08942 -0.2876 1.119 -0.0006594 0.000296 0.9867 -0.0004969 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7465 0.5478 0.4631 0.443 0.9638 0.9825 0.7505 0.8842 0.9596 0.6923 ] Network output: [ -0.04107 0.2763 0.8385 0.001659 -0.0007448 0.9741 0.00125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6381 0.6063 0.3867 0.2153 0.9797 0.9863 0.6388 0.9555 0.973 0.4165 ] Network output: [ -0.08917 0.3297 0.7962 -0.0002257 0.0001013 1.051 -0.0001701 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6631 0.6569 0.4434 0.1499 0.9764 0.984 0.6632 0.9468 0.9674 0.4507 ] Network output: [ 0.07947 0.7946 0.07873 -0.001279 0.0005742 0.9625 -0.000964 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06862 Epoch 1104 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03741 0.9808 0.9989 0.0002522 -0.0001132 -0.0535 0.0001901 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03653 -0.001846 0.02612 0.02785 0.9188 0.9314 0.07313 0.8497 0.8817 0.1729 ] Network output: [ 0.9665 0.04523 0.005828 -0.0001562 7.015e-05 0.01535 -0.0001178 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6497 0.04889 -0.007447 0.2864 0.959 0.9795 0.7467 0.8718 0.9527 0.6984 ] Network output: [ -0.004444 0.9066 1.064 0.0001906 -8.555e-05 0.03912 0.0001436 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0692 0.04349 0.06649 0.05364 0.9762 0.9827 0.07101 0.9471 0.9692 0.09795 ] Network output: [ 0.1167 -0.3834 1.19 2.579e-05 -1.159e-05 0.9601 1.95e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7434 0.5227 0.4416 0.4861 0.9638 0.9825 0.7474 0.8843 0.9597 0.698 ] Network output: [ -0.06786 0.1964 0.9595 0.001888 -0.0008477 0.9875 0.001423 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6311 0.5956 0.3903 0.263 0.9797 0.9863 0.6317 0.9553 0.9731 0.4243 ] Network output: [ -0.1129 0.2277 0.9207 0.0004293 -0.0001927 1.079 0.0003236 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6613 0.6543 0.4449 0.2157 0.9764 0.9841 0.6614 0.9469 0.9678 0.4532 ] Network output: [ 0.07322 0.7269 0.1435 -0.0004944 0.0002219 0.9812 -0.0003726 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06339 Epoch 1105 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04301 1.02 0.9527 8.673e-05 -3.894e-05 -0.05854 6.537e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03706 -0.00215 0.02204 0.02307 0.9189 0.9314 0.07424 0.8494 0.8813 0.1689 ] Network output: [ 1.021 0.1519 -0.1683 -0.000223 0.0001002 -0.02686 -0.0001682 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6537 0.04013 -0.0383 0.2484 0.9589 0.9794 0.7515 0.871 0.9523 0.6894 ] Network output: [ -0.002318 0.9401 1.028 4.714e-05 -2.117e-05 0.0369 3.555e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06989 0.04341 0.06054 0.04474 0.9761 0.9825 0.07173 0.9464 0.9685 0.09241 ] Network output: [ 0.1238 -0.2687 1.074 -0.0004887 0.0002194 0.945 -0.0003683 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7411 0.5231 0.4298 0.4429 0.9636 0.9824 0.7451 0.8837 0.9594 0.6915 ] Network output: [ -0.0639 0.2697 0.8841 0.001414 -0.000635 0.9797 0.001066 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6313 0.5962 0.3835 0.2271 0.9796 0.9861 0.6319 0.955 0.9727 0.4177 ] Network output: [ -0.1173 0.3023 0.863 -0.0003159 0.0001418 1.068 -0.000238 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6575 0.6507 0.4445 0.1736 0.9762 0.9839 0.6576 0.9467 0.9674 0.4528 ] Network output: [ 0.06249 0.7807 0.1143 -0.001234 0.0005542 0.975 -0.0009304 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06389 Epoch 1106 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02297 0.995 1.001 -4.322e-05 1.94e-05 -0.04215 -3.255e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03648 -0.0003401 0.03137 0.02623 0.9188 0.9314 0.07273 0.85 0.8818 0.171 ] Network output: [ 0.864 0.08515 0.08397 -0.00109 0.0004893 0.09845 -0.0008215 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6478 0.08672 0.04434 0.2744 0.959 0.9795 0.7438 0.872 0.9527 0.6965 ] Network output: [ -0.004786 0.9202 1.048 5.468e-05 -2.455e-05 0.04154 4.124e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07032 0.04679 0.07058 0.04922 0.9763 0.9828 0.07214 0.9478 0.9696 0.09837 ] Network output: [ 0.0893 -0.2995 1.131 -0.0006186 0.0002777 0.9873 -0.0004661 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7466 0.5477 0.4643 0.4471 0.9639 0.9825 0.7506 0.8842 0.9597 0.6924 ] Network output: [ -0.04164 0.2669 0.848 0.001719 -0.0007716 0.9753 0.001295 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6383 0.6065 0.3877 0.2198 0.9798 0.9863 0.639 0.9555 0.973 0.4173 ] Network output: [ -0.08888 0.3201 0.8043 -0.0001231 5.527e-05 1.053 -9.277e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6635 0.6573 0.4433 0.1556 0.9764 0.984 0.6637 0.9469 0.9675 0.4507 ] Network output: [ 0.07973 0.7904 0.08171 -0.001193 0.0005357 0.9636 -0.0008993 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06809 Epoch 1107 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03911 0.9843 0.9935 0.0002561 -0.000115 -0.05493 0.000193 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03657 -0.002008 0.02524 0.02737 0.9189 0.9315 0.07322 0.8497 0.8817 0.1724 ] Network output: [ 0.9815 0.05377 -0.02041 -9.01e-05 4.047e-05 0.003175 -6.798e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6504 0.04508 -0.01448 0.2823 0.9591 0.9795 0.7475 0.8717 0.9526 0.6972 ] Network output: [ -0.004279 0.9096 1.061 0.0001824 -8.188e-05 0.03875 0.0001375 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06917 0.04323 0.06548 0.05282 0.9762 0.9827 0.07098 0.947 0.9691 0.0972 ] Network output: [ 0.1195 -0.3766 1.182 -1.026e-05 4.591e-06 0.956 -7.673e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7428 0.5213 0.4392 0.4833 0.9638 0.9825 0.7468 0.8842 0.9597 0.6974 ] Network output: [ -0.06949 0.1982 0.9604 0.001839 -0.0008257 0.9878 0.001386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6308 0.5951 0.3904 0.2622 0.9797 0.9863 0.6315 0.9553 0.9731 0.4247 ] Network output: [ -0.115 0.229 0.9228 0.0003889 -0.0001746 1.08 0.0002931 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6608 0.6538 0.4453 0.2153 0.9764 0.9841 0.6609 0.9469 0.9678 0.4536 ] Network output: [ 0.07096 0.7295 0.1444 -0.000523 0.0002348 0.982 -0.0003942 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0623 Epoch 1108 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04083 1.02 0.9552 6.086e-05 -2.733e-05 -0.05675 4.588e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03701 -0.001946 0.02292 0.02306 0.9189 0.9315 0.07408 0.8494 0.8814 0.1686 ] Network output: [ 1.006 0.1515 -0.1504 -0.0003212 0.0001442 -0.01475 -0.0002422 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6533 0.04571 -0.02985 0.248 0.959 0.9795 0.7509 0.871 0.9523 0.6893 ] Network output: [ -0.002615 0.9403 1.028 3.655e-05 -1.641e-05 0.03749 2.757e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07 0.04388 0.06139 0.0445 0.9761 0.9826 0.07184 0.9466 0.9686 0.09269 ] Network output: [ 0.1194 -0.2628 1.072 -0.000576 0.0002586 0.9493 -0.0004341 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7416 0.5269 0.4342 0.4389 0.9636 0.9824 0.7457 0.8837 0.9594 0.691 ] Network output: [ -0.06087 0.2742 0.8743 0.001412 -0.000634 0.979 0.001064 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6325 0.598 0.3842 0.2231 0.9796 0.9862 0.6332 0.955 0.9727 0.4178 ] Network output: [ -0.1137 0.3102 0.8508 -0.0003395 0.0001524 1.065 -0.0002558 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6581 0.6514 0.4446 0.1678 0.9763 0.984 0.6583 0.9468 0.9675 0.4527 ] Network output: [ 0.06334 0.7871 0.1077 -0.001279 0.0005741 0.9733 -0.0009637 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06321 Epoch 1109 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02342 0.9915 1.004 -1.306e-05 5.86e-06 -0.04244 -9.825e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03643 -0.0004294 0.03134 0.02659 0.9189 0.9314 0.07263 0.8501 0.8819 0.1712 ] Network output: [ 0.8674 0.07519 0.09047 -0.001034 0.000464 0.09531 -0.000779 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6478 0.08505 0.04396 0.2769 0.959 0.9795 0.7437 0.872 0.9528 0.6969 ] Network output: [ -0.005008 0.9173 1.051 7.31e-05 -3.282e-05 0.04169 5.512e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0702 0.04662 0.07074 0.04999 0.9764 0.9829 0.07203 0.9478 0.9696 0.09862 ] Network output: [ 0.09032 -0.3123 1.143 -0.000566 0.0002541 0.9866 -0.0004265 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7466 0.5466 0.4644 0.4516 0.9639 0.9826 0.7506 0.8843 0.9597 0.6928 ] Network output: [ -0.04332 0.2562 0.8606 0.001771 -0.0007951 0.9771 0.001335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6383 0.6062 0.3888 0.2251 0.9798 0.9863 0.6389 0.9555 0.9731 0.4186 ] Network output: [ -0.08969 0.3087 0.8155 -1.964e-05 8.813e-06 1.055 -1.479e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6638 0.6575 0.4436 0.1623 0.9765 0.9841 0.6639 0.9469 0.9675 0.4509 ] Network output: [ 0.07951 0.7853 0.0861 -0.0011 0.0004939 0.9651 -0.0008291 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06705 Epoch 1110 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04058 0.9883 0.9877 0.0002546 -0.0001143 -0.05615 0.0001919 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03661 -0.002142 0.02444 0.02679 0.919 0.9315 0.07332 0.8496 0.8817 0.1718 ] Network output: [ 0.995 0.06437 -0.04693 -4.327e-05 1.944e-05 -0.00765 -3.269e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6511 0.04196 -0.02065 0.2775 0.9591 0.9795 0.7483 0.8715 0.9526 0.696 ] Network output: [ -0.004112 0.9131 1.057 0.0001718 -7.715e-05 0.03844 0.0001295 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06917 0.04305 0.06453 0.0518 0.9763 0.9827 0.07098 0.9469 0.9691 0.09644 ] Network output: [ 0.1216 -0.3668 1.171 -6.521e-05 2.926e-05 0.9524 -4.909e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7423 0.5204 0.4373 0.4791 0.9638 0.9825 0.7463 0.8841 0.9597 0.6967 ] Network output: [ -0.07055 0.2024 0.9581 0.001783 -0.0008006 0.9879 0.001344 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6307 0.595 0.3905 0.26 0.9797 0.9863 0.6314 0.9553 0.973 0.425 ] Network output: [ -0.1165 0.2335 0.9214 0.0003275 -0.000147 1.079 0.0002468 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6603 0.6533 0.4457 0.2128 0.9764 0.9841 0.6604 0.9469 0.9678 0.454 ] Network output: [ 0.06878 0.7349 0.1429 -0.0005806 0.0002607 0.9823 -0.0004376 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06125 Epoch 1111 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03839 1.019 0.9593 4.014e-05 -1.802e-05 -0.05472 3.026e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03694 -0.001722 0.02397 0.02315 0.919 0.9315 0.07387 0.8495 0.8815 0.1686 ] Network output: [ 0.9888 0.1482 -0.1268 -0.0004247 0.0001907 -0.0007648 -0.0003202 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6528 0.05179 -0.02 0.2485 0.959 0.9795 0.7501 0.8711 0.9524 0.6896 ] Network output: [ -0.002988 0.9394 1.028 3.065e-05 -1.377e-05 0.03818 2.312e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0701 0.04437 0.06249 0.04447 0.9762 0.9826 0.07193 0.9468 0.9687 0.09321 ] Network output: [ 0.1146 -0.2598 1.074 -0.0006525 0.0002929 0.9543 -0.0004917 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7423 0.5307 0.4393 0.4357 0.9637 0.9825 0.7463 0.8838 0.9595 0.6907 ] Network output: [ -0.05785 0.277 0.866 0.001421 -0.0006381 0.9785 0.001071 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6338 0.5999 0.3853 0.2198 0.9797 0.9862 0.6345 0.9551 0.9727 0.4181 ] Network output: [ -0.1098 0.317 0.839 -0.000349 0.0001567 1.062 -0.000263 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6589 0.6523 0.4449 0.1623 0.9764 0.984 0.659 0.9469 0.9675 0.4528 ] Network output: [ 0.06456 0.7929 0.101 -0.001311 0.0005885 0.9716 -0.000988 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06252 Epoch 1112 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02446 0.9883 1.006 2.529e-05 -1.136e-05 -0.04317 1.908e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03639 -0.0005692 0.03107 0.02689 0.9189 0.9315 0.07256 0.8501 0.8819 0.1714 ] Network output: [ 0.8752 0.06608 0.09097 -0.0009455 0.0004245 0.08877 -0.0007126 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6479 0.08207 0.04138 0.2789 0.9591 0.9795 0.7438 0.8721 0.9528 0.6973 ] Network output: [ -0.005193 0.9144 1.055 9.369e-05 -4.207e-05 0.04176 7.064e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07008 0.04634 0.07071 0.0507 0.9764 0.9829 0.07189 0.9478 0.9697 0.0988 ] Network output: [ 0.09221 -0.3254 1.154 -0.0005069 0.0002275 0.9848 -0.0003819 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7465 0.5448 0.4639 0.4562 0.964 0.9826 0.7504 0.8843 0.9598 0.6934 ] Network output: [ -0.04581 0.2448 0.8752 0.001816 -0.0008153 0.9791 0.001369 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.638 0.6057 0.3901 0.2307 0.9798 0.9864 0.6387 0.9556 0.9731 0.4201 ] Network output: [ -0.09128 0.2963 0.8287 8.188e-05 -3.676e-05 1.058 6.172e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.664 0.6577 0.444 0.1696 0.9765 0.9841 0.6641 0.947 0.9676 0.4514 ] Network output: [ 0.07889 0.7794 0.09157 -0.001003 0.0004502 0.9671 -0.0007558 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06576 Epoch 1113 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04179 0.9928 0.9818 0.0002486 -0.0001116 -0.05715 0.0001874 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03666 -0.002248 0.02374 0.02615 0.9191 0.9316 0.07342 0.8496 0.8816 0.1713 ] Network output: [ 1.007 0.07619 -0.07264 -1.347e-05 6.068e-06 -0.01701 -1.023e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6517 0.03949 -0.02588 0.2722 0.9591 0.9796 0.749 0.8714 0.9526 0.6948 ] Network output: [ -0.003957 0.917 1.053 0.0001597 -7.17e-05 0.03821 0.0001204 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06921 0.04293 0.06366 0.05066 0.9763 0.9827 0.07102 0.9469 0.969 0.09569 ] Network output: [ 0.1231 -0.355 1.159 -0.0001334 5.989e-05 0.9494 -0.0001005 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7419 0.5199 0.436 0.4738 0.9638 0.9825 0.7459 0.884 0.9597 0.696 ] Network output: [ -0.07106 0.2081 0.9533 0.001726 -0.0007749 0.9878 0.001301 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6308 0.5951 0.3906 0.2567 0.9797 0.9863 0.6315 0.9552 0.973 0.4251 ] Network output: [ -0.1176 0.2401 0.9174 0.0002555 -0.0001147 1.079 0.0001926 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6599 0.6529 0.4461 0.2088 0.9764 0.9841 0.66 0.947 0.9678 0.4544 ] Network output: [ 0.06672 0.742 0.1398 -0.0006555 0.0002943 0.9821 -0.000494 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06042 Epoch 1114 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03583 1.017 0.9644 2.302e-05 -1.034e-05 -0.05257 1.736e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03686 -0.001493 0.02512 0.02333 0.919 0.9316 0.07365 0.8496 0.8815 0.1686 ] Network output: [ 0.9702 0.1427 -0.09935 -0.0005312 0.0002385 0.01412 -0.0004004 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6523 0.05802 -0.009392 0.2498 0.9591 0.9795 0.7493 0.8712 0.9524 0.6901 ] Network output: [ -0.003403 0.9379 1.03 2.811e-05 -1.263e-05 0.03892 2.121e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07018 0.04486 0.06372 0.0446 0.9762 0.9827 0.07201 0.947 0.9689 0.09385 ] Network output: [ 0.1097 -0.2591 1.077 -0.0007168 0.0003218 0.9594 -0.0005402 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7429 0.5345 0.4446 0.4332 0.9638 0.9825 0.7469 0.8839 0.9595 0.6906 ] Network output: [ -0.05486 0.2785 0.859 0.001441 -0.0006471 0.9781 0.001086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6351 0.6017 0.3866 0.2169 0.9797 0.9862 0.6358 0.9552 0.9728 0.4187 ] Network output: [ -0.1058 0.3226 0.8281 -0.0003442 0.0001545 1.06 -0.0002594 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6598 0.6533 0.4452 0.1574 0.9764 0.984 0.6599 0.947 0.9675 0.4529 ] Network output: [ 0.06601 0.798 0.09461 -0.00133 0.0005973 0.97 -0.001003 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06204 Epoch 1115 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02587 0.9856 1.007 6.702e-05 -3.009e-05 -0.0442 5.053e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03636 -0.0007409 0.03063 0.02712 0.919 0.9316 0.07252 0.8501 0.882 0.1715 ] Network output: [ 0.8859 0.05809 0.08682 -0.0008378 0.0003761 0.07993 -0.0006314 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6482 0.07822 0.03724 0.2803 0.9591 0.9796 0.7441 0.8721 0.9529 0.6976 ] Network output: [ -0.005345 0.9119 1.057 0.0001143 -5.131e-05 0.04175 8.615e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06994 0.04601 0.0705 0.05131 0.9764 0.9829 0.07176 0.9478 0.9697 0.0989 ] Network output: [ 0.0947 -0.338 1.165 -0.0004464 0.0002004 0.9823 -0.0003364 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7462 0.5425 0.4628 0.4606 0.964 0.9826 0.7502 0.8844 0.9598 0.694 ] Network output: [ -0.04876 0.2332 0.8906 0.001854 -0.0008324 0.9813 0.001397 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6376 0.605 0.3914 0.2364 0.9799 0.9864 0.6383 0.9556 0.9732 0.4217 ] Network output: [ -0.09336 0.2836 0.8428 0.000179 -8.037e-05 1.061 0.0001349 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6641 0.6577 0.4445 0.177 0.9766 0.9841 0.6642 0.9471 0.9677 0.4519 ] Network output: [ 0.07799 0.7732 0.09788 -0.0009044 0.000406 0.9693 -0.0006816 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06449 Epoch 1116 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04271 0.9974 0.976 0.0002379 -0.0001068 -0.05792 0.0001793 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03671 -0.002327 0.02316 0.0255 0.9191 0.9317 0.07351 0.8496 0.8816 0.1707 ] Network output: [ 1.016 0.0885 -0.09648 5.192e-07 -2.143e-07 -0.02474 3.138e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6523 0.03769 -0.03007 0.2667 0.9592 0.9796 0.7497 0.8714 0.9526 0.6937 ] Network output: [ -0.003823 0.921 1.049 0.000146 -6.555e-05 0.03803 0.0001101 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06926 0.04286 0.06289 0.04947 0.9763 0.9827 0.07107 0.9468 0.9689 0.09497 ] Network output: [ 0.1241 -0.342 1.146 -0.0002103 9.44e-05 0.9469 -0.0001584 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7415 0.5199 0.4351 0.468 0.9638 0.9825 0.7455 0.884 0.9597 0.6952 ] Network output: [ -0.07106 0.2148 0.9466 0.001672 -0.0007506 0.9875 0.00126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.631 0.5954 0.3905 0.2527 0.9797 0.9863 0.6317 0.9552 0.973 0.425 ] Network output: [ -0.1182 0.248 0.9116 0.000181 -8.128e-05 1.078 0.0001364 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6595 0.6526 0.4465 0.2039 0.9765 0.9841 0.6597 0.947 0.9678 0.4547 ] Network output: [ 0.06482 0.7499 0.1358 -0.0007374 0.000331 0.9816 -0.0005558 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05991 Epoch 1117 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03325 1.014 0.97 7.867e-06 -3.535e-06 -0.05042 5.943e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03677 -0.001269 0.02628 0.02357 0.9191 0.9316 0.07341 0.8497 0.8816 0.1687 ] Network output: [ 0.9513 0.1358 -0.07017 -0.0006378 0.0002863 0.02907 -0.0004807 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6516 0.06408 0.001411 0.2515 0.9591 0.9796 0.7483 0.8714 0.9525 0.6908 ] Network output: [ -0.003828 0.9359 1.032 2.754e-05 -1.237e-05 0.03964 2.078e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07024 0.04531 0.065 0.04486 0.9763 0.9827 0.07207 0.9472 0.969 0.09454 ] Network output: [ 0.1051 -0.2602 1.082 -0.0007684 0.0003449 0.9645 -0.000579 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7436 0.538 0.4498 0.4315 0.9638 0.9825 0.7475 0.884 0.9596 0.6906 ] Network output: [ -0.05194 0.2786 0.8534 0.001471 -0.0006605 0.9779 0.001109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6364 0.6034 0.3879 0.2147 0.9798 0.9863 0.6371 0.9554 0.9729 0.4193 ] Network output: [ -0.1019 0.3267 0.8186 -0.0003252 0.000146 1.057 -0.0002451 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6607 0.6543 0.4455 0.1533 0.9765 0.9841 0.6608 0.9471 0.9676 0.453 ] Network output: [ 0.06753 0.802 0.08902 -0.001337 0.0006002 0.9685 -0.001008 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06188 Epoch 1118 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02749 0.9833 1.008 0.0001076 -4.833e-05 -0.0454 8.114e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03634 -0.0009288 0.03008 0.02729 0.9191 0.9316 0.0725 0.8502 0.882 0.1717 ] Network output: [ 0.8984 0.0515 0.0791 -0.000721 0.0003237 0.06968 -0.0005434 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6484 0.07391 0.0321 0.2812 0.9592 0.9796 0.7444 0.8721 0.9529 0.6978 ] Network output: [ -0.00546 0.9099 1.06 0.0001327 -5.959e-05 0.04168 0.0001001 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06981 0.04564 0.07015 0.0518 0.9765 0.9829 0.07161 0.9478 0.9697 0.0989 ] Network output: [ 0.09756 -0.3496 1.174 -0.0003895 0.0001748 0.9791 -0.0002935 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7458 0.5399 0.4613 0.4645 0.964 0.9827 0.7498 0.8844 0.9599 0.6947 ] Network output: [ -0.05185 0.2222 0.9057 0.001885 -0.0008461 0.9834 0.00142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6372 0.6042 0.3926 0.2418 0.9799 0.9864 0.6379 0.9556 0.9732 0.4233 ] Network output: [ -0.09567 0.2713 0.857 0.0002687 -0.0001206 1.064 0.0002025 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6641 0.6576 0.445 0.1842 0.9766 0.9842 0.6642 0.9472 0.9678 0.4525 ] Network output: [ 0.07688 0.7667 0.1047 -0.0008083 0.0003629 0.9715 -0.0006092 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06338 Epoch 1119 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0433 1.002 0.9707 0.0002224 -9.983e-05 -0.05843 0.0001676 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03675 -0.002378 0.02271 0.02488 0.9192 0.9317 0.07358 0.8496 0.8816 0.1701 ] Network output: [ 1.024 0.1006 -0.1174 -6.248e-07 2.991e-07 -0.03062 -5.473e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6529 0.0366 -0.03309 0.2614 0.9592 0.9796 0.7503 0.8713 0.9525 0.6925 ] Network output: [ -0.003717 0.925 1.045 0.0001307 -5.87e-05 0.03792 9.856e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06932 0.04286 0.06223 0.0483 0.9763 0.9827 0.07113 0.9468 0.9688 0.09427 ] Network output: [ 0.1246 -0.3285 1.133 -0.0002922 0.0001312 0.9451 -0.0002201 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7411 0.5202 0.4347 0.4618 0.9638 0.9825 0.7451 0.8839 0.9597 0.6943 ] Network output: [ -0.07056 0.222 0.9386 0.001623 -0.0007287 0.9871 0.001223 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6314 0.5958 0.3904 0.2483 0.9797 0.9863 0.6321 0.9552 0.973 0.4248 ] Network output: [ -0.1183 0.2563 0.9047 0.0001099 -4.933e-05 1.076 8.283e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6593 0.6524 0.4467 0.1986 0.9765 0.9841 0.6594 0.9471 0.9679 0.4548 ] Network output: [ 0.06311 0.7578 0.1317 -0.0008187 0.0003676 0.981 -0.0006171 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0597 Epoch 1120 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03078 1.011 0.9757 -6.128e-06 2.748e-06 -0.04837 -4.604e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03668 -0.001063 0.02741 0.02386 0.9191 0.9317 0.07317 0.8499 0.8817 0.1688 ] Network output: [ 0.9333 0.128 -0.0409 -0.00074 0.0003322 0.04331 -0.0005577 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.651 0.06972 0.01186 0.2536 0.9591 0.9796 0.7474 0.8715 0.9526 0.6915 ] Network output: [ -0.004236 0.9338 1.034 2.801e-05 -1.258e-05 0.04033 2.114e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07028 0.04573 0.06622 0.0452 0.9764 0.9828 0.0721 0.9474 0.9692 0.09521 ] Network output: [ 0.1009 -0.2629 1.089 -0.0008067 0.0003621 0.9692 -0.0006079 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7442 0.5412 0.4547 0.4305 0.9639 0.9826 0.7481 0.8841 0.9597 0.6906 ] Network output: [ -0.04916 0.2776 0.8491 0.00151 -0.0006777 0.9777 0.001138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6376 0.6051 0.3891 0.2133 0.9798 0.9863 0.6383 0.9555 0.9729 0.4198 ] Network output: [ -0.09816 0.3291 0.8108 -0.0002926 0.0001313 1.055 -0.0002205 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6615 0.6552 0.4456 0.1503 0.9765 0.9841 0.6617 0.9472 0.9677 0.453 ] Network output: [ 0.06904 0.8049 0.08453 -0.00133 0.000597 0.9671 -0.001002 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06204 Epoch 1121 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02918 0.9818 1.007 0.0001437 -6.453e-05 -0.04669 0.0001083 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03633 -0.001121 0.02945 0.02738 0.9192 0.9317 0.07249 0.8502 0.8821 0.1717 ] Network output: [ 0.9118 0.0466 0.06855 -0.0006039 0.0002711 0.05871 -0.0004551 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6488 0.06943 0.02638 0.2816 0.9592 0.9796 0.7448 0.8721 0.9529 0.6978 ] Network output: [ -0.005536 0.9085 1.062 0.0001475 -6.62e-05 0.04154 0.0001112 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06967 0.04526 0.06966 0.05213 0.9765 0.983 0.07148 0.9478 0.9697 0.09876 ] Network output: [ 0.1006 -0.3593 1.181 -0.0003406 0.0001529 0.9757 -0.0002567 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7454 0.5373 0.4595 0.4678 0.9641 0.9827 0.7493 0.8845 0.9599 0.6952 ] Network output: [ -0.05487 0.2123 0.9198 0.001907 -0.000856 0.9854 0.001437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6368 0.6034 0.3936 0.2467 0.9799 0.9864 0.6374 0.9557 0.9733 0.4247 ] Network output: [ -0.09805 0.2598 0.8706 0.000347 -0.0001558 1.067 0.0002615 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.664 0.6575 0.4454 0.1908 0.9766 0.9842 0.6642 0.9473 0.9679 0.453 ] Network output: [ 0.0756 0.7604 0.1118 -0.0007193 0.0003229 0.9737 -0.0005422 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06249 Epoch 1122 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04352 1.006 0.966 0.0002022 -9.079e-05 -0.05866 0.0001524 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03678 -0.002397 0.02241 0.0243 0.9193 0.9318 0.07363 0.8497 0.8817 0.1695 ] Network output: [ 1.028 0.1119 -0.1343 -1.623e-05 7.303e-06 -0.03438 -1.23e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6533 0.03628 -0.0348 0.2565 0.9592 0.9796 0.7507 0.8713 0.9525 0.6914 ] Network output: [ -0.003648 0.9289 1.041 0.0001142 -5.129e-05 0.03786 8.612e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06939 0.0429 0.0617 0.04719 0.9763 0.9827 0.07121 0.9468 0.9688 0.09363 ] Network output: [ 0.1246 -0.3152 1.121 -0.000376 0.0001688 0.9438 -0.0002833 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7409 0.5209 0.4347 0.4557 0.9638 0.9825 0.7448 0.8839 0.9596 0.6933 ] Network output: [ -0.0696 0.2293 0.9298 0.001582 -0.0007101 0.9866 0.001192 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6319 0.5965 0.3902 0.2438 0.9797 0.9863 0.6326 0.9552 0.9729 0.4244 ] Network output: [ -0.118 0.2646 0.897 4.567e-05 -2.051e-05 1.074 3.443e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6591 0.6522 0.4467 0.1932 0.9765 0.9841 0.6592 0.9472 0.9679 0.4548 ] Network output: [ 0.06166 0.7652 0.1277 -0.0008946 0.0004016 0.9801 -0.0006743 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05972 Epoch 1123 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02852 1.008 0.9812 -1.861e-05 8.351e-06 -0.04653 -1.401e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03659 -0.0008834 0.02843 0.02416 0.9192 0.9317 0.07294 0.85 0.8818 0.1688 ] Network output: [ 0.9169 0.1198 -0.01311 -0.0008319 0.0003735 0.05611 -0.000627 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6504 0.07466 0.02142 0.2559 0.9592 0.9796 0.7465 0.8716 0.9527 0.6921 ] Network output: [ -0.004609 0.9317 1.037 2.922e-05 -1.312e-05 0.04093 2.204e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0703 0.04608 0.06731 0.0456 0.9764 0.9829 0.07212 0.9476 0.9693 0.0958 ] Network output: [ 0.09732 -0.2668 1.095 -0.0008316 0.0003733 0.9733 -0.0006267 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7447 0.5439 0.4589 0.4302 0.964 0.9826 0.7487 0.8842 0.9597 0.6905 ] Network output: [ -0.04667 0.2754 0.8465 0.001554 -0.0006976 0.9777 0.001171 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6387 0.6065 0.39 0.2126 0.9798 0.9863 0.6393 0.9555 0.973 0.4202 ] Network output: [ -0.09482 0.3297 0.8053 -0.0002477 0.0001112 1.054 -0.0001867 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6624 0.6561 0.4456 0.1485 0.9766 0.9842 0.6625 0.9472 0.9677 0.4529 ] Network output: [ 0.07042 0.8064 0.08134 -0.00131 0.0005879 0.9661 -0.0009869 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06242 Epoch 1124 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03085 0.9811 1.006 0.0001732 -7.774e-05 -0.048 0.0001305 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03632 -0.001308 0.02878 0.02739 0.9193 0.9318 0.07249 0.8502 0.8821 0.1716 ] Network output: [ 0.9255 0.04367 0.0557 -0.0004929 0.0002213 0.04757 -0.0003715 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6491 0.06501 0.02038 0.2814 0.9593 0.9797 0.7452 0.8721 0.9529 0.6976 ] Network output: [ -0.005571 0.9079 1.063 0.0001575 -7.073e-05 0.04133 0.0001187 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06955 0.04489 0.06906 0.05229 0.9765 0.983 0.07135 0.9478 0.9696 0.0985 ] Network output: [ 0.1037 -0.3666 1.186 -0.0003038 0.0001364 0.972 -0.0002289 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7449 0.5348 0.4575 0.4703 0.9641 0.9827 0.7489 0.8845 0.9599 0.6954 ] Network output: [ -0.05766 0.2041 0.9319 0.001918 -0.0008612 0.9872 0.001446 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6364 0.6026 0.3944 0.2508 0.9799 0.9864 0.637 0.9557 0.9733 0.4259 ] Network output: [ -0.1004 0.25 0.8827 0.0004094 -0.0001838 1.07 0.0003086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6639 0.6573 0.4457 0.1966 0.9767 0.9843 0.664 0.9474 0.968 0.4533 ] Network output: [ 0.07418 0.7547 0.1186 -0.0006428 0.0002886 0.9757 -0.0004845 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06177 Epoch 1125 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04335 1.01 0.9623 0.0001787 -8.021e-05 -0.05859 0.0001347 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0368 -0.002384 0.02228 0.02381 0.9193 0.9318 0.07364 0.8497 0.8817 0.1689 ] Network output: [ 1.03 0.1216 -0.1463 -4.521e-05 2.031e-05 -0.03578 -3.414e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6536 0.03681 -0.03506 0.2523 0.9593 0.9796 0.7509 0.8712 0.9525 0.6903 ] Network output: [ -0.003623 0.9324 1.037 9.725e-05 -4.366e-05 0.03788 7.331e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06947 0.043 0.06134 0.04619 0.9763 0.9827 0.07128 0.9468 0.9688 0.09307 ] Network output: [ 0.124 -0.3026 1.109 -0.0004591 0.0002061 0.9432 -0.000346 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7407 0.5219 0.4352 0.4499 0.9639 0.9826 0.7447 0.8839 0.9596 0.6923 ] Network output: [ -0.06822 0.2363 0.9205 0.001548 -0.0006952 0.986 0.001167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6325 0.5973 0.3899 0.2394 0.9798 0.9863 0.6332 0.9552 0.9729 0.4239 ] Network output: [ -0.1172 0.2727 0.8889 -9.505e-06 4.264e-06 1.073 -7.149e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.659 0.6522 0.4466 0.188 0.9765 0.9842 0.6592 0.9472 0.9679 0.4546 ] Network output: [ 0.06053 0.7721 0.1237 -0.0009623 0.000432 0.9792 -0.0007253 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05984 Epoch 1126 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0266 1.005 0.9862 -2.82e-05 1.266e-05 -0.04498 -2.124e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03651 -0.0007407 0.0293 0.02448 0.9192 0.9317 0.07273 0.8501 0.8819 0.1689 ] Network output: [ 0.9032 0.1114 0.01181 -0.0009069 0.0004072 0.06676 -0.0006835 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6499 0.07867 0.02961 0.2583 0.9592 0.9796 0.7457 0.8718 0.9528 0.6926 ] Network output: [ -0.004936 0.9296 1.039 3.138e-05 -1.41e-05 0.04143 2.368e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0703 0.04635 0.06823 0.04605 0.9765 0.9829 0.07212 0.9477 0.9695 0.09628 ] Network output: [ 0.09451 -0.272 1.103 -0.0008436 0.0003787 0.9767 -0.0006357 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7451 0.5461 0.4624 0.4307 0.9641 0.9827 0.7491 0.8842 0.9598 0.6903 ] Network output: [ -0.04463 0.272 0.8458 0.001601 -0.000719 0.9779 0.001207 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6396 0.6077 0.3908 0.2129 0.9799 0.9864 0.6403 0.9556 0.973 0.4205 ] Network output: [ -0.09204 0.3284 0.8022 -0.0001927 8.652e-05 1.053 -0.0001452 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6631 0.6569 0.4455 0.1482 0.9767 0.9842 0.6632 0.9473 0.9677 0.4527 ] Network output: [ 0.0716 0.8067 0.07952 -0.001277 0.0005734 0.9654 -0.0009626 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06286 Epoch 1127 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03243 0.9812 1.004 0.0001951 -8.761e-05 -0.04928 0.0001471 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03632 -0.001483 0.0281 0.02731 0.9193 0.9318 0.0725 0.8502 0.8821 0.1714 ] Network output: [ 0.9389 0.04288 0.04097 -0.0003927 0.0001763 0.03666 -0.000296 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6495 0.06085 0.0144 0.2805 0.9593 0.9797 0.7456 0.872 0.9529 0.6972 ] Network output: [ -0.005568 0.908 1.063 0.0001628 -7.308e-05 0.04108 0.0001227 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06943 0.04456 0.06838 0.05225 0.9765 0.983 0.07123 0.9477 0.9696 0.09811 ] Network output: [ 0.1066 -0.371 1.188 -0.0002819 0.0001265 0.9684 -0.0002124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7444 0.5325 0.4554 0.4717 0.9641 0.9827 0.7483 0.8845 0.96 0.6955 ] Network output: [ -0.0601 0.1979 0.9415 0.001919 -0.0008613 0.9886 0.001446 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.636 0.6019 0.395 0.2539 0.9799 0.9864 0.6366 0.9557 0.9733 0.4267 ] Network output: [ -0.1026 0.2423 0.8928 0.0004524 -0.0002031 1.072 0.0003409 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6637 0.657 0.4459 0.2012 0.9767 0.9843 0.6638 0.9474 0.9681 0.4535 ] Network output: [ 0.07267 0.7502 0.1246 -0.0005842 0.0002623 0.9774 -0.0004403 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06113 Epoch 1128 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04279 1.014 0.9597 0.0001534 -6.887e-05 -0.05821 0.0001156 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03681 -0.002336 0.02233 0.02343 0.9194 0.9319 0.07362 0.8497 0.8817 0.1683 ] Network output: [ 1.029 0.1293 -0.1525 -8.594e-05 3.86e-05 -0.03467 -6.484e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6537 0.03821 -0.03376 0.249 0.9593 0.9797 0.751 0.8712 0.9525 0.6894 ] Network output: [ -0.003651 0.9353 1.034 8.084e-05 -3.63e-05 0.03798 6.095e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06954 0.04316 0.06117 0.04536 0.9763 0.9827 0.07135 0.9468 0.9688 0.09263 ] Network output: [ 0.1229 -0.2915 1.1 -0.0005395 0.0002422 0.9435 -0.0004065 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7407 0.5234 0.4362 0.4446 0.9639 0.9826 0.7446 0.8839 0.9596 0.6913 ] Network output: [ -0.06651 0.2426 0.9112 0.001523 -0.0006839 0.9854 0.001148 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6332 0.5982 0.3897 0.2353 0.9798 0.9863 0.6339 0.9553 0.9729 0.4234 ] Network output: [ -0.1159 0.2802 0.8805 -5.468e-05 2.454e-05 1.071 -4.119e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6591 0.6523 0.4464 0.1829 0.9766 0.9842 0.6592 0.9472 0.9679 0.4543 ] Network output: [ 0.05977 0.7783 0.1198 -0.001021 0.0004582 0.9782 -0.0007692 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0599 Epoch 1129 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02513 1.003 0.9906 -3.307e-05 1.484e-05 -0.04379 -2.491e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03643 -0.0006426 0.02998 0.02479 0.9193 0.9318 0.07254 0.8502 0.882 0.1689 ] Network output: [ 0.8928 0.1031 0.03268 -0.0009587 0.0004304 0.07471 -0.0007226 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6494 0.08155 0.03604 0.2607 0.9593 0.9797 0.7451 0.8719 0.9528 0.693 ] Network output: [ -0.005214 0.9275 1.041 3.503e-05 -1.573e-05 0.04182 2.642e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07028 0.04653 0.06895 0.04652 0.9765 0.9829 0.07209 0.9479 0.9696 0.09665 ] Network output: [ 0.09253 -0.2782 1.111 -0.0008435 0.0003787 0.9792 -0.0006356 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7454 0.5476 0.4651 0.432 0.9641 0.9827 0.7494 0.8843 0.9598 0.6901 ] Network output: [ -0.04321 0.2675 0.8473 0.001649 -0.0007404 0.9784 0.001243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6403 0.6086 0.3915 0.214 0.9799 0.9864 0.641 0.9557 0.9731 0.4209 ] Network output: [ -0.08994 0.3252 0.8019 -0.0001304 5.855e-05 1.052 -9.828e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6637 0.6576 0.4453 0.1492 0.9767 0.9842 0.6639 0.9474 0.9678 0.4524 ] Network output: [ 0.07251 0.8058 0.07904 -0.001234 0.0005542 0.9651 -0.0009303 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06317 Epoch 1130 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03388 0.9822 1.001 0.00021 -9.428e-05 -0.05049 0.0001583 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03633 -0.001641 0.02742 0.02715 0.9194 0.9319 0.07252 0.8502 0.8821 0.1711 ] Network output: [ 0.9517 0.04425 0.02476 -0.0003058 0.0001373 0.02629 -0.0002305 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6499 0.05706 0.008628 0.2789 0.9594 0.9797 0.7461 0.8719 0.9529 0.6965 ] Network output: [ -0.005534 0.9089 1.062 0.0001637 -7.348e-05 0.0408 0.0001234 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06934 0.04425 0.06764 0.05202 0.9765 0.983 0.07113 0.9476 0.9695 0.09763 ] Network output: [ 0.1093 -0.3725 1.188 -0.0002762 0.000124 0.9648 -0.0002081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7438 0.5305 0.4534 0.472 0.9641 0.9827 0.7478 0.8844 0.96 0.6953 ] Network output: [ -0.06214 0.1939 0.9484 0.001907 -0.0008563 0.9897 0.001438 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6357 0.6014 0.3953 0.2559 0.9799 0.9864 0.6363 0.9557 0.9733 0.4274 ] Network output: [ -0.1045 0.237 0.9005 0.0004738 -0.0002127 1.073 0.0003571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6634 0.6567 0.446 0.2044 0.9767 0.9843 0.6636 0.9475 0.9681 0.4537 ] Network output: [ 0.0711 0.7474 0.1293 -0.0005475 0.0002458 0.9788 -0.0004126 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06049 Epoch 1131 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04186 1.016 0.9585 0.0001284 -5.764e-05 -0.05752 9.676e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03679 -0.002257 0.02256 0.02315 0.9194 0.9319 0.07355 0.8498 0.8817 0.1678 ] Network output: [ 1.024 0.1347 -0.1529 -0.0001363 6.123e-05 -0.03112 -0.0001028 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6537 0.04044 -0.03093 0.2465 0.9593 0.9797 0.7508 0.8712 0.9525 0.6886 ] Network output: [ -0.003737 0.9375 1.032 6.614e-05 -2.97e-05 0.03817 4.987e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06961 0.04338 0.06121 0.0447 0.9764 0.9828 0.07142 0.9468 0.9688 0.09235 ] Network output: [ 0.1212 -0.2821 1.093 -0.0006153 0.0002762 0.9445 -0.0004637 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7407 0.5252 0.4379 0.4399 0.9639 0.9826 0.7447 0.8838 0.9597 0.6904 ] Network output: [ -0.06457 0.2481 0.9023 0.001507 -0.0006764 0.9848 0.001135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.634 0.5993 0.3897 0.2314 0.9798 0.9863 0.6347 0.9553 0.9729 0.4229 ] Network output: [ -0.1142 0.2871 0.872 -8.969e-05 4.026e-05 1.069 -6.758e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6593 0.6526 0.4461 0.1782 0.9766 0.9842 0.6594 0.9473 0.9679 0.454 ] Network output: [ 0.0594 0.784 0.1157 -0.001069 0.0004801 0.9771 -0.000806 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05981 Epoch 1132 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02417 1 0.9943 -3.161e-05 1.419e-05 -0.04302 -2.381e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03636 -0.0005931 0.03044 0.02509 0.9194 0.9319 0.07237 0.8503 0.8821 0.1689 ] Network output: [ 0.8862 0.0951 0.0488 -0.0009834 0.0004415 0.07961 -0.0007411 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6491 0.0832 0.04048 0.2629 0.9593 0.9797 0.7446 0.8719 0.9529 0.6933 ] Network output: [ -0.005449 0.9255 1.043 4.064e-05 -1.825e-05 0.04211 3.065e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07023 0.04661 0.06945 0.047 0.9766 0.983 0.07204 0.948 0.9697 0.09692 ] Network output: [ 0.09138 -0.2854 1.119 -0.0008327 0.0003738 0.9807 -0.0006275 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7456 0.5484 0.467 0.4338 0.9642 0.9827 0.7496 0.8843 0.9599 0.69 ] Network output: [ -0.04251 0.2618 0.851 0.001694 -0.0007606 0.9791 0.001277 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6409 0.6092 0.3922 0.216 0.9799 0.9864 0.6415 0.9557 0.9731 0.4213 ] Network output: [ -0.08861 0.3203 0.8042 -6.376e-05 2.862e-05 1.052 -4.804e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6642 0.6581 0.4451 0.1515 0.9767 0.9843 0.6644 0.9474 0.9678 0.4521 ] Network output: [ 0.07311 0.804 0.07979 -0.001183 0.0005312 0.9652 -0.0008918 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06321 Epoch 1133 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03518 0.9838 0.9983 0.0002188 -9.822e-05 -0.05159 0.0001649 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03634 -0.001781 0.02677 0.0269 0.9195 0.9319 0.07254 0.8501 0.8821 0.1707 ] Network output: [ 0.9636 0.04762 0.00751 -0.000233 0.0001046 0.01666 -0.0001757 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6503 0.05372 0.00324 0.2767 0.9594 0.9797 0.7465 0.8719 0.9529 0.6957 ] Network output: [ -0.005479 0.9104 1.061 0.0001611 -7.231e-05 0.04052 0.0001214 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06927 0.044 0.06688 0.05159 0.9765 0.983 0.07106 0.9476 0.9695 0.09708 ] Network output: [ 0.1116 -0.3711 1.185 -0.0002866 0.0001287 0.9614 -0.0002159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7433 0.5289 0.4515 0.4712 0.9641 0.9827 0.7473 0.8844 0.96 0.695 ] Network output: [ -0.06377 0.1922 0.9526 0.001886 -0.0008467 0.9905 0.001421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6355 0.601 0.3955 0.2567 0.9799 0.9865 0.6361 0.9556 0.9733 0.4279 ] Network output: [ -0.1062 0.2344 0.9054 0.0004744 -0.000213 1.075 0.0003576 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6632 0.6564 0.4462 0.206 0.9768 0.9843 0.6633 0.9475 0.9682 0.4539 ] Network output: [ 0.0695 0.7466 0.1324 -0.0005341 0.0002398 0.9798 -0.0004026 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05979 Epoch 1134 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04064 1.017 0.9585 0.0001052 -4.725e-05 -0.05656 7.932e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03677 -0.00215 0.02296 0.02298 0.9195 0.932 0.07345 0.8498 0.8817 0.1674 ] Network output: [ 1.017 0.1376 -0.1476 -0.0001941 8.717e-05 -0.0254 -0.0001464 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6536 0.04337 -0.0267 0.2449 0.9594 0.9797 0.7505 0.8712 0.9526 0.6881 ] Network output: [ -0.00388 0.9389 1.031 5.399e-05 -2.424e-05 0.03844 4.071e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06967 0.04363 0.06147 0.04423 0.9764 0.9828 0.07148 0.9469 0.9688 0.09223 ] Network output: [ 0.1189 -0.2748 1.088 -0.0006854 0.0003077 0.9463 -0.0005165 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7409 0.5273 0.4401 0.4359 0.964 0.9826 0.7448 0.8839 0.9597 0.6896 ] Network output: [ -0.0625 0.2525 0.8942 0.001498 -0.0006723 0.9844 0.001129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6349 0.6005 0.3898 0.228 0.9798 0.9863 0.6356 0.9553 0.9729 0.4226 ] Network output: [ -0.1122 0.2934 0.8635 -0.0001149 5.157e-05 1.067 -8.656e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6596 0.653 0.446 0.1737 0.9766 0.9842 0.6598 0.9473 0.9679 0.4537 ] Network output: [ 0.05942 0.7892 0.1115 -0.001109 0.000498 0.9759 -0.000836 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05955 Epoch 1135 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02372 0.9978 0.9973 -2.314e-05 1.039e-05 -0.04265 -1.743e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03631 -0.0005911 0.0307 0.02535 0.9194 0.9319 0.07223 0.8504 0.8821 0.1689 ] Network output: [ 0.8835 0.08747 0.06001 -0.0009801 0.00044 0.0815 -0.0007386 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6489 0.08364 0.04296 0.2648 0.9594 0.9797 0.7442 0.872 0.9529 0.6936 ] Network output: [ -0.005646 0.9236 1.046 4.841e-05 -2.174e-05 0.04231 3.651e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07017 0.0466 0.06975 0.04747 0.9766 0.983 0.07197 0.948 0.9697 0.0971 ] Network output: [ 0.09101 -0.2933 1.127 -0.0008134 0.0003651 0.9813 -0.0006129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7457 0.5485 0.4681 0.4361 0.9642 0.9827 0.7496 0.8843 0.9599 0.69 ] Network output: [ -0.04256 0.2552 0.8569 0.001735 -0.0007789 0.9801 0.001308 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6412 0.6096 0.3929 0.2187 0.98 0.9864 0.6419 0.9558 0.9732 0.4219 ] Network output: [ -0.08803 0.314 0.8088 4.575e-06 -2.056e-06 1.053 3.456e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6647 0.6585 0.445 0.1547 0.9768 0.9843 0.6648 0.9475 0.9679 0.452 ] Network output: [ 0.07339 0.8014 0.08157 -0.001126 0.0005056 0.9656 -0.0008488 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06294 Epoch 1136 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03631 0.986 0.9948 0.0002228 -0.0001 -0.05256 0.0001679 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03635 -0.001901 0.02616 0.02657 0.9195 0.932 0.07256 0.8501 0.8821 0.1703 ] Network output: [ 0.9745 0.05267 -0.01027 -0.0001738 7.806e-05 0.0079 -0.0001311 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6507 0.05085 -0.001652 0.2739 0.9594 0.9798 0.7469 0.8718 0.9529 0.6949 ] Network output: [ -0.005414 0.9124 1.059 0.000156 -7.003e-05 0.04026 0.0001176 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06921 0.04378 0.06614 0.05102 0.9765 0.983 0.071 0.9475 0.9694 0.09649 ] Network output: [ 0.1135 -0.3672 1.181 -0.0003112 0.0001397 0.9584 -0.0002345 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7428 0.5277 0.4498 0.4693 0.9642 0.9827 0.7468 0.8843 0.96 0.6945 ] Network output: [ -0.065 0.1923 0.9542 0.001857 -0.0008337 0.991 0.0014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6354 0.6007 0.3957 0.2564 0.9799 0.9865 0.636 0.9556 0.9733 0.4282 ] Network output: [ -0.1077 0.2342 0.9078 0.0004572 -0.0002053 1.075 0.0003446 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6629 0.6561 0.4463 0.2061 0.9768 0.9843 0.663 0.9475 0.9682 0.454 ] Network output: [ 0.06792 0.7478 0.1338 -0.0005424 0.0002435 0.9804 -0.0004088 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05903 Epoch 1137 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03918 1.018 0.9598 8.493e-05 -3.813e-05 -0.05538 6.401e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03673 -0.002023 0.0235 0.0229 0.9195 0.932 0.07332 0.8499 0.8818 0.1671 ] Network output: [ 1.008 0.1383 -0.1375 -0.0002571 0.0001154 -0.01795 -0.0001938 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6534 0.04684 -0.02131 0.2441 0.9594 0.9797 0.7501 0.8712 0.9526 0.6879 ] Network output: [ -0.004075 0.9395 1.03 4.475e-05 -2.009e-05 0.0388 3.375e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06973 0.04391 0.06191 0.04392 0.9764 0.9828 0.07154 0.947 0.9689 0.09227 ] Network output: [ 0.1162 -0.2696 1.085 -0.0007491 0.0003363 0.9487 -0.0005645 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7411 0.5295 0.4428 0.4325 0.964 0.9826 0.745 0.8839 0.9597 0.6891 ] Network output: [ -0.06038 0.2558 0.887 0.001496 -0.0006716 0.984 0.001127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6359 0.6018 0.3902 0.225 0.9798 0.9863 0.6365 0.9554 0.9729 0.4225 ] Network output: [ -0.1098 0.2989 0.8552 -0.0001307 5.869e-05 1.065 -9.852e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6601 0.6535 0.4459 0.1694 0.9767 0.9842 0.6602 0.9474 0.9679 0.4536 ] Network output: [ 0.05975 0.794 0.1072 -0.001141 0.0005122 0.9747 -0.0008598 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05914 Epoch 1138 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02374 0.9954 0.9997 -8.135e-06 3.649e-06 -0.04263 -6.117e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03626 -0.0006308 0.03077 0.02557 0.9195 0.932 0.07212 0.8504 0.8822 0.1689 ] Network output: [ 0.8843 0.08032 0.06664 -0.0009513 0.0004271 0.08066 -0.000717 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6488 0.08301 0.04367 0.2664 0.9594 0.9797 0.744 0.872 0.953 0.6938 ] Network output: [ -0.005814 0.9217 1.048 5.811e-05 -2.609e-05 0.04243 4.381e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.07008 0.04651 0.06989 0.04791 0.9766 0.9831 0.07188 0.9481 0.9698 0.09722 ] Network output: [ 0.09131 -0.3018 1.135 -0.0007877 0.0003536 0.9811 -0.0005936 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7457 0.548 0.4686 0.4386 0.9643 0.9828 0.7496 0.8844 0.9599 0.6901 ] Network output: [ -0.04328 0.2479 0.8646 0.001771 -0.0007949 0.9813 0.001334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6414 0.6097 0.3936 0.2218 0.98 0.9865 0.6421 0.9558 0.9732 0.4226 ] Network output: [ -0.0881 0.3068 0.8152 7.256e-05 -3.258e-05 1.055 5.469e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.665 0.6588 0.4451 0.1586 0.9768 0.9843 0.6651 0.9476 0.968 0.452 ] Network output: [ 0.07338 0.7983 0.08418 -0.001065 0.0004783 0.9664 -0.000803 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06239 Epoch 1139 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03728 0.9886 0.9911 0.0002231 -0.0001002 -0.05339 0.0001681 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03637 -0.002002 0.02561 0.02619 0.9196 0.9321 0.07259 0.8501 0.8821 0.1698 ] Network output: [ 0.9842 0.05899 -0.02806 -0.0001271 5.707e-05 7.197e-05 -9.584e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6511 0.04843 -0.005981 0.2706 0.9595 0.9798 0.7474 0.8717 0.9529 0.694 ] Network output: [ -0.005351 0.9148 1.057 0.0001492 -6.7e-05 0.04003 0.0001125 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06918 0.04361 0.06544 0.05032 0.9766 0.983 0.07096 0.9474 0.9694 0.09589 ] Network output: [ 0.115 -0.3614 1.174 -0.0003475 0.000156 0.9557 -0.0002618 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7424 0.5267 0.4485 0.4666 0.9642 0.9827 0.7463 0.8843 0.96 0.694 ] Network output: [ -0.06587 0.194 0.9538 0.001823 -0.0008186 0.9914 0.001374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6354 0.6006 0.3958 0.2552 0.98 0.9865 0.636 0.9556 0.9733 0.4284 ] Network output: [ -0.1088 0.236 0.908 0.0004268 -0.0001916 1.075 0.0003216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6626 0.6559 0.4465 0.2049 0.9768 0.9844 0.6628 0.9476 0.9682 0.4542 ] Network output: [ 0.06638 0.7507 0.1336 -0.0005682 0.0002551 0.9807 -0.0004283 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05827 Epoch 1140 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03756 1.017 0.962 6.758e-05 -3.034e-05 -0.05406 5.094e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03668 -0.001884 0.02414 0.02289 0.9196 0.9321 0.07316 0.8499 0.8818 0.1668 ] Network output: [ 0.9973 0.137 -0.1237 -0.0003235 0.0001452 -0.009309 -0.0002438 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6531 0.05064 -0.01509 0.2438 0.9594 0.9797 0.7496 0.8713 0.9526 0.6878 ] Network output: [ -0.00431 0.9394 1.03 3.826e-05 -1.718e-05 0.03921 2.885e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06978 0.04421 0.0625 0.04375 0.9765 0.9829 0.07158 0.9471 0.969 0.09245 ] Network output: [ 0.1133 -0.2663 1.085 -0.0008058 0.0003618 0.9515 -0.0006073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7414 0.5319 0.4458 0.4296 0.9641 0.9827 0.7453 0.8839 0.9597 0.6887 ] Network output: [ -0.05826 0.258 0.8808 0.001501 -0.0006738 0.9838 0.001131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6368 0.6031 0.3908 0.2223 0.9799 0.9864 0.6375 0.9554 0.973 0.4226 ] Network output: [ -0.1073 0.3037 0.8471 -0.0001378 6.187e-05 1.063 -0.0001039 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6606 0.6541 0.4459 0.1655 0.9767 0.9843 0.6608 0.9475 0.968 0.4535 ] Network output: [ 0.06034 0.7983 0.1028 -0.001164 0.0005228 0.9735 -0.0008776 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05868 Epoch 1141 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02414 0.9933 1.001 1.192e-05 -5.355e-06 -0.0429 8.998e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03623 -0.0007037 0.03069 0.02575 0.9195 0.932 0.07203 0.8505 0.8822 0.1689 ] Network output: [ 0.8878 0.07375 0.06933 -0.0009021 0.000405 0.0776 -0.0006799 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6488 0.08151 0.04293 0.2676 0.9595 0.9798 0.7439 0.8721 0.953 0.6941 ] Network output: [ -0.00596 0.9199 1.05 6.911e-05 -3.103e-05 0.0425 5.21e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06999 0.04636 0.06989 0.04831 0.9767 0.9831 0.07178 0.9481 0.9698 0.09728 ] Network output: [ 0.09214 -0.3104 1.143 -0.0007582 0.0003404 0.9802 -0.0005714 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7455 0.547 0.4686 0.4413 0.9643 0.9828 0.7494 0.8844 0.96 0.6904 ] Network output: [ -0.04452 0.2401 0.8735 0.001801 -0.0008087 0.9828 0.001358 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6415 0.6096 0.3945 0.2252 0.98 0.9865 0.6421 0.9558 0.9733 0.4235 ] Network output: [ -0.0887 0.2989 0.8229 0.0001388 -6.233e-05 1.056 0.0001046 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6652 0.6591 0.4452 0.1628 0.9769 0.9844 0.6654 0.9476 0.968 0.4522 ] Network output: [ 0.07313 0.7948 0.08742 -0.001003 0.0004502 0.9674 -0.0007557 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06165 Epoch 1142 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03808 0.9915 0.9873 0.0002204 -9.896e-05 -0.05408 0.0001661 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03639 -0.002086 0.02512 0.02578 0.9197 0.9321 0.07262 0.8501 0.8821 0.1693 ] Network output: [ 0.9928 0.06616 -0.04536 -9.156e-05 4.112e-05 -0.0068 -6.905e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6515 0.04644 -0.009713 0.2671 0.9595 0.9798 0.7478 0.8717 0.9529 0.6932 ] Network output: [ -0.005298 0.9174 1.054 0.0001414 -6.347e-05 0.03984 0.0001066 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06916 0.04348 0.06478 0.04954 0.9766 0.983 0.07094 0.9474 0.9693 0.0953 ] Network output: [ 0.1161 -0.3541 1.167 -0.0003926 0.0001762 0.9533 -0.0002958 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7419 0.5261 0.4475 0.4631 0.9642 0.9827 0.7458 0.8843 0.96 0.6935 ] Network output: [ -0.06641 0.1968 0.9517 0.001788 -0.0008026 0.9916 0.001347 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6355 0.6007 0.3958 0.2532 0.98 0.9865 0.6362 0.9556 0.9733 0.4285 ] Network output: [ -0.1097 0.2394 0.9065 0.000388 -0.0001742 1.075 0.0002924 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6624 0.6557 0.4467 0.2027 0.9768 0.9844 0.6626 0.9476 0.9682 0.4544 ] Network output: [ 0.06491 0.7547 0.1324 -0.0006063 0.0002722 0.9806 -0.000457 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05757 Epoch 1143 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03585 1.016 0.965 5.277e-05 -2.369e-05 -0.05265 3.978e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03662 -0.001739 0.02485 0.02294 0.9196 0.9321 0.07299 0.85 0.8819 0.1667 ] Network output: [ 0.9856 0.1344 -0.1073 -0.0003916 0.0001758 2.68e-05 -0.0002952 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6527 0.05459 -0.008332 0.2441 0.9595 0.9798 0.7489 0.8713 0.9527 0.688 ] Network output: [ -0.004572 0.9389 1.031 3.402e-05 -1.528e-05 0.03966 2.566e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06982 0.04451 0.06319 0.04369 0.9765 0.9829 0.07162 0.9473 0.9691 0.09273 ] Network output: [ 0.1102 -0.2645 1.086 -0.0008557 0.0003841 0.9545 -0.0006449 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7417 0.5342 0.4491 0.4273 0.9641 0.9827 0.7456 0.884 0.9598 0.6885 ] Network output: [ -0.05617 0.2592 0.8756 0.001512 -0.0006789 0.9837 0.00114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6378 0.6044 0.3915 0.22 0.9799 0.9864 0.6385 0.9555 0.973 0.4229 ] Network output: [ -0.1047 0.3077 0.8396 -0.0001366 6.134e-05 1.062 -0.000103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6612 0.6548 0.446 0.1618 0.9768 0.9843 0.6614 0.9476 0.968 0.4534 ] Network output: [ 0.06109 0.8022 0.09849 -0.001181 0.00053 0.9723 -0.0008897 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05826 Epoch 1144 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02481 0.9913 1.003 3.5e-05 -1.572e-05 -0.04338 2.639e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0362 -0.0008002 0.03049 0.02589 0.9196 0.9321 0.07196 0.8505 0.8823 0.1689 ] Network output: [ 0.8935 0.06783 0.06885 -0.0008383 0.0003764 0.07289 -0.0006318 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6488 0.07937 0.04111 0.2685 0.9595 0.9798 0.7439 0.8721 0.9531 0.6943 ] Network output: [ -0.006087 0.9182 1.052 8.056e-05 -3.617e-05 0.04251 6.074e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06989 0.04615 0.06978 0.04865 0.9767 0.9831 0.07168 0.9481 0.9698 0.09728 ] Network output: [ 0.09336 -0.319 1.15 -0.0007273 0.0003265 0.9788 -0.0005481 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7453 0.5456 0.4682 0.4438 0.9643 0.9828 0.7492 0.8845 0.96 0.6907 ] Network output: [ -0.04612 0.2321 0.8832 0.001828 -0.0008205 0.9844 0.001377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6415 0.6094 0.3953 0.2287 0.98 0.9865 0.6421 0.9559 0.9733 0.4245 ] Network output: [ -0.08965 0.2908 0.8313 0.0002025 -9.091e-05 1.058 0.0001526 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6654 0.6592 0.4454 0.1672 0.9769 0.9844 0.6656 0.9477 0.9681 0.4524 ] Network output: [ 0.07269 0.7911 0.09113 -0.0009396 0.0004218 0.9685 -0.0007081 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06083 Epoch 1145 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03871 0.9945 0.9836 0.0002151 -9.656e-05 -0.05462 0.0001621 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03641 -0.002152 0.0247 0.02534 0.9197 0.9322 0.07264 0.8501 0.8821 0.1688 ] Network output: [ 1 0.07378 -0.06173 -6.619e-05 2.973e-05 -0.01271 -4.994e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6519 0.04485 -0.01283 0.2634 0.9596 0.9798 0.7481 0.8716 0.9529 0.6924 ] Network output: [ -0.005258 0.9201 1.051 0.0001326 -5.955e-05 0.03969 9.998e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06915 0.04339 0.06419 0.04872 0.9766 0.983 0.07093 0.9474 0.9693 0.09473 ] Network output: [ 0.1169 -0.346 1.159 -0.0004439 0.0001993 0.9513 -0.0003345 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7415 0.5257 0.4468 0.4592 0.9642 0.9828 0.7454 0.8842 0.96 0.6929 ] Network output: [ -0.06664 0.2004 0.9483 0.001753 -0.0007869 0.9917 0.001321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6357 0.6009 0.3959 0.2508 0.98 0.9865 0.6363 0.9556 0.9732 0.4286 ] Network output: [ -0.1103 0.2437 0.9035 0.0003453 -0.000155 1.075 0.0002602 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6622 0.6555 0.4469 0.1997 0.9769 0.9844 0.6624 0.9477 0.9683 0.4545 ] Network output: [ 0.06353 0.7596 0.1303 -0.0006516 0.0002925 0.9804 -0.0004911 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05698 Epoch 1146 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03412 1.015 0.9683 3.981e-05 -1.787e-05 -0.05121 3.001e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03656 -0.001595 0.02558 0.02304 0.9197 0.9321 0.07281 0.8501 0.882 0.1666 ] Network output: [ 0.9736 0.1306 -0.08934 -0.0004601 0.0002066 0.009604 -0.0003468 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6523 0.05851 -0.001342 0.2447 0.9595 0.9798 0.7483 0.8714 0.9528 0.6883 ] Network output: [ -0.004847 0.938 1.032 3.14e-05 -1.41e-05 0.04011 2.368e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06985 0.04479 0.06393 0.04371 0.9765 0.9829 0.07164 0.9474 0.9692 0.09305 ] Network output: [ 0.1072 -0.264 1.088 -0.0008988 0.0004035 0.9575 -0.0006773 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7421 0.5364 0.4524 0.4253 0.9642 0.9827 0.746 0.8841 0.9598 0.6884 ] Network output: [ -0.05414 0.2596 0.8712 0.001529 -0.0006864 0.9837 0.001152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6388 0.6057 0.3923 0.218 0.9799 0.9864 0.6395 0.9556 0.9731 0.4232 ] Network output: [ -0.1021 0.311 0.8327 -0.0001278 5.738e-05 1.06 -9.632e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6619 0.6555 0.4461 0.1585 0.9768 0.9843 0.662 0.9477 0.968 0.4534 ] Network output: [ 0.06194 0.8056 0.09455 -0.001189 0.000534 0.9712 -0.0008964 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05793 Epoch 1147 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02566 0.9897 1.003 5.905e-05 -2.651e-05 -0.044 4.452e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03617 -0.0009117 0.03021 0.02599 0.9197 0.9321 0.0719 0.8506 0.8824 0.1689 ] Network output: [ 0.9006 0.06264 0.06592 -0.0007657 0.0003438 0.06705 -0.0005771 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6489 0.07682 0.03853 0.2691 0.9595 0.9798 0.744 0.8721 0.9531 0.6944 ] Network output: [ -0.0062 0.9168 1.053 9.161e-05 -4.113e-05 0.04249 6.906e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06978 0.04592 0.06958 0.04893 0.9767 0.9831 0.07157 0.9482 0.9698 0.09724 ] Network output: [ 0.09484 -0.3271 1.158 -0.0006972 0.000313 0.977 -0.0005254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.745 0.544 0.4676 0.4462 0.9644 0.9828 0.7489 0.8845 0.9601 0.6911 ] Network output: [ -0.04791 0.2242 0.8931 0.00185 -0.0008304 0.986 0.001394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6414 0.609 0.3962 0.2321 0.9801 0.9865 0.642 0.9559 0.9734 0.4256 ] Network output: [ -0.09083 0.2826 0.8401 0.0002627 -0.0001179 1.06 0.000198 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6656 0.6593 0.4457 0.1716 0.9769 0.9844 0.6657 0.9478 0.9682 0.4527 ] Network output: [ 0.07211 0.7873 0.09515 -0.0008774 0.0003939 0.9697 -0.0006613 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06003 Epoch 1148 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03916 0.9975 0.98 0.0002072 -9.302e-05 -0.05503 0.0001562 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03643 -0.002204 0.02435 0.02491 0.9198 0.9322 0.07266 0.8502 0.8821 0.1683 ] Network output: [ 1.006 0.08152 -0.07676 -5.013e-05 2.252e-05 -0.01763 -3.783e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6522 0.04363 -0.01532 0.2596 0.9596 0.9798 0.7484 0.8716 0.9529 0.6916 ] Network output: [ -0.005235 0.9228 1.049 0.0001232 -5.53e-05 0.03958 9.284e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06915 0.04332 0.06365 0.04788 0.9766 0.983 0.07093 0.9473 0.9692 0.09417 ] Network output: [ 0.1173 -0.3372 1.151 -0.0004994 0.0002242 0.9495 -0.0003763 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7411 0.5255 0.4463 0.455 0.9642 0.9828 0.745 0.8842 0.96 0.6923 ] Network output: [ -0.0666 0.2044 0.944 0.00172 -0.0007721 0.9918 0.001296 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.636 0.6012 0.3959 0.248 0.98 0.9865 0.6366 0.9556 0.9732 0.4285 ] Network output: [ -0.1106 0.2486 0.8997 0.000302 -0.0001356 1.074 0.0002276 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6621 0.6554 0.447 0.1964 0.9769 0.9844 0.6622 0.9478 0.9683 0.4546 ] Network output: [ 0.06224 0.7647 0.1279 -0.0006998 0.0003142 0.98 -0.0005274 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05652 Epoch 1149 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0324 1.013 0.9718 2.808e-05 -1.261e-05 -0.04979 2.117e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03649 -0.001456 0.02632 0.02316 0.9197 0.9322 0.07262 0.8502 0.8821 0.1665 ] Network output: [ 0.9617 0.1262 -0.0708 -0.0005274 0.0002368 0.01905 -0.0003975 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6518 0.06228 0.005626 0.2456 0.9595 0.9798 0.7476 0.8715 0.9528 0.6887 ] Network output: [ -0.005123 0.9369 1.033 2.983e-05 -1.339e-05 0.04057 2.25e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06987 0.04506 0.06467 0.04379 0.9766 0.983 0.07166 0.9476 0.9693 0.0934 ] Network output: [ 0.1043 -0.2646 1.092 -0.0009354 0.0004199 0.9605 -0.0007049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7424 0.5385 0.4557 0.4238 0.9642 0.9828 0.7463 0.8842 0.9599 0.6883 ] Network output: [ -0.05218 0.2593 0.8676 0.00155 -0.0006959 0.9838 0.001168 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6398 0.6069 0.3931 0.2164 0.98 0.9864 0.6404 0.9557 0.9731 0.4236 ] Network output: [ -0.0996 0.3135 0.8266 -0.0001121 5.032e-05 1.059 -8.447e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6625 0.6562 0.4462 0.1557 0.9769 0.9844 0.6627 0.9477 0.9681 0.4534 ] Network output: [ 0.06282 0.8083 0.09107 -0.001191 0.0005349 0.9701 -0.0008979 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05774 Epoch 1150 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02659 0.9883 1.004 8.233e-05 -3.696e-05 -0.0447 6.205e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03615 -0.001031 0.02988 0.02605 0.9198 0.9322 0.07185 0.8506 0.8824 0.1688 ] Network output: [ 0.9087 0.05824 0.06113 -0.0006892 0.0003094 0.06052 -0.0005194 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6491 0.07403 0.03546 0.2694 0.9596 0.9798 0.7441 0.8722 0.9531 0.6945 ] Network output: [ -0.006297 0.9157 1.055 0.0001015 -4.558e-05 0.04243 7.653e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06968 0.04567 0.06931 0.04914 0.9767 0.9831 0.07145 0.9482 0.9698 0.09713 ] Network output: [ 0.09649 -0.3345 1.164 -0.0006698 0.0003007 0.975 -0.0005047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7447 0.5423 0.4668 0.4484 0.9644 0.9829 0.7486 0.8846 0.9601 0.6915 ] Network output: [ -0.04977 0.2167 0.9028 0.001868 -0.0008386 0.9876 0.001408 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6413 0.6087 0.397 0.2353 0.9801 0.9865 0.6419 0.9559 0.9734 0.4266 ] Network output: [ -0.09212 0.2748 0.8488 0.0003186 -0.000143 1.062 0.0002401 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6657 0.6594 0.4459 0.1758 0.977 0.9844 0.6658 0.9479 0.9683 0.4529 ] Network output: [ 0.07143 0.7835 0.09936 -0.0008175 0.000367 0.971 -0.0006161 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05929 Epoch 1151 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03943 1.001 0.9767 0.0001969 -8.842e-05 -0.05529 0.0001484 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03644 -0.00224 0.02407 0.02449 0.9199 0.9323 0.07266 0.8502 0.8821 0.1678 ] Network output: [ 1.011 0.08909 -0.09013 -4.26e-05 1.914e-05 -0.02156 -3.215e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6525 0.04278 -0.01719 0.256 0.9596 0.9798 0.7486 0.8716 0.9529 0.6908 ] Network output: [ -0.00523 0.9255 1.046 0.0001131 -5.077e-05 0.0395 8.523e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06915 0.04328 0.06318 0.04706 0.9766 0.983 0.07093 0.9473 0.9692 0.09364 ] Network output: [ 0.1175 -0.3284 1.143 -0.0005572 0.0002501 0.9481 -0.0004199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7408 0.5255 0.4461 0.4507 0.9642 0.9828 0.7447 0.8842 0.96 0.6917 ] Network output: [ -0.06631 0.2087 0.9391 0.00169 -0.0007587 0.9917 0.001274 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6363 0.6015 0.3958 0.245 0.98 0.9865 0.637 0.9556 0.9732 0.4284 ] Network output: [ -0.1107 0.2538 0.8953 0.0002607 -0.000117 1.073 0.0001965 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.662 0.6553 0.447 0.1928 0.9769 0.9844 0.6621 0.9478 0.9683 0.4547 ] Network output: [ 0.06107 0.7699 0.1254 -0.0007475 0.0003356 0.9795 -0.0005634 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0562 Epoch 1152 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03076 1.012 0.9754 1.726e-05 -7.753e-06 -0.04843 1.302e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03643 -0.001327 0.02703 0.02329 0.9198 0.9322 0.07244 0.8504 0.8822 0.1664 ] Network output: [ 0.9503 0.1214 -0.05239 -0.0005917 0.0002656 0.02805 -0.000446 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6514 0.0658 0.01235 0.2466 0.9596 0.9798 0.7469 0.8717 0.9529 0.6891 ] Network output: [ -0.005388 0.9357 1.034 2.89e-05 -1.298e-05 0.04099 2.18e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06988 0.04531 0.06538 0.0439 0.9766 0.983 0.07166 0.9477 0.9694 0.09373 ] Network output: [ 0.1017 -0.2659 1.095 -0.0009658 0.0004336 0.9632 -0.0007278 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7427 0.5403 0.4587 0.4226 0.9643 0.9828 0.7465 0.8843 0.9599 0.6882 ] Network output: [ -0.05033 0.2584 0.8647 0.001575 -0.000707 0.984 0.001187 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6407 0.6081 0.3939 0.2151 0.98 0.9865 0.6413 0.9557 0.9732 0.4239 ] Network output: [ -0.09718 0.3151 0.8214 -9.034e-05 4.056e-05 1.057 -6.808e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6632 0.6569 0.4463 0.1533 0.9769 0.9844 0.6633 0.9478 0.9681 0.4534 ] Network output: [ 0.0637 0.8105 0.08815 -0.001187 0.0005329 0.9692 -0.0008945 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05767 Epoch 1153 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02755 0.9873 1.003 0.0001035 -4.648e-05 -0.04543 7.804e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03613 -0.001152 0.02951 0.02608 0.9198 0.9323 0.07181 0.8506 0.8825 0.1687 ] Network output: [ 0.9171 0.0547 0.05497 -0.0006125 0.000275 0.05366 -0.0004617 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6493 0.07116 0.03211 0.2694 0.9596 0.9799 0.7442 0.8722 0.9532 0.6945 ] Network output: [ -0.006379 0.9149 1.056 0.0001098 -4.93e-05 0.04235 8.277e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06957 0.04542 0.06897 0.04928 0.9768 0.9832 0.07134 0.9482 0.9699 0.09696 ] Network output: [ 0.09821 -0.3409 1.169 -0.0006465 0.0002902 0.9728 -0.0004872 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7443 0.5405 0.4658 0.4502 0.9644 0.9829 0.7482 0.8846 0.9602 0.6917 ] Network output: [ -0.0516 0.2098 0.9119 0.001882 -0.0008451 0.9892 0.001419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6412 0.6083 0.3977 0.2383 0.9801 0.9866 0.6418 0.956 0.9734 0.4275 ] Network output: [ -0.09346 0.2674 0.8571 0.0003691 -0.0001657 1.064 0.0002781 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6657 0.6594 0.4461 0.1797 0.977 0.9845 0.6659 0.948 0.9683 0.4532 ] Network output: [ 0.07068 0.7798 0.1036 -0.0007613 0.0003418 0.9721 -0.0005738 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05864 Epoch 1154 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03953 1.003 0.9737 0.0001847 -8.29e-05 -0.05542 0.0001392 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03645 -0.002262 0.02387 0.02409 0.9199 0.9323 0.07265 0.8502 0.8822 0.1673 ] Network output: [ 1.015 0.09622 -0.1015 -4.276e-05 1.921e-05 -0.02447 -3.227e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6527 0.04228 -0.01841 0.2526 0.9596 0.9799 0.7488 0.8716 0.9529 0.69 ] Network output: [ -0.005242 0.9282 1.043 0.0001026 -4.605e-05 0.03946 7.732e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06916 0.04326 0.06277 0.04627 0.9766 0.983 0.07093 0.9473 0.9692 0.09313 ] Network output: [ 0.1175 -0.3196 1.135 -0.0006156 0.0002764 0.9469 -0.0004639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7404 0.5256 0.4461 0.4464 0.9643 0.9828 0.7443 0.8842 0.96 0.6911 ] Network output: [ -0.06581 0.2129 0.9338 0.001664 -0.000747 0.9917 0.001254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6367 0.602 0.3957 0.2419 0.98 0.9865 0.6373 0.9556 0.9732 0.4282 ] Network output: [ -0.1106 0.259 0.8906 0.0002228 -0.0001 1.073 0.0001679 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6619 0.6552 0.447 0.1891 0.9769 0.9844 0.6621 0.9479 0.9683 0.4546 ] Network output: [ 0.06003 0.7749 0.1229 -0.0007927 0.0003559 0.9789 -0.0005974 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05598 Epoch 1155 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02922 1.01 0.9789 7.341e-06 -3.298e-06 -0.04717 5.542e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03636 -0.00121 0.02768 0.02344 0.9198 0.9323 0.07225 0.8505 0.8823 0.1663 ] Network output: [ 0.9397 0.1163 -0.03475 -0.0006509 0.0002922 0.03634 -0.0004906 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.651 0.06897 0.01863 0.2476 0.9596 0.9798 0.7462 0.8718 0.953 0.6894 ] Network output: [ -0.005635 0.9345 1.035 2.842e-05 -1.276e-05 0.04138 2.143e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06988 0.04552 0.06603 0.04404 0.9767 0.9831 0.07166 0.9478 0.9695 0.09402 ] Network output: [ 0.09937 -0.2678 1.099 -0.0009904 0.0004446 0.9657 -0.0007464 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7429 0.542 0.4615 0.4218 0.9643 0.9828 0.7468 0.8843 0.96 0.6881 ] Network output: [ -0.04862 0.2569 0.8627 0.001602 -0.0007192 0.9842 0.001207 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6416 0.6092 0.3945 0.2142 0.98 0.9865 0.6422 0.9558 0.9732 0.4242 ] Network output: [ -0.09495 0.3159 0.8173 -6.358e-05 2.854e-05 1.057 -4.791e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6638 0.6576 0.4462 0.1516 0.977 0.9844 0.6639 0.9479 0.9682 0.4533 ] Network output: [ 0.06452 0.812 0.08586 -0.001177 0.0005282 0.9683 -0.0008867 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05771 Epoch 1156 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0285 0.9867 1.003 0.0001219 -5.472e-05 -0.04617 9.187e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03612 -0.001271 0.02912 0.02607 0.9199 0.9323 0.07177 0.8507 0.8825 0.1686 ] Network output: [ 0.9256 0.05205 0.04779 -0.0005386 0.0002418 0.04675 -0.0004059 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6494 0.06831 0.02866 0.2691 0.9597 0.9799 0.7444 0.8722 0.9532 0.6944 ] Network output: [ -0.006447 0.9144 1.057 0.0001162 -5.216e-05 0.04224 8.757e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06946 0.04516 0.06858 0.04933 0.9768 0.9832 0.07123 0.9482 0.9699 0.09674 ] Network output: [ 0.09993 -0.3463 1.173 -0.0006286 0.0002822 0.9704 -0.0004737 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7439 0.5388 0.4648 0.4516 0.9645 0.9829 0.7478 0.8846 0.9602 0.6919 ] Network output: [ -0.05333 0.2036 0.9202 0.001893 -0.0008497 0.9906 0.001426 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6411 0.608 0.3983 0.2409 0.9801 0.9866 0.6417 0.956 0.9735 0.4283 ] Network output: [ -0.09477 0.2607 0.8648 0.000413 -0.0001854 1.066 0.0003113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6658 0.6594 0.4462 0.1832 0.977 0.9845 0.6659 0.948 0.9684 0.4533 ] Network output: [ 0.06988 0.7764 0.1078 -0.0007104 0.0003189 0.9732 -0.0005354 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05808 Epoch 1157 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03944 1.006 0.9711 0.0001708 -7.667e-05 -0.05541 0.0001287 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03645 -0.00227 0.02373 0.02374 0.92 0.9324 0.07262 0.8503 0.8822 0.1668 ] Network output: [ 1.017 0.1027 -0.1107 -4.972e-05 2.233e-05 -0.02633 -3.752e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6529 0.04213 -0.01899 0.2496 0.9597 0.9799 0.7488 0.8716 0.9529 0.6893 ] Network output: [ -0.005272 0.9307 1.041 9.193e-05 -4.127e-05 0.03944 6.929e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06916 0.04327 0.06243 0.04554 0.9766 0.983 0.07093 0.9473 0.9692 0.09266 ] Network output: [ 0.1172 -0.3112 1.128 -0.0006735 0.0003023 0.9461 -0.0005075 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7401 0.5258 0.4463 0.4422 0.9643 0.9828 0.744 0.8842 0.96 0.6904 ] Network output: [ -0.06511 0.217 0.9283 0.001642 -0.0007371 0.9916 0.001237 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6371 0.6025 0.3956 0.2389 0.98 0.9865 0.6378 0.9556 0.9732 0.428 ] Network output: [ -0.1103 0.264 0.8856 0.0001893 -8.5e-05 1.072 0.0001427 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6619 0.6553 0.447 0.1856 0.9769 0.9844 0.6621 0.9479 0.9684 0.4545 ] Network output: [ 0.05914 0.7796 0.1204 -0.0008338 0.0003743 0.9783 -0.0006284 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05584 Epoch 1158 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02783 1.008 0.9821 -1.429e-06 6.391e-07 -0.04603 -1.067e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0363 -0.00111 0.02828 0.02358 0.9199 0.9323 0.07208 0.8506 0.8824 0.1662 ] Network output: [ 0.9303 0.1113 -0.01841 -0.0007029 0.0003156 0.0437 -0.0005297 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6506 0.07172 0.02431 0.2488 0.9596 0.9799 0.7456 0.8719 0.953 0.6897 ] Network output: [ -0.00586 0.9333 1.037 2.833e-05 -1.272e-05 0.04172 2.137e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06987 0.04569 0.0666 0.04419 0.9767 0.9831 0.07164 0.948 0.9696 0.09426 ] Network output: [ 0.09738 -0.2702 1.103 -0.001009 0.0004532 0.9678 -0.0007608 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7431 0.5433 0.4639 0.4213 0.9644 0.9828 0.747 0.8844 0.96 0.688 ] Network output: [ -0.0471 0.2549 0.8614 0.00163 -0.0007319 0.9845 0.001229 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6423 0.6101 0.3951 0.2137 0.9801 0.9865 0.643 0.9559 0.9732 0.4244 ] Network output: [ -0.09295 0.3159 0.8141 -3.292e-05 1.478e-05 1.056 -2.48e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6644 0.6582 0.4462 0.1504 0.977 0.9844 0.6645 0.948 0.9682 0.4531 ] Network output: [ 0.06527 0.8129 0.0842 -0.001161 0.0005211 0.9676 -0.0008748 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05781 Epoch 1159 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02939 0.9864 1.002 0.000137 -6.15e-05 -0.04689 0.0001032 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0361 -0.001384 0.02874 0.02603 0.9199 0.9324 0.07173 0.8507 0.8825 0.1684 ] Network output: [ 0.934 0.05032 0.03988 -0.0004693 0.0002107 0.03998 -0.0003537 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6496 0.06557 0.02522 0.2686 0.9597 0.9799 0.7445 0.8722 0.9532 0.6942 ] Network output: [ -0.0065 0.9143 1.057 0.0001205 -5.412e-05 0.04211 9.087e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06936 0.04492 0.06816 0.04931 0.9768 0.9832 0.07113 0.9481 0.9698 0.09646 ] Network output: [ 0.1016 -0.3504 1.177 -0.0006169 0.0002769 0.9681 -0.0004649 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7435 0.5371 0.4637 0.4526 0.9645 0.9829 0.7473 0.8847 0.9602 0.692 ] Network output: [ -0.05489 0.1983 0.9273 0.001899 -0.0008523 0.9919 0.001431 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.641 0.6076 0.3988 0.243 0.9801 0.9866 0.6416 0.956 0.9735 0.429 ] Network output: [ -0.09601 0.2549 0.8717 0.0004494 -0.0002018 1.067 0.0003387 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6658 0.6593 0.4463 0.1862 0.9771 0.9845 0.6659 0.9481 0.9685 0.4534 ] Network output: [ 0.06904 0.7734 0.1116 -0.0006661 0.0002991 0.9742 -0.0005021 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0576 Epoch 1160 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0392 1.009 0.969 0.0001559 -6.999e-05 -0.05526 0.0001175 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03645 -0.002264 0.02367 0.02342 0.92 0.9324 0.07257 0.8503 0.8822 0.1663 ] Network output: [ 1.018 0.1083 -0.1175 -6.252e-05 2.808e-05 -0.02715 -4.717e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6529 0.04233 -0.01892 0.2468 0.9597 0.9799 0.7488 0.8716 0.9529 0.6886 ] Network output: [ -0.005318 0.9329 1.039 8.149e-05 -3.659e-05 0.03945 6.143e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06916 0.04329 0.06216 0.04487 0.9766 0.983 0.07093 0.9474 0.9692 0.09224 ] Network output: [ 0.1168 -0.3034 1.121 -0.0007296 0.0003276 0.9455 -0.0005498 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7399 0.5262 0.4467 0.4383 0.9643 0.9828 0.7438 0.8842 0.96 0.6897 ] Network output: [ -0.06426 0.2209 0.9228 0.001624 -0.0007291 0.9915 0.001224 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6376 0.603 0.3955 0.236 0.98 0.9865 0.6382 0.9557 0.9732 0.4277 ] Network output: [ -0.1097 0.2687 0.8807 0.0001607 -7.215e-05 1.071 0.0001211 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.662 0.6553 0.4468 0.1821 0.977 0.9845 0.6621 0.948 0.9684 0.4543 ] Network output: [ 0.05841 0.7839 0.1181 -0.0008701 0.0003906 0.9776 -0.0006558 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05575 Epoch 1161 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02663 1.007 0.9851 -8.623e-06 3.869e-06 -0.04505 -6.489e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03624 -0.001029 0.02879 0.02373 0.9199 0.9324 0.07191 0.8507 0.8824 0.1661 ] Network output: [ 0.9223 0.1063 -0.003789 -0.0007456 0.0003347 0.04993 -0.000562 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6502 0.07399 0.02925 0.2498 0.9597 0.9799 0.745 0.872 0.9531 0.6899 ] Network output: [ -0.00606 0.9323 1.038 2.871e-05 -1.289e-05 0.04201 2.165e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06984 0.04583 0.06707 0.04435 0.9768 0.9831 0.07161 0.9481 0.9697 0.09445 ] Network output: [ 0.09574 -0.273 1.108 -0.001024 0.0004595 0.9695 -0.0007713 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7432 0.5444 0.466 0.421 0.9644 0.9829 0.7471 0.8845 0.9601 0.6878 ] Network output: [ -0.04581 0.2525 0.861 0.001658 -0.0007445 0.9849 0.00125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.643 0.611 0.3956 0.2135 0.9801 0.9865 0.6437 0.9559 0.9733 0.4247 ] Network output: [ -0.09123 0.3151 0.8121 4.985e-07 -2.252e-07 1.055 3.815e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6649 0.6587 0.446 0.1498 0.9771 0.9845 0.665 0.9481 0.9683 0.4529 ] Network output: [ 0.0659 0.8133 0.08317 -0.001141 0.000512 0.9671 -0.0008596 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05793 Epoch 1162 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03022 0.9865 1.001 0.0001488 -6.679e-05 -0.04756 0.0001121 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03609 -0.001489 0.02835 0.02595 0.92 0.9324 0.0717 0.8507 0.8826 0.1681 ] Network output: [ 0.942 0.04948 0.03146 -0.0004058 0.0001822 0.03351 -0.0003059 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6498 0.063 0.02191 0.2678 0.9598 0.9799 0.7446 0.8722 0.9533 0.6939 ] Network output: [ -0.006539 0.9145 1.057 0.000123 -5.522e-05 0.04196 9.272e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06927 0.04469 0.06771 0.0492 0.9768 0.9832 0.07102 0.9481 0.9698 0.09613 ] Network output: [ 0.1031 -0.3533 1.179 -0.0006118 0.0002747 0.9658 -0.0004611 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.743 0.5356 0.4626 0.4531 0.9645 0.9829 0.7469 0.8847 0.9602 0.6919 ] Network output: [ -0.05627 0.1939 0.9333 0.0019 -0.000853 0.9931 0.001432 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6409 0.6074 0.3991 0.2447 0.9801 0.9866 0.6415 0.956 0.9735 0.4295 ] Network output: [ -0.09716 0.2501 0.8775 0.0004775 -0.0002144 1.069 0.0003599 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6657 0.6593 0.4463 0.1886 0.9771 0.9845 0.6659 0.9482 0.9685 0.4534 ] Network output: [ 0.06819 0.771 0.1151 -0.0006298 0.0002827 0.975 -0.0004746 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05716 Epoch 1163 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0388 1.011 0.9674 0.0001407 -6.316e-05 -0.055 0.000106 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03644 -0.002245 0.02369 0.02316 0.9201 0.9325 0.07251 0.8504 0.8823 0.1659 ] Network output: [ 1.018 0.1129 -0.1218 -8.013e-05 3.598e-05 -0.02694 -6.043e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.653 0.04286 -0.01822 0.2445 0.9597 0.9799 0.7487 0.8716 0.9529 0.688 ] Network output: [ -0.00538 0.9349 1.037 7.163e-05 -3.216e-05 0.03949 5.4e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06916 0.04333 0.06198 0.04428 0.9767 0.983 0.07092 0.9474 0.9692 0.09186 ] Network output: [ 0.1161 -0.2964 1.116 -0.0007832 0.0003516 0.9452 -0.0005902 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7397 0.5267 0.4472 0.4347 0.9643 0.9828 0.7436 0.8842 0.96 0.689 ] Network output: [ -0.06329 0.2243 0.9174 0.00161 -0.0007228 0.9914 0.001213 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6381 0.6036 0.3954 0.2333 0.98 0.9865 0.6388 0.9557 0.9732 0.4274 ] Network output: [ -0.1091 0.2731 0.8757 0.0001371 -6.157e-05 1.07 0.0001034 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.662 0.6554 0.4466 0.1789 0.977 0.9845 0.6622 0.948 0.9684 0.454 ] Network output: [ 0.05784 0.7878 0.1159 -0.0009013 0.0004046 0.977 -0.0006793 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05566 Epoch 1164 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02563 1.005 0.9877 -1.379e-05 6.191e-06 -0.04423 -1.039e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03618 -0.0009672 0.0292 0.02386 0.92 0.9324 0.07175 0.8508 0.8825 0.166 ] Network output: [ 0.9158 0.1015 0.008805 -0.0007775 0.0003491 0.0549 -0.000586 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6498 0.07574 0.03334 0.2509 0.9597 0.9799 0.7444 0.872 0.9532 0.6901 ] Network output: [ -0.006234 0.9312 1.039 2.964e-05 -1.331e-05 0.04225 2.235e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06981 0.04592 0.06744 0.0445 0.9768 0.9832 0.07157 0.9482 0.9698 0.09457 ] Network output: [ 0.09447 -0.2761 1.112 -0.001033 0.0004638 0.9709 -0.0007785 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7433 0.5452 0.4677 0.4211 0.9645 0.9829 0.7472 0.8845 0.9601 0.6876 ] Network output: [ -0.0448 0.2497 0.8614 0.001686 -0.0007567 0.9854 0.00127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6437 0.6117 0.396 0.2137 0.9801 0.9865 0.6443 0.956 0.9733 0.4248 ] Network output: [ -0.08983 0.3136 0.8112 3.553e-05 -1.595e-05 1.055 2.678e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6654 0.6592 0.4458 0.1497 0.9771 0.9845 0.6655 0.9481 0.9683 0.4526 ] Network output: [ 0.06642 0.8132 0.08272 -0.001117 0.0005013 0.9667 -0.0008415 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05801 Epoch 1165 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03096 0.9869 1 0.0001574 -7.067e-05 -0.04817 0.0001186 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03608 -0.001584 0.02799 0.02584 0.9201 0.9325 0.07166 0.8508 0.8826 0.1678 ] Network output: [ 0.9494 0.0495 0.02273 -0.0003489 0.0001567 0.02745 -0.000263 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6499 0.06065 0.01878 0.2668 0.9598 0.98 0.7447 0.8722 0.9533 0.6935 ] Network output: [ -0.006568 0.915 1.057 0.0001237 -5.556e-05 0.04181 9.327e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06917 0.04448 0.06725 0.04902 0.9768 0.9832 0.07093 0.9481 0.9698 0.09576 ] Network output: [ 0.1046 -0.3548 1.18 -0.0006135 0.0002754 0.9636 -0.0004623 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7426 0.5342 0.4616 0.4531 0.9645 0.9829 0.7465 0.8847 0.9602 0.6918 ] Network output: [ -0.05746 0.1906 0.938 0.001897 -0.0008518 0.9941 0.00143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6409 0.6071 0.3994 0.2459 0.9802 0.9866 0.6415 0.956 0.9735 0.4299 ] Network output: [ -0.0982 0.2464 0.8823 0.0004971 -0.0002232 1.07 0.0003746 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6657 0.6592 0.4463 0.1904 0.9771 0.9846 0.6658 0.9482 0.9686 0.4534 ] Network output: [ 0.06733 0.7692 0.118 -0.0006021 0.0002703 0.9757 -0.0004538 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05675 Epoch 1166 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03827 1.012 0.9664 0.0001257 -5.645e-05 -0.05461 9.477e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03642 -0.002214 0.02378 0.02294 0.9201 0.9325 0.07244 0.8504 0.8823 0.1654 ] Network output: [ 1.016 0.1165 -0.1236 -0.0001015 4.557e-05 -0.02578 -7.653e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6529 0.04369 -0.01694 0.2426 0.9598 0.9799 0.7485 0.8716 0.953 0.6874 ] Network output: [ -0.005459 0.9365 1.035 6.265e-05 -2.813e-05 0.03956 4.723e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06915 0.04339 0.06187 0.04378 0.9767 0.983 0.07091 0.9474 0.9692 0.09154 ] Network output: [ 0.1153 -0.2903 1.111 -0.0008335 0.0003742 0.9453 -0.0006281 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7395 0.5273 0.448 0.4314 0.9644 0.9828 0.7434 0.8842 0.96 0.6884 ] Network output: [ -0.06224 0.2273 0.9124 0.0016 -0.0007184 0.9913 0.001206 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6387 0.6043 0.3953 0.2309 0.98 0.9865 0.6393 0.9557 0.9732 0.4271 ] Network output: [ -0.1082 0.2771 0.8709 0.0001185 -5.321e-05 1.069 8.934e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6622 0.6556 0.4464 0.1759 0.977 0.9845 0.6623 0.9481 0.9684 0.4538 ] Network output: [ 0.05744 0.7913 0.1138 -0.0009275 0.0004164 0.9763 -0.000699 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05554 Epoch 1167 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02484 1.004 0.99 -1.659e-05 7.447e-06 -0.04359 -1.25e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03613 -0.0009263 0.02953 0.02398 0.92 0.9324 0.07161 0.8508 0.8826 0.1658 ] Network output: [ 0.911 0.09694 0.01919 -0.0007976 0.0003581 0.05855 -0.0006012 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6496 0.07696 0.03656 0.2518 0.9597 0.9799 0.744 0.8721 0.9532 0.6902 ] Network output: [ -0.006386 0.9303 1.04 3.121e-05 -1.401e-05 0.04243 2.354e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06976 0.04597 0.06771 0.04465 0.9768 0.9832 0.07152 0.9482 0.9698 0.09463 ] Network output: [ 0.09355 -0.2795 1.116 -0.001039 0.0004662 0.9718 -0.0007826 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7433 0.5456 0.4689 0.4213 0.9645 0.9829 0.7472 0.8846 0.9602 0.6874 ] Network output: [ -0.04409 0.2465 0.8627 0.001711 -0.0007681 0.986 0.001289 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6442 0.6123 0.3964 0.2142 0.9801 0.9866 0.6448 0.956 0.9733 0.425 ] Network output: [ -0.08875 0.3115 0.8113 7.115e-05 -3.194e-05 1.055 5.362e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6658 0.6597 0.4456 0.1501 0.9771 0.9845 0.6659 0.9482 0.9684 0.4524 ] Network output: [ 0.0668 0.8127 0.08278 -0.00109 0.0004892 0.9665 -0.0008213 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05801 Epoch 1168 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0316 0.9876 0.9986 0.0001633 -7.329e-05 -0.04872 0.000123 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03607 -0.00167 0.02764 0.0257 0.9201 0.9325 0.07162 0.8508 0.8826 0.1675 ] Network output: [ 0.9564 0.0503 0.01388 -0.0002988 0.0001342 0.02187 -0.0002252 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6501 0.05854 0.01591 0.2655 0.9598 0.98 0.7448 0.8722 0.9533 0.693 ] Network output: [ -0.00659 0.9158 1.056 0.000123 -5.524e-05 0.04165 9.274e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06909 0.04429 0.06678 0.04876 0.9768 0.9832 0.07084 0.9481 0.9698 0.09536 ] Network output: [ 0.1058 -0.3551 1.179 -0.0006217 0.0002791 0.9616 -0.0004685 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7422 0.5329 0.4606 0.4527 0.9646 0.9829 0.746 0.8847 0.9603 0.6916 ] Network output: [ -0.05844 0.1882 0.9414 0.001891 -0.0008488 0.9949 0.001425 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6409 0.607 0.3996 0.2466 0.9802 0.9866 0.6415 0.956 0.9735 0.4302 ] Network output: [ -0.09912 0.2438 0.8858 0.0005084 -0.0002282 1.071 0.0003831 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6657 0.6591 0.4462 0.1916 0.9772 0.9846 0.6658 0.9483 0.9686 0.4534 ] Network output: [ 0.06647 0.7682 0.1202 -0.0005833 0.0002619 0.9762 -0.0004396 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05634 Epoch 1169 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03762 1.013 0.9659 0.0001116 -5.01e-05 -0.05413 8.412e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03639 -0.002174 0.02392 0.02278 0.9202 0.9325 0.07234 0.8505 0.8824 0.165 ] Network output: [ 1.014 0.119 -0.1231 -0.0001256 5.639e-05 -0.02378 -9.468e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6528 0.04478 -0.01512 0.2411 0.9598 0.9799 0.7483 0.8716 0.953 0.687 ] Network output: [ -0.005554 0.9378 1.034 5.479e-05 -2.46e-05 0.03966 4.13e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06914 0.04347 0.06184 0.04335 0.9767 0.983 0.0709 0.9475 0.9692 0.09129 ] Network output: [ 0.1143 -0.2851 1.107 -0.0008801 0.0003951 0.9456 -0.0006633 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7394 0.528 0.4489 0.4284 0.9644 0.9828 0.7432 0.8842 0.96 0.6878 ] Network output: [ -0.06115 0.2297 0.9078 0.001594 -0.0007155 0.9913 0.001201 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6392 0.605 0.3953 0.2286 0.98 0.9865 0.6399 0.9557 0.9732 0.4268 ] Network output: [ -0.1072 0.2806 0.8663 0.0001047 -4.699e-05 1.068 7.889e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6624 0.6558 0.4462 0.173 0.9771 0.9845 0.6625 0.9481 0.9684 0.4535 ] Network output: [ 0.05721 0.7945 0.1117 -0.0009487 0.0004259 0.9756 -0.000715 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05538 Epoch 1170 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02428 1.003 0.9919 -1.686e-05 7.567e-06 -0.04313 -1.27e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03608 -0.0009056 0.02976 0.02409 0.9201 0.9325 0.07147 0.8509 0.8826 0.1656 ] Network output: [ 0.9079 0.09265 0.02733 -0.0008058 0.0003618 0.06089 -0.0006073 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6493 0.07765 0.03891 0.2526 0.9598 0.98 0.7436 0.8721 0.9532 0.6903 ] Network output: [ -0.006518 0.9294 1.041 3.345e-05 -1.502e-05 0.04257 2.522e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0697 0.04598 0.06788 0.04478 0.9769 0.9832 0.07146 0.9483 0.9699 0.09464 ] Network output: [ 0.09297 -0.2831 1.121 -0.001041 0.0004672 0.9723 -0.0007844 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7433 0.5458 0.4699 0.4218 0.9646 0.9829 0.7471 0.8846 0.9602 0.6873 ] Network output: [ -0.04368 0.243 0.8647 0.001734 -0.0007783 0.9867 0.001307 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6446 0.6127 0.3968 0.215 0.9802 0.9866 0.6453 0.956 0.9734 0.4253 ] Network output: [ -0.08799 0.3088 0.8123 0.0001065 -4.78e-05 1.055 8.024e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6661 0.66 0.4455 0.1508 0.9772 0.9845 0.6663 0.9482 0.9684 0.4522 ] Network output: [ 0.06706 0.8119 0.08328 -0.001061 0.0004763 0.9664 -0.0007995 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05792 Epoch 1171 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03215 0.9885 0.9971 0.0001667 -7.485e-05 -0.04919 0.0001257 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03606 -0.001745 0.02732 0.02554 0.9202 0.9326 0.07158 0.8508 0.8827 0.1671 ] Network output: [ 0.9627 0.05178 0.005069 -0.0002554 0.0001147 0.01681 -0.0001925 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6502 0.05667 0.01331 0.264 0.9599 0.98 0.7449 0.8722 0.9533 0.6925 ] Network output: [ -0.006609 0.9167 1.055 0.0001212 -5.44e-05 0.04151 9.134e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06901 0.04412 0.06632 0.04844 0.9769 0.9832 0.07075 0.9481 0.9698 0.09495 ] Network output: [ 0.1068 -0.3543 1.178 -0.0006359 0.0002855 0.9596 -0.0004792 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7417 0.5319 0.4597 0.4518 0.9646 0.9829 0.7456 0.8847 0.9603 0.6913 ] Network output: [ -0.05922 0.1867 0.9437 0.001881 -0.0008445 0.9957 0.001418 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6409 0.6069 0.3997 0.2468 0.9802 0.9866 0.6416 0.956 0.9735 0.4304 ] Network output: [ -0.0999 0.2423 0.8882 0.0005121 -0.0002299 1.071 0.0003859 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6656 0.659 0.4461 0.1921 0.9772 0.9846 0.6657 0.9483 0.9686 0.4533 ] Network output: [ 0.06564 0.768 0.1218 -0.0005731 0.0002573 0.9765 -0.0004319 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05592 Epoch 1172 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03689 1.014 0.966 9.862e-05 -4.428e-05 -0.05356 7.433e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03636 -0.002126 0.02413 0.02265 0.9202 0.9326 0.07224 0.8505 0.8824 0.1646 ] Network output: [ 1.01 0.1204 -0.1206 -0.0001515 6.8e-05 -0.02108 -0.0001142 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6527 0.04607 -0.01286 0.24 0.9598 0.98 0.748 0.8716 0.953 0.6866 ] Network output: [ -0.005663 0.9387 1.033 4.817e-05 -2.163e-05 0.03978 3.631e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06913 0.04355 0.06188 0.04301 0.9767 0.9831 0.07088 0.9475 0.9693 0.09109 ] Network output: [ 0.1131 -0.281 1.105 -0.000923 0.0004144 0.9461 -0.0006956 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7393 0.5288 0.45 0.4258 0.9644 0.9829 0.7431 0.8843 0.9601 0.6873 ] Network output: [ -0.06003 0.2316 0.9036 0.001591 -0.0007142 0.9914 0.001199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6398 0.6057 0.3953 0.2266 0.9801 0.9865 0.6404 0.9557 0.9732 0.4266 ] Network output: [ -0.1062 0.2838 0.8618 9.524e-05 -4.276e-05 1.067 7.179e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6626 0.6561 0.446 0.1704 0.9771 0.9845 0.6627 0.9482 0.9684 0.4532 ] Network output: [ 0.05711 0.7974 0.1096 -0.0009654 0.0004334 0.9749 -0.0007276 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05519 Epoch 1173 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02392 1.001 0.9935 -1.467e-05 6.586e-06 -0.04283 -1.105e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03604 -0.0009035 0.0299 0.02417 0.9202 0.9325 0.07135 0.851 0.8827 0.1655 ] Network output: [ 0.9064 0.08866 0.03331 -0.0008028 0.0003604 0.062 -0.000605 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6492 0.07785 0.04044 0.2532 0.9598 0.98 0.7432 0.8722 0.9533 0.6903 ] Network output: [ -0.006634 0.9287 1.042 3.63e-05 -1.63e-05 0.04266 2.737e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06964 0.04595 0.06797 0.0449 0.9769 0.9832 0.07139 0.9484 0.9699 0.0946 ] Network output: [ 0.09268 -0.2868 1.125 -0.001041 0.0004671 0.9724 -0.0007842 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7432 0.5457 0.4705 0.4223 0.9646 0.983 0.747 0.8846 0.9602 0.6871 ] Network output: [ -0.04356 0.2393 0.8675 0.001754 -0.0007873 0.9875 0.001322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.645 0.613 0.3971 0.2159 0.9802 0.9866 0.6456 0.9561 0.9734 0.4256 ] Network output: [ -0.08753 0.3058 0.8141 0.0001408 -6.322e-05 1.056 0.0001061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6664 0.6603 0.4453 0.1519 0.9772 0.9846 0.6666 0.9483 0.9684 0.452 ] Network output: [ 0.06718 0.8109 0.08414 -0.001031 0.0004628 0.9664 -0.0007769 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05774 Epoch 1174 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0326 0.9896 0.9955 0.0001682 -7.551e-05 -0.04958 0.0001268 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03605 -0.00181 0.02703 0.02535 0.9202 0.9326 0.07154 0.8508 0.8827 0.1668 ] Network output: [ 0.9683 0.05379 -0.003548 -0.0002183 9.799e-05 0.01229 -0.0001645 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6504 0.05505 0.01101 0.2624 0.9599 0.98 0.745 0.8721 0.9533 0.692 ] Network output: [ -0.006628 0.9179 1.055 0.0001185 -5.318e-05 0.04137 8.928e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06893 0.04397 0.06588 0.04807 0.9769 0.9832 0.07068 0.948 0.9698 0.09453 ] Network output: [ 0.1076 -0.3525 1.177 -0.0006553 0.0002942 0.9579 -0.0004938 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7413 0.5309 0.459 0.4505 0.9646 0.983 0.7451 0.8847 0.9603 0.6909 ] Network output: [ -0.05982 0.186 0.945 0.001869 -0.0008391 0.9963 0.001409 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.641 0.6069 0.3998 0.2466 0.9802 0.9866 0.6417 0.956 0.9735 0.4306 ] Network output: [ -0.1005 0.2417 0.8896 0.0005094 -0.0002287 1.072 0.0003839 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6655 0.659 0.4461 0.192 0.9772 0.9846 0.6657 0.9483 0.9687 0.4532 ] Network output: [ 0.06483 0.7685 0.1228 -0.0005705 0.0002561 0.9767 -0.00043 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05551 Epoch 1175 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03609 1.015 0.9664 8.698e-05 -3.905e-05 -0.05293 6.556e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03633 -0.002073 0.02437 0.02257 0.9203 0.9326 0.07212 0.8506 0.8825 0.1643 ] Network output: [ 1.006 0.1209 -0.1164 -0.0001783 8.006e-05 -0.01784 -0.0001344 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6525 0.04749 -0.01026 0.2391 0.9598 0.98 0.7476 0.8717 0.953 0.6863 ] Network output: [ -0.005784 0.9394 1.032 4.28e-05 -1.922e-05 0.03993 3.227e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06911 0.04364 0.06198 0.04273 0.9768 0.9831 0.07086 0.9476 0.9693 0.09095 ] Network output: [ 0.1119 -0.2778 1.103 -0.000962 0.0004319 0.9468 -0.000725 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7392 0.5296 0.4512 0.4235 0.9645 0.9829 0.7431 0.8843 0.9601 0.6869 ] Network output: [ -0.05893 0.2329 0.9 0.001591 -0.0007142 0.9915 0.001199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6404 0.6064 0.3954 0.2247 0.9801 0.9865 0.641 0.9558 0.9732 0.4265 ] Network output: [ -0.105 0.2865 0.8575 8.989e-05 -4.035e-05 1.066 6.775e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6629 0.6564 0.4458 0.168 0.9771 0.9845 0.663 0.9482 0.9685 0.453 ] Network output: [ 0.05714 0.8 0.1076 -0.0009779 0.000439 0.9742 -0.000737 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05496 Epoch 1176 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02373 1 0.9948 -1.033e-05 4.637e-06 -0.04266 -7.78e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.036 -0.0009176 0.02997 0.02424 0.9202 0.9326 0.07124 0.851 0.8828 0.1653 ] Network output: [ 0.9062 0.08499 0.03733 -0.0007898 0.0003546 0.06203 -0.0005953 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.649 0.07762 0.04125 0.2536 0.9598 0.98 0.7429 0.8722 0.9533 0.6903 ] Network output: [ -0.006738 0.9279 1.043 3.966e-05 -1.781e-05 0.04272 2.99e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06956 0.04589 0.06798 0.04499 0.9769 0.9832 0.07131 0.9484 0.97 0.09453 ] Network output: [ 0.09264 -0.2906 1.129 -0.001039 0.0004663 0.9722 -0.0007827 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.743 0.5453 0.4709 0.4229 0.9646 0.983 0.7468 0.8847 0.9603 0.6871 ] Network output: [ -0.04369 0.2354 0.8709 0.001771 -0.0007951 0.9884 0.001335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6453 0.6133 0.3975 0.217 0.9802 0.9866 0.6459 0.9561 0.9734 0.4259 ] Network output: [ -0.08733 0.3026 0.8165 0.0001738 -7.801e-05 1.056 0.000131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6667 0.6606 0.4452 0.1531 0.9772 0.9846 0.6668 0.9483 0.9685 0.4518 ] Network output: [ 0.0672 0.8097 0.08527 -0.001 0.0004491 0.9666 -0.000754 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05747 Epoch 1177 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03296 0.9908 0.9939 0.000168 -7.544e-05 -0.0499 0.0001266 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03604 -0.001865 0.02678 0.02515 0.9203 0.9327 0.07149 0.8509 0.8827 0.1664 ] Network output: [ 0.9733 0.05621 -0.01183 -0.000187 8.397e-05 0.008293 -0.000141 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6505 0.05365 0.009026 0.2607 0.9599 0.98 0.745 0.8721 0.9533 0.6915 ] Network output: [ -0.006651 0.9191 1.053 0.0001151 -5.168e-05 0.04125 8.676e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06887 0.04384 0.06546 0.04767 0.9769 0.9832 0.0706 0.948 0.9697 0.09411 ] Network output: [ 0.1083 -0.35 1.174 -0.000679 0.0003048 0.9563 -0.0005117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7409 0.5301 0.4584 0.4489 0.9646 0.983 0.7447 0.8847 0.9603 0.6906 ] Network output: [ -0.06026 0.1859 0.9454 0.001855 -0.000833 0.9968 0.001398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6412 0.6069 0.3998 0.2459 0.9802 0.9866 0.6418 0.956 0.9735 0.4307 ] Network output: [ -0.1011 0.242 0.89 0.0005019 -0.0002253 1.072 0.0003782 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6655 0.6589 0.446 0.1914 0.9772 0.9846 0.6656 0.9484 0.9687 0.4532 ] Network output: [ 0.06407 0.7697 0.1231 -0.0005741 0.0002577 0.9768 -0.0004327 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05509 Epoch 1178 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03525 1.015 0.9673 7.671e-05 -3.444e-05 -0.05225 5.782e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03629 -0.002017 0.02465 0.02251 0.9203 0.9327 0.07199 0.8507 0.8825 0.1639 ] Network output: [ 1.002 0.1207 -0.1108 -0.0002054 9.224e-05 -0.01424 -0.0001549 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6523 0.04899 -0.007405 0.2386 0.9599 0.98 0.7472 0.8717 0.9531 0.6861 ] Network output: [ -0.005916 0.9397 1.032 3.861e-05 -1.734e-05 0.04009 2.911e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06909 0.04372 0.06212 0.04251 0.9768 0.9831 0.07083 0.9476 0.9693 0.09085 ] Network output: [ 0.1106 -0.2755 1.103 -0.0009975 0.0004478 0.9477 -0.0007517 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7392 0.5303 0.4525 0.4215 0.9645 0.9829 0.743 0.8843 0.9601 0.6865 ] Network output: [ -0.05786 0.2337 0.8968 0.001594 -0.0007154 0.9917 0.001201 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.641 0.6071 0.3956 0.2231 0.9801 0.9865 0.6416 0.9558 0.9733 0.4265 ] Network output: [ -0.1039 0.2889 0.8535 8.821e-05 -3.96e-05 1.066 6.648e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6632 0.6567 0.4456 0.1657 0.9771 0.9846 0.6633 0.9483 0.9685 0.4528 ] Network output: [ 0.05727 0.8023 0.1057 -0.0009867 0.000443 0.9735 -0.0007436 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05471 Epoch 1179 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0237 0.9994 0.9958 -4.296e-06 1.927e-06 -0.04261 -3.229e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03597 -0.000945 0.02998 0.02429 0.9203 0.9326 0.07114 0.8511 0.8828 0.1651 ] Network output: [ 0.9072 0.08165 0.03963 -0.0007688 0.0003452 0.06116 -0.0005795 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6489 0.07704 0.04147 0.2539 0.9599 0.98 0.7427 0.8723 0.9534 0.6903 ] Network output: [ -0.006833 0.9273 1.044 4.335e-05 -1.947e-05 0.04275 3.268e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06948 0.04579 0.06792 0.04505 0.9769 0.9833 0.07122 0.9484 0.97 0.09442 ] Network output: [ 0.0928 -0.2943 1.133 -0.001036 0.000465 0.9717 -0.0007806 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7428 0.5447 0.471 0.4236 0.9647 0.983 0.7466 0.8847 0.9603 0.687 ] Network output: [ -0.04402 0.2314 0.8746 0.001786 -0.0008017 0.9893 0.001346 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6455 0.6134 0.3978 0.2182 0.9802 0.9866 0.6461 0.9561 0.9735 0.4262 ] Network output: [ -0.08734 0.2992 0.8193 0.000205 -9.203e-05 1.057 0.0001545 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6669 0.6608 0.4451 0.1545 0.9773 0.9846 0.6671 0.9484 0.9685 0.4517 ] Network output: [ 0.06712 0.8085 0.08661 -0.00097 0.0004355 0.9668 -0.000731 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05713 Epoch 1180 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03323 0.9921 0.9923 0.0001665 -7.477e-05 -0.05014 0.0001255 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03603 -0.001912 0.02655 0.02494 0.9204 0.9327 0.07144 0.8509 0.8827 0.166 ] Network output: [ 0.9776 0.05891 -0.01966 -0.0001612 7.237e-05 0.004806 -0.0001215 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6506 0.05245 0.007343 0.2588 0.9599 0.98 0.745 0.8721 0.9533 0.691 ] Network output: [ -0.006679 0.9204 1.052 0.0001113 -4.998e-05 0.04115 8.391e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0688 0.04372 0.06507 0.04723 0.9769 0.9832 0.07053 0.948 0.9697 0.09369 ] Network output: [ 0.1088 -0.347 1.172 -0.0007061 0.000317 0.9549 -0.0005321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7405 0.5295 0.4579 0.447 0.9646 0.983 0.7443 0.8847 0.9603 0.6902 ] Network output: [ -0.06056 0.1863 0.945 0.001841 -0.0008266 0.9973 0.001388 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6413 0.607 0.3999 0.245 0.9802 0.9866 0.642 0.956 0.9735 0.4307 ] Network output: [ -0.1014 0.2429 0.8897 0.0004908 -0.0002204 1.072 0.0003699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6655 0.6589 0.4459 0.1904 0.9772 0.9846 0.6656 0.9484 0.9687 0.4531 ] Network output: [ 0.06334 0.7713 0.123 -0.0005825 0.0002615 0.9767 -0.000439 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05468 Epoch 1181 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03439 1.015 0.9684 6.769e-05 -3.039e-05 -0.05155 5.102e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03625 -0.00196 0.02494 0.02248 0.9204 0.9327 0.07186 0.8507 0.8826 0.1637 ] Network output: [ 0.997 0.1198 -0.1043 -0.0002323 0.0001043 -0.01043 -0.0001751 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6521 0.05051 -0.00441 0.2382 0.9599 0.98 0.7468 0.8717 0.9531 0.686 ] Network output: [ -0.006055 0.9399 1.032 3.543e-05 -1.591e-05 0.04027 2.671e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06906 0.04381 0.0623 0.04233 0.9768 0.9831 0.0708 0.9477 0.9694 0.09079 ] Network output: [ 0.1092 -0.2739 1.103 -0.00103 0.0004623 0.9486 -0.000776 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7391 0.531 0.4538 0.4198 0.9645 0.9829 0.7429 0.8844 0.9601 0.6862 ] Network output: [ -0.05683 0.2341 0.8942 0.001598 -0.0007176 0.9919 0.001205 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6416 0.6078 0.3959 0.2217 0.9801 0.9865 0.6422 0.9558 0.9733 0.4265 ] Network output: [ -0.1027 0.2909 0.8498 8.98e-05 -4.031e-05 1.065 6.768e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6635 0.6571 0.4455 0.1636 0.9772 0.9846 0.6637 0.9483 0.9685 0.4526 ] Network output: [ 0.05748 0.8044 0.1039 -0.000992 0.0004453 0.9728 -0.0007476 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05445 Epoch 1182 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02378 0.9986 0.9965 2.906e-06 -1.307e-06 -0.04265 2.198e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03593 -0.0009827 0.02994 0.02431 0.9203 0.9327 0.07104 0.8511 0.8829 0.1649 ] Network output: [ 0.9091 0.07864 0.04051 -0.0007418 0.000333 0.05957 -0.0005591 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6489 0.07617 0.0412 0.2541 0.9599 0.98 0.7425 0.8723 0.9534 0.6903 ] Network output: [ -0.006922 0.9268 1.044 4.719e-05 -2.119e-05 0.04276 3.558e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06939 0.04568 0.06781 0.04509 0.977 0.9833 0.07113 0.9485 0.97 0.09428 ] Network output: [ 0.09312 -0.2979 1.136 -0.001033 0.0004636 0.971 -0.0007783 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7426 0.544 0.471 0.4241 0.9647 0.983 0.7464 0.8847 0.9603 0.687 ] Network output: [ -0.04451 0.2274 0.8786 0.001798 -0.0008074 0.9903 0.001355 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6457 0.6135 0.3982 0.2193 0.9802 0.9866 0.6463 0.9561 0.9735 0.4266 ] Network output: [ -0.08752 0.2958 0.8223 0.0002344 -0.0001052 1.058 0.0001767 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6671 0.6609 0.445 0.1559 0.9773 0.9846 0.6672 0.9485 0.9686 0.4516 ] Network output: [ 0.06696 0.8072 0.08807 -0.0009401 0.0004221 0.967 -0.0007085 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05675 Epoch 1183 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03342 0.9934 0.9907 0.0001639 -7.36e-05 -0.05031 0.0001236 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03602 -0.001951 0.02636 0.02473 0.9204 0.9327 0.07139 0.8509 0.8828 0.1656 ] Network output: [ 0.9814 0.06176 -0.02695 -0.0001402 6.297e-05 0.001797 -0.0001057 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6507 0.05144 0.005954 0.257 0.96 0.9801 0.745 0.8721 0.9533 0.6905 ] Network output: [ -0.006715 0.9218 1.051 0.0001073 -4.815e-05 0.04107 8.084e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06874 0.04362 0.06471 0.04678 0.9769 0.9832 0.07047 0.948 0.9697 0.09329 ] Network output: [ 0.1091 -0.3436 1.169 -0.0007357 0.0003303 0.9536 -0.0005544 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7401 0.5289 0.4575 0.445 0.9646 0.983 0.7439 0.8847 0.9603 0.6898 ] Network output: [ -0.06072 0.187 0.9441 0.001827 -0.0008202 0.9978 0.001377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6415 0.6072 0.3999 0.2439 0.9802 0.9866 0.6422 0.956 0.9735 0.4308 ] Network output: [ -0.1017 0.2443 0.8887 0.0004777 -0.0002145 1.072 0.00036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6655 0.6589 0.4458 0.189 0.9773 0.9847 0.6656 0.9485 0.9688 0.453 ] Network output: [ 0.06267 0.7732 0.1225 -0.0005941 0.0002667 0.9765 -0.0004478 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0543 Epoch 1184 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03354 1.014 0.9697 5.979e-05 -2.684e-05 -0.05084 4.507e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0362 -0.001904 0.02524 0.02246 0.9204 0.9327 0.07173 0.8508 0.8826 0.1634 ] Network output: [ 0.9921 0.1184 -0.09718 -0.0002585 0.0001161 -0.006566 -0.0001948 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6518 0.05199 -0.001358 0.238 0.9599 0.98 0.7463 0.8718 0.9532 0.6859 ] Network output: [ -0.006199 0.9399 1.032 3.31e-05 -1.486e-05 0.04044 2.495e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06902 0.04388 0.06249 0.04219 0.9768 0.9831 0.07076 0.9478 0.9694 0.09075 ] Network output: [ 0.1079 -0.2729 1.103 -0.001059 0.0004754 0.9495 -0.000798 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7391 0.5317 0.4552 0.4183 0.9646 0.9829 0.7429 0.8844 0.9602 0.6859 ] Network output: [ -0.05586 0.2341 0.892 0.001605 -0.0007207 0.9922 0.00121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6422 0.6084 0.3961 0.2204 0.9801 0.9866 0.6428 0.9559 0.9733 0.4265 ] Network output: [ -0.1015 0.2925 0.8464 9.425e-05 -4.231e-05 1.064 7.103e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6639 0.6574 0.4453 0.1616 0.9772 0.9846 0.664 0.9484 0.9686 0.4524 ] Network output: [ 0.05773 0.8062 0.1021 -0.0009942 0.0004463 0.9721 -0.0007493 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05421 Epoch 1185 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02394 0.9979 0.997 1.074e-05 -4.822e-06 -0.04275 8.1e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0359 -0.001028 0.02986 0.02432 0.9204 0.9327 0.07095 0.8512 0.8829 0.1647 ] Network output: [ 0.9117 0.07597 0.04025 -0.0007106 0.000319 0.05744 -0.0005356 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6488 0.0751 0.04058 0.2541 0.9599 0.9801 0.7423 0.8723 0.9534 0.6903 ] Network output: [ -0.007006 0.9264 1.045 5.101e-05 -2.29e-05 0.04276 3.845e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0693 0.04555 0.06766 0.0451 0.977 0.9833 0.07104 0.9485 0.97 0.09412 ] Network output: [ 0.09355 -0.3013 1.14 -0.00103 0.0004624 0.9701 -0.0007763 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7423 0.5431 0.4709 0.4247 0.9647 0.983 0.7461 0.8848 0.9603 0.687 ] Network output: [ -0.0451 0.2235 0.8828 0.001809 -0.0008121 0.9913 0.001363 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6459 0.6135 0.3986 0.2204 0.9803 0.9866 0.6465 0.9562 0.9735 0.427 ] Network output: [ -0.08781 0.2924 0.8255 0.0002619 -0.0001176 1.059 0.0001974 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6673 0.6611 0.4449 0.1572 0.9773 0.9847 0.6674 0.9485 0.9686 0.4516 ] Network output: [ 0.06675 0.8059 0.08962 -0.0009111 0.000409 0.9673 -0.0006867 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05634 Epoch 1186 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03353 0.9947 0.9893 0.0001604 -7.201e-05 -0.05042 0.0001209 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03601 -0.001984 0.0262 0.02451 0.9205 0.9328 0.07133 0.851 0.8828 0.1652 ] Network output: [ 0.9846 0.06465 -0.03363 -0.0001237 5.554e-05 -0.0007691 -9.325e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6507 0.05058 0.004844 0.2551 0.96 0.9801 0.7449 0.8721 0.9533 0.6901 ] Network output: [ -0.006759 0.9231 1.05 0.000103 -4.625e-05 0.041 7.765e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06868 0.04353 0.06437 0.04633 0.9769 0.9832 0.0704 0.948 0.9697 0.09289 ] Network output: [ 0.1093 -0.3399 1.166 -0.0007672 0.0003444 0.9524 -0.0005781 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7398 0.5284 0.4573 0.4428 0.9647 0.983 0.7435 0.8847 0.9603 0.6895 ] Network output: [ -0.06078 0.1879 0.9428 0.001813 -0.0008141 0.9982 0.001367 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6418 0.6073 0.3999 0.2425 0.9802 0.9866 0.6424 0.956 0.9735 0.4307 ] Network output: [ -0.1019 0.2461 0.8873 0.0004636 -0.0002081 1.072 0.0003494 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6655 0.6589 0.4457 0.1874 0.9773 0.9847 0.6656 0.9485 0.9688 0.4529 ] Network output: [ 0.06204 0.7754 0.1218 -0.0006077 0.0002728 0.9762 -0.000458 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05394 Epoch 1187 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03269 1.014 0.9711 5.281e-05 -2.371e-05 -0.05014 3.98e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03616 -0.001851 0.02555 0.02246 0.9204 0.9328 0.07159 0.8509 0.8827 0.1631 ] Network output: [ 0.9873 0.1167 -0.08978 -0.0002836 0.0001273 -0.002758 -0.0002138 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6516 0.05339 0.001674 0.2379 0.96 0.98 0.7458 0.8719 0.9532 0.6859 ] Network output: [ -0.006345 0.9397 1.032 3.143e-05 -1.411e-05 0.04062 2.37e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06899 0.04395 0.06269 0.04208 0.9769 0.9832 0.07072 0.9478 0.9695 0.09072 ] Network output: [ 0.1067 -0.2725 1.104 -0.001086 0.0004874 0.9504 -0.0008182 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.739 0.5323 0.4565 0.4169 0.9646 0.983 0.7428 0.8845 0.9602 0.6857 ] Network output: [ -0.05494 0.2337 0.8902 0.001614 -0.0007244 0.9926 0.001216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6427 0.6091 0.3964 0.2192 0.9802 0.9866 0.6434 0.9559 0.9733 0.4266 ] Network output: [ -0.1004 0.2939 0.8433 0.0001011 -4.54e-05 1.064 7.622e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6642 0.6578 0.4452 0.1598 0.9772 0.9846 0.6643 0.9485 0.9686 0.4522 ] Network output: [ 0.05801 0.8078 0.1006 -0.0009936 0.0004461 0.9715 -0.0007488 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05398 Epoch 1188 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02414 0.9973 0.9974 1.872e-05 -8.407e-06 -0.04288 1.412e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03588 -0.001078 0.02975 0.02432 0.9204 0.9328 0.07086 0.8512 0.883 0.1645 ] Network output: [ 0.9148 0.07363 0.03911 -0.000677 0.000304 0.05496 -0.0005103 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6488 0.07387 0.03972 0.2539 0.96 0.9801 0.7422 0.8724 0.9535 0.6902 ] Network output: [ -0.007088 0.926 1.046 5.464e-05 -2.453e-05 0.04274 4.119e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06921 0.04541 0.06748 0.04508 0.977 0.9833 0.07094 0.9485 0.97 0.09394 ] Network output: [ 0.09405 -0.3045 1.143 -0.001028 0.0004617 0.9691 -0.000775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.742 0.5421 0.4706 0.4251 0.9648 0.983 0.7457 0.8848 0.9604 0.687 ] Network output: [ -0.04576 0.2197 0.8869 0.001818 -0.0008162 0.9923 0.00137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.646 0.6135 0.3989 0.2215 0.9803 0.9867 0.6466 0.9562 0.9735 0.4274 ] Network output: [ -0.08817 0.2891 0.8287 0.0002875 -0.0001291 1.06 0.0002167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6674 0.6612 0.4449 0.1584 0.9773 0.9847 0.6676 0.9486 0.9687 0.4515 ] Network output: [ 0.06649 0.8047 0.0912 -0.0008833 0.0003965 0.9676 -0.0006657 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05593 Epoch 1189 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03356 0.996 0.9879 0.0001561 -7.009e-05 -0.05046 0.0001177 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.036 -0.002011 0.02607 0.0243 0.9205 0.9328 0.07126 0.851 0.8829 0.1648 ] Network output: [ 0.9873 0.06751 -0.03965 -0.000111 4.985e-05 -0.002928 -8.37e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6507 0.04985 0.003992 0.2533 0.96 0.9801 0.7449 0.8721 0.9534 0.6896 ] Network output: [ -0.00681 0.9245 1.049 9.869e-05 -4.431e-05 0.04096 7.439e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06862 0.04344 0.06406 0.04587 0.9769 0.9832 0.07034 0.948 0.9697 0.09251 ] Network output: [ 0.1093 -0.3362 1.163 -0.0007998 0.000359 0.9514 -0.0006027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7394 0.5279 0.4571 0.4405 0.9647 0.983 0.7432 0.8847 0.9603 0.6891 ] Network output: [ -0.06074 0.189 0.9412 0.001801 -0.0008085 0.9985 0.001357 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.642 0.6075 0.3999 0.2411 0.9802 0.9866 0.6426 0.956 0.9735 0.4307 ] Network output: [ -0.102 0.248 0.8855 0.0004495 -0.0002018 1.072 0.0003388 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6655 0.6589 0.4456 0.1856 0.9773 0.9847 0.6656 0.9486 0.9688 0.4528 ] Network output: [ 0.06147 0.7777 0.1209 -0.0006219 0.0002792 0.9759 -0.0004687 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05362 Epoch 1190 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03187 1.013 0.9725 4.658e-05 -2.091e-05 -0.04945 3.511e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03611 -0.001801 0.02584 0.02246 0.9205 0.9328 0.07144 0.851 0.8828 0.1629 ] Network output: [ 0.9827 0.1148 -0.08235 -0.0003073 0.000138 0.0009028 -0.0002316 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6513 0.05469 0.004625 0.2379 0.96 0.9801 0.7454 0.8719 0.9532 0.686 ] Network output: [ -0.006489 0.9395 1.033 3.028e-05 -1.359e-05 0.0408 2.283e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06894 0.04401 0.06289 0.04199 0.9769 0.9832 0.07067 0.9479 0.9696 0.0907 ] Network output: [ 0.1055 -0.2724 1.106 -0.00111 0.0004984 0.9513 -0.0008366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.739 0.5327 0.4578 0.4157 0.9646 0.983 0.7427 0.8845 0.9602 0.6855 ] Network output: [ -0.05408 0.2331 0.8887 0.001623 -0.0007287 0.993 0.001223 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6433 0.6097 0.3967 0.2182 0.9802 0.9866 0.6439 0.9559 0.9733 0.4267 ] Network output: [ -0.09928 0.2949 0.8405 0.00011 -4.94e-05 1.064 8.294e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6646 0.6582 0.4451 0.1582 0.9773 0.9846 0.6647 0.9485 0.9686 0.452 ] Network output: [ 0.05831 0.8092 0.09921 -0.0009907 0.0004448 0.9709 -0.0007466 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05377 Epoch 1191 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02438 0.9968 0.9976 2.648e-05 -1.189e-05 -0.04304 1.996e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03585 -0.00113 0.02963 0.0243 0.9205 0.9328 0.07078 0.8513 0.883 0.1643 ] Network output: [ 0.9181 0.0716 0.03731 -0.0006426 0.0002885 0.05225 -0.0004843 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6487 0.07256 0.03869 0.2537 0.96 0.9801 0.742 0.8724 0.9535 0.6901 ] Network output: [ -0.007166 0.9258 1.046 5.798e-05 -2.603e-05 0.04271 4.371e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06911 0.04526 0.06728 0.04504 0.977 0.9833 0.07084 0.9485 0.9701 0.09374 ] Network output: [ 0.09459 -0.3074 1.146 -0.001028 0.0004615 0.968 -0.0007747 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7417 0.5411 0.4704 0.4254 0.9648 0.9831 0.7454 0.8848 0.9604 0.687 ] Network output: [ -0.04643 0.2161 0.8909 0.001826 -0.0008196 0.9934 0.001376 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6461 0.6135 0.3992 0.2225 0.9803 0.9867 0.6468 0.9562 0.9735 0.4278 ] Network output: [ -0.08858 0.286 0.8318 0.0003113 -0.0001397 1.061 0.0002346 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6676 0.6613 0.4448 0.1596 0.9774 0.9847 0.6677 0.9486 0.9687 0.4515 ] Network output: [ 0.06621 0.8035 0.09278 -0.0008568 0.0003846 0.9678 -0.0006457 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05554 Epoch 1192 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03353 0.9973 0.9867 0.0001512 -6.789e-05 -0.05045 0.000114 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03598 -0.002033 0.02596 0.02409 0.9206 0.9329 0.07119 0.8511 0.8829 0.1644 ] Network output: [ 0.9895 0.07026 -0.04501 -0.0001018 4.569e-05 -0.004719 -7.672e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6507 0.04924 0.003376 0.2515 0.9601 0.9801 0.7447 0.8721 0.9534 0.6892 ] Network output: [ -0.006869 0.9258 1.047 9.437e-05 -4.237e-05 0.04092 7.113e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06856 0.04337 0.06378 0.04543 0.9769 0.9833 0.07027 0.948 0.9697 0.09214 ] Network output: [ 0.1093 -0.3324 1.16 -0.0008329 0.0003739 0.9504 -0.0006277 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.739 0.5275 0.4571 0.4382 0.9647 0.983 0.7428 0.8847 0.9604 0.6887 ] Network output: [ -0.06063 0.1901 0.9395 0.00179 -0.0008034 0.9989 0.001349 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6423 0.6078 0.3999 0.2396 0.9802 0.9866 0.6429 0.956 0.9735 0.4307 ] Network output: [ -0.102 0.2501 0.8835 0.000436 -0.0001958 1.072 0.0003286 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6655 0.6589 0.4455 0.1836 0.9773 0.9847 0.6657 0.9486 0.9688 0.4526 ] Network output: [ 0.06096 0.78 0.1199 -0.0006361 0.0002856 0.9756 -0.0004794 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05332 Epoch 1193 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03108 1.013 0.974 4.099e-05 -1.84e-05 -0.04879 3.09e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03607 -0.001756 0.02613 0.02247 0.9205 0.9329 0.0713 0.8511 0.8828 0.1627 ] Network output: [ 0.9783 0.1128 -0.0751 -0.0003295 0.0001479 0.004348 -0.0002483 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.651 0.05587 0.007448 0.2379 0.96 0.9801 0.7449 0.872 0.9533 0.686 ] Network output: [ -0.006631 0.9393 1.033 2.951e-05 -1.325e-05 0.04097 2.225e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06889 0.04405 0.06307 0.0419 0.9769 0.9832 0.07061 0.948 0.9696 0.09067 ] Network output: [ 0.1043 -0.2726 1.107 -0.001133 0.0005085 0.9521 -0.0008537 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7389 0.5331 0.459 0.4146 0.9647 0.983 0.7426 0.8846 0.9603 0.6854 ] Network output: [ -0.05329 0.2322 0.8876 0.001634 -0.0007334 0.9934 0.001231 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6438 0.6103 0.397 0.2173 0.9802 0.9866 0.6444 0.956 0.9734 0.4268 ] Network output: [ -0.09824 0.2957 0.838 0.0001206 -5.412e-05 1.063 9.086e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6649 0.6585 0.445 0.1567 0.9773 0.9847 0.665 0.9486 0.9687 0.4519 ] Network output: [ 0.05861 0.8104 0.09802 -0.0009857 0.0004425 0.9703 -0.0007428 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05359 Epoch 1194 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02462 0.9965 0.9976 3.372e-05 -1.514e-05 -0.04321 2.542e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03582 -0.001184 0.0295 0.02426 0.9205 0.9329 0.07069 0.8514 0.8831 0.164 ] Network output: [ 0.9216 0.06988 0.03506 -0.0006083 0.0002731 0.04944 -0.0004585 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6487 0.07119 0.03759 0.2533 0.96 0.9801 0.7419 0.8725 0.9535 0.69 ] Network output: [ -0.007243 0.9257 1.046 6.097e-05 -2.737e-05 0.04267 4.596e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06902 0.0451 0.06705 0.04497 0.977 0.9833 0.07073 0.9485 0.9701 0.09351 ] Network output: [ 0.09515 -0.3099 1.149 -0.001029 0.000462 0.9668 -0.0007755 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7413 0.54 0.4701 0.4256 0.9648 0.9831 0.745 0.8849 0.9604 0.687 ] Network output: [ -0.04711 0.2127 0.8946 0.001832 -0.0008225 0.9944 0.001381 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6463 0.6135 0.3995 0.2233 0.9803 0.9867 0.6469 0.9562 0.9736 0.4281 ] Network output: [ -0.089 0.2831 0.8347 0.0003331 -0.0001496 1.062 0.0002511 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6677 0.6614 0.4447 0.1606 0.9774 0.9847 0.6678 0.9487 0.9688 0.4514 ] Network output: [ 0.06591 0.8024 0.09433 -0.0008317 0.0003734 0.9681 -0.0006268 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05516 Epoch 1195 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03345 0.9985 0.9856 0.0001459 -6.549e-05 -0.05039 0.0001099 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03596 -0.00205 0.02588 0.02389 0.9206 0.9329 0.07112 0.8511 0.8829 0.164 ] Network output: [ 0.9913 0.07285 -0.04969 -9.541e-05 4.284e-05 -0.006176 -7.193e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6507 0.04872 0.002974 0.2498 0.9601 0.9801 0.7446 0.8722 0.9534 0.6888 ] Network output: [ -0.006933 0.927 1.046 9.012e-05 -4.046e-05 0.0409 6.793e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06849 0.04329 0.06351 0.04499 0.977 0.9833 0.0702 0.948 0.9697 0.09179 ] Network output: [ 0.1092 -0.3288 1.157 -0.0008663 0.0003889 0.9496 -0.0006528 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7387 0.5271 0.4571 0.436 0.9647 0.983 0.7424 0.8847 0.9604 0.6883 ] Network output: [ -0.06046 0.1912 0.9376 0.00178 -0.0007989 0.9993 0.001341 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6426 0.608 0.3999 0.238 0.9802 0.9867 0.6432 0.956 0.9735 0.4306 ] Network output: [ -0.1019 0.2522 0.8813 0.0004237 -0.0001902 1.072 0.0003193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6656 0.659 0.4454 0.1817 0.9774 0.9847 0.6657 0.9487 0.9689 0.4525 ] Network output: [ 0.0605 0.7822 0.119 -0.0006494 0.0002915 0.9752 -0.0004894 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05306 Epoch 1196 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03034 1.012 0.9754 3.592e-05 -1.613e-05 -0.04816 2.708e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03602 -0.001716 0.02639 0.02247 0.9206 0.9329 0.07116 0.8511 0.8829 0.1624 ] Network output: [ 0.9742 0.1107 -0.06815 -0.0003498 0.000157 0.00753 -0.0002636 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6507 0.0569 0.01011 0.2379 0.96 0.9801 0.7444 0.8721 0.9533 0.686 ] Network output: [ -0.006768 0.939 1.033 2.905e-05 -1.304e-05 0.04112 2.19e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06884 0.04408 0.06323 0.04182 0.9769 0.9832 0.07055 0.9481 0.9697 0.09062 ] Network output: [ 0.1033 -0.273 1.109 -0.001154 0.000518 0.9527 -0.0008696 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7388 0.5334 0.4602 0.4136 0.9647 0.983 0.7425 0.8846 0.9603 0.6852 ] Network output: [ -0.05256 0.2311 0.8867 0.001645 -0.0007383 0.9939 0.001239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6443 0.6108 0.3973 0.2165 0.9802 0.9866 0.6449 0.956 0.9734 0.4269 ] Network output: [ -0.09728 0.2962 0.8358 0.0001323 -5.939e-05 1.063 9.97e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6652 0.6589 0.4448 0.1553 0.9773 0.9847 0.6654 0.9486 0.9687 0.4517 ] Network output: [ 0.0589 0.8114 0.09703 -0.0009789 0.0004395 0.9698 -0.0007377 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05344 Epoch 1197 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02485 0.9962 0.9976 4.026e-05 -1.808e-05 -0.04337 3.035e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0358 -0.001237 0.02936 0.02422 0.9206 0.9329 0.07061 0.8514 0.8831 0.1638 ] Network output: [ 0.925 0.06843 0.03252 -0.0005751 0.0002582 0.04663 -0.0004334 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6487 0.06982 0.03646 0.2529 0.9601 0.9801 0.7417 0.8725 0.9535 0.6899 ] Network output: [ -0.007316 0.9256 1.047 6.356e-05 -2.854e-05 0.04263 4.791e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06892 0.04495 0.06682 0.04487 0.977 0.9834 0.07063 0.9486 0.9701 0.09328 ] Network output: [ 0.0957 -0.3122 1.151 -0.001032 0.0004632 0.9656 -0.0007776 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7409 0.5389 0.4697 0.4256 0.9648 0.9831 0.7447 0.8849 0.9604 0.6869 ] Network output: [ -0.04775 0.2095 0.8982 0.001838 -0.000825 0.9954 0.001385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6464 0.6134 0.3998 0.224 0.9803 0.9867 0.647 0.9562 0.9736 0.4284 ] Network output: [ -0.08942 0.2805 0.8374 0.0003532 -0.0001586 1.062 0.0002662 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6678 0.6615 0.4447 0.1614 0.9774 0.9847 0.6679 0.9487 0.9688 0.4513 ] Network output: [ 0.06561 0.8014 0.09583 -0.0008083 0.0003629 0.9683 -0.0006091 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05481 Epoch 1198 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03331 0.9997 0.9846 0.0001402 -6.294e-05 -0.05029 0.0001057 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03594 -0.002064 0.02583 0.02371 0.9207 0.933 0.07104 0.8512 0.883 0.1636 ] Network output: [ 0.9927 0.07524 -0.05372 -9.151e-05 4.109e-05 -0.007337 -6.9e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6507 0.04827 0.002763 0.2482 0.9601 0.9801 0.7444 0.8722 0.9534 0.6884 ] Network output: [ -0.007002 0.9282 1.045 8.602e-05 -3.862e-05 0.04088 6.483e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06843 0.04322 0.06327 0.04458 0.977 0.9833 0.07013 0.9481 0.9697 0.09144 ] Network output: [ 0.1091 -0.3253 1.155 -0.0008993 0.0004037 0.9488 -0.0006778 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7383 0.5267 0.4571 0.4338 0.9647 0.983 0.742 0.8847 0.9604 0.688 ] Network output: [ -0.06024 0.1922 0.9358 0.001771 -0.0007951 0.9997 0.001335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6429 0.6083 0.3998 0.2365 0.9803 0.9867 0.6435 0.9561 0.9735 0.4305 ] Network output: [ -0.1018 0.2542 0.8791 0.0004127 -0.0001853 1.072 0.0003111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6656 0.659 0.4452 0.1797 0.9774 0.9847 0.6658 0.9487 0.9689 0.4523 ] Network output: [ 0.0601 0.7844 0.118 -0.0006614 0.0002969 0.9747 -0.0004985 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05283 Epoch 1199 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02963 1.012 0.9767 3.132e-05 -1.406e-05 -0.04756 2.361e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03598 -0.001681 0.02664 0.02248 0.9206 0.9329 0.07103 0.8512 0.883 0.1622 ] Network output: [ 0.9705 0.1087 -0.06164 -0.0003681 0.0001653 0.01042 -0.0002774 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6505 0.05778 0.01259 0.2379 0.9601 0.9801 0.744 0.8721 0.9534 0.686 ] Network output: [ -0.006898 0.9388 1.034 2.882e-05 -1.294e-05 0.04127 2.173e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06878 0.0441 0.06336 0.04174 0.977 0.9833 0.07049 0.9481 0.9697 0.09057 ] Network output: [ 0.1024 -0.2735 1.111 -0.001174 0.0005269 0.9533 -0.0008845 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7386 0.5335 0.4612 0.4126 0.9647 0.983 0.7424 0.8847 0.9603 0.685 ] Network output: [ -0.05189 0.2299 0.8861 0.001656 -0.0007433 0.9944 0.001248 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6448 0.6113 0.3975 0.2158 0.9802 0.9866 0.6454 0.956 0.9734 0.427 ] Network output: [ -0.09639 0.2966 0.8339 0.0001449 -6.504e-05 1.063 0.0001092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6656 0.6592 0.4447 0.1541 0.9774 0.9847 0.6657 0.9487 0.9687 0.4515 ] Network output: [ 0.05917 0.8122 0.09622 -0.0009706 0.0004357 0.9693 -0.0007315 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0533 Epoch 1200 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02506 0.9961 0.9975 4.6e-05 -2.065e-05 -0.04351 3.468e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03577 -0.001289 0.02923 0.02416 0.9206 0.9329 0.07052 0.8515 0.8832 0.1635 ] Network output: [ 0.9284 0.06723 0.02981 -0.0005436 0.000244 0.04387 -0.0004097 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6486 0.06846 0.03536 0.2524 0.9601 0.9801 0.7416 0.8725 0.9536 0.6898 ] Network output: [ -0.007387 0.9257 1.047 6.575e-05 -2.952e-05 0.04259 4.956e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06882 0.04479 0.06657 0.04476 0.9771 0.9834 0.07052 0.9486 0.9701 0.09302 ] Network output: [ 0.09623 -0.314 1.153 -0.001036 0.0004653 0.9645 -0.0007811 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7406 0.5378 0.4694 0.4255 0.9649 0.9831 0.7443 0.8849 0.9605 0.6869 ] Network output: [ -0.04836 0.2065 0.9014 0.001842 -0.0008271 0.9963 0.001388 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6465 0.6134 0.4 0.2246 0.9803 0.9867 0.6471 0.9562 0.9736 0.4287 ] Network output: [ -0.08983 0.2781 0.8399 0.0003715 -0.0001668 1.063 0.00028 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6679 0.6615 0.4446 0.1621 0.9774 0.9848 0.668 0.9488 0.9688 0.4512 ] Network output: [ 0.06531 0.8005 0.09725 -0.0007864 0.000353 0.9684 -0.0005927 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05448 Epoch 1201 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03313 1.001 0.9837 0.0001343 -6.031e-05 -0.05015 0.0001012 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03592 -0.002076 0.02579 0.02353 0.9207 0.933 0.07096 0.8512 0.883 0.1632 ] Network output: [ 0.9938 0.07742 -0.05711 -8.961e-05 4.024e-05 -0.008235 -6.756e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6506 0.04789 0.00272 0.2467 0.9601 0.9801 0.7442 0.8722 0.9534 0.688 ] Network output: [ -0.007074 0.9294 1.044 8.211e-05 -3.686e-05 0.04087 6.189e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06836 0.04315 0.06305 0.04418 0.977 0.9833 0.07006 0.9481 0.9697 0.0911 ] Network output: [ 0.1089 -0.322 1.152 -0.0009319 0.0004183 0.9481 -0.0007023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.738 0.5263 0.4572 0.4316 0.9648 0.9831 0.7417 0.8848 0.9604 0.6876 ] Network output: [ -0.05999 0.1931 0.9339 0.001764 -0.0007919 1 0.001329 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6432 0.6085 0.3998 0.235 0.9803 0.9867 0.6438 0.9561 0.9735 0.4304 ] Network output: [ -0.1016 0.2562 0.8769 0.0004033 -0.0001811 1.072 0.000304 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6657 0.6591 0.4451 0.1778 0.9774 0.9847 0.6658 0.9488 0.9689 0.4521 ] Network output: [ 0.05974 0.7864 0.1171 -0.0006719 0.0003016 0.9743 -0.0005064 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05263 Epoch 1202 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02898 1.011 0.978 2.713e-05 -1.218e-05 -0.047 2.046e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03593 -0.001652 0.02687 0.02248 0.9207 0.933 0.07089 0.8513 0.883 0.1619 ] Network output: [ 0.9672 0.1067 -0.05563 -0.0003843 0.0001725 0.013 -0.0002896 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6502 0.05851 0.01486 0.238 0.9601 0.9801 0.7435 0.8722 0.9534 0.686 ] Network output: [ -0.007021 0.9386 1.034 2.877e-05 -1.292e-05 0.04139 2.169e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06871 0.0441 0.06347 0.04166 0.977 0.9833 0.07041 0.9482 0.9698 0.09049 ] Network output: [ 0.1016 -0.2741 1.112 -0.001192 0.0005353 0.9537 -0.0008986 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7385 0.5336 0.4621 0.4118 0.9648 0.983 0.7422 0.8847 0.9604 0.6848 ] Network output: [ -0.05129 0.2286 0.8857 0.001667 -0.0007482 0.995 0.001256 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6452 0.6117 0.3977 0.2152 0.9803 0.9866 0.6459 0.9561 0.9734 0.4271 ] Network output: [ -0.09557 0.2968 0.8322 0.000158 -7.092e-05 1.063 0.0001191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6659 0.6595 0.4445 0.1529 0.9774 0.9847 0.666 0.9487 0.9688 0.4513 ] Network output: [ 0.05943 0.8128 0.0956 -0.0009611 0.0004315 0.9688 -0.0007243 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05319 Epoch 1203 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02524 0.996 0.9973 5.089e-05 -2.285e-05 -0.04364 3.836e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03574 -0.001338 0.02911 0.0241 0.9207 0.933 0.07044 0.8515 0.8832 0.1632 ] Network output: [ 0.9317 0.06626 0.02703 -0.0005141 0.0002308 0.04122 -0.0003875 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6486 0.06714 0.03431 0.2518 0.9601 0.9802 0.7414 0.8726 0.9536 0.6896 ] Network output: [ -0.007456 0.9258 1.047 6.757e-05 -3.034e-05 0.04254 5.093e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06871 0.04463 0.06632 0.04462 0.9771 0.9834 0.07041 0.9486 0.9701 0.09276 ] Network output: [ 0.09674 -0.3156 1.155 -0.001043 0.0004681 0.9633 -0.0007858 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7402 0.5367 0.4691 0.4254 0.9649 0.9831 0.7439 0.885 0.9605 0.6868 ] Network output: [ -0.04892 0.2038 0.9043 0.001846 -0.0008288 0.9973 0.001391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6466 0.6134 0.4002 0.2251 0.9803 0.9867 0.6472 0.9563 0.9736 0.4289 ] Network output: [ -0.0902 0.2759 0.842 0.000388 -0.0001742 1.064 0.0002924 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6679 0.6616 0.4444 0.1626 0.9775 0.9848 0.6681 0.9489 0.9689 0.4511 ] Network output: [ 0.06502 0.7997 0.09859 -0.0007661 0.0003439 0.9685 -0.0005774 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05419 Epoch 1204 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03291 1.002 0.9829 0.0001284 -5.764e-05 -0.04998 9.677e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03589 -0.002085 0.02577 0.02337 0.9207 0.933 0.07087 0.8513 0.8831 0.1628 ] Network output: [ 0.9945 0.07935 -0.05991 -8.93e-05 4.01e-05 -0.008901 -6.733e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6505 0.04755 0.002825 0.2453 0.9602 0.9802 0.744 0.8723 0.9535 0.6877 ] Network output: [ -0.007148 0.9304 1.043 7.844e-05 -3.522e-05 0.04087 5.913e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06829 0.04308 0.06284 0.0438 0.977 0.9833 0.06998 0.9481 0.9698 0.09078 ] Network output: [ 0.1087 -0.3189 1.15 -0.0009636 0.0004326 0.9474 -0.0007262 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7376 0.5259 0.4574 0.4296 0.9648 0.9831 0.7413 0.8848 0.9604 0.6872 ] Network output: [ -0.05971 0.1939 0.9322 0.001758 -0.0007893 1 0.001325 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6435 0.6088 0.3998 0.2336 0.9803 0.9867 0.6441 0.9561 0.9735 0.4303 ] Network output: [ -0.1014 0.258 0.8748 0.0003955 -0.0001776 1.072 0.0002981 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6658 0.6592 0.4449 0.176 0.9774 0.9848 0.6659 0.9488 0.9689 0.4519 ] Network output: [ 0.05944 0.7882 0.1163 -0.0006806 0.0003056 0.9738 -0.000513 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05245 Epoch 1205 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02837 1.011 0.9791 2.335e-05 -1.048e-05 -0.04648 1.76e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03589 -0.001628 0.02707 0.02248 0.9207 0.933 0.07075 0.8514 0.8831 0.1617 ] Network output: [ 0.9643 0.1048 -0.05016 -0.0003982 0.0001788 0.01526 -0.0003002 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6499 0.05908 0.01693 0.2379 0.9601 0.9801 0.743 0.8723 0.9535 0.686 ] Network output: [ -0.007137 0.9384 1.034 2.888e-05 -1.297e-05 0.04151 2.177e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06864 0.04409 0.06355 0.04158 0.977 0.9833 0.07034 0.9483 0.9698 0.0904 ] Network output: [ 0.1008 -0.2748 1.114 -0.00121 0.0005433 0.954 -0.000912 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7383 0.5335 0.463 0.411 0.9648 0.9831 0.742 0.8848 0.9604 0.6847 ] Network output: [ -0.05076 0.2273 0.8855 0.001677 -0.0007531 0.9956 0.001264 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6457 0.6121 0.3979 0.2146 0.9803 0.9867 0.6463 0.9561 0.9735 0.4271 ] Network output: [ -0.09484 0.2968 0.8308 0.0001713 -7.691e-05 1.063 0.0001291 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6661 0.6598 0.4443 0.1519 0.9774 0.9847 0.6663 0.9488 0.9688 0.4511 ] Network output: [ 0.05966 0.8133 0.09514 -0.0009507 0.0004268 0.9684 -0.0007165 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05309 Epoch 1206 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0254 0.996 0.9971 5.493e-05 -2.466e-05 -0.04374 4.14e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03572 -0.001385 0.02899 0.02403 0.9207 0.933 0.07035 0.8516 0.8833 0.1629 ] Network output: [ 0.9348 0.0655 0.02426 -0.0004868 0.0002186 0.03872 -0.0003669 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6485 0.06586 0.03333 0.2512 0.9602 0.9802 0.7412 0.8726 0.9536 0.6894 ] Network output: [ -0.007521 0.926 1.047 6.904e-05 -3.1e-05 0.0425 5.204e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06861 0.04448 0.06607 0.04447 0.9771 0.9834 0.07031 0.9486 0.9701 0.09248 ] Network output: [ 0.0972 -0.3169 1.156 -0.001051 0.0004717 0.9621 -0.0007919 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7398 0.5357 0.4688 0.4251 0.9649 0.9831 0.7435 0.885 0.9605 0.6867 ] Network output: [ -0.04942 0.2013 0.9069 0.001849 -0.0008303 0.9982 0.001394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6468 0.6133 0.4003 0.2254 0.9803 0.9867 0.6474 0.9563 0.9736 0.4291 ] Network output: [ -0.09055 0.2741 0.8439 0.0004027 -0.0001808 1.065 0.0003035 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.668 0.6616 0.4443 0.163 0.9775 0.9848 0.6681 0.9489 0.9689 0.451 ] Network output: [ 0.06475 0.799 0.09984 -0.0007475 0.0003356 0.9686 -0.0005633 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05392 Epoch 1207 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03266 1.003 0.9823 0.0001225 -5.5e-05 -0.04978 9.233e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03587 -0.002092 0.02576 0.02322 0.9208 0.9331 0.07077 0.8514 0.8831 0.1624 ] Network output: [ 0.9951 0.08103 -0.06215 -9.019e-05 4.05e-05 -0.009365 -6.8e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6504 0.04725 0.003056 0.244 0.9602 0.9802 0.7437 0.8723 0.9535 0.6873 ] Network output: [ -0.007224 0.9314 1.042 7.507e-05 -3.371e-05 0.04087 5.659e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06821 0.04301 0.06265 0.04345 0.977 0.9833 0.0699 0.9481 0.9698 0.09046 ] Network output: [ 0.1084 -0.3161 1.148 -0.0009944 0.0004464 0.9468 -0.0007494 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7373 0.5255 0.4576 0.4276 0.9648 0.9831 0.741 0.8848 0.9604 0.6868 ] Network output: [ -0.05941 0.1946 0.9305 0.001754 -0.0007873 1.001 0.001322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6438 0.609 0.3997 0.2322 0.9803 0.9867 0.6444 0.9561 0.9736 0.4302 ] Network output: [ -0.1012 0.2597 0.8727 0.0003893 -0.0001748 1.072 0.0002934 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6659 0.6592 0.4447 0.1742 0.9774 0.9848 0.666 0.9489 0.969 0.4516 ] Network output: [ 0.05918 0.7899 0.1155 -0.0006876 0.0003087 0.9734 -0.0005182 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05229 Epoch 1208 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02781 1.01 0.9802 1.995e-05 -8.959e-06 -0.046 1.504e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03584 -0.001609 0.02725 0.02248 0.9208 0.933 0.07062 0.8515 0.8832 0.1614 ] Network output: [ 0.9617 0.103 -0.04527 -0.0004099 0.000184 0.01721 -0.000309 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6497 0.05951 0.01878 0.2379 0.9602 0.9802 0.7426 0.8723 0.9535 0.6859 ] Network output: [ -0.007244 0.9383 1.035 2.912e-05 -1.307e-05 0.0416 2.195e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06856 0.04406 0.0636 0.04149 0.977 0.9833 0.07026 0.9483 0.9698 0.09028 ] Network output: [ 0.1002 -0.2755 1.116 -0.001227 0.0005509 0.9542 -0.0009248 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7381 0.5333 0.4637 0.4102 0.9648 0.9831 0.7418 0.8848 0.9604 0.6845 ] Network output: [ -0.05029 0.2259 0.8854 0.001688 -0.0007577 0.9962 0.001272 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6461 0.6125 0.3981 0.2141 0.9803 0.9867 0.6467 0.9561 0.9735 0.4272 ] Network output: [ -0.09419 0.2967 0.8295 0.0001846 -8.288e-05 1.063 0.0001391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6664 0.66 0.4441 0.151 0.9774 0.9847 0.6665 0.9489 0.9688 0.4508 ] Network output: [ 0.05986 0.8137 0.09484 -0.0009395 0.0004218 0.968 -0.000708 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.053 Epoch 1209 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02551 0.9961 0.9969 5.814e-05 -2.61e-05 -0.04381 4.382e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03569 -0.001429 0.02888 0.02396 0.9208 0.9331 0.07026 0.8516 0.8833 0.1626 ] Network output: [ 0.9376 0.06492 0.02156 -0.000462 0.0002074 0.0364 -0.0003482 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6485 0.06464 0.03245 0.2506 0.9602 0.9802 0.741 0.8726 0.9537 0.6892 ] Network output: [ -0.007584 0.9263 1.047 7.02e-05 -3.152e-05 0.04245 5.291e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06851 0.04433 0.06582 0.0443 0.9771 0.9834 0.0702 0.9486 0.9701 0.0922 ] Network output: [ 0.09762 -0.3178 1.157 -0.00106 0.000476 0.961 -0.0007991 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7394 0.5346 0.4685 0.4246 0.9649 0.9831 0.7431 0.885 0.9605 0.6865 ] Network output: [ -0.04986 0.199 0.9092 0.001852 -0.0008314 0.9991 0.001396 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6469 0.6133 0.4004 0.2256 0.9803 0.9867 0.6475 0.9563 0.9736 0.4293 ] Network output: [ -0.09085 0.2725 0.8455 0.0004158 -0.0001867 1.065 0.0003134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6681 0.6617 0.4441 0.1632 0.9775 0.9848 0.6682 0.949 0.969 0.4508 ] Network output: [ 0.06449 0.7985 0.101 -0.0007304 0.0003279 0.9686 -0.0005505 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05368 Epoch 1210 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03238 1.003 0.9818 0.0001167 -5.241e-05 -0.04956 8.798e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03584 -0.002098 0.02577 0.02308 0.9208 0.9331 0.07067 0.8514 0.8832 0.162 ] Network output: [ 0.9953 0.08246 -0.06387 -9.196e-05 4.129e-05 -0.009656 -6.933e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6503 0.04698 0.003395 0.2428 0.9602 0.9802 0.7434 0.8723 0.9535 0.687 ] Network output: [ -0.0073 0.9324 1.042 7.202e-05 -3.234e-05 0.04088 5.429e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06813 0.04294 0.06246 0.04311 0.977 0.9833 0.06982 0.9481 0.9698 0.09015 ] Network output: [ 0.1081 -0.3135 1.147 -0.001024 0.0004598 0.9462 -0.0007718 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7369 0.525 0.4578 0.4258 0.9648 0.9831 0.7406 0.8848 0.9605 0.6865 ] Network output: [ -0.05911 0.1951 0.9289 0.00175 -0.0007858 1.001 0.001319 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.644 0.6092 0.3997 0.2309 0.9803 0.9867 0.6447 0.9561 0.9736 0.43 ] Network output: [ -0.1009 0.2612 0.8707 0.0003845 -0.0001726 1.072 0.0002898 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6659 0.6593 0.4444 0.1724 0.9775 0.9848 0.6661 0.9489 0.969 0.4514 ] Network output: [ 0.05897 0.7914 0.1149 -0.0006927 0.000311 0.9729 -0.0005221 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05215 Epoch 1211 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0273 1.01 0.9811 1.692e-05 -7.598e-06 -0.04556 1.276e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0358 -0.001595 0.02741 0.02247 0.9208 0.9331 0.07049 0.8515 0.8832 0.1611 ] Network output: [ 0.9595 0.1013 -0.04095 -0.0004193 0.0001883 0.01885 -0.0003161 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6494 0.05979 0.02043 0.2378 0.9602 0.9802 0.7422 0.8724 0.9535 0.6859 ] Network output: [ -0.007344 0.9382 1.035 2.948e-05 -1.324e-05 0.04169 2.222e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06848 0.04402 0.06361 0.04139 0.977 0.9833 0.07017 0.9484 0.9699 0.09015 ] Network output: [ 0.09969 -0.2761 1.117 -0.001244 0.0005583 0.9542 -0.0009372 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7379 0.5331 0.4643 0.4095 0.9649 0.9831 0.7416 0.8849 0.9604 0.6843 ] Network output: [ -0.04989 0.2244 0.8855 0.001698 -0.0007621 0.9968 0.001279 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6464 0.6128 0.3982 0.2136 0.9803 0.9867 0.647 0.9561 0.9735 0.4272 ] Network output: [ -0.09362 0.2965 0.8285 0.0001977 -8.875e-05 1.063 0.000149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6666 0.6602 0.4439 0.1501 0.9775 0.9848 0.6668 0.9489 0.9689 0.4506 ] Network output: [ 0.06005 0.8139 0.09468 -0.0009277 0.0004165 0.9676 -0.0006992 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05292 Epoch 1212 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0256 0.9963 0.9966 6.057e-05 -2.719e-05 -0.04385 4.565e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03566 -0.00147 0.02878 0.02388 0.9208 0.9331 0.07017 0.8517 0.8834 0.1623 ] Network output: [ 0.9402 0.0645 0.01898 -0.0004395 0.0001973 0.03426 -0.0003312 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6484 0.06348 0.03167 0.2499 0.9602 0.9802 0.7408 0.8727 0.9537 0.689 ] Network output: [ -0.007644 0.9266 1.047 7.109e-05 -3.192e-05 0.0424 5.358e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0684 0.04418 0.06557 0.04412 0.9771 0.9834 0.07009 0.9486 0.9701 0.0919 ] Network output: [ 0.098 -0.3185 1.158 -0.001071 0.000481 0.9599 -0.0008075 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.739 0.5335 0.4682 0.4241 0.9649 0.9831 0.7427 0.8851 0.9606 0.6863 ] Network output: [ -0.05024 0.197 0.9111 0.001854 -0.0008324 0.9999 0.001397 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.647 0.6133 0.4005 0.2257 0.9804 0.9867 0.6476 0.9563 0.9737 0.4294 ] Network output: [ -0.09112 0.2711 0.8468 0.0004273 -0.0001918 1.066 0.000322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6682 0.6617 0.4439 0.1632 0.9775 0.9848 0.6683 0.949 0.969 0.4506 ] Network output: [ 0.06425 0.798 0.102 -0.0007149 0.0003209 0.9686 -0.0005388 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05346 Epoch 1213 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03209 1.004 0.9814 0.0001112 -4.991e-05 -0.04931 8.379e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03581 -0.002102 0.02579 0.02295 0.9209 0.9331 0.07057 0.8515 0.8832 0.1616 ] Network output: [ 0.9954 0.08365 -0.06512 -9.432e-05 4.235e-05 -0.009798 -7.111e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6502 0.04672 0.003824 0.2417 0.9602 0.9802 0.7431 0.8724 0.9535 0.6867 ] Network output: [ -0.007376 0.9332 1.041 6.931e-05 -3.112e-05 0.04089 5.225e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06805 0.04287 0.06229 0.0428 0.977 0.9833 0.06973 0.9482 0.9698 0.08984 ] Network output: [ 0.1079 -0.3112 1.145 -0.001053 0.0004726 0.9457 -0.0007933 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7366 0.5245 0.4579 0.4241 0.9648 0.9831 0.7402 0.8849 0.9605 0.6861 ] Network output: [ -0.05881 0.1954 0.9275 0.001748 -0.0007848 1.002 0.001317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6443 0.6095 0.3996 0.2297 0.9803 0.9867 0.6449 0.9561 0.9736 0.4299 ] Network output: [ -0.1007 0.2626 0.8687 0.0003812 -0.0001711 1.072 0.0002873 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.666 0.6594 0.4442 0.1708 0.9775 0.9848 0.6662 0.949 0.969 0.4511 ] Network output: [ 0.05881 0.7928 0.1143 -0.0006961 0.0003125 0.9725 -0.0005246 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05203 Epoch 1214 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02683 1.01 0.9819 1.425e-05 -6.397e-06 -0.04515 1.074e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03576 -0.001586 0.02754 0.02245 0.9208 0.9331 0.07036 0.8516 0.8833 0.1608 ] Network output: [ 0.9578 0.09978 -0.03721 -0.0004265 0.0001915 0.02019 -0.0003215 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6492 0.05993 0.02187 0.2377 0.9602 0.9802 0.7417 0.8724 0.9536 0.6858 ] Network output: [ -0.007437 0.9382 1.035 2.994e-05 -1.344e-05 0.04175 2.257e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06839 0.04397 0.0636 0.04128 0.9771 0.9833 0.07007 0.9484 0.9699 0.08999 ] Network output: [ 0.09924 -0.2768 1.119 -0.00126 0.0005654 0.9542 -0.0009492 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7376 0.5327 0.4648 0.4088 0.9649 0.9831 0.7413 0.8849 0.9605 0.684 ] Network output: [ -0.04954 0.2229 0.8857 0.001707 -0.0007662 0.9974 0.001286 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6468 0.6131 0.3983 0.2132 0.9803 0.9867 0.6474 0.9562 0.9735 0.4272 ] Network output: [ -0.09312 0.2963 0.8277 0.0002104 -9.444e-05 1.063 0.0001585 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6668 0.6604 0.4436 0.1493 0.9775 0.9848 0.667 0.949 0.9689 0.4503 ] Network output: [ 0.06021 0.814 0.09464 -0.0009156 0.000411 0.9672 -0.00069 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05284 Epoch 1215 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02564 0.9965 0.9964 6.227e-05 -2.796e-05 -0.04386 4.694e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03563 -0.001508 0.02869 0.0238 0.9209 0.9331 0.07007 0.8517 0.8834 0.162 ] Network output: [ 0.9426 0.06421 0.01655 -0.0004193 0.0001882 0.03232 -0.000316 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6483 0.06238 0.03099 0.2492 0.9602 0.9802 0.7405 0.8727 0.9537 0.6887 ] Network output: [ -0.007701 0.927 1.046 7.174e-05 -3.221e-05 0.04235 5.408e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0683 0.04403 0.06531 0.04393 0.9771 0.9834 0.06997 0.9486 0.9701 0.0916 ] Network output: [ 0.09832 -0.3189 1.159 -0.001084 0.0004866 0.9588 -0.0008169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7386 0.5325 0.4679 0.4236 0.965 0.9832 0.7423 0.8851 0.9606 0.6861 ] Network output: [ -0.05056 0.1952 0.9128 0.001856 -0.0008331 1.001 0.001399 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6472 0.6133 0.4006 0.2256 0.9804 0.9867 0.6478 0.9563 0.9737 0.4295 ] Network output: [ -0.09135 0.27 0.8478 0.0004373 -0.0001963 1.067 0.0003295 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6682 0.6618 0.4437 0.1631 0.9776 0.9848 0.6684 0.949 0.969 0.4504 ] Network output: [ 0.06404 0.7977 0.1029 -0.0007008 0.0003146 0.9685 -0.0005282 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05326 Epoch 1216 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03178 1.005 0.9811 0.0001059 -4.753e-05 -0.04905 7.98e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03578 -0.002107 0.02582 0.02284 0.9209 0.9332 0.07046 0.8515 0.8833 0.1612 ] Network output: [ 0.9954 0.08459 -0.06595 -9.703e-05 4.357e-05 -0.009815 -7.315e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.65 0.04647 0.004329 0.2407 0.9603 0.9802 0.7428 0.8724 0.9536 0.6863 ] Network output: [ -0.007451 0.934 1.04 6.696e-05 -3.006e-05 0.04089 5.047e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06796 0.04279 0.06213 0.0425 0.9771 0.9833 0.06964 0.9482 0.9698 0.08955 ] Network output: [ 0.1076 -0.3091 1.144 -0.00108 0.0004848 0.9452 -0.0008139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7362 0.524 0.4581 0.4224 0.9649 0.9831 0.7399 0.8849 0.9605 0.6857 ] Network output: [ -0.05851 0.1956 0.9263 0.001747 -0.0007841 1.002 0.001316 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6446 0.6097 0.3996 0.2285 0.9803 0.9867 0.6452 0.9561 0.9736 0.4297 ] Network output: [ -0.1004 0.2639 0.8669 0.0003792 -0.0001702 1.072 0.0002858 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6661 0.6595 0.4439 0.1692 0.9775 0.9848 0.6663 0.949 0.969 0.4508 ] Network output: [ 0.05868 0.794 0.1138 -0.0006977 0.0003132 0.972 -0.0005258 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05192 Epoch 1217 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02641 1.009 0.9826 1.191e-05 -5.349e-06 -0.04477 8.983e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03572 -0.001582 0.02766 0.02243 0.9209 0.9332 0.07023 0.8517 0.8834 0.1605 ] Network output: [ 0.9563 0.09838 -0.03403 -0.0004315 0.0001937 0.02125 -0.0003252 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.649 0.05994 0.02312 0.2375 0.9602 0.9802 0.7413 0.8725 0.9536 0.6857 ] Network output: [ -0.007522 0.9382 1.035 3.05e-05 -1.369e-05 0.0418 2.299e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0683 0.0439 0.06357 0.04117 0.9771 0.9834 0.06998 0.9484 0.9699 0.08981 ] Network output: [ 0.09886 -0.2774 1.12 -0.001275 0.0005724 0.9541 -0.0009609 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7374 0.5322 0.4653 0.4081 0.9649 0.9831 0.741 0.8849 0.9605 0.6838 ] Network output: [ -0.04926 0.2215 0.886 0.001715 -0.00077 0.9981 0.001293 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6471 0.6133 0.3984 0.2127 0.9803 0.9867 0.6477 0.9562 0.9735 0.4272 ] Network output: [ -0.0927 0.296 0.827 0.0002225 -9.99e-05 1.063 0.0001677 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.667 0.6606 0.4434 0.1486 0.9775 0.9848 0.6672 0.949 0.9689 0.45 ] Network output: [ 0.06034 0.8141 0.09472 -0.0009031 0.0004054 0.9668 -0.0006806 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05277 Epoch 1218 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02566 0.9967 0.9961 6.331e-05 -2.843e-05 -0.04385 4.772e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0356 -0.001543 0.02861 0.02372 0.9209 0.9332 0.06997 0.8518 0.8835 0.1616 ] Network output: [ 0.9447 0.06404 0.01428 -0.0004014 0.0001802 0.03058 -0.0003025 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6482 0.06135 0.03042 0.2485 0.9603 0.9802 0.7403 0.8727 0.9537 0.6885 ] Network output: [ -0.007756 0.9274 1.046 7.221e-05 -3.242e-05 0.04231 5.442e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06819 0.04389 0.06507 0.04372 0.9771 0.9834 0.06986 0.9486 0.9702 0.0913 ] Network output: [ 0.0986 -0.3191 1.16 -0.001098 0.0004927 0.9578 -0.0008272 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7382 0.5315 0.4677 0.4229 0.965 0.9832 0.7418 0.8851 0.9606 0.6859 ] Network output: [ -0.05083 0.1936 0.9141 0.001857 -0.0008337 1.001 0.0014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6473 0.6133 0.4006 0.2255 0.9804 0.9867 0.6479 0.9563 0.9737 0.4295 ] Network output: [ -0.09154 0.2692 0.8485 0.0004457 -0.0002001 1.067 0.0003359 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6683 0.6618 0.4435 0.1628 0.9776 0.9848 0.6684 0.9491 0.969 0.4501 ] Network output: [ 0.06384 0.7974 0.1038 -0.0006881 0.0003089 0.9683 -0.0005186 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05309 Epoch 1219 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03146 1.005 0.9809 0.0001009 -4.529e-05 -0.04878 7.603e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03574 -0.00211 0.02586 0.02273 0.921 0.9332 0.07035 0.8516 0.8833 0.1608 ] Network output: [ 0.9952 0.08531 -0.06638 -9.991e-05 4.486e-05 -0.009726 -7.532e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6498 0.04622 0.004894 0.2398 0.9603 0.9802 0.7425 0.8724 0.9536 0.686 ] Network output: [ -0.007526 0.9347 1.04 6.495e-05 -2.916e-05 0.0409 4.896e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06787 0.04271 0.06197 0.04222 0.9771 0.9833 0.06954 0.9482 0.9698 0.08925 ] Network output: [ 0.1073 -0.3074 1.144 -0.001106 0.0004965 0.9447 -0.0008335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7359 0.5235 0.4583 0.4209 0.9649 0.9831 0.7395 0.8849 0.9605 0.6854 ] Network output: [ -0.05822 0.1956 0.9252 0.001746 -0.0007839 1.003 0.001316 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6449 0.6099 0.3995 0.2274 0.9803 0.9867 0.6455 0.9561 0.9736 0.4296 ] Network output: [ -0.1001 0.265 0.8652 0.0003784 -0.0001699 1.072 0.0002851 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6662 0.6596 0.4436 0.1678 0.9775 0.9848 0.6664 0.949 0.969 0.4505 ] Network output: [ 0.05859 0.795 0.1135 -0.0006978 0.0003133 0.9716 -0.0005259 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05182 Epoch 1220 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02603 1.009 0.9832 9.901e-06 -4.446e-06 -0.04443 7.467e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03568 -0.001582 0.02776 0.02241 0.9209 0.9332 0.0701 0.8517 0.8834 0.1602 ] Network output: [ 0.9552 0.0971 -0.03137 -0.0004344 0.000195 0.02203 -0.0003274 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6487 0.05983 0.02418 0.2373 0.9603 0.9802 0.7409 0.8725 0.9536 0.6855 ] Network output: [ -0.007602 0.9383 1.035 3.114e-05 -1.398e-05 0.04184 2.348e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0682 0.04382 0.0635 0.04105 0.9771 0.9834 0.06987 0.9485 0.97 0.08961 ] Network output: [ 0.09857 -0.2779 1.122 -0.00129 0.0005792 0.9538 -0.0009723 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7371 0.5317 0.4656 0.4075 0.9649 0.9831 0.7407 0.885 0.9605 0.6836 ] Network output: [ -0.04903 0.22 0.8863 0.001723 -0.0007735 0.9987 0.001298 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6474 0.6135 0.3985 0.2124 0.9803 0.9867 0.648 0.9562 0.9736 0.4272 ] Network output: [ -0.09235 0.2957 0.8264 0.0002341 -0.0001051 1.064 0.0001764 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6672 0.6608 0.4431 0.1479 0.9775 0.9848 0.6673 0.949 0.9689 0.4497 ] Network output: [ 0.06046 0.8141 0.09489 -0.0008905 0.0003998 0.9665 -0.0006711 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0527 Epoch 1221 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02563 0.997 0.9958 6.375e-05 -2.862e-05 -0.0438 4.805e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03557 -0.001576 0.02854 0.02363 0.921 0.9332 0.06987 0.8518 0.8835 0.1613 ] Network output: [ 0.9466 0.06397 0.01219 -0.0003856 0.0001731 0.02904 -0.0002906 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6481 0.06037 0.02996 0.2478 0.9603 0.9802 0.74 0.8728 0.9538 0.6882 ] Network output: [ -0.007809 0.9279 1.046 7.25e-05 -3.255e-05 0.04226 5.465e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06808 0.04375 0.06482 0.04351 0.9772 0.9834 0.06975 0.9486 0.9702 0.09098 ] Network output: [ 0.09884 -0.3191 1.16 -0.001112 0.0004994 0.9568 -0.0008383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7378 0.5305 0.4675 0.4221 0.965 0.9832 0.7414 0.8851 0.9606 0.6857 ] Network output: [ -0.05105 0.1922 0.9152 0.001858 -0.0008341 1.002 0.0014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6475 0.6133 0.4006 0.2252 0.9804 0.9867 0.6481 0.9563 0.9737 0.4295 ] Network output: [ -0.0917 0.2685 0.849 0.0004529 -0.0002033 1.068 0.0003413 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6683 0.6618 0.4432 0.1624 0.9776 0.9849 0.6685 0.9491 0.9691 0.4499 ] Network output: [ 0.06367 0.7973 0.1045 -0.0006767 0.0003038 0.9682 -0.00051 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05293 Epoch 1222 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03113 1.006 0.9808 9.619e-05 -4.318e-05 -0.04849 7.249e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03571 -0.002114 0.0259 0.02264 0.921 0.9332 0.07023 0.8517 0.8834 0.1604 ] Network output: [ 0.9949 0.0858 -0.06648 -0.0001028 4.616e-05 -0.009548 -7.75e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6497 0.04596 0.005509 0.239 0.9603 0.9802 0.7421 0.8725 0.9536 0.6858 ] Network output: [ -0.007599 0.9353 1.039 6.33e-05 -2.842e-05 0.04091 4.771e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06778 0.04263 0.06182 0.04196 0.9771 0.9834 0.06944 0.9482 0.9698 0.08897 ] Network output: [ 0.1071 -0.3058 1.143 -0.001131 0.0005076 0.9442 -0.0008521 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7355 0.5229 0.4585 0.4196 0.9649 0.9831 0.7392 0.8849 0.9605 0.685 ] Network output: [ -0.05794 0.1955 0.9242 0.001746 -0.000784 1.003 0.001316 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6452 0.6101 0.3994 0.2264 0.9803 0.9867 0.6458 0.9561 0.9736 0.4294 ] Network output: [ -0.09988 0.266 0.8637 0.0003786 -0.00017 1.072 0.0002853 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6663 0.6597 0.4433 0.1664 0.9775 0.9848 0.6665 0.9491 0.9691 0.4502 ] Network output: [ 0.05854 0.7958 0.1131 -0.0006963 0.0003126 0.9711 -0.0005248 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05174 Epoch 1223 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02568 1.009 0.9837 8.193e-06 -3.679e-06 -0.04411 6.179e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03564 -0.001586 0.02783 0.02238 0.921 0.9332 0.06997 0.8518 0.8835 0.1599 ] Network output: [ 0.9545 0.09595 -0.02923 -0.0004353 0.0001954 0.02257 -0.0003281 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6485 0.05959 0.02507 0.2371 0.9603 0.9802 0.7405 0.8726 0.9537 0.6854 ] Network output: [ -0.007676 0.9384 1.035 3.187e-05 -1.431e-05 0.04187 2.402e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0681 0.04373 0.06341 0.04092 0.9771 0.9834 0.06977 0.9485 0.97 0.08939 ] Network output: [ 0.09834 -0.2784 1.123 -0.001305 0.0005858 0.9535 -0.0009835 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7368 0.5311 0.4658 0.4068 0.965 0.9831 0.7404 0.885 0.9605 0.6833 ] Network output: [ -0.04886 0.2186 0.8868 0.00173 -0.0007766 0.9994 0.001304 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6476 0.6137 0.3985 0.212 0.9803 0.9867 0.6482 0.9562 0.9736 0.4271 ] Network output: [ -0.09206 0.2953 0.8259 0.000245 -0.00011 1.064 0.0001846 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6674 0.6609 0.4428 0.1472 0.9776 0.9848 0.6675 0.9491 0.969 0.4494 ] Network output: [ 0.06056 0.814 0.09516 -0.0008778 0.0003941 0.9661 -0.0006615 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05264 Epoch 1224 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02557 0.9973 0.9956 6.364e-05 -2.857e-05 -0.04373 4.796e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03554 -0.001605 0.02848 0.02355 0.921 0.9332 0.06977 0.8519 0.8836 0.1609 ] Network output: [ 0.9483 0.06399 0.01029 -0.0003719 0.0001669 0.02768 -0.0002803 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6479 0.05945 0.0296 0.2471 0.9603 0.9803 0.7397 0.8728 0.9538 0.6879 ] Network output: [ -0.00786 0.9284 1.045 7.267e-05 -3.263e-05 0.04222 5.477e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06797 0.04361 0.06458 0.04329 0.9772 0.9834 0.06963 0.9487 0.9702 0.09067 ] Network output: [ 0.09903 -0.3189 1.16 -0.001128 0.0005064 0.9558 -0.0008502 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7374 0.5295 0.4673 0.4213 0.965 0.9832 0.741 0.8852 0.9606 0.6854 ] Network output: [ -0.05121 0.191 0.9161 0.001858 -0.0008343 1.003 0.001401 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6476 0.6133 0.4005 0.2249 0.9804 0.9868 0.6482 0.9563 0.9737 0.4294 ] Network output: [ -0.09182 0.2681 0.8492 0.0004587 -0.0002059 1.068 0.0003457 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6684 0.6618 0.4429 0.1619 0.9776 0.9849 0.6685 0.9492 0.9691 0.4496 ] Network output: [ 0.06353 0.7972 0.1051 -0.0006664 0.0002992 0.9679 -0.0005022 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0528 Epoch 1225 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03079 1.006 0.9808 9.183e-05 -4.122e-05 -0.0482 6.921e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03567 -0.002118 0.02595 0.02255 0.921 0.9333 0.07012 0.8517 0.8835 0.1601 ] Network output: [ 0.9945 0.0861 -0.06626 -0.0001056 4.743e-05 -0.009297 -7.963e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6495 0.04569 0.006164 0.2383 0.9603 0.9803 0.7417 0.8725 0.9537 0.6855 ] Network output: [ -0.007672 0.9359 1.039 6.198e-05 -2.783e-05 0.04092 4.672e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06768 0.04254 0.06168 0.04172 0.9771 0.9834 0.06933 0.9483 0.9699 0.08868 ] Network output: [ 0.1068 -0.3045 1.143 -0.001154 0.0005182 0.9437 -0.0008699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7351 0.5223 0.4587 0.4183 0.9649 0.9831 0.7388 0.885 0.9605 0.6847 ] Network output: [ -0.05768 0.1953 0.9234 0.001747 -0.0007845 1.004 0.001317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6454 0.6102 0.3993 0.2255 0.9803 0.9867 0.646 0.9561 0.9736 0.4293 ] Network output: [ -0.09962 0.2668 0.8622 0.0003798 -0.0001705 1.072 0.0002862 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6664 0.6597 0.443 0.165 0.9776 0.9848 0.6666 0.9491 0.9691 0.4498 ] Network output: [ 0.05853 0.7966 0.1129 -0.0006934 0.0003113 0.9706 -0.0005226 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05166 Epoch 1226 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02537 1.009 0.9842 6.768e-06 -3.039e-06 -0.04383 5.105e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0356 -0.001594 0.02789 0.02234 0.921 0.9333 0.06985 0.8519 0.8835 0.1595 ] Network output: [ 0.954 0.09493 -0.02755 -0.0004344 0.000195 0.02288 -0.0003274 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6483 0.05924 0.02579 0.2368 0.9603 0.9802 0.7401 0.8726 0.9537 0.6852 ] Network output: [ -0.007746 0.9385 1.035 3.266e-05 -1.466e-05 0.04188 2.462e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06799 0.04363 0.0633 0.04078 0.9771 0.9834 0.06965 0.9485 0.97 0.08916 ] Network output: [ 0.09817 -0.2789 1.124 -0.00132 0.0005924 0.9531 -0.0009944 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7364 0.5303 0.466 0.4062 0.965 0.9832 0.74 0.885 0.9606 0.6831 ] Network output: [ -0.04873 0.2172 0.8873 0.001736 -0.0007794 1 0.001308 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6478 0.6138 0.3985 0.2117 0.9804 0.9867 0.6485 0.9562 0.9736 0.4271 ] Network output: [ -0.09184 0.295 0.8255 0.0002552 -0.0001146 1.064 0.0001923 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6675 0.661 0.4425 0.1466 0.9776 0.9848 0.6676 0.9491 0.969 0.4491 ] Network output: [ 0.06064 0.8139 0.0955 -0.0008649 0.0003883 0.9658 -0.0006519 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05257 Epoch 1227 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02549 0.9976 0.9953 6.303e-05 -2.83e-05 -0.04363 4.751e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03551 -0.001632 0.02844 0.02346 0.921 0.9333 0.06966 0.852 0.8836 0.1605 ] Network output: [ 0.9497 0.06409 0.008568 -0.00036 0.0001616 0.0265 -0.0002713 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6478 0.05858 0.02934 0.2464 0.9603 0.9803 0.7394 0.8728 0.9538 0.6876 ] Network output: [ -0.00791 0.9289 1.045 7.273e-05 -3.265e-05 0.04217 5.482e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06786 0.04348 0.06434 0.04307 0.9772 0.9835 0.06951 0.9487 0.9702 0.09035 ] Network output: [ 0.09918 -0.3185 1.161 -0.001145 0.0005139 0.9549 -0.0008627 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.737 0.5285 0.4671 0.4205 0.965 0.9832 0.7406 0.8852 0.9607 0.6851 ] Network output: [ -0.05134 0.1899 0.9167 0.001859 -0.0008345 1.004 0.001401 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6478 0.6133 0.4005 0.2245 0.9804 0.9868 0.6484 0.9563 0.9737 0.4294 ] Network output: [ -0.09191 0.2679 0.8492 0.0004633 -0.000208 1.069 0.0003492 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6684 0.6619 0.4427 0.1613 0.9776 0.9849 0.6686 0.9492 0.9691 0.4493 ] Network output: [ 0.06341 0.7972 0.1056 -0.0006572 0.000295 0.9677 -0.0004953 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05268 Epoch 1228 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03044 1.007 0.9808 8.779e-05 -3.941e-05 -0.04789 6.617e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03564 -0.002122 0.02601 0.02247 0.9211 0.9333 0.06999 0.8518 0.8835 0.1597 ] Network output: [ 0.9941 0.0862 -0.06577 -0.0001083 4.863e-05 -0.008983 -8.165e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6493 0.04541 0.006852 0.2377 0.9603 0.9803 0.7413 0.8725 0.9537 0.6852 ] Network output: [ -0.007743 0.9364 1.038 6.1e-05 -2.739e-05 0.04093 4.598e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06757 0.04245 0.06153 0.04149 0.9771 0.9834 0.06923 0.9483 0.9699 0.0884 ] Network output: [ 0.1065 -0.3035 1.142 -0.001177 0.0005282 0.9433 -0.0008867 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7348 0.5216 0.4589 0.4171 0.9649 0.9831 0.7384 0.885 0.9606 0.6844 ] Network output: [ -0.05743 0.1949 0.9227 0.001749 -0.0007852 1.004 0.001318 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6457 0.6104 0.3992 0.2246 0.9803 0.9867 0.6463 0.9561 0.9736 0.4291 ] Network output: [ -0.09936 0.2676 0.8608 0.0003818 -0.0001714 1.072 0.0002877 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6665 0.6598 0.4427 0.1638 0.9776 0.9849 0.6667 0.9491 0.9691 0.4495 ] Network output: [ 0.05855 0.7972 0.1128 -0.0006892 0.0003094 0.9702 -0.0005194 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0516 Epoch 1229 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02509 1.009 0.9845 5.606e-06 -2.518e-06 -0.04356 4.229e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03556 -0.001605 0.02794 0.0223 0.921 0.9333 0.06972 0.8519 0.8836 0.1592 ] Network output: [ 0.9538 0.09401 -0.02631 -0.0004318 0.0001939 0.02297 -0.0003254 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6481 0.05879 0.02637 0.2365 0.9603 0.9803 0.7397 0.8727 0.9537 0.685 ] Network output: [ -0.007812 0.9387 1.035 3.352e-05 -1.505e-05 0.04189 2.527e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06788 0.04352 0.06316 0.04064 0.9771 0.9834 0.06954 0.9486 0.97 0.08891 ] Network output: [ 0.09806 -0.2793 1.125 -0.001334 0.0005988 0.9526 -0.001005 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7361 0.5295 0.4661 0.4056 0.965 0.9832 0.7397 0.8851 0.9606 0.6828 ] Network output: [ -0.04866 0.2158 0.8879 0.001741 -0.0007818 1.001 0.001312 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6481 0.6139 0.3984 0.2113 0.9804 0.9867 0.6487 0.9562 0.9736 0.427 ] Network output: [ -0.09168 0.2946 0.8252 0.0002647 -0.0001188 1.065 0.0001995 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6676 0.6611 0.4422 0.146 0.9776 0.9848 0.6677 0.9492 0.969 0.4487 ] Network output: [ 0.06071 0.8137 0.09592 -0.0008521 0.0003825 0.9655 -0.0006422 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05252 Epoch 1230 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02537 0.998 0.9951 6.199e-05 -2.783e-05 -0.0435 4.672e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03548 -0.001657 0.0284 0.02337 0.9211 0.9333 0.06955 0.852 0.8837 0.1602 ] Network output: [ 0.9509 0.06425 0.007018 -0.0003499 0.0001571 0.02549 -0.0002637 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6476 0.05775 0.02916 0.2457 0.9604 0.9803 0.7391 0.8728 0.9538 0.6873 ] Network output: [ -0.007958 0.9294 1.045 7.27e-05 -3.264e-05 0.04213 5.48e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06775 0.04334 0.06411 0.04284 0.9772 0.9835 0.06939 0.9487 0.9702 0.09002 ] Network output: [ 0.0993 -0.318 1.161 -0.001162 0.0005217 0.9541 -0.0008758 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7365 0.5276 0.4669 0.4196 0.965 0.9832 0.7402 0.8852 0.9607 0.6848 ] Network output: [ -0.05142 0.189 0.9171 0.001859 -0.0008345 1.004 0.001401 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6479 0.6134 0.4004 0.224 0.9804 0.9868 0.6485 0.9563 0.9737 0.4293 ] Network output: [ -0.09198 0.2679 0.8489 0.0004668 -0.0002096 1.069 0.0003518 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6685 0.6619 0.4423 0.1605 0.9776 0.9849 0.6686 0.9492 0.9691 0.449 ] Network output: [ 0.06331 0.7973 0.1061 -0.0006489 0.0002913 0.9674 -0.000489 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05258 Epoch 1231 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0301 1.007 0.9809 8.407e-05 -3.774e-05 -0.04758 6.336e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0356 -0.002126 0.02607 0.0224 0.9211 0.9333 0.06987 0.8518 0.8836 0.1593 ] Network output: [ 0.9935 0.08614 -0.06504 -0.0001108 4.976e-05 -0.008616 -8.355e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6491 0.04511 0.007566 0.2371 0.9604 0.9803 0.7409 0.8726 0.9537 0.6849 ] Network output: [ -0.007813 0.9369 1.038 6.032e-05 -2.708e-05 0.04094 4.547e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06747 0.04236 0.06139 0.04128 0.9771 0.9834 0.06911 0.9483 0.9699 0.08813 ] Network output: [ 0.1063 -0.3027 1.142 -0.001198 0.0005378 0.9428 -0.0009028 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7344 0.5209 0.4591 0.416 0.965 0.9832 0.738 0.885 0.9606 0.684 ] Network output: [ -0.05721 0.1944 0.9222 0.001751 -0.0007861 1.005 0.00132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6459 0.6105 0.3991 0.2238 0.9803 0.9867 0.6465 0.9562 0.9736 0.4289 ] Network output: [ -0.09911 0.2682 0.8595 0.0003845 -0.0001726 1.072 0.0002898 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6666 0.6599 0.4423 0.1626 0.9776 0.9849 0.6667 0.9492 0.9691 0.4491 ] Network output: [ 0.05859 0.7976 0.1127 -0.0006839 0.000307 0.9697 -0.0005154 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05155 Epoch 1232 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02484 1.009 0.9848 4.69e-06 -2.106e-06 -0.04332 3.539e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03552 -0.00162 0.02797 0.02226 0.9211 0.9333 0.0696 0.852 0.8836 0.1588 ] Network output: [ 0.9538 0.09319 -0.02547 -0.0004277 0.000192 0.02287 -0.0003223 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6479 0.05823 0.02681 0.2362 0.9603 0.9803 0.7393 0.8727 0.9538 0.6848 ] Network output: [ -0.007874 0.939 1.035 3.445e-05 -1.547e-05 0.04188 2.597e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06777 0.0434 0.06301 0.04049 0.9772 0.9834 0.06941 0.9486 0.9701 0.08864 ] Network output: [ 0.098 -0.2797 1.126 -0.001348 0.0006052 0.952 -0.001016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7357 0.5287 0.4661 0.405 0.965 0.9832 0.7393 0.8851 0.9606 0.6825 ] Network output: [ -0.04863 0.2144 0.8885 0.001746 -0.000784 1.001 0.001316 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6482 0.614 0.3984 0.211 0.9804 0.9867 0.6488 0.9562 0.9736 0.4269 ] Network output: [ -0.09157 0.2943 0.825 0.0002735 -0.0001228 1.065 0.0002061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6677 0.6612 0.4419 0.1454 0.9776 0.9849 0.6678 0.9492 0.969 0.4484 ] Network output: [ 0.06077 0.8135 0.0964 -0.0008391 0.0003767 0.9652 -0.0006324 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05246 Epoch 1233 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02522 0.9983 0.9948 6.056e-05 -2.719e-05 -0.04335 4.564e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03544 -0.00168 0.02837 0.02328 0.9211 0.9333 0.06944 0.8521 0.8837 0.1598 ] Network output: [ 0.9519 0.06448 0.005629 -0.0003414 0.0001533 0.02463 -0.0002573 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6475 0.05696 0.02907 0.245 0.9604 0.9803 0.7388 0.8729 0.9538 0.687 ] Network output: [ -0.008005 0.93 1.044 7.262e-05 -3.26e-05 0.04209 5.473e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06763 0.04321 0.06387 0.0426 0.9772 0.9835 0.06927 0.9487 0.9702 0.08969 ] Network output: [ 0.09938 -0.3174 1.161 -0.00118 0.0005298 0.9532 -0.0008893 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7361 0.5266 0.4668 0.4186 0.9651 0.9832 0.7397 0.8852 0.9607 0.6845 ] Network output: [ -0.05147 0.1882 0.9173 0.001859 -0.0008344 1.005 0.001401 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6481 0.6134 0.4003 0.2234 0.9804 0.9868 0.6487 0.9563 0.9737 0.4291 ] Network output: [ -0.09202 0.2681 0.8485 0.0004692 -0.0002107 1.069 0.0003536 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6685 0.6619 0.442 0.1597 0.9777 0.9849 0.6686 0.9493 0.9692 0.4486 ] Network output: [ 0.06323 0.7974 0.1065 -0.0006415 0.000288 0.9671 -0.0004834 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05249 Epoch 1234 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02974 1.007 0.9811 8.064e-05 -3.62e-05 -0.04726 6.078e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03556 -0.00213 0.02613 0.02234 0.9212 0.9334 0.06975 0.8519 0.8836 0.1589 ] Network output: [ 0.993 0.08592 -0.06409 -0.0001132 5.083e-05 -0.008202 -8.534e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6488 0.04479 0.008302 0.2366 0.9604 0.9803 0.7405 0.8726 0.9537 0.6847 ] Network output: [ -0.007881 0.9373 1.038 5.994e-05 -2.691e-05 0.04094 4.518e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06736 0.04226 0.06126 0.04108 0.9771 0.9834 0.069 0.9483 0.9699 0.08785 ] Network output: [ 0.1061 -0.302 1.143 -0.001218 0.0005469 0.9424 -0.000918 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7341 0.5202 0.4592 0.415 0.965 0.9832 0.7377 0.885 0.9606 0.6837 ] Network output: [ -0.05699 0.1938 0.9218 0.001754 -0.0007872 1.006 0.001322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6461 0.6106 0.399 0.2231 0.9804 0.9867 0.6467 0.9562 0.9736 0.4288 ] Network output: [ -0.09887 0.2688 0.8583 0.0003878 -0.0001741 1.072 0.0002923 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6667 0.66 0.442 0.1614 0.9776 0.9849 0.6668 0.9492 0.9691 0.4488 ] Network output: [ 0.05867 0.798 0.1126 -0.0006774 0.0003041 0.9693 -0.0005105 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05151 Epoch 1235 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02462 1.009 0.985 4.003e-06 -1.798e-06 -0.0431 3.021e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03548 -0.001638 0.02799 0.02221 0.9211 0.9334 0.06948 0.852 0.8837 0.1585 ] Network output: [ 0.9541 0.09246 -0.02499 -0.0004221 0.0001895 0.02259 -0.0003181 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6477 0.05758 0.02712 0.2359 0.9604 0.9803 0.7389 0.8727 0.9538 0.6846 ] Network output: [ -0.007934 0.9392 1.035 3.545e-05 -1.592e-05 0.04187 2.672e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06765 0.04328 0.06284 0.04034 0.9772 0.9834 0.06929 0.9486 0.9701 0.08837 ] Network output: [ 0.09798 -0.28 1.127 -0.001362 0.0006115 0.9513 -0.001027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7353 0.5277 0.4661 0.4045 0.965 0.9832 0.7389 0.8851 0.9606 0.6823 ] Network output: [ -0.04864 0.213 0.8892 0.001751 -0.0007859 1.002 0.001319 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6484 0.614 0.3983 0.2107 0.9804 0.9867 0.649 0.9562 0.9736 0.4268 ] Network output: [ -0.09152 0.294 0.8248 0.0002817 -0.0001265 1.065 0.0002123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6678 0.6612 0.4415 0.1448 0.9776 0.9849 0.6679 0.9492 0.9691 0.448 ] Network output: [ 0.06082 0.8132 0.09694 -0.0008261 0.0003709 0.9649 -0.0006226 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05241 Epoch 1236 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02505 0.9987 0.9946 5.878e-05 -2.639e-05 -0.04318 4.43e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03541 -0.001702 0.02835 0.02319 0.9212 0.9334 0.06933 0.8521 0.8838 0.1594 ] Network output: [ 0.9528 0.06476 0.004382 -0.0003344 0.0001501 0.0239 -0.000252 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6473 0.0562 0.02905 0.2443 0.9604 0.9803 0.7384 0.8729 0.9539 0.6867 ] Network output: [ -0.008051 0.9305 1.044 7.248e-05 -3.254e-05 0.04206 5.463e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06751 0.04307 0.06364 0.04236 0.9772 0.9835 0.06915 0.9487 0.9702 0.08936 ] Network output: [ 0.09943 -0.3166 1.16 -0.001199 0.0005382 0.9524 -0.0009034 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7357 0.5256 0.4666 0.4177 0.9651 0.9832 0.7393 0.8852 0.9607 0.6842 ] Network output: [ -0.05149 0.1876 0.9173 0.001858 -0.0008342 1.006 0.0014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6482 0.6134 0.4001 0.2227 0.9804 0.9868 0.6488 0.9563 0.9737 0.429 ] Network output: [ -0.09205 0.2684 0.8479 0.0004707 -0.0002113 1.07 0.0003547 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6686 0.6619 0.4417 0.1587 0.9777 0.9849 0.6687 0.9493 0.9692 0.4483 ] Network output: [ 0.06317 0.7976 0.1068 -0.0006348 0.000285 0.9667 -0.0004784 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05242 Epoch 1237 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02939 1.007 0.9813 7.749e-05 -3.479e-05 -0.04693 5.84e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03552 -0.002135 0.0262 0.02228 0.9212 0.9334 0.06962 0.852 0.8836 0.1585 ] Network output: [ 0.9923 0.08557 -0.06294 -0.0001155 5.184e-05 -0.007745 -8.704e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6486 0.04445 0.009059 0.2362 0.9604 0.9803 0.7401 0.8726 0.9538 0.6844 ] Network output: [ -0.007948 0.9377 1.038 5.983e-05 -2.686e-05 0.04095 4.509e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06724 0.04215 0.06112 0.04088 0.9771 0.9834 0.06888 0.9484 0.9699 0.08758 ] Network output: [ 0.1058 -0.3016 1.143 -0.001237 0.0005555 0.9419 -0.0009326 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7337 0.5194 0.4594 0.4141 0.965 0.9832 0.7373 0.885 0.9606 0.6834 ] Network output: [ -0.0568 0.1932 0.9215 0.001756 -0.0007886 1.006 0.001324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6463 0.6107 0.3989 0.2224 0.9804 0.9867 0.6469 0.9562 0.9736 0.4286 ] Network output: [ -0.09863 0.2693 0.8571 0.0003917 -0.0001758 1.072 0.0002952 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6668 0.66 0.4416 0.1603 0.9776 0.9849 0.6669 0.9492 0.9692 0.4484 ] Network output: [ 0.05878 0.7982 0.1127 -0.00067 0.0003008 0.9688 -0.000505 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05148 Epoch 1238 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02441 1.009 0.9852 3.533e-06 -1.587e-06 -0.04289 2.667e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03544 -0.001659 0.028 0.02217 0.9212 0.9334 0.06935 0.8521 0.8837 0.1581 ] Network output: [ 0.9546 0.09182 -0.02483 -0.0004153 0.0001864 0.02215 -0.000313 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6475 0.05684 0.02733 0.2355 0.9604 0.9803 0.7385 0.8728 0.9538 0.6843 ] Network output: [ -0.007991 0.9395 1.035 3.652e-05 -1.64e-05 0.04185 2.753e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06752 0.04314 0.06265 0.04018 0.9772 0.9834 0.06915 0.9486 0.9701 0.08807 ] Network output: [ 0.09802 -0.2804 1.128 -0.001376 0.0006177 0.9506 -0.001037 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7349 0.5267 0.466 0.4039 0.965 0.9832 0.7385 0.8851 0.9606 0.682 ] Network output: [ -0.0487 0.2117 0.8899 0.001754 -0.0007875 1.003 0.001322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6485 0.614 0.3982 0.2104 0.9804 0.9867 0.6491 0.9562 0.9736 0.4267 ] Network output: [ -0.09151 0.2936 0.8247 0.0002894 -0.0001299 1.066 0.0002181 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6679 0.6613 0.4412 0.1443 0.9777 0.9849 0.668 0.9493 0.9691 0.4477 ] Network output: [ 0.06086 0.8129 0.09753 -0.000813 0.000365 0.9646 -0.0006127 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05236 Epoch 1239 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02486 0.9991 0.9944 5.67e-05 -2.546e-05 -0.04299 4.274e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03537 -0.001722 0.02833 0.0231 0.9212 0.9334 0.06921 0.8522 0.8838 0.159 ] Network output: [ 0.9535 0.0651 0.003257 -0.0003287 0.0001476 0.0233 -0.0002477 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6471 0.05546 0.0291 0.2436 0.9604 0.9803 0.7381 0.8729 0.9539 0.6864 ] Network output: [ -0.008097 0.9311 1.043 7.232e-05 -3.247e-05 0.04202 5.451e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06739 0.04294 0.06341 0.04212 0.9772 0.9835 0.06902 0.9487 0.9702 0.08903 ] Network output: [ 0.09946 -0.3157 1.16 -0.001218 0.0005468 0.9516 -0.0009178 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7353 0.5246 0.4665 0.4167 0.9651 0.9832 0.7389 0.8852 0.9607 0.6838 ] Network output: [ -0.05148 0.1871 0.9172 0.001857 -0.0008339 1.006 0.0014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6484 0.6134 0.4 0.222 0.9804 0.9868 0.649 0.9563 0.9737 0.4288 ] Network output: [ -0.09205 0.2689 0.8471 0.0004712 -0.0002116 1.07 0.0003551 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6686 0.6619 0.4413 0.1576 0.9777 0.9849 0.6687 0.9493 0.9692 0.4479 ] Network output: [ 0.06314 0.7978 0.107 -0.0006287 0.0002823 0.9663 -0.0004738 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05236 Epoch 1240 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02903 1.007 0.9816 7.458e-05 -3.348e-05 -0.0466 5.621e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03548 -0.002141 0.02626 0.02222 0.9212 0.9334 0.06949 0.852 0.8837 0.1582 ] Network output: [ 0.9916 0.08509 -0.06162 -0.0001177 5.283e-05 -0.007246 -8.869e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6484 0.04409 0.009835 0.2358 0.9604 0.9803 0.7397 0.8727 0.9538 0.6842 ] Network output: [ -0.008014 0.938 1.037 5.997e-05 -2.693e-05 0.04096 4.52e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06713 0.04204 0.06099 0.0407 0.9772 0.9834 0.06875 0.9484 0.9699 0.0873 ] Network output: [ 0.1056 -0.3013 1.143 -0.001256 0.0005638 0.9415 -0.0009465 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7333 0.5186 0.4595 0.4133 0.965 0.9832 0.7369 0.8851 0.9606 0.6831 ] Network output: [ -0.05662 0.1924 0.9213 0.00176 -0.0007901 1.007 0.001326 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6465 0.6108 0.3988 0.2217 0.9804 0.9867 0.6471 0.9562 0.9736 0.4284 ] Network output: [ -0.09841 0.2697 0.856 0.0003959 -0.0001777 1.073 0.0002984 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6669 0.6601 0.4413 0.1593 0.9776 0.9849 0.667 0.9493 0.9692 0.448 ] Network output: [ 0.05891 0.7984 0.1127 -0.0006618 0.0002971 0.9683 -0.0004987 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05146 Epoch 1241 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02423 1.009 0.9853 3.27e-06 -1.469e-06 -0.04269 2.468e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0354 -0.001683 0.02799 0.02211 0.9212 0.9334 0.06923 0.8521 0.8838 0.1577 ] Network output: [ 0.9552 0.09124 -0.02496 -0.0004074 0.0001829 0.02157 -0.000307 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6472 0.05601 0.02743 0.2351 0.9604 0.9803 0.7381 0.8728 0.9538 0.6841 ] Network output: [ -0.008047 0.9398 1.035 3.766e-05 -1.691e-05 0.04182 2.839e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06739 0.04299 0.06244 0.04001 0.9772 0.9835 0.06902 0.9486 0.9701 0.08777 ] Network output: [ 0.09809 -0.2807 1.129 -0.00139 0.0006238 0.9499 -0.001047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7345 0.5256 0.4659 0.4034 0.9651 0.9832 0.738 0.8851 0.9606 0.6817 ] Network output: [ -0.04879 0.2104 0.8907 0.001757 -0.000789 1.004 0.001324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6487 0.6139 0.3981 0.2101 0.9804 0.9867 0.6493 0.9562 0.9736 0.4265 ] Network output: [ -0.09155 0.2933 0.8247 0.0002965 -0.0001331 1.066 0.0002235 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6679 0.6613 0.4408 0.1437 0.9777 0.9849 0.668 0.9493 0.9691 0.4473 ] Network output: [ 0.0609 0.8125 0.09817 -0.0007998 0.0003591 0.9643 -0.0006027 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05232 Epoch 1242 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02465 0.9995 0.9942 5.437e-05 -2.441e-05 -0.04277 4.098e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03534 -0.001741 0.02832 0.02301 0.9212 0.9334 0.06909 0.8522 0.8838 0.1586 ] Network output: [ 0.9541 0.06549 0.002231 -0.0003242 0.0001455 0.0228 -0.0002443 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6469 0.05474 0.02919 0.2429 0.9604 0.9803 0.7377 0.8729 0.9539 0.6861 ] Network output: [ -0.008141 0.9317 1.043 7.215e-05 -3.239e-05 0.04199 5.438e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06727 0.0428 0.06318 0.04187 0.9772 0.9835 0.06889 0.9487 0.9702 0.08869 ] Network output: [ 0.09947 -0.3147 1.16 -0.001238 0.0005556 0.9509 -0.0009326 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7348 0.5236 0.4663 0.4156 0.9651 0.9832 0.7384 0.8852 0.9607 0.6835 ] Network output: [ -0.05145 0.1866 0.9169 0.001857 -0.0008335 1.007 0.001399 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6485 0.6134 0.3998 0.2212 0.9804 0.9868 0.6491 0.9563 0.9737 0.4286 ] Network output: [ -0.09205 0.2696 0.8461 0.0004709 -0.0002114 1.07 0.0003549 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6686 0.6619 0.441 0.1565 0.9777 0.9849 0.6687 0.9494 0.9692 0.4475 ] Network output: [ 0.06312 0.7981 0.1072 -0.0006232 0.0002798 0.9659 -0.0004697 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05232 Epoch 1243 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02866 1.007 0.982 7.189e-05 -3.228e-05 -0.04625 5.418e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03544 -0.002147 0.02634 0.02218 0.9213 0.9335 0.06935 0.8521 0.8837 0.1578 ] Network output: [ 0.9909 0.08452 -0.06013 -0.0001199 5.382e-05 -0.006704 -9.036e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6481 0.04371 0.01063 0.2355 0.9604 0.9803 0.7392 0.8727 0.9538 0.684 ] Network output: [ -0.008079 0.9383 1.037 6.036e-05 -2.71e-05 0.04096 4.549e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.067 0.04193 0.06085 0.04053 0.9772 0.9834 0.06863 0.9484 0.97 0.08703 ] Network output: [ 0.1054 -0.3011 1.144 -0.001274 0.0005718 0.9411 -0.0009599 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7329 0.5177 0.4597 0.4125 0.965 0.9832 0.7365 0.8851 0.9606 0.6827 ] Network output: [ -0.05645 0.1916 0.9211 0.001763 -0.0007917 1.007 0.001329 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6467 0.6109 0.3986 0.2211 0.9804 0.9867 0.6473 0.9562 0.9736 0.4282 ] Network output: [ -0.09819 0.2701 0.8549 0.0004005 -0.0001798 1.073 0.0003018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.667 0.6601 0.4409 0.1582 0.9777 0.9849 0.6671 0.9493 0.9692 0.4476 ] Network output: [ 0.05907 0.7985 0.1129 -0.0006527 0.000293 0.9679 -0.0004919 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05145 Epoch 1244 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02407 1.009 0.9853 3.207e-06 -1.441e-06 -0.04251 2.421e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03536 -0.001709 0.02798 0.02206 0.9212 0.9335 0.06911 0.8522 0.8838 0.1574 ] Network output: [ 0.9561 0.09072 -0.02534 -0.0003984 0.0001789 0.02085 -0.0003003 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.647 0.05511 0.02744 0.2348 0.9604 0.9803 0.7377 0.8728 0.9538 0.6838 ] Network output: [ -0.008102 0.9401 1.034 3.89e-05 -1.746e-05 0.04179 2.932e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06726 0.04283 0.06223 0.03985 0.9772 0.9835 0.06888 0.9486 0.9701 0.08746 ] Network output: [ 0.0982 -0.281 1.13 -0.001403 0.0006299 0.9491 -0.001057 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.734 0.5244 0.4657 0.4029 0.9651 0.9832 0.7376 0.8851 0.9607 0.6814 ] Network output: [ -0.04892 0.209 0.8916 0.00176 -0.0007903 1.004 0.001327 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6488 0.6139 0.3979 0.2098 0.9804 0.9867 0.6494 0.9562 0.9736 0.4264 ] Network output: [ -0.09162 0.293 0.8247 0.0003032 -0.0001361 1.067 0.0002285 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6679 0.6613 0.4404 0.1432 0.9777 0.9849 0.6681 0.9493 0.9691 0.4469 ] Network output: [ 0.06095 0.8121 0.09887 -0.0007863 0.000353 0.964 -0.0005926 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05228 Epoch 1245 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02442 0.9999 0.994 5.182e-05 -2.327e-05 -0.04254 3.906e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0353 -0.00176 0.02832 0.02292 0.9213 0.9335 0.06897 0.8523 0.8839 0.1582 ] Network output: [ 0.9545 0.06594 0.00128 -0.0003207 0.000144 0.02238 -0.0002417 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6467 0.05402 0.02934 0.2423 0.9605 0.9803 0.7373 0.873 0.9539 0.6858 ] Network output: [ -0.008184 0.9323 1.042 7.197e-05 -3.231e-05 0.04195 5.425e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06714 0.04266 0.06296 0.04162 0.9772 0.9835 0.06876 0.9487 0.9702 0.08834 ] Network output: [ 0.09946 -0.3136 1.159 -0.001258 0.0005646 0.9501 -0.0009478 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7344 0.5226 0.4662 0.4146 0.9651 0.9832 0.738 0.8853 0.9607 0.6831 ] Network output: [ -0.0514 0.1863 0.9166 0.001856 -0.0008331 1.007 0.001398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6487 0.6134 0.3996 0.2204 0.9804 0.9868 0.6493 0.9563 0.9737 0.4283 ] Network output: [ -0.09204 0.2704 0.845 0.0004698 -0.0002109 1.071 0.0003541 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6686 0.6619 0.4406 0.1553 0.9777 0.9849 0.6688 0.9494 0.9692 0.4471 ] Network output: [ 0.06313 0.7984 0.1074 -0.0006181 0.0002775 0.9655 -0.0004659 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0523 Epoch 1246 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02829 1.007 0.9823 6.938e-05 -3.115e-05 -0.0459 5.229e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03539 -0.002153 0.02641 0.02213 0.9213 0.9335 0.06922 0.8521 0.8838 0.1574 ] Network output: [ 0.9901 0.08385 -0.05849 -0.0001222 5.486e-05 -0.006119 -9.21e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6478 0.04331 0.01145 0.2352 0.9605 0.9803 0.7388 0.8728 0.9538 0.6837 ] Network output: [ -0.008142 0.9385 1.037 6.096e-05 -2.737e-05 0.04097 4.595e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06688 0.04181 0.06072 0.04037 0.9772 0.9834 0.06849 0.9484 0.97 0.08676 ] Network output: [ 0.1052 -0.3011 1.145 -0.001291 0.0005795 0.9406 -0.0009728 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7325 0.5168 0.4598 0.4118 0.9651 0.9832 0.7361 0.8851 0.9607 0.6824 ] Network output: [ -0.05629 0.1907 0.9211 0.001767 -0.0007935 1.008 0.001332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6469 0.6109 0.3985 0.2205 0.9804 0.9867 0.6475 0.9562 0.9736 0.428 ] Network output: [ -0.09798 0.2704 0.8539 0.0004052 -0.0001819 1.073 0.0003054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.667 0.6602 0.4405 0.1572 0.9777 0.9849 0.6672 0.9493 0.9692 0.4472 ] Network output: [ 0.05926 0.7985 0.113 -0.0006431 0.0002887 0.9674 -0.0004847 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05146 Epoch 1247 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02391 1.009 0.9853 3.341e-06 -1.501e-06 -0.04234 2.521e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03532 -0.001737 0.02796 0.02201 0.9213 0.9335 0.06898 0.8522 0.8839 0.157 ] Network output: [ 0.957 0.09024 -0.02593 -0.0003886 0.0001745 0.02003 -0.0002929 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6468 0.05412 0.02738 0.2344 0.9604 0.9803 0.7373 0.8728 0.9539 0.6835 ] Network output: [ -0.008155 0.9404 1.034 4.022e-05 -1.806e-05 0.04175 3.032e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06712 0.04267 0.062 0.03968 0.9772 0.9835 0.06873 0.9486 0.9701 0.08714 ] Network output: [ 0.09834 -0.2813 1.131 -0.001416 0.0006358 0.9482 -0.001067 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7336 0.5232 0.4655 0.4024 0.9651 0.9832 0.7371 0.8852 0.9607 0.6811 ] Network output: [ -0.04908 0.2077 0.8925 0.001763 -0.0007914 1.005 0.001329 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6488 0.6138 0.3978 0.2096 0.9804 0.9868 0.6494 0.9562 0.9736 0.4262 ] Network output: [ -0.09174 0.2926 0.8247 0.0003096 -0.000139 1.067 0.0002333 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6679 0.6613 0.44 0.1426 0.9777 0.9849 0.6681 0.9493 0.9691 0.4465 ] Network output: [ 0.06099 0.8116 0.09962 -0.0007726 0.0003469 0.9637 -0.0005823 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05226 Epoch 1248 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02418 1 0.9938 4.91e-05 -2.204e-05 -0.0423 3.701e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03526 -0.001778 0.02832 0.02283 0.9213 0.9335 0.06885 0.8523 0.8839 0.1577 ] Network output: [ 0.9549 0.06644 0.000376 -0.0003182 0.0001428 0.02203 -0.0002398 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6465 0.05329 0.02951 0.2416 0.9605 0.9803 0.7369 0.873 0.9539 0.6854 ] Network output: [ -0.008227 0.933 1.042 7.181e-05 -3.224e-05 0.04192 5.412e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06702 0.04252 0.06272 0.04137 0.9773 0.9835 0.06863 0.9487 0.9702 0.088 ] Network output: [ 0.09945 -0.3125 1.159 -0.001278 0.0005738 0.9494 -0.0009632 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.734 0.5215 0.4661 0.4135 0.9651 0.9832 0.7375 0.8853 0.9607 0.6827 ] Network output: [ -0.05135 0.1861 0.9161 0.001854 -0.0008325 1.008 0.001398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6488 0.6133 0.3994 0.2196 0.9804 0.9868 0.6494 0.9563 0.9737 0.4281 ] Network output: [ -0.09202 0.2713 0.8437 0.0004679 -0.0002101 1.071 0.0003527 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6686 0.6619 0.4402 0.154 0.9777 0.9849 0.6688 0.9494 0.9692 0.4467 ] Network output: [ 0.06314 0.7987 0.1075 -0.0006134 0.0002754 0.965 -0.0004623 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05229 Epoch 1249 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02791 1.007 0.9827 6.703e-05 -3.009e-05 -0.04554 5.052e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03535 -0.00216 0.02649 0.02209 0.9213 0.9335 0.06908 0.8522 0.8838 0.157 ] Network output: [ 0.9893 0.08312 -0.05671 -0.0001247 5.599e-05 -0.005487 -9.4e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6476 0.04289 0.01229 0.235 0.9605 0.9803 0.7383 0.8728 0.9538 0.6835 ] Network output: [ -0.008203 0.9388 1.037 6.177e-05 -2.773e-05 0.04098 4.656e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06675 0.04169 0.06059 0.04021 0.9772 0.9834 0.06836 0.9484 0.97 0.08649 ] Network output: [ 0.105 -0.3012 1.146 -0.001307 0.000587 0.9402 -0.0009853 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7322 0.5159 0.4599 0.4111 0.9651 0.9832 0.7357 0.8851 0.9607 0.6821 ] Network output: [ -0.05614 0.1897 0.9211 0.001772 -0.0007953 1.009 0.001335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6471 0.6109 0.3984 0.2199 0.9804 0.9868 0.6477 0.9562 0.9736 0.4278 ] Network output: [ -0.09778 0.2708 0.8529 0.0004101 -0.0001841 1.074 0.000309 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6671 0.6602 0.4401 0.1561 0.9777 0.9849 0.6672 0.9494 0.9692 0.4468 ] Network output: [ 0.05947 0.7984 0.1132 -0.0006329 0.0002841 0.9669 -0.000477 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05148 Epoch 1250 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02378 1.009 0.9853 3.667e-06 -1.647e-06 -0.04217 2.767e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03528 -0.001768 0.02794 0.02195 0.9213 0.9335 0.06886 0.8523 0.8839 0.1566 ] Network output: [ 0.9581 0.08979 -0.02669 -0.0003781 0.0001697 0.0191 -0.000285 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6466 0.05307 0.02724 0.234 0.9605 0.9803 0.737 0.8729 0.9539 0.6833 ] Network output: [ -0.008207 0.9408 1.034 4.166e-05 -1.87e-05 0.0417 3.14e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06697 0.0425 0.06176 0.03952 0.9772 0.9835 0.06858 0.9486 0.9701 0.08681 ] Network output: [ 0.09852 -0.2817 1.132 -0.001429 0.0006417 0.9473 -0.001077 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7331 0.5219 0.4653 0.402 0.9651 0.9832 0.7366 0.8852 0.9607 0.6808 ] Network output: [ -0.04928 0.2064 0.8935 0.001765 -0.0007924 1.006 0.00133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6489 0.6136 0.3976 0.2093 0.9804 0.9868 0.6495 0.9562 0.9736 0.426 ] Network output: [ -0.09189 0.2923 0.8248 0.0003156 -0.0001417 1.068 0.0002379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.668 0.6612 0.4396 0.1421 0.9777 0.9849 0.6681 0.9494 0.9691 0.4461 ] Network output: [ 0.06105 0.8111 0.1004 -0.0007586 0.0003406 0.9634 -0.0005717 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05224 Epoch 1251 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02392 1.001 0.9935 4.624e-05 -2.076e-05 -0.04204 3.485e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03522 -0.001796 0.02832 0.02274 0.9213 0.9335 0.06872 0.8523 0.884 0.1573 ] Network output: [ 0.9552 0.067 -0.000506 -0.0003163 0.000142 0.02173 -0.0002384 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6463 0.05256 0.02971 0.2409 0.9605 0.9804 0.7365 0.873 0.954 0.6851 ] Network output: [ -0.008269 0.9336 1.041 7.166e-05 -3.217e-05 0.04189 5.401e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06688 0.04238 0.06249 0.04112 0.9773 0.9835 0.06849 0.9487 0.9702 0.08764 ] Network output: [ 0.09943 -0.3113 1.159 -0.001299 0.0005831 0.9486 -0.0009788 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7335 0.5205 0.466 0.4124 0.9652 0.9833 0.737 0.8853 0.9607 0.6823 ] Network output: [ -0.05128 0.1859 0.9155 0.001853 -0.0008319 1.009 0.001397 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6489 0.6133 0.3991 0.2187 0.9804 0.9868 0.6495 0.9563 0.9737 0.4278 ] Network output: [ -0.092 0.2723 0.8424 0.0004654 -0.0002089 1.071 0.0003507 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6686 0.6619 0.4398 0.1527 0.9777 0.985 0.6688 0.9494 0.9692 0.4463 ] Network output: [ 0.06318 0.799 0.1076 -0.000609 0.0002734 0.9646 -0.000459 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05229 Epoch 1252 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02753 1.007 0.9832 6.478e-05 -2.908e-05 -0.04516 4.883e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0353 -0.002167 0.02657 0.02206 0.9214 0.9336 0.06894 0.8522 0.8839 0.1567 ] Network output: [ 0.9884 0.08233 -0.0548 -0.0001275 5.725e-05 -0.004806 -9.611e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6473 0.04246 0.01315 0.2348 0.9605 0.9804 0.7378 0.8728 0.9539 0.6833 ] Network output: [ -0.008262 0.939 1.037 6.276e-05 -2.818e-05 0.04099 4.731e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06662 0.04157 0.06046 0.04005 0.9772 0.9835 0.06822 0.9485 0.97 0.08621 ] Network output: [ 0.1048 -0.3014 1.147 -0.001324 0.0005942 0.9398 -0.0009975 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7318 0.5149 0.46 0.4105 0.9651 0.9832 0.7353 0.8851 0.9607 0.6818 ] Network output: [ -0.05599 0.1888 0.9211 0.001776 -0.0007973 1.009 0.001338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6472 0.6109 0.3982 0.2193 0.9804 0.9868 0.6478 0.9562 0.9736 0.4276 ] Network output: [ -0.09758 0.2711 0.8518 0.0004149 -0.0001863 1.074 0.0003127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6672 0.6603 0.4397 0.1551 0.9777 0.9849 0.6673 0.9494 0.9692 0.4463 ] Network output: [ 0.05971 0.7983 0.1134 -0.0006222 0.0002793 0.9664 -0.0004689 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05151 Epoch 1253 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02365 1.009 0.9853 4.184e-06 -1.879e-06 -0.04201 3.157e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03524 -0.001801 0.02791 0.0219 0.9214 0.9335 0.06873 0.8523 0.8839 0.1562 ] Network output: [ 0.9593 0.08936 -0.0276 -0.0003669 0.0001647 0.01809 -0.0002765 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6464 0.05195 0.02706 0.2336 0.9605 0.9803 0.7366 0.8729 0.9539 0.683 ] Network output: [ -0.008259 0.9411 1.034 4.321e-05 -1.94e-05 0.04166 3.257e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06683 0.04232 0.06152 0.03935 0.9772 0.9835 0.06843 0.9486 0.9701 0.08647 ] Network output: [ 0.09873 -0.2821 1.132 -0.001442 0.0006474 0.9463 -0.001087 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7326 0.5205 0.465 0.4016 0.9651 0.9832 0.7361 0.8852 0.9607 0.6805 ] Network output: [ -0.0495 0.205 0.8945 0.001767 -0.0007933 1.007 0.001332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.649 0.6135 0.3974 0.2091 0.9804 0.9868 0.6495 0.9562 0.9736 0.4258 ] Network output: [ -0.09207 0.292 0.8249 0.0003215 -0.0001443 1.069 0.0002423 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6679 0.6612 0.4392 0.1416 0.9777 0.9849 0.6681 0.9494 0.9692 0.4457 ] Network output: [ 0.06111 0.8105 0.1013 -0.0007442 0.0003341 0.963 -0.0005608 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05223 Epoch 1254 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02366 1.001 0.9933 4.328e-05 -1.943e-05 -0.04178 3.262e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03518 -0.001815 0.02832 0.02265 0.9214 0.9336 0.0686 0.8524 0.884 0.1569 ] Network output: [ 0.9555 0.06761 -0.001393 -0.0003151 0.0001415 0.02145 -0.0002375 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.646 0.0518 0.02992 0.2402 0.9605 0.9804 0.7361 0.873 0.954 0.6847 ] Network output: [ -0.00831 0.9343 1.041 7.156e-05 -3.213e-05 0.04185 5.394e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06675 0.04223 0.06225 0.04086 0.9773 0.9835 0.06835 0.9487 0.9702 0.08728 ] Network output: [ 0.09941 -0.31 1.158 -0.00132 0.0005926 0.9479 -0.0009947 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.733 0.5193 0.4658 0.4113 0.9652 0.9833 0.7366 0.8853 0.9608 0.682 ] Network output: [ -0.05121 0.1859 0.9148 0.001851 -0.0008312 1.009 0.001395 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.649 0.6132 0.3989 0.2177 0.9804 0.9868 0.6496 0.9563 0.9737 0.4275 ] Network output: [ -0.09199 0.2734 0.8409 0.0004621 -0.0002075 1.072 0.0003483 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6686 0.6618 0.4393 0.1513 0.9778 0.985 0.6688 0.9494 0.9692 0.4459 ] Network output: [ 0.06323 0.7993 0.1078 -0.0006048 0.0002715 0.9641 -0.0004558 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05231 Epoch 1255 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02714 1.007 0.9836 6.263e-05 -2.812e-05 -0.04478 4.72e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03526 -0.002174 0.02665 0.02202 0.9214 0.9336 0.0688 0.8523 0.8839 0.1563 ] Network output: [ 0.9874 0.0815 -0.05278 -0.0001307 5.869e-05 -0.004072 -9.853e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.647 0.04201 0.01404 0.2346 0.9605 0.9804 0.7373 0.8729 0.9539 0.6831 ] Network output: [ -0.008319 0.9392 1.037 6.393e-05 -2.87e-05 0.04099 4.818e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06649 0.04144 0.06032 0.0399 0.9772 0.9835 0.06808 0.9485 0.97 0.08594 ] Network output: [ 0.1046 -0.3016 1.148 -0.00134 0.0006014 0.9394 -0.001009 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7314 0.5139 0.4601 0.4099 0.9651 0.9832 0.7349 0.8852 0.9607 0.6815 ] Network output: [ -0.05585 0.1878 0.9211 0.001781 -0.0007994 1.01 0.001342 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6474 0.6109 0.398 0.2188 0.9804 0.9868 0.648 0.9562 0.9736 0.4274 ] Network output: [ -0.09738 0.2715 0.8507 0.0004197 -0.0001884 1.074 0.0003163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6673 0.6603 0.4393 0.154 0.9777 0.9849 0.6674 0.9494 0.9692 0.4459 ] Network output: [ 0.05997 0.7981 0.1136 -0.0006113 0.0002744 0.9659 -0.0004607 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05156 Epoch 1256 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02353 1.01 0.9853 4.891e-06 -2.196e-06 -0.04185 3.689e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0352 -0.001836 0.02787 0.02184 0.9214 0.9336 0.06861 0.8524 0.884 0.1558 ] Network output: [ 0.9606 0.08893 -0.02861 -0.0003552 0.0001595 0.01702 -0.0002677 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6462 0.05077 0.02683 0.2333 0.9605 0.9804 0.7362 0.8729 0.9539 0.6827 ] Network output: [ -0.00831 0.9414 1.034 4.489e-05 -2.015e-05 0.04161 3.384e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06667 0.04213 0.06126 0.03918 0.9772 0.9835 0.06827 0.9486 0.9701 0.08613 ] Network output: [ 0.09896 -0.2826 1.133 -0.001455 0.000653 0.9454 -0.001096 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7321 0.5191 0.4647 0.4012 0.9651 0.9832 0.7356 0.8852 0.9607 0.6802 ] Network output: [ -0.04975 0.2037 0.8956 0.001769 -0.0007942 1.007 0.001333 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.649 0.6133 0.3972 0.2089 0.9804 0.9868 0.6496 0.9562 0.9736 0.4256 ] Network output: [ -0.09228 0.2917 0.8251 0.0003272 -0.0001469 1.069 0.0002466 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6679 0.6611 0.4388 0.1411 0.9777 0.9849 0.668 0.9494 0.9692 0.4453 ] Network output: [ 0.06118 0.8098 0.1021 -0.0007294 0.0003274 0.9627 -0.0005497 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05223 Epoch 1257 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02339 1.002 0.9931 4.026e-05 -1.808e-05 -0.0415 3.035e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03514 -0.001834 0.02832 0.02256 0.9214 0.9336 0.06847 0.8524 0.884 0.1564 ] Network output: [ 0.9558 0.06828 -0.002308 -0.0003144 0.0001411 0.02119 -0.0002369 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6458 0.05101 0.03013 0.2396 0.9605 0.9804 0.7356 0.873 0.954 0.6844 ] Network output: [ -0.008351 0.9349 1.04 7.151e-05 -3.21e-05 0.04182 5.39e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06661 0.04208 0.06201 0.0406 0.9773 0.9835 0.0682 0.9487 0.9702 0.08692 ] Network output: [ 0.09939 -0.3087 1.157 -0.001341 0.0006021 0.9471 -0.001011 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7326 0.5182 0.4657 0.4102 0.9652 0.9833 0.7361 0.8853 0.9608 0.6816 ] Network output: [ -0.05115 0.1859 0.9141 0.00185 -0.0008304 1.01 0.001394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6491 0.6131 0.3986 0.2168 0.9804 0.9868 0.6497 0.9563 0.9737 0.4272 ] Network output: [ -0.09199 0.2746 0.8394 0.0004583 -0.0002058 1.072 0.0003454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6686 0.6618 0.4389 0.1498 0.9778 0.985 0.6687 0.9495 0.9692 0.4454 ] Network output: [ 0.06329 0.7995 0.1079 -0.0006006 0.0002696 0.9636 -0.0004526 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05234 Epoch 1258 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02674 1.007 0.9841 6.053e-05 -2.717e-05 -0.04438 4.562e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03521 -0.002181 0.02674 0.02199 0.9214 0.9336 0.06866 0.8523 0.884 0.1559 ] Network output: [ 0.9864 0.08065 -0.05065 -0.0001344 6.035e-05 -0.003284 -0.0001013 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6467 0.04154 0.01497 0.2345 0.9605 0.9804 0.7368 0.8729 0.9539 0.6829 ] Network output: [ -0.008374 0.9394 1.037 6.525e-05 -2.93e-05 0.041 4.918e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06635 0.04131 0.06019 0.03975 0.9772 0.9835 0.06794 0.9485 0.9701 0.08566 ] Network output: [ 0.1044 -0.3018 1.149 -0.001355 0.0006084 0.9389 -0.001021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.731 0.5128 0.4602 0.4093 0.9651 0.9832 0.7345 0.8852 0.9607 0.6812 ] Network output: [ -0.05571 0.1869 0.9211 0.001785 -0.0008015 1.011 0.001345 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6475 0.6109 0.3978 0.2182 0.9804 0.9868 0.6481 0.9562 0.9736 0.4271 ] Network output: [ -0.09719 0.2719 0.8496 0.0004243 -0.0001905 1.075 0.0003197 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6673 0.6603 0.4388 0.1529 0.9777 0.985 0.6674 0.9494 0.9692 0.4455 ] Network output: [ 0.06026 0.7979 0.1138 -0.0006 0.0002694 0.9653 -0.0004522 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05162 Epoch 1259 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02342 1.01 0.9852 5.782e-06 -2.596e-06 -0.04169 4.36e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03516 -0.001872 0.02783 0.02179 0.9214 0.9336 0.06848 0.8524 0.884 0.1554 ] Network output: [ 0.962 0.08849 -0.02968 -0.0003432 0.0001541 0.01589 -0.0002586 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6459 0.04953 0.02656 0.2329 0.9605 0.9804 0.7357 0.8729 0.9539 0.6824 ] Network output: [ -0.008361 0.9418 1.034 4.672e-05 -2.097e-05 0.04156 3.521e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06652 0.04193 0.061 0.03902 0.9772 0.9835 0.06811 0.9486 0.9701 0.08578 ] Network output: [ 0.09922 -0.2831 1.134 -0.001467 0.0006585 0.9443 -0.001105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7316 0.5176 0.4643 0.4009 0.9651 0.9832 0.7351 0.8852 0.9607 0.6799 ] Network output: [ -0.05003 0.2023 0.8967 0.001771 -0.0007951 1.008 0.001335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.649 0.6131 0.397 0.2087 0.9804 0.9868 0.6496 0.9562 0.9736 0.4254 ] Network output: [ -0.09251 0.2913 0.8252 0.0003328 -0.0001494 1.07 0.0002508 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6679 0.6611 0.4384 0.1405 0.9777 0.9849 0.668 0.9494 0.9692 0.4448 ] Network output: [ 0.06127 0.8091 0.1031 -0.0007141 0.0003206 0.9624 -0.0005381 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05224 Epoch 1260 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02312 1.002 0.9928 3.723e-05 -1.672e-05 -0.04122 2.806e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0351 -0.001854 0.02832 0.02246 0.9215 0.9336 0.06834 0.8525 0.8841 0.156 ] Network output: [ 0.956 0.069 -0.003276 -0.0003139 0.0001409 0.02092 -0.0002366 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6455 0.05019 0.03033 0.2389 0.9605 0.9804 0.7352 0.8731 0.954 0.684 ] Network output: [ -0.00839 0.9356 1.04 7.152e-05 -3.211e-05 0.04179 5.39e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06647 0.04192 0.06177 0.04035 0.9773 0.9835 0.06805 0.9487 0.9703 0.08655 ] Network output: [ 0.09938 -0.3073 1.157 -0.001363 0.0006118 0.9463 -0.001027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7321 0.517 0.4655 0.4091 0.9652 0.9833 0.7356 0.8853 0.9608 0.6811 ] Network output: [ -0.05109 0.1859 0.9133 0.001848 -0.0008295 1.01 0.001393 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6492 0.613 0.3983 0.2158 0.9804 0.9868 0.6498 0.9563 0.9737 0.4268 ] Network output: [ -0.092 0.2759 0.8378 0.000454 -0.0002038 1.072 0.0003421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6686 0.6617 0.4385 0.1483 0.9778 0.985 0.6687 0.9495 0.9693 0.445 ] Network output: [ 0.06336 0.7998 0.108 -0.0005964 0.0002678 0.963 -0.0004495 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05239 Epoch 1261 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02633 1.007 0.9846 5.846e-05 -2.624e-05 -0.04397 4.406e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03517 -0.002188 0.02683 0.02196 0.9215 0.9336 0.06852 0.8524 0.884 0.1555 ] Network output: [ 0.9853 0.07979 -0.04843 -0.0001387 6.226e-05 -0.002442 -0.0001045 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6464 0.04106 0.01592 0.2344 0.9606 0.9804 0.7363 0.8729 0.9539 0.6826 ] Network output: [ -0.008427 0.9395 1.037 6.672e-05 -2.995e-05 0.04101 5.029e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06621 0.04117 0.06006 0.03961 0.9772 0.9835 0.0678 0.9485 0.9701 0.08539 ] Network output: [ 0.1043 -0.3021 1.15 -0.001371 0.0006154 0.9385 -0.001033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7305 0.5117 0.4603 0.4088 0.9651 0.9832 0.734 0.8852 0.9607 0.6808 ] Network output: [ -0.05557 0.1859 0.9211 0.00179 -0.0008036 1.011 0.001349 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6477 0.6108 0.3976 0.2176 0.9804 0.9868 0.6483 0.9562 0.9736 0.4269 ] Network output: [ -0.09699 0.2724 0.8484 0.0004285 -0.0001924 1.075 0.000323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6674 0.6603 0.4384 0.1518 0.9778 0.985 0.6675 0.9494 0.9692 0.445 ] Network output: [ 0.06057 0.7976 0.114 -0.0005886 0.0002643 0.9648 -0.0004436 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0517 Epoch 1262 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02331 1.01 0.9852 6.851e-06 -3.076e-06 -0.04153 5.166e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03512 -0.00191 0.0278 0.02174 0.9215 0.9336 0.06835 0.8525 0.8841 0.155 ] Network output: [ 0.9633 0.08804 -0.03079 -0.0003309 0.0001485 0.01473 -0.0002494 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6457 0.04824 0.02628 0.2326 0.9605 0.9804 0.7353 0.873 0.9539 0.6821 ] Network output: [ -0.008411 0.9421 1.033 4.869e-05 -2.186e-05 0.0415 3.67e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06636 0.04173 0.06074 0.03886 0.9772 0.9835 0.06794 0.9486 0.9701 0.08543 ] Network output: [ 0.0995 -0.2837 1.135 -0.001479 0.0006639 0.9433 -0.001115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7311 0.5161 0.464 0.4006 0.9652 0.9833 0.7346 0.8852 0.9607 0.6796 ] Network output: [ -0.05033 0.2009 0.8979 0.001773 -0.000796 1.009 0.001336 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.649 0.6128 0.3968 0.2085 0.9804 0.9868 0.6496 0.9562 0.9736 0.4252 ] Network output: [ -0.09276 0.291 0.8255 0.0003384 -0.0001519 1.07 0.0002551 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6678 0.661 0.4379 0.14 0.9778 0.985 0.668 0.9494 0.9692 0.4444 ] Network output: [ 0.06137 0.8083 0.104 -0.0006983 0.0003135 0.9621 -0.0005262 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05227 Epoch 1263 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02285 1.003 0.9925 3.423e-05 -1.537e-05 -0.04094 2.58e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03506 -0.001876 0.02833 0.02237 0.9215 0.9336 0.06821 0.8525 0.8841 0.1556 ] Network output: [ 0.9563 0.06977 -0.004316 -0.0003136 0.0001408 0.02063 -0.0002364 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6453 0.04932 0.03052 0.2383 0.9606 0.9804 0.7348 0.8731 0.954 0.6837 ] Network output: [ -0.008429 0.9363 1.039 7.161e-05 -3.215e-05 0.04175 5.397e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06632 0.04176 0.06152 0.04008 0.9773 0.9835 0.0679 0.9487 0.9703 0.08617 ] Network output: [ 0.09939 -0.3059 1.156 -0.001385 0.0006216 0.9455 -0.001043 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7316 0.5157 0.4653 0.4081 0.9652 0.9833 0.7351 0.8853 0.9608 0.6807 ] Network output: [ -0.05104 0.186 0.9125 0.001846 -0.0008286 1.011 0.001391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6493 0.6129 0.398 0.2148 0.9804 0.9868 0.6499 0.9563 0.9737 0.4265 ] Network output: [ -0.09202 0.2772 0.8362 0.0004492 -0.0002016 1.072 0.0003385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6685 0.6616 0.438 0.1468 0.9778 0.985 0.6687 0.9495 0.9693 0.4445 ] Network output: [ 0.06345 0.8 0.1082 -0.0005921 0.0002658 0.9625 -0.0004463 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05245 Epoch 1264 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02591 1.007 0.9851 5.639e-05 -2.532e-05 -0.04355 4.25e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03512 -0.002196 0.02692 0.02193 0.9215 0.9337 0.06837 0.8524 0.8841 0.1551 ] Network output: [ 0.9841 0.07894 -0.04614 -0.0001435 6.444e-05 -0.001547 -0.0001082 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6461 0.04056 0.01689 0.2342 0.9606 0.9804 0.7358 0.8729 0.9539 0.6824 ] Network output: [ -0.008477 0.9397 1.037 6.831e-05 -3.067e-05 0.04102 5.149e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06607 0.04103 0.05993 0.03946 0.9772 0.9835 0.06765 0.9485 0.9701 0.08511 ] Network output: [ 0.1041 -0.3024 1.151 -0.001386 0.0006224 0.9381 -0.001045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7301 0.5106 0.4604 0.4083 0.9652 0.9833 0.7336 0.8852 0.9607 0.6805 ] Network output: [ -0.05543 0.185 0.9211 0.001795 -0.0008057 1.012 0.001353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6478 0.6108 0.3974 0.217 0.9804 0.9868 0.6484 0.9562 0.9736 0.4266 ] Network output: [ -0.0968 0.2729 0.8472 0.0004325 -0.0001941 1.075 0.0003259 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6674 0.6603 0.4379 0.1507 0.9778 0.985 0.6675 0.9495 0.9693 0.4445 ] Network output: [ 0.0609 0.7974 0.1143 -0.0005772 0.0002591 0.9642 -0.000435 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05179 Epoch 1265 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0232 1.01 0.9852 8.089e-06 -3.632e-06 -0.04137 6.099e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03508 -0.00195 0.02776 0.02169 0.9215 0.9337 0.06822 0.8525 0.8841 0.1546 ] Network output: [ 0.9647 0.08756 -0.03189 -0.0003185 0.000143 0.01356 -0.00024 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6455 0.0469 0.02599 0.2323 0.9606 0.9804 0.7349 0.873 0.954 0.6818 ] Network output: [ -0.00846 0.9424 1.033 5.083e-05 -2.282e-05 0.04145 3.831e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0662 0.04153 0.06047 0.0387 0.9773 0.9835 0.06777 0.9486 0.9701 0.08508 ] Network output: [ 0.0998 -0.2843 1.136 -0.001491 0.0006692 0.9422 -0.001123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7306 0.5144 0.4636 0.4004 0.9652 0.9833 0.734 0.8852 0.9607 0.6793 ] Network output: [ -0.05065 0.1994 0.8992 0.001775 -0.0007969 1.01 0.001338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.649 0.6125 0.3965 0.2084 0.9804 0.9868 0.6495 0.9562 0.9736 0.425 ] Network output: [ -0.09303 0.2906 0.8257 0.0003441 -0.0001545 1.071 0.0002593 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6678 0.6609 0.4375 0.1395 0.9778 0.985 0.6679 0.9494 0.9692 0.444 ] Network output: [ 0.0615 0.8074 0.1051 -0.0006819 0.0003061 0.9617 -0.0005139 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05231 Epoch 1266 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02258 1.003 0.9922 3.13e-05 -1.405e-05 -0.04065 2.359e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03502 -0.001898 0.02832 0.02228 0.9215 0.9337 0.06808 0.8526 0.8842 0.1551 ] Network output: [ 0.9566 0.07059 -0.005445 -0.0003134 0.0001407 0.02031 -0.0002362 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.645 0.0484 0.03069 0.2376 0.9606 0.9804 0.7343 0.8731 0.954 0.6833 ] Network output: [ -0.008467 0.937 1.039 7.18e-05 -3.223e-05 0.04171 5.411e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06617 0.04159 0.06126 0.03982 0.9773 0.9835 0.06774 0.9487 0.9703 0.08579 ] Network output: [ 0.09941 -0.3044 1.155 -0.001406 0.0006314 0.9446 -0.00106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7311 0.5144 0.4651 0.407 0.9652 0.9833 0.7346 0.8853 0.9608 0.6803 ] Network output: [ -0.05101 0.1861 0.9117 0.001843 -0.0008276 1.012 0.001389 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6493 0.6128 0.3976 0.2138 0.9804 0.9868 0.6499 0.9562 0.9737 0.4261 ] Network output: [ -0.09207 0.2786 0.8346 0.000444 -0.0001993 1.073 0.0003346 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6685 0.6615 0.4375 0.1452 0.9778 0.985 0.6686 0.9495 0.9693 0.444 ] Network output: [ 0.06355 0.8002 0.1084 -0.0005876 0.0002638 0.962 -0.0004429 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05252 Epoch 1267 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02548 1.007 0.9856 5.431e-05 -2.438e-05 -0.04312 4.093e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03507 -0.002204 0.02701 0.0219 0.9215 0.9337 0.06822 0.8525 0.8841 0.1547 ] Network output: [ 0.9828 0.07813 -0.0438 -0.0001491 6.693e-05 -0.0006015 -0.0001124 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6458 0.04005 0.0179 0.2342 0.9606 0.9804 0.7353 0.873 0.954 0.6822 ] Network output: [ -0.008524 0.9399 1.036 7.002e-05 -3.144e-05 0.04103 5.278e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06593 0.04089 0.05979 0.03931 0.9773 0.9835 0.0675 0.9485 0.9701 0.08482 ] Network output: [ 0.1039 -0.3027 1.151 -0.001402 0.0006295 0.9377 -0.001057 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7297 0.5094 0.4605 0.4077 0.9652 0.9833 0.7332 0.8852 0.9607 0.6802 ] Network output: [ -0.05529 0.1841 0.921 0.001799 -0.0008079 1.013 0.001356 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6479 0.6107 0.3972 0.2163 0.9804 0.9868 0.6485 0.9561 0.9736 0.4263 ] Network output: [ -0.09661 0.2735 0.8459 0.0004359 -0.0001957 1.076 0.0003285 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6674 0.6603 0.4375 0.1494 0.9778 0.985 0.6676 0.9495 0.9693 0.444 ] Network output: [ 0.06125 0.7971 0.1145 -0.0005658 0.000254 0.9636 -0.0004264 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0519 Epoch 1268 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02309 1.01 0.9851 9.48e-06 -4.257e-06 -0.0412 7.147e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03503 -0.00199 0.02772 0.02164 0.9215 0.9337 0.06809 0.8525 0.8841 0.1542 ] Network output: [ 0.9661 0.08706 -0.03295 -0.0003061 0.0001374 0.01239 -0.0002307 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6452 0.04553 0.0257 0.232 0.9606 0.9804 0.7345 0.873 0.954 0.6815 ] Network output: [ -0.008508 0.9427 1.033 5.314e-05 -2.386e-05 0.04139 4.005e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06603 0.04131 0.06021 0.03855 0.9773 0.9835 0.0676 0.9486 0.9701 0.08472 ] Network output: [ 0.1001 -0.2851 1.138 -0.001502 0.0006743 0.9412 -0.001132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.73 0.5127 0.4632 0.4002 0.9652 0.9833 0.7335 0.8852 0.9607 0.679 ] Network output: [ -0.05099 0.1979 0.9005 0.001777 -0.0007979 1.011 0.001339 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6489 0.6122 0.3963 0.2082 0.9804 0.9868 0.6495 0.9562 0.9736 0.4248 ] Network output: [ -0.09332 0.2902 0.8259 0.0003497 -0.000157 1.072 0.0002636 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6677 0.6607 0.437 0.139 0.9778 0.985 0.6679 0.9495 0.9692 0.4435 ] Network output: [ 0.06165 0.8065 0.1061 -0.0006651 0.0002986 0.9614 -0.0005012 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05236 Epoch 1269 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02231 1.004 0.9919 2.848e-05 -1.279e-05 -0.04036 2.147e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03498 -0.001923 0.02832 0.02218 0.9215 0.9337 0.06795 0.8526 0.8842 0.1546 ] Network output: [ 0.957 0.07144 -0.006672 -0.0003131 0.0001406 0.01993 -0.000236 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6448 0.04742 0.03083 0.237 0.9606 0.9804 0.7338 0.8731 0.954 0.6829 ] Network output: [ -0.008505 0.9377 1.038 7.21e-05 -3.237e-05 0.04167 5.434e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06602 0.04141 0.06099 0.03956 0.9773 0.9835 0.06758 0.9487 0.9703 0.0854 ] Network output: [ 0.09945 -0.303 1.155 -0.001428 0.0006412 0.9437 -0.001076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7306 0.513 0.4649 0.406 0.9652 0.9833 0.734 0.8853 0.9608 0.6799 ] Network output: [ -0.051 0.1862 0.911 0.001841 -0.0008265 1.012 0.001387 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6494 0.6126 0.3973 0.2128 0.9804 0.9868 0.6499 0.9562 0.9737 0.4257 ] Network output: [ -0.09214 0.28 0.8329 0.0004385 -0.0001968 1.073 0.0003304 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6684 0.6614 0.4371 0.1437 0.9778 0.985 0.6686 0.9495 0.9693 0.4435 ] Network output: [ 0.06366 0.8003 0.1086 -0.0005827 0.0002616 0.9614 -0.0004392 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05261 Epoch 1270 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02505 1.007 0.9861 5.219e-05 -2.343e-05 -0.04268 3.933e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03502 -0.002212 0.02711 0.02187 0.9216 0.9337 0.06807 0.8525 0.8841 0.1543 ] Network output: [ 0.9815 0.07736 -0.04143 -0.0001553 6.972e-05 0.0003868 -0.000117 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6454 0.03951 0.01893 0.2341 0.9606 0.9804 0.7347 0.873 0.954 0.682 ] Network output: [ -0.008569 0.9401 1.036 7.183e-05 -3.225e-05 0.04105 5.414e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06578 0.04075 0.05966 0.03917 0.9773 0.9835 0.06734 0.9485 0.9701 0.08453 ] Network output: [ 0.1037 -0.3029 1.152 -0.001418 0.0006367 0.9373 -0.001069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7293 0.5082 0.4606 0.4072 0.9652 0.9833 0.7327 0.8852 0.9607 0.6799 ] Network output: [ -0.05514 0.1833 0.9208 0.001804 -0.00081 1.013 0.00136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.648 0.6106 0.397 0.2157 0.9804 0.9868 0.6486 0.9561 0.9736 0.426 ] Network output: [ -0.09642 0.2742 0.8445 0.0004388 -0.000197 1.076 0.0003307 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6675 0.6603 0.437 0.1482 0.9778 0.985 0.6676 0.9495 0.9693 0.4436 ] Network output: [ 0.06162 0.7968 0.1147 -0.0005544 0.0002489 0.963 -0.0004178 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05203 Epoch 1271 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02298 1.01 0.9852 1.101e-05 -4.942e-06 -0.04103 8.298e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03499 -0.002031 0.02768 0.02159 0.9216 0.9337 0.06796 0.8526 0.8842 0.1538 ] Network output: [ 0.9675 0.08651 -0.03394 -0.0002939 0.000132 0.01124 -0.0002215 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.645 0.04411 0.02543 0.2318 0.9606 0.9804 0.734 0.873 0.954 0.6813 ] Network output: [ -0.008555 0.943 1.033 5.563e-05 -2.497e-05 0.04134 4.193e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06586 0.0411 0.05994 0.0384 0.9773 0.9835 0.06742 0.9486 0.9701 0.08436 ] Network output: [ 0.1004 -0.2859 1.139 -0.001513 0.0006794 0.9401 -0.00114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7295 0.511 0.4629 0.4001 0.9652 0.9833 0.7329 0.8852 0.9607 0.6787 ] Network output: [ -0.05134 0.1964 0.9019 0.00178 -0.000799 1.012 0.001341 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6489 0.6119 0.396 0.2081 0.9804 0.9868 0.6495 0.9561 0.9736 0.4245 ] Network output: [ -0.09362 0.2898 0.8262 0.0003554 -0.0001596 1.073 0.0002678 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6677 0.6606 0.4366 0.1385 0.9778 0.985 0.6678 0.9495 0.9692 0.443 ] Network output: [ 0.06182 0.8055 0.1072 -0.0006477 0.0002908 0.961 -0.0004881 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05243 Epoch 1272 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02205 1.004 0.9916 2.582e-05 -1.159e-05 -0.04008 1.946e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03493 -0.00195 0.02831 0.02209 0.9216 0.9337 0.06781 0.8526 0.8842 0.1542 ] Network output: [ 0.9575 0.07233 -0.008006 -0.0003125 0.0001403 0.0195 -0.0002355 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6445 0.04637 0.03093 0.2363 0.9606 0.9804 0.7334 0.8731 0.954 0.6825 ] Network output: [ -0.008542 0.9384 1.037 7.254e-05 -3.257e-05 0.04163 5.467e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06586 0.04122 0.06072 0.0393 0.9773 0.9835 0.06742 0.9487 0.9703 0.08501 ] Network output: [ 0.09952 -0.3016 1.154 -0.00145 0.000651 0.9428 -0.001093 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.73 0.5115 0.4647 0.4049 0.9652 0.9833 0.7335 0.8853 0.9608 0.6795 ] Network output: [ -0.05101 0.1863 0.9102 0.001838 -0.0008253 1.013 0.001386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6494 0.6124 0.3969 0.2118 0.9804 0.9868 0.65 0.9562 0.9737 0.4253 ] Network output: [ -0.09223 0.2815 0.8313 0.0004328 -0.0001943 1.074 0.0003262 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6684 0.6613 0.4366 0.1421 0.9778 0.985 0.6685 0.9495 0.9693 0.443 ] Network output: [ 0.06378 0.8004 0.1089 -0.0005774 0.0002592 0.9609 -0.0004352 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05271 Epoch 1273 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0246 1.007 0.9866 5.003e-05 -2.246e-05 -0.04222 3.771e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03497 -0.00222 0.02721 0.02184 0.9216 0.9337 0.06792 0.8526 0.8842 0.1539 ] Network output: [ 0.9802 0.07665 -0.03907 -0.0001622 7.28e-05 0.001409 -0.0001222 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6451 0.03896 0.01998 0.234 0.9606 0.9804 0.7342 0.873 0.954 0.6817 ] Network output: [ -0.008611 0.9402 1.036 7.374e-05 -3.311e-05 0.04106 5.558e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06563 0.0406 0.05952 0.03901 0.9773 0.9835 0.06718 0.9486 0.9701 0.08424 ] Network output: [ 0.1035 -0.303 1.153 -0.001434 0.000644 0.9368 -0.001081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7288 0.5069 0.4607 0.4066 0.9652 0.9833 0.7323 0.8852 0.9607 0.6795 ] Network output: [ -0.05499 0.1825 0.9206 0.001809 -0.000812 1.014 0.001363 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6481 0.6105 0.3967 0.2149 0.9804 0.9868 0.6487 0.9561 0.9736 0.4257 ] Network output: [ -0.09624 0.2749 0.843 0.000441 -0.000198 1.076 0.0003323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6675 0.6603 0.4365 0.1468 0.9778 0.985 0.6676 0.9495 0.9693 0.4431 ] Network output: [ 0.062 0.7964 0.1149 -0.0005432 0.0002439 0.9624 -0.0004094 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05217 Epoch 1274 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02286 1.01 0.9852 1.264e-05 -5.677e-06 -0.04084 9.532e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03495 -0.002072 0.02765 0.02155 0.9216 0.9337 0.06783 0.8526 0.8842 0.1534 ] Network output: [ 0.9688 0.08593 -0.03482 -0.0002822 0.0001267 0.01014 -0.0002127 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6447 0.04268 0.02519 0.2316 0.9606 0.9804 0.7336 0.873 0.954 0.681 ] Network output: [ -0.008601 0.9432 1.033 5.829e-05 -2.617e-05 0.04129 4.393e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06568 0.04087 0.05967 0.03825 0.9773 0.9835 0.06724 0.9486 0.9701 0.084 ] Network output: [ 0.1008 -0.2868 1.14 -0.001524 0.0006843 0.939 -0.001149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7289 0.5092 0.4625 0.4 0.9652 0.9833 0.7324 0.8852 0.9607 0.6784 ] Network output: [ -0.05169 0.1949 0.9033 0.001782 -0.0008001 1.012 0.001343 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6488 0.6116 0.3958 0.208 0.9804 0.9868 0.6494 0.9561 0.9736 0.4243 ] Network output: [ -0.09392 0.2895 0.8264 0.0003611 -0.0001621 1.073 0.0002721 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6676 0.6605 0.4361 0.138 0.9778 0.985 0.6677 0.9495 0.9692 0.4425 ] Network output: [ 0.06202 0.8045 0.1083 -0.0006299 0.0002828 0.9606 -0.0004747 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05252 Epoch 1275 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0218 1.005 0.9912 2.337e-05 -1.049e-05 -0.03979 1.761e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03489 -0.001979 0.0283 0.022 0.9216 0.9338 0.06768 0.8527 0.8843 0.1537 ] Network output: [ 0.958 0.07323 -0.009448 -0.0003116 0.0001399 0.01899 -0.0002349 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6442 0.04525 0.031 0.2357 0.9606 0.9804 0.7329 0.8731 0.9541 0.6821 ] Network output: [ -0.008578 0.9392 1.037 7.313e-05 -3.283e-05 0.04159 5.512e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0657 0.04103 0.06044 0.03904 0.9773 0.9835 0.06724 0.9487 0.9703 0.08461 ] Network output: [ 0.09962 -0.3003 1.153 -0.001472 0.0006608 0.9419 -0.001109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7295 0.5099 0.4644 0.404 0.9652 0.9833 0.7329 0.8853 0.9608 0.679 ] Network output: [ -0.05105 0.1864 0.9096 0.001836 -0.0008242 1.014 0.001384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6494 0.6121 0.3965 0.2108 0.9804 0.9868 0.65 0.9562 0.9736 0.4249 ] Network output: [ -0.09235 0.2829 0.8296 0.000427 -0.0001917 1.074 0.0003218 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6683 0.6612 0.4361 0.1406 0.9778 0.985 0.6684 0.9495 0.9693 0.4425 ] Network output: [ 0.06391 0.8003 0.1092 -0.0005715 0.0002566 0.9603 -0.0004307 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05282 Epoch 1276 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02416 1.007 0.9871 4.782e-05 -2.147e-05 -0.04176 3.604e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03492 -0.00223 0.0273 0.0218 0.9216 0.9338 0.06777 0.8526 0.8842 0.1535 ] Network output: [ 0.9788 0.07602 -0.03674 -0.0001696 7.616e-05 0.002453 -0.0001279 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6448 0.03837 0.02104 0.2339 0.9606 0.9804 0.7336 0.8731 0.954 0.6815 ] Network output: [ -0.008649 0.9404 1.036 7.572e-05 -3.4e-05 0.04107 5.707e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06548 0.04044 0.05937 0.03886 0.9773 0.9835 0.06702 0.9486 0.9701 0.08394 ] Network output: [ 0.1034 -0.3031 1.154 -0.001451 0.0006514 0.9364 -0.001094 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7284 0.5056 0.4608 0.4061 0.9652 0.9833 0.7318 0.8852 0.9608 0.6792 ] Network output: [ -0.05484 0.1819 0.9203 0.001813 -0.0008139 1.015 0.001366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6482 0.6104 0.3964 0.2142 0.9804 0.9868 0.6488 0.9561 0.9736 0.4254 ] Network output: [ -0.09606 0.2758 0.8413 0.0004425 -0.0001986 1.077 0.0003334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6675 0.6603 0.436 0.1454 0.9778 0.985 0.6676 0.9495 0.9693 0.4425 ] Network output: [ 0.0624 0.7961 0.1152 -0.0005321 0.0002389 0.9618 -0.000401 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05233 Epoch 1277 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02272 1.01 0.9853 1.437e-05 -6.45e-06 -0.04064 1.083e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0349 -0.002114 0.02763 0.02151 0.9216 0.9338 0.06769 0.8526 0.8842 0.153 ] Network output: [ 0.97 0.0853 -0.03557 -0.0002709 0.0001216 0.009102 -0.0002042 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6444 0.04122 0.02499 0.2314 0.9606 0.9804 0.7331 0.873 0.954 0.6807 ] Network output: [ -0.008646 0.9435 1.033 6.113e-05 -2.744e-05 0.04123 4.607e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06551 0.04065 0.05941 0.03811 0.9773 0.9835 0.06705 0.9486 0.9701 0.08365 ] Network output: [ 0.1011 -0.2877 1.141 -0.001535 0.0006892 0.9379 -0.001157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7284 0.5074 0.4622 0.3999 0.9652 0.9833 0.7318 0.8852 0.9607 0.6781 ] Network output: [ -0.05206 0.1933 0.9047 0.001785 -0.0008014 1.013 0.001345 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6488 0.6112 0.3955 0.2078 0.9804 0.9868 0.6493 0.9561 0.9736 0.424 ] Network output: [ -0.09422 0.2892 0.8266 0.0003668 -0.0001647 1.074 0.0002764 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6675 0.6604 0.4356 0.1375 0.9778 0.985 0.6676 0.9495 0.9692 0.4421 ] Network output: [ 0.06226 0.8034 0.1094 -0.0006116 0.0002746 0.9602 -0.0004609 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05262 Epoch 1278 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02156 1.006 0.9909 2.116e-05 -9.5e-06 -0.03951 1.595e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03485 -0.00201 0.02828 0.02191 0.9216 0.9338 0.06754 0.8527 0.8843 0.1532 ] Network output: [ 0.9586 0.07413 -0.01099 -0.0003103 0.0001393 0.01841 -0.0002339 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6439 0.04406 0.03103 0.2351 0.9606 0.9804 0.7324 0.8731 0.9541 0.6817 ] Network output: [ -0.008614 0.9399 1.036 7.39e-05 -3.318e-05 0.04154 5.57e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06553 0.04083 0.06016 0.03879 0.9773 0.9836 0.06707 0.9487 0.9702 0.0842 ] Network output: [ 0.09974 -0.299 1.153 -0.001493 0.0006704 0.9409 -0.001125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7289 0.5083 0.4641 0.403 0.9652 0.9833 0.7323 0.8853 0.9608 0.6786 ] Network output: [ -0.05112 0.1865 0.909 0.001833 -0.0008231 1.014 0.001382 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6494 0.6119 0.3962 0.2099 0.9804 0.9868 0.6499 0.9562 0.9736 0.4245 ] Network output: [ -0.0925 0.2843 0.8281 0.0004212 -0.0001891 1.074 0.0003175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6682 0.661 0.4355 0.139 0.9778 0.985 0.6683 0.9495 0.9692 0.442 ] Network output: [ 0.06406 0.8002 0.1097 -0.0005648 0.0002536 0.9597 -0.0004257 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05295 Epoch 1279 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0237 1.007 0.9876 4.557e-05 -2.046e-05 -0.04129 3.435e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03487 -0.00224 0.0274 0.02177 0.9217 0.9338 0.06762 0.8527 0.8842 0.1531 ] Network output: [ 0.9774 0.07548 -0.03448 -0.0001777 7.977e-05 0.003504 -0.0001339 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6444 0.03774 0.0221 0.2338 0.9606 0.9804 0.733 0.8731 0.954 0.6812 ] Network output: [ -0.008685 0.9407 1.036 7.778e-05 -3.492e-05 0.04108 5.862e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06532 0.04028 0.05922 0.0387 0.9773 0.9835 0.06686 0.9486 0.9702 0.08363 ] Network output: [ 0.1032 -0.3031 1.155 -0.001468 0.0006591 0.9359 -0.001106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7279 0.5042 0.4609 0.4055 0.9652 0.9833 0.7314 0.8852 0.9608 0.6788 ] Network output: [ -0.05469 0.1812 0.9199 0.001817 -0.0008158 1.016 0.001369 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6483 0.6102 0.3961 0.2134 0.9804 0.9868 0.6489 0.9561 0.9736 0.425 ] Network output: [ -0.0959 0.2769 0.8396 0.0004431 -0.0001989 1.077 0.000334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6675 0.6602 0.4355 0.144 0.9778 0.985 0.6676 0.9495 0.9693 0.442 ] Network output: [ 0.06282 0.7958 0.1154 -0.0005213 0.000234 0.9611 -0.0003929 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05251 Epoch 1280 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02258 1.01 0.9854 1.614e-05 -7.246e-06 -0.04042 1.217e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03486 -0.002156 0.02761 0.02147 0.9217 0.9338 0.06755 0.8527 0.8843 0.1525 ] Network output: [ 0.9711 0.08464 -0.03615 -0.0002605 0.0001169 0.00815 -0.0001963 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6441 0.03976 0.02485 0.2313 0.9606 0.9804 0.7326 0.8731 0.954 0.6804 ] Network output: [ -0.008689 0.9437 1.033 6.414e-05 -2.88e-05 0.04118 4.834e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06533 0.04042 0.05915 0.03797 0.9773 0.9835 0.06686 0.9486 0.9701 0.08329 ] Network output: [ 0.1014 -0.2887 1.143 -0.001546 0.0006941 0.9368 -0.001165 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7278 0.5055 0.4618 0.3998 0.9652 0.9833 0.7312 0.8852 0.9607 0.6778 ] Network output: [ -0.05242 0.1917 0.9061 0.001788 -0.0008028 1.014 0.001348 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6487 0.6109 0.3952 0.2077 0.9804 0.9868 0.6493 0.9561 0.9736 0.4238 ] Network output: [ -0.09452 0.2888 0.8268 0.0003724 -0.0001672 1.075 0.0002806 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6674 0.6602 0.4351 0.1369 0.9778 0.985 0.6675 0.9495 0.9692 0.4416 ] Network output: [ 0.06252 0.8022 0.1106 -0.0005929 0.0002662 0.9598 -0.0004469 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05275 Epoch 1281 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02133 1.006 0.9905 1.924e-05 -8.639e-06 -0.03923 1.45e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0348 -0.002043 0.02826 0.02182 0.9217 0.9338 0.0674 0.8527 0.8843 0.1528 ] Network output: [ 0.9593 0.07502 -0.01263 -0.0003085 0.0001385 0.01775 -0.0002325 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6436 0.04279 0.03101 0.2345 0.9606 0.9804 0.7319 0.8732 0.9541 0.6813 ] Network output: [ -0.008649 0.9406 1.036 7.488e-05 -3.362e-05 0.0415 5.644e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06535 0.04062 0.05987 0.03854 0.9773 0.9836 0.06689 0.9487 0.9702 0.08379 ] Network output: [ 0.0999 -0.2978 1.152 -0.001515 0.00068 0.9398 -0.001141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7283 0.5066 0.4638 0.4022 0.9653 0.9833 0.7318 0.8853 0.9608 0.6782 ] Network output: [ -0.05123 0.1865 0.9085 0.001831 -0.0008219 1.015 0.00138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6493 0.6116 0.3958 0.2089 0.9804 0.9868 0.6499 0.9561 0.9736 0.4241 ] Network output: [ -0.09269 0.2857 0.8266 0.0004156 -0.0001866 1.075 0.0003132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6681 0.6608 0.435 0.1375 0.9778 0.985 0.6682 0.9495 0.9692 0.4414 ] Network output: [ 0.06422 0.8 0.1102 -0.0005574 0.0002502 0.9592 -0.00042 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05309 Epoch 1282 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02325 1.007 0.988 4.33e-05 -1.944e-05 -0.04081 3.263e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03482 -0.002251 0.0275 0.02173 0.9217 0.9338 0.06746 0.8527 0.8843 0.1526 ] Network output: [ 0.976 0.07505 -0.03234 -0.0001862 8.357e-05 0.004545 -0.0001403 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.644 0.03708 0.02316 0.2337 0.9607 0.9804 0.7325 0.8731 0.954 0.681 ] Network output: [ -0.008717 0.9409 1.036 7.991e-05 -3.587e-05 0.04109 6.022e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06516 0.04012 0.05907 0.03853 0.9773 0.9835 0.06669 0.9486 0.9702 0.08332 ] Network output: [ 0.1031 -0.303 1.155 -0.001486 0.000667 0.9354 -0.00112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7275 0.5028 0.461 0.4049 0.9652 0.9833 0.7309 0.8852 0.9608 0.6784 ] Network output: [ -0.05454 0.1807 0.9195 0.001821 -0.0008174 1.016 0.001372 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6484 0.6101 0.3958 0.2126 0.9804 0.9868 0.6489 0.9561 0.9736 0.4246 ] Network output: [ -0.09574 0.278 0.8378 0.0004429 -0.0001989 1.077 0.0003338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6675 0.6602 0.435 0.1425 0.9778 0.985 0.6676 0.9495 0.9693 0.4415 ] Network output: [ 0.06325 0.7954 0.1156 -0.0005107 0.0002293 0.9604 -0.0003849 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0527 Epoch 1283 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02242 1.01 0.9855 1.793e-05 -8.051e-06 -0.04018 1.352e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03481 -0.002197 0.0276 0.02143 0.9217 0.9338 0.06741 0.8527 0.8843 0.1521 ] Network output: [ 0.9721 0.08395 -0.03654 -0.000251 0.0001127 0.007302 -0.0001891 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6438 0.03829 0.02478 0.2312 0.9606 0.9804 0.7322 0.8731 0.954 0.6801 ] Network output: [ -0.00873 0.9439 1.033 6.732e-05 -3.022e-05 0.04114 5.074e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06515 0.04019 0.0589 0.03784 0.9773 0.9835 0.06668 0.9486 0.9701 0.08293 ] Network output: [ 0.1018 -0.2897 1.144 -0.001557 0.000699 0.9357 -0.001173 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7272 0.5036 0.4615 0.3998 0.9652 0.9833 0.7307 0.8852 0.9607 0.6775 ] Network output: [ -0.05277 0.1902 0.9075 0.001791 -0.0008042 1.015 0.00135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6486 0.6105 0.395 0.2076 0.9804 0.9868 0.6492 0.9561 0.9736 0.4235 ] Network output: [ -0.09481 0.2886 0.8268 0.0003778 -0.0001696 1.076 0.0002847 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6673 0.6601 0.4346 0.1363 0.9778 0.985 0.6675 0.9495 0.9692 0.441 ] Network output: [ 0.06282 0.801 0.1117 -0.000574 0.0002577 0.9593 -0.0004326 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05289 Epoch 1284 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0211 1.007 0.9901 1.765e-05 -7.925e-06 -0.03895 1.331e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03476 -0.002079 0.02824 0.02173 0.9217 0.9338 0.06726 0.8528 0.8843 0.1523 ] Network output: [ 0.9601 0.07589 -0.01434 -0.0003061 0.0001374 0.01702 -0.0002307 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6433 0.04144 0.03096 0.2339 0.9607 0.9804 0.7314 0.8732 0.9541 0.6809 ] Network output: [ -0.008684 0.9413 1.035 7.608e-05 -3.416e-05 0.04145 5.734e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06518 0.04039 0.05958 0.03829 0.9773 0.9836 0.0667 0.9487 0.9702 0.08338 ] Network output: [ 0.1001 -0.2966 1.151 -0.001535 0.0006893 0.9388 -0.001157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7277 0.5048 0.4635 0.4013 0.9653 0.9833 0.7312 0.8853 0.9608 0.6777 ] Network output: [ -0.05137 0.1865 0.9081 0.001829 -0.0008209 1.016 0.001378 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6492 0.6112 0.3954 0.2081 0.9804 0.9868 0.6498 0.9561 0.9736 0.4237 ] Network output: [ -0.0929 0.287 0.8251 0.0004102 -0.0001842 1.075 0.0003091 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6679 0.6607 0.4345 0.136 0.9778 0.985 0.668 0.9495 0.9692 0.4409 ] Network output: [ 0.0644 0.7996 0.1107 -0.0005489 0.0002464 0.9586 -0.0004137 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05325 Epoch 1285 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02279 1.007 0.9883 4.101e-05 -1.841e-05 -0.04032 3.091e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03477 -0.002264 0.02759 0.02169 0.9217 0.9338 0.06731 0.8527 0.8843 0.1522 ] Network output: [ 0.9746 0.07474 -0.03034 -0.000195 8.752e-05 0.005555 -0.0001469 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6437 0.03636 0.0242 0.2336 0.9607 0.9804 0.7319 0.8731 0.9541 0.6807 ] Network output: [ -0.008747 0.9412 1.036 8.209e-05 -3.686e-05 0.04109 6.187e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.065 0.03995 0.0589 0.03836 0.9773 0.9835 0.06652 0.9486 0.9702 0.083 ] Network output: [ 0.103 -0.3028 1.156 -0.001504 0.0006751 0.9349 -0.001133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.727 0.5013 0.4611 0.4043 0.9652 0.9833 0.7304 0.8852 0.9608 0.678 ] Network output: [ -0.0544 0.1803 0.9189 0.001824 -0.000819 1.017 0.001375 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6484 0.6099 0.3955 0.2117 0.9804 0.9868 0.649 0.9561 0.9736 0.4242 ] Network output: [ -0.09561 0.2792 0.8359 0.0004419 -0.0001984 1.078 0.000333 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6674 0.6601 0.4344 0.1409 0.9778 0.985 0.6676 0.9495 0.9693 0.4409 ] Network output: [ 0.06368 0.795 0.1159 -0.0005002 0.0002246 0.9597 -0.000377 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05291 Epoch 1286 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02224 1.01 0.9857 1.971e-05 -8.848e-06 -0.03992 1.486e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03476 -0.002238 0.02759 0.0214 0.9217 0.9338 0.06727 0.8528 0.8843 0.1517 ] Network output: [ 0.973 0.08325 -0.03673 -0.0002425 0.0001089 0.006575 -0.0001828 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6435 0.03682 0.02479 0.2311 0.9607 0.9804 0.7317 0.8731 0.954 0.6799 ] Network output: [ -0.008768 0.9441 1.033 7.067e-05 -3.173e-05 0.04109 5.326e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06496 0.03995 0.05865 0.03771 0.9773 0.9835 0.06648 0.9486 0.9701 0.08258 ] Network output: [ 0.1021 -0.2908 1.146 -0.001568 0.000704 0.9347 -0.001182 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7267 0.5016 0.4612 0.3998 0.9652 0.9833 0.7301 0.8852 0.9607 0.6772 ] Network output: [ -0.05311 0.1886 0.9088 0.001795 -0.0008058 1.016 0.001353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6485 0.6101 0.3947 0.2074 0.9804 0.9868 0.6491 0.956 0.9736 0.4232 ] Network output: [ -0.09508 0.2884 0.8268 0.000383 -0.0001719 1.076 0.0002886 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6672 0.6599 0.4341 0.1356 0.9778 0.985 0.6674 0.9495 0.9692 0.4405 ] Network output: [ 0.06315 0.7998 0.1128 -0.0005549 0.0002491 0.9588 -0.0004182 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05305 Epoch 1287 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02089 1.007 0.9898 1.642e-05 -7.373e-06 -0.03868 1.238e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03471 -0.002118 0.02822 0.02164 0.9217 0.9339 0.06713 0.8528 0.8844 0.1518 ] Network output: [ 0.961 0.07671 -0.0161 -0.0003031 0.0001361 0.01622 -0.0002284 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.643 0.04001 0.03088 0.2334 0.9607 0.9804 0.7309 0.8732 0.9541 0.6805 ] Network output: [ -0.008718 0.9419 1.034 7.754e-05 -3.481e-05 0.0414 5.844e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06499 0.04017 0.05928 0.03805 0.9773 0.9836 0.06651 0.9487 0.9702 0.08296 ] Network output: [ 0.1003 -0.2957 1.151 -0.001556 0.0006985 0.9376 -0.001173 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7271 0.5029 0.4632 0.4006 0.9653 0.9833 0.7305 0.8852 0.9608 0.6773 ] Network output: [ -0.05155 0.1863 0.9079 0.001826 -0.0008199 1.016 0.001376 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6492 0.6108 0.395 0.2072 0.9804 0.9868 0.6497 0.9561 0.9736 0.4233 ] Network output: [ -0.09315 0.2883 0.8238 0.0004051 -0.0001819 1.076 0.0003053 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6678 0.6605 0.4339 0.1346 0.9778 0.985 0.6679 0.9495 0.9692 0.4404 ] Network output: [ 0.06461 0.7992 0.1114 -0.0005395 0.0002422 0.958 -0.0004066 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05342 Epoch 1288 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02234 1.007 0.9887 3.874e-05 -1.739e-05 -0.03984 2.92e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03472 -0.002279 0.02768 0.02164 0.9218 0.9339 0.06715 0.8528 0.8843 0.1517 ] Network output: [ 0.9733 0.07456 -0.02852 -0.0002039 9.155e-05 0.006517 -0.0001537 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6433 0.03558 0.02521 0.2334 0.9607 0.9805 0.7313 0.8731 0.9541 0.6804 ] Network output: [ -0.008773 0.9415 1.035 8.435e-05 -3.787e-05 0.0411 6.357e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06483 0.03977 0.05873 0.03818 0.9773 0.9835 0.06635 0.9486 0.9702 0.08266 ] Network output: [ 0.1028 -0.3025 1.156 -0.001522 0.0006835 0.9344 -0.001147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7265 0.4998 0.4611 0.4036 0.9653 0.9833 0.7299 0.8852 0.9608 0.6776 ] Network output: [ -0.05427 0.1799 0.9183 0.001827 -0.0008203 1.018 0.001377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6485 0.6096 0.3951 0.2108 0.9804 0.9868 0.649 0.9561 0.9736 0.4238 ] Network output: [ -0.09549 0.2806 0.8339 0.0004399 -0.0001975 1.078 0.0003315 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6674 0.66 0.4339 0.1392 0.9778 0.985 0.6675 0.9495 0.9693 0.4404 ] Network output: [ 0.06412 0.7946 0.1162 -0.00049 0.00022 0.959 -0.0003693 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05313 Epoch 1289 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02204 1.01 0.9859 2.144e-05 -9.624e-06 -0.03964 1.616e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03471 -0.002278 0.02759 0.02136 0.9218 0.9339 0.06712 0.8528 0.8843 0.1513 ] Network output: [ 0.9736 0.08253 -0.03671 -0.0002354 0.0001057 0.005983 -0.0001774 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6432 0.03536 0.02488 0.231 0.9607 0.9804 0.7311 0.8731 0.954 0.6796 ] Network output: [ -0.008804 0.9442 1.033 7.417e-05 -3.33e-05 0.04105 5.59e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06478 0.03972 0.0584 0.03757 0.9773 0.9835 0.06629 0.9485 0.9701 0.08222 ] Network output: [ 0.1024 -0.2918 1.147 -0.00158 0.0007091 0.9337 -0.00119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7261 0.4996 0.4609 0.3997 0.9652 0.9833 0.7295 0.8852 0.9607 0.6769 ] Network output: [ -0.05344 0.1871 0.9101 0.001799 -0.0008075 1.017 0.001355 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6484 0.6096 0.3944 0.2072 0.9804 0.9868 0.649 0.956 0.9736 0.4229 ] Network output: [ -0.09535 0.2883 0.8267 0.0003878 -0.0001741 1.077 0.0002923 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6671 0.6597 0.4336 0.1348 0.9778 0.985 0.6673 0.9495 0.9692 0.44 ] Network output: [ 0.06352 0.7985 0.1139 -0.0005358 0.0002405 0.9583 -0.0004038 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05323 Epoch 1290 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02068 1.008 0.9894 1.558e-05 -6.995e-06 -0.03841 1.174e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03467 -0.002159 0.02819 0.02156 0.9218 0.9339 0.06698 0.8528 0.8844 0.1513 ] Network output: [ 0.9619 0.07747 -0.01788 -0.0002995 0.0001345 0.01536 -0.0002257 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6427 0.03851 0.03077 0.2329 0.9607 0.9805 0.7304 0.8732 0.9541 0.6801 ] Network output: [ -0.008751 0.9426 1.034 7.927e-05 -3.559e-05 0.04134 5.974e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06481 0.03993 0.05897 0.03781 0.9773 0.9836 0.06632 0.9486 0.9702 0.08254 ] Network output: [ 0.1006 -0.2948 1.151 -0.001576 0.0007075 0.9365 -0.001188 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7265 0.5009 0.4628 0.3999 0.9653 0.9833 0.7299 0.8852 0.9608 0.6769 ] Network output: [ -0.05176 0.1861 0.9078 0.001824 -0.0008191 1.017 0.001375 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6491 0.6104 0.3946 0.2064 0.9804 0.9868 0.6496 0.9561 0.9736 0.4228 ] Network output: [ -0.09343 0.2895 0.8225 0.0004004 -0.0001798 1.076 0.0003018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6676 0.6603 0.4334 0.1332 0.9779 0.985 0.6678 0.9495 0.9692 0.4398 ] Network output: [ 0.06483 0.7986 0.1122 -0.0005289 0.0002374 0.9574 -0.0003986 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05361 Epoch 1291 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0219 1.007 0.989 3.652e-05 -1.64e-05 -0.03936 2.753e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03467 -0.002296 0.02777 0.0216 0.9218 0.9339 0.06699 0.8528 0.8844 0.1513 ] Network output: [ 0.9721 0.0745 -0.02693 -0.0002129 9.558e-05 0.007407 -0.0001605 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6429 0.03473 0.02618 0.2333 0.9607 0.9805 0.7307 0.8732 0.9541 0.6801 ] Network output: [ -0.008798 0.9419 1.035 8.667e-05 -3.891e-05 0.0411 6.532e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06466 0.03958 0.05855 0.03799 0.9773 0.9836 0.06617 0.9486 0.9702 0.08232 ] Network output: [ 0.1028 -0.302 1.156 -0.001542 0.0006921 0.9338 -0.001162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.726 0.4981 0.4611 0.403 0.9653 0.9833 0.7294 0.8852 0.9608 0.6772 ] Network output: [ -0.05416 0.1797 0.9177 0.00183 -0.0008214 1.018 0.001379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6485 0.6094 0.3948 0.2099 0.9804 0.9868 0.649 0.956 0.9736 0.4234 ] Network output: [ -0.09541 0.282 0.8319 0.000437 -0.0001962 1.079 0.0003293 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6674 0.6599 0.4333 0.1375 0.9779 0.985 0.6675 0.9495 0.9692 0.4398 ] Network output: [ 0.06457 0.7942 0.1165 -0.0004798 0.0002154 0.9582 -0.0003616 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05337 Epoch 1292 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02181 1.01 0.9861 2.309e-05 -1.037e-05 -0.03934 1.74e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03466 -0.002317 0.02761 0.02133 0.9218 0.9339 0.06697 0.8528 0.8844 0.1509 ] Network output: [ 0.9741 0.08182 -0.03647 -0.0002296 0.0001031 0.005535 -0.0001731 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6429 0.03391 0.02508 0.231 0.9607 0.9805 0.7306 0.8731 0.9541 0.6793 ] Network output: [ -0.008836 0.9444 1.033 7.782e-05 -3.494e-05 0.04102 5.865e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06459 0.03948 0.05817 0.03744 0.9773 0.9835 0.06609 0.9485 0.9701 0.08187 ] Network output: [ 0.1027 -0.2928 1.148 -0.001591 0.0007144 0.9326 -0.001199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7255 0.4976 0.4607 0.3997 0.9653 0.9833 0.7289 0.8852 0.9607 0.6766 ] Network output: [ -0.05374 0.1857 0.9113 0.001802 -0.0008092 1.018 0.001358 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6483 0.6092 0.3941 0.2069 0.9804 0.9868 0.6489 0.956 0.9735 0.4226 ] Network output: [ -0.09559 0.2884 0.8264 0.0003921 -0.000176 1.078 0.0002955 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.667 0.6596 0.433 0.134 0.9778 0.985 0.6672 0.9495 0.9692 0.4395 ] Network output: [ 0.06392 0.7973 0.115 -0.0005166 0.0002319 0.9578 -0.0003893 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05343 Epoch 1293 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02049 1.008 0.9891 1.514e-05 -6.799e-06 -0.03814 1.142e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03462 -0.002202 0.02816 0.02148 0.9218 0.9339 0.06684 0.8529 0.8844 0.1508 ] Network output: [ 0.9629 0.07816 -0.01966 -0.0002954 0.0001326 0.01445 -0.0002226 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6424 0.03693 0.03065 0.2324 0.9607 0.9805 0.7299 0.8732 0.9541 0.6797 ] Network output: [ -0.008783 0.9432 1.033 8.13e-05 -3.65e-05 0.04129 6.127e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06461 0.03968 0.05867 0.03759 0.9773 0.9836 0.06612 0.9486 0.9702 0.08212 ] Network output: [ 0.1009 -0.2941 1.151 -0.001595 0.0007162 0.9353 -0.001202 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7259 0.4989 0.4625 0.3993 0.9653 0.9833 0.7293 0.8852 0.9608 0.6765 ] Network output: [ -0.05202 0.1858 0.9079 0.001823 -0.0008184 1.018 0.001374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.649 0.61 0.3941 0.2057 0.9804 0.9868 0.6495 0.956 0.9736 0.4224 ] Network output: [ -0.09373 0.2906 0.8214 0.0003962 -0.0001779 1.077 0.0002986 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6675 0.66 0.4328 0.1318 0.9779 0.985 0.6676 0.9495 0.9692 0.4392 ] Network output: [ 0.06508 0.7979 0.113 -0.0005172 0.0002322 0.9568 -0.0003897 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05381 Epoch 1294 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02146 1.007 0.9892 3.441e-05 -1.545e-05 -0.03888 2.593e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03462 -0.002315 0.02784 0.02154 0.9218 0.9339 0.06684 0.8529 0.8844 0.1508 ] Network output: [ 0.971 0.07457 -0.02558 -0.0002217 9.953e-05 0.008208 -0.0001671 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6425 0.0338 0.0271 0.2331 0.9607 0.9805 0.7301 0.8732 0.9541 0.6798 ] Network output: [ -0.00882 0.9422 1.035 8.907e-05 -3.999e-05 0.0411 6.713e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06449 0.03939 0.05835 0.0378 0.9773 0.9836 0.06599 0.9486 0.9702 0.08197 ] Network output: [ 0.1027 -0.3015 1.157 -0.001561 0.0007009 0.9331 -0.001177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7255 0.4964 0.4611 0.4023 0.9653 0.9833 0.7289 0.8852 0.9608 0.6768 ] Network output: [ -0.05407 0.1795 0.917 0.001832 -0.0008224 1.019 0.00138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6485 0.6091 0.3944 0.2089 0.9804 0.9868 0.649 0.956 0.9736 0.423 ] Network output: [ -0.09535 0.2836 0.8298 0.0004333 -0.0001945 1.079 0.0003265 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6673 0.6597 0.4328 0.1357 0.9779 0.985 0.6674 0.9495 0.9692 0.4393 ] Network output: [ 0.06502 0.7937 0.1169 -0.0004696 0.0002108 0.9575 -0.0003539 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05363 Epoch 1295 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02157 1.01 0.9864 2.464e-05 -1.106e-05 -0.03901 1.857e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03461 -0.002355 0.02763 0.0213 0.9218 0.9339 0.06682 0.8528 0.8844 0.1504 ] Network output: [ 0.9744 0.08113 -0.03604 -0.0002253 0.0001012 0.005235 -0.0001698 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6425 0.03247 0.02536 0.231 0.9607 0.9805 0.7301 0.8731 0.9541 0.6791 ] Network output: [ -0.008866 0.9445 1.033 8.161e-05 -3.664e-05 0.04099 6.151e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0644 0.03924 0.05794 0.03731 0.9773 0.9835 0.0659 0.9485 0.9701 0.08152 ] Network output: [ 0.1029 -0.2938 1.15 -0.001603 0.0007198 0.9317 -0.001208 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.725 0.4955 0.4605 0.3997 0.9653 0.9833 0.7283 0.8852 0.9607 0.6762 ] Network output: [ -0.05403 0.1843 0.9124 0.001806 -0.000811 1.019 0.001361 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6483 0.6088 0.3938 0.2066 0.9804 0.9868 0.6488 0.956 0.9735 0.4223 ] Network output: [ -0.09582 0.2885 0.826 0.0003958 -0.0001777 1.079 0.0002983 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6669 0.6594 0.4325 0.1331 0.9778 0.985 0.6671 0.9495 0.9692 0.4389 ] Network output: [ 0.06436 0.796 0.1161 -0.0004976 0.0002234 0.9572 -0.000375 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05366 Epoch 1296 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0203 1.009 0.9888 1.512e-05 -6.79e-06 -0.03786 1.14e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03457 -0.002248 0.02814 0.0214 0.9218 0.9339 0.0667 0.8529 0.8844 0.1503 ] Network output: [ 0.964 0.07877 -0.0214 -0.0002909 0.0001306 0.01351 -0.0002192 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6421 0.03528 0.03052 0.232 0.9607 0.9805 0.7294 0.8732 0.9541 0.6793 ] Network output: [ -0.008814 0.9437 1.033 8.364e-05 -3.755e-05 0.04123 6.304e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06442 0.03943 0.05837 0.03737 0.9773 0.9836 0.06592 0.9486 0.9702 0.0817 ] Network output: [ 0.1012 -0.2936 1.151 -0.001614 0.0007246 0.934 -0.001216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7253 0.4967 0.4621 0.3988 0.9653 0.9833 0.7286 0.8852 0.9608 0.6761 ] Network output: [ -0.05231 0.1853 0.9081 0.001822 -0.0008178 1.019 0.001373 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6488 0.6095 0.3937 0.205 0.9804 0.9868 0.6494 0.956 0.9735 0.422 ] Network output: [ -0.09406 0.2916 0.8203 0.0003925 -0.0001762 1.078 0.0002958 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6673 0.6598 0.4323 0.1304 0.9779 0.985 0.6674 0.9495 0.9692 0.4387 ] Network output: [ 0.06536 0.7971 0.1139 -0.0005042 0.0002264 0.9562 -0.00038 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05402 Epoch 1297 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02104 1.007 0.9893 3.244e-05 -1.457e-05 -0.0384 2.445e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03456 -0.002337 0.02792 0.02149 0.9218 0.9339 0.06668 0.8529 0.8844 0.1504 ] Network output: [ 0.9699 0.07477 -0.02451 -0.0002302 0.0001033 0.008902 -0.0001735 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6422 0.03278 0.02797 0.2329 0.9607 0.9805 0.7295 0.8732 0.9541 0.6795 ] Network output: [ -0.00884 0.9426 1.034 9.157e-05 -4.111e-05 0.04109 6.901e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06431 0.03918 0.05815 0.0376 0.9773 0.9836 0.0658 0.9486 0.9702 0.08161 ] Network output: [ 0.1027 -0.3009 1.157 -0.001581 0.00071 0.9324 -0.001192 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7249 0.4946 0.4611 0.4016 0.9653 0.9833 0.7283 0.8852 0.9608 0.6764 ] Network output: [ -0.05401 0.1793 0.9163 0.001833 -0.0008231 1.02 0.001382 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6484 0.6088 0.394 0.2079 0.9804 0.9868 0.649 0.956 0.9735 0.4225 ] Network output: [ -0.09533 0.2852 0.8277 0.0004288 -0.0001925 1.08 0.0003231 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6672 0.6596 0.4322 0.1339 0.9779 0.985 0.6673 0.9495 0.9692 0.4387 ] Network output: [ 0.06548 0.7932 0.1173 -0.0004593 0.0002062 0.9567 -0.0003461 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0539 Epoch 1298 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0213 1.009 0.9867 2.607e-05 -1.17e-05 -0.03865 1.965e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03456 -0.002393 0.02766 0.02127 0.9218 0.9339 0.06667 0.8529 0.8844 0.15 ] Network output: [ 0.9745 0.08048 -0.03541 -0.0002226 9.992e-05 0.005082 -0.0001677 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6422 0.03105 0.02575 0.231 0.9607 0.9805 0.7295 0.8731 0.9541 0.6788 ] Network output: [ -0.008891 0.9447 1.033 8.553e-05 -3.84e-05 0.04096 6.446e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06421 0.03901 0.05771 0.03717 0.9773 0.9835 0.0657 0.9485 0.9701 0.08117 ] Network output: [ 0.1032 -0.2947 1.151 -0.001616 0.0007256 0.9307 -0.001218 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7244 0.4934 0.4603 0.3996 0.9653 0.9833 0.7278 0.8852 0.9607 0.6759 ] Network output: [ -0.05429 0.1829 0.9133 0.00181 -0.0008128 1.02 0.001364 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6482 0.6083 0.3935 0.2063 0.9804 0.9868 0.6487 0.956 0.9735 0.422 ] Network output: [ -0.09603 0.2888 0.8253 0.0003988 -0.000179 1.08 0.0003005 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6668 0.6592 0.432 0.1321 0.9779 0.985 0.667 0.9495 0.9692 0.4384 ] Network output: [ 0.06483 0.7947 0.1171 -0.0004788 0.000215 0.9565 -0.0003609 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0539 Epoch 1299 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02011 1.009 0.9886 1.552e-05 -6.968e-06 -0.03759 1.17e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03452 -0.002295 0.02811 0.02133 0.9219 0.9339 0.06656 0.8529 0.8844 0.1499 ] Network output: [ 0.965 0.07929 -0.02306 -0.000286 0.0001284 0.01255 -0.0002155 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6418 0.03357 0.0304 0.2317 0.9607 0.9805 0.7289 0.8732 0.9541 0.679 ] Network output: [ -0.008844 0.9443 1.033 8.631e-05 -3.875e-05 0.04118 6.505e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06422 0.03917 0.05806 0.03716 0.9773 0.9836 0.06571 0.9486 0.9702 0.08128 ] Network output: [ 0.1016 -0.2933 1.151 -0.001632 0.0007328 0.9328 -0.00123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7246 0.4945 0.4618 0.3984 0.9653 0.9833 0.728 0.8852 0.9607 0.6757 ] Network output: [ -0.05263 0.1848 0.9084 0.001821 -0.0008175 1.019 0.001372 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6487 0.609 0.3933 0.2044 0.9804 0.9868 0.6492 0.956 0.9735 0.4216 ] Network output: [ -0.09441 0.2926 0.8193 0.0003893 -0.0001748 1.079 0.0002934 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6672 0.6596 0.4317 0.1291 0.9779 0.985 0.6673 0.9495 0.9692 0.4381 ] Network output: [ 0.06567 0.7961 0.1149 -0.0004901 0.00022 0.9556 -0.0003694 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05425 Epoch 1300 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02063 1.007 0.9895 3.068e-05 -1.377e-05 -0.03793 2.312e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03451 -0.002363 0.02798 0.02143 0.9219 0.934 0.06652 0.8529 0.8845 0.1499 ] Network output: [ 0.9691 0.07508 -0.02371 -0.0002382 0.0001069 0.009474 -0.0001795 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6418 0.03166 0.02877 0.2326 0.9607 0.9805 0.7289 0.8732 0.9541 0.6792 ] Network output: [ -0.008858 0.9431 1.034 9.418e-05 -4.228e-05 0.04108 7.098e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06412 0.03897 0.05793 0.03739 0.9773 0.9836 0.06561 0.9486 0.9702 0.08124 ] Network output: [ 0.1027 -0.3002 1.157 -0.001602 0.0007192 0.9316 -0.001207 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7244 0.4927 0.4611 0.4009 0.9653 0.9833 0.7277 0.8852 0.9608 0.676 ] Network output: [ -0.05398 0.1792 0.9157 0.001835 -0.0008236 1.021 0.001383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6484 0.6084 0.3936 0.2069 0.9804 0.9868 0.649 0.956 0.9735 0.422 ] Network output: [ -0.09534 0.2869 0.8255 0.0004235 -0.0001901 1.08 0.0003192 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6671 0.6594 0.4317 0.1321 0.9779 0.985 0.6672 0.9495 0.9692 0.4381 ] Network output: [ 0.06594 0.7926 0.1177 -0.0004487 0.0002014 0.9559 -0.0003382 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05419 Epoch 1301 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02101 1.009 0.987 2.737e-05 -1.229e-05 -0.03827 2.063e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03451 -0.002429 0.0277 0.02124 0.9219 0.934 0.06651 0.8529 0.8844 0.1495 ] Network output: [ 0.9744 0.07988 -0.03464 -0.0002214 9.938e-05 0.005069 -0.0001668 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6418 0.02963 0.02623 0.231 0.9607 0.9805 0.7289 0.8731 0.9541 0.6786 ] Network output: [ -0.008914 0.9448 1.032 8.957e-05 -4.021e-05 0.04094 6.75e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06401 0.03877 0.05749 0.03703 0.9773 0.9836 0.0655 0.9485 0.9701 0.08082 ] Network output: [ 0.1034 -0.2955 1.152 -0.00163 0.0007316 0.9298 -0.001228 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7238 0.4912 0.4602 0.3995 0.9653 0.9833 0.7272 0.8852 0.9607 0.6756 ] Network output: [ -0.05453 0.1817 0.9142 0.001814 -0.0008146 1.021 0.001367 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6481 0.6079 0.3932 0.2058 0.9804 0.9868 0.6486 0.9559 0.9735 0.4216 ] Network output: [ -0.09622 0.2893 0.8245 0.0004009 -0.00018 1.08 0.0003021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6667 0.659 0.4314 0.1309 0.9779 0.985 0.6669 0.9495 0.9692 0.4378 ] Network output: [ 0.06533 0.7935 0.1181 -0.0004603 0.0002067 0.9559 -0.0003469 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05417 Epoch 1302 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01992 1.009 0.9884 1.632e-05 -7.326e-06 -0.03731 1.23e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03447 -0.002344 0.02809 0.02126 0.9219 0.934 0.06641 0.8529 0.8845 0.1494 ] Network output: [ 0.9661 0.07971 -0.02461 -0.0002809 0.0001261 0.01161 -0.0002117 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6414 0.0318 0.0303 0.2313 0.9607 0.9805 0.7284 0.8732 0.9541 0.6786 ] Network output: [ -0.008872 0.9447 1.032 8.932e-05 -4.01e-05 0.04112 6.732e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06402 0.0389 0.05776 0.03696 0.9773 0.9836 0.0655 0.9486 0.9702 0.08087 ] Network output: [ 0.1019 -0.2931 1.151 -0.00165 0.0007408 0.9315 -0.001244 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.724 0.4922 0.4615 0.398 0.9653 0.9833 0.7273 0.8852 0.9607 0.6753 ] Network output: [ -0.05297 0.1841 0.9089 0.001821 -0.0008173 1.02 0.001372 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6485 0.6084 0.393 0.2037 0.9804 0.9868 0.6491 0.9559 0.9735 0.4212 ] Network output: [ -0.09478 0.2935 0.8184 0.0003865 -0.0001735 1.079 0.0002913 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.667 0.6593 0.4312 0.1278 0.9779 0.985 0.6671 0.9495 0.9692 0.4376 ] Network output: [ 0.06602 0.7951 0.116 -0.0004749 0.0002132 0.955 -0.0003579 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0545 Epoch 1303 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02023 1.008 0.9895 2.917e-05 -1.31e-05 -0.03747 2.198e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03446 -0.002391 0.02804 0.02136 0.9219 0.934 0.06637 0.853 0.8845 0.1494 ] Network output: [ 0.9684 0.0755 -0.0232 -0.0002455 0.0001102 0.009913 -0.000185 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6414 0.03044 0.0295 0.2324 0.9607 0.9805 0.7283 0.8732 0.9541 0.6788 ] Network output: [ -0.008875 0.9435 1.034 9.694e-05 -4.352e-05 0.04107 7.306e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06393 0.03875 0.05771 0.03719 0.9773 0.9836 0.06541 0.9486 0.9702 0.08086 ] Network output: [ 0.1027 -0.2995 1.157 -0.001623 0.0007286 0.9308 -0.001223 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7238 0.4907 0.4611 0.4003 0.9653 0.9833 0.7271 0.8852 0.9608 0.6756 ] Network output: [ -0.05399 0.1791 0.9151 0.001835 -0.000824 1.021 0.001383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6483 0.608 0.3932 0.2059 0.9804 0.9868 0.6489 0.9559 0.9735 0.4216 ] Network output: [ -0.0954 0.2886 0.8234 0.0004177 -0.0001875 1.08 0.0003148 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.667 0.6592 0.4311 0.1302 0.9779 0.985 0.6671 0.9495 0.9692 0.4375 ] Network output: [ 0.06641 0.792 0.1183 -0.0004378 0.0001966 0.9551 -0.00033 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05449 Epoch 1304 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02069 1.009 0.9873 2.855e-05 -1.282e-05 -0.03787 2.152e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03446 -0.002465 0.02775 0.0212 0.9219 0.934 0.06636 0.8529 0.8845 0.1491 ] Network output: [ 0.9741 0.07937 -0.03374 -0.0002217 9.951e-05 0.005184 -0.0001671 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6415 0.02822 0.02679 0.231 0.9607 0.9805 0.7283 0.8731 0.9541 0.6783 ] Network output: [ -0.008932 0.9449 1.032 9.372e-05 -4.208e-05 0.04091 7.064e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06382 0.03853 0.05727 0.03689 0.9773 0.9836 0.0653 0.9485 0.9701 0.08046 ] Network output: [ 0.1036 -0.2962 1.153 -0.001644 0.000738 0.9288 -0.001239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7233 0.4891 0.4601 0.3994 0.9653 0.9833 0.7266 0.8851 0.9607 0.6753 ] Network output: [ -0.05475 0.1806 0.9149 0.001818 -0.0008164 1.021 0.00137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.648 0.6074 0.3928 0.2053 0.9804 0.9868 0.6485 0.9559 0.9735 0.4213 ] Network output: [ -0.09639 0.2899 0.8235 0.000402 -0.0001805 1.081 0.000303 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6666 0.6588 0.4309 0.1296 0.9779 0.985 0.6667 0.9495 0.9692 0.4373 ] Network output: [ 0.06586 0.7922 0.1191 -0.0004422 0.0001985 0.9551 -0.0003333 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05446 Epoch 1305 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01974 1.009 0.9882 1.75e-05 -7.857e-06 -0.03702 1.319e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03442 -0.002394 0.02807 0.02119 0.9219 0.934 0.06626 0.853 0.8845 0.1489 ] Network output: [ 0.9671 0.08004 -0.02602 -0.0002759 0.0001239 0.0107 -0.0002079 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6411 0.02999 0.03024 0.231 0.9607 0.9805 0.7278 0.8732 0.9541 0.6782 ] Network output: [ -0.008898 0.9452 1.032 9.267e-05 -4.161e-05 0.04107 6.984e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06381 0.03863 0.05747 0.03676 0.9773 0.9836 0.06529 0.9485 0.9702 0.08045 ] Network output: [ 0.1023 -0.2931 1.151 -0.001667 0.0007486 0.9302 -0.001257 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7233 0.4898 0.4611 0.3977 0.9653 0.9833 0.7267 0.8851 0.9607 0.6749 ] Network output: [ -0.05334 0.1833 0.9096 0.001821 -0.0008174 1.021 0.001372 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6483 0.6078 0.3926 0.2032 0.9804 0.9868 0.6489 0.9559 0.9735 0.4208 ] Network output: [ -0.09516 0.2943 0.8175 0.0003841 -0.0001724 1.08 0.0002895 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6668 0.659 0.4306 0.1265 0.9779 0.985 0.6669 0.9495 0.9692 0.437 ] Network output: [ 0.0664 0.7939 0.1171 -0.0004586 0.0002059 0.9543 -0.0003456 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05477 Epoch 1306 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01986 1.008 0.9895 2.797e-05 -1.256e-05 -0.03702 2.108e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03441 -0.002423 0.02809 0.0213 0.9219 0.934 0.06621 0.853 0.8845 0.1489 ] Network output: [ 0.9679 0.076 -0.02296 -0.0002521 0.0001132 0.01021 -0.00019 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.641 0.02912 0.03017 0.2321 0.9607 0.9805 0.7277 0.8732 0.9541 0.6785 ] Network output: [ -0.00889 0.944 1.033 9.987e-05 -4.483e-05 0.04105 7.527e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06374 0.03851 0.05747 0.03697 0.9773 0.9836 0.06521 0.9485 0.9702 0.08047 ] Network output: [ 0.1028 -0.2988 1.157 -0.001644 0.0007382 0.9299 -0.001239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7232 0.4886 0.461 0.3996 0.9653 0.9833 0.7265 0.8852 0.9607 0.6752 ] Network output: [ -0.05405 0.179 0.9145 0.001836 -0.0008242 1.022 0.001384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6483 0.6075 0.3928 0.2049 0.9804 0.9868 0.6488 0.9559 0.9735 0.4211 ] Network output: [ -0.0955 0.2904 0.8213 0.0004114 -0.0001847 1.081 0.00031 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6668 0.659 0.4305 0.1283 0.9779 0.985 0.667 0.9495 0.9692 0.4369 ] Network output: [ 0.06689 0.7913 0.1189 -0.0004264 0.0001914 0.9543 -0.0003213 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05481 Epoch 1307 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02036 1.009 0.9877 2.962e-05 -1.33e-05 -0.03744 2.233e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0344 -0.002501 0.02781 0.02116 0.9219 0.934 0.0662 0.853 0.8845 0.1486 ] Network output: [ 0.9737 0.07894 -0.03276 -0.0002234 0.0001003 0.005407 -0.0001683 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6411 0.0268 0.02744 0.231 0.9607 0.9805 0.7277 0.8732 0.9541 0.6781 ] Network output: [ -0.008947 0.9451 1.032 9.799e-05 -4.399e-05 0.0409 7.385e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06362 0.03828 0.05706 0.03674 0.9773 0.9836 0.06509 0.9485 0.9701 0.08011 ] Network output: [ 0.1038 -0.2968 1.154 -0.001659 0.0007447 0.9279 -0.00125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7227 0.4868 0.46 0.3992 0.9653 0.9833 0.726 0.8851 0.9607 0.6749 ] Network output: [ -0.05495 0.1796 0.9154 0.001822 -0.0008181 1.022 0.001373 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6479 0.6069 0.3925 0.2048 0.9804 0.9868 0.6484 0.9559 0.9735 0.4209 ] Network output: [ -0.09655 0.2907 0.8223 0.0004021 -0.0001805 1.082 0.000303 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6665 0.6586 0.4303 0.1283 0.9779 0.985 0.6666 0.9495 0.9692 0.4367 ] Network output: [ 0.06642 0.791 0.1201 -0.0004244 0.0001905 0.9544 -0.0003199 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05477 Epoch 1308 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01954 1.01 0.9881 1.904e-05 -8.546e-06 -0.03671 1.435e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03437 -0.002445 0.02806 0.02113 0.9219 0.934 0.06611 0.853 0.8845 0.1484 ] Network output: [ 0.968 0.08029 -0.02726 -0.0002711 0.0001217 0.009841 -0.0002043 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6408 0.02815 0.03024 0.2308 0.9607 0.9805 0.7273 0.8732 0.9541 0.6779 ] Network output: [ -0.008922 0.9455 1.032 9.638e-05 -4.327e-05 0.04102 7.264e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0636 0.03835 0.05718 0.03658 0.9773 0.9836 0.06507 0.9485 0.9702 0.08004 ] Network output: [ 0.1027 -0.2932 1.152 -0.001684 0.0007562 0.9289 -0.001269 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7227 0.4873 0.4608 0.3974 0.9653 0.9833 0.726 0.8851 0.9607 0.6746 ] Network output: [ -0.05373 0.1825 0.9103 0.001821 -0.0008176 1.022 0.001373 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6482 0.6072 0.3922 0.2026 0.9804 0.9868 0.6487 0.9559 0.9735 0.4204 ] Network output: [ -0.09555 0.2951 0.8167 0.000382 -0.0001715 1.081 0.0002879 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6666 0.6588 0.43 0.1252 0.9779 0.985 0.6667 0.9495 0.9692 0.4364 ] Network output: [ 0.06683 0.7926 0.1183 -0.0004413 0.0001981 0.9536 -0.0003326 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05506 Epoch 1309 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0195 1.008 0.9895 2.715e-05 -1.219e-05 -0.03658 2.046e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03435 -0.002459 0.02813 0.02123 0.9219 0.934 0.06605 0.853 0.8845 0.1484 ] Network output: [ 0.9675 0.07657 -0.02298 -0.0002579 0.0001158 0.01038 -0.0001944 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6406 0.02768 0.03077 0.2318 0.9607 0.9805 0.7271 0.8732 0.9541 0.6782 ] Network output: [ -0.008905 0.9444 1.033 0.000103 -4.624e-05 0.04103 7.762e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06354 0.03827 0.05723 0.03676 0.9773 0.9836 0.06501 0.9485 0.9702 0.08007 ] Network output: [ 0.103 -0.2981 1.156 -0.001666 0.0007478 0.9289 -0.001255 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7226 0.4864 0.4609 0.399 0.9653 0.9833 0.7259 0.8851 0.9607 0.6748 ] Network output: [ -0.05415 0.1789 0.914 0.001836 -0.0008243 1.023 0.001384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6482 0.607 0.3924 0.2039 0.9804 0.9868 0.6487 0.9559 0.9735 0.4206 ] Network output: [ -0.09565 0.2922 0.8192 0.0004047 -0.0001817 1.082 0.000305 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6667 0.6588 0.4299 0.1264 0.9779 0.985 0.6668 0.9495 0.9692 0.4363 ] Network output: [ 0.06737 0.7905 0.1196 -0.0004143 0.000186 0.9535 -0.0003122 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05514 Epoch 1310 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02002 1.009 0.988 3.061e-05 -1.374e-05 -0.037 2.307e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03435 -0.002537 0.02787 0.02112 0.922 0.934 0.06604 0.853 0.8845 0.1482 ] Network output: [ 0.9732 0.07863 -0.03174 -0.0002264 0.0001016 0.005717 -0.0001706 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6407 0.02536 0.02815 0.231 0.9607 0.9805 0.7271 0.8732 0.9541 0.6778 ] Network output: [ -0.008959 0.9453 1.032 0.0001024 -4.596e-05 0.04088 7.716e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06343 0.03803 0.05685 0.03659 0.9773 0.9836 0.06489 0.9485 0.9701 0.07975 ] Network output: [ 0.104 -0.2972 1.155 -0.001675 0.0007519 0.927 -0.001262 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7221 0.4845 0.46 0.3989 0.9653 0.9833 0.7254 0.8851 0.9607 0.6746 ] Network output: [ -0.05513 0.1786 0.9158 0.001826 -0.0008197 1.023 0.001376 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6478 0.6064 0.3922 0.2041 0.9804 0.9868 0.6483 0.9558 0.9735 0.4205 ] Network output: [ -0.09671 0.2917 0.821 0.000401 -0.00018 1.082 0.0003022 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6664 0.6584 0.4298 0.1267 0.9779 0.985 0.6665 0.9495 0.9692 0.4362 ] Network output: [ 0.067 0.7897 0.121 -0.000407 0.0001827 0.9536 -0.0003067 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0551 Epoch 1311 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01934 1.01 0.988 2.089e-05 -9.379e-06 -0.03639 1.575e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03432 -0.002497 0.02805 0.02107 0.922 0.934 0.06596 0.853 0.8845 0.1479 ] Network output: [ 0.9688 0.08046 -0.0283 -0.0002667 0.0001197 0.009063 -0.000201 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6404 0.02627 0.0303 0.2306 0.9607 0.9805 0.7267 0.8732 0.9541 0.6776 ] Network output: [ -0.008942 0.9459 1.031 0.0001004 -4.509e-05 0.04097 7.569e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06339 0.03806 0.0569 0.0364 0.9773 0.9836 0.06485 0.9485 0.9702 0.07964 ] Network output: [ 0.1031 -0.2934 1.153 -0.001701 0.0007637 0.9276 -0.001282 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.722 0.4847 0.4606 0.3972 0.9653 0.9833 0.7253 0.8851 0.9607 0.6742 ] Network output: [ -0.05413 0.1816 0.9111 0.001822 -0.0008181 1.023 0.001373 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.648 0.6066 0.3918 0.2021 0.9804 0.9868 0.6485 0.9558 0.9735 0.42 ] Network output: [ -0.09593 0.2959 0.8158 0.0003801 -0.0001707 1.082 0.0002865 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6664 0.6585 0.4295 0.1239 0.9779 0.985 0.6666 0.9495 0.9691 0.4359 ] Network output: [ 0.06729 0.7913 0.1195 -0.0004232 0.00019 0.9529 -0.000319 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05536 Epoch 1312 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01917 1.008 0.9894 2.674e-05 -1.2e-05 -0.03615 2.015e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0343 -0.002498 0.02817 0.02116 0.922 0.934 0.0659 0.853 0.8845 0.1479 ] Network output: [ 0.9673 0.07719 -0.02324 -0.0002628 0.000118 0.01041 -0.0001981 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6402 0.02614 0.03131 0.2316 0.9608 0.9805 0.7265 0.8732 0.9541 0.6778 ] Network output: [ -0.008917 0.9449 1.032 0.0001064 -4.775e-05 0.04101 8.016e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06334 0.03801 0.05697 0.03655 0.9773 0.9836 0.0648 0.9485 0.9702 0.07967 ] Network output: [ 0.1032 -0.2974 1.156 -0.001687 0.0007575 0.9278 -0.001272 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.722 0.484 0.4608 0.3984 0.9653 0.9833 0.7253 0.8851 0.9607 0.6743 ] Network output: [ -0.05429 0.1787 0.9137 0.001836 -0.0008243 1.024 0.001384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.648 0.6065 0.392 0.2029 0.9804 0.9868 0.6486 0.9558 0.9735 0.4202 ] Network output: [ -0.09584 0.2939 0.8172 0.0003979 -0.0001786 1.082 0.0002998 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6665 0.6586 0.4294 0.1245 0.9779 0.985 0.6667 0.9495 0.9691 0.4358 ] Network output: [ 0.06787 0.7896 0.1204 -0.0004014 0.0001802 0.9527 -0.0003025 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05549 Epoch 1313 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01966 1.009 0.9883 3.156e-05 -1.417e-05 -0.03654 2.378e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03429 -0.002574 0.02793 0.02108 0.922 0.9341 0.06588 0.853 0.8845 0.1477 ] Network output: [ 0.9726 0.07843 -0.03074 -0.0002305 0.0001035 0.006088 -0.0001737 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6403 0.0239 0.02891 0.2309 0.9608 0.9805 0.7265 0.8732 0.9541 0.6775 ] Network output: [ -0.008966 0.9455 1.032 0.0001069 -4.798e-05 0.04087 8.055e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06323 0.03778 0.05664 0.03643 0.9773 0.9836 0.06468 0.9485 0.9701 0.07939 ] Network output: [ 0.1042 -0.2975 1.156 -0.001692 0.0007595 0.926 -0.001275 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.4822 0.4599 0.3986 0.9653 0.9833 0.7248 0.8851 0.9607 0.6743 ] Network output: [ -0.0553 0.1778 0.9161 0.001829 -0.0008212 1.024 0.001379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6477 0.6059 0.3918 0.2034 0.9804 0.9868 0.6482 0.9558 0.9734 0.4201 ] Network output: [ -0.09686 0.2929 0.8194 0.0003989 -0.0001791 1.083 0.0003006 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6663 0.6582 0.4292 0.1251 0.9779 0.985 0.6664 0.9495 0.9691 0.4356 ] Network output: [ 0.06761 0.7885 0.1219 -0.0003899 0.000175 0.9528 -0.0002938 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05546 Epoch 1314 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01913 1.01 0.988 2.302e-05 -1.034e-05 -0.03606 1.735e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03427 -0.002549 0.02806 0.02101 0.922 0.9341 0.06581 0.853 0.8845 0.1475 ] Network output: [ 0.9696 0.08056 -0.02914 -0.000263 0.0001181 0.008382 -0.0001982 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6401 0.02437 0.03044 0.2304 0.9608 0.9805 0.7261 0.8732 0.9541 0.6773 ] Network output: [ -0.008959 0.9462 1.031 0.0001048 -4.706e-05 0.04093 7.9e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06318 0.03778 0.05662 0.03622 0.9773 0.9836 0.06463 0.9485 0.9701 0.07924 ] Network output: [ 0.1035 -0.2938 1.153 -0.001718 0.0007712 0.9264 -0.001295 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7214 0.4821 0.4604 0.397 0.9653 0.9833 0.7247 0.8851 0.9607 0.6739 ] Network output: [ -0.05453 0.1806 0.9119 0.001824 -0.0008188 1.024 0.001375 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6478 0.606 0.3915 0.2015 0.9804 0.9868 0.6483 0.9558 0.9734 0.4197 ] Network output: [ -0.09632 0.2968 0.8148 0.0003782 -0.0001698 1.083 0.0002851 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6663 0.6582 0.4289 0.1225 0.9779 0.985 0.6664 0.9494 0.9691 0.4353 ] Network output: [ 0.0678 0.7898 0.1208 -0.0004044 0.0001815 0.9522 -0.0003048 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05569 Epoch 1315 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01885 1.009 0.9894 2.678e-05 -1.202e-05 -0.03573 2.019e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03425 -0.002541 0.0282 0.02109 0.922 0.9341 0.06574 0.853 0.8845 0.1474 ] Network output: [ 0.9672 0.07783 -0.0237 -0.0002669 0.0001198 0.01031 -0.0002011 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6399 0.02448 0.0318 0.2313 0.9608 0.9805 0.7259 0.8732 0.9541 0.6775 ] Network output: [ -0.008929 0.9453 1.032 0.00011 -4.938e-05 0.04098 8.29e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06313 0.03774 0.05671 0.03633 0.9773 0.9836 0.06458 0.9485 0.9702 0.07926 ] Network output: [ 0.1034 -0.2968 1.156 -0.001709 0.0007671 0.9267 -0.001288 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7214 0.4816 0.4607 0.3978 0.9653 0.9833 0.7246 0.8851 0.9607 0.674 ] Network output: [ -0.05449 0.1785 0.9134 0.001836 -0.0008243 1.025 0.001384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6479 0.6059 0.3916 0.2019 0.9804 0.9868 0.6484 0.9558 0.9734 0.4197 ] Network output: [ -0.09607 0.2956 0.8152 0.0003909 -0.0001755 1.083 0.0002946 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6664 0.6583 0.4288 0.1226 0.9779 0.985 0.6665 0.9494 0.9691 0.4352 ] Network output: [ 0.06838 0.7885 0.1213 -0.0003876 0.000174 0.9518 -0.0002921 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05585 Epoch 1316 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01929 1.009 0.9885 3.25e-05 -1.459e-05 -0.03606 2.45e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03424 -0.002611 0.028 0.02104 0.922 0.9341 0.06572 0.853 0.8845 0.1472 ] Network output: [ 0.972 0.07836 -0.02981 -0.0002355 0.0001057 0.006491 -0.0001775 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6399 0.02239 0.02971 0.2309 0.9608 0.9805 0.7259 0.8732 0.9541 0.6773 ] Network output: [ -0.008971 0.9457 1.032 0.0001115 -5.006e-05 0.04086 8.404e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06302 0.03753 0.05642 0.03626 0.9773 0.9836 0.06447 0.9484 0.9701 0.07902 ] Network output: [ 0.1043 -0.2977 1.157 -0.00171 0.0007675 0.9251 -0.001288 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7209 0.4798 0.46 0.3983 0.9653 0.9833 0.7242 0.8851 0.9607 0.6739 ] Network output: [ -0.05547 0.1771 0.9162 0.001832 -0.0008226 1.025 0.001381 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6476 0.6053 0.3915 0.2026 0.9804 0.9868 0.6481 0.9558 0.9734 0.4197 ] Network output: [ -0.09701 0.2942 0.8177 0.0003955 -0.0001776 1.084 0.0002981 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6661 0.658 0.4287 0.1233 0.9779 0.985 0.6663 0.9494 0.9691 0.435 ] Network output: [ 0.06824 0.7872 0.1229 -0.0003729 0.0001674 0.9519 -0.0002811 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05584 Epoch 1317 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0189 1.01 0.9881 2.54e-05 -1.14e-05 -0.0357 1.914e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03422 -0.002602 0.02807 0.02096 0.922 0.9341 0.06565 0.853 0.8845 0.147 ] Network output: [ 0.9701 0.08063 -0.02977 -0.00026 0.0001167 0.007811 -0.000196 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6397 0.02246 0.03067 0.2303 0.9608 0.9805 0.7256 0.8732 0.9541 0.677 ] Network output: [ -0.008973 0.9465 1.031 0.0001095 -4.918e-05 0.04089 8.256e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06297 0.03749 0.05635 0.03605 0.9773 0.9836 0.06441 0.9484 0.9701 0.07884 ] Network output: [ 0.1039 -0.2941 1.154 -0.001735 0.0007787 0.9251 -0.001307 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7207 0.4794 0.4602 0.3969 0.9653 0.9833 0.724 0.8851 0.9607 0.6736 ] Network output: [ -0.05494 0.1796 0.9128 0.001826 -0.0008196 1.025 0.001376 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6476 0.6053 0.3912 0.201 0.9804 0.9868 0.6482 0.9558 0.9734 0.4193 ] Network output: [ -0.09669 0.2976 0.8139 0.0003762 -0.0001689 1.083 0.0002835 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6661 0.6579 0.4284 0.1211 0.9779 0.985 0.6662 0.9494 0.9691 0.4348 ] Network output: [ 0.06835 0.7883 0.1221 -0.0003849 0.0001728 0.9514 -0.0002901 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05604 Epoch 1318 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01854 1.009 0.9893 2.732e-05 -1.227e-05 -0.03531 2.059e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03419 -0.002587 0.02823 0.02102 0.922 0.9341 0.06558 0.8531 0.8846 0.1469 ] Network output: [ 0.9673 0.07849 -0.02432 -0.0002701 0.0001213 0.01011 -0.0002036 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.02273 0.03225 0.231 0.9608 0.9805 0.7252 0.8732 0.9541 0.6771 ] Network output: [ -0.008939 0.9458 1.032 0.0001139 -5.116e-05 0.04095 8.588e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06292 0.03747 0.05645 0.03612 0.9773 0.9836 0.06436 0.9485 0.9702 0.07886 ] Network output: [ 0.1037 -0.2962 1.156 -0.00173 0.0007768 0.9255 -0.001304 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7207 0.479 0.4607 0.3973 0.9653 0.9833 0.724 0.8851 0.9607 0.6736 ] Network output: [ -0.05473 0.1782 0.9134 0.001836 -0.0008243 1.025 0.001384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6477 0.6053 0.3912 0.201 0.9804 0.9868 0.6483 0.9558 0.9734 0.4193 ] Network output: [ -0.09634 0.2973 0.8133 0.0003839 -0.0001723 1.084 0.0002893 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6662 0.658 0.4283 0.1207 0.9779 0.985 0.6663 0.9494 0.9691 0.4346 ] Network output: [ 0.06892 0.7874 0.1223 -0.0003728 0.0001673 0.951 -0.0002809 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05623 Epoch 1319 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01892 1.009 0.9888 3.351e-05 -1.505e-05 -0.03558 2.526e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03419 -0.00265 0.02806 0.02098 0.922 0.9341 0.06555 0.8531 0.8846 0.1468 ] Network output: [ 0.9713 0.07842 -0.02897 -0.0002413 0.0001083 0.006901 -0.0001818 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.02084 0.03054 0.2308 0.9608 0.9805 0.7252 0.8732 0.9541 0.677 ] Network output: [ -0.008973 0.9459 1.032 0.0001163 -5.22e-05 0.04085 8.763e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06282 0.03727 0.05621 0.03608 0.9773 0.9836 0.06425 0.9484 0.9701 0.07865 ] Network output: [ 0.1045 -0.2977 1.157 -0.001728 0.000776 0.9241 -0.001303 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7203 0.4773 0.46 0.3979 0.9653 0.9833 0.7236 0.885 0.9607 0.6736 ] Network output: [ -0.05565 0.1764 0.9163 0.001835 -0.0008238 1.026 0.001383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6474 0.6048 0.3911 0.2017 0.9804 0.9868 0.648 0.9558 0.9734 0.4193 ] Network output: [ -0.09717 0.2957 0.8158 0.0003911 -0.0001756 1.084 0.0002947 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.666 0.6578 0.4281 0.1215 0.9779 0.985 0.6661 0.9494 0.9691 0.4345 ] Network output: [ 0.06889 0.7859 0.1238 -0.0003561 0.0001599 0.951 -0.0002684 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05623 Epoch 1320 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01865 1.01 0.9881 2.799e-05 -1.256e-05 -0.03533 2.109e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03417 -0.002654 0.02809 0.0209 0.922 0.9341 0.0655 0.8531 0.8846 0.1465 ] Network output: [ 0.9706 0.08067 -0.03019 -0.0002581 0.0001159 0.007362 -0.0001945 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6393 0.02053 0.031 0.2302 0.9608 0.9805 0.725 0.8731 0.9541 0.6767 ] Network output: [ -0.008983 0.9467 1.031 0.0001146 -5.144e-05 0.04086 8.636e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06275 0.0372 0.0561 0.03588 0.9773 0.9836 0.06418 0.9484 0.9701 0.07845 ] Network output: [ 0.1043 -0.2946 1.155 -0.001751 0.0007863 0.9238 -0.00132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7201 0.4767 0.4601 0.3967 0.9653 0.9833 0.7233 0.885 0.9607 0.6732 ] Network output: [ -0.05534 0.1785 0.9136 0.001828 -0.0008206 1.026 0.001378 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6474 0.6046 0.3908 0.2004 0.9804 0.9868 0.648 0.9557 0.9734 0.419 ] Network output: [ -0.09706 0.2986 0.8128 0.0003738 -0.0001678 1.084 0.0002817 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6659 0.6577 0.4279 0.1196 0.9779 0.985 0.666 0.9494 0.9691 0.4342 ] Network output: [ 0.06894 0.7867 0.1234 -0.000365 0.0001639 0.9506 -0.0002751 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05642 Epoch 1321 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01825 1.009 0.9892 2.837e-05 -1.274e-05 -0.0349 2.138e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03414 -0.002636 0.02825 0.02094 0.922 0.9341 0.06542 0.8531 0.8846 0.1464 ] Network output: [ 0.9675 0.07914 -0.02504 -0.0002727 0.0001224 0.009825 -0.0002055 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6391 0.02087 0.03267 0.2307 0.9608 0.9805 0.7246 0.8732 0.9541 0.6768 ] Network output: [ -0.008948 0.9462 1.031 0.0001182 -5.308e-05 0.04092 8.911e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06271 0.03718 0.05618 0.03591 0.9773 0.9836 0.06414 0.9484 0.9701 0.07845 ] Network output: [ 0.104 -0.2958 1.156 -0.001752 0.0007864 0.9243 -0.00132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.72 0.4763 0.4606 0.3968 0.9653 0.9833 0.7233 0.885 0.9607 0.6732 ] Network output: [ -0.05502 0.1778 0.9134 0.001836 -0.0008244 1.026 0.001384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6475 0.6047 0.3908 0.2001 0.9804 0.9868 0.6481 0.9557 0.9734 0.4189 ] Network output: [ -0.09665 0.299 0.8115 0.0003769 -0.0001692 1.084 0.000284 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.666 0.6577 0.4277 0.1188 0.9779 0.985 0.6661 0.9494 0.9691 0.434 ] Network output: [ 0.06948 0.7861 0.1233 -0.0003569 0.0001602 0.9501 -0.0002689 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05663 Epoch 1322 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01855 1.009 0.989 3.465e-05 -1.556e-05 -0.03509 2.611e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03413 -0.002691 0.02813 0.02093 0.9221 0.9341 0.06539 0.8531 0.8846 0.1463 ] Network output: [ 0.9707 0.07861 -0.02827 -0.0002475 0.0001111 0.007292 -0.0001865 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6391 0.01922 0.03138 0.2307 0.9608 0.9805 0.7246 0.8732 0.9541 0.6768 ] Network output: [ -0.008972 0.9461 1.031 0.0001212 -5.442e-05 0.04084 9.136e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06261 0.037 0.05599 0.0359 0.9773 0.9836 0.06404 0.9484 0.9701 0.07828 ] Network output: [ 0.1047 -0.2976 1.158 -0.001748 0.0007848 0.9231 -0.001317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7197 0.4747 0.4601 0.3975 0.9653 0.9833 0.723 0.885 0.9607 0.6732 ] Network output: [ -0.05583 0.1759 0.9164 0.001838 -0.0008249 1.027 0.001385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6473 0.6042 0.3908 0.2008 0.9804 0.9868 0.6479 0.9557 0.9734 0.4189 ] Network output: [ -0.09735 0.2973 0.8138 0.0003856 -0.0001731 1.085 0.0002906 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6659 0.6575 0.4276 0.1195 0.9779 0.985 0.666 0.9494 0.9691 0.4339 ] Network output: [ 0.06955 0.7846 0.1248 -0.0003393 0.0001523 0.9501 -0.0002557 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05666 Epoch 1323 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01839 1.01 0.9883 3.075e-05 -1.381e-05 -0.03493 2.318e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03411 -0.002706 0.02812 0.02085 0.9221 0.9341 0.06534 0.8531 0.8846 0.146 ] Network output: [ 0.9708 0.08071 -0.03043 -0.0002573 0.0001155 0.007036 -0.0001939 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6389 0.01859 0.03144 0.2301 0.9608 0.9805 0.7244 0.8731 0.9541 0.6765 ] Network output: [ -0.008988 0.9469 1.031 0.00012 -5.385e-05 0.04082 9.04e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06253 0.0369 0.05585 0.03571 0.9773 0.9836 0.06396 0.9484 0.9701 0.07807 ] Network output: [ 0.1047 -0.295 1.156 -0.001769 0.000794 0.9225 -0.001333 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7194 0.4739 0.46 0.3965 0.9653 0.9833 0.7227 0.885 0.9607 0.6729 ] Network output: [ -0.05573 0.1775 0.9144 0.00183 -0.0008218 1.027 0.00138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6472 0.6039 0.3905 0.1997 0.9804 0.9868 0.6478 0.9557 0.9734 0.4186 ] Network output: [ -0.09741 0.2996 0.8115 0.000371 -0.0001665 1.085 0.0002796 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6657 0.6574 0.4273 0.118 0.9779 0.985 0.6658 0.9494 0.9691 0.4337 ] Network output: [ 0.06957 0.7851 0.1247 -0.0003448 0.0001548 0.9497 -0.0002598 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05682 Epoch 1324 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01797 1.01 0.9891 2.995e-05 -1.345e-05 -0.03449 2.258e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03409 -0.002689 0.02828 0.02087 0.9221 0.9341 0.06527 0.8531 0.8846 0.1459 ] Network output: [ 0.9677 0.07977 -0.02584 -0.0002747 0.0001233 0.009476 -0.000207 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6387 0.01892 0.03309 0.2304 0.9608 0.9805 0.724 0.8732 0.9541 0.6765 ] Network output: [ -0.008954 0.9466 1.031 0.0001229 -5.517e-05 0.04089 9.262e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06249 0.03688 0.05592 0.0357 0.9773 0.9836 0.06391 0.9484 0.9701 0.07804 ] Network output: [ 0.1043 -0.2954 1.157 -0.001773 0.000796 0.923 -0.001336 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7194 0.4735 0.4605 0.3963 0.9653 0.9833 0.7226 0.885 0.9607 0.6729 ] Network output: [ -0.05535 0.1773 0.9136 0.001837 -0.0008245 1.027 0.001384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6474 0.604 0.3905 0.1992 0.9804 0.9868 0.6479 0.9557 0.9734 0.4185 ] Network output: [ -0.09699 0.3006 0.8097 0.00037 -0.0001661 1.085 0.0002788 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6658 0.6574 0.4272 0.1168 0.9779 0.985 0.6659 0.9494 0.9691 0.4335 ] Network output: [ 0.07007 0.7847 0.1245 -0.0003398 0.0001526 0.9492 -0.0002561 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05704 Epoch 1325 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01818 1.009 0.9891 3.598e-05 -1.615e-05 -0.03459 2.712e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03408 -0.002734 0.0282 0.02087 0.9221 0.9341 0.06523 0.8531 0.8846 0.1458 ] Network output: [ 0.9701 0.07892 -0.02774 -0.000254 0.000114 0.007641 -0.0001914 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6386 0.01752 0.03223 0.2306 0.9608 0.9805 0.7239 0.8731 0.9541 0.6765 ] Network output: [ -0.008968 0.9464 1.031 0.0001264 -5.672e-05 0.04083 9.523e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0624 0.03672 0.05577 0.03571 0.9773 0.9836 0.06382 0.9484 0.9701 0.0779 ] Network output: [ 0.105 -0.2974 1.158 -0.001769 0.0007941 0.922 -0.001333 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7191 0.472 0.4602 0.3971 0.9653 0.9833 0.7223 0.885 0.9607 0.6729 ] Network output: [ -0.05604 0.1753 0.9164 0.00184 -0.0008259 1.028 0.001386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6472 0.6035 0.3905 0.1998 0.9804 0.9868 0.6477 0.9557 0.9734 0.4186 ] Network output: [ -0.09755 0.299 0.8117 0.0003791 -0.0001702 1.086 0.0002857 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6657 0.6573 0.427 0.1174 0.9779 0.985 0.6658 0.9494 0.9691 0.4334 ] Network output: [ 0.07024 0.7833 0.1258 -0.0003223 0.0001447 0.9491 -0.0002429 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0571 Epoch 1326 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01812 1.01 0.9884 3.369e-05 -1.512e-05 -0.03451 2.539e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03406 -0.002759 0.02816 0.02079 0.9221 0.9341 0.06518 0.8531 0.8846 0.1455 ] Network output: [ 0.9709 0.08078 -0.0305 -0.0002577 0.0001157 0.006833 -0.0001942 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6385 0.01663 0.03197 0.23 0.9608 0.9805 0.7237 0.8731 0.9541 0.6762 ] Network output: [ -0.008989 0.9471 1.031 0.0001256 -5.64e-05 0.0408 9.468e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06232 0.0366 0.05561 0.03554 0.9773 0.9836 0.06373 0.9484 0.9701 0.07769 ] Network output: [ 0.1051 -0.2954 1.157 -0.001787 0.000802 0.9213 -0.001346 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7188 0.471 0.46 0.3963 0.9653 0.9833 0.722 0.885 0.9607 0.6726 ] Network output: [ -0.05611 0.1765 0.9152 0.001833 -0.000823 1.028 0.001382 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.647 0.6032 0.3902 0.199 0.9804 0.9868 0.6476 0.9557 0.9734 0.4183 ] Network output: [ -0.09775 0.3008 0.8101 0.0003674 -0.0001649 1.086 0.0002769 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6656 0.6571 0.4268 0.1162 0.9779 0.985 0.6657 0.9494 0.9691 0.4332 ] Network output: [ 0.07024 0.7834 0.126 -0.0003242 0.0001456 0.9488 -0.0002443 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05724 Epoch 1327 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0177 1.01 0.9891 3.207e-05 -1.44e-05 -0.03408 2.417e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03404 -0.002743 0.0283 0.0208 0.9221 0.9341 0.06511 0.8531 0.8846 0.1454 ] Network output: [ 0.968 0.08037 -0.02666 -0.0002763 0.000124 0.009091 -0.0002082 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6383 0.01689 0.03353 0.2302 0.9608 0.9805 0.7234 0.8731 0.9541 0.6762 ] Network output: [ -0.008958 0.9469 1.031 0.000128 -5.745e-05 0.04086 9.644e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06227 0.03658 0.05565 0.0355 0.9773 0.9836 0.06368 0.9484 0.9701 0.07763 ] Network output: [ 0.1047 -0.2952 1.157 -0.001794 0.0008055 0.9216 -0.001352 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7187 0.4706 0.4605 0.3959 0.9653 0.9833 0.7219 0.885 0.9607 0.6725 ] Network output: [ -0.05571 0.1767 0.914 0.001837 -0.0008248 1.028 0.001385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6472 0.6033 0.3901 0.1983 0.9804 0.9868 0.6477 0.9557 0.9734 0.4181 ] Network output: [ -0.09735 0.3022 0.808 0.0003631 -0.000163 1.086 0.0002736 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6656 0.6571 0.4266 0.1149 0.9779 0.985 0.6657 0.9494 0.9691 0.433 ] Network output: [ 0.07069 0.7832 0.1258 -0.0003217 0.0001444 0.9483 -0.0002425 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05748 Epoch 1328 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01782 1.009 0.9893 3.758e-05 -1.687e-05 -0.0341 2.832e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03402 -0.00278 0.02826 0.02081 0.9221 0.9341 0.06506 0.8531 0.8846 0.1453 ] Network output: [ 0.9695 0.07934 -0.0274 -0.0002606 0.000117 0.007932 -0.0001964 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6382 0.01574 0.03307 0.2304 0.9608 0.9805 0.7233 0.8731 0.9541 0.6762 ] Network output: [ -0.008962 0.9467 1.031 0.0001317 -5.913e-05 0.04082 9.926e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06219 0.03644 0.05555 0.03551 0.9773 0.9836 0.0636 0.9484 0.9701 0.07752 ] Network output: [ 0.1052 -0.2972 1.159 -0.00179 0.0008037 0.9209 -0.001349 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7184 0.4692 0.4603 0.3966 0.9653 0.9833 0.7217 0.885 0.9607 0.6726 ] Network output: [ -0.05627 0.1748 0.9164 0.001841 -0.0008267 1.029 0.001388 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.647 0.6029 0.3902 0.1988 0.9804 0.9868 0.6476 0.9557 0.9734 0.4182 ] Network output: [ -0.09777 0.3009 0.8095 0.0003717 -0.0001669 1.087 0.0002801 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6656 0.657 0.4265 0.1153 0.9779 0.985 0.6657 0.9494 0.9691 0.4328 ] Network output: [ 0.07094 0.7818 0.1269 -0.000305 0.0001369 0.9481 -0.0002298 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05756 Epoch 1329 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01782 1.01 0.9886 3.678e-05 -1.651e-05 -0.03406 2.772e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03401 -0.002811 0.02821 0.02074 0.9221 0.9342 0.06502 0.8531 0.8846 0.1451 ] Network output: [ 0.9709 0.08089 -0.03043 -0.0002593 0.0001164 0.006744 -0.0001955 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6381 0.01466 0.03261 0.2299 0.9608 0.9805 0.7231 0.8731 0.9541 0.676 ] Network output: [ -0.008985 0.9472 1.03 0.0001316 -5.908e-05 0.04078 9.918e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0621 0.0363 0.05537 0.03537 0.9773 0.9836 0.06351 0.9483 0.9701 0.07731 ] Network output: [ 0.1054 -0.2957 1.157 -0.001805 0.0008104 0.92 -0.00136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7181 0.468 0.4601 0.3961 0.9653 0.9833 0.7213 0.8849 0.9607 0.6724 ] Network output: [ -0.05649 0.1755 0.9159 0.001836 -0.0008242 1.029 0.001384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6469 0.6025 0.39 0.1983 0.9804 0.9868 0.6474 0.9556 0.9733 0.418 ] Network output: [ -0.09808 0.3021 0.8086 0.000363 -0.000163 1.087 0.0002736 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6654 0.6568 0.4263 0.1144 0.9779 0.985 0.6655 0.9494 0.969 0.4326 ] Network output: [ 0.07096 0.7817 0.1274 -0.0003035 0.0001362 0.9478 -0.0002287 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0577 Epoch 1330 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01744 1.01 0.989 3.471e-05 -1.558e-05 -0.03366 2.616e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03398 -0.0028 0.02833 0.02073 0.9221 0.9342 0.06495 0.8531 0.8846 0.1449 ] Network output: [ 0.9683 0.08094 -0.02746 -0.0002777 0.0001247 0.008695 -0.0002093 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6379 0.01479 0.03399 0.23 0.9608 0.9805 0.7228 0.8731 0.9541 0.676 ] Network output: [ -0.008958 0.9473 1.03 0.0001334 -5.991e-05 0.04083 0.0001006 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06205 0.03627 0.0554 0.0353 0.9773 0.9836 0.06345 0.9483 0.9701 0.07723 ] Network output: [ 0.1051 -0.295 1.157 -0.001815 0.000815 0.9203 -0.001368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.718 0.4675 0.4605 0.3955 0.9653 0.9833 0.7212 0.8849 0.9607 0.6722 ] Network output: [ -0.05611 0.176 0.9145 0.001838 -0.0008252 1.029 0.001385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.647 0.6025 0.3899 0.1974 0.9804 0.9868 0.6475 0.9556 0.9733 0.4178 ] Network output: [ -0.09773 0.3038 0.8063 0.0003562 -0.0001599 1.087 0.0002685 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6654 0.6568 0.4261 0.1129 0.9779 0.985 0.6655 0.9494 0.969 0.4325 ] Network output: [ 0.07135 0.7816 0.1271 -0.0003025 0.0001358 0.9473 -0.000228 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05794 Epoch 1331 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01746 1.009 0.9894 3.952e-05 -1.774e-05 -0.0336 2.978e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03397 -0.002828 0.02833 0.02074 0.9221 0.9342 0.0649 0.8531 0.8846 0.1448 ] Network output: [ 0.9691 0.07987 -0.02724 -0.0002672 0.0001199 0.008153 -0.0002014 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6378 0.01386 0.03391 0.2302 0.9608 0.9805 0.7226 0.8731 0.9541 0.676 ] Network output: [ -0.008953 0.9469 1.031 0.0001373 -6.166e-05 0.0408 0.0001035 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06197 0.03615 0.05532 0.03531 0.9773 0.9836 0.06337 0.9483 0.9701 0.07713 ] Network output: [ 0.1055 -0.2969 1.159 -0.001812 0.0008136 0.9197 -0.001366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7178 0.4663 0.4604 0.3961 0.9653 0.9833 0.721 0.8849 0.9607 0.6722 ] Network output: [ -0.05653 0.1743 0.9164 0.001843 -0.0008274 1.03 0.001389 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6469 0.6022 0.3899 0.1978 0.9804 0.9868 0.6474 0.9556 0.9733 0.4178 ] Network output: [ -0.09802 0.3028 0.8073 0.0003635 -0.0001632 1.087 0.000274 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6654 0.6567 0.426 0.113 0.9779 0.985 0.6655 0.9494 0.969 0.4323 ] Network output: [ 0.07166 0.7803 0.1281 -0.0002871 0.0001289 0.9471 -0.0002164 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05805 Epoch 1332 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01752 1.01 0.9888 4.004e-05 -1.798e-05 -0.0336 3.018e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03395 -0.002865 0.02826 0.02068 0.9221 0.9342 0.06486 0.8531 0.8846 0.1446 ] Network output: [ 0.9707 0.08106 -0.03027 -0.0002622 0.0001177 0.006754 -0.0001976 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6377 0.01266 0.03334 0.2298 0.9608 0.9805 0.7225 0.8731 0.9541 0.6758 ] Network output: [ -0.008976 0.9474 1.03 0.0001379 -6.19e-05 0.04076 0.0001039 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06188 0.036 0.05515 0.03519 0.9773 0.9836 0.06328 0.9483 0.9701 0.07693 ] Network output: [ 0.1058 -0.296 1.158 -0.001824 0.000819 0.9188 -0.001375 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7175 0.465 0.4602 0.3958 0.9653 0.9833 0.7207 0.8849 0.9606 0.6721 ] Network output: [ -0.05686 0.1745 0.9166 0.001839 -0.0008255 1.03 0.001386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6467 0.6018 0.3897 0.1974 0.9804 0.9868 0.6472 0.9556 0.9733 0.4177 ] Network output: [ -0.0984 0.3035 0.8069 0.0003577 -0.0001606 1.088 0.0002696 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6652 0.6565 0.4258 0.1124 0.9779 0.985 0.6654 0.9493 0.969 0.4322 ] Network output: [ 0.07171 0.7799 0.1287 -0.0002825 0.0001268 0.9468 -0.0002129 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05818 Epoch 1333 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01717 1.01 0.989 3.786e-05 -1.7e-05 -0.03324 2.853e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03393 -0.002859 0.02836 0.02065 0.9221 0.9342 0.06479 0.8531 0.8846 0.1444 ] Network output: [ 0.9686 0.08149 -0.02821 -0.0002792 0.0001253 0.008314 -0.0002104 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6375 0.01263 0.0345 0.2297 0.9608 0.9805 0.7222 0.8731 0.9541 0.6757 ] Network output: [ -0.008955 0.9475 1.03 0.0001394 -6.257e-05 0.0408 0.000105 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06182 0.03595 0.05514 0.0351 0.9773 0.9836 0.06322 0.9483 0.9701 0.07683 ] Network output: [ 0.1055 -0.2949 1.158 -0.001837 0.0008246 0.9189 -0.001384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7173 0.4644 0.4606 0.3952 0.9653 0.9833 0.7205 0.8849 0.9606 0.6719 ] Network output: [ -0.05654 0.1752 0.915 0.001839 -0.0008258 1.03 0.001386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6468 0.6017 0.3896 0.1965 0.9804 0.9868 0.6473 0.9556 0.9733 0.4174 ] Network output: [ -0.09813 0.3053 0.8045 0.0003492 -0.0001568 1.088 0.0002632 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6652 0.6565 0.4257 0.1109 0.9779 0.985 0.6654 0.9493 0.969 0.432 ] Network output: [ 0.07205 0.7799 0.1285 -0.0002822 0.0001267 0.9464 -0.0002127 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05842 Epoch 1334 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01712 1.01 0.9894 4.187e-05 -1.88e-05 -0.03311 3.155e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03391 -0.00288 0.02839 0.02067 0.9222 0.9342 0.06474 0.8531 0.8846 0.1443 ] Network output: [ 0.9687 0.08048 -0.02726 -0.0002736 0.0001228 0.008299 -0.0002062 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6374 0.01189 0.03475 0.23 0.9608 0.9805 0.722 0.8731 0.9541 0.6758 ] Network output: [ -0.008942 0.9472 1.03 0.0001433 -6.432e-05 0.04079 0.000108 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06175 0.03584 0.05509 0.03511 0.9773 0.9836 0.06315 0.9483 0.9701 0.07675 ] Network output: [ 0.1058 -0.2965 1.159 -0.001835 0.0008238 0.9185 -0.001383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7171 0.4633 0.4606 0.3956 0.9653 0.9833 0.7203 0.8849 0.9606 0.6719 ] Network output: [ -0.05682 0.1738 0.9165 0.001845 -0.0008281 1.031 0.00139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6467 0.6014 0.3896 0.1967 0.9804 0.9868 0.6472 0.9556 0.9733 0.4175 ] Network output: [ -0.0983 0.3047 0.805 0.0003547 -0.0001592 1.088 0.0002673 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6652 0.6564 0.4255 0.1107 0.9779 0.985 0.6653 0.9493 0.969 0.4318 ] Network output: [ 0.07241 0.7787 0.1293 -0.0002686 0.0001206 0.9461 -0.0002024 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05856 Epoch 1335 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0172 1.01 0.989 4.35e-05 -1.953e-05 -0.03312 3.278e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0339 -0.002918 0.02833 0.02062 0.9222 0.9342 0.0647 0.8531 0.8846 0.1441 ] Network output: [ 0.9704 0.08132 -0.03006 -0.0002661 0.0001195 0.006847 -0.0002006 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6373 0.01063 0.03416 0.2297 0.9608 0.9805 0.7218 0.8731 0.9541 0.6756 ] Network output: [ -0.008962 0.9475 1.03 0.0001445 -6.486e-05 0.04074 0.0001089 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06166 0.03569 0.05493 0.03501 0.9773 0.9836 0.06305 0.9483 0.9701 0.07656 ] Network output: [ 0.1062 -0.2961 1.159 -0.001845 0.0008281 0.9175 -0.00139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7168 0.4619 0.4604 0.3955 0.9653 0.9833 0.72 0.8849 0.9606 0.6718 ] Network output: [ -0.05722 0.1736 0.9171 0.001842 -0.0008268 1.031 0.001388 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6465 0.601 0.3895 0.1966 0.9804 0.9868 0.6471 0.9556 0.9733 0.4174 ] Network output: [ -0.09871 0.3051 0.805 0.0003514 -0.0001577 1.089 0.0002648 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6651 0.6563 0.4254 0.1102 0.9779 0.985 0.6652 0.9493 0.969 0.4317 ] Network output: [ 0.07249 0.7781 0.1301 -0.0002614 0.0001173 0.9458 -0.000197 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05869 Epoch 1336 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01691 1.01 0.989 4.151e-05 -1.864e-05 -0.03281 3.129e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03388 -0.00292 0.0284 0.02058 0.9222 0.9342 0.06463 0.8531 0.8846 0.1439 ] Network output: [ 0.9689 0.08201 -0.02888 -0.0002808 0.0001261 0.007971 -0.0002117 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6371 0.01041 0.03508 0.2295 0.9608 0.9805 0.7215 0.8731 0.9541 0.6755 ] Network output: [ -0.008947 0.9478 1.03 0.0001458 -6.543e-05 0.04077 0.0001098 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0616 0.03563 0.0549 0.0349 0.9773 0.9836 0.06299 0.9483 0.9701 0.07644 ] Network output: [ 0.106 -0.2949 1.158 -0.001858 0.0008342 0.9174 -0.0014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7166 0.4612 0.4607 0.3948 0.9653 0.9833 0.7198 0.8849 0.9606 0.6717 ] Network output: [ -0.05699 0.1743 0.9157 0.001841 -0.0008265 1.031 0.001387 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6465 0.6009 0.3894 0.1956 0.9804 0.9868 0.6471 0.9555 0.9733 0.4172 ] Network output: [ -0.09854 0.3069 0.8028 0.000342 -0.0001536 1.089 0.0002578 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6651 0.6562 0.4252 0.1088 0.9779 0.985 0.6652 0.9493 0.969 0.4315 ] Network output: [ 0.07279 0.778 0.13 -0.000261 0.0001172 0.9453 -0.0001967 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05892 Epoch 1337 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01679 1.01 0.9895 4.469e-05 -2.006e-05 -0.03262 3.368e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03386 -0.002935 0.02845 0.02059 0.9222 0.9342 0.06458 0.8532 0.8846 0.1438 ] Network output: [ 0.9684 0.08116 -0.02744 -0.0002797 0.0001256 0.00837 -0.0002108 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.637 0.009822 0.03558 0.2298 0.9608 0.9805 0.7213 0.8731 0.9541 0.6755 ] Network output: [ -0.008928 0.9475 1.03 0.0001495 -6.714e-05 0.04078 0.0001127 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06153 0.03553 0.05487 0.0349 0.9773 0.9836 0.06292 0.9483 0.9701 0.07636 ] Network output: [ 0.1061 -0.2961 1.159 -0.001858 0.0008343 0.9171 -0.001401 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7165 0.4601 0.4608 0.3951 0.9653 0.9833 0.7197 0.8849 0.9606 0.6717 ] Network output: [ -0.05715 0.1732 0.9167 0.001846 -0.0008287 1.032 0.001391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6465 0.6007 0.3894 0.1956 0.9804 0.9868 0.6471 0.9555 0.9733 0.4172 ] Network output: [ -0.09861 0.3068 0.8027 0.0003452 -0.000155 1.089 0.0002602 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6651 0.6562 0.4251 0.1083 0.9779 0.985 0.6652 0.9493 0.969 0.4314 ] Network output: [ 0.07318 0.777 0.1306 -0.0002492 0.0001119 0.945 -0.0001878 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05909 Epoch 1338 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01687 1.01 0.9892 4.718e-05 -2.118e-05 -0.03263 3.556e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03384 -0.002973 0.0284 0.02055 0.9222 0.9342 0.06453 0.8531 0.8846 0.1436 ] Network output: [ 0.97 0.08167 -0.02983 -0.0002711 0.0001217 0.007003 -0.0002043 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6369 0.008559 0.03506 0.2295 0.9608 0.9805 0.7212 0.8731 0.954 0.6754 ] Network output: [ -0.008944 0.9477 1.03 0.0001514 -6.797e-05 0.04073 0.0001141 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06144 0.03538 0.05472 0.03482 0.9773 0.9836 0.06282 0.9483 0.9701 0.07619 ] Network output: [ 0.1065 -0.2962 1.159 -0.001866 0.0008376 0.9162 -0.001406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7161 0.4587 0.4607 0.3951 0.9653 0.9833 0.7193 0.8849 0.9606 0.6716 ] Network output: [ -0.05759 0.1727 0.9176 0.001844 -0.000828 1.032 0.00139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6464 0.6002 0.3893 0.1956 0.9804 0.9868 0.6469 0.9555 0.9733 0.4171 ] Network output: [ -0.09902 0.3069 0.803 0.0003439 -0.0001544 1.09 0.0002592 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6649 0.656 0.4249 0.108 0.9779 0.985 0.665 0.9493 0.969 0.4312 ] Network output: [ 0.07331 0.7762 0.1315 -0.00024 0.0001077 0.9447 -0.0001809 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05922 Epoch 1339 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01664 1.01 0.9891 4.564e-05 -2.049e-05 -0.03236 3.44e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03382 -0.002982 0.02845 0.02051 0.9222 0.9342 0.06447 0.8532 0.8846 0.1434 ] Network output: [ 0.969 0.08253 -0.02945 -0.000283 0.000127 0.007687 -0.0002133 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6367 0.008134 0.03574 0.2293 0.9608 0.9805 0.7209 0.8731 0.954 0.6753 ] Network output: [ -0.008934 0.948 1.03 0.0001526 -6.851e-05 0.04075 0.000115 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06137 0.0353 0.05466 0.03471 0.9773 0.9836 0.06275 0.9483 0.9701 0.07605 ] Network output: [ 0.1064 -0.295 1.159 -0.00188 0.0008439 0.916 -0.001417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7159 0.4578 0.4609 0.3945 0.9653 0.9833 0.7191 0.8848 0.9606 0.6714 ] Network output: [ -0.05746 0.1734 0.9165 0.001843 -0.0008273 1.033 0.001389 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6463 0.6001 0.3892 0.1947 0.9804 0.9868 0.6469 0.9555 0.9733 0.4169 ] Network output: [ -0.09895 0.3086 0.801 0.0003344 -0.0001501 1.09 0.000252 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6649 0.6559 0.4248 0.1066 0.9779 0.985 0.665 0.9493 0.969 0.431 ] Network output: [ 0.07357 0.7761 0.1316 -0.0002389 0.0001072 0.9442 -0.00018 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05945 Epoch 1340 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01647 1.01 0.9895 4.804e-05 -2.157e-05 -0.03213 3.62e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03381 -0.002993 0.02851 0.02051 0.9222 0.9342 0.06442 0.8532 0.8846 0.1433 ] Network output: [ 0.9682 0.08189 -0.02777 -0.0002856 0.0001282 0.008375 -0.0002152 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6365 0.007653 0.03642 0.2295 0.9608 0.9805 0.7207 0.8731 0.954 0.6753 ] Network output: [ -0.00891 0.9477 1.03 0.0001562 -7.013e-05 0.04076 0.0001177 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06131 0.03521 0.05464 0.03469 0.9773 0.9836 0.06268 0.9483 0.9701 0.07597 ] Network output: [ 0.1064 -0.2958 1.159 -0.001882 0.000845 0.9158 -0.001418 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7158 0.4568 0.4611 0.3945 0.9653 0.9833 0.719 0.8848 0.9606 0.6714 ] Network output: [ -0.05753 0.1726 0.917 0.001847 -0.0008293 1.033 0.001392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6463 0.5999 0.3892 0.1945 0.9804 0.9868 0.6469 0.9555 0.9733 0.4169 ] Network output: [ -0.09894 0.3088 0.8004 0.0003352 -0.0001505 1.09 0.0002526 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6649 0.6559 0.4247 0.1059 0.9779 0.985 0.665 0.9493 0.969 0.4309 ] Network output: [ 0.07397 0.7752 0.132 -0.0002289 0.0001028 0.9438 -0.0001725 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05965 Epoch 1341 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01654 1.01 0.9893 5.113e-05 -2.296e-05 -0.03212 3.854e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03379 -0.00303 0.02847 0.02048 0.9222 0.9342 0.06437 0.8532 0.8846 0.1431 ] Network output: [ 0.9696 0.08212 -0.02963 -0.000277 0.0001244 0.007199 -0.0002088 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6364 0.006433 0.03603 0.2294 0.9608 0.9805 0.7205 0.8731 0.954 0.6752 ] Network output: [ -0.008921 0.9478 1.03 0.0001586 -7.122e-05 0.04072 0.0001196 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06122 0.03506 0.05451 0.03463 0.9773 0.9836 0.06259 0.9482 0.9701 0.07582 ] Network output: [ 0.1068 -0.2962 1.16 -0.001888 0.0008475 0.9149 -0.001423 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7155 0.4554 0.461 0.3946 0.9653 0.9833 0.7187 0.8848 0.9606 0.6714 ] Network output: [ -0.05796 0.1719 0.9181 0.001847 -0.0008292 1.033 0.001392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6462 0.5994 0.3891 0.1945 0.9804 0.9868 0.6467 0.9555 0.9733 0.4169 ] Network output: [ -0.09933 0.3087 0.8007 0.0003352 -0.0001505 1.091 0.0002526 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6648 0.6557 0.4245 0.1056 0.9779 0.985 0.6649 0.9493 0.969 0.4308 ] Network output: [ 0.07416 0.7743 0.1329 -0.0002182 9.798e-05 0.9436 -0.0001645 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05979 Epoch 1342 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01636 1.01 0.9892 5.024e-05 -2.255e-05 -0.0319 3.786e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03377 -0.003045 0.0285 0.02044 0.9222 0.9342 0.06432 0.8532 0.8846 0.1429 ] Network output: [ 0.9691 0.08305 -0.02991 -0.0002857 0.0001283 0.007475 -0.0002153 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6363 0.005819 0.03649 0.2291 0.9608 0.9805 0.7203 0.8731 0.954 0.6751 ] Network output: [ -0.008916 0.9481 1.03 0.0001599 -7.18e-05 0.04073 0.0001205 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06114 0.03496 0.05443 0.03452 0.9773 0.9836 0.06252 0.9482 0.9701 0.07567 ] Network output: [ 0.1068 -0.295 1.159 -0.001902 0.0008538 0.9145 -0.001433 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7153 0.4544 0.4612 0.3941 0.9653 0.9833 0.7184 0.8848 0.9606 0.6712 ] Network output: [ -0.05794 0.1724 0.9172 0.001845 -0.0008283 1.034 0.001391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6462 0.5992 0.389 0.1938 0.9804 0.9868 0.6467 0.9555 0.9732 0.4167 ] Network output: [ -0.09935 0.3103 0.7991 0.0003263 -0.0001465 1.091 0.0002459 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6647 0.6556 0.4244 0.1042 0.9779 0.985 0.6648 0.9493 0.9689 0.4306 ] Network output: [ 0.0744 0.774 0.1332 -0.0002159 9.693e-05 0.9431 -0.0001627 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06002 Epoch 1343 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01616 1.01 0.9896 5.196e-05 -2.333e-05 -0.03164 3.916e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03375 -0.003054 0.02857 0.02043 0.9222 0.9342 0.06425 0.8532 0.8846 0.1428 ] Network output: [ 0.968 0.08267 -0.02822 -0.0002912 0.0001308 0.008325 -0.0002195 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6361 0.005388 0.03729 0.2293 0.9608 0.9805 0.72 0.8731 0.954 0.6751 ] Network output: [ -0.00889 0.948 1.03 0.0001633 -7.333e-05 0.04075 0.0001231 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06108 0.03488 0.05441 0.03448 0.9773 0.9836 0.06245 0.9482 0.9701 0.07559 ] Network output: [ 0.1068 -0.2955 1.16 -0.001906 0.0008559 0.9143 -0.001437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7151 0.4534 0.4614 0.394 0.9653 0.9833 0.7183 0.8848 0.9606 0.6712 ] Network output: [ -0.05794 0.1719 0.9173 0.001849 -0.0008299 1.034 0.001393 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6462 0.599 0.389 0.1934 0.9804 0.9868 0.6467 0.9554 0.9732 0.4167 ] Network output: [ -0.09931 0.3109 0.7981 0.0003246 -0.0001457 1.091 0.0002447 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6647 0.6556 0.4243 0.1034 0.9779 0.985 0.6648 0.9493 0.9689 0.4305 ] Network output: [ 0.0748 0.7733 0.1336 -0.0002075 9.317e-05 0.9427 -0.0001564 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06024 Epoch 1344 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0162 1.01 0.9895 5.542e-05 -2.488e-05 -0.0316 4.176e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03374 -0.003088 0.02855 0.02041 0.9222 0.9342 0.06421 0.8532 0.8846 0.1426 ] Network output: [ 0.9691 0.08268 -0.02949 -0.0002836 0.0001273 0.007415 -0.0002137 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.636 0.004244 0.03706 0.2292 0.9608 0.9805 0.7199 0.8731 0.954 0.6751 ] Network output: [ -0.008893 0.948 1.03 0.0001662 -7.464e-05 0.04071 0.0001253 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.061 0.03474 0.0543 0.03443 0.9773 0.9836 0.06236 0.9482 0.9701 0.07545 ] Network output: [ 0.1072 -0.296 1.16 -0.001911 0.0008579 0.9135 -0.00144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7148 0.452 0.4614 0.3941 0.9653 0.9833 0.718 0.8848 0.9606 0.6712 ] Network output: [ -0.05835 0.171 0.9186 0.00185 -0.0008304 1.035 0.001394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.646 0.5986 0.389 0.1934 0.9804 0.9868 0.6466 0.9554 0.9732 0.4167 ] Network output: [ -0.09965 0.3108 0.7984 0.0003254 -0.0001461 1.091 0.0002452 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6646 0.6554 0.4242 0.103 0.9779 0.985 0.6648 0.9493 0.9689 0.4304 ] Network output: [ 0.07505 0.7723 0.1344 -0.000196 8.801e-05 0.9424 -0.0001478 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06039 Epoch 1345 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01608 1.01 0.9893 5.528e-05 -2.482e-05 -0.03142 4.166e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03372 -0.003109 0.02857 0.02036 0.9222 0.9342 0.06416 0.8532 0.8846 0.1424 ] Network output: [ 0.9691 0.0836 -0.03027 -0.0002893 0.0001299 0.007344 -0.000218 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6359 0.003462 0.03735 0.2289 0.9608 0.9805 0.7196 0.873 0.954 0.675 ] Network output: [ -0.008892 0.9483 1.029 0.0001677 -7.531e-05 0.04071 0.0001264 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06092 0.03463 0.05421 0.03432 0.9773 0.9836 0.06228 0.9482 0.9701 0.0753 ] Network output: [ 0.1073 -0.2951 1.16 -0.001924 0.0008639 0.913 -0.00145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7146 0.4508 0.4615 0.3937 0.9653 0.9833 0.7177 0.8848 0.9606 0.671 ] Network output: [ -0.05842 0.1714 0.918 0.001848 -0.0008294 1.035 0.001392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.646 0.5983 0.3889 0.1928 0.9804 0.9868 0.6465 0.9554 0.9732 0.4165 ] Network output: [ -0.09976 0.3121 0.797 0.0003173 -0.0001425 1.092 0.0002392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6646 0.6553 0.424 0.1018 0.9779 0.985 0.6647 0.9493 0.9689 0.4302 ] Network output: [ 0.07527 0.7719 0.1348 -0.0001922 8.627e-05 0.9419 -0.0001448 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06061 Epoch 1346 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01587 1.01 0.9896 5.65e-05 -2.536e-05 -0.03115 4.258e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0337 -0.003118 0.02864 0.02035 0.9222 0.9343 0.06409 0.8532 0.8846 0.1423 ] Network output: [ 0.9679 0.08348 -0.02874 -0.0002967 0.0001332 0.008237 -0.0002236 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6357 0.003032 0.03819 0.229 0.9608 0.9805 0.7194 0.873 0.954 0.675 ] Network output: [ -0.008865 0.9482 1.03 0.0001709 -7.674e-05 0.04073 0.0001288 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06085 0.03454 0.05419 0.03427 0.9773 0.9836 0.06221 0.9482 0.9701 0.07521 ] Network output: [ 0.1073 -0.2951 1.16 -0.001931 0.000867 0.9128 -0.001455 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7144 0.4499 0.4618 0.3934 0.9654 0.9833 0.7176 0.8848 0.9606 0.671 ] Network output: [ -0.05839 0.1711 0.9179 0.00185 -0.0008306 1.035 0.001394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.646 0.5982 0.3889 0.1923 0.9804 0.9868 0.6465 0.9554 0.9732 0.4165 ] Network output: [ -0.0997 0.313 0.7958 0.0003136 -0.0001408 1.092 0.0002363 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6646 0.6552 0.4239 0.1007 0.9779 0.985 0.6647 0.9493 0.9689 0.4301 ] Network output: [ 0.07567 0.7713 0.1352 -0.0001851 8.308e-05 0.9415 -0.0001395 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06085 Epoch 1347 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01587 1.01 0.9896 6.009e-05 -2.698e-05 -0.03107 4.529e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03368 -0.003149 0.02863 0.02033 0.9223 0.9343 0.06405 0.8532 0.8846 0.1421 ] Network output: [ 0.9686 0.08334 -0.02944 -0.0002908 0.0001306 0.007633 -0.0002192 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6356 0.001983 0.03816 0.229 0.9608 0.9805 0.7192 0.873 0.954 0.675 ] Network output: [ -0.00886 0.9481 1.03 0.0001742 -7.823e-05 0.04071 0.0001313 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06077 0.03441 0.0541 0.03422 0.9773 0.9836 0.06213 0.9482 0.9701 0.07509 ] Network output: [ 0.1076 -0.2958 1.161 -0.001935 0.0008687 0.9121 -0.001458 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7142 0.4485 0.4618 0.3936 0.9654 0.9833 0.7173 0.8847 0.9606 0.671 ] Network output: [ -0.05875 0.1702 0.919 0.001852 -0.0008314 1.036 0.001396 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6459 0.5978 0.3889 0.1922 0.9804 0.9868 0.6464 0.9554 0.9732 0.4165 ] Network output: [ -0.09998 0.3129 0.7959 0.0003144 -0.0001411 1.092 0.0002369 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6645 0.6551 0.4238 0.1003 0.9779 0.985 0.6646 0.9493 0.9689 0.43 ] Network output: [ 0.07597 0.7702 0.136 -0.0001732 7.778e-05 0.9411 -0.0001306 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06102 Epoch 1348 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01579 1.01 0.9894 6.077e-05 -2.728e-05 -0.03093 4.58e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03367 -0.003175 0.02864 0.02028 0.9223 0.9343 0.064 0.8532 0.8846 0.142 ] Network output: [ 0.9689 0.08418 -0.03054 -0.0002937 0.0001318 0.007299 -0.0002213 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6355 0.001064 0.03831 0.2287 0.9608 0.9805 0.719 0.873 0.954 0.6749 ] Network output: [ -0.008861 0.9484 1.029 0.000176 -7.903e-05 0.04069 0.0001327 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06069 0.03428 0.054 0.03412 0.9773 0.9836 0.06204 0.9482 0.97 0.07493 ] Network output: [ 0.1077 -0.2951 1.16 -0.001947 0.0008743 0.9115 -0.001468 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7139 0.4472 0.462 0.3932 0.9654 0.9833 0.717 0.8847 0.9606 0.6709 ] Network output: [ -0.05892 0.1703 0.9189 0.00185 -0.0008306 1.036 0.001394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6458 0.5975 0.3889 0.1917 0.9804 0.9868 0.6463 0.9554 0.9732 0.4164 ] Network output: [ -0.1002 0.314 0.7948 0.0003075 -0.000138 1.093 0.0002317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6644 0.655 0.4237 0.09924 0.9779 0.985 0.6645 0.9492 0.9689 0.4299 ] Network output: [ 0.07619 0.7697 0.1365 -0.0001677 7.527e-05 0.9407 -0.0001264 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06123 Epoch 1349 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01558 1.01 0.9896 6.167e-05 -2.769e-05 -0.03066 4.648e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03365 -0.003185 0.02871 0.02026 0.9223 0.9343 0.06394 0.8532 0.8846 0.1418 ] Network output: [ 0.9678 0.08431 -0.02931 -0.0003022 0.0001357 0.008132 -0.0002277 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6353 0.0005906 0.03914 0.2287 0.9608 0.9805 0.7187 0.873 0.954 0.6749 ] Network output: [ -0.008835 0.9484 1.029 0.000179 -8.038e-05 0.04072 0.0001349 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06062 0.0342 0.05398 0.03405 0.9773 0.9836 0.06198 0.9482 0.97 0.07484 ] Network output: [ 0.1077 -0.2949 1.16 -0.001956 0.0008782 0.9113 -0.001474 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7137 0.4462 0.4623 0.3929 0.9654 0.9833 0.7169 0.8847 0.9606 0.6708 ] Network output: [ -0.05888 0.1703 0.9185 0.001852 -0.0008313 1.037 0.001396 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6458 0.5973 0.3889 0.1911 0.9804 0.9868 0.6464 0.9554 0.9732 0.4163 ] Network output: [ -0.1001 0.3152 0.7934 0.000302 -0.0001356 1.093 0.0002276 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6644 0.6549 0.4236 0.09804 0.9779 0.985 0.6645 0.9492 0.9689 0.4298 ] Network output: [ 0.07658 0.7691 0.1369 -0.0001614 7.245e-05 0.9402 -0.0001216 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06149 Epoch 1350 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01554 1.01 0.9898 6.522e-05 -2.928e-05 -0.03054 4.915e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03363 -0.003212 0.02872 0.02025 0.9223 0.9343 0.06388 0.8532 0.8846 0.1417 ] Network output: [ 0.9682 0.08411 -0.02949 -0.0002985 0.000134 0.007839 -0.0002249 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6352 -0.0003595 0.03931 0.2287 0.9608 0.9805 0.7185 0.873 0.954 0.6749 ] Network output: [ -0.008823 0.9483 1.029 0.0001827 -8.2e-05 0.0407 0.0001377 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06055 0.03407 0.05391 0.03401 0.9773 0.9836 0.0619 0.9482 0.97 0.07472 ] Network output: [ 0.108 -0.2955 1.161 -0.00196 0.00088 0.9107 -0.001477 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7135 0.4449 0.4624 0.393 0.9654 0.9833 0.7166 0.8847 0.9605 0.6708 ] Network output: [ -0.05918 0.1694 0.9195 0.001854 -0.0008324 1.037 0.001397 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6457 0.5969 0.3889 0.191 0.9803 0.9868 0.6463 0.9554 0.9732 0.4164 ] Network output: [ -0.1003 0.3152 0.7933 0.0003022 -0.0001357 1.093 0.0002277 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6644 0.6548 0.4235 0.09747 0.9779 0.985 0.6645 0.9492 0.9689 0.4297 ] Network output: [ 0.07692 0.768 0.1377 -0.0001497 6.72e-05 0.9398 -0.0001128 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06168 Epoch 1351 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01549 1.01 0.9896 6.67e-05 -2.994e-05 -0.03042 5.027e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03362 -0.003241 0.02872 0.0202 0.9223 0.9343 0.06384 0.8532 0.8846 0.1415 ] Network output: [ 0.9687 0.08482 -0.03074 -0.000299 0.0001343 0.007337 -0.0002254 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6351 -0.001378 0.03938 0.2285 0.9608 0.9805 0.7183 0.873 0.954 0.6748 ] Network output: [ -0.008823 0.9484 1.029 0.0001849 -8.299e-05 0.04068 0.0001393 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06046 0.03394 0.05381 0.03392 0.9773 0.9836 0.06181 0.9481 0.97 0.07458 ] Network output: [ 0.1082 -0.295 1.161 -0.001971 0.000885 0.91 -0.001486 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7132 0.4435 0.4625 0.3927 0.9654 0.9833 0.7164 0.8847 0.9605 0.6708 ] Network output: [ -0.05942 0.1693 0.9197 0.001853 -0.0008318 1.037 0.001396 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6457 0.5966 0.3889 0.1906 0.9803 0.9868 0.6462 0.9553 0.9732 0.4163 ] Network output: [ -0.1006 0.3161 0.7925 0.0002965 -0.0001331 1.094 0.0002235 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6643 0.6547 0.4234 0.09651 0.9779 0.985 0.6644 0.9492 0.9689 0.4296 ] Network output: [ 0.07716 0.7674 0.1383 -0.0001424 6.394e-05 0.9394 -0.0001073 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0619 Epoch 1352 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01529 1.01 0.9897 6.75e-05 -3.03e-05 -0.03016 5.087e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0336 -0.003254 0.02878 0.02017 0.9223 0.9343 0.06378 0.8532 0.8846 0.1413 ] Network output: [ 0.9677 0.08517 -0.0299 -0.0003077 0.0001381 0.008029 -0.0002319 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6349 -0.001929 0.04015 0.2284 0.9608 0.9805 0.7181 0.873 0.954 0.6748 ] Network output: [ -0.008799 0.9485 1.029 0.0001877 -8.427e-05 0.0407 0.0001415 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0604 0.03384 0.05377 0.03384 0.9773 0.9836 0.06174 0.9481 0.97 0.07447 ] Network output: [ 0.1082 -0.2946 1.161 -0.001982 0.0008896 0.9097 -0.001493 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.713 0.4424 0.4628 0.3923 0.9654 0.9833 0.7162 0.8847 0.9605 0.6707 ] Network output: [ -0.0594 0.1693 0.9192 0.001854 -0.0008322 1.038 0.001397 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6457 0.5963 0.3889 0.1899 0.9803 0.9868 0.6462 0.9553 0.9732 0.4162 ] Network output: [ -0.1005 0.3173 0.791 0.0002897 -0.0001301 1.094 0.0002183 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6643 0.6547 0.4233 0.09522 0.9779 0.985 0.6644 0.9492 0.9689 0.4295 ] Network output: [ 0.07753 0.7668 0.1387 -0.0001365 6.127e-05 0.9389 -0.0001028 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06216 Epoch 1353 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01522 1.01 0.9899 7.088e-05 -3.182e-05 -0.03 5.342e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03358 -0.003278 0.02881 0.02016 0.9223 0.9343 0.06372 0.8532 0.8846 0.1412 ] Network output: [ 0.9677 0.08497 -0.02966 -0.0003064 0.0001376 0.008022 -0.0002309 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6348 -0.002789 0.04051 0.2284 0.9608 0.9805 0.7179 0.873 0.954 0.6748 ] Network output: [ -0.00878 0.9484 1.029 0.0001915 -8.599e-05 0.0407 0.0001444 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06032 0.03373 0.05372 0.0338 0.9773 0.9836 0.06166 0.9481 0.97 0.07437 ] Network output: [ 0.1084 -0.2952 1.161 -0.001986 0.0008917 0.9092 -0.001497 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7128 0.4411 0.463 0.3923 0.9654 0.9833 0.7159 0.8847 0.9605 0.6707 ] Network output: [ -0.05964 0.1685 0.92 0.001856 -0.0008334 1.038 0.001399 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6456 0.596 0.389 0.1897 0.9803 0.9868 0.6461 0.9553 0.9732 0.4163 ] Network output: [ -0.1007 0.3176 0.7906 0.0002888 -0.0001297 1.094 0.0002177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6643 0.6546 0.4233 0.0945 0.9779 0.985 0.6644 0.9492 0.9689 0.4294 ] Network output: [ 0.0779 0.7658 0.1394 -0.0001252 5.622e-05 0.9385 -9.438e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06238 Epoch 1354 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01519 1.01 0.9897 7.309e-05 -3.281e-05 -0.0299 5.509e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03356 -0.003309 0.02881 0.02012 0.9223 0.9343 0.06368 0.8532 0.8846 0.141 ] Network output: [ 0.9683 0.08554 -0.03089 -0.0003054 0.0001371 0.007451 -0.0002302 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6346 -0.003867 0.04057 0.2282 0.9608 0.9805 0.7177 0.873 0.954 0.6747 ] Network output: [ -0.008779 0.9485 1.029 0.0001942 -8.717e-05 0.04068 0.0001463 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06024 0.03359 0.05362 0.03371 0.9773 0.9836 0.06158 0.9481 0.97 0.07422 ] Network output: [ 0.1086 -0.2949 1.161 -0.001996 0.0008961 0.9084 -0.001504 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7125 0.4396 0.4631 0.3921 0.9654 0.9833 0.7157 0.8846 0.9605 0.6707 ] Network output: [ -0.05992 0.1682 0.9205 0.001856 -0.0008331 1.039 0.001398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6455 0.5956 0.389 0.1893 0.9803 0.9868 0.646 0.9553 0.9731 0.4163 ] Network output: [ -0.1009 0.3183 0.79 0.0002843 -0.0001276 1.095 0.0002143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6642 0.6544 0.4232 0.09362 0.9779 0.985 0.6643 0.9492 0.9689 0.4293 ] Network output: [ 0.07817 0.7649 0.1402 -0.0001164 5.227e-05 0.9381 -8.775e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0626 Epoch 1355 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01501 1.01 0.9898 7.399e-05 -3.322e-05 -0.02966 5.576e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03355 -0.003325 0.02886 0.02008 0.9223 0.9343 0.06362 0.8532 0.8846 0.1408 ] Network output: [ 0.9676 0.08605 -0.03048 -0.0003134 0.0001407 0.00795 -0.0002362 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6345 -0.004519 0.04125 0.2281 0.9608 0.9805 0.7174 0.873 0.954 0.6747 ] Network output: [ -0.008757 0.9486 1.029 0.000197 -8.843e-05 0.04069 0.0001485 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06017 0.03349 0.05358 0.03363 0.9773 0.9836 0.0615 0.9481 0.97 0.07411 ] Network output: [ 0.1086 -0.2944 1.161 -0.002008 0.0009012 0.9081 -0.001513 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7123 0.4385 0.4635 0.3917 0.9654 0.9833 0.7155 0.8846 0.9605 0.6706 ] Network output: [ -0.05994 0.1683 0.92 0.001856 -0.0008331 1.039 0.001399 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6455 0.5954 0.389 0.1887 0.9803 0.9868 0.646 0.9553 0.9731 0.4162 ] Network output: [ -0.101 0.3196 0.7885 0.0002767 -0.0001242 1.095 0.0002085 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6642 0.6544 0.4231 0.09229 0.9779 0.985 0.6643 0.9492 0.9688 0.4293 ] Network output: [ 0.07852 0.7643 0.1406 -0.0001103 4.953e-05 0.9376 -8.315e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06287 Epoch 1356 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01491 1.01 0.99 7.713e-05 -3.462e-05 -0.02947 5.813e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03353 -0.003347 0.02891 0.02006 0.9223 0.9343 0.06356 0.8532 0.8846 0.1407 ] Network output: [ 0.9673 0.08592 -0.02995 -0.0003146 0.0001412 0.008179 -0.0002371 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6344 -0.005308 0.04177 0.2281 0.9608 0.9805 0.7172 0.873 0.954 0.6747 ] Network output: [ -0.008732 0.9485 1.029 0.0002009 -9.02e-05 0.04069 0.0001514 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0601 0.03337 0.05354 0.03358 0.9773 0.9836 0.06143 0.9481 0.97 0.07401 ] Network output: [ 0.1088 -0.2948 1.161 -0.002013 0.0009037 0.9076 -0.001517 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7121 0.4372 0.4637 0.3916 0.9654 0.9833 0.7153 0.8846 0.9605 0.6706 ] Network output: [ -0.06013 0.1676 0.9206 0.001858 -0.0008343 1.04 0.001401 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6455 0.5951 0.3891 0.1884 0.9803 0.9868 0.646 0.9553 0.9731 0.4163 ] Network output: [ -0.1011 0.3201 0.7878 0.0002744 -0.0001232 1.095 0.0002068 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6642 0.6543 0.4231 0.09139 0.9779 0.985 0.6643 0.9492 0.9688 0.4292 ] Network output: [ 0.07893 0.7633 0.1413 -9.968e-05 4.475e-05 0.9371 -7.513e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06311 Epoch 1357 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01488 1.01 0.9899 7.997e-05 -3.59e-05 -0.02936 6.027e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03351 -0.003379 0.02891 0.02003 0.9223 0.9343 0.06352 0.8532 0.8846 0.1405 ] Network output: [ 0.9679 0.08634 -0.03102 -0.0003127 0.0001404 0.007632 -0.0002357 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6342 -0.006408 0.04187 0.2279 0.9608 0.9805 0.717 0.873 0.954 0.6747 ] Network output: [ -0.008727 0.9485 1.029 0.000204 -9.159e-05 0.04067 0.0001538 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.06001 0.03323 0.05345 0.0335 0.9773 0.9836 0.06134 0.9481 0.97 0.07388 ] Network output: [ 0.1091 -0.2947 1.162 -0.002022 0.0009077 0.9069 -0.001524 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7119 0.4357 0.4639 0.3915 0.9654 0.9833 0.715 0.8846 0.9605 0.6706 ] Network output: [ -0.06044 0.1671 0.9213 0.001858 -0.0008343 1.04 0.001401 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6454 0.5947 0.3891 0.188 0.9803 0.9868 0.6459 0.9553 0.9731 0.4163 ] Network output: [ -0.1013 0.3206 0.7873 0.0002707 -0.0001215 1.096 0.000204 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6641 0.6542 0.423 0.09054 0.9779 0.985 0.6642 0.9492 0.9688 0.4291 ] Network output: [ 0.07923 0.7624 0.1421 -8.961e-05 4.023e-05 0.9367 -6.753e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06333 Epoch 1358 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01473 1.01 0.9898 8.115e-05 -3.643e-05 -0.02914 6.116e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0335 -0.003399 0.02895 0.01999 0.9223 0.9343 0.06346 0.8532 0.8846 0.1403 ] Network output: [ 0.9674 0.08696 -0.03102 -0.0003196 0.0001435 0.007915 -0.0002408 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6341 -0.007175 0.04245 0.2277 0.9608 0.9805 0.7168 0.873 0.954 0.6747 ] Network output: [ -0.008708 0.9487 1.029 0.0002069 -9.287e-05 0.04068 0.0001559 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05994 0.03312 0.05339 0.03341 0.9773 0.9836 0.06127 0.9481 0.97 0.07376 ] Network output: [ 0.1091 -0.2941 1.161 -0.002034 0.0009131 0.9064 -0.001533 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7117 0.4344 0.4642 0.3911 0.9654 0.9833 0.7148 0.8846 0.9605 0.6705 ] Network output: [ -0.06052 0.1671 0.9209 0.001858 -0.0008341 1.041 0.0014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6454 0.5944 0.3891 0.1874 0.9803 0.9868 0.6459 0.9552 0.9731 0.4163 ] Network output: [ -0.1014 0.3219 0.7859 0.0002627 -0.0001179 1.096 0.000198 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6641 0.6541 0.423 0.08921 0.9779 0.985 0.6642 0.9492 0.9688 0.4291 ] Network output: [ 0.07957 0.7617 0.1426 -8.293e-05 3.723e-05 0.9361 -6.25e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06361 Epoch 1359 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0146 1.01 0.99 8.402e-05 -3.772e-05 -0.02893 6.332e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03348 -0.003418 0.02901 0.01996 0.9223 0.9343 0.06341 0.8532 0.8846 0.1402 ] Network output: [ 0.9669 0.08695 -0.03034 -0.0003229 0.0001449 0.00831 -0.0002433 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.634 -0.00792 0.0431 0.2277 0.9608 0.9805 0.7166 0.873 0.954 0.6747 ] Network output: [ -0.008678 0.9486 1.029 0.0002109 -9.466e-05 0.04069 0.0001589 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05987 0.03301 0.05336 0.03335 0.9773 0.9836 0.06119 0.9481 0.97 0.07367 ] Network output: [ 0.1092 -0.2943 1.162 -0.002041 0.0009161 0.906 -0.001538 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7115 0.4331 0.4645 0.3909 0.9654 0.9833 0.7146 0.8846 0.9605 0.6705 ] Network output: [ -0.06065 0.1666 0.9213 0.00186 -0.0008352 1.041 0.001402 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6454 0.5941 0.3892 0.1869 0.9803 0.9868 0.6459 0.9552 0.9731 0.4163 ] Network output: [ -0.1015 0.3226 0.7849 0.0002588 -0.0001162 1.096 0.000195 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6641 0.654 0.4229 0.08815 0.9779 0.985 0.6642 0.9492 0.9688 0.429 ] Network output: [ 0.07999 0.7608 0.1433 -7.291e-05 3.273e-05 0.9356 -5.495e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06388 Epoch 1360 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01457 1.01 0.99 8.736e-05 -3.922e-05 -0.02881 6.584e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03347 -0.00345 0.02902 0.01993 0.9223 0.9343 0.06336 0.8532 0.8846 0.14 ] Network output: [ 0.9674 0.08723 -0.03115 -0.0003209 0.0001441 0.007866 -0.0002418 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6338 -0.00901 0.04328 0.2276 0.9608 0.9805 0.7164 0.8729 0.954 0.6747 ] Network output: [ -0.008667 0.9486 1.029 0.0002144 -9.625e-05 0.04067 0.0001616 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05979 0.03287 0.05328 0.03328 0.9773 0.9836 0.06111 0.9481 0.97 0.07354 ] Network output: [ 0.1095 -0.2945 1.162 -0.002048 0.0009196 0.9052 -0.001544 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7112 0.4316 0.4648 0.3908 0.9654 0.9833 0.7143 0.8846 0.9605 0.6706 ] Network output: [ -0.06097 0.1659 0.922 0.001861 -0.0008355 1.042 0.001403 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6453 0.5938 0.3893 0.1867 0.9803 0.9868 0.6458 0.9552 0.9731 0.4164 ] Network output: [ -0.1017 0.3231 0.7845 0.0002555 -0.0001147 1.097 0.0001925 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6641 0.6539 0.4229 0.08729 0.9779 0.985 0.6642 0.9492 0.9688 0.429 ] Network output: [ 0.08033 0.7598 0.1441 -6.186e-05 2.777e-05 0.9352 -4.663e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06412 Epoch 1361 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01445 1.01 0.9899 8.897e-05 -3.994e-05 -0.02862 6.705e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03345 -0.003474 0.02905 0.01989 0.9224 0.9343 0.06331 0.8532 0.8846 0.1399 ] Network output: [ 0.9672 0.08791 -0.03151 -0.0003263 0.0001465 0.00794 -0.0002459 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6337 -0.009889 0.04377 0.2273 0.9609 0.9805 0.7162 0.8729 0.954 0.6747 ] Network output: [ -0.00865 0.9487 1.029 0.0002174 -9.758e-05 0.04067 0.0001638 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05971 0.03275 0.05322 0.03319 0.9773 0.9836 0.06103 0.948 0.97 0.07342 ] Network output: [ 0.1096 -0.2939 1.162 -0.002061 0.0009251 0.9047 -0.001553 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.711 0.4303 0.4651 0.3904 0.9654 0.9833 0.7141 0.8845 0.9605 0.6705 ] Network output: [ -0.06111 0.1659 0.9219 0.00186 -0.0008352 1.042 0.001402 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6453 0.5934 0.3894 0.186 0.9803 0.9868 0.6458 0.9552 0.9731 0.4164 ] Network output: [ -0.1018 0.3243 0.7832 0.0002476 -0.0001111 1.097 0.0001866 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.664 0.6538 0.4229 0.08598 0.9779 0.985 0.6641 0.9492 0.9688 0.429 ] Network output: [ 0.08067 0.759 0.1448 -5.427e-05 2.437e-05 0.9346 -4.091e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0644 Epoch 1362 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01431 1.01 0.9901 9.162e-05 -4.113e-05 -0.02838 6.905e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03343 -0.003493 0.02911 0.01986 0.9224 0.9343 0.06325 0.8532 0.8846 0.1397 ] Network output: [ 0.9665 0.08805 -0.03083 -0.0003313 0.0001487 0.008424 -0.0002497 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6336 -0.01062 0.0445 0.2273 0.9609 0.9805 0.7159 0.8729 0.954 0.6747 ] Network output: [ -0.008618 0.9486 1.029 0.0002214 -9.938e-05 0.04068 0.0001668 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05964 0.03264 0.0532 0.03312 0.9773 0.9836 0.06096 0.948 0.97 0.07333 ] Network output: [ 0.1097 -0.2938 1.162 -0.002069 0.0009288 0.9043 -0.001559 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7108 0.429 0.4655 0.3901 0.9654 0.9833 0.7139 0.8845 0.9605 0.6705 ] Network output: [ -0.06121 0.1655 0.9221 0.001862 -0.0008361 1.042 0.001403 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6453 0.5932 0.3895 0.1855 0.9803 0.9868 0.6458 0.9552 0.9731 0.4164 ] Network output: [ -0.1019 0.3253 0.782 0.000242 -0.0001086 1.097 0.0001824 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.664 0.6538 0.4229 0.08476 0.9779 0.9851 0.6641 0.9492 0.9688 0.4289 ] Network output: [ 0.0811 0.7581 0.1454 -4.476e-05 2.01e-05 0.9341 -3.374e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06469 Epoch 1363 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01427 1.01 0.9902 9.53e-05 -4.278e-05 -0.02825 7.182e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03342 -0.003523 0.02913 0.01983 0.9224 0.9344 0.0632 0.8532 0.8846 0.1396 ] Network output: [ 0.9668 0.08824 -0.03133 -0.0003299 0.0001481 0.008138 -0.0002486 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6334 -0.01168 0.04479 0.2272 0.9609 0.9805 0.7157 0.8729 0.954 0.6748 ] Network output: [ -0.0086 0.9486 1.029 0.0002253 -0.0001012 0.04067 0.0001698 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05957 0.03251 0.05313 0.03306 0.9773 0.9836 0.06088 0.948 0.97 0.07322 ] Network output: [ 0.11 -0.2941 1.162 -0.002076 0.000932 0.9036 -0.001565 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7106 0.4274 0.4657 0.39 0.9654 0.9833 0.7137 0.8845 0.9605 0.6706 ] Network output: [ -0.06152 0.1648 0.9228 0.001864 -0.0008367 1.043 0.001405 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6452 0.5928 0.3896 0.1852 0.9803 0.9868 0.6458 0.9552 0.9731 0.4165 ] Network output: [ -0.1021 0.3258 0.7815 0.0002386 -0.0001071 1.098 0.0001798 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.664 0.6537 0.4229 0.08384 0.9779 0.9851 0.6641 0.9492 0.9688 0.4289 ] Network output: [ 0.08147 0.757 0.1463 -3.308e-05 1.485e-05 0.9336 -2.493e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06494 Epoch 1364 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01417 1.01 0.9901 9.745e-05 -4.375e-05 -0.02808 7.344e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0334 -0.003551 0.02916 0.01979 0.9224 0.9344 0.06315 0.8532 0.8846 0.1394 ] Network output: [ 0.9668 0.08891 -0.03194 -0.0003337 0.0001498 0.008039 -0.0002515 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6333 -0.01266 0.04522 0.2269 0.9609 0.9805 0.7155 0.8729 0.9539 0.6747 ] Network output: [ -0.008584 0.9487 1.029 0.0002285 -0.0001026 0.04067 0.0001722 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05949 0.03238 0.05307 0.03297 0.9773 0.9836 0.0608 0.948 0.97 0.07309 ] Network output: [ 0.1101 -0.2936 1.162 -0.002088 0.0009374 0.9029 -0.001574 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7103 0.426 0.4661 0.3896 0.9654 0.9833 0.7134 0.8845 0.9605 0.6706 ] Network output: [ -0.06172 0.1646 0.9229 0.001863 -0.0008364 1.043 0.001404 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6452 0.5924 0.3897 0.1846 0.9803 0.9868 0.6457 0.9552 0.9731 0.4166 ] Network output: [ -0.1023 0.3269 0.7803 0.0002311 -0.0001037 1.098 0.0001741 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.664 0.6536 0.4229 0.08258 0.9779 0.9851 0.6641 0.9492 0.9688 0.4289 ] Network output: [ 0.08182 0.7562 0.147 -2.435e-05 1.093e-05 0.9331 -1.835e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06523 Epoch 1365 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01402 1.01 0.9902 9.996e-05 -4.487e-05 -0.02784 7.533e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03339 -0.00357 0.02922 0.01975 0.9224 0.9344 0.0631 0.8532 0.8846 0.1393 ] Network output: [ 0.9661 0.08921 -0.03138 -0.0003398 0.0001526 0.008532 -0.0002561 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6332 -0.01341 0.04598 0.2268 0.9609 0.9805 0.7153 0.8729 0.9539 0.6748 ] Network output: [ -0.00855 0.9487 1.029 0.0002325 -0.0001044 0.04068 0.0001752 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05942 0.03227 0.05304 0.03289 0.9773 0.9836 0.06072 0.948 0.97 0.073 ] Network output: [ 0.1102 -0.2933 1.162 -0.002098 0.0009417 0.9025 -0.001581 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7101 0.4246 0.4665 0.3892 0.9654 0.9833 0.7132 0.8845 0.9605 0.6706 ] Network output: [ -0.06181 0.1643 0.9229 0.001864 -0.000837 1.044 0.001405 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6452 0.5922 0.3898 0.184 0.9803 0.9868 0.6457 0.9551 0.9731 0.4167 ] Network output: [ -0.1023 0.328 0.779 0.000224 -0.0001006 1.099 0.0001688 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.664 0.6535 0.4229 0.08123 0.9779 0.9851 0.6641 0.9492 0.9688 0.4289 ] Network output: [ 0.08225 0.7552 0.1477 -1.511e-05 6.786e-06 0.9325 -1.139e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06554 Epoch 1366 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01396 1.01 0.9903 0.0001038 -4.661e-05 -0.02769 7.825e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03337 -0.003599 0.02925 0.01972 0.9224 0.9344 0.06305 0.8532 0.8846 0.1391 ] Network output: [ 0.9662 0.08935 -0.03157 -0.0003395 0.0001524 0.008436 -0.0002559 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6331 -0.01442 0.04641 0.2267 0.9609 0.9805 0.7151 0.8729 0.9539 0.6748 ] Network output: [ -0.008525 0.9486 1.029 0.0002368 -0.0001063 0.04067 0.0001785 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05934 0.03214 0.05299 0.03283 0.9773 0.9836 0.06065 0.948 0.97 0.0729 ] Network output: [ 0.1104 -0.2936 1.162 -0.002105 0.0009449 0.9019 -0.001586 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7099 0.4231 0.4668 0.3891 0.9654 0.9833 0.713 0.8845 0.9604 0.6706 ] Network output: [ -0.06209 0.1636 0.9237 0.001866 -0.0008378 1.044 0.001406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6452 0.5918 0.39 0.1836 0.9803 0.9868 0.6457 0.9551 0.9731 0.4168 ] Network output: [ -0.1025 0.3285 0.7783 0.0002201 -9.88e-05 1.099 0.0001659 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.664 0.6535 0.4229 0.08021 0.978 0.9851 0.6641 0.9492 0.9688 0.4289 ] Network output: [ 0.08266 0.7541 0.1485 -3.105e-06 1.395e-06 0.932 -2.343e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06581 Epoch 1367 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01389 1.01 0.9902 0.0001066 -4.785e-05 -0.02753 8.032e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03336 -0.003629 0.02928 0.01968 0.9224 0.9344 0.063 0.8532 0.8846 0.1389 ] Network output: [ 0.9664 0.08997 -0.03233 -0.0003419 0.0001535 0.008217 -0.0002576 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6329 -0.01548 0.0468 0.2265 0.9609 0.9805 0.7149 0.8729 0.9539 0.6749 ] Network output: [ -0.008508 0.9487 1.029 0.0002403 -0.0001079 0.04066 0.0001811 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05927 0.032 0.05293 0.03274 0.9773 0.9836 0.06056 0.948 0.97 0.07278 ] Network output: [ 0.1106 -0.2932 1.162 -0.002116 0.0009499 0.9012 -0.001595 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7097 0.4216 0.4672 0.3888 0.9654 0.9833 0.7127 0.8845 0.9604 0.6706 ] Network output: [ -0.06234 0.1633 0.924 0.001866 -0.0008376 1.045 0.001406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6451 0.5914 0.3901 0.1831 0.9803 0.9868 0.6457 0.9551 0.973 0.4169 ] Network output: [ -0.1027 0.3295 0.7773 0.000213 -9.561e-05 1.099 0.0001605 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.664 0.6534 0.4229 0.07899 0.978 0.9851 0.6641 0.9492 0.9688 0.4289 ] Network output: [ 0.08303 0.7532 0.1494 6.88e-06 -3.088e-06 0.9314 5.182e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06611 Epoch 1368 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01375 1.01 0.9903 0.0001091 -4.896e-05 -0.0273 8.22e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03334 -0.003651 0.02934 0.01964 0.9224 0.9344 0.06294 0.8532 0.8846 0.1388 ] Network output: [ 0.9657 0.09042 -0.03198 -0.0003485 0.0001565 0.00865 -0.0002626 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6328 -0.01629 0.04757 0.2264 0.9609 0.9805 0.7147 0.8729 0.9539 0.6749 ] Network output: [ -0.008475 0.9487 1.029 0.0002443 -0.0001097 0.04067 0.0001841 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0592 0.03189 0.0529 0.03266 0.9773 0.9836 0.06049 0.948 0.97 0.07268 ] Network output: [ 0.1107 -0.2928 1.162 -0.002127 0.0009548 0.9007 -0.001603 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7095 0.4202 0.4677 0.3883 0.9654 0.9833 0.7125 0.8844 0.9604 0.6707 ] Network output: [ -0.06245 0.1631 0.9239 0.001866 -0.0008379 1.045 0.001407 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6452 0.5912 0.3903 0.1824 0.9803 0.9868 0.6457 0.9551 0.973 0.417 ] Network output: [ -0.1028 0.3308 0.7759 0.0002047 -9.188e-05 1.1 0.0001542 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.664 0.6533 0.4229 0.07755 0.978 0.9851 0.6641 0.9492 0.9688 0.4289 ] Network output: [ 0.08346 0.7522 0.1501 1.614e-05 -7.246e-06 0.9309 1.216e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06643 Epoch 1369 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01367 1.01 0.9904 0.000113 -5.073e-05 -0.02711 8.516e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03333 -0.003678 0.02938 0.01961 0.9224 0.9344 0.06289 0.8532 0.8846 0.1387 ] Network output: [ 0.9656 0.09056 -0.03189 -0.0003497 0.000157 0.008748 -0.0002636 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6327 -0.01724 0.04814 0.2263 0.9609 0.9805 0.7145 0.8729 0.9539 0.675 ] Network output: [ -0.008442 0.9486 1.029 0.0002489 -0.0001117 0.04067 0.0001876 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05912 0.03176 0.05286 0.03259 0.9773 0.9836 0.06042 0.948 0.97 0.07258 ] Network output: [ 0.1109 -0.293 1.162 -0.002134 0.0009581 0.9001 -0.001608 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7093 0.4187 0.4681 0.3881 0.9654 0.9833 0.7123 0.8844 0.9604 0.6707 ] Network output: [ -0.06269 0.1624 0.9246 0.001868 -0.0008388 1.046 0.001408 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6452 0.5908 0.3905 0.182 0.9803 0.9868 0.6457 0.9551 0.973 0.4171 ] Network output: [ -0.1029 0.3315 0.775 0.0001997 -8.967e-05 1.1 0.0001505 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.664 0.6533 0.423 0.0764 0.978 0.9851 0.6641 0.9492 0.9688 0.429 ] Network output: [ 0.0839 0.7511 0.1509 2.824e-05 -1.268e-05 0.9303 2.128e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06673 Epoch 1370 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01361 1.01 0.9903 0.0001164 -5.224e-05 -0.02697 8.769e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03331 -0.00371 0.02941 0.01957 0.9224 0.9344 0.06284 0.8532 0.8846 0.1385 ] Network output: [ 0.9658 0.09111 -0.03267 -0.0003509 0.0001575 0.00848 -0.0002645 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6326 -0.01835 0.04853 0.226 0.9609 0.9805 0.7143 0.8729 0.9539 0.675 ] Network output: [ -0.008423 0.9486 1.029 0.0002528 -0.0001135 0.04066 0.0001905 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05904 0.03162 0.0528 0.03251 0.9773 0.9836 0.06034 0.948 0.97 0.07247 ] Network output: [ 0.1111 -0.2928 1.162 -0.002145 0.0009628 0.8994 -0.001616 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.709 0.417 0.4685 0.3878 0.9654 0.9833 0.7121 0.8844 0.9604 0.6708 ] Network output: [ -0.06298 0.1619 0.9251 0.001869 -0.0008389 1.047 0.001408 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6451 0.5904 0.3907 0.1815 0.9803 0.9868 0.6456 0.9551 0.973 0.4173 ] Network output: [ -0.1031 0.3323 0.7742 0.0001931 -8.667e-05 1.101 0.0001455 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.664 0.6532 0.4231 0.07519 0.978 0.9851 0.6641 0.9492 0.9688 0.429 ] Network output: [ 0.08429 0.75 0.1519 3.946e-05 -1.771e-05 0.9297 2.973e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06704 Epoch 1371 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01348 1.01 0.9903 0.000119 -5.34e-05 -0.02675 8.965e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0333 -0.003733 0.02946 0.01953 0.9224 0.9344 0.06279 0.8532 0.8846 0.1383 ] Network output: [ 0.9653 0.09169 -0.0326 -0.0003574 0.0001605 0.008798 -0.0002694 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6324 -0.01923 0.04927 0.2258 0.9609 0.9805 0.7141 0.8728 0.9539 0.6751 ] Network output: [ -0.00839 0.9486 1.029 0.0002568 -0.0001153 0.04067 0.0001935 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05897 0.0315 0.05277 0.03242 0.9773 0.9836 0.06026 0.948 0.97 0.07237 ] Network output: [ 0.1112 -0.2923 1.162 -0.002156 0.0009681 0.8989 -0.001625 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7088 0.4156 0.469 0.3874 0.9654 0.9833 0.7119 0.8844 0.9604 0.6708 ] Network output: [ -0.06312 0.1617 0.9251 0.001869 -0.0008389 1.047 0.001408 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6451 0.5901 0.3908 0.1808 0.9803 0.9868 0.6457 0.9551 0.973 0.4174 ] Network output: [ -0.1032 0.3336 0.7727 0.0001838 -8.254e-05 1.101 0.0001386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.664 0.6531 0.4231 0.07369 0.978 0.9851 0.6641 0.9492 0.9687 0.4291 ] Network output: [ 0.08472 0.749 0.1526 4.91e-05 -2.204e-05 0.9291 3.7e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06738 Epoch 1372 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01338 1.01 0.9905 0.0001229 -5.515e-05 -0.02654 9.259e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03328 -0.003759 0.02952 0.01949 0.9224 0.9344 0.06274 0.8532 0.8846 0.1382 ] Network output: [ 0.965 0.09188 -0.03231 -0.0003603 0.0001618 0.009065 -0.0002716 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6323 -0.02015 0.04997 0.2257 0.9609 0.9805 0.7138 0.8728 0.9539 0.6751 ] Network output: [ -0.008351 0.9485 1.029 0.0002616 -0.0001174 0.04067 0.0001971 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0589 0.03138 0.05274 0.03235 0.9773 0.9836 0.06019 0.948 0.97 0.07228 ] Network output: [ 0.1114 -0.2923 1.162 -0.002164 0.0009716 0.8983 -0.001631 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7086 0.4141 0.4695 0.3871 0.9654 0.9833 0.7117 0.8844 0.9604 0.6709 ] Network output: [ -0.06332 0.1611 0.9255 0.001871 -0.0008397 1.048 0.00141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6452 0.5898 0.3911 0.1803 0.9803 0.9868 0.6457 0.9551 0.973 0.4175 ] Network output: [ -0.1033 0.3345 0.7716 0.0001775 -7.97e-05 1.101 0.0001338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.664 0.6531 0.4232 0.0724 0.978 0.9851 0.6641 0.9492 0.9687 0.4291 ] Network output: [ 0.08519 0.7479 0.1535 6.112e-05 -2.744e-05 0.9285 4.606e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06771 Epoch 1373 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01333 1.01 0.9904 0.0001268 -5.691e-05 -0.0264 9.553e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03327 -0.003792 0.02955 0.01945 0.9224 0.9344 0.06269 0.8532 0.8846 0.1381 ] Network output: [ 0.9652 0.09234 -0.03299 -0.0003609 0.000162 0.008823 -0.000272 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6322 -0.02128 0.0504 0.2255 0.9609 0.9805 0.7137 0.8728 0.9539 0.6752 ] Network output: [ -0.008326 0.9485 1.029 0.0002659 -0.0001194 0.04066 0.0002004 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05883 0.03123 0.05269 0.03227 0.9773 0.9836 0.06011 0.948 0.97 0.07218 ] Network output: [ 0.1116 -0.2923 1.163 -0.002174 0.000976 0.8976 -0.001638 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7084 0.4124 0.4699 0.3868 0.9654 0.9833 0.7115 0.8844 0.9604 0.671 ] Network output: [ -0.06364 0.1605 0.9262 0.001871 -0.00084 1.048 0.00141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6451 0.5894 0.3913 0.1798 0.9803 0.9868 0.6457 0.955 0.973 0.4177 ] Network output: [ -0.1036 0.3353 0.7708 0.0001711 -7.681e-05 1.102 0.0001289 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.664 0.653 0.4233 0.07118 0.978 0.9851 0.6641 0.9492 0.9687 0.4292 ] Network output: [ 0.08561 0.7467 0.1545 7.346e-05 -3.298e-05 0.9279 5.536e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06802 Epoch 1374 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01322 1.01 0.9904 0.0001296 -5.819e-05 -0.02619 9.769e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03325 -0.003818 0.0296 0.01941 0.9225 0.9344 0.06264 0.8532 0.8846 0.1379 ] Network output: [ 0.9649 0.093 -0.0332 -0.0003667 0.0001646 0.008999 -0.0002763 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6321 -0.02225 0.0511 0.2253 0.9609 0.9805 0.7134 0.8728 0.9539 0.6753 ] Network output: [ -0.008294 0.9486 1.028 0.00027 -0.0001212 0.04066 0.0002035 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05875 0.03111 0.05266 0.03218 0.9773 0.9836 0.06003 0.9479 0.97 0.07208 ] Network output: [ 0.1117 -0.2917 1.162 -0.002186 0.0009814 0.897 -0.001648 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7082 0.4108 0.4704 0.3863 0.9654 0.9833 0.7112 0.8843 0.9604 0.671 ] Network output: [ -0.06382 0.1602 0.9263 0.001871 -0.0008399 1.049 0.00141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6452 0.5891 0.3915 0.1791 0.9803 0.9868 0.6457 0.955 0.973 0.4179 ] Network output: [ -0.1037 0.3366 0.7694 0.0001614 -7.244e-05 1.102 0.0001216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6641 0.6529 0.4234 0.06966 0.978 0.9851 0.6642 0.9492 0.9687 0.4293 ] Network output: [ 0.08604 0.7456 0.1553 8.382e-05 -3.763e-05 0.9273 6.317e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06838 Epoch 1375 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0131 1.01 0.9905 0.0001334 -5.99e-05 -0.02597 0.0001006 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03324 -0.003843 0.02967 0.01937 0.9225 0.9344 0.06259 0.8532 0.8846 0.1378 ] Network output: [ 0.9643 0.09329 -0.03283 -0.0003712 0.0001666 0.009385 -0.0002797 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.632 -0.02314 0.05191 0.2251 0.9609 0.9805 0.7132 0.8728 0.9539 0.6754 ] Network output: [ -0.008251 0.9485 1.028 0.0002749 -0.0001234 0.04067 0.0002072 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05869 0.03099 0.05264 0.03211 0.9773 0.9836 0.05996 0.9479 0.97 0.072 ] Network output: [ 0.1119 -0.2916 1.162 -0.002195 0.0009854 0.8964 -0.001654 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.708 0.4093 0.471 0.386 0.9654 0.9833 0.711 0.8843 0.9604 0.6711 ] Network output: [ -0.06399 0.1597 0.9266 0.001872 -0.0008406 1.049 0.001411 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6452 0.5888 0.3918 0.1785 0.9803 0.9868 0.6457 0.955 0.973 0.4181 ] Network output: [ -0.1038 0.3377 0.7681 0.0001534 -6.888e-05 1.102 0.0001156 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6641 0.6529 0.4235 0.06821 0.978 0.9851 0.6642 0.9492 0.9687 0.4294 ] Network output: [ 0.08653 0.7445 0.1562 9.575e-05 -4.299e-05 0.9266 7.216e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06873 Epoch 1376 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01305 1.01 0.9906 0.0001378 -6.185e-05 -0.02581 0.0001038 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03323 -0.003875 0.0297 0.01933 0.9225 0.9344 0.06255 0.8532 0.8846 0.1376 ] Network output: [ 0.9644 0.09367 -0.03331 -0.0003717 0.0001669 0.00924 -0.0002801 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6319 -0.02426 0.05243 0.225 0.9609 0.9805 0.7131 0.8728 0.9539 0.6755 ] Network output: [ -0.008219 0.9485 1.028 0.0002796 -0.0001255 0.04066 0.0002107 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05861 0.03085 0.0526 0.03203 0.9773 0.9836 0.05988 0.9479 0.97 0.0719 ] Network output: [ 0.1121 -0.2916 1.163 -0.002204 0.0009894 0.8957 -0.001661 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7078 0.4076 0.4715 0.3857 0.9654 0.9833 0.7108 0.8843 0.9604 0.6712 ] Network output: [ -0.06431 0.159 0.9273 0.001874 -0.0008411 1.05 0.001412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6452 0.5884 0.392 0.178 0.9803 0.9868 0.6457 0.955 0.973 0.4183 ] Network output: [ -0.104 0.3384 0.7672 0.0001468 -6.592e-05 1.103 0.0001107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6641 0.6529 0.4236 0.06695 0.978 0.9851 0.6642 0.9492 0.9687 0.4295 ] Network output: [ 0.08698 0.7432 0.1573 0.000109 -4.893e-05 0.926 8.214e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06907 Epoch 1377 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01296 1.01 0.9905 0.0001411 -6.332e-05 -0.02563 0.0001063 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03321 -0.003905 0.02975 0.01928 0.9225 0.9344 0.0625 0.8532 0.8846 0.1375 ] Network output: [ 0.9643 0.09437 -0.03377 -0.0003764 0.000169 0.009272 -0.0002837 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6318 -0.02532 0.05308 0.2247 0.9609 0.9805 0.7129 0.8728 0.9539 0.6755 ] Network output: [ -0.008188 0.9485 1.028 0.0002839 -0.0001275 0.04066 0.000214 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05854 0.03071 0.05256 0.03194 0.9773 0.9836 0.05981 0.9479 0.97 0.0718 ] Network output: [ 0.1123 -0.2911 1.163 -0.002216 0.0009949 0.895 -0.00167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7076 0.406 0.472 0.3852 0.9654 0.9833 0.7106 0.8843 0.9604 0.6713 ] Network output: [ -0.06454 0.1587 0.9275 0.001873 -0.000841 1.051 0.001412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6452 0.5881 0.3923 0.1773 0.9803 0.9868 0.6457 0.955 0.973 0.4185 ] Network output: [ -0.1041 0.3397 0.7659 0.000137 -6.149e-05 1.103 0.0001032 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6642 0.6528 0.4238 0.06543 0.978 0.9851 0.6643 0.9492 0.9687 0.4296 ] Network output: [ 0.08742 0.7421 0.1582 0.0001204 -5.404e-05 0.9254 9.071e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06943 Epoch 1378 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01284 1.01 0.9906 0.0001448 -6.499e-05 -0.0254 0.0001091 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0332 -0.00393 0.02982 0.01924 0.9225 0.9344 0.06245 0.8532 0.8846 0.1373 ] Network output: [ 0.9637 0.09479 -0.03343 -0.0003822 0.0001716 0.009712 -0.000288 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6316 -0.02622 0.05396 0.2245 0.9609 0.9805 0.7126 0.8728 0.9539 0.6756 ] Network output: [ -0.008141 0.9484 1.028 0.0002889 -0.0001297 0.04067 0.0002177 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05847 0.03059 0.05255 0.03186 0.9773 0.9837 0.05974 0.9479 0.97 0.07172 ] Network output: [ 0.1124 -0.2908 1.162 -0.002226 0.0009994 0.8945 -0.001678 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7074 0.4044 0.4726 0.3848 0.9654 0.9833 0.7104 0.8843 0.9604 0.6714 ] Network output: [ -0.0647 0.1582 0.9277 0.001874 -0.0008414 1.051 0.001412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6453 0.5878 0.3926 0.1766 0.9803 0.9868 0.6458 0.955 0.973 0.4187 ] Network output: [ -0.1042 0.3409 0.7644 0.0001274 -5.719e-05 1.104 9.6e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6642 0.6528 0.4239 0.06385 0.978 0.9851 0.6643 0.9492 0.9687 0.4297 ] Network output: [ 0.08792 0.7409 0.1592 0.0001323 -5.939e-05 0.9247 9.97e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06982 Epoch 1379 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01278 1.01 0.9906 0.0001494 -6.708e-05 -0.02523 0.0001126 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03319 -0.003961 0.02987 0.0192 0.9225 0.9345 0.0624 0.8532 0.8846 0.1372 ] Network output: [ 0.9636 0.09511 -0.03366 -0.0003833 0.0001721 0.009718 -0.0002888 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6315 -0.02731 0.05461 0.2243 0.9609 0.9805 0.7125 0.8728 0.9539 0.6758 ] Network output: [ -0.008102 0.9484 1.028 0.000294 -0.000132 0.04066 0.0002216 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0584 0.03045 0.05252 0.03178 0.9773 0.9837 0.05966 0.9479 0.97 0.07163 ] Network output: [ 0.1126 -0.2909 1.163 -0.002235 0.001003 0.8938 -0.001684 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7072 0.4027 0.4732 0.3845 0.9654 0.9833 0.7102 0.8843 0.9604 0.6715 ] Network output: [ -0.065 0.1575 0.9284 0.001876 -0.0008421 1.052 0.001414 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6453 0.5874 0.3929 0.1761 0.9803 0.9868 0.6458 0.955 0.973 0.419 ] Network output: [ -0.1044 0.3417 0.7635 0.0001202 -5.394e-05 1.104 9.056e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6643 0.6527 0.4241 0.0625 0.978 0.9851 0.6644 0.9492 0.9687 0.4299 ] Network output: [ 0.08841 0.7395 0.1602 0.0001462 -6.563e-05 0.924 0.0001102 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07017 Epoch 1380 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01271 1.01 0.9906 0.0001532 -6.878e-05 -0.02505 0.0001155 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03318 -0.003994 0.02991 0.01915 0.9225 0.9345 0.06235 0.8532 0.8846 0.137 ] Network output: [ 0.9636 0.0958 -0.0343 -0.0003867 0.0001736 0.009637 -0.0002914 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6314 -0.02844 0.05523 0.224 0.9609 0.9805 0.7123 0.8727 0.9539 0.6759 ] Network output: [ -0.008069 0.9484 1.028 0.0002985 -0.000134 0.04065 0.000225 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05832 0.03031 0.05248 0.03169 0.9773 0.9837 0.05959 0.9479 0.97 0.07154 ] Network output: [ 0.1128 -0.2905 1.163 -0.002246 0.001008 0.8931 -0.001693 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.707 0.401 0.4738 0.384 0.9654 0.9833 0.71 0.8842 0.9604 0.6716 ] Network output: [ -0.06528 0.157 0.9289 0.001876 -0.000842 1.052 0.001413 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6453 0.587 0.3932 0.1754 0.9803 0.9868 0.6458 0.955 0.973 0.4192 ] Network output: [ -0.1046 0.3429 0.7623 0.0001104 -4.955e-05 1.104 8.318e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6643 0.6527 0.4242 0.06099 0.978 0.9851 0.6644 0.9492 0.9687 0.43 ] Network output: [ 0.08887 0.7383 0.1613 0.0001588 -7.128e-05 0.9233 0.0001197 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07055 Epoch 1381 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01259 1.01 0.9906 0.0001569 -7.042e-05 -0.02482 0.0001182 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03316 -0.00402 0.02998 0.0191 0.9225 0.9345 0.0623 0.8532 0.8846 0.1369 ] Network output: [ 0.963 0.09636 -0.0341 -0.0003932 0.0001765 0.01006 -0.0002963 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6313 -0.02938 0.05614 0.2238 0.9609 0.9805 0.7121 0.8727 0.9539 0.676 ] Network output: [ -0.008021 0.9484 1.028 0.0003035 -0.0001362 0.04066 0.0002287 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05826 0.03019 0.05247 0.0316 0.9773 0.9837 0.05952 0.9479 0.97 0.07146 ] Network output: [ 0.1129 -0.29 1.162 -0.002257 0.001013 0.8925 -0.001701 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7068 0.3994 0.4745 0.3835 0.9654 0.9833 0.7098 0.8842 0.9603 0.6717 ] Network output: [ -0.06545 0.1566 0.929 0.001876 -0.0008422 1.053 0.001414 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6454 0.5867 0.3936 0.1746 0.9803 0.9868 0.6459 0.955 0.9729 0.4195 ] Network output: [ -0.1047 0.3442 0.7607 9.927e-05 -4.457e-05 1.105 7.482e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6644 0.6527 0.4244 0.0593 0.978 0.9851 0.6645 0.9492 0.9687 0.4302 ] Network output: [ 0.08937 0.7371 0.1623 0.0001709 -7.673e-05 0.9226 0.0001288 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07096 Epoch 1382 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01251 1.01 0.9907 0.0001617 -7.258e-05 -0.02463 0.0001218 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03315 -0.004049 0.03004 0.01906 0.9225 0.9345 0.06225 0.8532 0.8846 0.1368 ] Network output: [ 0.9628 0.09667 -0.03408 -0.0003954 0.0001775 0.01024 -0.000298 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6312 -0.03043 0.05692 0.2236 0.9609 0.9805 0.7119 0.8727 0.9539 0.6761 ] Network output: [ -0.007974 0.9482 1.028 0.0003089 -0.0001387 0.04066 0.0002328 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05819 0.03005 0.05246 0.03153 0.9773 0.9837 0.05944 0.9479 0.97 0.07138 ] Network output: [ 0.1131 -0.29 1.163 -0.002266 0.001017 0.8919 -0.001707 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7066 0.3977 0.4751 0.3832 0.9654 0.9833 0.7096 0.8842 0.9603 0.6718 ] Network output: [ -0.06572 0.1559 0.9296 0.001878 -0.0008429 1.054 0.001415 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6455 0.5864 0.3939 0.1741 0.9803 0.9868 0.646 0.9549 0.9729 0.4198 ] Network output: [ -0.1048 0.3452 0.7596 9.089e-05 -4.08e-05 1.105 6.85e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6644 0.6527 0.4246 0.05783 0.978 0.9851 0.6645 0.9492 0.9687 0.4304 ] Network output: [ 0.0899 0.7357 0.1634 0.0001852 -8.315e-05 0.9219 0.0001396 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07135 Epoch 1383 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01246 1.01 0.9906 0.000166 -7.452e-05 -0.02447 0.0001251 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03314 -0.004084 0.03008 0.01901 0.9225 0.9345 0.06221 0.8532 0.8846 0.1366 ] Network output: [ 0.9629 0.0973 -0.03477 -0.0003976 0.0001785 0.01011 -0.0002996 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6311 -0.03161 0.05755 0.2234 0.9609 0.9805 0.7117 0.8727 0.9538 0.6762 ] Network output: [ -0.007938 0.9482 1.028 0.0003138 -0.0001409 0.04065 0.0002365 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05811 0.02991 0.05243 0.03144 0.9773 0.9837 0.05937 0.9479 0.97 0.07129 ] Network output: [ 0.1134 -0.2897 1.163 -0.002276 0.001022 0.8911 -0.001716 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7064 0.3959 0.4757 0.3827 0.9654 0.9833 0.7094 0.8842 0.9603 0.672 ] Network output: [ -0.06604 0.1553 0.9302 0.001878 -0.000843 1.054 0.001415 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6455 0.586 0.3943 0.1734 0.9803 0.9868 0.646 0.9549 0.9729 0.4201 ] Network output: [ -0.105 0.3462 0.7585 8.126e-05 -3.648e-05 1.106 6.124e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6645 0.6526 0.4248 0.05633 0.978 0.9851 0.6646 0.9492 0.9687 0.4306 ] Network output: [ 0.09038 0.7343 0.1645 0.0001991 -8.939e-05 0.9212 0.0001501 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07173 Epoch 1384 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01235 1.01 0.9906 0.0001697 -7.619e-05 -0.02425 0.0001279 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03313 -0.004112 0.03015 0.01896 0.9225 0.9345 0.06216 0.8532 0.8846 0.1365 ] Network output: [ 0.9624 0.09799 -0.03481 -0.0004042 0.0001814 0.01044 -0.0003046 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.631 -0.03261 0.05846 0.2231 0.9609 0.9805 0.7115 0.8727 0.9538 0.6764 ] Network output: [ -0.00789 0.9482 1.028 0.0003187 -0.0001431 0.04065 0.0002402 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05805 0.02978 0.05242 0.03134 0.9773 0.9837 0.0593 0.9479 0.97 0.07121 ] Network output: [ 0.1135 -0.2892 1.162 -0.002288 0.001027 0.8905 -0.001725 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7062 0.3942 0.4764 0.3822 0.9654 0.9833 0.7092 0.8842 0.9603 0.6721 ] Network output: [ -0.06623 0.1549 0.9304 0.001877 -0.0008429 1.055 0.001415 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6456 0.5857 0.3947 0.1726 0.9803 0.9868 0.6461 0.9549 0.9729 0.4204 ] Network output: [ -0.1052 0.3477 0.7569 6.899e-05 -3.097e-05 1.106 5.2e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6646 0.6526 0.425 0.05456 0.9781 0.9851 0.6647 0.9492 0.9687 0.4308 ] Network output: [ 0.09089 0.733 0.1656 0.0002118 -9.507e-05 0.9205 0.0001596 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07216 Epoch 1385 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01226 1.01 0.9907 0.0001745 -7.834e-05 -0.02404 0.0001315 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03312 -0.00414 0.03022 0.01892 0.9225 0.9345 0.06211 0.8531 0.8846 0.1364 ] Network output: [ 0.9619 0.09835 -0.03459 -0.0004079 0.0001831 0.0108 -0.0003074 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6309 -0.03362 0.05938 0.2229 0.9609 0.9805 0.7113 0.8727 0.9538 0.6765 ] Network output: [ -0.007835 0.9481 1.028 0.0003243 -0.0001456 0.04065 0.0002444 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05798 0.02965 0.05241 0.03127 0.9774 0.9837 0.05923 0.9479 0.97 0.07114 ] Network output: [ 0.1137 -0.2891 1.162 -0.002297 0.001031 0.8899 -0.001731 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7061 0.3925 0.4772 0.3817 0.9654 0.9834 0.709 0.8842 0.9603 0.6723 ] Network output: [ -0.06646 0.1542 0.9309 0.001879 -0.0008435 1.055 0.001416 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6457 0.5853 0.3951 0.1719 0.9803 0.9868 0.6462 0.9549 0.9729 0.4207 ] Network output: [ -0.1053 0.3488 0.7555 5.895e-05 -2.647e-05 1.106 4.443e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6647 0.6526 0.4253 0.05294 0.9781 0.9851 0.6648 0.9492 0.9687 0.431 ] Network output: [ 0.09144 0.7316 0.1667 0.0002262 -0.0001016 0.9197 0.0001705 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07259 Epoch 1386 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01222 1.009 0.9907 0.0001794 -8.052e-05 -0.02388 0.0001352 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03311 -0.004175 0.03027 0.01887 0.9226 0.9345 0.06207 0.8531 0.8845 0.1362 ] Network output: [ 0.962 0.09889 -0.03519 -0.0004092 0.0001837 0.01069 -0.0003084 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6308 -0.03482 0.06006 0.2226 0.9609 0.9805 0.7111 0.8727 0.9538 0.6767 ] Network output: [ -0.007794 0.9481 1.028 0.0003296 -0.000148 0.04064 0.0002484 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05791 0.0295 0.05239 0.03118 0.9774 0.9837 0.05915 0.9479 0.97 0.07106 ] Network output: [ 0.1139 -0.2889 1.163 -0.002307 0.001035 0.8891 -0.001738 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7059 0.3907 0.4778 0.3813 0.9654 0.9834 0.7088 0.8842 0.9603 0.6724 ] Network output: [ -0.0668 0.1535 0.9316 0.00188 -0.0008438 1.056 0.001417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6457 0.5849 0.3955 0.1713 0.9803 0.9868 0.6462 0.9549 0.9729 0.4211 ] Network output: [ -0.1055 0.3497 0.7545 4.933e-05 -2.215e-05 1.107 3.718e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6647 0.6526 0.4255 0.05143 0.9781 0.9851 0.6648 0.9492 0.9687 0.4312 ] Network output: [ 0.09196 0.7301 0.168 0.0002414 -0.0001084 0.919 0.0001819 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07299 Epoch 1387 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01212 1.01 0.9906 0.0001833 -8.228e-05 -0.02368 0.0001381 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0331 -0.004206 0.03033 0.01882 0.9226 0.9345 0.06202 0.8531 0.8845 0.1361 ] Network output: [ 0.9616 0.09967 -0.03552 -0.0004151 0.0001864 0.01089 -0.0003128 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6307 -0.0359 0.06093 0.2223 0.9609 0.9806 0.711 0.8727 0.9538 0.6768 ] Network output: [ -0.007748 0.9481 1.028 0.0003345 -0.0001502 0.04064 0.0002521 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05784 0.02937 0.05238 0.03108 0.9774 0.9837 0.05908 0.9479 0.97 0.07098 ] Network output: [ 0.1141 -0.2883 1.162 -0.002319 0.001041 0.8884 -0.001748 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7057 0.3889 0.4786 0.3807 0.9654 0.9834 0.7086 0.8841 0.9603 0.6726 ] Network output: [ -0.06704 0.1531 0.9319 0.001879 -0.0008436 1.057 0.001416 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6458 0.5846 0.3959 0.1705 0.9803 0.9868 0.6463 0.9549 0.9729 0.4214 ] Network output: [ -0.1056 0.3512 0.7529 3.636e-05 -1.632e-05 1.107 2.74e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6648 0.6526 0.4258 0.04964 0.9781 0.9851 0.6649 0.9492 0.9687 0.4315 ] Network output: [ 0.09247 0.7288 0.1692 0.0002549 -0.0001144 0.9182 0.0001921 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07344 Epoch 1388 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01201 1.009 0.9907 0.0001879 -8.436e-05 -0.02345 0.0001416 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03308 -0.004234 0.03041 0.01877 0.9226 0.9345 0.06197 0.8531 0.8845 0.136 ] Network output: [ 0.961 0.1001 -0.0352 -0.0004204 0.0001888 0.01137 -0.0003169 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6307 -0.03688 0.06197 0.2221 0.9609 0.9806 0.7108 0.8727 0.9538 0.677 ] Network output: [ -0.007687 0.948 1.028 0.0003402 -0.0001528 0.04064 0.0002564 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05778 0.02924 0.05238 0.031 0.9774 0.9837 0.05901 0.9479 0.97 0.07091 ] Network output: [ 0.1142 -0.288 1.162 -0.002328 0.001045 0.8878 -0.001755 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7055 0.3872 0.4794 0.3802 0.9654 0.9834 0.7085 0.8841 0.9603 0.6727 ] Network output: [ -0.06724 0.1525 0.9322 0.00188 -0.000844 1.057 0.001417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6459 0.5843 0.3964 0.1697 0.9804 0.9868 0.6464 0.9549 0.9729 0.4218 ] Network output: [ -0.1057 0.3525 0.7514 2.432e-05 -1.092e-05 1.108 1.833e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6649 0.6526 0.4261 0.04787 0.9781 0.9851 0.665 0.9492 0.9687 0.4317 ] Network output: [ 0.09304 0.7273 0.1703 0.0002695 -0.000121 0.9174 0.0002031 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0739 Epoch 1389 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01197 1.009 0.9907 0.0001932 -8.674e-05 -0.02328 0.0001456 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03308 -0.004268 0.03047 0.01873 0.9226 0.9345 0.06193 0.8531 0.8845 0.1358 ] Network output: [ 0.9609 0.1006 -0.03559 -0.0004216 0.0001893 0.01139 -0.0003177 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6306 -0.03806 0.06275 0.2218 0.9609 0.9806 0.7106 0.8727 0.9538 0.6772 ] Network output: [ -0.007637 0.948 1.028 0.0003459 -0.0001553 0.04063 0.0002607 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05771 0.0291 0.05237 0.03092 0.9774 0.9837 0.05894 0.9479 0.97 0.07084 ] Network output: [ 0.1145 -0.288 1.162 -0.002337 0.001049 0.8871 -0.001761 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7053 0.3854 0.4801 0.3798 0.9654 0.9834 0.7083 0.8841 0.9603 0.6729 ] Network output: [ -0.06759 0.1516 0.933 0.001881 -0.0008445 1.058 0.001418 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.646 0.5839 0.3969 0.1691 0.9804 0.9868 0.6465 0.9549 0.9729 0.4222 ] Network output: [ -0.1059 0.3534 0.7503 1.427e-05 -6.408e-06 1.108 1.076e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.665 0.6526 0.4264 0.0463 0.9781 0.9852 0.6651 0.9492 0.9687 0.432 ] Network output: [ 0.0936 0.7257 0.1716 0.0002856 -0.0001282 0.9166 0.0002153 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07433 Epoch 1390 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0119 1.009 0.9906 0.0001975 -8.865e-05 -0.0231 0.0001488 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03307 -0.004302 0.03053 0.01867 0.9226 0.9345 0.06189 0.8531 0.8845 0.1357 ] Network output: [ 0.9608 0.1014 -0.03619 -0.0004261 0.0001913 0.01143 -0.0003211 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6305 -0.03924 0.06357 0.2215 0.9609 0.9806 0.7104 0.8726 0.9538 0.6774 ] Network output: [ -0.007592 0.948 1.028 0.000351 -0.0001576 0.04062 0.0002645 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05764 0.02895 0.05236 0.03082 0.9774 0.9837 0.05887 0.9479 0.97 0.07076 ] Network output: [ 0.1147 -0.2874 1.162 -0.002349 0.001054 0.8863 -0.00177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7051 0.3835 0.4809 0.3792 0.9654 0.9834 0.7081 0.8841 0.9603 0.6731 ] Network output: [ -0.06788 0.1511 0.9334 0.00188 -0.0008442 1.059 0.001417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6461 0.5836 0.3973 0.1684 0.9804 0.9868 0.6466 0.9549 0.9729 0.4226 ] Network output: [ -0.1061 0.3548 0.7489 1.117e-06 -5.014e-07 1.109 8.418e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6651 0.6526 0.4267 0.04453 0.9781 0.9852 0.6652 0.9492 0.9687 0.4323 ] Network output: [ 0.09412 0.7243 0.1729 0.0003004 -0.0001349 0.9158 0.0002264 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07479 Epoch 1391 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01179 1.009 0.9906 0.0002019 -9.064e-05 -0.02286 0.0001522 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03305 -0.00433 0.03061 0.01862 0.9226 0.9345 0.06184 0.8531 0.8845 0.1356 ] Network output: [ 0.9601 0.102 -0.03593 -0.0004327 0.0001943 0.01196 -0.0003261 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6304 -0.04023 0.06468 0.2212 0.9609 0.9806 0.7102 0.8726 0.9538 0.6776 ] Network output: [ -0.007528 0.9479 1.028 0.0003566 -0.0001601 0.04062 0.0002687 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05757 0.02882 0.05237 0.03073 0.9774 0.9837 0.0588 0.9479 0.97 0.0707 ] Network output: [ 0.1148 -0.2869 1.162 -0.002359 0.001059 0.8857 -0.001778 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.705 0.3818 0.4818 0.3786 0.9654 0.9834 0.7079 0.8841 0.9603 0.6733 ] Network output: [ -0.06807 0.1506 0.9336 0.001881 -0.0008443 1.06 0.001417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6462 0.5833 0.3978 0.1675 0.9804 0.9868 0.6467 0.9549 0.9729 0.423 ] Network output: [ -0.1062 0.3562 0.7472 -1.299e-05 5.833e-06 1.109 -9.792e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6652 0.6526 0.427 0.04261 0.9781 0.9852 0.6653 0.9493 0.9687 0.4326 ] Network output: [ 0.09471 0.7228 0.1742 0.0003151 -0.0001415 0.9149 0.0002375 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07528 Epoch 1392 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01173 1.009 0.9907 0.0002074 -9.312e-05 -0.02267 0.0001563 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03305 -0.004363 0.03068 0.01858 0.9226 0.9345 0.06179 0.8531 0.8845 0.1355 ] Network output: [ 0.9598 0.1024 -0.03601 -0.0004345 0.000195 0.01217 -0.0003274 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6303 -0.04135 0.06561 0.221 0.9609 0.9806 0.7101 0.8726 0.9538 0.6778 ] Network output: [ -0.007468 0.9478 1.028 0.0003626 -0.0001628 0.04061 0.0002733 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05751 0.02868 0.05237 0.03065 0.9774 0.9837 0.05873 0.9479 0.97 0.07064 ] Network output: [ 0.115 -0.2869 1.162 -0.002366 0.001062 0.885 -0.001783 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7048 0.3799 0.4826 0.3782 0.9654 0.9834 0.7078 0.8841 0.9603 0.6735 ] Network output: [ -0.06838 0.1497 0.9344 0.001882 -0.0008449 1.06 0.001418 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6463 0.5829 0.3984 0.1668 0.9804 0.9868 0.6468 0.9548 0.9729 0.4235 ] Network output: [ -0.1064 0.3573 0.746 -2.413e-05 1.083e-05 1.109 -1.819e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6654 0.6526 0.4273 0.04094 0.9781 0.9852 0.6655 0.9493 0.9687 0.4329 ] Network output: [ 0.0953 0.7211 0.1755 0.000332 -0.000149 0.9141 0.0002502 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07574 Epoch 1393 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01169 1.009 0.9906 0.0002122 -9.525e-05 -0.02251 0.0001599 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03304 -0.004399 0.03074 0.01852 0.9226 0.9346 0.06175 0.8531 0.8845 0.1353 ] Network output: [ 0.9599 0.1032 -0.03677 -0.0004372 0.0001963 0.01211 -0.0003295 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6303 -0.0426 0.06641 0.2206 0.9609 0.9806 0.7099 0.8726 0.9538 0.678 ] Network output: [ -0.007422 0.9478 1.028 0.0003679 -0.0001651 0.0406 0.0002772 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05744 0.02853 0.05236 0.03056 0.9774 0.9837 0.05866 0.9479 0.97 0.07057 ] Network output: [ 0.1153 -0.2865 1.162 -0.002377 0.001067 0.8842 -0.001792 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7046 0.378 0.4834 0.3776 0.9654 0.9834 0.7076 0.884 0.9603 0.6737 ] Network output: [ -0.06873 0.1491 0.9351 0.001882 -0.0008448 1.061 0.001418 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6464 0.5825 0.3989 0.1661 0.9804 0.9868 0.6469 0.9548 0.9729 0.424 ] Network output: [ -0.1066 0.3585 0.7447 -3.708e-05 1.664e-05 1.11 -2.794e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6655 0.6526 0.4277 0.0392 0.9781 0.9852 0.6656 0.9493 0.9687 0.4332 ] Network output: [ 0.09585 0.7196 0.1769 0.0003482 -0.0001563 0.9132 0.0002624 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07621 Epoch 1394 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01158 1.009 0.9905 0.0002164 -9.715e-05 -0.02228 0.0001631 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03303 -0.004429 0.03082 0.01846 0.9226 0.9346 0.06171 0.8531 0.8845 0.1352 ] Network output: [ 0.9592 0.104 -0.03676 -0.0004444 0.0001995 0.01257 -0.0003349 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6302 -0.04364 0.06753 0.2203 0.9609 0.9806 0.7097 0.8726 0.9538 0.6782 ] Network output: [ -0.007359 0.9478 1.028 0.0003733 -0.0001676 0.0406 0.0002813 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05737 0.0284 0.05237 0.03046 0.9774 0.9837 0.05859 0.9479 0.97 0.07051 ] Network output: [ 0.1154 -0.2858 1.162 -0.002389 0.001072 0.8835 -0.0018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7045 0.3762 0.4843 0.3769 0.9655 0.9834 0.7074 0.884 0.9603 0.6739 ] Network output: [ -0.06893 0.1486 0.9352 0.001881 -0.0008444 1.062 0.001418 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6466 0.5822 0.3994 0.1652 0.9804 0.9868 0.6471 0.9548 0.9729 0.4244 ] Network output: [ -0.1067 0.36 0.7429 -5.295e-05 2.377e-05 1.11 -3.99e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6656 0.6527 0.428 0.03719 0.9782 0.9852 0.6657 0.9493 0.9687 0.4336 ] Network output: [ 0.09643 0.718 0.1783 0.0003633 -0.0001631 0.9124 0.0002738 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07673 Epoch 1395 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0115 1.009 0.9906 0.0002219 -9.961e-05 -0.02207 0.0001672 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03302 -0.004459 0.0309 0.01842 0.9226 0.9346 0.06166 0.8531 0.8845 0.1351 ] Network output: [ 0.9587 0.1044 -0.03652 -0.0004476 0.000201 0.01302 -0.0003374 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6301 -0.0447 0.06863 0.2201 0.961 0.9806 0.7096 0.8726 0.9538 0.6784 ] Network output: [ -0.007289 0.9477 1.028 0.0003795 -0.0001704 0.0406 0.000286 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05731 0.02827 0.05239 0.03038 0.9774 0.9837 0.05853 0.9479 0.97 0.07046 ] Network output: [ 0.1156 -0.2857 1.162 -0.002396 0.001076 0.8829 -0.001806 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7043 0.3744 0.4853 0.3764 0.9655 0.9834 0.7073 0.884 0.9603 0.6742 ] Network output: [ -0.0692 0.1478 0.9358 0.001882 -0.0008451 1.062 0.001419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6467 0.5819 0.4 0.1644 0.9804 0.9869 0.6472 0.9548 0.9729 0.4249 ] Network output: [ -0.1068 0.3612 0.7415 -6.6e-05 2.963e-05 1.111 -4.974e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6657 0.6527 0.4284 0.03537 0.9782 0.9852 0.6658 0.9493 0.9687 0.4339 ] Network output: [ 0.09706 0.7163 0.1797 0.0003805 -0.0001708 0.9114 0.0002868 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07724 Epoch 1396 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01147 1.009 0.9905 0.0002272 -0.000102 -0.02191 0.0001712 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03301 -0.004497 0.03096 0.01836 0.9226 0.9346 0.06162 0.8531 0.8845 0.135 ] Network output: [ 0.9587 0.105 -0.03725 -0.0004486 0.0002014 0.01294 -0.0003381 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6301 -0.04598 0.06945 0.2198 0.961 0.9806 0.7094 0.8726 0.9538 0.6786 ] Network output: [ -0.007238 0.9477 1.028 0.0003851 -0.0001729 0.04058 0.0002902 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05724 0.02812 0.05239 0.03029 0.9774 0.9837 0.05845 0.9479 0.97 0.07039 ] Network output: [ 0.1159 -0.2854 1.162 -0.002405 0.00108 0.8821 -0.001812 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7042 0.3724 0.4861 0.3759 0.9655 0.9834 0.7071 0.884 0.9603 0.6744 ] Network output: [ -0.06958 0.1469 0.9367 0.001883 -0.0008452 1.063 0.001419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6468 0.5815 0.4006 0.1637 0.9804 0.9869 0.6473 0.9548 0.9729 0.4254 ] Network output: [ -0.1071 0.3623 0.7404 -7.864e-05 3.53e-05 1.111 -5.926e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6659 0.6527 0.4288 0.03366 0.9782 0.9852 0.666 0.9493 0.9687 0.4343 ] Network output: [ 0.09764 0.7146 0.1812 0.0003982 -0.0001788 0.9106 0.0003001 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07772 Epoch 1397 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01138 1.01 0.9904 0.0002314 -0.0001039 -0.0217 0.0001744 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.033 -0.00453 0.03104 0.0183 0.9227 0.9346 0.06157 0.8531 0.8845 0.1349 ] Network output: [ 0.9583 0.1059 -0.03761 -0.0004552 0.0002044 0.01324 -0.0003431 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.63 -0.04712 0.07052 0.2194 0.961 0.9806 0.7093 0.8726 0.9538 0.6789 ] Network output: [ -0.00718 0.9477 1.028 0.0003903 -0.0001752 0.04057 0.0002942 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05717 0.02798 0.05239 0.03018 0.9774 0.9837 0.05838 0.9479 0.97 0.07033 ] Network output: [ 0.1161 -0.2847 1.161 -0.002417 0.001085 0.8813 -0.001822 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.704 0.3705 0.4871 0.3752 0.9655 0.9834 0.7069 0.884 0.9603 0.6746 ] Network output: [ -0.06983 0.1465 0.9369 0.001881 -0.0008445 1.064 0.001418 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.647 0.5812 0.4012 0.1628 0.9804 0.9869 0.6475 0.9548 0.9729 0.4259 ] Network output: [ -0.1072 0.3639 0.7386 -9.554e-05 4.289e-05 1.112 -7.2e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.666 0.6528 0.4292 0.03163 0.9782 0.9852 0.6661 0.9493 0.9687 0.4347 ] Network output: [ 0.09823 0.713 0.1826 0.0004142 -0.0001859 0.9096 0.0003121 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07826 Epoch 1398 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01128 1.009 0.9904 0.0002365 -0.0001062 -0.02146 0.0001782 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03299 -0.004558 0.03113 0.01825 0.9227 0.9346 0.06153 0.8531 0.8845 0.1347 ] Network output: [ 0.9575 0.1064 -0.03718 -0.0004606 0.0002068 0.0139 -0.0003471 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6299 -0.04811 0.07178 0.2191 0.961 0.9806 0.7091 0.8726 0.9538 0.6791 ] Network output: [ -0.007102 0.9476 1.028 0.0003965 -0.000178 0.04057 0.0002988 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05711 0.02785 0.05242 0.0301 0.9774 0.9837 0.05832 0.9479 0.97 0.07028 ] Network output: [ 0.1162 -0.2843 1.161 -0.002425 0.001089 0.8807 -0.001827 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7039 0.3687 0.4881 0.3746 0.9655 0.9834 0.7068 0.884 0.9602 0.6749 ] Network output: [ -0.07004 0.1458 0.9373 0.001882 -0.0008448 1.065 0.001418 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6472 0.5809 0.4018 0.1619 0.9804 0.9869 0.6477 0.9548 0.9729 0.4265 ] Network output: [ -0.1073 0.3653 0.7369 -0.0001112 4.994e-05 1.112 -8.383e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6662 0.6528 0.4297 0.02962 0.9782 0.9852 0.6663 0.9494 0.9687 0.4351 ] Network output: [ 0.09888 0.7112 0.1841 0.0004313 -0.0001936 0.9087 0.0003251 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07881 Epoch 1399 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01125 1.009 0.9904 0.0002423 -0.0001088 -0.0213 0.0001826 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03299 -0.004595 0.0312 0.0182 0.9227 0.9346 0.06149 0.8531 0.8845 0.1346 ] Network output: [ 0.9574 0.1069 -0.03764 -0.0004605 0.0002068 0.01395 -0.0003471 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6299 -0.04936 0.07271 0.2188 0.961 0.9806 0.709 0.8725 0.9538 0.6794 ] Network output: [ -0.007041 0.9476 1.028 0.0004025 -0.0001807 0.04055 0.0003033 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05705 0.0277 0.05243 0.03002 0.9774 0.9837 0.05825 0.9479 0.97 0.07023 ] Network output: [ 0.1165 -0.2843 1.161 -0.002431 0.001091 0.8799 -0.001832 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7037 0.3667 0.489 0.3741 0.9655 0.9834 0.7066 0.8839 0.9602 0.6752 ] Network output: [ -0.07043 0.1448 0.9382 0.001883 -0.0008453 1.066 0.001419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6473 0.5805 0.4025 0.1612 0.9804 0.9869 0.6478 0.9548 0.9729 0.4271 ] Network output: [ -0.1075 0.3663 0.7358 -0.000124 5.567e-05 1.112 -9.345e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6663 0.6529 0.4301 0.0279 0.9782 0.9852 0.6664 0.9494 0.9687 0.4355 ] Network output: [ 0.09951 0.7094 0.1857 0.0004503 -0.0002022 0.9077 0.0003394 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07932 Epoch 1400 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0112 1.01 0.9902 0.0002467 -0.0001107 -0.02112 0.0001859 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03298 -0.004632 0.03127 0.01814 0.9227 0.9346 0.06144 0.8531 0.8845 0.1345 ] Network output: [ 0.9573 0.1079 -0.03842 -0.0004652 0.0002088 0.01401 -0.0003506 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6298 -0.05062 0.07368 0.2184 0.961 0.9806 0.7088 0.8725 0.9538 0.6796 ] Network output: [ -0.006988 0.9476 1.027 0.0004076 -0.000183 0.04053 0.0003072 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05698 0.02755 0.05243 0.02991 0.9774 0.9838 0.05818 0.9479 0.97 0.07017 ] Network output: [ 0.1167 -0.2836 1.161 -0.002443 0.001097 0.8791 -0.001841 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7035 0.3647 0.49 0.3735 0.9655 0.9834 0.7064 0.8839 0.9602 0.6754 ] Network output: [ -0.07076 0.1442 0.9387 0.001881 -0.0008446 1.066 0.001418 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6475 0.5802 0.4032 0.1603 0.9804 0.9869 0.648 0.9548 0.9729 0.4276 ] Network output: [ -0.1077 0.3677 0.7343 -0.0001409 6.326e-05 1.113 -0.0001062 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6665 0.6529 0.4306 0.02592 0.9782 0.9852 0.6666 0.9494 0.9687 0.436 ] Network output: [ 0.1001 0.7077 0.1872 0.0004676 -0.0002099 0.9067 0.0003524 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07987 Epoch 1401 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01108 1.01 0.9901 0.0002512 -0.0001128 -0.02087 0.0001893 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03297 -0.004659 0.03137 0.01809 0.9227 0.9346 0.0614 0.8531 0.8845 0.1344 ] Network output: [ 0.9564 0.1086 -0.03802 -0.0004726 0.0002122 0.01476 -0.0003562 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6297 -0.0516 0.07504 0.2181 0.961 0.9806 0.7086 0.8725 0.9538 0.6799 ] Network output: [ -0.006908 0.9476 1.027 0.0004134 -0.0001856 0.04054 0.0003116 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05692 0.02742 0.05247 0.02981 0.9774 0.9838 0.05812 0.9479 0.97 0.07012 ] Network output: [ 0.1169 -0.283 1.161 -0.002452 0.001101 0.8785 -0.001848 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7034 0.3629 0.491 0.3727 0.9655 0.9834 0.7063 0.8839 0.9602 0.6757 ] Network output: [ -0.07094 0.1437 0.9388 0.001881 -0.0008443 1.067 0.001417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6477 0.5799 0.4038 0.1593 0.9804 0.9869 0.6482 0.9548 0.9729 0.4282 ] Network output: [ -0.1078 0.3694 0.7323 -0.0001595 7.162e-05 1.113 -0.0001202 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6667 0.653 0.4311 0.02374 0.9782 0.9853 0.6668 0.9494 0.9687 0.4364 ] Network output: [ 0.1008 0.706 0.1888 0.0004847 -0.0002176 0.9057 0.0003653 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08046 Epoch 1402 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01103 1.01 0.9902 0.0002572 -0.0001155 -0.02068 0.0001939 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03296 -0.004693 0.03145 0.01804 0.9227 0.9346 0.06136 0.8531 0.8845 0.1343 ] Network output: [ 0.956 0.109 -0.038 -0.0004729 0.0002123 0.01511 -0.0003564 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6297 -0.05275 0.07615 0.2178 0.961 0.9806 0.7085 0.8725 0.9538 0.6802 ] Network output: [ -0.006833 0.9475 1.027 0.0004198 -0.0001884 0.04052 0.0003163 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05686 0.02729 0.0525 0.02974 0.9774 0.9838 0.05805 0.9479 0.97 0.07009 ] Network output: [ 0.1171 -0.283 1.161 -0.002456 0.001103 0.8778 -0.001851 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7033 0.3609 0.4921 0.3722 0.9655 0.9834 0.7062 0.8839 0.9602 0.676 ] Network output: [ -0.07127 0.1426 0.9397 0.001882 -0.0008451 1.068 0.001419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6479 0.5796 0.4045 0.1586 0.9804 0.9869 0.6484 0.9548 0.9729 0.4288 ] Network output: [ -0.108 0.3704 0.7311 -0.0001735 7.79e-05 1.114 -0.0001308 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6669 0.6531 0.4316 0.02192 0.9783 0.9853 0.667 0.9494 0.9688 0.4369 ] Network output: [ 0.1014 0.7041 0.1904 0.0005045 -0.0002265 0.9047 0.0003802 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08101 Epoch 1403 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01102 1.01 0.9899 0.0002621 -0.0001177 -0.02054 0.0001975 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03296 -0.004734 0.03151 0.01798 0.9227 0.9346 0.06132 0.8531 0.8845 0.1342 ] Network output: [ 0.9561 0.1099 -0.03907 -0.0004744 0.000213 0.01497 -0.0003575 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6296 -0.05411 0.07704 0.2174 0.961 0.9806 0.7084 0.8725 0.9538 0.6805 ] Network output: [ -0.006783 0.9476 1.027 0.0004249 -0.0001907 0.04049 0.0003202 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05678 0.02713 0.0525 0.02964 0.9774 0.9838 0.05798 0.9479 0.97 0.07002 ] Network output: [ 0.1174 -0.2825 1.161 -0.002466 0.001107 0.8769 -0.001858 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7031 0.3588 0.493 0.3716 0.9655 0.9834 0.706 0.8839 0.9602 0.6763 ] Network output: [ -0.07168 0.1418 0.9406 0.001881 -0.0008446 1.069 0.001418 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.648 0.5792 0.4053 0.1578 0.9804 0.9869 0.6485 0.9548 0.9729 0.4294 ] Network output: [ -0.1083 0.3716 0.7299 -0.0001894 8.503e-05 1.114 -0.0001427 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.667 0.6531 0.4321 0.02007 0.9783 0.9853 0.6671 0.9495 0.9688 0.4374 ] Network output: [ 0.102 0.7022 0.1921 0.0005235 -0.000235 0.9037 0.0003945 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08156 Epoch 1404 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01091 1.01 0.9898 0.0002659 -0.0001194 -0.02029 0.0002004 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03295 -0.004764 0.0316 0.01791 0.9227 0.9346 0.06127 0.8531 0.8845 0.1341 ] Network output: [ 0.9553 0.1109 -0.03906 -0.0004829 0.0002168 0.01559 -0.0003639 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6296 -0.05517 0.07838 0.217 0.961 0.9806 0.7082 0.8725 0.9538 0.6807 ] Network output: [ -0.00671 0.9476 1.027 0.0004301 -0.0001931 0.04049 0.0003242 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05672 0.02699 0.05253 0.02953 0.9775 0.9838 0.05791 0.9479 0.97 0.06998 ] Network output: [ 0.1176 -0.2816 1.16 -0.002477 0.001112 0.8762 -0.001867 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.703 0.3569 0.4941 0.3708 0.9655 0.9834 0.7058 0.8839 0.9602 0.6766 ] Network output: [ -0.07188 0.1414 0.9406 0.001879 -0.0008436 1.069 0.001416 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6482 0.5789 0.406 0.1568 0.9804 0.9869 0.6487 0.9548 0.9729 0.43 ] Network output: [ -0.1084 0.3734 0.7278 -0.0002104 9.447e-05 1.115 -0.0001586 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6672 0.6532 0.4326 0.01779 0.9783 0.9853 0.6673 0.9495 0.9688 0.4379 ] Network output: [ 0.1027 0.7004 0.1938 0.0005407 -0.0002428 0.9026 0.0004075 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08218 Epoch 1405 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01082 1.01 0.9898 0.0002717 -0.000122 -0.02005 0.0002047 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03294 -0.004792 0.03171 0.01787 0.9227 0.9347 0.06123 0.8531 0.8845 0.134 ] Network output: [ 0.9545 0.1112 -0.03847 -0.0004855 0.000218 0.01635 -0.0003659 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6295 -0.05618 0.07975 0.2168 0.961 0.9806 0.708 0.8725 0.9538 0.6811 ] Network output: [ -0.00662 0.9475 1.027 0.0004366 -0.000196 0.04049 0.000329 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05667 0.02687 0.05258 0.02945 0.9775 0.9838 0.05785 0.9479 0.97 0.06995 ] Network output: [ 0.1178 -0.2815 1.16 -0.00248 0.001114 0.8757 -0.001869 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7029 0.3551 0.4953 0.3702 0.9655 0.9834 0.7057 0.8839 0.9602 0.6769 ] Network output: [ -0.07212 0.1405 0.9412 0.001881 -0.0008443 1.07 0.001417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6485 0.5786 0.4067 0.1559 0.9804 0.9869 0.649 0.9548 0.9729 0.4307 ] Network output: [ -0.1085 0.3747 0.7262 -0.0002273 0.000102 1.115 -0.0001713 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6674 0.6533 0.4331 0.01576 0.9783 0.9853 0.6675 0.9495 0.9688 0.4384 ] Network output: [ 0.1034 0.6985 0.1954 0.0005605 -0.0002516 0.9016 0.0004224 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08279 Epoch 1406 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01083 1.01 0.9896 0.0002773 -0.0001245 -0.01993 0.000209 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03294 -0.004834 0.03177 0.01781 0.9228 0.9347 0.06119 0.8531 0.8845 0.1339 ] Network output: [ 0.9547 0.1119 -0.03946 -0.0004834 0.000217 0.0162 -0.0003643 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6295 -0.05756 0.08063 0.2165 0.961 0.9806 0.7079 0.8725 0.9538 0.6814 ] Network output: [ -0.006565 0.9475 1.027 0.0004421 -0.0001985 0.04045 0.0003332 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0566 0.0267 0.05258 0.02937 0.9775 0.9838 0.05778 0.9479 0.9701 0.0699 ] Network output: [ 0.1181 -0.2814 1.16 -0.002486 0.001116 0.8747 -0.001873 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7027 0.3529 0.4963 0.3697 0.9655 0.9834 0.7056 0.8838 0.9602 0.6773 ] Network output: [ -0.07258 0.1393 0.9424 0.001881 -0.0008445 1.071 0.001418 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6487 0.5782 0.4075 0.1553 0.9804 0.9869 0.6491 0.9548 0.9729 0.4314 ] Network output: [ -0.1088 0.3756 0.7253 -0.0002417 0.0001085 1.116 -0.0001821 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6676 0.6533 0.4337 0.01403 0.9783 0.9853 0.6677 0.9495 0.9688 0.4389 ] Network output: [ 0.1041 0.6965 0.1973 0.0005814 -0.000261 0.9005 0.0004382 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08334 Epoch 1407 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01075 1.01 0.9894 0.0002807 -0.000126 -0.01972 0.0002115 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03293 -0.00487 0.03185 0.01774 0.9228 0.9347 0.06114 0.8531 0.8845 0.1338 ] Network output: [ 0.9543 0.1131 -0.04017 -0.0004906 0.0002203 0.01645 -0.0003698 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6294 -0.05879 0.08182 0.216 0.961 0.9806 0.7078 0.8724 0.9537 0.6816 ] Network output: [ -0.006506 0.9476 1.027 0.0004466 -0.0002005 0.04044 0.0003366 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05653 0.02655 0.0526 0.02926 0.9775 0.9838 0.05771 0.9479 0.9701 0.06985 ] Network output: [ 0.1183 -0.2804 1.16 -0.002498 0.001122 0.8739 -0.001883 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7025 0.3509 0.4974 0.3689 0.9655 0.9834 0.7054 0.8838 0.9602 0.6776 ] Network output: [ -0.07287 0.1389 0.9426 0.001878 -0.000843 1.072 0.001415 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6489 0.5778 0.4083 0.1542 0.9804 0.9869 0.6493 0.9548 0.9729 0.4321 ] Network output: [ -0.1089 0.3773 0.7235 -0.0002634 0.0001183 1.116 -0.0001985 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6678 0.6534 0.4343 0.01181 0.9783 0.9853 0.6679 0.9495 0.9688 0.4394 ] Network output: [ 0.1047 0.6947 0.199 0.0005995 -0.0002691 0.8994 0.0004518 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08397 Epoch 1408 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01062 1.01 0.9894 0.0002854 -0.0001281 -0.01943 0.0002151 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03292 -0.004894 0.03197 0.01769 0.9228 0.9347 0.06109 0.8531 0.8845 0.1337 ] Network output: [ 0.953 0.1137 -0.03924 -0.0004974 0.0002233 0.01756 -0.0003749 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6294 -0.05967 0.08343 0.2157 0.961 0.9806 0.7076 0.8724 0.9537 0.682 ] Network output: [ -0.006406 0.9476 1.027 0.0004527 -0.0002032 0.04044 0.0003411 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05647 0.02644 0.05266 0.02916 0.9775 0.9838 0.05765 0.9479 0.9701 0.06983 ] Network output: [ 0.1185 -0.2799 1.159 -0.002503 0.001124 0.8734 -0.001887 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7024 0.3491 0.4986 0.3681 0.9655 0.9834 0.7053 0.8838 0.9602 0.6779 ] Network output: [ -0.07299 0.1383 0.9427 0.001877 -0.0008429 1.073 0.001415 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6491 0.5776 0.4091 0.1531 0.9804 0.9869 0.6496 0.9548 0.9729 0.4328 ] Network output: [ -0.109 0.379 0.7214 -0.0002847 0.0001278 1.116 -0.0002146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.668 0.6535 0.4348 0.009495 0.9784 0.9853 0.6681 0.9496 0.9688 0.44 ] Network output: [ 0.1054 0.6927 0.2007 0.0006185 -0.0002777 0.8982 0.0004662 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08464 Epoch 1409 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01062 1.01 0.9893 0.0002918 -0.000131 -0.01928 0.0002199 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03292 -0.004932 0.03204 0.01764 0.9228 0.9347 0.06105 0.8531 0.8845 0.1336 ] Network output: [ 0.9529 0.114 -0.03959 -0.0004932 0.0002214 0.01772 -0.0003717 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6294 -0.06095 0.08448 0.2154 0.961 0.9806 0.7075 0.8724 0.9537 0.6823 ] Network output: [ -0.006334 0.9475 1.027 0.0004587 -0.0002059 0.04041 0.0003457 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05641 0.02629 0.05269 0.0291 0.9775 0.9838 0.05758 0.9479 0.9701 0.0698 ] Network output: [ 0.1188 -0.2802 1.16 -0.002503 0.001124 0.8727 -0.001887 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7023 0.347 0.4997 0.3677 0.9655 0.9834 0.7052 0.8838 0.9602 0.6783 ] Network output: [ -0.07343 0.1369 0.944 0.00188 -0.000844 1.074 0.001417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6494 0.5773 0.4099 0.1525 0.9804 0.9869 0.6499 0.9548 0.9729 0.4335 ] Network output: [ -0.1092 0.3798 0.7205 -0.0002986 0.0001341 1.117 -0.0002251 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6683 0.6536 0.4354 0.007788 0.9784 0.9853 0.6684 0.9496 0.9688 0.4406 ] Network output: [ 0.1062 0.6906 0.2026 0.0006409 -0.0002877 0.897 0.000483 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08521 Epoch 1410 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01061 1.01 0.9889 0.0002954 -0.0001326 -0.01915 0.0002226 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03291 -0.004976 0.0321 0.01757 0.9228 0.9347 0.06101 0.8531 0.8845 0.1335 ] Network output: [ 0.9532 0.1152 -0.04113 -0.0004957 0.0002225 0.01747 -0.0003736 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6293 -0.0624 0.0854 0.215 0.961 0.9806 0.7073 0.8724 0.9537 0.6826 ] Network output: [ -0.006293 0.9477 1.026 0.0004627 -0.0002077 0.04038 0.0003487 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05633 0.02612 0.05269 0.02899 0.9775 0.9838 0.0575 0.9479 0.9701 0.06973 ] Network output: [ 0.1191 -0.2794 1.159 -0.002515 0.001129 0.8716 -0.001895 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7021 0.3447 0.5008 0.367 0.9655 0.9834 0.705 0.8838 0.9602 0.6786 ] Network output: [ -0.07387 0.1361 0.9448 0.001877 -0.0008426 1.074 0.001415 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6495 0.5768 0.4107 0.1517 0.9804 0.9869 0.65 0.9548 0.9729 0.4343 ] Network output: [ -0.1095 0.3811 0.7193 -0.0003184 0.0001429 1.117 -0.0002399 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6684 0.6536 0.436 0.005814 0.9784 0.9853 0.6685 0.9496 0.9688 0.4412 ] Network output: [ 0.1068 0.6887 0.2045 0.0006608 -0.0002967 0.8959 0.000498 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08582 Epoch 1411 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01044 1.01 0.9887 0.0002985 -0.000134 -0.01884 0.0002249 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0329 -0.005 0.03222 0.01751 0.9228 0.9347 0.06096 0.8531 0.8845 0.1334 ] Network output: [ 0.9518 0.1162 -0.04047 -0.0005068 0.0002275 0.0186 -0.000382 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6293 -0.0633 0.08709 0.2146 0.961 0.9806 0.7072 0.8724 0.9537 0.6829 ] Network output: [ -0.0062 0.9478 1.026 0.0004677 -0.00021 0.04038 0.0003525 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05627 0.026 0.05275 0.02887 0.9775 0.9838 0.05744 0.9479 0.9701 0.06971 ] Network output: [ 0.1192 -0.2782 1.158 -0.002524 0.001133 0.8711 -0.001902 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.702 0.3429 0.502 0.366 0.9656 0.9834 0.7048 0.8838 0.9602 0.6789 ] Network output: [ -0.07394 0.1359 0.9444 0.001874 -0.0008411 1.075 0.001412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6498 0.5766 0.4115 0.1504 0.9805 0.9869 0.6503 0.9548 0.9729 0.4349 ] Network output: [ -0.1096 0.3832 0.7168 -0.0003445 0.0001547 1.118 -0.0002597 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6687 0.6538 0.4367 0.003255 0.9784 0.9854 0.6688 0.9496 0.9688 0.4418 ] Network output: [ 0.1075 0.6868 0.2063 0.0006789 -0.0003048 0.8947 0.0005116 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08656 Epoch 1412 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01038 1.01 0.9888 0.0003049 -0.0001369 -0.01861 0.0002298 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03289 -0.005028 0.03233 0.01747 0.9228 0.9347 0.06092 0.8531 0.8845 0.1333 ] Network output: [ 0.9509 0.1162 -0.03966 -0.0005044 0.0002264 0.01948 -0.0003801 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6292 -0.06429 0.08854 0.2144 0.9611 0.9806 0.707 0.8724 0.9537 0.6834 ] Network output: [ -0.006097 0.9477 1.026 0.0004744 -0.000213 0.04037 0.0003575 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05622 0.02587 0.05281 0.02881 0.9775 0.9838 0.05738 0.9479 0.9701 0.0697 ] Network output: [ 0.1195 -0.2786 1.159 -0.00252 0.001131 0.8706 -0.001899 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.702 0.341 0.5032 0.3655 0.9656 0.9834 0.7048 0.8838 0.9602 0.6793 ] Network output: [ -0.07421 0.1346 0.9453 0.001877 -0.0008427 1.076 0.001415 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6501 0.5764 0.4124 0.1496 0.9805 0.9869 0.6506 0.9548 0.9729 0.4357 ] Network output: [ -0.1097 0.3841 0.7155 -0.0003611 0.0001621 1.118 -0.0002721 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6689 0.6539 0.4373 0.001337 0.9784 0.9854 0.669 0.9497 0.9689 0.4424 ] Network output: [ 0.1083 0.6846 0.2082 0.0007014 -0.0003149 0.8934 0.0005286 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08719 Epoch 1413 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01045 1.011 0.9884 0.0003096 -0.000139 -0.01854 0.0002333 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03289 -0.005078 0.03237 0.01741 0.9228 0.9347 0.06088 0.8531 0.8845 0.1332 ] Network output: [ 0.9518 0.1171 -0.04159 -0.000499 0.000224 0.01886 -0.0003761 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6292 -0.06591 0.08923 0.214 0.9611 0.9806 0.7069 0.8724 0.9537 0.6837 ] Network output: [ -0.006065 0.9478 1.026 0.0004785 -0.0002148 0.04032 0.0003606 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05614 0.02569 0.05281 0.02873 0.9775 0.9839 0.0573 0.9479 0.9701 0.06964 ] Network output: [ 0.1199 -0.2785 1.159 -0.002525 0.001133 0.8694 -0.001903 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7017 0.3386 0.5043 0.3651 0.9656 0.9834 0.7046 0.8837 0.9602 0.6798 ] Network output: [ -0.07481 0.1332 0.9469 0.001877 -0.0008426 1.077 0.001415 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6503 0.5759 0.4133 0.1491 0.9805 0.9869 0.6508 0.9548 0.9729 0.4366 ] Network output: [ -0.1101 0.3848 0.715 -0.0003761 0.0001689 1.119 -0.0002835 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6691 0.6539 0.4379 -0.0002528 0.9784 0.9854 0.6692 0.9497 0.9689 0.443 ] Network output: [ 0.1089 0.6826 0.2103 0.000724 -0.000325 0.8922 0.0005456 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08775 Epoch 1414 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01032 1.011 0.988 0.000311 -0.0001396 -0.01828 0.0002344 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03288 -0.00511 0.03246 0.01733 0.9229 0.9348 0.06083 0.8531 0.8845 0.1331 ] Network output: [ 0.951 0.1187 -0.04215 -0.0005114 0.0002296 0.01943 -0.0003854 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6292 -0.06705 0.09068 0.2134 0.9611 0.9806 0.7068 0.8724 0.9537 0.684 ] Network output: [ -0.006003 0.9481 1.026 0.0004817 -0.0002163 0.04031 0.0003631 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05606 0.02554 0.05284 0.0286 0.9775 0.9839 0.05722 0.9479 0.9701 0.06959 ] Network output: [ 0.1201 -0.2769 1.158 -0.00254 0.00114 0.8686 -0.001914 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7016 0.3366 0.5055 0.364 0.9656 0.9834 0.7044 0.8837 0.9602 0.6801 ] Network output: [ -0.07499 0.1332 0.9465 0.00187 -0.0008396 1.078 0.001409 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.5755 0.4142 0.1478 0.9805 0.987 0.651 0.9548 0.9729 0.4373 ] Network output: [ -0.1102 0.387 0.7126 -0.0004048 0.0001817 1.119 -0.0003051 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6693 0.654 0.4386 -0.002812 0.9785 0.9854 0.6694 0.9497 0.9689 0.4436 ] Network output: [ 0.1096 0.6807 0.2122 0.0007418 -0.000333 0.891 0.000559 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0885 Epoch 1415 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01014 1.011 0.9881 0.0003161 -0.0001419 -0.01793 0.0002382 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03287 -0.005126 0.03261 0.01729 0.9229 0.9348 0.06078 0.8531 0.8845 0.133 ] Network output: [ 0.949 0.1189 -0.04012 -0.0005165 0.0002319 0.0212 -0.0003893 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6291 -0.06769 0.09266 0.2133 0.9611 0.9806 0.7066 0.8724 0.9537 0.6844 ] Network output: [ -0.005871 0.948 1.025 0.0004882 -0.0002192 0.04032 0.0003679 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05602 0.02545 0.05293 0.02852 0.9775 0.9839 0.05717 0.948 0.9701 0.0696 ] Network output: [ 0.1202 -0.2767 1.158 -0.002537 0.001139 0.8684 -0.001912 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7016 0.335 0.5069 0.3632 0.9656 0.9834 0.7044 0.8837 0.9602 0.6805 ] Network output: [ -0.075 0.1325 0.9464 0.001871 -0.0008401 1.079 0.00141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6509 0.5754 0.415 0.1467 0.9805 0.987 0.6514 0.9548 0.9729 0.438 ] Network output: [ -0.1102 0.3887 0.7104 -0.0004286 0.0001924 1.119 -0.000323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6696 0.6542 0.4393 -0.005233 0.9785 0.9854 0.6697 0.9498 0.9689 0.4443 ] Network output: [ 0.1104 0.6785 0.2141 0.0007626 -0.0003424 0.8897 0.0005747 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08925 Epoch 1416 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01024 1.011 0.9879 0.0003227 -0.0001449 -0.01786 0.0002432 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03287 -0.005172 0.03266 0.01725 0.9229 0.9348 0.06074 0.8531 0.8845 0.1329 ] Network output: [ 0.9497 0.119 -0.04124 -0.0005033 0.0002259 0.02087 -0.0003793 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6291 -0.06919 0.09344 0.2131 0.9611 0.9806 0.7065 0.8724 0.9537 0.6848 ] Network output: [ -0.005817 0.9479 1.025 0.0004935 -0.0002216 0.04027 0.0003719 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05595 0.02528 0.05295 0.02847 0.9776 0.9839 0.0571 0.948 0.9701 0.06957 ] Network output: [ 0.1207 -0.2776 1.159 -0.00253 0.001136 0.8674 -0.001907 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7014 0.3326 0.5079 0.3632 0.9656 0.9834 0.7042 0.8837 0.9602 0.6809 ] Network output: [ -0.07562 0.1304 0.9487 0.001877 -0.0008425 1.08 0.001414 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6511 0.575 0.416 0.1464 0.9805 0.987 0.6516 0.9548 0.9729 0.439 ] Network output: [ -0.1105 0.3887 0.7104 -0.0004389 0.000197 1.12 -0.0003308 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6699 0.6543 0.44 -0.006529 0.9785 0.9854 0.6699 0.9498 0.9689 0.4449 ] Network output: [ 0.1112 0.6763 0.2162 0.0007879 -0.0003537 0.8883 0.0005938 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08979 Epoch 1417 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01023 1.011 0.9871 0.0003234 -0.0001452 -0.01775 0.0002438 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03286 -0.005222 0.0327 0.01716 0.9229 0.9348 0.0607 0.8531 0.8845 0.1328 ] Network output: [ 0.9503 0.121 -0.04385 -0.0005091 0.0002286 0.02024 -0.0003837 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6291 -0.07085 0.09426 0.2124 0.9611 0.9806 0.7064 0.8723 0.9537 0.6851 ] Network output: [ -0.005808 0.9484 1.025 0.000495 -0.0002222 0.04023 0.0003731 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05585 0.02509 0.05293 0.02834 0.9776 0.9839 0.057 0.948 0.9701 0.06949 ] Network output: [ 0.121 -0.2761 1.158 -0.002547 0.001143 0.8661 -0.001919 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7011 0.3301 0.5091 0.3623 0.9656 0.9834 0.7039 0.8837 0.9602 0.6813 ] Network output: [ -0.07611 0.13 0.9492 0.001869 -0.0008392 1.081 0.001409 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6513 0.5745 0.4169 0.1455 0.9805 0.987 0.6518 0.9548 0.9729 0.4398 ] Network output: [ -0.1109 0.3902 0.709 -0.0004637 0.0002082 1.121 -0.0003495 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.67 0.6543 0.4407 -0.00861 0.9785 0.9854 0.6701 0.9498 0.9689 0.4456 ] Network output: [ 0.1117 0.6744 0.2183 0.0008075 -0.0003625 0.887 0.0006085 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09046 Epoch 1418 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009948 1.012 0.987 0.0003251 -0.0001459 -0.0173 0.000245 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03285 -0.005232 0.03287 0.01711 0.9229 0.9348 0.06064 0.8531 0.8845 0.1327 ] Network output: [ 0.9476 0.1219 -0.04167 -0.0005266 0.0002364 0.02246 -0.0003969 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.629 -0.07134 0.09659 0.212 0.9611 0.9806 0.7061 0.8723 0.9537 0.6855 ] Network output: [ -0.005677 0.9485 1.025 0.0004996 -0.0002243 0.04026 0.0003765 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0558 0.025 0.05303 0.02822 0.9776 0.9839 0.05695 0.948 0.9702 0.06949 ] Network output: [ 0.121 -0.2745 1.156 -0.002555 0.001147 0.8659 -0.001925 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7011 0.3286 0.5105 0.361 0.9656 0.9834 0.7039 0.8837 0.9602 0.6816 ] Network output: [ -0.07589 0.1303 0.9478 0.001864 -0.0008366 1.081 0.001404 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6517 0.5744 0.4178 0.1438 0.9805 0.987 0.6522 0.9548 0.9729 0.4405 ] Network output: [ -0.1108 0.393 0.7057 -0.0004985 0.0002238 1.121 -0.0003757 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6703 0.6545 0.4414 -0.01166 0.9785 0.9854 0.6704 0.9498 0.9689 0.4463 ] Network output: [ 0.1125 0.6724 0.2202 0.000825 -0.0003704 0.8857 0.0006217 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09134 Epoch 1419 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009935 1.011 0.9872 0.0003333 -0.0001496 -0.01709 0.0002511 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03285 -0.005258 0.03298 0.01709 0.9229 0.9348 0.0606 0.8531 0.8845 0.1327 ] Network output: [ 0.9467 0.1211 -0.04023 -0.0005125 0.0002301 0.02354 -0.0003863 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.629 -0.07225 0.09806 0.2121 0.9611 0.9806 0.706 0.8723 0.9537 0.686 ] Network output: [ -0.005555 0.9482 1.025 0.0005069 -0.0002276 0.04024 0.000382 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05576 0.02488 0.05312 0.0282 0.9776 0.9839 0.0569 0.948 0.9702 0.06951 ] Network output: [ 0.1213 -0.2761 1.157 -0.002535 0.001138 0.8656 -0.00191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7011 0.3268 0.5117 0.3609 0.9656 0.9834 0.7039 0.8837 0.9602 0.6822 ] Network output: [ -0.07622 0.1281 0.9495 0.001873 -0.0008409 1.082 0.001412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6521 0.5742 0.4188 0.1434 0.9805 0.987 0.6525 0.9548 0.9729 0.4414 ] Network output: [ -0.1109 0.3931 0.7052 -0.00051 0.000229 1.121 -0.0003844 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6706 0.6547 0.4421 -0.01317 0.9786 0.9855 0.6707 0.9499 0.9689 0.447 ] Network output: [ 0.1135 0.6701 0.2223 0.000851 -0.000382 0.8842 0.0006413 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09196 Epoch 1420 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01013 1.012 0.9864 0.000336 -0.0001509 -0.01717 0.0002532 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03284 -0.005327 0.03296 0.01702 0.923 0.9348 0.06056 0.8531 0.8845 0.1326 ] Network output: [ 0.9492 0.1226 -0.04453 -0.0005006 0.0002248 0.02162 -0.0003773 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.629 -0.07444 0.09809 0.2115 0.9611 0.9806 0.706 0.8723 0.9537 0.6863 ] Network output: [ -0.005592 0.9486 1.025 0.0005081 -0.0002281 0.04015 0.0003829 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05565 0.02465 0.05306 0.02812 0.9776 0.9839 0.05678 0.948 0.9702 0.06942 ] Network output: [ 0.122 -0.276 1.158 -0.002541 0.001141 0.8639 -0.001915 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7008 0.3238 0.5127 0.3608 0.9656 0.9834 0.7036 0.8836 0.9602 0.6826 ] Network output: [ -0.07715 0.1262 0.9522 0.001872 -0.0008405 1.084 0.001411 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6521 0.5735 0.4198 0.1433 0.9805 0.987 0.6526 0.9548 0.9729 0.4423 ] Network output: [ -0.1116 0.393 0.7057 -0.000522 0.0002343 1.122 -0.0003934 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6707 0.6546 0.4428 -0.01425 0.9786 0.9855 0.6708 0.9499 0.969 0.4476 ] Network output: [ 0.114 0.668 0.2246 0.0008751 -0.0003929 0.8828 0.0006595 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09245 Epoch 1421 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00986 1.013 0.9857 0.0003328 -0.0001494 -0.01678 0.0002508 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03283 -0.00535 0.03309 0.01692 0.923 0.9348 0.0605 0.8531 0.8845 0.1324 ] Network output: [ 0.9474 0.1251 -0.04477 -0.0005278 0.0002369 0.02283 -0.0003978 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6289 -0.07538 0.1 0.2108 0.9611 0.9806 0.7057 0.8723 0.9537 0.6866 ] Network output: [ -0.005531 0.9492 1.024 0.0005086 -0.0002283 0.04017 0.0003833 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05557 0.02451 0.0531 0.02793 0.9776 0.9839 0.0567 0.948 0.9702 0.06936 ] Network output: [ 0.1221 -0.2728 1.155 -0.002568 0.001153 0.8631 -0.001935 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7006 0.3219 0.5142 0.3591 0.9656 0.9834 0.7034 0.8836 0.9602 0.6829 ] Network output: [ -0.07704 0.1275 0.9502 0.001857 -0.0008336 1.084 0.001399 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6524 0.5732 0.4206 0.1413 0.9805 0.987 0.6529 0.9548 0.9729 0.443 ] Network output: [ -0.1116 0.3964 0.7021 -0.0005647 0.0002535 1.122 -0.0004256 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6709 0.6547 0.4436 -0.01753 0.9786 0.9855 0.671 0.9499 0.969 0.4484 ] Network output: [ 0.1146 0.6662 0.2267 0.0008898 -0.0003995 0.8815 0.0006706 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09338 Epoch 1422 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00957 1.012 0.9862 0.0003394 -0.0001524 -0.01626 0.0002558 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03282 -0.005343 0.0333 0.01691 0.923 0.9349 0.06044 0.8531 0.8845 0.1324 ] Network output: [ 0.9436 0.1241 -0.0397 -0.0005296 0.0002378 0.0263 -0.0003991 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6288 -0.07532 0.1028 0.211 0.9611 0.9806 0.7055 0.8723 0.9537 0.6872 ] Network output: [ -0.005316 0.9489 1.024 0.0005169 -0.0002321 0.04022 0.0003896 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05555 0.02448 0.05328 0.02789 0.9776 0.9839 0.05668 0.948 0.9702 0.06943 ] Network output: [ 0.122 -0.2734 1.155 -0.002547 0.001144 0.8636 -0.00192 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7008 0.3208 0.5156 0.3583 0.9656 0.9835 0.7035 0.8836 0.9602 0.6833 ] Network output: [ -0.07669 0.1266 0.9495 0.001863 -0.0008363 1.085 0.001404 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.653 0.5733 0.4216 0.14 0.9805 0.987 0.6535 0.9548 0.9729 0.4438 ] Network output: [ -0.1112 0.3981 0.6993 -0.0005911 0.0002654 1.123 -0.0004455 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6714 0.6551 0.4444 -0.02016 0.9786 0.9855 0.6715 0.95 0.969 0.4491 ] Network output: [ 0.1157 0.6638 0.2285 0.0009121 -0.0004095 0.88 0.0006874 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09425 Epoch 1423 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009892 1.012 0.9858 0.0003478 -0.0001561 -0.01641 0.0002621 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03282 -0.00541 0.03328 0.01689 0.923 0.9349 0.06041 0.8531 0.8845 0.1323 ] Network output: [ 0.9464 0.1236 -0.04281 -0.0004931 0.0002214 0.0245 -0.0003716 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6289 -0.07742 0.1027 0.2109 0.9612 0.9807 0.7056 0.8723 0.9537 0.6877 ] Network output: [ -0.00532 0.9488 1.024 0.0005212 -0.000234 0.0401 0.0003928 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05546 0.02426 0.05324 0.02792 0.9776 0.984 0.05658 0.948 0.9702 0.06939 ] Network output: [ 0.1228 -0.2762 1.158 -0.002524 0.001133 0.8623 -0.001902 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7005 0.3179 0.5165 0.3592 0.9657 0.9835 0.7033 0.8836 0.9602 0.684 ] Network output: [ -0.07782 0.1226 0.9543 0.001877 -0.0008428 1.086 0.001415 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6531 0.5727 0.4227 0.1409 0.9805 0.987 0.6536 0.9548 0.9729 0.445 ] Network output: [ -0.1119 0.3961 0.7016 -0.0005858 0.000263 1.124 -0.0004414 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6716 0.6551 0.445 -0.02019 0.9786 0.9855 0.6717 0.95 0.969 0.4498 ] Network output: [ 0.1165 0.6616 0.2309 0.0009423 -0.000423 0.8784 0.0007102 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09458 Epoch 1424 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009913 1.014 0.9843 0.0003414 -0.0001533 -0.01637 0.0002573 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0328 -0.005479 0.03326 0.01675 0.923 0.9349 0.06036 0.8531 0.8845 0.1321 ] Network output: [ 0.9483 0.1275 -0.04866 -0.0005107 0.0002293 0.02249 -0.0003849 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6289 -0.07971 0.103 0.2098 0.9612 0.9807 0.7054 0.8723 0.9537 0.6878 ] Network output: [ -0.005418 0.9498 1.023 0.0005165 -0.0002319 0.04005 0.0003893 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05532 0.024 0.05315 0.02771 0.9776 0.984 0.05644 0.948 0.9702 0.06924 ] Network output: [ 0.1233 -0.2726 1.155 -0.002564 0.001151 0.8601 -0.001932 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7 0.3148 0.5177 0.358 0.9657 0.9835 0.7028 0.8836 0.9602 0.6843 ] Network output: [ -0.07849 0.1232 0.9544 0.001858 -0.0008341 1.087 0.0014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6531 0.5718 0.4236 0.1397 0.9805 0.987 0.6535 0.9548 0.9729 0.4458 ] Network output: [ -0.1126 0.3982 0.7002 -0.0006195 0.0002781 1.124 -0.0004669 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6715 0.6549 0.4458 -0.0224 0.9787 0.9855 0.6716 0.95 0.969 0.4505 ] Network output: [ 0.1168 0.6599 0.2333 0.0009586 -0.0004303 0.8771 0.0007224 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09528 Epoch 1425 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009273 1.014 0.9845 0.00034 -0.0001526 -0.01553 0.0002562 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03278 -0.005447 0.03356 0.0167 0.923 0.9349 0.06027 0.8531 0.8845 0.132 ] Network output: [ 0.9419 0.1284 -0.04208 -0.0005496 0.0002467 0.02771 -0.0004142 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6286 -0.07899 0.107 0.2095 0.9612 0.9807 0.705 0.8723 0.9537 0.6883 ] Network output: [ -0.005171 0.95 1.022 0.0005214 -0.0002341 0.04018 0.000393 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0553 0.02401 0.05336 0.02755 0.9777 0.984 0.05642 0.9481 0.9702 0.0693 ] Network output: [ 0.1229 -0.2696 1.152 -0.002573 0.001155 0.8609 -0.001939 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7002 0.3142 0.5195 0.3556 0.9657 0.9835 0.703 0.8836 0.9602 0.6845 ] Network output: [ -0.0774 0.1254 0.9495 0.001845 -0.0008283 1.087 0.00139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6537 0.5722 0.4244 0.1367 0.9806 0.987 0.6542 0.9548 0.9729 0.4463 ] Network output: [ -0.1119 0.4031 0.6941 -0.0006763 0.0003036 1.124 -0.0005097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6721 0.6553 0.4466 -0.02691 0.9787 0.9855 0.6722 0.9501 0.969 0.4513 ] Network output: [ 0.1177 0.6578 0.2352 0.0009719 -0.0004363 0.8756 0.0007324 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09653 Epoch 1426 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00938 1.013 0.9853 0.0003552 -0.0001594 -0.01536 0.0002677 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03279 -0.005464 0.03369 0.01676 0.923 0.9349 0.06024 0.8531 0.8845 0.1321 ] Network output: [ 0.9409 0.1248 -0.03828 -0.0005025 0.0002256 0.0295 -0.0003787 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6287 -0.07956 0.1085 0.2104 0.9612 0.9807 0.705 0.8723 0.9537 0.6891 ] Network output: [ -0.004982 0.9491 1.023 0.0005327 -0.0002391 0.04013 0.0004014 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05528 0.02393 0.0535 0.02767 0.9777 0.984 0.0564 0.9481 0.9703 0.06939 ] Network output: [ 0.1234 -0.2749 1.157 -0.002508 0.001126 0.8613 -0.00189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7004 0.3127 0.5206 0.3568 0.9657 0.9835 0.7031 0.8836 0.9602 0.6853 ] Network output: [ -0.07782 0.1207 0.9536 0.001875 -0.0008419 1.089 0.001413 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6543 0.5722 0.4255 0.1375 0.9806 0.987 0.6547 0.9548 0.9729 0.4475 ] Network output: [ -0.1119 0.4009 0.6955 -0.000668 0.0002999 1.125 -0.0005035 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6725 0.6556 0.4473 -0.02721 0.9787 0.9856 0.6726 0.9501 0.9691 0.452 ] Network output: [ 0.119 0.6552 0.2372 0.001005 -0.0004511 0.8738 0.0007572 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09698 Epoch 1427 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009988 1.014 0.9833 0.0003538 -0.0001588 -0.01593 0.0002666 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03278 -0.005594 0.03347 0.01665 0.9231 0.9349 0.06022 0.8531 0.8845 0.132 ] Network output: [ 0.9486 0.1278 -0.05007 -0.0004717 0.0002118 0.0232 -0.0003555 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6289 -0.08358 0.1062 0.2094 0.9612 0.9807 0.7051 0.8722 0.9537 0.6892 ] Network output: [ -0.005249 0.9501 1.023 0.0005265 -0.0002364 0.03993 0.0003968 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05509 0.02353 0.05325 0.0276 0.9777 0.984 0.0562 0.9481 0.9702 0.06919 ] Network output: [ 0.1246 -0.2752 1.158 -0.002525 0.001133 0.8579 -0.001903 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6996 0.3081 0.5212 0.3577 0.9657 0.9835 0.7023 0.8836 0.9602 0.6858 ] Network output: [ -0.07987 0.1172 0.9599 0.001875 -0.0008416 1.09 0.001413 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6539 0.5706 0.4267 0.1389 0.9806 0.9871 0.6544 0.9548 0.9729 0.4486 ] Network output: [ -0.1135 0.3985 0.6995 -0.0006618 0.0002971 1.126 -0.0004988 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6722 0.6551 0.4479 -0.02647 0.9787 0.9856 0.6723 0.9501 0.9691 0.4526 ] Network output: [ 0.1193 0.6535 0.2399 0.001031 -0.000463 0.8722 0.0007772 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09712 Epoch 1428 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009308 1.016 0.982 0.0003374 -0.0001515 -0.01517 0.0002543 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03275 -0.005597 0.03366 0.01648 0.9231 0.9349 0.06011 0.8531 0.8845 0.1317 ] Network output: [ 0.9442 0.1335 -0.04995 -0.0005506 0.0002472 0.02582 -0.000415 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6285 -0.08406 0.1094 0.2078 0.9612 0.9807 0.7046 0.8722 0.9537 0.6893 ] Network output: [ -0.005201 0.9515 1.021 0.0005199 -0.0002334 0.04005 0.0003918 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05498 0.02341 0.05331 0.02723 0.9777 0.984 0.05609 0.9481 0.9703 0.06908 ] Network output: [ 0.1243 -0.2666 1.15 -0.0026 0.001167 0.8568 -0.001959 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6993 0.3064 0.523 0.3539 0.9657 0.9835 0.7021 0.8835 0.9602 0.6858 ] Network output: [ -0.07898 0.1227 0.9527 0.001829 -0.0008212 1.09 0.001379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6542 0.5704 0.4273 0.1347 0.9806 0.9871 0.6546 0.9548 0.9729 0.449 ] Network output: [ -0.1133 0.4057 0.692 -0.0007454 0.0003346 1.126 -0.0005617 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6724 0.6552 0.4489 -0.032 0.9787 0.9856 0.6725 0.9502 0.9691 0.4535 ] Network output: [ 0.1195 0.652 0.2422 0.001035 -0.0004645 0.8711 0.0007797 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09851 Epoch 1429 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008594 1.014 0.9841 0.0003514 -0.0001578 -0.01406 0.0002648 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03274 -0.005506 0.03412 0.01658 0.9231 0.9349 0.06003 0.8531 0.8845 0.1318 ] Network output: [ 0.9341 0.1287 -0.03424 -0.0005471 0.0002456 0.0352 -0.0004123 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6284 -0.0815 0.115 0.2092 0.9612 0.9807 0.7042 0.8723 0.9538 0.6903 ] Network output: [ -0.004678 0.9503 1.021 0.0005371 -0.0002411 0.04022 0.0004048 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05507 0.02361 0.05373 0.02728 0.9777 0.984 0.05617 0.9482 0.9703 0.06934 ] Network output: [ 0.1238 -0.2695 1.152 -0.002529 0.001135 0.86 -0.001906 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7002 0.3075 0.5249 0.353 0.9657 0.9835 0.7029 0.8835 0.9602 0.6864 ] Network output: [ -0.07729 0.1218 0.9493 0.00185 -0.0008306 1.091 0.001394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6554 0.5716 0.4282 0.1326 0.9806 0.9871 0.6558 0.9548 0.9729 0.4497 ] Network output: [ -0.1118 0.4083 0.6867 -0.0007804 0.0003503 1.125 -0.0005881 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6734 0.6561 0.4498 -0.03569 0.9788 0.9856 0.6735 0.9502 0.9691 0.4544 ] Network output: [ 0.1212 0.6491 0.2436 0.001058 -0.0004752 0.8692 0.0007977 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09974 Epoch 1430 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009679 1.014 0.9835 0.0003694 -0.0001658 -0.01498 0.0002784 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03276 -0.00564 0.03387 0.01662 0.9231 0.935 0.06005 0.8531 0.8845 0.1319 ] Network output: [ 0.9436 0.1255 -0.04317 -0.0004322 0.000194 0.02867 -0.0003257 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6288 -0.08543 0.1117 0.2098 0.9612 0.9807 0.7046 0.8723 0.9537 0.6909 ] Network output: [ -0.004857 0.9498 1.022 0.0005416 -0.0002431 0.03992 0.0004082 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05493 0.02324 0.05355 0.02755 0.9777 0.984 0.05604 0.9481 0.9703 0.06927 ] Network output: [ 0.1254 -0.2791 1.161 -0.002447 0.001099 0.8577 -0.001844 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6997 0.3031 0.525 0.3573 0.9657 0.9835 0.7024 0.8835 0.9602 0.6875 ] Network output: [ -0.0801 0.1115 0.9629 0.001903 -0.0008541 1.094 0.001434 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6552 0.5703 0.4297 0.1373 0.9806 0.9871 0.6557 0.9548 0.973 0.4514 ] Network output: [ -0.1134 0.3997 0.6965 -0.0007127 0.00032 1.128 -0.0005372 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6733 0.6558 0.4502 -0.03143 0.9788 0.9856 0.6734 0.9503 0.9692 0.4548 ] Network output: [ 0.1222 0.6469 0.2461 0.001102 -0.0004949 0.8671 0.0008308 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09923 Epoch 1431 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009898 1.018 0.9794 0.0003413 -0.0001532 -0.01536 0.0002572 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03272 -0.005795 0.0336 0.01631 0.9231 0.935 0.05998 0.853 0.8845 0.1314 ] Network output: [ 0.9515 0.1363 -0.06183 -0.0004911 0.0002205 0.02056 -0.0003701 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6286 -0.09044 0.1094 0.2068 0.9612 0.9807 0.7044 0.8722 0.9537 0.6904 ] Network output: [ -0.005368 0.9528 1.02 0.0005177 -0.0002324 0.03979 0.0003901 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05462 0.0227 0.05315 0.02711 0.9777 0.984 0.05572 0.9481 0.9703 0.06885 ] Network output: [ 0.1264 -0.269 1.154 -0.00258 0.001158 0.8521 -0.001944 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6983 0.2973 0.526 0.3549 0.9657 0.9835 0.701 0.8835 0.9602 0.6875 ] Network output: [ -0.08178 0.1149 0.9626 0.001844 -0.0008276 1.094 0.001389 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6543 0.5681 0.4305 0.1358 0.9806 0.9871 0.6548 0.9547 0.9729 0.4521 ] Network output: [ -0.1154 0.403 0.6958 -0.0007656 0.0003437 1.129 -0.000577 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6726 0.6549 0.4509 -0.03363 0.9788 0.9856 0.6727 0.9503 0.9692 0.4555 ] Network output: [ 0.1215 0.6462 0.2493 0.001108 -0.0004973 0.866 0.0008348 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09989 Epoch 1432 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.007984 1.018 0.9808 0.0003302 -0.0001483 -0.0131 0.0002489 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03267 -0.005617 0.03434 0.01626 0.9231 0.935 0.05981 0.8531 0.8845 0.1313 ] Network output: [ 0.932 0.1378 -0.04028 -0.0006202 0.0002784 0.03603 -0.0004674 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6279 -0.08559 0.1194 0.2066 0.9612 0.9807 0.7034 0.8722 0.9537 0.691 ] Network output: [ -0.004698 0.953 1.018 0.0005257 -0.000236 0.04026 0.0003962 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05472 0.02306 0.05372 0.02673 0.9777 0.984 0.05582 0.9482 0.9703 0.06906 ] Network output: [ 0.1247 -0.2592 1.143 -0.002618 0.001175 0.8559 -0.001973 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6992 0.3002 0.5289 0.3483 0.9657 0.9835 0.7019 0.8835 0.9602 0.6873 ] Network output: [ -0.07756 0.1251 0.9449 0.001794 -0.0008053 1.092 0.001352 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6558 0.5701 0.4309 0.1275 0.9806 0.9871 0.6563 0.9548 0.9729 0.4519 ] Network output: [ -0.1127 0.4163 0.679 -0.0009064 0.0004069 1.126 -0.0006831 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6737 0.6561 0.4522 -0.04391 0.9788 0.9856 0.6738 0.9503 0.9691 0.4567 ] Network output: [ 0.1226 0.6437 0.2508 0.001105 -0.000496 0.8648 0.0008326 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1024 Epoch 1433 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008366 1.013 0.9844 0.0003763 -0.0001689 -0.01291 0.0002836 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03271 -0.00557 0.03461 0.0166 0.9232 0.935 0.05982 0.8531 0.8846 0.1317 ] Network output: [ 0.9285 0.1234 -0.02459 -0.0004583 0.0002057 0.04223 -0.0003454 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6284 -0.08401 0.1217 0.2104 0.9613 0.9807 0.7037 0.8723 0.9538 0.6928 ] Network output: [ -0.004142 0.9497 1.021 0.0005578 -0.0002504 0.04017 0.0004204 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05486 0.02323 0.0541 0.02733 0.9778 0.9841 0.05595 0.9483 0.9704 0.06945 ] Network output: [ 0.1252 -0.278 1.158 -0.002387 0.001072 0.86 -0.001799 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7003 0.3009 0.5296 0.3538 0.9658 0.9835 0.703 0.8835 0.9602 0.6887 ] Network output: [ -0.0778 0.1119 0.9557 0.001905 -0.0008551 1.096 0.001435 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6571 0.5711 0.4322 0.1318 0.9807 0.9871 0.6576 0.9548 0.973 0.4535 ] Network output: [ -0.1118 0.4073 0.6853 -0.0008278 0.0003716 1.128 -0.0006239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6748 0.657 0.4526 -0.0407 0.9789 0.9857 0.6748 0.9504 0.9692 0.4572 ] Network output: [ 0.1253 0.6402 0.252 0.001158 -0.00052 0.8619 0.0008729 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1024 Epoch 1434 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01069 1.017 0.9789 0.0003665 -0.0001646 -0.01553 0.0002762 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03272 -0.005945 0.03362 0.01636 0.9232 0.935 0.05988 0.853 0.8845 0.1314 ] Network output: [ 0.957 0.131 -0.06491 -0.0003431 0.000154 0.01853 -0.0002586 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.629 -0.09523 0.11 0.2081 0.9613 0.9807 0.7045 0.8721 0.9537 0.6923 ] Network output: [ -0.005293 0.9522 1.021 0.0005294 -0.0002377 0.03951 0.000399 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05438 0.02217 0.05317 0.02738 0.9778 0.9841 0.05546 0.9481 0.9703 0.06883 ] Network output: [ 0.1286 -0.282 1.165 -0.00243 0.001091 0.8501 -0.001831 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6978 0.2898 0.5287 0.3594 0.9658 0.9835 0.7005 0.8835 0.9602 0.6897 ] Network output: [ -0.08449 0.1004 0.9781 0.001922 -0.000863 1.098 0.001449 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6551 0.5665 0.4339 0.1398 0.9806 0.9871 0.6555 0.9548 0.973 0.4554 ] Network output: [ -0.1168 0.3946 0.7039 -0.0007261 0.000326 1.132 -0.0005472 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6732 0.6549 0.4527 -0.03222 0.9789 0.9857 0.6733 0.9504 0.9693 0.4572 ] Network output: [ 0.1245 0.64 0.2558 0.001196 -0.000537 0.8601 0.0009014 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1009 Epoch 1435 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008669 1.023 0.9747 0.0002997 -0.0001346 -0.0137 0.0002259 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03261 -0.005919 0.03397 0.01585 0.9232 0.935 0.05964 0.8531 0.8845 0.1306 ] Network output: [ 0.946 0.1507 -0.06859 -0.0006343 0.0002848 0.02329 -0.000478 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6278 -0.09536 0.1165 0.203 0.9613 0.9807 0.703 0.8721 0.9537 0.6912 ] Network output: [ -0.005419 0.957 1.016 0.000495 -0.0002222 0.03994 0.0003731 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05413 0.02199 0.05316 0.02627 0.9777 0.984 0.05521 0.9481 0.9703 0.06845 ] Network output: [ 0.1274 -0.2513 1.139 -0.002735 0.001228 0.8465 -0.002061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6969 0.2877 0.5317 0.3474 0.9657 0.9835 0.6996 0.8834 0.9601 0.6884 ] Network output: [ -0.08137 0.1223 0.9527 0.001748 -0.0007848 1.095 0.001317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6549 0.566 0.4338 0.1277 0.9806 0.9871 0.6553 0.9547 0.9729 0.4547 ] Network output: [ -0.1163 0.4162 0.6828 -0.0009607 0.0004313 1.13 -0.000724 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.673 0.6547 0.4543 -0.04596 0.9789 0.9857 0.6731 0.9504 0.9692 0.4587 ] Network output: [ 0.1231 0.6395 0.2589 0.001159 -0.0005201 0.8601 0.0008731 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1039 Epoch 1436 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.005946 1.016 0.9831 0.0003442 -0.0001545 -0.009893 0.0002594 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03259 -0.005463 0.03548 0.01632 0.9232 0.935 0.05947 0.8532 0.8846 0.1312 ] Network output: [ 0.9073 0.1319 -0.007616 -0.0006429 0.0002886 0.0584 -0.0004845 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6272 -0.08185 0.1346 0.2084 0.9613 0.9807 0.7019 0.8723 0.9538 0.6938 ] Network output: [ -0.0035 0.9523 1.016 0.0005523 -0.0002479 0.04068 0.0004162 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05469 0.0232 0.05459 0.02651 0.9778 0.9841 0.05577 0.9484 0.9705 0.06942 ] Network output: [ 0.1243 -0.2594 1.14 -0.002498 0.001121 0.8609 -0.001883 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7004 0.2989 0.5351 0.3435 0.9658 0.9835 0.7031 0.8834 0.9602 0.689 ] Network output: [ -0.07344 0.1256 0.9333 0.001804 -0.00081 1.095 0.00136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6588 0.5719 0.4341 0.1196 0.9807 0.9871 0.6592 0.9549 0.973 0.4546 ] Network output: [ -0.1097 0.4262 0.663 -0.001063 0.0004771 1.126 -0.000801 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.676 0.658 0.4555 -0.05648 0.9789 0.9857 0.6761 0.9504 0.9692 0.4599 ] Network output: [ 0.1272 0.634 0.2587 0.001185 -0.0005321 0.8577 0.0008932 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1073 Epoch 1437 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00991 1.012 0.9834 0.0004149 -0.0001863 -0.01362 0.0003127 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03271 -0.005807 0.03447 0.01674 0.9233 0.9351 0.0597 0.8531 0.8846 0.1318 ] Network output: [ 0.9403 0.1138 -0.03191 -0.0001949 8.749e-05 0.03675 -0.0001469 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.629 -0.09141 0.1202 0.213 0.9613 0.9807 0.7041 0.8722 0.9538 0.6953 ] Network output: [ -0.004142 0.9488 1.022 0.0005719 -0.0002568 0.03966 0.000431 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0545 0.02243 0.05398 0.02788 0.9779 0.9841 0.05558 0.9483 0.9705 0.06937 ] Network output: [ 0.1288 -0.3008 1.177 -0.002127 0.0009549 0.8571 -0.001603 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6996 0.2902 0.5324 0.3625 0.9658 0.9835 0.7023 0.8835 0.9602 0.6919 ] Network output: [ -0.0823 0.08766 0.9823 0.002048 -0.0009194 1.103 0.001543 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6579 0.5685 0.4366 0.1402 0.9807 0.9872 0.6584 0.9549 0.9731 0.458 ] Network output: [ -0.1141 0.3907 0.7017 -0.0007217 0.000324 1.133 -0.0005439 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6753 0.6568 0.4545 -0.03517 0.979 0.9858 0.6754 0.9505 0.9693 0.459 ] Network output: [ 0.1295 0.6322 0.2605 0.001282 -0.0005755 0.8534 0.0009661 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1032 Epoch 1438 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01178 1.026 0.9686 0.0003046 -0.0001367 -0.01672 0.0002295 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03262 -0.006419 0.03288 0.01571 0.9233 0.9351 0.05963 0.8529 0.8844 0.1303 ] Network output: [ 0.9812 0.1533 -0.1136 -0.0003813 0.0001712 -0.003776 -0.0002874 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6287 -0.1105 0.1035 0.2021 0.9613 0.9807 0.704 0.8719 0.9536 0.6919 ] Network output: [ -0.006663 0.9592 1.017 0.0004691 -0.0002106 0.03899 0.0003535 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05342 0.02049 0.05212 0.02664 0.9777 0.984 0.05449 0.9479 0.9702 0.06773 ] Network output: [ 0.1327 -0.2675 1.157 -0.002646 0.001188 0.8344 -0.001994 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6938 0.2705 0.532 0.3583 0.9657 0.9835 0.6965 0.8833 0.9601 0.6908 ] Network output: [ -0.09042 0.09746 0.9901 0.001847 -0.0008291 1.101 0.001392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6529 0.5596 0.4375 0.141 0.9806 0.9871 0.6534 0.9546 0.9729 0.4588 ] Network output: [ -0.1226 0.3932 0.7117 -0.0007852 0.0003525 1.137 -0.0005917 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6715 0.6522 0.455 -0.03335 0.9789 0.9857 0.6716 0.9504 0.9693 0.4594 ] Network output: [ 0.1244 0.6357 0.267 0.001257 -0.0005643 0.8537 0.0009472 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1034 Epoch 1439 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.004402 1.028 0.9732 0.0002382 -0.0001069 -0.008567 0.0001795 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03241 -0.005688 0.0354 0.01545 0.9232 0.9351 0.05907 0.8532 0.8846 0.1297 ] Network output: [ 0.9086 0.1646 -0.03717 -0.0009795 0.0004398 0.0513 -0.0007382 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6256 -0.09012 0.1379 0.1999 0.9613 0.9807 0.7 0.8721 0.9537 0.6923 ] Network output: [ -0.004338 0.9611 1.009 0.0004814 -0.0002161 0.04091 0.0003628 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05392 0.02213 0.05399 0.02495 0.9778 0.9841 0.05499 0.9482 0.9704 0.06842 ] Network output: [ 0.1257 -0.2187 1.107 -0.002948 0.001324 0.8485 -0.002222 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6973 0.2867 0.5396 0.33 0.9658 0.9835 0.7 0.8833 0.9601 0.6881 ] Network output: [ -0.07325 0.1474 0.9126 0.00157 -0.000705 1.093 0.001184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6574 0.5677 0.4356 0.1076 0.9806 0.9871 0.6578 0.9547 0.9729 0.4554 ] Network output: [ -0.1125 0.4465 0.6468 -0.001331 0.0005975 1.126 -0.001003 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6748 0.6563 0.4583 -0.07048 0.9789 0.9857 0.6749 0.9504 0.9691 0.4624 ] Network output: [ 0.1256 0.6305 0.2685 0.001189 -0.0005336 0.8548 0.0008957 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.112 Epoch 1440 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.004662 1.008 0.9915 0.0004298 -0.0001929 -0.006758 0.0003239 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03259 -0.005165 0.03682 0.01709 0.9233 0.9351 0.05922 0.8533 0.8847 0.1318 ] Network output: [ 0.878 0.09914 0.05273 -0.0003562 0.0001599 0.09065 -0.0002684 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6273 -0.07311 0.1507 0.2178 0.9614 0.9808 0.7013 0.8725 0.9539 0.6984 ] Network output: [ -0.001362 0.9462 1.018 0.0006257 -0.0002809 0.04085 0.0004715 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05498 0.02389 0.05583 0.0275 0.978 0.9842 0.05606 0.9487 0.9708 0.07025 ] Network output: [ 0.1248 -0.2937 1.162 -0.00195 0.0008752 0.874 -0.001469 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7036 0.3025 0.5392 0.3501 0.966 0.9836 0.7063 0.8834 0.9602 0.6918 ] Network output: [ -0.06856 0.1046 0.9392 0.002031 -0.0009119 1.102 0.001531 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6632 0.5759 0.4368 0.1215 0.9808 0.9872 0.6637 0.955 0.9731 0.4572 ] Network output: [ -0.1046 0.4168 0.6619 -0.001015 0.0004555 1.126 -0.0007647 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6792 0.6611 0.4572 -0.05826 0.9791 0.9858 0.6793 0.9506 0.9693 0.4616 ] Network output: [ 0.1352 0.6226 0.2645 0.001315 -0.0005903 0.8479 0.0009909 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1119 Epoch 1441 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01538 1.017 0.9729 0.0004167 -0.0001871 -0.01919 0.000314 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03273 -0.006633 0.03239 0.01659 0.9234 0.9352 0.05975 0.8528 0.8844 0.1312 ] Network output: [ 1.003 0.117 -0.1102 0.0002633 -0.0001182 -0.01115 0.0001985 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6307 -0.1163 0.09522 0.2124 0.9614 0.9808 0.7059 0.8718 0.9537 0.6956 ] Network output: [ -0.00639 0.9528 1.024 0.0005193 -0.0002331 0.03796 0.0003914 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05334 0.02002 0.05198 0.02863 0.9778 0.9841 0.05439 0.9479 0.9702 0.06802 ] Network output: [ 0.1377 -0.3296 1.209 -0.001949 0.0008748 0.8373 -0.001468 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6944 0.2632 0.5307 0.3827 0.9658 0.9835 0.697 0.8832 0.9602 0.6951 ] Network output: [ -0.09752 0.04461 1.047 0.002249 -0.00101 1.112 0.001695 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6541 0.5579 0.4412 0.1654 0.9807 0.9872 0.6546 0.9546 0.9731 0.4634 ] Network output: [ -0.1243 0.3458 0.7562 -0.0003287 0.0001476 1.145 -0.0002478 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6723 0.6522 0.4537 -0.008602 0.979 0.9858 0.6724 0.9505 0.9695 0.4583 ] Network output: [ 0.1311 0.6273 0.2724 0.00144 -0.0006466 0.8439 0.001085 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1038 Epoch 1442 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009004 1.046 0.9514 9.818e-05 -4.408e-05 -0.01481 7.399e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0323 -0.006802 0.03258 0.01431 0.9233 0.9351 0.05899 0.8528 0.8843 0.1276 ] Network output: [ 0.9854 0.21 -0.1663 -0.000994 0.0004462 -0.01853 -0.0007491 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6259 -0.1245 0.1069 0.1882 0.9612 0.9807 0.7007 0.8715 0.9535 0.6879 ] Network output: [ -0.008331 0.9749 1.004 0.0003312 -0.0001487 0.03926 0.0002496 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05209 0.01865 0.05081 0.02418 0.9776 0.984 0.05312 0.9475 0.97 0.06575 ] Network output: [ 0.1371 -0.1984 1.1 -0.003448 0.001548 0.81 -0.002599 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6879 0.2487 0.5375 0.3378 0.9656 0.9834 0.6905 0.8829 0.96 0.6877 ] Network output: [ -0.0895 0.1334 0.9575 0.001478 -0.0006637 1.094 0.001114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6491 0.5508 0.4379 0.1244 0.9805 0.987 0.6495 0.9542 0.9727 0.4578 ] Network output: [ -0.1279 0.422 0.6911 -0.001155 0.0005187 1.138 -0.0008707 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6683 0.648 0.4576 -0.05098 0.9788 0.9856 0.6684 0.9502 0.9692 0.4617 ] Network output: [ 0.12 0.634 0.2811 0.001224 -0.0005497 0.8499 0.0009227 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1113 Epoch 1443 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.004901 1.021 0.9862 0.0002439 -0.0001095 0.003566 0.0001838 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03216 -0.004534 0.03916 0.01603 0.9232 0.935 0.05821 0.8535 0.8848 0.1296 ] Network output: [ 0.8007 0.1421 0.1073 -0.001281 0.0005749 0.1442 -0.0009651 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6219 -0.05685 0.1881 0.208 0.9613 0.9807 0.6946 0.8724 0.9539 0.6961 ] Network output: [ 0.0005523 0.9581 1 0.0005558 -0.0002495 0.04301 0.0004189 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05469 0.02475 0.05665 0.02401 0.9779 0.9843 0.05575 0.9488 0.9709 0.06951 ] Network output: [ 0.1186 -0.1974 1.072 -0.002736 0.001228 0.8773 -0.002062 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7037 0.3111 0.5488 0.3059 0.9659 0.9836 0.7063 0.8829 0.96 0.6858 ] Network output: [ -0.04796 0.1826 0.8315 0.001492 -0.0006697 1.088 0.001124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6655 0.5805 0.4333 0.07446 0.9808 0.9872 0.666 0.9548 0.9728 0.4515 ] Network output: [ -0.09503 0.4865 0.5817 -0.001772 0.0007955 1.115 -0.001335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6806 0.6632 0.4605 -0.1064 0.9791 0.9857 0.6807 0.9503 0.9689 0.4644 ] Network output: [ 0.1348 0.6112 0.2768 0.001284 -0.0005764 0.8476 0.0009676 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1349 Epoch 1444 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009501 0.9923 1.001 0.000606 -0.0002721 -0.009901 0.0004567 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03275 -0.005335 0.03633 0.01889 0.9235 0.9353 0.05936 0.8529 0.8846 0.1329 ] Network output: [ 0.9001 0.03103 0.08194 0.0006932 -0.0003112 0.08965 0.0005224 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6301 -0.07699 0.1413 0.2384 0.9615 0.9808 0.7039 0.8721 0.9539 0.7027 ] Network output: [ 3.02e-06 0.9348 1.029 0.0007157 -0.0003213 0.03904 0.0005394 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05504 0.0236 0.05556 0.03117 0.9781 0.9844 0.05611 0.9486 0.9708 0.07047 ] Network output: [ 0.1353 -0.4023 1.247 -0.0006769 0.0003039 0.8818 -0.0005102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7057 0.2953 0.5339 0.3929 0.9661 0.9837 0.7083 0.883 0.9602 0.6963 ] Network output: [ -0.07538 0.0151 1.029 0.002803 -0.001259 1.118 0.002113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6652 0.5745 0.4384 0.1642 0.9809 0.9873 0.6657 0.9549 0.9732 0.4605 ] Network output: [ -0.105 0.3315 0.7393 -0.0001479 6.64e-05 1.138 -0.0001115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6801 0.6609 0.4518 -0.01136 0.9792 0.9859 0.6801 0.9506 0.9694 0.4565 ] Network output: [ 0.1462 0.6113 0.2706 0.00159 -0.0007139 0.8321 0.001198 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1129 Epoch 1445 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02407 1.049 0.9348 0.0001705 -7.655e-05 -0.03084 0.0001285 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03252 -0.008514 0.02686 0.01494 0.9236 0.9353 0.05963 0.8518 0.8837 0.1274 ] Network output: [ 1.143 0.186 -0.3357 0.0004724 -0.0002121 -0.1337 0.000356 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6311 -0.1734 0.03369 0.1961 0.9614 0.9807 0.7073 0.8704 0.9531 0.6865 ] Network output: [ -0.01316 0.9782 1.015 0.0002339 -0.000105 0.03418 0.0001763 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04999 0.01413 0.04551 0.02728 0.9775 0.9838 0.05099 0.9462 0.9692 0.06259 ] Network output: [ 0.1631 -0.2965 1.195 -0.002702 0.001213 0.7646 -0.002036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6762 0.1968 0.5199 0.4005 0.9654 0.9833 0.6788 0.8819 0.9597 0.6908 ] Network output: [ -0.124 0.01627 1.131 0.002157 -0.0009685 1.11 0.001626 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6373 0.5256 0.441 0.2013 0.9802 0.987 0.6377 0.9534 0.9726 0.4639 ] Network output: [ -0.1511 0.2769 0.8593 0.0002981 -0.0001338 1.167 0.0002246 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.659 0.6355 0.4477 0.04228 0.9786 0.9857 0.6591 0.9497 0.9693 0.4523 ] Network output: [ 0.1214 0.6278 0.3001 0.001592 -0.0007146 0.8358 0.0012 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1237 Epoch 1446 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.008754 1.074 0.9392 -0.0003179 0.0001427 0.003424 -0.0002395 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03141 -0.00576 0.03623 0.01288 0.9231 0.935 0.05709 0.8525 0.8841 0.1221 ] Network output: [ 0.8495 0.2875 -0.06894 -0.002772 0.001244 0.0712 -0.002089 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.615 -0.09861 0.1708 0.1752 0.961 0.9806 0.6878 0.8706 0.9532 0.678 ] Network output: [ -0.005254 0.9981 0.9696 0.0001572 -7.057e-05 0.04338 0.0001185 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05152 0.0199 0.05142 0.01911 0.9774 0.9839 0.05254 0.9472 0.9699 0.0635 ] Network output: [ 0.1354 -0.03837 0.9391 -0.004787 0.002149 0.809 -0.003607 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6876 0.2595 0.547 0.2697 0.9655 0.9834 0.6902 0.8816 0.9595 0.6724 ] Network output: [ -0.0513 0.2537 0.7829 0.0007569 -0.0003398 1.069 0.0005705 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6509 0.5561 0.4242 0.05698 0.9803 0.9869 0.6514 0.9535 0.9721 0.4404 ] Network output: [ -0.1112 0.5148 0.5787 -0.00209 0.0009382 1.12 -0.001575 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.668 0.6488 0.4548 -0.1123 0.9786 0.9854 0.6681 0.9493 0.9683 0.4583 ] Network output: [ 0.1179 0.619 0.3009 0.001177 -0.0005283 0.8491 0.0008869 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1497 Epoch 1447 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01558 0.986 1.027 0.0004824 -0.0002166 0.02017 0.0003636 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.032 -0.002249 0.04432 0.02008 0.9232 0.9351 0.0573 0.8534 0.8849 0.1307 ] Network output: [ 0.6336 -0.004948 0.4223 -0.0002451 0.00011 0.3143 -0.0001847 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6188 0.01354 0.252 0.2554 0.9613 0.9808 0.6893 0.8723 0.9541 0.7034 ] Network output: [ 0.0119 0.9363 1 0.0008151 -0.0003659 0.04321 0.0006143 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05639 0.03046 0.05906 0.02802 0.9782 0.9845 0.05745 0.949 0.9714 0.07003 ] Network output: [ 0.1096 -0.3241 1.15 -0.0004852 0.0002178 0.9525 -0.0003656 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.716 0.3673 0.545 0.3345 0.9661 0.9837 0.7186 0.8807 0.9595 0.6767 ] Network output: [ -0.002429 0.1286 0.8001 0.002566 -0.001152 1.087 0.001934 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6776 0.6034 0.415 0.0858 0.9809 0.9872 0.678 0.9542 0.9724 0.4324 ] Network output: [ -0.05596 0.4381 0.571 -0.001119 0.0005023 1.098 -0.0008432 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6877 0.6722 0.443 -0.08798 0.9791 0.9857 0.6878 0.9491 0.9681 0.447 ] Network output: [ 0.1613 0.586 0.2744 0.001571 -0.0007054 0.8234 0.001184 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1882 Epoch 1448 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02936 1.008 0.9679 0.0004935 -0.0002216 -0.03279 0.0003719 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03265 -0.008002 0.02732 0.01964 0.9237 0.9354 0.05966 0.8501 0.8828 0.1287 ] Network output: [ 1.128 0.02209 -0.1804 0.00235 -0.001055 -0.08805 0.001771 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6338 -0.1557 0.03421 0.2498 0.9614 0.9808 0.7096 0.8686 0.9527 0.6895 ] Network output: [ -0.007017 0.9511 1.034 0.0004117 -0.0001848 0.03081 0.0003103 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05121 0.01605 0.04605 0.03439 0.9776 0.984 0.05223 0.9457 0.9693 0.06361 ] Network output: [ 0.1863 -0.4983 1.327 -7.898e-05 3.546e-05 0.7981 -5.952e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6866 0.2129 0.5005 0.4726 0.9656 0.9834 0.6892 0.8803 0.9594 0.6927 ] Network output: [ -0.1218 -0.1328 1.256 0.003626 -0.001628 1.135 0.002733 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6446 0.5342 0.4344 0.2718 0.9804 0.9871 0.645 0.953 0.9727 0.4608 ] Network output: [ -0.1422 0.09138 1.015 0.002288 -0.001027 1.188 0.001724 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6629 0.6391 0.43 0.1431 0.9788 0.9858 0.663 0.949 0.9693 0.4353 ] Network output: [ 0.1436 0.579 0.3263 0.002345 -0.001053 0.817 0.001767 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1448 Epoch 1449 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009864 1.124 0.8759 -0.0007841 0.000352 -0.02322 -0.0005909 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03119 -0.009253 0.02534 0.01288 0.9233 0.9351 0.0574 0.8497 0.8822 0.114 ] Network output: [ 1.123 0.3343 -0.4302 -0.001599 0.0007179 -0.1575 -0.001205 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6183 -0.2024 0.04259 0.1801 0.9608 0.9805 0.6939 0.8668 0.9518 0.6519 ] Network output: [ -0.01726 1.041 0.9581 -0.0004101 0.0001841 0.03388 -0.0003091 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04615 0.009519 0.03894 0.02246 0.9766 0.9833 0.0471 0.9433 0.9677 0.05306 ] Network output: [ 0.2142 -0.1069 0.994 -0.004909 0.002204 0.6647 -0.003699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6566 0.1265 0.5027 0.3711 0.9648 0.9831 0.6592 0.8781 0.9585 0.6559 ] Network output: [ -0.09833 0.1176 1.025 0.001474 -0.0006618 1.06 0.001111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6137 0.4857 0.407 0.1895 0.9793 0.9864 0.6141 0.9505 0.9711 0.4268 ] Network output: [ -0.1683 0.2807 0.8847 0.0002979 -0.0001338 1.172 0.0002245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6374 0.6107 0.4275 0.06724 0.9776 0.985 0.6375 0.9467 0.9676 0.4315 ] Network output: [ 0.09757 0.6452 0.3343 0.001341 -0.0006018 0.8308 0.00101 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1424 Epoch 1450 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.03774 1.08 0.9525 -0.0006179 0.0002774 0.04085 -0.0004657 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03016 -0.002005 0.04219 0.01539 0.9227 0.9347 0.05404 0.8517 0.8836 0.1162 ] Network output: [ 0.5524 0.2307 0.3154 -0.003072 0.001379 0.3365 -0.002315 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5969 0.0132 0.2719 0.212 0.9605 0.9804 0.6653 0.869 0.9529 0.6691 ] Network output: [ 0.007312 1.011 0.9247 9.577e-05 -4.3e-05 0.05021 7.218e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05221 0.02809 0.05107 0.01808 0.9773 0.9839 0.05321 0.9465 0.97 0.05941 ] Network output: [ 0.09294 -0.01117 0.8854 -0.003465 0.001556 0.9258 -0.002612 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.696 0.3627 0.5372 0.2414 0.9654 0.9833 0.6986 0.8774 0.9583 0.6384 ] Network output: [ 0.03796 0.3274 0.5588 0.001167 -0.0005241 1.043 0.0008798 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6628 0.5928 0.3757 0.01087 0.9801 0.9866 0.6632 0.9517 0.9707 0.3882 ] Network output: [ -0.03684 0.5617 0.4308 -0.002183 0.0009801 1.072 -0.001645 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6717 0.6576 0.413 -0.1413 0.9781 0.985 0.6718 0.9461 0.966 0.4159 ] Network output: [ 0.1467 0.5848 0.3018 0.001357 -0.0006094 0.8255 0.001023 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2543 Epoch 1451 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.007497 0.9807 1.025 0.000306 -0.0001374 0.01084 0.0002306 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03145 -0.003162 0.03907 0.02279 0.9233 0.9351 0.05647 0.8497 0.8827 0.1238 ] Network output: [ 0.719 -0.08006 0.3872 0.001124 -0.0005047 0.2594 0.0008473 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.617 -0.01349 0.2001 0.2912 0.961 0.9806 0.6878 0.8675 0.9527 0.6847 ] Network output: [ 0.01219 0.9356 1.005 0.0005868 -0.0002634 0.03716 0.0004422 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05411 0.02726 0.05114 0.03359 0.9778 0.9842 0.05515 0.9459 0.97 0.0625 ] Network output: [ 0.1497 -0.5004 1.274 0.001313 -0.0005894 0.9326 0.0009893 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7093 0.3335 0.5058 0.4326 0.9658 0.9836 0.7119 0.8764 0.9583 0.6597 ] Network output: [ -0.008384 -0.04357 0.9775 0.004152 -0.001864 1.1 0.003129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6653 0.5837 0.3837 0.1975 0.9805 0.987 0.6658 0.9517 0.9715 0.4039 ] Network output: [ -0.06751 0.1861 0.8214 0.001494 -0.0006707 1.134 0.001126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6742 0.6568 0.4032 0.07493 0.9784 0.9854 0.6743 0.9461 0.967 0.4079 ] Network output: [ 0.1653 0.57 0.3071 0.001901 -0.0008533 0.7999 0.001432 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1585 Epoch 1452 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0301 1.085 0.8938 -0.00046 0.0002065 -0.04096 -0.0003467 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03143 -0.009885 0.01875 0.0172 0.9236 0.9354 0.0579 0.8463 0.8803 0.1148 ] Network output: [ 1.27 0.155 -0.4651 0.001978 -0.0008879 -0.2212 0.00149 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6258 -0.2172 -0.04664 0.2319 0.9609 0.9805 0.7024 0.863 0.9507 0.6502 ] Network output: [ -0.01734 1.022 0.9856 -0.0004868 0.0002185 0.02527 -0.0003669 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04523 0.008647 0.03321 0.02848 0.9766 0.9831 0.04615 0.9405 0.9665 0.05037 ] Network output: [ 0.2625 -0.3367 1.145 -0.001692 0.0007597 0.6595 -0.001275 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6534 0.1208 0.4434 0.4685 0.9647 0.983 0.656 0.8742 0.9576 0.6558 ] Network output: [ -0.1273 -0.03918 1.225 0.002471 -0.001109 1.079 0.001862 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6055 0.4765 0.3953 0.2847 0.9792 0.9864 0.6059 0.9484 0.9704 0.4241 ] Network output: [ -0.1931 0.04905 1.138 0.002248 -0.001009 1.208 0.001694 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6302 0.6027 0.4087 0.2095 0.9774 0.985 0.6303 0.9446 0.9671 0.4145 ] Network output: [ 0.1038 0.5765 0.4048 0.002073 -0.0009308 0.8195 0.001562 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.181 Epoch 1453 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.029 1.145 0.8855 -0.001442 0.0006472 0.02131 -0.001086 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02957 -0.004743 0.0325 0.0152 0.9228 0.9347 0.0536 0.8473 0.8808 0.1046 ] Network output: [ 0.758 0.3075 0.01255 -0.002864 0.001286 0.1523 -0.002158 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5956 -0.07303 0.1694 0.2171 0.9601 0.9801 0.666 0.8629 0.9508 0.6294 ] Network output: [ -0.006134 1.062 0.9022 -0.0006855 0.0003077 0.04508 -0.0005166 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04805 0.01988 0.03994 0.02124 0.9764 0.9832 0.049 0.9421 0.9676 0.04921 ] Network output: [ 0.1494 -0.071 0.9263 -0.003438 0.001543 0.8318 -0.002591 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6741 0.2676 0.4897 0.3372 0.9649 0.9831 0.6767 0.8733 0.9571 0.6136 ] Network output: [ 0.004979 0.2333 0.7194 0.001797 -0.0008067 1.045 0.001354 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6355 0.5462 0.3433 0.122 0.9792 0.9862 0.636 0.9486 0.9695 0.3582 ] Network output: [ -0.08352 0.396 0.6536 -0.0005376 0.0002414 1.115 -0.0004052 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6467 0.6285 0.3788 0.008846 0.9772 0.9845 0.6468 0.943 0.9648 0.3823 ] Network output: [ 0.1199 0.6397 0.3102 0.0008477 -0.0003806 0.8138 0.0006388 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1423 Epoch 1454 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.02771 1.062 0.9637 -0.0007616 0.0003419 0.02676 -0.0005739 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03007 -0.002475 0.03637 0.02022 0.9231 0.935 0.05392 0.8478 0.8814 0.1126 ] Network output: [ 0.6531 0.07487 0.3212 -0.000452 0.0002029 0.296 -0.0003407 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6015 0.00256 0.1987 0.2726 0.9605 0.9804 0.6705 0.8642 0.9515 0.6563 ] Network output: [ 0.004513 1.003 0.9446 -0.0002218 9.956e-05 0.04237 -0.0001671 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05096 0.02683 0.04421 0.0276 0.9771 0.9837 0.05194 0.9435 0.9687 0.05384 ] Network output: [ 0.1148 -0.315 1.134 -0.0001822 8.181e-05 0.9504 -0.0001373 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6944 0.3531 0.486 0.3953 0.9654 0.9833 0.697 0.873 0.9572 0.6272 ] Network output: [ 0.01903 0.1141 0.7916 0.002948 -0.001324 1.068 0.002222 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6529 0.581 0.343 0.1564 0.9799 0.9866 0.6534 0.9495 0.9701 0.3604 ] Network output: [ -0.05765 0.3005 0.7043 0.00022 -9.878e-05 1.111 0.0001658 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6611 0.6462 0.3757 0.04628 0.9776 0.9848 0.6612 0.9432 0.965 0.3799 ] Network output: [ 0.1501 0.6066 0.2986 0.001059 -0.0004755 0.799 0.0007982 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1461 Epoch 1455 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.007939 1.076 0.922 -0.0007686 0.0003451 -0.01666 -0.0005792 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03067 -0.006638 0.02425 0.01929 0.9235 0.9353 0.05583 0.8452 0.8798 0.1116 ] Network output: [ 1.041 0.09331 -0.1518 0.001627 -0.0007306 -0.01624 0.001226 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6157 -0.1242 0.03015 0.2599 0.9606 0.9804 0.6891 0.8614 0.9505 0.645 ] Network output: [ -0.01052 1.017 0.9705 -0.0006205 0.0002786 0.03081 -0.0004676 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04737 0.01665 0.03618 0.02925 0.9767 0.9833 0.04832 0.9408 0.967 0.05059 ] Network output: [ 0.1965 -0.3715 1.174 -0.0003135 0.0001407 0.8036 -0.0002362 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6714 0.2351 0.4409 0.4603 0.965 0.9832 0.674 0.8729 0.9572 0.6434 ] Network output: [ -0.07466 0.01736 1.058 0.002568 -0.001153 1.084 0.001935 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.625 0.5276 0.3628 0.2465 0.9795 0.9865 0.6255 0.9485 0.9701 0.3892 ] Network output: [ -0.1462 0.128 0.9943 0.00132 -0.0005927 1.175 0.000995 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6418 0.6215 0.3878 0.1701 0.9775 0.9849 0.6419 0.9434 0.966 0.3937 ] Network output: [ 0.1204 0.5939 0.3603 0.001315 -0.0005904 0.8103 0.0009911 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1034 Epoch 1456 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.009407 1.128 0.8878 -0.001379 0.0006192 -0.002433 -0.00104 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03008 -0.005674 0.02583 0.0168 0.9233 0.9351 0.05459 0.8455 0.8798 0.1061 ] Network output: [ 0.9387 0.2182 -0.1417 -0.0002303 0.0001034 0.04528 -0.0001735 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6067 -0.09854 0.06727 0.2353 0.9604 0.9803 0.6786 0.8611 0.9503 0.6318 ] Network output: [ -0.01274 1.056 0.93 -0.0009635 0.0004325 0.03577 -0.0007261 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04688 0.01817 0.03547 0.02451 0.9765 0.9831 0.04781 0.9406 0.9667 0.04774 ] Network output: [ 0.1604 -0.2066 1.052 -0.001817 0.0008159 0.8267 -0.00137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6678 0.261 0.4487 0.4059 0.9649 0.9831 0.6704 0.8724 0.9569 0.626 ] Network output: [ -0.04172 0.159 0.8712 0.001684 -0.000756 1.06 0.001269 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6253 0.5364 0.3426 0.1857 0.9793 0.9863 0.6257 0.948 0.9695 0.3647 ] Network output: [ -0.1243 0.2894 0.8156 -0.0001938 8.702e-05 1.143 -0.0001461 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6398 0.6216 0.3779 0.09354 0.9771 0.9845 0.6399 0.9425 0.965 0.383 ] Network output: [ 0.1133 0.643 0.3223 0.0005304 -0.0002381 0.8102 0.0003997 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09915 Epoch 1457 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.0206 1.097 0.9267 -0.001212 0.0005439 0.01224 -0.0009131 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02994 -0.003527 0.03044 0.01811 0.9233 0.9351 0.05387 0.8463 0.8804 0.109 ] Network output: [ 0.7822 0.156 0.09064 -0.000253 0.0001136 0.1879 -0.0001907 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6029 -0.03124 0.1257 0.2502 0.9605 0.9803 0.6728 0.8622 0.9508 0.6444 ] Network output: [ -0.005607 1.032 0.9375 -0.0007492 0.0003363 0.03838 -0.0005646 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04885 0.02375 0.03935 0.02513 0.9768 0.9834 0.04979 0.942 0.9676 0.05051 ] Network output: [ 0.1161 -0.2374 1.086 -0.001058 0.0004752 0.9149 -0.0007977 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6811 0.332 0.4604 0.3915 0.9652 0.9832 0.6837 0.8725 0.957 0.6284 ] Network output: [ -0.01194 0.1791 0.7894 0.001733 -0.000778 1.062 0.001306 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6399 0.5665 0.3382 0.1552 0.9797 0.9864 0.6404 0.9488 0.9697 0.359 ] Network output: [ -0.08964 0.3518 0.7073 -0.0008553 0.000384 1.117 -0.0006446 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6508 0.6357 0.3759 0.04632 0.9774 0.9846 0.6509 0.9426 0.9648 0.381 ] Network output: [ 0.1311 0.6371 0.297 0.0003921 -0.000176 0.8054 0.0002955 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1154 Epoch 1458 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.003675 1.072 0.9374 -0.0008945 0.0004016 -0.005494 -0.0006741 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03036 -0.004766 0.02644 0.01899 0.9235 0.9353 0.05484 0.8456 0.8801 0.1115 ] Network output: [ 0.925 0.09439 -0.02609 0.001368 -0.0006139 0.08729 0.001031 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6106 -0.06776 0.0614 0.2578 0.9607 0.9804 0.682 0.8617 0.9507 0.6496 ] Network output: [ -0.008646 1.015 0.9669 -0.0006834 0.0003068 0.03296 -0.0005151 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04827 0.02117 0.03752 0.0276 0.9769 0.9834 0.04921 0.9415 0.9673 0.05103 ] Network output: [ 0.1456 -0.334 1.163 -0.0001591 7.142e-05 0.8796 -0.0001199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.677 0.3018 0.4405 0.4332 0.9652 0.9832 0.6796 0.8728 0.9572 0.6422 ] Network output: [ -0.05046 0.09947 0.9308 0.001935 -0.0008687 1.079 0.001458 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6336 0.5534 0.3495 0.2015 0.9797 0.9865 0.6341 0.9488 0.9699 0.3754 ] Network output: [ -0.1228 0.2532 0.8465 -0.0001951 8.758e-05 1.145 -0.000147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6474 0.6308 0.3836 0.105 0.9775 0.9848 0.6474 0.9431 0.9654 0.3897 ] Network output: [ 0.1267 0.6243 0.3165 0.0005603 -0.0002515 0.8081 0.0004222 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09278 Epoch 1459 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.0006423 1.092 0.9161 -0.001061 0.0004761 -0.01132 -0.0007993 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03032 -0.005425 0.02381 0.01751 0.9235 0.9353 0.05492 0.8454 0.8798 0.1099 ] Network output: [ 0.9889 0.1485 -0.1498 0.001285 -0.0005769 0.02881 0.0009685 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6108 -0.08922 0.02972 0.2413 0.9606 0.9804 0.6827 0.8613 0.9504 0.6445 ] Network output: [ -0.01329 1.031 0.9604 -0.000867 0.0003892 0.03191 -0.0006534 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04715 0.01925 0.03509 0.02563 0.9767 0.9833 0.04808 0.9408 0.9667 0.04922 ] Network output: [ 0.1511 -0.2751 1.12 -0.0007809 0.0003506 0.8497 -0.0005885 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6683 0.2835 0.4313 0.4219 0.9651 0.9832 0.6709 0.8727 0.9571 0.6408 ] Network output: [ -0.06167 0.1389 0.9182 0.001405 -0.0006306 1.072 0.001059 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6267 0.5438 0.3482 0.1937 0.9796 0.9864 0.6272 0.9483 0.9697 0.375 ] Network output: [ -0.1362 0.2905 0.8337 -0.0007023 0.0003153 1.145 -0.0005293 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6418 0.6248 0.3848 0.09453 0.9774 0.9847 0.6418 0.9429 0.9652 0.3911 ] Network output: [ 0.1162 0.6401 0.3165 0.0003131 -0.0001406 0.8122 0.0002359 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09507 Epoch 1460 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01103 1.087 0.931 -0.001096 0.0004923 -0.0004281 -0.0008264 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03009 -0.004181 0.02702 0.01747 0.9234 0.9352 0.05425 0.8459 0.8801 0.1102 ] Network output: [ 0.8798 0.1519 -0.02375 0.0006246 -0.0002804 0.1147 0.0004707 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.606 -0.05131 0.07342 0.2412 0.9606 0.9804 0.6766 0.8618 0.9506 0.6482 ] Network output: [ -0.009937 1.025 0.9568 -0.0008012 0.0003597 0.0345 -0.0006038 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04814 0.0222 0.0375 0.02473 0.9769 0.9834 0.04908 0.9415 0.9672 0.05044 ] Network output: [ 0.1226 -0.2416 1.1 -0.001003 0.0004501 0.8925 -0.0007556 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6745 0.3196 0.4436 0.397 0.9652 0.9832 0.677 0.8728 0.9571 0.6391 ] Network output: [ -0.03881 0.1854 0.8294 0.001168 -0.0005246 1.068 0.0008806 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.635 0.56 0.3445 0.1598 0.9797 0.9864 0.6355 0.9487 0.9697 0.3693 ] Network output: [ -0.1132 0.3691 0.7268 -0.001457 0.0006539 1.124 -0.001098 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6476 0.6323 0.384 0.04465 0.9775 0.9847 0.6477 0.9429 0.965 0.39 ] Network output: [ 0.1237 0.6439 0.297 0.0001502 -6.742e-05 0.8123 0.0001132 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1053 Epoch 1461 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.005488 1.062 0.9505 -0.0008206 0.0003684 -0.005063 -0.0006185 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03026 -0.004273 0.02653 0.01837 0.9235 0.9353 0.05457 0.8459 0.8801 0.1128 ] Network output: [ 0.9069 0.09961 -0.01056 0.001429 -0.0006417 0.1029 0.001077 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6086 -0.05294 0.05975 0.25 0.9607 0.9804 0.6794 0.862 0.9507 0.6556 ] Network output: [ -0.009121 1.007 0.976 -0.0006435 0.0002889 0.03276 -0.000485 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04847 0.02229 0.03798 0.02638 0.977 0.9835 0.04941 0.9417 0.9673 0.05185 ] Network output: [ 0.1286 -0.3049 1.152 -0.0003061 0.0001374 0.8948 -0.0002306 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6761 0.3196 0.4383 0.4154 0.9653 0.9833 0.6787 0.8731 0.9572 0.6478 ] Network output: [ -0.05042 0.1433 0.8853 0.001378 -0.0006186 1.078 0.001038 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6363 0.5608 0.3509 0.1763 0.9798 0.9865 0.6367 0.949 0.9699 0.378 ] Network output: [ -0.1207 0.3323 0.7707 -0.001218 0.0005466 1.133 -0.0009176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6494 0.6339 0.3889 0.06146 0.9776 0.9848 0.6495 0.9433 0.9653 0.3954 ] Network output: [ 0.1273 0.6345 0.2989 0.0002657 -0.0001193 0.8132 0.0002002 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09896 Epoch 1462 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.001248 1.065 0.942 -0.0007812 0.0003507 -0.01304 -0.0005887 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03035 -0.005028 0.02391 0.01774 0.9236 0.9354 0.0549 0.8456 0.8799 0.1129 ] Network output: [ 0.9844 0.1128 -0.1139 0.001858 -0.0008341 0.03991 0.0014 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6105 -0.07642 0.02374 0.2421 0.9608 0.9805 0.6821 0.8618 0.9506 0.6554 ] Network output: [ -0.01243 1.01 0.9814 -0.0006892 0.0003094 0.03101 -0.0005194 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04767 0.02039 0.03613 0.02596 0.9769 0.9834 0.0486 0.9412 0.9669 0.05112 ] Network output: [ 0.1408 -0.3001 1.151 -0.0003702 0.0001662 0.8663 -0.000279 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6696 0.2993 0.4281 0.4202 0.9652 0.9832 0.6721 0.8731 0.9572 0.6515 ] Network output: [ -0.06786 0.1395 0.9213 0.001193 -0.0005358 1.08 0.0008994 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6309 0.5516 0.3546 0.1848 0.9797 0.9865 0.6313 0.9488 0.9698 0.3839 ] Network output: [ -0.1371 0.3247 0.8023 -0.00127 0.0005704 1.142 -0.0009575 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6455 0.6293 0.3925 0.07065 0.9776 0.9848 0.6456 0.9434 0.9654 0.3994 ] Network output: [ 0.1208 0.638 0.3046 0.0002297 -0.0001031 0.8166 0.0001731 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09707 Epoch 1463 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.004306 1.068 0.945 -0.0008435 0.0003787 -0.007972 -0.0006357 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03019 -0.004542 0.02529 0.01725 0.9235 0.9353 0.05452 0.8458 0.88 0.1127 ] Network output: [ 0.9368 0.1328 -0.07354 0.001353 -0.0006073 0.07266 0.001019 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6074 -0.06215 0.0428 0.2368 0.9607 0.9804 0.6783 0.862 0.9506 0.6563 ] Network output: [ -0.01173 1.011 0.9774 -0.000688 0.0003089 0.03252 -0.0005185 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04796 0.02141 0.03712 0.02483 0.9769 0.9834 0.04889 0.9414 0.967 0.05153 ] Network output: [ 0.1274 -0.2602 1.123 -0.0007718 0.0003465 0.8789 -0.0005816 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6706 0.3117 0.435 0.4006 0.9652 0.9832 0.6731 0.8732 0.9572 0.6505 ] Network output: [ -0.05746 0.1782 0.8649 0.0009088 -0.000408 1.076 0.0006849 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.634 0.5576 0.3537 0.1611 0.9798 0.9865 0.6345 0.9489 0.9697 0.3817 ] Network output: [ -0.1274 0.3829 0.734 -0.001847 0.0008294 1.13 -0.001392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6476 0.632 0.3938 0.03578 0.9776 0.9848 0.6477 0.9435 0.9653 0.4005 ] Network output: [ 0.1223 0.6418 0.2953 0.0001245 -5.59e-05 0.8189 9.384e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1041 Epoch 1464 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.004523 1.051 0.9616 -0.0006759 0.0003034 -0.006798 -0.0005094 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03022 -0.004238 0.02626 0.01784 0.9236 0.9353 0.0545 0.846 0.8802 0.1145 ] Network output: [ 0.9154 0.1022 -0.02053 0.001571 -0.0007051 0.09388 0.001184 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6072 -0.05213 0.05128 0.2427 0.9608 0.9805 0.6779 0.8623 0.9508 0.6624 ] Network output: [ -0.009984 0.9976 0.9875 -0.0005532 0.0002484 0.03261 -0.0004169 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0485 0.02232 0.03846 0.02564 0.9771 0.9835 0.04944 0.9418 0.9673 0.05301 ] Network output: [ 0.1237 -0.2885 1.148 -0.0004415 0.0001982 0.8916 -0.0003327 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6737 0.3207 0.4374 0.4038 0.9653 0.9833 0.6762 0.8734 0.9573 0.6555 ] Network output: [ -0.05695 0.165 0.8721 0.001002 -0.00045 1.081 0.0007554 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6377 0.5626 0.3577 0.1604 0.9799 0.9866 0.6381 0.9492 0.9699 0.3864 ] Network output: [ -0.1244 0.384 0.727 -0.001892 0.0008495 1.13 -0.001426 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6508 0.6355 0.3975 0.02954 0.9778 0.9849 0.6509 0.9438 0.9654 0.4044 ] Network output: [ 0.1273 0.635 0.2914 0.0002036 -9.14e-05 0.8198 0.0001534 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1056 Epoch 1465 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.001426 1.046 0.9615 -0.0005572 0.0002502 -0.0129 -0.0004199 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03032 -0.004771 0.02452 0.01775 0.9236 0.9354 0.05481 0.8459 0.8801 0.1155 ] Network output: [ 0.9721 0.09452 -0.08078 0.002068 -0.0009285 0.05038 0.001559 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.609 -0.06836 0.02525 0.2409 0.9608 0.9805 0.6802 0.8623 0.9507 0.6648 ] Network output: [ -0.01171 0.9937 0.9963 -0.0005201 0.0002335 0.03127 -0.000392 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0481 0.02106 0.03751 0.02602 0.9771 0.9835 0.04904 0.9415 0.9671 0.05307 ] Network output: [ 0.134 -0.3081 1.166 -0.0002438 0.0001095 0.8735 -0.0001838 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.67 0.3063 0.4303 0.4138 0.9653 0.9833 0.6725 0.8736 0.9573 0.6607 ] Network output: [ -0.07151 0.1445 0.9164 0.001021 -0.0004582 1.086 0.0007691 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6348 0.5567 0.3628 0.1726 0.9799 0.9866 0.6352 0.9491 0.9699 0.3935 ] Network output: [ -0.1365 0.3636 0.7633 -0.001774 0.0007963 1.139 -0.001337 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6491 0.6331 0.4015 0.04184 0.9778 0.985 0.6491 0.944 0.9656 0.4087 ] Network output: [ 0.125 0.6339 0.2952 0.0002539 -0.000114 0.8218 0.0001914 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1022 Epoch 1466 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -4.046e-06 1.051 0.9588 -0.0005978 0.0002684 -0.01204 -0.0004505 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03024 -0.004769 0.02457 0.01716 0.9236 0.9353 0.05467 0.846 0.8801 0.1153 ] Network output: [ 0.9683 0.1157 -0.09333 0.001821 -0.0008175 0.04834 0.001372 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6074 -0.06892 0.02601 0.2341 0.9608 0.9805 0.6785 0.8623 0.9507 0.6652 ] Network output: [ -0.01246 0.9966 0.9942 -0.00054 0.0002424 0.0319 -0.000407 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04795 0.0209 0.03755 0.02509 0.977 0.9835 0.04888 0.9415 0.967 0.05308 ] Network output: [ 0.1305 -0.2777 1.145 -0.0005861 0.0002631 0.8692 -0.0004417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.668 0.3047 0.4325 0.4019 0.9653 0.9833 0.6705 0.8736 0.9573 0.6611 ] Network output: [ -0.07063 0.1682 0.8924 0.0007847 -0.0003523 1.084 0.0005914 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6347 0.5565 0.3642 0.1597 0.9799 0.9866 0.6351 0.9491 0.9698 0.3947 ] Network output: [ -0.1359 0.3977 0.7307 -0.002134 0.0009579 1.135 -0.001608 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6489 0.6329 0.4042 0.0221 0.9778 0.985 0.649 0.9441 0.9656 0.4114 ] Network output: [ 0.1236 0.6361 0.293 0.0002095 -9.406e-05 0.8245 0.0001579 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1067 Epoch 1467 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.002821 1.042 0.9701 -0.0005228 0.0002347 -0.008515 -0.000394 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03018 -0.004378 0.02603 0.01742 0.9236 0.9353 0.05449 0.8462 0.8802 0.1165 ] Network output: [ 0.9304 0.1041 -0.03653 0.001689 -0.0007581 0.0784 0.001273 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6058 -0.05674 0.04266 0.2365 0.9608 0.9805 0.6764 0.8626 0.9508 0.6695 ] Network output: [ -0.01081 0.989 0.9979 -0.0004504 0.0002022 0.03288 -0.0003394 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04846 0.0219 0.03908 0.02523 0.9771 0.9835 0.0494 0.9419 0.9672 0.05436 ] Network output: [ 0.1235 -0.2799 1.148 -0.000557 0.0002501 0.8822 -0.0004198 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6707 0.3146 0.4384 0.3957 0.9653 0.9833 0.6732 0.8738 0.9574 0.6638 ] Network output: [ -0.0651 0.1742 0.8735 0.0007605 -0.0003414 1.086 0.0005731 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6386 0.5621 0.3672 0.1492 0.9799 0.9866 0.639 0.9493 0.9699 0.3973 ] Network output: [ -0.1293 0.4197 0.699 -0.002349 0.001054 1.13 -0.00177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6521 0.6364 0.4076 0.003899 0.9779 0.985 0.6522 0.9443 0.9656 0.4148 ] Network output: [ 0.128 0.6307 0.2885 0.0002671 -0.0001199 0.8258 0.0002013 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1116 Epoch 1468 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.001086 1.033 0.9752 -0.000383 0.000172 -0.01188 -0.0002887 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03027 -0.004643 0.02532 0.01767 0.9236 0.9354 0.0547 0.8462 0.8802 0.1178 ] Network output: [ 0.9596 0.08633 -0.05496 0.00209 -0.0009383 0.05794 0.001575 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6069 -0.06448 0.02954 0.2387 0.9609 0.9805 0.6779 0.8627 0.9509 0.6733 ] Network output: [ -0.01118 0.982 1.007 -0.0003749 0.0001683 0.03218 -0.0002826 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0484 0.02134 0.03903 0.02599 0.9772 0.9836 0.04934 0.9419 0.9672 0.05502 ] Network output: [ 0.1298 -0.3085 1.173 -0.0002625 0.0001178 0.8747 -0.0001978 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6696 0.3071 0.4353 0.4058 0.9653 0.9833 0.6721 0.874 0.9574 0.669 ] Network output: [ -0.07446 0.1499 0.9108 0.0008821 -0.000396 1.092 0.0006648 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6381 0.5597 0.3724 0.1597 0.98 0.9867 0.6386 0.9494 0.97 0.4041 ] Network output: [ -0.1357 0.4003 0.7256 -0.002193 0.0009844 1.137 -0.001652 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6522 0.6361 0.4115 0.01279 0.978 0.9851 0.6523 0.9446 0.9658 0.419 ] Network output: [ 0.129 0.6272 0.2896 0.0003591 -0.0001612 0.8266 0.0002706 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.109 Epoch 1469 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.002682 1.037 0.9701 -0.0003877 0.000174 -0.01382 -0.0002922 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03025 -0.004936 0.0245 0.01716 0.9236 0.9354 0.05473 0.8461 0.8801 0.1179 ] Network output: [ 0.9843 0.1025 -0.09739 0.002081 -0.000934 0.0348 0.001568 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6066 -0.07391 0.01817 0.2325 0.9609 0.9805 0.6777 0.8627 0.9508 0.6739 ] Network output: [ -0.01265 0.9845 1.007 -0.0003934 0.0001766 0.03217 -0.0002964 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04801 0.02048 0.03848 0.02544 0.9771 0.9835 0.04894 0.9417 0.967 0.05486 ] Network output: [ 0.1325 -0.2932 1.165 -0.0004648 0.0002087 0.8615 -0.0003503 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.666 0.2974 0.4342 0.4021 0.9653 0.9833 0.6685 0.874 0.9574 0.6708 ] Network output: [ -0.0802 0.1566 0.915 0.0007457 -0.0003348 1.092 0.000562 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6362 0.5558 0.3755 0.157 0.9799 0.9866 0.6366 0.9493 0.9699 0.4077 ] Network output: [ -0.1407 0.4114 0.7225 -0.002333 0.001047 1.138 -0.001758 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6508 0.6343 0.4148 0.006524 0.978 0.9851 0.6509 0.9447 0.9659 0.4223 ] Network output: [ 0.1267 0.6283 0.2911 0.0003667 -0.0001646 0.8287 0.0002763 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1108 Epoch 1470 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.0008446 1.035 0.9752 -0.0003843 0.0001725 -0.009895 -0.0002896 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03015 -0.004625 0.02586 0.01706 0.9235 0.9353 0.0545 0.8463 0.8802 0.1184 ] Network output: [ 0.9482 0.1072 -0.05665 0.001759 -0.0007897 0.0602 0.001326 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6044 -0.06464 0.03472 0.2313 0.9609 0.9805 0.6751 0.8629 0.9509 0.6764 ] Network output: [ -0.01161 0.9822 1.006 -0.0003525 0.0001583 0.0335 -0.0002657 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04832 0.02118 0.03977 0.02502 0.9771 0.9836 0.04926 0.9419 0.9672 0.05577 ] Network output: [ 0.1257 -0.276 1.152 -0.0006691 0.0003004 0.8695 -0.0005043 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6675 0.3039 0.4412 0.3904 0.9653 0.9833 0.67 0.8741 0.9574 0.672 ] Network output: [ -0.07374 0.1748 0.8841 0.0006206 -0.0002786 1.091 0.0004677 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6391 0.56 0.378 0.1417 0.98 0.9867 0.6396 0.9495 0.9699 0.4094 ] Network output: [ -0.1343 0.4434 0.6821 -0.002636 0.001183 1.132 -0.001987 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6531 0.6369 0.4184 -0.0166 0.9781 0.9851 0.6532 0.9448 0.9659 0.4257 ] Network output: [ 0.1293 0.6241 0.2884 0.0004087 -0.0001835 0.8304 0.000308 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1168 Epoch 1471 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0003725 1.025 0.9837 -0.0002569 0.0001153 -0.01015 -0.0001936 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03019 -0.00461 0.02623 0.01751 0.9236 0.9353 0.05457 0.8464 0.8802 0.1198 ] Network output: [ 0.947 0.08625 -0.0359 0.001968 -0.0008834 0.06371 0.001483 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6046 -0.06369 0.03587 0.2358 0.9609 0.9805 0.6753 0.8631 0.9509 0.6808 ] Network output: [ -0.0108 0.974 1.013 -0.000257 0.0001154 0.03356 -0.0001937 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04858 0.02133 0.0406 0.02585 0.9772 0.9836 0.04952 0.9421 0.9673 0.05689 ] Network output: [ 0.1274 -0.3025 1.174 -0.0003927 0.0001763 0.872 -0.0002959 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6686 0.3037 0.4424 0.3969 0.9654 0.9833 0.6711 0.8743 0.9575 0.6765 ] Network output: [ -0.07667 0.1556 0.9042 0.0007673 -0.0003445 1.097 0.0005782 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6409 0.5612 0.3828 0.1464 0.9801 0.9867 0.6414 0.9497 0.9701 0.415 ] Network output: [ -0.1347 0.4334 0.6906 -0.002535 0.001138 1.135 -0.00191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6549 0.6385 0.4221 -0.01548 0.9782 0.9852 0.6549 0.9451 0.9661 0.4297 ] Network output: [ 0.1329 0.6187 0.2873 0.0005219 -0.0002343 0.8303 0.0003933 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1166 Epoch 1472 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.004019 1.026 0.9791 -0.000215 9.653e-05 -0.01376 -0.000162 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03022 -0.005049 0.02497 0.01724 0.9236 0.9354 0.05473 0.8463 0.8802 0.1203 ] Network output: [ 0.9871 0.09232 -0.0884 0.002169 -0.0009736 0.03076 0.001634 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6053 -0.07729 0.01793 0.2323 0.9609 0.9805 0.6763 0.8631 0.9509 0.6822 ] Network output: [ -0.01237 0.9746 1.016 -0.0002552 0.0001146 0.03309 -0.0001923 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04812 0.02016 0.03981 0.02588 0.9772 0.9836 0.04906 0.9419 0.9671 0.05679 ] Network output: [ 0.1337 -0.3067 1.182 -0.0003976 0.0001785 0.8561 -0.0002996 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6648 0.2902 0.4394 0.4017 0.9653 0.9833 0.6673 0.8744 0.9575 0.6796 ] Network output: [ -0.08656 0.1439 0.9327 0.0007721 -0.0003466 1.1 0.0005819 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6384 0.5556 0.3871 0.1535 0.98 0.9867 0.6388 0.9496 0.9701 0.4204 ] Network output: [ -0.1424 0.4234 0.711 -0.002457 0.001103 1.14 -0.001852 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6532 0.6361 0.4254 -0.009847 0.9782 0.9852 0.6532 0.9452 0.9662 0.4331 ] Network output: [ 0.1312 0.6189 0.2901 0.0005756 -0.0002584 0.831 0.0004338 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1153 Epoch 1473 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00144 1.03 0.9773 -0.0002616 0.0001175 -0.01112 -0.0001972 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03011 -0.004962 0.02567 0.01679 0.9235 0.9353 0.05452 0.8464 0.8802 0.1202 ] Network output: [ 0.9689 0.1111 -0.08137 0.001802 -0.0008088 0.03973 0.001358 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6032 -0.07525 0.02687 0.2272 0.9609 0.9805 0.674 0.8631 0.9509 0.6829 ] Network output: [ -0.01241 0.9769 1.013 -0.0002629 0.000118 0.03429 -0.0001981 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04809 0.0202 0.04043 0.02504 0.9772 0.9836 0.04903 0.9419 0.9671 0.05713 ] Network output: [ 0.1296 -0.2773 1.16 -0.0007656 0.0003437 0.8548 -0.000577 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.664 0.2896 0.4451 0.3886 0.9653 0.9833 0.6665 0.8744 0.9575 0.6799 ] Network output: [ -0.08281 0.1666 0.9042 0.0005789 -0.0002599 1.097 0.0004363 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6392 0.5563 0.3896 0.1386 0.98 0.9867 0.6397 0.9496 0.97 0.4221 ] Network output: [ -0.1394 0.4547 0.6767 -0.00276 0.001239 1.136 -0.00208 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6538 0.6368 0.429 -0.03102 0.9782 0.9852 0.6539 0.9453 0.9661 0.4365 ] Network output: [ 0.1313 0.6165 0.2904 0.0006036 -0.000271 0.8329 0.0004549 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1207 Epoch 1474 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.0005341 1.021 0.987 -0.0001786 8.02e-05 -0.007986 -0.0001346 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03009 -0.004674 0.02712 0.01723 0.9235 0.9353 0.05441 0.8465 0.8803 0.1214 ] Network output: [ 0.9366 0.09478 -0.02714 0.001725 -0.0007743 0.06624 0.0013 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6022 -0.06614 0.04284 0.2319 0.9609 0.9805 0.6727 0.8634 0.951 0.6871 ] Network output: [ -0.01067 0.9698 1.016 -0.0001702 7.64e-05 0.03527 -0.0001282 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04859 0.02099 0.04206 0.02554 0.9773 0.9837 0.04954 0.9423 0.9674 0.05854 ] Network output: [ 0.1265 -0.2894 1.168 -0.0006388 0.0002868 0.8654 -0.0004814 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.667 0.2961 0.451 0.3869 0.9654 0.9833 0.6695 0.8746 0.9575 0.6828 ] Network output: [ -0.07842 0.1623 0.8967 0.0006601 -0.0002963 1.1 0.0004974 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.643 0.5609 0.3935 0.1331 0.9801 0.9868 0.6434 0.9498 0.9701 0.4258 ] Network output: [ -0.1338 0.4623 0.6597 -0.002807 0.00126 1.134 -0.002115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6569 0.6401 0.4328 -0.042 0.9784 0.9853 0.657 0.9456 0.9663 0.4403 ] Network output: [ 0.1362 0.6092 0.2885 0.0007212 -0.0003238 0.8328 0.0005435 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1241 Epoch 1475 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.003763 1.017 0.9864 -8.08e-05 3.627e-05 -0.01172 -6.089e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03017 -0.005068 0.02607 0.0174 0.9236 0.9353 0.05463 0.8464 0.8802 0.1224 ] Network output: [ 0.9742 0.08475 -0.06319 0.002081 -0.0009343 0.03852 0.001568 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6036 -0.0779 0.02637 0.2332 0.961 0.9806 0.6744 0.8634 0.951 0.6897 ] Network output: [ -0.01155 0.9665 1.021 -0.0001265 5.68e-05 0.0346 -9.534e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04833 0.02005 0.0416 0.02634 0.9773 0.9837 0.04927 0.9422 0.9673 0.05883 ] Network output: [ 0.1336 -0.3164 1.193 -0.0003917 0.0001758 0.8542 -0.0002952 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6648 0.2843 0.4479 0.3995 0.9654 0.9833 0.6672 0.8747 0.9576 0.6871 ] Network output: [ -0.08888 0.1332 0.9413 0.0008378 -0.0003761 1.107 0.0006314 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6414 0.5563 0.3985 0.1478 0.9801 0.9868 0.6419 0.9498 0.9702 0.4321 ] Network output: [ -0.1407 0.436 0.6937 -0.002544 0.001142 1.141 -0.001917 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6561 0.6385 0.4356 -0.02813 0.9784 0.9854 0.6562 0.9458 0.9664 0.4434 ] Network output: [ 0.1369 0.6081 0.2899 0.0008183 -0.0003674 0.8316 0.0006167 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1203 Epoch 1476 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00408 1.026 0.9776 -0.0001436 6.445e-05 -0.01228 -0.0001082 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03009 -0.005353 0.02548 0.01668 0.9235 0.9353 0.05456 0.8464 0.8801 0.122 ] Network output: [ 0.9915 0.1123 -0.1067 0.001859 -0.0008346 0.01897 0.001401 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6025 -0.08738 0.01928 0.2251 0.9609 0.9805 0.6734 0.8633 0.9509 0.6892 ] Network output: [ -0.01306 0.9725 1.018 -0.0001743 7.826e-05 0.03509 -0.0001314 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04781 0.01908 0.04106 0.02541 0.9772 0.9836 0.04874 0.9419 0.9671 0.05843 ] Network output: [ 0.1348 -0.2874 1.175 -0.0007941 0.0003565 0.8399 -0.0005984 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6607 0.2731 0.4497 0.3916 0.9653 0.9833 0.6632 0.8746 0.9575 0.6874 ] Network output: [ -0.09215 0.1475 0.935 0.0006558 -0.0002944 1.104 0.0004942 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6391 0.5516 0.4014 0.1413 0.98 0.9867 0.6396 0.9497 0.9701 0.4348 ] Network output: [ -0.1441 0.4519 0.6838 -0.002707 0.001215 1.141 -0.00204 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6544 0.6363 0.4388 -0.03787 0.9784 0.9854 0.6545 0.9458 0.9664 0.4465 ] Network output: [ 0.1342 0.6087 0.2934 0.0008386 -0.0003765 0.8329 0.000632 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.123 Epoch 1477 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.00094 1.023 0.9843 -0.0001455 6.531e-05 -0.006269 -0.0001096 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02997 -0.004903 0.02765 0.01676 0.9235 0.9353 0.05424 0.8466 0.8803 0.1225 ] Network output: [ 0.9365 0.113 -0.0396 0.001417 -0.000636 0.05941 0.001068 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6 -0.07385 0.04634 0.2261 0.9609 0.9805 0.6703 0.8635 0.951 0.6917 ] Network output: [ -0.0111 0.9698 1.015 -0.0001202 5.398e-05 0.03699 -9.062e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04834 0.02016 0.04308 0.02505 0.9773 0.9837 0.04928 0.9423 0.9674 0.05972 ] Network output: [ 0.1277 -0.2698 1.158 -0.0009979 0.000448 0.8528 -0.0007521 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6641 0.2829 0.4597 0.3773 0.9654 0.9833 0.6666 0.8747 0.9576 0.6881 ] Network output: [ -0.08135 0.1683 0.8935 0.0005574 -0.0002502 1.103 0.0004201 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6436 0.5578 0.404 0.1221 0.9801 0.9868 0.644 0.9499 0.9701 0.4361 ] Network output: [ -0.1345 0.4837 0.6385 -0.002987 0.001341 1.135 -0.002251 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6578 0.6402 0.4429 -0.06369 0.9785 0.9854 0.6579 0.946 0.9664 0.4503 ] Network output: [ 0.1384 0.6003 0.2928 0.0009344 -0.0004195 0.8339 0.0007042 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1302 Epoch 1478 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.001393 1.013 0.992 2.621e-06 -1.177e-06 -0.007297 1.976e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03007 -0.004954 0.02787 0.01753 0.9235 0.9353 0.05441 0.8466 0.8803 0.1241 ] Network output: [ 0.9419 0.08283 -0.01987 0.001769 -0.0007942 0.06047 0.001333 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6012 -0.07464 0.04514 0.2344 0.961 0.9806 0.6716 0.8637 0.9511 0.6962 ] Network output: [ -0.01014 0.9612 1.022 -1.469e-05 6.596e-06 0.03676 -1.107e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04862 0.02022 0.04383 0.02654 0.9773 0.9837 0.04956 0.9425 0.9675 0.06087 ] Network output: [ 0.1319 -0.3148 1.193 -0.000509 0.0002285 0.8562 -0.0003836 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6659 0.2811 0.4596 0.3923 0.9655 0.9834 0.6684 0.8749 0.9576 0.6927 ] Network output: [ -0.08575 0.1319 0.9318 0.0008811 -0.0003955 1.111 0.000664 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6454 0.5584 0.4087 0.1362 0.9802 0.9868 0.6458 0.9501 0.9703 0.4419 ] Network output: [ -0.1352 0.4553 0.6647 -0.002668 0.001198 1.14 -0.00201 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6595 0.6415 0.4453 -0.05156 0.9786 0.9855 0.6596 0.9463 0.9667 0.453 ] Network output: [ 0.1431 0.5961 0.2911 0.001073 -0.0004817 0.8309 0.0008086 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.127 Epoch 1479 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.006466 1.021 0.979 -1.336e-05 5.997e-06 -0.01271 -1.007e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03009 -0.005674 0.02564 0.01691 0.9235 0.9353 0.0546 0.8464 0.8801 0.1239 ] Network output: [ 1.006 0.1042 -0.1153 0.001951 -0.0008759 0.007038 0.00147 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6022 -0.09709 0.01616 0.2273 0.961 0.9806 0.6732 0.8634 0.9509 0.6955 ] Network output: [ -0.01304 0.9672 1.023 -6.945e-05 3.118e-05 0.03591 -5.234e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04766 0.01819 0.04198 0.02632 0.9772 0.9836 0.04859 0.942 0.9672 0.05989 ] Network output: [ 0.1399 -0.3112 1.198 -0.0006705 0.000301 0.8302 -0.0005053 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.659 0.2583 0.4552 0.3998 0.9654 0.9833 0.6615 0.8748 0.9576 0.6947 ] Network output: [ -0.09988 0.1168 0.9732 0.0008807 -0.0003954 1.113 0.0006637 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.64 0.5478 0.4127 0.1493 0.9801 0.9868 0.6404 0.9498 0.9702 0.4469 ] Network output: [ -0.1466 0.4358 0.6999 -0.002481 0.001114 1.147 -0.00187 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6555 0.6364 0.4472 -0.0378 0.9785 0.9855 0.6556 0.9463 0.9668 0.4549 ] Network output: [ 0.1392 0.6002 0.296 0.001108 -0.0004975 0.8299 0.0008352 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1242 Epoch 1480 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0006896 1.03 0.9751 -0.0001389 6.237e-05 -0.006742 -0.0001047 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02987 -0.005416 0.02724 0.01614 0.9234 0.9353 0.05415 0.8465 0.8801 0.123 ] Network output: [ 0.9619 0.1381 -0.08955 0.001192 -0.0005353 0.03247 0.0008986 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5986 -0.09042 0.03872 0.219 0.9609 0.9805 0.669 0.8634 0.9509 0.6947 ] Network output: [ -0.01256 0.9733 1.013 -0.0001076 4.83e-05 0.03813 -8.107e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04766 0.01852 0.04314 0.02462 0.9772 0.9836 0.0486 0.9421 0.9672 0.06015 ] Network output: [ 0.1327 -0.253 1.151 -0.00138 0.0006195 0.831 -0.00104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6589 0.2606 0.4661 0.3738 0.9653 0.9833 0.6614 0.8747 0.9575 0.6926 ] Network output: [ -0.08894 0.1637 0.9103 0.0005167 -0.000232 1.106 0.0003894 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6415 0.55 0.4142 0.1203 0.9801 0.9868 0.642 0.9498 0.9701 0.4463 ] Network output: [ -0.1396 0.4871 0.641 -0.002981 0.001338 1.139 -0.002246 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6567 0.6378 0.4516 -0.07262 0.9785 0.9855 0.6568 0.9463 0.9666 0.4589 ] Network output: [ 0.1388 0.5949 0.2995 0.001138 -0.000511 0.8327 0.0008578 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1324 Epoch 1481 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.003101 1.014 0.9927 1.745e-06 -7.833e-07 -0.0007622 1.315e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02988 -0.004774 0.03005 0.01731 0.9234 0.9352 0.05401 0.8468 0.8804 0.1248 ] Network output: [ 0.8959 0.09723 0.0256 0.001163 -0.000522 0.0901 0.0008763 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5977 -0.06992 0.07142 0.2321 0.9609 0.9806 0.6675 0.8639 0.9511 0.7005 ] Network output: [ -0.008669 0.9612 1.017 5.02e-05 -2.254e-05 0.03952 3.784e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04878 0.02041 0.04608 0.02591 0.9774 0.9838 0.04973 0.9428 0.9677 0.06246 ] Network output: [ 0.1287 -0.2868 1.168 -0.0009171 0.0004117 0.8573 -0.0006911 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6668 0.2783 0.474 0.3753 0.9655 0.9834 0.6693 0.8749 0.9576 0.6956 ] Network output: [ -0.07751 0.1505 0.8962 0.0007837 -0.0003518 1.111 0.0005906 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.649 0.5603 0.4172 0.1148 0.9802 0.9869 0.6495 0.9502 0.9703 0.4488 ] Network output: [ -0.1275 0.4874 0.6206 -0.002916 0.001309 1.135 -0.002198 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6625 0.6441 0.4546 -0.08283 0.9787 0.9856 0.6626 0.9466 0.9667 0.4618 ] Network output: [ 0.1482 0.5831 0.2958 0.001315 -0.0005902 0.8301 0.0009908 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1372 Epoch 1482 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.006185 1.012 0.9863 0.0001297 -5.821e-05 -0.009812 9.772e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03008 -0.005627 0.02716 0.01769 0.9235 0.9353 0.05452 0.8465 0.8802 0.1258 ] Network output: [ 0.9842 0.08078 -0.06783 0.001961 -0.0008805 0.02672 0.001478 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6015 -0.09535 0.03059 0.2358 0.961 0.9806 0.6722 0.8636 0.951 0.7023 ] Network output: [ -0.01117 0.9591 1.026 7.308e-05 -3.281e-05 0.0373 5.507e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04803 0.01833 0.04403 0.02774 0.9774 0.9838 0.04896 0.9424 0.9674 0.06193 ] Network output: [ 0.1421 -0.3455 1.224 -0.0003634 0.0001632 0.8358 -0.0002739 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6615 0.2543 0.4638 0.4083 0.9654 0.9834 0.664 0.875 0.9577 0.701 ] Network output: [ -0.09957 0.08401 0.9971 0.001232 -0.0005531 1.123 0.0009285 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6441 0.5494 0.4223 0.155 0.9802 0.9869 0.6446 0.95 0.9704 0.4566 ] Network output: [ -0.142 0.4192 0.7055 -0.002207 0.0009909 1.15 -0.001663 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.659 0.6392 0.454 -0.03966 0.9787 0.9856 0.659 0.9467 0.967 0.4617 ] Network output: [ 0.1479 0.5892 0.2965 0.001401 -0.0006291 0.8243 0.001056 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1257 Epoch 1483 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.006293 1.036 0.9623 -9.861e-05 4.427e-05 -0.01129 -7.432e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02986 -0.006264 0.02543 0.0158 0.9235 0.9353 0.05428 0.8462 0.8799 0.1236 ] Network output: [ 1.025 0.1538 -0.1785 0.001349 -0.0006057 -0.01976 0.001017 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5993 -0.1168 0.01309 0.2151 0.9609 0.9805 0.6703 0.8631 0.9508 0.6971 ] Network output: [ -0.01494 0.9771 1.015 -0.0001034 4.641e-05 0.03782 -7.792e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04659 0.01605 0.04191 0.02509 0.9771 0.9836 0.04751 0.9416 0.9669 0.05983 ] Network output: [ 0.1439 -0.2654 1.171 -0.001465 0.0006579 0.8009 -0.001104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6515 0.2286 0.4663 0.3881 0.9652 0.9833 0.654 0.8745 0.9575 0.6975 ] Network output: [ -0.1047 0.1267 0.9735 0.0007222 -0.0003242 1.112 0.0005442 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6366 0.5365 0.4237 0.1406 0.98 0.9867 0.637 0.9495 0.9701 0.4567 ] Network output: [ -0.1505 0.4514 0.6894 -0.002566 0.001152 1.15 -0.001934 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6531 0.6323 0.457 -0.05504 0.9786 0.9855 0.6532 0.9464 0.9668 0.4642 ] Network output: [ 0.1387 0.5946 0.3064 0.001339 -0.000601 0.8271 0.001009 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1307 Epoch 1484 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.007288 1.029 0.98 -0.0001337 6.002e-05 0.004685 -0.0001008 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02957 -0.004916 0.03108 0.01618 0.9233 0.9352 0.05348 0.8467 0.8802 0.1238 ] Network output: [ 0.8734 0.1475 0.01194 0.0002973 -0.0001335 0.09498 0.0002241 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5934 -0.07614 0.08731 0.22 0.9608 0.9805 0.6628 0.8636 0.951 0.7002 ] Network output: [ -0.009071 0.9715 1.004 2.8e-06 -1.257e-06 0.04227 2.111e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04818 0.01945 0.04683 0.02389 0.9773 0.9837 0.04912 0.9426 0.9676 0.06253 ] Network output: [ 0.1278 -0.2185 1.114 -0.001846 0.0008288 0.8418 -0.001391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6631 0.2641 0.4874 0.3487 0.9654 0.9834 0.6655 0.8746 0.9575 0.6951 ] Network output: [ -0.0715 0.19 0.8494 0.0004412 -0.0001981 1.105 0.0003325 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6483 0.556 0.4237 0.08843 0.9802 0.9868 0.6488 0.95 0.9702 0.4532 ] Network output: [ -0.1246 0.5252 0.5796 -0.00325 0.001459 1.131 -0.00245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.662 0.6429 0.4626 -0.1142 0.9787 0.9856 0.6621 0.9466 0.9667 0.4693 ] Network output: [ 0.1478 0.5732 0.3071 0.001501 -0.0006737 0.8302 0.001131 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1486 Epoch 1485 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.00142 1.001 1.001 0.0002211 -9.928e-05 0.001228 0.0001667 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02992 -0.004809 0.03145 0.01875 0.9235 0.9353 0.05403 0.8467 0.8804 0.1272 ] Network output: [ 0.882 0.05132 0.0814 0.001496 -0.0006716 0.1093 0.001127 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5982 -0.0702 0.08319 0.2484 0.961 0.9806 0.6679 0.8639 0.9512 0.7087 ] Network output: [ -0.006032 0.9503 1.022 0.0002424 -0.0001088 0.0404 0.0001827 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04918 0.02043 0.0481 0.02858 0.9775 0.9839 0.05013 0.9431 0.968 0.06462 ] Network output: [ 0.1375 -0.3585 1.219 -0.0001355 6.081e-05 0.864 -0.0001021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6702 0.2712 0.4795 0.3991 0.9656 0.9835 0.6727 0.8749 0.9577 0.703 ] Network output: [ -0.07886 0.08475 0.9528 0.001484 -0.0006661 1.126 0.001118 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6534 0.5609 0.4268 0.1367 0.9804 0.987 0.6539 0.9504 0.9705 0.4594 ] Network output: [ -0.1235 0.4371 0.6584 -0.002256 0.001013 1.142 -0.0017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6658 0.6464 0.4598 -0.0661 0.9789 0.9857 0.6659 0.947 0.967 0.4673 ] Network output: [ 0.1598 0.5723 0.2963 0.001681 -0.0007546 0.8187 0.001267 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1368 Epoch 1486 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01474 1.03 0.959 8.286e-05 -3.72e-05 -0.01782 6.245e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03001 -0.007012 0.0234 0.01685 0.9236 0.9354 0.05464 0.8458 0.8797 0.1252 ] Network output: [ 1.093 0.1204 -0.2343 0.002161 -0.0009702 -0.06351 0.001629 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6025 -0.1386 -0.01709 0.2271 0.961 0.9806 0.6741 0.8628 0.9507 0.7017 ] Network output: [ -0.01585 0.9726 1.023 -1.903e-05 8.543e-06 0.03608 -1.434e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04593 0.01419 0.04066 0.02776 0.9772 0.9836 0.04683 0.9412 0.9667 0.05991 ] Network output: [ 0.1604 -0.3444 1.237 -0.0006642 0.0002982 0.7835 -0.0005006 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6476 0.2029 0.4589 0.4281 0.9652 0.9833 0.65 0.8742 0.9574 0.7042 ] Network output: [ -0.1235 0.03713 1.088 0.001455 -0.0006533 1.128 0.001097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6335 0.5254 0.4313 0.1881 0.9799 0.9868 0.6339 0.9492 0.9702 0.467 ] Network output: [ -0.1606 0.3569 0.7907 -0.001494 0.0006706 1.167 -0.001126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6508 0.628 0.4565 -0.002705 0.9786 0.9856 0.6509 0.9464 0.9671 0.4642 ] Network output: [ 0.1447 0.5921 0.3107 0.001635 -0.000734 0.8145 0.001232 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1314 Epoch 1487 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.003886 1.061 0.9447 -0.0003974 0.0001784 0.0002177 -0.0002995 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02927 -0.006143 0.02785 0.01415 0.9233 0.9352 0.05317 0.8459 0.8796 0.1206 ] Network output: [ 0.9573 0.2335 -0.1613 -0.0002456 0.0001103 0.01227 -0.0001851 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5911 -0.1165 0.05144 0.198 0.9607 0.9804 0.6611 0.8623 0.9505 0.6927 ] Network output: [ -0.01447 0.9951 0.9907 -0.0002173 9.756e-05 0.04229 -0.0001638 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04583 0.0153 0.04316 0.02174 0.977 0.9835 0.04673 0.9413 0.9668 0.05926 ] Network output: [ 0.1415 -0.1471 1.069 -0.003003 0.001348 0.783 -0.002263 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6475 0.2138 0.4866 0.3417 0.9651 0.9832 0.65 0.8737 0.9572 0.6919 ] Network output: [ -0.08534 0.2025 0.8725 0.0001468 -6.592e-05 1.096 0.0001107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6357 0.5317 0.4266 0.09438 0.9798 0.9866 0.6361 0.949 0.9697 0.4554 ] Network output: [ -0.1414 0.5171 0.6133 -0.003155 0.001417 1.139 -0.002378 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6518 0.6303 0.4644 -0.1049 0.9785 0.9855 0.6519 0.9461 0.9665 0.4708 ] Network output: [ 0.1371 0.5826 0.323 0.00152 -0.0006822 0.8265 0.001145 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.149 Epoch 1488 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01852 1.009 1.007 4.028e-05 -1.808e-05 0.02191 3.035e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02934 -0.003448 0.03724 0.01848 0.9232 0.9351 0.05268 0.8469 0.8805 0.1256 ] Network output: [ 0.7112 0.07547 0.2681 8.252e-06 -3.704e-06 0.2341 6.218e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5885 -0.03008 0.1663 0.2478 0.9608 0.9805 0.6562 0.8639 0.9513 0.7088 ] Network output: [ 0.0003562 0.9569 0.9975 0.0002678 -0.0001202 0.04592 0.0002018 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04991 0.02332 0.05193 0.02562 0.9775 0.984 0.05086 0.9433 0.9684 0.06552 ] Network output: [ 0.1221 -0.2636 1.121 -0.0009969 0.0004475 0.8946 -0.0007513 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6767 0.2999 0.5033 0.3441 0.9657 0.9835 0.6792 0.8736 0.9573 0.6935 ] Network output: [ -0.03445 0.1757 0.7891 0.001049 -0.0004708 1.108 0.0007903 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6619 0.5759 0.4227 0.07087 0.9804 0.987 0.6624 0.9501 0.9701 0.45 ] Network output: [ -0.09215 0.5201 0.5332 -0.002971 0.001334 1.119 -0.002239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6714 0.6536 0.4628 -0.1333 0.979 0.9857 0.6715 0.9464 0.9664 0.4692 ] Network output: [ 0.1688 0.5448 0.3078 0.001953 -0.0008769 0.8177 0.001472 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1848 Epoch 1489 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01287 0.9999 0.9877 0.0003762 -0.0001689 -0.01178 0.0002835 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.03006 -0.006196 0.02682 0.02026 0.9236 0.9354 0.05449 0.8451 0.8794 0.1272 ] Network output: [ 1.015 0.0111 -0.04786 0.002836 -0.001273 0.01856 0.002137 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6031 -0.112 0.02064 0.2662 0.9611 0.9806 0.674 0.8621 0.9507 0.7083 ] Network output: [ -0.008658 0.9507 1.031 0.0002502 -0.0001123 0.03627 0.0001886 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04761 0.01677 0.04404 0.03255 0.9774 0.9838 0.04854 0.9417 0.9673 0.06263 ] Network output: [ 0.1702 -0.4731 1.31 0.001063 -0.0004772 0.8267 0.0008011 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6617 0.2258 0.4581 0.466 0.9655 0.9834 0.6641 0.8736 0.9574 0.7079 ] Network output: [ -0.1105 -0.05814 1.141 0.002664 -0.001196 1.149 0.002007 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6448 0.5397 0.4302 0.222 0.9802 0.987 0.6452 0.9494 0.9704 0.4675 ] Network output: [ -0.1446 0.2613 0.8517 -0.0002858 0.0001283 1.175 -0.0002154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6582 0.6357 0.4501 0.04056 0.9788 0.9858 0.6583 0.9464 0.9672 0.4583 ] Network output: [ 0.1647 0.5734 0.3077 0.002051 -0.0009207 0.7979 0.001546 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1331 Epoch 1490 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01566 1.087 0.9014 -0.0004983 0.0002237 -0.02194 -0.0003756 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02936 -0.008554 0.0188 0.01381 0.9235 0.9353 0.05379 0.8441 0.8784 0.1178 ] Network output: [ 1.189 0.2598 -0.468 0.001049 -0.000471 -0.1654 0.0007906 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5969 -0.1895 -0.06153 0.1959 0.9607 0.9804 0.6691 0.8601 0.9496 0.6835 ] Network output: [ -0.02295 1.018 0.9911 -0.0004768 0.000214 0.03493 -0.0003593 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04206 0.008478 0.03404 0.02332 0.9765 0.9831 0.04291 0.9382 0.965 0.05283 ] Network output: [ 0.1973 -0.1991 1.116 -0.00261 0.001172 0.6777 -0.001967 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.621 0.1243 0.4454 0.4087 0.9646 0.983 0.6234 0.871 0.9564 0.6869 ] Network output: [ -0.1279 0.08882 1.079 0.000817 -0.0003668 1.091 0.0006157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6086 0.4808 0.4212 0.1907 0.9791 0.9863 0.609 0.9465 0.9688 0.4557 ] Network output: [ -0.1868 0.3418 0.8503 -0.001338 0.0006009 1.176 -0.001009 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6301 0.6031 0.4511 0.02089 0.9779 0.9852 0.6302 0.9442 0.9661 0.4583 ] Network output: [ 0.1233 0.6087 0.3441 0.00155 -0.000696 0.807 0.001168 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.162 Epoch 1491 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.03325 1.07 0.9569 -0.0007134 0.0003203 0.03661 -0.0005377 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02817 -0.003494 0.03667 0.01456 0.9229 0.9348 0.05066 0.8453 0.8792 0.1161 ] Network output: [ 0.6564 0.2486 0.1894 -0.002341 0.001051 0.2396 -0.001764 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5726 -0.03762 0.1881 0.2079 0.9603 0.9803 0.6388 0.8608 0.9501 0.6867 ] Network output: [ -0.002608 1.004 0.948 -0.0002078 9.331e-05 0.05207 -0.0001566 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04732 0.02121 0.0482 0.01788 0.9769 0.9835 0.04824 0.9411 0.9671 0.0596 ] Network output: [ 0.1177 -0.01913 0.9146 -0.003768 0.001692 0.8539 -0.00284 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6613 0.2818 0.5113 0.2593 0.9652 0.9832 0.6637 0.8706 0.9562 0.6699 ] Network output: [ -0.006049 0.3241 0.6139 -0.0001801 8.083e-05 1.073 -0.0001357 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6515 0.5645 0.4066 0.002283 0.9798 0.9865 0.652 0.9481 0.9687 0.4283 ] Network output: [ -0.08031 0.6046 0.439 -0.003726 0.001673 1.102 -0.002808 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6606 0.643 0.4521 -0.1839 0.9784 0.9852 0.6607 0.9444 0.9649 0.4571 ] Network output: [ 0.1568 0.5315 0.3419 0.00203 -0.0009115 0.8211 0.00153 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.24 Epoch 1492 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01701 0.9724 1.038 0.0003585 -0.0001609 0.02514 0.0002702 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02929 -0.002565 0.03835 0.02331 0.9233 0.9352 0.05236 0.8446 0.8792 0.1249 ] Network output: [ 0.6568 -0.08708 0.4661 0.001723 -0.0007736 0.3145 0.001299 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5894 0.0001434 0.1809 0.3048 0.9608 0.9805 0.6564 0.8611 0.9506 0.7084 ] Network output: [ 0.009901 0.9332 1.007 0.0005249 -0.0002357 0.04211 0.0003956 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05059 0.02572 0.05061 0.03271 0.9776 0.9841 0.05155 0.9418 0.9681 0.06354 ] Network output: [ 0.1427 -0.49 1.271 0.002133 -0.0009577 0.9421 0.001608 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6853 0.3231 0.4778 0.4313 0.9657 0.9835 0.6878 0.8695 0.9562 0.6834 ] Network output: [ -0.01328 -0.004258 0.918 0.003289 -0.001476 1.126 0.002478 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.666 0.5835 0.3982 0.1599 0.9804 0.987 0.6665 0.9483 0.9694 0.4284 ] Network output: [ -0.0733 0.3212 0.6928 -0.0005715 0.0002566 1.13 -0.0004307 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6708 0.6532 0.4318 -0.01738 0.9787 0.9856 0.6709 0.944 0.9653 0.4394 ] Network output: [ 0.1905 0.5415 0.3036 0.002225 -0.000999 0.783 0.001677 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2024 Epoch 1493 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03441 1.06 0.9091 -0.0001825 8.194e-05 -0.03879 -0.0001376 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02953 -0.009441 0.01284 0.01833 0.9237 0.9355 0.05429 0.8403 0.8763 0.1176 ] Network output: [ 1.317 0.1113 -0.5036 0.00378 -0.001697 -0.2265 0.002849 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6037 -0.2132 -0.1351 0.2512 0.9607 0.9804 0.6773 0.8558 0.9483 0.6788 ] Network output: [ -0.02084 1.007 1.007 -0.0004679 0.0002101 0.0254 -0.0003526 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04113 0.006712 0.02839 0.02914 0.9764 0.983 0.04197 0.935 0.9636 0.05001 ] Network output: [ 0.2873 -0.399 1.193 0.0005629 -0.0002527 0.6338 0.0004242 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6174 0.09654 0.3707 0.5025 0.9644 0.9829 0.6198 0.8666 0.9553 0.6836 ] Network output: [ -0.1533 -0.09012 1.3 0.002356 -0.001058 1.106 0.001775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5974 0.4612 0.4054 0.3009 0.9789 0.9862 0.5978 0.9437 0.9678 0.4531 ] Network output: [ -0.2243 0.0562 1.173 0.001479 -0.0006642 1.225 0.001115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6191 0.5898 0.4312 0.1964 0.9776 0.9851 0.6192 0.9417 0.9655 0.4412 ] Network output: [ 0.1266 0.5721 0.3987 0.002191 -0.0009836 0.7849 0.001651 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2169 Epoch 1494 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.02701 1.144 0.8813 -0.001568 0.0007039 0.02222 -0.001182 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02743 -0.00551 0.02683 0.0139 0.9228 0.9348 0.04983 0.8403 0.8758 0.103 ] Network output: [ 0.8374 0.3382 -0.1049 -0.002035 0.0009134 0.08367 -0.001533 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5671 -0.1044 0.08149 0.2088 0.9597 0.98 0.6347 0.8538 0.9476 0.645 ] Network output: [ -0.0143 1.064 0.9132 -0.001 0.000449 0.04749 -0.0007538 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04283 0.01419 0.03567 0.01939 0.9757 0.9827 0.04369 0.9355 0.964 0.04778 ] Network output: [ 0.1732 -0.03853 0.9219 -0.003601 0.001616 0.7555 -0.002713 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6312 0.1969 0.4511 0.3388 0.9644 0.9829 0.6337 0.865 0.9544 0.6375 ] Network output: [ -0.02152 0.2474 0.7407 0.000583 -0.0002617 1.057 0.0004393 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6207 0.5156 0.3635 0.1051 0.9787 0.9859 0.6212 0.9436 0.9667 0.3882 ] Network output: [ -0.1149 0.4612 0.6329 -0.002082 0.0009346 1.127 -0.001569 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6311 0.6097 0.4094 -0.04454 0.9772 0.9845 0.6312 0.9401 0.963 0.4152 ] Network output: [ 0.1295 0.6033 0.3455 0.001308 -0.0005872 0.7975 0.0009858 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1643 Epoch 1495 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.04192 1.048 0.984 -0.0007789 0.0003497 0.0487 -0.000587 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02773 -0.001487 0.03744 0.02091 0.9229 0.9349 0.04944 0.842 0.8774 0.1124 ] Network output: [ 0.5299 0.05497 0.4919 -0.0003152 0.0001415 0.3919 -0.0002376 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5673 0.02864 0.2094 0.2905 0.9601 0.9802 0.6317 0.8566 0.9491 0.6785 ] Network output: [ 0.006506 0.9954 0.9408 -0.0002603 0.0001168 0.04968 -0.0001961 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04775 0.02616 0.04441 0.02671 0.9768 0.9836 0.04866 0.9384 0.9664 0.05443 ] Network output: [ 0.1021 -0.3133 1.141 0.0005476 -0.0002458 0.9704 0.0004127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.67 0.3508 0.4692 0.3873 0.9651 0.9832 0.6725 0.8639 0.9544 0.6423 ] Network output: [ 0.03717 0.1345 0.7101 0.002434 -0.001093 1.091 0.001835 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6563 0.5856 0.3528 0.1152 0.9796 0.9864 0.6568 0.945 0.9673 0.3763 ] Network output: [ -0.04032 0.4017 0.5733 -0.001254 0.0005628 1.101 -0.0009447 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6583 0.6436 0.3947 -0.04 0.9778 0.9848 0.6584 0.9398 0.9627 0.4008 ] Network output: [ 0.183 0.5567 0.3081 0.001745 -0.0007833 0.7763 0.001315 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2291 Epoch 1496 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009266 1.056 0.9351 -0.0006311 0.0002833 -0.01211 -0.0004756 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02858 -0.006607 0.02023 0.02087 0.9234 0.9353 0.05201 0.8377 0.8749 0.1113 ] Network output: [ 1.062 0.04935 -0.1456 0.002882 -0.001294 -0.01696 0.002172 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5899 -0.1308 -0.0358 0.2869 0.9602 0.9802 0.6604 0.8519 0.9475 0.6637 ] Network output: [ -0.01138 1.007 0.9824 -0.0006585 0.0002956 0.0309 -0.0004962 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04328 0.01337 0.03228 0.03042 0.9763 0.983 0.04415 0.9344 0.9639 0.04962 ] Network output: [ 0.2419 -0.4355 1.2 0.001416 -0.0006355 0.7578 0.001067 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6386 0.1892 0.3793 0.498 0.9647 0.983 0.6411 0.8637 0.9545 0.6636 ] Network output: [ -0.09139 -0.05555 1.137 0.002721 -0.001222 1.113 0.002051 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6172 0.5082 0.3714 0.2707 0.9791 0.9862 0.6177 0.9431 0.9673 0.4134 ] Network output: [ -0.1734 0.09123 1.06 0.001167 -0.0005239 1.2 0.0008794 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6301 0.6071 0.4062 0.177 0.9775 0.985 0.6302 0.9398 0.964 0.4158 ] Network output: [ 0.1435 0.5688 0.3743 0.001819 -0.0008165 0.7773 0.001371 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1292 Epoch 1497 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.009875 1.139 0.8726 -0.001607 0.0007214 0.001443 -0.001211 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02777 -0.006192 0.02016 0.01632 0.9232 0.9351 0.05052 0.8376 0.8744 0.1025 ] Network output: [ 0.9941 0.2476 -0.2355 0.000395 -0.0001773 0.001386 0.0002977 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5773 -0.1232 -0.01456 0.2404 0.9599 0.98 0.6463 0.8508 0.9468 0.6405 ] Network output: [ -0.01869 1.068 0.9262 -0.001298 0.0005828 0.03745 -0.0009783 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04154 0.01297 0.02977 0.02323 0.9757 0.9826 0.04237 0.9331 0.9628 0.04452 ] Network output: [ 0.2054 -0.1938 1.03 -0.001206 0.0005413 0.7482 -0.0009087 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6241 0.193 0.389 0.4235 0.9643 0.9828 0.6265 0.8623 0.9538 0.6381 ] Network output: [ -0.05681 0.1442 0.904 0.001162 -0.0005217 1.07 0.0008757 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6086 0.5049 0.3471 0.1905 0.9785 0.9858 0.609 0.9417 0.9661 0.3814 ] Network output: [ -0.1543 0.3041 0.8421 -0.0009612 0.0004315 1.158 -0.0007244 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6208 0.5995 0.3955 0.07728 0.9769 0.9844 0.6209 0.9382 0.9625 0.4036 ] Network output: [ 0.1237 0.6226 0.351 0.0008977 -0.000403 0.7827 0.0006765 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1202 Epoch 1498 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.03545 1.104 0.9273 -0.001507 0.0006766 0.03325 -0.001136 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02738 -0.002685 0.02995 0.0182 0.923 0.9349 0.04908 0.839 0.8754 0.1056 ] Network output: [ 0.6783 0.1723 0.2038 -0.0004987 0.0002239 0.2652 -0.0003759 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5666 -0.0112 0.1208 0.2631 0.9599 0.98 0.6319 0.8524 0.9476 0.6566 ] Network output: [ -0.005765 1.04 0.9219 -0.0009943 0.0004464 0.04549 -0.0007493 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04497 0.02224 0.03756 0.02376 0.9762 0.9831 0.04584 0.9356 0.9646 0.04909 ] Network output: [ 0.1155 -0.218 1.069 -0.0005061 0.0002272 0.9154 -0.0003814 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6513 0.3187 0.431 0.3863 0.9648 0.9831 0.6537 0.8623 0.9539 0.6361 ] Network output: [ 0.006735 0.1975 0.7152 0.001263 -0.000567 1.079 0.0009519 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6382 0.5642 0.3393 0.1234 0.9791 0.9861 0.6387 0.9433 0.9664 0.367 ] Network output: [ -0.07706 0.4291 0.6081 -0.002064 0.0009266 1.108 -0.001556 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6428 0.6278 0.3881 -0.02036 0.9773 0.9846 0.6429 0.9384 0.962 0.3952 ] Network output: [ 0.1578 0.5947 0.3142 0.0009726 -0.0004367 0.7794 0.000733 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1664 Epoch 1499 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.009368 1.065 0.9444 -0.0009932 0.0004459 0.005586 -0.0007485 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02807 -0.004443 0.02401 0.02062 0.9234 0.9352 0.05063 0.8378 0.8749 0.1095 ] Network output: [ 0.8933 0.0612 0.03776 0.002029 -0.0009109 0.1228 0.001529 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5812 -0.06394 0.02143 0.2875 0.9602 0.9802 0.649 0.8516 0.9475 0.6639 ] Network output: [ -0.009046 1.014 0.9658 -0.0008446 0.0003792 0.03539 -0.0006365 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04434 0.01861 0.03439 0.02876 0.9764 0.9831 0.04521 0.9349 0.9642 0.04963 ] Network output: [ 0.1779 -0.3949 1.188 0.001411 -0.0006333 0.8568 0.001063 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6474 0.2693 0.3878 0.4684 0.9648 0.9831 0.6498 0.8628 0.9542 0.6562 ] Network output: [ -0.05282 0.03969 0.9683 0.00211 -0.0009475 1.106 0.001591 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6285 0.542 0.3512 0.2175 0.9793 0.9863 0.6289 0.9433 0.9669 0.3898 ] Network output: [ -0.1361 0.2269 0.8808 -0.0004073 0.0001829 1.163 -0.000307 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.637 0.6191 0.3963 0.1047 0.9775 0.9848 0.6371 0.939 0.963 0.4059 ] Network output: [ 0.1515 0.5905 0.3348 0.001122 -0.0005035 0.7763 0.0008453 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1093 Epoch 1500 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.0008386 1.11 0.8947 -0.001362 0.0006113 -0.00822 -0.001026 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02804 -0.005889 0.01763 0.01773 0.9234 0.9353 0.05088 0.837 0.8742 0.1061 ] Network output: [ 1.046 0.1687 -0.2441 0.001986 -0.0008916 -0.009528 0.001497 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5827 -0.1117 -0.0564 0.2553 0.9601 0.9801 0.6519 0.8505 0.9468 0.6525 ] Network output: [ -0.01918 1.049 0.9515 -0.001259 0.0005653 0.03241 -0.0009489 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04175 0.01435 0.02862 0.02505 0.976 0.9827 0.04258 0.9329 0.9628 0.04552 ] Network output: [ 0.2051 -0.2866 1.1 0.0004051 -0.0001819 0.7775 0.0003053 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6258 0.223 0.3571 0.4523 0.9645 0.9829 0.6282 0.862 0.9539 0.6517 ] Network output: [ -0.0779 0.1077 0.9672 0.001126 -0.0005053 1.085 0.0008482 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6096 0.5152 0.3464 0.2112 0.9788 0.9859 0.61 0.9418 0.9661 0.3885 ] Network output: [ -0.1702 0.2733 0.8966 -0.001173 0.0005265 1.166 -0.0008839 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6219 0.6025 0.3986 0.101 0.9771 0.9846 0.622 0.9382 0.9626 0.4087 ] Network output: [ 0.1265 0.6192 0.3489 0.0006664 -0.0002992 0.7816 0.0005022 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1145 Epoch 1501 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.02484 1.108 0.9179 -0.001569 0.0007044 0.01712 -0.001182 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02751 -0.003453 0.02511 0.01753 0.9232 0.9351 0.04947 0.8379 0.8746 0.1056 ] Network output: [ 0.8027 0.1837 0.03797 0.0003645 -0.0001636 0.1744 0.0002747 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5708 -0.03533 0.04946 0.2544 0.96 0.9801 0.6371 0.8511 0.9471 0.6561 ] Network output: [ -0.01222 1.045 0.9349 -0.001205 0.0005408 0.03971 -0.0009079 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04383 0.02026 0.03422 0.02342 0.9762 0.9829 0.04468 0.9345 0.9637 0.0479 ] Network output: [ 0.1315 -0.2201 1.074 -0.0003399 0.0001526 0.8819 -0.0002562 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6413 0.302 0.3992 0.4001 0.9647 0.983 0.6437 0.8621 0.9538 0.6451 ] Network output: [ -0.02436 0.2035 0.7678 0.0006729 -0.0003021 1.08 0.0005071 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6293 0.5535 0.3397 0.1379 0.9791 0.9861 0.6297 0.9428 0.9662 0.3736 ] Network output: [ -0.1091 0.4308 0.6579 -0.002585 0.00116 1.119 -0.001948 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6361 0.6208 0.3935 -0.004889 0.9773 0.9846 0.6362 0.9383 0.962 0.4022 ] Network output: [ 0.1452 0.6106 0.3175 0.0005688 -0.0002554 0.7838 0.0004287 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1383 Epoch 1502 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01436 1.066 0.9506 -0.00109 0.0004895 0.007764 -0.0008218 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0279 -0.003734 0.02434 0.02003 0.9234 0.9353 0.05017 0.8379 0.8748 0.1101 ] Network output: [ 0.854 0.07717 0.06795 0.001821 -0.0008176 0.1543 0.001373 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5776 -0.04159 0.02588 0.2809 0.9602 0.9802 0.6446 0.8516 0.9475 0.6686 ] Network output: [ -0.01004 1.014 0.9668 -0.000921 0.0004135 0.0353 -0.0006941 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04448 0.02025 0.0348 0.02749 0.9765 0.9831 0.04534 0.935 0.9642 0.05011 ] Network output: [ 0.1529 -0.3591 1.174 0.001216 -0.0005458 0.8839 0.0009162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6467 0.2975 0.3843 0.4502 0.9649 0.9831 0.6491 0.8627 0.9542 0.6596 ] Network output: [ -0.04683 0.0984 0.8989 0.001437 -0.0006452 1.102 0.001083 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6313 0.5529 0.3478 0.1878 0.9794 0.9863 0.6318 0.9434 0.9668 0.3875 ] Network output: [ -0.1283 0.3181 0.787 -0.001599 0.0007179 1.145 -0.001205 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.639 0.623 0.3989 0.05645 0.9775 0.9848 0.6391 0.939 0.9627 0.4091 ] Network output: [ 0.152 0.5997 0.3181 0.0007735 -0.0003473 0.7812 0.000583 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1158 Epoch 1503 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0005809 1.086 0.9184 -0.001141 0.0005121 -0.0103 -0.0008597 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02808 -0.005362 0.01761 0.01846 0.9236 0.9354 0.05084 0.8371 0.8742 0.1091 ] Network output: [ 1.035 0.1259 -0.1965 0.002593 -0.001164 0.01066 0.001954 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5826 -0.09389 -0.06267 0.2623 0.9602 0.9802 0.6513 0.8508 0.947 0.6638 ] Network output: [ -0.01815 1.032 0.9695 -0.001127 0.0005059 0.03068 -0.0008492 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04235 0.01593 0.02943 0.02602 0.9762 0.9829 0.04319 0.9334 0.963 0.04733 ] Network output: [ 0.1928 -0.3284 1.141 0.001097 -0.0004923 0.806 0.0008265 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6286 0.2484 0.3482 0.4593 0.9646 0.983 0.631 0.8624 0.954 0.6629 ] Network output: [ -0.08334 0.1006 0.9738 0.001009 -0.0004528 1.096 0.0007601 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6149 0.5272 0.3492 0.2089 0.979 0.9861 0.6154 0.9423 0.9663 0.3952 ] Network output: [ -0.1701 0.2929 0.8773 -0.001633 0.0007332 1.163 -0.001231 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6264 0.6085 0.4038 0.08774 0.9773 0.9847 0.6265 0.9387 0.9628 0.4151 ] Network output: [ 0.1325 0.6155 0.3369 0.0005707 -0.0002562 0.785 0.0004301 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1121 Epoch 1504 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01622 1.097 0.9235 -0.001401 0.0006291 0.00578 -0.001056 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02766 -0.003947 0.02215 0.01748 0.9234 0.9352 0.04983 0.8376 0.8744 0.1077 ] Network output: [ 0.8856 0.17 -0.05181 0.001197 -0.0005373 0.1155 0.000902 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5735 -0.05039 0.001778 0.2522 0.9601 0.9801 0.6404 0.851 0.9471 0.6632 ] Network output: [ -0.01543 1.037 0.9529 -0.001183 0.000531 0.03575 -0.0008914 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04333 0.01912 0.03266 0.0238 0.9762 0.9829 0.04418 0.9341 0.9634 0.0483 ] Network output: [ 0.1444 -0.2431 1.095 5.814e-05 -2.61e-05 0.8598 4.382e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.635 0.2911 0.3776 0.4132 0.9647 0.983 0.6373 0.8623 0.9539 0.657 ] Network output: [ -0.0478 0.1928 0.8181 0.0004064 -0.0001825 1.086 0.0003063 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6255 0.548 0.3449 0.1492 0.9791 0.9861 0.6259 0.9427 0.9662 0.3846 ] Network output: [ -0.1313 0.4281 0.6941 -0.002888 0.001296 1.129 -0.002176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6338 0.6181 0.4021 0.00223 0.9774 0.9847 0.6339 0.9387 0.9623 0.4122 ] Network output: [ 0.14 0.6147 0.3179 0.0004326 -0.0001942 0.7891 0.000326 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1274 Epoch 1505 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01516 1.064 0.9561 -0.001064 0.0004775 0.00629 -0.0008016 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0278 -0.003545 0.024 0.01952 0.9235 0.9353 0.04999 0.838 0.8748 0.1114 ] Network output: [ 0.8519 0.09026 0.06019 0.001784 -0.0008007 0.1531 0.001344 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5751 -0.03567 0.01807 0.2743 0.9603 0.9802 0.6418 0.8518 0.9475 0.6748 ] Network output: [ -0.01144 1.012 0.9728 -0.0009154 0.0004109 0.03471 -0.0006898 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04444 0.02062 0.03505 0.02672 0.9765 0.9832 0.0453 0.9351 0.9641 0.051 ] Network output: [ 0.1432 -0.3336 1.165 0.001014 -0.0004552 0.8866 0.0007642 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6436 0.3053 0.3801 0.4385 0.9649 0.9831 0.646 0.8629 0.9542 0.6665 ] Network output: [ -0.0498 0.1337 0.8677 0.0009376 -0.0004209 1.102 0.0007066 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6326 0.5568 0.351 0.1689 0.9794 0.9863 0.633 0.9435 0.9667 0.3927 ] Network output: [ -0.1289 0.38 0.7308 -0.002419 0.001086 1.137 -0.001823 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6402 0.6248 0.4059 0.02145 0.9777 0.9849 0.6403 0.9393 0.9627 0.4167 ] Network output: [ 0.1515 0.6017 0.3099 0.0006452 -0.0002897 0.788 0.0004863 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1229 Epoch 1506 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -6.253e-05 1.068 0.938 -0.0009498 0.0004264 -0.01017 -0.0007158 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02804 -0.004947 0.01831 0.01894 0.9236 0.9354 0.05071 0.8374 0.8744 0.1119 ] Network output: [ 1.012 0.1013 -0.1454 0.002804 -0.001259 0.03185 0.002113 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5807 -0.08009 -0.05937 0.2664 0.9603 0.9802 0.6489 0.8513 0.9472 0.6743 ] Network output: [ -0.01711 1.017 0.9829 -0.0009784 0.0004392 0.0303 -0.0007374 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0429 0.01708 0.0308 0.02674 0.9764 0.983 0.04374 0.934 0.9633 0.04936 ] Network output: [ 0.1808 -0.3493 1.168 0.001373 -0.0006164 0.8249 0.001035 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6305 0.264 0.348 0.4603 0.9648 0.983 0.6328 0.8629 0.9542 0.673 ] Network output: [ -0.08514 0.1004 0.9687 0.000901 -0.0004045 1.105 0.000679 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6203 0.5361 0.3547 0.2016 0.9792 0.9862 0.6207 0.9428 0.9665 0.4031 ] Network output: [ -0.1665 0.3228 0.8422 -0.002059 0.0009244 1.16 -0.001552 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6311 0.6139 0.4108 0.06576 0.9775 0.9849 0.6311 0.9393 0.963 0.4228 ] Network output: [ 0.1386 0.611 0.3244 0.0005827 -0.0002616 0.7897 0.0004391 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1126 Epoch 1507 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.009648 1.085 0.9313 -0.001191 0.0005346 -0.001915 -0.0008974 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02774 -0.00433 0.02021 0.01758 0.9235 0.9353 0.05008 0.8376 0.8744 0.1103 ] Network output: [ 0.9425 0.1558 -0.1072 0.001801 -0.0008086 0.07369 0.001357 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5745 -0.06197 -0.03091 0.2516 0.9602 0.9802 0.6418 0.8513 0.9471 0.672 ] Network output: [ -0.01726 1.028 0.9689 -0.001087 0.0004879 0.03338 -0.000819 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04303 0.01824 0.03197 0.02437 0.9763 0.983 0.04387 0.9341 0.9632 0.04929 ] Network output: [ 0.1544 -0.266 1.117 0.0003893 -0.0001748 0.8419 0.0002934 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6299 0.2806 0.3636 0.4238 0.9648 0.983 0.6323 0.8628 0.9541 0.6686 ] Network output: [ -0.06571 0.1778 0.8612 0.0002915 -0.0001308 1.094 0.0002197 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6236 0.5441 0.3523 0.1576 0.9792 0.9861 0.6241 0.9428 0.9662 0.3968 ] Network output: [ -0.1468 0.4259 0.7186 -0.00306 0.001374 1.137 -0.002306 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6331 0.617 0.4115 0.003858 0.9775 0.9848 0.6331 0.9392 0.9626 0.4227 ] Network output: [ 0.1384 0.614 0.3166 0.0004418 -0.0001983 0.7943 0.0003329 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1241 Epoch 1508 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01488 1.061 0.9603 -0.0009969 0.0004476 0.004773 -0.0007513 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02771 -0.003543 0.02366 0.0191 0.9235 0.9353 0.04987 0.8381 0.8748 0.113 ] Network output: [ 0.858 0.1025 0.0449 0.001742 -0.0007821 0.1437 0.001313 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5727 -0.03584 0.009391 0.2684 0.9603 0.9802 0.6391 0.8521 0.9475 0.6813 ] Network output: [ -0.01265 1.008 0.9788 -0.0008731 0.000392 0.03462 -0.000658 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04434 0.02052 0.03542 0.02621 0.9766 0.9832 0.0452 0.9352 0.964 0.05211 ] Network output: [ 0.1392 -0.3134 1.157 0.0008034 -0.0003607 0.8809 0.0006055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.64 0.3047 0.3779 0.4296 0.965 0.9831 0.6424 0.8633 0.9543 0.6742 ] Network output: [ -0.05439 0.1568 0.851 0.0005814 -0.000261 1.103 0.0004382 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6334 0.5578 0.3569 0.1544 0.9795 0.9863 0.6338 0.9437 0.9667 0.4007 ] Network output: [ -0.131 0.4257 0.691 -0.002993 0.001344 1.133 -0.002256 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6412 0.6258 0.4145 -0.007184 0.9778 0.9849 0.6413 0.9398 0.9628 0.4257 ] Network output: [ 0.1514 0.5998 0.3053 0.0006448 -0.0002895 0.7946 0.000486 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1297 Epoch 1509 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.001452 1.055 0.9538 -0.0007891 0.0003542 -0.008781 -0.0005947 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02797 -0.004643 0.01935 0.01935 0.9236 0.9354 0.05054 0.8378 0.8746 0.1144 ] Network output: [ 0.9851 0.08559 -0.0961 0.00281 -0.001261 0.05177 0.002117 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5781 -0.07006 -0.05172 0.2699 0.9604 0.9803 0.6458 0.8519 0.9474 0.684 ] Network output: [ -0.01608 1.005 0.9929 -0.0008347 0.0003747 0.03079 -0.0006291 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04335 0.01787 0.03244 0.02742 0.9766 0.9831 0.0442 0.9345 0.9636 0.05146 ] Network output: [ 0.1711 -0.3617 1.188 0.001459 -0.0006552 0.8371 0.0011 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6316 0.2725 0.3519 0.4598 0.9649 0.9831 0.634 0.8635 0.9544 0.6822 ] Network output: [ -0.08537 0.1004 0.9617 0.0008389 -0.0003766 1.112 0.0006323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.625 0.5424 0.3616 0.1936 0.9794 0.9863 0.6254 0.9433 0.9667 0.4116 ] Network output: [ -0.1618 0.351 0.8064 -0.002382 0.00107 1.156 -0.001796 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6353 0.6185 0.4185 0.04286 0.9778 0.985 0.6354 0.94 0.9632 0.431 ] Network output: [ 0.1449 0.605 0.3137 0.0006775 -0.0003042 0.7944 0.0005106 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1155 Epoch 1510 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.004377 1.074 0.9378 -0.0009909 0.0004449 -0.007259 -0.0007468 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02778 -0.004681 0.01882 0.01776 0.9235 0.9354 0.05024 0.8377 0.8744 0.1128 ] Network output: [ 0.9856 0.1443 -0.1473 0.002225 -0.0009988 0.04084 0.001677 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5746 -0.07269 -0.05532 0.2522 0.9603 0.9802 0.6422 0.8517 0.9472 0.6807 ] Network output: [ -0.01844 1.019 0.9819 -0.0009736 0.0004371 0.03209 -0.0007338 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04275 0.01736 0.03166 0.02506 0.9764 0.983 0.04359 0.9341 0.9632 0.05044 ] Network output: [ 0.1632 -0.2875 1.138 0.0006441 -0.0002892 0.8252 0.0004854 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6253 0.2688 0.3542 0.434 0.9648 0.9831 0.6276 0.8632 0.9542 0.6791 ] Network output: [ -0.08032 0.1589 0.9022 0.0002871 -0.0001289 1.101 0.0002164 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6223 0.54 0.3604 0.1661 0.9792 0.9862 0.6227 0.9429 0.9663 0.4089 ] Network output: [ -0.1585 0.4193 0.7408 -0.003093 0.001388 1.144 -0.002331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6327 0.616 0.4206 0.005433 0.9776 0.9849 0.6328 0.9398 0.9629 0.4326 ] Network output: [ 0.1388 0.6112 0.3149 0.0005373 -0.0002412 0.7985 0.000405 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1237 Epoch 1511 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01431 1.06 0.9614 -0.0009379 0.0004211 0.003835 -0.0007069 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02761 -0.003653 0.02331 0.01868 0.9235 0.9353 0.04973 0.8383 0.8748 0.1143 ] Network output: [ 0.8678 0.1177 0.02286 0.001646 -0.0007391 0.1305 0.001241 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.57 -0.03972 0.0007453 0.2627 0.9603 0.9803 0.6364 0.8523 0.9475 0.6871 ] Network output: [ -0.01383 1.006 0.9828 -0.0008305 0.0003728 0.03509 -0.0006259 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04411 0.02007 0.03574 0.02577 0.9766 0.9832 0.04497 0.9352 0.9639 0.05312 ] Network output: [ 0.138 -0.2934 1.149 0.0005522 -0.0002479 0.8706 0.0004161 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6357 0.2988 0.377 0.4219 0.965 0.9831 0.638 0.8636 0.9543 0.6812 ] Network output: [ -0.05896 0.1739 0.8408 0.0003176 -0.0001426 1.104 0.0002394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6333 0.5567 0.3636 0.1426 0.9795 0.9863 0.6338 0.9438 0.9666 0.4091 ] Network output: [ -0.1333 0.4601 0.6616 -0.003391 0.001523 1.131 -0.002556 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6415 0.626 0.4233 -0.031 0.9779 0.985 0.6416 0.9402 0.963 0.4349 ] Network output: [ 0.1515 0.5962 0.3035 0.0007147 -0.0003208 0.8002 0.0005386 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1356 Epoch 1512 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.00388 1.044 0.9664 -0.0006696 0.0003006 -0.00579 -0.0005047 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02785 -0.004367 0.02077 0.01975 0.9236 0.9354 0.0503 0.8381 0.8748 0.1166 ] Network output: [ 0.9508 0.07584 -0.04214 0.002638 -0.001184 0.07536 0.001988 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5748 -0.06121 -0.03856 0.2734 0.9604 0.9803 0.6421 0.8524 0.9476 0.6924 ] Network output: [ -0.01488 0.9956 0.9992 -0.0007065 0.0003172 0.03209 -0.0005325 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04375 0.01852 0.03432 0.02805 0.9767 0.9832 0.04461 0.935 0.9638 0.05354 ] Network output: [ 0.1622 -0.3677 1.202 0.00142 -0.0006375 0.8473 0.00107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6327 0.278 0.3589 0.4575 0.965 0.9832 0.6351 0.8639 0.9545 0.6898 ] Network output: [ -0.08289 0.1016 0.9497 0.0008166 -0.0003666 1.118 0.0006154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6295 0.5475 0.3688 0.1843 0.9795 0.9864 0.6299 0.9438 0.9668 0.4196 ] Network output: [ -0.155 0.3774 0.769 -0.002622 0.001177 1.153 -0.001976 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6393 0.6225 0.4262 0.01948 0.9779 0.9851 0.6394 0.9406 0.9634 0.4389 ] Network output: [ 0.1514 0.5975 0.3049 0.0008275 -0.0003715 0.7981 0.0006236 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1201 Epoch 1513 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 9.184e-05 1.065 0.9427 -0.0008097 0.0003635 -0.01102 -0.0006102 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02779 -0.005017 0.01775 0.01809 0.9236 0.9354 0.05034 0.8379 0.8745 0.1152 ] Network output: [ 1.02 0.1331 -0.1773 0.002538 -0.001139 0.01442 0.001913 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5743 -0.08303 -0.07447 0.2547 0.9604 0.9803 0.6421 0.852 0.9473 0.6886 ] Network output: [ -0.01917 1.011 0.9923 -0.0008595 0.0003858 0.03146 -0.0006477 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04246 0.01647 0.03153 0.02597 0.9765 0.983 0.04329 0.9341 0.9631 0.05159 ] Network output: [ 0.1719 -0.3113 1.161 0.000875 -0.0003928 0.8099 0.0006594 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6212 0.2558 0.3473 0.4458 0.9648 0.9831 0.6235 0.8635 0.9543 0.6884 ] Network output: [ -0.0928 0.1338 0.9453 0.0003913 -0.0001757 1.108 0.0002949 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.621 0.5353 0.3682 0.1772 0.9793 0.9862 0.6215 0.943 0.9664 0.4203 ] Network output: [ -0.1678 0.4035 0.7677 -0.002965 0.001331 1.152 -0.002234 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6324 0.615 0.4286 0.01106 0.9778 0.985 0.6325 0.9402 0.9633 0.4414 ] Network output: [ 0.1407 0.6072 0.3133 0.0006924 -0.0003109 0.8008 0.0005219 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1241 Epoch 1514 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01318 1.062 0.9579 -0.0009049 0.0004063 0.002935 -0.000682 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02749 -0.003884 0.02267 0.01817 0.9235 0.9353 0.04959 0.8383 0.8747 0.1153 ] Network output: [ 0.8858 0.1383 -0.01439 0.001521 -0.0006828 0.1106 0.001146 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5675 -0.04769 -0.01089 0.2562 0.9603 0.9803 0.6338 0.8524 0.9475 0.6914 ] Network output: [ -0.01526 1.007 0.9844 -0.0008082 0.0003628 0.03582 -0.0006091 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04367 0.01923 0.03567 0.02526 0.9766 0.9832 0.04452 0.935 0.9638 0.05374 ] Network output: [ 0.1396 -0.272 1.139 0.0002603 -0.0001169 0.8551 0.0001962 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6302 0.2879 0.3759 0.4157 0.9649 0.9831 0.6326 0.8637 0.9543 0.687 ] Network output: [ -0.06407 0.1865 0.8375 0.0001179 -5.294e-05 1.105 8.887e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6318 0.5529 0.37 0.1341 0.9794 0.9863 0.6323 0.9437 0.9666 0.4169 ] Network output: [ -0.1368 0.4835 0.6445 -0.003638 0.001633 1.131 -0.002742 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6406 0.6246 0.4313 -0.04851 0.9779 0.9851 0.6407 0.9406 0.9631 0.4431 ] Network output: [ 0.1509 0.593 0.3042 0.0008122 -0.0003646 0.8044 0.0006121 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1396 Epoch 1515 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.007855 1.038 0.9762 -0.0006053 0.0002718 -0.0008321 -0.0004562 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02768 -0.004051 0.02266 0.02007 0.9236 0.9354 0.04995 0.8384 0.8749 0.1182 ] Network output: [ 0.9045 0.07255 0.02114 0.00227 -0.001019 0.1066 0.00171 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5708 -0.0514 -0.01817 0.2766 0.9605 0.9803 0.6374 0.8529 0.9477 0.6993 ] Network output: [ -0.0134 0.989 1.001 -0.0006036 0.000271 0.03414 -0.0004549 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04413 0.01921 0.03645 0.02847 0.9767 0.9833 0.04499 0.9355 0.9641 0.05546 ] Network output: [ 0.1529 -0.3645 1.206 0.00125 -0.0005612 0.8583 0.000942 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6342 0.2832 0.3689 0.4519 0.965 0.9832 0.6366 0.8642 0.9546 0.6953 ] Network output: [ -0.07611 0.1088 0.9256 0.0007974 -0.000358 1.121 0.0006009 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6339 0.5525 0.375 0.1715 0.9796 0.9865 0.6344 0.9441 0.967 0.4255 ] Network output: [ -0.1451 0.4061 0.7252 -0.002834 0.001272 1.147 -0.002136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.643 0.6264 0.433 -0.006365 0.9781 0.9852 0.6431 0.941 0.9636 0.4459 ] Network output: [ 0.1582 0.5888 0.298 0.001006 -0.0004519 0.8009 0.0007585 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1274 Epoch 1516 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.003603 1.056 0.9478 -0.0006428 0.0002886 -0.01323 -0.0004844 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02777 -0.005288 0.01713 0.01868 0.9236 0.9354 0.05037 0.8379 0.8745 0.1173 ] Network output: [ 1.043 0.1177 -0.1899 0.002779 -0.001247 -0.002149 0.002094 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5739 -0.09132 -0.0873 0.2606 0.9604 0.9803 0.6418 0.8523 0.9474 0.6959 ] Network output: [ -0.01928 1.004 1.001 -0.0007426 0.0003334 0.03126 -0.0005597 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04224 0.01573 0.03166 0.0272 0.9765 0.9831 0.04307 0.9341 0.9632 0.05278 ] Network output: [ 0.1799 -0.3408 1.187 0.001133 -0.0005088 0.7988 0.0008541 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6184 0.2439 0.3425 0.4604 0.9648 0.9831 0.6208 0.8638 0.9544 0.6966 ] Network output: [ -0.1028 0.1016 0.9905 0.0006108 -0.0002742 1.116 0.0004603 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6203 0.5312 0.3753 0.1916 0.9793 0.9863 0.6207 0.9431 0.9665 0.4304 ] Network output: [ -0.1746 0.3767 0.8006 -0.002672 0.0012 1.161 -0.002014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6325 0.6142 0.4349 0.02225 0.9779 0.9851 0.6326 0.9406 0.9635 0.4484 ] Network output: [ 0.1446 0.602 0.3114 0.0008968 -0.0004026 0.8012 0.0006758 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1245 Epoch 1517 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.0106 1.068 0.949 -0.0008944 0.0004015 0.0008685 -0.0006741 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02737 -0.004293 0.02132 0.01758 0.9234 0.9353 0.04948 0.8382 0.8745 0.1157 ] Network output: [ 0.9212 0.1633 -0.07791 0.001449 -0.0006507 0.07805 0.001092 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5656 -0.06145 -0.03052 0.2491 0.9603 0.9803 0.632 0.8522 0.9474 0.694 ] Network output: [ -0.01721 1.011 0.9844 -0.000813 0.000365 0.03624 -0.0006127 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04293 0.01786 0.03481 0.02476 0.9765 0.9831 0.04377 0.9346 0.9634 0.05369 ] Network output: [ 0.1456 -0.2526 1.129 -1.289e-05 5.787e-06 0.8321 -9.714e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6229 0.2705 0.3716 0.4139 0.9648 0.9831 0.6252 0.8637 0.9542 0.6914 ] Network output: [ -0.07159 0.1911 0.8483 -4.261e-06 1.913e-06 1.104 -3.211e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.628 0.5454 0.375 0.1328 0.9794 0.9863 0.6284 0.9434 0.9664 0.4235 ] Network output: [ -0.1435 0.4909 0.6481 -0.003703 0.001663 1.133 -0.002791 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6377 0.621 0.4375 -0.05486 0.9779 0.9851 0.6378 0.9407 0.9632 0.4496 ] Network output: [ 0.1487 0.5924 0.3073 0.0009038 -0.0004058 0.8065 0.0006812 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1404 Epoch 1518 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01351 1.037 0.9819 -0.0006152 0.0002762 0.005992 -0.0004637 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02744 -0.003684 0.02486 0.02016 0.9235 0.9353 0.04946 0.8386 0.875 0.119 ] Network output: [ 0.8468 0.08018 0.08856 0.001687 -0.0007573 0.1445 0.001271 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5659 -0.04036 0.008469 0.2778 0.9604 0.9803 0.6318 0.8531 0.9478 0.7039 ] Network output: [ -0.0118 0.9866 0.9979 -0.0005435 0.000244 0.03684 -0.0004096 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04442 0.01993 0.03857 0.02835 0.9768 0.9834 0.04528 0.9357 0.9643 0.05696 ] Network output: [ 0.1423 -0.3452 1.195 0.0009008 -0.0004044 0.8696 0.0006788 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6356 0.289 0.3812 0.44 0.9651 0.9832 0.6379 0.8642 0.9545 0.6979 ] Network output: [ -0.06433 0.1286 0.8826 0.0007156 -0.0003213 1.12 0.0005393 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.638 0.5574 0.3793 0.1523 0.9797 0.9865 0.6384 0.9443 0.967 0.4285 ] Network output: [ -0.1321 0.4419 0.6708 -0.003083 0.001384 1.139 -0.002323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6461 0.6296 0.4385 -0.03675 0.9782 0.9853 0.6462 0.9412 0.9635 0.4511 ] Network output: [ 0.1644 0.5791 0.294 0.001189 -0.0005337 0.803 0.000896 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1393 Epoch 1519 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.004967 1.045 0.9558 -0.0004926 0.0002211 -0.01288 -0.0003712 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02771 -0.00536 0.01746 0.01966 0.9236 0.9354 0.0503 0.8379 0.8745 0.1192 ] Network output: [ 1.041 0.09395 -0.1653 0.00291 -0.001306 0.001595 0.002193 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5729 -0.09331 -0.08775 0.2712 0.9605 0.9803 0.6407 0.8524 0.9475 0.7023 ] Network output: [ -0.01825 0.995 1.007 -0.0006171 0.000277 0.03162 -0.0004651 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0423 0.01552 0.03247 0.02883 0.9766 0.9831 0.04313 0.9343 0.9633 0.05416 ] Network output: [ 0.1846 -0.3768 1.215 0.001426 -0.0006401 0.7983 0.001075 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6186 0.2376 0.3417 0.4761 0.9648 0.9831 0.621 0.864 0.9544 0.7033 ] Network output: [ -0.1076 0.06542 1.029 0.000938 -0.0004211 1.125 0.0007069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6216 0.5299 0.381 0.2065 0.9794 0.9864 0.622 0.9433 0.9667 0.4382 ] Network output: [ -0.1762 0.3436 0.8307 -0.002262 0.001015 1.169 -0.001705 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6339 0.615 0.439 0.03587 0.978 0.9852 0.634 0.9409 0.9638 0.453 ] Network output: [ 0.1513 0.5944 0.3079 0.001144 -0.0005134 0.7997 0.0008618 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1239 Epoch 1520 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.005376 1.075 0.9359 -0.0008809 0.0003955 -0.003811 -0.0006639 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02729 -0.004936 0.01892 0.0171 0.9235 0.9353 0.04949 0.8378 0.8742 0.1157 ] Network output: [ 0.9828 0.1863 -0.1733 0.001559 -0.0006999 0.02773 0.001175 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5652 -0.08256 -0.06271 0.2436 0.9603 0.9802 0.632 0.8519 0.9471 0.6949 ] Network output: [ -0.01969 1.016 0.9845 -0.0008352 0.000375 0.03559 -0.0006294 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04184 0.01587 0.03284 0.02456 0.9764 0.983 0.04266 0.9338 0.9629 0.0528 ] Network output: [ 0.1595 -0.2456 1.127 -0.0001212 5.44e-05 0.7995 -9.133e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6135 0.2452 0.3604 0.422 0.9647 0.983 0.6159 0.8633 0.9541 0.6945 ] Network output: [ -0.08388 0.1784 0.8869 1.857e-05 -8.337e-06 1.103 1.399e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6211 0.5328 0.3777 0.1452 0.9792 0.9862 0.6215 0.9428 0.9662 0.4287 ] Network output: [ -0.1564 0.471 0.6872 -0.003494 0.001569 1.14 -0.002633 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6324 0.6145 0.4411 -0.04149 0.9778 0.985 0.6325 0.9405 0.9632 0.4536 ] Network output: [ 0.145 0.5962 0.3122 0.0009748 -0.0004376 0.8057 0.0007346 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1379 Epoch 1521 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01994 1.044 0.9797 -0.0007205 0.0003235 0.01345 -0.000543 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02712 -0.003356 0.02667 0.0197 0.9234 0.9352 0.04885 0.8386 0.8749 0.1185 ] Network output: [ 0.7915 0.1073 0.1354 0.0009316 -0.0004182 0.1781 0.0007021 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5601 -0.03109 0.03368 0.2736 0.9603 0.9803 0.6251 0.8529 0.9477 0.7051 ] Network output: [ -0.01077 0.991 0.9885 -0.0005563 0.0002497 0.03982 -0.0004192 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04438 0.02039 0.03997 0.02723 0.9767 0.9833 0.04524 0.9357 0.9643 0.05751 ] Network output: [ 0.1313 -0.3003 1.162 0.000304 -0.0001365 0.8765 0.0002291 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6349 0.2935 0.3932 0.4195 0.9651 0.9832 0.6372 0.8637 0.9543 0.6969 ] Network output: [ -0.04957 0.1667 0.8202 0.0004857 -0.000218 1.114 0.000366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6401 0.5605 0.3809 0.1257 0.9796 0.9865 0.6405 0.9442 0.9668 0.4278 ] Network output: [ -0.1181 0.4865 0.6083 -0.003416 0.001534 1.128 -0.002575 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6473 0.6311 0.4421 -0.07113 0.9782 0.9852 0.6474 0.9411 0.9633 0.4542 ] Network output: [ 0.1681 0.5697 0.2948 0.001342 -0.0006024 0.8047 0.001011 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1562 Epoch 1522 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.001873 1.033 0.9698 -0.0003836 0.0001722 -0.007867 -0.0002891 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02758 -0.005023 0.01959 0.02099 0.9236 0.9354 0.04998 0.8378 0.8745 0.1205 ] Network output: [ 0.9912 0.06203 -0.07509 0.002784 -0.00125 0.04199 0.002098 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5705 -0.08248 -0.06485 0.2864 0.9605 0.9804 0.6378 0.8524 0.9475 0.7075 ] Network output: [ -0.01537 0.9849 1.011 -0.0004826 0.0002166 0.03306 -0.0003637 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04286 0.01637 0.03459 0.03066 0.9767 0.9833 0.0437 0.9346 0.9637 0.05592 ] Network output: [ 0.181 -0.4129 1.241 0.001685 -0.0007565 0.8166 0.00127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6237 0.2438 0.3488 0.4876 0.965 0.9832 0.6261 0.8639 0.9545 0.7073 ] Network output: [ -0.1013 0.03637 1.038 0.00132 -0.0005928 1.134 0.0009951 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6266 0.5351 0.3839 0.2139 0.9795 0.9865 0.6271 0.9436 0.9669 0.4413 ] Network output: [ -0.1673 0.32 0.8358 -0.001884 0.0008458 1.171 -0.00142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6379 0.6189 0.4403 0.04157 0.9781 0.9853 0.638 0.9411 0.9639 0.4546 ] Network output: [ 0.1617 0.5839 0.3013 0.001405 -0.0006307 0.7971 0.001059 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1227 Epoch 1523 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.003133 1.08 0.9226 -0.0008171 0.0003668 -0.01173 -0.0006158 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0273 -0.005781 0.01548 0.01713 0.9235 0.9353 0.04968 0.8372 0.8738 0.1158 ] Network output: [ 1.069 0.1935 -0.2868 0.00198 -0.0008889 -0.03652 0.001492 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.567 -0.1095 -0.1069 0.2443 0.9603 0.9802 0.6346 0.8513 0.9469 0.6956 ] Network output: [ -0.02214 1.02 0.9875 -0.0008441 0.0003789 0.03325 -0.0006361 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04055 0.01346 0.02988 0.02516 0.9763 0.9829 0.04136 0.9328 0.9623 0.05135 ] Network output: [ 0.1861 -0.266 1.136 0.0001674 -7.517e-05 0.7585 0.0001262 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6033 0.2135 0.338 0.4453 0.9646 0.983 0.6056 0.8626 0.9539 0.697 ] Network output: [ -0.102 0.1383 0.9643 0.0002735 -0.0001228 1.103 0.0002061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6114 0.5156 0.3773 0.177 0.979 0.9861 0.6118 0.9419 0.9658 0.4329 ] Network output: [ -0.1778 0.4096 0.7782 -0.002891 0.001298 1.156 -0.002179 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6249 0.6053 0.4414 0.0004742 0.9777 0.985 0.625 0.94 0.9631 0.4548 ] Network output: [ 0.1407 0.6023 0.3193 0.001053 -0.0004728 0.8014 0.0007937 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1366 Epoch 1524 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.02467 1.062 0.9649 -0.0009257 0.0004156 0.01873 -0.0006976 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02676 -0.00325 0.02684 0.01841 0.9233 0.9351 0.0482 0.838 0.8744 0.1163 ] Network output: [ 0.7675 0.1608 0.1187 0.0001929 -8.659e-05 0.1863 0.0001454 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5541 -0.02954 0.04129 0.2606 0.9602 0.9802 0.6186 0.8519 0.9473 0.7012 ] Network output: [ -0.01153 1.004 0.9737 -0.0006747 0.0003029 0.04228 -0.0005085 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04369 0.02005 0.03954 0.0249 0.9765 0.9832 0.04454 0.9349 0.9639 0.05643 ] Network output: [ 0.1238 -0.2278 1.108 -0.0005047 0.0002266 0.8698 -0.0003804 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6293 0.2916 0.3984 0.3924 0.965 0.9832 0.6316 0.8627 0.9539 0.6926 ] Network output: [ -0.03758 0.2202 0.753 7.36e-05 -3.304e-05 1.102 5.547e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6378 0.5589 0.379 0.09679 0.9795 0.9864 0.6382 0.9435 0.9663 0.4237 ] Network output: [ -0.109 0.5331 0.5538 -0.003808 0.001709 1.116 -0.00287 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6446 0.6285 0.4428 -0.1024 0.9781 0.9851 0.6446 0.9406 0.9628 0.4542 ] Network output: [ 0.1664 0.5642 0.3026 0.001408 -0.0006323 0.8061 0.001061 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1729 Epoch 1525 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.0078 1.021 0.9894 -0.0003676 0.000165 0.003624 -0.0002771 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02729 -0.004114 0.02394 0.02235 0.9235 0.9354 0.04929 0.8376 0.8744 0.1209 ] Network output: [ 0.8762 0.0305 0.09586 0.002213 -0.0009936 0.1303 0.001668 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5651 -0.05371 -0.0105 0.3027 0.9605 0.9803 0.6312 0.8521 0.9476 0.711 ] Network output: [ -0.01026 0.9752 1.008 -0.0003627 0.0001628 0.03596 -0.0002733 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04393 0.01865 0.03812 0.03199 0.9768 0.9834 0.04479 0.9351 0.9642 0.05773 ] Network output: [ 0.1667 -0.4336 1.251 0.001812 -0.0008136 0.8566 0.001366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6333 0.2674 0.3647 0.4864 0.9651 0.9832 0.6357 0.8633 0.9543 0.7062 ] Network output: [ -0.07669 0.03255 0.9902 0.001645 -0.0007383 1.137 0.001239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6356 0.5485 0.3815 0.2031 0.9797 0.9866 0.636 0.9438 0.9669 0.4364 ] Network output: [ -0.1437 0.3268 0.791 -0.00176 0.00079 1.162 -0.001326 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6441 0.6261 0.4384 0.02649 0.9782 0.9854 0.6442 0.941 0.9636 0.4525 ] Network output: [ 0.1746 0.5709 0.2923 0.001619 -0.0007269 0.7942 0.00122 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1301 Epoch 1526 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01318 1.075 0.9166 -0.0006739 0.0003026 -0.02081 -0.0005079 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02737 -0.006558 0.01183 0.01819 0.9236 0.9354 0.04995 0.8363 0.8733 0.1164 ] Network output: [ 1.154 0.1686 -0.3729 0.002738 -0.001229 -0.09281 0.002063 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5703 -0.1335 -0.1522 0.2571 0.9603 0.9803 0.6388 0.8503 0.9466 0.6969 ] Network output: [ -0.02327 1.019 0.9946 -0.0008117 0.0003644 0.02955 -0.0006117 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03948 0.01144 0.02678 0.02686 0.9762 0.9828 0.04027 0.9315 0.9617 0.05006 ] Network output: [ 0.227 -0.3216 1.153 0.001053 -0.0004728 0.719 0.0007938 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5956 0.1851 0.3029 0.4819 0.9644 0.9829 0.5979 0.8614 0.9536 0.6995 ] Network output: [ -0.1219 0.07203 1.069 0.0007751 -0.000348 1.106 0.0005841 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6016 0.4987 0.373 0.2238 0.9788 0.986 0.6021 0.9408 0.9654 0.4362 ] Network output: [ -0.2057 0.3041 0.9193 -0.001905 0.0008554 1.18 -0.001436 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6171 0.5959 0.4388 0.06996 0.9775 0.985 0.6172 0.9391 0.963 0.4539 ] Network output: [ 0.1382 0.6027 0.3314 0.001205 -0.0005411 0.7945 0.0009084 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1444 Epoch 1527 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.02494 1.089 0.9373 -0.0012 0.0005385 0.01843 -0.000904 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02642 -0.003506 0.02432 0.01671 0.9232 0.9351 0.04767 0.8367 0.8733 0.1124 ] Network output: [ 0.8002 0.2277 0.01719 -0.000163 7.319e-05 0.154 -0.0001229 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5494 -0.0398 0.01574 0.2433 0.96 0.9801 0.6137 0.8499 0.9465 0.6915 ] Network output: [ -0.01464 1.025 0.9574 -0.000904 0.0004058 0.04276 -0.0006813 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04226 0.01862 0.03633 0.02232 0.9762 0.9829 0.04309 0.9332 0.9628 0.05328 ] Network output: [ 0.1279 -0.1561 1.052 -0.001116 0.0005009 0.8439 -0.0008409 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6174 0.2792 0.3858 0.3745 0.9647 0.983 0.6197 0.861 0.9533 0.6851 ] Network output: [ -0.03404 0.2629 0.7164 -0.000298 0.0001338 1.088 -0.0002246 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6294 0.5498 0.3701 0.08354 0.9792 0.9861 0.6299 0.9421 0.9654 0.4149 ] Network output: [ -0.1108 0.5554 0.5416 -0.004017 0.001803 1.108 -0.003027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6363 0.6203 0.4374 -0.11 0.9778 0.9849 0.6364 0.9394 0.962 0.4487 ] Network output: [ 0.158 0.5698 0.3149 0.001306 -0.0005863 0.8045 0.0009842 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1785 Epoch 1528 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.02242 1.019 1.004 -0.0005352 0.0002403 0.01909 -0.0004034 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02682 -0.002874 0.02874 0.02313 0.9234 0.9353 0.04821 0.8372 0.8741 0.1193 ] Network output: [ 0.7248 0.01996 0.2938 0.00123 -0.0005524 0.2417 0.0009273 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5563 -0.01484 0.05812 0.3144 0.9603 0.9803 0.6206 0.8513 0.9474 0.7102 ] Network output: [ -0.004579 0.9737 0.9945 -0.0003557 0.0001597 0.03942 -0.0002681 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0448 0.02156 0.0411 0.0318 0.9768 0.9835 0.04566 0.9349 0.9643 0.0581 ] Network output: [ 0.1436 -0.4188 1.234 0.001667 -0.0007484 0.9044 0.001256 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6412 0.3017 0.3818 0.47 0.9652 0.9833 0.6436 0.8614 0.9537 0.6967 ] Network output: [ -0.03707 0.06414 0.8874 0.001756 -0.0007883 1.13 0.001323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.644 0.5648 0.3707 0.1748 0.9797 0.9865 0.6445 0.9433 0.9664 0.4209 ] Network output: [ -0.1094 0.3633 0.7059 -0.001915 0.0008599 1.142 -0.001444 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.649 0.6326 0.4304 -0.004454 0.9782 0.9852 0.649 0.9399 0.9627 0.444 ] Network output: [ 0.186 0.558 0.2864 0.001732 -0.0007776 0.7907 0.001305 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1608 Epoch 1529 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01851 1.064 0.9223 -0.0005613 0.000252 -0.02528 -0.000423 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02731 -0.006775 0.01004 0.02019 0.9237 0.9355 0.0499 0.8348 0.8724 0.1163 ] Network output: [ 1.183 0.1177 -0.3667 0.003409 -0.00153 -0.1031 0.002569 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5713 -0.1398 -0.1735 0.2813 0.9603 0.9802 0.6402 0.8485 0.946 0.6962 ] Network output: [ -0.02165 1.014 1 -0.0007791 0.0003498 0.02621 -0.0005872 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03915 0.01091 0.025 0.0291 0.9762 0.9828 0.03994 0.9303 0.9612 0.04933 ] Network output: [ 0.2659 -0.3886 1.163 0.002166 -0.0009723 0.7025 0.001632 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5949 0.1759 0.2681 0.5163 0.9644 0.9829 0.5973 0.8596 0.9531 0.6995 ] Network output: [ -0.1295 0.01065 1.143 0.001335 -0.0005995 1.11 0.001006 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5972 0.4919 0.3635 0.2633 0.9787 0.986 0.5976 0.9396 0.965 0.4343 ] Network output: [ -0.2258 0.1962 1.049 -0.0009228 0.0004143 1.203 -0.0006954 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6128 0.591 0.4327 0.1387 0.9774 0.9849 0.6129 0.938 0.9626 0.4498 ] Network output: [ 0.1397 0.5912 0.3468 0.001423 -0.0006386 0.7884 0.001072 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1537 Epoch 1530 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.02082 1.114 0.9092 -0.001451 0.0006513 0.01231 -0.001093 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02621 -0.003983 0.02017 0.01614 0.9232 0.9351 0.04741 0.8346 0.8718 0.108 ] Network output: [ 0.8692 0.2634 -0.104 8.588e-05 -3.855e-05 0.1026 6.472e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5478 -0.05618 -0.03192 0.2399 0.9598 0.98 0.6124 0.847 0.9454 0.6789 ] Network output: [ -0.01793 1.046 0.9446 -0.001163 0.0005221 0.04003 -0.0008765 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04071 0.01681 0.03141 0.02167 0.9758 0.9826 0.04152 0.9308 0.9613 0.04898 ] Network output: [ 0.1498 -0.1449 1.03 -0.000865 0.0003883 0.8116 -0.0006519 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6048 0.2614 0.3515 0.3907 0.9644 0.9829 0.6071 0.8584 0.9525 0.6749 ] Network output: [ -0.03789 0.255 0.7419 -0.0001851 8.312e-05 1.078 -0.0001395 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6182 0.5365 0.3518 0.1076 0.9788 0.9859 0.6186 0.94 0.9643 0.3999 ] Network output: [ -0.124 0.5166 0.6038 -0.003644 0.001636 1.113 -0.002746 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6254 0.6089 0.4233 -0.06982 0.9773 0.9846 0.6254 0.9374 0.961 0.4355 ] Network output: [ 0.1481 0.589 0.322 0.001056 -0.0004743 0.7972 0.0007961 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1634 Epoch 1531 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.03327 1.039 0.9959 -0.0009198 0.0004129 0.0283 -0.0006932 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02629 -0.002133 0.02994 0.02273 0.9233 0.9352 0.04715 0.8359 0.8731 0.1153 ] Network output: [ 0.6413 0.05281 0.3678 0.0003765 -0.000169 0.2982 0.0002838 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5474 0.007533 0.08637 0.3147 0.9601 0.9802 0.6104 0.8491 0.9467 0.7018 ] Network output: [ -0.003259 0.9899 0.9732 -0.0005865 0.0002633 0.04099 -0.000442 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04442 0.02285 0.04036 0.03008 0.9766 0.9833 0.04527 0.9335 0.9637 0.05566 ] Network output: [ 0.1234 -0.3693 1.199 0.001246 -0.0005595 0.9289 0.0009392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6392 0.3235 0.3834 0.4538 0.965 0.9832 0.6416 0.8585 0.9528 0.6825 ] Network output: [ -0.009071 0.1122 0.7961 0.001554 -0.0006979 1.116 0.001171 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6443 0.5718 0.354 0.1533 0.9795 0.9864 0.6448 0.9418 0.9654 0.4007 ] Network output: [ -0.08726 0.396 0.6456 -0.00214 0.0009608 1.124 -0.001613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.647 0.6321 0.4167 -0.02022 0.9779 0.985 0.6471 0.938 0.9614 0.4298 ] Network output: [ 0.1884 0.557 0.2856 0.001637 -0.0007349 0.7872 0.001234 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1842 Epoch 1532 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01343 1.059 0.9319 -0.0006772 0.000304 -0.0209 -0.0005103 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02702 -0.006144 0.01142 0.02165 0.9237 0.9355 0.04925 0.833 0.8713 0.1143 ] Network output: [ 1.124 0.0862 -0.2682 0.003331 -0.001495 -0.05237 0.00251 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5673 -0.1203 -0.1559 0.3005 0.9602 0.9802 0.6354 0.8461 0.9454 0.6913 ] Network output: [ -0.01849 1.013 0.9954 -0.0008474 0.0003804 0.02544 -0.0006386 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03955 0.01237 0.02536 0.03016 0.9761 0.9827 0.04035 0.9294 0.9609 0.04881 ] Network output: [ 0.2668 -0.4158 1.165 0.00261 -0.001172 0.7278 0.001967 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6002 0.1959 0.2584 0.5274 0.9644 0.9829 0.6025 0.8576 0.9526 0.6939 ] Network output: [ -0.1152 0.001228 1.123 0.00156 -0.0007003 1.112 0.001176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6007 0.501 0.3509 0.2691 0.9787 0.986 0.6011 0.9388 0.9645 0.4227 ] Network output: [ -0.2191 0.1656 1.066 -0.0006485 0.0002911 1.204 -0.0004887 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6141 0.5935 0.4232 0.1593 0.9773 0.9848 0.6142 0.9369 0.962 0.4412 ] Network output: [ 0.1452 0.5824 0.3475 0.001456 -0.0006535 0.7857 0.001097 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1396 Epoch 1533 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01523 1.121 0.8983 -0.001552 0.000697 0.004416 -0.00117 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02618 -0.004292 0.01696 0.01698 0.9233 0.9352 0.04742 0.8327 0.8706 0.1059 ] Network output: [ 0.9246 0.2496 -0.1661 0.0006526 -0.000293 0.06991 0.0004918 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5494 -0.06562 -0.07113 0.2526 0.9597 0.98 0.6145 0.8445 0.9446 0.6725 ] Network output: [ -0.01915 1.055 0.9426 -0.001314 0.0005899 0.03541 -0.0009902 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03991 0.01594 0.02795 0.02284 0.9757 0.9825 0.0407 0.9289 0.9604 0.04639 ] Network output: [ 0.1747 -0.1887 1.043 -8.052e-06 3.615e-06 0.7958 -6.068e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5993 0.2537 0.3145 0.4252 0.9643 0.9828 0.6016 0.8561 0.9518 0.6698 ] Network output: [ -0.04666 0.2168 0.7991 0.0001429 -6.417e-05 1.078 0.0001077 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6109 0.5286 0.336 0.146 0.9785 0.9857 0.6113 0.9384 0.9635 0.3895 ] Network output: [ -0.1422 0.4472 0.6987 -0.003074 0.00138 1.126 -0.002317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6183 0.6018 0.4119 -0.01158 0.9771 0.9845 0.6184 0.9358 0.9603 0.4258 ] Network output: [ 0.1435 0.6038 0.3224 0.0008505 -0.0003818 0.7904 0.000641 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1416 Epoch 1534 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.03397 1.064 0.9737 -0.001256 0.000564 0.02565 -0.0009467 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02602 -0.002137 0.02736 0.02169 0.9233 0.9352 0.04668 0.8341 0.8719 0.1117 ] Network output: [ 0.6645 0.1002 0.2929 0.0001864 -8.368e-05 0.2785 0.0001405 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5441 0.006595 0.06007 0.3063 0.9599 0.9801 0.6069 0.8465 0.9458 0.6925 ] Network output: [ -0.00636 1.01 0.9595 -0.000887 0.0003982 0.03921 -0.0006685 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04333 0.02231 0.03723 0.02832 0.9763 0.9831 0.04417 0.9317 0.9626 0.05273 ] Network output: [ 0.1208 -0.3237 1.165 0.0009756 -0.000438 0.9216 0.0007353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.631 0.326 0.3644 0.4487 0.9649 0.9831 0.6333 0.8564 0.9522 0.6755 ] Network output: [ -0.008585 0.1501 0.7658 0.001131 -0.0005076 1.106 0.0008521 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6379 0.5683 0.3413 0.1486 0.9793 0.9862 0.6383 0.9404 0.9646 0.3894 ] Network output: [ -0.09128 0.4152 0.639 -0.002471 0.001109 1.118 -0.001862 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6405 0.6264 0.4087 -0.01791 0.9776 0.9848 0.6406 0.9366 0.9606 0.4222 ] Network output: [ 0.1813 0.5689 0.2877 0.001349 -0.0006056 0.7863 0.001017 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1728 Epoch 1535 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.004545 1.061 0.9398 -0.0008739 0.0003923 -0.01376 -0.0006586 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02671 -0.005174 0.01369 0.02208 0.9236 0.9354 0.04851 0.8321 0.8707 0.1128 ] Network output: [ 1.038 0.08149 -0.1609 0.002889 -0.001297 0.01554 0.002177 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.562 -0.08976 -0.1264 0.3077 0.9601 0.9801 0.6288 0.8447 0.945 0.6892 ] Network output: [ -0.01632 1.014 0.9882 -0.000948 0.0004256 0.02625 -0.0007144 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04017 0.0147 0.02671 0.03014 0.9761 0.9828 0.04097 0.9291 0.9609 0.04884 ] Network output: [ 0.2379 -0.4112 1.171 0.002589 -0.001162 0.7752 0.001951 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6059 0.2313 0.2647 0.5219 0.9645 0.9829 0.6082 0.8564 0.9523 0.6899 ] Network output: [ -0.09588 0.02901 1.056 0.001435 -0.0006442 1.113 0.001081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6074 0.5175 0.3415 0.2526 0.9789 0.986 0.6078 0.9386 0.9642 0.411 ] Network output: [ -0.1988 0.2032 1.001 -0.00105 0.0004713 1.189 -0.0007912 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6182 0.5998 0.4164 0.1385 0.9773 0.9848 0.6183 0.9362 0.9614 0.4345 ] Network output: [ 0.1519 0.5844 0.3315 0.001277 -0.0005731 0.7855 0.0009621 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1204 Epoch 1536 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.009385 1.114 0.9019 -0.0015 0.0006733 -0.003145 -0.00113 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02625 -0.004411 0.01477 0.01799 0.9235 0.9353 0.04757 0.8318 0.8701 0.1068 ] Network output: [ 0.9662 0.2196 -0.1967 0.001268 -0.0005691 0.0499 0.0009553 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5523 -0.06828 -0.1011 0.2647 0.9598 0.98 0.6177 0.8435 0.9444 0.6753 ] Network output: [ -0.01956 1.051 0.9513 -0.00134 0.0006016 0.03104 -0.00101 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03962 0.01581 0.02626 0.02416 0.9758 0.9825 0.04041 0.9282 0.96 0.04598 ] Network output: [ 0.1905 -0.2368 1.066 0.0008117 -0.0003644 0.7927 0.0006117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5978 0.2559 0.2861 0.4514 0.9643 0.9828 0.6001 0.8551 0.9516 0.6739 ] Network output: [ -0.05966 0.1869 0.8511 0.0002596 -0.0001165 1.082 0.0001956 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6074 0.5266 0.3301 0.1713 0.9786 0.9857 0.6078 0.9378 0.9632 0.3898 ] Network output: [ -0.1597 0.4024 0.7684 -0.002876 0.001291 1.137 -0.002167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6156 0.5994 0.4106 0.02444 0.977 0.9845 0.6157 0.9352 0.9601 0.4263 ] Network output: [ 0.1423 0.6087 0.3208 0.0007207 -0.0003235 0.7889 0.0005431 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1296 Epoch 1537 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.03 1.075 0.9601 -0.001386 0.0006223 0.01878 -0.001045 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02597 -0.002326 0.02448 0.02092 0.9234 0.9353 0.04664 0.833 0.8711 0.1106 ] Network output: [ 0.7166 0.1286 0.1985 0.0003789 -0.0001701 0.2413 0.0002855 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5443 0.000516 0.02258 0.2983 0.9599 0.9801 0.6074 0.8449 0.9452 0.6899 ] Network output: [ -0.009522 1.02 0.9582 -0.001055 0.0004736 0.03623 -0.000795 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04252 0.02159 0.03478 0.02729 0.9762 0.983 0.04335 0.9307 0.9619 0.05137 ] Network output: [ 0.1256 -0.2995 1.145 0.0009449 -0.0004242 0.9075 0.0007121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6238 0.3242 0.3426 0.4481 0.9648 0.9831 0.6261 0.8556 0.9519 0.6773 ] Network output: [ -0.019 0.1751 0.7655 0.0006983 -0.0003135 1.1 0.0005263 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6321 0.5641 0.3366 0.1472 0.9792 0.9861 0.6325 0.9397 0.9641 0.3885 ] Network output: [ -0.1037 0.4346 0.6446 -0.002903 0.001303 1.116 -0.002188 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6355 0.6218 0.4092 -0.01902 0.9775 0.9848 0.6356 0.9361 0.9603 0.4239 ] Network output: [ 0.174 0.5789 0.289 0.001095 -0.0004917 0.7885 0.0008255 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.159 Epoch 1538 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.001257 1.059 0.9489 -0.0009546 0.0004286 -0.00965 -0.0007194 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02653 -0.004438 0.01527 0.02227 0.9237 0.9355 0.04804 0.8319 0.8705 0.113 ] Network output: [ 0.9768 0.07994 -0.08632 0.002576 -0.001157 0.06331 0.001942 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5583 -0.06589 -0.1067 0.3103 0.9601 0.9802 0.6242 0.8442 0.945 0.6925 ] Network output: [ -0.01505 1.012 0.9875 -0.000973 0.0004368 0.02655 -0.0007333 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04066 0.01655 0.02806 0.03015 0.9762 0.9829 0.04147 0.9293 0.961 0.04966 ] Network output: [ 0.2113 -0.4055 1.181 0.002528 -0.001135 0.812 0.001905 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6096 0.2597 0.2707 0.5156 0.9646 0.983 0.6119 0.856 0.9522 0.6916 ] Network output: [ -0.08473 0.05508 1.005 0.001225 -0.0005497 1.114 0.0009229 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6128 0.5306 0.3384 0.236 0.979 0.9861 0.6133 0.9388 0.9642 0.4072 ] Network output: [ -0.1832 0.2504 0.9344 -0.001574 0.0007068 1.175 -0.001187 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6222 0.6055 0.4156 0.1101 0.9774 0.9848 0.6223 0.9362 0.9611 0.4341 ] Network output: [ 0.1577 0.5871 0.3147 0.001129 -0.0005068 0.7874 0.0008507 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1139 Epoch 1539 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.004007 1.102 0.9097 -0.001367 0.0006135 -0.009583 -0.00103 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02633 -0.004499 0.01308 0.01873 0.9236 0.9354 0.04773 0.8315 0.87 0.1088 ] Network output: [ 1 0.1949 -0.2201 0.00177 -0.0007945 0.03236 0.001334 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5543 -0.07002 -0.1254 0.2723 0.96 0.9801 0.62 0.8433 0.9444 0.6821 ] Network output: [ -0.01979 1.043 0.9632 -0.001288 0.0005782 0.02769 -0.0009706 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03947 0.01576 0.02538 0.02521 0.9759 0.9826 0.04025 0.928 0.9599 0.04653 ] Network output: [ 0.2008 -0.2729 1.087 0.001412 -0.0006337 0.79 0.001064 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5963 0.2582 0.2654 0.4687 0.9644 0.9829 0.5986 0.8549 0.9516 0.6817 ] Network output: [ -0.07251 0.1666 0.8923 0.0002694 -0.0001209 1.087 0.000203 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6056 0.526 0.3297 0.1863 0.9786 0.9858 0.606 0.9376 0.9632 0.3954 ] Network output: [ -0.174 0.3797 0.8119 -0.002877 0.001291 1.145 -0.002168 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6146 0.5987 0.4144 0.04191 0.9771 0.9846 0.6147 0.9352 0.9602 0.4318 ] Network output: [ 0.1423 0.6093 0.3183 0.0006678 -0.0002998 0.7905 0.0005032 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1255 Epoch 1540 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.02612 1.079 0.955 -0.001391 0.0006245 0.013 -0.001048 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02596 -0.002469 0.02243 0.02045 0.9235 0.9353 0.04666 0.8324 0.8706 0.111 ] Network output: [ 0.757 0.1444 0.1316 0.0005877 -0.0002638 0.2123 0.0004429 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5445 -0.003841 -0.006428 0.2923 0.96 0.9801 0.6077 0.8442 0.945 0.6919 ] Network output: [ -0.0116 1.023 0.962 -0.001108 0.0004975 0.0339 -0.0008351 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04203 0.02113 0.03341 0.02673 0.9762 0.9829 0.04285 0.9302 0.9615 0.05115 ] Network output: [ 0.1298 -0.2856 1.134 0.0009727 -0.0004367 0.896 0.0007331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6184 0.323 0.3262 0.4473 0.9648 0.9831 0.6207 0.8553 0.9518 0.6825 ] Network output: [ -0.02847 0.1927 0.7681 0.0003627 -0.0001628 1.098 0.0002734 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6286 0.5616 0.3362 0.1438 0.9792 0.9861 0.629 0.9394 0.9639 0.3921 ] Network output: [ -0.1134 0.4557 0.6429 -0.003299 0.001481 1.115 -0.002486 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6328 0.6194 0.4136 -0.02684 0.9775 0.9848 0.6329 0.9361 0.9602 0.4293 ] Network output: [ 0.1695 0.5838 0.2889 0.0009547 -0.0004286 0.7922 0.0007195 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1533 Epoch 1541 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.004724 1.054 0.9591 -0.0009455 0.0004245 -0.007438 -0.0007125 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0264 -0.003949 0.01642 0.02253 0.9237 0.9355 0.04774 0.8319 0.8705 0.1142 ] Network output: [ 0.9348 0.07584 -0.0307 0.002366 -0.001062 0.09493 0.001783 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5554 -0.04964 -0.09385 0.3129 0.9602 0.9802 0.6207 0.8442 0.945 0.6981 ] Network output: [ -0.01406 1.007 0.9907 -0.0009371 0.0004207 0.02659 -0.0007062 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04105 0.0178 0.02934 0.03044 0.9763 0.9829 0.04186 0.9296 0.9612 0.0509 ] Network output: [ 0.1931 -0.4045 1.193 0.00248 -0.001114 0.8357 0.001869 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6118 0.278 0.2748 0.5122 0.9648 0.9831 0.6141 0.856 0.9522 0.6961 ] Network output: [ -0.07888 0.07082 0.975 0.001068 -0.0004795 1.116 0.0008049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6171 0.5395 0.3389 0.2242 0.9791 0.9861 0.6176 0.9391 0.9642 0.4083 ] Network output: [ -0.1728 0.2868 0.885 -0.001974 0.0008861 1.166 -0.001487 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6256 0.6099 0.4181 0.08598 0.9776 0.9849 0.6257 0.9364 0.961 0.4371 ] Network output: [ 0.1629 0.5869 0.3012 0.001081 -0.0004855 0.7906 0.0008149 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1144 Epoch 1542 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0007458 1.092 0.9166 -0.001216 0.0005459 -0.01487 -0.0009163 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02637 -0.004623 0.01159 0.0193 0.9237 0.9355 0.04784 0.8314 0.8699 0.111 ] Network output: [ 1.03 0.1779 -0.2428 0.002141 -0.0009611 0.01461 0.001613 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5553 -0.07309 -0.1464 0.2777 0.9601 0.9801 0.6212 0.8433 0.9444 0.6892 ] Network output: [ -0.01998 1.036 0.9742 -0.001207 0.0005419 0.02525 -0.0009096 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0393 0.01556 0.02473 0.02611 0.976 0.9826 0.04008 0.9279 0.9598 0.04727 ] Network output: [ 0.2103 -0.2996 1.103 0.001849 -0.0008301 0.7837 0.001393 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5941 0.2572 0.2483 0.4821 0.9645 0.9829 0.5964 0.8549 0.9517 0.6898 ] Network output: [ -0.08372 0.1494 0.9275 0.0002831 -0.0001271 1.092 0.0002133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.604 0.5247 0.3309 0.1976 0.9787 0.9858 0.6044 0.9376 0.9632 0.4021 ] Network output: [ -0.1859 0.3633 0.8455 -0.002874 0.00129 1.151 -0.002166 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6139 0.598 0.4192 0.05319 0.9772 0.9846 0.6139 0.9354 0.9603 0.4382 ] Network output: [ 0.1427 0.6083 0.3159 0.000678 -0.0003044 0.7932 0.0005109 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1249 Epoch 1543 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.02336 1.08 0.9525 -0.001362 0.0006117 0.009106 -0.001027 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02592 -0.002559 0.02098 0.02009 0.9236 0.9354 0.04664 0.8322 0.8704 0.1117 ] Network output: [ 0.7838 0.1576 0.08576 0.0007035 -0.0003158 0.1919 0.0005302 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5438 -0.006565 -0.02731 0.2875 0.96 0.9801 0.607 0.8439 0.9448 0.6951 ] Network output: [ -0.01296 1.023 0.9657 -0.001116 0.0005009 0.03247 -0.0008409 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04167 0.0208 0.03258 0.02631 0.9762 0.9829 0.04248 0.9299 0.9612 0.05131 ] Network output: [ 0.1325 -0.273 1.125 0.0009691 -0.0004351 0.8867 0.0007303 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6137 0.3218 0.3142 0.4452 0.9648 0.9831 0.616 0.8552 0.9518 0.6878 ] Network output: [ -0.03463 0.2073 0.7665 0.0001144 -5.138e-05 1.096 8.625e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6264 0.5602 0.3372 0.1385 0.9792 0.9861 0.6269 0.9393 0.9637 0.3964 ] Network output: [ -0.1191 0.4761 0.635 -0.003608 0.00162 1.112 -0.002719 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6311 0.6179 0.4184 -0.03774 0.9776 0.9848 0.6312 0.9361 0.9602 0.435 ] Network output: [ 0.1668 0.5855 0.2884 0.0008956 -0.000402 0.7961 0.0006749 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1525 Epoch 1544 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.007652 1.048 0.9693 -0.0009113 0.0004091 -0.005353 -0.0006868 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02628 -0.003574 0.01753 0.02293 0.9238 0.9355 0.04747 0.832 0.8705 0.1156 ] Network output: [ 0.8974 0.0703 0.02221 0.002154 -0.000967 0.1215 0.001623 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5524 -0.0371 -0.08179 0.3166 0.9602 0.9802 0.6172 0.8443 0.9451 0.7038 ] Network output: [ -0.01302 1.001 0.9945 -0.0008801 0.0003951 0.02683 -0.0006633 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04138 0.01875 0.03066 0.03094 0.9764 0.983 0.0422 0.9299 0.9614 0.05225 ] Network output: [ 0.1798 -0.4063 1.204 0.002427 -0.00109 0.8527 0.001829 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6134 0.2906 0.2784 0.5109 0.9648 0.9831 0.6157 0.856 0.9522 0.7009 ] Network output: [ -0.07392 0.07947 0.9535 0.0009883 -0.0004437 1.119 0.0007448 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6208 0.5462 0.3405 0.2157 0.9793 0.9862 0.6213 0.9394 0.9643 0.4107 ] Network output: [ -0.1639 0.3124 0.8475 -0.002223 0.0009981 1.159 -0.001676 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6287 0.6136 0.4211 0.06704 0.9777 0.985 0.6288 0.9366 0.961 0.4407 ] Network output: [ 0.1681 0.5844 0.2902 0.001108 -0.0004975 0.7938 0.0008352 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1178 Epoch 1545 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.005111 1.084 0.9213 -0.001078 0.0004841 -0.01938 -0.0008126 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02638 -0.00479 0.01013 0.01984 0.9238 0.9356 0.04791 0.8313 0.8698 0.1128 ] Network output: [ 1.057 0.1659 -0.2667 0.00242 -0.001086 -0.003807 0.001824 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5556 -0.07773 -0.1662 0.2827 0.9602 0.9802 0.6217 0.8433 0.9445 0.6953 ] Network output: [ -0.02014 1.029 0.9831 -0.001128 0.0005062 0.02331 -0.0008498 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03905 0.01518 0.02401 0.02695 0.9761 0.9827 0.03983 0.9278 0.9597 0.04789 ] Network output: [ 0.2217 -0.3215 1.113 0.002215 -0.0009942 0.7737 0.001669 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5912 0.2531 0.2314 0.4946 0.9645 0.9829 0.5935 0.8549 0.9517 0.6967 ] Network output: [ -0.09346 0.1319 0.9614 0.0003362 -0.0001509 1.095 0.0002534 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.602 0.5221 0.3316 0.2091 0.9787 0.9859 0.6024 0.9374 0.9631 0.4082 ] Network output: [ -0.1972 0.3435 0.8813 -0.002792 0.001254 1.158 -0.002104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6127 0.5967 0.4235 0.06598 0.9773 0.9847 0.6128 0.9354 0.9604 0.4439 ] Network output: [ 0.1429 0.6068 0.3146 0.0007286 -0.0003271 0.7957 0.0005491 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1261 Epoch 1546 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.02157 1.082 0.9493 -0.001348 0.0006051 0.006657 -0.001016 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02584 -0.002609 0.01982 0.01974 0.9236 0.9354 0.04654 0.8319 0.8701 0.1121 ] Network output: [ 0.8016 0.1728 0.04975 0.0007303 -0.0003279 0.1771 0.0005504 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5422 -0.008167 -0.0436 0.2831 0.96 0.9801 0.6054 0.8435 0.9447 0.6976 ] Network output: [ -0.01397 1.024 0.9672 -0.001122 0.0005037 0.03168 -0.0008456 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04133 0.02053 0.03187 0.02585 0.9762 0.9829 0.04213 0.9295 0.9609 0.05138 ] Network output: [ 0.1345 -0.258 1.114 0.0009295 -0.0004173 0.8783 0.0007005 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6091 0.3207 0.3041 0.4419 0.9648 0.9831 0.6114 0.8549 0.9517 0.6918 ] Network output: [ -0.03762 0.2216 0.7597 -7.452e-05 3.345e-05 1.094 -5.616e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6246 0.559 0.3374 0.1324 0.9791 0.9861 0.625 0.939 0.9635 0.3994 ] Network output: [ -0.1219 0.4941 0.6246 -0.003834 0.001721 1.109 -0.00289 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6295 0.6164 0.4222 -0.04862 0.9776 0.9848 0.6296 0.9361 0.9601 0.4395 ] Network output: [ 0.1649 0.586 0.2884 0.0008742 -0.0003924 0.7994 0.0006588 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1542 Epoch 1547 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.011 1.042 0.9789 -0.000892 0.0004004 -0.002616 -0.0006722 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02613 -0.003221 0.0188 0.02342 0.9238 0.9356 0.04715 0.832 0.8705 0.1167 ] Network output: [ 0.8565 0.06438 0.08079 0.001887 -0.000847 0.1495 0.001422 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.549 -0.02534 -0.06735 0.3217 0.9603 0.9802 0.6132 0.8443 0.9452 0.7087 ] Network output: [ -0.01175 0.996 0.9968 -0.0008284 0.0003719 0.02734 -0.0006243 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04169 0.01962 0.03201 0.03153 0.9765 0.9831 0.04251 0.9302 0.9615 0.05349 ] Network output: [ 0.1688 -0.4087 1.213 0.002363 -0.001061 0.8678 0.001781 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.615 0.3014 0.2822 0.5105 0.9649 0.9832 0.6173 0.8558 0.9522 0.7044 ] Network output: [ -0.06733 0.08486 0.9329 0.0009719 -0.0004363 1.121 0.0007325 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6243 0.5521 0.3413 0.209 0.9794 0.9863 0.6248 0.9396 0.9643 0.4121 ] Network output: [ -0.1546 0.33 0.8163 -0.002353 0.001056 1.153 -0.001773 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6315 0.6169 0.4232 0.05237 0.9778 0.985 0.6316 0.9367 0.9609 0.4432 ] Network output: [ 0.1735 0.5805 0.2808 0.001175 -0.0005274 0.7964 0.0008853 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1232 Epoch 1548 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00923 1.077 0.9238 -0.0009663 0.0004338 -0.02343 -0.0007282 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02636 -0.004978 0.00859 0.02043 0.9239 0.9356 0.04794 0.8311 0.8696 0.1142 ] Network output: [ 1.084 0.1563 -0.2911 0.002642 -0.001186 -0.02197 0.001991 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5556 -0.08322 -0.1855 0.2886 0.9602 0.9802 0.6218 0.8432 0.9444 0.7001 ] Network output: [ -0.02018 1.025 0.9898 -0.001064 0.0004776 0.02153 -0.0008017 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03874 0.0147 0.02309 0.02778 0.9761 0.9827 0.03951 0.9275 0.9596 0.04823 ] Network output: [ 0.2365 -0.3412 1.118 0.002575 -0.001156 0.7608 0.001941 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5879 0.2472 0.2125 0.5078 0.9645 0.9829 0.5902 0.8546 0.9516 0.7021 ] Network output: [ -0.1017 0.1133 0.9949 0.0004311 -0.0001935 1.097 0.0003249 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5992 0.5184 0.3304 0.2222 0.9787 0.9859 0.5996 0.9371 0.963 0.4126 ] Network output: [ -0.2092 0.3164 0.9249 -0.002621 0.001177 1.166 -0.001975 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6107 0.5945 0.4262 0.08374 0.9773 0.9847 0.6108 0.9353 0.9604 0.4481 ] Network output: [ 0.1428 0.6048 0.3153 0.0007999 -0.0003591 0.7975 0.0006028 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1287 Epoch 1549 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.02047 1.086 0.9442 -0.001366 0.000613 0.005107 -0.001029 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02573 -0.002618 0.01871 0.01937 0.9237 0.9354 0.04638 0.8314 0.8696 0.112 ] Network output: [ 0.8139 0.1904 0.01817 0.0007028 -0.0003155 0.1664 0.0005297 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5401 -0.008562 -0.05778 0.279 0.96 0.9801 0.6031 0.8429 0.9444 0.6986 ] Network output: [ -0.01472 1.027 0.9662 -0.001147 0.0005149 0.03113 -0.0008643 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04096 0.02032 0.03101 0.0253 0.9762 0.9828 0.04176 0.929 0.9605 0.05112 ] Network output: [ 0.1366 -0.2408 1.1 0.0008914 -0.0004002 0.8708 0.0006718 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6044 0.3204 0.2937 0.4383 0.9647 0.983 0.6067 0.8544 0.9515 0.6938 ] Network output: [ -0.03777 0.2358 0.7487 -0.0002148 9.641e-05 1.09 -0.0001618 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6226 0.5579 0.3356 0.1266 0.9791 0.986 0.623 0.9386 0.9632 0.3999 ] Network output: [ -0.1227 0.508 0.6156 -0.003981 0.001787 1.106 -0.003 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6274 0.6146 0.4237 -0.05691 0.9776 0.9848 0.6275 0.9359 0.9598 0.4417 ] Network output: [ 0.1631 0.5865 0.2891 0.0008579 -0.0003852 0.8016 0.0006466 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1568 Epoch 1550 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01497 1.038 0.9874 -0.0009076 0.0004075 0.000705 -0.000684 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02594 -0.002851 0.02015 0.02397 0.9238 0.9356 0.04678 0.8319 0.8703 0.1173 ] Network output: [ 0.8108 0.05905 0.1451 0.001563 -0.0007015 0.1805 0.001178 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5451 -0.01314 -0.0507 0.3279 0.9603 0.9803 0.6088 0.8441 0.9451 0.7121 ] Network output: [ -0.01025 0.9924 0.997 -0.0007989 0.0003587 0.0279 -0.0006021 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04197 0.0205 0.03324 0.0321 0.9766 0.9832 0.04279 0.9302 0.9616 0.0544 ] Network output: [ 0.1589 -0.4103 1.219 0.002297 -0.001031 0.8828 0.001731 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6166 0.3122 0.2854 0.5107 0.965 0.9832 0.6189 0.8554 0.952 0.706 ] Network output: [ -0.05835 0.08947 0.9096 0.0009982 -0.0004481 1.122 0.0007523 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6275 0.5577 0.34 0.2033 0.9794 0.9863 0.628 0.9396 0.9641 0.4111 ] Network output: [ -0.1444 0.3409 0.7901 -0.002391 0.001074 1.148 -0.001802 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6338 0.6197 0.4231 0.04211 0.9778 0.985 0.6339 0.9366 0.9607 0.4437 ] Network output: [ 0.1789 0.5761 0.2729 0.00125 -0.0005613 0.7982 0.0009422 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1308 Epoch 1551 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01293 1.073 0.9245 -0.0008904 0.0003997 -0.02708 -0.0006711 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02631 -0.005148 0.007008 0.02108 0.924 0.9357 0.04792 0.8306 0.8693 0.1151 ] Network output: [ 1.107 0.148 -0.3125 0.002812 -0.001263 -0.03762 0.00212 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5552 -0.08816 -0.2039 0.2957 0.9602 0.9802 0.6216 0.8426 0.9442 0.703 ] Network output: [ -0.01998 1.022 0.9941 -0.001025 0.0004603 0.01967 -0.0007727 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03839 0.01424 0.02195 0.02854 0.9761 0.9827 0.03916 0.9269 0.9593 0.04821 ] Network output: [ 0.2546 -0.3588 1.115 0.002957 -0.001327 0.7466 0.002228 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5846 0.2415 0.1905 0.5211 0.9645 0.9829 0.5868 0.854 0.9515 0.7055 ] Network output: [ -0.1075 0.09577 1.025 0.0005472 -0.0002457 1.097 0.0004124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5959 0.5143 0.3264 0.236 0.9787 0.9858 0.5963 0.9366 0.9627 0.4144 ] Network output: [ -0.2216 0.2836 0.9751 -0.002398 0.001077 1.175 -0.001807 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6081 0.5917 0.4268 0.1062 0.9772 0.9847 0.6081 0.935 0.9603 0.4505 ] Network output: [ 0.142 0.6022 0.3186 0.0008713 -0.0003911 0.7987 0.0006566 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1326 Epoch 1552 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01999 1.092 0.9379 -0.001417 0.0006362 0.004112 -0.001068 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0256 -0.002564 0.01762 0.01909 0.9237 0.9355 0.04617 0.8308 0.8691 0.1114 ] Network output: [ 0.8203 0.2077 -0.00643 0.0006541 -0.0002936 0.1608 0.0004929 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5376 -0.006885 -0.07021 0.2764 0.96 0.9801 0.6005 0.8418 0.944 0.6978 ] Network output: [ -0.01514 1.032 0.9629 -0.001195 0.0005363 0.0305 -0.0009003 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04059 0.02025 0.02996 0.02477 0.9761 0.9828 0.04139 0.9282 0.96 0.05047 ] Network output: [ 0.1392 -0.2249 1.085 0.0009105 -0.0004088 0.8656 0.0006862 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6 0.3222 0.2814 0.4359 0.9647 0.983 0.6022 0.8535 0.9511 0.6936 ] Network output: [ -0.03524 0.2484 0.7351 -0.0002964 0.0001331 1.086 -0.0002234 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6205 0.5573 0.3309 0.1226 0.979 0.986 0.6209 0.9379 0.9627 0.3973 ] Network output: [ -0.1221 0.5156 0.6106 -0.004043 0.001815 1.101 -0.003047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.625 0.6125 0.4224 -0.0603 0.9775 0.9847 0.625 0.9353 0.9595 0.441 ] Network output: [ 0.1615 0.5877 0.2901 0.0008289 -0.0003721 0.8026 0.0006247 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1593 Epoch 1553 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.0189 1.037 0.9935 -0.0009636 0.0004326 0.003705 -0.0007262 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02574 -0.002492 0.02122 0.02451 0.9238 0.9356 0.04638 0.8315 0.87 0.1173 ] Network output: [ 0.7673 0.05563 0.2048 0.001235 -0.0005546 0.2099 0.000931 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5411 -0.001293 -0.0361 0.3346 0.9603 0.9803 0.6042 0.8434 0.9449 0.7136 ] Network output: [ -0.008772 0.991 0.9952 -0.0008044 0.0003611 0.02807 -0.0006062 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04213 0.02132 0.03401 0.03254 0.9766 0.9832 0.04296 0.9299 0.9615 0.05476 ] Network output: [ 0.1507 -0.4107 1.222 0.002256 -0.001013 0.8964 0.0017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6175 0.3229 0.2855 0.5119 0.965 0.9832 0.6199 0.8545 0.9517 0.7055 ] Network output: [ -0.04839 0.09383 0.8863 0.001042 -0.0004679 1.121 0.0007855 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6298 0.5627 0.336 0.1999 0.9794 0.9863 0.6303 0.9393 0.9639 0.4074 ] Network output: [ -0.1349 0.3443 0.7728 -0.002358 0.001059 1.143 -0.001777 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6351 0.6216 0.4205 0.03825 0.9778 0.985 0.6352 0.9362 0.9604 0.4416 ] Network output: [ 0.1835 0.5724 0.2668 0.001306 -0.0005864 0.799 0.0009843 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.139 Epoch 1554 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01574 1.071 0.9239 -0.0008653 0.0003885 -0.03016 -0.0006521 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02623 -0.005239 0.00551 0.02171 0.924 0.9357 0.04782 0.8299 0.8687 0.1152 ] Network output: [ 1.122 0.1423 -0.3275 0.002908 -0.001305 -0.04777 0.002191 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5544 -0.09066 -0.2199 0.3032 0.9603 0.9802 0.6209 0.8417 0.9439 0.7038 ] Network output: [ -0.01949 1.022 0.9956 -0.001022 0.0004586 0.01767 -0.0007699 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03806 0.01396 0.02066 0.02909 0.9761 0.9826 0.03882 0.9262 0.9588 0.04782 ] Network output: [ 0.2737 -0.371 1.103 0.003332 -0.001496 0.734 0.002511 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5816 0.2385 0.1663 0.5325 0.9645 0.9829 0.5839 0.853 0.9512 0.7065 ] Network output: [ -0.1097 0.08453 1.043 0.0006412 -0.0002878 1.094 0.0004832 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5925 0.511 0.3191 0.2473 0.9786 0.9858 0.593 0.9357 0.9622 0.413 ] Network output: [ -0.233 0.2526 1.023 -0.002206 0.0009906 1.181 -0.001663 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6049 0.5886 0.4253 0.1289 0.9771 0.9847 0.605 0.9343 0.9599 0.4508 ] Network output: [ 0.1405 0.5994 0.3238 0.0009142 -0.0004104 0.7996 0.000689 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.137 Epoch 1555 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.02029 1.098 0.9327 -0.001492 0.00067 0.003619 -0.001125 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02547 -0.002408 0.01675 0.01903 0.9237 0.9355 0.04592 0.8298 0.8683 0.1103 ] Network output: [ 0.8168 0.2195 -0.01463 0.000599 -0.0002689 0.1638 0.0004514 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.535 -0.001744 -0.07896 0.2769 0.9599 0.9801 0.5976 0.8405 0.9435 0.6958 ] Network output: [ -0.01503 1.037 0.9585 -0.001256 0.0005639 0.0296 -0.0009466 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04033 0.02046 0.02892 0.02446 0.976 0.9827 0.04113 0.9273 0.9594 0.04962 ] Network output: [ 0.1412 -0.2151 1.071 0.001024 -0.0004596 0.8654 0.0007716 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5967 0.3277 0.268 0.4366 0.9646 0.983 0.599 0.8522 0.9507 0.6917 ] Network output: [ -0.03029 0.2571 0.7206 -0.0003099 0.0001391 1.082 -0.0002336 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.619 0.5581 0.3236 0.1217 0.9789 0.9859 0.6194 0.9371 0.9621 0.392 ] Network output: [ -0.1198 0.516 0.6099 -0.004024 0.001806 1.097 -0.003032 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6228 0.6108 0.4182 -0.05802 0.9774 0.9846 0.6229 0.9346 0.9589 0.4375 ] Network output: [ 0.1608 0.5893 0.2899 0.0007859 -0.0003528 0.8025 0.0005923 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1612 Epoch 1556 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.02144 1.038 0.9961 -0.001046 0.0004695 0.00489 -0.0007882 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02556 -0.002219 0.02147 0.02498 0.9239 0.9356 0.04603 0.8307 0.8693 0.1167 ] Network output: [ 0.7392 0.05515 0.2417 0.001007 -0.0004519 0.2289 0.0007587 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5377 0.00782 -0.0309 0.3408 0.9602 0.9802 0.6003 0.8422 0.9446 0.7133 ] Network output: [ -0.007759 0.9922 0.9926 -0.0008476 0.0003805 0.02736 -0.0006388 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04211 0.0219 0.03391 0.03282 0.9765 0.9832 0.04293 0.9292 0.9612 0.05446 ] Network output: [ 0.1464 -0.4107 1.222 0.00228 -0.001024 0.9052 0.001718 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6171 0.3318 0.2793 0.5157 0.965 0.9832 0.6194 0.8531 0.9513 0.7037 ] Network output: [ -0.04095 0.09667 0.8707 0.001073 -0.0004818 1.119 0.0008088 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6304 0.5658 0.3294 0.2006 0.9794 0.9863 0.6308 0.9387 0.9634 0.4019 ] Network output: [ -0.1298 0.3382 0.7717 -0.002278 0.001023 1.141 -0.001717 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6349 0.6219 0.416 0.04332 0.9778 0.985 0.635 0.9355 0.9599 0.4379 ] Network output: [ 0.1862 0.5706 0.263 0.001318 -0.0005918 0.7994 0.0009935 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1438 Epoch 1557 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01722 1.072 0.9219 -0.0008986 0.0004034 -0.03245 -0.0006772 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02612 -0.005204 0.004242 0.02217 0.9241 0.9358 0.04765 0.8289 0.868 0.1147 ] Network output: [ 1.127 0.1424 -0.3351 0.002898 -0.001301 -0.05036 0.002184 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5529 -0.08912 -0.2321 0.3092 0.9602 0.9802 0.6194 0.8403 0.9435 0.7028 ] Network output: [ -0.01883 1.023 0.9943 -0.001056 0.0004741 0.01566 -0.0007958 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03779 0.01397 0.01941 0.02927 0.976 0.9826 0.03855 0.9252 0.9583 0.04714 ] Network output: [ 0.2892 -0.3741 1.084 0.003639 -0.001634 0.7263 0.002743 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5791 0.2406 0.1428 0.5396 0.9644 0.9829 0.5814 0.8517 0.9507 0.7056 ] Network output: [ -0.1075 0.0845 1.044 0.0006656 -0.0002988 1.089 0.0005016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5898 0.5097 0.3094 0.2529 0.9786 0.9857 0.5902 0.9348 0.9616 0.4084 ] Network output: [ -0.2406 0.234 1.055 -0.002141 0.000961 1.184 -0.001613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.602 0.586 0.4222 0.1451 0.977 0.9846 0.602 0.9334 0.9594 0.4495 ] Network output: [ 0.1383 0.5977 0.3287 0.0008982 -0.0004032 0.8007 0.0006769 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1397 Epoch 1558 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.02141 1.102 0.9311 -0.00157 0.0007047 0.00355 -0.001183 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02533 -0.002141 0.01628 0.0193 0.9238 0.9355 0.04566 0.8287 0.8675 0.1094 ] Network output: [ 0.8015 0.2226 -0.0002729 0.0005369 -0.000241 0.1768 0.0004046 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5325 0.007342 -0.08232 0.2812 0.9599 0.9801 0.5947 0.8389 0.943 0.6941 ] Network output: [ -0.01434 1.04 0.9548 -0.001314 0.0005897 0.02841 -0.00099 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04026 0.02103 0.02821 0.02452 0.976 0.9826 0.04105 0.9264 0.9589 0.04896 ] Network output: [ 0.1407 -0.2142 1.066 0.001208 -0.0005425 0.8721 0.0009106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5953 0.3376 0.2554 0.4406 0.9646 0.983 0.5976 0.8506 0.9502 0.6894 ] Network output: [ -0.0241 0.2612 0.7069 -0.0002785 0.000125 1.079 -0.0002099 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6187 0.5608 0.3153 0.1233 0.9789 0.9858 0.6192 0.9363 0.9616 0.3857 ] Network output: [ -0.116 0.5117 0.6108 -0.003962 0.001779 1.093 -0.002986 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6216 0.6102 0.4127 -0.05238 0.9773 0.9846 0.6217 0.9337 0.9583 0.433 ] Network output: [ 0.1616 0.5904 0.2872 0.0007401 -0.0003322 0.8021 0.0005577 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1627 Epoch 1559 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.02148 1.04 0.9953 -0.001122 0.0005035 0.003287 -0.0008452 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02542 -0.002092 0.02062 0.02536 0.9239 0.9357 0.04579 0.8296 0.8685 0.116 ] Network output: [ 0.7354 0.05693 0.2445 0.0009518 -0.0004273 0.2317 0.0007173 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5356 0.01246 -0.03954 0.3458 0.9602 0.9802 0.598 0.8407 0.9441 0.712 ] Network output: [ -0.007459 0.9946 0.9911 -0.0009118 0.0004093 0.02557 -0.0006871 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04187 0.02211 0.03291 0.033 0.9765 0.9831 0.04269 0.9283 0.9607 0.05373 ] Network output: [ 0.148 -0.4119 1.219 0.002394 -0.001075 0.9064 0.001804 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6149 0.3374 0.2652 0.5225 0.965 0.9832 0.6172 0.8517 0.9509 0.7024 ] Network output: [ -0.03901 0.09701 0.869 0.001062 -0.0004767 1.116 0.0008003 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6289 0.5664 0.3217 0.2061 0.9794 0.9862 0.6293 0.9379 0.9629 0.397 ] Network output: [ -0.1318 0.3239 0.7897 -0.002198 0.0009866 1.141 -0.001656 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6332 0.6207 0.4114 0.05652 0.9777 0.9849 0.6333 0.9346 0.9593 0.4345 ] Network output: [ 0.1862 0.5709 0.2617 0.001283 -0.0005762 0.8002 0.0009672 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1419 Epoch 1560 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01751 1.076 0.9191 -0.0009708 0.0004358 -0.03399 -0.0007316 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.026 -0.005039 0.003262 0.02238 0.9241 0.9358 0.04744 0.8278 0.8672 0.1138 ] Network output: [ 1.123 0.1488 -0.3366 0.002796 -0.001255 -0.04592 0.002107 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5511 -0.08332 -0.2404 0.3125 0.9602 0.9802 0.6173 0.8387 0.9429 0.7012 ] Network output: [ -0.01814 1.027 0.9914 -0.001113 0.0004996 0.01383 -0.0008387 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03764 0.01433 0.01837 0.02911 0.976 0.9826 0.0384 0.9243 0.9578 0.04642 ] Network output: [ 0.2972 -0.3683 1.064 0.003842 -0.001725 0.7257 0.002895 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5773 0.2484 0.1229 0.542 0.9644 0.9829 0.5796 0.8501 0.9503 0.7038 ] Network output: [ -0.1022 0.09526 1.028 0.0006087 -0.0002733 1.083 0.0004588 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5882 0.5111 0.2994 0.252 0.9785 0.9857 0.5886 0.9338 0.9609 0.4023 ] Network output: [ -0.2431 0.2327 1.064 -0.002229 0.001001 1.181 -0.00168 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5998 0.5845 0.4184 0.1507 0.9769 0.9845 0.5999 0.9324 0.9587 0.4473 ] Network output: [ 0.1362 0.5982 0.3306 0.0008216 -0.0003688 0.8022 0.0006192 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1395 Epoch 1561 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.02279 1.102 0.9339 -0.001626 0.0007298 0.003428 -0.001225 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02523 -0.001821 0.01616 0.01979 0.9238 0.9356 0.04543 0.8277 0.8666 0.109 ] Network output: [ 0.7795 0.2182 0.02961 0.0004702 -0.0002111 0.1951 0.0003543 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5303 0.01852 -0.08191 0.2878 0.9599 0.98 0.5922 0.8373 0.9425 0.6938 ] Network output: [ -0.01333 1.041 0.9537 -0.001351 0.0006063 0.02697 -0.001018 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04033 0.0218 0.02794 0.0249 0.976 0.9826 0.04112 0.9257 0.9585 0.04873 ] Network output: [ 0.1374 -0.2202 1.068 0.0014 -0.0006286 0.8833 0.001055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5954 0.3498 0.2451 0.4465 0.9647 0.983 0.5977 0.8491 0.9497 0.6884 ] Network output: [ -0.01872 0.2623 0.6963 -0.0002468 0.0001108 1.078 -0.000186 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6195 0.5649 0.3084 0.1258 0.9789 0.9858 0.62 0.9356 0.9611 0.3809 ] Network output: [ -0.1119 0.5066 0.6112 -0.00391 0.001755 1.09 -0.002946 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6215 0.6109 0.4081 -0.04688 0.9773 0.9845 0.6216 0.9329 0.9577 0.4295 ] Network output: [ 0.1638 0.5906 0.2821 0.0007036 -0.0003159 0.8025 0.0005303 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1642 Epoch 1562 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01924 1.041 0.993 -0.001162 0.0005217 -0.000571 -0.0008758 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02534 -0.002087 0.019 0.02568 0.924 0.9357 0.04567 0.8285 0.8677 0.1155 ] Network output: [ 0.7512 0.05932 0.2211 0.001039 -0.0004667 0.2214 0.0007834 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5347 0.01343 -0.05861 0.3497 0.9602 0.9802 0.5971 0.8391 0.9435 0.7112 ] Network output: [ -0.007645 0.9965 0.9918 -0.0009677 0.0004344 0.02304 -0.0007293 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04154 0.02205 0.03144 0.03317 0.9764 0.9831 0.04235 0.9274 0.9601 0.05299 ] Network output: [ 0.1545 -0.4146 1.215 0.002574 -0.001156 0.9014 0.00194 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6118 0.3403 0.2456 0.5309 0.965 0.9832 0.6141 0.8503 0.9505 0.7026 ] Network output: [ -0.04215 0.0959 0.8786 0.001003 -0.0004502 1.114 0.0007558 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6262 0.5654 0.3146 0.2139 0.9793 0.9862 0.6266 0.937 0.9624 0.394 ] Network output: [ -0.1395 0.3072 0.8193 -0.002161 0.0009703 1.144 -0.001629 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6308 0.6187 0.4084 0.07263 0.9776 0.9849 0.6309 0.9339 0.9589 0.433 ] Network output: [ 0.1844 0.5722 0.2621 0.001226 -0.0005503 0.802 0.0009238 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1355 Epoch 1563 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01724 1.079 0.9169 -0.001042 0.000468 -0.03511 -0.0007856 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0259 -0.004797 0.002522 0.02243 0.9242 0.9359 0.04723 0.8268 0.8664 0.1132 ] Network output: [ 1.112 0.1584 -0.334 0.00265 -0.00119 -0.03731 0.001997 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5492 -0.07476 -0.246 0.3138 0.9602 0.9802 0.6152 0.8372 0.9424 0.7003 ] Network output: [ -0.01746 1.029 0.9886 -0.001163 0.0005223 0.01224 -0.0008768 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03759 0.01491 0.01764 0.02883 0.976 0.9825 0.03835 0.9235 0.9572 0.04593 ] Network output: [ 0.2978 -0.3584 1.048 0.003951 -0.001774 0.731 0.002977 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5761 0.2598 0.1077 0.5415 0.9644 0.9829 0.5783 0.8487 0.9498 0.7028 ] Network output: [ -0.09626 0.1111 1.005 0.0005032 -0.0002259 1.078 0.0003793 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.588 0.5147 0.2909 0.2471 0.9785 0.9856 0.5884 0.933 0.9603 0.3968 ] Network output: [ -0.2414 0.244 1.054 -0.002413 0.001083 1.175 -0.001819 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5988 0.5844 0.4152 0.1475 0.9769 0.9845 0.5989 0.9316 0.9581 0.4455 ] Network output: [ 0.135 0.6001 0.3285 0.0007226 -0.0003244 0.8043 0.0005446 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1374 Epoch 1564 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.02369 1.099 0.9393 -0.001646 0.000739 0.002698 -0.001241 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02515 -0.001535 0.01612 0.02038 0.9239 0.9357 0.04526 0.8268 0.8659 0.1093 ] Network output: [ 0.7593 0.2103 0.06132 0.0004066 -0.0001826 0.2115 0.0003065 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5287 0.02887 -0.08119 0.2948 0.9599 0.9801 0.5904 0.836 0.9421 0.6954 ] Network output: [ -0.0123 1.039 0.9552 -0.001359 0.0006103 0.02535 -0.001025 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04047 0.02256 0.02795 0.02546 0.976 0.9827 0.04126 0.9251 0.9582 0.04892 ] Network output: [ 0.1331 -0.2294 1.075 0.001556 -0.0006983 0.8943 0.001172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5961 0.3612 0.2368 0.4533 0.9647 0.983 0.5984 0.8477 0.9493 0.6894 ] Network output: [ -0.0156 0.2616 0.6909 -0.0002343 0.0001052 1.078 -0.0001766 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6207 0.5689 0.3036 0.1286 0.9789 0.9858 0.6211 0.9351 0.9607 0.3785 ] Network output: [ -0.1089 0.5025 0.6118 -0.003879 0.001742 1.088 -0.002923 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6221 0.6121 0.4056 -0.04283 0.9773 0.9845 0.6222 0.9323 0.9573 0.4281 ] Network output: [ 0.1664 0.59 0.2759 0.0006855 -0.0003077 0.804 0.0005166 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1656 Epoch 1565 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01586 1.041 0.9908 -0.001163 0.0005221 -0.005332 -0.0008764 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02531 -0.002142 0.01714 0.02597 0.9241 0.9358 0.04565 0.8274 0.8669 0.1156 ] Network output: [ 0.7748 0.06177 0.1877 0.00117 -0.0005255 0.2056 0.0008821 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5346 0.01272 -0.08099 0.3527 0.9602 0.9802 0.5971 0.8376 0.943 0.7116 ] Network output: [ -0.007935 0.9972 0.9943 -0.0009971 0.0004476 0.02034 -0.0007514 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04122 0.0219 0.02997 0.03337 0.9764 0.983 0.04203 0.9265 0.9595 0.05252 ] Network output: [ 0.1626 -0.4174 1.21 0.002759 -0.001239 0.8939 0.00208 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6086 0.3419 0.225 0.5391 0.965 0.9832 0.6109 0.8491 0.9501 0.7045 ] Network output: [ -0.04772 0.09502 0.8924 0.0009159 -0.0004112 1.112 0.0006902 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6233 0.5639 0.309 0.2214 0.9793 0.9862 0.6238 0.9363 0.9619 0.3933 ] Network output: [ -0.1492 0.2934 0.8493 -0.002175 0.0009763 1.147 -0.001639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6284 0.6167 0.4073 0.08662 0.9776 0.9849 0.6285 0.9332 0.9585 0.4336 ] Network output: [ 0.182 0.5735 0.2623 0.001175 -0.0005274 0.8049 0.0008854 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1286 Epoch 1566 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01699 1.082 0.916 -0.001086 0.0004876 -0.03612 -0.0008185 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02583 -0.00454 0.001947 0.02245 0.9243 0.936 0.04707 0.826 0.8657 0.113 ] Network output: [ 1.099 0.1677 -0.3289 0.002495 -0.00112 -0.02766 0.00188 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5475 -0.06552 -0.2501 0.3143 0.9602 0.9802 0.6133 0.8359 0.942 0.7007 ] Network output: [ -0.01675 1.031 0.9871 -0.001188 0.0005333 0.01088 -0.0008952 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03763 0.01558 0.01719 0.02862 0.976 0.9825 0.03839 0.9228 0.9568 0.04575 ] Network output: [ 0.2936 -0.3486 1.038 0.003988 -0.00179 0.7392 0.003005 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5754 0.2722 0.09698 0.5403 0.9645 0.9829 0.5776 0.8475 0.9494 0.703 ] Network output: [ -0.09131 0.1265 0.9826 0.0003894 -0.0001748 1.075 0.0002935 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5886 0.5191 0.2848 0.2413 0.9785 0.9856 0.589 0.9324 0.9598 0.3931 ] Network output: [ -0.2377 0.26 1.037 -0.002612 0.001173 1.168 -0.001968 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5988 0.5852 0.4131 0.14 0.9769 0.9844 0.5989 0.9309 0.9576 0.4445 ] Network output: [ 0.1349 0.6021 0.3236 0.0006438 -0.000289 0.8071 0.0004852 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1348 Epoch 1567 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.02376 1.094 0.9455 -0.001631 0.0007324 0.001266 -0.001229 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0251 -0.001329 0.016 0.02097 0.9241 0.9358 0.04515 0.8261 0.8653 0.11 ] Network output: [ 0.7451 0.2022 0.0865 0.0003465 -0.0001556 0.2225 0.0002611 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5277 0.03678 -0.08208 0.3013 0.96 0.9801 0.5892 0.8348 0.9417 0.6983 ] Network output: [ -0.01138 1.035 0.9586 -0.001341 0.000602 0.02368 -0.001011 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0406 0.02315 0.02806 0.02611 0.9761 0.9827 0.0414 0.9247 0.9579 0.04936 ] Network output: [ 0.1291 -0.2393 1.085 0.001662 -0.0007461 0.9026 0.001252 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5968 0.3702 0.2296 0.4604 0.9648 0.9831 0.599 0.8466 0.949 0.6919 ] Network output: [ -0.01477 0.2596 0.6907 -0.0002321 0.0001042 1.078 -0.0001749 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6217 0.5721 0.3008 0.1317 0.979 0.9859 0.6221 0.9348 0.9604 0.3782 ] Network output: [ -0.1075 0.4988 0.614 -0.003852 0.001729 1.087 -0.002903 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.623 0.6134 0.4049 -0.03979 0.9773 0.9846 0.6231 0.9319 0.9569 0.4285 ] Network output: [ 0.1689 0.5891 0.2693 0.0006911 -0.0003103 0.8066 0.0005208 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1662 Epoch 1568 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01229 1.041 0.9893 -0.001135 0.0005094 -0.009988 -0.0008551 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02529 -0.002215 0.01538 0.02626 0.9243 0.9359 0.04567 0.8266 0.8662 0.116 ] Network output: [ 0.798 0.06466 0.155 0.001269 -0.0005696 0.1896 0.0009562 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5347 0.01157 -0.1019 0.3552 0.9603 0.9803 0.5974 0.8363 0.9426 0.713 ] Network output: [ -0.008122 0.9968 0.9975 -0.0009981 0.0004481 0.01789 -0.0007522 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04095 0.02176 0.02873 0.03361 0.9765 0.983 0.04176 0.9258 0.959 0.05233 ] Network output: [ 0.1697 -0.4192 1.205 0.002899 -0.001301 0.8864 0.002185 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6058 0.343 0.2065 0.5463 0.965 0.9832 0.6081 0.848 0.9498 0.7074 ] Network output: [ -0.05366 0.09502 0.9058 0.0008245 -0.0003701 1.11 0.0006213 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6209 0.5626 0.305 0.2274 0.9793 0.9862 0.6213 0.9357 0.9615 0.3939 ] Network output: [ -0.1582 0.2839 0.8741 -0.002212 0.0009929 1.149 -0.001667 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6266 0.6151 0.4075 0.09691 0.9776 0.9849 0.6267 0.9327 0.9582 0.4354 ] Network output: [ 0.1801 0.5744 0.2615 0.001148 -0.0005153 0.8086 0.000865 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1232 Epoch 1569 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01699 1.082 0.9163 -0.001097 0.0004925 -0.0371 -0.0008268 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02577 -0.004314 0.001478 0.0225 0.9244 0.936 0.04696 0.8253 0.865 0.1131 ] Network output: [ 1.088 0.1759 -0.323 0.002335 -0.001048 -0.01896 0.00176 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5462 -0.05705 -0.2533 0.3148 0.9602 0.9802 0.6117 0.8347 0.9416 0.7021 ] Network output: [ -0.016 1.031 0.9868 -0.001183 0.0005311 0.009719 -0.0008916 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03771 0.0162 0.01694 0.02854 0.976 0.9825 0.03846 0.9223 0.9564 0.04582 ] Network output: [ 0.2871 -0.3402 1.034 0.003965 -0.00178 0.7478 0.002988 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5751 0.2834 0.08964 0.5395 0.9645 0.9829 0.5773 0.8464 0.9491 0.7043 ] Network output: [ -0.08779 0.1395 0.9643 0.0002896 -0.00013 1.073 0.0002183 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5896 0.5233 0.2809 0.236 0.9786 0.9856 0.59 0.932 0.9594 0.391 ] Network output: [ -0.2334 0.2757 1.018 -0.002776 0.001246 1.162 -0.002092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5994 0.5865 0.4122 0.1314 0.9769 0.9844 0.5994 0.9305 0.9572 0.4445 ] Network output: [ 0.1358 0.6033 0.3171 0.0006045 -0.0002714 0.8104 0.0004555 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1323 Epoch 1570 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.02313 1.089 0.9514 -0.00159 0.0007138 -0.000562 -0.001198 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02507 -0.001202 0.01579 0.02152 0.9242 0.9359 0.0451 0.8254 0.8647 0.1109 ] Network output: [ 0.7367 0.1953 0.1041 0.0002798 -0.0001256 0.2284 0.0002109 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5271 0.04223 -0.08441 0.3069 0.9601 0.9801 0.5885 0.8338 0.9415 0.7016 ] Network output: [ -0.01054 1.031 0.9627 -0.001301 0.000584 0.02211 -0.0009803 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04072 0.02358 0.02819 0.02677 0.9762 0.9828 0.04151 0.9244 0.9577 0.04992 ] Network output: [ 0.1259 -0.2485 1.096 0.001719 -0.0007718 0.9081 0.001296 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5973 0.3767 0.2236 0.4674 0.9649 0.9831 0.5996 0.8457 0.9487 0.6951 ] Network output: [ -0.01542 0.257 0.6939 -0.0002272 0.000102 1.079 -0.0001712 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6225 0.5745 0.2994 0.1351 0.9791 0.9859 0.6229 0.9345 0.9601 0.3792 ] Network output: [ -0.1072 0.495 0.6176 -0.003809 0.00171 1.086 -0.002871 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6239 0.6146 0.4054 -0.03719 0.9774 0.9846 0.6239 0.9316 0.9567 0.43 ] Network output: [ 0.1711 0.5882 0.2626 0.0007217 -0.000324 0.8099 0.0005439 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1657 Epoch 1571 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.008909 1.039 0.9883 -0.001089 0.0004888 -0.01412 -0.0008205 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02529 -0.002293 0.01383 0.02653 0.9244 0.936 0.0457 0.8258 0.8655 0.1167 ] Network output: [ 0.8179 0.06825 0.1262 0.001309 -0.0005875 0.175 0.0009862 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.535 0.01031 -0.1198 0.3573 0.9603 0.9803 0.5978 0.8352 0.9423 0.7149 ] Network output: [ -0.008157 0.9957 1.001 -0.0009766 0.0004384 0.01582 -0.000736 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04074 0.02162 0.02774 0.03387 0.9765 0.983 0.04154 0.9252 0.9586 0.05232 ] Network output: [ 0.175 -0.4197 1.202 0.002975 -0.001335 0.8798 0.002242 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6035 0.344 0.1912 0.5525 0.965 0.9832 0.6058 0.8471 0.9495 0.7106 ] Network output: [ -0.05913 0.0957 0.9173 0.0007434 -0.0003337 1.108 0.0005603 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6188 0.5615 0.3021 0.2322 0.9793 0.9862 0.6193 0.9353 0.9612 0.3952 ] Network output: [ -0.1657 0.2775 0.8934 -0.002244 0.001008 1.151 -0.001691 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6253 0.614 0.4084 0.1043 0.9776 0.9849 0.6254 0.9323 0.9579 0.4377 ] Network output: [ 0.1787 0.575 0.2595 0.001149 -0.000516 0.8128 0.0008662 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1191 Epoch 1572 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01725 1.082 0.9172 -0.001083 0.0004862 -0.03802 -0.0008161 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02573 -0.004131 0.001081 0.02259 0.9245 0.9361 0.04687 0.8247 0.8644 0.1135 ] Network output: [ 1.077 0.1833 -0.3176 0.002165 -0.0009718 -0.01185 0.001631 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5451 -0.04991 -0.2558 0.3154 0.9603 0.9802 0.6105 0.8337 0.9413 0.7039 ] Network output: [ -0.01521 1.03 0.9874 -0.001155 0.0005186 0.008778 -0.0008706 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03779 0.01674 0.01684 0.02855 0.9761 0.9826 0.03855 0.9219 0.9561 0.04603 ] Network output: [ 0.2797 -0.3331 1.034 0.003892 -0.001747 0.7557 0.002933 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.575 0.2929 0.08491 0.5393 0.9646 0.983 0.5772 0.8454 0.9488 0.7062 ] Network output: [ -0.0854 0.15 0.95 0.0002104 -9.445e-05 1.072 0.0001586 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5906 0.527 0.2785 0.2316 0.9786 0.9856 0.591 0.9317 0.9591 0.3901 ] Network output: [ -0.2291 0.2893 1.001 -0.002891 0.001298 1.156 -0.002179 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6002 0.5878 0.412 0.1234 0.977 0.9845 0.6003 0.9301 0.9568 0.445 ] Network output: [ 0.1372 0.6038 0.3101 0.0006043 -0.0002713 0.8141 0.0004554 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1303 Epoch 1573 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.02209 1.084 0.9565 -0.001532 0.0006876 -0.002431 -0.001154 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02504 -0.001131 0.01553 0.02203 0.9243 0.936 0.04507 0.8249 0.8642 0.1119 ] Network output: [ 0.7317 0.1899 0.1163 0.000197 -8.844e-05 0.2311 0.0001485 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5268 0.04587 -0.08723 0.3119 0.9601 0.9802 0.5882 0.8329 0.9412 0.7049 ] Network output: [ -0.009739 1.027 0.9669 -0.001245 0.000559 0.02076 -0.0009383 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0408 0.02388 0.02833 0.02742 0.9763 0.9828 0.0416 0.9242 0.9575 0.05048 ] Network output: [ 0.1229 -0.2566 1.106 0.001732 -0.0007774 0.9119 0.001305 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5978 0.3813 0.2188 0.4742 0.9649 0.9832 0.6001 0.8448 0.9485 0.6985 ] Network output: [ -0.01672 0.254 0.6987 -0.0002123 9.53e-05 1.08 -0.00016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.623 0.5762 0.2986 0.1385 0.9791 0.986 0.6234 0.9343 0.9599 0.3805 ] Network output: [ -0.1073 0.4907 0.6221 -0.003742 0.00168 1.086 -0.00282 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6247 0.6156 0.4063 -0.03462 0.9775 0.9847 0.6248 0.9313 0.9565 0.4318 ] Network output: [ 0.1733 0.5872 0.256 0.0007745 -0.0003477 0.8134 0.0005837 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1646 Epoch 1574 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.005796 1.038 0.9873 -0.001033 0.0004637 -0.01767 -0.0007784 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02528 -0.002376 0.01247 0.02679 0.9245 0.9361 0.04573 0.8252 0.865 0.1173 ] Network output: [ 0.8349 0.07259 0.1009 0.001294 -0.000581 0.162 0.0009753 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5353 0.008912 -0.1349 0.3592 0.9604 0.9803 0.5983 0.8342 0.9419 0.717 ] Network output: [ -0.008064 0.9943 1.004 -0.0009394 0.0004217 0.01413 -0.000708 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04055 0.02149 0.02694 0.03414 0.9765 0.983 0.04135 0.9247 0.9582 0.05239 ] Network output: [ 0.1787 -0.4192 1.2 0.002993 -0.001343 0.8741 0.002255 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6016 0.3446 0.1787 0.5579 0.9651 0.9832 0.6039 0.8462 0.9493 0.7137 ] Network output: [ -0.06397 0.09677 0.9272 0.0006774 -0.0003041 1.107 0.0005105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.617 0.5605 0.3 0.2362 0.9793 0.9862 0.6175 0.9348 0.9609 0.3966 ] Network output: [ -0.1718 0.2724 0.9091 -0.002258 0.001014 1.153 -0.001701 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6242 0.6131 0.4095 0.1101 0.9776 0.9849 0.6243 0.932 0.9577 0.44 ] Network output: [ 0.1779 0.5752 0.2567 0.001176 -0.0005279 0.817 0.0008861 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.116 Epoch 1575 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01765 1.081 0.9181 -0.001053 0.0004727 -0.03876 -0.0007936 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02569 -0.003987 0.0007595 0.02269 0.9246 0.9362 0.04681 0.8241 0.8639 0.1139 ] Network output: [ 1.068 0.1902 -0.3128 0.00198 -0.0008888 -0.005959 0.001492 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5444 -0.04401 -0.2576 0.3162 0.9603 0.9803 0.6096 0.8327 0.941 0.7058 ] Network output: [ -0.01441 1.028 0.9881 -0.001112 0.0004992 0.008064 -0.000838 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03786 0.01718 0.01681 0.02862 0.9762 0.9826 0.03862 0.9216 0.9558 0.04627 ] Network output: [ 0.2718 -0.3265 1.035 0.003775 -0.001695 0.7629 0.002845 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5751 0.3008 0.08231 0.5395 0.9646 0.983 0.5774 0.8444 0.9485 0.7081 ] Network output: [ -0.08365 0.1588 0.9386 0.0001507 -6.767e-05 1.071 0.0001136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5915 0.5301 0.2772 0.2281 0.9787 0.9857 0.5919 0.9314 0.9588 0.3896 ] Network output: [ -0.2248 0.3005 0.9854 -0.002959 0.001328 1.152 -0.00223 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.601 0.5892 0.4121 0.1162 0.977 0.9845 0.6011 0.9298 0.9565 0.4456 ] Network output: [ 0.1391 0.6037 0.303 0.0006348 -0.000285 0.8178 0.0004784 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1285 Epoch 1576 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.02086 1.079 0.9608 -0.001464 0.0006571 -0.004148 -0.001103 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02502 -0.001099 0.01527 0.02251 0.9244 0.9361 0.04506 0.8244 0.8638 0.1128 ] Network output: [ 0.7286 0.1858 0.1252 9.553e-05 -4.288e-05 0.2321 7.199e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5267 0.04826 -0.08993 0.3164 0.9602 0.9802 0.5881 0.8321 0.941 0.7079 ] Network output: [ -0.008936 1.023 0.9708 -0.001179 0.0005294 0.01964 -0.0008887 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04087 0.02409 0.02846 0.02803 0.9763 0.9829 0.04166 0.9239 0.9574 0.051 ] Network output: [ 0.12 -0.2637 1.116 0.001708 -0.0007666 0.9146 0.001287 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5983 0.3847 0.2152 0.4807 0.965 0.9832 0.6006 0.844 0.9483 0.7017 ] Network output: [ -0.01822 0.2509 0.7045 -0.0001848 8.297e-05 1.08 -0.0001393 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6233 0.5773 0.2983 0.1421 0.9792 0.986 0.6237 0.9341 0.9597 0.3819 ] Network output: [ -0.1074 0.4855 0.6273 -0.003648 0.001638 1.087 -0.00275 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6253 0.6165 0.4072 -0.03158 0.9775 0.9847 0.6254 0.9311 0.9563 0.4335 ] Network output: [ 0.1754 0.5862 0.2494 0.000844 -0.0003789 0.817 0.0006361 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.163 Epoch 1577 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.002892 1.036 0.9862 -0.0009722 0.0004364 -0.02072 -0.0007326 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02527 -0.002467 0.01124 0.02703 0.9246 0.9362 0.04577 0.8246 0.8644 0.1179 ] Network output: [ 0.85 0.07773 0.07741 0.001239 -0.0005561 0.15 0.0009335 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5357 0.007245 -0.148 0.3608 0.9604 0.9803 0.5989 0.8332 0.9417 0.7188 ] Network output: [ -0.007887 0.9929 1.006 -0.0008924 0.0004006 0.01275 -0.0006725 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04036 0.02135 0.02623 0.03438 0.9766 0.9831 0.04116 0.9243 0.9579 0.05245 ] Network output: [ 0.1814 -0.4175 1.198 0.002966 -0.001331 0.8689 0.002235 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6001 0.3448 0.1685 0.5627 0.9651 0.9833 0.6024 0.8453 0.9491 0.7165 ] Network output: [ -0.06826 0.09824 0.9358 0.0006245 -0.0002803 1.105 0.0004706 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6153 0.5593 0.2983 0.2396 0.9794 0.9862 0.6157 0.9345 0.9606 0.3977 ] Network output: [ -0.177 0.2677 0.9227 -0.00225 0.00101 1.154 -0.001696 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6233 0.6123 0.4105 0.1153 0.9777 0.9849 0.6234 0.9316 0.9574 0.4419 ] Network output: [ 0.1774 0.5753 0.2537 0.00122 -0.0005476 0.8212 0.0009193 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1133 Epoch 1578 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01801 1.08 0.9191 -0.001015 0.0004556 -0.03922 -0.0007648 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02566 -0.003867 0.0005439 0.02281 0.9247 0.9363 0.04675 0.8236 0.8634 0.1143 ] Network output: [ 1.059 0.1968 -0.3077 0.001778 -0.0007984 -0.0003721 0.00134 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5438 -0.03891 -0.2583 0.317 0.9604 0.9803 0.609 0.8317 0.9407 0.7075 ] Network output: [ -0.01359 1.026 0.9889 -0.001059 0.0004754 0.007582 -0.0007981 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03792 0.01757 0.01688 0.0287 0.9762 0.9826 0.03868 0.9213 0.9556 0.04651 ] Network output: [ 0.2632 -0.3202 1.039 0.003619 -0.001625 0.7699 0.002727 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5755 0.3077 0.08181 0.5398 0.9647 0.983 0.5777 0.8436 0.9483 0.7099 ] Network output: [ -0.08211 0.1666 0.9285 0.0001067 -4.79e-05 1.069 8.041e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5922 0.5326 0.2765 0.2249 0.9787 0.9857 0.5926 0.9313 0.9586 0.3892 ] Network output: [ -0.2202 0.3101 0.9708 -0.002988 0.001341 1.148 -0.002252 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6019 0.5904 0.4122 0.1098 0.9771 0.9845 0.602 0.9295 0.9562 0.446 ] Network output: [ 0.1411 0.6033 0.2958 0.0006868 -0.0003083 0.8214 0.0005176 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1268 Epoch 1579 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.0195 1.074 0.9646 -0.00139 0.000624 -0.005697 -0.001047 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02501 -0.001098 0.01501 0.02296 0.9246 0.9362 0.04505 0.8239 0.8633 0.1136 ] Network output: [ 0.727 0.1824 0.1317 -2e-05 8.98e-06 0.2318 -1.508e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5268 0.04964 -0.09237 0.3207 0.9603 0.9803 0.5883 0.8313 0.9408 0.7105 ] Network output: [ -0.008132 1.019 0.9743 -0.001107 0.0004968 0.01871 -0.000834 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0409 0.02422 0.02857 0.02863 0.9764 0.9829 0.0417 0.9237 0.9572 0.05144 ] Network output: [ 0.1171 -0.2703 1.126 0.001656 -0.0007435 0.9168 0.001248 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.599 0.3871 0.2127 0.4873 0.9651 0.9832 0.6012 0.8432 0.9481 0.7044 ] Network output: [ -0.01986 0.2473 0.7112 -0.0001453 6.523e-05 1.081 -0.0001095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6233 0.5779 0.298 0.1462 0.9793 0.9861 0.6238 0.934 0.9596 0.383 ] Network output: [ -0.1076 0.4789 0.634 -0.00353 0.001585 1.088 -0.00266 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6258 0.6171 0.408 -0.02753 0.9776 0.9847 0.6259 0.9308 0.9561 0.4349 ] Network output: [ 0.1774 0.5855 0.243 0.0009248 -0.0004152 0.8204 0.000697 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1608 Epoch 1580 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.0001121 1.035 0.9848 -0.0009091 0.0004081 -0.02339 -0.0006851 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02526 -0.002567 0.0101 0.02723 0.9248 0.9363 0.0458 0.824 0.8639 0.1184 ] Network output: [ 0.8643 0.08387 0.05383 0.001155 -0.0005186 0.1383 0.0008706 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5362 0.00519 -0.1598 0.3621 0.9605 0.9804 0.5997 0.8323 0.9414 0.7203 ] Network output: [ -0.007675 0.9917 1.009 -0.0008395 0.0003769 0.01161 -0.0006326 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04017 0.02117 0.02557 0.03455 0.9766 0.9831 0.04096 0.9239 0.9576 0.05244 ] Network output: [ 0.1836 -0.4147 1.195 0.002905 -0.001304 0.864 0.002189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5988 0.3446 0.1598 0.5669 0.9651 0.9833 0.601 0.8445 0.9489 0.7187 ] Network output: [ -0.07211 0.1004 0.9433 0.0005795 -0.0002602 1.103 0.0004367 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6134 0.5578 0.2969 0.2426 0.9794 0.9862 0.6138 0.9341 0.9604 0.3984 ] Network output: [ -0.1815 0.2633 0.9352 -0.002229 0.001001 1.155 -0.00168 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6222 0.6114 0.4114 0.1202 0.9777 0.9849 0.6223 0.9313 0.9572 0.4434 ] Network output: [ 0.177 0.5753 0.2508 0.001273 -0.0005717 0.8252 0.0009597 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.111 Epoch 1581 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01819 1.079 0.9204 -0.0009726 0.0004366 -0.03928 -0.000733 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02562 -0.003756 0.0004819 0.02292 0.9248 0.9364 0.04669 0.8231 0.8629 0.1146 ] Network output: [ 1.049 0.203 -0.3007 0.001561 -0.000701 0.005864 0.001177 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5434 -0.03416 -0.2573 0.3178 0.9604 0.9803 0.6086 0.8308 0.9404 0.7088 ] Network output: [ -0.01276 1.025 0.9895 -0.001 0.0004489 0.007336 -0.0007536 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03799 0.01793 0.01705 0.02879 0.9763 0.9827 0.03875 0.9211 0.9554 0.04673 ] Network output: [ 0.2536 -0.314 1.043 0.003426 -0.001538 0.7775 0.002582 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5763 0.314 0.08371 0.5399 0.9647 0.9831 0.5785 0.8427 0.948 0.7112 ] Network output: [ -0.0805 0.1741 0.9187 7.385e-05 -3.315e-05 1.069 5.565e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5929 0.535 0.2764 0.2219 0.9788 0.9858 0.5934 0.9311 0.9584 0.3887 ] Network output: [ -0.2151 0.3187 0.9558 -0.002987 0.001341 1.143 -0.002251 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6028 0.5916 0.4123 0.1037 0.9771 0.9845 0.6029 0.9292 0.956 0.4461 ] Network output: [ 0.1435 0.6026 0.2884 0.0007538 -0.0003384 0.8249 0.0005681 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1249 Epoch 1582 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01799 1.07 0.9679 -0.001311 0.0005884 -0.007173 -0.0009877 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02499 -0.001128 0.01474 0.02341 0.9247 0.9363 0.04506 0.8235 0.8629 0.1143 ] Network output: [ 0.7273 0.1791 0.1355 -0.0001414 6.348e-05 0.2301 -0.0001066 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5272 0.04999 -0.09481 0.3249 0.9603 0.9803 0.5889 0.8305 0.9405 0.7127 ] Network output: [ -0.007342 1.015 0.9777 -0.001029 0.0004618 0.01791 -0.0007752 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04091 0.02428 0.02865 0.02922 0.9765 0.983 0.04171 0.9235 0.9571 0.05179 ] Network output: [ 0.1145 -0.2772 1.136 0.001586 -0.0007121 0.9184 0.001195 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5997 0.3886 0.211 0.4941 0.9651 0.9833 0.602 0.8425 0.9479 0.7068 ] Network output: [ -0.02186 0.2429 0.7195 -9.566e-05 4.295e-05 1.081 -7.21e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6231 0.578 0.2979 0.1511 0.9793 0.9861 0.6235 0.9338 0.9595 0.3837 ] Network output: [ -0.1083 0.4703 0.6431 -0.00339 0.001522 1.089 -0.002555 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6262 0.6175 0.4085 -0.02193 0.9776 0.9848 0.6263 0.9306 0.9559 0.4359 ] Network output: [ 0.1794 0.5849 0.2366 0.001013 -0.0004547 0.8237 0.0007633 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1578 Epoch 1583 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.002614 1.034 0.9827 -0.0008444 0.0003791 -0.02579 -0.0006364 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02526 -0.002681 0.009001 0.02736 0.9249 0.9364 0.04584 0.8235 0.8635 0.1188 ] Network output: [ 0.879 0.09122 0.02874 0.001053 -0.0004725 0.1264 0.0007933 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.537 0.002641 -0.1707 0.3628 0.9606 0.9804 0.6007 0.8315 0.9411 0.7213 ] Network output: [ -0.007468 0.9908 1.01 -0.0007825 0.0003513 0.01066 -0.0005897 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03996 0.02096 0.02491 0.03464 0.9767 0.9831 0.04075 0.9235 0.9573 0.05234 ] Network output: [ 0.1854 -0.4104 1.192 0.002818 -0.001265 0.859 0.002124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5976 0.3439 0.1525 0.5704 0.9652 0.9833 0.5999 0.8437 0.9486 0.7204 ] Network output: [ -0.07566 0.1035 0.9497 0.0005376 -0.0002413 1.1 0.0004051 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6113 0.5561 0.2955 0.2452 0.9794 0.9862 0.6118 0.9338 0.9602 0.3987 ] Network output: [ -0.1856 0.2595 0.9465 -0.002201 0.0009879 1.156 -0.001658 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.621 0.6102 0.412 0.1247 0.9777 0.9849 0.6211 0.9309 0.957 0.4446 ] Network output: [ 0.1764 0.5755 0.248 0.001331 -0.0005974 0.8291 0.001003 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1089 Epoch 1584 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01805 1.077 0.922 -0.0009278 0.0004165 -0.03891 -0.0006992 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02558 -0.003644 0.0006198 0.02304 0.9249 0.9365 0.04663 0.8226 0.8624 0.1148 ] Network output: [ 1.037 0.2085 -0.2906 0.00133 -0.0005972 0.01347 0.001002 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5432 -0.02942 -0.2543 0.3187 0.9605 0.9803 0.6084 0.8299 0.9401 0.7098 ] Network output: [ -0.01191 1.023 0.9901 -0.000936 0.0004202 0.007332 -0.0007054 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03808 0.0183 0.01737 0.02888 0.9763 0.9827 0.03884 0.9209 0.9552 0.04695 ] Network output: [ 0.2423 -0.3079 1.05 0.003194 -0.001434 0.7863 0.002407 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5775 0.3201 0.08841 0.5398 0.9648 0.9831 0.5797 0.842 0.9478 0.7123 ] Network output: [ -0.07869 0.1814 0.9085 4.895e-05 -2.198e-05 1.068 3.689e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5938 0.5373 0.2769 0.2188 0.9789 0.9858 0.5942 0.9311 0.9583 0.388 ] Network output: [ -0.2089 0.3272 0.9391 -0.002965 0.001331 1.139 -0.002235 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6039 0.593 0.4122 0.09735 0.9772 0.9846 0.6039 0.929 0.9557 0.4458 ] Network output: [ 0.1464 0.6019 0.2805 0.000832 -0.0003735 0.8282 0.000627 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1229 Epoch 1585 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01623 1.065 0.9709 -0.001225 0.0005498 -0.008691 -0.000923 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02498 -0.001195 0.01443 0.02386 0.9248 0.9363 0.04507 0.8231 0.8626 0.1149 ] Network output: [ 0.7303 0.1757 0.1361 -0.0002599 0.0001167 0.2265 -0.0001959 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.528 0.04916 -0.09765 0.329 0.9604 0.9803 0.5899 0.8299 0.9404 0.7145 ] Network output: [ -0.00659 1.011 0.9812 -0.0009452 0.0004244 0.01718 -0.0007124 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04089 0.02424 0.02867 0.02982 0.9766 0.983 0.04169 0.9234 0.9569 0.05207 ] Network output: [ 0.1125 -0.2847 1.147 0.001504 -0.0006754 0.9193 0.001134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6006 0.389 0.2099 0.5013 0.9652 0.9833 0.6029 0.8419 0.9478 0.7089 ] Network output: [ -0.02449 0.2374 0.7302 -3.86e-05 1.733e-05 1.081 -2.909e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6225 0.5774 0.2979 0.157 0.9794 0.9862 0.6229 0.9337 0.9594 0.3843 ] Network output: [ -0.1098 0.4593 0.6555 -0.003234 0.001452 1.092 -0.002437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6263 0.6176 0.4089 -0.01446 0.9777 0.9848 0.6264 0.9304 0.9558 0.4366 ] Network output: [ 0.1813 0.5847 0.2303 0.001106 -0.0004964 0.827 0.0008332 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1537 Epoch 1586 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.005324 1.034 0.98 -0.0007779 0.0003492 -0.028 -0.0005863 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02525 -0.002809 0.007934 0.02742 0.925 0.9365 0.04588 0.8231 0.863 0.1191 ] Network output: [ 0.8943 0.09996 0.001311 0.0009367 -0.0004205 0.114 0.0007059 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.538 -0.0004696 -0.1809 0.3628 0.9606 0.9804 0.602 0.8307 0.9408 0.7219 ] Network output: [ -0.007297 0.9902 1.012 -0.0007221 0.0003242 0.009873 -0.0005442 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03973 0.02071 0.02425 0.03463 0.9767 0.9831 0.04052 0.9231 0.957 0.05215 ] Network output: [ 0.1869 -0.4047 1.188 0.002709 -0.001216 0.854 0.002042 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5966 0.3428 0.1465 0.573 0.9652 0.9833 0.5989 0.843 0.9485 0.7217 ] Network output: [ -0.07898 0.1079 0.9548 0.0004956 -0.0002225 1.097 0.0003735 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.609 0.554 0.2944 0.247 0.9794 0.9862 0.6095 0.9335 0.9599 0.3987 ] Network output: [ -0.1893 0.2568 0.9563 -0.002172 0.0009753 1.157 -0.001637 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6197 0.6089 0.4127 0.1285 0.9777 0.9849 0.6198 0.9305 0.9568 0.4455 ] Network output: [ 0.1757 0.5761 0.2454 0.001388 -0.0006229 0.8328 0.001046 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1069 Epoch 1587 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01754 1.075 0.9242 -0.0008803 0.0003952 -0.03805 -0.0006635 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02554 -0.003525 0.0009895 0.02315 0.925 0.9365 0.04655 0.8222 0.862 0.1149 ] Network output: [ 1.022 0.2131 -0.2764 0.001087 -0.0004879 0.02285 0.0008191 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.543 -0.0245 -0.249 0.3196 0.9605 0.9804 0.6082 0.8291 0.9399 0.7106 ] Network output: [ -0.01104 1.02 0.9906 -0.0008673 0.0003894 0.007574 -0.0006536 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03818 0.01868 0.01786 0.02898 0.9764 0.9828 0.03895 0.9209 0.9551 0.04721 ] Network output: [ 0.2292 -0.3023 1.059 0.002925 -0.001313 0.7966 0.002204 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5792 0.3263 0.09618 0.5394 0.9649 0.9831 0.5814 0.8413 0.9476 0.713 ] Network output: [ -0.07663 0.1887 0.8975 3.047e-05 -1.368e-05 1.067 2.297e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5948 0.5397 0.2782 0.2154 0.979 0.9858 0.5952 0.9311 0.9582 0.3873 ] Network output: [ -0.2018 0.3362 0.9202 -0.002927 0.001314 1.135 -0.002206 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6051 0.5945 0.4122 0.09049 0.9772 0.9846 0.6052 0.9288 0.9555 0.4452 ] Network output: [ 0.1497 0.6011 0.272 0.0009191 -0.0004126 0.8314 0.0006927 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1206 Epoch 1588 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01416 1.061 0.9735 -0.001131 0.0005076 -0.01033 -0.0008521 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02497 -0.001302 0.01406 0.02431 0.9249 0.9364 0.04511 0.8228 0.8623 0.1155 ] Network output: [ 0.7365 0.172 0.1328 -0.0003683 0.0001653 0.2206 -0.0002776 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5292 0.04702 -0.1012 0.3332 0.9605 0.9804 0.5913 0.8293 0.9402 0.716 ] Network output: [ -0.005905 1.007 0.9848 -0.0008562 0.0003844 0.01648 -0.0006453 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04083 0.02411 0.02864 0.03043 0.9767 0.9831 0.04164 0.9233 0.9569 0.05227 ] Network output: [ 0.1112 -0.293 1.157 0.001416 -0.0006355 0.9191 0.001067 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6017 0.3882 0.2092 0.509 0.9653 0.9833 0.6039 0.8414 0.9477 0.7108 ] Network output: [ -0.02798 0.2307 0.744 2.319e-05 -1.041e-05 1.081 1.747e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6215 0.5763 0.298 0.164 0.9795 0.9862 0.622 0.9337 0.9594 0.3849 ] Network output: [ -0.1125 0.4459 0.6717 -0.003064 0.001376 1.095 -0.002309 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6262 0.6175 0.4093 -0.004952 0.9777 0.9848 0.6263 0.9302 0.9557 0.4372 ] Network output: [ 0.183 0.5847 0.2242 0.001202 -0.0005395 0.8301 0.0009057 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1485 Epoch 1589 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008016 1.034 0.9767 -0.0007093 0.0003184 -0.03003 -0.0005346 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02525 -0.002953 0.006908 0.0274 0.9251 0.9366 0.04594 0.8227 0.8627 0.1192 ] Network output: [ 0.9104 0.1101 -0.02862 0.0008111 -0.0003641 0.101 0.0006112 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5393 -0.004137 -0.1904 0.362 0.9607 0.9805 0.6036 0.83 0.9406 0.7221 ] Network output: [ -0.007179 0.9899 1.012 -0.0006583 0.0002955 0.009244 -0.0004961 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03948 0.02041 0.02361 0.03452 0.9767 0.9831 0.04027 0.9228 0.9567 0.05188 ] Network output: [ 0.1881 -0.3974 1.183 0.00258 -0.001158 0.8489 0.001944 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5957 0.3412 0.1419 0.5747 0.9652 0.9833 0.598 0.8424 0.9483 0.7225 ] Network output: [ -0.08208 0.1138 0.9585 0.0004527 -0.0002032 1.094 0.0003412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6066 0.5515 0.2936 0.2481 0.9794 0.9862 0.607 0.9332 0.9597 0.3983 ] Network output: [ -0.1926 0.2559 0.964 -0.002148 0.0009643 1.157 -0.001619 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6182 0.6074 0.4133 0.1313 0.9776 0.9849 0.6183 0.9302 0.9565 0.4461 ] Network output: [ 0.1747 0.5772 0.2429 0.001442 -0.0006472 0.8363 0.001087 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1053 Epoch 1590 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01662 1.073 0.9272 -0.0008298 0.0003725 -0.03672 -0.0006254 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02549 -0.003396 0.001603 0.02328 0.9251 0.9366 0.04647 0.8219 0.8617 0.1151 ] Network output: [ 1.005 0.2164 -0.2575 0.0008343 -0.0003746 0.0341 0.0006288 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.543 -0.01936 -0.2411 0.3205 0.9605 0.9804 0.6082 0.8285 0.9397 0.7112 ] Network output: [ -0.01017 1.018 0.9912 -0.0007936 0.0003563 0.00806 -0.0005981 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03832 0.01908 0.01855 0.0291 0.9765 0.9828 0.03909 0.9209 0.9551 0.04751 ] Network output: [ 0.2143 -0.2973 1.071 0.002624 -0.001178 0.8085 0.001978 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5815 0.3325 0.107 0.5385 0.9649 0.9832 0.5837 0.8408 0.9475 0.7136 ] Network output: [ -0.07431 0.196 0.8859 1.849e-05 -8.302e-06 1.067 1.394e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.596 0.5421 0.2803 0.2117 0.979 0.9859 0.5964 0.9312 0.9583 0.3867 ] Network output: [ -0.1936 0.3457 0.8988 -0.002877 0.001292 1.131 -0.002168 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6066 0.5962 0.4121 0.08297 0.9773 0.9846 0.6066 0.9287 0.9554 0.4445 ] Network output: [ 0.1534 0.6002 0.2627 0.001013 -0.0004549 0.8344 0.0007637 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1184 Epoch 1591 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01178 1.056 0.976 -0.001028 0.0004615 -0.01212 -0.0007748 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02496 -0.001451 0.01362 0.02477 0.925 0.9365 0.04516 0.8226 0.8621 0.1161 ] Network output: [ 0.7464 0.1679 0.1253 -0.0004612 0.000207 0.2122 -0.0003476 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5307 0.0435 -0.1057 0.3373 0.9605 0.9804 0.5932 0.8289 0.9402 0.7174 ] Network output: [ -0.005311 1.003 0.9888 -0.000762 0.0003421 0.01583 -0.0005743 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04074 0.02388 0.02855 0.03105 0.9767 0.9831 0.04154 0.9232 0.9568 0.05243 ] Network output: [ 0.1109 -0.3023 1.168 0.001325 -0.0005946 0.9178 0.0009982 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6027 0.3863 0.2086 0.5173 0.9653 0.9834 0.605 0.841 0.9476 0.7125 ] Network output: [ -0.03246 0.2228 0.7609 8.729e-05 -3.919e-05 1.082 6.579e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6201 0.5744 0.2985 0.172 0.9795 0.9863 0.6206 0.9337 0.9594 0.3855 ] Network output: [ -0.1164 0.43 0.692 -0.002886 0.001296 1.099 -0.002175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6259 0.6171 0.4098 0.006556 0.9777 0.9849 0.626 0.93 0.9556 0.4377 ] Network output: [ 0.1844 0.5849 0.2183 0.0013 -0.0005838 0.8333 0.00098 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1422 Epoch 1592 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01065 1.035 0.9729 -0.0006388 0.0002868 -0.03186 -0.0004814 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02526 -0.003107 0.005942 0.02728 0.9252 0.9367 0.04601 0.8224 0.8623 0.1192 ] Network output: [ 0.9271 0.1216 -0.06064 0.0006783 -0.0003045 0.08752 0.0005112 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5408 -0.008271 -0.1991 0.3602 0.9607 0.9805 0.6055 0.8294 0.9404 0.722 ] Network output: [ -0.007115 0.99 1.013 -0.0005914 0.0002655 0.008787 -0.0004457 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03923 0.02009 0.02299 0.03429 0.9768 0.9831 0.04001 0.9226 0.9565 0.05154 ] Network output: [ 0.189 -0.3886 1.177 0.002434 -0.001093 0.8439 0.001834 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5949 0.3393 0.1387 0.5754 0.9652 0.9833 0.5972 0.8418 0.9481 0.723 ] Network output: [ -0.08487 0.121 0.9606 0.0004093 -0.0001838 1.09 0.0003085 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6039 0.5489 0.293 0.2483 0.9794 0.9862 0.6043 0.933 0.9596 0.3978 ] Network output: [ -0.1953 0.257 0.969 -0.002129 0.0009556 1.156 -0.001604 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6165 0.6058 0.4139 0.1328 0.9776 0.9849 0.6166 0.9299 0.9563 0.4466 ] Network output: [ 0.1736 0.5788 0.2404 0.001491 -0.0006696 0.8397 0.001124 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1042 Epoch 1593 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0153 1.07 0.9311 -0.0007761 0.0003484 -0.03493 -0.0005849 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02543 -0.003259 0.002454 0.02341 0.9252 0.9367 0.04638 0.8217 0.8615 0.1152 ] Network output: [ 0.9853 0.2183 -0.2337 0.0005756 -0.0002584 0.04719 0.0004338 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.543 -0.01402 -0.2308 0.3216 0.9606 0.9804 0.6082 0.8279 0.9396 0.7117 ] Network output: [ -0.009288 1.015 0.992 -0.0007155 0.0003212 0.008777 -0.0005392 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03847 0.0195 0.01942 0.02926 0.9765 0.9829 0.03925 0.9211 0.9551 0.04787 ] Network output: [ 0.1981 -0.2932 1.085 0.002302 -0.001033 0.8216 0.001735 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5842 0.3388 0.1205 0.5373 0.965 0.9832 0.5865 0.8403 0.9474 0.714 ] Network output: [ -0.07167 0.2033 0.8735 1.413e-05 -6.343e-06 1.067 1.065e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5974 0.5446 0.2831 0.2077 0.9791 0.986 0.5978 0.9315 0.9583 0.3861 ] Network output: [ -0.1845 0.3558 0.8753 -0.002816 0.001264 1.126 -0.002122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6082 0.598 0.4122 0.07489 0.9773 0.9847 0.6082 0.9287 0.9553 0.4436 ] Network output: [ 0.1575 0.5995 0.2528 0.001112 -0.0004993 0.8372 0.0008382 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1165 Epoch 1594 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.009106 1.05 0.9781 -0.000918 0.0004121 -0.01402 -0.0006918 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02496 -0.001637 0.01311 0.02523 0.9251 0.9366 0.04523 0.8224 0.862 0.1167 ] Network output: [ 0.7597 0.1634 0.1136 -0.000535 0.0002402 0.2014 -0.0004032 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5325 0.0387 -0.1111 0.3413 0.9606 0.9804 0.5954 0.8286 0.9401 0.7185 ] Network output: [ -0.004828 0.9988 0.993 -0.0006639 0.000298 0.01522 -0.0005003 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0406 0.02355 0.02838 0.03167 0.9768 0.9832 0.04141 0.9233 0.9568 0.05252 ] Network output: [ 0.1117 -0.3122 1.179 0.001236 -0.0005549 0.9153 0.0009315 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6038 0.3831 0.2082 0.5258 0.9654 0.9834 0.6061 0.8408 0.9476 0.7142 ] Network output: [ -0.03787 0.2139 0.7807 0.0001517 -6.809e-05 1.082 0.0001143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6183 0.5718 0.2991 0.181 0.9796 0.9863 0.6188 0.9337 0.9595 0.3862 ] Network output: [ -0.1215 0.4119 0.7162 -0.002703 0.001213 1.104 -0.002037 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6253 0.6163 0.4103 0.01994 0.9778 0.9849 0.6254 0.9299 0.9556 0.4383 ] Network output: [ 0.1855 0.5853 0.2128 0.0014 -0.0006283 0.8366 0.001055 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1352 Epoch 1595 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01317 1.036 0.9686 -0.0005678 0.0002549 -0.03344 -0.0004279 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02526 -0.003266 0.005062 0.02708 0.9253 0.9367 0.04609 0.8222 0.8621 0.1191 ] Network output: [ 0.9439 0.1343 -0.0939 0.0005408 -0.0002428 0.07409 0.0004076 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5425 -0.01268 -0.2068 0.3576 0.9607 0.9805 0.6077 0.8289 0.9403 0.7214 ] Network output: [ -0.007097 0.9903 1.013 -0.0005227 0.0002347 0.008509 -0.0003939 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03896 0.01976 0.02241 0.03397 0.9768 0.9831 0.03975 0.9224 0.9563 0.05114 ] Network output: [ 0.1894 -0.3786 1.17 0.002275 -0.001021 0.8392 0.001714 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5942 0.3371 0.137 0.575 0.9653 0.9833 0.5965 0.8414 0.948 0.723 ] Network output: [ -0.08721 0.1297 0.9608 0.000367 -0.0001648 1.085 0.0002766 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6011 0.546 0.2927 0.2476 0.9794 0.9862 0.6016 0.9328 0.9595 0.3969 ] Network output: [ -0.1972 0.2601 0.971 -0.002115 0.0009493 1.155 -0.001594 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6147 0.6039 0.4145 0.1329 0.9776 0.9849 0.6148 0.9296 0.9562 0.4469 ] Network output: [ 0.1723 0.581 0.2378 0.001535 -0.0006891 0.8428 0.001157 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1035 Epoch 1596 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01362 1.067 0.9358 -0.0007203 0.0003233 -0.03272 -0.0005428 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02537 -0.003115 0.003518 0.02356 0.9253 0.9367 0.04627 0.8216 0.8613 0.1153 ] Network output: [ 0.9629 0.2187 -0.2051 0.0003141 -0.000141 0.06196 0.0002367 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.543 -0.00852 -0.2181 0.3228 0.9606 0.9804 0.6082 0.8276 0.9395 0.7122 ] Network output: [ -0.008397 1.012 0.9929 -0.0006343 0.0002847 0.009693 -0.000478 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03865 0.01992 0.02045 0.02944 0.9766 0.9829 0.03942 0.9213 0.9553 0.04829 ] Network output: [ 0.1814 -0.2901 1.1 0.001971 -0.0008848 0.8356 0.001485 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5874 0.345 0.136 0.5359 0.9651 0.9833 0.5897 0.8401 0.9474 0.7142 ] Network output: [ -0.06864 0.2102 0.8605 1.893e-05 -8.501e-06 1.067 1.427e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5988 0.5471 0.2863 0.2036 0.9792 0.986 0.5993 0.9318 0.9585 0.3855 ] Network output: [ -0.1748 0.3659 0.8506 -0.002747 0.001233 1.122 -0.00207 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6098 0.5998 0.4122 0.06658 0.9774 0.9847 0.6099 0.9287 0.9552 0.4426 ] Network output: [ 0.1618 0.5987 0.2427 0.001213 -0.0005444 0.8398 0.0009139 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1151 Epoch 1597 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.006204 1.045 0.9801 -0.0008029 0.0003604 -0.01602 -0.0006051 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02496 -0.001854 0.01252 0.02568 0.9252 0.9367 0.0453 0.8224 0.8619 0.1172 ] Network output: [ 0.7763 0.1587 0.09787 -0.0005878 0.0002639 0.1885 -0.000443 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5346 0.03277 -0.1176 0.3452 0.9607 0.9805 0.598 0.8284 0.9401 0.7194 ] Network output: [ -0.004463 0.9946 0.9974 -0.0005641 0.0002532 0.01465 -0.0004251 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04042 0.02313 0.02815 0.03228 0.9769 0.9832 0.04122 0.9233 0.9568 0.05256 ] Network output: [ 0.1137 -0.3225 1.188 0.001155 -0.0005185 0.9116 0.0008705 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6048 0.3789 0.2075 0.5344 0.9654 0.9834 0.6071 0.8407 0.9477 0.7157 ] Network output: [ -0.04407 0.2043 0.803 0.0002144 -9.627e-05 1.082 0.0001616 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.616 0.5685 0.2998 0.1907 0.9796 0.9863 0.6165 0.9338 0.9596 0.3869 ] Network output: [ -0.1279 0.3918 0.7441 -0.002519 0.001131 1.11 -0.001898 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6244 0.6152 0.4108 0.03499 0.9778 0.9849 0.6245 0.9298 0.9556 0.4388 ] Network output: [ 0.1863 0.5857 0.208 0.001498 -0.0006723 0.8398 0.001129 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1279 Epoch 1598 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01548 1.038 0.9641 -0.0004984 0.0002238 -0.03473 -0.0003756 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02527 -0.003421 0.004296 0.02679 0.9254 0.9368 0.04617 0.8221 0.8619 0.1189 ] Network output: [ 0.9598 0.1478 -0.1271 0.0004003 -0.0001797 0.06128 0.0003017 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5443 -0.01709 -0.2131 0.3542 0.9608 0.9805 0.6099 0.8285 0.9401 0.7205 ] Network output: [ -0.007101 0.9909 1.013 -0.000454 0.0002038 0.008408 -0.0003421 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0387 0.01942 0.0219 0.03354 0.9768 0.9831 0.03948 0.9223 0.9561 0.05067 ] Network output: [ 0.1892 -0.3674 1.163 0.002107 -0.000946 0.835 0.001588 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5937 0.335 0.1369 0.5736 0.9653 0.9834 0.596 0.841 0.9479 0.7225 ] Network output: [ -0.08888 0.1397 0.9586 0.0003273 -0.0001469 1.081 0.0002467 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5983 0.543 0.2926 0.2459 0.9794 0.9862 0.5987 0.9327 0.9594 0.3956 ] Network output: [ -0.1982 0.2654 0.9697 -0.002105 0.0009451 1.153 -0.001587 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6128 0.602 0.415 0.1316 0.9776 0.9849 0.6129 0.9293 0.956 0.4468 ] Network output: [ 0.171 0.5836 0.2352 0.001571 -0.0007052 0.8457 0.001184 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1034 Epoch 1599 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01162 1.063 0.9413 -0.0006636 0.0002979 -0.03018 -0.0005001 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02529 -0.002966 0.004757 0.02374 0.9253 0.9368 0.04615 0.8217 0.8612 0.1154 ] Network output: [ 0.9385 0.2173 -0.1721 5.338e-05 -2.396e-05 0.07813 4.023e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.543 -0.002954 -0.2034 0.3243 0.9607 0.9805 0.6083 0.8274 0.9395 0.7125 ] Network output: [ -0.007487 1.008 0.9939 -0.0005516 0.0002477 0.01075 -0.0004157 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03883 0.02036 0.02161 0.02966 0.9767 0.983 0.03961 0.9217 0.9554 0.04875 ] Network output: [ 0.1648 -0.2882 1.115 0.001646 -0.0007389 0.8501 0.00124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5909 0.3511 0.1527 0.5343 0.9652 0.9833 0.5932 0.84 0.9474 0.7142 ] Network output: [ -0.06521 0.2168 0.8472 3.446e-05 -1.547e-05 1.067 2.597e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6003 0.5495 0.2896 0.1995 0.9793 0.9861 0.6007 0.9323 0.9587 0.3848 ] Network output: [ -0.165 0.3754 0.826 -0.00267 0.001199 1.118 -0.002012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6114 0.6016 0.4122 0.05859 0.9774 0.9847 0.6115 0.9288 0.9552 0.4415 ] Network output: [ 0.1662 0.598 0.2326 0.001311 -0.0005886 0.8422 0.0009881 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1144 Epoch 1600 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.003136 1.04 0.9816 -0.0006862 0.000308 -0.01807 -0.0005171 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02496 -0.002094 0.01184 0.02612 0.9253 0.9367 0.04538 0.8224 0.8619 0.1177 ] Network output: [ 0.7955 0.1541 0.07846 -0.0006187 0.0002778 0.1738 -0.0004663 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5369 0.02595 -0.125 0.3489 0.9607 0.9805 0.6008 0.8284 0.9402 0.7199 ] Network output: [ -0.004216 0.9906 1.002 -0.0004656 0.000209 0.01412 -0.0003509 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0402 0.02263 0.02782 0.03284 0.9769 0.9833 0.04099 0.9234 0.9569 0.05252 ] Network output: [ 0.117 -0.3327 1.196 0.001087 -0.0004878 0.9066 0.0008189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6056 0.3738 0.2064 0.5428 0.9654 0.9834 0.608 0.8407 0.9477 0.7169 ] Network output: [ -0.05082 0.1945 0.8268 0.0002734 -0.0001227 1.081 0.000206 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6132 0.5646 0.3005 0.2007 0.9797 0.9864 0.6137 0.9339 0.9597 0.3875 ] Network output: [ -0.1353 0.3701 0.775 -0.002338 0.00105 1.116 -0.001762 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6232 0.6137 0.4113 0.05138 0.9778 0.9849 0.6233 0.9297 0.9557 0.4392 ] Network output: [ 0.1866 0.586 0.2042 0.001591 -0.0007143 0.8431 0.001199 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1207 Epoch 1601 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01746 1.039 0.9596 -0.0004338 0.0001947 -0.03565 -0.0003269 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02527 -0.003559 0.003687 0.02644 0.9254 0.9368 0.04624 0.8221 0.8618 0.1185 ] Network output: [ 0.9738 0.1617 -0.1583 0.0002576 -0.0001157 0.04998 0.0001942 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5462 -0.02109 -0.2178 0.3502 0.9608 0.9805 0.6122 0.8283 0.94 0.7192 ] Network output: [ -0.00709 0.9917 1.012 -0.0003876 0.000174 0.008469 -0.0002921 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03846 0.01912 0.02146 0.03305 0.9768 0.9831 0.03924 0.9222 0.956 0.05016 ] Network output: [ 0.1883 -0.3555 1.155 0.001934 -0.0008683 0.8319 0.001458 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5934 0.3332 0.1384 0.5712 0.9653 0.9834 0.5958 0.8407 0.9478 0.7216 ] Network output: [ -0.08966 0.151 0.9537 0.000292 -0.0001311 1.076 0.00022 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5956 0.5402 0.2924 0.2432 0.9794 0.9862 0.596 0.9327 0.9593 0.3939 ] Network output: [ -0.1981 0.2725 0.965 -0.002099 0.0009425 1.15 -0.001582 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6109 0.6001 0.4151 0.1289 0.9775 0.9849 0.611 0.9291 0.9558 0.4464 ] Network output: [ 0.1696 0.5865 0.2326 0.001598 -0.0007173 0.8482 0.001204 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1038 Epoch 1602 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009439 1.059 0.9475 -0.0006076 0.0002728 -0.02748 -0.0004579 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02521 -0.002818 0.006103 0.02396 0.9254 0.9368 0.04601 0.8218 0.8612 0.1155 ] Network output: [ 0.9129 0.2141 -0.1357 -0.0002012 9.032e-05 0.09507 -0.0001516 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5431 0.002491 -0.1874 0.3262 0.9607 0.9805 0.6084 0.8273 0.9396 0.7127 ] Network output: [ -0.006556 1.004 0.9951 -0.0004697 0.0002109 0.01185 -0.000354 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03901 0.02077 0.02283 0.02993 0.9767 0.9831 0.03979 0.9221 0.9557 0.04923 ] Network output: [ 0.1493 -0.2877 1.13 0.001343 -0.0006028 0.8644 0.001012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5947 0.3569 0.1696 0.533 0.9652 0.9833 0.597 0.8399 0.9474 0.7139 ] Network output: [ -0.0615 0.2226 0.8343 6.191e-05 -2.779e-05 1.066 4.666e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6016 0.5516 0.2926 0.1958 0.9794 0.9862 0.602 0.9327 0.9589 0.384 ] Network output: [ -0.1555 0.3834 0.8034 -0.002585 0.00116 1.114 -0.001948 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6129 0.6032 0.412 0.05179 0.9775 0.9848 0.613 0.9288 0.9552 0.4402 ] Network output: [ 0.1705 0.5974 0.2229 0.001404 -0.0006305 0.8444 0.001058 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1145 Epoch 1603 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 3.091e-05 1.035 0.9826 -0.000572 0.0002568 -0.02017 -0.0004311 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02495 -0.002348 0.01109 0.02652 0.9254 0.9368 0.04546 0.8225 0.8619 0.118 ] Network output: [ 0.817 0.1501 0.05549 -0.0006289 0.0002823 0.1578 -0.0004739 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5392 0.01854 -0.1332 0.3521 0.9608 0.9805 0.6038 0.8284 0.9402 0.72 ] Network output: [ -0.004077 0.9869 1.006 -0.0003717 0.0001669 0.0136 -0.0002802 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03992 0.02209 0.02739 0.03333 0.977 0.9833 0.04072 0.9236 0.9569 0.05238 ] Network output: [ 0.1216 -0.3419 1.202 0.001034 -0.0004642 0.9006 0.0007793 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6063 0.3682 0.2046 0.5508 0.9655 0.9835 0.6086 0.8408 0.9478 0.7177 ] Network output: [ -0.05777 0.1854 0.851 0.0003256 -0.0001462 1.08 0.0002454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.61 0.5602 0.301 0.2106 0.9797 0.9864 0.6104 0.9341 0.9598 0.388 ] Network output: [ -0.1435 0.3479 0.8077 -0.002166 0.0009726 1.123 -0.001633 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6215 0.6118 0.4117 0.06851 0.9777 0.985 0.6216 0.9296 0.9557 0.4394 ] Network output: [ 0.1864 0.5862 0.2014 0.001676 -0.0007525 0.8464 0.001263 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1141 Epoch 1604 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01895 1.041 0.9555 -0.0003768 0.0001691 -0.03612 -0.0002839 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02527 -0.003667 0.003291 0.02606 0.9255 0.9369 0.0463 0.8221 0.8617 0.118 ] Network output: [ 0.9843 0.1752 -0.1848 0.0001121 -5.033e-05 0.04136 8.449e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5479 -0.02418 -0.2201 0.346 0.9608 0.9806 0.6144 0.828 0.94 0.7174 ] Network output: [ -0.007014 0.9925 1.012 -0.0003256 0.0001462 0.008675 -0.0002454 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03825 0.01889 0.02115 0.0325 0.9768 0.9831 0.03903 0.9222 0.9559 0.04962 ] Network output: [ 0.1862 -0.3432 1.147 0.001757 -0.0007886 0.8305 0.001324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5936 0.3322 0.1416 0.568 0.9653 0.9834 0.5959 0.8405 0.9478 0.72 ] Network output: [ -0.08932 0.1631 0.9458 0.0002629 -0.000118 1.071 0.0001982 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5931 0.5378 0.2923 0.2396 0.9794 0.9862 0.5935 0.9326 0.9592 0.3916 ] Network output: [ -0.1966 0.2812 0.9567 -0.002095 0.0009403 1.147 -0.001579 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.609 0.5982 0.4149 0.1251 0.9775 0.9848 0.6091 0.9288 0.9557 0.4454 ] Network output: [ 0.1684 0.5896 0.2297 0.001616 -0.0007255 0.8505 0.001218 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1044 Epoch 1605 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.007278 1.054 0.954 -0.0005529 0.0002482 -0.02485 -0.0004167 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02513 -0.002686 0.007454 0.02423 0.9254 0.9368 0.04587 0.8219 0.8613 0.1156 ] Network output: [ 0.8879 0.2091 -0.09826 -0.0004414 0.0001982 0.1116 -0.0003327 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5433 0.00742 -0.171 0.3286 0.9607 0.9805 0.6086 0.8273 0.9396 0.7127 ] Network output: [ -0.005617 1 0.9963 -0.0003904 0.0001753 0.01287 -0.0002942 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03918 0.02115 0.02402 0.03024 0.9768 0.9831 0.03996 0.9225 0.9559 0.04967 ] Network output: [ 0.1358 -0.289 1.144 0.001075 -0.0004825 0.8779 0.00081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5986 0.3622 0.1857 0.5323 0.9653 0.9834 0.601 0.84 0.9475 0.7132 ] Network output: [ -0.05787 0.2271 0.8232 0.0001015 -4.558e-05 1.066 7.651e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6026 0.5533 0.2952 0.1932 0.9795 0.9863 0.603 0.9332 0.9591 0.3828 ] Network output: [ -0.147 0.3885 0.785 -0.002489 0.001118 1.11 -0.001876 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6141 0.6045 0.4115 0.04733 0.9775 0.9848 0.6142 0.9289 0.9552 0.4387 ] Network output: [ 0.1745 0.5969 0.2139 0.00149 -0.0006688 0.8464 0.001123 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1151 Epoch 1606 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.003223 1.031 0.9829 -0.0004648 0.0002086 -0.0223 -0.0003503 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02495 -0.002605 0.01026 0.02685 0.9254 0.9369 0.04554 0.8226 0.862 0.1182 ] Network output: [ 0.84 0.1476 0.02902 -0.0006228 0.0002796 0.1408 -0.0004693 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5417 0.01085 -0.1422 0.3547 0.9608 0.9806 0.6068 0.8284 0.9402 0.7196 ] Network output: [ -0.004026 0.9839 1.01 -0.0002857 0.0001283 0.01306 -0.0002153 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03962 0.02151 0.02684 0.03368 0.977 0.9833 0.04041 0.9236 0.9569 0.05211 ] Network output: [ 0.1272 -0.3492 1.205 0.000997 -0.0004476 0.8937 0.0007514 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6067 0.3624 0.2021 0.5577 0.9655 0.9835 0.6091 0.8408 0.9479 0.718 ] Network output: [ -0.0645 0.1779 0.8738 0.0003674 -0.0001649 1.079 0.0002769 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6063 0.5554 0.3011 0.2197 0.9797 0.9864 0.6068 0.9341 0.9599 0.388 ] Network output: [ -0.152 0.3265 0.8404 -0.002013 0.0009036 1.129 -0.001517 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6195 0.6095 0.4118 0.08541 0.9777 0.985 0.6196 0.9295 0.9557 0.4393 ] Network output: [ 0.1856 0.5864 0.1999 0.001748 -0.0007846 0.8497 0.001317 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1085 Epoch 1607 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01978 1.043 0.9523 -0.0003295 0.0001479 -0.03606 -0.0002483 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02525 -0.003729 0.003182 0.02569 0.9255 0.9369 0.04633 0.8221 0.8616 0.1174 ] Network output: [ 0.9895 0.1873 -0.2033 -3.79e-05 1.702e-05 0.03678 -2.856e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5495 -0.02583 -0.2194 0.342 0.9608 0.9806 0.6164 0.8279 0.9399 0.7152 ] Network output: [ -0.00682 0.9933 1.01 -0.0002693 0.0001209 0.009012 -0.000203 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03811 0.01877 0.02102 0.03197 0.9768 0.9831 0.03888 0.9222 0.9558 0.04911 ] Network output: [ 0.1823 -0.3314 1.141 0.001572 -0.0007059 0.8317 0.001185 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5943 0.3325 0.1471 0.5641 0.9653 0.9834 0.5966 0.8404 0.9477 0.718 ] Network output: [ -0.08772 0.1757 0.9346 0.0002421 -0.0001087 1.066 0.0001825 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.591 0.536 0.2921 0.2352 0.9794 0.9862 0.5915 0.9327 0.9592 0.3888 ] Network output: [ -0.1934 0.2911 0.9444 -0.002087 0.000937 1.143 -0.001573 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6075 0.5967 0.4142 0.1202 0.9774 0.9848 0.6076 0.9286 0.9555 0.4438 ] Network output: [ 0.1676 0.5926 0.2264 0.001628 -0.0007307 0.8524 0.001227 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.105 Epoch 1608 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.005452 1.049 0.9604 -0.0004986 0.0002238 -0.02265 -0.0003758 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02504 -0.002588 0.008674 0.02455 0.9254 0.9369 0.04574 0.8221 0.8614 0.1157 ] Network output: [ 0.8662 0.2027 -0.06342 -0.0006552 0.0002941 0.1257 -0.0004938 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5436 0.01121 -0.1559 0.3316 0.9607 0.9805 0.6091 0.8274 0.9397 0.7125 ] Network output: [ -0.004716 0.9966 0.9978 -0.0003149 0.0001414 0.01367 -0.0002373 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0393 0.02144 0.02507 0.0306 0.9769 0.9832 0.04009 0.923 0.9562 0.05002 ] Network output: [ 0.125 -0.2921 1.156 0.0008528 -0.0003829 0.8894 0.0006427 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6024 0.3663 0.1997 0.5327 0.9654 0.9834 0.6048 0.8401 0.9475 0.7123 ] Network output: [ -0.05494 0.2297 0.8157 0.0001517 -6.812e-05 1.065 0.0001144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6031 0.5542 0.2971 0.1925 0.9796 0.9863 0.6035 0.9336 0.9593 0.3813 ] Network output: [ -0.1406 0.3892 0.7737 -0.002382 0.001069 1.108 -0.001795 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6149 0.6053 0.4107 0.04644 0.9776 0.9848 0.6149 0.9289 0.9552 0.437 ] Network output: [ 0.1778 0.5966 0.2057 0.001565 -0.0007026 0.8483 0.001179 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1156 Epoch 1609 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.006347 1.028 0.9822 -0.0003687 0.0001655 -0.02443 -0.0002779 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02494 -0.002855 0.009385 0.02706 0.9255 0.9369 0.04563 0.8227 0.862 0.1182 ] Network output: [ 0.8636 0.1473 -0.0005958 -0.0006093 0.0002736 0.1235 -0.0004592 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5442 0.003258 -0.1513 0.3562 0.9609 0.9806 0.61 0.8284 0.9403 0.7185 ] Network output: [ -0.004042 0.9817 1.013 -0.00021 9.429e-05 0.01249 -0.0001583 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03929 0.02094 0.0262 0.03385 0.977 0.9833 0.04008 0.9237 0.9569 0.0517 ] Network output: [ 0.1335 -0.3536 1.204 0.0009696 -0.0004353 0.8864 0.0007308 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.607 0.3568 0.1993 0.563 0.9655 0.9835 0.6094 0.8409 0.9479 0.7177 ] Network output: [ -0.07053 0.1732 0.8932 0.0003951 -0.0001774 1.076 0.0002977 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6024 0.5504 0.3008 0.2273 0.9797 0.9864 0.6028 0.9342 0.96 0.3874 ] Network output: [ -0.1601 0.3079 0.8705 -0.001886 0.0008469 1.134 -0.001422 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6171 0.6069 0.4116 0.1007 0.9777 0.9849 0.6172 0.9293 0.9558 0.439 ] Network output: [ 0.1841 0.5867 0.1996 0.001801 -0.0008086 0.8528 0.001357 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1041 Epoch 1610 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01981 1.044 0.9506 -0.0002922 0.0001312 -0.03538 -0.0002202 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02522 -0.003734 0.003424 0.02538 0.9256 0.937 0.04632 0.8222 0.8615 0.1167 ] Network output: [ 0.9878 0.1968 -0.2105 -0.0001941 8.714e-05 0.03735 -0.0001463 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5508 -0.0256 -0.215 0.3387 0.9608 0.9806 0.618 0.8277 0.9398 0.7128 ] Network output: [ -0.006468 0.9937 1.009 -0.0002186 9.812e-05 0.009473 -0.0001647 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03804 0.01879 0.02114 0.03151 0.9769 0.9831 0.03881 0.9224 0.9558 0.04867 ] Network output: [ 0.1761 -0.321 1.138 0.001378 -0.0006185 0.8362 0.001038 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5958 0.3345 0.1553 0.5599 0.9653 0.9834 0.5982 0.8403 0.9477 0.7156 ] Network output: [ -0.08494 0.1882 0.9206 0.0002312 -0.0001038 1.062 0.0001742 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5897 0.5351 0.2922 0.2304 0.9795 0.9862 0.5901 0.9328 0.9592 0.3857 ] Network output: [ -0.1883 0.3017 0.9284 -0.002073 0.0009308 1.138 -0.001562 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6064 0.5957 0.4131 0.1145 0.9774 0.9848 0.6065 0.9285 0.9554 0.4416 ] Network output: [ 0.1675 0.5954 0.2221 0.001636 -0.0007347 0.8542 0.001233 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1052 Epoch 1611 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.004314 1.044 0.9663 -0.0004425 0.0001987 -0.02122 -0.0003335 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02496 -0.002549 0.00962 0.02492 0.9255 0.9369 0.04565 0.8224 0.8615 0.1157 ] Network output: [ 0.8509 0.1953 -0.03579 -0.0008285 0.000372 0.1353 -0.0006244 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5444 0.01311 -0.1439 0.335 0.9608 0.9805 0.6101 0.8276 0.9398 0.712 ] Network output: [ -0.003922 0.993 0.9997 -0.0002435 0.0001093 0.01414 -0.0001835 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03936 0.02159 0.02588 0.03101 0.9769 0.9832 0.04015 0.9234 0.9564 0.05024 ] Network output: [ 0.1177 -0.2974 1.167 0.0006813 -0.0003058 0.898 0.0005134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6059 0.3689 0.2109 0.5346 0.9654 0.9834 0.6083 0.8402 0.9476 0.7112 ] Network output: [ -0.05347 0.2297 0.8138 0.0002085 -9.358e-05 1.064 0.0001571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6028 0.5541 0.2983 0.194 0.9797 0.9864 0.6033 0.9339 0.9595 0.3796 ] Network output: [ -0.137 0.3846 0.7718 -0.002262 0.001015 1.108 -0.001704 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6151 0.6056 0.4096 0.05006 0.9776 0.9848 0.6152 0.9289 0.9552 0.4352 ] Network output: [ 0.1804 0.5967 0.1987 0.00163 -0.0007316 0.8504 0.001228 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1152 Epoch 1612 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009274 1.026 0.9804 -0.0002867 0.0001287 -0.02648 -0.0002161 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02493 -0.003087 0.008524 0.02714 0.9255 0.9369 0.04571 0.8227 0.862 0.118 ] Network output: [ 0.8866 0.1499 -0.03229 -0.0006012 0.0002699 0.1067 -0.0004531 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5467 -0.003844 -0.1599 0.3564 0.9609 0.9806 0.6131 0.8284 0.9403 0.7168 ] Network output: [ -0.004095 0.9806 1.015 -0.0001454 6.53e-05 0.01193 -0.0001096 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03896 0.02041 0.02552 0.03382 0.977 0.9833 0.03975 0.9237 0.9568 0.05117 ] Network output: [ 0.1394 -0.3542 1.2 0.0009403 -0.0004221 0.8794 0.0007087 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6071 0.3518 0.1967 0.5664 0.9655 0.9835 0.6095 0.8409 0.948 0.7167 ] Network output: [ -0.07541 0.1725 0.9075 0.0004069 -0.0001827 1.073 0.0003067 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5984 0.5456 0.3001 0.2326 0.9797 0.9864 0.5988 0.9342 0.96 0.3863 ] Network output: [ -0.167 0.2942 0.8947 -0.001794 0.0008055 1.138 -0.001352 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6145 0.6041 0.4113 0.113 0.9776 0.9849 0.6146 0.9292 0.9557 0.4383 ] Network output: [ 0.1821 0.5876 0.1998 0.001833 -0.0008229 0.8559 0.001381 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1011 Epoch 1613 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01903 1.044 0.9509 -0.0002624 0.0001178 -0.03411 -0.0001977 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02517 -0.003682 0.004036 0.02518 0.9256 0.937 0.04628 0.8223 0.8615 0.1161 ] Network output: [ 0.9787 0.2027 -0.2049 -0.0003566 0.0001601 0.04333 -0.0002688 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5519 -0.02348 -0.2067 0.3365 0.9608 0.9806 0.6194 0.8277 0.9398 0.7105 ] Network output: [ -0.005953 0.9936 1.008 -0.0001714 7.695e-05 0.01004 -0.0001292 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03806 0.01896 0.02155 0.03118 0.9769 0.9832 0.03883 0.9226 0.9559 0.04839 ] Network output: [ 0.1675 -0.3129 1.139 0.001172 -0.000526 0.8441 0.000883 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5982 0.3381 0.1663 0.5559 0.9653 0.9834 0.6005 0.8403 0.9477 0.7131 ] Network output: [ -0.08126 0.1998 0.905 0.000231 -0.0001037 1.059 0.0001741 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5891 0.5352 0.2926 0.2255 0.9795 0.9863 0.5895 0.9331 0.9593 0.3824 ] Network output: [ -0.1816 0.3124 0.9093 -0.00205 0.0009202 1.133 -0.001545 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6059 0.5953 0.4116 0.1085 0.9774 0.9847 0.606 0.9284 0.9553 0.4391 ] Network output: [ 0.1683 0.5978 0.2165 0.001647 -0.0007392 0.8559 0.001241 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1049 Epoch 1614 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00412 1.04 0.9712 -0.0003819 0.0001714 -0.02078 -0.0002878 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0249 -0.002583 0.01019 0.02531 0.9255 0.9369 0.04559 0.8226 0.8616 0.1157 ] Network output: [ 0.8445 0.1878 -0.01921 -0.0009509 0.0004269 0.1386 -0.0007166 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5456 0.01262 -0.1362 0.3387 0.9608 0.9806 0.6117 0.8278 0.9399 0.7113 ] Network output: [ -0.003306 0.9895 1.002 -0.0001759 7.897e-05 0.01422 -0.0001326 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03933 0.02157 0.02638 0.03144 0.977 0.9833 0.04013 0.9237 0.9566 0.05033 ] Network output: [ 0.114 -0.3043 1.176 0.0005579 -0.0002505 0.9029 0.0004205 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6089 0.3694 0.2189 0.5382 0.9655 0.9835 0.6113 0.8404 0.9477 0.71 ] Network output: [ -0.05401 0.2273 0.8186 0.0002657 -0.0001193 1.063 0.0002002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6017 0.5527 0.299 0.1981 0.9797 0.9864 0.6021 0.9342 0.9597 0.3781 ] Network output: [ -0.1367 0.3746 0.7801 -0.002132 0.0009573 1.11 -0.001607 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6147 0.6051 0.4086 0.05822 0.9775 0.9848 0.6148 0.9289 0.9552 0.4335 ] Network output: [ 0.182 0.597 0.193 0.001684 -0.0007561 0.8529 0.001269 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1134 Epoch 1615 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01185 1.026 0.9777 -0.0002193 9.844e-05 -0.0283 -0.0001652 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02493 -0.00329 0.007771 0.02707 0.9256 0.937 0.0458 0.8228 0.862 0.1176 ] Network output: [ 0.9073 0.1553 -0.06387 -0.0006102 0.0002739 0.0915 -0.0004599 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5492 -0.01006 -0.167 0.3552 0.9609 0.9806 0.6162 0.8284 0.9402 0.7146 ] Network output: [ -0.00415 0.9803 1.016 -9.078e-05 4.075e-05 0.01143 -6.842e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03866 0.01995 0.02489 0.03359 0.977 0.9833 0.03945 0.9238 0.9568 0.05057 ] Network output: [ 0.1441 -0.3512 1.193 0.000896 -0.0004023 0.8736 0.0006753 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6073 0.3479 0.1955 0.5676 0.9655 0.9835 0.6098 0.841 0.948 0.7151 ] Network output: [ -0.07884 0.1757 0.9154 0.0004049 -0.0001818 1.068 0.0003052 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5945 0.5412 0.2993 0.2354 0.9797 0.9864 0.595 0.9342 0.96 0.3845 ] Network output: [ -0.172 0.2867 0.9108 -0.001737 0.0007797 1.139 -0.001309 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6119 0.6013 0.4107 0.1213 0.9775 0.9849 0.612 0.929 0.9557 0.4373 ] Network output: [ 0.1798 0.5893 0.2 0.001844 -0.0008277 0.8587 0.00139 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09922 Epoch 1616 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01761 1.043 0.9531 -0.0002357 0.0001058 -0.03239 -0.0001776 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02511 -0.003588 0.004959 0.02511 0.9256 0.937 0.04621 0.8225 0.8615 0.1155 ] Network output: [ 0.9637 0.2047 -0.1878 -0.0005224 0.0002345 0.0536 -0.0003937 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5527 -0.01997 -0.195 0.3356 0.9609 0.9806 0.6205 0.8277 0.9398 0.7084 ] Network output: [ -0.005306 0.9926 1.007 -0.0001251 5.617e-05 0.01068 -9.429e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03814 0.01923 0.02223 0.03102 0.9769 0.9832 0.03892 0.9229 0.956 0.04828 ] Network output: [ 0.1571 -0.3078 1.143 0.0009604 -0.0004311 0.8547 0.0007238 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6013 0.3428 0.1796 0.5524 0.9653 0.9834 0.6037 0.8404 0.9477 0.7106 ] Network output: [ -0.07721 0.2099 0.8893 0.0002419 -0.0001086 1.056 0.0001823 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.589 0.5359 0.2935 0.221 0.9795 0.9863 0.5895 0.9334 0.9594 0.3794 ] Network output: [ -0.1741 0.3221 0.8891 -0.002014 0.0009044 1.129 -0.001518 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6059 0.5955 0.4101 0.1028 0.9773 0.9847 0.606 0.9283 0.9552 0.4364 ] Network output: [ 0.1698 0.5998 0.2097 0.001661 -0.0007455 0.8576 0.001251 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1042 Epoch 1617 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.004898 1.035 0.9748 -0.0003156 0.0001417 -0.02129 -0.0002379 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02485 -0.002691 0.01037 0.02569 0.9255 0.937 0.04558 0.8229 0.8618 0.1158 ] Network output: [ 0.8472 0.181 -0.01501 -0.001021 0.0004583 0.1355 -0.0007694 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5474 0.009722 -0.1331 0.3421 0.9608 0.9806 0.6139 0.828 0.94 0.7104 ] Network output: [ -0.002901 0.9864 1.005 -0.0001113 4.996e-05 0.01398 -8.386e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03922 0.02138 0.02657 0.03185 0.977 0.9833 0.04002 0.9241 0.9568 0.05028 ] Network output: [ 0.1137 -0.3121 1.182 0.0004753 -0.0002134 0.9042 0.0003582 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6113 0.3678 0.2241 0.5428 0.9655 0.9835 0.6137 0.8406 0.9478 0.709 ] Network output: [ -0.05657 0.2231 0.8294 0.0003176 -0.0001426 1.062 0.0002394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5996 0.5502 0.2994 0.204 0.9797 0.9864 0.6001 0.9345 0.9599 0.3768 ] Network output: [ -0.1395 0.3605 0.7968 -0.002001 0.0008984 1.114 -0.001508 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6138 0.6041 0.4077 0.06984 0.9775 0.9848 0.6139 0.9289 0.9552 0.4321 ] Network output: [ 0.1826 0.5975 0.1887 0.001729 -0.0007764 0.8557 0.001303 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1102 Epoch 1618 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01391 1.027 0.9747 -0.000165 7.407e-05 -0.02971 -0.0001243 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02492 -0.003453 0.007223 0.02688 0.9256 0.937 0.04588 0.823 0.862 0.1171 ] Network output: [ 0.924 0.1626 -0.09225 -0.0006428 0.0002886 0.07906 -0.0004844 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5516 -0.01501 -0.1714 0.3528 0.9609 0.9806 0.6192 0.8284 0.9402 0.7121 ] Network output: [ -0.004157 0.9808 1.016 -4.361e-05 1.958e-05 0.01109 -3.287e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03842 0.0196 0.02439 0.03324 0.977 0.9833 0.0392 0.9238 0.9567 0.04995 ] Network output: [ 0.1468 -0.3455 1.185 0.0008286 -0.000372 0.8698 0.0006245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6079 0.3453 0.1965 0.5668 0.9655 0.9835 0.6103 0.841 0.948 0.7131 ] Network output: [ -0.0807 0.1825 0.9171 0.0003948 -0.0001773 1.063 0.0002976 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5911 0.5375 0.2986 0.2358 0.9797 0.9864 0.5916 0.9343 0.9601 0.3823 ] Network output: [ -0.1746 0.2853 0.9178 -0.001706 0.0007661 1.139 -0.001286 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6094 0.5988 0.4101 0.1253 0.9775 0.9848 0.6095 0.9288 0.9556 0.4361 ] Network output: [ 0.1776 0.5918 0.1993 0.001838 -0.0008251 0.8613 0.001385 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09833 Epoch 1619 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01589 1.041 0.9569 -0.0002079 9.332e-05 -0.03051 -0.0001567 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02504 -0.003481 0.006056 0.02516 0.9256 0.937 0.04612 0.8227 0.8616 0.1151 ] Network output: [ 0.9456 0.2032 -0.1631 -0.0006839 0.000307 0.06595 -0.0005154 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5535 -0.01596 -0.1814 0.3358 0.9609 0.9806 0.6214 0.8278 0.9399 0.7067 ] Network output: [ -0.004589 0.9909 1.007 -7.772e-05 3.489e-05 0.01132 -5.857e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03826 0.01954 0.02307 0.03102 0.9769 0.9832 0.03904 0.9234 0.9562 0.04833 ] Network output: [ 0.1463 -0.3057 1.15 0.0007573 -0.00034 0.8663 0.0005707 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6049 0.3477 0.194 0.5498 0.9654 0.9834 0.6073 0.8405 0.9477 0.7084 ] Network output: [ -0.07338 0.2179 0.8756 0.0002632 -0.0001182 1.054 0.0001983 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5892 0.5368 0.2948 0.2175 0.9796 0.9863 0.5896 0.9339 0.9596 0.3768 ] Network output: [ -0.1666 0.3298 0.8706 -0.001967 0.000883 1.125 -0.001482 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6062 0.5959 0.4086 0.09836 0.9773 0.9847 0.6063 0.9284 0.9552 0.4339 ] Network output: [ 0.1718 0.6014 0.2023 0.001679 -0.0007536 0.8594 0.001265 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1035 Epoch 1620 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.006434 1.031 0.9772 -0.0002457 0.0001103 -0.0225 -0.0001852 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02482 -0.002852 0.01025 0.02603 0.9256 0.937 0.04561 0.8232 0.8619 0.1158 ] Network output: [ 0.8573 0.1755 -0.0216 -0.001048 0.0004707 0.1272 -0.0007901 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5495 0.005013 -0.1337 0.3449 0.9609 0.9806 0.6166 0.8283 0.9402 0.7092 ] Network output: [ -0.002681 0.9836 1.008 -4.993e-05 2.242e-05 0.01354 -3.763e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03905 0.02105 0.02652 0.0322 0.9771 0.9834 0.03985 0.9244 0.957 0.05011 ] Network output: [ 0.1158 -0.3194 1.187 0.0004224 -0.0001897 0.9025 0.0003184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6132 0.3648 0.227 0.5477 0.9655 0.9835 0.6156 0.8409 0.9479 0.708 ] Network output: [ -0.06052 0.2187 0.8437 0.0003606 -0.0001619 1.06 0.0002718 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5968 0.5467 0.2997 0.2106 0.9798 0.9865 0.5973 0.9348 0.9601 0.3758 ] Network output: [ -0.1443 0.3448 0.8184 -0.001877 0.0008427 1.118 -0.001415 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6123 0.6024 0.407 0.0831 0.9775 0.9848 0.6124 0.9288 0.9553 0.431 ] Network output: [ 0.1822 0.5982 0.1857 0.001765 -0.0007924 0.8588 0.00133 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1065 Epoch 1621 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01535 1.027 0.9719 -0.0001216 5.46e-05 -0.03058 -9.165e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02492 -0.003569 0.006951 0.02663 0.9257 0.9371 0.04595 0.8232 0.862 0.1165 ] Network output: [ 0.9353 0.1703 -0.1142 -0.0006984 0.0003135 0.07045 -0.0005263 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5538 -0.01842 -0.1727 0.3499 0.9609 0.9806 0.6219 0.8285 0.9402 0.7094 ] Network output: [ -0.00407 0.9814 1.016 -1.645e-06 7.386e-07 0.01093 -1.24e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03824 0.01937 0.0241 0.03282 0.977 0.9833 0.03902 0.924 0.9567 0.04939 ] Network output: [ 0.147 -0.3383 1.179 0.000738 -0.0003313 0.8686 0.0005562 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6089 0.3443 0.2001 0.5646 0.9654 0.9835 0.6113 0.8411 0.948 0.7106 ] Network output: [ -0.08106 0.1914 0.9135 0.0003841 -0.0001724 1.059 0.0002895 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5883 0.5346 0.2981 0.2344 0.9797 0.9864 0.5888 0.9344 0.9601 0.3798 ] Network output: [ -0.1749 0.2885 0.9169 -0.001691 0.0007591 1.138 -0.001274 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6073 0.5967 0.4092 0.1261 0.9774 0.9848 0.6074 0.9286 0.9555 0.4345 ] Network output: [ 0.1757 0.5948 0.1975 0.001822 -0.0008178 0.8637 0.001373 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09804 Epoch 1622 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01426 1.038 0.9616 -0.0001765 7.925e-05 -0.02879 -0.000133 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02497 -0.003389 0.007158 0.0253 0.9256 0.937 0.04603 0.823 0.8618 0.1149 ] Network output: [ 0.928 0.1992 -0.1363 -0.0008307 0.0003729 0.07777 -0.0006261 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5543 -0.01243 -0.1676 0.3369 0.9609 0.9806 0.6225 0.8281 0.94 0.7052 ] Network output: [ -0.003873 0.9886 1.007 -2.919e-05 1.31e-05 0.01187 -2.2e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03836 0.01981 0.02393 0.03115 0.977 0.9833 0.03915 0.9239 0.9565 0.04845 ] Network output: [ 0.1368 -0.3062 1.158 0.0005778 -0.0002594 0.8772 0.0004354 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6087 0.3521 0.2079 0.5483 0.9654 0.9835 0.6111 0.8408 0.9478 0.7063 ] Network output: [ -0.07032 0.2237 0.8654 0.0002935 -0.0001318 1.053 0.0002212 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5892 0.5374 0.2962 0.2155 0.9797 0.9864 0.5897 0.9343 0.9599 0.3745 ] Network output: [ -0.1604 0.3342 0.8567 -0.001906 0.0008559 1.122 -0.001437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6065 0.5962 0.4073 0.09621 0.9773 0.9847 0.6065 0.9284 0.9552 0.4315 ] Network output: [ 0.1738 0.6028 0.1951 0.001699 -0.0007626 0.8614 0.00128 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1029 Epoch 1623 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008377 1.028 0.9784 -0.0001771 7.951e-05 -0.02405 -0.0001335 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0248 -0.003039 0.009937 0.02629 0.9256 0.937 0.04567 0.8235 0.8621 0.1157 ] Network output: [ 0.8717 0.172 -0.03553 -0.00105 0.0004714 0.116 -0.0007914 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5518 -0.0005948 -0.1364 0.3469 0.9609 0.9806 0.6196 0.8286 0.9403 0.7077 ] Network output: [ -0.002582 0.9814 1.011 6.647e-06 -2.984e-06 0.01303 5.01e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03883 0.02066 0.02631 0.03243 0.9771 0.9834 0.03963 0.9246 0.9571 0.04986 ] Network output: [ 0.1194 -0.325 1.189 0.0003874 -0.0001739 0.8992 0.000292 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6147 0.3612 0.2288 0.5519 0.9655 0.9835 0.6171 0.8412 0.948 0.7068 ] Network output: [ -0.06491 0.2154 0.8583 0.0003934 -0.0001766 1.058 0.0002965 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5936 0.5427 0.2999 0.2169 0.9798 0.9865 0.594 0.935 0.9603 0.3748 ] Network output: [ -0.1499 0.3299 0.8406 -0.001768 0.0007936 1.122 -0.001332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6105 0.6004 0.4065 0.09603 0.9774 0.9848 0.6106 0.9288 0.9553 0.43 ] Network output: [ 0.1812 0.599 0.1838 0.00179 -0.0008035 0.8621 0.001349 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.103 Epoch 1624 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01612 1.028 0.9701 -8.694e-05 3.903e-05 -0.03087 -6.552e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0249 -0.003635 0.006977 0.02639 0.9257 0.9371 0.046 0.8234 0.8621 0.1159 ] Network output: [ 0.9406 0.1769 -0.1274 -0.0007716 0.0003464 0.06617 -0.0005815 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5558 -0.0202 -0.1706 0.3471 0.9609 0.9806 0.6244 0.8287 0.9403 0.7067 ] Network output: [ -0.003853 0.9818 1.015 3.654e-05 -1.64e-05 0.01096 2.754e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03813 0.01927 0.02405 0.03244 0.977 0.9833 0.03892 0.9242 0.9568 0.04892 ] Network output: [ 0.1451 -0.3313 1.174 0.0006306 -0.0002831 0.8703 0.0004752 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6104 0.3447 0.2061 0.5617 0.9654 0.9835 0.6129 0.8413 0.9481 0.708 ] Network output: [ -0.08019 0.2012 0.9063 0.0003788 -0.0001701 1.054 0.0002855 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5861 0.5325 0.2979 0.2319 0.9797 0.9864 0.5865 0.9346 0.9602 0.3771 ] Network output: [ -0.1732 0.2942 0.9104 -0.001679 0.0007536 1.135 -0.001265 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6057 0.595 0.4082 0.1247 0.9773 0.9848 0.6058 0.9286 0.9554 0.4326 ] Network output: [ 0.1745 0.5981 0.1946 0.001801 -0.0008086 0.8658 0.001357 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09801 Epoch 1625 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01304 1.035 0.9663 -0.0001414 6.347e-05 -0.02751 -0.0001066 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0249 -0.003336 0.008117 0.02551 0.9257 0.9371 0.04595 0.8234 0.862 0.1147 ] Network output: [ 0.914 0.1937 -0.1125 -0.0009526 0.0004277 0.08687 -0.0007179 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5553 -0.01014 -0.1554 0.3386 0.9609 0.9806 0.6238 0.8284 0.9401 0.7037 ] Network output: [ -0.00322 0.9861 1.008 1.889e-05 -8.479e-06 0.01225 1.423e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03843 0.02 0.02469 0.03135 0.977 0.9833 0.03923 0.9244 0.9568 0.04856 ] Network output: [ 0.1295 -0.3087 1.165 0.0004322 -0.000194 0.8861 0.0003257 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6123 0.3554 0.2201 0.5479 0.9655 0.9835 0.6147 0.8411 0.9479 0.7043 ] Network output: [ -0.06839 0.2274 0.8596 0.0003298 -0.0001481 1.051 0.0002485 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5887 0.5373 0.2973 0.215 0.9797 0.9864 0.5892 0.9348 0.9601 0.3725 ] Network output: [ -0.1562 0.3348 0.8493 -0.001834 0.0008236 1.121 -0.001383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6064 0.5962 0.4061 0.097 0.9773 0.9847 0.6065 0.9285 0.9552 0.4294 ] Network output: [ 0.1754 0.6039 0.1887 0.001717 -0.0007709 0.8636 0.001294 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1022 Epoch 1626 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01037 1.026 0.9787 -0.0001151 5.166e-05 -0.02563 -8.671e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02478 -0.003223 0.009579 0.02645 0.9257 0.9371 0.04573 0.8238 0.8623 0.1156 ] Network output: [ 0.887 0.1704 -0.05289 -0.001044 0.0004686 0.1041 -0.0007866 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5543 -0.006183 -0.1394 0.3478 0.9609 0.9806 0.6226 0.8289 0.9404 0.7059 ] Network output: [ -0.002526 0.9798 1.013 5.66e-05 -2.541e-05 0.01254 4.265e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03861 0.02028 0.02603 0.03254 0.9771 0.9834 0.03941 0.9249 0.9572 0.04952 ] Network output: [ 0.1231 -0.3283 1.188 0.0003585 -0.0001609 0.8956 0.0002702 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.616 0.3577 0.2304 0.5548 0.9655 0.9835 0.6185 0.8415 0.9481 0.7053 ] Network output: [ -0.06887 0.2142 0.8705 0.0004167 -0.0001871 1.055 0.000314 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5902 0.5386 0.2999 0.2218 0.9798 0.9865 0.5907 0.9352 0.9604 0.3736 ] Network output: [ -0.155 0.3179 0.8599 -0.001678 0.0007535 1.125 -0.001265 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6084 0.5982 0.406 0.1072 0.9774 0.9848 0.6085 0.9288 0.9554 0.429 ] Network output: [ 0.1797 0.6001 0.1825 0.001801 -0.0008086 0.8653 0.001357 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1002 Epoch 1627 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01628 1.028 0.9695 -5.889e-05 2.644e-05 -0.03067 -4.438e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02488 -0.003659 0.007274 0.02621 0.9257 0.9371 0.04602 0.8237 0.8622 0.1153 ] Network output: [ 0.9404 0.1813 -0.1315 -0.0008551 0.0003839 0.06592 -0.0006444 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5575 -0.02055 -0.1654 0.3449 0.961 0.9807 0.6266 0.8289 0.9403 0.704 ] Network output: [ -0.003506 0.9819 1.014 7.169e-05 -3.218e-05 0.01111 5.403e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03808 0.01927 0.0242 0.03214 0.9771 0.9833 0.03887 0.9245 0.9569 0.04856 ] Network output: [ 0.1414 -0.3256 1.171 0.0005158 -0.0002316 0.8743 0.0003887 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6126 0.3463 0.2142 0.5586 0.9655 0.9835 0.615 0.8415 0.9481 0.7052 ] Network output: [ -0.07849 0.2107 0.8972 0.0003821 -0.0001716 1.051 0.000288 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5844 0.5312 0.2979 0.2292 0.9797 0.9865 0.5849 0.9349 0.9603 0.3742 ] Network output: [ -0.1701 0.3006 0.901 -0.001661 0.0007457 1.132 -0.001252 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6044 0.5938 0.4069 0.1227 0.9773 0.9847 0.6045 0.9285 0.9554 0.4304 ] Network output: [ 0.1738 0.6011 0.1908 0.00178 -0.000799 0.8678 0.001341 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09798 Epoch 1628 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01241 1.031 0.9705 -0.0001036 4.65e-05 -0.02682 -7.806e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02483 -0.003329 0.008841 0.02575 0.9257 0.9371 0.0459 0.8238 0.8622 0.1146 ] Network output: [ 0.9054 0.188 -0.09513 -0.001043 0.0004683 0.09206 -0.0007862 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5565 -0.009412 -0.1459 0.3405 0.9609 0.9806 0.6254 0.8288 0.9403 0.7022 ] Network output: [ -0.002668 0.9836 1.01 6.448e-05 -2.895e-05 0.01241 4.859e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03844 0.02007 0.02527 0.03158 0.9771 0.9834 0.03924 0.9248 0.957 0.04861 ] Network output: [ 0.125 -0.3122 1.171 0.0003232 -0.0001451 0.8925 0.0002435 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6155 0.3572 0.23 0.5485 0.9655 0.9835 0.618 0.8415 0.9481 0.7023 ] Network output: [ -0.06772 0.2294 0.8582 0.0003679 -0.0001652 1.049 0.0002773 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5876 0.5363 0.2982 0.2161 0.9798 0.9865 0.5881 0.9352 0.9603 0.3706 ] Network output: [ -0.1541 0.3318 0.8487 -0.001755 0.0007878 1.121 -0.001322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6059 0.5957 0.405 0.1007 0.9773 0.9847 0.606 0.9286 0.9552 0.4275 ] Network output: [ 0.1763 0.605 0.1834 0.001731 -0.0007771 0.8661 0.001304 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1012 Epoch 1629 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01216 1.024 0.9783 -6.322e-05 2.838e-05 -0.02701 -4.764e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02476 -0.003384 0.00928 0.02651 0.9257 0.9371 0.0458 0.8241 0.8624 0.1153 ] Network output: [ 0.9008 0.1707 -0.0701 -0.001042 0.000468 0.09355 -0.0007856 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5566 -0.01104 -0.1416 0.3478 0.961 0.9807 0.6256 0.8292 0.9405 0.7036 ] Network output: [ -0.002449 0.9788 1.014 9.892e-05 -4.441e-05 0.01212 7.455e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03841 0.01995 0.02578 0.03252 0.9771 0.9834 0.03921 0.9252 0.9573 0.04914 ] Network output: [ 0.1263 -0.3293 1.185 0.0003268 -0.0001467 0.8928 0.0002463 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6174 0.355 0.2327 0.5563 0.9655 0.9835 0.6199 0.8418 0.9482 0.7034 ] Network output: [ -0.07182 0.2155 0.8786 0.0004327 -0.0001943 1.051 0.0003261 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.587 0.5349 0.2997 0.2251 0.9798 0.9865 0.5874 0.9354 0.9606 0.3721 ] Network output: [ -0.1588 0.3097 0.8741 -0.001609 0.0007225 1.127 -0.001213 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6064 0.596 0.4053 0.1157 0.9773 0.9848 0.6064 0.9287 0.9554 0.4278 ] Network output: [ 0.1779 0.6016 0.1814 0.001798 -0.0008074 0.8684 0.001355 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09834 Epoch 1630 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01603 1.028 0.9701 -3.52e-05 1.58e-05 -0.03017 -2.653e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02484 -0.003654 0.007762 0.02612 0.9257 0.9371 0.04602 0.824 0.8624 0.1148 ] Network output: [ 0.9361 0.1832 -0.1278 -0.0009404 0.0004222 0.06858 -0.0007087 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5591 -0.01991 -0.1581 0.3435 0.961 0.9807 0.6286 0.8291 0.9404 0.7014 ] Network output: [ -0.003058 0.9815 1.014 0.0001043 -4.682e-05 0.01131 7.859e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03808 0.01935 0.02451 0.03194 0.9771 0.9834 0.03887 0.9249 0.957 0.04831 ] Network output: [ 0.1369 -0.3217 1.17 0.000404 -0.0001814 0.88 0.0003045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6151 0.3487 0.2233 0.5559 0.9655 0.9835 0.6176 0.8418 0.9482 0.7023 ] Network output: [ -0.07644 0.219 0.8881 0.000394 -0.0001769 1.047 0.000297 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5831 0.5303 0.298 0.2268 0.9798 0.9865 0.5835 0.9352 0.9604 0.3715 ] Network output: [ -0.1665 0.306 0.8914 -0.001634 0.0007337 1.129 -0.001232 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6034 0.5928 0.4055 0.1212 0.9773 0.9847 0.6035 0.9285 0.9554 0.4281 ] Network output: [ 0.1735 0.6039 0.1864 0.001759 -0.0007898 0.8698 0.001326 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09782 Epoch 1631 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01239 1.028 0.9738 -6.497e-05 2.917e-05 -0.02673 -4.897e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02478 -0.003367 0.009315 0.02598 0.9257 0.9371 0.04587 0.8242 0.8624 0.1144 ] Network output: [ 0.9024 0.1828 -0.0853 -0.001102 0.0004949 0.09327 -0.0008308 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5581 -0.01013 -0.1391 0.3423 0.9609 0.9807 0.6274 0.8292 0.9404 0.7005 ] Network output: [ -0.002229 0.9814 1.011 0.000106 -4.757e-05 0.01236 7.986e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0384 0.02004 0.02564 0.03179 0.9771 0.9834 0.0392 0.9253 0.9572 0.04857 ] Network output: [ 0.1228 -0.316 1.175 0.0002465 -0.0001107 0.8964 0.0001858 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6183 0.3579 0.2378 0.5497 0.9655 0.9835 0.6208 0.8419 0.9482 0.7003 ] Network output: [ -0.06814 0.2305 0.8603 0.0004037 -0.0001812 1.047 0.0003043 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5859 0.5346 0.2987 0.2183 0.9798 0.9865 0.5864 0.9356 0.9606 0.3689 ] Network output: [ -0.1539 0.3262 0.8535 -0.001673 0.0007512 1.121 -0.001261 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.605 0.5948 0.4039 0.1066 0.9773 0.9847 0.6051 0.9286 0.9553 0.4257 ] Network output: [ 0.1764 0.6061 0.1794 0.001737 -0.00078 0.8688 0.001309 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09994 Epoch 1632 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01357 1.023 0.9777 -2.244e-05 1.008e-05 -0.02808 -1.691e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02474 -0.00351 0.009114 0.02652 0.9257 0.9371 0.04586 0.8244 0.8626 0.1149 ] Network output: [ 0.9114 0.1719 -0.08438 -0.001052 0.0004724 0.08534 -0.0007931 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5588 -0.01476 -0.142 0.3472 0.961 0.9807 0.6284 0.8295 0.9406 0.7011 ] Network output: [ -0.002306 0.9782 1.015 0.0001339 -6.012e-05 0.01179 0.0001009 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03825 0.01971 0.02561 0.03242 0.9771 0.9834 0.03905 0.9254 0.9574 0.04876 ] Network output: [ 0.1281 -0.3285 1.182 0.000288 -0.0001293 0.8915 0.000217 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6189 0.3535 0.236 0.5564 0.9655 0.9835 0.6214 0.8422 0.9483 0.7011 ] Network output: [ -0.07358 0.219 0.8823 0.0004444 -0.0001995 1.048 0.0003349 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.584 0.5317 0.2995 0.2268 0.9798 0.9865 0.5845 0.9357 0.9607 0.3702 ] Network output: [ -0.1611 0.3052 0.8826 -0.001557 0.0006992 1.128 -0.001174 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6044 0.5939 0.4043 0.1217 0.9773 0.9847 0.6045 0.9287 0.9554 0.4263 ] Network output: [ 0.1762 0.6036 0.1799 0.001783 -0.0008005 0.8713 0.001344 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0971 Epoch 1633 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01561 1.027 0.9717 -1.367e-05 6.137e-06 -0.02959 -1.03e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02479 -0.003639 0.008335 0.02611 0.9257 0.9371 0.04601 0.8244 0.8625 0.1144 ] Network output: [ 0.93 0.1829 -0.1196 -0.001019 0.0004575 0.07253 -0.000768 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5605 -0.01888 -0.1499 0.343 0.961 0.9807 0.6305 0.8295 0.9405 0.6989 ] Network output: [ -0.002559 0.9807 1.013 0.0001346 -6.043e-05 0.01147 0.0001014 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03809 0.01945 0.02489 0.03186 0.9771 0.9834 0.03888 0.9253 0.9572 0.04813 ] Network output: [ 0.1324 -0.3197 1.17 0.0003042 -0.0001366 0.8862 0.0002293 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.618 0.3513 0.2326 0.5539 0.9655 0.9835 0.6205 0.8421 0.9483 0.6995 ] Network output: [ -0.07454 0.226 0.8804 0.0004126 -0.0001852 1.044 0.0003109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5819 0.5294 0.2982 0.2251 0.9798 0.9865 0.5823 0.9356 0.9606 0.3688 ] Network output: [ -0.1632 0.3096 0.8836 -0.001598 0.0007176 1.127 -0.001205 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6025 0.592 0.404 0.1208 0.9772 0.9847 0.6026 0.9286 0.9553 0.4258 ] Network output: [ 0.1735 0.6063 0.182 0.001739 -0.0007809 0.8719 0.001311 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09749 Epoch 1634 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01283 1.025 0.9762 -2.784e-05 1.25e-05 -0.02708 -2.098e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02473 -0.003434 0.009587 0.02618 0.9257 0.9371 0.04587 0.8246 0.8627 0.1142 ] Network output: [ 0.9037 0.1786 -0.08206 -0.001136 0.0005101 0.09142 -0.0008562 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5598 -0.01184 -0.1347 0.3437 0.961 0.9807 0.6296 0.8296 0.9406 0.6985 ] Network output: [ -0.001882 0.9795 1.013 0.0001424 -6.392e-05 0.01217 0.0001073 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03832 0.01994 0.02585 0.03196 0.9771 0.9834 0.03912 0.9257 0.9574 0.04845 ] Network output: [ 0.1224 -0.3192 1.177 0.0001942 -8.717e-05 0.8985 0.0001463 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6207 0.3578 0.2439 0.5509 0.9655 0.9835 0.6233 0.8423 0.9483 0.6982 ] Network output: [ -0.06923 0.2315 0.8642 0.0004343 -0.000195 1.045 0.0003273 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5838 0.5323 0.299 0.2208 0.9798 0.9865 0.5842 0.9359 0.9608 0.3672 ] Network output: [ -0.1549 0.3197 0.8612 -0.001597 0.0007167 1.122 -0.001203 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6038 0.5935 0.4028 0.1134 0.9773 0.9847 0.6038 0.9287 0.9553 0.424 ] Network output: [ 0.1759 0.6073 0.1763 0.001735 -0.0007788 0.8716 0.001307 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0985 Epoch 1635 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01456 1.022 0.9773 8.586e-06 -3.855e-06 -0.0288 6.471e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02472 -0.0036 0.009108 0.02649 0.9258 0.9371 0.04591 0.8247 0.8628 0.1145 ] Network output: [ 0.9182 0.1733 -0.09408 -0.001073 0.0004818 0.07994 -0.0008088 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5609 -0.01723 -0.1404 0.3464 0.961 0.9807 0.6311 0.8299 0.9407 0.6984 ] Network output: [ -0.002078 0.9778 1.015 0.0001627 -7.306e-05 0.01156 0.0001227 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03813 0.01957 0.02555 0.03229 0.9772 0.9834 0.03893 0.9258 0.9575 0.0484 ] Network output: [ 0.1287 -0.3267 1.178 0.0002429 -0.0001091 0.892 0.0001831 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6207 0.3532 0.2406 0.5557 0.9655 0.9835 0.6233 0.8425 0.9484 0.6984 ] Network output: [ -0.07429 0.2239 0.8826 0.0004548 -0.0002042 1.044 0.0003427 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5815 0.5292 0.2992 0.2274 0.9798 0.9865 0.5819 0.936 0.9608 0.368 ] Network output: [ -0.1618 0.3035 0.8864 -0.001517 0.000681 1.128 -0.001143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6026 0.5921 0.4032 0.1257 0.9772 0.9847 0.6027 0.9287 0.9554 0.4245 ] Network output: [ 0.1748 0.6058 0.178 0.001758 -0.0007894 0.8739 0.001325 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09629 Epoch 1636 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01525 1.025 0.9738 7.143e-06 -3.207e-06 -0.02915 5.383e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02475 -0.003628 0.008894 0.02617 0.9258 0.9372 0.04599 0.8248 0.8627 0.114 ] Network output: [ 0.9242 0.1811 -0.11 -0.001084 0.0004866 0.07621 -0.0008169 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5619 -0.018 -0.1418 0.343 0.961 0.9807 0.6324 0.8299 0.9406 0.6966 ] Network output: [ -0.002059 0.9796 1.014 0.0001626 -7.298e-05 0.01155 0.0001225 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0381 0.01954 0.02528 0.03185 0.9771 0.9834 0.0389 0.9258 0.9574 0.048 ] Network output: [ 0.1287 -0.3192 1.171 0.0002225 -9.991e-05 0.892 0.0001677 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6208 0.3538 0.2414 0.5525 0.9655 0.9835 0.6234 0.8425 0.9484 0.6968 ] Network output: [ -0.07313 0.2315 0.875 0.0004347 -0.0001951 1.042 0.0003276 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5806 0.5285 0.2984 0.2244 0.9798 0.9865 0.581 0.936 0.9608 0.3664 ] Network output: [ -0.1606 0.311 0.8788 -0.001555 0.0006981 1.125 -0.001172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6016 0.5912 0.4026 0.122 0.9772 0.9847 0.6017 0.9286 0.9554 0.4236 ] Network output: [ 0.1734 0.6084 0.1778 0.001719 -0.0007717 0.8741 0.001295 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09698 Epoch 1637 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01354 1.023 0.9778 5.633e-06 -2.529e-06 -0.02769 4.245e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02469 -0.003514 0.009737 0.02634 0.9258 0.9372 0.04589 0.825 0.8629 0.114 ] Network output: [ 0.9075 0.1756 -0.08319 -0.001154 0.0005179 0.0879 -0.0008694 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5616 -0.01397 -0.1316 0.3446 0.961 0.9807 0.632 0.8301 0.9407 0.6964 ] Network output: [ -0.001586 0.9781 1.014 0.0001732 -7.777e-05 0.01189 0.0001306 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03823 0.01982 0.02595 0.03206 0.9772 0.9835 0.03903 0.9261 0.9576 0.04826 ] Network output: [ 0.1228 -0.3215 1.177 0.0001576 -7.076e-05 0.8997 0.0001188 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6229 0.3575 0.249 0.5518 0.9656 0.9836 0.6255 0.8427 0.9485 0.6959 ] Network output: [ -0.07053 0.2331 0.8682 0.0004588 -0.000206 1.042 0.0003458 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5814 0.5299 0.2991 0.2232 0.9799 0.9866 0.5819 0.9362 0.9609 0.3655 ] Network output: [ -0.1563 0.3137 0.8694 -0.001529 0.0006866 1.123 -0.001153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6023 0.592 0.4018 0.1202 0.9772 0.9847 0.6024 0.9287 0.9554 0.4224 ] Network output: [ 0.175 0.6086 0.1739 0.001722 -0.0007731 0.8745 0.001298 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09712 Epoch 1638 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01519 1.022 0.9774 3.223e-05 -1.447e-05 -0.02924 2.429e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02469 -0.003657 0.009247 0.02647 0.9258 0.9372 0.04595 0.8251 0.8629 0.1141 ] Network output: [ 0.9216 0.1742 -0.09891 -0.001101 0.0004942 0.07709 -0.0008296 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5628 -0.01861 -0.1371 0.3456 0.961 0.9807 0.6335 0.8302 0.9408 0.6957 ] Network output: [ -0.00177 0.9773 1.016 0.0001868 -8.385e-05 0.01139 0.0001408 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03806 0.0195 0.0256 0.03217 0.9772 0.9835 0.03886 0.9261 0.9576 0.04809 ] Network output: [ 0.1281 -0.3249 1.175 0.0001957 -8.785e-05 0.8941 0.0001475 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6228 0.3539 0.2462 0.5546 0.9656 0.9836 0.6254 0.8428 0.9485 0.6957 ] Network output: [ -0.07428 0.2294 0.8806 0.0004659 -0.0002092 1.04 0.0003511 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5793 0.5271 0.299 0.2274 0.9798 0.9866 0.5797 0.9363 0.961 0.3657 ] Network output: [ -0.1615 0.3033 0.8871 -0.001482 0.0006655 1.127 -0.001117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6011 0.5906 0.402 0.1286 0.9772 0.9847 0.6012 0.9287 0.9554 0.4226 ] Network output: [ 0.1735 0.6081 0.1755 0.001727 -0.0007754 0.8764 0.001302 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09568 Epoch 1639 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01509 1.023 0.9759 2.764e-05 -1.241e-05 -0.02897 2.083e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0247 -0.003633 0.009373 0.02627 0.9258 0.9372 0.04598 0.8252 0.863 0.1137 ] Network output: [ 0.9201 0.1784 -0.1018 -0.001132 0.000508 0.07861 -0.0008528 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5634 -0.01759 -0.1347 0.3435 0.961 0.9807 0.6343 0.8303 0.9408 0.6943 ] Network output: [ -0.001591 0.9783 1.014 0.0001877 -8.426e-05 0.01151 0.0001415 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03809 0.01959 0.02562 0.0319 0.9772 0.9834 0.03889 0.9262 0.9576 0.04788 ] Network output: [ 0.126 -0.3196 1.171 0.0001609 -7.224e-05 0.8969 0.0001213 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6236 0.3558 0.2492 0.5517 0.9656 0.9836 0.6261 0.8429 0.9485 0.6942 ] Network output: [ -0.07234 0.2358 0.8719 0.0004573 -0.0002053 1.039 0.0003447 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.579 0.5273 0.2985 0.2245 0.9798 0.9866 0.5795 0.9363 0.961 0.3642 ] Network output: [ -0.1589 0.3106 0.8772 -0.001508 0.0006768 1.124 -0.001136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6006 0.5902 0.4012 0.1246 0.9772 0.9847 0.6007 0.9287 0.9554 0.4215 ] Network output: [ 0.173 0.6102 0.1741 0.001696 -0.0007613 0.8765 0.001278 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0963 Epoch 1640 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01433 1.021 0.9789 3.397e-05 -1.525e-05 -0.02838 2.56e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02466 -0.003591 0.009843 0.02645 0.9258 0.9372 0.04592 0.8254 0.8631 0.1137 ] Network output: [ 0.912 0.1735 -0.0862 -0.001163 0.0005221 0.08401 -0.0008764 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5635 -0.01599 -0.1289 0.3451 0.961 0.9807 0.6344 0.8305 0.9409 0.694 ] Network output: [ -0.001303 0.9769 1.015 0.0001986 -8.918e-05 0.01159 0.0001497 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03813 0.01971 0.02601 0.0321 0.9772 0.9835 0.03894 0.9264 0.9578 0.04804 ] Network output: [ 0.1234 -0.3228 1.176 0.0001299 -5.832e-05 0.9008 9.79e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6251 0.3575 0.2537 0.5522 0.9656 0.9836 0.6276 0.8431 0.9486 0.6935 ] Network output: [ -0.07166 0.2355 0.8712 0.0004779 -0.0002146 1.039 0.0003602 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.579 0.5275 0.299 0.2251 0.9799 0.9866 0.5795 0.9365 0.9611 0.3637 ] Network output: [ -0.1575 0.3089 0.8763 -0.001473 0.0006614 1.124 -0.00111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6008 0.5904 0.4007 0.1261 0.9772 0.9847 0.6008 0.9287 0.9554 0.4208 ] Network output: [ 0.1738 0.6102 0.1718 0.0017 -0.000763 0.8774 0.001281 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09593 Epoch 1641 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01558 1.021 0.9779 5.088e-05 -2.284e-05 -0.02951 3.834e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02466 -0.003693 0.009486 0.02648 0.9258 0.9372 0.04597 0.8255 0.8631 0.1137 ] Network output: [ 0.9225 0.1741 -0.09975 -0.001129 0.0005069 0.07603 -0.0008509 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5646 -0.01922 -0.1327 0.345 0.961 0.9807 0.6358 0.8306 0.9409 0.6931 ] Network output: [ -0.001404 0.9768 1.016 0.0002071 -9.297e-05 0.01124 0.0001561 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03801 0.0195 0.02573 0.03209 0.9772 0.9835 0.03882 0.9265 0.9578 0.04785 ] Network output: [ 0.1269 -0.3234 1.173 0.0001515 -6.802e-05 0.8972 0.0001142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6251 0.3553 0.2523 0.5533 0.9656 0.9836 0.6277 0.8432 0.9486 0.6929 ] Network output: [ -0.0739 0.2348 0.8778 0.0004785 -0.0002148 1.037 0.0003606 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5774 0.5255 0.2988 0.2273 0.9799 0.9866 0.5778 0.9366 0.9611 0.3635 ] Network output: [ -0.1607 0.3036 0.8864 -0.001449 0.0006507 1.125 -0.001092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5997 0.5893 0.4007 0.131 0.9772 0.9847 0.5998 0.9287 0.9554 0.4207 ] Network output: [ 0.1724 0.6104 0.1727 0.001692 -0.0007596 0.8788 0.001275 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09511 Epoch 1642 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01519 1.021 0.9778 4.737e-05 -2.127e-05 -0.02906 3.57e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02465 -0.003654 0.009749 0.02639 0.9258 0.9372 0.04597 0.8256 0.8632 0.1134 ] Network output: [ 0.9181 0.1756 -0.09599 -0.001162 0.0005216 0.07946 -0.0008756 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.565 -0.01767 -0.1288 0.344 0.961 0.9807 0.6363 0.8307 0.9409 0.692 ] Network output: [ -0.001167 0.977 1.015 0.0002096 -9.409e-05 0.01138 0.0001579 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03806 0.01962 0.02589 0.03196 0.9772 0.9835 0.03887 0.9266 0.9578 0.04776 ] Network output: [ 0.1243 -0.3206 1.172 0.0001177 -5.284e-05 0.9008 8.871e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6261 0.3575 0.256 0.5513 0.9656 0.9836 0.6287 0.8433 0.9486 0.6916 ] Network output: [ -0.0721 0.2395 0.8708 0.0004783 -0.0002147 1.036 0.0003605 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5773 0.5258 0.2986 0.2252 0.9799 0.9866 0.5777 0.9367 0.9612 0.3622 ] Network output: [ -0.1581 0.3088 0.8782 -0.00146 0.0006554 1.123 -0.0011 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5995 0.5891 0.4 0.1283 0.9771 0.9847 0.5995 0.9287 0.9554 0.4196 ] Network output: [ 0.1724 0.612 0.1708 0.001669 -0.0007491 0.8791 0.001257 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09549 Epoch 1643 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01506 1.019 0.9797 5.68e-05 -2.55e-05 -0.02904 4.28e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02463 -0.003657 0.009957 0.02653 0.9258 0.9372 0.04595 0.8258 0.8633 0.1135 ] Network output: [ 0.9158 0.1721 -0.08914 -0.001169 0.0005249 0.08062 -0.0008812 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5653 -0.0176 -0.126 0.3452 0.961 0.9807 0.6368 0.8309 0.941 0.6915 ] Network output: [ -0.001004 0.9759 1.016 0.0002191 -9.834e-05 0.0113 0.0001651 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03805 0.01963 0.02607 0.03212 0.9772 0.9835 0.03886 0.9268 0.9579 0.04782 ] Network output: [ 0.1238 -0.3233 1.174 0.0001072 -4.811e-05 0.9023 8.076e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6272 0.3579 0.2585 0.5521 0.9656 0.9836 0.6298 0.8435 0.9487 0.691 ] Network output: [ -0.07246 0.2387 0.8729 0.000493 -0.0002213 1.035 0.0003716 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5768 0.5253 0.2988 0.2264 0.9799 0.9866 0.5772 0.9369 0.9613 0.3618 ] Network output: [ -0.1583 0.3056 0.8813 -0.001427 0.0006408 1.124 -0.001076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5992 0.5889 0.3996 0.1311 0.9771 0.9847 0.5993 0.9288 0.9555 0.4191 ] Network output: [ 0.1725 0.612 0.1697 0.001669 -0.0007492 0.8802 0.001258 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09493 Epoch 1644 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01585 1.019 0.9789 6.636e-05 -2.979e-05 -0.02972 5.001e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02463 -0.003718 0.00977 0.02652 0.9258 0.9372 0.04599 0.8259 0.8634 0.1133 ] Network output: [ 0.9222 0.1732 -0.09812 -0.001153 0.0005177 0.07586 -0.000869 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5662 -0.01941 -0.1277 0.3448 0.9611 0.9807 0.6379 0.831 0.941 0.6905 ] Network output: [ -0.001009 0.976 1.016 0.0002243 -0.0001007 0.01109 0.0001691 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03799 0.01952 0.02591 0.03206 0.9772 0.9835 0.03879 0.9269 0.9579 0.04765 ] Network output: [ 0.1255 -0.3226 1.171 0.0001146 -5.144e-05 0.9008 8.635e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6275 0.3571 0.2584 0.5522 0.9656 0.9836 0.6301 0.8436 0.9487 0.6901 ] Network output: [ -0.07348 0.2398 0.8752 0.0004919 -0.0002208 1.034 0.0003707 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5756 0.524 0.2986 0.2272 0.9799 0.9866 0.576 0.9369 0.9613 0.3613 ] Network output: [ -0.1597 0.3037 0.8857 -0.001416 0.0006359 1.124 -0.001067 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5984 0.588 0.3994 0.1335 0.9771 0.9847 0.5985 0.9287 0.9555 0.4188 ] Network output: [ 0.1715 0.6127 0.1698 0.001654 -0.0007426 0.8813 0.001247 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09451 Epoch 1645 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01549 1.019 0.9795 6.548e-05 -2.939e-05 -0.02936 4.934e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02461 -0.003686 0.01004 0.0265 0.9259 0.9372 0.04598 0.8261 0.8634 0.1132 ] Network output: [ 0.9179 0.173 -0.09257 -0.001178 0.0005288 0.07906 -0.0008878 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5666 -0.01809 -0.1239 0.3445 0.9611 0.9807 0.6384 0.8312 0.9411 0.6896 ] Network output: [ -0.0007803 0.9759 1.015 0.0002279 -0.0001023 0.01117 0.0001718 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03802 0.01962 0.0261 0.03202 0.9772 0.9835 0.03883 0.927 0.958 0.04762 ] Network output: [ 0.1234 -0.3215 1.171 8.923e-05 -4.006e-05 0.904 6.724e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6286 0.359 0.2619 0.551 0.9656 0.9836 0.6312 0.8438 0.9488 0.689 ] Network output: [ -0.07224 0.2428 0.8707 0.0004962 -0.0002228 1.033 0.000374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5754 0.5242 0.2986 0.2261 0.9799 0.9866 0.5759 0.9371 0.9614 0.3603 ] Network output: [ -0.1578 0.3065 0.8806 -0.001415 0.0006353 1.123 -0.001067 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5982 0.5879 0.3988 0.1323 0.9771 0.9846 0.5983 0.9288 0.9555 0.4179 ] Network output: [ 0.1715 0.6138 0.1681 0.001636 -0.0007346 0.8818 0.001233 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09463 Epoch 1646 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01569 1.018 0.9805 7.467e-05 -3.352e-05 -0.02961 5.627e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02459 -0.003709 0.0101 0.02661 0.9259 0.9372 0.04598 0.8262 0.8635 0.1132 ] Network output: [ 0.9186 0.1708 -0.09097 -0.001174 0.0005271 0.0781 -0.0008848 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5671 -0.01868 -0.1227 0.3452 0.9611 0.9808 0.6391 0.8313 0.9411 0.689 ] Network output: [ -0.0006788 0.9751 1.016 0.0002352 -0.0001056 0.01103 0.0001773 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03799 0.01959 0.02616 0.03212 0.9772 0.9835 0.0388 0.9272 0.9581 0.04762 ] Network output: [ 0.1238 -0.3233 1.172 8.819e-05 -3.959e-05 0.9044 6.646e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6293 0.3589 0.2634 0.5517 0.9656 0.9836 0.632 0.8439 0.9488 0.6883 ] Network output: [ -0.07292 0.2424 0.8733 0.0005056 -0.000227 1.032 0.000381 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5746 0.5234 0.2987 0.2273 0.9799 0.9866 0.5751 0.9372 0.9614 0.36 ] Network output: [ -0.1585 0.3035 0.8846 -0.001389 0.0006237 1.123 -0.001047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5978 0.5875 0.3985 0.1352 0.9771 0.9846 0.5979 0.9288 0.9555 0.4174 ] Network output: [ 0.1712 0.6139 0.1674 0.001631 -0.0007323 0.8829 0.001229 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09408 Epoch 1647 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01613 1.018 0.9801 7.977e-05 -3.581e-05 -0.02997 6.012e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02459 -0.003739 0.01005 0.02659 0.9259 0.9372 0.04601 0.8263 0.8636 0.113 ] Network output: [ 0.9216 0.1716 -0.09549 -0.001169 0.000525 0.07586 -0.0008814 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5678 -0.01945 -0.1227 0.3447 0.9611 0.9808 0.64 0.8315 0.9412 0.688 ] Network output: [ -0.0006086 0.9751 1.016 0.0002388 -0.0001072 0.01091 0.00018 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03796 0.01955 0.02611 0.03206 0.9772 0.9835 0.03877 0.9273 0.9581 0.04749 ] Network output: [ 0.1243 -0.3224 1.17 8.707e-05 -3.909e-05 0.9044 6.562e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6299 0.359 0.2644 0.5513 0.9656 0.9836 0.6325 0.8441 0.9489 0.6874 ] Network output: [ -0.07319 0.2442 0.8733 0.0005052 -0.0002268 1.031 0.0003808 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5738 0.5226 0.2985 0.2274 0.9799 0.9866 0.5743 0.9373 0.9615 0.3593 ] Network output: [ -0.1589 0.3033 0.8857 -0.001383 0.0006211 1.123 -0.001043 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5972 0.5868 0.3981 0.1364 0.9771 0.9846 0.5972 0.9288 0.9555 0.4169 ] Network output: [ 0.1705 0.6148 0.167 0.001614 -0.0007244 0.8838 0.001216 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09386 Epoch 1648 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01593 1.017 0.9809 8.124e-05 -3.647e-05 -0.02979 6.122e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02458 -0.003722 0.01026 0.02662 0.9259 0.9373 0.046 0.8265 0.8637 0.1129 ] Network output: [ 0.9187 0.1707 -0.09078 -0.001184 0.0005315 0.07792 -0.0008922 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5682 -0.01863 -0.1196 0.3448 0.9611 0.9808 0.6406 0.8316 0.9412 0.6872 ] Network output: [ -0.0004151 0.9748 1.016 0.0002427 -0.0001089 0.01092 0.0001829 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03798 0.01962 0.02626 0.03208 0.9772 0.9835 0.03879 0.9275 0.9582 0.04748 ] Network output: [ 0.1229 -0.3223 1.17 7.156e-05 -3.213e-05 0.9068 5.393e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6308 0.3604 0.2672 0.5507 0.9656 0.9836 0.6335 0.8442 0.9489 0.6864 ] Network output: [ -0.07254 0.2461 0.871 0.000511 -0.0002294 1.03 0.0003851 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5735 0.5225 0.2985 0.2271 0.9799 0.9866 0.5739 0.9374 0.9615 0.3586 ] Network output: [ -0.1578 0.3043 0.8834 -0.001375 0.0006173 1.122 -0.001036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5969 0.5866 0.3976 0.1364 0.9771 0.9846 0.597 0.9289 0.9555 0.4162 ] Network output: [ 0.1704 0.6156 0.1656 0.001599 -0.0007177 0.8845 0.001205 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09377 Epoch 1649 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01623 1.017 0.9814 8.86e-05 -3.978e-05 -0.03012 6.677e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02456 -0.003749 0.01028 0.02668 0.9259 0.9373 0.04601 0.8266 0.8637 0.1129 ] Network output: [ 0.9204 0.1694 -0.09143 -0.001177 0.0005283 0.07636 -0.0008869 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5688 -0.01931 -0.119 0.3452 0.9611 0.9808 0.6413 0.8318 0.9413 0.6865 ] Network output: [ -0.0003297 0.9742 1.017 0.0002479 -0.0001113 0.01078 0.0001868 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03795 0.01958 0.02629 0.03213 0.9773 0.9835 0.03876 0.9276 0.9583 0.04745 ] Network output: [ 0.1234 -0.3232 1.17 7.34e-05 -3.295e-05 0.9069 5.532e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6315 0.3604 0.2684 0.5511 0.9656 0.9836 0.6342 0.8444 0.949 0.6857 ] Network output: [ -0.07315 0.2463 0.873 0.0005165 -0.0002319 1.029 0.0003892 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5727 0.5217 0.2985 0.228 0.9799 0.9866 0.5731 0.9375 0.9616 0.3582 ] Network output: [ -0.1585 0.3021 0.8866 -0.001356 0.0006089 1.123 -0.001022 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5964 0.5861 0.3974 0.1388 0.9771 0.9846 0.5965 0.9289 0.9555 0.4158 ] Network output: [ 0.1699 0.616 0.1651 0.001589 -0.0007132 0.8856 0.001197 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09332 Epoch 1650 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01646 1.016 0.9813 9.153e-05 -4.109e-05 -0.0303 6.898e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02456 -0.003761 0.01032 0.02668 0.9259 0.9373 0.04603 0.8268 0.8638 0.1128 ] Network output: [ 0.9214 0.1697 -0.09283 -0.001177 0.0005286 0.07562 -0.0008873 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5694 -0.01948 -0.118 0.3449 0.9611 0.9808 0.6421 0.8319 0.9413 0.6856 ] Network output: [ -0.0002158 0.9742 1.017 0.0002506 -0.0001125 0.01071 0.0001888 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03794 0.01958 0.0263 0.0321 0.9773 0.9835 0.03875 0.9277 0.9583 0.04737 ] Network output: [ 0.1233 -0.3225 1.168 6.919e-05 -3.106e-05 0.9078 5.214e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6322 0.361 0.2699 0.5506 0.9656 0.9836 0.6349 0.8445 0.949 0.6848 ] Network output: [ -0.07309 0.2482 0.8722 0.0005175 -0.0002323 1.028 0.00039 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.572 0.5212 0.2984 0.2279 0.9799 0.9866 0.5725 0.9376 0.9616 0.3575 ] Network output: [ -0.1583 0.3025 0.8864 -0.001351 0.0006066 1.122 -0.001018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5959 0.5857 0.397 0.1395 0.9771 0.9846 0.596 0.9289 0.9556 0.4152 ] Network output: [ 0.1694 0.6169 0.1643 0.001571 -0.0007051 0.8865 0.001184 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09315 Epoch 1651 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01642 1.016 0.982 9.431e-05 -4.234e-05 -0.03029 7.108e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02454 -0.003757 0.01046 0.02672 0.9259 0.9373 0.04603 0.8269 0.8639 0.1127 ] Network output: [ 0.9199 0.1686 -0.08977 -0.001183 0.0005311 0.07652 -0.0008915 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5698 -0.01911 -0.1158 0.345 0.9611 0.9808 0.6427 0.8321 0.9414 0.6849 ] Network output: [ -5.774e-05 0.9737 1.017 0.0002541 -0.0001141 0.01067 0.0001915 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03794 0.01962 0.02642 0.03213 0.9773 0.9835 0.03876 0.9279 0.9584 0.04735 ] Network output: [ 0.1226 -0.3228 1.168 6.164e-05 -2.767e-05 0.9094 4.645e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6331 0.362 0.2721 0.5503 0.9657 0.9836 0.6357 0.8447 0.9491 0.6839 ] Network output: [ -0.07288 0.2495 0.8714 0.0005228 -0.0002347 1.027 0.000394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5716 0.5209 0.2984 0.228 0.98 0.9866 0.572 0.9378 0.9617 0.3569 ] Network output: [ -0.1578 0.3024 0.886 -0.00134 0.0006016 1.122 -0.00101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5956 0.5854 0.3966 0.1403 0.9771 0.9846 0.5957 0.9289 0.9556 0.4146 ] Network output: [ 0.1691 0.6175 0.1632 0.001556 -0.0006987 0.8873 0.001173 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09294 Epoch 1652 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01672 1.015 0.9823 9.957e-05 -4.47e-05 -0.03058 7.504e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02453 -0.003779 0.01047 0.02676 0.9259 0.9373 0.04604 0.8271 0.864 0.1126 ] Network output: [ 0.9215 0.1679 -0.0908 -0.001176 0.0005281 0.07515 -0.0008866 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5704 -0.01961 -0.1151 0.3452 0.9611 0.9808 0.6435 0.8323 0.9414 0.6841 ] Network output: [ 3.451e-05 0.9734 1.017 0.0002577 -0.0001157 0.01055 0.0001942 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03792 0.0196 0.02643 0.03216 0.9773 0.9836 0.03874 0.928 0.9584 0.04731 ] Network output: [ 0.123 -0.3231 1.168 6.359e-05 -2.855e-05 0.9098 4.792e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6337 0.3622 0.2733 0.5504 0.9657 0.9836 0.6364 0.8448 0.9491 0.6831 ] Network output: [ -0.07329 0.2502 0.8725 0.0005261 -0.0002362 1.026 0.0003965 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5708 0.5202 0.2984 0.2286 0.98 0.9866 0.5713 0.9379 0.9618 0.3564 ] Network output: [ -0.1582 0.3011 0.8881 -0.001327 0.0005957 1.122 -0.001 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5951 0.5849 0.3963 0.1421 0.977 0.9846 0.5952 0.929 0.9556 0.4142 ] Network output: [ 0.1686 0.6181 0.1627 0.001543 -0.0006925 0.8883 0.001163 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09259 Epoch 1653 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01685 1.015 0.9825 0.0001017 -4.565e-05 -0.0307 7.664e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02452 -0.003785 0.01054 0.02678 0.9259 0.9373 0.04605 0.8272 0.8641 0.1125 ] Network output: [ 0.9215 0.1677 -0.09057 -0.001178 0.0005286 0.07504 -0.0008874 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5709 -0.01952 -0.1137 0.345 0.9611 0.9808 0.6442 0.8324 0.9415 0.6832 ] Network output: [ 0.0001656 0.9732 1.017 0.0002598 -0.0001166 0.01048 0.0001958 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03792 0.01962 0.02649 0.03215 0.9773 0.9836 0.03874 0.9281 0.9585 0.04726 ] Network output: [ 0.1226 -0.3228 1.167 5.985e-05 -2.687e-05 0.9109 4.51e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6344 0.363 0.2751 0.5499 0.9657 0.9836 0.6371 0.845 0.9491 0.6822 ] Network output: [ -0.07316 0.2518 0.8716 0.0005281 -0.0002371 1.025 0.000398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5703 0.5198 0.2983 0.2285 0.98 0.9866 0.5707 0.938 0.9618 0.3558 ] Network output: [ -0.1578 0.3015 0.8877 -0.001321 0.0005929 1.121 -0.0009953 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5947 0.5845 0.3959 0.1428 0.977 0.9846 0.5948 0.929 0.9556 0.4136 ] Network output: [ 0.1682 0.6189 0.1618 0.001525 -0.0006845 0.8891 0.001149 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09242 Epoch 1654 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01694 1.014 0.9831 0.0001048 -4.704e-05 -0.0308 7.897e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02451 -0.003789 0.01064 0.02682 0.926 0.9373 0.04606 0.8274 0.8641 0.1125 ] Network output: [ 0.9211 0.1667 -0.08891 -0.001177 0.0005285 0.07514 -0.0008872 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5714 -0.01942 -0.1121 0.3452 0.9611 0.9808 0.6448 0.8326 0.9416 0.6825 ] Network output: [ 0.0002998 0.9728 1.017 0.0002627 -0.0001179 0.01041 0.0001979 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03791 0.01964 0.02656 0.03218 0.9773 0.9836 0.03873 0.9283 0.9585 0.04724 ] Network output: [ 0.1223 -0.3231 1.167 5.753e-05 -2.583e-05 0.9121 4.336e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6352 0.3638 0.2768 0.5498 0.9657 0.9836 0.6379 0.8452 0.9492 0.6814 ] Network output: [ -0.07318 0.2529 0.8715 0.0005323 -0.000239 1.024 0.0004012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5697 0.5194 0.2984 0.2288 0.98 0.9867 0.5702 0.9381 0.9619 0.3553 ] Network output: [ -0.1577 0.301 0.8882 -0.00131 0.0005879 1.121 -0.0009869 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5943 0.5841 0.3955 0.1439 0.977 0.9846 0.5944 0.929 0.9556 0.4131 ] Network output: [ 0.1678 0.6195 0.1609 0.00151 -0.0006778 0.89 0.001138 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09216 Epoch 1655 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01719 1.014 0.9833 0.0001083 -4.864e-05 -0.03104 8.165e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0245 -0.003805 0.01067 0.02686 0.926 0.9373 0.04607 0.8275 0.8642 0.1124 ] Network output: [ 0.9222 0.1662 -0.08954 -0.001172 0.0005261 0.07419 -0.0008832 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5719 -0.01968 -0.1112 0.3452 0.9612 0.9808 0.6455 0.8328 0.9416 0.6817 ] Network output: [ 0.0004047 0.9725 1.017 0.000265 -0.000119 0.01031 0.0001997 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0379 0.01963 0.02659 0.0322 0.9773 0.9836 0.03872 0.9284 0.9586 0.0472 ] Network output: [ 0.1225 -0.3232 1.166 5.917e-05 -2.656e-05 0.9127 4.459e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6359 0.3642 0.2781 0.5497 0.9657 0.9836 0.6386 0.8453 0.9492 0.6806 ] Network output: [ -0.07341 0.2539 0.872 0.0005346 -0.00024 1.023 0.0004029 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5691 0.5189 0.2983 0.2292 0.98 0.9867 0.5695 0.9382 0.9619 0.3548 ] Network output: [ -0.1578 0.3003 0.8894 -0.0013 0.0005835 1.121 -0.0009795 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5938 0.5837 0.3952 0.1453 0.977 0.9846 0.5939 0.9291 0.9557 0.4126 ] Network output: [ 0.1673 0.6202 0.1603 0.001494 -0.0006706 0.891 0.001126 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09188 Epoch 1656 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01731 1.013 0.9836 0.0001102 -4.948e-05 -0.03116 8.306e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02449 -0.003809 0.01075 0.02688 0.926 0.9373 0.04608 0.8277 0.8643 0.1124 ] Network output: [ 0.9221 0.1657 -0.08873 -0.001171 0.0005258 0.07419 -0.0008827 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5724 -0.01954 -0.1097 0.3452 0.9612 0.9808 0.6462 0.8329 0.9417 0.6809 ] Network output: [ 0.0005375 0.9722 1.018 0.0002667 -0.0001197 0.01025 0.000201 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0379 0.01966 0.02666 0.03221 0.9773 0.9836 0.03872 0.9286 0.9587 0.04717 ] Network output: [ 0.1221 -0.3231 1.165 5.745e-05 -2.579e-05 0.9139 4.33e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6366 0.3651 0.2798 0.5493 0.9657 0.9837 0.6393 0.8455 0.9493 0.6797 ] Network output: [ -0.07334 0.2553 0.8714 0.000537 -0.0002411 1.022 0.0004047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5685 0.5185 0.2983 0.2292 0.98 0.9867 0.569 0.9383 0.962 0.3543 ] Network output: [ -0.1575 0.3003 0.8892 -0.001293 0.0005803 1.12 -0.0009742 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5935 0.5833 0.3949 0.1461 0.977 0.9846 0.5936 0.9291 0.9557 0.4121 ] Network output: [ 0.1669 0.6209 0.1594 0.001476 -0.0006628 0.8918 0.001113 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09168 Epoch 1657 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01746 1.013 0.9841 0.000113 -5.072e-05 -0.03132 8.514e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02448 -0.003817 0.01081 0.02693 0.926 0.9374 0.04609 0.8279 0.8644 0.1123 ] Network output: [ 0.9222 0.1648 -0.0879 -0.001168 0.0005243 0.07389 -0.0008801 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5729 -0.01954 -0.1084 0.3453 0.9612 0.9808 0.6468 0.8331 0.9417 0.6802 ] Network output: [ 0.0006599 0.9718 1.018 0.0002687 -0.0001206 0.01017 0.0002025 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03789 0.01967 0.02672 0.03224 0.9773 0.9836 0.03871 0.9287 0.9587 0.04715 ] Network output: [ 0.122 -0.3233 1.165 5.817e-05 -2.612e-05 0.9148 4.384e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6373 0.3658 0.2813 0.5492 0.9657 0.9837 0.64 0.8457 0.9493 0.6789 ] Network output: [ -0.07345 0.2563 0.8716 0.00054 -0.0002424 1.021 0.000407 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.568 0.5181 0.2983 0.2295 0.98 0.9867 0.5684 0.9385 0.9621 0.3538 ] Network output: [ -0.1575 0.2998 0.89 -0.001283 0.0005758 1.12 -0.0009667 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5931 0.583 0.3945 0.1473 0.977 0.9846 0.5932 0.9292 0.9557 0.4116 ] Network output: [ 0.1665 0.6216 0.1586 0.00146 -0.0006556 0.8928 0.001101 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09141 Epoch 1658 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01767 1.012 0.9843 0.0001154 -5.18e-05 -0.03151 8.696e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02447 -0.003827 0.01086 0.02696 0.926 0.9374 0.0461 0.828 0.8645 0.1123 ] Network output: [ 0.9228 0.1643 -0.08801 -0.001163 0.0005221 0.07329 -0.0008765 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5734 -0.01962 -0.1074 0.3453 0.9612 0.9808 0.6475 0.8333 0.9418 0.6794 ] Network output: [ 0.0007744 0.9715 1.018 0.0002701 -0.0001213 0.01009 0.0002036 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03788 0.01968 0.02676 0.03226 0.9773 0.9836 0.03871 0.9288 0.9588 0.04712 ] Network output: [ 0.122 -0.3233 1.164 5.998e-05 -2.693e-05 0.9157 4.52e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.638 0.3664 0.2827 0.549 0.9657 0.9837 0.6407 0.8458 0.9494 0.6781 ] Network output: [ -0.07357 0.2574 0.8717 0.0005419 -0.0002433 1.02 0.0004084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5674 0.5176 0.2983 0.2298 0.98 0.9867 0.5678 0.9386 0.9621 0.3534 ] Network output: [ -0.1575 0.2994 0.8906 -0.001275 0.0005722 1.12 -0.0009606 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5926 0.5826 0.3942 0.1484 0.977 0.9846 0.5927 0.9292 0.9557 0.4112 ] Network output: [ 0.166 0.6223 0.1579 0.001443 -0.0006478 0.8937 0.001088 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09117 Epoch 1659 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0178 1.012 0.9847 0.0001171 -5.257e-05 -0.03164 8.825e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02446 -0.003832 0.01093 0.02699 0.926 0.9374 0.04611 0.8282 0.8646 0.1122 ] Network output: [ 0.9228 0.1637 -0.08716 -0.00116 0.0005209 0.07318 -0.0008744 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5739 -0.01949 -0.106 0.3454 0.9612 0.9808 0.6481 0.8334 0.9418 0.6786 ] Network output: [ 0.000903 0.9712 1.018 0.0002713 -0.0001218 0.01002 0.0002045 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03788 0.0197 0.02683 0.03229 0.9774 0.9836 0.03871 0.929 0.9588 0.0471 ] Network output: [ 0.1218 -0.3234 1.163 6.05e-05 -2.716e-05 0.9168 4.559e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6387 0.3672 0.2843 0.5487 0.9657 0.9837 0.6414 0.846 0.9494 0.6772 ] Network output: [ -0.07357 0.2586 0.8714 0.0005441 -0.0002443 1.019 0.00041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5668 0.5172 0.2983 0.2299 0.98 0.9867 0.5673 0.9387 0.9622 0.3529 ] Network output: [ -0.1572 0.2993 0.8908 -0.001267 0.0005688 1.119 -0.0009549 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5923 0.5822 0.3939 0.1493 0.977 0.9846 0.5923 0.9293 0.9558 0.4107 ] Network output: [ 0.1656 0.623 0.1571 0.001425 -0.0006399 0.8946 0.001074 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09095 Epoch 1660 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01798 1.011 0.985 0.0001193 -5.355e-05 -0.03182 8.99e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02445 -0.00384 0.01098 0.02704 0.926 0.9374 0.04612 0.8283 0.8646 0.1122 ] Network output: [ 0.9231 0.163 -0.08668 -0.001155 0.0005186 0.07276 -0.0008705 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5744 -0.01949 -0.1049 0.3455 0.9612 0.9809 0.6488 0.8336 0.9419 0.6779 ] Network output: [ 0.001021 0.9709 1.018 0.0002726 -0.0001224 0.009944 0.0002054 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03787 0.01971 0.02688 0.03232 0.9774 0.9836 0.0387 0.9291 0.9589 0.04708 ] Network output: [ 0.1217 -0.3235 1.163 6.291e-05 -2.824e-05 0.9176 4.741e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6394 0.3679 0.2857 0.5486 0.9657 0.9837 0.6421 0.8462 0.9495 0.6764 ] Network output: [ -0.07369 0.2596 0.8716 0.0005463 -0.0002452 1.018 0.0004117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5663 0.5168 0.2983 0.2302 0.98 0.9867 0.5667 0.9388 0.9622 0.3525 ] Network output: [ -0.1572 0.2989 0.8915 -0.001258 0.000565 1.119 -0.0009484 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5919 0.5818 0.3936 0.1504 0.977 0.9846 0.5919 0.9293 0.9558 0.4103 ] Network output: [ 0.1651 0.6237 0.1563 0.001408 -0.0006323 0.8955 0.001061 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09069 Epoch 1661 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01817 1.011 0.9853 0.000121 -5.432e-05 -0.03199 9.119e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02444 -0.003848 0.01103 0.02707 0.9261 0.9374 0.04613 0.8285 0.8647 0.1121 ] Network output: [ 0.9235 0.1624 -0.08642 -0.00115 0.0005163 0.07235 -0.0008667 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5748 -0.01946 -0.1038 0.3455 0.9612 0.9809 0.6494 0.8338 0.942 0.6772 ] Network output: [ 0.00114 0.9706 1.018 0.0002734 -0.0001227 0.009872 0.000206 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03787 0.01973 0.02693 0.03234 0.9774 0.9836 0.0387 0.9293 0.959 0.04706 ] Network output: [ 0.1216 -0.3235 1.162 6.539e-05 -2.936e-05 0.9185 4.928e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.64 0.3687 0.2871 0.5484 0.9658 0.9837 0.6428 0.8463 0.9495 0.6756 ] Network output: [ -0.07378 0.2607 0.8716 0.0005479 -0.000246 1.018 0.0004129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5657 0.5164 0.2983 0.2305 0.98 0.9867 0.5662 0.9389 0.9623 0.3521 ] Network output: [ -0.1571 0.2986 0.8919 -0.001251 0.0005617 1.119 -0.000943 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5915 0.5815 0.3933 0.1514 0.977 0.9846 0.5916 0.9294 0.9558 0.4098 ] Network output: [ 0.1646 0.6244 0.1556 0.001391 -0.0006243 0.8964 0.001048 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09046 Epoch 1662 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01832 1.01 0.9856 0.0001225 -5.499e-05 -0.03214 9.231e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02442 -0.003854 0.01109 0.02711 0.9261 0.9374 0.04614 0.8286 0.8648 0.1121 ] Network output: [ 0.9236 0.1618 -0.0857 -0.001145 0.0005142 0.07212 -0.0008633 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5753 -0.01934 -0.1025 0.3455 0.9612 0.9809 0.65 0.834 0.942 0.6764 ] Network output: [ 0.001264 0.9702 1.019 0.0002741 -0.0001231 0.009805 0.0002066 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03787 0.01975 0.02699 0.03237 0.9774 0.9836 0.0387 0.9294 0.959 0.04705 ] Network output: [ 0.1214 -0.3236 1.161 6.775e-05 -3.041e-05 0.9196 5.106e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6407 0.3695 0.2885 0.5482 0.9658 0.9837 0.6435 0.8465 0.9496 0.6749 ] Network output: [ -0.07383 0.2617 0.8715 0.0005497 -0.0002468 1.017 0.0004143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5652 0.516 0.2983 0.2307 0.9801 0.9867 0.5656 0.9391 0.9623 0.3516 ] Network output: [ -0.157 0.2984 0.8922 -0.001244 0.0005584 1.118 -0.0009374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5911 0.5811 0.393 0.1524 0.977 0.9846 0.5912 0.9294 0.9558 0.4094 ] Network output: [ 0.1642 0.6251 0.1548 0.001373 -0.0006163 0.8973 0.001035 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09023 Epoch 1663 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01851 1.01 0.986 0.0001241 -5.572e-05 -0.03232 9.353e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02441 -0.003861 0.01114 0.02715 0.9261 0.9374 0.04615 0.8288 0.8649 0.112 ] Network output: [ 0.9239 0.1611 -0.08531 -0.00114 0.0005116 0.0717 -0.0008588 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5758 -0.0193 -0.1014 0.3456 0.9613 0.9809 0.6507 0.8342 0.9421 0.6757 ] Network output: [ 0.001382 0.9699 1.019 0.0002747 -0.0001233 0.009733 0.000207 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03787 0.01977 0.02705 0.0324 0.9774 0.9837 0.0387 0.9295 0.9591 0.04704 ] Network output: [ 0.1214 -0.3237 1.161 7.122e-05 -3.197e-05 0.9204 5.367e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6414 0.3702 0.2898 0.548 0.9658 0.9837 0.6441 0.8467 0.9497 0.6741 ] Network output: [ -0.07394 0.2627 0.8717 0.0005513 -0.0002475 1.016 0.0004155 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5647 0.5156 0.2983 0.231 0.9801 0.9867 0.5651 0.9392 0.9624 0.3513 ] Network output: [ -0.1569 0.2981 0.8928 -0.001236 0.000555 1.118 -0.0009317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5907 0.5807 0.3928 0.1534 0.977 0.9846 0.5908 0.9295 0.9559 0.409 ] Network output: [ 0.1637 0.6258 0.1541 0.001355 -0.0006083 0.8982 0.001021 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08998 Epoch 1664 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01869 1.009 0.9862 0.0001254 -5.628e-05 -0.03249 9.448e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0244 -0.003868 0.01119 0.02719 0.9261 0.9374 0.04616 0.829 0.865 0.112 ] Network output: [ 0.9242 0.1605 -0.08486 -0.001134 0.000509 0.07136 -0.0008545 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5762 -0.01921 -0.1003 0.3456 0.9613 0.9809 0.6513 0.8343 0.9421 0.675 ] Network output: [ 0.001502 0.9696 1.019 0.0002751 -0.0001235 0.009668 0.0002073 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03786 0.01979 0.02711 0.03243 0.9774 0.9837 0.0387 0.9297 0.9592 0.04703 ] Network output: [ 0.1213 -0.3238 1.16 7.46e-05 -3.349e-05 0.9214 5.622e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.642 0.371 0.2912 0.5478 0.9658 0.9837 0.6448 0.8469 0.9497 0.6733 ] Network output: [ -0.07402 0.2638 0.8717 0.0005527 -0.0002481 1.015 0.0004165 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5641 0.5153 0.2984 0.2312 0.9801 0.9867 0.5646 0.9393 0.9625 0.3509 ] Network output: [ -0.1568 0.2979 0.8932 -0.00123 0.000552 1.117 -0.0009267 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5903 0.5804 0.3925 0.1544 0.977 0.9846 0.5904 0.9295 0.9559 0.4086 ] Network output: [ 0.1632 0.6265 0.1533 0.001337 -0.0006001 0.8991 0.001007 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08976 Epoch 1665 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01886 1.009 0.9866 0.0001266 -5.682e-05 -0.03265 9.539e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02439 -0.003873 0.01124 0.02723 0.9261 0.9375 0.04618 0.8291 0.8651 0.112 ] Network output: [ 0.9244 0.1599 -0.08426 -0.001128 0.0005064 0.07105 -0.0008501 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5767 -0.01909 -0.09916 0.3457 0.9613 0.9809 0.6519 0.8345 0.9422 0.6743 ] Network output: [ 0.001622 0.9693 1.019 0.0002753 -0.0001236 0.009605 0.0002075 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03786 0.01981 0.02717 0.03246 0.9774 0.9837 0.0387 0.9298 0.9592 0.04702 ] Network output: [ 0.1212 -0.3239 1.16 7.828e-05 -3.514e-05 0.9223 5.9e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6427 0.3719 0.2925 0.5476 0.9658 0.9837 0.6455 0.847 0.9498 0.6725 ] Network output: [ -0.0741 0.2647 0.8717 0.0005541 -0.0002488 1.014 0.0004176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5636 0.5149 0.2984 0.2314 0.9801 0.9867 0.5641 0.9394 0.9625 0.3505 ] Network output: [ -0.1566 0.2976 0.8936 -0.001223 0.0005489 1.117 -0.0009214 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5899 0.5801 0.3922 0.1554 0.977 0.9846 0.59 0.9296 0.9559 0.4082 ] Network output: [ 0.1628 0.6272 0.1526 0.001319 -0.000592 0.9 0.0009938 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08952 Epoch 1666 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01904 1.008 0.9868 0.0001277 -5.734e-05 -0.03282 9.626e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02438 -0.00388 0.01129 0.02727 0.9261 0.9375 0.04619 0.8293 0.8652 0.112 ] Network output: [ 0.9247 0.1593 -0.08384 -0.001121 0.0005034 0.07066 -0.0008451 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5771 -0.019 -0.09809 0.3457 0.9613 0.9809 0.6525 0.8347 0.9423 0.6736 ] Network output: [ 0.001739 0.9689 1.019 0.0002754 -0.0001236 0.009541 0.0002076 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03786 0.01983 0.02722 0.0325 0.9774 0.9837 0.0387 0.93 0.9593 0.04702 ] Network output: [ 0.1211 -0.3239 1.159 8.253e-05 -3.705e-05 0.9232 6.22e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6433 0.3727 0.2938 0.5474 0.9658 0.9837 0.6461 0.8472 0.9498 0.6718 ] Network output: [ -0.0742 0.2657 0.8718 0.0005553 -0.0002493 1.013 0.0004185 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5631 0.5146 0.2984 0.2317 0.9801 0.9867 0.5636 0.9395 0.9626 0.3501 ] Network output: [ -0.1565 0.2973 0.8941 -0.001216 0.0005458 1.117 -0.0009163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5896 0.5797 0.3919 0.1564 0.977 0.9846 0.5897 0.9296 0.956 0.4078 ] Network output: [ 0.1623 0.6279 0.1519 0.0013 -0.0005838 0.9009 0.00098 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08929 Epoch 1667 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01922 1.008 0.9871 0.0001287 -5.776e-05 -0.03298 9.697e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02437 -0.003886 0.01134 0.02731 0.9261 0.9375 0.0462 0.8295 0.8653 0.1119 ] Network output: [ 0.9249 0.1587 -0.08333 -0.001115 0.0005006 0.07033 -0.0008403 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5775 -0.01887 -0.097 0.3458 0.9613 0.9809 0.6531 0.8349 0.9423 0.6729 ] Network output: [ 0.001858 0.9686 1.019 0.0002753 -0.0001236 0.009482 0.0002075 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03786 0.01985 0.02728 0.03253 0.9774 0.9837 0.0387 0.9301 0.9593 0.04702 ] Network output: [ 0.121 -0.324 1.158 8.678e-05 -3.896e-05 0.9241 6.54e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.644 0.3735 0.2951 0.5472 0.9658 0.9837 0.6468 0.8474 0.9499 0.671 ] Network output: [ -0.07429 0.2667 0.8719 0.0005564 -0.0002498 1.012 0.0004193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5626 0.5142 0.2985 0.2319 0.9801 0.9867 0.5631 0.9397 0.9626 0.3498 ] Network output: [ -0.1564 0.2971 0.8944 -0.001209 0.000543 1.116 -0.0009115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5892 0.5794 0.3917 0.1573 0.977 0.9846 0.5893 0.9297 0.956 0.4074 ] Network output: [ 0.1618 0.6287 0.1511 0.001282 -0.0005755 0.9019 0.0009661 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08906 Epoch 1668 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0194 1.007 0.9874 0.0001296 -5.817e-05 -0.03314 9.766e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02437 -0.003891 0.01139 0.02735 0.9262 0.9375 0.04621 0.8296 0.8654 0.1119 ] Network output: [ 0.9251 0.158 -0.08281 -0.001108 0.0004976 0.06999 -0.0008353 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.578 -0.01874 -0.09593 0.3458 0.9613 0.9809 0.6537 0.8351 0.9424 0.6722 ] Network output: [ 0.001975 0.9683 1.019 0.0002752 -0.0001235 0.009425 0.0002074 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03786 0.01988 0.02734 0.03257 0.9774 0.9837 0.0387 0.9303 0.9594 0.04702 ] Network output: [ 0.1209 -0.3241 1.158 9.138e-05 -4.102e-05 0.925 6.886e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6446 0.3743 0.2964 0.547 0.9658 0.9837 0.6474 0.8476 0.9499 0.6703 ] Network output: [ -0.07438 0.2676 0.872 0.0005574 -0.0002503 1.011 0.0004201 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5621 0.5139 0.2985 0.2322 0.9801 0.9868 0.5626 0.9398 0.9627 0.3495 ] Network output: [ -0.1563 0.2969 0.8948 -0.001203 0.0005401 1.116 -0.0009066 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5888 0.5791 0.3914 0.1582 0.977 0.9846 0.5889 0.9297 0.956 0.407 ] Network output: [ 0.1613 0.6294 0.1504 0.001264 -0.0005673 0.9028 0.0009523 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08883 Epoch 1669 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01959 1.007 0.9877 0.0001304 -5.854e-05 -0.03331 9.827e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02436 -0.003897 0.01143 0.02739 0.9262 0.9375 0.04622 0.8298 0.8654 0.1119 ] Network output: [ 0.9254 0.1574 -0.08235 -0.001101 0.0004944 0.06961 -0.00083 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5784 -0.01861 -0.0949 0.3459 0.9613 0.9809 0.6543 0.8353 0.9424 0.6715 ] Network output: [ 0.002091 0.968 1.02 0.0002749 -0.0001234 0.00937 0.0002072 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03786 0.0199 0.0274 0.03261 0.9775 0.9837 0.0387 0.9304 0.9595 0.04702 ] Network output: [ 0.1208 -0.3241 1.157 9.625e-05 -4.321e-05 0.9259 7.253e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6452 0.3752 0.2976 0.5468 0.9658 0.9837 0.648 0.8478 0.95 0.6695 ] Network output: [ -0.07448 0.2685 0.8721 0.0005583 -0.0002507 1.011 0.0004208 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5616 0.5136 0.2986 0.2325 0.9801 0.9868 0.5621 0.9399 0.9627 0.3492 ] Network output: [ -0.1561 0.2967 0.8952 -0.001197 0.0005373 1.115 -0.0009019 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5885 0.5787 0.3912 0.1592 0.977 0.9846 0.5886 0.9298 0.956 0.4067 ] Network output: [ 0.1608 0.6301 0.1497 0.001245 -0.000559 0.9037 0.0009384 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0886 Epoch 1670 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01977 1.007 0.9879 0.0001311 -5.885e-05 -0.03347 9.879e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02435 -0.003902 0.01148 0.02744 0.9262 0.9375 0.04624 0.83 0.8655 0.1119 ] Network output: [ 0.9256 0.1568 -0.08183 -0.001094 0.0004913 0.06928 -0.0008247 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5788 -0.01845 -0.09385 0.3459 0.9614 0.9809 0.6549 0.8355 0.9425 0.6708 ] Network output: [ 0.002208 0.9677 1.02 0.0002745 -0.0001232 0.009318 0.0002069 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03786 0.01993 0.02746 0.03265 0.9775 0.9837 0.0387 0.9306 0.9595 0.04703 ] Network output: [ 0.1207 -0.3242 1.156 0.0001012 -4.543e-05 0.9268 7.627e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6458 0.3761 0.2989 0.5466 0.9659 0.9838 0.6487 0.8479 0.95 0.6688 ] Network output: [ -0.07457 0.2694 0.8722 0.0005592 -0.000251 1.01 0.0004214 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5612 0.5133 0.2986 0.2327 0.9801 0.9868 0.5616 0.94 0.9628 0.3489 ] Network output: [ -0.156 0.2965 0.8956 -0.001191 0.0005345 1.115 -0.0008973 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5881 0.5784 0.391 0.1601 0.977 0.9846 0.5882 0.9299 0.9561 0.4063 ] Network output: [ 0.1603 0.6308 0.149 0.001227 -0.0005507 0.9046 0.0009244 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08837 Epoch 1671 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01996 1.006 0.9882 0.0001317 -5.915e-05 -0.03363 9.929e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02434 -0.003907 0.01152 0.02748 0.9262 0.9375 0.04625 0.8301 0.8656 0.1119 ] Network output: [ 0.9259 0.1562 -0.08134 -0.001087 0.000488 0.06892 -0.0008192 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5792 -0.0183 -0.09283 0.346 0.9614 0.981 0.6554 0.8357 0.9426 0.6702 ] Network output: [ 0.002323 0.9673 1.02 0.000274 -0.000123 0.009268 0.0002065 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03787 0.01995 0.02752 0.03269 0.9775 0.9837 0.03871 0.9307 0.9596 0.04703 ] Network output: [ 0.1206 -0.3243 1.156 0.0001064 -4.778e-05 0.9277 8.02e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6465 0.3769 0.3001 0.5464 0.9659 0.9838 0.6493 0.8481 0.9501 0.6681 ] Network output: [ -0.07467 0.2703 0.8724 0.0005599 -0.0002514 1.009 0.000422 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5607 0.513 0.2987 0.233 0.9801 0.9868 0.5611 0.9402 0.9629 0.3486 ] Network output: [ -0.1559 0.2963 0.896 -0.001185 0.0005318 1.115 -0.0008928 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5878 0.5781 0.3908 0.161 0.977 0.9846 0.5879 0.9299 0.9561 0.406 ] Network output: [ 0.1598 0.6315 0.1483 0.001208 -0.0005423 0.9055 0.0009104 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08815 Epoch 1672 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02014 1.006 0.9884 0.0001323 -5.94e-05 -0.03379 9.971e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02433 -0.003913 0.01156 0.02752 0.9262 0.9375 0.04626 0.8303 0.8657 0.1119 ] Network output: [ 0.9261 0.1556 -0.08086 -0.00108 0.0004847 0.06856 -0.0008137 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5797 -0.01813 -0.09182 0.3461 0.9614 0.981 0.656 0.8358 0.9426 0.6695 ] Network output: [ 0.002438 0.967 1.02 0.0002734 -0.0001227 0.009221 0.000206 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03787 0.01998 0.02759 0.03273 0.9775 0.9837 0.03871 0.9308 0.9596 0.04704 ] Network output: [ 0.1205 -0.3243 1.155 0.0001118 -5.018e-05 0.9286 8.424e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6471 0.3778 0.3013 0.5462 0.9659 0.9838 0.6499 0.8483 0.9501 0.6674 ] Network output: [ -0.07477 0.2712 0.8725 0.0005606 -0.0002517 1.008 0.0004225 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5602 0.5127 0.2988 0.2333 0.9802 0.9868 0.5607 0.9403 0.9629 0.3483 ] Network output: [ -0.1557 0.2961 0.8963 -0.001179 0.0005292 1.114 -0.0008883 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5874 0.5778 0.3905 0.1619 0.9769 0.9846 0.5875 0.93 0.9562 0.4057 ] Network output: [ 0.1593 0.6323 0.1476 0.001189 -0.000534 0.9064 0.0008964 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08792 Epoch 1673 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02033 1.005 0.9887 0.0001328 -5.962e-05 -0.03395 0.0001001 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02432 -0.003917 0.01161 0.02757 0.9262 0.9376 0.04627 0.8305 0.8658 0.1119 ] Network output: [ 0.9264 0.155 -0.08035 -0.001072 0.0004813 0.06821 -0.000808 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5801 -0.01795 -0.09082 0.3461 0.9614 0.981 0.6566 0.836 0.9427 0.6688 ] Network output: [ 0.002552 0.9667 1.02 0.0002727 -0.0001224 0.009177 0.0002055 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03787 0.02 0.02765 0.03277 0.9775 0.9837 0.03871 0.931 0.9597 0.04705 ] Network output: [ 0.1204 -0.3244 1.155 0.0001173 -5.264e-05 0.9294 8.837e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6477 0.3787 0.3024 0.546 0.9659 0.9838 0.6505 0.8485 0.9502 0.6666 ] Network output: [ -0.07486 0.272 0.8727 0.0005612 -0.0002519 1.007 0.0004229 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5598 0.5124 0.2989 0.2335 0.9802 0.9868 0.5602 0.9404 0.963 0.348 ] Network output: [ -0.1556 0.296 0.8967 -0.001173 0.0005266 1.114 -0.000884 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5871 0.5775 0.3903 0.1628 0.9769 0.9846 0.5872 0.9301 0.9562 0.4053 ] Network output: [ 0.1588 0.633 0.1469 0.001171 -0.0005256 0.9073 0.0008824 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0877 Epoch 1674 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02052 1.005 0.9889 0.0001333 -5.982e-05 -0.03411 0.0001004 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02431 -0.003922 0.01165 0.02761 0.9263 0.9376 0.04629 0.8306 0.8659 0.1119 ] Network output: [ 0.9266 0.1544 -0.07986 -0.001065 0.0004779 0.06784 -0.0008023 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5805 -0.01777 -0.08984 0.3462 0.9614 0.981 0.6571 0.8362 0.9428 0.6682 ] Network output: [ 0.002666 0.9664 1.02 0.000272 -0.0001221 0.009136 0.000205 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03787 0.02003 0.02771 0.03282 0.9775 0.9838 0.03872 0.9311 0.9598 0.04707 ] Network output: [ 0.1204 -0.3245 1.154 0.0001229 -5.517e-05 0.9303 9.261e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6482 0.3796 0.3036 0.5458 0.9659 0.9838 0.6511 0.8487 0.9503 0.6659 ] Network output: [ -0.07497 0.2729 0.8728 0.0005617 -0.0002522 1.007 0.0004233 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5593 0.5121 0.2989 0.2338 0.9802 0.9868 0.5598 0.9405 0.963 0.3478 ] Network output: [ -0.1555 0.2958 0.897 -0.001167 0.000524 1.113 -0.0008796 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5868 0.5772 0.3901 0.1637 0.9769 0.9846 0.5869 0.9301 0.9562 0.405 ] Network output: [ 0.1583 0.6337 0.1462 0.001152 -0.0005173 0.9082 0.0008684 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08747 Epoch 1675 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02071 1.004 0.9892 0.0001336 -5.999e-05 -0.03426 0.0001007 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0243 -0.003927 0.01169 0.02766 0.9263 0.9376 0.0463 0.8308 0.866 0.1119 ] Network output: [ 0.9269 0.1538 -0.07937 -0.001057 0.0004745 0.06749 -0.0007965 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5809 -0.01758 -0.08887 0.3462 0.9614 0.981 0.6577 0.8364 0.9428 0.6676 ] Network output: [ 0.002778 0.966 1.02 0.0002711 -0.0001217 0.009097 0.0002043 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03788 0.02006 0.02778 0.03286 0.9775 0.9838 0.03872 0.9313 0.9598 0.04708 ] Network output: [ 0.1203 -0.3245 1.153 0.0001286 -5.774e-05 0.9312 9.692e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6488 0.3805 0.3048 0.5456 0.9659 0.9838 0.6517 0.8489 0.9503 0.6652 ] Network output: [ -0.07507 0.2737 0.873 0.0005622 -0.0002524 1.006 0.0004237 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5589 0.5119 0.299 0.2341 0.9802 0.9868 0.5593 0.9406 0.9631 0.3475 ] Network output: [ -0.1553 0.2956 0.8973 -0.001162 0.0005215 1.113 -0.0008754 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5864 0.5769 0.3899 0.1646 0.9769 0.9846 0.5865 0.9302 0.9563 0.4047 ] Network output: [ 0.1578 0.6345 0.1455 0.001134 -0.000509 0.9091 0.0008544 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08725 Epoch 1676 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0209 1.004 0.9894 0.000134 -6.015e-05 -0.03442 0.000101 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02429 -0.003932 0.01173 0.0277 0.9263 0.9376 0.04631 0.831 0.8661 0.1119 ] Network output: [ 0.9271 0.1533 -0.07887 -0.001049 0.000471 0.06713 -0.0007907 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5813 -0.01738 -0.0879 0.3463 0.9614 0.981 0.6582 0.8366 0.9429 0.6669 ] Network output: [ 0.00289 0.9657 1.021 0.0002703 -0.0001213 0.009062 0.0002037 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03788 0.02009 0.02784 0.03291 0.9775 0.9838 0.03873 0.9314 0.9599 0.0471 ] Network output: [ 0.1202 -0.3246 1.153 0.0001344 -6.034e-05 0.932 0.0001013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6494 0.3814 0.3059 0.5454 0.9659 0.9838 0.6523 0.8491 0.9504 0.6646 ] Network output: [ -0.07517 0.2745 0.8732 0.0005626 -0.0002526 1.005 0.000424 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5584 0.5116 0.2991 0.2344 0.9802 0.9868 0.5589 0.9408 0.9631 0.3473 ] Network output: [ -0.1552 0.2955 0.8977 -0.001156 0.000519 1.112 -0.0008712 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5861 0.5766 0.3897 0.1654 0.9769 0.9846 0.5862 0.9303 0.9563 0.4044 ] Network output: [ 0.1573 0.6352 0.1448 0.001115 -0.0005006 0.91 0.0008404 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08703 Epoch 1677 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02109 1.003 0.9896 0.0001343 -6.028e-05 -0.03457 0.0001012 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02428 -0.003936 0.01177 0.02775 0.9263 0.9376 0.04633 0.8311 0.8662 0.1119 ] Network output: [ 0.9273 0.1527 -0.07838 -0.001041 0.0004675 0.06676 -0.0007848 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5816 -0.01718 -0.08696 0.3463 0.9615 0.981 0.6588 0.8368 0.943 0.6663 ] Network output: [ 0.003001 0.9654 1.021 0.0002693 -0.0001209 0.009029 0.000203 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03789 0.02012 0.02791 0.03296 0.9776 0.9838 0.03874 0.9316 0.96 0.04712 ] Network output: [ 0.1201 -0.3246 1.152 0.0001403 -6.299e-05 0.9329 0.0001057 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.65 0.3823 0.307 0.5453 0.966 0.9838 0.6529 0.8493 0.9504 0.6639 ] Network output: [ -0.07527 0.2753 0.8734 0.000563 -0.0002527 1.004 0.0004243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.558 0.5114 0.2992 0.2346 0.9802 0.9868 0.5585 0.9409 0.9632 0.3471 ] Network output: [ -0.155 0.2953 0.898 -0.001151 0.0005165 1.112 -0.0008671 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5858 0.5763 0.3896 0.1663 0.9769 0.9846 0.5859 0.9304 0.9563 0.4041 ] Network output: [ 0.1568 0.6359 0.1441 0.001097 -0.0004923 0.9109 0.0008265 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0868 Epoch 1678 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02128 1.003 0.9898 0.0001345 -6.039e-05 -0.03472 0.0001014 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02427 -0.003941 0.01181 0.02779 0.9263 0.9376 0.04634 0.8313 0.8663 0.1119 ] Network output: [ 0.9276 0.1521 -0.07789 -0.001034 0.000464 0.0664 -0.0007789 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.582 -0.01697 -0.08602 0.3464 0.9615 0.981 0.6593 0.837 0.943 0.6657 ] Network output: [ 0.003111 0.9651 1.021 0.0002683 -0.0001205 0.009 0.0002022 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03789 0.02015 0.02797 0.033 0.9776 0.9838 0.03874 0.9317 0.96 0.04715 ] Network output: [ 0.12 -0.3247 1.152 0.0001463 -6.566e-05 0.9337 0.0001102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6506 0.3832 0.3081 0.5451 0.966 0.9838 0.6535 0.8494 0.9505 0.6632 ] Network output: [ -0.07537 0.276 0.8736 0.0005633 -0.0002529 1.003 0.0004245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5576 0.5111 0.2993 0.2349 0.9802 0.9868 0.5581 0.941 0.9633 0.3469 ] Network output: [ -0.1548 0.2952 0.8983 -0.001145 0.0005141 1.112 -0.000863 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5855 0.576 0.3894 0.1671 0.977 0.9846 0.5856 0.9304 0.9564 0.4038 ] Network output: [ 0.1563 0.6367 0.1434 0.001078 -0.000484 0.9118 0.0008126 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08658 Epoch 1679 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02147 1.002 0.99 0.0001347 -6.049e-05 -0.03487 0.0001016 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02426 -0.003945 0.01184 0.02784 0.9263 0.9376 0.04636 0.8315 0.8664 0.1119 ] Network output: [ 0.9278 0.1515 -0.0774 -0.001026 0.0004605 0.06604 -0.000773 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5824 -0.01675 -0.08509 0.3465 0.9615 0.981 0.6599 0.8372 0.9431 0.6651 ] Network output: [ 0.003221 0.9648 1.021 0.0002673 -0.00012 0.008973 0.0002014 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0379 0.02018 0.02804 0.03305 0.9776 0.9838 0.03875 0.9319 0.9601 0.04717 ] Network output: [ 0.12 -0.3247 1.151 0.0001522 -6.835e-05 0.9345 0.0001147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6511 0.3841 0.3092 0.5449 0.966 0.9838 0.654 0.8496 0.9506 0.6625 ] Network output: [ -0.07547 0.2768 0.8738 0.0005636 -0.000253 1.003 0.0004247 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5572 0.5109 0.2995 0.2352 0.9802 0.9868 0.5577 0.9411 0.9633 0.3467 ] Network output: [ -0.1547 0.295 0.8986 -0.00114 0.0005117 1.111 -0.000859 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5851 0.5757 0.3892 0.168 0.977 0.9846 0.5852 0.9305 0.9564 0.4036 ] Network output: [ 0.1558 0.6374 0.1427 0.00106 -0.0004758 0.9126 0.0007987 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08636 Epoch 1680 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02166 1.002 0.9902 0.0001349 -6.058e-05 -0.03502 0.0001017 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02426 -0.003949 0.01188 0.02789 0.9264 0.9377 0.04637 0.8317 0.8665 0.1119 ] Network output: [ 0.9281 0.151 -0.07692 -0.001018 0.0004569 0.06567 -0.000767 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5828 -0.01652 -0.08417 0.3465 0.9615 0.981 0.6604 0.8374 0.9431 0.6644 ] Network output: [ 0.003329 0.9644 1.021 0.0002662 -0.0001195 0.008949 0.0002006 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03791 0.02021 0.0281 0.03311 0.9776 0.9838 0.03876 0.932 0.9601 0.0472 ] Network output: [ 0.1199 -0.3248 1.15 0.0001583 -7.106e-05 0.9354 0.0001193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6517 0.385 0.3103 0.5447 0.966 0.9838 0.6546 0.8498 0.9506 0.6619 ] Network output: [ -0.07558 0.2775 0.874 0.0005638 -0.0002531 1.002 0.0004249 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5568 0.5107 0.2996 0.2355 0.9802 0.9868 0.5573 0.9413 0.9634 0.3465 ] Network output: [ -0.1545 0.2949 0.8989 -0.001135 0.0005093 1.111 -0.000855 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5848 0.5755 0.3891 0.1688 0.977 0.9846 0.5849 0.9306 0.9565 0.4033 ] Network output: [ 0.1552 0.6382 0.1421 0.001041 -0.0004675 0.9135 0.0007848 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08614 Epoch 1681 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02185 1.002 0.9904 0.0001351 -6.066e-05 -0.03516 0.0001018 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02425 -0.003953 0.01192 0.02794 0.9264 0.9377 0.04639 0.8318 0.8666 0.1119 ] Network output: [ 0.9283 0.1504 -0.07643 -0.00101 0.0004534 0.06531 -0.0007611 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5831 -0.01629 -0.08326 0.3466 0.9615 0.9811 0.6609 0.8376 0.9432 0.6638 ] Network output: [ 0.003437 0.9641 1.021 0.0002651 -0.000119 0.008929 0.0001998 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03791 0.02024 0.02817 0.03316 0.9776 0.9838 0.03877 0.9321 0.9602 0.04723 ] Network output: [ 0.1198 -0.3249 1.15 0.0001643 -7.377e-05 0.9362 0.0001238 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6523 0.386 0.3114 0.5445 0.966 0.9838 0.6552 0.85 0.9507 0.6612 ] Network output: [ -0.07568 0.2782 0.8743 0.000564 -0.0002532 1.001 0.0004251 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5564 0.5105 0.2997 0.2358 0.9802 0.9868 0.5569 0.9414 0.9634 0.3464 ] Network output: [ -0.1543 0.2947 0.8992 -0.001129 0.000507 1.11 -0.0008511 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5845 0.5752 0.3889 0.1696 0.977 0.9846 0.5846 0.9307 0.9565 0.4031 ] Network output: [ 0.1547 0.6389 0.1414 0.001023 -0.0004593 0.9144 0.000771 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08592 Epoch 1682 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02204 1.001 0.9906 0.0001353 -6.072e-05 -0.03531 0.0001019 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02424 -0.003957 0.01195 0.02799 0.9264 0.9377 0.0464 0.832 0.8667 0.112 ] Network output: [ 0.9285 0.1499 -0.07595 -0.001002 0.0004498 0.06494 -0.0007551 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5835 -0.01606 -0.08236 0.3466 0.9615 0.9811 0.6614 0.8378 0.9433 0.6632 ] Network output: [ 0.003543 0.9638 1.021 0.000264 -0.0001185 0.008911 0.0001989 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03792 0.02027 0.02824 0.03321 0.9776 0.9838 0.03878 0.9323 0.9603 0.04726 ] Network output: [ 0.1197 -0.3249 1.149 0.0001704 -7.65e-05 0.937 0.0001284 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6528 0.3869 0.3125 0.5444 0.966 0.9839 0.6557 0.8502 0.9507 0.6606 ] Network output: [ -0.07579 0.2789 0.8745 0.0005642 -0.0002533 1 0.0004252 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.556 0.5103 0.2999 0.2361 0.9803 0.9869 0.5565 0.9415 0.9635 0.3462 ] Network output: [ -0.1542 0.2946 0.8994 -0.001124 0.0005046 1.11 -0.0008471 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5842 0.575 0.3888 0.1705 0.977 0.9846 0.5843 0.9307 0.9565 0.4028 ] Network output: [ 0.1542 0.6397 0.1407 0.001005 -0.0004511 0.9153 0.0007573 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08571 Epoch 1683 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02224 1.001 0.9908 0.0001354 -6.078e-05 -0.03545 0.000102 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02423 -0.003961 0.01199 0.02803 0.9264 0.9377 0.04642 0.8322 0.8668 0.112 ] Network output: [ 0.9288 0.1493 -0.07547 -0.0009941 0.0004463 0.06457 -0.0007492 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5839 -0.01581 -0.08147 0.3467 0.9616 0.9811 0.662 0.838 0.9434 0.6627 ] Network output: [ 0.003649 0.9635 1.021 0.0002628 -0.000118 0.008896 0.0001981 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03793 0.0203 0.02831 0.03327 0.9776 0.9838 0.03879 0.9324 0.9603 0.04729 ] Network output: [ 0.1196 -0.325 1.149 0.0001765 -7.922e-05 0.9378 0.000133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6533 0.3878 0.3135 0.5442 0.966 0.9839 0.6563 0.8504 0.9508 0.6599 ] Network output: [ -0.07589 0.2796 0.8748 0.0005643 -0.0002534 0.9998 0.0004253 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5556 0.5101 0.3 0.2364 0.9803 0.9869 0.5561 0.9416 0.9636 0.3461 ] Network output: [ -0.154 0.2945 0.8997 -0.001119 0.0005023 1.109 -0.0008433 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5839 0.5747 0.3886 0.1713 0.977 0.9846 0.584 0.9308 0.9566 0.4026 ] Network output: [ 0.1537 0.6404 0.1401 0.0009866 -0.0004429 0.9162 0.0007436 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08549 Epoch 1684 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02243 1 0.991 0.0001355 -6.083e-05 -0.03559 0.0001021 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02422 -0.003965 0.01202 0.02808 0.9264 0.9377 0.04643 0.8323 0.8669 0.112 ] Network output: [ 0.929 0.1488 -0.07499 -0.0009862 0.0004428 0.0642 -0.0007433 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5842 -0.01556 -0.08058 0.3468 0.9616 0.9811 0.6625 0.8382 0.9434 0.6621 ] Network output: [ 0.003753 0.9632 1.021 0.0002616 -0.0001175 0.008885 0.0001972 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03794 0.02034 0.02838 0.03332 0.9776 0.9839 0.03879 0.9326 0.9604 0.04732 ] Network output: [ 0.1196 -0.325 1.148 0.0001825 -8.194e-05 0.9386 0.0001375 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6539 0.3888 0.3146 0.544 0.966 0.9839 0.6568 0.8506 0.9508 0.6593 ] Network output: [ -0.07599 0.2802 0.875 0.0005645 -0.0002534 0.999 0.0004254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5553 0.5099 0.3002 0.2367 0.9803 0.9869 0.5557 0.9417 0.9636 0.3459 ] Network output: [ -0.1538 0.2944 0.9 -0.001114 0.0005 1.109 -0.0008394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5837 0.5744 0.3885 0.1721 0.977 0.9846 0.5838 0.9309 0.9566 0.4023 ] Network output: [ 0.1532 0.6412 0.1394 0.0009685 -0.0004348 0.9171 0.0007299 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08527 Epoch 1685 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02262 0.9999 0.9912 0.0001356 -6.088e-05 -0.03573 0.0001022 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02421 -0.003969 0.01206 0.02813 0.9265 0.9377 0.04645 0.8325 0.867 0.112 ] Network output: [ 0.9292 0.1482 -0.07451 -0.0009784 0.0004392 0.06383 -0.0007374 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5846 -0.01531 -0.07971 0.3468 0.9616 0.9811 0.663 0.8384 0.9435 0.6615 ] Network output: [ 0.003857 0.9629 1.022 0.0002604 -0.0001169 0.008876 0.0001963 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03794 0.02037 0.02845 0.03338 0.9777 0.9839 0.03881 0.9327 0.9605 0.04736 ] Network output: [ 0.1195 -0.325 1.147 0.0001886 -8.465e-05 0.9394 0.0001421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6544 0.3897 0.3156 0.5438 0.9661 0.9839 0.6574 0.8508 0.9509 0.6587 ] Network output: [ -0.0761 0.2809 0.8753 0.0005646 -0.0002535 0.9983 0.0004255 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5549 0.5097 0.3003 0.237 0.9803 0.9869 0.5554 0.9419 0.9637 0.3458 ] Network output: [ -0.1536 0.2943 0.9002 -0.001109 0.0004977 1.108 -0.0008355 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5834 0.5742 0.3884 0.1729 0.977 0.9846 0.5835 0.931 0.9567 0.4021 ] Network output: [ 0.1526 0.6419 0.1387 0.0009505 -0.0004267 0.9179 0.0007164 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08505 Epoch 1686 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02281 0.9995 0.9913 0.0001357 -6.092e-05 -0.03586 0.0001023 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02421 -0.003973 0.01209 0.02818 0.9265 0.9378 0.04646 0.8327 0.8671 0.1121 ] Network output: [ 0.9295 0.1477 -0.07404 -0.0009706 0.0004357 0.06347 -0.0007315 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5849 -0.01505 -0.07884 0.3469 0.9616 0.9811 0.6635 0.8386 0.9436 0.6609 ] Network output: [ 0.00396 0.9626 1.022 0.0002592 -0.0001164 0.00887 0.0001954 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03795 0.0204 0.02852 0.03344 0.9777 0.9839 0.03882 0.9329 0.9605 0.0474 ] Network output: [ 0.1194 -0.3251 1.147 0.0001946 -8.735e-05 0.9402 0.0001466 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.655 0.3907 0.3166 0.5437 0.9661 0.9839 0.6579 0.851 0.951 0.658 ] Network output: [ -0.0762 0.2815 0.8756 0.0005647 -0.0002535 0.9977 0.0004255 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5545 0.5095 0.3005 0.2373 0.9803 0.9869 0.555 0.942 0.9637 0.3457 ] Network output: [ -0.1535 0.2942 0.9005 -0.001104 0.0004955 1.108 -0.0008317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5831 0.574 0.3882 0.1737 0.977 0.9846 0.5832 0.9311 0.9567 0.4019 ] Network output: [ 0.1521 0.6427 0.1381 0.0009326 -0.0004187 0.9188 0.0007028 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08484 Epoch 1687 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.023 0.9991 0.9915 0.0001358 -6.097e-05 -0.036 0.0001023 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0242 -0.003976 0.01213 0.02823 0.9265 0.9378 0.04648 0.8329 0.8672 0.1121 ] Network output: [ 0.9297 0.1472 -0.07356 -0.0009629 0.0004323 0.06309 -0.0007256 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5853 -0.01478 -0.07798 0.3469 0.9616 0.9811 0.664 0.8388 0.9436 0.6604 ] Network output: [ 0.004061 0.9622 1.022 0.000258 -0.0001158 0.008868 0.0001944 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03796 0.02044 0.0286 0.0335 0.9777 0.9839 0.03883 0.933 0.9606 0.04744 ] Network output: [ 0.1193 -0.3251 1.146 0.0002005 -9.003e-05 0.941 0.0001511 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6555 0.3916 0.3177 0.5435 0.9661 0.9839 0.6584 0.8512 0.951 0.6574 ] Network output: [ -0.07631 0.2821 0.8758 0.0005647 -0.0002535 0.997 0.0004256 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5542 0.5093 0.3007 0.2376 0.9803 0.9869 0.5547 0.9421 0.9638 0.3456 ] Network output: [ -0.1533 0.2941 0.9007 -0.001099 0.0004932 1.107 -0.0008279 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5828 0.5737 0.3881 0.1744 0.977 0.9846 0.5829 0.9312 0.9568 0.4017 ] Network output: [ 0.1516 0.6434 0.1374 0.0009148 -0.0004107 0.9197 0.0006894 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08462 Epoch 1688 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0232 0.9986 0.9916 0.0001359 -6.101e-05 -0.03613 0.0001024 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02419 -0.00398 0.01216 0.02828 0.9265 0.9378 0.04649 0.833 0.8673 0.1121 ] Network output: [ 0.9299 0.1466 -0.07309 -0.0009552 0.0004288 0.06272 -0.0007198 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5856 -0.01451 -0.07713 0.347 0.9616 0.9811 0.6645 0.839 0.9437 0.6598 ] Network output: [ 0.004162 0.9619 1.022 0.0002568 -0.0001153 0.008868 0.0001935 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03797 0.02047 0.02867 0.03356 0.9777 0.9839 0.03884 0.9332 0.9607 0.04748 ] Network output: [ 0.1193 -0.3252 1.146 0.0002065 -9.27e-05 0.9418 0.0001556 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.656 0.3926 0.3187 0.5433 0.9661 0.9839 0.659 0.8514 0.9511 0.6568 ] Network output: [ -0.07641 0.2827 0.8761 0.0005648 -0.0002536 0.9963 0.0004257 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5538 0.5092 0.3008 0.2379 0.9803 0.9869 0.5543 0.9422 0.9638 0.3455 ] Network output: [ -0.1531 0.294 0.9009 -0.001094 0.0004909 1.107 -0.0008241 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5825 0.5735 0.388 0.1752 0.977 0.9846 0.5826 0.9312 0.9568 0.4015 ] Network output: [ 0.1511 0.6442 0.1368 0.000897 -0.0004027 0.9205 0.000676 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08441 Epoch 1689 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02339 0.9982 0.9918 0.000136 -6.105e-05 -0.03626 0.0001025 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02418 -0.003984 0.01219 0.02833 0.9265 0.9378 0.04651 0.8332 0.8673 0.1122 ] Network output: [ 0.9302 0.1461 -0.07263 -0.0009475 0.0004254 0.06235 -0.0007141 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.586 -0.01424 -0.07628 0.3471 0.9617 0.9811 0.665 0.8392 0.9438 0.6592 ] Network output: [ 0.004261 0.9616 1.022 0.0002556 -0.0001147 0.008871 0.0001926 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03798 0.02051 0.02874 0.03362 0.9777 0.9839 0.03885 0.9333 0.9607 0.04753 ] Network output: [ 0.1192 -0.3252 1.145 0.0002124 -9.534e-05 0.9426 0.00016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6565 0.3936 0.3197 0.5432 0.9661 0.9839 0.6595 0.8516 0.9512 0.6562 ] Network output: [ -0.07652 0.2833 0.8764 0.0005649 -0.0002536 0.9956 0.0004257 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5535 0.509 0.301 0.2383 0.9803 0.9869 0.554 0.9423 0.9639 0.3455 ] Network output: [ -0.1529 0.2939 0.9012 -0.001089 0.0004887 1.106 -0.0008203 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5823 0.5733 0.3879 0.176 0.977 0.9847 0.5824 0.9313 0.9569 0.4013 ] Network output: [ 0.1505 0.6449 0.1362 0.0008794 -0.0003948 0.9214 0.0006628 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08419 Epoch 1690 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02358 0.9978 0.9919 0.0001361 -6.109e-05 -0.03639 0.0001026 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02418 -0.003987 0.01223 0.02838 0.9266 0.9378 0.04652 0.8334 0.8674 0.1122 ] Network output: [ 0.9304 0.1456 -0.07216 -0.0009399 0.000422 0.06198 -0.0007084 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5863 -0.01396 -0.07544 0.3471 0.9617 0.9812 0.6655 0.8394 0.9438 0.6587 ] Network output: [ 0.004359 0.9613 1.022 0.0002543 -0.0001142 0.008877 0.0001917 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03799 0.02054 0.02882 0.03368 0.9777 0.9839 0.03886 0.9334 0.9608 0.04757 ] Network output: [ 0.1191 -0.3253 1.145 0.0002182 -9.796e-05 0.9433 0.0001644 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.657 0.3945 0.3207 0.543 0.9661 0.9839 0.66 0.8518 0.9512 0.6556 ] Network output: [ -0.07662 0.2838 0.8768 0.0005649 -0.0002536 0.995 0.0004257 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5532 0.5089 0.3012 0.2386 0.9804 0.9869 0.5537 0.9425 0.964 0.3454 ] Network output: [ -0.1527 0.2938 0.9014 -0.001084 0.0004864 1.106 -0.0008166 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.582 0.573 0.3878 0.1768 0.977 0.9847 0.5821 0.9314 0.9569 0.4011 ] Network output: [ 0.15 0.6457 0.1355 0.0008619 -0.0003869 0.9223 0.0006495 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08398 Epoch 1691 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02377 0.9974 0.9921 0.0001362 -6.114e-05 -0.03651 0.0001026 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02417 -0.00399 0.01226 0.02844 0.9266 0.9378 0.04654 0.8336 0.8675 0.1122 ] Network output: [ 0.9306 0.1451 -0.0717 -0.0009324 0.0004186 0.06161 -0.0007027 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5866 -0.01368 -0.07461 0.3472 0.9617 0.9812 0.666 0.8396 0.9439 0.6581 ] Network output: [ 0.004457 0.961 1.022 0.0002531 -0.0001136 0.008887 0.0001908 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03801 0.02058 0.02889 0.03374 0.9777 0.9839 0.03888 0.9336 0.9608 0.04762 ] Network output: [ 0.119 -0.3253 1.144 0.000224 -0.0001006 0.9441 0.0001688 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6576 0.3955 0.3217 0.5428 0.9662 0.9839 0.6605 0.852 0.9513 0.655 ] Network output: [ -0.07672 0.2844 0.8771 0.000565 -0.0002536 0.9943 0.0004258 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5529 0.5087 0.3014 0.2389 0.9804 0.9869 0.5533 0.9426 0.964 0.3453 ] Network output: [ -0.1525 0.2937 0.9016 -0.001079 0.0004842 1.105 -0.0008128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5818 0.5728 0.3878 0.1775 0.977 0.9847 0.5819 0.9315 0.957 0.4009 ] Network output: [ 0.1495 0.6465 0.1349 0.0008445 -0.0003791 0.9231 0.0006364 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08377 Epoch 1692 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02396 0.997 0.9922 0.0001363 -6.118e-05 -0.03663 0.0001027 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02416 -0.003994 0.01229 0.02849 0.9266 0.9379 0.04656 0.8337 0.8676 0.1123 ] Network output: [ 0.9308 0.1446 -0.07124 -0.000925 0.0004153 0.06124 -0.0006971 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.587 -0.01339 -0.07378 0.3472 0.9617 0.9812 0.6664 0.8399 0.944 0.6576 ] Network output: [ 0.004553 0.9607 1.022 0.0002519 -0.0001131 0.008898 0.0001899 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03802 0.02061 0.02897 0.03381 0.9777 0.9839 0.03889 0.9337 0.9609 0.04767 ] Network output: [ 0.119 -0.3253 1.144 0.0002297 -0.0001031 0.9448 0.0001731 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6581 0.3965 0.3227 0.5427 0.9662 0.9839 0.661 0.8522 0.9513 0.6545 ] Network output: [ -0.07683 0.2849 0.8774 0.000565 -0.0002537 0.9937 0.0004258 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5525 0.5086 0.3016 0.2392 0.9804 0.9869 0.553 0.9427 0.9641 0.3453 ] Network output: [ -0.1523 0.2936 0.9018 -0.001074 0.0004819 1.105 -0.000809 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5815 0.5726 0.3877 0.1783 0.977 0.9847 0.5816 0.9316 0.957 0.4008 ] Network output: [ 0.149 0.6472 0.1343 0.0008271 -0.0003713 0.924 0.0006234 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08355 Epoch 1693 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02416 0.9966 0.9924 0.0001364 -6.124e-05 -0.03675 0.0001028 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02415 -0.003997 0.01232 0.02854 0.9266 0.9379 0.04657 0.8339 0.8677 0.1123 ] Network output: [ 0.9311 0.144 -0.07078 -0.0009177 0.000412 0.06086 -0.0006916 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5873 -0.0131 -0.07296 0.3473 0.9617 0.9812 0.6669 0.8401 0.944 0.6571 ] Network output: [ 0.004648 0.9604 1.022 0.0002507 -0.0001126 0.008913 0.0001889 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03803 0.02065 0.02905 0.03387 0.9778 0.9839 0.0389 0.9339 0.961 0.04772 ] Network output: [ 0.1189 -0.3254 1.143 0.0002354 -0.0001057 0.9456 0.0001774 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6586 0.3975 0.3237 0.5425 0.9662 0.984 0.6616 0.8524 0.9514 0.6539 ] Network output: [ -0.07693 0.2854 0.8777 0.0005651 -0.0002537 0.993 0.0004259 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5522 0.5085 0.3019 0.2396 0.9804 0.9869 0.5527 0.9428 0.9641 0.3453 ] Network output: [ -0.1521 0.2935 0.902 -0.001069 0.0004797 1.104 -0.0008053 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5813 0.5724 0.3876 0.179 0.977 0.9847 0.5814 0.9317 0.9571 0.4006 ] Network output: [ 0.1484 0.648 0.1336 0.0008099 -0.0003636 0.9248 0.0006104 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08334 Epoch 1694 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02435 0.9963 0.9925 0.0001365 -6.129e-05 -0.03687 0.0001029 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02415 -0.004001 0.01235 0.02859 0.9266 0.9379 0.04659 0.8341 0.8678 0.1124 ] Network output: [ 0.9313 0.1435 -0.07032 -0.0009104 0.0004087 0.06049 -0.0006861 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5876 -0.0128 -0.07214 0.3474 0.9618 0.9812 0.6674 0.8403 0.9441 0.6565 ] Network output: [ 0.004742 0.9601 1.023 0.0002495 -0.000112 0.008931 0.0001881 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03804 0.02069 0.02913 0.03394 0.9778 0.984 0.03892 0.934 0.961 0.04777 ] Network output: [ 0.1188 -0.3254 1.142 0.0002409 -0.0001082 0.9463 0.0001816 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6591 0.3984 0.3246 0.5423 0.9662 0.984 0.6621 0.8526 0.9515 0.6533 ] Network output: [ -0.07704 0.2859 0.8781 0.0005651 -0.0002537 0.9924 0.0004259 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5519 0.5083 0.3021 0.2399 0.9804 0.9869 0.5524 0.943 0.9642 0.3453 ] Network output: [ -0.1519 0.2934 0.9022 -0.001064 0.0004775 1.104 -0.0008015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.581 0.5722 0.3875 0.1797 0.977 0.9847 0.5811 0.9318 0.9571 0.4005 ] Network output: [ 0.1479 0.6488 0.133 0.0007928 -0.0003559 0.9257 0.0005975 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08313 Epoch 1695 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02454 0.9959 0.9926 0.0001367 -6.135e-05 -0.03699 0.000103 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02414 -0.004004 0.01239 0.02864 0.9267 0.9379 0.04661 0.8343 0.8679 0.1124 ] Network output: [ 0.9315 0.143 -0.06987 -0.0009032 0.0004055 0.06012 -0.0006807 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.588 -0.0125 -0.07132 0.3474 0.9618 0.9812 0.6679 0.8405 0.9442 0.656 ] Network output: [ 0.004834 0.9598 1.023 0.0002484 -0.0001115 0.008951 0.0001872 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03806 0.02073 0.0292 0.034 0.9778 0.984 0.03893 0.9342 0.9611 0.04782 ] Network output: [ 0.1187 -0.3254 1.142 0.0002465 -0.0001106 0.9471 0.0001857 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6596 0.3994 0.3256 0.5422 0.9662 0.984 0.6626 0.8528 0.9515 0.6527 ] Network output: [ -0.07714 0.2864 0.8784 0.0005652 -0.0002537 0.9918 0.0004259 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5516 0.5082 0.3023 0.2402 0.9804 0.987 0.5521 0.9431 0.9643 0.3452 ] Network output: [ -0.1517 0.2933 0.9024 -0.001059 0.0004752 1.103 -0.0007978 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5808 0.572 0.3875 0.1805 0.977 0.9847 0.5809 0.9319 0.9572 0.4003 ] Network output: [ 0.1474 0.6495 0.1324 0.0007759 -0.0003483 0.9265 0.0005847 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08292 Epoch 1696 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02473 0.9955 0.9927 0.0001368 -6.142e-05 -0.0371 0.0001031 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02413 -0.004007 0.01242 0.0287 0.9267 0.9379 0.04662 0.8344 0.868 0.1125 ] Network output: [ 0.9317 0.1426 -0.06942 -0.0008961 0.0004023 0.05975 -0.0006753 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5883 -0.0122 -0.07052 0.3475 0.9618 0.9812 0.6683 0.8407 0.9442 0.6555 ] Network output: [ 0.004926 0.9595 1.023 0.0002472 -0.000111 0.008974 0.0001863 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03807 0.02076 0.02928 0.03407 0.9778 0.984 0.03895 0.9343 0.9612 0.04788 ] Network output: [ 0.1186 -0.3254 1.141 0.0002519 -0.0001131 0.9478 0.0001898 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6601 0.4004 0.3266 0.542 0.9662 0.984 0.6631 0.853 0.9516 0.6522 ] Network output: [ -0.07724 0.2869 0.8788 0.0005653 -0.0002538 0.9912 0.000426 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5514 0.5081 0.3025 0.2406 0.9804 0.987 0.5518 0.9432 0.9643 0.3452 ] Network output: [ -0.1515 0.2933 0.9026 -0.001054 0.000473 1.103 -0.000794 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5805 0.5718 0.3874 0.1812 0.977 0.9847 0.5806 0.932 0.9572 0.4002 ] Network output: [ 0.1468 0.6503 0.1318 0.000759 -0.0003407 0.9273 0.000572 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08271 Epoch 1697 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02492 0.9951 0.9928 0.000137 -6.149e-05 -0.03722 0.0001032 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02413 -0.00401 0.01245 0.02875 0.9267 0.9379 0.04664 0.8346 0.8681 0.1125 ] Network output: [ 0.932 0.1421 -0.06897 -0.0008891 0.0003992 0.05937 -0.0006701 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5886 -0.01189 -0.06971 0.3475 0.9618 0.9812 0.6688 0.8409 0.9443 0.655 ] Network output: [ 0.005016 0.9592 1.023 0.0002461 -0.0001105 0.009 0.0001854 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03808 0.0208 0.02936 0.03414 0.9778 0.984 0.03896 0.9344 0.9612 0.04794 ] Network output: [ 0.1186 -0.3255 1.141 0.0002572 -0.0001155 0.9485 0.0001939 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6605 0.4014 0.3275 0.5418 0.9663 0.984 0.6636 0.8532 0.9516 0.6516 ] Network output: [ -0.07734 0.2873 0.8791 0.0005653 -0.0002538 0.9906 0.0004261 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5511 0.508 0.3028 0.2409 0.9804 0.987 0.5515 0.9433 0.9644 0.3452 ] Network output: [ -0.1512 0.2932 0.9027 -0.001049 0.0004708 1.102 -0.0007903 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5803 0.5716 0.3874 0.1819 0.977 0.9847 0.5804 0.9321 0.9573 0.4001 ] Network output: [ 0.1463 0.6511 0.1312 0.0007423 -0.0003332 0.9282 0.0005594 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08249 Epoch 1698 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02511 0.9947 0.9929 0.0001371 -6.157e-05 -0.03733 0.0001034 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02412 -0.004014 0.01248 0.0288 0.9267 0.938 0.04666 0.8348 0.8682 0.1126 ] Network output: [ 0.9322 0.1416 -0.06852 -0.0008822 0.0003961 0.059 -0.0006649 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5889 -0.01158 -0.06891 0.3476 0.9618 0.9812 0.6693 0.8411 0.9444 0.6545 ] Network output: [ 0.005105 0.9589 1.023 0.0002449 -0.00011 0.009028 0.0001846 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0381 0.02084 0.02945 0.03421 0.9778 0.984 0.03898 0.9346 0.9613 0.048 ] Network output: [ 0.1185 -0.3255 1.14 0.0002625 -0.0001178 0.9493 0.0001978 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.661 0.4024 0.3285 0.5417 0.9663 0.984 0.6641 0.8534 0.9517 0.6511 ] Network output: [ -0.07745 0.2877 0.8795 0.0005654 -0.0002538 0.99 0.0004261 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5508 0.5079 0.303 0.2413 0.9804 0.987 0.5513 0.9434 0.9644 0.3453 ] Network output: [ -0.151 0.2931 0.9029 -0.001044 0.0004685 1.102 -0.0007865 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5801 0.5714 0.3874 0.1826 0.977 0.9847 0.5802 0.9322 0.9573 0.3999 ] Network output: [ 0.1458 0.6518 0.1306 0.0007256 -0.0003258 0.929 0.0005469 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08228 Epoch 1699 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02529 0.9944 0.993 0.0001373 -6.166e-05 -0.03744 0.0001035 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02411 -0.004017 0.01251 0.02886 0.9267 0.938 0.04668 0.8349 0.8683 0.1126 ] Network output: [ 0.9324 0.1411 -0.06808 -0.0008754 0.000393 0.05863 -0.0006597 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5892 -0.01127 -0.06812 0.3476 0.9618 0.9813 0.6697 0.8413 0.9445 0.654 ] Network output: [ 0.005193 0.9586 1.023 0.0002438 -0.0001095 0.009059 0.0001838 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03811 0.02088 0.02953 0.03428 0.9778 0.984 0.03899 0.9347 0.9613 0.04806 ] Network output: [ 0.1184 -0.3255 1.14 0.0002677 -0.0001202 0.95 0.0002017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6615 0.4034 0.3294 0.5415 0.9663 0.984 0.6645 0.8536 0.9518 0.6505 ] Network output: [ -0.07755 0.2882 0.8799 0.0005655 -0.0002539 0.9894 0.0004262 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5505 0.5078 0.3033 0.2416 0.9805 0.987 0.551 0.9436 0.9645 0.3453 ] Network output: [ -0.1508 0.2931 0.9031 -0.001039 0.0004663 1.101 -0.0007828 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5799 0.5712 0.3873 0.1833 0.977 0.9847 0.58 0.9323 0.9574 0.3998 ] Network output: [ 0.1452 0.6526 0.13 0.0007091 -0.0003184 0.9298 0.0005344 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08207 Epoch 1700 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02548 0.994 0.9931 0.0001375 -6.175e-05 -0.03754 0.0001037 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02411 -0.00402 0.01254 0.02891 0.9268 0.938 0.04669 0.8351 0.8684 0.1127 ] Network output: [ 0.9326 0.1406 -0.06764 -0.0008687 0.00039 0.05826 -0.0006547 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5896 -0.01095 -0.06732 0.3477 0.9619 0.9813 0.6702 0.8415 0.9445 0.6535 ] Network output: [ 0.00528 0.9583 1.023 0.0002428 -0.000109 0.009093 0.0001829 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03813 0.02092 0.02961 0.03435 0.9779 0.984 0.03901 0.9349 0.9614 0.04812 ] Network output: [ 0.1183 -0.3255 1.139 0.0002728 -0.0001225 0.9507 0.0002056 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.662 0.4044 0.3304 0.5414 0.9663 0.984 0.665 0.8538 0.9518 0.65 ] Network output: [ -0.07765 0.2886 0.8802 0.0005657 -0.0002539 0.9888 0.0004263 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5503 0.5077 0.3036 0.242 0.9805 0.987 0.5507 0.9437 0.9645 0.3453 ] Network output: [ -0.1506 0.293 0.9032 -0.001034 0.0004641 1.101 -0.000779 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5796 0.571 0.3873 0.184 0.9771 0.9847 0.5797 0.9323 0.9574 0.3997 ] Network output: [ 0.1447 0.6534 0.1294 0.0006927 -0.000311 0.9306 0.0005221 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08186 Epoch 1701 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02567 0.9936 0.9932 0.0001378 -6.185e-05 -0.03765 0.0001038 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0241 -0.004023 0.01257 0.02896 0.9268 0.938 0.04671 0.8353 0.8685 0.1128 ] Network output: [ 0.9328 0.1401 -0.0672 -0.0008621 0.000387 0.05789 -0.0006497 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5899 -0.01063 -0.06653 0.3478 0.9619 0.9813 0.6706 0.8417 0.9446 0.653 ] Network output: [ 0.005365 0.958 1.023 0.0002417 -0.0001085 0.009129 0.0001821 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03814 0.02096 0.0297 0.03442 0.9779 0.984 0.03903 0.935 0.9615 0.04818 ] Network output: [ 0.1183 -0.3256 1.139 0.0002778 -0.0001247 0.9514 0.0002094 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6625 0.4053 0.3313 0.5412 0.9663 0.984 0.6655 0.854 0.9519 0.6495 ] Network output: [ -0.07775 0.289 0.8806 0.0005658 -0.000254 0.9882 0.0004264 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.55 0.5076 0.3038 0.2423 0.9805 0.987 0.5505 0.9438 0.9646 0.3454 ] Network output: [ -0.1503 0.2929 0.9034 -0.001029 0.0004618 1.1 -0.0007752 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5794 0.5709 0.3873 0.1847 0.9771 0.9847 0.5795 0.9324 0.9575 0.3996 ] Network output: [ 0.1442 0.6542 0.1288 0.0006765 -0.0003037 0.9315 0.0005098 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08165 Epoch 1702 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02585 0.9933 0.9933 0.000138 -6.196e-05 -0.03775 0.000104 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0241 -0.004026 0.0126 0.02902 0.9268 0.938 0.04673 0.8355 0.8686 0.1128 ] Network output: [ 0.9331 0.1397 -0.06677 -0.0008556 0.0003841 0.05752 -0.0006448 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5902 -0.01031 -0.06575 0.3478 0.9619 0.9813 0.6711 0.8419 0.9447 0.6525 ] Network output: [ 0.005449 0.9577 1.023 0.0002407 -0.000108 0.009167 0.0001814 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03816 0.021 0.02978 0.0345 0.9779 0.984 0.03905 0.9352 0.9615 0.04825 ] Network output: [ 0.1182 -0.3256 1.138 0.0002828 -0.0001269 0.9521 0.0002131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6629 0.4063 0.3323 0.541 0.9663 0.984 0.666 0.8542 0.952 0.6489 ] Network output: [ -0.07785 0.2893 0.881 0.0005659 -0.0002541 0.9876 0.0004265 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5498 0.5076 0.3041 0.2427 0.9805 0.987 0.5502 0.9439 0.9647 0.3454 ] Network output: [ -0.1501 0.2929 0.9035 -0.001024 0.0004596 1.1 -0.0007715 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5792 0.5707 0.3873 0.1854 0.9771 0.9847 0.5793 0.9325 0.9575 0.3995 ] Network output: [ 0.1436 0.655 0.1282 0.0006603 -0.0002964 0.9323 0.0004976 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08145 Epoch 1703 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02604 0.9929 0.9934 0.0001383 -6.208e-05 -0.03785 0.0001042 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02409 -0.004029 0.01264 0.02907 0.9268 0.9381 0.04675 0.8356 0.8687 0.1129 ] Network output: [ 0.9333 0.1392 -0.06633 -0.0008492 0.0003812 0.05715 -0.00064 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5905 -0.009981 -0.06497 0.3479 0.9619 0.9813 0.6715 0.8421 0.9447 0.652 ] Network output: [ 0.005532 0.9574 1.023 0.0002396 -0.0001076 0.009208 0.0001806 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03818 0.02104 0.02987 0.03457 0.9779 0.9841 0.03906 0.9353 0.9616 0.04831 ] Network output: [ 0.1181 -0.3256 1.138 0.0002876 -0.0001291 0.9528 0.0002167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6634 0.4073 0.3332 0.5409 0.9664 0.9841 0.6665 0.8544 0.952 0.6484 ] Network output: [ -0.07795 0.2897 0.8814 0.0005661 -0.0002541 0.9871 0.0004266 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5495 0.5075 0.3044 0.243 0.9805 0.987 0.55 0.944 0.9647 0.3455 ] Network output: [ -0.1499 0.2928 0.9036 -0.001019 0.0004573 1.099 -0.0007677 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.579 0.5705 0.3873 0.1861 0.9771 0.9847 0.5791 0.9326 0.9576 0.3994 ] Network output: [ 0.1431 0.6558 0.1276 0.0006443 -0.0002892 0.9331 0.0004856 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08124 Epoch 1704 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02623 0.9926 0.9935 0.0001386 -6.22e-05 -0.03795 0.0001044 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02408 -0.004032 0.01267 0.02913 0.9269 0.9381 0.04677 0.8358 0.8688 0.113 ] Network output: [ 0.9335 0.1387 -0.0659 -0.0008429 0.0003784 0.05678 -0.0006352 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5908 -0.009651 -0.06419 0.3479 0.9619 0.9813 0.672 0.8423 0.9448 0.6515 ] Network output: [ 0.005614 0.9571 1.023 0.0002386 -0.0001071 0.009252 0.0001798 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03819 0.02108 0.02995 0.03464 0.9779 0.9841 0.03908 0.9354 0.9617 0.04838 ] Network output: [ 0.118 -0.3256 1.137 0.0002924 -0.0001313 0.9535 0.0002203 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6639 0.4083 0.3342 0.5407 0.9664 0.9841 0.667 0.8546 0.9521 0.6479 ] Network output: [ -0.07804 0.2901 0.8818 0.0005663 -0.0002542 0.9865 0.0004268 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5493 0.5074 0.3047 0.2434 0.9805 0.987 0.5498 0.9442 0.9648 0.3456 ] Network output: [ -0.1496 0.2928 0.9038 -0.001014 0.0004551 1.099 -0.0007639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5788 0.5704 0.3873 0.1868 0.9771 0.9847 0.5789 0.9327 0.9576 0.3993 ] Network output: [ 0.1426 0.6565 0.127 0.0006284 -0.0002821 0.9339 0.0004736 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08103 Epoch 1705 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02641 0.9922 0.9936 0.0001389 -6.234e-05 -0.03804 0.0001046 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02408 -0.004035 0.0127 0.02918 0.9269 0.9381 0.04679 0.836 0.8689 0.113 ] Network output: [ 0.9337 0.1383 -0.06548 -0.0008367 0.0003756 0.05641 -0.0006306 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5911 -0.009318 -0.06341 0.348 0.962 0.9813 0.6724 0.8425 0.9449 0.6511 ] Network output: [ 0.005695 0.9568 1.023 0.0002377 -0.0001067 0.009298 0.0001791 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03821 0.02112 0.03004 0.03472 0.9779 0.9841 0.0391 0.9356 0.9617 0.04845 ] Network output: [ 0.118 -0.3256 1.137 0.000297 -0.0001334 0.9541 0.0002239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6643 0.4093 0.3351 0.5406 0.9664 0.9841 0.6674 0.8548 0.9521 0.6474 ] Network output: [ -0.07814 0.2904 0.8822 0.0005665 -0.0002543 0.986 0.0004269 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5491 0.5074 0.305 0.2438 0.9805 0.987 0.5495 0.9443 0.9648 0.3456 ] Network output: [ -0.1494 0.2927 0.9039 -0.001009 0.0004528 1.098 -0.0007602 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5786 0.5702 0.3873 0.1875 0.9771 0.9848 0.5787 0.9328 0.9577 0.3993 ] Network output: [ 0.142 0.6573 0.1264 0.0006126 -0.000275 0.9347 0.0004617 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08082 Epoch 1706 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02659 0.9919 0.9937 0.0001392 -6.248e-05 -0.03814 0.0001049 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02407 -0.004038 0.01273 0.02924 0.9269 0.9381 0.0468 0.8362 0.869 0.1131 ] Network output: [ 0.9339 0.1378 -0.06505 -0.0008306 0.0003729 0.05604 -0.000626 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5914 -0.008983 -0.06264 0.348 0.962 0.9813 0.6729 0.8428 0.945 0.6506 ] Network output: [ 0.005774 0.9565 1.024 0.0002367 -0.0001063 0.009346 0.0001784 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03823 0.02116 0.03013 0.0348 0.9779 0.9841 0.03912 0.9357 0.9618 0.04852 ] Network output: [ 0.1179 -0.3256 1.136 0.0003016 -0.0001354 0.9548 0.0002273 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6648 0.4103 0.336 0.5404 0.9664 0.9841 0.6679 0.855 0.9522 0.6469 ] Network output: [ -0.07824 0.2907 0.8826 0.0005667 -0.0002544 0.9854 0.0004271 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5488 0.5073 0.3053 0.2441 0.9805 0.987 0.5493 0.9444 0.9649 0.3457 ] Network output: [ -0.1492 0.2927 0.904 -0.001004 0.0004506 1.098 -0.0007564 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5784 0.57 0.3873 0.1882 0.9771 0.9848 0.5785 0.9329 0.9577 0.3992 ] Network output: [ 0.1415 0.6581 0.1258 0.0005969 -0.000268 0.9355 0.0004499 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08061 Epoch 1707 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02678 0.9915 0.9937 0.0001395 -6.263e-05 -0.03823 0.0001051 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02407 -0.004041 0.01276 0.02929 0.9269 0.9381 0.04682 0.8363 0.8691 0.1132 ] Network output: [ 0.9341 0.1374 -0.06463 -0.0008246 0.0003702 0.05567 -0.0006215 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5917 -0.008645 -0.06186 0.3481 0.962 0.9814 0.6733 0.843 0.945 0.6501 ] Network output: [ 0.005852 0.9562 1.024 0.0002358 -0.0001059 0.009396 0.0001777 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03824 0.0212 0.03022 0.03487 0.978 0.9841 0.03914 0.9359 0.9619 0.04859 ] Network output: [ 0.1178 -0.3257 1.136 0.0003061 -0.0001374 0.9555 0.0002307 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6653 0.4113 0.3369 0.5403 0.9664 0.9841 0.6684 0.8552 0.9523 0.6464 ] Network output: [ -0.07833 0.291 0.8831 0.000567 -0.0002545 0.9849 0.0004273 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5486 0.5073 0.3056 0.2445 0.9806 0.9871 0.5491 0.9445 0.965 0.3458 ] Network output: [ -0.1489 0.2926 0.9041 -0.0009986 0.0004483 1.097 -0.0007526 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5782 0.5699 0.3873 0.1888 0.9771 0.9848 0.5783 0.9331 0.9578 0.3991 ] Network output: [ 0.141 0.6589 0.1252 0.0005814 -0.000261 0.9363 0.0004382 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0804 Epoch 1708 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02696 0.9912 0.9938 0.0001399 -6.278e-05 -0.03832 0.0001054 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02406 -0.004044 0.01279 0.02935 0.9269 0.9381 0.04684 0.8365 0.8692 0.1132 ] Network output: [ 0.9343 0.1369 -0.06421 -0.0008188 0.0003676 0.05531 -0.0006171 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.592 -0.008304 -0.06109 0.3481 0.962 0.9814 0.6738 0.8432 0.9451 0.6497 ] Network output: [ 0.005928 0.9559 1.024 0.0002349 -0.0001055 0.009449 0.000177 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03826 0.02124 0.03031 0.03495 0.978 0.9841 0.03916 0.936 0.9619 0.04867 ] Network output: [ 0.1177 -0.3257 1.135 0.0003106 -0.0001394 0.9562 0.000234 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6657 0.4123 0.3379 0.5401 0.9664 0.9841 0.6688 0.8554 0.9523 0.6459 ] Network output: [ -0.07843 0.2913 0.8835 0.0005672 -0.0002546 0.9843 0.0004275 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5484 0.5073 0.3059 0.2449 0.9806 0.9871 0.5489 0.9446 0.965 0.3459 ] Network output: [ -0.1487 0.2926 0.9042 -0.0009936 0.000446 1.096 -0.0007488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.578 0.5697 0.3873 0.1895 0.9771 0.9848 0.5782 0.9332 0.9579 0.3991 ] Network output: [ 0.1404 0.6597 0.1246 0.000566 -0.0002541 0.9371 0.0004265 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0802 Epoch 1709 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02714 0.9908 0.9939 0.0001402 -6.295e-05 -0.03841 0.0001057 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02406 -0.004047 0.01282 0.0294 0.927 0.9382 0.04686 0.8367 0.8693 0.1133 ] Network output: [ 0.9345 0.1365 -0.06379 -0.000813 0.000365 0.05494 -0.0006127 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5923 -0.007961 -0.06033 0.3482 0.962 0.9814 0.6742 0.8434 0.9452 0.6492 ] Network output: [ 0.006004 0.9556 1.024 0.000234 -0.0001051 0.009504 0.0001764 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03828 0.02128 0.0304 0.03503 0.978 0.9841 0.03918 0.9362 0.962 0.04874 ] Network output: [ 0.1177 -0.3257 1.135 0.0003149 -0.0001414 0.9568 0.0002373 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6662 0.4133 0.3388 0.54 0.9665 0.9841 0.6693 0.8556 0.9524 0.6454 ] Network output: [ -0.07853 0.2916 0.8839 0.0005675 -0.0002548 0.9838 0.0004277 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5482 0.5072 0.3062 0.2452 0.9806 0.9871 0.5487 0.9448 0.9651 0.3461 ] Network output: [ -0.1484 0.2925 0.9044 -0.0009885 0.0004438 1.096 -0.000745 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5779 0.5696 0.3873 0.1902 0.9771 0.9848 0.578 0.9333 0.9579 0.399 ] Network output: [ 0.1399 0.6605 0.1241 0.0005507 -0.0002472 0.9379 0.000415 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07999 Epoch 1710 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02732 0.9905 0.9939 0.0001406 -6.313e-05 -0.03849 0.000106 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02405 -0.00405 0.01285 0.02946 0.927 0.9382 0.04688 0.8369 0.8694 0.1134 ] Network output: [ 0.9348 0.136 -0.06338 -0.0008073 0.0003624 0.05458 -0.0006084 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5926 -0.007615 -0.05956 0.3482 0.9621 0.9814 0.6746 0.8436 0.9452 0.6488 ] Network output: [ 0.006078 0.9554 1.024 0.0002332 -0.0001047 0.009561 0.0001758 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0383 0.02133 0.03049 0.03511 0.978 0.9841 0.0392 0.9363 0.962 0.04882 ] Network output: [ 0.1176 -0.3257 1.134 0.0003191 -0.0001433 0.9575 0.0002405 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6666 0.4143 0.3397 0.5398 0.9665 0.9841 0.6698 0.8558 0.9525 0.6449 ] Network output: [ -0.07862 0.2919 0.8844 0.0005678 -0.0002549 0.9833 0.0004279 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.548 0.5072 0.3066 0.2456 0.9806 0.9871 0.5485 0.9449 0.9651 0.3462 ] Network output: [ -0.1482 0.2925 0.9045 -0.0009835 0.0004415 1.095 -0.0007412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5777 0.5695 0.3874 0.1908 0.9771 0.9848 0.5778 0.9334 0.958 0.399 ] Network output: [ 0.1394 0.6613 0.1235 0.0005355 -0.0002404 0.9386 0.0004036 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07978 Epoch 1711 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0275 0.9902 0.994 0.000141 -6.331e-05 -0.03858 0.0001063 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02405 -0.004053 0.01288 0.02952 0.927 0.9382 0.0469 0.837 0.8695 0.1135 ] Network output: [ 0.935 0.1356 -0.06297 -0.0008018 0.0003599 0.05422 -0.0006042 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5929 -0.007266 -0.0588 0.3483 0.9621 0.9814 0.6751 0.8438 0.9453 0.6483 ] Network output: [ 0.00615 0.9551 1.024 0.0002324 -0.0001043 0.00962 0.0001751 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03832 0.02137 0.03058 0.03519 0.978 0.9841 0.03922 0.9364 0.9621 0.0489 ] Network output: [ 0.1175 -0.3257 1.134 0.0003233 -0.0001451 0.9581 0.0002437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6671 0.4153 0.3406 0.5397 0.9665 0.9841 0.6702 0.856 0.9525 0.6444 ] Network output: [ -0.07871 0.2921 0.8848 0.0005681 -0.0002551 0.9828 0.0004282 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5478 0.5072 0.3069 0.246 0.9806 0.9871 0.5483 0.945 0.9652 0.3463 ] Network output: [ -0.1479 0.2925 0.9045 -0.0009784 0.0004392 1.095 -0.0007374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5775 0.5693 0.3874 0.1915 0.9772 0.9848 0.5776 0.9335 0.958 0.399 ] Network output: [ 0.1388 0.6621 0.1229 0.0005204 -0.0002336 0.9394 0.0003922 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07957 Epoch 1712 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02768 0.9899 0.994 0.0001414 -6.35e-05 -0.03866 0.0001066 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02404 -0.004056 0.01291 0.02957 0.927 0.9382 0.04692 0.8372 0.8696 0.1135 ] Network output: [ 0.9352 0.1351 -0.06256 -0.0007963 0.0003575 0.05385 -0.0006001 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5932 -0.006915 -0.05804 0.3483 0.9621 0.9814 0.6755 0.844 0.9454 0.6479 ] Network output: [ 0.006222 0.9548 1.024 0.0002316 -0.000104 0.009681 0.0001745 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03834 0.02141 0.03067 0.03527 0.978 0.9842 0.03924 0.9366 0.9622 0.04897 ] Network output: [ 0.1174 -0.3257 1.133 0.0003274 -0.000147 0.9588 0.0002467 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6675 0.4163 0.3415 0.5395 0.9665 0.9841 0.6707 0.8562 0.9526 0.6439 ] Network output: [ -0.0788 0.2924 0.8853 0.0005685 -0.0002552 0.9823 0.0004284 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5476 0.5072 0.3072 0.2464 0.9806 0.9871 0.5481 0.9451 0.9652 0.3465 ] Network output: [ -0.1476 0.2924 0.9046 -0.0009734 0.000437 1.094 -0.0007335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5774 0.5692 0.3875 0.1921 0.9772 0.9848 0.5775 0.9336 0.9581 0.3989 ] Network output: [ 0.1383 0.6629 0.1224 0.0005055 -0.0002269 0.9402 0.0003809 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07937 Epoch 1713 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02785 0.9895 0.9941 0.0001419 -6.37e-05 -0.03874 0.0001069 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02404 -0.004058 0.01295 0.02963 0.9271 0.9382 0.04694 0.8374 0.8697 0.1136 ] Network output: [ 0.9354 0.1347 -0.06215 -0.000791 0.0003551 0.05349 -0.0005961 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5935 -0.006562 -0.05728 0.3484 0.9621 0.9814 0.6759 0.8442 0.9454 0.6475 ] Network output: [ 0.006292 0.9545 1.024 0.0002308 -0.0001036 0.009744 0.000174 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03836 0.02145 0.03077 0.03535 0.9781 0.9842 0.03926 0.9367 0.9622 0.04906 ] Network output: [ 0.1174 -0.3257 1.133 0.0003314 -0.0001488 0.9594 0.0002498 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.668 0.4173 0.3424 0.5394 0.9665 0.9842 0.6711 0.8564 0.9527 0.6435 ] Network output: [ -0.0789 0.2926 0.8857 0.0005689 -0.0002554 0.9818 0.0004287 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5475 0.5071 0.3076 0.2468 0.9806 0.9871 0.5479 0.9452 0.9653 0.3466 ] Network output: [ -0.1474 0.2924 0.9047 -0.0009683 0.0004347 1.094 -0.0007297 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5772 0.5691 0.3875 0.1928 0.9772 0.9848 0.5773 0.9337 0.9581 0.3989 ] Network output: [ 0.1378 0.6637 0.1218 0.0004906 -0.0002203 0.941 0.0003697 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07916 Epoch 1714 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02803 0.9892 0.9941 0.0001423 -6.391e-05 -0.03882 0.0001073 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02404 -0.004061 0.01298 0.02969 0.9271 0.9383 0.04696 0.8375 0.8698 0.1137 ] Network output: [ 0.9356 0.1343 -0.06175 -0.0007857 0.0003527 0.05313 -0.0005921 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5937 -0.006206 -0.05652 0.3484 0.9621 0.9814 0.6763 0.8444 0.9455 0.647 ] Network output: [ 0.006361 0.9542 1.024 0.0002301 -0.0001033 0.00981 0.0001734 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03838 0.0215 0.03086 0.03544 0.9781 0.9842 0.03929 0.9369 0.9623 0.04914 ] Network output: [ 0.1173 -0.3257 1.132 0.0003353 -0.0001505 0.9601 0.0002527 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6684 0.4184 0.3434 0.5392 0.9665 0.9842 0.6716 0.8566 0.9527 0.643 ] Network output: [ -0.07899 0.2928 0.8862 0.0005692 -0.0002556 0.9813 0.000429 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5473 0.5071 0.3079 0.2472 0.9806 0.9871 0.5478 0.9454 0.9654 0.3468 ] Network output: [ -0.1471 0.2924 0.9048 -0.0009632 0.0004324 1.093 -0.0007259 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.577 0.569 0.3876 0.1934 0.9772 0.9848 0.5771 0.9338 0.9582 0.3989 ] Network output: [ 0.1372 0.6645 0.1212 0.0004759 -0.0002136 0.9417 0.0003586 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07895 Epoch 1715 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0282 0.9889 0.9942 0.0001428 -6.412e-05 -0.0389 0.0001076 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02403 -0.004064 0.01301 0.02974 0.9271 0.9383 0.04698 0.8377 0.87 0.1138 ] Network output: [ 0.9358 0.1338 -0.06135 -0.0007806 0.0003504 0.05278 -0.0005883 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.594 -0.005848 -0.05576 0.3485 0.9622 0.9815 0.6768 0.8446 0.9456 0.6466 ] Network output: [ 0.006429 0.954 1.024 0.0002294 -0.000103 0.009877 0.0001728 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0384 0.02154 0.03096 0.03552 0.9781 0.9842 0.03931 0.937 0.9624 0.04922 ] Network output: [ 0.1172 -0.3257 1.132 0.0003392 -0.0001523 0.9607 0.0002556 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6689 0.4194 0.3443 0.5391 0.9666 0.9842 0.672 0.8568 0.9528 0.6425 ] Network output: [ -0.07907 0.293 0.8866 0.0005697 -0.0002557 0.9808 0.0004293 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5471 0.5071 0.3083 0.2476 0.9807 0.9871 0.5476 0.9455 0.9654 0.3469 ] Network output: [ -0.1469 0.2923 0.9049 -0.0009581 0.0004301 1.093 -0.0007221 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5769 0.5688 0.3877 0.194 0.9772 0.9848 0.577 0.9339 0.9583 0.3989 ] Network output: [ 0.1367 0.6653 0.1207 0.0004613 -0.0002071 0.9425 0.0003476 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07875 Epoch 1716 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02838 0.9886 0.9942 0.0001433 -6.434e-05 -0.03898 0.000108 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02403 -0.004067 0.01304 0.0298 0.9271 0.9383 0.047 0.8379 0.8701 0.1139 ] Network output: [ 0.936 0.1334 -0.06095 -0.0007755 0.0003482 0.05242 -0.0005844 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5943 -0.005488 -0.05501 0.3485 0.9622 0.9815 0.6772 0.8448 0.9457 0.6462 ] Network output: [ 0.006495 0.9537 1.024 0.0002287 -0.0001027 0.009946 0.0001723 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03842 0.02158 0.03106 0.0356 0.9781 0.9842 0.03933 0.9371 0.9624 0.0493 ] Network output: [ 0.1171 -0.3257 1.131 0.000343 -0.000154 0.9613 0.0002585 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6693 0.4204 0.3452 0.5389 0.9666 0.9842 0.6725 0.857 0.9528 0.6421 ] Network output: [ -0.07916 0.2932 0.8871 0.0005701 -0.0002559 0.9803 0.0004296 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.547 0.5071 0.3087 0.248 0.9807 0.9871 0.5474 0.9456 0.9655 0.3471 ] Network output: [ -0.1466 0.2923 0.905 -0.000953 0.0004279 1.092 -0.0007182 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5767 0.5687 0.3877 0.1947 0.9772 0.9849 0.5768 0.934 0.9583 0.3989 ] Network output: [ 0.1362 0.6661 0.1201 0.0004468 -0.0002006 0.9432 0.0003367 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07854 Epoch 1717 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02855 0.9883 0.9943 0.0001438 -6.457e-05 -0.03905 0.0001084 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02402 -0.00407 0.01307 0.02986 0.9271 0.9383 0.04702 0.8381 0.8702 0.114 ] Network output: [ 0.9362 0.133 -0.06055 -0.0007706 0.0003459 0.05207 -0.0005807 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5946 -0.005125 -0.05425 0.3486 0.9622 0.9815 0.6776 0.8451 0.9457 0.6458 ] Network output: [ 0.00656 0.9534 1.024 0.000228 -0.0001024 0.01002 0.0001718 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03845 0.02163 0.03115 0.03569 0.9781 0.9842 0.03936 0.9373 0.9625 0.04939 ] Network output: [ 0.1171 -0.3257 1.131 0.0003467 -0.0001556 0.962 0.0002613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6697 0.4214 0.3461 0.5388 0.9666 0.9842 0.6729 0.8572 0.9529 0.6416 ] Network output: [ -0.07925 0.2934 0.8876 0.0005706 -0.0002561 0.9798 0.00043 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5468 0.5072 0.309 0.2483 0.9807 0.9871 0.5473 0.9457 0.9655 0.3473 ] Network output: [ -0.1463 0.2923 0.905 -0.000948 0.0004256 1.092 -0.0007144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5766 0.5686 0.3878 0.1953 0.9772 0.9849 0.5767 0.9341 0.9584 0.3989 ] Network output: [ 0.1356 0.6669 0.1196 0.0004324 -0.0001941 0.944 0.0003259 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07834 Epoch 1718 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02872 0.988 0.9943 0.0001444 -6.481e-05 -0.03912 0.0001088 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02402 -0.004072 0.0131 0.02991 0.9272 0.9383 0.04704 0.8382 0.8703 0.114 ] Network output: [ 0.9364 0.1326 -0.06016 -0.0007657 0.0003437 0.05171 -0.0005771 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5949 -0.00476 -0.0535 0.3486 0.9622 0.9815 0.678 0.8453 0.9458 0.6453 ] Network output: [ 0.006623 0.9531 1.024 0.0002273 -0.0001021 0.01009 0.0001713 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03847 0.02167 0.03125 0.03577 0.9781 0.9842 0.03938 0.9374 0.9626 0.04947 ] Network output: [ 0.117 -0.3257 1.131 0.0003503 -0.0001573 0.9626 0.000264 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6702 0.4224 0.347 0.5386 0.9666 0.9842 0.6733 0.8574 0.953 0.6412 ] Network output: [ -0.07934 0.2936 0.8881 0.000571 -0.0002564 0.9793 0.0004303 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5466 0.5072 0.3094 0.2487 0.9807 0.9872 0.5471 0.9458 0.9656 0.3475 ] Network output: [ -0.1461 0.2922 0.9051 -0.0009429 0.0004233 1.091 -0.0007106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5764 0.5685 0.3879 0.1959 0.9772 0.9849 0.5765 0.9342 0.9584 0.3989 ] Network output: [ 0.1351 0.6678 0.119 0.0004181 -0.0001877 0.9447 0.0003151 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07813 Epoch 1719 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0289 0.9877 0.9943 0.0001449 -6.505e-05 -0.0392 0.0001092 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02402 -0.004075 0.01314 0.02997 0.9272 0.9384 0.04706 0.8384 0.8704 0.1141 ] Network output: [ 0.9366 0.1321 -0.05977 -0.0007609 0.0003416 0.05136 -0.0005735 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5952 -0.004393 -0.05275 0.3487 0.9622 0.9815 0.6784 0.8455 0.9459 0.6449 ] Network output: [ 0.006686 0.9529 1.025 0.0002267 -0.0001018 0.01016 0.0001708 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03849 0.02172 0.03135 0.03586 0.9781 0.9842 0.0394 0.9375 0.9626 0.04956 ] Network output: [ 0.1169 -0.3257 1.13 0.0003539 -0.0001589 0.9632 0.0002667 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6706 0.4234 0.3479 0.5385 0.9666 0.9842 0.6738 0.8576 0.953 0.6407 ] Network output: [ -0.07942 0.2937 0.8885 0.0005715 -0.0002566 0.9789 0.0004307 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5465 0.5072 0.3098 0.2491 0.9807 0.9872 0.547 0.946 0.9657 0.3477 ] Network output: [ -0.1458 0.2922 0.9052 -0.0009378 0.000421 1.09 -0.0007067 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5763 0.5684 0.388 0.1965 0.9772 0.9849 0.5764 0.9343 0.9585 0.3989 ] Network output: [ 0.1346 0.6686 0.1185 0.0004039 -0.0001813 0.9455 0.0003044 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07793 Epoch 1720 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02907 0.9874 0.9943 0.0001455 -6.53e-05 -0.03927 0.0001096 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02401 -0.004078 0.01317 0.03003 0.9272 0.9384 0.04708 0.8386 0.8705 0.1142 ] Network output: [ 0.9368 0.1317 -0.05938 -0.0007563 0.0003395 0.05101 -0.0005699 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5955 -0.004024 -0.052 0.3487 0.9623 0.9815 0.6789 0.8457 0.9459 0.6445 ] Network output: [ 0.006747 0.9526 1.025 0.0002261 -0.0001015 0.01024 0.0001704 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03851 0.02176 0.03145 0.03595 0.9782 0.9843 0.03943 0.9377 0.9627 0.04965 ] Network output: [ 0.1168 -0.3257 1.13 0.0003574 -0.0001604 0.9638 0.0002693 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6711 0.4244 0.3488 0.5384 0.9666 0.9842 0.6742 0.8578 0.9531 0.6403 ] Network output: [ -0.07951 0.2939 0.889 0.0005721 -0.0002568 0.9784 0.0004311 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5464 0.5072 0.3102 0.2496 0.9807 0.9872 0.5469 0.9461 0.9657 0.3479 ] Network output: [ -0.1455 0.2922 0.9052 -0.0009327 0.0004187 1.09 -0.0007029 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5762 0.5683 0.3881 0.1971 0.9773 0.9849 0.5763 0.9344 0.9586 0.399 ] Network output: [ 0.134 0.6694 0.1179 0.0003899 -0.000175 0.9462 0.0002938 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07772 Epoch 1721 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02923 0.9871 0.9944 0.000146 -6.555e-05 -0.03933 0.00011 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02401 -0.00408 0.0132 0.03009 0.9272 0.9384 0.0471 0.8387 0.8706 0.1143 ] Network output: [ 0.937 0.1313 -0.05899 -0.0007517 0.0003375 0.05066 -0.0005665 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5957 -0.003652 -0.05125 0.3487 0.9623 0.9815 0.6793 0.8459 0.946 0.6441 ] Network output: [ 0.006807 0.9523 1.025 0.0002255 -0.0001012 0.01032 0.0001699 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03854 0.02181 0.03155 0.03603 0.9782 0.9843 0.03945 0.9378 0.9627 0.04974 ] Network output: [ 0.1168 -0.3257 1.129 0.0003608 -0.000162 0.9644 0.0002719 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6715 0.4254 0.3497 0.5382 0.9667 0.9842 0.6747 0.8581 0.9532 0.6399 ] Network output: [ -0.07959 0.294 0.8895 0.0005726 -0.0002571 0.978 0.0004315 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5462 0.5072 0.3106 0.25 0.9807 0.9872 0.5467 0.9462 0.9658 0.3481 ] Network output: [ -0.1452 0.2922 0.9053 -0.0009276 0.0004164 1.089 -0.0006991 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.576 0.5682 0.3882 0.1977 0.9773 0.9849 0.5761 0.9345 0.9586 0.399 ] Network output: [ 0.1335 0.6702 0.1174 0.0003759 -0.0001688 0.9469 0.0002833 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07752 Epoch 1722 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0294 0.9868 0.9944 0.0001466 -6.582e-05 -0.0394 0.0001105 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02401 -0.004083 0.01323 0.03014 0.9273 0.9384 0.04713 0.8389 0.8707 0.1144 ] Network output: [ 0.9372 0.1309 -0.05861 -0.0007472 0.0003354 0.05032 -0.0005631 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.596 -0.003279 -0.0505 0.3488 0.9623 0.9815 0.6797 0.8461 0.9461 0.6437 ] Network output: [ 0.006865 0.9521 1.025 0.0002249 -0.000101 0.0104 0.0001695 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03856 0.02185 0.03165 0.03612 0.9782 0.9843 0.03948 0.938 0.9628 0.04983 ] Network output: [ 0.1167 -0.3257 1.129 0.0003642 -0.0001635 0.965 0.0002745 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6719 0.4264 0.3506 0.5381 0.9667 0.9843 0.6751 0.8583 0.9532 0.6394 ] Network output: [ -0.07967 0.2942 0.89 0.0005732 -0.0002573 0.9775 0.000432 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5461 0.5073 0.311 0.2504 0.9808 0.9872 0.5466 0.9463 0.9658 0.3483 ] Network output: [ -0.145 0.2921 0.9053 -0.0009225 0.0004141 1.089 -0.0006952 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5759 0.5681 0.3883 0.1983 0.9773 0.9849 0.576 0.9347 0.9587 0.399 ] Network output: [ 0.133 0.671 0.1169 0.000362 -0.0001625 0.9477 0.0002728 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07731 Epoch 1723 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02957 0.9865 0.9944 0.0001472 -6.608e-05 -0.03947 0.0001109 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.024 -0.004086 0.01327 0.0302 0.9273 0.9384 0.04715 0.8391 0.8708 0.1145 ] Network output: [ 0.9374 0.1305 -0.05823 -0.0007428 0.0003335 0.04997 -0.0005598 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5963 -0.002904 -0.04976 0.3488 0.9623 0.9816 0.6801 0.8463 0.9462 0.6433 ] Network output: [ 0.006922 0.9518 1.025 0.0002244 -0.0001007 0.01048 0.0001691 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03858 0.0219 0.03176 0.03621 0.9782 0.9843 0.0395 0.9381 0.9629 0.04992 ] Network output: [ 0.1166 -0.3257 1.128 0.0003675 -0.000165 0.9656 0.0002769 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6723 0.4274 0.3515 0.5379 0.9667 0.9843 0.6755 0.8585 0.9533 0.639 ] Network output: [ -0.07975 0.2943 0.8905 0.0005738 -0.0002576 0.9771 0.0004324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.546 0.5073 0.3114 0.2508 0.9808 0.9872 0.5465 0.9464 0.9659 0.3485 ] Network output: [ -0.1447 0.2921 0.9054 -0.0009174 0.0004118 1.088 -0.0006914 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5758 0.568 0.3884 0.199 0.9773 0.9849 0.5759 0.9348 0.9588 0.3991 ] Network output: [ 0.1324 0.6719 0.1163 0.0003483 -0.0001564 0.9484 0.0002625 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07711 Epoch 1724 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02973 0.9862 0.9944 0.0001478 -6.636e-05 -0.03953 0.0001114 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.024 -0.004088 0.0133 0.03026 0.9273 0.9384 0.04717 0.8392 0.8709 0.1146 ] Network output: [ 0.9376 0.1301 -0.05785 -0.0007385 0.0003315 0.04963 -0.0005565 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5966 -0.002526 -0.04901 0.3489 0.9624 0.9816 0.6805 0.8465 0.9462 0.6429 ] Network output: [ 0.006978 0.9515 1.025 0.0002239 -0.0001005 0.01056 0.0001687 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03861 0.02195 0.03186 0.0363 0.9782 0.9843 0.03953 0.9382 0.9629 0.05002 ] Network output: [ 0.1166 -0.3257 1.128 0.0003707 -0.0001664 0.9662 0.0002794 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6728 0.4284 0.3524 0.5378 0.9667 0.9843 0.676 0.8587 0.9534 0.6386 ] Network output: [ -0.07983 0.2944 0.891 0.0005744 -0.0002579 0.9766 0.0004329 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5459 0.5074 0.3118 0.2512 0.9808 0.9872 0.5464 0.9465 0.9659 0.3488 ] Network output: [ -0.1444 0.2921 0.9054 -0.0009123 0.0004096 1.088 -0.0006875 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5757 0.568 0.3885 0.1996 0.9773 0.9849 0.5758 0.9349 0.9588 0.3991 ] Network output: [ 0.1319 0.6727 0.1158 0.0003346 -0.0001502 0.9491 0.0002522 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0769 Epoch 1725 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0299 0.9859 0.9945 0.0001484 -6.664e-05 -0.03959 0.0001119 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.024 -0.004091 0.01333 0.03032 0.9273 0.9385 0.04719 0.8394 0.871 0.1147 ] Network output: [ 0.9378 0.1297 -0.05747 -0.0007343 0.0003296 0.04929 -0.0005534 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5969 -0.002147 -0.04826 0.3489 0.9624 0.9816 0.6809 0.8467 0.9463 0.6426 ] Network output: [ 0.007032 0.9513 1.025 0.0002234 -0.0001003 0.01064 0.0001683 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03863 0.02199 0.03196 0.03639 0.9782 0.9843 0.03956 0.9384 0.963 0.05011 ] Network output: [ 0.1165 -0.3257 1.127 0.0003739 -0.0001679 0.9668 0.0002818 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6732 0.4294 0.3533 0.5377 0.9667 0.9843 0.6764 0.8589 0.9534 0.6382 ] Network output: [ -0.07991 0.2945 0.8915 0.000575 -0.0002581 0.9762 0.0004333 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5458 0.5074 0.3122 0.2516 0.9808 0.9872 0.5462 0.9467 0.966 0.349 ] Network output: [ -0.1441 0.2921 0.9054 -0.0009072 0.0004073 1.087 -0.0006837 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5756 0.5679 0.3886 0.2001 0.9773 0.9849 0.5757 0.935 0.9589 0.3992 ] Network output: [ 0.1314 0.6735 0.1153 0.0003211 -0.0001441 0.9498 0.000242 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0767 Epoch 1726 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03006 0.9857 0.9945 0.0001491 -6.692e-05 -0.03965 0.0001123 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02399 -0.004094 0.01337 0.03037 0.9274 0.9385 0.04721 0.8396 0.8711 0.1148 ] Network output: [ 0.938 0.1293 -0.0571 -0.0007301 0.0003278 0.04895 -0.0005502 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5971 -0.001765 -0.04752 0.3489 0.9624 0.9816 0.6813 0.8469 0.9464 0.6422 ] Network output: [ 0.007085 0.951 1.025 0.0002229 -0.0001001 0.01073 0.000168 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03866 0.02204 0.03207 0.03648 0.9783 0.9843 0.03958 0.9385 0.9631 0.05021 ] Network output: [ 0.1164 -0.3256 1.127 0.000377 -0.0001693 0.9674 0.0002841 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6736 0.4304 0.3542 0.5375 0.9668 0.9843 0.6768 0.8591 0.9535 0.6378 ] Network output: [ -0.07999 0.2945 0.892 0.0005757 -0.0002584 0.9757 0.0004338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5456 0.5075 0.3127 0.252 0.9808 0.9872 0.5461 0.9468 0.9661 0.3493 ] Network output: [ -0.1438 0.2921 0.9055 -0.0009021 0.000405 1.086 -0.0006798 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5754 0.5678 0.3888 0.2007 0.9773 0.985 0.5755 0.9351 0.9589 0.3992 ] Network output: [ 0.1308 0.6743 0.1147 0.0003076 -0.0001381 0.9505 0.0002318 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0765 Epoch 1727 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03022 0.9854 0.9945 0.0001497 -6.721e-05 -0.03971 0.0001128 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02399 -0.004096 0.0134 0.03043 0.9274 0.9385 0.04723 0.8398 0.8712 0.1149 ] Network output: [ 0.9381 0.1289 -0.05672 -0.0007261 0.000326 0.04861 -0.0005472 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5974 -0.001382 -0.04678 0.349 0.9624 0.9816 0.6817 0.8471 0.9464 0.6418 ] Network output: [ 0.007137 0.9507 1.025 0.0002224 -9.984e-05 0.01082 0.0001676 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03868 0.02209 0.03217 0.03657 0.9783 0.9843 0.03961 0.9386 0.9631 0.0503 ] Network output: [ 0.1164 -0.3256 1.126 0.0003801 -0.0001706 0.968 0.0002864 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.674 0.4315 0.355 0.5374 0.9668 0.9843 0.6772 0.8593 0.9536 0.6374 ] Network output: [ -0.08006 0.2946 0.8925 0.0005763 -0.0002587 0.9753 0.0004343 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5455 0.5075 0.3131 0.2524 0.9808 0.9872 0.546 0.9469 0.9661 0.3495 ] Network output: [ -0.1436 0.292 0.9055 -0.000897 0.0004027 1.086 -0.000676 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5753 0.5677 0.3889 0.2013 0.9773 0.985 0.5754 0.9352 0.959 0.3993 ] Network output: [ 0.1303 0.6752 0.1142 0.0002943 -0.0001321 0.9512 0.0002218 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07629 Epoch 1728 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03039 0.9851 0.9945 0.0001504 -6.75e-05 -0.03977 0.0001133 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02399 -0.004099 0.01343 0.03049 0.9274 0.9385 0.04726 0.8399 0.8713 0.115 ] Network output: [ 0.9383 0.1285 -0.05636 -0.0007221 0.0003242 0.04827 -0.0005442 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5977 -0.0009963 -0.04604 0.349 0.9624 0.9816 0.6821 0.8473 0.9465 0.6414 ] Network output: [ 0.007188 0.9505 1.025 0.0002219 -9.964e-05 0.01091 0.0001673 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03871 0.02213 0.03228 0.03666 0.9783 0.9844 0.03964 0.9388 0.9632 0.0504 ] Network output: [ 0.1163 -0.3256 1.126 0.0003831 -0.000172 0.9686 0.0002887 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6744 0.4325 0.3559 0.5372 0.9668 0.9843 0.6777 0.8595 0.9536 0.637 ] Network output: [ -0.08014 0.2947 0.8931 0.000577 -0.000259 0.9749 0.0004349 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5455 0.5076 0.3135 0.2529 0.9808 0.9872 0.546 0.947 0.9662 0.3498 ] Network output: [ -0.1433 0.292 0.9055 -0.0008919 0.0004004 1.085 -0.0006722 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5752 0.5677 0.389 0.2019 0.9774 0.985 0.5753 0.9353 0.9591 0.3994 ] Network output: [ 0.1298 0.676 0.1137 0.000281 -0.0001262 0.9519 0.0002118 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07609 Epoch 1729 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03054 0.9849 0.9945 0.000151 -6.78e-05 -0.03983 0.0001138 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02399 -0.004101 0.01347 0.03055 0.9274 0.9385 0.04728 0.8401 0.8714 0.1151 ] Network output: [ 0.9385 0.1281 -0.05599 -0.0007182 0.0003224 0.04794 -0.0005412 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.598 -0.0006091 -0.04529 0.3491 0.9625 0.9816 0.6825 0.8475 0.9466 0.6411 ] Network output: [ 0.007237 0.9502 1.025 0.0002215 -9.944e-05 0.011 0.0001669 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03873 0.02218 0.03238 0.03675 0.9783 0.9844 0.03966 0.9389 0.9633 0.0505 ] Network output: [ 0.1162 -0.3256 1.126 0.0003861 -0.0001733 0.9691 0.000291 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6749 0.4335 0.3568 0.5371 0.9668 0.9843 0.6781 0.8597 0.9537 0.6366 ] Network output: [ -0.08021 0.2947 0.8936 0.0005777 -0.0002594 0.9745 0.0004354 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5454 0.5077 0.314 0.2533 0.9808 0.9873 0.5459 0.9471 0.9662 0.35 ] Network output: [ -0.143 0.292 0.9056 -0.0008868 0.0003981 1.085 -0.0006683 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5751 0.5676 0.3892 0.2025 0.9774 0.985 0.5752 0.9354 0.9591 0.3994 ] Network output: [ 0.1292 0.6768 0.1132 0.0002679 -0.0001203 0.9526 0.0002019 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07589 Epoch 1730 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0307 0.9846 0.9945 0.0001517 -6.81e-05 -0.03988 0.0001143 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02398 -0.004104 0.0135 0.03061 0.9274 0.9386 0.0473 0.8403 0.8715 0.1152 ] Network output: [ 0.9387 0.1277 -0.05562 -0.0007144 0.0003207 0.04761 -0.0005384 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5982 -0.00022 -0.04455 0.3491 0.9625 0.9816 0.6829 0.8477 0.9467 0.6407 ] Network output: [ 0.007285 0.95 1.025 0.0002211 -9.925e-05 0.01109 0.0001666 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03876 0.02223 0.03249 0.03685 0.9783 0.9844 0.03969 0.9391 0.9633 0.0506 ] Network output: [ 0.1162 -0.3256 1.125 0.000389 -0.0001746 0.9697 0.0002931 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6753 0.4345 0.3577 0.537 0.9668 0.9843 0.6785 0.8599 0.9537 0.6362 ] Network output: [ -0.08028 0.2947 0.8941 0.0005784 -0.0002597 0.9741 0.0004359 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5453 0.5077 0.3144 0.2537 0.9809 0.9873 0.5458 0.9472 0.9663 0.3503 ] Network output: [ -0.1427 0.292 0.9056 -0.0008817 0.0003958 1.084 -0.0006645 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.575 0.5675 0.3893 0.2031 0.9774 0.985 0.5751 0.9356 0.9592 0.3995 ] Network output: [ 0.1287 0.6777 0.1127 0.0002548 -0.0001144 0.9533 0.000192 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07568 Epoch 1731 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03086 0.9843 0.9945 0.0001524 -6.84e-05 -0.03994 0.0001148 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02398 -0.004106 0.01353 0.03067 0.9275 0.9386 0.04732 0.8404 0.8716 0.1153 ] Network output: [ 0.9389 0.1273 -0.05526 -0.0007106 0.000319 0.04728 -0.0005355 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5985 0.000171 -0.04381 0.3491 0.9625 0.9817 0.6833 0.848 0.9467 0.6403 ] Network output: [ 0.007332 0.9497 1.025 0.0002207 -9.907e-05 0.01118 0.0001663 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03879 0.02227 0.0326 0.03694 0.9783 0.9844 0.03972 0.9392 0.9634 0.0507 ] Network output: [ 0.1161 -0.3256 1.125 0.0003918 -0.0001759 0.9703 0.0002953 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6757 0.4355 0.3586 0.5368 0.9669 0.9844 0.6789 0.8601 0.9538 0.6358 ] Network output: [ -0.08035 0.2948 0.8946 0.0005792 -0.00026 0.9737 0.0004365 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5452 0.5078 0.3148 0.2541 0.9809 0.9873 0.5457 0.9473 0.9664 0.3506 ] Network output: [ -0.1424 0.292 0.9056 -0.0008766 0.0003935 1.084 -0.0006606 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5749 0.5675 0.3895 0.2036 0.9774 0.985 0.5751 0.9357 0.9593 0.3996 ] Network output: [ 0.1282 0.6785 0.1121 0.0002418 -0.0001086 0.954 0.0001822 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07548 Epoch 1732 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03102 0.9841 0.9945 0.0001531 -6.871e-05 -0.03999 0.0001153 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02398 -0.004109 0.01357 0.03072 0.9275 0.9386 0.04735 0.8406 0.8717 0.1154 ] Network output: [ 0.9391 0.1269 -0.0549 -0.000707 0.0003174 0.04695 -0.0005328 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5988 0.0005637 -0.04308 0.3492 0.9625 0.9817 0.6837 0.8482 0.9468 0.64 ] Network output: [ 0.007377 0.9495 1.025 0.0002203 -9.889e-05 0.01127 0.000166 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03881 0.02232 0.03271 0.03703 0.9784 0.9844 0.03975 0.9393 0.9634 0.0508 ] Network output: [ 0.116 -0.3256 1.124 0.0003947 -0.0001772 0.9708 0.0002974 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6761 0.4365 0.3595 0.5367 0.9669 0.9844 0.6793 0.8603 0.9539 0.6354 ] Network output: [ -0.08042 0.2948 0.8952 0.00058 -0.0002604 0.9733 0.0004371 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5451 0.5079 0.3153 0.2546 0.9809 0.9873 0.5456 0.9475 0.9664 0.3509 ] Network output: [ -0.1421 0.292 0.9056 -0.0008715 0.0003913 1.083 -0.0006568 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5749 0.5674 0.3896 0.2042 0.9774 0.985 0.575 0.9358 0.9593 0.3997 ] Network output: [ 0.1276 0.6794 0.1116 0.0002289 -0.0001028 0.9547 0.0001725 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07528 Epoch 1733 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03117 0.9838 0.9945 0.0001537 -6.902e-05 -0.04004 0.0001159 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02398 -0.004111 0.0136 0.03078 0.9275 0.9386 0.04737 0.8408 0.8718 0.1155 ] Network output: [ 0.9393 0.1265 -0.05454 -0.0007034 0.0003158 0.04662 -0.0005301 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5991 0.0009583 -0.04234 0.3492 0.9625 0.9817 0.6841 0.8484 0.9469 0.6396 ] Network output: [ 0.007421 0.9492 1.025 0.0002199 -9.872e-05 0.01137 0.0001657 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03884 0.02237 0.03282 0.03713 0.9784 0.9844 0.03978 0.9395 0.9635 0.05091 ] Network output: [ 0.116 -0.3255 1.124 0.0003974 -0.0001784 0.9714 0.0002995 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6765 0.4375 0.3603 0.5366 0.9669 0.9844 0.6798 0.8605 0.9539 0.635 ] Network output: [ -0.08049 0.2948 0.8957 0.0005807 -0.0002607 0.9729 0.0004377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5451 0.508 0.3158 0.255 0.9809 0.9873 0.5456 0.9476 0.9665 0.3512 ] Network output: [ -0.1418 0.2919 0.9056 -0.0008665 0.000389 1.083 -0.000653 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5748 0.5674 0.3898 0.2048 0.9774 0.985 0.5749 0.9359 0.9594 0.3998 ] Network output: [ 0.1271 0.6802 0.1111 0.0002162 -9.704e-05 0.9553 0.0001629 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07508 Epoch 1734 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03133 0.9836 0.9945 0.0001544 -6.934e-05 -0.0401 0.0001164 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02398 -0.004114 0.01364 0.03084 0.9275 0.9386 0.04739 0.8409 0.8719 0.1156 ] Network output: [ 0.9395 0.1261 -0.05419 -0.0006998 0.0003142 0.0463 -0.0005274 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5993 0.001355 -0.0416 0.3492 0.9626 0.9817 0.6845 0.8486 0.9469 0.6393 ] Network output: [ 0.007464 0.949 1.026 0.0002195 -9.856e-05 0.01146 0.0001655 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03887 0.02242 0.03293 0.03722 0.9784 0.9844 0.03981 0.9396 0.9636 0.05101 ] Network output: [ 0.1159 -0.3255 1.123 0.0004002 -0.0001796 0.9719 0.0003016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6769 0.4385 0.3612 0.5364 0.9669 0.9844 0.6802 0.8607 0.954 0.6347 ] Network output: [ -0.08055 0.2948 0.8962 0.0005815 -0.0002611 0.9725 0.0004383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.545 0.5081 0.3162 0.2554 0.9809 0.9873 0.5455 0.9477 0.9665 0.3515 ] Network output: [ -0.1415 0.2919 0.9056 -0.0008614 0.0003867 1.082 -0.0006492 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5747 0.5673 0.39 0.2053 0.9774 0.985 0.5748 0.936 0.9594 0.3999 ] Network output: [ 0.1266 0.6811 0.1106 0.0002035 -9.134e-05 0.956 0.0001533 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07487 Epoch 1735 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03148 0.9833 0.9945 0.0001552 -6.965e-05 -0.04015 0.0001169 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02398 -0.004116 0.01367 0.0309 0.9276 0.9387 0.04741 0.8411 0.872 0.1157 ] Network output: [ 0.9396 0.1257 -0.05384 -0.0006964 0.0003126 0.04598 -0.0005248 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5996 0.001753 -0.04086 0.3493 0.9626 0.9817 0.6849 0.8488 0.947 0.6389 ] Network output: [ 0.007506 0.9487 1.026 0.0002192 -9.84e-05 0.01156 0.0001652 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0389 0.02247 0.03304 0.03732 0.9784 0.9844 0.03984 0.9397 0.9636 0.05112 ] Network output: [ 0.1158 -0.3255 1.123 0.0004029 -0.0001809 0.9725 0.0003036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6773 0.4395 0.3621 0.5363 0.9669 0.9844 0.6806 0.8609 0.9541 0.6343 ] Network output: [ -0.08062 0.2948 0.8968 0.0005824 -0.0002614 0.9721 0.0004389 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5449 0.5082 0.3167 0.2559 0.9809 0.9873 0.5454 0.9478 0.9666 0.3518 ] Network output: [ -0.1412 0.2919 0.9056 -0.0008563 0.0003844 1.081 -0.0006454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5746 0.5673 0.3901 0.2059 0.9775 0.9851 0.5747 0.9361 0.9595 0.4 ] Network output: [ 0.126 0.6819 0.1101 0.0001909 -8.568e-05 0.9567 0.0001438 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07467 Epoch 1736 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03163 0.9831 0.9945 0.0001559 -6.997e-05 -0.04019 0.0001175 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02397 -0.004119 0.01371 0.03096 0.9276 0.9387 0.04744 0.8413 0.8721 0.1158 ] Network output: [ 0.9398 0.1254 -0.05348 -0.000693 0.0003111 0.04566 -0.0005223 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5999 0.002153 -0.04013 0.3493 0.9626 0.9817 0.6853 0.849 0.9471 0.6386 ] Network output: [ 0.007546 0.9485 1.026 0.0002188 -9.825e-05 0.01166 0.0001649 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03893 0.02252 0.03315 0.03741 0.9784 0.9845 0.03987 0.9399 0.9637 0.05122 ] Network output: [ 0.1158 -0.3255 1.123 0.0004055 -0.000182 0.973 0.0003056 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6777 0.4405 0.363 0.5362 0.9669 0.9844 0.681 0.8611 0.9541 0.6339 ] Network output: [ -0.08068 0.2947 0.8973 0.0005832 -0.0002618 0.9717 0.0004395 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5449 0.5083 0.3172 0.2563 0.9809 0.9873 0.5454 0.9479 0.9666 0.3521 ] Network output: [ -0.1409 0.2919 0.9056 -0.0008513 0.0003822 1.081 -0.0006416 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5745 0.5673 0.3903 0.2065 0.9775 0.9851 0.5746 0.9363 0.9596 0.4001 ] Network output: [ 0.1255 0.6827 0.1096 0.0001783 -8.006e-05 0.9573 0.0001344 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07447 Epoch 1737 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03178 0.9828 0.9945 0.0001566 -7.03e-05 -0.04024 0.000118 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02397 -0.004121 0.01374 0.03102 0.9276 0.9387 0.04746 0.8414 0.8722 0.1159 ] Network output: [ 0.94 0.125 -0.05314 -0.0006897 0.0003096 0.04534 -0.0005198 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6001 0.002554 -0.03939 0.3493 0.9626 0.9817 0.6857 0.8492 0.9472 0.6383 ] Network output: [ 0.007585 0.9482 1.026 0.0002185 -9.81e-05 0.01176 0.0001647 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03896 0.02257 0.03327 0.03751 0.9784 0.9845 0.0399 0.94 0.9638 0.05133 ] Network output: [ 0.1157 -0.3255 1.122 0.0004081 -0.0001832 0.9736 0.0003076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6781 0.4415 0.3638 0.5361 0.967 0.9844 0.6814 0.8613 0.9542 0.6336 ] Network output: [ -0.08074 0.2947 0.8978 0.000584 -0.0002622 0.9713 0.0004401 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5448 0.5084 0.3176 0.2567 0.981 0.9873 0.5453 0.948 0.9667 0.3524 ] Network output: [ -0.1406 0.2919 0.9056 -0.0008462 0.0003799 1.08 -0.0006378 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5745 0.5672 0.3905 0.207 0.9775 0.9851 0.5746 0.9364 0.9596 0.4003 ] Network output: [ 0.125 0.6836 0.1091 0.0001659 -7.448e-05 0.958 0.000125 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07427 Epoch 1738 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03193 0.9826 0.9945 0.0001573 -7.062e-05 -0.04029 0.0001185 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02397 -0.004124 0.01378 0.03107 0.9276 0.9387 0.04748 0.8416 0.8723 0.116 ] Network output: [ 0.9402 0.1246 -0.05279 -0.0006864 0.0003082 0.04503 -0.0005173 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6004 0.002958 -0.03866 0.3494 0.9627 0.9818 0.6861 0.8494 0.9472 0.6379 ] Network output: [ 0.007623 0.948 1.026 0.0002182 -9.795e-05 0.01186 0.0001644 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03898 0.02262 0.03338 0.03761 0.9784 0.9845 0.03993 0.9401 0.9638 0.05144 ] Network output: [ 0.1156 -0.3255 1.122 0.0004107 -0.0001844 0.9741 0.0003095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6785 0.4425 0.3647 0.5359 0.967 0.9844 0.6818 0.8615 0.9543 0.6332 ] Network output: [ -0.0808 0.2947 0.8984 0.0005849 -0.0002626 0.9709 0.0004408 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5448 0.5085 0.3181 0.2572 0.981 0.9873 0.5453 0.9481 0.9668 0.3527 ] Network output: [ -0.1403 0.2919 0.9056 -0.0008412 0.0003776 1.08 -0.000634 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5744 0.5672 0.3907 0.2076 0.9775 0.9851 0.5745 0.9365 0.9597 0.4004 ] Network output: [ 0.1245 0.6845 0.1086 0.0001536 -6.894e-05 0.9587 0.0001157 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07407 Epoch 1739 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03208 0.9824 0.9944 0.000158 -7.094e-05 -0.04033 0.0001191 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02397 -0.004126 0.01381 0.03113 0.9277 0.9387 0.04751 0.8418 0.8724 0.1161 ] Network output: [ 0.9404 0.1242 -0.05245 -0.0006832 0.0003067 0.04472 -0.0005149 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6007 0.003363 -0.03793 0.3494 0.9627 0.9818 0.6865 0.8496 0.9473 0.6376 ] Network output: [ 0.007659 0.9478 1.026 0.0002179 -9.781e-05 0.01196 0.0001642 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03901 0.02267 0.03349 0.0377 0.9785 0.9845 0.03996 0.9403 0.9639 0.05154 ] Network output: [ 0.1156 -0.3254 1.121 0.0004132 -0.0001855 0.9746 0.0003114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6789 0.4435 0.3656 0.5358 0.967 0.9844 0.6822 0.8617 0.9543 0.6329 ] Network output: [ -0.08086 0.2946 0.8989 0.0005858 -0.000263 0.9706 0.0004415 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5448 0.5086 0.3186 0.2576 0.981 0.9873 0.5453 0.9483 0.9668 0.353 ] Network output: [ -0.14 0.2919 0.9056 -0.0008362 0.0003754 1.079 -0.0006302 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5743 0.5672 0.3909 0.2081 0.9775 0.9851 0.5744 0.9366 0.9598 0.4005 ] Network output: [ 0.1239 0.6853 0.1081 0.0001413 -6.343e-05 0.9593 0.0001065 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07387 Epoch 1740 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03222 0.9822 0.9944 0.0001588 -7.127e-05 -0.04038 0.0001196 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02397 -0.004128 0.01385 0.03119 0.9277 0.9388 0.04753 0.8419 0.8725 0.1162 ] Network output: [ 0.9405 0.1239 -0.0521 -0.0006801 0.0003053 0.04441 -0.0005125 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6009 0.003769 -0.0372 0.3494 0.9627 0.9818 0.6869 0.8498 0.9474 0.6373 ] Network output: [ 0.007695 0.9475 1.026 0.0002176 -9.768e-05 0.01207 0.000164 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03904 0.02272 0.03361 0.0378 0.9785 0.9845 0.03999 0.9404 0.9639 0.05165 ] Network output: [ 0.1155 -0.3254 1.121 0.0004157 -0.0001866 0.9752 0.0003133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6793 0.4445 0.3664 0.5357 0.967 0.9845 0.6826 0.8619 0.9544 0.6325 ] Network output: [ -0.08092 0.2945 0.8995 0.0005867 -0.0002634 0.9702 0.0004421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5447 0.5087 0.3191 0.2581 0.981 0.9874 0.5452 0.9484 0.9669 0.3534 ] Network output: [ -0.1397 0.2919 0.9056 -0.0008311 0.0003731 1.079 -0.0006264 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5743 0.5671 0.3911 0.2087 0.9775 0.9851 0.5744 0.9367 0.9598 0.4007 ] Network output: [ 0.1234 0.6862 0.1076 0.0001291 -5.797e-05 0.96 9.731e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07367 Epoch 1741 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03237 0.9819 0.9944 0.0001595 -7.16e-05 -0.04042 0.0001202 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02397 -0.004131 0.01388 0.03125 0.9277 0.9388 0.04755 0.8421 0.8726 0.1164 ] Network output: [ 0.9407 0.1235 -0.05176 -0.000677 0.0003039 0.0441 -0.0005102 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6012 0.004178 -0.03646 0.3495 0.9627 0.9818 0.6873 0.85 0.9474 0.637 ] Network output: [ 0.007729 0.9473 1.026 0.0002173 -9.755e-05 0.01217 0.0001637 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03907 0.02277 0.03372 0.0379 0.9785 0.9845 0.04002 0.9405 0.964 0.05176 ] Network output: [ 0.1155 -0.3254 1.12 0.0004182 -0.0001877 0.9757 0.0003152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6797 0.4455 0.3673 0.5356 0.967 0.9845 0.683 0.8621 0.9544 0.6322 ] Network output: [ -0.08097 0.2945 0.9 0.0005876 -0.0002638 0.9699 0.0004428 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5447 0.5088 0.3196 0.2585 0.981 0.9874 0.5452 0.9485 0.9669 0.3537 ] Network output: [ -0.1394 0.2918 0.9056 -0.0008261 0.0003709 1.078 -0.0006226 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5742 0.5671 0.3913 0.2092 0.9776 0.9851 0.5743 0.9368 0.9599 0.4008 ] Network output: [ 0.1229 0.687 0.1071 0.000117 -5.254e-05 0.9606 8.82e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07347 Epoch 1742 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03251 0.9817 0.9944 0.0001602 -7.193e-05 -0.04046 0.0001207 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02397 -0.004133 0.01392 0.03131 0.9277 0.9388 0.04758 0.8422 0.8727 0.1165 ] Network output: [ 0.9409 0.1231 -0.05143 -0.000674 0.0003026 0.0438 -0.000508 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6015 0.004588 -0.03573 0.3495 0.9627 0.9818 0.6876 0.8502 0.9475 0.6367 ] Network output: [ 0.007761 0.9471 1.026 0.000217 -9.742e-05 0.01227 0.0001635 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0391 0.02282 0.03384 0.038 0.9785 0.9845 0.04006 0.9407 0.9641 0.05187 ] Network output: [ 0.1154 -0.3254 1.12 0.0004206 -0.0001888 0.9762 0.000317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6801 0.4465 0.3682 0.5354 0.9671 0.9845 0.6834 0.8623 0.9545 0.6318 ] Network output: [ -0.08103 0.2944 0.9006 0.0005885 -0.0002642 0.9695 0.0004435 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5447 0.5089 0.3201 0.2589 0.981 0.9874 0.5452 0.9486 0.967 0.3541 ] Network output: [ -0.1391 0.2918 0.9056 -0.0008211 0.0003686 1.077 -0.0006188 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5742 0.5671 0.3915 0.2098 0.9776 0.9851 0.5743 0.937 0.96 0.4009 ] Network output: [ 0.1223 0.6879 0.1066 0.000105 -4.715e-05 0.9612 7.915e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07327 Epoch 1743 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03265 0.9815 0.9944 0.000161 -7.226e-05 -0.04051 0.0001213 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02397 -0.004135 0.01396 0.03137 0.9278 0.9388 0.0476 0.8424 0.8728 0.1166 ] Network output: [ 0.9411 0.1228 -0.05109 -0.0006711 0.0003013 0.04349 -0.0005057 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6017 0.004999 -0.035 0.3495 0.9628 0.9818 0.688 0.8504 0.9476 0.6363 ] Network output: [ 0.007793 0.9468 1.026 0.0002167 -9.729e-05 0.01238 0.0001633 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03914 0.02287 0.03396 0.0381 0.9785 0.9845 0.04009 0.9408 0.9641 0.05199 ] Network output: [ 0.1153 -0.3254 1.12 0.000423 -0.0001899 0.9767 0.0003188 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6805 0.4475 0.369 0.5353 0.9671 0.9845 0.6838 0.8625 0.9546 0.6315 ] Network output: [ -0.08108 0.2943 0.9011 0.0005894 -0.0002646 0.9691 0.0004442 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5447 0.5091 0.3206 0.2594 0.981 0.9874 0.5452 0.9487 0.9671 0.3544 ] Network output: [ -0.1388 0.2918 0.9056 -0.0008161 0.0003664 1.077 -0.0006151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5741 0.5671 0.3917 0.2103 0.9776 0.9851 0.5742 0.9371 0.96 0.4011 ] Network output: [ 0.1218 0.6887 0.1061 9.311e-05 -4.18e-05 0.9619 7.017e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07307 Epoch 1744 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03279 0.9813 0.9943 0.0001617 -7.259e-05 -0.04055 0.0001219 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02397 -0.004138 0.01399 0.03143 0.9278 0.9389 0.04763 0.8426 0.8729 0.1167 ] Network output: [ 0.9412 0.1224 -0.05076 -0.0006682 0.0003 0.04319 -0.0005035 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.602 0.005412 -0.03428 0.3496 0.9628 0.9818 0.6884 0.8506 0.9476 0.636 ] Network output: [ 0.007823 0.9466 1.026 0.0002164 -9.717e-05 0.01249 0.0001631 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03917 0.02292 0.03407 0.0382 0.9785 0.9846 0.04012 0.9409 0.9642 0.0521 ] Network output: [ 0.1153 -0.3253 1.119 0.0004254 -0.000191 0.9772 0.0003206 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6809 0.4485 0.3699 0.5352 0.9671 0.9845 0.6842 0.8627 0.9546 0.6312 ] Network output: [ -0.08113 0.2942 0.9017 0.0005904 -0.000265 0.9688 0.0004449 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5446 0.5092 0.3211 0.2598 0.9811 0.9874 0.5451 0.9488 0.9671 0.3548 ] Network output: [ -0.1385 0.2918 0.9056 -0.0008112 0.0003642 1.076 -0.0006113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5741 0.5671 0.3919 0.2108 0.9776 0.9852 0.5742 0.9372 0.9601 0.4013 ] Network output: [ 0.1213 0.6896 0.1057 8.127e-05 -3.648e-05 0.9625 6.124e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07287 Epoch 1745 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03293 0.9811 0.9943 0.0001624 -7.292e-05 -0.04059 0.0001224 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02397 -0.00414 0.01403 0.03149 0.9278 0.9389 0.04765 0.8427 0.873 0.1168 ] Network output: [ 0.9414 0.122 -0.05043 -0.0006653 0.0002987 0.04289 -0.0005014 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6023 0.005827 -0.03355 0.3496 0.9628 0.9818 0.6888 0.8508 0.9477 0.6357 ] Network output: [ 0.007852 0.9464 1.026 0.0002162 -9.705e-05 0.01259 0.0001629 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0392 0.02297 0.03419 0.0383 0.9786 0.9846 0.04015 0.941 0.9643 0.05221 ] Network output: [ 0.1152 -0.3253 1.119 0.0004277 -0.000192 0.9778 0.0003224 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6813 0.4495 0.3708 0.5351 0.9671 0.9845 0.6846 0.8628 0.9547 0.6309 ] Network output: [ -0.08118 0.2941 0.9022 0.0005913 -0.0002655 0.9684 0.0004456 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5446 0.5093 0.3216 0.2603 0.9811 0.9874 0.5451 0.9489 0.9672 0.3551 ] Network output: [ -0.1382 0.2918 0.9056 -0.0008062 0.0003619 1.076 -0.0006076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.574 0.5671 0.3921 0.2114 0.9776 0.9852 0.5741 0.9373 0.9602 0.4014 ] Network output: [ 0.1208 0.6905 0.1052 6.951e-05 -3.12e-05 0.9631 5.238e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07267 Epoch 1746 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03307 0.9808 0.9943 0.0001632 -7.325e-05 -0.04062 0.000123 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02397 -0.004142 0.01406 0.03154 0.9278 0.9389 0.04767 0.8429 0.8731 0.1169 ] Network output: [ 0.9416 0.1217 -0.0501 -0.0006625 0.0002974 0.0426 -0.0004993 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6025 0.006243 -0.03282 0.3496 0.9628 0.9819 0.6892 0.851 0.9478 0.6354 ] Network output: [ 0.00788 0.9461 1.026 0.0002159 -9.693e-05 0.0127 0.0001627 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03923 0.02302 0.03431 0.0384 0.9786 0.9846 0.04019 0.9412 0.9643 0.05233 ] Network output: [ 0.1152 -0.3253 1.118 0.0004301 -0.0001931 0.9783 0.0003241 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6817 0.4505 0.3716 0.535 0.9671 0.9845 0.685 0.863 0.9548 0.6305 ] Network output: [ -0.08123 0.294 0.9028 0.0005923 -0.0002659 0.9681 0.0004464 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5446 0.5095 0.3221 0.2607 0.9811 0.9874 0.5451 0.949 0.9672 0.3555 ] Network output: [ -0.1379 0.2918 0.9055 -0.0008012 0.0003597 1.075 -0.0006038 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.574 0.5671 0.3923 0.2119 0.9776 0.9852 0.5741 0.9374 0.9602 0.4016 ] Network output: [ 0.1202 0.6913 0.1047 5.783e-05 -2.596e-05 0.9637 4.358e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07247 Epoch 1747 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03321 0.9806 0.9943 0.0001639 -7.358e-05 -0.04066 0.0001235 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02397 -0.004144 0.0141 0.0316 0.9279 0.9389 0.0477 0.8431 0.8732 0.117 ] Network output: [ 0.9417 0.1213 -0.04977 -0.0006598 0.0002962 0.0423 -0.0004972 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6028 0.006661 -0.0321 0.3496 0.9629 0.9819 0.6895 0.8512 0.9479 0.6351 ] Network output: [ 0.007907 0.9459 1.026 0.0002157 -9.682e-05 0.01281 0.0001625 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03926 0.02307 0.03443 0.0385 0.9786 0.9846 0.04022 0.9413 0.9644 0.05244 ] Network output: [ 0.1151 -0.3253 1.118 0.0004323 -0.0001941 0.9788 0.0003258 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6821 0.4515 0.3725 0.5348 0.9672 0.9845 0.6854 0.8632 0.9548 0.6302 ] Network output: [ -0.08127 0.2938 0.9034 0.0005933 -0.0002663 0.9678 0.0004471 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5446 0.5096 0.3226 0.2612 0.9811 0.9874 0.5451 0.9492 0.9673 0.3558 ] Network output: [ -0.1376 0.2918 0.9055 -0.0007963 0.0003575 1.075 -0.0006001 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.574 0.5671 0.3926 0.2124 0.9777 0.9852 0.5741 0.9375 0.9603 0.4018 ] Network output: [ 0.1197 0.6922 0.1042 4.623e-05 -2.076e-05 0.9643 3.484e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07227 Epoch 1748 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03335 0.9804 0.9942 0.0001646 -7.391e-05 -0.0407 0.0001241 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02397 -0.004147 0.01414 0.03166 0.9279 0.9389 0.04772 0.8432 0.8733 0.1172 ] Network output: [ 0.9419 0.1209 -0.04945 -0.0006571 0.000295 0.04201 -0.0004952 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6031 0.00708 -0.03137 0.3497 0.9629 0.9819 0.6899 0.8514 0.9479 0.6348 ] Network output: [ 0.007932 0.9457 1.026 0.0002154 -9.671e-05 0.01292 0.0001623 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03929 0.02312 0.03455 0.0386 0.9786 0.9846 0.04026 0.9414 0.9644 0.05256 ] Network output: [ 0.1151 -0.3252 1.118 0.0004346 -0.0001951 0.9793 0.0003275 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6825 0.4525 0.3733 0.5347 0.9672 0.9845 0.6858 0.8634 0.9549 0.6299 ] Network output: [ -0.08132 0.2937 0.9039 0.0005943 -0.0002668 0.9674 0.0004478 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5446 0.5098 0.3232 0.2616 0.9811 0.9874 0.5451 0.9493 0.9673 0.3562 ] Network output: [ -0.1373 0.2918 0.9055 -0.0007914 0.0003553 1.074 -0.0005964 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5739 0.5671 0.3928 0.2129 0.9777 0.9852 0.574 0.9377 0.9604 0.4019 ] Network output: [ 0.1192 0.6931 0.1037 3.472e-05 -1.559e-05 0.965 2.616e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07207 Epoch 1749 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03348 0.9802 0.9942 0.0001654 -7.424e-05 -0.04073 0.0001246 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02397 -0.004149 0.01417 0.03172 0.9279 0.939 0.04775 0.8434 0.8734 0.1173 ] Network output: [ 0.9421 0.1206 -0.04913 -0.0006545 0.0002938 0.04172 -0.0004932 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6033 0.007501 -0.03065 0.3497 0.9629 0.9819 0.6903 0.8516 0.948 0.6346 ] Network output: [ 0.007956 0.9455 1.026 0.0002152 -9.66e-05 0.01303 0.0001622 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03933 0.02317 0.03467 0.0387 0.9786 0.9846 0.04029 0.9416 0.9645 0.05267 ] Network output: [ 0.115 -0.3252 1.117 0.0004369 -0.0001961 0.9798 0.0003292 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6829 0.4535 0.3742 0.5346 0.9672 0.9846 0.6862 0.8636 0.9549 0.6296 ] Network output: [ -0.08136 0.2936 0.9045 0.0005952 -0.0002672 0.9671 0.0004486 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5446 0.5099 0.3237 0.2621 0.9811 0.9874 0.5451 0.9494 0.9674 0.3566 ] Network output: [ -0.137 0.2918 0.9055 -0.0007864 0.0003531 1.073 -0.0005927 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5739 0.5671 0.393 0.2135 0.9777 0.9852 0.574 0.9378 0.9604 0.4021 ] Network output: [ 0.1187 0.694 0.1033 2.328e-05 -1.045e-05 0.9656 1.754e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07187 Epoch 1750 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03362 0.98 0.9942 0.0001661 -7.456e-05 -0.04077 0.0001252 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02397 -0.004151 0.01421 0.03178 0.9279 0.939 0.04777 0.8435 0.8735 0.1174 ] Network output: [ 0.9422 0.1202 -0.04881 -0.0006519 0.0002927 0.04144 -0.0004913 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6036 0.007923 -0.02993 0.3497 0.9629 0.9819 0.6907 0.8518 0.9481 0.6343 ] Network output: [ 0.00798 0.9452 1.027 0.0002149 -9.649e-05 0.01315 0.000162 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03936 0.02323 0.03479 0.0388 0.9786 0.9846 0.04032 0.9417 0.9646 0.05279 ] Network output: [ 0.1149 -0.3252 1.117 0.0004391 -0.0001971 0.9802 0.0003309 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6833 0.4545 0.375 0.5345 0.9672 0.9846 0.6866 0.8638 0.955 0.6293 ] Network output: [ -0.0814 0.2934 0.905 0.0005963 -0.0002677 0.9668 0.0004494 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5447 0.5101 0.3242 0.2625 0.9811 0.9875 0.5452 0.9495 0.9675 0.357 ] Network output: [ -0.1366 0.2918 0.9054 -0.0007815 0.0003509 1.073 -0.000589 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5739 0.5671 0.3933 0.214 0.9777 0.9852 0.574 0.9379 0.9605 0.4023 ] Network output: [ 0.1181 0.6948 0.1028 1.192e-05 -5.353e-06 0.9662 8.985e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07168 Epoch 1751 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03375 0.9798 0.9941 0.0001668 -7.489e-05 -0.0408 0.0001257 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02397 -0.004153 0.01425 0.03184 0.928 0.939 0.0478 0.8437 0.8735 0.1175 ] Network output: [ 0.9424 0.1199 -0.04849 -0.0006493 0.0002915 0.04115 -0.0004894 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6038 0.008346 -0.0292 0.3498 0.963 0.9819 0.691 0.852 0.9481 0.634 ] Network output: [ 0.008001 0.945 1.027 0.0002147 -9.638e-05 0.01326 0.0001618 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03939 0.02328 0.03491 0.0389 0.9787 0.9846 0.04036 0.9418 0.9646 0.05291 ] Network output: [ 0.1149 -0.3252 1.116 0.0004413 -0.0001981 0.9807 0.0003325 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6836 0.4555 0.3759 0.5344 0.9672 0.9846 0.687 0.864 0.9551 0.629 ] Network output: [ -0.08144 0.2933 0.9056 0.0005973 -0.0002681 0.9665 0.0004501 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5447 0.5102 0.3247 0.263 0.9812 0.9875 0.5452 0.9496 0.9675 0.3574 ] Network output: [ -0.1363 0.2917 0.9054 -0.0007766 0.0003487 1.072 -0.0005853 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5739 0.5671 0.3935 0.2145 0.9777 0.9853 0.574 0.938 0.9606 0.4025 ] Network output: [ 0.1176 0.6957 0.1023 6.447e-07 -2.894e-07 0.9668 4.858e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07148 Epoch 1752 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03388 0.9796 0.9941 0.0001675 -7.522e-05 -0.04084 0.0001263 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02397 -0.004155 0.01429 0.0319 0.928 0.939 0.04782 0.8439 0.8736 0.1176 ] Network output: [ 0.9426 0.1195 -0.04817 -0.0006468 0.0002904 0.04087 -0.0004875 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6041 0.008771 -0.02848 0.3498 0.963 0.9819 0.6914 0.8522 0.9482 0.6337 ] Network output: [ 0.008022 0.9448 1.027 0.0002144 -9.627e-05 0.01337 0.0001616 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03943 0.02333 0.03503 0.03901 0.9787 0.9847 0.0404 0.942 0.9647 0.05303 ] Network output: [ 0.1148 -0.3251 1.116 0.0004434 -0.0001991 0.9812 0.0003342 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.684 0.4565 0.3767 0.5343 0.9673 0.9846 0.6874 0.8642 0.9551 0.6287 ] Network output: [ -0.08148 0.2931 0.9062 0.0005983 -0.0002686 0.9661 0.0004509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5447 0.5104 0.3253 0.2635 0.9812 0.9875 0.5452 0.9497 0.9676 0.3577 ] Network output: [ -0.136 0.2917 0.9054 -0.0007718 0.0003465 1.072 -0.0005816 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5738 0.5671 0.3938 0.215 0.9777 0.9853 0.574 0.9381 0.9606 0.4027 ] Network output: [ 0.1171 0.6966 0.1019 -1.055e-05 4.738e-06 0.9674 -7.954e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07128 Epoch 1753 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03401 0.9795 0.9941 0.0001683 -7.554e-05 -0.04087 0.0001268 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02397 -0.004158 0.01432 0.03195 0.928 0.939 0.04785 0.844 0.8737 0.1178 ] Network output: [ 0.9427 0.1192 -0.04786 -0.0006444 0.0002893 0.04059 -0.0004856 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6044 0.009198 -0.02776 0.3498 0.963 0.982 0.6918 0.8524 0.9483 0.6334 ] Network output: [ 0.008042 0.9446 1.027 0.0002142 -9.617e-05 0.01349 0.0001614 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03946 0.02338 0.03515 0.03911 0.9787 0.9847 0.04043 0.9421 0.9647 0.05315 ] Network output: [ 0.1148 -0.3251 1.116 0.0004456 -0.0002 0.9817 0.0003358 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6844 0.4575 0.3776 0.5342 0.9673 0.9846 0.6878 0.8644 0.9552 0.6284 ] Network output: [ -0.08151 0.2929 0.9067 0.0005993 -0.0002691 0.9658 0.0004517 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5447 0.5106 0.3258 0.2639 0.9812 0.9875 0.5452 0.9498 0.9676 0.3581 ] Network output: [ -0.1357 0.2917 0.9054 -0.0007669 0.0003443 1.071 -0.000578 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5738 0.5671 0.394 0.2155 0.9778 0.9853 0.5739 0.9383 0.9607 0.4029 ] Network output: [ 0.1166 0.6975 0.1014 -2.168e-05 9.731e-06 0.9679 -1.634e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07109 Epoch 1754 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03414 0.9793 0.994 0.000169 -7.587e-05 -0.0409 0.0001274 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02397 -0.00416 0.01436 0.03201 0.928 0.9391 0.04787 0.8442 0.8738 0.1179 ] Network output: [ 0.9429 0.1188 -0.04755 -0.000642 0.0002882 0.04032 -0.0004838 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6046 0.009625 -0.02705 0.3498 0.963 0.982 0.6922 0.8526 0.9483 0.6332 ] Network output: [ 0.00806 0.9444 1.027 0.000214 -9.606e-05 0.0136 0.0001613 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0395 0.02344 0.03528 0.03921 0.9787 0.9847 0.04047 0.9422 0.9648 0.05327 ] Network output: [ 0.1147 -0.3251 1.115 0.0004477 -0.000201 0.9822 0.0003374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6848 0.4585 0.3784 0.5341 0.9673 0.9846 0.6882 0.8646 0.9553 0.6281 ] Network output: [ -0.08155 0.2927 0.9073 0.0006004 -0.0002695 0.9655 0.0004525 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5447 0.5107 0.3263 0.2644 0.9812 0.9875 0.5453 0.9499 0.9677 0.3585 ] Network output: [ -0.1354 0.2917 0.9053 -0.000762 0.0003421 1.071 -0.0005743 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5738 0.5671 0.3943 0.216 0.9778 0.9853 0.5739 0.9384 0.9608 0.4031 ] Network output: [ 0.116 0.6983 0.1009 -3.272e-05 1.469e-05 0.9685 -2.466e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07089 Epoch 1755 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03426 0.9791 0.994 0.0001697 -7.619e-05 -0.04093 0.0001279 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02397 -0.004162 0.0144 0.03207 0.9281 0.9391 0.0479 0.8443 0.8739 0.118 ] Network output: [ 0.9431 0.1185 -0.04724 -0.0006396 0.0002872 0.04004 -0.0004821 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6049 0.01005 -0.02633 0.3499 0.963 0.982 0.6925 0.8528 0.9484 0.6329 ] Network output: [ 0.008077 0.9442 1.027 0.0002137 -9.596e-05 0.01372 0.0001611 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03953 0.02349 0.0354 0.03932 0.9787 0.9847 0.0405 0.9423 0.9649 0.05339 ] Network output: [ 0.1147 -0.3251 1.115 0.0004498 -0.0002019 0.9826 0.000339 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6852 0.4595 0.3792 0.534 0.9673 0.9846 0.6885 0.8648 0.9553 0.6279 ] Network output: [ -0.08158 0.2926 0.9078 0.0006014 -0.00027 0.9652 0.0004533 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5448 0.5109 0.3269 0.2648 0.9812 0.9875 0.5453 0.95 0.9677 0.3589 ] Network output: [ -0.1351 0.2917 0.9053 -0.0007572 0.0003399 1.07 -0.0005706 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5738 0.5671 0.3945 0.2165 0.9778 0.9853 0.5739 0.9385 0.9608 0.4033 ] Network output: [ 0.1155 0.6992 0.1005 -4.368e-05 1.961e-05 0.9691 -3.292e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07069 Epoch 1756 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03439 0.9789 0.994 0.0001704 -7.651e-05 -0.04096 0.0001284 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02397 -0.004164 0.01444 0.03213 0.9281 0.9391 0.04792 0.8445 0.874 0.1181 ] Network output: [ 0.9432 0.1181 -0.04693 -0.0006373 0.0002861 0.03977 -0.0004803 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6051 0.01048 -0.02561 0.3499 0.9631 0.982 0.6929 0.853 0.9485 0.6327 ] Network output: [ 0.008093 0.9439 1.027 0.0002135 -9.586e-05 0.01383 0.0001609 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03957 0.02354 0.03552 0.03942 0.9788 0.9847 0.04054 0.9425 0.9649 0.05351 ] Network output: [ 0.1146 -0.325 1.114 0.0004519 -0.0002029 0.9831 0.0003406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6855 0.4605 0.3801 0.5339 0.9673 0.9846 0.6889 0.865 0.9554 0.6276 ] Network output: [ -0.08161 0.2924 0.9084 0.0006025 -0.0002705 0.9649 0.000454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5448 0.5111 0.3274 0.2653 0.9812 0.9875 0.5453 0.9502 0.9678 0.3593 ] Network output: [ -0.1348 0.2917 0.9053 -0.0007524 0.0003378 1.07 -0.000567 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5738 0.5672 0.3948 0.2171 0.9778 0.9853 0.5739 0.9386 0.9609 0.4035 ] Network output: [ 0.115 0.7001 0.1 -5.457e-05 2.45e-05 0.9697 -4.113e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0705 Epoch 1757 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03451 0.9787 0.9939 0.0001711 -7.683e-05 -0.04099 0.000129 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02398 -0.004166 0.01447 0.03219 0.9281 0.9391 0.04795 0.8447 0.8741 0.1183 ] Network output: [ 0.9434 0.1178 -0.04663 -0.0006351 0.0002851 0.0395 -0.0004786 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6054 0.01092 -0.0249 0.3499 0.9631 0.982 0.6933 0.8532 0.9485 0.6324 ] Network output: [ 0.008108 0.9437 1.027 0.0002133 -9.575e-05 0.01395 0.0001607 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0396 0.0236 0.03565 0.03952 0.9788 0.9847 0.04058 0.9426 0.965 0.05363 ] Network output: [ 0.1146 -0.325 1.114 0.000454 -0.0002038 0.9836 0.0003421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6859 0.4615 0.3809 0.5338 0.9674 0.9846 0.6893 0.8652 0.9554 0.6273 ] Network output: [ -0.08164 0.2922 0.909 0.0006035 -0.000271 0.9646 0.0004548 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5449 0.5113 0.328 0.2658 0.9812 0.9875 0.5454 0.9503 0.9678 0.3598 ] Network output: [ -0.1345 0.2917 0.9052 -0.0007476 0.0003356 1.069 -0.0005634 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5738 0.5672 0.3951 0.2176 0.9778 0.9853 0.5739 0.9387 0.961 0.4037 ] Network output: [ 0.1145 0.701 0.09955 -6.538e-05 2.935e-05 0.9702 -4.927e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0703 Epoch 1758 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03464 0.9786 0.9939 0.0001718 -7.714e-05 -0.04102 0.0001295 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02398 -0.004168 0.01451 0.03225 0.9281 0.9391 0.04797 0.8448 0.8742 0.1184 ] Network output: [ 0.9435 0.1174 -0.04632 -0.0006328 0.0002841 0.03923 -0.0004769 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6056 0.01135 -0.02418 0.3499 0.9631 0.982 0.6936 0.8534 0.9486 0.6321 ] Network output: [ 0.008122 0.9435 1.027 0.0002131 -9.565e-05 0.01407 0.0001606 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03964 0.02365 0.03577 0.03963 0.9788 0.9847 0.04061 0.9427 0.965 0.05375 ] Network output: [ 0.1145 -0.325 1.114 0.000456 -0.0002047 0.984 0.0003437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6863 0.4624 0.3818 0.5336 0.9674 0.9847 0.6897 0.8654 0.9555 0.6271 ] Network output: [ -0.08167 0.292 0.9095 0.0006046 -0.0002714 0.9643 0.0004557 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5449 0.5115 0.3285 0.2662 0.9813 0.9875 0.5454 0.9504 0.9679 0.3602 ] Network output: [ -0.1341 0.2917 0.9052 -0.0007428 0.0003335 1.068 -0.0005598 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5738 0.5672 0.3953 0.2181 0.9778 0.9853 0.5739 0.9389 0.9611 0.4039 ] Network output: [ 0.114 0.7019 0.09909 -7.611e-05 3.417e-05 0.9708 -5.736e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07011 Epoch 1759 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03476 0.9784 0.9938 0.0001725 -7.746e-05 -0.04105 0.00013 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02398 -0.00417 0.01455 0.0323 0.9282 0.9392 0.048 0.845 0.8743 0.1185 ] Network output: [ 0.9437 0.1171 -0.04602 -0.0006306 0.0002831 0.03897 -0.0004753 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6059 0.01178 -0.02347 0.3499 0.9631 0.982 0.694 0.8536 0.9487 0.6319 ] Network output: [ 0.008134 0.9433 1.027 0.0002128 -9.555e-05 0.01418 0.0001604 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03967 0.02371 0.0359 0.03973 0.9788 0.9847 0.04065 0.9428 0.9651 0.05388 ] Network output: [ 0.1145 -0.3249 1.113 0.000458 -0.0002056 0.9845 0.0003452 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6867 0.4634 0.3826 0.5335 0.9674 0.9847 0.6901 0.8656 0.9556 0.6268 ] Network output: [ -0.08169 0.2917 0.9101 0.0006057 -0.0002719 0.964 0.0004565 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.545 0.5116 0.3291 0.2667 0.9813 0.9875 0.5455 0.9505 0.968 0.3606 ] Network output: [ -0.1338 0.2916 0.9051 -0.000738 0.0003313 1.068 -0.0005562 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5738 0.5673 0.3956 0.2186 0.9779 0.9854 0.5739 0.939 0.9611 0.4041 ] Network output: [ 0.1134 0.7028 0.09864 -8.676e-05 3.895e-05 0.9714 -6.539e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06992 Epoch 1760 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03488 0.9782 0.9938 0.0001732 -7.777e-05 -0.04108 0.0001306 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02398 -0.004172 0.01459 0.03236 0.9282 0.9392 0.04803 0.8451 0.8744 0.1186 ] Network output: [ 0.9438 0.1168 -0.04572 -0.0006285 0.0002821 0.03871 -0.0004736 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6062 0.01222 -0.02276 0.35 0.9632 0.982 0.6944 0.8538 0.9488 0.6317 ] Network output: [ 0.008146 0.9431 1.027 0.0002126 -9.544e-05 0.0143 0.0001602 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03971 0.02376 0.03602 0.03984 0.9788 0.9848 0.04069 0.943 0.9652 0.054 ] Network output: [ 0.1145 -0.3249 1.113 0.00046 -0.0002065 0.9849 0.0003467 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.687 0.4644 0.3834 0.5334 0.9674 0.9847 0.6904 0.8658 0.9556 0.6265 ] Network output: [ -0.08171 0.2915 0.9107 0.0006068 -0.0002724 0.9637 0.0004573 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.545 0.5118 0.3296 0.2671 0.9813 0.9876 0.5455 0.9506 0.968 0.361 ] Network output: [ -0.1335 0.2916 0.9051 -0.0007332 0.0003292 1.067 -0.0005526 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5738 0.5673 0.3959 0.219 0.9779 0.9854 0.5739 0.9391 0.9612 0.4044 ] Network output: [ 0.1129 0.7036 0.09819 -9.734e-05 4.37e-05 0.9719 -7.336e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06972 Epoch 1761 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.035 0.9781 0.9937 0.0001739 -7.808e-05 -0.0411 0.0001311 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02398 -0.004174 0.01463 0.03242 0.9282 0.9392 0.04805 0.8453 0.8745 0.1188 ] Network output: [ 0.944 0.1164 -0.04543 -0.0006264 0.0002812 0.03845 -0.000472 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6064 0.01266 -0.02205 0.35 0.9632 0.9821 0.6947 0.854 0.9488 0.6314 ] Network output: [ 0.008156 0.9429 1.027 0.0002124 -9.534e-05 0.01442 0.00016 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03974 0.02381 0.03615 0.03994 0.9788 0.9848 0.04073 0.9431 0.9652 0.05413 ] Network output: [ 0.1144 -0.3249 1.113 0.000462 -0.0002074 0.9854 0.0003482 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6874 0.4654 0.3842 0.5333 0.9674 0.9847 0.6908 0.866 0.9557 0.6263 ] Network output: [ -0.08174 0.2913 0.9112 0.0006078 -0.0002729 0.9634 0.0004581 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5451 0.512 0.3302 0.2676 0.9813 0.9876 0.5456 0.9507 0.9681 0.3614 ] Network output: [ -0.1332 0.2916 0.9051 -0.0007285 0.000327 1.067 -0.000549 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5738 0.5674 0.3962 0.2195 0.9779 0.9854 0.5739 0.9392 0.9613 0.4046 ] Network output: [ 0.1124 0.7045 0.09774 -0.0001078 4.841e-05 0.9725 -8.127e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06953 Epoch 1762 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03512 0.9779 0.9937 0.0001746 -7.839e-05 -0.04113 0.0001316 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02398 -0.004176 0.01467 0.03248 0.9282 0.9392 0.04808 0.8454 0.8746 0.1189 ] Network output: [ 0.9442 0.1161 -0.04513 -0.0006243 0.0002803 0.03819 -0.0004705 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6067 0.01309 -0.02134 0.35 0.9632 0.9821 0.6951 0.8542 0.9489 0.6312 ] Network output: [ 0.008166 0.9427 1.027 0.0002121 -9.523e-05 0.01454 0.0001599 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03978 0.02387 0.03627 0.04005 0.9789 0.9848 0.04077 0.9432 0.9653 0.05425 ] Network output: [ 0.1144 -0.3249 1.112 0.000464 -0.0002083 0.9858 0.0003497 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6878 0.4664 0.3851 0.5333 0.9675 0.9847 0.6912 0.8661 0.9558 0.626 ] Network output: [ -0.08175 0.291 0.9118 0.0006089 -0.0002734 0.9631 0.0004589 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5451 0.5122 0.3308 0.2681 0.9813 0.9876 0.5457 0.9508 0.9681 0.3619 ] Network output: [ -0.1329 0.2916 0.905 -0.0007237 0.0003249 1.066 -0.0005454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5738 0.5674 0.3964 0.22 0.9779 0.9854 0.5739 0.9393 0.9613 0.4048 ] Network output: [ 0.1119 0.7054 0.09729 -0.0001183 5.309e-05 0.973 -8.912e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06934 Epoch 1763 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03524 0.9777 0.9937 0.0001753 -7.869e-05 -0.04115 0.0001321 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02399 -0.004178 0.0147 0.03254 0.9283 0.9392 0.0481 0.8456 0.8747 0.119 ] Network output: [ 0.9443 0.1158 -0.04484 -0.0006222 0.0002793 0.03794 -0.0004689 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6069 0.01353 -0.02063 0.35 0.9632 0.9821 0.6954 0.8544 0.949 0.6309 ] Network output: [ 0.008174 0.9425 1.027 0.0002119 -9.513e-05 0.01466 0.0001597 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03982 0.02392 0.0364 0.04015 0.9789 0.9848 0.0408 0.9433 0.9654 0.05438 ] Network output: [ 0.1143 -0.3248 1.112 0.000466 -0.0002092 0.9863 0.0003512 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6882 0.4674 0.3859 0.5332 0.9675 0.9847 0.6916 0.8663 0.9558 0.6258 ] Network output: [ -0.08177 0.2908 0.9124 0.00061 -0.0002739 0.9629 0.0004597 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5452 0.5124 0.3313 0.2685 0.9813 0.9876 0.5457 0.9509 0.9682 0.3623 ] Network output: [ -0.1326 0.2916 0.905 -0.000719 0.0003228 1.066 -0.0005419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5738 0.5674 0.3967 0.2205 0.9779 0.9854 0.574 0.9395 0.9614 0.4051 ] Network output: [ 0.1114 0.7063 0.09684 -0.0001286 5.773e-05 0.9736 -9.692e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06914 Epoch 1764 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03535 0.9776 0.9936 0.0001759 -7.899e-05 -0.04118 0.0001326 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02399 -0.00418 0.01474 0.03259 0.9283 0.9393 0.04813 0.8458 0.8748 0.1191 ] Network output: [ 0.9445 0.1154 -0.04455 -0.0006202 0.0002784 0.03768 -0.0004674 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6072 0.01397 -0.01993 0.3501 0.9632 0.9821 0.6958 0.8546 0.949 0.6307 ] Network output: [ 0.008181 0.9423 1.027 0.0002117 -9.502e-05 0.01478 0.0001595 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03986 0.02398 0.03653 0.04026 0.9789 0.9848 0.04084 0.9435 0.9654 0.0545 ] Network output: [ 0.1143 -0.3248 1.111 0.0004679 -0.0002101 0.9867 0.0003526 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6885 0.4683 0.3867 0.5331 0.9675 0.9847 0.6919 0.8665 0.9559 0.6255 ] Network output: [ -0.08179 0.2906 0.9129 0.0006111 -0.0002744 0.9626 0.0004606 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5453 0.5126 0.3319 0.269 0.9813 0.9876 0.5458 0.951 0.9682 0.3627 ] Network output: [ -0.1323 0.2916 0.9049 -0.0007143 0.0003207 1.065 -0.0005383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5739 0.5675 0.397 0.221 0.978 0.9854 0.574 0.9396 0.9615 0.4053 ] Network output: [ 0.1108 0.7072 0.0964 -0.0001389 6.234e-05 0.9741 -0.0001047 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06895 Epoch 1765 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03547 0.9774 0.9936 0.0001766 -7.929e-05 -0.0412 0.0001331 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02399 -0.004182 0.01478 0.03265 0.9283 0.9393 0.04816 0.8459 0.8749 0.1193 ] Network output: [ 0.9446 0.1151 -0.04426 -0.0006183 0.0002776 0.03743 -0.0004659 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6074 0.01441 -0.01922 0.3501 0.9633 0.9821 0.6962 0.8548 0.9491 0.6305 ] Network output: [ 0.008187 0.9421 1.027 0.0002114 -9.491e-05 0.0149 0.0001593 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03989 0.02403 0.03666 0.04036 0.9789 0.9848 0.04088 0.9436 0.9655 0.05463 ] Network output: [ 0.1142 -0.3248 1.111 0.0004698 -0.0002109 0.9871 0.0003541 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6889 0.4693 0.3875 0.533 0.9675 0.9847 0.6923 0.8667 0.9559 0.6253 ] Network output: [ -0.0818 0.2903 0.9135 0.0006122 -0.0002748 0.9623 0.0004614 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5454 0.5128 0.3325 0.2695 0.9814 0.9876 0.5459 0.9511 0.9683 0.3632 ] Network output: [ -0.1319 0.2915 0.9049 -0.0007096 0.0003186 1.065 -0.0005348 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5739 0.5675 0.3973 0.2215 0.978 0.9854 0.574 0.9397 0.9615 0.4055 ] Network output: [ 0.1103 0.7081 0.09596 -0.000149 6.691e-05 0.9747 -0.0001123 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06876 Epoch 1766 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03558 0.9773 0.9935 0.0001773 -7.958e-05 -0.04123 0.0001336 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02399 -0.004184 0.01482 0.03271 0.9283 0.9393 0.04818 0.8461 0.875 0.1194 ] Network output: [ 0.9448 0.1148 -0.04397 -0.0006163 0.0002767 0.03719 -0.0004645 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6077 0.01486 -0.01852 0.3501 0.9633 0.9821 0.6965 0.855 0.9492 0.6303 ] Network output: [ 0.008192 0.9419 1.028 0.0002112 -9.48e-05 0.01502 0.0001591 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03993 0.02409 0.03678 0.04047 0.9789 0.9848 0.04092 0.9437 0.9655 0.05475 ] Network output: [ 0.1142 -0.3247 1.111 0.0004717 -0.0002118 0.9876 0.0003555 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6893 0.4703 0.3883 0.5329 0.9675 0.9847 0.6927 0.8669 0.956 0.6251 ] Network output: [ -0.08181 0.29 0.914 0.0006133 -0.0002753 0.962 0.0004622 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5454 0.5131 0.333 0.2699 0.9814 0.9876 0.546 0.9512 0.9684 0.3636 ] Network output: [ -0.1316 0.2915 0.9048 -0.000705 0.0003165 1.064 -0.0005313 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5739 0.5676 0.3976 0.222 0.978 0.9855 0.574 0.9398 0.9616 0.4058 ] Network output: [ 0.1098 0.709 0.09552 -0.0001592 7.145e-05 0.9752 -0.0001199 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06857 Epoch 1767 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03569 0.9771 0.9935 0.0001779 -7.988e-05 -0.04125 0.0001341 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02399 -0.004186 0.01486 0.03277 0.9284 0.9393 0.04821 0.8462 0.8751 0.1195 ] Network output: [ 0.9449 0.1144 -0.04369 -0.0006144 0.0002758 0.03694 -0.000463 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6079 0.0153 -0.01782 0.3501 0.9633 0.9821 0.6969 0.8551 0.9492 0.63 ] Network output: [ 0.008196 0.9417 1.028 0.0002109 -9.469e-05 0.01515 0.000159 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03997 0.02414 0.03691 0.04058 0.9789 0.9849 0.04096 0.9438 0.9656 0.05488 ] Network output: [ 0.1142 -0.3247 1.11 0.0004736 -0.0002126 0.988 0.0003569 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6896 0.4713 0.3892 0.5328 0.9676 0.9848 0.693 0.8671 0.9561 0.6248 ] Network output: [ -0.08182 0.2898 0.9146 0.0006144 -0.0002758 0.9618 0.000463 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5455 0.5133 0.3336 0.2704 0.9814 0.9876 0.546 0.9513 0.9684 0.364 ] Network output: [ -0.1313 0.2915 0.9048 -0.0007003 0.0003144 1.063 -0.0005278 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5739 0.5677 0.3979 0.2225 0.978 0.9855 0.5741 0.9399 0.9617 0.406 ] Network output: [ 0.1093 0.7099 0.09508 -0.0001692 7.595e-05 0.9757 -0.0001275 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06838 Epoch 1768 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0358 0.977 0.9934 0.0001786 -8.016e-05 -0.04127 0.0001346 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.024 -0.004188 0.0149 0.03282 0.9284 0.9394 0.04823 0.8464 0.8752 0.1196 ] Network output: [ 0.9451 0.1141 -0.0434 -0.0006126 0.000275 0.0367 -0.0004616 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6082 0.01574 -0.01712 0.3501 0.9633 0.9822 0.6972 0.8553 0.9493 0.6298 ] Network output: [ 0.008199 0.9415 1.028 0.0002107 -9.458e-05 0.01527 0.0001588 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04001 0.0242 0.03704 0.04068 0.979 0.9849 0.041 0.944 0.9657 0.05501 ] Network output: [ 0.1141 -0.3247 1.11 0.0004755 -0.0002135 0.9884 0.0003583 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.69 0.4722 0.39 0.5327 0.9676 0.9848 0.6934 0.8673 0.9561 0.6246 ] Network output: [ -0.08183 0.2895 0.9152 0.0006155 -0.0002763 0.9615 0.0004639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5456 0.5135 0.3342 0.2709 0.9814 0.9876 0.5461 0.9514 0.9685 0.3645 ] Network output: [ -0.131 0.2915 0.9048 -0.0006957 0.0003123 1.063 -0.0005243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.574 0.5677 0.3982 0.2229 0.978 0.9855 0.5741 0.9401 0.9617 0.4063 ] Network output: [ 0.1088 0.7108 0.09465 -0.0001791 8.042e-05 0.9763 -0.000135 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06819 Epoch 1769 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03591 0.9769 0.9933 0.0001792 -8.045e-05 -0.04129 0.0001351 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.024 -0.00419 0.01494 0.03288 0.9284 0.9394 0.04826 0.8465 0.8753 0.1198 ] Network output: [ 0.9452 0.1138 -0.04312 -0.0006107 0.0002742 0.03646 -0.0004603 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6084 0.01619 -0.01642 0.3502 0.9634 0.9822 0.6976 0.8555 0.9494 0.6296 ] Network output: [ 0.008201 0.9413 1.028 0.0002104 -9.447e-05 0.01539 0.0001586 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04005 0.02426 0.03717 0.04079 0.979 0.9849 0.04104 0.9441 0.9657 0.05514 ] Network output: [ 0.1141 -0.3246 1.11 0.0004773 -0.0002143 0.9889 0.0003597 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6903 0.4732 0.3908 0.5326 0.9676 0.9848 0.6938 0.8675 0.9562 0.6244 ] Network output: [ -0.08184 0.2892 0.9157 0.0006166 -0.0002768 0.9613 0.0004647 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5457 0.5137 0.3348 0.2713 0.9814 0.9876 0.5462 0.9515 0.9685 0.3649 ] Network output: [ -0.1307 0.2915 0.9047 -0.0006911 0.0003102 1.062 -0.0005208 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.574 0.5678 0.3985 0.2234 0.978 0.9855 0.5741 0.9402 0.9618 0.4065 ] Network output: [ 0.1083 0.7117 0.09421 -0.000189 8.485e-05 0.9768 -0.0001424 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.068 Epoch 1770 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03602 0.9767 0.9933 0.0001798 -8.073e-05 -0.04131 0.0001355 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.024 -0.004192 0.01497 0.03294 0.9284 0.9394 0.04829 0.8467 0.8754 0.1199 ] Network output: [ 0.9453 0.1135 -0.04284 -0.0006089 0.0002734 0.03622 -0.0004589 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6087 0.01664 -0.01572 0.3502 0.9634 0.9822 0.6979 0.8557 0.9494 0.6294 ] Network output: [ 0.008201 0.9411 1.028 0.0002102 -9.435e-05 0.01551 0.0001584 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04009 0.02431 0.0373 0.0409 0.979 0.9849 0.04108 0.9442 0.9658 0.05527 ] Network output: [ 0.114 -0.3246 1.109 0.0004791 -0.0002151 0.9893 0.0003611 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6907 0.4742 0.3916 0.5325 0.9676 0.9848 0.6941 0.8677 0.9562 0.6242 ] Network output: [ -0.08184 0.2889 0.9163 0.0006177 -0.0002773 0.961 0.0004655 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5458 0.5139 0.3353 0.2718 0.9814 0.9877 0.5463 0.9516 0.9686 0.3654 ] Network output: [ -0.1304 0.2914 0.9047 -0.0006865 0.0003082 1.062 -0.0005173 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.574 0.5678 0.3988 0.2239 0.9781 0.9855 0.5742 0.9403 0.9619 0.4068 ] Network output: [ 0.1078 0.7126 0.09378 -0.0001988 8.925e-05 0.9773 -0.0001498 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06781 Epoch 1771 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03613 0.9766 0.9932 0.0001805 -8.101e-05 -0.04133 0.000136 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.024 -0.004194 0.01501 0.03299 0.9285 0.9394 0.04831 0.8468 0.8755 0.12 ] Network output: [ 0.9455 0.1131 -0.04257 -0.0006071 0.0002726 0.03598 -0.0004576 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.609 0.01708 -0.01503 0.3502 0.9634 0.9822 0.6983 0.8559 0.9495 0.6292 ] Network output: [ 0.008201 0.941 1.028 0.0002099 -9.424e-05 0.01564 0.0001582 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04012 0.02437 0.03743 0.041 0.979 0.9849 0.04112 0.9443 0.9658 0.0554 ] Network output: [ 0.114 -0.3246 1.109 0.000481 -0.0002159 0.9897 0.0003625 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6911 0.4751 0.3924 0.5324 0.9676 0.9848 0.6945 0.8678 0.9563 0.624 ] Network output: [ -0.08185 0.2887 0.9168 0.0006188 -0.0002778 0.9607 0.0004664 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5459 0.5141 0.3359 0.2723 0.9814 0.9877 0.5464 0.9518 0.9686 0.3659 ] Network output: [ -0.13 0.2914 0.9046 -0.0006819 0.0003061 1.061 -0.0005139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5741 0.5679 0.3991 0.2244 0.9781 0.9855 0.5742 0.9404 0.9619 0.4071 ] Network output: [ 0.1073 0.7135 0.09335 -0.0002085 9.36e-05 0.9778 -0.0001571 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06762 Epoch 1772 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03623 0.9764 0.9932 0.0001811 -8.129e-05 -0.04135 0.0001365 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02401 -0.004196 0.01505 0.03305 0.9285 0.9394 0.04834 0.847 0.8756 0.1202 ] Network output: [ 0.9456 0.1128 -0.04229 -0.0006054 0.0002718 0.03575 -0.0004563 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6092 0.01753 -0.01433 0.3502 0.9634 0.9822 0.6986 0.8561 0.9496 0.629 ] Network output: [ 0.0082 0.9408 1.028 0.0002096 -9.412e-05 0.01576 0.000158 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04016 0.02443 0.03756 0.04111 0.979 0.9849 0.04116 0.9444 0.9659 0.05552 ] Network output: [ 0.114 -0.3245 1.108 0.0004828 -0.0002167 0.9901 0.0003638 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6914 0.4761 0.3932 0.5323 0.9677 0.9848 0.6949 0.868 0.9564 0.6237 ] Network output: [ -0.08185 0.2884 0.9174 0.0006199 -0.0002783 0.9605 0.0004672 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.546 0.5144 0.3365 0.2728 0.9815 0.9877 0.5465 0.9519 0.9687 0.3663 ] Network output: [ -0.1297 0.2914 0.9046 -0.0006773 0.0003041 1.061 -0.0005104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5741 0.568 0.3994 0.2248 0.9781 0.9855 0.5742 0.9405 0.962 0.4073 ] Network output: [ 0.1067 0.7144 0.09292 -0.0002181 9.793e-05 0.9783 -0.0001644 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06743 Epoch 1773 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03634 0.9763 0.9931 0.0001817 -8.156e-05 -0.04137 0.0001369 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02401 -0.004198 0.01509 0.03311 0.9285 0.9395 0.04837 0.8471 0.8757 0.1203 ] Network output: [ 0.9458 0.1125 -0.04202 -0.0006037 0.000271 0.03552 -0.000455 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6095 0.01798 -0.01364 0.3502 0.9635 0.9822 0.699 0.8563 0.9496 0.6288 ] Network output: [ 0.008198 0.9406 1.028 0.0002094 -9.4e-05 0.01589 0.0001578 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0402 0.02448 0.03769 0.04122 0.979 0.9849 0.04121 0.9446 0.9659 0.05565 ] Network output: [ 0.1139 -0.3245 1.108 0.0004845 -0.0002175 0.9905 0.0003652 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6918 0.4771 0.394 0.5323 0.9677 0.9848 0.6952 0.8682 0.9564 0.6235 ] Network output: [ -0.08185 0.2881 0.9179 0.000621 -0.0002788 0.9602 0.000468 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5461 0.5146 0.3371 0.2732 0.9815 0.9877 0.5466 0.952 0.9687 0.3668 ] Network output: [ -0.1294 0.2914 0.9045 -0.0006728 0.000302 1.06 -0.000507 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5742 0.5681 0.3998 0.2253 0.9781 0.9856 0.5743 0.9407 0.9621 0.4076 ] Network output: [ 0.1062 0.7153 0.0925 -0.0002277 0.0001022 0.9788 -0.0001716 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06724 Epoch 1774 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03644 0.9762 0.9931 0.0001823 -8.182e-05 -0.04139 0.0001374 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02401 -0.0042 0.01513 0.03317 0.9285 0.9395 0.04839 0.8473 0.8758 0.1204 ] Network output: [ 0.9459 0.1122 -0.04175 -0.000602 0.0002703 0.03529 -0.0004537 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6097 0.01843 -0.01295 0.3503 0.9635 0.9822 0.6993 0.8565 0.9497 0.6286 ] Network output: [ 0.008194 0.9404 1.028 0.0002091 -9.388e-05 0.01601 0.0001576 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04024 0.02454 0.03782 0.04133 0.9791 0.9849 0.04125 0.9447 0.966 0.05578 ] Network output: [ 0.1139 -0.3245 1.108 0.0004863 -0.0002183 0.9909 0.0003665 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6921 0.478 0.3948 0.5322 0.9677 0.9848 0.6956 0.8684 0.9565 0.6233 ] Network output: [ -0.08185 0.2878 0.9185 0.0006221 -0.0002793 0.96 0.0004688 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5462 0.5148 0.3377 0.2737 0.9815 0.9877 0.5467 0.9521 0.9688 0.3672 ] Network output: [ -0.1291 0.2913 0.9045 -0.0006682 0.0003 1.06 -0.0005036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5742 0.5681 0.4001 0.2258 0.9781 0.9856 0.5743 0.9408 0.9621 0.4079 ] Network output: [ 0.1057 0.7162 0.09207 -0.0002372 0.0001065 0.9793 -0.0001787 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06705 Epoch 1775 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03654 0.9761 0.993 0.0001829 -8.209e-05 -0.04141 0.0001378 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02402 -0.004201 0.01517 0.03322 0.9286 0.9395 0.04842 0.8474 0.8759 0.1205 ] Network output: [ 0.9461 0.1119 -0.04148 -0.0006004 0.0002695 0.03506 -0.0004525 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.61 0.01888 -0.01226 0.3503 0.9635 0.9823 0.6997 0.8567 0.9498 0.6284 ] Network output: [ 0.00819 0.9402 1.028 0.0002088 -9.375e-05 0.01614 0.0001574 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04028 0.0246 0.03795 0.04143 0.9791 0.985 0.04129 0.9448 0.9661 0.05591 ] Network output: [ 0.1139 -0.3245 1.107 0.000488 -0.0002191 0.9913 0.0003678 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6925 0.479 0.3956 0.5321 0.9677 0.9848 0.696 0.8686 0.9566 0.6231 ] Network output: [ -0.08184 0.2874 0.919 0.0006232 -0.0002798 0.9597 0.0004697 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5463 0.5151 0.3382 0.2742 0.9815 0.9877 0.5468 0.9522 0.9689 0.3677 ] Network output: [ -0.1288 0.2913 0.9044 -0.0006637 0.000298 1.059 -0.0005002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5743 0.5682 0.4004 0.2262 0.9782 0.9856 0.5744 0.9409 0.9622 0.4081 ] Network output: [ 0.1052 0.7171 0.09165 -0.0002465 0.0001107 0.9798 -0.0001858 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06687 Epoch 1776 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03664 0.9759 0.993 0.0001834 -8.235e-05 -0.04143 0.0001382 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02402 -0.004203 0.01521 0.03328 0.9286 0.9395 0.04845 0.8476 0.876 0.1207 ] Network output: [ 0.9462 0.1115 -0.04121 -0.0005987 0.0002688 0.03484 -0.0004512 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6102 0.01933 -0.01157 0.3503 0.9635 0.9823 0.7 0.8569 0.9498 0.6282 ] Network output: [ 0.008185 0.94 1.028 0.0002085 -9.363e-05 0.01626 0.0001572 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04032 0.02465 0.03808 0.04154 0.9791 0.985 0.04133 0.9449 0.9661 0.05605 ] Network output: [ 0.1138 -0.3244 1.107 0.0004898 -0.0002199 0.9917 0.0003691 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6928 0.4799 0.3964 0.532 0.9677 0.9849 0.6963 0.8688 0.9566 0.6229 ] Network output: [ -0.08184 0.2871 0.9196 0.0006243 -0.0002803 0.9595 0.0004705 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5464 0.5153 0.3388 0.2746 0.9815 0.9877 0.547 0.9523 0.9689 0.3682 ] Network output: [ -0.1285 0.2913 0.9044 -0.0006592 0.0002959 1.059 -0.0004968 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5743 0.5683 0.4007 0.2267 0.9782 0.9856 0.5744 0.941 0.9623 0.4084 ] Network output: [ 0.1047 0.718 0.09123 -0.0002558 0.0001149 0.9803 -0.0001928 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06668 Epoch 1777 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03674 0.9758 0.9929 0.000184 -8.26e-05 -0.04144 0.0001387 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02402 -0.004205 0.01525 0.03333 0.9286 0.9395 0.04847 0.8477 0.8761 0.1208 ] Network output: [ 0.9463 0.1112 -0.04094 -0.0005971 0.0002681 0.03462 -0.00045 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6104 0.01978 -0.01088 0.3503 0.9635 0.9823 0.7004 0.8571 0.9499 0.628 ] Network output: [ 0.008179 0.9399 1.028 0.0002083 -9.35e-05 0.01639 0.000157 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04037 0.02471 0.03821 0.04165 0.9791 0.985 0.04137 0.945 0.9662 0.05618 ] Network output: [ 0.1138 -0.3244 1.107 0.0004915 -0.0002206 0.9921 0.0003704 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6932 0.4809 0.3971 0.5319 0.9678 0.9849 0.6967 0.869 0.9567 0.6228 ] Network output: [ -0.08183 0.2868 0.9201 0.0006254 -0.0002808 0.9593 0.0004713 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5466 0.5156 0.3394 0.2751 0.9815 0.9877 0.5471 0.9524 0.969 0.3686 ] Network output: [ -0.1281 0.2913 0.9043 -0.0006547 0.0002939 1.058 -0.0004934 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5744 0.5684 0.401 0.2272 0.9782 0.9856 0.5745 0.9411 0.9624 0.4087 ] Network output: [ 0.1042 0.7189 0.09081 -0.0002651 0.000119 0.9808 -0.0001998 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0665 Epoch 1778 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03684 0.9757 0.9928 0.0001846 -8.286e-05 -0.04146 0.0001391 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02403 -0.004207 0.01529 0.03339 0.9286 0.9396 0.0485 0.8479 0.8762 0.1209 ] Network output: [ 0.9465 0.1109 -0.04068 -0.0005956 0.0002674 0.0344 -0.0004488 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6107 0.02024 -0.0102 0.3503 0.9636 0.9823 0.7007 0.8572 0.95 0.6278 ] Network output: [ 0.008172 0.9397 1.028 0.000208 -9.336e-05 0.01652 0.0001567 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04041 0.02477 0.03835 0.04176 0.9791 0.985 0.04142 0.9452 0.9662 0.05631 ] Network output: [ 0.1137 -0.3244 1.106 0.0004932 -0.0002214 0.9925 0.0003717 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6936 0.4818 0.3979 0.5319 0.9678 0.9849 0.697 0.8691 0.9567 0.6226 ] Network output: [ -0.08182 0.2865 0.9207 0.0006265 -0.0002813 0.959 0.0004722 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5467 0.5158 0.34 0.2756 0.9816 0.9877 0.5472 0.9525 0.969 0.3691 ] Network output: [ -0.1278 0.2912 0.9043 -0.0006503 0.0002919 1.057 -0.00049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5744 0.5685 0.4014 0.2276 0.9782 0.9856 0.5745 0.9412 0.9624 0.409 ] Network output: [ 0.1037 0.7198 0.0904 -0.0002742 0.0001231 0.9813 -0.0002067 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06631 Epoch 1779 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03694 0.9756 0.9928 0.0001851 -8.311e-05 -0.04147 0.0001395 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02403 -0.004209 0.01533 0.03345 0.9287 0.9396 0.04853 0.848 0.8763 0.1211 ] Network output: [ 0.9466 0.1106 -0.04042 -0.000594 0.0002667 0.03418 -0.0004477 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6109 0.02069 -0.009514 0.3503 0.9636 0.9823 0.701 0.8574 0.95 0.6277 ] Network output: [ 0.008163 0.9395 1.028 0.0002077 -9.323e-05 0.01664 0.0001565 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04045 0.02482 0.03848 0.04186 0.9792 0.985 0.04146 0.9453 0.9663 0.05644 ] Network output: [ 0.1137 -0.3243 1.106 0.0004948 -0.0002221 0.9929 0.0003729 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6939 0.4828 0.3987 0.5318 0.9678 0.9849 0.6974 0.8693 0.9568 0.6224 ] Network output: [ -0.08181 0.2862 0.9212 0.0006276 -0.0002817 0.9588 0.000473 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5468 0.5161 0.3406 0.276 0.9816 0.9877 0.5473 0.9526 0.9691 0.3696 ] Network output: [ -0.1275 0.2912 0.9042 -0.0006458 0.0002899 1.057 -0.0004867 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5745 0.5686 0.4017 0.2281 0.9782 0.9856 0.5746 0.9414 0.9625 0.4093 ] Network output: [ 0.1032 0.7207 0.08998 -0.0002833 0.0001272 0.9818 -0.0002135 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06613 Epoch 1780 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03703 0.9755 0.9927 0.0001857 -8.335e-05 -0.04149 0.0001399 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02403 -0.004211 0.01537 0.0335 0.9287 0.9396 0.04855 0.8482 0.8763 0.1212 ] Network output: [ 0.9467 0.1103 -0.04016 -0.0005925 0.000266 0.03396 -0.0004466 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6112 0.02115 -0.008833 0.3504 0.9636 0.9823 0.7014 0.8576 0.9501 0.6275 ] Network output: [ 0.008154 0.9393 1.028 0.0002074 -9.309e-05 0.01677 0.0001563 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04049 0.02488 0.03861 0.04197 0.9792 0.985 0.0415 0.9454 0.9664 0.05657 ] Network output: [ 0.1137 -0.3243 1.106 0.0004965 -0.0002229 0.9933 0.0003742 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6943 0.4837 0.3995 0.5317 0.9678 0.9849 0.6977 0.8695 0.9569 0.6222 ] Network output: [ -0.08179 0.2858 0.9218 0.0006287 -0.0002822 0.9586 0.0004738 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5469 0.5163 0.3412 0.2765 0.9816 0.9878 0.5475 0.9527 0.9691 0.37 ] Network output: [ -0.1272 0.2912 0.9042 -0.0006414 0.0002879 1.056 -0.0004834 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5746 0.5686 0.402 0.2286 0.9783 0.9857 0.5747 0.9415 0.9626 0.4095 ] Network output: [ 0.1027 0.7216 0.08957 -0.0002922 0.0001312 0.9823 -0.0002202 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06594 Epoch 1781 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03712 0.9754 0.9926 0.0001862 -8.359e-05 -0.0415 0.0001403 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02404 -0.004212 0.01541 0.03356 0.9287 0.9396 0.04858 0.8483 0.8764 0.1213 ] Network output: [ 0.9469 0.11 -0.0399 -0.000591 0.0002653 0.03375 -0.0004454 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6114 0.0216 -0.008153 0.3504 0.9636 0.9823 0.7017 0.8578 0.9502 0.6273 ] Network output: [ 0.008144 0.9392 1.028 0.0002071 -9.295e-05 0.0169 0.000156 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04053 0.02494 0.03874 0.04208 0.9792 0.985 0.04155 0.9455 0.9664 0.0567 ] Network output: [ 0.1136 -0.3243 1.105 0.0004981 -0.0002236 0.9936 0.0003754 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6946 0.4847 0.4003 0.5316 0.9678 0.9849 0.6981 0.8697 0.9569 0.622 ] Network output: [ -0.08178 0.2855 0.9223 0.0006298 -0.0002827 0.9583 0.0004746 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5471 0.5166 0.3418 0.277 0.9816 0.9878 0.5476 0.9528 0.9692 0.3705 ] Network output: [ -0.1269 0.2911 0.9042 -0.000637 0.000286 1.056 -0.00048 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5746 0.5687 0.4024 0.229 0.9783 0.9857 0.5747 0.9416 0.9626 0.4098 ] Network output: [ 0.1022 0.7225 0.08916 -0.0003011 0.0001352 0.9827 -0.0002269 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06576 Epoch 1782 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03722 0.9753 0.9926 0.0001867 -8.383e-05 -0.04152 0.0001407 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02404 -0.004214 0.01544 0.03361 0.9287 0.9397 0.04861 0.8485 0.8765 0.1215 ] Network output: [ 0.947 0.1097 -0.03965 -0.0005896 0.0002647 0.03354 -0.0004443 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6117 0.02206 -0.007475 0.3504 0.9637 0.9823 0.7021 0.858 0.9502 0.6272 ] Network output: [ 0.008133 0.939 1.029 0.0002067 -9.281e-05 0.01702 0.0001558 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04057 0.025 0.03887 0.04219 0.9792 0.9851 0.04159 0.9456 0.9665 0.05684 ] Network output: [ 0.1136 -0.3242 1.105 0.0004997 -0.0002243 0.994 0.0003766 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6949 0.4856 0.401 0.5316 0.9679 0.9849 0.6984 0.8699 0.957 0.6219 ] Network output: [ -0.08176 0.2851 0.9228 0.0006308 -0.0002832 0.9581 0.0004754 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5472 0.5168 0.3424 0.2774 0.9816 0.9878 0.5477 0.9529 0.9692 0.371 ] Network output: [ -0.1266 0.2911 0.9041 -0.0006326 0.000284 1.055 -0.0004767 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5747 0.5688 0.4027 0.2295 0.9783 0.9857 0.5748 0.9417 0.9627 0.4101 ] Network output: [ 0.1017 0.7234 0.08875 -0.0003099 0.0001391 0.9832 -0.0002336 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06557 Epoch 1783 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03731 0.9752 0.9925 0.0001872 -8.406e-05 -0.04153 0.0001411 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02405 -0.004216 0.01548 0.03367 0.9288 0.9397 0.04864 0.8486 0.8766 0.1216 ] Network output: [ 0.9471 0.1094 -0.03939 -0.0005882 0.000264 0.03333 -0.0004433 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6119 0.02251 -0.006799 0.3504 0.9637 0.9824 0.7024 0.8582 0.9503 0.627 ] Network output: [ 0.008122 0.9388 1.029 0.0002064 -9.267e-05 0.01715 0.0001556 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04061 0.02506 0.03901 0.04229 0.9792 0.9851 0.04163 0.9458 0.9665 0.05697 ] Network output: [ 0.1136 -0.3242 1.105 0.0005013 -0.0002251 0.9944 0.0003778 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6953 0.4866 0.4018 0.5315 0.9679 0.9849 0.6988 0.8701 0.957 0.6217 ] Network output: [ -0.08174 0.2848 0.9234 0.0006319 -0.0002837 0.9579 0.0004762 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5473 0.5171 0.343 0.2779 0.9816 0.9878 0.5479 0.953 0.9693 0.3715 ] Network output: [ -0.1263 0.2911 0.9041 -0.0006282 0.000282 1.055 -0.0004734 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5748 0.5689 0.403 0.2299 0.9783 0.9857 0.5749 0.9418 0.9628 0.4104 ] Network output: [ 0.1012 0.7243 0.08835 -0.0003187 0.0001431 0.9837 -0.0002402 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06539 Epoch 1784 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0374 0.975 0.9925 0.0001877 -8.428e-05 -0.04155 0.0001415 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02405 -0.004218 0.01552 0.03372 0.9288 0.9397 0.04866 0.8488 0.8767 0.1217 ] Network output: [ 0.9473 0.1091 -0.03914 -0.0005868 0.0002634 0.03313 -0.0004422 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6122 0.02297 -0.006124 0.3504 0.9637 0.9824 0.7027 0.8584 0.9504 0.6268 ] Network output: [ 0.008109 0.9387 1.029 0.0002061 -9.252e-05 0.01728 0.0001553 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04066 0.02511 0.03914 0.0424 0.9792 0.9851 0.04168 0.9459 0.9666 0.0571 ] Network output: [ 0.1136 -0.3242 1.104 0.0005029 -0.0002258 0.9948 0.000379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6956 0.4875 0.4026 0.5314 0.9679 0.985 0.6991 0.8702 0.9571 0.6215 ] Network output: [ -0.08172 0.2844 0.9239 0.000633 -0.0002842 0.9577 0.000477 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5475 0.5173 0.3436 0.2784 0.9816 0.9878 0.548 0.9531 0.9693 0.372 ] Network output: [ -0.126 0.291 0.904 -0.0006238 0.0002801 1.054 -0.0004701 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5748 0.569 0.4034 0.2304 0.9783 0.9857 0.5749 0.942 0.9628 0.4107 ] Network output: [ 0.1007 0.7252 0.08794 -0.0003273 0.0001469 0.9841 -0.0002467 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06521 Epoch 1785 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03749 0.9749 0.9924 0.0001882 -8.451e-05 -0.04156 0.0001419 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02405 -0.00422 0.01556 0.03378 0.9288 0.9397 0.04869 0.8489 0.8768 0.1218 ] Network output: [ 0.9474 0.1088 -0.03889 -0.0005854 0.0002628 0.03292 -0.0004412 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6124 0.02343 -0.005451 0.3504 0.9637 0.9824 0.7031 0.8585 0.9504 0.6267 ] Network output: [ 0.008095 0.9385 1.029 0.0002058 -9.237e-05 0.01741 0.0001551 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0407 0.02517 0.03927 0.04251 0.9793 0.9851 0.04172 0.946 0.9667 0.05723 ] Network output: [ 0.1135 -0.3241 1.104 0.0005044 -0.0002265 0.9951 0.0003802 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.696 0.4884 0.4033 0.5314 0.9679 0.985 0.6995 0.8704 0.9572 0.6214 ] Network output: [ -0.0817 0.2841 0.9244 0.000634 -0.0002846 0.9574 0.0004778 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5476 0.5176 0.3442 0.2788 0.9817 0.9878 0.5481 0.9532 0.9694 0.3724 ] Network output: [ -0.1256 0.291 0.904 -0.0006195 0.0002781 1.054 -0.0004668 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5749 0.5691 0.4037 0.2308 0.9784 0.9857 0.575 0.9421 0.9629 0.411 ] Network output: [ 0.1002 0.7261 0.08754 -0.0003358 0.0001508 0.9846 -0.0002531 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06503 Epoch 1786 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03758 0.9749 0.9923 0.0001887 -8.473e-05 -0.04157 0.0001422 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02406 -0.004221 0.0156 0.03383 0.9288 0.9397 0.04872 0.8491 0.8769 0.122 ] Network output: [ 0.9475 0.1085 -0.03864 -0.000584 0.0002622 0.03272 -0.0004401 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6127 0.02388 -0.004781 0.3505 0.9638 0.9824 0.7034 0.8587 0.9505 0.6265 ] Network output: [ 0.008081 0.9383 1.029 0.0002054 -9.222e-05 0.01754 0.0001548 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04074 0.02523 0.0394 0.04262 0.9793 0.9851 0.04177 0.9461 0.9667 0.05737 ] Network output: [ 0.1135 -0.3241 1.104 0.000506 -0.0002271 0.9955 0.0003813 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6963 0.4894 0.4041 0.5313 0.9679 0.985 0.6998 0.8706 0.9572 0.6212 ] Network output: [ -0.08167 0.2837 0.925 0.0006351 -0.0002851 0.9572 0.0004786 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5478 0.5178 0.3447 0.2793 0.9817 0.9878 0.5483 0.9533 0.9695 0.3729 ] Network output: [ -0.1253 0.2909 0.9039 -0.0006151 0.0002762 1.053 -0.0004636 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.575 0.5692 0.4041 0.2313 0.9784 0.9857 0.5751 0.9422 0.963 0.4113 ] Network output: [ 0.09969 0.727 0.08714 -0.0003443 0.0001546 0.9851 -0.0002595 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06485 Epoch 1787 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03767 0.9748 0.9923 0.0001892 -8.494e-05 -0.04158 0.0001426 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02406 -0.004223 0.01564 0.03389 0.9289 0.9398 0.04874 0.8492 0.877 0.1221 ] Network output: [ 0.9477 0.1082 -0.0384 -0.0005827 0.0002616 0.03252 -0.0004391 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6129 0.02434 -0.004111 0.3505 0.9638 0.9824 0.7038 0.8589 0.9506 0.6264 ] Network output: [ 0.008065 0.9382 1.029 0.0002051 -9.206e-05 0.01766 0.0001545 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04079 0.02529 0.03954 0.04272 0.9793 0.9851 0.04181 0.9462 0.9668 0.0575 ] Network output: [ 0.1135 -0.3241 1.103 0.0005075 -0.0002278 0.9958 0.0003824 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6967 0.4903 0.4049 0.5312 0.968 0.985 0.7002 0.8708 0.9573 0.621 ] Network output: [ -0.08165 0.2834 0.9255 0.0006361 -0.0002856 0.957 0.0004794 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5479 0.5181 0.3453 0.2798 0.9817 0.9878 0.5484 0.9534 0.9695 0.3734 ] Network output: [ -0.125 0.2909 0.9039 -0.0006108 0.0002742 1.053 -0.0004603 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5751 0.5693 0.4044 0.2317 0.9784 0.9858 0.5752 0.9423 0.963 0.4116 ] Network output: [ 0.0992 0.7279 0.08674 -0.0003527 0.0001583 0.9855 -0.0002658 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06467 Epoch 1788 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03775 0.9747 0.9922 0.0001897 -8.515e-05 -0.04159 0.0001429 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02407 -0.004225 0.01568 0.03394 0.9289 0.9398 0.04877 0.8494 0.8771 0.1222 ] Network output: [ 0.9478 0.1079 -0.03815 -0.0005814 0.000261 0.03232 -0.0004381 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6132 0.0248 -0.003444 0.3505 0.9638 0.9824 0.7041 0.8591 0.9506 0.6262 ] Network output: [ 0.008049 0.938 1.029 0.0002047 -9.19e-05 0.01779 0.0001543 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04083 0.02535 0.03967 0.04283 0.9793 0.9851 0.04186 0.9463 0.9668 0.05763 ] Network output: [ 0.1134 -0.324 1.103 0.000509 -0.0002285 0.9962 0.0003836 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.697 0.4912 0.4056 0.5312 0.968 0.985 0.7005 0.871 0.9573 0.6209 ] Network output: [ -0.08162 0.283 0.926 0.0006372 -0.0002861 0.9568 0.0004802 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5481 0.5184 0.3459 0.2802 0.9817 0.9878 0.5486 0.9535 0.9696 0.3739 ] Network output: [ -0.1247 0.2909 0.9038 -0.0006065 0.0002723 1.052 -0.0004571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5751 0.5694 0.4047 0.2322 0.9784 0.9858 0.5753 0.9424 0.9631 0.4119 ] Network output: [ 0.0987 0.7288 0.08634 -0.000361 0.0001621 0.986 -0.000272 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06449 Epoch 1789 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03784 0.9746 0.9921 0.0001901 -8.536e-05 -0.04161 0.0001433 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02407 -0.004227 0.01572 0.034 0.9289 0.9398 0.0488 0.8495 0.8772 0.1224 ] Network output: [ 0.9479 0.1076 -0.03791 -0.0005801 0.0002604 0.03212 -0.0004372 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6134 0.02526 -0.002779 0.3505 0.9638 0.9824 0.7044 0.8593 0.9507 0.6261 ] Network output: [ 0.008032 0.9378 1.029 0.0002044 -9.174e-05 0.01792 0.000154 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04087 0.02541 0.0398 0.04294 0.9793 0.9852 0.0419 0.9464 0.9669 0.05777 ] Network output: [ 0.1134 -0.324 1.103 0.0005104 -0.0002291 0.9966 0.0003847 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6973 0.4922 0.4064 0.5311 0.968 0.985 0.7009 0.8711 0.9574 0.6207 ] Network output: [ -0.08159 0.2826 0.9266 0.0006382 -0.0002865 0.9566 0.000481 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5482 0.5187 0.3465 0.2807 0.9817 0.9879 0.5488 0.9536 0.9696 0.3744 ] Network output: [ -0.1244 0.2908 0.9038 -0.0006023 0.0002704 1.052 -0.0004539 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5752 0.5696 0.4051 0.2326 0.9784 0.9858 0.5753 0.9425 0.9632 0.4122 ] Network output: [ 0.09821 0.7297 0.08595 -0.0003692 0.0001657 0.9864 -0.0002782 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06431 Epoch 1790 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03792 0.9745 0.9921 0.0001906 -8.556e-05 -0.04162 0.0001436 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02408 -0.004229 0.01576 0.03405 0.9289 0.9398 0.04883 0.8496 0.8773 0.1225 ] Network output: [ 0.948 0.1073 -0.03767 -0.0005788 0.0002599 0.03193 -0.0004362 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6136 0.02572 -0.002115 0.3505 0.9638 0.9825 0.7047 0.8595 0.9508 0.6259 ] Network output: [ 0.008014 0.9377 1.029 0.000204 -9.158e-05 0.01805 0.0001537 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04092 0.02547 0.03994 0.04305 0.9794 0.9852 0.04195 0.9466 0.9669 0.0579 ] Network output: [ 0.1134 -0.324 1.102 0.0005119 -0.0002298 0.9969 0.0003858 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6977 0.4931 0.4071 0.531 0.968 0.985 0.7012 0.8713 0.9574 0.6206 ] Network output: [ -0.08156 0.2823 0.9271 0.0006393 -0.000287 0.9564 0.0004818 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5484 0.5189 0.3471 0.2812 0.9817 0.9879 0.5489 0.9537 0.9697 0.3749 ] Network output: [ -0.1241 0.2908 0.9038 -0.000598 0.0002685 1.051 -0.0004507 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5753 0.5697 0.4054 0.233 0.9785 0.9858 0.5754 0.9427 0.9632 0.4125 ] Network output: [ 0.09772 0.7306 0.08555 -0.0003773 0.0001694 0.9868 -0.0002843 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06413 Epoch 1791 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.038 0.9744 0.992 0.000191 -8.576e-05 -0.04163 0.000144 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02408 -0.00423 0.0158 0.03411 0.929 0.9398 0.04885 0.8498 0.8774 0.1226 ] Network output: [ 0.9482 0.107 -0.03743 -0.0005776 0.0002593 0.03174 -0.0004353 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6139 0.02618 -0.001454 0.3505 0.9639 0.9825 0.7051 0.8596 0.9508 0.6258 ] Network output: [ 0.007995 0.9375 1.029 0.0002036 -9.141e-05 0.01818 0.0001535 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04096 0.02553 0.04007 0.04315 0.9794 0.9852 0.04199 0.9467 0.967 0.05803 ] Network output: [ 0.1134 -0.3239 1.102 0.0005133 -0.0002304 0.9973 0.0003868 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.698 0.494 0.4079 0.531 0.9681 0.985 0.7015 0.8715 0.9575 0.6204 ] Network output: [ -0.08153 0.2819 0.9276 0.0006403 -0.0002874 0.9562 0.0004825 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5485 0.5192 0.3477 0.2816 0.9817 0.9879 0.5491 0.9538 0.9697 0.3754 ] Network output: [ -0.1238 0.2907 0.9037 -0.0005938 0.0002666 1.051 -0.0004475 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5754 0.5698 0.4058 0.2335 0.9785 0.9858 0.5755 0.9428 0.9633 0.4129 ] Network output: [ 0.09723 0.7315 0.08516 -0.0003853 0.000173 0.9873 -0.0002904 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06395 Epoch 1792 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03808 0.9743 0.9919 0.0001915 -8.595e-05 -0.04164 0.0001443 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02408 -0.004232 0.01584 0.03416 0.929 0.9399 0.04888 0.8499 0.8775 0.1228 ] Network output: [ 0.9483 0.1067 -0.03719 -0.0005764 0.0002588 0.03155 -0.0004344 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6141 0.02664 -0.0007944 0.3505 0.9639 0.9825 0.7054 0.8598 0.9509 0.6256 ] Network output: [ 0.007976 0.9374 1.029 0.0002032 -9.124e-05 0.01831 0.0001532 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.041 0.02558 0.0402 0.04326 0.9794 0.9852 0.04204 0.9468 0.9671 0.05817 ] Network output: [ 0.1133 -0.3239 1.102 0.0005147 -0.0002311 0.9976 0.0003879 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6984 0.4949 0.4086 0.5309 0.9681 0.985 0.7019 0.8717 0.9576 0.6203 ] Network output: [ -0.08149 0.2815 0.9281 0.0006413 -0.0002879 0.956 0.0004833 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5487 0.5195 0.3483 0.2821 0.9818 0.9879 0.5492 0.9539 0.9698 0.3758 ] Network output: [ -0.1235 0.2907 0.9037 -0.0005895 0.0002647 1.05 -0.0004443 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5755 0.5699 0.4061 0.2339 0.9785 0.9858 0.5756 0.9429 0.9634 0.4132 ] Network output: [ 0.09674 0.7324 0.08477 -0.0003932 0.0001765 0.9877 -0.0002964 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06378 Epoch 1793 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03816 0.9742 0.9919 0.0001919 -8.614e-05 -0.04165 0.0001446 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02409 -0.004234 0.01588 0.03421 0.929 0.9399 0.04891 0.8501 0.8776 0.1229 ] Network output: [ 0.9484 0.1064 -0.03696 -0.0005752 0.0002582 0.03136 -0.0004335 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6144 0.0271 -0.0001367 0.3506 0.9639 0.9825 0.7057 0.86 0.9509 0.6255 ] Network output: [ 0.007955 0.9372 1.029 0.0002029 -9.107e-05 0.01844 0.0001529 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04105 0.02564 0.04034 0.04337 0.9794 0.9852 0.04208 0.9469 0.9671 0.0583 ] Network output: [ 0.1133 -0.3238 1.101 0.000516 -0.0002317 0.9979 0.0003889 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6987 0.4959 0.4094 0.5309 0.9681 0.9851 0.7022 0.8719 0.9576 0.6202 ] Network output: [ -0.08146 0.2811 0.9286 0.0006423 -0.0002884 0.9558 0.0004841 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5489 0.5198 0.3489 0.2826 0.9818 0.9879 0.5494 0.954 0.9698 0.3763 ] Network output: [ -0.1232 0.2906 0.9036 -0.0005853 0.0002628 1.05 -0.0004411 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5756 0.57 0.4065 0.2344 0.9785 0.9858 0.5757 0.943 0.9634 0.4135 ] Network output: [ 0.09625 0.7333 0.08439 -0.0004011 0.0001801 0.9881 -0.0003023 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0636 Epoch 1794 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03824 0.9742 0.9918 0.0001923 -8.632e-05 -0.04165 0.0001449 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02409 -0.004236 0.01591 0.03427 0.929 0.9399 0.04894 0.8502 0.8776 0.123 ] Network output: [ 0.9485 0.1061 -0.03672 -0.000574 0.0002577 0.03117 -0.0004326 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6146 0.02756 0.0005189 0.3506 0.9639 0.9825 0.7061 0.8602 0.951 0.6254 ] Network output: [ 0.007934 0.9371 1.029 0.0002025 -9.089e-05 0.01857 0.0001526 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04109 0.0257 0.04047 0.04347 0.9794 0.9852 0.04213 0.947 0.9672 0.05843 ] Network output: [ 0.1133 -0.3238 1.101 0.0005174 -0.0002323 0.9983 0.0003899 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.699 0.4968 0.4101 0.5308 0.9681 0.9851 0.7026 0.872 0.9577 0.62 ] Network output: [ -0.08142 0.2808 0.9291 0.0006433 -0.0002888 0.9556 0.0004848 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.549 0.52 0.3495 0.283 0.9818 0.9879 0.5496 0.9541 0.9699 0.3768 ] Network output: [ -0.1229 0.2906 0.9036 -0.0005812 0.0002609 1.049 -0.000438 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5757 0.5701 0.4068 0.2348 0.9785 0.9859 0.5758 0.9431 0.9635 0.4138 ] Network output: [ 0.09576 0.7342 0.084 -0.0004088 0.0001835 0.9886 -0.0003081 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06343 Epoch 1795 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03832 0.9741 0.9917 0.0001927 -8.65e-05 -0.04166 0.0001452 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0241 -0.004237 0.01595 0.03432 0.9291 0.9399 0.04896 0.8504 0.8777 0.1231 ] Network output: [ 0.9487 0.1058 -0.03649 -0.0005729 0.0002572 0.03099 -0.0004317 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6149 0.02802 0.001173 0.3506 0.964 0.9825 0.7064 0.8604 0.9511 0.6252 ] Network output: [ 0.007912 0.9369 1.029 0.0002021 -9.071e-05 0.0187 0.0001523 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04114 0.02576 0.0406 0.04358 0.9794 0.9852 0.04218 0.9471 0.9672 0.05857 ] Network output: [ 0.1133 -0.3238 1.101 0.0005187 -0.0002329 0.9986 0.0003909 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6994 0.4977 0.4109 0.5308 0.9681 0.9851 0.7029 0.8722 0.9577 0.6199 ] Network output: [ -0.08138 0.2804 0.9296 0.0006443 -0.0002893 0.9554 0.0004856 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5492 0.5203 0.3501 0.2835 0.9818 0.9879 0.5497 0.9542 0.9699 0.3773 ] Network output: [ -0.1225 0.2905 0.9035 -0.000577 0.000259 1.049 -0.0004348 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5758 0.5703 0.4072 0.2352 0.9786 0.9859 0.5759 0.9433 0.9636 0.4141 ] Network output: [ 0.09528 0.7351 0.08362 -0.0004165 0.000187 0.989 -0.0003139 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06325 Epoch 1796 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03839 0.974 0.9917 0.0001931 -8.668e-05 -0.04167 0.0001455 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0241 -0.004239 0.01599 0.03437 0.9291 0.94 0.04899 0.8505 0.8778 0.1233 ] Network output: [ 0.9488 0.1056 -0.03626 -0.0005718 0.0002567 0.03081 -0.0004309 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6151 0.02848 0.001824 0.3506 0.964 0.9825 0.7067 0.8605 0.9511 0.6251 ] Network output: [ 0.007889 0.9368 1.029 0.0002016 -9.053e-05 0.01883 0.000152 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04118 0.02582 0.04074 0.04369 0.9795 0.9852 0.04222 0.9472 0.9673 0.0587 ] Network output: [ 0.1132 -0.3237 1.1 0.00052 -0.0002335 0.999 0.0003919 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6997 0.4986 0.4116 0.5307 0.9682 0.9851 0.7032 0.8724 0.9578 0.6198 ] Network output: [ -0.08134 0.28 0.9301 0.0006453 -0.0002897 0.9552 0.0004863 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5494 0.5206 0.3507 0.2839 0.9818 0.9879 0.5499 0.9543 0.97 0.3778 ] Network output: [ -0.1222 0.2905 0.9035 -0.0005728 0.0002572 1.048 -0.0004317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5759 0.5704 0.4076 0.2357 0.9786 0.9859 0.576 0.9434 0.9637 0.4144 ] Network output: [ 0.09479 0.736 0.08324 -0.000424 0.0001904 0.9894 -0.0003196 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06308 Epoch 1797 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03847 0.9739 0.9916 0.0001935 -8.685e-05 -0.04168 0.0001458 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02411 -0.004241 0.01603 0.03442 0.9291 0.94 0.04902 0.8506 0.8779 0.1234 ] Network output: [ 0.9489 0.1053 -0.03603 -0.0005707 0.0002562 0.03063 -0.0004301 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6153 0.02894 0.002474 0.3506 0.964 0.9825 0.707 0.8607 0.9512 0.625 ] Network output: [ 0.007866 0.9366 1.03 0.0002012 -9.034e-05 0.01896 0.0001517 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04123 0.02588 0.04087 0.04379 0.9795 0.9853 0.04227 0.9473 0.9673 0.05883 ] Network output: [ 0.1132 -0.3237 1.1 0.0005213 -0.000234 0.9993 0.0003929 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7 0.4995 0.4123 0.5307 0.9682 0.9851 0.7036 0.8726 0.9579 0.6197 ] Network output: [ -0.0813 0.2796 0.9306 0.0006463 -0.0002901 0.955 0.000487 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5496 0.5209 0.3513 0.2844 0.9818 0.9879 0.5501 0.9544 0.97 0.3783 ] Network output: [ -0.1219 0.2904 0.9035 -0.0005687 0.0002553 1.048 -0.0004286 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.576 0.5705 0.4079 0.2361 0.9786 0.9859 0.5761 0.9435 0.9637 0.4147 ] Network output: [ 0.09431 0.7369 0.08286 -0.0004315 0.0001937 0.9898 -0.0003252 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0629 Epoch 1798 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03854 0.9739 0.9915 0.0001938 -8.702e-05 -0.04168 0.0001461 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02411 -0.004243 0.01607 0.03448 0.9291 0.94 0.04905 0.8508 0.878 0.1235 ] Network output: [ 0.949 0.105 -0.03581 -0.0005696 0.0002557 0.03045 -0.0004293 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6156 0.0294 0.003122 0.3506 0.964 0.9826 0.7074 0.8609 0.9513 0.6249 ] Network output: [ 0.007841 0.9365 1.03 0.0002008 -9.015e-05 0.01909 0.0001513 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04127 0.02594 0.041 0.0439 0.9795 0.9853 0.04232 0.9475 0.9674 0.05897 ] Network output: [ 0.1132 -0.3237 1.1 0.0005225 -0.0002346 0.9996 0.0003938 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7004 0.5004 0.4131 0.5306 0.9682 0.9851 0.7039 0.8727 0.9579 0.6195 ] Network output: [ -0.08125 0.2792 0.9311 0.0006472 -0.0002906 0.9548 0.0004878 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5497 0.5212 0.3519 0.2849 0.9819 0.9879 0.5503 0.9545 0.9701 0.3788 ] Network output: [ -0.1216 0.2904 0.9034 -0.0005646 0.0002535 1.047 -0.0004255 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5761 0.5706 0.4083 0.2365 0.9786 0.9859 0.5762 0.9436 0.9638 0.4151 ] Network output: [ 0.09383 0.7378 0.08248 -0.0004389 0.000197 0.9902 -0.0003308 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06273 Epoch 1799 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03862 0.9738 0.9915 0.0001942 -8.718e-05 -0.04169 0.0001463 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02412 -0.004245 0.01611 0.03453 0.9292 0.94 0.04907 0.8509 0.8781 0.1237 ] Network output: [ 0.9491 0.1047 -0.03558 -0.0005685 0.0002552 0.03027 -0.0004285 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6158 0.02987 0.003767 0.3506 0.9641 0.9826 0.7077 0.8611 0.9513 0.6247 ] Network output: [ 0.007816 0.9363 1.03 0.0002004 -8.996e-05 0.01922 0.000151 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04132 0.026 0.04114 0.04401 0.9795 0.9853 0.04236 0.9476 0.9674 0.0591 ] Network output: [ 0.1132 -0.3236 1.099 0.0005237 -0.0002351 0.9999 0.0003947 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7007 0.5013 0.4138 0.5306 0.9682 0.9851 0.7042 0.8729 0.958 0.6194 ] Network output: [ -0.08121 0.2788 0.9316 0.0006482 -0.000291 0.9546 0.0004885 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5499 0.5215 0.3525 0.2853 0.9819 0.988 0.5504 0.9546 0.9701 0.3793 ] Network output: [ -0.1213 0.2903 0.9034 -0.0005605 0.0002516 1.047 -0.0004224 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5762 0.5708 0.4086 0.237 0.9787 0.9859 0.5763 0.9437 0.9639 0.4154 ] Network output: [ 0.09335 0.7387 0.0821 -0.0004462 0.0002003 0.9906 -0.0003363 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06256 Epoch 1800 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03869 0.9737 0.9914 0.0001945 -8.734e-05 -0.0417 0.0001466 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02412 -0.004246 0.01615 0.03458 0.9292 0.94 0.0491 0.8511 0.8782 0.1238 ] Network output: [ 0.9493 0.1044 -0.03536 -0.0005675 0.0002548 0.0301 -0.0004277 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.616 0.03033 0.004411 0.3507 0.9641 0.9826 0.708 0.8612 0.9514 0.6246 ] Network output: [ 0.00779 0.9362 1.03 0.0001999 -8.976e-05 0.01935 0.0001507 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04137 0.02606 0.04127 0.04411 0.9795 0.9853 0.04241 0.9477 0.9675 0.05923 ] Network output: [ 0.1131 -0.3236 1.099 0.0005249 -0.0002357 1 0.0003956 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.701 0.5022 0.4145 0.5305 0.9682 0.9851 0.7046 0.8731 0.958 0.6193 ] Network output: [ -0.08116 0.2784 0.9321 0.0006491 -0.0002914 0.9544 0.0004892 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5501 0.5217 0.3531 0.2858 0.9819 0.988 0.5506 0.9546 0.9702 0.3798 ] Network output: [ -0.121 0.2902 0.9034 -0.0005564 0.0002498 1.046 -0.0004193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5763 0.5709 0.409 0.2374 0.9787 0.986 0.5764 0.9438 0.9639 0.4157 ] Network output: [ 0.09287 0.7396 0.08173 -0.0004534 0.0002035 0.9911 -0.0003417 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06239 Epoch 1801 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03876 0.9737 0.9913 0.0001949 -8.749e-05 -0.0417 0.0001469 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02413 -0.004248 0.01619 0.03463 0.9292 0.9401 0.04913 0.8512 0.8783 0.1239 ] Network output: [ 0.9494 0.1041 -0.03514 -0.0005665 0.0002543 0.02992 -0.0004269 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6163 0.03079 0.005052 0.3507 0.9641 0.9826 0.7083 0.8614 0.9515 0.6245 ] Network output: [ 0.007764 0.936 1.03 0.0001995 -8.956e-05 0.01949 0.0001504 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04141 0.02612 0.0414 0.04422 0.9795 0.9853 0.04246 0.9478 0.9676 0.05937 ] Network output: [ 0.1131 -0.3236 1.099 0.0005261 -0.0002362 1.001 0.0003965 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7013 0.5031 0.4152 0.5305 0.9683 0.9852 0.7049 0.8733 0.9581 0.6192 ] Network output: [ -0.08111 0.278 0.9326 0.0006501 -0.0002918 0.9542 0.0004899 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5503 0.522 0.3537 0.2862 0.9819 0.988 0.5508 0.9547 0.9703 0.3803 ] Network output: [ -0.1207 0.2902 0.9033 -0.0005524 0.000248 1.046 -0.0004163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5764 0.571 0.4094 0.2378 0.9787 0.986 0.5765 0.9439 0.964 0.416 ] Network output: [ 0.0924 0.7405 0.08136 -0.0004605 0.0002067 0.9915 -0.000347 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06222 Epoch 1802 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03883 0.9736 0.9913 0.0001952 -8.764e-05 -0.04171 0.0001471 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02414 -0.00425 0.01622 0.03469 0.9292 0.9401 0.04916 0.8513 0.8784 0.124 ] Network output: [ 0.9495 0.1039 -0.03492 -0.0005655 0.0002539 0.02975 -0.0004262 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6165 0.03125 0.005691 0.3507 0.9641 0.9826 0.7086 0.8616 0.9515 0.6244 ] Network output: [ 0.007736 0.9359 1.03 0.0001991 -8.936e-05 0.01962 0.00015 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04146 0.02618 0.04154 0.04432 0.9796 0.9853 0.04251 0.9479 0.9676 0.0595 ] Network output: [ 0.1131 -0.3235 1.099 0.0005272 -0.0002367 1.001 0.0003973 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7017 0.504 0.4159 0.5304 0.9683 0.9852 0.7052 0.8734 0.9581 0.6191 ] Network output: [ -0.08106 0.2776 0.9331 0.000651 -0.0002923 0.9541 0.0004906 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5505 0.5223 0.3543 0.2867 0.9819 0.988 0.551 0.9548 0.9703 0.3808 ] Network output: [ -0.1204 0.2901 0.9033 -0.0005483 0.0002462 1.045 -0.0004132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5765 0.5712 0.4097 0.2382 0.9787 0.986 0.5766 0.9441 0.9641 0.4164 ] Network output: [ 0.09192 0.7414 0.08099 -0.0004675 0.0002099 0.9919 -0.0003523 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06204 Epoch 1803 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0389 0.9735 0.9912 0.0001955 -8.778e-05 -0.04171 0.0001474 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02414 -0.004252 0.01626 0.03474 0.9293 0.9401 0.04918 0.8515 0.8785 0.1242 ] Network output: [ 0.9496 0.1036 -0.0347 -0.0005645 0.0002534 0.02958 -0.0004254 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6168 0.03171 0.006329 0.3507 0.9641 0.9826 0.709 0.8618 0.9516 0.6243 ] Network output: [ 0.007708 0.9357 1.03 0.0001986 -8.916e-05 0.01975 0.0001497 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0415 0.02624 0.04167 0.04443 0.9796 0.9853 0.04255 0.948 0.9677 0.05963 ] Network output: [ 0.1131 -0.3235 1.098 0.0005284 -0.0002372 1.001 0.0003982 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.702 0.5049 0.4167 0.5304 0.9683 0.9852 0.7055 0.8736 0.9582 0.619 ] Network output: [ -0.08101 0.2772 0.9336 0.0006519 -0.0002927 0.9539 0.0004913 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5507 0.5226 0.3549 0.2872 0.9819 0.988 0.5512 0.9549 0.9704 0.3813 ] Network output: [ -0.1201 0.2901 0.9033 -0.0005443 0.0002444 1.045 -0.0004102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5766 0.5713 0.4101 0.2387 0.9787 0.986 0.5767 0.9442 0.9641 0.4167 ] Network output: [ 0.09145 0.7423 0.08062 -0.0004744 0.000213 0.9922 -0.0003575 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06188 Epoch 1804 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03896 0.9735 0.9911 0.0001959 -8.792e-05 -0.04172 0.0001476 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02415 -0.004254 0.0163 0.03479 0.9293 0.9401 0.04921 0.8516 0.8786 0.1243 ] Network output: [ 0.9497 0.1033 -0.03448 -0.0005635 0.000253 0.02942 -0.0004247 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.617 0.03217 0.006964 0.3507 0.9642 0.9826 0.7093 0.8619 0.9516 0.6242 ] Network output: [ 0.00768 0.9356 1.03 0.0001981 -8.895e-05 0.01988 0.0001493 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04155 0.0263 0.0418 0.04453 0.9796 0.9854 0.0426 0.9481 0.9677 0.05977 ] Network output: [ 0.1131 -0.3235 1.098 0.0005294 -0.0002377 1.002 0.000399 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7023 0.5058 0.4174 0.5303 0.9683 0.9852 0.7059 0.8738 0.9583 0.6189 ] Network output: [ -0.08096 0.2768 0.9341 0.0006528 -0.0002931 0.9537 0.000492 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5509 0.5229 0.3555 0.2876 0.9819 0.988 0.5514 0.955 0.9704 0.3818 ] Network output: [ -0.1198 0.29 0.9032 -0.0005403 0.0002426 1.044 -0.0004072 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5767 0.5714 0.4104 0.2391 0.9788 0.986 0.5768 0.9443 0.9642 0.417 ] Network output: [ 0.09098 0.7432 0.08025 -0.0004812 0.000216 0.9926 -0.0003627 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06171 Epoch 1805 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03903 0.9734 0.991 0.0001962 -8.806e-05 -0.04172 0.0001478 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02415 -0.004255 0.01634 0.03484 0.9293 0.9402 0.04924 0.8518 0.8786 0.1244 ] Network output: [ 0.9498 0.1031 -0.03427 -0.0005626 0.0002526 0.02925 -0.000424 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6172 0.03263 0.007597 0.3507 0.9642 0.9827 0.7096 0.8621 0.9517 0.6241 ] Network output: [ 0.00765 0.9355 1.03 0.0001977 -8.874e-05 0.02001 0.000149 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0416 0.02636 0.04193 0.04464 0.9796 0.9854 0.04265 0.9482 0.9678 0.0599 ] Network output: [ 0.113 -0.3234 1.098 0.0005305 -0.0002382 1.002 0.0003998 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7026 0.5067 0.4181 0.5303 0.9683 0.9852 0.7062 0.8739 0.9583 0.6188 ] Network output: [ -0.0809 0.2764 0.9346 0.0006537 -0.0002935 0.9535 0.0004926 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.551 0.5232 0.3561 0.2881 0.982 0.988 0.5516 0.9551 0.9705 0.3822 ] Network output: [ -0.1195 0.2899 0.9032 -0.0005363 0.0002408 1.044 -0.0004042 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5768 0.5716 0.4108 0.2395 0.9788 0.986 0.5769 0.9444 0.9643 0.4173 ] Network output: [ 0.09051 0.7441 0.07989 -0.0004879 0.000219 0.993 -0.0003677 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06154 Epoch 1806 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03909 0.9734 0.991 0.0001964 -8.819e-05 -0.04173 0.000148 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02416 -0.004257 0.01638 0.03489 0.9294 0.9402 0.04927 0.8519 0.8787 0.1246 ] Network output: [ 0.95 0.1028 -0.03406 -0.0005617 0.0002522 0.02909 -0.0004233 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6175 0.03309 0.008227 0.3507 0.9642 0.9827 0.7099 0.8623 0.9518 0.624 ] Network output: [ 0.00762 0.9353 1.03 0.0001972 -8.852e-05 0.02014 0.0001486 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04164 0.02642 0.04207 0.04474 0.9796 0.9854 0.0427 0.9483 0.9678 0.06003 ] Network output: [ 0.113 -0.3234 1.097 0.0005315 -0.0002386 1.002 0.0004006 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.703 0.5076 0.4188 0.5302 0.9684 0.9852 0.7065 0.8741 0.9584 0.6187 ] Network output: [ -0.08084 0.276 0.935 0.0006546 -0.0002939 0.9534 0.0004933 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5512 0.5235 0.3567 0.2885 0.982 0.988 0.5518 0.9552 0.9705 0.3827 ] Network output: [ -0.1192 0.2899 0.9032 -0.0005324 0.000239 1.043 -0.0004012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5769 0.5717 0.4112 0.2399 0.9788 0.986 0.5771 0.9445 0.9643 0.4177 ] Network output: [ 0.09004 0.745 0.07952 -0.0004946 0.000222 0.9934 -0.0003727 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06137 Epoch 1807 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03916 0.9733 0.9909 0.0001967 -8.832e-05 -0.04173 0.0001483 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02416 -0.004259 0.01641 0.03494 0.9294 0.9402 0.04929 0.852 0.8788 0.1247 ] Network output: [ 0.9501 0.1025 -0.03385 -0.0005608 0.0002517 0.02892 -0.0004226 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6177 0.03355 0.008856 0.3507 0.9642 0.9827 0.7102 0.8625 0.9518 0.6239 ] Network output: [ 0.007589 0.9352 1.03 0.0001967 -8.83e-05 0.02027 0.0001482 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04169 0.02648 0.0422 0.04485 0.9797 0.9854 0.04275 0.9484 0.9679 0.06016 ] Network output: [ 0.113 -0.3233 1.097 0.0005325 -0.0002391 1.002 0.0004013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7033 0.5084 0.4195 0.5302 0.9684 0.9852 0.7068 0.8743 0.9584 0.6186 ] Network output: [ -0.08079 0.2756 0.9355 0.0006554 -0.0002942 0.9532 0.000494 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5514 0.5238 0.3572 0.289 0.982 0.988 0.552 0.9553 0.9706 0.3832 ] Network output: [ -0.1189 0.2898 0.9032 -0.0005284 0.0002372 1.043 -0.0003982 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5771 0.5718 0.4115 0.2404 0.9788 0.9861 0.5772 0.9446 0.9644 0.418 ] Network output: [ 0.08957 0.7459 0.07916 -0.0005011 0.000225 0.9938 -0.0003776 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0612 Epoch 1808 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03922 0.9733 0.9908 0.000197 -8.844e-05 -0.04173 0.0001485 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02417 -0.004261 0.01645 0.03499 0.9294 0.9402 0.04932 0.8522 0.8789 0.1248 ] Network output: [ 0.9502 0.1022 -0.03364 -0.0005599 0.0002514 0.02876 -0.0004219 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6179 0.03401 0.009482 0.3508 0.9643 0.9827 0.7105 0.8626 0.9519 0.6238 ] Network output: [ 0.007558 0.9351 1.03 0.0001962 -8.808e-05 0.0204 0.0001479 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04174 0.02654 0.04233 0.04495 0.9797 0.9854 0.0428 0.9486 0.9679 0.0603 ] Network output: [ 0.113 -0.3233 1.097 0.0005335 -0.0002395 1.003 0.000402 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7036 0.5093 0.4202 0.5301 0.9684 0.9852 0.7072 0.8745 0.9585 0.6185 ] Network output: [ -0.08073 0.2751 0.936 0.0006563 -0.0002946 0.953 0.0004946 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5516 0.5241 0.3578 0.2894 0.982 0.9881 0.5522 0.9554 0.9706 0.3837 ] Network output: [ -0.1186 0.2897 0.9031 -0.0005245 0.0002355 1.042 -0.0003953 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5772 0.572 0.4119 0.2408 0.9788 0.9861 0.5773 0.9447 0.9645 0.4183 ] Network output: [ 0.0891 0.7467 0.0788 -0.0005075 0.0002278 0.9942 -0.0003825 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06104 Epoch 1809 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03928 0.9732 0.9908 0.0001973 -8.856e-05 -0.04173 0.0001487 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02417 -0.004263 0.01649 0.03504 0.9294 0.9402 0.04935 0.8523 0.879 0.1249 ] Network output: [ 0.9503 0.102 -0.03343 -0.000559 0.000251 0.0286 -0.0004213 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6182 0.03447 0.01011 0.3508 0.9643 0.9827 0.7108 0.8628 0.952 0.6237 ] Network output: [ 0.007526 0.9349 1.03 0.0001957 -8.786e-05 0.02054 0.0001475 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04178 0.0266 0.04246 0.04505 0.9797 0.9854 0.04284 0.9487 0.968 0.06043 ] Network output: [ 0.113 -0.3233 1.097 0.0005344 -0.0002399 1.003 0.0004027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7039 0.5102 0.4209 0.5301 0.9684 0.9852 0.7075 0.8746 0.9585 0.6184 ] Network output: [ -0.08067 0.2747 0.9364 0.0006571 -0.000295 0.9528 0.0004952 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5518 0.5244 0.3584 0.2899 0.982 0.9881 0.5524 0.9555 0.9707 0.3842 ] Network output: [ -0.1183 0.2896 0.9031 -0.0005206 0.0002337 1.042 -0.0003923 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5773 0.5721 0.4123 0.2412 0.9789 0.9861 0.5774 0.9449 0.9645 0.4187 ] Network output: [ 0.08864 0.7476 0.07845 -0.0005138 0.0002307 0.9945 -0.0003872 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06087 Epoch 1810 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03934 0.9732 0.9907 0.0001975 -8.868e-05 -0.04174 0.0001489 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02418 -0.004265 0.01653 0.03509 0.9295 0.9403 0.04938 0.8524 0.8791 0.1251 ] Network output: [ 0.9504 0.1017 -0.03323 -0.0005582 0.0002506 0.02845 -0.0004207 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6184 0.03493 0.01073 0.3508 0.9643 0.9827 0.7111 0.863 0.952 0.6236 ] Network output: [ 0.007493 0.9348 1.03 0.0001952 -8.763e-05 0.02067 0.0001471 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04183 0.02666 0.0426 0.04516 0.9797 0.9854 0.04289 0.9488 0.9681 0.06056 ] Network output: [ 0.1129 -0.3232 1.096 0.0005353 -0.0002403 1.003 0.0004034 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7042 0.5111 0.4216 0.5301 0.9684 0.9853 0.7078 0.8748 0.9586 0.6183 ] Network output: [ -0.0806 0.2743 0.9369 0.000658 -0.0002954 0.9527 0.0004959 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.552 0.5247 0.359 0.2903 0.982 0.9881 0.5526 0.9556 0.9707 0.3847 ] Network output: [ -0.118 0.2896 0.9031 -0.0005167 0.000232 1.041 -0.0003894 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5774 0.5723 0.4126 0.2416 0.9789 0.9861 0.5775 0.945 0.9646 0.419 ] Network output: [ 0.08818 0.7485 0.07809 -0.0005201 0.0002335 0.9949 -0.0003919 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06071 Epoch 1811 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0394 0.9731 0.9906 0.0001978 -8.879e-05 -0.04174 0.000149 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02419 -0.004266 0.01656 0.03514 0.9295 0.9403 0.0494 0.8526 0.8792 0.1252 ] Network output: [ 0.9505 0.1014 -0.03302 -0.0005573 0.0002502 0.02829 -0.00042 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6186 0.03539 0.01135 0.3508 0.9643 0.9827 0.7115 0.8632 0.9521 0.6235 ] Network output: [ 0.00746 0.9347 1.03 0.0001947 -8.74e-05 0.0208 0.0001467 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04188 0.02673 0.04273 0.04526 0.9797 0.9854 0.04294 0.9489 0.9681 0.06069 ] Network output: [ 0.1129 -0.3232 1.096 0.0005362 -0.0002407 1.004 0.0004041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7045 0.5119 0.4223 0.53 0.9685 0.9853 0.7081 0.875 0.9587 0.6182 ] Network output: [ -0.08054 0.2739 0.9374 0.0006588 -0.0002957 0.9525 0.0004965 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5522 0.525 0.3596 0.2908 0.9821 0.9881 0.5528 0.9557 0.9708 0.3852 ] Network output: [ -0.1178 0.2895 0.9031 -0.0005128 0.0002302 1.041 -0.0003865 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5775 0.5724 0.413 0.242 0.9789 0.9861 0.5777 0.9451 0.9647 0.4193 ] Network output: [ 0.08772 0.7494 0.07774 -0.0005262 0.0002362 0.9953 -0.0003966 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06055 Epoch 1812 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03946 0.9731 0.9906 0.000198 -8.889e-05 -0.04174 0.0001492 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02419 -0.004268 0.0166 0.03519 0.9295 0.9403 0.04943 0.8527 0.8793 0.1253 ] Network output: [ 0.9506 0.1012 -0.03282 -0.0005565 0.0002498 0.02813 -0.0004194 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6189 0.03585 0.01197 0.3508 0.9644 0.9827 0.7118 0.8633 0.9521 0.6234 ] Network output: [ 0.007426 0.9345 1.03 0.0001942 -8.717e-05 0.02093 0.0001463 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04193 0.02679 0.04286 0.04536 0.9797 0.9855 0.04299 0.949 0.9682 0.06082 ] Network output: [ 0.1129 -0.3232 1.096 0.000537 -0.0002411 1.004 0.0004047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7049 0.5128 0.423 0.53 0.9685 0.9853 0.7084 0.8751 0.9587 0.6182 ] Network output: [ -0.08048 0.2735 0.9378 0.0006596 -0.0002961 0.9523 0.0004971 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5525 0.5253 0.3602 0.2912 0.9821 0.9881 0.553 0.9558 0.9708 0.3857 ] Network output: [ -0.1175 0.2894 0.903 -0.000509 0.0002285 1.04 -0.0003836 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5777 0.5726 0.4134 0.2424 0.9789 0.9861 0.5778 0.9452 0.9647 0.4197 ] Network output: [ 0.08726 0.7503 0.07739 -0.0005322 0.0002389 0.9956 -0.0004011 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06038 Epoch 1813 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03952 0.973 0.9905 0.0001982 -8.9e-05 -0.04174 0.0001494 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0242 -0.00427 0.01664 0.03524 0.9295 0.9403 0.04946 0.8528 0.8794 0.1254 ] Network output: [ 0.9507 0.1009 -0.03262 -0.0005557 0.0002495 0.02798 -0.0004188 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6191 0.03631 0.01258 0.3508 0.9644 0.9828 0.7121 0.8635 0.9522 0.6234 ] Network output: [ 0.007391 0.9344 1.031 0.0001936 -8.693e-05 0.02106 0.0001459 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04197 0.02685 0.04299 0.04546 0.9798 0.9855 0.04304 0.9491 0.9682 0.06095 ] Network output: [ 0.1129 -0.3231 1.095 0.0005378 -0.0002414 1.004 0.0004053 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7052 0.5137 0.4236 0.53 0.9685 0.9853 0.7088 0.8753 0.9588 0.6181 ] Network output: [ -0.08041 0.2731 0.9383 0.0006604 -0.0002965 0.9522 0.0004977 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5527 0.5257 0.3608 0.2916 0.9821 0.9881 0.5532 0.9559 0.9709 0.3862 ] Network output: [ -0.1172 0.2893 0.903 -0.0005051 0.0002268 1.04 -0.0003807 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5778 0.5727 0.4137 0.2429 0.9789 0.9861 0.5779 0.9453 0.9648 0.42 ] Network output: [ 0.0868 0.7512 0.07704 -0.0005382 0.0002416 0.996 -0.0004056 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06022 Epoch 1814 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03957 0.973 0.9904 0.0001985 -8.91e-05 -0.04174 0.0001496 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0242 -0.004272 0.01668 0.03528 0.9296 0.9403 0.04949 0.853 0.8795 0.1256 ] Network output: [ 0.9508 0.1007 -0.03242 -0.0005549 0.0002491 0.02783 -0.0004182 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6193 0.03677 0.01319 0.3508 0.9644 0.9828 0.7124 0.8637 0.9523 0.6233 ] Network output: [ 0.007356 0.9343 1.031 0.0001931 -8.669e-05 0.02119 0.0001455 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04202 0.02691 0.04312 0.04557 0.9798 0.9855 0.04309 0.9492 0.9683 0.06108 ] Network output: [ 0.1129 -0.3231 1.095 0.0005386 -0.0002418 1.004 0.0004059 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7055 0.5145 0.4243 0.5299 0.9685 0.9853 0.7091 0.8755 0.9588 0.618 ] Network output: [ -0.08034 0.2726 0.9387 0.0006611 -0.0002968 0.952 0.0004983 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5529 0.526 0.3614 0.2921 0.9821 0.9881 0.5534 0.9559 0.9709 0.3867 ] Network output: [ -0.1169 0.2892 0.903 -0.0005013 0.0002251 1.039 -0.0003778 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5779 0.5729 0.4141 0.2433 0.979 0.9862 0.578 0.9454 0.9649 0.4203 ] Network output: [ 0.08635 0.752 0.07669 -0.000544 0.0002442 0.9964 -0.00041 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06006 Epoch 1815 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03963 0.9729 0.9903 0.0001987 -8.919e-05 -0.04174 0.0001497 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02421 -0.004274 0.01671 0.03533 0.9296 0.9404 0.04951 0.8531 0.8795 0.1257 ] Network output: [ 0.9509 0.1004 -0.03222 -0.0005542 0.0002488 0.02768 -0.0004176 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6196 0.03723 0.0138 0.3508 0.9644 0.9828 0.7127 0.8638 0.9523 0.6232 ] Network output: [ 0.00732 0.9342 1.031 0.0001926 -8.645e-05 0.02133 0.0001451 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04207 0.02697 0.04325 0.04567 0.9798 0.9855 0.04314 0.9493 0.9683 0.06121 ] Network output: [ 0.1128 -0.323 1.095 0.0005393 -0.0002421 1.005 0.0004065 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7058 0.5154 0.425 0.5299 0.9685 0.9853 0.7094 0.8756 0.9589 0.6179 ] Network output: [ -0.08027 0.2722 0.9392 0.0006619 -0.0002971 0.9519 0.0004988 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5531 0.5263 0.3619 0.2925 0.9821 0.9881 0.5536 0.956 0.971 0.3872 ] Network output: [ -0.1166 0.2892 0.903 -0.0004975 0.0002234 1.039 -0.000375 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5781 0.573 0.4145 0.2437 0.979 0.9862 0.5782 0.9455 0.9649 0.4207 ] Network output: [ 0.0859 0.7529 0.07634 -0.0005498 0.0002468 0.9967 -0.0004143 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0599 Epoch 1816 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03968 0.9729 0.9903 0.0001989 -8.928e-05 -0.04174 0.0001499 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02422 -0.004276 0.01675 0.03538 0.9296 0.9404 0.04954 0.8532 0.8796 0.1258 ] Network output: [ 0.951 0.1002 -0.03203 -0.0005534 0.0002485 0.02753 -0.0004171 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6198 0.03768 0.01441 0.3508 0.9644 0.9828 0.713 0.864 0.9524 0.6231 ] Network output: [ 0.007284 0.934 1.031 0.000192 -8.62e-05 0.02146 0.0001447 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04212 0.02703 0.04338 0.04577 0.9798 0.9855 0.04319 0.9494 0.9684 0.06134 ] Network output: [ 0.1128 -0.323 1.095 0.00054 -0.0002424 1.005 0.000407 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7061 0.5162 0.4257 0.5299 0.9686 0.9853 0.7097 0.8758 0.9589 0.6179 ] Network output: [ -0.0802 0.2718 0.9396 0.0006626 -0.0002975 0.9517 0.0004994 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5533 0.5266 0.3625 0.293 0.9821 0.9881 0.5538 0.9561 0.971 0.3877 ] Network output: [ -0.1163 0.2891 0.903 -0.0004938 0.0002217 1.039 -0.0003721 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5782 0.5732 0.4148 0.2441 0.979 0.9862 0.5783 0.9456 0.965 0.421 ] Network output: [ 0.08545 0.7538 0.076 -0.0005554 0.0002493 0.9971 -0.0004186 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05974 Epoch 1817 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03973 0.9729 0.9902 0.0001991 -8.937e-05 -0.04174 0.00015 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02422 -0.004278 0.01679 0.03543 0.9296 0.9404 0.04957 0.8534 0.8797 0.1259 ] Network output: [ 0.9512 0.0999 -0.03184 -0.0005527 0.0002481 0.02738 -0.0004165 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.62 0.03814 0.01502 0.3508 0.9645 0.9828 0.7133 0.8642 0.9524 0.623 ] Network output: [ 0.007247 0.9339 1.031 0.0001915 -8.595e-05 0.02159 0.0001443 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04217 0.02709 0.04351 0.04587 0.9798 0.9855 0.04324 0.9495 0.9684 0.06147 ] Network output: [ 0.1128 -0.323 1.094 0.0005407 -0.0002427 1.005 0.0004075 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7064 0.5171 0.4264 0.5298 0.9686 0.9853 0.71 0.876 0.959 0.6178 ] Network output: [ -0.08013 0.2714 0.94 0.0006634 -0.0002978 0.9516 0.0004999 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5535 0.5269 0.3631 0.2934 0.9821 0.9881 0.554 0.9562 0.9711 0.3882 ] Network output: [ -0.116 0.289 0.9029 -0.00049 0.00022 1.038 -0.0003693 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5783 0.5733 0.4152 0.2445 0.979 0.9862 0.5784 0.9457 0.9651 0.4213 ] Network output: [ 0.085 0.7546 0.07566 -0.0005609 0.0002518 0.9974 -0.0004227 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05958 Epoch 1818 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03979 0.9728 0.9901 0.0001992 -8.945e-05 -0.04174 0.0001502 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02423 -0.00428 0.01682 0.03548 0.9297 0.9404 0.0496 0.8535 0.8798 0.1261 ] Network output: [ 0.9513 0.09965 -0.03164 -0.000552 0.0002478 0.02724 -0.000416 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6203 0.0386 0.01562 0.3509 0.9645 0.9828 0.7136 0.8643 0.9525 0.623 ] Network output: [ 0.007209 0.9338 1.031 0.0001909 -8.57e-05 0.02172 0.0001439 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04221 0.02715 0.04364 0.04597 0.9799 0.9855 0.04329 0.9496 0.9685 0.0616 ] Network output: [ 0.1128 -0.3229 1.094 0.0005414 -0.000243 1.006 0.000408 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7067 0.5179 0.427 0.5298 0.9686 0.9853 0.7103 0.8761 0.959 0.6177 ] Network output: [ -0.08006 0.2709 0.9405 0.0006641 -0.0002981 0.9514 0.0005005 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5537 0.5272 0.3637 0.2938 0.9822 0.9882 0.5543 0.9563 0.9711 0.3886 ] Network output: [ -0.1157 0.2889 0.9029 -0.0004863 0.0002183 1.038 -0.0003665 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5784 0.5735 0.4156 0.2449 0.979 0.9862 0.5786 0.9459 0.9651 0.4217 ] Network output: [ 0.08455 0.7555 0.07532 -0.0005664 0.0002543 0.9978 -0.0004268 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05942 Epoch 1819 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03984 0.9728 0.9901 0.0001994 -8.953e-05 -0.04174 0.0001503 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02423 -0.004282 0.01686 0.03552 0.9297 0.9405 0.04962 0.8536 0.8799 0.1262 ] Network output: [ 0.9514 0.09939 -0.03145 -0.0005513 0.0002475 0.02709 -0.0004155 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6205 0.03905 0.01622 0.3509 0.9645 0.9828 0.7139 0.8645 0.9526 0.6229 ] Network output: [ 0.007171 0.9337 1.031 0.0001903 -8.545e-05 0.02185 0.0001434 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04226 0.02721 0.04377 0.04607 0.9799 0.9856 0.04334 0.9497 0.9685 0.06173 ] Network output: [ 0.1128 -0.3229 1.094 0.000542 -0.0002433 1.006 0.0004084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.707 0.5188 0.4277 0.5298 0.9686 0.9854 0.7106 0.8763 0.9591 0.6177 ] Network output: [ -0.07999 0.2705 0.9409 0.0006648 -0.0002985 0.9512 0.000501 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5539 0.5275 0.3643 0.2943 0.9822 0.9882 0.5545 0.9564 0.9712 0.3891 ] Network output: [ -0.1155 0.2888 0.9029 -0.0004826 0.0002166 1.037 -0.0003637 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5786 0.5736 0.4159 0.2453 0.9791 0.9862 0.5787 0.946 0.9652 0.422 ] Network output: [ 0.0841 0.7564 0.07498 -0.0005717 0.0002567 0.9981 -0.0004309 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05926 Epoch 1820 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03989 0.9728 0.99 0.0001996 -8.96e-05 -0.04174 0.0001504 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02424 -0.004284 0.0169 0.03557 0.9297 0.9405 0.04965 0.8538 0.88 0.1263 ] Network output: [ 0.9515 0.09914 -0.03126 -0.0005506 0.0002472 0.02695 -0.0004149 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6207 0.03951 0.01682 0.3509 0.9645 0.9829 0.7142 0.8647 0.9526 0.6228 ] Network output: [ 0.007133 0.9336 1.031 0.0001898 -8.519e-05 0.02198 0.000143 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04231 0.02727 0.0439 0.04617 0.9799 0.9856 0.04339 0.9498 0.9686 0.06186 ] Network output: [ 0.1127 -0.3228 1.093 0.0005425 -0.0002436 1.006 0.0004089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7073 0.5196 0.4283 0.5297 0.9686 0.9854 0.7109 0.8765 0.9592 0.6176 ] Network output: [ -0.07991 0.2701 0.9413 0.0006655 -0.0002988 0.9511 0.0005015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5542 0.5278 0.3648 0.2947 0.9822 0.9882 0.5547 0.9565 0.9712 0.3896 ] Network output: [ -0.1152 0.2887 0.9029 -0.0004789 0.000215 1.037 -0.0003609 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5787 0.5738 0.4163 0.2457 0.9791 0.9863 0.5788 0.9461 0.9652 0.4224 ] Network output: [ 0.08366 0.7572 0.07464 -0.000577 0.000259 0.9984 -0.0004348 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05911 Epoch 1821 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03994 0.9727 0.9899 0.0001997 -8.967e-05 -0.04174 0.0001505 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02425 -0.004286 0.01693 0.03562 0.9297 0.9405 0.04968 0.8539 0.8801 0.1264 ] Network output: [ 0.9516 0.0989 -0.03108 -0.0005499 0.0002469 0.02681 -0.0004144 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6209 0.03996 0.01742 0.3509 0.9646 0.9829 0.7145 0.8648 0.9527 0.6228 ] Network output: [ 0.007094 0.9335 1.031 0.0001892 -8.493e-05 0.02212 0.0001426 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04236 0.02733 0.04403 0.04627 0.9799 0.9856 0.04344 0.9499 0.9686 0.06199 ] Network output: [ 0.1127 -0.3228 1.093 0.0005431 -0.0002438 1.006 0.0004093 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7076 0.5204 0.429 0.5297 0.9687 0.9854 0.7112 0.8766 0.9592 0.6175 ] Network output: [ -0.07984 0.2697 0.9418 0.0006662 -0.0002991 0.951 0.000502 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5544 0.5281 0.3654 0.2951 0.9822 0.9882 0.5549 0.9566 0.9713 0.3901 ] Network output: [ -0.1149 0.2886 0.9029 -0.0004752 0.0002133 1.036 -0.0003581 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5789 0.5739 0.4167 0.2461 0.9791 0.9863 0.579 0.9462 0.9653 0.4227 ] Network output: [ 0.08322 0.7581 0.0743 -0.0005821 0.0002613 0.9988 -0.0004387 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05895 Epoch 1822 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03998 0.9727 0.9899 0.0001999 -8.974e-05 -0.04174 0.0001506 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02425 -0.004288 0.01697 0.03566 0.9298 0.9405 0.0497 0.854 0.8802 0.1265 ] Network output: [ 0.9517 0.09865 -0.03089 -0.0005493 0.0002466 0.02667 -0.0004139 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6212 0.04041 0.01801 0.3509 0.9646 0.9829 0.7148 0.865 0.9527 0.6227 ] Network output: [ 0.007054 0.9333 1.031 0.0001886 -8.467e-05 0.02225 0.0001421 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04241 0.02739 0.04416 0.04637 0.9799 0.9856 0.04349 0.95 0.9687 0.06212 ] Network output: [ 0.1127 -0.3228 1.093 0.0005436 -0.000244 1.007 0.0004097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7079 0.5213 0.4297 0.5297 0.9687 0.9854 0.7116 0.8768 0.9593 0.6175 ] Network output: [ -0.07976 0.2692 0.9422 0.0006668 -0.0002994 0.9508 0.0005025 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5546 0.5285 0.366 0.2956 0.9822 0.9882 0.5551 0.9567 0.9713 0.3906 ] Network output: [ -0.1146 0.2885 0.9029 -0.0004715 0.0002117 1.036 -0.0003554 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.579 0.5741 0.417 0.2465 0.9791 0.9863 0.5791 0.9463 0.9654 0.423 ] Network output: [ 0.08278 0.759 0.07397 -0.0005871 0.0002636 0.9991 -0.0004425 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0588 Epoch 1823 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04003 0.9727 0.9898 0.0002 -8.98e-05 -0.04173 0.0001507 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02426 -0.00429 0.017 0.03571 0.9298 0.9405 0.04973 0.8542 0.8802 0.1267 ] Network output: [ 0.9518 0.0984 -0.03071 -0.0005486 0.0002463 0.02653 -0.0004135 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6214 0.04087 0.01861 0.3509 0.9646 0.9829 0.7151 0.8652 0.9528 0.6226 ] Network output: [ 0.007014 0.9332 1.031 0.000188 -8.44e-05 0.02238 0.0001417 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04246 0.02745 0.04428 0.04647 0.9799 0.9856 0.04354 0.9501 0.9688 0.06224 ] Network output: [ 0.1127 -0.3227 1.093 0.000544 -0.0002442 1.007 0.00041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7082 0.5221 0.4303 0.5296 0.9687 0.9854 0.7119 0.8769 0.9593 0.6174 ] Network output: [ -0.07968 0.2688 0.9426 0.0006675 -0.0002997 0.9507 0.000503 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5548 0.5288 0.3665 0.296 0.9822 0.9882 0.5554 0.9567 0.9714 0.3911 ] Network output: [ -0.1143 0.2884 0.9029 -0.0004679 0.0002101 1.035 -0.0003526 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5791 0.5743 0.4174 0.2469 0.9791 0.9863 0.5792 0.9464 0.9654 0.4234 ] Network output: [ 0.08234 0.7598 0.07364 -0.0005921 0.0002658 0.9994 -0.0004462 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05864 Epoch 1824 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04008 0.9727 0.9897 0.0002002 -8.986e-05 -0.04173 0.0001508 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02427 -0.004292 0.01704 0.03575 0.9298 0.9406 0.04976 0.8543 0.8803 0.1268 ] Network output: [ 0.9519 0.09816 -0.03053 -0.000548 0.000246 0.02639 -0.000413 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6216 0.04132 0.0192 0.3509 0.9646 0.9829 0.7154 0.8653 0.9529 0.6226 ] Network output: [ 0.006973 0.9331 1.031 0.0001874 -8.413e-05 0.02251 0.0001412 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04251 0.02752 0.04441 0.04656 0.98 0.9856 0.04359 0.9502 0.9688 0.06237 ] Network output: [ 0.1127 -0.3227 1.092 0.0005445 -0.0002444 1.007 0.0004103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7085 0.5229 0.431 0.5296 0.9687 0.9854 0.7122 0.8771 0.9594 0.6174 ] Network output: [ -0.0796 0.2684 0.943 0.0006681 -0.0002999 0.9505 0.0005035 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5551 0.5291 0.3671 0.2964 0.9822 0.9882 0.5556 0.9568 0.9714 0.3916 ] Network output: [ -0.1141 0.2883 0.9029 -0.0004643 0.0002084 1.035 -0.0003499 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5793 0.5744 0.4178 0.2473 0.9792 0.9863 0.5794 0.9465 0.9655 0.4237 ] Network output: [ 0.08191 0.7607 0.07331 -0.0005969 0.000268 0.9998 -0.0004499 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05849 Epoch 1825 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04012 0.9726 0.9897 0.0002003 -8.991e-05 -0.04173 0.0001509 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02427 -0.004294 0.01707 0.0358 0.9298 0.9406 0.04979 0.8544 0.8804 0.1269 ] Network output: [ 0.952 0.09791 -0.03035 -0.0005474 0.0002457 0.02626 -0.0004125 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6219 0.04177 0.01978 0.3509 0.9647 0.9829 0.7157 0.8655 0.9529 0.6225 ] Network output: [ 0.006932 0.933 1.031 0.0001868 -8.386e-05 0.02264 0.0001408 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04255 0.02758 0.04454 0.04666 0.98 0.9856 0.04364 0.9503 0.9689 0.0625 ] Network output: [ 0.1127 -0.3226 1.092 0.0005449 -0.0002446 1.007 0.0004106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7089 0.5237 0.4316 0.5296 0.9687 0.9854 0.7125 0.8773 0.9594 0.6173 ] Network output: [ -0.07952 0.268 0.9434 0.0006687 -0.0003002 0.9504 0.000504 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5553 0.5294 0.3677 0.2969 0.9823 0.9882 0.5558 0.9569 0.9715 0.392 ] Network output: [ -0.1138 0.2883 0.9029 -0.0004607 0.0002068 1.035 -0.0003472 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5794 0.5746 0.4181 0.2477 0.9792 0.9863 0.5795 0.9466 0.9656 0.424 ] Network output: [ 0.08147 0.7615 0.07298 -0.0006017 0.0002701 1 -0.0004534 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05834 Epoch 1826 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04016 0.9726 0.9896 0.0002004 -8.997e-05 -0.04172 0.000151 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02428 -0.004296 0.01711 0.03585 0.9299 0.9406 0.04981 0.8546 0.8805 0.127 ] Network output: [ 0.9521 0.09767 -0.03017 -0.0005468 0.0002455 0.02612 -0.0004121 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6221 0.04222 0.02037 0.3509 0.9647 0.9829 0.716 0.8656 0.953 0.6225 ] Network output: [ 0.006891 0.9329 1.031 0.0001862 -8.359e-05 0.02277 0.0001403 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0426 0.02764 0.04467 0.04676 0.98 0.9857 0.04369 0.9504 0.9689 0.06262 ] Network output: [ 0.1126 -0.3226 1.092 0.0005452 -0.0002448 1.008 0.0004109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7091 0.5246 0.4323 0.5296 0.9688 0.9854 0.7128 0.8774 0.9595 0.6173 ] Network output: [ -0.07944 0.2675 0.9438 0.0006693 -0.0003005 0.9502 0.0005044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5555 0.5297 0.3682 0.2973 0.9823 0.9882 0.556 0.957 0.9715 0.3925 ] Network output: [ -0.1135 0.2882 0.9029 -0.0004571 0.0002052 1.034 -0.0003445 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5796 0.5747 0.4185 0.2481 0.9792 0.9863 0.5797 0.9467 0.9656 0.4244 ] Network output: [ 0.08104 0.7624 0.07266 -0.0006063 0.0002722 1 -0.0004569 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05818 Epoch 1827 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04021 0.9726 0.9895 0.0002005 -9.001e-05 -0.04172 0.0001511 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02428 -0.004298 0.01714 0.03589 0.9299 0.9406 0.04984 0.8547 0.8806 0.1271 ] Network output: [ 0.9522 0.09743 -0.02999 -0.0005462 0.0002452 0.02599 -0.0004116 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6223 0.04267 0.02095 0.3509 0.9647 0.9829 0.7163 0.8658 0.953 0.6224 ] Network output: [ 0.006849 0.9328 1.031 0.0001856 -8.331e-05 0.0229 0.0001399 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04265 0.0277 0.04479 0.04686 0.98 0.9857 0.04374 0.9505 0.969 0.06275 ] Network output: [ 0.1126 -0.3225 1.092 0.0005456 -0.0002449 1.008 0.0004112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7094 0.5254 0.4329 0.5295 0.9688 0.9854 0.7131 0.8776 0.9595 0.6172 ] Network output: [ -0.07936 0.2671 0.9442 0.0006699 -0.0003007 0.9501 0.0005049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5557 0.53 0.3688 0.2977 0.9823 0.9882 0.5563 0.9571 0.9716 0.393 ] Network output: [ -0.1132 0.2881 0.9029 -0.0004536 0.0002036 1.034 -0.0003418 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5797 0.5749 0.4189 0.2485 0.9792 0.9864 0.5798 0.9468 0.9657 0.4247 ] Network output: [ 0.08061 0.7632 0.07233 -0.0006109 0.0002742 1.001 -0.0004604 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05803 Epoch 1828 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04025 0.9726 0.9895 0.0002006 -9.006e-05 -0.04172 0.0001512 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02429 -0.0043 0.01718 0.03593 0.9299 0.9407 0.04987 0.8548 0.8807 0.1272 ] Network output: [ 0.9523 0.09719 -0.02981 -0.0005456 0.0002449 0.02585 -0.0004112 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6225 0.04312 0.02153 0.3509 0.9647 0.983 0.7165 0.866 0.9531 0.6224 ] Network output: [ 0.006806 0.9327 1.031 0.000185 -8.303e-05 0.02303 0.0001394 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0427 0.02776 0.04492 0.04695 0.98 0.9857 0.04379 0.9506 0.969 0.06287 ] Network output: [ 0.1126 -0.3225 1.091 0.0005459 -0.0002451 1.008 0.0004114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7097 0.5262 0.4335 0.5295 0.9688 0.9855 0.7134 0.8777 0.9596 0.6172 ] Network output: [ -0.07927 0.2667 0.9446 0.0006705 -0.000301 0.95 0.0005053 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.556 0.5304 0.3694 0.2981 0.9823 0.9883 0.5565 0.9572 0.9716 0.3935 ] Network output: [ -0.113 0.2879 0.9029 -0.0004501 0.000202 1.033 -0.0003392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5798 0.5751 0.4192 0.2489 0.9793 0.9864 0.58 0.9469 0.9658 0.425 ] Network output: [ 0.08019 0.7641 0.07201 -0.0006153 0.0002762 1.001 -0.0004637 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05788 Epoch 1829 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04029 0.9726 0.9894 0.0002007 -9.01e-05 -0.04171 0.0001512 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0243 -0.004302 0.01721 0.03598 0.9299 0.9407 0.04989 0.8549 0.8808 0.1274 ] Network output: [ 0.9524 0.09696 -0.02964 -0.000545 0.0002447 0.02572 -0.0004108 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6228 0.04357 0.02211 0.3509 0.9647 0.983 0.7168 0.8661 0.9532 0.6223 ] Network output: [ 0.006763 0.9326 1.031 0.0001843 -8.275e-05 0.02317 0.0001389 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04275 0.02782 0.04505 0.04705 0.9801 0.9857 0.04384 0.9507 0.9691 0.063 ] Network output: [ 0.1126 -0.3225 1.091 0.0005461 -0.0002452 1.008 0.0004116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.71 0.527 0.4342 0.5295 0.9688 0.9855 0.7137 0.8779 0.9596 0.6171 ] Network output: [ -0.07919 0.2663 0.945 0.000671 -0.0003012 0.9498 0.0005057 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5562 0.5307 0.3699 0.2985 0.9823 0.9883 0.5567 0.9573 0.9717 0.394 ] Network output: [ -0.1127 0.2878 0.9029 -0.0004466 0.0002005 1.033 -0.0003365 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.58 0.5752 0.4196 0.2493 0.9793 0.9864 0.5801 0.947 0.9658 0.4254 ] Network output: [ 0.07976 0.7649 0.07169 -0.0006196 0.0002782 1.001 -0.000467 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05773 Epoch 1830 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04033 0.9725 0.9893 0.0002008 -9.014e-05 -0.04171 0.0001513 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0243 -0.004304 0.01725 0.03602 0.93 0.9407 0.04992 0.8551 0.8808 0.1275 ] Network output: [ 0.9525 0.09672 -0.02947 -0.0005445 0.0002444 0.02559 -0.0004103 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.623 0.04401 0.02268 0.3509 0.9648 0.983 0.7171 0.8663 0.9532 0.6223 ] Network output: [ 0.00672 0.9325 1.032 0.0001837 -8.246e-05 0.0233 0.0001384 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0428 0.02788 0.04517 0.04714 0.9801 0.9857 0.04389 0.9508 0.9691 0.06312 ] Network output: [ 0.1126 -0.3224 1.091 0.0005463 -0.0002453 1.009 0.0004117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7103 0.5278 0.4348 0.5295 0.9689 0.9855 0.714 0.8781 0.9597 0.6171 ] Network output: [ -0.0791 0.2658 0.9454 0.0006715 -0.0003015 0.9497 0.0005061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5564 0.531 0.3705 0.299 0.9823 0.9883 0.557 0.9574 0.9717 0.3944 ] Network output: [ -0.1124 0.2877 0.9029 -0.0004431 0.0001989 1.032 -0.0003339 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5801 0.5754 0.42 0.2497 0.9793 0.9864 0.5802 0.9472 0.9659 0.4257 ] Network output: [ 0.07934 0.7658 0.07137 -0.0006239 0.0002801 1.002 -0.0004702 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05758 Epoch 1831 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04037 0.9725 0.9893 0.0002009 -9.017e-05 -0.0417 0.0001514 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02431 -0.004306 0.01728 0.03607 0.93 0.9407 0.04995 0.8552 0.8809 0.1276 ] Network output: [ 0.9526 0.09649 -0.0293 -0.0005439 0.0002442 0.02546 -0.0004099 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6232 0.04446 0.02326 0.3509 0.9648 0.983 0.7174 0.8664 0.9533 0.6222 ] Network output: [ 0.006676 0.9324 1.032 0.000183 -8.218e-05 0.02343 0.000138 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04285 0.02794 0.0453 0.04724 0.9801 0.9857 0.04394 0.9509 0.9692 0.06325 ] Network output: [ 0.1126 -0.3224 1.091 0.0005465 -0.0002453 1.009 0.0004119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7106 0.5286 0.4354 0.5294 0.9689 0.9855 0.7143 0.8782 0.9597 0.6171 ] Network output: [ -0.07902 0.2654 0.9458 0.0006721 -0.0003017 0.9496 0.0005065 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5567 0.5313 0.371 0.2994 0.9824 0.9883 0.5572 0.9574 0.9718 0.3949 ] Network output: [ -0.1121 0.2876 0.9029 -0.0004396 0.0001974 1.032 -0.0003313 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5803 0.5756 0.4203 0.2501 0.9793 0.9864 0.5804 0.9473 0.9659 0.426 ] Network output: [ 0.07892 0.7666 0.07105 -0.000628 0.0002819 1.002 -0.0004733 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05743 Epoch 1832 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04041 0.9725 0.9892 0.0002009 -9.02e-05 -0.0417 0.0001514 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02432 -0.004308 0.01732 0.03611 0.93 0.9407 0.04997 0.8553 0.881 0.1277 ] Network output: [ 0.9527 0.09625 -0.02913 -0.0005434 0.000244 0.02533 -0.0004095 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6234 0.0449 0.02383 0.351 0.9648 0.983 0.7177 0.8666 0.9533 0.6222 ] Network output: [ 0.006632 0.9323 1.032 0.0001824 -8.189e-05 0.02356 0.0001375 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0429 0.028 0.04542 0.04733 0.9801 0.9857 0.04399 0.951 0.9692 0.06337 ] Network output: [ 0.1125 -0.3223 1.09 0.0005466 -0.0002454 1.009 0.000412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7109 0.5294 0.4361 0.5294 0.9689 0.9855 0.7146 0.8784 0.9598 0.617 ] Network output: [ -0.07893 0.265 0.9462 0.0006726 -0.0003019 0.9494 0.0005069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5569 0.5316 0.3716 0.2998 0.9824 0.9883 0.5574 0.9575 0.9718 0.3954 ] Network output: [ -0.1119 0.2875 0.9029 -0.0004362 0.0001958 1.032 -0.0003287 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5804 0.5757 0.4207 0.2504 0.9793 0.9864 0.5805 0.9474 0.966 0.4264 ] Network output: [ 0.0785 0.7674 0.07074 -0.0006321 0.0002838 1.002 -0.0004763 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05729 Epoch 1833 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04045 0.9725 0.9891 0.000201 -9.023e-05 -0.04169 0.0001515 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02432 -0.004311 0.01735 0.03615 0.9301 0.9408 0.05 0.8554 0.8811 0.1278 ] Network output: [ 0.9528 0.09602 -0.02896 -0.0005429 0.0002437 0.02521 -0.0004091 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6236 0.04535 0.0244 0.351 0.9648 0.983 0.718 0.8668 0.9534 0.6221 ] Network output: [ 0.006588 0.9322 1.032 0.0001818 -8.16e-05 0.02369 0.000137 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04295 0.02806 0.04554 0.04743 0.9801 0.9857 0.04405 0.9511 0.9693 0.06349 ] Network output: [ 0.1125 -0.3223 1.09 0.0005468 -0.0002455 1.009 0.0004121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7112 0.5302 0.4367 0.5294 0.9689 0.9855 0.7148 0.8785 0.9599 0.617 ] Network output: [ -0.07884 0.2646 0.9466 0.0006731 -0.0003022 0.9493 0.0005072 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5571 0.532 0.3722 0.3002 0.9824 0.9883 0.5577 0.9576 0.9719 0.3959 ] Network output: [ -0.1116 0.2874 0.9029 -0.0004327 0.0001943 1.031 -0.0003261 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5806 0.5759 0.421 0.2508 0.9794 0.9864 0.5807 0.9475 0.9661 0.4267 ] Network output: [ 0.07809 0.7683 0.07043 -0.000636 0.0002855 1.003 -0.0004793 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05714 Epoch 1834 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04048 0.9725 0.9891 0.000201 -9.025e-05 -0.04169 0.0001515 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02433 -0.004313 0.01738 0.03619 0.9301 0.9408 0.05002 0.8556 0.8812 0.1279 ] Network output: [ 0.9529 0.09579 -0.02879 -0.0005424 0.0002435 0.02508 -0.0004087 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6239 0.04579 0.02496 0.351 0.9649 0.983 0.7183 0.8669 0.9535 0.6221 ] Network output: [ 0.006543 0.9321 1.032 0.0001811 -8.13e-05 0.02382 0.0001365 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.043 0.02812 0.04567 0.04752 0.9801 0.9858 0.0441 0.9512 0.9693 0.06361 ] Network output: [ 0.1125 -0.3222 1.09 0.0005468 -0.0002455 1.01 0.0004121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7115 0.531 0.4373 0.5294 0.9689 0.9855 0.7151 0.8787 0.9599 0.6169 ] Network output: [ -0.07875 0.2641 0.9469 0.0006735 -0.0003024 0.9492 0.0005076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5574 0.5323 0.3727 0.3006 0.9824 0.9883 0.5579 0.9577 0.9719 0.3963 ] Network output: [ -0.1113 0.2873 0.9029 -0.0004293 0.0001927 1.031 -0.0003236 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5807 0.5761 0.4214 0.2512 0.9794 0.9865 0.5808 0.9476 0.9661 0.4271 ] Network output: [ 0.07767 0.7691 0.07011 -0.0006399 0.0002873 1.003 -0.0004822 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.057 Epoch 1835 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04052 0.9725 0.989 0.0002011 -9.027e-05 -0.04168 0.0001515 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02434 -0.004315 0.01742 0.03624 0.9301 0.9408 0.05005 0.8557 0.8813 0.128 ] Network output: [ 0.953 0.09556 -0.02863 -0.0005419 0.0002433 0.02495 -0.0004084 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6241 0.04623 0.02552 0.351 0.9649 0.9831 0.7186 0.8671 0.9535 0.6221 ] Network output: [ 0.006497 0.932 1.032 0.0001804 -8.1e-05 0.02395 0.000136 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04305 0.02818 0.04579 0.04761 0.9802 0.9858 0.04415 0.9513 0.9694 0.06373 ] Network output: [ 0.1125 -0.3222 1.09 0.0005469 -0.0002455 1.01 0.0004121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7118 0.5318 0.4379 0.5294 0.969 0.9855 0.7154 0.8788 0.96 0.6169 ] Network output: [ -0.07866 0.2637 0.9473 0.000674 -0.0003026 0.949 0.0005079 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5576 0.5326 0.3732 0.301 0.9824 0.9883 0.5581 0.9578 0.972 0.3968 ] Network output: [ -0.1111 0.2872 0.9029 -0.0004259 0.0001912 1.03 -0.000321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5809 0.5762 0.4218 0.2516 0.9794 0.9865 0.581 0.9477 0.9662 0.4274 ] Network output: [ 0.07726 0.7699 0.0698 -0.0006436 0.0002889 1.003 -0.000485 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05685 Epoch 1836 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04055 0.9725 0.9889 0.0002011 -9.029e-05 -0.04167 0.0001516 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02434 -0.004317 0.01745 0.03628 0.9301 0.9408 0.05008 0.8558 0.8814 0.1282 ] Network output: [ 0.953 0.09534 -0.02846 -0.0005414 0.000243 0.02483 -0.000408 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6243 0.04667 0.02609 0.351 0.9649 0.9831 0.7188 0.8672 0.9536 0.622 ] Network output: [ 0.006452 0.9319 1.032 0.0001798 -8.07e-05 0.02408 0.0001355 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0431 0.02824 0.04591 0.0477 0.9802 0.9858 0.0442 0.9514 0.9694 0.06385 ] Network output: [ 0.1125 -0.3222 1.089 0.0005469 -0.0002455 1.01 0.0004121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7121 0.5325 0.4385 0.5294 0.969 0.9855 0.7157 0.879 0.96 0.6169 ] Network output: [ -0.07857 0.2633 0.9477 0.0006744 -0.0003028 0.9489 0.0005083 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5578 0.5329 0.3738 0.3014 0.9824 0.9883 0.5584 0.9579 0.972 0.3973 ] Network output: [ -0.1108 0.2871 0.903 -0.0004226 0.0001897 1.03 -0.0003185 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.581 0.5764 0.4221 0.252 0.9794 0.9865 0.5811 0.9478 0.9663 0.4277 ] Network output: [ 0.07685 0.7707 0.0695 -0.0006473 0.0002906 1.003 -0.0004878 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05671 Epoch 1837 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04059 0.9724 0.9889 0.0002011 -9.03e-05 -0.04167 0.0001516 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02435 -0.004319 0.01748 0.03632 0.9302 0.9409 0.0501 0.8559 0.8814 0.1283 ] Network output: [ 0.9531 0.09511 -0.0283 -0.0005409 0.0002428 0.02471 -0.0004076 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6245 0.04711 0.02664 0.351 0.9649 0.9831 0.7191 0.8674 0.9536 0.622 ] Network output: [ 0.006406 0.9318 1.032 0.0001791 -8.04e-05 0.02421 0.000135 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04315 0.0283 0.04604 0.0478 0.9802 0.9858 0.04425 0.9515 0.9695 0.06398 ] Network output: [ 0.1124 -0.3221 1.089 0.0005468 -0.0002455 1.01 0.0004121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7124 0.5333 0.4391 0.5293 0.969 0.9856 0.716 0.8791 0.9601 0.6169 ] Network output: [ -0.07848 0.2629 0.9481 0.0006748 -0.000303 0.9488 0.0005086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5581 0.5332 0.3743 0.3018 0.9824 0.9884 0.5586 0.9579 0.972 0.3977 ] Network output: [ -0.1106 0.287 0.903 -0.0004193 0.0001882 1.029 -0.000316 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5812 0.5766 0.4225 0.2524 0.9794 0.9865 0.5813 0.9479 0.9663 0.4281 ] Network output: [ 0.07644 0.7716 0.06919 -0.0006508 0.0002922 1.004 -0.0004905 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05656 Epoch 1838 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04062 0.9724 0.9888 0.0002012 -9.031e-05 -0.04166 0.0001516 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02436 -0.004322 0.01752 0.03636 0.9302 0.9409 0.05013 0.8561 0.8815 0.1284 ] Network output: [ 0.9532 0.09489 -0.02814 -0.0005404 0.0002426 0.02459 -0.0004073 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6247 0.04755 0.0272 0.351 0.9649 0.9831 0.7194 0.8675 0.9537 0.6219 ] Network output: [ 0.006359 0.9317 1.032 0.0001784 -8.01e-05 0.02434 0.0001345 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0432 0.02836 0.04616 0.04789 0.9802 0.9858 0.0443 0.9516 0.9695 0.06409 ] Network output: [ 0.1124 -0.3221 1.089 0.0005467 -0.0002454 1.011 0.000412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7127 0.5341 0.4398 0.5293 0.969 0.9856 0.7163 0.8793 0.9601 0.6168 ] Network output: [ -0.07839 0.2625 0.9484 0.0006752 -0.0003031 0.9487 0.0005089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5583 0.5336 0.3749 0.3022 0.9825 0.9884 0.5588 0.958 0.9721 0.3982 ] Network output: [ -0.1103 0.2868 0.903 -0.0004159 0.0001867 1.029 -0.0003135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5813 0.5767 0.4228 0.2528 0.9795 0.9865 0.5814 0.948 0.9664 0.4284 ] Network output: [ 0.07604 0.7724 0.06889 -0.0006543 0.0002937 1.004 -0.0004931 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05642 Epoch 1839 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04065 0.9724 0.9887 0.0002012 -9.032e-05 -0.04165 0.0001516 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02436 -0.004324 0.01755 0.0364 0.9302 0.9409 0.05016 0.8562 0.8816 0.1285 ] Network output: [ 0.9533 0.09466 -0.02798 -0.0005399 0.0002424 0.02446 -0.0004069 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.625 0.04799 0.02775 0.351 0.965 0.9831 0.7197 0.8677 0.9537 0.6219 ] Network output: [ 0.006313 0.9316 1.032 0.0001777 -7.979e-05 0.02447 0.000134 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04325 0.02842 0.04628 0.04798 0.9802 0.9858 0.04435 0.9517 0.9696 0.06421 ] Network output: [ 0.1124 -0.322 1.089 0.0005466 -0.0002454 1.011 0.0004119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7129 0.5349 0.4404 0.5293 0.969 0.9856 0.7166 0.8794 0.9602 0.6168 ] Network output: [ -0.0783 0.262 0.9488 0.0006756 -0.0003033 0.9485 0.0005092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5585 0.5339 0.3754 0.3026 0.9825 0.9884 0.5591 0.9581 0.9721 0.3986 ] Network output: [ -0.11 0.2867 0.903 -0.0004126 0.0001852 1.029 -0.000311 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5815 0.5769 0.4232 0.2531 0.9795 0.9865 0.5816 0.9481 0.9664 0.4287 ] Network output: [ 0.07564 0.7732 0.06858 -0.0006576 0.0002952 1.004 -0.0004956 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05628 Epoch 1840 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04068 0.9724 0.9887 0.0002012 -9.032e-05 -0.04164 0.0001516 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02437 -0.004326 0.01758 0.03644 0.9302 0.9409 0.05018 0.8563 0.8817 0.1286 ] Network output: [ 0.9534 0.09444 -0.02783 -0.0005395 0.0002422 0.02434 -0.0004066 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6252 0.04843 0.0283 0.351 0.965 0.9831 0.72 0.8678 0.9538 0.6219 ] Network output: [ 0.006265 0.9315 1.032 0.0001771 -7.949e-05 0.0246 0.0001334 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04329 0.02848 0.0464 0.04807 0.9802 0.9858 0.0444 0.9518 0.9696 0.06433 ] Network output: [ 0.1124 -0.322 1.088 0.0005465 -0.0002453 1.011 0.0004118 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7132 0.5356 0.441 0.5293 0.9691 0.9856 0.7169 0.8796 0.9602 0.6168 ] Network output: [ -0.0782 0.2616 0.9491 0.000676 -0.0003035 0.9484 0.0005095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5588 0.5342 0.376 0.303 0.9825 0.9884 0.5593 0.9582 0.9722 0.3991 ] Network output: [ -0.1098 0.2866 0.903 -0.0004094 0.0001838 1.028 -0.0003085 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5816 0.5771 0.4236 0.2535 0.9795 0.9865 0.5817 0.9482 0.9665 0.429 ] Network output: [ 0.07524 0.774 0.06828 -0.0006609 0.0002967 1.005 -0.0004981 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05614 Epoch 1841 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04071 0.9724 0.9886 0.0002012 -9.033e-05 -0.04164 0.0001516 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02438 -0.004329 0.01761 0.03648 0.9303 0.9409 0.05021 0.8564 0.8818 0.1287 ] Network output: [ 0.9535 0.09422 -0.02767 -0.000539 0.000242 0.02423 -0.0004062 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6254 0.04886 0.02885 0.351 0.965 0.9831 0.7202 0.868 0.9539 0.6219 ] Network output: [ 0.006218 0.9314 1.032 0.0001764 -7.918e-05 0.02473 0.0001329 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04334 0.02854 0.04652 0.04816 0.9803 0.9859 0.04445 0.9519 0.9697 0.06445 ] Network output: [ 0.1124 -0.3219 1.088 0.0005463 -0.0002452 1.011 0.0004117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7135 0.5364 0.4416 0.5293 0.9691 0.9856 0.7172 0.8797 0.9603 0.6168 ] Network output: [ -0.07811 0.2612 0.9495 0.0006764 -0.0003036 0.9483 0.0005097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.559 0.5345 0.3765 0.3034 0.9825 0.9884 0.5596 0.9583 0.9722 0.3996 ] Network output: [ -0.1095 0.2865 0.9031 -0.0004061 0.0001823 1.028 -0.0003061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5818 0.5773 0.4239 0.2539 0.9795 0.9866 0.5819 0.9483 0.9666 0.4294 ] Network output: [ 0.07484 0.7748 0.06798 -0.0006641 0.0002981 1.005 -0.0005005 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.056 Epoch 1842 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04074 0.9724 0.9886 0.0002012 -9.032e-05 -0.04163 0.0001516 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02438 -0.004331 0.01765 0.03652 0.9303 0.941 0.05023 0.8566 0.8819 0.1288 ] Network output: [ 0.9536 0.094 -0.02752 -0.0005386 0.0002418 0.02411 -0.0004059 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6256 0.0493 0.0294 0.351 0.965 0.9831 0.7205 0.8682 0.9539 0.6218 ] Network output: [ 0.00617 0.9313 1.032 0.0001757 -7.886e-05 0.02486 0.0001324 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04339 0.0286 0.04664 0.04824 0.9803 0.9859 0.0445 0.952 0.9697 0.06457 ] Network output: [ 0.1124 -0.3219 1.088 0.000546 -0.0002451 1.011 0.0004115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7138 0.5372 0.4422 0.5293 0.9691 0.9856 0.7174 0.8799 0.9603 0.6168 ] Network output: [ -0.07802 0.2608 0.9498 0.0006767 -0.0003038 0.9482 0.00051 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5593 0.5348 0.377 0.3038 0.9825 0.9884 0.5598 0.9583 0.9723 0.4 ] Network output: [ -0.1093 0.2864 0.9031 -0.0004029 0.0001809 1.027 -0.0003036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5819 0.5774 0.4243 0.2543 0.9795 0.9866 0.582 0.9484 0.9666 0.4297 ] Network output: [ 0.07444 0.7756 0.06768 -0.0006671 0.0002995 1.005 -0.0005028 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05586 Epoch 1843 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04077 0.9724 0.9885 0.0002012 -9.032e-05 -0.04162 0.0001516 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02439 -0.004333 0.01768 0.03656 0.9303 0.941 0.05026 0.8567 0.8819 0.1289 ] Network output: [ 0.9537 0.09378 -0.02736 -0.0005382 0.0002416 0.02399 -0.0004056 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6258 0.04973 0.02994 0.351 0.9651 0.9832 0.7208 0.8683 0.954 0.6218 ] Network output: [ 0.006122 0.9312 1.032 0.000175 -7.855e-05 0.02499 0.0001319 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04344 0.02866 0.04676 0.04833 0.9803 0.9859 0.04456 0.9521 0.9698 0.06468 ] Network output: [ 0.1123 -0.3218 1.088 0.0005458 -0.000245 1.012 0.0004113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7141 0.5379 0.4427 0.5292 0.9691 0.9856 0.7177 0.88 0.9604 0.6167 ] Network output: [ -0.07792 0.2604 0.9502 0.000677 -0.0003039 0.9481 0.0005102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5595 0.5352 0.3776 0.3042 0.9825 0.9884 0.56 0.9584 0.9723 0.4005 ] Network output: [ -0.109 0.2862 0.9031 -0.0003997 0.0001794 1.027 -0.0003012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5821 0.5776 0.4246 0.2546 0.9796 0.9866 0.5822 0.9485 0.9667 0.43 ] Network output: [ 0.07405 0.7764 0.06739 -0.0006701 0.0003008 1.005 -0.000505 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05572 Epoch 1844 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0408 0.9724 0.9884 0.0002012 -9.031e-05 -0.04161 0.0001516 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0244 -0.004336 0.01771 0.0366 0.9303 0.941 0.05028 0.8568 0.882 0.129 ] Network output: [ 0.9538 0.09357 -0.02721 -0.0005377 0.0002414 0.02387 -0.0004053 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.626 0.05016 0.03048 0.351 0.9651 0.9832 0.7211 0.8685 0.954 0.6218 ] Network output: [ 0.006074 0.9312 1.032 0.0001743 -7.823e-05 0.02512 0.0001313 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04349 0.02872 0.04688 0.04842 0.9803 0.9859 0.04461 0.9521 0.9698 0.0648 ] Network output: [ 0.1123 -0.3218 1.087 0.0005455 -0.0002449 1.012 0.0004111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7143 0.5387 0.4433 0.5292 0.9691 0.9856 0.718 0.8802 0.9604 0.6167 ] Network output: [ -0.07782 0.26 0.9505 0.0006773 -0.0003041 0.9479 0.0005104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5597 0.5355 0.3781 0.3046 0.9825 0.9884 0.5603 0.9585 0.9724 0.4009 ] Network output: [ -0.1088 0.2861 0.9031 -0.0003965 0.000178 1.027 -0.0002988 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5822 0.5778 0.425 0.255 0.9796 0.9866 0.5824 0.9486 0.9667 0.4304 ] Network output: [ 0.07366 0.7772 0.06709 -0.000673 0.0003021 1.006 -0.0005072 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05558 Epoch 1845 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04082 0.9724 0.9884 0.0002011 -9.03e-05 -0.0416 0.0001516 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0244 -0.004338 0.01774 0.03664 0.9304 0.941 0.05031 0.8569 0.8821 0.1291 ] Network output: [ 0.9539 0.09335 -0.02706 -0.0005373 0.0002412 0.02376 -0.0004049 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6262 0.05059 0.03102 0.351 0.9651 0.9832 0.7213 0.8686 0.9541 0.6218 ] Network output: [ 0.006026 0.9311 1.032 0.0001736 -7.792e-05 0.02525 0.0001308 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04354 0.02878 0.04699 0.04851 0.9803 0.9859 0.04466 0.9522 0.9699 0.06492 ] Network output: [ 0.1123 -0.3218 1.087 0.0005451 -0.0002447 1.012 0.0004108 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7146 0.5394 0.4439 0.5292 0.9692 0.9856 0.7183 0.8803 0.9605 0.6167 ] Network output: [ -0.07773 0.2595 0.9508 0.0006776 -0.0003042 0.9478 0.0005107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.56 0.5358 0.3786 0.305 0.9826 0.9884 0.5605 0.9586 0.9724 0.4014 ] Network output: [ -0.1085 0.286 0.9032 -0.0003933 0.0001766 1.026 -0.0002964 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5824 0.5779 0.4253 0.2554 0.9796 0.9866 0.5825 0.9487 0.9668 0.4307 ] Network output: [ 0.07327 0.778 0.0668 -0.0006758 0.0003034 1.006 -0.0005093 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05545 Epoch 1846 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04085 0.9724 0.9883 0.0002011 -9.029e-05 -0.04159 0.0001516 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02441 -0.004341 0.01777 0.03668 0.9304 0.9411 0.05034 0.857 0.8822 0.1292 ] Network output: [ 0.954 0.09314 -0.02691 -0.0005369 0.000241 0.02364 -0.0004046 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6265 0.05102 0.03156 0.351 0.9651 0.9832 0.7216 0.8688 0.9541 0.6217 ] Network output: [ 0.005977 0.931 1.032 0.0001728 -7.76e-05 0.02538 0.0001303 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04359 0.02884 0.04711 0.04859 0.9804 0.9859 0.04471 0.9523 0.9699 0.06503 ] Network output: [ 0.1123 -0.3217 1.087 0.0005448 -0.0002446 1.012 0.0004105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7149 0.5402 0.4445 0.5292 0.9692 0.9857 0.7186 0.8805 0.9605 0.6167 ] Network output: [ -0.07763 0.2591 0.9512 0.0006779 -0.0003043 0.9477 0.0005109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5602 0.5361 0.3791 0.3054 0.9826 0.9884 0.5608 0.9587 0.9725 0.4018 ] Network output: [ -0.1083 0.2859 0.9032 -0.0003902 0.0001752 1.026 -0.0002941 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5825 0.5781 0.4257 0.2558 0.9796 0.9866 0.5827 0.9488 0.9669 0.431 ] Network output: [ 0.07288 0.7788 0.06651 -0.0006784 0.0003046 1.006 -0.0005113 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05531 Epoch 1847 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04088 0.9724 0.9883 0.0002011 -9.027e-05 -0.04158 0.0001515 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02442 -0.004343 0.0178 0.03672 0.9304 0.9411 0.05036 0.8572 0.8823 0.1293 ] Network output: [ 0.9541 0.09293 -0.02677 -0.0005365 0.0002409 0.02353 -0.0004043 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6267 0.05145 0.03209 0.351 0.9651 0.9832 0.7219 0.8689 0.9542 0.6217 ] Network output: [ 0.005928 0.9309 1.032 0.0001721 -7.727e-05 0.02551 0.0001297 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04364 0.0289 0.04723 0.04868 0.9804 0.9859 0.04476 0.9524 0.97 0.06514 ] Network output: [ 0.1123 -0.3217 1.087 0.0005443 -0.0002444 1.013 0.0004102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7152 0.5409 0.4451 0.5292 0.9692 0.9857 0.7188 0.8806 0.9606 0.6167 ] Network output: [ -0.07753 0.2587 0.9515 0.0006781 -0.0003044 0.9476 0.0005111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5605 0.5364 0.3796 0.3058 0.9826 0.9885 0.561 0.9587 0.9725 0.4023 ] Network output: [ -0.108 0.2857 0.9032 -0.0003871 0.0001738 1.026 -0.0002917 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5827 0.5783 0.426 0.2561 0.9796 0.9866 0.5828 0.9489 0.9669 0.4313 ] Network output: [ 0.0725 0.7796 0.06622 -0.000681 0.0003057 1.006 -0.0005133 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05517 Epoch 1848 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0409 0.9724 0.9882 0.000201 -9.025e-05 -0.04157 0.0001515 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02442 -0.004346 0.01783 0.03676 0.9304 0.9411 0.05039 0.8573 0.8823 0.1294 ] Network output: [ 0.9542 0.09272 -0.02662 -0.0005361 0.0002407 0.02342 -0.000404 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6269 0.05187 0.03262 0.351 0.9652 0.9832 0.7221 0.8691 0.9542 0.6217 ] Network output: [ 0.005879 0.9308 1.032 0.0001714 -7.695e-05 0.02563 0.0001292 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04369 0.02896 0.04734 0.04876 0.9804 0.9859 0.04481 0.9525 0.97 0.06526 ] Network output: [ 0.1122 -0.3216 1.087 0.0005439 -0.0002442 1.013 0.0004099 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7155 0.5417 0.4457 0.5292 0.9692 0.9857 0.7191 0.8808 0.9606 0.6167 ] Network output: [ -0.07743 0.2583 0.9518 0.0006784 -0.0003045 0.9475 0.0005112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5607 0.5368 0.3802 0.3062 0.9826 0.9885 0.5612 0.9588 0.9726 0.4027 ] Network output: [ -0.1078 0.2856 0.9033 -0.000384 0.0001724 1.025 -0.0002894 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5829 0.5785 0.4264 0.2565 0.9797 0.9867 0.583 0.949 0.967 0.4317 ] Network output: [ 0.07212 0.7804 0.06594 -0.0006836 0.0003069 1.007 -0.0005151 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05504 Epoch 1849 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04092 0.9724 0.9881 0.000201 -9.023e-05 -0.04156 0.0001515 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02443 -0.004348 0.01786 0.03679 0.9305 0.9411 0.05041 0.8574 0.8824 0.1295 ] Network output: [ 0.9542 0.09251 -0.02648 -0.0005357 0.0002405 0.0233 -0.0004037 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6271 0.0523 0.03315 0.351 0.9652 0.9832 0.7224 0.8692 0.9543 0.6217 ] Network output: [ 0.005829 0.9307 1.033 0.0001707 -7.662e-05 0.02576 0.0001286 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04374 0.02902 0.04746 0.04885 0.9804 0.986 0.04486 0.9526 0.9701 0.06537 ] Network output: [ 0.1122 -0.3216 1.086 0.0005434 -0.000244 1.013 0.0004095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7157 0.5424 0.4462 0.5292 0.9692 0.9857 0.7194 0.8809 0.9607 0.6167 ] Network output: [ -0.07733 0.2579 0.9522 0.0006786 -0.0003046 0.9474 0.0005114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5609 0.5371 0.3807 0.3066 0.9826 0.9885 0.5615 0.9589 0.9726 0.4032 ] Network output: [ -0.1075 0.2855 0.9033 -0.0003809 0.000171 1.025 -0.0002871 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.583 0.5786 0.4267 0.2569 0.9797 0.9867 0.5831 0.9491 0.967 0.432 ] Network output: [ 0.07174 0.7812 0.06565 -0.000686 0.000308 1.007 -0.000517 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05491 Epoch 1850 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04095 0.9724 0.9881 0.0002009 -9.021e-05 -0.04155 0.0001514 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02444 -0.004351 0.01789 0.03683 0.9305 0.9411 0.05044 0.8575 0.8825 0.1296 ] Network output: [ 0.9543 0.0923 -0.02633 -0.0005353 0.0002403 0.02319 -0.0004034 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6273 0.05272 0.03367 0.351 0.9652 0.9832 0.7227 0.8694 0.9544 0.6217 ] Network output: [ 0.005779 0.9307 1.033 0.00017 -7.63e-05 0.02589 0.0001281 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04379 0.02908 0.04757 0.04893 0.9804 0.986 0.04491 0.9527 0.9701 0.06548 ] Network output: [ 0.1122 -0.3215 1.086 0.0005429 -0.0002437 1.013 0.0004091 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.716 0.5431 0.4468 0.5291 0.9693 0.9857 0.7197 0.8811 0.9607 0.6167 ] Network output: [ -0.07723 0.2575 0.9525 0.0006788 -0.0003047 0.9473 0.0005116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5612 0.5374 0.3812 0.3069 0.9826 0.9885 0.5617 0.959 0.9727 0.4036 ] Network output: [ -0.1073 0.2853 0.9033 -0.0003779 0.0001696 1.024 -0.0002848 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5832 0.5788 0.4271 0.2572 0.9797 0.9867 0.5833 0.9492 0.9671 0.4323 ] Network output: [ 0.07136 0.7819 0.06537 -0.0006883 0.000309 1.007 -0.0005187 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05477 Epoch 1851 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04097 0.9724 0.988 0.0002009 -9.018e-05 -0.04154 0.0001514 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02444 -0.004353 0.01792 0.03687 0.9305 0.9412 0.05046 0.8576 0.8826 0.1297 ] Network output: [ 0.9544 0.0921 -0.02619 -0.0005349 0.0002402 0.02308 -0.0004031 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6275 0.05314 0.0342 0.351 0.9652 0.9833 0.7229 0.8695 0.9544 0.6216 ] Network output: [ 0.005729 0.9306 1.033 0.0001692 -7.597e-05 0.02602 0.0001275 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04384 0.02914 0.04769 0.04902 0.9804 0.986 0.04496 0.9528 0.9702 0.06559 ] Network output: [ 0.1122 -0.3215 1.086 0.0005423 -0.0002435 1.013 0.0004087 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7163 0.5439 0.4474 0.5291 0.9693 0.9857 0.7199 0.8812 0.9608 0.6166 ] Network output: [ -0.07713 0.2571 0.9528 0.000679 -0.0003048 0.9472 0.0005117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5614 0.5377 0.3817 0.3073 0.9827 0.9885 0.562 0.9591 0.9727 0.404 ] Network output: [ -0.107 0.2852 0.9034 -0.0003748 0.0001683 1.024 -0.0002825 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5833 0.579 0.4274 0.2576 0.9797 0.9867 0.5834 0.9493 0.9672 0.4326 ] Network output: [ 0.07099 0.7827 0.06508 -0.0006905 0.00031 1.007 -0.0005204 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05464 Epoch 1852 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04099 0.9724 0.988 0.0002008 -9.015e-05 -0.04153 0.0001513 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02445 -0.004356 0.01795 0.0369 0.9305 0.9412 0.05049 0.8578 0.8827 0.1298 ] Network output: [ 0.9545 0.09189 -0.02605 -0.0005346 0.00024 0.02297 -0.0004029 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6277 0.05356 0.03472 0.351 0.9653 0.9833 0.7232 0.8696 0.9545 0.6216 ] Network output: [ 0.005679 0.9305 1.033 0.0001685 -7.564e-05 0.02615 0.000127 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04389 0.0292 0.0478 0.0491 0.9805 0.986 0.04502 0.9529 0.9702 0.0657 ] Network output: [ 0.1122 -0.3214 1.086 0.0005417 -0.0002432 1.014 0.0004083 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7165 0.5446 0.4479 0.5291 0.9693 0.9857 0.7202 0.8814 0.9608 0.6166 ] Network output: [ -0.07703 0.2567 0.9531 0.0006791 -0.0003049 0.9471 0.0005118 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5617 0.538 0.3822 0.3077 0.9827 0.9885 0.5622 0.9591 0.9727 0.4045 ] Network output: [ -0.1068 0.2851 0.9034 -0.0003718 0.0001669 1.024 -0.0002802 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5835 0.5792 0.4277 0.258 0.9797 0.9867 0.5836 0.9494 0.9672 0.4329 ] Network output: [ 0.07062 0.7835 0.0648 -0.0006927 0.000311 1.008 -0.000522 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05451 Epoch 1853 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04101 0.9724 0.9879 0.0002007 -9.012e-05 -0.04152 0.0001513 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02445 -0.004359 0.01798 0.03694 0.9306 0.9412 0.05051 0.8579 0.8828 0.1299 ] Network output: [ 0.9546 0.09169 -0.02591 -0.0005342 0.0002398 0.02286 -0.0004026 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6279 0.05398 0.03524 0.351 0.9653 0.9833 0.7235 0.8698 0.9545 0.6216 ] Network output: [ 0.005629 0.9304 1.033 0.0001677 -7.531e-05 0.02627 0.0001264 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04394 0.02926 0.04791 0.04918 0.9805 0.986 0.04507 0.953 0.9703 0.06581 ] Network output: [ 0.1121 -0.3214 1.085 0.0005411 -0.0002429 1.014 0.0004078 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7168 0.5453 0.4485 0.5291 0.9693 0.9857 0.7205 0.8815 0.9609 0.6166 ] Network output: [ -0.07693 0.2563 0.9534 0.0006793 -0.000305 0.9469 0.0005119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5619 0.5384 0.3827 0.3081 0.9827 0.9885 0.5625 0.9592 0.9728 0.4049 ] Network output: [ -0.1066 0.2849 0.9034 -0.0003689 0.0001656 1.023 -0.000278 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5836 0.5793 0.4281 0.2583 0.9798 0.9867 0.5838 0.9495 0.9673 0.4333 ] Network output: [ 0.07025 0.7843 0.06453 -0.0006947 0.0003119 1.008 -0.0005236 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05438 Epoch 1854 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04103 0.9724 0.9879 0.0002007 -9.009e-05 -0.04151 0.0001512 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02446 -0.004361 0.01801 0.03698 0.9306 0.9412 0.05053 0.858 0.8828 0.13 ] Network output: [ 0.9547 0.09148 -0.02577 -0.0005338 0.0002396 0.02275 -0.0004023 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6281 0.0544 0.03575 0.351 0.9653 0.9833 0.7237 0.8699 0.9546 0.6216 ] Network output: [ 0.005578 0.9303 1.033 0.000167 -7.497e-05 0.0264 0.0001259 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04399 0.02932 0.04803 0.04926 0.9805 0.986 0.04512 0.9531 0.9703 0.06592 ] Network output: [ 0.1121 -0.3213 1.085 0.0005404 -0.0002426 1.014 0.0004073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7171 0.546 0.449 0.5291 0.9693 0.9857 0.7207 0.8817 0.9609 0.6166 ] Network output: [ -0.07683 0.2559 0.9537 0.0006794 -0.000305 0.9468 0.000512 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5622 0.5387 0.3832 0.3084 0.9827 0.9885 0.5627 0.9593 0.9728 0.4053 ] Network output: [ -0.1063 0.2848 0.9035 -0.0003659 0.0001643 1.023 -0.0002758 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5838 0.5795 0.4284 0.2587 0.9798 0.9867 0.5839 0.9496 0.9673 0.4336 ] Network output: [ 0.06988 0.785 0.06425 -0.0006967 0.0003128 1.008 -0.000525 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05425 Epoch 1855 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04105 0.9724 0.9878 0.0002006 -9.005e-05 -0.0415 0.0001512 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02447 -0.004364 0.01804 0.03701 0.9306 0.9413 0.05056 0.8581 0.8829 0.1301 ] Network output: [ 0.9548 0.09128 -0.02564 -0.0005334 0.0002395 0.02264 -0.000402 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6283 0.05481 0.03627 0.351 0.9653 0.9833 0.724 0.8701 0.9546 0.6216 ] Network output: [ 0.005527 0.9303 1.033 0.0001663 -7.464e-05 0.02653 0.0001253 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04404 0.02938 0.04814 0.04934 0.9805 0.986 0.04517 0.9531 0.9704 0.06603 ] Network output: [ 0.1121 -0.3213 1.085 0.0005397 -0.0002423 1.014 0.0004067 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7173 0.5467 0.4496 0.5291 0.9693 0.9858 0.721 0.8818 0.961 0.6166 ] Network output: [ -0.07673 0.2555 0.954 0.0006796 -0.0003051 0.9467 0.0005121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5624 0.539 0.3837 0.3088 0.9827 0.9885 0.5629 0.9594 0.9729 0.4058 ] Network output: [ -0.1061 0.2847 0.9035 -0.000363 0.0001629 1.023 -0.0002735 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.584 0.5797 0.4288 0.259 0.9798 0.9868 0.5841 0.9497 0.9674 0.4339 ] Network output: [ 0.06952 0.7858 0.06397 -0.0006985 0.0003136 1.008 -0.0005264 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05412 Epoch 1856 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04107 0.9724 0.9877 0.0002005 -9.001e-05 -0.04149 0.0001511 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02447 -0.004367 0.01807 0.03705 0.9306 0.9413 0.05058 0.8582 0.883 0.1302 ] Network output: [ 0.9549 0.09108 -0.0255 -0.0005331 0.0002393 0.02254 -0.0004017 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6285 0.05523 0.03678 0.351 0.9653 0.9833 0.7242 0.8702 0.9547 0.6216 ] Network output: [ 0.005477 0.9302 1.033 0.0001655 -7.43e-05 0.02666 0.0001247 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04409 0.02943 0.04825 0.04942 0.9805 0.986 0.04522 0.9532 0.9704 0.06614 ] Network output: [ 0.1121 -0.3212 1.085 0.000539 -0.000242 1.014 0.0004062 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7176 0.5474 0.4501 0.5291 0.9694 0.9858 0.7213 0.8819 0.961 0.6166 ] Network output: [ -0.07662 0.2551 0.9543 0.0006797 -0.0003051 0.9466 0.0005122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5626 0.5393 0.3842 0.3092 0.9827 0.9885 0.5632 0.9594 0.9729 0.4062 ] Network output: [ -0.1059 0.2845 0.9036 -0.0003601 0.0001616 1.022 -0.0002713 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5841 0.5799 0.4291 0.2594 0.9798 0.9868 0.5842 0.9498 0.9674 0.4342 ] Network output: [ 0.06916 0.7865 0.0637 -0.0007003 0.0003144 1.009 -0.0005278 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05399 Epoch 1857 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04109 0.9724 0.9877 0.0002004 -8.997e-05 -0.04147 0.000151 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02448 -0.004369 0.0181 0.03708 0.9307 0.9413 0.05061 0.8583 0.8831 0.1303 ] Network output: [ 0.9549 0.09089 -0.02537 -0.0005327 0.0002391 0.02243 -0.0004015 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6288 0.05564 0.03729 0.351 0.9654 0.9833 0.7245 0.8704 0.9547 0.6216 ] Network output: [ 0.005425 0.9301 1.033 0.0001648 -7.397e-05 0.02678 0.0001242 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04414 0.02949 0.04836 0.0495 0.9805 0.9861 0.04527 0.9533 0.9705 0.06624 ] Network output: [ 0.112 -0.3212 1.085 0.0005382 -0.0002416 1.015 0.0004056 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7179 0.5481 0.4507 0.5291 0.9694 0.9858 0.7215 0.8821 0.9611 0.6166 ] Network output: [ -0.07652 0.2547 0.9546 0.0006797 -0.0003052 0.9465 0.0005123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5629 0.5396 0.3847 0.3095 0.9827 0.9886 0.5634 0.9595 0.973 0.4066 ] Network output: [ -0.1056 0.2844 0.9036 -0.0003572 0.0001603 1.022 -0.0002692 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5843 0.58 0.4294 0.2598 0.9798 0.9868 0.5844 0.9499 0.9675 0.4345 ] Network output: [ 0.0688 0.7873 0.06343 -0.000702 0.0003152 1.009 -0.0005291 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05387 Epoch 1858 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0411 0.9724 0.9876 0.0002003 -8.993e-05 -0.04146 0.000151 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02449 -0.004372 0.01813 0.03711 0.9307 0.9413 0.05063 0.8585 0.8832 0.1304 ] Network output: [ 0.955 0.09069 -0.02524 -0.0005323 0.000239 0.02232 -0.0004012 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.629 0.05605 0.03779 0.351 0.9654 0.9833 0.7248 0.8705 0.9548 0.6216 ] Network output: [ 0.005374 0.9301 1.033 0.000164 -7.363e-05 0.02691 0.0001236 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04419 0.02955 0.04847 0.04958 0.9806 0.9861 0.04532 0.9534 0.9705 0.06635 ] Network output: [ 0.112 -0.3211 1.084 0.0005374 -0.0002412 1.015 0.000405 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7181 0.5488 0.4512 0.5291 0.9694 0.9858 0.7218 0.8822 0.9611 0.6166 ] Network output: [ -0.07642 0.2543 0.9549 0.0006798 -0.0003052 0.9464 0.0005123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5631 0.5399 0.3852 0.3099 0.9828 0.9886 0.5637 0.9596 0.973 0.4071 ] Network output: [ -0.1054 0.2842 0.9037 -0.0003543 0.0001591 1.021 -0.000267 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5844 0.5802 0.4298 0.2601 0.9799 0.9868 0.5845 0.95 0.9676 0.4348 ] Network output: [ 0.06844 0.7881 0.06316 -0.0007036 0.0003159 1.009 -0.0005303 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05374 Epoch 1859 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04112 0.9724 0.9876 0.0002002 -8.988e-05 -0.04145 0.0001509 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02449 -0.004375 0.01816 0.03715 0.9307 0.9413 0.05066 0.8586 0.8832 0.1305 ] Network output: [ 0.9551 0.09049 -0.02511 -0.000532 0.0002388 0.02222 -0.0004009 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6292 0.05646 0.03829 0.351 0.9654 0.9834 0.725 0.8707 0.9548 0.6216 ] Network output: [ 0.005323 0.93 1.033 0.0001632 -7.329e-05 0.02703 0.000123 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04424 0.02961 0.04858 0.04966 0.9806 0.9861 0.04537 0.9535 0.9705 0.06646 ] Network output: [ 0.112 -0.3211 1.084 0.0005365 -0.0002409 1.015 0.0004043 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7184 0.5495 0.4518 0.529 0.9694 0.9858 0.7221 0.8824 0.9612 0.6167 ] Network output: [ -0.07631 0.2539 0.9552 0.0006799 -0.0003052 0.9463 0.0005124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5634 0.5403 0.3857 0.3102 0.9828 0.9886 0.5639 0.9597 0.9731 0.4075 ] Network output: [ -0.1052 0.2841 0.9037 -0.0003515 0.0001578 1.021 -0.0002649 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5846 0.5804 0.4301 0.2605 0.9799 0.9868 0.5847 0.9501 0.9676 0.4351 ] Network output: [ 0.06808 0.7888 0.06289 -0.0007052 0.0003166 1.009 -0.0005314 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05362 Epoch 1860 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04114 0.9724 0.9875 0.0002001 -8.983e-05 -0.04144 0.0001508 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0245 -0.004378 0.01819 0.03718 0.9307 0.9414 0.05068 0.8587 0.8833 0.1306 ] Network output: [ 0.9552 0.0903 -0.02498 -0.0005316 0.0002387 0.02211 -0.0004006 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6294 0.05687 0.0388 0.351 0.9654 0.9834 0.7253 0.8708 0.9549 0.6216 ] Network output: [ 0.005271 0.9299 1.033 0.0001625 -7.295e-05 0.02716 0.0001225 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04429 0.02967 0.04869 0.04974 0.9806 0.9861 0.04542 0.9536 0.9706 0.06656 ] Network output: [ 0.112 -0.321 1.084 0.0005356 -0.0002405 1.015 0.0004037 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7187 0.5502 0.4523 0.529 0.9694 0.9858 0.7223 0.8825 0.9612 0.6167 ] Network output: [ -0.07621 0.2535 0.9554 0.0006799 -0.0003052 0.9462 0.0005124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5636 0.5406 0.3862 0.3106 0.9828 0.9886 0.5642 0.9597 0.9731 0.4079 ] Network output: [ -0.1049 0.284 0.9038 -0.0003486 0.0001565 1.021 -0.0002627 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5847 0.5805 0.4304 0.2608 0.9799 0.9868 0.5849 0.9502 0.9677 0.4355 ] Network output: [ 0.06773 0.7895 0.06262 -0.0007066 0.0003172 1.009 -0.0005325 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05349 Epoch 1861 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04115 0.9725 0.9875 0.0002 -8.978e-05 -0.04142 0.0001507 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02451 -0.00438 0.01821 0.03722 0.9308 0.9414 0.0507 0.8588 0.8834 0.1307 ] Network output: [ 0.9553 0.09011 -0.02485 -0.0005313 0.0002385 0.02201 -0.0004004 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6296 0.05728 0.03929 0.351 0.9654 0.9834 0.7255 0.8709 0.9549 0.6216 ] Network output: [ 0.00522 0.9298 1.033 0.0001617 -7.26e-05 0.02729 0.0001219 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04433 0.02973 0.0488 0.04981 0.9806 0.9861 0.04547 0.9537 0.9706 0.06666 ] Network output: [ 0.112 -0.321 1.084 0.0005347 -0.0002401 1.015 0.000403 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7189 0.5509 0.4529 0.529 0.9695 0.9858 0.7226 0.8827 0.9613 0.6167 ] Network output: [ -0.07611 0.2531 0.9557 0.0006799 -0.0003053 0.9461 0.0005124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5639 0.5409 0.3867 0.311 0.9828 0.9886 0.5644 0.9598 0.9731 0.4083 ] Network output: [ -0.1047 0.2838 0.9038 -0.0003458 0.0001553 1.02 -0.0002606 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5849 0.5807 0.4308 0.2612 0.9799 0.9868 0.585 0.9502 0.9677 0.4358 ] Network output: [ 0.06738 0.7903 0.06236 -0.000708 0.0003178 1.01 -0.0005335 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05337 Epoch 1862 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04116 0.9725 0.9874 0.0001999 -8.973e-05 -0.04141 0.0001506 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02451 -0.004383 0.01824 0.03725 0.9308 0.9414 0.05073 0.8589 0.8835 0.1308 ] Network output: [ 0.9554 0.08991 -0.02472 -0.0005309 0.0002383 0.02191 -0.0004001 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6298 0.05768 0.03979 0.3509 0.9655 0.9834 0.7258 0.8711 0.955 0.6216 ] Network output: [ 0.005168 0.9298 1.033 0.000161 -7.226e-05 0.02741 0.0001213 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04438 0.02978 0.0489 0.04989 0.9806 0.9861 0.04552 0.9538 0.9707 0.06677 ] Network output: [ 0.1119 -0.3209 1.084 0.0005338 -0.0002396 1.016 0.0004023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7192 0.5516 0.4534 0.529 0.9695 0.9858 0.7228 0.8828 0.9613 0.6167 ] Network output: [ -0.076 0.2527 0.956 0.0006799 -0.0003053 0.946 0.0005124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5641 0.5412 0.3871 0.3113 0.9828 0.9886 0.5646 0.9599 0.9732 0.4087 ] Network output: [ -0.1045 0.2837 0.9039 -0.0003431 0.000154 1.02 -0.0002585 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5851 0.5809 0.4311 0.2615 0.9799 0.9869 0.5852 0.9503 0.9678 0.4361 ] Network output: [ 0.06703 0.791 0.06209 -0.0007092 0.0003184 1.01 -0.0005345 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05324 Epoch 1863 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04118 0.9725 0.9874 0.0001997 -8.967e-05 -0.0414 0.0001505 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02452 -0.004386 0.01827 0.03728 0.9308 0.9414 0.05075 0.859 0.8836 0.1309 ] Network output: [ 0.9555 0.08972 -0.0246 -0.0005306 0.0002382 0.0218 -0.0003998 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.63 0.05809 0.04028 0.3509 0.9655 0.9834 0.726 0.8712 0.9551 0.6216 ] Network output: [ 0.005116 0.9297 1.033 0.0001602 -7.192e-05 0.02754 0.0001207 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04443 0.02984 0.04901 0.04996 0.9806 0.9861 0.04557 0.9538 0.9707 0.06687 ] Network output: [ 0.1119 -0.3209 1.083 0.0005328 -0.0002392 1.016 0.0004015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7194 0.5523 0.4539 0.529 0.9695 0.9858 0.7231 0.8829 0.9614 0.6167 ] Network output: [ -0.0759 0.2523 0.9563 0.0006799 -0.0003053 0.946 0.0005124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5643 0.5415 0.3876 0.3117 0.9828 0.9886 0.5649 0.96 0.9732 0.4091 ] Network output: [ -0.1043 0.2835 0.9039 -0.0003403 0.0001528 1.02 -0.0002565 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5852 0.5811 0.4314 0.2619 0.98 0.9869 0.5853 0.9504 0.9678 0.4364 ] Network output: [ 0.06669 0.7918 0.06183 -0.0007104 0.0003189 1.01 -0.0005354 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05312 Epoch 1864 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04119 0.9725 0.9873 0.0001996 -8.962e-05 -0.04138 0.0001504 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02453 -0.004389 0.01829 0.03731 0.9309 0.9415 0.05077 0.8591 0.8836 0.131 ] Network output: [ 0.9555 0.08954 -0.02447 -0.0005302 0.000238 0.0217 -0.0003996 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6302 0.05849 0.04077 0.3509 0.9655 0.9834 0.7263 0.8714 0.9551 0.6216 ] Network output: [ 0.005064 0.9296 1.033 0.0001594 -7.157e-05 0.02766 0.0001201 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04448 0.0299 0.04911 0.05004 0.9807 0.9862 0.04562 0.9539 0.9708 0.06697 ] Network output: [ 0.1119 -0.3208 1.083 0.0005318 -0.0002387 1.016 0.0004008 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7197 0.553 0.4544 0.529 0.9695 0.9859 0.7234 0.8831 0.9614 0.6167 ] Network output: [ -0.07579 0.2519 0.9565 0.0006799 -0.0003052 0.9459 0.0005124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5646 0.5418 0.3881 0.312 0.9828 0.9886 0.5651 0.96 0.9733 0.4096 ] Network output: [ -0.104 0.2834 0.904 -0.0003376 0.0001516 1.019 -0.0002544 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5854 0.5812 0.4318 0.2622 0.98 0.9869 0.5855 0.9505 0.9679 0.4367 ] Network output: [ 0.06635 0.7925 0.06157 -0.0007116 0.0003194 1.01 -0.0005363 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.053 Epoch 1865 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0412 0.9725 0.9873 0.0001995 -8.956e-05 -0.04137 0.0001503 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02453 -0.004392 0.01832 0.03734 0.9309 0.9415 0.0508 0.8592 0.8837 0.131 ] Network output: [ 0.9556 0.08935 -0.02435 -0.0005298 0.0002379 0.0216 -0.0003993 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6304 0.05889 0.04126 0.3509 0.9655 0.9834 0.7265 0.8715 0.9552 0.6216 ] Network output: [ 0.005012 0.9296 1.033 0.0001586 -7.122e-05 0.02779 0.0001196 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04453 0.02996 0.04922 0.05011 0.9807 0.9862 0.04567 0.954 0.9708 0.06707 ] Network output: [ 0.1119 -0.3208 1.083 0.0005307 -0.0002383 1.016 0.0004 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7199 0.5536 0.455 0.529 0.9695 0.9859 0.7236 0.8832 0.9615 0.6167 ] Network output: [ -0.07569 0.2516 0.9568 0.0006799 -0.0003052 0.9458 0.0005124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5648 0.5421 0.3886 0.3124 0.9829 0.9886 0.5654 0.9601 0.9733 0.41 ] Network output: [ -0.1038 0.2832 0.9041 -0.0003349 0.0001503 1.019 -0.0002524 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5855 0.5814 0.4321 0.2626 0.98 0.9869 0.5856 0.9506 0.968 0.437 ] Network output: [ 0.06601 0.7932 0.06131 -0.0007126 0.0003199 1.011 -0.000537 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05288 Epoch 1866 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04122 0.9725 0.9872 0.0001994 -8.95e-05 -0.04135 0.0001502 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02454 -0.004395 0.01835 0.03738 0.9309 0.9415 0.05082 0.8594 0.8838 0.1311 ] Network output: [ 0.9557 0.08916 -0.02423 -0.0005295 0.0002377 0.0215 -0.000399 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6306 0.05929 0.04175 0.3509 0.9656 0.9834 0.7268 0.8716 0.9552 0.6216 ] Network output: [ 0.00496 0.9295 1.033 0.0001579 -7.087e-05 0.02791 0.000119 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04458 0.03001 0.04932 0.05018 0.9807 0.9862 0.04572 0.9541 0.9709 0.06717 ] Network output: [ 0.1118 -0.3207 1.083 0.0005296 -0.0002378 1.016 0.0003991 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7202 0.5543 0.4555 0.529 0.9696 0.9859 0.7239 0.8833 0.9615 0.6167 ] Network output: [ -0.07558 0.2512 0.9571 0.0006798 -0.0003052 0.9457 0.0005123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5651 0.5424 0.389 0.3127 0.9829 0.9886 0.5656 0.9602 0.9734 0.4104 ] Network output: [ -0.1036 0.2831 0.9041 -0.0003322 0.0001491 1.019 -0.0002504 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5857 0.5816 0.4324 0.2629 0.98 0.9869 0.5858 0.9507 0.968 0.4373 ] Network output: [ 0.06567 0.7939 0.06105 -0.0007135 0.0003203 1.011 -0.0005378 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05276 Epoch 1867 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04123 0.9725 0.9872 0.0001992 -8.944e-05 -0.04134 0.0001501 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02454 -0.004398 0.01837 0.03741 0.9309 0.9415 0.05084 0.8595 0.8839 0.1312 ] Network output: [ 0.9558 0.08898 -0.02411 -0.0005291 0.0002376 0.0214 -0.0003988 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6308 0.05968 0.04223 0.3509 0.9656 0.9835 0.727 0.8718 0.9553 0.6216 ] Network output: [ 0.004908 0.9295 1.033 0.0001571 -7.053e-05 0.02803 0.0001184 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04463 0.03007 0.04943 0.05026 0.9807 0.9862 0.04577 0.9542 0.9709 0.06727 ] Network output: [ 0.1118 -0.3207 1.083 0.0005285 -0.0002373 1.017 0.0003983 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7204 0.555 0.456 0.529 0.9696 0.9859 0.7241 0.8835 0.9616 0.6167 ] Network output: [ -0.07547 0.2508 0.9573 0.0006798 -0.0003052 0.9456 0.0005123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5653 0.5428 0.3895 0.313 0.9829 0.9887 0.5659 0.9603 0.9734 0.4108 ] Network output: [ -0.1034 0.2829 0.9042 -0.0003295 0.0001479 1.018 -0.0002484 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5858 0.5818 0.4327 0.2633 0.98 0.9869 0.586 0.9508 0.9681 0.4376 ] Network output: [ 0.06534 0.7947 0.0608 -0.0007144 0.0003207 1.011 -0.0005384 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05264 Epoch 1868 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04124 0.9725 0.9871 0.0001991 -8.937e-05 -0.04132 0.00015 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02455 -0.004401 0.0184 0.03744 0.931 0.9415 0.05086 0.8596 0.8839 0.1313 ] Network output: [ 0.9559 0.08879 -0.02399 -0.0005288 0.0002374 0.0213 -0.0003985 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.631 0.06008 0.04271 0.3509 0.9656 0.9835 0.7273 0.8719 0.9553 0.6216 ] Network output: [ 0.004856 0.9294 1.033 0.0001563 -7.018e-05 0.02816 0.0001178 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04468 0.03013 0.04953 0.05033 0.9807 0.9862 0.04582 0.9543 0.971 0.06737 ] Network output: [ 0.1118 -0.3206 1.082 0.0005273 -0.0002367 1.017 0.0003974 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7207 0.5557 0.4565 0.529 0.9696 0.9859 0.7244 0.8836 0.9616 0.6167 ] Network output: [ -0.07537 0.2504 0.9576 0.0006797 -0.0003051 0.9455 0.0005122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5656 0.5431 0.39 0.3134 0.9829 0.9887 0.5661 0.9603 0.9734 0.4112 ] Network output: [ -0.1032 0.2828 0.9042 -0.0003269 0.0001468 1.018 -0.0002464 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.586 0.5819 0.433 0.2636 0.9801 0.9869 0.5861 0.9509 0.9681 0.4379 ] Network output: [ 0.065 0.7954 0.06054 -0.0007152 0.0003211 1.011 -0.000539 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05252 Epoch 1869 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04125 0.9725 0.9871 0.0001989 -8.93e-05 -0.04131 0.0001499 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02456 -0.004404 0.01843 0.03747 0.931 0.9416 0.05089 0.8597 0.884 0.1314 ] Network output: [ 0.956 0.08861 -0.02387 -0.0005284 0.0002372 0.0212 -0.0003982 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6312 0.06047 0.04319 0.3509 0.9656 0.9835 0.7275 0.8721 0.9554 0.6216 ] Network output: [ 0.004804 0.9293 1.033 0.0001555 -6.983e-05 0.02828 0.0001172 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04472 0.03018 0.04963 0.0504 0.9807 0.9862 0.04587 0.9543 0.971 0.06747 ] Network output: [ 0.1118 -0.3206 1.082 0.0005262 -0.0002362 1.017 0.0003965 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7209 0.5563 0.457 0.5289 0.9696 0.9859 0.7246 0.8838 0.9616 0.6168 ] Network output: [ -0.07526 0.2501 0.9578 0.0006796 -0.0003051 0.9454 0.0005121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5658 0.5434 0.3904 0.3137 0.9829 0.9887 0.5663 0.9604 0.9735 0.4116 ] Network output: [ -0.103 0.2826 0.9043 -0.0003243 0.0001456 1.018 -0.0002444 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5862 0.5821 0.4334 0.2639 0.9801 0.987 0.5863 0.951 0.9682 0.4382 ] Network output: [ 0.06467 0.7961 0.06029 -0.0007159 0.0003214 1.011 -0.0005396 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0524 Epoch 1870 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04126 0.9726 0.987 0.0001988 -8.924e-05 -0.0413 0.0001498 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02456 -0.004407 0.01845 0.0375 0.931 0.9416 0.05091 0.8598 0.8841 0.1315 ] Network output: [ 0.956 0.08843 -0.02375 -0.0005281 0.0002371 0.0211 -0.000398 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6314 0.06086 0.04367 0.3509 0.9656 0.9835 0.7278 0.8722 0.9554 0.6216 ] Network output: [ 0.004751 0.9293 1.033 0.0001548 -6.947e-05 0.0284 0.0001166 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04477 0.03024 0.04973 0.05047 0.9808 0.9862 0.04592 0.9544 0.9711 0.06756 ] Network output: [ 0.1117 -0.3205 1.082 0.0005249 -0.0002357 1.017 0.0003956 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7212 0.557 0.4575 0.5289 0.9696 0.9859 0.7249 0.8839 0.9617 0.6168 ] Network output: [ -0.07515 0.2497 0.9581 0.0006795 -0.000305 0.9453 0.0005121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.566 0.5437 0.3909 0.3141 0.9829 0.9887 0.5666 0.9605 0.9735 0.412 ] Network output: [ -0.1027 0.2825 0.9044 -0.0003217 0.0001444 1.017 -0.0002424 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5863 0.5823 0.4337 0.2643 0.9801 0.987 0.5864 0.9511 0.9682 0.4385 ] Network output: [ 0.06434 0.7968 0.06004 -0.0007166 0.0003217 1.012 -0.00054 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05228 Epoch 1871 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04127 0.9726 0.987 0.0001986 -8.917e-05 -0.04128 0.0001497 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02457 -0.00441 0.01848 0.03753 0.931 0.9416 0.05093 0.8599 0.8842 0.1316 ] Network output: [ 0.9561 0.08825 -0.02364 -0.0005277 0.0002369 0.021 -0.0003977 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6315 0.06125 0.04414 0.3509 0.9657 0.9835 0.728 0.8723 0.9555 0.6216 ] Network output: [ 0.004699 0.9292 1.033 0.000154 -6.912e-05 0.02853 0.000116 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04482 0.0303 0.04984 0.05054 0.9808 0.9862 0.04597 0.9545 0.9711 0.06766 ] Network output: [ 0.1117 -0.3205 1.082 0.0005237 -0.0002351 1.017 0.0003947 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7214 0.5576 0.458 0.5289 0.9697 0.9859 0.7251 0.884 0.9617 0.6168 ] Network output: [ -0.07505 0.2493 0.9583 0.0006793 -0.000305 0.9452 0.000512 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5663 0.544 0.3913 0.3144 0.9829 0.9887 0.5668 0.9605 0.9736 0.4124 ] Network output: [ -0.1025 0.2823 0.9044 -0.0003191 0.0001433 1.017 -0.0002405 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5865 0.5824 0.434 0.2646 0.9801 0.987 0.5866 0.9512 0.9683 0.4388 ] Network output: [ 0.06402 0.7975 0.05979 -0.0007172 0.000322 1.012 -0.0005405 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05217 Epoch 1872 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04127 0.9726 0.9869 0.0001985 -8.909e-05 -0.04126 0.0001496 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02458 -0.004413 0.0185 0.03756 0.9311 0.9416 0.05095 0.86 0.8843 0.1316 ] Network output: [ 0.9562 0.08807 -0.02352 -0.0005274 0.0002367 0.0209 -0.0003974 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6317 0.06164 0.04461 0.3508 0.9657 0.9835 0.7282 0.8725 0.9555 0.6216 ] Network output: [ 0.004647 0.9292 1.034 0.0001532 -6.877e-05 0.02865 0.0001154 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04487 0.03035 0.04994 0.05061 0.9808 0.9863 0.04602 0.9546 0.9711 0.06775 ] Network output: [ 0.1117 -0.3204 1.082 0.0005224 -0.0002345 1.017 0.0003937 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7217 0.5583 0.4585 0.5289 0.9697 0.9859 0.7254 0.8842 0.9618 0.6168 ] Network output: [ -0.07494 0.2489 0.9586 0.0006792 -0.0003049 0.9451 0.0005119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5665 0.5443 0.3918 0.3147 0.983 0.9887 0.5671 0.9606 0.9736 0.4128 ] Network output: [ -0.1023 0.2822 0.9045 -0.0003166 0.0001421 1.017 -0.0002386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5866 0.5826 0.4343 0.2649 0.9801 0.987 0.5867 0.9512 0.9683 0.4391 ] Network output: [ 0.0637 0.7982 0.05954 -0.0007177 0.0003222 1.012 -0.0005409 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05205 Epoch 1873 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04128 0.9726 0.9869 0.0001983 -8.902e-05 -0.04125 0.0001494 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02458 -0.004416 0.01853 0.03759 0.9311 0.9417 0.05098 0.8601 0.8843 0.1317 ] Network output: [ 0.9563 0.08789 -0.02341 -0.000527 0.0002366 0.0208 -0.0003972 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6319 0.06203 0.04508 0.3508 0.9657 0.9835 0.7285 0.8726 0.9556 0.6216 ] Network output: [ 0.004594 0.9291 1.034 0.0001524 -6.842e-05 0.02877 0.0001149 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04492 0.03041 0.05003 0.05068 0.9808 0.9863 0.04607 0.9547 0.9712 0.06785 ] Network output: [ 0.1117 -0.3204 1.082 0.0005211 -0.0002339 1.018 0.0003927 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7219 0.5589 0.459 0.5289 0.9697 0.986 0.7256 0.8843 0.9618 0.6168 ] Network output: [ -0.07483 0.2486 0.9588 0.000679 -0.0003048 0.9451 0.0005117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5668 0.5446 0.3922 0.3151 0.983 0.9887 0.5673 0.9607 0.9737 0.4131 ] Network output: [ -0.1021 0.282 0.9046 -0.0003141 0.000141 1.016 -0.0002367 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5868 0.5828 0.4346 0.2653 0.9801 0.987 0.5869 0.9513 0.9684 0.4394 ] Network output: [ 0.06338 0.7989 0.0593 -0.0007181 0.0003224 1.012 -0.0005412 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05194 Epoch 1874 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04129 0.9726 0.9869 0.0001981 -8.894e-05 -0.04123 0.0001493 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02459 -0.004419 0.01855 0.03761 0.9311 0.9417 0.051 0.8602 0.8844 0.1318 ] Network output: [ 0.9564 0.08772 -0.0233 -0.0005266 0.0002364 0.0207 -0.0003969 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6321 0.06241 0.04555 0.3508 0.9657 0.9835 0.7287 0.8727 0.9556 0.6216 ] Network output: [ 0.004542 0.929 1.034 0.0001516 -6.806e-05 0.02889 0.0001143 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04497 0.03046 0.05013 0.05074 0.9808 0.9863 0.04612 0.9547 0.9712 0.06794 ] Network output: [ 0.1116 -0.3203 1.081 0.0005198 -0.0002333 1.018 0.0003917 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.5596 0.4595 0.5289 0.9697 0.986 0.7259 0.8844 0.9619 0.6168 ] Network output: [ -0.07473 0.2482 0.959 0.0006789 -0.0003048 0.945 0.0005116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.567 0.5449 0.3927 0.3154 0.983 0.9887 0.5676 0.9608 0.9737 0.4135 ] Network output: [ -0.1019 0.2819 0.9047 -0.0003116 0.0001399 1.016 -0.0002348 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5869 0.583 0.4349 0.2656 0.9802 0.987 0.5871 0.9514 0.9684 0.4396 ] Network output: [ 0.06306 0.7996 0.05905 -0.0007184 0.0003225 1.012 -0.0005414 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05182 Epoch 1875 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04129 0.9726 0.9868 0.0001979 -8.887e-05 -0.04122 0.0001492 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02459 -0.004422 0.01858 0.03764 0.9311 0.9417 0.05102 0.8603 0.8845 0.1319 ] Network output: [ 0.9564 0.08754 -0.02319 -0.0005263 0.0002363 0.02061 -0.0003966 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6323 0.06279 0.04601 0.3508 0.9657 0.9836 0.729 0.8729 0.9557 0.6216 ] Network output: [ 0.004489 0.929 1.034 0.0001508 -6.771e-05 0.02901 0.0001137 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04501 0.03052 0.05023 0.05081 0.9808 0.9863 0.04617 0.9548 0.9713 0.06803 ] Network output: [ 0.1116 -0.3203 1.081 0.0005184 -0.0002327 1.018 0.0003907 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7224 0.5602 0.46 0.5289 0.9697 0.986 0.7261 0.8846 0.9619 0.6169 ] Network output: [ -0.07462 0.2478 0.9593 0.0006787 -0.0003047 0.9449 0.0005115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5672 0.5452 0.3931 0.3157 0.983 0.9887 0.5678 0.9608 0.9737 0.4139 ] Network output: [ -0.1017 0.2817 0.9047 -0.0003091 0.0001388 1.016 -0.000233 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5871 0.5831 0.4352 0.2659 0.9802 0.987 0.5872 0.9515 0.9685 0.4399 ] Network output: [ 0.06274 0.8003 0.05881 -0.0007187 0.0003227 1.013 -0.0005417 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05171 Epoch 1876 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0413 0.9726 0.9868 0.0001978 -8.879e-05 -0.0412 0.000149 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0246 -0.004426 0.0186 0.03767 0.9312 0.9417 0.05104 0.8605 0.8846 0.132 ] Network output: [ 0.9565 0.08737 -0.02308 -0.0005259 0.0002361 0.02051 -0.0003963 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6325 0.06317 0.04647 0.3508 0.9658 0.9836 0.7292 0.873 0.9557 0.6216 ] Network output: [ 0.004437 0.9289 1.034 0.00015 -6.735e-05 0.02914 0.0001131 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04506 0.03058 0.05033 0.05088 0.9809 0.9863 0.04622 0.9549 0.9713 0.06812 ] Network output: [ 0.1116 -0.3202 1.081 0.000517 -0.0002321 1.018 0.0003896 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7227 0.5608 0.4605 0.5289 0.9698 0.986 0.7263 0.8847 0.962 0.6169 ] Network output: [ -0.07451 0.2475 0.9595 0.0006785 -0.0003046 0.9448 0.0005113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5675 0.5455 0.3936 0.316 0.983 0.9887 0.568 0.9609 0.9738 0.4143 ] Network output: [ -0.1015 0.2815 0.9048 -0.0003067 0.0001377 1.015 -0.0002311 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5872 0.5833 0.4355 0.2663 0.9802 0.987 0.5874 0.9516 0.9686 0.4402 ] Network output: [ 0.06243 0.8009 0.05857 -0.0007189 0.0003228 1.013 -0.0005418 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0516 Epoch 1877 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04131 0.9726 0.9867 0.0001976 -8.871e-05 -0.04119 0.0001489 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0246 -0.004429 0.01862 0.0377 0.9312 0.9417 0.05106 0.8606 0.8846 0.132 ] Network output: [ 0.9566 0.0872 -0.02297 -0.0005255 0.0002359 0.02041 -0.000396 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6327 0.06355 0.04693 0.3508 0.9658 0.9836 0.7294 0.8731 0.9558 0.6216 ] Network output: [ 0.004384 0.9289 1.034 0.0001492 -6.7e-05 0.02926 0.0001125 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04511 0.03063 0.05042 0.05094 0.9809 0.9863 0.04627 0.955 0.9714 0.06821 ] Network output: [ 0.1115 -0.3201 1.081 0.0005155 -0.0002314 1.018 0.0003885 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7229 0.5615 0.461 0.5289 0.9698 0.986 0.7266 0.8848 0.962 0.6169 ] Network output: [ -0.07441 0.2471 0.9597 0.0006783 -0.0003045 0.9447 0.0005112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5677 0.5458 0.394 0.3163 0.983 0.9888 0.5683 0.961 0.9738 0.4147 ] Network output: [ -0.1013 0.2814 0.9049 -0.0003042 0.0001366 1.015 -0.0002293 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5874 0.5835 0.4358 0.2666 0.9802 0.9871 0.5875 0.9517 0.9686 0.4405 ] Network output: [ 0.06212 0.8016 0.05833 -0.0007191 0.0003228 1.013 -0.0005419 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05149 Epoch 1878 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04131 0.9727 0.9867 0.0001974 -8.862e-05 -0.04117 0.0001488 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02461 -0.004432 0.01865 0.03772 0.9312 0.9418 0.05108 0.8607 0.8847 0.1321 ] Network output: [ 0.9567 0.08702 -0.02286 -0.0005251 0.0002358 0.02032 -0.0003958 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6329 0.06393 0.04739 0.3508 0.9658 0.9836 0.7297 0.8733 0.9558 0.6216 ] Network output: [ 0.004332 0.9288 1.034 0.0001484 -6.664e-05 0.02938 0.0001119 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04516 0.03069 0.05052 0.05101 0.9809 0.9863 0.04632 0.9551 0.9714 0.0683 ] Network output: [ 0.1115 -0.3201 1.081 0.0005141 -0.0002308 1.019 0.0003874 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7231 0.5621 0.4615 0.5289 0.9698 0.986 0.7268 0.8849 0.9621 0.6169 ] Network output: [ -0.0743 0.2468 0.9599 0.000678 -0.0003044 0.9446 0.000511 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.568 0.5461 0.3945 0.3167 0.983 0.9888 0.5685 0.961 0.9739 0.415 ] Network output: [ -0.1011 0.2812 0.905 -0.0003018 0.0001355 1.015 -0.0002275 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5876 0.5836 0.4361 0.2669 0.9802 0.9871 0.5877 0.9518 0.9687 0.4408 ] Network output: [ 0.06181 0.8023 0.05809 -0.0007192 0.0003229 1.013 -0.000542 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05138 Epoch 1879 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04131 0.9727 0.9866 0.0001972 -8.854e-05 -0.04115 0.0001486 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02462 -0.004435 0.01867 0.03775 0.9312 0.9418 0.05111 0.8608 0.8848 0.1322 ] Network output: [ 0.9568 0.08685 -0.02276 -0.0005248 0.0002356 0.02022 -0.0003955 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6331 0.06431 0.04784 0.3507 0.9658 0.9836 0.7299 0.8734 0.9559 0.6217 ] Network output: [ 0.00428 0.9288 1.034 0.0001476 -6.628e-05 0.0295 0.0001113 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0452 0.03074 0.05062 0.05107 0.9809 0.9863 0.04637 0.9551 0.9714 0.06839 ] Network output: [ 0.1115 -0.32 1.08 0.0005126 -0.0002301 1.019 0.0003863 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7234 0.5627 0.462 0.5289 0.9698 0.986 0.7271 0.8851 0.9621 0.617 ] Network output: [ -0.07419 0.2464 0.9602 0.0006778 -0.0003043 0.9446 0.0005108 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5682 0.5464 0.3949 0.317 0.9831 0.9888 0.5687 0.9611 0.9739 0.4154 ] Network output: [ -0.1009 0.2811 0.905 -0.0002994 0.0001344 1.014 -0.0002257 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5877 0.5838 0.4364 0.2673 0.9803 0.9871 0.5878 0.9519 0.9687 0.4411 ] Network output: [ 0.06151 0.803 0.05785 -0.0007192 0.0003229 1.013 -0.000542 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05126 Epoch 1880 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04132 0.9727 0.9866 0.000197 -8.845e-05 -0.04114 0.0001485 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02462 -0.004438 0.0187 0.03778 0.9313 0.9418 0.05113 0.8609 0.8849 0.1323 ] Network output: [ 0.9569 0.08669 -0.02265 -0.0005244 0.0002354 0.02013 -0.0003952 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6333 0.06468 0.04829 0.3507 0.9659 0.9836 0.7301 0.8735 0.9559 0.6217 ] Network output: [ 0.004227 0.9287 1.034 0.0001469 -6.593e-05 0.02962 0.0001107 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04525 0.0308 0.05071 0.05113 0.9809 0.9864 0.04641 0.9552 0.9715 0.06848 ] Network output: [ 0.1115 -0.32 1.08 0.0005111 -0.0002294 1.019 0.0003852 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7236 0.5633 0.4625 0.5288 0.9698 0.986 0.7273 0.8852 0.9622 0.617 ] Network output: [ -0.07408 0.2461 0.9604 0.0006775 -0.0003042 0.9445 0.0005106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5684 0.5467 0.3953 0.3173 0.9831 0.9888 0.569 0.9612 0.9739 0.4158 ] Network output: [ -0.1007 0.2809 0.9051 -0.0002971 0.0001334 1.014 -0.0002239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5879 0.584 0.4367 0.2676 0.9803 0.9871 0.588 0.9519 0.9688 0.4414 ] Network output: [ 0.0612 0.8037 0.05762 -0.0007191 0.0003228 1.013 -0.000542 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05116 Epoch 1881 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04132 0.9727 0.9866 0.0001968 -8.837e-05 -0.04112 0.0001483 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02463 -0.004442 0.01872 0.0378 0.9313 0.9418 0.05115 0.861 0.8849 0.1323 ] Network output: [ 0.9569 0.08652 -0.02255 -0.000524 0.0002352 0.02003 -0.0003949 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6334 0.06505 0.04874 0.3507 0.9659 0.9836 0.7303 0.8737 0.956 0.6217 ] Network output: [ 0.004175 0.9287 1.034 0.0001461 -6.557e-05 0.02974 0.0001101 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0453 0.03085 0.0508 0.0512 0.9809 0.9864 0.04646 0.9553 0.9715 0.06857 ] Network output: [ 0.1114 -0.3199 1.08 0.0005095 -0.0002287 1.019 0.000384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7238 0.5639 0.4629 0.5288 0.9698 0.986 0.7275 0.8853 0.9622 0.617 ] Network output: [ -0.07398 0.2457 0.9606 0.0006773 -0.000304 0.9444 0.0005104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5687 0.547 0.3957 0.3176 0.9831 0.9888 0.5692 0.9612 0.974 0.4162 ] Network output: [ -0.1005 0.2807 0.9052 -0.0002947 0.0001323 1.014 -0.0002221 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.588 0.5841 0.437 0.2679 0.9803 0.9871 0.5881 0.952 0.9688 0.4416 ] Network output: [ 0.0609 0.8043 0.05738 -0.000719 0.0003228 1.014 -0.0005419 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05105 Epoch 1882 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04132 0.9727 0.9865 0.0001966 -8.828e-05 -0.0411 0.0001482 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02463 -0.004445 0.01874 0.03783 0.9313 0.9418 0.05117 0.8611 0.885 0.1324 ] Network output: [ 0.957 0.08635 -0.02244 -0.0005236 0.0002351 0.01994 -0.0003946 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6336 0.06542 0.04919 0.3507 0.9659 0.9836 0.7306 0.8738 0.956 0.6217 ] Network output: [ 0.004123 0.9286 1.034 0.0001453 -6.521e-05 0.02986 0.0001095 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04535 0.0309 0.0509 0.05126 0.981 0.9864 0.04651 0.9554 0.9716 0.06866 ] Network output: [ 0.1114 -0.3199 1.08 0.000508 -0.000228 1.019 0.0003828 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7241 0.5645 0.4634 0.5288 0.9699 0.986 0.7278 0.8855 0.9622 0.617 ] Network output: [ -0.07387 0.2454 0.9608 0.000677 -0.0003039 0.9443 0.0005102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5689 0.5473 0.3962 0.3179 0.9831 0.9888 0.5695 0.9613 0.974 0.4165 ] Network output: [ -0.1003 0.2806 0.9053 -0.0002924 0.0001313 1.014 -0.0002204 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5882 0.5843 0.4373 0.2682 0.9803 0.9871 0.5883 0.9521 0.9689 0.4419 ] Network output: [ 0.0606 0.805 0.05715 -0.0007188 0.0003227 1.014 -0.0005417 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05094 Epoch 1883 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04133 0.9727 0.9865 0.0001964 -8.819e-05 -0.04108 0.000148 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02464 -0.004448 0.01876 0.03785 0.9313 0.9419 0.05119 0.8612 0.8851 0.1325 ] Network output: [ 0.9571 0.08619 -0.02234 -0.0005232 0.0002349 0.01985 -0.0003943 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6338 0.06579 0.04963 0.3507 0.9659 0.9837 0.7308 0.8739 0.9561 0.6217 ] Network output: [ 0.004071 0.9286 1.034 0.0001445 -6.485e-05 0.02997 0.0001089 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04539 0.03096 0.05099 0.05132 0.981 0.9864 0.04656 0.9555 0.9716 0.06874 ] Network output: [ 0.1114 -0.3198 1.08 0.0005064 -0.0002273 1.019 0.0003816 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7243 0.5651 0.4639 0.5288 0.9699 0.9861 0.728 0.8856 0.9623 0.6171 ] Network output: [ -0.07376 0.245 0.961 0.0006767 -0.0003038 0.9443 0.00051 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5691 0.5476 0.3966 0.3182 0.9831 0.9888 0.5697 0.9614 0.9741 0.4169 ] Network output: [ -0.1001 0.2804 0.9054 -0.0002901 0.0001303 1.013 -0.0002187 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5883 0.5845 0.4376 0.2685 0.9803 0.9871 0.5884 0.9522 0.9689 0.4422 ] Network output: [ 0.06031 0.8056 0.05692 -0.0007186 0.0003226 1.014 -0.0005415 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05083 Epoch 1884 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04133 0.9728 0.9865 0.0001962 -8.81e-05 -0.04107 0.0001479 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02465 -0.004452 0.01879 0.03788 0.9314 0.9419 0.05121 0.8613 0.8852 0.1325 ] Network output: [ 0.9572 0.08602 -0.02224 -0.0005228 0.0002347 0.01975 -0.000394 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.634 0.06616 0.05007 0.3507 0.9659 0.9837 0.731 0.874 0.9561 0.6217 ] Network output: [ 0.004018 0.9285 1.034 0.0001437 -6.45e-05 0.03009 0.0001083 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04544 0.03101 0.05108 0.05138 0.981 0.9864 0.04661 0.9555 0.9717 0.06883 ] Network output: [ 0.1113 -0.3198 1.08 0.0005047 -0.0002266 1.02 0.0003804 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7245 0.5658 0.4643 0.5288 0.9699 0.9861 0.7282 0.8857 0.9623 0.6171 ] Network output: [ -0.07365 0.2447 0.9612 0.0006764 -0.0003036 0.9442 0.0005097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5694 0.5479 0.397 0.3185 0.9831 0.9888 0.5699 0.9614 0.9741 0.4173 ] Network output: [ -0.0999 0.2803 0.9054 -0.0002879 0.0001292 1.013 -0.0002169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5885 0.5846 0.4379 0.2689 0.9804 0.9871 0.5886 0.9523 0.969 0.4425 ] Network output: [ 0.06001 0.8063 0.05669 -0.0007183 0.0003225 1.014 -0.0005413 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05072 Epoch 1885 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04133 0.9728 0.9864 0.000196 -8.8e-05 -0.04105 0.0001477 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02465 -0.004455 0.01881 0.0379 0.9314 0.9419 0.05123 0.8614 0.8852 0.1326 ] Network output: [ 0.9572 0.08586 -0.02214 -0.0005224 0.0002345 0.01966 -0.0003937 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6342 0.06652 0.05051 0.3506 0.966 0.9837 0.7313 0.8742 0.9562 0.6218 ] Network output: [ 0.003966 0.9285 1.034 0.0001429 -6.414e-05 0.03021 0.0001077 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04549 0.03107 0.05117 0.05144 0.981 0.9864 0.04666 0.9556 0.9717 0.06891 ] Network output: [ 0.1113 -0.3197 1.079 0.0005031 -0.0002258 1.02 0.0003791 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7248 0.5663 0.4648 0.5288 0.9699 0.9861 0.7285 0.8858 0.9624 0.6171 ] Network output: [ -0.07355 0.2443 0.9614 0.0006761 -0.0003035 0.9441 0.0005095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5696 0.5482 0.3974 0.3188 0.9831 0.9888 0.5702 0.9615 0.9741 0.4176 ] Network output: [ -0.09971 0.2801 0.9055 -0.0002856 0.0001282 1.013 -0.0002153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5886 0.5848 0.4382 0.2692 0.9804 0.9872 0.5887 0.9524 0.969 0.4427 ] Network output: [ 0.05972 0.8069 0.05646 -0.0007179 0.0003223 1.014 -0.000541 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05062 Epoch 1886 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04133 0.9728 0.9864 0.0001958 -8.791e-05 -0.04103 0.0001476 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02466 -0.004459 0.01883 0.03793 0.9314 0.9419 0.05125 0.8615 0.8853 0.1327 ] Network output: [ 0.9573 0.0857 -0.02204 -0.000522 0.0002343 0.01957 -0.0003934 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6344 0.06688 0.05095 0.3506 0.966 0.9837 0.7315 0.8743 0.9562 0.6218 ] Network output: [ 0.003914 0.9284 1.034 0.0001421 -6.378e-05 0.03033 0.0001071 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04553 0.03112 0.05126 0.0515 0.981 0.9864 0.0467 0.9557 0.9717 0.06899 ] Network output: [ 0.1113 -0.3197 1.079 0.0005014 -0.0002251 1.02 0.0003779 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.725 0.5669 0.4653 0.5288 0.9699 0.9861 0.7287 0.886 0.9624 0.6171 ] Network output: [ -0.07344 0.244 0.9616 0.0006757 -0.0003034 0.944 0.0005092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5698 0.5485 0.3978 0.3191 0.9831 0.9888 0.5704 0.9616 0.9742 0.418 ] Network output: [ -0.09952 0.2799 0.9056 -0.0002834 0.0001272 1.012 -0.0002136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5888 0.585 0.4385 0.2695 0.9804 0.9872 0.5889 0.9524 0.9691 0.443 ] Network output: [ 0.05943 0.8076 0.05624 -0.0007175 0.0003221 1.014 -0.0005407 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05051 Epoch 1887 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04133 0.9728 0.9863 0.0001956 -8.781e-05 -0.04101 0.0001474 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02466 -0.004462 0.01885 0.03795 0.9314 0.942 0.05127 0.8616 0.8854 0.1328 ] Network output: [ 0.9574 0.08554 -0.02194 -0.0005216 0.0002341 0.01948 -0.0003931 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6345 0.06725 0.05138 0.3506 0.966 0.9837 0.7317 0.8744 0.9563 0.6218 ] Network output: [ 0.003862 0.9284 1.034 0.0001413 -6.342e-05 0.03045 0.0001065 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04558 0.03117 0.05135 0.05155 0.981 0.9864 0.04675 0.9558 0.9718 0.06908 ] Network output: [ 0.1112 -0.3196 1.079 0.0004997 -0.0002243 1.02 0.0003766 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7252 0.5675 0.4657 0.5288 0.97 0.9861 0.7289 0.8861 0.9625 0.6172 ] Network output: [ -0.07333 0.2437 0.9618 0.0006754 -0.0003032 0.944 0.000509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5701 0.5488 0.3982 0.3194 0.9832 0.9888 0.5706 0.9616 0.9742 0.4183 ] Network output: [ -0.09933 0.2798 0.9057 -0.0002812 0.0001262 1.012 -0.0002119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5889 0.5851 0.4388 0.2698 0.9804 0.9872 0.589 0.9525 0.9691 0.4433 ] Network output: [ 0.05914 0.8082 0.05601 -0.000717 0.0003219 1.015 -0.0005403 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05041 Epoch 1888 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04133 0.9728 0.9863 0.0001954 -8.771e-05 -0.041 0.0001472 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02467 -0.004466 0.01887 0.03797 0.9315 0.942 0.05129 0.8617 0.8855 0.1328 ] Network output: [ 0.9575 0.08538 -0.02185 -0.0005211 0.000234 0.01938 -0.0003928 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6347 0.06761 0.05182 0.3506 0.966 0.9837 0.7319 0.8745 0.9563 0.6218 ] Network output: [ 0.00381 0.9284 1.034 0.0001405 -6.306e-05 0.03056 0.0001059 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04563 0.03123 0.05144 0.05161 0.9811 0.9864 0.0468 0.9558 0.9718 0.06916 ] Network output: [ 0.1112 -0.3195 1.079 0.0004979 -0.0002235 1.02 0.0003753 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7255 0.5681 0.4662 0.5288 0.97 0.9861 0.7292 0.8862 0.9625 0.6172 ] Network output: [ -0.07322 0.2433 0.962 0.000675 -0.000303 0.9439 0.0005087 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5703 0.549 0.3987 0.3197 0.9832 0.9889 0.5709 0.9617 0.9743 0.4187 ] Network output: [ -0.09914 0.2796 0.9058 -0.000279 0.0001253 1.012 -0.0002103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5891 0.5853 0.4391 0.2701 0.9804 0.9872 0.5892 0.9526 0.9692 0.4435 ] Network output: [ 0.05886 0.8089 0.05579 -0.0007164 0.0003216 1.015 -0.0005399 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0503 Epoch 1889 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04133 0.9728 0.9863 0.0001952 -8.762e-05 -0.04098 0.0001471 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02467 -0.004469 0.01889 0.038 0.9315 0.942 0.05131 0.8618 0.8855 0.1329 ] Network output: [ 0.9576 0.08522 -0.02175 -0.0005207 0.0002338 0.01929 -0.0003924 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6349 0.06796 0.05225 0.3506 0.966 0.9837 0.7321 0.8747 0.9564 0.6218 ] Network output: [ 0.003759 0.9283 1.034 0.0001397 -6.27e-05 0.03068 0.0001053 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04567 0.03128 0.05152 0.05167 0.9811 0.9865 0.04685 0.9559 0.9719 0.06924 ] Network output: [ 0.1112 -0.3195 1.079 0.0004962 -0.0002228 1.02 0.0003739 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7257 0.5687 0.4666 0.5287 0.97 0.9861 0.7294 0.8863 0.9626 0.6172 ] Network output: [ -0.07312 0.243 0.9622 0.0006747 -0.0003029 0.9438 0.0005084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5705 0.5493 0.3991 0.32 0.9832 0.9889 0.5711 0.9618 0.9743 0.419 ] Network output: [ -0.09895 0.2794 0.9059 -0.0002769 0.0001243 1.011 -0.0002086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5892 0.5854 0.4393 0.2704 0.9804 0.9872 0.5893 0.9527 0.9692 0.4438 ] Network output: [ 0.05858 0.8095 0.05557 -0.0007158 0.0003214 1.015 -0.0005395 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0502 Epoch 1890 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04133 0.9728 0.9862 0.0001949 -8.752e-05 -0.04096 0.0001469 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02468 -0.004473 0.01891 0.03802 0.9315 0.942 0.05133 0.8619 0.8856 0.133 ] Network output: [ 0.9576 0.08506 -0.02166 -0.0005203 0.0002336 0.0192 -0.0003921 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6351 0.06832 0.05267 0.3505 0.9661 0.9837 0.7324 0.8748 0.9564 0.6218 ] Network output: [ 0.003707 0.9283 1.034 0.0001389 -6.234e-05 0.0308 0.0001047 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04572 0.03133 0.05161 0.05172 0.9811 0.9865 0.04689 0.956 0.9719 0.06932 ] Network output: [ 0.1111 -0.3194 1.079 0.0004944 -0.000222 1.021 0.0003726 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7259 0.5693 0.4671 0.5287 0.97 0.9861 0.7296 0.8865 0.9626 0.6173 ] Network output: [ -0.07301 0.2427 0.9623 0.0006743 -0.0003027 0.9437 0.0005082 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5708 0.5496 0.3995 0.3203 0.9832 0.9889 0.5713 0.9618 0.9743 0.4194 ] Network output: [ -0.09877 0.2793 0.906 -0.0002747 0.0001233 1.011 -0.000207 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5894 0.5856 0.4396 0.2707 0.9805 0.9872 0.5895 0.9528 0.9693 0.4441 ] Network output: [ 0.0583 0.8101 0.05535 -0.0007152 0.0003211 1.015 -0.000539 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0501 Epoch 1891 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04133 0.9729 0.9862 0.0001947 -8.741e-05 -0.04094 0.0001467 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02468 -0.004476 0.01893 0.03804 0.9315 0.942 0.05135 0.862 0.8857 0.133 ] Network output: [ 0.9577 0.08491 -0.02156 -0.0005198 0.0002334 0.01911 -0.0003918 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6353 0.06867 0.0531 0.3505 0.9661 0.9837 0.7326 0.8749 0.9564 0.6219 ] Network output: [ 0.003655 0.9282 1.034 0.0001381 -6.198e-05 0.03091 0.000104 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04576 0.03139 0.0517 0.05178 0.9811 0.9865 0.04694 0.9561 0.972 0.0694 ] Network output: [ 0.1111 -0.3194 1.078 0.0004926 -0.0002211 1.021 0.0003712 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7261 0.5699 0.4675 0.5287 0.97 0.9861 0.7298 0.8866 0.9626 0.6173 ] Network output: [ -0.0729 0.2423 0.9625 0.0006739 -0.0003025 0.9437 0.0005079 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.571 0.5499 0.3999 0.3206 0.9832 0.9889 0.5716 0.9619 0.9744 0.4197 ] Network output: [ -0.09859 0.2791 0.9061 -0.0002726 0.0001224 1.011 -0.0002054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5895 0.5858 0.4399 0.271 0.9805 0.9872 0.5896 0.9529 0.9693 0.4443 ] Network output: [ 0.05802 0.8108 0.05513 -0.0007145 0.0003207 1.015 -0.0005384 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05 Epoch 1892 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04133 0.9729 0.9862 0.0001945 -8.731e-05 -0.04092 0.0001466 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02469 -0.00448 0.01896 0.03806 0.9316 0.9421 0.05137 0.8621 0.8857 0.1331 ] Network output: [ 0.9578 0.08475 -0.02147 -0.0005194 0.0002332 0.01902 -0.0003914 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6354 0.06902 0.05352 0.3505 0.9661 0.9838 0.7328 0.875 0.9565 0.6219 ] Network output: [ 0.003604 0.9282 1.034 0.0001373 -6.162e-05 0.03103 0.0001034 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04581 0.03144 0.05178 0.05183 0.9811 0.9865 0.04699 0.9561 0.972 0.06947 ] Network output: [ 0.1111 -0.3193 1.078 0.0004908 -0.0002203 1.021 0.0003699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7263 0.5704 0.468 0.5287 0.9701 0.9861 0.7301 0.8867 0.9627 0.6173 ] Network output: [ -0.0728 0.242 0.9627 0.0006735 -0.0003024 0.9436 0.0005076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5712 0.5502 0.4003 0.3208 0.9832 0.9889 0.5718 0.962 0.9744 0.4201 ] Network output: [ -0.0984 0.2789 0.9062 -0.0002705 0.0001214 1.011 -0.0002039 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5897 0.5859 0.4402 0.2713 0.9805 0.9872 0.5898 0.9529 0.9694 0.4446 ] Network output: [ 0.05775 0.8114 0.05491 -0.0007137 0.0003204 1.015 -0.0005379 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0499 Epoch 1893 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04133 0.9729 0.9861 0.0001943 -8.721e-05 -0.0409 0.0001464 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02469 -0.004483 0.01898 0.03809 0.9316 0.9421 0.05139 0.8622 0.8858 0.1331 ] Network output: [ 0.9579 0.0846 -0.02138 -0.0005189 0.000233 0.01893 -0.0003911 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6356 0.06937 0.05394 0.3505 0.9661 0.9838 0.733 0.8752 0.9565 0.6219 ] Network output: [ 0.003553 0.9281 1.034 0.0001365 -6.126e-05 0.03114 0.0001028 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04586 0.03149 0.05187 0.05189 0.9811 0.9865 0.04703 0.9562 0.972 0.06955 ] Network output: [ 0.111 -0.3193 1.078 0.0004889 -0.0002195 1.021 0.0003685 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7266 0.571 0.4684 0.5287 0.9701 0.9862 0.7303 0.8868 0.9627 0.6173 ] Network output: [ -0.07269 0.2417 0.9629 0.0006731 -0.0003022 0.9435 0.0005073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5715 0.5505 0.4006 0.3211 0.9832 0.9889 0.572 0.962 0.9745 0.4204 ] Network output: [ -0.09822 0.2788 0.9063 -0.0002684 0.0001205 1.01 -0.0002023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5898 0.5861 0.4405 0.2716 0.9805 0.9873 0.5899 0.953 0.9694 0.4449 ] Network output: [ 0.05747 0.812 0.05469 -0.0007129 0.00032 1.015 -0.0005372 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04979 Epoch 1894 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04132 0.9729 0.9861 0.000194 -8.71e-05 -0.04088 0.0001462 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0247 -0.004487 0.019 0.03811 0.9316 0.9421 0.05141 0.8623 0.8859 0.1332 ] Network output: [ 0.9579 0.08445 -0.02129 -0.0005185 0.0002328 0.01884 -0.0003907 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6358 0.06972 0.05436 0.3504 0.9661 0.9838 0.7332 0.8753 0.9566 0.6219 ] Network output: [ 0.003501 0.9281 1.034 0.0001357 -6.09e-05 0.03126 0.0001022 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0459 0.03154 0.05195 0.05194 0.9812 0.9865 0.04708 0.9563 0.9721 0.06963 ] Network output: [ 0.111 -0.3192 1.078 0.000487 -0.0002187 1.021 0.0003671 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7268 0.5716 0.4689 0.5287 0.9701 0.9862 0.7305 0.8869 0.9628 0.6174 ] Network output: [ -0.07258 0.2414 0.9631 0.0006727 -0.000302 0.9435 0.0005069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5717 0.5508 0.401 0.3214 0.9833 0.9889 0.5722 0.9621 0.9745 0.4207 ] Network output: [ -0.09804 0.2786 0.9064 -0.0002664 0.0001196 1.01 -0.0002008 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.59 0.5862 0.4407 0.2719 0.9805 0.9873 0.5901 0.9531 0.9695 0.4451 ] Network output: [ 0.0572 0.8126 0.05448 -0.000712 0.0003196 1.016 -0.0005366 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0497 Epoch 1895 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04132 0.9729 0.9861 0.0001938 -8.7e-05 -0.04086 0.000146 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0247 -0.00449 0.01901 0.03813 0.9316 0.9421 0.05142 0.8624 0.886 0.1333 ] Network output: [ 0.958 0.0843 -0.0212 -0.000518 0.0002326 0.01875 -0.0003904 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.636 0.07007 0.05478 0.3504 0.9662 0.9838 0.7334 0.8754 0.9566 0.622 ] Network output: [ 0.00345 0.9281 1.034 0.0001349 -6.054e-05 0.03137 0.0001016 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04595 0.03159 0.05203 0.05199 0.9812 0.9865 0.04713 0.9564 0.9721 0.0697 ] Network output: [ 0.111 -0.3191 1.078 0.0004852 -0.0002178 1.021 0.0003656 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.727 0.5721 0.4693 0.5287 0.9701 0.9862 0.7307 0.8871 0.9628 0.6174 ] Network output: [ -0.07247 0.2411 0.9632 0.0006722 -0.0003018 0.9434 0.0005066 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5719 0.551 0.4014 0.3217 0.9833 0.9889 0.5725 0.9622 0.9745 0.4211 ] Network output: [ -0.09786 0.2784 0.9065 -0.0002644 0.0001187 1.01 -0.0001992 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5901 0.5864 0.441 0.2722 0.9805 0.9873 0.5902 0.9532 0.9695 0.4454 ] Network output: [ 0.05693 0.8132 0.05426 -0.0007111 0.0003192 1.016 -0.0005359 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0496 Epoch 1896 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04132 0.9729 0.9861 0.0001935 -8.689e-05 -0.04085 0.0001459 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02471 -0.004494 0.01903 0.03815 0.9317 0.9421 0.05144 0.8625 0.886 0.1333 ] Network output: [ 0.9581 0.08415 -0.02111 -0.0005175 0.0002323 0.01866 -0.00039 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6361 0.07041 0.05519 0.3504 0.9662 0.9838 0.7336 0.8755 0.9567 0.622 ] Network output: [ 0.003399 0.928 1.034 0.0001341 -6.018e-05 0.03149 0.000101 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04599 0.03165 0.05211 0.05204 0.9812 0.9865 0.04717 0.9564 0.9722 0.06978 ] Network output: [ 0.1109 -0.3191 1.078 0.0004832 -0.0002169 1.022 0.0003642 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7272 0.5727 0.4697 0.5287 0.9701 0.9862 0.7309 0.8872 0.9628 0.6174 ] Network output: [ -0.07237 0.2407 0.9634 0.0006718 -0.0003016 0.9433 0.0005063 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5721 0.5513 0.4018 0.322 0.9833 0.9889 0.5727 0.9622 0.9746 0.4214 ] Network output: [ -0.09769 0.2783 0.9066 -0.0002624 0.0001178 1.009 -0.0001977 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5903 0.5866 0.4413 0.2725 0.9806 0.9873 0.5904 0.9533 0.9696 0.4456 ] Network output: [ 0.05667 0.8139 0.05405 -0.0007101 0.0003188 1.016 -0.0005352 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0495 Epoch 1897 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04131 0.973 0.986 0.0001933 -8.678e-05 -0.04083 0.0001457 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02471 -0.004498 0.01905 0.03817 0.9317 0.9422 0.05146 0.8626 0.8861 0.1334 ] Network output: [ 0.9582 0.084 -0.02102 -0.0005171 0.0002321 0.01857 -0.0003897 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6363 0.07075 0.0556 0.3504 0.9662 0.9838 0.7339 0.8757 0.9567 0.622 ] Network output: [ 0.003348 0.928 1.034 0.0001333 -5.983e-05 0.0316 0.0001004 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04604 0.0317 0.05219 0.05209 0.9812 0.9866 0.04722 0.9565 0.9722 0.06985 ] Network output: [ 0.1109 -0.319 1.078 0.0004813 -0.0002161 1.022 0.0003627 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7274 0.5733 0.4702 0.5286 0.9701 0.9862 0.7311 0.8873 0.9629 0.6175 ] Network output: [ -0.07226 0.2404 0.9636 0.0006713 -0.0003014 0.9433 0.0005059 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5724 0.5516 0.4022 0.3222 0.9833 0.9889 0.5729 0.9623 0.9746 0.4217 ] Network output: [ -0.09751 0.2781 0.9067 -0.0002604 0.0001169 1.009 -0.0001962 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5904 0.5867 0.4415 0.2728 0.9806 0.9873 0.5905 0.9533 0.9696 0.4459 ] Network output: [ 0.0564 0.8145 0.05384 -0.0007091 0.0003183 1.016 -0.0005344 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0494 Epoch 1898 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04131 0.973 0.986 0.0001931 -8.667e-05 -0.04081 0.0001455 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02472 -0.004501 0.01907 0.03819 0.9317 0.9422 0.05148 0.8627 0.8862 0.1335 ] Network output: [ 0.9583 0.08385 -0.02094 -0.0005166 0.0002319 0.01848 -0.0003893 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6365 0.0711 0.05601 0.3503 0.9662 0.9838 0.7341 0.8758 0.9568 0.622 ] Network output: [ 0.003298 0.928 1.034 0.0001325 -5.947e-05 0.03171 9.983e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04608 0.03175 0.05228 0.05214 0.9812 0.9866 0.04727 0.9566 0.9722 0.06992 ] Network output: [ 0.1109 -0.319 1.077 0.0004793 -0.0002152 1.022 0.0003612 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7276 0.5738 0.4706 0.5286 0.9702 0.9862 0.7314 0.8874 0.9629 0.6175 ] Network output: [ -0.07216 0.2401 0.9637 0.0006709 -0.0003012 0.9432 0.0005056 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5726 0.5519 0.4026 0.3225 0.9833 0.989 0.5731 0.9623 0.9747 0.4221 ] Network output: [ -0.09734 0.2779 0.9068 -0.0002584 0.000116 1.009 -0.0001948 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5906 0.5869 0.4418 0.2731 0.9806 0.9873 0.5907 0.9534 0.9697 0.4461 ] Network output: [ 0.05614 0.8151 0.05363 -0.000708 0.0003179 1.016 -0.0005336 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0493 Epoch 1899 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0413 0.973 0.986 0.0001928 -8.656e-05 -0.04079 0.0001453 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02472 -0.004505 0.01909 0.03821 0.9317 0.9422 0.0515 0.8628 0.8862 0.1335 ] Network output: [ 0.9583 0.0837 -0.02085 -0.0005161 0.0002317 0.01839 -0.0003889 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6366 0.07143 0.05642 0.3503 0.9662 0.9838 0.7343 0.8759 0.9568 0.6221 ] Network output: [ 0.003247 0.9279 1.034 0.0001317 -5.911e-05 0.03182 9.922e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04613 0.0318 0.05236 0.05219 0.9812 0.9866 0.04731 0.9567 0.9723 0.07 ] Network output: [ 0.1108 -0.3189 1.077 0.0004774 -0.0002143 1.022 0.0003597 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7279 0.5744 0.471 0.5286 0.9702 0.9862 0.7316 0.8875 0.963 0.6175 ] Network output: [ -0.07205 0.2398 0.9639 0.0006704 -0.000301 0.9431 0.0005052 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5728 0.5522 0.4029 0.3228 0.9833 0.989 0.5734 0.9624 0.9747 0.4224 ] Network output: [ -0.09716 0.2777 0.9069 -0.0002565 0.0001151 1.009 -0.0001933 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5907 0.587 0.4421 0.2734 0.9806 0.9873 0.5908 0.9535 0.9697 0.4464 ] Network output: [ 0.05588 0.8157 0.05342 -0.0007069 0.0003173 1.016 -0.0005327 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04921 Epoch 1900 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0413 0.973 0.9859 0.0001926 -8.644e-05 -0.04077 0.0001451 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02473 -0.004509 0.01911 0.03823 0.9318 0.9422 0.05151 0.8629 0.8863 0.1336 ] Network output: [ 0.9584 0.08356 -0.02076 -0.0005156 0.0002315 0.01831 -0.0003885 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6368 0.07177 0.05682 0.3503 0.9663 0.9838 0.7345 0.876 0.9569 0.6221 ] Network output: [ 0.003197 0.9279 1.034 0.0001309 -5.875e-05 0.03194 9.862e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04617 0.03185 0.05243 0.05224 0.9813 0.9866 0.04736 0.9567 0.9723 0.07007 ] Network output: [ 0.1108 -0.3189 1.077 0.0004754 -0.0002134 1.022 0.0003582 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7281 0.5749 0.4714 0.5286 0.9702 0.9862 0.7318 0.8877 0.963 0.6176 ] Network output: [ -0.07194 0.2395 0.964 0.0006699 -0.0003007 0.9431 0.0005049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.573 0.5524 0.4033 0.323 0.9833 0.989 0.5736 0.9625 0.9747 0.4227 ] Network output: [ -0.09699 0.2776 0.907 -0.0002546 0.0001143 1.008 -0.0001918 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5908 0.5872 0.4423 0.2737 0.9806 0.9873 0.591 0.9536 0.9698 0.4466 ] Network output: [ 0.05562 0.8163 0.05322 -0.0007057 0.0003168 1.016 -0.0005319 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04911 Epoch 1901 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04129 0.973 0.9859 0.0001923 -8.633e-05 -0.04075 0.0001449 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02473 -0.004513 0.01913 0.03825 0.9318 0.9423 0.05153 0.863 0.8864 0.1336 ] Network output: [ 0.9585 0.08341 -0.02068 -0.0005151 0.0002312 0.01822 -0.0003882 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.637 0.07211 0.05722 0.3502 0.9663 0.9839 0.7347 0.8761 0.9569 0.6221 ] Network output: [ 0.003146 0.9279 1.034 0.0001301 -5.839e-05 0.03205 9.802e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04621 0.0319 0.05251 0.05229 0.9813 0.9866 0.0474 0.9568 0.9724 0.07014 ] Network output: [ 0.1107 -0.3188 1.077 0.0004733 -0.0002125 1.022 0.0003567 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7283 0.5754 0.4719 0.5286 0.9702 0.9862 0.732 0.8878 0.9631 0.6176 ] Network output: [ -0.07184 0.2392 0.9642 0.0006694 -0.0003005 0.943 0.0005045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5733 0.5527 0.4037 0.3233 0.9833 0.989 0.5738 0.9625 0.9748 0.423 ] Network output: [ -0.09682 0.2774 0.9071 -0.0002527 0.0001134 1.008 -0.0001904 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.591 0.5873 0.4426 0.274 0.9807 0.9874 0.5911 0.9536 0.9698 0.4469 ] Network output: [ 0.05537 0.8168 0.05301 -0.0007045 0.0003163 1.017 -0.000531 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04901 Epoch 1902 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04129 0.973 0.9859 0.000192 -8.621e-05 -0.04073 0.0001447 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02474 -0.004516 0.01915 0.03826 0.9318 0.9423 0.05155 0.8631 0.8865 0.1337 ] Network output: [ 0.9586 0.08327 -0.0206 -0.0005145 0.000231 0.01813 -0.0003878 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6371 0.07244 0.05762 0.3502 0.9663 0.9839 0.7349 0.8763 0.957 0.6221 ] Network output: [ 0.003096 0.9278 1.034 0.0001293 -5.803e-05 0.03216 9.742e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04626 0.03195 0.05259 0.05234 0.9813 0.9866 0.04745 0.9569 0.9724 0.07021 ] Network output: [ 0.1107 -0.3187 1.077 0.0004713 -0.0002116 1.022 0.0003552 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7285 0.576 0.4723 0.5286 0.9702 0.9862 0.7322 0.8879 0.9631 0.6176 ] Network output: [ -0.07173 0.2389 0.9643 0.0006689 -0.0003003 0.943 0.0005041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5735 0.553 0.4041 0.3236 0.9834 0.989 0.574 0.9626 0.9748 0.4234 ] Network output: [ -0.09665 0.2772 0.9072 -0.0002508 0.0001126 1.008 -0.000189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5911 0.5875 0.4429 0.2743 0.9807 0.9874 0.5912 0.9537 0.9698 0.4471 ] Network output: [ 0.05512 0.8174 0.05281 -0.0007033 0.0003157 1.017 -0.00053 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04892 Epoch 1903 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04128 0.9731 0.9859 0.0001918 -8.61e-05 -0.04071 0.0001445 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02474 -0.00452 0.01916 0.03828 0.9318 0.9423 0.05157 0.8632 0.8865 0.1337 ] Network output: [ 0.9586 0.08313 -0.02052 -0.000514 0.0002308 0.01804 -0.0003874 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6373 0.07277 0.05802 0.3502 0.9663 0.9839 0.7351 0.8764 0.957 0.6222 ] Network output: [ 0.003046 0.9278 1.034 0.0001285 -5.767e-05 0.03227 9.682e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0463 0.032 0.05267 0.05238 0.9813 0.9866 0.04749 0.9569 0.9724 0.07028 ] Network output: [ 0.1107 -0.3187 1.077 0.0004692 -0.0002107 1.023 0.0003536 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7287 0.5765 0.4727 0.5286 0.9702 0.9863 0.7324 0.888 0.9631 0.6177 ] Network output: [ -0.07163 0.2386 0.9645 0.0006684 -0.0003001 0.9429 0.0005037 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5737 0.5532 0.4044 0.3238 0.9834 0.989 0.5743 0.9626 0.9748 0.4237 ] Network output: [ -0.09648 0.2771 0.9073 -0.0002489 0.0001117 1.008 -0.0001876 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5913 0.5876 0.4431 0.2746 0.9807 0.9874 0.5914 0.9538 0.9699 0.4474 ] Network output: [ 0.05486 0.818 0.05261 -0.000702 0.0003151 1.017 -0.000529 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04883 Epoch 1904 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04128 0.9731 0.9858 0.0001915 -8.598e-05 -0.04069 0.0001443 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02475 -0.004524 0.01918 0.0383 0.9319 0.9423 0.05158 0.8633 0.8866 0.1338 ] Network output: [ 0.9587 0.08299 -0.02043 -0.0005135 0.0002305 0.01796 -0.000387 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6375 0.0731 0.05842 0.3501 0.9663 0.9839 0.7353 0.8765 0.957 0.6222 ] Network output: [ 0.002996 0.9278 1.034 0.0001277 -5.732e-05 0.03238 9.622e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04635 0.03205 0.05274 0.05243 0.9813 0.9866 0.04754 0.957 0.9725 0.07034 ] Network output: [ 0.1106 -0.3186 1.077 0.0004672 -0.0002097 1.023 0.0003521 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7289 0.577 0.4731 0.5285 0.9703 0.9863 0.7326 0.8881 0.9632 0.6177 ] Network output: [ -0.07152 0.2383 0.9646 0.0006679 -0.0002998 0.9428 0.0005033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5739 0.5535 0.4048 0.3241 0.9834 0.989 0.5745 0.9627 0.9749 0.424 ] Network output: [ -0.09632 0.2769 0.9074 -0.0002471 0.0001109 1.007 -0.0001862 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5914 0.5878 0.4434 0.2749 0.9807 0.9874 0.5915 0.9539 0.9699 0.4476 ] Network output: [ 0.05462 0.8186 0.0524 -0.0007006 0.0003145 1.017 -0.000528 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04873 Epoch 1905 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04127 0.9731 0.9858 0.0001913 -8.586e-05 -0.04067 0.0001441 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02475 -0.004528 0.0192 0.03832 0.9319 0.9423 0.0516 0.8634 0.8867 0.1338 ] Network output: [ 0.9588 0.08285 -0.02035 -0.0005129 0.0002303 0.01787 -0.0003866 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6376 0.07343 0.05881 0.3501 0.9664 0.9839 0.7355 0.8766 0.9571 0.6222 ] Network output: [ 0.002947 0.9277 1.034 0.0001269 -5.696e-05 0.03249 9.562e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04639 0.0321 0.05282 0.05247 0.9813 0.9867 0.04758 0.9571 0.9725 0.07041 ] Network output: [ 0.1106 -0.3186 1.076 0.0004651 -0.0002088 1.023 0.0003505 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7291 0.5776 0.4735 0.5285 0.9703 0.9863 0.7328 0.8882 0.9632 0.6178 ] Network output: [ -0.07142 0.238 0.9648 0.0006673 -0.0002996 0.9428 0.0005029 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5741 0.5538 0.4051 0.3243 0.9834 0.989 0.5747 0.9628 0.9749 0.4243 ] Network output: [ -0.09615 0.2767 0.9075 -0.0002453 0.0001101 1.007 -0.0001848 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5916 0.5879 0.4436 0.2752 0.9807 0.9874 0.5917 0.9539 0.97 0.4478 ] Network output: [ 0.05437 0.8192 0.0522 -0.0006993 0.0003139 1.017 -0.000527 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04864 Epoch 1906 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04126 0.9731 0.9858 0.000191 -8.574e-05 -0.04065 0.0001439 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02476 -0.004532 0.01922 0.03833 0.9319 0.9424 0.05162 0.8635 0.8867 0.1339 ] Network output: [ 0.9588 0.08271 -0.02027 -0.0005124 0.00023 0.01778 -0.0003861 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6378 0.07375 0.0592 0.3501 0.9664 0.9839 0.7357 0.8767 0.9571 0.6222 ] Network output: [ 0.002897 0.9277 1.034 0.0001261 -5.66e-05 0.0326 9.502e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04643 0.03215 0.05289 0.05252 0.9813 0.9867 0.04763 0.9571 0.9726 0.07048 ] Network output: [ 0.1105 -0.3185 1.076 0.000463 -0.0002078 1.023 0.0003489 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7293 0.5781 0.4739 0.5285 0.9703 0.9863 0.733 0.8884 0.9633 0.6178 ] Network output: [ -0.07131 0.2377 0.9649 0.0006668 -0.0002994 0.9427 0.0005025 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5744 0.5541 0.4055 0.3246 0.9834 0.989 0.5749 0.9628 0.9749 0.4246 ] Network output: [ -0.09599 0.2765 0.9076 -0.0002435 0.0001093 1.007 -0.0001835 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5917 0.5881 0.4439 0.2754 0.9807 0.9874 0.5918 0.954 0.97 0.4481 ] Network output: [ 0.05412 0.8197 0.05201 -0.0006979 0.0003133 1.017 -0.0005259 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04855 Epoch 1907 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04125 0.9731 0.9858 0.0001907 -8.562e-05 -0.04063 0.0001437 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02476 -0.004536 0.01923 0.03835 0.9319 0.9424 0.05163 0.8636 0.8868 0.1339 ] Network output: [ 0.9589 0.08257 -0.02019 -0.0005118 0.0002298 0.0177 -0.0003857 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.638 0.07408 0.05959 0.35 0.9664 0.9839 0.7359 0.8768 0.9572 0.6223 ] Network output: [ 0.002848 0.9277 1.034 0.0001253 -5.625e-05 0.03271 9.442e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04648 0.0322 0.05297 0.05256 0.9814 0.9867 0.04767 0.9572 0.9726 0.07054 ] Network output: [ 0.1105 -0.3185 1.076 0.0004608 -0.0002069 1.023 0.0003473 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7295 0.5786 0.4743 0.5285 0.9703 0.9863 0.7332 0.8885 0.9633 0.6178 ] Network output: [ -0.07121 0.2374 0.9651 0.0006663 -0.0002991 0.9427 0.0005021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5746 0.5543 0.4059 0.3248 0.9834 0.989 0.5751 0.9629 0.975 0.4249 ] Network output: [ -0.09582 0.2764 0.9077 -0.0002417 0.0001085 1.007 -0.0001821 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5918 0.5882 0.4441 0.2757 0.9808 0.9874 0.5919 0.9541 0.9701 0.4483 ] Network output: [ 0.05388 0.8203 0.05181 -0.0006964 0.0003126 1.017 -0.0005248 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04846 Epoch 1908 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04125 0.9731 0.9858 0.0001905 -8.55e-05 -0.04061 0.0001435 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02476 -0.004539 0.01925 0.03837 0.932 0.9424 0.05165 0.8637 0.8869 0.134 ] Network output: [ 0.959 0.08243 -0.02012 -0.0005112 0.0002295 0.01761 -0.0003853 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6381 0.0744 0.05998 0.35 0.9664 0.9839 0.7361 0.877 0.9572 0.6223 ] Network output: [ 0.002799 0.9276 1.034 0.0001245 -5.589e-05 0.03282 9.382e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04652 0.03225 0.05304 0.0526 0.9814 0.9867 0.04771 0.9573 0.9726 0.07061 ] Network output: [ 0.1105 -0.3184 1.076 0.0004587 -0.0002059 1.023 0.0003457 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7297 0.5791 0.4747 0.5285 0.9703 0.9863 0.7334 0.8886 0.9633 0.6179 ] Network output: [ -0.0711 0.2371 0.9652 0.0006657 -0.0002989 0.9426 0.0005017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5748 0.5546 0.4062 0.3251 0.9834 0.989 0.5753 0.9629 0.975 0.4252 ] Network output: [ -0.09566 0.2762 0.9078 -0.0002399 0.0001077 1.006 -0.0001808 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.592 0.5884 0.4444 0.276 0.9808 0.9874 0.5921 0.9542 0.9701 0.4485 ] Network output: [ 0.05364 0.8209 0.05161 -0.0006949 0.000312 1.017 -0.0005237 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04837 Epoch 1909 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04124 0.9731 0.9857 0.0001902 -8.538e-05 -0.04059 0.0001433 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02477 -0.004543 0.01927 0.03838 0.932 0.9424 0.05167 0.8638 0.8869 0.134 ] Network output: [ 0.9591 0.0823 -0.02004 -0.0005107 0.0002293 0.01752 -0.0003849 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6383 0.07472 0.06036 0.35 0.9664 0.9839 0.7363 0.8771 0.9573 0.6223 ] Network output: [ 0.00275 0.9276 1.034 0.0001237 -5.553e-05 0.03293 9.322e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04656 0.03229 0.05311 0.05264 0.9814 0.9867 0.04776 0.9574 0.9727 0.07067 ] Network output: [ 0.1104 -0.3183 1.076 0.0004565 -0.0002049 1.024 0.000344 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7299 0.5796 0.4751 0.5284 0.9704 0.9863 0.7336 0.8887 0.9634 0.6179 ] Network output: [ -0.071 0.2368 0.9653 0.0006652 -0.0002986 0.9425 0.0005013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.575 0.5549 0.4066 0.3253 0.9835 0.9891 0.5756 0.963 0.9751 0.4255 ] Network output: [ -0.0955 0.276 0.9079 -0.0002382 0.0001069 1.006 -0.0001795 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5921 0.5885 0.4446 0.2763 0.9808 0.9874 0.5922 0.9542 0.9702 0.4488 ] Network output: [ 0.0534 0.8214 0.05142 -0.0006934 0.0003113 1.018 -0.0005226 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04828 Epoch 1910 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04123 0.9732 0.9857 0.0001899 -8.525e-05 -0.04057 0.0001431 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02477 -0.004547 0.01928 0.0384 0.932 0.9424 0.05168 0.8639 0.887 0.1341 ] Network output: [ 0.9591 0.08216 -0.01996 -0.0005101 0.000229 0.01744 -0.0003844 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6384 0.07504 0.06074 0.3499 0.9664 0.984 0.7365 0.8772 0.9573 0.6223 ] Network output: [ 0.002701 0.9276 1.034 0.0001229 -5.518e-05 0.03303 9.263e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0466 0.03234 0.05318 0.05269 0.9814 0.9867 0.0478 0.9574 0.9727 0.07073 ] Network output: [ 0.1104 -0.3183 1.076 0.0004543 -0.000204 1.024 0.0003424 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7301 0.5801 0.4755 0.5284 0.9704 0.9863 0.7338 0.8888 0.9634 0.6179 ] Network output: [ -0.07089 0.2365 0.9655 0.0006646 -0.0002984 0.9425 0.0005009 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5752 0.5551 0.4069 0.3255 0.9835 0.9891 0.5758 0.9631 0.9751 0.4258 ] Network output: [ -0.09534 0.2759 0.908 -0.0002365 0.0001062 1.006 -0.0001782 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5922 0.5887 0.4449 0.2766 0.9808 0.9875 0.5924 0.9543 0.9702 0.449 ] Network output: [ 0.05317 0.822 0.05123 -0.0006918 0.0003106 1.018 -0.0005214 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04819 Epoch 1911 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04122 0.9732 0.9857 0.0001896 -8.513e-05 -0.04055 0.0001429 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02478 -0.004551 0.0193 0.03841 0.932 0.9425 0.0517 0.864 0.8871 0.1341 ] Network output: [ 0.9592 0.08203 -0.01989 -0.0005095 0.0002287 0.01735 -0.000384 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6386 0.07536 0.06112 0.3499 0.9665 0.984 0.7367 0.8773 0.9574 0.6224 ] Network output: [ 0.002653 0.9276 1.034 0.0001221 -5.482e-05 0.03314 9.203e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04665 0.03239 0.05325 0.05273 0.9814 0.9867 0.04785 0.9575 0.9727 0.07079 ] Network output: [ 0.1103 -0.3182 1.076 0.0004521 -0.000203 1.024 0.0003408 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7303 0.5807 0.4759 0.5284 0.9704 0.9863 0.734 0.8889 0.9635 0.618 ] Network output: [ -0.07079 0.2363 0.9656 0.000664 -0.0002981 0.9424 0.0005004 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5754 0.5554 0.4072 0.3258 0.9835 0.9891 0.576 0.9631 0.9751 0.4261 ] Network output: [ -0.09519 0.2757 0.9082 -0.0002348 0.0001054 1.006 -0.0001769 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5924 0.5888 0.4451 0.2768 0.9808 0.9875 0.5925 0.9544 0.9703 0.4492 ] Network output: [ 0.05293 0.8226 0.05103 -0.0006902 0.0003099 1.018 -0.0005202 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0481 Epoch 1912 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04121 0.9732 0.9857 0.0001893 -8.5e-05 -0.04053 0.0001427 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02478 -0.004555 0.01931 0.03843 0.932 0.9425 0.05172 0.8641 0.8871 0.1342 ] Network output: [ 0.9593 0.0819 -0.01981 -0.0005089 0.0002285 0.01727 -0.0003835 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6388 0.07567 0.0615 0.3499 0.9665 0.984 0.7369 0.8774 0.9574 0.6224 ] Network output: [ 0.002604 0.9275 1.035 0.0001213 -5.447e-05 0.03325 9.144e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04669 0.03244 0.05332 0.05277 0.9814 0.9867 0.04789 0.9576 0.9728 0.07086 ] Network output: [ 0.1103 -0.3182 1.075 0.0004499 -0.000202 1.024 0.0003391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7305 0.5812 0.4763 0.5284 0.9704 0.9863 0.7342 0.889 0.9635 0.618 ] Network output: [ -0.07069 0.236 0.9657 0.0006634 -0.0002978 0.9424 0.0005 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5756 0.5556 0.4076 0.326 0.9835 0.9891 0.5762 0.9632 0.9752 0.4264 ] Network output: [ -0.09503 0.2755 0.9083 -0.0002331 0.0001046 1.005 -0.0001757 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5925 0.589 0.4453 0.2771 0.9808 0.9875 0.5926 0.9545 0.9703 0.4495 ] Network output: [ 0.0527 0.8231 0.05084 -0.0006886 0.0003091 1.018 -0.0005189 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04801 Epoch 1913 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0412 0.9732 0.9857 0.0001891 -8.488e-05 -0.0405 0.0001425 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02479 -0.004559 0.01933 0.03844 0.9321 0.9425 0.05173 0.8642 0.8872 0.1342 ] Network output: [ 0.9594 0.08176 -0.01974 -0.0005083 0.0002282 0.01718 -0.000383 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6389 0.07598 0.06188 0.3498 0.9665 0.984 0.737 0.8775 0.9574 0.6224 ] Network output: [ 0.002556 0.9275 1.035 0.0001205 -5.411e-05 0.03336 9.084e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04673 0.03249 0.05339 0.05281 0.9815 0.9867 0.04793 0.9576 0.9728 0.07092 ] Network output: [ 0.1103 -0.3181 1.075 0.0004477 -0.000201 1.024 0.0003374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7307 0.5817 0.4767 0.5284 0.9704 0.9863 0.7344 0.8891 0.9635 0.6181 ] Network output: [ -0.07058 0.2357 0.9658 0.0006628 -0.0002976 0.9423 0.0004995 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5759 0.5559 0.4079 0.3263 0.9835 0.9891 0.5764 0.9632 0.9752 0.4267 ] Network output: [ -0.09487 0.2753 0.9084 -0.0002314 0.0001039 1.005 -0.0001744 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5926 0.5891 0.4456 0.2774 0.9809 0.9875 0.5928 0.9545 0.9703 0.4497 ] Network output: [ 0.05247 0.8237 0.05065 -0.0006869 0.0003084 1.018 -0.0005177 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04792 Epoch 1914 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04119 0.9732 0.9856 0.0001888 -8.475e-05 -0.04048 0.0001423 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02479 -0.004563 0.01935 0.03846 0.9321 0.9425 0.05175 0.8643 0.8873 0.1343 ] Network output: [ 0.9594 0.08163 -0.01967 -0.0005076 0.0002279 0.0171 -0.0003826 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6391 0.07629 0.06225 0.3498 0.9665 0.984 0.7372 0.8776 0.9575 0.6225 ] Network output: [ 0.002508 0.9275 1.035 0.0001198 -5.376e-05 0.03346 9.025e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04677 0.03253 0.05346 0.05284 0.9815 0.9868 0.04797 0.9577 0.9729 0.07098 ] Network output: [ 0.1102 -0.318 1.075 0.0004455 -0.0002 1.024 0.0003357 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7309 0.5822 0.4771 0.5283 0.9704 0.9864 0.7346 0.8893 0.9636 0.6181 ] Network output: [ -0.07048 0.2354 0.966 0.0006622 -0.0002973 0.9423 0.0004991 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5761 0.5561 0.4083 0.3265 0.9835 0.9891 0.5766 0.9633 0.9752 0.427 ] Network output: [ -0.09472 0.2752 0.9085 -0.0002298 0.0001032 1.005 -0.0001732 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5928 0.5893 0.4458 0.2776 0.9809 0.9875 0.5929 0.9546 0.9704 0.4499 ] Network output: [ 0.05224 0.8242 0.05047 -0.0006852 0.0003076 1.018 -0.0005164 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04783 Epoch 1915 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04119 0.9732 0.9856 0.0001885 -8.462e-05 -0.04046 0.000142 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02479 -0.004567 0.01936 0.03847 0.9321 0.9426 0.05176 0.8644 0.8873 0.1343 ] Network output: [ 0.9595 0.0815 -0.01959 -0.000507 0.0002276 0.01702 -0.0003821 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6392 0.0766 0.06262 0.3497 0.9665 0.984 0.7374 0.8778 0.9575 0.6225 ] Network output: [ 0.00246 0.9274 1.035 0.000119 -5.341e-05 0.03357 8.966e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04682 0.03258 0.05353 0.05288 0.9815 0.9868 0.04802 0.9578 0.9729 0.07103 ] Network output: [ 0.1102 -0.318 1.075 0.0004432 -0.000199 1.024 0.000334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7311 0.5826 0.4775 0.5283 0.9705 0.9864 0.7348 0.8894 0.9636 0.6181 ] Network output: [ -0.07038 0.2352 0.9661 0.0006616 -0.000297 0.9422 0.0004986 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5763 0.5564 0.4086 0.3267 0.9835 0.9891 0.5768 0.9633 0.9753 0.4273 ] Network output: [ -0.09457 0.275 0.9086 -0.0002282 0.0001024 1.005 -0.000172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5929 0.5894 0.446 0.2779 0.9809 0.9875 0.593 0.9547 0.9704 0.4501 ] Network output: [ 0.05202 0.8247 0.05028 -0.0006835 0.0003068 1.018 -0.0005151 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04775 Epoch 1916 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04118 0.9732 0.9856 0.0001882 -8.449e-05 -0.04044 0.0001418 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0248 -0.004571 0.01938 0.03848 0.9321 0.9426 0.05178 0.8645 0.8874 0.1344 ] Network output: [ 0.9596 0.08138 -0.01952 -0.0005064 0.0002273 0.01693 -0.0003816 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6394 0.07691 0.06299 0.3497 0.9666 0.984 0.7376 0.8779 0.9576 0.6225 ] Network output: [ 0.002413 0.9274 1.035 0.0001182 -5.306e-05 0.03367 8.906e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04686 0.03263 0.0536 0.05292 0.9815 0.9868 0.04806 0.9578 0.9729 0.07109 ] Network output: [ 0.1101 -0.3179 1.075 0.000441 -0.000198 1.025 0.0003323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7313 0.5831 0.4778 0.5283 0.9705 0.9864 0.735 0.8895 0.9637 0.6182 ] Network output: [ -0.07027 0.2349 0.9662 0.000661 -0.0002968 0.9422 0.0004982 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5765 0.5567 0.4089 0.327 0.9835 0.9891 0.577 0.9634 0.9753 0.4276 ] Network output: [ -0.09441 0.2748 0.9087 -0.0002266 0.0001017 1.004 -0.0001707 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.593 0.5896 0.4463 0.2782 0.9809 0.9875 0.5932 0.9547 0.9705 0.4503 ] Network output: [ 0.05179 0.8253 0.05009 -0.0006817 0.000306 1.018 -0.0005138 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04766 Epoch 1917 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04117 0.9733 0.9856 0.0001879 -8.436e-05 -0.04042 0.0001416 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0248 -0.004575 0.01939 0.0385 0.9322 0.9426 0.05179 0.8645 0.8875 0.1344 ] Network output: [ 0.9596 0.08125 -0.01945 -0.0005057 0.000227 0.01685 -0.0003811 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.07722 0.06336 0.3496 0.9666 0.984 0.7378 0.878 0.9576 0.6225 ] Network output: [ 0.002366 0.9274 1.035 0.0001174 -5.27e-05 0.03378 8.847e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0469 0.03268 0.05366 0.05295 0.9815 0.9868 0.0481 0.9579 0.973 0.07115 ] Network output: [ 0.1101 -0.3179 1.075 0.0004387 -0.0001969 1.025 0.0003306 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7315 0.5836 0.4782 0.5283 0.9705 0.9864 0.7352 0.8896 0.9637 0.6182 ] Network output: [ -0.07017 0.2346 0.9663 0.0006604 -0.0002965 0.9421 0.0004977 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5767 0.5569 0.4092 0.3272 0.9836 0.9891 0.5772 0.9635 0.9753 0.4278 ] Network output: [ -0.09426 0.2746 0.9088 -0.000225 0.000101 1.004 -0.0001696 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5932 0.5897 0.4465 0.2784 0.9809 0.9875 0.5933 0.9548 0.9705 0.4506 ] Network output: [ 0.05157 0.8258 0.04991 -0.0006799 0.0003052 1.018 -0.0005124 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04758 Epoch 1918 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04115 0.9733 0.9856 0.0001876 -8.423e-05 -0.0404 0.0001414 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0248 -0.004579 0.0194 0.03851 0.9322 0.9426 0.05181 0.8646 0.8875 0.1344 ] Network output: [ 0.9597 0.08112 -0.01938 -0.0005051 0.0002267 0.01676 -0.0003806 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6397 0.07752 0.06372 0.3496 0.9666 0.984 0.738 0.8781 0.9577 0.6226 ] Network output: [ 0.002318 0.9274 1.035 0.0001166 -5.235e-05 0.03388 8.788e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04694 0.03272 0.05373 0.05299 0.9815 0.9868 0.04814 0.958 0.973 0.07121 ] Network output: [ 0.11 -0.3178 1.075 0.0004364 -0.0001959 1.025 0.0003289 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7317 0.5841 0.4786 0.5282 0.9705 0.9864 0.7354 0.8897 0.9637 0.6182 ] Network output: [ -0.07007 0.2343 0.9664 0.0006598 -0.0002962 0.9421 0.0004972 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5769 0.5572 0.4096 0.3274 0.9836 0.9891 0.5775 0.9635 0.9754 0.4281 ] Network output: [ -0.09411 0.2745 0.909 -0.0002234 0.0001003 1.004 -0.0001684 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5933 0.5898 0.4467 0.2787 0.9809 0.9876 0.5934 0.9549 0.9706 0.4508 ] Network output: [ 0.05135 0.8263 0.04973 -0.0006781 0.0003044 1.018 -0.000511 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04749 Epoch 1919 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04114 0.9733 0.9856 0.0001873 -8.409e-05 -0.04038 0.0001412 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02481 -0.004583 0.01942 0.03852 0.9322 0.9426 0.05182 0.8647 0.8876 0.1345 ] Network output: [ 0.9598 0.081 -0.01931 -0.0005044 0.0002264 0.01668 -0.0003801 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6398 0.07782 0.06409 0.3496 0.9666 0.9841 0.7382 0.8782 0.9577 0.6226 ] Network output: [ 0.002272 0.9274 1.035 0.0001158 -5.2e-05 0.03398 8.73e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04698 0.03277 0.05379 0.05303 0.9815 0.9868 0.04819 0.958 0.973 0.07126 ] Network output: [ 0.11 -0.3177 1.075 0.0004341 -0.0001949 1.025 0.0003271 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7318 0.5846 0.479 0.5282 0.9705 0.9864 0.7356 0.8898 0.9638 0.6183 ] Network output: [ -0.06997 0.2341 0.9665 0.0006592 -0.0002959 0.942 0.0004968 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5771 0.5574 0.4099 0.3276 0.9836 0.9891 0.5777 0.9636 0.9754 0.4284 ] Network output: [ -0.09397 0.2743 0.9091 -0.0002219 9.96e-05 1.004 -0.0001672 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5934 0.59 0.447 0.279 0.9809 0.9876 0.5936 0.955 0.9706 0.451 ] Network output: [ 0.05113 0.8269 0.04954 -0.0006762 0.0003036 1.019 -0.0005096 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04741 Epoch 1920 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04113 0.9733 0.9856 0.000187 -8.396e-05 -0.04036 0.0001409 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02481 -0.004587 0.01943 0.03853 0.9322 0.9427 0.05183 0.8648 0.8877 0.1345 ] Network output: [ 0.9599 0.08087 -0.01925 -0.0005037 0.0002261 0.0166 -0.0003796 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.64 0.07812 0.06445 0.3495 0.9666 0.9841 0.7383 0.8783 0.9577 0.6226 ] Network output: [ 0.002225 0.9273 1.035 0.0001151 -5.165e-05 0.03409 8.671e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04702 0.03281 0.05386 0.05306 0.9816 0.9868 0.04823 0.9581 0.9731 0.07132 ] Network output: [ 0.11 -0.3177 1.074 0.0004318 -0.0001938 1.025 0.0003254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.732 0.5851 0.4793 0.5282 0.9705 0.9864 0.7358 0.8899 0.9638 0.6183 ] Network output: [ -0.06987 0.2338 0.9666 0.0006585 -0.0002956 0.9419 0.0004963 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5773 0.5577 0.4102 0.3278 0.9836 0.9891 0.5779 0.9636 0.9754 0.4287 ] Network output: [ -0.09382 0.2741 0.9092 -0.0002203 9.892e-05 1.003 -0.0001661 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5936 0.5901 0.4472 0.2792 0.981 0.9876 0.5937 0.955 0.9707 0.4512 ] Network output: [ 0.05092 0.8274 0.04936 -0.0006744 0.0003027 1.019 -0.0005082 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04732 Epoch 1921 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04112 0.9733 0.9855 0.0001867 -8.382e-05 -0.04033 0.0001407 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02482 -0.004591 0.01945 0.03855 0.9323 0.9427 0.05185 0.8649 0.8877 0.1346 ] Network output: [ 0.9599 0.08075 -0.01918 -0.000503 0.0002258 0.01651 -0.0003791 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6401 0.07842 0.06481 0.3495 0.9667 0.9841 0.7385 0.8784 0.9578 0.6227 ] Network output: [ 0.002178 0.9273 1.035 0.0001143 -5.13e-05 0.03419 8.612e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04706 0.03286 0.05392 0.05309 0.9816 0.9868 0.04827 0.9581 0.9731 0.07137 ] Network output: [ 0.1099 -0.3176 1.074 0.0004295 -0.0001928 1.025 0.0003237 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7322 0.5855 0.4797 0.5282 0.9706 0.9864 0.7359 0.89 0.9638 0.6184 ] Network output: [ -0.06976 0.2336 0.9668 0.0006579 -0.0002954 0.9419 0.0004958 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5775 0.5579 0.4105 0.3281 0.9836 0.9892 0.5781 0.9637 0.9755 0.4289 ] Network output: [ -0.09367 0.2739 0.9093 -0.0002188 9.824e-05 1.003 -0.0001649 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5937 0.5903 0.4474 0.2795 0.981 0.9876 0.5938 0.9551 0.9707 0.4514 ] Network output: [ 0.0507 0.8279 0.04918 -0.0006725 0.0003019 1.019 -0.0005068 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04724 Epoch 1922 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04111 0.9733 0.9855 0.0001864 -8.369e-05 -0.04031 0.0001405 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02482 -0.004595 0.01946 0.03856 0.9323 0.9427 0.05186 0.865 0.8878 0.1346 ] Network output: [ 0.96 0.08063 -0.01911 -0.0005023 0.0002255 0.01643 -0.0003786 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6403 0.07872 0.06516 0.3494 0.9667 0.9841 0.7387 0.8785 0.9578 0.6227 ] Network output: [ 0.002132 0.9273 1.035 0.0001135 -5.095e-05 0.03429 8.554e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0471 0.0329 0.05398 0.05313 0.9816 0.9868 0.04831 0.9582 0.9732 0.07142 ] Network output: [ 0.1099 -0.3176 1.074 0.0004271 -0.0001917 1.025 0.0003219 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7324 0.586 0.4801 0.5281 0.9706 0.9864 0.7361 0.8901 0.9639 0.6184 ] Network output: [ -0.06966 0.2333 0.9669 0.0006573 -0.0002951 0.9419 0.0004953 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5777 0.5582 0.4108 0.3283 0.9836 0.9892 0.5783 0.9637 0.9755 0.4292 ] Network output: [ -0.09353 0.2737 0.9094 -0.0002173 9.757e-05 1.003 -0.0001638 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5938 0.5904 0.4476 0.2797 0.981 0.9876 0.5939 0.9552 0.9707 0.4516 ] Network output: [ 0.05049 0.8284 0.04901 -0.0006705 0.000301 1.019 -0.0005053 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04716 Epoch 1923 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0411 0.9733 0.9855 0.0001861 -8.355e-05 -0.04029 0.0001403 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02482 -0.004599 0.01947 0.03857 0.9323 0.9427 0.05188 0.8651 0.8879 0.1346 ] Network output: [ 0.9601 0.0805 -0.01904 -0.0005016 0.0002252 0.01635 -0.000378 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6404 0.07901 0.06552 0.3494 0.9667 0.9841 0.7389 0.8786 0.9579 0.6227 ] Network output: [ 0.002086 0.9273 1.035 0.0001127 -5.06e-05 0.0344 8.495e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04714 0.03295 0.05404 0.05316 0.9816 0.9869 0.04835 0.9583 0.9732 0.07147 ] Network output: [ 0.1098 -0.3175 1.074 0.0004248 -0.0001907 1.026 0.0003201 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7326 0.5865 0.4804 0.5281 0.9706 0.9864 0.7363 0.8902 0.9639 0.6184 ] Network output: [ -0.06956 0.233 0.967 0.0006566 -0.0002948 0.9418 0.0004948 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5779 0.5584 0.4112 0.3285 0.9836 0.9892 0.5785 0.9638 0.9755 0.4295 ] Network output: [ -0.09338 0.2736 0.9096 -0.0002159 9.69e-05 1.003 -0.0001627 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.594 0.5905 0.4479 0.28 0.981 0.9876 0.5941 0.9552 0.9708 0.4518 ] Network output: [ 0.05028 0.8289 0.04883 -0.0006686 0.0003001 1.019 -0.0005039 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04707 Epoch 1924 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04109 0.9733 0.9855 0.0001858 -8.341e-05 -0.04027 0.00014 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02483 -0.004604 0.01949 0.03858 0.9323 0.9427 0.05189 0.8652 0.8879 0.1347 ] Network output: [ 0.9601 0.08038 -0.01898 -0.0005009 0.0002249 0.01627 -0.0003775 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6406 0.07931 0.06587 0.3493 0.9667 0.9841 0.7391 0.8787 0.9579 0.6228 ] Network output: [ 0.00204 0.9273 1.035 0.0001119 -5.026e-05 0.0345 8.437e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04718 0.033 0.05411 0.05319 0.9816 0.9869 0.04839 0.9583 0.9732 0.07153 ] Network output: [ 0.1098 -0.3174 1.074 0.0004224 -0.0001896 1.026 0.0003183 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7328 0.5869 0.4808 0.5281 0.9706 0.9865 0.7365 0.8903 0.964 0.6185 ] Network output: [ -0.06946 0.2328 0.9671 0.000656 -0.0002945 0.9418 0.0004944 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5781 0.5586 0.4115 0.3287 0.9836 0.9892 0.5787 0.9638 0.9756 0.4297 ] Network output: [ -0.09324 0.2734 0.9097 -0.0002144 9.625e-05 1.003 -0.0001616 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5941 0.5907 0.4481 0.2802 0.981 0.9876 0.5942 0.9553 0.9708 0.452 ] Network output: [ 0.05007 0.8295 0.04865 -0.0006666 0.0002993 1.019 -0.0005024 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04699 Epoch 1925 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04107 0.9734 0.9855 0.0001855 -8.327e-05 -0.04025 0.0001398 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02483 -0.004608 0.0195 0.03859 0.9324 0.9428 0.0519 0.8653 0.888 0.1347 ] Network output: [ 0.9602 0.08026 -0.01891 -0.0005002 0.0002246 0.01618 -0.000377 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6407 0.0796 0.06622 0.3493 0.9667 0.9841 0.7392 0.8788 0.958 0.6228 ] Network output: [ 0.001995 0.9272 1.035 0.0001112 -4.991e-05 0.0346 8.378e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04722 0.03304 0.05417 0.05322 0.9816 0.9869 0.04843 0.9584 0.9733 0.07158 ] Network output: [ 0.1097 -0.3174 1.074 0.00042 -0.0001886 1.026 0.0003166 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7329 0.5874 0.4812 0.5281 0.9706 0.9865 0.7367 0.8904 0.964 0.6185 ] Network output: [ -0.06936 0.2325 0.9672 0.0006553 -0.0002942 0.9417 0.0004939 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5783 0.5589 0.4118 0.3289 0.9837 0.9892 0.5789 0.9639 0.9756 0.43 ] Network output: [ -0.0931 0.2732 0.9098 -0.0002129 9.56e-05 1.002 -0.0001605 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5942 0.5908 0.4483 0.2805 0.981 0.9876 0.5943 0.9554 0.9709 0.4522 ] Network output: [ 0.04986 0.83 0.04848 -0.0006646 0.0002984 1.019 -0.0005009 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04691 Epoch 1926 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04106 0.9734 0.9855 0.0001852 -8.313e-05 -0.04023 0.0001396 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02483 -0.004612 0.01951 0.0386 0.9324 0.9428 0.05192 0.8654 0.8881 0.1348 ] Network output: [ 0.9603 0.08014 -0.01885 -0.0004995 0.0002242 0.0161 -0.0003764 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6409 0.07989 0.06657 0.3492 0.9667 0.9841 0.7394 0.879 0.958 0.6228 ] Network output: [ 0.00195 0.9272 1.035 0.0001104 -4.956e-05 0.0347 8.32e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04726 0.03308 0.05423 0.05325 0.9816 0.9869 0.04847 0.9585 0.9733 0.07163 ] Network output: [ 0.1097 -0.3173 1.074 0.0004177 -0.0001875 1.026 0.0003148 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7331 0.5878 0.4815 0.528 0.9706 0.9865 0.7368 0.8905 0.964 0.6186 ] Network output: [ -0.06926 0.2323 0.9672 0.0006546 -0.0002939 0.9417 0.0004934 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5785 0.5591 0.4121 0.3291 0.9837 0.9892 0.5791 0.964 0.9756 0.4303 ] Network output: [ -0.09296 0.273 0.9099 -0.0002115 9.496e-05 1.002 -0.0001594 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5943 0.5909 0.4485 0.2807 0.9811 0.9876 0.5944 0.9554 0.9709 0.4524 ] Network output: [ 0.04966 0.8305 0.0483 -0.0006626 0.0002974 1.019 -0.0004993 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04683 Epoch 1927 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04105 0.9734 0.9855 0.0001849 -8.299e-05 -0.04021 0.0001393 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02484 -0.004616 0.01953 0.03861 0.9324 0.9428 0.05193 0.8654 0.8881 0.1348 ] Network output: [ 0.9604 0.08003 -0.01879 -0.0004987 0.0002239 0.01602 -0.0003759 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.641 0.08017 0.06692 0.3492 0.9668 0.9841 0.7396 0.8791 0.958 0.6229 ] Network output: [ 0.001904 0.9272 1.035 0.0001096 -4.922e-05 0.0348 8.262e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0473 0.03313 0.05429 0.05328 0.9817 0.9869 0.04851 0.9585 0.9733 0.07168 ] Network output: [ 0.1096 -0.3173 1.074 0.0004153 -0.0001864 1.026 0.000313 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7333 0.5883 0.4819 0.528 0.9707 0.9865 0.737 0.8906 0.9641 0.6186 ] Network output: [ -0.06916 0.232 0.9673 0.000654 -0.0002936 0.9416 0.0004929 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5787 0.5594 0.4124 0.3293 0.9837 0.9892 0.5793 0.964 0.9757 0.4305 ] Network output: [ -0.09282 0.2729 0.91 -0.0002101 9.433e-05 1.002 -0.0001583 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5945 0.5911 0.4487 0.281 0.9811 0.9876 0.5946 0.9555 0.9709 0.4526 ] Network output: [ 0.04945 0.831 0.04813 -0.0006605 0.0002965 1.019 -0.0004978 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04675 Epoch 1928 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04104 0.9734 0.9855 0.0001846 -8.285e-05 -0.04018 0.0001391 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02484 -0.00462 0.01954 0.03862 0.9324 0.9428 0.05194 0.8655 0.8882 0.1348 ] Network output: [ 0.9604 0.07991 -0.01872 -0.000498 0.0002236 0.01594 -0.0003753 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6412 0.08046 0.06726 0.3491 0.9668 0.9842 0.7398 0.8792 0.9581 0.6229 ] Network output: [ 0.00186 0.9272 1.035 0.0001089 -4.887e-05 0.0349 8.204e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04734 0.03317 0.05434 0.05331 0.9817 0.9869 0.04855 0.9586 0.9734 0.07172 ] Network output: [ 0.1096 -0.3172 1.073 0.0004129 -0.0001854 1.026 0.0003112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7335 0.5887 0.4822 0.528 0.9707 0.9865 0.7372 0.8908 0.9641 0.6186 ] Network output: [ -0.06906 0.2318 0.9674 0.0006533 -0.0002933 0.9416 0.0004924 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5789 0.5596 0.4127 0.3295 0.9837 0.9892 0.5795 0.9641 0.9757 0.4308 ] Network output: [ -0.09268 0.2727 0.9102 -0.0002087 9.37e-05 1.002 -0.0001573 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5946 0.5912 0.4489 0.2812 0.9811 0.9877 0.5947 0.9556 0.971 0.4528 ] Network output: [ 0.04925 0.8315 0.04796 -0.0006584 0.0002956 1.019 -0.0004962 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04667 Epoch 1929 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04102 0.9734 0.9855 0.0001842 -8.271e-05 -0.04016 0.0001388 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02484 -0.004624 0.01955 0.03863 0.9325 0.9428 0.05196 0.8656 0.8883 0.1349 ] Network output: [ 0.9605 0.07979 -0.01866 -0.0004972 0.0002232 0.01586 -0.0003747 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6413 0.08075 0.0676 0.3491 0.9668 0.9842 0.7399 0.8793 0.9581 0.6229 ] Network output: [ 0.001815 0.9272 1.035 0.0001081 -4.853e-05 0.035 8.147e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04738 0.03322 0.0544 0.05334 0.9817 0.9869 0.04859 0.9586 0.9734 0.07177 ] Network output: [ 0.1095 -0.3171 1.073 0.0004105 -0.0001843 1.026 0.0003094 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7337 0.5892 0.4826 0.5279 0.9707 0.9865 0.7374 0.8909 0.9641 0.6187 ] Network output: [ -0.06896 0.2315 0.9675 0.0006526 -0.000293 0.9415 0.0004918 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5791 0.5598 0.413 0.3297 0.9837 0.9892 0.5797 0.9641 0.9757 0.431 ] Network output: [ -0.09254 0.2725 0.9103 -0.0002073 9.308e-05 1.001 -0.0001563 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5947 0.5913 0.4491 0.2815 0.9811 0.9877 0.5948 0.9556 0.971 0.453 ] Network output: [ 0.04905 0.832 0.04779 -0.0006564 0.0002947 1.019 -0.0004947 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04659 Epoch 1930 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04101 0.9734 0.9855 0.0001839 -8.257e-05 -0.04014 0.0001386 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02484 -0.004629 0.01956 0.03864 0.9325 0.9429 0.05197 0.8657 0.8883 0.1349 ] Network output: [ 0.9606 0.07968 -0.0186 -0.0004964 0.0002229 0.01578 -0.0003741 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6415 0.08103 0.06794 0.349 0.9668 0.9842 0.7401 0.8794 0.9582 0.623 ] Network output: [ 0.001771 0.9271 1.035 0.0001073 -4.819e-05 0.0351 8.089e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04742 0.03326 0.05446 0.05336 0.9817 0.9869 0.04863 0.9587 0.9734 0.07182 ] Network output: [ 0.1095 -0.3171 1.073 0.0004081 -0.0001832 1.027 0.0003075 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7338 0.5896 0.4829 0.5279 0.9707 0.9865 0.7376 0.891 0.9642 0.6187 ] Network output: [ -0.06886 0.2313 0.9676 0.000652 -0.0002927 0.9415 0.0004913 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5793 0.5601 0.4133 0.3299 0.9837 0.9892 0.5799 0.9642 0.9758 0.4313 ] Network output: [ -0.09241 0.2723 0.9104 -0.000206 9.247e-05 1.001 -0.0001552 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5948 0.5915 0.4493 0.2817 0.9811 0.9877 0.5949 0.9557 0.9711 0.4532 ] Network output: [ 0.04885 0.8325 0.04762 -0.0006543 0.0002937 1.02 -0.0004931 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04651 Epoch 1931 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.041 0.9734 0.9854 0.0001836 -8.242e-05 -0.04012 0.0001384 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02485 -0.004633 0.01958 0.03865 0.9325 0.9429 0.05198 0.8658 0.8884 0.1349 ] Network output: [ 0.9606 0.07957 -0.01854 -0.0004957 0.0002225 0.0157 -0.0003735 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6416 0.08131 0.06828 0.349 0.9668 0.9842 0.7403 0.8795 0.9582 0.623 ] Network output: [ 0.001726 0.9271 1.035 0.0001066 -4.784e-05 0.03519 8.032e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04745 0.0333 0.05451 0.05339 0.9817 0.9869 0.04867 0.9588 0.9735 0.07186 ] Network output: [ 0.1094 -0.317 1.073 0.0004057 -0.0001821 1.027 0.0003057 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.734 0.5901 0.4833 0.5279 0.9707 0.9865 0.7377 0.8911 0.9642 0.6188 ] Network output: [ -0.06877 0.2311 0.9677 0.0006513 -0.0002924 0.9414 0.0004908 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5795 0.5603 0.4136 0.3301 0.9837 0.9892 0.58 0.9642 0.9758 0.4315 ] Network output: [ -0.09227 0.2722 0.9105 -0.0002046 9.187e-05 1.001 -0.0001542 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5949 0.5916 0.4496 0.2819 0.9811 0.9877 0.5951 0.9558 0.9711 0.4534 ] Network output: [ 0.04866 0.8329 0.04745 -0.0006521 0.0002928 1.02 -0.0004915 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04644 Epoch 1932 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04098 0.9734 0.9854 0.0001833 -8.228e-05 -0.0401 0.0001381 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02485 -0.004637 0.01959 0.03865 0.9325 0.9429 0.05199 0.8659 0.8885 0.1349 ] Network output: [ 0.9607 0.07945 -0.01848 -0.0004949 0.0002222 0.01562 -0.000373 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6417 0.08159 0.06862 0.3489 0.9669 0.9842 0.7404 0.8796 0.9582 0.623 ] Network output: [ 0.001683 0.9271 1.035 0.0001058 -4.75e-05 0.03529 7.974e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04749 0.03335 0.05457 0.05342 0.9817 0.987 0.04871 0.9588 0.9735 0.07191 ] Network output: [ 0.1094 -0.317 1.073 0.0004032 -0.000181 1.027 0.0003039 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7342 0.5905 0.4836 0.5278 0.9707 0.9865 0.7379 0.8912 0.9643 0.6188 ] Network output: [ -0.06867 0.2308 0.9678 0.0006506 -0.0002921 0.9414 0.0004903 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5797 0.5605 0.4138 0.3303 0.9837 0.9892 0.5802 0.9643 0.9758 0.4318 ] Network output: [ -0.09214 0.272 0.9107 -0.0002033 9.127e-05 1.001 -0.0001532 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5951 0.5917 0.4498 0.2822 0.9811 0.9877 0.5952 0.9558 0.9711 0.4536 ] Network output: [ 0.04846 0.8334 0.04729 -0.00065 0.0002918 1.02 -0.0004898 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04636 Epoch 1933 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04097 0.9734 0.9854 0.0001829 -8.213e-05 -0.04007 0.0001379 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02485 -0.004641 0.0196 0.03866 0.9325 0.9429 0.052 0.866 0.8885 0.135 ] Network output: [ 0.9608 0.07934 -0.01842 -0.0004941 0.0002218 0.01554 -0.0003724 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6419 0.08187 0.06895 0.3489 0.9669 0.9842 0.7406 0.8797 0.9583 0.6231 ] Network output: [ 0.001639 0.9271 1.035 0.000105 -4.716e-05 0.03539 7.917e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04753 0.03339 0.05463 0.05344 0.9817 0.987 0.04875 0.9589 0.9735 0.07195 ] Network output: [ 0.1094 -0.3169 1.073 0.0004008 -0.0001799 1.027 0.0003021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7343 0.5909 0.4839 0.5278 0.9708 0.9865 0.7381 0.8913 0.9643 0.6188 ] Network output: [ -0.06857 0.2306 0.9679 0.0006499 -0.0002918 0.9413 0.0004898 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5799 0.5608 0.4141 0.3305 0.9837 0.9893 0.5804 0.9643 0.9759 0.432 ] Network output: [ -0.092 0.2718 0.9108 -0.000202 9.069e-05 1.001 -0.0001522 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5952 0.5918 0.45 0.2824 0.9812 0.9877 0.5953 0.9559 0.9712 0.4538 ] Network output: [ 0.04827 0.8339 0.04712 -0.0006478 0.0002908 1.02 -0.0004882 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04628 Epoch 1934 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04095 0.9735 0.9854 0.0001826 -8.199e-05 -0.04005 0.0001376 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02486 -0.004645 0.01961 0.03867 0.9326 0.9429 0.05202 0.8661 0.8886 0.135 ] Network output: [ 0.9608 0.07923 -0.01836 -0.0004933 0.0002214 0.01546 -0.0003717 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.642 0.08214 0.06929 0.3488 0.9669 0.9842 0.7408 0.8798 0.9583 0.6231 ] Network output: [ 0.001596 0.9271 1.035 0.0001043 -4.682e-05 0.03548 7.86e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04757 0.03343 0.05468 0.05347 0.9818 0.987 0.04879 0.9589 0.9736 0.072 ] Network output: [ 0.1093 -0.3168 1.073 0.0003984 -0.0001788 1.027 0.0003002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7345 0.5914 0.4843 0.5278 0.9708 0.9865 0.7382 0.8914 0.9643 0.6189 ] Network output: [ -0.06847 0.2303 0.9679 0.0006492 -0.0002915 0.9413 0.0004893 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5801 0.561 0.4144 0.3307 0.9838 0.9893 0.5806 0.9644 0.9759 0.4323 ] Network output: [ -0.09187 0.2716 0.9109 -0.0002007 9.01e-05 1 -0.0001513 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5953 0.592 0.4502 0.2827 0.9812 0.9877 0.5954 0.956 0.9712 0.454 ] Network output: [ 0.04808 0.8344 0.04696 -0.0006456 0.0002899 1.02 -0.0004866 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04621 Epoch 1935 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04094 0.9735 0.9854 0.0001823 -8.184e-05 -0.04003 0.0001374 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02486 -0.00465 0.01962 0.03868 0.9326 0.943 0.05203 0.8661 0.8887 0.135 ] Network output: [ 0.9609 0.07912 -0.01831 -0.0004925 0.0002211 0.01538 -0.0003711 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6421 0.08241 0.06962 0.3487 0.9669 0.9842 0.7409 0.8799 0.9584 0.6231 ] Network output: [ 0.001552 0.9271 1.035 0.0001035 -4.648e-05 0.03558 7.803e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04761 0.03347 0.05473 0.05349 0.9818 0.987 0.04883 0.959 0.9736 0.07204 ] Network output: [ 0.1093 -0.3168 1.073 0.0003959 -0.0001777 1.027 0.0002984 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7347 0.5918 0.4846 0.5277 0.9708 0.9865 0.7384 0.8915 0.9644 0.6189 ] Network output: [ -0.06837 0.2301 0.968 0.0006485 -0.0002911 0.9413 0.0004887 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5803 0.5612 0.4147 0.3309 0.9838 0.9893 0.5808 0.9644 0.9759 0.4325 ] Network output: [ -0.09174 0.2715 0.911 -0.0001994 8.953e-05 1 -0.0001503 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5954 0.5921 0.4504 0.2829 0.9812 0.9877 0.5955 0.956 0.9713 0.4542 ] Network output: [ 0.04789 0.8349 0.04679 -0.0006434 0.0002889 1.02 -0.0004849 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04613 Epoch 1936 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04093 0.9735 0.9854 0.000182 -8.169e-05 -0.04001 0.0001371 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02486 -0.004654 0.01963 0.03868 0.9326 0.943 0.05204 0.8662 0.8887 0.1351 ] Network output: [ 0.961 0.07901 -0.01825 -0.0004916 0.0002207 0.0153 -0.0003705 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6423 0.08269 0.06995 0.3487 0.9669 0.9842 0.7411 0.88 0.9584 0.6232 ] Network output: [ 0.001509 0.9271 1.035 0.0001028 -4.614e-05 0.03568 7.746e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04764 0.03352 0.05479 0.05352 0.9818 0.987 0.04886 0.9591 0.9736 0.07208 ] Network output: [ 0.1092 -0.3167 1.073 0.0003935 -0.0001766 1.027 0.0002965 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7348 0.5922 0.4849 0.5277 0.9708 0.9866 0.7386 0.8916 0.9644 0.619 ] Network output: [ -0.06828 0.2299 0.9681 0.0006478 -0.0002908 0.9412 0.0004882 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5804 0.5615 0.415 0.331 0.9838 0.9893 0.581 0.9645 0.976 0.4328 ] Network output: [ -0.09161 0.2713 0.9112 -0.0001982 8.896e-05 1 -0.0001493 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5955 0.5922 0.4506 0.2831 0.9812 0.9877 0.5956 0.9561 0.9713 0.4544 ] Network output: [ 0.0477 0.8353 0.04663 -0.0006412 0.0002879 1.02 -0.0004833 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04605 Epoch 1937 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04091 0.9735 0.9854 0.0001816 -8.154e-05 -0.03999 0.0001369 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02486 -0.004658 0.01964 0.03869 0.9326 0.943 0.05205 0.8663 0.8888 0.1351 ] Network output: [ 0.961 0.0789 -0.01819 -0.0004908 0.0002203 0.01522 -0.0003699 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6424 0.08296 0.07027 0.3486 0.9669 0.9842 0.7413 0.8801 0.9584 0.6232 ] Network output: [ 0.001467 0.927 1.035 0.000102 -4.58e-05 0.03577 7.689e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04768 0.03356 0.05484 0.05354 0.9818 0.987 0.0489 0.9591 0.9737 0.07213 ] Network output: [ 0.1092 -0.3166 1.072 0.000391 -0.0001755 1.027 0.0002947 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.735 0.5927 0.4853 0.5277 0.9708 0.9866 0.7387 0.8917 0.9644 0.619 ] Network output: [ -0.06818 0.2296 0.9682 0.0006471 -0.0002905 0.9412 0.0004877 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5806 0.5617 0.4153 0.3312 0.9838 0.9893 0.5812 0.9645 0.976 0.433 ] Network output: [ -0.09148 0.2711 0.9113 -0.0001969 8.84e-05 0.9998 -0.0001484 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5957 0.5924 0.4508 0.2834 0.9812 0.9878 0.5958 0.9561 0.9713 0.4546 ] Network output: [ 0.04751 0.8358 0.04647 -0.000639 0.0002869 1.02 -0.0004816 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04598 Epoch 1938 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0409 0.9735 0.9854 0.0001813 -8.139e-05 -0.03996 0.0001366 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02487 -0.004662 0.01965 0.0387 0.9327 0.943 0.05206 0.8664 0.8888 0.1351 ] Network output: [ 0.9611 0.07879 -0.01814 -0.0004899 0.00022 0.01514 -0.0003692 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6426 0.08323 0.0706 0.3486 0.967 0.9843 0.7414 0.8802 0.9585 0.6232 ] Network output: [ 0.001424 0.927 1.035 0.0001013 -4.547e-05 0.03587 7.633e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04772 0.0336 0.05489 0.05356 0.9818 0.987 0.04894 0.9592 0.9737 0.07217 ] Network output: [ 0.1091 -0.3166 1.072 0.0003885 -0.0001744 1.028 0.0002928 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7352 0.5931 0.4856 0.5276 0.9708 0.9866 0.7389 0.8918 0.9645 0.619 ] Network output: [ -0.06808 0.2294 0.9682 0.0006464 -0.0002902 0.9411 0.0004872 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5808 0.5619 0.4155 0.3314 0.9838 0.9893 0.5814 0.9646 0.976 0.4332 ] Network output: [ -0.09135 0.2709 0.9114 -0.0001957 8.784e-05 0.9996 -0.0001475 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5958 0.5925 0.4509 0.2836 0.9812 0.9878 0.5959 0.9562 0.9714 0.4547 ] Network output: [ 0.04733 0.8363 0.04631 -0.0006368 0.0002859 1.02 -0.0004799 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04591 Epoch 1939 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04088 0.9735 0.9854 0.000181 -8.124e-05 -0.03994 0.0001364 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02487 -0.004667 0.01966 0.0387 0.9327 0.943 0.05207 0.8665 0.8889 0.1351 ] Network output: [ 0.9612 0.07869 -0.01808 -0.0004891 0.0002196 0.01506 -0.0003686 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6427 0.08349 0.07092 0.3485 0.967 0.9843 0.7416 0.8803 0.9585 0.6232 ] Network output: [ 0.001382 0.927 1.035 0.0001005 -4.513e-05 0.03596 7.576e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04775 0.03364 0.05494 0.05358 0.9818 0.987 0.04898 0.9592 0.9737 0.07221 ] Network output: [ 0.1091 -0.3165 1.072 0.0003861 -0.0001733 1.028 0.000291 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7353 0.5935 0.4859 0.5276 0.9708 0.9866 0.7391 0.8919 0.9645 0.6191 ] Network output: [ -0.06799 0.2292 0.9683 0.0006457 -0.0002899 0.9411 0.0004866 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.581 0.5621 0.4158 0.3316 0.9838 0.9893 0.5816 0.9646 0.9761 0.4335 ] Network output: [ -0.09122 0.2707 0.9116 -0.0001944 8.73e-05 0.9994 -0.0001465 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5959 0.5926 0.4511 0.2838 0.9813 0.9878 0.596 0.9563 0.9714 0.4549 ] Network output: [ 0.04714 0.8367 0.04615 -0.0006345 0.0002849 1.02 -0.0004782 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04583 Epoch 1940 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04087 0.9735 0.9854 0.0001806 -8.109e-05 -0.03992 0.0001361 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02487 -0.004671 0.01968 0.03871 0.9327 0.9431 0.05208 0.8666 0.889 0.1352 ] Network output: [ 0.9612 0.07858 -0.01802 -0.0004882 0.0002192 0.01498 -0.0003679 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6428 0.08376 0.07124 0.3485 0.967 0.9843 0.7417 0.8804 0.9586 0.6233 ] Network output: [ 0.00134 0.927 1.035 9.978e-05 -4.48e-05 0.03605 7.52e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04779 0.03368 0.05499 0.0536 0.9818 0.987 0.04902 0.9593 0.9738 0.07225 ] Network output: [ 0.109 -0.3165 1.072 0.0003836 -0.0001722 1.028 0.0002891 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7355 0.5939 0.4862 0.5276 0.9709 0.9866 0.7392 0.892 0.9645 0.6191 ] Network output: [ -0.06789 0.229 0.9684 0.000645 -0.0002896 0.9411 0.0004861 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5812 0.5624 0.4161 0.3318 0.9838 0.9893 0.5817 0.9647 0.9761 0.4337 ] Network output: [ -0.0911 0.2706 0.9117 -0.0001932 8.675e-05 0.9992 -0.0001456 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.596 0.5927 0.4513 0.284 0.9813 0.9878 0.5961 0.9563 0.9715 0.4551 ] Network output: [ 0.04696 0.8372 0.04599 -0.0006323 0.0002838 1.02 -0.0004765 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04576 Epoch 1941 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04085 0.9735 0.9854 0.0001803 -8.093e-05 -0.0399 0.0001359 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02487 -0.004675 0.01969 0.03871 0.9327 0.9431 0.05209 0.8667 0.889 0.1352 ] Network output: [ 0.9613 0.07848 -0.01797 -0.0004874 0.0002188 0.0149 -0.0003673 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6429 0.08402 0.07156 0.3484 0.967 0.9843 0.7419 0.8805 0.9586 0.6233 ] Network output: [ 0.001299 0.927 1.035 9.904e-05 -4.446e-05 0.03615 7.464e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04783 0.03372 0.05504 0.05362 0.9819 0.987 0.04905 0.9594 0.9738 0.07228 ] Network output: [ 0.109 -0.3164 1.072 0.0003811 -0.0001711 1.028 0.0002872 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7357 0.5943 0.4866 0.5275 0.9709 0.9866 0.7394 0.8921 0.9646 0.6192 ] Network output: [ -0.0678 0.2287 0.9684 0.0006443 -0.0002893 0.941 0.0004856 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5814 0.5626 0.4163 0.3319 0.9838 0.9893 0.5819 0.9647 0.9761 0.4339 ] Network output: [ -0.09097 0.2704 0.9118 -0.0001921 8.622e-05 0.999 -0.0001447 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5961 0.5928 0.4515 0.2843 0.9813 0.9878 0.5962 0.9564 0.9715 0.4553 ] Network output: [ 0.04678 0.8377 0.04584 -0.00063 0.0002828 1.02 -0.0004748 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04568 Epoch 1942 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04084 0.9735 0.9854 0.0001799 -8.078e-05 -0.03987 0.0001356 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02488 -0.00468 0.0197 0.03872 0.9328 0.9431 0.05211 0.8667 0.8891 0.1352 ] Network output: [ 0.9614 0.07837 -0.01792 -0.0004865 0.0002184 0.01482 -0.0003666 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6431 0.08429 0.07188 0.3483 0.967 0.9843 0.742 0.8806 0.9586 0.6233 ] Network output: [ 0.001257 0.927 1.035 9.829e-05 -4.413e-05 0.03624 7.408e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04786 0.03376 0.05509 0.05364 0.9819 0.9871 0.04909 0.9594 0.9738 0.07232 ] Network output: [ 0.1089 -0.3163 1.072 0.0003786 -0.00017 1.028 0.0002853 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7358 0.5947 0.4869 0.5275 0.9709 0.9866 0.7395 0.8921 0.9646 0.6192 ] Network output: [ -0.0677 0.2285 0.9685 0.0006436 -0.0002889 0.941 0.000485 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5816 0.5628 0.4166 0.3321 0.9839 0.9893 0.5821 0.9648 0.9762 0.4342 ] Network output: [ -0.09085 0.2702 0.9119 -0.0001909 8.569e-05 0.9988 -0.0001438 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5962 0.593 0.4517 0.2845 0.9813 0.9878 0.5963 0.9565 0.9715 0.4555 ] Network output: [ 0.0466 0.8381 0.04568 -0.0006277 0.0002818 1.02 -0.0004731 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04561 Epoch 1943 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04082 0.9735 0.9854 0.0001796 -8.063e-05 -0.03985 0.0001353 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02488 -0.004684 0.01971 0.03872 0.9328 0.9431 0.05212 0.8668 0.8891 0.1352 ] Network output: [ 0.9614 0.07827 -0.01786 -0.0004856 0.000218 0.01474 -0.000366 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6432 0.08455 0.0722 0.3483 0.9671 0.9843 0.7422 0.8807 0.9587 0.6234 ] Network output: [ 0.001216 0.927 1.035 9.755e-05 -4.379e-05 0.03633 7.352e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0479 0.0338 0.05514 0.05366 0.9819 0.9871 0.04913 0.9595 0.9739 0.07236 ] Network output: [ 0.1089 -0.3163 1.072 0.0003761 -0.0001689 1.028 0.0002835 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.736 0.5951 0.4872 0.5274 0.9709 0.9866 0.7397 0.8922 0.9646 0.6192 ] Network output: [ -0.0676 0.2283 0.9686 0.0006429 -0.0002886 0.9409 0.0004845 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5817 0.563 0.4169 0.3323 0.9839 0.9893 0.5823 0.9648 0.9762 0.4344 ] Network output: [ -0.09072 0.27 0.9121 -0.0001897 8.517e-05 0.9986 -0.000143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5963 0.5931 0.4519 0.2847 0.9813 0.9878 0.5964 0.9565 0.9716 0.4556 ] Network output: [ 0.04643 0.8386 0.04553 -0.0006254 0.0002808 1.021 -0.0004713 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04554 Epoch 1944 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0408 0.9735 0.9854 0.0001792 -8.047e-05 -0.03983 0.0001351 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02488 -0.004688 0.01971 0.03873 0.9328 0.9431 0.05213 0.8669 0.8892 0.1353 ] Network output: [ 0.9615 0.07817 -0.01781 -0.0004847 0.0002176 0.01467 -0.0003653 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6433 0.08481 0.07251 0.3482 0.9671 0.9843 0.7424 0.8808 0.9587 0.6234 ] Network output: [ 0.001175 0.927 1.035 9.681e-05 -4.346e-05 0.03642 7.296e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04793 0.03384 0.05518 0.05368 0.9819 0.9871 0.04916 0.9595 0.9739 0.0724 ] Network output: [ 0.1088 -0.3162 1.072 0.0003736 -0.0001677 1.028 0.0002816 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7361 0.5955 0.4875 0.5274 0.9709 0.9866 0.7399 0.8923 0.9647 0.6193 ] Network output: [ -0.06751 0.2281 0.9686 0.0006422 -0.0002883 0.9409 0.000484 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5819 0.5632 0.4171 0.3324 0.9839 0.9893 0.5825 0.9649 0.9762 0.4346 ] Network output: [ -0.0906 0.2699 0.9122 -0.0001886 8.465e-05 0.9984 -0.0001421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5964 0.5932 0.4521 0.2849 0.9813 0.9878 0.5966 0.9566 0.9716 0.4558 ] Network output: [ 0.04625 0.839 0.04537 -0.0006231 0.0002797 1.021 -0.0004696 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04547 Epoch 1945 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04079 0.9735 0.9854 0.0001789 -8.031e-05 -0.03981 0.0001348 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02488 -0.004693 0.01972 0.03873 0.9328 0.9432 0.05214 0.867 0.8893 0.1353 ] Network output: [ 0.9616 0.07806 -0.01776 -0.0004838 0.0002172 0.01459 -0.0003646 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6435 0.08506 0.07282 0.3481 0.9671 0.9843 0.7425 0.8809 0.9587 0.6234 ] Network output: [ 0.001135 0.9269 1.035 9.608e-05 -4.313e-05 0.03652 7.241e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04797 0.03388 0.05523 0.0537 0.9819 0.9871 0.0492 0.9596 0.9739 0.07243 ] Network output: [ 0.1088 -0.3162 1.072 0.0003712 -0.0001666 1.029 0.0002797 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7363 0.5959 0.4878 0.5274 0.9709 0.9866 0.74 0.8924 0.9647 0.6193 ] Network output: [ -0.06742 0.2279 0.9687 0.0006415 -0.000288 0.9409 0.0004834 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5821 0.5635 0.4174 0.3326 0.9839 0.9894 0.5827 0.9649 0.9763 0.4348 ] Network output: [ -0.09048 0.2697 0.9123 -0.0001874 8.414e-05 0.9982 -0.0001412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5966 0.5933 0.4523 0.2851 0.9813 0.9878 0.5967 0.9566 0.9717 0.456 ] Network output: [ 0.04608 0.8395 0.04522 -0.0006208 0.0002787 1.021 -0.0004678 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0454 Epoch 1946 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04077 0.9736 0.9854 0.0001785 -8.016e-05 -0.03978 0.0001346 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02488 -0.004697 0.01973 0.03874 0.9328 0.9432 0.05214 0.8671 0.8893 0.1353 ] Network output: [ 0.9616 0.07796 -0.01771 -0.0004829 0.0002168 0.01451 -0.0003639 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6436 0.08532 0.07313 0.3481 0.9671 0.9843 0.7427 0.881 0.9588 0.6235 ] Network output: [ 0.001094 0.9269 1.035 9.534e-05 -4.28e-05 0.03661 7.185e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.048 0.03392 0.05528 0.05372 0.9819 0.9871 0.04923 0.9596 0.974 0.07247 ] Network output: [ 0.1087 -0.3161 1.072 0.0003687 -0.0001655 1.029 0.0002778 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7364 0.5963 0.4881 0.5273 0.971 0.9866 0.7402 0.8925 0.9647 0.6194 ] Network output: [ -0.06732 0.2277 0.9688 0.0006408 -0.0002877 0.9408 0.0004829 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5823 0.5637 0.4177 0.3328 0.9839 0.9894 0.5828 0.965 0.9763 0.4351 ] Network output: [ -0.09036 0.2695 0.9125 -0.0001863 8.364e-05 0.998 -0.0001404 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5967 0.5934 0.4524 0.2853 0.9814 0.9878 0.5968 0.9567 0.9717 0.4562 ] Network output: [ 0.04591 0.8399 0.04507 -0.0006185 0.0002776 1.021 -0.0004661 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04533 Epoch 1947 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04076 0.9736 0.9854 0.0001782 -8e-05 -0.03976 0.0001343 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02489 -0.004701 0.01974 0.03874 0.9329 0.9432 0.05215 0.8672 0.8894 0.1353 ] Network output: [ 0.9617 0.07786 -0.01766 -0.000482 0.0002164 0.01443 -0.0003632 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6437 0.08557 0.07344 0.348 0.9671 0.9843 0.7428 0.8811 0.9588 0.6235 ] Network output: [ 0.001054 0.9269 1.035 9.461e-05 -4.247e-05 0.0367 7.13e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04804 0.03396 0.05532 0.05373 0.9819 0.9871 0.04927 0.9597 0.974 0.0725 ] Network output: [ 0.1087 -0.316 1.071 0.0003662 -0.0001644 1.029 0.0002759 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7366 0.5967 0.4885 0.5273 0.971 0.9867 0.7403 0.8926 0.9648 0.6194 ] Network output: [ -0.06723 0.2274 0.9688 0.00064 -0.0002873 0.9408 0.0004824 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5825 0.5639 0.4179 0.3329 0.9839 0.9894 0.583 0.965 0.9763 0.4353 ] Network output: [ -0.09024 0.2693 0.9126 -0.0001852 8.314e-05 0.9978 -0.0001396 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5968 0.5935 0.4526 0.2856 0.9814 0.9879 0.5969 0.9568 0.9717 0.4563 ] Network output: [ 0.04573 0.8403 0.04492 -0.0006161 0.0002766 1.021 -0.0004643 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04526 Epoch 1948 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04074 0.9736 0.9854 0.0001778 -7.984e-05 -0.03974 0.000134 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02489 -0.004706 0.01975 0.03874 0.9329 0.9432 0.05216 0.8672 0.8895 0.1353 ] Network output: [ 0.9618 0.07776 -0.01761 -0.000481 0.0002159 0.01436 -0.0003625 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6438 0.08583 0.07375 0.3479 0.9671 0.9844 0.743 0.8812 0.9589 0.6235 ] Network output: [ 0.001014 0.9269 1.035 9.388e-05 -4.214e-05 0.03679 7.075e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04807 0.034 0.05537 0.05375 0.982 0.9871 0.04931 0.9598 0.974 0.07254 ] Network output: [ 0.1086 -0.316 1.071 0.0003636 -0.0001633 1.029 0.0002741 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7368 0.5971 0.4888 0.5272 0.971 0.9867 0.7405 0.8927 0.9648 0.6194 ] Network output: [ -0.06713 0.2272 0.9689 0.0006393 -0.000287 0.9408 0.0004818 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5826 0.5641 0.4182 0.3331 0.9839 0.9894 0.5832 0.9651 0.9763 0.4355 ] Network output: [ -0.09012 0.2692 0.9127 -0.0001841 8.264e-05 0.9976 -0.0001387 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5969 0.5937 0.4528 0.2858 0.9814 0.9879 0.597 0.9568 0.9718 0.4565 ] Network output: [ 0.04557 0.8408 0.04477 -0.0006138 0.0002755 1.021 -0.0004626 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04519 Epoch 1949 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04072 0.9736 0.9854 0.0001775 -7.968e-05 -0.03972 0.0001338 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02489 -0.00471 0.01976 0.03875 0.9329 0.9432 0.05217 0.8673 0.8895 0.1354 ] Network output: [ 0.9618 0.07767 -0.01756 -0.0004801 0.0002155 0.01428 -0.0003618 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.644 0.08608 0.07405 0.3479 0.9672 0.9844 0.7431 0.8813 0.9589 0.6236 ] Network output: [ 0.0009744 0.9269 1.035 9.315e-05 -4.182e-05 0.03688 7.02e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04811 0.03404 0.05541 0.05377 0.982 0.9871 0.04934 0.9598 0.9741 0.07257 ] Network output: [ 0.1085 -0.3159 1.071 0.0003611 -0.0001621 1.029 0.0002722 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7369 0.5975 0.4891 0.5272 0.971 0.9867 0.7406 0.8928 0.9648 0.6195 ] Network output: [ -0.06704 0.227 0.9689 0.0006386 -0.0002867 0.9407 0.0004813 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5828 0.5643 0.4184 0.3333 0.9839 0.9894 0.5834 0.9651 0.9764 0.4357 ] Network output: [ -0.09 0.269 0.9129 -0.000183 8.216e-05 0.9974 -0.0001379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.597 0.5938 0.453 0.286 0.9814 0.9879 0.5971 0.9569 0.9718 0.4567 ] Network output: [ 0.0454 0.8412 0.04462 -0.0006114 0.0002745 1.021 -0.0004608 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04512 Epoch 1950 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04071 0.9736 0.9854 0.0001771 -7.952e-05 -0.03969 0.0001335 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02489 -0.004714 0.01977 0.03875 0.9329 0.9433 0.05218 0.8674 0.8896 0.1354 ] Network output: [ 0.9619 0.07757 -0.01751 -0.0004791 0.0002151 0.0142 -0.0003611 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6441 0.08633 0.07436 0.3478 0.9672 0.9844 0.7432 0.8814 0.9589 0.6236 ] Network output: [ 0.000935 0.9269 1.035 9.242e-05 -4.149e-05 0.03697 6.965e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04814 0.03408 0.05546 0.05378 0.982 0.9871 0.04938 0.9599 0.9741 0.0726 ] Network output: [ 0.1085 -0.3159 1.071 0.0003586 -0.000161 1.029 0.0002703 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7371 0.5979 0.4894 0.5271 0.971 0.9867 0.7408 0.8929 0.9649 0.6195 ] Network output: [ -0.06695 0.2268 0.969 0.0006379 -0.0002864 0.9407 0.0004807 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.583 0.5645 0.4187 0.3334 0.9839 0.9894 0.5835 0.9652 0.9764 0.4359 ] Network output: [ -0.08988 0.2688 0.913 -0.0001819 8.168e-05 0.9972 -0.0001371 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5971 0.5939 0.4531 0.2862 0.9814 0.9879 0.5972 0.9569 0.9718 0.4568 ] Network output: [ 0.04523 0.8417 0.04447 -0.0006091 0.0002734 1.021 -0.000459 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04505 Epoch 1951 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04069 0.9736 0.9854 0.0001768 -7.936e-05 -0.03967 0.0001332 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02489 -0.004719 0.01978 0.03875 0.933 0.9433 0.05219 0.8675 0.8896 0.1354 ] Network output: [ 0.962 0.07747 -0.01746 -0.0004782 0.0002147 0.01412 -0.0003604 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6442 0.08657 0.07466 0.3477 0.9672 0.9844 0.7434 0.8815 0.959 0.6236 ] Network output: [ 0.0008959 0.9269 1.035 9.17e-05 -4.117e-05 0.03705 6.91e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04818 0.03412 0.0555 0.0538 0.982 0.9871 0.04941 0.9599 0.9741 0.07263 ] Network output: [ 0.1084 -0.3158 1.071 0.0003561 -0.0001599 1.029 0.0002684 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7372 0.5983 0.4897 0.5271 0.971 0.9867 0.7409 0.893 0.9649 0.6195 ] Network output: [ -0.06685 0.2266 0.969 0.0006372 -0.0002861 0.9407 0.0004802 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5832 0.5647 0.4189 0.3336 0.984 0.9894 0.5837 0.9652 0.9764 0.4361 ] Network output: [ -0.08977 0.2686 0.9131 -0.0001809 8.12e-05 0.9971 -0.0001363 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5972 0.594 0.4533 0.2864 0.9814 0.9879 0.5973 0.957 0.9719 0.457 ] Network output: [ 0.04507 0.8421 0.04433 -0.0006067 0.0002724 1.021 -0.0004572 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04498 Epoch 1952 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04067 0.9736 0.9854 0.0001764 -7.92e-05 -0.03965 0.000133 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02489 -0.004723 0.01979 0.03875 0.933 0.9433 0.0522 0.8676 0.8897 0.1354 ] Network output: [ 0.962 0.07738 -0.01741 -0.0004772 0.0002142 0.01405 -0.0003596 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6443 0.08682 0.07496 0.3477 0.9672 0.9844 0.7435 0.8816 0.959 0.6237 ] Network output: [ 0.000857 0.9269 1.035 9.097e-05 -4.084e-05 0.03714 6.856e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04821 0.03415 0.05554 0.05381 0.982 0.9872 0.04944 0.96 0.9742 0.07266 ] Network output: [ 0.1084 -0.3157 1.071 0.0003536 -0.0001587 1.029 0.0002665 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7374 0.5986 0.49 0.5271 0.971 0.9867 0.7411 0.8931 0.9649 0.6196 ] Network output: [ -0.06676 0.2264 0.9691 0.0006365 -0.0002857 0.9406 0.0004797 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5833 0.5649 0.4191 0.3337 0.984 0.9894 0.5839 0.9653 0.9765 0.4363 ] Network output: [ -0.08965 0.2684 0.9133 -0.0001798 8.073e-05 0.9969 -0.0001355 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5973 0.5941 0.4535 0.2866 0.9814 0.9879 0.5974 0.957 0.9719 0.4571 ] Network output: [ 0.0449 0.8425 0.04418 -0.0006043 0.0002713 1.021 -0.0004555 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04491 Epoch 1953 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04065 0.9736 0.9854 0.0001761 -7.904e-05 -0.03963 0.0001327 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0249 -0.004727 0.01979 0.03876 0.933 0.9433 0.05221 0.8676 0.8898 0.1354 ] Network output: [ 0.9621 0.07728 -0.01737 -0.0004762 0.0002138 0.01397 -0.0003589 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6445 0.08706 0.07526 0.3476 0.9672 0.9844 0.7437 0.8817 0.959 0.6237 ] Network output: [ 0.0008184 0.9269 1.035 9.025e-05 -4.052e-05 0.03723 6.802e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04824 0.03419 0.05558 0.05382 0.982 0.9872 0.04948 0.96 0.9742 0.07269 ] Network output: [ 0.1083 -0.3157 1.071 0.0003511 -0.0001576 1.03 0.0002646 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7375 0.599 0.4903 0.527 0.9711 0.9867 0.7412 0.8932 0.965 0.6196 ] Network output: [ -0.06667 0.2262 0.9691 0.0006358 -0.0002854 0.9406 0.0004791 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5835 0.5651 0.4194 0.3339 0.984 0.9894 0.584 0.9653 0.9765 0.4365 ] Network output: [ -0.08954 0.2683 0.9134 -0.0001788 8.027e-05 0.9967 -0.0001347 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5974 0.5942 0.4537 0.2868 0.9814 0.9879 0.5975 0.9571 0.9719 0.4573 ] Network output: [ 0.04474 0.8429 0.04404 -0.000602 0.0002702 1.021 -0.0004537 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04484 Epoch 1954 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04064 0.9736 0.9855 0.0001757 -7.887e-05 -0.0396 0.0001324 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0249 -0.004732 0.0198 0.03876 0.933 0.9433 0.05222 0.8677 0.8898 0.1354 ] Network output: [ 0.9622 0.07719 -0.01732 -0.0004753 0.0002134 0.0139 -0.0003582 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6446 0.08731 0.07555 0.3475 0.9672 0.9844 0.7438 0.8817 0.9591 0.6237 ] Network output: [ 0.00078 0.9269 1.035 8.953e-05 -4.019e-05 0.03732 6.748e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04828 0.03423 0.05562 0.05383 0.982 0.9872 0.04951 0.9601 0.9742 0.07272 ] Network output: [ 0.1083 -0.3156 1.071 0.0003486 -0.0001565 1.03 0.0002627 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7376 0.5994 0.4906 0.527 0.9711 0.9867 0.7414 0.8933 0.965 0.6197 ] Network output: [ -0.06658 0.226 0.9692 0.000635 -0.0002851 0.9406 0.0004786 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5837 0.5653 0.4196 0.334 0.984 0.9894 0.5842 0.9654 0.9765 0.4368 ] Network output: [ -0.08942 0.2681 0.9135 -0.0001778 7.981e-05 0.9965 -0.000134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5975 0.5943 0.4538 0.287 0.9815 0.9879 0.5976 0.9572 0.972 0.4575 ] Network output: [ 0.04458 0.8434 0.04389 -0.0005996 0.0002692 1.021 -0.0004519 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04478 Epoch 1955 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04062 0.9736 0.9855 0.0001753 -7.871e-05 -0.03958 0.0001321 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0249 -0.004736 0.01981 0.03876 0.933 0.9434 0.05222 0.8678 0.8899 0.1355 ] Network output: [ 0.9622 0.0771 -0.01727 -0.0004743 0.0002129 0.01382 -0.0003574 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6447 0.08755 0.07585 0.3474 0.9673 0.9844 0.744 0.8818 0.9591 0.6238 ] Network output: [ 0.0007419 0.9269 1.035 8.882e-05 -3.987e-05 0.0374 6.694e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04831 0.03427 0.05566 0.05385 0.982 0.9872 0.04955 0.9601 0.9743 0.07275 ] Network output: [ 0.1082 -0.3156 1.071 0.0003461 -0.0001554 1.03 0.0002608 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7378 0.5998 0.4909 0.5269 0.9711 0.9867 0.7415 0.8934 0.965 0.6197 ] Network output: [ -0.06649 0.2258 0.9692 0.0006343 -0.0002848 0.9405 0.000478 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5838 0.5655 0.4199 0.3342 0.984 0.9894 0.5844 0.9654 0.9765 0.437 ] Network output: [ -0.08931 0.2679 0.9137 -0.0001768 7.935e-05 0.9963 -0.0001332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5976 0.5944 0.454 0.2872 0.9815 0.9879 0.5977 0.9572 0.972 0.4576 ] Network output: [ 0.04442 0.8438 0.04375 -0.0005972 0.0002681 1.021 -0.0004501 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04471 Epoch 1956 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0406 0.9736 0.9855 0.000175 -7.854e-05 -0.03956 0.0001319 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0249 -0.00474 0.01982 0.03876 0.9331 0.9434 0.05223 0.8679 0.8899 0.1355 ] Network output: [ 0.9623 0.077 -0.01723 -0.0004733 0.0002125 0.01374 -0.0003567 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6448 0.08779 0.07614 0.3474 0.9673 0.9844 0.7441 0.8819 0.9592 0.6238 ] Network output: [ 0.0007041 0.9269 1.035 8.81e-05 -3.955e-05 0.03749 6.64e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04834 0.0343 0.0557 0.05386 0.9821 0.9872 0.04958 0.9602 0.9743 0.07278 ] Network output: [ 0.1082 -0.3155 1.071 0.0003435 -0.0001542 1.03 0.0002589 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7379 0.6001 0.4911 0.5269 0.9711 0.9867 0.7417 0.8935 0.9651 0.6197 ] Network output: [ -0.06639 0.2256 0.9693 0.0006336 -0.0002844 0.9405 0.0004775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.584 0.5658 0.4201 0.3343 0.984 0.9894 0.5846 0.9654 0.9766 0.4372 ] Network output: [ -0.0892 0.2677 0.9138 -0.0001758 7.89e-05 0.9962 -0.0001325 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5977 0.5946 0.4542 0.2874 0.9815 0.9879 0.5978 0.9573 0.9721 0.4578 ] Network output: [ 0.04426 0.8442 0.04361 -0.0005948 0.000267 1.021 -0.0004483 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04464 Epoch 1957 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04059 0.9736 0.9855 0.0001746 -7.838e-05 -0.03954 0.0001316 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0249 -0.004745 0.01982 0.03876 0.9331 0.9434 0.05224 0.868 0.89 0.1355 ] Network output: [ 0.9623 0.07691 -0.01718 -0.0004723 0.000212 0.01367 -0.0003559 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6449 0.08803 0.07643 0.3473 0.9673 0.9844 0.7442 0.882 0.9592 0.6238 ] Network output: [ 0.0006665 0.9269 1.035 8.739e-05 -3.923e-05 0.03757 6.586e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04838 0.03434 0.05574 0.05387 0.9821 0.9872 0.04961 0.9602 0.9743 0.07281 ] Network output: [ 0.1081 -0.3154 1.07 0.000341 -0.0001531 1.03 0.000257 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7381 0.6005 0.4914 0.5268 0.9711 0.9867 0.7418 0.8935 0.9651 0.6198 ] Network output: [ -0.0663 0.2254 0.9693 0.0006329 -0.0002841 0.9405 0.000477 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5842 0.566 0.4203 0.3344 0.984 0.9894 0.5847 0.9655 0.9766 0.4374 ] Network output: [ -0.08909 0.2676 0.9139 -0.0001748 7.846e-05 0.996 -0.0001317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5978 0.5947 0.4543 0.2876 0.9815 0.9879 0.5979 0.9573 0.9721 0.4579 ] Network output: [ 0.0441 0.8446 0.04347 -0.0005924 0.000266 1.021 -0.0004465 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04458 Epoch 1958 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04057 0.9736 0.9855 0.0001742 -7.821e-05 -0.03951 0.0001313 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0249 -0.004749 0.01983 0.03876 0.9331 0.9434 0.05225 0.868 0.89 0.1355 ] Network output: [ 0.9624 0.07682 -0.01714 -0.0004712 0.0002116 0.01359 -0.0003551 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.645 0.08827 0.07672 0.3472 0.9673 0.9845 0.7444 0.8821 0.9592 0.6239 ] Network output: [ 0.0006291 0.9269 1.035 8.668e-05 -3.891e-05 0.03766 6.533e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04841 0.03438 0.05578 0.05388 0.9821 0.9872 0.04965 0.9603 0.9743 0.07284 ] Network output: [ 0.1081 -0.3154 1.07 0.0003385 -0.000152 1.03 0.0002551 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7382 0.6009 0.4917 0.5268 0.9711 0.9867 0.7419 0.8936 0.9651 0.6198 ] Network output: [ -0.06621 0.2252 0.9693 0.0006322 -0.0002838 0.9404 0.0004764 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5843 0.5662 0.4206 0.3346 0.984 0.9895 0.5849 0.9655 0.9766 0.4376 ] Network output: [ -0.08897 0.2674 0.9141 -0.0001738 7.802e-05 0.9958 -0.000131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5979 0.5948 0.4545 0.2878 0.9815 0.988 0.598 0.9574 0.9721 0.4581 ] Network output: [ 0.04395 0.845 0.04333 -0.0005901 0.0002649 1.021 -0.0004447 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04451 Epoch 1959 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04055 0.9736 0.9855 0.0001738 -7.805e-05 -0.03949 0.000131 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0249 -0.004754 0.01984 0.03876 0.9331 0.9434 0.05226 0.8681 0.8901 0.1355 ] Network output: [ 0.9625 0.07673 -0.01709 -0.0004702 0.0002111 0.01352 -0.0003544 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6452 0.0885 0.07701 0.3471 0.9673 0.9845 0.7445 0.8822 0.9593 0.6239 ] Network output: [ 0.000592 0.9269 1.035 8.597e-05 -3.86e-05 0.03774 6.479e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04844 0.03441 0.05582 0.05389 0.9821 0.9872 0.04968 0.9604 0.9744 0.07286 ] Network output: [ 0.108 -0.3153 1.07 0.000336 -0.0001508 1.03 0.0002532 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7384 0.6012 0.492 0.5267 0.9712 0.9868 0.7421 0.8937 0.9652 0.6199 ] Network output: [ -0.06612 0.225 0.9694 0.0006315 -0.0002835 0.9404 0.0004759 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5845 0.5663 0.4208 0.3347 0.984 0.9895 0.5851 0.9656 0.9767 0.4377 ] Network output: [ -0.08886 0.2672 0.9142 -0.0001728 7.759e-05 0.9956 -0.0001302 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.598 0.5949 0.4546 0.288 0.9815 0.988 0.5981 0.9574 0.9722 0.4582 ] Network output: [ 0.04379 0.8454 0.04319 -0.0005877 0.0002638 1.021 -0.0004429 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04445 Epoch 1960 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04053 0.9736 0.9855 0.0001735 -7.788e-05 -0.03947 0.0001307 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0249 -0.004758 0.01985 0.03876 0.9332 0.9435 0.05226 0.8682 0.8902 0.1355 ] Network output: [ 0.9625 0.07664 -0.01705 -0.0004692 0.0002106 0.01344 -0.0003536 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6453 0.08874 0.0773 0.3471 0.9673 0.9845 0.7446 0.8823 0.9593 0.6239 ] Network output: [ 0.0005552 0.9269 1.035 8.527e-05 -3.828e-05 0.03783 6.426e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04847 0.03445 0.05586 0.0539 0.9821 0.9872 0.04971 0.9604 0.9744 0.07289 ] Network output: [ 0.108 -0.3152 1.07 0.0003335 -0.0001497 1.03 0.0002513 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7385 0.6016 0.4923 0.5267 0.9712 0.9868 0.7422 0.8938 0.9652 0.6199 ] Network output: [ -0.06603 0.2248 0.9694 0.0006308 -0.0002832 0.9404 0.0004754 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5847 0.5665 0.421 0.3349 0.9841 0.9895 0.5852 0.9656 0.9767 0.4379 ] Network output: [ -0.08876 0.267 0.9143 -0.0001719 7.716e-05 0.9955 -0.0001295 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5981 0.595 0.4548 0.2882 0.9815 0.988 0.5982 0.9575 0.9722 0.4584 ] Network output: [ 0.04364 0.8458 0.04305 -0.0005853 0.0002627 1.021 -0.0004411 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04438 Epoch 1961 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04051 0.9736 0.9855 0.0001731 -7.771e-05 -0.03945 0.0001305 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004762 0.01985 0.03876 0.9332 0.9435 0.05227 0.8683 0.8902 0.1355 ] Network output: [ 0.9626 0.07655 -0.01701 -0.0004682 0.0002102 0.01337 -0.0003528 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6454 0.08897 0.07758 0.347 0.9674 0.9845 0.7448 0.8824 0.9593 0.624 ] Network output: [ 0.0005186 0.9268 1.035 8.456e-05 -3.796e-05 0.03791 6.373e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0485 0.03449 0.0559 0.05391 0.9821 0.9872 0.04975 0.9605 0.9744 0.07292 ] Network output: [ 0.1079 -0.3152 1.07 0.0003309 -0.0001486 1.031 0.0002494 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7386 0.6019 0.4926 0.5266 0.9712 0.9868 0.7424 0.8939 0.9652 0.6199 ] Network output: [ -0.06594 0.2246 0.9695 0.00063 -0.0002828 0.9404 0.0004748 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5848 0.5667 0.4212 0.335 0.9841 0.9895 0.5854 0.9657 0.9767 0.4381 ] Network output: [ -0.08865 0.2669 0.9145 -0.0001709 7.674e-05 0.9953 -0.0001288 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5982 0.5951 0.455 0.2884 0.9816 0.988 0.5983 0.9576 0.9722 0.4585 ] Network output: [ 0.04349 0.8462 0.04292 -0.0005829 0.0002617 1.021 -0.0004393 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04432 Epoch 1962 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0405 0.9736 0.9855 0.0001727 -7.754e-05 -0.03942 0.0001302 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004767 0.01986 0.03876 0.9332 0.9435 0.05228 0.8683 0.8903 0.1355 ] Network output: [ 0.9627 0.07647 -0.01696 -0.0004671 0.0002097 0.01329 -0.000352 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6455 0.0892 0.07786 0.3469 0.9674 0.9845 0.7449 0.8825 0.9594 0.624 ] Network output: [ 0.0004822 0.9268 1.035 8.386e-05 -3.765e-05 0.03799 6.32e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04854 0.03452 0.05593 0.05392 0.9821 0.9873 0.04978 0.9605 0.9745 0.07294 ] Network output: [ 0.1079 -0.3151 1.07 0.0003284 -0.0001474 1.031 0.0002475 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7388 0.6023 0.4928 0.5265 0.9712 0.9868 0.7425 0.894 0.9653 0.62 ] Network output: [ -0.06585 0.2245 0.9695 0.0006293 -0.0002825 0.9403 0.0004743 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.585 0.5669 0.4215 0.3351 0.9841 0.9895 0.5855 0.9657 0.9767 0.4383 ] Network output: [ -0.08854 0.2667 0.9146 -0.00017 7.632e-05 0.9951 -0.0001281 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5983 0.5952 0.4551 0.2886 0.9816 0.988 0.5984 0.9576 0.9723 0.4587 ] Network output: [ 0.04334 0.8466 0.04278 -0.0005805 0.0002606 1.022 -0.0004375 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04425 Epoch 1963 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04048 0.9736 0.9855 0.0001723 -7.737e-05 -0.0394 0.0001299 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004771 0.01987 0.03876 0.9332 0.9435 0.05228 0.8684 0.8903 0.1355 ] Network output: [ 0.9627 0.07638 -0.01692 -0.0004661 0.0002092 0.01322 -0.0003512 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6456 0.08943 0.07814 0.3468 0.9674 0.9845 0.745 0.8826 0.9594 0.624 ] Network output: [ 0.0004461 0.9268 1.035 8.316e-05 -3.734e-05 0.03808 6.268e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04857 0.03456 0.05597 0.05392 0.9822 0.9873 0.04981 0.9606 0.9745 0.07296 ] Network output: [ 0.1078 -0.3151 1.07 0.0003259 -0.0001463 1.031 0.0002456 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7389 0.6026 0.4931 0.5265 0.9712 0.9868 0.7426 0.8941 0.9653 0.62 ] Network output: [ -0.06576 0.2243 0.9695 0.0006286 -0.0002822 0.9403 0.0004737 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5852 0.5671 0.4217 0.3353 0.9841 0.9895 0.5857 0.9658 0.9768 0.4385 ] Network output: [ -0.08843 0.2665 0.9147 -0.0001691 7.59e-05 0.9949 -0.0001274 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5984 0.5953 0.4553 0.2888 0.9816 0.988 0.5985 0.9577 0.9723 0.4588 ] Network output: [ 0.04319 0.847 0.04265 -0.0005781 0.0002595 1.022 -0.0004357 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04419 Epoch 1964 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04046 0.9736 0.9855 0.000172 -7.72e-05 -0.03938 0.0001296 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004776 0.01987 0.03876 0.9332 0.9435 0.05229 0.8685 0.8904 0.1356 ] Network output: [ 0.9628 0.07629 -0.01688 -0.000465 0.0002088 0.01315 -0.0003504 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6457 0.08966 0.07842 0.3467 0.9674 0.9845 0.7452 0.8827 0.9594 0.6241 ] Network output: [ 0.0004103 0.9268 1.035 8.247e-05 -3.702e-05 0.03816 6.215e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0486 0.03459 0.056 0.05393 0.9822 0.9873 0.04984 0.9606 0.9745 0.07299 ] Network output: [ 0.1077 -0.315 1.07 0.0003234 -0.0001452 1.031 0.0002437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.739 0.603 0.4934 0.5264 0.9712 0.9868 0.7428 0.8942 0.9653 0.62 ] Network output: [ -0.06567 0.2241 0.9696 0.0006279 -0.0002819 0.9403 0.0004732 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5853 0.5673 0.4219 0.3354 0.9841 0.9895 0.5859 0.9658 0.9768 0.4387 ] Network output: [ -0.08833 0.2663 0.9149 -0.0001682 7.549e-05 0.9948 -0.0001267 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5985 0.5954 0.4554 0.2889 0.9816 0.988 0.5986 0.9577 0.9723 0.459 ] Network output: [ 0.04304 0.8474 0.04251 -0.0005757 0.0002584 1.022 -0.0004338 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04412 Epoch 1965 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04044 0.9736 0.9855 0.0001716 -7.703e-05 -0.03936 0.0001293 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.00478 0.01988 0.03876 0.9333 0.9435 0.0523 0.8686 0.8904 0.1356 ] Network output: [ 0.9628 0.07621 -0.01684 -0.0004639 0.0002083 0.01307 -0.0003496 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6458 0.08988 0.0787 0.3467 0.9674 0.9845 0.7453 0.8827 0.9595 0.6241 ] Network output: [ 0.0003747 0.9268 1.035 8.177e-05 -3.671e-05 0.03824 6.163e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04863 0.03463 0.05604 0.05394 0.9822 0.9873 0.04987 0.9607 0.9746 0.07301 ] Network output: [ 0.1077 -0.3149 1.07 0.0003209 -0.000144 1.031 0.0002418 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7392 0.6033 0.4937 0.5264 0.9712 0.9868 0.7429 0.8942 0.9654 0.6201 ] Network output: [ -0.06559 0.2239 0.9696 0.0006272 -0.0002816 0.9402 0.0004727 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5855 0.5675 0.4221 0.3355 0.9841 0.9895 0.586 0.9658 0.9768 0.4389 ] Network output: [ -0.08822 0.2662 0.915 -0.0001673 7.509e-05 0.9946 -0.000126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5986 0.5955 0.4556 0.2891 0.9816 0.988 0.5987 0.9578 0.9724 0.4591 ] Network output: [ 0.0429 0.8478 0.04238 -0.0005733 0.0002574 1.022 -0.000432 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04406 Epoch 1966 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04042 0.9736 0.9856 0.0001712 -7.686e-05 -0.03933 0.000129 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004784 0.01989 0.03876 0.9333 0.9436 0.0523 0.8687 0.8905 0.1356 ] Network output: [ 0.9629 0.07612 -0.0168 -0.0004629 0.0002078 0.013 -0.0003488 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6459 0.09011 0.07898 0.3466 0.9674 0.9845 0.7454 0.8828 0.9595 0.6241 ] Network output: [ 0.0003394 0.9268 1.034 8.108e-05 -3.64e-05 0.03832 6.111e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04866 0.03466 0.05607 0.05394 0.9822 0.9873 0.0499 0.9607 0.9746 0.07303 ] Network output: [ 0.1076 -0.3149 1.07 0.0003183 -0.0001429 1.031 0.0002399 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7393 0.6037 0.4939 0.5263 0.9713 0.9868 0.743 0.8943 0.9654 0.6201 ] Network output: [ -0.0655 0.2237 0.9696 0.0006265 -0.0002813 0.9402 0.0004722 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5856 0.5677 0.4223 0.3356 0.9841 0.9895 0.5862 0.9659 0.9769 0.4391 ] Network output: [ -0.08812 0.266 0.9151 -0.0001664 7.468e-05 0.9944 -0.0001254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5987 0.5956 0.4557 0.2893 0.9816 0.988 0.5988 0.9578 0.9724 0.4592 ] Network output: [ 0.04275 0.8482 0.04225 -0.0005709 0.0002563 1.022 -0.0004302 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.044 Epoch 1967 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0404 0.9736 0.9856 0.0001708 -7.668e-05 -0.03931 0.0001287 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004789 0.01989 0.03875 0.9333 0.9436 0.05231 0.8687 0.8906 0.1356 ] Network output: [ 0.963 0.07604 -0.01676 -0.0004618 0.0002073 0.01293 -0.000348 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.646 0.09033 0.07925 0.3465 0.9675 0.9845 0.7456 0.8829 0.9595 0.6242 ] Network output: [ 0.0003043 0.9268 1.034 8.039e-05 -3.609e-05 0.0384 6.059e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04869 0.0347 0.05611 0.05395 0.9822 0.9873 0.04994 0.9608 0.9746 0.07305 ] Network output: [ 0.1076 -0.3148 1.07 0.0003158 -0.0001418 1.031 0.000238 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7394 0.604 0.4942 0.5263 0.9713 0.9868 0.7432 0.8944 0.9654 0.6202 ] Network output: [ -0.06541 0.2235 0.9696 0.0006258 -0.0002809 0.9402 0.0004716 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5858 0.5679 0.4226 0.3358 0.9841 0.9895 0.5863 0.9659 0.9769 0.4392 ] Network output: [ -0.08801 0.2658 0.9153 -0.0001655 7.429e-05 0.9943 -0.0001247 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5988 0.5957 0.4559 0.2895 0.9816 0.988 0.5989 0.9579 0.9724 0.4594 ] Network output: [ 0.04261 0.8486 0.04211 -0.0005685 0.0002552 1.022 -0.0004284 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04394 Epoch 1968 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04038 0.9736 0.9856 0.0001704 -7.651e-05 -0.03929 0.0001284 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004793 0.0199 0.03875 0.9333 0.9436 0.05232 0.8688 0.8906 0.1356 ] Network output: [ 0.963 0.07596 -0.01672 -0.0004607 0.0002068 0.01285 -0.0003472 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6462 0.09055 0.07953 0.3464 0.9675 0.9845 0.7457 0.883 0.9596 0.6242 ] Network output: [ 0.0002694 0.9268 1.034 7.97e-05 -3.578e-05 0.03848 6.007e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04872 0.03473 0.05614 0.05395 0.9822 0.9873 0.04997 0.9608 0.9746 0.07308 ] Network output: [ 0.1075 -0.3148 1.07 0.0003133 -0.0001407 1.031 0.0002361 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7396 0.6043 0.4945 0.5262 0.9713 0.9868 0.7433 0.8945 0.9655 0.6202 ] Network output: [ -0.06532 0.2234 0.9697 0.0006251 -0.0002806 0.9402 0.0004711 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5859 0.5681 0.4228 0.3359 0.9841 0.9895 0.5865 0.966 0.9769 0.4394 ] Network output: [ -0.08791 0.2656 0.9154 -0.0001646 7.39e-05 0.9941 -0.000124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5989 0.5958 0.456 0.2897 0.9816 0.9881 0.599 0.9579 0.9725 0.4595 ] Network output: [ 0.04246 0.849 0.04198 -0.0005661 0.0002541 1.022 -0.0004266 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04387 Epoch 1969 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04037 0.9736 0.9856 0.00017 -7.634e-05 -0.03927 0.0001281 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004797 0.0199 0.03875 0.9334 0.9436 0.05232 0.8689 0.8907 0.1356 ] Network output: [ 0.9631 0.07587 -0.01668 -0.0004596 0.0002063 0.01278 -0.0003464 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6463 0.09078 0.0798 0.3463 0.9675 0.9846 0.7458 0.8831 0.9596 0.6242 ] Network output: [ 0.0002348 0.9268 1.034 7.902e-05 -3.547e-05 0.03856 5.955e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04875 0.03476 0.05617 0.05396 0.9822 0.9873 0.05 0.9609 0.9747 0.0731 ] Network output: [ 0.1075 -0.3147 1.069 0.0003108 -0.0001395 1.032 0.0002342 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7397 0.6047 0.4947 0.5261 0.9713 0.9868 0.7434 0.8946 0.9655 0.6202 ] Network output: [ -0.06523 0.2232 0.9697 0.0006244 -0.0002803 0.9401 0.0004706 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5861 0.5683 0.423 0.336 0.9841 0.9895 0.5867 0.966 0.9769 0.4396 ] Network output: [ -0.08781 0.2655 0.9155 -0.0001637 7.351e-05 0.994 -0.0001234 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.599 0.5959 0.4562 0.2898 0.9817 0.9881 0.5991 0.958 0.9725 0.4597 ] Network output: [ 0.04232 0.8494 0.04185 -0.0005637 0.0002531 1.022 -0.0004248 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04381 Epoch 1970 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04035 0.9736 0.9856 0.0001697 -7.616e-05 -0.03924 0.0001279 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004802 0.01991 0.03875 0.9334 0.9436 0.05233 0.869 0.8907 0.1356 ] Network output: [ 0.9631 0.07579 -0.01664 -0.0004585 0.0002058 0.01271 -0.0003455 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6464 0.091 0.08007 0.3462 0.9675 0.9846 0.7459 0.8832 0.9596 0.6243 ] Network output: [ 0.0002005 0.9268 1.034 7.834e-05 -3.517e-05 0.03864 5.904e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04878 0.0348 0.0562 0.05396 0.9822 0.9873 0.05003 0.9609 0.9747 0.07312 ] Network output: [ 0.1074 -0.3146 1.069 0.0003083 -0.0001384 1.032 0.0002323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7398 0.605 0.495 0.5261 0.9713 0.9868 0.7435 0.8947 0.9655 0.6203 ] Network output: [ -0.06515 0.223 0.9697 0.0006237 -0.00028 0.9401 0.00047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5863 0.5685 0.4232 0.3361 0.9842 0.9895 0.5868 0.9661 0.977 0.4398 ] Network output: [ -0.08771 0.2653 0.9157 -0.0001629 7.312e-05 0.9938 -0.0001228 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.599 0.596 0.4563 0.29 0.9817 0.9881 0.5992 0.958 0.9725 0.4598 ] Network output: [ 0.04218 0.8498 0.04173 -0.0005613 0.000252 1.022 -0.000423 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04375 Epoch 1971 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04033 0.9736 0.9856 0.0001693 -7.599e-05 -0.03922 0.0001276 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004806 0.01991 0.03874 0.9334 0.9437 0.05233 0.869 0.8908 0.1356 ] Network output: [ 0.9632 0.07571 -0.0166 -0.0004574 0.0002053 0.01263 -0.0003447 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6465 0.09121 0.08034 0.3462 0.9675 0.9846 0.7461 0.8833 0.9597 0.6243 ] Network output: [ 0.0001663 0.9268 1.034 7.765e-05 -3.486e-05 0.03872 5.852e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04881 0.03483 0.05624 0.05397 0.9823 0.9873 0.05006 0.961 0.9747 0.07314 ] Network output: [ 0.1074 -0.3146 1.069 0.0003058 -0.0001373 1.032 0.0002304 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7399 0.6053 0.4953 0.526 0.9713 0.9868 0.7437 0.8948 0.9655 0.6203 ] Network output: [ -0.06506 0.2228 0.9698 0.000623 -0.0002797 0.9401 0.0004695 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5864 0.5686 0.4234 0.3362 0.9842 0.9895 0.587 0.9661 0.977 0.44 ] Network output: [ -0.0876 0.2651 0.9158 -0.000162 7.274e-05 0.9936 -0.0001221 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5991 0.5961 0.4564 0.2902 0.9817 0.9881 0.5992 0.9581 0.9726 0.4599 ] Network output: [ 0.04204 0.8501 0.0416 -0.0005589 0.0002509 1.022 -0.0004212 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04369 Epoch 1972 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04031 0.9736 0.9856 0.0001689 -7.581e-05 -0.0392 0.0001273 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004811 0.01992 0.03874 0.9334 0.9437 0.05234 0.8691 0.8908 0.1356 ] Network output: [ 0.9633 0.07563 -0.01656 -0.0004563 0.0002048 0.01256 -0.0003439 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6466 0.09143 0.0806 0.3461 0.9675 0.9846 0.7462 0.8834 0.9597 0.6243 ] Network output: [ 0.0001325 0.9268 1.034 7.698e-05 -3.456e-05 0.0388 5.801e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04884 0.03487 0.05627 0.05397 0.9823 0.9874 0.05009 0.961 0.9748 0.07315 ] Network output: [ 0.1073 -0.3145 1.069 0.0003033 -0.0001361 1.032 0.0002285 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7401 0.6057 0.4955 0.526 0.9713 0.9869 0.7438 0.8948 0.9656 0.6203 ] Network output: [ -0.06497 0.2226 0.9698 0.0006223 -0.0002794 0.9401 0.000469 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5866 0.5688 0.4236 0.3364 0.9842 0.9896 0.5871 0.9661 0.977 0.4401 ] Network output: [ -0.0875 0.2649 0.9159 -0.0001612 7.237e-05 0.9935 -0.0001215 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5992 0.5962 0.4566 0.2904 0.9817 0.9881 0.5993 0.9581 0.9726 0.4601 ] Network output: [ 0.0419 0.8505 0.04147 -0.0005565 0.0002499 1.022 -0.0004194 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04363 Epoch 1973 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04029 0.9736 0.9857 0.0001685 -7.563e-05 -0.03918 0.000127 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004815 0.01992 0.03874 0.9334 0.9437 0.05234 0.8692 0.8909 0.1356 ] Network output: [ 0.9633 0.07555 -0.01652 -0.0004552 0.0002043 0.01249 -0.000343 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6467 0.09165 0.08087 0.346 0.9675 0.9846 0.7463 0.8834 0.9598 0.6243 ] Network output: [ 9.886e-05 0.9268 1.034 7.63e-05 -3.425e-05 0.03888 5.75e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04887 0.0349 0.0563 0.05397 0.9823 0.9874 0.05012 0.9611 0.9748 0.07317 ] Network output: [ 0.1072 -0.3145 1.069 0.0003008 -0.000135 1.032 0.0002267 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7402 0.606 0.4958 0.5259 0.9714 0.9869 0.7439 0.8949 0.9656 0.6204 ] Network output: [ -0.06489 0.2225 0.9698 0.0006216 -0.0002791 0.94 0.0004685 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5867 0.569 0.4238 0.3365 0.9842 0.9896 0.5873 0.9662 0.977 0.4403 ] Network output: [ -0.0874 0.2648 0.9161 -0.0001604 7.199e-05 0.9933 -0.0001209 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5993 0.5963 0.4567 0.2905 0.9817 0.9881 0.5994 0.9582 0.9726 0.4602 ] Network output: [ 0.04177 0.8509 0.04134 -0.0005542 0.0002488 1.022 -0.0004176 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04357 Epoch 1974 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04027 0.9736 0.9857 0.0001681 -7.546e-05 -0.03915 0.0001267 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004819 0.01993 0.03873 0.9335 0.9437 0.05235 0.8693 0.891 0.1356 ] Network output: [ 0.9634 0.07547 -0.01649 -0.000454 0.0002038 0.01242 -0.0003422 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6468 0.09186 0.08113 0.3459 0.9676 0.9846 0.7464 0.8835 0.9598 0.6244 ] Network output: [ 6.548e-05 0.9268 1.034 7.563e-05 -3.395e-05 0.03895 5.699e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0489 0.03493 0.05633 0.05397 0.9823 0.9874 0.05015 0.9611 0.9748 0.07319 ] Network output: [ 0.1072 -0.3144 1.069 0.0002983 -0.0001339 1.032 0.0002248 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7403 0.6063 0.496 0.5258 0.9714 0.9869 0.744 0.895 0.9656 0.6204 ] Network output: [ -0.0648 0.2223 0.9698 0.0006209 -0.0002788 0.94 0.000468 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5869 0.5692 0.424 0.3366 0.9842 0.9896 0.5874 0.9662 0.9771 0.4405 ] Network output: [ -0.0873 0.2646 0.9162 -0.0001595 7.162e-05 0.9932 -0.0001202 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5994 0.5964 0.4569 0.2907 0.9817 0.9881 0.5995 0.9582 0.9727 0.4603 ] Network output: [ 0.04163 0.8513 0.04122 -0.0005518 0.0002477 1.022 -0.0004158 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04351 Epoch 1975 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04025 0.9736 0.9857 0.0001677 -7.528e-05 -0.03913 0.0001264 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004824 0.01993 0.03873 0.9335 0.9437 0.05235 0.8693 0.891 0.1356 ] Network output: [ 0.9634 0.0754 -0.01645 -0.0004529 0.0002033 0.01235 -0.0003413 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6469 0.09207 0.0814 0.3458 0.9676 0.9846 0.7465 0.8836 0.9598 0.6244 ] Network output: [ 3.234e-05 0.9268 1.034 7.495e-05 -3.365e-05 0.03903 5.649e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04893 0.03496 0.05636 0.05398 0.9823 0.9874 0.05018 0.9612 0.9748 0.07321 ] Network output: [ 0.1071 -0.3143 1.069 0.0002958 -0.0001328 1.032 0.0002229 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7404 0.6066 0.4963 0.5258 0.9714 0.9869 0.7442 0.8951 0.9657 0.6204 ] Network output: [ -0.06471 0.2221 0.9698 0.0006203 -0.0002785 0.94 0.0004674 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.587 0.5694 0.4242 0.3367 0.9842 0.9896 0.5876 0.9663 0.9771 0.4406 ] Network output: [ -0.08721 0.2644 0.9163 -0.0001587 7.126e-05 0.993 -0.0001196 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5995 0.5965 0.457 0.2909 0.9817 0.9881 0.5996 0.9583 0.9727 0.4604 ] Network output: [ 0.04149 0.8516 0.04109 -0.0005494 0.0002467 1.022 -0.0004141 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04345 Epoch 1976 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04023 0.9736 0.9857 0.0001673 -7.51e-05 -0.03911 0.0001261 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004828 0.01994 0.03873 0.9335 0.9438 0.05236 0.8694 0.8911 0.1356 ] Network output: [ 0.9635 0.07532 -0.01641 -0.0004518 0.0002028 0.01227 -0.0003405 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.647 0.09228 0.08166 0.3457 0.9676 0.9846 0.7467 0.8837 0.9599 0.6244 ] Network output: [ -5.534e-07 0.9268 1.034 7.429e-05 -3.335e-05 0.03911 5.598e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04895 0.035 0.05638 0.05398 0.9823 0.9874 0.0502 0.9612 0.9749 0.07322 ] Network output: [ 0.1071 -0.3143 1.069 0.0002933 -0.0001317 1.032 0.000221 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7406 0.6069 0.4965 0.5257 0.9714 0.9869 0.7443 0.8952 0.9657 0.6205 ] Network output: [ -0.06463 0.222 0.9699 0.0006196 -0.0002782 0.94 0.0004669 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5872 0.5695 0.4244 0.3368 0.9842 0.9896 0.5877 0.9663 0.9771 0.4408 ] Network output: [ -0.08711 0.2642 0.9165 -0.0001579 7.09e-05 0.9929 -0.000119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5996 0.5965 0.4571 0.2911 0.9817 0.9881 0.5997 0.9583 0.9727 0.4606 ] Network output: [ 0.04136 0.852 0.04097 -0.000547 0.0002456 1.022 -0.0004123 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04339 Epoch 1977 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04021 0.9736 0.9857 0.0001669 -7.492e-05 -0.03909 0.0001258 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004833 0.01994 0.03872 0.9335 0.9438 0.05236 0.8695 0.8911 0.1356 ] Network output: [ 0.9636 0.07524 -0.01638 -0.0004506 0.0002023 0.0122 -0.0003396 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6471 0.09249 0.08192 0.3456 0.9676 0.9846 0.7468 0.8838 0.9599 0.6245 ] Network output: [ -3.321e-05 0.9268 1.034 7.362e-05 -3.305e-05 0.03918 5.548e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04898 0.03503 0.05641 0.05398 0.9823 0.9874 0.05023 0.9613 0.9749 0.07324 ] Network output: [ 0.107 -0.3142 1.069 0.0002908 -0.0001305 1.032 0.0002191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7407 0.6072 0.4968 0.5256 0.9714 0.9869 0.7444 0.8953 0.9657 0.6205 ] Network output: [ -0.06454 0.2218 0.9699 0.0006189 -0.0002778 0.9399 0.0004664 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5873 0.5697 0.4246 0.3369 0.9842 0.9896 0.5879 0.9664 0.9772 0.441 ] Network output: [ -0.08701 0.2641 0.9166 -0.0001571 7.054e-05 0.9927 -0.0001184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5997 0.5966 0.4573 0.2912 0.9818 0.9881 0.5998 0.9584 0.9728 0.4607 ] Network output: [ 0.04123 0.8524 0.04085 -0.0005447 0.0002445 1.022 -0.0004105 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04333 Epoch 1978 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04019 0.9736 0.9857 0.0001665 -7.474e-05 -0.03906 0.0001255 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004837 0.01995 0.03872 0.9336 0.9438 0.05237 0.8695 0.8912 0.1356 ] Network output: [ 0.9636 0.07517 -0.01634 -0.0004495 0.0002018 0.01213 -0.0003387 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6472 0.0927 0.08217 0.3455 0.9676 0.9846 0.7469 0.8839 0.9599 0.6245 ] Network output: [ -6.563e-05 0.9268 1.034 7.295e-05 -3.275e-05 0.03926 5.498e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04901 0.03506 0.05644 0.05398 0.9823 0.9874 0.05026 0.9613 0.9749 0.07326 ] Network output: [ 0.107 -0.3142 1.069 0.0002883 -0.0001294 1.033 0.0002172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7408 0.6076 0.497 0.5256 0.9714 0.9869 0.7445 0.8953 0.9658 0.6205 ] Network output: [ -0.06446 0.2216 0.9699 0.0006182 -0.0002775 0.9399 0.0004659 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5875 0.5699 0.4248 0.337 0.9842 0.9896 0.588 0.9664 0.9772 0.4411 ] Network output: [ -0.08691 0.2639 0.9167 -0.0001563 7.019e-05 0.9926 -0.0001178 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5997 0.5967 0.4574 0.2914 0.9818 0.9881 0.5999 0.9584 0.9728 0.4608 ] Network output: [ 0.0411 0.8527 0.04073 -0.0005423 0.0002435 1.022 -0.0004087 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04327 Epoch 1979 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04017 0.9736 0.9857 0.0001661 -7.456e-05 -0.03904 0.0001252 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004841 0.01995 0.03871 0.9336 0.9438 0.05237 0.8696 0.8912 0.1356 ] Network output: [ 0.9637 0.07509 -0.01631 -0.0004483 0.0002013 0.01206 -0.0003379 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6473 0.09291 0.08243 0.3454 0.9676 0.9846 0.747 0.884 0.96 0.6245 ] Network output: [ -9.782e-05 0.9268 1.034 7.229e-05 -3.245e-05 0.03933 5.448e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04904 0.03509 0.05647 0.05398 0.9824 0.9874 0.05029 0.9614 0.9749 0.07327 ] Network output: [ 0.1069 -0.3141 1.069 0.0002858 -0.0001283 1.033 0.0002154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7409 0.6079 0.4973 0.5255 0.9714 0.9869 0.7446 0.8954 0.9658 0.6206 ] Network output: [ -0.06437 0.2215 0.9699 0.0006176 -0.0002772 0.9399 0.0004654 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5876 0.5701 0.425 0.3371 0.9842 0.9896 0.5882 0.9664 0.9772 0.4413 ] Network output: [ -0.08682 0.2637 0.9169 -0.0001556 6.984e-05 0.9924 -0.0001172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5998 0.5968 0.4575 0.2915 0.9818 0.9881 0.5999 0.9585 0.9728 0.4609 ] Network output: [ 0.04096 0.8531 0.04061 -0.00054 0.0002424 1.022 -0.0004069 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04322 Epoch 1980 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04015 0.9736 0.9858 0.0001657 -7.438e-05 -0.03902 0.0001249 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004846 0.01996 0.03871 0.9336 0.9438 0.05238 0.8697 0.8913 0.1356 ] Network output: [ 0.9637 0.07502 -0.01627 -0.0004472 0.0002008 0.01199 -0.000337 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6474 0.09312 0.08269 0.3453 0.9677 0.9847 0.7471 0.884 0.96 0.6246 ] Network output: [ -0.0001298 0.9268 1.034 7.163e-05 -3.216e-05 0.03941 5.398e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04907 0.03512 0.05649 0.05398 0.9824 0.9874 0.05032 0.9614 0.975 0.07329 ] Network output: [ 0.1069 -0.314 1.069 0.0002833 -0.0001272 1.033 0.0002135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.741 0.6082 0.4975 0.5254 0.9715 0.9869 0.7448 0.8955 0.9658 0.6206 ] Network output: [ -0.06429 0.2213 0.9699 0.0006169 -0.0002769 0.9399 0.0004649 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5878 0.5702 0.4252 0.3372 0.9843 0.9896 0.5883 0.9665 0.9772 0.4415 ] Network output: [ -0.08672 0.2635 0.917 -0.0001548 6.949e-05 0.9923 -0.0001167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5999 0.5969 0.4577 0.2917 0.9818 0.9882 0.6 0.9585 0.9729 0.4611 ] Network output: [ 0.04083 0.8535 0.04048 -0.0005376 0.0002414 1.022 -0.0004052 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04316 Epoch 1981 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04013 0.9736 0.9858 0.0001653 -7.42e-05 -0.039 0.0001246 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.00485 0.01996 0.0387 0.9336 0.9438 0.05238 0.8698 0.8913 0.1356 ] Network output: [ 0.9638 0.07494 -0.01624 -0.000446 0.0002002 0.01192 -0.0003361 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6475 0.09332 0.08294 0.3453 0.9677 0.9847 0.7472 0.8841 0.96 0.6246 ] Network output: [ -0.0001615 0.9268 1.034 7.098e-05 -3.186e-05 0.03948 5.349e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04909 0.03515 0.05652 0.05397 0.9824 0.9874 0.05035 0.9614 0.975 0.0733 ] Network output: [ 0.1068 -0.314 1.069 0.0002808 -0.0001261 1.033 0.0002116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7412 0.6085 0.4978 0.5254 0.9715 0.9869 0.7449 0.8956 0.9658 0.6207 ] Network output: [ -0.0642 0.2211 0.9699 0.0006162 -0.0002766 0.9399 0.0004644 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5879 0.5704 0.4254 0.3373 0.9843 0.9896 0.5884 0.9665 0.9773 0.4416 ] Network output: [ -0.08663 0.2634 0.9172 -0.000154 6.914e-05 0.9921 -0.0001161 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6 0.597 0.4578 0.2919 0.9818 0.9882 0.6001 0.9586 0.9729 0.4612 ] Network output: [ 0.04071 0.8538 0.04037 -0.0005353 0.0002403 1.022 -0.0004034 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0431 Epoch 1982 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04011 0.9736 0.9858 0.0001649 -7.402e-05 -0.03897 0.0001243 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004854 0.01997 0.0387 0.9336 0.9439 0.05238 0.8698 0.8914 0.1356 ] Network output: [ 0.9638 0.07487 -0.01621 -0.0004448 0.0001997 0.01185 -0.0003352 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6476 0.09352 0.08319 0.3452 0.9677 0.9847 0.7473 0.8842 0.96 0.6246 ] Network output: [ -0.000193 0.9268 1.034 7.032e-05 -3.157e-05 0.03955 5.3e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04912 0.03518 0.05655 0.05397 0.9824 0.9874 0.05037 0.9615 0.975 0.07331 ] Network output: [ 0.1067 -0.3139 1.068 0.0002783 -0.0001249 1.033 0.0002097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7413 0.6088 0.498 0.5253 0.9715 0.9869 0.745 0.8957 0.9659 0.6207 ] Network output: [ -0.06412 0.221 0.9699 0.0006156 -0.0002763 0.9398 0.0004639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.588 0.5706 0.4255 0.3374 0.9843 0.9896 0.5886 0.9666 0.9773 0.4418 ] Network output: [ -0.08654 0.2632 0.9173 -0.0001533 6.88e-05 0.992 -0.0001155 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6001 0.5971 0.4579 0.292 0.9818 0.9882 0.6002 0.9586 0.9729 0.4613 ] Network output: [ 0.04058 0.8542 0.04025 -0.0005329 0.0002393 1.022 -0.0004016 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04304 Epoch 1983 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04009 0.9736 0.9858 0.0001645 -7.383e-05 -0.03895 0.0001239 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004859 0.01997 0.03869 0.9337 0.9439 0.05239 0.8699 0.8914 0.1356 ] Network output: [ 0.9639 0.0748 -0.01617 -0.0004437 0.0001992 0.01178 -0.0003344 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6477 0.09373 0.08344 0.3451 0.9677 0.9847 0.7475 0.8843 0.9601 0.6247 ] Network output: [ -0.0002242 0.9269 1.034 6.967e-05 -3.128e-05 0.03963 5.25e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04915 0.03522 0.05657 0.05397 0.9824 0.9875 0.0504 0.9615 0.9751 0.07333 ] Network output: [ 0.1067 -0.3139 1.068 0.0002758 -0.0001238 1.033 0.0002079 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7414 0.6091 0.4983 0.5252 0.9715 0.9869 0.7451 0.8957 0.9659 0.6207 ] Network output: [ -0.06403 0.2208 0.9699 0.0006149 -0.000276 0.9398 0.0004634 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5882 0.5708 0.4257 0.3375 0.9843 0.9896 0.5887 0.9666 0.9773 0.4419 ] Network output: [ -0.08644 0.263 0.9174 -0.0001525 6.847e-05 0.9918 -0.0001149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6002 0.5972 0.4581 0.2922 0.9818 0.9882 0.6003 0.9587 0.9729 0.4614 ] Network output: [ 0.04045 0.8545 0.04013 -0.0005306 0.0002382 1.022 -0.0003999 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04299 Epoch 1984 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04007 0.9736 0.9858 0.0001641 -7.365e-05 -0.03893 0.0001236 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004863 0.01997 0.03869 0.9337 0.9439 0.05239 0.87 0.8915 0.1356 ] Network output: [ 0.964 0.07473 -0.01614 -0.0004425 0.0001986 0.01171 -0.0003335 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6477 0.09393 0.08369 0.345 0.9677 0.9847 0.7476 0.8844 0.9601 0.6247 ] Network output: [ -0.0002552 0.9269 1.034 6.902e-05 -3.098e-05 0.0397 5.201e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04917 0.03525 0.0566 0.05397 0.9824 0.9875 0.05043 0.9616 0.9751 0.07334 ] Network output: [ 0.1066 -0.3138 1.068 0.0002734 -0.0001227 1.033 0.000206 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7415 0.6094 0.4985 0.5252 0.9715 0.9869 0.7452 0.8958 0.9659 0.6208 ] Network output: [ -0.06395 0.2206 0.97 0.0006142 -0.0002758 0.9398 0.0004629 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5883 0.5709 0.4259 0.3376 0.9843 0.9896 0.5889 0.9666 0.9773 0.4421 ] Network output: [ -0.08635 0.2629 0.9176 -0.0001518 6.813e-05 0.9917 -0.0001144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6002 0.5973 0.4582 0.2923 0.9818 0.9882 0.6003 0.9587 0.973 0.4615 ] Network output: [ 0.04033 0.8549 0.04001 -0.0005283 0.0002372 1.022 -0.0003981 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04293 Epoch 1985 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04005 0.9736 0.9859 0.0001636 -7.347e-05 -0.03891 0.0001233 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004868 0.01998 0.03868 0.9337 0.9439 0.05239 0.87 0.8915 0.1356 ] Network output: [ 0.964 0.07465 -0.01611 -0.0004413 0.0001981 0.01164 -0.0003326 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6478 0.09413 0.08394 0.3449 0.9677 0.9847 0.7477 0.8844 0.9601 0.6247 ] Network output: [ -0.000286 0.9269 1.034 6.837e-05 -3.069e-05 0.03977 5.153e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0492 0.03528 0.05662 0.05396 0.9824 0.9875 0.05046 0.9616 0.9751 0.07335 ] Network output: [ 0.1066 -0.3137 1.068 0.0002709 -0.0001216 1.033 0.0002041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7416 0.6097 0.4987 0.5251 0.9715 0.987 0.7453 0.8959 0.966 0.6208 ] Network output: [ -0.06387 0.2205 0.97 0.0006136 -0.0002755 0.9398 0.0004624 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5885 0.5711 0.4261 0.3377 0.9843 0.9896 0.589 0.9667 0.9774 0.4422 ] Network output: [ -0.08626 0.2627 0.9177 -0.000151 6.78e-05 0.9915 -0.0001138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6003 0.5973 0.4583 0.2925 0.9819 0.9882 0.6004 0.9588 0.973 0.4617 ] Network output: [ 0.0402 0.8552 0.0399 -0.000526 0.0002361 1.022 -0.0003964 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04287 Epoch 1986 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04003 0.9736 0.9859 0.0001632 -7.328e-05 -0.03888 0.000123 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004872 0.01998 0.03867 0.9337 0.9439 0.0524 0.8701 0.8916 0.1356 ] Network output: [ 0.9641 0.07458 -0.01608 -0.0004401 0.0001976 0.01157 -0.0003317 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6479 0.09432 0.08419 0.3448 0.9677 0.9847 0.7478 0.8845 0.9602 0.6247 ] Network output: [ -0.0003166 0.9269 1.034 6.772e-05 -3.04e-05 0.03984 5.104e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04923 0.03531 0.05665 0.05396 0.9824 0.9875 0.05048 0.9617 0.9751 0.07336 ] Network output: [ 0.1065 -0.3137 1.068 0.0002684 -0.0001205 1.034 0.0002023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7417 0.61 0.499 0.525 0.9715 0.987 0.7454 0.896 0.966 0.6208 ] Network output: [ -0.06378 0.2203 0.97 0.0006129 -0.0002752 0.9398 0.0004619 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5886 0.5713 0.4263 0.3378 0.9843 0.9897 0.5892 0.9667 0.9774 0.4424 ] Network output: [ -0.08616 0.2625 0.9178 -0.0001503 6.747e-05 0.9914 -0.0001133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6004 0.5974 0.4584 0.2926 0.9819 0.9882 0.6005 0.9588 0.973 0.4618 ] Network output: [ 0.04008 0.8556 0.03978 -0.0005236 0.0002351 1.022 -0.0003946 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04282 Epoch 1987 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04001 0.9736 0.9859 0.0001628 -7.31e-05 -0.03886 0.0001227 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004876 0.01998 0.03867 0.9337 0.944 0.0524 0.8702 0.8917 0.1356 ] Network output: [ 0.9641 0.07451 -0.01604 -0.0004389 0.000197 0.0115 -0.0003308 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.648 0.09452 0.08443 0.3447 0.9678 0.9847 0.7479 0.8846 0.9602 0.6248 ] Network output: [ -0.0003469 0.9269 1.034 6.708e-05 -3.011e-05 0.03992 5.055e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04925 0.03534 0.05667 0.05396 0.9824 0.9875 0.05051 0.9617 0.9752 0.07337 ] Network output: [ 0.1065 -0.3136 1.068 0.000266 -0.0001194 1.034 0.0002004 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7418 0.6103 0.4992 0.5249 0.9716 0.987 0.7455 0.8961 0.966 0.6209 ] Network output: [ -0.0637 0.2202 0.97 0.0006123 -0.0002749 0.9397 0.0004614 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5887 0.5714 0.4265 0.3379 0.9843 0.9897 0.5893 0.9668 0.9774 0.4425 ] Network output: [ -0.08607 0.2623 0.918 -0.0001496 6.714e-05 0.9912 -0.0001127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6005 0.5975 0.4585 0.2928 0.9819 0.9882 0.6006 0.9589 0.9731 0.4619 ] Network output: [ 0.03995 0.8559 0.03966 -0.0005213 0.000234 1.022 -0.0003929 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04276 Epoch 1988 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03999 0.9736 0.9859 0.0001624 -7.291e-05 -0.03884 0.0001224 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004881 0.01999 0.03866 0.9338 0.944 0.0524 0.8703 0.8917 0.1356 ] Network output: [ 0.9642 0.07445 -0.01601 -0.0004377 0.0001965 0.01143 -0.0003299 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6481 0.09472 0.08467 0.3446 0.9678 0.9847 0.748 0.8847 0.9602 0.6248 ] Network output: [ -0.000377 0.9269 1.034 6.644e-05 -2.983e-05 0.03999 5.007e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04928 0.03537 0.05669 0.05395 0.9825 0.9875 0.05054 0.9618 0.9752 0.07338 ] Network output: [ 0.1064 -0.3136 1.068 0.0002635 -0.0001183 1.034 0.0001986 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7419 0.6106 0.4995 0.5249 0.9716 0.987 0.7457 0.8961 0.966 0.6209 ] Network output: [ -0.06362 0.22 0.97 0.0006116 -0.0002746 0.9397 0.000461 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5889 0.5716 0.4266 0.338 0.9843 0.9897 0.5894 0.9668 0.9774 0.4427 ] Network output: [ -0.08598 0.2622 0.9181 -0.0001488 6.682e-05 0.9911 -0.0001122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6005 0.5976 0.4587 0.2929 0.9819 0.9882 0.6007 0.9589 0.9731 0.462 ] Network output: [ 0.03983 0.8563 0.03955 -0.000519 0.000233 1.022 -0.0003912 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04271 Epoch 1989 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03997 0.9736 0.9859 0.000162 -7.272e-05 -0.03882 0.0001221 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004885 0.01999 0.03865 0.9338 0.944 0.0524 0.8703 0.8918 0.1356 ] Network output: [ 0.9642 0.07438 -0.01598 -0.0004365 0.000196 0.01136 -0.000329 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6482 0.09491 0.08492 0.3445 0.9678 0.9847 0.7481 0.8848 0.9603 0.6248 ] Network output: [ -0.0004069 0.9269 1.034 6.58e-05 -2.954e-05 0.04006 4.959e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0493 0.03539 0.05671 0.05395 0.9825 0.9875 0.05056 0.9618 0.9752 0.07339 ] Network output: [ 0.1063 -0.3135 1.068 0.000261 -0.0001172 1.034 0.0001967 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.742 0.6108 0.4997 0.5248 0.9716 0.987 0.7458 0.8962 0.9661 0.6209 ] Network output: [ -0.06354 0.2199 0.97 0.000611 -0.0002743 0.9397 0.0004605 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.589 0.5718 0.4268 0.3381 0.9843 0.9897 0.5896 0.9668 0.9775 0.4428 ] Network output: [ -0.08589 0.262 0.9182 -0.0001481 6.65e-05 0.991 -0.0001116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6006 0.5977 0.4588 0.2931 0.9819 0.9882 0.6007 0.959 0.9731 0.4621 ] Network output: [ 0.03971 0.8566 0.03944 -0.0005167 0.000232 1.022 -0.0003894 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04265 Epoch 1990 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03995 0.9736 0.986 0.0001616 -7.254e-05 -0.0388 0.0001218 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004889 0.01999 0.03864 0.9338 0.944 0.05241 0.8704 0.8918 0.1356 ] Network output: [ 0.9643 0.07431 -0.01595 -0.0004353 0.0001954 0.01129 -0.0003281 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6483 0.09511 0.08516 0.3444 0.9678 0.9847 0.7482 0.8848 0.9603 0.6249 ] Network output: [ -0.0004366 0.9269 1.034 6.517e-05 -2.925e-05 0.04013 4.911e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04933 0.03542 0.05674 0.05394 0.9825 0.9875 0.05059 0.9619 0.9752 0.0734 ] Network output: [ 0.1063 -0.3134 1.068 0.0002586 -0.0001161 1.034 0.0001949 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7421 0.6111 0.4999 0.5247 0.9716 0.987 0.7459 0.8963 0.9661 0.621 ] Network output: [ -0.06345 0.2197 0.97 0.0006104 -0.000274 0.9397 0.00046 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5892 0.5719 0.427 0.3381 0.9844 0.9897 0.5897 0.9669 0.9775 0.443 ] Network output: [ -0.0858 0.2618 0.9184 -0.0001474 6.618e-05 0.9908 -0.0001111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6007 0.5978 0.4589 0.2932 0.9819 0.9882 0.6008 0.959 0.9732 0.4622 ] Network output: [ 0.03959 0.857 0.03932 -0.0005145 0.000231 1.022 -0.0003877 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0426 Epoch 1991 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03993 0.9736 0.986 0.0001612 -7.235e-05 -0.03877 0.0001215 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004894 0.02 0.03864 0.9338 0.944 0.05241 0.8705 0.8919 0.1356 ] Network output: [ 0.9643 0.07424 -0.01592 -0.0004341 0.0001949 0.01123 -0.0003271 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6484 0.0953 0.0854 0.3443 0.9678 0.9848 0.7483 0.8849 0.9603 0.6249 ] Network output: [ -0.000466 0.9269 1.034 6.453e-05 -2.897e-05 0.0402 4.863e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04935 0.03545 0.05676 0.05394 0.9825 0.9875 0.05061 0.9619 0.9753 0.07341 ] Network output: [ 0.1062 -0.3134 1.068 0.0002561 -0.000115 1.034 0.000193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7423 0.6114 0.5001 0.5246 0.9716 0.987 0.746 0.8964 0.9661 0.621 ] Network output: [ -0.06337 0.2196 0.97 0.0006097 -0.0002737 0.9397 0.0004595 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5893 0.5721 0.4272 0.3382 0.9844 0.9897 0.5898 0.9669 0.9775 0.4431 ] Network output: [ -0.08571 0.2617 0.9185 -0.0001467 6.587e-05 0.9907 -0.0001106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6008 0.5978 0.459 0.2934 0.9819 0.9882 0.6009 0.9591 0.9732 0.4623 ] Network output: [ 0.03947 0.8573 0.03921 -0.0005122 0.0002299 1.022 -0.000386 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04254 Epoch 1992 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03991 0.9736 0.986 0.0001607 -7.216e-05 -0.03875 0.0001211 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02491 -0.004898 0.02 0.03863 0.9338 0.9441 0.05241 0.8705 0.8919 0.1356 ] Network output: [ 0.9644 0.07418 -0.01589 -0.0004329 0.0001943 0.01116 -0.0003262 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6485 0.09549 0.08563 0.3442 0.9678 0.9848 0.7484 0.885 0.9604 0.6249 ] Network output: [ -0.0004952 0.9269 1.034 6.39e-05 -2.869e-05 0.04026 4.816e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04938 0.03548 0.05678 0.05393 0.9825 0.9875 0.05064 0.9619 0.9753 0.07342 ] Network output: [ 0.1062 -0.3133 1.068 0.0002537 -0.0001139 1.034 0.0001912 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7424 0.6117 0.5004 0.5245 0.9716 0.987 0.7461 0.8964 0.9661 0.621 ] Network output: [ -0.06329 0.2194 0.97 0.0006091 -0.0002735 0.9397 0.000459 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5894 0.5722 0.4273 0.3383 0.9844 0.9897 0.59 0.9669 0.9775 0.4433 ] Network output: [ -0.08563 0.2615 0.9186 -0.000146 6.556e-05 0.9905 -0.00011 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6009 0.5979 0.4591 0.2935 0.9819 0.9883 0.601 0.9591 0.9732 0.4624 ] Network output: [ 0.03935 0.8576 0.0391 -0.0005099 0.0002289 1.022 -0.0003843 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04249 Epoch 1993 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03989 0.9736 0.986 0.0001603 -7.197e-05 -0.03873 0.0001208 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0249 -0.004902 0.02 0.03862 0.9339 0.9441 0.05241 0.8706 0.892 0.1356 ] Network output: [ 0.9645 0.07411 -0.01586 -0.0004316 0.0001938 0.01109 -0.0003253 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6486 0.09568 0.08587 0.3441 0.9679 0.9848 0.7485 0.8851 0.9604 0.6249 ] Network output: [ -0.0005242 0.9269 1.034 6.327e-05 -2.84e-05 0.04033 4.768e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0494 0.03551 0.0568 0.05392 0.9825 0.9875 0.05066 0.962 0.9753 0.07343 ] Network output: [ 0.1061 -0.3132 1.068 0.0002512 -0.0001128 1.034 0.0001893 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7425 0.612 0.5006 0.5245 0.9716 0.987 0.7462 0.8965 0.9662 0.6211 ] Network output: [ -0.06321 0.2192 0.97 0.0006085 -0.0002732 0.9396 0.0004586 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5896 0.5724 0.4275 0.3384 0.9844 0.9897 0.5901 0.967 0.9776 0.4434 ] Network output: [ -0.08554 0.2613 0.9188 -0.0001453 6.524e-05 0.9904 -0.0001095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6009 0.598 0.4592 0.2937 0.9819 0.9883 0.601 0.9592 0.9732 0.4625 ] Network output: [ 0.03924 0.858 0.03899 -0.0005077 0.0002279 1.023 -0.0003826 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04243 Epoch 1994 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03987 0.9736 0.986 0.0001599 -7.179e-05 -0.03871 0.0001205 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0249 -0.004907 0.02 0.03861 0.9339 0.9441 0.05242 0.8707 0.892 0.1356 ] Network output: [ 0.9645 0.07404 -0.01583 -0.0004304 0.0001932 0.01102 -0.0003244 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6486 0.09587 0.08611 0.3439 0.9679 0.9848 0.7486 0.8852 0.9604 0.625 ] Network output: [ -0.000553 0.9269 1.034 6.264e-05 -2.812e-05 0.0404 4.721e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04943 0.03554 0.05682 0.05392 0.9825 0.9875 0.05069 0.962 0.9753 0.07343 ] Network output: [ 0.106 -0.3132 1.068 0.0002488 -0.0001117 1.034 0.0001875 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7426 0.6122 0.5008 0.5244 0.9716 0.987 0.7463 0.8966 0.9662 0.6211 ] Network output: [ -0.06313 0.2191 0.97 0.0006079 -0.0002729 0.9396 0.0004581 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5897 0.5726 0.4277 0.3385 0.9844 0.9897 0.5902 0.967 0.9776 0.4436 ] Network output: [ -0.08545 0.2611 0.9189 -0.0001446 6.494e-05 0.9903 -0.000109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.601 0.5981 0.4594 0.2938 0.982 0.9883 0.6011 0.9592 0.9733 0.4627 ] Network output: [ 0.03912 0.8583 0.03888 -0.0005054 0.0002269 1.023 -0.0003809 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04238 Epoch 1995 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03985 0.9736 0.9861 0.0001595 -7.16e-05 -0.03869 0.0001202 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0249 -0.004911 0.02001 0.0386 0.9339 0.9441 0.05242 0.8707 0.8921 0.1355 ] Network output: [ 0.9646 0.07398 -0.0158 -0.0004292 0.0001927 0.01095 -0.0003234 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6487 0.09605 0.08634 0.3438 0.9679 0.9848 0.7487 0.8852 0.9605 0.625 ] Network output: [ -0.0005816 0.9269 1.034 6.202e-05 -2.784e-05 0.04047 4.674e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04945 0.03557 0.05684 0.05391 0.9825 0.9876 0.05071 0.9621 0.9754 0.07344 ] Network output: [ 0.106 -0.3131 1.068 0.0002464 -0.0001106 1.035 0.0001857 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7427 0.6125 0.501 0.5243 0.9717 0.987 0.7464 0.8967 0.9662 0.6211 ] Network output: [ -0.06305 0.219 0.97 0.0006073 -0.0002726 0.9396 0.0004576 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5898 0.5727 0.4278 0.3385 0.9844 0.9897 0.5904 0.9671 0.9776 0.4437 ] Network output: [ -0.08536 0.261 0.919 -0.000144 6.463e-05 0.9901 -0.0001085 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6011 0.5982 0.4595 0.2939 0.982 0.9883 0.6012 0.9593 0.9733 0.4628 ] Network output: [ 0.039 0.8586 0.03877 -0.0005032 0.0002259 1.023 -0.0003792 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04233 Epoch 1996 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03983 0.9736 0.9861 0.0001591 -7.141e-05 -0.03866 0.0001199 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0249 -0.004915 0.02001 0.0386 0.9339 0.9441 0.05242 0.8708 0.8921 0.1355 ] Network output: [ 0.9646 0.07392 -0.01578 -0.000428 0.0001921 0.01089 -0.0003225 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6488 0.09624 0.08657 0.3437 0.9679 0.9848 0.7488 0.8853 0.9605 0.625 ] Network output: [ -0.0006099 0.9269 1.034 6.14e-05 -2.756e-05 0.04054 4.627e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04948 0.03559 0.05686 0.0539 0.9826 0.9876 0.05074 0.9621 0.9754 0.07345 ] Network output: [ 0.1059 -0.3131 1.068 0.0002439 -0.0001095 1.035 0.0001838 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7428 0.6128 0.5013 0.5242 0.9717 0.987 0.7465 0.8967 0.9663 0.6211 ] Network output: [ -0.06297 0.2188 0.97 0.0006066 -0.0002723 0.9396 0.0004572 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.59 0.5729 0.428 0.3386 0.9844 0.9897 0.5905 0.9671 0.9776 0.4438 ] Network output: [ -0.08528 0.2608 0.9192 -0.0001433 6.433e-05 0.99 -0.000108 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6012 0.5982 0.4596 0.2941 0.982 0.9883 0.6013 0.9593 0.9733 0.4629 ] Network output: [ 0.03889 0.859 0.03867 -0.0005009 0.0002249 1.023 -0.0003775 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04227 Epoch 1997 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03981 0.9736 0.9861 0.0001586 -7.122e-05 -0.03864 0.0001195 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0249 -0.004919 0.02001 0.03859 0.9339 0.9441 0.05242 0.8709 0.8922 0.1355 ] Network output: [ 0.9647 0.07385 -0.01575 -0.0004267 0.0001916 0.01082 -0.0003216 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6489 0.09642 0.08681 0.3436 0.9679 0.9848 0.7489 0.8854 0.9605 0.6251 ] Network output: [ -0.000638 0.9269 1.034 6.078e-05 -2.729e-05 0.0406 4.58e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0495 0.03562 0.05688 0.05389 0.9826 0.9876 0.05076 0.9622 0.9754 0.07345 ] Network output: [ 0.1059 -0.313 1.068 0.0002415 -0.0001084 1.035 0.000182 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7429 0.6131 0.5015 0.5241 0.9717 0.987 0.7466 0.8968 0.9663 0.6212 ] Network output: [ -0.06289 0.2187 0.97 0.000606 -0.0002721 0.9396 0.0004567 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5901 0.573 0.4282 0.3387 0.9844 0.9897 0.5906 0.9671 0.9776 0.444 ] Network output: [ -0.08519 0.2606 0.9193 -0.0001426 6.403e-05 0.9899 -0.0001075 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6012 0.5983 0.4597 0.2942 0.982 0.9883 0.6013 0.9593 0.9734 0.463 ] Network output: [ 0.03877 0.8593 0.03856 -0.0004987 0.0002239 1.023 -0.0003758 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04222 Epoch 1998 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03979 0.9736 0.9861 0.0001582 -7.102e-05 -0.03862 0.0001192 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0249 -0.004924 0.02001 0.03858 0.934 0.9442 0.05242 0.8709 0.8922 0.1355 ] Network output: [ 0.9647 0.07379 -0.01572 -0.0004255 0.000191 0.01075 -0.0003206 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.649 0.09661 0.08704 0.3435 0.9679 0.9848 0.749 0.8855 0.9606 0.6251 ] Network output: [ -0.000666 0.9269 1.034 6.016e-05 -2.701e-05 0.04067 4.534e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04952 0.03565 0.0569 0.05388 0.9826 0.9876 0.05079 0.9622 0.9754 0.07346 ] Network output: [ 0.1058 -0.313 1.067 0.0002391 -0.0001073 1.035 0.0001802 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.743 0.6133 0.5017 0.5241 0.9717 0.987 0.7467 0.8969 0.9663 0.6212 ] Network output: [ -0.06281 0.2185 0.97 0.0006054 -0.0002718 0.9396 0.0004563 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5902 0.5732 0.4283 0.3388 0.9844 0.9897 0.5908 0.9672 0.9777 0.4441 ] Network output: [ -0.08511 0.2605 0.9194 -0.000142 6.373e-05 0.9898 -0.000107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6013 0.5984 0.4598 0.2943 0.982 0.9883 0.6014 0.9594 0.9734 0.4631 ] Network output: [ 0.03866 0.8596 0.03845 -0.0004965 0.0002229 1.023 -0.0003742 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04217 Epoch 1999 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03977 0.9735 0.9862 0.0001578 -7.083e-05 -0.0386 0.0001189 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0249 -0.004928 0.02002 0.03857 0.934 0.9442 0.05242 0.871 0.8923 0.1355 ] Network output: [ 0.9648 0.07373 -0.01569 -0.0004242 0.0001905 0.01068 -0.0003197 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6491 0.09679 0.08726 0.3434 0.9679 0.9848 0.7491 0.8855 0.9606 0.6251 ] Network output: [ -0.0006937 0.9269 1.034 5.955e-05 -2.673e-05 0.04073 4.488e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04955 0.03568 0.05691 0.05387 0.9826 0.9876 0.05081 0.9623 0.9755 0.07347 ] Network output: [ 0.1058 -0.3129 1.067 0.0002367 -0.0001063 1.035 0.0001784 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7431 0.6136 0.5019 0.524 0.9717 0.9871 0.7468 0.897 0.9663 0.6212 ] Network output: [ -0.06273 0.2184 0.97 0.0006048 -0.0002715 0.9396 0.0004558 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5904 0.5733 0.4285 0.3388 0.9844 0.9897 0.5909 0.9672 0.9777 0.4442 ] Network output: [ -0.08502 0.2603 0.9196 -0.0001413 6.343e-05 0.9896 -0.0001065 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6014 0.5985 0.4599 0.2945 0.982 0.9883 0.6015 0.9594 0.9734 0.4632 ] Network output: [ 0.03855 0.8599 0.03835 -0.0004943 0.0002219 1.023 -0.0003725 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04212 Epoch 2000 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03975 0.9735 0.9862 0.0001574 -7.064e-05 -0.03858 0.0001186 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0249 -0.004932 0.02002 0.03856 0.934 0.9442 0.05242 0.8711 0.8923 0.1355 ] Network output: [ 0.9648 0.07367 -0.01567 -0.000423 0.0001899 0.01062 -0.0003188 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6491 0.09697 0.08749 0.3433 0.968 0.9848 0.7492 0.8856 0.9606 0.6251 ] Network output: [ -0.0007212 0.9269 1.034 5.893e-05 -2.646e-05 0.0408 4.442e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04957 0.0357 0.05693 0.05386 0.9826 0.9876 0.05083 0.9623 0.9755 0.07347 ] Network output: [ 0.1057 -0.3128 1.067 0.0002343 -0.0001052 1.035 0.0001765 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7432 0.6138 0.5021 0.5239 0.9717 0.9871 0.7469 0.897 0.9664 0.6213 ] Network output: [ -0.06265 0.2182 0.97 0.0006043 -0.0002713 0.9395 0.0004554 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5905 0.5735 0.4286 0.3389 0.9844 0.9897 0.591 0.9672 0.9777 0.4444 ] Network output: [ -0.08494 0.2601 0.9197 -0.0001406 6.314e-05 0.9895 -0.000106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6014 0.5985 0.46 0.2946 0.982 0.9883 0.6015 0.9595 0.9734 0.4633 ] Network output: [ 0.03844 0.8602 0.03824 -0.0004921 0.0002209 1.023 -0.0003709 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04206 Epoch 2001 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03972 0.9735 0.9862 0.0001569 -7.045e-05 -0.03855 0.0001183 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0249 -0.004937 0.02002 0.03855 0.934 0.9442 0.05243 0.8711 0.8924 0.1355 ] Network output: [ 0.9649 0.07361 -0.01564 -0.0004217 0.0001893 0.01055 -0.0003178 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6492 0.09715 0.08772 0.3432 0.968 0.9848 0.7493 0.8857 0.9606 0.6252 ] Network output: [ -0.0007485 0.927 1.034 5.832e-05 -2.618e-05 0.04086 4.396e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04959 0.03573 0.05695 0.05385 0.9826 0.9876 0.05086 0.9623 0.9755 0.07347 ] Network output: [ 0.1056 -0.3128 1.067 0.0002319 -0.0001041 1.035 0.0001747 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7433 0.6141 0.5023 0.5238 0.9717 0.9871 0.747 0.8971 0.9664 0.6213 ] Network output: [ -0.06257 0.2181 0.97 0.0006037 -0.000271 0.9395 0.0004549 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5906 0.5736 0.4288 0.339 0.9845 0.9898 0.5912 0.9673 0.9777 0.4445 ] Network output: [ -0.08485 0.2599 0.9198 -0.00014 6.285e-05 0.9894 -0.0001055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6015 0.5986 0.4601 0.2947 0.982 0.9883 0.6016 0.9595 0.9735 0.4634 ] Network output: [ 0.03833 0.8606 0.03814 -0.0004899 0.0002199 1.023 -0.0003692 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04201 Epoch 2002 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0397 0.9735 0.9862 0.0001565 -7.026e-05 -0.03853 0.0001179 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02489 -0.004941 0.02002 0.03854 0.934 0.9442 0.05243 0.8712 0.8924 0.1355 ] Network output: [ 0.9649 0.07355 -0.01561 -0.0004205 0.0001888 0.01049 -0.0003169 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6493 0.09733 0.08794 0.3431 0.968 0.9848 0.7494 0.8858 0.9607 0.6252 ] Network output: [ -0.0007756 0.927 1.034 5.772e-05 -2.591e-05 0.04093 4.35e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04962 0.03576 0.05696 0.05384 0.9826 0.9876 0.05088 0.9624 0.9755 0.07348 ] Network output: [ 0.1056 -0.3127 1.067 0.0002294 -0.000103 1.035 0.0001729 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7433 0.6144 0.5025 0.5237 0.9717 0.9871 0.7471 0.8972 0.9664 0.6213 ] Network output: [ -0.06249 0.2179 0.97 0.0006031 -0.0002707 0.9395 0.0004545 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5907 0.5738 0.4289 0.339 0.9845 0.9898 0.5913 0.9673 0.9778 0.4446 ] Network output: [ -0.08477 0.2598 0.92 -0.0001393 6.255e-05 0.9892 -0.000105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6016 0.5987 0.4602 0.2949 0.9821 0.9883 0.6017 0.9596 0.9735 0.4635 ] Network output: [ 0.03822 0.8609 0.03803 -0.0004877 0.000219 1.023 -0.0003676 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04196 Epoch 2003 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03968 0.9735 0.9863 0.0001561 -7.006e-05 -0.03851 0.0001176 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02489 -0.004945 0.02002 0.03853 0.9341 0.9442 0.05243 0.8713 0.8925 0.1355 ] Network output: [ 0.965 0.07349 -0.01559 -0.0004192 0.0001882 0.01042 -0.0003159 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6494 0.09751 0.08817 0.343 0.968 0.9849 0.7494 0.8858 0.9607 0.6252 ] Network output: [ -0.0008025 0.927 1.034 5.711e-05 -2.564e-05 0.04099 4.304e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04964 0.03578 0.05698 0.05383 0.9826 0.9876 0.0509 0.9624 0.9756 0.07348 ] Network output: [ 0.1055 -0.3127 1.067 0.0002271 -0.0001019 1.035 0.0001711 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7434 0.6146 0.5028 0.5236 0.9718 0.9871 0.7472 0.8973 0.9664 0.6214 ] Network output: [ -0.06241 0.2178 0.97 0.0006025 -0.0002705 0.9395 0.0004541 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5909 0.5739 0.4291 0.3391 0.9845 0.9898 0.5914 0.9673 0.9778 0.4448 ] Network output: [ -0.08469 0.2596 0.9201 -0.0001387 6.227e-05 0.9891 -0.0001045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6016 0.5988 0.4603 0.295 0.9821 0.9883 0.6018 0.9596 0.9735 0.4636 ] Network output: [ 0.03811 0.8612 0.03793 -0.0004856 0.000218 1.023 -0.0003659 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04191 Epoch 2004 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03966 0.9735 0.9863 0.0001556 -6.987e-05 -0.03849 0.0001173 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02489 -0.004949 0.02002 0.03852 0.9341 0.9443 0.05243 0.8713 0.8925 0.1355 ] Network output: [ 0.965 0.07343 -0.01556 -0.000418 0.0001876 0.01035 -0.000315 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6495 0.09769 0.08839 0.3429 0.968 0.9849 0.7495 0.8859 0.9607 0.6253 ] Network output: [ -0.0008292 0.927 1.034 5.651e-05 -2.537e-05 0.04106 4.259e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04966 0.03581 0.057 0.05382 0.9826 0.9876 0.05093 0.9625 0.9756 0.07349 ] Network output: [ 0.1055 -0.3126 1.067 0.0002247 -0.0001009 1.035 0.0001693 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7435 0.6149 0.503 0.5235 0.9718 0.9871 0.7473 0.8973 0.9665 0.6214 ] Network output: [ -0.06233 0.2177 0.9699 0.0006019 -0.0002702 0.9395 0.0004536 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.591 0.5741 0.4293 0.3392 0.9845 0.9898 0.5915 0.9674 0.9778 0.4449 ] Network output: [ -0.0846 0.2594 0.9202 -0.0001381 6.198e-05 0.989 -0.000104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6017 0.5988 0.4604 0.2951 0.9821 0.9883 0.6018 0.9597 0.9736 0.4636 ] Network output: [ 0.038 0.8615 0.03783 -0.0004834 0.000217 1.023 -0.0003643 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04186 Epoch 2005 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03964 0.9735 0.9863 0.0001552 -6.968e-05 -0.03847 0.000117 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02489 -0.004954 0.02003 0.03851 0.9341 0.9443 0.05243 0.8714 0.8926 0.1354 ] Network output: [ 0.9651 0.07337 -0.01554 -0.0004167 0.0001871 0.01029 -0.000314 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6495 0.09786 0.08861 0.3427 0.968 0.9849 0.7496 0.886 0.9608 0.6253 ] Network output: [ -0.0008557 0.927 1.034 5.591e-05 -2.51e-05 0.04112 4.213e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04968 0.03583 0.05701 0.05381 0.9827 0.9876 0.05095 0.9625 0.9756 0.07349 ] Network output: [ 0.1054 -0.3125 1.067 0.0002223 -9.978e-05 1.036 0.0001675 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7436 0.6151 0.5032 0.5235 0.9718 0.9871 0.7473 0.8974 0.9665 0.6214 ] Network output: [ -0.06225 0.2175 0.9699 0.0006014 -0.00027 0.9395 0.0004532 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5911 0.5742 0.4294 0.3392 0.9845 0.9898 0.5917 0.9674 0.9778 0.445 ] Network output: [ -0.08452 0.2593 0.9203 -0.0001374 6.169e-05 0.9889 -0.0001036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6018 0.5989 0.4605 0.2953 0.9821 0.9884 0.6019 0.9597 0.9736 0.4637 ] Network output: [ 0.03789 0.8618 0.03772 -0.0004813 0.0002161 1.023 -0.0003627 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04181 Epoch 2006 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03962 0.9735 0.9863 0.0001548 -6.948e-05 -0.03844 0.0001166 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02489 -0.004958 0.02003 0.0385 0.9341 0.9443 0.05243 0.8715 0.8926 0.1354 ] Network output: [ 0.9651 0.07331 -0.01551 -0.0004154 0.0001865 0.01022 -0.0003131 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6496 0.09804 0.08883 0.3426 0.968 0.9849 0.7497 0.8861 0.9608 0.6253 ] Network output: [ -0.000882 0.927 1.034 5.531e-05 -2.483e-05 0.04118 4.168e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04971 0.03586 0.05703 0.0538 0.9827 0.9876 0.05097 0.9625 0.9756 0.07349 ] Network output: [ 0.1053 -0.3125 1.067 0.0002199 -9.871e-05 1.036 0.0001657 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7437 0.6154 0.5034 0.5234 0.9718 0.9871 0.7474 0.8975 0.9665 0.6215 ] Network output: [ -0.06217 0.2174 0.9699 0.0006008 -0.0002697 0.9395 0.0004528 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5912 0.5744 0.4296 0.3393 0.9845 0.9898 0.5918 0.9675 0.9779 0.4452 ] Network output: [ -0.08444 0.2591 0.9205 -0.0001368 6.141e-05 0.9887 -0.0001031 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6018 0.599 0.4606 0.2954 0.9821 0.9884 0.602 0.9597 0.9736 0.4638 ] Network output: [ 0.03779 0.8621 0.03762 -0.0004791 0.0002151 1.023 -0.0003611 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04176 Epoch 2007 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0396 0.9735 0.9864 0.0001543 -6.929e-05 -0.03842 0.0001163 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02489 -0.004962 0.02003 0.03849 0.9341 0.9443 0.05243 0.8715 0.8927 0.1354 ] Network output: [ 0.9652 0.07325 -0.01549 -0.0004142 0.0001859 0.01016 -0.0003121 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6497 0.09821 0.08905 0.3425 0.9681 0.9849 0.7498 0.8861 0.9608 0.6253 ] Network output: [ -0.0009081 0.927 1.034 5.472e-05 -2.456e-05 0.04124 4.124e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04973 0.03588 0.05704 0.05379 0.9827 0.9877 0.05099 0.9626 0.9757 0.07349 ] Network output: [ 0.1053 -0.3124 1.067 0.0002175 -9.764e-05 1.036 0.0001639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7438 0.6156 0.5036 0.5233 0.9718 0.9871 0.7475 0.8975 0.9665 0.6215 ] Network output: [ -0.06209 0.2172 0.9699 0.0006002 -0.0002695 0.9395 0.0004524 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5914 0.5745 0.4297 0.3393 0.9845 0.9898 0.5919 0.9675 0.9779 0.4453 ] Network output: [ -0.08436 0.2589 0.9206 -0.0001362 6.113e-05 0.9886 -0.0001026 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6019 0.5991 0.4607 0.2955 0.9821 0.9884 0.602 0.9598 0.9736 0.4639 ] Network output: [ 0.03768 0.8625 0.03752 -0.000477 0.0002141 1.023 -0.0003595 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04171 Epoch 2008 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03958 0.9735 0.9864 0.0001539 -6.909e-05 -0.0384 0.000116 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02488 -0.004966 0.02003 0.03848 0.9342 0.9443 0.05243 0.8716 0.8927 0.1354 ] Network output: [ 0.9652 0.0732 -0.01547 -0.0004129 0.0001854 0.01009 -0.0003112 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6498 0.09838 0.08927 0.3424 0.9681 0.9849 0.7499 0.8862 0.9609 0.6254 ] Network output: [ -0.000934 0.927 1.034 5.412e-05 -2.43e-05 0.04131 4.079e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04975 0.03591 0.05706 0.05377 0.9827 0.9877 0.05102 0.9626 0.9757 0.07349 ] Network output: [ 0.1052 -0.3124 1.067 0.0002151 -9.658e-05 1.036 0.0001621 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7439 0.6159 0.5038 0.5232 0.9718 0.9871 0.7476 0.8976 0.9666 0.6215 ] Network output: [ -0.06201 0.2171 0.9699 0.0005997 -0.0002692 0.9395 0.0004519 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5915 0.5747 0.4299 0.3394 0.9845 0.9898 0.592 0.9675 0.9779 0.4454 ] Network output: [ -0.08428 0.2588 0.9207 -0.0001355 6.085e-05 0.9885 -0.0001021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.602 0.5991 0.4608 0.2956 0.9821 0.9884 0.6021 0.9598 0.9737 0.464 ] Network output: [ 0.03757 0.8628 0.03742 -0.0004749 0.0002132 1.023 -0.0003579 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04166 Epoch 2009 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03956 0.9735 0.9864 0.0001535 -6.89e-05 -0.03838 0.0001157 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02488 -0.004971 0.02003 0.03846 0.9342 0.9444 0.05243 0.8717 0.8928 0.1354 ] Network output: [ 0.9653 0.07314 -0.01544 -0.0004116 0.0001848 0.01003 -0.0003102 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6498 0.09855 0.08948 0.3423 0.9681 0.9849 0.75 0.8863 0.9609 0.6254 ] Network output: [ -0.0009597 0.927 1.034 5.353e-05 -2.403e-05 0.04137 4.034e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04977 0.03593 0.05707 0.05376 0.9827 0.9877 0.05104 0.9627 0.9757 0.0735 ] Network output: [ 0.1052 -0.3123 1.067 0.0002128 -9.551e-05 1.036 0.0001603 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.744 0.6161 0.504 0.5231 0.9718 0.9871 0.7477 0.8977 0.9666 0.6215 ] Network output: [ -0.06194 0.217 0.9699 0.0005991 -0.000269 0.9395 0.0004515 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5916 0.5748 0.43 0.3395 0.9845 0.9898 0.5922 0.9676 0.9779 0.4455 ] Network output: [ -0.0842 0.2586 0.9209 -0.0001349 6.057e-05 0.9884 -0.0001017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.602 0.5992 0.4609 0.2957 0.9821 0.9884 0.6022 0.9599 0.9737 0.4641 ] Network output: [ 0.03747 0.8631 0.03732 -0.0004728 0.0002123 1.023 -0.0003563 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04161 Epoch 2010 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03953 0.9735 0.9864 0.000153 -6.87e-05 -0.03836 0.0001153 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02488 -0.004975 0.02003 0.03845 0.9342 0.9444 0.05243 0.8717 0.8928 0.1354 ] Network output: [ 0.9654 0.07308 -0.01542 -0.0004103 0.0001842 0.009963 -0.0003092 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6499 0.09872 0.0897 0.3422 0.9681 0.9849 0.7501 0.8864 0.9609 0.6254 ] Network output: [ -0.0009852 0.927 1.034 5.294e-05 -2.377e-05 0.04143 3.99e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04979 0.03596 0.05708 0.05375 0.9827 0.9877 0.05106 0.9627 0.9757 0.0735 ] Network output: [ 0.1051 -0.3122 1.067 0.0002104 -9.445e-05 1.036 0.0001586 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7441 0.6164 0.5042 0.523 0.9718 0.9871 0.7478 0.8977 0.9666 0.6216 ] Network output: [ -0.06186 0.2168 0.9699 0.0005986 -0.0002687 0.9394 0.0004511 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5917 0.575 0.4301 0.3395 0.9845 0.9898 0.5923 0.9676 0.9779 0.4456 ] Network output: [ -0.08412 0.2584 0.921 -0.0001343 6.029e-05 0.9883 -0.0001012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6021 0.5993 0.461 0.2959 0.9821 0.9884 0.6022 0.9599 0.9737 0.4642 ] Network output: [ 0.03737 0.8634 0.03723 -0.0004707 0.0002113 1.023 -0.0003547 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04156 Epoch 2011 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03951 0.9735 0.9865 0.0001526 -6.85e-05 -0.03834 0.000115 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02488 -0.004979 0.02003 0.03844 0.9342 0.9444 0.05243 0.8718 0.8929 0.1354 ] Network output: [ 0.9654 0.07303 -0.0154 -0.0004091 0.0001836 0.009898 -0.0003083 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.65 0.09889 0.08991 0.342 0.9681 0.9849 0.7501 0.8864 0.9609 0.6254 ] Network output: [ -0.001011 0.927 1.034 5.236e-05 -2.35e-05 0.04149 3.946e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04981 0.03598 0.0571 0.05373 0.9827 0.9877 0.05108 0.9627 0.9758 0.0735 ] Network output: [ 0.1051 -0.3122 1.067 0.000208 -9.339e-05 1.036 0.0001568 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7442 0.6166 0.5044 0.5229 0.9719 0.9871 0.7479 0.8978 0.9667 0.6216 ] Network output: [ -0.06178 0.2167 0.9699 0.000598 -0.0002685 0.9394 0.0004507 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5918 0.5751 0.4303 0.3396 0.9845 0.9898 0.5924 0.9676 0.978 0.4458 ] Network output: [ -0.08404 0.2583 0.9211 -0.0001337 6.001e-05 0.9881 -0.0001007 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6022 0.5993 0.4611 0.296 0.9821 0.9884 0.6023 0.96 0.9737 0.4643 ] Network output: [ 0.03726 0.8637 0.03713 -0.0004686 0.0002104 1.023 -0.0003532 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04151 Epoch 2012 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03949 0.9735 0.9865 0.0001522 -6.831e-05 -0.03832 0.0001147 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02488 -0.004983 0.02003 0.03843 0.9342 0.9444 0.05243 0.8719 0.8929 0.1354 ] Network output: [ 0.9655 0.07297 -0.01537 -0.0004078 0.0001831 0.009834 -0.0003073 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.65 0.09906 0.09012 0.3419 0.9681 0.9849 0.7502 0.8865 0.961 0.6255 ] Network output: [ -0.001036 0.9271 1.034 5.177e-05 -2.324e-05 0.04155 3.902e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04983 0.03601 0.05711 0.05372 0.9827 0.9877 0.0511 0.9628 0.9758 0.0735 ] Network output: [ 0.105 -0.3121 1.067 0.0002057 -9.233e-05 1.036 0.000155 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7442 0.6169 0.5046 0.5228 0.9719 0.9871 0.748 0.8979 0.9667 0.6216 ] Network output: [ -0.0617 0.2166 0.9699 0.0005975 -0.0002682 0.9394 0.0004503 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.592 0.5753 0.4304 0.3396 0.9846 0.9898 0.5925 0.9677 0.978 0.4459 ] Network output: [ -0.08396 0.2581 0.9213 -0.0001331 5.974e-05 0.988 -0.0001003 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6022 0.5994 0.4612 0.2961 0.9822 0.9884 0.6023 0.96 0.9738 0.4644 ] Network output: [ 0.03716 0.864 0.03703 -0.0004665 0.0002094 1.023 -0.0003516 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04146 Epoch 2013 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03947 0.9734 0.9865 0.0001517 -6.811e-05 -0.03829 0.0001143 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02487 -0.004987 0.02004 0.03842 0.9343 0.9444 0.05243 0.8719 0.893 0.1353 ] Network output: [ 0.9655 0.07292 -0.01535 -0.0004065 0.0001825 0.00977 -0.0003063 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6501 0.09923 0.09034 0.3418 0.9681 0.9849 0.7503 0.8866 0.961 0.6255 ] Network output: [ -0.001061 0.9271 1.034 5.119e-05 -2.298e-05 0.04161 3.858e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04985 0.03603 0.05712 0.0537 0.9827 0.9877 0.05112 0.9628 0.9758 0.0735 ] Network output: [ 0.1049 -0.3121 1.067 0.0002033 -9.128e-05 1.036 0.0001532 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7443 0.6171 0.5048 0.5227 0.9719 0.9872 0.748 0.898 0.9667 0.6217 ] Network output: [ -0.06163 0.2164 0.9698 0.000597 -0.000268 0.9394 0.0004499 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5921 0.5754 0.4306 0.3397 0.9846 0.9898 0.5926 0.9677 0.978 0.446 ] Network output: [ -0.08388 0.2579 0.9214 -0.0001325 5.947e-05 0.9879 -9.983e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6023 0.5995 0.4613 0.2962 0.9822 0.9884 0.6024 0.96 0.9738 0.4645 ] Network output: [ 0.03706 0.8643 0.03693 -0.0004645 0.0002085 1.023 -0.0003501 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04141 Epoch 2014 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03945 0.9734 0.9866 0.0001513 -6.791e-05 -0.03827 0.000114 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02487 -0.004992 0.02004 0.0384 0.9343 0.9444 0.05242 0.872 0.893 0.1353 ] Network output: [ 0.9656 0.07287 -0.01533 -0.0004052 0.0001819 0.009706 -0.0003054 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6502 0.09939 0.09055 0.3417 0.9682 0.9849 0.7504 0.8866 0.961 0.6255 ] Network output: [ -0.001085 0.9271 1.034 5.061e-05 -2.272e-05 0.04167 3.814e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04988 0.03606 0.05713 0.05369 0.9827 0.9877 0.05114 0.9629 0.9758 0.0735 ] Network output: [ 0.1049 -0.312 1.067 0.000201 -9.022e-05 1.036 0.0001515 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7444 0.6173 0.505 0.5226 0.9719 0.9872 0.7481 0.898 0.9667 0.6217 ] Network output: [ -0.06155 0.2163 0.9698 0.0005965 -0.0002678 0.9394 0.0004495 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5922 0.5755 0.4307 0.3397 0.9846 0.9898 0.5928 0.9677 0.978 0.4461 ] Network output: [ -0.0838 0.2578 0.9215 -0.0001319 5.919e-05 0.9878 -9.937e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6024 0.5995 0.4614 0.2963 0.9822 0.9884 0.6025 0.9601 0.9738 0.4645 ] Network output: [ 0.03696 0.8646 0.03684 -0.0004624 0.0002076 1.023 -0.0003485 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04136 Epoch 2015 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03943 0.9734 0.9866 0.0001508 -6.771e-05 -0.03825 0.0001137 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02487 -0.004996 0.02004 0.03839 0.9343 0.9445 0.05242 0.8721 0.8931 0.1353 ] Network output: [ 0.9656 0.07281 -0.01531 -0.0004039 0.0001813 0.009643 -0.0003044 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6503 0.09956 0.09076 0.3416 0.9682 0.985 0.7505 0.8867 0.9611 0.6256 ] Network output: [ -0.00111 0.9271 1.034 5.003e-05 -2.246e-05 0.04173 3.771e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0499 0.03608 0.05715 0.05367 0.9828 0.9877 0.05116 0.9629 0.9759 0.07349 ] Network output: [ 0.1048 -0.3119 1.067 0.0001986 -8.917e-05 1.037 0.0001497 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7445 0.6176 0.5052 0.5225 0.9719 0.9872 0.7482 0.8981 0.9668 0.6217 ] Network output: [ -0.06147 0.2162 0.9698 0.0005959 -0.0002675 0.9394 0.0004491 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5923 0.5757 0.4309 0.3398 0.9846 0.9898 0.5929 0.9678 0.9781 0.4462 ] Network output: [ -0.08372 0.2576 0.9216 -0.0001312 5.892e-05 0.9877 -9.891e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6024 0.5996 0.4615 0.2964 0.9822 0.9884 0.6025 0.9601 0.9739 0.4646 ] Network output: [ 0.03686 0.8649 0.03674 -0.0004604 0.0002067 1.023 -0.000347 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04131 Epoch 2016 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03941 0.9734 0.9866 0.0001504 -6.752e-05 -0.03823 0.0001133 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02487 -0.005 0.02004 0.03838 0.9343 0.9445 0.05242 0.8721 0.8931 0.1353 ] Network output: [ 0.9657 0.07276 -0.01529 -0.0004026 0.0001808 0.009579 -0.0003034 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6503 0.09972 0.09096 0.3414 0.9682 0.985 0.7505 0.8868 0.9611 0.6256 ] Network output: [ -0.001134 0.9271 1.034 4.946e-05 -2.22e-05 0.04178 3.727e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04992 0.0361 0.05716 0.05366 0.9828 0.9877 0.05118 0.9629 0.9759 0.07349 ] Network output: [ 0.1048 -0.3119 1.066 0.0001963 -8.812e-05 1.037 0.0001479 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7446 0.6178 0.5054 0.5224 0.9719 0.9872 0.7483 0.8982 0.9668 0.6217 ] Network output: [ -0.0614 0.216 0.9698 0.0005954 -0.0002673 0.9394 0.0004487 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5924 0.5758 0.431 0.3398 0.9846 0.9898 0.593 0.9678 0.9781 0.4463 ] Network output: [ -0.08365 0.2574 0.9218 -0.0001306 5.865e-05 0.9876 -9.846e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6025 0.5997 0.4616 0.2965 0.9822 0.9884 0.6026 0.9602 0.9739 0.4647 ] Network output: [ 0.03676 0.8652 0.03665 -0.0004584 0.0002058 1.023 -0.0003454 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04126 Epoch 2017 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03938 0.9734 0.9866 0.0001499 -6.732e-05 -0.03821 0.000113 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02487 -0.005004 0.02004 0.03837 0.9343 0.9445 0.05242 0.8722 0.8932 0.1353 ] Network output: [ 0.9657 0.07271 -0.01526 -0.0004013 0.0001802 0.009516 -0.0003025 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6504 0.09988 0.09117 0.3413 0.9682 0.985 0.7506 0.8869 0.9611 0.6256 ] Network output: [ -0.001159 0.9271 1.034 4.889e-05 -2.195e-05 0.04184 3.684e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04994 0.03613 0.05717 0.05364 0.9828 0.9877 0.05121 0.963 0.9759 0.07349 ] Network output: [ 0.1047 -0.3118 1.066 0.000194 -8.707e-05 1.037 0.0001462 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7447 0.618 0.5056 0.5223 0.9719 0.9872 0.7484 0.8982 0.9668 0.6218 ] Network output: [ -0.06132 0.2159 0.9698 0.0005949 -0.0002671 0.9394 0.0004484 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5926 0.576 0.4311 0.3399 0.9846 0.9899 0.5931 0.9678 0.9781 0.4465 ] Network output: [ -0.08357 0.2573 0.9219 -0.00013 5.838e-05 0.9875 -9.801e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6025 0.5997 0.4616 0.2966 0.9822 0.9884 0.6027 0.9602 0.9739 0.4648 ] Network output: [ 0.03666 0.8655 0.03655 -0.0004564 0.0002049 1.023 -0.0003439 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04122 Epoch 2018 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03936 0.9734 0.9867 0.0001495 -6.712e-05 -0.03819 0.0001127 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02486 -0.005008 0.02004 0.03835 0.9344 0.9445 0.05242 0.8722 0.8932 0.1353 ] Network output: [ 0.9658 0.07266 -0.01524 -0.0004 0.0001796 0.009453 -0.0003015 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6505 0.1 0.09138 0.3412 0.9682 0.985 0.7507 0.8869 0.9611 0.6256 ] Network output: [ -0.001183 0.9271 1.034 4.832e-05 -2.169e-05 0.0419 3.641e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04995 0.03615 0.05718 0.05363 0.9828 0.9877 0.05122 0.963 0.9759 0.07349 ] Network output: [ 0.1046 -0.3118 1.066 0.0001916 -8.603e-05 1.037 0.0001444 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7447 0.6182 0.5057 0.5222 0.9719 0.9872 0.7484 0.8983 0.9668 0.6218 ] Network output: [ -0.06124 0.2158 0.9698 0.0005944 -0.0002669 0.9394 0.000448 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5927 0.5761 0.4313 0.3399 0.9846 0.9899 0.5932 0.9679 0.9781 0.4466 ] Network output: [ -0.08349 0.2571 0.922 -0.0001295 5.812e-05 0.9873 -9.756e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6026 0.5998 0.4617 0.2968 0.9822 0.9885 0.6027 0.9602 0.9739 0.4649 ] Network output: [ 0.03656 0.8658 0.03646 -0.0004544 0.000204 1.023 -0.0003424 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04117 Epoch 2019 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03934 0.9734 0.9867 0.0001491 -6.692e-05 -0.03817 0.0001123 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02486 -0.005012 0.02004 0.03834 0.9344 0.9445 0.05242 0.8723 0.8932 0.1352 ] Network output: [ 0.9658 0.07261 -0.01522 -0.0003988 0.000179 0.009391 -0.0003005 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6505 0.1002 0.09158 0.3411 0.9682 0.985 0.7508 0.887 0.9612 0.6257 ] Network output: [ -0.001206 0.9271 1.034 4.775e-05 -2.144e-05 0.04196 3.599e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04997 0.03617 0.05719 0.05361 0.9828 0.9878 0.05124 0.9631 0.9759 0.07349 ] Network output: [ 0.1046 -0.3117 1.066 0.0001893 -8.499e-05 1.037 0.0001427 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7448 0.6185 0.5059 0.5221 0.972 0.9872 0.7485 0.8984 0.9669 0.6218 ] Network output: [ -0.06117 0.2156 0.9697 0.0005939 -0.0002666 0.9394 0.0004476 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5928 0.5762 0.4314 0.34 0.9846 0.9899 0.5933 0.9679 0.9781 0.4467 ] Network output: [ -0.08341 0.2569 0.9222 -0.0001289 5.785e-05 0.9872 -9.711e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6027 0.5999 0.4618 0.2969 0.9822 0.9885 0.6028 0.9603 0.974 0.4649 ] Network output: [ 0.03646 0.866 0.03637 -0.0004524 0.0002031 1.023 -0.0003409 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04112 Epoch 2020 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03932 0.9734 0.9867 0.0001486 -6.672e-05 -0.03814 0.000112 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02486 -0.005016 0.02004 0.03833 0.9344 0.9445 0.05242 0.8724 0.8933 0.1352 ] Network output: [ 0.9658 0.07256 -0.0152 -0.0003975 0.0001784 0.009328 -0.0002995 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6506 0.1004 0.09178 0.3409 0.9682 0.985 0.7508 0.8871 0.9612 0.6257 ] Network output: [ -0.00123 0.9271 1.033 4.718e-05 -2.118e-05 0.04201 3.556e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04999 0.03619 0.0572 0.05359 0.9828 0.9878 0.05126 0.9631 0.976 0.07348 ] Network output: [ 0.1045 -0.3116 1.066 0.000187 -8.394e-05 1.037 0.0001409 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7449 0.6187 0.5061 0.522 0.972 0.9872 0.7486 0.8984 0.9669 0.6219 ] Network output: [ -0.06109 0.2155 0.9697 0.0005934 -0.0002664 0.9394 0.0004472 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5929 0.5764 0.4315 0.34 0.9846 0.9899 0.5934 0.9679 0.9782 0.4468 ] Network output: [ -0.08334 0.2568 0.9223 -0.0001283 5.758e-05 0.9871 -9.667e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6027 0.5999 0.4619 0.297 0.9822 0.9885 0.6028 0.9603 0.974 0.465 ] Network output: [ 0.03637 0.8663 0.03628 -0.0004504 0.0002022 1.023 -0.0003394 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04107 Epoch 2021 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0393 0.9734 0.9868 0.0001482 -6.652e-05 -0.03812 0.0001117 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02486 -0.00502 0.02004 0.03831 0.9344 0.9446 0.05241 0.8724 0.8933 0.1352 ] Network output: [ 0.9659 0.07251 -0.01518 -0.0003962 0.0001779 0.009266 -0.0002986 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6507 0.1005 0.09199 0.3408 0.9683 0.985 0.7509 0.8871 0.9612 0.6257 ] Network output: [ -0.001254 0.9272 1.033 4.662e-05 -2.093e-05 0.04207 3.513e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05001 0.03622 0.05721 0.05358 0.9828 0.9878 0.05128 0.9631 0.976 0.07348 ] Network output: [ 0.1045 -0.3116 1.066 0.0001847 -8.291e-05 1.037 0.0001392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.745 0.6189 0.5063 0.5219 0.972 0.9872 0.7487 0.8985 0.9669 0.6219 ] Network output: [ -0.06101 0.2154 0.9697 0.0005929 -0.0002662 0.9394 0.0004469 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.593 0.5765 0.4317 0.34 0.9846 0.9899 0.5936 0.968 0.9782 0.4469 ] Network output: [ -0.08326 0.2566 0.9224 -0.0001277 5.732e-05 0.987 -9.622e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6028 0.6 0.462 0.2971 0.9823 0.9885 0.6029 0.9604 0.974 0.4651 ] Network output: [ 0.03627 0.8666 0.03619 -0.0004484 0.0002013 1.023 -0.0003379 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04103 Epoch 2022 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03928 0.9734 0.9868 0.0001477 -6.632e-05 -0.0381 0.0001113 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02485 -0.005025 0.02004 0.0383 0.9344 0.9446 0.05241 0.8725 0.8934 0.1352 ] Network output: [ 0.9659 0.07246 -0.01516 -0.0003949 0.0001773 0.009204 -0.0002976 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6507 0.1007 0.09219 0.3407 0.9683 0.985 0.751 0.8872 0.9612 0.6257 ] Network output: [ -0.001277 0.9272 1.033 4.606e-05 -2.068e-05 0.04213 3.471e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05003 0.03624 0.05722 0.05356 0.9828 0.9878 0.0513 0.9632 0.976 0.07348 ] Network output: [ 0.1044 -0.3115 1.066 0.0001824 -8.187e-05 1.037 0.0001374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.745 0.6191 0.5065 0.5218 0.972 0.9872 0.7487 0.8986 0.9669 0.6219 ] Network output: [ -0.06094 0.2153 0.9697 0.0005925 -0.000266 0.9394 0.0004465 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5931 0.5766 0.4318 0.3401 0.9846 0.9899 0.5937 0.968 0.9782 0.447 ] Network output: [ -0.08319 0.2564 0.9225 -0.0001271 5.705e-05 0.9869 -9.578e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6028 0.6001 0.4621 0.2972 0.9823 0.9885 0.603 0.9604 0.974 0.4652 ] Network output: [ 0.03617 0.8669 0.03609 -0.0004465 0.0002004 1.023 -0.0003365 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04098 Epoch 2023 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03925 0.9734 0.9868 0.0001473 -6.612e-05 -0.03808 0.000111 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02485 -0.005029 0.02004 0.03829 0.9345 0.9446 0.05241 0.8725 0.8934 0.1352 ] Network output: [ 0.966 0.07241 -0.01514 -0.0003936 0.0001767 0.009142 -0.0002966 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6508 0.1008 0.09239 0.3406 0.9683 0.985 0.7511 0.8873 0.9613 0.6258 ] Network output: [ -0.0013 0.9272 1.033 4.55e-05 -2.043e-05 0.04218 3.429e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05005 0.03626 0.05723 0.05354 0.9828 0.9878 0.05132 0.9632 0.976 0.07347 ] Network output: [ 0.1043 -0.3115 1.066 0.0001801 -8.083e-05 1.037 0.0001357 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7451 0.6194 0.5067 0.5217 0.972 0.9872 0.7488 0.8986 0.9669 0.6219 ] Network output: [ -0.06086 0.2151 0.9696 0.000592 -0.0002658 0.9394 0.0004461 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5932 0.5768 0.4319 0.3401 0.9847 0.9899 0.5938 0.968 0.9782 0.4471 ] Network output: [ -0.08311 0.2563 0.9227 -0.0001265 5.679e-05 0.9868 -9.533e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6029 0.6001 0.4621 0.2973 0.9823 0.9885 0.603 0.9604 0.9741 0.4653 ] Network output: [ 0.03608 0.8672 0.036 -0.0004445 0.0001996 1.023 -0.000335 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04093 Epoch 2024 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03923 0.9734 0.9868 0.0001468 -6.592e-05 -0.03806 0.0001107 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02485 -0.005033 0.02004 0.03827 0.9345 0.9446 0.05241 0.8726 0.8935 0.1351 ] Network output: [ 0.966 0.07236 -0.01513 -0.0003923 0.0001761 0.00908 -0.0002956 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6508 0.101 0.09259 0.3404 0.9683 0.985 0.7511 0.8873 0.9613 0.6258 ] Network output: [ -0.001323 0.9272 1.033 4.494e-05 -2.018e-05 0.04224 3.387e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05007 0.03628 0.05723 0.05352 0.9829 0.9878 0.05134 0.9633 0.9761 0.07347 ] Network output: [ 0.1043 -0.3114 1.066 0.0001778 -7.98e-05 1.037 0.000134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7452 0.6196 0.5069 0.5216 0.972 0.9872 0.7489 0.8987 0.967 0.622 ] Network output: [ -0.06079 0.215 0.9696 0.0005915 -0.0002656 0.9394 0.0004458 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5933 0.5769 0.432 0.3401 0.9847 0.9899 0.5939 0.9681 0.9782 0.4472 ] Network output: [ -0.08304 0.2561 0.9228 -0.0001259 5.653e-05 0.9867 -9.489e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.603 0.6002 0.4622 0.2974 0.9823 0.9885 0.6031 0.9605 0.9741 0.4653 ] Network output: [ 0.03598 0.8675 0.03591 -0.0004426 0.0001987 1.023 -0.0003335 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04089 Epoch 2025 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03921 0.9733 0.9869 0.0001464 -6.572e-05 -0.03804 0.0001103 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02485 -0.005037 0.02004 0.03826 0.9345 0.9446 0.05241 0.8727 0.8935 0.1351 ] Network output: [ 0.9661 0.07232 -0.01511 -0.000391 0.0001755 0.009018 -0.0002946 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6509 0.1012 0.09278 0.3403 0.9683 0.985 0.7512 0.8874 0.9613 0.6258 ] Network output: [ -0.001346 0.9272 1.033 4.439e-05 -1.993e-05 0.04229 3.345e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05009 0.03631 0.05724 0.0535 0.9829 0.9878 0.05136 0.9633 0.9761 0.07346 ] Network output: [ 0.1042 -0.3113 1.066 0.0001755 -7.877e-05 1.038 0.0001322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7453 0.6198 0.507 0.5215 0.972 0.9872 0.749 0.8988 0.967 0.622 ] Network output: [ -0.06071 0.2149 0.9696 0.0005911 -0.0002653 0.9394 0.0004454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5935 0.577 0.4322 0.3402 0.9847 0.9899 0.594 0.9681 0.9783 0.4473 ] Network output: [ -0.08296 0.2559 0.9229 -0.0001253 5.627e-05 0.9866 -9.445e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.603 0.6002 0.4623 0.2975 0.9823 0.9885 0.6031 0.9605 0.9741 0.4654 ] Network output: [ 0.03589 0.8678 0.03583 -0.0004407 0.0001978 1.023 -0.0003321 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04084 Epoch 2026 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03919 0.9733 0.9869 0.0001459 -6.552e-05 -0.03802 0.00011 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02484 -0.005041 0.02004 0.03824 0.9345 0.9446 0.0524 0.8727 0.8936 0.1351 ] Network output: [ 0.9661 0.07227 -0.01509 -0.0003897 0.0001749 0.008957 -0.0002937 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.651 0.1013 0.09298 0.3402 0.9683 0.985 0.7513 0.8875 0.9614 0.6258 ] Network output: [ -0.001369 0.9272 1.033 4.384e-05 -1.968e-05 0.04235 3.304e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0501 0.03633 0.05725 0.05349 0.9829 0.9878 0.05138 0.9633 0.9761 0.07346 ] Network output: [ 0.1042 -0.3113 1.066 0.0001732 -7.774e-05 1.038 0.0001305 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7453 0.62 0.5072 0.5214 0.972 0.9872 0.749 0.8988 0.967 0.622 ] Network output: [ -0.06064 0.2148 0.9696 0.0005906 -0.0002651 0.9394 0.0004451 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5936 0.5772 0.4323 0.3402 0.9847 0.9899 0.5941 0.9681 0.9783 0.4474 ] Network output: [ -0.08289 0.2558 0.923 -0.0001247 5.6e-05 0.9865 -9.401e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6031 0.6003 0.4624 0.2976 0.9823 0.9885 0.6032 0.9606 0.9741 0.4655 ] Network output: [ 0.0358 0.868 0.03574 -0.0004388 0.000197 1.023 -0.0003307 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04079 Epoch 2027 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03917 0.9733 0.9869 0.0001455 -6.531e-05 -0.038 0.0001096 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02484 -0.005045 0.02004 0.03823 0.9345 0.9447 0.0524 0.8728 0.8936 0.1351 ] Network output: [ 0.9662 0.07222 -0.01507 -0.0003884 0.0001743 0.008896 -0.0002927 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.651 0.1015 0.09318 0.34 0.9683 0.985 0.7513 0.8875 0.9614 0.6259 ] Network output: [ -0.001391 0.9272 1.033 4.329e-05 -1.943e-05 0.0424 3.262e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05012 0.03635 0.05726 0.05347 0.9829 0.9878 0.0514 0.9634 0.9761 0.07345 ] Network output: [ 0.1041 -0.3112 1.066 0.0001709 -7.672e-05 1.038 0.0001288 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7454 0.6202 0.5074 0.5213 0.9721 0.9872 0.7491 0.8989 0.967 0.622 ] Network output: [ -0.06056 0.2146 0.9696 0.0005901 -0.0002649 0.9393 0.0004448 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5937 0.5773 0.4324 0.3403 0.9847 0.9899 0.5942 0.9681 0.9783 0.4475 ] Network output: [ -0.08282 0.2556 0.9232 -0.0001242 5.574e-05 0.9864 -9.358e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6031 0.6004 0.4625 0.2977 0.9823 0.9885 0.6032 0.9606 0.9742 0.4655 ] Network output: [ 0.03571 0.8683 0.03565 -0.0004369 0.0001961 1.023 -0.0003292 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04075 Epoch 2028 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03914 0.9733 0.987 0.000145 -6.511e-05 -0.03798 0.0001093 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02484 -0.005049 0.02004 0.03821 0.9346 0.9447 0.0524 0.8729 0.8937 0.1351 ] Network output: [ 0.9662 0.07218 -0.01505 -0.0003871 0.0001738 0.008835 -0.0002917 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6511 0.1016 0.09337 0.3399 0.9683 0.9851 0.7514 0.8876 0.9614 0.6259 ] Network output: [ -0.001413 0.9273 1.033 4.274e-05 -1.919e-05 0.04245 3.221e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05014 0.03637 0.05726 0.05345 0.9829 0.9878 0.05141 0.9634 0.9761 0.07345 ] Network output: [ 0.104 -0.3112 1.066 0.0001686 -7.569e-05 1.038 0.0001271 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7455 0.6204 0.5076 0.5212 0.9721 0.9873 0.7492 0.899 0.9671 0.6221 ] Network output: [ -0.06049 0.2145 0.9695 0.0005897 -0.0002647 0.9393 0.0004444 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5938 0.5774 0.4326 0.3403 0.9847 0.9899 0.5943 0.9682 0.9783 0.4476 ] Network output: [ -0.08274 0.2554 0.9233 -0.0001236 5.548e-05 0.9863 -9.314e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6032 0.6004 0.4625 0.2978 0.9823 0.9885 0.6033 0.9606 0.9742 0.4656 ] Network output: [ 0.03561 0.8686 0.03556 -0.000435 0.0001953 1.023 -0.0003278 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0407 Epoch 2029 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03912 0.9733 0.987 0.0001446 -6.491e-05 -0.03796 0.000109 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02484 -0.005053 0.02004 0.0382 0.9346 0.9447 0.0524 0.8729 0.8937 0.135 ] Network output: [ 0.9663 0.07213 -0.01504 -0.0003857 0.0001732 0.008774 -0.0002907 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6512 0.1018 0.09356 0.3398 0.9684 0.9851 0.7515 0.8877 0.9614 0.6259 ] Network output: [ -0.001436 0.9273 1.033 4.22e-05 -1.894e-05 0.04251 3.18e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05016 0.03639 0.05727 0.05343 0.9829 0.9878 0.05143 0.9634 0.9762 0.07344 ] Network output: [ 0.104 -0.3111 1.066 0.0001663 -7.467e-05 1.038 0.0001254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7455 0.6206 0.5078 0.5211 0.9721 0.9873 0.7493 0.899 0.9671 0.6221 ] Network output: [ -0.06042 0.2144 0.9695 0.0005893 -0.0002645 0.9393 0.0004441 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5939 0.5775 0.4327 0.3403 0.9847 0.9899 0.5944 0.9682 0.9783 0.4477 ] Network output: [ -0.08267 0.2553 0.9234 -0.000123 5.522e-05 0.9862 -9.27e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6032 0.6005 0.4626 0.2978 0.9823 0.9885 0.6033 0.9607 0.9742 0.4657 ] Network output: [ 0.03552 0.8689 0.03547 -0.0004331 0.0001944 1.023 -0.0003264 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04066 Epoch 2030 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0391 0.9733 0.987 0.0001441 -6.471e-05 -0.03793 0.0001086 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02483 -0.005057 0.02004 0.03818 0.9346 0.9447 0.05239 0.873 0.8938 0.135 ] Network output: [ 0.9663 0.07208 -0.01502 -0.0003844 0.0001726 0.008714 -0.0002897 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6512 0.1019 0.09376 0.3396 0.9684 0.9851 0.7515 0.8877 0.9615 0.6259 ] Network output: [ -0.001458 0.9273 1.033 4.166e-05 -1.87e-05 0.04256 3.139e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05017 0.03641 0.05728 0.05341 0.9829 0.9878 0.05145 0.9635 0.9762 0.07343 ] Network output: [ 0.1039 -0.311 1.066 0.0001641 -7.365e-05 1.038 0.0001236 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7456 0.6208 0.5079 0.521 0.9721 0.9873 0.7493 0.8991 0.9671 0.6221 ] Network output: [ -0.06034 0.2143 0.9695 0.0005888 -0.0002643 0.9393 0.0004438 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.594 0.5777 0.4328 0.3403 0.9847 0.9899 0.5946 0.9682 0.9784 0.4478 ] Network output: [ -0.0826 0.2551 0.9235 -0.0001224 5.496e-05 0.9861 -9.227e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6033 0.6005 0.4627 0.2979 0.9823 0.9885 0.6034 0.9607 0.9742 0.4658 ] Network output: [ 0.03543 0.8691 0.03539 -0.0004312 0.0001936 1.023 -0.000325 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04061 Epoch 2031 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03908 0.9733 0.9871 0.0001437 -6.451e-05 -0.03791 0.0001083 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02483 -0.005061 0.02003 0.03817 0.9346 0.9447 0.05239 0.873 0.8938 0.135 ] Network output: [ 0.9664 0.07204 -0.015 -0.0003831 0.000172 0.008654 -0.0002887 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6513 0.1021 0.09395 0.3395 0.9684 0.9851 0.7516 0.8878 0.9615 0.626 ] Network output: [ -0.001479 0.9273 1.033 4.111e-05 -1.846e-05 0.04261 3.099e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05019 0.03643 0.05728 0.05339 0.9829 0.9878 0.05147 0.9635 0.9762 0.07343 ] Network output: [ 0.1039 -0.311 1.066 0.0001618 -7.264e-05 1.038 0.0001219 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7457 0.6211 0.5081 0.5209 0.9721 0.9873 0.7494 0.8991 0.9671 0.6221 ] Network output: [ -0.06027 0.2141 0.9694 0.0005884 -0.0002642 0.9393 0.0004434 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5941 0.5778 0.4329 0.3404 0.9847 0.9899 0.5947 0.9683 0.9784 0.4479 ] Network output: [ -0.08252 0.2549 0.9237 -0.0001219 5.471e-05 0.986 -9.183e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6033 0.6006 0.4628 0.298 0.9824 0.9885 0.6035 0.9608 0.9743 0.4658 ] Network output: [ 0.03534 0.8694 0.0353 -0.0004294 0.0001928 1.023 -0.0003236 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04056 Epoch 2032 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03906 0.9733 0.9871 0.0001432 -6.43e-05 -0.03789 0.0001079 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02483 -0.005065 0.02003 0.03815 0.9346 0.9447 0.05239 0.8731 0.8939 0.135 ] Network output: [ 0.9664 0.072 -0.01499 -0.0003818 0.0001714 0.008593 -0.0002878 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6513 0.1022 0.09414 0.3394 0.9684 0.9851 0.7517 0.8879 0.9615 0.626 ] Network output: [ -0.001501 0.9273 1.033 4.058e-05 -1.822e-05 0.04266 3.058e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05021 0.03645 0.05729 0.05337 0.9829 0.9879 0.05148 0.9635 0.9762 0.07342 ] Network output: [ 0.1038 -0.3109 1.066 0.0001595 -7.162e-05 1.038 0.0001202 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7457 0.6213 0.5083 0.5207 0.9721 0.9873 0.7495 0.8992 0.9672 0.6222 ] Network output: [ -0.06019 0.214 0.9694 0.000588 -0.000264 0.9393 0.0004431 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5942 0.5779 0.433 0.3404 0.9847 0.9899 0.5948 0.9683 0.9784 0.448 ] Network output: [ -0.08245 0.2548 0.9238 -0.0001213 5.445e-05 0.9859 -9.14e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6034 0.6007 0.4628 0.2981 0.9824 0.9886 0.6035 0.9608 0.9743 0.4659 ] Network output: [ 0.03525 0.8697 0.03522 -0.0004275 0.0001919 1.023 -0.0003222 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04052 Epoch 2033 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03903 0.9733 0.9871 0.0001428 -6.41e-05 -0.03787 0.0001076 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02482 -0.005069 0.02003 0.03814 0.9347 0.9448 0.05238 0.8732 0.8939 0.135 ] Network output: [ 0.9665 0.07195 -0.01497 -0.0003805 0.0001708 0.008534 -0.0002868 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6514 0.1024 0.09433 0.3392 0.9684 0.9851 0.7517 0.8879 0.9615 0.626 ] Network output: [ -0.001523 0.9273 1.033 4.004e-05 -1.798e-05 0.04271 3.018e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05023 0.03647 0.05729 0.05335 0.9829 0.9879 0.0515 0.9636 0.9763 0.07341 ] Network output: [ 0.1037 -0.3109 1.066 0.0001573 -7.061e-05 1.038 0.0001185 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7458 0.6215 0.5085 0.5206 0.9721 0.9873 0.7495 0.8993 0.9672 0.6222 ] Network output: [ -0.06012 0.2139 0.9694 0.0005875 -0.0002638 0.9393 0.0004428 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5943 0.578 0.4332 0.3404 0.9847 0.9899 0.5949 0.9683 0.9784 0.4481 ] Network output: [ -0.08238 0.2546 0.9239 -0.0001207 5.419e-05 0.9858 -9.097e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6035 0.6007 0.4629 0.2982 0.9824 0.9886 0.6036 0.9608 0.9743 0.466 ] Network output: [ 0.03516 0.87 0.03513 -0.0004257 0.0001911 1.023 -0.0003208 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04047 Epoch 2034 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03901 0.9733 0.9872 0.0001423 -6.39e-05 -0.03785 0.0001073 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02482 -0.005073 0.02003 0.03812 0.9347 0.9448 0.05238 0.8732 0.8939 0.1349 ] Network output: [ 0.9665 0.07191 -0.01495 -0.0003792 0.0001702 0.008474 -0.0002858 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6514 0.1025 0.09452 0.3391 0.9684 0.9851 0.7518 0.888 0.9616 0.626 ] Network output: [ -0.001544 0.9273 1.033 3.951e-05 -1.774e-05 0.04277 2.977e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05024 0.03649 0.0573 0.05332 0.983 0.9879 0.05152 0.9636 0.9763 0.07341 ] Network output: [ 0.1037 -0.3108 1.066 0.000155 -6.96e-05 1.038 0.0001168 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7459 0.6217 0.5086 0.5205 0.9721 0.9873 0.7496 0.8993 0.9672 0.6222 ] Network output: [ -0.06005 0.2138 0.9694 0.0005871 -0.0002636 0.9393 0.0004425 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5944 0.5782 0.4333 0.3404 0.9847 0.99 0.595 0.9684 0.9784 0.4482 ] Network output: [ -0.08231 0.2544 0.924 -0.0001201 5.393e-05 0.9857 -9.053e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6035 0.6008 0.463 0.2983 0.9824 0.9886 0.6036 0.9609 0.9743 0.466 ] Network output: [ 0.03507 0.8702 0.03505 -0.0004239 0.0001903 1.023 -0.0003195 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04043 Epoch 2035 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03899 0.9732 0.9872 0.0001419 -6.369e-05 -0.03783 0.0001069 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02482 -0.005077 0.02003 0.03811 0.9347 0.9448 0.05238 0.8733 0.894 0.1349 ] Network output: [ 0.9666 0.07187 -0.01494 -0.0003779 0.0001697 0.008414 -0.0002848 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6515 0.1026 0.0947 0.339 0.9684 0.9851 0.7519 0.8881 0.9616 0.626 ] Network output: [ -0.001565 0.9273 1.033 3.898e-05 -1.75e-05 0.04282 2.937e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05026 0.03651 0.05731 0.0533 0.983 0.9879 0.05153 0.9637 0.9763 0.0734 ] Network output: [ 0.1036 -0.3108 1.066 0.0001528 -6.859e-05 1.038 0.0001151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7459 0.6219 0.5088 0.5204 0.9722 0.9873 0.7496 0.8994 0.9672 0.6222 ] Network output: [ -0.05997 0.2137 0.9693 0.0005867 -0.0002634 0.9393 0.0004422 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5945 0.5783 0.4334 0.3405 0.9847 0.99 0.5951 0.9684 0.9785 0.4483 ] Network output: [ -0.08224 0.2543 0.9242 -0.0001196 5.367e-05 0.9856 -9.01e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6036 0.6008 0.4631 0.2984 0.9824 0.9886 0.6037 0.9609 0.9744 0.4661 ] Network output: [ 0.03498 0.8705 0.03497 -0.0004221 0.0001895 1.023 -0.0003181 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04039 Epoch 2036 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03897 0.9732 0.9872 0.0001414 -6.349e-05 -0.03781 0.0001066 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02481 -0.005081 0.02003 0.03809 0.9347 0.9448 0.05237 0.8733 0.894 0.1349 ] Network output: [ 0.9666 0.07182 -0.01492 -0.0003766 0.0001691 0.008355 -0.0002838 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6515 0.1028 0.09489 0.3388 0.9685 0.9851 0.7519 0.8881 0.9616 0.6261 ] Network output: [ -0.001586 0.9274 1.033 3.845e-05 -1.726e-05 0.04287 2.897e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05027 0.03653 0.05731 0.05328 0.983 0.9879 0.05155 0.9637 0.9763 0.07339 ] Network output: [ 0.1036 -0.3107 1.066 0.0001505 -6.758e-05 1.039 0.0001134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.746 0.6221 0.509 0.5203 0.9722 0.9873 0.7497 0.8995 0.9673 0.6223 ] Network output: [ -0.0599 0.2135 0.9693 0.0005863 -0.0002632 0.9393 0.0004419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5946 0.5784 0.4335 0.3405 0.9848 0.99 0.5952 0.9684 0.9785 0.4484 ] Network output: [ -0.08217 0.2541 0.9243 -0.000119 5.342e-05 0.9855 -8.967e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6036 0.6009 0.4631 0.2985 0.9824 0.9886 0.6037 0.9609 0.9744 0.4662 ] Network output: [ 0.0349 0.8708 0.03488 -0.0004203 0.0001887 1.023 -0.0003168 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04034 Epoch 2037 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03895 0.9732 0.9873 0.000141 -6.328e-05 -0.03779 0.0001062 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02481 -0.005085 0.02003 0.03808 0.9347 0.9448 0.05237 0.8734 0.8941 0.1349 ] Network output: [ 0.9667 0.07178 -0.01491 -0.0003753 0.0001685 0.008296 -0.0002828 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6516 0.1029 0.09507 0.3387 0.9685 0.9851 0.752 0.8882 0.9616 0.6261 ] Network output: [ -0.001607 0.9274 1.033 3.792e-05 -1.702e-05 0.04292 2.858e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05029 0.03655 0.05731 0.05326 0.983 0.9879 0.05157 0.9637 0.9763 0.07338 ] Network output: [ 0.1035 -0.3106 1.066 0.0001483 -6.658e-05 1.039 0.0001118 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7461 0.6223 0.5091 0.5202 0.9722 0.9873 0.7498 0.8995 0.9673 0.6223 ] Network output: [ -0.05983 0.2134 0.9693 0.0005859 -0.000263 0.9393 0.0004416 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5947 0.5785 0.4336 0.3405 0.9848 0.99 0.5953 0.9685 0.9785 0.4485 ] Network output: [ -0.08209 0.2539 0.9244 -0.0001184 5.316e-05 0.9854 -8.924e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6037 0.6009 0.4632 0.2986 0.9824 0.9886 0.6038 0.961 0.9744 0.4662 ] Network output: [ 0.03481 0.871 0.0348 -0.0004186 0.0001879 1.023 -0.0003154 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0403 Epoch 2038 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03892 0.9732 0.9873 0.0001405 -6.308e-05 -0.03777 0.0001059 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02481 -0.005089 0.02003 0.03806 0.9347 0.9448 0.05236 0.8734 0.8941 0.1348 ] Network output: [ 0.9667 0.07174 -0.01489 -0.000374 0.0001679 0.008237 -0.0002819 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6517 0.1031 0.09526 0.3386 0.9685 0.9851 0.752 0.8883 0.9617 0.6261 ] Network output: [ -0.001628 0.9274 1.033 3.739e-05 -1.679e-05 0.04297 2.818e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05031 0.03657 0.05732 0.05324 0.983 0.9879 0.05158 0.9638 0.9764 0.07337 ] Network output: [ 0.1034 -0.3106 1.066 0.0001461 -6.557e-05 1.039 0.0001101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7461 0.6224 0.5093 0.5201 0.9722 0.9873 0.7498 0.8996 0.9673 0.6223 ] Network output: [ -0.05975 0.2133 0.9692 0.0005855 -0.0002629 0.9393 0.0004413 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5948 0.5787 0.4337 0.3405 0.9848 0.99 0.5954 0.9685 0.9785 0.4486 ] Network output: [ -0.08202 0.2538 0.9245 -0.0001178 5.29e-05 0.9853 -8.881e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6037 0.601 0.4633 0.2986 0.9824 0.9886 0.6038 0.961 0.9744 0.4663 ] Network output: [ 0.03472 0.8713 0.03472 -0.0004168 0.0001871 1.023 -0.0003141 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04025 Epoch 2039 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0389 0.9732 0.9873 0.0001401 -6.288e-05 -0.03775 0.0001055 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02481 -0.005093 0.02003 0.03804 0.9348 0.9449 0.05236 0.8735 0.8942 0.1348 ] Network output: [ 0.9667 0.0717 -0.01488 -0.0003727 0.0001673 0.008179 -0.0002809 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6517 0.1032 0.09544 0.3384 0.9685 0.9851 0.7521 0.8883 0.9617 0.6261 ] Network output: [ -0.001649 0.9274 1.033 3.687e-05 -1.655e-05 0.04301 2.779e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05032 0.03659 0.05732 0.05321 0.983 0.9879 0.0516 0.9638 0.9764 0.07336 ] Network output: [ 0.1034 -0.3105 1.066 0.0001438 -6.457e-05 1.039 0.0001084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7462 0.6226 0.5095 0.5199 0.9722 0.9873 0.7499 0.8996 0.9673 0.6223 ] Network output: [ -0.05968 0.2132 0.9692 0.0005852 -0.0002627 0.9393 0.000441 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5949 0.5788 0.4338 0.3406 0.9848 0.99 0.5955 0.9685 0.9785 0.4487 ] Network output: [ -0.08195 0.2536 0.9246 -0.0001173 5.264e-05 0.9852 -8.837e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6038 0.6011 0.4633 0.2987 0.9824 0.9886 0.6039 0.9611 0.9744 0.4664 ] Network output: [ 0.03464 0.8716 0.03464 -0.000415 0.0001863 1.023 -0.0003128 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04021 Epoch 2040 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03888 0.9732 0.9873 0.0001396 -6.267e-05 -0.03773 0.0001052 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0248 -0.005097 0.02002 0.03803 0.9348 0.9449 0.05236 0.8736 0.8942 0.1348 ] Network output: [ 0.9668 0.07166 -0.01486 -0.0003714 0.0001667 0.00812 -0.0002799 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6518 0.1034 0.09562 0.3383 0.9685 0.9851 0.7522 0.8884 0.9617 0.6262 ] Network output: [ -0.001669 0.9274 1.033 3.635e-05 -1.632e-05 0.04306 2.739e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05034 0.03661 0.05733 0.05319 0.983 0.9879 0.05161 0.9638 0.9764 0.07336 ] Network output: [ 0.1033 -0.3105 1.065 0.0001416 -6.357e-05 1.039 0.0001067 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7463 0.6228 0.5096 0.5198 0.9722 0.9873 0.75 0.8997 0.9673 0.6224 ] Network output: [ -0.05961 0.2131 0.9692 0.0005848 -0.0002625 0.9393 0.0004407 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.595 0.5789 0.434 0.3406 0.9848 0.99 0.5956 0.9685 0.9786 0.4488 ] Network output: [ -0.08188 0.2535 0.9248 -0.0001167 5.239e-05 0.9851 -8.794e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6038 0.6011 0.4634 0.2988 0.9824 0.9886 0.6039 0.9611 0.9745 0.4664 ] Network output: [ 0.03455 0.8718 0.03456 -0.0004133 0.0001855 1.023 -0.0003115 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04016 Epoch 2041 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03886 0.9732 0.9874 0.0001391 -6.247e-05 -0.03771 0.0001049 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0248 -0.005101 0.02002 0.03801 0.9348 0.9449 0.05235 0.8736 0.8943 0.1348 ] Network output: [ 0.9668 0.07162 -0.01485 -0.0003701 0.0001661 0.008062 -0.0002789 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6518 0.1035 0.0958 0.3381 0.9685 0.9852 0.7522 0.8885 0.9618 0.6262 ] Network output: [ -0.00169 0.9274 1.033 3.583e-05 -1.609e-05 0.04311 2.7e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05035 0.03663 0.05733 0.05317 0.983 0.9879 0.05163 0.9639 0.9764 0.07335 ] Network output: [ 0.1033 -0.3104 1.065 0.0001394 -6.258e-05 1.039 0.000105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7463 0.623 0.5098 0.5197 0.9722 0.9873 0.75 0.8998 0.9674 0.6224 ] Network output: [ -0.05954 0.2129 0.9692 0.0005844 -0.0002624 0.9394 0.0004404 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5951 0.579 0.4341 0.3406 0.9848 0.99 0.5957 0.9686 0.9786 0.4489 ] Network output: [ -0.08181 0.2533 0.9249 -0.0001161 5.213e-05 0.985 -8.751e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6039 0.6012 0.4635 0.2989 0.9825 0.9886 0.604 0.9611 0.9745 0.4665 ] Network output: [ 0.03447 0.8721 0.03448 -0.0004116 0.0001848 1.023 -0.0003102 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04012 Epoch 2042 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03884 0.9732 0.9874 0.0001387 -6.226e-05 -0.03769 0.0001045 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0248 -0.005104 0.02002 0.03799 0.9348 0.9449 0.05235 0.8737 0.8943 0.1347 ] Network output: [ 0.9669 0.07158 -0.01484 -0.0003688 0.0001656 0.008004 -0.0002779 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6519 0.1036 0.09598 0.338 0.9685 0.9852 0.7523 0.8885 0.9618 0.6262 ] Network output: [ -0.00171 0.9275 1.033 3.531e-05 -1.585e-05 0.04316 2.661e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05037 0.03664 0.05733 0.05314 0.983 0.9879 0.05164 0.9639 0.9764 0.07334 ] Network output: [ 0.1032 -0.3103 1.065 0.0001372 -6.158e-05 1.039 0.0001034 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7464 0.6232 0.5099 0.5196 0.9722 0.9873 0.7501 0.8998 0.9674 0.6224 ] Network output: [ -0.05946 0.2128 0.9691 0.000584 -0.0002622 0.9394 0.0004402 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5952 0.5791 0.4342 0.3406 0.9848 0.99 0.5958 0.9686 0.9786 0.449 ] Network output: [ -0.08174 0.2531 0.925 -0.0001155 5.187e-05 0.9849 -8.708e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6039 0.6012 0.4635 0.299 0.9825 0.9886 0.604 0.9612 0.9745 0.4665 ] Network output: [ 0.03438 0.8724 0.0344 -0.0004099 0.000184 1.023 -0.0003089 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04008 Epoch 2043 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03881 0.9732 0.9874 0.0001382 -6.206e-05 -0.03767 0.0001042 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02479 -0.005108 0.02002 0.03798 0.9348 0.9449 0.05234 0.8737 0.8943 0.1347 ] Network output: [ 0.9669 0.07154 -0.01482 -0.0003675 0.000165 0.007946 -0.0002769 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6519 0.1038 0.09616 0.3378 0.9685 0.9852 0.7523 0.8886 0.9618 0.6262 ] Network output: [ -0.00173 0.9275 1.033 3.48e-05 -1.562e-05 0.04321 2.623e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05038 0.03666 0.05733 0.05312 0.983 0.9879 0.05166 0.9639 0.9765 0.07333 ] Network output: [ 0.1031 -0.3103 1.065 0.000135 -6.059e-05 1.039 0.0001017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7464 0.6234 0.5101 0.5195 0.9722 0.9873 0.7501 0.8999 0.9674 0.6224 ] Network output: [ -0.05939 0.2127 0.9691 0.0005837 -0.000262 0.9394 0.0004399 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5953 0.5793 0.4343 0.3406 0.9848 0.99 0.5959 0.9686 0.9786 0.4491 ] Network output: [ -0.08168 0.253 0.9251 -0.000115 5.162e-05 0.9848 -8.665e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.604 0.6013 0.4636 0.299 0.9825 0.9886 0.6041 0.9612 0.9745 0.4666 ] Network output: [ 0.0343 0.8726 0.03432 -0.0004082 0.0001832 1.023 -0.0003076 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04003 Epoch 2044 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03879 0.9731 0.9875 0.0001378 -6.185e-05 -0.03765 0.0001038 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02479 -0.005112 0.02002 0.03796 0.9349 0.9449 0.05234 0.8738 0.8944 0.1347 ] Network output: [ 0.967 0.0715 -0.01481 -0.0003661 0.0001644 0.007888 -0.0002759 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.652 0.1039 0.09634 0.3377 0.9686 0.9852 0.7524 0.8887 0.9618 0.6263 ] Network output: [ -0.00175 0.9275 1.033 3.429e-05 -1.539e-05 0.04325 2.584e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0504 0.03668 0.05734 0.0531 0.9831 0.9879 0.05167 0.964 0.9765 0.07332 ] Network output: [ 0.1031 -0.3102 1.065 0.0001328 -5.96e-05 1.039 0.0001 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7465 0.6236 0.5103 0.5193 0.9723 0.9874 0.7502 0.8999 0.9674 0.6225 ] Network output: [ -0.05932 0.2126 0.9691 0.0005833 -0.0002619 0.9394 0.0004396 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5954 0.5794 0.4344 0.3406 0.9848 0.99 0.596 0.9687 0.9786 0.4491 ] Network output: [ -0.08161 0.2528 0.9252 -0.0001144 5.136e-05 0.9847 -8.622e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.604 0.6013 0.4637 0.2991 0.9825 0.9886 0.6041 0.9612 0.9746 0.4667 ] Network output: [ 0.03421 0.8729 0.03424 -0.0004065 0.0001825 1.023 -0.0003063 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03999 Epoch 2045 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03877 0.9731 0.9875 0.0001373 -6.165e-05 -0.03763 0.0001035 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02479 -0.005116 0.02002 0.03794 0.9349 0.945 0.05233 0.8738 0.8944 0.1347 ] Network output: [ 0.967 0.07146 -0.0148 -0.0003648 0.0001638 0.007831 -0.000275 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.652 0.1041 0.09652 0.3376 0.9686 0.9852 0.7524 0.8887 0.9619 0.6263 ] Network output: [ -0.00177 0.9275 1.033 3.378e-05 -1.516e-05 0.0433 2.546e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05041 0.0367 0.05734 0.05307 0.9831 0.9879 0.05169 0.964 0.9765 0.07331 ] Network output: [ 0.103 -0.3102 1.065 0.0001305 -5.861e-05 1.039 9.839e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7465 0.6238 0.5104 0.5192 0.9723 0.9874 0.7502 0.9 0.9675 0.6225 ] Network output: [ -0.05925 0.2125 0.969 0.000583 -0.0002617 0.9394 0.0004394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5955 0.5795 0.4345 0.3406 0.9848 0.99 0.5961 0.9687 0.9787 0.4492 ] Network output: [ -0.08154 0.2526 0.9254 -0.0001138 5.11e-05 0.9846 -8.578e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6041 0.6014 0.4637 0.2992 0.9825 0.9886 0.6042 0.9613 0.9746 0.4667 ] Network output: [ 0.03413 0.8731 0.03416 -0.0004048 0.0001817 1.023 -0.0003051 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03995 Epoch 2046 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03875 0.9731 0.9876 0.0001369 -6.144e-05 -0.03761 0.0001031 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02478 -0.00512 0.02002 0.03792 0.9349 0.945 0.05233 0.8739 0.8945 0.1346 ] Network output: [ 0.9671 0.07143 -0.01478 -0.0003635 0.0001632 0.007773 -0.000274 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6521 0.1042 0.09669 0.3374 0.9686 0.9852 0.7525 0.8888 0.9619 0.6263 ] Network output: [ -0.001789 0.9275 1.033 3.327e-05 -1.494e-05 0.04335 2.507e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05043 0.03672 0.05734 0.05305 0.9831 0.988 0.0517 0.964 0.9765 0.07329 ] Network output: [ 0.103 -0.3101 1.065 0.0001284 -5.762e-05 1.039 9.673e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7466 0.6239 0.5106 0.5191 0.9723 0.9874 0.7503 0.9001 0.9675 0.6225 ] Network output: [ -0.05918 0.2124 0.969 0.0005826 -0.0002616 0.9394 0.0004391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5956 0.5796 0.4346 0.3406 0.9848 0.99 0.5962 0.9687 0.9787 0.4493 ] Network output: [ -0.08147 0.2525 0.9255 -0.0001133 5.084e-05 0.9845 -8.535e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6041 0.6014 0.4638 0.2993 0.9825 0.9886 0.6042 0.9613 0.9746 0.4668 ] Network output: [ 0.03405 0.8734 0.03408 -0.0004031 0.000181 1.023 -0.0003038 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03991 Epoch 2047 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03872 0.9731 0.9876 0.0001364 -6.123e-05 -0.03759 0.0001028 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02478 -0.005124 0.02001 0.03791 0.9349 0.945 0.05232 0.874 0.8945 0.1346 ] Network output: [ 0.9671 0.07139 -0.01477 -0.0003622 0.0001626 0.007716 -0.000273 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6521 0.1043 0.09687 0.3373 0.9686 0.9852 0.7525 0.8888 0.9619 0.6263 ] Network output: [ -0.001809 0.9275 1.033 3.276e-05 -1.471e-05 0.04339 2.469e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05044 0.03673 0.05734 0.05302 0.9831 0.988 0.05172 0.9641 0.9765 0.07328 ] Network output: [ 0.1029 -0.31 1.065 0.0001262 -5.664e-05 1.039 9.507e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7467 0.6241 0.5107 0.519 0.9723 0.9874 0.7504 0.9001 0.9675 0.6225 ] Network output: [ -0.0591 0.2123 0.9689 0.0005823 -0.0002614 0.9394 0.0004388 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5957 0.5797 0.4347 0.3407 0.9848 0.99 0.5963 0.9687 0.9787 0.4494 ] Network output: [ -0.0814 0.2523 0.9256 -0.0001127 5.059e-05 0.9844 -8.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6042 0.6015 0.4638 0.2993 0.9825 0.9886 0.6043 0.9613 0.9746 0.4668 ] Network output: [ 0.03397 0.8736 0.03401 -0.0004015 0.0001802 1.023 -0.0003026 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03986 Epoch 2048 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0387 0.9731 0.9876 0.0001359 -6.103e-05 -0.03757 0.0001024 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02477 -0.005128 0.02001 0.03789 0.9349 0.945 0.05232 0.874 0.8946 0.1346 ] Network output: [ 0.9671 0.07135 -0.01476 -0.0003609 0.000162 0.00766 -0.000272 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6522 0.1045 0.09704 0.3371 0.9686 0.9852 0.7526 0.8889 0.9619 0.6263 ] Network output: [ -0.001828 0.9276 1.033 3.226e-05 -1.448e-05 0.04344 2.431e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05045 0.03675 0.05734 0.053 0.9831 0.988 0.05173 0.9641 0.9766 0.07327 ] Network output: [ 0.1028 -0.31 1.065 0.000124 -5.565e-05 1.04 9.342e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7467 0.6243 0.5109 0.5189 0.9723 0.9874 0.7504 0.9002 0.9675 0.6226 ] Network output: [ -0.05903 0.2121 0.9689 0.000582 -0.0002613 0.9394 0.0004386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5958 0.5798 0.4348 0.3407 0.9849 0.99 0.5964 0.9688 0.9787 0.4495 ] Network output: [ -0.08133 0.2521 0.9257 -0.0001121 5.033e-05 0.9844 -8.448e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6042 0.6015 0.4639 0.2994 0.9825 0.9887 0.6043 0.9614 0.9747 0.4669 ] Network output: [ 0.03388 0.8739 0.03393 -0.0003998 0.0001795 1.023 -0.0003013 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03982 Epoch 2049 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03868 0.9731 0.9877 0.0001355 -6.082e-05 -0.03755 0.0001021 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02477 -0.005131 0.02001 0.03787 0.935 0.945 0.05231 0.8741 0.8946 0.1346 ] Network output: [ 0.9672 0.07131 -0.01475 -0.0003596 0.0001615 0.007603 -0.000271 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6522 0.1046 0.09722 0.337 0.9686 0.9852 0.7526 0.889 0.962 0.6264 ] Network output: [ -0.001847 0.9276 1.033 3.176e-05 -1.426e-05 0.04348 2.393e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05047 0.03677 0.05734 0.05297 0.9831 0.988 0.05175 0.9641 0.9766 0.07326 ] Network output: [ 0.1028 -0.3099 1.065 0.0001218 -5.467e-05 1.04 9.178e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7468 0.6245 0.5111 0.5187 0.9723 0.9874 0.7505 0.9002 0.9675 0.6226 ] Network output: [ -0.05896 0.212 0.9689 0.0005817 -0.0002611 0.9394 0.0004384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5959 0.58 0.4349 0.3407 0.9849 0.99 0.5965 0.9688 0.9787 0.4496 ] Network output: [ -0.08126 0.252 0.9258 -0.0001115 5.007e-05 0.9843 -8.405e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6043 0.6016 0.464 0.2995 0.9825 0.9887 0.6044 0.9614 0.9747 0.4669 ] Network output: [ 0.0338 0.8741 0.03385 -0.0003982 0.0001788 1.023 -0.0003001 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03978 Epoch 2050 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03866 0.9731 0.9877 0.000135 -6.062e-05 -0.03753 0.0001018 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02477 -0.005135 0.02001 0.03785 0.935 0.945 0.05231 0.8741 0.8946 0.1345 ] Network output: [ 0.9672 0.07128 -0.01474 -0.0003583 0.0001609 0.007546 -0.00027 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6522 0.1047 0.09739 0.3368 0.9686 0.9852 0.7527 0.889 0.962 0.6264 ] Network output: [ -0.001867 0.9276 1.033 3.126e-05 -1.403e-05 0.04353 2.356e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05048 0.03679 0.05735 0.05295 0.9831 0.988 0.05176 0.9642 0.9766 0.07325 ] Network output: [ 0.1027 -0.3099 1.065 0.0001196 -5.369e-05 1.04 9.013e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7468 0.6247 0.5112 0.5186 0.9723 0.9874 0.7505 0.9003 0.9676 0.6226 ] Network output: [ -0.05889 0.2119 0.9688 0.0005813 -0.000261 0.9394 0.0004381 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.596 0.5801 0.435 0.3407 0.9849 0.99 0.5966 0.9688 0.9788 0.4497 ] Network output: [ -0.0812 0.2518 0.9259 -0.000111 4.981e-05 0.9842 -8.362e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6043 0.6016 0.464 0.2995 0.9825 0.9887 0.6044 0.9614 0.9747 0.467 ] Network output: [ 0.03372 0.8744 0.03378 -0.0003966 0.000178 1.023 -0.0002989 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03974 Epoch 2051 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03863 0.9731 0.9877 0.0001346 -6.041e-05 -0.03751 0.0001014 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02476 -0.005139 0.02001 0.03783 0.935 0.945 0.0523 0.8742 0.8947 0.1345 ] Network output: [ 0.9673 0.07124 -0.01472 -0.000357 0.0001603 0.00749 -0.0002691 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6523 0.1049 0.09756 0.3367 0.9686 0.9852 0.7527 0.8891 0.962 0.6264 ] Network output: [ -0.001886 0.9276 1.033 3.076e-05 -1.381e-05 0.04357 2.318e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05049 0.0368 0.05735 0.05292 0.9831 0.988 0.05177 0.9642 0.9766 0.07324 ] Network output: [ 0.1027 -0.3098 1.065 0.0001174 -5.271e-05 1.04 8.849e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7469 0.6248 0.5114 0.5185 0.9723 0.9874 0.7506 0.9004 0.9676 0.6226 ] Network output: [ -0.05882 0.2118 0.9688 0.000581 -0.0002608 0.9394 0.0004379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5961 0.5802 0.4351 0.3407 0.9849 0.99 0.5967 0.9689 0.9788 0.4497 ] Network output: [ -0.08113 0.2517 0.9261 -0.0001104 4.955e-05 0.9841 -8.318e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6043 0.6017 0.4641 0.2996 0.9825 0.9887 0.6045 0.9615 0.9747 0.467 ] Network output: [ 0.03364 0.8746 0.0337 -0.000395 0.0001773 1.023 -0.0002977 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03969 Epoch 2052 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03861 0.9731 0.9878 0.0001341 -6.02e-05 -0.03749 0.0001011 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02476 -0.005143 0.02 0.03782 0.935 0.9451 0.0523 0.8742 0.8947 0.1345 ] Network output: [ 0.9673 0.07121 -0.01471 -0.0003557 0.0001597 0.007434 -0.0002681 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6523 0.105 0.09773 0.3365 0.9687 0.9852 0.7528 0.8891 0.962 0.6264 ] Network output: [ -0.001904 0.9276 1.033 3.027e-05 -1.359e-05 0.04362 2.281e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05051 0.03682 0.05735 0.05289 0.9831 0.988 0.05179 0.9642 0.9766 0.07322 ] Network output: [ 0.1026 -0.3097 1.065 0.0001152 -5.174e-05 1.04 8.685e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7469 0.625 0.5115 0.5184 0.9723 0.9874 0.7506 0.9004 0.9676 0.6227 ] Network output: [ -0.05875 0.2117 0.9688 0.0005807 -0.0002607 0.9394 0.0004377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5962 0.5803 0.4352 0.3407 0.9849 0.9901 0.5968 0.9689 0.9788 0.4498 ] Network output: [ -0.08106 0.2515 0.9262 -0.0001098 4.929e-05 0.984 -8.275e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6044 0.6017 0.4641 0.2997 0.9826 0.9887 0.6045 0.9615 0.9747 0.4671 ] Network output: [ 0.03356 0.8749 0.03363 -0.0003934 0.0001766 1.023 -0.0002965 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03965 Epoch 2053 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03859 0.973 0.9878 0.0001336 -6e-05 -0.03747 0.0001007 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02476 -0.005146 0.02 0.0378 0.935 0.9451 0.05229 0.8743 0.8948 0.1344 ] Network output: [ 0.9674 0.07117 -0.0147 -0.0003544 0.0001591 0.007378 -0.0002671 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6524 0.1051 0.0979 0.3364 0.9687 0.9852 0.7528 0.8892 0.9621 0.6265 ] Network output: [ -0.001923 0.9276 1.033 2.977e-05 -1.337e-05 0.04366 2.244e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05052 0.03684 0.05735 0.05287 0.9831 0.988 0.0518 0.9643 0.9767 0.07321 ] Network output: [ 0.1025 -0.3097 1.065 0.0001131 -5.076e-05 1.04 8.522e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.747 0.6252 0.5117 0.5182 0.9724 0.9874 0.7507 0.9005 0.9676 0.6227 ] Network output: [ -0.05868 0.2116 0.9687 0.0005804 -0.0002606 0.9394 0.0004374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5963 0.5804 0.4353 0.3407 0.9849 0.9901 0.5969 0.9689 0.9788 0.4499 ] Network output: [ -0.081 0.2513 0.9263 -0.0001092 4.903e-05 0.9839 -8.231e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6044 0.6018 0.4642 0.2997 0.9826 0.9887 0.6045 0.9615 0.9748 0.4671 ] Network output: [ 0.03348 0.8751 0.03355 -0.0003918 0.0001759 1.023 -0.0002953 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03961 Epoch 2054 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03857 0.973 0.9878 0.0001332 -5.979e-05 -0.03745 0.0001004 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02475 -0.00515 0.02 0.03778 0.935 0.9451 0.05229 0.8743 0.8948 0.1344 ] Network output: [ 0.9674 0.07114 -0.01469 -0.0003531 0.0001585 0.007322 -0.0002661 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6524 0.1053 0.09807 0.3362 0.9687 0.9852 0.7529 0.8893 0.9621 0.6265 ] Network output: [ -0.001942 0.9277 1.033 2.928e-05 -1.315e-05 0.04371 2.207e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05053 0.03685 0.05735 0.05284 0.9831 0.988 0.05181 0.9643 0.9767 0.0732 ] Network output: [ 0.1025 -0.3096 1.065 0.0001109 -4.979e-05 1.04 8.358e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.747 0.6253 0.5118 0.5181 0.9724 0.9874 0.7507 0.9005 0.9676 0.6227 ] Network output: [ -0.05861 0.2115 0.9687 0.0005801 -0.0002604 0.9394 0.0004372 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5964 0.5805 0.4354 0.3407 0.9849 0.9901 0.597 0.9689 0.9788 0.45 ] Network output: [ -0.08093 0.2512 0.9264 -0.0001086 4.877e-05 0.9838 -8.187e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6045 0.6018 0.4642 0.2998 0.9826 0.9887 0.6046 0.9616 0.9748 0.4672 ] Network output: [ 0.0334 0.8754 0.03348 -0.0003903 0.0001752 1.023 -0.0002941 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03957 Epoch 2055 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03854 0.973 0.9879 0.0001327 -5.958e-05 -0.03743 0.0001 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02475 -0.005154 0.02 0.03776 0.9351 0.9451 0.05228 0.8744 0.8949 0.1344 ] Network output: [ 0.9674 0.0711 -0.01468 -0.0003518 0.0001579 0.007267 -0.0002651 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6525 0.1054 0.09824 0.3361 0.9687 0.9853 0.7529 0.8893 0.9621 0.6265 ] Network output: [ -0.00196 0.9277 1.033 2.879e-05 -1.293e-05 0.04375 2.17e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05055 0.03687 0.05735 0.05282 0.9832 0.988 0.05183 0.9643 0.9767 0.07319 ] Network output: [ 0.1024 -0.3096 1.065 0.0001087 -4.882e-05 1.04 8.195e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7471 0.6255 0.512 0.518 0.9724 0.9874 0.7508 0.9006 0.9677 0.6227 ] Network output: [ -0.05854 0.2114 0.9686 0.0005798 -0.0002603 0.9394 0.000437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5965 0.5806 0.4355 0.3407 0.9849 0.9901 0.597 0.969 0.9788 0.4501 ] Network output: [ -0.08086 0.251 0.9265 -0.0001081 4.851e-05 0.9838 -8.143e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6045 0.6019 0.4643 0.2999 0.9826 0.9887 0.6046 0.9616 0.9748 0.4672 ] Network output: [ 0.03332 0.8756 0.03341 -0.0003887 0.0001745 1.023 -0.0002929 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03953 Epoch 2056 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03852 0.973 0.9879 0.0001323 -5.938e-05 -0.03741 9.968e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02474 -0.005158 0.01999 0.03774 0.9351 0.9451 0.05228 0.8745 0.8949 0.1344 ] Network output: [ 0.9675 0.07107 -0.01467 -0.0003505 0.0001574 0.007212 -0.0002642 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6525 0.1055 0.0984 0.3359 0.9687 0.9853 0.753 0.8894 0.9621 0.6265 ] Network output: [ -0.001979 0.9277 1.033 2.83e-05 -1.271e-05 0.04379 2.133e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05056 0.03688 0.05734 0.05279 0.9832 0.988 0.05184 0.9644 0.9767 0.07317 ] Network output: [ 0.1024 -0.3095 1.065 0.0001066 -4.785e-05 1.04 8.033e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7471 0.6257 0.5121 0.5178 0.9724 0.9874 0.7508 0.9006 0.9677 0.6227 ] Network output: [ -0.05846 0.2112 0.9686 0.0005796 -0.0002602 0.9394 0.0004368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5966 0.5807 0.4356 0.3407 0.9849 0.9901 0.5971 0.969 0.9789 0.4501 ] Network output: [ -0.0808 0.2509 0.9266 -0.0001075 4.825e-05 0.9837 -8.099e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6046 0.6019 0.4644 0.2999 0.9826 0.9887 0.6047 0.9616 0.9748 0.4673 ] Network output: [ 0.03325 0.8759 0.03333 -0.0003872 0.0001738 1.023 -0.0002918 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03949 Epoch 2057 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0385 0.973 0.9879 0.0001318 -5.917e-05 -0.03739 9.933e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02474 -0.005161 0.01999 0.03772 0.9351 0.9451 0.05227 0.8745 0.8949 0.1343 ] Network output: [ 0.9675 0.07104 -0.01466 -0.0003492 0.0001568 0.007157 -0.0002632 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6525 0.1056 0.09857 0.3358 0.9687 0.9853 0.753 0.8894 0.9621 0.6265 ] Network output: [ -0.001997 0.9277 1.033 2.782e-05 -1.249e-05 0.04383 2.097e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05057 0.0369 0.05734 0.05276 0.9832 0.988 0.05185 0.9644 0.9767 0.07316 ] Network output: [ 0.1023 -0.3094 1.065 0.0001044 -4.688e-05 1.04 7.87e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7472 0.6258 0.5123 0.5177 0.9724 0.9874 0.7509 0.9007 0.9677 0.6228 ] Network output: [ -0.05839 0.2111 0.9686 0.0005793 -0.0002601 0.9394 0.0004366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5967 0.5808 0.4357 0.3407 0.9849 0.9901 0.5972 0.969 0.9789 0.4502 ] Network output: [ -0.08073 0.2507 0.9267 -0.0001069 4.799e-05 0.9836 -8.055e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6046 0.602 0.4644 0.3 0.9826 0.9887 0.6047 0.9617 0.9749 0.4673 ] Network output: [ 0.03317 0.8761 0.03326 -0.0003856 0.0001731 1.023 -0.0002906 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03944 Epoch 2058 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03848 0.973 0.988 0.0001313 -5.896e-05 -0.03737 9.898e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02474 -0.005165 0.01999 0.0377 0.9351 0.9452 0.05226 0.8746 0.895 0.1343 ] Network output: [ 0.9676 0.071 -0.01465 -0.0003479 0.0001562 0.007102 -0.0002622 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6526 0.1058 0.09873 0.3356 0.9687 0.9853 0.7531 0.8895 0.9622 0.6266 ] Network output: [ -0.002015 0.9277 1.033 2.734e-05 -1.227e-05 0.04388 2.06e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05058 0.03692 0.05734 0.05273 0.9832 0.988 0.05186 0.9644 0.9768 0.07315 ] Network output: [ 0.1022 -0.3094 1.065 0.0001023 -4.592e-05 1.04 7.708e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7472 0.626 0.5124 0.5176 0.9724 0.9874 0.7509 0.9008 0.9677 0.6228 ] Network output: [ -0.05832 0.211 0.9685 0.000579 -0.0002599 0.9394 0.0004364 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5968 0.5809 0.4358 0.3407 0.9849 0.9901 0.5973 0.969 0.9789 0.4503 ] Network output: [ -0.08066 0.2505 0.9269 -0.0001063 4.772e-05 0.9835 -8.011e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6047 0.602 0.4645 0.3 0.9826 0.9887 0.6048 0.9617 0.9749 0.4674 ] Network output: [ 0.03309 0.8764 0.03319 -0.0003841 0.0001724 1.023 -0.0002895 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0394 Epoch 2059 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03845 0.973 0.988 0.0001309 -5.876e-05 -0.03735 9.864e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02473 -0.005169 0.01999 0.03768 0.9351 0.9452 0.05226 0.8746 0.895 0.1343 ] Network output: [ 0.9676 0.07097 -0.01464 -0.0003466 0.0001556 0.007047 -0.0002612 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6526 0.1059 0.0989 0.3355 0.9687 0.9853 0.7531 0.8896 0.9622 0.6266 ] Network output: [ -0.002033 0.9277 1.033 2.685e-05 -1.206e-05 0.04392 2.024e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0506 0.03693 0.05734 0.05271 0.9832 0.988 0.05188 0.9645 0.9768 0.07313 ] Network output: [ 0.1022 -0.3093 1.065 0.0001001 -4.495e-05 1.04 7.546e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7473 0.6262 0.5126 0.5174 0.9724 0.9874 0.7509 0.9008 0.9677 0.6228 ] Network output: [ -0.05825 0.2109 0.9685 0.0005788 -0.0002598 0.9395 0.0004362 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5969 0.5811 0.4359 0.3407 0.9849 0.9901 0.5974 0.9691 0.9789 0.4504 ] Network output: [ -0.0806 0.2504 0.927 -0.0001057 4.746e-05 0.9834 -7.967e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6047 0.6021 0.4645 0.3001 0.9826 0.9887 0.6048 0.9617 0.9749 0.4674 ] Network output: [ 0.03301 0.8766 0.03312 -0.0003826 0.0001718 1.023 -0.0002883 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03936 Epoch 2060 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03843 0.973 0.988 0.0001304 -5.855e-05 -0.03733 9.829e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02473 -0.005172 0.01998 0.03766 0.9351 0.9452 0.05225 0.8747 0.8951 0.1342 ] Network output: [ 0.9676 0.07094 -0.01463 -0.0003454 0.000155 0.006992 -0.0002603 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6527 0.106 0.09906 0.3353 0.9688 0.9853 0.7531 0.8896 0.9622 0.6266 ] Network output: [ -0.002051 0.9278 1.032 2.637e-05 -1.184e-05 0.04396 1.988e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05061 0.03695 0.05734 0.05268 0.9832 0.9881 0.05189 0.9645 0.9768 0.07312 ] Network output: [ 0.1021 -0.3093 1.065 9.799e-05 -4.399e-05 1.041 7.385e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7473 0.6263 0.5127 0.5173 0.9724 0.9874 0.751 0.9009 0.9678 0.6228 ] Network output: [ -0.05818 0.2108 0.9684 0.0005785 -0.0002597 0.9395 0.000436 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.597 0.5812 0.436 0.3407 0.9849 0.9901 0.5975 0.9691 0.9789 0.4504 ] Network output: [ -0.08053 0.2502 0.9271 -0.0001051 4.72e-05 0.9833 -7.923e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6047 0.6021 0.4646 0.3002 0.9826 0.9887 0.6049 0.9618 0.9749 0.4675 ] Network output: [ 0.03294 0.8768 0.03305 -0.0003811 0.0001711 1.023 -0.0002872 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03932 Epoch 2061 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03841 0.973 0.9881 0.00013 -5.834e-05 -0.03731 9.794e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02472 -0.005176 0.01998 0.03764 0.9352 0.9452 0.05224 0.8747 0.8951 0.1342 ] Network output: [ 0.9677 0.07091 -0.01462 -0.0003441 0.0001545 0.006938 -0.0002593 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6527 0.1061 0.09922 0.3352 0.9688 0.9853 0.7532 0.8897 0.9622 0.6266 ] Network output: [ -0.002069 0.9278 1.032 2.59e-05 -1.163e-05 0.044 1.952e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05062 0.03696 0.05734 0.05265 0.9832 0.9881 0.0519 0.9645 0.9768 0.0731 ] Network output: [ 0.1021 -0.3092 1.065 9.585e-05 -4.303e-05 1.041 7.224e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7473 0.6265 0.5128 0.5172 0.9724 0.9875 0.751 0.9009 0.9678 0.6228 ] Network output: [ -0.05811 0.2107 0.9684 0.0005783 -0.0002596 0.9395 0.0004358 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5971 0.5813 0.4361 0.3407 0.9849 0.9901 0.5976 0.9691 0.979 0.4505 ] Network output: [ -0.08047 0.25 0.9272 -0.0001045 4.693e-05 0.9833 -7.878e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6048 0.6021 0.4646 0.3002 0.9826 0.9887 0.6049 0.9618 0.9749 0.4675 ] Network output: [ 0.03286 0.8771 0.03298 -0.0003796 0.0001704 1.023 -0.0002861 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03928 Epoch 2062 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03839 0.9729 0.9881 0.0001295 -5.814e-05 -0.03729 9.759e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02472 -0.00518 0.01998 0.03763 0.9352 0.9452 0.05224 0.8748 0.8951 0.1342 ] Network output: [ 0.9677 0.07088 -0.01461 -0.0003428 0.0001539 0.006884 -0.0002583 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6527 0.1063 0.09939 0.335 0.9688 0.9853 0.7532 0.8897 0.9623 0.6267 ] Network output: [ -0.002087 0.9278 1.032 2.542e-05 -1.141e-05 0.04404 1.916e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05063 0.03698 0.05734 0.05262 0.9832 0.9881 0.05191 0.9646 0.9768 0.07309 ] Network output: [ 0.102 -0.3091 1.065 9.371e-05 -4.207e-05 1.041 7.063e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7474 0.6266 0.513 0.517 0.9724 0.9875 0.7511 0.901 0.9678 0.6229 ] Network output: [ -0.05804 0.2106 0.9684 0.000578 -0.0002595 0.9395 0.0004356 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5971 0.5814 0.4362 0.3407 0.985 0.9901 0.5977 0.9692 0.979 0.4506 ] Network output: [ -0.0804 0.2499 0.9273 -0.0001039 4.667e-05 0.9832 -7.834e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6048 0.6022 0.4647 0.3003 0.9826 0.9887 0.6049 0.9618 0.975 0.4676 ] Network output: [ 0.03278 0.8773 0.03291 -0.0003782 0.0001698 1.023 -0.000285 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03924 Epoch 2063 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03836 0.9729 0.9881 0.000129 -5.793e-05 -0.03727 9.724e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02472 -0.005183 0.01998 0.03761 0.9352 0.9452 0.05223 0.8748 0.8952 0.1342 ] Network output: [ 0.9678 0.07084 -0.01461 -0.0003415 0.0001533 0.00683 -0.0002574 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6528 0.1064 0.09955 0.3348 0.9688 0.9853 0.7533 0.8898 0.9623 0.6267 ] Network output: [ -0.002104 0.9278 1.032 2.495e-05 -1.12e-05 0.04408 1.88e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05064 0.03699 0.05733 0.05259 0.9832 0.9881 0.05192 0.9646 0.9769 0.07307 ] Network output: [ 0.1019 -0.3091 1.065 9.158e-05 -4.111e-05 1.041 6.902e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7474 0.6268 0.5131 0.5169 0.9725 0.9875 0.7511 0.901 0.9678 0.6229 ] Network output: [ -0.05797 0.2105 0.9683 0.0005778 -0.0002594 0.9395 0.0004354 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5972 0.5815 0.4363 0.3407 0.985 0.9901 0.5978 0.9692 0.979 0.4507 ] Network output: [ -0.08034 0.2497 0.9274 -0.0001034 4.64e-05 0.9831 -7.789e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6049 0.6022 0.4647 0.3003 0.9827 0.9887 0.605 0.9619 0.975 0.4676 ] Network output: [ 0.03271 0.8776 0.03284 -0.0003767 0.0001691 1.023 -0.0002839 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0392 Epoch 2064 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03834 0.9729 0.9882 0.0001286 -5.772e-05 -0.03726 9.69e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02471 -0.005187 0.01997 0.03759 0.9352 0.9453 0.05223 0.8749 0.8952 0.1341 ] Network output: [ 0.9678 0.07081 -0.0146 -0.0003402 0.0001527 0.006776 -0.0002564 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6528 0.1065 0.09971 0.3347 0.9688 0.9853 0.7533 0.8899 0.9623 0.6267 ] Network output: [ -0.002122 0.9278 1.032 2.448e-05 -1.099e-05 0.04412 1.845e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05065 0.03701 0.05733 0.05256 0.9832 0.9881 0.05193 0.9646 0.9769 0.07306 ] Network output: [ 0.1019 -0.309 1.065 8.945e-05 -4.016e-05 1.041 6.741e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7475 0.6269 0.5133 0.5168 0.9725 0.9875 0.7512 0.9011 0.9679 0.6229 ] Network output: [ -0.0579 0.2104 0.9683 0.0005775 -0.0002593 0.9395 0.0004353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5973 0.5816 0.4363 0.3406 0.985 0.9901 0.5979 0.9692 0.979 0.4507 ] Network output: [ -0.08027 0.2496 0.9275 -0.0001028 4.613e-05 0.983 -7.744e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6049 0.6023 0.4648 0.3004 0.9827 0.9888 0.605 0.9619 0.975 0.4677 ] Network output: [ 0.03263 0.8778 0.03277 -0.0003753 0.0001685 1.023 -0.0002828 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03916 Epoch 2065 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03832 0.9729 0.9882 0.0001281 -5.751e-05 -0.03724 9.655e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02471 -0.005191 0.01997 0.03757 0.9352 0.9453 0.05222 0.8749 0.8953 0.1341 ] Network output: [ 0.9679 0.07078 -0.01459 -0.0003389 0.0001522 0.006723 -0.0002554 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6529 0.1066 0.09986 0.3345 0.9688 0.9853 0.7533 0.8899 0.9623 0.6267 ] Network output: [ -0.002139 0.9279 1.032 2.401e-05 -1.078e-05 0.04416 1.809e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05066 0.03702 0.05733 0.05253 0.9832 0.9881 0.05195 0.9646 0.9769 0.07304 ] Network output: [ 0.1018 -0.309 1.065 8.733e-05 -3.92e-05 1.041 6.581e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7475 0.6271 0.5134 0.5166 0.9725 0.9875 0.7512 0.9011 0.9679 0.6229 ] Network output: [ -0.05783 0.2103 0.9682 0.0005773 -0.0002592 0.9395 0.0004351 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5974 0.5817 0.4364 0.3406 0.985 0.9901 0.598 0.9692 0.979 0.4508 ] Network output: [ -0.08021 0.2494 0.9276 -0.0001022 4.587e-05 0.983 -7.699e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.605 0.6023 0.4648 0.3004 0.9827 0.9888 0.6051 0.9619 0.975 0.4677 ] Network output: [ 0.03256 0.878 0.0327 -0.0003738 0.0001678 1.023 -0.0002817 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03912 Epoch 2066 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03829 0.9729 0.9883 0.0001276 -5.731e-05 -0.03722 9.62e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0247 -0.005194 0.01997 0.03755 0.9353 0.9453 0.05221 0.875 0.8953 0.1341 ] Network output: [ 0.9679 0.07075 -0.01458 -0.0003376 0.0001516 0.006669 -0.0002545 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6529 0.1067 0.1 0.3344 0.9688 0.9853 0.7534 0.89 0.9624 0.6267 ] Network output: [ -0.002157 0.9279 1.032 2.354e-05 -1.057e-05 0.0442 1.774e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05068 0.03704 0.05732 0.05251 0.9833 0.9881 0.05196 0.9647 0.9769 0.07303 ] Network output: [ 0.1017 -0.3089 1.065 8.52e-05 -3.825e-05 1.041 6.421e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7475 0.6273 0.5135 0.5165 0.9725 0.9875 0.7512 0.9012 0.9679 0.623 ] Network output: [ -0.05777 0.2102 0.9682 0.0005771 -0.0002591 0.9395 0.0004349 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5975 0.5818 0.4365 0.3406 0.985 0.9901 0.598 0.9693 0.979 0.4509 ] Network output: [ -0.08014 0.2492 0.9278 -0.0001016 4.56e-05 0.9829 -7.654e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.605 0.6024 0.4649 0.3005 0.9827 0.9888 0.6051 0.962 0.975 0.4678 ] Network output: [ 0.03248 0.8783 0.03263 -0.0003724 0.0001672 1.023 -0.0002807 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03908 Epoch 2067 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03827 0.9729 0.9883 0.0001272 -5.71e-05 -0.0372 9.585e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0247 -0.005198 0.01997 0.03753 0.9353 0.9453 0.0522 0.875 0.8953 0.134 ] Network output: [ 0.9679 0.07072 -0.01457 -0.0003364 0.000151 0.006616 -0.0002535 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6529 0.1069 0.1002 0.3342 0.9688 0.9853 0.7534 0.89 0.9624 0.6268 ] Network output: [ -0.002174 0.9279 1.032 2.307e-05 -1.036e-05 0.04424 1.739e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05069 0.03705 0.05732 0.05248 0.9833 0.9881 0.05197 0.9647 0.9769 0.07301 ] Network output: [ 0.1017 -0.3088 1.065 8.308e-05 -3.73e-05 1.041 6.261e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7476 0.6274 0.5137 0.5164 0.9725 0.9875 0.7513 0.9012 0.9679 0.623 ] Network output: [ -0.0577 0.21 0.9682 0.0005769 -0.000259 0.9395 0.0004348 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5976 0.5819 0.4366 0.3406 0.985 0.9901 0.5981 0.9693 0.9791 0.4509 ] Network output: [ -0.08008 0.2491 0.9279 -0.000101 4.533e-05 0.9828 -7.609e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.605 0.6024 0.4649 0.3005 0.9827 0.9888 0.6051 0.962 0.9751 0.4678 ] Network output: [ 0.03241 0.8785 0.03257 -0.000371 0.0001666 1.023 -0.0002796 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03904 Epoch 2068 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03825 0.9729 0.9883 0.0001267 -5.689e-05 -0.03718 9.55e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02469 -0.005201 0.01996 0.03751 0.9353 0.9453 0.0522 0.8751 0.8954 0.134 ] Network output: [ 0.968 0.07069 -0.01457 -0.0003351 0.0001504 0.006563 -0.0002525 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.653 0.107 0.1003 0.3341 0.9688 0.9853 0.7535 0.8901 0.9624 0.6268 ] Network output: [ -0.002191 0.9279 1.032 2.261e-05 -1.015e-05 0.04428 1.704e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0507 0.03706 0.05732 0.05245 0.9833 0.9881 0.05198 0.9647 0.9769 0.073 ] Network output: [ 0.1016 -0.3088 1.065 8.097e-05 -3.635e-05 1.041 6.102e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7476 0.6276 0.5138 0.5162 0.9725 0.9875 0.7513 0.9013 0.9679 0.623 ] Network output: [ -0.05763 0.2099 0.9681 0.0005767 -0.0002589 0.9396 0.0004346 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5977 0.582 0.4367 0.3406 0.985 0.9901 0.5982 0.9693 0.9791 0.451 ] Network output: [ -0.08001 0.2489 0.928 -0.0001004 4.506e-05 0.9827 -7.564e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6051 0.6025 0.465 0.3006 0.9827 0.9888 0.6052 0.962 0.9751 0.4678 ] Network output: [ 0.03234 0.8787 0.0325 -0.0003696 0.0001659 1.023 -0.0002785 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.039 Epoch 2069 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03823 0.9729 0.9884 0.0001263 -5.668e-05 -0.03716 9.516e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02469 -0.005205 0.01996 0.03748 0.9353 0.9453 0.05219 0.8751 0.8954 0.134 ] Network output: [ 0.968 0.07067 -0.01456 -0.0003338 0.0001499 0.00651 -0.0002516 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.653 0.1071 0.1005 0.3339 0.9689 0.9853 0.7535 0.8901 0.9624 0.6268 ] Network output: [ -0.002208 0.9279 1.032 2.214e-05 -9.942e-06 0.04432 1.669e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05071 0.03708 0.05731 0.05242 0.9833 0.9881 0.05199 0.9648 0.977 0.07298 ] Network output: [ 0.1016 -0.3087 1.065 7.885e-05 -3.54e-05 1.041 5.943e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7477 0.6277 0.514 0.5161 0.9725 0.9875 0.7513 0.9014 0.968 0.623 ] Network output: [ -0.05756 0.2098 0.9681 0.0005765 -0.0002588 0.9396 0.0004345 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5978 0.5821 0.4368 0.3406 0.985 0.9901 0.5983 0.9693 0.9791 0.4511 ] Network output: [ -0.07995 0.2488 0.9281 -9.976e-05 4.479e-05 0.9827 -7.518e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6051 0.6025 0.465 0.3006 0.9827 0.9888 0.6052 0.9621 0.9751 0.4679 ] Network output: [ 0.03226 0.879 0.03243 -0.0003682 0.0001653 1.023 -0.0002775 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03896 Epoch 2070 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0382 0.9728 0.9884 0.0001258 -5.648e-05 -0.03714 9.481e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02468 -0.005208 0.01996 0.03746 0.9353 0.9453 0.05218 0.8752 0.8955 0.1339 ] Network output: [ 0.9681 0.07064 -0.01455 -0.0003325 0.0001493 0.006458 -0.0002506 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.653 0.1072 0.1006 0.3337 0.9689 0.9854 0.7535 0.8902 0.9625 0.6268 ] Network output: [ -0.002225 0.928 1.032 2.168e-05 -9.735e-06 0.04435 1.634e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05072 0.03709 0.05731 0.05239 0.9833 0.9881 0.052 0.9648 0.977 0.07296 ] Network output: [ 0.1015 -0.3087 1.065 7.674e-05 -3.445e-05 1.041 5.784e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7477 0.6279 0.5141 0.5159 0.9725 0.9875 0.7514 0.9014 0.968 0.623 ] Network output: [ -0.05749 0.2097 0.968 0.0005763 -0.0002587 0.9396 0.0004343 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5978 0.5822 0.4369 0.3406 0.985 0.9901 0.5984 0.9694 0.9791 0.4512 ] Network output: [ -0.07989 0.2486 0.9282 -9.915e-05 4.451e-05 0.9826 -7.472e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6052 0.6025 0.465 0.3007 0.9827 0.9888 0.6053 0.9621 0.9751 0.4679 ] Network output: [ 0.03219 0.8792 0.03236 -0.0003668 0.0001647 1.023 -0.0002765 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03892 Epoch 2071 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03818 0.9728 0.9884 0.0001253 -5.627e-05 -0.03712 9.446e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02468 -0.005212 0.01995 0.03744 0.9353 0.9454 0.05218 0.8752 0.8955 0.1339 ] Network output: [ 0.9681 0.07061 -0.01455 -0.0003312 0.0001487 0.006405 -0.0002496 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6531 0.1073 0.1008 0.3336 0.9689 0.9854 0.7536 0.8902 0.9625 0.6268 ] Network output: [ -0.002242 0.928 1.032 2.123e-05 -9.529e-06 0.04439 1.6e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05073 0.03711 0.05731 0.05236 0.9833 0.9881 0.05201 0.9648 0.977 0.07295 ] Network output: [ 0.1014 -0.3086 1.065 7.464e-05 -3.351e-05 1.041 5.625e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7477 0.628 0.5142 0.5158 0.9725 0.9875 0.7514 0.9015 0.968 0.6231 ] Network output: [ -0.05742 0.2096 0.968 0.0005761 -0.0002586 0.9396 0.0004342 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5979 0.5823 0.4369 0.3406 0.985 0.9901 0.5985 0.9694 0.9791 0.4512 ] Network output: [ -0.07982 0.2484 0.9283 -9.854e-05 4.424e-05 0.9825 -7.427e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6052 0.6026 0.4651 0.3007 0.9827 0.9888 0.6053 0.9621 0.9751 0.468 ] Network output: [ 0.03212 0.8794 0.0323 -0.0003655 0.0001641 1.023 -0.0002754 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03888 Epoch 2072 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03816 0.9728 0.9885 0.0001249 -5.606e-05 -0.0371 9.411e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02468 -0.005215 0.01995 0.03742 0.9354 0.9454 0.05217 0.8753 0.8955 0.1339 ] Network output: [ 0.9681 0.07058 -0.01454 -0.00033 0.0001481 0.006353 -0.0002487 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6531 0.1075 0.101 0.3334 0.9689 0.9854 0.7536 0.8903 0.9625 0.6269 ] Network output: [ -0.002258 0.928 1.032 2.077e-05 -9.324e-06 0.04443 1.565e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05074 0.03712 0.0573 0.05233 0.9833 0.9881 0.05202 0.9649 0.977 0.07293 ] Network output: [ 0.1014 -0.3085 1.065 7.253e-05 -3.256e-05 1.041 5.466e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7478 0.6281 0.5144 0.5156 0.9726 0.9875 0.7515 0.9015 0.968 0.6231 ] Network output: [ -0.05735 0.2095 0.9679 0.0005759 -0.0002585 0.9396 0.000434 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.598 0.5824 0.437 0.3405 0.985 0.9902 0.5986 0.9694 0.9791 0.4513 ] Network output: [ -0.07976 0.2483 0.9284 -9.793e-05 4.397e-05 0.9824 -7.381e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6052 0.6026 0.4651 0.3008 0.9827 0.9888 0.6053 0.9622 0.9752 0.468 ] Network output: [ 0.03205 0.8797 0.03223 -0.0003641 0.0001635 1.023 -0.0002744 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03884 Epoch 2073 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03814 0.9728 0.9885 0.0001244 -5.585e-05 -0.03709 9.376e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02467 -0.005219 0.01995 0.0374 0.9354 0.9454 0.05216 0.8753 0.8956 0.1338 ] Network output: [ 0.9682 0.07055 -0.01453 -0.0003287 0.0001476 0.006301 -0.0002477 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6531 0.1076 0.1011 0.3332 0.9689 0.9854 0.7536 0.8904 0.9625 0.6269 ] Network output: [ -0.002275 0.928 1.032 2.031e-05 -9.119e-06 0.04447 1.531e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05075 0.03713 0.0573 0.05229 0.9833 0.9881 0.05203 0.9649 0.977 0.07292 ] Network output: [ 0.1013 -0.3085 1.065 7.043e-05 -3.162e-05 1.041 5.308e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7478 0.6283 0.5145 0.5155 0.9726 0.9875 0.7515 0.9016 0.968 0.6231 ] Network output: [ -0.05728 0.2094 0.9679 0.0005757 -0.0002585 0.9396 0.0004339 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5981 0.5825 0.4371 0.3405 0.985 0.9902 0.5987 0.9694 0.9792 0.4514 ] Network output: [ -0.0797 0.2481 0.9285 -9.732e-05 4.369e-05 0.9824 -7.334e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6053 0.6027 0.4652 0.3008 0.9827 0.9888 0.6054 0.9622 0.9752 0.468 ] Network output: [ 0.03197 0.8799 0.03217 -0.0003628 0.0001629 1.023 -0.0002734 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0388 Epoch 2074 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03811 0.9728 0.9885 0.000124 -5.565e-05 -0.03707 9.341e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02467 -0.005222 0.01994 0.03738 0.9354 0.9454 0.05215 0.8754 0.8956 0.1338 ] Network output: [ 0.9682 0.07053 -0.01453 -0.0003274 0.000147 0.006249 -0.0002468 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6532 0.1077 0.1013 0.3331 0.9689 0.9854 0.7537 0.8904 0.9625 0.6269 ] Network output: [ -0.002291 0.928 1.032 1.986e-05 -8.916e-06 0.0445 1.497e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05076 0.03714 0.05729 0.05226 0.9833 0.9881 0.05204 0.9649 0.9771 0.0729 ] Network output: [ 0.1013 -0.3084 1.065 6.834e-05 -3.068e-05 1.042 5.15e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7478 0.6284 0.5146 0.5154 0.9726 0.9875 0.7515 0.9016 0.9681 0.6231 ] Network output: [ -0.05721 0.2093 0.9678 0.0005756 -0.0002584 0.9396 0.0004338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5982 0.5826 0.4372 0.3405 0.985 0.9902 0.5987 0.9695 0.9792 0.4514 ] Network output: [ -0.07963 0.248 0.9286 -9.671e-05 4.341e-05 0.9823 -7.288e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6053 0.6027 0.4652 0.3009 0.9827 0.9888 0.6054 0.9622 0.9752 0.4681 ] Network output: [ 0.0319 0.8801 0.0321 -0.0003615 0.0001623 1.023 -0.0002724 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03876 Epoch 2075 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03809 0.9728 0.9886 0.0001235 -5.544e-05 -0.03705 9.306e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02466 -0.005226 0.01994 0.03736 0.9354 0.9454 0.05215 0.8755 0.8957 0.1338 ] Network output: [ 0.9682 0.0705 -0.01452 -0.0003262 0.0001464 0.006198 -0.0002458 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6532 0.1078 0.1014 0.3329 0.9689 0.9854 0.7537 0.8905 0.9626 0.6269 ] Network output: [ -0.002308 0.9281 1.032 1.941e-05 -8.713e-06 0.04454 1.463e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05077 0.03716 0.05729 0.05223 0.9833 0.9882 0.05205 0.9649 0.9771 0.07288 ] Network output: [ 0.1012 -0.3084 1.065 6.624e-05 -2.974e-05 1.042 4.992e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7479 0.6286 0.5148 0.5152 0.9726 0.9875 0.7516 0.9017 0.9681 0.6231 ] Network output: [ -0.05714 0.2092 0.9678 0.0005754 -0.0002583 0.9396 0.0004336 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5983 0.5827 0.4373 0.3405 0.985 0.9902 0.5988 0.9695 0.9792 0.4515 ] Network output: [ -0.07957 0.2478 0.9287 -9.609e-05 4.314e-05 0.9822 -7.241e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6054 0.6028 0.4653 0.3009 0.9828 0.9888 0.6055 0.9623 0.9752 0.4681 ] Network output: [ 0.03183 0.8803 0.03204 -0.0003602 0.0001617 1.022 -0.0002714 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03872 Epoch 2076 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03807 0.9728 0.9886 0.000123 -5.523e-05 -0.03703 9.272e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02466 -0.005229 0.01994 0.03734 0.9354 0.9454 0.05214 0.8755 0.8957 0.1337 ] Network output: [ 0.9683 0.07047 -0.01452 -0.0003249 0.0001459 0.006146 -0.0002449 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6532 0.1079 0.1016 0.3327 0.9689 0.9854 0.7537 0.8905 0.9626 0.627 ] Network output: [ -0.002324 0.9281 1.032 1.896e-05 -8.511e-06 0.04457 1.429e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05078 0.03717 0.05728 0.0522 0.9833 0.9882 0.05206 0.965 0.9771 0.07286 ] Network output: [ 0.1011 -0.3083 1.065 6.415e-05 -2.88e-05 1.042 4.835e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7479 0.6287 0.5149 0.5151 0.9726 0.9875 0.7516 0.9017 0.9681 0.6232 ] Network output: [ -0.05708 0.2091 0.9677 0.0005752 -0.0002582 0.9397 0.0004335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5984 0.5828 0.4374 0.3405 0.9851 0.9902 0.5989 0.9695 0.9792 0.4516 ] Network output: [ -0.07951 0.2476 0.9288 -9.547e-05 4.286e-05 0.9822 -7.195e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6054 0.6028 0.4653 0.3009 0.9828 0.9888 0.6055 0.9623 0.9752 0.4682 ] Network output: [ 0.03176 0.8806 0.03198 -0.0003588 0.0001611 1.022 -0.0002704 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03868 Epoch 2077 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03804 0.9728 0.9887 0.0001226 -5.502e-05 -0.03701 9.237e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02465 -0.005233 0.01993 0.03732 0.9354 0.9455 0.05213 0.8756 0.8957 0.1337 ] Network output: [ 0.9683 0.07045 -0.01451 -0.0003236 0.0001453 0.006095 -0.0002439 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6533 0.108 0.1017 0.3326 0.969 0.9854 0.7538 0.8906 0.9626 0.627 ] Network output: [ -0.00234 0.9281 1.032 1.851e-05 -8.31e-06 0.04461 1.395e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05079 0.03718 0.05728 0.05217 0.9833 0.9882 0.05207 0.965 0.9771 0.07285 ] Network output: [ 0.1011 -0.3082 1.065 6.206e-05 -2.786e-05 1.042 4.677e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7479 0.6288 0.515 0.5149 0.9726 0.9875 0.7516 0.9018 0.9681 0.6232 ] Network output: [ -0.05701 0.209 0.9677 0.0005751 -0.0002582 0.9397 0.0004334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5984 0.5829 0.4374 0.3404 0.9851 0.9902 0.599 0.9695 0.9792 0.4516 ] Network output: [ -0.07944 0.2475 0.9289 -9.484e-05 4.258e-05 0.9821 -7.148e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6054 0.6028 0.4653 0.301 0.9828 0.9888 0.6055 0.9623 0.9753 0.4682 ] Network output: [ 0.03169 0.8808 0.03191 -0.0003576 0.0001605 1.022 -0.0002695 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03864 Epoch 2078 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03802 0.9727 0.9887 0.0001221 -5.482e-05 -0.03699 9.202e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02465 -0.005236 0.01993 0.03729 0.9355 0.9455 0.05212 0.8756 0.8958 0.1337 ] Network output: [ 0.9684 0.07042 -0.01451 -0.0003224 0.0001447 0.006044 -0.000243 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6533 0.1081 0.1019 0.3324 0.969 0.9854 0.7538 0.8906 0.9626 0.627 ] Network output: [ -0.002357 0.9281 1.032 1.806e-05 -8.11e-06 0.04464 1.361e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05079 0.0372 0.05727 0.05214 0.9834 0.9882 0.05208 0.965 0.9771 0.07283 ] Network output: [ 0.101 -0.3082 1.065 5.997e-05 -2.692e-05 1.042 4.52e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.748 0.629 0.5152 0.5148 0.9726 0.9876 0.7517 0.9018 0.9681 0.6232 ] Network output: [ -0.05694 0.2089 0.9676 0.000575 -0.0002581 0.9397 0.0004333 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5985 0.583 0.4375 0.3404 0.9851 0.9902 0.5991 0.9696 0.9792 0.4517 ] Network output: [ -0.07938 0.2473 0.929 -9.422e-05 4.23e-05 0.982 -7.101e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6055 0.6029 0.4654 0.301 0.9828 0.9888 0.6056 0.9624 0.9753 0.4682 ] Network output: [ 0.03162 0.881 0.03185 -0.0003563 0.0001599 1.022 -0.0002685 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0386 Epoch 2079 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.038 0.9727 0.9887 0.0001216 -5.461e-05 -0.03697 9.167e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02464 -0.00524 0.01993 0.03727 0.9355 0.9455 0.05211 0.8757 0.8958 0.1336 ] Network output: [ 0.9684 0.0704 -0.0145 -0.0003211 0.0001442 0.005993 -0.000242 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6533 0.1082 0.102 0.3322 0.969 0.9854 0.7538 0.8907 0.9627 0.627 ] Network output: [ -0.002373 0.9281 1.032 1.762e-05 -7.91e-06 0.04468 1.328e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0508 0.03721 0.05727 0.05211 0.9834 0.9882 0.05209 0.9651 0.9771 0.07281 ] Network output: [ 0.101 -0.3081 1.065 5.789e-05 -2.599e-05 1.042 4.363e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.748 0.6291 0.5153 0.5146 0.9726 0.9876 0.7517 0.9019 0.9681 0.6232 ] Network output: [ -0.05687 0.2088 0.9676 0.0005748 -0.0002581 0.9397 0.0004332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5986 0.5831 0.4376 0.3404 0.9851 0.9902 0.5992 0.9696 0.9793 0.4517 ] Network output: [ -0.07932 0.2472 0.9292 -9.359e-05 4.202e-05 0.982 -7.053e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6055 0.6029 0.4654 0.3011 0.9828 0.9888 0.6056 0.9624 0.9753 0.4683 ] Network output: [ 0.03155 0.8812 0.03179 -0.000355 0.0001594 1.022 -0.0002675 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03857 Epoch 2080 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03798 0.9727 0.9888 0.0001212 -5.44e-05 -0.03696 9.132e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02464 -0.005243 0.01992 0.03725 0.9355 0.9455 0.05211 0.8757 0.8959 0.1336 ] Network output: [ 0.9684 0.07037 -0.0145 -0.0003199 0.0001436 0.005942 -0.0002411 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6533 0.1084 0.1021 0.3321 0.969 0.9854 0.7538 0.8907 0.9627 0.627 ] Network output: [ -0.002389 0.9282 1.032 1.718e-05 -7.711e-06 0.04471 1.294e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05081 0.03722 0.05726 0.05207 0.9834 0.9882 0.05209 0.9651 0.9772 0.07279 ] Network output: [ 0.1009 -0.3081 1.065 5.581e-05 -2.506e-05 1.042 4.206e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.748 0.6292 0.5154 0.5145 0.9726 0.9876 0.7517 0.9019 0.9682 0.6232 ] Network output: [ -0.0568 0.2087 0.9675 0.0005747 -0.000258 0.9397 0.0004331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5987 0.5832 0.4377 0.3404 0.9851 0.9902 0.5992 0.9696 0.9793 0.4518 ] Network output: [ -0.07926 0.247 0.9293 -9.296e-05 4.173e-05 0.9819 -7.006e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6055 0.603 0.4655 0.3011 0.9828 0.9888 0.6057 0.9624 0.9753 0.4683 ] Network output: [ 0.03148 0.8815 0.03172 -0.0003538 0.0001588 1.022 -0.0002666 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03853 Epoch 2081 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03795 0.9727 0.9888 0.0001207 -5.419e-05 -0.03694 9.097e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02463 -0.005246 0.01992 0.03723 0.9355 0.9455 0.0521 0.8758 0.8959 0.1336 ] Network output: [ 0.9685 0.07034 -0.01449 -0.0003186 0.000143 0.005892 -0.0002401 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6534 0.1085 0.1023 0.3319 0.969 0.9854 0.7539 0.8908 0.9627 0.6271 ] Network output: [ -0.002404 0.9282 1.032 1.674e-05 -7.513e-06 0.04475 1.261e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05082 0.03723 0.05726 0.05204 0.9834 0.9882 0.0521 0.9651 0.9772 0.07278 ] Network output: [ 0.1008 -0.308 1.065 5.373e-05 -2.412e-05 1.042 4.049e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7481 0.6294 0.5155 0.5143 0.9726 0.9876 0.7517 0.902 0.9682 0.6233 ] Network output: [ -0.05673 0.2086 0.9675 0.0005746 -0.0002579 0.9397 0.000433 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5988 0.5833 0.4378 0.3403 0.9851 0.9902 0.5993 0.9696 0.9793 0.4519 ] Network output: [ -0.07919 0.2468 0.9294 -9.233e-05 4.145e-05 0.9818 -6.958e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6056 0.603 0.4655 0.3011 0.9828 0.9889 0.6057 0.9624 0.9753 0.4683 ] Network output: [ 0.03141 0.8817 0.03166 -0.0003525 0.0001583 1.022 -0.0002657 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03849 Epoch 2082 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03793 0.9727 0.9888 0.0001203 -5.399e-05 -0.03692 9.063e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02463 -0.00525 0.01992 0.03721 0.9355 0.9455 0.05209 0.8758 0.8959 0.1335 ] Network output: [ 0.9685 0.07032 -0.01449 -0.0003174 0.0001425 0.005841 -0.0002392 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6534 0.1086 0.1024 0.3317 0.969 0.9854 0.7539 0.8908 0.9627 0.6271 ] Network output: [ -0.00242 0.9282 1.032 1.63e-05 -7.316e-06 0.04478 1.228e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05083 0.03724 0.05725 0.05201 0.9834 0.9882 0.05211 0.9651 0.9772 0.07276 ] Network output: [ 0.1008 -0.3079 1.065 5.166e-05 -2.319e-05 1.042 3.893e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7481 0.6295 0.5157 0.5142 0.9727 0.9876 0.7518 0.902 0.9682 0.6233 ] Network output: [ -0.05667 0.2085 0.9674 0.0005744 -0.0002579 0.9398 0.0004329 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5989 0.5834 0.4378 0.3403 0.9851 0.9902 0.5994 0.9697 0.9793 0.4519 ] Network output: [ -0.07913 0.2467 0.9295 -9.169e-05 4.116e-05 0.9818 -6.91e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6056 0.603 0.4655 0.3012 0.9828 0.9889 0.6057 0.9625 0.9754 0.4684 ] Network output: [ 0.03134 0.8819 0.0316 -0.0003513 0.0001577 1.022 -0.0002647 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03845 Epoch 2083 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03791 0.9727 0.9889 0.0001198 -5.378e-05 -0.0369 9.028e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02462 -0.005253 0.01991 0.03719 0.9355 0.9455 0.05208 0.8759 0.896 0.1335 ] Network output: [ 0.9686 0.0703 -0.01448 -0.0003161 0.0001419 0.005791 -0.0002382 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6534 0.1087 0.1026 0.3316 0.969 0.9854 0.7539 0.8909 0.9627 0.6271 ] Network output: [ -0.002436 0.9282 1.032 1.586e-05 -7.119e-06 0.04482 1.195e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05084 0.03726 0.05724 0.05198 0.9834 0.9882 0.05212 0.9652 0.9772 0.07274 ] Network output: [ 0.1007 -0.3079 1.065 4.958e-05 -2.226e-05 1.042 3.737e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7481 0.6296 0.5158 0.514 0.9727 0.9876 0.7518 0.9021 0.9682 0.6233 ] Network output: [ -0.0566 0.2084 0.9674 0.0005743 -0.0002578 0.9398 0.0004328 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5989 0.5835 0.4379 0.3403 0.9851 0.9902 0.5995 0.9697 0.9793 0.452 ] Network output: [ -0.07907 0.2465 0.9296 -9.105e-05 4.088e-05 0.9817 -6.862e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6057 0.6031 0.4656 0.3012 0.9828 0.9889 0.6058 0.9625 0.9754 0.4684 ] Network output: [ 0.03127 0.8821 0.03154 -0.00035 0.0001572 1.022 -0.0002638 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03841 Epoch 2084 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03788 0.9727 0.9889 0.0001193 -5.357e-05 -0.03688 8.993e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02462 -0.005256 0.01991 0.03716 0.9356 0.9456 0.05207 0.8759 0.896 0.1335 ] Network output: [ 0.9686 0.07027 -0.01448 -0.0003149 0.0001414 0.005741 -0.0002373 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6534 0.1088 0.1027 0.3314 0.969 0.9854 0.7539 0.891 0.9628 0.6271 ] Network output: [ -0.002452 0.9283 1.032 1.542e-05 -6.923e-06 0.04485 1.162e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05085 0.03727 0.05724 0.05194 0.9834 0.9882 0.05213 0.9652 0.9772 0.07272 ] Network output: [ 0.1006 -0.3078 1.065 4.751e-05 -2.133e-05 1.042 3.581e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7481 0.6298 0.5159 0.5139 0.9727 0.9876 0.7518 0.9021 0.9682 0.6233 ] Network output: [ -0.05653 0.2083 0.9673 0.0005742 -0.0002578 0.9398 0.0004327 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.599 0.5836 0.438 0.3403 0.9851 0.9902 0.5996 0.9697 0.9793 0.4521 ] Network output: [ -0.07901 0.2464 0.9297 -9.041e-05 4.059e-05 0.9816 -6.814e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6057 0.6031 0.4656 0.3012 0.9828 0.9889 0.6058 0.9625 0.9754 0.4684 ] Network output: [ 0.03121 0.8823 0.03148 -0.0003488 0.0001566 1.022 -0.0002629 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03837 Epoch 2085 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03786 0.9727 0.989 0.0001189 -5.336e-05 -0.03686 8.958e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02461 -0.00526 0.0199 0.03714 0.9356 0.9456 0.05206 0.876 0.8961 0.1334 ] Network output: [ 0.9686 0.07025 -0.01448 -0.0003136 0.0001408 0.005691 -0.0002364 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6535 0.1089 0.1029 0.3312 0.969 0.9855 0.754 0.891 0.9628 0.6271 ] Network output: [ -0.002467 0.9283 1.032 1.499e-05 -6.728e-06 0.04488 1.129e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05085 0.03728 0.05723 0.05191 0.9834 0.9882 0.05214 0.9652 0.9772 0.0727 ] Network output: [ 0.1006 -0.3077 1.065 4.544e-05 -2.04e-05 1.042 3.425e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7482 0.6299 0.516 0.5137 0.9727 0.9876 0.7518 0.9022 0.9683 0.6233 ] Network output: [ -0.05646 0.2082 0.9673 0.0005741 -0.0002577 0.9398 0.0004327 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5991 0.5837 0.4381 0.3402 0.9851 0.9902 0.5996 0.9697 0.9793 0.4521 ] Network output: [ -0.07894 0.2462 0.9298 -8.976e-05 4.03e-05 0.9816 -6.765e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6057 0.6032 0.4657 0.3013 0.9828 0.9889 0.6058 0.9626 0.9754 0.4685 ] Network output: [ 0.03114 0.8826 0.03142 -0.0003476 0.0001561 1.022 -0.000262 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03833 Epoch 2086 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03784 0.9727 0.989 0.0001184 -5.316e-05 -0.03685 8.923e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02461 -0.005263 0.0199 0.03712 0.9356 0.9456 0.05205 0.876 0.8961 0.1334 ] Network output: [ 0.9687 0.07022 -0.01447 -0.0003124 0.0001402 0.005642 -0.0002354 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6535 0.109 0.103 0.3311 0.9691 0.9855 0.754 0.8911 0.9628 0.6272 ] Network output: [ -0.002483 0.9283 1.032 1.455e-05 -6.534e-06 0.04492 1.097e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05086 0.03729 0.05722 0.05188 0.9834 0.9882 0.05214 0.9653 0.9773 0.07268 ] Network output: [ 0.1005 -0.3077 1.065 4.338e-05 -1.947e-05 1.042 3.269e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7482 0.63 0.5162 0.5136 0.9727 0.9876 0.7519 0.9022 0.9683 0.6234 ] Network output: [ -0.05639 0.208 0.9672 0.000574 -0.0002577 0.9398 0.0004326 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5992 0.5838 0.4381 0.3402 0.9851 0.9902 0.5997 0.9697 0.9794 0.4522 ] Network output: [ -0.07888 0.246 0.9299 -8.912e-05 4.001e-05 0.9815 -6.716e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6058 0.6032 0.4657 0.3013 0.9828 0.9889 0.6059 0.9626 0.9754 0.4685 ] Network output: [ 0.03107 0.8828 0.03136 -0.0003464 0.0001555 1.022 -0.0002611 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0383 Epoch 2087 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03781 0.9726 0.989 0.0001179 -5.295e-05 -0.03683 8.889e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0246 -0.005266 0.0199 0.0371 0.9356 0.9456 0.05205 0.876 0.8961 0.1334 ] Network output: [ 0.9687 0.0702 -0.01447 -0.0003111 0.0001397 0.005592 -0.0002345 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6535 0.1091 0.1031 0.3309 0.9691 0.9855 0.754 0.8911 0.9628 0.6272 ] Network output: [ -0.002498 0.9283 1.032 1.412e-05 -6.34e-06 0.04495 1.064e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05087 0.0373 0.05722 0.05184 0.9834 0.9882 0.05215 0.9653 0.9773 0.07266 ] Network output: [ 0.1005 -0.3076 1.064 4.131e-05 -1.855e-05 1.042 3.113e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7482 0.6302 0.5163 0.5134 0.9727 0.9876 0.7519 0.9023 0.9683 0.6234 ] Network output: [ -0.05633 0.2079 0.9672 0.0005739 -0.0002577 0.9398 0.0004325 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5993 0.5839 0.4382 0.3402 0.9851 0.9902 0.5998 0.9698 0.9794 0.4522 ] Network output: [ -0.07882 0.2459 0.93 -8.847e-05 3.972e-05 0.9814 -6.667e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6058 0.6032 0.4657 0.3013 0.9829 0.9889 0.6059 0.9626 0.9755 0.4685 ] Network output: [ 0.031 0.883 0.0313 -0.0003453 0.000155 1.022 -0.0002602 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03826 Epoch 2088 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03779 0.9726 0.9891 0.0001175 -5.274e-05 -0.03681 8.854e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0246 -0.00527 0.01989 0.03707 0.9356 0.9456 0.05204 0.8761 0.8962 0.1333 ] Network output: [ 0.9687 0.07018 -0.01447 -0.0003099 0.0001391 0.005543 -0.0002335 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6535 0.1092 0.1033 0.3307 0.9691 0.9855 0.754 0.8912 0.9628 0.6272 ] Network output: [ -0.002513 0.9283 1.032 1.369e-05 -6.147e-06 0.04498 1.032e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05088 0.03731 0.05721 0.05181 0.9834 0.9882 0.05216 0.9653 0.9773 0.07264 ] Network output: [ 0.1004 -0.3076 1.064 3.925e-05 -1.762e-05 1.042 2.958e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7483 0.6303 0.5164 0.5133 0.9727 0.9876 0.7519 0.9023 0.9683 0.6234 ] Network output: [ -0.05626 0.2078 0.9671 0.0005738 -0.0002576 0.9399 0.0004325 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5993 0.584 0.4383 0.3401 0.9851 0.9902 0.5999 0.9698 0.9794 0.4523 ] Network output: [ -0.07876 0.2457 0.9301 -8.781e-05 3.942e-05 0.9814 -6.618e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6058 0.6033 0.4658 0.3014 0.9829 0.9889 0.6059 0.9626 0.9755 0.4686 ] Network output: [ 0.03094 0.8832 0.03124 -0.0003441 0.0001545 1.022 -0.0002593 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03822 Epoch 2089 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03777 0.9726 0.9891 0.000117 -5.253e-05 -0.03679 8.819e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02459 -0.005273 0.01989 0.03705 0.9356 0.9456 0.05203 0.8761 0.8962 0.1333 ] Network output: [ 0.9688 0.07016 -0.01447 -0.0003087 0.0001386 0.005494 -0.0002326 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6536 0.1093 0.1034 0.3305 0.9691 0.9855 0.7541 0.8912 0.9629 0.6272 ] Network output: [ -0.002529 0.9284 1.032 1.327e-05 -5.955e-06 0.04501 9.997e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05088 0.03732 0.0572 0.05178 0.9834 0.9882 0.05217 0.9653 0.9773 0.07262 ] Network output: [ 0.1003 -0.3075 1.064 3.719e-05 -1.67e-05 1.042 2.803e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7483 0.6304 0.5165 0.5131 0.9727 0.9876 0.7519 0.9024 0.9683 0.6234 ] Network output: [ -0.05619 0.2077 0.9671 0.0005738 -0.0002576 0.9399 0.0004324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5994 0.584 0.4384 0.3401 0.9851 0.9902 0.6 0.9698 0.9794 0.4523 ] Network output: [ -0.0787 0.2456 0.9302 -8.715e-05 3.913e-05 0.9813 -6.568e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6059 0.6033 0.4658 0.3014 0.9829 0.9889 0.606 0.9627 0.9755 0.4686 ] Network output: [ 0.03087 0.8834 0.03118 -0.0003429 0.000154 1.022 -0.0002585 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03818 Epoch 2090 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03774 0.9726 0.9892 0.0001166 -5.233e-05 -0.03678 8.784e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02459 -0.005276 0.01988 0.03703 0.9357 0.9456 0.05202 0.8762 0.8962 0.1333 ] Network output: [ 0.9688 0.07013 -0.01446 -0.0003074 0.000138 0.005445 -0.0002317 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6536 0.1094 0.1036 0.3304 0.9691 0.9855 0.7541 0.8913 0.9629 0.6272 ] Network output: [ -0.002544 0.9284 1.032 1.284e-05 -5.764e-06 0.04504 9.676e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05089 0.03733 0.05719 0.05174 0.9835 0.9882 0.05217 0.9654 0.9773 0.07261 ] Network output: [ 0.1003 -0.3074 1.064 3.513e-05 -1.577e-05 1.043 2.648e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7483 0.6305 0.5167 0.5129 0.9727 0.9876 0.752 0.9024 0.9684 0.6234 ] Network output: [ -0.05612 0.2076 0.967 0.0005737 -0.0002576 0.9399 0.0004324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5995 0.5841 0.4384 0.3401 0.9851 0.9902 0.6 0.9698 0.9794 0.4524 ] Network output: [ -0.07864 0.2454 0.9303 -8.649e-05 3.883e-05 0.9812 -6.518e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6059 0.6034 0.4658 0.3014 0.9829 0.9889 0.606 0.9627 0.9755 0.4686 ] Network output: [ 0.0308 0.8836 0.03112 -0.0003418 0.0001534 1.022 -0.0002576 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03814 Epoch 2091 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03772 0.9726 0.9892 0.0001161 -5.212e-05 -0.03676 8.749e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02458 -0.00528 0.01988 0.03701 0.9357 0.9457 0.05201 0.8762 0.8963 0.1332 ] Network output: [ 0.9689 0.07011 -0.01446 -0.0003062 0.0001375 0.005397 -0.0002308 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6536 0.1095 0.1037 0.3302 0.9691 0.9855 0.7541 0.8913 0.9629 0.6273 ] Network output: [ -0.002559 0.9284 1.032 1.241e-05 -5.573e-06 0.04507 9.355e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0509 0.03734 0.05719 0.05171 0.9835 0.9883 0.05218 0.9654 0.9773 0.07259 ] Network output: [ 0.1002 -0.3074 1.064 3.307e-05 -1.485e-05 1.043 2.493e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7483 0.6306 0.5168 0.5128 0.9727 0.9876 0.752 0.9025 0.9684 0.6235 ] Network output: [ -0.05606 0.2075 0.967 0.0005736 -0.0002575 0.9399 0.0004323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5996 0.5842 0.4385 0.34 0.9852 0.9902 0.6001 0.9699 0.9794 0.4525 ] Network output: [ -0.07858 0.2452 0.9304 -8.583e-05 3.853e-05 0.9812 -6.468e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6059 0.6034 0.4659 0.3014 0.9829 0.9889 0.606 0.9627 0.9755 0.4686 ] Network output: [ 0.03074 0.8838 0.03107 -0.0003407 0.0001529 1.022 -0.0002567 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03811 Epoch 2092 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0377 0.9726 0.9892 0.0001156 -5.191e-05 -0.03674 8.715e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02458 -0.005283 0.01988 0.03698 0.9357 0.9457 0.052 0.8763 0.8963 0.1332 ] Network output: [ 0.9689 0.07009 -0.01446 -0.000305 0.0001369 0.005348 -0.0002298 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6536 0.1096 0.1038 0.33 0.9691 0.9855 0.7541 0.8914 0.9629 0.6273 ] Network output: [ -0.002574 0.9284 1.032 1.199e-05 -5.383e-06 0.0451 9.036e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0509 0.03735 0.05718 0.05168 0.9835 0.9883 0.05219 0.9654 0.9774 0.07257 ] Network output: [ 0.1002 -0.3073 1.064 3.102e-05 -1.393e-05 1.043 2.338e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7483 0.6308 0.5169 0.5126 0.9728 0.9876 0.752 0.9025 0.9684 0.6235 ] Network output: [ -0.05599 0.2074 0.9669 0.0005736 -0.0002575 0.9399 0.0004323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5997 0.5843 0.4386 0.34 0.9852 0.9902 0.6002 0.9699 0.9795 0.4525 ] Network output: [ -0.07851 0.2451 0.9305 -8.516e-05 3.823e-05 0.9811 -6.418e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.606 0.6034 0.4659 0.3015 0.9829 0.9889 0.6061 0.9628 0.9756 0.4687 ] Network output: [ 0.03067 0.8841 0.03101 -0.0003395 0.0001524 1.022 -0.0002559 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03807 Epoch 2093 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03768 0.9726 0.9893 0.0001152 -5.171e-05 -0.03672 8.68e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02457 -0.005286 0.01987 0.03696 0.9357 0.9457 0.05199 0.8763 0.8964 0.1332 ] Network output: [ 0.9689 0.07007 -0.01446 -0.0003037 0.0001364 0.0053 -0.0002289 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6536 0.1097 0.104 0.3298 0.9691 0.9855 0.7541 0.8914 0.963 0.6273 ] Network output: [ -0.002589 0.9285 1.032 1.157e-05 -5.193e-06 0.04514 8.718e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05091 0.03736 0.05717 0.05164 0.9835 0.9883 0.05219 0.9655 0.9774 0.07255 ] Network output: [ 0.1001 -0.3072 1.064 2.897e-05 -1.3e-05 1.043 2.183e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7484 0.6309 0.517 0.5125 0.9728 0.9876 0.752 0.9026 0.9684 0.6235 ] Network output: [ -0.05592 0.2073 0.9669 0.0005735 -0.0002575 0.94 0.0004322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5997 0.5844 0.4386 0.34 0.9852 0.9903 0.6003 0.9699 0.9795 0.4526 ] Network output: [ -0.07845 0.2449 0.9306 -8.449e-05 3.793e-05 0.9811 -6.368e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.606 0.6035 0.4659 0.3015 0.9829 0.9889 0.6061 0.9628 0.9756 0.4687 ] Network output: [ 0.0306 0.8843 0.03095 -0.0003384 0.0001519 1.022 -0.0002551 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03803 Epoch 2094 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03765 0.9726 0.9893 0.0001147 -5.15e-05 -0.0367 8.645e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02457 -0.005289 0.01987 0.03694 0.9357 0.9457 0.05198 0.8764 0.8964 0.1331 ] Network output: [ 0.969 0.07005 -0.01445 -0.0003025 0.0001358 0.005251 -0.000228 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6537 0.1098 0.1041 0.3296 0.9691 0.9855 0.7542 0.8915 0.963 0.6273 ] Network output: [ -0.002604 0.9285 1.032 1.115e-05 -5.005e-06 0.04517 8.402e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05092 0.03737 0.05716 0.05161 0.9835 0.9883 0.0522 0.9655 0.9774 0.07253 ] Network output: [ 0.1 -0.3072 1.064 2.692e-05 -1.208e-05 1.043 2.029e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7484 0.631 0.5171 0.5123 0.9728 0.9876 0.7521 0.9026 0.9684 0.6235 ] Network output: [ -0.05585 0.2072 0.9668 0.0005735 -0.0002575 0.94 0.0004322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5998 0.5845 0.4387 0.3399 0.9852 0.9903 0.6004 0.9699 0.9795 0.4526 ] Network output: [ -0.07839 0.2448 0.9307 -8.382e-05 3.763e-05 0.981 -6.317e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.606 0.6035 0.466 0.3015 0.9829 0.9889 0.6061 0.9628 0.9756 0.4687 ] Network output: [ 0.03054 0.8845 0.03089 -0.0003373 0.0001514 1.022 -0.0002542 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03799 Epoch 2095 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03763 0.9726 0.9893 0.0001143 -5.129e-05 -0.03669 8.611e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02456 -0.005292 0.01986 0.03691 0.9357 0.9457 0.05197 0.8764 0.8964 0.1331 ] Network output: [ 0.969 0.07003 -0.01445 -0.0003013 0.0001353 0.005204 -0.0002271 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6537 0.1099 0.1042 0.3295 0.9692 0.9855 0.7542 0.8915 0.963 0.6273 ] Network output: [ -0.002619 0.9285 1.032 1.073e-05 -4.817e-06 0.0452 8.086e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05092 0.03738 0.05715 0.05157 0.9835 0.9883 0.05221 0.9655 0.9774 0.07251 ] Network output: [ 0.09997 -0.3071 1.064 2.487e-05 -1.116e-05 1.043 1.874e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7484 0.6311 0.5173 0.5121 0.9728 0.9876 0.7521 0.9027 0.9684 0.6235 ] Network output: [ -0.05579 0.2071 0.9668 0.0005734 -0.0002574 0.94 0.0004322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5999 0.5846 0.4388 0.3399 0.9852 0.9903 0.6004 0.9699 0.9795 0.4527 ] Network output: [ -0.07833 0.2446 0.9308 -8.314e-05 3.733e-05 0.981 -6.266e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6061 0.6035 0.466 0.3015 0.9829 0.9889 0.6062 0.9628 0.9756 0.4688 ] Network output: [ 0.03047 0.8847 0.03084 -0.0003362 0.0001509 1.022 -0.0002534 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03796 Epoch 2096 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03761 0.9725 0.9894 0.0001138 -5.109e-05 -0.03667 8.576e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02456 -0.005296 0.01986 0.03689 0.9358 0.9457 0.05196 0.8765 0.8965 0.1331 ] Network output: [ 0.969 0.07001 -0.01445 -0.0003001 0.0001347 0.005156 -0.0002261 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6537 0.11 0.1044 0.3293 0.9692 0.9855 0.7542 0.8916 0.963 0.6274 ] Network output: [ -0.002634 0.9285 1.032 1.031e-05 -4.629e-06 0.04523 7.771e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05093 0.03739 0.05715 0.05154 0.9835 0.9883 0.05221 0.9655 0.9774 0.07249 ] Network output: [ 0.09991 -0.3071 1.064 2.282e-05 -1.025e-05 1.043 1.72e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7484 0.6312 0.5174 0.512 0.9728 0.9876 0.7521 0.9027 0.9685 0.6236 ] Network output: [ -0.05572 0.207 0.9667 0.0005734 -0.0002574 0.94 0.0004321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6 0.5847 0.4388 0.3399 0.9852 0.9903 0.6005 0.97 0.9795 0.4527 ] Network output: [ -0.07827 0.2444 0.9309 -8.246e-05 3.702e-05 0.9809 -6.215e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6061 0.6036 0.466 0.3016 0.9829 0.9889 0.6062 0.9629 0.9756 0.4688 ] Network output: [ 0.03041 0.8849 0.03078 -0.0003352 0.0001505 1.022 -0.0002526 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03792 Epoch 2097 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03758 0.9725 0.9894 0.0001133 -5.088e-05 -0.03665 8.541e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02455 -0.005299 0.01986 0.03687 0.9358 0.9457 0.05195 0.8765 0.8965 0.133 ] Network output: [ 0.9691 0.06999 -0.01445 -0.0002989 0.0001342 0.005108 -0.0002252 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6537 0.1101 0.1045 0.3291 0.9692 0.9855 0.7542 0.8916 0.963 0.6274 ] Network output: [ -0.002648 0.9285 1.032 9.896e-06 -4.443e-06 0.04525 7.458e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05094 0.0374 0.05714 0.0515 0.9835 0.9883 0.05222 0.9656 0.9774 0.07246 ] Network output: [ 0.09984 -0.307 1.064 2.078e-05 -9.327e-06 1.043 1.566e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7484 0.6313 0.5175 0.5118 0.9728 0.9877 0.7521 0.9028 0.9685 0.6236 ] Network output: [ -0.05565 0.2069 0.9667 0.0005734 -0.0002574 0.9401 0.0004321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6 0.5848 0.4389 0.3398 0.9852 0.9903 0.6006 0.97 0.9795 0.4528 ] Network output: [ -0.07821 0.2443 0.931 -8.178e-05 3.671e-05 0.9808 -6.163e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6061 0.6036 0.466 0.3016 0.9829 0.9889 0.6063 0.9629 0.9756 0.4688 ] Network output: [ 0.03034 0.8851 0.03073 -0.0003341 0.00015 1.022 -0.0002518 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03788 Epoch 2098 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03756 0.9725 0.9895 0.0001129 -5.067e-05 -0.03663 8.506e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02455 -0.005302 0.01985 0.03684 0.9358 0.9458 0.05194 0.8766 0.8965 0.133 ] Network output: [ 0.9691 0.06997 -0.01445 -0.0002976 0.0001336 0.005061 -0.0002243 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6537 0.1102 0.1047 0.3289 0.9692 0.9855 0.7542 0.8917 0.9631 0.6274 ] Network output: [ -0.002663 0.9286 1.032 9.482e-06 -4.257e-06 0.04528 7.146e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05094 0.03741 0.05713 0.05147 0.9835 0.9883 0.05223 0.9656 0.9775 0.07244 ] Network output: [ 0.09978 -0.3069 1.064 1.873e-05 -8.409e-06 1.043 1.412e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7485 0.6315 0.5176 0.5117 0.9728 0.9877 0.7521 0.9028 0.9685 0.6236 ] Network output: [ -0.05559 0.2068 0.9666 0.0005734 -0.0002574 0.9401 0.0004321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6001 0.5849 0.439 0.3398 0.9852 0.9903 0.6007 0.97 0.9795 0.4528 ] Network output: [ -0.07815 0.2441 0.9311 -8.109e-05 3.64e-05 0.9808 -6.111e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6062 0.6036 0.4661 0.3016 0.9829 0.9889 0.6063 0.9629 0.9757 0.4688 ] Network output: [ 0.03028 0.8853 0.03067 -0.000333 0.0001495 1.022 -0.000251 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03784 Epoch 2099 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03754 0.9725 0.9895 0.0001124 -5.047e-05 -0.03662 8.472e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02454 -0.005305 0.01985 0.03682 0.9358 0.9458 0.05193 0.8766 0.8966 0.1329 ] Network output: [ 0.9691 0.06995 -0.01445 -0.0002964 0.0001331 0.005013 -0.0002234 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6538 0.1103 0.1048 0.3287 0.9692 0.9855 0.7543 0.8917 0.9631 0.6274 ] Network output: [ -0.002678 0.9286 1.031 9.069e-06 -4.071e-06 0.04531 6.835e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05095 0.03742 0.05712 0.05143 0.9835 0.9883 0.05223 0.9656 0.9775 0.07242 ] Network output: [ 0.09972 -0.3069 1.064 1.669e-05 -7.492e-06 1.043 1.258e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7485 0.6316 0.5177 0.5115 0.9728 0.9877 0.7521 0.9029 0.9685 0.6236 ] Network output: [ -0.05552 0.2067 0.9666 0.0005734 -0.0002574 0.9401 0.0004321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6002 0.5849 0.4391 0.3397 0.9852 0.9903 0.6007 0.97 0.9796 0.4529 ] Network output: [ -0.07809 0.244 0.9312 -8.04e-05 3.609e-05 0.9807 -6.059e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6062 0.6037 0.4661 0.3016 0.9829 0.989 0.6063 0.963 0.9757 0.4689 ] Network output: [ 0.03022 0.8855 0.03062 -0.000332 0.000149 1.022 -0.0002502 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03781 Epoch 2100 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03751 0.9725 0.9895 0.000112 -5.026e-05 -0.0366 8.437e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02454 -0.005308 0.01984 0.0368 0.9358 0.9458 0.05192 0.8767 0.8966 0.1329 ] Network output: [ 0.9692 0.06993 -0.01445 -0.0002952 0.0001325 0.004966 -0.0002225 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6538 0.1104 0.1049 0.3286 0.9692 0.9855 0.7543 0.8918 0.9631 0.6274 ] Network output: [ -0.002692 0.9286 1.031 8.658e-06 -3.887e-06 0.04534 6.525e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05096 0.03743 0.05711 0.0514 0.9835 0.9883 0.05224 0.9656 0.9775 0.0724 ] Network output: [ 0.09966 -0.3068 1.064 1.465e-05 -6.576e-06 1.043 1.104e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7485 0.6317 0.5178 0.5113 0.9728 0.9877 0.7522 0.9029 0.9685 0.6236 ] Network output: [ -0.05545 0.2066 0.9665 0.0005733 -0.0002574 0.9401 0.0004321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6003 0.585 0.4391 0.3397 0.9852 0.9903 0.6008 0.9701 0.9796 0.453 ] Network output: [ -0.07803 0.2438 0.9313 -7.97e-05 3.578e-05 0.9807 -6.007e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6062 0.6037 0.4661 0.3016 0.983 0.989 0.6064 0.963 0.9757 0.4689 ] Network output: [ 0.03015 0.8857 0.03056 -0.0003309 0.0001486 1.022 -0.0002494 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03777 Epoch 2101 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03749 0.9725 0.9896 0.0001115 -5.005e-05 -0.03658 8.403e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02453 -0.005311 0.01984 0.03677 0.9358 0.9458 0.05191 0.8767 0.8966 0.1329 ] Network output: [ 0.9692 0.06991 -0.01445 -0.000294 0.000132 0.004919 -0.0002216 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6538 0.1105 0.105 0.3284 0.9692 0.9856 0.7543 0.8918 0.9631 0.6275 ] Network output: [ -0.002707 0.9286 1.031 8.248e-06 -3.703e-06 0.04537 6.216e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05096 0.03744 0.0571 0.05136 0.9835 0.9883 0.05224 0.9657 0.9775 0.07238 ] Network output: [ 0.09959 -0.3067 1.064 1.261e-05 -5.66e-06 1.043 9.502e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7485 0.6318 0.5179 0.5112 0.9728 0.9877 0.7522 0.903 0.9685 0.6237 ] Network output: [ -0.05538 0.2065 0.9665 0.0005733 -0.0002574 0.9401 0.0004321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6003 0.5851 0.4392 0.3397 0.9852 0.9903 0.6009 0.9701 0.9796 0.453 ] Network output: [ -0.07797 0.2436 0.9313 -7.901e-05 3.547e-05 0.9806 -5.954e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6063 0.6038 0.4662 0.3017 0.983 0.989 0.6064 0.963 0.9757 0.4689 ] Network output: [ 0.03009 0.8859 0.03051 -0.0003299 0.0001481 1.022 -0.0002486 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03773 Epoch 2102 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03747 0.9725 0.9896 0.000111 -4.985e-05 -0.03657 8.368e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02452 -0.005314 0.01983 0.03675 0.9359 0.9458 0.0519 0.8768 0.8967 0.1328 ] Network output: [ 0.9692 0.06989 -0.01445 -0.0002928 0.0001315 0.004873 -0.0002207 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6538 0.1106 0.1052 0.3282 0.9692 0.9856 0.7543 0.8919 0.9631 0.6275 ] Network output: [ -0.002721 0.9287 1.031 7.839e-06 -3.519e-06 0.0454 5.908e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05097 0.03745 0.05709 0.05132 0.9836 0.9883 0.05225 0.9657 0.9775 0.07236 ] Network output: [ 0.09953 -0.3067 1.064 1.057e-05 -4.745e-06 1.043 7.966e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7485 0.6319 0.5181 0.511 0.9728 0.9877 0.7522 0.903 0.9686 0.6237 ] Network output: [ -0.05532 0.2064 0.9664 0.0005733 -0.0002574 0.9402 0.0004321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6004 0.5852 0.4392 0.3396 0.9852 0.9903 0.601 0.9701 0.9796 0.4531 ] Network output: [ -0.07791 0.2435 0.9314 -7.83e-05 3.515e-05 0.9806 -5.901e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6063 0.6038 0.4662 0.3017 0.983 0.989 0.6064 0.963 0.9757 0.4689 ] Network output: [ 0.03003 0.8861 0.03045 -0.0003289 0.0001477 1.022 -0.0002479 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0377 Epoch 2103 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03744 0.9725 0.9896 0.0001106 -4.964e-05 -0.03655 8.333e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02452 -0.005318 0.01983 0.03672 0.9359 0.9458 0.05189 0.8768 0.8967 0.1328 ] Network output: [ 0.9693 0.06987 -0.01445 -0.0002916 0.0001309 0.004826 -0.0002198 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6538 0.1107 0.1053 0.328 0.9692 0.9856 0.7543 0.8919 0.9632 0.6275 ] Network output: [ -0.002735 0.9287 1.031 7.432e-06 -3.337e-06 0.04542 5.601e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05097 0.03746 0.05708 0.05129 0.9836 0.9883 0.05225 0.9657 0.9775 0.07234 ] Network output: [ 0.09947 -0.3066 1.064 8.533e-06 -3.831e-06 1.043 6.431e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7485 0.632 0.5182 0.5108 0.9729 0.9877 0.7522 0.903 0.9686 0.6237 ] Network output: [ -0.05525 0.2063 0.9663 0.0005734 -0.0002574 0.9402 0.0004321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6005 0.5853 0.4393 0.3396 0.9852 0.9903 0.601 0.9701 0.9796 0.4531 ] Network output: [ -0.07785 0.2433 0.9315 -7.76e-05 3.484e-05 0.9805 -5.848e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6063 0.6038 0.4662 0.3017 0.983 0.989 0.6064 0.9631 0.9758 0.469 ] Network output: [ 0.02996 0.8864 0.0304 -0.0003279 0.0001472 1.022 -0.0002471 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03766 Epoch 2104 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03742 0.9725 0.9897 0.0001101 -4.944e-05 -0.03653 8.299e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02451 -0.005321 0.01982 0.0367 0.9359 0.9458 0.05188 0.8769 0.8968 0.1328 ] Network output: [ 0.9693 0.06985 -0.01445 -0.0002904 0.0001304 0.00478 -0.0002189 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6538 0.1108 0.1054 0.3278 0.9692 0.9856 0.7543 0.892 0.9632 0.6275 ] Network output: [ -0.00275 0.9287 1.031 7.026e-06 -3.154e-06 0.04545 5.295e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05098 0.03747 0.05707 0.05125 0.9836 0.9883 0.05226 0.9657 0.9775 0.07232 ] Network output: [ 0.09941 -0.3065 1.064 6.497e-06 -2.917e-06 1.043 4.897e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7486 0.6321 0.5183 0.5107 0.9729 0.9877 0.7522 0.9031 0.9686 0.6237 ] Network output: [ -0.05518 0.2062 0.9663 0.0005734 -0.0002574 0.9402 0.0004321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6006 0.5854 0.4394 0.3395 0.9852 0.9903 0.6011 0.9701 0.9796 0.4532 ] Network output: [ -0.07778 0.2432 0.9316 -7.689e-05 3.452e-05 0.9805 -5.795e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6064 0.6039 0.4662 0.3017 0.983 0.989 0.6065 0.9631 0.9758 0.469 ] Network output: [ 0.0299 0.8866 0.03035 -0.0003269 0.0001468 1.022 -0.0002464 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03762 Epoch 2105 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0374 0.9724 0.9897 0.0001097 -4.923e-05 -0.03652 8.264e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02451 -0.005324 0.01982 0.03667 0.9359 0.9459 0.05187 0.8769 0.8968 0.1327 ] Network output: [ 0.9694 0.06983 -0.01445 -0.0002892 0.0001298 0.004733 -0.000218 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6539 0.1109 0.1056 0.3277 0.9693 0.9856 0.7543 0.892 0.9632 0.6275 ] Network output: [ -0.002764 0.9287 1.031 6.622e-06 -2.973e-06 0.04548 4.991e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05098 0.03748 0.05706 0.05122 0.9836 0.9883 0.05226 0.9658 0.9776 0.0723 ] Network output: [ 0.09934 -0.3065 1.064 4.463e-06 -2.003e-06 1.043 3.363e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7486 0.6322 0.5184 0.5105 0.9729 0.9877 0.7522 0.9031 0.9686 0.6237 ] Network output: [ -0.05512 0.2061 0.9662 0.0005734 -0.0002574 0.9402 0.0004321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6006 0.5855 0.4394 0.3395 0.9852 0.9903 0.6012 0.9702 0.9796 0.4532 ] Network output: [ -0.07772 0.243 0.9317 -7.618e-05 3.42e-05 0.9804 -5.741e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6064 0.6039 0.4663 0.3017 0.983 0.989 0.6065 0.9631 0.9758 0.469 ] Network output: [ 0.02984 0.8868 0.03029 -0.0003259 0.0001463 1.022 -0.0002456 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03758 Epoch 2106 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03737 0.9724 0.9898 0.0001092 -4.902e-05 -0.0365 8.23e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0245 -0.005327 0.01982 0.03665 0.9359 0.9459 0.05186 0.8769 0.8968 0.1327 ] Network output: [ 0.9694 0.06981 -0.01445 -0.000288 0.0001293 0.004687 -0.0002171 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6539 0.111 0.1057 0.3275 0.9693 0.9856 0.7543 0.8921 0.9632 0.6276 ] Network output: [ -0.002778 0.9288 1.031 6.219e-06 -2.792e-06 0.04551 4.687e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05099 0.03749 0.05705 0.05118 0.9836 0.9883 0.05227 0.9658 0.9776 0.07228 ] Network output: [ 0.09928 -0.3064 1.064 2.429e-06 -1.09e-06 1.043 1.831e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7486 0.6323 0.5185 0.5103 0.9729 0.9877 0.7522 0.9032 0.9686 0.6238 ] Network output: [ -0.05505 0.206 0.9662 0.0005734 -0.0002574 0.9403 0.0004321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6007 0.5856 0.4395 0.3394 0.9853 0.9903 0.6013 0.9702 0.9797 0.4533 ] Network output: [ -0.07766 0.2428 0.9318 -7.546e-05 3.388e-05 0.9804 -5.687e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6064 0.6039 0.4663 0.3017 0.983 0.989 0.6065 0.9631 0.9758 0.469 ] Network output: [ 0.02977 0.887 0.03024 -0.0003249 0.0001459 1.022 -0.0002449 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03755 Epoch 2107 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03735 0.9724 0.9898 0.0001087 -4.882e-05 -0.03648 8.195e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0245 -0.00533 0.01981 0.03663 0.9359 0.9459 0.05185 0.877 0.8969 0.1326 ] Network output: [ 0.9694 0.0698 -0.01445 -0.0002868 0.0001288 0.004642 -0.0002162 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6539 0.1111 0.1058 0.3273 0.9693 0.9856 0.7544 0.8921 0.9632 0.6276 ] Network output: [ -0.002792 0.9288 1.031 5.817e-06 -2.612e-06 0.04553 4.384e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05099 0.03749 0.05704 0.05114 0.9836 0.9883 0.05227 0.9658 0.9776 0.07225 ] Network output: [ 0.09922 -0.3063 1.064 3.965e-07 -1.78e-07 1.043 2.988e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7486 0.6324 0.5186 0.5101 0.9729 0.9877 0.7523 0.9032 0.9687 0.6238 ] Network output: [ -0.05498 0.2059 0.9661 0.0005735 -0.0002574 0.9403 0.0004322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6008 0.5856 0.4396 0.3394 0.9853 0.9903 0.6013 0.9702 0.9797 0.4533 ] Network output: [ -0.0776 0.2427 0.9319 -7.474e-05 3.355e-05 0.9803 -5.632e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6065 0.604 0.4663 0.3017 0.983 0.989 0.6066 0.9632 0.9758 0.469 ] Network output: [ 0.02971 0.8872 0.03019 -0.000324 0.0001454 1.022 -0.0002442 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03751 Epoch 2108 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03733 0.9724 0.9898 0.0001083 -4.861e-05 -0.03646 8.161e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02449 -0.005333 0.01981 0.0366 0.936 0.9459 0.05184 0.877 0.8969 0.1326 ] Network output: [ 0.9695 0.06978 -0.01445 -0.0002857 0.0001282 0.004596 -0.0002153 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6539 0.1112 0.1059 0.3271 0.9693 0.9856 0.7544 0.8922 0.9633 0.6276 ] Network output: [ -0.002806 0.9288 1.031 5.417e-06 -2.432e-06 0.04556 4.083e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.051 0.0375 0.05703 0.05111 0.9836 0.9884 0.05228 0.9658 0.9776 0.07223 ] Network output: [ 0.09916 -0.3063 1.064 -1.635e-06 7.341e-07 1.044 -1.232e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7486 0.6325 0.5187 0.51 0.9729 0.9877 0.7523 0.9033 0.9687 0.6238 ] Network output: [ -0.05492 0.2058 0.9661 0.0005735 -0.0002575 0.9403 0.0004322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6009 0.5857 0.4396 0.3393 0.9853 0.9903 0.6014 0.9702 0.9797 0.4534 ] Network output: [ -0.07754 0.2425 0.932 -7.401e-05 3.323e-05 0.9803 -5.578e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6065 0.604 0.4663 0.3017 0.983 0.989 0.6066 0.9632 0.9758 0.4691 ] Network output: [ 0.02965 0.8874 0.03014 -0.000323 0.000145 1.022 -0.0002434 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03747 Epoch 2109 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0373 0.9724 0.9899 0.0001078 -4.841e-05 -0.03645 8.126e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02449 -0.005336 0.0198 0.03658 0.936 0.9459 0.05183 0.8771 0.8969 0.1326 ] Network output: [ 0.9695 0.06976 -0.01445 -0.0002845 0.0001277 0.00455 -0.0002144 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6539 0.1113 0.1061 0.3269 0.9693 0.9856 0.7544 0.8922 0.9633 0.6276 ] Network output: [ -0.00282 0.9288 1.031 5.018e-06 -2.253e-06 0.04558 3.782e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.051 0.03751 0.05702 0.05107 0.9836 0.9884 0.05228 0.9659 0.9776 0.07221 ] Network output: [ 0.09909 -0.3062 1.064 -3.666e-06 1.646e-06 1.044 -2.763e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7486 0.6326 0.5188 0.5098 0.9729 0.9877 0.7523 0.9033 0.9687 0.6238 ] Network output: [ -0.05485 0.2057 0.966 0.0005735 -0.0002575 0.9403 0.0004322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6009 0.5858 0.4397 0.3393 0.9853 0.9903 0.6015 0.9702 0.9797 0.4534 ] Network output: [ -0.07748 0.2424 0.9321 -7.328e-05 3.29e-05 0.9802 -5.523e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6065 0.604 0.4664 0.3018 0.983 0.989 0.6066 0.9632 0.9759 0.4691 ] Network output: [ 0.02959 0.8876 0.03008 -0.0003221 0.0001446 1.022 -0.0002427 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03744 Epoch 2110 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03728 0.9724 0.9899 0.0001074 -4.82e-05 -0.03643 8.092e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02448 -0.005339 0.0198 0.03655 0.936 0.9459 0.05182 0.8771 0.897 0.1325 ] Network output: [ 0.9695 0.06974 -0.01445 -0.0002833 0.0001272 0.004505 -0.0002135 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6539 0.1114 0.1062 0.3267 0.9693 0.9856 0.7544 0.8923 0.9633 0.6276 ] Network output: [ -0.002834 0.9289 1.031 4.621e-06 -2.074e-06 0.04561 3.482e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05101 0.03752 0.05701 0.05103 0.9836 0.9884 0.05229 0.9659 0.9776 0.07219 ] Network output: [ 0.09903 -0.3062 1.064 -5.696e-06 2.557e-06 1.044 -4.292e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7486 0.6327 0.5189 0.5096 0.9729 0.9877 0.7523 0.9034 0.9687 0.6238 ] Network output: [ -0.05478 0.2056 0.9659 0.0005736 -0.0002575 0.9404 0.0004323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.601 0.5859 0.4398 0.3392 0.9853 0.9903 0.6015 0.9703 0.9797 0.4535 ] Network output: [ -0.07742 0.2422 0.9322 -7.255e-05 3.257e-05 0.9802 -5.468e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6066 0.6041 0.4664 0.3018 0.983 0.989 0.6067 0.9633 0.9759 0.4691 ] Network output: [ 0.02953 0.8878 0.03003 -0.0003211 0.0001442 1.022 -0.000242 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0374 Epoch 2111 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03726 0.9724 0.99 0.0001069 -4.8e-05 -0.03641 8.057e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02447 -0.005342 0.01979 0.03653 0.936 0.9459 0.05181 0.8772 0.897 0.1325 ] Network output: [ 0.9696 0.06973 -0.01445 -0.0002821 0.0001267 0.00446 -0.0002126 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6539 0.1115 0.1063 0.3265 0.9693 0.9856 0.7544 0.8923 0.9633 0.6277 ] Network output: [ -0.002848 0.9289 1.031 4.224e-06 -1.896e-06 0.04563 3.184e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05101 0.03753 0.057 0.051 0.9836 0.9884 0.05229 0.9659 0.9777 0.07217 ] Network output: [ 0.09897 -0.3061 1.064 -7.725e-06 3.468e-06 1.044 -5.822e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7486 0.6328 0.519 0.5095 0.9729 0.9877 0.7523 0.9034 0.9687 0.6239 ] Network output: [ -0.05472 0.2055 0.9659 0.0005737 -0.0002575 0.9404 0.0004323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6011 0.586 0.4398 0.3392 0.9853 0.9903 0.6016 0.9703 0.9797 0.4535 ] Network output: [ -0.07736 0.242 0.9323 -7.181e-05 3.224e-05 0.9801 -5.412e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6066 0.6041 0.4664 0.3018 0.983 0.989 0.6067 0.9633 0.9759 0.4691 ] Network output: [ 0.02947 0.888 0.02998 -0.0003202 0.0001438 1.022 -0.0002413 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03736 Epoch 2112 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03723 0.9724 0.99 0.0001065 -4.779e-05 -0.0364 8.023e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02447 -0.005345 0.01979 0.0365 0.936 0.946 0.0518 0.8772 0.897 0.1325 ] Network output: [ 0.9696 0.06971 -0.01445 -0.0002809 0.0001261 0.004415 -0.0002117 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6539 0.1116 0.1064 0.3263 0.9693 0.9856 0.7544 0.8924 0.9633 0.6277 ] Network output: [ -0.002862 0.9289 1.031 3.829e-06 -1.719e-06 0.04566 2.886e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05102 0.03753 0.05699 0.05096 0.9836 0.9884 0.0523 0.9659 0.9777 0.07214 ] Network output: [ 0.09891 -0.306 1.064 -9.753e-06 4.379e-06 1.044 -7.35e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6329 0.5192 0.5093 0.9729 0.9877 0.7523 0.9035 0.9687 0.6239 ] Network output: [ -0.05465 0.2054 0.9658 0.0005737 -0.0002576 0.9404 0.0004324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6012 0.5861 0.4399 0.3391 0.9853 0.9903 0.6017 0.9703 0.9797 0.4536 ] Network output: [ -0.0773 0.2419 0.9324 -7.107e-05 3.191e-05 0.9801 -5.356e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6066 0.6041 0.4664 0.3018 0.983 0.989 0.6067 0.9633 0.9759 0.4691 ] Network output: [ 0.0294 0.8882 0.02993 -0.0003193 0.0001433 1.022 -0.0002406 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03733 Epoch 2113 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03721 0.9724 0.99 0.000106 -4.759e-05 -0.03638 7.988e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02446 -0.005348 0.01978 0.03648 0.936 0.946 0.05179 0.8773 0.8971 0.1324 ] Network output: [ 0.9696 0.06969 -0.01446 -0.0002798 0.0001256 0.00437 -0.0002108 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1117 0.1066 0.3261 0.9693 0.9856 0.7544 0.8924 0.9634 0.6277 ] Network output: [ -0.002876 0.9289 1.031 3.436e-06 -1.542e-06 0.04568 2.589e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05102 0.03754 0.05698 0.05092 0.9836 0.9884 0.0523 0.966 0.9777 0.07212 ] Network output: [ 0.09884 -0.306 1.064 -1.178e-05 5.289e-06 1.044 -8.878e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.633 0.5193 0.5091 0.973 0.9877 0.7523 0.9035 0.9688 0.6239 ] Network output: [ -0.05458 0.2053 0.9658 0.0005738 -0.0002576 0.9404 0.0004324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6012 0.5861 0.4399 0.3391 0.9853 0.9903 0.6018 0.9703 0.9798 0.4536 ] Network output: [ -0.07724 0.2417 0.9325 -7.033e-05 3.157e-05 0.98 -5.3e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6067 0.6042 0.4665 0.3018 0.9831 0.989 0.6068 0.9633 0.9759 0.4692 ] Network output: [ 0.02934 0.8884 0.02988 -0.0003184 0.0001429 1.022 -0.0002399 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03729 Epoch 2114 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03719 0.9723 0.9901 0.0001055 -4.738e-05 -0.03637 7.954e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02446 -0.005351 0.01978 0.03645 0.9361 0.946 0.05178 0.8773 0.8971 0.1324 ] Network output: [ 0.9697 0.06968 -0.01446 -0.0002786 0.0001251 0.004325 -0.00021 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1117 0.1067 0.326 0.9694 0.9856 0.7544 0.8924 0.9634 0.6277 ] Network output: [ -0.00289 0.929 1.031 3.043e-06 -1.366e-06 0.04571 2.293e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05102 0.03755 0.05697 0.05088 0.9836 0.9884 0.0523 0.966 0.9777 0.0721 ] Network output: [ 0.09878 -0.3059 1.064 -1.381e-05 6.199e-06 1.044 -1.041e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6331 0.5194 0.5089 0.973 0.9877 0.7523 0.9035 0.9688 0.6239 ] Network output: [ -0.05452 0.2052 0.9657 0.0005739 -0.0002576 0.9405 0.0004325 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6013 0.5862 0.44 0.339 0.9853 0.9903 0.6018 0.9704 0.9798 0.4536 ] Network output: [ -0.07718 0.2416 0.9325 -6.958e-05 3.124e-05 0.98 -5.244e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6067 0.6042 0.4665 0.3018 0.9831 0.989 0.6068 0.9634 0.9759 0.4692 ] Network output: [ 0.02928 0.8886 0.02983 -0.0003175 0.0001425 1.022 -0.0002393 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03725 Epoch 2115 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03716 0.9723 0.9901 0.0001051 -4.718e-05 -0.03635 7.92e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02445 -0.005354 0.01977 0.03643 0.9361 0.946 0.05177 0.8773 0.8971 0.1323 ] Network output: [ 0.9697 0.06966 -0.01446 -0.0002774 0.0001245 0.004281 -0.0002091 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1118 0.1068 0.3258 0.9694 0.9856 0.7544 0.8925 0.9634 0.6277 ] Network output: [ -0.002903 0.929 1.031 2.652e-06 -1.191e-06 0.04573 1.999e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05103 0.03756 0.05696 0.05085 0.9837 0.9884 0.05231 0.966 0.9777 0.07208 ] Network output: [ 0.09872 -0.3058 1.064 -1.583e-05 7.109e-06 1.044 -1.193e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6332 0.5195 0.5088 0.973 0.9877 0.7523 0.9036 0.9688 0.6239 ] Network output: [ -0.05445 0.2051 0.9656 0.000574 -0.0002577 0.9405 0.0004326 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6014 0.5863 0.4401 0.339 0.9853 0.9904 0.6019 0.9704 0.9798 0.4537 ] Network output: [ -0.07712 0.2414 0.9326 -6.882e-05 3.09e-05 0.9799 -5.187e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6067 0.6042 0.4665 0.3018 0.9831 0.989 0.6068 0.9634 0.976 0.4692 ] Network output: [ 0.02922 0.8888 0.02978 -0.0003166 0.0001421 1.022 -0.0002386 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03722 Epoch 2116 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03714 0.9723 0.9902 0.0001046 -4.697e-05 -0.03633 7.885e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02445 -0.005356 0.01977 0.0364 0.9361 0.946 0.05176 0.8774 0.8972 0.1323 ] Network output: [ 0.9697 0.06965 -0.01446 -0.0002763 0.000124 0.004236 -0.0002082 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1119 0.1069 0.3256 0.9694 0.9856 0.7544 0.8925 0.9634 0.6278 ] Network output: [ -0.002917 0.929 1.031 2.262e-06 -1.015e-06 0.04576 1.705e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05103 0.03756 0.05695 0.05081 0.9837 0.9884 0.05231 0.966 0.9777 0.07205 ] Network output: [ 0.09865 -0.3058 1.064 -1.786e-05 8.018e-06 1.044 -1.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6333 0.5196 0.5086 0.973 0.9877 0.7523 0.9036 0.9688 0.6239 ] Network output: [ -0.05439 0.205 0.9656 0.000574 -0.0002577 0.9405 0.0004326 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6014 0.5864 0.4401 0.3389 0.9853 0.9904 0.602 0.9704 0.9798 0.4537 ] Network output: [ -0.07706 0.2413 0.9327 -6.807e-05 3.056e-05 0.9799 -5.13e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6068 0.6043 0.4665 0.3018 0.9831 0.989 0.6069 0.9634 0.976 0.4692 ] Network output: [ 0.02916 0.889 0.02973 -0.0003157 0.0001417 1.022 -0.0002379 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03718 Epoch 2117 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03712 0.9723 0.9902 0.0001042 -4.677e-05 -0.03632 7.851e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02444 -0.005359 0.01976 0.03638 0.9361 0.946 0.05174 0.8774 0.8972 0.1323 ] Network output: [ 0.9698 0.06963 -0.01446 -0.0002751 0.0001235 0.004192 -0.0002073 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.112 0.107 0.3254 0.9694 0.9857 0.7544 0.8926 0.9634 0.6278 ] Network output: [ -0.002931 0.929 1.031 1.873e-06 -8.409e-07 0.04578 1.412e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05103 0.03757 0.05694 0.05077 0.9837 0.9884 0.05232 0.9661 0.9777 0.07203 ] Network output: [ 0.09859 -0.3057 1.064 -1.989e-05 8.928e-06 1.044 -1.499e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6334 0.5197 0.5084 0.973 0.9878 0.7523 0.9037 0.9688 0.624 ] Network output: [ -0.05432 0.2049 0.9655 0.0005741 -0.0002578 0.9406 0.0004327 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6015 0.5865 0.4402 0.3389 0.9853 0.9904 0.602 0.9704 0.9798 0.4538 ] Network output: [ -0.077 0.2411 0.9328 -6.73e-05 3.022e-05 0.9798 -5.072e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6068 0.6043 0.4665 0.3018 0.9831 0.989 0.6069 0.9634 0.976 0.4692 ] Network output: [ 0.0291 0.8892 0.02968 -0.0003149 0.0001413 1.022 -0.0002373 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03715 Epoch 2118 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03709 0.9723 0.9902 0.0001037 -4.656e-05 -0.0363 7.817e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02443 -0.005362 0.01976 0.03635 0.9361 0.946 0.05173 0.8775 0.8972 0.1322 ] Network output: [ 0.9698 0.06961 -0.01447 -0.0002739 0.000123 0.004148 -0.0002065 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1121 0.1072 0.3252 0.9694 0.9857 0.7544 0.8926 0.9635 0.6278 ] Network output: [ -0.002944 0.9291 1.031 1.486e-06 -6.669e-07 0.0458 1.12e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05104 0.03758 0.05692 0.05073 0.9837 0.9884 0.05232 0.9661 0.9778 0.07201 ] Network output: [ 0.09853 -0.3056 1.064 -2.191e-05 9.837e-06 1.044 -1.651e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6335 0.5198 0.5082 0.973 0.9878 0.7523 0.9037 0.9688 0.624 ] Network output: [ -0.05425 0.2048 0.9655 0.0005742 -0.0002578 0.9406 0.0004328 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6016 0.5866 0.4402 0.3388 0.9853 0.9904 0.6021 0.9704 0.9798 0.4538 ] Network output: [ -0.07694 0.2409 0.9329 -6.654e-05 2.987e-05 0.9798 -5.015e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6068 0.6043 0.4666 0.3018 0.9831 0.9891 0.6069 0.9635 0.976 0.4693 ] Network output: [ 0.02904 0.8893 0.02963 -0.000314 0.000141 1.022 -0.0002366 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03711 Epoch 2119 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03707 0.9723 0.9903 0.0001033 -4.636e-05 -0.03628 7.782e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02443 -0.005365 0.01975 0.03633 0.9361 0.9461 0.05172 0.8775 0.8973 0.1322 ] Network output: [ 0.9698 0.0696 -0.01447 -0.0002728 0.0001225 0.004104 -0.0002056 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1122 0.1073 0.325 0.9694 0.9857 0.7544 0.8927 0.9635 0.6278 ] Network output: [ -0.002958 0.9291 1.031 1.099e-06 -4.935e-07 0.04583 8.284e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05104 0.03758 0.05691 0.05069 0.9837 0.9884 0.05232 0.9661 0.9778 0.07199 ] Network output: [ 0.09846 -0.3056 1.064 -2.394e-05 1.075e-05 1.044 -1.804e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6336 0.5199 0.508 0.973 0.9878 0.7524 0.9038 0.9689 0.624 ] Network output: [ -0.05419 0.2047 0.9654 0.0005744 -0.0002578 0.9406 0.0004329 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6016 0.5866 0.4403 0.3387 0.9853 0.9904 0.6022 0.9705 0.9798 0.4539 ] Network output: [ -0.07688 0.2408 0.933 -6.577e-05 2.953e-05 0.9797 -4.956e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6068 0.6044 0.4666 0.3018 0.9831 0.9891 0.6069 0.9635 0.976 0.4693 ] Network output: [ 0.02898 0.8895 0.02959 -0.0003131 0.0001406 1.022 -0.000236 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03707 Epoch 2120 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03705 0.9723 0.9903 0.0001028 -4.615e-05 -0.03627 7.748e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02442 -0.005368 0.01975 0.0363 0.9361 0.9461 0.05171 0.8776 0.8973 0.1322 ] Network output: [ 0.9699 0.06958 -0.01447 -0.0002716 0.0001219 0.004061 -0.0002047 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1123 0.1074 0.3248 0.9694 0.9857 0.7544 0.8927 0.9635 0.6278 ] Network output: [ -0.002971 0.9291 1.031 7.141e-07 -3.206e-07 0.04585 5.382e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05104 0.03759 0.0569 0.05066 0.9837 0.9884 0.05233 0.9661 0.9778 0.07196 ] Network output: [ 0.0984 -0.3055 1.064 -2.596e-05 1.165e-05 1.044 -1.956e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6336 0.52 0.5079 0.973 0.9878 0.7524 0.9038 0.9689 0.624 ] Network output: [ -0.05412 0.2046 0.9654 0.0005745 -0.0002579 0.9406 0.0004329 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6017 0.5867 0.4403 0.3387 0.9853 0.9904 0.6023 0.9705 0.9799 0.4539 ] Network output: [ -0.07682 0.2406 0.9331 -6.499e-05 2.918e-05 0.9797 -4.898e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6069 0.6044 0.4666 0.3018 0.9831 0.9891 0.607 0.9635 0.976 0.4693 ] Network output: [ 0.02892 0.8897 0.02954 -0.0003123 0.0001402 1.022 -0.0002354 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03704 Epoch 2121 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03702 0.9723 0.9904 0.0001024 -4.595e-05 -0.03625 7.714e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02442 -0.005371 0.01974 0.03628 0.9362 0.9461 0.0517 0.8776 0.8974 0.1321 ] Network output: [ 0.9699 0.06957 -0.01448 -0.0002705 0.0001214 0.004017 -0.0002038 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1124 0.1075 0.3246 0.9694 0.9857 0.7544 0.8928 0.9635 0.6279 ] Network output: [ -0.002985 0.9291 1.031 3.301e-07 -1.482e-07 0.04587 2.488e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05105 0.0376 0.05689 0.05062 0.9837 0.9884 0.05233 0.9662 0.9778 0.07194 ] Network output: [ 0.09834 -0.3054 1.064 -2.799e-05 1.256e-05 1.044 -2.109e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6337 0.5201 0.5077 0.973 0.9878 0.7524 0.9038 0.9689 0.624 ] Network output: [ -0.05405 0.2045 0.9653 0.0005746 -0.000258 0.9407 0.000433 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6018 0.5868 0.4404 0.3386 0.9853 0.9904 0.6023 0.9705 0.9799 0.454 ] Network output: [ -0.07677 0.2405 0.9332 -6.421e-05 2.883e-05 0.9797 -4.839e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6069 0.6044 0.4666 0.3018 0.9831 0.9891 0.607 0.9635 0.9761 0.4693 ] Network output: [ 0.02887 0.8899 0.02949 -0.0003115 0.0001398 1.022 -0.0002347 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.037 Epoch 2122 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.037 0.9723 0.9904 0.0001019 -4.575e-05 -0.03624 7.68e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02441 -0.005374 0.01974 0.03625 0.9362 0.9461 0.05169 0.8777 0.8974 0.1321 ] Network output: [ 0.9699 0.06956 -0.01448 -0.0002693 0.0001209 0.003974 -0.000203 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1124 0.1076 0.3244 0.9694 0.9857 0.7544 0.8928 0.9635 0.6279 ] Network output: [ -0.002998 0.9292 1.031 -5.274e-08 2.368e-08 0.04589 -3.975e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05105 0.0376 0.05688 0.05058 0.9837 0.9884 0.05233 0.9662 0.9778 0.07192 ] Network output: [ 0.09827 -0.3054 1.065 -3.001e-05 1.347e-05 1.044 -2.262e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6338 0.5202 0.5075 0.973 0.9878 0.7524 0.9039 0.9689 0.6241 ] Network output: [ -0.05399 0.2044 0.9652 0.0005747 -0.000258 0.9407 0.0004331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6019 0.5869 0.4404 0.3386 0.9853 0.9904 0.6024 0.9705 0.9799 0.454 ] Network output: [ -0.07671 0.2403 0.9332 -6.343e-05 2.848e-05 0.9796 -4.78e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6069 0.6045 0.4666 0.3018 0.9831 0.9891 0.607 0.9636 0.9761 0.4693 ] Network output: [ 0.02881 0.8901 0.02944 -0.0003107 0.0001395 1.022 -0.0002341 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03696 Epoch 2123 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03697 0.9723 0.9904 0.0001014 -4.554e-05 -0.03622 7.645e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0244 -0.005376 0.01973 0.03622 0.9362 0.9461 0.05168 0.8777 0.8974 0.132 ] Network output: [ 0.97 0.06954 -0.01448 -0.0002682 0.0001204 0.003931 -0.0002021 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1125 0.1077 0.3242 0.9694 0.9857 0.7544 0.8929 0.9635 0.6279 ] Network output: [ -0.003012 0.9292 1.031 -4.344e-07 1.95e-07 0.04592 -3.274e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05105 0.03761 0.05686 0.05054 0.9837 0.9884 0.05233 0.9662 0.9778 0.07189 ] Network output: [ 0.09821 -0.3053 1.065 -3.203e-05 1.438e-05 1.044 -2.414e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6339 0.5203 0.5073 0.973 0.9878 0.7524 0.9039 0.9689 0.6241 ] Network output: [ -0.05392 0.2043 0.9652 0.0005748 -0.0002581 0.9407 0.0004332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6019 0.587 0.4405 0.3385 0.9854 0.9904 0.6025 0.9705 0.9799 0.454 ] Network output: [ -0.07665 0.2401 0.9333 -6.264e-05 2.812e-05 0.9796 -4.721e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.607 0.6045 0.4667 0.3018 0.9831 0.9891 0.6071 0.9636 0.9761 0.4693 ] Network output: [ 0.02875 0.8903 0.02939 -0.0003098 0.0001391 1.022 -0.0002335 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03693 Epoch 2124 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03695 0.9722 0.9905 0.000101 -4.534e-05 -0.0362 7.611e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0244 -0.005379 0.01973 0.0362 0.9362 0.9461 0.05166 0.8777 0.8975 0.132 ] Network output: [ 0.97 0.06953 -0.01449 -0.000267 0.0001199 0.003888 -0.0002013 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1126 0.1079 0.324 0.9695 0.9857 0.7544 0.8929 0.9636 0.6279 ] Network output: [ -0.003025 0.9292 1.031 -8.15e-07 3.659e-07 0.04594 -6.142e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05106 0.03762 0.05685 0.0505 0.9837 0.9884 0.05234 0.9662 0.9778 0.07187 ] Network output: [ 0.09815 -0.3052 1.065 -3.406e-05 1.529e-05 1.044 -2.567e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.634 0.5204 0.5071 0.973 0.9878 0.7524 0.904 0.9689 0.6241 ] Network output: [ -0.05386 0.2042 0.9651 0.000575 -0.0002581 0.9408 0.0004333 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.602 0.587 0.4406 0.3384 0.9854 0.9904 0.6025 0.9705 0.9799 0.4541 ] Network output: [ -0.07659 0.24 0.9334 -6.185e-05 2.777e-05 0.9795 -4.661e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.607 0.6045 0.4667 0.3018 0.9831 0.9891 0.6071 0.9636 0.9761 0.4693 ] Network output: [ 0.02869 0.8905 0.02935 -0.000309 0.0001387 1.022 -0.0002329 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03689 Epoch 2125 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03693 0.9722 0.9905 0.0001005 -4.514e-05 -0.03619 7.577e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02439 -0.005382 0.01972 0.03617 0.9362 0.9461 0.05165 0.8778 0.8975 0.132 ] Network output: [ 0.97 0.06951 -0.01449 -0.0002659 0.0001194 0.003845 -0.0002004 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.1127 0.108 0.3238 0.9695 0.9857 0.7544 0.893 0.9636 0.628 ] Network output: [ -0.003038 0.9292 1.031 -1.194e-06 5.362e-07 0.04596 -9.001e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05106 0.03762 0.05684 0.05046 0.9837 0.9884 0.05234 0.9662 0.9779 0.07185 ] Network output: [ 0.09808 -0.3051 1.065 -3.608e-05 1.62e-05 1.044 -2.719e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6341 0.5205 0.5069 0.9731 0.9878 0.7524 0.904 0.9689 0.6241 ] Network output: [ -0.05379 0.2041 0.965 0.0005751 -0.0002582 0.9408 0.0004334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6021 0.5871 0.4406 0.3384 0.9854 0.9904 0.6026 0.9706 0.9799 0.4541 ] Network output: [ -0.07653 0.2398 0.9335 -6.105e-05 2.741e-05 0.9795 -4.601e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.607 0.6046 0.4667 0.3018 0.9831 0.9891 0.6071 0.9636 0.9761 0.4694 ] Network output: [ 0.02863 0.8907 0.0293 -0.0003083 0.0001384 1.021 -0.0002323 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03686 Epoch 2126 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0369 0.9722 0.9905 0.0001001 -4.493e-05 -0.03617 7.543e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02439 -0.005385 0.01972 0.03615 0.9362 0.9461 0.05164 0.8778 0.8975 0.1319 ] Network output: [ 0.9701 0.0695 -0.01449 -0.0002648 0.0001189 0.003802 -0.0001995 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.1128 0.1081 0.3236 0.9695 0.9857 0.7544 0.893 0.9636 0.628 ] Network output: [ -0.003052 0.9293 1.031 -1.573e-06 7.061e-07 0.04598 -1.185e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05106 0.03763 0.05683 0.05042 0.9837 0.9885 0.05234 0.9663 0.9779 0.07182 ] Network output: [ 0.09802 -0.3051 1.065 -3.811e-05 1.711e-05 1.044 -2.872e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6342 0.5206 0.5068 0.9731 0.9878 0.7524 0.9041 0.969 0.6241 ] Network output: [ -0.05372 0.204 0.965 0.0005753 -0.0002583 0.9408 0.0004335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6021 0.5872 0.4407 0.3383 0.9854 0.9904 0.6027 0.9706 0.9799 0.4542 ] Network output: [ -0.07647 0.2397 0.9336 -6.025e-05 2.705e-05 0.9794 -4.541e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6071 0.6046 0.4667 0.3018 0.9831 0.9891 0.6072 0.9637 0.9761 0.4694 ] Network output: [ 0.02857 0.8909 0.02926 -0.0003075 0.000138 1.021 -0.0002317 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03682 Epoch 2127 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03688 0.9722 0.9906 9.963e-05 -4.473e-05 -0.03616 7.509e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02438 -0.005388 0.01971 0.03612 0.9363 0.9462 0.05163 0.8779 0.8976 0.1319 ] Network output: [ 0.9701 0.06948 -0.0145 -0.0002636 0.0001184 0.00376 -0.0001987 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.1129 0.1082 0.3234 0.9695 0.9857 0.7544 0.893 0.9636 0.628 ] Network output: [ -0.003065 0.9293 1.031 -1.95e-06 8.754e-07 0.046 -1.47e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05106 0.03763 0.05682 0.05038 0.9837 0.9885 0.05234 0.9663 0.9779 0.0718 ] Network output: [ 0.09795 -0.305 1.065 -4.013e-05 1.802e-05 1.044 -3.024e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6342 0.5207 0.5066 0.9731 0.9878 0.7524 0.9041 0.969 0.6242 ] Network output: [ -0.05366 0.2039 0.9649 0.0005754 -0.0002583 0.9409 0.0004337 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6022 0.5873 0.4407 0.3382 0.9854 0.9904 0.6027 0.9706 0.98 0.4542 ] Network output: [ -0.07641 0.2395 0.9337 -5.944e-05 2.669e-05 0.9794 -4.48e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6071 0.6046 0.4667 0.3018 0.9832 0.9891 0.6072 0.9637 0.9762 0.4694 ] Network output: [ 0.02851 0.8911 0.02921 -0.0003067 0.0001377 1.021 -0.0002311 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03678 Epoch 2128 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03686 0.9722 0.9906 9.918e-05 -4.453e-05 -0.03614 7.475e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02437 -0.00539 0.01971 0.03609 0.9363 0.9462 0.05162 0.8779 0.8976 0.1318 ] Network output: [ 0.9701 0.06947 -0.0145 -0.0002625 0.0001179 0.003717 -0.0001978 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.1129 0.1083 0.3232 0.9695 0.9857 0.7544 0.8931 0.9636 0.628 ] Network output: [ -0.003078 0.9293 1.031 -2.326e-06 1.044e-06 0.04602 -1.753e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05107 0.03764 0.0568 0.05035 0.9837 0.9885 0.05235 0.9663 0.9779 0.07178 ] Network output: [ 0.09789 -0.3049 1.065 -4.216e-05 1.893e-05 1.044 -3.177e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6343 0.5208 0.5064 0.9731 0.9878 0.7524 0.9041 0.969 0.6242 ] Network output: [ -0.05359 0.2038 0.9649 0.0005756 -0.0002584 0.9409 0.0004338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6023 0.5874 0.4408 0.3382 0.9854 0.9904 0.6028 0.9706 0.98 0.4543 ] Network output: [ -0.07635 0.2393 0.9338 -5.863e-05 2.632e-05 0.9794 -4.419e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6071 0.6047 0.4667 0.3018 0.9832 0.9891 0.6072 0.9637 0.9762 0.4694 ] Network output: [ 0.02846 0.8913 0.02916 -0.0003059 0.0001373 1.021 -0.0002306 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03675 Epoch 2129 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03683 0.9722 0.9907 9.873e-05 -4.432e-05 -0.03613 7.441e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02437 -0.005393 0.0197 0.03607 0.9363 0.9462 0.05161 0.878 0.8976 0.1318 ] Network output: [ 0.9702 0.06946 -0.01451 -0.0002614 0.0001173 0.003675 -0.000197 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.113 0.1084 0.323 0.9695 0.9857 0.7544 0.8931 0.9637 0.628 ] Network output: [ -0.003091 0.9294 1.031 -2.701e-06 1.213e-06 0.04604 -2.036e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05107 0.03765 0.05679 0.05031 0.9838 0.9885 0.05235 0.9663 0.9779 0.07175 ] Network output: [ 0.09783 -0.3049 1.065 -4.418e-05 1.983e-05 1.044 -3.33e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6344 0.5209 0.5062 0.9731 0.9878 0.7524 0.9042 0.969 0.6242 ] Network output: [ -0.05353 0.2037 0.9648 0.0005758 -0.0002585 0.9409 0.0004339 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6023 0.5874 0.4408 0.3381 0.9854 0.9904 0.6029 0.9706 0.98 0.4543 ] Network output: [ -0.07629 0.2392 0.9338 -5.782e-05 2.596e-05 0.9793 -4.357e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6071 0.6047 0.4668 0.3018 0.9832 0.9891 0.6072 0.9637 0.9762 0.4694 ] Network output: [ 0.0284 0.8915 0.02912 -0.0003052 0.000137 1.021 -0.00023 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03671 Epoch 2130 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03681 0.9722 0.9907 9.828e-05 -4.412e-05 -0.03611 7.407e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02436 -0.005396 0.0197 0.03604 0.9363 0.9462 0.05159 0.878 0.8977 0.1318 ] Network output: [ 0.9702 0.06945 -0.01451 -0.0002603 0.0001168 0.003633 -0.0001961 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.1131 0.1085 0.3228 0.9695 0.9857 0.7544 0.8932 0.9637 0.6281 ] Network output: [ -0.003105 0.9294 1.031 -3.075e-06 1.381e-06 0.04606 -2.318e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05107 0.03765 0.05678 0.05027 0.9838 0.9885 0.05235 0.9664 0.9779 0.07173 ] Network output: [ 0.09776 -0.3048 1.065 -4.621e-05 2.074e-05 1.045 -3.482e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6345 0.521 0.506 0.9731 0.9878 0.7524 0.9042 0.969 0.6242 ] Network output: [ -0.05346 0.2036 0.9647 0.0005759 -0.0002586 0.941 0.000434 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6024 0.5875 0.4409 0.3381 0.9854 0.9904 0.6029 0.9707 0.98 0.4543 ] Network output: [ -0.07623 0.239 0.9339 -5.7e-05 2.559e-05 0.9793 -4.296e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6072 0.6047 0.4668 0.3018 0.9832 0.9891 0.6073 0.9638 0.9762 0.4694 ] Network output: [ 0.02834 0.8916 0.02907 -0.0003044 0.0001367 1.021 -0.0002294 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03668 Epoch 2131 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03679 0.9722 0.9907 9.783e-05 -4.392e-05 -0.0361 7.373e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02435 -0.005398 0.01969 0.03602 0.9363 0.9462 0.05158 0.878 0.8977 0.1317 ] Network output: [ 0.9702 0.06943 -0.01452 -0.0002591 0.0001163 0.003591 -0.0001953 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.1132 0.1087 0.3226 0.9695 0.9857 0.7544 0.8932 0.9637 0.6281 ] Network output: [ -0.003118 0.9294 1.031 -3.448e-06 1.548e-06 0.04608 -2.599e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05107 0.03766 0.05677 0.05023 0.9838 0.9885 0.05235 0.9664 0.9779 0.07171 ] Network output: [ 0.0977 -0.3047 1.065 -4.823e-05 2.165e-05 1.045 -3.635e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6346 0.5211 0.5058 0.9731 0.9878 0.7524 0.9043 0.969 0.6242 ] Network output: [ -0.05339 0.2035 0.9647 0.0005761 -0.0002586 0.941 0.0004342 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6025 0.5876 0.4409 0.338 0.9854 0.9904 0.603 0.9707 0.98 0.4544 ] Network output: [ -0.07617 0.2389 0.934 -5.617e-05 2.522e-05 0.9793 -4.233e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6072 0.6047 0.4668 0.3018 0.9832 0.9891 0.6073 0.9638 0.9762 0.4694 ] Network output: [ 0.02829 0.8918 0.02903 -0.0003037 0.0001363 1.021 -0.0002289 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03664 Epoch 2132 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03676 0.9722 0.9908 9.738e-05 -4.372e-05 -0.03608 7.339e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02435 -0.005401 0.01969 0.03599 0.9363 0.9462 0.05157 0.8781 0.8977 0.1317 ] Network output: [ 0.9703 0.06942 -0.01452 -0.000258 0.0001158 0.00355 -0.0001945 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.1133 0.1088 0.3224 0.9695 0.9857 0.7544 0.8933 0.9637 0.6281 ] Network output: [ -0.003131 0.9294 1.031 -3.82e-06 1.715e-06 0.0461 -2.879e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05107 0.03766 0.05675 0.05019 0.9838 0.9885 0.05235 0.9664 0.978 0.07168 ] Network output: [ 0.09763 -0.3047 1.065 -5.026e-05 2.256e-05 1.045 -3.788e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6346 0.5212 0.5056 0.9731 0.9878 0.7524 0.9043 0.9691 0.6243 ] Network output: [ -0.05333 0.2034 0.9646 0.0005763 -0.0002587 0.941 0.0004343 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6025 0.5877 0.441 0.3379 0.9854 0.9904 0.6031 0.9707 0.98 0.4544 ] Network output: [ -0.07611 0.2387 0.9341 -5.534e-05 2.485e-05 0.9792 -4.171e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6072 0.6048 0.4668 0.3018 0.9832 0.9891 0.6073 0.9638 0.9762 0.4695 ] Network output: [ 0.02823 0.892 0.02898 -0.000303 0.000136 1.021 -0.0002283 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03661 Epoch 2133 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03674 0.9722 0.9908 9.693e-05 -4.351e-05 -0.03606 7.305e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02434 -0.005404 0.01968 0.03596 0.9363 0.9462 0.05156 0.8781 0.8978 0.1316 ] Network output: [ 0.9703 0.06941 -0.01452 -0.0002569 0.0001153 0.003508 -0.0001936 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.1134 0.1089 0.3222 0.9695 0.9857 0.7544 0.8933 0.9637 0.6281 ] Network output: [ -0.003144 0.9295 1.031 -4.191e-06 1.882e-06 0.04612 -3.158e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05108 0.03767 0.05674 0.05015 0.9838 0.9885 0.05235 0.9664 0.978 0.07166 ] Network output: [ 0.09757 -0.3046 1.065 -5.229e-05 2.347e-05 1.045 -3.94e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6347 0.5213 0.5054 0.9731 0.9878 0.7524 0.9043 0.9691 0.6243 ] Network output: [ -0.05326 0.2033 0.9645 0.0005765 -0.0002588 0.9411 0.0004345 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6026 0.5877 0.441 0.3379 0.9854 0.9904 0.6031 0.9707 0.98 0.4545 ] Network output: [ -0.07605 0.2385 0.9342 -5.451e-05 2.447e-05 0.9792 -4.108e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6073 0.6048 0.4668 0.3017 0.9832 0.9891 0.6074 0.9638 0.9763 0.4695 ] Network output: [ 0.02817 0.8922 0.02894 -0.0003022 0.0001357 1.021 -0.0002278 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03657 Epoch 2134 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03671 0.9722 0.9909 9.648e-05 -4.331e-05 -0.03605 7.271e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02434 -0.005406 0.01967 0.03594 0.9364 0.9463 0.05155 0.8782 0.8978 0.1316 ] Network output: [ 0.9703 0.06939 -0.01453 -0.0002558 0.0001148 0.003467 -0.0001928 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.1134 0.109 0.322 0.9696 0.9857 0.7544 0.8933 0.9638 0.6281 ] Network output: [ -0.003157 0.9295 1.031 -4.561e-06 2.048e-06 0.04614 -3.437e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05108 0.03767 0.05673 0.05011 0.9838 0.9885 0.05236 0.9665 0.978 0.07163 ] Network output: [ 0.09751 -0.3045 1.065 -5.431e-05 2.438e-05 1.045 -4.093e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6348 0.5214 0.5053 0.9731 0.9878 0.7523 0.9044 0.9691 0.6243 ] Network output: [ -0.0532 0.2032 0.9645 0.0005767 -0.0002589 0.9411 0.0004346 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6027 0.5878 0.4411 0.3378 0.9854 0.9904 0.6032 0.9707 0.98 0.4545 ] Network output: [ -0.07599 0.2384 0.9342 -5.367e-05 2.41e-05 0.9791 -4.045e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6073 0.6048 0.4668 0.3017 0.9832 0.9891 0.6074 0.9639 0.9763 0.4695 ] Network output: [ 0.02811 0.8924 0.02889 -0.0003015 0.0001354 1.021 -0.0002272 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03653 Epoch 2135 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03669 0.9721 0.9909 9.603e-05 -4.311e-05 -0.03603 7.237e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02433 -0.005409 0.01967 0.03591 0.9364 0.9463 0.05153 0.8782 0.8978 0.1316 ] Network output: [ 0.9703 0.06938 -0.01454 -0.0002547 0.0001143 0.003426 -0.0001919 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.1135 0.1091 0.3218 0.9696 0.9858 0.7544 0.8934 0.9638 0.6282 ] Network output: [ -0.00317 0.9295 1.031 -4.93e-06 2.213e-06 0.04616 -3.715e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05108 0.03768 0.05671 0.05007 0.9838 0.9885 0.05236 0.9665 0.978 0.07161 ] Network output: [ 0.09744 -0.3044 1.065 -5.634e-05 2.529e-05 1.045 -4.246e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6349 0.5215 0.5051 0.9731 0.9878 0.7523 0.9044 0.9691 0.6243 ] Network output: [ -0.05313 0.2031 0.9644 0.0005769 -0.000259 0.9411 0.0004348 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6027 0.5879 0.4411 0.3377 0.9854 0.9904 0.6033 0.9708 0.9801 0.4545 ] Network output: [ -0.07593 0.2382 0.9343 -5.283e-05 2.372e-05 0.9791 -3.981e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6073 0.6049 0.4668 0.3017 0.9832 0.9891 0.6074 0.9639 0.9763 0.4695 ] Network output: [ 0.02806 0.8926 0.02885 -0.0003008 0.0001351 1.021 -0.0002267 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0365 Epoch 2136 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03667 0.9721 0.9909 9.558e-05 -4.291e-05 -0.03602 7.203e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02432 -0.005412 0.01966 0.03588 0.9364 0.9463 0.05152 0.8782 0.8979 0.1315 ] Network output: [ 0.9704 0.06937 -0.01454 -0.0002536 0.0001138 0.003385 -0.0001911 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.1136 0.1092 0.3216 0.9696 0.9858 0.7544 0.8934 0.9638 0.6282 ] Network output: [ -0.003183 0.9295 1.031 -5.298e-06 2.378e-06 0.04618 -3.993e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05108 0.03768 0.0567 0.05003 0.9838 0.9885 0.05236 0.9665 0.978 0.07159 ] Network output: [ 0.09738 -0.3044 1.065 -5.837e-05 2.62e-05 1.045 -4.399e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6349 0.5216 0.5049 0.9732 0.9878 0.7523 0.9045 0.9691 0.6243 ] Network output: [ -0.05306 0.203 0.9644 0.0005771 -0.0002591 0.9412 0.0004349 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6028 0.588 0.4412 0.3376 0.9854 0.9904 0.6033 0.9708 0.9801 0.4546 ] Network output: [ -0.07587 0.2381 0.9344 -5.198e-05 2.334e-05 0.9791 -3.917e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6073 0.6049 0.4669 0.3017 0.9832 0.9891 0.6074 0.9639 0.9763 0.4695 ] Network output: [ 0.028 0.8928 0.02881 -0.0003001 0.0001347 1.021 -0.0002262 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03646 Epoch 2137 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03664 0.9721 0.991 9.513e-05 -4.271e-05 -0.036 7.169e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02432 -0.005414 0.01966 0.03586 0.9364 0.9463 0.05151 0.8783 0.8979 0.1315 ] Network output: [ 0.9704 0.06936 -0.01455 -0.0002525 0.0001133 0.003344 -0.0001903 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.1137 0.1093 0.3214 0.9696 0.9858 0.7544 0.8935 0.9638 0.6282 ] Network output: [ -0.003196 0.9296 1.031 -5.665e-06 2.543e-06 0.0462 -4.269e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05108 0.03769 0.05669 0.04999 0.9838 0.9885 0.05236 0.9665 0.978 0.07156 ] Network output: [ 0.09731 -0.3043 1.065 -6.04e-05 2.712e-05 1.045 -4.552e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.635 0.5217 0.5047 0.9732 0.9878 0.7523 0.9045 0.9691 0.6244 ] Network output: [ -0.053 0.2029 0.9643 0.0005773 -0.0002592 0.9412 0.0004351 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6029 0.5881 0.4412 0.3376 0.9854 0.9904 0.6034 0.9708 0.9801 0.4546 ] Network output: [ -0.07581 0.2379 0.9345 -5.113e-05 2.295e-05 0.979 -3.853e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6074 0.6049 0.4669 0.3017 0.9832 0.9891 0.6075 0.9639 0.9763 0.4695 ] Network output: [ 0.02795 0.893 0.02876 -0.0002994 0.0001344 1.021 -0.0002257 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03643 Epoch 2138 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03662 0.9721 0.991 9.468e-05 -4.25e-05 -0.03599 7.135e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02431 -0.005417 0.01965 0.03583 0.9364 0.9463 0.0515 0.8783 0.8979 0.1314 ] Network output: [ 0.9704 0.06935 -0.01455 -0.0002514 0.0001129 0.003303 -0.0001894 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.1138 0.1094 0.3212 0.9696 0.9858 0.7544 0.8935 0.9638 0.6282 ] Network output: [ -0.003209 0.9296 1.031 -6.031e-06 2.707e-06 0.04621 -4.545e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05108 0.03769 0.05667 0.04995 0.9838 0.9885 0.05236 0.9665 0.978 0.07154 ] Network output: [ 0.09725 -0.3042 1.065 -6.243e-05 2.803e-05 1.045 -4.705e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6351 0.5218 0.5045 0.9732 0.9879 0.7523 0.9045 0.9692 0.6244 ] Network output: [ -0.05293 0.2028 0.9642 0.0005775 -0.0002593 0.9412 0.0004353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6029 0.5881 0.4413 0.3375 0.9854 0.9904 0.6035 0.9708 0.9801 0.4547 ] Network output: [ -0.07575 0.2377 0.9346 -5.027e-05 2.257e-05 0.979 -3.788e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6074 0.605 0.4669 0.3017 0.9832 0.9892 0.6075 0.9639 0.9763 0.4695 ] Network output: [ 0.02789 0.8931 0.02872 -0.0002988 0.0001341 1.021 -0.0002252 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03639 Epoch 2139 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0366 0.9721 0.9911 9.423e-05 -4.23e-05 -0.03597 7.101e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0243 -0.005419 0.01965 0.0358 0.9364 0.9463 0.05148 0.8784 0.8979 0.1314 ] Network output: [ 0.9705 0.06933 -0.01456 -0.0002503 0.0001124 0.003262 -0.0001886 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.1138 0.1095 0.321 0.9696 0.9858 0.7544 0.8936 0.9638 0.6283 ] Network output: [ -0.003222 0.9296 1.031 -6.396e-06 2.871e-06 0.04623 -4.82e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05108 0.03769 0.05666 0.04991 0.9838 0.9885 0.05236 0.9666 0.978 0.07151 ] Network output: [ 0.09718 -0.3042 1.065 -6.446e-05 2.894e-05 1.045 -4.858e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6352 0.5219 0.5043 0.9732 0.9879 0.7523 0.9046 0.9692 0.6244 ] Network output: [ -0.05287 0.2026 0.9642 0.0005778 -0.0002594 0.9413 0.0004354 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.603 0.5882 0.4413 0.3374 0.9854 0.9904 0.6035 0.9708 0.9801 0.4547 ] Network output: [ -0.07569 0.2376 0.9346 -4.941e-05 2.218e-05 0.979 -3.723e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6074 0.605 0.4669 0.3017 0.9832 0.9892 0.6075 0.964 0.9763 0.4695 ] Network output: [ 0.02783 0.8933 0.02868 -0.0002981 0.0001338 1.021 -0.0002247 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03635 Epoch 2140 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03657 0.9721 0.9911 9.378e-05 -4.21e-05 -0.03596 7.068e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0243 -0.005422 0.01964 0.03578 0.9365 0.9463 0.05147 0.8784 0.898 0.1314 ] Network output: [ 0.9705 0.06932 -0.01456 -0.0002492 0.0001119 0.003222 -0.0001878 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.1139 0.1096 0.3208 0.9696 0.9858 0.7544 0.8936 0.9639 0.6283 ] Network output: [ -0.003235 0.9297 1.031 -6.76e-06 3.035e-06 0.04625 -5.095e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05108 0.0377 0.05665 0.04986 0.9838 0.9885 0.05236 0.9666 0.9781 0.07149 ] Network output: [ 0.09712 -0.3041 1.065 -6.649e-05 2.985e-05 1.045 -5.011e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6352 0.522 0.5041 0.9732 0.9879 0.7523 0.9046 0.9692 0.6244 ] Network output: [ -0.0528 0.2025 0.9641 0.000578 -0.0002595 0.9413 0.0004356 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6031 0.5883 0.4414 0.3374 0.9855 0.9905 0.6036 0.9708 0.9801 0.4547 ] Network output: [ -0.07563 0.2374 0.9347 -4.854e-05 2.179e-05 0.9789 -3.658e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6075 0.605 0.4669 0.3017 0.9832 0.9892 0.6076 0.964 0.9764 0.4695 ] Network output: [ 0.02778 0.8935 0.02864 -0.0002974 0.0001335 1.021 -0.0002242 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03632 Epoch 2141 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03655 0.9721 0.9911 9.333e-05 -4.19e-05 -0.03594 7.034e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02429 -0.005425 0.01964 0.03575 0.9365 0.9463 0.05146 0.8785 0.898 0.1313 ] Network output: [ 0.9705 0.06931 -0.01457 -0.0002481 0.0001114 0.003182 -0.000187 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.114 0.1097 0.3206 0.9696 0.9858 0.7544 0.8936 0.9639 0.6283 ] Network output: [ -0.003248 0.9297 1.031 -7.123e-06 3.198e-06 0.04627 -5.368e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05108 0.0377 0.05663 0.04982 0.9838 0.9885 0.05236 0.9666 0.9781 0.07146 ] Network output: [ 0.09705 -0.304 1.065 -6.853e-05 3.076e-05 1.045 -5.164e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6353 0.522 0.5039 0.9732 0.9879 0.7523 0.9047 0.9692 0.6244 ] Network output: [ -0.05273 0.2024 0.964 0.0005783 -0.0002596 0.9413 0.0004358 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6031 0.5884 0.4414 0.3373 0.9855 0.9905 0.6037 0.9709 0.9801 0.4548 ] Network output: [ -0.07557 0.2372 0.9348 -4.766e-05 2.14e-05 0.9789 -3.592e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6075 0.6051 0.4669 0.3016 0.9833 0.9892 0.6076 0.964 0.9764 0.4695 ] Network output: [ 0.02772 0.8937 0.02859 -0.0002968 0.0001332 1.021 -0.0002237 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03628 Epoch 2142 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03652 0.9721 0.9912 9.289e-05 -4.17e-05 -0.03593 7e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02428 -0.005427 0.01963 0.03572 0.9365 0.9464 0.05145 0.8785 0.898 0.1313 ] Network output: [ 0.9706 0.0693 -0.01458 -0.000247 0.0001109 0.003141 -0.0001861 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.1141 0.1098 0.3204 0.9696 0.9858 0.7544 0.8937 0.9639 0.6283 ] Network output: [ -0.003261 0.9297 1.03 -7.486e-06 3.361e-06 0.04628 -5.641e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05109 0.03771 0.05662 0.04978 0.9838 0.9885 0.05236 0.9666 0.9781 0.07144 ] Network output: [ 0.09699 -0.3039 1.065 -7.056e-05 3.168e-05 1.045 -5.318e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6354 0.5221 0.5037 0.9732 0.9879 0.7523 0.9047 0.9692 0.6245 ] Network output: [ -0.05267 0.2023 0.964 0.0005785 -0.0002597 0.9414 0.000436 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6032 0.5884 0.4415 0.3372 0.9855 0.9905 0.6037 0.9709 0.9801 0.4548 ] Network output: [ -0.07551 0.2371 0.9349 -4.679e-05 2.1e-05 0.9789 -3.526e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6075 0.6051 0.4669 0.3016 0.9833 0.9892 0.6076 0.964 0.9764 0.4695 ] Network output: [ 0.02767 0.8939 0.02855 -0.0002961 0.0001329 1.021 -0.0002232 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03625 Epoch 2143 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0365 0.9721 0.9912 9.244e-05 -4.15e-05 -0.03592 6.967e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02428 -0.00543 0.01963 0.03569 0.9365 0.9464 0.05143 0.8785 0.8981 0.1312 ] Network output: [ 0.9706 0.06929 -0.01458 -0.0002459 0.0001104 0.003102 -0.0001853 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.1141 0.1099 0.3202 0.9696 0.9858 0.7544 0.8937 0.9639 0.6283 ] Network output: [ -0.003274 0.9297 1.03 -7.847e-06 3.523e-06 0.0463 -5.914e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05109 0.03771 0.0566 0.04974 0.9839 0.9885 0.05236 0.9666 0.9781 0.07142 ] Network output: [ 0.09692 -0.3039 1.065 -7.259e-05 3.259e-05 1.045 -5.471e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6354 0.5222 0.5035 0.9732 0.9879 0.7523 0.9047 0.9692 0.6245 ] Network output: [ -0.0526 0.2022 0.9639 0.0005788 -0.0002598 0.9414 0.0004362 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6033 0.5885 0.4415 0.3371 0.9855 0.9905 0.6038 0.9709 0.9802 0.4548 ] Network output: [ -0.07545 0.2369 0.935 -4.59e-05 2.061e-05 0.9788 -3.46e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6075 0.6051 0.4669 0.3016 0.9833 0.9892 0.6076 0.9641 0.9764 0.4696 ] Network output: [ 0.02761 0.8941 0.02851 -0.0002955 0.0001327 1.021 -0.0002227 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03621 Epoch 2144 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03648 0.9721 0.9913 9.199e-05 -4.13e-05 -0.0359 6.933e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02427 -0.005432 0.01962 0.03567 0.9365 0.9464 0.05142 0.8786 0.8981 0.1312 ] Network output: [ 0.9706 0.06928 -0.01459 -0.0002448 0.0001099 0.003062 -0.0001845 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.1142 0.11 0.32 0.9697 0.9858 0.7543 0.8938 0.9639 0.6284 ] Network output: [ -0.003286 0.9298 1.03 -8.208e-06 3.685e-06 0.04632 -6.185e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05109 0.03772 0.05659 0.0497 0.9839 0.9885 0.05236 0.9667 0.9781 0.07139 ] Network output: [ 0.09686 -0.3038 1.065 -7.463e-05 3.35e-05 1.045 -5.624e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7487 0.6355 0.5223 0.5033 0.9732 0.9879 0.7523 0.9048 0.9692 0.6245 ] Network output: [ -0.05254 0.2021 0.9638 0.000579 -0.0002599 0.9415 0.0004364 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6033 0.5886 0.4416 0.337 0.9855 0.9905 0.6039 0.9709 0.9802 0.4549 ] Network output: [ -0.0754 0.2368 0.935 -4.502e-05 2.021e-05 0.9788 -3.393e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6076 0.6051 0.4669 0.3016 0.9833 0.9892 0.6077 0.9641 0.9764 0.4696 ] Network output: [ 0.02756 0.8942 0.02847 -0.0002949 0.0001324 1.021 -0.0002222 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03618 Epoch 2145 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03645 0.9721 0.9913 9.155e-05 -4.11e-05 -0.03589 6.899e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02427 -0.005435 0.01961 0.03564 0.9365 0.9464 0.05141 0.8786 0.8981 0.1312 ] Network output: [ 0.9707 0.06927 -0.0146 -0.0002438 0.0001094 0.003022 -0.0001837 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.1143 0.1101 0.3197 0.9697 0.9858 0.7543 0.8938 0.9639 0.6284 ] Network output: [ -0.003299 0.9298 1.03 -8.567e-06 3.846e-06 0.04633 -6.456e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05109 0.03772 0.05658 0.04966 0.9839 0.9886 0.05236 0.9667 0.9781 0.07137 ] Network output: [ 0.09679 -0.3037 1.065 -7.667e-05 3.442e-05 1.045 -5.778e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7486 0.6356 0.5224 0.5031 0.9732 0.9879 0.7523 0.9048 0.9693 0.6245 ] Network output: [ -0.05247 0.202 0.9638 0.0005793 -0.0002601 0.9415 0.0004366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6034 0.5887 0.4416 0.337 0.9855 0.9905 0.6039 0.9709 0.9802 0.4549 ] Network output: [ -0.07534 0.2366 0.9351 -4.412e-05 1.981e-05 0.9788 -3.325e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6076 0.6052 0.467 0.3016 0.9833 0.9892 0.6077 0.9641 0.9764 0.4696 ] Network output: [ 0.0275 0.8944 0.02843 -0.0002943 0.0001321 1.021 -0.0002218 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03614 Epoch 2146 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03643 0.9721 0.9913 9.11e-05 -4.09e-05 -0.03587 6.866e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02426 -0.005437 0.01961 0.03561 0.9365 0.9464 0.05139 0.8787 0.8982 0.1311 ] Network output: [ 0.9707 0.06926 -0.0146 -0.0002427 0.0001089 0.002983 -0.0001829 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.1144 0.1102 0.3195 0.9697 0.9858 0.7543 0.8939 0.964 0.6284 ] Network output: [ -0.003312 0.9298 1.03 -8.926e-06 4.007e-06 0.04635 -6.727e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05109 0.03772 0.05656 0.04962 0.9839 0.9886 0.05236 0.9667 0.9781 0.07134 ] Network output: [ 0.09673 -0.3037 1.065 -7.871e-05 3.533e-05 1.045 -5.932e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7486 0.6356 0.5225 0.5029 0.9732 0.9879 0.7523 0.9049 0.9693 0.6245 ] Network output: [ -0.0524 0.2019 0.9637 0.0005795 -0.0002602 0.9415 0.0004368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6035 0.5887 0.4417 0.3369 0.9855 0.9905 0.604 0.971 0.9802 0.455 ] Network output: [ -0.07528 0.2364 0.9352 -4.323e-05 1.941e-05 0.9788 -3.258e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6076 0.6052 0.467 0.3015 0.9833 0.9892 0.6077 0.9641 0.9765 0.4696 ] Network output: [ 0.02745 0.8946 0.02839 -0.0002936 0.0001318 1.021 -0.0002213 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03611 Epoch 2147 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0364 0.972 0.9914 9.066e-05 -4.07e-05 -0.03586 6.832e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02425 -0.00544 0.0196 0.03559 0.9366 0.9464 0.05138 0.8787 0.8982 0.1311 ] Network output: [ 0.9707 0.06925 -0.01461 -0.0002416 0.0001085 0.002943 -0.0001821 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.1144 0.1103 0.3193 0.9697 0.9858 0.7543 0.8939 0.964 0.6284 ] Network output: [ -0.003325 0.9299 1.03 -9.284e-06 4.168e-06 0.04636 -6.997e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05109 0.03773 0.05655 0.04958 0.9839 0.9886 0.05236 0.9667 0.9782 0.07132 ] Network output: [ 0.09666 -0.3036 1.065 -8.075e-05 3.625e-05 1.045 -6.085e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7486 0.6357 0.5226 0.5027 0.9732 0.9879 0.7522 0.9049 0.9693 0.6246 ] Network output: [ -0.05234 0.2018 0.9636 0.0005798 -0.0002603 0.9416 0.000437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6035 0.5888 0.4417 0.3368 0.9855 0.9905 0.6041 0.971 0.9802 0.455 ] Network output: [ -0.07522 0.2363 0.9353 -4.232e-05 1.9e-05 0.9787 -3.19e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6077 0.6052 0.467 0.3015 0.9833 0.9892 0.6078 0.9642 0.9765 0.4696 ] Network output: [ 0.02739 0.8948 0.02835 -0.000293 0.0001316 1.021 -0.0002208 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03607 Epoch 2148 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03638 0.972 0.9914 9.021e-05 -4.05e-05 -0.03584 6.799e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02425 -0.005442 0.0196 0.03556 0.9366 0.9464 0.05137 0.8787 0.8982 0.131 ] Network output: [ 0.9707 0.06924 -0.01462 -0.0002405 0.000108 0.002904 -0.0001813 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1145 0.1105 0.3191 0.9697 0.9858 0.7543 0.8939 0.964 0.6285 ] Network output: [ -0.003338 0.9299 1.03 -9.641e-06 4.328e-06 0.04638 -7.266e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05109 0.03773 0.05653 0.04954 0.9839 0.9886 0.05236 0.9668 0.9782 0.07129 ] Network output: [ 0.0966 -0.3035 1.065 -8.279e-05 3.717e-05 1.045 -6.239e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7486 0.6357 0.5227 0.5025 0.9733 0.9879 0.7522 0.9049 0.9693 0.6246 ] Network output: [ -0.05227 0.2017 0.9636 0.0005801 -0.0002604 0.9416 0.0004372 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6036 0.5889 0.4418 0.3367 0.9855 0.9905 0.6041 0.971 0.9802 0.455 ] Network output: [ -0.07516 0.2361 0.9353 -4.142e-05 1.859e-05 0.9787 -3.121e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6077 0.6053 0.467 0.3015 0.9833 0.9892 0.6078 0.9642 0.9765 0.4696 ] Network output: [ 0.02734 0.895 0.02831 -0.0002925 0.0001313 1.021 -0.0002204 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03603 Epoch 2149 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03636 0.972 0.9914 8.977e-05 -4.03e-05 -0.03583 6.765e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02424 -0.005445 0.01959 0.03553 0.9366 0.9464 0.05135 0.8788 0.8983 0.131 ] Network output: [ 0.9708 0.06923 -0.01462 -0.0002395 0.0001075 0.002865 -0.0001805 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1146 0.1106 0.3189 0.9697 0.9858 0.7543 0.894 0.964 0.6285 ] Network output: [ -0.00335 0.9299 1.03 -9.997e-06 4.488e-06 0.0464 -7.534e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05109 0.03773 0.05652 0.04949 0.9839 0.9886 0.05236 0.9668 0.9782 0.07127 ] Network output: [ 0.09653 -0.3034 1.065 -8.483e-05 3.808e-05 1.045 -6.393e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7486 0.6358 0.5228 0.5023 0.9733 0.9879 0.7522 0.905 0.9693 0.6246 ] Network output: [ -0.05221 0.2016 0.9635 0.0005804 -0.0002606 0.9417 0.0004374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6037 0.589 0.4418 0.3367 0.9855 0.9905 0.6042 0.971 0.9802 0.4551 ] Network output: [ -0.0751 0.236 0.9354 -4.05e-05 1.818e-05 0.9787 -3.052e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6077 0.6053 0.467 0.3015 0.9833 0.9892 0.6078 0.9642 0.9765 0.4696 ] Network output: [ 0.02728 0.8951 0.02827 -0.0002919 0.000131 1.021 -0.00022 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.036 Epoch 2150 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03633 0.972 0.9915 8.932e-05 -4.01e-05 -0.03581 6.732e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02423 -0.005447 0.01959 0.0355 0.9366 0.9465 0.05134 0.8788 0.8983 0.131 ] Network output: [ 0.9708 0.06922 -0.01463 -0.0002384 0.000107 0.002826 -0.0001797 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1146 0.1107 0.3187 0.9697 0.9858 0.7543 0.894 0.964 0.6285 ] Network output: [ -0.003363 0.93 1.03 -1.035e-05 4.648e-06 0.04641 -7.802e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05109 0.03774 0.05651 0.04945 0.9839 0.9886 0.05236 0.9668 0.9782 0.07124 ] Network output: [ 0.09646 -0.3034 1.065 -8.688e-05 3.9e-05 1.045 -6.547e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7486 0.6359 0.5229 0.5021 0.9733 0.9879 0.7522 0.905 0.9693 0.6246 ] Network output: [ -0.05214 0.2015 0.9634 0.0005807 -0.0002607 0.9417 0.0004376 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6037 0.589 0.4419 0.3366 0.9855 0.9905 0.6043 0.971 0.9802 0.4551 ] Network output: [ -0.07504 0.2358 0.9355 -3.958e-05 1.777e-05 0.9786 -2.983e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6077 0.6053 0.467 0.3015 0.9833 0.9892 0.6078 0.9642 0.9765 0.4696 ] Network output: [ 0.02723 0.8953 0.02823 -0.0002913 0.0001308 1.021 -0.0002195 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03596 Epoch 2151 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03631 0.972 0.9915 8.888e-05 -3.99e-05 -0.0358 6.698e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02423 -0.005449 0.01958 0.03548 0.9366 0.9465 0.05133 0.8788 0.8983 0.1309 ] Network output: [ 0.9708 0.06921 -0.01464 -0.0002373 0.0001065 0.002787 -0.0001789 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1147 0.1108 0.3185 0.9697 0.9858 0.7543 0.8941 0.964 0.6285 ] Network output: [ -0.003376 0.93 1.03 -1.071e-05 4.807e-06 0.04643 -8.07e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05109 0.03774 0.05649 0.04941 0.9839 0.9886 0.05236 0.9668 0.9782 0.07122 ] Network output: [ 0.0964 -0.3033 1.065 -8.892e-05 3.992e-05 1.045 -6.701e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7486 0.6359 0.523 0.5019 0.9733 0.9879 0.7522 0.9051 0.9693 0.6247 ] Network output: [ -0.05207 0.2014 0.9634 0.000581 -0.0002608 0.9417 0.0004378 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6038 0.5891 0.4419 0.3365 0.9855 0.9905 0.6043 0.971 0.9802 0.4551 ] Network output: [ -0.07498 0.2356 0.9356 -3.866e-05 1.736e-05 0.9786 -2.914e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6078 0.6054 0.467 0.3014 0.9833 0.9892 0.6079 0.9642 0.9765 0.4696 ] Network output: [ 0.02718 0.8955 0.02819 -0.0002907 0.0001305 1.021 -0.0002191 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03593 Epoch 2152 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03628 0.972 0.9916 8.843e-05 -3.97e-05 -0.03579 6.665e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02422 -0.005452 0.01957 0.03545 0.9366 0.9465 0.05132 0.8789 0.8984 0.1309 ] Network output: [ 0.9709 0.0692 -0.01465 -0.0002363 0.0001061 0.002749 -0.0001781 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1148 0.1109 0.3183 0.9697 0.9858 0.7543 0.8941 0.9641 0.6285 ] Network output: [ -0.003389 0.93 1.03 -1.106e-05 4.966e-06 0.04644 -8.336e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05109 0.03774 0.05648 0.04937 0.9839 0.9886 0.05236 0.9668 0.9782 0.07119 ] Network output: [ 0.09633 -0.3032 1.065 -9.097e-05 4.084e-05 1.045 -6.856e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7486 0.636 0.523 0.5017 0.9733 0.9879 0.7522 0.9051 0.9694 0.6247 ] Network output: [ -0.05201 0.2013 0.9633 0.0005813 -0.000261 0.9418 0.0004381 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6039 0.5892 0.4419 0.3364 0.9855 0.9905 0.6044 0.9711 0.9803 0.4552 ] Network output: [ -0.07492 0.2355 0.9356 -3.773e-05 1.694e-05 0.9786 -2.844e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6078 0.6054 0.467 0.3014 0.9833 0.9892 0.6079 0.9643 0.9765 0.4696 ] Network output: [ 0.02712 0.8957 0.02815 -0.0002902 0.0001303 1.021 -0.0002187 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03589 Epoch 2153 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03626 0.972 0.9916 8.799e-05 -3.95e-05 -0.03577 6.631e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02421 -0.005454 0.01957 0.03542 0.9366 0.9465 0.0513 0.8789 0.8984 0.1308 ] Network output: [ 0.9709 0.06919 -0.01465 -0.0002352 0.0001056 0.00271 -0.0001773 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1149 0.1109 0.318 0.9697 0.9859 0.7542 0.8941 0.9641 0.6286 ] Network output: [ -0.003401 0.93 1.03 -1.141e-05 5.124e-06 0.04645 -8.602e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05109 0.03775 0.05646 0.04933 0.9839 0.9886 0.05236 0.9669 0.9782 0.07117 ] Network output: [ 0.09627 -0.3031 1.065 -9.302e-05 4.176e-05 1.045 -7.01e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7486 0.636 0.5231 0.5015 0.9733 0.9879 0.7522 0.9051 0.9694 0.6247 ] Network output: [ -0.05194 0.2012 0.9632 0.0005816 -0.0002611 0.9418 0.0004383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6039 0.5892 0.442 0.3363 0.9855 0.9905 0.6045 0.9711 0.9803 0.4552 ] Network output: [ -0.07486 0.2353 0.9357 -3.68e-05 1.652e-05 0.9786 -2.773e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6078 0.6054 0.467 0.3014 0.9833 0.9892 0.6079 0.9643 0.9766 0.4696 ] Network output: [ 0.02707 0.8959 0.02811 -0.0002896 0.00013 1.021 -0.0002182 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03586 Epoch 2154 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03624 0.972 0.9916 8.755e-05 -3.93e-05 -0.03576 6.598e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02421 -0.005457 0.01956 0.03539 0.9367 0.9465 0.05129 0.879 0.8984 0.1308 ] Network output: [ 0.9709 0.06918 -0.01466 -0.0002342 0.0001051 0.002672 -0.0001765 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1149 0.111 0.3178 0.9697 0.9859 0.7542 0.8942 0.9641 0.6286 ] Network output: [ -0.003414 0.9301 1.03 -1.177e-05 5.283e-06 0.04647 -8.868e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05109 0.03775 0.05645 0.04928 0.9839 0.9886 0.05236 0.9669 0.9782 0.07114 ] Network output: [ 0.0962 -0.3031 1.065 -9.507e-05 4.268e-05 1.045 -7.165e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7485 0.6361 0.5232 0.5013 0.9733 0.9879 0.7522 0.9052 0.9694 0.6247 ] Network output: [ -0.05188 0.2011 0.9632 0.0005819 -0.0002612 0.9419 0.0004385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.604 0.5893 0.442 0.3362 0.9855 0.9905 0.6045 0.9711 0.9803 0.4552 ] Network output: [ -0.0748 0.2351 0.9358 -3.586e-05 1.61e-05 0.9785 -2.703e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6079 0.6054 0.467 0.3014 0.9833 0.9892 0.608 0.9643 0.9766 0.4696 ] Network output: [ 0.02702 0.896 0.02807 -0.000289 0.0001298 1.021 -0.0002178 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03582 Epoch 2155 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03621 0.972 0.9917 8.71e-05 -3.91e-05 -0.03574 6.564e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0242 -0.005459 0.01956 0.03536 0.9367 0.9465 0.05128 0.879 0.8985 0.1308 ] Network output: [ 0.971 0.06917 -0.01467 -0.0002331 0.0001047 0.002634 -0.0001757 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.115 0.1111 0.3176 0.9698 0.9859 0.7542 0.8942 0.9641 0.6286 ] Network output: [ -0.003427 0.9301 1.03 -1.212e-05 5.44e-06 0.04648 -9.133e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05108 0.03775 0.05643 0.04924 0.9839 0.9886 0.05236 0.9669 0.9783 0.07112 ] Network output: [ 0.09613 -0.303 1.065 -9.712e-05 4.36e-05 1.045 -7.319e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7485 0.6362 0.5233 0.501 0.9733 0.9879 0.7521 0.9052 0.9694 0.6247 ] Network output: [ -0.05181 0.201 0.9631 0.0005822 -0.0002614 0.9419 0.0004388 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.604 0.5894 0.4421 0.3362 0.9855 0.9905 0.6046 0.9711 0.9803 0.4553 ] Network output: [ -0.07474 0.235 0.9358 -3.492e-05 1.568e-05 0.9785 -2.631e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6079 0.6055 0.467 0.3013 0.9833 0.9892 0.608 0.9643 0.9766 0.4696 ] Network output: [ 0.02696 0.8962 0.02803 -0.0002885 0.0001295 1.021 -0.0002174 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03578 Epoch 2156 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03619 0.972 0.9917 8.666e-05 -3.891e-05 -0.03573 6.531e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02419 -0.005461 0.01955 0.03534 0.9367 0.9465 0.05126 0.879 0.8985 0.1307 ] Network output: [ 0.971 0.06916 -0.01468 -0.0002321 0.0001042 0.002596 -0.0001749 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1151 0.1112 0.3174 0.9698 0.9859 0.7542 0.8943 0.9641 0.6286 ] Network output: [ -0.003439 0.9301 1.03 -1.247e-05 5.598e-06 0.0465 -9.397e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05108 0.03775 0.05642 0.0492 0.9839 0.9886 0.05236 0.9669 0.9783 0.07109 ] Network output: [ 0.09607 -0.3029 1.065 -9.918e-05 4.452e-05 1.045 -7.474e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7485 0.6362 0.5234 0.5008 0.9733 0.9879 0.7521 0.9052 0.9694 0.6248 ] Network output: [ -0.05174 0.2009 0.963 0.0005826 -0.0002615 0.9419 0.000439 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6041 0.5895 0.4421 0.3361 0.9855 0.9905 0.6046 0.9711 0.9803 0.4553 ] Network output: [ -0.07468 0.2348 0.9359 -3.397e-05 1.525e-05 0.9785 -2.56e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6079 0.6055 0.467 0.3013 0.9834 0.9892 0.608 0.9644 0.9766 0.4696 ] Network output: [ 0.02691 0.8964 0.02799 -0.000288 0.0001293 1.021 -0.000217 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03575 Epoch 2157 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03616 0.972 0.9918 8.622e-05 -3.871e-05 -0.03572 6.498e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02419 -0.005464 0.01954 0.03531 0.9367 0.9466 0.05125 0.8791 0.8985 0.1307 ] Network output: [ 0.971 0.06915 -0.01469 -0.000231 0.0001037 0.002558 -0.0001741 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1151 0.1113 0.3172 0.9698 0.9859 0.7542 0.8943 0.9641 0.6287 ] Network output: [ -0.003452 0.9302 1.03 -1.282e-05 5.755e-06 0.04651 -9.661e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05108 0.03776 0.0564 0.04916 0.9839 0.9886 0.05236 0.9669 0.9783 0.07107 ] Network output: [ 0.096 -0.3028 1.065 -0.0001012 4.545e-05 1.045 -7.629e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7485 0.6363 0.5235 0.5006 0.9733 0.9879 0.7521 0.9053 0.9694 0.6248 ] Network output: [ -0.05168 0.2008 0.963 0.0005829 -0.0002617 0.942 0.0004393 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6042 0.5895 0.4422 0.336 0.9855 0.9905 0.6047 0.9711 0.9803 0.4553 ] Network output: [ -0.07462 0.2347 0.936 -3.301e-05 1.482e-05 0.9785 -2.488e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6079 0.6055 0.467 0.3013 0.9834 0.9892 0.608 0.9644 0.9766 0.4696 ] Network output: [ 0.02686 0.8966 0.02796 -0.0002874 0.000129 1.021 -0.0002166 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03571 Epoch 2158 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03614 0.972 0.9918 8.578e-05 -3.851e-05 -0.0357 6.465e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02418 -0.005466 0.01954 0.03528 0.9367 0.9466 0.05123 0.8791 0.8985 0.1306 ] Network output: [ 0.971 0.06914 -0.01469 -0.00023 0.0001032 0.00252 -0.0001733 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1152 0.1114 0.317 0.9698 0.9859 0.7542 0.8943 0.9642 0.6287 ] Network output: [ -0.003465 0.9302 1.03 -1.317e-05 5.912e-06 0.04652 -9.924e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05108 0.03776 0.05639 0.04912 0.984 0.9886 0.05236 0.967 0.9783 0.07104 ] Network output: [ 0.09593 -0.3027 1.065 -0.0001033 4.637e-05 1.045 -7.784e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7485 0.6363 0.5236 0.5004 0.9733 0.9879 0.7521 0.9053 0.9694 0.6248 ] Network output: [ -0.05161 0.2007 0.9629 0.0005832 -0.0002618 0.942 0.0004395 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6042 0.5896 0.4422 0.3359 0.9855 0.9905 0.6048 0.9712 0.9803 0.4554 ] Network output: [ -0.07456 0.2345 0.9361 -3.205e-05 1.439e-05 0.9784 -2.416e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.608 0.6056 0.4671 0.3012 0.9834 0.9892 0.6081 0.9644 0.9766 0.4696 ] Network output: [ 0.0268 0.8967 0.02792 -0.0002869 0.0001288 1.021 -0.0002162 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03568 Epoch 2159 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03612 0.972 0.9918 8.534e-05 -3.831e-05 -0.03569 6.431e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02417 -0.005468 0.01953 0.03525 0.9367 0.9466 0.05122 0.8792 0.8986 0.1306 ] Network output: [ 0.9711 0.06913 -0.0147 -0.0002289 0.0001028 0.002483 -0.0001725 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1153 0.1115 0.3167 0.9698 0.9859 0.7541 0.8944 0.9642 0.6287 ] Network output: [ -0.003477 0.9302 1.03 -1.352e-05 6.068e-06 0.04654 -1.019e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05108 0.03776 0.05637 0.04907 0.984 0.9886 0.05236 0.967 0.9783 0.07102 ] Network output: [ 0.09587 -0.3027 1.065 -0.0001054 4.73e-05 1.045 -7.94e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7485 0.6364 0.5237 0.5002 0.9733 0.9879 0.7521 0.9053 0.9695 0.6248 ] Network output: [ -0.05154 0.2006 0.9628 0.0005836 -0.000262 0.9421 0.0004398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6043 0.5897 0.4422 0.3358 0.9856 0.9905 0.6048 0.9712 0.9803 0.4554 ] Network output: [ -0.0745 0.2343 0.9361 -3.109e-05 1.396e-05 0.9784 -2.343e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.608 0.6056 0.4671 0.3012 0.9834 0.9892 0.6081 0.9644 0.9766 0.4696 ] Network output: [ 0.02675 0.8969 0.02788 -0.0002864 0.0001286 1.021 -0.0002159 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03564 Epoch 2160 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03609 0.972 0.9919 8.49e-05 -3.811e-05 -0.03568 6.398e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02417 -0.005471 0.01953 0.03522 0.9367 0.9466 0.05121 0.8792 0.8986 0.1306 ] Network output: [ 0.9711 0.06912 -0.01471 -0.0002279 0.0001023 0.002446 -0.0001718 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.1153 0.1116 0.3165 0.9698 0.9859 0.7541 0.8944 0.9642 0.6287 ] Network output: [ -0.00349 0.9303 1.03 -1.387e-05 6.225e-06 0.04655 -1.045e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05108 0.03776 0.05636 0.04903 0.984 0.9886 0.05235 0.967 0.9783 0.07099 ] Network output: [ 0.0958 -0.3026 1.065 -0.0001074 4.822e-05 1.045 -8.095e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7484 0.6364 0.5237 0.5 0.9733 0.988 0.7521 0.9054 0.9695 0.6248 ] Network output: [ -0.05148 0.2005 0.9627 0.0005839 -0.0002621 0.9421 0.0004401 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6044 0.5897 0.4423 0.3357 0.9856 0.9905 0.6049 0.9712 0.9804 0.4554 ] Network output: [ -0.07444 0.2342 0.9362 -3.012e-05 1.352e-05 0.9784 -2.27e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.608 0.6056 0.4671 0.3012 0.9834 0.9893 0.6081 0.9644 0.9767 0.4696 ] Network output: [ 0.0267 0.8971 0.02784 -0.0002859 0.0001284 1.021 -0.0002155 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03561 Epoch 2161 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03607 0.972 0.9919 8.446e-05 -3.792e-05 -0.03566 6.365e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02416 -0.005473 0.01952 0.0352 0.9368 0.9466 0.05119 0.8792 0.8986 0.1305 ] Network output: [ 0.9711 0.06911 -0.01472 -0.0002269 0.0001018 0.002408 -0.000171 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6539 0.1154 0.1117 0.3163 0.9698 0.9859 0.7541 0.8945 0.9642 0.6288 ] Network output: [ -0.003503 0.9303 1.03 -1.421e-05 6.381e-06 0.04656 -1.071e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05108 0.03777 0.05634 0.04899 0.984 0.9886 0.05235 0.967 0.9783 0.07096 ] Network output: [ 0.09573 -0.3025 1.065 -0.0001095 4.915e-05 1.045 -8.251e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7484 0.6365 0.5238 0.4998 0.9734 0.988 0.752 0.9054 0.9695 0.6249 ] Network output: [ -0.05141 0.2004 0.9627 0.0005843 -0.0002623 0.9422 0.0004403 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6044 0.5898 0.4423 0.3356 0.9856 0.9905 0.605 0.9712 0.9804 0.4555 ] Network output: [ -0.07438 0.234 0.9363 -2.914e-05 1.308e-05 0.9784 -2.196e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.608 0.6057 0.4671 0.3012 0.9834 0.9893 0.6081 0.9645 0.9767 0.4696 ] Network output: [ 0.02664 0.8973 0.0278 -0.0002854 0.0001281 1.02 -0.0002151 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03557 Epoch 2162 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03604 0.9719 0.992 8.402e-05 -3.772e-05 -0.03565 6.332e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02415 -0.005475 0.01951 0.03517 0.9368 0.9466 0.05118 0.8793 0.8987 0.1305 ] Network output: [ 0.9712 0.0691 -0.01473 -0.0002258 0.0001014 0.002371 -0.0001702 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6539 0.1155 0.1118 0.3161 0.9698 0.9859 0.7541 0.8945 0.9642 0.6288 ] Network output: [ -0.003515 0.9303 1.03 -1.456e-05 6.536e-06 0.04657 -1.097e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05108 0.03777 0.05633 0.04894 0.984 0.9886 0.05235 0.967 0.9783 0.07094 ] Network output: [ 0.09567 -0.3024 1.065 -0.0001115 5.008e-05 1.045 -8.406e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7484 0.6365 0.5239 0.4996 0.9734 0.988 0.752 0.9055 0.9695 0.6249 ] Network output: [ -0.05135 0.2003 0.9626 0.0005847 -0.0002625 0.9422 0.0004406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6045 0.5899 0.4424 0.3355 0.9856 0.9905 0.605 0.9712 0.9804 0.4555 ] Network output: [ -0.07432 0.2338 0.9363 -2.816e-05 1.264e-05 0.9783 -2.122e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6081 0.6057 0.4671 0.3011 0.9834 0.9893 0.6082 0.9645 0.9767 0.4697 ] Network output: [ 0.02659 0.8974 0.02777 -0.0002849 0.0001279 1.02 -0.0002147 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03554 Epoch 2163 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03602 0.9719 0.992 8.358e-05 -3.752e-05 -0.03564 6.299e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02415 -0.005477 0.01951 0.03514 0.9368 0.9466 0.05117 0.8793 0.8987 0.1304 ] Network output: [ 0.9712 0.0691 -0.01474 -0.0002248 0.0001009 0.002334 -0.0001694 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6539 0.1155 0.1119 0.3159 0.9698 0.9859 0.7541 0.8945 0.9642 0.6288 ] Network output: [ -0.003528 0.9303 1.03 -1.491e-05 6.691e-06 0.04659 -1.123e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05108 0.03777 0.05631 0.0489 0.984 0.9886 0.05235 0.9671 0.9784 0.07091 ] Network output: [ 0.0956 -0.3024 1.065 -0.0001136 5.1e-05 1.045 -8.562e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7484 0.6366 0.524 0.4993 0.9734 0.988 0.752 0.9055 0.9695 0.6249 ] Network output: [ -0.05128 0.2002 0.9625 0.000585 -0.0002626 0.9422 0.0004409 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6045 0.59 0.4424 0.3355 0.9856 0.9905 0.6051 0.9712 0.9804 0.4555 ] Network output: [ -0.07426 0.2337 0.9364 -2.717e-05 1.22e-05 0.9783 -2.048e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6081 0.6057 0.4671 0.3011 0.9834 0.9893 0.6082 0.9645 0.9767 0.4697 ] Network output: [ 0.02654 0.8976 0.02773 -0.0002845 0.0001277 1.02 -0.0002144 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0355 Epoch 2164 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03599 0.9719 0.992 8.314e-05 -3.732e-05 -0.03562 6.266e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02414 -0.00548 0.0195 0.03511 0.9368 0.9466 0.05115 0.8793 0.8987 0.1304 ] Network output: [ 0.9712 0.06909 -0.01475 -0.0002238 0.0001005 0.002297 -0.0001687 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6539 0.1156 0.112 0.3156 0.9698 0.9859 0.754 0.8946 0.9643 0.6288 ] Network output: [ -0.00354 0.9304 1.03 -1.525e-05 6.846e-06 0.0466 -1.149e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05107 0.03777 0.0563 0.04886 0.984 0.9886 0.05235 0.9671 0.9784 0.07089 ] Network output: [ 0.09553 -0.3023 1.065 -0.0001157 5.193e-05 1.045 -8.718e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7484 0.6366 0.5241 0.4991 0.9734 0.988 0.752 0.9055 0.9695 0.6249 ] Network output: [ -0.05121 0.2 0.9625 0.0005854 -0.0002628 0.9423 0.0004412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6046 0.59 0.4425 0.3354 0.9856 0.9905 0.6051 0.9712 0.9804 0.4556 ] Network output: [ -0.0742 0.2335 0.9365 -2.618e-05 1.175e-05 0.9783 -1.973e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6081 0.6057 0.4671 0.3011 0.9834 0.9893 0.6082 0.9645 0.9767 0.4697 ] Network output: [ 0.02649 0.8978 0.02769 -0.000284 0.0001275 1.02 -0.000214 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03546 Epoch 2165 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03597 0.9719 0.9921 8.27e-05 -3.713e-05 -0.03561 6.233e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02413 -0.005482 0.0195 0.03508 0.9368 0.9467 0.05114 0.8794 0.8988 0.1303 ] Network output: [ 0.9713 0.06908 -0.01476 -0.0002228 0.0001 0.002261 -0.0001679 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6539 0.1157 0.1121 0.3154 0.9698 0.9859 0.754 0.8946 0.9643 0.6289 ] Network output: [ -0.003553 0.9304 1.03 -1.559e-05 7.001e-06 0.04661 -1.175e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05107 0.03777 0.05628 0.04882 0.984 0.9886 0.05235 0.9671 0.9784 0.07086 ] Network output: [ 0.09546 -0.3022 1.065 -0.0001178 5.287e-05 1.045 -8.875e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7484 0.6367 0.5242 0.4989 0.9734 0.988 0.752 0.9056 0.9695 0.625 ] Network output: [ -0.05115 0.1999 0.9624 0.0005858 -0.000263 0.9423 0.0004415 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6047 0.5901 0.4425 0.3353 0.9856 0.9905 0.6052 0.9713 0.9804 0.4556 ] Network output: [ -0.07414 0.2333 0.9365 -2.518e-05 1.13e-05 0.9783 -1.898e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6082 0.6058 0.4671 0.301 0.9834 0.9893 0.6083 0.9646 0.9767 0.4697 ] Network output: [ 0.02644 0.898 0.02766 -0.0002835 0.0001273 1.02 -0.0002137 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03543 Epoch 2166 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03595 0.9719 0.9921 8.226e-05 -3.693e-05 -0.0356 6.2e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02413 -0.005484 0.01949 0.03505 0.9368 0.9467 0.05112 0.8794 0.8988 0.1303 ] Network output: [ 0.9713 0.06907 -0.01477 -0.0002217 9.955e-05 0.002224 -0.0001671 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6539 0.1157 0.1122 0.3152 0.9699 0.9859 0.754 0.8947 0.9643 0.6289 ] Network output: [ -0.003566 0.9304 1.03 -1.594e-05 7.156e-06 0.04662 -1.201e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05107 0.03778 0.05627 0.04877 0.984 0.9887 0.05234 0.9671 0.9784 0.07084 ] Network output: [ 0.0954 -0.3021 1.065 -0.0001198 5.38e-05 1.046 -9.031e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7483 0.6367 0.5242 0.4987 0.9734 0.988 0.7519 0.9056 0.9696 0.625 ] Network output: [ -0.05108 0.1998 0.9623 0.0005862 -0.0002632 0.9424 0.0004418 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6047 0.5902 0.4425 0.3352 0.9856 0.9905 0.6053 0.9713 0.9804 0.4556 ] Network output: [ -0.07408 0.2332 0.9366 -2.418e-05 1.085e-05 0.9783 -1.822e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6082 0.6058 0.4671 0.301 0.9834 0.9893 0.6083 0.9646 0.9767 0.4697 ] Network output: [ 0.02638 0.8981 0.02762 -0.0002831 0.0001271 1.02 -0.0002133 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03539 Epoch 2167 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03592 0.9719 0.9922 8.182e-05 -3.673e-05 -0.03558 6.167e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02412 -0.005486 0.01948 0.03502 0.9368 0.9467 0.05111 0.8795 0.8988 0.1303 ] Network output: [ 0.9713 0.06906 -0.01478 -0.0002207 9.909e-05 0.002188 -0.0001663 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6539 0.1158 0.1123 0.315 0.9699 0.9859 0.754 0.8947 0.9643 0.6289 ] Network output: [ -0.003578 0.9305 1.03 -1.628e-05 7.31e-06 0.04663 -1.227e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05107 0.03778 0.05625 0.04873 0.984 0.9887 0.05234 0.9671 0.9784 0.07081 ] Network output: [ 0.09533 -0.302 1.065 -0.0001219 5.473e-05 1.046 -9.188e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7483 0.6368 0.5243 0.4985 0.9734 0.988 0.7519 0.9056 0.9696 0.625 ] Network output: [ -0.05101 0.1997 0.9623 0.0005866 -0.0002633 0.9424 0.0004421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6048 0.5902 0.4426 0.3351 0.9856 0.9906 0.6053 0.9713 0.9804 0.4557 ] Network output: [ -0.07402 0.233 0.9367 -2.317e-05 1.04e-05 0.9782 -1.746e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6082 0.6058 0.4671 0.301 0.9834 0.9893 0.6083 0.9646 0.9768 0.4697 ] Network output: [ 0.02633 0.8983 0.02759 -0.0002826 0.0001269 1.02 -0.000213 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03536 Epoch 2168 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0359 0.9719 0.9922 8.139e-05 -3.654e-05 -0.03557 6.134e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02411 -0.005488 0.01948 0.035 0.9369 0.9467 0.0511 0.8795 0.8988 0.1302 ] Network output: [ 0.9713 0.06905 -0.01478 -0.0002197 9.864e-05 0.002152 -0.0001656 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6539 0.1159 0.1124 0.3148 0.9699 0.9859 0.754 0.8947 0.9643 0.6289 ] Network output: [ -0.003591 0.9305 1.03 -1.663e-05 7.464e-06 0.04664 -1.253e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05107 0.03778 0.05623 0.04869 0.984 0.9887 0.05234 0.9671 0.9784 0.07079 ] Network output: [ 0.09526 -0.3019 1.065 -0.000124 5.566e-05 1.046 -9.344e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7483 0.6368 0.5244 0.4983 0.9734 0.988 0.7519 0.9057 0.9696 0.625 ] Network output: [ -0.05095 0.1996 0.9622 0.000587 -0.0002635 0.9425 0.0004424 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6049 0.5903 0.4426 0.335 0.9856 0.9906 0.6054 0.9713 0.9804 0.4557 ] Network output: [ -0.07396 0.2329 0.9367 -2.216e-05 9.947e-06 0.9782 -1.67e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6082 0.6059 0.4671 0.3009 0.9834 0.9893 0.6083 0.9646 0.9768 0.4697 ] Network output: [ 0.02628 0.8985 0.02755 -0.0002822 0.0001267 1.02 -0.0002126 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03532 Epoch 2169 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03587 0.9719 0.9922 8.095e-05 -3.634e-05 -0.03556 6.101e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0241 -0.005491 0.01947 0.03497 0.9369 0.9467 0.05108 0.8795 0.8989 0.1302 ] Network output: [ 0.9714 0.06905 -0.01479 -0.0002187 9.819e-05 0.002116 -0.0001648 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6538 0.1159 0.1125 0.3145 0.9699 0.9859 0.7539 0.8948 0.9643 0.6289 ] Network output: [ -0.003604 0.9305 1.03 -1.697e-05 7.617e-06 0.04665 -1.279e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05106 0.03778 0.05622 0.04864 0.984 0.9887 0.05234 0.9672 0.9784 0.07076 ] Network output: [ 0.09519 -0.3019 1.065 -0.0001261 5.66e-05 1.046 -9.501e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7483 0.6369 0.5245 0.498 0.9734 0.988 0.7519 0.9057 0.9696 0.625 ] Network output: [ -0.05088 0.1995 0.9621 0.0005874 -0.0002637 0.9425 0.0004427 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6049 0.5904 0.4427 0.3349 0.9856 0.9906 0.6055 0.9713 0.9805 0.4557 ] Network output: [ -0.07389 0.2327 0.9368 -2.114e-05 9.489e-06 0.9782 -1.593e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6083 0.6059 0.4671 0.3009 0.9834 0.9893 0.6084 0.9646 0.9768 0.4697 ] Network output: [ 0.02623 0.8986 0.02752 -0.0002817 0.0001265 1.02 -0.0002123 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03529 Epoch 2170 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03585 0.9719 0.9923 8.051e-05 -3.615e-05 -0.03554 6.068e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0241 -0.005493 0.01947 0.03494 0.9369 0.9467 0.05107 0.8796 0.8989 0.1301 ] Network output: [ 0.9714 0.06904 -0.0148 -0.0002177 9.773e-05 0.00208 -0.0001641 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6538 0.116 0.1125 0.3143 0.9699 0.9859 0.7539 0.8948 0.9644 0.629 ] Network output: [ -0.003616 0.9306 1.03 -1.731e-05 7.77e-06 0.04666 -1.304e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05106 0.03778 0.0562 0.0486 0.984 0.9887 0.05233 0.9672 0.9784 0.07074 ] Network output: [ 0.09513 -0.3018 1.065 -0.0001282 5.754e-05 1.046 -9.658e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7482 0.6369 0.5246 0.4978 0.9734 0.988 0.7518 0.9057 0.9696 0.6251 ] Network output: [ -0.05082 0.1994 0.962 0.0005878 -0.0002639 0.9426 0.000443 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.605 0.5905 0.4427 0.3348 0.9856 0.9906 0.6055 0.9713 0.9805 0.4557 ] Network output: [ -0.07383 0.2325 0.9369 -2.011e-05 9.029e-06 0.9782 -1.516e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6083 0.6059 0.4671 0.3008 0.9834 0.9893 0.6084 0.9647 0.9768 0.4697 ] Network output: [ 0.02618 0.8988 0.02748 -0.0002813 0.0001263 1.02 -0.000212 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03525 Epoch 2171 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03582 0.9719 0.9923 8.008e-05 -3.595e-05 -0.03553 6.035e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02409 -0.005495 0.01946 0.03491 0.9369 0.9467 0.05105 0.8796 0.8989 0.1301 ] Network output: [ 0.9714 0.06903 -0.01481 -0.0002167 9.728e-05 0.002044 -0.0001633 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6538 0.1161 0.1126 0.3141 0.9699 0.9859 0.7539 0.8948 0.9644 0.629 ] Network output: [ -0.003629 0.9306 1.03 -1.765e-05 7.924e-06 0.04667 -1.33e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05106 0.03778 0.05619 0.04855 0.984 0.9887 0.05233 0.9672 0.9784 0.07071 ] Network output: [ 0.09506 -0.3017 1.065 -0.0001302 5.847e-05 1.046 -9.816e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7482 0.6369 0.5247 0.4976 0.9734 0.988 0.7518 0.9058 0.9696 0.6251 ] Network output: [ -0.05075 0.1993 0.962 0.0005882 -0.0002641 0.9426 0.0004433 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.605 0.5905 0.4427 0.3347 0.9856 0.9906 0.6056 0.9714 0.9805 0.4558 ] Network output: [ -0.07377 0.2324 0.9369 -1.908e-05 8.566e-06 0.9782 -1.438e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6083 0.6059 0.4671 0.3008 0.9834 0.9893 0.6084 0.9647 0.9768 0.4697 ] Network output: [ 0.02613 0.899 0.02745 -0.0002809 0.0001261 1.02 -0.0002117 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03521 Epoch 2172 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0358 0.9719 0.9923 7.964e-05 -3.575e-05 -0.03552 6.002e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02408 -0.005497 0.01945 0.03488 0.9369 0.9467 0.05104 0.8796 0.899 0.1301 ] Network output: [ 0.9715 0.06902 -0.01482 -0.0002157 9.683e-05 0.002008 -0.0001626 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6538 0.1161 0.1127 0.3139 0.9699 0.9859 0.7539 0.8949 0.9644 0.629 ] Network output: [ -0.003641 0.9306 1.03 -1.799e-05 8.076e-06 0.04668 -1.356e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05106 0.03778 0.05617 0.04851 0.984 0.9887 0.05233 0.9672 0.9785 0.07068 ] Network output: [ 0.09499 -0.3016 1.065 -0.0001323 5.941e-05 1.046 -9.973e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7482 0.637 0.5247 0.4974 0.9734 0.988 0.7518 0.9058 0.9696 0.6251 ] Network output: [ -0.05068 0.1992 0.9619 0.0005886 -0.0002642 0.9427 0.0004436 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6051 0.5906 0.4428 0.3346 0.9856 0.9906 0.6056 0.9714 0.9805 0.4558 ] Network output: [ -0.07371 0.2322 0.937 -1.804e-05 8.101e-06 0.9782 -1.36e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6084 0.606 0.4671 0.3008 0.9835 0.9893 0.6085 0.9647 0.9768 0.4697 ] Network output: [ 0.02608 0.8992 0.02741 -0.0002805 0.0001259 1.02 -0.0002114 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03518 Epoch 2173 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03577 0.9719 0.9924 7.921e-05 -3.556e-05 -0.03551 5.969e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02408 -0.005499 0.01945 0.03485 0.9369 0.9467 0.05103 0.8797 0.899 0.13 ] Network output: [ 0.9715 0.06902 -0.01483 -0.0002147 9.639e-05 0.001973 -0.0001618 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6538 0.1162 0.1128 0.3136 0.9699 0.986 0.7538 0.8949 0.9644 0.6291 ] Network output: [ -0.003654 0.9307 1.03 -1.833e-05 8.229e-06 0.04669 -1.381e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05106 0.03778 0.05616 0.04847 0.984 0.9887 0.05233 0.9672 0.9785 0.07066 ] Network output: [ 0.09492 -0.3015 1.066 -0.0001344 6.035e-05 1.046 -0.0001013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7482 0.637 0.5248 0.4972 0.9735 0.988 0.7518 0.9058 0.9696 0.6251 ] Network output: [ -0.05062 0.1991 0.9618 0.000589 -0.0002644 0.9427 0.0004439 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6052 0.5907 0.4428 0.3345 0.9856 0.9906 0.6057 0.9714 0.9805 0.4558 ] Network output: [ -0.07365 0.232 0.9371 -1.7e-05 7.633e-06 0.9781 -1.281e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6084 0.606 0.4671 0.3007 0.9835 0.9893 0.6085 0.9647 0.9768 0.4697 ] Network output: [ 0.02602 0.8993 0.02738 -0.00028 0.0001257 1.02 -0.000211 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03514 Epoch 2174 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03575 0.9719 0.9924 7.877e-05 -3.536e-05 -0.03549 5.936e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02407 -0.005501 0.01944 0.03482 0.9369 0.9468 0.05101 0.8797 0.899 0.13 ] Network output: [ 0.9715 0.06901 -0.01484 -0.0002137 9.594e-05 0.001937 -0.0001611 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6538 0.1163 0.1129 0.3134 0.9699 0.986 0.7538 0.895 0.9644 0.6291 ] Network output: [ -0.003667 0.9307 1.03 -1.867e-05 8.381e-06 0.0467 -1.407e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05105 0.03779 0.05614 0.04842 0.9841 0.9887 0.05232 0.9673 0.9785 0.07063 ] Network output: [ 0.09485 -0.3015 1.066 -0.0001365 6.129e-05 1.046 -0.0001029 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7482 0.6371 0.5249 0.4969 0.9735 0.988 0.7518 0.9059 0.9697 0.6252 ] Network output: [ -0.05055 0.199 0.9618 0.0005895 -0.0002646 0.9428 0.0004442 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6052 0.5907 0.4428 0.3344 0.9856 0.9906 0.6058 0.9714 0.9805 0.4559 ] Network output: [ -0.07359 0.2319 0.9371 -1.595e-05 7.163e-06 0.9781 -1.202e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6084 0.606 0.4671 0.3007 0.9835 0.9893 0.6085 0.9647 0.9769 0.4697 ] Network output: [ 0.02597 0.8995 0.02734 -0.0002796 0.0001255 1.02 -0.0002107 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03511 Epoch 2175 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03573 0.9719 0.9925 7.834e-05 -3.517e-05 -0.03548 5.904e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02406 -0.005503 0.01943 0.03479 0.937 0.9468 0.051 0.8798 0.8991 0.1299 ] Network output: [ 0.9715 0.069 -0.01485 -0.0002127 9.549e-05 0.001902 -0.0001603 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6538 0.1163 0.113 0.3132 0.9699 0.986 0.7538 0.895 0.9644 0.6291 ] Network output: [ -0.003679 0.9307 1.03 -1.901e-05 8.533e-06 0.04671 -1.432e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05105 0.03779 0.05612 0.04838 0.9841 0.9887 0.05232 0.9673 0.9785 0.07061 ] Network output: [ 0.09478 -0.3014 1.066 -0.0001386 6.223e-05 1.046 -0.0001045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7481 0.6371 0.525 0.4967 0.9735 0.988 0.7517 0.9059 0.9697 0.6252 ] Network output: [ -0.05048 0.1989 0.9617 0.0005899 -0.0002648 0.9428 0.0004446 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6053 0.5908 0.4429 0.3343 0.9856 0.9906 0.6058 0.9714 0.9805 0.4559 ] Network output: [ -0.07353 0.2317 0.9372 -1.49e-05 6.69e-06 0.9781 -1.123e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6084 0.6061 0.4671 0.3006 0.9835 0.9893 0.6085 0.9648 0.9769 0.4697 ] Network output: [ 0.02592 0.8997 0.02731 -0.0002792 0.0001254 1.02 -0.0002104 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03507 Epoch 2176 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0357 0.9719 0.9925 7.79e-05 -3.497e-05 -0.03547 5.871e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02406 -0.005505 0.01943 0.03476 0.937 0.9468 0.05098 0.8798 0.8991 0.1299 ] Network output: [ 0.9716 0.06899 -0.01486 -0.0002117 9.505e-05 0.001867 -0.0001596 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6537 0.1164 0.1131 0.3129 0.9699 0.986 0.7538 0.895 0.9645 0.6291 ] Network output: [ -0.003692 0.9308 1.03 -1.935e-05 8.685e-06 0.04672 -1.458e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05105 0.03779 0.05611 0.04834 0.9841 0.9887 0.05232 0.9673 0.9785 0.07058 ] Network output: [ 0.09472 -0.3013 1.066 -0.0001407 6.318e-05 1.046 -0.0001061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7481 0.6371 0.5251 0.4965 0.9735 0.988 0.7517 0.9059 0.9697 0.6252 ] Network output: [ -0.05042 0.1988 0.9616 0.0005904 -0.000265 0.9429 0.0004449 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6054 0.5909 0.4429 0.3342 0.9856 0.9906 0.6059 0.9714 0.9805 0.4559 ] Network output: [ -0.07347 0.2315 0.9373 -1.384e-05 6.215e-06 0.9781 -1.043e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6085 0.6061 0.4671 0.3006 0.9835 0.9893 0.6086 0.9648 0.9769 0.4697 ] Network output: [ 0.02587 0.8998 0.02727 -0.0002789 0.0001252 1.02 -0.0002102 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03503 Epoch 2177 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03568 0.9719 0.9925 7.747e-05 -3.478e-05 -0.03545 5.838e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02405 -0.005507 0.01942 0.03474 0.937 0.9468 0.05097 0.8798 0.8991 0.1298 ] Network output: [ 0.9716 0.06898 -0.01488 -0.0002107 9.46e-05 0.001832 -0.0001588 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6537 0.1164 0.1132 0.3127 0.9699 0.986 0.7537 0.8951 0.9645 0.6292 ] Network output: [ -0.003705 0.9308 1.03 -1.968e-05 8.837e-06 0.04673 -1.483e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05104 0.03779 0.05609 0.04829 0.9841 0.9887 0.05232 0.9673 0.9785 0.07055 ] Network output: [ 0.09465 -0.3012 1.066 -0.0001428 6.412e-05 1.046 -0.0001076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7481 0.6372 0.5252 0.4963 0.9735 0.988 0.7517 0.906 0.9697 0.6252 ] Network output: [ -0.05035 0.1987 0.9615 0.0005908 -0.0002652 0.9429 0.0004452 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6054 0.5909 0.443 0.3341 0.9856 0.9906 0.6059 0.9714 0.9805 0.456 ] Network output: [ -0.07341 0.2314 0.9373 -1.278e-05 5.737e-06 0.9781 -9.63e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6085 0.6061 0.4671 0.3006 0.9835 0.9893 0.6086 0.9648 0.9769 0.4697 ] Network output: [ 0.02582 0.9 0.02724 -0.0002785 0.000125 1.02 -0.0002099 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.035 Epoch 2178 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03565 0.9719 0.9926 7.703e-05 -3.458e-05 -0.03544 5.806e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02404 -0.005509 0.01942 0.03471 0.937 0.9468 0.05095 0.8799 0.8991 0.1298 ] Network output: [ 0.9716 0.06898 -0.01489 -0.0002097 9.416e-05 0.001797 -0.0001581 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6537 0.1165 0.1132 0.3125 0.97 0.986 0.7537 0.8951 0.9645 0.6292 ] Network output: [ -0.003717 0.9308 1.03 -2.002e-05 8.988e-06 0.04674 -1.509e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05104 0.03779 0.05607 0.04825 0.9841 0.9887 0.05231 0.9673 0.9785 0.07053 ] Network output: [ 0.09458 -0.3011 1.066 -0.0001449 6.507e-05 1.046 -0.0001092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7481 0.6372 0.5252 0.496 0.9735 0.988 0.7517 0.906 0.9697 0.6253 ] Network output: [ -0.05028 0.1985 0.9615 0.0005913 -0.0002654 0.943 0.0004456 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6055 0.591 0.443 0.334 0.9856 0.9906 0.606 0.9715 0.9805 0.456 ] Network output: [ -0.07335 0.2312 0.9374 -1.171e-05 5.256e-06 0.9781 -8.824e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6085 0.6062 0.4671 0.3005 0.9835 0.9893 0.6086 0.9648 0.9769 0.4697 ] Network output: [ 0.02577 0.9002 0.02721 -0.0002781 0.0001248 1.02 -0.0002096 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03496 Epoch 2179 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03563 0.9719 0.9926 7.66e-05 -3.439e-05 -0.03543 5.773e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02404 -0.005511 0.01941 0.03468 0.937 0.9468 0.05094 0.8799 0.8992 0.1298 ] Network output: [ 0.9717 0.06897 -0.0149 -0.0002088 9.372e-05 0.001763 -0.0001573 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6537 0.1166 0.1133 0.3123 0.97 0.986 0.7537 0.8951 0.9645 0.6292 ] Network output: [ -0.00373 0.9309 1.03 -2.036e-05 9.139e-06 0.04675 -1.534e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05104 0.03779 0.05606 0.0482 0.9841 0.9887 0.05231 0.9674 0.9785 0.0705 ] Network output: [ 0.09451 -0.301 1.066 -0.0001471 6.602e-05 1.046 -0.0001108 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.748 0.6373 0.5253 0.4958 0.9735 0.988 0.7516 0.906 0.9697 0.6253 ] Network output: [ -0.05022 0.1984 0.9614 0.0005917 -0.0002656 0.943 0.0004459 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6055 0.5911 0.443 0.3339 0.9857 0.9906 0.6061 0.9715 0.9806 0.456 ] Network output: [ -0.07329 0.231 0.9374 -1.063e-05 4.774e-06 0.9781 -8.013e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6085 0.6062 0.4671 0.3005 0.9835 0.9893 0.6086 0.9648 0.9769 0.4697 ] Network output: [ 0.02572 0.9003 0.02717 -0.0002777 0.0001247 1.02 -0.0002093 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03493 Epoch 2180 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0356 0.9719 0.9927 7.617e-05 -3.419e-05 -0.03542 5.74e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02403 -0.005513 0.0194 0.03465 0.937 0.9468 0.05092 0.8799 0.8992 0.1297 ] Network output: [ 0.9717 0.06896 -0.01491 -0.0002078 9.328e-05 0.001728 -0.0001566 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6537 0.1166 0.1134 0.312 0.97 0.986 0.7537 0.8952 0.9645 0.6292 ] Network output: [ -0.003743 0.9309 1.03 -2.069e-05 9.29e-06 0.04676 -1.559e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05104 0.03779 0.05604 0.04816 0.9841 0.9887 0.05231 0.9674 0.9785 0.07048 ] Network output: [ 0.09444 -0.3009 1.066 -0.0001492 6.697e-05 1.046 -0.0001124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.748 0.6373 0.5254 0.4956 0.9735 0.988 0.7516 0.9061 0.9697 0.6253 ] Network output: [ -0.05015 0.1983 0.9613 0.0005922 -0.0002659 0.9431 0.0004463 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6056 0.5911 0.4431 0.3338 0.9857 0.9906 0.6061 0.9715 0.9806 0.456 ] Network output: [ -0.07323 0.2309 0.9375 -9.552e-06 4.288e-06 0.978 -7.198e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6086 0.6062 0.4671 0.3004 0.9835 0.9893 0.6087 0.9649 0.9769 0.4697 ] Network output: [ 0.02567 0.9005 0.02714 -0.0002774 0.0001245 1.02 -0.000209 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03489 Epoch 2181 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03558 0.9719 0.9927 7.574e-05 -3.4e-05 -0.03541 5.708e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02402 -0.005515 0.0194 0.03462 0.937 0.9468 0.05091 0.88 0.8992 0.1297 ] Network output: [ 0.9717 0.06896 -0.01492 -0.0002068 9.285e-05 0.001694 -0.0001559 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6537 0.1167 0.1135 0.3118 0.97 0.986 0.7536 0.8952 0.9645 0.6293 ] Network output: [ -0.003755 0.9309 1.03 -2.103e-05 9.441e-06 0.04676 -1.585e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05103 0.03779 0.05603 0.04811 0.9841 0.9887 0.0523 0.9674 0.9786 0.07045 ] Network output: [ 0.09437 -0.3009 1.066 -0.0001513 6.792e-05 1.046 -0.000114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.748 0.6373 0.5255 0.4953 0.9735 0.988 0.7516 0.9061 0.9698 0.6253 ] Network output: [ -0.05008 0.1982 0.9613 0.0005927 -0.0002661 0.9431 0.0004466 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6057 0.5912 0.4431 0.3337 0.9857 0.9906 0.6062 0.9715 0.9806 0.4561 ] Network output: [ -0.07316 0.2307 0.9376 -8.465e-06 3.8e-06 0.978 -6.379e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6086 0.6062 0.4671 0.3004 0.9835 0.9893 0.6087 0.9649 0.9769 0.4696 ] Network output: [ 0.02562 0.9007 0.02711 -0.000277 0.0001244 1.02 -0.0002088 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03485 Epoch 2182 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03555 0.9719 0.9927 7.53e-05 -3.381e-05 -0.03539 5.675e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02401 -0.005517 0.01939 0.03459 0.937 0.9469 0.0509 0.88 0.8993 0.1296 ] Network output: [ 0.9717 0.06895 -0.01493 -0.0002058 9.241e-05 0.00166 -0.0001551 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6536 0.1167 0.1136 0.3116 0.97 0.986 0.7536 0.8952 0.9645 0.6293 ] Network output: [ -0.003768 0.931 1.03 -2.136e-05 9.591e-06 0.04677 -1.61e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05103 0.03779 0.05601 0.04807 0.9841 0.9887 0.0523 0.9674 0.9786 0.07043 ] Network output: [ 0.0943 -0.3008 1.066 -0.0001534 6.887e-05 1.046 -0.0001156 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7479 0.6374 0.5256 0.4951 0.9735 0.988 0.7515 0.9061 0.9698 0.6254 ] Network output: [ -0.05002 0.1981 0.9612 0.0005931 -0.0002663 0.9432 0.000447 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6057 0.5913 0.4431 0.3336 0.9857 0.9906 0.6063 0.9715 0.9806 0.4561 ] Network output: [ -0.0731 0.2305 0.9376 -7.372e-06 3.309e-06 0.978 -5.556e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6086 0.6063 0.4671 0.3003 0.9835 0.9893 0.6087 0.9649 0.977 0.4696 ] Network output: [ 0.02557 0.9009 0.02708 -0.0002766 0.0001242 1.02 -0.0002085 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03482 Epoch 2183 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03553 0.9719 0.9928 7.487e-05 -3.361e-05 -0.03538 5.643e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02401 -0.005519 0.01938 0.03456 0.9371 0.9469 0.05088 0.88 0.8993 0.1296 ] Network output: [ 0.9718 0.06894 -0.01494 -0.0002049 9.197e-05 0.001625 -0.0001544 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6536 0.1168 0.1137 0.3113 0.97 0.986 0.7536 0.8953 0.9646 0.6293 ] Network output: [ -0.003781 0.931 1.03 -2.17e-05 9.741e-06 0.04678 -1.635e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05103 0.03779 0.05599 0.04803 0.9841 0.9887 0.0523 0.9674 0.9786 0.0704 ] Network output: [ 0.09423 -0.3007 1.066 -0.0001555 6.982e-05 1.046 -0.0001172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7479 0.6374 0.5256 0.4949 0.9735 0.988 0.7515 0.9062 0.9698 0.6254 ] Network output: [ -0.04995 0.198 0.9611 0.0005936 -0.0002665 0.9432 0.0004474 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6058 0.5913 0.4432 0.3335 0.9857 0.9906 0.6063 0.9715 0.9806 0.4561 ] Network output: [ -0.07304 0.2304 0.9377 -6.273e-06 2.816e-06 0.978 -4.728e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6087 0.6063 0.4671 0.3003 0.9835 0.9893 0.6088 0.9649 0.977 0.4696 ] Network output: [ 0.02552 0.901 0.02704 -0.0002763 0.000124 1.02 -0.0002082 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03478 Epoch 2184 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0355 0.9719 0.9928 7.444e-05 -3.342e-05 -0.03537 5.61e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.024 -0.005521 0.01938 0.03453 0.9371 0.9469 0.05087 0.8801 0.8993 0.1296 ] Network output: [ 0.9718 0.06893 -0.01495 -0.0002039 9.154e-05 0.001591 -0.0001537 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6536 0.1169 0.1137 0.3111 0.97 0.986 0.7535 0.8953 0.9646 0.6293 ] Network output: [ -0.003793 0.931 1.03 -2.203e-05 9.891e-06 0.04679 -1.66e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05102 0.03779 0.05598 0.04798 0.9841 0.9887 0.05229 0.9674 0.9786 0.07037 ] Network output: [ 0.09416 -0.3006 1.066 -0.0001577 7.078e-05 1.046 -0.0001188 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7479 0.6374 0.5257 0.4946 0.9735 0.9881 0.7515 0.9062 0.9698 0.6254 ] Network output: [ -0.04988 0.1979 0.961 0.0005941 -0.0002667 0.9433 0.0004477 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6058 0.5914 0.4432 0.3334 0.9857 0.9906 0.6064 0.9716 0.9806 0.4562 ] Network output: [ -0.07298 0.2302 0.9377 -5.169e-06 2.321e-06 0.978 -3.896e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6087 0.6063 0.4671 0.3002 0.9835 0.9894 0.6088 0.9649 0.977 0.4696 ] Network output: [ 0.02547 0.9012 0.02701 -0.000276 0.0001239 1.02 -0.000208 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03475 Epoch 2185 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03548 0.9719 0.9928 7.401e-05 -3.323e-05 -0.03536 5.578e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02399 -0.005523 0.01937 0.0345 0.9371 0.9469 0.05085 0.8801 0.8993 0.1295 ] Network output: [ 0.9718 0.06893 -0.01496 -0.0002029 9.111e-05 0.001558 -0.0001529 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6536 0.1169 0.1138 0.3109 0.97 0.986 0.7535 0.8954 0.9646 0.6294 ] Network output: [ -0.003806 0.9311 1.03 -2.237e-05 1.004e-05 0.04679 -1.686e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05102 0.03779 0.05596 0.04794 0.9841 0.9887 0.05229 0.9675 0.9786 0.07035 ] Network output: [ 0.09409 -0.3005 1.066 -0.0001598 7.173e-05 1.046 -0.0001204 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7479 0.6375 0.5258 0.4944 0.9735 0.9881 0.7515 0.9062 0.9698 0.6254 ] Network output: [ -0.04981 0.1978 0.961 0.0005946 -0.0002669 0.9433 0.0004481 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6059 0.5915 0.4433 0.3333 0.9857 0.9906 0.6064 0.9716 0.9806 0.4562 ] Network output: [ -0.07292 0.23 0.9378 -4.059e-06 1.822e-06 0.978 -3.059e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6087 0.6064 0.4671 0.3002 0.9835 0.9894 0.6088 0.965 0.977 0.4696 ] Network output: [ 0.02542 0.9014 0.02698 -0.0002756 0.0001237 1.02 -0.0002077 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03471 Epoch 2186 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03546 0.9718 0.9929 7.358e-05 -3.303e-05 -0.03535 5.545e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02399 -0.005525 0.01937 0.03447 0.9371 0.9469 0.05084 0.8801 0.8994 0.1295 ] Network output: [ 0.9719 0.06892 -0.01497 -0.000202 9.067e-05 0.001524 -0.0001522 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6536 0.117 0.1139 0.3106 0.97 0.986 0.7535 0.8954 0.9646 0.6294 ] Network output: [ -0.003819 0.9311 1.03 -2.27e-05 1.019e-05 0.0468 -1.711e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05102 0.03779 0.05594 0.04789 0.9841 0.9887 0.05228 0.9675 0.9786 0.07032 ] Network output: [ 0.09402 -0.3004 1.066 -0.0001619 7.269e-05 1.046 -0.000122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7478 0.6375 0.5259 0.4942 0.9735 0.9881 0.7514 0.9063 0.9698 0.6255 ] Network output: [ -0.04975 0.1977 0.9609 0.0005951 -0.0002672 0.9434 0.0004485 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.606 0.5916 0.4433 0.3332 0.9857 0.9906 0.6065 0.9716 0.9806 0.4562 ] Network output: [ -0.07286 0.2299 0.9379 -2.943e-06 1.321e-06 0.978 -2.218e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6087 0.6064 0.4671 0.3001 0.9835 0.9894 0.6088 0.965 0.977 0.4696 ] Network output: [ 0.02537 0.9015 0.02695 -0.0002753 0.0001236 1.02 -0.0002075 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03467 Epoch 2187 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03543 0.9718 0.9929 7.315e-05 -3.284e-05 -0.03533 5.513e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02398 -0.005527 0.01936 0.03444 0.9371 0.9469 0.05082 0.8802 0.8994 0.1294 ] Network output: [ 0.9719 0.06891 -0.01499 -0.000201 9.024e-05 0.00149 -0.0001515 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6535 0.117 0.114 0.3104 0.97 0.986 0.7534 0.8954 0.9646 0.6294 ] Network output: [ -0.003831 0.9311 1.03 -2.303e-05 1.034e-05 0.04681 -1.736e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05101 0.03779 0.05593 0.04785 0.9841 0.9888 0.05228 0.9675 0.9786 0.0703 ] Network output: [ 0.09395 -0.3003 1.066 -0.0001641 7.365e-05 1.046 -0.0001236 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7478 0.6375 0.5259 0.4939 0.9736 0.9881 0.7514 0.9063 0.9698 0.6255 ] Network output: [ -0.04968 0.1976 0.9608 0.0005956 -0.0002674 0.9434 0.0004489 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.606 0.5916 0.4433 0.3331 0.9857 0.9906 0.6066 0.9716 0.9806 0.4562 ] Network output: [ -0.0728 0.2297 0.9379 -1.822e-06 8.177e-07 0.978 -1.373e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6088 0.6064 0.4671 0.3001 0.9835 0.9894 0.6089 0.965 0.977 0.4696 ] Network output: [ 0.02532 0.9017 0.02692 -0.000275 0.0001235 1.02 -0.0002072 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03464 Epoch 2188 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03541 0.9718 0.993 7.272e-05 -3.265e-05 -0.03532 5.481e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02397 -0.005529 0.01935 0.03441 0.9371 0.9469 0.05081 0.8802 0.8994 0.1294 ] Network output: [ 0.9719 0.06891 -0.015 -0.0002001 8.981e-05 0.001457 -0.0001508 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6535 0.1171 0.1141 0.3102 0.97 0.986 0.7534 0.8955 0.9646 0.6294 ] Network output: [ -0.003844 0.9312 1.03 -2.336e-05 1.049e-05 0.04681 -1.761e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05101 0.03779 0.05591 0.0478 0.9841 0.9888 0.05228 0.9675 0.9786 0.07027 ] Network output: [ 0.09388 -0.3002 1.066 -0.0001662 7.461e-05 1.046 -0.0001252 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7478 0.6376 0.526 0.4937 0.9736 0.9881 0.7514 0.9063 0.9698 0.6255 ] Network output: [ -0.04961 0.1974 0.9607 0.0005961 -0.0002676 0.9435 0.0004493 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6061 0.5917 0.4434 0.333 0.9857 0.9906 0.6066 0.9716 0.9807 0.4563 ] Network output: [ -0.07273 0.2295 0.938 -6.941e-07 3.116e-07 0.978 -5.231e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6088 0.6064 0.4671 0.3 0.9836 0.9894 0.6089 0.965 0.977 0.4696 ] Network output: [ 0.02527 0.9019 0.02688 -0.0002747 0.0001233 1.02 -0.000207 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0346 Epoch 2189 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03538 0.9718 0.993 7.229e-05 -3.246e-05 -0.03531 5.448e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02396 -0.005531 0.01935 0.03438 0.9371 0.9469 0.05079 0.8803 0.8994 0.1293 ] Network output: [ 0.9719 0.0689 -0.01501 -0.0001991 8.939e-05 0.001424 -0.0001501 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6535 0.1171 0.1142 0.3099 0.9701 0.986 0.7534 0.8955 0.9647 0.6295 ] Network output: [ -0.003857 0.9312 1.03 -2.37e-05 1.064e-05 0.04682 -1.786e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.051 0.03779 0.05589 0.04776 0.9841 0.9888 0.05227 0.9675 0.9786 0.07024 ] Network output: [ 0.09381 -0.3002 1.066 -0.0001683 7.557e-05 1.046 -0.0001269 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7477 0.6376 0.5261 0.4935 0.9736 0.9881 0.7513 0.9064 0.9699 0.6255 ] Network output: [ -0.04955 0.1973 0.9607 0.0005967 -0.0002679 0.9435 0.0004497 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6061 0.5918 0.4434 0.3329 0.9857 0.9906 0.6067 0.9716 0.9807 0.4563 ] Network output: [ -0.07267 0.2294 0.938 4.392e-07 -1.972e-07 0.9779 3.31e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6088 0.6065 0.4671 0.3 0.9836 0.9894 0.6089 0.965 0.9771 0.4696 ] Network output: [ 0.02522 0.902 0.02685 -0.0002744 0.0001232 1.02 -0.0002068 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03456 Epoch 2190 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03536 0.9718 0.993 7.186e-05 -3.226e-05 -0.0353 5.416e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02396 -0.005533 0.01934 0.03435 0.9372 0.947 0.05078 0.8803 0.8995 0.1293 ] Network output: [ 0.972 0.06889 -0.01502 -0.0001982 8.896e-05 0.001391 -0.0001493 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6535 0.1172 0.1142 0.3097 0.9701 0.986 0.7534 0.8955 0.9647 0.6295 ] Network output: [ -0.00387 0.9312 1.03 -2.403e-05 1.079e-05 0.04683 -1.811e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.051 0.03779 0.05588 0.04771 0.9842 0.9888 0.05227 0.9675 0.9787 0.07022 ] Network output: [ 0.09374 -0.3001 1.066 -0.0001705 7.654e-05 1.046 -0.0001285 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7477 0.6376 0.5262 0.4932 0.9736 0.9881 0.7513 0.9064 0.9699 0.6256 ] Network output: [ -0.04948 0.1972 0.9606 0.0005972 -0.0002681 0.9436 0.0004501 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6062 0.5918 0.4434 0.3328 0.9857 0.9906 0.6067 0.9716 0.9807 0.4563 ] Network output: [ -0.07261 0.2292 0.9381 1.578e-06 -7.086e-07 0.9779 1.189e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6089 0.6065 0.4671 0.2999 0.9836 0.9894 0.609 0.9651 0.9771 0.4696 ] Network output: [ 0.02517 0.9022 0.02682 -0.0002741 0.000123 1.02 -0.0002065 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03453 Epoch 2191 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03533 0.9718 0.9931 7.144e-05 -3.207e-05 -0.03529 5.384e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02395 -0.005535 0.01933 0.03432 0.9372 0.947 0.05076 0.8803 0.8995 0.1293 ] Network output: [ 0.972 0.06889 -0.01503 -0.0001972 8.853e-05 0.001358 -0.0001486 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6535 0.1173 0.1143 0.3094 0.9701 0.986 0.7533 0.8956 0.9647 0.6295 ] Network output: [ -0.003882 0.9313 1.03 -2.436e-05 1.094e-05 0.04683 -1.836e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.051 0.03779 0.05586 0.04767 0.9842 0.9888 0.05226 0.9676 0.9787 0.07019 ] Network output: [ 0.09367 -0.3 1.066 -0.0001726 7.75e-05 1.046 -0.0001301 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7477 0.6376 0.5263 0.493 0.9736 0.9881 0.7513 0.9064 0.9699 0.6256 ] Network output: [ -0.04941 0.1971 0.9605 0.0005977 -0.0002683 0.9436 0.0004505 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6063 0.5919 0.4435 0.3327 0.9857 0.9906 0.6068 0.9716 0.9807 0.4564 ] Network output: [ -0.07255 0.229 0.9382 2.723e-06 -1.223e-06 0.9779 2.052e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6089 0.6065 0.4671 0.2999 0.9836 0.9894 0.609 0.9651 0.9771 0.4696 ] Network output: [ 0.02512 0.9023 0.02679 -0.0002738 0.0001229 1.019 -0.0002063 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03449 Epoch 2192 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03531 0.9718 0.9931 7.101e-05 -3.188e-05 -0.03528 5.351e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02394 -0.005537 0.01933 0.03429 0.9372 0.947 0.05075 0.8804 0.8995 0.1292 ] Network output: [ 0.972 0.06888 -0.01504 -0.0001963 8.811e-05 0.001325 -0.0001479 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6534 0.1173 0.1144 0.3092 0.9701 0.986 0.7533 0.8956 0.9647 0.6296 ] Network output: [ -0.003895 0.9313 1.03 -2.469e-05 1.108e-05 0.04684 -1.861e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05099 0.03779 0.05584 0.04762 0.9842 0.9888 0.05226 0.9676 0.9787 0.07017 ] Network output: [ 0.0936 -0.2999 1.066 -0.0001748 7.847e-05 1.046 -0.0001317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7476 0.6377 0.5263 0.4928 0.9736 0.9881 0.7512 0.9065 0.9699 0.6256 ] Network output: [ -0.04934 0.197 0.9604 0.0005982 -0.0002686 0.9437 0.0004509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6063 0.592 0.4435 0.3326 0.9857 0.9906 0.6069 0.9717 0.9807 0.4564 ] Network output: [ -0.07249 0.2288 0.9382 3.874e-06 -1.739e-06 0.9779 2.92e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6089 0.6066 0.4671 0.2998 0.9836 0.9894 0.609 0.9651 0.9771 0.4696 ] Network output: [ 0.02507 0.9025 0.02676 -0.0002735 0.0001228 1.019 -0.0002061 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03446 Epoch 2193 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03528 0.9718 0.9932 7.058e-05 -3.169e-05 -0.03526 5.319e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02394 -0.005538 0.01932 0.03426 0.9372 0.947 0.05073 0.8804 0.8996 0.1292 ] Network output: [ 0.972 0.06887 -0.01506 -0.0001953 8.769e-05 0.001292 -0.0001472 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6534 0.1174 0.1145 0.309 0.9701 0.9861 0.7533 0.8956 0.9647 0.6296 ] Network output: [ -0.003908 0.9313 1.03 -2.502e-05 1.123e-05 0.04684 -1.886e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05099 0.03779 0.05583 0.04758 0.9842 0.9888 0.05225 0.9676 0.9787 0.07014 ] Network output: [ 0.09353 -0.2998 1.066 -0.0001769 7.944e-05 1.046 -0.0001334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7476 0.6377 0.5264 0.4925 0.9736 0.9881 0.7512 0.9065 0.9699 0.6256 ] Network output: [ -0.04928 0.1969 0.9604 0.0005988 -0.0002688 0.9437 0.0004513 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6064 0.592 0.4435 0.3324 0.9857 0.9906 0.6069 0.9717 0.9807 0.4564 ] Network output: [ -0.07242 0.2287 0.9383 5.031e-06 -2.259e-06 0.9779 3.791e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6089 0.6066 0.4671 0.2998 0.9836 0.9894 0.609 0.9651 0.9771 0.4696 ] Network output: [ 0.02503 0.9027 0.02673 -0.0002732 0.0001227 1.019 -0.0002059 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03442 Epoch 2194 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03526 0.9718 0.9932 7.015e-05 -3.149e-05 -0.03525 5.287e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02393 -0.00554 0.01931 0.03423 0.9372 0.947 0.05072 0.8804 0.8996 0.1291 ] Network output: [ 0.9721 0.06887 -0.01507 -0.0001944 8.726e-05 0.001259 -0.0001465 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6534 0.1174 0.1146 0.3087 0.9701 0.9861 0.7532 0.8957 0.9647 0.6296 ] Network output: [ -0.003921 0.9314 1.03 -2.535e-05 1.138e-05 0.04685 -1.91e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05098 0.03779 0.05581 0.04753 0.9842 0.9888 0.05225 0.9676 0.9787 0.07011 ] Network output: [ 0.09346 -0.2997 1.066 -0.0001791 8.041e-05 1.046 -0.000135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7476 0.6377 0.5265 0.4923 0.9736 0.9881 0.7512 0.9065 0.9699 0.6257 ] Network output: [ -0.04921 0.1968 0.9603 0.0005993 -0.0002691 0.9438 0.0004517 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6064 0.5921 0.4436 0.3323 0.9857 0.9906 0.607 0.9717 0.9807 0.4564 ] Network output: [ -0.07236 0.2285 0.9383 6.193e-06 -2.78e-06 0.9779 4.668e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.609 0.6066 0.4671 0.2997 0.9836 0.9894 0.6091 0.9651 0.9771 0.4696 ] Network output: [ 0.02498 0.9028 0.0267 -0.0002729 0.0001225 1.019 -0.0002057 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03438 Epoch 2195 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03523 0.9718 0.9932 6.973e-05 -3.13e-05 -0.03524 5.255e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02392 -0.005542 0.01931 0.0342 0.9372 0.947 0.0507 0.8805 0.8996 0.1291 ] Network output: [ 0.9721 0.06886 -0.01508 -0.0001934 8.684e-05 0.001227 -0.0001458 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6534 0.1175 0.1146 0.3085 0.9701 0.9861 0.7532 0.8957 0.9647 0.6296 ] Network output: [ -0.003934 0.9314 1.029 -2.568e-05 1.153e-05 0.04685 -1.935e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05098 0.03778 0.05579 0.04748 0.9842 0.9888 0.05225 0.9676 0.9787 0.07009 ] Network output: [ 0.09339 -0.2996 1.066 -0.0001813 8.138e-05 1.046 -0.0001366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7475 0.6377 0.5266 0.492 0.9736 0.9881 0.7511 0.9066 0.9699 0.6257 ] Network output: [ -0.04914 0.1967 0.9602 0.0005999 -0.0002693 0.9439 0.0004521 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6065 0.5922 0.4436 0.3322 0.9857 0.9906 0.607 0.9717 0.9807 0.4565 ] Network output: [ -0.0723 0.2283 0.9384 7.362e-06 -3.305e-06 0.9779 5.548e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.609 0.6067 0.4671 0.2997 0.9836 0.9894 0.6091 0.9651 0.9771 0.4696 ] Network output: [ 0.02493 0.903 0.02667 -0.0002727 0.0001224 1.019 -0.0002055 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03435 Epoch 2196 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03521 0.9718 0.9933 6.93e-05 -3.111e-05 -0.03523 5.223e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02391 -0.005544 0.0193 0.03417 0.9372 0.947 0.05069 0.8805 0.8996 0.1291 ] Network output: [ 0.9721 0.06885 -0.01509 -0.0001925 8.642e-05 0.001194 -0.0001451 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6533 0.1175 0.1147 0.3083 0.9701 0.9861 0.7531 0.8957 0.9648 0.6297 ] Network output: [ -0.003947 0.9314 1.029 -2.601e-05 1.168e-05 0.04686 -1.96e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05098 0.03778 0.05578 0.04744 0.9842 0.9888 0.05224 0.9676 0.9787 0.07006 ] Network output: [ 0.09332 -0.2995 1.066 -0.0001834 8.235e-05 1.046 -0.0001382 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7475 0.6378 0.5266 0.4918 0.9736 0.9881 0.7511 0.9066 0.9699 0.6257 ] Network output: [ -0.04907 0.1965 0.9601 0.0006004 -0.0002696 0.9439 0.0004525 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6066 0.5922 0.4436 0.3321 0.9857 0.9907 0.6071 0.9717 0.9807 0.4565 ] Network output: [ -0.07224 0.2282 0.9384 8.536e-06 -3.832e-06 0.9779 6.433e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.609 0.6067 0.4671 0.2996 0.9836 0.9894 0.6091 0.9652 0.9771 0.4696 ] Network output: [ 0.02488 0.9032 0.02664 -0.0002724 0.0001223 1.019 -0.0002053 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03431 Epoch 2197 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03518 0.9718 0.9933 6.888e-05 -3.092e-05 -0.03522 5.191e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02391 -0.005546 0.01929 0.03414 0.9373 0.947 0.05067 0.8805 0.8997 0.129 ] Network output: [ 0.9722 0.06885 -0.0151 -0.0001916 8.601e-05 0.001162 -0.0001444 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6533 0.1176 0.1148 0.308 0.9701 0.9861 0.7531 0.8958 0.9648 0.6297 ] Network output: [ -0.003959 0.9315 1.029 -2.634e-05 1.182e-05 0.04686 -1.985e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05097 0.03778 0.05576 0.04739 0.9842 0.9888 0.05224 0.9677 0.9787 0.07004 ] Network output: [ 0.09324 -0.2994 1.066 -0.0001856 8.333e-05 1.046 -0.0001399 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7475 0.6378 0.5267 0.4915 0.9736 0.9881 0.7511 0.9066 0.97 0.6258 ] Network output: [ -0.04901 0.1964 0.9601 0.000601 -0.0002698 0.944 0.0004529 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6066 0.5923 0.4437 0.332 0.9857 0.9907 0.6072 0.9717 0.9807 0.4565 ] Network output: [ -0.07217 0.228 0.9385 9.717e-06 -4.362e-06 0.9779 7.323e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6091 0.6067 0.4671 0.2996 0.9836 0.9894 0.6092 0.9652 0.9772 0.4696 ] Network output: [ 0.02483 0.9033 0.02661 -0.0002721 0.0001222 1.019 -0.0002051 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03427 Epoch 2198 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03516 0.9718 0.9933 6.845e-05 -3.073e-05 -0.03521 5.159e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0239 -0.005547 0.01929 0.03411 0.9373 0.947 0.05066 0.8806 0.8997 0.129 ] Network output: [ 0.9722 0.06884 -0.01512 -0.0001906 8.559e-05 0.00113 -0.0001437 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6533 0.1176 0.1149 0.3078 0.9701 0.9861 0.7531 0.8958 0.9648 0.6297 ] Network output: [ -0.003972 0.9315 1.029 -2.667e-05 1.197e-05 0.04687 -2.01e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05097 0.03778 0.05574 0.04735 0.9842 0.9888 0.05223 0.9677 0.9787 0.07001 ] Network output: [ 0.09317 -0.2993 1.066 -0.0001878 8.431e-05 1.046 -0.0001415 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7474 0.6378 0.5268 0.4913 0.9736 0.9881 0.751 0.9067 0.97 0.6258 ] Network output: [ -0.04894 0.1963 0.96 0.0006016 -0.0002701 0.944 0.0004534 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6067 0.5924 0.4437 0.3319 0.9857 0.9907 0.6072 0.9717 0.9808 0.4565 ] Network output: [ -0.07211 0.2278 0.9386 1.09e-05 -4.895e-06 0.9779 8.217e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6091 0.6068 0.4671 0.2995 0.9836 0.9894 0.6092 0.9652 0.9772 0.4696 ] Network output: [ 0.02478 0.9035 0.02658 -0.0002719 0.0001221 1.019 -0.0002049 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03424 Epoch 2199 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03513 0.9718 0.9934 6.803e-05 -3.054e-05 -0.0352 5.127e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02389 -0.005549 0.01928 0.03408 0.9373 0.9471 0.05064 0.8806 0.8997 0.1289 ] Network output: [ 0.9722 0.06883 -0.01513 -0.0001897 8.517e-05 0.001098 -0.000143 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6533 0.1177 0.1149 0.3075 0.9701 0.9861 0.753 0.8958 0.9648 0.6298 ] Network output: [ -0.003985 0.9316 1.029 -2.699e-05 1.212e-05 0.04687 -2.034e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05096 0.03778 0.05573 0.0473 0.9842 0.9888 0.05223 0.9677 0.9788 0.06998 ] Network output: [ 0.0931 -0.2992 1.066 -0.00019 8.529e-05 1.046 -0.0001432 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7474 0.6378 0.5269 0.4911 0.9736 0.9881 0.751 0.9067 0.97 0.6258 ] Network output: [ -0.04887 0.1962 0.9599 0.0006021 -0.0002703 0.9441 0.0004538 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6068 0.5924 0.4437 0.3318 0.9857 0.9907 0.6073 0.9718 0.9808 0.4566 ] Network output: [ -0.07205 0.2276 0.9386 1.21e-05 -5.43e-06 0.9779 9.115e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6091 0.6068 0.4671 0.2994 0.9836 0.9894 0.6092 0.9652 0.9772 0.4696 ] Network output: [ 0.02473 0.9037 0.02655 -0.0002716 0.0001219 1.019 -0.0002047 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0342 Epoch 2200 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03511 0.9718 0.9934 6.76e-05 -3.035e-05 -0.03519 5.095e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02389 -0.005551 0.01928 0.03405 0.9373 0.9471 0.05063 0.8806 0.8998 0.1289 ] Network output: [ 0.9722 0.06883 -0.01514 -0.0001888 8.476e-05 0.001066 -0.0001423 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6532 0.1177 0.115 0.3073 0.9701 0.9861 0.753 0.8959 0.9648 0.6298 ] Network output: [ -0.003998 0.9316 1.029 -2.732e-05 1.227e-05 0.04688 -2.059e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05096 0.03778 0.05571 0.04726 0.9842 0.9888 0.05222 0.9677 0.9788 0.06996 ] Network output: [ 0.09303 -0.2991 1.066 -0.0001922 8.627e-05 1.046 -0.0001448 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7474 0.6379 0.5269 0.4908 0.9737 0.9881 0.7509 0.9067 0.97 0.6258 ] Network output: [ -0.0488 0.1961 0.9598 0.0006027 -0.0002706 0.9441 0.0004542 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6068 0.5925 0.4438 0.3317 0.9858 0.9907 0.6073 0.9718 0.9808 0.4566 ] Network output: [ -0.07199 0.2275 0.9387 1.329e-05 -5.968e-06 0.9779 1.002e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6091 0.6068 0.4671 0.2994 0.9836 0.9894 0.6092 0.9652 0.9772 0.4696 ] Network output: [ 0.02468 0.9038 0.02652 -0.0002714 0.0001218 1.019 -0.0002045 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03416 Epoch 2201 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03508 0.9718 0.9935 6.718e-05 -3.016e-05 -0.03518 5.063e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02388 -0.005553 0.01927 0.03402 0.9373 0.9471 0.05061 0.8807 0.8998 0.1288 ] Network output: [ 0.9723 0.06882 -0.01515 -0.0001879 8.435e-05 0.001035 -0.0001416 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6532 0.1178 0.1151 0.307 0.9702 0.9861 0.753 0.8959 0.9648 0.6298 ] Network output: [ -0.004011 0.9316 1.029 -2.765e-05 1.241e-05 0.04688 -2.084e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05095 0.03778 0.05569 0.04721 0.9842 0.9888 0.05222 0.9677 0.9788 0.06993 ] Network output: [ 0.09296 -0.299 1.067 -0.0001944 8.725e-05 1.046 -0.0001465 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7473 0.6379 0.527 0.4906 0.9737 0.9881 0.7509 0.9068 0.97 0.6259 ] Network output: [ -0.04873 0.196 0.9598 0.0006033 -0.0002709 0.9442 0.0004547 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6069 0.5926 0.4438 0.3315 0.9858 0.9907 0.6074 0.9718 0.9808 0.4566 ] Network output: [ -0.07192 0.2273 0.9387 1.45e-05 -6.509e-06 0.9779 1.093e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6092 0.6068 0.4671 0.2993 0.9836 0.9894 0.6093 0.9653 0.9772 0.4696 ] Network output: [ 0.02464 0.904 0.02649 -0.0002712 0.0001217 1.019 -0.0002044 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03413 Epoch 2202 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03506 0.9718 0.9935 6.675e-05 -2.997e-05 -0.03517 5.031e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02387 -0.005554 0.01926 0.03399 0.9373 0.9471 0.05059 0.8807 0.8998 0.1288 ] Network output: [ 0.9723 0.06881 -0.01517 -0.000187 8.393e-05 0.001003 -0.0001409 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6532 0.1178 0.1152 0.3068 0.9702 0.9861 0.7529 0.8959 0.9648 0.6299 ] Network output: [ -0.004024 0.9317 1.029 -2.798e-05 1.256e-05 0.04689 -2.108e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05095 0.03778 0.05568 0.04716 0.9842 0.9888 0.05221 0.9677 0.9788 0.0699 ] Network output: [ 0.09289 -0.2989 1.067 -0.0001965 8.824e-05 1.046 -0.0001481 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7473 0.6379 0.5271 0.4903 0.9737 0.9881 0.7509 0.9068 0.97 0.6259 ] Network output: [ -0.04867 0.1959 0.9597 0.0006039 -0.0002711 0.9443 0.0004551 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6069 0.5926 0.4438 0.3314 0.9858 0.9907 0.6075 0.9718 0.9808 0.4566 ] Network output: [ -0.07186 0.2271 0.9388 1.571e-05 -7.052e-06 0.9779 1.184e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6092 0.6069 0.4671 0.2993 0.9836 0.9894 0.6093 0.9653 0.9772 0.4696 ] Network output: [ 0.02459 0.9041 0.02646 -0.0002709 0.0001216 1.019 -0.0002042 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03409 Epoch 2203 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03503 0.9718 0.9935 6.633e-05 -2.978e-05 -0.03516 4.999e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02386 -0.005556 0.01926 0.03396 0.9373 0.9471 0.05058 0.8807 0.8998 0.1288 ] Network output: [ 0.9723 0.06881 -0.01518 -0.000186 8.352e-05 0.0009717 -0.0001402 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6532 0.1179 0.1152 0.3066 0.9702 0.9861 0.7529 0.896 0.9649 0.6299 ] Network output: [ -0.004037 0.9317 1.029 -2.83e-05 1.271e-05 0.04689 -2.133e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05094 0.03778 0.05566 0.04712 0.9842 0.9888 0.05221 0.9678 0.9788 0.06988 ] Network output: [ 0.09281 -0.2988 1.067 -0.0001987 8.922e-05 1.046 -0.0001498 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7473 0.6379 0.5272 0.4901 0.9737 0.9881 0.7508 0.9068 0.97 0.6259 ] Network output: [ -0.0486 0.1957 0.9596 0.0006045 -0.0002714 0.9443 0.0004556 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.607 0.5927 0.4439 0.3313 0.9858 0.9907 0.6075 0.9718 0.9808 0.4567 ] Network output: [ -0.0718 0.227 0.9388 1.692e-05 -7.598e-06 0.9779 1.275e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6092 0.6069 0.4671 0.2992 0.9836 0.9894 0.6093 0.9653 0.9772 0.4696 ] Network output: [ 0.02454 0.9043 0.02643 -0.0002707 0.0001215 1.019 -0.000204 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03405 Epoch 2204 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03501 0.9718 0.9936 6.591e-05 -2.959e-05 -0.03514 4.967e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02386 -0.005558 0.01925 0.03393 0.9373 0.9471 0.05056 0.8808 0.8999 0.1287 ] Network output: [ 0.9723 0.0688 -0.01519 -0.0001851 8.311e-05 0.0009405 -0.0001395 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6531 0.118 0.1153 0.3063 0.9702 0.9861 0.7529 0.896 0.9649 0.6299 ] Network output: [ -0.00405 0.9317 1.029 -2.863e-05 1.285e-05 0.04689 -2.158e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05094 0.03777 0.05564 0.04707 0.9842 0.9888 0.0522 0.9678 0.9788 0.06985 ] Network output: [ 0.09274 -0.2987 1.067 -0.0002009 9.021e-05 1.046 -0.0001514 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7472 0.6379 0.5272 0.4898 0.9737 0.9881 0.7508 0.9068 0.97 0.6259 ] Network output: [ -0.04853 0.1956 0.9595 0.0006051 -0.0002717 0.9444 0.000456 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6071 0.5928 0.4439 0.3312 0.9858 0.9907 0.6076 0.9718 0.9808 0.4567 ] Network output: [ -0.07173 0.2268 0.9389 1.815e-05 -8.147e-06 0.9779 1.368e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6093 0.6069 0.4671 0.2991 0.9836 0.9894 0.6094 0.9653 0.9772 0.4695 ] Network output: [ 0.02449 0.9045 0.02641 -0.0002705 0.0001214 1.019 -0.0002038 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03401 Epoch 2205 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03498 0.9718 0.9936 6.548e-05 -2.94e-05 -0.03513 4.935e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02385 -0.005559 0.01924 0.0339 0.9374 0.9471 0.05055 0.8808 0.8999 0.1287 ] Network output: [ 0.9724 0.06879 -0.01521 -0.0001842 8.271e-05 0.0009093 -0.0001388 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6531 0.118 0.1154 0.3061 0.9702 0.9861 0.7528 0.896 0.9649 0.6299 ] Network output: [ -0.004063 0.9318 1.029 -2.895e-05 1.3e-05 0.0469 -2.182e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05093 0.03777 0.05562 0.04703 0.9842 0.9888 0.0522 0.9678 0.9788 0.06983 ] Network output: [ 0.09267 -0.2987 1.067 -0.0002031 9.12e-05 1.046 -0.0001531 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7472 0.6379 0.5273 0.4896 0.9737 0.9881 0.7508 0.9069 0.9701 0.626 ] Network output: [ -0.04846 0.1955 0.9594 0.0006057 -0.0002719 0.9444 0.0004565 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6071 0.5928 0.4439 0.3311 0.9858 0.9907 0.6076 0.9718 0.9808 0.4567 ] Network output: [ -0.07167 0.2266 0.9389 1.937e-05 -8.698e-06 0.9779 1.46e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6093 0.607 0.4671 0.2991 0.9837 0.9894 0.6094 0.9653 0.9773 0.4695 ] Network output: [ 0.02444 0.9046 0.02638 -0.0002703 0.0001213 1.019 -0.0002037 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03398 Epoch 2206 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03496 0.9718 0.9936 6.506e-05 -2.921e-05 -0.03512 4.903e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02384 -0.005561 0.01924 0.03387 0.9374 0.9471 0.05053 0.8808 0.8999 0.1286 ] Network output: [ 0.9724 0.06879 -0.01522 -0.0001833 8.23e-05 0.0008783 -0.0001382 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6531 0.1181 0.1155 0.3058 0.9702 0.9861 0.7528 0.8961 0.9649 0.63 ] Network output: [ -0.004076 0.9318 1.029 -2.928e-05 1.315e-05 0.0469 -2.207e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05093 0.03777 0.05561 0.04698 0.9842 0.9888 0.05219 0.9678 0.9788 0.0698 ] Network output: [ 0.09259 -0.2986 1.067 -0.0002054 9.219e-05 1.046 -0.0001548 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7472 0.638 0.5274 0.4893 0.9737 0.9881 0.7507 0.9069 0.9701 0.626 ] Network output: [ -0.04839 0.1954 0.9594 0.0006063 -0.0002722 0.9445 0.000457 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6072 0.5929 0.444 0.331 0.9858 0.9907 0.6077 0.9719 0.9808 0.4567 ] Network output: [ -0.07161 0.2264 0.939 2.061e-05 -9.252e-06 0.9779 1.553e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6093 0.607 0.4671 0.299 0.9837 0.9894 0.6094 0.9654 0.9773 0.4695 ] Network output: [ 0.0244 0.9048 0.02635 -0.0002701 0.0001212 1.019 -0.0002035 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03394 Epoch 2207 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03493 0.9718 0.9937 6.464e-05 -2.902e-05 -0.03511 4.872e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02383 -0.005563 0.01923 0.03384 0.9374 0.9471 0.05052 0.8809 0.8999 0.1286 ] Network output: [ 0.9724 0.06878 -0.01523 -0.0001824 8.189e-05 0.0008474 -0.0001375 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6531 0.1181 0.1155 0.3056 0.9702 0.9861 0.7527 0.8961 0.9649 0.63 ] Network output: [ -0.004089 0.9318 1.029 -2.961e-05 1.329e-05 0.0469 -2.231e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05092 0.03777 0.05559 0.04693 0.9843 0.9888 0.05219 0.9678 0.9788 0.06977 ] Network output: [ 0.09252 -0.2985 1.067 -0.0002076 9.319e-05 1.046 -0.0001564 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7471 0.638 0.5275 0.4891 0.9737 0.9881 0.7507 0.9069 0.9701 0.626 ] Network output: [ -0.04833 0.1953 0.9593 0.000607 -0.0002725 0.9446 0.0004574 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6072 0.593 0.444 0.3308 0.9858 0.9907 0.6078 0.9719 0.9808 0.4568 ] Network output: [ -0.07154 0.2263 0.939 2.185e-05 -9.809e-06 0.9779 1.647e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6094 0.607 0.467 0.2989 0.9837 0.9894 0.6095 0.9654 0.9773 0.4695 ] Network output: [ 0.02435 0.905 0.02632 -0.0002699 0.0001211 1.019 -0.0002034 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0339 Epoch 2208 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03491 0.9718 0.9937 6.422e-05 -2.883e-05 -0.0351 4.84e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02383 -0.005564 0.01922 0.03381 0.9374 0.9472 0.0505 0.8809 0.9 0.1286 ] Network output: [ 0.9725 0.06878 -0.01524 -0.0001815 8.149e-05 0.0008166 -0.0001368 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.653 0.1182 0.1156 0.3053 0.9702 0.9861 0.7527 0.8961 0.9649 0.63 ] Network output: [ -0.004102 0.9319 1.029 -2.993e-05 1.344e-05 0.0469 -2.256e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05092 0.03777 0.05557 0.04689 0.9843 0.9888 0.05218 0.9678 0.9788 0.06975 ] Network output: [ 0.09245 -0.2984 1.067 -0.0002098 9.418e-05 1.046 -0.0001581 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7471 0.638 0.5275 0.4888 0.9737 0.9881 0.7506 0.907 0.9701 0.6261 ] Network output: [ -0.04826 0.1952 0.9592 0.0006076 -0.0002728 0.9446 0.0004579 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6073 0.593 0.444 0.3307 0.9858 0.9907 0.6078 0.9719 0.9808 0.4568 ] Network output: [ -0.07148 0.2261 0.9391 2.31e-05 -1.037e-05 0.9779 1.741e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6094 0.6071 0.467 0.2989 0.9837 0.9895 0.6095 0.9654 0.9773 0.4695 ] Network output: [ 0.0243 0.9051 0.02629 -0.0002697 0.0001211 1.019 -0.0002032 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03387 Epoch 2209 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03488 0.9718 0.9938 6.38e-05 -2.864e-05 -0.03509 4.808e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02382 -0.005566 0.01922 0.03378 0.9374 0.9472 0.05049 0.8809 0.9 0.1285 ] Network output: [ 0.9725 0.06877 -0.01526 -0.0001806 8.108e-05 0.0007859 -0.0001361 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.653 0.1182 0.1157 0.3051 0.9702 0.9861 0.7527 0.8962 0.9649 0.6301 ] Network output: [ -0.004115 0.9319 1.029 -3.026e-05 1.358e-05 0.04691 -2.28e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05091 0.03776 0.05556 0.04684 0.9843 0.9888 0.05217 0.9679 0.9789 0.06972 ] Network output: [ 0.09238 -0.2983 1.067 -0.000212 9.518e-05 1.046 -0.0001598 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.747 0.638 0.5276 0.4885 0.9737 0.9882 0.7506 0.907 0.9701 0.6261 ] Network output: [ -0.04819 0.195 0.9591 0.0006082 -0.000273 0.9447 0.0004584 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6074 0.5931 0.4441 0.3306 0.9858 0.9907 0.6079 0.9719 0.9809 0.4568 ] Network output: [ -0.07142 0.2259 0.9391 2.435e-05 -1.093e-05 0.9779 1.835e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6094 0.6071 0.467 0.2988 0.9837 0.9895 0.6095 0.9654 0.9773 0.4695 ] Network output: [ 0.02425 0.9053 0.02626 -0.0002695 0.000121 1.019 -0.0002031 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03383 Epoch 2210 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03485 0.9718 0.9938 6.338e-05 -2.845e-05 -0.03508 4.776e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02381 -0.005568 0.01921 0.03375 0.9374 0.9472 0.05047 0.881 0.9 0.1285 ] Network output: [ 0.9725 0.06876 -0.01527 -0.0001797 8.068e-05 0.0007554 -0.0001354 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.653 0.1183 0.1157 0.3048 0.9702 0.9861 0.7526 0.8962 0.965 0.6301 ] Network output: [ -0.004129 0.932 1.029 -3.058e-05 1.373e-05 0.04691 -2.305e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05091 0.03776 0.05554 0.04679 0.9843 0.9888 0.05217 0.9679 0.9789 0.0697 ] Network output: [ 0.0923 -0.2982 1.067 -0.0002142 9.618e-05 1.046 -0.0001615 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.747 0.638 0.5277 0.4883 0.9737 0.9882 0.7506 0.907 0.9701 0.6261 ] Network output: [ -0.04812 0.1949 0.9591 0.0006088 -0.0002733 0.9447 0.0004588 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6074 0.5932 0.4441 0.3305 0.9858 0.9907 0.6079 0.9719 0.9809 0.4568 ] Network output: [ -0.07135 0.2257 0.9392 2.561e-05 -1.15e-05 0.9779 1.93e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6094 0.6071 0.467 0.2988 0.9837 0.9895 0.6095 0.9654 0.9773 0.4695 ] Network output: [ 0.02421 0.9054 0.02624 -0.0002693 0.0001209 1.019 -0.0002029 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03379 Epoch 2211 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03483 0.9718 0.9938 6.296e-05 -2.826e-05 -0.03507 4.745e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0238 -0.005569 0.0192 0.03372 0.9374 0.9472 0.05045 0.881 0.9 0.1284 ] Network output: [ 0.9725 0.06876 -0.01528 -0.0001788 8.028e-05 0.000725 -0.0001348 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6529 0.1183 0.1158 0.3046 0.9702 0.9861 0.7526 0.8962 0.965 0.6301 ] Network output: [ -0.004142 0.932 1.029 -3.09e-05 1.387e-05 0.04691 -2.329e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0509 0.03776 0.05552 0.04675 0.9843 0.9889 0.05216 0.9679 0.9789 0.06967 ] Network output: [ 0.09223 -0.2981 1.067 -0.0002165 9.718e-05 1.046 -0.0001631 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.747 0.638 0.5278 0.488 0.9737 0.9882 0.7505 0.9071 0.9701 0.6261 ] Network output: [ -0.04805 0.1948 0.959 0.0006095 -0.0002736 0.9448 0.0004593 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6075 0.5932 0.4441 0.3303 0.9858 0.9907 0.608 0.9719 0.9809 0.4569 ] Network output: [ -0.07129 0.2256 0.9392 2.687e-05 -1.206e-05 0.9779 2.025e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6095 0.6072 0.467 0.2987 0.9837 0.9895 0.6096 0.9654 0.9773 0.4695 ] Network output: [ 0.02416 0.9056 0.02621 -0.0002691 0.0001208 1.019 -0.0002028 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03375 Epoch 2212 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0348 0.9718 0.9939 6.254e-05 -2.808e-05 -0.03506 4.713e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0238 -0.005571 0.0192 0.03369 0.9374 0.9472 0.05044 0.881 0.9001 0.1284 ] Network output: [ 0.9726 0.06875 -0.0153 -0.0001779 7.988e-05 0.0006948 -0.0001341 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6529 0.1184 0.1159 0.3043 0.9702 0.9861 0.7525 0.8963 0.965 0.6302 ] Network output: [ -0.004155 0.932 1.029 -3.123e-05 1.402e-05 0.04691 -2.353e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0509 0.03776 0.0555 0.0467 0.9843 0.9889 0.05216 0.9679 0.9789 0.06964 ] Network output: [ 0.09215 -0.298 1.067 -0.0002187 9.819e-05 1.046 -0.0001648 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7469 0.6381 0.5278 0.4878 0.9737 0.9882 0.7505 0.9071 0.9701 0.6262 ] Network output: [ -0.04798 0.1947 0.9589 0.0006101 -0.0002739 0.9449 0.0004598 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6075 0.5933 0.4442 0.3302 0.9858 0.9907 0.6081 0.9719 0.9809 0.4569 ] Network output: [ -0.07122 0.2254 0.9393 2.814e-05 -1.263e-05 0.9779 2.121e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6095 0.6072 0.467 0.2986 0.9837 0.9895 0.6096 0.9655 0.9773 0.4695 ] Network output: [ 0.02411 0.9058 0.02618 -0.0002689 0.0001207 1.019 -0.0002027 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03372 Epoch 2213 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03478 0.9718 0.9939 6.212e-05 -2.789e-05 -0.03505 4.681e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02379 -0.005572 0.01919 0.03366 0.9375 0.9472 0.05042 0.881 0.9001 0.1283 ] Network output: [ 0.9726 0.06874 -0.01531 -0.000177 7.948e-05 0.0006646 -0.0001334 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6529 0.1184 0.116 0.3041 0.9702 0.9861 0.7525 0.8963 0.965 0.6302 ] Network output: [ -0.004168 0.9321 1.029 -3.155e-05 1.417e-05 0.04691 -2.378e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05089 0.03776 0.05549 0.04666 0.9843 0.9889 0.05215 0.9679 0.9789 0.06962 ] Network output: [ 0.09208 -0.2979 1.067 -0.000221 9.919e-05 1.046 -0.0001665 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7469 0.6381 0.5279 0.4875 0.9737 0.9882 0.7504 0.9071 0.9701 0.6262 ] Network output: [ -0.04792 0.1946 0.9588 0.0006108 -0.0002742 0.9449 0.0004603 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6076 0.5934 0.4442 0.3301 0.9858 0.9907 0.6081 0.9719 0.9809 0.4569 ] Network output: [ -0.07116 0.2252 0.9393 2.942e-05 -1.321e-05 0.9779 2.217e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6095 0.6072 0.467 0.2986 0.9837 0.9895 0.6096 0.9655 0.9773 0.4695 ] Network output: [ 0.02406 0.9059 0.02616 -0.0002687 0.0001206 1.019 -0.0002025 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03368 Epoch 2214 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03475 0.9718 0.9939 6.17e-05 -2.77e-05 -0.03504 4.65e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02378 -0.005574 0.01918 0.03363 0.9375 0.9472 0.05041 0.8811 0.9001 0.1283 ] Network output: [ 0.9726 0.06874 -0.01532 -0.0001762 7.908e-05 0.0006346 -0.0001328 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6529 0.1184 0.116 0.3038 0.9703 0.9861 0.7524 0.8963 0.965 0.6302 ] Network output: [ -0.004181 0.9321 1.029 -3.188e-05 1.431e-05 0.04692 -2.402e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05089 0.03775 0.05547 0.04661 0.9843 0.9889 0.05214 0.9679 0.9789 0.06959 ] Network output: [ 0.09201 -0.2977 1.067 -0.0002232 0.0001002 1.046 -0.0001682 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7468 0.6381 0.528 0.4873 0.9737 0.9882 0.7504 0.9071 0.9702 0.6262 ] Network output: [ -0.04785 0.1944 0.9588 0.0006114 -0.0002745 0.945 0.0004608 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6077 0.5934 0.4442 0.33 0.9858 0.9907 0.6082 0.972 0.9809 0.4569 ] Network output: [ -0.0711 0.225 0.9394 3.07e-05 -1.378e-05 0.9779 2.314e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6096 0.6072 0.467 0.2985 0.9837 0.9895 0.6097 0.9655 0.9774 0.4695 ] Network output: [ 0.02402 0.9061 0.02613 -0.0002686 0.0001206 1.019 -0.0002024 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03364 Epoch 2215 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03473 0.9718 0.994 6.128e-05 -2.751e-05 -0.03503 4.618e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02378 -0.005575 0.01918 0.03359 0.9375 0.9472 0.05039 0.8811 0.9001 0.1283 ] Network output: [ 0.9726 0.06873 -0.01534 -0.0001753 7.869e-05 0.0006047 -0.0001321 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6528 0.1185 0.1161 0.3036 0.9703 0.9862 0.7524 0.8964 0.965 0.6303 ] Network output: [ -0.004195 0.9321 1.029 -3.22e-05 1.446e-05 0.04692 -2.427e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05088 0.03775 0.05545 0.04656 0.9843 0.9889 0.05214 0.9679 0.9789 0.06957 ] Network output: [ 0.09193 -0.2976 1.067 -0.0002255 0.0001012 1.046 -0.0001699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7468 0.6381 0.5281 0.487 0.9738 0.9882 0.7503 0.9072 0.9702 0.6263 ] Network output: [ -0.04778 0.1943 0.9587 0.0006121 -0.0002748 0.945 0.0004613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6077 0.5935 0.4443 0.3299 0.9858 0.9907 0.6082 0.972 0.9809 0.457 ] Network output: [ -0.07103 0.2249 0.9394 3.199e-05 -1.436e-05 0.9779 2.411e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6096 0.6073 0.467 0.2984 0.9837 0.9895 0.6097 0.9655 0.9774 0.4695 ] Network output: [ 0.02397 0.9062 0.0261 -0.0002684 0.0001205 1.019 -0.0002023 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0336 Epoch 2216 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0347 0.9718 0.994 6.086e-05 -2.732e-05 -0.03502 4.587e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02377 -0.005577 0.01917 0.03356 0.9375 0.9472 0.05038 0.8811 0.9002 0.1282 ] Network output: [ 0.9727 0.06872 -0.01535 -0.0001744 7.829e-05 0.000575 -0.0001314 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6528 0.1185 0.1162 0.3033 0.9703 0.9862 0.7524 0.8964 0.965 0.6303 ] Network output: [ -0.004208 0.9322 1.029 -3.252e-05 1.46e-05 0.04692 -2.451e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05087 0.03775 0.05543 0.04652 0.9843 0.9889 0.05213 0.968 0.9789 0.06954 ] Network output: [ 0.09186 -0.2975 1.067 -0.0002277 0.0001022 1.046 -0.0001716 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7467 0.6381 0.5281 0.4867 0.9738 0.9882 0.7503 0.9072 0.9702 0.6263 ] Network output: [ -0.04771 0.1942 0.9586 0.0006128 -0.0002751 0.9451 0.0004618 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6078 0.5935 0.4443 0.3297 0.9858 0.9907 0.6083 0.972 0.9809 0.457 ] Network output: [ -0.07097 0.2247 0.9395 3.329e-05 -1.494e-05 0.9779 2.509e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6096 0.6073 0.467 0.2983 0.9837 0.9895 0.6097 0.9655 0.9774 0.4695 ] Network output: [ 0.02392 0.9064 0.02607 -0.0002683 0.0001204 1.019 -0.0002022 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03357 Epoch 2217 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03468 0.9718 0.9941 6.045e-05 -2.714e-05 -0.03501 4.555e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02376 -0.005578 0.01916 0.03353 0.9375 0.9473 0.05036 0.8812 0.9002 0.1282 ] Network output: [ 0.9727 0.06872 -0.01536 -0.0001735 7.79e-05 0.0005454 -0.0001308 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6528 0.1186 0.1162 0.3031 0.9703 0.9862 0.7523 0.8964 0.9651 0.6303 ] Network output: [ -0.004221 0.9322 1.029 -3.284e-05 1.475e-05 0.04692 -2.475e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05087 0.03775 0.05542 0.04647 0.9843 0.9889 0.05213 0.968 0.9789 0.06951 ] Network output: [ 0.09178 -0.2974 1.067 -0.00023 0.0001032 1.046 -0.0001733 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7467 0.6381 0.5282 0.4865 0.9738 0.9882 0.7503 0.9072 0.9702 0.6263 ] Network output: [ -0.04764 0.1941 0.9585 0.0006135 -0.0002754 0.9452 0.0004623 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6078 0.5936 0.4443 0.3296 0.9858 0.9907 0.6084 0.972 0.9809 0.457 ] Network output: [ -0.0709 0.2245 0.9395 3.459e-05 -1.553e-05 0.9779 2.607e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6097 0.6073 0.467 0.2983 0.9837 0.9895 0.6098 0.9655 0.9774 0.4694 ] Network output: [ 0.02387 0.9066 0.02605 -0.0002681 0.0001204 1.019 -0.0002021 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03353 Epoch 2218 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03465 0.9718 0.9941 6.003e-05 -2.695e-05 -0.035 4.524e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02375 -0.00558 0.01916 0.0335 0.9375 0.9473 0.05034 0.8812 0.9002 0.1281 ] Network output: [ 0.9727 0.06871 -0.01538 -0.0001726 7.751e-05 0.0005159 -0.0001301 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6527 0.1186 0.1163 0.3028 0.9703 0.9862 0.7523 0.8965 0.9651 0.6304 ] Network output: [ -0.004235 0.9323 1.029 -3.317e-05 1.489e-05 0.04692 -2.5e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05086 0.03774 0.0554 0.04642 0.9843 0.9889 0.05212 0.968 0.9789 0.06949 ] Network output: [ 0.09171 -0.2973 1.067 -0.0002322 0.0001043 1.046 -0.000175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7467 0.6381 0.5283 0.4862 0.9738 0.9882 0.7502 0.9073 0.9702 0.6264 ] Network output: [ -0.04757 0.194 0.9584 0.0006141 -0.0002757 0.9452 0.0004628 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6079 0.5937 0.4444 0.3295 0.9858 0.9907 0.6084 0.972 0.9809 0.457 ] Network output: [ -0.07084 0.2243 0.9396 3.59e-05 -1.611e-05 0.9779 2.705e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6097 0.6074 0.467 0.2982 0.9837 0.9895 0.6098 0.9656 0.9774 0.4694 ] Network output: [ 0.02383 0.9067 0.02602 -0.000268 0.0001203 1.019 -0.0002019 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03349 Epoch 2219 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03463 0.9718 0.9941 5.961e-05 -2.676e-05 -0.03499 4.493e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02375 -0.005581 0.01915 0.03347 0.9375 0.9473 0.05033 0.8812 0.9002 0.1281 ] Network output: [ 0.9728 0.0687 -0.01539 -0.0001718 7.711e-05 0.0004865 -0.0001295 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6527 0.1187 0.1164 0.3026 0.9703 0.9862 0.7522 0.8965 0.9651 0.6304 ] Network output: [ -0.004248 0.9323 1.029 -3.349e-05 1.503e-05 0.04692 -2.524e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05086 0.03774 0.05538 0.04637 0.9843 0.9889 0.05211 0.968 0.979 0.06946 ] Network output: [ 0.09163 -0.2972 1.067 -0.0002345 0.0001053 1.046 -0.0001767 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7466 0.6381 0.5283 0.486 0.9738 0.9882 0.7502 0.9073 0.9702 0.6264 ] Network output: [ -0.0475 0.1938 0.9584 0.0006148 -0.000276 0.9453 0.0004633 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.608 0.5937 0.4444 0.3294 0.9858 0.9907 0.6085 0.972 0.9809 0.4571 ] Network output: [ -0.07077 0.2242 0.9396 3.721e-05 -1.67e-05 0.9779 2.804e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6097 0.6074 0.467 0.2981 0.9837 0.9895 0.6098 0.9656 0.9774 0.4694 ] Network output: [ 0.02378 0.9069 0.026 -0.0002678 0.0001202 1.018 -0.0002018 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03345 Epoch 2220 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0346 0.9719 0.9942 5.92e-05 -2.658e-05 -0.03498 4.461e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02374 -0.005583 0.01914 0.03344 0.9375 0.9473 0.05031 0.8813 0.9003 0.1281 ] Network output: [ 0.9728 0.0687 -0.01541 -0.0001709 7.672e-05 0.0004572 -0.0001288 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6527 0.1187 0.1164 0.3023 0.9703 0.9862 0.7522 0.8965 0.9651 0.6304 ] Network output: [ -0.004261 0.9323 1.029 -3.381e-05 1.518e-05 0.04692 -2.548e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05085 0.03774 0.05537 0.04633 0.9843 0.9889 0.05211 0.968 0.979 0.06944 ] Network output: [ 0.09156 -0.2971 1.067 -0.0002368 0.0001063 1.046 -0.0001784 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7466 0.6381 0.5284 0.4857 0.9738 0.9882 0.7501 0.9073 0.9702 0.6264 ] Network output: [ -0.04743 0.1937 0.9583 0.0006155 -0.0002763 0.9454 0.0004639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.608 0.5938 0.4444 0.3292 0.9858 0.9907 0.6085 0.972 0.981 0.4571 ] Network output: [ -0.07071 0.224 0.9397 3.853e-05 -1.73e-05 0.9779 2.904e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6097 0.6074 0.467 0.2981 0.9837 0.9895 0.6098 0.9656 0.9774 0.4694 ] Network output: [ 0.02373 0.907 0.02597 -0.0002677 0.0001202 1.018 -0.0002017 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03341 Epoch 2221 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03458 0.9719 0.9942 5.878e-05 -2.639e-05 -0.03498 4.43e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02373 -0.005584 0.01914 0.03341 0.9376 0.9473 0.0503 0.8813 0.9003 0.128 ] Network output: [ 0.9728 0.06869 -0.01542 -0.00017 7.633e-05 0.0004281 -0.0001281 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6526 0.1188 0.1165 0.3021 0.9703 0.9862 0.7521 0.8965 0.9651 0.6305 ] Network output: [ -0.004275 0.9324 1.029 -3.413e-05 1.532e-05 0.04692 -2.572e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05084 0.03774 0.05535 0.04628 0.9843 0.9889 0.0521 0.968 0.979 0.06941 ] Network output: [ 0.09148 -0.297 1.067 -0.0002391 0.0001073 1.046 -0.0001802 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7465 0.6381 0.5285 0.4854 0.9738 0.9882 0.7501 0.9073 0.9702 0.6265 ] Network output: [ -0.04736 0.1936 0.9582 0.0006162 -0.0002766 0.9454 0.0004644 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6081 0.5939 0.4444 0.3291 0.9858 0.9907 0.6086 0.972 0.981 0.4571 ] Network output: [ -0.07064 0.2238 0.9397 3.986e-05 -1.789e-05 0.9779 3.004e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6098 0.6075 0.467 0.298 0.9837 0.9895 0.6099 0.9656 0.9774 0.4694 ] Network output: [ 0.02369 0.9072 0.02594 -0.0002676 0.0001201 1.018 -0.0002016 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03338 Epoch 2222 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03455 0.9719 0.9942 5.836e-05 -2.62e-05 -0.03497 4.399e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02372 -0.005586 0.01913 0.03338 0.9376 0.9473 0.05028 0.8813 0.9003 0.128 ] Network output: [ 0.9728 0.06868 -0.01543 -0.0001692 7.595e-05 0.0003991 -0.0001275 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6526 0.1188 0.1166 0.3018 0.9703 0.9862 0.7521 0.8966 0.9651 0.6305 ] Network output: [ -0.004288 0.9324 1.029 -3.445e-05 1.547e-05 0.04692 -2.597e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05084 0.03773 0.05533 0.04623 0.9843 0.9889 0.05209 0.968 0.979 0.06938 ] Network output: [ 0.09141 -0.2969 1.067 -0.0002413 0.0001083 1.046 -0.0001819 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7465 0.6381 0.5286 0.4852 0.9738 0.9882 0.75 0.9074 0.9703 0.6265 ] Network output: [ -0.04729 0.1935 0.9581 0.0006169 -0.000277 0.9455 0.0004649 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6081 0.5939 0.4445 0.329 0.9858 0.9907 0.6087 0.972 0.981 0.4571 ] Network output: [ -0.07058 0.2236 0.9398 4.119e-05 -1.849e-05 0.9779 3.104e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6098 0.6075 0.4669 0.2979 0.9837 0.9895 0.6099 0.9656 0.9775 0.4694 ] Network output: [ 0.02364 0.9074 0.02592 -0.0002674 0.0001201 1.018 -0.0002015 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03334 Epoch 2223 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03452 0.9719 0.9943 5.795e-05 -2.602e-05 -0.03496 4.367e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02372 -0.005587 0.01912 0.03335 0.9376 0.9473 0.05026 0.8814 0.9004 0.1279 ] Network output: [ 0.9729 0.06868 -0.01545 -0.0001683 7.556e-05 0.0003702 -0.0001268 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6526 0.1189 0.1166 0.3016 0.9703 0.9862 0.752 0.8966 0.9651 0.6305 ] Network output: [ -0.004302 0.9325 1.029 -3.478e-05 1.561e-05 0.04692 -2.621e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05083 0.03773 0.05531 0.04619 0.9843 0.9889 0.05209 0.9681 0.979 0.06936 ] Network output: [ 0.09133 -0.2968 1.068 -0.0002436 0.0001094 1.046 -0.0001836 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7464 0.6382 0.5286 0.4849 0.9738 0.9882 0.75 0.9074 0.9703 0.6265 ] Network output: [ -0.04722 0.1934 0.958 0.0006176 -0.0002773 0.9456 0.0004655 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6082 0.594 0.4445 0.3288 0.9859 0.9907 0.6087 0.9721 0.981 0.4572 ] Network output: [ -0.07051 0.2234 0.9398 4.253e-05 -1.909e-05 0.978 3.205e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6098 0.6075 0.4669 0.2978 0.9838 0.9895 0.6099 0.9657 0.9775 0.4694 ] Network output: [ 0.02359 0.9075 0.02589 -0.0002673 0.00012 1.018 -0.0002014 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0333 Epoch 2224 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0345 0.9719 0.9943 5.753e-05 -2.583e-05 -0.03495 4.336e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02371 -0.005589 0.01912 0.03332 0.9376 0.9473 0.05025 0.8814 0.9004 0.1279 ] Network output: [ 0.9729 0.06867 -0.01546 -0.0001674 7.517e-05 0.0003415 -0.0001262 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6526 0.1189 0.1167 0.3013 0.9703 0.9862 0.752 0.8966 0.9651 0.6306 ] Network output: [ -0.004315 0.9325 1.029 -3.51e-05 1.576e-05 0.04692 -2.645e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05083 0.03773 0.0553 0.04614 0.9843 0.9889 0.05208 0.9681 0.979 0.06933 ] Network output: [ 0.09126 -0.2967 1.068 -0.0002459 0.0001104 1.046 -0.0001853 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7464 0.6382 0.5287 0.4846 0.9738 0.9882 0.7499 0.9074 0.9703 0.6265 ] Network output: [ -0.04715 0.1932 0.958 0.0006183 -0.0002776 0.9456 0.000466 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6083 0.5941 0.4445 0.3287 0.9859 0.9907 0.6088 0.9721 0.981 0.4572 ] Network output: [ -0.07045 0.2233 0.9399 4.387e-05 -1.97e-05 0.978 3.306e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6099 0.6076 0.4669 0.2978 0.9838 0.9895 0.61 0.9657 0.9775 0.4694 ] Network output: [ 0.02355 0.9077 0.02587 -0.0002672 0.00012 1.018 -0.0002014 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03326 Epoch 2225 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03447 0.9719 0.9944 5.712e-05 -2.564e-05 -0.03494 4.305e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0237 -0.00559 0.01911 0.03329 0.9376 0.9473 0.05023 0.8814 0.9004 0.1278 ] Network output: [ 0.9729 0.06866 -0.01547 -0.0001666 7.479e-05 0.0003129 -0.0001255 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6525 0.119 0.1168 0.3011 0.9703 0.9862 0.752 0.8967 0.9652 0.6306 ] Network output: [ -0.004329 0.9325 1.029 -3.542e-05 1.59e-05 0.04692 -2.669e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05082 0.03772 0.05528 0.04609 0.9843 0.9889 0.05207 0.9681 0.979 0.06931 ] Network output: [ 0.09118 -0.2966 1.068 -0.0002482 0.0001114 1.046 -0.0001871 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7463 0.6382 0.5288 0.4844 0.9738 0.9882 0.7499 0.9075 0.9703 0.6266 ] Network output: [ -0.04708 0.1931 0.9579 0.0006191 -0.0002779 0.9457 0.0004665 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6083 0.5941 0.4446 0.3286 0.9859 0.9907 0.6088 0.9721 0.981 0.4572 ] Network output: [ -0.07038 0.2231 0.9399 4.522e-05 -2.03e-05 0.978 3.408e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6099 0.6076 0.4669 0.2977 0.9838 0.9895 0.61 0.9657 0.9775 0.4694 ] Network output: [ 0.0235 0.9078 0.02584 -0.0002671 0.0001199 1.018 -0.0002013 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03322 Epoch 2226 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03445 0.9719 0.9944 5.671e-05 -2.546e-05 -0.03493 4.274e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02369 -0.005592 0.0191 0.03325 0.9376 0.9474 0.05022 0.8815 0.9004 0.1278 ] Network output: [ 0.9729 0.06866 -0.01549 -0.0001657 7.44e-05 0.0002844 -0.0001249 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6525 0.119 0.1168 0.3008 0.9703 0.9862 0.7519 0.8967 0.9652 0.6306 ] Network output: [ -0.004342 0.9326 1.029 -3.574e-05 1.604e-05 0.04692 -2.693e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05081 0.03772 0.05526 0.04604 0.9844 0.9889 0.05207 0.9681 0.979 0.06928 ] Network output: [ 0.0911 -0.2965 1.068 -0.0002505 0.0001125 1.046 -0.0001888 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7463 0.6382 0.5288 0.4841 0.9738 0.9882 0.7498 0.9075 0.9703 0.6266 ] Network output: [ -0.04702 0.193 0.9578 0.0006198 -0.0002782 0.9458 0.0004671 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6084 0.5942 0.4446 0.3284 0.9859 0.9907 0.6089 0.9721 0.981 0.4572 ] Network output: [ -0.07032 0.2229 0.9399 4.658e-05 -2.091e-05 0.978 3.51e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6099 0.6076 0.4669 0.2976 0.9838 0.9895 0.61 0.9657 0.9775 0.4694 ] Network output: [ 0.02345 0.908 0.02582 -0.000267 0.0001199 1.018 -0.0002012 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03318 Epoch 2227 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03442 0.9719 0.9944 5.629e-05 -2.527e-05 -0.03492 4.242e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02369 -0.005593 0.0191 0.03322 0.9376 0.9474 0.0502 0.8815 0.9005 0.1278 ] Network output: [ 0.973 0.06865 -0.0155 -0.0001649 7.402e-05 0.000256 -0.0001243 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6525 0.119 0.1169 0.3005 0.9704 0.9862 0.7519 0.8967 0.9652 0.6307 ] Network output: [ -0.004356 0.9326 1.029 -3.606e-05 1.619e-05 0.04692 -2.718e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05081 0.03772 0.05524 0.046 0.9844 0.9889 0.05206 0.9681 0.979 0.06925 ] Network output: [ 0.09103 -0.2964 1.068 -0.0002528 0.0001135 1.046 -0.0001905 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7463 0.6382 0.5289 0.4838 0.9738 0.9882 0.7498 0.9075 0.9703 0.6266 ] Network output: [ -0.04695 0.1929 0.9577 0.0006205 -0.0002786 0.9458 0.0004676 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6084 0.5943 0.4446 0.3283 0.9859 0.9907 0.609 0.9721 0.981 0.4573 ] Network output: [ -0.07025 0.2227 0.94 4.794e-05 -2.152e-05 0.978 3.613e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.61 0.6077 0.4669 0.2975 0.9838 0.9895 0.6101 0.9657 0.9775 0.4693 ] Network output: [ 0.02341 0.9081 0.02579 -0.0002669 0.0001198 1.018 -0.0002011 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03315 Epoch 2228 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0344 0.9719 0.9945 5.588e-05 -2.509e-05 -0.03491 4.211e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02368 -0.005594 0.01909 0.03319 0.9376 0.9474 0.05018 0.8815 0.9005 0.1277 ] Network output: [ 0.973 0.06864 -0.01552 -0.000164 7.364e-05 0.0002277 -0.0001236 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6524 0.1191 0.117 0.3003 0.9704 0.9862 0.7518 0.8968 0.9652 0.6307 ] Network output: [ -0.00437 0.9327 1.029 -3.638e-05 1.633e-05 0.04692 -2.742e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0508 0.03771 0.05523 0.04595 0.9844 0.9889 0.05205 0.9681 0.979 0.06923 ] Network output: [ 0.09095 -0.2963 1.068 -0.0002552 0.0001145 1.046 -0.0001923 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7462 0.6382 0.529 0.4835 0.9738 0.9882 0.7498 0.9075 0.9703 0.6267 ] Network output: [ -0.04688 0.1927 0.9576 0.0006212 -0.0002789 0.9459 0.0004682 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6085 0.5943 0.4447 0.3282 0.9859 0.9907 0.609 0.9721 0.981 0.4573 ] Network output: [ -0.07019 0.2225 0.94 4.931e-05 -2.214e-05 0.978 3.716e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.61 0.6077 0.4669 0.2975 0.9838 0.9895 0.6101 0.9657 0.9775 0.4693 ] Network output: [ 0.02336 0.9083 0.02577 -0.0002668 0.0001198 1.018 -0.000201 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03311 Epoch 2229 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03437 0.9719 0.9945 5.547e-05 -2.49e-05 -0.0349 4.18e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02367 -0.005596 0.01908 0.03316 0.9377 0.9474 0.05017 0.8815 0.9005 0.1277 ] Network output: [ 0.973 0.06863 -0.01553 -0.0001632 7.326e-05 0.0001996 -0.000123 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6524 0.1191 0.117 0.3 0.9704 0.9862 0.7518 0.8968 0.9652 0.6307 ] Network output: [ -0.004383 0.9327 1.029 -3.67e-05 1.648e-05 0.04691 -2.766e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05079 0.03771 0.05521 0.0459 0.9844 0.9889 0.05205 0.9682 0.979 0.0692 ] Network output: [ 0.09087 -0.2961 1.068 -0.0002575 0.0001156 1.046 -0.000194 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7462 0.6382 0.5291 0.4833 0.9738 0.9882 0.7497 0.9076 0.9703 0.6267 ] Network output: [ -0.04681 0.1926 0.9576 0.000622 -0.0002792 0.946 0.0004687 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6086 0.5944 0.4447 0.328 0.9859 0.9908 0.6091 0.9721 0.981 0.4573 ] Network output: [ -0.07012 0.2224 0.9401 5.069e-05 -2.276e-05 0.978 3.82e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.61 0.6077 0.4669 0.2974 0.9838 0.9895 0.6101 0.9658 0.9775 0.4693 ] Network output: [ 0.02332 0.9085 0.02574 -0.0002667 0.0001197 1.018 -0.000201 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03307 Epoch 2230 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03434 0.9719 0.9945 5.505e-05 -2.472e-05 -0.03489 4.149e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02366 -0.005597 0.01908 0.03313 0.9377 0.9474 0.05015 0.8816 0.9005 0.1276 ] Network output: [ 0.973 0.06863 -0.01554 -0.0001623 7.288e-05 0.0001716 -0.0001223 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6523 0.1192 0.1171 0.2998 0.9704 0.9862 0.7517 0.8968 0.9652 0.6308 ] Network output: [ -0.004397 0.9327 1.029 -3.702e-05 1.662e-05 0.04691 -2.79e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05079 0.03771 0.05519 0.04585 0.9844 0.9889 0.05204 0.9682 0.9791 0.06918 ] Network output: [ 0.0908 -0.296 1.068 -0.0002598 0.0001166 1.045 -0.0001958 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7461 0.6382 0.5291 0.483 0.9739 0.9882 0.7497 0.9076 0.9703 0.6267 ] Network output: [ -0.04674 0.1925 0.9575 0.0006227 -0.0002796 0.946 0.0004693 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6086 0.5945 0.4447 0.3279 0.9859 0.9908 0.6092 0.9721 0.981 0.4573 ] Network output: [ -0.07005 0.2222 0.9401 5.207e-05 -2.338e-05 0.978 3.924e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6101 0.6078 0.4669 0.2973 0.9838 0.9895 0.6102 0.9658 0.9775 0.4693 ] Network output: [ 0.02327 0.9086 0.02572 -0.0002666 0.0001197 1.018 -0.0002009 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03303 Epoch 2231 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03432 0.9719 0.9946 5.464e-05 -2.453e-05 -0.03489 4.118e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02366 -0.005598 0.01907 0.0331 0.9377 0.9474 0.05013 0.8816 0.9005 0.1276 ] Network output: [ 0.9731 0.06862 -0.01556 -0.0001615 7.25e-05 0.0001437 -0.0001217 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6523 0.1192 0.1172 0.2995 0.9704 0.9862 0.7517 0.8968 0.9652 0.6308 ] Network output: [ -0.004411 0.9328 1.029 -3.734e-05 1.676e-05 0.04691 -2.814e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05078 0.0377 0.05517 0.04581 0.9844 0.9889 0.05203 0.9682 0.9791 0.06915 ] Network output: [ 0.09072 -0.2959 1.068 -0.0002621 0.0001177 1.045 -0.0001975 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7461 0.6382 0.5292 0.4827 0.9739 0.9882 0.7496 0.9076 0.9704 0.6268 ] Network output: [ -0.04667 0.1924 0.9574 0.0006235 -0.0002799 0.9461 0.0004699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6087 0.5945 0.4447 0.3278 0.9859 0.9908 0.6092 0.9722 0.9811 0.4574 ] Network output: [ -0.06999 0.222 0.9402 5.346e-05 -2.4e-05 0.978 4.029e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6101 0.6078 0.4669 0.2972 0.9838 0.9895 0.6102 0.9658 0.9776 0.4693 ] Network output: [ 0.02322 0.9088 0.02569 -0.0002665 0.0001196 1.018 -0.0002008 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03299 Epoch 2232 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03429 0.9719 0.9946 5.423e-05 -2.435e-05 -0.03488 4.087e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02365 -0.0056 0.01906 0.03307 0.9377 0.9474 0.05012 0.8816 0.9006 0.1276 ] Network output: [ 0.9731 0.06861 -0.01557 -0.0001607 7.213e-05 0.000116 -0.0001211 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6523 0.1193 0.1172 0.2992 0.9704 0.9862 0.7516 0.8969 0.9653 0.6308 ] Network output: [ -0.004424 0.9328 1.029 -3.766e-05 1.691e-05 0.04691 -2.838e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05077 0.0377 0.05515 0.04576 0.9844 0.9889 0.05203 0.9682 0.9791 0.06912 ] Network output: [ 0.09064 -0.2958 1.068 -0.0002645 0.0001187 1.045 -0.0001993 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.746 0.6382 0.5293 0.4825 0.9739 0.9882 0.7496 0.9077 0.9704 0.6268 ] Network output: [ -0.0466 0.1922 0.9573 0.0006242 -0.0002802 0.9462 0.0004704 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6087 0.5946 0.4448 0.3276 0.9859 0.9908 0.6093 0.9722 0.9811 0.4574 ] Network output: [ -0.06992 0.2218 0.9402 5.485e-05 -2.463e-05 0.978 4.134e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6101 0.6078 0.4669 0.2972 0.9838 0.9895 0.6102 0.9658 0.9776 0.4693 ] Network output: [ 0.02318 0.9089 0.02567 -0.0002664 0.0001196 1.018 -0.0002008 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03295 Epoch 2233 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03427 0.9719 0.9947 5.382e-05 -2.416e-05 -0.03487 4.056e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02364 -0.005601 0.01906 0.03304 0.9377 0.9474 0.0501 0.8817 0.9006 0.1275 ] Network output: [ 0.9731 0.06861 -0.01559 -0.0001598 7.175e-05 8.831e-05 -0.0001205 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6522 0.1193 0.1173 0.299 0.9704 0.9862 0.7516 0.8969 0.9653 0.6309 ] Network output: [ -0.004438 0.9329 1.029 -3.798e-05 1.705e-05 0.04691 -2.862e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05077 0.0377 0.05514 0.04571 0.9844 0.9889 0.05202 0.9682 0.9791 0.0691 ] Network output: [ 0.09057 -0.2957 1.068 -0.0002668 0.0001198 1.045 -0.0002011 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.746 0.6382 0.5293 0.4822 0.9739 0.9882 0.7495 0.9077 0.9704 0.6268 ] Network output: [ -0.04653 0.1921 0.9572 0.000625 -0.0002806 0.9462 0.000471 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6088 0.5947 0.4448 0.3275 0.9859 0.9908 0.6093 0.9722 0.9811 0.4574 ] Network output: [ -0.06986 0.2216 0.9403 5.626e-05 -2.525e-05 0.9781 4.24e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6102 0.6079 0.4669 0.2971 0.9838 0.9895 0.6103 0.9658 0.9776 0.4693 ] Network output: [ 0.02313 0.9091 0.02564 -0.0002663 0.0001196 1.018 -0.0002007 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03291 Epoch 2234 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03424 0.9719 0.9947 5.341e-05 -2.398e-05 -0.03486 4.025e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02363 -0.005602 0.01905 0.033 0.9377 0.9474 0.05009 0.8817 0.9006 0.1275 ] Network output: [ 0.9731 0.0686 -0.0156 -0.000159 7.138e-05 6.079e-05 -0.0001198 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6522 0.1193 0.1174 0.2987 0.9704 0.9862 0.7515 0.8969 0.9653 0.6309 ] Network output: [ -0.004452 0.9329 1.029 -3.83e-05 1.719e-05 0.0469 -2.886e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05076 0.03769 0.05512 0.04566 0.9844 0.9889 0.05201 0.9682 0.9791 0.06907 ] Network output: [ 0.09049 -0.2956 1.068 -0.0002692 0.0001208 1.045 -0.0002028 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7459 0.6382 0.5294 0.4819 0.9739 0.9882 0.7495 0.9077 0.9704 0.6269 ] Network output: [ -0.04645 0.192 0.9572 0.0006258 -0.0002809 0.9463 0.0004716 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6089 0.5947 0.4448 0.3274 0.9859 0.9908 0.6094 0.9722 0.9811 0.4574 ] Network output: [ -0.06979 0.2214 0.9403 5.766e-05 -2.589e-05 0.9781 4.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6102 0.6079 0.4668 0.297 0.9838 0.9895 0.6103 0.9658 0.9776 0.4693 ] Network output: [ 0.02309 0.9092 0.02562 -0.0002663 0.0001195 1.018 -0.0002007 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03287 Epoch 2235 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03421 0.9719 0.9947 5.3e-05 -2.379e-05 -0.03485 3.994e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02363 -0.005604 0.01904 0.03297 0.9377 0.9475 0.05007 0.8817 0.9006 0.1274 ] Network output: [ 0.9732 0.06859 -0.01562 -0.0001582 7.101e-05 3.339e-05 -0.0001192 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6522 0.1194 0.1174 0.2985 0.9704 0.9862 0.7515 0.897 0.9653 0.6309 ] Network output: [ -0.004466 0.9329 1.029 -3.862e-05 1.734e-05 0.0469 -2.91e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05075 0.03769 0.0551 0.04561 0.9844 0.9889 0.052 0.9682 0.9791 0.06905 ] Network output: [ 0.09041 -0.2955 1.068 -0.0002715 0.0001219 1.045 -0.0002046 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7459 0.6382 0.5295 0.4816 0.9739 0.9882 0.7494 0.9077 0.9704 0.6269 ] Network output: [ -0.04638 0.1918 0.9571 0.0006265 -0.0002813 0.9464 0.0004722 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6089 0.5948 0.4449 0.3272 0.9859 0.9908 0.6095 0.9722 0.9811 0.4574 ] Network output: [ -0.06972 0.2213 0.9403 5.908e-05 -2.652e-05 0.9781 4.452e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6102 0.6079 0.4668 0.2969 0.9838 0.9896 0.6103 0.9659 0.9776 0.4693 ] Network output: [ 0.02304 0.9094 0.0256 -0.0002662 0.0001195 1.018 -0.0002006 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03283 Epoch 2236 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03419 0.9719 0.9948 5.259e-05 -2.361e-05 -0.03484 3.963e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02362 -0.005605 0.01904 0.03294 0.9377 0.9475 0.05005 0.8818 0.9007 0.1274 ] Network output: [ 0.9732 0.06858 -0.01563 -0.0001573 7.063e-05 6.118e-06 -0.0001186 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6521 0.1194 0.1175 0.2982 0.9704 0.9862 0.7514 0.897 0.9653 0.631 ] Network output: [ -0.00448 0.933 1.029 -3.893e-05 1.748e-05 0.0469 -2.934e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05075 0.03769 0.05508 0.04557 0.9844 0.989 0.052 0.9682 0.9791 0.06902 ] Network output: [ 0.09033 -0.2953 1.068 -0.0002739 0.0001229 1.045 -0.0002064 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7458 0.6382 0.5295 0.4813 0.9739 0.9882 0.7494 0.9078 0.9704 0.6269 ] Network output: [ -0.04631 0.1917 0.957 0.0006273 -0.0002816 0.9465 0.0004728 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.609 0.5949 0.4449 0.3271 0.9859 0.9908 0.6095 0.9722 0.9811 0.4575 ] Network output: [ -0.06966 0.2211 0.9404 6.05e-05 -2.716e-05 0.9781 4.559e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6103 0.608 0.4668 0.2968 0.9838 0.9896 0.6104 0.9659 0.9776 0.4692 ] Network output: [ 0.02299 0.9095 0.02557 -0.0002661 0.0001195 1.018 -0.0002006 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03279 Epoch 2237 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03416 0.9719 0.9948 5.218e-05 -2.343e-05 -0.03484 3.932e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02361 -0.005606 0.01903 0.03291 0.9378 0.9475 0.05004 0.8818 0.9007 0.1273 ] Network output: [ 0.9732 0.06858 -0.01564 -0.0001565 7.026e-05 -2.104e-05 -0.000118 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6521 0.1195 0.1175 0.2979 0.9704 0.9863 0.7514 0.897 0.9653 0.631 ] Network output: [ -0.004494 0.933 1.029 -3.925e-05 1.762e-05 0.0469 -2.958e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05074 0.03768 0.05507 0.04552 0.9844 0.989 0.05199 0.9683 0.9791 0.069 ] Network output: [ 0.09025 -0.2952 1.068 -0.0002762 0.000124 1.045 -0.0002082 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7458 0.6382 0.5296 0.4811 0.9739 0.9883 0.7493 0.9078 0.9704 0.627 ] Network output: [ -0.04624 0.1916 0.9569 0.0006281 -0.000282 0.9465 0.0004734 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6091 0.5949 0.4449 0.327 0.9859 0.9908 0.6096 0.9722 0.9811 0.4575 ] Network output: [ -0.06959 0.2209 0.9404 6.192e-05 -2.78e-05 0.9781 4.667e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6103 0.608 0.4668 0.2967 0.9838 0.9896 0.6104 0.9659 0.9776 0.4692 ] Network output: [ 0.02295 0.9097 0.02555 -0.0002661 0.0001195 1.018 -0.0002005 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03276 Epoch 2238 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03414 0.9719 0.9948 5.177e-05 -2.324e-05 -0.03483 3.902e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02361 -0.005607 0.01902 0.03288 0.9378 0.9475 0.05002 0.8818 0.9007 0.1273 ] Network output: [ 0.9733 0.06857 -0.01566 -0.0001557 6.989e-05 -4.807e-05 -0.0001173 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6521 0.1195 0.1176 0.2977 0.9704 0.9863 0.7513 0.8971 0.9653 0.6311 ] Network output: [ -0.004508 0.9331 1.029 -3.957e-05 1.777e-05 0.04689 -2.982e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05073 0.03768 0.05505 0.04547 0.9844 0.989 0.05198 0.9683 0.9791 0.06897 ] Network output: [ 0.09018 -0.2951 1.068 -0.0002786 0.0001251 1.045 -0.00021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7457 0.6382 0.5297 0.4808 0.9739 0.9883 0.7493 0.9078 0.9704 0.627 ] Network output: [ -0.04617 0.1915 0.9568 0.0006289 -0.0002823 0.9466 0.000474 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6091 0.595 0.4449 0.3268 0.9859 0.9908 0.6096 0.9722 0.9811 0.4575 ] Network output: [ -0.06952 0.2207 0.9405 6.335e-05 -2.844e-05 0.9781 4.775e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6103 0.608 0.4668 0.2967 0.9838 0.9896 0.6104 0.9659 0.9776 0.4692 ] Network output: [ 0.0229 0.9099 0.02553 -0.000266 0.0001194 1.018 -0.0002005 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03272 Epoch 2239 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03411 0.9719 0.9949 5.136e-05 -2.306e-05 -0.03482 3.871e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0236 -0.005609 0.01902 0.03285 0.9378 0.9475 0.05 0.8818 0.9007 0.1273 ] Network output: [ 0.9733 0.06856 -0.01567 -0.0001549 6.953e-05 -7.498e-05 -0.0001167 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.652 0.1195 0.1177 0.2974 0.9704 0.9863 0.7513 0.8971 0.9653 0.6311 ] Network output: [ -0.004522 0.9331 1.029 -3.989e-05 1.791e-05 0.04689 -3.006e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05072 0.03768 0.05503 0.04542 0.9844 0.989 0.05198 0.9683 0.9791 0.06894 ] Network output: [ 0.0901 -0.295 1.068 -0.000281 0.0001261 1.045 -0.0002117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7457 0.6382 0.5298 0.4805 0.9739 0.9883 0.7492 0.9078 0.9704 0.6271 ] Network output: [ -0.0461 0.1913 0.9568 0.0006297 -0.0002827 0.9467 0.0004746 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6092 0.5951 0.445 0.3267 0.9859 0.9908 0.6097 0.9722 0.9811 0.4575 ] Network output: [ -0.06945 0.2205 0.9405 6.479e-05 -2.909e-05 0.9781 4.883e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6104 0.6081 0.4668 0.2966 0.9838 0.9896 0.6105 0.9659 0.9776 0.4692 ] Network output: [ 0.02286 0.91 0.0255 -0.000266 0.0001194 1.018 -0.0002005 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03268 Epoch 2240 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03408 0.9719 0.9949 5.095e-05 -2.287e-05 -0.03481 3.84e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02359 -0.00561 0.01901 0.03281 0.9378 0.9475 0.04999 0.8819 0.9008 0.1272 ] Network output: [ 0.9733 0.06855 -0.01569 -0.000154 6.916e-05 -0.0001018 -0.0001161 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.652 0.1196 0.1177 0.2971 0.9705 0.9863 0.7512 0.8971 0.9654 0.6311 ] Network output: [ -0.004536 0.9331 1.029 -4.021e-05 1.805e-05 0.04689 -3.03e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05072 0.03767 0.05501 0.04537 0.9844 0.989 0.05197 0.9683 0.9791 0.06892 ] Network output: [ 0.09002 -0.2949 1.068 -0.0002833 0.0001272 1.045 -0.0002135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7456 0.6382 0.5298 0.4802 0.9739 0.9883 0.7491 0.9079 0.9704 0.6271 ] Network output: [ -0.04603 0.1912 0.9567 0.0006305 -0.0002831 0.9467 0.0004752 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6092 0.5951 0.445 0.3265 0.9859 0.9908 0.6098 0.9723 0.9811 0.4576 ] Network output: [ -0.06939 0.2203 0.9405 6.624e-05 -2.974e-05 0.9782 4.992e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6104 0.6081 0.4668 0.2965 0.9838 0.9896 0.6105 0.9659 0.9777 0.4692 ] Network output: [ 0.02281 0.9102 0.02548 -0.0002659 0.0001194 1.018 -0.0002004 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03264 Epoch 2241 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03406 0.972 0.9949 5.055e-05 -2.269e-05 -0.0348 3.809e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02358 -0.005611 0.019 0.03278 0.9378 0.9475 0.04997 0.8819 0.9008 0.1272 ] Network output: [ 0.9733 0.06854 -0.0157 -0.0001532 6.879e-05 -0.0001284 -0.0001155 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.652 0.1196 0.1178 0.2969 0.9705 0.9863 0.7512 0.8971 0.9654 0.6312 ] Network output: [ -0.00455 0.9332 1.029 -4.053e-05 1.819e-05 0.04688 -3.054e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05071 0.03767 0.055 0.04533 0.9844 0.989 0.05196 0.9683 0.9791 0.06889 ] Network output: [ 0.08994 -0.2947 1.068 -0.0002857 0.0001283 1.045 -0.0002153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7456 0.6381 0.5299 0.4799 0.9739 0.9883 0.7491 0.9079 0.9705 0.6271 ] Network output: [ -0.04596 0.1911 0.9566 0.0006313 -0.0002834 0.9468 0.0004758 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6093 0.5952 0.445 0.3264 0.9859 0.9908 0.6098 0.9723 0.9811 0.4576 ] Network output: [ -0.06932 0.2201 0.9406 6.769e-05 -3.039e-05 0.9782 5.101e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6104 0.6081 0.4668 0.2964 0.9839 0.9896 0.6105 0.966 0.9777 0.4692 ] Network output: [ 0.02277 0.9103 0.02546 -0.0002659 0.0001194 1.018 -0.0002004 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0326 Epoch 2242 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03403 0.972 0.995 5.014e-05 -2.251e-05 -0.0348 3.779e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02358 -0.005612 0.019 0.03275 0.9378 0.9475 0.04995 0.8819 0.9008 0.1271 ] Network output: [ 0.9734 0.06854 -0.01572 -0.0001524 6.843e-05 -0.000155 -0.0001149 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6519 0.1197 0.1178 0.2966 0.9705 0.9863 0.7511 0.8972 0.9654 0.6312 ] Network output: [ -0.004564 0.9332 1.029 -4.084e-05 1.834e-05 0.04688 -3.078e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0507 0.03766 0.05498 0.04528 0.9844 0.989 0.05195 0.9683 0.9792 0.06887 ] Network output: [ 0.08986 -0.2946 1.069 -0.0002881 0.0001293 1.045 -0.0002171 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7455 0.6381 0.53 0.4796 0.9739 0.9883 0.749 0.9079 0.9705 0.6272 ] Network output: [ -0.04589 0.1909 0.9565 0.0006321 -0.0002838 0.9469 0.0004764 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6094 0.5953 0.4451 0.3263 0.9859 0.9908 0.6099 0.9723 0.9811 0.4576 ] Network output: [ -0.06925 0.22 0.9406 6.915e-05 -3.104e-05 0.9782 5.211e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6105 0.6082 0.4668 0.2963 0.9839 0.9896 0.6106 0.966 0.9777 0.4692 ] Network output: [ 0.02272 0.9105 0.02543 -0.0002659 0.0001194 1.018 -0.0002004 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03256 Epoch 2243 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03401 0.972 0.995 4.973e-05 -2.233e-05 -0.03479 3.748e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02357 -0.005613 0.01899 0.03272 0.9378 0.9475 0.04994 0.882 0.9008 0.1271 ] Network output: [ 0.9734 0.06853 -0.01573 -0.0001516 6.806e-05 -0.0001814 -0.0001143 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6519 0.1197 0.1179 0.2963 0.9705 0.9863 0.751 0.8972 0.9654 0.6312 ] Network output: [ -0.004578 0.9333 1.029 -4.116e-05 1.848e-05 0.04688 -3.102e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0507 0.03766 0.05496 0.04523 0.9844 0.989 0.05194 0.9683 0.9792 0.06884 ] Network output: [ 0.08978 -0.2945 1.069 -0.0002905 0.0001304 1.045 -0.0002189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7455 0.6381 0.53 0.4794 0.9739 0.9883 0.749 0.9079 0.9705 0.6272 ] Network output: [ -0.04582 0.1908 0.9564 0.0006329 -0.0002841 0.947 0.000477 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6094 0.5953 0.4451 0.3261 0.9859 0.9908 0.6099 0.9723 0.9812 0.4576 ] Network output: [ -0.06918 0.2198 0.9407 7.061e-05 -3.17e-05 0.9782 5.322e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6105 0.6082 0.4668 0.2962 0.9839 0.9896 0.6106 0.966 0.9777 0.4692 ] Network output: [ 0.02267 0.9106 0.02541 -0.0002658 0.0001193 1.018 -0.0002003 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03252 Epoch 2244 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03398 0.972 0.995 4.933e-05 -2.214e-05 -0.03478 3.717e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02356 -0.005615 0.01898 0.03269 0.9378 0.9475 0.04992 0.882 0.9009 0.1271 ] Network output: [ 0.9734 0.06852 -0.01574 -0.0001508 6.77e-05 -0.0002077 -0.0001136 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6518 0.1197 0.118 0.2961 0.9705 0.9863 0.751 0.8972 0.9654 0.6313 ] Network output: [ -0.004592 0.9333 1.029 -4.148e-05 1.862e-05 0.04687 -3.126e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05069 0.03766 0.05494 0.04518 0.9844 0.989 0.05194 0.9684 0.9792 0.06882 ] Network output: [ 0.0897 -0.2944 1.069 -0.0002929 0.0001315 1.045 -0.0002208 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7454 0.6381 0.5301 0.4791 0.9739 0.9883 0.7489 0.908 0.9705 0.6272 ] Network output: [ -0.04575 0.1907 0.9564 0.0006338 -0.0002845 0.947 0.0004776 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6095 0.5954 0.4451 0.326 0.9859 0.9908 0.61 0.9723 0.9812 0.4577 ] Network output: [ -0.06912 0.2196 0.9407 7.208e-05 -3.236e-05 0.9782 5.432e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6105 0.6083 0.4667 0.2961 0.9839 0.9896 0.6106 0.966 0.9777 0.4692 ] Network output: [ 0.02263 0.9108 0.02539 -0.0002658 0.0001193 1.017 -0.0002003 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03248 Epoch 2245 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03395 0.972 0.9951 4.892e-05 -2.196e-05 -0.03477 3.687e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02355 -0.005616 0.01898 0.03266 0.9378 0.9476 0.04991 0.882 0.9009 0.127 ] Network output: [ 0.9734 0.06851 -0.01576 -0.00015 6.734e-05 -0.0002339 -0.000113 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6518 0.1198 0.118 0.2958 0.9705 0.9863 0.7509 0.8973 0.9654 0.6313 ] Network output: [ -0.004607 0.9334 1.029 -4.18e-05 1.876e-05 0.04687 -3.15e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05068 0.03765 0.05493 0.04513 0.9845 0.989 0.05193 0.9684 0.9792 0.06879 ] Network output: [ 0.08962 -0.2943 1.069 -0.0002953 0.0001326 1.045 -0.0002226 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7454 0.6381 0.5302 0.4788 0.974 0.9883 0.7489 0.908 0.9705 0.6273 ] Network output: [ -0.04567 0.1905 0.9563 0.0006346 -0.0002849 0.9471 0.0004782 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6095 0.5955 0.4451 0.3258 0.9859 0.9908 0.6101 0.9723 0.9812 0.4577 ] Network output: [ -0.06905 0.2194 0.9408 7.356e-05 -3.302e-05 0.9783 5.544e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6106 0.6083 0.4667 0.2961 0.9839 0.9896 0.6107 0.966 0.9777 0.4691 ] Network output: [ 0.02258 0.9109 0.02536 -0.0002658 0.0001193 1.017 -0.0002003 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03244 Epoch 2246 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03393 0.972 0.9951 4.851e-05 -2.178e-05 -0.03477 3.656e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02355 -0.005617 0.01897 0.03262 0.9379 0.9476 0.04989 0.882 0.9009 0.127 ] Network output: [ 0.9735 0.0685 -0.01577 -0.0001492 6.698e-05 -0.00026 -0.0001124 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6518 0.1198 0.1181 0.2955 0.9705 0.9863 0.7509 0.8973 0.9654 0.6314 ] Network output: [ -0.004621 0.9334 1.029 -4.211e-05 1.891e-05 0.04686 -3.174e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05067 0.03765 0.05491 0.04508 0.9845 0.989 0.05192 0.9684 0.9792 0.06876 ] Network output: [ 0.08954 -0.2941 1.069 -0.0002977 0.0001337 1.045 -0.0002244 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7453 0.6381 0.5302 0.4785 0.974 0.9883 0.7488 0.908 0.9705 0.6273 ] Network output: [ -0.0456 0.1904 0.9562 0.0006354 -0.0002853 0.9472 0.0004789 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6096 0.5955 0.4452 0.3257 0.9859 0.9908 0.6101 0.9723 0.9812 0.4577 ] Network output: [ -0.06898 0.2192 0.9408 7.504e-05 -3.369e-05 0.9783 5.655e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6106 0.6083 0.4667 0.296 0.9839 0.9896 0.6107 0.966 0.9777 0.4691 ] Network output: [ 0.02254 0.9111 0.02534 -0.0002657 0.0001193 1.017 -0.0002003 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0324 Epoch 2247 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0339 0.972 0.9952 4.811e-05 -2.16e-05 -0.03476 3.626e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02354 -0.005618 0.01896 0.03259 0.9379 0.9476 0.04987 0.8821 0.9009 0.1269 ] Network output: [ 0.9735 0.0685 -0.01579 -0.0001484 6.662e-05 -0.000286 -0.0001118 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6517 0.1199 0.1181 0.2953 0.9705 0.9863 0.7508 0.8973 0.9654 0.6314 ] Network output: [ -0.004635 0.9334 1.029 -4.243e-05 1.905e-05 0.04686 -3.198e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05067 0.03764 0.05489 0.04504 0.9845 0.989 0.05191 0.9684 0.9792 0.06874 ] Network output: [ 0.08946 -0.294 1.069 -0.0003002 0.0001348 1.045 -0.0002262 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7452 0.6381 0.5303 0.4782 0.974 0.9883 0.7488 0.9081 0.9705 0.6273 ] Network output: [ -0.04553 0.1903 0.9561 0.0006363 -0.0002856 0.9473 0.0004795 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6097 0.5956 0.4452 0.3255 0.9859 0.9908 0.6102 0.9723 0.9812 0.4577 ] Network output: [ -0.06891 0.219 0.9408 7.653e-05 -3.436e-05 0.9783 5.768e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6106 0.6084 0.4667 0.2959 0.9839 0.9896 0.6107 0.966 0.9777 0.4691 ] Network output: [ 0.02249 0.9113 0.02532 -0.0002657 0.0001193 1.017 -0.0002003 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03236 Epoch 2248 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03387 0.972 0.9952 4.77e-05 -2.142e-05 -0.03475 3.595e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02353 -0.005619 0.01896 0.03256 0.9379 0.9476 0.04986 0.8821 0.901 0.1269 ] Network output: [ 0.9735 0.06849 -0.0158 -0.0001476 6.626e-05 -0.0003118 -0.0001112 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6517 0.1199 0.1182 0.295 0.9705 0.9863 0.7508 0.8973 0.9655 0.6314 ] Network output: [ -0.00465 0.9335 1.029 -4.275e-05 1.919e-05 0.04685 -3.221e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05066 0.03764 0.05487 0.04499 0.9845 0.989 0.05191 0.9684 0.9792 0.06871 ] Network output: [ 0.08938 -0.2939 1.069 -0.0003026 0.0001358 1.045 -0.000228 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7452 0.6381 0.5304 0.4779 0.974 0.9883 0.7487 0.9081 0.9705 0.6274 ] Network output: [ -0.04546 0.1901 0.956 0.0006371 -0.000286 0.9473 0.0004801 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6097 0.5957 0.4452 0.3254 0.986 0.9908 0.6103 0.9723 0.9812 0.4577 ] Network output: [ -0.06884 0.2188 0.9409 7.803e-05 -3.503e-05 0.9783 5.88e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6107 0.6084 0.4667 0.2958 0.9839 0.9896 0.6108 0.9661 0.9777 0.4691 ] Network output: [ 0.02245 0.9114 0.0253 -0.0002657 0.0001193 1.017 -0.0002003 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03232 Epoch 2249 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03385 0.972 0.9952 4.73e-05 -2.123e-05 -0.03474 3.565e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02352 -0.00562 0.01895 0.03253 0.9379 0.9476 0.04984 0.8821 0.901 0.1269 ] Network output: [ 0.9735 0.06848 -0.01582 -0.0001468 6.59e-05 -0.0003375 -0.0001106 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6516 0.1199 0.1183 0.2947 0.9705 0.9863 0.7507 0.8974 0.9655 0.6315 ] Network output: [ -0.004664 0.9335 1.029 -4.306e-05 1.933e-05 0.04685 -3.245e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05065 0.03763 0.05485 0.04494 0.9845 0.989 0.0519 0.9684 0.9792 0.06869 ] Network output: [ 0.0893 -0.2938 1.069 -0.000305 0.0001369 1.045 -0.0002299 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7451 0.6381 0.5305 0.4776 0.974 0.9883 0.7487 0.9081 0.9705 0.6274 ] Network output: [ -0.04539 0.19 0.9559 0.0006379 -0.0002864 0.9474 0.0004808 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6098 0.5957 0.4453 0.3252 0.986 0.9908 0.6103 0.9723 0.9812 0.4578 ] Network output: [ -0.06877 0.2186 0.9409 7.953e-05 -3.57e-05 0.9783 5.994e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6107 0.6084 0.4667 0.2957 0.9839 0.9896 0.6108 0.9661 0.9778 0.4691 ] Network output: [ 0.0224 0.9116 0.02527 -0.0002657 0.0001193 1.017 -0.0002002 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03228 Epoch 2250 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03382 0.972 0.9953 4.689e-05 -2.105e-05 -0.03474 3.534e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02352 -0.005621 0.01894 0.0325 0.9379 0.9476 0.04982 0.8822 0.901 0.1268 ] Network output: [ 0.9736 0.06847 -0.01583 -0.000146 6.555e-05 -0.0003631 -0.00011 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6516 0.12 0.1183 0.2945 0.9705 0.9863 0.7507 0.8974 0.9655 0.6315 ] Network output: [ -0.004678 0.9336 1.029 -4.338e-05 1.947e-05 0.04684 -3.269e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05064 0.03763 0.05484 0.04489 0.9845 0.989 0.05189 0.9684 0.9792 0.06866 ] Network output: [ 0.08922 -0.2936 1.069 -0.0003075 0.000138 1.045 -0.0002317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7451 0.6381 0.5305 0.4773 0.974 0.9883 0.7486 0.9081 0.9706 0.6275 ] Network output: [ -0.04532 0.1899 0.9559 0.0006388 -0.0002868 0.9475 0.0004814 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6099 0.5958 0.4453 0.3251 0.986 0.9908 0.6104 0.9724 0.9812 0.4578 ] Network output: [ -0.06871 0.2184 0.9409 8.104e-05 -3.638e-05 0.9784 6.107e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6108 0.6085 0.4667 0.2956 0.9839 0.9896 0.6109 0.9661 0.9778 0.4691 ] Network output: [ 0.02236 0.9117 0.02525 -0.0002657 0.0001193 1.017 -0.0002002 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03223 Epoch 2251 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0338 0.972 0.9953 4.649e-05 -2.087e-05 -0.03473 3.504e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02351 -0.005622 0.01894 0.03247 0.9379 0.9476 0.04981 0.8822 0.901 0.1268 ] Network output: [ 0.9736 0.06846 -0.01585 -0.0001452 6.519e-05 -0.0003886 -0.0001094 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6516 0.12 0.1184 0.2942 0.9705 0.9863 0.7506 0.8974 0.9655 0.6315 ] Network output: [ -0.004693 0.9336 1.029 -4.369e-05 1.962e-05 0.04684 -3.293e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05064 0.03762 0.05482 0.04484 0.9845 0.989 0.05188 0.9684 0.9792 0.06864 ] Network output: [ 0.08914 -0.2935 1.069 -0.0003099 0.0001391 1.045 -0.0002335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.745 0.6381 0.5306 0.477 0.974 0.9883 0.7485 0.9082 0.9706 0.6275 ] Network output: [ -0.04524 0.1897 0.9558 0.0006397 -0.0002872 0.9476 0.0004821 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6099 0.5959 0.4453 0.325 0.986 0.9908 0.6104 0.9724 0.9812 0.4578 ] Network output: [ -0.06864 0.2183 0.941 8.255e-05 -3.706e-05 0.9784 6.221e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6108 0.6085 0.4667 0.2955 0.9839 0.9896 0.6109 0.9661 0.9778 0.4691 ] Network output: [ 0.02231 0.9119 0.02523 -0.0002657 0.0001193 1.017 -0.0002002 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03219 Epoch 2252 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03377 0.972 0.9953 4.609e-05 -2.069e-05 -0.03472 3.473e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0235 -0.005623 0.01893 0.03243 0.9379 0.9476 0.04979 0.8822 0.9011 0.1267 ] Network output: [ 0.9736 0.06845 -0.01586 -0.0001444 6.484e-05 -0.0004139 -0.0001088 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6515 0.12 0.1184 0.2939 0.9705 0.9863 0.7505 0.8975 0.9655 0.6316 ] Network output: [ -0.004707 0.9337 1.029 -4.401e-05 1.976e-05 0.04683 -3.317e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05063 0.03762 0.0548 0.04479 0.9845 0.989 0.05187 0.9685 0.9792 0.06861 ] Network output: [ 0.08906 -0.2934 1.069 -0.0003123 0.0001402 1.045 -0.0002354 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.745 0.6381 0.5307 0.4767 0.974 0.9883 0.7485 0.9082 0.9706 0.6275 ] Network output: [ -0.04517 0.1896 0.9557 0.0006405 -0.0002876 0.9477 0.0004827 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.61 0.5959 0.4453 0.3248 0.986 0.9908 0.6105 0.9724 0.9812 0.4578 ] Network output: [ -0.06857 0.2181 0.941 8.407e-05 -3.774e-05 0.9784 6.336e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6108 0.6085 0.4667 0.2954 0.9839 0.9896 0.6109 0.9661 0.9778 0.4691 ] Network output: [ 0.02227 0.912 0.02521 -0.0002657 0.0001193 1.017 -0.0002002 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03215 Epoch 2253 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03374 0.972 0.9954 4.569e-05 -2.051e-05 -0.03471 3.443e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02349 -0.005624 0.01892 0.0324 0.9379 0.9476 0.04977 0.8822 0.9011 0.1267 ] Network output: [ 0.9736 0.06844 -0.01587 -0.0001436 6.449e-05 -0.0004392 -0.0001083 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6515 0.1201 0.1185 0.2936 0.9705 0.9863 0.7505 0.8975 0.9655 0.6316 ] Network output: [ -0.004722 0.9337 1.029 -4.433e-05 1.99e-05 0.04683 -3.341e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05062 0.03762 0.05478 0.04474 0.9845 0.989 0.05186 0.9685 0.9793 0.06859 ] Network output: [ 0.08898 -0.2933 1.069 -0.0003148 0.0001413 1.045 -0.0002372 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7449 0.638 0.5307 0.4765 0.974 0.9883 0.7484 0.9082 0.9706 0.6276 ] Network output: [ -0.0451 0.1895 0.9556 0.0006414 -0.0002879 0.9477 0.0004834 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.61 0.596 0.4454 0.3247 0.986 0.9908 0.6106 0.9724 0.9812 0.4579 ] Network output: [ -0.0685 0.2179 0.9411 8.56e-05 -3.843e-05 0.9784 6.451e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6109 0.6086 0.4666 0.2953 0.9839 0.9896 0.611 0.9661 0.9778 0.469 ] Network output: [ 0.02222 0.9122 0.02519 -0.0002657 0.0001193 1.017 -0.0002002 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03211 Epoch 2254 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03372 0.9721 0.9954 4.528e-05 -2.033e-05 -0.03471 3.413e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02349 -0.005625 0.01892 0.03237 0.938 0.9476 0.04976 0.8823 0.9011 0.1267 ] Network output: [ 0.9737 0.06843 -0.01589 -0.0001429 6.414e-05 -0.0004643 -0.0001077 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6514 0.1201 0.1185 0.2934 0.9706 0.9863 0.7504 0.8975 0.9655 0.6317 ] Network output: [ -0.004737 0.9338 1.029 -4.464e-05 2.004e-05 0.04682 -3.364e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05061 0.03761 0.05477 0.04469 0.9845 0.989 0.05186 0.9685 0.9793 0.06856 ] Network output: [ 0.08889 -0.2931 1.069 -0.0003173 0.0001424 1.045 -0.0002391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7449 0.638 0.5308 0.4762 0.974 0.9883 0.7484 0.9082 0.9706 0.6276 ] Network output: [ -0.04503 0.1893 0.9555 0.0006423 -0.0002883 0.9478 0.000484 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6101 0.5961 0.4454 0.3245 0.986 0.9908 0.6106 0.9724 0.9812 0.4579 ] Network output: [ -0.06843 0.2177 0.9411 8.714e-05 -3.912e-05 0.9784 6.567e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6109 0.6086 0.4666 0.2952 0.9839 0.9896 0.611 0.9662 0.9778 0.469 ] Network output: [ 0.02218 0.9123 0.02516 -0.0002657 0.0001193 1.017 -0.0002003 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03207 Epoch 2255 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03369 0.9721 0.9954 4.488e-05 -2.015e-05 -0.0347 3.382e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02348 -0.005627 0.01891 0.03234 0.938 0.9477 0.04974 0.8823 0.9011 0.1266 ] Network output: [ 0.9737 0.06842 -0.0159 -0.0001421 6.379e-05 -0.0004893 -0.0001071 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6514 0.1202 0.1186 0.2931 0.9706 0.9863 0.7504 0.8975 0.9655 0.6317 ] Network output: [ -0.004751 0.9338 1.029 -4.496e-05 2.018e-05 0.04682 -3.388e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0506 0.03761 0.05475 0.04465 0.9845 0.989 0.05185 0.9685 0.9793 0.06854 ] Network output: [ 0.08881 -0.293 1.069 -0.0003197 0.0001435 1.045 -0.000241 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7448 0.638 0.5309 0.4759 0.974 0.9883 0.7483 0.9083 0.9706 0.6276 ] Network output: [ -0.04496 0.1892 0.9555 0.0006431 -0.0002887 0.9479 0.0004847 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6102 0.5961 0.4454 0.3244 0.986 0.9908 0.6107 0.9724 0.9812 0.4579 ] Network output: [ -0.06836 0.2175 0.9411 8.868e-05 -3.981e-05 0.9785 6.683e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6109 0.6087 0.4666 0.2951 0.9839 0.9896 0.611 0.9662 0.9778 0.469 ] Network output: [ 0.02213 0.9125 0.02514 -0.0002657 0.0001193 1.017 -0.0002003 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03203 Epoch 2256 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03366 0.9721 0.9955 4.448e-05 -1.997e-05 -0.03469 3.352e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02347 -0.005628 0.0189 0.03231 0.938 0.9477 0.04972 0.8823 0.9011 0.1266 ] Network output: [ 0.9737 0.06841 -0.01592 -0.0001413 6.344e-05 -0.0005142 -0.0001065 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6514 0.1202 0.1187 0.2928 0.9706 0.9863 0.7503 0.8976 0.9656 0.6318 ] Network output: [ -0.004766 0.9338 1.029 -4.527e-05 2.032e-05 0.04681 -3.412e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0506 0.0376 0.05473 0.0446 0.9845 0.989 0.05184 0.9685 0.9793 0.06851 ] Network output: [ 0.08873 -0.2929 1.069 -0.0003222 0.0001446 1.045 -0.0002428 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7447 0.638 0.5309 0.4756 0.974 0.9883 0.7483 0.9083 0.9706 0.6277 ] Network output: [ -0.04488 0.1891 0.9554 0.000644 -0.0002891 0.948 0.0004854 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6102 0.5962 0.4454 0.3242 0.986 0.9908 0.6108 0.9724 0.9813 0.4579 ] Network output: [ -0.06829 0.2173 0.9412 9.022e-05 -4.05e-05 0.9785 6.799e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.611 0.6087 0.4666 0.295 0.9839 0.9896 0.6111 0.9662 0.9778 0.469 ] Network output: [ 0.02209 0.9126 0.02512 -0.0002658 0.0001193 1.017 -0.0002003 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03199 Epoch 2257 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03364 0.9721 0.9955 4.408e-05 -1.979e-05 -0.03469 3.322e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02346 -0.005629 0.0189 0.03227 0.938 0.9477 0.04971 0.8824 0.9012 0.1265 ] Network output: [ 0.9737 0.06841 -0.01593 -0.0001405 6.309e-05 -0.000539 -0.0001059 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6513 0.1202 0.1187 0.2926 0.9706 0.9863 0.7503 0.8976 0.9656 0.6318 ] Network output: [ -0.004781 0.9339 1.029 -4.559e-05 2.047e-05 0.0468 -3.436e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05059 0.0376 0.05471 0.04455 0.9845 0.989 0.05183 0.9685 0.9793 0.06849 ] Network output: [ 0.08865 -0.2927 1.069 -0.0003247 0.0001458 1.045 -0.0002447 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7447 0.638 0.531 0.4753 0.974 0.9883 0.7482 0.9083 0.9706 0.6277 ] Network output: [ -0.04481 0.1889 0.9553 0.0006449 -0.0002895 0.948 0.000486 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6103 0.5963 0.4455 0.3241 0.986 0.9908 0.6108 0.9724 0.9813 0.4579 ] Network output: [ -0.06822 0.2171 0.9412 9.178e-05 -4.12e-05 0.9785 6.917e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.611 0.6087 0.4666 0.295 0.9839 0.9896 0.6111 0.9662 0.9778 0.469 ] Network output: [ 0.02204 0.9128 0.0251 -0.0002658 0.0001193 1.017 -0.0002003 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03195 Epoch 2258 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03361 0.9721 0.9955 4.368e-05 -1.961e-05 -0.03468 3.292e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02346 -0.00563 0.01889 0.03224 0.938 0.9477 0.04969 0.8824 0.9012 0.1265 ] Network output: [ 0.9738 0.0684 -0.01595 -0.0001398 6.274e-05 -0.0005636 -0.0001053 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6513 0.1203 0.1188 0.2923 0.9706 0.9863 0.7502 0.8976 0.9656 0.6318 ] Network output: [ -0.004795 0.9339 1.029 -4.59e-05 2.061e-05 0.0468 -3.459e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05058 0.03759 0.0547 0.0445 0.9845 0.989 0.05182 0.9685 0.9793 0.06846 ] Network output: [ 0.08857 -0.2926 1.069 -0.0003272 0.0001469 1.045 -0.0002466 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7446 0.638 0.5311 0.475 0.974 0.9883 0.7481 0.9083 0.9706 0.6278 ] Network output: [ -0.04474 0.1888 0.9552 0.0006458 -0.0002899 0.9481 0.0004867 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6104 0.5963 0.4455 0.3239 0.986 0.9908 0.6109 0.9724 0.9813 0.458 ] Network output: [ -0.06815 0.2169 0.9412 9.334e-05 -4.19e-05 0.9785 7.034e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.611 0.6088 0.4666 0.2949 0.9839 0.9896 0.6111 0.9662 0.9778 0.469 ] Network output: [ 0.022 0.913 0.02508 -0.0002658 0.0001193 1.017 -0.0002003 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03191 Epoch 2259 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03358 0.9721 0.9956 4.328e-05 -1.943e-05 -0.03467 3.262e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02345 -0.005631 0.01888 0.03221 0.938 0.9477 0.04967 0.8824 0.9012 0.1264 ] Network output: [ 0.9738 0.06839 -0.01596 -0.000139 6.24e-05 -0.0005882 -0.0001047 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6512 0.1203 0.1188 0.292 0.9706 0.9863 0.7501 0.8976 0.9656 0.6319 ] Network output: [ -0.00481 0.934 1.029 -4.622e-05 2.075e-05 0.04679 -3.483e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05057 0.03759 0.05468 0.04445 0.9845 0.989 0.05181 0.9685 0.9793 0.06843 ] Network output: [ 0.08848 -0.2925 1.069 -0.0003297 0.000148 1.045 -0.0002484 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7446 0.638 0.5311 0.4747 0.974 0.9883 0.7481 0.9083 0.9706 0.6278 ] Network output: [ -0.04466 0.1886 0.9551 0.0006467 -0.0002903 0.9482 0.0004874 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6104 0.5964 0.4455 0.3238 0.986 0.9908 0.6109 0.9725 0.9813 0.458 ] Network output: [ -0.06808 0.2167 0.9413 9.49e-05 -4.26e-05 0.9786 7.152e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6111 0.6088 0.4666 0.2948 0.9839 0.9896 0.6112 0.9662 0.9779 0.469 ] Network output: [ 0.02195 0.9131 0.02506 -0.0002658 0.0001193 1.017 -0.0002003 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03187 Epoch 2260 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03356 0.9721 0.9956 4.288e-05 -1.925e-05 -0.03467 3.231e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02344 -0.005631 0.01888 0.03218 0.938 0.9477 0.04966 0.8824 0.9012 0.1264 ] Network output: [ 0.9738 0.06838 -0.01598 -0.0001382 6.205e-05 -0.0006126 -0.0001042 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6512 0.1203 0.1189 0.2917 0.9706 0.9863 0.7501 0.8977 0.9656 0.6319 ] Network output: [ -0.004825 0.934 1.029 -4.653e-05 2.089e-05 0.04678 -3.507e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05056 0.03758 0.05466 0.0444 0.9845 0.989 0.05181 0.9686 0.9793 0.06841 ] Network output: [ 0.0884 -0.2923 1.07 -0.0003322 0.0001491 1.045 -0.0002503 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7445 0.6379 0.5312 0.4744 0.974 0.9883 0.748 0.9084 0.9707 0.6278 ] Network output: [ -0.04459 0.1885 0.955 0.0006476 -0.0002907 0.9483 0.0004881 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6105 0.5965 0.4456 0.3236 0.986 0.9908 0.611 0.9725 0.9813 0.458 ] Network output: [ -0.06801 0.2165 0.9413 9.647e-05 -4.331e-05 0.9786 7.271e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6111 0.6089 0.4666 0.2947 0.984 0.9896 0.6112 0.9663 0.9779 0.4689 ] Network output: [ 0.02191 0.9133 0.02504 -0.0002659 0.0001194 1.017 -0.0002004 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03182 Epoch 2261 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03353 0.9721 0.9956 4.248e-05 -1.907e-05 -0.03466 3.201e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02343 -0.005632 0.01887 0.03215 0.938 0.9477 0.04964 0.8825 0.9013 0.1264 ] Network output: [ 0.9739 0.06837 -0.01599 -0.0001375 6.171e-05 -0.0006369 -0.0001036 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6511 0.1204 0.1189 0.2915 0.9706 0.9863 0.75 0.8977 0.9656 0.632 ] Network output: [ -0.00484 0.9341 1.029 -4.685e-05 2.103e-05 0.04678 -3.531e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05056 0.03758 0.05464 0.04435 0.9845 0.989 0.0518 0.9686 0.9793 0.06838 ] Network output: [ 0.08832 -0.2922 1.07 -0.0003347 0.0001502 1.045 -0.0002522 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7444 0.6379 0.5313 0.4741 0.9741 0.9883 0.748 0.9084 0.9707 0.6279 ] Network output: [ -0.04452 0.1884 0.955 0.0006485 -0.0002912 0.9484 0.0004888 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6105 0.5965 0.4456 0.3234 0.986 0.9908 0.6111 0.9725 0.9813 0.458 ] Network output: [ -0.06794 0.2163 0.9413 9.805e-05 -4.402e-05 0.9786 7.39e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6112 0.6089 0.4665 0.2946 0.984 0.9896 0.6113 0.9663 0.9779 0.4689 ] Network output: [ 0.02186 0.9134 0.02502 -0.0002659 0.0001194 1.017 -0.0002004 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03178 Epoch 2262 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0335 0.9721 0.9957 4.208e-05 -1.889e-05 -0.03466 3.171e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02343 -0.005633 0.01886 0.03211 0.938 0.9477 0.04962 0.8825 0.9013 0.1263 ] Network output: [ 0.9739 0.06836 -0.016 -0.0001367 6.137e-05 -0.0006611 -0.000103 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6511 0.1204 0.119 0.2912 0.9706 0.9864 0.75 0.8977 0.9656 0.632 ] Network output: [ -0.004855 0.9341 1.029 -4.716e-05 2.117e-05 0.04677 -3.554e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05055 0.03757 0.05462 0.0443 0.9845 0.989 0.05179 0.9686 0.9793 0.06836 ] Network output: [ 0.08823 -0.2921 1.07 -0.0003372 0.0001514 1.045 -0.0002541 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7444 0.6379 0.5313 0.4738 0.9741 0.9883 0.7479 0.9084 0.9707 0.6279 ] Network output: [ -0.04445 0.1882 0.9549 0.0006495 -0.0002916 0.9484 0.0004895 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6106 0.5966 0.4456 0.3233 0.986 0.9908 0.6111 0.9725 0.9813 0.4581 ] Network output: [ -0.06787 0.2161 0.9414 9.964e-05 -4.473e-05 0.9786 7.509e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6112 0.6089 0.4665 0.2945 0.984 0.9896 0.6113 0.9663 0.9779 0.4689 ] Network output: [ 0.02182 0.9136 0.025 -0.0002659 0.0001194 1.017 -0.0002004 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03174 Epoch 2263 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03348 0.9721 0.9957 4.168e-05 -1.871e-05 -0.03465 3.141e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02342 -0.005634 0.01886 0.03208 0.9381 0.9477 0.0496 0.8825 0.9013 0.1263 ] Network output: [ 0.9739 0.06835 -0.01602 -0.0001359 6.103e-05 -0.0006851 -0.0001024 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6511 0.1204 0.119 0.2909 0.9706 0.9864 0.7499 0.8977 0.9656 0.632 ] Network output: [ -0.00487 0.9342 1.029 -4.748e-05 2.131e-05 0.04676 -3.578e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05054 0.03757 0.05461 0.04425 0.9845 0.9891 0.05178 0.9686 0.9793 0.06833 ] Network output: [ 0.08815 -0.2919 1.07 -0.0003397 0.0001525 1.045 -0.000256 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7443 0.6379 0.5314 0.4734 0.9741 0.9883 0.7478 0.9084 0.9707 0.628 ] Network output: [ -0.04437 0.1881 0.9548 0.0006504 -0.000292 0.9485 0.0004901 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6107 0.5967 0.4456 0.3231 0.986 0.9908 0.6112 0.9725 0.9813 0.4581 ] Network output: [ -0.0678 0.2159 0.9414 0.0001012 -4.545e-05 0.9787 7.629e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6112 0.609 0.4665 0.2944 0.984 0.9896 0.6113 0.9663 0.9779 0.4689 ] Network output: [ 0.02178 0.9137 0.02497 -0.000266 0.0001194 1.017 -0.0002004 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0317 Epoch 2264 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03345 0.9722 0.9958 4.128e-05 -1.853e-05 -0.03464 3.111e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02341 -0.005635 0.01885 0.03205 0.9381 0.9477 0.04959 0.8826 0.9013 0.1262 ] Network output: [ 0.9739 0.06833 -0.01603 -0.0001352 6.069e-05 -0.0007091 -0.0001019 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.651 0.1205 0.1191 0.2906 0.9706 0.9864 0.7498 0.8978 0.9656 0.6321 ] Network output: [ -0.004885 0.9342 1.029 -4.779e-05 2.145e-05 0.04676 -3.602e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05053 0.03756 0.05459 0.0442 0.9845 0.9891 0.05177 0.9686 0.9793 0.06831 ] Network output: [ 0.08807 -0.2918 1.07 -0.0003422 0.0001536 1.044 -0.0002579 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7443 0.6379 0.5315 0.4731 0.9741 0.9883 0.7478 0.9085 0.9707 0.628 ] Network output: [ -0.0443 0.1879 0.9547 0.0006513 -0.0002924 0.9486 0.0004908 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6107 0.5967 0.4457 0.323 0.986 0.9908 0.6113 0.9725 0.9813 0.4581 ] Network output: [ -0.06773 0.2157 0.9414 0.0001028 -4.616e-05 0.9787 7.749e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6113 0.609 0.4665 0.2943 0.984 0.9897 0.6114 0.9663 0.9779 0.4689 ] Network output: [ 0.02173 0.9139 0.02495 -0.000266 0.0001194 1.017 -0.0002005 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03166 Epoch 2265 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03342 0.9722 0.9958 4.089e-05 -1.836e-05 -0.03464 3.081e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0234 -0.005636 0.01884 0.03202 0.9381 0.9478 0.04957 0.8826 0.9013 0.1262 ] Network output: [ 0.974 0.06832 -0.01605 -0.0001344 6.035e-05 -0.0007329 -0.0001013 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.651 0.1205 0.1191 0.2903 0.9706 0.9864 0.7498 0.8978 0.9657 0.6321 ] Network output: [ -0.0049 0.9343 1.029 -4.81e-05 2.16e-05 0.04675 -3.625e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05052 0.03756 0.05457 0.04415 0.9846 0.9891 0.05176 0.9686 0.9793 0.06828 ] Network output: [ 0.08798 -0.2916 1.07 -0.0003447 0.0001548 1.044 -0.0002598 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7442 0.6379 0.5315 0.4728 0.9741 0.9883 0.7477 0.9085 0.9707 0.628 ] Network output: [ -0.04423 0.1878 0.9546 0.0006522 -0.0002928 0.9487 0.0004915 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6108 0.5968 0.4457 0.3228 0.986 0.9909 0.6113 0.9725 0.9813 0.4581 ] Network output: [ -0.06766 0.2155 0.9415 0.0001044 -4.688e-05 0.9787 7.87e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6113 0.609 0.4665 0.2942 0.984 0.9897 0.6114 0.9663 0.9779 0.4689 ] Network output: [ 0.02169 0.914 0.02493 -0.0002661 0.0001194 1.017 -0.0002005 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03161 Epoch 2266 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0334 0.9722 0.9958 4.049e-05 -1.818e-05 -0.03463 3.051e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0234 -0.005637 0.01884 0.03198 0.9381 0.9478 0.04955 0.8826 0.9014 0.1262 ] Network output: [ 0.974 0.06831 -0.01606 -0.0001337 6.001e-05 -0.0007566 -0.0001007 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6509 0.1205 0.1192 0.2901 0.9706 0.9864 0.7497 0.8978 0.9657 0.6322 ] Network output: [ -0.004915 0.9343 1.029 -4.842e-05 2.174e-05 0.04674 -3.649e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05051 0.03755 0.05455 0.04411 0.9846 0.9891 0.05175 0.9686 0.9794 0.06826 ] Network output: [ 0.0879 -0.2915 1.07 -0.0003473 0.0001559 1.044 -0.0002617 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7441 0.6378 0.5316 0.4725 0.9741 0.9884 0.7476 0.9085 0.9707 0.6281 ] Network output: [ -0.04415 0.1876 0.9545 0.0006532 -0.0002932 0.9488 0.0004923 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6109 0.5969 0.4457 0.3227 0.986 0.9909 0.6114 0.9725 0.9813 0.4582 ] Network output: [ -0.06759 0.2153 0.9415 0.000106 -4.761e-05 0.9788 7.992e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6114 0.6091 0.4665 0.2941 0.984 0.9897 0.6115 0.9663 0.9779 0.4689 ] Network output: [ 0.02164 0.9142 0.02491 -0.0002661 0.0001195 1.017 -0.0002006 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03157 Epoch 2267 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03337 0.9722 0.9959 4.009e-05 -1.8e-05 -0.03463 3.021e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02339 -0.005638 0.01883 0.03195 0.9381 0.9478 0.04954 0.8826 0.9014 0.1261 ] Network output: [ 0.974 0.0683 -0.01608 -0.0001329 5.968e-05 -0.0007802 -0.0001002 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6509 0.1206 0.1193 0.2898 0.9706 0.9864 0.7496 0.8979 0.9657 0.6322 ] Network output: [ -0.00493 0.9343 1.029 -4.873e-05 2.188e-05 0.04673 -3.672e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05051 0.03754 0.05454 0.04406 0.9846 0.9891 0.05174 0.9686 0.9794 0.06823 ] Network output: [ 0.08781 -0.2914 1.07 -0.0003498 0.000157 1.044 -0.0002636 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7441 0.6378 0.5317 0.4722 0.9741 0.9884 0.7476 0.9085 0.9707 0.6281 ] Network output: [ -0.04408 0.1875 0.9545 0.0006541 -0.0002937 0.9489 0.000493 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6109 0.5969 0.4457 0.3225 0.986 0.9909 0.6115 0.9725 0.9813 0.4582 ] Network output: [ -0.06752 0.2151 0.9415 0.0001077 -4.833e-05 0.9788 8.113e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6114 0.6091 0.4665 0.294 0.984 0.9897 0.6115 0.9664 0.9779 0.4688 ] Network output: [ 0.0216 0.9143 0.02489 -0.0002662 0.0001195 1.016 -0.0002006 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03153 Epoch 2268 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03334 0.9722 0.9959 3.97e-05 -1.782e-05 -0.03462 2.992e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02338 -0.005639 0.01882 0.03192 0.9381 0.9478 0.04952 0.8827 0.9014 0.1261 ] Network output: [ 0.974 0.06829 -0.01609 -0.0001322 5.934e-05 -0.0008037 -9.962e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6508 0.1206 0.1193 0.2895 0.9707 0.9864 0.7496 0.8979 0.9657 0.6323 ] Network output: [ -0.004945 0.9344 1.029 -4.904e-05 2.202e-05 0.04673 -3.696e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0505 0.03754 0.05452 0.04401 0.9846 0.9891 0.05174 0.9687 0.9794 0.06821 ] Network output: [ 0.08773 -0.2912 1.07 -0.0003524 0.0001582 1.044 -0.0002655 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.744 0.6378 0.5318 0.4719 0.9741 0.9884 0.7475 0.9086 0.9707 0.6282 ] Network output: [ -0.044 0.1874 0.9544 0.0006551 -0.0002941 0.9489 0.0004937 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.611 0.597 0.4458 0.3224 0.986 0.9909 0.6115 0.9725 0.9813 0.4582 ] Network output: [ -0.06744 0.2149 0.9416 0.0001093 -4.906e-05 0.9788 8.236e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6114 0.6092 0.4665 0.2939 0.984 0.9897 0.6115 0.9664 0.978 0.4688 ] Network output: [ 0.02155 0.9145 0.02487 -0.0002662 0.0001195 1.016 -0.0002006 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03149 Epoch 2269 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03331 0.9722 0.9959 3.93e-05 -1.764e-05 -0.03461 2.962e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02338 -0.00564 0.01882 0.03189 0.9381 0.9478 0.0495 0.8827 0.9014 0.126 ] Network output: [ 0.9741 0.06828 -0.01611 -0.0001314 5.901e-05 -0.0008271 -9.906e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6508 0.1206 0.1194 0.2892 0.9707 0.9864 0.7495 0.8979 0.9657 0.6323 ] Network output: [ -0.004961 0.9344 1.029 -4.936e-05 2.216e-05 0.04672 -3.72e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05049 0.03753 0.0545 0.04396 0.9846 0.9891 0.05173 0.9687 0.9794 0.06818 ] Network output: [ 0.08764 -0.2911 1.07 -0.0003549 0.0001593 1.044 -0.0002675 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.744 0.6378 0.5318 0.4716 0.9741 0.9884 0.7475 0.9086 0.9707 0.6282 ] Network output: [ -0.04393 0.1872 0.9543 0.000656 -0.0002945 0.949 0.0004944 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6111 0.5971 0.4458 0.3222 0.986 0.9909 0.6116 0.9725 0.9813 0.4582 ] Network output: [ -0.06737 0.2147 0.9416 0.0001109 -4.979e-05 0.9788 8.359e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6115 0.6092 0.4664 0.2938 0.984 0.9897 0.6116 0.9664 0.978 0.4688 ] Network output: [ 0.02151 0.9146 0.02485 -0.0002663 0.0001196 1.016 -0.0002007 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03144 Epoch 2270 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03329 0.9722 0.996 3.89e-05 -1.747e-05 -0.03461 2.932e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02337 -0.00564 0.01881 0.03186 0.9381 0.9478 0.04949 0.8827 0.9015 0.126 ] Network output: [ 0.9741 0.06827 -0.01612 -0.0001307 5.867e-05 -0.0008503 -9.85e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6508 0.1207 0.1194 0.289 0.9707 0.9864 0.7495 0.8979 0.9657 0.6323 ] Network output: [ -0.004976 0.9345 1.029 -4.967e-05 2.23e-05 0.04671 -3.743e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05048 0.03753 0.05448 0.04391 0.9846 0.9891 0.05172 0.9687 0.9794 0.06816 ] Network output: [ 0.08756 -0.2909 1.07 -0.0003575 0.0001605 1.044 -0.0002694 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7439 0.6377 0.5319 0.4713 0.9741 0.9884 0.7474 0.9086 0.9707 0.6283 ] Network output: [ -0.04386 0.1871 0.9542 0.000657 -0.0002949 0.9491 0.0004951 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6111 0.5971 0.4458 0.322 0.986 0.9909 0.6117 0.9726 0.9814 0.4582 ] Network output: [ -0.0673 0.2145 0.9416 0.0001125 -5.053e-05 0.9789 8.482e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6115 0.6093 0.4664 0.2937 0.984 0.9897 0.6116 0.9664 0.978 0.4688 ] Network output: [ 0.02146 0.9148 0.02483 -0.0002664 0.0001196 1.016 -0.0002007 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0314 Epoch 2271 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03326 0.9722 0.996 3.851e-05 -1.729e-05 -0.0346 2.902e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02336 -0.005641 0.0188 0.03182 0.9381 0.9478 0.04947 0.8827 0.9015 0.126 ] Network output: [ 0.9741 0.06826 -0.01613 -0.00013 5.834e-05 -0.0008735 -9.794e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6507 0.1207 0.1195 0.2887 0.9707 0.9864 0.7494 0.898 0.9657 0.6324 ] Network output: [ -0.004991 0.9345 1.029 -4.998e-05 2.244e-05 0.0467 -3.767e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05047 0.03752 0.05447 0.04386 0.9846 0.9891 0.05171 0.9687 0.9794 0.06813 ] Network output: [ 0.08747 -0.2908 1.07 -0.00036 0.0001616 1.044 -0.0002713 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7438 0.6377 0.532 0.471 0.9741 0.9884 0.7473 0.9086 0.9708 0.6283 ] Network output: [ -0.04378 0.1869 0.9541 0.0006579 -0.0002954 0.9492 0.0004958 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6112 0.5972 0.4459 0.3219 0.986 0.9909 0.6117 0.9726 0.9814 0.4583 ] Network output: [ -0.06723 0.2143 0.9417 0.0001142 -5.126e-05 0.9789 8.606e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6116 0.6093 0.4664 0.2936 0.984 0.9897 0.6117 0.9664 0.978 0.4688 ] Network output: [ 0.02142 0.9149 0.02481 -0.0002664 0.0001196 1.016 -0.0002008 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03136 Epoch 2272 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03323 0.9723 0.996 3.811e-05 -1.711e-05 -0.0346 2.872e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02335 -0.005642 0.0188 0.03179 0.9382 0.9478 0.04945 0.8828 0.9015 0.1259 ] Network output: [ 0.9741 0.06825 -0.01615 -0.0001292 5.801e-05 -0.0008965 -9.739e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6507 0.1207 0.1195 0.2884 0.9707 0.9864 0.7493 0.898 0.9657 0.6324 ] Network output: [ -0.005007 0.9346 1.029 -5.029e-05 2.258e-05 0.04669 -3.79e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05046 0.03752 0.05445 0.04381 0.9846 0.9891 0.0517 0.9687 0.9794 0.06811 ] Network output: [ 0.08739 -0.2907 1.07 -0.0003626 0.0001628 1.044 -0.0002733 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7438 0.6377 0.532 0.4707 0.9741 0.9884 0.7473 0.9087 0.9708 0.6283 ] Network output: [ -0.04371 0.1868 0.954 0.0006589 -0.0002958 0.9493 0.0004966 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6113 0.5973 0.4459 0.3217 0.986 0.9909 0.6118 0.9726 0.9814 0.4583 ] Network output: [ -0.06716 0.2141 0.9417 0.0001158 -5.2e-05 0.9789 8.73e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6116 0.6093 0.4664 0.2934 0.984 0.9897 0.6117 0.9664 0.978 0.4688 ] Network output: [ 0.02138 0.9151 0.02479 -0.0002665 0.0001196 1.016 -0.0002008 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03132 Epoch 2273 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03321 0.9723 0.9961 3.772e-05 -1.693e-05 -0.03459 2.843e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02335 -0.005643 0.01879 0.03176 0.9382 0.9478 0.04944 0.8828 0.9015 0.1259 ] Network output: [ 0.9742 0.06823 -0.01616 -0.0001285 5.768e-05 -0.0009194 -9.683e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6506 0.1208 0.1196 0.2881 0.9707 0.9864 0.7493 0.898 0.9657 0.6325 ] Network output: [ -0.005022 0.9346 1.029 -5.061e-05 2.272e-05 0.04668 -3.814e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05045 0.03751 0.05443 0.04376 0.9846 0.9891 0.05169 0.9687 0.9794 0.06808 ] Network output: [ 0.0873 -0.2905 1.07 -0.0003652 0.0001639 1.044 -0.0002752 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7437 0.6377 0.5321 0.4703 0.9741 0.9884 0.7472 0.9087 0.9708 0.6284 ] Network output: [ -0.04363 0.1866 0.954 0.0006599 -0.0002962 0.9494 0.0004973 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6113 0.5973 0.4459 0.3216 0.986 0.9909 0.6118 0.9726 0.9814 0.4583 ] Network output: [ -0.06708 0.2139 0.9417 0.0001175 -5.275e-05 0.979 8.855e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6116 0.6094 0.4664 0.2933 0.984 0.9897 0.6117 0.9664 0.978 0.4688 ] Network output: [ 0.02133 0.9153 0.02477 -0.0002666 0.0001197 1.016 -0.0002009 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03127 Epoch 2274 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03318 0.9723 0.9961 3.733e-05 -1.676e-05 -0.03459 2.813e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02334 -0.005644 0.01878 0.03173 0.9382 0.9478 0.04942 0.8828 0.9015 0.1258 ] Network output: [ 0.9742 0.06822 -0.01618 -0.0001278 5.736e-05 -0.0009422 -9.628e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6506 0.1208 0.1196 0.2878 0.9707 0.9864 0.7492 0.898 0.9658 0.6325 ] Network output: [ -0.005038 0.9347 1.029 -5.092e-05 2.286e-05 0.04667 -3.837e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05045 0.03751 0.05441 0.04371 0.9846 0.9891 0.05168 0.9687 0.9794 0.06806 ] Network output: [ 0.08722 -0.2904 1.07 -0.0003678 0.0001651 1.044 -0.0002772 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7437 0.6377 0.5322 0.47 0.9741 0.9884 0.7471 0.9087 0.9708 0.6284 ] Network output: [ -0.04356 0.1865 0.9539 0.0006609 -0.0002967 0.9495 0.000498 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6114 0.5974 0.4459 0.3214 0.9861 0.9909 0.6119 0.9726 0.9814 0.4583 ] Network output: [ -0.06701 0.2137 0.9418 0.0001192 -5.349e-05 0.979 8.98e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6117 0.6094 0.4664 0.2932 0.984 0.9897 0.6118 0.9665 0.978 0.4687 ] Network output: [ 0.02129 0.9154 0.02475 -0.0002667 0.0001197 1.016 -0.000201 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03123 Epoch 2275 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03315 0.9723 0.9961 3.693e-05 -1.658e-05 -0.03458 2.783e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02333 -0.005644 0.01878 0.03169 0.9382 0.9479 0.0494 0.8828 0.9016 0.1258 ] Network output: [ 0.9742 0.06821 -0.01619 -0.000127 5.703e-05 -0.0009649 -9.573e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6505 0.1208 0.1197 0.2875 0.9707 0.9864 0.7491 0.8981 0.9658 0.6326 ] Network output: [ -0.005053 0.9347 1.029 -5.123e-05 2.3e-05 0.04667 -3.861e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05044 0.0375 0.0544 0.04366 0.9846 0.9891 0.05167 0.9687 0.9794 0.06803 ] Network output: [ 0.08713 -0.2902 1.07 -0.0003704 0.0001663 1.044 -0.0002791 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7436 0.6376 0.5322 0.4697 0.9741 0.9884 0.7471 0.9087 0.9708 0.6285 ] Network output: [ -0.04349 0.1863 0.9538 0.0006618 -0.0002971 0.9495 0.0004988 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6115 0.5975 0.446 0.3212 0.9861 0.9909 0.612 0.9726 0.9814 0.4584 ] Network output: [ -0.06694 0.2135 0.9418 0.0001208 -5.424e-05 0.979 9.106e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6117 0.6095 0.4664 0.2931 0.984 0.9897 0.6118 0.9665 0.978 0.4687 ] Network output: [ 0.02124 0.9156 0.02473 -0.0002667 0.0001197 1.016 -0.000201 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03119 Epoch 2276 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03313 0.9723 0.9962 3.654e-05 -1.64e-05 -0.03458 2.754e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02332 -0.005645 0.01877 0.03166 0.9382 0.9479 0.04939 0.8829 0.9016 0.1258 ] Network output: [ 0.9742 0.0682 -0.0162 -0.0001263 5.67e-05 -0.0009874 -9.519e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6505 0.1209 0.1197 0.2873 0.9707 0.9864 0.7491 0.8981 0.9658 0.6326 ] Network output: [ -0.005069 0.9348 1.028 -5.154e-05 2.314e-05 0.04666 -3.884e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05043 0.0375 0.05438 0.04361 0.9846 0.9891 0.05166 0.9688 0.9794 0.06801 ] Network output: [ 0.08704 -0.2901 1.07 -0.000373 0.0001674 1.044 -0.0002811 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7435 0.6376 0.5323 0.4694 0.9741 0.9884 0.747 0.9087 0.9708 0.6285 ] Network output: [ -0.04341 0.1862 0.9537 0.0006628 -0.0002976 0.9496 0.0004995 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6115 0.5975 0.446 0.3211 0.9861 0.9909 0.612 0.9726 0.9814 0.4584 ] Network output: [ -0.06687 0.2133 0.9418 0.0001225 -5.499e-05 0.9791 9.232e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6118 0.6095 0.4663 0.293 0.984 0.9897 0.6119 0.9665 0.978 0.4687 ] Network output: [ 0.0212 0.9157 0.02471 -0.0002668 0.0001198 1.016 -0.0002011 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03114 Epoch 2277 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0331 0.9723 0.9962 3.615e-05 -1.623e-05 -0.03457 2.724e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02332 -0.005646 0.01876 0.03163 0.9382 0.9479 0.04937 0.8829 0.9016 0.1257 ] Network output: [ 0.9743 0.06818 -0.01622 -0.0001256 5.638e-05 -0.00101 -9.464e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6504 0.1209 0.1198 0.287 0.9707 0.9864 0.749 0.8981 0.9658 0.6327 ] Network output: [ -0.005084 0.9348 1.028 -5.185e-05 2.328e-05 0.04665 -3.908e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05042 0.03749 0.05436 0.04356 0.9846 0.9891 0.05165 0.9688 0.9794 0.06799 ] Network output: [ 0.08696 -0.2899 1.071 -0.0003756 0.0001686 1.044 -0.000283 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7435 0.6376 0.5324 0.4691 0.9741 0.9884 0.7469 0.9088 0.9708 0.6286 ] Network output: [ -0.04334 0.186 0.9536 0.0006638 -0.000298 0.9497 0.0005003 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6116 0.5976 0.446 0.3209 0.9861 0.9909 0.6121 0.9726 0.9814 0.4584 ] Network output: [ -0.06679 0.2131 0.9419 0.0001242 -5.575e-05 0.9791 9.358e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6118 0.6095 0.4663 0.2929 0.984 0.9897 0.6119 0.9665 0.978 0.4687 ] Network output: [ 0.02116 0.9159 0.02469 -0.0002669 0.0001198 1.016 -0.0002012 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0311 Epoch 2278 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03307 0.9723 0.9962 3.576e-05 -1.605e-05 -0.03457 2.695e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02331 -0.005647 0.01876 0.0316 0.9382 0.9479 0.04935 0.8829 0.9016 0.1257 ] Network output: [ 0.9743 0.06817 -0.01623 -0.0001249 5.605e-05 -0.001032 -9.41e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6504 0.1209 0.1198 0.2867 0.9707 0.9864 0.7489 0.8981 0.9658 0.6327 ] Network output: [ -0.0051 0.9349 1.028 -5.217e-05 2.342e-05 0.04664 -3.931e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05041 0.03748 0.05434 0.04351 0.9846 0.9891 0.05164 0.9688 0.9794 0.06796 ] Network output: [ 0.08687 -0.2898 1.071 -0.0003782 0.0001698 1.044 -0.000285 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7434 0.6376 0.5324 0.4688 0.9742 0.9884 0.7469 0.9088 0.9708 0.6286 ] Network output: [ -0.04326 0.1859 0.9535 0.0006648 -0.0002985 0.9498 0.000501 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6116 0.5977 0.446 0.3207 0.9861 0.9909 0.6122 0.9726 0.9814 0.4584 ] Network output: [ -0.06672 0.2129 0.9419 0.0001259 -5.65e-05 0.9791 9.485e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6118 0.6096 0.4663 0.2928 0.984 0.9897 0.6119 0.9665 0.978 0.4687 ] Network output: [ 0.02111 0.916 0.02468 -0.000267 0.0001199 1.016 -0.0002012 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03106 Epoch 2279 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03304 0.9724 0.9963 3.537e-05 -1.588e-05 -0.03456 2.665e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0233 -0.005647 0.01875 0.03156 0.9382 0.9479 0.04933 0.8829 0.9017 0.1256 ] Network output: [ 0.9743 0.06816 -0.01625 -0.0001241 5.573e-05 -0.001054 -9.356e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6503 0.1209 0.1199 0.2864 0.9707 0.9864 0.7489 0.8982 0.9658 0.6328 ] Network output: [ -0.005116 0.9349 1.028 -5.248e-05 2.356e-05 0.04663 -3.955e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0504 0.03748 0.05432 0.04346 0.9846 0.9891 0.05164 0.9688 0.9795 0.06794 ] Network output: [ 0.08678 -0.2896 1.071 -0.0003808 0.000171 1.044 -0.000287 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7433 0.6375 0.5325 0.4684 0.9742 0.9884 0.7468 0.9088 0.9708 0.6286 ] Network output: [ -0.04319 0.1857 0.9535 0.0006658 -0.0002989 0.9499 0.0005018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6117 0.5977 0.4461 0.3206 0.9861 0.9909 0.6122 0.9726 0.9814 0.4584 ] Network output: [ -0.06665 0.2127 0.9419 0.0001276 -5.726e-05 0.9792 9.613e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6119 0.6096 0.4663 0.2927 0.984 0.9897 0.612 0.9665 0.9781 0.4687 ] Network output: [ 0.02107 0.9162 0.02466 -0.0002671 0.0001199 1.016 -0.0002013 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03101 Epoch 2280 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03302 0.9724 0.9963 3.498e-05 -1.57e-05 -0.03456 2.636e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02329 -0.005648 0.01875 0.03153 0.9382 0.9479 0.04932 0.883 0.9017 0.1256 ] Network output: [ 0.9743 0.06815 -0.01626 -0.0001234 5.541e-05 -0.001077 -9.302e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6503 0.121 0.1199 0.2861 0.9707 0.9864 0.7488 0.8982 0.9658 0.6328 ] Network output: [ -0.005132 0.935 1.028 -5.279e-05 2.37e-05 0.04662 -3.978e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05039 0.03747 0.05431 0.04341 0.9846 0.9891 0.05163 0.9688 0.9795 0.06791 ] Network output: [ 0.08669 -0.2895 1.071 -0.0003834 0.0001721 1.044 -0.000289 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7433 0.6375 0.5326 0.4681 0.9742 0.9884 0.7468 0.9088 0.9708 0.6287 ] Network output: [ -0.04311 0.1856 0.9534 0.0006668 -0.0002994 0.95 0.0005026 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6118 0.5978 0.4461 0.3204 0.9861 0.9909 0.6123 0.9726 0.9814 0.4585 ] Network output: [ -0.06657 0.2125 0.942 0.0001293 -5.803e-05 0.9792 9.741e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6119 0.6097 0.4663 0.2926 0.9841 0.9897 0.612 0.9665 0.9781 0.4687 ] Network output: [ 0.02102 0.9163 0.02464 -0.0002672 0.00012 1.016 -0.0002014 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03097 Epoch 2281 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03299 0.9724 0.9963 3.459e-05 -1.553e-05 -0.03455 2.606e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02329 -0.005649 0.01874 0.0315 0.9383 0.9479 0.0493 0.883 0.9017 0.1256 ] Network output: [ 0.9744 0.06813 -0.01627 -0.0001227 5.509e-05 -0.001099 -9.248e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6503 0.121 0.12 0.2858 0.9707 0.9864 0.7487 0.8982 0.9658 0.6328 ] Network output: [ -0.005147 0.935 1.028 -5.31e-05 2.384e-05 0.04661 -4.002e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05038 0.03747 0.05429 0.04336 0.9846 0.9891 0.05162 0.9688 0.9795 0.06789 ] Network output: [ 0.08661 -0.2893 1.071 -0.0003861 0.0001733 1.044 -0.0002909 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7432 0.6375 0.5326 0.4678 0.9742 0.9884 0.7467 0.9089 0.9709 0.6287 ] Network output: [ -0.04304 0.1854 0.9533 0.0006679 -0.0002998 0.9501 0.0005033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6118 0.5979 0.4461 0.3203 0.9861 0.9909 0.6124 0.9727 0.9814 0.4585 ] Network output: [ -0.0665 0.2123 0.942 0.000131 -5.879e-05 0.9792 9.87e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.612 0.6097 0.4663 0.2925 0.9841 0.9897 0.6121 0.9666 0.9781 0.4686 ] Network output: [ 0.02098 0.9165 0.02462 -0.0002673 0.00012 1.016 -0.0002014 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03092 Epoch 2282 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03296 0.9724 0.9964 3.42e-05 -1.535e-05 -0.03455 2.577e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02328 -0.00565 0.01873 0.03147 0.9383 0.9479 0.04928 0.883 0.9017 0.1255 ] Network output: [ 0.9744 0.06812 -0.01629 -0.000122 5.477e-05 -0.00112 -9.195e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6502 0.121 0.12 0.2855 0.9707 0.9864 0.7487 0.8982 0.9658 0.6329 ] Network output: [ -0.005163 0.9351 1.028 -5.341e-05 2.398e-05 0.0466 -4.025e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05038 0.03746 0.05427 0.04331 0.9846 0.9891 0.05161 0.9688 0.9795 0.06786 ] Network output: [ 0.08652 -0.2892 1.071 -0.0003887 0.0001745 1.044 -0.0002929 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7431 0.6375 0.5327 0.4675 0.9742 0.9884 0.7466 0.9089 0.9709 0.6288 ] Network output: [ -0.04296 0.1853 0.9532 0.0006689 -0.0003003 0.9502 0.0005041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6119 0.598 0.4461 0.3201 0.9861 0.9909 0.6124 0.9727 0.9814 0.4585 ] Network output: [ -0.06643 0.2121 0.942 0.0001327 -5.956e-05 0.9793 9.999e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.612 0.6098 0.4663 0.2924 0.9841 0.9897 0.6121 0.9666 0.9781 0.4686 ] Network output: [ 0.02094 0.9166 0.0246 -0.0002674 0.00012 1.016 -0.0002015 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03088 Epoch 2283 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03293 0.9724 0.9964 3.381e-05 -1.518e-05 -0.03454 2.548e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02327 -0.00565 0.01873 0.03144 0.9383 0.9479 0.04927 0.883 0.9017 0.1255 ] Network output: [ 0.9744 0.06811 -0.0163 -0.0001213 5.446e-05 -0.001142 -9.141e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6502 0.1211 0.1201 0.2853 0.9708 0.9864 0.7486 0.8983 0.9659 0.6329 ] Network output: [ -0.005179 0.9351 1.028 -5.372e-05 2.412e-05 0.04659 -4.048e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05037 0.03745 0.05425 0.04326 0.9846 0.9891 0.0516 0.9688 0.9795 0.06784 ] Network output: [ 0.08643 -0.289 1.071 -0.0003913 0.0001757 1.044 -0.0002949 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7431 0.6374 0.5328 0.4671 0.9742 0.9884 0.7465 0.9089 0.9709 0.6288 ] Network output: [ -0.04288 0.1851 0.9531 0.0006699 -0.0003007 0.9502 0.0005049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.612 0.598 0.4462 0.3199 0.9861 0.9909 0.6125 0.9727 0.9814 0.4585 ] Network output: [ -0.06635 0.2119 0.9421 0.0001344 -6.033e-05 0.9793 0.0001013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6121 0.6098 0.4663 0.2923 0.9841 0.9897 0.6122 0.9666 0.9781 0.4686 ] Network output: [ 0.02089 0.9168 0.02458 -0.0002675 0.0001201 1.016 -0.0002016 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03084 Epoch 2284 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03291 0.9724 0.9964 3.342e-05 -1.5e-05 -0.03454 2.518e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02327 -0.005651 0.01872 0.0314 0.9383 0.9479 0.04925 0.8831 0.9018 0.1254 ] Network output: [ 0.9744 0.06809 -0.01631 -0.0001206 5.414e-05 -0.001164 -9.088e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6501 0.1211 0.1201 0.285 0.9708 0.9864 0.7485 0.8983 0.9659 0.633 ] Network output: [ -0.005195 0.9352 1.028 -5.403e-05 2.426e-05 0.04658 -4.072e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05036 0.03745 0.05424 0.04321 0.9846 0.9891 0.05159 0.9688 0.9795 0.06781 ] Network output: [ 0.08634 -0.2889 1.071 -0.000394 0.0001769 1.044 -0.0002969 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.743 0.6374 0.5328 0.4668 0.9742 0.9884 0.7465 0.9089 0.9709 0.6289 ] Network output: [ -0.04281 0.185 0.953 0.0006709 -0.0003012 0.9503 0.0005056 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.612 0.5981 0.4462 0.3198 0.9861 0.9909 0.6126 0.9727 0.9815 0.4586 ] Network output: [ -0.06628 0.2117 0.9421 0.0001361 -6.111e-05 0.9793 0.0001026 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6121 0.6098 0.4662 0.2921 0.9841 0.9897 0.6122 0.9666 0.9781 0.4686 ] Network output: [ 0.02085 0.9169 0.02456 -0.0002676 0.0001201 1.016 -0.0002017 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03079 Epoch 2285 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03288 0.9724 0.9965 3.303e-05 -1.483e-05 -0.03453 2.489e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02326 -0.005652 0.01871 0.03137 0.9383 0.948 0.04923 0.8831 0.9018 0.1254 ] Network output: [ 0.9745 0.06808 -0.01633 -0.0001199 5.382e-05 -0.001185 -9.035e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6501 0.1211 0.1202 0.2847 0.9708 0.9864 0.7485 0.8983 0.9659 0.633 ] Network output: [ -0.005211 0.9352 1.028 -5.434e-05 2.439e-05 0.04657 -4.095e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05035 0.03744 0.05422 0.04316 0.9846 0.9891 0.05158 0.9689 0.9795 0.06779 ] Network output: [ 0.08625 -0.2887 1.071 -0.0003967 0.0001781 1.044 -0.0002989 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7429 0.6374 0.5329 0.4665 0.9742 0.9884 0.7464 0.9089 0.9709 0.6289 ] Network output: [ -0.04273 0.1848 0.953 0.000672 -0.0003017 0.9504 0.0005064 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6121 0.5982 0.4462 0.3196 0.9861 0.9909 0.6126 0.9727 0.9815 0.4586 ] Network output: [ -0.06621 0.2115 0.9421 0.0001378 -6.189e-05 0.9794 0.0001039 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6121 0.6099 0.4662 0.292 0.9841 0.9897 0.6122 0.9666 0.9781 0.4686 ] Network output: [ 0.0208 0.9171 0.02454 -0.0002677 0.0001202 1.016 -0.0002018 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03075 Epoch 2286 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03285 0.9725 0.9965 3.264e-05 -1.465e-05 -0.03453 2.46e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02325 -0.005652 0.01871 0.03134 0.9383 0.948 0.04921 0.8831 0.9018 0.1254 ] Network output: [ 0.9745 0.06806 -0.01634 -0.0001192 5.351e-05 -0.001207 -8.983e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.65 0.1211 0.1202 0.2844 0.9708 0.9864 0.7484 0.8983 0.9659 0.6331 ] Network output: [ -0.005227 0.9353 1.028 -5.465e-05 2.453e-05 0.04655 -4.118e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05034 0.03744 0.0542 0.04311 0.9846 0.9891 0.05157 0.9689 0.9795 0.06776 ] Network output: [ 0.08617 -0.2885 1.071 -0.0003993 0.0001793 1.043 -0.0003009 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7429 0.6373 0.533 0.4662 0.9742 0.9884 0.7463 0.909 0.9709 0.629 ] Network output: [ -0.04266 0.1847 0.9529 0.000673 -0.0003021 0.9505 0.0005072 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6122 0.5982 0.4463 0.3194 0.9861 0.9909 0.6127 0.9727 0.9815 0.4586 ] Network output: [ -0.06613 0.2113 0.9421 0.0001396 -6.267e-05 0.9794 0.0001052 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6122 0.6099 0.4662 0.2919 0.9841 0.9897 0.6123 0.9666 0.9781 0.4686 ] Network output: [ 0.02076 0.9172 0.02452 -0.0002678 0.0001202 1.016 -0.0002018 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0307 Epoch 2287 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03282 0.9725 0.9965 3.225e-05 -1.448e-05 -0.03452 2.431e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02324 -0.005653 0.0187 0.03131 0.9383 0.948 0.0492 0.8831 0.9018 0.1253 ] Network output: [ 0.9745 0.06805 -0.01636 -0.0001185 5.32e-05 -0.001228 -8.93e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.65 0.1212 0.1203 0.2841 0.9708 0.9864 0.7483 0.8984 0.9659 0.6331 ] Network output: [ -0.005243 0.9353 1.028 -5.496e-05 2.467e-05 0.04654 -4.142e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05033 0.03743 0.05418 0.04306 0.9847 0.9891 0.05156 0.9689 0.9795 0.06774 ] Network output: [ 0.08608 -0.2884 1.071 -0.000402 0.0001805 1.043 -0.000303 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7428 0.6373 0.533 0.4658 0.9742 0.9884 0.7463 0.909 0.9709 0.629 ] Network output: [ -0.04258 0.1845 0.9528 0.000674 -0.0003026 0.9506 0.000508 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6122 0.5983 0.4463 0.3192 0.9861 0.9909 0.6128 0.9727 0.9815 0.4586 ] Network output: [ -0.06606 0.2111 0.9422 0.0001413 -6.345e-05 0.9795 0.0001065 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6122 0.61 0.4662 0.2918 0.9841 0.9897 0.6123 0.9666 0.9781 0.4686 ] Network output: [ 0.02072 0.9174 0.02451 -0.0002679 0.0001203 1.016 -0.0002019 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03066 Epoch 2288 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0328 0.9725 0.9966 3.187e-05 -1.431e-05 -0.03452 2.402e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02324 -0.005654 0.01869 0.03127 0.9383 0.948 0.04918 0.8832 0.9018 0.1253 ] Network output: [ 0.9746 0.06803 -0.01637 -0.0001178 5.289e-05 -0.001249 -8.878e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6499 0.1212 0.1203 0.2838 0.9708 0.9865 0.7483 0.8984 0.9659 0.6332 ] Network output: [ -0.00526 0.9354 1.028 -5.526e-05 2.481e-05 0.04653 -4.165e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05032 0.03742 0.05417 0.04301 0.9847 0.9891 0.05155 0.9689 0.9795 0.06772 ] Network output: [ 0.08599 -0.2882 1.071 -0.0004047 0.0001817 1.043 -0.000305 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7427 0.6373 0.5331 0.4655 0.9742 0.9884 0.7462 0.909 0.9709 0.6291 ] Network output: [ -0.04251 0.1844 0.9527 0.0006751 -0.0003031 0.9507 0.0005088 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6123 0.5984 0.4463 0.3191 0.9861 0.9909 0.6128 0.9727 0.9815 0.4587 ] Network output: [ -0.06598 0.2108 0.9422 0.0001431 -6.423e-05 0.9795 0.0001078 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6123 0.61 0.4662 0.2917 0.9841 0.9897 0.6124 0.9667 0.9781 0.4685 ] Network output: [ 0.02067 0.9175 0.02449 -0.0002681 0.0001203 1.016 -0.000202 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03061 Epoch 2289 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03277 0.9725 0.9966 3.148e-05 -1.413e-05 -0.03452 2.372e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02323 -0.005654 0.01869 0.03124 0.9383 0.948 0.04916 0.8832 0.9019 0.1253 ] Network output: [ 0.9746 0.06802 -0.01638 -0.0001171 5.258e-05 -0.00127 -8.826e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6499 0.1212 0.1203 0.2835 0.9708 0.9865 0.7482 0.8984 0.9659 0.6332 ] Network output: [ -0.005276 0.9354 1.028 -5.557e-05 2.495e-05 0.04652 -4.188e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05031 0.03742 0.05415 0.04296 0.9847 0.9891 0.05154 0.9689 0.9795 0.06769 ] Network output: [ 0.0859 -0.2881 1.071 -0.0004074 0.0001829 1.043 -0.000307 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7427 0.6373 0.5332 0.4652 0.9742 0.9884 0.7461 0.909 0.9709 0.6291 ] Network output: [ -0.04243 0.1842 0.9526 0.0006761 -0.0003035 0.9508 0.0005096 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6124 0.5984 0.4463 0.3189 0.9861 0.9909 0.6129 0.9727 0.9815 0.4587 ] Network output: [ -0.06591 0.2106 0.9422 0.0001448 -6.502e-05 0.9795 0.0001092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6123 0.6101 0.4662 0.2916 0.9841 0.9897 0.6124 0.9667 0.9782 0.4685 ] Network output: [ 0.02063 0.9177 0.02447 -0.0002682 0.0001204 1.015 -0.0002021 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03057 Epoch 2290 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03274 0.9725 0.9966 3.109e-05 -1.396e-05 -0.03451 2.343e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02322 -0.005655 0.01868 0.03121 0.9384 0.948 0.04915 0.8832 0.9019 0.1252 ] Network output: [ 0.9746 0.068 -0.0164 -0.0001164 5.227e-05 -0.001291 -8.774e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6498 0.1213 0.1204 0.2832 0.9708 0.9865 0.7481 0.8984 0.9659 0.6333 ] Network output: [ -0.005292 0.9355 1.028 -5.588e-05 2.509e-05 0.04651 -4.211e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0503 0.03741 0.05413 0.04291 0.9847 0.9891 0.05153 0.9689 0.9795 0.06767 ] Network output: [ 0.08581 -0.2879 1.071 -0.00041 0.0001841 1.043 -0.000309 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7426 0.6372 0.5333 0.4648 0.9742 0.9884 0.7461 0.9091 0.9709 0.6291 ] Network output: [ -0.04235 0.184 0.9525 0.0006772 -0.000304 0.9509 0.0005104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6125 0.5985 0.4464 0.3187 0.9861 0.9909 0.613 0.9727 0.9815 0.4587 ] Network output: [ -0.06583 0.2104 0.9423 0.0001466 -6.581e-05 0.9796 0.0001105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6124 0.6101 0.4662 0.2915 0.9841 0.9897 0.6125 0.9667 0.9782 0.4685 ] Network output: [ 0.02059 0.9178 0.02445 -0.0002683 0.0001204 1.015 -0.0002022 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03052 Epoch 2291 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03271 0.9725 0.9967 3.071e-05 -1.379e-05 -0.03451 2.314e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02321 -0.005655 0.01867 0.03117 0.9384 0.948 0.04913 0.8832 0.9019 0.1252 ] Network output: [ 0.9746 0.06799 -0.01641 -0.0001157 5.196e-05 -0.001312 -8.722e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6498 0.1213 0.1204 0.2829 0.9708 0.9865 0.7481 0.8984 0.9659 0.6333 ] Network output: [ -0.005308 0.9355 1.028 -5.619e-05 2.523e-05 0.0465 -4.235e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05029 0.0374 0.05412 0.04286 0.9847 0.9891 0.05152 0.9689 0.9795 0.06764 ] Network output: [ 0.08572 -0.2877 1.071 -0.0004127 0.0001853 1.043 -0.0003111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7425 0.6372 0.5333 0.4645 0.9742 0.9884 0.746 0.9091 0.9709 0.6292 ] Network output: [ -0.04228 0.1839 0.9525 0.0006783 -0.0003045 0.951 0.0005112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6125 0.5986 0.4464 0.3186 0.9861 0.9909 0.613 0.9727 0.9815 0.4587 ] Network output: [ -0.06576 0.2102 0.9423 0.0001484 -6.661e-05 0.9796 0.0001118 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6124 0.6102 0.4661 0.2913 0.9841 0.9897 0.6125 0.9667 0.9782 0.4685 ] Network output: [ 0.02054 0.918 0.02443 -0.0002684 0.0001205 1.015 -0.0002023 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03048 Epoch 2292 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03269 0.9726 0.9967 3.032e-05 -1.361e-05 -0.0345 2.285e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02321 -0.005656 0.01867 0.03114 0.9384 0.948 0.04911 0.8833 0.9019 0.1251 ] Network output: [ 0.9747 0.06797 -0.01642 -0.0001151 5.165e-05 -0.001333 -8.671e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6497 0.1213 0.1205 0.2827 0.9708 0.9865 0.748 0.8985 0.966 0.6334 ] Network output: [ -0.005325 0.9356 1.028 -5.65e-05 2.536e-05 0.04649 -4.258e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05028 0.0374 0.0541 0.04281 0.9847 0.9891 0.05151 0.9689 0.9795 0.06762 ] Network output: [ 0.08563 -0.2876 1.072 -0.0004154 0.0001865 1.043 -0.0003131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7425 0.6372 0.5334 0.4642 0.9742 0.9884 0.7459 0.9091 0.971 0.6292 ] Network output: [ -0.0422 0.1837 0.9524 0.0006793 -0.000305 0.9511 0.000512 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6126 0.5987 0.4464 0.3184 0.9861 0.9909 0.6131 0.9727 0.9815 0.4587 ] Network output: [ -0.06568 0.21 0.9423 0.0001501 -6.74e-05 0.9797 0.0001132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6124 0.6102 0.4661 0.2912 0.9841 0.9897 0.6125 0.9667 0.9782 0.4685 ] Network output: [ 0.0205 0.9182 0.02441 -0.0002686 0.0001206 1.015 -0.0002024 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03043 Epoch 2293 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03266 0.9726 0.9967 2.994e-05 -1.344e-05 -0.0345 2.256e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0232 -0.005656 0.01866 0.03111 0.9384 0.948 0.04909 0.8833 0.9019 0.1251 ] Network output: [ 0.9747 0.06796 -0.01644 -0.0001144 5.135e-05 -0.001353 -8.619e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6497 0.1213 0.1205 0.2824 0.9708 0.9865 0.7479 0.8985 0.966 0.6334 ] Network output: [ -0.005341 0.9356 1.028 -5.681e-05 2.55e-05 0.04648 -4.281e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05028 0.03739 0.05408 0.04276 0.9847 0.9892 0.0515 0.9689 0.9796 0.0676 ] Network output: [ 0.08554 -0.2874 1.072 -0.0004182 0.0001877 1.043 -0.0003151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7424 0.6371 0.5335 0.4638 0.9742 0.9884 0.7459 0.9091 0.971 0.6293 ] Network output: [ -0.04212 0.1836 0.9523 0.0006804 -0.0003055 0.9512 0.0005128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6127 0.5987 0.4464 0.3182 0.9861 0.9909 0.6132 0.9728 0.9815 0.4588 ] Network output: [ -0.06561 0.2098 0.9423 0.0001519 -6.82e-05 0.9797 0.0001145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6125 0.6102 0.4661 0.2911 0.9841 0.9897 0.6126 0.9667 0.9782 0.4685 ] Network output: [ 0.02046 0.9183 0.0244 -0.0002687 0.0001206 1.015 -0.0002025 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03039 Epoch 2294 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03263 0.9726 0.9968 2.955e-05 -1.327e-05 -0.0345 2.227e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02319 -0.005657 0.01866 0.03108 0.9384 0.948 0.04908 0.8833 0.902 0.1251 ] Network output: [ 0.9747 0.06794 -0.01645 -0.0001137 5.104e-05 -0.001374 -8.568e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6496 0.1214 0.1206 0.2821 0.9708 0.9865 0.7479 0.8985 0.966 0.6335 ] Network output: [ -0.005358 0.9357 1.028 -5.711e-05 2.564e-05 0.04646 -4.304e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05027 0.03739 0.05406 0.04271 0.9847 0.9892 0.05149 0.9689 0.9796 0.06757 ] Network output: [ 0.08545 -0.2873 1.072 -0.0004209 0.0001889 1.043 -0.0003172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7423 0.6371 0.5335 0.4635 0.9742 0.9884 0.7458 0.9091 0.971 0.6293 ] Network output: [ -0.04205 0.1834 0.9522 0.0006815 -0.0003059 0.9513 0.0005136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6127 0.5988 0.4465 0.318 0.9861 0.9909 0.6132 0.9728 0.9815 0.4588 ] Network output: [ -0.06553 0.2096 0.9424 0.0001537 -6.901e-05 0.9797 0.0001158 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6125 0.6103 0.4661 0.291 0.9841 0.9898 0.6126 0.9667 0.9782 0.4684 ] Network output: [ 0.02041 0.9185 0.02438 -0.0002688 0.0001207 1.015 -0.0002026 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03034 Epoch 2295 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0326 0.9726 0.9968 2.917e-05 -1.31e-05 -0.03449 2.198e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02319 -0.005658 0.01865 0.03104 0.9384 0.948 0.04906 0.8833 0.902 0.125 ] Network output: [ 0.9747 0.06793 -0.01646 -0.000113 5.074e-05 -0.001394 -8.517e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6496 0.1214 0.1206 0.2818 0.9708 0.9865 0.7478 0.8985 0.966 0.6335 ] Network output: [ -0.005374 0.9357 1.028 -5.742e-05 2.578e-05 0.04645 -4.327e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05026 0.03738 0.05405 0.04266 0.9847 0.9892 0.05148 0.969 0.9796 0.06755 ] Network output: [ 0.08536 -0.2871 1.072 -0.0004236 0.0001902 1.043 -0.0003192 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7423 0.6371 0.5336 0.4632 0.9743 0.9884 0.7457 0.9092 0.971 0.6294 ] Network output: [ -0.04197 0.1832 0.9521 0.0006826 -0.0003064 0.9513 0.0005144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6128 0.5989 0.4465 0.3179 0.9861 0.9909 0.6133 0.9728 0.9815 0.4588 ] Network output: [ -0.06545 0.2094 0.9424 0.0001555 -6.981e-05 0.9798 0.0001172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6126 0.6103 0.4661 0.2909 0.9841 0.9898 0.6127 0.9668 0.9782 0.4684 ] Network output: [ 0.02037 0.9186 0.02436 -0.000269 0.0001207 1.015 -0.0002027 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03029 Epoch 2296 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03257 0.9726 0.9968 2.879e-05 -1.292e-05 -0.03449 2.169e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02318 -0.005658 0.01864 0.03101 0.9384 0.9481 0.04904 0.8834 0.902 0.125 ] Network output: [ 0.9748 0.06791 -0.01647 -0.0001123 5.044e-05 -0.001414 -8.467e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6495 0.1214 0.1207 0.2815 0.9708 0.9865 0.7477 0.8986 0.966 0.6336 ] Network output: [ -0.005391 0.9358 1.028 -5.773e-05 2.592e-05 0.04644 -4.35e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05025 0.03737 0.05403 0.04261 0.9847 0.9892 0.05147 0.969 0.9796 0.06752 ] Network output: [ 0.08526 -0.2869 1.072 -0.0004263 0.0001914 1.043 -0.0003213 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7422 0.637 0.5337 0.4628 0.9743 0.9884 0.7457 0.9092 0.971 0.6294 ] Network output: [ -0.04189 0.1831 0.952 0.0006836 -0.0003069 0.9514 0.0005152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6129 0.5989 0.4465 0.3177 0.9861 0.9909 0.6134 0.9728 0.9815 0.4588 ] Network output: [ -0.06538 0.2091 0.9424 0.0001573 -7.062e-05 0.9798 0.0001186 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6126 0.6104 0.4661 0.2908 0.9841 0.9898 0.6127 0.9668 0.9782 0.4684 ] Network output: [ 0.02032 0.9188 0.02434 -0.0002691 0.0001208 1.015 -0.0002028 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03025 Epoch 2297 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03255 0.9727 0.9969 2.84e-05 -1.275e-05 -0.03449 2.141e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02317 -0.005659 0.01864 0.03098 0.9384 0.9481 0.04903 0.8834 0.902 0.1249 ] Network output: [ 0.9748 0.06789 -0.01649 -0.0001117 5.014e-05 -0.001435 -8.416e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6495 0.1214 0.1207 0.2812 0.9708 0.9865 0.7476 0.8986 0.966 0.6336 ] Network output: [ -0.005407 0.9358 1.028 -5.803e-05 2.605e-05 0.04643 -4.373e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05024 0.03737 0.05401 0.04256 0.9847 0.9892 0.05146 0.969 0.9796 0.0675 ] Network output: [ 0.08517 -0.2868 1.072 -0.0004291 0.0001926 1.043 -0.0003234 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7421 0.637 0.5337 0.4625 0.9743 0.9884 0.7456 0.9092 0.971 0.6295 ] Network output: [ -0.04182 0.1829 0.952 0.0006847 -0.0003074 0.9515 0.000516 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6129 0.599 0.4465 0.3175 0.9861 0.9909 0.6135 0.9728 0.9815 0.4589 ] Network output: [ -0.0653 0.2089 0.9424 0.0001591 -7.143e-05 0.9799 0.0001199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6127 0.6104 0.466 0.2906 0.9841 0.9898 0.6128 0.9668 0.9782 0.4684 ] Network output: [ 0.02028 0.9189 0.02432 -0.0002692 0.0001209 1.015 -0.0002029 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0302 Epoch 2298 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03252 0.9727 0.9969 2.802e-05 -1.258e-05 -0.03448 2.112e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02316 -0.005659 0.01863 0.03095 0.9384 0.9481 0.04901 0.8834 0.9021 0.1249 ] Network output: [ 0.9748 0.06788 -0.0165 -0.000111 4.984e-05 -0.001455 -8.366e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6494 0.1215 0.1208 0.2809 0.9709 0.9865 0.7476 0.8986 0.966 0.6337 ] Network output: [ -0.005424 0.9359 1.028 -5.834e-05 2.619e-05 0.04641 -4.397e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05023 0.03736 0.05399 0.0425 0.9847 0.9892 0.05145 0.969 0.9796 0.06748 ] Network output: [ 0.08508 -0.2866 1.072 -0.0004318 0.0001939 1.043 -0.0003254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.742 0.637 0.5338 0.4622 0.9743 0.9885 0.7455 0.9092 0.971 0.6295 ] Network output: [ -0.04174 0.1828 0.9519 0.0006858 -0.0003079 0.9516 0.0005169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.613 0.5991 0.4466 0.3173 0.9861 0.9909 0.6135 0.9728 0.9815 0.4589 ] Network output: [ -0.06523 0.2087 0.9425 0.0001609 -7.224e-05 0.9799 0.0001213 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6127 0.6105 0.466 0.2905 0.9841 0.9898 0.6128 0.9668 0.9782 0.4684 ] Network output: [ 0.02024 0.9191 0.02431 -0.0002694 0.0001209 1.015 -0.000203 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03016 Epoch 2299 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03249 0.9727 0.9969 2.764e-05 -1.241e-05 -0.03448 2.083e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02316 -0.00566 0.01862 0.03091 0.9385 0.9481 0.04899 0.8834 0.9021 0.1249 ] Network output: [ 0.9748 0.06786 -0.01651 -0.0001103 4.954e-05 -0.001475 -8.316e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6494 0.1215 0.1208 0.2806 0.9709 0.9865 0.7475 0.8986 0.966 0.6337 ] Network output: [ -0.005441 0.936 1.028 -5.864e-05 2.633e-05 0.0464 -4.42e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05022 0.03735 0.05398 0.04245 0.9847 0.9892 0.05144 0.969 0.9796 0.06745 ] Network output: [ 0.08499 -0.2864 1.072 -0.0004346 0.0001951 1.043 -0.0003275 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.742 0.6369 0.5339 0.4618 0.9743 0.9885 0.7454 0.9092 0.971 0.6296 ] Network output: [ -0.04166 0.1826 0.9518 0.0006869 -0.0003084 0.9517 0.0005177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6131 0.5992 0.4466 0.3172 0.9861 0.9909 0.6136 0.9728 0.9816 0.4589 ] Network output: [ -0.06515 0.2085 0.9425 0.0001627 -7.306e-05 0.98 0.0001226 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6128 0.6105 0.466 0.2904 0.9841 0.9898 0.6129 0.9668 0.9782 0.4684 ] Network output: [ 0.02019 0.9192 0.02429 -0.0002695 0.000121 1.015 -0.0002031 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03011 Epoch 2300 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03246 0.9727 0.997 2.726e-05 -1.224e-05 -0.03448 2.054e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02315 -0.00566 0.01862 0.03088 0.9385 0.9481 0.04897 0.8835 0.9021 0.1248 ] Network output: [ 0.9749 0.06784 -0.01653 -0.0001097 4.924e-05 -0.001494 -8.266e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6493 0.1215 0.1209 0.2803 0.9709 0.9865 0.7474 0.8987 0.966 0.6338 ] Network output: [ -0.005458 0.936 1.028 -5.895e-05 2.646e-05 0.04639 -4.443e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05021 0.03735 0.05396 0.0424 0.9847 0.9892 0.05143 0.969 0.9796 0.06743 ] Network output: [ 0.0849 -0.2863 1.072 -0.0004373 0.0001963 1.043 -0.0003296 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7419 0.6369 0.5339 0.4615 0.9743 0.9885 0.7454 0.9093 0.971 0.6296 ] Network output: [ -0.04158 0.1824 0.9517 0.000688 -0.0003089 0.9518 0.0005185 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6131 0.5992 0.4466 0.317 0.9861 0.9909 0.6137 0.9728 0.9816 0.4589 ] Network output: [ -0.06507 0.2083 0.9425 0.0001646 -7.388e-05 0.98 0.000124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6128 0.6106 0.466 0.2903 0.9842 0.9898 0.6129 0.9668 0.9783 0.4683 ] Network output: [ 0.02015 0.9194 0.02427 -0.0002697 0.0001211 1.015 -0.0002032 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03006 Epoch 2301 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03244 0.9727 0.997 2.688e-05 -1.207e-05 -0.03447 2.026e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02314 -0.005661 0.01861 0.03085 0.9385 0.9481 0.04896 0.8835 0.9021 0.1248 ] Network output: [ 0.9749 0.06782 -0.01654 -0.000109 4.895e-05 -0.001514 -8.217e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6493 0.1216 0.1209 0.28 0.9709 0.9865 0.7474 0.8987 0.9661 0.6338 ] Network output: [ -0.005475 0.9361 1.028 -5.925e-05 2.66e-05 0.04638 -4.466e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0502 0.03734 0.05394 0.04235 0.9847 0.9892 0.05142 0.969 0.9796 0.0674 ] Network output: [ 0.0848 -0.2861 1.072 -0.0004401 0.0001976 1.043 -0.0003317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7418 0.6368 0.534 0.4611 0.9743 0.9885 0.7453 0.9093 0.971 0.6297 ] Network output: [ -0.04151 0.1823 0.9516 0.0006891 -0.0003094 0.9519 0.0005194 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6132 0.5993 0.4467 0.3168 0.9861 0.9909 0.6137 0.9728 0.9816 0.459 ] Network output: [ -0.065 0.2081 0.9426 0.0001664 -7.47e-05 0.98 0.0001254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6129 0.6106 0.466 0.2902 0.9842 0.9898 0.613 0.9668 0.9783 0.4683 ] Network output: [ 0.02011 0.9195 0.02425 -0.0002698 0.0001211 1.015 -0.0002033 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03002 Epoch 2302 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03241 0.9727 0.997 2.65e-05 -1.19e-05 -0.03447 1.997e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02314 -0.005661 0.01861 0.03082 0.9385 0.9481 0.04894 0.8835 0.9021 0.1247 ] Network output: [ 0.9749 0.06781 -0.01655 -0.0001084 4.865e-05 -0.001534 -8.167e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6492 0.1216 0.1209 0.2797 0.9709 0.9865 0.7473 0.8987 0.9661 0.6339 ] Network output: [ -0.005491 0.9361 1.028 -5.956e-05 2.674e-05 0.04636 -4.489e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05019 0.03733 0.05392 0.0423 0.9847 0.9892 0.05141 0.969 0.9796 0.06738 ] Network output: [ 0.08471 -0.2859 1.072 -0.0004429 0.0001988 1.043 -0.0003338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7418 0.6368 0.5341 0.4608 0.9743 0.9885 0.7452 0.9093 0.971 0.6297 ] Network output: [ -0.04143 0.1821 0.9515 0.0006903 -0.0003099 0.952 0.0005202 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6133 0.5994 0.4467 0.3166 0.9861 0.9909 0.6138 0.9728 0.9816 0.459 ] Network output: [ -0.06492 0.2078 0.9426 0.0001682 -7.553e-05 0.9801 0.0001268 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6129 0.6107 0.466 0.29 0.9842 0.9898 0.613 0.9669 0.9783 0.4683 ] Network output: [ 0.02006 0.9197 0.02424 -0.00027 0.0001212 1.015 -0.0002035 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02997 Epoch 2303 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03238 0.9728 0.997 2.612e-05 -1.173e-05 -0.03447 1.968e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02313 -0.005661 0.0186 0.03078 0.9385 0.9481 0.04892 0.8835 0.9022 0.1247 ] Network output: [ 0.9749 0.06779 -0.01656 -0.0001077 4.836e-05 -0.001553 -8.118e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6492 0.1216 0.121 0.2794 0.9709 0.9865 0.7472 0.8987 0.9661 0.6339 ] Network output: [ -0.005508 0.9362 1.028 -5.986e-05 2.687e-05 0.04635 -4.511e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05018 0.03733 0.05391 0.04225 0.9847 0.9892 0.0514 0.969 0.9796 0.06736 ] Network output: [ 0.08462 -0.2857 1.072 -0.0004456 0.0002001 1.042 -0.0003359 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7417 0.6368 0.5341 0.4604 0.9743 0.9885 0.7451 0.9093 0.971 0.6298 ] Network output: [ -0.04135 0.1819 0.9515 0.0006914 -0.0003104 0.9521 0.000521 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6134 0.5994 0.4467 0.3165 0.9862 0.9909 0.6139 0.9728 0.9816 0.459 ] Network output: [ -0.06484 0.2076 0.9426 0.0001701 -7.635e-05 0.9801 0.0001282 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.613 0.6107 0.466 0.2899 0.9842 0.9898 0.6131 0.9669 0.9783 0.4683 ] Network output: [ 0.02002 0.9198 0.02422 -0.0002701 0.0001213 1.015 -0.0002036 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02992 Epoch 2304 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03235 0.9728 0.9971 2.574e-05 -1.156e-05 -0.03446 1.94e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02312 -0.005662 0.01859 0.03075 0.9385 0.9481 0.04891 0.8836 0.9022 0.1247 ] Network output: [ 0.975 0.06777 -0.01658 -0.0001071 4.807e-05 -0.001572 -8.069e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6491 0.1216 0.121 0.2791 0.9709 0.9865 0.7471 0.8987 0.9661 0.634 ] Network output: [ -0.005525 0.9362 1.028 -6.017e-05 2.701e-05 0.04634 -4.534e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05017 0.03732 0.05389 0.0422 0.9847 0.9892 0.05139 0.969 0.9796 0.06733 ] Network output: [ 0.08452 -0.2856 1.072 -0.0004484 0.0002013 1.042 -0.000338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7416 0.6367 0.5342 0.4601 0.9743 0.9885 0.7451 0.9093 0.9711 0.6298 ] Network output: [ -0.04127 0.1818 0.9514 0.0006925 -0.0003109 0.9522 0.0005219 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6134 0.5995 0.4467 0.3163 0.9862 0.9909 0.6139 0.9728 0.9816 0.459 ] Network output: [ -0.06476 0.2074 0.9426 0.0001719 -7.719e-05 0.9802 0.0001296 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.613 0.6108 0.4659 0.2898 0.9842 0.9898 0.6131 0.9669 0.9783 0.4683 ] Network output: [ 0.01998 0.92 0.0242 -0.0002703 0.0001213 1.015 -0.0002037 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02988 Epoch 2305 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03232 0.9728 0.9971 2.536e-05 -1.139e-05 -0.03446 1.911e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02311 -0.005662 0.01859 0.03072 0.9385 0.9481 0.04889 0.8836 0.9022 0.1246 ] Network output: [ 0.975 0.06775 -0.01659 -0.0001064 4.778e-05 -0.001592 -8.02e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6491 0.1216 0.1211 0.2788 0.9709 0.9865 0.7471 0.8988 0.9661 0.634 ] Network output: [ -0.005542 0.9363 1.028 -6.047e-05 2.715e-05 0.04632 -4.557e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05016 0.03731 0.05387 0.04215 0.9847 0.9892 0.05138 0.9691 0.9796 0.06731 ] Network output: [ 0.08443 -0.2854 1.072 -0.0004512 0.0002026 1.042 -0.0003401 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7415 0.6367 0.5343 0.4598 0.9743 0.9885 0.745 0.9094 0.9711 0.6299 ] Network output: [ -0.04119 0.1816 0.9513 0.0006936 -0.0003114 0.9523 0.0005227 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6135 0.5996 0.4468 0.3161 0.9862 0.9909 0.614 0.9729 0.9816 0.459 ] Network output: [ -0.06469 0.2072 0.9427 0.0001738 -7.802e-05 0.9802 0.000131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6131 0.6108 0.4659 0.2897 0.9842 0.9898 0.6132 0.9669 0.9783 0.4683 ] Network output: [ 0.01993 0.9201 0.02418 -0.0002704 0.0001214 1.015 -0.0002038 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02983 Epoch 2306 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03229 0.9728 0.9971 2.498e-05 -1.122e-05 -0.03446 1.883e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02311 -0.005663 0.01858 0.03069 0.9385 0.9481 0.04887 0.8836 0.9022 0.1246 ] Network output: [ 0.975 0.06773 -0.0166 -0.0001058 4.749e-05 -0.001611 -7.971e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.649 0.1217 0.1211 0.2785 0.9709 0.9865 0.747 0.8988 0.9661 0.6341 ] Network output: [ -0.00556 0.9363 1.028 -6.077e-05 2.728e-05 0.04631 -4.58e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05015 0.03731 0.05386 0.0421 0.9847 0.9892 0.05137 0.9691 0.9796 0.06728 ] Network output: [ 0.08434 -0.2852 1.072 -0.000454 0.0002038 1.042 -0.0003422 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7415 0.6367 0.5343 0.4594 0.9743 0.9885 0.7449 0.9094 0.9711 0.6299 ] Network output: [ -0.04112 0.1814 0.9512 0.0006948 -0.0003119 0.9524 0.0005236 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6136 0.5997 0.4468 0.3159 0.9862 0.991 0.6141 0.9729 0.9816 0.4591 ] Network output: [ -0.06461 0.207 0.9427 0.0001756 -7.885e-05 0.9803 0.0001324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6131 0.6109 0.4659 0.2895 0.9842 0.9898 0.6132 0.9669 0.9783 0.4683 ] Network output: [ 0.01989 0.9203 0.02417 -0.0002706 0.0001215 1.015 -0.0002039 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02978 Epoch 2307 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03227 0.9729 0.9972 2.461e-05 -1.105e-05 -0.03445 1.854e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0231 -0.005663 0.01857 0.03065 0.9385 0.9482 0.04885 0.8836 0.9022 0.1246 ] Network output: [ 0.9751 0.06771 -0.01661 -0.0001051 4.72e-05 -0.00163 -7.923e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.649 0.1217 0.1212 0.2782 0.9709 0.9865 0.7469 0.8988 0.9661 0.6342 ] Network output: [ -0.005577 0.9364 1.028 -6.108e-05 2.742e-05 0.04629 -4.603e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05014 0.0373 0.05384 0.04205 0.9847 0.9892 0.05136 0.9691 0.9796 0.06726 ] Network output: [ 0.08424 -0.285 1.072 -0.0004568 0.0002051 1.042 -0.0003443 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7414 0.6366 0.5344 0.4591 0.9743 0.9885 0.7449 0.9094 0.9711 0.63 ] Network output: [ -0.04104 0.1813 0.9511 0.0006959 -0.0003124 0.9525 0.0005245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6136 0.5997 0.4468 0.3157 0.9862 0.991 0.6142 0.9729 0.9816 0.4591 ] Network output: [ -0.06453 0.2067 0.9427 0.0001775 -7.969e-05 0.9803 0.0001338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6132 0.6109 0.4659 0.2894 0.9842 0.9898 0.6133 0.9669 0.9783 0.4682 ] Network output: [ 0.01985 0.9204 0.02415 -0.0002708 0.0001216 1.015 -0.0002041 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02973 Epoch 2308 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03224 0.9729 0.9972 2.423e-05 -1.088e-05 -0.03445 1.826e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02309 -0.005664 0.01857 0.03062 0.9385 0.9482 0.04884 0.8837 0.9022 0.1245 ] Network output: [ 0.9751 0.06769 -0.01662 -0.0001045 4.691e-05 -0.001649 -7.875e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6489 0.1217 0.1212 0.2779 0.9709 0.9865 0.7469 0.8988 0.9661 0.6342 ] Network output: [ -0.005594 0.9364 1.028 -6.138e-05 2.756e-05 0.04628 -4.626e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05013 0.03729 0.05382 0.042 0.9847 0.9892 0.05135 0.9691 0.9797 0.06724 ] Network output: [ 0.08415 -0.2849 1.073 -0.0004596 0.0002063 1.042 -0.0003464 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7413 0.6366 0.5345 0.4587 0.9743 0.9885 0.7448 0.9094 0.9711 0.63 ] Network output: [ -0.04096 0.1811 0.951 0.000697 -0.0003129 0.9526 0.0005253 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6137 0.5998 0.4468 0.3156 0.9862 0.991 0.6142 0.9729 0.9816 0.4591 ] Network output: [ -0.06445 0.2065 0.9427 0.0001794 -8.053e-05 0.9804 0.0001352 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6132 0.611 0.4659 0.2893 0.9842 0.9898 0.6133 0.9669 0.9783 0.4682 ] Network output: [ 0.01981 0.9206 0.02413 -0.0002709 0.0001216 1.015 -0.0002042 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02969 Epoch 2309 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03221 0.9729 0.9972 2.385e-05 -1.071e-05 -0.03445 1.798e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02309 -0.005664 0.01856 0.03059 0.9386 0.9482 0.04882 0.8837 0.9023 0.1245 ] Network output: [ 0.9751 0.06767 -0.01664 -0.0001039 4.662e-05 -0.001668 -7.827e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6489 0.1217 0.1213 0.2776 0.9709 0.9865 0.7468 0.8989 0.9661 0.6343 ] Network output: [ -0.005611 0.9365 1.028 -6.168e-05 2.769e-05 0.04627 -4.648e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05012 0.03729 0.0538 0.04195 0.9847 0.9892 0.05134 0.9691 0.9797 0.06721 ] Network output: [ 0.08405 -0.2847 1.073 -0.0004625 0.0002076 1.042 -0.0003485 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7413 0.6365 0.5345 0.4584 0.9743 0.9885 0.7447 0.9095 0.9711 0.6301 ] Network output: [ -0.04088 0.1809 0.951 0.0006982 -0.0003134 0.9527 0.0005262 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6138 0.5999 0.4469 0.3154 0.9862 0.991 0.6143 0.9729 0.9816 0.4591 ] Network output: [ -0.06437 0.2063 0.9428 0.0001813 -8.138e-05 0.9804 0.0001366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6133 0.611 0.4659 0.2892 0.9842 0.9898 0.6134 0.9669 0.9783 0.4682 ] Network output: [ 0.01976 0.9207 0.02411 -0.0002711 0.0001217 1.015 -0.0002043 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02964 Epoch 2310 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03218 0.9729 0.9973 2.348e-05 -1.054e-05 -0.03445 1.769e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02308 -0.005664 0.01856 0.03055 0.9386 0.9482 0.0488 0.8837 0.9023 0.1244 ] Network output: [ 0.9751 0.06765 -0.01665 -0.0001032 4.634e-05 -0.001686 -7.779e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6488 0.1218 0.1213 0.2773 0.9709 0.9865 0.7467 0.8989 0.9661 0.6343 ] Network output: [ -0.005628 0.9366 1.028 -6.198e-05 2.783e-05 0.04625 -4.671e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05012 0.03728 0.05379 0.0419 0.9848 0.9892 0.05133 0.9691 0.9797 0.06719 ] Network output: [ 0.08396 -0.2845 1.073 -0.0004653 0.0002089 1.042 -0.0003507 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7412 0.6365 0.5346 0.458 0.9743 0.9885 0.7446 0.9095 0.9711 0.6301 ] Network output: [ -0.0408 0.1808 0.9509 0.0006993 -0.000314 0.9528 0.000527 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6138 0.5999 0.4469 0.3152 0.9862 0.991 0.6144 0.9729 0.9816 0.4592 ] Network output: [ -0.0643 0.2061 0.9428 0.0001832 -8.222e-05 0.9805 0.000138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6133 0.6111 0.4659 0.289 0.9842 0.9898 0.6134 0.967 0.9783 0.4682 ] Network output: [ 0.01972 0.9209 0.0241 -0.0002713 0.0001218 1.014 -0.0002044 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02959 Epoch 2311 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03215 0.9729 0.9973 2.31e-05 -1.037e-05 -0.03444 1.741e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02307 -0.005665 0.01855 0.03052 0.9386 0.9482 0.04879 0.8837 0.9023 0.1244 ] Network output: [ 0.9752 0.06763 -0.01666 -0.0001026 4.606e-05 -0.001705 -7.732e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6488 0.1218 0.1213 0.277 0.9709 0.9865 0.7466 0.8989 0.9662 0.6344 ] Network output: [ -0.005646 0.9366 1.028 -6.228e-05 2.796e-05 0.04624 -4.694e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05011 0.03727 0.05377 0.04184 0.9848 0.9892 0.05132 0.9691 0.9797 0.06717 ] Network output: [ 0.08386 -0.2843 1.073 -0.0004681 0.0002102 1.042 -0.0003528 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7411 0.6365 0.5347 0.4577 0.9743 0.9885 0.7446 0.9095 0.9711 0.6302 ] Network output: [ -0.04072 0.1806 0.9508 0.0007005 -0.0003145 0.9529 0.0005279 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6139 0.6 0.4469 0.315 0.9862 0.991 0.6144 0.9729 0.9816 0.4592 ] Network output: [ -0.06422 0.2059 0.9428 0.000185 -8.307e-05 0.9805 0.0001395 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6134 0.6111 0.4658 0.2889 0.9842 0.9898 0.6135 0.967 0.9784 0.4682 ] Network output: [ 0.01968 0.921 0.02408 -0.0002714 0.0001219 1.014 -0.0002046 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02954 Epoch 2312 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03212 0.973 0.9973 2.273e-05 -1.02e-05 -0.03444 1.713e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02307 -0.005665 0.01854 0.03049 0.9386 0.9482 0.04877 0.8837 0.9023 0.1244 ] Network output: [ 0.9752 0.06761 -0.01667 -0.000102 4.578e-05 -0.001723 -7.684e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6487 0.1218 0.1214 0.2767 0.9709 0.9865 0.7466 0.8989 0.9662 0.6344 ] Network output: [ -0.005663 0.9367 1.028 -6.258e-05 2.81e-05 0.04622 -4.717e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0501 0.03727 0.05375 0.04179 0.9848 0.9892 0.05131 0.9691 0.9797 0.06714 ] Network output: [ 0.08377 -0.2841 1.073 -0.000471 0.0002114 1.042 -0.0003549 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.741 0.6364 0.5348 0.4573 0.9743 0.9885 0.7445 0.9095 0.9711 0.6302 ] Network output: [ -0.04065 0.1804 0.9507 0.0007017 -0.000315 0.953 0.0005288 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.614 0.6001 0.4469 0.3148 0.9862 0.991 0.6145 0.9729 0.9816 0.4592 ] Network output: [ -0.06414 0.2056 0.9428 0.0001869 -8.393e-05 0.9806 0.0001409 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6134 0.6112 0.4658 0.2888 0.9842 0.9898 0.6135 0.967 0.9784 0.4682 ] Network output: [ 0.01963 0.9212 0.02406 -0.0002716 0.0001219 1.014 -0.0002047 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0295 Epoch 2313 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0321 0.973 0.9974 2.235e-05 -1.004e-05 -0.03444 1.685e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02306 -0.005665 0.01854 0.03046 0.9386 0.9482 0.04875 0.8838 0.9023 0.1243 ] Network output: [ 0.9752 0.06759 -0.01668 -0.0001013 4.55e-05 -0.001741 -7.637e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6487 0.1218 0.1214 0.2764 0.9709 0.9865 0.7465 0.8989 0.9662 0.6345 ] Network output: [ -0.005681 0.9367 1.028 -6.288e-05 2.823e-05 0.04621 -4.739e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05009 0.03726 0.05374 0.04174 0.9848 0.9892 0.0513 0.9691 0.9797 0.06712 ] Network output: [ 0.08367 -0.2839 1.073 -0.0004738 0.0002127 1.042 -0.0003571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.741 0.6364 0.5348 0.4569 0.9743 0.9885 0.7444 0.9095 0.9711 0.6303 ] Network output: [ -0.04057 0.1803 0.9506 0.0007028 -0.0003155 0.9531 0.0005297 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6141 0.6002 0.447 0.3146 0.9862 0.991 0.6146 0.9729 0.9816 0.4592 ] Network output: [ -0.06406 0.2054 0.9429 0.0001888 -8.478e-05 0.9806 0.0001423 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6135 0.6112 0.4658 0.2887 0.9842 0.9898 0.6136 0.967 0.9784 0.4681 ] Network output: [ 0.01959 0.9213 0.02405 -0.0002718 0.000122 1.014 -0.0002048 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02945 Epoch 2314 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03207 0.973 0.9974 2.198e-05 -9.868e-06 -0.03444 1.657e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02305 -0.005666 0.01853 0.03042 0.9386 0.9482 0.04873 0.8838 0.9024 0.1243 ] Network output: [ 0.9752 0.06757 -0.01669 -0.0001007 4.522e-05 -0.00176 -7.59e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6486 0.1219 0.1215 0.2761 0.971 0.9865 0.7464 0.899 0.9662 0.6345 ] Network output: [ -0.005698 0.9368 1.028 -6.318e-05 2.837e-05 0.04619 -4.762e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05008 0.03725 0.05372 0.04169 0.9848 0.9892 0.05129 0.9691 0.9797 0.0671 ] Network output: [ 0.08358 -0.2838 1.073 -0.0004767 0.000214 1.042 -0.0003592 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7409 0.6363 0.5349 0.4566 0.9744 0.9885 0.7443 0.9095 0.9711 0.6303 ] Network output: [ -0.04049 0.1801 0.9505 0.000704 -0.000316 0.9532 0.0005306 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6141 0.6002 0.447 0.3145 0.9862 0.991 0.6147 0.9729 0.9816 0.4593 ] Network output: [ -0.06398 0.2052 0.9429 0.0001908 -8.564e-05 0.9807 0.0001438 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6135 0.6113 0.4658 0.2885 0.9842 0.9898 0.6136 0.967 0.9784 0.4681 ] Network output: [ 0.01955 0.9215 0.02403 -0.0002719 0.0001221 1.014 -0.000205 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0294 Epoch 2315 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03204 0.973 0.9974 2.161e-05 -9.7e-06 -0.03444 1.628e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02304 -0.005666 0.01853 0.03039 0.9386 0.9482 0.04872 0.8838 0.9024 0.1242 ] Network output: [ 0.9753 0.06755 -0.0167 -0.0001001 4.494e-05 -0.001778 -7.544e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6486 0.1219 0.1215 0.2758 0.971 0.9865 0.7463 0.899 0.9662 0.6346 ] Network output: [ -0.005716 0.9368 1.028 -6.348e-05 2.85e-05 0.04618 -4.784e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05007 0.03725 0.0537 0.04164 0.9848 0.9892 0.05128 0.9692 0.9797 0.06707 ] Network output: [ 0.08348 -0.2836 1.073 -0.0004795 0.0002153 1.042 -0.0003614 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7408 0.6363 0.535 0.4562 0.9744 0.9885 0.7443 0.9096 0.9711 0.6304 ] Network output: [ -0.04041 0.1799 0.9505 0.0007052 -0.0003166 0.9533 0.0005314 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6142 0.6003 0.447 0.3143 0.9862 0.991 0.6147 0.9729 0.9816 0.4593 ] Network output: [ -0.0639 0.205 0.9429 0.0001927 -8.65e-05 0.9807 0.0001452 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6136 0.6113 0.4658 0.2884 0.9842 0.9898 0.6137 0.967 0.9784 0.4681 ] Network output: [ 0.0195 0.9216 0.02401 -0.0002721 0.0001222 1.014 -0.0002051 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02935 Epoch 2316 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03201 0.9731 0.9974 2.124e-05 -9.533e-06 -0.03443 1.6e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02304 -0.005666 0.01852 0.03036 0.9386 0.9482 0.0487 0.8838 0.9024 0.1242 ] Network output: [ 0.9753 0.06753 -0.01672 -9.948e-05 4.466e-05 -0.001796 -7.497e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6485 0.1219 0.1216 0.2755 0.971 0.9866 0.7463 0.899 0.9662 0.6346 ] Network output: [ -0.005733 0.9369 1.028 -6.378e-05 2.863e-05 0.04616 -4.807e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05006 0.03724 0.05369 0.04159 0.9848 0.9892 0.05127 0.9692 0.9797 0.06705 ] Network output: [ 0.08339 -0.2834 1.073 -0.0004824 0.0002166 1.042 -0.0003635 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7407 0.6363 0.535 0.4559 0.9744 0.9885 0.7442 0.9096 0.9712 0.6305 ] Network output: [ -0.04033 0.1797 0.9504 0.0007063 -0.0003171 0.9534 0.0005323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6143 0.6004 0.4471 0.3141 0.9862 0.991 0.6148 0.9729 0.9817 0.4593 ] Network output: [ -0.06382 0.2047 0.9429 0.0001946 -8.736e-05 0.9808 0.0001467 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6136 0.6114 0.4658 0.2883 0.9842 0.9898 0.6137 0.967 0.9784 0.4681 ] Network output: [ 0.01946 0.9218 0.024 -0.0002723 0.0001222 1.014 -0.0002052 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0293 Epoch 2317 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03198 0.9731 0.9975 2.086e-05 -9.366e-06 -0.03443 1.572e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02303 -0.005667 0.01851 0.03033 0.9386 0.9482 0.04868 0.8839 0.9024 0.1242 ] Network output: [ 0.9753 0.06751 -0.01673 -9.887e-05 4.439e-05 -0.001814 -7.451e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6485 0.1219 0.1216 0.2752 0.971 0.9866 0.7462 0.899 0.9662 0.6347 ] Network output: [ -0.005751 0.937 1.028 -6.408e-05 2.877e-05 0.04615 -4.829e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05005 0.03723 0.05367 0.04154 0.9848 0.9892 0.05126 0.9692 0.9797 0.06703 ] Network output: [ 0.08329 -0.2832 1.073 -0.0004853 0.0002179 1.042 -0.0003657 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7407 0.6362 0.5351 0.4555 0.9744 0.9885 0.7441 0.9096 0.9712 0.6305 ] Network output: [ -0.04025 0.1796 0.9503 0.0007075 -0.0003176 0.9535 0.0005332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6144 0.6005 0.4471 0.3139 0.9862 0.991 0.6149 0.9729 0.9817 0.4593 ] Network output: [ -0.06374 0.2045 0.943 0.0001965 -8.823e-05 0.9808 0.0001481 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6137 0.6114 0.4657 0.2881 0.9842 0.9898 0.6138 0.9671 0.9784 0.4681 ] Network output: [ 0.01942 0.922 0.02398 -0.0002725 0.0001223 1.014 -0.0002054 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02925 Epoch 2318 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03195 0.9731 0.9975 2.049e-05 -9.2e-06 -0.03443 1.544e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02302 -0.005667 0.01851 0.03029 0.9386 0.9483 0.04867 0.8839 0.9024 0.1241 ] Network output: [ 0.9753 0.06748 -0.01674 -9.826e-05 4.411e-05 -0.001831 -7.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6484 0.122 0.1216 0.2749 0.971 0.9866 0.7461 0.8991 0.9662 0.6347 ] Network output: [ -0.005769 0.937 1.028 -6.438e-05 2.89e-05 0.04613 -4.852e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05004 0.03722 0.05365 0.04149 0.9848 0.9892 0.05125 0.9692 0.9797 0.067 ] Network output: [ 0.08319 -0.283 1.073 -0.0004881 0.0002191 1.041 -0.0003679 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7406 0.6362 0.5352 0.4552 0.9744 0.9885 0.744 0.9096 0.9712 0.6306 ] Network output: [ -0.04017 0.1794 0.9502 0.0007087 -0.0003182 0.9536 0.0005341 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6144 0.6005 0.4471 0.3137 0.9862 0.991 0.6149 0.9729 0.9817 0.4594 ] Network output: [ -0.06366 0.2043 0.943 0.0001985 -8.909e-05 0.9809 0.0001496 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6137 0.6115 0.4657 0.288 0.9842 0.9898 0.6138 0.9671 0.9784 0.4681 ] Network output: [ 0.01937 0.9221 0.02396 -0.0002727 0.0001224 1.014 -0.0002055 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0292 Epoch 2319 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03193 0.9731 0.9975 2.012e-05 -9.033e-06 -0.03443 1.516e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02302 -0.005667 0.0185 0.03026 0.9387 0.9483 0.04865 0.8839 0.9025 0.1241 ] Network output: [ 0.9754 0.06746 -0.01675 -9.765e-05 4.384e-05 -0.001849 -7.359e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6484 0.122 0.1217 0.2746 0.971 0.9866 0.746 0.8991 0.9662 0.6348 ] Network output: [ -0.005787 0.9371 1.028 -6.468e-05 2.904e-05 0.04612 -4.874e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05003 0.03722 0.05363 0.04144 0.9848 0.9892 0.05124 0.9692 0.9797 0.06698 ] Network output: [ 0.08309 -0.2828 1.073 -0.000491 0.0002204 1.041 -0.0003701 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7405 0.6361 0.5352 0.4548 0.9744 0.9885 0.744 0.9096 0.9712 0.6306 ] Network output: [ -0.04009 0.1792 0.9501 0.0007099 -0.0003187 0.9537 0.000535 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6145 0.6006 0.4471 0.3135 0.9862 0.991 0.615 0.973 0.9817 0.4594 ] Network output: [ -0.06358 0.204 0.943 0.0002004 -8.996e-05 0.9809 0.000151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6138 0.6115 0.4657 0.2879 0.9842 0.9898 0.6139 0.9671 0.9784 0.468 ] Network output: [ 0.01933 0.9223 0.02395 -0.0002729 0.0001225 1.014 -0.0002056 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02916 Epoch 2320 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0319 0.9731 0.9976 1.975e-05 -8.867e-06 -0.03443 1.489e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02301 -0.005667 0.0185 0.03023 0.9387 0.9483 0.04863 0.8839 0.9025 0.1241 ] Network output: [ 0.9754 0.06744 -0.01676 -9.705e-05 4.357e-05 -0.001866 -7.314e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6483 0.122 0.1217 0.2743 0.971 0.9866 0.7459 0.8991 0.9662 0.6349 ] Network output: [ -0.005805 0.9371 1.028 -6.497e-05 2.917e-05 0.0461 -4.897e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05002 0.03721 0.05362 0.04139 0.9848 0.9892 0.05123 0.9692 0.9797 0.06696 ] Network output: [ 0.083 -0.2826 1.073 -0.0004939 0.0002217 1.041 -0.0003722 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7404 0.6361 0.5353 0.4544 0.9744 0.9885 0.7439 0.9097 0.9712 0.6307 ] Network output: [ -0.04001 0.179 0.95 0.0007111 -0.0003192 0.9538 0.0005359 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6146 0.6007 0.4472 0.3133 0.9862 0.991 0.6151 0.973 0.9817 0.4594 ] Network output: [ -0.0635 0.2038 0.943 0.0002023 -9.084e-05 0.981 0.0001525 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6138 0.6116 0.4657 0.2877 0.9842 0.9898 0.6139 0.9671 0.9784 0.468 ] Network output: [ 0.01929 0.9224 0.02393 -0.000273 0.0001226 1.014 -0.0002058 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02911 Epoch 2321 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03187 0.9732 0.9976 1.938e-05 -8.701e-06 -0.03442 1.461e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.023 -0.005668 0.01849 0.03019 0.9387 0.9483 0.04861 0.884 0.9025 0.124 ] Network output: [ 0.9754 0.06742 -0.01677 -9.645e-05 4.33e-05 -0.001884 -7.269e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6482 0.122 0.1218 0.274 0.971 0.9866 0.7459 0.8991 0.9663 0.6349 ] Network output: [ -0.005822 0.9372 1.028 -6.527e-05 2.93e-05 0.04609 -4.919e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05001 0.0372 0.0536 0.04133 0.9848 0.9892 0.05122 0.9692 0.9797 0.06694 ] Network output: [ 0.0829 -0.2824 1.073 -0.0004968 0.000223 1.041 -0.0003744 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7404 0.636 0.5354 0.4541 0.9744 0.9885 0.7438 0.9097 0.9712 0.6307 ] Network output: [ -0.03993 0.1789 0.95 0.0007123 -0.0003198 0.9539 0.0005368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6147 0.6008 0.4472 0.3131 0.9862 0.991 0.6152 0.973 0.9817 0.4594 ] Network output: [ -0.06342 0.2036 0.943 0.0002043 -9.171e-05 0.981 0.000154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6139 0.6117 0.4657 0.2876 0.9843 0.9898 0.614 0.9671 0.9784 0.468 ] Network output: [ 0.01925 0.9226 0.02391 -0.0002732 0.0001227 1.014 -0.0002059 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02906 Epoch 2322 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03184 0.9732 0.9976 1.901e-05 -8.536e-06 -0.03442 1.433e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.023 -0.005668 0.01849 0.03016 0.9387 0.9483 0.0486 0.884 0.9025 0.124 ] Network output: [ 0.9754 0.06739 -0.01678 -9.585e-05 4.303e-05 -0.001901 -7.223e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6482 0.122 0.1218 0.2737 0.971 0.9866 0.7458 0.8991 0.9663 0.635 ] Network output: [ -0.00584 0.9372 1.028 -6.556e-05 2.943e-05 0.04607 -4.941e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.05 0.0372 0.05358 0.04128 0.9848 0.9892 0.05121 0.9692 0.9797 0.06691 ] Network output: [ 0.0828 -0.2822 1.073 -0.0004997 0.0002243 1.041 -0.0003766 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7403 0.636 0.5354 0.4537 0.9744 0.9885 0.7437 0.9097 0.9712 0.6308 ] Network output: [ -0.03985 0.1787 0.9499 0.0007135 -0.0003203 0.954 0.0005377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6147 0.6008 0.4472 0.313 0.9862 0.991 0.6152 0.973 0.9817 0.4595 ] Network output: [ -0.06334 0.2034 0.9431 0.0002062 -9.259e-05 0.9811 0.0001554 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6139 0.6117 0.4657 0.2875 0.9843 0.9898 0.614 0.9671 0.9785 0.468 ] Network output: [ 0.0192 0.9227 0.0239 -0.0002734 0.0001227 1.014 -0.0002061 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02901 Epoch 2323 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03181 0.9732 0.9977 1.864e-05 -8.37e-06 -0.03442 1.405e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02299 -0.005668 0.01848 0.03013 0.9387 0.9483 0.04858 0.884 0.9025 0.1239 ] Network output: [ 0.9755 0.06737 -0.01679 -9.525e-05 4.276e-05 -0.001918 -7.179e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6481 0.1221 0.1219 0.2734 0.971 0.9866 0.7457 0.8992 0.9663 0.635 ] Network output: [ -0.005858 0.9373 1.028 -6.586e-05 2.957e-05 0.04606 -4.963e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04999 0.03719 0.05357 0.04123 0.9848 0.9892 0.0512 0.9692 0.9798 0.06689 ] Network output: [ 0.0827 -0.282 1.074 -0.0005026 0.0002257 1.041 -0.0003788 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7402 0.636 0.5355 0.4533 0.9744 0.9885 0.7436 0.9097 0.9712 0.6308 ] Network output: [ -0.03977 0.1785 0.9498 0.0007147 -0.0003209 0.9541 0.0005386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6148 0.6009 0.4472 0.3128 0.9862 0.991 0.6153 0.973 0.9817 0.4595 ] Network output: [ -0.06326 0.2031 0.9431 0.0002082 -9.347e-05 0.9812 0.0001569 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.614 0.6118 0.4657 0.2873 0.9843 0.9898 0.6141 0.9671 0.9785 0.468 ] Network output: [ 0.01916 0.9229 0.02388 -0.0002736 0.0001228 1.014 -0.0002062 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02896 Epoch 2324 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03178 0.9732 0.9977 1.828e-05 -8.205e-06 -0.03442 1.377e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02298 -0.005668 0.01847 0.0301 0.9387 0.9483 0.04856 0.884 0.9026 0.1239 ] Network output: [ 0.9755 0.06734 -0.0168 -9.466e-05 4.25e-05 -0.001935 -7.134e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6481 0.1221 0.1219 0.2731 0.971 0.9866 0.7456 0.8992 0.9663 0.6351 ] Network output: [ -0.005877 0.9374 1.028 -6.616e-05 2.97e-05 0.04604 -4.986e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04998 0.03718 0.05355 0.04118 0.9848 0.9892 0.05119 0.9692 0.9798 0.06687 ] Network output: [ 0.0826 -0.2818 1.074 -0.0005056 0.000227 1.041 -0.000381 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7401 0.6359 0.5356 0.453 0.9744 0.9885 0.7436 0.9097 0.9712 0.6309 ] Network output: [ -0.03969 0.1783 0.9497 0.0007159 -0.0003214 0.9542 0.0005395 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6149 0.601 0.4473 0.3126 0.9862 0.991 0.6154 0.973 0.9817 0.4595 ] Network output: [ -0.06318 0.2029 0.9431 0.0002102 -9.436e-05 0.9812 0.0001584 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.614 0.6118 0.4656 0.2872 0.9843 0.9898 0.6141 0.9671 0.9785 0.468 ] Network output: [ 0.01912 0.923 0.02386 -0.0002738 0.0001229 1.014 -0.0002063 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02891 Epoch 2325 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03175 0.9733 0.9977 1.791e-05 -8.04e-06 -0.03442 1.35e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02298 -0.005668 0.01847 0.03006 0.9387 0.9483 0.04855 0.884 0.9026 0.1239 ] Network output: [ 0.9755 0.06732 -0.01681 -9.407e-05 4.223e-05 -0.001952 -7.089e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.648 0.1221 0.1219 0.2728 0.971 0.9866 0.7456 0.8992 0.9663 0.6351 ] Network output: [ -0.005895 0.9374 1.028 -6.645e-05 2.983e-05 0.04602 -5.008e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04997 0.03718 0.05353 0.04113 0.9848 0.9893 0.05118 0.9692 0.9798 0.06684 ] Network output: [ 0.08251 -0.2816 1.074 -0.0005085 0.0002283 1.041 -0.0003832 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7401 0.6359 0.5357 0.4526 0.9744 0.9885 0.7435 0.9098 0.9712 0.6309 ] Network output: [ -0.03961 0.1782 0.9496 0.0007171 -0.0003219 0.9544 0.0005405 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6149 0.6011 0.4473 0.3124 0.9862 0.991 0.6155 0.973 0.9817 0.4595 ] Network output: [ -0.0631 0.2027 0.9431 0.0002122 -9.524e-05 0.9813 0.0001599 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6141 0.6119 0.4656 0.2871 0.9843 0.9898 0.6142 0.9672 0.9785 0.4679 ] Network output: [ 0.01907 0.9232 0.02385 -0.000274 0.000123 1.014 -0.0002065 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02886 Epoch 2326 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03172 0.9733 0.9977 1.754e-05 -7.876e-06 -0.03442 1.322e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02297 -0.005669 0.01846 0.03003 0.9387 0.9483 0.04853 0.8841 0.9026 0.1238 ] Network output: [ 0.9756 0.0673 -0.01682 -9.348e-05 4.197e-05 -0.001969 -7.045e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.648 0.1221 0.122 0.2725 0.971 0.9866 0.7455 0.8992 0.9663 0.6352 ] Network output: [ -0.005913 0.9375 1.028 -6.674e-05 2.996e-05 0.04601 -5.03e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04996 0.03717 0.05352 0.04108 0.9848 0.9893 0.05117 0.9693 0.9798 0.06682 ] Network output: [ 0.08241 -0.2814 1.074 -0.0005114 0.0002296 1.041 -0.0003854 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.74 0.6358 0.5357 0.4522 0.9744 0.9885 0.7434 0.9098 0.9712 0.631 ] Network output: [ -0.03953 0.178 0.9495 0.0007184 -0.0003225 0.9545 0.0005414 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.615 0.6011 0.4473 0.3122 0.9862 0.991 0.6155 0.973 0.9817 0.4596 ] Network output: [ -0.06301 0.2024 0.9432 0.0002141 -9.613e-05 0.9813 0.0001614 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6142 0.6119 0.4656 0.2869 0.9843 0.9899 0.6143 0.9672 0.9785 0.4679 ] Network output: [ 0.01903 0.9233 0.02383 -0.0002742 0.0001231 1.014 -0.0002066 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02881 Epoch 2327 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0317 0.9733 0.9978 1.718e-05 -7.712e-06 -0.03442 1.295e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02296 -0.005669 0.01846 0.03 0.9387 0.9483 0.04851 0.8841 0.9026 0.1238 ] Network output: [ 0.9756 0.06727 -0.01683 -9.29e-05 4.171e-05 -0.001986 -7.001e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6479 0.1221 0.122 0.2722 0.971 0.9866 0.7454 0.8992 0.9663 0.6353 ] Network output: [ -0.005931 0.9375 1.028 -6.704e-05 3.009e-05 0.04599 -5.052e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04995 0.03716 0.0535 0.04103 0.9848 0.9893 0.05116 0.9693 0.9798 0.0668 ] Network output: [ 0.08231 -0.2813 1.074 -0.0005144 0.0002309 1.041 -0.0003876 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7399 0.6358 0.5358 0.4519 0.9744 0.9885 0.7433 0.9098 0.9712 0.6311 ] Network output: [ -0.03945 0.1778 0.9495 0.0007196 -0.000323 0.9546 0.0005423 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6151 0.6012 0.4474 0.312 0.9862 0.991 0.6156 0.973 0.9817 0.4596 ] Network output: [ -0.06293 0.2022 0.9432 0.0002161 -9.702e-05 0.9814 0.0001629 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6142 0.612 0.4656 0.2868 0.9843 0.9899 0.6143 0.9672 0.9785 0.4679 ] Network output: [ 0.01899 0.9235 0.02381 -0.0002744 0.0001232 1.014 -0.0002068 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02876 Epoch 2328 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03167 0.9733 0.9978 1.681e-05 -7.548e-06 -0.03441 1.267e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02296 -0.005669 0.01845 0.02997 0.9387 0.9483 0.04849 0.8841 0.9026 0.1238 ] Network output: [ 0.9756 0.06725 -0.01684 -9.232e-05 4.145e-05 -0.002002 -6.958e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6479 0.1222 0.1221 0.2719 0.971 0.9866 0.7453 0.8993 0.9663 0.6353 ] Network output: [ -0.005949 0.9376 1.028 -6.733e-05 3.023e-05 0.04597 -5.074e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04994 0.03715 0.05348 0.04098 0.9848 0.9893 0.05115 0.9693 0.9798 0.06678 ] Network output: [ 0.08221 -0.281 1.074 -0.0005173 0.0002322 1.041 -0.0003899 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7398 0.6357 0.5359 0.4515 0.9744 0.9885 0.7433 0.9098 0.9713 0.6311 ] Network output: [ -0.03937 0.1776 0.9494 0.0007208 -0.0003236 0.9547 0.0005432 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6152 0.6013 0.4474 0.3118 0.9862 0.991 0.6157 0.973 0.9817 0.4596 ] Network output: [ -0.06285 0.202 0.9432 0.0002181 -9.792e-05 0.9814 0.0001644 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6143 0.612 0.4656 0.2867 0.9843 0.9899 0.6144 0.9672 0.9785 0.4679 ] Network output: [ 0.01895 0.9236 0.0238 -0.0002746 0.0001233 1.014 -0.0002069 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02871 Epoch 2329 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03164 0.9734 0.9978 1.645e-05 -7.384e-06 -0.03441 1.24e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02295 -0.005669 0.01845 0.02993 0.9388 0.9484 0.04848 0.8841 0.9027 0.1237 ] Network output: [ 0.9756 0.06722 -0.01685 -9.174e-05 4.119e-05 -0.002019 -6.914e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6478 0.1222 0.1221 0.2716 0.971 0.9866 0.7452 0.8993 0.9663 0.6354 ] Network output: [ -0.005968 0.9377 1.028 -6.762e-05 3.036e-05 0.04596 -5.096e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04993 0.03715 0.05347 0.04092 0.9848 0.9893 0.05114 0.9693 0.9798 0.06675 ] Network output: [ 0.08211 -0.2808 1.074 -0.0005203 0.0002336 1.041 -0.0003921 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7398 0.6357 0.5359 0.4511 0.9744 0.9885 0.7432 0.9098 0.9713 0.6312 ] Network output: [ -0.03929 0.1774 0.9493 0.000722 -0.0003241 0.9548 0.0005441 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6152 0.6014 0.4474 0.3116 0.9862 0.991 0.6158 0.973 0.9817 0.4596 ] Network output: [ -0.06277 0.2017 0.9432 0.0002201 -9.882e-05 0.9815 0.0001659 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6143 0.6121 0.4656 0.2865 0.9843 0.9899 0.6144 0.9672 0.9785 0.4679 ] Network output: [ 0.0189 0.9238 0.02378 -0.0002748 0.0001234 1.014 -0.0002071 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02866 Epoch 2330 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03161 0.9734 0.9979 1.608e-05 -7.221e-06 -0.03441 1.212e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02294 -0.005669 0.01844 0.0299 0.9388 0.9484 0.04846 0.8841 0.9027 0.1237 ] Network output: [ 0.9757 0.06719 -0.01686 -9.117e-05 4.093e-05 -0.002035 -6.871e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6478 0.1222 0.1222 0.2713 0.971 0.9866 0.7452 0.8993 0.9663 0.6354 ] Network output: [ -0.005986 0.9377 1.028 -6.791e-05 3.049e-05 0.04594 -5.118e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04992 0.03714 0.05345 0.04087 0.9848 0.9893 0.05113 0.9693 0.9798 0.06673 ] Network output: [ 0.08201 -0.2806 1.074 -0.0005232 0.0002349 1.041 -0.0003943 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7397 0.6356 0.536 0.4508 0.9744 0.9885 0.7431 0.9098 0.9713 0.6312 ] Network output: [ -0.0392 0.1772 0.9492 0.0007233 -0.0003247 0.9549 0.0005451 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6153 0.6015 0.4474 0.3114 0.9862 0.991 0.6158 0.973 0.9817 0.4597 ] Network output: [ -0.06269 0.2015 0.9432 0.0002221 -9.971e-05 0.9815 0.0001674 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6144 0.6121 0.4655 0.2864 0.9843 0.9899 0.6145 0.9672 0.9785 0.4679 ] Network output: [ 0.01886 0.9239 0.02377 -0.000275 0.0001235 1.013 -0.0002072 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02861 Epoch 2331 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03158 0.9734 0.9979 1.572e-05 -7.057e-06 -0.03441 1.185e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02294 -0.005669 0.01843 0.02987 0.9388 0.9484 0.04844 0.8842 0.9027 0.1236 ] Network output: [ 0.9757 0.06717 -0.01687 -9.06e-05 4.067e-05 -0.002052 -6.828e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6477 0.1222 0.1222 0.271 0.9711 0.9866 0.7451 0.8993 0.9663 0.6355 ] Network output: [ -0.006005 0.9378 1.028 -6.82e-05 3.062e-05 0.04592 -5.14e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04991 0.03713 0.05343 0.04082 0.9848 0.9893 0.05112 0.9693 0.9798 0.06671 ] Network output: [ 0.08191 -0.2804 1.074 -0.0005262 0.0002362 1.04 -0.0003966 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7396 0.6356 0.5361 0.4504 0.9744 0.9885 0.743 0.9099 0.9713 0.6313 ] Network output: [ -0.03912 0.1771 0.9491 0.0007245 -0.0003253 0.955 0.000546 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6154 0.6015 0.4475 0.3112 0.9862 0.991 0.6159 0.973 0.9817 0.4597 ] Network output: [ -0.0626 0.2012 0.9433 0.0002241 -0.0001006 0.9816 0.0001689 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6144 0.6122 0.4655 0.2862 0.9843 0.9899 0.6145 0.9672 0.9785 0.4678 ] Network output: [ 0.01882 0.9241 0.02375 -0.0002752 0.0001235 1.013 -0.0002074 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02856 Epoch 2332 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03155 0.9735 0.9979 1.536e-05 -6.895e-06 -0.03441 1.157e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02293 -0.00567 0.01843 0.02984 0.9388 0.9484 0.04843 0.8842 0.9027 0.1236 ] Network output: [ 0.9757 0.06714 -0.01688 -9.003e-05 4.042e-05 -0.002068 -6.785e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6477 0.1222 0.1222 0.2707 0.9711 0.9866 0.745 0.8993 0.9664 0.6356 ] Network output: [ -0.006023 0.9379 1.028 -6.849e-05 3.075e-05 0.04591 -5.162e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0499 0.03712 0.05342 0.04077 0.9848 0.9893 0.05111 0.9693 0.9798 0.06668 ] Network output: [ 0.08181 -0.2802 1.074 -0.0005292 0.0002376 1.04 -0.0003988 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7395 0.6355 0.5361 0.45 0.9744 0.9885 0.7429 0.9099 0.9713 0.6313 ] Network output: [ -0.03904 0.1769 0.9491 0.0007258 -0.0003258 0.9551 0.000547 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6155 0.6016 0.4475 0.311 0.9862 0.991 0.616 0.973 0.9817 0.4597 ] Network output: [ -0.06252 0.201 0.9433 0.0002261 -0.0001015 0.9817 0.0001704 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6145 0.6123 0.4655 0.2861 0.9843 0.9899 0.6146 0.9672 0.9785 0.4678 ] Network output: [ 0.01878 0.9242 0.02373 -0.0002754 0.0001236 1.013 -0.0002075 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0285 Epoch 2333 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03152 0.9735 0.9979 1.5e-05 -6.732e-06 -0.03441 1.13e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02292 -0.00567 0.01842 0.0298 0.9388 0.9484 0.04841 0.8842 0.9027 0.1236 ] Network output: [ 0.9757 0.06711 -0.01689 -8.947e-05 4.016e-05 -0.002084 -6.742e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6476 0.1223 0.1223 0.2704 0.9711 0.9866 0.7449 0.8994 0.9664 0.6356 ] Network output: [ -0.006042 0.9379 1.028 -6.878e-05 3.088e-05 0.04589 -5.184e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04989 0.03712 0.0534 0.04072 0.9848 0.9893 0.0511 0.9693 0.9798 0.06666 ] Network output: [ 0.08171 -0.28 1.074 -0.0005322 0.0002389 1.04 -0.000401 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7395 0.6355 0.5362 0.4496 0.9745 0.9885 0.7429 0.9099 0.9713 0.6314 ] Network output: [ -0.03896 0.1767 0.949 0.000727 -0.0003264 0.9552 0.0005479 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6156 0.6017 0.4475 0.3108 0.9862 0.991 0.6161 0.9731 0.9817 0.4597 ] Network output: [ -0.06244 0.2008 0.9433 0.0002282 -0.0001024 0.9817 0.0001719 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6145 0.6123 0.4655 0.286 0.9843 0.9899 0.6146 0.9673 0.9785 0.4678 ] Network output: [ 0.01873 0.9244 0.02372 -0.0002756 0.0001237 1.013 -0.0002077 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02845 Epoch 2334 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03149 0.9735 0.998 1.463e-05 -6.57e-06 -0.03441 1.103e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02292 -0.00567 0.01842 0.02977 0.9388 0.9484 0.04839 0.8842 0.9027 0.1235 ] Network output: [ 0.9758 0.06709 -0.01689 -8.89e-05 3.991e-05 -0.0021 -6.7e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6476 0.1223 0.1223 0.27 0.9711 0.9866 0.7449 0.8994 0.9664 0.6357 ] Network output: [ -0.00606 0.938 1.028 -6.907e-05 3.101e-05 0.04587 -5.205e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04988 0.03711 0.05338 0.04067 0.9849 0.9893 0.05109 0.9693 0.9798 0.06664 ] Network output: [ 0.08161 -0.2798 1.074 -0.0005351 0.0002402 1.04 -0.0004033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7394 0.6354 0.5363 0.4493 0.9745 0.9886 0.7428 0.9099 0.9713 0.6314 ] Network output: [ -0.03888 0.1765 0.9489 0.0007283 -0.0003269 0.9553 0.0005488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6156 0.6018 0.4475 0.3106 0.9863 0.991 0.6161 0.9731 0.9818 0.4598 ] Network output: [ -0.06236 0.2005 0.9433 0.0002302 -0.0001033 0.9818 0.0001735 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6146 0.6124 0.4655 0.2858 0.9843 0.9899 0.6147 0.9673 0.9786 0.4678 ] Network output: [ 0.01869 0.9245 0.0237 -0.0002758 0.0001238 1.013 -0.0002079 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0284 Epoch 2335 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03146 0.9735 0.998 1.427e-05 -6.408e-06 -0.03441 1.076e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02291 -0.00567 0.01841 0.02974 0.9388 0.9484 0.04837 0.8843 0.9028 0.1235 ] Network output: [ 0.9758 0.06706 -0.0169 -8.835e-05 3.966e-05 -0.002115 -6.658e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6475 0.1223 0.1224 0.2697 0.9711 0.9866 0.7448 0.8994 0.9664 0.6357 ] Network output: [ -0.006079 0.938 1.028 -6.936e-05 3.114e-05 0.04585 -5.227e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04987 0.0371 0.05337 0.04062 0.9849 0.9893 0.05108 0.9693 0.9798 0.06662 ] Network output: [ 0.0815 -0.2796 1.074 -0.0005381 0.0002416 1.04 -0.0004056 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7393 0.6354 0.5364 0.4489 0.9745 0.9886 0.7427 0.9099 0.9713 0.6315 ] Network output: [ -0.0388 0.1763 0.9488 0.0007295 -0.0003275 0.9554 0.0005498 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6157 0.6018 0.4476 0.3104 0.9863 0.991 0.6162 0.9731 0.9818 0.4598 ] Network output: [ -0.06227 0.2003 0.9434 0.0002322 -0.0001042 0.9818 0.000175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6147 0.6124 0.4655 0.2857 0.9843 0.9899 0.6148 0.9673 0.9786 0.4678 ] Network output: [ 0.01865 0.9247 0.02369 -0.000276 0.0001239 1.013 -0.000208 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02835 Epoch 2336 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03143 0.9736 0.998 1.391e-05 -6.246e-06 -0.03441 1.049e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0229 -0.00567 0.01841 0.0297 0.9388 0.9484 0.04836 0.8843 0.9028 0.1235 ] Network output: [ 0.9758 0.06703 -0.01691 -8.779e-05 3.941e-05 -0.002131 -6.616e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6474 0.1223 0.1224 0.2694 0.9711 0.9866 0.7447 0.8994 0.9664 0.6358 ] Network output: [ -0.006098 0.9381 1.028 -6.965e-05 3.127e-05 0.04584 -5.249e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04986 0.0371 0.05335 0.04056 0.9849 0.9893 0.05107 0.9693 0.9798 0.06659 ] Network output: [ 0.0814 -0.2794 1.074 -0.0005411 0.0002429 1.04 -0.0004078 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7392 0.6353 0.5364 0.4485 0.9745 0.9886 0.7426 0.91 0.9713 0.6316 ] Network output: [ -0.03872 0.1761 0.9487 0.0007308 -0.0003281 0.9555 0.0005507 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6158 0.6019 0.4476 0.3103 0.9863 0.991 0.6163 0.9731 0.9818 0.4598 ] Network output: [ -0.06219 0.2001 0.9434 0.0002342 -0.0001052 0.9819 0.0001765 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6147 0.6125 0.4654 0.2855 0.9843 0.9899 0.6148 0.9673 0.9786 0.4678 ] Network output: [ 0.0186 0.9248 0.02367 -0.0002762 0.000124 1.013 -0.0002082 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0283 Epoch 2337 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03141 0.9736 0.9981 1.355e-05 -6.085e-06 -0.03441 1.021e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0229 -0.00567 0.0184 0.02967 0.9388 0.9484 0.04834 0.8843 0.9028 0.1234 ] Network output: [ 0.9759 0.067 -0.01692 -8.724e-05 3.916e-05 -0.002147 -6.575e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6474 0.1223 0.1224 0.2691 0.9711 0.9866 0.7446 0.8994 0.9664 0.6359 ] Network output: [ -0.006116 0.9382 1.028 -6.993e-05 3.14e-05 0.04582 -5.27e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04985 0.03709 0.05333 0.04051 0.9849 0.9893 0.05106 0.9694 0.9798 0.06657 ] Network output: [ 0.0813 -0.2792 1.074 -0.0005441 0.0002443 1.04 -0.0004101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7391 0.6353 0.5365 0.4481 0.9745 0.9886 0.7425 0.91 0.9713 0.6316 ] Network output: [ -0.03863 0.1759 0.9487 0.000732 -0.0003286 0.9557 0.0005517 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6159 0.602 0.4476 0.3101 0.9863 0.991 0.6164 0.9731 0.9818 0.4598 ] Network output: [ -0.06211 0.1998 0.9434 0.0002363 -0.0001061 0.982 0.0001781 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6148 0.6126 0.4654 0.2854 0.9843 0.9899 0.6149 0.9673 0.9786 0.4677 ] Network output: [ 0.01856 0.925 0.02365 -0.0002764 0.0001241 1.013 -0.0002083 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02825 Epoch 2338 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03138 0.9736 0.9981 1.319e-05 -5.924e-06 -0.03441 9.944e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02289 -0.00567 0.0184 0.02964 0.9388 0.9484 0.04832 0.8843 0.9028 0.1234 ] Network output: [ 0.9759 0.06697 -0.01693 -8.669e-05 3.892e-05 -0.002162 -6.533e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6473 0.1224 0.1225 0.2688 0.9711 0.9866 0.7445 0.8995 0.9664 0.6359 ] Network output: [ -0.006135 0.9382 1.028 -7.022e-05 3.152e-05 0.0458 -5.292e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04984 0.03708 0.05332 0.04046 0.9849 0.9893 0.05104 0.9694 0.9798 0.06655 ] Network output: [ 0.0812 -0.279 1.075 -0.0005472 0.0002456 1.04 -0.0004124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7391 0.6353 0.5366 0.4478 0.9745 0.9886 0.7425 0.91 0.9713 0.6317 ] Network output: [ -0.03855 0.1758 0.9486 0.0007333 -0.0003292 0.9558 0.0005526 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6159 0.6021 0.4477 0.3099 0.9863 0.991 0.6165 0.9731 0.9818 0.4599 ] Network output: [ -0.06202 0.1996 0.9434 0.0002383 -0.000107 0.982 0.0001796 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6148 0.6126 0.4654 0.2853 0.9843 0.9899 0.6149 0.9673 0.9786 0.4677 ] Network output: [ 0.01852 0.9252 0.02364 -0.0002766 0.0001242 1.013 -0.0002085 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0282 Epoch 2339 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03135 0.9737 0.9981 1.284e-05 -5.763e-06 -0.03441 9.674e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02288 -0.00567 0.01839 0.02961 0.9389 0.9484 0.04831 0.8843 0.9028 0.1234 ] Network output: [ 0.9759 0.06694 -0.01694 -8.614e-05 3.867e-05 -0.002177 -6.492e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6473 0.1224 0.1225 0.2685 0.9711 0.9866 0.7445 0.8995 0.9664 0.636 ] Network output: [ -0.006154 0.9383 1.028 -7.05e-05 3.165e-05 0.04578 -5.313e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04983 0.03707 0.0533 0.04041 0.9849 0.9893 0.05103 0.9694 0.9798 0.06653 ] Network output: [ 0.0811 -0.2788 1.075 -0.0005502 0.000247 1.04 -0.0004146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.739 0.6352 0.5366 0.4474 0.9745 0.9886 0.7424 0.91 0.9713 0.6317 ] Network output: [ -0.03847 0.1756 0.9485 0.0007346 -0.0003298 0.9559 0.0005536 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.616 0.6022 0.4477 0.3097 0.9863 0.991 0.6165 0.9731 0.9818 0.4599 ] Network output: [ -0.06194 0.1993 0.9434 0.0002404 -0.0001079 0.9821 0.0001812 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6149 0.6127 0.4654 0.2851 0.9843 0.9899 0.615 0.9673 0.9786 0.4677 ] Network output: [ 0.01848 0.9253 0.02362 -0.0002768 0.0001243 1.013 -0.0002086 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02814 Epoch 2340 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03132 0.9737 0.9981 1.248e-05 -5.602e-06 -0.03441 9.405e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02288 -0.00567 0.01838 0.02957 0.9389 0.9484 0.04829 0.8844 0.9029 0.1233 ] Network output: [ 0.9759 0.06691 -0.01694 -8.56e-05 3.843e-05 -0.002193 -6.451e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6472 0.1224 0.1226 0.2682 0.9711 0.9866 0.7444 0.8995 0.9664 0.636 ] Network output: [ -0.006173 0.9384 1.028 -7.079e-05 3.178e-05 0.04576 -5.335e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04982 0.03707 0.05329 0.04036 0.9849 0.9893 0.05102 0.9694 0.9799 0.06651 ] Network output: [ 0.08099 -0.2786 1.075 -0.0005532 0.0002484 1.04 -0.0004169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7389 0.6352 0.5367 0.447 0.9745 0.9886 0.7423 0.91 0.9713 0.6318 ] Network output: [ -0.03839 0.1754 0.9484 0.0007358 -0.0003303 0.956 0.0005546 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6161 0.6022 0.4477 0.3095 0.9863 0.991 0.6166 0.9731 0.9818 0.4599 ] Network output: [ -0.06186 0.1991 0.9435 0.0002424 -0.0001088 0.9822 0.0001827 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.615 0.6127 0.4654 0.285 0.9843 0.9899 0.6151 0.9673 0.9786 0.4677 ] Network output: [ 0.01843 0.9255 0.02361 -0.0002771 0.0001244 1.013 -0.0002088 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02809 Epoch 2341 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03129 0.9737 0.9982 1.212e-05 -5.442e-06 -0.03441 9.136e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02287 -0.00567 0.01838 0.02954 0.9389 0.9485 0.04827 0.8844 0.9029 0.1233 ] Network output: [ 0.976 0.06688 -0.01695 -8.506e-05 3.819e-05 -0.002208 -6.411e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6472 0.1224 0.1226 0.2679 0.9711 0.9866 0.7443 0.8995 0.9664 0.6361 ] Network output: [ -0.006192 0.9384 1.028 -7.107e-05 3.191e-05 0.04575 -5.356e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04981 0.03706 0.05327 0.04031 0.9849 0.9893 0.05101 0.9694 0.9799 0.06648 ] Network output: [ 0.08089 -0.2783 1.075 -0.0005562 0.0002497 1.04 -0.0004192 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7388 0.6351 0.5368 0.4466 0.9745 0.9886 0.7422 0.91 0.9714 0.6319 ] Network output: [ -0.03831 0.1752 0.9483 0.0007371 -0.0003309 0.9561 0.0005555 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6162 0.6023 0.4477 0.3093 0.9863 0.991 0.6167 0.9731 0.9818 0.4599 ] Network output: [ -0.06177 0.1988 0.9435 0.0002445 -0.0001098 0.9822 0.0001843 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.615 0.6128 0.4654 0.2848 0.9843 0.9899 0.6151 0.9674 0.9786 0.4677 ] Network output: [ 0.01839 0.9256 0.02359 -0.0002773 0.0001245 1.013 -0.000209 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02804 Epoch 2342 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03126 0.9737 0.9982 1.177e-05 -5.282e-06 -0.03441 8.868e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02286 -0.00567 0.01837 0.02951 0.9389 0.9485 0.04825 0.8844 0.9029 0.1232 ] Network output: [ 0.976 0.06685 -0.01696 -8.453e-05 3.795e-05 -0.002223 -6.37e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6471 0.1224 0.1226 0.2676 0.9711 0.9866 0.7442 0.8995 0.9664 0.6362 ] Network output: [ -0.006211 0.9385 1.028 -7.136e-05 3.203e-05 0.04573 -5.378e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0498 0.03705 0.05325 0.04025 0.9849 0.9893 0.051 0.9694 0.9799 0.06646 ] Network output: [ 0.08079 -0.2781 1.075 -0.0005593 0.0002511 1.039 -0.0004215 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7387 0.6351 0.5368 0.4462 0.9745 0.9886 0.7421 0.9101 0.9714 0.6319 ] Network output: [ -0.03822 0.175 0.9483 0.0007384 -0.0003315 0.9562 0.0005565 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6163 0.6024 0.4478 0.3091 0.9863 0.991 0.6168 0.9731 0.9818 0.46 ] Network output: [ -0.06169 0.1986 0.9435 0.0002466 -0.0001107 0.9823 0.0001858 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6151 0.6129 0.4654 0.2847 0.9843 0.9899 0.6152 0.9674 0.9786 0.4677 ] Network output: [ 0.01835 0.9258 0.02357 -0.0002775 0.0001246 1.013 -0.0002091 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02799 Epoch 2343 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03123 0.9738 0.9982 1.141e-05 -5.123e-06 -0.03441 8.6e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02286 -0.00567 0.01837 0.02948 0.9389 0.9485 0.04824 0.8844 0.9029 0.1232 ] Network output: [ 0.976 0.06682 -0.01697 -8.399e-05 3.771e-05 -0.002238 -6.33e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6471 0.1224 0.1227 0.2673 0.9711 0.9866 0.7441 0.8996 0.9665 0.6362 ] Network output: [ -0.00623 0.9385 1.028 -7.164e-05 3.216e-05 0.04571 -5.399e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04979 0.03704 0.05324 0.0402 0.9849 0.9893 0.05099 0.9694 0.9799 0.06644 ] Network output: [ 0.08068 -0.2779 1.075 -0.0005623 0.0002524 1.039 -0.0004238 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7387 0.635 0.5369 0.4458 0.9745 0.9886 0.7421 0.9101 0.9714 0.632 ] Network output: [ -0.03814 0.1748 0.9482 0.0007397 -0.0003321 0.9563 0.0005575 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6163 0.6025 0.4478 0.3089 0.9863 0.991 0.6168 0.9731 0.9818 0.46 ] Network output: [ -0.0616 0.1984 0.9435 0.0002487 -0.0001116 0.9823 0.0001874 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6151 0.6129 0.4653 0.2845 0.9844 0.9899 0.6152 0.9674 0.9786 0.4676 ] Network output: [ 0.01831 0.9259 0.02356 -0.0002777 0.0001247 1.013 -0.0002093 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02794 Epoch 2344 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0312 0.9738 0.9983 1.106e-05 -4.964e-06 -0.03441 8.332e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02285 -0.00567 0.01836 0.02944 0.9389 0.9485 0.04822 0.8844 0.9029 0.1232 ] Network output: [ 0.976 0.06679 -0.01697 -8.346e-05 3.747e-05 -0.002252 -6.29e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.647 0.1225 0.1227 0.2669 0.9711 0.9866 0.7441 0.8996 0.9665 0.6363 ] Network output: [ -0.006249 0.9386 1.028 -7.192e-05 3.229e-05 0.04569 -5.42e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04978 0.03704 0.05322 0.04015 0.9849 0.9893 0.05098 0.9694 0.9799 0.06642 ] Network output: [ 0.08058 -0.2777 1.075 -0.0005654 0.0002538 1.039 -0.0004261 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7386 0.6349 0.537 0.4455 0.9745 0.9886 0.742 0.9101 0.9714 0.632 ] Network output: [ -0.03806 0.1746 0.9481 0.000741 -0.0003327 0.9564 0.0005584 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6164 0.6026 0.4478 0.3087 0.9863 0.991 0.6169 0.9731 0.9818 0.46 ] Network output: [ -0.06152 0.1981 0.9435 0.0002507 -0.0001126 0.9824 0.000189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6152 0.613 0.4653 0.2844 0.9844 0.9899 0.6153 0.9674 0.9786 0.4676 ] Network output: [ 0.01826 0.9261 0.02354 -0.0002779 0.0001248 1.013 -0.0002094 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02788 Epoch 2345 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03117 0.9738 0.9983 1.07e-05 -4.805e-06 -0.03441 8.066e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02284 -0.00567 0.01836 0.02941 0.9389 0.9485 0.0482 0.8845 0.9029 0.1231 ] Network output: [ 0.9761 0.06676 -0.01698 -8.294e-05 3.723e-05 -0.002267 -6.25e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.647 0.1225 0.1228 0.2666 0.9711 0.9866 0.744 0.8996 0.9665 0.6363 ] Network output: [ -0.006268 0.9387 1.028 -7.22e-05 3.241e-05 0.04567 -5.441e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04977 0.03703 0.0532 0.0401 0.9849 0.9893 0.05097 0.9694 0.9799 0.06639 ] Network output: [ 0.08048 -0.2775 1.075 -0.0005684 0.0002552 1.039 -0.0004284 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7385 0.6349 0.5371 0.4451 0.9745 0.9886 0.7419 0.9101 0.9714 0.6321 ] Network output: [ -0.03798 0.1744 0.948 0.0007423 -0.0003332 0.9565 0.0005594 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6165 0.6026 0.4479 0.3085 0.9863 0.991 0.617 0.9731 0.9818 0.46 ] Network output: [ -0.06143 0.1979 0.9436 0.0002528 -0.0001135 0.9825 0.0001905 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6153 0.613 0.4653 0.2843 0.9844 0.9899 0.6154 0.9674 0.9786 0.4676 ] Network output: [ 0.01822 0.9262 0.02353 -0.0002781 0.0001249 1.013 -0.0002096 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02783 Epoch 2346 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03114 0.9739 0.9983 1.035e-05 -4.646e-06 -0.03441 7.799e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02284 -0.00567 0.01835 0.02938 0.9389 0.9485 0.04819 0.8845 0.903 0.1231 ] Network output: [ 0.9761 0.06673 -0.01699 -8.241e-05 3.7e-05 -0.002281 -6.211e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6469 0.1225 0.1228 0.2663 0.9711 0.9867 0.7439 0.8996 0.9665 0.6364 ] Network output: [ -0.006288 0.9387 1.028 -7.248e-05 3.254e-05 0.04565 -5.462e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04976 0.03702 0.05319 0.04005 0.9849 0.9893 0.05096 0.9694 0.9799 0.06637 ] Network output: [ 0.08037 -0.2772 1.075 -0.0005715 0.0002566 1.039 -0.0004307 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7384 0.6348 0.5371 0.4447 0.9745 0.9886 0.7418 0.9101 0.9714 0.6322 ] Network output: [ -0.03789 0.1742 0.9479 0.0007436 -0.0003338 0.9567 0.0005604 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6166 0.6027 0.4479 0.3083 0.9863 0.991 0.6171 0.9731 0.9818 0.4601 ] Network output: [ -0.06135 0.1976 0.9436 0.0002549 -0.0001144 0.9825 0.0001921 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6153 0.6131 0.4653 0.2841 0.9844 0.9899 0.6154 0.9674 0.9787 0.4676 ] Network output: [ 0.01818 0.9264 0.02351 -0.0002783 0.000125 1.013 -0.0002098 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02778 Epoch 2347 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03111 0.9739 0.9983 9.996e-06 -4.488e-06 -0.03441 7.534e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02283 -0.00567 0.01835 0.02935 0.9389 0.9485 0.04817 0.8845 0.903 0.1231 ] Network output: [ 0.9761 0.0667 -0.017 -8.189e-05 3.677e-05 -0.002296 -6.172e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6469 0.1225 0.1228 0.266 0.9711 0.9867 0.7438 0.8996 0.9665 0.6365 ] Network output: [ -0.006307 0.9388 1.028 -7.276e-05 3.266e-05 0.04564 -5.483e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04975 0.03701 0.05317 0.04 0.9849 0.9893 0.05095 0.9694 0.9799 0.06635 ] Network output: [ 0.08027 -0.277 1.075 -0.0005746 0.0002579 1.039 -0.000433 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7383 0.6348 0.5372 0.4443 0.9745 0.9886 0.7417 0.9101 0.9714 0.6322 ] Network output: [ -0.03781 0.174 0.9479 0.0007449 -0.0003344 0.9568 0.0005614 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6167 0.6028 0.4479 0.308 0.9863 0.991 0.6172 0.9731 0.9818 0.4601 ] Network output: [ -0.06126 0.1974 0.9436 0.000257 -0.0001154 0.9826 0.0001937 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6154 0.6132 0.4653 0.284 0.9844 0.9899 0.6155 0.9674 0.9787 0.4676 ] Network output: [ 0.01814 0.9265 0.0235 -0.0002786 0.0001251 1.013 -0.0002099 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02772 Epoch 2348 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03108 0.9739 0.9984 9.645e-06 -4.33e-06 -0.03441 7.268e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02282 -0.00567 0.01834 0.02931 0.9389 0.9485 0.04815 0.8845 0.903 0.123 ] Network output: [ 0.9762 0.06667 -0.017 -8.138e-05 3.653e-05 -0.00231 -6.133e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6468 0.1225 0.1229 0.2657 0.9712 0.9867 0.7437 0.8997 0.9665 0.6365 ] Network output: [ -0.006326 0.9389 1.028 -7.304e-05 3.279e-05 0.04562 -5.504e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04974 0.03701 0.05316 0.03995 0.9849 0.9893 0.05094 0.9694 0.9799 0.06633 ] Network output: [ 0.08016 -0.2768 1.075 -0.0005776 0.0002593 1.039 -0.0004353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7383 0.6347 0.5373 0.4439 0.9745 0.9886 0.7417 0.9102 0.9714 0.6323 ] Network output: [ -0.03773 0.1738 0.9478 0.0007462 -0.000335 0.9569 0.0005623 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6167 0.6029 0.4479 0.3078 0.9863 0.991 0.6172 0.9731 0.9818 0.4601 ] Network output: [ -0.06118 0.1971 0.9436 0.0002591 -0.0001163 0.9827 0.0001953 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6154 0.6132 0.4653 0.2838 0.9844 0.9899 0.6155 0.9674 0.9787 0.4676 ] Network output: [ 0.01809 0.9267 0.02348 -0.0002788 0.0001252 1.013 -0.0002101 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02767 Epoch 2349 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03105 0.974 0.9984 9.293e-06 -4.172e-06 -0.03441 7.004e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02282 -0.00567 0.01834 0.02928 0.939 0.9485 0.04814 0.8845 0.903 0.123 ] Network output: [ 0.9762 0.06663 -0.01701 -8.087e-05 3.63e-05 -0.002324 -6.094e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6467 0.1226 0.1229 0.2654 0.9712 0.9867 0.7436 0.8997 0.9665 0.6366 ] Network output: [ -0.006346 0.9389 1.028 -7.331e-05 3.291e-05 0.0456 -5.525e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04973 0.037 0.05314 0.03989 0.9849 0.9893 0.05093 0.9695 0.9799 0.06631 ] Network output: [ 0.08006 -0.2766 1.075 -0.0005807 0.0002607 1.039 -0.0004376 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7382 0.6347 0.5373 0.4435 0.9745 0.9886 0.7416 0.9102 0.9714 0.6323 ] Network output: [ -0.03764 0.1736 0.9477 0.0007475 -0.0003356 0.957 0.0005633 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6168 0.603 0.448 0.3076 0.9863 0.991 0.6173 0.9732 0.9818 0.4601 ] Network output: [ -0.06109 0.1969 0.9436 0.0002612 -0.0001173 0.9827 0.0001969 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6155 0.6133 0.4652 0.2837 0.9844 0.9899 0.6156 0.9674 0.9787 0.4675 ] Network output: [ 0.01805 0.9268 0.02346 -0.000279 0.0001253 1.012 -0.0002103 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02762 Epoch 2350 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03102 0.974 0.9984 8.943e-06 -4.015e-06 -0.03441 6.74e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02281 -0.00567 0.01833 0.02925 0.939 0.9485 0.04812 0.8846 0.903 0.123 ] Network output: [ 0.9762 0.0666 -0.01702 -8.036e-05 3.607e-05 -0.002338 -6.056e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6467 0.1226 0.123 0.2651 0.9712 0.9867 0.7436 0.8997 0.9665 0.6367 ] Network output: [ -0.006365 0.939 1.028 -7.359e-05 3.304e-05 0.04558 -5.546e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04972 0.03699 0.05312 0.03984 0.9849 0.9893 0.05092 0.9695 0.9799 0.06629 ] Network output: [ 0.07995 -0.2763 1.075 -0.0005838 0.0002621 1.039 -0.00044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7381 0.6346 0.5374 0.4431 0.9745 0.9886 0.7415 0.9102 0.9714 0.6324 ] Network output: [ -0.03756 0.1734 0.9476 0.0007488 -0.0003362 0.9571 0.0005643 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6169 0.603 0.448 0.3074 0.9863 0.991 0.6174 0.9732 0.9818 0.4602 ] Network output: [ -0.06101 0.1966 0.9437 0.0002633 -0.0001182 0.9828 0.0001984 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6156 0.6133 0.4652 0.2835 0.9844 0.9899 0.6157 0.9675 0.9787 0.4675 ] Network output: [ 0.01801 0.927 0.02345 -0.0002792 0.0001254 1.012 -0.0002104 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02756 Epoch 2351 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03099 0.974 0.9984 8.593e-06 -3.858e-06 -0.03441 6.476e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02281 -0.00567 0.01833 0.02922 0.939 0.9485 0.0481 0.8846 0.903 0.1229 ] Network output: [ 0.9762 0.06657 -0.01702 -7.985e-05 3.585e-05 -0.002352 -6.018e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6466 0.1226 0.123 0.2648 0.9712 0.9867 0.7435 0.8997 0.9665 0.6367 ] Network output: [ -0.006385 0.9391 1.028 -7.387e-05 3.316e-05 0.04556 -5.567e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04971 0.03698 0.05311 0.03979 0.9849 0.9893 0.05091 0.9695 0.9799 0.06626 ] Network output: [ 0.07984 -0.2761 1.075 -0.0005869 0.0002635 1.039 -0.0004423 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.738 0.6346 0.5375 0.4427 0.9745 0.9886 0.7414 0.9102 0.9714 0.6325 ] Network output: [ -0.03748 0.1732 0.9475 0.0007501 -0.0003367 0.9572 0.0005653 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.617 0.6031 0.448 0.3072 0.9863 0.991 0.6175 0.9732 0.9818 0.4602 ] Network output: [ -0.06092 0.1964 0.9437 0.0002654 -0.0001192 0.9829 0.0002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6156 0.6134 0.4652 0.2834 0.9844 0.9899 0.6157 0.9675 0.9787 0.4675 ] Network output: [ 0.01797 0.9271 0.02343 -0.0002794 0.0001255 1.012 -0.0002106 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02751 Epoch 2352 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03096 0.9741 0.9985 8.244e-06 -3.701e-06 -0.03441 6.213e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0228 -0.00567 0.01832 0.02918 0.939 0.9485 0.04808 0.8846 0.9031 0.1229 ] Network output: [ 0.9763 0.06653 -0.01703 -7.935e-05 3.562e-05 -0.002366 -5.98e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6466 0.1226 0.123 0.2644 0.9712 0.9867 0.7434 0.8997 0.9665 0.6368 ] Network output: [ -0.006404 0.9391 1.028 -7.414e-05 3.328e-05 0.04554 -5.588e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0497 0.03698 0.05309 0.03974 0.9849 0.9893 0.0509 0.9695 0.9799 0.06624 ] Network output: [ 0.07974 -0.2759 1.075 -0.00059 0.0002649 1.039 -0.0004446 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7379 0.6345 0.5376 0.4423 0.9745 0.9886 0.7413 0.9102 0.9714 0.6325 ] Network output: [ -0.03739 0.173 0.9475 0.0007514 -0.0003373 0.9573 0.0005663 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6171 0.6032 0.4481 0.307 0.9863 0.9911 0.6176 0.9732 0.9818 0.4602 ] Network output: [ -0.06083 0.1961 0.9437 0.0002676 -0.0001201 0.9829 0.0002016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6157 0.6135 0.4652 0.2832 0.9844 0.9899 0.6158 0.9675 0.9787 0.4675 ] Network output: [ 0.01793 0.9273 0.02342 -0.0002797 0.0001255 1.012 -0.0002108 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02746 Epoch 2353 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03093 0.9741 0.9985 7.896e-06 -3.545e-06 -0.03441 5.951e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02279 -0.00567 0.01832 0.02915 0.939 0.9486 0.04807 0.8846 0.9031 0.1228 ] Network output: [ 0.9763 0.0665 -0.01703 -7.885e-05 3.54e-05 -0.00238 -5.942e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6465 0.1226 0.1231 0.2641 0.9712 0.9867 0.7433 0.8998 0.9665 0.6368 ] Network output: [ -0.006424 0.9392 1.028 -7.442e-05 3.341e-05 0.04552 -5.608e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04969 0.03697 0.05308 0.03969 0.9849 0.9893 0.05089 0.9695 0.9799 0.06622 ] Network output: [ 0.07963 -0.2757 1.076 -0.0005931 0.0002663 1.038 -0.000447 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7379 0.6345 0.5376 0.4419 0.9746 0.9886 0.7412 0.9102 0.9714 0.6326 ] Network output: [ -0.03731 0.1728 0.9474 0.0007527 -0.0003379 0.9575 0.0005673 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6171 0.6033 0.4481 0.3068 0.9863 0.9911 0.6177 0.9732 0.9819 0.4603 ] Network output: [ -0.06075 0.1959 0.9437 0.0002697 -0.0001211 0.983 0.0002032 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6158 0.6135 0.4652 0.2831 0.9844 0.9899 0.6159 0.9675 0.9787 0.4675 ] Network output: [ 0.01788 0.9274 0.0234 -0.0002799 0.0001256 1.012 -0.0002109 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0274 Epoch 2354 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0309 0.9741 0.9985 7.549e-06 -3.389e-06 -0.03441 5.689e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02279 -0.00567 0.01831 0.02912 0.939 0.9486 0.04805 0.8846 0.9031 0.1228 ] Network output: [ 0.9763 0.06646 -0.01704 -7.835e-05 3.518e-05 -0.002393 -5.905e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6465 0.1226 0.1231 0.2638 0.9712 0.9867 0.7432 0.8998 0.9666 0.6369 ] Network output: [ -0.006444 0.9393 1.028 -7.469e-05 3.353e-05 0.0455 -5.629e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04968 0.03696 0.05306 0.03964 0.9849 0.9893 0.05088 0.9695 0.9799 0.0662 ] Network output: [ 0.07953 -0.2754 1.076 -0.0005962 0.0002677 1.038 -0.0004493 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7378 0.6344 0.5377 0.4416 0.9746 0.9886 0.7412 0.9103 0.9714 0.6326 ] Network output: [ -0.03723 0.1726 0.9473 0.000754 -0.0003385 0.9576 0.0005683 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6172 0.6034 0.4481 0.3066 0.9863 0.9911 0.6177 0.9732 0.9819 0.4603 ] Network output: [ -0.06066 0.1956 0.9437 0.0002718 -0.000122 0.9831 0.0002048 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6158 0.6136 0.4652 0.2829 0.9844 0.9899 0.6159 0.9675 0.9787 0.4675 ] Network output: [ 0.01784 0.9276 0.02339 -0.0002801 0.0001257 1.012 -0.0002111 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02735 Epoch 2355 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03087 0.9742 0.9985 7.202e-06 -3.233e-06 -0.03441 5.428e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02278 -0.005669 0.01831 0.02909 0.939 0.9486 0.04803 0.8847 0.9031 0.1228 ] Network output: [ 0.9764 0.06643 -0.01704 -7.786e-05 3.495e-05 -0.002407 -5.868e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6464 0.1227 0.1232 0.2635 0.9712 0.9867 0.7432 0.8998 0.9666 0.637 ] Network output: [ -0.006463 0.9393 1.028 -7.496e-05 3.365e-05 0.04548 -5.649e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04967 0.03695 0.05304 0.03958 0.9849 0.9893 0.05087 0.9695 0.9799 0.06618 ] Network output: [ 0.07942 -0.2752 1.076 -0.0005993 0.0002691 1.038 -0.0004517 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7377 0.6344 0.5378 0.4412 0.9746 0.9886 0.7411 0.9103 0.9715 0.6327 ] Network output: [ -0.03714 0.1724 0.9472 0.0007554 -0.0003391 0.9577 0.0005693 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6173 0.6034 0.4481 0.3064 0.9863 0.9911 0.6178 0.9732 0.9819 0.4603 ] Network output: [ -0.06057 0.1954 0.9438 0.0002739 -0.000123 0.9831 0.0002065 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6159 0.6137 0.4651 0.2828 0.9844 0.9899 0.616 0.9675 0.9787 0.4674 ] Network output: [ 0.0178 0.9277 0.02337 -0.0002803 0.0001258 1.012 -0.0002113 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02729 Epoch 2356 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03084 0.9742 0.9986 6.856e-06 -3.078e-06 -0.03441 5.167e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02277 -0.005669 0.0183 0.02905 0.939 0.9486 0.04802 0.8847 0.9031 0.1227 ] Network output: [ 0.9764 0.06639 -0.01705 -7.737e-05 3.473e-05 -0.00242 -5.831e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6464 0.1227 0.1232 0.2632 0.9712 0.9867 0.7431 0.8998 0.9666 0.637 ] Network output: [ -0.006483 0.9394 1.028 -7.523e-05 3.377e-05 0.04546 -5.67e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04966 0.03695 0.05303 0.03953 0.9849 0.9893 0.05086 0.9695 0.9799 0.06615 ] Network output: [ 0.07931 -0.275 1.076 -0.0006024 0.0002705 1.038 -0.000454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7376 0.6343 0.5378 0.4408 0.9746 0.9886 0.741 0.9103 0.9715 0.6328 ] Network output: [ -0.03706 0.1722 0.9471 0.0007567 -0.0003397 0.9578 0.0005703 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6174 0.6035 0.4482 0.3062 0.9863 0.9911 0.6179 0.9732 0.9819 0.4603 ] Network output: [ -0.06049 0.1951 0.9438 0.0002761 -0.0001239 0.9832 0.0002081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6159 0.6137 0.4651 0.2826 0.9844 0.9899 0.616 0.9675 0.9787 0.4674 ] Network output: [ 0.01776 0.9279 0.02336 -0.0002805 0.0001259 1.012 -0.0002114 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02724 Epoch 2357 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03081 0.9742 0.9986 6.511e-06 -2.923e-06 -0.03441 4.907e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02277 -0.005669 0.0183 0.02902 0.939 0.9486 0.048 0.8847 0.9031 0.1227 ] Network output: [ 0.9764 0.06636 -0.01706 -7.689e-05 3.452e-05 -0.002433 -5.794e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6463 0.1227 0.1232 0.2629 0.9712 0.9867 0.743 0.8998 0.9666 0.6371 ] Network output: [ -0.006503 0.9395 1.028 -7.55e-05 3.39e-05 0.04544 -5.69e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04965 0.03694 0.05301 0.03948 0.9849 0.9893 0.05085 0.9695 0.9799 0.06613 ] Network output: [ 0.0792 -0.2747 1.076 -0.0006056 0.0002719 1.038 -0.0004564 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7375 0.6343 0.5379 0.4404 0.9746 0.9886 0.7409 0.9103 0.9715 0.6328 ] Network output: [ -0.03697 0.172 0.9471 0.000758 -0.0003403 0.9579 0.0005713 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6175 0.6036 0.4482 0.306 0.9863 0.9911 0.618 0.9732 0.9819 0.4604 ] Network output: [ -0.0604 0.1949 0.9438 0.0002782 -0.0001249 0.9833 0.0002097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.616 0.6138 0.4651 0.2825 0.9844 0.9899 0.6161 0.9675 0.9787 0.4674 ] Network output: [ 0.01771 0.9281 0.02334 -0.0002808 0.000126 1.012 -0.0002116 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02719 Epoch 2358 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03078 0.9743 0.9986 6.167e-06 -2.768e-06 -0.03441 4.647e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02276 -0.005669 0.01829 0.02899 0.939 0.9486 0.04798 0.8847 0.9032 0.1227 ] Network output: [ 0.9764 0.06632 -0.01706 -7.64e-05 3.43e-05 -0.002446 -5.758e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6463 0.1227 0.1233 0.2626 0.9712 0.9867 0.7429 0.8998 0.9666 0.6372 ] Network output: [ -0.006523 0.9395 1.028 -7.577e-05 3.402e-05 0.04542 -5.71e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04964 0.03693 0.053 0.03943 0.9849 0.9893 0.05084 0.9695 0.98 0.06611 ] Network output: [ 0.0791 -0.2745 1.076 -0.0006087 0.0002733 1.038 -0.0004587 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7375 0.6342 0.538 0.44 0.9746 0.9886 0.7408 0.9103 0.9715 0.6329 ] Network output: [ -0.03689 0.1718 0.947 0.0007594 -0.0003409 0.958 0.0005723 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6175 0.6037 0.4482 0.3058 0.9863 0.9911 0.6181 0.9732 0.9819 0.4604 ] Network output: [ -0.06031 0.1946 0.9438 0.0002804 -0.0001259 0.9833 0.0002113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6161 0.6139 0.4651 0.2823 0.9844 0.9899 0.6162 0.9676 0.9787 0.4674 ] Network output: [ 0.01767 0.9282 0.02333 -0.000281 0.0001261 1.012 -0.0002118 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02713 Epoch 2359 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03076 0.9743 0.9986 5.823e-06 -2.614e-06 -0.03442 4.388e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02276 -0.005669 0.01829 0.02896 0.939 0.9486 0.04797 0.8847 0.9032 0.1226 ] Network output: [ 0.9765 0.06628 -0.01706 -7.592e-05 3.409e-05 -0.002459 -5.722e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6462 0.1227 0.1233 0.2622 0.9712 0.9867 0.7428 0.8999 0.9666 0.6372 ] Network output: [ -0.006543 0.9396 1.028 -7.604e-05 3.414e-05 0.0454 -5.731e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04963 0.03692 0.05298 0.03938 0.9849 0.9893 0.05082 0.9695 0.98 0.06609 ] Network output: [ 0.07899 -0.2742 1.076 -0.0006119 0.0002747 1.038 -0.0004611 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7374 0.6341 0.5381 0.4396 0.9746 0.9886 0.7408 0.9103 0.9715 0.633 ] Network output: [ -0.03681 0.1716 0.9469 0.0007607 -0.0003415 0.9582 0.0005733 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6176 0.6038 0.4483 0.3056 0.9863 0.9911 0.6181 0.9732 0.9819 0.4604 ] Network output: [ -0.06023 0.1943 0.9438 0.0002825 -0.0001268 0.9834 0.0002129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6161 0.6139 0.4651 0.2822 0.9844 0.9899 0.6162 0.9676 0.9788 0.4674 ] Network output: [ 0.01763 0.9284 0.02331 -0.0002812 0.0001263 1.012 -0.0002119 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02708 Epoch 2360 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03073 0.9743 0.9987 5.48e-06 -2.46e-06 -0.03442 4.13e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02275 -0.005669 0.01828 0.02892 0.9391 0.9486 0.04795 0.8848 0.9032 0.1226 ] Network output: [ 0.9765 0.06624 -0.01707 -7.545e-05 3.387e-05 -0.002472 -5.686e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6461 0.1227 0.1234 0.2619 0.9712 0.9867 0.7427 0.8999 0.9666 0.6373 ] Network output: [ -0.006563 0.9397 1.028 -7.631e-05 3.426e-05 0.04538 -5.751e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04962 0.03692 0.05296 0.03933 0.985 0.9893 0.05081 0.9695 0.98 0.06607 ] Network output: [ 0.07888 -0.274 1.076 -0.000615 0.0002761 1.038 -0.0004635 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7373 0.6341 0.5381 0.4392 0.9746 0.9886 0.7407 0.9104 0.9715 0.633 ] Network output: [ -0.03672 0.1714 0.9468 0.000762 -0.0003421 0.9583 0.0005743 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6177 0.6039 0.4483 0.3054 0.9863 0.9911 0.6182 0.9732 0.9819 0.4604 ] Network output: [ -0.06014 0.1941 0.9439 0.0002847 -0.0001278 0.9835 0.0002145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6162 0.614 0.4651 0.282 0.9844 0.99 0.6163 0.9676 0.9788 0.4674 ] Network output: [ 0.01759 0.9285 0.02329 -0.0002814 0.0001264 1.012 -0.0002121 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02702 Epoch 2361 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0307 0.9744 0.9987 5.138e-06 -2.307e-06 -0.03442 3.872e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02274 -0.005668 0.01828 0.02889 0.9391 0.9486 0.04793 0.8848 0.9032 0.1226 ] Network output: [ 0.9765 0.06621 -0.01707 -7.498e-05 3.366e-05 -0.002485 -5.651e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6461 0.1227 0.1234 0.2616 0.9712 0.9867 0.7427 0.8999 0.9666 0.6374 ] Network output: [ -0.006583 0.9397 1.028 -7.657e-05 3.438e-05 0.04536 -5.771e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04961 0.03691 0.05295 0.03927 0.985 0.9894 0.0508 0.9695 0.98 0.06605 ] Network output: [ 0.07877 -0.2738 1.076 -0.0006182 0.0002775 1.038 -0.0004659 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7372 0.634 0.5382 0.4388 0.9746 0.9886 0.7406 0.9104 0.9715 0.6331 ] Network output: [ -0.03664 0.1712 0.9468 0.0007634 -0.0003427 0.9584 0.0005753 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6178 0.6039 0.4483 0.3052 0.9863 0.9911 0.6183 0.9732 0.9819 0.4605 ] Network output: [ -0.06005 0.1938 0.9439 0.0002868 -0.0001288 0.9836 0.0002162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6163 0.6141 0.4651 0.2819 0.9844 0.99 0.6164 0.9676 0.9788 0.4673 ] Network output: [ 0.01755 0.9287 0.02328 -0.0002817 0.0001265 1.012 -0.0002123 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02697 Epoch 2362 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03067 0.9744 0.9987 4.797e-06 -2.153e-06 -0.03442 3.615e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02274 -0.005668 0.01827 0.02886 0.9391 0.9486 0.04792 0.8848 0.9032 0.1225 ] Network output: [ 0.9766 0.06617 -0.01708 -7.451e-05 3.345e-05 -0.002498 -5.615e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.646 0.1228 0.1234 0.2613 0.9712 0.9867 0.7426 0.8999 0.9666 0.6374 ] Network output: [ -0.006603 0.9398 1.028 -7.684e-05 3.449e-05 0.04534 -5.791e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0496 0.0369 0.05293 0.03922 0.985 0.9894 0.05079 0.9696 0.98 0.06603 ] Network output: [ 0.07866 -0.2735 1.076 -0.0006213 0.0002789 1.038 -0.0004682 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7371 0.634 0.5383 0.4384 0.9746 0.9886 0.7405 0.9104 0.9715 0.6331 ] Network output: [ -0.03655 0.171 0.9467 0.0007647 -0.0003433 0.9585 0.0005763 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6179 0.604 0.4483 0.3049 0.9863 0.9911 0.6184 0.9732 0.9819 0.4605 ] Network output: [ -0.05996 0.1936 0.9439 0.000289 -0.0001298 0.9836 0.0002178 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6163 0.6141 0.465 0.2817 0.9844 0.99 0.6164 0.9676 0.9788 0.4673 ] Network output: [ 0.0175 0.9288 0.02326 -0.0002819 0.0001266 1.012 -0.0002124 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02691 Epoch 2363 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03064 0.9744 0.9987 4.456e-06 -2.001e-06 -0.03442 3.358e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02273 -0.005668 0.01827 0.02883 0.9391 0.9486 0.0479 0.8848 0.9032 0.1225 ] Network output: [ 0.9766 0.06613 -0.01708 -7.405e-05 3.324e-05 -0.00251 -5.58e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.646 0.1228 0.1235 0.261 0.9712 0.9867 0.7425 0.8999 0.9666 0.6375 ] Network output: [ -0.006623 0.9399 1.028 -7.71e-05 3.461e-05 0.04532 -5.811e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04959 0.03689 0.05292 0.03917 0.985 0.9894 0.05078 0.9696 0.98 0.066 ] Network output: [ 0.07856 -0.2733 1.076 -0.0006245 0.0002803 1.037 -0.0004706 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7371 0.6339 0.5383 0.438 0.9746 0.9886 0.7404 0.9104 0.9715 0.6332 ] Network output: [ -0.03647 0.1708 0.9466 0.0007661 -0.0003439 0.9586 0.0005773 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.618 0.6041 0.4484 0.3047 0.9863 0.9911 0.6185 0.9732 0.9819 0.4605 ] Network output: [ -0.05987 0.1933 0.9439 0.0002912 -0.0001307 0.9837 0.0002195 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6164 0.6142 0.465 0.2815 0.9844 0.99 0.6165 0.9676 0.9788 0.4673 ] Network output: [ 0.01746 0.929 0.02325 -0.0002821 0.0001267 1.012 -0.0002126 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02686 Epoch 2364 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03061 0.9745 0.9988 4.117e-06 -1.848e-06 -0.03442 3.102e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02273 -0.005668 0.01826 0.02879 0.9391 0.9486 0.04788 0.8848 0.9033 0.1225 ] Network output: [ 0.9766 0.06609 -0.01709 -7.359e-05 3.304e-05 -0.002523 -5.546e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6459 0.1228 0.1235 0.2607 0.9712 0.9867 0.7424 0.9 0.9666 0.6376 ] Network output: [ -0.006643 0.94 1.028 -7.736e-05 3.473e-05 0.0453 -5.83e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04958 0.03689 0.0529 0.03912 0.985 0.9894 0.05077 0.9696 0.98 0.06598 ] Network output: [ 0.07845 -0.273 1.076 -0.0006276 0.0002818 1.037 -0.000473 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.737 0.6339 0.5384 0.4376 0.9746 0.9886 0.7403 0.9104 0.9715 0.6333 ] Network output: [ -0.03638 0.1706 0.9465 0.0007674 -0.0003445 0.9588 0.0005784 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.618 0.6042 0.4484 0.3045 0.9863 0.9911 0.6186 0.9732 0.9819 0.4605 ] Network output: [ -0.05979 0.1931 0.9439 0.0002934 -0.0001317 0.9838 0.0002211 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6165 0.6143 0.465 0.2814 0.9844 0.99 0.6166 0.9676 0.9788 0.4673 ] Network output: [ 0.01742 0.9291 0.02323 -0.0002823 0.0001268 1.012 -0.0002128 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0268 Epoch 2365 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03058 0.9745 0.9988 3.778e-06 -1.696e-06 -0.03442 2.847e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02272 -0.005668 0.01826 0.02876 0.9391 0.9486 0.04786 0.8849 0.9033 0.1224 ] Network output: [ 0.9766 0.06605 -0.01709 -7.313e-05 3.283e-05 -0.002535 -5.511e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6459 0.1228 0.1236 0.2603 0.9712 0.9867 0.7423 0.9 0.9666 0.6376 ] Network output: [ -0.006663 0.94 1.028 -7.763e-05 3.485e-05 0.04528 -5.85e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04957 0.03688 0.05289 0.03907 0.985 0.9894 0.05076 0.9696 0.98 0.06596 ] Network output: [ 0.07834 -0.2728 1.076 -0.0006308 0.0002832 1.037 -0.0004754 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7369 0.6338 0.5385 0.4372 0.9746 0.9886 0.7403 0.9104 0.9715 0.6333 ] Network output: [ -0.0363 0.1704 0.9465 0.0007688 -0.0003451 0.9589 0.0005794 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6181 0.6043 0.4484 0.3043 0.9863 0.9911 0.6186 0.9732 0.9819 0.4606 ] Network output: [ -0.0597 0.1928 0.944 0.0002955 -0.0001327 0.9838 0.0002227 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6165 0.6143 0.465 0.2812 0.9845 0.99 0.6166 0.9676 0.9788 0.4673 ] Network output: [ 0.01738 0.9293 0.02322 -0.0002826 0.0001269 1.012 -0.000213 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02675 Epoch 2366 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03055 0.9745 0.9988 3.44e-06 -1.544e-06 -0.03443 2.592e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02271 -0.005667 0.01826 0.02873 0.9391 0.9487 0.04785 0.8849 0.9033 0.1224 ] Network output: [ 0.9767 0.06601 -0.01709 -7.268e-05 3.263e-05 -0.002547 -5.477e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6458 0.1228 0.1236 0.26 0.9713 0.9867 0.7422 0.9 0.9667 0.6377 ] Network output: [ -0.006683 0.9401 1.028 -7.789e-05 3.497e-05 0.04526 -5.87e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04956 0.03687 0.05287 0.03901 0.985 0.9894 0.05075 0.9696 0.98 0.06594 ] Network output: [ 0.07823 -0.2725 1.076 -0.000634 0.0002846 1.037 -0.0004778 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7368 0.6338 0.5386 0.4367 0.9746 0.9886 0.7402 0.9105 0.9715 0.6334 ] Network output: [ -0.03621 0.1702 0.9464 0.0007702 -0.0003457 0.959 0.0005804 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6182 0.6044 0.4485 0.3041 0.9863 0.9911 0.6187 0.9733 0.9819 0.4606 ] Network output: [ -0.05961 0.1925 0.944 0.0002977 -0.0001337 0.9839 0.0002244 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6166 0.6144 0.465 0.2811 0.9845 0.99 0.6167 0.9676 0.9788 0.4673 ] Network output: [ 0.01734 0.9294 0.0232 -0.0002828 0.000127 1.012 -0.0002131 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02669 Epoch 2367 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03052 0.9746 0.9988 3.102e-06 -1.393e-06 -0.03443 2.338e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02271 -0.005667 0.01825 0.0287 0.9391 0.9487 0.04783 0.8849 0.9033 0.1223 ] Network output: [ 0.9767 0.06597 -0.0171 -7.223e-05 3.242e-05 -0.002559 -5.443e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6458 0.1228 0.1236 0.2597 0.9713 0.9867 0.7422 0.9 0.9667 0.6377 ] Network output: [ -0.006704 0.9402 1.028 -7.815e-05 3.508e-05 0.04524 -5.889e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04955 0.03686 0.05285 0.03896 0.985 0.9894 0.05074 0.9696 0.98 0.06592 ] Network output: [ 0.07812 -0.2723 1.076 -0.0006372 0.0002861 1.037 -0.0004802 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7367 0.6337 0.5386 0.4363 0.9746 0.9886 0.7401 0.9105 0.9715 0.6335 ] Network output: [ -0.03613 0.17 0.9463 0.0007715 -0.0003464 0.9591 0.0005814 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6183 0.6044 0.4485 0.3039 0.9863 0.9911 0.6188 0.9733 0.9819 0.4606 ] Network output: [ -0.05952 0.1923 0.944 0.0002999 -0.0001346 0.984 0.000226 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6167 0.6145 0.465 0.2809 0.9845 0.99 0.6168 0.9677 0.9788 0.4672 ] Network output: [ 0.01729 0.9296 0.02319 -0.000283 0.0001271 1.011 -0.0002133 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02663 Epoch 2368 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03049 0.9746 0.9989 2.766e-06 -1.242e-06 -0.03443 2.085e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0227 -0.005667 0.01825 0.02867 0.9391 0.9487 0.04781 0.8849 0.9033 0.1223 ] Network output: [ 0.9767 0.06593 -0.0171 -7.178e-05 3.222e-05 -0.002571 -5.41e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6457 0.1228 0.1237 0.2594 0.9713 0.9867 0.7421 0.9 0.9667 0.6378 ] Network output: [ -0.006724 0.9402 1.028 -7.84e-05 3.52e-05 0.04521 -5.909e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04954 0.03685 0.05284 0.03891 0.985 0.9894 0.05073 0.9696 0.98 0.0659 ] Network output: [ 0.07801 -0.2721 1.076 -0.0006404 0.0002875 1.037 -0.0004826 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7366 0.6336 0.5387 0.4359 0.9746 0.9886 0.74 0.9105 0.9715 0.6335 ] Network output: [ -0.03604 0.1698 0.9462 0.0007729 -0.000347 0.9592 0.0005825 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6184 0.6045 0.4485 0.3037 0.9864 0.9911 0.6189 0.9733 0.9819 0.4607 ] Network output: [ -0.05943 0.192 0.944 0.0003021 -0.0001356 0.9841 0.0002277 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6167 0.6145 0.4649 0.2808 0.9845 0.99 0.6168 0.9677 0.9788 0.4672 ] Network output: [ 0.01725 0.9297 0.02317 -0.0002832 0.0001272 1.011 -0.0002135 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02658 Epoch 2369 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03045 0.9746 0.9989 2.431e-06 -1.091e-06 -0.03443 1.832e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0227 -0.005667 0.01824 0.02863 0.9391 0.9487 0.0478 0.8849 0.9033 0.1223 ] Network output: [ 0.9768 0.06589 -0.0171 -7.134e-05 3.203e-05 -0.002583 -5.376e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6457 0.1229 0.1237 0.2591 0.9713 0.9867 0.742 0.9 0.9667 0.6379 ] Network output: [ -0.006745 0.9403 1.028 -7.866e-05 3.531e-05 0.04519 -5.928e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04953 0.03685 0.05282 0.03886 0.985 0.9894 0.05072 0.9696 0.98 0.06588 ] Network output: [ 0.0779 -0.2718 1.077 -0.0006436 0.0002889 1.037 -0.000485 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7366 0.6336 0.5388 0.4355 0.9746 0.9886 0.7399 0.9105 0.9716 0.6336 ] Network output: [ -0.03596 0.1696 0.9462 0.0007742 -0.0003476 0.9594 0.0005835 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6185 0.6046 0.4485 0.3035 0.9864 0.9911 0.619 0.9733 0.9819 0.4607 ] Network output: [ -0.05934 0.1917 0.944 0.0003043 -0.0001366 0.9841 0.0002293 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6168 0.6146 0.4649 0.2806 0.9845 0.99 0.6169 0.9677 0.9788 0.4672 ] Network output: [ 0.01721 0.9299 0.02316 -0.0002835 0.0001273 1.011 -0.0002136 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02652 Epoch 2370 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03042 0.9747 0.9989 2.096e-06 -9.409e-07 -0.03443 1.58e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02269 -0.005666 0.01824 0.0286 0.9391 0.9487 0.04778 0.885 0.9034 0.1222 ] Network output: [ 0.9768 0.06585 -0.0171 -7.09e-05 3.183e-05 -0.002595 -5.343e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6456 0.1229 0.1238 0.2588 0.9713 0.9867 0.7419 0.9001 0.9667 0.6379 ] Network output: [ -0.006765 0.9404 1.028 -7.892e-05 3.543e-05 0.04517 -5.948e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04952 0.03684 0.05281 0.03881 0.985 0.9894 0.05071 0.9696 0.98 0.06586 ] Network output: [ 0.07779 -0.2715 1.077 -0.0006468 0.0002904 1.037 -0.0004874 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7365 0.6335 0.5389 0.4351 0.9746 0.9886 0.7398 0.9105 0.9716 0.6337 ] Network output: [ -0.03587 0.1693 0.9461 0.0007756 -0.0003482 0.9595 0.0005845 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6186 0.6047 0.4486 0.3032 0.9864 0.9911 0.6191 0.9733 0.9819 0.4607 ] Network output: [ -0.05925 0.1915 0.9441 0.0003065 -0.0001376 0.9842 0.000231 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6169 0.6147 0.4649 0.2805 0.9845 0.99 0.617 0.9677 0.9788 0.4672 ] Network output: [ 0.01717 0.93 0.02314 -0.0002837 0.0001274 1.011 -0.0002138 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02647 Epoch 2371 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03039 0.9747 0.9989 1.762e-06 -7.91e-07 -0.03443 1.328e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02268 -0.005666 0.01823 0.02857 0.9392 0.9487 0.04776 0.885 0.9034 0.1222 ] Network output: [ 0.9768 0.06581 -0.01711 -7.046e-05 3.163e-05 -0.002606 -5.31e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6456 0.1229 0.1238 0.2584 0.9713 0.9867 0.7418 0.9001 0.9667 0.638 ] Network output: [ -0.006786 0.9405 1.028 -7.917e-05 3.554e-05 0.04515 -5.967e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04951 0.03683 0.05279 0.03876 0.985 0.9894 0.0507 0.9696 0.98 0.06584 ] Network output: [ 0.07767 -0.2713 1.077 -0.00065 0.0002918 1.037 -0.0004898 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7364 0.6335 0.5389 0.4347 0.9746 0.9886 0.7398 0.9105 0.9716 0.6337 ] Network output: [ -0.03579 0.1691 0.946 0.000777 -0.0003488 0.9596 0.0005856 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6186 0.6048 0.4486 0.303 0.9864 0.9911 0.6192 0.9733 0.9819 0.4607 ] Network output: [ -0.05916 0.1912 0.9441 0.0003087 -0.0001386 0.9843 0.0002327 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.617 0.6147 0.4649 0.2803 0.9845 0.99 0.6171 0.9677 0.9788 0.4672 ] Network output: [ 0.01713 0.9302 0.02313 -0.0002839 0.0001275 1.011 -0.000214 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02641 Epoch 2372 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03036 0.9748 0.999 1.429e-06 -6.415e-07 -0.03444 1.077e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02268 -0.005666 0.01823 0.02854 0.9392 0.9487 0.04775 0.885 0.9034 0.1222 ] Network output: [ 0.9768 0.06576 -0.01711 -7.003e-05 3.144e-05 -0.002618 -5.278e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6455 0.1229 0.1238 0.2581 0.9713 0.9867 0.7417 0.9001 0.9667 0.6381 ] Network output: [ -0.006806 0.9405 1.028 -7.943e-05 3.566e-05 0.04513 -5.986e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0495 0.03682 0.05278 0.0387 0.985 0.9894 0.05069 0.9696 0.98 0.06581 ] Network output: [ 0.07756 -0.271 1.077 -0.0006532 0.0002932 1.037 -0.0004923 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7363 0.6334 0.539 0.4343 0.9746 0.9887 0.7397 0.9106 0.9716 0.6338 ] Network output: [ -0.0357 0.1689 0.9459 0.0007784 -0.0003494 0.9597 0.0005866 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6187 0.6049 0.4486 0.3028 0.9864 0.9911 0.6192 0.9733 0.9819 0.4608 ] Network output: [ -0.05907 0.191 0.9441 0.0003109 -0.0001396 0.9844 0.0002343 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.617 0.6148 0.4649 0.2801 0.9845 0.99 0.6171 0.9677 0.9789 0.4672 ] Network output: [ 0.01708 0.9303 0.02311 -0.0002842 0.0001276 1.011 -0.0002141 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02635 Epoch 2373 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03033 0.9748 0.999 1.097e-06 -4.924e-07 -0.03444 8.266e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02267 -0.005666 0.01822 0.0285 0.9392 0.9487 0.04773 0.885 0.9034 0.1221 ] Network output: [ 0.9769 0.06572 -0.01711 -6.96e-05 3.125e-05 -0.002629 -5.246e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6454 0.1229 0.1239 0.2578 0.9713 0.9867 0.7416 0.9001 0.9667 0.6382 ] Network output: [ -0.006827 0.9406 1.028 -7.968e-05 3.577e-05 0.04511 -6.005e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04949 0.03682 0.05276 0.03865 0.985 0.9894 0.05068 0.9696 0.98 0.06579 ] Network output: [ 0.07745 -0.2708 1.077 -0.0006564 0.0002947 1.036 -0.0004947 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7362 0.6334 0.5391 0.4339 0.9747 0.9887 0.7396 0.9106 0.9716 0.6338 ] Network output: [ -0.03562 0.1687 0.9459 0.0007797 -0.00035 0.9598 0.0005876 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6188 0.605 0.4487 0.3026 0.9864 0.9911 0.6193 0.9733 0.9819 0.4608 ] Network output: [ -0.05898 0.1907 0.9441 0.0003132 -0.0001406 0.9844 0.000236 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6171 0.6149 0.4649 0.28 0.9845 0.99 0.6172 0.9677 0.9789 0.4671 ] Network output: [ 0.01704 0.9305 0.0231 -0.0002844 0.0001277 1.011 -0.0002143 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0263 Epoch 2374 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0303 0.9748 0.999 7.655e-07 -3.437e-07 -0.03444 5.769e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02267 -0.005665 0.01822 0.02847 0.9392 0.9487 0.04771 0.885 0.9034 0.1221 ] Network output: [ 0.9769 0.06568 -0.01711 -6.918e-05 3.106e-05 -0.00264 -5.214e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6454 0.1229 0.1239 0.2575 0.9713 0.9867 0.7416 0.9001 0.9667 0.6382 ] Network output: [ -0.006847 0.9407 1.028 -7.993e-05 3.588e-05 0.04509 -6.024e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04948 0.03681 0.05275 0.0386 0.985 0.9894 0.05067 0.9696 0.98 0.06577 ] Network output: [ 0.07734 -0.2705 1.077 -0.0006596 0.0002961 1.036 -0.0004971 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7362 0.6333 0.5391 0.4335 0.9747 0.9887 0.7395 0.9106 0.9716 0.6339 ] Network output: [ -0.03553 0.1685 0.9458 0.0007811 -0.0003507 0.96 0.0005887 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6189 0.605 0.4487 0.3024 0.9864 0.9911 0.6194 0.9733 0.982 0.4608 ] Network output: [ -0.05889 0.1904 0.9441 0.0003154 -0.0001416 0.9845 0.0002377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6172 0.6149 0.4648 0.2798 0.9845 0.99 0.6173 0.9677 0.9789 0.4671 ] Network output: [ 0.017 0.9307 0.02308 -0.0002846 0.0001278 1.011 -0.0002145 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02624 Epoch 2375 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03027 0.9749 0.999 4.351e-07 -1.953e-07 -0.03444 3.279e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02266 -0.005665 0.01822 0.02844 0.9392 0.9487 0.0477 0.885 0.9034 0.1221 ] Network output: [ 0.9769 0.06563 -0.01711 -6.876e-05 3.087e-05 -0.002651 -5.182e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6453 0.1229 0.124 0.2572 0.9713 0.9867 0.7415 0.9001 0.9667 0.6383 ] Network output: [ -0.006868 0.9407 1.028 -8.018e-05 3.6e-05 0.04506 -6.043e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04947 0.0368 0.05273 0.03855 0.985 0.9894 0.05066 0.9696 0.98 0.06575 ] Network output: [ 0.07723 -0.2703 1.077 -0.0006629 0.0002976 1.036 -0.0004995 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7361 0.6332 0.5392 0.4331 0.9747 0.9887 0.7394 0.9106 0.9716 0.634 ] Network output: [ -0.03544 0.1683 0.9457 0.0007825 -0.0003513 0.9601 0.0005897 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.619 0.6051 0.4487 0.3022 0.9864 0.9911 0.6195 0.9733 0.982 0.4609 ] Network output: [ -0.0588 0.1902 0.9441 0.0003176 -0.0001426 0.9846 0.0002394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6172 0.615 0.4648 0.2797 0.9845 0.99 0.6173 0.9678 0.9789 0.4671 ] Network output: [ 0.01696 0.9308 0.02306 -0.0002848 0.0001279 1.011 -0.0002147 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02618 Epoch 2376 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03024 0.9749 0.9991 1.056e-07 -4.74e-08 -0.03444 7.957e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02266 -0.005665 0.01821 0.02841 0.9392 0.9487 0.04768 0.8851 0.9035 0.122 ] Network output: [ 0.977 0.06559 -0.01712 -6.834e-05 3.068e-05 -0.002662 -5.151e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6453 0.123 0.124 0.2568 0.9713 0.9867 0.7414 0.9002 0.9667 0.6384 ] Network output: [ -0.006889 0.9408 1.028 -8.043e-05 3.611e-05 0.04504 -6.062e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04946 0.03679 0.05271 0.0385 0.985 0.9894 0.05065 0.9697 0.98 0.06573 ] Network output: [ 0.07711 -0.27 1.077 -0.0006661 0.000299 1.036 -0.000502 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.736 0.6332 0.5393 0.4326 0.9747 0.9887 0.7393 0.9106 0.9716 0.634 ] Network output: [ -0.03536 0.1681 0.9456 0.0007839 -0.0003519 0.9602 0.0005908 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6191 0.6052 0.4488 0.302 0.9864 0.9911 0.6196 0.9733 0.982 0.4609 ] Network output: [ -0.05871 0.1899 0.9442 0.0003198 -0.0001436 0.9847 0.000241 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6173 0.6151 0.4648 0.2795 0.9845 0.99 0.6174 0.9678 0.9789 0.4671 ] Network output: [ 0.01692 0.931 0.02305 -0.0002851 0.000128 1.011 -0.0002148 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02613 Epoch 2377 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03021 0.9749 0.9991 -2.231e-07 1.001e-07 -0.03445 -1.681e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02265 -0.005664 0.01821 0.02838 0.9392 0.9487 0.04766 0.8851 0.9035 0.122 ] Network output: [ 0.977 0.06554 -0.01712 -6.793e-05 3.05e-05 -0.002673 -5.12e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6452 0.123 0.124 0.2565 0.9713 0.9867 0.7413 0.9002 0.9667 0.6384 ] Network output: [ -0.00691 0.9409 1.028 -8.068e-05 3.622e-05 0.04502 -6.08e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04946 0.03679 0.0527 0.03845 0.985 0.9894 0.05064 0.9697 0.98 0.06571 ] Network output: [ 0.077 -0.2698 1.077 -0.0006693 0.0003005 1.036 -0.0005044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7359 0.6331 0.5394 0.4322 0.9747 0.9887 0.7393 0.9106 0.9716 0.6341 ] Network output: [ -0.03527 0.1678 0.9456 0.0007853 -0.0003525 0.9603 0.0005918 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6192 0.6053 0.4488 0.3017 0.9864 0.9911 0.6197 0.9733 0.982 0.4609 ] Network output: [ -0.05862 0.1896 0.9442 0.0003221 -0.0001446 0.9847 0.0002427 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6174 0.6152 0.4648 0.2793 0.9845 0.99 0.6175 0.9678 0.9789 0.4671 ] Network output: [ 0.01687 0.9311 0.02303 -0.0002853 0.0001281 1.011 -0.000215 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02607 Epoch 2378 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03018 0.975 0.9991 -5.509e-07 2.473e-07 -0.03445 -4.151e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02264 -0.005664 0.0182 0.02834 0.9392 0.9487 0.04765 0.8851 0.9035 0.122 ] Network output: [ 0.977 0.0655 -0.01712 -6.752e-05 3.031e-05 -0.002684 -5.089e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6452 0.123 0.1241 0.2562 0.9713 0.9868 0.7412 0.9002 0.9668 0.6385 ] Network output: [ -0.00693 0.941 1.028 -8.093e-05 3.633e-05 0.045 -6.099e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04945 0.03678 0.05268 0.03839 0.985 0.9894 0.05063 0.9697 0.9801 0.06569 ] Network output: [ 0.07689 -0.2695 1.077 -0.0006726 0.0003019 1.036 -0.0005069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7358 0.6331 0.5394 0.4318 0.9747 0.9887 0.7392 0.9106 0.9716 0.6342 ] Network output: [ -0.03519 0.1676 0.9455 0.0007866 -0.0003532 0.9605 0.0005928 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6193 0.6054 0.4488 0.3015 0.9864 0.9911 0.6198 0.9733 0.982 0.4609 ] Network output: [ -0.05853 0.1894 0.9442 0.0003243 -0.0001456 0.9848 0.0002444 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6175 0.6152 0.4648 0.2792 0.9845 0.99 0.6175 0.9678 0.9789 0.4671 ] Network output: [ 0.01683 0.9313 0.02302 -0.0002855 0.0001282 1.011 -0.0002152 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02601 Epoch 2379 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03015 0.975 0.9991 -8.777e-07 3.94e-07 -0.03445 -6.615e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02264 -0.005664 0.0182 0.02831 0.9392 0.9488 0.04763 0.8851 0.9035 0.1219 ] Network output: [ 0.977 0.06545 -0.01712 -6.712e-05 3.013e-05 -0.002695 -5.058e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6451 0.123 0.1241 0.2559 0.9713 0.9868 0.7411 0.9002 0.9668 0.6386 ] Network output: [ -0.006951 0.941 1.028 -8.117e-05 3.644e-05 0.04498 -6.117e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04944 0.03677 0.05267 0.03834 0.985 0.9894 0.05062 0.9697 0.9801 0.06567 ] Network output: [ 0.07678 -0.2692 1.077 -0.0006758 0.0003034 1.036 -0.0005093 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7357 0.633 0.5395 0.4314 0.9747 0.9887 0.7391 0.9107 0.9716 0.6342 ] Network output: [ -0.0351 0.1674 0.9454 0.000788 -0.0003538 0.9606 0.0005939 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6193 0.6055 0.4488 0.3013 0.9864 0.9911 0.6198 0.9733 0.982 0.461 ] Network output: [ -0.05844 0.1891 0.9442 0.0003265 -0.0001466 0.9849 0.0002461 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6175 0.6153 0.4648 0.279 0.9845 0.99 0.6176 0.9678 0.9789 0.467 ] Network output: [ 0.01679 0.9314 0.023 -0.0002857 0.0001283 1.011 -0.0002153 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02595 Epoch 2380 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03012 0.9751 0.9991 -1.204e-06 5.404e-07 -0.03445 -9.072e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02263 -0.005663 0.0182 0.02828 0.9392 0.9488 0.04761 0.8851 0.9035 0.1219 ] Network output: [ 0.9771 0.06541 -0.01712 -6.672e-05 2.995e-05 -0.002705 -5.028e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6451 0.123 0.1242 0.2556 0.9713 0.9868 0.7411 0.9002 0.9668 0.6386 ] Network output: [ -0.006972 0.9411 1.028 -8.141e-05 3.655e-05 0.04495 -6.136e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04943 0.03676 0.05265 0.03829 0.985 0.9894 0.05061 0.9697 0.9801 0.06565 ] Network output: [ 0.07666 -0.269 1.077 -0.0006791 0.0003049 1.036 -0.0005118 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7357 0.6329 0.5396 0.431 0.9747 0.9887 0.739 0.9107 0.9716 0.6343 ] Network output: [ -0.03502 0.1672 0.9453 0.0007894 -0.0003544 0.9607 0.0005949 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6194 0.6056 0.4489 0.3011 0.9864 0.9911 0.6199 0.9733 0.982 0.461 ] Network output: [ -0.05835 0.1888 0.9442 0.0003288 -0.0001476 0.985 0.0002478 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6176 0.6154 0.4648 0.2789 0.9845 0.99 0.6177 0.9678 0.9789 0.467 ] Network output: [ 0.01675 0.9316 0.02299 -0.000286 0.0001284 1.011 -0.0002155 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0259 Epoch 2381 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03009 0.9751 0.9992 -1.529e-06 6.863e-07 -0.03446 -1.152e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02263 -0.005663 0.01819 0.02825 0.9392 0.9488 0.0476 0.8852 0.9035 0.1219 ] Network output: [ 0.9771 0.06536 -0.01712 -6.632e-05 2.977e-05 -0.002716 -4.998e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.645 0.123 0.1242 0.2552 0.9713 0.9868 0.741 0.9002 0.9668 0.6387 ] Network output: [ -0.006993 0.9412 1.028 -8.166e-05 3.666e-05 0.04493 -6.154e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04942 0.03676 0.05264 0.03824 0.985 0.9894 0.05059 0.9697 0.9801 0.06563 ] Network output: [ 0.07655 -0.2687 1.077 -0.0006823 0.0003063 1.036 -0.0005142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7356 0.6329 0.5397 0.4306 0.9747 0.9887 0.7389 0.9107 0.9716 0.6344 ] Network output: [ -0.03493 0.167 0.9453 0.0007908 -0.000355 0.9608 0.000596 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6195 0.6057 0.4489 0.3009 0.9864 0.9911 0.62 0.9733 0.982 0.461 ] Network output: [ -0.05826 0.1885 0.9443 0.000331 -0.0001486 0.9851 0.0002495 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6177 0.6155 0.4647 0.2787 0.9845 0.99 0.6178 0.9678 0.9789 0.467 ] Network output: [ 0.01671 0.9317 0.02297 -0.0002862 0.0001285 1.011 -0.0002157 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02584 Epoch 2382 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03006 0.9751 0.9992 -1.853e-06 8.319e-07 -0.03446 -1.396e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02262 -0.005662 0.01819 0.02822 0.9393 0.9488 0.04758 0.8852 0.9036 0.1218 ] Network output: [ 0.9771 0.06531 -0.01712 -6.593e-05 2.96e-05 -0.002726 -4.969e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.645 0.123 0.1243 0.2549 0.9713 0.9868 0.7409 0.9003 0.9668 0.6388 ] Network output: [ -0.007014 0.9413 1.028 -8.19e-05 3.677e-05 0.04491 -6.172e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04941 0.03675 0.05262 0.03819 0.985 0.9894 0.05058 0.9697 0.9801 0.06561 ] Network output: [ 0.07644 -0.2684 1.077 -0.0006856 0.0003078 1.035 -0.0005167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7355 0.6328 0.5397 0.4302 0.9747 0.9887 0.7388 0.9107 0.9716 0.6344 ] Network output: [ -0.03484 0.1668 0.9452 0.0007922 -0.0003557 0.9609 0.000597 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6196 0.6057 0.4489 0.3006 0.9864 0.9911 0.6201 0.9733 0.982 0.4611 ] Network output: [ -0.05817 0.1883 0.9443 0.0003333 -0.0001496 0.9851 0.0002512 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6177 0.6155 0.4647 0.2785 0.9845 0.99 0.6178 0.9678 0.9789 0.467 ] Network output: [ 0.01667 0.9319 0.02296 -0.0002864 0.0001286 1.011 -0.0002158 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02578 Epoch 2383 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03003 0.9752 0.9992 -2.176e-06 9.77e-07 -0.03446 -1.64e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02262 -0.005662 0.01818 0.02819 0.9393 0.9488 0.04756 0.8852 0.9036 0.1218 ] Network output: [ 0.9772 0.06526 -0.01712 -6.554e-05 2.942e-05 -0.002736 -4.939e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6449 0.1231 0.1243 0.2546 0.9713 0.9868 0.7408 0.9003 0.9668 0.6388 ] Network output: [ -0.007035 0.9413 1.028 -8.214e-05 3.687e-05 0.04489 -6.19e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0494 0.03674 0.05261 0.03814 0.985 0.9894 0.05057 0.9697 0.9801 0.06559 ] Network output: [ 0.07632 -0.2682 1.077 -0.0006888 0.0003092 1.035 -0.0005191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7354 0.6328 0.5398 0.4297 0.9747 0.9887 0.7387 0.9107 0.9716 0.6345 ] Network output: [ -0.03476 0.1665 0.9451 0.0007936 -0.0003563 0.9611 0.0005981 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6197 0.6058 0.449 0.3004 0.9864 0.9911 0.6202 0.9733 0.982 0.4611 ] Network output: [ -0.05808 0.188 0.9443 0.0003355 -0.0001506 0.9852 0.0002529 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6178 0.6156 0.4647 0.2784 0.9845 0.99 0.6179 0.9678 0.9789 0.467 ] Network output: [ 0.01663 0.932 0.02294 -0.0002866 0.0001287 1.011 -0.000216 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02572 Epoch 2384 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03 0.9752 0.9992 -2.499e-06 1.122e-06 -0.03446 -1.883e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02261 -0.005662 0.01818 0.02815 0.9393 0.9488 0.04755 0.8852 0.9036 0.1218 ] Network output: [ 0.9772 0.06522 -0.01712 -6.515e-05 2.925e-05 -0.002746 -4.91e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6449 0.1231 0.1243 0.2543 0.9714 0.9868 0.7407 0.9003 0.9668 0.6389 ] Network output: [ -0.007056 0.9414 1.027 -8.238e-05 3.698e-05 0.04486 -6.208e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04939 0.03673 0.05259 0.03809 0.985 0.9894 0.05056 0.9697 0.9801 0.06557 ] Network output: [ 0.07621 -0.2679 1.077 -0.0006921 0.0003107 1.035 -0.0005216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7353 0.6327 0.5399 0.4293 0.9747 0.9887 0.7387 0.9107 0.9716 0.6346 ] Network output: [ -0.03467 0.1663 0.9451 0.000795 -0.0003569 0.9612 0.0005991 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6198 0.6059 0.449 0.3002 0.9864 0.9911 0.6203 0.9733 0.982 0.4611 ] Network output: [ -0.05798 0.1877 0.9443 0.0003378 -0.0001516 0.9853 0.0002546 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6179 0.6157 0.4647 0.2782 0.9845 0.99 0.618 0.9679 0.9789 0.467 ] Network output: [ 0.01658 0.9322 0.02293 -0.0002868 0.0001288 1.011 -0.0002162 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02567 Epoch 2385 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02997 0.9753 0.9993 -2.82e-06 1.266e-06 -0.03447 -2.125e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02261 -0.005661 0.01818 0.02812 0.9393 0.9488 0.04753 0.8852 0.9036 0.1217 ] Network output: [ 0.9772 0.06517 -0.01712 -6.477e-05 2.908e-05 -0.002756 -4.881e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6448 0.1231 0.1244 0.254 0.9714 0.9868 0.7406 0.9003 0.9668 0.639 ] Network output: [ -0.007078 0.9415 1.027 -8.261e-05 3.709e-05 0.04484 -6.226e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04938 0.03673 0.05258 0.03803 0.985 0.9894 0.05055 0.9697 0.9801 0.06555 ] Network output: [ 0.07609 -0.2676 1.077 -0.0006954 0.0003122 1.035 -0.0005241 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7352 0.6326 0.54 0.4289 0.9747 0.9887 0.7386 0.9107 0.9717 0.6346 ] Network output: [ -0.03458 0.1661 0.945 0.0007964 -0.0003575 0.9613 0.0006002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6199 0.606 0.449 0.3 0.9864 0.9911 0.6204 0.9733 0.982 0.4611 ] Network output: [ -0.05789 0.1875 0.9443 0.0003401 -0.0001527 0.9854 0.0002563 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.618 0.6157 0.4647 0.278 0.9845 0.99 0.6181 0.9679 0.979 0.4669 ] Network output: [ 0.01654 0.9323 0.02291 -0.0002871 0.0001289 1.011 -0.0002163 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02561 Epoch 2386 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02994 0.9753 0.9993 -3.14e-06 1.41e-06 -0.03447 -2.367e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0226 -0.005661 0.01817 0.02809 0.9393 0.9488 0.04751 0.8852 0.9036 0.1217 ] Network output: [ 0.9773 0.06512 -0.01712 -6.439e-05 2.891e-05 -0.002766 -4.853e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6447 0.1231 0.1244 0.2536 0.9714 0.9868 0.7406 0.9003 0.9668 0.639 ] Network output: [ -0.007099 0.9416 1.027 -8.285e-05 3.719e-05 0.04482 -6.244e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04937 0.03672 0.05256 0.03798 0.985 0.9894 0.05054 0.9697 0.9801 0.06553 ] Network output: [ 0.07598 -0.2674 1.078 -0.0006987 0.0003137 1.035 -0.0005265 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7352 0.6326 0.54 0.4285 0.9747 0.9887 0.7385 0.9108 0.9717 0.6347 ] Network output: [ -0.0345 0.1659 0.9449 0.0007978 -0.0003582 0.9614 0.0006013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.62 0.6061 0.4491 0.2998 0.9864 0.9911 0.6205 0.9734 0.982 0.4612 ] Network output: [ -0.0578 0.1872 0.9444 0.0003423 -0.0001537 0.9855 0.000258 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.618 0.6158 0.4647 0.2779 0.9845 0.99 0.6181 0.9679 0.979 0.4669 ] Network output: [ 0.0165 0.9325 0.0229 -0.0002873 0.000129 1.01 -0.0002165 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02555 Epoch 2387 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02991 0.9753 0.9993 -3.46e-06 1.553e-06 -0.03447 -2.608e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02259 -0.00566 0.01817 0.02806 0.9393 0.9488 0.0475 0.8853 0.9036 0.1217 ] Network output: [ 0.9773 0.06507 -0.01712 -6.402e-05 2.874e-05 -0.002776 -4.825e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6447 0.1231 0.1245 0.2533 0.9714 0.9868 0.7405 0.9003 0.9668 0.6391 ] Network output: [ -0.00712 0.9416 1.027 -8.308e-05 3.73e-05 0.0448 -6.261e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04936 0.03671 0.05255 0.03793 0.985 0.9894 0.05053 0.9697 0.9801 0.0655 ] Network output: [ 0.07586 -0.2671 1.078 -0.0007019 0.0003151 1.035 -0.000529 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7351 0.6325 0.5401 0.428 0.9747 0.9887 0.7384 0.9108 0.9717 0.6348 ] Network output: [ -0.03441 0.1656 0.9449 0.0007992 -0.0003588 0.9616 0.0006023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.62 0.6062 0.4491 0.2995 0.9864 0.9911 0.6206 0.9734 0.982 0.4612 ] Network output: [ -0.05771 0.1869 0.9444 0.0003446 -0.0001547 0.9855 0.0002597 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6181 0.6159 0.4646 0.2777 0.9846 0.99 0.6182 0.9679 0.979 0.4669 ] Network output: [ 0.01646 0.9326 0.02288 -0.0002875 0.0001291 1.01 -0.0002167 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02549 Epoch 2388 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02988 0.9754 0.9993 -3.779e-06 1.696e-06 -0.03447 -2.848e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02259 -0.00566 0.01817 0.02803 0.9393 0.9488 0.04748 0.8853 0.9036 0.1216 ] Network output: [ 0.9773 0.06502 -0.01711 -6.365e-05 2.858e-05 -0.002785 -4.797e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6446 0.1231 0.1245 0.253 0.9714 0.9868 0.7404 0.9004 0.9668 0.6392 ] Network output: [ -0.007141 0.9417 1.027 -8.332e-05 3.74e-05 0.04477 -6.279e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04935 0.0367 0.05253 0.03788 0.9851 0.9894 0.05052 0.9697 0.9801 0.06548 ] Network output: [ 0.07575 -0.2668 1.078 -0.0007052 0.0003166 1.035 -0.0005315 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.735 0.6325 0.5402 0.4276 0.9747 0.9887 0.7383 0.9108 0.9717 0.6348 ] Network output: [ -0.03432 0.1654 0.9448 0.0008006 -0.0003594 0.9617 0.0006034 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6201 0.6063 0.4491 0.2993 0.9864 0.9911 0.6206 0.9734 0.982 0.4612 ] Network output: [ -0.05762 0.1866 0.9444 0.0003469 -0.0001557 0.9856 0.0002614 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6182 0.616 0.4646 0.2775 0.9846 0.99 0.6183 0.9679 0.979 0.4669 ] Network output: [ 0.01642 0.9328 0.02287 -0.0002877 0.0001292 1.01 -0.0002168 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02543 Epoch 2389 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02985 0.9754 0.9993 -4.096e-06 1.839e-06 -0.03448 -3.087e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02258 -0.00566 0.01816 0.02799 0.9393 0.9488 0.04747 0.8853 0.9037 0.1216 ] Network output: [ 0.9773 0.06497 -0.01711 -6.329e-05 2.841e-05 -0.002795 -4.769e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6446 0.1231 0.1245 0.2527 0.9714 0.9868 0.7403 0.9004 0.9668 0.6392 ] Network output: [ -0.007163 0.9418 1.027 -8.355e-05 3.751e-05 0.04475 -6.296e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04934 0.03669 0.05252 0.03783 0.9851 0.9894 0.05051 0.9697 0.9801 0.06546 ] Network output: [ 0.07563 -0.2665 1.078 -0.0007085 0.0003181 1.035 -0.000534 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7349 0.6324 0.5403 0.4272 0.9747 0.9887 0.7382 0.9108 0.9717 0.6349 ] Network output: [ -0.03424 0.1652 0.9447 0.000802 -0.0003601 0.9618 0.0006044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6202 0.6064 0.4492 0.2991 0.9864 0.9911 0.6207 0.9734 0.982 0.4613 ] Network output: [ -0.05752 0.1864 0.9444 0.0003491 -0.0001567 0.9857 0.0002631 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6183 0.616 0.4646 0.2774 0.9846 0.99 0.6184 0.9679 0.979 0.4669 ] Network output: [ 0.01638 0.9329 0.02285 -0.0002879 0.0001293 1.01 -0.000217 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02537 Epoch 2390 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02982 0.9755 0.9994 -4.413e-06 1.981e-06 -0.03448 -3.326e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02258 -0.005659 0.01816 0.02796 0.9393 0.9488 0.04745 0.8853 0.9037 0.1215 ] Network output: [ 0.9774 0.06492 -0.01711 -6.292e-05 2.825e-05 -0.002804 -4.742e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6445 0.1231 0.1246 0.2524 0.9714 0.9868 0.7402 0.9004 0.9669 0.6393 ] Network output: [ -0.007184 0.9419 1.027 -8.378e-05 3.761e-05 0.04473 -6.314e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04933 0.03669 0.0525 0.03778 0.9851 0.9894 0.0505 0.9698 0.9801 0.06544 ] Network output: [ 0.07551 -0.2663 1.078 -0.0007118 0.0003196 1.035 -0.0005364 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7348 0.6323 0.5403 0.4268 0.9747 0.9887 0.7382 0.9108 0.9717 0.635 ] Network output: [ -0.03415 0.165 0.9446 0.0008034 -0.0003607 0.9619 0.0006055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6203 0.6065 0.4492 0.2989 0.9864 0.9911 0.6208 0.9734 0.982 0.4613 ] Network output: [ -0.05743 0.1861 0.9444 0.0003514 -0.0001578 0.9858 0.0002648 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6183 0.6161 0.4646 0.2772 0.9846 0.99 0.6184 0.9679 0.979 0.4669 ] Network output: [ 0.01633 0.9331 0.02284 -0.0002882 0.0001294 1.01 -0.0002172 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02532 Epoch 2391 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02978 0.9755 0.9994 -4.728e-06 2.123e-06 -0.03448 -3.564e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02257 -0.005659 0.01815 0.02793 0.9393 0.9488 0.04743 0.8853 0.9037 0.1215 ] Network output: [ 0.9774 0.06486 -0.01711 -6.257e-05 2.809e-05 -0.002813 -4.715e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6445 0.1232 0.1246 0.252 0.9714 0.9868 0.7401 0.9004 0.9669 0.6394 ] Network output: [ -0.007205 0.942 1.027 -8.401e-05 3.771e-05 0.0447 -6.331e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04932 0.03668 0.05249 0.03772 0.9851 0.9894 0.05049 0.9698 0.9801 0.06542 ] Network output: [ 0.0754 -0.266 1.078 -0.0007151 0.000321 1.034 -0.0005389 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7348 0.6323 0.5404 0.4264 0.9747 0.9887 0.7381 0.9108 0.9717 0.635 ] Network output: [ -0.03406 0.1648 0.9446 0.0008049 -0.0003613 0.9621 0.0006066 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6204 0.6065 0.4492 0.2987 0.9864 0.9911 0.6209 0.9734 0.982 0.4613 ] Network output: [ -0.05734 0.1858 0.9444 0.0003537 -0.0001588 0.9859 0.0002666 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6184 0.6162 0.4646 0.277 0.9846 0.99 0.6185 0.9679 0.979 0.4668 ] Network output: [ 0.01629 0.9333 0.02282 -0.0002884 0.0001295 1.01 -0.0002173 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02526 Epoch 2392 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02975 0.9756 0.9994 -5.043e-06 2.264e-06 -0.03449 -3.801e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02257 -0.005658 0.01815 0.0279 0.9393 0.9489 0.04742 0.8853 0.9037 0.1215 ] Network output: [ 0.9774 0.06481 -0.01711 -6.221e-05 2.793e-05 -0.002822 -4.689e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6444 0.1232 0.1247 0.2517 0.9714 0.9868 0.74 0.9004 0.9669 0.6395 ] Network output: [ -0.007227 0.942 1.027 -8.423e-05 3.782e-05 0.04468 -6.348e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04931 0.03667 0.05247 0.03767 0.9851 0.9894 0.05048 0.9698 0.9801 0.0654 ] Network output: [ 0.07528 -0.2657 1.078 -0.0007184 0.0003225 1.034 -0.0005414 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7347 0.6322 0.5405 0.4259 0.9747 0.9887 0.738 0.9108 0.9717 0.6351 ] Network output: [ -0.03398 0.1645 0.9445 0.0008063 -0.000362 0.9622 0.0006076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6205 0.6066 0.4492 0.2984 0.9864 0.9911 0.621 0.9734 0.982 0.4613 ] Network output: [ -0.05725 0.1855 0.9445 0.000356 -0.0001598 0.9859 0.0002683 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6185 0.6163 0.4646 0.2769 0.9846 0.99 0.6186 0.9679 0.979 0.4668 ] Network output: [ 0.01625 0.9334 0.0228 -0.0002886 0.0001296 1.01 -0.0002175 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0252 Epoch 2393 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02972 0.9756 0.9994 -5.357e-06 2.405e-06 -0.03449 -4.037e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02256 -0.005658 0.01815 0.02787 0.9394 0.9489 0.0474 0.8854 0.9037 0.1214 ] Network output: [ 0.9775 0.06476 -0.0171 -6.186e-05 2.777e-05 -0.002831 -4.662e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6444 0.1232 0.1247 0.2514 0.9714 0.9868 0.74 0.9004 0.9669 0.6395 ] Network output: [ -0.007248 0.9421 1.027 -8.446e-05 3.792e-05 0.04466 -6.365e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0493 0.03666 0.05246 0.03762 0.9851 0.9894 0.05047 0.9698 0.9801 0.06538 ] Network output: [ 0.07517 -0.2654 1.078 -0.0007217 0.000324 1.034 -0.0005439 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7346 0.6322 0.5406 0.4255 0.9747 0.9887 0.7379 0.9109 0.9717 0.6352 ] Network output: [ -0.03389 0.1643 0.9444 0.0008077 -0.0003626 0.9623 0.0006087 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6206 0.6067 0.4493 0.2982 0.9864 0.9911 0.6211 0.9734 0.982 0.4614 ] Network output: [ -0.05715 0.1853 0.9445 0.0003583 -0.0001608 0.986 0.00027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6186 0.6164 0.4645 0.2767 0.9846 0.99 0.6187 0.9679 0.979 0.4668 ] Network output: [ 0.01621 0.9336 0.02279 -0.0002888 0.0001297 1.01 -0.0002177 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02514 Epoch 2394 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02969 0.9756 0.9994 -5.67e-06 2.545e-06 -0.03449 -4.273e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02256 -0.005657 0.01814 0.02784 0.9394 0.9489 0.04738 0.8854 0.9037 0.1214 ] Network output: [ 0.9775 0.06471 -0.0171 -6.152e-05 2.762e-05 -0.00284 -4.636e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6443 0.1232 0.1248 0.2511 0.9714 0.9868 0.7399 0.9005 0.9669 0.6396 ] Network output: [ -0.00727 0.9422 1.027 -8.468e-05 3.802e-05 0.04463 -6.382e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04929 0.03666 0.05244 0.03757 0.9851 0.9894 0.05046 0.9698 0.9801 0.06536 ] Network output: [ 0.07505 -0.2651 1.078 -0.000725 0.0003255 1.034 -0.0005464 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7345 0.6321 0.5406 0.4251 0.9747 0.9887 0.7378 0.9109 0.9717 0.6352 ] Network output: [ -0.0338 0.1641 0.9444 0.0008091 -0.0003632 0.9625 0.0006098 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6207 0.6068 0.4493 0.298 0.9864 0.9911 0.6212 0.9734 0.982 0.4614 ] Network output: [ -0.05706 0.185 0.9445 0.0003606 -0.0001619 0.9861 0.0002717 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6186 0.6164 0.4645 0.2765 0.9846 0.99 0.6187 0.968 0.979 0.4668 ] Network output: [ 0.01617 0.9337 0.02277 -0.000289 0.0001298 1.01 -0.0002178 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02508 Epoch 2395 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02966 0.9757 0.9995 -5.982e-06 2.685e-06 -0.0345 -4.508e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02255 -0.005657 0.01814 0.02781 0.9394 0.9489 0.04737 0.8854 0.9038 0.1214 ] Network output: [ 0.9775 0.06465 -0.0171 -6.118e-05 2.747e-05 -0.002849 -4.611e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6443 0.1232 0.1248 0.2508 0.9714 0.9868 0.7398 0.9005 0.9669 0.6397 ] Network output: [ -0.007291 0.9423 1.027 -8.49e-05 3.812e-05 0.04461 -6.399e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04928 0.03665 0.05243 0.03752 0.9851 0.9894 0.05045 0.9698 0.9801 0.06534 ] Network output: [ 0.07493 -0.2649 1.078 -0.0007283 0.000327 1.034 -0.0005489 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7344 0.632 0.5407 0.4246 0.9748 0.9887 0.7377 0.9109 0.9717 0.6353 ] Network output: [ -0.03372 0.1638 0.9443 0.0008105 -0.0003639 0.9626 0.0006108 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6208 0.6069 0.4493 0.2978 0.9864 0.9911 0.6213 0.9734 0.982 0.4614 ] Network output: [ -0.05697 0.1847 0.9445 0.0003628 -0.0001629 0.9862 0.0002735 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6187 0.6165 0.4645 0.2764 0.9846 0.99 0.6188 0.968 0.979 0.4668 ] Network output: [ 0.01613 0.9339 0.02276 -0.0002892 0.0001299 1.01 -0.000218 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02502 Epoch 2396 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02963 0.9757 0.9995 -6.292e-06 2.825e-06 -0.0345 -4.742e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02255 -0.005656 0.01814 0.02777 0.9394 0.9489 0.04735 0.8854 0.9038 0.1213 ] Network output: [ 0.9776 0.0646 -0.0171 -6.084e-05 2.731e-05 -0.002858 -4.585e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6442 0.1232 0.1248 0.2504 0.9714 0.9868 0.7397 0.9005 0.9669 0.6397 ] Network output: [ -0.007313 0.9423 1.027 -8.513e-05 3.822e-05 0.04459 -6.415e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04927 0.03664 0.05241 0.03747 0.9851 0.9894 0.05044 0.9698 0.9801 0.06532 ] Network output: [ 0.07481 -0.2646 1.078 -0.0007317 0.0003285 1.034 -0.0005514 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7343 0.632 0.5408 0.4242 0.9748 0.9887 0.7376 0.9109 0.9717 0.6354 ] Network output: [ -0.03363 0.1636 0.9442 0.0008119 -0.0003645 0.9627 0.0006119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6209 0.607 0.4494 0.2975 0.9864 0.9911 0.6214 0.9734 0.982 0.4615 ] Network output: [ -0.05687 0.1844 0.9445 0.0003651 -0.0001639 0.9863 0.0002752 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6188 0.6166 0.4645 0.2762 0.9846 0.99 0.6189 0.968 0.979 0.4668 ] Network output: [ 0.01609 0.934 0.02274 -0.0002895 0.00013 1.01 -0.0002181 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02496 Epoch 2397 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0296 0.9758 0.9995 -6.602e-06 2.964e-06 -0.0345 -4.976e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02254 -0.005656 0.01813 0.02774 0.9394 0.9489 0.04733 0.8854 0.9038 0.1213 ] Network output: [ 0.9776 0.06454 -0.01709 -6.051e-05 2.716e-05 -0.002866 -4.56e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6442 0.1232 0.1249 0.2501 0.9714 0.9868 0.7396 0.9005 0.9669 0.6398 ] Network output: [ -0.007335 0.9424 1.027 -8.534e-05 3.831e-05 0.04456 -6.432e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04926 0.03663 0.0524 0.03742 0.9851 0.9894 0.05043 0.9698 0.9801 0.0653 ] Network output: [ 0.0747 -0.2643 1.078 -0.000735 0.00033 1.034 -0.0005539 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7343 0.6319 0.5409 0.4238 0.9748 0.9887 0.7376 0.9109 0.9717 0.6354 ] Network output: [ -0.03354 0.1634 0.9442 0.0008134 -0.0003651 0.9628 0.000613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.621 0.6071 0.4494 0.2973 0.9864 0.9911 0.6215 0.9734 0.982 0.4615 ] Network output: [ -0.05678 0.1841 0.9446 0.0003674 -0.000165 0.9864 0.0002769 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6189 0.6167 0.4645 0.276 0.9846 0.9901 0.619 0.968 0.979 0.4667 ] Network output: [ 0.01605 0.9342 0.02273 -0.0002897 0.00013 1.01 -0.0002183 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0249 Epoch 2398 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02957 0.9758 0.9995 -6.911e-06 3.103e-06 -0.03451 -5.208e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02254 -0.005655 0.01813 0.02771 0.9394 0.9489 0.04732 0.8854 0.9038 0.1213 ] Network output: [ 0.9776 0.06449 -0.01709 -6.018e-05 2.702e-05 -0.002875 -4.535e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6441 0.1232 0.1249 0.2498 0.9714 0.9868 0.7395 0.9005 0.9669 0.6399 ] Network output: [ -0.007356 0.9425 1.027 -8.556e-05 3.841e-05 0.04454 -6.448e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04925 0.03663 0.05238 0.03736 0.9851 0.9894 0.05042 0.9698 0.9801 0.06528 ] Network output: [ 0.07458 -0.264 1.078 -0.0007383 0.0003315 1.034 -0.0005564 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7342 0.6319 0.5409 0.4234 0.9748 0.9887 0.7375 0.9109 0.9717 0.6355 ] Network output: [ -0.03345 0.1632 0.9441 0.0008148 -0.0003658 0.963 0.000614 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.621 0.6072 0.4494 0.2971 0.9864 0.9911 0.6216 0.9734 0.9821 0.4615 ] Network output: [ -0.05669 0.1839 0.9446 0.0003697 -0.000166 0.9864 0.0002787 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.619 0.6167 0.4645 0.2759 0.9846 0.9901 0.6191 0.968 0.979 0.4667 ] Network output: [ 0.016 0.9343 0.02271 -0.0002899 0.0001301 1.01 -0.0002185 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02484 Epoch 2399 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02954 0.9759 0.9995 -7.219e-06 3.241e-06 -0.03451 -5.44e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02253 -0.005655 0.01813 0.02768 0.9394 0.9489 0.0473 0.8855 0.9038 0.1212 ] Network output: [ 0.9776 0.06443 -0.01708 -5.986e-05 2.687e-05 -0.002883 -4.511e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6441 0.1232 0.125 0.2495 0.9714 0.9868 0.7394 0.9005 0.9669 0.6399 ] Network output: [ -0.007378 0.9426 1.027 -8.578e-05 3.851e-05 0.04452 -6.465e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04924 0.03662 0.05237 0.03731 0.9851 0.9894 0.05041 0.9698 0.9802 0.06526 ] Network output: [ 0.07446 -0.2637 1.078 -0.0007416 0.0003329 1.033 -0.0005589 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7341 0.6318 0.541 0.4229 0.9748 0.9887 0.7374 0.9109 0.9717 0.6356 ] Network output: [ -0.03337 0.1629 0.944 0.0008162 -0.0003664 0.9631 0.0006151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6211 0.6073 0.4495 0.2969 0.9864 0.9911 0.6216 0.9734 0.9821 0.4615 ] Network output: [ -0.05659 0.1836 0.9446 0.0003721 -0.000167 0.9865 0.0002804 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.619 0.6168 0.4645 0.2757 0.9846 0.9901 0.6191 0.968 0.9791 0.4667 ] Network output: [ 0.01596 0.9345 0.0227 -0.0002901 0.0001302 1.01 -0.0002186 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02478 Epoch 2400 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02951 0.9759 0.9996 -7.525e-06 3.378e-06 -0.03451 -5.671e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02253 -0.005654 0.01812 0.02765 0.9394 0.9489 0.04729 0.8855 0.9038 0.1212 ] Network output: [ 0.9777 0.06438 -0.01708 -5.954e-05 2.673e-05 -0.002891 -4.487e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.644 0.1233 0.125 0.2492 0.9714 0.9868 0.7394 0.9005 0.9669 0.64 ] Network output: [ -0.0074 0.9427 1.027 -8.599e-05 3.861e-05 0.04449 -6.481e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04923 0.03661 0.05235 0.03726 0.9851 0.9895 0.0504 0.9698 0.9802 0.06524 ] Network output: [ 0.07434 -0.2634 1.078 -0.000745 0.0003344 1.033 -0.0005614 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.734 0.6317 0.5411 0.4225 0.9748 0.9887 0.7373 0.911 0.9718 0.6357 ] Network output: [ -0.03328 0.1627 0.944 0.0008176 -0.0003671 0.9632 0.0006162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6212 0.6074 0.4495 0.2966 0.9864 0.9911 0.6217 0.9734 0.9821 0.4616 ] Network output: [ -0.0565 0.1833 0.9446 0.0003744 -0.0001681 0.9866 0.0002821 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6191 0.6169 0.4644 0.2755 0.9846 0.9901 0.6192 0.968 0.9791 0.4667 ] Network output: [ 0.01592 0.9346 0.02268 -0.0002903 0.0001303 1.01 -0.0002188 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02472 Epoch 2401 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02948 0.976 0.9996 -7.831e-06 3.516e-06 -0.03452 -5.902e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02252 -0.005653 0.01812 0.02762 0.9394 0.9489 0.04727 0.8855 0.9038 0.1212 ] Network output: [ 0.9777 0.06432 -0.01708 -5.922e-05 2.659e-05 -0.002899 -4.463e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.644 0.1233 0.125 0.2488 0.9714 0.9868 0.7393 0.9006 0.9669 0.6401 ] Network output: [ -0.007422 0.9427 1.027 -8.621e-05 3.87e-05 0.04447 -6.497e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04922 0.0366 0.05234 0.03721 0.9851 0.9895 0.05039 0.9698 0.9802 0.06522 ] Network output: [ 0.07422 -0.2631 1.078 -0.0007483 0.0003359 1.033 -0.0005639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7339 0.6317 0.5412 0.4221 0.9748 0.9887 0.7372 0.911 0.9718 0.6357 ] Network output: [ -0.03319 0.1625 0.9439 0.000819 -0.0003677 0.9633 0.0006173 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6213 0.6075 0.4495 0.2964 0.9864 0.9911 0.6218 0.9734 0.9821 0.4616 ] Network output: [ -0.0564 0.183 0.9446 0.0003767 -0.0001691 0.9867 0.0002839 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6192 0.617 0.4644 0.2754 0.9846 0.9901 0.6193 0.968 0.9791 0.4667 ] Network output: [ 0.01588 0.9348 0.02267 -0.0002905 0.0001304 1.01 -0.0002189 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02466 Epoch 2402 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02945 0.976 0.9996 -8.136e-06 3.653e-06 -0.03452 -6.131e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02252 -0.005653 0.01812 0.02759 0.9394 0.9489 0.04725 0.8855 0.9039 0.1211 ] Network output: [ 0.9777 0.06426 -0.01707 -5.891e-05 2.645e-05 -0.002907 -4.439e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6439 0.1233 0.1251 0.2485 0.9714 0.9868 0.7392 0.9006 0.9669 0.6402 ] Network output: [ -0.007443 0.9428 1.027 -8.642e-05 3.88e-05 0.04444 -6.513e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04921 0.0366 0.05233 0.03716 0.9851 0.9895 0.05038 0.9698 0.9802 0.0652 ] Network output: [ 0.07411 -0.2628 1.078 -0.0007516 0.0003374 1.033 -0.0005665 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7338 0.6316 0.5412 0.4216 0.9748 0.9887 0.7371 0.911 0.9718 0.6358 ] Network output: [ -0.0331 0.1622 0.9438 0.0008205 -0.0003683 0.9635 0.0006183 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6214 0.6075 0.4496 0.2962 0.9864 0.9911 0.6219 0.9734 0.9821 0.4616 ] Network output: [ -0.05631 0.1827 0.9447 0.000379 -0.0001701 0.9868 0.0002856 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6193 0.6171 0.4644 0.2752 0.9846 0.9901 0.6194 0.968 0.9791 0.4667 ] Network output: [ 0.01584 0.9349 0.02265 -0.0002907 0.0001305 1.01 -0.0002191 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0246 Epoch 2403 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02941 0.976 0.9996 -8.44e-06 3.789e-06 -0.03452 -6.36e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02251 -0.005652 0.01812 0.02756 0.9394 0.9489 0.04724 0.8855 0.9039 0.1211 ] Network output: [ 0.9778 0.0642 -0.01707 -5.86e-05 2.631e-05 -0.002915 -4.416e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6439 0.1233 0.1251 0.2482 0.9714 0.9868 0.7391 0.9006 0.9669 0.6402 ] Network output: [ -0.007465 0.9429 1.027 -8.663e-05 3.889e-05 0.04442 -6.528e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0492 0.03659 0.05231 0.03711 0.9851 0.9895 0.05037 0.9698 0.9802 0.06518 ] Network output: [ 0.07399 -0.2626 1.079 -0.000755 0.0003389 1.033 -0.000569 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7338 0.6316 0.5413 0.4212 0.9748 0.9887 0.7371 0.911 0.9718 0.6359 ] Network output: [ -0.03302 0.162 0.9438 0.0008219 -0.000369 0.9636 0.0006194 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6215 0.6076 0.4496 0.296 0.9864 0.9911 0.622 0.9734 0.9821 0.4617 ] Network output: [ -0.05622 0.1824 0.9447 0.0003813 -0.0001712 0.9869 0.0002874 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6194 0.6171 0.4644 0.275 0.9846 0.9901 0.6194 0.9681 0.9791 0.4666 ] Network output: [ 0.0158 0.9351 0.02264 -0.0002909 0.0001306 1.01 -0.0002193 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02454 Epoch 2404 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02938 0.9761 0.9996 -8.742e-06 3.925e-06 -0.03453 -6.588e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02251 -0.005652 0.01811 0.02752 0.9394 0.9489 0.04722 0.8855 0.9039 0.1211 ] Network output: [ 0.9778 0.06415 -0.01706 -5.83e-05 2.617e-05 -0.002923 -4.393e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6438 0.1233 0.1252 0.2479 0.9715 0.9868 0.739 0.9006 0.967 0.6403 ] Network output: [ -0.007487 0.943 1.027 -8.683e-05 3.898e-05 0.0444 -6.544e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0492 0.03658 0.0523 0.03706 0.9851 0.9895 0.05036 0.9698 0.9802 0.06516 ] Network output: [ 0.07387 -0.2623 1.079 -0.0007583 0.0003404 1.033 -0.0005715 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7337 0.6315 0.5414 0.4208 0.9748 0.9887 0.737 0.911 0.9718 0.6359 ] Network output: [ -0.03293 0.1618 0.9437 0.0008233 -0.0003696 0.9637 0.0006205 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6216 0.6077 0.4496 0.2957 0.9865 0.9911 0.6221 0.9734 0.9821 0.4617 ] Network output: [ -0.05612 0.1822 0.9447 0.0003836 -0.0001722 0.9869 0.0002891 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6194 0.6172 0.4644 0.2748 0.9846 0.9901 0.6195 0.9681 0.9791 0.4666 ] Network output: [ 0.01576 0.9352 0.02262 -0.0002911 0.0001307 1.009 -0.0002194 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02448 Epoch 2405 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02935 0.9761 0.9996 -9.044e-06 4.06e-06 -0.03453 -6.816e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0225 -0.005651 0.01811 0.02749 0.9395 0.949 0.0472 0.8856 0.9039 0.121 ] Network output: [ 0.9778 0.06409 -0.01706 -5.8e-05 2.604e-05 -0.00293 -4.371e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6438 0.1233 0.1252 0.2476 0.9715 0.9868 0.7389 0.9006 0.967 0.6404 ] Network output: [ -0.007509 0.9431 1.027 -8.704e-05 3.908e-05 0.04437 -6.56e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04919 0.03657 0.05228 0.03701 0.9851 0.9895 0.05035 0.9698 0.9802 0.06514 ] Network output: [ 0.07375 -0.262 1.079 -0.0007617 0.0003419 1.033 -0.000574 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7336 0.6314 0.5415 0.4203 0.9748 0.9887 0.7369 0.911 0.9718 0.636 ] Network output: [ -0.03284 0.1615 0.9436 0.0008248 -0.0003703 0.9639 0.0006216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6217 0.6078 0.4496 0.2955 0.9865 0.9912 0.6222 0.9734 0.9821 0.4617 ] Network output: [ -0.05603 0.1819 0.9447 0.0003859 -0.0001733 0.987 0.0002909 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6195 0.6173 0.4644 0.2747 0.9846 0.9901 0.6196 0.9681 0.9791 0.4666 ] Network output: [ 0.01572 0.9354 0.0226 -0.0002913 0.0001308 1.009 -0.0002196 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02442 Epoch 2406 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02932 0.9762 0.9997 -9.344e-06 4.195e-06 -0.03453 -7.042e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0225 -0.005651 0.01811 0.02746 0.9395 0.949 0.04719 0.8856 0.9039 0.121 ] Network output: [ 0.9779 0.06403 -0.01705 -5.77e-05 2.59e-05 -0.002938 -4.349e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6437 0.1233 0.1253 0.2472 0.9715 0.9868 0.7389 0.9006 0.967 0.6404 ] Network output: [ -0.007531 0.9431 1.027 -8.724e-05 3.917e-05 0.04435 -6.575e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04918 0.03657 0.05227 0.03695 0.9851 0.9895 0.05034 0.9699 0.9802 0.06512 ] Network output: [ 0.07363 -0.2617 1.079 -0.000765 0.0003434 1.033 -0.0005765 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7335 0.6314 0.5415 0.4199 0.9748 0.9887 0.7368 0.911 0.9718 0.6361 ] Network output: [ -0.03275 0.1613 0.9436 0.0008262 -0.0003709 0.964 0.0006226 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6218 0.6079 0.4497 0.2953 0.9865 0.9912 0.6223 0.9734 0.9821 0.4618 ] Network output: [ -0.05593 0.1816 0.9447 0.0003883 -0.0001743 0.9871 0.0002926 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6196 0.6174 0.4643 0.2745 0.9846 0.9901 0.6197 0.9681 0.9791 0.4666 ] Network output: [ 0.01568 0.9355 0.02259 -0.0002915 0.0001309 1.009 -0.0002197 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02436 Epoch 2407 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02929 0.9762 0.9997 -9.644e-06 4.329e-06 -0.03454 -7.268e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02249 -0.00565 0.0181 0.02743 0.9395 0.949 0.04717 0.8856 0.9039 0.121 ] Network output: [ 0.9779 0.06397 -0.01704 -5.741e-05 2.577e-05 -0.002945 -4.327e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6437 0.1233 0.1253 0.2469 0.9715 0.9868 0.7388 0.9007 0.967 0.6405 ] Network output: [ -0.007553 0.9432 1.027 -8.745e-05 3.926e-05 0.04432 -6.59e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04917 0.03656 0.05225 0.0369 0.9851 0.9895 0.05033 0.9699 0.9802 0.0651 ] Network output: [ 0.07351 -0.2614 1.079 -0.0007684 0.000345 1.033 -0.0005791 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7334 0.6313 0.5416 0.4195 0.9748 0.9887 0.7367 0.911 0.9718 0.6361 ] Network output: [ -0.03267 0.1611 0.9435 0.0008276 -0.0003715 0.9641 0.0006237 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6219 0.608 0.4497 0.295 0.9865 0.9912 0.6224 0.9735 0.9821 0.4618 ] Network output: [ -0.05584 0.1813 0.9447 0.0003906 -0.0001753 0.9872 0.0002944 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6197 0.6175 0.4643 0.2743 0.9846 0.9901 0.6198 0.9681 0.9791 0.4666 ] Network output: [ 0.01564 0.9357 0.02257 -0.0002917 0.000131 1.009 -0.0002199 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0243 Epoch 2408 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02926 0.9763 0.9997 -9.942e-06 4.463e-06 -0.03454 -7.493e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02249 -0.005649 0.0181 0.0274 0.9395 0.949 0.04716 0.8856 0.9039 0.1209 ] Network output: [ 0.9779 0.06391 -0.01704 -5.712e-05 2.565e-05 -0.002952 -4.305e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6436 0.1233 0.1254 0.2466 0.9715 0.9868 0.7387 0.9007 0.967 0.6406 ] Network output: [ -0.007575 0.9433 1.027 -8.765e-05 3.935e-05 0.0443 -6.605e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04916 0.03655 0.05224 0.03685 0.9851 0.9895 0.05032 0.9699 0.9802 0.06508 ] Network output: [ 0.07339 -0.2611 1.079 -0.0007717 0.0003465 1.032 -0.0005816 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7334 0.6312 0.5417 0.419 0.9748 0.9887 0.7366 0.9111 0.9718 0.6362 ] Network output: [ -0.03258 0.1608 0.9435 0.000829 -0.0003722 0.9642 0.0006248 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.622 0.6081 0.4497 0.2948 0.9865 0.9912 0.6225 0.9735 0.9821 0.4618 ] Network output: [ -0.05574 0.181 0.9448 0.0003929 -0.0001764 0.9873 0.0002961 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6198 0.6175 0.4643 0.2741 0.9846 0.9901 0.6198 0.9681 0.9791 0.4666 ] Network output: [ 0.01559 0.9358 0.02256 -0.000292 0.0001311 1.009 -0.00022 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02424 Epoch 2409 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02923 0.9763 0.9997 -1.024e-05 4.597e-06 -0.03454 -7.717e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02248 -0.005649 0.0181 0.02737 0.9395 0.949 0.04714 0.8856 0.904 0.1209 ] Network output: [ 0.978 0.06385 -0.01703 -5.684e-05 2.552e-05 -0.002959 -4.284e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6436 0.1234 0.1254 0.2463 0.9715 0.9868 0.7386 0.9007 0.967 0.6407 ] Network output: [ -0.007597 0.9434 1.027 -8.785e-05 3.944e-05 0.04428 -6.62e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04915 0.03654 0.05222 0.0368 0.9851 0.9895 0.05031 0.9699 0.9802 0.06506 ] Network output: [ 0.07327 -0.2608 1.079 -0.0007751 0.000348 1.032 -0.0005841 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7333 0.6312 0.5418 0.4186 0.9748 0.9887 0.7366 0.9111 0.9718 0.6363 ] Network output: [ -0.03249 0.1606 0.9434 0.0008305 -0.0003728 0.9644 0.0006259 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6221 0.6082 0.4498 0.2946 0.9865 0.9912 0.6226 0.9735 0.9821 0.4618 ] Network output: [ -0.05565 0.1807 0.9448 0.0003952 -0.0001774 0.9874 0.0002979 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6198 0.6176 0.4643 0.274 0.9846 0.9901 0.6199 0.9681 0.9791 0.4665 ] Network output: [ 0.01555 0.936 0.02254 -0.0002922 0.0001312 1.009 -0.0002202 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02418 Epoch 2410 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0292 0.9764 0.9997 -1.054e-05 4.73e-06 -0.03455 -7.94e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02248 -0.005648 0.0181 0.02734 0.9395 0.949 0.04712 0.8856 0.904 0.1209 ] Network output: [ 0.978 0.06379 -0.01702 -5.656e-05 2.539e-05 -0.002966 -4.263e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6435 0.1234 0.1254 0.246 0.9715 0.9868 0.7385 0.9007 0.967 0.6407 ] Network output: [ -0.007619 0.9435 1.027 -8.804e-05 3.953e-05 0.04425 -6.635e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04914 0.03654 0.05221 0.03675 0.9851 0.9895 0.0503 0.9699 0.9802 0.06504 ] Network output: [ 0.07315 -0.2605 1.079 -0.0007784 0.0003495 1.032 -0.0005867 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7332 0.6311 0.5418 0.4182 0.9748 0.9887 0.7365 0.9111 0.9718 0.6363 ] Network output: [ -0.0324 0.1604 0.9433 0.0008319 -0.0003735 0.9645 0.000627 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6222 0.6083 0.4498 0.2944 0.9865 0.9912 0.6227 0.9735 0.9821 0.4619 ] Network output: [ -0.05555 0.1804 0.9448 0.0003976 -0.0001785 0.9875 0.0002996 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6199 0.6177 0.4643 0.2738 0.9847 0.9901 0.62 0.9681 0.9791 0.4665 ] Network output: [ 0.01551 0.9361 0.02253 -0.0002923 0.0001312 1.009 -0.0002203 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02412 Epoch 2411 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02917 0.9764 0.9998 -1.083e-05 4.862e-06 -0.03455 -8.163e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02247 -0.005648 0.01809 0.02731 0.9395 0.949 0.04711 0.8857 0.904 0.1208 ] Network output: [ 0.978 0.06372 -0.01702 -5.629e-05 2.527e-05 -0.002973 -4.242e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6435 0.1234 0.1255 0.2456 0.9715 0.9868 0.7384 0.9007 0.967 0.6408 ] Network output: [ -0.007641 0.9436 1.027 -8.824e-05 3.961e-05 0.04423 -6.65e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04913 0.03653 0.05219 0.0367 0.9851 0.9895 0.05029 0.9699 0.9802 0.06502 ] Network output: [ 0.07303 -0.2602 1.079 -0.0007818 0.000351 1.032 -0.0005892 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7331 0.6311 0.5419 0.4177 0.9748 0.9887 0.7364 0.9111 0.9718 0.6364 ] Network output: [ -0.03232 0.1601 0.9433 0.0008333 -0.0003741 0.9646 0.000628 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6223 0.6084 0.4498 0.2941 0.9865 0.9912 0.6228 0.9735 0.9821 0.4619 ] Network output: [ -0.05546 0.1802 0.9448 0.0003999 -0.0001795 0.9876 0.0003014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.62 0.6178 0.4643 0.2736 0.9847 0.9901 0.6201 0.9681 0.9791 0.4665 ] Network output: [ 0.01547 0.9363 0.02251 -0.0002925 0.0001313 1.009 -0.0002205 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02406 Epoch 2412 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02913 0.9765 0.9998 -1.113e-05 4.995e-06 -0.03456 -8.384e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02247 -0.005647 0.01809 0.02728 0.9395 0.949 0.04709 0.8857 0.904 0.1208 ] Network output: [ 0.9781 0.06366 -0.01701 -5.602e-05 2.515e-05 -0.00298 -4.222e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6434 0.1234 0.1255 0.2453 0.9715 0.9868 0.7384 0.9007 0.967 0.6409 ] Network output: [ -0.007664 0.9436 1.027 -8.843e-05 3.97e-05 0.0442 -6.664e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04912 0.03652 0.05218 0.03665 0.9851 0.9895 0.05028 0.9699 0.9802 0.065 ] Network output: [ 0.0729 -0.2599 1.079 -0.0007852 0.0003525 1.032 -0.0005917 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.733 0.631 0.542 0.4173 0.9748 0.9887 0.7363 0.9111 0.9718 0.6365 ] Network output: [ -0.03223 0.1599 0.9432 0.0008348 -0.0003748 0.9648 0.0006291 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6224 0.6085 0.4499 0.2939 0.9865 0.9912 0.6229 0.9735 0.9821 0.4619 ] Network output: [ -0.05536 0.1799 0.9448 0.0004022 -0.0001806 0.9876 0.0003031 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6201 0.6179 0.4643 0.2735 0.9847 0.9901 0.6202 0.9682 0.9791 0.4665 ] Network output: [ 0.01543 0.9365 0.0225 -0.0002927 0.0001314 1.009 -0.0002206 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.024 Epoch 2413 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0291 0.9765 0.9998 -1.142e-05 5.126e-06 -0.03456 -8.605e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02246 -0.005646 0.01809 0.02725 0.9395 0.949 0.04708 0.8857 0.904 0.1208 ] Network output: [ 0.9781 0.0636 -0.017 -5.576e-05 2.503e-05 -0.002987 -4.202e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6434 0.1234 0.1256 0.245 0.9715 0.9868 0.7383 0.9007 0.967 0.6409 ] Network output: [ -0.007686 0.9437 1.027 -8.862e-05 3.979e-05 0.04418 -6.679e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04911 0.03651 0.05217 0.0366 0.9851 0.9895 0.05027 0.9699 0.9802 0.06498 ] Network output: [ 0.07278 -0.2596 1.079 -0.0007885 0.000354 1.032 -0.0005943 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.733 0.6309 0.5421 0.4168 0.9748 0.9887 0.7362 0.9111 0.9718 0.6366 ] Network output: [ -0.03214 0.1596 0.9431 0.0008362 -0.0003754 0.9649 0.0006302 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6225 0.6086 0.4499 0.2937 0.9865 0.9912 0.623 0.9735 0.9821 0.462 ] Network output: [ -0.05526 0.1796 0.9449 0.0004046 -0.0001816 0.9877 0.0003049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6202 0.618 0.4642 0.2733 0.9847 0.9901 0.6203 0.9682 0.9791 0.4665 ] Network output: [ 0.01539 0.9366 0.02248 -0.0002929 0.0001315 1.009 -0.0002208 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02394 Epoch 2414 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02907 0.9766 0.9998 -1.171e-05 5.257e-06 -0.03456 -8.825e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02246 -0.005646 0.01808 0.02722 0.9395 0.949 0.04706 0.8857 0.904 0.1207 ] Network output: [ 0.9781 0.06353 -0.017 -5.55e-05 2.491e-05 -0.002993 -4.182e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6433 0.1234 0.1256 0.2447 0.9715 0.9869 0.7382 0.9008 0.967 0.641 ] Network output: [ -0.007708 0.9438 1.027 -8.881e-05 3.987e-05 0.04415 -6.693e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0491 0.03651 0.05215 0.03655 0.9851 0.9895 0.05026 0.9699 0.9802 0.06496 ] Network output: [ 0.07266 -0.2593 1.079 -0.0007919 0.0003555 1.032 -0.0005968 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7329 0.6309 0.5421 0.4164 0.9748 0.9887 0.7361 0.9111 0.9718 0.6366 ] Network output: [ -0.03205 0.1594 0.9431 0.0008376 -0.000376 0.965 0.0006313 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6226 0.6087 0.4499 0.2934 0.9865 0.9912 0.6231 0.9735 0.9821 0.462 ] Network output: [ -0.05517 0.1793 0.9449 0.0004069 -0.0001827 0.9878 0.0003067 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6202 0.618 0.4642 0.2731 0.9847 0.9901 0.6203 0.9682 0.9792 0.4665 ] Network output: [ 0.01535 0.9368 0.02247 -0.0002931 0.0001316 1.009 -0.0002209 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02388 Epoch 2415 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02904 0.9766 0.9998 -1.2e-05 5.388e-06 -0.03457 -9.044e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02245 -0.005645 0.01808 0.02718 0.9395 0.949 0.04704 0.8857 0.904 0.1207 ] Network output: [ 0.9781 0.06347 -0.01699 -5.524e-05 2.48e-05 -0.003 -4.163e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6433 0.1234 0.1257 0.2444 0.9715 0.9869 0.7381 0.9008 0.967 0.6411 ] Network output: [ -0.00773 0.9439 1.027 -8.9e-05 3.996e-05 0.04413 -6.707e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04909 0.0365 0.05214 0.0365 0.9851 0.9895 0.05025 0.9699 0.9802 0.06495 ] Network output: [ 0.07254 -0.259 1.079 -0.0007953 0.000357 1.032 -0.0005994 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7328 0.6308 0.5422 0.416 0.9748 0.9888 0.7361 0.9112 0.9718 0.6367 ] Network output: [ -0.03196 0.1592 0.943 0.0008391 -0.0003767 0.9652 0.0006323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6226 0.6088 0.45 0.2932 0.9865 0.9912 0.6232 0.9735 0.9821 0.462 ] Network output: [ -0.05507 0.179 0.9449 0.0004093 -0.0001837 0.9879 0.0003084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6203 0.6181 0.4642 0.2729 0.9847 0.9901 0.6204 0.9682 0.9792 0.4665 ] Network output: [ 0.01531 0.9369 0.02245 -0.0002933 0.0001317 1.009 -0.0002211 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02382 Epoch 2416 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02901 0.9767 0.9998 -1.229e-05 5.518e-06 -0.03457 -9.263e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02245 -0.005644 0.01808 0.02715 0.9395 0.949 0.04703 0.8857 0.9041 0.1207 ] Network output: [ 0.9782 0.06341 -0.01698 -5.499e-05 2.469e-05 -0.003006 -4.144e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6432 0.1234 0.1257 0.244 0.9715 0.9869 0.738 0.9008 0.967 0.6412 ] Network output: [ -0.007752 0.944 1.027 -8.919e-05 4.004e-05 0.0441 -6.721e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04908 0.03649 0.05212 0.03644 0.9852 0.9895 0.05024 0.9699 0.9802 0.06493 ] Network output: [ 0.07242 -0.2586 1.079 -0.0007987 0.0003585 1.031 -0.0006019 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7327 0.6308 0.5423 0.4155 0.9748 0.9888 0.736 0.9112 0.9718 0.6368 ] Network output: [ -0.03188 0.1589 0.943 0.0008405 -0.0003773 0.9653 0.0006334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6227 0.6089 0.45 0.293 0.9865 0.9912 0.6232 0.9735 0.9821 0.462 ] Network output: [ -0.05498 0.1787 0.9449 0.0004116 -0.0001848 0.988 0.0003102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6204 0.6182 0.4642 0.2728 0.9847 0.9901 0.6205 0.9682 0.9792 0.4664 ] Network output: [ 0.01527 0.9371 0.02243 -0.0002935 0.0001318 1.009 -0.0002212 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02376 Epoch 2417 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02898 0.9767 0.9999 -1.258e-05 5.647e-06 -0.03458 -9.48e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02244 -0.005644 0.01808 0.02712 0.9396 0.949 0.04701 0.8858 0.9041 0.1206 ] Network output: [ 0.9782 0.06334 -0.01697 -5.474e-05 2.458e-05 -0.003012 -4.125e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6432 0.1234 0.1258 0.2437 0.9715 0.9869 0.7379 0.9008 0.967 0.6412 ] Network output: [ -0.007775 0.9441 1.027 -8.937e-05 4.012e-05 0.04408 -6.735e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04907 0.03649 0.05211 0.03639 0.9852 0.9895 0.05023 0.9699 0.9802 0.06491 ] Network output: [ 0.0723 -0.2583 1.079 -0.000802 0.0003601 1.031 -0.0006044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7326 0.6307 0.5424 0.4151 0.9748 0.9888 0.7359 0.9112 0.9719 0.6368 ] Network output: [ -0.03179 0.1587 0.9429 0.0008419 -0.000378 0.9654 0.0006345 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6228 0.609 0.45 0.2927 0.9865 0.9912 0.6233 0.9735 0.9821 0.4621 ] Network output: [ -0.05488 0.1784 0.9449 0.0004139 -0.0001858 0.9881 0.000312 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6205 0.6183 0.4642 0.2726 0.9847 0.9901 0.6206 0.9682 0.9792 0.4664 ] Network output: [ 0.01523 0.9372 0.02242 -0.0002937 0.0001319 1.009 -0.0002213 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02369 Epoch 2418 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02895 0.9768 0.9999 -1.287e-05 5.776e-06 -0.03458 -9.697e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02244 -0.005643 0.01808 0.02709 0.9396 0.949 0.047 0.8858 0.9041 0.1206 ] Network output: [ 0.9782 0.06327 -0.01696 -5.45e-05 2.447e-05 -0.003019 -4.107e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6431 0.1234 0.1258 0.2434 0.9715 0.9869 0.7378 0.9008 0.9671 0.6413 ] Network output: [ -0.007797 0.9441 1.027 -8.955e-05 4.02e-05 0.04405 -6.749e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04907 0.03648 0.05209 0.03634 0.9852 0.9895 0.05022 0.9699 0.9802 0.06489 ] Network output: [ 0.07217 -0.258 1.079 -0.0008054 0.0003616 1.031 -0.000607 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7325 0.6306 0.5424 0.4146 0.9749 0.9888 0.7358 0.9112 0.9719 0.6369 ] Network output: [ -0.0317 0.1584 0.9429 0.0008434 -0.0003786 0.9656 0.0006356 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6229 0.609 0.4501 0.2925 0.9865 0.9912 0.6234 0.9735 0.9821 0.4621 ] Network output: [ -0.05478 0.1781 0.945 0.0004163 -0.0001869 0.9882 0.0003137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6206 0.6184 0.4642 0.2724 0.9847 0.9901 0.6207 0.9682 0.9792 0.4664 ] Network output: [ 0.01519 0.9374 0.0224 -0.0002939 0.0001319 1.009 -0.0002215 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02363 Epoch 2419 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02892 0.9768 0.9999 -1.315e-05 5.905e-06 -0.03458 -9.913e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02244 -0.005642 0.01807 0.02706 0.9396 0.9491 0.04698 0.8858 0.9041 0.1206 ] Network output: [ 0.9783 0.06321 -0.01695 -5.426e-05 2.436e-05 -0.003025 -4.089e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6431 0.1235 0.1258 0.2431 0.9715 0.9869 0.7378 0.9008 0.9671 0.6414 ] Network output: [ -0.007819 0.9442 1.027 -8.973e-05 4.028e-05 0.04403 -6.763e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04906 0.03647 0.05208 0.03629 0.9852 0.9895 0.05021 0.9699 0.9802 0.06487 ] Network output: [ 0.07205 -0.2577 1.079 -0.0008088 0.0003631 1.031 -0.0006095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7325 0.6306 0.5425 0.4142 0.9749 0.9888 0.7357 0.9112 0.9719 0.637 ] Network output: [ -0.03161 0.1582 0.9428 0.0008448 -0.0003793 0.9657 0.0006367 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.623 0.6091 0.4501 0.2923 0.9865 0.9912 0.6235 0.9735 0.9821 0.4621 ] Network output: [ -0.05469 0.1778 0.945 0.0004186 -0.0001879 0.9883 0.0003155 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6207 0.6185 0.4641 0.2722 0.9847 0.9901 0.6208 0.9682 0.9792 0.4664 ] Network output: [ 0.01515 0.9375 0.02239 -0.0002941 0.000132 1.009 -0.0002216 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02357 Epoch 2420 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02889 0.9769 0.9999 -1.344e-05 6.033e-06 -0.03459 -1.013e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02243 -0.005641 0.01807 0.02703 0.9396 0.9491 0.04697 0.8858 0.9041 0.1205 ] Network output: [ 0.9783 0.06314 -0.01694 -5.403e-05 2.425e-05 -0.00303 -4.072e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.643 0.1235 0.1259 0.2428 0.9715 0.9869 0.7377 0.9008 0.9671 0.6414 ] Network output: [ -0.007842 0.9443 1.027 -8.991e-05 4.036e-05 0.044 -6.776e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04905 0.03646 0.05207 0.03624 0.9852 0.9895 0.0502 0.9699 0.9802 0.06485 ] Network output: [ 0.07193 -0.2574 1.079 -0.0008122 0.0003646 1.031 -0.0006121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7324 0.6305 0.5426 0.4138 0.9749 0.9888 0.7356 0.9112 0.9719 0.637 ] Network output: [ -0.03153 0.1579 0.9427 0.0008462 -0.0003799 0.9658 0.0006378 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6231 0.6092 0.4501 0.292 0.9865 0.9912 0.6236 0.9735 0.9821 0.4622 ] Network output: [ -0.05459 0.1775 0.945 0.000421 -0.000189 0.9884 0.0003173 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6208 0.6185 0.4641 0.272 0.9847 0.9901 0.6208 0.9682 0.9792 0.4664 ] Network output: [ 0.01511 0.9377 0.02237 -0.0002943 0.0001321 1.009 -0.0002218 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02351 Epoch 2421 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02885 0.9769 0.9999 -1.372e-05 6.161e-06 -0.03459 -1.034e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02243 -0.005641 0.01807 0.027 0.9396 0.9491 0.04695 0.8858 0.9041 0.1205 ] Network output: [ 0.9783 0.06307 -0.01693 -5.38e-05 2.415e-05 -0.003036 -4.054e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.643 0.1235 0.1259 0.2424 0.9715 0.9869 0.7376 0.9009 0.9671 0.6415 ] Network output: [ -0.007864 0.9444 1.027 -9.009e-05 4.044e-05 0.04398 -6.789e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04904 0.03646 0.05205 0.03619 0.9852 0.9895 0.05019 0.9699 0.9802 0.06483 ] Network output: [ 0.07181 -0.2571 1.079 -0.0008156 0.0003661 1.031 -0.0006146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7323 0.6304 0.5427 0.4133 0.9749 0.9888 0.7356 0.9112 0.9719 0.6371 ] Network output: [ -0.03144 0.1577 0.9427 0.0008477 -0.0003806 0.9659 0.0006388 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6232 0.6093 0.4501 0.2918 0.9865 0.9912 0.6237 0.9735 0.9821 0.4622 ] Network output: [ -0.05449 0.1773 0.945 0.0004233 -0.00019 0.9884 0.000319 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6208 0.6186 0.4641 0.2719 0.9847 0.9901 0.6209 0.9682 0.9792 0.4664 ] Network output: [ 0.01507 0.9378 0.02236 -0.0002944 0.0001322 1.008 -0.0002219 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02345 Epoch 2422 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02882 0.977 0.9999 -1.401e-05 6.288e-06 -0.0346 -1.056e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02242 -0.00564 0.01807 0.02697 0.9396 0.9491 0.04693 0.8858 0.9041 0.1205 ] Network output: [ 0.9784 0.06301 -0.01692 -5.357e-05 2.405e-05 -0.003042 -4.037e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6429 0.1235 0.126 0.2421 0.9715 0.9869 0.7375 0.9009 0.9671 0.6416 ] Network output: [ -0.007887 0.9445 1.027 -9.026e-05 4.052e-05 0.04395 -6.802e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04903 0.03645 0.05204 0.03614 0.9852 0.9895 0.05018 0.97 0.9803 0.06481 ] Network output: [ 0.07168 -0.2568 1.079 -0.0008189 0.0003677 1.031 -0.0006172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7322 0.6304 0.5428 0.4129 0.9749 0.9888 0.7355 0.9112 0.9719 0.6372 ] Network output: [ -0.03135 0.1575 0.9426 0.0008491 -0.0003812 0.9661 0.0006399 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6233 0.6094 0.4502 0.2916 0.9865 0.9912 0.6238 0.9735 0.9821 0.4622 ] Network output: [ -0.0544 0.177 0.945 0.0004257 -0.0001911 0.9885 0.0003208 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6209 0.6187 0.4641 0.2717 0.9847 0.9901 0.621 0.9683 0.9792 0.4663 ] Network output: [ 0.01503 0.938 0.02234 -0.0002946 0.0001323 1.008 -0.000222 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02339 Epoch 2423 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02879 0.977 0.9999 -1.429e-05 6.414e-06 -0.0346 -1.077e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02242 -0.005639 0.01806 0.02694 0.9396 0.9491 0.04692 0.8859 0.9042 0.1204 ] Network output: [ 0.9784 0.06294 -0.01691 -5.335e-05 2.395e-05 -0.003047 -4.021e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6429 0.1235 0.126 0.2418 0.9715 0.9869 0.7374 0.9009 0.9671 0.6417 ] Network output: [ -0.007909 0.9446 1.027 -9.043e-05 4.06e-05 0.04393 -6.815e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04902 0.03644 0.05202 0.03609 0.9852 0.9895 0.05017 0.97 0.9803 0.06479 ] Network output: [ 0.07156 -0.2565 1.08 -0.0008223 0.0003692 1.03 -0.0006197 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7321 0.6303 0.5428 0.4124 0.9749 0.9888 0.7354 0.9113 0.9719 0.6372 ] Network output: [ -0.03126 0.1572 0.9426 0.0008505 -0.0003818 0.9662 0.000641 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6234 0.6095 0.4502 0.2914 0.9865 0.9912 0.6239 0.9735 0.9821 0.4623 ] Network output: [ -0.0543 0.1767 0.9451 0.000428 -0.0001922 0.9886 0.0003226 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.621 0.6188 0.4641 0.2715 0.9847 0.9901 0.6211 0.9683 0.9792 0.4663 ] Network output: [ 0.01499 0.9381 0.02232 -0.0002948 0.0001323 1.008 -0.0002222 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02333 Epoch 2424 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02876 0.9771 1 -1.457e-05 6.54e-06 -0.0346 -1.098e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02241 -0.005639 0.01806 0.02691 0.9396 0.9491 0.0469 0.8859 0.9042 0.1204 ] Network output: [ 0.9784 0.06287 -0.0169 -5.313e-05 2.385e-05 -0.003053 -4.004e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6429 0.1235 0.1261 0.2415 0.9716 0.9869 0.7373 0.9009 0.9671 0.6417 ] Network output: [ -0.007932 0.9447 1.027 -9.06e-05 4.068e-05 0.0439 -6.828e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04901 0.03643 0.05201 0.03604 0.9852 0.9895 0.05016 0.97 0.9803 0.06477 ] Network output: [ 0.07144 -0.2561 1.08 -0.0008257 0.0003707 1.03 -0.0006223 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7321 0.6303 0.5429 0.412 0.9749 0.9888 0.7353 0.9113 0.9719 0.6373 ] Network output: [ -0.03117 0.157 0.9425 0.000852 -0.0003825 0.9663 0.0006421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6235 0.6096 0.4502 0.2911 0.9865 0.9912 0.624 0.9735 0.9821 0.4623 ] Network output: [ -0.0542 0.1764 0.9451 0.0004304 -0.0001932 0.9887 0.0003244 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6211 0.6189 0.4641 0.2713 0.9847 0.9901 0.6212 0.9683 0.9792 0.4663 ] Network output: [ 0.01495 0.9383 0.02231 -0.000295 0.0001324 1.008 -0.0002223 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02326 Epoch 2425 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02873 0.9771 1 -1.485e-05 6.666e-06 -0.03461 -1.119e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02241 -0.005638 0.01806 0.02688 0.9396 0.9491 0.04689 0.8859 0.9042 0.1204 ] Network output: [ 0.9785 0.0628 -0.01689 -5.292e-05 2.376e-05 -0.003058 -3.988e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6428 0.1235 0.1261 0.2412 0.9716 0.9869 0.7373 0.9009 0.9671 0.6418 ] Network output: [ -0.007954 0.9448 1.027 -9.077e-05 4.075e-05 0.04388 -6.841e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.049 0.03643 0.052 0.03599 0.9852 0.9895 0.05015 0.97 0.9803 0.06475 ] Network output: [ 0.07131 -0.2558 1.08 -0.0008291 0.0003722 1.03 -0.0006248 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.732 0.6302 0.543 0.4115 0.9749 0.9888 0.7352 0.9113 0.9719 0.6374 ] Network output: [ -0.03109 0.1567 0.9424 0.0008534 -0.0003831 0.9665 0.0006432 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6236 0.6097 0.4503 0.2909 0.9865 0.9912 0.6241 0.9735 0.9822 0.4623 ] Network output: [ -0.05411 0.1761 0.9451 0.0004327 -0.0001943 0.9888 0.0003261 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6212 0.619 0.464 0.2712 0.9847 0.9901 0.6213 0.9683 0.9792 0.4663 ] Network output: [ 0.01491 0.9384 0.02229 -0.0002951 0.0001325 1.008 -0.0002224 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0232 Epoch 2426 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0287 0.9772 1 -1.513e-05 6.791e-06 -0.03461 -1.14e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02241 -0.005637 0.01806 0.02685 0.9396 0.9491 0.04687 0.8859 0.9042 0.1203 ] Network output: [ 0.9785 0.06273 -0.01688 -5.272e-05 2.367e-05 -0.003063 -3.973e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6428 0.1235 0.1262 0.2408 0.9716 0.9869 0.7372 0.9009 0.9671 0.6419 ] Network output: [ -0.007977 0.9448 1.027 -9.094e-05 4.083e-05 0.04385 -6.853e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04899 0.03642 0.05198 0.03594 0.9852 0.9895 0.05014 0.97 0.9803 0.06473 ] Network output: [ 0.07119 -0.2555 1.08 -0.0008325 0.0003737 1.03 -0.0006274 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7319 0.6301 0.5431 0.4111 0.9749 0.9888 0.7352 0.9113 0.9719 0.6375 ] Network output: [ -0.031 0.1565 0.9424 0.0008548 -0.0003838 0.9666 0.0006442 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6237 0.6098 0.4503 0.2907 0.9865 0.9912 0.6242 0.9735 0.9822 0.4624 ] Network output: [ -0.05401 0.1758 0.9451 0.0004351 -0.0001953 0.9889 0.0003279 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6213 0.6191 0.464 0.271 0.9847 0.9901 0.6214 0.9683 0.9792 0.4663 ] Network output: [ 0.01487 0.9386 0.02228 -0.0002953 0.0001326 1.008 -0.0002226 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02314 Epoch 2427 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02867 0.9772 1 -1.54e-05 6.915e-06 -0.03462 -1.161e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0224 -0.005636 0.01806 0.02682 0.9396 0.9491 0.04685 0.8859 0.9042 0.1203 ] Network output: [ 0.9785 0.06266 -0.01687 -5.251e-05 2.358e-05 -0.003068 -3.958e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6427 0.1235 0.1262 0.2405 0.9716 0.9869 0.7371 0.9009 0.9671 0.642 ] Network output: [ -0.007999 0.9449 1.027 -9.11e-05 4.09e-05 0.04383 -6.866e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04898 0.03641 0.05197 0.03589 0.9852 0.9895 0.05013 0.97 0.9803 0.06471 ] Network output: [ 0.07106 -0.2552 1.08 -0.0008359 0.0003753 1.03 -0.0006299 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7318 0.6301 0.5431 0.4106 0.9749 0.9888 0.7351 0.9113 0.9719 0.6375 ] Network output: [ -0.03091 0.1562 0.9423 0.0008563 -0.0003844 0.9667 0.0006453 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6238 0.6099 0.4503 0.2904 0.9865 0.9912 0.6243 0.9735 0.9822 0.4624 ] Network output: [ -0.05391 0.1755 0.9451 0.0004374 -0.0001964 0.989 0.0003297 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6214 0.6191 0.464 0.2708 0.9847 0.9901 0.6214 0.9683 0.9792 0.4663 ] Network output: [ 0.01483 0.9387 0.02226 -0.0002955 0.0001327 1.008 -0.0002227 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02308 Epoch 2428 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02863 0.9773 1 -1.568e-05 7.039e-06 -0.03462 -1.182e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0224 -0.005636 0.01806 0.02679 0.9396 0.9491 0.04684 0.8859 0.9042 0.1203 ] Network output: [ 0.9786 0.06259 -0.01686 -5.231e-05 2.349e-05 -0.003073 -3.943e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6427 0.1235 0.1263 0.2402 0.9716 0.9869 0.737 0.901 0.9671 0.642 ] Network output: [ -0.008022 0.945 1.027 -9.127e-05 4.097e-05 0.0438 -6.878e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04897 0.0364 0.05195 0.03584 0.9852 0.9895 0.05012 0.97 0.9803 0.06469 ] Network output: [ 0.07094 -0.2549 1.08 -0.0008393 0.0003768 1.03 -0.0006325 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7317 0.63 0.5432 0.4102 0.9749 0.9888 0.735 0.9113 0.9719 0.6376 ] Network output: [ -0.03082 0.156 0.9423 0.0008577 -0.0003851 0.9669 0.0006464 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6239 0.61 0.4504 0.2902 0.9865 0.9912 0.6244 0.9735 0.9822 0.4624 ] Network output: [ -0.05381 0.1752 0.9452 0.0004398 -0.0001974 0.9891 0.0003315 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6214 0.6192 0.464 0.2706 0.9847 0.9901 0.6215 0.9683 0.9792 0.4662 ] Network output: [ 0.01479 0.9389 0.02224 -0.0002957 0.0001327 1.008 -0.0002228 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02302 Epoch 2429 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0286 0.9773 1 -1.595e-05 7.163e-06 -0.03463 -1.202e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02239 -0.005635 0.01805 0.02676 0.9397 0.9491 0.04682 0.8859 0.9042 0.1203 ] Network output: [ 0.9786 0.06252 -0.01685 -5.212e-05 2.34e-05 -0.003078 -3.928e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6426 0.1235 0.1263 0.2399 0.9716 0.9869 0.7369 0.901 0.9671 0.6421 ] Network output: [ -0.008044 0.9451 1.027 -9.143e-05 4.104e-05 0.04377 -6.89e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04897 0.0364 0.05194 0.03579 0.9852 0.9895 0.05011 0.97 0.9803 0.06467 ] Network output: [ 0.07082 -0.2545 1.08 -0.0008427 0.0003783 1.03 -0.0006351 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7317 0.6299 0.5433 0.4098 0.9749 0.9888 0.7349 0.9113 0.9719 0.6377 ] Network output: [ -0.03073 0.1557 0.9422 0.0008591 -0.0003857 0.967 0.0006475 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.624 0.6101 0.4504 0.29 0.9865 0.9912 0.6245 0.9735 0.9822 0.4624 ] Network output: [ -0.05372 0.1749 0.9452 0.0004422 -0.0001985 0.9892 0.0003332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6215 0.6193 0.464 0.2704 0.9847 0.9901 0.6216 0.9683 0.9793 0.4662 ] Network output: [ 0.01475 0.939 0.02223 -0.0002958 0.0001328 1.008 -0.0002229 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02295 Epoch 2430 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02857 0.9774 1 -1.623e-05 7.286e-06 -0.03463 -1.223e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02239 -0.005634 0.01805 0.02673 0.9397 0.9491 0.04681 0.886 0.9043 0.1202 ] Network output: [ 0.9786 0.06244 -0.01684 -5.193e-05 2.331e-05 -0.003083 -3.914e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6426 0.1236 0.1264 0.2396 0.9716 0.9869 0.7369 0.901 0.9671 0.6422 ] Network output: [ -0.008067 0.9452 1.027 -9.158e-05 4.112e-05 0.04375 -6.902e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04896 0.03639 0.05193 0.03574 0.9852 0.9895 0.0501 0.97 0.9803 0.06465 ] Network output: [ 0.07069 -0.2542 1.08 -0.000846 0.0003798 1.03 -0.0006376 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7316 0.6299 0.5434 0.4093 0.9749 0.9888 0.7348 0.9113 0.9719 0.6377 ] Network output: [ -0.03065 0.1555 0.9422 0.0008606 -0.0003863 0.9671 0.0006485 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6241 0.6102 0.4504 0.2897 0.9865 0.9912 0.6246 0.9735 0.9822 0.4625 ] Network output: [ -0.05362 0.1746 0.9452 0.0004445 -0.0001996 0.9893 0.000335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6216 0.6194 0.464 0.2703 0.9847 0.9901 0.6217 0.9683 0.9793 0.4662 ] Network output: [ 0.01471 0.9392 0.02221 -0.000296 0.0001329 1.008 -0.0002231 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02289 Epoch 2431 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02854 0.9774 1 -1.65e-05 7.408e-06 -0.03464 -1.244e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02238 -0.005633 0.01805 0.0267 0.9397 0.9491 0.04679 0.886 0.9043 0.1202 ] Network output: [ 0.9787 0.06237 -0.01682 -5.175e-05 2.323e-05 -0.003088 -3.9e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6425 0.1236 0.1264 0.2393 0.9716 0.9869 0.7368 0.901 0.9671 0.6422 ] Network output: [ -0.00809 0.9453 1.027 -9.174e-05 4.119e-05 0.04372 -6.914e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04895 0.03638 0.05191 0.03569 0.9852 0.9895 0.05009 0.97 0.9803 0.06463 ] Network output: [ 0.07057 -0.2539 1.08 -0.0008494 0.0003813 1.029 -0.0006402 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7315 0.6298 0.5434 0.4089 0.9749 0.9888 0.7347 0.9114 0.9719 0.6378 ] Network output: [ -0.03056 0.1552 0.9421 0.000862 -0.000387 0.9673 0.0006496 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6242 0.6103 0.4505 0.2895 0.9865 0.9912 0.6247 0.9735 0.9822 0.4625 ] Network output: [ -0.05352 0.1743 0.9452 0.0004469 -0.0002006 0.9893 0.0003368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6217 0.6195 0.464 0.2701 0.9847 0.9901 0.6218 0.9683 0.9793 0.4662 ] Network output: [ 0.01467 0.9393 0.0222 -0.0002962 0.000133 1.008 -0.0002232 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02283 Epoch 2432 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02851 0.9775 1 -1.677e-05 7.53e-06 -0.03464 -1.264e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02238 -0.005632 0.01805 0.02667 0.9397 0.9491 0.04678 0.886 0.9043 0.1202 ] Network output: [ 0.9787 0.0623 -0.01681 -5.157e-05 2.315e-05 -0.003092 -3.886e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6425 0.1236 0.1265 0.2389 0.9716 0.9869 0.7367 0.901 0.9672 0.6423 ] Network output: [ -0.008112 0.9454 1.027 -9.189e-05 4.125e-05 0.0437 -6.925e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04894 0.03638 0.0519 0.03564 0.9852 0.9895 0.05009 0.97 0.9803 0.06462 ] Network output: [ 0.07044 -0.2536 1.08 -0.0008528 0.0003829 1.029 -0.0006427 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7314 0.6298 0.5435 0.4084 0.9749 0.9888 0.7347 0.9114 0.9719 0.6379 ] Network output: [ -0.03047 0.155 0.9421 0.0008634 -0.0003876 0.9674 0.0006507 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6243 0.6104 0.4505 0.2892 0.9865 0.9912 0.6248 0.9736 0.9822 0.4625 ] Network output: [ -0.05342 0.174 0.9452 0.0004492 -0.0002017 0.9894 0.0003386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6218 0.6196 0.4639 0.2699 0.9847 0.9901 0.6219 0.9684 0.9793 0.4662 ] Network output: [ 0.01463 0.9395 0.02218 -0.0002963 0.000133 1.008 -0.0002233 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02277 Epoch 2433 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02848 0.9775 1 -1.704e-05 7.651e-06 -0.03464 -1.284e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02238 -0.005632 0.01805 0.02664 0.9397 0.9492 0.04676 0.886 0.9043 0.1201 ] Network output: [ 0.9787 0.06222 -0.0168 -5.139e-05 2.307e-05 -0.003097 -3.873e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6424 0.1236 0.1265 0.2386 0.9716 0.9869 0.7366 0.901 0.9672 0.6424 ] Network output: [ -0.008135 0.9455 1.027 -9.205e-05 4.132e-05 0.04367 -6.937e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04893 0.03637 0.05188 0.03559 0.9852 0.9895 0.05008 0.97 0.9803 0.0646 ] Network output: [ 0.07032 -0.2532 1.08 -0.0008562 0.0003844 1.029 -0.0006453 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7313 0.6297 0.5436 0.408 0.9749 0.9888 0.7346 0.9114 0.9719 0.6379 ] Network output: [ -0.03038 0.1547 0.942 0.0008648 -0.0003883 0.9675 0.0006518 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6244 0.6105 0.4505 0.289 0.9865 0.9912 0.6249 0.9736 0.9822 0.4626 ] Network output: [ -0.05333 0.1737 0.9453 0.0004516 -0.0002027 0.9895 0.0003403 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6219 0.6197 0.4639 0.2697 0.9847 0.9901 0.622 0.9684 0.9793 0.4662 ] Network output: [ 0.01459 0.9396 0.02216 -0.0002965 0.0001331 1.008 -0.0002234 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02271 Epoch 2434 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02845 0.9776 1 -1.731e-05 7.772e-06 -0.03465 -1.305e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02237 -0.005631 0.01805 0.02661 0.9397 0.9492 0.04675 0.886 0.9043 0.1201 ] Network output: [ 0.9788 0.06215 -0.01679 -5.122e-05 2.3e-05 -0.003101 -3.86e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6424 0.1236 0.1266 0.2383 0.9716 0.9869 0.7365 0.901 0.9672 0.6425 ] Network output: [ -0.008158 0.9455 1.027 -9.219e-05 4.139e-05 0.04365 -6.948e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04892 0.03636 0.05187 0.03554 0.9852 0.9895 0.05007 0.97 0.9803 0.06458 ] Network output: [ 0.07019 -0.2529 1.08 -0.0008596 0.0003859 1.029 -0.0006478 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7313 0.6296 0.5437 0.4075 0.9749 0.9888 0.7345 0.9114 0.9719 0.638 ] Network output: [ -0.03029 0.1545 0.942 0.0008663 -0.0003889 0.9677 0.0006528 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6245 0.6106 0.4506 0.2888 0.9865 0.9912 0.625 0.9736 0.9822 0.4626 ] Network output: [ -0.05323 0.1734 0.9453 0.000454 -0.0002038 0.9896 0.0003421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.622 0.6198 0.4639 0.2695 0.9848 0.9901 0.6221 0.9684 0.9793 0.4661 ] Network output: [ 0.01455 0.9398 0.02215 -0.0002966 0.0001332 1.008 -0.0002236 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02264 Epoch 2435 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02841 0.9776 1 -1.758e-05 7.892e-06 -0.03465 -1.325e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02237 -0.00563 0.01804 0.02658 0.9397 0.9492 0.04673 0.886 0.9043 0.1201 ] Network output: [ 0.9788 0.06207 -0.01677 -5.106e-05 2.292e-05 -0.003105 -3.848e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6424 0.1236 0.1266 0.238 0.9716 0.9869 0.7364 0.9011 0.9672 0.6425 ] Network output: [ -0.00818 0.9456 1.027 -9.234e-05 4.146e-05 0.04362 -6.959e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04891 0.03635 0.05186 0.03549 0.9852 0.9895 0.05006 0.97 0.9803 0.06456 ] Network output: [ 0.07007 -0.2526 1.08 -0.000863 0.0003874 1.029 -0.0006504 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7312 0.6296 0.5438 0.4071 0.9749 0.9888 0.7344 0.9114 0.972 0.6381 ] Network output: [ -0.03021 0.1542 0.9419 0.0008677 -0.0003895 0.9678 0.0006539 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6246 0.6107 0.4506 0.2885 0.9865 0.9912 0.6251 0.9736 0.9822 0.4626 ] Network output: [ -0.05313 0.1731 0.9453 0.0004563 -0.0002049 0.9897 0.0003439 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6221 0.6198 0.4639 0.2694 0.9848 0.9902 0.6222 0.9684 0.9793 0.4661 ] Network output: [ 0.01451 0.9399 0.02213 -0.0002968 0.0001332 1.008 -0.0002237 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02258 Epoch 2436 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02838 0.9777 1 -1.785e-05 8.012e-06 -0.03466 -1.345e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02236 -0.005629 0.01804 0.02655 0.9397 0.9492 0.04672 0.886 0.9043 0.12 ] Network output: [ 0.9788 0.062 -0.01676 -5.089e-05 2.285e-05 -0.003109 -3.836e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6423 0.1236 0.1267 0.2377 0.9716 0.9869 0.7364 0.9011 0.9672 0.6426 ] Network output: [ -0.008203 0.9457 1.027 -9.249e-05 4.152e-05 0.0436 -6.97e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0489 0.03635 0.05184 0.03544 0.9852 0.9895 0.05005 0.97 0.9803 0.06454 ] Network output: [ 0.06994 -0.2523 1.08 -0.0008664 0.000389 1.029 -0.000653 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7311 0.6295 0.5438 0.4066 0.9749 0.9888 0.7343 0.9114 0.972 0.6382 ] Network output: [ -0.03012 0.154 0.9419 0.0008691 -0.0003902 0.9679 0.000655 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6247 0.6108 0.4506 0.2883 0.9865 0.9912 0.6252 0.9736 0.9822 0.4627 ] Network output: [ -0.05303 0.1728 0.9453 0.0004587 -0.0002059 0.9898 0.0003457 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6221 0.6199 0.4639 0.2692 0.9848 0.9902 0.6222 0.9684 0.9793 0.4661 ] Network output: [ 0.01447 0.9401 0.02212 -0.000297 0.0001333 1.008 -0.0002238 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02252 Epoch 2437 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02835 0.9777 1 -1.811e-05 8.131e-06 -0.03466 -1.365e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02236 -0.005628 0.01804 0.02652 0.9397 0.9492 0.0467 0.8861 0.9043 0.12 ] Network output: [ 0.9789 0.06192 -0.01675 -5.074e-05 2.278e-05 -0.003113 -3.824e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6423 0.1236 0.1267 0.2373 0.9716 0.9869 0.7363 0.9011 0.9672 0.6427 ] Network output: [ -0.008226 0.9458 1.027 -9.263e-05 4.159e-05 0.04357 -6.981e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0489 0.03634 0.05183 0.03539 0.9852 0.9895 0.05004 0.97 0.9803 0.06452 ] Network output: [ 0.06982 -0.2519 1.08 -0.0008698 0.0003905 1.029 -0.0006555 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.731 0.6295 0.5439 0.4062 0.9749 0.9888 0.7343 0.9114 0.972 0.6382 ] Network output: [ -0.03003 0.1537 0.9418 0.0008705 -0.0003908 0.968 0.0006561 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6248 0.6109 0.4507 0.2881 0.9865 0.9912 0.6253 0.9736 0.9822 0.4627 ] Network output: [ -0.05294 0.1725 0.9453 0.000461 -0.000207 0.9899 0.0003474 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6222 0.62 0.4639 0.269 0.9848 0.9902 0.6223 0.9684 0.9793 0.4661 ] Network output: [ 0.01443 0.9402 0.0221 -0.0002971 0.0001334 1.008 -0.0002239 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02246 Epoch 2438 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02832 0.9778 1 -1.838e-05 8.25e-06 -0.03467 -1.385e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02236 -0.005628 0.01804 0.02649 0.9397 0.9492 0.04668 0.8861 0.9044 0.12 ] Network output: [ 0.9789 0.06185 -0.01673 -5.058e-05 2.271e-05 -0.003117 -3.812e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6422 0.1236 0.1268 0.237 0.9716 0.9869 0.7362 0.9011 0.9672 0.6427 ] Network output: [ -0.008248 0.9459 1.027 -9.277e-05 4.165e-05 0.04354 -6.992e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04889 0.03633 0.05182 0.03534 0.9852 0.9895 0.05003 0.97 0.9803 0.0645 ] Network output: [ 0.06969 -0.2516 1.08 -0.0008732 0.000392 1.028 -0.0006581 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.731 0.6294 0.544 0.4057 0.9749 0.9888 0.7342 0.9114 0.972 0.6383 ] Network output: [ -0.02994 0.1535 0.9418 0.000872 -0.0003915 0.9682 0.0006571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6249 0.611 0.4507 0.2878 0.9865 0.9912 0.6254 0.9736 0.9822 0.4627 ] Network output: [ -0.05284 0.1722 0.9454 0.0004634 -0.000208 0.99 0.0003492 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6223 0.6201 0.4638 0.2688 0.9848 0.9902 0.6224 0.9684 0.9793 0.4661 ] Network output: [ 0.01439 0.9404 0.02208 -0.0002973 0.0001334 1.008 -0.000224 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02239 Epoch 2439 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02829 0.9779 1 -1.864e-05 8.368e-06 -0.03467 -1.405e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02235 -0.005627 0.01804 0.02646 0.9397 0.9492 0.04667 0.8861 0.9044 0.1199 ] Network output: [ 0.9789 0.06177 -0.01672 -5.044e-05 2.264e-05 -0.003121 -3.801e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6422 0.1236 0.1268 0.2367 0.9716 0.9869 0.7361 0.9011 0.9672 0.6428 ] Network output: [ -0.008271 0.946 1.027 -9.291e-05 4.171e-05 0.04352 -7.002e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04888 0.03633 0.0518 0.03529 0.9852 0.9895 0.05002 0.97 0.9803 0.06448 ] Network output: [ 0.06956 -0.2513 1.08 -0.0008766 0.0003935 1.028 -0.0006606 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7309 0.6293 0.5441 0.4053 0.9749 0.9888 0.7341 0.9114 0.972 0.6384 ] Network output: [ -0.02986 0.1532 0.9417 0.0008734 -0.0003921 0.9683 0.0006582 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.625 0.6111 0.4507 0.2876 0.9865 0.9912 0.6255 0.9736 0.9822 0.4627 ] Network output: [ -0.05274 0.1719 0.9454 0.0004658 -0.0002091 0.9901 0.000351 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6224 0.6202 0.4638 0.2686 0.9848 0.9902 0.6225 0.9684 0.9793 0.4661 ] Network output: [ 0.01435 0.9405 0.02207 -0.0002974 0.0001335 1.007 -0.0002241 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02233 Epoch 2440 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02826 0.9779 1 -1.89e-05 8.485e-06 -0.03468 -1.424e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02235 -0.005626 0.01804 0.02643 0.9397 0.9492 0.04665 0.8861 0.9044 0.1199 ] Network output: [ 0.979 0.06169 -0.0167 -5.029e-05 2.258e-05 -0.003125 -3.79e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6421 0.1236 0.1269 0.2364 0.9716 0.9869 0.736 0.9011 0.9672 0.6429 ] Network output: [ -0.008294 0.9461 1.027 -9.305e-05 4.177e-05 0.04349 -7.012e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04887 0.03632 0.05179 0.03524 0.9852 0.9895 0.05001 0.9701 0.9803 0.06446 ] Network output: [ 0.06944 -0.2509 1.08 -0.00088 0.0003951 1.028 -0.0006632 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7308 0.6293 0.5441 0.4048 0.9749 0.9888 0.734 0.9115 0.972 0.6384 ] Network output: [ -0.02977 0.153 0.9417 0.0008748 -0.0003927 0.9684 0.0006593 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6251 0.6112 0.4508 0.2874 0.9865 0.9912 0.6256 0.9736 0.9822 0.4628 ] Network output: [ -0.05264 0.1716 0.9454 0.0004681 -0.0002102 0.9902 0.0003528 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6225 0.6203 0.4638 0.2685 0.9848 0.9902 0.6226 0.9684 0.9793 0.466 ] Network output: [ 0.01431 0.9407 0.02205 -0.0002975 0.0001336 1.007 -0.0002242 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02227 Epoch 2441 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02822 0.978 1 -1.916e-05 8.602e-06 -0.03468 -1.444e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02234 -0.005625 0.01804 0.0264 0.9398 0.9492 0.04664 0.8861 0.9044 0.1199 ] Network output: [ 0.979 0.06161 -0.01669 -5.015e-05 2.252e-05 -0.003128 -3.78e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6421 0.1236 0.1269 0.2361 0.9716 0.9869 0.736 0.9011 0.9672 0.643 ] Network output: [ -0.008317 0.9462 1.027 -9.318e-05 4.183e-05 0.04347 -7.022e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04886 0.03631 0.05177 0.03519 0.9852 0.9895 0.05 0.9701 0.9803 0.06444 ] Network output: [ 0.06931 -0.2506 1.08 -0.0008834 0.0003966 1.028 -0.0006657 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7307 0.6292 0.5442 0.4044 0.975 0.9888 0.7339 0.9115 0.972 0.6385 ] Network output: [ -0.02968 0.1527 0.9416 0.0008762 -0.0003934 0.9686 0.0006604 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6252 0.6113 0.4508 0.2871 0.9865 0.9912 0.6257 0.9736 0.9822 0.4628 ] Network output: [ -0.05254 0.1713 0.9454 0.0004705 -0.0002112 0.9903 0.0003546 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6226 0.6204 0.4638 0.2683 0.9848 0.9902 0.6227 0.9684 0.9793 0.466 ] Network output: [ 0.01427 0.9408 0.02204 -0.0002977 0.0001336 1.007 -0.0002244 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02221 Epoch 2442 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02819 0.978 1 -1.942e-05 8.719e-06 -0.03469 -1.464e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02234 -0.005624 0.01804 0.02637 0.9398 0.9492 0.04662 0.8861 0.9044 0.1198 ] Network output: [ 0.979 0.06153 -0.01667 -5.002e-05 2.246e-05 -0.003132 -3.77e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6421 0.1237 0.127 0.2358 0.9716 0.9869 0.7359 0.9011 0.9672 0.643 ] Network output: [ -0.008339 0.9463 1.027 -9.331e-05 4.189e-05 0.04344 -7.032e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04885 0.0363 0.05176 0.03514 0.9852 0.9896 0.04999 0.9701 0.9803 0.06442 ] Network output: [ 0.06918 -0.2503 1.08 -0.0008868 0.0003981 1.028 -0.0006683 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7306 0.6291 0.5443 0.4039 0.975 0.9888 0.7339 0.9115 0.972 0.6386 ] Network output: [ -0.02959 0.1525 0.9416 0.0008776 -0.000394 0.9687 0.0006614 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6253 0.6114 0.4508 0.2869 0.9865 0.9912 0.6258 0.9736 0.9822 0.4628 ] Network output: [ -0.05244 0.171 0.9454 0.0004728 -0.0002123 0.9904 0.0003563 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6227 0.6205 0.4638 0.2681 0.9848 0.9902 0.6228 0.9685 0.9793 0.466 ] Network output: [ 0.01423 0.941 0.02202 -0.0002978 0.0001337 1.007 -0.0002245 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02214 Epoch 2443 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02816 0.9781 1 -1.968e-05 8.835e-06 -0.03469 -1.483e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02234 -0.005623 0.01804 0.02634 0.9398 0.9492 0.04661 0.8861 0.9044 0.1198 ] Network output: [ 0.9791 0.06145 -0.01666 -4.989e-05 2.24e-05 -0.003135 -3.76e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.642 0.1237 0.127 0.2355 0.9716 0.9869 0.7358 0.9012 0.9672 0.6431 ] Network output: [ -0.008362 0.9464 1.027 -9.344e-05 4.195e-05 0.04341 -7.042e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04884 0.0363 0.05175 0.03509 0.9852 0.9896 0.04998 0.9701 0.9803 0.0644 ] Network output: [ 0.06906 -0.2499 1.08 -0.0008902 0.0003996 1.028 -0.0006709 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7306 0.6291 0.5444 0.4035 0.975 0.9888 0.7338 0.9115 0.972 0.6386 ] Network output: [ -0.02951 0.1522 0.9415 0.0008791 -0.0003946 0.9688 0.0006625 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6254 0.6115 0.4508 0.2867 0.9866 0.9912 0.6259 0.9736 0.9822 0.4629 ] Network output: [ -0.05235 0.1707 0.9455 0.0004752 -0.0002133 0.9905 0.0003581 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6228 0.6206 0.4638 0.2679 0.9848 0.9902 0.6229 0.9685 0.9793 0.466 ] Network output: [ 0.01419 0.9411 0.022 -0.000298 0.0001338 1.007 -0.0002246 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02208 Epoch 2444 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02813 0.9781 1 -1.994e-05 8.95e-06 -0.03469 -1.502e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02233 -0.005622 0.01803 0.02632 0.9398 0.9492 0.04659 0.8862 0.9044 0.1198 ] Network output: [ 0.9791 0.06137 -0.01664 -4.977e-05 2.234e-05 -0.003138 -3.751e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.642 0.1237 0.1271 0.2351 0.9717 0.9869 0.7357 0.9012 0.9672 0.6432 ] Network output: [ -0.008385 0.9464 1.027 -9.357e-05 4.201e-05 0.04339 -7.052e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04883 0.03629 0.05173 0.03504 0.9852 0.9896 0.04997 0.9701 0.9803 0.06439 ] Network output: [ 0.06893 -0.2496 1.08 -0.0008935 0.0004011 1.028 -0.0006734 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7305 0.629 0.5444 0.403 0.975 0.9888 0.7337 0.9115 0.972 0.6387 ] Network output: [ -0.02942 0.152 0.9415 0.0008805 -0.0003953 0.969 0.0006636 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6255 0.6116 0.4509 0.2864 0.9866 0.9912 0.626 0.9736 0.9822 0.4629 ] Network output: [ -0.05225 0.1704 0.9455 0.0004775 -0.0002144 0.9906 0.0003599 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6229 0.6207 0.4637 0.2677 0.9848 0.9902 0.623 0.9685 0.9793 0.466 ] Network output: [ 0.01415 0.9413 0.02199 -0.0002981 0.0001338 1.007 -0.0002247 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02202 Epoch 2445 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0281 0.9782 1 -2.019e-05 9.065e-06 -0.0347 -1.522e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02233 -0.005622 0.01803 0.02629 0.9398 0.9492 0.04658 0.8862 0.9045 0.1197 ] Network output: [ 0.9791 0.06129 -0.01663 -4.965e-05 2.229e-05 -0.003141 -3.742e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6419 0.1237 0.1271 0.2348 0.9717 0.9869 0.7356 0.9012 0.9672 0.6432 ] Network output: [ -0.008408 0.9465 1.027 -9.369e-05 4.206e-05 0.04336 -7.061e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04883 0.03628 0.05172 0.03499 0.9852 0.9896 0.04996 0.9701 0.9803 0.06437 ] Network output: [ 0.0688 -0.2493 1.08 -0.0008969 0.0004027 1.028 -0.000676 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7304 0.629 0.5445 0.4026 0.975 0.9888 0.7336 0.9115 0.972 0.6388 ] Network output: [ -0.02933 0.1517 0.9414 0.0008819 -0.0003959 0.9691 0.0006646 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6256 0.6117 0.4509 0.2862 0.9866 0.9912 0.6261 0.9736 0.9822 0.4629 ] Network output: [ -0.05215 0.1701 0.9455 0.0004799 -0.0002154 0.9906 0.0003617 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.623 0.6207 0.4637 0.2675 0.9848 0.9902 0.6231 0.9685 0.9794 0.466 ] Network output: [ 0.01412 0.9414 0.02197 -0.0002982 0.0001339 1.007 -0.0002248 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02196 Epoch 2446 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02807 0.9782 1 -2.045e-05 9.179e-06 -0.0347 -1.541e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02232 -0.005621 0.01803 0.02626 0.9398 0.9492 0.04656 0.8862 0.9045 0.1197 ] Network output: [ 0.9792 0.06121 -0.01661 -4.953e-05 2.224e-05 -0.003144 -3.733e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6419 0.1237 0.1272 0.2345 0.9717 0.9869 0.7356 0.9012 0.9672 0.6433 ] Network output: [ -0.008431 0.9466 1.027 -9.382e-05 4.212e-05 0.04334 -7.07e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04882 0.03628 0.05171 0.03494 0.9853 0.9896 0.04995 0.9701 0.9804 0.06435 ] Network output: [ 0.06868 -0.2489 1.08 -0.0009003 0.0004042 1.027 -0.0006785 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7303 0.6289 0.5446 0.4021 0.975 0.9888 0.7336 0.9115 0.972 0.6388 ] Network output: [ -0.02924 0.1515 0.9414 0.0008833 -0.0003965 0.9692 0.0006657 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6257 0.6118 0.4509 0.2859 0.9866 0.9912 0.6262 0.9736 0.9822 0.463 ] Network output: [ -0.05205 0.1698 0.9455 0.0004823 -0.0002165 0.9907 0.0003635 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6231 0.6208 0.4637 0.2674 0.9848 0.9902 0.6231 0.9685 0.9794 0.4659 ] Network output: [ 0.01408 0.9416 0.02195 -0.0002984 0.000134 1.007 -0.0002249 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02189 Epoch 2447 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02804 0.9783 1 -2.07e-05 9.293e-06 -0.03471 -1.56e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02232 -0.00562 0.01803 0.02623 0.9398 0.9492 0.04655 0.8862 0.9045 0.1197 ] Network output: [ 0.9792 0.06113 -0.01659 -4.942e-05 2.219e-05 -0.003147 -3.724e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6418 0.1237 0.1272 0.2342 0.9717 0.9869 0.7355 0.9012 0.9673 0.6434 ] Network output: [ -0.008453 0.9467 1.026 -9.394e-05 4.217e-05 0.04331 -7.079e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04881 0.03627 0.05169 0.03489 0.9853 0.9896 0.04994 0.9701 0.9804 0.06433 ] Network output: [ 0.06855 -0.2486 1.081 -0.0009037 0.0004057 1.027 -0.0006811 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7303 0.6288 0.5447 0.4017 0.975 0.9888 0.7335 0.9115 0.972 0.6389 ] Network output: [ -0.02916 0.1512 0.9413 0.0008847 -0.0003972 0.9694 0.0006667 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6258 0.6119 0.451 0.2857 0.9866 0.9912 0.6263 0.9736 0.9822 0.463 ] Network output: [ -0.05195 0.1695 0.9455 0.0004846 -0.0002176 0.9908 0.0003652 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6231 0.6209 0.4637 0.2672 0.9848 0.9902 0.6232 0.9685 0.9794 0.4659 ] Network output: [ 0.01404 0.9417 0.02194 -0.0002985 0.000134 1.007 -0.000225 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02183 Epoch 2448 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.028 0.9783 1 -2.095e-05 9.406e-06 -0.03471 -1.579e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02232 -0.005619 0.01803 0.0262 0.9398 0.9493 0.04653 0.8862 0.9045 0.1196 ] Network output: [ 0.9792 0.06105 -0.01658 -4.931e-05 2.214e-05 -0.00315 -3.716e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6418 0.1237 0.1273 0.2339 0.9717 0.9869 0.7354 0.9012 0.9673 0.6435 ] Network output: [ -0.008476 0.9468 1.026 -9.406e-05 4.222e-05 0.04328 -7.088e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0488 0.03626 0.05168 0.03484 0.9853 0.9896 0.04994 0.9701 0.9804 0.06431 ] Network output: [ 0.06842 -0.2482 1.081 -0.0009071 0.0004072 1.027 -0.0006836 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7302 0.6288 0.5448 0.4012 0.975 0.9888 0.7334 0.9115 0.972 0.639 ] Network output: [ -0.02907 0.151 0.9413 0.0008861 -0.0003978 0.9695 0.0006678 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6259 0.612 0.451 0.2855 0.9866 0.9912 0.6264 0.9736 0.9822 0.463 ] Network output: [ -0.05185 0.1692 0.9456 0.000487 -0.0002186 0.9909 0.000367 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6232 0.621 0.4637 0.267 0.9848 0.9902 0.6233 0.9685 0.9794 0.4659 ] Network output: [ 0.014 0.9419 0.02192 -0.0002986 0.0001341 1.007 -0.0002251 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02177 Epoch 2449 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02797 0.9784 1 -2.12e-05 9.519e-06 -0.03472 -1.598e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02231 -0.005618 0.01803 0.02617 0.9398 0.9493 0.04652 0.8862 0.9045 0.1196 ] Network output: [ 0.9793 0.06097 -0.01656 -4.921e-05 2.209e-05 -0.003153 -3.709e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6418 0.1237 0.1273 0.2336 0.9717 0.9869 0.7353 0.9012 0.9673 0.6435 ] Network output: [ -0.008499 0.9469 1.026 -9.417e-05 4.228e-05 0.04326 -7.097e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04879 0.03626 0.05167 0.03479 0.9853 0.9896 0.04993 0.9701 0.9804 0.06429 ] Network output: [ 0.06829 -0.2479 1.081 -0.0009105 0.0004088 1.027 -0.0006862 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7301 0.6287 0.5448 0.4008 0.975 0.9888 0.7333 0.9116 0.972 0.639 ] Network output: [ -0.02898 0.1507 0.9412 0.0008875 -0.0003984 0.9696 0.0006689 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.626 0.6121 0.451 0.2852 0.9866 0.9912 0.6265 0.9736 0.9822 0.463 ] Network output: [ -0.05175 0.1689 0.9456 0.0004893 -0.0002197 0.991 0.0003688 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6233 0.6211 0.4637 0.2668 0.9848 0.9902 0.6234 0.9685 0.9794 0.4659 ] Network output: [ 0.01396 0.942 0.02191 -0.0002988 0.0001341 1.007 -0.0002252 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02171 Epoch 2450 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02794 0.9785 1 -2.145e-05 9.631e-06 -0.03472 -1.617e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02231 -0.005617 0.01803 0.02614 0.9398 0.9493 0.0465 0.8862 0.9045 0.1196 ] Network output: [ 0.9793 0.06088 -0.01654 -4.912e-05 2.205e-05 -0.003156 -3.701e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6417 0.1237 0.1274 0.2333 0.9717 0.9869 0.7352 0.9013 0.9673 0.6436 ] Network output: [ -0.008522 0.947 1.026 -9.428e-05 4.233e-05 0.04323 -7.106e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04878 0.03625 0.05165 0.03474 0.9853 0.9896 0.04992 0.9701 0.9804 0.06427 ] Network output: [ 0.06817 -0.2475 1.081 -0.0009139 0.0004103 1.027 -0.0006887 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.73 0.6287 0.5449 0.4003 0.975 0.9888 0.7332 0.9116 0.972 0.6391 ] Network output: [ -0.02889 0.1504 0.9412 0.0008889 -0.0003991 0.9698 0.0006699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6261 0.6122 0.4511 0.285 0.9866 0.9912 0.6266 0.9736 0.9822 0.4631 ] Network output: [ -0.05165 0.1686 0.9456 0.0004917 -0.0002207 0.9911 0.0003706 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6234 0.6212 0.4637 0.2666 0.9848 0.9902 0.6235 0.9685 0.9794 0.4659 ] Network output: [ 0.01392 0.9422 0.02189 -0.0002989 0.0001342 1.007 -0.0002252 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02164 Epoch 2451 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02791 0.9785 1 -2.17e-05 9.742e-06 -0.03473 -1.635e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02231 -0.005616 0.01803 0.02611 0.9398 0.9493 0.04649 0.8863 0.9045 0.1195 ] Network output: [ 0.9793 0.0608 -0.01653 -4.902e-05 2.201e-05 -0.003158 -3.695e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6417 0.1237 0.1274 0.2329 0.9717 0.9869 0.7352 0.9013 0.9673 0.6437 ] Network output: [ -0.008545 0.9471 1.026 -9.44e-05 4.238e-05 0.0432 -7.114e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04877 0.03624 0.05164 0.03469 0.9853 0.9896 0.04991 0.9701 0.9804 0.06425 ] Network output: [ 0.06804 -0.2472 1.081 -0.0009173 0.0004118 1.027 -0.0006913 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.73 0.6286 0.545 0.3999 0.975 0.9888 0.7332 0.9116 0.972 0.6392 ] Network output: [ -0.02881 0.1502 0.9411 0.0008903 -0.0003997 0.9699 0.000671 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6262 0.6123 0.4511 0.2848 0.9866 0.9912 0.6267 0.9736 0.9822 0.4631 ] Network output: [ -0.05155 0.1683 0.9456 0.000494 -0.0002218 0.9912 0.0003723 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6235 0.6213 0.4636 0.2664 0.9848 0.9902 0.6236 0.9685 0.9794 0.4659 ] Network output: [ 0.01388 0.9423 0.02187 -0.000299 0.0001342 1.007 -0.0002253 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02158 Epoch 2452 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02788 0.9786 1 -2.195e-05 9.853e-06 -0.03473 -1.654e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0223 -0.005615 0.01803 0.02608 0.9398 0.9493 0.04647 0.8863 0.9045 0.1195 ] Network output: [ 0.9794 0.06072 -0.01651 -4.894e-05 2.197e-05 -0.003161 -3.688e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6416 0.1237 0.1275 0.2326 0.9717 0.987 0.7351 0.9013 0.9673 0.6437 ] Network output: [ -0.008568 0.9472 1.026 -9.45e-05 4.243e-05 0.04318 -7.122e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04877 0.03623 0.05162 0.03464 0.9853 0.9896 0.0499 0.9701 0.9804 0.06423 ] Network output: [ 0.06791 -0.2469 1.081 -0.0009206 0.0004133 1.027 -0.0006938 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7299 0.6285 0.5451 0.3994 0.975 0.9888 0.7331 0.9116 0.972 0.6393 ] Network output: [ -0.02872 0.1499 0.9411 0.0008917 -0.0004003 0.97 0.000672 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6263 0.6124 0.4511 0.2845 0.9866 0.9912 0.6268 0.9736 0.9822 0.4631 ] Network output: [ -0.05146 0.168 0.9456 0.0004964 -0.0002229 0.9913 0.0003741 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6236 0.6214 0.4636 0.2663 0.9848 0.9902 0.6237 0.9685 0.9794 0.4658 ] Network output: [ 0.01384 0.9424 0.02186 -0.0002991 0.0001343 1.007 -0.0002254 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02152 Epoch 2453 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02785 0.9786 1 -2.219e-05 9.964e-06 -0.03474 -1.673e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0223 -0.005614 0.01803 0.02605 0.9398 0.9493 0.04646 0.8863 0.9046 0.1195 ] Network output: [ 0.9794 0.06063 -0.01649 -4.885e-05 2.193e-05 -0.003163 -3.682e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6416 0.1237 0.1275 0.2323 0.9717 0.987 0.735 0.9013 0.9673 0.6438 ] Network output: [ -0.008591 0.9473 1.026 -9.461e-05 4.247e-05 0.04315 -7.13e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04876 0.03623 0.05161 0.03459 0.9853 0.9896 0.04989 0.9701 0.9804 0.06421 ] Network output: [ 0.06778 -0.2465 1.081 -0.000924 0.0004148 1.026 -0.0006964 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7298 0.6285 0.5451 0.399 0.975 0.9888 0.733 0.9116 0.972 0.6393 ] Network output: [ -0.02863 0.1497 0.9411 0.0008931 -0.000401 0.9702 0.0006731 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6264 0.6125 0.4512 0.2843 0.9866 0.9912 0.6269 0.9736 0.9822 0.4632 ] Network output: [ -0.05136 0.1677 0.9457 0.0004987 -0.0002239 0.9914 0.0003759 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6237 0.6215 0.4636 0.2661 0.9848 0.9902 0.6238 0.9686 0.9794 0.4658 ] Network output: [ 0.01381 0.9426 0.02184 -0.0002992 0.0001343 1.007 -0.0002255 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02146 Epoch 2454 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02781 0.9787 1 -2.244e-05 1.007e-05 -0.03474 -1.691e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0223 -0.005613 0.01803 0.02603 0.9399 0.9493 0.04644 0.8863 0.9046 0.1194 ] Network output: [ 0.9794 0.06054 -0.01647 -4.877e-05 2.19e-05 -0.003165 -3.676e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6416 0.1237 0.1276 0.232 0.9717 0.987 0.7349 0.9013 0.9673 0.6439 ] Network output: [ -0.008613 0.9474 1.026 -9.471e-05 4.252e-05 0.04313 -7.138e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04875 0.03622 0.0516 0.03455 0.9853 0.9896 0.04988 0.9701 0.9804 0.0642 ] Network output: [ 0.06766 -0.2462 1.081 -0.0009274 0.0004163 1.026 -0.0006989 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7297 0.6284 0.5452 0.3985 0.975 0.9888 0.7329 0.9116 0.9721 0.6394 ] Network output: [ -0.02855 0.1494 0.941 0.0008945 -0.0004016 0.9703 0.0006741 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6265 0.6126 0.4512 0.2841 0.9866 0.9912 0.627 0.9736 0.9822 0.4632 ] Network output: [ -0.05126 0.1674 0.9457 0.0005011 -0.000225 0.9915 0.0003776 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6238 0.6216 0.4636 0.2659 0.9848 0.9902 0.6239 0.9686 0.9794 0.4658 ] Network output: [ 0.01377 0.9427 0.02182 -0.0002994 0.0001344 1.007 -0.0002256 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02139 Epoch 2455 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02778 0.9787 1 -2.268e-05 1.018e-05 -0.03475 -1.709e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02229 -0.005612 0.01803 0.026 0.9399 0.9493 0.04643 0.8863 0.9046 0.1194 ] Network output: [ 0.9795 0.06046 -0.01645 -4.87e-05 2.186e-05 -0.003167 -3.67e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6415 0.1237 0.1276 0.2317 0.9717 0.987 0.7349 0.9013 0.9673 0.644 ] Network output: [ -0.008636 0.9475 1.026 -9.482e-05 4.257e-05 0.0431 -7.146e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04874 0.03621 0.05158 0.0345 0.9853 0.9896 0.04987 0.9701 0.9804 0.06418 ] Network output: [ 0.06753 -0.2458 1.081 -0.0009308 0.0004179 1.026 -0.0007015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7297 0.6284 0.5453 0.3981 0.975 0.9888 0.7329 0.9116 0.9721 0.6395 ] Network output: [ -0.02846 0.1492 0.941 0.0008959 -0.0004022 0.9704 0.0006752 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6266 0.6127 0.4512 0.2838 0.9866 0.9912 0.6271 0.9736 0.9822 0.4632 ] Network output: [ -0.05116 0.1671 0.9457 0.0005034 -0.000226 0.9916 0.0003794 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6239 0.6217 0.4636 0.2657 0.9848 0.9902 0.624 0.9686 0.9794 0.4658 ] Network output: [ 0.01373 0.9429 0.02181 -0.0002995 0.0001344 1.007 -0.0002257 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02133 Epoch 2456 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02775 0.9788 1 -2.292e-05 1.029e-05 -0.03475 -1.728e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02229 -0.005612 0.01803 0.02597 0.9399 0.9493 0.04641 0.8863 0.9046 0.1194 ] Network output: [ 0.9795 0.06037 -0.01643 -4.863e-05 2.183e-05 -0.003169 -3.665e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6415 0.1238 0.1277 0.2314 0.9717 0.987 0.7348 0.9013 0.9673 0.644 ] Network output: [ -0.008659 0.9476 1.026 -9.492e-05 4.261e-05 0.04307 -7.153e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04873 0.03621 0.05157 0.03445 0.9853 0.9896 0.04986 0.9701 0.9804 0.06416 ] Network output: [ 0.0674 -0.2455 1.081 -0.0009341 0.0004194 1.026 -0.000704 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7296 0.6283 0.5454 0.3976 0.975 0.9888 0.7328 0.9116 0.9721 0.6395 ] Network output: [ -0.02837 0.1489 0.9409 0.0008973 -0.0004028 0.9706 0.0006762 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6267 0.6128 0.4513 0.2836 0.9866 0.9912 0.6272 0.9736 0.9822 0.4632 ] Network output: [ -0.05106 0.1668 0.9457 0.0005058 -0.0002271 0.9917 0.0003812 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.624 0.6218 0.4636 0.2655 0.9848 0.9902 0.6241 0.9686 0.9794 0.4658 ] Network output: [ 0.01369 0.943 0.02179 -0.0002996 0.0001345 1.007 -0.0002258 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02127 Epoch 2457 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02772 0.9789 1 -2.317e-05 1.04e-05 -0.03476 -1.746e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02229 -0.005611 0.01803 0.02594 0.9399 0.9493 0.0464 0.8863 0.9046 0.1193 ] Network output: [ 0.9796 0.06029 -0.01641 -4.857e-05 2.18e-05 -0.003171 -3.66e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6415 0.1238 0.1278 0.2311 0.9717 0.987 0.7347 0.9013 0.9673 0.6441 ] Network output: [ -0.008682 0.9477 1.026 -9.501e-05 4.265e-05 0.04305 -7.16e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04872 0.0362 0.05156 0.0344 0.9853 0.9896 0.04985 0.9701 0.9804 0.06414 ] Network output: [ 0.06727 -0.2451 1.081 -0.0009375 0.0004209 1.026 -0.0007065 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7295 0.6282 0.5455 0.3972 0.975 0.9888 0.7327 0.9116 0.9721 0.6396 ] Network output: [ -0.02829 0.1486 0.9409 0.0008987 -0.0004035 0.9707 0.0006773 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6268 0.6129 0.4513 0.2833 0.9866 0.9912 0.6273 0.9736 0.9823 0.4633 ] Network output: [ -0.05096 0.1664 0.9458 0.0005081 -0.0002281 0.9918 0.000383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6241 0.6219 0.4635 0.2653 0.9848 0.9902 0.6242 0.9686 0.9794 0.4658 ] Network output: [ 0.01365 0.9432 0.02177 -0.0002997 0.0001345 1.007 -0.0002258 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0212 Epoch 2458 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02769 0.9789 1 -2.34e-05 1.051e-05 -0.03476 -1.764e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02228 -0.00561 0.01803 0.02591 0.9399 0.9493 0.04638 0.8864 0.9046 0.1193 ] Network output: [ 0.9796 0.0602 -0.0164 -4.851e-05 2.178e-05 -0.003173 -3.656e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6414 0.1238 0.1278 0.2308 0.9717 0.987 0.7346 0.9014 0.9673 0.6442 ] Network output: [ -0.008705 0.9477 1.026 -9.511e-05 4.27e-05 0.04302 -7.167e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04872 0.03619 0.05154 0.03435 0.9853 0.9896 0.04984 0.9701 0.9804 0.06412 ] Network output: [ 0.06714 -0.2448 1.081 -0.0009409 0.0004224 1.026 -0.0007091 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7294 0.6282 0.5455 0.3967 0.975 0.9888 0.7326 0.9117 0.9721 0.6397 ] Network output: [ -0.0282 0.1484 0.9409 0.0009001 -0.0004041 0.9708 0.0006783 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6269 0.613 0.4513 0.2831 0.9866 0.9912 0.6274 0.9736 0.9823 0.4633 ] Network output: [ -0.05086 0.1661 0.9458 0.0005105 -0.0002292 0.9919 0.0003847 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6242 0.6219 0.4635 0.2651 0.9849 0.9902 0.6243 0.9686 0.9794 0.4657 ] Network output: [ 0.01361 0.9433 0.02176 -0.0002998 0.0001346 1.006 -0.0002259 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02114 Epoch 2459 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02766 0.979 1 -2.364e-05 1.061e-05 -0.03476 -1.782e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02228 -0.005609 0.01803 0.02588 0.9399 0.9493 0.04637 0.8864 0.9046 0.1193 ] Network output: [ 0.9796 0.06011 -0.01638 -4.845e-05 2.175e-05 -0.003175 -3.652e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6414 0.1238 0.1279 0.2305 0.9717 0.987 0.7345 0.9014 0.9673 0.6442 ] Network output: [ -0.008728 0.9478 1.026 -9.52e-05 4.274e-05 0.04299 -7.174e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04871 0.03619 0.05153 0.0343 0.9853 0.9896 0.04983 0.9702 0.9804 0.0641 ] Network output: [ 0.06701 -0.2444 1.081 -0.0009442 0.0004239 1.026 -0.0007116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7294 0.6281 0.5456 0.3963 0.975 0.9888 0.7326 0.9117 0.9721 0.6397 ] Network output: [ -0.02811 0.1481 0.9408 0.0009015 -0.0004047 0.971 0.0006794 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.627 0.6131 0.4513 0.2829 0.9866 0.9912 0.6275 0.9736 0.9823 0.4633 ] Network output: [ -0.05076 0.1658 0.9458 0.0005128 -0.0002302 0.992 0.0003865 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6243 0.622 0.4635 0.265 0.9849 0.9902 0.6244 0.9686 0.9794 0.4657 ] Network output: [ 0.01358 0.9435 0.02174 -0.0002999 0.0001346 1.006 -0.000226 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02108 Epoch 2460 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02762 0.979 1 -2.388e-05 1.072e-05 -0.03477 -1.8e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02228 -0.005608 0.01803 0.02586 0.9399 0.9493 0.04635 0.8864 0.9046 0.1193 ] Network output: [ 0.9797 0.06002 -0.01636 -4.84e-05 2.173e-05 -0.003176 -3.648e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6413 0.1238 0.1279 0.2301 0.9717 0.987 0.7345 0.9014 0.9673 0.6443 ] Network output: [ -0.008751 0.9479 1.026 -9.529e-05 4.278e-05 0.04297 -7.181e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0487 0.03618 0.05152 0.03425 0.9853 0.9896 0.04983 0.9702 0.9804 0.06408 ] Network output: [ 0.06688 -0.2441 1.081 -0.0009476 0.0004254 1.025 -0.0007141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7293 0.6281 0.5457 0.3958 0.975 0.9888 0.7325 0.9117 0.9721 0.6398 ] Network output: [ -0.02803 0.1479 0.9408 0.0009028 -0.0004053 0.9711 0.0006804 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6271 0.6132 0.4514 0.2826 0.9866 0.9912 0.6276 0.9736 0.9823 0.4634 ] Network output: [ -0.05066 0.1655 0.9458 0.0005152 -0.0002313 0.9921 0.0003883 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6244 0.6221 0.4635 0.2648 0.9849 0.9902 0.6244 0.9686 0.9794 0.4657 ] Network output: [ 0.01354 0.9436 0.02172 -0.0003 0.0001347 1.006 -0.0002261 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02102 Epoch 2461 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02759 0.9791 1 -2.412e-05 1.083e-05 -0.03477 -1.817e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02227 -0.005607 0.01803 0.02583 0.9399 0.9493 0.04634 0.8864 0.9047 0.1192 ] Network output: [ 0.9797 0.05993 -0.01634 -4.836e-05 2.171e-05 -0.003178 -3.644e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6413 0.1238 0.128 0.2298 0.9717 0.987 0.7344 0.9014 0.9673 0.6444 ] Network output: [ -0.008773 0.948 1.026 -9.537e-05 4.282e-05 0.04294 -7.188e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04869 0.03617 0.0515 0.0342 0.9853 0.9896 0.04982 0.9702 0.9804 0.06406 ] Network output: [ 0.06676 -0.2437 1.081 -0.000951 0.0004269 1.025 -0.0007167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7292 0.628 0.5458 0.3954 0.975 0.9888 0.7324 0.9117 0.9721 0.6399 ] Network output: [ -0.02794 0.1476 0.9407 0.0009042 -0.0004059 0.9712 0.0006815 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6272 0.6133 0.4514 0.2824 0.9866 0.9912 0.6277 0.9737 0.9823 0.4634 ] Network output: [ -0.05056 0.1652 0.9458 0.0005175 -0.0002323 0.9922 0.00039 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6244 0.6222 0.4635 0.2646 0.9849 0.9902 0.6245 0.9686 0.9794 0.4657 ] Network output: [ 0.0135 0.9438 0.02171 -0.0003001 0.0001347 1.006 -0.0002261 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02095 Epoch 2462 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02756 0.9792 1 -2.435e-05 1.093e-05 -0.03478 -1.835e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02227 -0.005606 0.01803 0.0258 0.9399 0.9493 0.04632 0.8864 0.9047 0.1192 ] Network output: [ 0.9797 0.05984 -0.01631 -4.832e-05 2.169e-05 -0.00318 -3.641e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6413 0.1238 0.128 0.2295 0.9717 0.987 0.7343 0.9014 0.9673 0.6445 ] Network output: [ -0.008796 0.9481 1.026 -9.546e-05 4.285e-05 0.04291 -7.194e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04868 0.03617 0.05149 0.03416 0.9853 0.9896 0.04981 0.9702 0.9804 0.06404 ] Network output: [ 0.06663 -0.2434 1.081 -0.0009543 0.0004284 1.025 -0.0007192 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7292 0.6279 0.5458 0.3949 0.975 0.9889 0.7323 0.9117 0.9721 0.6399 ] Network output: [ -0.02785 0.1474 0.9407 0.0009056 -0.0004066 0.9713 0.0006825 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6273 0.6134 0.4514 0.2822 0.9866 0.9912 0.6278 0.9737 0.9823 0.4634 ] Network output: [ -0.05046 0.1649 0.9459 0.0005199 -0.0002334 0.9923 0.0003918 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6245 0.6223 0.4635 0.2644 0.9849 0.9902 0.6246 0.9686 0.9795 0.4657 ] Network output: [ 0.01346 0.9439 0.02169 -0.0003002 0.0001348 1.006 -0.0002262 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02089 Epoch 2463 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02753 0.9792 1 -2.458e-05 1.104e-05 -0.03478 -1.853e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02227 -0.005605 0.01803 0.02577 0.9399 0.9494 0.04631 0.8864 0.9047 0.1192 ] Network output: [ 0.9798 0.05975 -0.01629 -4.828e-05 2.167e-05 -0.003181 -3.639e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6412 0.1238 0.1281 0.2292 0.9717 0.987 0.7342 0.9014 0.9674 0.6445 ] Network output: [ -0.008819 0.9482 1.026 -9.554e-05 4.289e-05 0.04289 -7.2e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04867 0.03616 0.05148 0.03411 0.9853 0.9896 0.0498 0.9702 0.9804 0.06402 ] Network output: [ 0.0665 -0.243 1.081 -0.0009577 0.0004299 1.025 -0.0007217 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7291 0.6279 0.5459 0.3944 0.975 0.9889 0.7323 0.9117 0.9721 0.64 ] Network output: [ -0.02777 0.1471 0.9407 0.000907 -0.0004072 0.9715 0.0006835 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6274 0.6135 0.4515 0.2819 0.9866 0.9912 0.6279 0.9737 0.9823 0.4635 ] Network output: [ -0.05036 0.1646 0.9459 0.0005222 -0.0002344 0.9924 0.0003935 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6246 0.6224 0.4634 0.2642 0.9849 0.9902 0.6247 0.9686 0.9795 0.4657 ] Network output: [ 0.01342 0.9441 0.02167 -0.0003003 0.0001348 1.006 -0.0002263 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02083 Epoch 2464 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0275 0.9793 1 -2.482e-05 1.114e-05 -0.03479 -1.87e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02226 -0.005604 0.01803 0.02574 0.9399 0.9494 0.04629 0.8864 0.9047 0.1191 ] Network output: [ 0.9798 0.05966 -0.01627 -4.825e-05 2.166e-05 -0.003182 -3.636e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6412 0.1238 0.1281 0.2289 0.9717 0.987 0.7342 0.9014 0.9674 0.6446 ] Network output: [ -0.008842 0.9483 1.026 -9.562e-05 4.293e-05 0.04286 -7.206e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04867 0.03615 0.05146 0.03406 0.9853 0.9896 0.04979 0.9702 0.9804 0.06401 ] Network output: [ 0.06637 -0.2426 1.081 -0.000961 0.0004314 1.025 -0.0007243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.729 0.6278 0.546 0.394 0.975 0.9889 0.7322 0.9117 0.9721 0.6401 ] Network output: [ -0.02768 0.1468 0.9406 0.0009083 -0.0004078 0.9716 0.0006846 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6275 0.6136 0.4515 0.2817 0.9866 0.9912 0.628 0.9737 0.9823 0.4635 ] Network output: [ -0.05027 0.1643 0.9459 0.0005245 -0.0002355 0.9925 0.0003953 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6247 0.6225 0.4634 0.264 0.9849 0.9902 0.6248 0.9687 0.9795 0.4656 ] Network output: [ 0.01339 0.9442 0.02166 -0.0003003 0.0001348 1.006 -0.0002263 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02077 Epoch 2465 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02747 0.9793 1 -2.505e-05 1.124e-05 -0.03479 -1.888e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02226 -0.005603 0.01803 0.02572 0.9399 0.9494 0.04628 0.8864 0.9047 0.1191 ] Network output: [ 0.9798 0.05957 -0.01625 -4.822e-05 2.165e-05 -0.003183 -3.634e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6412 0.1238 0.1282 0.2286 0.9717 0.987 0.7341 0.9014 0.9674 0.6447 ] Network output: [ -0.008865 0.9484 1.026 -9.57e-05 4.296e-05 0.04283 -7.212e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04866 0.03615 0.05145 0.03401 0.9853 0.9896 0.04978 0.9702 0.9804 0.06399 ] Network output: [ 0.06624 -0.2423 1.081 -0.0009644 0.0004329 1.025 -0.0007268 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7289 0.6278 0.5461 0.3935 0.9751 0.9889 0.7321 0.9117 0.9721 0.6401 ] Network output: [ -0.0276 0.1466 0.9406 0.0009097 -0.0004084 0.9717 0.0006856 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6276 0.6137 0.4515 0.2814 0.9866 0.9912 0.6281 0.9737 0.9823 0.4635 ] Network output: [ -0.05017 0.164 0.9459 0.0005269 -0.0002365 0.9925 0.0003971 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6248 0.6226 0.4634 0.2638 0.9849 0.9902 0.6249 0.9687 0.9795 0.4656 ] Network output: [ 0.01335 0.9443 0.02164 -0.0003004 0.0001349 1.006 -0.0002264 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0207 Epoch 2466 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02743 0.9794 1 -2.528e-05 1.135e-05 -0.0348 -1.905e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02226 -0.005602 0.01803 0.02569 0.9399 0.9494 0.04626 0.8865 0.9047 0.1191 ] Network output: [ 0.9799 0.05948 -0.01623 -4.82e-05 2.164e-05 -0.003184 -3.633e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6411 0.1238 0.1283 0.2283 0.9718 0.987 0.734 0.9014 0.9674 0.6447 ] Network output: [ -0.008887 0.9485 1.026 -9.577e-05 4.3e-05 0.04281 -7.218e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04865 0.03614 0.05144 0.03396 0.9853 0.9896 0.04977 0.9702 0.9804 0.06397 ] Network output: [ 0.06611 -0.2419 1.081 -0.0009677 0.0004345 1.025 -0.0007293 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7289 0.6277 0.5461 0.3931 0.9751 0.9889 0.732 0.9117 0.9721 0.6402 ] Network output: [ -0.02751 0.1463 0.9405 0.0009111 -0.000409 0.9719 0.0006866 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6277 0.6138 0.4516 0.2812 0.9866 0.9913 0.6282 0.9737 0.9823 0.4635 ] Network output: [ -0.05007 0.1637 0.946 0.0005292 -0.0002376 0.9926 0.0003988 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6249 0.6227 0.4634 0.2637 0.9849 0.9902 0.625 0.9687 0.9795 0.4656 ] Network output: [ 0.01331 0.9445 0.02162 -0.0003005 0.0001349 1.006 -0.0002265 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02064 Epoch 2467 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0274 0.9794 1 -2.55e-05 1.145e-05 -0.0348 -1.922e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02225 -0.005601 0.01803 0.02566 0.94 0.9494 0.04625 0.8865 0.9047 0.119 ] Network output: [ 0.9799 0.05939 -0.01621 -4.818e-05 2.163e-05 -0.003185 -3.631e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6411 0.1238 0.1283 0.228 0.9718 0.987 0.7339 0.9015 0.9674 0.6448 ] Network output: [ -0.00891 0.9486 1.026 -9.584e-05 4.303e-05 0.04278 -7.223e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04864 0.03613 0.05142 0.03391 0.9853 0.9896 0.04976 0.9702 0.9804 0.06395 ] Network output: [ 0.06598 -0.2416 1.081 -0.0009711 0.000436 1.024 -0.0007318 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7288 0.6277 0.5462 0.3926 0.9751 0.9889 0.732 0.9117 0.9721 0.6403 ] Network output: [ -0.02742 0.1461 0.9405 0.0009124 -0.0004096 0.972 0.0006876 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6279 0.6139 0.4516 0.281 0.9866 0.9913 0.6283 0.9737 0.9823 0.4636 ] Network output: [ -0.04997 0.1634 0.946 0.0005315 -0.0002386 0.9927 0.0004006 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.625 0.6228 0.4634 0.2635 0.9849 0.9902 0.6251 0.9687 0.9795 0.4656 ] Network output: [ 0.01327 0.9446 0.0216 -0.0003006 0.0001349 1.006 -0.0002265 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02058 Epoch 2468 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02737 0.9795 1 -2.573e-05 1.155e-05 -0.03481 -1.939e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02225 -0.0056 0.01803 0.02563 0.94 0.9494 0.04623 0.8865 0.9047 0.119 ] Network output: [ 0.9799 0.05929 -0.01619 -4.817e-05 2.163e-05 -0.003186 -3.63e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6411 0.1238 0.1284 0.2277 0.9718 0.987 0.7339 0.9015 0.9674 0.6449 ] Network output: [ -0.008933 0.9487 1.026 -9.591e-05 4.306e-05 0.04276 -7.228e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04863 0.03613 0.05141 0.03387 0.9853 0.9896 0.04975 0.9702 0.9804 0.06393 ] Network output: [ 0.06585 -0.2412 1.081 -0.0009744 0.0004375 1.024 -0.0007344 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7287 0.6276 0.5463 0.3922 0.9751 0.9889 0.7319 0.9118 0.9721 0.6403 ] Network output: [ -0.02734 0.1458 0.9405 0.0009138 -0.0004102 0.9721 0.0006887 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.628 0.614 0.4516 0.2807 0.9866 0.9913 0.6284 0.9737 0.9823 0.4636 ] Network output: [ -0.04987 0.1631 0.946 0.0005338 -0.0002397 0.9928 0.0004023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6251 0.6229 0.4634 0.2633 0.9849 0.9902 0.6252 0.9687 0.9795 0.4656 ] Network output: [ 0.01324 0.9448 0.02159 -0.0003007 0.000135 1.006 -0.0002266 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02051 Epoch 2469 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02734 0.9796 1 -2.596e-05 1.165e-05 -0.03481 -1.956e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02225 -0.005599 0.01803 0.0256 0.94 0.9494 0.04622 0.8865 0.9047 0.119 ] Network output: [ 0.98 0.0592 -0.01616 -4.816e-05 2.162e-05 -0.003187 -3.63e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.641 0.1238 0.1284 0.2274 0.9718 0.987 0.7338 0.9015 0.9674 0.6449 ] Network output: [ -0.008956 0.9488 1.026 -9.598e-05 4.309e-05 0.04273 -7.233e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04863 0.03612 0.0514 0.03382 0.9853 0.9896 0.04975 0.9702 0.9804 0.06391 ] Network output: [ 0.06572 -0.2409 1.081 -0.0009778 0.000439 1.024 -0.0007369 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7287 0.6275 0.5464 0.3917 0.9751 0.9889 0.7318 0.9118 0.9721 0.6404 ] Network output: [ -0.02725 0.1455 0.9404 0.0009152 -0.0004108 0.9723 0.0006897 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6281 0.6141 0.4517 0.2805 0.9866 0.9913 0.6285 0.9737 0.9823 0.4636 ] Network output: [ -0.04977 0.1628 0.946 0.0005362 -0.0002407 0.9929 0.0004041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6252 0.623 0.4633 0.2631 0.9849 0.9902 0.6253 0.9687 0.9795 0.4655 ] Network output: [ 0.0132 0.9449 0.02157 -0.0003007 0.000135 1.006 -0.0002266 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02045 Epoch 2470 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02731 0.9796 1 -2.618e-05 1.175e-05 -0.03482 -1.973e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02224 -0.005598 0.01803 0.02558 0.94 0.9494 0.0462 0.8865 0.9048 0.1189 ] Network output: [ 0.98 0.05911 -0.01614 -4.816e-05 2.162e-05 -0.003188 -3.629e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.641 0.1238 0.1285 0.2271 0.9718 0.987 0.7337 0.9015 0.9674 0.645 ] Network output: [ -0.008979 0.9489 1.026 -9.604e-05 4.312e-05 0.0427 -7.238e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04862 0.03611 0.05138 0.03377 0.9853 0.9896 0.04974 0.9702 0.9804 0.06389 ] Network output: [ 0.06559 -0.2405 1.081 -0.0009811 0.0004404 1.024 -0.0007394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7286 0.6275 0.5465 0.3913 0.9751 0.9889 0.7317 0.9118 0.9721 0.6405 ] Network output: [ -0.02717 0.1453 0.9404 0.0009165 -0.0004115 0.9724 0.0006907 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6282 0.6142 0.4517 0.2803 0.9866 0.9913 0.6287 0.9737 0.9823 0.4637 ] Network output: [ -0.04967 0.1625 0.946 0.0005385 -0.0002417 0.993 0.0004058 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6253 0.6231 0.4633 0.2629 0.9849 0.9902 0.6254 0.9687 0.9795 0.4655 ] Network output: [ 0.01316 0.9451 0.02155 -0.0003008 0.000135 1.006 -0.0002267 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02039 Epoch 2471 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02728 0.9797 1 -2.641e-05 1.185e-05 -0.03482 -1.99e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02224 -0.005597 0.01803 0.02555 0.94 0.9494 0.04619 0.8865 0.9048 0.1189 ] Network output: [ 0.98 0.05901 -0.01612 -4.816e-05 2.162e-05 -0.003189 -3.629e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.641 0.1239 0.1286 0.2268 0.9718 0.987 0.7336 0.9015 0.9674 0.6451 ] Network output: [ -0.009001 0.949 1.026 -9.611e-05 4.315e-05 0.04268 -7.243e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04861 0.03611 0.05137 0.03372 0.9853 0.9896 0.04973 0.9702 0.9804 0.06387 ] Network output: [ 0.06546 -0.2401 1.081 -0.0009844 0.0004419 1.024 -0.0007419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7285 0.6274 0.5465 0.3908 0.9751 0.9889 0.7317 0.9118 0.9721 0.6405 ] Network output: [ -0.02708 0.145 0.9404 0.0009179 -0.0004121 0.9725 0.0006917 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6283 0.6143 0.4517 0.28 0.9866 0.9913 0.6288 0.9737 0.9823 0.4637 ] Network output: [ -0.04957 0.1622 0.9461 0.0005408 -0.0002428 0.9931 0.0004076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6254 0.6232 0.4633 0.2627 0.9849 0.9902 0.6255 0.9687 0.9795 0.4655 ] Network output: [ 0.01312 0.9452 0.02154 -0.0003009 0.0001351 1.006 -0.0002268 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02033 Epoch 2472 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02724 0.9797 1 -2.663e-05 1.195e-05 -0.03482 -2.007e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02224 -0.005596 0.01803 0.02552 0.94 0.9494 0.04618 0.8865 0.9048 0.1189 ] Network output: [ 0.9801 0.05892 -0.0161 -4.816e-05 2.162e-05 -0.003189 -3.63e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6409 0.1239 0.1286 0.2265 0.9718 0.987 0.7336 0.9015 0.9674 0.6451 ] Network output: [ -0.009024 0.9491 1.026 -9.617e-05 4.317e-05 0.04265 -7.247e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0486 0.0361 0.05136 0.03368 0.9853 0.9896 0.04972 0.9702 0.9804 0.06385 ] Network output: [ 0.06533 -0.2398 1.081 -0.0009877 0.0004434 1.024 -0.0007444 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7284 0.6274 0.5466 0.3904 0.9751 0.9889 0.7316 0.9118 0.9721 0.6406 ] Network output: [ -0.027 0.1447 0.9403 0.0009192 -0.0004127 0.9727 0.0006927 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6284 0.6144 0.4518 0.2798 0.9866 0.9913 0.6289 0.9737 0.9823 0.4637 ] Network output: [ -0.04947 0.1619 0.9461 0.0005431 -0.0002438 0.9932 0.0004093 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6255 0.6233 0.4633 0.2625 0.9849 0.9902 0.6256 0.9687 0.9795 0.4655 ] Network output: [ 0.01309 0.9454 0.02152 -0.0003009 0.0001351 1.006 -0.0002268 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02026 Epoch 2473 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02721 0.9798 1 -2.685e-05 1.205e-05 -0.03483 -2.023e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02224 -0.005595 0.01803 0.02549 0.94 0.9494 0.04616 0.8865 0.9048 0.1188 ] Network output: [ 0.9801 0.05882 -0.01607 -4.817e-05 2.163e-05 -0.00319 -3.631e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6409 0.1239 0.1287 0.2262 0.9718 0.987 0.7335 0.9015 0.9674 0.6452 ] Network output: [ -0.009047 0.9492 1.026 -9.622e-05 4.32e-05 0.04262 -7.252e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04859 0.03609 0.05134 0.03363 0.9853 0.9896 0.04971 0.9702 0.9805 0.06384 ] Network output: [ 0.0652 -0.2394 1.081 -0.0009911 0.0004449 1.024 -0.0007469 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7284 0.6273 0.5467 0.3899 0.9751 0.9889 0.7315 0.9118 0.9721 0.6407 ] Network output: [ -0.02691 0.1445 0.9403 0.0009206 -0.0004133 0.9728 0.0006938 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6285 0.6145 0.4518 0.2795 0.9866 0.9913 0.629 0.9737 0.9823 0.4637 ] Network output: [ -0.04937 0.1615 0.9461 0.0005454 -0.0002449 0.9933 0.0004111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6256 0.6234 0.4633 0.2624 0.9849 0.9902 0.6257 0.9687 0.9795 0.4655 ] Network output: [ 0.01305 0.9455 0.0215 -0.000301 0.0001351 1.006 -0.0002269 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0202 Epoch 2474 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02718 0.9799 1 -2.707e-05 1.215e-05 -0.03483 -2.04e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02223 -0.005594 0.01803 0.02547 0.94 0.9494 0.04615 0.8866 0.9048 0.1188 ] Network output: [ 0.9802 0.05873 -0.01605 -4.819e-05 2.163e-05 -0.00319 -3.632e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6409 0.1239 0.1287 0.2259 0.9718 0.987 0.7334 0.9015 0.9674 0.6453 ] Network output: [ -0.00907 0.9493 1.026 -9.628e-05 4.322e-05 0.0426 -7.256e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04859 0.03609 0.05133 0.03358 0.9853 0.9896 0.0497 0.9702 0.9805 0.06382 ] Network output: [ 0.06507 -0.2391 1.081 -0.0009944 0.0004464 1.023 -0.0007494 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7283 0.6273 0.5468 0.3895 0.9751 0.9889 0.7315 0.9118 0.9721 0.6407 ] Network output: [ -0.02683 0.1442 0.9403 0.0009219 -0.0004139 0.9729 0.0006948 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6286 0.6146 0.4518 0.2793 0.9866 0.9913 0.6291 0.9737 0.9823 0.4638 ] Network output: [ -0.04927 0.1612 0.9461 0.0005477 -0.0002459 0.9934 0.0004128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6257 0.6235 0.4633 0.2622 0.9849 0.9902 0.6258 0.9687 0.9795 0.4655 ] Network output: [ 0.01301 0.9456 0.02148 -0.0003011 0.0001352 1.006 -0.0002269 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02014 Epoch 2475 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02715 0.9799 1.001 -2.729e-05 1.225e-05 -0.03484 -2.056e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02223 -0.005593 0.01804 0.02544 0.94 0.9494 0.04613 0.8866 0.9048 0.1188 ] Network output: [ 0.9802 0.05863 -0.01602 -4.821e-05 2.164e-05 -0.00319 -3.633e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6408 0.1239 0.1288 0.2256 0.9718 0.987 0.7334 0.9016 0.9674 0.6454 ] Network output: [ -0.009092 0.9494 1.026 -9.633e-05 4.325e-05 0.04257 -7.26e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04858 0.03608 0.05132 0.03353 0.9853 0.9896 0.04969 0.9702 0.9805 0.0638 ] Network output: [ 0.06495 -0.2387 1.081 -0.0009977 0.0004479 1.023 -0.0007519 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7282 0.6272 0.5468 0.389 0.9751 0.9889 0.7314 0.9118 0.9722 0.6408 ] Network output: [ -0.02674 0.144 0.9402 0.0009232 -0.0004145 0.973 0.0006958 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6287 0.6147 0.4518 0.2791 0.9866 0.9913 0.6292 0.9737 0.9823 0.4638 ] Network output: [ -0.04917 0.1609 0.9462 0.0005501 -0.0002469 0.9935 0.0004145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6258 0.6236 0.4632 0.262 0.9849 0.9902 0.6259 0.9688 0.9795 0.4654 ] Network output: [ 0.01298 0.9458 0.02147 -0.0003011 0.0001352 1.006 -0.0002269 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02008 Epoch 2476 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02712 0.98 1.001 -2.75e-05 1.235e-05 -0.03484 -2.073e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02223 -0.005591 0.01804 0.02541 0.94 0.9494 0.04612 0.8866 0.9048 0.1187 ] Network output: [ 0.9802 0.05853 -0.016 -4.823e-05 2.165e-05 -0.003191 -3.635e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6408 0.1239 0.1289 0.2253 0.9718 0.987 0.7333 0.9016 0.9674 0.6454 ] Network output: [ -0.009115 0.9495 1.026 -9.638e-05 4.327e-05 0.04254 -7.263e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04857 0.03607 0.0513 0.03348 0.9853 0.9896 0.04968 0.9702 0.9805 0.06378 ] Network output: [ 0.06482 -0.2383 1.081 -0.001001 0.0004494 1.023 -0.0007544 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7282 0.6271 0.5469 0.3886 0.9751 0.9889 0.7313 0.9118 0.9722 0.6409 ] Network output: [ -0.02666 0.1437 0.9402 0.0009246 -0.0004151 0.9732 0.0006968 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6288 0.6148 0.4519 0.2788 0.9866 0.9913 0.6293 0.9737 0.9823 0.4638 ] Network output: [ -0.04907 0.1606 0.9462 0.0005524 -0.000248 0.9936 0.0004163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6259 0.6237 0.4632 0.2618 0.9849 0.9903 0.626 0.9688 0.9795 0.4654 ] Network output: [ 0.01294 0.9459 0.02145 -0.0003012 0.0001352 1.006 -0.000227 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02002 Epoch 2477 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02709 0.98 1.001 -2.772e-05 1.244e-05 -0.03485 -2.089e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02222 -0.00559 0.01804 0.02538 0.94 0.9494 0.0461 0.8866 0.9048 0.1187 ] Network output: [ 0.9803 0.05843 -0.01598 -4.826e-05 2.167e-05 -0.003191 -3.637e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6408 0.1239 0.1289 0.225 0.9718 0.987 0.7332 0.9016 0.9674 0.6455 ] Network output: [ -0.009138 0.9496 1.026 -9.642e-05 4.329e-05 0.04252 -7.267e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04856 0.03607 0.05129 0.03344 0.9853 0.9896 0.04968 0.9702 0.9805 0.06376 ] Network output: [ 0.06469 -0.238 1.081 -0.001004 0.0004509 1.023 -0.0007569 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7281 0.6271 0.547 0.3881 0.9751 0.9889 0.7312 0.9118 0.9722 0.6409 ] Network output: [ -0.02657 0.1434 0.9402 0.0009259 -0.0004157 0.9733 0.0006978 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6289 0.6149 0.4519 0.2786 0.9866 0.9913 0.6294 0.9737 0.9823 0.4639 ] Network output: [ -0.04897 0.1603 0.9462 0.0005547 -0.000249 0.9937 0.000418 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.626 0.6238 0.4632 0.2616 0.9849 0.9903 0.6261 0.9688 0.9795 0.4654 ] Network output: [ 0.0129 0.9461 0.02143 -0.0003012 0.0001352 1.005 -0.000227 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01995 Epoch 2478 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02705 0.9801 1.001 -2.793e-05 1.254e-05 -0.03485 -2.105e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02222 -0.005589 0.01804 0.02536 0.94 0.9494 0.04609 0.8866 0.9049 0.1187 ] Network output: [ 0.9803 0.05834 -0.01595 -4.83e-05 2.168e-05 -0.003191 -3.64e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6407 0.1239 0.129 0.2247 0.9718 0.987 0.7331 0.9016 0.9674 0.6456 ] Network output: [ -0.00916 0.9497 1.026 -9.647e-05 4.331e-05 0.04249 -7.27e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04855 0.03606 0.05128 0.03339 0.9854 0.9896 0.04967 0.9702 0.9805 0.06374 ] Network output: [ 0.06456 -0.2376 1.081 -0.001008 0.0004524 1.023 -0.0007594 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.728 0.627 0.5471 0.3877 0.9751 0.9889 0.7312 0.9119 0.9722 0.641 ] Network output: [ -0.02649 0.1432 0.9401 0.0009272 -0.0004163 0.9734 0.0006988 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.629 0.615 0.4519 0.2784 0.9866 0.9913 0.6295 0.9737 0.9823 0.4639 ] Network output: [ -0.04887 0.16 0.9462 0.000557 -0.00025 0.9938 0.0004197 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6261 0.6239 0.4632 0.2614 0.9849 0.9903 0.6262 0.9688 0.9795 0.4654 ] Network output: [ 0.01287 0.9462 0.02142 -0.0003013 0.0001353 1.005 -0.0002271 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01989 Epoch 2479 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02702 0.9802 1.001 -2.815e-05 1.264e-05 -0.03486 -2.121e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02222 -0.005588 0.01804 0.02533 0.94 0.9495 0.04607 0.8866 0.9049 0.1186 ] Network output: [ 0.9803 0.05824 -0.01593 -4.833e-05 2.17e-05 -0.003191 -3.643e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6407 0.1239 0.129 0.2244 0.9718 0.987 0.7331 0.9016 0.9674 0.6456 ] Network output: [ -0.009183 0.9498 1.026 -9.651e-05 4.333e-05 0.04246 -7.273e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04855 0.03605 0.05127 0.03334 0.9854 0.9896 0.04966 0.9703 0.9805 0.06372 ] Network output: [ 0.06443 -0.2372 1.082 -0.001011 0.0004538 1.023 -0.0007619 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.728 0.627 0.5471 0.3872 0.9751 0.9889 0.7311 0.9119 0.9722 0.641 ] Network output: [ -0.0264 0.1429 0.9401 0.0009285 -0.0004169 0.9736 0.0006998 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6291 0.6151 0.452 0.2781 0.9866 0.9913 0.6296 0.9737 0.9823 0.4639 ] Network output: [ -0.04878 0.1597 0.9462 0.0005593 -0.0002511 0.9939 0.0004215 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6262 0.624 0.4632 0.2612 0.9849 0.9903 0.6263 0.9688 0.9796 0.4654 ] Network output: [ 0.01283 0.9464 0.0214 -0.0003013 0.0001353 1.005 -0.0002271 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01983 Epoch 2480 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02699 0.9802 1.001 -2.836e-05 1.273e-05 -0.03486 -2.137e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02222 -0.005587 0.01804 0.0253 0.9401 0.9495 0.04606 0.8866 0.9049 0.1186 ] Network output: [ 0.9804 0.05814 -0.0159 -4.838e-05 2.172e-05 -0.003191 -3.646e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6407 0.1239 0.1291 0.2241 0.9718 0.987 0.733 0.9016 0.9674 0.6457 ] Network output: [ -0.009206 0.9499 1.026 -9.655e-05 4.334e-05 0.04244 -7.276e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04854 0.03605 0.05125 0.0333 0.9854 0.9896 0.04965 0.9703 0.9805 0.0637 ] Network output: [ 0.0643 -0.2369 1.082 -0.001014 0.0004553 1.023 -0.0007643 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7279 0.6269 0.5472 0.3868 0.9751 0.9889 0.731 0.9119 0.9722 0.6411 ] Network output: [ -0.02632 0.1426 0.9401 0.0009299 -0.0004175 0.9737 0.0007008 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6292 0.6152 0.452 0.2779 0.9866 0.9913 0.6297 0.9737 0.9823 0.4639 ] Network output: [ -0.04868 0.1594 0.9463 0.0005615 -0.0002521 0.994 0.0004232 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6263 0.624 0.4632 0.2611 0.9849 0.9903 0.6264 0.9688 0.9796 0.4654 ] Network output: [ 0.01279 0.9465 0.02138 -0.0003014 0.0001353 1.005 -0.0002271 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01977 Epoch 2481 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02696 0.9803 1.001 -2.857e-05 1.283e-05 -0.03486 -2.153e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02221 -0.005586 0.01804 0.02528 0.9401 0.9495 0.04605 0.8866 0.9049 0.1186 ] Network output: [ 0.9804 0.05804 -0.01588 -4.842e-05 2.174e-05 -0.00319 -3.649e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6406 0.1239 0.1292 0.2238 0.9718 0.987 0.7329 0.9016 0.9675 0.6458 ] Network output: [ -0.009228 0.95 1.026 -9.658e-05 4.336e-05 0.04241 -7.279e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04853 0.03604 0.05124 0.03325 0.9854 0.9896 0.04964 0.9703 0.9805 0.06368 ] Network output: [ 0.06417 -0.2365 1.082 -0.001017 0.0004568 1.022 -0.0007668 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7278 0.6269 0.5473 0.3863 0.9751 0.9889 0.731 0.9119 0.9722 0.6412 ] Network output: [ -0.02623 0.1424 0.9401 0.0009312 -0.000418 0.9738 0.0007018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6293 0.6153 0.452 0.2776 0.9866 0.9913 0.6298 0.9737 0.9823 0.464 ] Network output: [ -0.04858 0.1591 0.9463 0.0005638 -0.0002531 0.9941 0.0004249 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6264 0.6241 0.4631 0.2609 0.9849 0.9903 0.6265 0.9688 0.9796 0.4653 ] Network output: [ 0.01276 0.9466 0.02136 -0.0003014 0.0001353 1.005 -0.0002272 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0197 Epoch 2482 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02693 0.9804 1.001 -2.878e-05 1.292e-05 -0.03487 -2.169e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02221 -0.005585 0.01804 0.02525 0.9401 0.9495 0.04603 0.8867 0.9049 0.1186 ] Network output: [ 0.9805 0.05794 -0.01585 -4.847e-05 2.176e-05 -0.00319 -3.653e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6406 0.1239 0.1292 0.2235 0.9718 0.987 0.7329 0.9016 0.9675 0.6458 ] Network output: [ -0.009251 0.9501 1.026 -9.662e-05 4.338e-05 0.04238 -7.281e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04852 0.03603 0.05123 0.0332 0.9854 0.9896 0.04963 0.9703 0.9805 0.06366 ] Network output: [ 0.06404 -0.2361 1.082 -0.001021 0.0004583 1.022 -0.0007693 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7278 0.6268 0.5474 0.3859 0.9751 0.9889 0.7309 0.9119 0.9722 0.6412 ] Network output: [ -0.02615 0.1421 0.94 0.0009325 -0.0004186 0.9739 0.0007028 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6294 0.6154 0.4521 0.2774 0.9866 0.9913 0.6299 0.9737 0.9823 0.464 ] Network output: [ -0.04848 0.1588 0.9463 0.0005661 -0.0002542 0.9942 0.0004266 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6265 0.6242 0.4631 0.2607 0.9849 0.9903 0.6266 0.9688 0.9796 0.4653 ] Network output: [ 0.01272 0.9468 0.02135 -0.0003014 0.0001353 1.005 -0.0002272 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01964 Epoch 2483 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0269 0.9804 1.001 -2.898e-05 1.301e-05 -0.03487 -2.184e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02221 -0.005584 0.01805 0.02522 0.9401 0.9495 0.04602 0.8867 0.9049 0.1185 ] Network output: [ 0.9805 0.05784 -0.01582 -4.853e-05 2.179e-05 -0.00319 -3.657e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6406 0.1239 0.1293 0.2232 0.9718 0.987 0.7328 0.9016 0.9675 0.6459 ] Network output: [ -0.009273 0.9502 1.026 -9.665e-05 4.339e-05 0.04236 -7.284e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04851 0.03603 0.05121 0.03316 0.9854 0.9896 0.04962 0.9703 0.9805 0.06364 ] Network output: [ 0.06391 -0.2358 1.082 -0.001024 0.0004597 1.022 -0.0007718 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7277 0.6268 0.5474 0.3854 0.9751 0.9889 0.7308 0.9119 0.9722 0.6413 ] Network output: [ -0.02607 0.1419 0.94 0.0009338 -0.0004192 0.9741 0.0007037 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6295 0.6155 0.4521 0.2772 0.9866 0.9913 0.63 0.9737 0.9823 0.464 ] Network output: [ -0.04838 0.1585 0.9463 0.0005684 -0.0002552 0.9943 0.0004284 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6266 0.6243 0.4631 0.2605 0.985 0.9903 0.6267 0.9688 0.9796 0.4653 ] Network output: [ 0.01268 0.9469 0.02133 -0.0003015 0.0001353 1.005 -0.0002272 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01958 Epoch 2484 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02686 0.9805 1.001 -2.919e-05 1.311e-05 -0.03488 -2.2e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0222 -0.005583 0.01805 0.0252 0.9401 0.9495 0.046 0.8867 0.9049 0.1185 ] Network output: [ 0.9805 0.05774 -0.0158 -4.859e-05 2.181e-05 -0.003189 -3.662e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6406 0.1239 0.1294 0.2229 0.9718 0.987 0.7327 0.9017 0.9675 0.646 ] Network output: [ -0.009296 0.9503 1.026 -9.668e-05 4.34e-05 0.04233 -7.286e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04851 0.03602 0.0512 0.03311 0.9854 0.9896 0.04962 0.9703 0.9805 0.06363 ] Network output: [ 0.06378 -0.2354 1.082 -0.001027 0.0004612 1.022 -0.0007742 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7276 0.6267 0.5475 0.385 0.9751 0.9889 0.7308 0.9119 0.9722 0.6414 ] Network output: [ -0.02598 0.1416 0.94 0.0009351 -0.0004198 0.9742 0.0007047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6296 0.6156 0.4521 0.2769 0.9867 0.9913 0.6301 0.9737 0.9823 0.464 ] Network output: [ -0.04828 0.1582 0.9464 0.0005707 -0.0002562 0.9944 0.0004301 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6267 0.6244 0.4631 0.2603 0.985 0.9903 0.6268 0.9688 0.9796 0.4653 ] Network output: [ 0.01265 0.9471 0.02131 -0.0003015 0.0001354 1.005 -0.0002272 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01952 Epoch 2485 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02683 0.9805 1.001 -2.94e-05 1.32e-05 -0.03488 -2.215e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0222 -0.005582 0.01805 0.02517 0.9401 0.9495 0.04599 0.8867 0.9049 0.1185 ] Network output: [ 0.9806 0.05764 -0.01577 -4.866e-05 2.184e-05 -0.003189 -3.667e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 0.1239 0.1294 0.2226 0.9718 0.987 0.7326 0.9017 0.9675 0.646 ] Network output: [ -0.009318 0.9504 1.026 -9.67e-05 4.341e-05 0.0423 -7.288e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0485 0.03601 0.05119 0.03306 0.9854 0.9896 0.04961 0.9703 0.9805 0.06361 ] Network output: [ 0.06365 -0.235 1.082 -0.001031 0.0004627 1.022 -0.0007767 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7276 0.6266 0.5476 0.3845 0.9751 0.9889 0.7307 0.9119 0.9722 0.6414 ] Network output: [ -0.0259 0.1413 0.9399 0.0009364 -0.0004204 0.9743 0.0007057 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6297 0.6157 0.4521 0.2767 0.9867 0.9913 0.6302 0.9737 0.9823 0.4641 ] Network output: [ -0.04818 0.1579 0.9464 0.000573 -0.0002572 0.9945 0.0004318 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6268 0.6245 0.4631 0.2601 0.985 0.9903 0.6269 0.9688 0.9796 0.4653 ] Network output: [ 0.01261 0.9472 0.02129 -0.0003015 0.0001354 1.005 -0.0002273 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01946 Epoch 2486 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0268 0.9806 1.001 -2.96e-05 1.329e-05 -0.03489 -2.231e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0222 -0.005581 0.01805 0.02514 0.9401 0.9495 0.04597 0.8867 0.9049 0.1184 ] Network output: [ 0.9806 0.05754 -0.01575 -4.873e-05 2.188e-05 -0.003188 -3.672e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 0.1239 0.1295 0.2223 0.9718 0.987 0.7326 0.9017 0.9675 0.6461 ] Network output: [ -0.009341 0.9505 1.026 -9.673e-05 4.342e-05 0.04228 -7.29e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04849 0.03601 0.05117 0.03302 0.9854 0.9896 0.0496 0.9703 0.9805 0.06359 ] Network output: [ 0.06352 -0.2347 1.082 -0.001034 0.0004641 1.022 -0.0007791 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7275 0.6266 0.5477 0.3841 0.9751 0.9889 0.7306 0.9119 0.9722 0.6415 ] Network output: [ -0.02581 0.1411 0.9399 0.0009377 -0.000421 0.9745 0.0007067 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6298 0.6158 0.4522 0.2765 0.9867 0.9913 0.6303 0.9737 0.9823 0.4641 ] Network output: [ -0.04808 0.1576 0.9464 0.0005752 -0.0002582 0.9946 0.0004335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6269 0.6246 0.463 0.2599 0.985 0.9903 0.627 0.9689 0.9796 0.4652 ] Network output: [ 0.01258 0.9473 0.02128 -0.0003016 0.0001354 1.005 -0.0002273 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01939 Epoch 2487 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02677 0.9807 1.001 -2.98e-05 1.338e-05 -0.03489 -2.246e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0222 -0.00558 0.01805 0.02512 0.9401 0.9495 0.04596 0.8867 0.905 0.1184 ] Network output: [ 0.9806 0.05743 -0.01572 -4.88e-05 2.191e-05 -0.003188 -3.678e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 0.1239 0.1295 0.222 0.9718 0.987 0.7325 0.9017 0.9675 0.6462 ] Network output: [ -0.009363 0.9506 1.026 -9.675e-05 4.343e-05 0.04225 -7.291e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04848 0.036 0.05116 0.03297 0.9854 0.9896 0.04959 0.9703 0.9805 0.06357 ] Network output: [ 0.06339 -0.2343 1.082 -0.001037 0.0004656 1.022 -0.0007816 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7274 0.6265 0.5477 0.3836 0.9751 0.9889 0.7306 0.9119 0.9722 0.6416 ] Network output: [ -0.02573 0.1408 0.9399 0.000939 -0.0004216 0.9746 0.0007077 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6299 0.6159 0.4522 0.2762 0.9867 0.9913 0.6304 0.9737 0.9823 0.4641 ] Network output: [ -0.04798 0.1572 0.9464 0.0005775 -0.0002593 0.9946 0.0004352 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.627 0.6247 0.463 0.2597 0.985 0.9903 0.6271 0.9689 0.9796 0.4652 ] Network output: [ 0.01254 0.9475 0.02126 -0.0003016 0.0001354 1.005 -0.0002273 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01933 Epoch 2488 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02674 0.9807 1.001 -3.001e-05 1.347e-05 -0.03489 -2.261e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02219 -0.005579 0.01805 0.02509 0.9401 0.9495 0.04595 0.8867 0.905 0.1184 ] Network output: [ 0.9807 0.05733 -0.01569 -4.888e-05 2.194e-05 -0.003187 -3.684e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6404 0.1239 0.1296 0.2217 0.9719 0.987 0.7324 0.9017 0.9675 0.6462 ] Network output: [ -0.009386 0.9507 1.025 -9.676e-05 4.344e-05 0.04222 -7.292e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04848 0.03599 0.05115 0.03292 0.9854 0.9897 0.04958 0.9703 0.9805 0.06355 ] Network output: [ 0.06326 -0.2339 1.082 -0.00104 0.000467 1.021 -0.000784 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7274 0.6265 0.5478 0.3832 0.9751 0.9889 0.7305 0.912 0.9722 0.6416 ] Network output: [ -0.02565 0.1405 0.9399 0.0009403 -0.0004221 0.9747 0.0007086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.63 0.616 0.4522 0.276 0.9867 0.9913 0.6305 0.9737 0.9823 0.4642 ] Network output: [ -0.04788 0.1569 0.9464 0.0005798 -0.0002603 0.9947 0.0004369 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6271 0.6248 0.463 0.2596 0.985 0.9903 0.6271 0.9689 0.9796 0.4652 ] Network output: [ 0.0125 0.9476 0.02124 -0.0003016 0.0001354 1.005 -0.0002273 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01927 Epoch 2489 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02671 0.9808 1.001 -3.021e-05 1.356e-05 -0.0349 -2.276e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02219 -0.005577 0.01806 0.02506 0.9401 0.9495 0.04593 0.8867 0.905 0.1183 ] Network output: [ 0.9807 0.05723 -0.01566 -4.896e-05 2.198e-05 -0.003186 -3.69e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6404 0.1239 0.1297 0.2214 0.9719 0.987 0.7324 0.9017 0.9675 0.6463 ] Network output: [ -0.009408 0.9508 1.025 -9.678e-05 4.345e-05 0.0422 -7.294e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04847 0.03599 0.05113 0.03288 0.9854 0.9897 0.04957 0.9703 0.9805 0.06353 ] Network output: [ 0.06313 -0.2336 1.082 -0.001044 0.0004685 1.021 -0.0007865 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7273 0.6264 0.5479 0.3828 0.9751 0.9889 0.7304 0.912 0.9722 0.6417 ] Network output: [ -0.02556 0.1403 0.9398 0.0009416 -0.0004227 0.9748 0.0007096 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6301 0.6161 0.4523 0.2758 0.9867 0.9913 0.6306 0.9737 0.9823 0.4642 ] Network output: [ -0.04778 0.1566 0.9465 0.000582 -0.0002613 0.9948 0.0004386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6272 0.6249 0.463 0.2594 0.985 0.9903 0.6272 0.9689 0.9796 0.4652 ] Network output: [ 0.01247 0.9478 0.02122 -0.0003016 0.0001354 1.005 -0.0002273 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01921 Epoch 2490 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02667 0.9809 1.001 -3.041e-05 1.365e-05 -0.0349 -2.291e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02219 -0.005576 0.01806 0.02504 0.9401 0.9495 0.04592 0.8868 0.905 0.1183 ] Network output: [ 0.9807 0.05712 -0.01564 -4.905e-05 2.202e-05 -0.003185 -3.697e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6404 0.1239 0.1297 0.2211 0.9719 0.987 0.7323 0.9017 0.9675 0.6464 ] Network output: [ -0.00943 0.9509 1.025 -9.679e-05 4.345e-05 0.04217 -7.295e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04846 0.03598 0.05112 0.03283 0.9854 0.9897 0.04957 0.9703 0.9805 0.06351 ] Network output: [ 0.063 -0.2332 1.082 -0.001047 0.00047 1.021 -0.0007889 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7272 0.6264 0.548 0.3823 0.9751 0.9889 0.7304 0.912 0.9722 0.6417 ] Network output: [ -0.02548 0.14 0.9398 0.0009429 -0.0004233 0.975 0.0007106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6302 0.6162 0.4523 0.2755 0.9867 0.9913 0.6307 0.9737 0.9823 0.4642 ] Network output: [ -0.04769 0.1563 0.9465 0.0005843 -0.0002623 0.9949 0.0004403 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6273 0.625 0.463 0.2592 0.985 0.9903 0.6273 0.9689 0.9796 0.4652 ] Network output: [ 0.01243 0.9479 0.02121 -0.0003016 0.0001354 1.005 -0.0002273 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01915 Epoch 2491 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02664 0.9809 1.001 -3.06e-05 1.374e-05 -0.03491 -2.306e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02219 -0.005575 0.01806 0.02501 0.9401 0.9495 0.0459 0.8868 0.905 0.1183 ] Network output: [ 0.9808 0.05702 -0.01561 -4.914e-05 2.206e-05 -0.003185 -3.704e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6404 0.124 0.1298 0.2208 0.9719 0.987 0.7322 0.9017 0.9675 0.6464 ] Network output: [ -0.009453 0.951 1.025 -9.68e-05 4.346e-05 0.04215 -7.295e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04845 0.03598 0.05111 0.03278 0.9854 0.9897 0.04956 0.9703 0.9805 0.06349 ] Network output: [ 0.06287 -0.2328 1.082 -0.00105 0.0004714 1.021 -0.0007914 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7272 0.6263 0.548 0.3819 0.9752 0.9889 0.7303 0.912 0.9722 0.6418 ] Network output: [ -0.0254 0.1398 0.9398 0.0009441 -0.0004239 0.9751 0.0007115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6303 0.6163 0.4523 0.2753 0.9867 0.9913 0.6308 0.9737 0.9823 0.4642 ] Network output: [ -0.04759 0.156 0.9465 0.0005865 -0.0002633 0.995 0.000442 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6274 0.6251 0.463 0.259 0.985 0.9903 0.6274 0.9689 0.9796 0.4651 ] Network output: [ 0.0124 0.948 0.02119 -0.0003016 0.0001354 1.005 -0.0002273 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01909 Epoch 2492 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02661 0.981 1.001 -3.08e-05 1.383e-05 -0.03491 -2.321e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02218 -0.005574 0.01806 0.02499 0.9401 0.9495 0.04589 0.8868 0.905 0.1182 ] Network output: [ 0.9808 0.05692 -0.01558 -4.924e-05 2.211e-05 -0.003184 -3.711e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6403 0.124 0.1299 0.2205 0.9719 0.987 0.7322 0.9017 0.9675 0.6465 ] Network output: [ -0.009475 0.9511 1.025 -9.681e-05 4.346e-05 0.04212 -7.296e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04844 0.03597 0.05109 0.03274 0.9854 0.9897 0.04955 0.9703 0.9805 0.06347 ] Network output: [ 0.06274 -0.2324 1.082 -0.001053 0.0004729 1.021 -0.0007938 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7271 0.6263 0.5481 0.3814 0.9752 0.9889 0.7302 0.912 0.9722 0.6419 ] Network output: [ -0.02532 0.1395 0.9398 0.0009454 -0.0004244 0.9752 0.0007125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6304 0.6164 0.4523 0.2751 0.9867 0.9913 0.6309 0.9737 0.9823 0.4643 ] Network output: [ -0.04749 0.1557 0.9465 0.0005888 -0.0002643 0.9951 0.0004437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6275 0.6252 0.4629 0.2588 0.985 0.9903 0.6275 0.9689 0.9796 0.4651 ] Network output: [ 0.01236 0.9482 0.02117 -0.0003017 0.0001354 1.005 -0.0002273 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01903 Epoch 2493 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02658 0.981 1.001 -3.1e-05 1.391e-05 -0.03492 -2.336e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02218 -0.005573 0.01806 0.02496 0.9401 0.9495 0.04588 0.8868 0.905 0.1182 ] Network output: [ 0.9809 0.05681 -0.01555 -4.934e-05 2.215e-05 -0.003182 -3.719e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6403 0.124 0.1299 0.2202 0.9719 0.987 0.7321 0.9018 0.9675 0.6465 ] Network output: [ -0.009497 0.9512 1.025 -9.681e-05 4.346e-05 0.04209 -7.296e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04844 0.03596 0.05108 0.03269 0.9854 0.9897 0.04954 0.9703 0.9805 0.06345 ] Network output: [ 0.06261 -0.2321 1.082 -0.001057 0.0004743 1.021 -0.0007962 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.727 0.6262 0.5482 0.381 0.9752 0.9889 0.7302 0.912 0.9722 0.6419 ] Network output: [ -0.02523 0.1392 0.9398 0.0009467 -0.000425 0.9753 0.0007134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6305 0.6165 0.4524 0.2748 0.9867 0.9913 0.631 0.9737 0.9823 0.4643 ] Network output: [ -0.04739 0.1554 0.9466 0.000591 -0.0002653 0.9952 0.0004454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6276 0.6253 0.4629 0.2586 0.985 0.9903 0.6276 0.9689 0.9796 0.4651 ] Network output: [ 0.01233 0.9483 0.02115 -0.0003017 0.0001354 1.005 -0.0002273 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01896 Epoch 2494 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02655 0.9811 1.001 -3.119e-05 1.4e-05 -0.03492 -2.351e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02218 -0.005572 0.01806 0.02493 0.9402 0.9495 0.04586 0.8868 0.905 0.1182 ] Network output: [ 0.9809 0.0567 -0.01552 -4.945e-05 2.22e-05 -0.003181 -3.726e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6403 0.124 0.13 0.2199 0.9719 0.987 0.732 0.9018 0.9675 0.6466 ] Network output: [ -0.00952 0.9513 1.025 -9.682e-05 4.346e-05 0.04207 -7.296e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04843 0.03596 0.05107 0.03265 0.9854 0.9897 0.04953 0.9703 0.9805 0.06343 ] Network output: [ 0.06248 -0.2317 1.082 -0.00106 0.0004757 1.021 -0.0007986 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.727 0.6262 0.5483 0.3805 0.9752 0.9889 0.7301 0.912 0.9722 0.642 ] Network output: [ -0.02515 0.139 0.9397 0.0009479 -0.0004256 0.9755 0.0007144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6307 0.6166 0.4524 0.2746 0.9867 0.9913 0.6311 0.9738 0.9823 0.4643 ] Network output: [ -0.04729 0.1551 0.9466 0.0005933 -0.0002663 0.9953 0.0004471 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6276 0.6254 0.4629 0.2584 0.985 0.9903 0.6277 0.9689 0.9796 0.4651 ] Network output: [ 0.01229 0.9484 0.02114 -0.0003017 0.0001354 1.005 -0.0002273 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0189 Epoch 2495 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02652 0.9812 1.001 -3.138e-05 1.409e-05 -0.03492 -2.365e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02218 -0.005571 0.01807 0.02491 0.9402 0.9496 0.04585 0.8868 0.905 0.1181 ] Network output: [ 0.9809 0.0566 -0.01549 -4.956e-05 2.225e-05 -0.00318 -3.735e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6403 0.124 0.1301 0.2196 0.9719 0.9871 0.732 0.9018 0.9675 0.6467 ] Network output: [ -0.009542 0.9514 1.025 -9.682e-05 4.346e-05 0.04204 -7.296e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04842 0.03595 0.05105 0.0326 0.9854 0.9897 0.04952 0.9703 0.9805 0.06341 ] Network output: [ 0.06235 -0.2313 1.082 -0.001063 0.0004772 1.02 -0.0008011 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7269 0.6261 0.5483 0.3801 0.9752 0.9889 0.73 0.912 0.9722 0.642 ] Network output: [ -0.02507 0.1387 0.9397 0.0009492 -0.0004261 0.9756 0.0007153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6308 0.6167 0.4524 0.2743 0.9867 0.9913 0.6312 0.9738 0.9824 0.4643 ] Network output: [ -0.04719 0.1548 0.9466 0.0005955 -0.0002673 0.9954 0.0004488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6277 0.6255 0.4629 0.2582 0.985 0.9903 0.6278 0.9689 0.9796 0.4651 ] Network output: [ 0.01226 0.9486 0.02112 -0.0003017 0.0001354 1.005 -0.0002273 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01884 Epoch 2496 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02649 0.9812 1.001 -3.157e-05 1.417e-05 -0.03493 -2.38e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02217 -0.00557 0.01807 0.02488 0.9402 0.9496 0.04583 0.8868 0.905 0.1181 ] Network output: [ 0.981 0.05649 -0.01546 -4.967e-05 2.23e-05 -0.003179 -3.743e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6402 0.124 0.1301 0.2193 0.9719 0.9871 0.7319 0.9018 0.9675 0.6467 ] Network output: [ -0.009564 0.9515 1.025 -9.681e-05 4.346e-05 0.04201 -7.296e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04841 0.03594 0.05104 0.03255 0.9854 0.9897 0.04952 0.9703 0.9805 0.0634 ] Network output: [ 0.06222 -0.231 1.082 -0.001066 0.0004786 1.02 -0.0008035 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7269 0.6261 0.5484 0.3796 0.9752 0.9889 0.73 0.912 0.9722 0.6421 ] Network output: [ -0.02499 0.1384 0.9397 0.0009504 -0.0004267 0.9757 0.0007163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6309 0.6168 0.4525 0.2741 0.9867 0.9913 0.6313 0.9738 0.9824 0.4644 ] Network output: [ -0.04709 0.1545 0.9466 0.0005977 -0.0002683 0.9955 0.0004505 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6278 0.6256 0.4629 0.2581 0.985 0.9903 0.6279 0.9689 0.9796 0.4651 ] Network output: [ 0.01222 0.9487 0.0211 -0.0003017 0.0001354 1.005 -0.0002273 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01878 Epoch 2497 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02645 0.9813 1.001 -3.176e-05 1.426e-05 -0.03493 -2.394e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02217 -0.005568 0.01807 0.02486 0.9402 0.9496 0.04582 0.8868 0.9051 0.1181 ] Network output: [ 0.981 0.05639 -0.01544 -4.979e-05 2.235e-05 -0.003178 -3.752e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6402 0.124 0.1302 0.219 0.9719 0.9871 0.7318 0.9018 0.9675 0.6468 ] Network output: [ -0.009586 0.9516 1.025 -9.681e-05 4.346e-05 0.04199 -7.296e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04841 0.03594 0.05103 0.03251 0.9854 0.9897 0.04951 0.9703 0.9805 0.06338 ] Network output: [ 0.06209 -0.2306 1.082 -0.001069 0.0004801 1.02 -0.0008059 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7268 0.626 0.5485 0.3792 0.9752 0.9889 0.7299 0.912 0.9723 0.6422 ] Network output: [ -0.02491 0.1382 0.9397 0.0009517 -0.0004272 0.9758 0.0007172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.631 0.6169 0.4525 0.2739 0.9867 0.9913 0.6314 0.9738 0.9824 0.4644 ] Network output: [ -0.047 0.1542 0.9467 0.0006 -0.0002693 0.9956 0.0004522 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6279 0.6257 0.4628 0.2579 0.985 0.9903 0.628 0.9689 0.9796 0.465 ] Network output: [ 0.01219 0.9489 0.02108 -0.0003016 0.0001354 1.004 -0.0002273 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01872 Epoch 2498 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02642 0.9814 1.001 -3.195e-05 1.435e-05 -0.03493 -2.408e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02217 -0.005567 0.01807 0.02483 0.9402 0.9496 0.04581 0.8868 0.9051 0.118 ] Network output: [ 0.9811 0.05628 -0.01541 -4.991e-05 2.241e-05 -0.003176 -3.762e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6402 0.124 0.1303 0.2187 0.9719 0.9871 0.7318 0.9018 0.9675 0.6469 ] Network output: [ -0.009608 0.9517 1.025 -9.68e-05 4.346e-05 0.04196 -7.295e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0484 0.03593 0.05101 0.03246 0.9854 0.9897 0.0495 0.9703 0.9805 0.06336 ] Network output: [ 0.06196 -0.2302 1.082 -0.001073 0.0004815 1.02 -0.0008083 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7267 0.6259 0.5486 0.3787 0.9752 0.9889 0.7298 0.912 0.9723 0.6422 ] Network output: [ -0.02482 0.1379 0.9396 0.0009529 -0.0004278 0.976 0.0007182 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6311 0.617 0.4525 0.2736 0.9867 0.9913 0.6315 0.9738 0.9824 0.4644 ] Network output: [ -0.0469 0.1539 0.9467 0.0006022 -0.0002703 0.9957 0.0004538 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.628 0.6258 0.4628 0.2577 0.985 0.9903 0.6281 0.969 0.9797 0.465 ] Network output: [ 0.01215 0.949 0.02106 -0.0003016 0.0001354 1.004 -0.0002273 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01866 Epoch 2499 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02639 0.9814 1.001 -3.214e-05 1.443e-05 -0.03494 -2.422e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02217 -0.005566 0.01808 0.0248 0.9402 0.9496 0.04579 0.8869 0.9051 0.118 ] Network output: [ 0.9811 0.05617 -0.01538 -5.004e-05 2.247e-05 -0.003175 -3.771e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6402 0.124 0.1303 0.2185 0.9719 0.9871 0.7317 0.9018 0.9676 0.6469 ] Network output: [ -0.00963 0.9518 1.025 -9.679e-05 4.345e-05 0.04193 -7.294e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04839 0.03592 0.051 0.03242 0.9854 0.9897 0.04949 0.9703 0.9805 0.06334 ] Network output: [ 0.06183 -0.2298 1.082 -0.001076 0.0004829 1.02 -0.0008107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7267 0.6259 0.5486 0.3783 0.9752 0.9889 0.7298 0.9121 0.9723 0.6423 ] Network output: [ -0.02474 0.1376 0.9396 0.0009542 -0.0004284 0.9761 0.0007191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6312 0.6171 0.4525 0.2734 0.9867 0.9913 0.6317 0.9738 0.9824 0.4644 ] Network output: [ -0.0468 0.1536 0.9467 0.0006044 -0.0002713 0.9958 0.0004555 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6281 0.6259 0.4628 0.2575 0.985 0.9903 0.6282 0.969 0.9797 0.465 ] Network output: [ 0.01212 0.9491 0.02105 -0.0003016 0.0001354 1.004 -0.0002273 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0186 Epoch 2500 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02636 0.9815 1.001 -3.233e-05 1.451e-05 -0.03494 -2.436e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02216 -0.005565 0.01808 0.02478 0.9402 0.9496 0.04578 0.8869 0.9051 0.118 ] Network output: [ 0.9811 0.05606 -0.01535 -5.017e-05 2.252e-05 -0.003173 -3.781e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6402 0.124 0.1304 0.2182 0.9719 0.9871 0.7316 0.9018 0.9676 0.647 ] Network output: [ -0.009652 0.9519 1.025 -9.677e-05 4.345e-05 0.04191 -7.293e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04838 0.03592 0.05099 0.03237 0.9854 0.9897 0.04948 0.9704 0.9805 0.06332 ] Network output: [ 0.0617 -0.2295 1.082 -0.001079 0.0004843 1.02 -0.0008131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7266 0.6258 0.5487 0.3779 0.9752 0.9889 0.7297 0.9121 0.9723 0.6423 ] Network output: [ -0.02466 0.1374 0.9396 0.0009554 -0.0004289 0.9762 0.00072 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6313 0.6172 0.4526 0.2732 0.9867 0.9913 0.6318 0.9738 0.9824 0.4645 ] Network output: [ -0.0467 0.1533 0.9467 0.0006066 -0.0002723 0.9959 0.0004572 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6282 0.626 0.4628 0.2573 0.985 0.9903 0.6283 0.969 0.9797 0.465 ] Network output: [ 0.01208 0.9493 0.02103 -0.0003016 0.0001354 1.004 -0.0002273 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01854 Epoch 2501 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02633 0.9816 1.001 -3.251e-05 1.46e-05 -0.03495 -2.45e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02216 -0.005564 0.01808 0.02475 0.9402 0.9496 0.04576 0.8869 0.9051 0.118 ] Network output: [ 0.9812 0.05595 -0.01532 -5.031e-05 2.259e-05 -0.003172 -3.792e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6401 0.124 0.1305 0.2179 0.9719 0.9871 0.7316 0.9018 0.9676 0.6471 ] Network output: [ -0.009674 0.952 1.025 -9.676e-05 4.344e-05 0.04188 -7.292e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04838 0.03591 0.05098 0.03233 0.9854 0.9897 0.04947 0.9704 0.9805 0.0633 ] Network output: [ 0.06157 -0.2291 1.082 -0.001082 0.0004858 1.02 -0.0008155 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7266 0.6258 0.5488 0.3774 0.9752 0.9889 0.7296 0.9121 0.9723 0.6424 ] Network output: [ -0.02458 0.1371 0.9396 0.0009566 -0.0004295 0.9763 0.000721 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6314 0.6173 0.4526 0.2729 0.9867 0.9913 0.6319 0.9738 0.9824 0.4645 ] Network output: [ -0.0466 0.153 0.9467 0.0006088 -0.0002733 0.996 0.0004588 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6283 0.6261 0.4628 0.2571 0.985 0.9903 0.6284 0.969 0.9797 0.465 ] Network output: [ 0.01205 0.9494 0.02101 -0.0003016 0.0001354 1.004 -0.0002273 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01848 Epoch 2502 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0263 0.9816 1.001 -3.27e-05 1.468e-05 -0.03495 -2.464e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02216 -0.005563 0.01808 0.02473 0.9402 0.9496 0.04575 0.8869 0.9051 0.1179 ] Network output: [ 0.9812 0.05584 -0.01528 -5.045e-05 2.265e-05 -0.00317 -3.802e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6401 0.124 0.1305 0.2176 0.9719 0.9871 0.7315 0.9018 0.9676 0.6471 ] Network output: [ -0.009696 0.9521 1.025 -9.674e-05 4.343e-05 0.04185 -7.29e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04837 0.03591 0.05096 0.03228 0.9854 0.9897 0.04947 0.9704 0.9806 0.06328 ] Network output: [ 0.06144 -0.2287 1.082 -0.001085 0.0004872 1.019 -0.0008178 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7265 0.6257 0.5489 0.377 0.9752 0.9889 0.7296 0.9121 0.9723 0.6425 ] Network output: [ -0.0245 0.1369 0.9396 0.0009579 -0.00043 0.9765 0.0007219 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6315 0.6174 0.4526 0.2727 0.9867 0.9913 0.632 0.9738 0.9824 0.4645 ] Network output: [ -0.04651 0.1527 0.9468 0.000611 -0.0002743 0.9961 0.0004605 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6284 0.6262 0.4628 0.2569 0.985 0.9903 0.6285 0.969 0.9797 0.4649 ] Network output: [ 0.01201 0.9495 0.02099 -0.0003016 0.0001354 1.004 -0.0002273 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01842 Epoch 2503 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02626 0.9817 1.001 -3.288e-05 1.476e-05 -0.03495 -2.478e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02216 -0.005562 0.01809 0.0247 0.9402 0.9496 0.04574 0.8869 0.9051 0.1179 ] Network output: [ 0.9812 0.05573 -0.01525 -5.06e-05 2.271e-05 -0.003168 -3.813e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6401 0.124 0.1306 0.2173 0.9719 0.9871 0.7314 0.9019 0.9676 0.6472 ] Network output: [ -0.009718 0.9522 1.025 -9.672e-05 4.342e-05 0.04183 -7.289e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04836 0.0359 0.05095 0.03224 0.9854 0.9897 0.04946 0.9704 0.9806 0.06326 ] Network output: [ 0.06131 -0.2283 1.082 -0.001088 0.0004886 1.019 -0.0008202 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7264 0.6257 0.5489 0.3765 0.9752 0.9889 0.7295 0.9121 0.9723 0.6425 ] Network output: [ -0.02442 0.1366 0.9396 0.0009591 -0.0004306 0.9766 0.0007228 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6316 0.6175 0.4527 0.2725 0.9867 0.9913 0.6321 0.9738 0.9824 0.4645 ] Network output: [ -0.04641 0.1524 0.9468 0.0006132 -0.0002753 0.9962 0.0004621 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6285 0.6263 0.4627 0.2568 0.985 0.9903 0.6286 0.969 0.9797 0.4649 ] Network output: [ 0.01198 0.9497 0.02098 -0.0003015 0.0001354 1.004 -0.0002272 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01836 Epoch 2504 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02623 0.9817 1.001 -3.306e-05 1.484e-05 -0.03496 -2.492e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02216 -0.00556 0.01809 0.02468 0.9402 0.9496 0.04572 0.8869 0.9051 0.1179 ] Network output: [ 0.9813 0.05562 -0.01522 -5.075e-05 2.278e-05 -0.003167 -3.824e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6401 0.124 0.1307 0.217 0.9719 0.9871 0.7314 0.9019 0.9676 0.6472 ] Network output: [ -0.00974 0.9523 1.025 -9.669e-05 4.341e-05 0.0418 -7.287e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04835 0.03589 0.05094 0.03219 0.9854 0.9897 0.04945 0.9704 0.9806 0.06324 ] Network output: [ 0.06118 -0.228 1.082 -0.001091 0.00049 1.019 -0.0008226 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7264 0.6256 0.549 0.3761 0.9752 0.9889 0.7295 0.9121 0.9723 0.6426 ] Network output: [ -0.02434 0.1363 0.9395 0.0009603 -0.0004311 0.9767 0.0007237 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6317 0.6176 0.4527 0.2722 0.9867 0.9913 0.6322 0.9738 0.9824 0.4646 ] Network output: [ -0.04631 0.1521 0.9468 0.0006154 -0.0002763 0.9963 0.0004638 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6286 0.6264 0.4627 0.2566 0.985 0.9903 0.6287 0.969 0.9797 0.4649 ] Network output: [ 0.01194 0.9498 0.02096 -0.0003015 0.0001354 1.004 -0.0002272 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0183 Epoch 2505 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0262 0.9818 1.001 -3.324e-05 1.492e-05 -0.03496 -2.505e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02215 -0.005559 0.01809 0.02465 0.9402 0.9496 0.04571 0.8869 0.9051 0.1178 ] Network output: [ 0.9813 0.05551 -0.01519 -5.09e-05 2.285e-05 -0.003165 -3.836e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.64 0.124 0.1308 0.2167 0.9719 0.9871 0.7313 0.9019 0.9676 0.6473 ] Network output: [ -0.009762 0.9524 1.025 -9.667e-05 4.34e-05 0.04178 -7.285e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04835 0.03589 0.05092 0.03215 0.9854 0.9897 0.04944 0.9704 0.9806 0.06322 ] Network output: [ 0.06105 -0.2276 1.082 -0.001095 0.0004914 1.019 -0.000825 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7263 0.6256 0.5491 0.3757 0.9752 0.9889 0.7294 0.9121 0.9723 0.6426 ] Network output: [ -0.02426 0.1361 0.9395 0.0009615 -0.0004317 0.9768 0.0007246 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6318 0.6177 0.4527 0.272 0.9867 0.9913 0.6323 0.9738 0.9824 0.4646 ] Network output: [ -0.04621 0.1518 0.9468 0.0006176 -0.0002773 0.9964 0.0004655 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6287 0.6265 0.4627 0.2564 0.985 0.9903 0.6288 0.969 0.9797 0.4649 ] Network output: [ 0.01191 0.9499 0.02094 -0.0003015 0.0001353 1.004 -0.0002272 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01824 Epoch 2506 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02617 0.9819 1.001 -3.342e-05 1.5e-05 -0.03496 -2.519e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02215 -0.005558 0.01809 0.02463 0.9402 0.9496 0.0457 0.8869 0.9052 0.1178 ] Network output: [ 0.9814 0.0554 -0.01516 -5.106e-05 2.292e-05 -0.003163 -3.848e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.64 0.124 0.1308 0.2165 0.9719 0.9871 0.7313 0.9019 0.9676 0.6474 ] Network output: [ -0.009784 0.9525 1.025 -9.664e-05 4.338e-05 0.04175 -7.283e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04834 0.03588 0.05091 0.0321 0.9854 0.9897 0.04943 0.9704 0.9806 0.0632 ] Network output: [ 0.06092 -0.2272 1.082 -0.001098 0.0004928 1.019 -0.0008273 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7263 0.6255 0.5492 0.3752 0.9752 0.9889 0.7293 0.9121 0.9723 0.6427 ] Network output: [ -0.02418 0.1358 0.9395 0.0009627 -0.0004322 0.977 0.0007255 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6319 0.6178 0.4527 0.2718 0.9867 0.9913 0.6324 0.9738 0.9824 0.4646 ] Network output: [ -0.04611 0.1514 0.9469 0.0006198 -0.0002782 0.9964 0.0004671 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6288 0.6266 0.4627 0.2562 0.985 0.9903 0.6289 0.969 0.9797 0.4649 ] Network output: [ 0.01188 0.9501 0.02092 -0.0003014 0.0001353 1.004 -0.0002272 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01818 Epoch 2507 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02614 0.9819 1.001 -3.36e-05 1.508e-05 -0.03497 -2.532e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02215 -0.005557 0.0181 0.0246 0.9403 0.9496 0.04568 0.887 0.9052 0.1178 ] Network output: [ 0.9814 0.05529 -0.01513 -5.122e-05 2.299e-05 -0.003161 -3.86e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.64 0.124 0.1309 0.2162 0.9719 0.9871 0.7312 0.9019 0.9676 0.6474 ] Network output: [ -0.009805 0.9526 1.025 -9.66e-05 4.337e-05 0.04172 -7.28e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04833 0.03587 0.0509 0.03206 0.9854 0.9897 0.04942 0.9704 0.9806 0.06318 ] Network output: [ 0.06079 -0.2268 1.082 -0.001101 0.0004942 1.019 -0.0008297 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7262 0.6255 0.5492 0.3748 0.9752 0.9889 0.7293 0.9121 0.9723 0.6427 ] Network output: [ -0.0241 0.1355 0.9395 0.0009639 -0.0004327 0.9771 0.0007264 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.632 0.6179 0.4528 0.2716 0.9867 0.9913 0.6325 0.9738 0.9824 0.4646 ] Network output: [ -0.04602 0.1511 0.9469 0.000622 -0.0002792 0.9965 0.0004687 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6289 0.6267 0.4627 0.256 0.985 0.9903 0.629 0.969 0.9797 0.4648 ] Network output: [ 0.01184 0.9502 0.0209 -0.0003014 0.0001353 1.004 -0.0002271 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01812 Epoch 2508 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02611 0.982 1.001 -3.378e-05 1.516e-05 -0.03497 -2.546e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02215 -0.005556 0.0181 0.02458 0.9403 0.9496 0.04567 0.887 0.9052 0.1177 ] Network output: [ 0.9814 0.05518 -0.0151 -5.139e-05 2.307e-05 -0.003159 -3.873e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.64 0.124 0.131 0.2159 0.9719 0.9871 0.7311 0.9019 0.9676 0.6475 ] Network output: [ -0.009827 0.9527 1.025 -9.657e-05 4.335e-05 0.0417 -7.278e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04832 0.03587 0.05088 0.03201 0.9854 0.9897 0.04942 0.9704 0.9806 0.06316 ] Network output: [ 0.06066 -0.2265 1.082 -0.001104 0.0004956 1.019 -0.000832 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7261 0.6254 0.5493 0.3743 0.9752 0.9889 0.7292 0.9121 0.9723 0.6428 ] Network output: [ -0.02402 0.1353 0.9395 0.0009651 -0.0004333 0.9772 0.0007273 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6321 0.618 0.4528 0.2713 0.9867 0.9913 0.6326 0.9738 0.9824 0.4647 ] Network output: [ -0.04592 0.1508 0.9469 0.0006242 -0.0002802 0.9966 0.0004704 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.629 0.6268 0.4626 0.2558 0.985 0.9903 0.6291 0.969 0.9797 0.4648 ] Network output: [ 0.01181 0.9503 0.02089 -0.0003014 0.0001353 1.004 -0.0002271 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01806 Epoch 2509 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02608 0.9821 1.001 -3.395e-05 1.524e-05 -0.03497 -2.559e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02215 -0.005555 0.0181 0.02455 0.9403 0.9496 0.04566 0.887 0.9052 0.1177 ] Network output: [ 0.9815 0.05507 -0.01506 -5.156e-05 2.315e-05 -0.003157 -3.885e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.64 0.124 0.131 0.2156 0.9719 0.9871 0.7311 0.9019 0.9676 0.6475 ] Network output: [ -0.009849 0.9528 1.025 -9.653e-05 4.334e-05 0.04167 -7.275e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04832 0.03586 0.05087 0.03197 0.9854 0.9897 0.04941 0.9704 0.9806 0.06314 ] Network output: [ 0.06053 -0.2261 1.082 -0.001107 0.000497 1.018 -0.0008344 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7261 0.6254 0.5494 0.3739 0.9752 0.9889 0.7292 0.9121 0.9723 0.6429 ] Network output: [ -0.02394 0.135 0.9395 0.0009663 -0.0004338 0.9773 0.0007282 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6322 0.6181 0.4528 0.2711 0.9867 0.9913 0.6327 0.9738 0.9824 0.4647 ] Network output: [ -0.04582 0.1505 0.9469 0.0006263 -0.0002812 0.9967 0.000472 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6291 0.6269 0.4626 0.2556 0.9851 0.9903 0.6292 0.969 0.9797 0.4648 ] Network output: [ 0.01177 0.9505 0.02087 -0.0003013 0.0001353 1.004 -0.0002271 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.018 Epoch 2510 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02604 0.9821 1.001 -3.413e-05 1.532e-05 -0.03498 -2.572e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02214 -0.005553 0.01811 0.02453 0.9403 0.9496 0.04564 0.887 0.9052 0.1177 ] Network output: [ 0.9815 0.05496 -0.01503 -5.173e-05 2.322e-05 -0.003155 -3.899e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6399 0.124 0.1311 0.2153 0.9719 0.9871 0.731 0.9019 0.9676 0.6476 ] Network output: [ -0.00987 0.9529 1.025 -9.649e-05 4.332e-05 0.04165 -7.272e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04831 0.03586 0.05086 0.03193 0.9854 0.9897 0.0494 0.9704 0.9806 0.06312 ] Network output: [ 0.0604 -0.2257 1.082 -0.00111 0.0004984 1.018 -0.0008367 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.726 0.6253 0.5494 0.3735 0.9752 0.9889 0.7291 0.9121 0.9723 0.6429 ] Network output: [ -0.02386 0.1347 0.9395 0.0009675 -0.0004343 0.9775 0.0007291 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6323 0.6182 0.4528 0.2709 0.9867 0.9913 0.6328 0.9738 0.9824 0.4647 ] Network output: [ -0.04572 0.1502 0.9469 0.0006285 -0.0002821 0.9968 0.0004736 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6292 0.627 0.4626 0.2555 0.9851 0.9903 0.6293 0.9691 0.9797 0.4648 ] Network output: [ 0.01174 0.9506 0.02085 -0.0003013 0.0001353 1.004 -0.000227 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01794 Epoch 2511 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02601 0.9822 1.001 -3.43e-05 1.54e-05 -0.03498 -2.585e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02214 -0.005552 0.01811 0.0245 0.9403 0.9496 0.04563 0.887 0.9052 0.1176 ] Network output: [ 0.9815 0.05484 -0.015 -5.191e-05 2.33e-05 -0.003153 -3.912e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6399 0.124 0.1312 0.215 0.9719 0.9871 0.731 0.9019 0.9676 0.6477 ] Network output: [ -0.009892 0.953 1.025 -9.645e-05 4.33e-05 0.04162 -7.269e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0483 0.03585 0.05084 0.03188 0.9855 0.9897 0.04939 0.9704 0.9806 0.0631 ] Network output: [ 0.06027 -0.2253 1.082 -0.001113 0.0004998 1.018 -0.000839 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.726 0.6253 0.5495 0.373 0.9752 0.9889 0.729 0.9122 0.9723 0.643 ] Network output: [ -0.02378 0.1345 0.9394 0.0009687 -0.0004349 0.9776 0.00073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6324 0.6183 0.4529 0.2706 0.9867 0.9913 0.6329 0.9738 0.9824 0.4647 ] Network output: [ -0.04563 0.1499 0.947 0.0006306 -0.0002831 0.9969 0.0004753 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6293 0.6271 0.4626 0.2553 0.9851 0.9903 0.6294 0.9691 0.9797 0.4647 ] Network output: [ 0.01171 0.9507 0.02083 -0.0003012 0.0001352 1.004 -0.000227 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01788 Epoch 2512 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02598 0.9823 1.001 -3.447e-05 1.548e-05 -0.03498 -2.598e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02214 -0.005551 0.01811 0.02448 0.9403 0.9497 0.04561 0.887 0.9052 0.1176 ] Network output: [ 0.9816 0.05473 -0.01497 -5.209e-05 2.339e-05 -0.003151 -3.926e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6399 0.124 0.1313 0.2148 0.972 0.9871 0.7309 0.9019 0.9676 0.6477 ] Network output: [ -0.009913 0.9531 1.025 -9.64e-05 4.328e-05 0.04159 -7.265e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04829 0.03584 0.05083 0.03184 0.9855 0.9897 0.04938 0.9704 0.9806 0.06308 ] Network output: [ 0.06014 -0.2249 1.082 -0.001116 0.0005012 1.018 -0.0008414 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7259 0.6252 0.5496 0.3726 0.9752 0.9889 0.729 0.9122 0.9723 0.643 ] Network output: [ -0.0237 0.1342 0.9394 0.0009698 -0.0004354 0.9777 0.0007309 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6325 0.6184 0.4529 0.2704 0.9867 0.9913 0.633 0.9738 0.9824 0.4648 ] Network output: [ -0.04553 0.1496 0.947 0.0006328 -0.0002841 0.997 0.0004769 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6294 0.6272 0.4626 0.2551 0.9851 0.9903 0.6295 0.9691 0.9797 0.4647 ] Network output: [ 0.01167 0.9509 0.02081 -0.0003012 0.0001352 1.004 -0.000227 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01782 Epoch 2513 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02595 0.9823 1.001 -3.465e-05 1.555e-05 -0.03499 -2.611e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02214 -0.00555 0.01812 0.02445 0.9403 0.9497 0.0456 0.887 0.9052 0.1176 ] Network output: [ 0.9816 0.05462 -0.01493 -5.228e-05 2.347e-05 -0.003149 -3.94e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6399 0.124 0.1313 0.2145 0.972 0.9871 0.7308 0.902 0.9676 0.6478 ] Network output: [ -0.009935 0.9532 1.025 -9.636e-05 4.326e-05 0.04157 -7.262e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04829 0.03584 0.05082 0.03179 0.9855 0.9897 0.04938 0.9704 0.9806 0.06306 ] Network output: [ 0.06002 -0.2246 1.082 -0.001119 0.0005026 1.018 -0.0008437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7259 0.6252 0.5497 0.3722 0.9752 0.9889 0.7289 0.9122 0.9723 0.6431 ] Network output: [ -0.02362 0.134 0.9394 0.000971 -0.0004359 0.9778 0.0007318 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6326 0.6185 0.4529 0.2702 0.9867 0.9913 0.6331 0.9738 0.9824 0.4648 ] Network output: [ -0.04543 0.1493 0.947 0.0006349 -0.000285 0.9971 0.0004785 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6295 0.6273 0.4625 0.2549 0.9851 0.9903 0.6296 0.9691 0.9797 0.4647 ] Network output: [ 0.01164 0.951 0.0208 -0.0003011 0.0001352 1.004 -0.0002269 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01776 Epoch 2514 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02592 0.9824 1.001 -3.482e-05 1.563e-05 -0.03499 -2.624e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02214 -0.005549 0.01812 0.02443 0.9403 0.9497 0.04559 0.887 0.9052 0.1175 ] Network output: [ 0.9817 0.0545 -0.0149 -5.247e-05 2.356e-05 -0.003147 -3.954e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6399 0.124 0.1314 0.2142 0.972 0.9871 0.7308 0.902 0.9676 0.6478 ] Network output: [ -0.009956 0.9533 1.025 -9.631e-05 4.323e-05 0.04154 -7.258e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04828 0.03583 0.0508 0.03175 0.9855 0.9897 0.04937 0.9704 0.9806 0.06305 ] Network output: [ 0.05989 -0.2242 1.082 -0.001123 0.0005039 1.018 -0.000846 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7258 0.6251 0.5497 0.3717 0.9752 0.9889 0.7289 0.9122 0.9723 0.6431 ] Network output: [ -0.02354 0.1337 0.9394 0.0009722 -0.0004364 0.9779 0.0007327 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6327 0.6186 0.4529 0.2699 0.9867 0.9913 0.6332 0.9738 0.9824 0.4648 ] Network output: [ -0.04534 0.149 0.947 0.0006371 -0.000286 0.9972 0.0004801 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6296 0.6274 0.4625 0.2547 0.9851 0.9903 0.6297 0.9691 0.9797 0.4647 ] Network output: [ 0.01161 0.9511 0.02078 -0.000301 0.0001352 1.004 -0.0002269 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0177 Epoch 2515 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02589 0.9825 1.001 -3.498e-05 1.571e-05 -0.03499 -2.636e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02213 -0.005548 0.01812 0.02441 0.9403 0.9497 0.04557 0.887 0.9052 0.1175 ] Network output: [ 0.9817 0.05439 -0.01487 -5.267e-05 2.364e-05 -0.003144 -3.969e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6399 0.124 0.1315 0.2139 0.972 0.9871 0.7307 0.902 0.9676 0.6479 ] Network output: [ -0.009978 0.9534 1.025 -9.625e-05 4.321e-05 0.04152 -7.254e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04827 0.03583 0.05079 0.03171 0.9855 0.9897 0.04936 0.9704 0.9806 0.06303 ] Network output: [ 0.05976 -0.2238 1.082 -0.001126 0.0005053 1.018 -0.0008483 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7257 0.6251 0.5498 0.3713 0.9752 0.989 0.7288 0.9122 0.9723 0.6432 ] Network output: [ -0.02347 0.1334 0.9394 0.0009733 -0.000437 0.9781 0.0007335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6328 0.6187 0.453 0.2697 0.9867 0.9913 0.6333 0.9738 0.9824 0.4648 ] Network output: [ -0.04524 0.1487 0.9471 0.0006392 -0.000287 0.9973 0.0004817 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6297 0.6275 0.4625 0.2545 0.9851 0.9903 0.6298 0.9691 0.9797 0.4647 ] Network output: [ 0.01157 0.9512 0.02076 -0.000301 0.0001351 1.004 -0.0002268 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01764 Epoch 2516 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02586 0.9825 1.001 -3.515e-05 1.578e-05 -0.035 -2.649e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02213 -0.005546 0.01813 0.02438 0.9403 0.9497 0.04556 0.887 0.9053 0.1175 ] Network output: [ 0.9817 0.05428 -0.01483 -5.287e-05 2.373e-05 -0.003142 -3.984e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6398 0.124 0.1316 0.2136 0.972 0.9871 0.7307 0.902 0.9676 0.648 ] Network output: [ -0.009999 0.9535 1.025 -9.62e-05 4.319e-05 0.04149 -7.25e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04827 0.03582 0.05078 0.03166 0.9855 0.9897 0.04935 0.9704 0.9806 0.06301 ] Network output: [ 0.05963 -0.2234 1.082 -0.001129 0.0005067 1.018 -0.0008506 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7257 0.625 0.5499 0.3709 0.9752 0.989 0.7287 0.9122 0.9723 0.6432 ] Network output: [ -0.02339 0.1332 0.9394 0.0009745 -0.0004375 0.9782 0.0007344 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6329 0.6188 0.453 0.2695 0.9867 0.9913 0.6334 0.9738 0.9824 0.4648 ] Network output: [ -0.04514 0.1484 0.9471 0.0006413 -0.0002879 0.9974 0.0004833 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6298 0.6276 0.4625 0.2543 0.9851 0.9903 0.6299 0.9691 0.9797 0.4646 ] Network output: [ 0.01154 0.9514 0.02074 -0.0003009 0.0001351 1.004 -0.0002268 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01758 Epoch 2517 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02582 0.9826 1.001 -3.532e-05 1.586e-05 -0.035 -2.662e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02213 -0.005545 0.01813 0.02436 0.9403 0.9497 0.04555 0.8871 0.9053 0.1174 ] Network output: [ 0.9818 0.05416 -0.0148 -5.307e-05 2.383e-05 -0.00314 -4e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6398 0.124 0.1316 0.2134 0.972 0.9871 0.7306 0.902 0.9676 0.648 ] Network output: [ -0.01002 0.9536 1.025 -9.614e-05 4.316e-05 0.04146 -7.245e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04826 0.03581 0.05076 0.03162 0.9855 0.9897 0.04934 0.9704 0.9806 0.06299 ] Network output: [ 0.0595 -0.2231 1.082 -0.001132 0.0005081 1.017 -0.0008529 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7256 0.625 0.5499 0.3704 0.9752 0.989 0.7287 0.9122 0.9723 0.6433 ] Network output: [ -0.02331 0.1329 0.9394 0.0009756 -0.000438 0.9783 0.0007353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.633 0.6189 0.453 0.2693 0.9867 0.9913 0.6335 0.9738 0.9824 0.4649 ] Network output: [ -0.04505 0.1481 0.9471 0.0006435 -0.0002889 0.9975 0.0004849 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6299 0.6277 0.4625 0.2542 0.9851 0.9903 0.63 0.9691 0.9797 0.4646 ] Network output: [ 0.01151 0.9515 0.02072 -0.0003009 0.0001351 1.004 -0.0002267 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01752 Epoch 2518 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02579 0.9826 1.001 -3.548e-05 1.593e-05 -0.035 -2.674e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02213 -0.005544 0.01813 0.02433 0.9403 0.9497 0.04553 0.8871 0.9053 0.1174 ] Network output: [ 0.9818 0.05404 -0.01477 -5.328e-05 2.392e-05 -0.003137 -4.015e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6398 0.124 0.1317 0.2131 0.972 0.9871 0.7305 0.902 0.9676 0.6481 ] Network output: [ -0.01004 0.9537 1.025 -9.608e-05 4.313e-05 0.04144 -7.241e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04825 0.03581 0.05075 0.03157 0.9855 0.9897 0.04934 0.9704 0.9806 0.06297 ] Network output: [ 0.05937 -0.2227 1.082 -0.001135 0.0005094 1.017 -0.0008552 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7256 0.625 0.55 0.37 0.9753 0.989 0.7286 0.9122 0.9723 0.6433 ] Network output: [ -0.02323 0.1327 0.9394 0.0009768 -0.0004385 0.9784 0.0007361 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6331 0.619 0.453 0.269 0.9867 0.9913 0.6336 0.9738 0.9824 0.4649 ] Network output: [ -0.04495 0.1478 0.9471 0.0006456 -0.0002898 0.9976 0.0004865 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.63 0.6278 0.4624 0.254 0.9851 0.9903 0.6301 0.9691 0.9797 0.4646 ] Network output: [ 0.01147 0.9516 0.0207 -0.0003008 0.000135 1.003 -0.0002267 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01747 Epoch 2519 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02576 0.9827 1.001 -3.565e-05 1.6e-05 -0.03501 -2.686e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02213 -0.005543 0.01814 0.02431 0.9403 0.9497 0.04552 0.8871 0.9053 0.1174 ] Network output: [ 0.9819 0.05393 -0.01473 -5.349e-05 2.401e-05 -0.003135 -4.031e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6398 0.124 0.1318 0.2128 0.972 0.9871 0.7305 0.902 0.9677 0.6481 ] Network output: [ -0.01006 0.9538 1.025 -9.601e-05 4.31e-05 0.04141 -7.236e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04824 0.0358 0.05074 0.03153 0.9855 0.9897 0.04933 0.9704 0.9806 0.06295 ] Network output: [ 0.05924 -0.2223 1.082 -0.001138 0.0005108 1.017 -0.0008574 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7255 0.6249 0.5501 0.3696 0.9753 0.989 0.7286 0.9122 0.9723 0.6434 ] Network output: [ -0.02316 0.1324 0.9394 0.0009779 -0.000439 0.9785 0.000737 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6332 0.6191 0.4531 0.2688 0.9867 0.9913 0.6337 0.9738 0.9824 0.4649 ] Network output: [ -0.04485 0.1475 0.9472 0.0006477 -0.0002908 0.9977 0.0004881 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6301 0.6279 0.4624 0.2538 0.9851 0.9903 0.6302 0.9691 0.9798 0.4646 ] Network output: [ 0.01144 0.9518 0.02069 -0.0003007 0.000135 1.003 -0.0002266 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01741 Epoch 2520 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02573 0.9828 1.001 -3.581e-05 1.608e-05 -0.03501 -2.699e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02212 -0.005542 0.01814 0.02428 0.9403 0.9497 0.04551 0.8871 0.9053 0.1173 ] Network output: [ 0.9819 0.05381 -0.0147 -5.371e-05 2.411e-05 -0.003133 -4.048e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6398 0.124 0.1319 0.2125 0.972 0.9871 0.7304 0.902 0.9677 0.6482 ] Network output: [ -0.01008 0.9539 1.025 -9.595e-05 4.307e-05 0.04139 -7.231e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04824 0.03579 0.05072 0.03149 0.9855 0.9897 0.04932 0.9704 0.9806 0.06293 ] Network output: [ 0.05912 -0.2219 1.082 -0.001141 0.0005121 1.017 -0.0008597 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7255 0.6249 0.5502 0.3691 0.9753 0.989 0.7285 0.9122 0.9723 0.6434 ] Network output: [ -0.02308 0.1321 0.9393 0.0009791 -0.0004395 0.9787 0.0007378 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6333 0.6192 0.4531 0.2686 0.9867 0.9913 0.6338 0.9738 0.9824 0.4649 ] Network output: [ -0.04476 0.1472 0.9472 0.0006498 -0.0002917 0.9977 0.0004897 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6302 0.628 0.4624 0.2536 0.9851 0.9903 0.6303 0.9691 0.9798 0.4646 ] Network output: [ 0.01141 0.9519 0.02067 -0.0003006 0.000135 1.003 -0.0002266 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01735 Epoch 2521 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0257 0.9828 1.001 -3.597e-05 1.615e-05 -0.03501 -2.711e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02212 -0.00554 0.01814 0.02426 0.9403 0.9497 0.04549 0.8871 0.9053 0.1173 ] Network output: [ 0.9819 0.0537 -0.01466 -5.393e-05 2.421e-05 -0.00313 -4.064e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6398 0.124 0.1319 0.2123 0.972 0.9871 0.7304 0.902 0.9677 0.6482 ] Network output: [ -0.0101 0.954 1.024 -9.588e-05 4.304e-05 0.04136 -7.226e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04823 0.03579 0.05071 0.03144 0.9855 0.9897 0.04931 0.9704 0.9806 0.06291 ] Network output: [ 0.05899 -0.2216 1.082 -0.001144 0.0005135 1.017 -0.000862 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7254 0.6248 0.5502 0.3687 0.9753 0.989 0.7285 0.9122 0.9723 0.6435 ] Network output: [ -0.023 0.1319 0.9393 0.0009802 -0.00044 0.9788 0.0007387 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6334 0.6193 0.4531 0.2683 0.9867 0.9913 0.6339 0.9738 0.9824 0.465 ] Network output: [ -0.04466 0.1469 0.9472 0.0006519 -0.0002927 0.9978 0.0004913 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6303 0.6281 0.4624 0.2534 0.9851 0.9904 0.6304 0.9691 0.9798 0.4645 ] Network output: [ 0.01138 0.952 0.02065 -0.0003005 0.0001349 1.003 -0.0002265 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01729 Epoch 2522 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02567 0.9829 1.001 -3.613e-05 1.622e-05 -0.03501 -2.723e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02212 -0.005539 0.01815 0.02424 0.9404 0.9497 0.04548 0.8871 0.9053 0.1173 ] Network output: [ 0.982 0.05358 -0.01463 -5.415e-05 2.431e-05 -0.003128 -4.081e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6397 0.124 0.132 0.212 0.972 0.9871 0.7303 0.902 0.9677 0.6483 ] Network output: [ -0.01013 0.9541 1.024 -9.581e-05 4.301e-05 0.04133 -7.22e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04822 0.03578 0.0507 0.0314 0.9855 0.9897 0.0493 0.9705 0.9806 0.06289 ] Network output: [ 0.05886 -0.2212 1.082 -0.001147 0.0005148 1.017 -0.0008642 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7254 0.6248 0.5503 0.3683 0.9753 0.989 0.7284 0.9122 0.9724 0.6435 ] Network output: [ -0.02292 0.1316 0.9393 0.0009813 -0.0004405 0.9789 0.0007395 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6335 0.6194 0.4531 0.2681 0.9867 0.9913 0.634 0.9738 0.9824 0.465 ] Network output: [ -0.04457 0.1466 0.9472 0.000654 -0.0002936 0.9979 0.0004929 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6304 0.6282 0.4623 0.2532 0.9851 0.9904 0.6305 0.9691 0.9798 0.4645 ] Network output: [ 0.01134 0.9521 0.02063 -0.0003005 0.0001349 1.003 -0.0002264 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01723 Epoch 2523 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02564 0.983 1.001 -3.629e-05 1.629e-05 -0.03502 -2.735e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02212 -0.005538 0.01815 0.02421 0.9404 0.9497 0.04547 0.8871 0.9053 0.1172 ] Network output: [ 0.982 0.05346 -0.01459 -5.438e-05 2.441e-05 -0.003125 -4.098e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6397 0.124 0.1321 0.2117 0.972 0.9871 0.7302 0.902 0.9677 0.6483 ] Network output: [ -0.01015 0.9542 1.024 -9.573e-05 4.298e-05 0.04131 -7.215e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04821 0.03578 0.05068 0.03136 0.9855 0.9897 0.0493 0.9705 0.9806 0.06287 ] Network output: [ 0.05873 -0.2208 1.082 -0.00115 0.0005162 1.017 -0.0008665 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7253 0.6247 0.5504 0.3679 0.9753 0.989 0.7284 0.9123 0.9724 0.6436 ] Network output: [ -0.02285 0.1314 0.9393 0.0009824 -0.000441 0.979 0.0007404 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6336 0.6195 0.4532 0.2679 0.9867 0.9913 0.6341 0.9738 0.9824 0.465 ] Network output: [ -0.04447 0.1463 0.9472 0.0006561 -0.0002945 0.998 0.0004945 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6305 0.6283 0.4623 0.253 0.9851 0.9904 0.6306 0.9692 0.9798 0.4645 ] Network output: [ 0.01131 0.9523 0.02061 -0.0003004 0.0001348 1.003 -0.0002264 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01717 Epoch 2524 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02561 0.983 1.001 -3.645e-05 1.636e-05 -0.03502 -2.747e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02212 -0.005537 0.01816 0.02419 0.9404 0.9497 0.04545 0.8871 0.9053 0.1172 ] Network output: [ 0.9821 0.05335 -0.01456 -5.461e-05 2.452e-05 -0.003122 -4.116e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6397 0.124 0.1322 0.2115 0.972 0.9871 0.7302 0.9021 0.9677 0.6484 ] Network output: [ -0.01017 0.9543 1.024 -9.566e-05 4.294e-05 0.04128 -7.209e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04821 0.03577 0.05067 0.03132 0.9855 0.9897 0.04929 0.9705 0.9806 0.06285 ] Network output: [ 0.05861 -0.2204 1.082 -0.001153 0.0005175 1.016 -0.0008687 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7253 0.6247 0.5504 0.3674 0.9753 0.989 0.7283 0.9123 0.9724 0.6436 ] Network output: [ -0.02277 0.1311 0.9393 0.0009835 -0.0004415 0.9791 0.0007412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6337 0.6196 0.4532 0.2677 0.9867 0.9913 0.6342 0.9738 0.9824 0.465 ] Network output: [ -0.04437 0.1461 0.9473 0.0006582 -0.0002955 0.9981 0.000496 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6306 0.6284 0.4623 0.2529 0.9851 0.9904 0.6307 0.9692 0.9798 0.4645 ] Network output: [ 0.01128 0.9524 0.0206 -0.0003003 0.0001348 1.003 -0.0002263 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01712 Epoch 2525 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02557 0.9831 1.001 -3.661e-05 1.643e-05 -0.03502 -2.759e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02212 -0.005536 0.01816 0.02417 0.9404 0.9497 0.04544 0.8871 0.9053 0.1172 ] Network output: [ 0.9821 0.05323 -0.01452 -5.485e-05 2.462e-05 -0.00312 -4.133e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6397 0.124 0.1322 0.2112 0.972 0.9871 0.7301 0.9021 0.9677 0.6485 ] Network output: [ -0.01019 0.9544 1.024 -9.558e-05 4.291e-05 0.04126 -7.203e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0482 0.03576 0.05066 0.03127 0.9855 0.9897 0.04928 0.9705 0.9806 0.06283 ] Network output: [ 0.05848 -0.22 1.082 -0.001156 0.0005188 1.016 -0.000871 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7252 0.6246 0.5505 0.367 0.9753 0.989 0.7282 0.9123 0.9724 0.6437 ] Network output: [ -0.0227 0.1308 0.9393 0.0009846 -0.000442 0.9792 0.000742 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6338 0.6197 0.4532 0.2674 0.9867 0.9913 0.6343 0.9738 0.9824 0.465 ] Network output: [ -0.04428 0.1458 0.9473 0.0006603 -0.0002964 0.9982 0.0004976 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6307 0.6285 0.4623 0.2527 0.9851 0.9904 0.6308 0.9692 0.9798 0.4644 ] Network output: [ 0.01125 0.9525 0.02058 -0.0003002 0.0001348 1.003 -0.0002262 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01706 Epoch 2526 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02554 0.9832 1.001 -3.676e-05 1.65e-05 -0.03502 -2.771e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02211 -0.005534 0.01816 0.02414 0.9404 0.9497 0.04543 0.8872 0.9054 0.1171 ] Network output: [ 0.9821 0.05311 -0.01449 -5.509e-05 2.473e-05 -0.003117 -4.151e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6397 0.124 0.1323 0.2109 0.972 0.9871 0.7301 0.9021 0.9677 0.6485 ] Network output: [ -0.01021 0.9545 1.024 -9.55e-05 4.287e-05 0.04123 -7.197e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04819 0.03576 0.05064 0.03123 0.9855 0.9897 0.04927 0.9705 0.9806 0.06281 ] Network output: [ 0.05835 -0.2197 1.082 -0.001159 0.0005202 1.016 -0.0008732 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7252 0.6246 0.5506 0.3666 0.9753 0.989 0.7282 0.9123 0.9724 0.6437 ] Network output: [ -0.02262 0.1306 0.9393 0.0009857 -0.0004425 0.9794 0.0007429 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6339 0.6198 0.4532 0.2672 0.9867 0.9913 0.6344 0.9738 0.9824 0.4651 ] Network output: [ -0.04418 0.1455 0.9473 0.0006623 -0.0002973 0.9983 0.0004992 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6308 0.6286 0.4623 0.2525 0.9851 0.9904 0.6309 0.9692 0.9798 0.4644 ] Network output: [ 0.01122 0.9527 0.02056 -0.0003001 0.0001347 1.003 -0.0002262 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.017 Epoch 2527 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02551 0.9832 1.001 -3.692e-05 1.657e-05 -0.03503 -2.782e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02211 -0.005533 0.01817 0.02412 0.9404 0.9497 0.04541 0.8872 0.9054 0.1171 ] Network output: [ 0.9822 0.05299 -0.01445 -5.533e-05 2.484e-05 -0.003114 -4.17e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6397 0.124 0.1324 0.2106 0.972 0.9871 0.73 0.9021 0.9677 0.6486 ] Network output: [ -0.01023 0.9546 1.024 -9.541e-05 4.283e-05 0.04121 -7.191e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04819 0.03575 0.05063 0.03119 0.9855 0.9897 0.04926 0.9705 0.9806 0.06279 ] Network output: [ 0.05822 -0.2193 1.082 -0.001162 0.0005215 1.016 -0.0008754 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7251 0.6245 0.5506 0.3662 0.9753 0.989 0.7281 0.9123 0.9724 0.6438 ] Network output: [ -0.02254 0.1303 0.9393 0.0009868 -0.000443 0.9795 0.0007437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.634 0.6199 0.4533 0.267 0.9867 0.9913 0.6345 0.9738 0.9824 0.4651 ] Network output: [ -0.04409 0.1452 0.9473 0.0006644 -0.0002983 0.9984 0.0005007 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6309 0.6287 0.4622 0.2523 0.9851 0.9904 0.631 0.9692 0.9798 0.4644 ] Network output: [ 0.01118 0.9528 0.02054 -0.0003 0.0001347 1.003 -0.0002261 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01694 Epoch 2528 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02548 0.9833 1.001 -3.707e-05 1.664e-05 -0.03503 -2.794e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02211 -0.005532 0.01817 0.0241 0.9404 0.9497 0.0454 0.8872 0.9054 0.1171 ] Network output: [ 0.9822 0.05287 -0.01441 -5.558e-05 2.495e-05 -0.003112 -4.188e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6397 0.124 0.1325 0.2104 0.972 0.9871 0.73 0.9021 0.9677 0.6486 ] Network output: [ -0.01025 0.9547 1.024 -9.533e-05 4.28e-05 0.04118 -7.184e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04818 0.03575 0.05062 0.03115 0.9855 0.9897 0.04926 0.9705 0.9806 0.06277 ] Network output: [ 0.0581 -0.2189 1.082 -0.001165 0.0005228 1.016 -0.0008777 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7251 0.6245 0.5507 0.3657 0.9753 0.989 0.7281 0.9123 0.9724 0.6438 ] Network output: [ -0.02247 0.1301 0.9393 0.0009879 -0.0004435 0.9796 0.0007445 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6341 0.62 0.4533 0.2668 0.9867 0.9913 0.6346 0.9738 0.9824 0.4651 ] Network output: [ -0.04399 0.1449 0.9474 0.0006664 -0.0002992 0.9985 0.0005023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.631 0.6288 0.4622 0.2521 0.9851 0.9904 0.6311 0.9692 0.9798 0.4644 ] Network output: [ 0.01115 0.9529 0.02052 -0.0002999 0.0001346 1.003 -0.000226 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01689 Epoch 2529 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02545 0.9834 1.001 -3.722e-05 1.671e-05 -0.03503 -2.805e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02211 -0.005531 0.01818 0.02407 0.9404 0.9497 0.04539 0.8872 0.9054 0.1171 ] Network output: [ 0.9823 0.05275 -0.01438 -5.583e-05 2.506e-05 -0.003109 -4.207e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6397 0.124 0.1326 0.2101 0.972 0.9871 0.7299 0.9021 0.9677 0.6487 ] Network output: [ -0.01027 0.9548 1.024 -9.524e-05 4.276e-05 0.04115 -7.177e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04817 0.03574 0.0506 0.0311 0.9855 0.9897 0.04925 0.9705 0.9806 0.06275 ] Network output: [ 0.05797 -0.2185 1.082 -0.001168 0.0005241 1.016 -0.0008799 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.725 0.6244 0.5508 0.3653 0.9753 0.989 0.728 0.9123 0.9724 0.6439 ] Network output: [ -0.02239 0.1298 0.9393 0.000989 -0.000444 0.9797 0.0007453 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6342 0.6201 0.4533 0.2665 0.9868 0.9913 0.6347 0.9738 0.9824 0.4651 ] Network output: [ -0.0439 0.1446 0.9474 0.0006685 -0.0003001 0.9986 0.0005038 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6311 0.6289 0.4622 0.2519 0.9851 0.9904 0.6312 0.9692 0.9798 0.4644 ] Network output: [ 0.01112 0.953 0.0205 -0.0002998 0.0001346 1.003 -0.0002259 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01683 Epoch 2530 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02542 0.9834 1.001 -3.738e-05 1.678e-05 -0.03503 -2.817e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02211 -0.00553 0.01818 0.02405 0.9404 0.9498 0.04537 0.8872 0.9054 0.117 ] Network output: [ 0.9823 0.05263 -0.01434 -5.608e-05 2.518e-05 -0.003106 -4.227e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6396 0.124 0.1326 0.2098 0.972 0.9871 0.7299 0.9021 0.9677 0.6487 ] Network output: [ -0.01029 0.9549 1.024 -9.515e-05 4.271e-05 0.04113 -7.17e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04816 0.03573 0.05059 0.03106 0.9855 0.9897 0.04924 0.9705 0.9806 0.06273 ] Network output: [ 0.05784 -0.2182 1.082 -0.00117 0.0005255 1.016 -0.0008821 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.725 0.6244 0.5509 0.3649 0.9753 0.989 0.728 0.9123 0.9724 0.6439 ] Network output: [ -0.02232 0.1295 0.9393 0.0009901 -0.0004445 0.9798 0.0007461 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6343 0.6202 0.4533 0.2663 0.9868 0.9913 0.6348 0.9738 0.9824 0.4651 ] Network output: [ -0.0438 0.1443 0.9474 0.0006705 -0.000301 0.9987 0.0005053 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6312 0.629 0.4622 0.2518 0.9851 0.9904 0.6313 0.9692 0.9798 0.4643 ] Network output: [ 0.01109 0.9532 0.02049 -0.0002997 0.0001345 1.003 -0.0002258 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01677 Epoch 2531 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02539 0.9835 1.001 -3.753e-05 1.685e-05 -0.03504 -2.828e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0221 -0.005528 0.01818 0.02403 0.9404 0.9498 0.04536 0.8872 0.9054 0.117 ] Network output: [ 0.9823 0.05251 -0.0143 -5.634e-05 2.529e-05 -0.003104 -4.246e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6396 0.124 0.1327 0.2096 0.972 0.9871 0.7298 0.9021 0.9677 0.6488 ] Network output: [ -0.01031 0.955 1.024 -9.505e-05 4.267e-05 0.0411 -7.163e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04816 0.03573 0.05058 0.03102 0.9855 0.9897 0.04923 0.9705 0.9806 0.06271 ] Network output: [ 0.05772 -0.2178 1.082 -0.001173 0.0005268 1.015 -0.0008843 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7249 0.6244 0.5509 0.3645 0.9753 0.989 0.7279 0.9123 0.9724 0.644 ] Network output: [ -0.02224 0.1293 0.9393 0.0009911 -0.000445 0.9799 0.0007469 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6344 0.6203 0.4533 0.2661 0.9868 0.9913 0.6349 0.9738 0.9824 0.4652 ] Network output: [ -0.04371 0.144 0.9474 0.0006726 -0.0003019 0.9987 0.0005069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6313 0.6291 0.4622 0.2516 0.9851 0.9904 0.6314 0.9692 0.9798 0.4643 ] Network output: [ 0.01106 0.9533 0.02047 -0.0002996 0.0001345 1.003 -0.0002258 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01672 Epoch 2532 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02536 0.9836 1.001 -3.768e-05 1.691e-05 -0.03504 -2.839e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0221 -0.005527 0.01819 0.024 0.9404 0.9498 0.04535 0.8872 0.9054 0.117 ] Network output: [ 0.9824 0.0524 -0.01427 -5.66e-05 2.541e-05 -0.003101 -4.266e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6396 0.124 0.1328 0.2093 0.972 0.9871 0.7298 0.9021 0.9677 0.6488 ] Network output: [ -0.01033 0.9551 1.024 -9.495e-05 4.263e-05 0.04108 -7.156e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04815 0.03572 0.05056 0.03098 0.9855 0.9897 0.04922 0.9705 0.9806 0.06269 ] Network output: [ 0.05759 -0.2174 1.082 -0.001176 0.0005281 1.015 -0.0008865 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7249 0.6243 0.551 0.3641 0.9753 0.989 0.7279 0.9123 0.9724 0.644 ] Network output: [ -0.02217 0.129 0.9393 0.0009922 -0.0004454 0.9801 0.0007477 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6345 0.6204 0.4534 0.2659 0.9868 0.9913 0.635 0.9738 0.9824 0.4652 ] Network output: [ -0.04362 0.1437 0.9474 0.0006746 -0.0003029 0.9988 0.0005084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6314 0.6292 0.4621 0.2514 0.9851 0.9904 0.6315 0.9692 0.9798 0.4643 ] Network output: [ 0.01103 0.9534 0.02045 -0.0002994 0.0001344 1.003 -0.0002257 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01666 Epoch 2533 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02533 0.9836 1.001 -3.782e-05 1.698e-05 -0.03504 -2.851e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0221 -0.005526 0.01819 0.02398 0.9404 0.9498 0.04533 0.8872 0.9054 0.1169 ] Network output: [ 0.9824 0.05227 -0.01423 -5.687e-05 2.553e-05 -0.003098 -4.286e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6396 0.124 0.1329 0.209 0.972 0.9871 0.7297 0.9021 0.9677 0.6489 ] Network output: [ -0.01035 0.9552 1.024 -9.485e-05 4.258e-05 0.04105 -7.149e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04814 0.03572 0.05055 0.03094 0.9855 0.9897 0.04922 0.9705 0.9806 0.06266 ] Network output: [ 0.05746 -0.217 1.082 -0.001179 0.0005294 1.015 -0.0008886 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7248 0.6243 0.5511 0.3636 0.9753 0.989 0.7278 0.9123 0.9724 0.6441 ] Network output: [ -0.0221 0.1288 0.9393 0.0009932 -0.0004459 0.9802 0.0007485 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6346 0.6205 0.4534 0.2656 0.9868 0.9913 0.6351 0.9739 0.9824 0.4652 ] Network output: [ -0.04352 0.1434 0.9475 0.0006766 -0.0003038 0.9989 0.0005099 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6315 0.6293 0.4621 0.2512 0.9851 0.9904 0.6316 0.9692 0.9798 0.4643 ] Network output: [ 0.011 0.9535 0.02043 -0.0002993 0.0001344 1.003 -0.0002256 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0166 Epoch 2534 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02529 0.9837 1.001 -3.797e-05 1.705e-05 -0.03504 -2.862e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0221 -0.005525 0.0182 0.02396 0.9404 0.9498 0.04532 0.8872 0.9054 0.1169 ] Network output: [ 0.9825 0.05215 -0.01419 -5.714e-05 2.565e-05 -0.003095 -4.306e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6396 0.124 0.133 0.2088 0.972 0.9871 0.7297 0.9021 0.9677 0.6489 ] Network output: [ -0.01037 0.9553 1.024 -9.475e-05 4.254e-05 0.04103 -7.141e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04813 0.03571 0.05054 0.03089 0.9855 0.9897 0.04921 0.9705 0.9807 0.06264 ] Network output: [ 0.05734 -0.2166 1.082 -0.001182 0.0005307 1.015 -0.0008908 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7248 0.6242 0.5511 0.3632 0.9753 0.989 0.7278 0.9123 0.9724 0.6441 ] Network output: [ -0.02202 0.1285 0.9393 0.0009943 -0.0004464 0.9803 0.0007493 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6347 0.6206 0.4534 0.2654 0.9868 0.9913 0.6352 0.9739 0.9824 0.4652 ] Network output: [ -0.04343 0.1431 0.9475 0.0006787 -0.0003047 0.999 0.0005115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6316 0.6294 0.4621 0.251 0.9851 0.9904 0.6317 0.9692 0.9798 0.4642 ] Network output: [ 0.01097 0.9536 0.02041 -0.0002992 0.0001343 1.003 -0.0002255 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01655 Epoch 2535 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02526 0.9838 1.001 -3.812e-05 1.711e-05 -0.03504 -2.873e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0221 -0.005523 0.0182 0.02393 0.9404 0.9498 0.04531 0.8872 0.9054 0.1169 ] Network output: [ 0.9825 0.05203 -0.01416 -5.741e-05 2.578e-05 -0.003092 -4.327e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6396 0.124 0.133 0.2085 0.972 0.9871 0.7296 0.9022 0.9677 0.649 ] Network output: [ -0.01039 0.9554 1.024 -9.465e-05 4.249e-05 0.041 -7.133e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04813 0.0357 0.05052 0.03085 0.9855 0.9897 0.0492 0.9705 0.9807 0.06262 ] Network output: [ 0.05721 -0.2163 1.082 -0.001185 0.0005319 1.015 -0.000893 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7247 0.6242 0.5512 0.3628 0.9753 0.989 0.7277 0.9123 0.9724 0.6442 ] Network output: [ -0.02195 0.1283 0.9393 0.0009953 -0.0004468 0.9804 0.0007501 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6348 0.6207 0.4534 0.2652 0.9868 0.9913 0.6353 0.9739 0.9824 0.4652 ] Network output: [ -0.04333 0.1428 0.9475 0.0006807 -0.0003056 0.9991 0.000513 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6317 0.6295 0.4621 0.2509 0.9851 0.9904 0.6318 0.9692 0.9798 0.4642 ] Network output: [ 0.01093 0.9538 0.02039 -0.0002991 0.0001343 1.003 -0.0002254 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01649 Epoch 2536 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02523 0.9838 1.001 -3.826e-05 1.718e-05 -0.03504 -2.884e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0221 -0.005522 0.01821 0.02391 0.9405 0.9498 0.0453 0.8873 0.9054 0.1168 ] Network output: [ 0.9825 0.05191 -0.01412 -5.769e-05 2.59e-05 -0.003089 -4.348e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6396 0.124 0.1331 0.2083 0.972 0.9871 0.7295 0.9022 0.9677 0.649 ] Network output: [ -0.01041 0.9555 1.024 -9.454e-05 4.244e-05 0.04098 -7.125e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04812 0.0357 0.05051 0.03081 0.9855 0.9897 0.04919 0.9705 0.9807 0.0626 ] Network output: [ 0.05709 -0.2159 1.082 -0.001188 0.0005332 1.015 -0.0008951 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7247 0.6241 0.5513 0.3624 0.9753 0.989 0.7277 0.9124 0.9724 0.6442 ] Network output: [ -0.02188 0.128 0.9393 0.0009964 -0.0004473 0.9805 0.0007509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6349 0.6208 0.4535 0.265 0.9868 0.9913 0.6354 0.9739 0.9824 0.4653 ] Network output: [ -0.04324 0.1425 0.9475 0.0006827 -0.0003065 0.9992 0.0005145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6318 0.6296 0.462 0.2507 0.9851 0.9904 0.6319 0.9693 0.9798 0.4642 ] Network output: [ 0.0109 0.9539 0.02038 -0.000299 0.0001342 1.003 -0.0002253 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01644 Epoch 2537 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0252 0.9839 1.001 -3.841e-05 1.724e-05 -0.03505 -2.894e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0221 -0.005521 0.01821 0.02389 0.9405 0.9498 0.04528 0.8873 0.9055 0.1168 ] Network output: [ 0.9826 0.05179 -0.01408 -5.797e-05 2.603e-05 -0.003087 -4.369e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6396 0.124 0.1332 0.208 0.9721 0.9871 0.7295 0.9022 0.9677 0.6491 ] Network output: [ -0.01043 0.9556 1.024 -9.443e-05 4.239e-05 0.04095 -7.117e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04811 0.03569 0.0505 0.03077 0.9855 0.9897 0.04918 0.9705 0.9807 0.06258 ] Network output: [ 0.05696 -0.2155 1.082 -0.001191 0.0005345 1.015 -0.0008973 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7246 0.6241 0.5513 0.362 0.9753 0.989 0.7276 0.9124 0.9724 0.6443 ] Network output: [ -0.0218 0.1277 0.9393 0.0009974 -0.0004478 0.9806 0.0007517 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.635 0.6209 0.4535 0.2648 0.9868 0.9913 0.6355 0.9739 0.9824 0.4653 ] Network output: [ -0.04315 0.1423 0.9476 0.0006847 -0.0003074 0.9993 0.000516 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6319 0.6297 0.462 0.2505 0.9852 0.9904 0.632 0.9693 0.9798 0.4642 ] Network output: [ 0.01087 0.954 0.02036 -0.0002988 0.0001342 1.003 -0.0002252 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01638 Epoch 2538 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02517 0.984 1.001 -3.855e-05 1.731e-05 -0.03505 -2.905e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02209 -0.00552 0.01822 0.02387 0.9405 0.9498 0.04527 0.8873 0.9055 0.1168 ] Network output: [ 0.9826 0.05167 -0.01404 -5.826e-05 2.615e-05 -0.003084 -4.39e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6396 0.124 0.1333 0.2077 0.9721 0.9871 0.7294 0.9022 0.9677 0.6491 ] Network output: [ -0.01045 0.9557 1.024 -9.432e-05 4.234e-05 0.04093 -7.108e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04811 0.03568 0.05048 0.03073 0.9855 0.9898 0.04918 0.9705 0.9807 0.06256 ] Network output: [ 0.05683 -0.2151 1.082 -0.001193 0.0005358 1.014 -0.0008994 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7246 0.6241 0.5514 0.3616 0.9753 0.989 0.7276 0.9124 0.9724 0.6443 ] Network output: [ -0.02173 0.1275 0.9393 0.0009984 -0.0004482 0.9807 0.0007524 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6351 0.621 0.4535 0.2645 0.9868 0.9914 0.6356 0.9739 0.9824 0.4653 ] Network output: [ -0.04305 0.142 0.9476 0.0006867 -0.0003083 0.9994 0.0005175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.632 0.6298 0.462 0.2503 0.9852 0.9904 0.6321 0.9693 0.9798 0.4641 ] Network output: [ 0.01084 0.9541 0.02034 -0.0002987 0.0001341 1.003 -0.0002251 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01632 Epoch 2539 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02514 0.984 1.001 -3.869e-05 1.737e-05 -0.03505 -2.916e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02209 -0.005519 0.01822 0.02384 0.9405 0.9498 0.04526 0.8873 0.9055 0.1167 ] Network output: [ 0.9826 0.05155 -0.01401 -5.855e-05 2.628e-05 -0.003081 -4.412e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6396 0.124 0.1334 0.2075 0.9721 0.9871 0.7294 0.9022 0.9677 0.6492 ] Network output: [ -0.01047 0.9558 1.024 -9.421e-05 4.229e-05 0.0409 -7.1e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0481 0.03568 0.05047 0.03069 0.9855 0.9898 0.04917 0.9705 0.9807 0.06254 ] Network output: [ 0.05671 -0.2148 1.082 -0.001196 0.0005371 1.014 -0.0009016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7245 0.624 0.5515 0.3611 0.9753 0.989 0.7275 0.9124 0.9724 0.6444 ] Network output: [ -0.02166 0.1272 0.9393 0.0009994 -0.0004487 0.9809 0.0007532 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6352 0.6211 0.4535 0.2643 0.9868 0.9914 0.6357 0.9739 0.9824 0.4653 ] Network output: [ -0.04296 0.1417 0.9476 0.0006886 -0.0003092 0.9994 0.000519 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6321 0.6299 0.462 0.2501 0.9852 0.9904 0.6322 0.9693 0.9798 0.4641 ] Network output: [ 0.01081 0.9543 0.02032 -0.0002986 0.000134 1.003 -0.000225 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01627 Epoch 2540 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02511 0.9841 1.001 -3.883e-05 1.743e-05 -0.03505 -2.927e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02209 -0.005517 0.01823 0.02382 0.9405 0.9498 0.04524 0.8873 0.9055 0.1167 ] Network output: [ 0.9827 0.05143 -0.01397 -5.884e-05 2.641e-05 -0.003078 -4.434e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.1334 0.2072 0.9721 0.9871 0.7293 0.9022 0.9678 0.6492 ] Network output: [ -0.01049 0.9559 1.024 -9.409e-05 4.224e-05 0.04088 -7.091e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04809 0.03567 0.05045 0.03065 0.9855 0.9898 0.04916 0.9705 0.9807 0.06252 ] Network output: [ 0.05658 -0.2144 1.082 -0.001199 0.0005383 1.014 -0.0009037 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7245 0.624 0.5515 0.3607 0.9753 0.989 0.7275 0.9124 0.9724 0.6444 ] Network output: [ -0.02158 0.127 0.9393 0.001 -0.0004491 0.981 0.000754 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6353 0.6212 0.4535 0.2641 0.9868 0.9914 0.6358 0.9739 0.9824 0.4653 ] Network output: [ -0.04287 0.1414 0.9476 0.0006906 -0.00031 0.9995 0.0005205 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6322 0.63 0.4619 0.2499 0.9852 0.9904 0.6323 0.9693 0.9798 0.4641 ] Network output: [ 0.01078 0.9544 0.0203 -0.0002984 0.000134 1.003 -0.0002249 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01621 Epoch 2541 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02508 0.9842 1.001 -3.897e-05 1.75e-05 -0.03505 -2.937e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02209 -0.005516 0.01823 0.0238 0.9405 0.9498 0.04523 0.8873 0.9055 0.1167 ] Network output: [ 0.9827 0.0513 -0.01393 -5.913e-05 2.655e-05 -0.003075 -4.456e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.1335 0.207 0.9721 0.9871 0.7293 0.9022 0.9678 0.6493 ] Network output: [ -0.01051 0.956 1.024 -9.397e-05 4.219e-05 0.04085 -7.082e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04808 0.03567 0.05044 0.0306 0.9855 0.9898 0.04915 0.9705 0.9807 0.0625 ] Network output: [ 0.05646 -0.214 1.082 -0.001202 0.0005396 1.014 -0.0009058 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7244 0.6239 0.5516 0.3603 0.9753 0.989 0.7274 0.9124 0.9724 0.6444 ] Network output: [ -0.02151 0.1267 0.9393 0.001001 -0.0004496 0.9811 0.0007547 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6354 0.6213 0.4536 0.2639 0.9868 0.9914 0.6359 0.9739 0.9824 0.4653 ] Network output: [ -0.04277 0.1411 0.9476 0.0006926 -0.0003109 0.9996 0.000522 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6323 0.6301 0.4619 0.2498 0.9852 0.9904 0.6324 0.9693 0.9799 0.4641 ] Network output: [ 0.01075 0.9545 0.02028 -0.0002983 0.0001339 1.003 -0.0002248 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01616 Epoch 2542 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02505 0.9842 1.001 -3.911e-05 1.756e-05 -0.03505 -2.948e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02209 -0.005515 0.01824 0.02378 0.9405 0.9498 0.04522 0.8873 0.9055 0.1166 ] Network output: [ 0.9828 0.05118 -0.01389 -5.943e-05 2.668e-05 -0.003072 -4.479e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.1336 0.2067 0.9721 0.9871 0.7292 0.9022 0.9678 0.6493 ] Network output: [ -0.01053 0.9561 1.024 -9.385e-05 4.213e-05 0.04083 -7.073e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04808 0.03566 0.05043 0.03056 0.9855 0.9898 0.04914 0.9705 0.9807 0.06248 ] Network output: [ 0.05634 -0.2136 1.082 -0.001205 0.0005409 1.014 -0.0009079 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7244 0.6239 0.5517 0.3599 0.9753 0.989 0.7274 0.9124 0.9724 0.6445 ] Network output: [ -0.02144 0.1265 0.9393 0.001002 -0.00045 0.9812 0.0007555 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6355 0.6214 0.4536 0.2637 0.9868 0.9914 0.636 0.9739 0.9824 0.4654 ] Network output: [ -0.04268 0.1408 0.9477 0.0006946 -0.0003118 0.9997 0.0005234 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6324 0.6302 0.4619 0.2496 0.9852 0.9904 0.6325 0.9693 0.9799 0.464 ] Network output: [ 0.01072 0.9546 0.02027 -0.0002981 0.0001338 1.002 -0.0002247 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0161 Epoch 2543 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02502 0.9843 1.001 -3.925e-05 1.762e-05 -0.03506 -2.958e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02209 -0.005514 0.01824 0.02376 0.9405 0.9498 0.0452 0.8873 0.9055 0.1166 ] Network output: [ 0.9828 0.05106 -0.01385 -5.973e-05 2.682e-05 -0.003069 -4.502e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.1337 0.2064 0.9721 0.9872 0.7292 0.9022 0.9678 0.6494 ] Network output: [ -0.01055 0.9562 1.024 -9.373e-05 4.208e-05 0.0408 -7.064e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04807 0.03565 0.05041 0.03052 0.9855 0.9898 0.04914 0.9705 0.9807 0.06246 ] Network output: [ 0.05621 -0.2133 1.082 -0.001208 0.0005421 1.014 -0.00091 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7243 0.6238 0.5517 0.3595 0.9753 0.989 0.7273 0.9124 0.9724 0.6445 ] Network output: [ -0.02137 0.1262 0.9393 0.001003 -0.0004505 0.9813 0.0007562 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6356 0.6215 0.4536 0.2634 0.9868 0.9914 0.6361 0.9739 0.9825 0.4654 ] Network output: [ -0.04259 0.1405 0.9477 0.0006965 -0.0003127 0.9998 0.0005249 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6325 0.6302 0.4619 0.2494 0.9852 0.9904 0.6326 0.9693 0.9799 0.464 ] Network output: [ 0.01069 0.9547 0.02025 -0.000298 0.0001338 1.002 -0.0002246 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01605 Epoch 2544 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02499 0.9844 1.001 -3.939e-05 1.768e-05 -0.03506 -2.968e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02208 -0.005512 0.01825 0.02373 0.9405 0.9498 0.04519 0.8873 0.9055 0.1166 ] Network output: [ 0.9828 0.05094 -0.01381 -6.004e-05 2.695e-05 -0.003066 -4.525e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.1338 0.2062 0.9721 0.9872 0.7291 0.9022 0.9678 0.6494 ] Network output: [ -0.01057 0.9563 1.024 -9.36e-05 4.202e-05 0.04078 -7.054e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04806 0.03565 0.0504 0.03048 0.9855 0.9898 0.04913 0.9705 0.9807 0.06244 ] Network output: [ 0.05609 -0.2129 1.082 -0.00121 0.0005434 1.014 -0.0009121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7243 0.6238 0.5518 0.3591 0.9753 0.989 0.7273 0.9124 0.9724 0.6446 ] Network output: [ -0.0213 0.126 0.9393 0.001004 -0.0004509 0.9814 0.000757 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6357 0.6216 0.4536 0.2632 0.9868 0.9914 0.6362 0.9739 0.9825 0.4654 ] Network output: [ -0.0425 0.1402 0.9477 0.0006985 -0.0003136 0.9999 0.0005264 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6326 0.6303 0.4618 0.2492 0.9852 0.9904 0.6327 0.9693 0.9799 0.464 ] Network output: [ 0.01066 0.9548 0.02023 -0.0002978 0.0001337 1.002 -0.0002245 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01599 Epoch 2545 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02495 0.9844 1.001 -3.952e-05 1.774e-05 -0.03506 -2.979e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02208 -0.005511 0.01825 0.02371 0.9405 0.9498 0.04518 0.8873 0.9055 0.1165 ] Network output: [ 0.9829 0.05081 -0.01377 -6.035e-05 2.709e-05 -0.003063 -4.548e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.1339 0.2059 0.9721 0.9872 0.7291 0.9022 0.9678 0.6495 ] Network output: [ -0.01059 0.9564 1.024 -9.347e-05 4.196e-05 0.04075 -7.044e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04806 0.03564 0.05039 0.03044 0.9855 0.9898 0.04912 0.9705 0.9807 0.06242 ] Network output: [ 0.05596 -0.2125 1.082 -0.001213 0.0005446 1.014 -0.0009142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7243 0.6238 0.5519 0.3587 0.9753 0.989 0.7272 0.9124 0.9724 0.6446 ] Network output: [ -0.02123 0.1257 0.9393 0.001005 -0.0004514 0.9815 0.0007577 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6358 0.6217 0.4536 0.263 0.9868 0.9914 0.6363 0.9739 0.9825 0.4654 ] Network output: [ -0.0424 0.14 0.9477 0.0007004 -0.0003144 1 0.0005278 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6327 0.6304 0.4618 0.249 0.9852 0.9904 0.6328 0.9693 0.9799 0.464 ] Network output: [ 0.01063 0.955 0.02021 -0.0002977 0.0001336 1.002 -0.0002243 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01594 Epoch 2546 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02492 0.9845 1.001 -3.966e-05 1.78e-05 -0.03506 -2.989e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02208 -0.00551 0.01826 0.02369 0.9405 0.9498 0.04517 0.8873 0.9055 0.1165 ] Network output: [ 0.9829 0.05069 -0.01373 -6.066e-05 2.723e-05 -0.00306 -4.572e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.1339 0.2057 0.9721 0.9872 0.7291 0.9022 0.9678 0.6495 ] Network output: [ -0.01061 0.9565 1.024 -9.334e-05 4.19e-05 0.04073 -7.034e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04805 0.03564 0.05037 0.0304 0.9856 0.9898 0.04911 0.9706 0.9807 0.0624 ] Network output: [ 0.05584 -0.2121 1.082 -0.001216 0.0005458 1.013 -0.0009163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7242 0.6237 0.5519 0.3583 0.9754 0.989 0.7272 0.9124 0.9724 0.6447 ] Network output: [ -0.02116 0.1255 0.9393 0.001006 -0.0004518 0.9816 0.0007585 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6359 0.6218 0.4537 0.2628 0.9868 0.9914 0.6364 0.9739 0.9825 0.4654 ] Network output: [ -0.04231 0.1397 0.9477 0.0007023 -0.0003153 1 0.0005293 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6328 0.6305 0.4618 0.2489 0.9852 0.9904 0.6329 0.9693 0.9799 0.4639 ] Network output: [ 0.0106 0.9551 0.02019 -0.0002975 0.0001336 1.002 -0.0002242 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01589 Epoch 2547 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02489 0.9845 1.001 -3.979e-05 1.786e-05 -0.03506 -2.999e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02208 -0.005509 0.01826 0.02367 0.9405 0.9498 0.04515 0.8874 0.9055 0.1165 ] Network output: [ 0.983 0.05056 -0.01369 -6.098e-05 2.738e-05 -0.003057 -4.596e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.134 0.2054 0.9721 0.9872 0.729 0.9023 0.9678 0.6496 ] Network output: [ -0.01063 0.9566 1.024 -9.321e-05 4.184e-05 0.0407 -7.024e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04804 0.03563 0.05036 0.03036 0.9856 0.9898 0.04911 0.9706 0.9807 0.06238 ] Network output: [ 0.05572 -0.2118 1.082 -0.001219 0.0005471 1.013 -0.0009184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7242 0.6237 0.552 0.3579 0.9754 0.989 0.7272 0.9124 0.9724 0.6447 ] Network output: [ -0.02109 0.1252 0.9393 0.001007 -0.0004522 0.9817 0.0007592 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.636 0.6219 0.4537 0.2626 0.9868 0.9914 0.6365 0.9739 0.9825 0.4654 ] Network output: [ -0.04222 0.1394 0.9478 0.0007043 -0.0003162 1 0.0005308 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6329 0.6306 0.4618 0.2487 0.9852 0.9904 0.633 0.9693 0.9799 0.4639 ] Network output: [ 0.01057 0.9552 0.02018 -0.0002974 0.0001335 1.002 -0.0002241 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01583 Epoch 2548 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02486 0.9846 1.001 -3.992e-05 1.792e-05 -0.03506 -3.009e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02208 -0.005508 0.01827 0.02365 0.9405 0.9499 0.04514 0.8874 0.9056 0.1164 ] Network output: [ 0.983 0.05044 -0.01365 -6.13e-05 2.752e-05 -0.003054 -4.62e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.1341 0.2052 0.9721 0.9872 0.729 0.9023 0.9678 0.6496 ] Network output: [ -0.01065 0.9567 1.023 -9.307e-05 4.178e-05 0.04068 -7.014e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04803 0.03562 0.05035 0.03032 0.9856 0.9898 0.0491 0.9706 0.9807 0.06236 ] Network output: [ 0.05559 -0.2114 1.082 -0.001221 0.0005483 1.013 -0.0009204 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7241 0.6236 0.552 0.3575 0.9754 0.989 0.7271 0.9124 0.9724 0.6447 ] Network output: [ -0.02102 0.125 0.9393 0.001008 -0.0004527 0.9818 0.0007599 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6361 0.622 0.4537 0.2623 0.9868 0.9914 0.6366 0.9739 0.9825 0.4655 ] Network output: [ -0.04213 0.1391 0.9478 0.0007062 -0.000317 1 0.0005322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.633 0.6307 0.4617 0.2485 0.9852 0.9904 0.6331 0.9693 0.9799 0.4639 ] Network output: [ 0.01055 0.9553 0.02016 -0.0002972 0.0001334 1.002 -0.000224 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01578 Epoch 2549 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02483 0.9847 1.001 -4.006e-05 1.798e-05 -0.03506 -3.019e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02208 -0.005506 0.01827 0.02362 0.9405 0.9499 0.04513 0.8874 0.9056 0.1164 ] Network output: [ 0.983 0.05032 -0.01361 -6.162e-05 2.766e-05 -0.003051 -4.644e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.1342 0.2049 0.9721 0.9872 0.7289 0.9023 0.9678 0.6496 ] Network output: [ -0.01067 0.9568 1.023 -9.293e-05 4.172e-05 0.04065 -7.004e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04803 0.03562 0.05033 0.03028 0.9856 0.9898 0.04909 0.9706 0.9807 0.06233 ] Network output: [ 0.05547 -0.211 1.082 -0.001224 0.0005495 1.013 -0.0009225 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7241 0.6236 0.5521 0.3571 0.9754 0.989 0.7271 0.9124 0.9724 0.6448 ] Network output: [ -0.02095 0.1247 0.9393 0.001009 -0.0004531 0.982 0.0007606 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6362 0.622 0.4537 0.2621 0.9868 0.9914 0.6367 0.9739 0.9825 0.4655 ] Network output: [ -0.04204 0.1388 0.9478 0.0007081 -0.0003179 1 0.0005336 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6331 0.6308 0.4617 0.2483 0.9852 0.9904 0.6332 0.9693 0.9799 0.4639 ] Network output: [ 0.01052 0.9554 0.02014 -0.0002971 0.0001334 1.002 -0.0002239 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01572 Epoch 2550 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0248 0.9847 1.001 -4.019e-05 1.804e-05 -0.03506 -3.029e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02208 -0.005505 0.01828 0.0236 0.9405 0.9499 0.04512 0.8874 0.9056 0.1164 ] Network output: [ 0.9831 0.05019 -0.01357 -6.195e-05 2.781e-05 -0.003049 -4.669e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.1343 0.2047 0.9721 0.9872 0.7289 0.9023 0.9678 0.6497 ] Network output: [ -0.01069 0.9569 1.023 -9.279e-05 4.166e-05 0.04063 -6.993e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04802 0.03561 0.05032 0.03024 0.9856 0.9898 0.04908 0.9706 0.9807 0.06231 ] Network output: [ 0.05535 -0.2107 1.082 -0.001227 0.0005507 1.013 -0.0009245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.724 0.6236 0.5522 0.3567 0.9754 0.989 0.727 0.9125 0.9725 0.6448 ] Network output: [ -0.02088 0.1245 0.9393 0.00101 -0.0004535 0.9821 0.0007613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6363 0.6221 0.4537 0.2619 0.9868 0.9914 0.6368 0.9739 0.9825 0.4655 ] Network output: [ -0.04195 0.1386 0.9478 0.00071 -0.0003187 1 0.0005351 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6332 0.6309 0.4617 0.2482 0.9852 0.9904 0.6333 0.9694 0.9799 0.4638 ] Network output: [ 0.01049 0.9555 0.02012 -0.0002969 0.0001333 1.002 -0.0002237 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01567 Epoch 2551 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02477 0.9848 1.001 -4.032e-05 1.81e-05 -0.03506 -3.038e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02208 -0.005504 0.01828 0.02358 0.9405 0.9499 0.0451 0.8874 0.9056 0.1163 ] Network output: [ 0.9831 0.05007 -0.01353 -6.228e-05 2.796e-05 -0.003046 -4.694e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.1344 0.2044 0.9721 0.9872 0.7288 0.9023 0.9678 0.6497 ] Network output: [ -0.01071 0.9571 1.023 -9.265e-05 4.159e-05 0.0406 -6.982e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04801 0.03561 0.0503 0.0302 0.9856 0.9898 0.04907 0.9706 0.9807 0.06229 ] Network output: [ 0.05522 -0.2103 1.082 -0.001229 0.000552 1.013 -0.0009266 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.724 0.6235 0.5522 0.3563 0.9754 0.989 0.727 0.9125 0.9725 0.6449 ] Network output: [ -0.02081 0.1242 0.9393 0.001011 -0.000454 0.9822 0.0007621 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6364 0.6222 0.4538 0.2617 0.9868 0.9914 0.6369 0.9739 0.9825 0.4655 ] Network output: [ -0.04186 0.1383 0.9478 0.0007119 -0.0003196 1 0.0005365 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6333 0.631 0.4617 0.248 0.9852 0.9904 0.6334 0.9694 0.9799 0.4638 ] Network output: [ 0.01046 0.9557 0.0201 -0.0002967 0.0001332 1.002 -0.0002236 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01562 Epoch 2552 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02474 0.9849 1.001 -4.045e-05 1.816e-05 -0.03506 -3.048e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02207 -0.005503 0.01829 0.02356 0.9406 0.9499 0.04509 0.8874 0.9056 0.1163 ] Network output: [ 0.9832 0.04994 -0.01349 -6.261e-05 2.811e-05 -0.003043 -4.719e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.1345 0.2042 0.9721 0.9872 0.7288 0.9023 0.9678 0.6498 ] Network output: [ -0.01073 0.9572 1.023 -9.25e-05 4.153e-05 0.04058 -6.971e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04801 0.0356 0.05029 0.03016 0.9856 0.9898 0.04907 0.9706 0.9807 0.06227 ] Network output: [ 0.0551 -0.2099 1.082 -0.001232 0.0005532 1.013 -0.0009286 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.724 0.6235 0.5523 0.3559 0.9754 0.989 0.7269 0.9125 0.9725 0.6449 ] Network output: [ -0.02074 0.124 0.9394 0.001012 -0.0004544 0.9823 0.0007628 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6365 0.6223 0.4538 0.2615 0.9868 0.9914 0.637 0.9739 0.9825 0.4655 ] Network output: [ -0.04177 0.138 0.9479 0.0007138 -0.0003204 1.001 0.0005379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6334 0.6311 0.4616 0.2478 0.9852 0.9904 0.6335 0.9694 0.9799 0.4638 ] Network output: [ 0.01043 0.9558 0.02008 -0.0002965 0.0001331 1.002 -0.0002235 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01556 Epoch 2553 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02471 0.9849 1.001 -4.057e-05 1.822e-05 -0.03507 -3.058e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02207 -0.005501 0.01829 0.02354 0.9406 0.9499 0.04508 0.8874 0.9056 0.1163 ] Network output: [ 0.9832 0.04982 -0.01345 -6.295e-05 2.826e-05 -0.00304 -4.744e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.1346 0.2039 0.9721 0.9872 0.7287 0.9023 0.9678 0.6498 ] Network output: [ -0.01074 0.9573 1.023 -9.236e-05 4.146e-05 0.04056 -6.96e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.048 0.03559 0.05028 0.03012 0.9856 0.9898 0.04906 0.9706 0.9807 0.06225 ] Network output: [ 0.05498 -0.2095 1.082 -0.001235 0.0005544 1.012 -0.0009306 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7239 0.6234 0.5524 0.3555 0.9754 0.989 0.7269 0.9125 0.9725 0.6449 ] Network output: [ -0.02067 0.1237 0.9394 0.001013 -0.0004548 0.9824 0.0007635 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6366 0.6224 0.4538 0.2613 0.9868 0.9914 0.6371 0.9739 0.9825 0.4655 ] Network output: [ -0.04167 0.1377 0.9479 0.0007157 -0.0003213 1.001 0.0005394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6335 0.6312 0.4616 0.2476 0.9852 0.9904 0.6336 0.9694 0.9799 0.4638 ] Network output: [ 0.0104 0.9559 0.02007 -0.0002964 0.000133 1.002 -0.0002234 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01551 Epoch 2554 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02468 0.985 1.001 -4.07e-05 1.827e-05 -0.03507 -3.067e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02207 -0.0055 0.0183 0.02352 0.9406 0.9499 0.04506 0.8874 0.9056 0.1162 ] Network output: [ 0.9832 0.04969 -0.01341 -6.329e-05 2.841e-05 -0.003037 -4.77e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.1346 0.2037 0.9721 0.9872 0.7287 0.9023 0.9678 0.6499 ] Network output: [ -0.01076 0.9574 1.023 -9.221e-05 4.139e-05 0.04053 -6.949e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04799 0.03559 0.05026 0.03008 0.9856 0.9898 0.04905 0.9706 0.9807 0.06223 ] Network output: [ 0.05486 -0.2092 1.082 -0.001238 0.0005556 1.012 -0.0009326 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7239 0.6234 0.5524 0.3551 0.9754 0.989 0.7268 0.9125 0.9725 0.645 ] Network output: [ -0.0206 0.1235 0.9394 0.001014 -0.0004552 0.9825 0.0007642 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6367 0.6225 0.4538 0.2611 0.9868 0.9914 0.6372 0.9739 0.9825 0.4655 ] Network output: [ -0.04158 0.1374 0.9479 0.0007176 -0.0003221 1.001 0.0005408 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6336 0.6313 0.4616 0.2474 0.9852 0.9904 0.6337 0.9694 0.9799 0.4637 ] Network output: [ 0.01037 0.956 0.02005 -0.0002962 0.000133 1.002 -0.0002232 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01546 Epoch 2555 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02465 0.9851 1.001 -4.083e-05 1.833e-05 -0.03507 -3.077e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02207 -0.005499 0.01831 0.0235 0.9406 0.9499 0.04505 0.8874 0.9056 0.1162 ] Network output: [ 0.9833 0.04957 -0.01337 -6.363e-05 2.857e-05 -0.003034 -4.795e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.1347 0.2034 0.9721 0.9872 0.7286 0.9023 0.9678 0.6499 ] Network output: [ -0.01078 0.9575 1.023 -9.205e-05 4.133e-05 0.04051 -6.937e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04798 0.03558 0.05025 0.03004 0.9856 0.9898 0.04904 0.9706 0.9807 0.06221 ] Network output: [ 0.05473 -0.2088 1.082 -0.00124 0.0005568 1.012 -0.0009346 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7238 0.6234 0.5525 0.3547 0.9754 0.989 0.7268 0.9125 0.9725 0.645 ] Network output: [ -0.02053 0.1232 0.9394 0.001015 -0.0004556 0.9826 0.0007649 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6368 0.6226 0.4538 0.2609 0.9868 0.9914 0.6373 0.9739 0.9825 0.4656 ] Network output: [ -0.04149 0.1372 0.9479 0.0007194 -0.000323 1.001 0.0005422 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6337 0.6314 0.4616 0.2473 0.9852 0.9904 0.6337 0.9694 0.9799 0.4637 ] Network output: [ 0.01034 0.9561 0.02003 -0.000296 0.0001329 1.002 -0.0002231 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01541 Epoch 2556 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02462 0.9851 1.001 -4.095e-05 1.839e-05 -0.03507 -3.086e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02207 -0.005498 0.01831 0.02347 0.9406 0.9499 0.04504 0.8874 0.9056 0.1162 ] Network output: [ 0.9833 0.04944 -0.01333 -6.398e-05 2.872e-05 -0.003031 -4.822e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.1348 0.2032 0.9721 0.9872 0.7286 0.9023 0.9678 0.6499 ] Network output: [ -0.0108 0.9576 1.023 -9.19e-05 4.126e-05 0.04048 -6.926e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04798 0.03558 0.05023 0.03001 0.9856 0.9898 0.04903 0.9706 0.9807 0.06219 ] Network output: [ 0.05461 -0.2084 1.082 -0.001243 0.0005579 1.012 -0.0009366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7238 0.6233 0.5526 0.3543 0.9754 0.989 0.7268 0.9125 0.9725 0.645 ] Network output: [ -0.02046 0.123 0.9394 0.001016 -0.000456 0.9827 0.0007655 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6369 0.6227 0.4538 0.2606 0.9868 0.9914 0.6374 0.9739 0.9825 0.4656 ] Network output: [ -0.0414 0.1369 0.9479 0.0007213 -0.0003238 1.001 0.0005436 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6338 0.6315 0.4615 0.2471 0.9852 0.9904 0.6338 0.9694 0.9799 0.4637 ] Network output: [ 0.01032 0.9562 0.02001 -0.0002958 0.0001328 1.002 -0.0002229 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01535 Epoch 2557 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02459 0.9852 1.001 -4.108e-05 1.844e-05 -0.03507 -3.096e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02207 -0.005496 0.01832 0.02345 0.9406 0.9499 0.04503 0.8874 0.9056 0.1161 ] Network output: [ 0.9834 0.04932 -0.01329 -6.433e-05 2.888e-05 -0.003028 -4.848e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.1349 0.2029 0.9721 0.9872 0.7286 0.9023 0.9678 0.65 ] Network output: [ -0.01082 0.9577 1.023 -9.174e-05 4.119e-05 0.04046 -6.914e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04797 0.03557 0.05022 0.02997 0.9856 0.9898 0.04903 0.9706 0.9807 0.06216 ] Network output: [ 0.05449 -0.2081 1.082 -0.001245 0.0005591 1.012 -0.0009386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7238 0.6233 0.5526 0.3539 0.9754 0.989 0.7267 0.9125 0.9725 0.6451 ] Network output: [ -0.0204 0.1227 0.9394 0.001017 -0.0004564 0.9828 0.0007662 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.637 0.6228 0.4539 0.2604 0.9868 0.9914 0.6375 0.9739 0.9825 0.4656 ] Network output: [ -0.04131 0.1366 0.948 0.0007231 -0.0003246 1.001 0.000545 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6339 0.6316 0.4615 0.2469 0.9852 0.9904 0.6339 0.9694 0.9799 0.4636 ] Network output: [ 0.01029 0.9563 0.01999 -0.0002956 0.0001327 1.002 -0.0002228 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0153 Epoch 2558 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02456 0.9853 1.001 -4.12e-05 1.85e-05 -0.03507 -3.105e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02207 -0.005495 0.01832 0.02343 0.9406 0.9499 0.04501 0.8875 0.9056 0.1161 ] Network output: [ 0.9834 0.04919 -0.01325 -6.468e-05 2.904e-05 -0.003025 -4.874e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.135 0.2027 0.9721 0.9872 0.7285 0.9023 0.9678 0.65 ] Network output: [ -0.01084 0.9578 1.023 -9.158e-05 4.112e-05 0.04043 -6.902e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04796 0.03556 0.05021 0.02993 0.9856 0.9898 0.04902 0.9706 0.9807 0.06214 ] Network output: [ 0.05437 -0.2077 1.082 -0.001248 0.0005603 1.012 -0.0009406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7237 0.6232 0.5527 0.3535 0.9754 0.989 0.7267 0.9125 0.9725 0.6451 ] Network output: [ -0.02033 0.1225 0.9394 0.001018 -0.0004568 0.9829 0.0007669 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6371 0.6229 0.4539 0.2602 0.9868 0.9914 0.6376 0.9739 0.9825 0.4656 ] Network output: [ -0.04123 0.1363 0.948 0.000725 -0.0003255 1.001 0.0005464 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6339 0.6317 0.4615 0.2467 0.9852 0.9904 0.634 0.9694 0.9799 0.4636 ] Network output: [ 0.01026 0.9565 0.01998 -0.0002954 0.0001326 1.002 -0.0002227 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01525 Epoch 2559 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02453 0.9853 1.001 -4.132e-05 1.855e-05 -0.03507 -3.114e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02207 -0.005494 0.01833 0.02341 0.9406 0.9499 0.045 0.8875 0.9057 0.1161 ] Network output: [ 0.9835 0.04906 -0.01321 -6.504e-05 2.92e-05 -0.003023 -4.901e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.1351 0.2025 0.9721 0.9872 0.7285 0.9024 0.9678 0.6501 ] Network output: [ -0.01086 0.9579 1.023 -9.142e-05 4.104e-05 0.04041 -6.89e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04796 0.03556 0.05019 0.02989 0.9856 0.9898 0.04901 0.9706 0.9807 0.06212 ] Network output: [ 0.05425 -0.2073 1.082 -0.001251 0.0005615 1.012 -0.0009426 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7237 0.6232 0.5527 0.3531 0.9754 0.989 0.7266 0.9125 0.9725 0.6451 ] Network output: [ -0.02026 0.1222 0.9394 0.001018 -0.0004572 0.983 0.0007676 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6372 0.623 0.4539 0.26 0.9868 0.9914 0.6377 0.9739 0.9825 0.4656 ] Network output: [ -0.04114 0.1361 0.948 0.0007268 -0.0003263 1.001 0.0005478 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.634 0.6318 0.4615 0.2466 0.9852 0.9904 0.6341 0.9694 0.9799 0.4636 ] Network output: [ 0.01023 0.9566 0.01996 -0.0002953 0.0001326 1.002 -0.0002225 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0152 Epoch 2560 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0245 0.9854 1.001 -4.144e-05 1.861e-05 -0.03507 -3.123e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02206 -0.005493 0.01833 0.02339 0.9406 0.9499 0.04499 0.8875 0.9057 0.116 ] Network output: [ 0.9835 0.04894 -0.01317 -6.539e-05 2.936e-05 -0.00302 -4.928e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.1352 0.2022 0.9721 0.9872 0.7284 0.9024 0.9678 0.6501 ] Network output: [ -0.01087 0.958 1.023 -9.126e-05 4.097e-05 0.04039 -6.878e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04795 0.03555 0.05018 0.02985 0.9856 0.9898 0.049 0.9706 0.9807 0.0621 ] Network output: [ 0.05413 -0.207 1.082 -0.001253 0.0005626 1.012 -0.0009445 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7236 0.6232 0.5528 0.3527 0.9754 0.989 0.7266 0.9125 0.9725 0.6452 ] Network output: [ -0.0202 0.122 0.9394 0.001019 -0.0004576 0.9831 0.0007682 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6373 0.6231 0.4539 0.2598 0.9868 0.9914 0.6378 0.9739 0.9825 0.4656 ] Network output: [ -0.04105 0.1358 0.948 0.0007286 -0.0003271 1.001 0.0005491 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6341 0.6319 0.4614 0.2464 0.9852 0.9904 0.6342 0.9694 0.9799 0.4636 ] Network output: [ 0.01021 0.9567 0.01994 -0.0002951 0.0001325 1.002 -0.0002224 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01515 Epoch 2561 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02446 0.9855 1.001 -4.156e-05 1.866e-05 -0.03507 -3.132e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02206 -0.005492 0.01834 0.02337 0.9406 0.9499 0.04498 0.8875 0.9057 0.116 ] Network output: [ 0.9835 0.04881 -0.01312 -6.576e-05 2.952e-05 -0.003017 -4.956e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.1353 0.202 0.9721 0.9872 0.7284 0.9024 0.9678 0.6501 ] Network output: [ -0.01089 0.9581 1.023 -9.109e-05 4.089e-05 0.04036 -6.865e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04794 0.03554 0.05016 0.02981 0.9856 0.9898 0.04899 0.9706 0.9807 0.06208 ] Network output: [ 0.05401 -0.2066 1.082 -0.001256 0.0005638 1.011 -0.0009465 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7236 0.6231 0.5529 0.3523 0.9754 0.989 0.7266 0.9125 0.9725 0.6452 ] Network output: [ -0.02013 0.1217 0.9394 0.00102 -0.000458 0.9832 0.0007689 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6374 0.6232 0.4539 0.2596 0.9868 0.9914 0.6378 0.9739 0.9825 0.4656 ] Network output: [ -0.04096 0.1355 0.948 0.0007305 -0.0003279 1.001 0.0005505 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6342 0.632 0.4614 0.2462 0.9852 0.9904 0.6343 0.9694 0.9799 0.4635 ] Network output: [ 0.01018 0.9568 0.01992 -0.0002949 0.0001324 1.002 -0.0002222 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0151 Epoch 2562 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02443 0.9855 1 -4.168e-05 1.871e-05 -0.03507 -3.141e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02206 -0.00549 0.01835 0.02335 0.9406 0.9499 0.04496 0.8875 0.9057 0.116 ] Network output: [ 0.9836 0.04869 -0.01308 -6.612e-05 2.968e-05 -0.003014 -4.983e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.1354 0.2017 0.9721 0.9872 0.7283 0.9024 0.9678 0.6502 ] Network output: [ -0.01091 0.9582 1.023 -9.092e-05 4.082e-05 0.04034 -6.852e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04793 0.03554 0.05015 0.02977 0.9856 0.9898 0.04899 0.9706 0.9807 0.06206 ] Network output: [ 0.05389 -0.2062 1.082 -0.001258 0.000565 1.011 -0.0009484 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7236 0.6231 0.5529 0.3519 0.9754 0.989 0.7265 0.9125 0.9725 0.6452 ] Network output: [ -0.02006 0.1215 0.9395 0.001021 -0.0004584 0.9833 0.0007696 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6375 0.6233 0.4539 0.2594 0.9868 0.9914 0.6379 0.9739 0.9825 0.4656 ] Network output: [ -0.04087 0.1352 0.9481 0.0007323 -0.0003287 1.001 0.0005519 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6343 0.6321 0.4614 0.246 0.9852 0.9904 0.6344 0.9694 0.9799 0.4635 ] Network output: [ 0.01015 0.9569 0.0199 -0.0002947 0.0001323 1.002 -0.0002221 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01504 Epoch 2563 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0244 0.9856 1 -4.18e-05 1.877e-05 -0.03507 -3.15e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02206 -0.005489 0.01835 0.02333 0.9406 0.9499 0.04495 0.8875 0.9057 0.1159 ] Network output: [ 0.9836 0.04856 -0.01304 -6.649e-05 2.985e-05 -0.003012 -5.011e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.124 0.1354 0.2015 0.9722 0.9872 0.7283 0.9024 0.9679 0.6502 ] Network output: [ -0.01093 0.9583 1.023 -9.075e-05 4.074e-05 0.04031 -6.84e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04793 0.03553 0.05014 0.02974 0.9856 0.9898 0.04898 0.9706 0.9807 0.06204 ] Network output: [ 0.05377 -0.2059 1.082 -0.001261 0.0005661 1.011 -0.0009503 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7235 0.6231 0.553 0.3516 0.9754 0.989 0.7265 0.9125 0.9725 0.6453 ] Network output: [ -0.02 0.1213 0.9395 0.001022 -0.0004588 0.9834 0.0007702 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6376 0.6233 0.454 0.2592 0.9868 0.9914 0.638 0.9739 0.9825 0.4656 ] Network output: [ -0.04078 0.135 0.9481 0.0007341 -0.0003296 1.001 0.0005532 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6344 0.6322 0.4614 0.2459 0.9852 0.9904 0.6345 0.9694 0.9799 0.4635 ] Network output: [ 0.01012 0.957 0.01989 -0.0002945 0.0001322 1.002 -0.0002219 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01499 Epoch 2564 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02437 0.9857 1 -4.192e-05 1.882e-05 -0.03507 -3.159e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02206 -0.005488 0.01836 0.02331 0.9406 0.9499 0.04494 0.8875 0.9057 0.1159 ] Network output: [ 0.9837 0.04843 -0.013 -6.686e-05 3.002e-05 -0.003009 -5.039e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.1239 0.1355 0.2013 0.9722 0.9872 0.7283 0.9024 0.9679 0.6503 ] Network output: [ -0.01095 0.9584 1.023 -9.058e-05 4.067e-05 0.04029 -6.827e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04792 0.03553 0.05012 0.0297 0.9856 0.9898 0.04897 0.9706 0.9807 0.06201 ] Network output: [ 0.05365 -0.2055 1.082 -0.001264 0.0005673 1.011 -0.0009523 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7235 0.623 0.553 0.3512 0.9754 0.989 0.7264 0.9125 0.9725 0.6453 ] Network output: [ -0.01993 0.121 0.9395 0.001023 -0.0004592 0.9835 0.0007709 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6377 0.6234 0.454 0.259 0.9868 0.9914 0.6381 0.9739 0.9825 0.4657 ] Network output: [ -0.04069 0.1347 0.9481 0.0007359 -0.0003304 1.002 0.0005546 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6345 0.6323 0.4613 0.2457 0.9852 0.9904 0.6346 0.9694 0.9799 0.4634 ] Network output: [ 0.0101 0.9571 0.01987 -0.0002943 0.0001321 1.002 -0.0002218 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01494 Epoch 2565 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02434 0.9857 1 -4.204e-05 1.887e-05 -0.03507 -3.168e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02206 -0.005487 0.01837 0.02329 0.9406 0.9499 0.04493 0.8875 0.9057 0.1159 ] Network output: [ 0.9837 0.04831 -0.01296 -6.723e-05 3.018e-05 -0.003006 -5.067e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.1239 0.1356 0.201 0.9722 0.9872 0.7282 0.9024 0.9679 0.6503 ] Network output: [ -0.01097 0.9585 1.023 -9.041e-05 4.059e-05 0.04027 -6.813e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04791 0.03552 0.05011 0.02966 0.9856 0.9898 0.04896 0.9706 0.9807 0.06199 ] Network output: [ 0.05353 -0.2051 1.082 -0.001266 0.0005684 1.011 -0.0009542 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7235 0.623 0.5531 0.3508 0.9754 0.989 0.7264 0.9125 0.9725 0.6453 ] Network output: [ -0.01987 0.1208 0.9395 0.001024 -0.0004596 0.9836 0.0007715 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6378 0.6235 0.454 0.2588 0.9868 0.9914 0.6382 0.9739 0.9825 0.4657 ] Network output: [ -0.0406 0.1344 0.9481 0.0007377 -0.0003312 1.002 0.0005559 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6346 0.6324 0.4613 0.2455 0.9852 0.9904 0.6347 0.9695 0.9799 0.4634 ] Network output: [ 0.01007 0.9572 0.01985 -0.0002941 0.000132 1.002 -0.0002216 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01489 Epoch 2566 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02431 0.9858 1 -4.216e-05 1.892e-05 -0.03507 -3.177e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02206 -0.005485 0.01837 0.02327 0.9406 0.9499 0.04491 0.8875 0.9057 0.1158 ] Network output: [ 0.9837 0.04818 -0.01291 -6.761e-05 3.035e-05 -0.003004 -5.095e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.1239 0.1357 0.2008 0.9722 0.9872 0.7282 0.9024 0.9679 0.6503 ] Network output: [ -0.01098 0.9586 1.023 -9.023e-05 4.051e-05 0.04024 -6.8e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04791 0.03551 0.05009 0.02962 0.9856 0.9898 0.04896 0.9706 0.9807 0.06197 ] Network output: [ 0.05341 -0.2048 1.082 -0.001269 0.0005695 1.011 -0.0009561 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7234 0.6229 0.5532 0.3504 0.9754 0.989 0.7264 0.9126 0.9725 0.6454 ] Network output: [ -0.0198 0.1205 0.9395 0.001025 -0.00046 0.9837 0.0007721 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6379 0.6236 0.454 0.2585 0.9868 0.9914 0.6383 0.9739 0.9825 0.4657 ] Network output: [ -0.04052 0.1342 0.9481 0.0007395 -0.000332 1.002 0.0005573 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6347 0.6325 0.4613 0.2453 0.9852 0.9904 0.6348 0.9695 0.98 0.4634 ] Network output: [ 0.01004 0.9573 0.01983 -0.0002938 0.0001319 1.002 -0.0002214 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01484 Epoch 2567 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02428 0.9859 1 -4.227e-05 1.898e-05 -0.03507 -3.186e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02206 -0.005484 0.01838 0.02325 0.9407 0.95 0.0449 0.8875 0.9057 0.1158 ] Network output: [ 0.9838 0.04805 -0.01287 -6.799e-05 3.052e-05 -0.003001 -5.124e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.1239 0.1358 0.2005 0.9722 0.9872 0.7281 0.9024 0.9679 0.6504 ] Network output: [ -0.011 0.9587 1.023 -9.005e-05 4.043e-05 0.04022 -6.787e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0479 0.03551 0.05008 0.02958 0.9856 0.9898 0.04895 0.9706 0.9807 0.06195 ] Network output: [ 0.05329 -0.2044 1.082 -0.001271 0.0005707 1.011 -0.000958 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7234 0.6229 0.5532 0.35 0.9754 0.989 0.7263 0.9126 0.9725 0.6454 ] Network output: [ -0.01974 0.1203 0.9395 0.001025 -0.0004603 0.9838 0.0007728 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.638 0.6237 0.454 0.2583 0.9868 0.9914 0.6384 0.9739 0.9825 0.4657 ] Network output: [ -0.04043 0.1339 0.9482 0.0007412 -0.0003328 1.002 0.0005586 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6348 0.6325 0.4612 0.2452 0.9853 0.9904 0.6349 0.9695 0.98 0.4634 ] Network output: [ 0.01002 0.9574 0.01982 -0.0002936 0.0001318 1.002 -0.0002213 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01479 Epoch 2568 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02425 0.9859 1 -4.239e-05 1.903e-05 -0.03507 -3.194e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02205 -0.005483 0.01838 0.02323 0.9407 0.95 0.04489 0.8875 0.9057 0.1158 ] Network output: [ 0.9838 0.04792 -0.01283 -6.837e-05 3.07e-05 -0.002998 -5.153e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.1239 0.1359 0.2003 0.9722 0.9872 0.7281 0.9024 0.9679 0.6504 ] Network output: [ -0.01102 0.9588 1.023 -8.987e-05 4.035e-05 0.04019 -6.773e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04789 0.0355 0.05007 0.02955 0.9856 0.9898 0.04894 0.9706 0.9808 0.06193 ] Network output: [ 0.05318 -0.2041 1.082 -0.001274 0.0005718 1.011 -0.0009599 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7233 0.6229 0.5533 0.3496 0.9754 0.989 0.7263 0.9126 0.9725 0.6454 ] Network output: [ -0.01967 0.12 0.9395 0.001026 -0.0004607 0.9839 0.0007734 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.638 0.6238 0.454 0.2581 0.9868 0.9914 0.6385 0.9739 0.9825 0.4657 ] Network output: [ -0.04034 0.1336 0.9482 0.000743 -0.0003336 1.002 0.0005599 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6349 0.6326 0.4612 0.245 0.9853 0.9904 0.635 0.9695 0.98 0.4633 ] Network output: [ 0.009989 0.9575 0.0198 -0.0002934 0.0001317 1.001 -0.0002211 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01474 Epoch 2569 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02422 0.986 1 -4.25e-05 1.908e-05 -0.03506 -3.203e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02205 -0.005482 0.01839 0.02321 0.9407 0.95 0.04488 0.8876 0.9057 0.1157 ] Network output: [ 0.9839 0.0478 -0.01279 -6.876e-05 3.087e-05 -0.002996 -5.182e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.1239 0.136 0.2001 0.9722 0.9872 0.7281 0.9024 0.9679 0.6504 ] Network output: [ -0.01104 0.9589 1.023 -8.969e-05 4.026e-05 0.04017 -6.759e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04788 0.0355 0.05005 0.02951 0.9856 0.9898 0.04893 0.9706 0.9808 0.0619 ] Network output: [ 0.05306 -0.2037 1.082 -0.001276 0.0005729 1.01 -0.0009617 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7233 0.6228 0.5533 0.3493 0.9754 0.989 0.7262 0.9126 0.9725 0.6455 ] Network output: [ -0.01961 0.1198 0.9396 0.001027 -0.0004611 0.984 0.000774 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6381 0.6239 0.454 0.2579 0.9868 0.9914 0.6386 0.9739 0.9825 0.4657 ] Network output: [ -0.04026 0.1334 0.9482 0.0007448 -0.0003343 1.002 0.0005613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.635 0.6327 0.4612 0.2448 0.9853 0.9904 0.6351 0.9695 0.98 0.4633 ] Network output: [ 0.009963 0.9576 0.01978 -0.0002932 0.0001316 1.001 -0.000221 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01469 Epoch 2570 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02419 0.986 1 -4.261e-05 1.913e-05 -0.03506 -3.211e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02205 -0.00548 0.0184 0.02319 0.9407 0.95 0.04486 0.8876 0.9057 0.1157 ] Network output: [ 0.9839 0.04767 -0.01274 -6.915e-05 3.104e-05 -0.002993 -5.211e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.1239 0.1361 0.1998 0.9722 0.9872 0.728 0.9024 0.9679 0.6505 ] Network output: [ -0.01105 0.959 1.023 -8.95e-05 4.018e-05 0.04015 -6.745e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04788 0.03549 0.05004 0.02947 0.9856 0.9898 0.04892 0.9706 0.9808 0.06188 ] Network output: [ 0.05294 -0.2033 1.082 -0.001279 0.000574 1.01 -0.0009636 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7233 0.6228 0.5534 0.3489 0.9754 0.989 0.7262 0.9126 0.9725 0.6455 ] Network output: [ -0.01954 0.1196 0.9396 0.001028 -0.0004614 0.9841 0.0007746 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6382 0.624 0.454 0.2577 0.9868 0.9914 0.6387 0.9739 0.9825 0.4657 ] Network output: [ -0.04017 0.1331 0.9482 0.0007465 -0.0003351 1.002 0.0005626 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6351 0.6328 0.4612 0.2447 0.9853 0.9904 0.6352 0.9695 0.98 0.4633 ] Network output: [ 0.009936 0.9578 0.01976 -0.000293 0.0001315 1.001 -0.0002208 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01464 Epoch 2571 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02416 0.9861 1 -4.272e-05 1.918e-05 -0.03506 -3.22e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02205 -0.005479 0.0184 0.02317 0.9407 0.95 0.04485 0.8876 0.9057 0.1157 ] Network output: [ 0.9839 0.04754 -0.0127 -6.954e-05 3.122e-05 -0.002991 -5.241e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.1239 0.1362 0.1996 0.9722 0.9872 0.728 0.9024 0.9679 0.6505 ] Network output: [ -0.01107 0.9591 1.023 -8.931e-05 4.01e-05 0.04012 -6.731e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04787 0.03548 0.05002 0.02943 0.9856 0.9898 0.04892 0.9706 0.9808 0.06186 ] Network output: [ 0.05282 -0.203 1.082 -0.001281 0.0005751 1.01 -0.0009655 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7232 0.6228 0.5534 0.3485 0.9754 0.989 0.7262 0.9126 0.9725 0.6455 ] Network output: [ -0.01948 0.1193 0.9396 0.001029 -0.0004618 0.9842 0.0007752 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6383 0.6241 0.4541 0.2575 0.9868 0.9914 0.6388 0.9739 0.9825 0.4657 ] Network output: [ -0.04008 0.1328 0.9482 0.0007483 -0.0003359 1.002 0.0005639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6352 0.6329 0.4611 0.2445 0.9853 0.9904 0.6353 0.9695 0.98 0.4632 ] Network output: [ 0.00991 0.9579 0.01974 -0.0002928 0.0001314 1.001 -0.0002206 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01459 Epoch 2572 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02413 0.9862 1 -4.284e-05 1.923e-05 -0.03506 -3.228e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02205 -0.005478 0.01841 0.02315 0.9407 0.95 0.04484 0.8876 0.9058 0.1156 ] Network output: [ 0.984 0.04741 -0.01266 -6.993e-05 3.14e-05 -0.002988 -5.271e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.1239 0.1363 0.1994 0.9722 0.9872 0.7279 0.9025 0.9679 0.6505 ] Network output: [ -0.01109 0.9592 1.023 -8.912e-05 4.001e-05 0.0401 -6.717e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04786 0.03548 0.05001 0.0294 0.9856 0.9898 0.04891 0.9707 0.9808 0.06184 ] Network output: [ 0.05271 -0.2026 1.082 -0.001284 0.0005762 1.01 -0.0009673 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7232 0.6227 0.5535 0.3481 0.9754 0.989 0.7261 0.9126 0.9725 0.6455 ] Network output: [ -0.01942 0.1191 0.9396 0.001029 -0.0004622 0.9843 0.0007758 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6384 0.6242 0.4541 0.2573 0.9868 0.9914 0.6389 0.9739 0.9825 0.4657 ] Network output: [ -0.04 0.1326 0.9482 0.00075 -0.0003367 1.002 0.0005652 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6353 0.633 0.4611 0.2443 0.9853 0.9905 0.6354 0.9695 0.98 0.4632 ] Network output: [ 0.009884 0.958 0.01973 -0.0002925 0.0001313 1.001 -0.0002205 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01454 Epoch 2573 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0241 0.9862 1 -4.295e-05 1.928e-05 -0.03506 -3.237e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02205 -0.005477 0.01842 0.02313 0.9407 0.95 0.04483 0.8876 0.9058 0.1156 ] Network output: [ 0.984 0.04729 -0.01262 -7.033e-05 3.157e-05 -0.002986 -5.3e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.1239 0.1364 0.1992 0.9722 0.9872 0.7279 0.9025 0.9679 0.6506 ] Network output: [ -0.01111 0.9593 1.023 -8.893e-05 3.993e-05 0.04008 -6.702e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04786 0.03547 0.04999 0.02936 0.9856 0.9898 0.0489 0.9707 0.9808 0.06182 ] Network output: [ 0.05259 -0.2023 1.082 -0.001286 0.0005773 1.01 -0.0009692 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7232 0.6227 0.5536 0.3478 0.9754 0.989 0.7261 0.9126 0.9725 0.6456 ] Network output: [ -0.01935 0.1188 0.9396 0.00103 -0.0004625 0.9844 0.0007764 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6385 0.6242 0.4541 0.2571 0.9868 0.9914 0.639 0.9739 0.9825 0.4657 ] Network output: [ -0.03991 0.1323 0.9483 0.0007517 -0.0003375 1.002 0.0005665 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6354 0.6331 0.4611 0.2441 0.9853 0.9905 0.6355 0.9695 0.98 0.4632 ] Network output: [ 0.009858 0.9581 0.01971 -0.0002923 0.0001312 1.001 -0.0002203 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01449 Epoch 2574 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02407 0.9863 1 -4.306e-05 1.933e-05 -0.03506 -3.245e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02205 -0.005476 0.01842 0.02311 0.9407 0.95 0.04481 0.8876 0.9058 0.1156 ] Network output: [ 0.9841 0.04716 -0.01257 -7.073e-05 3.175e-05 -0.002983 -5.331e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.1239 0.1365 0.1989 0.9722 0.9872 0.7279 0.9025 0.9679 0.6506 ] Network output: [ -0.01112 0.9594 1.022 -8.874e-05 3.984e-05 0.04005 -6.688e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04785 0.03546 0.04998 0.02932 0.9856 0.9898 0.04889 0.9707 0.9808 0.06179 ] Network output: [ 0.05247 -0.2019 1.082 -0.001288 0.0005784 1.01 -0.000971 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7231 0.6227 0.5536 0.3474 0.9754 0.989 0.7261 0.9126 0.9725 0.6456 ] Network output: [ -0.01929 0.1186 0.9396 0.001031 -0.0004629 0.9845 0.000777 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6386 0.6243 0.4541 0.2569 0.9868 0.9914 0.6391 0.9739 0.9825 0.4657 ] Network output: [ -0.03982 0.1321 0.9483 0.0007534 -0.0003382 1.002 0.0005678 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6355 0.6332 0.461 0.244 0.9853 0.9905 0.6355 0.9695 0.98 0.4632 ] Network output: [ 0.009833 0.9582 0.01969 -0.0002921 0.0001311 1.001 -0.0002201 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01445 Epoch 2575 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02404 0.9864 1 -4.316e-05 1.938e-05 -0.03506 -3.253e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02205 -0.005474 0.01843 0.02309 0.9407 0.95 0.0448 0.8876 0.9058 0.1155 ] Network output: [ 0.9841 0.04703 -0.01253 -7.114e-05 3.194e-05 -0.002981 -5.361e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.1239 0.1366 0.1987 0.9722 0.9872 0.7278 0.9025 0.9679 0.6506 ] Network output: [ -0.01114 0.9595 1.022 -8.854e-05 3.975e-05 0.04003 -6.673e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04784 0.03546 0.04996 0.02929 0.9856 0.9898 0.04888 0.9707 0.9808 0.06177 ] Network output: [ 0.05236 -0.2015 1.082 -0.001291 0.0005795 1.01 -0.0009728 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7231 0.6226 0.5537 0.347 0.9754 0.989 0.726 0.9126 0.9725 0.6456 ] Network output: [ -0.01923 0.1184 0.9397 0.001032 -0.0004632 0.9846 0.0007776 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6387 0.6244 0.4541 0.2567 0.9868 0.9914 0.6392 0.9739 0.9825 0.4658 ] Network output: [ -0.03974 0.1318 0.9483 0.0007551 -0.000339 1.002 0.0005691 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6356 0.6333 0.461 0.2438 0.9853 0.9905 0.6356 0.9695 0.98 0.4631 ] Network output: [ 0.009807 0.9583 0.01967 -0.0002918 0.000131 1.001 -0.0002199 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0144 Epoch 2576 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02401 0.9864 1 -4.327e-05 1.943e-05 -0.03506 -3.261e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02205 -0.005473 0.01844 0.02307 0.9407 0.95 0.04479 0.8876 0.9058 0.1155 ] Network output: [ 0.9841 0.0469 -0.01248 -7.154e-05 3.212e-05 -0.002979 -5.392e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.1239 0.1367 0.1985 0.9722 0.9872 0.7278 0.9025 0.9679 0.6507 ] Network output: [ -0.01116 0.9596 1.022 -8.835e-05 3.966e-05 0.04001 -6.658e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04783 0.03545 0.04995 0.02925 0.9856 0.9898 0.04888 0.9707 0.9808 0.06175 ] Network output: [ 0.05224 -0.2012 1.082 -0.001293 0.0005806 1.01 -0.0009746 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7231 0.6226 0.5537 0.3466 0.9755 0.989 0.726 0.9126 0.9725 0.6457 ] Network output: [ -0.01917 0.1181 0.9397 0.001033 -0.0004636 0.9847 0.0007782 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6388 0.6245 0.4541 0.2565 0.9868 0.9914 0.6392 0.9739 0.9825 0.4658 ] Network output: [ -0.03965 0.1315 0.9483 0.0007568 -0.0003398 1.003 0.0005704 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6356 0.6334 0.461 0.2436 0.9853 0.9905 0.6357 0.9695 0.98 0.4631 ] Network output: [ 0.009782 0.9584 0.01966 -0.0002916 0.0001309 1.001 -0.0002198 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01435 Epoch 2577 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02398 0.9865 1 -4.338e-05 1.947e-05 -0.03506 -3.269e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02204 -0.005472 0.01844 0.02305 0.9407 0.95 0.04478 0.8876 0.9058 0.1155 ] Network output: [ 0.9842 0.04678 -0.01244 -7.195e-05 3.23e-05 -0.002976 -5.422e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.1239 0.1368 0.1982 0.9722 0.9872 0.7278 0.9025 0.9679 0.6507 ] Network output: [ -0.01117 0.9597 1.022 -8.815e-05 3.957e-05 0.03998 -6.643e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04783 0.03545 0.04994 0.02921 0.9856 0.9898 0.04887 0.9707 0.9808 0.06173 ] Network output: [ 0.05212 -0.2008 1.082 -0.001296 0.0005817 1.009 -0.0009764 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.723 0.6226 0.5538 0.3463 0.9755 0.9891 0.726 0.9126 0.9725 0.6457 ] Network output: [ -0.01911 0.1179 0.9397 0.001033 -0.0004639 0.9848 0.0007788 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6389 0.6246 0.4541 0.2563 0.9868 0.9914 0.6393 0.9739 0.9825 0.4658 ] Network output: [ -0.03957 0.1313 0.9483 0.0007585 -0.0003405 1.003 0.0005717 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6357 0.6335 0.461 0.2435 0.9853 0.9905 0.6358 0.9695 0.98 0.4631 ] Network output: [ 0.009756 0.9585 0.01964 -0.0002914 0.0001308 1.001 -0.0002196 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0143 Epoch 2578 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02395 0.9866 1 -4.349e-05 1.952e-05 -0.03505 -3.277e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02204 -0.005471 0.01845 0.02303 0.9407 0.95 0.04476 0.8876 0.9058 0.1154 ] Network output: [ 0.9842 0.04665 -0.0124 -7.236e-05 3.249e-05 -0.002974 -5.453e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.1238 0.1369 0.198 0.9722 0.9872 0.7277 0.9025 0.9679 0.6507 ] Network output: [ -0.01119 0.9598 1.022 -8.794e-05 3.948e-05 0.03996 -6.628e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04782 0.03544 0.04992 0.02918 0.9856 0.9898 0.04886 0.9707 0.9808 0.0617 ] Network output: [ 0.05201 -0.2005 1.082 -0.001298 0.0005827 1.009 -0.0009782 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.723 0.6225 0.5538 0.3459 0.9755 0.9891 0.7259 0.9126 0.9725 0.6457 ] Network output: [ -0.01904 0.1177 0.9397 0.001034 -0.0004643 0.9849 0.0007794 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.639 0.6247 0.4541 0.2561 0.9869 0.9914 0.6394 0.9739 0.9825 0.4658 ] Network output: [ -0.03948 0.131 0.9483 0.0007602 -0.0003413 1.003 0.0005729 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6358 0.6336 0.4609 0.2433 0.9853 0.9905 0.6359 0.9695 0.98 0.463 ] Network output: [ 0.009731 0.9586 0.01962 -0.0002911 0.0001307 1.001 -0.0002194 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01425 Epoch 2579 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02392 0.9866 1 -4.359e-05 1.957e-05 -0.03505 -3.285e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02204 -0.00547 0.01846 0.02301 0.9407 0.95 0.04475 0.8876 0.9058 0.1154 ] Network output: [ 0.9843 0.04652 -0.01235 -7.277e-05 3.267e-05 -0.002972 -5.484e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.1238 0.1369 0.1978 0.9722 0.9872 0.7277 0.9025 0.9679 0.6508 ] Network output: [ -0.01121 0.9599 1.022 -8.774e-05 3.939e-05 0.03994 -6.612e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04781 0.03543 0.04991 0.02914 0.9856 0.9898 0.04885 0.9707 0.9808 0.06168 ] Network output: [ 0.05189 -0.2001 1.082 -0.0013 0.0005838 1.009 -0.00098 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.723 0.6225 0.5539 0.3455 0.9755 0.9891 0.7259 0.9126 0.9725 0.6457 ] Network output: [ -0.01898 0.1174 0.9397 0.001035 -0.0004646 0.985 0.0007799 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6391 0.6248 0.4542 0.2559 0.9869 0.9914 0.6395 0.9739 0.9825 0.4658 ] Network output: [ -0.0394 0.1308 0.9484 0.0007619 -0.000342 1.003 0.0005742 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6359 0.6337 0.4609 0.2431 0.9853 0.9905 0.636 0.9695 0.98 0.463 ] Network output: [ 0.009706 0.9587 0.01961 -0.0002909 0.0001306 1.001 -0.0002192 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01421 Epoch 2580 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02389 0.9867 1 -4.37e-05 1.962e-05 -0.03505 -3.293e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02204 -0.005468 0.01847 0.02299 0.9407 0.95 0.04474 0.8876 0.9058 0.1154 ] Network output: [ 0.9843 0.04639 -0.01231 -7.319e-05 3.286e-05 -0.00297 -5.516e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.1238 0.137 0.1976 0.9722 0.9872 0.7277 0.9025 0.9679 0.6508 ] Network output: [ -0.01122 0.96 1.022 -8.753e-05 3.93e-05 0.03991 -6.597e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04781 0.03543 0.04989 0.0291 0.9856 0.9898 0.04884 0.9707 0.9808 0.06166 ] Network output: [ 0.05178 -0.1998 1.082 -0.001303 0.0005849 1.009 -0.0009818 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7229 0.6225 0.554 0.3452 0.9755 0.9891 0.7259 0.9126 0.9725 0.6458 ] Network output: [ -0.01892 0.1172 0.9398 0.001036 -0.0004649 0.9851 0.0007805 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6391 0.6249 0.4542 0.2557 0.9869 0.9914 0.6396 0.9739 0.9825 0.4658 ] Network output: [ -0.03931 0.1305 0.9484 0.0007636 -0.0003428 1.003 0.0005755 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.636 0.6337 0.4609 0.2429 0.9853 0.9905 0.6361 0.9695 0.98 0.463 ] Network output: [ 0.009681 0.9588 0.01959 -0.0002906 0.0001305 1.001 -0.000219 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01416 Epoch 2581 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02386 0.9868 1 -4.38e-05 1.966e-05 -0.03505 -3.301e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02204 -0.005467 0.01847 0.02297 0.9407 0.95 0.04473 0.8877 0.9058 0.1153 ] Network output: [ 0.9843 0.04626 -0.01227 -7.361e-05 3.305e-05 -0.002968 -5.547e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.1238 0.1371 0.1973 0.9722 0.9872 0.7276 0.9025 0.9679 0.6508 ] Network output: [ -0.01124 0.9601 1.022 -8.733e-05 3.92e-05 0.03989 -6.581e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0478 0.03542 0.04988 0.02907 0.9856 0.9898 0.04884 0.9707 0.9808 0.06164 ] Network output: [ 0.05166 -0.1994 1.082 -0.001305 0.0005859 1.009 -0.0009836 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7229 0.6224 0.554 0.3448 0.9755 0.9891 0.7258 0.9126 0.9725 0.6458 ] Network output: [ -0.01886 0.117 0.9398 0.001036 -0.0004653 0.9852 0.0007811 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6392 0.6249 0.4542 0.2555 0.9869 0.9914 0.6397 0.9739 0.9825 0.4658 ] Network output: [ -0.03923 0.1303 0.9484 0.0007652 -0.0003435 1.003 0.0005767 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6361 0.6338 0.4608 0.2428 0.9853 0.9905 0.6362 0.9695 0.98 0.4629 ] Network output: [ 0.009656 0.9589 0.01957 -0.0002904 0.0001304 1.001 -0.0002188 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01411 Epoch 2582 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02383 0.9868 1 -4.39e-05 1.971e-05 -0.03505 -3.309e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02204 -0.005466 0.01848 0.02295 0.9407 0.95 0.04471 0.8877 0.9058 0.1153 ] Network output: [ 0.9844 0.04614 -0.01222 -7.403e-05 3.324e-05 -0.002966 -5.579e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.1238 0.1372 0.1971 0.9722 0.9872 0.7276 0.9025 0.9679 0.6509 ] Network output: [ -0.01126 0.9602 1.022 -8.712e-05 3.911e-05 0.03987 -6.565e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04779 0.03541 0.04986 0.02903 0.9856 0.9898 0.04883 0.9707 0.9808 0.06161 ] Network output: [ 0.05155 -0.1991 1.082 -0.001307 0.0005869 1.009 -0.0009853 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7229 0.6224 0.5541 0.3444 0.9755 0.9891 0.7258 0.9126 0.9726 0.6458 ] Network output: [ -0.0188 0.1167 0.9398 0.001037 -0.0004656 0.9853 0.0007816 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6393 0.625 0.4542 0.2553 0.9869 0.9914 0.6398 0.974 0.9825 0.4658 ] Network output: [ -0.03915 0.13 0.9484 0.0007669 -0.0003443 1.003 0.0005779 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6362 0.6339 0.4608 0.2426 0.9853 0.9905 0.6363 0.9696 0.98 0.4629 ] Network output: [ 0.009632 0.959 0.01955 -0.0002901 0.0001303 1.001 -0.0002187 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01406 Epoch 2583 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0238 0.9869 1 -4.401e-05 1.976e-05 -0.03505 -3.317e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02204 -0.005465 0.01849 0.02293 0.9408 0.95 0.0447 0.8877 0.9058 0.1153 ] Network output: [ 0.9844 0.04601 -0.01218 -7.446e-05 3.343e-05 -0.002964 -5.611e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6395 0.1238 0.1373 0.1969 0.9722 0.9872 0.7276 0.9025 0.9679 0.6509 ] Network output: [ -0.01127 0.9602 1.022 -8.69e-05 3.901e-05 0.03985 -6.549e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04778 0.03541 0.04985 0.029 0.9856 0.9898 0.04882 0.9707 0.9808 0.06159 ] Network output: [ 0.05144 -0.1987 1.082 -0.00131 0.000588 1.009 -0.0009871 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7229 0.6224 0.5541 0.3441 0.9755 0.9891 0.7258 0.9127 0.9726 0.6458 ] Network output: [ -0.01874 0.1165 0.9398 0.001038 -0.0004659 0.9854 0.0007822 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6394 0.6251 0.4542 0.2551 0.9869 0.9914 0.6399 0.974 0.9825 0.4658 ] Network output: [ -0.03906 0.1298 0.9484 0.0007685 -0.000345 1.003 0.0005792 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6363 0.634 0.4608 0.2424 0.9853 0.9905 0.6364 0.9696 0.98 0.4629 ] Network output: [ 0.009607 0.9591 0.01954 -0.0002899 0.0001301 1.001 -0.0002185 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01402 Epoch 2584 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02377 0.9869 1 -4.411e-05 1.98e-05 -0.03504 -3.324e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02204 -0.005464 0.01849 0.02292 0.9408 0.95 0.04469 0.8877 0.9059 0.1152 ] Network output: [ 0.9845 0.04588 -0.01213 -7.488e-05 3.362e-05 -0.002962 -5.643e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6396 0.1238 0.1374 0.1967 0.9722 0.9872 0.7275 0.9025 0.9679 0.6509 ] Network output: [ -0.01129 0.9603 1.022 -8.669e-05 3.892e-05 0.03982 -6.533e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04778 0.0354 0.04983 0.02896 0.9857 0.9898 0.04881 0.9707 0.9808 0.06157 ] Network output: [ 0.05132 -0.1984 1.082 -0.001312 0.000589 1.009 -0.0009888 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7228 0.6223 0.5542 0.3437 0.9755 0.9891 0.7257 0.9127 0.9726 0.6458 ] Network output: [ -0.01868 0.1163 0.9398 0.001039 -0.0004663 0.9855 0.0007827 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6395 0.6252 0.4542 0.2549 0.9869 0.9914 0.64 0.974 0.9825 0.4658 ] Network output: [ -0.03898 0.1295 0.9484 0.0007702 -0.0003458 1.003 0.0005804 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6364 0.6341 0.4607 0.2423 0.9853 0.9905 0.6365 0.9696 0.98 0.4628 ] Network output: [ 0.009583 0.9592 0.01952 -0.0002896 0.00013 1.001 -0.0002183 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01397 Epoch 2585 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02374 0.987 1 -4.421e-05 1.985e-05 -0.03504 -3.332e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02204 -0.005462 0.0185 0.0229 0.9408 0.95 0.04468 0.8877 0.9059 0.1152 ] Network output: [ 0.9845 0.04575 -0.01209 -7.531e-05 3.381e-05 -0.00296 -5.676e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6396 0.1238 0.1375 0.1965 0.9722 0.9872 0.7275 0.9025 0.9679 0.6509 ] Network output: [ -0.0113 0.9604 1.022 -8.647e-05 3.882e-05 0.0398 -6.517e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04777 0.0354 0.04982 0.02893 0.9857 0.9898 0.0488 0.9707 0.9808 0.06155 ] Network output: [ 0.05121 -0.198 1.082 -0.001314 0.0005901 1.008 -0.0009905 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7228 0.6223 0.5542 0.3434 0.9755 0.9891 0.7257 0.9127 0.9726 0.6459 ] Network output: [ -0.01862 0.116 0.9399 0.001039 -0.0004666 0.9856 0.0007833 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6396 0.6253 0.4542 0.2547 0.9869 0.9914 0.64 0.974 0.9825 0.4658 ] Network output: [ -0.0389 0.1293 0.9484 0.0007718 -0.0003465 1.003 0.0005816 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6365 0.6342 0.4607 0.2421 0.9853 0.9905 0.6366 0.9696 0.98 0.4628 ] Network output: [ 0.009559 0.9593 0.0195 -0.0002894 0.0001299 1.001 -0.0002181 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01392 Epoch 2586 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02371 0.9871 1 -4.431e-05 1.989e-05 -0.03504 -3.339e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02204 -0.005461 0.01851 0.02288 0.9408 0.95 0.04467 0.8877 0.9059 0.1151 ] Network output: [ 0.9845 0.04562 -0.01204 -7.574e-05 3.4e-05 -0.002958 -5.708e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6396 0.1238 0.1376 0.1962 0.9722 0.9872 0.7275 0.9025 0.9679 0.651 ] Network output: [ -0.01132 0.9605 1.022 -8.626e-05 3.872e-05 0.03978 -6.501e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04776 0.03539 0.0498 0.02889 0.9857 0.9898 0.0488 0.9707 0.9808 0.06152 ] Network output: [ 0.0511 -0.1977 1.082 -0.001317 0.0005911 1.008 -0.0009922 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7228 0.6223 0.5543 0.343 0.9755 0.9891 0.7257 0.9127 0.9726 0.6459 ] Network output: [ -0.01856 0.1158 0.9399 0.00104 -0.0004669 0.9857 0.0007838 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6397 0.6254 0.4542 0.2546 0.9869 0.9914 0.6401 0.974 0.9825 0.4658 ] Network output: [ -0.03881 0.129 0.9485 0.0007734 -0.0003472 1.003 0.0005829 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6366 0.6343 0.4607 0.2419 0.9853 0.9905 0.6366 0.9696 0.98 0.4628 ] Network output: [ 0.009535 0.9594 0.01949 -0.0002891 0.0001298 1.001 -0.0002179 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01388 Epoch 2587 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02368 0.9871 1 -4.441e-05 1.994e-05 -0.03504 -3.347e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02203 -0.00546 0.01852 0.02286 0.9408 0.9501 0.04465 0.8877 0.9059 0.1151 ] Network output: [ 0.9846 0.04549 -0.012 -7.618e-05 3.42e-05 -0.002956 -5.741e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6396 0.1238 0.1377 0.196 0.9722 0.9872 0.7274 0.9026 0.9679 0.651 ] Network output: [ -0.01134 0.9606 1.022 -8.604e-05 3.863e-05 0.03975 -6.484e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04775 0.03538 0.04979 0.02886 0.9857 0.9898 0.04879 0.9707 0.9808 0.0615 ] Network output: [ 0.05098 -0.1973 1.082 -0.001319 0.0005921 1.008 -0.0009939 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7227 0.6222 0.5543 0.3427 0.9755 0.9891 0.7256 0.9127 0.9726 0.6459 ] Network output: [ -0.01851 0.1156 0.9399 0.001041 -0.0004672 0.9858 0.0007843 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6398 0.6255 0.4542 0.2544 0.9869 0.9914 0.6402 0.974 0.9825 0.4658 ] Network output: [ -0.03873 0.1288 0.9485 0.000775 -0.0003479 1.003 0.0005841 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6367 0.6344 0.4606 0.2418 0.9853 0.9905 0.6367 0.9696 0.98 0.4627 ] Network output: [ 0.009511 0.9595 0.01947 -0.0002889 0.0001297 1.001 -0.0002177 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01383 Epoch 2588 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02365 0.9872 1 -4.451e-05 1.998e-05 -0.03504 -3.354e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02203 -0.005459 0.01852 0.02284 0.9408 0.9501 0.04464 0.8877 0.9059 0.1151 ] Network output: [ 0.9846 0.04537 -0.01195 -7.662e-05 3.44e-05 -0.002955 -5.774e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6396 0.1237 0.1378 0.1958 0.9722 0.9872 0.7274 0.9026 0.9679 0.651 ] Network output: [ -0.01135 0.9607 1.022 -8.582e-05 3.853e-05 0.03973 -6.467e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04775 0.03538 0.04977 0.02882 0.9857 0.9898 0.04878 0.9707 0.9808 0.06148 ] Network output: [ 0.05087 -0.197 1.082 -0.001321 0.0005931 1.008 -0.0009956 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7227 0.6222 0.5544 0.3423 0.9755 0.9891 0.7256 0.9127 0.9726 0.6459 ] Network output: [ -0.01845 0.1154 0.9399 0.001041 -0.0004675 0.9859 0.0007848 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6399 0.6255 0.4542 0.2542 0.9869 0.9914 0.6403 0.974 0.9825 0.4658 ] Network output: [ -0.03865 0.1285 0.9485 0.0007766 -0.0003487 1.003 0.0005853 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6367 0.6345 0.4606 0.2416 0.9853 0.9905 0.6368 0.9696 0.98 0.4627 ] Network output: [ 0.009487 0.9596 0.01945 -0.0002886 0.0001296 1.001 -0.0002175 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01379 Epoch 2589 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02362 0.9873 1 -4.461e-05 2.003e-05 -0.03503 -3.362e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02203 -0.005458 0.01853 0.02282 0.9408 0.9501 0.04463 0.8877 0.9059 0.115 ] Network output: [ 0.9847 0.04524 -0.01191 -7.705e-05 3.459e-05 -0.002953 -5.807e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6396 0.1237 0.1379 0.1956 0.9722 0.9872 0.7274 0.9026 0.9679 0.651 ] Network output: [ -0.01137 0.9608 1.022 -8.559e-05 3.843e-05 0.03971 -6.45e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04774 0.03537 0.04976 0.02879 0.9857 0.9898 0.04877 0.9707 0.9808 0.06145 ] Network output: [ 0.05076 -0.1966 1.082 -0.001323 0.0005941 1.008 -0.0009973 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7227 0.6222 0.5544 0.3419 0.9755 0.9891 0.7256 0.9127 0.9726 0.6459 ] Network output: [ -0.01839 0.1151 0.9399 0.001042 -0.0004678 0.986 0.0007854 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6399 0.6256 0.4542 0.254 0.9869 0.9914 0.6404 0.974 0.9825 0.4658 ] Network output: [ -0.03857 0.1283 0.9485 0.0007782 -0.0003494 1.004 0.0005865 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6368 0.6346 0.4606 0.2414 0.9853 0.9905 0.6369 0.9696 0.98 0.4627 ] Network output: [ 0.009463 0.9597 0.01944 -0.0002883 0.0001294 1.001 -0.0002173 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01374 Epoch 2590 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02359 0.9873 1 -4.471e-05 2.007e-05 -0.03503 -3.369e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02203 -0.005456 0.01854 0.0228 0.9408 0.9501 0.04462 0.8877 0.9059 0.115 ] Network output: [ 0.9847 0.04511 -0.01186 -7.75e-05 3.479e-05 -0.002951 -5.84e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6396 0.1237 0.138 0.1954 0.9722 0.9872 0.7273 0.9026 0.968 0.6511 ] Network output: [ -0.01138 0.9609 1.022 -8.537e-05 3.832e-05 0.03969 -6.433e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04773 0.03536 0.04974 0.02875 0.9857 0.9898 0.04876 0.9707 0.9808 0.06143 ] Network output: [ 0.05065 -0.1963 1.082 -0.001326 0.0005951 1.008 -0.000999 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7227 0.6221 0.5545 0.3416 0.9755 0.9891 0.7256 0.9127 0.9726 0.646 ] Network output: [ -0.01833 0.1149 0.94 0.001043 -0.0004681 0.986 0.0007859 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.64 0.6257 0.4543 0.2538 0.9869 0.9914 0.6405 0.974 0.9825 0.4658 ] Network output: [ -0.03848 0.128 0.9485 0.0007798 -0.0003501 1.004 0.0005877 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6369 0.6346 0.4605 0.2413 0.9853 0.9905 0.637 0.9696 0.98 0.4626 ] Network output: [ 0.00944 0.9598 0.01942 -0.0002881 0.0001293 1.001 -0.0002171 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01369 Epoch 2591 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02356 0.9874 1 -4.48e-05 2.011e-05 -0.03503 -3.377e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02203 -0.005455 0.01855 0.02279 0.9408 0.9501 0.0446 0.8877 0.9059 0.115 ] Network output: [ 0.9847 0.04498 -0.01182 -7.794e-05 3.499e-05 -0.00295 -5.874e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6396 0.1237 0.1381 0.1952 0.9723 0.9872 0.7273 0.9026 0.968 0.6511 ] Network output: [ -0.0114 0.961 1.022 -8.514e-05 3.822e-05 0.03966 -6.416e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04772 0.03536 0.04973 0.02872 0.9857 0.9898 0.04875 0.9707 0.9808 0.06141 ] Network output: [ 0.05054 -0.1959 1.082 -0.001328 0.0005961 1.008 -0.001001 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7226 0.6221 0.5545 0.3412 0.9755 0.9891 0.7255 0.9127 0.9726 0.646 ] Network output: [ -0.01827 0.1147 0.94 0.001043 -0.0004684 0.9861 0.0007864 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6401 0.6258 0.4543 0.2536 0.9869 0.9914 0.6406 0.974 0.9825 0.4658 ] Network output: [ -0.0384 0.1278 0.9485 0.0007814 -0.0003508 1.004 0.0005889 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.637 0.6347 0.4605 0.2411 0.9853 0.9905 0.6371 0.9696 0.98 0.4626 ] Network output: [ 0.009416 0.9599 0.0194 -0.0002878 0.0001292 1.001 -0.0002169 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01365 Epoch 2592 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02353 0.9875 1 -4.49e-05 2.016e-05 -0.03503 -3.384e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02203 -0.005454 0.01855 0.02277 0.9408 0.9501 0.04459 0.8877 0.9059 0.1149 ] Network output: [ 0.9848 0.04485 -0.01177 -7.839e-05 3.519e-05 -0.002948 -5.908e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6396 0.1237 0.1382 0.195 0.9723 0.9872 0.7273 0.9026 0.968 0.6511 ] Network output: [ -0.01141 0.9611 1.022 -8.491e-05 3.812e-05 0.03964 -6.399e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04772 0.03535 0.04971 0.02868 0.9857 0.9898 0.04875 0.9707 0.9808 0.06138 ] Network output: [ 0.05043 -0.1956 1.082 -0.00133 0.0005971 1.008 -0.001002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7226 0.6221 0.5546 0.3409 0.9755 0.9891 0.7255 0.9127 0.9726 0.646 ] Network output: [ -0.01822 0.1144 0.94 0.001044 -0.0004687 0.9862 0.0007869 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6402 0.6259 0.4543 0.2534 0.9869 0.9914 0.6407 0.974 0.9825 0.4658 ] Network output: [ -0.03832 0.1275 0.9485 0.000783 -0.0003515 1.004 0.0005901 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6371 0.6348 0.4605 0.241 0.9853 0.9905 0.6372 0.9696 0.98 0.4626 ] Network output: [ 0.009393 0.9599 0.01939 -0.0002875 0.0001291 1.001 -0.0002167 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01361 Epoch 2593 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0235 0.9875 1 -4.5e-05 2.02e-05 -0.03502 -3.391e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02203 -0.005453 0.01856 0.02275 0.9408 0.9501 0.04458 0.8877 0.9059 0.1149 ] Network output: [ 0.9848 0.04472 -0.01173 -7.884e-05 3.539e-05 -0.002947 -5.941e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6396 0.1237 0.1383 0.1948 0.9723 0.9872 0.7272 0.9026 0.968 0.6511 ] Network output: [ -0.01143 0.9612 1.022 -8.468e-05 3.802e-05 0.03962 -6.382e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04771 0.03534 0.0497 0.02865 0.9857 0.9898 0.04874 0.9707 0.9808 0.06136 ] Network output: [ 0.05032 -0.1952 1.082 -0.001332 0.0005981 1.008 -0.001004 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7226 0.622 0.5546 0.3406 0.9755 0.9891 0.7255 0.9127 0.9726 0.646 ] Network output: [ -0.01816 0.1142 0.94 0.001045 -0.000469 0.9863 0.0007874 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6403 0.626 0.4543 0.2532 0.9869 0.9914 0.6407 0.974 0.9825 0.4658 ] Network output: [ -0.03824 0.1273 0.9485 0.0007845 -0.0003522 1.004 0.0005912 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6372 0.6349 0.4604 0.2408 0.9853 0.9905 0.6373 0.9696 0.98 0.4625 ] Network output: [ 0.00937 0.96 0.01937 -0.0002873 0.000129 1.001 -0.0002165 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01356 Epoch 2594 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02347 0.9876 1 -4.509e-05 2.024e-05 -0.03502 -3.398e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02203 -0.005452 0.01857 0.02273 0.9408 0.9501 0.04457 0.8878 0.9059 0.1149 ] Network output: [ 0.9849 0.0446 -0.01168 -7.929e-05 3.56e-05 -0.002946 -5.975e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6396 0.1237 0.1384 0.1945 0.9723 0.9872 0.7272 0.9026 0.968 0.6512 ] Network output: [ -0.01144 0.9613 1.022 -8.445e-05 3.791e-05 0.0396 -6.364e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0477 0.03534 0.04968 0.02861 0.9857 0.9899 0.04873 0.9707 0.9808 0.06134 ] Network output: [ 0.05021 -0.1949 1.082 -0.001334 0.000599 1.007 -0.001006 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7226 0.622 0.5547 0.3402 0.9755 0.9891 0.7254 0.9127 0.9726 0.646 ] Network output: [ -0.01811 0.114 0.9401 0.001045 -0.0004693 0.9864 0.0007879 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6404 0.626 0.4543 0.253 0.9869 0.9914 0.6408 0.974 0.9825 0.4658 ] Network output: [ -0.03816 0.1271 0.9486 0.0007861 -0.0003529 1.004 0.0005924 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6373 0.635 0.4604 0.2406 0.9853 0.9905 0.6374 0.9696 0.98 0.4625 ] Network output: [ 0.009347 0.9601 0.01935 -0.000287 0.0001288 1.001 -0.0002163 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01352 Epoch 2595 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02345 0.9876 1 -4.519e-05 2.029e-05 -0.03502 -3.405e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02203 -0.00545 0.01858 0.02271 0.9408 0.9501 0.04456 0.8878 0.9059 0.1148 ] Network output: [ 0.9849 0.04447 -0.01164 -7.974e-05 3.58e-05 -0.002944 -6.01e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6397 0.1237 0.1385 0.1943 0.9723 0.9872 0.7272 0.9026 0.968 0.6512 ] Network output: [ -0.01146 0.9614 1.022 -8.421e-05 3.781e-05 0.03958 -6.347e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04769 0.03533 0.04967 0.02858 0.9857 0.9899 0.04872 0.9707 0.9808 0.06132 ] Network output: [ 0.0501 -0.1946 1.082 -0.001337 0.0006 1.007 -0.001007 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7225 0.622 0.5547 0.3399 0.9755 0.9891 0.7254 0.9127 0.9726 0.646 ] Network output: [ -0.01805 0.1138 0.9401 0.001046 -0.0004696 0.9865 0.0007884 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6405 0.6261 0.4543 0.2528 0.9869 0.9914 0.6409 0.974 0.9825 0.4658 ] Network output: [ -0.03808 0.1268 0.9486 0.0007876 -0.0003536 1.004 0.0005936 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6374 0.6351 0.4604 0.2405 0.9853 0.9905 0.6374 0.9696 0.9801 0.4625 ] Network output: [ 0.009324 0.9602 0.01934 -0.0002867 0.0001287 1.001 -0.0002161 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01347 Epoch 2596 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02342 0.9877 1 -4.528e-05 2.033e-05 -0.03501 -3.412e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02203 -0.005449 0.01858 0.0227 0.9408 0.9501 0.04454 0.8878 0.9059 0.1148 ] Network output: [ 0.9849 0.04434 -0.01159 -8.02e-05 3.6e-05 -0.002943 -6.044e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6397 0.1236 0.1386 0.1941 0.9723 0.9872 0.7271 0.9026 0.968 0.6512 ] Network output: [ -0.01147 0.9615 1.022 -8.398e-05 3.77e-05 0.03955 -6.329e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04769 0.03532 0.04965 0.02854 0.9857 0.9899 0.04871 0.9707 0.9808 0.06129 ] Network output: [ 0.04999 -0.1942 1.082 -0.001339 0.000601 1.007 -0.001009 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7225 0.6219 0.5548 0.3395 0.9755 0.9891 0.7254 0.9127 0.9726 0.6461 ] Network output: [ -0.01799 0.1135 0.9401 0.001047 -0.0004699 0.9866 0.0007889 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6406 0.6262 0.4543 0.2526 0.9869 0.9914 0.641 0.974 0.9825 0.4658 ] Network output: [ -0.038 0.1266 0.9486 0.0007892 -0.0003543 1.004 0.0005947 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6375 0.6352 0.4603 0.2403 0.9853 0.9905 0.6375 0.9696 0.9801 0.4624 ] Network output: [ 0.009302 0.9603 0.01932 -0.0002864 0.0001286 1.001 -0.0002159 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01343 Epoch 2597 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02339 0.9878 1 -4.537e-05 2.037e-05 -0.03501 -3.42e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02202 -0.005448 0.01859 0.02268 0.9408 0.9501 0.04453 0.8878 0.9059 0.1148 ] Network output: [ 0.985 0.04421 -0.01155 -8.066e-05 3.621e-05 -0.002942 -6.079e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6397 0.1236 0.1387 0.1939 0.9723 0.9872 0.7271 0.9026 0.968 0.6512 ] Network output: [ -0.01149 0.9616 1.022 -8.374e-05 3.759e-05 0.03953 -6.311e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04768 0.03532 0.04964 0.02851 0.9857 0.9899 0.04871 0.9707 0.9808 0.06127 ] Network output: [ 0.04988 -0.1939 1.082 -0.001341 0.0006019 1.007 -0.00101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7225 0.6219 0.5548 0.3392 0.9755 0.9891 0.7254 0.9127 0.9726 0.6461 ] Network output: [ -0.01794 0.1133 0.9401 0.001047 -0.0004702 0.9867 0.0007893 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6406 0.6263 0.4543 0.2525 0.9869 0.9914 0.6411 0.974 0.9825 0.4658 ] Network output: [ -0.03792 0.1264 0.9486 0.0007907 -0.000355 1.004 0.0005959 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6375 0.6353 0.4603 0.2401 0.9853 0.9905 0.6376 0.9696 0.9801 0.4624 ] Network output: [ 0.009279 0.9604 0.0193 -0.0002862 0.0001285 1.001 -0.0002157 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01338 Epoch 2598 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02336 0.9878 1 -4.547e-05 2.041e-05 -0.03501 -3.427e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02202 -0.005447 0.0186 0.02266 0.9408 0.9501 0.04452 0.8878 0.906 0.1147 ] Network output: [ 0.985 0.04408 -0.0115 -8.112e-05 3.642e-05 -0.002941 -6.113e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6397 0.1236 0.1388 0.1937 0.9723 0.9872 0.7271 0.9026 0.968 0.6512 ] Network output: [ -0.0115 0.9617 1.021 -8.35e-05 3.749e-05 0.03951 -6.293e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04767 0.03531 0.04962 0.02848 0.9857 0.9899 0.0487 0.9707 0.9808 0.06124 ] Network output: [ 0.04977 -0.1935 1.082 -0.001343 0.0006029 1.007 -0.001012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7225 0.6219 0.5549 0.3388 0.9755 0.9891 0.7253 0.9127 0.9726 0.6461 ] Network output: [ -0.01788 0.1131 0.9402 0.001048 -0.0004705 0.9868 0.0007898 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6407 0.6264 0.4543 0.2523 0.9869 0.9914 0.6412 0.974 0.9825 0.4658 ] Network output: [ -0.03784 0.1261 0.9486 0.0007922 -0.0003556 1.004 0.000597 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6376 0.6354 0.4603 0.24 0.9853 0.9905 0.6377 0.9696 0.9801 0.4624 ] Network output: [ 0.009257 0.9605 0.01929 -0.0002859 0.0001283 1.001 -0.0002154 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01334 Epoch 2599 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02333 0.9879 1 -4.556e-05 2.045e-05 -0.035 -3.433e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02202 -0.005446 0.01861 0.02264 0.9408 0.9501 0.04451 0.8878 0.906 0.1147 ] Network output: [ 0.9851 0.04396 -0.01145 -8.158e-05 3.663e-05 -0.00294 -6.148e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6397 0.1236 0.139 0.1935 0.9723 0.9873 0.7271 0.9026 0.968 0.6513 ] Network output: [ -0.01152 0.9618 1.021 -8.326e-05 3.738e-05 0.03949 -6.275e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04766 0.0353 0.04961 0.02844 0.9857 0.9899 0.04869 0.9708 0.9808 0.06122 ] Network output: [ 0.04966 -0.1932 1.082 -0.001345 0.0006038 1.007 -0.001014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7224 0.6218 0.5549 0.3385 0.9755 0.9891 0.7253 0.9127 0.9726 0.6461 ] Network output: [ -0.01783 0.1129 0.9402 0.001049 -0.0004708 0.9869 0.0007903 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6408 0.6265 0.4543 0.2521 0.9869 0.9914 0.6413 0.974 0.9825 0.4658 ] Network output: [ -0.03776 0.1259 0.9486 0.0007937 -0.0003563 1.004 0.0005982 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6377 0.6354 0.4602 0.2398 0.9853 0.9905 0.6378 0.9696 0.9801 0.4623 ] Network output: [ 0.009235 0.9606 0.01927 -0.0002856 0.0001282 1.001 -0.0002152 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0133 Epoch 2600 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0233 0.988 1 -4.565e-05 2.049e-05 -0.035 -3.44e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02202 -0.005445 0.01862 0.02263 0.9409 0.9501 0.0445 0.8878 0.906 0.1147 ] Network output: [ 0.9851 0.04383 -0.01141 -8.205e-05 3.683e-05 -0.002939 -6.183e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6397 0.1236 0.1391 0.1933 0.9723 0.9873 0.727 0.9026 0.968 0.6513 ] Network output: [ -0.01153 0.9619 1.021 -8.301e-05 3.727e-05 0.03947 -6.256e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04766 0.0353 0.04959 0.02841 0.9857 0.9899 0.04868 0.9708 0.9808 0.0612 ] Network output: [ 0.04955 -0.1929 1.082 -0.001347 0.0006048 1.007 -0.001015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7224 0.6218 0.555 0.3382 0.9755 0.9891 0.7253 0.9127 0.9726 0.6461 ] Network output: [ -0.01777 0.1127 0.9402 0.001049 -0.000471 0.9869 0.0007907 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6409 0.6265 0.4543 0.2519 0.9869 0.9914 0.6413 0.974 0.9825 0.4658 ] Network output: [ -0.03768 0.1256 0.9486 0.0007952 -0.000357 1.004 0.0005993 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6378 0.6355 0.4602 0.2397 0.9854 0.9905 0.6379 0.9697 0.9801 0.4623 ] Network output: [ 0.009213 0.9607 0.01926 -0.0002853 0.0001281 1 -0.000215 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01325 Epoch 2601 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02327 0.988 1 -4.574e-05 2.053e-05 -0.035 -3.447e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02202 -0.005443 0.01863 0.02261 0.9409 0.9501 0.04448 0.8878 0.906 0.1146 ] Network output: [ 0.9851 0.0437 -0.01136 -8.252e-05 3.704e-05 -0.002938 -6.219e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6397 0.1236 0.1392 0.1931 0.9723 0.9873 0.727 0.9026 0.968 0.6513 ] Network output: [ -0.01155 0.962 1.021 -8.277e-05 3.716e-05 0.03944 -6.238e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04765 0.03529 0.04958 0.02838 0.9857 0.9899 0.04867 0.9708 0.9808 0.06117 ] Network output: [ 0.04945 -0.1925 1.082 -0.001349 0.0006057 1.007 -0.001017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7224 0.6218 0.555 0.3378 0.9755 0.9891 0.7253 0.9127 0.9726 0.6461 ] Network output: [ -0.01772 0.1124 0.9402 0.00105 -0.0004713 0.987 0.0007912 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.641 0.6266 0.4543 0.2517 0.9869 0.9914 0.6414 0.974 0.9825 0.4658 ] Network output: [ -0.0376 0.1254 0.9486 0.0007967 -0.0003577 1.004 0.0006004 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6379 0.6356 0.4602 0.2395 0.9854 0.9905 0.638 0.9697 0.9801 0.4622 ] Network output: [ 0.009191 0.9608 0.01924 -0.000285 0.000128 1 -0.0002148 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01321 Epoch 2602 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02324 0.9881 1 -4.583e-05 2.058e-05 -0.03499 -3.454e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02202 -0.005442 0.01863 0.02259 0.9409 0.9501 0.04447 0.8878 0.906 0.1146 ] Network output: [ 0.9852 0.04357 -0.01132 -8.299e-05 3.726e-05 -0.002938 -6.254e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6397 0.1236 0.1393 0.1929 0.9723 0.9873 0.727 0.9026 0.968 0.6513 ] Network output: [ -0.01156 0.9621 1.021 -8.252e-05 3.705e-05 0.03942 -6.219e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04764 0.03528 0.04956 0.02834 0.9857 0.9899 0.04866 0.9708 0.9808 0.06115 ] Network output: [ 0.04934 -0.1922 1.082 -0.001351 0.0006066 1.006 -0.001018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7224 0.6217 0.5551 0.3375 0.9755 0.9891 0.7252 0.9128 0.9726 0.6461 ] Network output: [ -0.01767 0.1122 0.9403 0.00105 -0.0004716 0.9871 0.0007917 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6411 0.6267 0.4543 0.2515 0.9869 0.9914 0.6415 0.974 0.9825 0.4658 ] Network output: [ -0.03753 0.1252 0.9486 0.0007982 -0.0003583 1.004 0.0006015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.638 0.6357 0.4601 0.2393 0.9854 0.9905 0.6381 0.9697 0.9801 0.4622 ] Network output: [ 0.009169 0.9609 0.01922 -0.0002847 0.0001278 1 -0.0002146 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01317 Epoch 2603 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02321 0.9881 1 -4.592e-05 2.062e-05 -0.03499 -3.461e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02202 -0.005441 0.01864 0.02257 0.9409 0.9501 0.04446 0.8878 0.906 0.1145 ] Network output: [ 0.9852 0.04344 -0.01127 -8.346e-05 3.747e-05 -0.002937 -6.29e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6398 0.1235 0.1394 0.1927 0.9723 0.9873 0.727 0.9027 0.968 0.6513 ] Network output: [ -0.01158 0.9622 1.021 -8.227e-05 3.694e-05 0.0394 -6.2e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04763 0.03528 0.04955 0.02831 0.9857 0.9899 0.04866 0.9708 0.9808 0.06113 ] Network output: [ 0.04923 -0.1919 1.082 -0.001353 0.0006076 1.006 -0.00102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7223 0.6217 0.5551 0.3372 0.9755 0.9891 0.7252 0.9128 0.9726 0.6461 ] Network output: [ -0.01761 0.112 0.9403 0.001051 -0.0004719 0.9872 0.0007921 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6411 0.6268 0.4543 0.2514 0.9869 0.9914 0.6416 0.974 0.9825 0.4658 ] Network output: [ -0.03745 0.125 0.9487 0.0007997 -0.000359 1.005 0.0006027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6381 0.6358 0.4601 0.2392 0.9854 0.9905 0.6381 0.9697 0.9801 0.4622 ] Network output: [ 0.009147 0.9609 0.01921 -0.0002844 0.0001277 1 -0.0002144 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01313 Epoch 2604 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02318 0.9882 1 -4.601e-05 2.066e-05 -0.03499 -3.467e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02202 -0.00544 0.01865 0.02256 0.9409 0.9501 0.04445 0.8878 0.906 0.1145 ] Network output: [ 0.9853 0.04332 -0.01122 -8.393e-05 3.768e-05 -0.002936 -6.325e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6398 0.1235 0.1395 0.1925 0.9723 0.9873 0.7269 0.9027 0.968 0.6514 ] Network output: [ -0.01159 0.9623 1.021 -8.202e-05 3.682e-05 0.03938 -6.182e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04763 0.03527 0.04953 0.02828 0.9857 0.9899 0.04865 0.9708 0.9808 0.0611 ] Network output: [ 0.04913 -0.1915 1.082 -0.001355 0.0006085 1.006 -0.001021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7223 0.6217 0.5552 0.3368 0.9755 0.9891 0.7252 0.9128 0.9726 0.6461 ] Network output: [ -0.01756 0.1118 0.9403 0.001052 -0.0004721 0.9873 0.0007925 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6412 0.6269 0.4543 0.2512 0.9869 0.9914 0.6417 0.974 0.9825 0.4658 ] Network output: [ -0.03737 0.1247 0.9487 0.0008011 -0.0003597 1.005 0.0006038 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6381 0.6359 0.4601 0.239 0.9854 0.9905 0.6382 0.9697 0.9801 0.4621 ] Network output: [ 0.009126 0.961 0.01919 -0.0002841 0.0001276 1 -0.0002141 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01308 Epoch 2605 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02315 0.9883 1 -4.61e-05 2.07e-05 -0.03498 -3.474e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02202 -0.005439 0.01866 0.02254 0.9409 0.9501 0.04444 0.8878 0.906 0.1145 ] Network output: [ 0.9853 0.04319 -0.01118 -8.441e-05 3.789e-05 -0.002936 -6.361e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6398 0.1235 0.1396 0.1923 0.9723 0.9873 0.7269 0.9027 0.968 0.6514 ] Network output: [ -0.01161 0.9624 1.021 -8.177e-05 3.671e-05 0.03936 -6.163e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04762 0.03526 0.04952 0.02824 0.9857 0.9899 0.04864 0.9708 0.9808 0.06108 ] Network output: [ 0.04902 -0.1912 1.082 -0.001357 0.0006094 1.006 -0.001023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7223 0.6217 0.5552 0.3365 0.9755 0.9891 0.7252 0.9128 0.9726 0.6462 ] Network output: [ -0.01751 0.1116 0.9404 0.001052 -0.0004724 0.9874 0.000793 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6413 0.6269 0.4543 0.251 0.9869 0.9914 0.6418 0.974 0.9825 0.4658 ] Network output: [ -0.03729 0.1245 0.9487 0.0008026 -0.0003603 1.005 0.0006049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6382 0.636 0.46 0.2389 0.9854 0.9905 0.6383 0.9697 0.9801 0.4621 ] Network output: [ 0.009105 0.9611 0.01918 -0.0002838 0.0001274 1 -0.0002139 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01304 Epoch 2606 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02312 0.9883 1 -4.619e-05 2.073e-05 -0.03498 -3.481e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02202 -0.005438 0.01867 0.02252 0.9409 0.9501 0.04442 0.8878 0.906 0.1144 ] Network output: [ 0.9853 0.04306 -0.01113 -8.489e-05 3.811e-05 -0.002936 -6.397e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6398 0.1235 0.1397 0.1921 0.9723 0.9873 0.7269 0.9027 0.968 0.6514 ] Network output: [ -0.01162 0.9625 1.021 -8.152e-05 3.66e-05 0.03934 -6.144e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04761 0.03526 0.0495 0.02821 0.9857 0.9899 0.04863 0.9708 0.9808 0.06105 ] Network output: [ 0.04891 -0.1909 1.081 -0.001359 0.0006103 1.006 -0.001025 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7223 0.6216 0.5552 0.3362 0.9755 0.9891 0.7251 0.9128 0.9726 0.6462 ] Network output: [ -0.01746 0.1114 0.9404 0.001053 -0.0004726 0.9875 0.0007934 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6414 0.627 0.4543 0.2508 0.9869 0.9914 0.6418 0.974 0.9825 0.4658 ] Network output: [ -0.03721 0.1243 0.9487 0.0008041 -0.000361 1.005 0.000606 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6383 0.636 0.46 0.2387 0.9854 0.9905 0.6384 0.9697 0.9801 0.4621 ] Network output: [ 0.009083 0.9612 0.01916 -0.0002835 0.0001273 1 -0.0002137 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.013 Epoch 2607 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02309 0.9884 1 -4.627e-05 2.077e-05 -0.03498 -3.487e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02202 -0.005437 0.01867 0.0225 0.9409 0.9501 0.04441 0.8878 0.906 0.1144 ] Network output: [ 0.9854 0.04293 -0.01109 -8.537e-05 3.833e-05 -0.002935 -6.434e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6398 0.1235 0.1398 0.1919 0.9723 0.9873 0.7269 0.9027 0.968 0.6514 ] Network output: [ -0.01163 0.9626 1.021 -8.126e-05 3.648e-05 0.03931 -6.124e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0476 0.03525 0.04949 0.02818 0.9857 0.9899 0.04862 0.9708 0.9809 0.06103 ] Network output: [ 0.04881 -0.1906 1.081 -0.001361 0.0006112 1.006 -0.001026 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.6216 0.5553 0.3358 0.9755 0.9891 0.7251 0.9128 0.9726 0.6462 ] Network output: [ -0.0174 0.1111 0.9404 0.001053 -0.0004729 0.9875 0.0007938 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6415 0.6271 0.4543 0.2506 0.9869 0.9914 0.6419 0.974 0.9825 0.4658 ] Network output: [ -0.03714 0.124 0.9487 0.0008055 -0.0003616 1.005 0.0006071 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6384 0.6361 0.46 0.2385 0.9854 0.9905 0.6385 0.9697 0.9801 0.462 ] Network output: [ 0.009062 0.9613 0.01915 -0.0002832 0.0001272 1 -0.0002135 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01296 Epoch 2608 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02306 0.9884 1 -4.636e-05 2.081e-05 -0.03497 -3.494e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02201 -0.005435 0.01868 0.02249 0.9409 0.9502 0.0444 0.8879 0.906 0.1144 ] Network output: [ 0.9854 0.04281 -0.01104 -8.585e-05 3.854e-05 -0.002935 -6.47e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6398 0.1235 0.1399 0.1917 0.9723 0.9873 0.7268 0.9027 0.968 0.6514 ] Network output: [ -0.01165 0.9626 1.021 -8.101e-05 3.637e-05 0.03929 -6.105e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0476 0.03524 0.04947 0.02815 0.9857 0.9899 0.04861 0.9708 0.9809 0.06101 ] Network output: [ 0.0487 -0.1902 1.081 -0.001363 0.0006121 1.006 -0.001028 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.6216 0.5553 0.3355 0.9755 0.9891 0.7251 0.9128 0.9726 0.6462 ] Network output: [ -0.01735 0.1109 0.9404 0.001054 -0.0004731 0.9876 0.0007943 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6416 0.6272 0.4543 0.2505 0.9869 0.9914 0.642 0.974 0.9825 0.4658 ] Network output: [ -0.03706 0.1238 0.9487 0.0008069 -0.0003623 1.005 0.0006081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6385 0.6362 0.4599 0.2384 0.9854 0.9905 0.6386 0.9697 0.9801 0.462 ] Network output: [ 0.009041 0.9614 0.01913 -0.0002829 0.000127 1 -0.0002132 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01292 Epoch 2609 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02303 0.9885 1 -4.645e-05 2.085e-05 -0.03497 -3.5e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02201 -0.005434 0.01869 0.02247 0.9409 0.9502 0.04439 0.8879 0.906 0.1143 ] Network output: [ 0.9854 0.04268 -0.01099 -8.634e-05 3.876e-05 -0.002935 -6.507e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6398 0.1234 0.14 0.1915 0.9723 0.9873 0.7268 0.9027 0.968 0.6514 ] Network output: [ -0.01166 0.9627 1.021 -8.075e-05 3.625e-05 0.03927 -6.086e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04759 0.03524 0.04945 0.02811 0.9857 0.9899 0.04861 0.9708 0.9809 0.06098 ] Network output: [ 0.0486 -0.1899 1.081 -0.001365 0.000613 1.006 -0.001029 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.6215 0.5554 0.3352 0.9756 0.9891 0.7251 0.9128 0.9726 0.6462 ] Network output: [ -0.0173 0.1107 0.9405 0.001054 -0.0004734 0.9877 0.0007947 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6416 0.6272 0.4543 0.2503 0.9869 0.9914 0.6421 0.974 0.9825 0.4658 ] Network output: [ -0.03698 0.1236 0.9487 0.0008084 -0.0003629 1.005 0.0006092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6386 0.6363 0.4599 0.2382 0.9854 0.9905 0.6387 0.9697 0.9801 0.462 ] Network output: [ 0.009021 0.9615 0.01912 -0.0002826 0.0001269 1 -0.000213 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01288 Epoch 2610 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02301 0.9886 1 -4.653e-05 2.089e-05 -0.03496 -3.507e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02201 -0.005433 0.0187 0.02245 0.9409 0.9502 0.04438 0.8879 0.906 0.1143 ] Network output: [ 0.9855 0.04255 -0.01095 -8.682e-05 3.898e-05 -0.002935 -6.543e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6399 0.1234 0.1401 0.1913 0.9723 0.9873 0.7268 0.9027 0.968 0.6514 ] Network output: [ -0.01167 0.9628 1.021 -8.049e-05 3.613e-05 0.03925 -6.066e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04758 0.03523 0.04944 0.02808 0.9857 0.9899 0.0486 0.9708 0.9809 0.06096 ] Network output: [ 0.04849 -0.1896 1.081 -0.001367 0.0006139 1.006 -0.00103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.6215 0.5554 0.3349 0.9756 0.9891 0.725 0.9128 0.9726 0.6462 ] Network output: [ -0.01725 0.1105 0.9405 0.001055 -0.0004736 0.9878 0.0007951 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6417 0.6273 0.4543 0.2501 0.9869 0.9914 0.6422 0.974 0.9825 0.4658 ] Network output: [ -0.03691 0.1234 0.9487 0.0008098 -0.0003635 1.005 0.0006103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6387 0.6364 0.4598 0.2381 0.9854 0.9905 0.6387 0.9697 0.9801 0.4619 ] Network output: [ 0.009 0.9615 0.0191 -0.0002823 0.0001267 1 -0.0002128 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01284 Epoch 2611 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02298 0.9886 1 -4.662e-05 2.093e-05 -0.03496 -3.513e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02201 -0.005432 0.01871 0.02244 0.9409 0.9502 0.04436 0.8879 0.906 0.1143 ] Network output: [ 0.9855 0.04242 -0.0109 -8.731e-05 3.92e-05 -0.002935 -6.58e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6399 0.1234 0.1402 0.1911 0.9723 0.9873 0.7268 0.9027 0.968 0.6515 ] Network output: [ -0.01169 0.9629 1.021 -8.023e-05 3.602e-05 0.03923 -6.046e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04757 0.03522 0.04942 0.02805 0.9857 0.9899 0.04859 0.9708 0.9809 0.06093 ] Network output: [ 0.04839 -0.1893 1.081 -0.001369 0.0006147 1.005 -0.001032 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.6215 0.5555 0.3346 0.9756 0.9891 0.725 0.9128 0.9726 0.6462 ] Network output: [ -0.0172 0.1103 0.9405 0.001056 -0.0004739 0.9879 0.0007955 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6418 0.6274 0.4543 0.2499 0.9869 0.9914 0.6422 0.974 0.9825 0.4658 ] Network output: [ -0.03683 0.1232 0.9487 0.0008112 -0.0003642 1.005 0.0006113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6387 0.6365 0.4598 0.2379 0.9854 0.9905 0.6388 0.9697 0.9801 0.4619 ] Network output: [ 0.00898 0.9616 0.01909 -0.000282 0.0001266 1 -0.0002125 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0128 Epoch 2612 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02295 0.9887 1 -4.67e-05 2.097e-05 -0.03495 -3.52e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02201 -0.005431 0.01872 0.02242 0.9409 0.9502 0.04435 0.8879 0.9061 0.1142 ] Network output: [ 0.9856 0.0423 -0.01085 -8.781e-05 3.942e-05 -0.002935 -6.617e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6399 0.1234 0.1403 0.1909 0.9723 0.9873 0.7267 0.9027 0.968 0.6515 ] Network output: [ -0.0117 0.963 1.021 -7.997e-05 3.59e-05 0.03921 -6.026e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04757 0.03522 0.04941 0.02802 0.9857 0.9899 0.04858 0.9708 0.9809 0.06091 ] Network output: [ 0.04829 -0.1889 1.081 -0.001371 0.0006156 1.005 -0.001033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7221 0.6214 0.5555 0.3342 0.9756 0.9891 0.725 0.9128 0.9726 0.6462 ] Network output: [ -0.01715 0.1101 0.9406 0.001056 -0.0004741 0.988 0.0007959 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6419 0.6275 0.4543 0.2498 0.9869 0.9914 0.6423 0.974 0.9825 0.4658 ] Network output: [ -0.03676 0.1229 0.9487 0.0008126 -0.0003648 1.005 0.0006124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6388 0.6365 0.4598 0.2378 0.9854 0.9905 0.6389 0.9697 0.9801 0.4618 ] Network output: [ 0.008959 0.9617 0.01907 -0.0002817 0.0001265 1 -0.0002123 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01276 Epoch 2613 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02292 0.9887 1 -4.678e-05 2.1e-05 -0.03495 -3.526e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02201 -0.00543 0.01873 0.0224 0.9409 0.9502 0.04434 0.8879 0.9061 0.1142 ] Network output: [ 0.9856 0.04217 -0.01081 -8.83e-05 3.964e-05 -0.002935 -6.655e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6399 0.1234 0.1404 0.1907 0.9723 0.9873 0.7267 0.9027 0.968 0.6515 ] Network output: [ -0.01171 0.9631 1.021 -7.97e-05 3.578e-05 0.03919 -6.007e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04756 0.03521 0.04939 0.02799 0.9857 0.9899 0.04857 0.9708 0.9809 0.06088 ] Network output: [ 0.04818 -0.1886 1.081 -0.001373 0.0006165 1.005 -0.001035 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7221 0.6214 0.5556 0.3339 0.9756 0.9891 0.725 0.9128 0.9726 0.6462 ] Network output: [ -0.0171 0.1099 0.9406 0.001057 -0.0004744 0.988 0.0007963 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.642 0.6276 0.4543 0.2496 0.9869 0.9914 0.6424 0.974 0.9826 0.4658 ] Network output: [ -0.03668 0.1227 0.9487 0.000814 -0.0003654 1.005 0.0006135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6389 0.6366 0.4597 0.2376 0.9854 0.9905 0.639 0.9697 0.9801 0.4618 ] Network output: [ 0.008939 0.9618 0.01906 -0.0002814 0.0001263 1 -0.0002121 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01272 Epoch 2614 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02289 0.9888 1 -4.687e-05 2.104e-05 -0.03494 -3.532e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02201 -0.005429 0.01873 0.02239 0.9409 0.9502 0.04433 0.8879 0.9061 0.1142 ] Network output: [ 0.9856 0.04204 -0.01076 -8.88e-05 3.986e-05 -0.002935 -6.692e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6399 0.1234 0.1405 0.1906 0.9723 0.9873 0.7267 0.9027 0.968 0.6515 ] Network output: [ -0.01173 0.9632 1.021 -7.944e-05 3.566e-05 0.03917 -5.986e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04755 0.0352 0.04938 0.02795 0.9857 0.9899 0.04856 0.9708 0.9809 0.06086 ] Network output: [ 0.04808 -0.1883 1.081 -0.001375 0.0006173 1.005 -0.001036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7221 0.6214 0.5556 0.3336 0.9756 0.9891 0.725 0.9128 0.9726 0.6462 ] Network output: [ -0.01705 0.1097 0.9406 0.001057 -0.0004746 0.9881 0.0007967 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.642 0.6276 0.4543 0.2494 0.9869 0.9914 0.6425 0.974 0.9826 0.4658 ] Network output: [ -0.03661 0.1225 0.9487 0.0008154 -0.0003661 1.005 0.0006145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.639 0.6367 0.4597 0.2375 0.9854 0.9905 0.6391 0.9697 0.9801 0.4618 ] Network output: [ 0.008919 0.9619 0.01904 -0.0002811 0.0001262 1 -0.0002118 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01268 Epoch 2615 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02286 0.9889 1 -4.695e-05 2.108e-05 -0.03494 -3.538e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02201 -0.005428 0.01874 0.02237 0.9409 0.9502 0.04432 0.8879 0.9061 0.1141 ] Network output: [ 0.9857 0.04192 -0.01071 -8.929e-05 4.009e-05 -0.002936 -6.729e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6399 0.1233 0.1407 0.1904 0.9723 0.9873 0.7267 0.9027 0.968 0.6515 ] Network output: [ -0.01174 0.9633 1.021 -7.917e-05 3.554e-05 0.03915 -5.966e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04754 0.0352 0.04936 0.02792 0.9857 0.9899 0.04856 0.9708 0.9809 0.06084 ] Network output: [ 0.04798 -0.188 1.081 -0.001377 0.0006182 1.005 -0.001038 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7221 0.6214 0.5556 0.3333 0.9756 0.9891 0.7249 0.9128 0.9726 0.6462 ] Network output: [ -0.017 0.1095 0.9407 0.001058 -0.0004748 0.9882 0.0007971 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6421 0.6277 0.4543 0.2492 0.9869 0.9914 0.6426 0.974 0.9826 0.4658 ] Network output: [ -0.03653 0.1223 0.9487 0.0008168 -0.0003667 1.005 0.0006155 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6391 0.6368 0.4597 0.2373 0.9854 0.9905 0.6392 0.9697 0.9801 0.4617 ] Network output: [ 0.008899 0.962 0.01903 -0.0002808 0.0001261 1 -0.0002116 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01264 Epoch 2616 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02283 0.9889 1 -4.703e-05 2.112e-05 -0.03493 -3.545e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02201 -0.005426 0.01875 0.02235 0.9409 0.9502 0.0443 0.8879 0.9061 0.1141 ] Network output: [ 0.9857 0.04179 -0.01066 -8.979e-05 4.031e-05 -0.002936 -6.767e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6399 0.1233 0.1408 0.1902 0.9723 0.9873 0.7266 0.9027 0.968 0.6515 ] Network output: [ -0.01175 0.9634 1.021 -7.89e-05 3.542e-05 0.03912 -5.946e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04753 0.03519 0.04934 0.02789 0.9857 0.9899 0.04855 0.9708 0.9809 0.06081 ] Network output: [ 0.04788 -0.1877 1.081 -0.001379 0.000619 1.005 -0.001039 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7221 0.6213 0.5557 0.333 0.9756 0.9891 0.7249 0.9128 0.9726 0.6462 ] Network output: [ -0.01695 0.1092 0.9407 0.001058 -0.0004751 0.9883 0.0007975 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6422 0.6278 0.4543 0.2491 0.9869 0.9914 0.6426 0.974 0.9826 0.4658 ] Network output: [ -0.03646 0.1221 0.9487 0.0008181 -0.0003673 1.005 0.0006166 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6392 0.6369 0.4596 0.2371 0.9854 0.9905 0.6392 0.9697 0.9801 0.4617 ] Network output: [ 0.00888 0.962 0.01901 -0.0002805 0.0001259 1 -0.0002114 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0126 Epoch 2617 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0228 0.989 1 -4.712e-05 2.115e-05 -0.03493 -3.551e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02201 -0.005425 0.01876 0.02234 0.9409 0.9502 0.04429 0.8879 0.9061 0.114 ] Network output: [ 0.9858 0.04166 -0.01062 -9.03e-05 4.054e-05 -0.002937 -6.805e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.64 0.1233 0.1409 0.19 0.9723 0.9873 0.7266 0.9027 0.968 0.6515 ] Network output: [ -0.01177 0.9635 1.021 -7.863e-05 3.53e-05 0.0391 -5.926e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04753 0.03518 0.04933 0.02786 0.9857 0.9899 0.04854 0.9708 0.9809 0.06079 ] Network output: [ 0.04778 -0.1873 1.081 -0.001381 0.0006199 1.005 -0.001041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7221 0.6213 0.5557 0.3327 0.9756 0.9891 0.7249 0.9128 0.9726 0.6462 ] Network output: [ -0.0169 0.109 0.9407 0.001059 -0.0004753 0.9884 0.0007979 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6423 0.6279 0.4543 0.2489 0.9869 0.9914 0.6427 0.974 0.9826 0.4658 ] Network output: [ -0.03638 0.1219 0.9488 0.0008195 -0.0003679 1.005 0.0006176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6392 0.637 0.4596 0.237 0.9854 0.9905 0.6393 0.9697 0.9801 0.4616 ] Network output: [ 0.00886 0.9621 0.019 -0.0002801 0.0001258 1 -0.0002111 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01256 Epoch 2618 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02277 0.989 1 -4.72e-05 2.119e-05 -0.03492 -3.557e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02201 -0.005424 0.01877 0.02232 0.941 0.9502 0.04428 0.8879 0.9061 0.114 ] Network output: [ 0.9858 0.04154 -0.01057 -9.08e-05 4.076e-05 -0.002937 -6.843e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.64 0.1233 0.141 0.1898 0.9723 0.9873 0.7266 0.9027 0.968 0.6515 ] Network output: [ -0.01178 0.9636 1.021 -7.836e-05 3.518e-05 0.03908 -5.905e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04752 0.03518 0.04931 0.02783 0.9857 0.9899 0.04853 0.9708 0.9809 0.06076 ] Network output: [ 0.04768 -0.187 1.081 -0.001383 0.0006207 1.005 -0.001042 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.722 0.6213 0.5558 0.3324 0.9756 0.9891 0.7249 0.9128 0.9726 0.6462 ] Network output: [ -0.01685 0.1088 0.9407 0.001059 -0.0004755 0.9884 0.0007982 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6424 0.6279 0.4543 0.2487 0.9869 0.9914 0.6428 0.974 0.9826 0.4658 ] Network output: [ -0.03631 0.1216 0.9488 0.0008208 -0.0003685 1.006 0.0006186 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6393 0.637 0.4595 0.2368 0.9854 0.9905 0.6394 0.9697 0.9801 0.4616 ] Network output: [ 0.008841 0.9622 0.01898 -0.0002798 0.0001256 1 -0.0002109 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01252 Epoch 2619 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02274 0.9891 1 -4.728e-05 2.122e-05 -0.03492 -3.563e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.022 -0.005423 0.01878 0.02231 0.941 0.9502 0.04427 0.8879 0.9061 0.114 ] Network output: [ 0.9858 0.04141 -0.01052 -9.131e-05 4.099e-05 -0.002938 -6.881e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.64 0.1233 0.1411 0.1896 0.9723 0.9873 0.7266 0.9027 0.968 0.6515 ] Network output: [ -0.01179 0.9637 1.021 -7.808e-05 3.505e-05 0.03906 -5.885e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04751 0.03517 0.0493 0.0278 0.9857 0.9899 0.04852 0.9708 0.9809 0.06074 ] Network output: [ 0.04757 -0.1867 1.081 -0.001384 0.0006215 1.005 -0.001043 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.722 0.6212 0.5558 0.3321 0.9756 0.9891 0.7248 0.9128 0.9726 0.6462 ] Network output: [ -0.0168 0.1086 0.9408 0.00106 -0.0004757 0.9885 0.0007986 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6424 0.628 0.4543 0.2485 0.9869 0.9914 0.6429 0.974 0.9826 0.4658 ] Network output: [ -0.03624 0.1214 0.9488 0.0008222 -0.0003691 1.006 0.0006196 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6394 0.6371 0.4595 0.2367 0.9854 0.9905 0.6395 0.9698 0.9801 0.4616 ] Network output: [ 0.008821 0.9623 0.01897 -0.0002795 0.0001255 1 -0.0002106 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01248 Epoch 2620 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02272 0.9892 1 -4.736e-05 2.126e-05 -0.03491 -3.569e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.022 -0.005422 0.01879 0.02229 0.941 0.9502 0.04426 0.8879 0.9061 0.1139 ] Network output: [ 0.9859 0.04129 -0.01048 -9.181e-05 4.122e-05 -0.002939 -6.919e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.64 0.1232 0.1412 0.1894 0.9723 0.9873 0.7266 0.9027 0.9681 0.6515 ] Network output: [ -0.0118 0.9638 1.02 -7.781e-05 3.493e-05 0.03904 -5.864e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0475 0.03516 0.04928 0.02777 0.9857 0.9899 0.04851 0.9708 0.9809 0.06071 ] Network output: [ 0.04747 -0.1864 1.081 -0.001386 0.0006223 1.005 -0.001045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.722 0.6212 0.5558 0.3317 0.9756 0.9891 0.7248 0.9128 0.9726 0.6462 ] Network output: [ -0.01676 0.1084 0.9408 0.00106 -0.0004759 0.9886 0.000799 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6425 0.6281 0.4543 0.2484 0.9869 0.9914 0.643 0.974 0.9826 0.4657 ] Network output: [ -0.03616 0.1212 0.9488 0.0008235 -0.0003697 1.006 0.0006206 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6395 0.6372 0.4595 0.2365 0.9854 0.9905 0.6396 0.9698 0.9801 0.4615 ] Network output: [ 0.008802 0.9624 0.01896 -0.0002792 0.0001253 0.9999 -0.0002104 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01244 Epoch 2621 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02269 0.9892 1 -4.744e-05 2.13e-05 -0.03491 -3.575e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.022 -0.005421 0.0188 0.02227 0.941 0.9502 0.04424 0.8879 0.9061 0.1139 ] Network output: [ 0.9859 0.04116 -0.01043 -9.232e-05 4.145e-05 -0.00294 -6.958e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.64 0.1232 0.1413 0.1892 0.9724 0.9873 0.7265 0.9028 0.9681 0.6516 ] Network output: [ -0.01182 0.9638 1.02 -7.753e-05 3.481e-05 0.03902 -5.843e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0475 0.03515 0.04926 0.02774 0.9857 0.9899 0.0485 0.9708 0.9809 0.06069 ] Network output: [ 0.04737 -0.1861 1.081 -0.001388 0.0006232 1.004 -0.001046 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.722 0.6212 0.5559 0.3314 0.9756 0.9891 0.7248 0.9128 0.9726 0.6462 ] Network output: [ -0.01671 0.1082 0.9408 0.001061 -0.0004762 0.9887 0.0007993 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6426 0.6282 0.4543 0.2482 0.9869 0.9914 0.643 0.974 0.9826 0.4657 ] Network output: [ -0.03609 0.121 0.9488 0.0008248 -0.0003703 1.006 0.0006216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6396 0.6373 0.4594 0.2364 0.9854 0.9905 0.6397 0.9698 0.9801 0.4615 ] Network output: [ 0.008783 0.9624 0.01894 -0.0002789 0.0001252 0.9999 -0.0002102 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0124 Epoch 2622 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02266 0.9893 1 -4.752e-05 2.133e-05 -0.0349 -3.581e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.022 -0.00542 0.01881 0.02226 0.941 0.9502 0.04423 0.888 0.9061 0.1139 ] Network output: [ 0.986 0.04103 -0.01038 -9.284e-05 4.168e-05 -0.002941 -6.996e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6401 0.1232 0.1414 0.1891 0.9724 0.9873 0.7265 0.9028 0.9681 0.6516 ] Network output: [ -0.01183 0.9639 1.02 -7.725e-05 3.468e-05 0.039 -5.822e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04749 0.03515 0.04925 0.02771 0.9857 0.9899 0.0485 0.9708 0.9809 0.06066 ] Network output: [ 0.04728 -0.1858 1.081 -0.00139 0.000624 1.004 -0.001047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.722 0.6212 0.5559 0.3311 0.9756 0.9891 0.7248 0.9128 0.9727 0.6462 ] Network output: [ -0.01666 0.108 0.9409 0.001061 -0.0004764 0.9888 0.0007997 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6427 0.6282 0.4543 0.248 0.9869 0.9914 0.6431 0.974 0.9826 0.4657 ] Network output: [ -0.03602 0.1208 0.9488 0.0008262 -0.0003709 1.006 0.0006226 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6397 0.6374 0.4594 0.2362 0.9854 0.9905 0.6397 0.9698 0.9801 0.4614 ] Network output: [ 0.008764 0.9625 0.01893 -0.0002785 0.000125 0.9999 -0.0002099 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01236 Epoch 2623 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02263 0.9893 1 -4.76e-05 2.137e-05 -0.0349 -3.587e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.022 -0.005419 0.01882 0.02224 0.941 0.9502 0.04422 0.888 0.9061 0.1138 ] Network output: [ 0.986 0.04091 -0.01033 -9.335e-05 4.191e-05 -0.002942 -7.035e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6401 0.1232 0.1415 0.1889 0.9724 0.9873 0.7265 0.9028 0.9681 0.6516 ] Network output: [ -0.01184 0.964 1.02 -7.697e-05 3.456e-05 0.03898 -5.801e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04748 0.03514 0.04923 0.02768 0.9857 0.9899 0.04849 0.9708 0.9809 0.06064 ] Network output: [ 0.04718 -0.1855 1.081 -0.001392 0.0006248 1.004 -0.001049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7219 0.6211 0.556 0.3308 0.9756 0.9891 0.7248 0.9128 0.9727 0.6462 ] Network output: [ -0.01662 0.1078 0.9409 0.001062 -0.0004766 0.9888 0.0008 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6427 0.6283 0.4543 0.2479 0.9869 0.9914 0.6432 0.974 0.9826 0.4657 ] Network output: [ -0.03594 0.1206 0.9488 0.0008275 -0.0003715 1.006 0.0006236 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6397 0.6374 0.4593 0.2361 0.9854 0.9905 0.6398 0.9698 0.9801 0.4614 ] Network output: [ 0.008746 0.9626 0.01892 -0.0002782 0.0001249 0.9999 -0.0002097 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01233 Epoch 2624 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0226 0.9894 1 -4.768e-05 2.14e-05 -0.03489 -3.593e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.022 -0.005418 0.01882 0.02223 0.941 0.9502 0.04421 0.888 0.9061 0.1138 ] Network output: [ 0.986 0.04078 -0.01029 -9.387e-05 4.214e-05 -0.002944 -7.074e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6401 0.1232 0.1416 0.1887 0.9724 0.9873 0.7265 0.9028 0.9681 0.6516 ] Network output: [ -0.01185 0.9641 1.02 -7.669e-05 3.443e-05 0.03896 -5.78e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04747 0.03513 0.04922 0.02765 0.9857 0.9899 0.04848 0.9708 0.9809 0.06061 ] Network output: [ 0.04708 -0.1852 1.081 -0.001393 0.0006256 1.004 -0.00105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7219 0.6211 0.556 0.3305 0.9756 0.9891 0.7248 0.9128 0.9727 0.6462 ] Network output: [ -0.01657 0.1076 0.9409 0.001062 -0.0004768 0.9889 0.0008004 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6428 0.6284 0.4543 0.2477 0.9869 0.9914 0.6433 0.974 0.9826 0.4657 ] Network output: [ -0.03587 0.1204 0.9488 0.0008288 -0.0003721 1.006 0.0006246 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6398 0.6375 0.4593 0.2359 0.9854 0.9905 0.6399 0.9698 0.9801 0.4614 ] Network output: [ 0.008727 0.9627 0.0189 -0.0002779 0.0001248 0.9998 -0.0002094 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01229 Epoch 2625 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02257 0.9895 1 -4.775e-05 2.144e-05 -0.03489 -3.599e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.022 -0.005417 0.01883 0.02221 0.941 0.9502 0.0442 0.888 0.9061 0.1138 ] Network output: [ 0.9861 0.04066 -0.01024 -9.438e-05 4.237e-05 -0.002945 -7.113e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6401 0.1231 0.1418 0.1885 0.9724 0.9873 0.7265 0.9028 0.9681 0.6516 ] Network output: [ -0.01186 0.9642 1.02 -7.641e-05 3.43e-05 0.03894 -5.758e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04746 0.03513 0.0492 0.02762 0.9857 0.9899 0.04847 0.9708 0.9809 0.06059 ] Network output: [ 0.04698 -0.1849 1.081 -0.001395 0.0006264 1.004 -0.001051 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7219 0.6211 0.556 0.3302 0.9756 0.9891 0.7247 0.9128 0.9727 0.6462 ] Network output: [ -0.01652 0.1074 0.941 0.001062 -0.000477 0.989 0.0008007 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6429 0.6284 0.4543 0.2475 0.9869 0.9914 0.6433 0.974 0.9826 0.4657 ] Network output: [ -0.0358 0.1202 0.9488 0.0008301 -0.0003726 1.006 0.0006256 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6399 0.6376 0.4593 0.2358 0.9854 0.9905 0.64 0.9698 0.9801 0.4613 ] Network output: [ 0.008709 0.9627 0.01889 -0.0002776 0.0001246 0.9998 -0.0002092 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01225 Epoch 2626 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02254 0.9895 1 -4.783e-05 2.147e-05 -0.03488 -3.605e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.022 -0.005416 0.01884 0.0222 0.941 0.9502 0.04418 0.888 0.9061 0.1137 ] Network output: [ 0.9861 0.04053 -0.01019 -9.49e-05 4.261e-05 -0.002947 -7.152e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6401 0.1231 0.1419 0.1883 0.9724 0.9873 0.7264 0.9028 0.9681 0.6516 ] Network output: [ -0.01188 0.9643 1.02 -7.613e-05 3.418e-05 0.03892 -5.737e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04746 0.03512 0.04918 0.02759 0.9857 0.9899 0.04846 0.9708 0.9809 0.06056 ] Network output: [ 0.04688 -0.1846 1.081 -0.001397 0.0006271 1.004 -0.001053 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7219 0.621 0.5561 0.3299 0.9756 0.9891 0.7247 0.9129 0.9727 0.6462 ] Network output: [ -0.01648 0.1072 0.941 0.001063 -0.0004772 0.9891 0.0008011 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.643 0.6285 0.4543 0.2474 0.9869 0.9914 0.6434 0.974 0.9826 0.4657 ] Network output: [ -0.03573 0.12 0.9488 0.0008314 -0.0003732 1.006 0.0006265 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.64 0.6377 0.4592 0.2356 0.9854 0.9905 0.6401 0.9698 0.9801 0.4613 ] Network output: [ 0.008691 0.9628 0.01888 -0.0002772 0.0001245 0.9998 -0.0002089 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01221 Epoch 2627 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02251 0.9896 1 -4.791e-05 2.151e-05 -0.03487 -3.611e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.022 -0.005414 0.01885 0.02218 0.941 0.9502 0.04417 0.888 0.9061 0.1137 ] Network output: [ 0.9861 0.04041 -0.01014 -9.543e-05 4.284e-05 -0.002948 -7.192e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6402 0.1231 0.142 0.1882 0.9724 0.9873 0.7264 0.9028 0.9681 0.6516 ] Network output: [ -0.01189 0.9644 1.02 -7.584e-05 3.405e-05 0.0389 -5.716e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04745 0.03511 0.04917 0.02756 0.9858 0.9899 0.04845 0.9708 0.9809 0.06054 ] Network output: [ 0.04678 -0.1842 1.081 -0.001399 0.0006279 1.004 -0.001054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7219 0.621 0.5561 0.3297 0.9756 0.9891 0.7247 0.9129 0.9727 0.6462 ] Network output: [ -0.01643 0.107 0.941 0.001063 -0.0004774 0.9891 0.0008014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.643 0.6286 0.4543 0.2472 0.9869 0.9914 0.6435 0.974 0.9826 0.4657 ] Network output: [ -0.03566 0.1198 0.9488 0.0008326 -0.0003738 1.006 0.0006275 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6401 0.6378 0.4592 0.2355 0.9854 0.9905 0.6401 0.9698 0.9801 0.4612 ] Network output: [ 0.008672 0.9629 0.01886 -0.0002769 0.0001243 0.9998 -0.0002087 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01218 Epoch 2628 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02249 0.9896 1 -4.798e-05 2.154e-05 -0.03487 -3.616e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.022 -0.005413 0.01886 0.02216 0.941 0.9502 0.04416 0.888 0.9062 0.1136 ] Network output: [ 0.9862 0.04028 -0.0101 -9.595e-05 4.307e-05 -0.00295 -7.231e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6402 0.1231 0.1421 0.188 0.9724 0.9873 0.7264 0.9028 0.9681 0.6516 ] Network output: [ -0.0119 0.9645 1.02 -7.555e-05 3.392e-05 0.03888 -5.694e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04744 0.0351 0.04915 0.02753 0.9858 0.9899 0.04844 0.9708 0.9809 0.06051 ] Network output: [ 0.04669 -0.1839 1.081 -0.0014 0.0006287 1.004 -0.001055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7219 0.621 0.5561 0.3294 0.9756 0.9891 0.7247 0.9129 0.9727 0.6462 ] Network output: [ -0.01639 0.1068 0.9411 0.001064 -0.0004776 0.9892 0.0008017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6431 0.6287 0.4543 0.247 0.9869 0.9914 0.6436 0.974 0.9826 0.4657 ] Network output: [ -0.03559 0.1196 0.9488 0.0008339 -0.0003744 1.006 0.0006285 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6401 0.6378 0.4591 0.2353 0.9854 0.9905 0.6402 0.9698 0.9801 0.4612 ] Network output: [ 0.008655 0.963 0.01885 -0.0002766 0.0001242 0.9998 -0.0002084 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01214 Epoch 2629 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02246 0.9897 1 -4.806e-05 2.158e-05 -0.03486 -3.622e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.022 -0.005412 0.01887 0.02215 0.941 0.9502 0.04415 0.888 0.9062 0.1136 ] Network output: [ 0.9862 0.04016 -0.01005 -9.647e-05 4.331e-05 -0.002952 -7.271e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6402 0.123 0.1422 0.1878 0.9724 0.9873 0.7264 0.9028 0.9681 0.6516 ] Network output: [ -0.01191 0.9646 1.02 -7.527e-05 3.379e-05 0.03886 -5.672e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04743 0.0351 0.04913 0.0275 0.9858 0.9899 0.04843 0.9708 0.9809 0.06048 ] Network output: [ 0.04659 -0.1836 1.081 -0.001402 0.0006295 1.004 -0.001057 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7219 0.621 0.5562 0.3291 0.9756 0.9891 0.7247 0.9129 0.9727 0.6462 ] Network output: [ -0.01634 0.1066 0.9411 0.001064 -0.0004778 0.9893 0.000802 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6432 0.6287 0.4543 0.2469 0.9869 0.9915 0.6436 0.974 0.9826 0.4657 ] Network output: [ -0.03552 0.1194 0.9488 0.0008352 -0.0003749 1.006 0.0006294 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6402 0.6379 0.4591 0.2352 0.9854 0.9905 0.6403 0.9698 0.9801 0.4611 ] Network output: [ 0.008637 0.963 0.01884 -0.0002762 0.000124 0.9997 -0.0002082 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01211 Epoch 2630 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02243 0.9898 1 -4.814e-05 2.161e-05 -0.03486 -3.628e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02199 -0.005411 0.01888 0.02213 0.941 0.9503 0.04414 0.888 0.9062 0.1136 ] Network output: [ 0.9863 0.04004 -0.01 -9.7e-05 4.355e-05 -0.002954 -7.31e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6402 0.123 0.1423 0.1876 0.9724 0.9873 0.7264 0.9028 0.9681 0.6516 ] Network output: [ -0.01192 0.9647 1.02 -7.498e-05 3.366e-05 0.03884 -5.65e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04742 0.03509 0.04912 0.02747 0.9858 0.9899 0.04843 0.9709 0.9809 0.06046 ] Network output: [ 0.04649 -0.1833 1.081 -0.001404 0.0006302 1.004 -0.001058 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7218 0.6209 0.5562 0.3288 0.9756 0.9891 0.7247 0.9129 0.9727 0.6462 ] Network output: [ -0.0163 0.1064 0.9411 0.001065 -0.000478 0.9894 0.0008024 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6433 0.6288 0.4543 0.2467 0.9869 0.9915 0.6437 0.974 0.9826 0.4656 ] Network output: [ -0.03545 0.1192 0.9488 0.0008364 -0.0003755 1.006 0.0006304 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6403 0.638 0.4591 0.235 0.9854 0.9905 0.6404 0.9698 0.9801 0.4611 ] Network output: [ 0.008619 0.9631 0.01882 -0.0002759 0.0001239 0.9997 -0.0002079 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01207 Epoch 2631 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0224 0.9898 1 -4.821e-05 2.164e-05 -0.03485 -3.633e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02199 -0.00541 0.01889 0.02212 0.941 0.9503 0.04413 0.888 0.9062 0.1135 ] Network output: [ 0.9863 0.03991 -0.009953 -9.753e-05 4.379e-05 -0.002956 -7.35e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6402 0.123 0.1424 0.1875 0.9724 0.9873 0.7263 0.9028 0.9681 0.6516 ] Network output: [ -0.01193 0.9647 1.02 -7.469e-05 3.353e-05 0.03882 -5.629e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04742 0.03508 0.0491 0.02744 0.9858 0.9899 0.04842 0.9709 0.9809 0.06043 ] Network output: [ 0.0464 -0.183 1.081 -0.001406 0.000631 1.003 -0.001059 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7218 0.6209 0.5562 0.3285 0.9756 0.9891 0.7246 0.9129 0.9727 0.6462 ] Network output: [ -0.01625 0.1062 0.9412 0.001065 -0.0004781 0.9894 0.0008027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6433 0.6289 0.4543 0.2466 0.9869 0.9915 0.6438 0.974 0.9826 0.4656 ] Network output: [ -0.03538 0.119 0.9488 0.0008377 -0.0003761 1.006 0.0006313 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6404 0.6381 0.459 0.2349 0.9854 0.9905 0.6404 0.9698 0.9802 0.4611 ] Network output: [ 0.008602 0.9632 0.01881 -0.0002756 0.0001237 0.9997 -0.0002077 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01203 Epoch 2632 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02237 0.9899 1 -4.829e-05 2.168e-05 -0.03484 -3.639e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02199 -0.005409 0.0189 0.0221 0.941 0.9503 0.04411 0.888 0.9062 0.1135 ] Network output: [ 0.9863 0.03979 -0.009905 -9.806e-05 4.402e-05 -0.002958 -7.39e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6403 0.123 0.1426 0.1873 0.9724 0.9873 0.7263 0.9028 0.9681 0.6516 ] Network output: [ -0.01195 0.9648 1.02 -7.439e-05 3.34e-05 0.0388 -5.607e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04741 0.03507 0.04909 0.02741 0.9858 0.9899 0.04841 0.9709 0.9809 0.06041 ] Network output: [ 0.0463 -0.1827 1.081 -0.001407 0.0006317 1.003 -0.001061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7218 0.6209 0.5563 0.3282 0.9756 0.9891 0.7246 0.9129 0.9727 0.6462 ] Network output: [ -0.01621 0.106 0.9412 0.001065 -0.0004783 0.9895 0.000803 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6434 0.6289 0.4543 0.2464 0.9869 0.9915 0.6439 0.974 0.9826 0.4656 ] Network output: [ -0.03531 0.1188 0.9488 0.0008389 -0.0003766 1.006 0.0006322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6404 0.6381 0.459 0.2348 0.9854 0.9905 0.6405 0.9698 0.9802 0.461 ] Network output: [ 0.008584 0.9633 0.0188 -0.0002752 0.0001236 0.9997 -0.0002074 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.012 Epoch 2633 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02234 0.9899 1 -4.836e-05 2.171e-05 -0.03484 -3.645e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02199 -0.005408 0.01891 0.02209 0.941 0.9503 0.0441 0.888 0.9062 0.1135 ] Network output: [ 0.9864 0.03966 -0.009857 -9.86e-05 4.426e-05 -0.002961 -7.431e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6403 0.1229 0.1427 0.1871 0.9724 0.9873 0.7263 0.9028 0.9681 0.6516 ] Network output: [ -0.01196 0.9649 1.02 -7.41e-05 3.327e-05 0.03878 -5.584e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0474 0.03507 0.04907 0.02738 0.9858 0.9899 0.0484 0.9709 0.9809 0.06038 ] Network output: [ 0.04621 -0.1824 1.081 -0.001409 0.0006325 1.003 -0.001062 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7218 0.6208 0.5563 0.3279 0.9756 0.9891 0.7246 0.9129 0.9727 0.6462 ] Network output: [ -0.01617 0.1058 0.9412 0.001066 -0.0004785 0.9896 0.0008033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6435 0.629 0.4543 0.2462 0.9869 0.9915 0.6439 0.974 0.9826 0.4656 ] Network output: [ -0.03524 0.1186 0.9488 0.0008401 -0.0003772 1.007 0.0006331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6405 0.6382 0.4589 0.2346 0.9854 0.9905 0.6406 0.9698 0.9802 0.461 ] Network output: [ 0.008567 0.9633 0.01879 -0.0002749 0.0001234 0.9996 -0.0002072 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01196 Epoch 2634 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02232 0.99 1 -4.844e-05 2.174e-05 -0.03483 -3.65e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02199 -0.005407 0.01892 0.02207 0.941 0.9503 0.04409 0.888 0.9062 0.1134 ] Network output: [ 0.9864 0.03954 -0.00981 -9.913e-05 4.45e-05 -0.002963 -7.471e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6403 0.1229 0.1428 0.187 0.9724 0.9873 0.7263 0.9028 0.9681 0.6516 ] Network output: [ -0.01197 0.965 1.02 -7.381e-05 3.313e-05 0.03876 -5.562e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04739 0.03506 0.04905 0.02735 0.9858 0.9899 0.04839 0.9709 0.9809 0.06036 ] Network output: [ 0.04611 -0.1822 1.081 -0.00141 0.0006332 1.003 -0.001063 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7218 0.6208 0.5563 0.3276 0.9756 0.9891 0.7246 0.9129 0.9727 0.6462 ] Network output: [ -0.01612 0.1056 0.9413 0.001066 -0.0004787 0.9897 0.0008036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6436 0.6291 0.4543 0.2461 0.9869 0.9915 0.644 0.974 0.9826 0.4656 ] Network output: [ -0.03517 0.1184 0.9488 0.0008413 -0.0003777 1.007 0.0006341 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6406 0.6383 0.4589 0.2345 0.9854 0.9906 0.6407 0.9698 0.9802 0.4609 ] Network output: [ 0.00855 0.9634 0.01877 -0.0002746 0.0001233 0.9996 -0.0002069 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01193 Epoch 2635 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02229 0.99 1 -4.851e-05 2.178e-05 -0.03482 -3.656e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02199 -0.005406 0.01893 0.02206 0.941 0.9503 0.04408 0.888 0.9062 0.1134 ] Network output: [ 0.9865 0.03942 -0.009762 -9.967e-05 4.475e-05 -0.002966 -7.511e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6403 0.1229 0.1429 0.1868 0.9724 0.9873 0.7263 0.9028 0.9681 0.6516 ] Network output: [ -0.01198 0.9651 1.02 -7.351e-05 3.3e-05 0.03874 -5.54e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04738 0.03505 0.04904 0.02732 0.9858 0.9899 0.04838 0.9709 0.9809 0.06033 ] Network output: [ 0.04602 -0.1819 1.081 -0.001412 0.000634 1.003 -0.001064 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7218 0.6208 0.5564 0.3274 0.9756 0.9891 0.7246 0.9129 0.9727 0.6461 ] Network output: [ -0.01608 0.1054 0.9413 0.001067 -0.0004789 0.9897 0.0008039 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6436 0.6291 0.4543 0.2459 0.9869 0.9915 0.6441 0.974 0.9826 0.4656 ] Network output: [ -0.0351 0.1182 0.9488 0.0008426 -0.0003783 1.007 0.000635 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6407 0.6384 0.4588 0.2343 0.9854 0.9906 0.6408 0.9698 0.9802 0.4609 ] Network output: [ 0.008533 0.9635 0.01876 -0.0002742 0.0001231 0.9996 -0.0002067 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01189 Epoch 2636 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02226 0.9901 1 -4.858e-05 2.181e-05 -0.03482 -3.661e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02199 -0.005405 0.01894 0.02204 0.9411 0.9503 0.04407 0.888 0.9062 0.1133 ] Network output: [ 0.9865 0.03929 -0.009714 -0.0001002 4.499e-05 -0.002968 -7.552e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6403 0.1229 0.143 0.1866 0.9724 0.9873 0.7263 0.9028 0.9681 0.6516 ] Network output: [ -0.01199 0.9652 1.02 -7.321e-05 3.287e-05 0.03872 -5.518e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04737 0.03504 0.04902 0.02729 0.9858 0.9899 0.04837 0.9709 0.9809 0.0603 ] Network output: [ 0.04593 -0.1816 1.081 -0.001414 0.0006347 1.003 -0.001065 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7218 0.6208 0.5564 0.3271 0.9756 0.9891 0.7246 0.9129 0.9727 0.6461 ] Network output: [ -0.01604 0.1053 0.9414 0.001067 -0.000479 0.9898 0.0008042 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6437 0.6292 0.4542 0.2458 0.987 0.9915 0.6442 0.974 0.9826 0.4656 ] Network output: [ -0.03503 0.118 0.9488 0.0008438 -0.0003788 1.007 0.0006359 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6408 0.6385 0.4588 0.2342 0.9854 0.9906 0.6408 0.9698 0.9802 0.4608 ] Network output: [ 0.008517 0.9635 0.01875 -0.0002739 0.0001229 0.9996 -0.0002064 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01186 Epoch 2637 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02223 0.9902 1 -4.866e-05 2.184e-05 -0.03481 -3.667e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02199 -0.005404 0.01895 0.02203 0.9411 0.9503 0.04405 0.888 0.9062 0.1133 ] Network output: [ 0.9865 0.03917 -0.009666 -0.0001007 4.523e-05 -0.002971 -7.593e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6404 0.1228 0.1431 0.1865 0.9724 0.9873 0.7262 0.9028 0.9681 0.6516 ] Network output: [ -0.012 0.9653 1.02 -7.291e-05 3.273e-05 0.0387 -5.495e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04737 0.03504 0.049 0.02726 0.9858 0.9899 0.04836 0.9709 0.9809 0.06028 ] Network output: [ 0.04583 -0.1813 1.081 -0.001415 0.0006354 1.003 -0.001067 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7217 0.6207 0.5564 0.3268 0.9756 0.9891 0.7245 0.9129 0.9727 0.6461 ] Network output: [ -0.016 0.1051 0.9414 0.001067 -0.0004792 0.9899 0.0008044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6438 0.6293 0.4542 0.2456 0.987 0.9915 0.6442 0.974 0.9826 0.4656 ] Network output: [ -0.03496 0.1178 0.9488 0.000845 -0.0003793 1.007 0.0006368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6408 0.6385 0.4588 0.234 0.9855 0.9906 0.6409 0.9698 0.9802 0.4608 ] Network output: [ 0.0085 0.9636 0.01874 -0.0002735 0.0001228 0.9996 -0.0002061 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01182 Epoch 2638 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0222 0.9902 1 -4.873e-05 2.188e-05 -0.0348 -3.672e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02199 -0.005403 0.01896 0.02202 0.9411 0.9503 0.04404 0.8881 0.9062 0.1133 ] Network output: [ 0.9866 0.03905 -0.009618 -0.0001013 4.547e-05 -0.002974 -7.634e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6404 0.1228 0.1432 0.1863 0.9724 0.9873 0.7262 0.9028 0.9681 0.6516 ] Network output: [ -0.01201 0.9654 1.02 -7.262e-05 3.26e-05 0.03868 -5.473e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04736 0.03503 0.04899 0.02724 0.9858 0.9899 0.04835 0.9709 0.9809 0.06025 ] Network output: [ 0.04574 -0.181 1.081 -0.001417 0.0006361 1.003 -0.001068 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7217 0.6207 0.5565 0.3265 0.9756 0.9891 0.7245 0.9129 0.9727 0.6461 ] Network output: [ -0.01596 0.1049 0.9414 0.001068 -0.0004794 0.99 0.0008047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6439 0.6293 0.4542 0.2455 0.987 0.9915 0.6443 0.974 0.9826 0.4655 ] Network output: [ -0.0349 0.1176 0.9488 0.0008461 -0.0003799 1.007 0.0006377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6409 0.6386 0.4587 0.2339 0.9855 0.9906 0.641 0.9698 0.9802 0.4607 ] Network output: [ 0.008484 0.9637 0.01873 -0.0002732 0.0001226 0.9995 -0.0002059 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01179 Epoch 2639 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02217 0.9903 1 -4.88e-05 2.191e-05 -0.0348 -3.678e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02199 -0.005402 0.01897 0.022 0.9411 0.9503 0.04403 0.8881 0.9062 0.1132 ] Network output: [ 0.9866 0.03893 -0.009569 -0.0001018 4.572e-05 -0.002977 -7.675e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6404 0.1228 0.1434 0.1861 0.9724 0.9873 0.7262 0.9028 0.9681 0.6516 ] Network output: [ -0.01202 0.9654 1.02 -7.231e-05 3.246e-05 0.03866 -5.45e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04735 0.03502 0.04897 0.02721 0.9858 0.9899 0.04834 0.9709 0.9809 0.06023 ] Network output: [ 0.04565 -0.1807 1.081 -0.001419 0.0006368 1.003 -0.001069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7217 0.6207 0.5565 0.3262 0.9756 0.9891 0.7245 0.9129 0.9727 0.6461 ] Network output: [ -0.01591 0.1047 0.9415 0.001068 -0.0004795 0.99 0.000805 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6439 0.6294 0.4542 0.2453 0.987 0.9915 0.6444 0.974 0.9826 0.4655 ] Network output: [ -0.03483 0.1175 0.9488 0.0008473 -0.0003804 1.007 0.0006386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.641 0.6387 0.4587 0.2338 0.9855 0.9906 0.6411 0.9698 0.9802 0.4607 ] Network output: [ 0.008467 0.9637 0.01871 -0.0002728 0.0001225 0.9995 -0.0002056 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01175 Epoch 2640 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02215 0.9903 1 -4.887e-05 2.194e-05 -0.03479 -3.683e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02199 -0.005401 0.01898 0.02199 0.9411 0.9503 0.04402 0.8881 0.9062 0.1132 ] Network output: [ 0.9866 0.03881 -0.009521 -0.0001024 4.596e-05 -0.00298 -7.716e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6404 0.1227 0.1435 0.186 0.9724 0.9873 0.7262 0.9028 0.9681 0.6516 ] Network output: [ -0.01203 0.9655 1.02 -7.201e-05 3.233e-05 0.03864 -5.427e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04734 0.03501 0.04895 0.02718 0.9858 0.9899 0.04834 0.9709 0.9809 0.0602 ] Network output: [ 0.04556 -0.1804 1.081 -0.00142 0.0006375 1.003 -0.00107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7217 0.6207 0.5565 0.326 0.9756 0.9891 0.7245 0.9129 0.9727 0.6461 ] Network output: [ -0.01587 0.1045 0.9415 0.001069 -0.0004797 0.9901 0.0008053 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.644 0.6295 0.4542 0.2452 0.987 0.9915 0.6444 0.974 0.9826 0.4655 ] Network output: [ -0.03476 0.1173 0.9488 0.0008485 -0.0003809 1.007 0.0006395 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6411 0.6388 0.4586 0.2336 0.9855 0.9906 0.6411 0.9698 0.9802 0.4606 ] Network output: [ 0.008451 0.9638 0.0187 -0.0002725 0.0001223 0.9995 -0.0002054 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01172 Epoch 2641 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02212 0.9904 1 -4.894e-05 2.197e-05 -0.03478 -3.689e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02198 -0.0054 0.01899 0.02197 0.9411 0.9503 0.04401 0.8881 0.9062 0.1132 ] Network output: [ 0.9867 0.03868 -0.009473 -0.0001029 4.621e-05 -0.002984 -7.757e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6404 0.1227 0.1436 0.1858 0.9724 0.9873 0.7262 0.9029 0.9681 0.6516 ] Network output: [ -0.01204 0.9656 1.02 -7.171e-05 3.219e-05 0.03862 -5.404e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04733 0.03501 0.04894 0.02715 0.9858 0.9899 0.04833 0.9709 0.9809 0.06017 ] Network output: [ 0.04546 -0.1801 1.081 -0.001422 0.0006382 1.002 -0.001071 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7217 0.6206 0.5566 0.3257 0.9756 0.9891 0.7245 0.9129 0.9727 0.6461 ] Network output: [ -0.01583 0.1043 0.9415 0.001069 -0.0004799 0.9902 0.0008055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6441 0.6295 0.4542 0.245 0.987 0.9915 0.6445 0.974 0.9826 0.4655 ] Network output: [ -0.03469 0.1171 0.9488 0.0008497 -0.0003814 1.007 0.0006403 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6411 0.6388 0.4586 0.2335 0.9855 0.9906 0.6412 0.9698 0.9802 0.4606 ] Network output: [ 0.008435 0.9639 0.01869 -0.0002721 0.0001222 0.9995 -0.0002051 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01169 Epoch 2642 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02209 0.9904 1 -4.901e-05 2.2e-05 -0.03477 -3.694e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02198 -0.005399 0.019 0.02196 0.9411 0.9503 0.044 0.8881 0.9062 0.1131 ] Network output: [ 0.9867 0.03856 -0.009425 -0.0001035 4.646e-05 -0.002987 -7.799e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 0.1227 0.1437 0.1856 0.9724 0.9873 0.7262 0.9029 0.9681 0.6516 ] Network output: [ -0.01205 0.9657 1.02 -7.14e-05 3.206e-05 0.0386 -5.381e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04732 0.035 0.04892 0.02712 0.9858 0.9899 0.04832 0.9709 0.9809 0.06015 ] Network output: [ 0.04537 -0.1798 1.081 -0.001423 0.0006389 1.002 -0.001073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7217 0.6206 0.5566 0.3254 0.9756 0.9891 0.7245 0.9129 0.9727 0.6461 ] Network output: [ -0.01579 0.1041 0.9416 0.001069 -0.00048 0.9902 0.0008058 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6441 0.6296 0.4542 0.2448 0.987 0.9915 0.6446 0.974 0.9826 0.4655 ] Network output: [ -0.03463 0.1169 0.9488 0.0008508 -0.000382 1.007 0.0006412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6412 0.6389 0.4585 0.2333 0.9855 0.9906 0.6413 0.9699 0.9802 0.4606 ] Network output: [ 0.008419 0.9639 0.01868 -0.0002718 0.000122 0.9994 -0.0002048 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01165 Epoch 2643 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02206 0.9905 1 -4.908e-05 2.204e-05 -0.03477 -3.699e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02198 -0.005398 0.01901 0.02194 0.9411 0.9503 0.04398 0.8881 0.9062 0.1131 ] Network output: [ 0.9868 0.03844 -0.009377 -0.000104 4.671e-05 -0.002991 -7.84e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 0.1227 0.1438 0.1855 0.9724 0.9873 0.7262 0.9029 0.9681 0.6516 ] Network output: [ -0.01206 0.9658 1.019 -7.11e-05 3.192e-05 0.03858 -5.358e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04732 0.03499 0.0489 0.02709 0.9858 0.9899 0.04831 0.9709 0.9809 0.06012 ] Network output: [ 0.04528 -0.1796 1.081 -0.001425 0.0006396 1.002 -0.001074 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7217 0.6206 0.5566 0.3252 0.9756 0.9891 0.7245 0.9129 0.9727 0.6461 ] Network output: [ -0.01575 0.1039 0.9416 0.00107 -0.0004802 0.9903 0.0008061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6442 0.6297 0.4542 0.2447 0.987 0.9915 0.6446 0.974 0.9826 0.4655 ] Network output: [ -0.03456 0.1167 0.9487 0.000852 -0.0003825 1.007 0.0006421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6413 0.639 0.4585 0.2332 0.9855 0.9906 0.6414 0.9699 0.9802 0.4605 ] Network output: [ 0.008404 0.964 0.01867 -0.0002714 0.0001219 0.9994 -0.0002046 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01162 Epoch 2644 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02203 0.9906 0.9999 -4.915e-05 2.207e-05 -0.03476 -3.704e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02198 -0.005397 0.01902 0.02193 0.9411 0.9503 0.04397 0.8881 0.9063 0.113 ] Network output: [ 0.9868 0.03832 -0.009329 -0.0001046 4.695e-05 -0.002994 -7.882e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 0.1226 0.144 0.1853 0.9724 0.9873 0.7261 0.9029 0.9681 0.6516 ] Network output: [ -0.01207 0.9659 1.019 -7.079e-05 3.178e-05 0.03856 -5.335e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04731 0.03498 0.04889 0.02707 0.9858 0.9899 0.0483 0.9709 0.9809 0.0601 ] Network output: [ 0.04519 -0.1793 1.081 -0.001426 0.0006403 1.002 -0.001075 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7217 0.6205 0.5567 0.3249 0.9756 0.9891 0.7245 0.9129 0.9727 0.646 ] Network output: [ -0.01571 0.1038 0.9416 0.00107 -0.0004803 0.9904 0.0008063 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6443 0.6297 0.4542 0.2445 0.987 0.9915 0.6447 0.974 0.9826 0.4654 ] Network output: [ -0.0345 0.1165 0.9487 0.0008531 -0.000383 1.007 0.0006429 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6414 0.6391 0.4584 0.233 0.9855 0.9906 0.6414 0.9699 0.9802 0.4605 ] Network output: [ 0.008388 0.9641 0.01866 -0.0002711 0.0001217 0.9994 -0.0002043 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01159 Epoch 2645 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.022 0.9906 0.9999 -4.922e-05 2.21e-05 -0.03475 -3.71e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02198 -0.005396 0.01903 0.02191 0.9411 0.9503 0.04396 0.8881 0.9063 0.113 ] Network output: [ 0.9868 0.0382 -0.00928 -0.0001051 4.72e-05 -0.002998 -7.924e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6405 0.1226 0.1441 0.1852 0.9724 0.9873 0.7261 0.9029 0.9681 0.6515 ] Network output: [ -0.01208 0.966 1.019 -7.048e-05 3.164e-05 0.03854 -5.312e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0473 0.03498 0.04887 0.02704 0.9858 0.9899 0.04829 0.9709 0.9809 0.06007 ] Network output: [ 0.0451 -0.179 1.081 -0.001428 0.0006409 1.002 -0.001076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7217 0.6205 0.5567 0.3246 0.9757 0.9891 0.7244 0.9129 0.9727 0.646 ] Network output: [ -0.01567 0.1036 0.9417 0.00107 -0.0004805 0.9904 0.0008066 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6443 0.6298 0.4542 0.2444 0.987 0.9915 0.6448 0.974 0.9826 0.4654 ] Network output: [ -0.03443 0.1164 0.9487 0.0008542 -0.0003835 1.007 0.0006438 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6414 0.6391 0.4584 0.2329 0.9855 0.9906 0.6415 0.9699 0.9802 0.4604 ] Network output: [ 0.008373 0.9641 0.01865 -0.0002707 0.0001215 0.9994 -0.000204 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01155 Epoch 2646 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02198 0.9907 0.9999 -4.929e-05 2.213e-05 -0.03474 -3.715e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02198 -0.005395 0.01904 0.0219 0.9411 0.9503 0.04395 0.8881 0.9063 0.113 ] Network output: [ 0.9869 0.03808 -0.009232 -0.0001057 4.745e-05 -0.003002 -7.966e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6406 0.1226 0.1442 0.185 0.9724 0.9873 0.7261 0.9029 0.9681 0.6515 ] Network output: [ -0.01209 0.966 1.019 -7.017e-05 3.15e-05 0.03853 -5.288e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04729 0.03497 0.04885 0.02701 0.9858 0.9899 0.04828 0.9709 0.9809 0.06004 ] Network output: [ 0.04501 -0.1787 1.081 -0.001429 0.0006416 1.002 -0.001077 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6205 0.5567 0.3244 0.9757 0.9891 0.7244 0.9129 0.9727 0.646 ] Network output: [ -0.01563 0.1034 0.9417 0.001071 -0.0004806 0.9905 0.0008068 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6444 0.6299 0.4541 0.2442 0.987 0.9915 0.6449 0.974 0.9826 0.4654 ] Network output: [ -0.03436 0.1162 0.9487 0.0008554 -0.000384 1.007 0.0006446 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6415 0.6392 0.4583 0.2328 0.9855 0.9906 0.6416 0.9699 0.9802 0.4604 ] Network output: [ 0.008358 0.9642 0.01864 -0.0002704 0.0001214 0.9994 -0.0002038 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01152 Epoch 2647 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02195 0.9907 0.9999 -4.936e-05 2.216e-05 -0.03474 -3.72e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02198 -0.005394 0.01905 0.02189 0.9411 0.9503 0.04394 0.8881 0.9063 0.1129 ] Network output: [ 0.9869 0.03796 -0.009184 -0.0001063 4.771e-05 -0.003006 -8.008e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6406 0.1225 0.1443 0.1848 0.9724 0.9873 0.7261 0.9029 0.9681 0.6515 ] Network output: [ -0.0121 0.9661 1.019 -6.986e-05 3.136e-05 0.03851 -5.265e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04728 0.03496 0.04883 0.02698 0.9858 0.9899 0.04827 0.9709 0.9809 0.06002 ] Network output: [ 0.04492 -0.1784 1.081 -0.001431 0.0006423 1.002 -0.001078 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6205 0.5568 0.3241 0.9757 0.9891 0.7244 0.9129 0.9727 0.646 ] Network output: [ -0.0156 0.1032 0.9418 0.001071 -0.0004808 0.9906 0.000807 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6445 0.6299 0.4541 0.2441 0.987 0.9915 0.6449 0.974 0.9826 0.4654 ] Network output: [ -0.0343 0.116 0.9487 0.0008565 -0.0003845 1.007 0.0006455 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6416 0.6393 0.4583 0.2326 0.9855 0.9906 0.6417 0.9699 0.9802 0.4603 ] Network output: [ 0.008343 0.9642 0.01863 -0.00027 0.0001212 0.9993 -0.0002035 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01149 Epoch 2648 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02192 0.9908 0.9999 -4.943e-05 2.219e-05 -0.03473 -3.725e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02198 -0.005393 0.01906 0.02187 0.9411 0.9503 0.04392 0.8881 0.9063 0.1129 ] Network output: [ 0.9869 0.03784 -0.009135 -0.0001068 4.796e-05 -0.003011 -8.051e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6406 0.1225 0.1444 0.1847 0.9724 0.9873 0.7261 0.9029 0.9681 0.6515 ] Network output: [ -0.01211 0.9662 1.019 -6.955e-05 3.122e-05 0.03849 -5.241e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04727 0.03495 0.04882 0.02696 0.9858 0.9899 0.04826 0.9709 0.9809 0.05999 ] Network output: [ 0.04484 -0.1782 1.081 -0.001432 0.0006429 1.002 -0.001079 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6204 0.5568 0.3239 0.9757 0.9891 0.7244 0.9129 0.9727 0.646 ] Network output: [ -0.01556 0.103 0.9418 0.001071 -0.0004809 0.9907 0.0008073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6446 0.63 0.4541 0.244 0.987 0.9915 0.645 0.974 0.9826 0.4654 ] Network output: [ -0.03424 0.1158 0.9487 0.0008576 -0.000385 1.007 0.0006463 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6417 0.6393 0.4583 0.2325 0.9855 0.9906 0.6417 0.9699 0.9802 0.4603 ] Network output: [ 0.008328 0.9643 0.01862 -0.0002697 0.0001211 0.9993 -0.0002032 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01146 Epoch 2649 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02189 0.9908 0.9999 -4.95e-05 2.222e-05 -0.03472 -3.73e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02198 -0.005392 0.01907 0.02186 0.9411 0.9503 0.04391 0.8881 0.9063 0.1129 ] Network output: [ 0.987 0.03772 -0.009087 -0.0001074 4.821e-05 -0.003015 -8.093e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6406 0.1225 0.1446 0.1845 0.9724 0.9873 0.7261 0.9029 0.9681 0.6515 ] Network output: [ -0.01212 0.9663 1.019 -6.924e-05 3.108e-05 0.03847 -5.218e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04726 0.03494 0.0488 0.02693 0.9858 0.9899 0.04825 0.9709 0.9809 0.05996 ] Network output: [ 0.04475 -0.1779 1.081 -0.001434 0.0006436 1.002 -0.00108 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6204 0.5568 0.3236 0.9757 0.9891 0.7244 0.9129 0.9727 0.646 ] Network output: [ -0.01552 0.1028 0.9418 0.001071 -0.000481 0.9907 0.0008075 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6446 0.6301 0.4541 0.2438 0.987 0.9915 0.6451 0.974 0.9826 0.4654 ] Network output: [ -0.03417 0.1157 0.9487 0.0008587 -0.0003855 1.007 0.0006471 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6417 0.6394 0.4582 0.2324 0.9855 0.9906 0.6418 0.9699 0.9802 0.4602 ] Network output: [ 0.008313 0.9644 0.01861 -0.0002693 0.0001209 0.9993 -0.000203 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01143 Epoch 2650 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02186 0.9909 0.9999 -4.957e-05 2.225e-05 -0.03471 -3.736e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02198 -0.005391 0.01908 0.02185 0.9411 0.9503 0.0439 0.8881 0.9063 0.1128 ] Network output: [ 0.987 0.0376 -0.009039 -0.000108 4.847e-05 -0.00302 -8.136e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6407 0.1224 0.1447 0.1844 0.9724 0.9873 0.7261 0.9029 0.9681 0.6515 ] Network output: [ -0.01213 0.9664 1.019 -6.892e-05 3.094e-05 0.03845 -5.194e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04726 0.03494 0.04878 0.0269 0.9858 0.9899 0.04824 0.9709 0.981 0.05994 ] Network output: [ 0.04466 -0.1776 1.081 -0.001435 0.0006442 1.002 -0.001081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6204 0.5568 0.3233 0.9757 0.9891 0.7244 0.9129 0.9727 0.6459 ] Network output: [ -0.01548 0.1027 0.9419 0.001072 -0.0004812 0.9908 0.0008077 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6447 0.6301 0.4541 0.2437 0.987 0.9915 0.6451 0.974 0.9826 0.4653 ] Network output: [ -0.03411 0.1155 0.9487 0.0008598 -0.000386 1.008 0.000648 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6418 0.6395 0.4582 0.2322 0.9855 0.9906 0.6419 0.9699 0.9802 0.4602 ] Network output: [ 0.008298 0.9644 0.0186 -0.000269 0.0001207 0.9993 -0.0002027 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01139 Epoch 2651 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02184 0.9909 0.9999 -4.964e-05 2.228e-05 -0.0347 -3.741e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02198 -0.00539 0.01909 0.02183 0.9411 0.9503 0.04389 0.8881 0.9063 0.1128 ] Network output: [ 0.987 0.03748 -0.00899 -0.0001085 4.872e-05 -0.003024 -8.179e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6407 0.1224 0.1448 0.1842 0.9724 0.9873 0.726 0.9029 0.9681 0.6515 ] Network output: [ -0.01214 0.9665 1.019 -6.861e-05 3.08e-05 0.03843 -5.17e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04725 0.03493 0.04877 0.02688 0.9858 0.9899 0.04823 0.9709 0.981 0.05991 ] Network output: [ 0.04457 -0.1773 1.081 -0.001436 0.0006449 1.002 -0.001083 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6204 0.5569 0.3231 0.9757 0.9891 0.7244 0.9129 0.9727 0.6459 ] Network output: [ -0.01544 0.1025 0.9419 0.001072 -0.0004813 0.9909 0.000808 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6448 0.6302 0.4541 0.2435 0.987 0.9915 0.6452 0.974 0.9826 0.4653 ] Network output: [ -0.03404 0.1153 0.9487 0.0008609 -0.0003865 1.008 0.0006488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6419 0.6396 0.4581 0.2321 0.9855 0.9906 0.642 0.9699 0.9802 0.4601 ] Network output: [ 0.008284 0.9645 0.01859 -0.0002686 0.0001206 0.9993 -0.0002024 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01136 Epoch 2652 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02181 0.991 0.9999 -4.97e-05 2.231e-05 -0.03469 -3.746e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02198 -0.00539 0.0191 0.02182 0.9411 0.9503 0.04388 0.8881 0.9063 0.1127 ] Network output: [ 0.9871 0.03736 -0.008942 -0.0001091 4.898e-05 -0.003029 -8.222e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6407 0.1224 0.1449 0.1841 0.9724 0.9873 0.726 0.9029 0.9681 0.6515 ] Network output: [ -0.01215 0.9665 1.019 -6.829e-05 3.066e-05 0.03841 -5.147e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04724 0.03492 0.04875 0.02685 0.9858 0.9899 0.04822 0.9709 0.981 0.05988 ] Network output: [ 0.04449 -0.1771 1.081 -0.001438 0.0006455 1.001 -0.001084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6203 0.5569 0.3228 0.9757 0.9891 0.7244 0.9129 0.9727 0.6459 ] Network output: [ -0.01541 0.1023 0.9419 0.001072 -0.0004814 0.9909 0.0008082 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6448 0.6302 0.4541 0.2434 0.987 0.9915 0.6453 0.974 0.9826 0.4653 ] Network output: [ -0.03398 0.1152 0.9487 0.0008619 -0.000387 1.008 0.0006496 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6419 0.6396 0.4581 0.232 0.9855 0.9906 0.642 0.9699 0.9802 0.4601 ] Network output: [ 0.00827 0.9645 0.01858 -0.0002682 0.0001204 0.9992 -0.0002022 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01133 Epoch 2653 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02178 0.991 0.9999 -4.977e-05 2.234e-05 -0.03469 -3.751e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02197 -0.005389 0.01911 0.0218 0.9411 0.9504 0.04387 0.8881 0.9063 0.1127 ] Network output: [ 0.9871 0.03724 -0.008893 -0.0001097 4.923e-05 -0.003034 -8.265e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6407 0.1223 0.145 0.1839 0.9724 0.9873 0.726 0.9029 0.9681 0.6515 ] Network output: [ -0.01216 0.9666 1.019 -6.797e-05 3.052e-05 0.03839 -5.123e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04723 0.03491 0.04873 0.02682 0.9858 0.9899 0.04821 0.9709 0.981 0.05986 ] Network output: [ 0.0444 -0.1768 1.081 -0.001439 0.0006461 1.001 -0.001085 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6203 0.5569 0.3226 0.9757 0.9891 0.7244 0.9129 0.9727 0.6459 ] Network output: [ -0.01537 0.1021 0.942 0.001073 -0.0004816 0.991 0.0008084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6449 0.6303 0.4541 0.2432 0.987 0.9915 0.6453 0.9741 0.9826 0.4653 ] Network output: [ -0.03392 0.115 0.9487 0.000863 -0.0003874 1.008 0.0006504 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.642 0.6397 0.458 0.2318 0.9855 0.9906 0.6421 0.9699 0.9802 0.46 ] Network output: [ 0.008256 0.9646 0.01857 -0.0002679 0.0001203 0.9992 -0.0002019 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0113 Epoch 2654 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02175 0.9911 0.9999 -4.984e-05 2.237e-05 -0.03468 -3.756e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02197 -0.005388 0.01912 0.02179 0.9411 0.9504 0.04385 0.8881 0.9063 0.1127 ] Network output: [ 0.9872 0.03712 -0.008845 -0.0001102 4.949e-05 -0.003039 -8.308e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6408 0.1223 0.1452 0.1838 0.9724 0.9873 0.726 0.9029 0.9681 0.6515 ] Network output: [ -0.01216 0.9667 1.019 -6.765e-05 3.037e-05 0.03837 -5.099e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04722 0.0349 0.04871 0.0268 0.9858 0.9899 0.04821 0.9709 0.981 0.05983 ] Network output: [ 0.04431 -0.1765 1.081 -0.001441 0.0006467 1.001 -0.001086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6203 0.5569 0.3223 0.9757 0.9892 0.7243 0.9129 0.9727 0.6458 ] Network output: [ -0.01534 0.102 0.942 0.001073 -0.0004817 0.9911 0.0008086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.645 0.6304 0.454 0.2431 0.987 0.9915 0.6454 0.9741 0.9826 0.4653 ] Network output: [ -0.03385 0.1148 0.9487 0.0008641 -0.0003879 1.008 0.0006512 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6421 0.6398 0.458 0.2317 0.9855 0.9906 0.6422 0.9699 0.9802 0.46 ] Network output: [ 0.008242 0.9647 0.01856 -0.0002675 0.0001201 0.9992 -0.0002016 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01127 Epoch 2655 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02172 0.9912 0.9999 -4.99e-05 2.24e-05 -0.03467 -3.761e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02197 -0.005387 0.01913 0.02178 0.9412 0.9504 0.04384 0.8882 0.9063 0.1126 ] Network output: [ 0.9872 0.037 -0.008796 -0.0001108 4.975e-05 -0.003045 -8.351e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6408 0.1223 0.1453 0.1836 0.9725 0.9873 0.726 0.9029 0.9681 0.6514 ] Network output: [ -0.01217 0.9668 1.019 -6.733e-05 3.023e-05 0.03836 -5.074e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04721 0.0349 0.0487 0.02677 0.9858 0.9899 0.0482 0.9709 0.981 0.0598 ] Network output: [ 0.04423 -0.1763 1.081 -0.001442 0.0006473 1.001 -0.001087 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6202 0.557 0.3221 0.9757 0.9892 0.7243 0.9129 0.9727 0.6458 ] Network output: [ -0.0153 0.1018 0.9421 0.001073 -0.0004818 0.9911 0.0008088 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.645 0.6304 0.454 0.243 0.987 0.9915 0.6455 0.9741 0.9826 0.4652 ] Network output: [ -0.03379 0.1147 0.9486 0.0008651 -0.0003884 1.008 0.000652 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6422 0.6398 0.4579 0.2316 0.9855 0.9906 0.6422 0.9699 0.9802 0.4599 ] Network output: [ 0.008228 0.9647 0.01855 -0.0002672 0.0001199 0.9992 -0.0002013 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01124 Epoch 2656 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0217 0.9912 0.9999 -4.997e-05 2.243e-05 -0.03466 -3.766e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02197 -0.005386 0.01914 0.02176 0.9412 0.9504 0.04383 0.8882 0.9063 0.1126 ] Network output: [ 0.9872 0.03689 -0.008748 -0.0001114 5.001e-05 -0.00305 -8.395e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6408 0.1222 0.1454 0.1835 0.9725 0.9873 0.726 0.9029 0.9682 0.6514 ] Network output: [ -0.01218 0.9669 1.019 -6.701e-05 3.008e-05 0.03834 -5.05e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0472 0.03489 0.04868 0.02675 0.9858 0.9899 0.04819 0.9709 0.981 0.05977 ] Network output: [ 0.04414 -0.176 1.081 -0.001443 0.000648 1.001 -0.001088 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6202 0.557 0.3218 0.9757 0.9892 0.7243 0.913 0.9727 0.6458 ] Network output: [ -0.01526 0.1016 0.9421 0.001073 -0.0004819 0.9912 0.000809 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6451 0.6305 0.454 0.2428 0.987 0.9915 0.6455 0.9741 0.9826 0.4652 ] Network output: [ -0.03373 0.1145 0.9486 0.0008662 -0.0003889 1.008 0.0006528 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6422 0.6399 0.4579 0.2314 0.9855 0.9906 0.6423 0.9699 0.9802 0.4599 ] Network output: [ 0.008214 0.9648 0.01854 -0.0002668 0.0001198 0.9992 -0.0002011 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01121 Epoch 2657 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02167 0.9913 0.9998 -5.003e-05 2.246e-05 -0.03465 -3.771e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02197 -0.005385 0.01915 0.02175 0.9412 0.9504 0.04382 0.8882 0.9063 0.1125 ] Network output: [ 0.9873 0.03677 -0.008699 -0.000112 5.027e-05 -0.003055 -8.439e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6408 0.1222 0.1455 0.1833 0.9725 0.9873 0.726 0.9029 0.9682 0.6514 ] Network output: [ -0.01219 0.967 1.019 -6.669e-05 2.994e-05 0.03832 -5.026e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0472 0.03488 0.04866 0.02672 0.9858 0.9899 0.04818 0.9709 0.981 0.05975 ] Network output: [ 0.04406 -0.1757 1.081 -0.001445 0.0006486 1.001 -0.001089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6202 0.557 0.3216 0.9757 0.9892 0.7243 0.913 0.9727 0.6458 ] Network output: [ -0.01523 0.1014 0.9421 0.001074 -0.000482 0.9913 0.0008092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6452 0.6306 0.454 0.2427 0.987 0.9915 0.6456 0.9741 0.9826 0.4652 ] Network output: [ -0.03367 0.1143 0.9486 0.0008672 -0.0003893 1.008 0.0006536 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6423 0.64 0.4578 0.2313 0.9855 0.9906 0.6424 0.9699 0.9802 0.4598 ] Network output: [ 0.008201 0.9648 0.01853 -0.0002664 0.0001196 0.9992 -0.0002008 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01118 Epoch 2658 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02164 0.9913 0.9998 -5.01e-05 2.249e-05 -0.03464 -3.776e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02197 -0.005384 0.01916 0.02174 0.9412 0.9504 0.04381 0.8882 0.9063 0.1125 ] Network output: [ 0.9873 0.03665 -0.008651 -0.0001126 5.053e-05 -0.003061 -8.482e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6409 0.1222 0.1456 0.1832 0.9725 0.9873 0.726 0.9029 0.9682 0.6514 ] Network output: [ -0.0122 0.967 1.019 -6.637e-05 2.979e-05 0.0383 -5.002e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04719 0.03487 0.04864 0.02669 0.9858 0.9899 0.04817 0.9709 0.981 0.05972 ] Network output: [ 0.04398 -0.1755 1.081 -0.001446 0.0006492 1.001 -0.00109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6202 0.557 0.3214 0.9757 0.9892 0.7243 0.913 0.9727 0.6458 ] Network output: [ -0.01519 0.1013 0.9422 0.001074 -0.0004822 0.9913 0.0008094 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6452 0.6306 0.454 0.2425 0.987 0.9915 0.6456 0.9741 0.9826 0.4652 ] Network output: [ -0.03361 0.1142 0.9486 0.0008682 -0.0003898 1.008 0.0006543 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6424 0.6401 0.4578 0.2312 0.9855 0.9906 0.6424 0.9699 0.9802 0.4598 ] Network output: [ 0.008187 0.9649 0.01852 -0.0002661 0.0001194 0.9991 -0.0002005 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01115 Epoch 2659 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02161 0.9914 0.9998 -5.016e-05 2.252e-05 -0.03463 -3.781e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02197 -0.005383 0.01917 0.02172 0.9412 0.9504 0.0438 0.8882 0.9063 0.1125 ] Network output: [ 0.9873 0.03653 -0.008602 -0.0001131 5.079e-05 -0.003067 -8.526e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6409 0.1221 0.1458 0.183 0.9725 0.9873 0.726 0.9029 0.9682 0.6514 ] Network output: [ -0.01221 0.9671 1.019 -6.604e-05 2.965e-05 0.03828 -4.977e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04718 0.03486 0.04863 0.02667 0.9858 0.9899 0.04816 0.9709 0.981 0.05969 ] Network output: [ 0.04389 -0.1752 1.081 -0.001447 0.0006497 1.001 -0.001091 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6201 0.5571 0.3211 0.9757 0.9892 0.7243 0.913 0.9727 0.6457 ] Network output: [ -0.01516 0.1011 0.9422 0.001074 -0.0004823 0.9914 0.0008096 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6453 0.6307 0.454 0.2424 0.987 0.9915 0.6457 0.9741 0.9826 0.4651 ] Network output: [ -0.03354 0.114 0.9486 0.0008693 -0.0003902 1.008 0.0006551 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6424 0.6401 0.4577 0.231 0.9855 0.9906 0.6425 0.9699 0.9802 0.4597 ] Network output: [ 0.008174 0.9649 0.01851 -0.0002657 0.0001193 0.9991 -0.0002002 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01112 Epoch 2660 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02159 0.9914 0.9998 -5.023e-05 2.255e-05 -0.03462 -3.785e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02197 -0.005382 0.01918 0.02171 0.9412 0.9504 0.04378 0.8882 0.9063 0.1124 ] Network output: [ 0.9874 0.03642 -0.008553 -0.0001137 5.105e-05 -0.003073 -8.57e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6409 0.1221 0.1459 0.1829 0.9725 0.9873 0.726 0.9029 0.9682 0.6514 ] Network output: [ -0.01221 0.9672 1.019 -6.572e-05 2.95e-05 0.03826 -4.953e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04717 0.03486 0.04861 0.02664 0.9858 0.9899 0.04815 0.9709 0.981 0.05967 ] Network output: [ 0.04381 -0.175 1.081 -0.001449 0.0006503 1.001 -0.001092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6201 0.5571 0.3209 0.9757 0.9892 0.7243 0.913 0.9727 0.6457 ] Network output: [ -0.01512 0.1009 0.9423 0.001075 -0.0004824 0.9914 0.0008098 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6453 0.6307 0.4539 0.2423 0.987 0.9915 0.6458 0.9741 0.9826 0.4651 ] Network output: [ -0.03348 0.1139 0.9486 0.0008703 -0.0003907 1.008 0.0006559 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6425 0.6402 0.4577 0.2309 0.9855 0.9906 0.6426 0.9699 0.9802 0.4597 ] Network output: [ 0.008161 0.965 0.01851 -0.0002653 0.0001191 0.9991 -0.0002 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01109 Epoch 2661 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02156 0.9915 0.9998 -5.029e-05 2.258e-05 -0.03462 -3.79e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02197 -0.005381 0.0192 0.0217 0.9412 0.9504 0.04377 0.8882 0.9063 0.1124 ] Network output: [ 0.9874 0.0363 -0.008505 -0.0001143 5.132e-05 -0.003079 -8.615e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.641 0.1221 0.146 0.1828 0.9725 0.9873 0.7259 0.9029 0.9682 0.6513 ] Network output: [ -0.01222 0.9673 1.019 -6.539e-05 2.936e-05 0.03825 -4.928e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04716 0.03485 0.04859 0.02662 0.9858 0.9899 0.04814 0.9709 0.981 0.05964 ] Network output: [ 0.04373 -0.1747 1.081 -0.00145 0.0006509 1.001 -0.001093 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6201 0.5571 0.3206 0.9757 0.9892 0.7243 0.913 0.9727 0.6457 ] Network output: [ -0.01509 0.1007 0.9423 0.001075 -0.0004825 0.9915 0.00081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6454 0.6308 0.4539 0.2421 0.987 0.9915 0.6458 0.9741 0.9826 0.4651 ] Network output: [ -0.03342 0.1137 0.9486 0.0008713 -0.0003912 1.008 0.0006566 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6426 0.6403 0.4576 0.2308 0.9855 0.9906 0.6427 0.9699 0.9802 0.4596 ] Network output: [ 0.008148 0.965 0.0185 -0.000265 0.000119 0.9991 -0.0001997 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01106 Epoch 2662 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02153 0.9915 0.9998 -5.036e-05 2.261e-05 -0.03461 -3.795e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02197 -0.005381 0.01921 0.02169 0.9412 0.9504 0.04376 0.8882 0.9064 0.1123 ] Network output: [ 0.9874 0.03618 -0.008456 -0.0001149 5.158e-05 -0.003086 -8.659e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.641 0.122 0.1461 0.1826 0.9725 0.9873 0.7259 0.9029 0.9682 0.6513 ] Network output: [ -0.01223 0.9674 1.019 -6.506e-05 2.921e-05 0.03823 -4.903e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04715 0.03484 0.04857 0.02659 0.9858 0.99 0.04813 0.9709 0.981 0.05961 ] Network output: [ 0.04364 -0.1745 1.081 -0.001451 0.0006515 1.001 -0.001094 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.62 0.5571 0.3204 0.9757 0.9892 0.7243 0.913 0.9727 0.6457 ] Network output: [ -0.01506 0.1006 0.9423 0.001075 -0.0004826 0.9916 0.0008101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6455 0.6308 0.4539 0.242 0.987 0.9915 0.6459 0.9741 0.9826 0.4651 ] Network output: [ -0.03336 0.1135 0.9486 0.0008723 -0.0003916 1.008 0.0006574 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6426 0.6403 0.4576 0.2306 0.9855 0.9906 0.6427 0.9699 0.9802 0.4596 ] Network output: [ 0.008135 0.9651 0.01849 -0.0002646 0.0001188 0.9991 -0.0001994 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01103 Epoch 2663 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0215 0.9916 0.9998 -5.042e-05 2.264e-05 -0.0346 -3.8e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02196 -0.00538 0.01922 0.02167 0.9412 0.9504 0.04375 0.8882 0.9064 0.1123 ] Network output: [ 0.9875 0.03607 -0.008407 -0.0001155 5.185e-05 -0.003092 -8.704e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.641 0.122 0.1463 0.1825 0.9725 0.9873 0.7259 0.9029 0.9682 0.6513 ] Network output: [ -0.01224 0.9674 1.019 -6.473e-05 2.906e-05 0.03821 -4.878e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04714 0.03483 0.04856 0.02657 0.9858 0.99 0.04812 0.9709 0.981 0.05958 ] Network output: [ 0.04356 -0.1742 1.081 -0.001452 0.000652 1.001 -0.001095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.62 0.5572 0.3202 0.9757 0.9892 0.7243 0.913 0.9727 0.6456 ] Network output: [ -0.01502 0.1004 0.9424 0.001075 -0.0004827 0.9916 0.0008103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6455 0.6309 0.4539 0.2419 0.987 0.9915 0.646 0.9741 0.9826 0.465 ] Network output: [ -0.0333 0.1134 0.9485 0.0008733 -0.0003921 1.008 0.0006581 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6427 0.6404 0.4575 0.2305 0.9855 0.9906 0.6428 0.9699 0.9802 0.4595 ] Network output: [ 0.008123 0.9651 0.01848 -0.0002642 0.0001186 0.999 -0.0001991 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01101 Epoch 2664 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02148 0.9916 0.9998 -5.048e-05 2.266e-05 -0.03459 -3.805e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02196 -0.005379 0.01923 0.02166 0.9412 0.9504 0.04374 0.8882 0.9064 0.1123 ] Network output: [ 0.9875 0.03595 -0.008359 -0.0001161 5.211e-05 -0.003099 -8.748e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.641 0.1219 0.1464 0.1823 0.9725 0.9873 0.7259 0.9029 0.9682 0.6513 ] Network output: [ -0.01225 0.9675 1.019 -6.44e-05 2.891e-05 0.03819 -4.854e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04713 0.03482 0.04854 0.02654 0.9858 0.99 0.04811 0.971 0.981 0.05955 ] Network output: [ 0.04348 -0.1739 1.081 -0.001454 0.0006526 1 -0.001096 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.62 0.5572 0.3199 0.9757 0.9892 0.7243 0.913 0.9727 0.6456 ] Network output: [ -0.01499 0.1002 0.9424 0.001075 -0.0004828 0.9917 0.0008105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6456 0.631 0.4539 0.2417 0.987 0.9915 0.646 0.9741 0.9826 0.465 ] Network output: [ -0.03324 0.1132 0.9485 0.0008743 -0.0003925 1.008 0.0006589 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6428 0.6405 0.4575 0.2304 0.9855 0.9906 0.6429 0.9699 0.9802 0.4595 ] Network output: [ 0.00811 0.9652 0.01848 -0.0002639 0.0001185 0.999 -0.0001989 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01098 Epoch 2665 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02145 0.9917 0.9998 -5.055e-05 2.269e-05 -0.03458 -3.809e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02196 -0.005378 0.01924 0.02165 0.9412 0.9504 0.04373 0.8882 0.9064 0.1122 ] Network output: [ 0.9876 0.03584 -0.00831 -0.0001167 5.238e-05 -0.003105 -8.793e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6411 0.1219 0.1465 0.1822 0.9725 0.9873 0.7259 0.9029 0.9682 0.6513 ] Network output: [ -0.01225 0.9676 1.018 -6.407e-05 2.876e-05 0.03817 -4.829e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04712 0.03481 0.04852 0.02652 0.9858 0.99 0.0481 0.971 0.981 0.05953 ] Network output: [ 0.0434 -0.1737 1.081 -0.001455 0.0006532 1 -0.001096 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.62 0.5572 0.3197 0.9757 0.9892 0.7243 0.913 0.9727 0.6456 ] Network output: [ -0.01496 0.1001 0.9425 0.001076 -0.0004829 0.9918 0.0008106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6457 0.631 0.4539 0.2416 0.987 0.9915 0.6461 0.9741 0.9826 0.465 ] Network output: [ -0.03318 0.1131 0.9485 0.0008753 -0.0003929 1.008 0.0006596 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6428 0.6405 0.4574 0.2302 0.9855 0.9906 0.6429 0.9699 0.9802 0.4594 ] Network output: [ 0.008098 0.9653 0.01847 -0.0002635 0.0001183 0.999 -0.0001986 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01095 Epoch 2666 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02142 0.9917 0.9998 -5.061e-05 2.272e-05 -0.03457 -3.814e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02196 -0.005377 0.01925 0.02163 0.9412 0.9504 0.04371 0.8882 0.9064 0.1122 ] Network output: [ 0.9876 0.03572 -0.008261 -0.0001173 5.265e-05 -0.003112 -8.838e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6411 0.1219 0.1466 0.1821 0.9725 0.9873 0.7259 0.903 0.9682 0.6513 ] Network output: [ -0.01226 0.9677 1.018 -6.374e-05 2.862e-05 0.03816 -4.804e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04711 0.03481 0.0485 0.02649 0.9858 0.99 0.04809 0.971 0.981 0.0595 ] Network output: [ 0.04332 -0.1734 1.081 -0.001456 0.0006537 1 -0.001097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6199 0.5572 0.3195 0.9757 0.9892 0.7242 0.913 0.9727 0.6455 ] Network output: [ -0.01493 0.09992 0.9425 0.001076 -0.000483 0.9918 0.0008108 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6457 0.6311 0.4538 0.2415 0.987 0.9915 0.6462 0.9741 0.9826 0.465 ] Network output: [ -0.03313 0.1129 0.9485 0.0008762 -0.0003934 1.008 0.0006603 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6429 0.6406 0.4574 0.2301 0.9855 0.9906 0.643 0.9699 0.9802 0.4594 ] Network output: [ 0.008086 0.9653 0.01846 -0.0002631 0.0001181 0.999 -0.0001983 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01092 Epoch 2667 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02139 0.9918 0.9998 -5.067e-05 2.275e-05 -0.03456 -3.819e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02196 -0.005376 0.01926 0.02162 0.9412 0.9504 0.0437 0.8882 0.9064 0.1122 ] Network output: [ 0.9876 0.03561 -0.008213 -0.0001179 5.292e-05 -0.003119 -8.883e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6411 0.1218 0.1468 0.1819 0.9725 0.9873 0.7259 0.903 0.9682 0.6512 ] Network output: [ -0.01227 0.9678 1.018 -6.341e-05 2.847e-05 0.03814 -4.779e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04711 0.0348 0.04848 0.02647 0.9858 0.99 0.04808 0.971 0.981 0.05947 ] Network output: [ 0.04324 -0.1732 1.081 -0.001457 0.0006543 1 -0.001098 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6199 0.5572 0.3193 0.9757 0.9892 0.7242 0.913 0.9727 0.6455 ] Network output: [ -0.01489 0.09975 0.9425 0.001076 -0.0004831 0.9919 0.000811 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6458 0.6311 0.4538 0.2413 0.987 0.9915 0.6462 0.9741 0.9826 0.4649 ] Network output: [ -0.03307 0.1128 0.9485 0.0008772 -0.0003938 1.008 0.0006611 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.643 0.6407 0.4573 0.23 0.9855 0.9906 0.6431 0.9699 0.9802 0.4593 ] Network output: [ 0.008074 0.9654 0.01846 -0.0002628 0.000118 0.999 -0.000198 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01089 Epoch 2668 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02137 0.9918 0.9998 -5.074e-05 2.278e-05 -0.03455 -3.824e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02196 -0.005376 0.01927 0.02161 0.9412 0.9504 0.04369 0.8882 0.9064 0.1121 ] Network output: [ 0.9877 0.03549 -0.008164 -0.0001185 5.319e-05 -0.003127 -8.929e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6411 0.1218 0.1469 0.1818 0.9725 0.9874 0.7259 0.903 0.9682 0.6512 ] Network output: [ -0.01228 0.9678 1.018 -6.307e-05 2.832e-05 0.03812 -4.753e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0471 0.03479 0.04847 0.02644 0.9858 0.99 0.04807 0.971 0.981 0.05944 ] Network output: [ 0.04316 -0.173 1.081 -0.001459 0.0006548 1 -0.001099 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6199 0.5573 0.319 0.9757 0.9892 0.7242 0.913 0.9727 0.6455 ] Network output: [ -0.01486 0.09959 0.9426 0.001076 -0.0004832 0.9919 0.0008111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6458 0.6312 0.4538 0.2412 0.987 0.9915 0.6463 0.9741 0.9826 0.4649 ] Network output: [ -0.03301 0.1126 0.9485 0.0008781 -0.0003942 1.008 0.0006618 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6431 0.6407 0.4573 0.2299 0.9855 0.9906 0.6431 0.9699 0.9802 0.4592 ] Network output: [ 0.008062 0.9654 0.01845 -0.0002624 0.0001178 0.999 -0.0001977 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01087 Epoch 2669 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02134 0.9919 0.9998 -5.08e-05 2.28e-05 -0.03454 -3.828e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02196 -0.005375 0.01928 0.0216 0.9412 0.9504 0.04368 0.8882 0.9064 0.1121 ] Network output: [ 0.9877 0.03538 -0.008115 -0.0001191 5.346e-05 -0.003134 -8.974e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6412 0.1217 0.147 0.1817 0.9725 0.9874 0.7259 0.903 0.9682 0.6512 ] Network output: [ -0.01228 0.9679 1.018 -6.274e-05 2.817e-05 0.0381 -4.728e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04709 0.03478 0.04845 0.02642 0.9858 0.99 0.04806 0.971 0.981 0.05942 ] Network output: [ 0.04308 -0.1727 1.081 -0.00146 0.0006553 1 -0.0011 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6199 0.5573 0.3188 0.9757 0.9892 0.7242 0.913 0.9727 0.6454 ] Network output: [ -0.01483 0.09943 0.9426 0.001076 -0.0004833 0.992 0.0008113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6459 0.6312 0.4538 0.2411 0.987 0.9915 0.6463 0.9741 0.9826 0.4649 ] Network output: [ -0.03295 0.1125 0.9484 0.0008791 -0.0003947 1.009 0.0006625 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6431 0.6408 0.4572 0.2297 0.9855 0.9906 0.6432 0.9699 0.9802 0.4592 ] Network output: [ 0.008051 0.9654 0.01844 -0.000262 0.0001176 0.9989 -0.0001975 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01084 Epoch 2670 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02131 0.9919 0.9998 -5.086e-05 2.283e-05 -0.03453 -3.833e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02196 -0.005374 0.01929 0.02159 0.9412 0.9504 0.04367 0.8882 0.9064 0.112 ] Network output: [ 0.9877 0.03527 -0.008066 -0.0001197 5.373e-05 -0.003142 -9.02e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6412 0.1217 0.1472 0.1815 0.9725 0.9874 0.7259 0.903 0.9682 0.6512 ] Network output: [ -0.01229 0.968 1.018 -6.24e-05 2.801e-05 0.03809 -4.703e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04708 0.03477 0.04843 0.02639 0.9858 0.99 0.04805 0.971 0.981 0.05939 ] Network output: [ 0.043 -0.1725 1.081 -0.001461 0.0006559 0.9999 -0.001101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6198 0.5573 0.3186 0.9757 0.9892 0.7242 0.913 0.9727 0.6454 ] Network output: [ -0.0148 0.09926 0.9426 0.001077 -0.0004834 0.9921 0.0008114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.646 0.6313 0.4538 0.2409 0.987 0.9915 0.6464 0.9741 0.9826 0.4649 ] Network output: [ -0.03289 0.1124 0.9484 0.00088 -0.0003951 1.009 0.0006632 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6432 0.6409 0.4571 0.2296 0.9855 0.9906 0.6433 0.9699 0.9802 0.4591 ] Network output: [ 0.008039 0.9655 0.01844 -0.0002616 0.0001175 0.9989 -0.0001972 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01081 Epoch 2671 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02128 0.992 0.9997 -5.092e-05 2.286e-05 -0.03452 -3.838e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02196 -0.005373 0.01931 0.02157 0.9412 0.9504 0.04365 0.8882 0.9064 0.112 ] Network output: [ 0.9878 0.03515 -0.008018 -0.0001203 5.4e-05 -0.00315 -9.066e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6412 0.1217 0.1473 0.1814 0.9725 0.9874 0.7259 0.903 0.9682 0.6511 ] Network output: [ -0.0123 0.9681 1.018 -6.206e-05 2.786e-05 0.03807 -4.677e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04707 0.03476 0.04841 0.02637 0.9858 0.99 0.04804 0.971 0.981 0.05936 ] Network output: [ 0.04292 -0.1722 1.081 -0.001462 0.0006564 0.9999 -0.001102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6198 0.5573 0.3184 0.9757 0.9892 0.7242 0.913 0.9727 0.6454 ] Network output: [ -0.01477 0.0991 0.9427 0.001077 -0.0004834 0.9921 0.0008116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.646 0.6313 0.4537 0.2408 0.987 0.9915 0.6465 0.9741 0.9826 0.4648 ] Network output: [ -0.03284 0.1122 0.9484 0.000881 -0.0003955 1.009 0.0006639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6432 0.6409 0.4571 0.2295 0.9855 0.9906 0.6433 0.97 0.9802 0.4591 ] Network output: [ 0.008028 0.9655 0.01843 -0.0002613 0.0001173 0.9989 -0.0001969 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01079 Epoch 2672 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02126 0.9921 0.9997 -5.098e-05 2.289e-05 -0.03451 -3.842e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02196 -0.005372 0.01932 0.02156 0.9412 0.9504 0.04364 0.8882 0.9064 0.112 ] Network output: [ 0.9878 0.03504 -0.007969 -0.0001209 5.428e-05 -0.003157 -9.112e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6413 0.1216 0.1474 0.1813 0.9725 0.9874 0.7259 0.903 0.9682 0.6511 ] Network output: [ -0.0123 0.9681 1.018 -6.173e-05 2.771e-05 0.03805 -4.652e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04706 0.03475 0.04839 0.02635 0.9858 0.99 0.04803 0.971 0.981 0.05933 ] Network output: [ 0.04285 -0.172 1.081 -0.001463 0.0006569 0.9998 -0.001103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6198 0.5573 0.3182 0.9757 0.9892 0.7242 0.913 0.9727 0.6453 ] Network output: [ -0.01474 0.09894 0.9427 0.001077 -0.0004835 0.9922 0.0008117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6461 0.6314 0.4537 0.2407 0.987 0.9915 0.6465 0.9741 0.9826 0.4648 ] Network output: [ -0.03278 0.1121 0.9484 0.0008819 -0.0003959 1.009 0.0006646 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6433 0.641 0.457 0.2294 0.9855 0.9906 0.6434 0.97 0.9802 0.459 ] Network output: [ 0.008017 0.9656 0.01843 -0.0002609 0.0001171 0.9989 -0.0001966 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01076 Epoch 2673 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02123 0.9921 0.9997 -5.104e-05 2.291e-05 -0.0345 -3.847e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02196 -0.005372 0.01933 0.02155 0.9412 0.9504 0.04363 0.8883 0.9064 0.1119 ] Network output: [ 0.9878 0.03493 -0.00792 -0.0001215 5.455e-05 -0.003166 -9.158e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6413 0.1216 0.1475 0.1811 0.9725 0.9874 0.7258 0.903 0.9682 0.6511 ] Network output: [ -0.01231 0.9682 1.018 -6.139e-05 2.756e-05 0.03803 -4.626e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04705 0.03475 0.04838 0.02632 0.9858 0.99 0.04802 0.971 0.981 0.0593 ] Network output: [ 0.04277 -0.1717 1.081 -0.001464 0.0006574 0.9997 -0.001104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6197 0.5573 0.3179 0.9757 0.9892 0.7242 0.913 0.9727 0.6453 ] Network output: [ -0.01471 0.09879 0.9428 0.001077 -0.0004836 0.9923 0.0008118 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6461 0.6315 0.4537 0.2406 0.987 0.9915 0.6466 0.9741 0.9826 0.4648 ] Network output: [ -0.03272 0.1119 0.9484 0.0008828 -0.0003963 1.009 0.0006653 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6434 0.641 0.457 0.2292 0.9855 0.9906 0.6435 0.97 0.9802 0.459 ] Network output: [ 0.008006 0.9656 0.01842 -0.0002605 0.000117 0.9989 -0.0001963 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01073 Epoch 2674 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0212 0.9922 0.9997 -5.11e-05 2.294e-05 -0.03449 -3.851e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02195 -0.005371 0.01934 0.02154 0.9412 0.9504 0.04362 0.8883 0.9064 0.1119 ] Network output: [ 0.9879 0.03482 -0.007871 -0.0001221 5.483e-05 -0.003174 -9.204e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6413 0.1215 0.1477 0.181 0.9725 0.9874 0.7258 0.903 0.9682 0.6511 ] Network output: [ -0.01232 0.9683 1.018 -6.105e-05 2.741e-05 0.03802 -4.601e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04704 0.03474 0.04836 0.0263 0.9858 0.99 0.04801 0.971 0.981 0.05927 ] Network output: [ 0.04269 -0.1715 1.081 -0.001466 0.0006579 0.9996 -0.001104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6197 0.5574 0.3177 0.9757 0.9892 0.7242 0.913 0.9727 0.6453 ] Network output: [ -0.01468 0.09863 0.9428 0.001077 -0.0004837 0.9923 0.000812 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6462 0.6315 0.4537 0.2404 0.987 0.9915 0.6466 0.9741 0.9826 0.4647 ] Network output: [ -0.03267 0.1118 0.9483 0.0008837 -0.0003967 1.009 0.000666 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6434 0.6411 0.4569 0.2291 0.9855 0.9906 0.6435 0.97 0.9802 0.4589 ] Network output: [ 0.007995 0.9657 0.01841 -0.0002601 0.0001168 0.9989 -0.000196 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01071 Epoch 2675 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02117 0.9922 0.9997 -5.116e-05 2.297e-05 -0.03448 -3.856e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02195 -0.00537 0.01935 0.02152 0.9413 0.9504 0.04361 0.8883 0.9064 0.1118 ] Network output: [ 0.9879 0.0347 -0.007822 -0.0001227 5.511e-05 -0.003182 -9.251e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6413 0.1215 0.1478 0.1809 0.9725 0.9874 0.7258 0.903 0.9682 0.651 ] Network output: [ -0.01232 0.9684 1.018 -6.071e-05 2.725e-05 0.038 -4.575e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04703 0.03473 0.04834 0.02628 0.9858 0.99 0.048 0.971 0.981 0.05925 ] Network output: [ 0.04262 -0.1713 1.081 -0.001467 0.0006584 0.9995 -0.001105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6197 0.5574 0.3175 0.9757 0.9892 0.7242 0.913 0.9728 0.6452 ] Network output: [ -0.01465 0.09847 0.9428 0.001078 -0.0004838 0.9924 0.0008121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6463 0.6316 0.4536 0.2403 0.987 0.9915 0.6467 0.9741 0.9826 0.4647 ] Network output: [ -0.03261 0.1117 0.9483 0.0008846 -0.0003971 1.009 0.0006667 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6435 0.6412 0.4569 0.229 0.9855 0.9906 0.6436 0.97 0.9802 0.4589 ] Network output: [ 0.007984 0.9657 0.01841 -0.0002598 0.0001166 0.9988 -0.0001958 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01068 Epoch 2676 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02115 0.9923 0.9997 -5.122e-05 2.3e-05 -0.03446 -3.86e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02195 -0.005369 0.01936 0.02151 0.9413 0.9504 0.0436 0.8883 0.9064 0.1118 ] Network output: [ 0.9879 0.03459 -0.007774 -0.0001234 5.538e-05 -0.003191 -9.297e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6414 0.1214 0.1479 0.1808 0.9725 0.9874 0.7258 0.903 0.9682 0.651 ] Network output: [ -0.01233 0.9685 1.018 -6.036e-05 2.71e-05 0.03798 -4.549e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04702 0.03472 0.04832 0.02625 0.9858 0.99 0.04799 0.971 0.981 0.05922 ] Network output: [ 0.04254 -0.171 1.081 -0.001468 0.0006589 0.9995 -0.001106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6197 0.5574 0.3173 0.9757 0.9892 0.7242 0.913 0.9728 0.6452 ] Network output: [ -0.01462 0.09832 0.9429 0.001078 -0.0004838 0.9924 0.0008122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6463 0.6316 0.4536 0.2402 0.987 0.9915 0.6467 0.9741 0.9826 0.4647 ] Network output: [ -0.03255 0.1115 0.9483 0.0008855 -0.0003975 1.009 0.0006674 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6436 0.6412 0.4568 0.2289 0.9855 0.9906 0.6436 0.97 0.9802 0.4588 ] Network output: [ 0.007974 0.9658 0.0184 -0.0002594 0.0001164 0.9988 -0.0001955 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01066 Epoch 2677 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02112 0.9923 0.9997 -5.128e-05 2.302e-05 -0.03445 -3.865e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02195 -0.005368 0.01937 0.0215 0.9413 0.9504 0.04358 0.8883 0.9064 0.1118 ] Network output: [ 0.988 0.03448 -0.007725 -0.000124 5.566e-05 -0.0032 -9.344e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6414 0.1214 0.1481 0.1806 0.9725 0.9874 0.7258 0.903 0.9682 0.651 ] Network output: [ -0.01234 0.9685 1.018 -6.002e-05 2.695e-05 0.03796 -4.523e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04701 0.03471 0.0483 0.02623 0.9859 0.99 0.04798 0.971 0.981 0.05919 ] Network output: [ 0.04246 -0.1708 1.081 -0.001469 0.0006594 0.9994 -0.001107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6196 0.5574 0.3171 0.9757 0.9892 0.7242 0.913 0.9728 0.6452 ] Network output: [ -0.01459 0.09816 0.9429 0.001078 -0.0004839 0.9925 0.0008123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6464 0.6317 0.4536 0.2401 0.987 0.9915 0.6468 0.9741 0.9826 0.4647 ] Network output: [ -0.0325 0.1114 0.9483 0.0008864 -0.0003979 1.009 0.000668 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6436 0.6413 0.4568 0.2288 0.9855 0.9906 0.6437 0.97 0.9802 0.4587 ] Network output: [ 0.007963 0.9658 0.0184 -0.000259 0.0001163 0.9988 -0.0001952 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01063 Epoch 2678 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02109 0.9924 0.9997 -5.134e-05 2.305e-05 -0.03444 -3.869e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02195 -0.005368 0.01938 0.02149 0.9413 0.9505 0.04357 0.8883 0.9064 0.1117 ] Network output: [ 0.988 0.03437 -0.007676 -0.0001246 5.594e-05 -0.003209 -9.391e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6414 0.1214 0.1482 0.1805 0.9725 0.9874 0.7258 0.903 0.9682 0.651 ] Network output: [ -0.01234 0.9686 1.018 -5.968e-05 2.679e-05 0.03795 -4.497e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.047 0.0347 0.04829 0.02621 0.9859 0.99 0.04797 0.971 0.981 0.05916 ] Network output: [ 0.04239 -0.1706 1.081 -0.00147 0.0006599 0.9993 -0.001108 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6196 0.5574 0.3169 0.9757 0.9892 0.7242 0.913 0.9728 0.6451 ] Network output: [ -0.01457 0.09801 0.943 0.001078 -0.000484 0.9925 0.0008124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6464 0.6317 0.4536 0.24 0.987 0.9915 0.6469 0.9741 0.9826 0.4646 ] Network output: [ -0.03244 0.1112 0.9483 0.0008873 -0.0003983 1.009 0.0006687 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6437 0.6414 0.4567 0.2286 0.9855 0.9906 0.6438 0.97 0.9802 0.4587 ] Network output: [ 0.007953 0.9658 0.0184 -0.0002586 0.0001161 0.9988 -0.0001949 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01061 Epoch 2679 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02106 0.9924 0.9997 -5.14e-05 2.308e-05 -0.03443 -3.874e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02195 -0.005367 0.0194 0.02148 0.9413 0.9505 0.04356 0.8883 0.9064 0.1117 ] Network output: [ 0.988 0.03426 -0.007627 -0.0001252 5.622e-05 -0.003218 -9.438e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6415 0.1213 0.1483 0.1804 0.9725 0.9874 0.7258 0.903 0.9682 0.6509 ] Network output: [ -0.01235 0.9687 1.018 -5.933e-05 2.664e-05 0.03793 -4.471e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04699 0.03469 0.04827 0.02618 0.9859 0.99 0.04796 0.971 0.981 0.05913 ] Network output: [ 0.04231 -0.1703 1.081 -0.001471 0.0006604 0.9992 -0.001109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6196 0.5574 0.3167 0.9757 0.9892 0.7242 0.913 0.9728 0.6451 ] Network output: [ -0.01454 0.09785 0.943 0.001078 -0.000484 0.9926 0.0008125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6465 0.6318 0.4536 0.2398 0.987 0.9915 0.6469 0.9741 0.9826 0.4646 ] Network output: [ -0.03239 0.1111 0.9482 0.0008882 -0.0003987 1.009 0.0006694 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6438 0.6414 0.4567 0.2285 0.9855 0.9906 0.6438 0.97 0.9802 0.4586 ] Network output: [ 0.007943 0.9659 0.01839 -0.0002583 0.0001159 0.9988 -0.0001946 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01058 Epoch 2680 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02104 0.9925 0.9997 -5.146e-05 2.31e-05 -0.03442 -3.878e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02195 -0.005366 0.01941 0.02147 0.9413 0.9505 0.04355 0.8883 0.9064 0.1116 ] Network output: [ 0.9881 0.03415 -0.007578 -0.0001259 5.651e-05 -0.003227 -9.486e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6415 0.1213 0.1485 0.1803 0.9725 0.9874 0.7258 0.903 0.9682 0.6509 ] Network output: [ -0.01235 0.9688 1.018 -5.898e-05 2.648e-05 0.03791 -4.445e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04698 0.03468 0.04825 0.02616 0.9859 0.99 0.04795 0.971 0.981 0.0591 ] Network output: [ 0.04224 -0.1701 1.08 -0.001472 0.0006608 0.9991 -0.001109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6195 0.5575 0.3165 0.9757 0.9892 0.7242 0.913 0.9728 0.6451 ] Network output: [ -0.01451 0.0977 0.943 0.001078 -0.0004841 0.9927 0.0008127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6465 0.6318 0.4535 0.2397 0.987 0.9915 0.647 0.9741 0.9826 0.4646 ] Network output: [ -0.03233 0.111 0.9482 0.0008891 -0.0003991 1.009 0.00067 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6438 0.6415 0.4566 0.2284 0.9855 0.9906 0.6439 0.97 0.9802 0.4586 ] Network output: [ 0.007933 0.9659 0.01839 -0.0002579 0.0001158 0.9988 -0.0001943 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01056 Epoch 2681 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02101 0.9925 0.9997 -5.152e-05 2.313e-05 -0.03441 -3.882e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02195 -0.005366 0.01942 0.02145 0.9413 0.9505 0.04354 0.8883 0.9065 0.1116 ] Network output: [ 0.9881 0.03404 -0.007529 -0.0001265 5.679e-05 -0.003237 -9.533e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6415 0.1212 0.1486 0.1801 0.9725 0.9874 0.7258 0.903 0.9682 0.6509 ] Network output: [ -0.01236 0.9688 1.018 -5.864e-05 2.632e-05 0.03789 -4.419e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04697 0.03467 0.04823 0.02614 0.9859 0.99 0.04794 0.971 0.981 0.05907 ] Network output: [ 0.04217 -0.1699 1.08 -0.001473 0.0006613 0.9991 -0.00111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6195 0.5575 0.3163 0.9757 0.9892 0.7242 0.913 0.9728 0.645 ] Network output: [ -0.01448 0.09755 0.9431 0.001078 -0.0004842 0.9927 0.0008128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6466 0.6319 0.4535 0.2396 0.987 0.9915 0.647 0.9741 0.9826 0.4645 ] Network output: [ -0.03228 0.1109 0.9482 0.0008899 -0.0003995 1.009 0.0006707 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6439 0.6415 0.4565 0.2283 0.9855 0.9906 0.644 0.97 0.9802 0.4585 ] Network output: [ 0.007924 0.966 0.01838 -0.0002575 0.0001156 0.9988 -0.0001941 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01053 Epoch 2682 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02098 0.9926 0.9997 -5.158e-05 2.315e-05 -0.0344 -3.887e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02195 -0.005365 0.01943 0.02144 0.9413 0.9505 0.04353 0.8883 0.9065 0.1116 ] Network output: [ 0.9881 0.03393 -0.00748 -0.0001271 5.707e-05 -0.003246 -9.581e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6416 0.1212 0.1487 0.18 0.9725 0.9874 0.7258 0.903 0.9682 0.6508 ] Network output: [ -0.01237 0.9689 1.018 -5.829e-05 2.617e-05 0.03788 -4.393e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04696 0.03466 0.04821 0.02611 0.9859 0.99 0.04793 0.971 0.981 0.05905 ] Network output: [ 0.04209 -0.1697 1.08 -0.001474 0.0006618 0.999 -0.001111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6195 0.5575 0.3161 0.9757 0.9892 0.7242 0.913 0.9728 0.645 ] Network output: [ -0.01446 0.0974 0.9431 0.001079 -0.0004842 0.9928 0.0008129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6467 0.6319 0.4535 0.2395 0.987 0.9915 0.6471 0.9741 0.9826 0.4645 ] Network output: [ -0.03223 0.1107 0.9482 0.0008908 -0.0003999 1.009 0.0006713 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6439 0.6416 0.4565 0.2282 0.9855 0.9906 0.644 0.97 0.9802 0.4585 ] Network output: [ 0.007914 0.966 0.01838 -0.0002571 0.0001154 0.9987 -0.0001938 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01051 Epoch 2683 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02096 0.9926 0.9997 -5.163e-05 2.318e-05 -0.03439 -3.891e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02195 -0.005364 0.01944 0.02143 0.9413 0.9505 0.04351 0.8883 0.9065 0.1115 ] Network output: [ 0.9882 0.03382 -0.007432 -0.0001278 5.736e-05 -0.003256 -9.629e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6416 0.1211 0.1488 0.1799 0.9725 0.9874 0.7258 0.903 0.9682 0.6508 ] Network output: [ -0.01237 0.969 1.018 -5.794e-05 2.601e-05 0.03786 -4.367e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04696 0.03466 0.04819 0.02609 0.9859 0.99 0.04792 0.971 0.981 0.05902 ] Network output: [ 0.04202 -0.1694 1.08 -0.001475 0.0006622 0.9989 -0.001112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6195 0.5575 0.3159 0.9757 0.9892 0.7242 0.913 0.9728 0.6449 ] Network output: [ -0.01443 0.09725 0.9432 0.001079 -0.0004843 0.9928 0.000813 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6467 0.632 0.4534 0.2394 0.987 0.9915 0.6471 0.9741 0.9826 0.4645 ] Network output: [ -0.03217 0.1106 0.9481 0.0008916 -0.0004003 1.009 0.000672 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.644 0.6417 0.4564 0.228 0.9855 0.9906 0.6441 0.97 0.9803 0.4584 ] Network output: [ 0.007905 0.966 0.01838 -0.0002567 0.0001153 0.9987 -0.0001935 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01048 Epoch 2684 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02093 0.9927 0.9997 -5.169e-05 2.321e-05 -0.03437 -3.896e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02194 -0.005363 0.01945 0.02142 0.9413 0.9505 0.0435 0.8883 0.9065 0.1115 ] Network output: [ 0.9882 0.03371 -0.007383 -0.0001284 5.765e-05 -0.003266 -9.677e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6416 0.1211 0.149 0.1798 0.9725 0.9874 0.7258 0.903 0.9682 0.6508 ] Network output: [ -0.01238 0.9691 1.018 -5.759e-05 2.585e-05 0.03784 -4.34e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04695 0.03465 0.04817 0.02607 0.9859 0.99 0.04791 0.971 0.981 0.05899 ] Network output: [ 0.04195 -0.1692 1.08 -0.001476 0.0006627 0.9988 -0.001112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6194 0.5575 0.3157 0.9757 0.9892 0.7242 0.913 0.9728 0.6449 ] Network output: [ -0.0144 0.0971 0.9432 0.001079 -0.0004843 0.9929 0.0008131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6468 0.632 0.4534 0.2393 0.987 0.9915 0.6472 0.9741 0.9826 0.4644 ] Network output: [ -0.03212 0.1105 0.9481 0.0008925 -0.0004007 1.009 0.0006726 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6441 0.6417 0.4564 0.2279 0.9856 0.9906 0.6441 0.97 0.9803 0.4583 ] Network output: [ 0.007896 0.9661 0.01837 -0.0002564 0.0001151 0.9987 -0.0001932 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01046 Epoch 2685 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0209 0.9927 0.9996 -5.175e-05 2.323e-05 -0.03436 -3.9e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02194 -0.005363 0.01947 0.02141 0.9413 0.9505 0.04349 0.8883 0.9065 0.1114 ] Network output: [ 0.9882 0.0336 -0.007334 -0.000129 5.793e-05 -0.003276 -9.725e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6417 0.121 0.1491 0.1797 0.9725 0.9874 0.7258 0.903 0.9682 0.6507 ] Network output: [ -0.01238 0.9691 1.018 -5.724e-05 2.57e-05 0.03783 -4.314e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04694 0.03464 0.04816 0.02605 0.9859 0.99 0.0479 0.971 0.981 0.05896 ] Network output: [ 0.04188 -0.169 1.08 -0.001477 0.0006631 0.9988 -0.001113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6194 0.5575 0.3155 0.9757 0.9892 0.7242 0.913 0.9728 0.6448 ] Network output: [ -0.01438 0.09696 0.9432 0.001079 -0.0004844 0.9929 0.0008131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6468 0.6321 0.4534 0.2391 0.987 0.9915 0.6472 0.9741 0.9826 0.4644 ] Network output: [ -0.03207 0.1104 0.9481 0.0008933 -0.000401 1.009 0.0006732 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6441 0.6418 0.4563 0.2278 0.9856 0.9906 0.6442 0.97 0.9803 0.4583 ] Network output: [ 0.007886 0.9661 0.01837 -0.000256 0.0001149 0.9987 -0.0001929 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01044 Epoch 2686 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02087 0.9928 0.9996 -5.18e-05 2.326e-05 -0.03435 -3.904e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02194 -0.005362 0.01948 0.0214 0.9413 0.9505 0.04348 0.8883 0.9065 0.1114 ] Network output: [ 0.9883 0.0335 -0.007285 -0.0001297 5.822e-05 -0.003287 -9.774e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6417 0.121 0.1492 0.1796 0.9725 0.9874 0.7258 0.903 0.9682 0.6507 ] Network output: [ -0.01239 0.9692 1.018 -5.689e-05 2.554e-05 0.03781 -4.287e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04693 0.03463 0.04814 0.02603 0.9859 0.99 0.04789 0.971 0.981 0.05893 ] Network output: [ 0.04181 -0.1688 1.08 -0.001478 0.0006636 0.9987 -0.001114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6194 0.5575 0.3153 0.9757 0.9892 0.7242 0.913 0.9728 0.6448 ] Network output: [ -0.01435 0.09681 0.9433 0.001079 -0.0004844 0.993 0.0008132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6469 0.6321 0.4534 0.239 0.987 0.9915 0.6473 0.9741 0.9826 0.4644 ] Network output: [ -0.03201 0.1102 0.948 0.0008941 -0.0004014 1.009 0.0006738 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6442 0.6418 0.4563 0.2277 0.9856 0.9906 0.6443 0.97 0.9803 0.4582 ] Network output: [ 0.007878 0.9662 0.01837 -0.0002556 0.0001147 0.9987 -0.0001926 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01041 Epoch 2687 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02085 0.9928 0.9996 -5.186e-05 2.328e-05 -0.03434 -3.908e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02194 -0.005361 0.01949 0.02139 0.9413 0.9505 0.04347 0.8883 0.9065 0.1113 ] Network output: [ 0.9883 0.03339 -0.007236 -0.0001303 5.851e-05 -0.003297 -9.822e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6417 0.1209 0.1494 0.1794 0.9725 0.9874 0.7258 0.903 0.9682 0.6507 ] Network output: [ -0.01239 0.9693 1.017 -5.653e-05 2.538e-05 0.03779 -4.261e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04692 0.03462 0.04812 0.026 0.9859 0.99 0.04788 0.971 0.981 0.0589 ] Network output: [ 0.04174 -0.1686 1.08 -0.001479 0.000664 0.9986 -0.001115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6193 0.5575 0.3151 0.9758 0.9892 0.7242 0.913 0.9728 0.6448 ] Network output: [ -0.01433 0.09666 0.9433 0.001079 -0.0004845 0.9931 0.0008133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6469 0.6322 0.4533 0.2389 0.987 0.9915 0.6474 0.9741 0.9826 0.4643 ] Network output: [ -0.03196 0.1101 0.948 0.000895 -0.0004018 1.009 0.0006745 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6443 0.6419 0.4562 0.2276 0.9856 0.9906 0.6443 0.97 0.9803 0.4582 ] Network output: [ 0.007869 0.9662 0.01837 -0.0002552 0.0001146 0.9987 -0.0001923 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01039 Epoch 2688 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02082 0.9929 0.9996 -5.192e-05 2.331e-05 -0.03433 -3.913e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02194 -0.005361 0.0195 0.02138 0.9413 0.9505 0.04346 0.8883 0.9065 0.1113 ] Network output: [ 0.9883 0.03328 -0.007187 -0.000131 5.88e-05 -0.003308 -9.871e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6418 0.1209 0.1495 0.1793 0.9725 0.9874 0.7258 0.903 0.9682 0.6506 ] Network output: [ -0.0124 0.9694 1.017 -5.618e-05 2.522e-05 0.03778 -4.234e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04691 0.03461 0.0481 0.02598 0.9859 0.99 0.04787 0.971 0.981 0.05887 ] Network output: [ 0.04167 -0.1684 1.08 -0.00148 0.0006644 0.9986 -0.001115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6193 0.5576 0.3149 0.9758 0.9892 0.7242 0.913 0.9728 0.6447 ] Network output: [ -0.0143 0.09652 0.9434 0.001079 -0.0004845 0.9931 0.0008134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.647 0.6322 0.4533 0.2388 0.987 0.9915 0.6474 0.9741 0.9826 0.4643 ] Network output: [ -0.03191 0.11 0.948 0.0008958 -0.0004021 1.009 0.0006751 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6443 0.642 0.4561 0.2275 0.9856 0.9906 0.6444 0.97 0.9803 0.4581 ] Network output: [ 0.00786 0.9662 0.01837 -0.0002548 0.0001144 0.9987 -0.000192 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01037 Epoch 2689 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02079 0.9929 0.9996 -5.197e-05 2.333e-05 -0.03432 -3.917e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02194 -0.00536 0.01951 0.02136 0.9413 0.9505 0.04344 0.8883 0.9065 0.1113 ] Network output: [ 0.9884 0.03317 -0.007138 -0.0001316 5.909e-05 -0.003319 -9.92e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6418 0.1208 0.1497 0.1792 0.9725 0.9874 0.7258 0.903 0.9682 0.6506 ] Network output: [ -0.0124 0.9694 1.017 -5.582e-05 2.506e-05 0.03776 -4.207e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0469 0.0346 0.04808 0.02596 0.9859 0.99 0.04786 0.971 0.981 0.05884 ] Network output: [ 0.0416 -0.1681 1.08 -0.001481 0.0006648 0.9985 -0.001116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6193 0.5576 0.3147 0.9758 0.9892 0.7242 0.913 0.9728 0.6447 ] Network output: [ -0.01428 0.09638 0.9434 0.001079 -0.0004846 0.9932 0.0008135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.647 0.6323 0.4533 0.2387 0.987 0.9915 0.6475 0.9741 0.9826 0.4643 ] Network output: [ -0.03186 0.1099 0.948 0.0008966 -0.0004025 1.01 0.0006757 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6444 0.642 0.4561 0.2274 0.9856 0.9906 0.6444 0.97 0.9803 0.458 ] Network output: [ 0.007852 0.9663 0.01836 -0.0002544 0.0001142 0.9986 -0.0001918 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01035 Epoch 2690 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02077 0.993 0.9996 -5.203e-05 2.336e-05 -0.0343 -3.921e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02194 -0.005359 0.01953 0.02135 0.9413 0.9505 0.04343 0.8883 0.9065 0.1112 ] Network output: [ 0.9884 0.03307 -0.007089 -0.0001323 5.939e-05 -0.00333 -9.969e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6418 0.1208 0.1498 0.1791 0.9725 0.9874 0.7258 0.903 0.9682 0.6506 ] Network output: [ -0.01241 0.9695 1.017 -5.547e-05 2.49e-05 0.03774 -4.18e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04689 0.03459 0.04806 0.02594 0.9859 0.99 0.04785 0.971 0.981 0.05881 ] Network output: [ 0.04153 -0.1679 1.08 -0.001482 0.0006653 0.9984 -0.001117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6192 0.5576 0.3146 0.9758 0.9892 0.7242 0.913 0.9728 0.6446 ] Network output: [ -0.01425 0.09623 0.9434 0.001079 -0.0004846 0.9932 0.0008135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6471 0.6323 0.4533 0.2386 0.987 0.9915 0.6475 0.9741 0.9826 0.4642 ] Network output: [ -0.03181 0.1098 0.9479 0.0008974 -0.0004029 1.01 0.0006763 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6444 0.6421 0.456 0.2272 0.9856 0.9906 0.6445 0.97 0.9803 0.458 ] Network output: [ 0.007844 0.9663 0.01836 -0.0002541 0.0001141 0.9986 -0.0001915 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01032 Epoch 2691 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02074 0.993 0.9996 -5.209e-05 2.338e-05 -0.03429 -3.925e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02194 -0.005359 0.01954 0.02134 0.9413 0.9505 0.04342 0.8883 0.9065 0.1112 ] Network output: [ 0.9884 0.03296 -0.00704 -0.0001329 5.968e-05 -0.003342 -0.0001002 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6418 0.1207 0.1499 0.179 0.9725 0.9874 0.7258 0.903 0.9682 0.6505 ] Network output: [ -0.01241 0.9696 1.017 -5.511e-05 2.474e-05 0.03773 -4.153e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04688 0.03458 0.04804 0.02592 0.9859 0.99 0.04783 0.971 0.981 0.05878 ] Network output: [ 0.04146 -0.1677 1.08 -0.001483 0.0006657 0.9983 -0.001117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6192 0.5576 0.3144 0.9758 0.9892 0.7242 0.913 0.9728 0.6446 ] Network output: [ -0.01423 0.09609 0.9435 0.00108 -0.0004847 0.9933 0.0008136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6471 0.6324 0.4532 0.2385 0.987 0.9915 0.6476 0.9741 0.9826 0.4642 ] Network output: [ -0.03176 0.1097 0.9479 0.0008982 -0.0004032 1.01 0.0006769 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6445 0.6421 0.456 0.2271 0.9856 0.9906 0.6446 0.97 0.9803 0.4579 ] Network output: [ 0.007836 0.9663 0.01836 -0.0002537 0.0001139 0.9986 -0.0001912 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0103 Epoch 2692 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02071 0.993 0.9996 -5.214e-05 2.341e-05 -0.03428 -3.93e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02194 -0.005358 0.01955 0.02133 0.9413 0.9505 0.04341 0.8883 0.9065 0.1111 ] Network output: [ 0.9885 0.03286 -0.006991 -0.0001336 5.998e-05 -0.003353 -0.0001007 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6419 0.1207 0.1501 0.1789 0.9725 0.9874 0.7258 0.903 0.9682 0.6505 ] Network output: [ -0.01242 0.9696 1.017 -5.475e-05 2.458e-05 0.03771 -4.126e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04687 0.03457 0.04802 0.0259 0.9859 0.99 0.04782 0.971 0.981 0.05875 ] Network output: [ 0.04139 -0.1675 1.08 -0.001484 0.0006661 0.9983 -0.001118 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6192 0.5576 0.3142 0.9758 0.9892 0.7242 0.913 0.9728 0.6445 ] Network output: [ -0.01421 0.09595 0.9435 0.00108 -0.0004847 0.9933 0.0008137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6472 0.6324 0.4532 0.2384 0.987 0.9915 0.6476 0.9741 0.9826 0.4641 ] Network output: [ -0.03171 0.1095 0.9479 0.000899 -0.0004036 1.01 0.0006775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6445 0.6422 0.4559 0.227 0.9856 0.9906 0.6446 0.97 0.9803 0.4578 ] Network output: [ 0.007828 0.9664 0.01836 -0.0002533 0.0001137 0.9986 -0.0001909 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01028 Epoch 2693 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02069 0.9931 0.9996 -5.22e-05 2.343e-05 -0.03427 -3.934e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02194 -0.005358 0.01956 0.02132 0.9413 0.9505 0.0434 0.8883 0.9065 0.1111 ] Network output: [ 0.9885 0.03275 -0.006942 -0.0001343 6.027e-05 -0.003365 -0.0001012 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6419 0.1206 0.1502 0.1788 0.9726 0.9874 0.7258 0.903 0.9682 0.6505 ] Network output: [ -0.01242 0.9697 1.017 -5.439e-05 2.442e-05 0.0377 -4.099e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04686 0.03456 0.04801 0.02587 0.9859 0.99 0.04781 0.971 0.981 0.05872 ] Network output: [ 0.04132 -0.1673 1.08 -0.001485 0.0006665 0.9982 -0.001119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6192 0.5576 0.314 0.9758 0.9892 0.7242 0.913 0.9728 0.6445 ] Network output: [ -0.01418 0.09581 0.9436 0.00108 -0.0004847 0.9934 0.0008137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6472 0.6324 0.4532 0.2383 0.987 0.9915 0.6477 0.9741 0.9826 0.4641 ] Network output: [ -0.03166 0.1094 0.9478 0.0008998 -0.0004039 1.01 0.0006781 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6446 0.6423 0.4558 0.2269 0.9856 0.9906 0.6447 0.97 0.9803 0.4578 ] Network output: [ 0.00782 0.9664 0.01836 -0.0002529 0.0001135 0.9986 -0.0001906 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01026 Epoch 2694 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02066 0.9931 0.9996 -5.225e-05 2.346e-05 -0.03425 -3.938e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02193 -0.005357 0.01957 0.02131 0.9413 0.9505 0.04339 0.8884 0.9065 0.1111 ] Network output: [ 0.9885 0.03265 -0.006894 -0.0001349 6.057e-05 -0.003377 -0.0001017 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6419 0.1206 0.1503 0.1787 0.9726 0.9874 0.7258 0.903 0.9682 0.6504 ] Network output: [ -0.01243 0.9698 1.017 -5.404e-05 2.426e-05 0.03768 -4.072e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04685 0.03455 0.04799 0.02585 0.9859 0.99 0.0478 0.971 0.981 0.05869 ] Network output: [ 0.04125 -0.1671 1.08 -0.001485 0.0006669 0.9981 -0.001119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6191 0.5576 0.3138 0.9758 0.9892 0.7242 0.913 0.9728 0.6444 ] Network output: [ -0.01416 0.09567 0.9436 0.00108 -0.0004848 0.9934 0.0008138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6473 0.6325 0.4531 0.2382 0.987 0.9915 0.6477 0.9741 0.9826 0.4641 ] Network output: [ -0.03161 0.1093 0.9478 0.0009005 -0.0004043 1.01 0.0006787 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6447 0.6423 0.4558 0.2268 0.9856 0.9906 0.6447 0.97 0.9803 0.4577 ] Network output: [ 0.007812 0.9664 0.01836 -0.0002525 0.0001134 0.9986 -0.0001903 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01024 Epoch 2695 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02063 0.9932 0.9996 -5.231e-05 2.348e-05 -0.03424 -3.942e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02193 -0.005356 0.01959 0.0213 0.9413 0.9505 0.04337 0.8884 0.9065 0.111 ] Network output: [ 0.9886 0.03254 -0.006845 -0.0001356 6.087e-05 -0.003389 -0.0001022 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.642 0.1205 0.1505 0.1786 0.9726 0.9874 0.7258 0.903 0.9682 0.6504 ] Network output: [ -0.01243 0.9699 1.017 -5.367e-05 2.41e-05 0.03766 -4.045e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04684 0.03454 0.04797 0.02583 0.9859 0.99 0.04779 0.971 0.981 0.05866 ] Network output: [ 0.04119 -0.1669 1.08 -0.001486 0.0006673 0.9981 -0.00112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6191 0.5576 0.3137 0.9758 0.9892 0.7242 0.913 0.9728 0.6444 ] Network output: [ -0.01414 0.09553 0.9437 0.00108 -0.0004848 0.9935 0.0008139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6473 0.6325 0.4531 0.2381 0.987 0.9915 0.6478 0.9741 0.9826 0.464 ] Network output: [ -0.03156 0.1092 0.9478 0.0009013 -0.0004046 1.01 0.0006792 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6447 0.6424 0.4557 0.2267 0.9856 0.9906 0.6448 0.97 0.9803 0.4577 ] Network output: [ 0.007805 0.9664 0.01836 -0.0002521 0.0001132 0.9986 -0.00019 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01022 Epoch 2696 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02061 0.9932 0.9996 -5.236e-05 2.351e-05 -0.03423 -3.946e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02193 -0.005356 0.0196 0.02129 0.9414 0.9505 0.04336 0.8884 0.9065 0.111 ] Network output: [ 0.9886 0.03244 -0.006796 -0.0001363 6.117e-05 -0.003401 -0.0001027 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.642 0.1204 0.1506 0.1785 0.9726 0.9874 0.7258 0.903 0.9682 0.6503 ] Network output: [ -0.01243 0.9699 1.017 -5.331e-05 2.393e-05 0.03765 -4.018e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04683 0.03453 0.04795 0.02581 0.9859 0.99 0.04778 0.971 0.981 0.05864 ] Network output: [ 0.04112 -0.1667 1.08 -0.001487 0.0006677 0.998 -0.001121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6191 0.5576 0.3135 0.9758 0.9892 0.7242 0.913 0.9728 0.6443 ] Network output: [ -0.01411 0.0954 0.9437 0.00108 -0.0004848 0.9935 0.0008139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6474 0.6326 0.4531 0.238 0.987 0.9915 0.6478 0.9741 0.9826 0.464 ] Network output: [ -0.03151 0.1091 0.9477 0.0009021 -0.000405 1.01 0.0006798 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6448 0.6424 0.4557 0.2266 0.9856 0.9906 0.6449 0.97 0.9803 0.4576 ] Network output: [ 0.007798 0.9665 0.01836 -0.0002517 0.000113 0.9985 -0.0001897 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01019 Epoch 2697 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02058 0.9933 0.9996 -5.241e-05 2.353e-05 -0.03422 -3.95e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02193 -0.005355 0.01961 0.02128 0.9414 0.9505 0.04335 0.8884 0.9065 0.1109 ] Network output: [ 0.9886 0.03233 -0.006747 -0.0001369 6.147e-05 -0.003414 -0.0001032 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.642 0.1204 0.1507 0.1784 0.9726 0.9874 0.7258 0.903 0.9682 0.6503 ] Network output: [ -0.01244 0.97 1.017 -5.295e-05 2.377e-05 0.03763 -3.99e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04682 0.03452 0.04793 0.02579 0.9859 0.99 0.04777 0.971 0.981 0.05861 ] Network output: [ 0.04105 -0.1665 1.08 -0.001488 0.000668 0.9979 -0.001121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.619 0.5576 0.3133 0.9758 0.9892 0.7242 0.913 0.9728 0.6443 ] Network output: [ -0.01409 0.09526 0.9437 0.00108 -0.0004849 0.9936 0.000814 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6474 0.6326 0.453 0.2379 0.987 0.9915 0.6479 0.9741 0.9826 0.4639 ] Network output: [ -0.03146 0.109 0.9477 0.0009028 -0.0004053 1.01 0.0006804 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6448 0.6425 0.4556 0.2265 0.9856 0.9906 0.6449 0.97 0.9803 0.4575 ] Network output: [ 0.007791 0.9665 0.01836 -0.0002513 0.0001128 0.9985 -0.0001894 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01017 Epoch 2698 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02055 0.9933 0.9996 -5.247e-05 2.355e-05 -0.0342 -3.954e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02193 -0.005355 0.01962 0.02127 0.9414 0.9505 0.04334 0.8884 0.9065 0.1109 ] Network output: [ 0.9887 0.03223 -0.006698 -0.0001376 6.178e-05 -0.003426 -0.0001037 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6421 0.1203 0.1509 0.1783 0.9726 0.9874 0.7258 0.9031 0.9682 0.6503 ] Network output: [ -0.01244 0.9701 1.017 -5.259e-05 2.361e-05 0.03761 -3.963e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04681 0.03451 0.04791 0.02577 0.9859 0.99 0.04776 0.971 0.981 0.05858 ] Network output: [ 0.04099 -0.1663 1.08 -0.001489 0.0006684 0.9978 -0.001122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.619 0.5576 0.3132 0.9758 0.9892 0.7242 0.913 0.9728 0.6442 ] Network output: [ -0.01407 0.09513 0.9438 0.00108 -0.0004849 0.9936 0.000814 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6475 0.6327 0.453 0.2378 0.987 0.9915 0.6479 0.9741 0.9826 0.4639 ] Network output: [ -0.03141 0.1089 0.9477 0.0009036 -0.0004056 1.01 0.000681 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6449 0.6425 0.4555 0.2264 0.9856 0.9906 0.645 0.97 0.9803 0.4575 ] Network output: [ 0.007784 0.9665 0.01836 -0.000251 0.0001127 0.9985 -0.0001891 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01015 Epoch 2699 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02052 0.9934 0.9995 -5.252e-05 2.358e-05 -0.03419 -3.958e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02193 -0.005354 0.01964 0.02126 0.9414 0.9505 0.04333 0.8884 0.9065 0.1109 ] Network output: [ 0.9887 0.03213 -0.006649 -0.0001383 6.208e-05 -0.003439 -0.0001042 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6421 0.1203 0.151 0.1782 0.9726 0.9874 0.7258 0.9031 0.9682 0.6502 ] Network output: [ -0.01245 0.9701 1.017 -5.222e-05 2.344e-05 0.0376 -3.936e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0468 0.0345 0.04789 0.02575 0.9859 0.99 0.04775 0.971 0.981 0.05855 ] Network output: [ 0.04092 -0.1661 1.08 -0.00149 0.0006688 0.9978 -0.001123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.619 0.5576 0.313 0.9758 0.9892 0.7242 0.913 0.9728 0.6442 ] Network output: [ -0.01405 0.09499 0.9438 0.00108 -0.0004849 0.9937 0.000814 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6475 0.6327 0.453 0.2377 0.987 0.9915 0.648 0.9741 0.9826 0.4639 ] Network output: [ -0.03136 0.1088 0.9476 0.0009043 -0.000406 1.01 0.0006815 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6449 0.6426 0.4555 0.2263 0.9856 0.9906 0.645 0.97 0.9803 0.4574 ] Network output: [ 0.007777 0.9666 0.01836 -0.0002506 0.0001125 0.9985 -0.0001888 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01013 Epoch 2700 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0205 0.9934 0.9995 -5.257e-05 2.36e-05 -0.03418 -3.962e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02193 -0.005354 0.01965 0.02125 0.9414 0.9505 0.04332 0.8884 0.9065 0.1108 ] Network output: [ 0.9887 0.03203 -0.0066 -0.000139 6.239e-05 -0.003452 -0.0001047 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6421 0.1202 0.1512 0.1781 0.9726 0.9874 0.7258 0.9031 0.9682 0.6502 ] Network output: [ -0.01245 0.9702 1.017 -5.186e-05 2.328e-05 0.03758 -3.908e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04679 0.03449 0.04787 0.02573 0.9859 0.99 0.04774 0.971 0.9811 0.05852 ] Network output: [ 0.04086 -0.1659 1.08 -0.001491 0.0006691 0.9977 -0.001123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6189 0.5577 0.3128 0.9758 0.9892 0.7242 0.913 0.9728 0.6441 ] Network output: [ -0.01403 0.09486 0.9439 0.00108 -0.000485 0.9937 0.0008141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6476 0.6327 0.4529 0.2376 0.987 0.9915 0.648 0.9741 0.9826 0.4638 ] Network output: [ -0.03131 0.1087 0.9476 0.0009051 -0.0004063 1.01 0.0006821 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.645 0.6426 0.4554 0.2262 0.9856 0.9906 0.6451 0.97 0.9803 0.4573 ] Network output: [ 0.00777 0.9666 0.01836 -0.0002502 0.0001123 0.9985 -0.0001885 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01011 Epoch 2701 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02047 0.9935 0.9995 -5.263e-05 2.363e-05 -0.03416 -3.966e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02193 -0.005353 0.01966 0.02124 0.9414 0.9505 0.0433 0.8884 0.9065 0.1108 ] Network output: [ 0.9888 0.03192 -0.006551 -0.0001396 6.269e-05 -0.003466 -0.0001052 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6422 0.1202 0.1513 0.178 0.9726 0.9874 0.7258 0.9031 0.9682 0.6501 ] Network output: [ -0.01245 0.9703 1.017 -5.149e-05 2.312e-05 0.03757 -3.88e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04678 0.03448 0.04785 0.02571 0.9859 0.99 0.04773 0.971 0.9811 0.05849 ] Network output: [ 0.04079 -0.1657 1.08 -0.001491 0.0006695 0.9976 -0.001124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6189 0.5577 0.3127 0.9758 0.9892 0.7242 0.913 0.9728 0.6441 ] Network output: [ -0.01401 0.09473 0.9439 0.00108 -0.000485 0.9938 0.0008141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6476 0.6328 0.4529 0.2375 0.987 0.9915 0.6481 0.9741 0.9826 0.4638 ] Network output: [ -0.03126 0.1086 0.9476 0.0009058 -0.0004066 1.01 0.0006826 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6451 0.6427 0.4553 0.226 0.9856 0.9906 0.6451 0.97 0.9803 0.4573 ] Network output: [ 0.007764 0.9666 0.01837 -0.0002498 0.0001121 0.9985 -0.0001883 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01009 Epoch 2702 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02044 0.9935 0.9995 -5.268e-05 2.365e-05 -0.03415 -3.97e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02193 -0.005352 0.01967 0.02123 0.9414 0.9505 0.04329 0.8884 0.9065 0.1107 ] Network output: [ 0.9888 0.03182 -0.006502 -0.0001403 6.3e-05 -0.003479 -0.0001058 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6422 0.1201 0.1514 0.1779 0.9726 0.9874 0.7258 0.9031 0.9682 0.6501 ] Network output: [ -0.01246 0.9704 1.017 -5.112e-05 2.295e-05 0.03755 -3.853e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04677 0.03447 0.04783 0.02569 0.9859 0.99 0.04772 0.971 0.9811 0.05846 ] Network output: [ 0.04073 -0.1655 1.08 -0.001492 0.0006699 0.9976 -0.001124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6189 0.5577 0.3125 0.9758 0.9892 0.7242 0.913 0.9728 0.644 ] Network output: [ -0.01399 0.0946 0.9439 0.00108 -0.000485 0.9938 0.0008142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6477 0.6328 0.4529 0.2374 0.987 0.9915 0.6481 0.9741 0.9826 0.4637 ] Network output: [ -0.03122 0.1085 0.9475 0.0009065 -0.000407 1.01 0.0006832 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6451 0.6428 0.4553 0.2259 0.9856 0.9906 0.6452 0.97 0.9803 0.4572 ] Network output: [ 0.007758 0.9666 0.01837 -0.0002494 0.000112 0.9985 -0.000188 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01007 Epoch 2703 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02042 0.9936 0.9995 -5.273e-05 2.367e-05 -0.03414 -3.974e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02192 -0.005352 0.01969 0.02122 0.9414 0.9505 0.04328 0.8884 0.9066 0.1107 ] Network output: [ 0.9888 0.03172 -0.006453 -0.000141 6.331e-05 -0.003493 -0.0001063 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6422 0.12 0.1516 0.1778 0.9726 0.9874 0.7258 0.9031 0.9682 0.65 ] Network output: [ -0.01246 0.9704 1.017 -5.075e-05 2.278e-05 0.03754 -3.825e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04676 0.03446 0.04781 0.02567 0.9859 0.99 0.0477 0.971 0.9811 0.05842 ] Network output: [ 0.04067 -0.1653 1.08 -0.001493 0.0006702 0.9975 -0.001125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6188 0.5577 0.3123 0.9758 0.9892 0.7242 0.913 0.9728 0.644 ] Network output: [ -0.01397 0.09447 0.944 0.00108 -0.000485 0.9939 0.0008142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6477 0.6329 0.4528 0.2373 0.987 0.9915 0.6482 0.9741 0.9826 0.4637 ] Network output: [ -0.03117 0.1084 0.9475 0.0009072 -0.0004073 1.01 0.0006837 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6452 0.6428 0.4552 0.2258 0.9856 0.9906 0.6452 0.97 0.9803 0.4571 ] Network output: [ 0.007752 0.9667 0.01837 -0.000249 0.0001118 0.9985 -0.0001877 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01005 Epoch 2704 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02039 0.9936 0.9995 -5.278e-05 2.37e-05 -0.03412 -3.978e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02192 -0.005351 0.0197 0.02121 0.9414 0.9505 0.04327 0.8884 0.9066 0.1106 ] Network output: [ 0.9889 0.03162 -0.006404 -0.0001417 6.362e-05 -0.003507 -0.0001068 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6423 0.12 0.1517 0.1777 0.9726 0.9874 0.7258 0.9031 0.9683 0.65 ] Network output: [ -0.01246 0.9705 1.017 -5.038e-05 2.262e-05 0.03752 -3.797e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04675 0.03445 0.04779 0.02565 0.9859 0.99 0.04769 0.971 0.9811 0.05839 ] Network output: [ 0.0406 -0.1651 1.08 -0.001494 0.0006706 0.9974 -0.001126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6188 0.5577 0.3122 0.9758 0.9892 0.7242 0.913 0.9728 0.6439 ] Network output: [ -0.01395 0.09434 0.944 0.00108 -0.000485 0.9939 0.0008142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6478 0.6329 0.4528 0.2372 0.987 0.9915 0.6482 0.9741 0.9826 0.4637 ] Network output: [ -0.03112 0.1083 0.9474 0.0009079 -0.0004076 1.01 0.0006843 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6452 0.6429 0.4551 0.2257 0.9856 0.9906 0.6453 0.97 0.9803 0.4571 ] Network output: [ 0.007746 0.9667 0.01837 -0.0002486 0.0001116 0.9984 -0.0001874 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01004 Epoch 2705 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02036 0.9937 0.9995 -5.284e-05 2.372e-05 -0.03411 -3.982e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02192 -0.005351 0.01971 0.0212 0.9414 0.9506 0.04326 0.8884 0.9066 0.1106 ] Network output: [ 0.9889 0.03152 -0.006355 -0.0001424 6.393e-05 -0.003521 -0.0001073 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6423 0.1199 0.1519 0.1776 0.9726 0.9874 0.7258 0.9031 0.9683 0.65 ] Network output: [ -0.01247 0.9706 1.017 -5.001e-05 2.245e-05 0.0375 -3.769e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04673 0.03444 0.04777 0.02563 0.9859 0.99 0.04768 0.9711 0.9811 0.05836 ] Network output: [ 0.04054 -0.165 1.08 -0.001494 0.0006709 0.9974 -0.001126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6188 0.5577 0.312 0.9758 0.9892 0.7242 0.913 0.9728 0.6438 ] Network output: [ -0.01393 0.09421 0.944 0.00108 -0.000485 0.994 0.0008142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6478 0.633 0.4528 0.2371 0.987 0.9915 0.6483 0.9741 0.9826 0.4636 ] Network output: [ -0.03108 0.1082 0.9474 0.0009087 -0.0004079 1.01 0.0006848 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6453 0.6429 0.4551 0.2256 0.9856 0.9906 0.6454 0.97 0.9803 0.457 ] Network output: [ 0.00774 0.9667 0.01838 -0.0002482 0.0001114 0.9984 -0.0001871 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01002 Epoch 2706 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02034 0.9937 0.9995 -5.289e-05 2.374e-05 -0.03409 -3.986e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02192 -0.00535 0.01972 0.02119 0.9414 0.9506 0.04325 0.8884 0.9066 0.1106 ] Network output: [ 0.9889 0.03142 -0.006306 -0.0001431 6.425e-05 -0.003535 -0.0001079 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6424 0.1199 0.152 0.1775 0.9726 0.9874 0.7258 0.9031 0.9683 0.6499 ] Network output: [ -0.01247 0.9706 1.017 -4.964e-05 2.229e-05 0.03749 -3.741e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04672 0.03443 0.04776 0.02561 0.9859 0.99 0.04767 0.9711 0.9811 0.05833 ] Network output: [ 0.04048 -0.1648 1.08 -0.001495 0.0006712 0.9973 -0.001127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6188 0.5577 0.3119 0.9758 0.9892 0.7242 0.913 0.9728 0.6438 ] Network output: [ -0.01391 0.09408 0.9441 0.00108 -0.0004851 0.994 0.0008143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6479 0.633 0.4527 0.237 0.987 0.9915 0.6483 0.9741 0.9826 0.4636 ] Network output: [ -0.03103 0.1081 0.9474 0.0009094 -0.0004082 1.01 0.0006853 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6453 0.643 0.455 0.2255 0.9856 0.9906 0.6454 0.97 0.9803 0.4569 ] Network output: [ 0.007734 0.9667 0.01838 -0.0002478 0.0001113 0.9984 -0.0001868 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009998 Epoch 2707 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02031 0.9937 0.9995 -5.294e-05 2.377e-05 -0.03408 -3.99e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02192 -0.00535 0.01974 0.02118 0.9414 0.9506 0.04323 0.8884 0.9066 0.1105 ] Network output: [ 0.989 0.03132 -0.006257 -0.0001438 6.456e-05 -0.00355 -0.0001084 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6424 0.1198 0.1522 0.1774 0.9726 0.9874 0.7258 0.9031 0.9683 0.6499 ] Network output: [ -0.01247 0.9707 1.017 -4.927e-05 2.212e-05 0.03747 -3.713e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04671 0.03442 0.04774 0.02559 0.9859 0.99 0.04766 0.9711 0.9811 0.0583 ] Network output: [ 0.04042 -0.1646 1.08 -0.001496 0.0006716 0.9973 -0.001127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6187 0.5577 0.3117 0.9758 0.9892 0.7242 0.913 0.9728 0.6437 ] Network output: [ -0.01389 0.09396 0.9441 0.00108 -0.0004851 0.9941 0.0008143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6479 0.633 0.4527 0.2369 0.987 0.9915 0.6483 0.9741 0.9826 0.4635 ] Network output: [ -0.03099 0.108 0.9473 0.00091 -0.0004086 1.01 0.0006858 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6454 0.643 0.4549 0.2254 0.9856 0.9906 0.6455 0.97 0.9803 0.4569 ] Network output: [ 0.007729 0.9667 0.01838 -0.0002475 0.0001111 0.9984 -0.0001865 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00998 Epoch 2708 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02028 0.9938 0.9995 -5.299e-05 2.379e-05 -0.03407 -3.994e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02192 -0.005349 0.01975 0.02117 0.9414 0.9506 0.04322 0.8884 0.9066 0.1105 ] Network output: [ 0.989 0.03122 -0.006208 -0.0001445 6.488e-05 -0.003565 -0.0001089 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6424 0.1197 0.1523 0.1773 0.9726 0.9874 0.7258 0.9031 0.9683 0.6498 ] Network output: [ -0.01248 0.9708 1.017 -4.89e-05 2.195e-05 0.03746 -3.685e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0467 0.03441 0.04772 0.02557 0.9859 0.99 0.04765 0.9711 0.9811 0.05827 ] Network output: [ 0.04036 -0.1644 1.08 -0.001497 0.0006719 0.9972 -0.001128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6187 0.5577 0.3116 0.9758 0.9892 0.7242 0.913 0.9728 0.6437 ] Network output: [ -0.01387 0.09383 0.9442 0.001081 -0.0004851 0.9941 0.0008143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.648 0.6331 0.4527 0.2368 0.987 0.9915 0.6484 0.9741 0.9826 0.4635 ] Network output: [ -0.03094 0.1079 0.9473 0.0009107 -0.0004089 1.01 0.0006864 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6454 0.6431 0.4549 0.2253 0.9856 0.9906 0.6455 0.97 0.9803 0.4568 ] Network output: [ 0.007724 0.9668 0.01839 -0.0002471 0.0001109 0.9984 -0.0001862 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009961 Epoch 2709 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02026 0.9938 0.9995 -5.304e-05 2.381e-05 -0.03405 -3.997e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02192 -0.005349 0.01976 0.02116 0.9414 0.9506 0.04321 0.8884 0.9066 0.1104 ] Network output: [ 0.989 0.03112 -0.006159 -0.0001452 6.52e-05 -0.00358 -0.0001094 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6425 0.1197 0.1524 0.1772 0.9726 0.9874 0.7258 0.9031 0.9683 0.6498 ] Network output: [ -0.01248 0.9708 1.016 -4.852e-05 2.178e-05 0.03744 -3.657e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04669 0.0344 0.0477 0.02555 0.9859 0.99 0.04764 0.9711 0.9811 0.05824 ] Network output: [ 0.0403 -0.1642 1.08 -0.001497 0.0006722 0.9971 -0.001128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6187 0.5577 0.3114 0.9758 0.9892 0.7242 0.913 0.9728 0.6436 ] Network output: [ -0.01386 0.09371 0.9442 0.001081 -0.0004851 0.9942 0.0008143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.648 0.6331 0.4526 0.2367 0.987 0.9915 0.6484 0.9741 0.9826 0.4634 ] Network output: [ -0.03089 0.1078 0.9472 0.0009114 -0.0004092 1.01 0.0006869 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6455 0.6431 0.4548 0.2252 0.9856 0.9906 0.6456 0.97 0.9803 0.4567 ] Network output: [ 0.007719 0.9668 0.01839 -0.0002467 0.0001107 0.9984 -0.0001859 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009944 Epoch 2710 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02023 0.9939 0.9995 -5.309e-05 2.383e-05 -0.03404 -4.001e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02192 -0.005349 0.01977 0.02115 0.9414 0.9506 0.0432 0.8884 0.9066 0.1104 ] Network output: [ 0.989 0.03103 -0.00611 -0.0001459 6.552e-05 -0.003595 -0.00011 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6425 0.1196 0.1526 0.1771 0.9726 0.9874 0.7258 0.9031 0.9683 0.6497 ] Network output: [ -0.01248 0.9709 1.016 -4.815e-05 2.161e-05 0.03743 -3.628e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04668 0.03439 0.04768 0.02553 0.9859 0.99 0.04763 0.9711 0.9811 0.05821 ] Network output: [ 0.04024 -0.164 1.08 -0.001498 0.0006725 0.9971 -0.001129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6186 0.5577 0.3113 0.9758 0.9892 0.7242 0.913 0.9728 0.6436 ] Network output: [ -0.01384 0.09359 0.9442 0.001081 -0.0004851 0.9942 0.0008143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6481 0.6332 0.4526 0.2366 0.987 0.9915 0.6485 0.9741 0.9826 0.4634 ] Network output: [ -0.03085 0.1078 0.9472 0.0009121 -0.0004095 1.01 0.0006874 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6455 0.6432 0.4547 0.2251 0.9856 0.9906 0.6456 0.97 0.9803 0.4567 ] Network output: [ 0.007714 0.9668 0.0184 -0.0002463 0.0001106 0.9984 -0.0001856 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009926 Epoch 2711 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0202 0.9939 0.9995 -5.314e-05 2.386e-05 -0.03402 -4.005e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02192 -0.005348 0.01979 0.02114 0.9414 0.9506 0.04319 0.8884 0.9066 0.1103 ] Network output: [ 0.9891 0.03093 -0.006061 -0.0001467 6.584e-05 -0.00361 -0.0001105 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6425 0.1195 0.1527 0.177 0.9726 0.9874 0.7258 0.9031 0.9683 0.6497 ] Network output: [ -0.01248 0.971 1.016 -4.777e-05 2.145e-05 0.03741 -3.6e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04667 0.03438 0.04766 0.02552 0.9859 0.99 0.04761 0.9711 0.9811 0.05818 ] Network output: [ 0.04018 -0.1639 1.08 -0.001499 0.0006728 0.997 -0.001129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6186 0.5577 0.3111 0.9758 0.9892 0.7242 0.913 0.9728 0.6435 ] Network output: [ -0.01382 0.09346 0.9443 0.001081 -0.0004851 0.9943 0.0008143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6481 0.6332 0.4525 0.2365 0.987 0.9915 0.6485 0.9741 0.9826 0.4633 ] Network output: [ -0.03081 0.1077 0.9472 0.0009128 -0.0004098 1.01 0.0006879 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6456 0.6432 0.4547 0.225 0.9856 0.9906 0.6457 0.97 0.9803 0.4566 ] Network output: [ 0.007709 0.9668 0.0184 -0.0002459 0.0001104 0.9984 -0.0001853 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009908 Epoch 2712 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02018 0.994 0.9995 -5.319e-05 2.388e-05 -0.03401 -4.009e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02191 -0.005348 0.0198 0.02113 0.9414 0.9506 0.04317 0.8884 0.9066 0.1103 ] Network output: [ 0.9891 0.03083 -0.006012 -0.0001474 6.616e-05 -0.003626 -0.0001111 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6426 0.1195 0.1529 0.1769 0.9726 0.9874 0.7258 0.9031 0.9683 0.6496 ] Network output: [ -0.01249 0.971 1.016 -4.739e-05 2.128e-05 0.0374 -3.572e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04666 0.03437 0.04764 0.0255 0.9859 0.99 0.0476 0.9711 0.9811 0.05815 ] Network output: [ 0.04012 -0.1637 1.08 -0.001499 0.0006731 0.9969 -0.00113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6186 0.5577 0.311 0.9758 0.9892 0.7242 0.913 0.9728 0.6434 ] Network output: [ -0.0138 0.09334 0.9443 0.001081 -0.0004851 0.9943 0.0008143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6482 0.6332 0.4525 0.2364 0.987 0.9915 0.6486 0.9741 0.9826 0.4633 ] Network output: [ -0.03076 0.1076 0.9471 0.0009134 -0.0004101 1.011 0.0006884 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6456 0.6433 0.4546 0.2249 0.9856 0.9906 0.6457 0.9701 0.9803 0.4565 ] Network output: [ 0.007705 0.9668 0.01841 -0.0002455 0.0001102 0.9984 -0.000185 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009891 Epoch 2713 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02015 0.994 0.9995 -5.324e-05 2.39e-05 -0.034 -4.012e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02191 -0.005347 0.01981 0.02112 0.9414 0.9506 0.04316 0.8884 0.9066 0.1103 ] Network output: [ 0.9891 0.03073 -0.005963 -0.0001481 6.649e-05 -0.003642 -0.0001116 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6426 0.1194 0.153 0.1768 0.9726 0.9874 0.7258 0.9031 0.9683 0.6496 ] Network output: [ -0.01249 0.9711 1.016 -4.701e-05 2.111e-05 0.03738 -3.543e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04665 0.03436 0.04762 0.02548 0.9859 0.99 0.04759 0.9711 0.9811 0.05812 ] Network output: [ 0.04006 -0.1635 1.08 -0.0015 0.0006734 0.9969 -0.00113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6185 0.5577 0.3109 0.9758 0.9892 0.7242 0.9131 0.9728 0.6434 ] Network output: [ -0.01379 0.09322 0.9444 0.001081 -0.0004851 0.9944 0.0008143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6482 0.6333 0.4525 0.2364 0.987 0.9915 0.6486 0.9741 0.9826 0.4632 ] Network output: [ -0.03072 0.1075 0.9471 0.0009141 -0.0004104 1.011 0.0006889 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6457 0.6433 0.4545 0.2248 0.9856 0.9906 0.6458 0.9701 0.9803 0.4565 ] Network output: [ 0.007701 0.9669 0.01841 -0.0002451 0.00011 0.9983 -0.0001847 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009874 Epoch 2714 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02013 0.9941 0.9994 -5.329e-05 2.392e-05 -0.03398 -4.016e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02191 -0.005347 0.01983 0.02111 0.9414 0.9506 0.04315 0.8884 0.9066 0.1102 ] Network output: [ 0.9892 0.03064 -0.005914 -0.0001488 6.681e-05 -0.003658 -0.0001122 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6426 0.1193 0.1532 0.1768 0.9726 0.9874 0.7258 0.9031 0.9683 0.6495 ] Network output: [ -0.01249 0.9712 1.016 -4.663e-05 2.094e-05 0.03737 -3.514e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04664 0.03435 0.0476 0.02546 0.9859 0.99 0.04758 0.9711 0.9811 0.05809 ] Network output: [ 0.04 -0.1634 1.08 -0.001501 0.0006737 0.9968 -0.001131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6185 0.5577 0.3107 0.9758 0.9892 0.7242 0.9131 0.9728 0.6433 ] Network output: [ -0.01377 0.0931 0.9444 0.001081 -0.0004851 0.9944 0.0008143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6482 0.6333 0.4524 0.2363 0.987 0.9915 0.6487 0.9741 0.9826 0.4632 ] Network output: [ -0.03067 0.1074 0.947 0.0009148 -0.0004107 1.011 0.0006894 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6457 0.6434 0.4545 0.2247 0.9856 0.9906 0.6458 0.9701 0.9803 0.4564 ] Network output: [ 0.007696 0.9669 0.01842 -0.0002447 0.0001099 0.9983 -0.0001844 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009857 Epoch 2715 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0201 0.9941 0.9994 -5.334e-05 2.395e-05 -0.03397 -4.02e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02191 -0.005346 0.01984 0.0211 0.9414 0.9506 0.04314 0.8884 0.9066 0.1102 ] Network output: [ 0.9892 0.03054 -0.005865 -0.0001496 6.714e-05 -0.003674 -0.0001127 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6427 0.1193 0.1533 0.1767 0.9726 0.9874 0.7258 0.9031 0.9683 0.6495 ] Network output: [ -0.01249 0.9712 1.016 -4.625e-05 2.076e-05 0.03735 -3.486e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04663 0.03434 0.04758 0.02544 0.9859 0.99 0.04757 0.9711 0.9811 0.05806 ] Network output: [ 0.03994 -0.1632 1.08 -0.001501 0.000674 0.9968 -0.001131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6185 0.5577 0.3106 0.9758 0.9892 0.7242 0.9131 0.9728 0.6432 ] Network output: [ -0.01376 0.09299 0.9444 0.001081 -0.0004851 0.9945 0.0008143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6483 0.6333 0.4524 0.2362 0.987 0.9915 0.6487 0.9741 0.9826 0.4631 ] Network output: [ -0.03063 0.1074 0.947 0.0009154 -0.000411 1.011 0.0006899 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6458 0.6434 0.4544 0.2246 0.9856 0.9906 0.6459 0.9701 0.9803 0.4563 ] Network output: [ 0.007692 0.9669 0.01842 -0.0002443 0.0001097 0.9983 -0.0001841 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00984 Epoch 2716 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02007 0.9942 0.9994 -5.339e-05 2.397e-05 -0.03395 -4.023e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02191 -0.005346 0.01985 0.02109 0.9414 0.9506 0.04313 0.8884 0.9066 0.1101 ] Network output: [ 0.9892 0.03045 -0.005816 -0.0001503 6.747e-05 -0.003691 -0.0001133 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6427 0.1192 0.1535 0.1766 0.9726 0.9874 0.7258 0.9031 0.9683 0.6494 ] Network output: [ -0.01249 0.9713 1.016 -4.587e-05 2.059e-05 0.03734 -3.457e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04662 0.03433 0.04756 0.02542 0.9859 0.99 0.04756 0.9711 0.9811 0.05802 ] Network output: [ 0.03989 -0.163 1.08 -0.001502 0.0006743 0.9967 -0.001132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6184 0.5577 0.3105 0.9758 0.9892 0.7242 0.9131 0.9728 0.6432 ] Network output: [ -0.01374 0.09287 0.9445 0.001081 -0.0004851 0.9945 0.0008143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6483 0.6334 0.4523 0.2361 0.987 0.9915 0.6487 0.9741 0.9826 0.4631 ] Network output: [ -0.03059 0.1073 0.9469 0.000916 -0.0004112 1.011 0.0006904 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6458 0.6435 0.4543 0.2245 0.9856 0.9906 0.6459 0.9701 0.9803 0.4563 ] Network output: [ 0.007689 0.9669 0.01843 -0.0002439 0.0001095 0.9983 -0.0001838 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009824 Epoch 2717 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02005 0.9942 0.9994 -5.344e-05 2.399e-05 -0.03394 -4.027e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02191 -0.005346 0.01987 0.02109 0.9414 0.9506 0.04312 0.8885 0.9066 0.1101 ] Network output: [ 0.9893 0.03035 -0.005767 -0.000151 6.78e-05 -0.003708 -0.0001138 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6427 0.1191 0.1536 0.1765 0.9726 0.9874 0.7258 0.9031 0.9683 0.6493 ] Network output: [ -0.0125 0.9714 1.016 -4.549e-05 2.042e-05 0.03732 -3.428e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04661 0.03432 0.04754 0.02541 0.9859 0.99 0.04755 0.9711 0.9811 0.05799 ] Network output: [ 0.03983 -0.1628 1.08 -0.001503 0.0006746 0.9966 -0.001132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6184 0.5577 0.3103 0.9758 0.9892 0.7242 0.9131 0.9728 0.6431 ] Network output: [ -0.01372 0.09275 0.9445 0.001081 -0.0004851 0.9946 0.0008143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6484 0.6334 0.4523 0.236 0.987 0.9915 0.6488 0.9741 0.9826 0.463 ] Network output: [ -0.03055 0.1072 0.9469 0.0009167 -0.0004115 1.011 0.0006908 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6459 0.6435 0.4543 0.2245 0.9856 0.9906 0.646 0.9701 0.9803 0.4562 ] Network output: [ 0.007685 0.9669 0.01844 -0.0002435 0.0001093 0.9983 -0.0001835 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009808 Epoch 2718 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02002 0.9942 0.9994 -5.348e-05 2.401e-05 -0.03392 -4.031e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02191 -0.005345 0.01988 0.02108 0.9415 0.9506 0.0431 0.8885 0.9066 0.11 ] Network output: [ 0.9893 0.03026 -0.005718 -0.0001518 6.813e-05 -0.003725 -0.0001144 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6428 0.1191 0.1538 0.1764 0.9726 0.9874 0.7258 0.9031 0.9683 0.6493 ] Network output: [ -0.0125 0.9714 1.016 -4.511e-05 2.025e-05 0.03731 -3.399e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0466 0.03431 0.04752 0.02539 0.9859 0.99 0.04753 0.9711 0.9811 0.05796 ] Network output: [ 0.03977 -0.1627 1.08 -0.001503 0.0006748 0.9966 -0.001133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.6184 0.5577 0.3102 0.9758 0.9892 0.7242 0.9131 0.9728 0.643 ] Network output: [ -0.01371 0.09264 0.9446 0.001081 -0.0004851 0.9946 0.0008143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6484 0.6334 0.4523 0.2359 0.9871 0.9915 0.6488 0.9741 0.9826 0.463 ] Network output: [ -0.0305 0.1071 0.9468 0.0009173 -0.0004118 1.011 0.0006913 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6459 0.6436 0.4542 0.2244 0.9856 0.9906 0.646 0.9701 0.9803 0.4561 ] Network output: [ 0.007682 0.9669 0.01844 -0.0002431 0.0001091 0.9983 -0.0001832 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009792 Epoch 2719 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01999 0.9943 0.9994 -5.353e-05 2.403e-05 -0.03391 -4.034e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02191 -0.005345 0.01989 0.02107 0.9415 0.9506 0.04309 0.8885 0.9066 0.11 ] Network output: [ 0.9893 0.03016 -0.005669 -0.0001525 6.847e-05 -0.003742 -0.0001149 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6428 0.119 0.1539 0.1764 0.9726 0.9874 0.7258 0.9031 0.9683 0.6492 ] Network output: [ -0.0125 0.9715 1.016 -4.472e-05 2.008e-05 0.03729 -3.37e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04659 0.0343 0.0475 0.02537 0.9859 0.99 0.04752 0.9711 0.9811 0.05793 ] Network output: [ 0.03972 -0.1625 1.08 -0.001504 0.0006751 0.9965 -0.001133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6183 0.5577 0.3101 0.9758 0.9892 0.7242 0.9131 0.9728 0.643 ] Network output: [ -0.01369 0.09252 0.9446 0.001081 -0.0004851 0.9947 0.0008143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6485 0.6335 0.4522 0.2359 0.9871 0.9915 0.6489 0.9741 0.9826 0.4629 ] Network output: [ -0.03046 0.1071 0.9468 0.0009179 -0.0004121 1.011 0.0006918 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.646 0.6436 0.4541 0.2243 0.9856 0.9906 0.6461 0.9701 0.9803 0.456 ] Network output: [ 0.007678 0.9669 0.01845 -0.0002427 0.000109 0.9983 -0.0001829 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009776 Epoch 2720 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01997 0.9943 0.9994 -5.358e-05 2.405e-05 -0.03389 -4.038e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02191 -0.005345 0.01991 0.02106 0.9415 0.9506 0.04308 0.8885 0.9066 0.11 ] Network output: [ 0.9893 0.03007 -0.00562 -0.0001533 6.88e-05 -0.003759 -0.0001155 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6429 0.1189 0.1541 0.1763 0.9726 0.9874 0.7258 0.9031 0.9683 0.6492 ] Network output: [ -0.0125 0.9716 1.016 -4.434e-05 1.99e-05 0.03728 -3.341e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04657 0.03429 0.04748 0.02535 0.9859 0.99 0.04751 0.9711 0.9811 0.0579 ] Network output: [ 0.03966 -0.1624 1.08 -0.001504 0.0006754 0.9965 -0.001134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6183 0.5577 0.3099 0.9758 0.9892 0.7242 0.9131 0.9728 0.6429 ] Network output: [ -0.01368 0.09241 0.9446 0.00108 -0.0004851 0.9947 0.0008143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6485 0.6335 0.4522 0.2358 0.9871 0.9915 0.6489 0.9741 0.9826 0.4629 ] Network output: [ -0.03042 0.107 0.9467 0.0009186 -0.0004124 1.011 0.0006923 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.646 0.6437 0.4541 0.2242 0.9856 0.9906 0.6461 0.9701 0.9803 0.456 ] Network output: [ 0.007675 0.9669 0.01846 -0.0002423 0.0001088 0.9983 -0.0001826 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009761 Epoch 2721 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01994 0.9944 0.9994 -5.363e-05 2.408e-05 -0.03387 -4.042e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0219 -0.005344 0.01992 0.02105 0.9415 0.9506 0.04307 0.8885 0.9066 0.1099 ] Network output: [ 0.9894 0.02997 -0.005571 -0.000154 6.914e-05 -0.003777 -0.0001161 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6429 0.1189 0.1542 0.1762 0.9726 0.9874 0.7258 0.9031 0.9683 0.6491 ] Network output: [ -0.0125 0.9716 1.016 -4.395e-05 1.973e-05 0.03726 -3.312e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04656 0.03427 0.04746 0.02534 0.9859 0.99 0.0475 0.9711 0.9811 0.05787 ] Network output: [ 0.03961 -0.1622 1.08 -0.001505 0.0006756 0.9964 -0.001134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6182 0.5577 0.3098 0.9758 0.9892 0.7242 0.9131 0.9728 0.6428 ] Network output: [ -0.01367 0.0923 0.9447 0.00108 -0.0004851 0.9948 0.0008143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6485 0.6335 0.4521 0.2357 0.9871 0.9915 0.649 0.9741 0.9826 0.4628 ] Network output: [ -0.03038 0.1069 0.9467 0.0009192 -0.0004127 1.011 0.0006927 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6461 0.6437 0.454 0.2241 0.9856 0.9906 0.6462 0.9701 0.9803 0.4559 ] Network output: [ 0.007672 0.9669 0.01847 -0.0002419 0.0001086 0.9983 -0.0001823 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009746 Epoch 2722 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01991 0.9944 0.9994 -5.367e-05 2.41e-05 -0.03386 -4.045e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0219 -0.005344 0.01993 0.02104 0.9415 0.9506 0.04306 0.8885 0.9066 0.1099 ] Network output: [ 0.9894 0.02988 -0.005522 -0.0001548 6.948e-05 -0.003795 -0.0001166 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6429 0.1188 0.1544 0.1761 0.9726 0.9874 0.7258 0.9031 0.9683 0.6491 ] Network output: [ -0.0125 0.9717 1.016 -4.356e-05 1.956e-05 0.03725 -3.283e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04655 0.03426 0.04744 0.02532 0.9859 0.99 0.04749 0.9711 0.9811 0.05784 ] Network output: [ 0.03955 -0.162 1.08 -0.001506 0.0006759 0.9963 -0.001135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6182 0.5577 0.3097 0.9758 0.9892 0.7242 0.9131 0.9728 0.6428 ] Network output: [ -0.01365 0.09219 0.9447 0.00108 -0.0004851 0.9948 0.0008143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6486 0.6336 0.4521 0.2356 0.9871 0.9915 0.649 0.9741 0.9826 0.4628 ] Network output: [ -0.03034 0.1069 0.9466 0.0009198 -0.0004129 1.011 0.0006932 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6461 0.6438 0.4539 0.224 0.9856 0.9906 0.6462 0.9701 0.9803 0.4558 ] Network output: [ 0.00767 0.967 0.01848 -0.0002415 0.0001084 0.9982 -0.000182 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00973 Epoch 2723 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01989 0.9945 0.9994 -5.372e-05 2.412e-05 -0.03384 -4.049e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0219 -0.005344 0.01995 0.02103 0.9415 0.9506 0.04305 0.8885 0.9066 0.1098 ] Network output: [ 0.9894 0.02979 -0.005473 -0.0001555 6.982e-05 -0.003813 -0.0001172 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.643 0.1187 0.1545 0.176 0.9726 0.9874 0.7258 0.9031 0.9683 0.649 ] Network output: [ -0.01251 0.9717 1.016 -4.317e-05 1.938e-05 0.03723 -3.254e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04654 0.03425 0.04742 0.0253 0.9859 0.99 0.04748 0.9711 0.9811 0.0578 ] Network output: [ 0.0395 -0.1619 1.08 -0.001506 0.0006761 0.9963 -0.001135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6182 0.5577 0.3096 0.9758 0.9892 0.7242 0.9131 0.9728 0.6427 ] Network output: [ -0.01364 0.09208 0.9447 0.00108 -0.000485 0.9948 0.0008142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6486 0.6336 0.4521 0.2355 0.9871 0.9915 0.649 0.9741 0.9826 0.4627 ] Network output: [ -0.0303 0.1068 0.9466 0.0009204 -0.0004132 1.011 0.0006936 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6462 0.6438 0.4538 0.2239 0.9856 0.9906 0.6463 0.9701 0.9803 0.4557 ] Network output: [ 0.007667 0.967 0.01849 -0.0002411 0.0001082 0.9982 -0.0001817 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009716 Epoch 2724 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01986 0.9945 0.9994 -5.377e-05 2.414e-05 -0.03383 -4.052e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0219 -0.005343 0.01996 0.02102 0.9415 0.9506 0.04303 0.8885 0.9066 0.1098 ] Network output: [ 0.9895 0.0297 -0.005424 -0.0001563 7.016e-05 -0.003831 -0.0001178 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.643 0.1186 0.1547 0.176 0.9726 0.9874 0.7258 0.9031 0.9683 0.649 ] Network output: [ -0.01251 0.9718 1.016 -4.278e-05 1.921e-05 0.03722 -3.224e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04653 0.03424 0.0474 0.02528 0.9859 0.99 0.04746 0.9711 0.9811 0.05777 ] Network output: [ 0.03945 -0.1617 1.08 -0.001507 0.0006764 0.9962 -0.001135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6181 0.5577 0.3095 0.9758 0.9892 0.7242 0.9131 0.9728 0.6426 ] Network output: [ -0.01363 0.09197 0.9448 0.00108 -0.000485 0.9949 0.0008142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6487 0.6336 0.452 0.2355 0.9871 0.9915 0.6491 0.9741 0.9826 0.4627 ] Network output: [ -0.03026 0.1067 0.9465 0.000921 -0.0004135 1.011 0.0006941 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6462 0.6438 0.4538 0.2238 0.9856 0.9906 0.6463 0.9701 0.9803 0.4557 ] Network output: [ 0.007665 0.967 0.0185 -0.0002407 0.0001081 0.9982 -0.0001814 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009701 Epoch 2725 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01983 0.9945 0.9994 -5.381e-05 2.416e-05 -0.03381 -4.056e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0219 -0.005343 0.01997 0.02102 0.9415 0.9506 0.04302 0.8885 0.9066 0.1097 ] Network output: [ 0.9895 0.02961 -0.005375 -0.0001571 7.051e-05 -0.00385 -0.0001184 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.643 0.1186 0.1548 0.1759 0.9726 0.9874 0.7258 0.9031 0.9683 0.6489 ] Network output: [ -0.01251 0.9719 1.016 -4.239e-05 1.903e-05 0.0372 -3.195e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04652 0.03423 0.04738 0.02527 0.9859 0.99 0.04745 0.9711 0.9811 0.05774 ] Network output: [ 0.03939 -0.1616 1.081 -0.001507 0.0006766 0.9962 -0.001136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6181 0.5576 0.3094 0.9758 0.9892 0.7242 0.9131 0.9728 0.6426 ] Network output: [ -0.01361 0.09186 0.9448 0.00108 -0.000485 0.9949 0.0008142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6487 0.6337 0.452 0.2354 0.9871 0.9915 0.6491 0.9741 0.9826 0.4626 ] Network output: [ -0.03022 0.1067 0.9465 0.0009216 -0.0004137 1.011 0.0006945 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6463 0.6439 0.4537 0.2237 0.9856 0.9906 0.6464 0.9701 0.9803 0.4556 ] Network output: [ 0.007663 0.967 0.01851 -0.0002403 0.0001079 0.9982 -0.0001811 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009687 Epoch 2726 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01981 0.9946 0.9994 -5.386e-05 2.418e-05 -0.0338 -4.059e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0219 -0.005343 0.01999 0.02101 0.9415 0.9506 0.04301 0.8885 0.9066 0.1097 ] Network output: [ 0.9895 0.02952 -0.005326 -0.0001578 7.086e-05 -0.003869 -0.0001189 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6431 0.1185 0.155 0.1758 0.9726 0.9874 0.7258 0.9031 0.9683 0.6488 ] Network output: [ -0.01251 0.9719 1.016 -4.2e-05 1.886e-05 0.03719 -3.165e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04651 0.03422 0.04736 0.02525 0.9859 0.99 0.04744 0.9711 0.9811 0.05771 ] Network output: [ 0.03934 -0.1614 1.081 -0.001508 0.0006769 0.9961 -0.001136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6181 0.5576 0.3092 0.9758 0.9892 0.7242 0.9131 0.9728 0.6425 ] Network output: [ -0.0136 0.09176 0.9449 0.00108 -0.000485 0.995 0.0008142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6487 0.6337 0.4519 0.2353 0.9871 0.9915 0.6492 0.9741 0.9826 0.4626 ] Network output: [ -0.03018 0.1066 0.9464 0.0009222 -0.000414 1.011 0.000695 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6463 0.6439 0.4536 0.2236 0.9856 0.9906 0.6464 0.9701 0.9803 0.4555 ] Network output: [ 0.007661 0.967 0.01852 -0.0002399 0.0001077 0.9982 -0.0001808 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009673 Epoch 2727 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01978 0.9946 0.9994 -5.39e-05 2.42e-05 -0.03378 -4.062e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0219 -0.005343 0.02 0.021 0.9415 0.9506 0.043 0.8885 0.9067 0.1097 ] Network output: [ 0.9895 0.02942 -0.005277 -0.0001586 7.12e-05 -0.003888 -0.0001195 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6431 0.1184 0.1551 0.1758 0.9726 0.9874 0.7258 0.9031 0.9683 0.6488 ] Network output: [ -0.01251 0.972 1.016 -4.161e-05 1.868e-05 0.03718 -3.136e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0465 0.03421 0.04733 0.02523 0.9859 0.99 0.04743 0.9711 0.9811 0.05768 ] Network output: [ 0.03929 -0.1613 1.081 -0.001508 0.0006771 0.9961 -0.001137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.618 0.5576 0.3091 0.9758 0.9892 0.7242 0.9131 0.9728 0.6424 ] Network output: [ -0.01359 0.09165 0.9449 0.00108 -0.000485 0.995 0.0008141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6488 0.6337 0.4519 0.2353 0.9871 0.9915 0.6492 0.9741 0.9826 0.4625 ] Network output: [ -0.03014 0.1065 0.9464 0.0009228 -0.0004143 1.011 0.0006954 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6464 0.644 0.4535 0.2235 0.9856 0.9906 0.6464 0.9701 0.9803 0.4554 ] Network output: [ 0.007659 0.967 0.01853 -0.0002395 0.0001075 0.9982 -0.0001805 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009659 Epoch 2728 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01976 0.9947 0.9994 -5.395e-05 2.422e-05 -0.03376 -4.066e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0219 -0.005342 0.02001 0.02099 0.9415 0.9506 0.04299 0.8885 0.9067 0.1096 ] Network output: [ 0.9896 0.02933 -0.005227 -0.0001594 7.155e-05 -0.003907 -0.0001201 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6432 0.1183 0.1553 0.1757 0.9726 0.9874 0.7258 0.9031 0.9683 0.6487 ] Network output: [ -0.01251 0.9721 1.016 -4.121e-05 1.85e-05 0.03716 -3.106e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04648 0.0342 0.04731 0.02522 0.9859 0.99 0.04742 0.9711 0.9811 0.05764 ] Network output: [ 0.03924 -0.1611 1.081 -0.001509 0.0006773 0.996 -0.001137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.618 0.5576 0.309 0.9758 0.9892 0.7242 0.9131 0.9728 0.6423 ] Network output: [ -0.01358 0.09155 0.9449 0.00108 -0.0004849 0.9951 0.0008141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6488 0.6338 0.4518 0.2352 0.9871 0.9915 0.6492 0.9741 0.9826 0.4625 ] Network output: [ -0.0301 0.1065 0.9463 0.0009233 -0.0004145 1.011 0.0006959 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6464 0.644 0.4535 0.2235 0.9856 0.9906 0.6465 0.9701 0.9803 0.4554 ] Network output: [ 0.007658 0.967 0.01854 -0.0002391 0.0001073 0.9982 -0.0001802 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009645 Epoch 2729 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01973 0.9947 0.9994 -5.399e-05 2.424e-05 -0.03375 -4.069e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02189 -0.005342 0.02003 0.02098 0.9415 0.9506 0.04298 0.8885 0.9067 0.1096 ] Network output: [ 0.9896 0.02925 -0.005178 -0.0001602 7.191e-05 -0.003927 -0.0001207 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6432 0.1183 0.1554 0.1756 0.9726 0.9874 0.7258 0.9031 0.9683 0.6486 ] Network output: [ -0.01251 0.9721 1.016 -4.082e-05 1.833e-05 0.03715 -3.076e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04647 0.03419 0.04729 0.0252 0.9859 0.99 0.0474 0.9711 0.9811 0.05761 ] Network output: [ 0.03919 -0.161 1.081 -0.001509 0.0006775 0.9959 -0.001137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.618 0.5576 0.3089 0.9758 0.9892 0.7243 0.9131 0.9728 0.6423 ] Network output: [ -0.01357 0.09144 0.945 0.00108 -0.0004849 0.9951 0.000814 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6489 0.6338 0.4518 0.2351 0.9871 0.9915 0.6493 0.9741 0.9826 0.4624 ] Network output: [ -0.03006 0.1064 0.9463 0.0009239 -0.0004148 1.011 0.0006963 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6465 0.6441 0.4534 0.2234 0.9856 0.9906 0.6465 0.9701 0.9803 0.4553 ] Network output: [ 0.007656 0.967 0.01855 -0.0002387 0.0001072 0.9982 -0.0001799 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009632 Epoch 2730 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0197 0.9948 0.9994 -5.404e-05 2.426e-05 -0.03373 -4.073e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02189 -0.005342 0.02004 0.02097 0.9415 0.9506 0.04296 0.8885 0.9067 0.1095 ] Network output: [ 0.9896 0.02916 -0.005129 -0.000161 7.226e-05 -0.003947 -0.0001213 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6432 0.1182 0.1556 0.1756 0.9726 0.9874 0.7258 0.9031 0.9683 0.6486 ] Network output: [ -0.01251 0.9722 1.016 -4.042e-05 1.815e-05 0.03713 -3.046e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04646 0.03417 0.04727 0.02518 0.9859 0.99 0.04739 0.9711 0.9811 0.05758 ] Network output: [ 0.03913 -0.1609 1.081 -0.00151 0.0006778 0.9959 -0.001138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6179 0.5576 0.3088 0.9758 0.9892 0.7243 0.9131 0.9728 0.6422 ] Network output: [ -0.01355 0.09134 0.945 0.00108 -0.0004849 0.9952 0.000814 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6489 0.6338 0.4517 0.235 0.9871 0.9915 0.6493 0.9741 0.9826 0.4624 ] Network output: [ -0.03002 0.1064 0.9462 0.0009245 -0.000415 1.011 0.0006967 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6465 0.6441 0.4533 0.2233 0.9856 0.9906 0.6466 0.9701 0.9803 0.4552 ] Network output: [ 0.007655 0.967 0.01856 -0.0002383 0.000107 0.9982 -0.0001796 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009619 Epoch 2731 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01968 0.9948 0.9993 -5.408e-05 2.428e-05 -0.03371 -4.076e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02189 -0.005342 0.02005 0.02097 0.9415 0.9506 0.04295 0.8885 0.9067 0.1095 ] Network output: [ 0.9897 0.02907 -0.00508 -0.0001618 7.262e-05 -0.003967 -0.0001219 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6433 0.1181 0.1557 0.1755 0.9726 0.9874 0.7258 0.9031 0.9683 0.6485 ] Network output: [ -0.01251 0.9722 1.015 -4.003e-05 1.797e-05 0.03712 -3.016e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04645 0.03416 0.04725 0.02517 0.9859 0.99 0.04738 0.9711 0.9811 0.05755 ] Network output: [ 0.03908 -0.1607 1.081 -0.00151 0.000678 0.9958 -0.001138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6179 0.5576 0.3087 0.9758 0.9892 0.7243 0.9131 0.9728 0.6421 ] Network output: [ -0.01354 0.09124 0.945 0.00108 -0.0004849 0.9952 0.000814 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6489 0.6339 0.4517 0.235 0.9871 0.9915 0.6493 0.9741 0.9826 0.4623 ] Network output: [ -0.02998 0.1063 0.9461 0.000925 -0.0004153 1.011 0.0006971 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6466 0.6442 0.4532 0.2232 0.9856 0.9906 0.6466 0.9701 0.9803 0.4551 ] Network output: [ 0.007654 0.967 0.01857 -0.0002379 0.0001068 0.9982 -0.0001793 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009606 Epoch 2732 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01965 0.9948 0.9993 -5.413e-05 2.43e-05 -0.0337 -4.079e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02189 -0.005342 0.02007 0.02096 0.9415 0.9506 0.04294 0.8885 0.9067 0.1094 ] Network output: [ 0.9897 0.02898 -0.005031 -0.0001626 7.298e-05 -0.003987 -0.0001225 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6433 0.118 0.1559 0.1754 0.9726 0.9874 0.7258 0.9031 0.9683 0.6485 ] Network output: [ -0.01251 0.9723 1.015 -3.963e-05 1.779e-05 0.0371 -2.986e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04644 0.03415 0.04723 0.02515 0.9859 0.99 0.04737 0.9711 0.9811 0.05752 ] Network output: [ 0.03904 -0.1606 1.081 -0.001511 0.0006782 0.9958 -0.001138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7216 0.6178 0.5576 0.3086 0.9758 0.9892 0.7243 0.9131 0.9728 0.642 ] Network output: [ -0.01353 0.09114 0.9451 0.00108 -0.0004849 0.9952 0.0008139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.649 0.6339 0.4516 0.2349 0.9871 0.9915 0.6494 0.9741 0.9826 0.4622 ] Network output: [ -0.02995 0.1063 0.9461 0.0009256 -0.0004155 1.011 0.0006976 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6466 0.6442 0.4532 0.2231 0.9856 0.9907 0.6467 0.9701 0.9803 0.4551 ] Network output: [ 0.007653 0.967 0.01859 -0.0002375 0.0001066 0.9981 -0.000179 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009593 Epoch 2733 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01962 0.9949 0.9993 -5.417e-05 2.432e-05 -0.03368 -4.083e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02189 -0.005341 0.02008 0.02095 0.9415 0.9506 0.04293 0.8885 0.9067 0.1094 ] Network output: [ 0.9897 0.02889 -0.004982 -0.0001634 7.334e-05 -0.004008 -0.0001231 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6433 0.1179 0.156 0.1754 0.9726 0.9874 0.7258 0.9031 0.9683 0.6484 ] Network output: [ -0.01251 0.9724 1.015 -3.923e-05 1.761e-05 0.03709 -2.956e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04643 0.03414 0.04721 0.02514 0.9859 0.99 0.04736 0.9711 0.9811 0.05748 ] Network output: [ 0.03899 -0.1604 1.081 -0.001511 0.0006784 0.9957 -0.001139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7217 0.6178 0.5576 0.3085 0.9758 0.9892 0.7243 0.9131 0.9728 0.642 ] Network output: [ -0.01352 0.09104 0.9451 0.00108 -0.0004848 0.9953 0.0008139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.649 0.6339 0.4516 0.2348 0.9871 0.9915 0.6494 0.9741 0.9826 0.4622 ] Network output: [ -0.02991 0.1062 0.946 0.0009262 -0.0004158 1.011 0.000698 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6466 0.6442 0.4531 0.223 0.9856 0.9907 0.6467 0.9701 0.9803 0.455 ] Network output: [ 0.007653 0.967 0.0186 -0.0002371 0.0001064 0.9981 -0.0001787 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00958 Epoch 2734 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0196 0.9949 0.9993 -5.421e-05 2.434e-05 -0.03366 -4.086e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02189 -0.005341 0.0201 0.02094 0.9415 0.9507 0.04292 0.8885 0.9067 0.1093 ] Network output: [ 0.9897 0.0288 -0.004933 -0.0001642 7.37e-05 -0.004029 -0.0001237 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6434 0.1179 0.1562 0.1753 0.9726 0.9874 0.7258 0.9031 0.9683 0.6483 ] Network output: [ -0.01251 0.9724 1.015 -3.883e-05 1.743e-05 0.03708 -2.926e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04642 0.03413 0.04719 0.02512 0.9859 0.99 0.04734 0.9711 0.9811 0.05745 ] Network output: [ 0.03894 -0.1603 1.081 -0.001512 0.0006786 0.9957 -0.001139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7217 0.6178 0.5576 0.3084 0.9758 0.9892 0.7243 0.9131 0.9728 0.6419 ] Network output: [ -0.01351 0.09094 0.9451 0.00108 -0.0004848 0.9953 0.0008138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.649 0.6339 0.4515 0.2348 0.9871 0.9915 0.6494 0.9741 0.9826 0.4621 ] Network output: [ -0.02987 0.1062 0.946 0.0009267 -0.000416 1.011 0.0006984 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6467 0.6443 0.453 0.223 0.9856 0.9907 0.6468 0.9701 0.9803 0.4549 ] Network output: [ 0.007652 0.967 0.01861 -0.0002367 0.0001063 0.9981 -0.0001784 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009568 Epoch 2735 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01957 0.995 0.9993 -5.426e-05 2.436e-05 -0.03365 -4.089e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02189 -0.005341 0.02011 0.02093 0.9415 0.9507 0.0429 0.8885 0.9067 0.1093 ] Network output: [ 0.9898 0.02872 -0.004884 -0.000165 7.407e-05 -0.00405 -0.0001243 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6434 0.1178 0.1563 0.1752 0.9726 0.9874 0.7259 0.9031 0.9683 0.6483 ] Network output: [ -0.01251 0.9725 1.015 -3.843e-05 1.725e-05 0.03706 -2.896e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0464 0.03412 0.04717 0.0251 0.9859 0.99 0.04733 0.9711 0.9811 0.05742 ] Network output: [ 0.03889 -0.1602 1.081 -0.001512 0.0006788 0.9956 -0.001139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7217 0.6177 0.5576 0.3083 0.9759 0.9892 0.7243 0.9131 0.9728 0.6418 ] Network output: [ -0.0135 0.09084 0.9452 0.00108 -0.0004848 0.9954 0.0008138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6491 0.634 0.4515 0.2347 0.9871 0.9915 0.6495 0.9741 0.9826 0.4621 ] Network output: [ -0.02984 0.1061 0.9459 0.0009272 -0.0004163 1.011 0.0006988 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6467 0.6443 0.4529 0.2229 0.9856 0.9907 0.6468 0.9701 0.9803 0.4548 ] Network output: [ 0.007652 0.967 0.01863 -0.0002363 0.0001061 0.9981 -0.0001781 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009556 Epoch 2736 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01955 0.995 0.9993 -5.43e-05 2.438e-05 -0.03363 -4.092e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02189 -0.005341 0.02012 0.02093 0.9415 0.9507 0.04289 0.8885 0.9067 0.1093 ] Network output: [ 0.9898 0.02863 -0.004834 -0.0001658 7.443e-05 -0.004071 -0.000125 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6435 0.1177 0.1565 0.1752 0.9727 0.9874 0.7259 0.9031 0.9683 0.6482 ] Network output: [ -0.01251 0.9726 1.015 -3.802e-05 1.707e-05 0.03705 -2.865e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04639 0.0341 0.04715 0.02509 0.9859 0.99 0.04732 0.9711 0.9811 0.05739 ] Network output: [ 0.03884 -0.16 1.081 -0.001512 0.000679 0.9956 -0.00114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7217 0.6177 0.5576 0.3082 0.9759 0.9892 0.7243 0.9131 0.9728 0.6417 ] Network output: [ -0.0135 0.09075 0.9452 0.00108 -0.0004847 0.9954 0.0008137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6491 0.634 0.4514 0.2347 0.9871 0.9915 0.6495 0.9741 0.9826 0.462 ] Network output: [ -0.0298 0.1061 0.9458 0.0009278 -0.0004165 1.011 0.0006992 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6468 0.6444 0.4529 0.2228 0.9856 0.9907 0.6468 0.9701 0.9803 0.4547 ] Network output: [ 0.007652 0.967 0.01864 -0.0002359 0.0001059 0.9981 -0.0001778 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009544 Epoch 2737 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01952 0.995 0.9993 -5.434e-05 2.44e-05 -0.03361 -4.095e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02188 -0.005341 0.02014 0.02092 0.9415 0.9507 0.04288 0.8885 0.9067 0.1092 ] Network output: [ 0.9898 0.02855 -0.004785 -0.0001666 7.48e-05 -0.004093 -0.0001256 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6435 0.1176 0.1567 0.1751 0.9727 0.9874 0.7259 0.9031 0.9683 0.6481 ] Network output: [ -0.01251 0.9726 1.015 -3.762e-05 1.689e-05 0.03704 -2.835e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04638 0.03409 0.04713 0.02507 0.9859 0.99 0.04731 0.9711 0.9811 0.05735 ] Network output: [ 0.03879 -0.1599 1.081 -0.001513 0.0006791 0.9955 -0.00114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7217 0.6177 0.5575 0.3081 0.9759 0.9892 0.7243 0.9131 0.9728 0.6416 ] Network output: [ -0.01349 0.09065 0.9453 0.00108 -0.0004847 0.9955 0.0008137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6491 0.634 0.4514 0.2346 0.9871 0.9915 0.6496 0.9741 0.9826 0.4619 ] Network output: [ -0.02976 0.106 0.9458 0.0009283 -0.0004168 1.011 0.0006996 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6468 0.6444 0.4528 0.2227 0.9856 0.9907 0.6469 0.9701 0.9803 0.4547 ] Network output: [ 0.007652 0.967 0.01866 -0.0002355 0.0001057 0.9981 -0.0001775 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009533 Epoch 2738 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01949 0.9951 0.9993 -5.439e-05 2.442e-05 -0.0336 -4.099e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02188 -0.005341 0.02015 0.02091 0.9415 0.9507 0.04287 0.8885 0.9067 0.1092 ] Network output: [ 0.9899 0.02846 -0.004736 -0.0001675 7.517e-05 -0.004115 -0.0001262 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6435 0.1175 0.1568 0.1751 0.9727 0.9874 0.7259 0.9031 0.9683 0.648 ] Network output: [ -0.01251 0.9727 1.015 -3.721e-05 1.671e-05 0.03702 -2.804e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04637 0.03408 0.04711 0.02506 0.9859 0.99 0.04729 0.9711 0.9811 0.05732 ] Network output: [ 0.03875 -0.1598 1.081 -0.001513 0.0006793 0.9955 -0.00114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7217 0.6176 0.5575 0.308 0.9759 0.9892 0.7243 0.9131 0.9728 0.6416 ] Network output: [ -0.01348 0.09056 0.9453 0.00108 -0.0004847 0.9955 0.0008136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6492 0.634 0.4513 0.2345 0.9871 0.9915 0.6496 0.9741 0.9826 0.4619 ] Network output: [ -0.02973 0.106 0.9457 0.0009289 -0.000417 1.012 0.0007 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6469 0.6445 0.4527 0.2226 0.9856 0.9907 0.6469 0.9701 0.9803 0.4546 ] Network output: [ 0.007652 0.967 0.01867 -0.0002351 0.0001055 0.9981 -0.0001771 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009521 Epoch 2739 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01947 0.9951 0.9993 -5.443e-05 2.443e-05 -0.03358 -4.102e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02188 -0.005341 0.02017 0.0209 0.9415 0.9507 0.04286 0.8885 0.9067 0.1091 ] Network output: [ 0.9899 0.02838 -0.004687 -0.0001683 7.555e-05 -0.004137 -0.0001268 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6436 0.1174 0.157 0.175 0.9727 0.9874 0.7259 0.9031 0.9683 0.648 ] Network output: [ -0.01251 0.9727 1.015 -3.681e-05 1.652e-05 0.03701 -2.774e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04636 0.03407 0.04708 0.02504 0.9859 0.99 0.04728 0.9711 0.9811 0.05729 ] Network output: [ 0.0387 -0.1596 1.081 -0.001514 0.0006795 0.9954 -0.001141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7217 0.6176 0.5575 0.308 0.9759 0.9892 0.7243 0.9131 0.9728 0.6415 ] Network output: [ -0.01347 0.09047 0.9453 0.00108 -0.0004846 0.9955 0.0008136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6492 0.6341 0.4513 0.2345 0.9871 0.9915 0.6496 0.9741 0.9826 0.4618 ] Network output: [ -0.02969 0.1059 0.9456 0.0009294 -0.0004172 1.012 0.0007004 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6469 0.6445 0.4526 0.2226 0.9856 0.9907 0.647 0.9701 0.9803 0.4545 ] Network output: [ 0.007653 0.967 0.01869 -0.0002346 0.0001053 0.9981 -0.0001768 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00951 Epoch 2740 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01944 0.9952 0.9993 -5.447e-05 2.445e-05 -0.03356 -4.105e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02188 -0.005341 0.02018 0.0209 0.9415 0.9507 0.04285 0.8885 0.9067 0.1091 ] Network output: [ 0.9899 0.02829 -0.004638 -0.0001691 7.593e-05 -0.004159 -0.0001275 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6436 0.1173 0.1571 0.175 0.9727 0.9874 0.7259 0.9031 0.9683 0.6479 ] Network output: [ -0.01251 0.9728 1.015 -3.64e-05 1.634e-05 0.03699 -2.743e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04635 0.03406 0.04706 0.02503 0.9859 0.99 0.04727 0.9711 0.9811 0.05725 ] Network output: [ 0.03866 -0.1595 1.081 -0.001514 0.0006797 0.9954 -0.001141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7217 0.6175 0.5575 0.3079 0.9759 0.9892 0.7243 0.9131 0.9728 0.6414 ] Network output: [ -0.01346 0.09038 0.9454 0.001079 -0.0004846 0.9956 0.0008135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6492 0.6341 0.4512 0.2344 0.9871 0.9915 0.6497 0.9741 0.9826 0.4618 ] Network output: [ -0.02966 0.1059 0.9456 0.0009299 -0.0004175 1.012 0.0007008 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6469 0.6445 0.4525 0.2225 0.9856 0.9907 0.647 0.9701 0.9803 0.4544 ] Network output: [ 0.007654 0.967 0.01871 -0.0002342 0.0001052 0.9981 -0.0001765 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009499 Epoch 2741 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01941 0.9952 0.9993 -5.451e-05 2.447e-05 -0.03354 -4.108e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02188 -0.005341 0.0202 0.02089 0.9415 0.9507 0.04283 0.8885 0.9067 0.109 ] Network output: [ 0.9899 0.02821 -0.004588 -0.00017 7.63e-05 -0.004182 -0.0001281 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6437 0.1173 0.1573 0.1749 0.9727 0.9874 0.7259 0.9031 0.9683 0.6478 ] Network output: [ -0.01251 0.9728 1.015 -3.599e-05 1.616e-05 0.03698 -2.712e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04633 0.03404 0.04704 0.02501 0.9859 0.99 0.04726 0.9711 0.9811 0.05722 ] Network output: [ 0.03861 -0.1594 1.081 -0.001514 0.0006798 0.9953 -0.001141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7217 0.6175 0.5575 0.3078 0.9759 0.9892 0.7243 0.9131 0.9728 0.6413 ] Network output: [ -0.01345 0.09029 0.9454 0.001079 -0.0004846 0.9956 0.0008134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6493 0.6341 0.4512 0.2344 0.9871 0.9915 0.6497 0.9741 0.9826 0.4617 ] Network output: [ -0.02962 0.1059 0.9455 0.0009304 -0.0004177 1.012 0.0007012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.647 0.6446 0.4525 0.2224 0.9856 0.9907 0.6471 0.9701 0.9803 0.4543 ] Network output: [ 0.007655 0.9669 0.01872 -0.0002338 0.000105 0.9981 -0.0001762 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009489 Epoch 2742 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01939 0.9952 0.9993 -5.455e-05 2.449e-05 -0.03352 -4.111e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02188 -0.00534 0.02021 0.02088 0.9416 0.9507 0.04282 0.8885 0.9067 0.109 ] Network output: [ 0.99 0.02813 -0.004539 -0.0001708 7.668e-05 -0.004205 -0.0001287 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6437 0.1172 0.1575 0.1748 0.9727 0.9874 0.7259 0.9031 0.9683 0.6478 ] Network output: [ -0.01251 0.9729 1.015 -3.558e-05 1.597e-05 0.03697 -2.681e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04632 0.03403 0.04702 0.025 0.9859 0.99 0.04724 0.9711 0.9811 0.05719 ] Network output: [ 0.03857 -0.1593 1.081 -0.001515 0.00068 0.9953 -0.001142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7217 0.6175 0.5575 0.3077 0.9759 0.9892 0.7244 0.9131 0.9728 0.6412 ] Network output: [ -0.01345 0.0902 0.9454 0.001079 -0.0004845 0.9957 0.0008134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6493 0.6341 0.4511 0.2343 0.9871 0.9915 0.6497 0.9741 0.9826 0.4616 ] Network output: [ -0.02959 0.1058 0.9455 0.0009309 -0.0004179 1.012 0.0007016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.647 0.6446 0.4524 0.2223 0.9856 0.9907 0.6471 0.9701 0.9803 0.4542 ] Network output: [ 0.007656 0.9669 0.01874 -0.0002334 0.0001048 0.9981 -0.0001759 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009478 Epoch 2743 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01936 0.9953 0.9993 -5.459e-05 2.451e-05 -0.03351 -4.114e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02188 -0.00534 0.02022 0.02087 0.9416 0.9507 0.04281 0.8885 0.9067 0.1089 ] Network output: [ 0.99 0.02804 -0.00449 -0.0001717 7.707e-05 -0.004228 -0.0001294 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6437 0.1171 0.1576 0.1748 0.9727 0.9874 0.7259 0.9031 0.9683 0.6477 ] Network output: [ -0.01251 0.973 1.015 -3.517e-05 1.579e-05 0.03695 -2.65e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04631 0.03402 0.047 0.02498 0.986 0.99 0.04723 0.9711 0.9811 0.05715 ] Network output: [ 0.03852 -0.1592 1.081 -0.001515 0.0006802 0.9952 -0.001142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7217 0.6174 0.5575 0.3076 0.9759 0.9892 0.7244 0.9131 0.9728 0.6411 ] Network output: [ -0.01344 0.09011 0.9455 0.001079 -0.0004845 0.9957 0.0008133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6493 0.6342 0.4511 0.2343 0.9871 0.9915 0.6498 0.9741 0.9826 0.4616 ] Network output: [ -0.02955 0.1058 0.9454 0.0009314 -0.0004182 1.012 0.000702 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6471 0.6446 0.4523 0.2223 0.9856 0.9907 0.6471 0.9701 0.9803 0.4542 ] Network output: [ 0.007657 0.9669 0.01876 -0.000233 0.0001046 0.9981 -0.0001756 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009468 Epoch 2744 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01934 0.9953 0.9993 -5.463e-05 2.453e-05 -0.03349 -4.117e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02188 -0.00534 0.02024 0.02087 0.9416 0.9507 0.0428 0.8886 0.9067 0.1089 ] Network output: [ 0.99 0.02796 -0.004441 -0.0001725 7.745e-05 -0.004251 -0.00013 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6438 0.117 0.1578 0.1747 0.9727 0.9874 0.7259 0.9031 0.9683 0.6476 ] Network output: [ -0.01251 0.973 1.015 -3.476e-05 1.56e-05 0.03694 -2.619e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0463 0.03401 0.04698 0.02497 0.986 0.99 0.04722 0.9711 0.9811 0.05712 ] Network output: [ 0.03848 -0.159 1.081 -0.001515 0.0006803 0.9952 -0.001142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7218 0.6174 0.5575 0.3076 0.9759 0.9892 0.7244 0.9131 0.9728 0.6411 ] Network output: [ -0.01343 0.09002 0.9455 0.001079 -0.0004844 0.9957 0.0008132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6494 0.6342 0.451 0.2342 0.9871 0.9915 0.6498 0.9741 0.9826 0.4615 ] Network output: [ -0.02952 0.1058 0.9453 0.0009319 -0.0004184 1.012 0.0007023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6471 0.6447 0.4522 0.2222 0.9856 0.9907 0.6472 0.9701 0.9803 0.4541 ] Network output: [ 0.007659 0.9669 0.01878 -0.0002326 0.0001044 0.998 -0.0001753 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009458 Epoch 2745 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01931 0.9954 0.9993 -5.467e-05 2.454e-05 -0.03347 -4.12e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02188 -0.00534 0.02025 0.02086 0.9416 0.9507 0.04279 0.8886 0.9067 0.1088 ] Network output: [ 0.99 0.02788 -0.004391 -0.0001734 7.784e-05 -0.004275 -0.0001307 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6438 0.1169 0.158 0.1747 0.9727 0.9874 0.7259 0.9031 0.9683 0.6475 ] Network output: [ -0.01251 0.9731 1.015 -3.434e-05 1.542e-05 0.03693 -2.588e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04629 0.034 0.04696 0.02496 0.986 0.99 0.04721 0.9711 0.9811 0.05709 ] Network output: [ 0.03843 -0.1589 1.081 -0.001516 0.0006805 0.9951 -0.001142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7218 0.6173 0.5574 0.3075 0.9759 0.9892 0.7244 0.9131 0.9728 0.641 ] Network output: [ -0.01343 0.08994 0.9455 0.001079 -0.0004844 0.9958 0.0008132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6494 0.6342 0.451 0.2341 0.9871 0.9915 0.6498 0.9741 0.9826 0.4614 ] Network output: [ -0.02949 0.1057 0.9452 0.0009324 -0.0004186 1.012 0.0007027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6471 0.6447 0.4521 0.2221 0.9856 0.9907 0.6472 0.9701 0.9803 0.454 ] Network output: [ 0.007661 0.9669 0.0188 -0.0002322 0.0001042 0.998 -0.000175 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009448 Epoch 2746 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01928 0.9954 0.9993 -5.471e-05 2.456e-05 -0.03345 -4.123e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02187 -0.00534 0.02027 0.02085 0.9416 0.9507 0.04278 0.8886 0.9067 0.1088 ] Network output: [ 0.9901 0.0278 -0.004342 -0.0001743 7.823e-05 -0.004299 -0.0001313 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6439 0.1168 0.1581 0.1746 0.9727 0.9874 0.7259 0.9031 0.9683 0.6475 ] Network output: [ -0.01251 0.9731 1.015 -3.393e-05 1.523e-05 0.03691 -2.557e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04627 0.03398 0.04694 0.02494 0.986 0.99 0.04719 0.9711 0.9811 0.05705 ] Network output: [ 0.03839 -0.1588 1.081 -0.001516 0.0006806 0.9951 -0.001143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7218 0.6173 0.5574 0.3074 0.9759 0.9892 0.7244 0.9131 0.9728 0.6409 ] Network output: [ -0.01342 0.08985 0.9456 0.001079 -0.0004844 0.9958 0.0008131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6494 0.6342 0.4509 0.2341 0.9871 0.9915 0.6498 0.9741 0.9826 0.4614 ] Network output: [ -0.02945 0.1057 0.9452 0.0009329 -0.0004188 1.012 0.0007031 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6472 0.6448 0.452 0.222 0.9856 0.9907 0.6473 0.9701 0.9803 0.4539 ] Network output: [ 0.007663 0.9669 0.01881 -0.0002317 0.000104 0.998 -0.0001747 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009439 Epoch 2747 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01926 0.9954 0.9993 -5.475e-05 2.458e-05 -0.03343 -4.126e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02187 -0.00534 0.02028 0.02085 0.9416 0.9507 0.04276 0.8886 0.9067 0.1088 ] Network output: [ 0.9901 0.02772 -0.004293 -0.0001751 7.863e-05 -0.004323 -0.000132 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6439 0.1167 0.1583 0.1746 0.9727 0.9874 0.7259 0.9031 0.9683 0.6474 ] Network output: [ -0.0125 0.9732 1.015 -3.351e-05 1.504e-05 0.0369 -2.526e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04626 0.03397 0.04691 0.02493 0.986 0.99 0.04718 0.9711 0.9811 0.05702 ] Network output: [ 0.03835 -0.1587 1.081 -0.001516 0.0006807 0.995 -0.001143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7218 0.6173 0.5574 0.3073 0.9759 0.9892 0.7244 0.9131 0.9728 0.6408 ] Network output: [ -0.01342 0.08977 0.9456 0.001079 -0.0004843 0.9959 0.000813 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6495 0.6343 0.4508 0.234 0.9871 0.9915 0.6499 0.9741 0.9826 0.4613 ] Network output: [ -0.02942 0.1057 0.9451 0.0009334 -0.000419 1.012 0.0007034 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6472 0.6448 0.452 0.222 0.9856 0.9907 0.6473 0.9701 0.9803 0.4538 ] Network output: [ 0.007665 0.9669 0.01883 -0.0002313 0.0001039 0.998 -0.0001743 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00943 Epoch 2748 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01923 0.9955 0.9992 -5.479e-05 2.46e-05 -0.03342 -4.129e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02187 -0.00534 0.0203 0.02084 0.9416 0.9507 0.04275 0.8886 0.9067 0.1087 ] Network output: [ 0.9901 0.02764 -0.004244 -0.000176 7.902e-05 -0.004348 -0.0001327 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6439 0.1166 0.1584 0.1746 0.9727 0.9874 0.7259 0.9031 0.9683 0.6473 ] Network output: [ -0.0125 0.9733 1.015 -3.309e-05 1.486e-05 0.03689 -2.494e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04625 0.03396 0.04689 0.02491 0.986 0.99 0.04717 0.9711 0.9811 0.05699 ] Network output: [ 0.03831 -0.1586 1.081 -0.001517 0.0006809 0.995 -0.001143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7218 0.6172 0.5574 0.3073 0.9759 0.9892 0.7244 0.9131 0.9728 0.6407 ] Network output: [ -0.01341 0.08968 0.9456 0.001079 -0.0004843 0.9959 0.000813 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6495 0.6343 0.4508 0.234 0.9871 0.9915 0.6499 0.9741 0.9826 0.4612 ] Network output: [ -0.02939 0.1057 0.945 0.0009339 -0.0004193 1.012 0.0007038 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6473 0.6448 0.4519 0.2219 0.9856 0.9907 0.6473 0.9701 0.9803 0.4537 ] Network output: [ 0.007668 0.9669 0.01885 -0.0002309 0.0001037 0.998 -0.000174 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009421 Epoch 2749 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0192 0.9955 0.9992 -5.483e-05 2.461e-05 -0.0334 -4.132e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02187 -0.005341 0.02031 0.02083 0.9416 0.9507 0.04274 0.8886 0.9067 0.1087 ] Network output: [ 0.9901 0.02756 -0.004194 -0.0001769 7.942e-05 -0.004373 -0.0001333 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.644 0.1165 0.1586 0.1745 0.9727 0.9874 0.726 0.9031 0.9683 0.6472 ] Network output: [ -0.0125 0.9733 1.015 -3.268e-05 1.467e-05 0.03688 -2.463e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04624 0.03395 0.04687 0.0249 0.986 0.99 0.04715 0.9711 0.9811 0.05695 ] Network output: [ 0.03826 -0.1585 1.081 -0.001517 0.000681 0.9949 -0.001143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7218 0.6172 0.5574 0.3072 0.9759 0.9892 0.7244 0.9131 0.9728 0.6406 ] Network output: [ -0.01341 0.0896 0.9457 0.001079 -0.0004842 0.9959 0.0008129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6495 0.6343 0.4507 0.234 0.9871 0.9915 0.6499 0.9741 0.9826 0.4612 ] Network output: [ -0.02936 0.1056 0.945 0.0009344 -0.0004195 1.012 0.0007042 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6473 0.6449 0.4518 0.2218 0.9856 0.9907 0.6474 0.9701 0.9803 0.4537 ] Network output: [ 0.00767 0.9668 0.01888 -0.0002305 0.0001035 0.998 -0.0001737 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009412 Epoch 2750 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01918 0.9956 0.9992 -5.487e-05 2.463e-05 -0.03338 -4.135e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02187 -0.005341 0.02033 0.02082 0.9416 0.9507 0.04273 0.8886 0.9067 0.1086 ] Network output: [ 0.9902 0.02748 -0.004145 -0.0001778 7.982e-05 -0.004398 -0.000134 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.644 0.1164 0.1588 0.1745 0.9727 0.9874 0.726 0.9031 0.9683 0.6472 ] Network output: [ -0.0125 0.9734 1.015 -3.226e-05 1.448e-05 0.03686 -2.431e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04623 0.03393 0.04685 0.02489 0.986 0.99 0.04714 0.9711 0.9811 0.05692 ] Network output: [ 0.03822 -0.1584 1.081 -0.001517 0.0006811 0.9949 -0.001143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7218 0.6171 0.5574 0.3072 0.9759 0.9892 0.7244 0.9131 0.9728 0.6405 ] Network output: [ -0.0134 0.08952 0.9457 0.001079 -0.0004842 0.996 0.0008128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6496 0.6343 0.4507 0.2339 0.9871 0.9915 0.65 0.9741 0.9826 0.4611 ] Network output: [ -0.02932 0.1056 0.9449 0.0009349 -0.0004197 1.012 0.0007045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6473 0.6449 0.4517 0.2218 0.9856 0.9907 0.6474 0.9701 0.9803 0.4536 ] Network output: [ 0.007673 0.9668 0.0189 -0.0002301 0.0001033 0.998 -0.0001734 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009403 Epoch 2751 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01915 0.9956 0.9992 -5.49e-05 2.465e-05 -0.03336 -4.138e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02187 -0.005341 0.02034 0.02082 0.9416 0.9507 0.04272 0.8886 0.9067 0.1086 ] Network output: [ 0.9902 0.0274 -0.004095 -0.0001787 8.023e-05 -0.004423 -0.0001347 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6441 0.1163 0.1589 0.1744 0.9727 0.9874 0.726 0.9031 0.9683 0.6471 ] Network output: [ -0.0125 0.9734 1.015 -3.183e-05 1.429e-05 0.03685 -2.399e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04621 0.03392 0.04683 0.02487 0.986 0.99 0.04713 0.9711 0.9811 0.05688 ] Network output: [ 0.03818 -0.1583 1.081 -0.001517 0.0006813 0.9948 -0.001144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7218 0.6171 0.5573 0.3071 0.9759 0.9892 0.7244 0.9131 0.9728 0.6404 ] Network output: [ -0.0134 0.08944 0.9457 0.001078 -0.0004841 0.996 0.0008127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6496 0.6343 0.4506 0.2339 0.9871 0.9915 0.65 0.9741 0.9826 0.461 ] Network output: [ -0.02929 0.1056 0.9448 0.0009353 -0.0004199 1.012 0.0007049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6474 0.6449 0.4516 0.2217 0.9856 0.9907 0.6474 0.9701 0.9803 0.4535 ] Network output: [ 0.007676 0.9668 0.01892 -0.0002297 0.0001031 0.998 -0.0001731 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009395 Epoch 2752 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01913 0.9956 0.9992 -5.494e-05 2.467e-05 -0.03334 -4.141e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02187 -0.005341 0.02036 0.02081 0.9416 0.9507 0.0427 0.8886 0.9067 0.1085 ] Network output: [ 0.9902 0.02732 -0.004046 -0.0001796 8.063e-05 -0.004449 -0.0001354 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6441 0.1162 0.1591 0.1744 0.9727 0.9874 0.726 0.9031 0.9683 0.647 ] Network output: [ -0.0125 0.9735 1.015 -3.141e-05 1.41e-05 0.03684 -2.367e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0462 0.03391 0.04681 0.02486 0.986 0.99 0.04711 0.9711 0.9811 0.05685 ] Network output: [ 0.03814 -0.1582 1.081 -0.001518 0.0006814 0.9948 -0.001144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7219 0.617 0.5573 0.307 0.9759 0.9892 0.7244 0.9131 0.9728 0.6403 ] Network output: [ -0.01339 0.08937 0.9458 0.001078 -0.0004841 0.9961 0.0008127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6496 0.6344 0.4506 0.2338 0.9871 0.9915 0.65 0.9741 0.9826 0.461 ] Network output: [ -0.02926 0.1056 0.9447 0.0009358 -0.0004201 1.012 0.0007052 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6474 0.645 0.4515 0.2216 0.9857 0.9907 0.6475 0.9701 0.9803 0.4534 ] Network output: [ 0.00768 0.9668 0.01894 -0.0002292 0.0001029 0.998 -0.0001728 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009387 Epoch 2753 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0191 0.9957 0.9992 -5.498e-05 2.468e-05 -0.03332 -4.143e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02187 -0.005341 0.02037 0.0208 0.9416 0.9507 0.04269 0.8886 0.9067 0.1085 ] Network output: [ 0.9902 0.02724 -0.003997 -0.0001805 8.104e-05 -0.004475 -0.000136 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6441 0.1162 0.1593 0.1743 0.9727 0.9874 0.726 0.9031 0.9683 0.6469 ] Network output: [ -0.01249 0.9735 1.015 -3.099e-05 1.391e-05 0.03682 -2.335e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04619 0.0339 0.04678 0.02485 0.986 0.99 0.0471 0.9711 0.9811 0.05682 ] Network output: [ 0.0381 -0.1581 1.081 -0.001518 0.0006815 0.9947 -0.001144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7219 0.617 0.5573 0.307 0.9759 0.9892 0.7245 0.9131 0.9728 0.6402 ] Network output: [ -0.01339 0.08929 0.9458 0.001078 -0.000484 0.9961 0.0008126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6496 0.6344 0.4505 0.2338 0.9871 0.9915 0.6501 0.9741 0.9826 0.4609 ] Network output: [ -0.02923 0.1056 0.9447 0.0009363 -0.0004203 1.012 0.0007056 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6474 0.645 0.4515 0.2215 0.9857 0.9907 0.6475 0.9701 0.9803 0.4533 ] Network output: [ 0.007683 0.9668 0.01897 -0.0002288 0.0001027 0.998 -0.0001724 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009379 Epoch 2754 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01907 0.9957 0.9992 -5.502e-05 2.47e-05 -0.0333 -4.146e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02186 -0.005341 0.02039 0.0208 0.9416 0.9507 0.04268 0.8886 0.9067 0.1084 ] Network output: [ 0.9903 0.02717 -0.003947 -0.0001814 8.145e-05 -0.004501 -0.0001367 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6442 0.1161 0.1595 0.1743 0.9727 0.9874 0.726 0.9031 0.9683 0.6468 ] Network output: [ -0.01249 0.9736 1.014 -3.056e-05 1.372e-05 0.03681 -2.303e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04618 0.03388 0.04676 0.02483 0.986 0.99 0.04709 0.9711 0.9811 0.05678 ] Network output: [ 0.03806 -0.158 1.081 -0.001518 0.0006816 0.9947 -0.001144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7219 0.6169 0.5573 0.3069 0.9759 0.9892 0.7245 0.9131 0.9728 0.6401 ] Network output: [ -0.01339 0.08921 0.9458 0.001078 -0.000484 0.9961 0.0008125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6497 0.6344 0.4504 0.2337 0.9871 0.9915 0.6501 0.9741 0.9826 0.4608 ] Network output: [ -0.0292 0.1055 0.9446 0.0009367 -0.0004205 1.012 0.0007059 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6475 0.645 0.4514 0.2215 0.9857 0.9907 0.6476 0.9701 0.9803 0.4532 ] Network output: [ 0.007687 0.9667 0.01899 -0.0002284 0.0001025 0.998 -0.0001721 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009371 Epoch 2755 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01905 0.9958 0.9992 -5.505e-05 2.471e-05 -0.03328 -4.149e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02186 -0.005341 0.0204 0.02079 0.9416 0.9507 0.04267 0.8886 0.9067 0.1084 ] Network output: [ 0.9903 0.02709 -0.003898 -0.0001824 8.187e-05 -0.004528 -0.0001374 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6442 0.116 0.1596 0.1743 0.9727 0.9874 0.726 0.9031 0.9683 0.6468 ] Network output: [ -0.01249 0.9736 1.014 -3.014e-05 1.353e-05 0.0368 -2.271e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04616 0.03387 0.04674 0.02482 0.986 0.9901 0.04708 0.9711 0.9811 0.05675 ] Network output: [ 0.03803 -0.1579 1.081 -0.001518 0.0006817 0.9947 -0.001144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7219 0.6169 0.5573 0.3069 0.9759 0.9892 0.7245 0.9131 0.9728 0.64 ] Network output: [ -0.01338 0.08914 0.9459 0.001078 -0.0004839 0.9962 0.0008124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6497 0.6344 0.4504 0.2337 0.9871 0.9915 0.6501 0.9741 0.9826 0.4607 ] Network output: [ -0.02917 0.1055 0.9445 0.0009372 -0.0004207 1.012 0.0007063 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6475 0.6451 0.4513 0.2214 0.9857 0.9907 0.6476 0.9701 0.9803 0.4531 ] Network output: [ 0.007691 0.9667 0.01901 -0.000228 0.0001023 0.998 -0.0001718 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009364 Epoch 2756 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01902 0.9958 0.9992 -5.509e-05 2.473e-05 -0.03326 -4.152e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02186 -0.005341 0.02042 0.02079 0.9416 0.9507 0.04266 0.8886 0.9068 0.1083 ] Network output: [ 0.9903 0.02702 -0.003848 -0.0001833 8.229e-05 -0.004555 -0.0001381 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6443 0.1159 0.1598 0.1742 0.9727 0.9874 0.726 0.9031 0.9683 0.6467 ] Network output: [ -0.01249 0.9737 1.014 -2.971e-05 1.334e-05 0.03679 -2.239e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04615 0.03386 0.04672 0.02481 0.986 0.9901 0.04706 0.9711 0.9811 0.05671 ] Network output: [ 0.03799 -0.1578 1.081 -0.001519 0.0006818 0.9946 -0.001145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7219 0.6169 0.5573 0.3068 0.9759 0.9892 0.7245 0.9131 0.9728 0.6399 ] Network output: [ -0.01338 0.08907 0.9459 0.001078 -0.0004839 0.9962 0.0008123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6497 0.6344 0.4503 0.2337 0.9871 0.9915 0.6501 0.9741 0.9826 0.4607 ] Network output: [ -0.02914 0.1055 0.9444 0.0009376 -0.0004209 1.012 0.0007066 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6475 0.6451 0.4512 0.2214 0.9857 0.9907 0.6476 0.9701 0.9803 0.453 ] Network output: [ 0.007695 0.9667 0.01904 -0.0002275 0.0001021 0.998 -0.0001715 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009357 Epoch 2757 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.019 0.9958 0.9992 -5.512e-05 2.475e-05 -0.03324 -4.154e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02186 -0.005341 0.02043 0.02078 0.9416 0.9507 0.04264 0.8886 0.9068 0.1083 ] Network output: [ 0.9903 0.02694 -0.003799 -0.0001842 8.271e-05 -0.004582 -0.0001388 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6443 0.1158 0.16 0.1742 0.9727 0.9874 0.726 0.9031 0.9683 0.6466 ] Network output: [ -0.01249 0.9738 1.014 -2.928e-05 1.314e-05 0.03677 -2.207e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04614 0.03384 0.0467 0.02479 0.986 0.9901 0.04705 0.9711 0.9811 0.05668 ] Network output: [ 0.03795 -0.1577 1.081 -0.001519 0.0006819 0.9946 -0.001145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7219 0.6168 0.5572 0.3068 0.9759 0.9892 0.7245 0.9131 0.9728 0.6399 ] Network output: [ -0.01338 0.08899 0.9459 0.001078 -0.0004838 0.9962 0.0008122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6498 0.6345 0.4502 0.2336 0.9871 0.9915 0.6502 0.9741 0.9826 0.4606 ] Network output: [ -0.02911 0.1055 0.9444 0.0009381 -0.0004211 1.012 0.000707 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6476 0.6451 0.4511 0.2213 0.9857 0.9907 0.6477 0.9701 0.9803 0.4529 ] Network output: [ 0.0077 0.9667 0.01906 -0.0002271 0.000102 0.998 -0.0001712 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00935 Epoch 2758 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01897 0.9959 0.9992 -5.516e-05 2.476e-05 -0.03322 -4.157e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02186 -0.005342 0.02045 0.02077 0.9416 0.9507 0.04263 0.8886 0.9068 0.1082 ] Network output: [ 0.9904 0.02686 -0.003749 -0.0001852 8.313e-05 -0.004609 -0.0001396 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6444 0.1157 0.1601 0.1742 0.9727 0.9874 0.726 0.9031 0.9683 0.6465 ] Network output: [ -0.01248 0.9738 1.014 -2.885e-05 1.295e-05 0.03676 -2.174e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04613 0.03383 0.04667 0.02478 0.986 0.9901 0.04704 0.9711 0.9811 0.05664 ] Network output: [ 0.03791 -0.1576 1.081 -0.001519 0.000682 0.9945 -0.001145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7219 0.6168 0.5572 0.3067 0.9759 0.9892 0.7245 0.913 0.9728 0.6398 ] Network output: [ -0.01338 0.08892 0.9459 0.001078 -0.0004838 0.9963 0.0008121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6498 0.6345 0.4502 0.2336 0.9871 0.9915 0.6502 0.9741 0.9826 0.4605 ] Network output: [ -0.02908 0.1055 0.9443 0.0009385 -0.0004213 1.012 0.0007073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6476 0.6452 0.451 0.2212 0.9857 0.9907 0.6477 0.9701 0.9803 0.4528 ] Network output: [ 0.007704 0.9666 0.01909 -0.0002267 0.0001018 0.9979 -0.0001708 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009343 Epoch 2759 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01894 0.9959 0.9992 -5.519e-05 2.478e-05 -0.0332 -4.16e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02186 -0.005342 0.02046 0.02077 0.9416 0.9507 0.04262 0.8886 0.9068 0.1082 ] Network output: [ 0.9904 0.02679 -0.0037 -0.0001861 8.356e-05 -0.004637 -0.0001403 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6444 0.1155 0.1603 0.1741 0.9727 0.9874 0.726 0.9031 0.9683 0.6464 ] Network output: [ -0.01248 0.9739 1.014 -2.842e-05 1.276e-05 0.03675 -2.142e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04611 0.03382 0.04665 0.02477 0.986 0.9901 0.04702 0.9712 0.9811 0.05661 ] Network output: [ 0.03788 -0.1575 1.081 -0.001519 0.0006821 0.9945 -0.001145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7219 0.6167 0.5572 0.3067 0.9759 0.9892 0.7245 0.913 0.9728 0.6397 ] Network output: [ -0.01338 0.08885 0.946 0.001078 -0.0004837 0.9963 0.0008121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6498 0.6345 0.4501 0.2335 0.9871 0.9915 0.6502 0.9741 0.9826 0.4605 ] Network output: [ -0.02905 0.1055 0.9442 0.000939 -0.0004215 1.012 0.0007076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6477 0.6452 0.4509 0.2212 0.9857 0.9907 0.6477 0.9701 0.9803 0.4528 ] Network output: [ 0.007709 0.9666 0.01912 -0.0002262 0.0001016 0.9979 -0.0001705 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009337 Epoch 2760 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01892 0.9959 0.9992 -5.523e-05 2.479e-05 -0.03318 -4.162e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02186 -0.005342 0.02048 0.02076 0.9416 0.9507 0.04261 0.8886 0.9068 0.1081 ] Network output: [ 0.9904 0.02672 -0.00365 -0.0001871 8.399e-05 -0.004665 -0.000141 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6444 0.1154 0.1605 0.1741 0.9727 0.9874 0.7261 0.9031 0.9683 0.6463 ] Network output: [ -0.01248 0.9739 1.014 -2.798e-05 1.256e-05 0.03674 -2.109e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0461 0.0338 0.04663 0.02476 0.986 0.9901 0.04701 0.9712 0.9811 0.05658 ] Network output: [ 0.03784 -0.1574 1.081 -0.00152 0.0006822 0.9944 -0.001145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.722 0.6167 0.5572 0.3067 0.9759 0.9892 0.7245 0.913 0.9728 0.6396 ] Network output: [ -0.01337 0.08879 0.946 0.001077 -0.0004837 0.9963 0.000812 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6498 0.6345 0.4501 0.2335 0.9871 0.9915 0.6502 0.9741 0.9826 0.4604 ] Network output: [ -0.02903 0.1055 0.9441 0.0009394 -0.0004217 1.012 0.000708 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6477 0.6452 0.4508 0.2211 0.9857 0.9907 0.6478 0.9701 0.9803 0.4527 ] Network output: [ 0.007714 0.9666 0.01914 -0.0002258 0.0001014 0.9979 -0.0001702 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009331 Epoch 2761 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01889 0.996 0.9992 -5.526e-05 2.481e-05 -0.03316 -4.165e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02185 -0.005342 0.02049 0.02075 0.9416 0.9507 0.0426 0.8886 0.9068 0.1081 ] Network output: [ 0.9904 0.02664 -0.003601 -0.000188 8.442e-05 -0.004693 -0.0001417 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6445 0.1153 0.1607 0.1741 0.9727 0.9874 0.7261 0.9031 0.9683 0.6462 ] Network output: [ -0.01248 0.974 1.014 -2.755e-05 1.237e-05 0.03672 -2.076e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04609 0.03379 0.04661 0.02474 0.986 0.9901 0.047 0.9712 0.9811 0.05654 ] Network output: [ 0.0378 -0.1573 1.081 -0.00152 0.0006823 0.9944 -0.001145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.722 0.6166 0.5572 0.3066 0.9759 0.9892 0.7245 0.913 0.9728 0.6395 ] Network output: [ -0.01337 0.08872 0.946 0.001077 -0.0004836 0.9964 0.0008119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6499 0.6345 0.45 0.2335 0.9871 0.9915 0.6503 0.9741 0.9826 0.4603 ] Network output: [ -0.029 0.1055 0.944 0.0009398 -0.0004219 1.012 0.0007083 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6477 0.6453 0.4507 0.221 0.9857 0.9907 0.6478 0.9701 0.9803 0.4526 ] Network output: [ 0.00772 0.9665 0.01917 -0.0002254 0.0001012 0.9979 -0.0001699 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009325 Epoch 2762 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01886 0.996 0.9992 -5.53e-05 2.483e-05 -0.03314 -4.167e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02185 -0.005343 0.02051 0.02075 0.9416 0.9507 0.04259 0.8886 0.9068 0.1081 ] Network output: [ 0.9905 0.02657 -0.003551 -0.000189 8.486e-05 -0.004722 -0.0001425 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6445 0.1152 0.1608 0.174 0.9727 0.9875 0.7261 0.9031 0.9683 0.6462 ] Network output: [ -0.01247 0.974 1.014 -2.711e-05 1.217e-05 0.03671 -2.043e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04608 0.03378 0.04659 0.02473 0.986 0.9901 0.04698 0.9712 0.9811 0.05651 ] Network output: [ 0.03777 -0.1572 1.081 -0.00152 0.0006823 0.9944 -0.001145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.722 0.6166 0.5571 0.3066 0.9759 0.9892 0.7246 0.913 0.9728 0.6394 ] Network output: [ -0.01337 0.08865 0.9461 0.001077 -0.0004836 0.9964 0.0008118 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6499 0.6345 0.4499 0.2334 0.9871 0.9915 0.6503 0.9741 0.9826 0.4602 ] Network output: [ -0.02897 0.1055 0.9439 0.0009403 -0.0004221 1.012 0.0007086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6478 0.6453 0.4506 0.221 0.9857 0.9907 0.6478 0.9701 0.9803 0.4525 ] Network output: [ 0.007725 0.9665 0.0192 -0.0002249 0.000101 0.9979 -0.0001695 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009319 Epoch 2763 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01884 0.9961 0.9992 -5.533e-05 2.484e-05 -0.03312 -4.17e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02185 -0.005343 0.02052 0.02074 0.9416 0.9507 0.04257 0.8886 0.9068 0.108 ] Network output: [ 0.9905 0.0265 -0.003501 -0.00019 8.53e-05 -0.004751 -0.0001432 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6446 0.1151 0.161 0.174 0.9727 0.9875 0.7261 0.9031 0.9683 0.6461 ] Network output: [ -0.01247 0.9741 1.014 -2.667e-05 1.197e-05 0.0367 -2.01e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04606 0.03376 0.04656 0.02472 0.986 0.9901 0.04697 0.9712 0.9812 0.05647 ] Network output: [ 0.03773 -0.1572 1.081 -0.00152 0.0006824 0.9943 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.722 0.6165 0.5571 0.3066 0.9759 0.9892 0.7246 0.913 0.9728 0.6393 ] Network output: [ -0.01337 0.08859 0.9461 0.001077 -0.0004835 0.9965 0.0008117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6499 0.6345 0.4499 0.2334 0.9871 0.9915 0.6503 0.9741 0.9826 0.4601 ] Network output: [ -0.02894 0.1055 0.9439 0.0009407 -0.0004223 1.012 0.0007089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6478 0.6453 0.4506 0.2209 0.9857 0.9907 0.6479 0.9701 0.9803 0.4524 ] Network output: [ 0.007731 0.9665 0.01923 -0.0002245 0.0001008 0.9979 -0.0001692 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009314 Epoch 2764 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01881 0.9961 0.9992 -5.536e-05 2.486e-05 -0.0331 -4.172e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02185 -0.005343 0.02054 0.02074 0.9416 0.9507 0.04256 0.8886 0.9068 0.108 ] Network output: [ 0.9905 0.02643 -0.003452 -0.000191 8.574e-05 -0.00478 -0.0001439 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6446 0.115 0.1612 0.174 0.9727 0.9875 0.7261 0.9031 0.9683 0.646 ] Network output: [ -0.01247 0.9741 1.014 -2.623e-05 1.178e-05 0.03669 -1.977e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04605 0.03375 0.04654 0.02471 0.986 0.9901 0.04695 0.9712 0.9812 0.05644 ] Network output: [ 0.0377 -0.1571 1.081 -0.00152 0.0006825 0.9943 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.722 0.6165 0.5571 0.3065 0.9759 0.9892 0.7246 0.913 0.9728 0.6391 ] Network output: [ -0.01337 0.08852 0.9461 0.001077 -0.0004835 0.9965 0.0008116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6499 0.6346 0.4498 0.2334 0.9871 0.9915 0.6503 0.9741 0.9826 0.4601 ] Network output: [ -0.02891 0.1055 0.9438 0.0009411 -0.0004225 1.012 0.0007092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6478 0.6454 0.4505 0.2209 0.9857 0.9907 0.6479 0.9701 0.9803 0.4523 ] Network output: [ 0.007737 0.9664 0.01926 -0.0002241 0.0001006 0.9979 -0.0001689 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009309 Epoch 2765 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01879 0.9961 0.9992 -5.54e-05 2.487e-05 -0.03308 -4.175e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02185 -0.005343 0.02055 0.02073 0.9416 0.9507 0.04255 0.8886 0.9068 0.1079 ] Network output: [ 0.9905 0.02636 -0.003402 -0.000192 8.619e-05 -0.00481 -0.0001447 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6447 0.1149 0.1614 0.174 0.9727 0.9875 0.7261 0.9031 0.9683 0.6459 ] Network output: [ -0.01246 0.9742 1.014 -2.579e-05 1.158e-05 0.03668 -1.944e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04604 0.03374 0.04652 0.0247 0.986 0.9901 0.04694 0.9712 0.9812 0.0564 ] Network output: [ 0.03767 -0.157 1.081 -0.00152 0.0006825 0.9942 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.722 0.6164 0.5571 0.3065 0.9759 0.9892 0.7246 0.913 0.9728 0.639 ] Network output: [ -0.01337 0.08846 0.9461 0.001077 -0.0004834 0.9965 0.0008115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6499 0.6346 0.4497 0.2334 0.9871 0.9915 0.6504 0.9741 0.9826 0.46 ] Network output: [ -0.02889 0.1055 0.9437 0.0009415 -0.0004227 1.012 0.0007096 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6478 0.6454 0.4504 0.2208 0.9857 0.9907 0.6479 0.9701 0.9803 0.4522 ] Network output: [ 0.007744 0.9664 0.01929 -0.0002236 0.0001004 0.9979 -0.0001685 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009304 Epoch 2766 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01876 0.9962 0.9991 -5.543e-05 2.488e-05 -0.03306 -4.177e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02185 -0.005344 0.02057 0.02073 0.9417 0.9508 0.04254 0.8886 0.9068 0.1079 ] Network output: [ 0.9906 0.02629 -0.003352 -0.000193 8.664e-05 -0.00484 -0.0001454 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6447 0.1148 0.1615 0.1739 0.9727 0.9875 0.7261 0.9031 0.9683 0.6458 ] Network output: [ -0.01246 0.9742 1.014 -2.535e-05 1.138e-05 0.03666 -1.911e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04602 0.03372 0.0465 0.02469 0.986 0.9901 0.04693 0.9712 0.9812 0.05636 ] Network output: [ 0.03763 -0.1569 1.081 -0.00152 0.0006826 0.9942 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.722 0.6164 0.557 0.3065 0.9759 0.9892 0.7246 0.913 0.9728 0.6389 ] Network output: [ -0.01337 0.0884 0.9462 0.001077 -0.0004833 0.9966 0.0008114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.65 0.6346 0.4497 0.2333 0.9871 0.9915 0.6504 0.9741 0.9826 0.4599 ] Network output: [ -0.02886 0.1055 0.9436 0.0009419 -0.0004229 1.012 0.0007099 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6479 0.6454 0.4503 0.2208 0.9857 0.9907 0.6479 0.9701 0.9803 0.4521 ] Network output: [ 0.00775 0.9664 0.01932 -0.0002232 0.0001002 0.9979 -0.0001682 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009299 Epoch 2767 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01873 0.9962 0.9991 -5.546e-05 2.49e-05 -0.03304 -4.18e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02185 -0.005344 0.02058 0.02072 0.9417 0.9508 0.04253 0.8886 0.9068 0.1078 ] Network output: [ 0.9906 0.02622 -0.003302 -0.000194 8.709e-05 -0.00487 -0.0001462 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6448 0.1147 0.1617 0.1739 0.9727 0.9875 0.7261 0.9031 0.9683 0.6457 ] Network output: [ -0.01246 0.9743 1.014 -2.491e-05 1.118e-05 0.03665 -1.877e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04601 0.03371 0.04647 0.02467 0.986 0.9901 0.04691 0.9712 0.9812 0.05633 ] Network output: [ 0.0376 -0.1569 1.081 -0.001521 0.0006827 0.9942 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7221 0.6163 0.557 0.3065 0.9759 0.9893 0.7246 0.913 0.9728 0.6388 ] Network output: [ -0.01337 0.08834 0.9462 0.001077 -0.0004833 0.9966 0.0008113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.65 0.6346 0.4496 0.2333 0.9871 0.9915 0.6504 0.9741 0.9826 0.4598 ] Network output: [ -0.02884 0.1055 0.9435 0.0009423 -0.0004231 1.013 0.0007102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6479 0.6455 0.4502 0.2207 0.9857 0.9907 0.648 0.9701 0.9803 0.452 ] Network output: [ 0.007757 0.9663 0.01935 -0.0002227 1e-04 0.9979 -0.0001679 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009295 Epoch 2768 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01871 0.9962 0.9991 -5.549e-05 2.491e-05 -0.03302 -4.182e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02185 -0.005344 0.0206 0.02071 0.9417 0.9508 0.04251 0.8886 0.9068 0.1078 ] Network output: [ 0.9906 0.02615 -0.003253 -0.000195 8.754e-05 -0.0049 -0.000147 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6448 0.1146 0.1619 0.1739 0.9727 0.9875 0.7261 0.9031 0.9683 0.6456 ] Network output: [ -0.01245 0.9743 1.014 -2.446e-05 1.098e-05 0.03664 -1.844e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.046 0.0337 0.04645 0.02466 0.986 0.9901 0.0469 0.9712 0.9812 0.05629 ] Network output: [ 0.03757 -0.1568 1.081 -0.001521 0.0006827 0.9941 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7221 0.6163 0.557 0.3064 0.9759 0.9893 0.7246 0.913 0.9728 0.6387 ] Network output: [ -0.01338 0.08828 0.9462 0.001076 -0.0004832 0.9966 0.0008112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.65 0.6346 0.4495 0.2333 0.9871 0.9915 0.6504 0.9741 0.9826 0.4598 ] Network output: [ -0.02881 0.1055 0.9434 0.0009428 -0.0004232 1.013 0.0007105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6479 0.6455 0.4501 0.2207 0.9857 0.9907 0.648 0.9701 0.9803 0.4519 ] Network output: [ 0.007764 0.9663 0.01939 -0.0002223 9.98e-05 0.9979 -0.0001675 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00929 Epoch 2769 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01868 0.9963 0.9991 -5.553e-05 2.493e-05 -0.033 -4.185e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02184 -0.005345 0.02062 0.02071 0.9417 0.9508 0.0425 0.8886 0.9068 0.1077 ] Network output: [ 0.9906 0.02608 -0.003203 -0.000196 8.8e-05 -0.004931 -0.0001477 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6448 0.1145 0.1621 0.1739 0.9727 0.9875 0.7262 0.9031 0.9683 0.6455 ] Network output: [ -0.01245 0.9744 1.014 -2.401e-05 1.078e-05 0.03663 -1.81e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04598 0.03368 0.04643 0.02465 0.986 0.9901 0.04689 0.9712 0.9812 0.05626 ] Network output: [ 0.03753 -0.1567 1.081 -0.001521 0.0006828 0.9941 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7221 0.6162 0.557 0.3064 0.9759 0.9893 0.7246 0.913 0.9728 0.6386 ] Network output: [ -0.01338 0.08823 0.9463 0.001076 -0.0004832 0.9967 0.0008111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.65 0.6346 0.4494 0.2333 0.9871 0.9915 0.6504 0.9741 0.9826 0.4597 ] Network output: [ -0.02878 0.1055 0.9433 0.0009432 -0.0004234 1.013 0.0007108 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.648 0.6455 0.45 0.2206 0.9857 0.9907 0.648 0.9701 0.9803 0.4518 ] Network output: [ 0.007772 0.9663 0.01942 -0.0002219 9.96e-05 0.9979 -0.0001672 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009286 Epoch 2770 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01865 0.9963 0.9991 -5.556e-05 2.494e-05 -0.03298 -4.187e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02184 -0.005345 0.02063 0.0207 0.9417 0.9508 0.04249 0.8886 0.9068 0.1077 ] Network output: [ 0.9907 0.02601 -0.003153 -0.0001971 8.847e-05 -0.004962 -0.0001485 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6449 0.1144 0.1623 0.1739 0.9727 0.9875 0.7262 0.9031 0.9683 0.6454 ] Network output: [ -0.01245 0.9744 1.014 -2.357e-05 1.058e-05 0.03662 -1.776e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04597 0.03367 0.04641 0.02464 0.986 0.9901 0.04687 0.9712 0.9812 0.05622 ] Network output: [ 0.0375 -0.1566 1.081 -0.001521 0.0006828 0.994 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7221 0.6162 0.5569 0.3064 0.9759 0.9893 0.7247 0.913 0.9728 0.6385 ] Network output: [ -0.01338 0.08817 0.9463 0.001076 -0.0004831 0.9967 0.000811 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6501 0.6346 0.4494 0.2332 0.9871 0.9915 0.6505 0.9741 0.9826 0.4596 ] Network output: [ -0.02876 0.1055 0.9432 0.0009436 -0.0004236 1.013 0.0007111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.648 0.6455 0.4499 0.2206 0.9857 0.9907 0.6481 0.9701 0.9803 0.4517 ] Network output: [ 0.007779 0.9662 0.01945 -0.0002214 9.94e-05 0.9979 -0.0001669 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009283 Epoch 2771 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01863 0.9963 0.9991 -5.559e-05 2.496e-05 -0.03295 -4.189e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02184 -0.005346 0.02065 0.0207 0.9417 0.9508 0.04248 0.8886 0.9068 0.1076 ] Network output: [ 0.9907 0.02594 -0.003103 -0.0001981 8.893e-05 -0.004994 -0.0001493 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6449 0.1142 0.1624 0.1739 0.9727 0.9875 0.7262 0.9031 0.9683 0.6453 ] Network output: [ -0.01244 0.9745 1.014 -2.312e-05 1.038e-05 0.0366 -1.742e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04596 0.03365 0.04638 0.02463 0.986 0.9901 0.04686 0.9712 0.9812 0.05619 ] Network output: [ 0.03747 -0.1566 1.081 -0.001521 0.0006829 0.994 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7221 0.6161 0.5569 0.3064 0.9759 0.9893 0.7247 0.913 0.9728 0.6384 ] Network output: [ -0.01338 0.08812 0.9463 0.001076 -0.000483 0.9967 0.0008109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6501 0.6346 0.4493 0.2332 0.9871 0.9915 0.6505 0.9741 0.9826 0.4595 ] Network output: [ -0.02873 0.1055 0.9432 0.000944 -0.0004238 1.013 0.0007114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.648 0.6456 0.4498 0.2205 0.9857 0.9907 0.6481 0.9701 0.9803 0.4516 ] Network output: [ 0.007787 0.9662 0.01949 -0.000221 9.919e-05 0.9979 -0.0001665 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009279 Epoch 2772 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0186 0.9964 0.9991 -5.562e-05 2.497e-05 -0.03293 -4.192e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02184 -0.005346 0.02066 0.02069 0.9417 0.9508 0.04247 0.8886 0.9068 0.1076 ] Network output: [ 0.9907 0.02588 -0.003053 -0.0001991 8.94e-05 -0.005026 -0.0001501 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.645 0.1141 0.1626 0.1738 0.9727 0.9875 0.7262 0.9031 0.9683 0.6452 ] Network output: [ -0.01244 0.9745 1.014 -2.266e-05 1.018e-05 0.03659 -1.708e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04595 0.03364 0.04636 0.02462 0.986 0.9901 0.04684 0.9712 0.9812 0.05615 ] Network output: [ 0.03744 -0.1565 1.081 -0.001521 0.0006829 0.994 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7221 0.6161 0.5569 0.3064 0.9759 0.9893 0.7247 0.913 0.9728 0.6383 ] Network output: [ -0.01339 0.08806 0.9463 0.001076 -0.000483 0.9968 0.0008108 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6501 0.6346 0.4492 0.2332 0.9871 0.9915 0.6505 0.9741 0.9826 0.4594 ] Network output: [ -0.02871 0.1055 0.9431 0.0009444 -0.000424 1.013 0.0007117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6481 0.6456 0.4497 0.2205 0.9857 0.9907 0.6481 0.9701 0.9803 0.4515 ] Network output: [ 0.007795 0.9661 0.01952 -0.0002205 9.899e-05 0.9979 -0.0001662 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009276 Epoch 2773 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01858 0.9964 0.9991 -5.565e-05 2.498e-05 -0.03291 -4.194e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02184 -0.005347 0.02068 0.02069 0.9417 0.9508 0.04245 0.8886 0.9068 0.1075 ] Network output: [ 0.9907 0.02581 -0.003003 -0.0002002 8.988e-05 -0.005058 -0.0001509 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.645 0.114 0.1628 0.1738 0.9727 0.9875 0.7262 0.9031 0.9683 0.6451 ] Network output: [ -0.01243 0.9746 1.014 -2.221e-05 9.972e-06 0.03658 -1.674e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04593 0.03363 0.04634 0.02461 0.986 0.9901 0.04683 0.9712 0.9812 0.05611 ] Network output: [ 0.03741 -0.1565 1.082 -0.001521 0.0006829 0.9939 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7221 0.616 0.5569 0.3064 0.9759 0.9893 0.7247 0.913 0.9728 0.6382 ] Network output: [ -0.01339 0.08801 0.9464 0.001076 -0.0004829 0.9968 0.0008107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6501 0.6347 0.4492 0.2332 0.9871 0.9915 0.6505 0.9741 0.9826 0.4593 ] Network output: [ -0.02869 0.1056 0.943 0.0009447 -0.0004241 1.013 0.000712 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6481 0.6456 0.4496 0.2204 0.9857 0.9907 0.6482 0.9701 0.9803 0.4514 ] Network output: [ 0.007804 0.9661 0.01956 -0.00022 9.879e-05 0.9979 -0.0001658 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009273 Epoch 2774 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01855 0.9965 0.9991 -5.568e-05 2.5e-05 -0.03289 -4.196e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02184 -0.005347 0.0207 0.02068 0.9417 0.9508 0.04244 0.8886 0.9068 0.1075 ] Network output: [ 0.9907 0.02574 -0.002953 -0.0002013 9.036e-05 -0.00509 -0.0001517 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6451 0.1139 0.163 0.1738 0.9727 0.9875 0.7262 0.9031 0.9683 0.645 ] Network output: [ -0.01243 0.9746 1.014 -2.176e-05 9.767e-06 0.03657 -1.64e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04592 0.03361 0.04631 0.0246 0.986 0.9901 0.04682 0.9712 0.9812 0.05608 ] Network output: [ 0.03738 -0.1564 1.082 -0.001521 0.000683 0.9939 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.616 0.5568 0.3064 0.9759 0.9893 0.7247 0.913 0.9728 0.6381 ] Network output: [ -0.01339 0.08796 0.9464 0.001076 -0.0004829 0.9968 0.0008106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6501 0.6347 0.4491 0.2332 0.9871 0.9915 0.6505 0.9741 0.9826 0.4592 ] Network output: [ -0.02866 0.1056 0.9429 0.0009451 -0.0004243 1.013 0.0007123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6481 0.6456 0.4495 0.2204 0.9857 0.9907 0.6482 0.9701 0.9803 0.4513 ] Network output: [ 0.007812 0.966 0.0196 -0.0002196 9.858e-05 0.9979 -0.0001655 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00927 Epoch 2775 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01852 0.9965 0.9991 -5.571e-05 2.501e-05 -0.03287 -4.198e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02184 -0.005348 0.02071 0.02068 0.9417 0.9508 0.04243 0.8886 0.9068 0.1074 ] Network output: [ 0.9908 0.02568 -0.002903 -0.0002023 9.084e-05 -0.005123 -0.0001525 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6451 0.1138 0.1632 0.1738 0.9727 0.9875 0.7262 0.9031 0.9683 0.6449 ] Network output: [ -0.01243 0.9747 1.014 -2.13e-05 9.563e-06 0.03656 -1.605e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0459 0.0336 0.04629 0.02459 0.986 0.9901 0.0468 0.9712 0.9812 0.05604 ] Network output: [ 0.03735 -0.1563 1.082 -0.001521 0.000683 0.9939 -0.001147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.6159 0.5568 0.3064 0.9759 0.9893 0.7247 0.913 0.9728 0.638 ] Network output: [ -0.0134 0.08791 0.9464 0.001075 -0.0004828 0.9969 0.0008105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6502 0.6347 0.449 0.2331 0.9871 0.9915 0.6506 0.9741 0.9826 0.4592 ] Network output: [ -0.02864 0.1056 0.9428 0.0009455 -0.0004245 1.013 0.0007126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6481 0.6457 0.4494 0.2203 0.9857 0.9907 0.6482 0.9701 0.9803 0.4512 ] Network output: [ 0.007821 0.966 0.01964 -0.0002191 9.838e-05 0.9979 -0.0001651 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009268 Epoch 2776 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0185 0.9965 0.9991 -5.574e-05 2.502e-05 -0.03285 -4.201e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02184 -0.005348 0.02073 0.02067 0.9417 0.9508 0.04242 0.8886 0.9068 0.1074 ] Network output: [ 0.9908 0.02561 -0.002853 -0.0002034 9.132e-05 -0.005156 -0.0001533 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6452 0.1137 0.1634 0.1738 0.9727 0.9875 0.7263 0.9031 0.9683 0.6448 ] Network output: [ -0.01242 0.9747 1.013 -2.084e-05 9.357e-06 0.03655 -1.571e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04589 0.03358 0.04627 0.02458 0.986 0.9901 0.04679 0.9712 0.9812 0.05601 ] Network output: [ 0.03732 -0.1563 1.082 -0.001521 0.000683 0.9938 -0.001147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.6159 0.5568 0.3064 0.9759 0.9893 0.7247 0.913 0.9728 0.6378 ] Network output: [ -0.0134 0.08786 0.9464 0.001075 -0.0004827 0.9969 0.0008104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6502 0.6347 0.4489 0.2331 0.9871 0.9915 0.6506 0.9741 0.9826 0.4591 ] Network output: [ -0.02862 0.1056 0.9427 0.0009459 -0.0004246 1.013 0.0007129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6482 0.6457 0.4493 0.2203 0.9857 0.9907 0.6482 0.9701 0.9803 0.4511 ] Network output: [ 0.00783 0.9659 0.01967 -0.0002187 9.817e-05 0.9978 -0.0001648 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009266 Epoch 2777 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01847 0.9966 0.9991 -5.577e-05 2.504e-05 -0.03282 -4.203e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02183 -0.005349 0.02074 0.02067 0.9417 0.9508 0.04241 0.8887 0.9068 0.1073 ] Network output: [ 0.9908 0.02555 -0.002803 -0.0002045 9.181e-05 -0.00519 -0.0001541 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6452 0.1135 0.1636 0.1738 0.9727 0.9875 0.7263 0.9031 0.9683 0.6447 ] Network output: [ -0.01242 0.9748 1.013 -2.038e-05 9.15e-06 0.03654 -1.536e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04588 0.03357 0.04624 0.02457 0.986 0.9901 0.04677 0.9712 0.9812 0.05597 ] Network output: [ 0.0373 -0.1562 1.082 -0.001521 0.000683 0.9938 -0.001147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.6158 0.5567 0.3064 0.9759 0.9893 0.7248 0.913 0.9728 0.6377 ] Network output: [ -0.0134 0.08782 0.9464 0.001075 -0.0004827 0.9969 0.0008103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6502 0.6347 0.4489 0.2331 0.9871 0.9915 0.6506 0.9741 0.9826 0.459 ] Network output: [ -0.02859 0.1056 0.9426 0.0009463 -0.0004248 1.013 0.0007131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6482 0.6457 0.4492 0.2202 0.9857 0.9907 0.6483 0.9701 0.9803 0.451 ] Network output: [ 0.00784 0.9659 0.01971 -0.0002182 9.796e-05 0.9978 -0.0001644 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009264 Epoch 2778 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01844 0.9966 0.9991 -5.58e-05 2.505e-05 -0.0328 -4.205e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02183 -0.005349 0.02076 0.02066 0.9417 0.9508 0.04239 0.8887 0.9068 0.1073 ] Network output: [ 0.9908 0.02549 -0.002753 -0.0002056 9.23e-05 -0.005223 -0.000155 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6453 0.1134 0.1637 0.1738 0.9727 0.9875 0.7263 0.9031 0.9683 0.6446 ] Network output: [ -0.01241 0.9748 1.013 -1.992e-05 8.943e-06 0.03652 -1.501e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04586 0.03355 0.04622 0.02456 0.986 0.9901 0.04676 0.9712 0.9812 0.05593 ] Network output: [ 0.03727 -0.1562 1.082 -0.001521 0.000683 0.9937 -0.001147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.6158 0.5567 0.3064 0.9759 0.9893 0.7248 0.913 0.9728 0.6376 ] Network output: [ -0.01341 0.08777 0.9465 0.001075 -0.0004826 0.997 0.0008101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6502 0.6347 0.4488 0.2331 0.9871 0.9915 0.6506 0.9741 0.9826 0.4589 ] Network output: [ -0.02857 0.1057 0.9425 0.0009467 -0.000425 1.013 0.0007134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6482 0.6457 0.4491 0.2202 0.9857 0.9907 0.6483 0.9701 0.9803 0.4509 ] Network output: [ 0.00785 0.9658 0.01975 -0.0002177 9.775e-05 0.9978 -0.0001641 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009262 Epoch 2779 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01842 0.9966 0.9991 -5.582e-05 2.506e-05 -0.03278 -4.207e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02183 -0.00535 0.02078 0.02066 0.9417 0.9508 0.04238 0.8887 0.9068 0.1072 ] Network output: [ 0.9908 0.02542 -0.002703 -0.0002067 9.28e-05 -0.005257 -0.0001558 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6453 0.1133 0.1639 0.1738 0.9727 0.9875 0.7263 0.9031 0.9683 0.6445 ] Network output: [ -0.01241 0.9749 1.013 -1.946e-05 8.735e-06 0.03651 -1.466e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04585 0.03354 0.0462 0.02455 0.986 0.9901 0.04674 0.9712 0.9812 0.0559 ] Network output: [ 0.03724 -0.1561 1.082 -0.001521 0.0006831 0.9937 -0.001147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7222 0.6157 0.5567 0.3064 0.9759 0.9893 0.7248 0.913 0.9728 0.6375 ] Network output: [ -0.01341 0.08773 0.9465 0.001075 -0.0004825 0.997 0.00081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6502 0.6347 0.4487 0.2331 0.9871 0.9915 0.6506 0.9741 0.9826 0.4588 ] Network output: [ -0.02855 0.1057 0.9424 0.000947 -0.0004252 1.013 0.0007137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6482 0.6458 0.449 0.2201 0.9857 0.9907 0.6483 0.9701 0.9803 0.4508 ] Network output: [ 0.00786 0.9658 0.01979 -0.0002173 9.754e-05 0.9978 -0.0001637 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009261 Epoch 2780 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01839 0.9967 0.9991 -5.585e-05 2.507e-05 -0.03276 -4.209e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02183 -0.00535 0.02079 0.02065 0.9417 0.9508 0.04237 0.8887 0.9068 0.1072 ] Network output: [ 0.9909 0.02536 -0.002653 -0.0002078 9.33e-05 -0.005292 -0.0001566 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6453 0.1132 0.1641 0.1738 0.9727 0.9875 0.7263 0.9031 0.9683 0.6444 ] Network output: [ -0.0124 0.9749 1.013 -1.899e-05 8.527e-06 0.0365 -1.431e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04584 0.03353 0.04617 0.02454 0.986 0.9901 0.04673 0.9712 0.9812 0.05586 ] Network output: [ 0.03722 -0.1561 1.082 -0.001522 0.0006831 0.9937 -0.001147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7223 0.6157 0.5567 0.3064 0.9759 0.9893 0.7248 0.913 0.9728 0.6374 ] Network output: [ -0.01342 0.08768 0.9465 0.001075 -0.0004825 0.997 0.0008099 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6502 0.6347 0.4486 0.2331 0.9871 0.9915 0.6506 0.9741 0.9826 0.4587 ] Network output: [ -0.02853 0.1057 0.9423 0.0009474 -0.0004253 1.013 0.000714 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6483 0.6458 0.4489 0.2201 0.9857 0.9907 0.6483 0.9701 0.9803 0.4507 ] Network output: [ 0.00787 0.9657 0.01984 -0.0002168 9.733e-05 0.9978 -0.0001634 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00926 Epoch 2781 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01837 0.9967 0.9991 -5.588e-05 2.509e-05 -0.03273 -4.211e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02183 -0.005351 0.02081 0.02065 0.9417 0.9508 0.04236 0.8887 0.9068 0.1071 ] Network output: [ 0.9909 0.0253 -0.002602 -0.000209 9.381e-05 -0.005327 -0.0001575 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6454 0.113 0.1643 0.1738 0.9727 0.9875 0.7263 0.9031 0.9683 0.6443 ] Network output: [ -0.0124 0.975 1.013 -1.853e-05 8.317e-06 0.03649 -1.396e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04582 0.03351 0.04615 0.02453 0.986 0.9901 0.04672 0.9712 0.9812 0.05582 ] Network output: [ 0.03719 -0.156 1.082 -0.001522 0.0006831 0.9936 -0.001147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7223 0.6156 0.5566 0.3064 0.9759 0.9893 0.7248 0.913 0.9728 0.6373 ] Network output: [ -0.01342 0.08764 0.9465 0.001075 -0.0004824 0.997 0.0008098 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6503 0.6347 0.4485 0.2331 0.9871 0.9915 0.6507 0.9741 0.9826 0.4586 ] Network output: [ -0.0285 0.1058 0.9422 0.0009478 -0.0004255 1.013 0.0007143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6483 0.6458 0.4488 0.2201 0.9857 0.9907 0.6484 0.9701 0.9803 0.4506 ] Network output: [ 0.00788 0.9657 0.01988 -0.0002163 9.712e-05 0.9978 -0.000163 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009259 Epoch 2782 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01834 0.9967 0.9991 -5.591e-05 2.51e-05 -0.03271 -4.213e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02183 -0.005351 0.02083 0.02064 0.9417 0.9508 0.04235 0.8887 0.9068 0.1071 ] Network output: [ 0.9909 0.02524 -0.002552 -0.0002101 9.432e-05 -0.005362 -0.0001583 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6454 0.1129 0.1645 0.1738 0.9727 0.9875 0.7263 0.9031 0.9683 0.6442 ] Network output: [ -0.01239 0.975 1.013 -1.806e-05 8.107e-06 0.03648 -1.361e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04581 0.0335 0.04613 0.02452 0.986 0.9901 0.0467 0.9712 0.9812 0.05579 ] Network output: [ 0.03716 -0.156 1.082 -0.001522 0.0006831 0.9936 -0.001147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7223 0.6156 0.5566 0.3064 0.9759 0.9893 0.7248 0.913 0.9728 0.6371 ] Network output: [ -0.01343 0.0876 0.9466 0.001074 -0.0004823 0.9971 0.0008097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6503 0.6347 0.4485 0.2331 0.9871 0.9915 0.6507 0.9741 0.9826 0.4585 ] Network output: [ -0.02848 0.1058 0.9421 0.0009481 -0.0004257 1.013 0.0007145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6483 0.6458 0.4487 0.22 0.9857 0.9907 0.6484 0.9701 0.9803 0.4505 ] Network output: [ 0.007891 0.9656 0.01992 -0.0002159 9.691e-05 0.9978 -0.0001627 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009258 Epoch 2783 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01831 0.9968 0.9991 -5.593e-05 2.511e-05 -0.03269 -4.215e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02183 -0.005352 0.02084 0.02064 0.9417 0.9508 0.04233 0.8887 0.9068 0.107 ] Network output: [ 0.9909 0.02518 -0.002502 -0.0002112 9.483e-05 -0.005397 -0.0001592 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6455 0.1128 0.1647 0.1738 0.9727 0.9875 0.7264 0.9031 0.9683 0.6441 ] Network output: [ -0.01239 0.9751 1.013 -1.759e-05 7.896e-06 0.03647 -1.326e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0458 0.03348 0.0461 0.02451 0.986 0.9901 0.04669 0.9712 0.9812 0.05575 ] Network output: [ 0.03714 -0.1559 1.082 -0.001522 0.0006831 0.9936 -0.001147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7223 0.6155 0.5566 0.3064 0.9759 0.9893 0.7249 0.913 0.9728 0.637 ] Network output: [ -0.01344 0.08757 0.9466 0.001074 -0.0004823 0.9971 0.0008096 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6503 0.6347 0.4484 0.2331 0.9871 0.9915 0.6507 0.9741 0.9826 0.4584 ] Network output: [ -0.02846 0.1058 0.942 0.0009485 -0.0004258 1.013 0.0007148 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6483 0.6459 0.4486 0.22 0.9857 0.9907 0.6484 0.9701 0.9803 0.4503 ] Network output: [ 0.007903 0.9655 0.01997 -0.0002154 9.669e-05 0.9978 -0.0001623 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009258 Epoch 2784 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01829 0.9968 0.9991 -5.596e-05 2.512e-05 -0.03266 -4.217e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02183 -0.005353 0.02086 0.02063 0.9417 0.9508 0.04232 0.8887 0.9068 0.107 ] Network output: [ 0.991 0.02512 -0.002452 -0.0002124 9.535e-05 -0.005433 -0.0001601 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6455 0.1127 0.1649 0.1738 0.9727 0.9875 0.7264 0.9031 0.9683 0.644 ] Network output: [ -0.01238 0.9751 1.013 -1.712e-05 7.684e-06 0.03646 -1.29e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04578 0.03347 0.04608 0.0245 0.986 0.9901 0.04667 0.9712 0.9812 0.05571 ] Network output: [ 0.03711 -0.1559 1.082 -0.001522 0.0006831 0.9935 -0.001147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7223 0.6155 0.5565 0.3064 0.9759 0.9893 0.7249 0.913 0.9728 0.6369 ] Network output: [ -0.01344 0.08753 0.9466 0.001074 -0.0004822 0.9971 0.0008095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6503 0.6347 0.4483 0.2331 0.9871 0.9915 0.6507 0.9741 0.9826 0.4583 ] Network output: [ -0.02844 0.1059 0.9419 0.0009489 -0.000426 1.013 0.0007151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6484 0.6459 0.4485 0.2199 0.9857 0.9907 0.6484 0.9701 0.9803 0.4502 ] Network output: [ 0.007914 0.9655 0.02001 -0.0002149 9.648e-05 0.9978 -0.000162 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009258 Epoch 2785 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01826 0.9968 0.999 -5.599e-05 2.513e-05 -0.03264 -4.219e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02182 -0.005353 0.02088 0.02063 0.9417 0.9508 0.04231 0.8887 0.9068 0.1069 ] Network output: [ 0.991 0.02506 -0.002401 -0.0002136 9.587e-05 -0.00547 -0.0001609 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6456 0.1125 0.1651 0.1738 0.9727 0.9875 0.7264 0.9031 0.9683 0.6439 ] Network output: [ -0.01238 0.9752 1.013 -1.664e-05 7.472e-06 0.03645 -1.254e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04577 0.03345 0.04606 0.02449 0.986 0.9901 0.04666 0.9712 0.9812 0.05567 ] Network output: [ 0.03709 -0.1559 1.082 -0.001521 0.0006831 0.9935 -0.001147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7223 0.6154 0.5565 0.3065 0.9759 0.9893 0.7249 0.913 0.9728 0.6368 ] Network output: [ -0.01345 0.08749 0.9466 0.001074 -0.0004821 0.9972 0.0008094 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6503 0.6347 0.4482 0.2331 0.9871 0.9915 0.6507 0.9741 0.9826 0.4583 ] Network output: [ -0.02842 0.1059 0.9418 0.0009492 -0.0004261 1.013 0.0007154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6484 0.6459 0.4483 0.2199 0.9857 0.9907 0.6485 0.9701 0.9803 0.4501 ] Network output: [ 0.007926 0.9654 0.02006 -0.0002144 9.626e-05 0.9978 -0.0001616 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009258 Epoch 2786 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01823 0.9969 0.999 -5.601e-05 2.515e-05 -0.03262 -4.221e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02182 -0.005354 0.02089 0.02063 0.9417 0.9508 0.0423 0.8887 0.9068 0.1069 ] Network output: [ 0.991 0.025 -0.002351 -0.0002147 9.64e-05 -0.005506 -0.0001618 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6456 0.1124 0.1653 0.1738 0.9727 0.9875 0.7264 0.9031 0.9683 0.6438 ] Network output: [ -0.01237 0.9752 1.013 -1.617e-05 7.258e-06 0.03644 -1.218e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04575 0.03344 0.04603 0.02449 0.986 0.9901 0.04664 0.9712 0.9812 0.05564 ] Network output: [ 0.03706 -0.1558 1.082 -0.001521 0.000683 0.9935 -0.001147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7224 0.6153 0.5565 0.3065 0.9759 0.9893 0.7249 0.913 0.9728 0.6367 ] Network output: [ -0.01346 0.08746 0.9466 0.001074 -0.0004821 0.9972 0.0008093 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6503 0.6347 0.4481 0.2331 0.9871 0.9915 0.6507 0.9741 0.9826 0.4582 ] Network output: [ -0.0284 0.1059 0.9417 0.0009496 -0.0004263 1.013 0.0007156 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6484 0.6459 0.4482 0.2199 0.9857 0.9907 0.6485 0.9701 0.9803 0.45 ] Network output: [ 0.007938 0.9653 0.02011 -0.0002139 9.604e-05 0.9978 -0.0001612 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009258 Epoch 2787 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01821 0.9969 0.999 -5.604e-05 2.516e-05 -0.03259 -4.223e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02182 -0.005355 0.02091 0.02062 0.9417 0.9508 0.04229 0.8887 0.9068 0.1068 ] Network output: [ 0.991 0.02494 -0.0023 -0.0002159 9.693e-05 -0.005543 -0.0001627 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6457 0.1123 0.1655 0.1738 0.9727 0.9875 0.7264 0.9031 0.9683 0.6437 ] Network output: [ -0.01237 0.9753 1.013 -1.569e-05 7.044e-06 0.03643 -1.182e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04574 0.03342 0.04601 0.02448 0.986 0.9901 0.04663 0.9712 0.9812 0.0556 ] Network output: [ 0.03704 -0.1558 1.082 -0.001521 0.000683 0.9935 -0.001147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7224 0.6153 0.5564 0.3065 0.9759 0.9893 0.7249 0.913 0.9728 0.6365 ] Network output: [ -0.01347 0.08743 0.9467 0.001074 -0.000482 0.9972 0.0008092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6503 0.6347 0.448 0.2331 0.9871 0.9915 0.6507 0.9741 0.9826 0.4581 ] Network output: [ -0.02838 0.106 0.9416 0.0009499 -0.0004265 1.013 0.0007159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6484 0.6459 0.4481 0.2198 0.9857 0.9907 0.6485 0.9701 0.9803 0.4499 ] Network output: [ 0.00795 0.9653 0.02015 -0.0002134 9.582e-05 0.9978 -0.0001609 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009259 Epoch 2788 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01818 0.9969 0.999 -5.606e-05 2.517e-05 -0.03257 -4.225e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02182 -0.005356 0.02093 0.02062 0.9417 0.9508 0.04227 0.8887 0.9068 0.1068 ] Network output: [ 0.991 0.02489 -0.00225 -0.0002171 9.746e-05 -0.00558 -0.0001636 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6457 0.1121 0.1657 0.1738 0.9728 0.9875 0.7264 0.9031 0.9683 0.6436 ] Network output: [ -0.01236 0.9753 1.013 -1.521e-05 6.829e-06 0.03642 -1.146e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04573 0.03341 0.04598 0.02447 0.986 0.9901 0.04661 0.9712 0.9812 0.05556 ] Network output: [ 0.03702 -0.1558 1.082 -0.001521 0.000683 0.9934 -0.001147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7224 0.6152 0.5564 0.3065 0.9759 0.9893 0.7249 0.913 0.9728 0.6364 ] Network output: [ -0.01347 0.0874 0.9467 0.001074 -0.0004819 0.9973 0.000809 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6504 0.6347 0.448 0.2331 0.9871 0.9915 0.6508 0.9741 0.9826 0.458 ] Network output: [ -0.02836 0.106 0.9414 0.0009503 -0.0004266 1.013 0.0007162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6485 0.646 0.448 0.2198 0.9857 0.9907 0.6485 0.9701 0.9803 0.4498 ] Network output: [ 0.007963 0.9652 0.0202 -0.0002129 9.56e-05 0.9978 -0.0001605 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00926 Epoch 2789 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01815 0.997 0.999 -5.609e-05 2.518e-05 -0.03254 -4.227e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02182 -0.005356 0.02094 0.02061 0.9417 0.9508 0.04226 0.8887 0.9068 0.1067 ] Network output: [ 0.991 0.02483 -0.002199 -0.0002183 9.8e-05 -0.005618 -0.0001645 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6458 0.112 0.1659 0.1738 0.9728 0.9875 0.7264 0.9031 0.9683 0.6435 ] Network output: [ -0.01236 0.9754 1.013 -1.473e-05 6.613e-06 0.03641 -1.11e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04571 0.03339 0.04596 0.02446 0.986 0.9901 0.0466 0.9712 0.9812 0.05552 ] Network output: [ 0.037 -0.1557 1.082 -0.001521 0.000683 0.9934 -0.001147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7224 0.6152 0.5564 0.3066 0.9759 0.9893 0.725 0.913 0.9728 0.6363 ] Network output: [ -0.01348 0.08737 0.9467 0.001073 -0.0004819 0.9973 0.0008089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6504 0.6347 0.4479 0.2331 0.9871 0.9915 0.6508 0.9741 0.9826 0.4579 ] Network output: [ -0.02835 0.1061 0.9413 0.0009506 -0.0004268 1.013 0.0007164 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6485 0.646 0.4479 0.2198 0.9857 0.9907 0.6485 0.9701 0.9803 0.4497 ] Network output: [ 0.007976 0.9651 0.02025 -0.0002125 9.538e-05 0.9978 -0.0001601 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009261 Epoch 2790 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01813 0.997 0.999 -5.611e-05 2.519e-05 -0.03252 -4.229e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02182 -0.005357 0.02096 0.02061 0.9417 0.9508 0.04225 0.8887 0.9069 0.1067 ] Network output: [ 0.9911 0.02478 -0.002148 -0.0002195 9.855e-05 -0.005656 -0.0001654 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6458 0.1119 0.1661 0.1738 0.9728 0.9875 0.7265 0.9031 0.9683 0.6434 ] Network output: [ -0.01235 0.9754 1.013 -1.425e-05 6.396e-06 0.0364 -1.074e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0457 0.03337 0.04594 0.02445 0.986 0.9901 0.04658 0.9712 0.9812 0.05548 ] Network output: [ 0.03697 -0.1557 1.082 -0.001521 0.000683 0.9934 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7224 0.6151 0.5563 0.3066 0.9759 0.9893 0.725 0.913 0.9728 0.6361 ] Network output: [ -0.01349 0.08734 0.9467 0.001073 -0.0004818 0.9973 0.0008088 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6504 0.6347 0.4478 0.2331 0.9871 0.9915 0.6508 0.9741 0.9826 0.4578 ] Network output: [ -0.02833 0.1061 0.9412 0.000951 -0.0004269 1.013 0.0007167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6485 0.646 0.4478 0.2197 0.9857 0.9907 0.6486 0.9701 0.9803 0.4496 ] Network output: [ 0.007989 0.9651 0.0203 -0.000212 9.515e-05 0.9978 -0.0001597 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009263 Epoch 2791 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0181 0.997 0.999 -5.614e-05 2.52e-05 -0.0325 -4.231e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02182 -0.005358 0.02098 0.0206 0.9417 0.9508 0.04224 0.8887 0.9069 0.1066 ] Network output: [ 0.9911 0.02472 -0.002098 -0.0002207 9.91e-05 -0.005694 -0.0001664 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6459 0.1117 0.1663 0.1738 0.9728 0.9875 0.7265 0.9031 0.9683 0.6432 ] Network output: [ -0.01235 0.9755 1.013 -1.376e-05 6.178e-06 0.03639 -1.037e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04568 0.03336 0.04591 0.02444 0.986 0.9901 0.04657 0.9712 0.9812 0.05545 ] Network output: [ 0.03695 -0.1557 1.082 -0.001521 0.0006829 0.9933 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7225 0.615 0.5563 0.3066 0.9759 0.9893 0.725 0.913 0.9728 0.636 ] Network output: [ -0.0135 0.08731 0.9467 0.001073 -0.0004817 0.9973 0.0008087 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6504 0.6347 0.4477 0.2331 0.9871 0.9915 0.6508 0.9741 0.9826 0.4577 ] Network output: [ -0.02831 0.1062 0.9411 0.0009513 -0.0004271 1.013 0.0007169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6485 0.646 0.4477 0.2197 0.9857 0.9907 0.6486 0.9701 0.9803 0.4494 ] Network output: [ 0.008003 0.965 0.02036 -0.0002115 9.493e-05 0.9978 -0.0001594 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009265 Epoch 2792 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01807 0.9971 0.999 -5.616e-05 2.521e-05 -0.03247 -4.233e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02181 -0.005359 0.02099 0.0206 0.9418 0.9508 0.04223 0.8887 0.9069 0.1066 ] Network output: [ 0.9911 0.02467 -0.002047 -0.000222 9.965e-05 -0.005733 -0.0001673 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6459 0.1116 0.1665 0.1738 0.9728 0.9875 0.7265 0.9031 0.9684 0.6431 ] Network output: [ -0.01234 0.9755 1.013 -1.328e-05 5.96e-06 0.03638 -1e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04567 0.03334 0.04589 0.02444 0.986 0.9901 0.04655 0.9712 0.9812 0.05541 ] Network output: [ 0.03693 -0.1556 1.082 -0.001521 0.0006829 0.9933 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7225 0.615 0.5563 0.3067 0.9759 0.9893 0.725 0.913 0.9728 0.6359 ] Network output: [ -0.01351 0.08729 0.9467 0.001073 -0.0004817 0.9974 0.0008086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6504 0.6347 0.4476 0.2331 0.9871 0.9915 0.6508 0.9741 0.9826 0.4576 ] Network output: [ -0.02829 0.1062 0.941 0.0009516 -0.0004272 1.013 0.0007172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6485 0.646 0.4476 0.2197 0.9857 0.9907 0.6486 0.9701 0.9803 0.4493 ] Network output: [ 0.008017 0.9649 0.02041 -0.0002109 9.47e-05 0.9978 -0.000159 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009267 Epoch 2793 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01805 0.9971 0.999 -5.619e-05 2.522e-05 -0.03245 -4.234e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02181 -0.00536 0.02101 0.0206 0.9418 0.9508 0.04221 0.8887 0.9069 0.1065 ] Network output: [ 0.9911 0.02461 -0.001996 -0.0002232 0.0001002 -0.005772 -0.0001682 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.646 0.1114 0.1667 0.1738 0.9728 0.9875 0.7265 0.9031 0.9684 0.643 ] Network output: [ -0.01233 0.9756 1.013 -1.279e-05 5.741e-06 0.03637 -9.637e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04565 0.03333 0.04586 0.02443 0.986 0.9901 0.04654 0.9712 0.9812 0.05537 ] Network output: [ 0.03691 -0.1556 1.082 -0.001521 0.0006829 0.9933 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7225 0.6149 0.5562 0.3067 0.9759 0.9893 0.725 0.913 0.9728 0.6358 ] Network output: [ -0.01352 0.08726 0.9467 0.001073 -0.0004816 0.9974 0.0008085 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6504 0.6347 0.4475 0.2331 0.9871 0.9915 0.6508 0.9741 0.9826 0.4575 ] Network output: [ -0.02828 0.1063 0.9409 0.000952 -0.0004274 1.013 0.0007174 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6486 0.6461 0.4475 0.2197 0.9857 0.9907 0.6486 0.9701 0.9803 0.4492 ] Network output: [ 0.008031 0.9648 0.02046 -0.0002104 9.447e-05 0.9978 -0.0001586 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009269 Epoch 2794 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01802 0.9972 0.999 -5.621e-05 2.523e-05 -0.03242 -4.236e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02181 -0.005361 0.02103 0.02059 0.9418 0.9508 0.0422 0.8887 0.9069 0.1065 ] Network output: [ 0.9911 0.02456 -0.001945 -0.0002245 0.0001008 -0.005812 -0.0001692 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.646 0.1113 0.1669 0.1739 0.9728 0.9875 0.7265 0.9031 0.9684 0.6429 ] Network output: [ -0.01233 0.9756 1.013 -1.23e-05 5.52e-06 0.03635 -9.267e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04564 0.03331 0.04584 0.02442 0.986 0.9901 0.04652 0.9712 0.9812 0.05533 ] Network output: [ 0.03689 -0.1556 1.082 -0.001521 0.0006828 0.9933 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7225 0.6149 0.5562 0.3068 0.9759 0.9893 0.725 0.913 0.9728 0.6356 ] Network output: [ -0.01353 0.08724 0.9468 0.001073 -0.0004815 0.9974 0.0008084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6504 0.6347 0.4474 0.2331 0.9871 0.9915 0.6508 0.9741 0.9826 0.4574 ] Network output: [ -0.02826 0.1063 0.9408 0.0009523 -0.0004275 1.013 0.0007177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6486 0.6461 0.4473 0.2196 0.9857 0.9907 0.6486 0.9701 0.9803 0.4491 ] Network output: [ 0.008046 0.9648 0.02052 -0.0002099 9.424e-05 0.9978 -0.0001582 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009272 Epoch 2795 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01799 0.9972 0.999 -5.623e-05 2.525e-05 -0.0324 -4.238e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02181 -0.005361 0.02105 0.02059 0.9418 0.9508 0.04219 0.8887 0.9069 0.1064 ] Network output: [ 0.9912 0.02451 -0.001895 -0.0002257 0.0001013 -0.005852 -0.0001701 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6461 0.1112 0.1671 0.1739 0.9728 0.9875 0.7265 0.9031 0.9684 0.6428 ] Network output: [ -0.01232 0.9756 1.013 -1.18e-05 5.299e-06 0.03634 -8.896e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04562 0.0333 0.04581 0.02441 0.986 0.9901 0.04651 0.9712 0.9812 0.05529 ] Network output: [ 0.03687 -0.1556 1.082 -0.001521 0.0006828 0.9932 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7225 0.6148 0.5562 0.3068 0.976 0.9893 0.7251 0.913 0.9728 0.6355 ] Network output: [ -0.01354 0.08722 0.9468 0.001072 -0.0004815 0.9974 0.0008082 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6504 0.6347 0.4473 0.2331 0.9871 0.9915 0.6508 0.9741 0.9826 0.4573 ] Network output: [ -0.02824 0.1064 0.9407 0.0009527 -0.0004277 1.013 0.000718 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6486 0.6461 0.4472 0.2196 0.9857 0.9907 0.6487 0.9701 0.9803 0.449 ] Network output: [ 0.008061 0.9647 0.02058 -0.0002094 9.401e-05 0.9978 -0.0001578 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009275 Epoch 2796 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01797 0.9972 0.999 -5.626e-05 2.526e-05 -0.03237 -4.24e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02181 -0.005362 0.02106 0.02059 0.9418 0.9508 0.04218 0.8887 0.9069 0.1064 ] Network output: [ 0.9912 0.02445 -0.001844 -0.000227 0.0001019 -0.005892 -0.0001711 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6461 0.111 0.1673 0.1739 0.9728 0.9875 0.7266 0.9031 0.9684 0.6427 ] Network output: [ -0.01231 0.9757 1.013 -1.131e-05 5.077e-06 0.03633 -8.523e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04561 0.03328 0.04579 0.02441 0.986 0.9901 0.04649 0.9712 0.9812 0.05525 ] Network output: [ 0.03685 -0.1556 1.082 -0.001521 0.0006827 0.9932 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7226 0.6147 0.5561 0.3069 0.976 0.9893 0.7251 0.913 0.9728 0.6354 ] Network output: [ -0.01355 0.0872 0.9468 0.001072 -0.0004814 0.9975 0.0008081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6504 0.6347 0.4472 0.2331 0.9871 0.9915 0.6508 0.9741 0.9826 0.4572 ] Network output: [ -0.02823 0.1065 0.9405 0.000953 -0.0004278 1.013 0.0007182 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6486 0.6461 0.4471 0.2196 0.9857 0.9907 0.6487 0.9701 0.9803 0.4489 ] Network output: [ 0.008076 0.9646 0.02063 -0.0002089 9.378e-05 0.9978 -0.0001574 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009278 Epoch 2797 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01794 0.9973 0.999 -5.628e-05 2.527e-05 -0.03235 -4.241e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02181 -0.005363 0.02108 0.02058 0.9418 0.9508 0.04217 0.8887 0.9069 0.1063 ] Network output: [ 0.9912 0.0244 -0.001793 -0.0002283 0.0001025 -0.005933 -0.0001721 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6462 0.1109 0.1675 0.1739 0.9728 0.9875 0.7266 0.9031 0.9684 0.6425 ] Network output: [ -0.01231 0.9757 1.013 -1.081e-05 4.854e-06 0.03632 -8.149e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0456 0.03326 0.04577 0.0244 0.986 0.9901 0.04648 0.9712 0.9812 0.05521 ] Network output: [ 0.03683 -0.1555 1.083 -0.001521 0.0006827 0.9932 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7226 0.6147 0.5561 0.3069 0.976 0.9893 0.7251 0.913 0.9728 0.6352 ] Network output: [ -0.01356 0.08719 0.9468 0.001072 -0.0004813 0.9975 0.000808 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6504 0.6347 0.4472 0.2332 0.9871 0.9915 0.6508 0.9741 0.9826 0.457 ] Network output: [ -0.02821 0.1065 0.9404 0.0009533 -0.000428 1.013 0.0007185 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6486 0.6461 0.447 0.2196 0.9857 0.9907 0.6487 0.9701 0.9803 0.4487 ] Network output: [ 0.008092 0.9645 0.02069 -0.0002084 9.354e-05 0.9978 -0.000157 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009281 Epoch 2798 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01791 0.9973 0.999 -5.63e-05 2.528e-05 -0.03232 -4.243e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02181 -0.005364 0.0211 0.02058 0.9418 0.9508 0.04215 0.8887 0.9069 0.1063 ] Network output: [ 0.9912 0.02435 -0.001742 -0.0002296 0.0001031 -0.005974 -0.000173 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6462 0.1107 0.1677 0.1739 0.9728 0.9875 0.7266 0.9031 0.9684 0.6424 ] Network output: [ -0.0123 0.9758 1.012 -1.031e-05 4.631e-06 0.03632 -7.774e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04558 0.03325 0.04574 0.02439 0.986 0.9901 0.04646 0.9712 0.9812 0.05518 ] Network output: [ 0.03682 -0.1555 1.083 -0.001521 0.0006826 0.9931 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7226 0.6146 0.556 0.307 0.976 0.9893 0.7251 0.913 0.9728 0.6351 ] Network output: [ -0.01357 0.08717 0.9468 0.001072 -0.0004813 0.9975 0.0008079 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6504 0.6347 0.4471 0.2332 0.9871 0.9915 0.6508 0.9741 0.9826 0.4569 ] Network output: [ -0.02819 0.1066 0.9403 0.0009536 -0.0004281 1.013 0.0007187 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6486 0.6461 0.4469 0.2195 0.9857 0.9907 0.6487 0.9701 0.9803 0.4486 ] Network output: [ 0.008108 0.9644 0.02075 -0.0002078 9.33e-05 0.9978 -0.0001566 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009285 Epoch 2799 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01789 0.9973 0.999 -5.633e-05 2.529e-05 -0.03229 -4.245e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02181 -0.005365 0.02112 0.02058 0.9418 0.9509 0.04214 0.8887 0.9069 0.1062 ] Network output: [ 0.9912 0.0243 -0.001691 -0.0002309 0.0001037 -0.006015 -0.000174 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6463 0.1106 0.1679 0.174 0.9728 0.9875 0.7266 0.9031 0.9684 0.6423 ] Network output: [ -0.01229 0.9758 1.012 -9.814e-06 4.406e-06 0.03631 -7.396e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04557 0.03323 0.04572 0.02439 0.986 0.9901 0.04644 0.9712 0.9812 0.05514 ] Network output: [ 0.0368 -0.1555 1.083 -0.00152 0.0006826 0.9931 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7226 0.6146 0.556 0.307 0.976 0.9893 0.7251 0.913 0.9728 0.635 ] Network output: [ -0.01358 0.08716 0.9468 0.001072 -0.0004812 0.9975 0.0008078 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.6347 0.447 0.2332 0.9871 0.9915 0.6508 0.9741 0.9826 0.4568 ] Network output: [ -0.02818 0.1066 0.9402 0.000954 -0.0004283 1.013 0.0007189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6487 0.6461 0.4468 0.2195 0.9857 0.9907 0.6487 0.9701 0.9803 0.4485 ] Network output: [ 0.008124 0.9643 0.02081 -0.0002073 9.307e-05 0.9978 -0.0001562 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009289 Epoch 2800 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01786 0.9974 0.999 -5.635e-05 2.53e-05 -0.03227 -4.247e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0218 -0.005366 0.02113 0.02057 0.9418 0.9509 0.04213 0.8887 0.9069 0.1061 ] Network output: [ 0.9912 0.02425 -0.00164 -0.0002322 0.0001043 -0.006057 -0.000175 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6463 0.1104 0.1681 0.174 0.9728 0.9875 0.7266 0.9031 0.9684 0.6422 ] Network output: [ -0.01229 0.9759 1.012 -9.312e-06 4.18e-06 0.0363 -7.018e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04555 0.03322 0.04569 0.02438 0.986 0.9901 0.04643 0.9712 0.9812 0.0551 ] Network output: [ 0.03678 -0.1555 1.083 -0.00152 0.0006825 0.9931 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7226 0.6145 0.556 0.3071 0.976 0.9893 0.7252 0.913 0.9728 0.6348 ] Network output: [ -0.0136 0.08714 0.9468 0.001072 -0.0004811 0.9976 0.0008077 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.6347 0.4469 0.2332 0.9871 0.9915 0.6509 0.9741 0.9826 0.4567 ] Network output: [ -0.02817 0.1067 0.9401 0.0009543 -0.0004284 1.013 0.0007192 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6487 0.6462 0.4466 0.2195 0.9857 0.9907 0.6487 0.9701 0.9803 0.4484 ] Network output: [ 0.008141 0.9642 0.02088 -0.0002068 9.282e-05 0.9978 -0.0001558 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009293 Epoch 2801 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01783 0.9974 0.999 -5.637e-05 2.531e-05 -0.03224 -4.248e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0218 -0.005367 0.02115 0.02057 0.9418 0.9509 0.04212 0.8887 0.9069 0.1061 ] Network output: [ 0.9913 0.0242 -0.001588 -0.0002336 0.0001049 -0.006099 -0.000176 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6464 0.1103 0.1684 0.174 0.9728 0.9875 0.7267 0.9031 0.9684 0.6421 ] Network output: [ -0.01228 0.9759 1.012 -8.807e-06 3.954e-06 0.03629 -6.638e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04554 0.0332 0.04567 0.02437 0.986 0.9901 0.04641 0.9712 0.9812 0.05506 ] Network output: [ 0.03677 -0.1555 1.083 -0.00152 0.0006824 0.9931 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7227 0.6144 0.5559 0.3072 0.976 0.9893 0.7252 0.913 0.9728 0.6347 ] Network output: [ -0.01361 0.08713 0.9468 0.001072 -0.0004811 0.9976 0.0008076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.6347 0.4468 0.2332 0.9871 0.9915 0.6509 0.9741 0.9826 0.4566 ] Network output: [ -0.02815 0.1068 0.9399 0.0009546 -0.0004286 1.013 0.0007194 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6487 0.6462 0.4465 0.2195 0.9857 0.9907 0.6488 0.9701 0.9803 0.4482 ] Network output: [ 0.008158 0.9641 0.02094 -0.0002062 9.258e-05 0.9978 -0.0001554 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009298 Epoch 2802 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01781 0.9974 0.999 -5.639e-05 2.532e-05 -0.03222 -4.25e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0218 -0.005368 0.02117 0.02057 0.9418 0.9509 0.04211 0.8887 0.9069 0.106 ] Network output: [ 0.9913 0.02416 -0.001537 -0.0002349 0.0001055 -0.006142 -0.000177 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6464 0.1101 0.1686 0.1741 0.9728 0.9875 0.7267 0.9031 0.9684 0.6419 ] Network output: [ -0.01227 0.976 1.012 -8.301e-06 3.727e-06 0.03628 -6.256e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04552 0.03318 0.04564 0.02437 0.986 0.9901 0.0464 0.9712 0.9812 0.05502 ] Network output: [ 0.03675 -0.1555 1.083 -0.00152 0.0006824 0.993 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7227 0.6144 0.5559 0.3072 0.976 0.9893 0.7252 0.913 0.9728 0.6345 ] Network output: [ -0.01362 0.08713 0.9469 0.001071 -0.000481 0.9976 0.0008075 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.6347 0.4467 0.2333 0.9871 0.9915 0.6509 0.9741 0.9826 0.4565 ] Network output: [ -0.02814 0.1069 0.9398 0.0009549 -0.0004287 1.013 0.0007197 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6487 0.6462 0.4464 0.2195 0.9857 0.9907 0.6488 0.9701 0.9803 0.4481 ] Network output: [ 0.008176 0.964 0.021 -0.0002057 9.234e-05 0.9978 -0.000155 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009303 Epoch 2803 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01778 0.9975 0.999 -5.641e-05 2.533e-05 -0.03219 -4.252e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0218 -0.00537 0.02119 0.02056 0.9418 0.9509 0.04209 0.8887 0.9069 0.106 ] Network output: [ 0.9913 0.02411 -0.001486 -0.0002363 0.0001061 -0.006185 -0.0001781 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6465 0.11 0.1688 0.1741 0.9728 0.9875 0.7267 0.9031 0.9684 0.6418 ] Network output: [ -0.01227 0.976 1.012 -7.792e-06 3.498e-06 0.03627 -5.872e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04551 0.03317 0.04562 0.02436 0.986 0.9901 0.04638 0.9712 0.9812 0.05498 ] Network output: [ 0.03673 -0.1555 1.083 -0.00152 0.0006823 0.993 -0.001145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7227 0.6143 0.5558 0.3073 0.976 0.9893 0.7252 0.913 0.9728 0.6344 ] Network output: [ -0.01363 0.08712 0.9469 0.001071 -0.0004809 0.9976 0.0008073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.6346 0.4466 0.2333 0.9871 0.9915 0.6509 0.9741 0.9826 0.4564 ] Network output: [ -0.02812 0.1069 0.9397 0.0009553 -0.0004289 1.014 0.0007199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6487 0.6462 0.4463 0.2195 0.9857 0.9907 0.6488 0.9701 0.9803 0.448 ] Network output: [ 0.008194 0.9639 0.02107 -0.0002051 9.209e-05 0.9978 -0.0001546 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009308 Epoch 2804 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01775 0.9975 0.9989 -5.644e-05 2.534e-05 -0.03216 -4.253e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0218 -0.005371 0.02121 0.02056 0.9418 0.9509 0.04208 0.8887 0.9069 0.1059 ] Network output: [ 0.9913 0.02406 -0.001435 -0.0002376 0.0001067 -0.006228 -0.0001791 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6465 0.1098 0.169 0.1741 0.9728 0.9875 0.7267 0.9031 0.9684 0.6417 ] Network output: [ -0.01226 0.9761 1.012 -7.281e-06 3.269e-06 0.03626 -5.488e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04549 0.03315 0.04559 0.02435 0.986 0.9901 0.04637 0.9712 0.9812 0.05494 ] Network output: [ 0.03672 -0.1555 1.083 -0.00152 0.0006822 0.993 -0.001145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7227 0.6142 0.5558 0.3074 0.976 0.9893 0.7252 0.913 0.9728 0.6343 ] Network output: [ -0.01365 0.08711 0.9469 0.001071 -0.0004809 0.9977 0.0008072 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.6346 0.4465 0.2333 0.9871 0.9915 0.6509 0.9741 0.9826 0.4563 ] Network output: [ -0.02811 0.107 0.9396 0.0009556 -0.000429 1.014 0.0007202 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6487 0.6462 0.4461 0.2195 0.9857 0.9907 0.6488 0.9701 0.9803 0.4479 ] Network output: [ 0.008212 0.9638 0.02114 -0.0002046 9.184e-05 0.9978 -0.0001542 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009314 Epoch 2805 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01772 0.9975 0.9989 -5.646e-05 2.535e-05 -0.03214 -4.255e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0218 -0.005372 0.02122 0.02056 0.9418 0.9509 0.04207 0.8887 0.9069 0.1059 ] Network output: [ 0.9913 0.02402 -0.001383 -0.000239 0.0001073 -0.006272 -0.0001801 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6466 0.1097 0.1692 0.1742 0.9728 0.9875 0.7267 0.9031 0.9684 0.6416 ] Network output: [ -0.01225 0.9761 1.012 -6.769e-06 3.039e-06 0.03625 -5.101e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04548 0.03313 0.04556 0.02435 0.986 0.9901 0.04635 0.9712 0.9812 0.0549 ] Network output: [ 0.0367 -0.1555 1.083 -0.00152 0.0006822 0.993 -0.001145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7228 0.6142 0.5558 0.3075 0.976 0.9893 0.7253 0.913 0.9728 0.6341 ] Network output: [ -0.01366 0.08711 0.9469 0.001071 -0.0004808 0.9977 0.0008071 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.6346 0.4464 0.2333 0.9871 0.9915 0.6509 0.9741 0.9826 0.4562 ] Network output: [ -0.0281 0.1071 0.9394 0.0009559 -0.0004291 1.014 0.0007204 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6487 0.6462 0.446 0.2194 0.9857 0.9907 0.6488 0.9701 0.9803 0.4477 ] Network output: [ 0.00823 0.9637 0.0212 -0.000204 9.159e-05 0.9978 -0.0001538 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00932 Epoch 2806 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0177 0.9975 0.9989 -5.648e-05 2.536e-05 -0.03211 -4.256e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0218 -0.005373 0.02124 0.02055 0.9418 0.9509 0.04206 0.8887 0.9069 0.1058 ] Network output: [ 0.9913 0.02397 -0.001332 -0.0002404 0.0001079 -0.006316 -0.0001812 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6466 0.1095 0.1694 0.1742 0.9728 0.9875 0.7268 0.9031 0.9684 0.6414 ] Network output: [ -0.01224 0.9761 1.012 -6.254e-06 2.807e-06 0.03624 -4.713e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04546 0.03312 0.04554 0.02434 0.986 0.9901 0.04633 0.9712 0.9812 0.05486 ] Network output: [ 0.03669 -0.1555 1.083 -0.001519 0.0006821 0.993 -0.001145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7228 0.6141 0.5557 0.3075 0.976 0.9893 0.7253 0.913 0.9728 0.634 ] Network output: [ -0.01368 0.08711 0.9469 0.001071 -0.0004807 0.9977 0.000807 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.6346 0.4463 0.2334 0.9871 0.9915 0.6509 0.9741 0.9826 0.456 ] Network output: [ -0.02808 0.1072 0.9393 0.0009562 -0.0004293 1.014 0.0007206 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6488 0.6462 0.4459 0.2194 0.9857 0.9907 0.6488 0.9701 0.9803 0.4476 ] Network output: [ 0.008249 0.9636 0.02127 -0.0002034 9.134e-05 0.9978 -0.0001533 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009326 Epoch 2807 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01767 0.9976 0.9989 -5.65e-05 2.536e-05 -0.03208 -4.258e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02179 -0.005374 0.02126 0.02055 0.9418 0.9509 0.04204 0.8887 0.9069 0.1058 ] Network output: [ 0.9914 0.02393 -0.00128 -0.0002418 0.0001086 -0.00636 -0.0001823 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6467 0.1093 0.1697 0.1742 0.9728 0.9875 0.7268 0.9031 0.9684 0.6413 ] Network output: [ -0.01224 0.9762 1.012 -5.737e-06 2.575e-06 0.03623 -4.323e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04545 0.0331 0.04551 0.02434 0.986 0.9901 0.04632 0.9712 0.9812 0.05482 ] Network output: [ 0.03668 -0.1555 1.083 -0.001519 0.000682 0.9929 -0.001145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7228 0.614 0.5557 0.3076 0.976 0.9893 0.7253 0.913 0.9728 0.6338 ] Network output: [ -0.01369 0.08711 0.9469 0.001071 -0.0004807 0.9977 0.0008069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.6346 0.4462 0.2334 0.9871 0.9915 0.6509 0.9741 0.9826 0.4559 ] Network output: [ -0.02807 0.1072 0.9392 0.0009565 -0.0004294 1.014 0.0007209 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6488 0.6462 0.4458 0.2194 0.9857 0.9907 0.6488 0.9701 0.9803 0.4475 ] Network output: [ 0.008269 0.9635 0.02135 -0.0002029 9.108e-05 0.9978 -0.0001529 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009332 Epoch 2808 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01764 0.9976 0.9989 -5.652e-05 2.537e-05 -0.03206 -4.26e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02179 -0.005375 0.02128 0.02055 0.9418 0.9509 0.04203 0.8887 0.9069 0.1057 ] Network output: [ 0.9914 0.02389 -0.001229 -0.0002433 0.0001092 -0.006405 -0.0001833 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6467 0.1092 0.1699 0.1743 0.9728 0.9875 0.7268 0.9031 0.9684 0.6412 ] Network output: [ -0.01223 0.9762 1.012 -5.217e-06 2.342e-06 0.03622 -3.932e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04543 0.03308 0.04549 0.02433 0.986 0.9901 0.0463 0.9712 0.9812 0.05478 ] Network output: [ 0.03666 -0.1555 1.083 -0.001519 0.0006819 0.9929 -0.001145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7228 0.614 0.5556 0.3077 0.976 0.9893 0.7253 0.913 0.9728 0.6337 ] Network output: [ -0.01371 0.08711 0.9469 0.001071 -0.0004806 0.9978 0.0008068 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.6346 0.4461 0.2334 0.9871 0.9915 0.6509 0.9741 0.9826 0.4558 ] Network output: [ -0.02806 0.1073 0.9391 0.0009568 -0.0004296 1.014 0.0007211 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6488 0.6462 0.4456 0.2194 0.9857 0.9907 0.6488 0.9701 0.9803 0.4474 ] Network output: [ 0.008289 0.9634 0.02142 -0.0002023 9.082e-05 0.9978 -0.0001525 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009339 Epoch 2809 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01762 0.9976 0.9989 -5.654e-05 2.538e-05 -0.03203 -4.261e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02179 -0.005377 0.0213 0.02055 0.9418 0.9509 0.04202 0.8887 0.9069 0.1057 ] Network output: [ 0.9914 0.02384 -0.001177 -0.0002447 0.0001098 -0.006451 -0.0001844 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6468 0.109 0.1701 0.1743 0.9728 0.9875 0.7268 0.9031 0.9684 0.641 ] Network output: [ -0.01222 0.9763 1.012 -4.696e-06 2.108e-06 0.03621 -3.539e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04541 0.03307 0.04546 0.02433 0.986 0.9901 0.04628 0.9712 0.9812 0.05474 ] Network output: [ 0.03665 -0.1555 1.083 -0.001519 0.0006818 0.9929 -0.001145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7229 0.6139 0.5556 0.3078 0.976 0.9893 0.7254 0.913 0.9728 0.6335 ] Network output: [ -0.01372 0.08711 0.9469 0.00107 -0.0004805 0.9978 0.0008067 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.6346 0.446 0.2335 0.9871 0.9915 0.6509 0.9741 0.9826 0.4557 ] Network output: [ -0.02805 0.1074 0.9389 0.0009571 -0.0004297 1.014 0.0007213 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6488 0.6462 0.4455 0.2194 0.9857 0.9907 0.6489 0.9701 0.9803 0.4472 ] Network output: [ 0.008309 0.9633 0.02149 -0.0002017 9.056e-05 0.9978 -0.000152 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009346 Epoch 2810 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01759 0.9977 0.9989 -5.656e-05 2.539e-05 -0.032 -4.263e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02179 -0.005378 0.02132 0.02054 0.9418 0.9509 0.04201 0.8887 0.9069 0.1056 ] Network output: [ 0.9914 0.0238 -0.001126 -0.0002461 0.0001105 -0.006497 -0.0001855 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6468 0.1089 0.1703 0.1743 0.9728 0.9875 0.7268 0.9031 0.9684 0.6409 ] Network output: [ -0.01221 0.9763 1.012 -4.172e-06 1.873e-06 0.0362 -3.144e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0454 0.03305 0.04544 0.02432 0.986 0.9901 0.04627 0.9712 0.9812 0.0547 ] Network output: [ 0.03664 -0.1555 1.083 -0.001519 0.0006818 0.9929 -0.001144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7229 0.6138 0.5555 0.3079 0.976 0.9893 0.7254 0.913 0.9728 0.6334 ] Network output: [ -0.01374 0.08712 0.9469 0.00107 -0.0004805 0.9978 0.0008066 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.6346 0.4459 0.2335 0.9871 0.9915 0.6509 0.9741 0.9826 0.4556 ] Network output: [ -0.02804 0.1075 0.9388 0.0009574 -0.0004298 1.014 0.0007216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6488 0.6463 0.4454 0.2194 0.9857 0.9907 0.6489 0.9701 0.9803 0.4471 ] Network output: [ 0.00833 0.9632 0.02157 -0.0002011 9.03e-05 0.9978 -0.0001516 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009353 Epoch 2811 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01756 0.9977 0.9989 -5.658e-05 2.54e-05 -0.03197 -4.264e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02179 -0.005379 0.02133 0.02054 0.9418 0.9509 0.042 0.8887 0.9069 0.1056 ] Network output: [ 0.9914 0.02376 -0.001074 -0.0002476 0.0001112 -0.006543 -0.0001866 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6469 0.1087 0.1705 0.1744 0.9728 0.9875 0.7269 0.9031 0.9684 0.6408 ] Network output: [ -0.01221 0.9764 1.012 -3.647e-06 1.637e-06 0.03619 -2.748e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04538 0.03303 0.04541 0.02432 0.986 0.9901 0.04625 0.9712 0.9812 0.05465 ] Network output: [ 0.03662 -0.1556 1.083 -0.001518 0.0006817 0.9929 -0.001144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7229 0.6138 0.5555 0.308 0.976 0.9893 0.7254 0.913 0.9728 0.6332 ] Network output: [ -0.01375 0.08713 0.9469 0.00107 -0.0004804 0.9978 0.0008065 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.6345 0.4457 0.2335 0.9871 0.9915 0.6509 0.974 0.9826 0.4554 ] Network output: [ -0.02803 0.1076 0.9387 0.0009577 -0.00043 1.014 0.0007218 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6488 0.6463 0.4452 0.2194 0.9857 0.9907 0.6489 0.9701 0.9803 0.447 ] Network output: [ 0.008351 0.9631 0.02164 -0.0002005 9.003e-05 0.9978 -0.0001511 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009361 Epoch 2812 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01753 0.9977 0.9989 -5.66e-05 2.541e-05 -0.03195 -4.266e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02179 -0.00538 0.02135 0.02054 0.9418 0.9509 0.04198 0.8887 0.9069 0.1055 ] Network output: [ 0.9914 0.02372 -0.001022 -0.0002491 0.0001118 -0.006589 -0.0001877 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.647 0.1085 0.1708 0.1744 0.9728 0.9875 0.7269 0.9031 0.9684 0.6406 ] Network output: [ -0.0122 0.9764 1.012 -3.119e-06 1.4e-06 0.03618 -2.35e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04537 0.03301 0.04538 0.02431 0.986 0.9901 0.04624 0.9712 0.9812 0.05461 ] Network output: [ 0.03661 -0.1556 1.083 -0.001518 0.0006816 0.9928 -0.001144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7229 0.6137 0.5555 0.3081 0.976 0.9893 0.7254 0.913 0.9728 0.6331 ] Network output: [ -0.01377 0.08714 0.9469 0.00107 -0.0004803 0.9978 0.0008064 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.6345 0.4456 0.2336 0.9871 0.9915 0.6509 0.974 0.9826 0.4553 ] Network output: [ -0.02802 0.1077 0.9385 0.0009581 -0.0004301 1.014 0.000722 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6488 0.6463 0.4451 0.2194 0.9857 0.9907 0.6489 0.9701 0.9803 0.4468 ] Network output: [ 0.008372 0.963 0.02172 -0.0001999 8.976e-05 0.9978 -0.0001507 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009369 Epoch 2813 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01751 0.9978 0.9989 -5.663e-05 2.542e-05 -0.03192 -4.268e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02179 -0.005382 0.02137 0.02054 0.9418 0.9509 0.04197 0.8887 0.9069 0.1054 ] Network output: [ 0.9915 0.02368 -0.0009703 -0.0002506 0.0001125 -0.006636 -0.0001888 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.647 0.1084 0.171 0.1745 0.9728 0.9875 0.7269 0.9031 0.9684 0.6405 ] Network output: [ -0.01219 0.9764 1.012 -2.589e-06 1.162e-06 0.03618 -1.951e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04535 0.033 0.04536 0.02431 0.986 0.9901 0.04622 0.9712 0.9812 0.05457 ] Network output: [ 0.0366 -0.1556 1.083 -0.001518 0.0006815 0.9928 -0.001144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.723 0.6136 0.5554 0.3082 0.976 0.9893 0.7254 0.913 0.9728 0.6329 ] Network output: [ -0.01378 0.08715 0.9469 0.00107 -0.0004803 0.9979 0.0008063 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.6345 0.4455 0.2336 0.9871 0.9915 0.6509 0.974 0.9826 0.4552 ] Network output: [ -0.02801 0.1078 0.9384 0.0009584 -0.0004302 1.014 0.0007222 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6488 0.6463 0.445 0.2194 0.9857 0.9907 0.6489 0.97 0.9803 0.4467 ] Network output: [ 0.008394 0.9628 0.0218 -0.0001993 8.949e-05 0.9978 -0.0001502 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009378 Epoch 2814 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01748 0.9978 0.9989 -5.665e-05 2.543e-05 -0.03189 -4.269e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02179 -0.005383 0.02139 0.02053 0.9418 0.9509 0.04196 0.8887 0.9069 0.1054 ] Network output: [ 0.9915 0.02364 -0.0009184 -0.0002521 0.0001132 -0.006684 -0.00019 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6471 0.1082 0.1712 0.1745 0.9728 0.9875 0.7269 0.9031 0.9684 0.6404 ] Network output: [ -0.01218 0.9765 1.012 -2.056e-06 9.232e-07 0.03617 -1.55e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04534 0.03298 0.04533 0.0243 0.986 0.9901 0.0462 0.9712 0.9812 0.05453 ] Network output: [ 0.03659 -0.1556 1.083 -0.001518 0.0006814 0.9928 -0.001144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.723 0.6135 0.5554 0.3083 0.976 0.9893 0.7255 0.913 0.9728 0.6328 ] Network output: [ -0.0138 0.08716 0.9469 0.00107 -0.0004802 0.9979 0.0008061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.6345 0.4454 0.2337 0.9871 0.9915 0.6509 0.974 0.9826 0.4551 ] Network output: [ -0.028 0.1079 0.9383 0.0009587 -0.0004304 1.014 0.0007225 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6488 0.6463 0.4448 0.2194 0.9857 0.9907 0.6489 0.97 0.9803 0.4465 ] Network output: [ 0.008416 0.9627 0.02188 -0.0001987 8.922e-05 0.9978 -0.0001498 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009386 Epoch 2815 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01745 0.9978 0.9989 -5.667e-05 2.544e-05 -0.03186 -4.271e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02178 -0.005385 0.02141 0.02053 0.9418 0.9509 0.04195 0.8887 0.9069 0.1053 ] Network output: [ 0.9915 0.0236 -0.0008665 -0.0002536 0.0001138 -0.006732 -0.0001911 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6471 0.108 0.1715 0.1746 0.9728 0.9875 0.7269 0.9031 0.9684 0.6402 ] Network output: [ -0.01217 0.9765 1.012 -1.522e-06 6.832e-07 0.03616 -1.147e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04532 0.03296 0.04531 0.0243 0.986 0.9901 0.04619 0.9712 0.9812 0.05449 ] Network output: [ 0.03658 -0.1556 1.084 -0.001518 0.0006813 0.9928 -0.001144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.723 0.6135 0.5553 0.3084 0.976 0.9893 0.7255 0.913 0.9728 0.6326 ] Network output: [ -0.01382 0.08717 0.9469 0.00107 -0.0004802 0.9979 0.000806 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.6345 0.4453 0.2337 0.9871 0.9915 0.6509 0.974 0.9826 0.4549 ] Network output: [ -0.02799 0.108 0.9381 0.000959 -0.0004305 1.014 0.0007227 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6488 0.6463 0.4447 0.2194 0.9857 0.9907 0.6489 0.97 0.9803 0.4464 ] Network output: [ 0.008439 0.9626 0.02197 -0.0001981 8.894e-05 0.9978 -0.0001493 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009395 Epoch 2816 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01742 0.9979 0.9989 -5.669e-05 2.545e-05 -0.03183 -4.272e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02178 -0.005386 0.02143 0.02053 0.9418 0.9509 0.04193 0.8887 0.9069 0.1053 ] Network output: [ 0.9915 0.02357 -0.0008145 -0.0002551 0.0001145 -0.00678 -0.0001923 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6472 0.1079 0.1717 0.1746 0.9728 0.9875 0.727 0.9031 0.9684 0.6401 ] Network output: [ -0.01216 0.9766 1.012 -9.85e-07 4.422e-07 0.03615 -7.424e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0453 0.03294 0.04528 0.02429 0.986 0.9901 0.04617 0.9712 0.9812 0.05445 ] Network output: [ 0.03657 -0.1557 1.084 -0.001517 0.0006812 0.9928 -0.001143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.723 0.6134 0.5553 0.3085 0.976 0.9893 0.7255 0.9129 0.9728 0.6325 ] Network output: [ -0.01384 0.08719 0.9469 0.001069 -0.0004801 0.9979 0.0008059 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.6345 0.4452 0.2337 0.9871 0.9915 0.6509 0.974 0.9826 0.4548 ] Network output: [ -0.02798 0.1081 0.938 0.0009593 -0.0004306 1.014 0.0007229 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6488 0.6463 0.4446 0.2194 0.9857 0.9907 0.6489 0.97 0.9803 0.4463 ] Network output: [ 0.008462 0.9624 0.02205 -0.0001975 8.866e-05 0.9978 -0.0001488 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009404 Epoch 2817 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0174 0.9979 0.9989 -5.671e-05 2.546e-05 -0.0318 -4.274e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02178 -0.005387 0.02145 0.02053 0.9418 0.9509 0.04192 0.8887 0.9069 0.1052 ] Network output: [ 0.9915 0.02353 -0.0007625 -0.0002566 0.0001152 -0.006829 -0.0001934 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6472 0.1077 0.1719 0.1747 0.9728 0.9875 0.727 0.9031 0.9684 0.64 ] Network output: [ -0.01216 0.9766 1.012 -4.46e-07 2.002e-07 0.03614 -3.361e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04529 0.03292 0.04525 0.02429 0.986 0.9901 0.04615 0.9712 0.9812 0.05441 ] Network output: [ 0.03656 -0.1557 1.084 -0.001517 0.0006811 0.9928 -0.001143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7231 0.6133 0.5552 0.3086 0.976 0.9893 0.7255 0.9129 0.9728 0.6323 ] Network output: [ -0.01385 0.08721 0.9469 0.001069 -0.00048 0.9979 0.0008058 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.6344 0.4451 0.2338 0.9871 0.9915 0.6509 0.974 0.9826 0.4547 ] Network output: [ -0.02797 0.1082 0.9378 0.0009596 -0.0004308 1.014 0.0007232 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6488 0.6463 0.4444 0.2194 0.9857 0.9907 0.6489 0.97 0.9803 0.4461 ] Network output: [ 0.008486 0.9623 0.02214 -0.0001969 8.838e-05 0.9978 -0.0001484 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009414 Epoch 2818 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01737 0.9979 0.9989 -5.673e-05 2.547e-05 -0.03177 -4.276e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02178 -0.005389 0.02147 0.02053 0.9419 0.9509 0.04191 0.8887 0.9069 0.1052 ] Network output: [ 0.9915 0.02349 -0.0007104 -0.0002582 0.0001159 -0.006878 -0.0001946 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6473 0.1075 0.1722 0.1747 0.9728 0.9875 0.727 0.9031 0.9684 0.6398 ] Network output: [ -0.01215 0.9767 1.012 9.521e-08 -4.274e-08 0.03613 7.175e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04527 0.03291 0.04523 0.02429 0.986 0.9901 0.04614 0.9712 0.9812 0.05436 ] Network output: [ 0.03655 -0.1557 1.084 -0.001517 0.0006809 0.9927 -0.001143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7231 0.6132 0.5552 0.3087 0.976 0.9893 0.7256 0.9129 0.9728 0.6322 ] Network output: [ -0.01387 0.08723 0.9469 0.001069 -0.00048 0.998 0.0008057 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.6344 0.445 0.2338 0.9871 0.9915 0.6509 0.974 0.9826 0.4546 ] Network output: [ -0.02796 0.1083 0.9377 0.0009599 -0.0004309 1.014 0.0007234 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6489 0.6463 0.4443 0.2194 0.9857 0.9907 0.6489 0.97 0.9803 0.446 ] Network output: [ 0.00851 0.9622 0.02223 -0.0001962 8.809e-05 0.9978 -0.0001479 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009424 Epoch 2819 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01734 0.998 0.9989 -5.675e-05 2.548e-05 -0.03174 -4.277e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02178 -0.00539 0.02149 0.02052 0.9419 0.9509 0.0419 0.8888 0.9069 0.1051 ] Network output: [ 0.9915 0.02346 -0.0006583 -0.0002598 0.0001166 -0.006927 -0.0001958 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6473 0.1073 0.1724 0.1748 0.9728 0.9875 0.727 0.9031 0.9684 0.6397 ] Network output: [ -0.01214 0.9767 1.011 6.387e-07 -2.867e-07 0.03612 4.813e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04526 0.03289 0.0452 0.02428 0.986 0.9901 0.04612 0.9712 0.9812 0.05432 ] Network output: [ 0.03655 -0.1557 1.084 -0.001517 0.0006808 0.9927 -0.001143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7231 0.6132 0.5551 0.3089 0.976 0.9893 0.7256 0.9129 0.9728 0.632 ] Network output: [ -0.01389 0.08726 0.9469 0.001069 -0.0004799 0.998 0.0008056 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.6344 0.4448 0.2339 0.9871 0.9915 0.6508 0.974 0.9826 0.4544 ] Network output: [ -0.02796 0.1084 0.9376 0.0009602 -0.000431 1.014 0.0007236 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6489 0.6463 0.4441 0.2194 0.9857 0.9907 0.6489 0.97 0.9803 0.4458 ] Network output: [ 0.008535 0.962 0.02232 -0.0001956 8.78e-05 0.9978 -0.0001474 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009434 Epoch 2820 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01731 0.998 0.9989 -5.678e-05 2.549e-05 -0.03172 -4.279e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02178 -0.005392 0.0215 0.02052 0.9419 0.9509 0.04189 0.8888 0.9069 0.1051 ] Network output: [ 0.9915 0.02342 -0.0006061 -0.0002614 0.0001173 -0.006977 -0.000197 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6474 0.1072 0.1726 0.1749 0.9728 0.9875 0.7271 0.9031 0.9684 0.6395 ] Network output: [ -0.01213 0.9767 1.011 1.184e-06 -5.317e-07 0.03612 8.926e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04524 0.03287 0.04517 0.02428 0.986 0.9901 0.0461 0.9712 0.9812 0.05428 ] Network output: [ 0.03654 -0.1558 1.084 -0.001516 0.0006807 0.9927 -0.001143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7231 0.6131 0.5551 0.309 0.976 0.9893 0.7256 0.9129 0.9728 0.6318 ] Network output: [ -0.01391 0.08728 0.9469 0.001069 -0.0004798 0.998 0.0008055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6505 0.6344 0.4447 0.2339 0.9871 0.9915 0.6508 0.974 0.9826 0.4543 ] Network output: [ -0.02795 0.1085 0.9374 0.0009604 -0.0004312 1.014 0.0007238 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6489 0.6463 0.444 0.2194 0.9857 0.9907 0.6489 0.97 0.9803 0.4457 ] Network output: [ 0.00856 0.9619 0.02241 -0.0001949 8.751e-05 0.9978 -0.0001469 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009445 Epoch 2821 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01728 0.998 0.9988 -5.68e-05 2.55e-05 -0.03169 -4.28e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02178 -0.005394 0.02152 0.02052 0.9419 0.9509 0.04187 0.8888 0.9069 0.105 ] Network output: [ 0.9916 0.02339 -0.0005539 -0.000263 0.0001181 -0.007028 -0.0001982 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6475 0.107 0.1729 0.1749 0.9728 0.9875 0.7271 0.9031 0.9684 0.6394 ] Network output: [ -0.01212 0.9768 1.011 1.732e-06 -7.778e-07 0.03611 1.306e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04522 0.03285 0.04514 0.02427 0.986 0.9901 0.04608 0.9712 0.9812 0.05424 ] Network output: [ 0.03653 -0.1558 1.084 -0.001516 0.0006806 0.9927 -0.001143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7232 0.613 0.555 0.3091 0.976 0.9893 0.7256 0.9129 0.9728 0.6317 ] Network output: [ -0.01393 0.08731 0.9469 0.001069 -0.0004798 0.998 0.0008054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6504 0.6344 0.4446 0.234 0.9871 0.9915 0.6508 0.974 0.9826 0.4542 ] Network output: [ -0.02794 0.1086 0.9373 0.0009607 -0.0004313 1.014 0.000724 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6489 0.6463 0.4439 0.2195 0.9857 0.9907 0.6489 0.97 0.9803 0.4455 ] Network output: [ 0.008586 0.9618 0.0225 -0.0001943 8.722e-05 0.9978 -0.0001464 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009456 Epoch 2822 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01726 0.9981 0.9988 -5.682e-05 2.551e-05 -0.03166 -4.282e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02178 -0.005395 0.02154 0.02052 0.9419 0.9509 0.04186 0.8888 0.9069 0.1049 ] Network output: [ 0.9916 0.02336 -0.0005016 -0.0002646 0.0001188 -0.007079 -0.0001994 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6475 0.1068 0.1731 0.175 0.9728 0.9875 0.7271 0.9031 0.9684 0.6393 ] Network output: [ -0.01211 0.9768 1.011 2.283e-06 -1.025e-06 0.0361 1.72e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04521 0.03283 0.04512 0.02427 0.986 0.9901 0.04607 0.9712 0.9812 0.05419 ] Network output: [ 0.03652 -0.1558 1.084 -0.001516 0.0006805 0.9927 -0.001142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7232 0.6129 0.555 0.3092 0.976 0.9893 0.7257 0.9129 0.9728 0.6315 ] Network output: [ -0.01395 0.08734 0.9469 0.001069 -0.0004797 0.998 0.0008053 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6504 0.6343 0.4445 0.2341 0.9871 0.9915 0.6508 0.974 0.9826 0.454 ] Network output: [ -0.02794 0.1087 0.9371 0.000961 -0.0004314 1.014 0.0007243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6489 0.6463 0.4437 0.2195 0.9857 0.9907 0.6489 0.97 0.9803 0.4454 ] Network output: [ 0.008612 0.9616 0.0226 -0.0001936 8.692e-05 0.9978 -0.0001459 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009467 Epoch 2823 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01723 0.9981 0.9988 -5.684e-05 2.552e-05 -0.03163 -4.284e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02177 -0.005397 0.02156 0.02052 0.9419 0.9509 0.04185 0.8888 0.9069 0.1049 ] Network output: [ 0.9916 0.02333 -0.0004493 -0.0002662 0.0001195 -0.00713 -0.0002006 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6476 0.1066 0.1733 0.1751 0.9728 0.9875 0.7271 0.9031 0.9684 0.6391 ] Network output: [ -0.0121 0.9769 1.011 2.835e-06 -1.273e-06 0.03609 2.137e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04519 0.03281 0.04509 0.02427 0.986 0.9901 0.04605 0.9712 0.9812 0.05415 ] Network output: [ 0.03652 -0.1559 1.084 -0.001515 0.0006804 0.9927 -0.001142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7232 0.6129 0.5549 0.3094 0.976 0.9893 0.7257 0.9129 0.9728 0.6313 ] Network output: [ -0.01397 0.08737 0.9469 0.001068 -0.0004797 0.9981 0.0008052 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6504 0.6343 0.4444 0.2341 0.9871 0.9915 0.6508 0.974 0.9826 0.4539 ] Network output: [ -0.02793 0.1088 0.937 0.0009613 -0.0004316 1.014 0.0007245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6489 0.6463 0.4436 0.2195 0.9857 0.9907 0.6489 0.97 0.9803 0.4452 ] Network output: [ 0.008639 0.9615 0.02269 -0.0001929 8.661e-05 0.9978 -0.0001454 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009479 Epoch 2824 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0172 0.9981 0.9988 -5.687e-05 2.553e-05 -0.0316 -4.286e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02177 -0.005398 0.02158 0.02052 0.9419 0.9509 0.04184 0.8888 0.9069 0.1048 ] Network output: [ 0.9916 0.0233 -0.0003969 -0.0002679 0.0001203 -0.007182 -0.0002019 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6476 0.1064 0.1736 0.1751 0.9728 0.9875 0.7272 0.9031 0.9684 0.639 ] Network output: [ -0.01209 0.9769 1.011 3.39e-06 -1.522e-06 0.03608 2.555e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04517 0.0328 0.04506 0.02427 0.986 0.9901 0.04603 0.9712 0.9812 0.05411 ] Network output: [ 0.03651 -0.1559 1.084 -0.001515 0.0006802 0.9927 -0.001142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7232 0.6128 0.5549 0.3095 0.976 0.9893 0.7257 0.9129 0.9728 0.6312 ] Network output: [ -0.01399 0.0874 0.9469 0.001068 -0.0004796 0.9981 0.0008051 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6504 0.6343 0.4442 0.2342 0.9871 0.9915 0.6508 0.974 0.9826 0.4537 ] Network output: [ -0.02793 0.1089 0.9368 0.0009616 -0.0004317 1.014 0.0007247 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6489 0.6463 0.4434 0.2195 0.9857 0.9907 0.6489 0.97 0.9803 0.4451 ] Network output: [ 0.008666 0.9613 0.02279 -0.0001922 8.631e-05 0.9978 -0.0001449 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009491 Epoch 2825 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01717 0.9982 0.9988 -5.689e-05 2.554e-05 -0.03156 -4.287e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02177 -0.0054 0.0216 0.02052 0.9419 0.9509 0.04182 0.8888 0.9069 0.1048 ] Network output: [ 0.9916 0.02327 -0.0003444 -0.0002695 0.000121 -0.007234 -0.0002031 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6477 0.1062 0.1738 0.1752 0.9728 0.9875 0.7272 0.9031 0.9684 0.6388 ] Network output: [ -0.01208 0.9769 1.011 3.947e-06 -1.772e-06 0.03608 2.975e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04516 0.03278 0.04503 0.02426 0.986 0.9901 0.04601 0.9712 0.9812 0.05406 ] Network output: [ 0.03651 -0.156 1.084 -0.001515 0.0006801 0.9927 -0.001142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7233 0.6127 0.5548 0.3097 0.976 0.9893 0.7258 0.9129 0.9728 0.631 ] Network output: [ -0.01401 0.08744 0.9468 0.001068 -0.0004796 0.9981 0.000805 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6504 0.6343 0.4441 0.2342 0.9871 0.9915 0.6508 0.974 0.9826 0.4536 ] Network output: [ -0.02792 0.1091 0.9367 0.0009619 -0.0004318 1.014 0.0007249 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6489 0.6463 0.4433 0.2195 0.9857 0.9907 0.6489 0.97 0.9803 0.4449 ] Network output: [ 0.008694 0.9611 0.02289 -0.0001916 8.6e-05 0.9978 -0.0001444 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009503 Epoch 2826 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01714 0.9982 0.9988 -5.691e-05 2.555e-05 -0.03153 -4.289e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02177 -0.005402 0.02162 0.02051 0.9419 0.9509 0.04181 0.8888 0.9069 0.1047 ] Network output: [ 0.9916 0.02324 -0.0002919 -0.0002712 0.0001218 -0.007286 -0.0002044 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6477 0.1061 0.1741 0.1753 0.9728 0.9875 0.7272 0.9031 0.9684 0.6387 ] Network output: [ -0.01207 0.977 1.011 4.507e-06 -2.023e-06 0.03607 3.396e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04514 0.03276 0.04501 0.02426 0.986 0.9901 0.046 0.9712 0.9812 0.05402 ] Network output: [ 0.0365 -0.156 1.084 -0.001515 0.00068 0.9926 -0.001141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7233 0.6126 0.5548 0.3098 0.976 0.9893 0.7258 0.9129 0.9728 0.6308 ] Network output: [ -0.01403 0.08748 0.9468 0.001068 -0.0004795 0.9981 0.0008049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6504 0.6342 0.444 0.2343 0.9871 0.9915 0.6508 0.974 0.9826 0.4535 ] Network output: [ -0.02792 0.1092 0.9365 0.0009622 -0.000432 1.014 0.0007251 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6489 0.6463 0.4431 0.2195 0.9857 0.9907 0.6489 0.97 0.9803 0.4448 ] Network output: [ 0.008722 0.961 0.023 -0.0001909 8.568e-05 0.9978 -0.0001438 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009516 Epoch 2827 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01712 0.9982 0.9988 -5.694e-05 2.556e-05 -0.0315 -4.291e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02177 -0.005404 0.02164 0.02051 0.9419 0.9509 0.0418 0.8888 0.9069 0.1046 ] Network output: [ 0.9916 0.02321 -0.0002394 -0.0002729 0.0001225 -0.007339 -0.0002057 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6478 0.1059 0.1743 0.1753 0.9728 0.9875 0.7272 0.9031 0.9684 0.6385 ] Network output: [ -0.01206 0.977 1.011 5.068e-06 -2.275e-06 0.03606 3.82e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04512 0.03274 0.04498 0.02426 0.986 0.9901 0.04598 0.9712 0.9812 0.05397 ] Network output: [ 0.0365 -0.1561 1.084 -0.001514 0.0006798 0.9926 -0.001141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7233 0.6125 0.5547 0.3099 0.976 0.9893 0.7258 0.9129 0.9728 0.6307 ] Network output: [ -0.01405 0.08752 0.9468 0.001068 -0.0004794 0.9981 0.0008048 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6504 0.6342 0.4439 0.2344 0.9871 0.9915 0.6508 0.974 0.9826 0.4533 ] Network output: [ -0.02791 0.1093 0.9364 0.0009625 -0.0004321 1.014 0.0007254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6489 0.6463 0.443 0.2196 0.9857 0.9907 0.6489 0.97 0.9803 0.4446 ] Network output: [ 0.008751 0.9608 0.0231 -0.0001901 8.536e-05 0.9978 -0.0001433 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009529 Epoch 2828 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01709 0.9983 0.9988 -5.696e-05 2.557e-05 -0.03147 -4.293e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02177 -0.005405 0.02166 0.02051 0.9419 0.9509 0.04179 0.8888 0.9069 0.1046 ] Network output: [ 0.9916 0.02318 -0.0001868 -0.0002746 0.0001233 -0.007393 -0.000207 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6479 0.1057 0.1746 0.1754 0.9728 0.9875 0.7273 0.9031 0.9684 0.6384 ] Network output: [ -0.01205 0.9771 1.011 5.632e-06 -2.528e-06 0.03605 4.244e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04511 0.03272 0.04495 0.02425 0.986 0.9901 0.04596 0.9712 0.9812 0.05393 ] Network output: [ 0.03649 -0.1561 1.084 -0.001514 0.0006797 0.9926 -0.001141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7234 0.6125 0.5547 0.3101 0.976 0.9893 0.7258 0.9129 0.9728 0.6305 ] Network output: [ -0.01407 0.08756 0.9468 0.001068 -0.0004794 0.9981 0.0008048 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6504 0.6342 0.4437 0.2344 0.9871 0.9915 0.6508 0.974 0.9826 0.4532 ] Network output: [ -0.02791 0.1094 0.9362 0.0009628 -0.0004322 1.014 0.0007256 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6489 0.6463 0.4428 0.2196 0.9857 0.9907 0.6489 0.97 0.9803 0.4445 ] Network output: [ 0.00878 0.9607 0.02321 -0.0001894 8.504e-05 0.9978 -0.0001428 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009543 Epoch 2829 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01706 0.9983 0.9988 -5.699e-05 2.558e-05 -0.03144 -4.295e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02177 -0.005407 0.02168 0.02051 0.9419 0.9509 0.04177 0.8888 0.9069 0.1045 ] Network output: [ 0.9917 0.02316 -0.0001342 -0.0002763 0.0001241 -0.007446 -0.0002083 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6479 0.1055 0.1748 0.1755 0.9728 0.9875 0.7273 0.9031 0.9684 0.6382 ] Network output: [ -0.01204 0.9771 1.011 6.198e-06 -2.783e-06 0.03604 4.671e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04509 0.0327 0.04492 0.02425 0.986 0.9901 0.04594 0.9712 0.9812 0.05389 ] Network output: [ 0.03649 -0.1561 1.084 -0.001514 0.0006796 0.9926 -0.001141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7234 0.6124 0.5546 0.3103 0.976 0.9893 0.7259 0.9129 0.9728 0.6303 ] Network output: [ -0.0141 0.08761 0.9468 0.001068 -0.0004793 0.9981 0.0008047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6504 0.6341 0.4436 0.2345 0.9871 0.9915 0.6507 0.974 0.9826 0.453 ] Network output: [ -0.0279 0.1096 0.9361 0.0009631 -0.0004324 1.014 0.0007258 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6489 0.6463 0.4427 0.2196 0.9857 0.9907 0.6489 0.97 0.9802 0.4443 ] Network output: [ 0.00881 0.9605 0.02332 -0.0001887 8.471e-05 0.9978 -0.0001422 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009557 Epoch 2830 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01703 0.9983 0.9988 -5.701e-05 2.559e-05 -0.03141 -4.297e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02176 -0.005409 0.0217 0.02051 0.9419 0.9509 0.04176 0.8888 0.9069 0.1045 ] Network output: [ 0.9917 0.02313 -8.152e-05 -0.0002781 0.0001248 -0.007501 -0.0002096 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.648 0.1053 0.1751 0.1756 0.9728 0.9875 0.7273 0.9031 0.9684 0.6381 ] Network output: [ -0.01203 0.9772 1.011 6.767e-06 -3.038e-06 0.03604 5.099e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04507 0.03268 0.04489 0.02425 0.986 0.9901 0.04593 0.9712 0.9812 0.05384 ] Network output: [ 0.03649 -0.1562 1.084 -0.001513 0.0006794 0.9926 -0.001141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7234 0.6123 0.5545 0.3104 0.976 0.9893 0.7259 0.9129 0.9728 0.6302 ] Network output: [ -0.01412 0.08766 0.9468 0.001068 -0.0004793 0.9982 0.0008046 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6503 0.6341 0.4435 0.2346 0.9871 0.9915 0.6507 0.974 0.9826 0.4529 ] Network output: [ -0.0279 0.1097 0.9359 0.0009633 -0.0004325 1.014 0.000726 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6489 0.6463 0.4425 0.2196 0.9857 0.9907 0.6489 0.97 0.9802 0.4442 ] Network output: [ 0.008841 0.9603 0.02343 -0.000188 8.438e-05 0.9978 -0.0001417 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009571 Epoch 2831 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.017 0.9984 0.9988 -5.704e-05 2.561e-05 -0.03138 -4.299e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02176 -0.005411 0.02172 0.02051 0.9419 0.9509 0.04175 0.8888 0.9069 0.1044 ] Network output: [ 0.9917 0.02311 -2.881e-05 -0.0002798 0.0001256 -0.007555 -0.0002109 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.648 0.1051 0.1753 0.1757 0.9728 0.9875 0.7273 0.9031 0.9684 0.6379 ] Network output: [ -0.01202 0.9772 1.011 7.337e-06 -3.294e-06 0.03603 5.53e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04506 0.03266 0.04487 0.02425 0.986 0.9901 0.04591 0.9712 0.9812 0.0538 ] Network output: [ 0.03648 -0.1563 1.085 -0.001513 0.0006793 0.9926 -0.00114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7235 0.6122 0.5545 0.3106 0.976 0.9893 0.7259 0.9129 0.9728 0.63 ] Network output: [ -0.01414 0.08771 0.9468 0.001067 -0.0004792 0.9982 0.0008045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6503 0.6341 0.4433 0.2347 0.9871 0.9915 0.6507 0.974 0.9826 0.4527 ] Network output: [ -0.0279 0.1098 0.9358 0.0009636 -0.0004326 1.014 0.0007262 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6489 0.6463 0.4423 0.2197 0.9857 0.9907 0.6489 0.97 0.9802 0.444 ] Network output: [ 0.008872 0.9601 0.02355 -0.0001872 8.405e-05 0.9978 -0.0001411 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009585 Epoch 2832 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01697 0.9984 0.9988 -5.707e-05 2.562e-05 -0.03135 -4.301e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02176 -0.005413 0.02174 0.02051 0.9419 0.9509 0.04174 0.8888 0.9069 0.1044 ] Network output: [ 0.9917 0.02308 2.395e-05 -0.0002816 0.0001264 -0.007611 -0.0002122 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6481 0.1049 0.1756 0.1757 0.9728 0.9875 0.7274 0.9031 0.9684 0.6377 ] Network output: [ -0.01201 0.9772 1.011 7.91e-06 -3.551e-06 0.03602 5.961e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04504 0.03264 0.04484 0.02425 0.986 0.9901 0.04589 0.9712 0.9812 0.05375 ] Network output: [ 0.03648 -0.1563 1.085 -0.001513 0.0006792 0.9926 -0.00114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7235 0.6121 0.5544 0.3107 0.976 0.9893 0.7259 0.9129 0.9728 0.6298 ] Network output: [ -0.01416 0.08776 0.9467 0.001067 -0.0004792 0.9982 0.0008044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6503 0.634 0.4432 0.2347 0.9871 0.9915 0.6507 0.974 0.9826 0.4526 ] Network output: [ -0.0279 0.11 0.9356 0.0009639 -0.0004327 1.014 0.0007264 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6489 0.6463 0.4422 0.2197 0.9857 0.9907 0.6489 0.97 0.9802 0.4438 ] Network output: [ 0.008903 0.9599 0.02366 -0.0001865 8.371e-05 0.9978 -0.0001405 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0096 Epoch 2833 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01694 0.9984 0.9988 -5.709e-05 2.563e-05 -0.03131 -4.303e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02176 -0.005415 0.02176 0.02051 0.9419 0.9509 0.04172 0.8888 0.9069 0.1043 ] Network output: [ 0.9917 0.02306 7.674e-05 -0.0002834 0.0001272 -0.007666 -0.0002136 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6482 0.1047 0.1758 0.1758 0.9728 0.9875 0.7274 0.9031 0.9684 0.6376 ] Network output: [ -0.012 0.9773 1.011 8.485e-06 -3.809e-06 0.03601 6.395e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04502 0.03262 0.04481 0.02425 0.986 0.9901 0.04587 0.9712 0.9812 0.05371 ] Network output: [ 0.03648 -0.1564 1.085 -0.001512 0.000679 0.9926 -0.00114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7235 0.612 0.5544 0.3109 0.976 0.9893 0.726 0.9129 0.9728 0.6296 ] Network output: [ -0.01419 0.08782 0.9467 0.001067 -0.0004791 0.9982 0.0008043 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6503 0.634 0.4431 0.2348 0.9871 0.9915 0.6507 0.974 0.9826 0.4524 ] Network output: [ -0.02789 0.1101 0.9354 0.0009642 -0.0004329 1.014 0.0007267 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6488 0.6462 0.442 0.2197 0.9857 0.9907 0.6489 0.97 0.9802 0.4437 ] Network output: [ 0.008936 0.9598 0.02378 -0.0001857 8.336e-05 0.9978 -0.0001399 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009616 Epoch 2834 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01691 0.9985 0.9988 -5.712e-05 2.564e-05 -0.03128 -4.305e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02176 -0.005417 0.02178 0.02051 0.9419 0.9509 0.04171 0.8888 0.907 0.1042 ] Network output: [ 0.9917 0.02304 0.0001296 -0.0002852 0.000128 -0.007722 -0.000215 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6482 0.1045 0.1761 0.1759 0.9728 0.9875 0.7274 0.9031 0.9684 0.6374 ] Network output: [ -0.01199 0.9773 1.011 9.063e-06 -4.069e-06 0.03601 6.83e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.045 0.0326 0.04478 0.02424 0.986 0.9901 0.04585 0.9712 0.9812 0.05366 ] Network output: [ 0.03648 -0.1564 1.085 -0.001512 0.0006789 0.9926 -0.00114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7235 0.612 0.5543 0.3111 0.976 0.9893 0.726 0.9129 0.9728 0.6295 ] Network output: [ -0.01421 0.08788 0.9467 0.001067 -0.0004791 0.9982 0.0008042 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6503 0.634 0.4429 0.2349 0.9871 0.9915 0.6507 0.974 0.9826 0.4523 ] Network output: [ -0.02789 0.1102 0.9353 0.0009645 -0.000433 1.014 0.0007269 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6488 0.6462 0.4419 0.2198 0.9857 0.9907 0.6489 0.97 0.9802 0.4435 ] Network output: [ 0.008969 0.9596 0.0239 -0.0001849 8.301e-05 0.9978 -0.0001394 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009632 Epoch 2835 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01689 0.9985 0.9988 -5.715e-05 2.566e-05 -0.03125 -4.307e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02176 -0.005419 0.0218 0.02051 0.9419 0.951 0.0417 0.8888 0.907 0.1042 ] Network output: [ 0.9917 0.02302 0.0001824 -0.0002871 0.0001289 -0.007779 -0.0002163 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6483 0.1043 0.1764 0.176 0.9728 0.9875 0.7275 0.9031 0.9684 0.6373 ] Network output: [ -0.01198 0.9774 1.011 9.642e-06 -4.329e-06 0.036 7.267e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04499 0.03258 0.04475 0.02424 0.986 0.9901 0.04584 0.9712 0.9812 0.05361 ] Network output: [ 0.03648 -0.1565 1.085 -0.001512 0.0006787 0.9926 -0.001139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7236 0.6119 0.5543 0.3113 0.976 0.9893 0.726 0.9129 0.9728 0.6293 ] Network output: [ -0.01423 0.08794 0.9467 0.001067 -0.000479 0.9982 0.0008041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6503 0.6339 0.4428 0.235 0.9871 0.9915 0.6506 0.974 0.9826 0.4521 ] Network output: [ -0.02789 0.1104 0.9351 0.0009648 -0.0004331 1.014 0.0007271 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6488 0.6462 0.4417 0.2198 0.9857 0.9907 0.6489 0.97 0.9802 0.4433 ] Network output: [ 0.009002 0.9594 0.02403 -0.0001841 8.266e-05 0.9978 -0.0001388 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009648 Epoch 2836 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01686 0.9985 0.9987 -5.718e-05 2.567e-05 -0.03122 -4.309e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02176 -0.005421 0.02182 0.02051 0.9419 0.951 0.04169 0.8888 0.907 0.1041 ] Network output: [ 0.9917 0.023 0.0002353 -0.0002889 0.0001297 -0.007836 -0.0002177 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6483 0.1041 0.1766 0.1761 0.9728 0.9875 0.7275 0.9031 0.9684 0.6371 ] Network output: [ -0.01197 0.9774 1.011 1.022e-05 -4.59e-06 0.03599 7.705e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04497 0.03256 0.04472 0.02424 0.986 0.9901 0.04582 0.9712 0.9813 0.05357 ] Network output: [ 0.03648 -0.1566 1.085 -0.001511 0.0006786 0.9926 -0.001139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7236 0.6118 0.5542 0.3114 0.976 0.9893 0.7261 0.9129 0.9728 0.6291 ] Network output: [ -0.01426 0.088 0.9466 0.001067 -0.000479 0.9982 0.0008041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6502 0.6339 0.4426 0.2351 0.9871 0.9915 0.6506 0.974 0.9826 0.452 ] Network output: [ -0.02789 0.1105 0.935 0.0009651 -0.0004332 1.014 0.0007273 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6488 0.6462 0.4415 0.2199 0.9857 0.9907 0.6489 0.97 0.9802 0.4432 ] Network output: [ 0.009037 0.9592 0.02415 -0.0001833 8.23e-05 0.9978 -0.0001382 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009664 Epoch 2837 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01683 0.9986 0.9987 -5.721e-05 2.569e-05 -0.03118 -4.312e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02176 -0.005423 0.02184 0.02051 0.9419 0.951 0.04167 0.8888 0.907 0.104 ] Network output: [ 0.9917 0.02298 0.0002883 -0.0002908 0.0001305 -0.007893 -0.0002191 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6484 0.1039 0.1769 0.1762 0.9728 0.9875 0.7275 0.9031 0.9684 0.6369 ] Network output: [ -0.01196 0.9774 1.011 1.081e-05 -4.852e-06 0.03598 8.145e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04495 0.03254 0.04469 0.02424 0.986 0.9901 0.0458 0.9712 0.9813 0.05352 ] Network output: [ 0.03648 -0.1566 1.085 -0.001511 0.0006784 0.9926 -0.001139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7236 0.6117 0.5541 0.3116 0.976 0.9893 0.7261 0.9129 0.9728 0.6289 ] Network output: [ -0.01428 0.08807 0.9466 0.001067 -0.0004789 0.9982 0.000804 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6502 0.6339 0.4425 0.2351 0.9871 0.9915 0.6506 0.974 0.9826 0.4518 ] Network output: [ -0.02789 0.1107 0.9348 0.0009653 -0.0004334 1.014 0.0007275 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6488 0.6462 0.4414 0.2199 0.9857 0.9907 0.6489 0.97 0.9802 0.443 ] Network output: [ 0.009071 0.959 0.02428 -0.0001825 8.194e-05 0.9979 -0.0001375 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009681 Epoch 2838 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0168 0.9986 0.9987 -5.725e-05 2.57e-05 -0.03115 -4.314e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02175 -0.005425 0.02186 0.02051 0.9419 0.951 0.04166 0.8888 0.907 0.104 ] Network output: [ 0.9917 0.02296 0.0003412 -0.0002926 0.0001314 -0.007951 -0.0002205 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6485 0.1037 0.1771 0.1763 0.9728 0.9875 0.7275 0.9031 0.9684 0.6368 ] Network output: [ -0.01195 0.9775 1.01 1.139e-05 -5.115e-06 0.03598 8.587e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04493 0.03252 0.04466 0.02424 0.986 0.9901 0.04578 0.9712 0.9813 0.05348 ] Network output: [ 0.03648 -0.1567 1.085 -0.001511 0.0006783 0.9926 -0.001139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7237 0.6116 0.5541 0.3118 0.976 0.9893 0.7261 0.9129 0.9728 0.6287 ] Network output: [ -0.01431 0.08814 0.9466 0.001067 -0.0004789 0.9982 0.0008039 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6502 0.6338 0.4424 0.2352 0.9871 0.9915 0.6506 0.974 0.9826 0.4516 ] Network output: [ -0.02789 0.1108 0.9346 0.0009656 -0.0004335 1.014 0.0007277 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6488 0.6462 0.4412 0.2199 0.9857 0.9907 0.6489 0.97 0.9802 0.4428 ] Network output: [ 0.009107 0.9588 0.02442 -0.0001817 8.157e-05 0.9979 -0.0001369 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009699 Epoch 2839 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01677 0.9986 0.9987 -5.728e-05 2.572e-05 -0.03112 -4.317e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02175 -0.005427 0.02188 0.02051 0.9419 0.951 0.04165 0.8888 0.907 0.1039 ] Network output: [ 0.9917 0.02294 0.0003942 -0.0002945 0.0001322 -0.008009 -0.000222 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6485 0.1035 0.1774 0.1764 0.9728 0.9875 0.7276 0.9031 0.9684 0.6366 ] Network output: [ -0.01194 0.9775 1.01 1.198e-05 -5.379e-06 0.03597 9.03e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04491 0.0325 0.04463 0.02424 0.986 0.9901 0.04576 0.9712 0.9813 0.05343 ] Network output: [ 0.03648 -0.1568 1.085 -0.00151 0.0006781 0.9926 -0.001138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7237 0.6115 0.554 0.312 0.976 0.9893 0.7262 0.9129 0.9728 0.6285 ] Network output: [ -0.01433 0.08821 0.9466 0.001067 -0.0004788 0.9982 0.0008038 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6502 0.6338 0.4422 0.2353 0.9871 0.9915 0.6506 0.974 0.9826 0.4515 ] Network output: [ -0.02789 0.111 0.9345 0.0009659 -0.0004336 1.014 0.0007279 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6488 0.6462 0.441 0.22 0.9857 0.9907 0.6489 0.97 0.9802 0.4427 ] Network output: [ 0.009143 0.9586 0.02455 -0.0001808 8.119e-05 0.9979 -0.0001363 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009716 Epoch 2840 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01674 0.9987 0.9987 -5.732e-05 2.573e-05 -0.03108 -4.319e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02175 -0.005429 0.02191 0.02051 0.9419 0.951 0.04164 0.8888 0.907 0.1039 ] Network output: [ 0.9917 0.02293 0.0004472 -0.0002965 0.0001331 -0.008067 -0.0002234 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6486 0.1032 0.1777 0.1765 0.9728 0.9875 0.7276 0.9031 0.9684 0.6364 ] Network output: [ -0.01192 0.9776 1.01 1.257e-05 -5.644e-06 0.03596 9.475e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0449 0.03248 0.0446 0.02424 0.986 0.9901 0.04574 0.9712 0.9813 0.05338 ] Network output: [ 0.03648 -0.1568 1.085 -0.00151 0.0006779 0.9926 -0.001138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7237 0.6114 0.554 0.3122 0.976 0.9893 0.7262 0.9129 0.9728 0.6284 ] Network output: [ -0.01436 0.08828 0.9465 0.001067 -0.0004788 0.9982 0.0008038 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6502 0.6337 0.4421 0.2354 0.9871 0.9915 0.6505 0.974 0.9826 0.4513 ] Network output: [ -0.02789 0.1111 0.9343 0.0009662 -0.0004338 1.014 0.0007281 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6488 0.6462 0.4409 0.22 0.9857 0.9907 0.6489 0.97 0.9802 0.4425 ] Network output: [ 0.00918 0.9583 0.02469 -0.00018 8.081e-05 0.9979 -0.0001357 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009735 Epoch 2841 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01671 0.9987 0.9987 -5.735e-05 2.575e-05 -0.03105 -4.322e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02175 -0.005431 0.02193 0.02051 0.9419 0.951 0.04162 0.8888 0.907 0.1038 ] Network output: [ 0.9918 0.02291 0.0005002 -0.0002984 0.000134 -0.008126 -0.0002249 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6487 0.103 0.178 0.1766 0.9728 0.9875 0.7276 0.9031 0.9684 0.6363 ] Network output: [ -0.01191 0.9776 1.01 1.316e-05 -5.91e-06 0.03596 9.921e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04488 0.03246 0.04457 0.02424 0.986 0.9901 0.04572 0.9712 0.9813 0.05333 ] Network output: [ 0.03648 -0.1569 1.085 -0.00151 0.0006778 0.9926 -0.001138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7238 0.6113 0.5539 0.3124 0.976 0.9893 0.7262 0.9129 0.9728 0.6282 ] Network output: [ -0.01439 0.08836 0.9465 0.001066 -0.0004787 0.9983 0.0008037 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6501 0.6337 0.4419 0.2355 0.9871 0.9915 0.6505 0.974 0.9826 0.4511 ] Network output: [ -0.0279 0.1113 0.9341 0.0009665 -0.0004339 1.014 0.0007284 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6488 0.6462 0.4407 0.2201 0.9857 0.9907 0.6488 0.97 0.9802 0.4423 ] Network output: [ 0.009218 0.9581 0.02483 -0.0001791 8.042e-05 0.9979 -0.000135 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009753 Epoch 2842 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01668 0.9987 0.9987 -5.739e-05 2.576e-05 -0.03101 -4.325e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02175 -0.005433 0.02195 0.02051 0.9419 0.951 0.04161 0.8888 0.907 0.1037 ] Network output: [ 0.9918 0.0229 0.0005532 -0.0003003 0.0001348 -0.008186 -0.0002263 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6487 0.1028 0.1782 0.1767 0.9728 0.9875 0.7277 0.9031 0.9684 0.6361 ] Network output: [ -0.0119 0.9776 1.01 1.376e-05 -6.177e-06 0.03595 1.037e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04486 0.03244 0.04454 0.02424 0.986 0.9901 0.0457 0.9712 0.9813 0.05329 ] Network output: [ 0.03648 -0.157 1.085 -0.001509 0.0006776 0.9926 -0.001138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7238 0.6112 0.5538 0.3126 0.976 0.9893 0.7263 0.9129 0.9728 0.628 ] Network output: [ -0.01441 0.08844 0.9465 0.001066 -0.0004787 0.9983 0.0008036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6501 0.6337 0.4418 0.2356 0.9871 0.9915 0.6505 0.974 0.9826 0.451 ] Network output: [ -0.0279 0.1114 0.934 0.0009667 -0.000434 1.014 0.0007286 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6488 0.6461 0.4405 0.2201 0.9857 0.9907 0.6488 0.97 0.9802 0.4421 ] Network output: [ 0.009256 0.9579 0.02497 -0.0001783 8.003e-05 0.9979 -0.0001343 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009772 Epoch 2843 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01665 0.9988 0.9987 -5.743e-05 2.578e-05 -0.03098 -4.328e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02175 -0.005436 0.02197 0.02051 0.9419 0.951 0.0416 0.8888 0.907 0.1037 ] Network output: [ 0.9918 0.02288 0.0006063 -0.0003023 0.0001357 -0.008246 -0.0002278 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6488 0.1026 0.1785 0.1768 0.9728 0.9875 0.7277 0.9031 0.9684 0.6359 ] Network output: [ -0.01189 0.9777 1.01 1.436e-05 -6.445e-06 0.03594 1.082e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04484 0.03242 0.04451 0.02424 0.986 0.9901 0.04568 0.9712 0.9813 0.05324 ] Network output: [ 0.03649 -0.1571 1.085 -0.001509 0.0006775 0.9926 -0.001137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7238 0.6112 0.5538 0.3128 0.976 0.9893 0.7263 0.9129 0.9728 0.6278 ] Network output: [ -0.01444 0.08853 0.9464 0.001066 -0.0004787 0.9983 0.0008035 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6501 0.6336 0.4416 0.2357 0.9871 0.9915 0.6505 0.974 0.9826 0.4508 ] Network output: [ -0.0279 0.1116 0.9338 0.000967 -0.0004341 1.014 0.0007288 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6487 0.6461 0.4403 0.2202 0.9857 0.9907 0.6488 0.97 0.9802 0.4419 ] Network output: [ 0.009295 0.9577 0.02512 -0.0001774 7.963e-05 0.9979 -0.0001337 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009792 Epoch 2844 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01662 0.9988 0.9987 -5.747e-05 2.58e-05 -0.03094 -4.331e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02175 -0.005438 0.02199 0.02051 0.942 0.951 0.04159 0.8888 0.907 0.1036 ] Network output: [ 0.9918 0.02287 0.0006593 -0.0003043 0.0001366 -0.008306 -0.0002293 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6489 0.1024 0.1788 0.1769 0.9728 0.9875 0.7277 0.9031 0.9684 0.6358 ] Network output: [ -0.01188 0.9777 1.01 1.495e-05 -6.713e-06 0.03593 1.127e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04482 0.0324 0.04448 0.02424 0.986 0.9901 0.04567 0.9712 0.9813 0.05319 ] Network output: [ 0.03649 -0.1571 1.085 -0.001509 0.0006773 0.9926 -0.001137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7239 0.6111 0.5537 0.313 0.976 0.9893 0.7263 0.9129 0.9728 0.6276 ] Network output: [ -0.01447 0.08861 0.9464 0.001066 -0.0004786 0.9983 0.0008035 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6501 0.6336 0.4414 0.2358 0.9871 0.9915 0.6504 0.974 0.9826 0.4506 ] Network output: [ -0.0279 0.1118 0.9336 0.0009673 -0.0004343 1.014 0.000729 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6487 0.6461 0.4401 0.2203 0.9857 0.9907 0.6488 0.97 0.9802 0.4418 ] Network output: [ 0.009335 0.9574 0.02527 -0.0001765 7.922e-05 0.9979 -0.000133 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009812 Epoch 2845 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01659 0.9988 0.9987 -5.751e-05 2.582e-05 -0.03091 -4.334e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02175 -0.00544 0.02201 0.02051 0.942 0.951 0.04157 0.8888 0.907 0.1035 ] Network output: [ 0.9918 0.02286 0.0007124 -0.0003063 0.0001375 -0.008367 -0.0002308 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6489 0.1022 0.179 0.177 0.9728 0.9875 0.7278 0.9031 0.9684 0.6356 ] Network output: [ -0.01187 0.9778 1.01 1.555e-05 -6.983e-06 0.03593 1.172e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0448 0.03237 0.04445 0.02424 0.986 0.9901 0.04565 0.9712 0.9813 0.05314 ] Network output: [ 0.03649 -0.1572 1.086 -0.001508 0.0006771 0.9926 -0.001137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7239 0.611 0.5536 0.3133 0.976 0.9893 0.7264 0.9129 0.9728 0.6274 ] Network output: [ -0.01449 0.0887 0.9464 0.001066 -0.0004786 0.9983 0.0008034 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.65 0.6335 0.4413 0.2359 0.9871 0.9915 0.6504 0.974 0.9826 0.4504 ] Network output: [ -0.02791 0.1119 0.9335 0.0009676 -0.0004344 1.014 0.0007292 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6487 0.6461 0.44 0.2203 0.9857 0.9907 0.6488 0.97 0.9802 0.4416 ] Network output: [ 0.009376 0.9572 0.02542 -0.0001755 7.881e-05 0.9979 -0.0001323 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009832 Epoch 2846 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01656 0.9989 0.9987 -5.756e-05 2.584e-05 -0.03087 -4.338e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02174 -0.005443 0.02203 0.02051 0.942 0.951 0.04156 0.8888 0.907 0.1035 ] Network output: [ 0.9918 0.02285 0.0007654 -0.0003083 0.0001384 -0.008428 -0.0002324 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.649 0.1019 0.1793 0.1772 0.9728 0.9875 0.7278 0.9031 0.9684 0.6354 ] Network output: [ -0.01185 0.9778 1.01 1.616e-05 -7.253e-06 0.03592 1.218e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04479 0.03235 0.04442 0.02424 0.986 0.9901 0.04563 0.9712 0.9813 0.05309 ] Network output: [ 0.0365 -0.1573 1.086 -0.001508 0.000677 0.9926 -0.001136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.724 0.6109 0.5536 0.3135 0.976 0.9893 0.7264 0.9129 0.9728 0.6272 ] Network output: [ -0.01452 0.0888 0.9463 0.001066 -0.0004785 0.9983 0.0008033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.65 0.6335 0.4411 0.236 0.9871 0.9915 0.6504 0.974 0.9826 0.4503 ] Network output: [ -0.02791 0.1121 0.9333 0.0009679 -0.0004345 1.014 0.0007294 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6487 0.6461 0.4398 0.2204 0.9857 0.9907 0.6488 0.97 0.9802 0.4414 ] Network output: [ 0.009417 0.9569 0.02558 -0.0001746 7.839e-05 0.9979 -0.0001316 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009853 Epoch 2847 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01653 0.9989 0.9987 -5.76e-05 2.586e-05 -0.03084 -4.341e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02174 -0.005445 0.02205 0.02051 0.942 0.951 0.04155 0.8888 0.907 0.1034 ] Network output: [ 0.9918 0.02284 0.0008185 -0.0003104 0.0001393 -0.00849 -0.0002339 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6491 0.1017 0.1796 0.1773 0.9728 0.9875 0.7278 0.9031 0.9684 0.6352 ] Network output: [ -0.01184 0.9779 1.01 1.676e-05 -7.524e-06 0.03591 1.263e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04477 0.03233 0.04439 0.02424 0.986 0.9901 0.04561 0.9713 0.9813 0.05304 ] Network output: [ 0.0365 -0.1574 1.086 -0.001508 0.0006768 0.9926 -0.001136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.724 0.6108 0.5535 0.3137 0.976 0.9893 0.7264 0.9128 0.9728 0.627 ] Network output: [ -0.01455 0.08889 0.9463 0.001066 -0.0004785 0.9983 0.0008033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.65 0.6334 0.441 0.2361 0.9871 0.9915 0.6504 0.974 0.9826 0.4501 ] Network output: [ -0.02792 0.1123 0.9331 0.0009681 -0.0004346 1.014 0.0007296 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6487 0.646 0.4396 0.2204 0.9857 0.9907 0.6487 0.9699 0.9802 0.4412 ] Network output: [ 0.00946 0.9567 0.02574 -0.0001737 7.796e-05 0.9979 -0.0001309 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009875 Epoch 2848 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0165 0.9989 0.9986 -5.765e-05 2.588e-05 -0.0308 -4.345e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02174 -0.005447 0.02208 0.02051 0.942 0.951 0.04153 0.8888 0.907 0.1034 ] Network output: [ 0.9918 0.02283 0.0008715 -0.0003125 0.0001403 -0.008552 -0.0002355 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6491 0.1015 0.1799 0.1774 0.9729 0.9875 0.7279 0.9031 0.9684 0.6351 ] Network output: [ -0.01183 0.9779 1.01 1.737e-05 -7.797e-06 0.03591 1.309e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04475 0.03231 0.04436 0.02424 0.986 0.9901 0.04559 0.9713 0.9813 0.05299 ] Network output: [ 0.0365 -0.1575 1.086 -0.001507 0.0006766 0.9926 -0.001136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.724 0.6107 0.5534 0.3139 0.976 0.9893 0.7265 0.9128 0.9728 0.6268 ] Network output: [ -0.01458 0.08899 0.9462 0.001066 -0.0004785 0.9983 0.0008032 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6499 0.6334 0.4408 0.2363 0.9871 0.9915 0.6503 0.974 0.9826 0.4499 ] Network output: [ -0.02792 0.1124 0.9329 0.0009684 -0.0004348 1.014 0.0007298 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6487 0.646 0.4394 0.2205 0.9857 0.9907 0.6487 0.9699 0.9802 0.441 ] Network output: [ 0.009503 0.9564 0.0259 -0.0001727 7.753e-05 0.998 -0.0001301 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009896 Epoch 2849 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01647 0.999 0.9986 -5.77e-05 2.59e-05 -0.03077 -4.348e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02174 -0.00545 0.0221 0.02051 0.942 0.951 0.04152 0.8888 0.907 0.1033 ] Network output: [ 0.9918 0.02283 0.0009246 -0.0003145 0.0001412 -0.008614 -0.0002371 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6492 0.1012 0.1802 0.1775 0.9729 0.9875 0.7279 0.9031 0.9684 0.6349 ] Network output: [ -0.01182 0.9779 1.01 1.797e-05 -8.069e-06 0.0359 1.355e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04473 0.03229 0.04432 0.02424 0.986 0.9901 0.04557 0.9713 0.9813 0.05294 ] Network output: [ 0.03651 -0.1576 1.086 -0.001507 0.0006765 0.9926 -0.001136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7241 0.6106 0.5534 0.3142 0.976 0.9893 0.7265 0.9128 0.9728 0.6266 ] Network output: [ -0.0146 0.0891 0.9462 0.001066 -0.0004784 0.9983 0.0008032 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6499 0.6333 0.4406 0.2364 0.9871 0.9915 0.6503 0.974 0.9826 0.4497 ] Network output: [ -0.02793 0.1126 0.9327 0.0009687 -0.0004349 1.014 0.00073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6486 0.646 0.4392 0.2206 0.9857 0.9907 0.6487 0.9699 0.9802 0.4408 ] Network output: [ 0.009547 0.9562 0.02607 -0.0001717 7.709e-05 0.998 -0.0001294 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009919 Epoch 2850 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01644 0.999 0.9986 -5.775e-05 2.593e-05 -0.03073 -4.352e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02174 -0.005452 0.02212 0.02051 0.942 0.951 0.04151 0.8888 0.907 0.1032 ] Network output: [ 0.9918 0.02282 0.0009776 -0.0003167 0.0001422 -0.008677 -0.0002386 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6493 0.101 0.1805 0.1776 0.9729 0.9875 0.7279 0.9031 0.9684 0.6347 ] Network output: [ -0.0118 0.978 1.01 1.858e-05 -8.343e-06 0.03589 1.401e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04471 0.03227 0.04429 0.02424 0.986 0.9901 0.04555 0.9713 0.9813 0.05289 ] Network output: [ 0.03651 -0.1577 1.086 -0.001506 0.0006763 0.9926 -0.001135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7241 0.6105 0.5533 0.3144 0.976 0.9893 0.7265 0.9128 0.9728 0.6264 ] Network output: [ -0.01463 0.0892 0.9461 0.001066 -0.0004784 0.9983 0.0008031 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6499 0.6333 0.4405 0.2365 0.9871 0.9915 0.6503 0.974 0.9826 0.4495 ] Network output: [ -0.02793 0.1128 0.9326 0.000969 -0.000435 1.014 0.0007302 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6486 0.646 0.439 0.2207 0.9857 0.9907 0.6487 0.9699 0.9802 0.4406 ] Network output: [ 0.009591 0.9559 0.02624 -0.0001707 7.664e-05 0.998 -0.0001286 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009941 Epoch 2851 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01641 0.999 0.9986 -5.781e-05 2.595e-05 -0.03069 -4.356e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02174 -0.005455 0.02214 0.02052 0.942 0.951 0.0415 0.8888 0.907 0.1032 ] Network output: [ 0.9918 0.02282 0.001031 -0.0003188 0.0001431 -0.00874 -0.0002402 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6493 0.1008 0.1807 0.1778 0.9729 0.9875 0.728 0.9031 0.9684 0.6345 ] Network output: [ -0.01179 0.978 1.01 1.919e-05 -8.617e-06 0.03589 1.447e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04469 0.03224 0.04426 0.02425 0.986 0.9901 0.04553 0.9713 0.9813 0.05284 ] Network output: [ 0.03652 -0.1578 1.086 -0.001506 0.0006761 0.9926 -0.001135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7241 0.6104 0.5532 0.3147 0.976 0.9893 0.7266 0.9128 0.9728 0.6262 ] Network output: [ -0.01466 0.08931 0.9461 0.001066 -0.0004784 0.9983 0.000803 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6498 0.6332 0.4403 0.2366 0.9871 0.9915 0.6502 0.974 0.9826 0.4493 ] Network output: [ -0.02794 0.113 0.9324 0.0009692 -0.0004351 1.014 0.0007305 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6486 0.6459 0.4388 0.2207 0.9857 0.9907 0.6487 0.9699 0.9802 0.4404 ] Network output: [ 0.009637 0.9556 0.02642 -0.0001697 7.618e-05 0.998 -0.0001279 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009965 Epoch 2852 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01637 0.9991 0.9986 -5.786e-05 2.598e-05 -0.03066 -4.361e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02174 -0.005457 0.02216 0.02052 0.942 0.951 0.04148 0.8888 0.907 0.1031 ] Network output: [ 0.9918 0.02281 0.001084 -0.0003209 0.0001441 -0.008804 -0.0002419 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6494 0.1005 0.181 0.1779 0.9729 0.9875 0.728 0.9031 0.9684 0.6343 ] Network output: [ -0.01178 0.9781 1.01 1.981e-05 -8.892e-06 0.03588 1.493e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04467 0.03222 0.04423 0.02425 0.986 0.9901 0.04551 0.9713 0.9813 0.05279 ] Network output: [ 0.03653 -0.1579 1.086 -0.001506 0.0006759 0.9926 -0.001135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7242 0.6103 0.5532 0.3149 0.976 0.9893 0.7266 0.9128 0.9728 0.626 ] Network output: [ -0.01469 0.08943 0.946 0.001065 -0.0004783 0.9983 0.000803 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6498 0.6332 0.4401 0.2367 0.9871 0.9915 0.6502 0.974 0.9826 0.4491 ] Network output: [ -0.02795 0.1132 0.9322 0.0009695 -0.0004353 1.014 0.0007307 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6486 0.6459 0.4386 0.2208 0.9857 0.9907 0.6486 0.9699 0.9802 0.4402 ] Network output: [ 0.009684 0.9553 0.0266 -0.0001687 7.571e-05 0.998 -0.0001271 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009988 Epoch 2853 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01634 0.9991 0.9986 -5.792e-05 2.6e-05 -0.03062 -4.365e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02173 -0.00546 0.02218 0.02052 0.942 0.951 0.04147 0.8888 0.907 0.103 ] Network output: [ 0.9918 0.02281 0.001136 -0.0003231 0.000145 -0.008868 -0.0002435 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6495 0.1003 0.1813 0.178 0.9729 0.9875 0.728 0.9031 0.9684 0.6341 ] Network output: [ -0.01177 0.9781 1.01 2.042e-05 -9.168e-06 0.03587 1.539e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04465 0.0322 0.04419 0.02425 0.986 0.9901 0.04549 0.9713 0.9813 0.05274 ] Network output: [ 0.03653 -0.158 1.086 -0.001505 0.0006758 0.9926 -0.001134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7242 0.6102 0.5531 0.3152 0.976 0.9893 0.7266 0.9128 0.9728 0.6258 ] Network output: [ -0.01472 0.08955 0.946 0.001065 -0.0004783 0.9983 0.0008029 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6498 0.6331 0.4399 0.2369 0.9871 0.9915 0.6502 0.974 0.9826 0.4489 ] Network output: [ -0.02795 0.1134 0.932 0.0009698 -0.0004354 1.014 0.0007309 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6486 0.6459 0.4384 0.2209 0.9857 0.9907 0.6486 0.9699 0.9802 0.44 ] Network output: [ 0.009731 0.9551 0.02678 -0.0001676 7.524e-05 0.998 -0.0001263 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01001 Epoch 2854 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01631 0.9991 0.9986 -5.798e-05 2.603e-05 -0.03058 -4.37e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02173 -0.005463 0.02221 0.02052 0.942 0.951 0.04146 0.8888 0.907 0.103 ] Network output: [ 0.9918 0.02281 0.001189 -0.0003253 0.000146 -0.008932 -0.0002451 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6495 0.1001 0.1816 0.1782 0.9729 0.9875 0.7281 0.9031 0.9684 0.634 ] Network output: [ -0.01175 0.9782 1.01 2.104e-05 -9.444e-06 0.03587 1.585e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04463 0.03218 0.04416 0.02425 0.986 0.9901 0.04547 0.9713 0.9813 0.05269 ] Network output: [ 0.03654 -0.1581 1.086 -0.001505 0.0006756 0.9926 -0.001134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7243 0.6101 0.553 0.3154 0.976 0.9893 0.7267 0.9128 0.9728 0.6256 ] Network output: [ -0.01475 0.08967 0.9459 0.001065 -0.0004783 0.9983 0.0008029 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6497 0.6331 0.4398 0.237 0.9871 0.9915 0.6501 0.974 0.9826 0.4487 ] Network output: [ -0.02796 0.1135 0.9318 0.0009701 -0.0004355 1.014 0.0007311 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6485 0.6459 0.4382 0.221 0.9857 0.9907 0.6486 0.9699 0.9802 0.4398 ] Network output: [ 0.00978 0.9548 0.02697 -0.0001665 7.476e-05 0.998 -0.0001255 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01004 Epoch 2855 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01628 0.9992 0.9986 -5.805e-05 2.606e-05 -0.03055 -4.375e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02173 -0.005465 0.02223 0.02052 0.942 0.951 0.04144 0.8888 0.907 0.1029 ] Network output: [ 0.9918 0.02281 0.001242 -0.0003275 0.000147 -0.008997 -0.0002468 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6496 0.09983 0.1819 0.1783 0.9729 0.9875 0.7281 0.9031 0.9684 0.6338 ] Network output: [ -0.01174 0.9782 1.01 2.165e-05 -9.721e-06 0.03586 1.632e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04461 0.03215 0.04413 0.02425 0.986 0.9901 0.04545 0.9713 0.9813 0.05264 ] Network output: [ 0.03655 -0.1582 1.086 -0.001504 0.0006754 0.9927 -0.001134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7243 0.61 0.553 0.3157 0.976 0.9893 0.7267 0.9128 0.9728 0.6254 ] Network output: [ -0.01478 0.08979 0.9459 0.001065 -0.0004782 0.9982 0.0008028 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6497 0.633 0.4396 0.2371 0.9871 0.9915 0.6501 0.974 0.9826 0.4485 ] Network output: [ -0.02797 0.1137 0.9317 0.0009703 -0.0004356 1.014 0.0007313 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6485 0.6458 0.438 0.2211 0.9857 0.9907 0.6486 0.9699 0.9802 0.4396 ] Network output: [ 0.009829 0.9545 0.02716 -0.0001654 7.426e-05 0.9981 -0.0001247 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01006 Epoch 2856 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01625 0.9992 0.9986 -5.812e-05 2.609e-05 -0.03051 -4.38e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02173 -0.005468 0.02225 0.02052 0.942 0.951 0.04143 0.8888 0.907 0.1028 ] Network output: [ 0.9918 0.02281 0.001295 -0.0003297 0.000148 -0.009062 -0.0002485 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6497 0.09958 0.1822 0.1785 0.9729 0.9875 0.7281 0.9031 0.9684 0.6336 ] Network output: [ -0.01173 0.9782 1.009 2.227e-05 -9.999e-06 0.03585 1.678e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04459 0.03213 0.04409 0.02426 0.986 0.9901 0.04542 0.9713 0.9813 0.05259 ] Network output: [ 0.03655 -0.1583 1.086 -0.001504 0.0006752 0.9927 -0.001134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7243 0.6099 0.5529 0.316 0.976 0.9893 0.7268 0.9128 0.9728 0.6251 ] Network output: [ -0.01481 0.08992 0.9458 0.001065 -0.0004782 0.9982 0.0008028 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6497 0.6329 0.4394 0.2373 0.9871 0.9915 0.65 0.974 0.9826 0.4483 ] Network output: [ -0.02798 0.1139 0.9315 0.0009706 -0.0004357 1.015 0.0007315 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6485 0.6458 0.4378 0.2212 0.9857 0.9907 0.6485 0.9699 0.9802 0.4394 ] Network output: [ 0.00988 0.9542 0.02735 -0.0001643 7.376e-05 0.9981 -0.0001238 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01009 Epoch 2857 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01622 0.9993 0.9985 -5.819e-05 2.612e-05 -0.03047 -4.385e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02173 -0.005471 0.02227 0.02053 0.942 0.951 0.04142 0.8888 0.907 0.1028 ] Network output: [ 0.9918 0.02282 0.001348 -0.000332 0.000149 -0.009127 -0.0002502 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6497 0.09933 0.1825 0.1786 0.9729 0.9875 0.7282 0.9031 0.9684 0.6334 ] Network output: [ -0.01171 0.9783 1.009 2.289e-05 -1.028e-05 0.03585 1.725e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04457 0.03211 0.04406 0.02426 0.986 0.9901 0.0454 0.9713 0.9813 0.05253 ] Network output: [ 0.03656 -0.1584 1.086 -0.001504 0.0006751 0.9927 -0.001133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7244 0.6098 0.5528 0.3162 0.976 0.9893 0.7268 0.9128 0.9728 0.6249 ] Network output: [ -0.01484 0.09006 0.9457 0.001065 -0.0004782 0.9982 0.0008027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6496 0.6329 0.4392 0.2374 0.9871 0.9915 0.65 0.974 0.9826 0.4481 ] Network output: [ -0.02799 0.1141 0.9313 0.0009709 -0.0004359 1.015 0.0007317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6484 0.6458 0.4376 0.2212 0.9857 0.9907 0.6485 0.9699 0.9802 0.4392 ] Network output: [ 0.009931 0.9538 0.02755 -0.0001632 7.325e-05 0.9981 -0.000123 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01011 Epoch 2858 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01618 0.9993 0.9985 -5.826e-05 2.616e-05 -0.03043 -4.391e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02173 -0.005474 0.02229 0.02053 0.942 0.951 0.0414 0.8888 0.907 0.1027 ] Network output: [ 0.9918 0.02282 0.0014 -0.0003342 0.00015 -0.009193 -0.0002519 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6498 0.09909 0.1828 0.1788 0.9729 0.9875 0.7282 0.9031 0.9684 0.6332 ] Network output: [ -0.0117 0.9783 1.009 2.351e-05 -1.056e-05 0.03584 1.772e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04455 0.03208 0.04403 0.02426 0.986 0.9901 0.04538 0.9713 0.9813 0.05248 ] Network output: [ 0.03657 -0.1585 1.087 -0.001503 0.0006749 0.9927 -0.001133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7244 0.6097 0.5527 0.3165 0.976 0.9893 0.7268 0.9128 0.9728 0.6247 ] Network output: [ -0.01488 0.09019 0.9457 0.001065 -0.0004782 0.9982 0.0008027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6496 0.6328 0.439 0.2375 0.9871 0.9915 0.65 0.974 0.9826 0.4479 ] Network output: [ -0.028 0.1143 0.9311 0.0009712 -0.000436 1.015 0.0007319 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6484 0.6457 0.4374 0.2213 0.9857 0.9907 0.6485 0.9699 0.9802 0.439 ] Network output: [ 0.009984 0.9535 0.02776 -0.000162 7.273e-05 0.9981 -0.0001221 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01014 Epoch 2859 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01615 0.9993 0.9985 -5.834e-05 2.619e-05 -0.03039 -4.397e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02173 -0.005476 0.02232 0.02053 0.942 0.951 0.04139 0.8888 0.907 0.1026 ] Network output: [ 0.9918 0.02283 0.001453 -0.0003365 0.0001511 -0.009259 -0.0002536 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6499 0.09883 0.1831 0.1789 0.9729 0.9875 0.7283 0.9031 0.9684 0.633 ] Network output: [ -0.01169 0.9784 1.009 2.413e-05 -1.083e-05 0.03584 1.819e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04453 0.03206 0.04399 0.02427 0.986 0.9901 0.04536 0.9713 0.9813 0.05243 ] Network output: [ 0.03658 -0.1586 1.087 -0.001503 0.0006747 0.9927 -0.001133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7245 0.6096 0.5527 0.3168 0.976 0.9893 0.7269 0.9128 0.9728 0.6245 ] Network output: [ -0.01491 0.09033 0.9456 0.001065 -0.0004781 0.9982 0.0008027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6495 0.6327 0.4388 0.2377 0.9871 0.9915 0.6499 0.974 0.9826 0.4477 ] Network output: [ -0.02801 0.1145 0.9309 0.0009714 -0.0004361 1.015 0.0007321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6484 0.6457 0.4372 0.2214 0.9857 0.9907 0.6485 0.9699 0.9802 0.4388 ] Network output: [ 0.01004 0.9532 0.02797 -0.0001608 7.22e-05 0.9981 -0.0001212 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01017 Epoch 2860 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01612 0.9994 0.9985 -5.842e-05 2.623e-05 -0.03036 -4.403e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02173 -0.005479 0.02234 0.02053 0.942 0.951 0.04138 0.8888 0.907 0.1025 ] Network output: [ 0.9918 0.02283 0.001505 -0.0003388 0.0001521 -0.009326 -0.0002553 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.65 0.09858 0.1834 0.1791 0.9729 0.9875 0.7283 0.9031 0.9684 0.6328 ] Network output: [ -0.01167 0.9784 1.009 2.476e-05 -1.111e-05 0.03583 1.866e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04451 0.03204 0.04396 0.02427 0.986 0.9901 0.04534 0.9713 0.9813 0.05237 ] Network output: [ 0.03659 -0.1587 1.087 -0.001503 0.0006745 0.9927 -0.001132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7245 0.6095 0.5526 0.3171 0.976 0.9893 0.7269 0.9128 0.9728 0.6243 ] Network output: [ -0.01494 0.09048 0.9455 0.001065 -0.0004781 0.9982 0.0008026 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6495 0.6327 0.4386 0.2378 0.9871 0.9915 0.6499 0.974 0.9826 0.4475 ] Network output: [ -0.02802 0.1147 0.9307 0.0009717 -0.0004362 1.015 0.0007323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6484 0.6457 0.437 0.2216 0.9857 0.9907 0.6484 0.9699 0.9802 0.4385 ] Network output: [ 0.01009 0.9528 0.02819 -0.0001596 7.166e-05 0.9981 -0.0001203 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0102 Epoch 2861 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01609 0.9994 0.9985 -5.851e-05 2.627e-05 -0.03032 -4.409e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02172 -0.005482 0.02236 0.02053 0.942 0.951 0.04136 0.8888 0.907 0.1025 ] Network output: [ 0.9918 0.02284 0.001557 -0.0003412 0.0001532 -0.009393 -0.0002571 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.65 0.09833 0.1837 0.1792 0.9729 0.9875 0.7283 0.903 0.9684 0.6326 ] Network output: [ -0.01166 0.9785 1.009 2.538e-05 -1.139e-05 0.03582 1.913e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04449 0.03201 0.04392 0.02427 0.986 0.9901 0.04532 0.9713 0.9813 0.05232 ] Network output: [ 0.0366 -0.1588 1.087 -0.001502 0.0006744 0.9928 -0.001132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7245 0.6093 0.5525 0.3174 0.976 0.9893 0.727 0.9128 0.9728 0.624 ] Network output: [ -0.01497 0.09063 0.9455 0.001065 -0.0004781 0.9982 0.0008026 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6494 0.6326 0.4384 0.238 0.9871 0.9915 0.6498 0.974 0.9826 0.4473 ] Network output: [ -0.02803 0.1149 0.9305 0.000972 -0.0004364 1.015 0.0007325 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6483 0.6456 0.4368 0.2217 0.9857 0.9907 0.6484 0.9699 0.9802 0.4383 ] Network output: [ 0.01015 0.9525 0.02841 -0.0001584 7.111e-05 0.9982 -0.0001194 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01023 Epoch 2862 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01605 0.9994 0.9985 -5.86e-05 2.631e-05 -0.03028 -4.416e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02172 -0.005485 0.02238 0.02054 0.942 0.951 0.04135 0.8888 0.907 0.1024 ] Network output: [ 0.9918 0.02285 0.00161 -0.0003435 0.0001542 -0.00946 -0.0002589 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6501 0.09807 0.184 0.1794 0.9729 0.9875 0.7284 0.903 0.9684 0.6324 ] Network output: [ -0.01164 0.9785 1.009 2.6e-05 -1.167e-05 0.03582 1.96e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04447 0.03199 0.04389 0.02428 0.986 0.9901 0.0453 0.9713 0.9813 0.05227 ] Network output: [ 0.03661 -0.1589 1.087 -0.001502 0.0006742 0.9928 -0.001132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7246 0.6092 0.5524 0.3177 0.976 0.9893 0.727 0.9128 0.9728 0.6238 ] Network output: [ -0.015 0.09079 0.9454 0.001065 -0.0004781 0.9982 0.0008026 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6494 0.6325 0.4382 0.2381 0.9871 0.9915 0.6498 0.974 0.9826 0.4471 ] Network output: [ -0.02804 0.1151 0.9303 0.0009723 -0.0004365 1.015 0.0007327 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6483 0.6456 0.4365 0.2218 0.9857 0.9907 0.6483 0.9699 0.9802 0.4381 ] Network output: [ 0.0102 0.9521 0.02863 -0.0001571 7.055e-05 0.9982 -0.0001184 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01026 Epoch 2863 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01602 0.9995 0.9985 -5.869e-05 2.635e-05 -0.03024 -4.423e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02172 -0.005488 0.02241 0.02054 0.942 0.951 0.04134 0.8888 0.907 0.1023 ] Network output: [ 0.9918 0.02286 0.001662 -0.0003459 0.0001553 -0.009527 -0.0002607 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6502 0.09781 0.1844 0.1795 0.9729 0.9875 0.7284 0.903 0.9684 0.6322 ] Network output: [ -0.01163 0.9786 1.009 2.663e-05 -1.195e-05 0.03581 2.007e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04445 0.03196 0.04385 0.02428 0.986 0.9901 0.04527 0.9713 0.9813 0.05221 ] Network output: [ 0.03662 -0.159 1.087 -0.001501 0.000674 0.9928 -0.001131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7246 0.6091 0.5523 0.318 0.976 0.9893 0.727 0.9128 0.9728 0.6236 ] Network output: [ -0.01504 0.09095 0.9453 0.001065 -0.0004781 0.9982 0.0008025 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6493 0.6325 0.438 0.2383 0.9871 0.9915 0.6497 0.974 0.9826 0.4469 ] Network output: [ -0.02806 0.1154 0.9301 0.0009725 -0.0004366 1.015 0.0007329 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6482 0.6455 0.4363 0.2219 0.9857 0.9907 0.6483 0.9699 0.9802 0.4379 ] Network output: [ 0.01026 0.9518 0.02886 -0.0001559 6.997e-05 0.9982 -0.0001175 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01029 Epoch 2864 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01599 0.9995 0.9985 -5.879e-05 2.639e-05 -0.0302 -4.431e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02172 -0.005491 0.02243 0.02054 0.942 0.951 0.04133 0.8888 0.907 0.1023 ] Network output: [ 0.9918 0.02287 0.001714 -0.0003483 0.0001564 -0.009595 -0.0002625 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6503 0.09755 0.1847 0.1797 0.9729 0.9875 0.7285 0.903 0.9684 0.632 ] Network output: [ -0.01162 0.9786 1.009 2.725e-05 -1.223e-05 0.0358 2.054e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04443 0.03194 0.04382 0.02428 0.986 0.9901 0.04525 0.9713 0.9813 0.05216 ] Network output: [ 0.03663 -0.1591 1.087 -0.001501 0.0006738 0.9928 -0.001131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7247 0.609 0.5523 0.3183 0.976 0.9893 0.7271 0.9128 0.9728 0.6234 ] Network output: [ -0.01507 0.09111 0.9452 0.001065 -0.000478 0.9981 0.0008025 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6493 0.6324 0.4378 0.2384 0.9871 0.9915 0.6497 0.974 0.9826 0.4466 ] Network output: [ -0.02807 0.1156 0.93 0.0009728 -0.0004367 1.015 0.0007331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6482 0.6455 0.4361 0.222 0.9857 0.9907 0.6483 0.9699 0.9802 0.4376 ] Network output: [ 0.01032 0.9514 0.0291 -0.0001546 6.939e-05 0.9982 -0.0001165 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01032 Epoch 2865 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01595 0.9996 0.9984 -5.889e-05 2.644e-05 -0.03016 -4.439e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02172 -0.005494 0.02245 0.02055 0.942 0.951 0.04131 0.8888 0.907 0.1022 ] Network output: [ 0.9918 0.02289 0.001766 -0.0003507 0.0001574 -0.009663 -0.0002643 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6503 0.09728 0.185 0.1799 0.9729 0.9875 0.7285 0.903 0.9684 0.6318 ] Network output: [ -0.0116 0.9787 1.009 2.788e-05 -1.251e-05 0.0358 2.101e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04441 0.03192 0.04378 0.02429 0.986 0.9901 0.04523 0.9713 0.9813 0.0521 ] Network output: [ 0.03664 -0.1592 1.087 -0.001501 0.0006736 0.9928 -0.001131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7247 0.6089 0.5522 0.3186 0.976 0.9893 0.7271 0.9128 0.9728 0.6231 ] Network output: [ -0.0151 0.09128 0.9451 0.001065 -0.000478 0.9981 0.0008025 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6492 0.6323 0.4376 0.2386 0.9871 0.9915 0.6496 0.974 0.9826 0.4464 ] Network output: [ -0.02808 0.1158 0.9298 0.0009731 -0.0004368 1.015 0.0007333 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6482 0.6455 0.4359 0.2221 0.9857 0.9907 0.6482 0.9699 0.9802 0.4374 ] Network output: [ 0.01038 0.951 0.02935 -0.0001532 6.879e-05 0.9982 -0.0001155 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01035 Epoch 2866 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01592 0.9996 0.9984 -5.9e-05 2.649e-05 -0.03012 -4.447e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02172 -0.005497 0.02247 0.02055 0.942 0.951 0.0413 0.8888 0.907 0.1021 ] Network output: [ 0.9918 0.0229 0.001817 -0.0003531 0.0001585 -0.009731 -0.0002661 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6504 0.09701 0.1853 0.1801 0.9729 0.9875 0.7285 0.903 0.9684 0.6316 ] Network output: [ -0.01159 0.9787 1.009 2.85e-05 -1.279e-05 0.03579 2.148e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04439 0.03189 0.04374 0.02429 0.986 0.9901 0.04521 0.9713 0.9813 0.05204 ] Network output: [ 0.03665 -0.1594 1.087 -0.0015 0.0006735 0.9929 -0.001131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7248 0.6088 0.5521 0.319 0.976 0.9893 0.7272 0.9128 0.9728 0.6229 ] Network output: [ -0.01514 0.09145 0.945 0.001065 -0.000478 0.9981 0.0008024 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6492 0.6322 0.4374 0.2388 0.9871 0.9915 0.6496 0.974 0.9826 0.4462 ] Network output: [ -0.0281 0.116 0.9296 0.0009733 -0.000437 1.015 0.0007335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6481 0.6454 0.4356 0.2223 0.9857 0.9907 0.6482 0.9699 0.9802 0.4372 ] Network output: [ 0.01044 0.9506 0.02959 -0.0001519 6.817e-05 0.9983 -0.0001144 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01038 Epoch 2867 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01589 0.9997 0.9984 -5.912e-05 2.654e-05 -0.03008 -4.455e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02172 -0.0055 0.0225 0.02055 0.942 0.951 0.04129 0.8888 0.907 0.102 ] Network output: [ 0.9918 0.02292 0.001869 -0.0003556 0.0001596 -0.0098 -0.000268 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6505 0.09675 0.1856 0.1802 0.9729 0.9875 0.7286 0.903 0.9684 0.6314 ] Network output: [ -0.01157 0.9788 1.009 2.912e-05 -1.308e-05 0.03578 2.195e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04436 0.03187 0.04371 0.0243 0.986 0.9901 0.04519 0.9713 0.9813 0.05199 ] Network output: [ 0.03666 -0.1595 1.087 -0.0015 0.0006733 0.9929 -0.00113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7248 0.6087 0.552 0.3193 0.976 0.9893 0.7272 0.9128 0.9728 0.6227 ] Network output: [ -0.01517 0.09163 0.945 0.001065 -0.000478 0.9981 0.0008024 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6491 0.6322 0.4372 0.2389 0.9871 0.9915 0.6495 0.974 0.9826 0.4459 ] Network output: [ -0.02811 0.1162 0.9294 0.0009736 -0.0004371 1.015 0.0007337 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6481 0.6454 0.4354 0.2224 0.9857 0.9907 0.6482 0.9698 0.9802 0.4369 ] Network output: [ 0.01051 0.9502 0.02985 -0.0001505 6.755e-05 0.9983 -0.0001134 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01041 Epoch 2868 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01585 0.9997 0.9984 -5.924e-05 2.659e-05 -0.03003 -4.464e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02171 -0.005503 0.02252 0.02055 0.942 0.951 0.04127 0.8888 0.907 0.102 ] Network output: [ 0.9918 0.02293 0.00192 -0.0003581 0.0001608 -0.009869 -0.0002699 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6506 0.09647 0.1859 0.1804 0.9729 0.9875 0.7286 0.903 0.9684 0.6311 ] Network output: [ -0.01156 0.9788 1.009 2.975e-05 -1.336e-05 0.03578 2.242e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04434 0.03184 0.04367 0.0243 0.986 0.9901 0.04516 0.9713 0.9813 0.05193 ] Network output: [ 0.03667 -0.1596 1.087 -0.001499 0.0006731 0.9929 -0.00113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7249 0.6086 0.5519 0.3196 0.9761 0.9893 0.7273 0.9128 0.9728 0.6224 ] Network output: [ -0.0152 0.09182 0.9449 0.001065 -0.000478 0.9981 0.0008024 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6491 0.6321 0.437 0.2391 0.9871 0.9915 0.6495 0.974 0.9826 0.4457 ] Network output: [ -0.02813 0.1165 0.9292 0.0009739 -0.0004372 1.015 0.0007339 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.648 0.6453 0.4352 0.2225 0.9857 0.9907 0.6481 0.9698 0.9801 0.4367 ] Network output: [ 0.01057 0.9498 0.03011 -0.000149 6.691e-05 0.9983 -0.0001123 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01044 Epoch 2869 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01582 0.9997 0.9984 -5.936e-05 2.665e-05 -0.02999 -4.474e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02171 -0.005507 0.02254 0.02056 0.942 0.951 0.04126 0.8888 0.907 0.1019 ] Network output: [ 0.9918 0.02295 0.001971 -0.0003606 0.0001619 -0.009938 -0.0002717 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6506 0.0962 0.1863 0.1806 0.9729 0.9875 0.7287 0.903 0.9684 0.6309 ] Network output: [ -0.01154 0.9789 1.009 3.037e-05 -1.364e-05 0.03577 2.289e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04432 0.03182 0.04363 0.02431 0.986 0.9901 0.04514 0.9713 0.9813 0.05187 ] Network output: [ 0.03668 -0.1597 1.087 -0.001499 0.0006729 0.9929 -0.00113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7249 0.6084 0.5518 0.32 0.9761 0.9893 0.7273 0.9128 0.9728 0.6222 ] Network output: [ -0.01524 0.09201 0.9448 0.001065 -0.000478 0.998 0.0008024 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.649 0.632 0.4368 0.2393 0.9871 0.9915 0.6494 0.974 0.9826 0.4454 ] Network output: [ -0.02815 0.1167 0.929 0.0009741 -0.0004373 1.015 0.0007341 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.648 0.6453 0.4349 0.2227 0.9857 0.9907 0.6481 0.9698 0.9801 0.4364 ] Network output: [ 0.01064 0.9494 0.03038 -0.0001476 6.626e-05 0.9983 -0.0001112 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01048 Epoch 2870 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01578 0.9998 0.9984 -5.949e-05 2.671e-05 -0.02995 -4.484e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02171 -0.00551 0.02256 0.02056 0.9421 0.9511 0.04124 0.8888 0.907 0.1018 ] Network output: [ 0.9918 0.02297 0.002022 -0.0003631 0.000163 -0.01001 -0.0002736 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6507 0.09593 0.1866 0.1808 0.9729 0.9875 0.7287 0.903 0.9684 0.6307 ] Network output: [ -0.01153 0.9789 1.008 3.1e-05 -1.392e-05 0.03576 2.336e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0443 0.03179 0.04359 0.02431 0.986 0.9901 0.04512 0.9713 0.9813 0.05181 ] Network output: [ 0.03669 -0.1598 1.087 -0.001499 0.0006727 0.993 -0.001129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7249 0.6083 0.5517 0.3203 0.9761 0.9893 0.7274 0.9128 0.9728 0.622 ] Network output: [ -0.01527 0.0922 0.9447 0.001065 -0.000478 0.998 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.649 0.6319 0.4365 0.2395 0.9871 0.9915 0.6493 0.974 0.9826 0.4452 ] Network output: [ -0.02816 0.1169 0.9288 0.0009744 -0.0004374 1.015 0.0007343 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6479 0.6452 0.4347 0.2228 0.9857 0.9907 0.648 0.9698 0.9801 0.4362 ] Network output: [ 0.01071 0.949 0.03066 -0.0001461 6.559e-05 0.9984 -0.0001101 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01051 Epoch 2871 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01575 0.9998 0.9983 -5.963e-05 2.677e-05 -0.02991 -4.494e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02171 -0.005513 0.02259 0.02057 0.9421 0.9511 0.04123 0.8888 0.907 0.1018 ] Network output: [ 0.9918 0.023 0.002072 -0.0003657 0.0001642 -0.01008 -0.0002756 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6508 0.09565 0.1869 0.181 0.9729 0.9875 0.7288 0.903 0.9684 0.6305 ] Network output: [ -0.01151 0.979 1.008 3.162e-05 -1.419e-05 0.03576 2.383e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04428 0.03176 0.04356 0.02432 0.986 0.9901 0.04509 0.9713 0.9813 0.05175 ] Network output: [ 0.0367 -0.16 1.087 -0.001498 0.0006726 0.993 -0.001129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.725 0.6082 0.5517 0.3207 0.9761 0.9893 0.7274 0.9128 0.9728 0.6217 ] Network output: [ -0.01531 0.0924 0.9446 0.001065 -0.0004779 0.998 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6489 0.6318 0.4363 0.2396 0.9871 0.9915 0.6493 0.974 0.9826 0.4449 ] Network output: [ -0.02818 0.1172 0.9286 0.0009747 -0.0004376 1.015 0.0007345 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6479 0.6452 0.4344 0.2229 0.9857 0.9907 0.648 0.9698 0.9801 0.4359 ] Network output: [ 0.01077 0.9485 0.03094 -0.0001446 6.491e-05 0.9984 -0.000109 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01055 Epoch 2872 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01571 0.9999 0.9983 -5.978e-05 2.684e-05 -0.02987 -4.505e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02171 -0.005516 0.02261 0.02057 0.9421 0.9511 0.04122 0.8889 0.907 0.1017 ] Network output: [ 0.9918 0.02302 0.002123 -0.0003682 0.0001653 -0.01015 -0.0002775 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6509 0.09537 0.1872 0.1812 0.9729 0.9875 0.7288 0.903 0.9684 0.6303 ] Network output: [ -0.0115 0.979 1.008 3.224e-05 -1.447e-05 0.03575 2.43e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04425 0.03174 0.04352 0.02432 0.986 0.9901 0.04507 0.9713 0.9813 0.05169 ] Network output: [ 0.03671 -0.1601 1.088 -0.001498 0.0006724 0.993 -0.001129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.725 0.6081 0.5516 0.321 0.9761 0.9893 0.7274 0.9127 0.9728 0.6215 ] Network output: [ -0.01534 0.09261 0.9444 0.001065 -0.0004779 0.998 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6488 0.6318 0.4361 0.2398 0.9871 0.9915 0.6492 0.974 0.9826 0.4447 ] Network output: [ -0.0282 0.1174 0.9284 0.0009749 -0.0004377 1.015 0.0007347 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6479 0.6451 0.4342 0.2231 0.9857 0.9906 0.6479 0.9698 0.9801 0.4357 ] Network output: [ 0.01085 0.9481 0.03123 -0.000143 6.421e-05 0.9984 -0.0001078 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01058 Epoch 2873 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01568 0.9999 0.9983 -5.993e-05 2.69e-05 -0.02983 -4.516e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02171 -0.00552 0.02263 0.02057 0.9421 0.9511 0.0412 0.8889 0.907 0.1016 ] Network output: [ 0.9917 0.02304 0.002173 -0.0003708 0.0001665 -0.01022 -0.0002795 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.651 0.09508 0.1876 0.1814 0.9729 0.9875 0.7288 0.903 0.9684 0.6301 ] Network output: [ -0.01148 0.9791 1.008 3.286e-05 -1.475e-05 0.03574 2.476e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04423 0.03171 0.04348 0.02433 0.986 0.9901 0.04505 0.9713 0.9813 0.05163 ] Network output: [ 0.03673 -0.1602 1.088 -0.001497 0.0006722 0.9931 -0.001128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7251 0.608 0.5515 0.3214 0.9761 0.9893 0.7275 0.9127 0.9728 0.6212 ] Network output: [ -0.01537 0.09282 0.9443 0.001065 -0.0004779 0.9979 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6488 0.6317 0.4358 0.24 0.9871 0.9915 0.6491 0.974 0.9826 0.4444 ] Network output: [ -0.02822 0.1176 0.9282 0.0009752 -0.0004378 1.015 0.0007349 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6478 0.6451 0.4339 0.2233 0.9857 0.9906 0.6479 0.9698 0.9801 0.4354 ] Network output: [ 0.01092 0.9476 0.03153 -0.0001414 6.349e-05 0.9985 -0.0001066 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01062 Epoch 2874 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01564 1 0.9983 -6.009e-05 2.698e-05 -0.02978 -4.529e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02171 -0.005523 0.02266 0.02058 0.9421 0.9511 0.04119 0.8889 0.907 0.1015 ] Network output: [ 0.9917 0.02307 0.002223 -0.0003734 0.0001676 -0.01029 -0.0002814 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.651 0.0948 0.1879 0.1816 0.9729 0.9875 0.7289 0.903 0.9684 0.6298 ] Network output: [ -0.01147 0.9791 1.008 3.348e-05 -1.503e-05 0.03574 2.523e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04421 0.03169 0.04344 0.02433 0.986 0.9901 0.04502 0.9713 0.9813 0.05157 ] Network output: [ 0.03674 -0.1603 1.088 -0.001497 0.000672 0.9931 -0.001128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7251 0.6079 0.5514 0.3218 0.9761 0.9893 0.7275 0.9127 0.9728 0.621 ] Network output: [ -0.01541 0.09304 0.9442 0.001065 -0.0004779 0.9979 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6487 0.6316 0.4356 0.2402 0.9871 0.9915 0.6491 0.974 0.9826 0.4442 ] Network output: [ -0.02823 0.1179 0.928 0.0009754 -0.0004379 1.015 0.0007351 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6477 0.645 0.4337 0.2234 0.9857 0.9906 0.6478 0.9698 0.9801 0.4352 ] Network output: [ 0.01099 0.9471 0.03184 -0.0001398 6.276e-05 0.9985 -0.0001054 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01066 Epoch 2875 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0156 1 0.9983 -6.026e-05 2.705e-05 -0.02974 -4.541e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0217 -0.005526 0.02268 0.02058 0.9421 0.9511 0.04118 0.8889 0.907 0.1014 ] Network output: [ 0.9917 0.0231 0.002272 -0.0003761 0.0001688 -0.01036 -0.0002834 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6511 0.09451 0.1882 0.1818 0.9729 0.9875 0.7289 0.903 0.9684 0.6296 ] Network output: [ -0.01145 0.9792 1.008 3.409e-05 -1.531e-05 0.03573 2.569e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04419 0.03166 0.0434 0.02434 0.986 0.9901 0.045 0.9713 0.9813 0.05151 ] Network output: [ 0.03675 -0.1605 1.088 -0.001497 0.0006719 0.9931 -0.001128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7252 0.6077 0.5513 0.3221 0.9761 0.9893 0.7276 0.9127 0.9728 0.6207 ] Network output: [ -0.01544 0.09327 0.9441 0.001065 -0.0004779 0.9979 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6486 0.6315 0.4354 0.2404 0.9871 0.9915 0.649 0.974 0.9826 0.4439 ] Network output: [ -0.02825 0.1181 0.9278 0.0009757 -0.000438 1.015 0.0007353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6477 0.6449 0.4334 0.2236 0.9856 0.9906 0.6478 0.9698 0.9801 0.4349 ] Network output: [ 0.01107 0.9466 0.03216 -0.0001381 6.201e-05 0.9985 -0.0001041 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0107 Epoch 2876 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01557 1 0.9982 -6.043e-05 2.713e-05 -0.0297 -4.554e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0217 -0.00553 0.0227 0.02059 0.9421 0.9511 0.04116 0.8889 0.907 0.1014 ] Network output: [ 0.9917 0.02313 0.002321 -0.0003787 0.00017 -0.01043 -0.0002854 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6512 0.09422 0.1886 0.182 0.9729 0.9875 0.729 0.903 0.9684 0.6294 ] Network output: [ -0.01144 0.9793 1.008 3.471e-05 -1.558e-05 0.03572 2.616e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04416 0.03163 0.04336 0.02435 0.9861 0.9901 0.04498 0.9713 0.9813 0.05145 ] Network output: [ 0.03676 -0.1606 1.088 -0.001496 0.0006717 0.9932 -0.001128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7252 0.6076 0.5512 0.3225 0.9761 0.9893 0.7276 0.9127 0.9728 0.6205 ] Network output: [ -0.01548 0.0935 0.944 0.001065 -0.0004779 0.9978 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6486 0.6314 0.4351 0.2406 0.9871 0.9915 0.6489 0.974 0.9826 0.4436 ] Network output: [ -0.02828 0.1184 0.9275 0.0009759 -0.0004381 1.015 0.0007355 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6476 0.6449 0.4331 0.2238 0.9856 0.9906 0.6477 0.9698 0.9801 0.4346 ] Network output: [ 0.01114 0.9461 0.03248 -0.0001364 6.124e-05 0.9986 -0.0001028 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01073 Epoch 2877 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01553 1 0.9982 -6.062e-05 2.721e-05 -0.02965 -4.568e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0217 -0.005533 0.02273 0.02059 0.9421 0.9511 0.04115 0.8889 0.907 0.1013 ] Network output: [ 0.9917 0.02316 0.00237 -0.0003814 0.0001712 -0.0105 -0.0002874 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6513 0.09393 0.1889 0.1822 0.9729 0.9875 0.729 0.903 0.9684 0.6291 ] Network output: [ -0.01142 0.9793 1.008 3.532e-05 -1.586e-05 0.03571 2.662e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04414 0.03161 0.04331 0.02435 0.9861 0.9901 0.04495 0.9713 0.9813 0.05139 ] Network output: [ 0.03677 -0.1607 1.088 -0.001496 0.0006715 0.9932 -0.001127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7253 0.6075 0.5511 0.3229 0.9761 0.9893 0.7277 0.9127 0.9728 0.6202 ] Network output: [ -0.01552 0.09374 0.9438 0.001065 -0.0004779 0.9978 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6485 0.6313 0.4349 0.2408 0.9871 0.9915 0.6489 0.974 0.9826 0.4434 ] Network output: [ -0.0283 0.1186 0.9273 0.0009762 -0.0004383 1.015 0.0007357 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6476 0.6448 0.4328 0.2239 0.9856 0.9906 0.6476 0.9698 0.9801 0.4343 ] Network output: [ 0.01122 0.9456 0.03281 -0.0001347 6.046e-05 0.9986 -0.0001015 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01077 Epoch 2878 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01549 1 0.9982 -6.081e-05 2.73e-05 -0.02961 -4.583e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0217 -0.005537 0.02275 0.0206 0.9421 0.9511 0.04113 0.8889 0.907 0.1012 ] Network output: [ 0.9917 0.02319 0.002419 -0.0003841 0.0001724 -0.01057 -0.0002895 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6514 0.09363 0.1893 0.1824 0.9729 0.9875 0.7291 0.903 0.9684 0.6289 ] Network output: [ -0.0114 0.9794 1.008 3.593e-05 -1.613e-05 0.03571 2.708e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04412 0.03158 0.04327 0.02436 0.9861 0.9901 0.04493 0.9713 0.9813 0.05133 ] Network output: [ 0.03679 -0.1609 1.088 -0.001495 0.0006714 0.9933 -0.001127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7253 0.6074 0.551 0.3233 0.9761 0.9893 0.7277 0.9127 0.9728 0.6199 ] Network output: [ -0.01555 0.09399 0.9437 0.001065 -0.0004779 0.9977 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6484 0.6312 0.4346 0.241 0.9871 0.9915 0.6488 0.974 0.9826 0.4431 ] Network output: [ -0.02832 0.1189 0.9271 0.0009764 -0.0004384 1.015 0.0007359 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6475 0.6448 0.4326 0.2241 0.9856 0.9906 0.6476 0.9698 0.9801 0.4341 ] Network output: [ 0.0113 0.9451 0.03316 -0.0001329 5.965e-05 0.9986 -0.0001001 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01082 Epoch 2879 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01546 1 0.9982 -6.101e-05 2.739e-05 -0.02956 -4.598e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0217 -0.005541 0.02277 0.0206 0.9421 0.9511 0.04112 0.8889 0.907 0.1011 ] Network output: [ 0.9917 0.02323 0.002467 -0.0003868 0.0001737 -0.01064 -0.0002915 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6515 0.09334 0.1896 0.1827 0.9729 0.9875 0.7291 0.903 0.9684 0.6287 ] Network output: [ -0.01139 0.9794 1.008 3.654e-05 -1.64e-05 0.0357 2.754e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04409 0.03155 0.04323 0.02437 0.9861 0.9901 0.0449 0.9713 0.9813 0.05126 ] Network output: [ 0.0368 -0.161 1.088 -0.001495 0.0006712 0.9933 -0.001127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7254 0.6072 0.5509 0.3237 0.9761 0.9893 0.7278 0.9127 0.9728 0.6197 ] Network output: [ -0.01559 0.09424 0.9436 0.001065 -0.0004779 0.9977 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6483 0.6311 0.4343 0.2412 0.9871 0.9915 0.6487 0.974 0.9826 0.4428 ] Network output: [ -0.02834 0.1192 0.9269 0.0009767 -0.0004385 1.015 0.0007361 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6474 0.6447 0.4323 0.2243 0.9856 0.9906 0.6475 0.9698 0.9801 0.4338 ] Network output: [ 0.01138 0.9445 0.03351 -0.000131 5.883e-05 0.9987 -9.875e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01086 Epoch 2880 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01542 1 0.9981 -6.123e-05 2.749e-05 -0.02952 -4.614e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0217 -0.005544 0.0228 0.02061 0.9421 0.9511 0.04111 0.8889 0.907 0.101 ] Network output: [ 0.9917 0.02327 0.002514 -0.0003896 0.0001749 -0.01071 -0.0002936 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6515 0.09304 0.1899 0.1829 0.9729 0.9875 0.7292 0.903 0.9684 0.6284 ] Network output: [ -0.01137 0.9795 1.008 3.714e-05 -1.667e-05 0.03569 2.799e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04407 0.03152 0.04319 0.02437 0.9861 0.9901 0.04488 0.9713 0.9813 0.0512 ] Network output: [ 0.03681 -0.1611 1.088 -0.001495 0.000671 0.9933 -0.001126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7255 0.6071 0.5508 0.3242 0.9761 0.9893 0.7278 0.9127 0.9728 0.6194 ] Network output: [ -0.01562 0.0945 0.9434 0.001065 -0.0004779 0.9976 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6483 0.631 0.4341 0.2415 0.9871 0.9915 0.6486 0.974 0.9826 0.4425 ] Network output: [ -0.02836 0.1194 0.9267 0.0009769 -0.0004386 1.015 0.0007363 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6474 0.6446 0.432 0.2245 0.9856 0.9906 0.6474 0.9698 0.9801 0.4335 ] Network output: [ 0.01147 0.944 0.03387 -0.0001291 5.798e-05 0.9987 -9.733e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0109 Epoch 2881 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01538 1 0.9981 -6.145e-05 2.759e-05 -0.02948 -4.631e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0217 -0.005548 0.02282 0.02061 0.9421 0.9511 0.04109 0.8889 0.907 0.101 ] Network output: [ 0.9917 0.02331 0.002561 -0.0003923 0.0001761 -0.01078 -0.0002957 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6516 0.09274 0.1903 0.1831 0.9729 0.9875 0.7292 0.903 0.9684 0.6282 ] Network output: [ -0.01136 0.9796 1.008 3.774e-05 -1.694e-05 0.03568 2.844e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04404 0.0315 0.04314 0.02438 0.9861 0.9901 0.04485 0.9713 0.9813 0.05113 ] Network output: [ 0.03682 -0.1612 1.088 -0.001494 0.0006709 0.9934 -0.001126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7255 0.607 0.5507 0.3246 0.9761 0.9893 0.7279 0.9127 0.9728 0.6191 ] Network output: [ -0.01566 0.09477 0.9433 0.001065 -0.0004779 0.9976 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6482 0.6309 0.4338 0.2417 0.9871 0.9915 0.6486 0.974 0.9826 0.4422 ] Network output: [ -0.02839 0.1197 0.9265 0.0009772 -0.0004387 1.015 0.0007364 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6473 0.6445 0.4317 0.2247 0.9856 0.9906 0.6474 0.9697 0.9801 0.4332 ] Network output: [ 0.01155 0.9434 0.03425 -0.0001272 5.711e-05 0.9987 -9.587e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01094 Epoch 2882 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01534 1 0.9981 -6.169e-05 2.769e-05 -0.02943 -4.649e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02169 -0.005551 0.02284 0.02062 0.9421 0.9511 0.04108 0.8889 0.907 0.1009 ] Network output: [ 0.9916 0.02335 0.002608 -0.0003951 0.0001774 -0.01085 -0.0002978 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6517 0.09243 0.1906 0.1834 0.9729 0.9875 0.7293 0.903 0.9684 0.628 ] Network output: [ -0.01134 0.9796 1.008 3.833e-05 -1.721e-05 0.03568 2.889e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04402 0.03147 0.0431 0.02439 0.9861 0.9901 0.04483 0.9713 0.9813 0.05107 ] Network output: [ 0.03684 -0.1614 1.088 -0.001494 0.0006707 0.9934 -0.001126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7256 0.6069 0.5506 0.325 0.9761 0.9893 0.7279 0.9127 0.9728 0.6189 ] Network output: [ -0.01569 0.09504 0.9431 0.001065 -0.0004779 0.9976 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6481 0.6308 0.4335 0.2419 0.9871 0.9915 0.6485 0.974 0.9826 0.4419 ] Network output: [ -0.02841 0.12 0.9263 0.0009774 -0.0004388 1.015 0.0007366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6472 0.6445 0.4314 0.2249 0.9856 0.9906 0.6473 0.9697 0.9801 0.4329 ] Network output: [ 0.01164 0.9428 0.03463 -0.0001252 5.622e-05 0.9988 -9.437e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01099 Epoch 2883 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0153 1 0.9981 -6.194e-05 2.781e-05 -0.02938 -4.668e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02169 -0.005555 0.02286 0.02062 0.9421 0.9511 0.04106 0.8889 0.907 0.1008 ] Network output: [ 0.9916 0.02339 0.002654 -0.0003979 0.0001787 -0.01092 -0.0002999 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6518 0.09213 0.191 0.1836 0.9729 0.9875 0.7293 0.903 0.9684 0.6277 ] Network output: [ -0.01132 0.9797 1.007 3.892e-05 -1.747e-05 0.03567 2.933e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.044 0.03144 0.04306 0.0244 0.9861 0.9901 0.0448 0.9713 0.9813 0.051 ] Network output: [ 0.03685 -0.1615 1.088 -0.001494 0.0006705 0.9935 -0.001126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7256 0.6067 0.5504 0.3255 0.9761 0.9893 0.728 0.9127 0.9728 0.6186 ] Network output: [ -0.01573 0.09532 0.943 0.001065 -0.0004779 0.9975 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.648 0.6307 0.4333 0.2421 0.9871 0.9915 0.6484 0.974 0.9826 0.4416 ] Network output: [ -0.02844 0.1202 0.9261 0.0009777 -0.0004389 1.015 0.0007368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6472 0.6444 0.4311 0.2251 0.9856 0.9906 0.6472 0.9697 0.9801 0.4326 ] Network output: [ 0.01173 0.9422 0.03503 -0.0001232 5.53e-05 0.9988 -9.284e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01103 Epoch 2884 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01526 1.001 0.998 -6.219e-05 2.792e-05 -0.02934 -4.687e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02169 -0.005559 0.02289 0.02063 0.9421 0.9511 0.04105 0.8889 0.907 0.1007 ] Network output: [ 0.9916 0.02343 0.002699 -0.0004008 0.0001799 -0.01098 -0.000302 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6519 0.09182 0.1913 0.1839 0.9729 0.9875 0.7294 0.903 0.9684 0.6275 ] Network output: [ -0.01131 0.9798 1.007 3.951e-05 -1.774e-05 0.03566 2.978e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04397 0.03141 0.04301 0.0244 0.9861 0.9901 0.04478 0.9713 0.9813 0.05093 ] Network output: [ 0.03686 -0.1616 1.088 -0.001493 0.0006704 0.9935 -0.001125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7257 0.6066 0.5503 0.3259 0.9761 0.9893 0.7281 0.9127 0.9728 0.6183 ] Network output: [ -0.01577 0.09562 0.9428 0.001065 -0.0004779 0.9974 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6479 0.6306 0.433 0.2424 0.9871 0.9915 0.6483 0.974 0.9826 0.4413 ] Network output: [ -0.02846 0.1205 0.9259 0.0009779 -0.000439 1.015 0.000737 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6471 0.6443 0.4308 0.2253 0.9856 0.9906 0.6472 0.9697 0.9801 0.4323 ] Network output: [ 0.01183 0.9415 0.03544 -0.0001211 5.436e-05 0.9989 -9.126e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01108 Epoch 2885 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01522 1.001 0.998 -6.247e-05 2.804e-05 -0.02929 -4.708e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02169 -0.005563 0.02291 0.02064 0.9421 0.9511 0.04104 0.8889 0.9071 0.1006 ] Network output: [ 0.9916 0.02348 0.002744 -0.0004036 0.0001812 -0.01105 -0.0003042 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.652 0.09151 0.1917 0.1841 0.9729 0.9875 0.7294 0.903 0.9684 0.6272 ] Network output: [ -0.01129 0.9799 1.007 4.009e-05 -1.8e-05 0.03565 3.021e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04395 0.03138 0.04297 0.02441 0.9861 0.9901 0.04475 0.9713 0.9813 0.05086 ] Network output: [ 0.03687 -0.1618 1.088 -0.001493 0.0006702 0.9936 -0.001125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7257 0.6065 0.5502 0.3264 0.9761 0.9893 0.7281 0.9127 0.9728 0.618 ] Network output: [ -0.0158 0.09592 0.9426 0.001065 -0.0004779 0.9974 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6478 0.6304 0.4327 0.2426 0.9871 0.9915 0.6482 0.974 0.9826 0.441 ] Network output: [ -0.02849 0.1208 0.9257 0.0009781 -0.0004391 1.015 0.0007371 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.647 0.6442 0.4305 0.2256 0.9856 0.9906 0.6471 0.9697 0.9801 0.432 ] Network output: [ 0.01192 0.9409 0.03586 -0.0001189 5.34e-05 0.9989 -8.963e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01113 Epoch 2886 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01518 1.001 0.998 -6.275e-05 2.817e-05 -0.02925 -4.729e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02169 -0.005567 0.02293 0.02064 0.9421 0.9511 0.04102 0.8889 0.9071 0.1005 ] Network output: [ 0.9916 0.02353 0.002789 -0.0004065 0.0001825 -0.01112 -0.0003064 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6521 0.09119 0.192 0.1844 0.9729 0.9875 0.7295 0.903 0.9684 0.627 ] Network output: [ -0.01127 0.9799 1.007 4.066e-05 -1.826e-05 0.03564 3.065e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04392 0.03135 0.04292 0.02442 0.9861 0.9901 0.04472 0.9713 0.9813 0.05079 ] Network output: [ 0.03688 -0.1619 1.088 -0.001493 0.0006701 0.9937 -0.001125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7258 0.6063 0.5501 0.3268 0.9761 0.9893 0.7282 0.9127 0.9728 0.6177 ] Network output: [ -0.01584 0.09622 0.9425 0.001065 -0.0004779 0.9973 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6478 0.6303 0.4324 0.2429 0.9871 0.9915 0.6481 0.9739 0.9826 0.4407 ] Network output: [ -0.02852 0.1211 0.9255 0.0009783 -0.0004392 1.014 0.0007373 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6469 0.6441 0.4302 0.2258 0.9856 0.9906 0.647 0.9697 0.9801 0.4317 ] Network output: [ 0.01202 0.9402 0.03629 -0.0001167 5.24e-05 0.999 -8.797e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01118 Epoch 2887 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01514 1.001 0.9979 -6.305e-05 2.831e-05 -0.0292 -4.752e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02169 -0.00557 0.02296 0.02065 0.9421 0.9511 0.04101 0.8889 0.9071 0.1005 ] Network output: [ 0.9916 0.02358 0.002832 -0.0004094 0.0001838 -0.01119 -0.0003086 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6522 0.09088 0.1924 0.1847 0.9729 0.9875 0.7295 0.903 0.9684 0.6267 ] Network output: [ -0.01126 0.98 1.007 4.123e-05 -1.851e-05 0.03563 3.107e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0439 0.03133 0.04287 0.02443 0.9861 0.9901 0.0447 0.9713 0.9813 0.05072 ] Network output: [ 0.0369 -0.162 1.088 -0.001492 0.0006699 0.9937 -0.001125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7258 0.6062 0.55 0.3273 0.9761 0.9893 0.7282 0.9127 0.9728 0.6174 ] Network output: [ -0.01587 0.09654 0.9423 0.001065 -0.0004779 0.9973 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6477 0.6302 0.4321 0.2431 0.9871 0.9915 0.648 0.9739 0.9826 0.4404 ] Network output: [ -0.02854 0.1213 0.9253 0.0009786 -0.0004393 1.014 0.0007375 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6469 0.6441 0.4299 0.226 0.9856 0.9906 0.6469 0.9697 0.9801 0.4314 ] Network output: [ 0.01212 0.9395 0.03674 -0.0001145 5.138e-05 0.999 -8.626e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01123 Epoch 2888 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0151 1.001 0.9979 -6.337e-05 2.845e-05 -0.02915 -4.776e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02168 -0.005574 0.02298 0.02065 0.9421 0.9511 0.04099 0.8889 0.9071 0.1004 ] Network output: [ 0.9915 0.02363 0.002875 -0.0004123 0.0001851 -0.01126 -0.0003108 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6522 0.09056 0.1928 0.1849 0.9729 0.9875 0.7296 0.903 0.9684 0.6264 ] Network output: [ -0.01124 0.9801 1.007 4.179e-05 -1.876e-05 0.03562 3.15e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04387 0.0313 0.04282 0.02444 0.9861 0.9901 0.04467 0.9713 0.9813 0.05065 ] Network output: [ 0.03691 -0.1622 1.089 -0.001492 0.0006698 0.9938 -0.001124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7259 0.6061 0.5499 0.3278 0.9761 0.9893 0.7283 0.9127 0.9728 0.6172 ] Network output: [ -0.01591 0.09687 0.9421 0.001065 -0.0004779 0.9972 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6476 0.6301 0.4318 0.2434 0.9871 0.9915 0.6479 0.9739 0.9826 0.44 ] Network output: [ -0.02857 0.1216 0.925 0.0009788 -0.0004394 1.014 0.0007376 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6468 0.644 0.4296 0.2263 0.9856 0.9906 0.6468 0.9697 0.9801 0.431 ] Network output: [ 0.01222 0.9388 0.0372 -0.0001121 5.033e-05 0.9991 -8.449e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01128 Epoch 2889 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01506 1.001 0.9979 -6.37e-05 2.86e-05 -0.02911 -4.801e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02168 -0.005578 0.023 0.02066 0.9421 0.9511 0.04098 0.8889 0.9071 0.1003 ] Network output: [ 0.9915 0.02369 0.002918 -0.0004153 0.0001864 -0.01133 -0.000313 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6523 0.09024 0.1931 0.1852 0.9729 0.9875 0.7297 0.903 0.9684 0.6262 ] Network output: [ -0.01122 0.9802 1.007 4.234e-05 -1.901e-05 0.03561 3.191e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04384 0.03127 0.04278 0.02445 0.9861 0.9901 0.04464 0.9713 0.9813 0.05058 ] Network output: [ 0.03692 -0.1623 1.089 -0.001492 0.0006696 0.9938 -0.001124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.726 0.6059 0.5497 0.3283 0.9761 0.9893 0.7283 0.9127 0.9728 0.6169 ] Network output: [ -0.01594 0.09721 0.9419 0.001065 -0.0004779 0.9971 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6475 0.63 0.4315 0.2437 0.9871 0.9915 0.6478 0.9739 0.9826 0.4397 ] Network output: [ -0.0286 0.1219 0.9248 0.000979 -0.0004395 1.014 0.0007378 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6467 0.6439 0.4292 0.2265 0.9856 0.9906 0.6468 0.9697 0.9801 0.4307 ] Network output: [ 0.01233 0.9381 0.03768 -0.0001097 4.925e-05 0.9991 -8.268e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01133 Epoch 2890 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01501 1.001 0.9978 -6.405e-05 2.876e-05 -0.02906 -4.827e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02168 -0.005582 0.02303 0.02067 0.9421 0.9511 0.04096 0.8889 0.9071 0.1002 ] Network output: [ 0.9915 0.02375 0.002959 -0.0004183 0.0001878 -0.0114 -0.0003152 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6524 0.08992 0.1935 0.1855 0.9729 0.9875 0.7297 0.903 0.9684 0.6259 ] Network output: [ -0.01121 0.9803 1.007 4.289e-05 -1.925e-05 0.0356 3.232e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04382 0.03124 0.04273 0.02446 0.9861 0.9901 0.04462 0.9713 0.9813 0.05051 ] Network output: [ 0.03693 -0.1624 1.089 -0.001491 0.0006695 0.9939 -0.001124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.726 0.6058 0.5496 0.3288 0.9761 0.9893 0.7284 0.9127 0.9728 0.6166 ] Network output: [ -0.01598 0.09755 0.9417 0.001065 -0.0004779 0.9971 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6474 0.6298 0.4312 0.2439 0.9871 0.9915 0.6477 0.9739 0.9826 0.4393 ] Network output: [ -0.02863 0.1222 0.9246 0.0009792 -0.0004396 1.014 0.000738 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6466 0.6438 0.4289 0.2268 0.9856 0.9906 0.6467 0.9697 0.9801 0.4304 ] Network output: [ 0.01243 0.9373 0.03817 -0.0001072 4.814e-05 0.9992 -8.082e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01138 Epoch 2891 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01497 1.001 0.9978 -6.442e-05 2.892e-05 -0.02901 -4.855e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02168 -0.005586 0.02305 0.02068 0.9421 0.9511 0.04095 0.8889 0.9071 0.1001 ] Network output: [ 0.9915 0.02381 0.003 -0.0004212 0.0001891 -0.01146 -0.0003175 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6525 0.08959 0.1938 0.1858 0.9729 0.9875 0.7298 0.903 0.9684 0.6256 ] Network output: [ -0.01119 0.9803 1.007 4.343e-05 -1.95e-05 0.03559 3.273e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04379 0.03121 0.04268 0.02447 0.9861 0.9901 0.04459 0.9713 0.9813 0.05044 ] Network output: [ 0.03694 -0.1625 1.089 -0.001491 0.0006694 0.994 -0.001124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7261 0.6057 0.5495 0.3293 0.9761 0.9893 0.7285 0.9126 0.9728 0.6163 ] Network output: [ -0.01601 0.09791 0.9415 0.001065 -0.0004779 0.997 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6473 0.6297 0.4308 0.2442 0.9871 0.9915 0.6476 0.9739 0.9826 0.439 ] Network output: [ -0.02866 0.1225 0.9244 0.0009794 -0.0004397 1.014 0.0007381 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6465 0.6437 0.4286 0.2271 0.9856 0.9906 0.6466 0.9697 0.98 0.43 ] Network output: [ 0.01254 0.9365 0.03868 -0.0001047 4.7e-05 0.9993 -7.889e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01144 Epoch 2892 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01493 1.001 0.9978 -6.48e-05 2.909e-05 -0.02897 -4.884e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02168 -0.00559 0.02307 0.02068 0.9421 0.9511 0.04093 0.8889 0.9071 0.1 ] Network output: [ 0.9914 0.02387 0.003039 -0.0004242 0.0001905 -0.01153 -0.0003197 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6526 0.08926 0.1942 0.1861 0.9729 0.9875 0.7298 0.903 0.9684 0.6254 ] Network output: [ -0.01117 0.9804 1.007 4.395e-05 -1.973e-05 0.03558 3.312e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04376 0.03118 0.04263 0.02448 0.9861 0.9901 0.04456 0.9713 0.9813 0.05036 ] Network output: [ 0.03695 -0.1627 1.089 -0.001491 0.0006692 0.994 -0.001123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7261 0.6055 0.5494 0.3298 0.9761 0.9893 0.7285 0.9126 0.9728 0.616 ] Network output: [ -0.01605 0.09828 0.9413 0.001065 -0.0004779 0.9969 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6471 0.6296 0.4305 0.2445 0.9871 0.9915 0.6475 0.9739 0.9826 0.4386 ] Network output: [ -0.0287 0.1228 0.9242 0.0009796 -0.0004398 1.014 0.0007383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6464 0.6436 0.4282 0.2274 0.9856 0.9906 0.6465 0.9697 0.98 0.4297 ] Network output: [ 0.01266 0.9357 0.03921 -0.0001021 4.582e-05 0.9993 -7.692e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01149 Epoch 2893 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01488 1.001 0.9977 -6.521e-05 2.927e-05 -0.02892 -4.914e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02167 -0.005594 0.02309 0.02069 0.9421 0.9511 0.04092 0.8889 0.9071 0.09991 ] Network output: [ 0.9914 0.02393 0.003078 -0.0004273 0.0001918 -0.01159 -0.000322 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6527 0.08893 0.1946 0.1864 0.9729 0.9875 0.7299 0.903 0.9684 0.6251 ] Network output: [ -0.01116 0.9805 1.006 4.447e-05 -1.996e-05 0.03557 3.351e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04374 0.03115 0.04257 0.02449 0.9861 0.9901 0.04453 0.9713 0.9813 0.05029 ] Network output: [ 0.03696 -0.1628 1.089 -0.00149 0.0006691 0.9941 -0.001123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7262 0.6054 0.5492 0.3304 0.9761 0.9893 0.7286 0.9126 0.9728 0.6156 ] Network output: [ -0.01608 0.09866 0.941 0.001065 -0.0004779 0.9968 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.647 0.6294 0.4302 0.2448 0.9871 0.9915 0.6474 0.9739 0.9826 0.4383 ] Network output: [ -0.02873 0.1231 0.924 0.0009798 -0.0004399 1.014 0.0007384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6463 0.6435 0.4279 0.2276 0.9856 0.9906 0.6464 0.9696 0.98 0.4293 ] Network output: [ 0.01277 0.9349 0.03975 -9.936e-05 4.46e-05 0.9994 -7.488e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01155 Epoch 2894 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01484 1.001 0.9977 -6.563e-05 2.947e-05 -0.02887 -4.946e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02167 -0.005598 0.02312 0.0207 0.9422 0.9511 0.0409 0.8889 0.9071 0.09982 ] Network output: [ 0.9914 0.024 0.003116 -0.0004303 0.0001932 -0.01166 -0.0003243 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6528 0.0886 0.1949 0.1867 0.9729 0.9875 0.7299 0.903 0.9684 0.6248 ] Network output: [ -0.01114 0.9806 1.006 4.497e-05 -2.019e-05 0.03556 3.389e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04371 0.03112 0.04252 0.0245 0.9861 0.9901 0.04451 0.9713 0.9814 0.05021 ] Network output: [ 0.03697 -0.1629 1.089 -0.00149 0.000669 0.9942 -0.001123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7263 0.6052 0.5491 0.3309 0.9761 0.9893 0.7286 0.9126 0.9728 0.6153 ] Network output: [ -0.01612 0.09905 0.9408 0.001065 -0.0004779 0.9967 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6469 0.6293 0.4298 0.2451 0.9871 0.9915 0.6473 0.9739 0.9826 0.4379 ] Network output: [ -0.02876 0.1234 0.9238 0.00098 -0.00044 1.014 0.0007386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6462 0.6434 0.4275 0.2279 0.9856 0.9906 0.6463 0.9696 0.98 0.4289 ] Network output: [ 0.01289 0.934 0.04031 -9.657e-05 4.335e-05 0.9995 -7.278e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01161 Epoch 2895 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01479 1.001 0.9976 -6.608e-05 2.967e-05 -0.02882 -4.98e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02167 -0.005603 0.02314 0.02071 0.9422 0.9511 0.04089 0.8889 0.9071 0.09972 ] Network output: [ 0.9914 0.02407 0.003153 -0.0004334 0.0001946 -0.01172 -0.0003266 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6529 0.08827 0.1953 0.187 0.9729 0.9875 0.73 0.903 0.9684 0.6245 ] Network output: [ -0.01112 0.9807 1.006 4.547e-05 -2.041e-05 0.03555 3.426e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04368 0.03108 0.04247 0.02451 0.9861 0.9901 0.04448 0.9713 0.9814 0.05013 ] Network output: [ 0.03698 -0.163 1.089 -0.00149 0.0006689 0.9943 -0.001123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7263 0.6051 0.5489 0.3315 0.9761 0.9893 0.7287 0.9126 0.9728 0.615 ] Network output: [ -0.01615 0.09946 0.9406 0.001065 -0.0004779 0.9966 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6468 0.6291 0.4295 0.2454 0.9871 0.9915 0.6472 0.9739 0.9826 0.4375 ] Network output: [ -0.0288 0.1237 0.9236 0.0009802 -0.00044 1.014 0.0007387 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6461 0.6433 0.4271 0.2283 0.9856 0.9906 0.6462 0.9696 0.98 0.4286 ] Network output: [ 0.01302 0.9332 0.0409 -9.369e-05 4.206e-05 0.9995 -7.061e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01167 Epoch 2896 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01475 1.001 0.9976 -6.655e-05 2.988e-05 -0.02877 -5.015e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02167 -0.005607 0.02316 0.02072 0.9422 0.9511 0.04087 0.8889 0.9071 0.09963 ] Network output: [ 0.9913 0.02414 0.003188 -0.0004365 0.000196 -0.01179 -0.000329 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.653 0.08793 0.1957 0.1873 0.9729 0.9875 0.7301 0.903 0.9684 0.6242 ] Network output: [ -0.0111 0.9808 1.006 4.595e-05 -2.063e-05 0.03554 3.463e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04365 0.03105 0.04241 0.02452 0.9861 0.9901 0.04445 0.9713 0.9814 0.05005 ] Network output: [ 0.03698 -0.1631 1.089 -0.00149 0.0006688 0.9943 -0.001123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7264 0.605 0.5488 0.3321 0.9761 0.9893 0.7288 0.9126 0.9728 0.6147 ] Network output: [ -0.01619 0.09987 0.9403 0.001065 -0.0004779 0.9965 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6467 0.629 0.4291 0.2457 0.9871 0.9915 0.647 0.9739 0.9826 0.4371 ] Network output: [ -0.02883 0.124 0.9234 0.0009803 -0.0004401 1.014 0.0007388 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.646 0.6432 0.4268 0.2286 0.9856 0.9906 0.6461 0.9696 0.98 0.4282 ] Network output: [ 0.01314 0.9322 0.0415 -9.072e-05 4.073e-05 0.9996 -6.837e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01174 Epoch 2897 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0147 1.002 0.9975 -6.705e-05 3.01e-05 -0.02872 -5.053e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02167 -0.005611 0.02318 0.02073 0.9422 0.9511 0.04086 0.8889 0.9071 0.09953 ] Network output: [ 0.9913 0.02422 0.003222 -0.0004396 0.0001974 -0.01185 -0.0003313 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6531 0.08759 0.196 0.1877 0.9729 0.9875 0.7301 0.903 0.9684 0.6239 ] Network output: [ -0.01109 0.9809 1.006 4.641e-05 -2.084e-05 0.03552 3.498e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04363 0.03102 0.04236 0.02453 0.9861 0.9901 0.04442 0.9713 0.9814 0.04997 ] Network output: [ 0.03699 -0.1633 1.089 -0.001489 0.0006686 0.9944 -0.001122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7265 0.6048 0.5486 0.3326 0.9761 0.9893 0.7288 0.9126 0.9728 0.6144 ] Network output: [ -0.01622 0.1003 0.94 0.001065 -0.0004779 0.9964 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6466 0.6288 0.4287 0.246 0.9871 0.9915 0.6469 0.9739 0.9826 0.4367 ] Network output: [ -0.02887 0.1244 0.9232 0.0009805 -0.0004402 1.014 0.0007389 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6459 0.643 0.4264 0.2289 0.9856 0.9906 0.646 0.9696 0.98 0.4278 ] Network output: [ 0.01327 0.9313 0.04212 -8.766e-05 3.935e-05 0.9997 -6.606e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0118 Epoch 2898 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01465 1.002 0.9975 -6.756e-05 3.033e-05 -0.02868 -5.092e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02166 -0.005615 0.0232 0.02074 0.9422 0.9511 0.04084 0.8889 0.9071 0.09943 ] Network output: [ 0.9913 0.0243 0.003256 -0.0004427 0.0001988 -0.01191 -0.0003337 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6532 0.08725 0.1964 0.188 0.9729 0.9875 0.7302 0.903 0.9684 0.6236 ] Network output: [ -0.01107 0.981 1.006 4.687e-05 -2.104e-05 0.03551 3.532e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0436 0.03099 0.0423 0.02454 0.9861 0.9901 0.04439 0.9713 0.9814 0.04989 ] Network output: [ 0.037 -0.1634 1.089 -0.001489 0.0006685 0.9945 -0.001122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7265 0.6047 0.5485 0.3332 0.9761 0.9893 0.7289 0.9126 0.9728 0.614 ] Network output: [ -0.01625 0.1007 0.9398 0.001065 -0.0004779 0.9963 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6464 0.6287 0.4284 0.2464 0.9871 0.9915 0.6468 0.9739 0.9826 0.4363 ] Network output: [ -0.0289 0.1247 0.9229 0.0009807 -0.0004403 1.014 0.0007391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6458 0.6429 0.426 0.2292 0.9856 0.9906 0.6458 0.9696 0.98 0.4274 ] Network output: [ 0.0134 0.9303 0.04277 -8.449e-05 3.793e-05 0.9998 -6.368e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01187 Epoch 2899 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0146 1.002 0.9974 -6.811e-05 3.058e-05 -0.02863 -5.133e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02166 -0.005619 0.02323 0.02075 0.9422 0.9511 0.04083 0.8889 0.9071 0.09933 ] Network output: [ 0.9912 0.02438 0.003287 -0.0004459 0.0002002 -0.01197 -0.000336 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6533 0.08691 0.1968 0.1884 0.9729 0.9875 0.7303 0.903 0.9684 0.6233 ] Network output: [ -0.01105 0.9812 1.006 4.73e-05 -2.124e-05 0.03549 3.565e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04357 0.03096 0.04224 0.02456 0.9861 0.9901 0.04436 0.9713 0.9814 0.04981 ] Network output: [ 0.037 -0.1635 1.089 -0.001489 0.0006685 0.9946 -0.001122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7266 0.6045 0.5483 0.3339 0.9761 0.9893 0.729 0.9126 0.9728 0.6137 ] Network output: [ -0.01629 0.1012 0.9395 0.001065 -0.0004779 0.9962 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6463 0.6285 0.428 0.2467 0.9871 0.9915 0.6467 0.9739 0.9826 0.4359 ] Network output: [ -0.02894 0.125 0.9227 0.0009808 -0.0004403 1.014 0.0007392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6457 0.6428 0.4256 0.2296 0.9856 0.9906 0.6457 0.9696 0.98 0.427 ] Network output: [ 0.01354 0.9293 0.04344 -8.122e-05 3.646e-05 0.9999 -6.121e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01193 Epoch 2900 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01455 1.002 0.9974 -6.869e-05 3.084e-05 -0.02858 -5.176e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02166 -0.005624 0.02325 0.02076 0.9422 0.9511 0.04081 0.8889 0.9071 0.09923 ] Network output: [ 0.9912 0.02446 0.003318 -0.0004491 0.0002016 -0.01203 -0.0003384 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6534 0.08657 0.1972 0.1887 0.9729 0.9875 0.7303 0.903 0.9684 0.623 ] Network output: [ -0.01103 0.9813 1.005 4.772e-05 -2.142e-05 0.03548 3.596e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04354 0.03093 0.04218 0.02457 0.9861 0.9901 0.04433 0.9713 0.9814 0.04972 ] Network output: [ 0.03701 -0.1636 1.089 -0.001489 0.0006684 0.9947 -0.001122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7267 0.6044 0.5482 0.3345 0.9761 0.9893 0.729 0.9126 0.9728 0.6133 ] Network output: [ -0.01632 0.1017 0.9392 0.001065 -0.0004779 0.9961 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6462 0.6284 0.4276 0.247 0.9871 0.9915 0.6465 0.9739 0.9826 0.4355 ] Network output: [ -0.02898 0.1253 0.9225 0.0009809 -0.0004404 1.014 0.0007393 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6455 0.6427 0.4252 0.23 0.9856 0.9906 0.6456 0.9696 0.98 0.4266 ] Network output: [ 0.01368 0.9282 0.04414 -7.784e-05 3.495e-05 0.9999 -5.867e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.012 Epoch 2901 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0145 1.002 0.9973 -6.929e-05 3.111e-05 -0.02853 -5.222e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02166 -0.005628 0.02327 0.02077 0.9422 0.9511 0.04079 0.8889 0.9071 0.09913 ] Network output: [ 0.9912 0.02455 0.003347 -0.0004522 0.000203 -0.01209 -0.0003408 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6535 0.08622 0.1975 0.1891 0.9729 0.9875 0.7304 0.9029 0.9684 0.6227 ] Network output: [ -0.01102 0.9814 1.005 4.812e-05 -2.16e-05 0.03546 3.627e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04351 0.03089 0.04213 0.02458 0.9861 0.9901 0.0443 0.9713 0.9814 0.04964 ] Network output: [ 0.03701 -0.1637 1.089 -0.001489 0.0006683 0.9948 -0.001122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7267 0.6043 0.548 0.3351 0.9761 0.9893 0.7291 0.9126 0.9728 0.613 ] Network output: [ -0.01635 0.1022 0.9389 0.001065 -0.0004779 0.996 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.646 0.6282 0.4272 0.2474 0.9871 0.9915 0.6464 0.9739 0.9826 0.4351 ] Network output: [ -0.02902 0.1257 0.9223 0.0009811 -0.0004404 1.014 0.0007394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6454 0.6425 0.4248 0.2303 0.9856 0.9906 0.6455 0.9696 0.98 0.4262 ] Network output: [ 0.01383 0.9271 0.04486 -7.435e-05 3.338e-05 1 -5.603e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01208 Epoch 2902 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01445 1.002 0.9972 -6.993e-05 3.139e-05 -0.02848 -5.27e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02166 -0.005632 0.02329 0.02078 0.9422 0.9512 0.04078 0.8889 0.9071 0.09902 ] Network output: [ 0.9911 0.02464 0.003374 -0.0004555 0.0002045 -0.01214 -0.0003432 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6536 0.08587 0.1979 0.1894 0.9729 0.9875 0.7304 0.9029 0.9684 0.6224 ] Network output: [ -0.011 0.9815 1.005 4.851e-05 -2.178e-05 0.03545 3.656e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04348 0.03086 0.04206 0.02459 0.9861 0.9901 0.04427 0.9713 0.9814 0.04955 ] Network output: [ 0.03702 -0.1638 1.089 -0.001488 0.0006682 0.9949 -0.001122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7268 0.6041 0.5478 0.3358 0.9761 0.9893 0.7292 0.9126 0.9728 0.6127 ] Network output: [ -0.01638 0.1027 0.9386 0.001065 -0.0004779 0.9959 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6459 0.628 0.4268 0.2478 0.9871 0.9915 0.6463 0.9739 0.9826 0.4347 ] Network output: [ -0.02906 0.126 0.9221 0.0009812 -0.0004405 1.014 0.0007395 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6453 0.6424 0.4244 0.2307 0.9856 0.9906 0.6453 0.9695 0.98 0.4258 ] Network output: [ 0.01398 0.926 0.04561 -7.073e-05 3.175e-05 1 -5.33e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01215 Epoch 2903 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0144 1.002 0.9972 -7.06e-05 3.169e-05 -0.02843 -5.321e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02165 -0.005636 0.02331 0.02079 0.9422 0.9512 0.04076 0.8889 0.9071 0.09892 ] Network output: [ 0.9911 0.02473 0.0034 -0.0004587 0.0002059 -0.0122 -0.0003457 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6537 0.08552 0.1983 0.1898 0.9729 0.9875 0.7305 0.9029 0.9684 0.6221 ] Network output: [ -0.01098 0.9817 1.005 4.887e-05 -2.194e-05 0.03543 3.683e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04345 0.03083 0.042 0.02461 0.9861 0.9901 0.04424 0.9713 0.9814 0.04946 ] Network output: [ 0.03702 -0.1639 1.089 -0.001488 0.0006681 0.995 -0.001122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7269 0.604 0.5477 0.3364 0.9761 0.9893 0.7292 0.9126 0.9728 0.6123 ] Network output: [ -0.01641 0.1032 0.9382 0.001065 -0.0004779 0.9957 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6458 0.6279 0.4263 0.2481 0.9871 0.9915 0.6461 0.9739 0.9826 0.4342 ] Network output: [ -0.0291 0.1263 0.9219 0.0009813 -0.0004405 1.014 0.0007395 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6451 0.6423 0.424 0.2311 0.9856 0.9906 0.6452 0.9695 0.98 0.4253 ] Network output: [ 0.01413 0.9248 0.04639 -6.698e-05 3.007e-05 1 -5.048e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01223 Epoch 2904 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01435 1.002 0.9971 -7.13e-05 3.201e-05 -0.02838 -5.374e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02165 -0.005641 0.02333 0.0208 0.9422 0.9512 0.04075 0.8889 0.9071 0.09881 ] Network output: [ 0.9911 0.02483 0.003424 -0.0004619 0.0002074 -0.01225 -0.0003481 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6538 0.08517 0.1987 0.1902 0.9729 0.9875 0.7306 0.9029 0.9684 0.6218 ] Network output: [ -0.01096 0.9818 1.005 4.921e-05 -2.209e-05 0.03541 3.708e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04342 0.03079 0.04194 0.02462 0.9861 0.9901 0.04421 0.9713 0.9814 0.04937 ] Network output: [ 0.03702 -0.1639 1.089 -0.001488 0.0006681 0.9951 -0.001122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7269 0.6038 0.5475 0.3371 0.9761 0.9893 0.7293 0.9126 0.9728 0.6119 ] Network output: [ -0.01644 0.1037 0.9379 0.001065 -0.0004779 0.9956 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6456 0.6277 0.4259 0.2485 0.9871 0.9915 0.646 0.9739 0.9826 0.4337 ] Network output: [ -0.02914 0.1267 0.9217 0.0009814 -0.0004406 1.014 0.0007396 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.645 0.6421 0.4235 0.2316 0.9856 0.9906 0.6451 0.9695 0.98 0.4249 ] Network output: [ 0.01429 0.9236 0.0472 -6.31e-05 2.833e-05 1 -4.756e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01231 Epoch 2905 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01429 1.002 0.997 -7.205e-05 3.234e-05 -0.02833 -5.43e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02165 -0.005645 0.02335 0.02081 0.9422 0.9512 0.04073 0.8889 0.9071 0.0987 ] Network output: [ 0.991 0.02493 0.003446 -0.0004652 0.0002088 -0.0123 -0.0003506 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6539 0.08482 0.199 0.1906 0.9729 0.9875 0.7307 0.9029 0.9684 0.6214 ] Network output: [ -0.01095 0.982 1.005 4.953e-05 -2.223e-05 0.03539 3.732e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04339 0.03076 0.04187 0.02463 0.9861 0.9901 0.04417 0.9713 0.9814 0.04928 ] Network output: [ 0.03702 -0.164 1.089 -0.001488 0.000668 0.9952 -0.001121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.727 0.6037 0.5473 0.3378 0.9761 0.9893 0.7294 0.9126 0.9728 0.6116 ] Network output: [ -0.01647 0.1043 0.9375 0.001065 -0.0004779 0.9954 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6455 0.6275 0.4255 0.2489 0.9871 0.9915 0.6458 0.9739 0.9826 0.4333 ] Network output: [ -0.02919 0.127 0.9215 0.0009815 -0.0004406 1.014 0.0007397 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6449 0.642 0.4231 0.232 0.9856 0.9906 0.6449 0.9695 0.98 0.4244 ] Network output: [ 0.01446 0.9223 0.04805 -5.908e-05 2.652e-05 1 -4.452e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01239 Epoch 2906 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01424 1.003 0.9969 -7.283e-05 3.27e-05 -0.02828 -5.489e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02165 -0.00565 0.02337 0.02082 0.9422 0.9512 0.04071 0.8889 0.9071 0.09859 ] Network output: [ 0.991 0.02504 0.003466 -0.0004685 0.0002103 -0.01235 -0.0003531 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.654 0.08446 0.1994 0.191 0.9729 0.9875 0.7307 0.9029 0.9684 0.6211 ] Network output: [ -0.01093 0.9821 1.005 4.982e-05 -2.236e-05 0.03537 3.754e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04336 0.03073 0.04181 0.02465 0.9861 0.9901 0.04414 0.9713 0.9814 0.04919 ] Network output: [ 0.03702 -0.1641 1.089 -0.001488 0.000668 0.9953 -0.001121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7271 0.6035 0.5471 0.3385 0.9761 0.9893 0.7294 0.9126 0.9728 0.6112 ] Network output: [ -0.01649 0.1049 0.9372 0.001065 -0.0004779 0.9953 0.0008023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6453 0.6273 0.425 0.2493 0.9871 0.9915 0.6457 0.9739 0.9826 0.4328 ] Network output: [ -0.02923 0.1274 0.9213 0.0009816 -0.0004407 1.014 0.0007397 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6447 0.6418 0.4226 0.2324 0.9856 0.9906 0.6448 0.9695 0.98 0.424 ] Network output: [ 0.01463 0.921 0.04893 -5.491e-05 2.465e-05 1.001 -4.138e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01248 Epoch 2907 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01418 1.003 0.9969 -7.365e-05 3.307e-05 -0.02823 -5.551e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02164 -0.005654 0.02339 0.02083 0.9422 0.9512 0.0407 0.8889 0.9071 0.09848 ] Network output: [ 0.9909 0.02514 0.003484 -0.0004718 0.0002118 -0.0124 -0.0003556 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6541 0.08411 0.1998 0.1915 0.9729 0.9875 0.7308 0.9029 0.9684 0.6208 ] Network output: [ -0.01091 0.9823 1.004 5.008e-05 -2.248e-05 0.03535 3.774e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04333 0.03069 0.04174 0.02466 0.9861 0.9901 0.04411 0.9713 0.9814 0.0491 ] Network output: [ 0.03701 -0.1642 1.089 -0.001488 0.000668 0.9954 -0.001121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7272 0.6034 0.5469 0.3393 0.9761 0.9893 0.7295 0.9126 0.9728 0.6108 ] Network output: [ -0.01652 0.1054 0.9368 0.001065 -0.0004779 0.9951 0.0008022 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6451 0.6271 0.4246 0.2497 0.9871 0.9915 0.6455 0.9739 0.9826 0.4323 ] Network output: [ -0.02928 0.1277 0.9211 0.0009816 -0.0004407 1.014 0.0007398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6446 0.6417 0.4221 0.2329 0.9856 0.9906 0.6446 0.9695 0.9799 0.4235 ] Network output: [ 0.0148 0.9196 0.04984 -5.058e-05 2.271e-05 1.001 -3.812e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01257 Epoch 2908 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01412 1.003 0.9968 -7.452e-05 3.346e-05 -0.02818 -5.616e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02164 -0.005658 0.02341 0.02085 0.9422 0.9512 0.04068 0.8889 0.9071 0.09836 ] Network output: [ 0.9909 0.02526 0.0035 -0.0004751 0.0002133 -0.01244 -0.0003581 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6542 0.08375 0.2002 0.1919 0.9729 0.9875 0.7309 0.9029 0.9684 0.6204 ] Network output: [ -0.01089 0.9825 1.004 5.032e-05 -2.259e-05 0.03533 3.792e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04329 0.03066 0.04167 0.02468 0.9861 0.9901 0.04408 0.9713 0.9814 0.049 ] Network output: [ 0.03701 -0.1642 1.089 -0.001488 0.0006679 0.9955 -0.001121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7272 0.6033 0.5467 0.34 0.9761 0.9893 0.7296 0.9125 0.9728 0.6104 ] Network output: [ -0.01654 0.1061 0.9364 0.001064 -0.0004779 0.995 0.0008022 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.645 0.6269 0.4241 0.2501 0.9871 0.9915 0.6453 0.9739 0.9826 0.4318 ] Network output: [ -0.02932 0.1281 0.9209 0.0009817 -0.0004407 1.014 0.0007398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6444 0.6415 0.4217 0.2334 0.9856 0.9906 0.6445 0.9695 0.9799 0.423 ] Network output: [ 0.01498 0.9182 0.0508 -4.608e-05 2.069e-05 1.001 -3.473e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01266 Epoch 2909 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01406 1.003 0.9967 -7.544e-05 3.387e-05 -0.02813 -5.685e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02164 -0.005663 0.02343 0.02086 0.9422 0.9512 0.04066 0.8889 0.9071 0.09825 ] Network output: [ 0.9908 0.02537 0.003513 -0.0004785 0.0002148 -0.01248 -0.0003606 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6543 0.08339 0.2005 0.1923 0.9729 0.9875 0.7309 0.9029 0.9684 0.6201 ] Network output: [ -0.01088 0.9826 1.004 5.053e-05 -2.268e-05 0.03531 3.808e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04326 0.03062 0.0416 0.02469 0.9861 0.9901 0.04404 0.9713 0.9814 0.0489 ] Network output: [ 0.037 -0.1643 1.089 -0.001488 0.0006679 0.9957 -0.001121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7273 0.6031 0.5465 0.3408 0.9761 0.9893 0.7297 0.9125 0.9728 0.61 ] Network output: [ -0.01657 0.1067 0.936 0.001064 -0.0004779 0.9948 0.0008022 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6448 0.6267 0.4236 0.2506 0.9871 0.9915 0.6452 0.9739 0.9826 0.4313 ] Network output: [ -0.02937 0.1284 0.9207 0.0009817 -0.0004407 1.014 0.0007398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6442 0.6413 0.4212 0.2339 0.9855 0.9906 0.6443 0.9694 0.9799 0.4225 ] Network output: [ 0.01517 0.9167 0.05179 -4.141e-05 1.859e-05 1.001 -3.121e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01275 Epoch 2910 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.014 1.003 0.9966 -7.64e-05 3.43e-05 -0.02809 -5.758e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02164 -0.005667 0.02345 0.02087 0.9422 0.9512 0.04065 0.8889 0.9071 0.09813 ] Network output: [ 0.9908 0.0255 0.003524 -0.0004818 0.0002163 -0.01252 -0.0003631 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6544 0.08303 0.2009 0.1928 0.9729 0.9875 0.731 0.9029 0.9684 0.6197 ] Network output: [ -0.01086 0.9828 1.004 5.07e-05 -2.276e-05 0.03528 3.821e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04323 0.03059 0.04153 0.02471 0.9861 0.9901 0.04401 0.9713 0.9814 0.0488 ] Network output: [ 0.03699 -0.1643 1.088 -0.001488 0.0006679 0.9958 -0.001121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7274 0.603 0.5463 0.3416 0.9761 0.9893 0.7297 0.9125 0.9728 0.6096 ] Network output: [ -0.01659 0.1074 0.9355 0.001064 -0.0004778 0.9946 0.0008021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6446 0.6265 0.4231 0.251 0.9871 0.9915 0.645 0.9739 0.9826 0.4308 ] Network output: [ -0.02942 0.1288 0.9205 0.0009817 -0.0004407 1.013 0.0007398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6441 0.6412 0.4207 0.2344 0.9855 0.9906 0.6441 0.9694 0.9799 0.422 ] Network output: [ 0.01536 0.9152 0.05283 -3.655e-05 1.641e-05 1.001 -2.755e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01285 Epoch 2911 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01394 1.003 0.9965 -7.742e-05 3.476e-05 -0.02804 -5.835e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02163 -0.005672 0.02347 0.02089 0.9422 0.9512 0.04063 0.8889 0.9071 0.09801 ] Network output: [ 0.9907 0.02562 0.003533 -0.0004852 0.0002178 -0.01256 -0.0003657 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6546 0.08267 0.2013 0.1933 0.9729 0.9875 0.7311 0.9029 0.9684 0.6193 ] Network output: [ -0.01084 0.983 1.004 5.084e-05 -2.282e-05 0.03526 3.831e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0432 0.03055 0.04145 0.02472 0.9861 0.9901 0.04397 0.9713 0.9814 0.0487 ] Network output: [ 0.03698 -0.1644 1.088 -0.001488 0.0006679 0.9959 -0.001121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7275 0.6028 0.5461 0.3424 0.9761 0.9893 0.7298 0.9125 0.9728 0.6092 ] Network output: [ -0.01661 0.108 0.9351 0.001064 -0.0004778 0.9944 0.0008021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6444 0.6263 0.4226 0.2515 0.9871 0.9915 0.6448 0.9739 0.9826 0.4302 ] Network output: [ -0.02947 0.1292 0.9204 0.0009817 -0.0004407 1.013 0.0007398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6439 0.641 0.4202 0.235 0.9855 0.9906 0.644 0.9694 0.9799 0.4215 ] Network output: [ 0.01556 0.9135 0.05392 -3.15e-05 1.414e-05 1.001 -2.374e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01296 Epoch 2912 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01388 1.004 0.9964 -7.849e-05 3.524e-05 -0.02799 -5.916e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02163 -0.005676 0.02348 0.0209 0.9422 0.9512 0.04061 0.8889 0.9071 0.09789 ] Network output: [ 0.9907 0.02575 0.003538 -0.0004886 0.0002194 -0.0126 -0.0003682 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6547 0.08231 0.2017 0.1938 0.9729 0.9875 0.7312 0.9029 0.9684 0.619 ] Network output: [ -0.01083 0.9832 1.003 5.094e-05 -2.287e-05 0.03523 3.839e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04316 0.03052 0.04137 0.02474 0.9861 0.9901 0.04394 0.9713 0.9814 0.04859 ] Network output: [ 0.03697 -0.1644 1.088 -0.001488 0.000668 0.9961 -0.001121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7276 0.6027 0.5459 0.3432 0.9761 0.9893 0.7299 0.9125 0.9728 0.6088 ] Network output: [ -0.01663 0.1088 0.9346 0.001064 -0.0004778 0.9942 0.000802 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6443 0.6261 0.4221 0.252 0.9871 0.9915 0.6446 0.9739 0.9826 0.4297 ] Network output: [ -0.02952 0.1295 0.9202 0.0009817 -0.0004407 1.013 0.0007398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6437 0.6408 0.4196 0.2355 0.9855 0.9906 0.6438 0.9694 0.9799 0.421 ] Network output: [ 0.01577 0.9118 0.05506 -2.624e-05 1.178e-05 1.001 -1.977e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01306 Epoch 2913 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01381 1.004 0.9962 -7.963e-05 3.575e-05 -0.02794 -6.001e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02163 -0.00568 0.0235 0.02092 0.9422 0.9512 0.0406 0.8889 0.9071 0.09776 ] Network output: [ 0.9906 0.02589 0.003541 -0.000492 0.0002209 -0.01263 -0.0003708 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6548 0.08195 0.202 0.1943 0.9729 0.9875 0.7312 0.9029 0.9684 0.6186 ] Network output: [ -0.01081 0.9835 1.003 5.1e-05 -2.29e-05 0.0352 3.844e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04313 0.03048 0.0413 0.02476 0.9861 0.9901 0.04391 0.9713 0.9814 0.04849 ] Network output: [ 0.03695 -0.1644 1.088 -0.001488 0.000668 0.9962 -0.001121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7276 0.6025 0.5456 0.344 0.9761 0.9893 0.73 0.9125 0.9728 0.6084 ] Network output: [ -0.01665 0.1095 0.9341 0.001064 -0.0004777 0.994 0.000802 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6441 0.6259 0.4215 0.2525 0.9871 0.9915 0.6444 0.9739 0.9826 0.4291 ] Network output: [ -0.02957 0.1299 0.92 0.0009816 -0.0004407 1.013 0.0007398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6435 0.6406 0.4191 0.2361 0.9855 0.9906 0.6436 0.9694 0.9799 0.4204 ] Network output: [ 0.01598 0.9101 0.05624 -2.075e-05 9.318e-06 1.002 -1.564e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01318 Epoch 2914 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01374 1.004 0.9961 -8.082e-05 3.628e-05 -0.02789 -6.091e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02163 -0.005685 0.02352 0.02094 0.9422 0.9512 0.04058 0.8889 0.9071 0.09763 ] Network output: [ 0.9905 0.02603 0.003541 -0.0004955 0.0002224 -0.01265 -0.0003734 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6549 0.08158 0.2024 0.1948 0.9729 0.9875 0.7313 0.9029 0.9684 0.6182 ] Network output: [ -0.01079 0.9837 1.003 5.102e-05 -2.291e-05 0.03517 3.845e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0431 0.03045 0.04122 0.02478 0.9861 0.9901 0.04387 0.9713 0.9814 0.04838 ] Network output: [ 0.03693 -0.1644 1.088 -0.001488 0.0006681 0.9964 -0.001121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7277 0.6024 0.5454 0.3449 0.9761 0.9893 0.7301 0.9125 0.9728 0.6079 ] Network output: [ -0.01666 0.1103 0.9336 0.001064 -0.0004777 0.9938 0.0008019 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6439 0.6257 0.421 0.253 0.9871 0.9915 0.6442 0.9739 0.9826 0.4285 ] Network output: [ -0.02962 0.1303 0.9198 0.0009816 -0.0004407 1.013 0.0007397 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6433 0.6404 0.4185 0.2367 0.9855 0.9906 0.6434 0.9694 0.9799 0.4199 ] Network output: [ 0.0162 0.9082 0.05749 -1.504e-05 6.752e-06 1.002 -1.134e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0133 Epoch 2915 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01368 1.004 0.996 -8.208e-05 3.685e-05 -0.02785 -6.186e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02162 -0.005689 0.02353 0.02095 0.9423 0.9512 0.04056 0.8889 0.9071 0.0975 ] Network output: [ 0.9905 0.02618 0.003537 -0.0004989 0.000224 -0.01268 -0.000376 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.655 0.08122 0.2028 0.1953 0.9729 0.9875 0.7314 0.9029 0.9684 0.6178 ] Network output: [ -0.01078 0.9839 1.003 5.1e-05 -2.29e-05 0.03514 3.843e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04306 0.03041 0.04113 0.02479 0.9861 0.9901 0.04383 0.9713 0.9814 0.04826 ] Network output: [ 0.03691 -0.1644 1.088 -0.001488 0.0006681 0.9965 -0.001122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7278 0.6023 0.5451 0.3458 0.9761 0.9893 0.7301 0.9125 0.9728 0.6075 ] Network output: [ -0.01668 0.111 0.933 0.001064 -0.0004777 0.9936 0.0008018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6437 0.6254 0.4204 0.2535 0.9871 0.9915 0.644 0.9739 0.9826 0.4279 ] Network output: [ -0.02967 0.1307 0.9196 0.0009815 -0.0004406 1.013 0.0007397 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6431 0.6402 0.418 0.2373 0.9855 0.9906 0.6432 0.9693 0.9799 0.4193 ] Network output: [ 0.01643 0.9063 0.05879 -9.08e-06 4.076e-06 1.002 -6.843e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01342 Epoch 2916 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01361 1.004 0.9959 -8.342e-05 3.745e-05 -0.0278 -6.286e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02162 -0.005694 0.02355 0.02097 0.9423 0.9512 0.04054 0.889 0.9071 0.09737 ] Network output: [ 0.9904 0.02633 0.003531 -0.0005024 0.0002255 -0.0127 -0.0003786 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6551 0.08086 0.2031 0.1959 0.9729 0.9875 0.7315 0.9029 0.9684 0.6174 ] Network output: [ -0.01076 0.9842 1.002 5.093e-05 -2.286e-05 0.0351 3.838e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04303 0.03038 0.04105 0.02481 0.9861 0.9901 0.0438 0.9713 0.9814 0.04815 ] Network output: [ 0.03689 -0.1644 1.088 -0.001488 0.0006682 0.9967 -0.001122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7279 0.6021 0.5449 0.3467 0.9761 0.9893 0.7302 0.9125 0.9728 0.607 ] Network output: [ -0.01669 0.1119 0.9325 0.001064 -0.0004776 0.9934 0.0008018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6435 0.6252 0.4198 0.254 0.9871 0.9915 0.6438 0.9739 0.9826 0.4273 ] Network output: [ -0.02973 0.1311 0.9195 0.0009813 -0.0004406 1.013 0.0007396 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6429 0.64 0.4174 0.238 0.9855 0.9906 0.643 0.9693 0.9799 0.4187 ] Network output: [ 0.01667 0.9043 0.06016 -2.856e-06 1.282e-06 1.002 -2.153e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01355 Epoch 2917 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01353 1.005 0.9957 -8.482e-05 3.808e-05 -0.02776 -6.393e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02162 -0.005698 0.02357 0.02099 0.9423 0.9512 0.04052 0.889 0.9071 0.09724 ] Network output: [ 0.9903 0.02649 0.00352 -0.0005059 0.0002271 -0.01272 -0.0003813 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6553 0.08049 0.2035 0.1964 0.973 0.9875 0.7316 0.9029 0.9684 0.617 ] Network output: [ -0.01075 0.9845 1.002 5.08e-05 -2.281e-05 0.03507 3.829e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04299 0.03034 0.04096 0.02483 0.9861 0.9901 0.04376 0.9713 0.9814 0.04803 ] Network output: [ 0.03686 -0.1643 1.088 -0.001489 0.0006683 0.9968 -0.001122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.728 0.602 0.5446 0.3477 0.9761 0.9893 0.7303 0.9125 0.9728 0.6066 ] Network output: [ -0.0167 0.1127 0.9319 0.001064 -0.0004775 0.9931 0.0008017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6432 0.625 0.4192 0.2546 0.9871 0.9915 0.6436 0.9739 0.9826 0.4266 ] Network output: [ -0.02978 0.1315 0.9193 0.0009812 -0.0004405 1.013 0.0007395 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6427 0.6398 0.4168 0.2387 0.9855 0.9906 0.6428 0.9693 0.9799 0.4181 ] Network output: [ 0.01692 0.9022 0.0616 3.646e-06 -1.637e-06 1.002 2.748e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01369 Epoch 2918 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01346 1.005 0.9955 -8.631e-05 3.875e-05 -0.02771 -6.505e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02161 -0.005702 0.02358 0.02101 0.9423 0.9512 0.04051 0.889 0.9071 0.0971 ] Network output: [ 0.9902 0.02665 0.003506 -0.0005094 0.0002287 -0.01273 -0.0003839 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6554 0.08013 0.2039 0.197 0.973 0.9875 0.7317 0.9029 0.9684 0.6166 ] Network output: [ -0.01073 0.9847 1.002 5.063e-05 -2.273e-05 0.03503 3.815e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04295 0.0303 0.04087 0.02485 0.9861 0.9901 0.04372 0.9713 0.9814 0.04791 ] Network output: [ 0.03683 -0.1643 1.088 -0.001489 0.0006684 0.997 -0.001122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7281 0.6019 0.5443 0.3487 0.9761 0.9893 0.7304 0.9125 0.9728 0.6061 ] Network output: [ -0.0167 0.1136 0.9313 0.001064 -0.0004775 0.9929 0.0008015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.643 0.6247 0.4186 0.2552 0.9871 0.9915 0.6434 0.9739 0.9826 0.426 ] Network output: [ -0.02984 0.1319 0.9191 0.000981 -0.0004404 1.013 0.0007393 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6425 0.6396 0.4162 0.2394 0.9855 0.9906 0.6426 0.9693 0.9799 0.4175 ] Network output: [ 0.01717 0.9 0.0631 1.045e-05 -4.689e-06 1.003 7.872e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01383 Epoch 2919 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01338 1.005 0.9954 -8.788e-05 3.945e-05 -0.02767 -6.623e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02161 -0.005707 0.02359 0.02103 0.9423 0.9512 0.04049 0.889 0.9072 0.09696 ] Network output: [ 0.9902 0.02683 0.003487 -0.0005129 0.0002303 -0.01273 -0.0003866 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6555 0.07977 0.2042 0.1976 0.973 0.9875 0.7318 0.9029 0.9684 0.6161 ] Network output: [ -0.01072 0.985 1.002 5.039e-05 -2.262e-05 0.03499 3.798e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04292 0.03027 0.04078 0.02487 0.9861 0.9901 0.04369 0.9713 0.9814 0.04779 ] Network output: [ 0.0368 -0.1642 1.087 -0.001489 0.0006686 0.9972 -0.001122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7282 0.6017 0.544 0.3497 0.9761 0.9893 0.7305 0.9125 0.9728 0.6056 ] Network output: [ -0.01671 0.1145 0.9306 0.001063 -0.0004774 0.9926 0.0008014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6428 0.6245 0.4179 0.2557 0.9871 0.9915 0.6431 0.9739 0.9826 0.4253 ] Network output: [ -0.0299 0.1323 0.919 0.0009808 -0.0004403 1.013 0.0007392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6423 0.6393 0.4155 0.2401 0.9855 0.9905 0.6423 0.9693 0.9798 0.4168 ] Network output: [ 0.01744 0.8976 0.06469 1.756e-05 -7.884e-06 1.003 1.323e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01398 Epoch 2920 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0133 1.005 0.9952 -8.954e-05 4.02e-05 -0.02762 -6.748e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02161 -0.005711 0.02361 0.02105 0.9423 0.9512 0.04047 0.889 0.9072 0.09681 ] Network output: [ 0.9901 0.02701 0.003465 -0.0005165 0.0002319 -0.01274 -0.0003893 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6556 0.0794 0.2046 0.1982 0.973 0.9875 0.7319 0.9029 0.9684 0.6157 ] Network output: [ -0.0107 0.9854 1.001 5.009e-05 -2.249e-05 0.03495 3.775e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04288 0.03023 0.04069 0.02489 0.9861 0.9901 0.04365 0.9713 0.9814 0.04766 ] Network output: [ 0.03676 -0.1641 1.087 -0.00149 0.0006687 0.9974 -0.001123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7283 0.6016 0.5437 0.3507 0.9761 0.9893 0.7306 0.9125 0.9728 0.6051 ] Network output: [ -0.01671 0.1155 0.9299 0.001063 -0.0004773 0.9923 0.0008013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6426 0.6242 0.4173 0.2564 0.9871 0.9915 0.6429 0.9739 0.9826 0.4246 ] Network output: [ -0.02996 0.1327 0.9188 0.0009806 -0.0004402 1.012 0.000739 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6421 0.6391 0.4149 0.2409 0.9855 0.9905 0.6421 0.9693 0.9798 0.4162 ] Network output: [ 0.01772 0.8952 0.06636 2.501e-05 -1.123e-05 1.003 1.885e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01414 Epoch 2921 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01322 1.006 0.995 -9.13e-05 4.099e-05 -0.02758 -6.881e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0216 -0.005715 0.02362 0.02107 0.9423 0.9512 0.04045 0.889 0.9072 0.09667 ] Network output: [ 0.99 0.02719 0.003438 -0.0005201 0.0002335 -0.01273 -0.000392 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6558 0.07904 0.2049 0.1989 0.973 0.9875 0.7319 0.9029 0.9684 0.6153 ] Network output: [ -0.01069 0.9857 1.001 4.973e-05 -2.232e-05 0.0349 3.748e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04285 0.03019 0.04059 0.02491 0.9861 0.9901 0.04361 0.9713 0.9814 0.04754 ] Network output: [ 0.03672 -0.164 1.087 -0.00149 0.0006689 0.9976 -0.001123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7284 0.6015 0.5434 0.3518 0.9761 0.9893 0.7307 0.9125 0.9728 0.6046 ] Network output: [ -0.0167 0.1165 0.9292 0.001063 -0.0004772 0.992 0.0008011 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6423 0.6239 0.4166 0.257 0.9871 0.9915 0.6427 0.9739 0.9826 0.4239 ] Network output: [ -0.03002 0.1331 0.9187 0.0009804 -0.0004401 1.012 0.0007388 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6418 0.6388 0.4142 0.2417 0.9855 0.9905 0.6419 0.9692 0.9798 0.4155 ] Network output: [ 0.018 0.8926 0.06812 3.282e-05 -1.473e-05 1.003 2.474e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01431 Epoch 2922 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01314 1.006 0.9948 -9.316e-05 4.183e-05 -0.02754 -7.021e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0216 -0.00572 0.02363 0.02109 0.9423 0.9512 0.04043 0.889 0.9072 0.09652 ] Network output: [ 0.9899 0.02739 0.003406 -0.0005237 0.0002351 -0.01273 -0.0003947 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6559 0.07868 0.2053 0.1996 0.973 0.9875 0.732 0.9029 0.9684 0.6148 ] Network output: [ -0.01067 0.9861 1.001 4.929e-05 -2.213e-05 0.03485 3.715e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04281 0.03016 0.04049 0.02494 0.9861 0.9901 0.04357 0.9713 0.9814 0.0474 ] Network output: [ 0.03668 -0.1638 1.087 -0.00149 0.0006691 0.9978 -0.001123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7285 0.6013 0.5431 0.3529 0.9761 0.9893 0.7308 0.9124 0.9728 0.6041 ] Network output: [ -0.0167 0.1176 0.9285 0.001063 -0.0004771 0.9917 0.000801 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6421 0.6237 0.4159 0.2576 0.9871 0.9915 0.6424 0.9739 0.9826 0.4232 ] Network output: [ -0.03008 0.1335 0.9185 0.0009801 -0.00044 1.012 0.0007386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6416 0.6386 0.4135 0.2425 0.9855 0.9905 0.6416 0.9692 0.9798 0.4148 ] Network output: [ 0.0183 0.8899 0.06997 4.101e-05 -1.841e-05 1.004 3.091e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01449 Epoch 2923 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01305 1.006 0.9946 -9.514e-05 4.271e-05 -0.0275 -7.17e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0216 -0.005724 0.02364 0.02111 0.9423 0.9512 0.04042 0.889 0.9072 0.09636 ] Network output: [ 0.9898 0.02759 0.003369 -0.0005273 0.0002367 -0.01271 -0.0003974 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.656 0.07832 0.2056 0.2003 0.973 0.9875 0.7321 0.9029 0.9684 0.6143 ] Network output: [ -0.01066 0.9864 1 4.878e-05 -2.19e-05 0.0348 3.676e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04277 0.03012 0.04038 0.02496 0.986 0.9901 0.04353 0.9713 0.9814 0.04727 ] Network output: [ 0.03663 -0.1637 1.086 -0.001491 0.0006693 0.998 -0.001124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7286 0.6012 0.5428 0.3541 0.9761 0.9893 0.7309 0.9124 0.9727 0.6035 ] Network output: [ -0.01669 0.1187 0.9277 0.001063 -0.000477 0.9914 0.0008008 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6418 0.6234 0.4151 0.2583 0.9871 0.9915 0.6422 0.9739 0.9826 0.4224 ] Network output: [ -0.03014 0.134 0.9184 0.0009797 -0.0004398 1.012 0.0007383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6413 0.6383 0.4128 0.2434 0.9855 0.9905 0.6414 0.9692 0.9798 0.4141 ] Network output: [ 0.01862 0.887 0.07193 4.962e-05 -2.227e-05 1.004 3.739e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01468 Epoch 2924 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01296 1.007 0.9944 -9.722e-05 4.365e-05 -0.02747 -7.327e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02159 -0.005728 0.02365 0.02114 0.9423 0.9512 0.0404 0.889 0.9072 0.0962 ] Network output: [ 0.9897 0.0278 0.003328 -0.000531 0.0002384 -0.01269 -0.0004002 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6562 0.07797 0.206 0.201 0.973 0.9875 0.7322 0.9029 0.9684 0.6138 ] Network output: [ -0.01065 0.9868 0.9999 4.819e-05 -2.163e-05 0.03475 3.632e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04273 0.03008 0.04028 0.02498 0.986 0.9901 0.04349 0.9713 0.9814 0.04713 ] Network output: [ 0.03657 -0.1635 1.086 -0.001491 0.0006696 0.9982 -0.001124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7287 0.6011 0.5424 0.3552 0.9761 0.9893 0.731 0.9124 0.9727 0.603 ] Network output: [ -0.01667 0.1198 0.9268 0.001062 -0.0004769 0.991 0.0008006 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6415 0.6231 0.4144 0.259 0.9871 0.9915 0.6419 0.9739 0.9826 0.4216 ] Network output: [ -0.03021 0.1344 0.9183 0.0009793 -0.0004397 1.012 0.0007381 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.641 0.6381 0.4121 0.2443 0.9855 0.9905 0.6411 0.9692 0.9798 0.4134 ] Network output: [ 0.01894 0.884 0.07399 5.866e-05 -2.633e-05 1.004 4.42e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01489 Epoch 2925 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01287 1.007 0.9941 -9.943e-05 4.464e-05 -0.02743 -7.493e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02159 -0.005732 0.02366 0.02116 0.9423 0.9512 0.04038 0.889 0.9072 0.09604 ] Network output: [ 0.9896 0.02802 0.003281 -0.0005347 0.00024 -0.01266 -0.0004029 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6563 0.07761 0.2063 0.2017 0.973 0.9875 0.7323 0.9029 0.9684 0.6133 ] Network output: [ -0.01064 0.9873 0.9995 4.751e-05 -2.133e-05 0.03469 3.581e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04269 0.03004 0.04017 0.02501 0.986 0.9901 0.04345 0.9713 0.9814 0.04698 ] Network output: [ 0.03651 -0.1633 1.086 -0.001492 0.0006698 0.9984 -0.001124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7288 0.601 0.5421 0.3565 0.9761 0.9893 0.7311 0.9124 0.9727 0.6024 ] Network output: [ -0.01666 0.121 0.926 0.001062 -0.0004768 0.9907 0.0008004 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6413 0.6228 0.4136 0.2598 0.9871 0.9915 0.6416 0.9739 0.9826 0.4208 ] Network output: [ -0.03027 0.1349 0.9181 0.0009789 -0.0004395 1.012 0.0007377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6407 0.6378 0.4113 0.2453 0.9854 0.9905 0.6408 0.9691 0.9798 0.4126 ] Network output: [ 0.01928 0.8809 0.07618 6.816e-05 -3.06e-05 1.005 5.137e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01511 Epoch 2926 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01277 1.008 0.9939 -0.0001018 4.569e-05 -0.0274 -7.67e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02159 -0.005737 0.02366 0.02119 0.9423 0.9512 0.04036 0.889 0.9072 0.09588 ] Network output: [ 0.9895 0.02825 0.003228 -0.0005384 0.0002417 -0.01263 -0.0004057 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6564 0.07726 0.2066 0.2025 0.973 0.9875 0.7325 0.9029 0.9684 0.6128 ] Network output: [ -0.01063 0.9877 0.9991 4.674e-05 -2.098e-05 0.03462 3.522e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04265 0.03001 0.04005 0.02503 0.986 0.9901 0.04341 0.9713 0.9814 0.04684 ] Network output: [ 0.03645 -0.1631 1.085 -0.001493 0.0006701 0.9987 -0.001125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7289 0.6009 0.5417 0.3577 0.9761 0.9893 0.7312 0.9124 0.9727 0.6018 ] Network output: [ -0.01663 0.1223 0.925 0.001062 -0.0004766 0.9903 0.0008001 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.641 0.6225 0.4128 0.2605 0.9871 0.9915 0.6413 0.9739 0.9826 0.42 ] Network output: [ -0.03034 0.1353 0.918 0.0009784 -0.0004393 1.011 0.0007374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6405 0.6375 0.4106 0.2463 0.9854 0.9905 0.6405 0.9691 0.9798 0.4118 ] Network output: [ 0.01964 0.8775 0.07849 7.817e-05 -3.509e-05 1.005 5.891e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01534 Epoch 2927 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01267 1.008 0.9936 -0.0001043 4.68e-05 -0.02737 -7.857e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02158 -0.005741 0.02367 0.02122 0.9423 0.9512 0.04034 0.889 0.9072 0.09571 ] Network output: [ 0.9894 0.02849 0.003169 -0.0005421 0.0002434 -0.01259 -0.0004086 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6566 0.07692 0.207 0.2033 0.973 0.9875 0.7326 0.9029 0.9684 0.6123 ] Network output: [ -0.01062 0.9882 0.9987 4.587e-05 -2.059e-05 0.03456 3.457e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04262 0.02997 0.03994 0.02506 0.986 0.9901 0.04337 0.9713 0.9814 0.04669 ] Network output: [ 0.03638 -0.1628 1.085 -0.001493 0.0006705 0.9989 -0.001126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.729 0.6008 0.5413 0.359 0.9761 0.9893 0.7313 0.9124 0.9727 0.6013 ] Network output: [ -0.01661 0.1236 0.9241 0.001061 -0.0004765 0.9899 0.0007998 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6407 0.6222 0.412 0.2613 0.987 0.9915 0.6411 0.9739 0.9826 0.4191 ] Network output: [ -0.03041 0.1358 0.9179 0.0009779 -0.000439 1.011 0.000737 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6402 0.6372 0.4097 0.2473 0.9854 0.9905 0.6402 0.9691 0.9798 0.411 ] Network output: [ 0.02001 0.874 0.08094 8.871e-05 -3.983e-05 1.005 6.686e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01559 Epoch 2928 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01256 1.008 0.9933 -0.0001069 4.799e-05 -0.02734 -8.056e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02158 -0.005745 0.02367 0.02125 0.9423 0.9513 0.04032 0.889 0.9072 0.09553 ] Network output: [ 0.9892 0.02873 0.003105 -0.0005459 0.0002451 -0.01254 -0.0004114 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6567 0.07657 0.2073 0.2042 0.973 0.9876 0.7327 0.9029 0.9684 0.6117 ] Network output: [ -0.01061 0.9887 0.9982 4.49e-05 -2.016e-05 0.03449 3.384e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04258 0.02993 0.03982 0.02509 0.986 0.9901 0.04333 0.9713 0.9815 0.04653 ] Network output: [ 0.0363 -0.1625 1.085 -0.001494 0.0006708 0.9992 -0.001126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7291 0.6007 0.5409 0.3604 0.9761 0.9893 0.7315 0.9124 0.9727 0.6006 ] Network output: [ -0.01657 0.125 0.923 0.001061 -0.0004763 0.9894 0.0007996 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6404 0.6219 0.4111 0.2621 0.987 0.9915 0.6408 0.9739 0.9826 0.4182 ] Network output: [ -0.03048 0.1363 0.9177 0.0009773 -0.0004387 1.011 0.0007365 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6398 0.6369 0.4089 0.2484 0.9854 0.9905 0.6399 0.9691 0.9797 0.4102 ] Network output: [ 0.0204 0.8702 0.08354 9.983e-05 -4.482e-05 1.006 7.524e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01586 Epoch 2929 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01246 1.009 0.993 -0.0001097 4.924e-05 -0.02732 -8.266e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02158 -0.005749 0.02368 0.02128 0.9423 0.9513 0.0403 0.889 0.9072 0.09535 ] Network output: [ 0.9891 0.02899 0.003034 -0.0005498 0.0002468 -0.01248 -0.0004143 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6569 0.07623 0.2076 0.205 0.973 0.9876 0.7328 0.9029 0.9684 0.6112 ] Network output: [ -0.0106 0.9892 0.9977 4.381e-05 -1.967e-05 0.03441 3.302e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04254 0.0299 0.03969 0.02512 0.986 0.9901 0.04329 0.9713 0.9815 0.04637 ] Network output: [ 0.03621 -0.1621 1.084 -0.001495 0.0006712 0.9994 -0.001127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7293 0.6006 0.5404 0.3618 0.9761 0.9893 0.7316 0.9124 0.9727 0.6 ] Network output: [ -0.01654 0.1264 0.9219 0.00106 -0.0004761 0.989 0.0007992 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6401 0.6216 0.4102 0.263 0.987 0.9915 0.6405 0.9739 0.9826 0.4173 ] Network output: [ -0.03055 0.1368 0.9176 0.0009766 -0.0004384 1.011 0.000736 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6395 0.6365 0.4081 0.2495 0.9854 0.9905 0.6396 0.969 0.9797 0.4093 ] Network output: [ 0.0208 0.8662 0.0863 0.0001116 -5.009e-05 1.006 8.408e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01616 Epoch 2930 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01234 1.01 0.9926 -0.0001126 5.057e-05 -0.02729 -8.489e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02157 -0.005753 0.02368 0.02131 0.9423 0.9513 0.04028 0.889 0.9072 0.09517 ] Network output: [ 0.989 0.02926 0.002956 -0.0005536 0.0002486 -0.01242 -0.0004172 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.657 0.07589 0.2079 0.2059 0.973 0.9876 0.7329 0.9029 0.9684 0.6106 ] Network output: [ -0.01059 0.9898 0.9972 4.261e-05 -1.913e-05 0.03433 3.211e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0425 0.02986 0.03956 0.02515 0.986 0.9901 0.04325 0.9713 0.9815 0.0462 ] Network output: [ 0.03612 -0.1617 1.084 -0.001496 0.0006717 0.9997 -0.001128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7294 0.6005 0.54 0.3633 0.9761 0.9893 0.7317 0.9124 0.9727 0.5994 ] Network output: [ -0.01649 0.128 0.9208 0.00106 -0.0004759 0.9885 0.0007989 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6398 0.6212 0.4093 0.2639 0.987 0.9915 0.6401 0.9739 0.9826 0.4163 ] Network output: [ -0.03062 0.1373 0.9175 0.0009759 -0.0004381 1.01 0.0007355 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6392 0.6362 0.4072 0.2507 0.9854 0.9905 0.6392 0.969 0.9797 0.4084 ] Network output: [ 0.02123 0.862 0.08923 0.000124 -5.566e-05 1.007 9.343e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01647 Epoch 2931 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01223 1.01 0.9923 -0.0001158 5.198e-05 -0.02728 -8.726e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02157 -0.005757 0.02368 0.02135 0.9423 0.9513 0.04027 0.889 0.9072 0.09498 ] Network output: [ 0.9888 0.02954 0.002872 -0.0005576 0.0002503 -0.01234 -0.0004202 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6572 0.07556 0.2082 0.2069 0.973 0.9876 0.7331 0.9029 0.9684 0.61 ] Network output: [ -0.01058 0.9904 0.9967 4.128e-05 -1.853e-05 0.03424 3.111e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04246 0.02982 0.03943 0.02518 0.986 0.9901 0.04321 0.9713 0.9815 0.04603 ] Network output: [ 0.03602 -0.1613 1.083 -0.001497 0.0006721 1 -0.001128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7295 0.6004 0.5395 0.3648 0.9761 0.9893 0.7318 0.9124 0.9727 0.5987 ] Network output: [ -0.01644 0.1296 0.9196 0.001059 -0.0004756 0.988 0.0007985 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6395 0.6209 0.4083 0.2648 0.987 0.9915 0.6398 0.9739 0.9826 0.4153 ] Network output: [ -0.0307 0.1378 0.9174 0.0009751 -0.0004378 1.01 0.0007349 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6388 0.6358 0.4063 0.252 0.9854 0.9905 0.6389 0.969 0.9797 0.4075 ] Network output: [ 0.02167 0.8576 0.09235 0.0001371 -6.154e-05 1.007 0.0001033 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01682 Epoch 2932 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0121 1.011 0.9919 -0.0001191 5.348e-05 -0.02726 -8.978e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02157 -0.005761 0.02368 0.02138 0.9424 0.9513 0.04025 0.889 0.9072 0.09478 ] Network output: [ 0.9887 0.02984 0.002781 -0.0005616 0.0002521 -0.01226 -0.0004232 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6574 0.07524 0.2085 0.2079 0.973 0.9876 0.7332 0.9028 0.9684 0.6094 ] Network output: [ -0.01058 0.9911 0.9961 3.981e-05 -1.787e-05 0.03415 3e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04242 0.02979 0.03929 0.02521 0.986 0.9901 0.04317 0.9713 0.9815 0.04585 ] Network output: [ 0.03591 -0.1608 1.083 -0.001498 0.0006727 1 -0.001129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7297 0.6004 0.539 0.3664 0.9761 0.9893 0.732 0.9124 0.9727 0.598 ] Network output: [ -0.01638 0.1313 0.9183 0.001059 -0.0004754 0.9875 0.000798 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6391 0.6205 0.4074 0.2658 0.987 0.9915 0.6395 0.9739 0.9826 0.4143 ] Network output: [ -0.03078 0.1384 0.9173 0.0009742 -0.0004374 1.01 0.0007342 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6385 0.6355 0.4053 0.2533 0.9854 0.9905 0.6385 0.969 0.9797 0.4065 ] Network output: [ 0.02214 0.8528 0.09567 0.000151 -6.777e-05 1.008 0.0001138 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01719 Epoch 2933 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01198 1.011 0.9915 -0.0001227 5.507e-05 -0.02725 -9.245e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02156 -0.005764 0.02368 0.02142 0.9424 0.9513 0.04023 0.889 0.9072 0.09458 ] Network output: [ 0.9885 0.03014 0.002683 -0.0005656 0.0002539 -0.01216 -0.0004263 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6575 0.07492 0.2088 0.209 0.973 0.9876 0.7333 0.9028 0.9684 0.6088 ] Network output: [ -0.01057 0.9918 0.9955 3.82e-05 -1.715e-05 0.03405 2.879e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04238 0.02975 0.03914 0.02524 0.986 0.9901 0.04313 0.9714 0.9815 0.04567 ] Network output: [ 0.03579 -0.1603 1.082 -0.0015 0.0006732 1.001 -0.00113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7298 0.6003 0.5385 0.368 0.9761 0.9893 0.7321 0.9123 0.9727 0.5973 ] Network output: [ -0.01632 0.133 0.9169 0.001058 -0.0004751 0.987 0.0007975 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6388 0.6202 0.4063 0.2668 0.987 0.9915 0.6391 0.9739 0.9826 0.4132 ] Network output: [ -0.03085 0.139 0.9172 0.0009732 -0.0004369 1.009 0.0007334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6381 0.6351 0.4043 0.2547 0.9854 0.9905 0.6382 0.9689 0.9797 0.4055 ] Network output: [ 0.02263 0.8477 0.09921 0.0001657 -7.438e-05 1.008 0.0001249 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0176 Epoch 2934 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01184 1.012 0.991 -0.0001264 5.676e-05 -0.02725 -9.529e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02156 -0.005768 0.02367 0.02146 0.9424 0.9513 0.04021 0.889 0.9072 0.09437 ] Network output: [ 0.9884 0.03046 0.002578 -0.0005697 0.0002558 -0.01206 -0.0004294 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6577 0.0746 0.209 0.2101 0.973 0.9876 0.7335 0.9028 0.9684 0.6081 ] Network output: [ -0.01057 0.9925 0.9948 3.643e-05 -1.635e-05 0.03395 2.745e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04234 0.02972 0.039 0.02528 0.986 0.9901 0.04309 0.9714 0.9815 0.04549 ] Network output: [ 0.03566 -0.1597 1.081 -0.001501 0.0006738 1.001 -0.001131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.73 0.6003 0.538 0.3698 0.9761 0.9893 0.7323 0.9123 0.9727 0.5965 ] Network output: [ -0.01625 0.1349 0.9155 0.001058 -0.0004748 0.9864 0.000797 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6384 0.6198 0.4053 0.2678 0.987 0.9915 0.6388 0.9739 0.9826 0.4121 ] Network output: [ -0.03094 0.1396 0.9171 0.0009721 -0.0004364 1.009 0.0007326 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6377 0.6347 0.4033 0.2561 0.9854 0.9905 0.6378 0.9689 0.9797 0.4045 ] Network output: [ 0.02314 0.8424 0.103 0.0001813 -8.138e-05 1.009 0.0001366 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01805 Epoch 2935 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01171 1.013 0.9905 -0.0001304 5.856e-05 -0.02725 -9.83e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02156 -0.005772 0.02367 0.02151 0.9424 0.9513 0.04019 0.889 0.9072 0.09416 ] Network output: [ 0.9882 0.03079 0.002466 -0.0005739 0.0002576 -0.01194 -0.0004325 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6579 0.0743 0.2093 0.2112 0.973 0.9876 0.7336 0.9028 0.9684 0.6074 ] Network output: [ -0.01057 0.9933 0.9941 3.45e-05 -1.549e-05 0.03384 2.6e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0423 0.02969 0.03884 0.02532 0.986 0.9901 0.04305 0.9714 0.9815 0.04529 ] Network output: [ 0.03552 -0.159 1.081 -0.001502 0.0006745 1.001 -0.001132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7301 0.6003 0.5374 0.3716 0.9761 0.9893 0.7324 0.9123 0.9727 0.5958 ] Network output: [ -0.01617 0.1369 0.9139 0.001057 -0.0004744 0.9858 0.0007964 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6381 0.6194 0.4041 0.2689 0.987 0.9915 0.6384 0.9739 0.9826 0.411 ] Network output: [ -0.03102 0.1402 0.917 0.0009709 -0.0004359 1.009 0.0007317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6373 0.6343 0.4022 0.2576 0.9854 0.9905 0.6374 0.9689 0.9796 0.4035 ] Network output: [ 0.02368 0.8366 0.107 0.0001978 -8.88e-05 1.01 0.0001491 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01854 Epoch 2936 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01156 1.014 0.99 -0.0001347 6.046e-05 -0.02726 -0.0001015 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02155 -0.005776 0.02366 0.02155 0.9424 0.9513 0.04017 0.889 0.9073 0.09394 ] Network output: [ 0.988 0.03113 0.002348 -0.0005782 0.0002596 -0.01182 -0.0004357 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6581 0.074 0.2096 0.2124 0.973 0.9876 0.7338 0.9028 0.9684 0.6067 ] Network output: [ -0.01057 0.9942 0.9934 3.239e-05 -1.454e-05 0.03372 2.441e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04226 0.02965 0.03868 0.02536 0.986 0.9901 0.04301 0.9714 0.9815 0.04509 ] Network output: [ 0.03537 -0.1583 1.08 -0.001504 0.0006752 1.002 -0.001134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7303 0.6003 0.5369 0.3734 0.9761 0.9893 0.7326 0.9123 0.9727 0.595 ] Network output: [ -0.01608 0.139 0.9123 0.001056 -0.000474 0.9852 0.0007957 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6377 0.6191 0.403 0.27 0.987 0.9915 0.6381 0.9739 0.9826 0.4098 ] Network output: [ -0.03111 0.1409 0.9168 0.0009695 -0.0004352 1.008 0.0007307 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6369 0.6339 0.4011 0.2592 0.9853 0.9905 0.6369 0.9688 0.9796 0.4024 ] Network output: [ 0.02425 0.8305 0.1113 0.0002154 -9.669e-05 1.011 0.0001623 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01908 Epoch 2937 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01141 1.014 0.9895 -0.0001392 6.249e-05 -0.02727 -0.0001049 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02155 -0.005779 0.02365 0.0216 0.9424 0.9513 0.04016 0.889 0.9073 0.09371 ] Network output: [ 0.9878 0.03148 0.002223 -0.0005825 0.0002615 -0.01168 -0.000439 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6583 0.07371 0.2098 0.2137 0.973 0.9876 0.734 0.9028 0.9684 0.606 ] Network output: [ -0.01057 0.9951 0.9926 3.01e-05 -1.351e-05 0.03359 2.269e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04222 0.02962 0.03852 0.0254 0.986 0.9901 0.04297 0.9714 0.9815 0.04488 ] Network output: [ 0.03521 -0.1575 1.079 -0.001506 0.000676 1.002 -0.001135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7305 0.6003 0.5362 0.3754 0.9761 0.9893 0.7328 0.9123 0.9727 0.5941 ] Network output: [ -0.01598 0.1413 0.9105 0.001055 -0.0004736 0.9845 0.000795 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6373 0.6187 0.4018 0.2712 0.987 0.9915 0.6377 0.9739 0.9826 0.4086 ] Network output: [ -0.0312 0.1417 0.9167 0.000968 -0.0004346 1.008 0.0007295 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6364 0.6334 0.4 0.2608 0.9853 0.9904 0.6365 0.9688 0.9796 0.4012 ] Network output: [ 0.02484 0.824 0.1159 0.0002341 -0.0001051 1.011 0.0001764 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01968 Epoch 2938 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01125 1.015 0.9889 -0.000144 6.464e-05 -0.02729 -0.0001085 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02155 -0.005783 0.02364 0.02165 0.9424 0.9513 0.04014 0.889 0.9073 0.09347 ] Network output: [ 0.9876 0.03185 0.002093 -0.000587 0.0002635 -0.01152 -0.0004424 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6585 0.07343 0.21 0.215 0.973 0.9876 0.7342 0.9028 0.9684 0.6053 ] Network output: [ -0.01058 0.9961 0.9918 2.762e-05 -1.24e-05 0.03345 2.082e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04219 0.02959 0.03834 0.02544 0.986 0.9901 0.04293 0.9714 0.9815 0.04467 ] Network output: [ 0.03503 -0.1566 1.078 -0.001508 0.0006769 1.002 -0.001136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7307 0.6003 0.5356 0.3774 0.9761 0.9893 0.733 0.9123 0.9727 0.5933 ] Network output: [ -0.01588 0.1436 0.9087 0.001054 -0.0004731 0.9838 0.0007942 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.637 0.6183 0.4005 0.2725 0.987 0.9915 0.6373 0.9739 0.9826 0.4073 ] Network output: [ -0.03129 0.1424 0.9165 0.0009663 -0.0004338 1.008 0.0007282 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.636 0.633 0.3988 0.2626 0.9853 0.9904 0.636 0.9688 0.9796 0.4 ] Network output: [ 0.02547 0.817 0.1209 0.0002539 -0.000114 1.012 0.0001914 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02034 Epoch 2939 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01109 1.016 0.9883 -0.0001491 6.692e-05 -0.02732 -0.0001123 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02155 -0.005786 0.02362 0.02171 0.9424 0.9513 0.04013 0.889 0.9073 0.09323 ] Network output: [ 0.9874 0.03222 0.001957 -0.0005916 0.0002656 -0.01136 -0.0004458 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6587 0.07316 0.2103 0.2164 0.973 0.9876 0.7343 0.9028 0.9684 0.6045 ] Network output: [ -0.01058 0.9971 0.9909 2.493e-05 -1.119e-05 0.03331 1.879e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04215 0.02956 0.03817 0.02549 0.986 0.9901 0.0429 0.9714 0.9815 0.04445 ] Network output: [ 0.03484 -0.1556 1.077 -0.00151 0.0006778 1.003 -0.001138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7309 0.6003 0.5349 0.3795 0.9761 0.9893 0.7332 0.9123 0.9727 0.5924 ] Network output: [ -0.01577 0.1461 0.9067 0.001053 -0.0004725 0.983 0.0007932 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6366 0.6179 0.3993 0.2737 0.987 0.9915 0.6369 0.9739 0.9826 0.406 ] Network output: [ -0.03139 0.1433 0.9164 0.0009644 -0.000433 1.007 0.0007268 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6355 0.6325 0.3976 0.2644 0.9853 0.9904 0.6356 0.9687 0.9796 0.3988 ] Network output: [ 0.02613 0.8095 0.1261 0.0002751 -0.0001235 1.013 0.0002073 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02107 Epoch 2940 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01092 1.017 0.9876 -0.0001544 6.934e-05 -0.02736 -0.0001164 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02154 -0.00579 0.02361 0.02177 0.9424 0.9513 0.04011 0.8891 0.9073 0.09298 ] Network output: [ 0.9872 0.0326 0.001818 -0.0005962 0.0002677 -0.01118 -0.0004493 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6589 0.07291 0.2105 0.2179 0.973 0.9876 0.7346 0.9028 0.9684 0.6037 ] Network output: [ -0.01059 0.9982 0.9899 2.202e-05 -9.886e-06 0.03315 1.66e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04212 0.02954 0.03798 0.02554 0.986 0.9901 0.04286 0.9714 0.9815 0.04422 ] Network output: [ 0.03463 -0.1545 1.076 -0.001512 0.0006788 1.003 -0.00114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7311 0.6004 0.5342 0.3818 0.9761 0.9893 0.7334 0.9123 0.9727 0.5915 ] Network output: [ -0.01564 0.1488 0.9045 0.001051 -0.0004719 0.9822 0.0007922 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6361 0.6175 0.3979 0.2751 0.987 0.9915 0.6365 0.9739 0.9826 0.4046 ] Network output: [ -0.0315 0.1442 0.9162 0.0009623 -0.000432 1.007 0.0007252 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.635 0.632 0.3963 0.2662 0.9853 0.9904 0.6351 0.9687 0.9796 0.3975 ] Network output: [ 0.02683 0.8016 0.1318 0.0002976 -0.0001336 1.014 0.0002243 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02188 Epoch 2941 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01074 1.018 0.9869 -0.0001602 7.19e-05 -0.0274 -0.0001207 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02154 -0.005794 0.02359 0.02183 0.9424 0.9513 0.0401 0.8891 0.9073 0.09272 ] Network output: [ 0.9869 0.03299 0.001676 -0.000601 0.0002698 -0.01099 -0.000453 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6592 0.07266 0.2107 0.2195 0.973 0.9876 0.7348 0.9028 0.9684 0.6028 ] Network output: [ -0.0106 0.9994 0.9889 1.888e-05 -8.478e-06 0.03299 1.423e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04208 0.02951 0.03779 0.02559 0.986 0.9901 0.04283 0.9714 0.9815 0.04398 ] Network output: [ 0.03441 -0.1533 1.075 -0.001515 0.0006799 1.004 -0.001141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7313 0.6004 0.5335 0.3841 0.9761 0.9893 0.7336 0.9123 0.9727 0.5905 ] Network output: [ -0.01551 0.1517 0.9023 0.00105 -0.0004712 0.9814 0.000791 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6357 0.617 0.3965 0.2765 0.987 0.9915 0.6361 0.9739 0.9826 0.4031 ] Network output: [ -0.03161 0.1452 0.916 0.0009599 -0.000431 1.006 0.0007234 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6345 0.6315 0.395 0.2682 0.9853 0.9904 0.6346 0.9687 0.9796 0.3962 ] Network output: [ 0.02756 0.793 0.1378 0.0003216 -0.0001444 1.015 0.0002423 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02279 Epoch 2942 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01055 1.02 0.9861 -0.0001662 7.462e-05 -0.02746 -0.0001253 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02154 -0.005797 0.02357 0.0219 0.9424 0.9513 0.04008 0.8891 0.9073 0.09246 ] Network output: [ 0.9867 0.03339 0.001533 -0.000606 0.0002721 -0.01078 -0.0004567 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6594 0.07242 0.2109 0.2212 0.973 0.9876 0.735 0.9028 0.9684 0.602 ] Network output: [ -0.01062 1.001 0.9878 1.551e-05 -6.963e-06 0.03281 1.169e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04205 0.02949 0.03759 0.02565 0.986 0.9901 0.04279 0.9714 0.9815 0.04374 ] Network output: [ 0.03417 -0.152 1.073 -0.001517 0.0006811 1.004 -0.001143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7316 0.6005 0.5328 0.3866 0.9761 0.9893 0.7339 0.9123 0.9727 0.5896 ] Network output: [ -0.01537 0.1547 0.8998 0.001048 -0.0004704 0.9805 0.0007897 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6353 0.6166 0.3951 0.278 0.987 0.9915 0.6356 0.9739 0.9826 0.4017 ] Network output: [ -0.03172 0.1463 0.9157 0.0009573 -0.0004298 1.005 0.0007214 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.634 0.631 0.3937 0.2703 0.9853 0.9904 0.634 0.9686 0.9795 0.3948 ] Network output: [ 0.02833 0.7839 0.1443 0.0003471 -0.0001558 1.017 0.0002616 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0238 Epoch 2943 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01035 1.021 0.9853 -0.0001726 7.75e-05 -0.02753 -0.0001301 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02154 -0.005801 0.02355 0.02197 0.9424 0.9513 0.04007 0.8891 0.9073 0.09218 ] Network output: [ 0.9865 0.03378 0.001392 -0.0006111 0.0002743 -0.01056 -0.0004605 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6597 0.0722 0.211 0.2229 0.973 0.9876 0.7353 0.9028 0.9684 0.6011 ] Network output: [ -0.01063 1.002 0.9867 1.189e-05 -5.336e-06 0.03262 8.958e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04202 0.02947 0.03739 0.0257 0.986 0.9901 0.04276 0.9714 0.9815 0.04349 ] Network output: [ 0.03391 -0.1506 1.072 -0.00152 0.0006824 1.005 -0.001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7318 0.6007 0.532 0.3891 0.9761 0.9893 0.7341 0.9122 0.9727 0.5885 ] Network output: [ -0.01522 0.158 0.8972 0.001046 -0.0004695 0.9795 0.0007882 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6349 0.6162 0.3936 0.2795 0.987 0.9915 0.6352 0.9739 0.9826 0.4001 ] Network output: [ -0.03185 0.1475 0.9154 0.0009543 -0.0004284 1.005 0.0007192 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6334 0.6304 0.3923 0.2724 0.9852 0.9904 0.6335 0.9686 0.9795 0.3934 ] Network output: [ 0.02914 0.7741 0.1513 0.0003743 -0.0001681 1.018 0.0002821 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02493 Epoch 2944 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01015 1.022 0.9845 -0.0001794 8.053e-05 -0.02761 -0.0001352 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02154 -0.005805 0.02352 0.02205 0.9424 0.9513 0.04006 0.8891 0.9074 0.0919 ] Network output: [ 0.9862 0.03418 0.001256 -0.0006163 0.0002767 -0.01033 -0.0004645 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.66 0.07199 0.2112 0.2248 0.973 0.9876 0.7355 0.9028 0.9684 0.6002 ] Network output: [ -0.01065 1.003 0.9855 8.007e-06 -3.594e-06 0.03242 6.034e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.042 0.02945 0.03717 0.02577 0.986 0.9901 0.04273 0.9714 0.9816 0.04323 ] Network output: [ 0.03363 -0.149 1.071 -0.001523 0.0006839 1.005 -0.001148 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7321 0.6008 0.5311 0.3918 0.9761 0.9893 0.7344 0.9122 0.9728 0.5875 ] Network output: [ -0.01506 0.1615 0.8944 0.001044 -0.0004685 0.9785 0.0007865 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6344 0.6157 0.392 0.2811 0.987 0.9915 0.6348 0.9739 0.9826 0.3985 ] Network output: [ -0.03198 0.1488 0.915 0.0009509 -0.0004269 1.004 0.0007167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6328 0.6298 0.3908 0.2747 0.9852 0.9904 0.6329 0.9685 0.9795 0.3919 ] Network output: [ 0.03 0.7637 0.1587 0.0004034 -0.0001811 1.019 0.000304 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0262 Epoch 2945 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009938 1.024 0.9835 -0.0001865 8.374e-05 -0.0277 -0.0001406 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02154 -0.005809 0.0235 0.02214 0.9425 0.9513 0.04005 0.8891 0.9074 0.09161 ] Network output: [ 0.9859 0.03456 0.001129 -0.0006217 0.0002791 -0.01008 -0.0004686 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6603 0.07179 0.2114 0.2268 0.973 0.9876 0.7358 0.9028 0.9684 0.5992 ] Network output: [ -0.01067 1.005 0.9842 3.864e-06 -1.735e-06 0.03221 2.912e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04197 0.02943 0.03696 0.02583 0.986 0.9901 0.04271 0.9714 0.9816 0.04296 ] Network output: [ 0.03333 -0.1472 1.069 -0.001527 0.0006854 1.006 -0.001151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7324 0.601 0.5303 0.3946 0.9761 0.9893 0.7347 0.9122 0.9728 0.5864 ] Network output: [ -0.0149 0.1652 0.8914 0.001041 -0.0004674 0.9774 0.0007846 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.634 0.6153 0.3904 0.2827 0.987 0.9915 0.6343 0.9739 0.9826 0.3968 ] Network output: [ -0.03212 0.1503 0.9146 0.0009472 -0.0004252 1.003 0.0007138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6322 0.6292 0.3893 0.277 0.9852 0.9904 0.6323 0.9685 0.9795 0.3904 ] Network output: [ 0.0309 0.7525 0.1667 0.0004343 -0.000195 1.021 0.0003273 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02762 Epoch 2946 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009718 1.025 0.9826 -0.000194 8.711e-05 -0.0278 -0.0001462 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02155 -0.005813 0.02347 0.02223 0.9425 0.9514 0.04005 0.8891 0.9074 0.09131 ] Network output: [ 0.9857 0.03493 0.001016 -0.0006273 0.0002816 -0.009824 -0.0004728 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6606 0.07161 0.2115 0.2288 0.973 0.9876 0.7361 0.9028 0.9684 0.5982 ] Network output: [ -0.0107 1.007 0.9829 -5.469e-07 2.455e-07 0.03199 -4.121e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04195 0.02942 0.03673 0.0259 0.986 0.9901 0.04269 0.9714 0.9816 0.04269 ] Network output: [ 0.033 -0.1453 1.067 -0.001531 0.0006872 1.006 -0.001154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7327 0.6012 0.5294 0.3975 0.9761 0.9893 0.735 0.9122 0.9728 0.5853 ] Network output: [ -0.01472 0.1692 0.8882 0.001038 -0.0004661 0.9763 0.0007824 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6335 0.6148 0.3887 0.2845 0.987 0.9915 0.6338 0.9739 0.9826 0.3951 ] Network output: [ -0.03228 0.152 0.9141 0.0009429 -0.0004233 1.002 0.0007106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6316 0.6286 0.3877 0.2794 0.9852 0.9904 0.6317 0.9685 0.9795 0.3888 ] Network output: [ 0.03185 0.7406 0.1752 0.0004672 -0.0002097 1.022 0.0003521 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02921 Epoch 2947 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00949 1.027 0.9816 -0.0002019 9.065e-05 -0.02792 -0.0001522 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02155 -0.005818 0.02344 0.02233 0.9425 0.9514 0.04004 0.8891 0.9074 0.091 ] Network output: [ 0.9854 0.03527 0.0009221 -0.000633 0.0002842 -0.00955 -0.0004771 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6609 0.07144 0.2116 0.2311 0.973 0.9876 0.7364 0.9028 0.9684 0.5972 ] Network output: [ -0.01072 1.008 0.9814 -5.226e-06 2.346e-06 0.03175 -3.939e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04193 0.02942 0.0365 0.02598 0.986 0.9901 0.04267 0.9714 0.9816 0.0424 ] Network output: [ 0.03265 -0.1433 1.065 -0.001535 0.0006891 1.007 -0.001157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7331 0.6015 0.5284 0.4006 0.9761 0.9893 0.7354 0.9122 0.9728 0.5841 ] Network output: [ -0.01455 0.1734 0.8848 0.001035 -0.0004645 0.9751 0.0007798 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.633 0.6144 0.387 0.2862 0.987 0.9915 0.6334 0.9739 0.9826 0.3934 ] Network output: [ -0.03245 0.1538 0.9135 0.000938 -0.0004211 1.001 0.0007069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.631 0.6279 0.3861 0.2819 0.9852 0.9904 0.631 0.9684 0.9794 0.3872 ] Network output: [ 0.03285 0.7278 0.1843 0.0005022 -0.0002254 1.024 0.0003784 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.031 Epoch 2948 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009254 1.028 0.9805 -0.0002102 9.435e-05 -0.02805 -0.0001584 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02156 -0.005823 0.0234 0.02243 0.9425 0.9514 0.04004 0.8891 0.9074 0.09068 ] Network output: [ 0.9851 0.03557 0.0008561 -0.0006389 0.0002868 -0.009264 -0.0004815 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6613 0.07129 0.2118 0.2334 0.973 0.9876 0.7368 0.9028 0.9684 0.5962 ] Network output: [ -0.01075 1.01 0.9799 -1.017e-05 4.566e-06 0.0315 -7.665e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04192 0.02941 0.03626 0.02606 0.986 0.9901 0.04266 0.9714 0.9816 0.04211 ] Network output: [ 0.03227 -0.141 1.063 -0.001539 0.0006911 1.007 -0.00116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7335 0.6018 0.5275 0.4038 0.9761 0.9893 0.7357 0.9122 0.9728 0.5829 ] Network output: [ -0.01438 0.178 0.8811 0.001031 -0.0004628 0.9738 0.0007769 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6325 0.6139 0.3852 0.2881 0.987 0.9915 0.6329 0.9739 0.9826 0.3915 ] Network output: [ -0.03264 0.1559 0.9128 0.0009325 -0.0004186 1 0.0007028 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6303 0.6273 0.3844 0.2844 0.9852 0.9903 0.6303 0.9684 0.9794 0.3855 ] Network output: [ 0.0339 0.7143 0.194 0.0005393 -0.0002421 1.026 0.0004064 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.033 Epoch 2949 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009012 1.03 0.9794 -0.0002187 9.82e-05 -0.02819 -0.0001649 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02156 -0.005828 0.02337 0.02254 0.9425 0.9514 0.04004 0.8891 0.9074 0.09035 ] Network output: [ 0.9848 0.03582 0.0008267 -0.0006449 0.0002895 -0.008965 -0.0004861 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6616 0.07114 0.2119 0.2359 0.973 0.9876 0.7372 0.9028 0.9685 0.5951 ] Network output: [ -0.01078 1.012 0.9783 -1.537e-05 6.902e-06 0.03124 -1.159e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04191 0.02941 0.03601 0.02614 0.986 0.9901 0.04265 0.9714 0.9816 0.04181 ] Network output: [ 0.03186 -0.1385 1.061 -0.001545 0.0006934 1.008 -0.001164 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7339 0.6021 0.5265 0.4071 0.9761 0.9893 0.7361 0.9122 0.9728 0.5817 ] Network output: [ -0.0142 0.183 0.8772 0.001027 -0.0004608 0.9724 0.0007736 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.632 0.6134 0.3834 0.29 0.987 0.9915 0.6324 0.9739 0.9827 0.3896 ] Network output: [ -0.03285 0.1582 0.9119 0.0009263 -0.0004158 0.9993 0.0006981 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6295 0.6265 0.3827 0.2871 0.9851 0.9903 0.6296 0.9683 0.9794 0.3838 ] Network output: [ 0.035 0.6999 0.2042 0.0005787 -0.0002598 1.028 0.0004361 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03523 Epoch 2950 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008764 1.032 0.9783 -0.0002277 0.0001022 -0.02835 -0.0001716 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02157 -0.005833 0.02333 0.02267 0.9425 0.9514 0.04004 0.8891 0.9075 0.09002 ] Network output: [ 0.9846 0.03601 0.000845 -0.0006511 0.0002923 -0.008655 -0.0004907 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.662 0.07101 0.212 0.2385 0.973 0.9876 0.7376 0.9028 0.9685 0.594 ] Network output: [ -0.01082 1.014 0.9766 -2.082e-05 9.347e-06 0.03098 -1.569e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04191 0.02942 0.03576 0.02623 0.986 0.9901 0.04264 0.9714 0.9816 0.04151 ] Network output: [ 0.03142 -0.1357 1.058 -0.00155 0.0006959 1.008 -0.001168 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7343 0.6025 0.5255 0.4106 0.9761 0.9893 0.7366 0.9122 0.9728 0.5804 ] Network output: [ -0.01404 0.1883 0.873 0.001021 -0.0004586 0.971 0.0007698 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6315 0.6129 0.3815 0.2919 0.987 0.9915 0.6319 0.9739 0.9827 0.3877 ] Network output: [ -0.03309 0.1609 0.911 0.0009192 -0.0004126 0.9981 0.0006927 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6288 0.6258 0.3809 0.2898 0.9851 0.9903 0.6288 0.9683 0.9794 0.382 ] Network output: [ 0.03616 0.6846 0.2151 0.0006204 -0.0002785 1.031 0.0004675 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03774 Epoch 2951 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008511 1.033 0.9771 -0.0002368 0.0001063 -0.02851 -0.0001785 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02158 -0.005839 0.02329 0.0228 0.9425 0.9514 0.04005 0.8891 0.9075 0.08968 ] Network output: [ 0.9843 0.03611 0.0009242 -0.0006573 0.0002951 -0.008336 -0.0004953 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6625 0.07089 0.212 0.2413 0.973 0.9876 0.738 0.9028 0.9685 0.5929 ] Network output: [ -0.01085 1.016 0.9749 -2.649e-05 1.189e-05 0.0307 -1.996e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04191 0.02943 0.0355 0.02633 0.986 0.9902 0.04264 0.9714 0.9816 0.0412 ] Network output: [ 0.03095 -0.1327 1.056 -0.001556 0.0006987 1.009 -0.001173 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7348 0.6029 0.5244 0.4142 0.9761 0.9893 0.737 0.9122 0.9728 0.5791 ] Network output: [ -0.01388 0.1939 0.8685 0.001016 -0.0004559 0.9695 0.0007654 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.631 0.6124 0.3795 0.2939 0.987 0.9915 0.6313 0.9739 0.9827 0.3857 ] Network output: [ -0.03335 0.1638 0.9098 0.0009111 -0.000409 0.9968 0.0006867 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.628 0.625 0.3791 0.2925 0.9851 0.9903 0.628 0.9682 0.9794 0.3802 ] Network output: [ 0.03737 0.6685 0.2265 0.0006643 -0.0002982 1.033 0.0005006 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04052 Epoch 2952 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008256 1.035 0.9759 -0.0002462 0.0001105 -0.02868 -0.0001856 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02159 -0.005846 0.02325 0.02294 0.9425 0.9514 0.04006 0.8892 0.9075 0.08934 ] Network output: [ 0.9841 0.0361 0.00108 -0.0006635 0.0002979 -0.008008 -0.0005 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6629 0.07077 0.2121 0.2443 0.973 0.9876 0.7385 0.9028 0.9685 0.5917 ] Network output: [ -0.01089 1.018 0.9731 -3.235e-05 1.452e-05 0.03042 -2.438e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04192 0.02944 0.03524 0.02643 0.986 0.9902 0.04265 0.9715 0.9817 0.04088 ] Network output: [ 0.03045 -0.1295 1.053 -0.001563 0.0007017 1.01 -0.001178 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7352 0.6033 0.5233 0.418 0.9761 0.9893 0.7375 0.9122 0.9728 0.5778 ] Network output: [ -0.01375 0.2 0.8637 0.001009 -0.0004529 0.9678 0.0007603 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6304 0.6119 0.3775 0.2959 0.987 0.9915 0.6308 0.9739 0.9827 0.3836 ] Network output: [ -0.03365 0.1672 0.9084 0.0009021 -0.000405 0.9954 0.0006798 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6271 0.6241 0.3772 0.2952 0.9851 0.9903 0.6272 0.9682 0.9793 0.3783 ] Network output: [ 0.03863 0.6516 0.2385 0.0007105 -0.000319 1.036 0.0005354 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04361 Epoch 2953 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008 1.037 0.9747 -0.0002558 0.0001148 -0.02885 -0.0001928 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0216 -0.005853 0.0232 0.02309 0.9425 0.9514 0.04007 0.8892 0.9075 0.08899 ] Network output: [ 0.9838 0.03596 0.00133 -0.0006696 0.0003006 -0.007673 -0.0005046 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6634 0.07065 0.2122 0.2474 0.973 0.9876 0.739 0.9028 0.9685 0.5906 ] Network output: [ -0.01093 1.02 0.9713 -3.835e-05 1.722e-05 0.03013 -2.89e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04193 0.02946 0.03498 0.02653 0.986 0.9902 0.04266 0.9715 0.9817 0.04056 ] Network output: [ 0.02991 -0.1259 1.05 -0.001571 0.0007051 1.01 -0.001184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7358 0.6038 0.5222 0.4219 0.9761 0.9893 0.7381 0.9122 0.9728 0.5765 ] Network output: [ -0.01364 0.2066 0.8587 0.001001 -0.0004494 0.9661 0.0007544 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6299 0.6113 0.3755 0.298 0.987 0.9915 0.6302 0.9739 0.9827 0.3816 ] Network output: [ -0.03399 0.1709 0.9069 0.0008918 -0.0004004 0.9938 0.0006721 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6262 0.6232 0.3753 0.298 0.9851 0.9903 0.6262 0.9681 0.9793 0.3764 ] Network output: [ 0.03995 0.6339 0.2509 0.0007588 -0.0003406 1.038 0.0005718 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04703 Epoch 2954 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.007746 1.039 0.9735 -0.0002654 0.0001191 -0.02903 -0.0002 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02161 -0.005861 0.02316 0.02325 0.9425 0.9514 0.04009 0.8892 0.9076 0.08864 ] Network output: [ 0.9836 0.03564 0.001694 -0.0006756 0.0003033 -0.007333 -0.0005091 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6639 0.07054 0.2122 0.2507 0.973 0.9876 0.7395 0.9028 0.9685 0.5894 ] Network output: [ -0.01097 1.022 0.9694 -4.446e-05 1.996e-05 0.02985 -3.35e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04195 0.02948 0.03471 0.02664 0.986 0.9902 0.04268 0.9715 0.9817 0.04024 ] Network output: [ 0.02934 -0.1221 1.046 -0.001579 0.0007088 1.011 -0.00119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7363 0.6043 0.5211 0.4258 0.9761 0.9893 0.7386 0.9122 0.9728 0.5751 ] Network output: [ -0.01356 0.2135 0.8534 0.0009922 -0.0004454 0.9642 0.0007478 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6292 0.6108 0.3734 0.3001 0.987 0.9915 0.6296 0.9739 0.9827 0.3795 ] Network output: [ -0.03438 0.1751 0.9052 0.0008802 -0.0003952 0.9921 0.0006634 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6252 0.6222 0.3734 0.3007 0.985 0.9903 0.6253 0.9681 0.9793 0.3745 ] Network output: [ 0.04132 0.6156 0.2638 0.0008091 -0.0003632 1.041 0.0006097 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05077 Epoch 2955 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.007497 1.041 0.9724 -0.0002749 0.0001234 -0.02919 -0.0002072 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02163 -0.00587 0.02312 0.02342 0.9425 0.9514 0.0401 0.8892 0.9076 0.08829 ] Network output: [ 0.9835 0.03512 0.002196 -0.0006813 0.0003058 -0.006989 -0.0005134 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6644 0.07042 0.2123 0.2541 0.973 0.9876 0.74 0.9028 0.9685 0.5882 ] Network output: [ -0.01101 1.025 0.9675 -5.059e-05 2.271e-05 0.02958 -3.813e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04197 0.02951 0.03444 0.02676 0.986 0.9902 0.0427 0.9715 0.9817 0.03991 ] Network output: [ 0.02873 -0.118 1.043 -0.001588 0.0007128 1.011 -0.001197 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7369 0.6048 0.52 0.4299 0.9761 0.9893 0.7392 0.9122 0.9728 0.5737 ] Network output: [ -0.01353 0.221 0.8479 0.000982 -0.0004409 0.9622 0.0007401 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6286 0.6101 0.3713 0.3021 0.987 0.9915 0.6289 0.9739 0.9827 0.3773 ] Network output: [ -0.03481 0.1797 0.9032 0.0008672 -0.0003893 0.9903 0.0006536 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6242 0.6212 0.3714 0.3034 0.985 0.9903 0.6242 0.968 0.9793 0.3725 ] Network output: [ 0.04274 0.5967 0.2769 0.0008611 -0.0003866 1.044 0.0006489 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05484 Epoch 2956 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.007257 1.042 0.9713 -0.0002842 0.0001276 -0.02934 -0.0002142 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02164 -0.00588 0.02307 0.0236 0.9426 0.9514 0.04012 0.8892 0.9076 0.08793 ] Network output: [ 0.9833 0.03436 0.002858 -0.0006865 0.0003082 -0.006641 -0.0005173 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6649 0.07029 0.2123 0.2577 0.973 0.9876 0.7406 0.9028 0.9685 0.5871 ] Network output: [ -0.01104 1.027 0.9656 -5.668e-05 2.545e-05 0.02932 -4.272e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04199 0.02954 0.03417 0.02688 0.986 0.9902 0.04273 0.9715 0.9818 0.03959 ] Network output: [ 0.02809 -0.1136 1.039 -0.001598 0.0007172 1.012 -0.001204 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7375 0.6054 0.5189 0.4341 0.9761 0.9893 0.7398 0.9122 0.9728 0.5724 ] Network output: [ -0.01356 0.2288 0.8421 0.0009705 -0.0004357 0.9601 0.0007314 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6279 0.6094 0.3692 0.3041 0.987 0.9915 0.6282 0.9739 0.9828 0.3751 ] Network output: [ -0.03531 0.1848 0.901 0.0008528 -0.0003828 0.9883 0.0006427 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.623 0.62 0.3694 0.306 0.985 0.9902 0.6231 0.9679 0.9793 0.3705 ] Network output: [ 0.0442 0.5775 0.2901 0.0009146 -0.0004106 1.048 0.0006893 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05924 Epoch 2957 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00703 1.044 0.9704 -0.0002931 0.0001316 -0.02946 -0.0002209 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02166 -0.005891 0.02302 0.02379 0.9426 0.9514 0.04014 0.8892 0.9077 0.08758 ] Network output: [ 0.9832 0.03332 0.003708 -0.000691 0.0003102 -0.006291 -0.0005208 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6655 0.07013 0.2123 0.2615 0.973 0.9876 0.7412 0.9029 0.9686 0.5859 ] Network output: [ -0.01108 1.029 0.9638 -6.263e-05 2.812e-05 0.02908 -4.72e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04202 0.02957 0.03391 0.027 0.986 0.9902 0.04275 0.9715 0.9818 0.03927 ] Network output: [ 0.02742 -0.1089 1.035 -0.001608 0.0007221 1.013 -0.001212 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7381 0.6059 0.5178 0.4383 0.9761 0.9893 0.7404 0.9122 0.9728 0.571 ] Network output: [ -0.01365 0.2372 0.8361 0.0009574 -0.0004298 0.9579 0.0007216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6271 0.6087 0.3671 0.3061 0.987 0.9915 0.6274 0.9739 0.9828 0.373 ] Network output: [ -0.03586 0.1904 0.8986 0.0008367 -0.0003756 0.9861 0.0006306 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6218 0.6188 0.3674 0.3086 0.985 0.9902 0.6218 0.9679 0.9793 0.3685 ] Network output: [ 0.04571 0.558 0.3034 0.0009691 -0.0004351 1.051 0.0007304 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06393 Epoch 2958 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00682 1.045 0.9695 -0.0003015 0.0001354 -0.02954 -0.0002273 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02167 -0.005902 0.02297 0.024 0.9426 0.9515 0.04016 0.8892 0.9077 0.08723 ] Network output: [ 0.9832 0.03195 0.00477 -0.0006947 0.0003119 -0.005939 -0.0005236 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.666 0.06995 0.2123 0.2653 0.973 0.9876 0.7417 0.9029 0.9686 0.5848 ] Network output: [ -0.01111 1.031 0.962 -6.835e-05 3.069e-05 0.02887 -5.151e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04204 0.0296 0.03364 0.02713 0.986 0.9902 0.04278 0.9716 0.9818 0.03895 ] Network output: [ 0.02672 -0.104 1.031 -0.00162 0.0007273 1.013 -0.001221 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7387 0.6065 0.5167 0.4425 0.9761 0.9893 0.741 0.9122 0.9729 0.5697 ] Network output: [ -0.01382 0.2459 0.83 0.0009428 -0.0004232 0.9555 0.0007105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6262 0.6078 0.365 0.308 0.987 0.9915 0.6265 0.974 0.9828 0.3708 ] Network output: [ -0.03649 0.1966 0.896 0.000819 -0.0003677 0.9838 0.0006172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6204 0.6175 0.3654 0.311 0.9849 0.9902 0.6205 0.9678 0.9792 0.3665 ] Network output: [ 0.04725 0.5386 0.3164 0.001024 -0.0004598 1.055 0.0007719 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06887 Epoch 2959 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.006632 1.046 0.9689 -0.0003092 0.0001388 -0.02958 -0.000233 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02169 -0.005914 0.02292 0.02421 0.9426 0.9515 0.04018 0.8893 0.9078 0.08689 ] Network output: [ 0.9832 0.03022 0.006067 -0.0006973 0.000313 -0.005583 -0.0005255 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6666 0.06974 0.2123 0.2693 0.973 0.9876 0.7423 0.9029 0.9686 0.5837 ] Network output: [ -0.01113 1.033 0.9604 -7.373e-05 3.31e-05 0.0287 -5.557e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04207 0.02963 0.03338 0.02726 0.986 0.9902 0.0428 0.9716 0.9819 0.03864 ] Network output: [ 0.026 -0.09878 1.026 -0.001632 0.0007328 1.014 -0.00123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7394 0.607 0.5156 0.4468 0.9761 0.9893 0.7417 0.9122 0.9729 0.5684 ] Network output: [ -0.01407 0.2551 0.8239 0.0009264 -0.0004159 0.9529 0.0006982 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6252 0.6069 0.3628 0.3098 0.987 0.9915 0.6255 0.974 0.9828 0.3686 ] Network output: [ -0.0372 0.2032 0.8932 0.0007996 -0.000359 0.9813 0.0006026 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6189 0.616 0.3634 0.3132 0.9849 0.9902 0.619 0.9677 0.9792 0.3645 ] Network output: [ 0.04882 0.5194 0.3289 0.00108 -0.0004846 1.058 0.0008136 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07401 Epoch 2960 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.006472 1.047 0.9684 -0.0003159 0.0001418 -0.02954 -0.0002381 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0217 -0.005926 0.02287 0.02442 0.9426 0.9515 0.04019 0.8893 0.9078 0.08655 ] Network output: [ 0.9833 0.0281 0.00762 -0.0006984 0.0003136 -0.00522 -0.0005264 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6671 0.06947 0.2123 0.2734 0.973 0.9876 0.7428 0.9029 0.9686 0.5826 ] Network output: [ -0.01115 1.035 0.9588 -7.867e-05 3.532e-05 0.02859 -5.929e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04208 0.02965 0.03312 0.02739 0.986 0.9902 0.04282 0.9716 0.9819 0.03833 ] Network output: [ 0.02527 -0.09341 1.022 -0.001646 0.0007388 1.014 -0.00124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.74 0.6075 0.5146 0.451 0.9761 0.9893 0.7423 0.9122 0.9729 0.5672 ] Network output: [ -0.01442 0.2646 0.8177 0.0009083 -0.0004078 0.9502 0.0006845 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.624 0.6058 0.3607 0.3115 0.987 0.9915 0.6244 0.974 0.9829 0.3665 ] Network output: [ -0.03799 0.2102 0.8904 0.0007785 -0.0003495 0.9786 0.0005867 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6173 0.6143 0.3614 0.3153 0.9849 0.9902 0.6173 0.9677 0.9792 0.3624 ] Network output: [ 0.05041 0.5007 0.3409 0.001134 -0.0005093 1.062 0.0008549 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07929 Epoch 2961 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.006344 1.047 0.9682 -0.0003216 0.0001444 -0.02943 -0.0002423 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02171 -0.00594 0.02282 0.02465 0.9426 0.9515 0.0402 0.8893 0.9079 0.08622 ] Network output: [ 0.9835 0.02554 0.009446 -0.000698 0.0003133 -0.004846 -0.000526 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6676 0.06916 0.2122 0.2775 0.973 0.9876 0.7433 0.9029 0.9687 0.5816 ] Network output: [ -0.01115 1.036 0.9575 -8.305e-05 3.729e-05 0.02853 -6.259e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04209 0.02966 0.03287 0.02751 0.986 0.9902 0.04282 0.9716 0.9819 0.03803 ] Network output: [ 0.02453 -0.0879 1.017 -0.001659 0.000745 1.015 -0.001251 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7406 0.608 0.5135 0.4551 0.9762 0.9893 0.7429 0.9122 0.9729 0.566 ] Network output: [ -0.01488 0.2744 0.8117 0.0008886 -0.0003989 0.9473 0.0006697 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6227 0.6045 0.3586 0.313 0.987 0.9915 0.623 0.974 0.9829 0.3643 ] Network output: [ -0.03886 0.2177 0.8874 0.0007559 -0.0003394 0.9757 0.0005697 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6154 0.6125 0.3594 0.3171 0.9849 0.9902 0.6155 0.9676 0.9792 0.3604 ] Network output: [ 0.052 0.4828 0.352 0.001188 -0.0005334 1.066 0.0008954 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08462 Epoch 2962 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.006252 1.047 0.9682 -0.0003259 0.0001463 -0.02922 -0.0002456 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02171 -0.005953 0.02277 0.02488 0.9426 0.9515 0.0402 0.8893 0.9079 0.0859 ] Network output: [ 0.9838 0.02253 0.01155 -0.0006955 0.0003123 -0.004455 -0.0005242 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.668 0.06879 0.2121 0.2817 0.973 0.9876 0.7438 0.9029 0.9687 0.5806 ] Network output: [ -0.01115 1.037 0.9563 -8.68e-05 3.897e-05 0.02854 -6.542e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04208 0.02966 0.03263 0.02764 0.986 0.9902 0.04282 0.9717 0.982 0.03774 ] Network output: [ 0.0238 -0.08231 1.012 -0.001674 0.0007514 1.016 -0.001261 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7411 0.6083 0.5125 0.4591 0.9762 0.9893 0.7434 0.9122 0.9729 0.5648 ] Network output: [ -0.01544 0.2844 0.8058 0.0008672 -0.0003893 0.9442 0.0006536 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6212 0.603 0.3565 0.3144 0.987 0.9915 0.6215 0.974 0.9829 0.3621 ] Network output: [ -0.03981 0.2255 0.8845 0.0007318 -0.0003286 0.9726 0.0005515 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6134 0.6105 0.3574 0.3187 0.9848 0.9901 0.6135 0.9675 0.9791 0.3585 ] Network output: [ 0.0536 0.4659 0.362 0.00124 -0.0005569 1.07 0.0009348 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08994 Epoch 2963 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0062 1.047 0.9685 -0.0003289 0.0001476 -0.0289 -0.0002478 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02171 -0.005966 0.02271 0.02511 0.9427 0.9515 0.04019 0.8893 0.908 0.08559 ] Network output: [ 0.9841 0.01906 0.01394 -0.000691 0.0003102 -0.004039 -0.0005207 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6684 0.06834 0.2119 0.2858 0.973 0.9876 0.7442 0.9029 0.9687 0.5796 ] Network output: [ -0.01113 1.038 0.9554 -8.984e-05 4.033e-05 0.02863 -6.771e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04206 0.02964 0.03239 0.02775 0.986 0.9902 0.04279 0.9717 0.982 0.03746 ] Network output: [ 0.02308 -0.07671 1.008 -0.001688 0.000758 1.016 -0.001272 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7416 0.6086 0.5116 0.463 0.9762 0.9893 0.7439 0.9123 0.973 0.5637 ] Network output: [ -0.0161 0.2945 0.8002 0.0008445 -0.0003791 0.9409 0.0006364 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6195 0.6013 0.3544 0.3155 0.987 0.9915 0.6198 0.974 0.983 0.36 ] Network output: [ -0.04085 0.2335 0.8816 0.0007065 -0.0003172 0.9694 0.0005324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6112 0.6083 0.3555 0.3201 0.9848 0.9901 0.6112 0.9674 0.9791 0.3565 ] Network output: [ 0.05518 0.4501 0.3709 0.001291 -0.0005794 1.074 0.0009727 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09515 Epoch 2964 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.006192 1.046 0.969 -0.0003303 0.0001483 -0.02845 -0.0002489 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0217 -0.005979 0.02265 0.02534 0.9427 0.9515 0.04016 0.8894 0.908 0.08528 ] Network output: [ 0.9845 0.01512 0.0166 -0.0006841 0.0003071 -0.003586 -0.0005156 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6688 0.06783 0.2117 0.29 0.973 0.9876 0.7445 0.903 0.9687 0.5787 ] Network output: [ -0.01109 1.038 0.9548 -9.213e-05 4.136e-05 0.02881 -6.943e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04202 0.02961 0.03215 0.02786 0.986 0.9902 0.04275 0.9717 0.982 0.03718 ] Network output: [ 0.0224 -0.07117 1.003 -0.001703 0.0007646 1.017 -0.001284 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.742 0.6088 0.5106 0.4667 0.9762 0.9893 0.7443 0.9123 0.973 0.5626 ] Network output: [ -0.01686 0.3048 0.7948 0.0008205 -0.0003683 0.9375 0.0006183 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6175 0.5994 0.3523 0.3165 0.987 0.9915 0.6178 0.974 0.983 0.3579 ] Network output: [ -0.04197 0.2418 0.8789 0.00068 -0.0003053 0.9661 0.0005124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6087 0.6058 0.3535 0.3212 0.9848 0.9901 0.6088 0.9673 0.9791 0.3545 ] Network output: [ 0.05673 0.4356 0.3785 0.001338 -0.0006009 1.078 0.001009 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1002 Epoch 2965 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00623 1.044 0.9699 -0.0003302 0.0001483 -0.02787 -0.0002489 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02169 -0.005991 0.02259 0.02556 0.9427 0.9515 0.04013 0.8894 0.9081 0.08497 ] Network output: [ 0.985 0.01075 0.01952 -0.0006748 0.0003029 -0.003088 -0.0005085 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.669 0.06725 0.2115 0.294 0.973 0.9876 0.7448 0.903 0.9688 0.5778 ] Network output: [ -0.01103 1.038 0.9544 -9.364e-05 4.204e-05 0.02909 -7.057e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04195 0.02955 0.03192 0.02796 0.986 0.9902 0.04268 0.9717 0.9821 0.03691 ] Network output: [ 0.02176 -0.06575 0.9981 -0.001718 0.0007711 1.017 -0.001295 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7424 0.6089 0.5097 0.4702 0.9762 0.9893 0.7446 0.9123 0.973 0.5616 ] Network output: [ -0.01771 0.315 0.7899 0.0007955 -0.0003571 0.9337 0.0005995 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6153 0.5972 0.3501 0.3172 0.987 0.9915 0.6156 0.9741 0.983 0.3557 ] Network output: [ -0.04316 0.2502 0.8763 0.0006525 -0.000293 0.9625 0.0004918 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.606 0.6031 0.3515 0.3221 0.9847 0.9901 0.6061 0.9672 0.979 0.3525 ] Network output: [ 0.05825 0.4224 0.3848 0.001383 -0.0006211 1.082 0.001043 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.105 Epoch 2966 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.006313 1.042 0.971 -0.0003286 0.0001475 -0.02715 -0.0002477 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02167 -0.006003 0.02253 0.02579 0.9427 0.9515 0.04008 0.8894 0.9081 0.08467 ] Network output: [ 0.9856 0.005955 0.02268 -0.0006629 0.0002976 -0.002531 -0.0004996 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6692 0.06659 0.2112 0.298 0.973 0.9876 0.7449 0.903 0.9688 0.5769 ] Network output: [ -0.01096 1.038 0.9542 -9.439e-05 4.238e-05 0.02945 -7.114e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04185 0.02948 0.0317 0.02805 0.986 0.9902 0.04258 0.9717 0.9821 0.03665 ] Network output: [ 0.02117 -0.06052 0.9935 -0.001732 0.0007775 1.018 -0.001305 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7426 0.6088 0.5088 0.4736 0.9762 0.9893 0.7449 0.9123 0.973 0.5607 ] Network output: [ -0.01863 0.3252 0.7854 0.0007698 -0.0003456 0.9298 0.0005801 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6127 0.5947 0.348 0.3177 0.987 0.9915 0.6131 0.9741 0.983 0.3535 ] Network output: [ -0.04442 0.2586 0.8739 0.0006244 -0.0002803 0.9589 0.0004706 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.6031 0.6002 0.3496 0.3227 0.9847 0.9901 0.6031 0.9671 0.979 0.3506 ] Network output: [ 0.05971 0.4107 0.3898 0.001425 -0.0006399 1.086 0.001074 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1096 Epoch 2967 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.006444 1.04 0.9723 -0.0003256 0.0001462 -0.02628 -0.0002454 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02164 -0.006013 0.02246 0.026 0.9427 0.9516 0.04001 0.8894 0.9082 0.08437 ] Network output: [ 0.9862 0.0007865 0.02604 -0.0006486 0.0002912 -0.001906 -0.0004888 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6692 0.06587 0.2108 0.3019 0.9731 0.9876 0.745 0.903 0.9688 0.5761 ] Network output: [ -0.01086 1.037 0.9543 -9.442e-05 4.239e-05 0.02992 -7.116e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04173 0.02938 0.03147 0.02813 0.986 0.9902 0.04246 0.9717 0.9821 0.03639 ] Network output: [ 0.02065 -0.05551 0.989 -0.001745 0.0007836 1.018 -0.001315 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7427 0.6086 0.5079 0.4767 0.9762 0.9893 0.745 0.9123 0.9731 0.5597 ] Network output: [ -0.01961 0.3353 0.7813 0.0007436 -0.0003338 0.9257 0.0005604 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6099 0.5919 0.3458 0.318 0.987 0.9915 0.6103 0.9741 0.9831 0.3513 ] Network output: [ -0.04573 0.267 0.8719 0.0005958 -0.0002675 0.9551 0.000449 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5999 0.597 0.3476 0.323 0.9846 0.99 0.5999 0.9669 0.979 0.3486 ] Network output: [ 0.06111 0.4003 0.3935 0.001464 -0.0006573 1.09 0.001103 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1138 Epoch 2968 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.006619 1.037 0.9739 -0.0003213 0.0001442 -0.02526 -0.0002421 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0216 -0.006022 0.02239 0.02621 0.9427 0.9516 0.03993 0.8894 0.9082 0.08408 ] Network output: [ 0.9869 -0.004709 0.02957 -0.0006318 0.0002837 -0.001203 -0.0004762 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6692 0.06507 0.2104 0.3056 0.9731 0.9876 0.7449 0.903 0.9689 0.5753 ] Network output: [ -0.01074 1.036 0.9546 -9.381e-05 4.212e-05 0.03048 -7.07e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04158 0.02926 0.03125 0.02819 0.986 0.9902 0.0423 0.9717 0.9821 0.03614 ] Network output: [ 0.0202 -0.05078 0.9846 -0.001758 0.0007894 1.019 -0.001325 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7428 0.6082 0.507 0.4795 0.9762 0.9893 0.7451 0.9123 0.9731 0.5589 ] Network output: [ -0.02064 0.3453 0.7776 0.0007173 -0.000322 0.9213 0.0005406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6068 0.5889 0.3436 0.3179 0.987 0.9915 0.6071 0.9741 0.9831 0.349 ] Network output: [ -0.0471 0.2753 0.8701 0.000567 -0.0002545 0.9511 0.0004273 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5964 0.5935 0.3456 0.3231 0.9846 0.99 0.5965 0.9668 0.9789 0.3466 ] Network output: [ 0.06243 0.3912 0.396 0.0015 -0.0006733 1.094 0.00113 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1178 Epoch 2969 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.006837 1.033 0.9757 -0.0003157 0.0001417 -0.0241 -0.0002379 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02155 -0.006029 0.02231 0.0264 0.9428 0.9516 0.03983 0.8895 0.9083 0.08378 ] Network output: [ 0.9876 -0.01048 0.03323 -0.0006128 0.0002751 -0.0004137 -0.0004619 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6691 0.06422 0.2099 0.3092 0.9731 0.9876 0.7447 0.9031 0.9689 0.5745 ] Network output: [ -0.01059 1.034 0.9552 -9.265e-05 4.159e-05 0.03113 -6.982e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0414 0.02911 0.03103 0.02823 0.986 0.9902 0.04212 0.9717 0.9822 0.03589 ] Network output: [ 0.01983 -0.04635 0.9804 -0.00177 0.0007947 1.019 -0.001334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7427 0.6077 0.5061 0.4822 0.9762 0.9893 0.745 0.9123 0.9731 0.558 ] Network output: [ -0.02168 0.355 0.7744 0.0006912 -0.0003103 0.9167 0.0005209 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.6034 0.5855 0.3412 0.3177 0.987 0.9915 0.6037 0.9741 0.9831 0.3467 ] Network output: [ -0.0485 0.2834 0.8686 0.000538 -0.0002415 0.9471 0.0004055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5927 0.5899 0.3436 0.323 0.9845 0.99 0.5928 0.9666 0.9788 0.3446 ] Network output: [ 0.06367 0.3833 0.3974 0.001532 -0.0006878 1.098 0.001155 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1215 Epoch 2970 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.007095 1.03 0.9776 -0.0003092 0.0001388 -0.0228 -0.000233 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0215 -0.006034 0.02223 0.02659 0.9428 0.9516 0.03972 0.8895 0.9083 0.08348 ] Network output: [ 0.9883 -0.01646 0.03699 -0.0005918 0.0002657 0.0004656 -0.000446 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6689 0.06331 0.2094 0.3126 0.9731 0.9876 0.7444 0.9031 0.9689 0.5737 ] Network output: [ -0.01043 1.033 0.9559 -9.103e-05 4.087e-05 0.03187 -6.86e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04119 0.02894 0.03081 0.02826 0.986 0.9902 0.0419 0.9717 0.9822 0.03564 ] Network output: [ 0.01953 -0.04222 0.9762 -0.001781 0.0007996 1.02 -0.001342 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7425 0.6071 0.5053 0.4846 0.9762 0.9893 0.7448 0.9123 0.9731 0.5571 ] Network output: [ -0.02274 0.3646 0.7716 0.0006654 -0.0002987 0.9119 0.0005014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5997 0.5818 0.3389 0.3172 0.987 0.9915 0.6 0.9741 0.9831 0.3443 ] Network output: [ -0.04993 0.2914 0.8675 0.0005091 -0.0002286 0.943 0.0003837 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5888 0.5859 0.3415 0.3227 0.9845 0.9899 0.5888 0.9664 0.9788 0.3425 ] Network output: [ 0.06482 0.3765 0.3977 0.001561 -0.0007009 1.102 0.001177 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1249 Epoch 2971 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.007391 1.026 0.9797 -0.0003018 0.0001355 -0.02137 -0.0002275 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02144 -0.006038 0.02214 0.02676 0.9428 0.9516 0.03958 0.8895 0.9084 0.08317 ] Network output: [ 0.989 -0.02261 0.0408 -0.0005688 0.0002554 0.001437 -0.0004287 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6686 0.06236 0.2088 0.3159 0.9731 0.9876 0.744 0.9031 0.969 0.5729 ] Network output: [ -0.01023 1.031 0.9568 -8.907e-05 3.999e-05 0.0327 -6.713e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04095 0.02874 0.03059 0.02827 0.986 0.9902 0.04166 0.9717 0.9822 0.03539 ] Network output: [ 0.01932 -0.03841 0.9722 -0.001791 0.000804 1.02 -0.00135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7422 0.6063 0.5044 0.4868 0.9762 0.9894 0.7445 0.9123 0.9732 0.5563 ] Network output: [ -0.02378 0.374 0.7692 0.0006401 -0.0002874 0.9069 0.0004824 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5957 0.5779 0.3364 0.3164 0.987 0.9915 0.596 0.9741 0.9831 0.3418 ] Network output: [ -0.05138 0.2992 0.8667 0.0004805 -0.0002157 0.9388 0.0003621 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5846 0.5818 0.3394 0.3221 0.9844 0.9899 0.5847 0.9662 0.9787 0.3404 ] Network output: [ 0.06587 0.3708 0.3972 0.001587 -0.0007126 1.107 0.001196 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1281 Epoch 2972 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.007721 1.021 0.9818 -0.0002938 0.0001319 -0.01982 -0.0002214 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02137 -0.00604 0.02205 0.02692 0.9428 0.9516 0.03944 0.8895 0.9085 0.08286 ] Network output: [ 0.9898 -0.02886 0.04464 -0.0005442 0.0002443 0.0025 -0.0004101 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6682 0.06136 0.2082 0.319 0.9731 0.9876 0.7435 0.9031 0.969 0.5722 ] Network output: [ -0.01002 1.028 0.9578 -8.688e-05 3.9e-05 0.0336 -6.548e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04068 0.02853 0.03036 0.02826 0.986 0.9902 0.04139 0.9717 0.9822 0.03513 ] Network output: [ 0.01919 -0.0349 0.9684 -0.001799 0.0008078 1.021 -0.001356 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7418 0.6054 0.5035 0.4889 0.9762 0.9894 0.7441 0.9123 0.9732 0.5555 ] Network output: [ -0.02481 0.3832 0.7672 0.0006156 -0.0002763 0.9017 0.0004639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5915 0.5737 0.3338 0.3154 0.987 0.9915 0.5918 0.9741 0.9832 0.3392 ] Network output: [ -0.05284 0.3068 0.8662 0.0004521 -0.000203 0.9344 0.0003407 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5803 0.5774 0.3373 0.3213 0.9844 0.9899 0.5803 0.966 0.9786 0.3383 ] Network output: [ 0.06682 0.366 0.396 0.00161 -0.0007229 1.111 0.001214 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.131 Epoch 2973 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008082 1.017 0.984 -0.0002854 0.0001281 -0.01815 -0.0002151 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02129 -0.00604 0.02196 0.02707 0.9428 0.9516 0.03928 0.8896 0.9085 0.08255 ] Network output: [ 0.9905 -0.03517 0.04847 -0.0005182 0.0002326 0.00365 -0.0003905 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6677 0.06032 0.2075 0.322 0.9731 0.9876 0.7428 0.9031 0.969 0.5714 ] Network output: [ -0.009784 1.026 0.9589 -8.457e-05 3.797e-05 0.03457 -6.374e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04039 0.02829 0.03013 0.02824 0.986 0.9902 0.04109 0.9717 0.9823 0.03488 ] Network output: [ 0.01913 -0.03169 0.9646 -0.001807 0.0008112 1.021 -0.001362 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7413 0.6043 0.5025 0.4907 0.9762 0.9894 0.7436 0.9123 0.9732 0.5547 ] Network output: [ -0.0258 0.3922 0.7654 0.0005918 -0.0002657 0.8963 0.000446 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.587 0.5692 0.3312 0.3142 0.987 0.9915 0.5873 0.974 0.9832 0.3365 ] Network output: [ -0.0543 0.3142 0.8661 0.0004241 -0.0001904 0.9301 0.0003197 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5757 0.5728 0.3352 0.3204 0.9843 0.9898 0.5757 0.9658 0.9785 0.3362 ] Network output: [ 0.06766 0.3619 0.394 0.001631 -0.0007321 1.115 0.001229 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1338 Epoch 2974 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008471 1.012 0.9863 -0.0002767 0.0001242 -0.01638 -0.0002085 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02121 -0.006038 0.02186 0.02721 0.9429 0.9517 0.03911 0.8896 0.9086 0.08222 ] Network output: [ 0.9912 -0.04149 0.05227 -0.000491 0.0002204 0.004881 -0.00037 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6671 0.05925 0.2068 0.3248 0.9731 0.9876 0.7421 0.9032 0.969 0.5706 ] Network output: [ -0.009526 1.023 0.9601 -8.224e-05 3.692e-05 0.03561 -6.198e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.04008 0.02804 0.0299 0.02821 0.986 0.9902 0.04078 0.9717 0.9823 0.03462 ] Network output: [ 0.01914 -0.02875 0.961 -0.001813 0.0008141 1.022 -0.001367 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7407 0.6031 0.5016 0.4923 0.9762 0.9894 0.7429 0.9123 0.9732 0.5539 ] Network output: [ -0.02675 0.4011 0.764 0.0005689 -0.0002554 0.8908 0.0004288 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5823 0.5646 0.3285 0.3128 0.987 0.9915 0.5826 0.974 0.9832 0.3338 ] Network output: [ -0.05576 0.3214 0.8661 0.0003967 -0.0001781 0.9256 0.0002989 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5709 0.5681 0.333 0.3193 0.9842 0.9898 0.571 0.9656 0.9784 0.334 ] Network output: [ 0.0684 0.3586 0.3915 0.001649 -0.0007401 1.12 0.001242 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1364 Epoch 2975 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008884 1.007 0.9885 -0.0002678 0.0001202 -0.01451 -0.0002018 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02112 -0.006035 0.02176 0.02734 0.9429 0.9517 0.03892 0.8896 0.9086 0.0819 ] Network output: [ 0.9919 -0.04779 0.05602 -0.0004627 0.0002077 0.006187 -0.0003487 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6664 0.05816 0.206 0.3275 0.9731 0.9876 0.7413 0.9032 0.9691 0.5698 ] Network output: [ -0.009249 1.02 0.9614 -7.997e-05 3.59e-05 0.03671 -6.027e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03975 0.02777 0.02967 0.02815 0.986 0.9902 0.04044 0.9717 0.9823 0.03436 ] Network output: [ 0.01921 -0.02608 0.9575 -0.001819 0.0008166 1.023 -0.001371 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.74 0.6019 0.5007 0.4938 0.9762 0.9894 0.7422 0.9123 0.9732 0.553 ] Network output: [ -0.02766 0.4097 0.7629 0.000547 -0.0002456 0.885 0.0004122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5773 0.5597 0.3256 0.3112 0.9869 0.9915 0.5776 0.974 0.9832 0.3309 ] Network output: [ -0.05721 0.3283 0.8665 0.0003697 -0.000166 0.9211 0.0002786 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.566 0.5632 0.3307 0.3181 0.9842 0.9898 0.5661 0.9653 0.9783 0.3317 ] Network output: [ 0.06904 0.3559 0.3886 0.001664 -0.000747 1.124 0.001254 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1389 Epoch 2976 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00932 1.002 0.9907 -0.0002589 0.0001162 -0.01255 -0.0001951 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02103 -0.00603 0.02165 0.02745 0.9429 0.9517 0.03873 0.8896 0.9087 0.08157 ] Network output: [ 0.9925 -0.05404 0.0597 -0.0004336 0.0001947 0.007559 -0.0003268 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6657 0.05704 0.2052 0.33 0.9731 0.9876 0.7404 0.9032 0.9691 0.569 ] Network output: [ -0.008953 1.017 0.9627 -7.784e-05 3.494e-05 0.03787 -5.866e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0394 0.02749 0.02943 0.02809 0.986 0.9902 0.04008 0.9717 0.9823 0.0341 ] Network output: [ 0.01933 -0.02365 0.9541 -0.001824 0.0008186 1.023 -0.001374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7392 0.6005 0.4997 0.4951 0.9762 0.9894 0.7415 0.9123 0.9732 0.5522 ] Network output: [ -0.02852 0.4181 0.7619 0.000526 -0.0002361 0.8791 0.0003964 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5722 0.5546 0.3227 0.3094 0.9869 0.9915 0.5725 0.974 0.9832 0.328 ] Network output: [ -0.05865 0.3351 0.8671 0.0003433 -0.0001541 0.9166 0.0002588 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.561 0.5582 0.3285 0.3167 0.9841 0.9897 0.5611 0.9651 0.9782 0.3295 ] Network output: [ 0.06958 0.3537 0.3852 0.001677 -0.000753 1.129 0.001264 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1412 Epoch 2977 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009775 0.997 0.9929 -0.0002502 0.0001123 -0.01052 -0.0001885 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02093 -0.006024 0.02154 0.02755 0.9429 0.9517 0.03853 0.8897 0.9087 0.08123 ] Network output: [ 0.9931 -0.0602 0.06329 -0.0004038 0.0001813 0.008986 -0.0003043 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6649 0.05591 0.2044 0.3324 0.9731 0.9876 0.7394 0.9032 0.9691 0.5682 ] Network output: [ -0.00864 1.014 0.964 -7.59e-05 3.407e-05 0.03908 -5.72e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03904 0.0272 0.02919 0.02801 0.986 0.9902 0.03971 0.9717 0.9823 0.03383 ] Network output: [ 0.0195 -0.02143 0.9509 -0.001827 0.0008203 1.024 -0.001377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7383 0.599 0.4987 0.4963 0.9762 0.9894 0.7406 0.9122 0.9733 0.5514 ] Network output: [ -0.02933 0.4264 0.7612 0.0005059 -0.0002271 0.8731 0.0003813 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5669 0.5493 0.3198 0.3074 0.9869 0.9915 0.5672 0.9739 0.9832 0.325 ] Network output: [ -0.06007 0.3416 0.8678 0.0003176 -0.0001426 0.912 0.0002393 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5559 0.553 0.3262 0.3152 0.984 0.9897 0.5559 0.9648 0.9781 0.3272 ] Network output: [ 0.07003 0.352 0.3815 0.001689 -0.0007581 1.133 0.001273 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1435 Epoch 2978 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01025 0.9918 0.9951 -0.0002416 0.0001084 -0.008413 -0.0001821 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02083 -0.006016 0.02143 0.02764 0.943 0.9517 0.03832 0.8897 0.9088 0.08089 ] Network output: [ 0.9937 -0.06626 0.06679 -0.0003735 0.0001677 0.01046 -0.0002815 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6641 0.05476 0.2035 0.3346 0.9731 0.9876 0.7384 0.9032 0.9691 0.5675 ] Network output: [ -0.00831 1.011 0.9653 -7.42e-05 3.331e-05 0.04033 -5.592e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03867 0.0269 0.02895 0.02792 0.986 0.9902 0.03933 0.9716 0.9823 0.03356 ] Network output: [ 0.01971 -0.01941 0.9477 -0.00183 0.0008216 1.025 -0.001379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7374 0.5974 0.4977 0.4973 0.9762 0.9894 0.7396 0.9122 0.9733 0.5506 ] Network output: [ -0.03009 0.4345 0.7607 0.0004867 -0.0002185 0.8669 0.0003668 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5614 0.5439 0.3167 0.3053 0.9869 0.9915 0.5617 0.9739 0.9832 0.3219 ] Network output: [ -0.06146 0.3479 0.8688 0.0002924 -0.0001313 0.9074 0.0002204 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5506 0.5478 0.3238 0.3136 0.9839 0.9896 0.5507 0.9645 0.978 0.3248 ] Network output: [ 0.07039 0.3508 0.3775 0.001698 -0.0007623 1.138 0.00128 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1457 Epoch 2979 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01074 0.9866 0.9972 -0.0002332 0.0001047 -0.006246 -0.0001758 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02073 -0.006008 0.02131 0.02773 0.943 0.9518 0.03811 0.8897 0.9089 0.08054 ] Network output: [ 0.9943 -0.0722 0.07017 -0.0003427 0.0001539 0.01197 -0.0002583 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6632 0.0536 0.2026 0.3368 0.9731 0.9876 0.7372 0.9032 0.9692 0.5667 ] Network output: [ -0.007967 1.007 0.9666 -7.278e-05 3.267e-05 0.04163 -5.485e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03828 0.02659 0.02871 0.02782 0.986 0.9902 0.03894 0.9716 0.9823 0.0333 ] Network output: [ 0.01995 -0.01756 0.9446 -0.001832 0.0008226 1.026 -0.001381 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7364 0.5958 0.4967 0.4982 0.9762 0.9894 0.7386 0.9122 0.9733 0.5498 ] Network output: [ -0.0308 0.4425 0.7604 0.0004684 -0.0002103 0.8606 0.000353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5559 0.5384 0.3136 0.303 0.9869 0.9915 0.5562 0.9739 0.9832 0.3188 ] Network output: [ -0.06283 0.354 0.87 0.0002679 -0.0001203 0.9028 0.0002019 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5453 0.5425 0.3215 0.3119 0.9838 0.9896 0.5454 0.9642 0.9779 0.3225 ] Network output: [ 0.07066 0.3499 0.3732 0.001706 -0.0007659 1.143 0.001286 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1478 Epoch 2980 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01124 0.9813 0.9993 -0.0002252 0.0001011 -0.004022 -0.0001697 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02062 -0.005998 0.02119 0.0278 0.943 0.9518 0.03789 0.8898 0.9089 0.0802 ] Network output: [ 0.9949 -0.078 0.07344 -0.0003117 0.0001399 0.01351 -0.0002349 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6622 0.05244 0.2016 0.3389 0.9732 0.9876 0.7361 0.9032 0.9692 0.5659 ] Network output: [ -0.00761 1.004 0.968 -7.166e-05 3.217e-05 0.04297 -5.401e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03789 0.02627 0.02846 0.02771 0.986 0.9902 0.03854 0.9716 0.9823 0.03303 ] Network output: [ 0.02021 -0.01587 0.9417 -0.001834 0.0008233 1.026 -0.001382 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7354 0.5941 0.4957 0.499 0.9762 0.9894 0.7376 0.9122 0.9733 0.549 ] Network output: [ -0.03146 0.4503 0.7602 0.0004509 -0.0002024 0.8542 0.0003398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5502 0.5328 0.3104 0.3006 0.9869 0.9915 0.5505 0.9738 0.9832 0.3156 ] Network output: [ -0.06418 0.3599 0.8713 0.0002439 -0.0001095 0.8982 0.0001838 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.54 0.5371 0.3191 0.3102 0.9838 0.9895 0.54 0.9639 0.9778 0.3201 ] Network output: [ 0.07086 0.3493 0.3688 0.001712 -0.0007687 1.147 0.00129 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1499 Epoch 2981 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01175 0.976 1.001 -0.0002175 9.763e-05 -0.001746 -0.0001639 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02052 -0.005987 0.02107 0.02787 0.9431 0.9518 0.03766 0.8898 0.909 0.07984 ] Network output: [ 0.9954 -0.08365 0.0766 -0.0002804 0.0001259 0.01507 -0.0002113 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6613 0.05127 0.2007 0.3408 0.9732 0.9877 0.7349 0.9033 0.9692 0.5651 ] Network output: [ -0.007241 1.001 0.9693 -7.086e-05 3.181e-05 0.04435 -5.34e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03749 0.02595 0.02821 0.02759 0.986 0.9902 0.03813 0.9715 0.9823 0.03276 ] Network output: [ 0.02049 -0.01432 0.9388 -0.001835 0.0008237 1.027 -0.001383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7343 0.5923 0.4947 0.4998 0.9762 0.9894 0.7365 0.9122 0.9733 0.5482 ] Network output: [ -0.03207 0.4579 0.7602 0.0004341 -0.0001949 0.8478 0.0003272 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5444 0.5271 0.3072 0.2982 0.9869 0.9915 0.5447 0.9738 0.9832 0.3124 ] Network output: [ -0.06549 0.3657 0.8727 0.0002207 -9.906e-05 0.8935 0.0001663 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5346 0.5317 0.3167 0.3083 0.9837 0.9895 0.5346 0.9636 0.9776 0.3177 ] Network output: [ 0.07098 0.3491 0.3642 0.001717 -0.000771 1.152 0.001294 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1519 Epoch 2982 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01228 0.9707 1.003 -0.0002101 9.433e-05 0.0005781 -0.0001584 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02041 -0.005976 0.02094 0.02792 0.9431 0.9518 0.03743 0.8898 0.909 0.07949 ] Network output: [ 0.9959 -0.08916 0.07963 -0.0002489 0.0001117 0.01664 -0.0001876 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6602 0.0501 0.1996 0.3427 0.9732 0.9877 0.7336 0.9033 0.9692 0.5644 ] Network output: [ -0.006861 0.9971 0.9706 -7.039e-05 3.16e-05 0.04577 -5.305e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03709 0.02563 0.02797 0.02746 0.986 0.9902 0.03772 0.9715 0.9823 0.03248 ] Network output: [ 0.02078 -0.01288 0.9361 -0.001835 0.0008239 1.028 -0.001383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7331 0.5905 0.4937 0.5004 0.9762 0.9894 0.7353 0.9122 0.9733 0.5475 ] Network output: [ -0.03264 0.4654 0.7603 0.0004181 -0.0001877 0.8412 0.0003151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5386 0.5213 0.304 0.2956 0.9869 0.9915 0.5389 0.9738 0.9832 0.3091 ] Network output: [ -0.06677 0.3712 0.8742 0.000198 -8.888e-05 0.8889 0.0001492 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5291 0.5263 0.3143 0.3065 0.9836 0.9894 0.5292 0.9633 0.9775 0.3154 ] Network output: [ 0.07103 0.3491 0.3594 0.001721 -0.0007727 1.156 0.001297 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1539 Epoch 2983 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01281 0.9654 1.005 -0.0002031 9.12e-05 0.002946 -0.0001531 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0203 -0.005964 0.02081 0.02797 0.9431 0.9519 0.0372 0.8898 0.9091 0.07914 ] Network output: [ 0.9964 -0.0945 0.08255 -0.0002173 9.758e-05 0.01821 -0.0001638 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6592 0.04894 0.1986 0.3444 0.9732 0.9877 0.7323 0.9033 0.9693 0.5636 ] Network output: [ -0.006471 0.9936 0.9719 -7.024e-05 3.154e-05 0.04723 -5.294e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03668 0.02531 0.02772 0.02733 0.986 0.9902 0.03731 0.9715 0.9824 0.03221 ] Network output: [ 0.02107 -0.01154 0.9335 -0.001835 0.000824 1.028 -0.001383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7319 0.5887 0.4927 0.501 0.9762 0.9894 0.7341 0.9122 0.9733 0.5467 ] Network output: [ -0.03317 0.4727 0.7606 0.0004026 -0.0001808 0.8347 0.0003034 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5327 0.5155 0.3007 0.293 0.9869 0.9915 0.533 0.9737 0.9832 0.3058 ] Network output: [ -0.06802 0.3766 0.8758 0.0001759 -7.898e-05 0.8844 0.0001326 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5237 0.5209 0.312 0.3045 0.9835 0.9894 0.5238 0.963 0.9773 0.313 ] Network output: [ 0.07102 0.3494 0.3545 0.001724 -0.0007739 1.161 0.001299 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1558 Epoch 2984 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01336 0.9601 1.007 -0.0001966 8.824e-05 0.005354 -0.0001481 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02019 -0.005952 0.02068 0.02802 0.9431 0.9519 0.03697 0.8899 0.9091 0.07879 ] Network output: [ 0.9969 -0.09968 0.08534 -0.0001857 8.338e-05 0.01978 -0.00014 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6581 0.04778 0.1975 0.3461 0.9732 0.9877 0.731 0.9033 0.9693 0.5629 ] Network output: [ -0.006072 0.99 0.9731 -7.042e-05 3.161e-05 0.04872 -5.307e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03628 0.02499 0.02747 0.02719 0.986 0.9902 0.0369 0.9715 0.9824 0.03194 ] Network output: [ 0.02137 -0.0103 0.9309 -0.001835 0.0008238 1.029 -0.001383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7307 0.5868 0.4916 0.5015 0.9763 0.9894 0.7329 0.9122 0.9734 0.5459 ] Network output: [ -0.03366 0.4799 0.7609 0.0003878 -0.0001741 0.8281 0.0002922 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5268 0.5097 0.2974 0.2903 0.9869 0.9915 0.5271 0.9737 0.9832 0.3025 ] Network output: [ -0.06924 0.3817 0.8775 0.0001545 -6.934e-05 0.8798 0.0001164 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5183 0.5154 0.3096 0.3026 0.9834 0.9893 0.5183 0.9627 0.9772 0.3106 ] Network output: [ 0.07095 0.3499 0.3496 0.001726 -0.0007747 1.166 0.001301 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1578 Epoch 2985 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01391 0.9548 1.009 -0.0001903 8.545e-05 0.0078 -0.0001434 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.02009 -0.005939 0.02055 0.02806 0.9432 0.9519 0.03674 0.8899 0.9092 0.07844 ] Network output: [ 0.9974 -0.1047 0.088 -0.0001541 6.919e-05 0.02135 -0.0001161 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.657 0.04663 0.1964 0.3477 0.9732 0.9877 0.7296 0.9033 0.9693 0.5621 ] Network output: [ -0.005666 0.9865 0.9743 -7.091e-05 3.183e-05 0.05025 -5.344e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03588 0.02467 0.02723 0.02705 0.986 0.9902 0.03648 0.9714 0.9824 0.03168 ] Network output: [ 0.02167 -0.009122 0.9285 -0.001834 0.0008236 1.03 -0.001383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7294 0.5849 0.4906 0.5019 0.9763 0.9894 0.7316 0.9121 0.9734 0.5452 ] Network output: [ -0.03411 0.4869 0.7614 0.0003734 -0.0001676 0.8214 0.0002814 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5208 0.5038 0.2941 0.2875 0.9869 0.9915 0.5211 0.9737 0.9832 0.2992 ] Network output: [ -0.07042 0.3868 0.8793 0.0001336 -5.997e-05 0.8753 0.0001007 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5129 0.51 0.3072 0.3006 0.9833 0.9893 0.5129 0.9624 0.9771 0.3082 ] Network output: [ 0.07083 0.3507 0.3445 0.001727 -0.0007752 1.17 0.001301 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1596 Epoch 2986 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01447 0.9495 1.011 -0.0001845 8.283e-05 0.01028 -0.000139 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01998 -0.005925 0.02042 0.02809 0.9432 0.9519 0.03651 0.8899 0.9092 0.07809 ] Network output: [ 0.9978 -0.1095 0.09054 -0.0001225 5.501e-05 0.0229 -9.234e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6559 0.04549 0.1953 0.3493 0.9732 0.9877 0.7282 0.9033 0.9693 0.5614 ] Network output: [ -0.005253 0.9829 0.9755 -7.17e-05 3.219e-05 0.05182 -5.404e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03547 0.02434 0.02698 0.0269 0.986 0.9902 0.03607 0.9714 0.9824 0.03141 ] Network output: [ 0.02195 -0.008012 0.9261 -0.001834 0.0008231 1.03 -0.001382 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7282 0.583 0.4896 0.5023 0.9763 0.9894 0.7303 0.9121 0.9734 0.5445 ] Network output: [ -0.03454 0.4937 0.762 0.0003595 -0.0001614 0.8148 0.0002709 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5149 0.4979 0.2908 0.2847 0.9869 0.9914 0.5152 0.9736 0.9832 0.2959 ] Network output: [ -0.07157 0.3916 0.8812 0.0001133 -5.085e-05 0.8708 8.537e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5075 0.5047 0.3048 0.2986 0.9832 0.9892 0.5076 0.962 0.9769 0.3059 ] Network output: [ 0.07067 0.3516 0.3394 0.001727 -0.0007752 1.175 0.001301 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1615 Epoch 2987 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9442 1.012 -0.000179 8.036e-05 0.0128 -0.0001349 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01987 -0.005912 0.02029 0.02811 0.9433 0.952 0.03628 0.89 0.9093 0.07774 ] Network output: [ 0.9982 -0.1142 0.09296 -9.099e-05 4.085e-05 0.02443 -6.858e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6548 0.04436 0.1942 0.3507 0.9732 0.9877 0.7268 0.9033 0.9694 0.5607 ] Network output: [ -0.004834 0.9792 0.9767 -7.278e-05 3.267e-05 0.05342 -5.485e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03507 0.02403 0.02674 0.02675 0.9859 0.9902 0.03567 0.9713 0.9824 0.03115 ] Network output: [ 0.02223 -0.006955 0.9239 -0.001832 0.0008226 1.031 -0.001381 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7269 0.581 0.4885 0.5027 0.9763 0.9894 0.729 0.9121 0.9734 0.5438 ] Network output: [ -0.03494 0.5004 0.7627 0.000346 -0.0001553 0.8081 0.0002608 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.5089 0.492 0.2875 0.2819 0.9869 0.9914 0.5092 0.9736 0.9832 0.2926 ] Network output: [ -0.07268 0.3963 0.8831 9.351e-05 -4.198e-05 0.8664 7.047e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.5022 0.4994 0.3025 0.2966 0.9831 0.9892 0.5023 0.9617 0.9768 0.3035 ] Network output: [ 0.07046 0.3527 0.3342 0.001726 -0.000775 1.179 0.001301 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1633 Epoch 2988 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0156 0.939 1.014 -0.0001739 7.806e-05 0.01534 -0.000131 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01977 -0.005898 0.02015 0.02814 0.9433 0.952 0.03605 0.89 0.9093 0.07739 ] Network output: [ 0.9987 -0.1187 0.09526 -5.955e-05 2.673e-05 0.02594 -4.488e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6536 0.04325 0.193 0.3521 0.9733 0.9877 0.7254 0.9034 0.9694 0.56 ] Network output: [ -0.00441 0.9756 0.9779 -7.412e-05 3.328e-05 0.05505 -5.586e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03468 0.02371 0.0265 0.0266 0.9859 0.9902 0.03526 0.9713 0.9824 0.03089 ] Network output: [ 0.0225 -0.005941 0.9218 -0.001831 0.000822 1.032 -0.00138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7255 0.5791 0.4875 0.503 0.9763 0.9894 0.7277 0.9121 0.9734 0.5431 ] Network output: [ -0.03531 0.5069 0.7636 0.0003329 -0.0001494 0.8015 0.0002509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.503 0.4862 0.2843 0.2791 0.9869 0.9914 0.5033 0.9735 0.9832 0.2893 ] Network output: [ -0.07376 0.4008 0.885 7.428e-05 -3.335e-05 0.862 5.598e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.497 0.4941 0.3002 0.2946 0.983 0.9891 0.497 0.9614 0.9766 0.3012 ] Network output: [ 0.07022 0.3541 0.329 0.001725 -0.0007744 1.184 0.0013 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1651 Epoch 2989 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01618 0.9337 1.015 -0.0001691 7.59e-05 0.01792 -0.0001274 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01966 -0.005883 0.02002 0.02815 0.9433 0.952 0.03583 0.89 0.9094 0.07705 ] Network output: [ 0.9991 -0.1231 0.09743 -2.822e-05 1.267e-05 0.02743 -2.126e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6525 0.04215 0.1918 0.3535 0.9733 0.9877 0.7239 0.9034 0.9694 0.5594 ] Network output: [ -0.003981 0.9719 0.979 -7.572e-05 3.399e-05 0.05672 -5.706e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03429 0.0234 0.02626 0.02644 0.9859 0.9902 0.03486 0.9713 0.9823 0.03063 ] Network output: [ 0.02275 -0.004963 0.9197 -0.001829 0.0008213 1.032 -0.001379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7242 0.5771 0.4865 0.5033 0.9763 0.9894 0.7264 0.9121 0.9734 0.5424 ] Network output: [ -0.03566 0.5132 0.7645 0.00032 -0.0001437 0.7949 0.0002412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4971 0.4804 0.281 0.2762 0.9868 0.9914 0.4974 0.9735 0.9832 0.286 ] Network output: [ -0.07481 0.4052 0.887 5.556e-05 -2.494e-05 0.8577 4.187e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4918 0.4889 0.2979 0.2926 0.983 0.9891 0.4918 0.961 0.9765 0.2989 ] Network output: [ 0.06995 0.3556 0.3237 0.001723 -0.0007737 1.188 0.001299 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1669 Epoch 2990 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01675 0.9285 1.017 -0.0001646 7.388e-05 0.02053 -0.000124 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01956 -0.005869 0.01988 0.02817 0.9434 0.9521 0.0356 0.8901 0.9094 0.07671 ] Network output: [ 0.9995 -0.1273 0.09949 2.99e-06 -1.342e-06 0.02889 2.254e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6514 0.04107 0.1906 0.3548 0.9733 0.9877 0.7225 0.9034 0.9694 0.5587 ] Network output: [ -0.003549 0.9683 0.9801 -7.755e-05 3.481e-05 0.05843 -5.844e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0339 0.0231 0.02602 0.02629 0.9859 0.9902 0.03447 0.9712 0.9823 0.03037 ] Network output: [ 0.02298 -0.004013 0.9178 -0.001828 0.0008205 1.033 -0.001377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7229 0.5751 0.4855 0.5035 0.9763 0.9894 0.725 0.9121 0.9734 0.5417 ] Network output: [ -0.03599 0.5194 0.7655 0.0003074 -0.000138 0.7883 0.0002317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4912 0.4746 0.2778 0.2734 0.9868 0.9914 0.4915 0.9734 0.9832 0.2828 ] Network output: [ -0.07582 0.4094 0.889 3.733e-05 -1.676e-05 0.8534 2.813e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4866 0.4838 0.2956 0.2906 0.9829 0.989 0.4867 0.9607 0.9763 0.2967 ] Network output: [ 0.06965 0.3572 0.3184 0.001721 -0.0007727 1.192 0.001297 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1686 Epoch 2991 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01733 0.9233 1.018 -0.0001603 7.198e-05 0.02316 -0.0001208 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01946 -0.005854 0.01974 0.02818 0.9434 0.9521 0.03538 0.8901 0.9095 0.07637 ] Network output: [ 0.9998 -0.1313 0.1014 3.405e-05 -1.529e-05 0.03032 2.566e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6502 0.04002 0.1894 0.3561 0.9733 0.9877 0.7211 0.9034 0.9694 0.5581 ] Network output: [ -0.003114 0.9646 0.9812 -7.958e-05 3.573e-05 0.06016 -5.998e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03352 0.0228 0.02579 0.02613 0.9859 0.9902 0.03408 0.9712 0.9823 0.03012 ] Network output: [ 0.0232 -0.003087 0.916 -0.001826 0.0008196 1.033 -0.001376 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7215 0.5732 0.4845 0.5037 0.9763 0.9894 0.7237 0.9121 0.9734 0.5411 ] Network output: [ -0.0363 0.5253 0.7667 0.000295 -0.0001324 0.7818 0.0002223 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4854 0.4688 0.2746 0.2705 0.9868 0.9914 0.4856 0.9734 0.9832 0.2796 ] Network output: [ -0.0768 0.4134 0.891 1.958e-05 -8.789e-06 0.8492 1.475e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4816 0.4787 0.2934 0.2886 0.9828 0.9889 0.4816 0.9604 0.9762 0.2944 ] Network output: [ 0.06932 0.359 0.3131 0.001718 -0.0007715 1.196 0.001295 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1703 Epoch 2992 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01792 0.9182 1.02 -0.0001564 7.021e-05 0.02582 -0.0001179 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01935 -0.005839 0.0196 0.02819 0.9435 0.9521 0.03516 0.8901 0.9095 0.07604 ] Network output: [ 1 -0.1352 0.1033 6.495e-05 -2.916e-05 0.03172 4.895e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6491 0.03898 0.1881 0.3573 0.9733 0.9877 0.7196 0.9034 0.9695 0.5575 ] Network output: [ -0.002677 0.9609 0.9822 -8.181e-05 3.673e-05 0.06194 -6.165e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03314 0.0225 0.02556 0.02597 0.9859 0.9902 0.0337 0.9712 0.9823 0.02987 ] Network output: [ 0.0234 -0.00218 0.9142 -0.001824 0.0008186 1.034 -0.001374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7202 0.5712 0.4835 0.5039 0.9763 0.9894 0.7223 0.9121 0.9734 0.5405 ] Network output: [ -0.0366 0.5311 0.7679 0.0002828 -0.000127 0.7753 0.0002131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4796 0.4631 0.2714 0.2677 0.9868 0.9914 0.4799 0.9734 0.9831 0.2764 ] Network output: [ -0.07774 0.4173 0.8931 2.28e-06 -1.024e-06 0.8451 1.718e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4766 0.4737 0.2912 0.2866 0.9827 0.9889 0.4766 0.9601 0.976 0.2922 ] Network output: [ 0.06898 0.361 0.3078 0.001715 -0.0007701 1.2 0.001293 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.172 Epoch 2993 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01851 0.913 1.021 -0.0001527 6.855e-05 0.0285 -0.0001151 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01926 -0.005824 0.01947 0.02819 0.9435 0.9521 0.03494 0.8902 0.9096 0.07572 ] Network output: [ 1.001 -0.1389 0.105 9.566e-05 -4.295e-05 0.0331 7.21e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6479 0.03797 0.1869 0.3584 0.9733 0.9877 0.7182 0.9035 0.9695 0.5569 ] Network output: [ -0.002238 0.9572 0.9832 -8.421e-05 3.78e-05 0.06374 -6.346e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03277 0.02221 0.02533 0.02582 0.9859 0.9902 0.03332 0.9711 0.9823 0.02963 ] Network output: [ 0.02358 -0.001286 0.9126 -0.001821 0.0008176 1.034 -0.001373 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7189 0.5693 0.4825 0.5041 0.9763 0.9894 0.721 0.912 0.9735 0.5398 ] Network output: [ -0.03689 0.5368 0.7693 0.0002708 -0.0001216 0.7689 0.0002041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4739 0.4575 0.2683 0.2649 0.9868 0.9914 0.4741 0.9733 0.9831 0.2733 ] Network output: [ -0.07865 0.421 0.8952 -1.458e-05 6.546e-06 0.8411 -1.099e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4717 0.4689 0.289 0.2847 0.9826 0.9888 0.4717 0.9597 0.9759 0.2901 ] Network output: [ 0.06862 0.3631 0.3024 0.001712 -0.0007686 1.204 0.00129 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1736 Epoch 2994 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01909 0.9079 1.022 -0.0001492 6.699e-05 0.03121 -0.0001125 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01916 -0.005809 0.01933 0.02819 0.9435 0.9522 0.03472 0.8902 0.9096 0.07539 ] Network output: [ 1.001 -0.1424 0.1066 0.0001262 -5.665e-05 0.03443 9.51e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6468 0.03698 0.1856 0.3596 0.9734 0.9877 0.7167 0.9035 0.9695 0.5563 ] Network output: [ -0.001798 0.9535 0.9842 -8.676e-05 3.895e-05 0.06557 -6.538e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03241 0.02193 0.0251 0.02566 0.9859 0.9902 0.03295 0.9711 0.9823 0.02939 ] Network output: [ 0.02374 -0.0004045 0.911 -0.001819 0.0008166 1.035 -0.001371 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7175 0.5674 0.4815 0.5042 0.9764 0.9894 0.7196 0.912 0.9735 0.5393 ] Network output: [ -0.03717 0.5422 0.7707 0.0002588 -0.0001162 0.7625 0.0001951 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4682 0.4519 0.2652 0.2621 0.9868 0.9914 0.4685 0.9733 0.9831 0.2702 ] Network output: [ -0.07954 0.4246 0.8972 -3.102e-05 1.393e-05 0.8371 -2.338e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4669 0.464 0.2869 0.2828 0.9825 0.9888 0.4669 0.9594 0.9758 0.2879 ] Network output: [ 0.06824 0.3654 0.2971 0.001708 -0.0007669 1.208 0.001287 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1752 Epoch 2995 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01968 0.9028 1.023 -0.000146 6.552e-05 0.03394 -0.00011 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01906 -0.005794 0.01919 0.02819 0.9436 0.9522 0.03451 0.8902 0.9097 0.07508 ] Network output: [ 1.001 -0.1458 0.108 0.0001565 -7.026e-05 0.03574 0.000118 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6457 0.03602 0.1843 0.3606 0.9734 0.9877 0.7153 0.9035 0.9695 0.5558 ] Network output: [ -0.001357 0.9498 0.9852 -8.944e-05 4.015e-05 0.06744 -6.741e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03206 0.02165 0.02488 0.0255 0.9859 0.9902 0.03259 0.9711 0.9823 0.02916 ] Network output: [ 0.02388 0.0004686 0.9095 -0.001816 0.0008155 1.035 -0.001369 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7162 0.5655 0.4805 0.5043 0.9764 0.9894 0.7183 0.912 0.9735 0.5387 ] Network output: [ -0.03744 0.5474 0.7722 0.000247 -0.0001109 0.7562 0.0001861 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4626 0.4464 0.2622 0.2594 0.9868 0.9914 0.4629 0.9732 0.9831 0.2671 ] Network output: [ -0.08039 0.4281 0.8993 -4.706e-05 2.113e-05 0.8332 -3.547e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4621 0.4593 0.2848 0.2809 0.9824 0.9887 0.4622 0.9591 0.9756 0.2859 ] Network output: [ 0.06786 0.3678 0.2917 0.001704 -0.0007651 1.212 0.001284 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1768 Epoch 2996 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02027 0.8977 1.024 -0.0001429 6.414e-05 0.03669 -0.0001077 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01897 -0.005778 0.01906 0.02819 0.9436 0.9522 0.0343 0.8903 0.9097 0.07476 ] Network output: [ 1.002 -0.149 0.1094 0.0001866 -8.378e-05 0.03701 0.0001406 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6445 0.03509 0.183 0.3617 0.9734 0.9878 0.7139 0.9035 0.9695 0.5552 ] Network output: [ -0.0009169 0.946 0.9861 -9.224e-05 4.141e-05 0.06933 -6.951e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03171 0.02137 0.02467 0.02535 0.9859 0.9902 0.03223 0.971 0.9823 0.02892 ] Network output: [ 0.024 0.001334 0.9081 -0.001814 0.0008143 1.035 -0.001367 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7149 0.5636 0.4795 0.5044 0.9764 0.9894 0.7169 0.912 0.9735 0.5381 ] Network output: [ -0.0377 0.5525 0.7739 0.0002353 -0.0001056 0.75 0.0001773 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4571 0.441 0.2592 0.2567 0.9868 0.9914 0.4573 0.9732 0.9831 0.2642 ] Network output: [ -0.08121 0.4314 0.9014 -6.272e-05 2.816e-05 0.8294 -4.727e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4575 0.4547 0.2827 0.279 0.9824 0.9887 0.4575 0.9587 0.9755 0.2838 ] Network output: [ 0.06747 0.3703 0.2863 0.0017 -0.0007631 1.215 0.001281 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1783 Epoch 2997 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02087 0.8927 1.026 -0.00014 6.283e-05 0.03946 -0.0001055 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01888 -0.005763 0.01892 0.02819 0.9437 0.9523 0.0341 0.8903 0.9097 0.07446 ] Network output: [ 1.002 -0.1521 0.1107 0.0002165 -9.719e-05 0.03825 0.0001631 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6434 0.03419 0.1817 0.3627 0.9734 0.9878 0.7125 0.9035 0.9696 0.5547 ] Network output: [ -0.0004767 0.9423 0.987 -9.512e-05 4.271e-05 0.07126 -7.169e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03137 0.02111 0.02445 0.02519 0.9859 0.9902 0.03188 0.971 0.9823 0.0287 ] Network output: [ 0.02409 0.002194 0.9068 -0.001811 0.0008131 1.035 -0.001365 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7135 0.5618 0.4785 0.5045 0.9764 0.9894 0.7156 0.912 0.9735 0.5376 ] Network output: [ -0.03795 0.5574 0.7756 0.0002236 -0.0001004 0.7438 0.0001685 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4517 0.4356 0.2563 0.254 0.9868 0.9914 0.4519 0.9732 0.9831 0.2612 ] Network output: [ -0.08201 0.4345 0.9035 -7.801e-05 3.502e-05 0.8256 -5.879e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.453 0.4501 0.2807 0.2772 0.9823 0.9886 0.453 0.9584 0.9753 0.2818 ] Network output: [ 0.06707 0.373 0.281 0.001695 -0.0007611 1.219 0.001278 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1797 Epoch 2998 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02146 0.8877 1.027 -0.0001372 6.159e-05 0.04224 -0.0001034 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01879 -0.005748 0.01878 0.02818 0.9437 0.9523 0.0339 0.8903 0.9098 0.07416 ] Network output: [ 1.002 -0.1551 0.1119 0.0002461 -0.0001105 0.03945 0.0001855 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6423 0.03331 0.1803 0.3637 0.9734 0.9878 0.711 0.9036 0.9696 0.5542 ] Network output: [ -3.745e-05 0.9386 0.9879 -9.809e-05 4.404e-05 0.07321 -7.393e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03103 0.02085 0.02424 0.02504 0.9859 0.9902 0.03154 0.971 0.9823 0.02848 ] Network output: [ 0.02417 0.003049 0.9056 -0.001808 0.0008119 1.036 -0.001363 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7122 0.56 0.4776 0.5046 0.9764 0.9894 0.7143 0.912 0.9735 0.5371 ] Network output: [ -0.03821 0.5621 0.7774 0.0002119 -9.515e-05 0.7378 0.0001597 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4463 0.4303 0.2534 0.2514 0.9868 0.9914 0.4465 0.9731 0.9831 0.2583 ] Network output: [ -0.08278 0.4376 0.9057 -9.294e-05 4.172e-05 0.822 -7.004e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4485 0.4457 0.2787 0.2754 0.9822 0.9886 0.4485 0.9581 0.9752 0.2799 ] Network output: [ 0.06667 0.3757 0.2756 0.001691 -0.000759 1.222 0.001274 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1811 Epoch 2999 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02205 0.8827 1.028 -0.0001346 6.041e-05 0.04505 -0.0001014 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01871 -0.005732 0.01865 0.02817 0.9438 0.9523 0.0337 0.8903 0.9098 0.07386 ] Network output: [ 1.003 -0.1579 0.1129 0.0002755 -0.0001237 0.04062 0.0002076 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6412 0.03246 0.179 0.3646 0.9735 0.9878 0.7097 0.9036 0.9696 0.5537 ] Network output: [ 0.0004006 0.9349 0.9887 -0.0001011 4.54e-05 0.07519 -7.621e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0307 0.02059 0.02404 0.02489 0.9859 0.9902 0.0312 0.9709 0.9823 0.02826 ] Network output: [ 0.02423 0.003899 0.9045 -0.001806 0.0008106 1.036 -0.001361 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7109 0.5582 0.4766 0.5047 0.9764 0.9894 0.713 0.912 0.9735 0.5366 ] Network output: [ -0.03846 0.5666 0.7793 0.0002003 -8.994e-05 0.7318 0.000151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.441 0.4251 0.2506 0.2488 0.9868 0.9914 0.4412 0.9731 0.9831 0.2555 ] Network output: [ -0.08352 0.4404 0.9078 -0.0001075 4.828e-05 0.8184 -8.104e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4441 0.4413 0.2768 0.2736 0.9821 0.9886 0.4442 0.9578 0.975 0.2779 ] Network output: [ 0.06626 0.3786 0.2703 0.001686 -0.0007567 1.225 0.00127 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1825 Epoch 3000 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02264 0.8778 1.029 -0.000132 5.927e-05 0.04786 -9.951e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01862 -0.005717 0.01851 0.02816 0.9438 0.9524 0.03351 0.8904 0.9099 0.07358 ] Network output: [ 1.003 -0.1606 0.1139 0.0003046 -0.0001367 0.04176 0.0002295 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6402 0.03164 0.1776 0.3655 0.9735 0.9878 0.7083 0.9036 0.9696 0.5533 ] Network output: [ 0.0008371 0.9312 0.9895 -0.0001042 4.678e-05 0.07719 -7.852e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03038 0.02035 0.02384 0.02474 0.9859 0.9902 0.03088 0.9709 0.9823 0.02805 ] Network output: [ 0.02427 0.004744 0.9034 -0.001803 0.0008093 1.036 -0.001359 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7096 0.5564 0.4757 0.5048 0.9764 0.9895 0.7117 0.912 0.9735 0.5361 ] Network output: [ -0.03871 0.5709 0.7813 0.0001888 -8.475e-05 0.726 0.0001423 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4357 0.42 0.2478 0.2462 0.9868 0.9914 0.436 0.973 0.9831 0.2527 ] Network output: [ -0.08423 0.4432 0.9099 -0.0001218 5.468e-05 0.8149 -9.18e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4399 0.437 0.2749 0.2719 0.982 0.9885 0.4399 0.9575 0.9749 0.2761 ] Network output: [ 0.06586 0.3816 0.265 0.00168 -0.0007544 1.229 0.001266 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1838 Epoch 3001 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02323 0.8729 1.029 -0.0001296 5.819e-05 0.05069 -9.768e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01854 -0.005701 0.01838 0.02815 0.9438 0.9524 0.03332 0.8904 0.9099 0.07329 ] Network output: [ 1.003 -0.1631 0.1148 0.0003334 -0.0001497 0.04286 0.0002513 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6391 0.03085 0.1763 0.3664 0.9735 0.9878 0.7069 0.9036 0.9696 0.5528 ] Network output: [ 0.001272 0.9275 0.9903 -0.0001073 4.817e-05 0.07922 -8.086e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.03007 0.0201 0.02364 0.0246 0.9859 0.9902 0.03056 0.9709 0.9823 0.02784 ] Network output: [ 0.02429 0.005583 0.9025 -0.0018 0.0008079 1.036 -0.001356 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7084 0.5547 0.4747 0.5049 0.9765 0.9895 0.7104 0.912 0.9735 0.5357 ] Network output: [ -0.03895 0.5751 0.7833 0.0001772 -7.957e-05 0.7202 0.0001336 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4306 0.415 0.2451 0.2437 0.9868 0.9914 0.4308 0.973 0.9831 0.25 ] Network output: [ -0.08493 0.4458 0.912 -0.0001358 6.095e-05 0.8114 -0.0001023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4357 0.4328 0.2731 0.2702 0.982 0.9885 0.4357 0.9572 0.9748 0.2742 ] Network output: [ 0.06546 0.3847 0.2597 0.001675 -0.000752 1.232 0.001262 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1851 Epoch 3002 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02381 0.8681 1.03 -0.0001273 5.714e-05 0.05353 -9.592e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01846 -0.005686 0.01824 0.02814 0.9439 0.9524 0.03313 0.8904 0.91 0.07302 ] Network output: [ 1.004 -0.1655 0.1155 0.0003619 -0.0001625 0.04393 0.0002727 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6381 0.03009 0.1749 0.3673 0.9735 0.9878 0.7056 0.9036 0.9696 0.5524 ] Network output: [ 0.001704 0.9238 0.9911 -0.0001104 4.957e-05 0.08127 -8.321e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02976 0.01987 0.02345 0.02445 0.9859 0.9902 0.03024 0.9708 0.9823 0.02764 ] Network output: [ 0.02429 0.006416 0.9016 -0.001796 0.0008065 1.036 -0.001354 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7071 0.553 0.4738 0.5049 0.9765 0.9895 0.7091 0.912 0.9735 0.5352 ] Network output: [ -0.0392 0.5791 0.7855 0.0001657 -7.44e-05 0.7145 0.0001249 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4255 0.41 0.2425 0.2413 0.9868 0.9914 0.4257 0.973 0.9831 0.2474 ] Network output: [ -0.08559 0.4483 0.9141 -0.0001494 6.708e-05 0.8081 -0.0001126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4316 0.4287 0.2713 0.2685 0.9819 0.9884 0.4316 0.9568 0.9746 0.2725 ] Network output: [ 0.06506 0.3879 0.2544 0.00167 -0.0007496 1.234 0.001258 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1863 Epoch 3003 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0244 0.8633 1.031 -0.000125 5.612e-05 0.05639 -9.421e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01838 -0.005671 0.01811 0.02813 0.9439 0.9525 0.03295 0.8905 0.91 0.07274 ] Network output: [ 1.004 -0.1678 0.1162 0.0003901 -0.0001751 0.04496 0.000294 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6371 0.02936 0.1735 0.3681 0.9735 0.9878 0.7042 0.9037 0.9697 0.552 ] Network output: [ 0.002134 0.9202 0.9918 -0.0001135 5.097e-05 0.08333 -8.556e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02946 0.01964 0.02326 0.02431 0.9859 0.9902 0.02994 0.9708 0.9823 0.02744 ] Network output: [ 0.02427 0.007242 0.9007 -0.001793 0.0008051 1.036 -0.001351 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7059 0.5513 0.4729 0.505 0.9765 0.9895 0.7079 0.912 0.9736 0.5348 ] Network output: [ -0.03945 0.5829 0.7877 0.0001542 -6.923e-05 0.709 0.0001162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4206 0.4051 0.2399 0.2389 0.9868 0.9914 0.4208 0.9729 0.9831 0.2448 ] Network output: [ -0.08624 0.4507 0.9163 -0.0001628 7.308e-05 0.8048 -0.0001227 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4276 0.4247 0.2695 0.2669 0.9818 0.9884 0.4276 0.9565 0.9745 0.2707 ] Network output: [ 0.06466 0.3912 0.2492 0.001664 -0.000747 1.237 0.001254 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1875 Epoch 3004 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02498 0.8585 1.032 -0.0001228 5.513e-05 0.05924 -9.254e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0183 -0.005655 0.01798 0.02812 0.944 0.9525 0.03277 0.8905 0.91 0.07248 ] Network output: [ 1.004 -0.1699 0.1168 0.000418 -0.0001877 0.04596 0.000315 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.636 0.02866 0.1721 0.3689 0.9736 0.9878 0.7029 0.9037 0.9697 0.5516 ] Network output: [ 0.002562 0.9165 0.9925 -0.0001166 5.237e-05 0.08542 -8.791e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02917 0.01941 0.02307 0.02417 0.9859 0.9902 0.02964 0.9708 0.9823 0.02725 ] Network output: [ 0.02424 0.00806 0.9 -0.00179 0.0008036 1.036 -0.001349 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7046 0.5497 0.472 0.505 0.9765 0.9895 0.7066 0.912 0.9736 0.5344 ] Network output: [ -0.03971 0.5865 0.79 0.0001427 -6.408e-05 0.7035 0.0001076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4157 0.4003 0.2374 0.2365 0.9868 0.9914 0.4159 0.9729 0.983 0.2423 ] Network output: [ -0.08687 0.453 0.9184 -0.0001759 7.895e-05 0.8017 -0.0001325 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4237 0.4208 0.2678 0.2653 0.9817 0.9883 0.4237 0.9562 0.9744 0.269 ] Network output: [ 0.06427 0.3946 0.2439 0.001658 -0.0007444 1.24 0.00125 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1886 Epoch 3005 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02557 0.8538 1.032 -0.0001206 5.415e-05 0.06211 -9.091e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01823 -0.00564 0.01785 0.02811 0.944 0.9526 0.0326 0.8905 0.9101 0.07222 ] Network output: [ 1.005 -0.1719 0.1173 0.0004456 -0.0002 0.04693 0.0003358 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6351 0.02799 0.1707 0.3697 0.9736 0.9878 0.7017 0.9037 0.9697 0.5512 ] Network output: [ 0.002986 0.9129 0.9931 -0.0001197 5.375e-05 0.08752 -9.023e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02888 0.0192 0.02289 0.02404 0.9859 0.9902 0.02934 0.9707 0.9823 0.02706 ] Network output: [ 0.02419 0.008868 0.8993 -0.001787 0.0008021 1.036 -0.001346 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7034 0.5481 0.4711 0.5051 0.9765 0.9895 0.7054 0.912 0.9736 0.534 ] Network output: [ -0.03996 0.5899 0.7924 0.0001313 -5.892e-05 0.6982 9.892e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4109 0.3956 0.2349 0.2342 0.9868 0.9914 0.4111 0.9729 0.983 0.2398 ] Network output: [ -0.08747 0.4551 0.9205 -0.0001887 8.47e-05 0.7986 -0.0001422 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4198 0.417 0.2662 0.2637 0.9817 0.9883 0.4199 0.956 0.9742 0.2674 ] Network output: [ 0.06389 0.398 0.2388 0.001652 -0.0007418 1.242 0.001245 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1896 Epoch 3006 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02615 0.8492 1.033 -0.0001185 5.319e-05 0.06498 -8.93e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01815 -0.005625 0.01772 0.0281 0.9441 0.9526 0.03243 0.8906 0.9101 0.07197 ] Network output: [ 1.005 -0.1738 0.1177 0.0004728 -0.0002123 0.04787 0.0003563 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6341 0.02735 0.1693 0.3705 0.9736 0.9878 0.7004 0.9037 0.9697 0.5508 ] Network output: [ 0.003408 0.9093 0.9938 -0.0001228 5.512e-05 0.08964 -9.253e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0286 0.01898 0.02271 0.0239 0.9859 0.9902 0.02906 0.9707 0.9823 0.02688 ] Network output: [ 0.02412 0.009664 0.8987 -0.001783 0.0008006 1.036 -0.001344 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.7022 0.5466 0.4702 0.5051 0.9765 0.9895 0.7042 0.912 0.9736 0.5336 ] Network output: [ -0.04022 0.5932 0.7948 0.0001198 -5.378e-05 0.6929 9.028e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4061 0.391 0.2325 0.232 0.9868 0.9914 0.4063 0.9728 0.983 0.2374 ] Network output: [ -0.08806 0.4571 0.9226 -0.0002012 9.033e-05 0.7956 -0.0001516 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4161 0.4133 0.2646 0.2622 0.9816 0.9883 0.4161 0.9557 0.9741 0.2658 ] Network output: [ 0.06351 0.4016 0.2336 0.001646 -0.0007391 1.244 0.001241 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1907 Epoch 3007 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02673 0.8446 1.034 -0.0001164 5.225e-05 0.06785 -8.771e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01808 -0.00561 0.01759 0.02808 0.9441 0.9526 0.03226 0.8906 0.9102 0.07172 ] Network output: [ 1.005 -0.1756 0.118 0.0004997 -0.0002243 0.04877 0.0003766 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6331 0.02674 0.1679 0.3712 0.9736 0.9879 0.6991 0.9038 0.9697 0.5505 ] Network output: [ 0.003827 0.9057 0.9944 -0.0001258 5.647e-05 0.09178 -9.48e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02833 0.01878 0.02254 0.02377 0.9859 0.9902 0.02878 0.9707 0.9823 0.0267 ] Network output: [ 0.02404 0.01045 0.8982 -0.00178 0.000799 1.036 -0.001341 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.701 0.545 0.4693 0.5052 0.9766 0.9895 0.703 0.912 0.9736 0.5333 ] Network output: [ -0.04048 0.5963 0.7973 0.0001084 -4.865e-05 0.6878 8.166e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.4015 0.3865 0.2302 0.2298 0.9868 0.9914 0.4017 0.9728 0.983 0.2351 ] Network output: [ -0.08862 0.459 0.9247 -0.0002135 9.585e-05 0.7926 -0.0001609 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4124 0.4096 0.263 0.2608 0.9815 0.9882 0.4125 0.9554 0.974 0.2642 ] Network output: [ 0.06314 0.4052 0.2285 0.00164 -0.0007363 1.247 0.001236 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1916 Epoch 3008 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0273 0.84 1.034 -0.0001143 5.13e-05 0.07072 -8.612e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01801 -0.005595 0.01746 0.02807 0.9442 0.9527 0.03209 0.8906 0.9102 0.07148 ] Network output: [ 1.006 -0.1773 0.1182 0.0005262 -0.0002362 0.04964 0.0003965 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6322 0.02615 0.1665 0.372 0.9737 0.9879 0.6979 0.9038 0.9698 0.5501 ] Network output: [ 0.004242 0.9022 0.9949 -0.0001287 5.78e-05 0.09392 -9.703e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02806 0.01858 0.02237 0.02365 0.9859 0.9902 0.02851 0.9706 0.9823 0.02652 ] Network output: [ 0.02394 0.01122 0.8977 -0.001776 0.0007974 1.036 -0.001339 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6999 0.5436 0.4684 0.5052 0.9766 0.9895 0.7018 0.912 0.9736 0.5329 ] Network output: [ -0.04074 0.5993 0.7999 9.695e-05 -4.352e-05 0.6828 7.306e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3969 0.382 0.2279 0.2277 0.9868 0.9914 0.3971 0.9728 0.983 0.2328 ] Network output: [ -0.08917 0.4608 0.9269 -0.0002256 0.0001013 0.7898 -0.00017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4088 0.406 0.2615 0.2593 0.9815 0.9882 0.4089 0.9551 0.9739 0.2627 ] Network output: [ 0.06278 0.4089 0.2235 0.001634 -0.0007335 1.249 0.001231 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1926 Epoch 3009 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02788 0.8356 1.035 -0.0001122 5.036e-05 0.07358 -8.454e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01794 -0.00558 0.01733 0.02805 0.9442 0.9527 0.03193 0.8907 0.9102 0.07125 ] Network output: [ 1.006 -0.1789 0.1184 0.0005523 -0.0002479 0.05048 0.0004162 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6313 0.02559 0.1651 0.3727 0.9737 0.9879 0.6967 0.9038 0.9698 0.5498 ] Network output: [ 0.004654 0.8986 0.9954 -0.0001316 5.91e-05 0.09607 -9.92e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0278 0.01838 0.02221 0.02352 0.986 0.9902 0.02824 0.9706 0.9823 0.02635 ] Network output: [ 0.02383 0.01197 0.8973 -0.001773 0.0007958 1.036 -0.001336 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6987 0.5421 0.4676 0.5052 0.9766 0.9895 0.7006 0.912 0.9736 0.5326 ] Network output: [ -0.04101 0.6021 0.8025 8.556e-05 -3.841e-05 0.6778 6.448e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3925 0.3777 0.2257 0.2256 0.9868 0.9914 0.3927 0.9727 0.983 0.2306 ] Network output: [ -0.08971 0.4625 0.929 -0.0002374 0.0001066 0.787 -0.0001789 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4054 0.4026 0.26 0.2579 0.9814 0.9881 0.4054 0.9548 0.9737 0.2612 ] Network output: [ 0.06242 0.4126 0.2184 0.001628 -0.0007307 1.251 0.001227 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1934 Epoch 3010 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02845 0.8311 1.035 -0.0001101 4.942e-05 0.07645 -8.296e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01788 -0.005565 0.01721 0.02804 0.9443 0.9527 0.03178 0.8907 0.9103 0.07102 ] Network output: [ 1.006 -0.1803 0.1185 0.000578 -0.0002595 0.05128 0.0004356 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6304 0.02506 0.1637 0.3734 0.9737 0.9879 0.6955 0.9038 0.9698 0.5495 ] Network output: [ 0.005063 0.8951 0.9959 -0.0001345 6.036e-05 0.09823 -0.0001013 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02755 0.01819 0.02205 0.0234 0.986 0.9902 0.02798 0.9706 0.9823 0.02619 ] Network output: [ 0.0237 0.01271 0.8969 -0.001769 0.0007941 1.036 -0.001333 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6976 0.5407 0.4667 0.5053 0.9766 0.9895 0.6995 0.912 0.9736 0.5323 ] Network output: [ -0.04129 0.6047 0.8051 7.421e-05 -3.331e-05 0.673 5.593e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3881 0.3734 0.2235 0.2236 0.9868 0.9914 0.3883 0.9727 0.983 0.2284 ] Network output: [ -0.09022 0.4641 0.9311 -0.0002489 0.0001118 0.7843 -0.0001876 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.4019 0.3991 0.2585 0.2566 0.9813 0.9881 0.402 0.9545 0.9736 0.2598 ] Network output: [ 0.06207 0.4164 0.2135 0.001621 -0.0007278 1.253 0.001222 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1942 Epoch 3011 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02902 0.8268 1.035 -0.000108 4.847e-05 0.07931 -8.137e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01781 -0.00555 0.01709 0.02803 0.9443 0.9528 0.03162 0.8907 0.9103 0.0708 ] Network output: [ 1.007 -0.1816 0.1185 0.0006033 -0.0002709 0.05206 0.0004547 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6295 0.02456 0.1623 0.374 0.9737 0.9879 0.6943 0.9039 0.9698 0.5492 ] Network output: [ 0.005468 0.8917 0.9964 -0.0001372 6.159e-05 0.1004 -0.0001034 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0273 0.018 0.02189 0.02328 0.986 0.9902 0.02772 0.9706 0.9822 0.02603 ] Network output: [ 0.02356 0.01342 0.8967 -0.001765 0.0007924 1.036 -0.00133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6965 0.5393 0.4659 0.5053 0.9766 0.9895 0.6984 0.912 0.9736 0.532 ] Network output: [ -0.04156 0.6072 0.8079 6.289e-05 -2.823e-05 0.6684 4.74e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3838 0.3692 0.2214 0.2216 0.9868 0.9914 0.384 0.9727 0.983 0.2263 ] Network output: [ -0.09073 0.4656 0.9332 -0.0002603 0.0001169 0.7816 -0.0001962 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3986 0.3958 0.2571 0.2552 0.9813 0.9881 0.3986 0.9543 0.9735 0.2584 ] Network output: [ 0.06174 0.4203 0.2086 0.001615 -0.0007248 1.254 0.001217 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.195 Epoch 3012 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02958 0.8225 1.036 -0.0001058 4.751e-05 0.08216 -7.976e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01775 -0.005535 0.01696 0.02801 0.9444 0.9528 0.03147 0.8908 0.9103 0.07058 ] Network output: [ 1.007 -0.1829 0.1184 0.0006282 -0.000282 0.0528 0.0004735 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6286 0.02408 0.1609 0.3747 0.9738 0.9879 0.6932 0.9039 0.9698 0.549 ] Network output: [ 0.00587 0.8883 0.9968 -0.0001398 6.278e-05 0.1026 -0.0001054 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02705 0.01782 0.02173 0.02316 0.986 0.9902 0.02748 0.9705 0.9822 0.02587 ] Network output: [ 0.02341 0.01412 0.8964 -0.001761 0.0007907 1.035 -0.001327 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6954 0.538 0.465 0.5054 0.9766 0.9895 0.6973 0.912 0.9736 0.5317 ] Network output: [ -0.04185 0.6095 0.8106 5.162e-05 -2.317e-05 0.6638 3.89e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3795 0.3651 0.2194 0.2197 0.9868 0.9914 0.3797 0.9726 0.983 0.2243 ] Network output: [ -0.09122 0.4669 0.9353 -0.0002714 0.0001218 0.7791 -0.0002045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3953 0.3926 0.2557 0.254 0.9812 0.988 0.3954 0.954 0.9734 0.257 ] Network output: [ 0.06141 0.4242 0.2037 0.001608 -0.0007219 1.256 0.001212 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1958 Epoch 3013 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03014 0.8182 1.036 -0.0001037 4.655e-05 0.085 -7.814e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01769 -0.00552 0.01684 0.028 0.9444 0.9528 0.03133 0.8908 0.9104 0.07037 ] Network output: [ 1.007 -0.184 0.1183 0.0006527 -0.000293 0.05351 0.0004919 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6278 0.02363 0.1595 0.3753 0.9738 0.9879 0.6921 0.9039 0.9698 0.5487 ] Network output: [ 0.006269 0.8849 0.9972 -0.0001424 6.393e-05 0.1047 -0.0001073 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02682 0.01765 0.02159 0.02305 0.986 0.9902 0.02723 0.9705 0.9822 0.02572 ] Network output: [ 0.02325 0.01479 0.8963 -0.001757 0.0007889 1.035 -0.001324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6943 0.5367 0.4642 0.5054 0.9767 0.9895 0.6962 0.912 0.9737 0.5314 ] Network output: [ -0.04214 0.6117 0.8135 4.039e-05 -1.813e-05 0.6593 3.044e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3754 0.361 0.2174 0.2178 0.9868 0.9914 0.3756 0.9726 0.983 0.2223 ] Network output: [ -0.09169 0.4682 0.9374 -0.0002823 0.0001267 0.7766 -0.0002128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3922 0.3894 0.2544 0.2527 0.9812 0.988 0.3922 0.9538 0.9733 0.2557 ] Network output: [ 0.06109 0.4281 0.1989 0.001601 -0.0007189 1.257 0.001207 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1964 Epoch 3014 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0307 0.8141 1.036 -0.0001015 4.557e-05 0.08783 -7.649e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01763 -0.005506 0.01672 0.02799 0.9445 0.9529 0.03118 0.8908 0.9104 0.07017 ] Network output: [ 1.008 -0.185 0.1181 0.0006767 -0.0003038 0.05419 0.00051 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.627 0.0232 0.1581 0.3759 0.9738 0.9879 0.691 0.9039 0.9699 0.5485 ] Network output: [ 0.006664 0.8816 0.9976 -0.0001448 6.503e-05 0.1069 -0.0001092 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02658 0.01748 0.02144 0.02294 0.986 0.9902 0.027 0.9705 0.9822 0.02557 ] Network output: [ 0.02308 0.01544 0.8962 -0.001753 0.0007872 1.035 -0.001321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6932 0.5354 0.4634 0.5055 0.9767 0.9895 0.6951 0.912 0.9737 0.5312 ] Network output: [ -0.04243 0.6137 0.8163 2.922e-05 -1.312e-05 0.655 2.202e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3713 0.3571 0.2155 0.216 0.9868 0.9914 0.3715 0.9726 0.983 0.2204 ] Network output: [ -0.09216 0.4694 0.9395 -0.000293 0.0001315 0.7742 -0.0002208 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3891 0.3863 0.2531 0.2515 0.9811 0.988 0.3891 0.9535 0.9732 0.2545 ] Network output: [ 0.06078 0.4321 0.1941 0.001595 -0.0007158 1.259 0.001202 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1971 Epoch 3015 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03126 0.81 1.036 -9.927e-05 4.457e-05 0.09065 -7.482e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01758 -0.005492 0.0166 0.02797 0.9445 0.9529 0.03105 0.8908 0.9105 0.06997 ] Network output: [ 1.008 -0.186 0.1178 0.0007003 -0.0003144 0.05484 0.0005278 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6262 0.02279 0.1567 0.3766 0.9738 0.9879 0.6899 0.9039 0.9699 0.5482 ] Network output: [ 0.007056 0.8783 0.9979 -0.0001472 6.608e-05 0.1091 -0.0001109 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02636 0.01731 0.0213 0.02283 0.986 0.9902 0.02677 0.9705 0.9822 0.02543 ] Network output: [ 0.0229 0.01606 0.8961 -0.001749 0.0007854 1.035 -0.001318 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6922 0.5342 0.4626 0.5055 0.9767 0.9896 0.6941 0.912 0.9737 0.5309 ] Network output: [ -0.04273 0.6156 0.8192 1.811e-05 -8.13e-06 0.6507 1.365e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3674 0.3532 0.2136 0.2142 0.9868 0.9914 0.3675 0.9725 0.983 0.2185 ] Network output: [ -0.09261 0.4705 0.9416 -0.0003035 0.0001362 0.7719 -0.0002287 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.386 0.3832 0.2519 0.2503 0.9811 0.9879 0.386 0.9532 0.9731 0.2532 ] Network output: [ 0.06048 0.4361 0.1894 0.001588 -0.0007128 1.26 0.001197 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1977 Epoch 3016 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03182 0.8059 1.037 -9.701e-05 4.355e-05 0.09345 -7.311e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01752 -0.005477 0.01649 0.02796 0.9446 0.953 0.03091 0.8909 0.9105 0.06977 ] Network output: [ 1.008 -0.1868 0.1175 0.0007235 -0.0003248 0.05546 0.0005452 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6254 0.02241 0.1554 0.3771 0.9739 0.9879 0.6888 0.904 0.9699 0.548 ] Network output: [ 0.007444 0.875 0.9982 -0.0001494 6.709e-05 0.1113 -0.0001126 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02614 0.01715 0.02116 0.02272 0.986 0.9902 0.02654 0.9704 0.9822 0.02529 ] Network output: [ 0.02271 0.01666 0.8961 -0.001745 0.0007835 1.035 -0.001315 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6912 0.533 0.4618 0.5056 0.9767 0.9896 0.693 0.912 0.9737 0.5307 ] Network output: [ -0.04303 0.6173 0.8221 7.065e-06 -3.172e-06 0.6466 5.324e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3634 0.3494 0.2118 0.2125 0.9868 0.9914 0.3636 0.9725 0.983 0.2167 ] Network output: [ -0.09305 0.4715 0.9437 -0.0003137 0.0001409 0.7696 -0.0002365 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.383 0.3803 0.2507 0.2492 0.981 0.9879 0.3831 0.953 0.973 0.252 ] Network output: [ 0.06019 0.4402 0.1847 0.001581 -0.0007097 1.261 0.001191 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1982 Epoch 3017 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03237 0.802 1.037 -9.471e-05 4.252e-05 0.09624 -7.137e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01747 -0.005463 0.01637 0.02795 0.9446 0.953 0.03078 0.8909 0.9105 0.06959 ] Network output: [ 1.009 -0.1876 0.1171 0.0007461 -0.000335 0.05605 0.0005623 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6246 0.02205 0.154 0.3777 0.9739 0.988 0.6878 0.904 0.9699 0.5478 ] Network output: [ 0.007829 0.8718 0.9985 -0.0001516 6.804e-05 0.1134 -0.0001142 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02592 0.01699 0.02103 0.02262 0.986 0.9902 0.02632 0.9704 0.9822 0.02516 ] Network output: [ 0.02251 0.01723 0.8961 -0.001741 0.0007817 1.035 -0.001312 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6902 0.5318 0.461 0.5056 0.9767 0.9896 0.692 0.912 0.9737 0.5305 ] Network output: [ -0.04334 0.619 0.8251 -3.906e-06 1.754e-06 0.6426 -2.944e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3596 0.3457 0.21 0.2108 0.9868 0.9914 0.3598 0.9725 0.983 0.215 ] Network output: [ -0.09348 0.4724 0.9458 -0.0003238 0.0001454 0.7674 -0.000244 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3802 0.3774 0.2495 0.2481 0.981 0.9879 0.3802 0.9528 0.9729 0.2509 ] Network output: [ 0.05991 0.4443 0.1801 0.001574 -0.0007065 1.262 0.001186 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1987 Epoch 3018 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03291 0.7981 1.037 -9.235e-05 4.146e-05 0.099 -6.96e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01741 -0.005449 0.01626 0.02793 0.9447 0.953 0.03065 0.8909 0.9106 0.0694 ] Network output: [ 1.009 -0.1883 0.1166 0.0007683 -0.0003449 0.05661 0.000579 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6238 0.02172 0.1526 0.3783 0.9739 0.988 0.6868 0.904 0.9699 0.5476 ] Network output: [ 0.008211 0.8686 0.9987 -0.0001536 6.894e-05 0.1156 -0.0001157 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02571 0.01684 0.02089 0.02252 0.986 0.9902 0.02611 0.9704 0.9822 0.02503 ] Network output: [ 0.0223 0.01776 0.8962 -0.001737 0.0007798 1.034 -0.001309 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6892 0.5307 0.4602 0.5057 0.9768 0.9896 0.691 0.9121 0.9737 0.5303 ] Network output: [ -0.04366 0.6205 0.8281 -1.48e-05 6.644e-06 0.6387 -1.115e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3559 0.342 0.2083 0.2092 0.9868 0.9914 0.356 0.9725 0.983 0.2133 ] Network output: [ -0.0939 0.4733 0.9479 -0.0003337 0.0001498 0.7653 -0.0002515 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3773 0.3746 0.2484 0.2471 0.9809 0.9879 0.3774 0.9525 0.9728 0.2498 ] Network output: [ 0.05964 0.4484 0.1756 0.001567 -0.0007034 1.263 0.001181 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1992 Epoch 3019 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03346 0.7943 1.037 -8.995e-05 4.038e-05 0.1018 -6.779e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01736 -0.005436 0.01615 0.02792 0.9447 0.9531 0.03052 0.891 0.9106 0.06923 ] Network output: [ 1.009 -0.1888 0.1161 0.00079 -0.0003547 0.05714 0.0005954 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6231 0.0214 0.1512 0.3788 0.9739 0.988 0.6858 0.904 0.9699 0.5474 ] Network output: [ 0.00859 0.8655 0.9989 -0.0001555 6.979e-05 0.1177 -0.0001172 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02551 0.01669 0.02077 0.02242 0.986 0.9902 0.0259 0.9704 0.9822 0.0249 ] Network output: [ 0.02208 0.01827 0.8964 -0.001733 0.0007779 1.034 -0.001306 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6882 0.5296 0.4595 0.5057 0.9768 0.9896 0.69 0.9121 0.9737 0.5301 ] Network output: [ -0.04398 0.6218 0.8312 -2.561e-05 1.15e-05 0.6349 -1.93e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3522 0.3385 0.2067 0.2077 0.9868 0.9914 0.3523 0.9724 0.983 0.2116 ] Network output: [ -0.09431 0.474 0.95 -0.0003433 0.0001541 0.7632 -0.0002588 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3746 0.3718 0.2473 0.246 0.9809 0.9878 0.3746 0.9523 0.9727 0.2487 ] Network output: [ 0.05937 0.4525 0.1711 0.00156 -0.0007002 1.264 0.001175 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1996 Epoch 3020 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.034 0.7905 1.037 -8.749e-05 3.928e-05 0.1045 -6.594e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01731 -0.005422 0.01604 0.02791 0.9448 0.9531 0.0304 0.891 0.9106 0.06906 ] Network output: [ 1.01 -0.1893 0.1155 0.0008112 -0.0003642 0.05764 0.0006114 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6224 0.02111 0.1499 0.3794 0.974 0.988 0.6848 0.9041 0.9699 0.5473 ] Network output: [ 0.008966 0.8624 0.9991 -0.0001572 7.058e-05 0.1199 -0.0001185 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02531 0.01654 0.02064 0.02233 0.986 0.9902 0.02569 0.9704 0.9822 0.02478 ] Network output: [ 0.02186 0.01874 0.8966 -0.001728 0.000776 1.034 -0.001303 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6873 0.5286 0.4587 0.5058 0.9768 0.9896 0.6891 0.9121 0.9737 0.5299 ] Network output: [ -0.0443 0.6231 0.8342 -3.632e-05 1.631e-05 0.6312 -2.737e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3486 0.335 0.2051 0.2062 0.9868 0.9914 0.3487 0.9724 0.9829 0.21 ] Network output: [ -0.09472 0.4747 0.9521 -0.0003528 0.0001584 0.7612 -0.0002659 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3719 0.3691 0.2462 0.245 0.9808 0.9878 0.3719 0.9521 0.9726 0.2477 ] Network output: [ 0.05912 0.4567 0.1666 0.001553 -0.000697 1.265 0.00117 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2 Epoch 3021 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03454 0.7869 1.037 -8.498e-05 3.815e-05 0.1072 -6.404e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01727 -0.005409 0.01593 0.0279 0.9448 0.9531 0.03027 0.891 0.9107 0.06889 ] Network output: [ 1.01 -0.1897 0.1149 0.0008319 -0.0003735 0.05812 0.000627 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6217 0.02083 0.1485 0.3799 0.974 0.988 0.6839 0.9041 0.97 0.5471 ] Network output: [ 0.009338 0.8594 0.9992 -0.0001588 7.131e-05 0.122 -0.0001197 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02511 0.0164 0.02052 0.02224 0.986 0.9902 0.02549 0.9703 0.9822 0.02466 ] Network output: [ 0.02163 0.01918 0.8968 -0.001724 0.000774 1.034 -0.001299 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6863 0.5275 0.458 0.5059 0.9768 0.9896 0.6881 0.9121 0.9737 0.5298 ] Network output: [ -0.04464 0.6242 0.8373 -4.694e-05 2.107e-05 0.6276 -3.538e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3451 0.3315 0.2035 0.2047 0.9868 0.9914 0.3452 0.9724 0.9829 0.2085 ] Network output: [ -0.09511 0.4753 0.9542 -0.0003621 0.0001625 0.7593 -0.0002729 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3692 0.3665 0.2452 0.2441 0.9808 0.9878 0.3692 0.9518 0.9725 0.2466 ] Network output: [ 0.05888 0.4608 0.1623 0.001545 -0.0006938 1.265 0.001165 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2003 Epoch 3022 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03508 0.7833 1.036 -8.241e-05 3.7e-05 0.1099 -6.211e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01722 -0.005395 0.01582 0.02789 0.9449 0.9532 0.03016 0.891 0.9107 0.06873 ] Network output: [ 1.01 -0.1901 0.1142 0.0008521 -0.0003826 0.05856 0.0006422 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.621 0.02057 0.1472 0.3804 0.974 0.988 0.6829 0.9041 0.97 0.547 ] Network output: [ 0.009708 0.8564 0.9993 -0.0001603 7.198e-05 0.1241 -0.0001208 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02492 0.01627 0.02041 0.02215 0.986 0.9902 0.0253 0.9703 0.9822 0.02455 ] Network output: [ 0.0214 0.01959 0.8971 -0.00172 0.000772 1.034 -0.001296 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6854 0.5266 0.4573 0.5059 0.9768 0.9896 0.6872 0.9121 0.9737 0.5296 ] Network output: [ -0.04497 0.6252 0.8404 -5.746e-05 2.58e-05 0.6241 -4.331e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3416 0.3282 0.202 0.2033 0.9868 0.9914 0.3417 0.9724 0.9829 0.207 ] Network output: [ -0.0955 0.4758 0.9563 -0.0003711 0.0001666 0.7574 -0.0002797 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3666 0.3639 0.2442 0.2432 0.9807 0.9878 0.3667 0.9516 0.9724 0.2457 ] Network output: [ 0.05865 0.465 0.158 0.001538 -0.0006906 1.266 0.001159 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2006 Epoch 3023 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03562 0.7798 1.036 -7.979e-05 3.582e-05 0.1125 -6.013e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01717 -0.005382 0.01571 0.02788 0.9449 0.9532 0.03004 0.8911 0.9107 0.06858 ] Network output: [ 1.011 -0.1903 0.1135 0.0008718 -0.0003914 0.05897 0.000657 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6203 0.02033 0.1459 0.381 0.974 0.988 0.682 0.9041 0.97 0.5469 ] Network output: [ 0.01007 0.8535 0.9994 -0.0001617 7.26e-05 0.1262 -0.0001219 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02473 0.01613 0.02029 0.02207 0.986 0.9902 0.02511 0.9703 0.9822 0.02444 ] Network output: [ 0.02117 0.01995 0.8974 -0.001715 0.0007701 1.033 -0.001293 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6845 0.5256 0.4565 0.506 0.9769 0.9896 0.6863 0.9121 0.9737 0.5295 ] Network output: [ -0.04532 0.6262 0.8435 -6.787e-05 3.047e-05 0.6207 -5.115e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3382 0.3249 0.2006 0.2019 0.9869 0.9915 0.3384 0.9723 0.9829 0.2056 ] Network output: [ -0.09588 0.4763 0.9583 -0.00038 0.0001706 0.7556 -0.0002864 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3641 0.3614 0.2433 0.2423 0.9807 0.9877 0.3641 0.9514 0.9723 0.2447 ] Network output: [ 0.05842 0.4692 0.1537 0.001531 -0.0006874 1.266 0.001154 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2009 Epoch 3024 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03615 0.7763 1.036 -7.71e-05 3.461e-05 0.1151 -5.811e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01713 -0.005369 0.01561 0.02787 0.9449 0.9533 0.02993 0.8911 0.9107 0.06843 ] Network output: [ 1.011 -0.1905 0.1127 0.000891 -0.0004 0.05936 0.0006715 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6197 0.02011 0.1445 0.3815 0.9741 0.988 0.6811 0.9042 0.97 0.5467 ] Network output: [ 0.01044 0.8507 0.9995 -0.0001629 7.315e-05 0.1283 -0.0001228 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02455 0.016 0.02018 0.02198 0.9861 0.9902 0.02492 0.9703 0.9822 0.02433 ] Network output: [ 0.02092 0.02029 0.8978 -0.001711 0.000768 1.033 -0.001289 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6836 0.5247 0.4558 0.5061 0.9769 0.9896 0.6854 0.9121 0.9738 0.5294 ] Network output: [ -0.04567 0.627 0.8467 -7.817e-05 3.509e-05 0.6174 -5.891e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3349 0.3217 0.1992 0.2006 0.9869 0.9915 0.335 0.9723 0.9829 0.2042 ] Network output: [ -0.09625 0.4766 0.9604 -0.0003887 0.0001745 0.7539 -0.0002929 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3617 0.3589 0.2424 0.2414 0.9806 0.9877 0.3617 0.9512 0.9722 0.2439 ] Network output: [ 0.05821 0.4734 0.1495 0.001524 -0.0006841 1.267 0.001148 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2011 Epoch 3025 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03668 0.7729 1.036 -7.436e-05 3.338e-05 0.1177 -5.604e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01709 -0.005357 0.01551 0.02786 0.945 0.9533 0.02982 0.8911 0.9108 0.06829 ] Network output: [ 1.011 -0.1906 0.1119 0.0009096 -0.0004084 0.05972 0.0006855 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6191 0.01991 0.1432 0.382 0.9741 0.988 0.6803 0.9042 0.97 0.5466 ] Network output: [ 0.0108 0.8478 0.9995 -0.000164 7.364e-05 0.1304 -0.0001236 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02437 0.01588 0.02008 0.0219 0.9861 0.9902 0.02474 0.9703 0.9822 0.02423 ] Network output: [ 0.02068 0.02058 0.8982 -0.001706 0.000766 1.033 -0.001286 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6827 0.5237 0.4551 0.5062 0.9769 0.9896 0.6845 0.9121 0.9738 0.5293 ] Network output: [ -0.04602 0.6277 0.8498 -8.836e-05 3.967e-05 0.6142 -6.659e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3316 0.3185 0.1978 0.1993 0.9869 0.9915 0.3318 0.9723 0.9829 0.2029 ] Network output: [ -0.09662 0.4769 0.9625 -0.0003972 0.0001783 0.7522 -0.0002994 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3593 0.3565 0.2415 0.2406 0.9806 0.9877 0.3593 0.951 0.9721 0.243 ] Network output: [ 0.058 0.4776 0.1454 0.001517 -0.0006808 1.267 0.001143 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2013 Epoch 3026 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0372 0.7696 1.035 -7.155e-05 3.212e-05 0.1203 -5.392e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01704 -0.005344 0.01541 0.02785 0.945 0.9533 0.02971 0.8911 0.9108 0.06815 ] Network output: [ 1.012 -0.1907 0.1111 0.0009277 -0.0004165 0.06004 0.0006992 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6184 0.01972 0.142 0.3824 0.9741 0.9881 0.6794 0.9042 0.97 0.5465 ] Network output: [ 0.01116 0.8451 0.9994 -0.000165 7.408e-05 0.1325 -0.0001244 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0242 0.01576 0.01998 0.02183 0.9861 0.9903 0.02456 0.9702 0.9822 0.02413 ] Network output: [ 0.02043 0.02084 0.8986 -0.001702 0.0007639 1.033 -0.001282 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6819 0.5229 0.4545 0.5063 0.9769 0.9896 0.6836 0.9121 0.9738 0.5292 ] Network output: [ -0.04638 0.6283 0.853 -9.842e-05 4.418e-05 0.6111 -7.417e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3285 0.3155 0.1966 0.1981 0.9869 0.9915 0.3286 0.9723 0.9829 0.2016 ] Network output: [ -0.09698 0.4772 0.9645 -0.0004055 0.0001821 0.7506 -0.0003056 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3569 0.3542 0.2407 0.2398 0.9806 0.9877 0.3569 0.9508 0.9721 0.2422 ] Network output: [ 0.0578 0.4818 0.1413 0.001509 -0.0006776 1.267 0.001137 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2014 Epoch 3027 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03773 0.7664 1.035 -6.869e-05 3.084e-05 0.1229 -5.177e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.017 -0.005332 0.01531 0.02785 0.9451 0.9534 0.02961 0.8912 0.9108 0.06802 ] Network output: [ 1.012 -0.1906 0.1102 0.0009453 -0.0004244 0.06035 0.0007124 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6178 0.01954 0.1407 0.3829 0.9741 0.9881 0.6786 0.9042 0.97 0.5465 ] Network output: [ 0.01152 0.8424 0.9994 -0.0001658 7.445e-05 0.1345 -0.000125 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02403 0.01564 0.01988 0.02175 0.9861 0.9903 0.02439 0.9702 0.9822 0.02403 ] Network output: [ 0.02018 0.02106 0.8991 -0.001697 0.0007619 1.033 -0.001279 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.681 0.522 0.4538 0.5064 0.9769 0.9897 0.6828 0.9121 0.9738 0.5291 ] Network output: [ -0.04674 0.6288 0.8562 -0.0001084 4.865e-05 0.6081 -8.166e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3254 0.3125 0.1953 0.1969 0.9869 0.9915 0.3255 0.9723 0.9829 0.2004 ] Network output: [ -0.09734 0.4774 0.9666 -0.0004136 0.0001857 0.749 -0.0003117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3546 0.3519 0.2399 0.239 0.9805 0.9876 0.3546 0.9506 0.972 0.2414 ] Network output: [ 0.05762 0.486 0.1373 0.001502 -0.0006743 1.268 0.001132 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2016 Epoch 3028 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03825 0.7633 1.035 -6.576e-05 2.952e-05 0.1254 -4.956e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01697 -0.00532 0.01521 0.02784 0.9451 0.9534 0.02951 0.8912 0.9109 0.06789 ] Network output: [ 1.012 -0.1905 0.1093 0.0009623 -0.000432 0.06062 0.0007252 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6173 0.01938 0.1394 0.3834 0.9742 0.9881 0.6778 0.9043 0.9701 0.5464 ] Network output: [ 0.01187 0.8397 0.9993 -0.0001665 7.476e-05 0.1365 -0.0001255 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02386 0.01552 0.01978 0.02168 0.9861 0.9903 0.02422 0.9702 0.9822 0.02394 ] Network output: [ 0.01993 0.02124 0.8996 -0.001692 0.0007598 1.032 -0.001275 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6802 0.5212 0.4531 0.5065 0.977 0.9897 0.682 0.9121 0.9738 0.529 ] Network output: [ -0.04711 0.6292 0.8593 -0.0001182 5.305e-05 0.6052 -8.905e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3223 0.3095 0.1941 0.1958 0.9869 0.9915 0.3224 0.9722 0.9829 0.1992 ] Network output: [ -0.09769 0.4775 0.9687 -0.0004216 0.0001893 0.7475 -0.0003177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3524 0.3497 0.2391 0.2383 0.9805 0.9876 0.3524 0.9504 0.9719 0.2406 ] Network output: [ 0.05744 0.4902 0.1334 0.001495 -0.000671 1.268 0.001126 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2016 Epoch 3029 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03877 0.7602 1.034 -6.278e-05 2.818e-05 0.1278 -4.731e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01693 -0.005308 0.01512 0.02783 0.9452 0.9534 0.02941 0.8912 0.9109 0.06776 ] Network output: [ 1.013 -0.1903 0.1083 0.0009788 -0.0004394 0.06087 0.0007376 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6167 0.01923 0.1382 0.3838 0.9742 0.9881 0.677 0.9043 0.9701 0.5463 ] Network output: [ 0.01222 0.8371 0.9992 -0.0001671 7.5e-05 0.1385 -0.0001259 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0237 0.01541 0.01969 0.02161 0.9861 0.9903 0.02405 0.9702 0.9822 0.02385 ] Network output: [ 0.01967 0.02138 0.9001 -0.001688 0.0007577 1.032 -0.001272 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6794 0.5204 0.4525 0.5066 0.977 0.9897 0.6812 0.9121 0.9738 0.5289 ] Network output: [ -0.04749 0.6295 0.8625 -0.0001278 5.739e-05 0.6024 -9.634e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3193 0.3066 0.1929 0.1947 0.9869 0.9915 0.3195 0.9722 0.9829 0.198 ] Network output: [ -0.09803 0.4775 0.9707 -0.0004293 0.0001927 0.7461 -0.0003236 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3502 0.3475 0.2383 0.2376 0.9805 0.9876 0.3502 0.9502 0.9718 0.2399 ] Network output: [ 0.05726 0.4944 0.1295 0.001487 -0.0006678 1.268 0.001121 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2017 Epoch 3030 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03929 0.7572 1.034 -5.973e-05 2.682e-05 0.1303 -4.502e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01689 -0.005296 0.01503 0.02783 0.9452 0.9535 0.02932 0.8912 0.9109 0.06765 ] Network output: [ 1.013 -0.1901 0.1073 0.0009947 -0.0004466 0.06109 0.0007496 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6162 0.0191 0.1369 0.3843 0.9742 0.9881 0.6762 0.9043 0.9701 0.5463 ] Network output: [ 0.01257 0.8346 0.9991 -0.0001675 7.519e-05 0.1405 -0.0001262 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02355 0.0153 0.0196 0.02155 0.9861 0.9903 0.02389 0.9702 0.9822 0.02377 ] Network output: [ 0.01941 0.02148 0.9007 -0.001683 0.0007555 1.032 -0.001268 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6786 0.5196 0.4519 0.5067 0.977 0.9897 0.6804 0.9121 0.9738 0.5289 ] Network output: [ -0.04787 0.6297 0.8657 -0.0001374 6.167e-05 0.5997 -0.0001035 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3164 0.3038 0.1918 0.1937 0.9869 0.9915 0.3165 0.9722 0.9829 0.1969 ] Network output: [ -0.09837 0.4775 0.9728 -0.0004369 0.0001961 0.7447 -0.0003293 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.348 0.3454 0.2376 0.2369 0.9804 0.9876 0.3481 0.95 0.9718 0.2392 ] Network output: [ 0.0571 0.4986 0.1257 0.00148 -0.0006645 1.268 0.001115 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2017 Epoch 3031 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0398 0.7543 1.033 -5.663e-05 2.542e-05 0.1327 -4.268e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01686 -0.005285 0.01493 0.02782 0.9453 0.9535 0.02922 0.8913 0.9109 0.06753 ] Network output: [ 1.013 -0.1898 0.1063 0.00101 -0.0004535 0.06128 0.0007612 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6156 0.01898 0.1357 0.3847 0.9742 0.9881 0.6755 0.9043 0.9701 0.5462 ] Network output: [ 0.01292 0.8321 0.9989 -0.0001678 7.532e-05 0.1425 -0.0001264 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02339 0.01519 0.01951 0.02149 0.9861 0.9903 0.02374 0.9702 0.9822 0.02369 ] Network output: [ 0.01916 0.02155 0.9013 -0.001678 0.0007534 1.032 -0.001265 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6779 0.5189 0.4512 0.5068 0.977 0.9897 0.6796 0.9122 0.9738 0.5288 ] Network output: [ -0.04825 0.6298 0.869 -0.0001468 6.59e-05 0.5971 -0.0001106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3135 0.3011 0.1908 0.1927 0.9869 0.9915 0.3137 0.9722 0.9829 0.1959 ] Network output: [ -0.09871 0.4774 0.9748 -0.0004443 0.0001995 0.7433 -0.0003348 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.346 0.3433 0.2369 0.2363 0.9804 0.9876 0.346 0.9499 0.9717 0.2385 ] Network output: [ 0.05694 0.5028 0.1219 0.001473 -0.0006612 1.267 0.00111 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2017 Epoch 3032 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04032 0.7515 1.033 -5.347e-05 2.4e-05 0.1351 -4.03e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01682 -0.005273 0.01484 0.02782 0.9453 0.9535 0.02913 0.8913 0.911 0.06742 ] Network output: [ 1.013 -0.1894 0.1052 0.001025 -0.0004601 0.06145 0.0007724 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6151 0.01887 0.1345 0.3852 0.9743 0.9881 0.6748 0.9043 0.9701 0.5462 ] Network output: [ 0.01326 0.8297 0.9987 -0.0001679 7.538e-05 0.1444 -0.0001265 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02324 0.01509 0.01943 0.02143 0.9861 0.9903 0.02358 0.9702 0.9822 0.02361 ] Network output: [ 0.0189 0.02157 0.9019 -0.001673 0.0007512 1.032 -0.001261 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6771 0.5182 0.4506 0.5069 0.977 0.9897 0.6788 0.9122 0.9738 0.5288 ] Network output: [ -0.04864 0.6299 0.8722 -0.000156 7.005e-05 0.5946 -0.0001176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3108 0.2984 0.1898 0.1917 0.9869 0.9915 0.3109 0.9722 0.9829 0.1949 ] Network output: [ -0.09904 0.4773 0.9769 -0.0004515 0.0002027 0.7421 -0.0003402 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3439 0.3413 0.2363 0.2357 0.9804 0.9876 0.3439 0.9497 0.9716 0.2379 ] Network output: [ 0.0568 0.5069 0.1182 0.001466 -0.000658 1.267 0.001105 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2016 Epoch 3033 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04083 0.7487 1.032 -5.025e-05 2.256e-05 0.1374 -3.787e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01679 -0.005262 0.01476 0.02782 0.9454 0.9536 0.02904 0.8913 0.911 0.06732 ] Network output: [ 1.014 -0.189 0.1041 0.001039 -0.0004665 0.06159 0.0007831 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6146 0.01877 0.1334 0.3856 0.9743 0.9881 0.674 0.9044 0.9701 0.5462 ] Network output: [ 0.01361 0.8273 0.9985 -0.0001679 7.539e-05 0.1463 -0.0001266 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0231 0.01498 0.01935 0.02137 0.9861 0.9903 0.02343 0.9701 0.9822 0.02353 ] Network output: [ 0.01864 0.02155 0.9025 -0.001668 0.000749 1.032 -0.001257 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6764 0.5175 0.4501 0.507 0.9771 0.9897 0.6781 0.9122 0.9738 0.5288 ] Network output: [ -0.04904 0.6299 0.8754 -0.0001652 7.414e-05 0.5922 -0.0001245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.308 0.2957 0.1888 0.1908 0.9869 0.9915 0.3081 0.9722 0.9829 0.1939 ] Network output: [ -0.09937 0.4771 0.9789 -0.0004585 0.0002058 0.7408 -0.0003455 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3419 0.3393 0.2357 0.2351 0.9803 0.9875 0.342 0.9495 0.9715 0.2373 ] Network output: [ 0.05665 0.5111 0.1146 0.001458 -0.0006547 1.267 0.001099 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2015 Epoch 3034 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04134 0.746 1.031 -4.698e-05 2.109e-05 0.1397 -3.541e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01676 -0.005252 0.01467 0.02781 0.9454 0.9536 0.02896 0.8913 0.911 0.06722 ] Network output: [ 1.014 -0.1885 0.103 0.001053 -0.0004727 0.0617 0.0007935 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6141 0.01868 0.1322 0.386 0.9743 0.9881 0.6734 0.9044 0.9701 0.5462 ] Network output: [ 0.01395 0.825 0.9982 -0.0001678 7.534e-05 0.1482 -0.0001265 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02295 0.01489 0.01927 0.02131 0.9862 0.9903 0.02329 0.9701 0.9822 0.02346 ] Network output: [ 0.01839 0.02149 0.9032 -0.001664 0.0007468 1.032 -0.001254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6756 0.5168 0.4495 0.5071 0.9771 0.9897 0.6773 0.9122 0.9738 0.5288 ] Network output: [ -0.04944 0.6297 0.8786 -0.0001741 7.817e-05 0.5898 -0.0001312 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3053 0.2931 0.1878 0.1899 0.9869 0.9915 0.3055 0.9721 0.9829 0.193 ] Network output: [ -0.0997 0.4769 0.9809 -0.0004653 0.0002089 0.7397 -0.0003507 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.34 0.3373 0.2351 0.2345 0.9803 0.9875 0.34 0.9494 0.9715 0.2367 ] Network output: [ 0.05652 0.5152 0.111 0.001451 -0.0006515 1.267 0.001094 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2014 Epoch 3035 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04184 0.7434 1.031 -4.365e-05 1.96e-05 0.142 -3.29e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01673 -0.005241 0.01459 0.02781 0.9455 0.9536 0.02887 0.8914 0.911 0.06713 ] Network output: [ 1.014 -0.188 0.1018 0.001066 -0.0004786 0.06179 0.0008034 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6136 0.01861 0.1311 0.3864 0.9743 0.9881 0.6727 0.9044 0.9702 0.5462 ] Network output: [ 0.01429 0.8227 0.9979 -0.0001676 7.522e-05 0.1501 -0.0001263 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02281 0.01479 0.01919 0.02126 0.9862 0.9903 0.02314 0.9701 0.9822 0.0234 ] Network output: [ 0.01813 0.02139 0.9039 -0.001659 0.0007446 1.032 -0.00125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6749 0.5162 0.4489 0.5073 0.9771 0.9897 0.6766 0.9122 0.9738 0.5288 ] Network output: [ -0.04984 0.6296 0.8818 -0.0001829 8.213e-05 0.5875 -0.0001379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3027 0.2906 0.1869 0.1891 0.9869 0.9915 0.3029 0.9721 0.9829 0.1921 ] Network output: [ -0.1 0.4766 0.983 -0.0004719 0.0002119 0.7385 -0.0003557 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3381 0.3354 0.2345 0.234 0.9803 0.9875 0.3381 0.9492 0.9714 0.2361 ] Network output: [ 0.0564 0.5193 0.1075 0.001444 -0.0006483 1.266 0.001088 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2013 Epoch 3036 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04235 0.7409 1.03 -4.028e-05 1.808e-05 0.1442 -3.035e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0167 -0.005231 0.01451 0.02781 0.9455 0.9537 0.02879 0.8914 0.9111 0.06704 ] Network output: [ 1.015 -0.1874 0.1007 0.001079 -0.0004842 0.06186 0.0008129 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6132 0.01854 0.1299 0.3868 0.9744 0.9882 0.672 0.9044 0.9702 0.5462 ] Network output: [ 0.01463 0.8205 0.9976 -0.0001672 7.505e-05 0.1519 -0.000126 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02268 0.0147 0.01912 0.02121 0.9862 0.9903 0.023 0.9701 0.9822 0.02333 ] Network output: [ 0.01788 0.02125 0.9046 -0.001654 0.0007424 1.032 -0.001246 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6742 0.5155 0.4484 0.5074 0.9771 0.9897 0.6759 0.9122 0.9739 0.5288 ] Network output: [ -0.05025 0.6293 0.885 -0.0001916 8.602e-05 0.5854 -0.0001444 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.3002 0.2882 0.1861 0.1883 0.9869 0.9915 0.3003 0.9721 0.9829 0.1913 ] Network output: [ -0.1003 0.4763 0.985 -0.0004784 0.0002148 0.7375 -0.0003605 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3362 0.3336 0.234 0.2335 0.9803 0.9875 0.3363 0.949 0.9714 0.2356 ] Network output: [ 0.05628 0.5234 0.104 0.001437 -0.000645 1.266 0.001083 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2011 Epoch 3037 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04285 0.7384 1.029 -3.685e-05 1.654e-05 0.1464 -2.777e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01667 -0.005221 0.01442 0.02781 0.9455 0.9537 0.02871 0.8914 0.9111 0.06695 ] Network output: [ 1.015 -0.1868 0.09949 0.001091 -0.0004896 0.0619 0.0008219 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6127 0.01848 0.1288 0.3872 0.9744 0.9882 0.6714 0.9045 0.9702 0.5462 ] Network output: [ 0.01497 0.8184 0.9973 -0.0001667 7.482e-05 0.1537 -0.0001256 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02254 0.01461 0.01906 0.02116 0.9862 0.9903 0.02287 0.9701 0.9822 0.02327 ] Network output: [ 0.01763 0.02107 0.9053 -0.001649 0.0007401 1.032 -0.001242 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6736 0.5149 0.4479 0.5075 0.9771 0.9897 0.6752 0.9122 0.9739 0.5288 ] Network output: [ -0.05067 0.629 0.8883 -0.0002001 8.985e-05 0.5833 -0.0001508 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2977 0.2858 0.1853 0.1875 0.9869 0.9915 0.2978 0.9721 0.9829 0.1905 ] Network output: [ -0.1007 0.4759 0.987 -0.0004847 0.0002176 0.7364 -0.0003653 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3344 0.3318 0.2335 0.2331 0.9802 0.9875 0.3344 0.9489 0.9713 0.2351 ] Network output: [ 0.05616 0.5275 0.1006 0.00143 -0.0006418 1.265 0.001077 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2009 Epoch 3038 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04335 0.7361 1.029 -3.337e-05 1.498e-05 0.1485 -2.515e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01664 -0.005211 0.01435 0.02781 0.9456 0.9537 0.02864 0.8914 0.9111 0.06687 ] Network output: [ 1.015 -0.1861 0.09828 0.001102 -0.0004948 0.06191 0.0008306 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6123 0.01842 0.1277 0.3876 0.9744 0.9882 0.6708 0.9045 0.9702 0.5462 ] Network output: [ 0.0153 0.8163 0.997 -0.000166 7.454e-05 0.1555 -0.0001251 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02241 0.01452 0.01899 0.02112 0.9862 0.9903 0.02274 0.9701 0.9822 0.02321 ] Network output: [ 0.01738 0.02085 0.9061 -0.001644 0.0007379 1.032 -0.001239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6729 0.5144 0.4473 0.5077 0.9772 0.9897 0.6745 0.9122 0.9739 0.5288 ] Network output: [ -0.05109 0.6286 0.8915 -0.0002085 9.361e-05 0.5812 -0.0001571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2952 0.2834 0.1845 0.1868 0.987 0.9915 0.2954 0.9721 0.9829 0.1897 ] Network output: [ -0.101 0.4755 0.989 -0.0004908 0.0002203 0.7355 -0.0003699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3326 0.33 0.233 0.2326 0.9802 0.9875 0.3327 0.9487 0.9712 0.2347 ] Network output: [ 0.05606 0.5316 0.09722 0.001423 -0.0006387 1.265 0.001072 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2007 Epoch 3039 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04385 0.7337 1.028 -2.985e-05 1.34e-05 0.1506 -2.249e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01661 -0.005201 0.01427 0.02781 0.9456 0.9538 0.02856 0.8914 0.9111 0.06679 ] Network output: [ 1.015 -0.1853 0.09705 0.001113 -0.0004997 0.0619 0.0008388 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6119 0.01838 0.1267 0.388 0.9744 0.9882 0.6702 0.9045 0.9702 0.5463 ] Network output: [ 0.01564 0.8142 0.9966 -0.0001653 7.42e-05 0.1572 -0.0001246 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02229 0.01443 0.01893 0.02108 0.9862 0.9903 0.02261 0.9701 0.9822 0.02316 ] Network output: [ 0.01713 0.02059 0.9068 -0.001639 0.0007356 1.032 -0.001235 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6722 0.5138 0.4468 0.5078 0.9772 0.9898 0.6739 0.9122 0.9739 0.5288 ] Network output: [ -0.05151 0.6281 0.8947 -0.0002167 9.729e-05 0.5793 -0.0001633 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2928 0.2811 0.1838 0.1861 0.987 0.9915 0.293 0.9721 0.9829 0.189 ] Network output: [ -0.1013 0.475 0.991 -0.0004967 0.000223 0.7345 -0.0003743 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3309 0.3283 0.2325 0.2322 0.9802 0.9875 0.3309 0.9486 0.9712 0.2342 ] Network output: [ 0.05596 0.5356 0.09393 0.001416 -0.0006355 1.264 0.001067 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2005 Epoch 3040 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04435 0.7315 1.027 -2.628e-05 1.18e-05 0.1527 -1.981e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01659 -0.005192 0.0142 0.02781 0.9457 0.9538 0.02849 0.8915 0.9112 0.06672 ] Network output: [ 1.016 -0.1845 0.0958 0.001123 -0.0005043 0.06187 0.0008466 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6115 0.01834 0.1256 0.3884 0.9745 0.9882 0.6696 0.9045 0.9702 0.5463 ] Network output: [ 0.01597 0.8123 0.9962 -0.0001644 7.38e-05 0.1589 -0.0001239 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02216 0.01435 0.01887 0.02104 0.9862 0.9903 0.02248 0.9701 0.9822 0.0231 ] Network output: [ 0.01688 0.02029 0.9076 -0.001633 0.0007333 1.032 -0.001231 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6716 0.5132 0.4464 0.508 0.9772 0.9898 0.6732 0.9122 0.9739 0.5289 ] Network output: [ -0.05194 0.6276 0.8979 -0.0002248 0.0001009 0.5774 -0.0001694 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2905 0.2789 0.1831 0.1855 0.987 0.9915 0.2906 0.9721 0.9829 0.1883 ] Network output: [ -0.1016 0.4745 0.993 -0.0005024 0.0002256 0.7336 -0.0003787 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3292 0.3266 0.2321 0.2318 0.9802 0.9874 0.3293 0.9485 0.9711 0.2338 ] Network output: [ 0.05587 0.5396 0.09068 0.001409 -0.0006324 1.264 0.001062 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2002 Epoch 3041 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04484 0.7293 1.026 -2.267e-05 1.018e-05 0.1547 -1.708e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01656 -0.005183 0.01412 0.02782 0.9457 0.9538 0.02842 0.8915 0.9112 0.06665 ] Network output: [ 1.016 -0.1837 0.09454 0.001133 -0.0005087 0.06181 0.000854 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6111 0.01831 0.1246 0.3888 0.9745 0.9882 0.669 0.9045 0.9702 0.5463 ] Network output: [ 0.0163 0.8103 0.9958 -0.0001634 7.335e-05 0.1606 -0.0001231 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02204 0.01427 0.01881 0.021 0.9862 0.9903 0.02236 0.9701 0.9822 0.02306 ] Network output: [ 0.01664 0.01995 0.9084 -0.001628 0.000731 1.032 -0.001227 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.671 0.5127 0.4459 0.5081 0.9772 0.9898 0.6726 0.9123 0.9739 0.5289 ] Network output: [ -0.05237 0.627 0.9011 -0.0002327 0.0001045 0.5757 -0.0001754 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2882 0.2767 0.1824 0.1848 0.987 0.9915 0.2883 0.9721 0.9829 0.1877 ] Network output: [ -0.1019 0.4739 0.995 -0.000508 0.0002281 0.7328 -0.0003828 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3276 0.325 0.2317 0.2315 0.9801 0.9874 0.3276 0.9483 0.9711 0.2335 ] Network output: [ 0.05578 0.5436 0.0875 0.001402 -0.0006293 1.263 0.001056 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1999 Epoch 3042 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04533 0.7272 1.025 -1.902e-05 8.538e-06 0.1567 -1.433e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01654 -0.005174 0.01405 0.02782 0.9458 0.9539 0.02835 0.8915 0.9112 0.06659 ] Network output: [ 1.016 -0.1828 0.09326 0.001142 -0.0005129 0.06174 0.000861 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6107 0.01829 0.1236 0.3892 0.9745 0.9882 0.6685 0.9046 0.9702 0.5464 ] Network output: [ 0.01663 0.8085 0.9953 -0.0001623 7.285e-05 0.1623 -0.0001223 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02193 0.01419 0.01876 0.02097 0.9862 0.9904 0.02224 0.9701 0.9822 0.02301 ] Network output: [ 0.01641 0.01957 0.9092 -0.001623 0.0007287 1.032 -0.001223 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6704 0.5122 0.4454 0.5083 0.9772 0.9898 0.672 0.9123 0.9739 0.529 ] Network output: [ -0.05281 0.6264 0.9043 -0.0002404 0.0001079 0.574 -0.0001812 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.286 0.2745 0.1818 0.1843 0.987 0.9915 0.2861 0.972 0.9829 0.1871 ] Network output: [ -0.1022 0.4733 0.997 -0.0005134 0.0002305 0.732 -0.0003869 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.326 0.3234 0.2313 0.2311 0.9801 0.9874 0.326 0.9482 0.971 0.2331 ] Network output: [ 0.0557 0.5476 0.08437 0.001395 -0.0006262 1.262 0.001051 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1996 Epoch 3043 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04583 0.7252 1.024 -1.533e-05 6.882e-06 0.1586 -1.155e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01651 -0.005165 0.01398 0.02782 0.9458 0.9539 0.02829 0.8915 0.9112 0.06653 ] Network output: [ 1.016 -0.1818 0.09197 0.001151 -0.0005168 0.06163 0.0008676 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6103 0.01827 0.1227 0.3895 0.9745 0.9882 0.6679 0.9046 0.9702 0.5465 ] Network output: [ 0.01696 0.8066 0.9949 -0.000161 7.23e-05 0.1639 -0.0001214 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02181 0.01411 0.01871 0.02093 0.9863 0.9904 0.02212 0.97 0.9822 0.02297 ] Network output: [ 0.01617 0.01916 0.91 -0.001618 0.0007263 1.032 -0.001219 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6698 0.5117 0.445 0.5084 0.9773 0.9898 0.6714 0.9123 0.9739 0.529 ] Network output: [ -0.05325 0.6257 0.9075 -0.000248 0.0001113 0.5723 -0.0001869 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2838 0.2724 0.1812 0.1837 0.987 0.9915 0.2839 0.972 0.9829 0.1865 ] Network output: [ -0.1025 0.4726 0.999 -0.0005186 0.0002328 0.7313 -0.0003908 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3244 0.3219 0.231 0.2308 0.9801 0.9874 0.3245 0.9481 0.971 0.2328 ] Network output: [ 0.05562 0.5515 0.08129 0.001388 -0.0006231 1.262 0.001046 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1992 Epoch 3044 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04632 0.7233 1.024 -1.16e-05 5.209e-06 0.1605 -8.745e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01649 -0.005157 0.01392 0.02783 0.9458 0.9539 0.02822 0.8915 0.9113 0.06647 ] Network output: [ 1.017 -0.1809 0.09066 0.001159 -0.0005205 0.06151 0.0008738 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.61 0.01826 0.1217 0.3899 0.9745 0.9882 0.6674 0.9046 0.9703 0.5465 ] Network output: [ 0.01729 0.8049 0.9944 -0.0001597 7.17e-05 0.1655 -0.0001204 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0217 0.01404 0.01866 0.0209 0.9863 0.9904 0.02201 0.97 0.9822 0.02293 ] Network output: [ 0.01594 0.0187 0.9108 -0.001613 0.000724 1.032 -0.001215 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6692 0.5112 0.4446 0.5086 0.9773 0.9898 0.6708 0.9123 0.9739 0.5291 ] Network output: [ -0.05369 0.6249 0.9107 -0.0002555 0.0001147 0.5708 -0.0001925 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2817 0.2704 0.1807 0.1832 0.987 0.9915 0.2818 0.972 0.9829 0.186 ] Network output: [ -0.1028 0.4719 1.001 -0.0005236 0.0002351 0.7306 -0.0003946 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3229 0.3203 0.2307 0.2305 0.9801 0.9874 0.3229 0.9479 0.9709 0.2325 ] Network output: [ 0.05556 0.5554 0.07827 0.001381 -0.0006201 1.261 0.001041 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1988 Epoch 3045 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0468 0.7214 1.023 -7.844e-06 3.521e-06 0.1624 -5.912e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01647 -0.005149 0.01385 0.02783 0.9459 0.954 0.02816 0.8916 0.9113 0.06642 ] Network output: [ 1.017 -0.1798 0.08935 0.001167 -0.0005239 0.06136 0.0008795 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6097 0.01825 0.1208 0.3903 0.9746 0.9882 0.6669 0.9046 0.9703 0.5466 ] Network output: [ 0.01762 0.8031 0.9939 -0.0001582 7.104e-05 0.1671 -0.0001193 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02159 0.01396 0.01861 0.02088 0.9863 0.9904 0.0219 0.97 0.9822 0.02289 ] Network output: [ 0.01571 0.01821 0.9117 -0.001607 0.0007216 1.032 -0.001211 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6686 0.5108 0.4442 0.5088 0.9773 0.9898 0.6702 0.9123 0.9739 0.5292 ] Network output: [ -0.05414 0.6241 0.9139 -0.0002628 0.000118 0.5693 -0.000198 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2796 0.2684 0.1801 0.1828 0.987 0.9915 0.2797 0.972 0.9829 0.1855 ] Network output: [ -0.1031 0.4712 1.003 -0.0005285 0.0002373 0.7299 -0.0003983 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3214 0.3189 0.2304 0.2303 0.9801 0.9874 0.3214 0.9478 0.9709 0.2322 ] Network output: [ 0.05549 0.5593 0.0753 0.001374 -0.000617 1.26 0.001036 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1984 Epoch 3046 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04729 0.7196 1.022 -4.053e-06 1.82e-06 0.1642 -3.055e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01645 -0.005141 0.01379 0.02784 0.9459 0.954 0.0281 0.8916 0.9113 0.06637 ] Network output: [ 1.017 -0.1788 0.08802 0.001174 -0.0005271 0.0612 0.0008849 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6093 0.01825 0.1199 0.3906 0.9746 0.9883 0.6664 0.9046 0.9703 0.5467 ] Network output: [ 0.01794 0.8015 0.9934 -0.0001567 7.034e-05 0.1686 -0.0001181 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02148 0.01389 0.01857 0.02085 0.9863 0.9904 0.02179 0.97 0.9822 0.02286 ] Network output: [ 0.01549 0.01768 0.9125 -0.001602 0.0007193 1.032 -0.001207 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6681 0.5104 0.4438 0.5089 0.9773 0.9898 0.6697 0.9123 0.9739 0.5293 ] Network output: [ -0.05459 0.6232 0.917 -0.0002699 0.0001212 0.5678 -0.0002034 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2776 0.2664 0.1797 0.1823 0.987 0.9916 0.2777 0.972 0.9829 0.185 ] Network output: [ -0.1034 0.4704 1.005 -0.0005332 0.0002394 0.7293 -0.0004018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.32 0.3174 0.2301 0.23 0.9801 0.9874 0.32 0.9477 0.9708 0.2319 ] Network output: [ 0.05543 0.5632 0.07238 0.001368 -0.0006141 1.259 0.001031 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.198 Epoch 3047 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04778 0.7178 1.021 -2.337e-07 1.049e-07 0.166 -1.761e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01643 -0.005133 0.01373 0.02785 0.946 0.954 0.02804 0.8916 0.9113 0.06632 ] Network output: [ 1.017 -0.1777 0.08669 0.001181 -0.0005301 0.06101 0.0008899 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.609 0.01825 0.119 0.391 0.9746 0.9883 0.666 0.9047 0.9703 0.5468 ] Network output: [ 0.01827 0.7999 0.9928 -0.000155 6.96e-05 0.1701 -0.0001168 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02138 0.01382 0.01853 0.02083 0.9863 0.9904 0.02168 0.97 0.9822 0.02282 ] Network output: [ 0.01528 0.01711 0.9133 -0.001597 0.0007169 1.032 -0.001203 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6675 0.5099 0.4434 0.5091 0.9773 0.9898 0.6691 0.9123 0.9739 0.5294 ] Network output: [ -0.05505 0.6223 0.9202 -0.0002769 0.0001243 0.5665 -0.0002087 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2756 0.2645 0.1792 0.1819 0.987 0.9916 0.2757 0.972 0.9829 0.1846 ] Network output: [ -0.1037 0.4696 1.007 -0.0005377 0.0002414 0.7287 -0.0004052 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3185 0.316 0.2299 0.2298 0.98 0.9874 0.3186 0.9476 0.9708 0.2317 ] Network output: [ 0.05538 0.567 0.06951 0.001361 -0.0006111 1.258 0.001026 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1976 Epoch 3048 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04826 0.7162 1.02 3.613e-06 -1.622e-06 0.1677 2.723e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01641 -0.005126 0.01367 0.02785 0.946 0.954 0.02799 0.8916 0.9113 0.06628 ] Network output: [ 1.018 -0.1765 0.08535 0.001187 -0.0005328 0.0608 0.0008945 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6087 0.01825 0.1181 0.3913 0.9746 0.9883 0.6655 0.9047 0.9703 0.5469 ] Network output: [ 0.01859 0.7983 0.9923 -0.0001533 6.881e-05 0.1716 -0.0001155 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02128 0.01376 0.01849 0.02081 0.9863 0.9904 0.02158 0.97 0.9822 0.0228 ] Network output: [ 0.01507 0.01651 0.9142 -0.001591 0.0007145 1.033 -0.001199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.667 0.5095 0.4431 0.5093 0.9774 0.9898 0.6686 0.9123 0.9739 0.5295 ] Network output: [ -0.05551 0.6213 0.9233 -0.0002837 0.0001274 0.5652 -0.0002138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2737 0.2627 0.1788 0.1815 0.987 0.9916 0.2738 0.972 0.9829 0.1842 ] Network output: [ -0.1039 0.4688 1.009 -0.000542 0.0002433 0.7282 -0.0004085 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3172 0.3146 0.2297 0.2296 0.98 0.9874 0.3172 0.9475 0.9708 0.2315 ] Network output: [ 0.05533 0.5708 0.0667 0.001355 -0.0006082 1.257 0.001021 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1971 Epoch 3049 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04874 0.7145 1.019 7.483e-06 -3.359e-06 0.1694 5.64e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01639 -0.005118 0.01361 0.02786 0.946 0.9541 0.02793 0.8916 0.9114 0.06625 ] Network output: [ 1.018 -0.1753 0.084 0.001192 -0.0005353 0.06057 0.0008987 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6084 0.01826 0.1173 0.3916 0.9747 0.9883 0.6651 0.9047 0.9703 0.547 ] Network output: [ 0.01892 0.7968 0.9917 -0.0001514 6.797e-05 0.173 -0.0001141 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02118 0.01369 0.01846 0.02079 0.9863 0.9904 0.02148 0.97 0.9822 0.02277 ] Network output: [ 0.01486 0.01587 0.915 -0.001586 0.0007121 1.033 -0.001195 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6665 0.5091 0.4427 0.5095 0.9774 0.9898 0.668 0.9123 0.9739 0.5296 ] Network output: [ -0.05597 0.6203 0.9265 -0.0002904 0.0001304 0.564 -0.0002188 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2718 0.2609 0.1784 0.1812 0.987 0.9916 0.2719 0.972 0.9829 0.1839 ] Network output: [ -0.1042 0.4679 1.011 -0.0005461 0.0002452 0.7277 -0.0004116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3158 0.3133 0.2295 0.2294 0.98 0.9874 0.3158 0.9474 0.9707 0.2313 ] Network output: [ 0.05529 0.5746 0.06393 0.001348 -0.0006053 1.256 0.001016 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1966 Epoch 3050 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04922 0.713 1.018 1.138e-05 -5.107e-06 0.171 8.573e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01637 -0.005111 0.01356 0.02787 0.9461 0.9541 0.02788 0.8916 0.9114 0.06621 ] Network output: [ 1.018 -0.1741 0.08264 0.001198 -0.0005376 0.06032 0.0009025 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6081 0.01827 0.1165 0.392 0.9747 0.9883 0.6646 0.9047 0.9703 0.5471 ] Network output: [ 0.01924 0.7954 0.9911 -0.0001494 6.709e-05 0.1744 -0.0001126 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02109 0.01363 0.01842 0.02077 0.9863 0.9904 0.02138 0.97 0.9822 0.02275 ] Network output: [ 0.01466 0.01519 0.9159 -0.001581 0.0007096 1.033 -0.001191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.666 0.5087 0.4424 0.5097 0.9774 0.9898 0.6675 0.9123 0.974 0.5297 ] Network output: [ -0.05644 0.6192 0.9296 -0.0002969 0.0001333 0.5628 -0.0002237 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.27 0.2591 0.1781 0.1809 0.987 0.9916 0.2701 0.972 0.9829 0.1836 ] Network output: [ -0.1045 0.467 1.013 -0.0005501 0.000247 0.7272 -0.0004146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3145 0.312 0.2293 0.2293 0.98 0.9874 0.3145 0.9473 0.9707 0.2311 ] Network output: [ 0.05525 0.5784 0.06121 0.001342 -0.0006024 1.255 0.001011 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1961 Epoch 3051 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0497 0.7115 1.017 1.529e-05 -6.863e-06 0.1726 1.152e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01636 -0.005105 0.0135 0.02788 0.9461 0.9541 0.02783 0.8917 0.9114 0.06618 ] Network output: [ 1.018 -0.1729 0.08128 0.001202 -0.0005396 0.06005 0.0009059 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6079 0.01828 0.1157 0.3923 0.9747 0.9883 0.6642 0.9047 0.9703 0.5472 ] Network output: [ 0.01956 0.794 0.9905 -0.0001474 6.617e-05 0.1758 -0.0001111 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02099 0.01357 0.01839 0.02076 0.9863 0.9904 0.02129 0.97 0.9822 0.02273 ] Network output: [ 0.01447 0.01448 0.9168 -0.001575 0.0007072 1.033 -0.001187 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6655 0.5084 0.4421 0.5099 0.9774 0.9898 0.667 0.9123 0.974 0.5298 ] Network output: [ -0.05691 0.6181 0.9327 -0.0003032 0.0001361 0.5617 -0.0002285 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2682 0.2574 0.1778 0.1806 0.9871 0.9916 0.2683 0.972 0.9829 0.1833 ] Network output: [ -0.1048 0.466 1.014 -0.000554 0.0002487 0.7268 -0.0004175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3132 0.3107 0.2291 0.2292 0.98 0.9874 0.3133 0.9472 0.9707 0.231 ] Network output: [ 0.05521 0.5821 0.05854 0.001336 -0.0005996 1.254 0.001007 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1956 Epoch 3052 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05017 0.7101 1.015 1.922e-05 -8.626e-06 0.1742 1.448e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01634 -0.005098 0.01345 0.02789 0.9462 0.9542 0.02778 0.8917 0.9114 0.06615 ] Network output: [ 1.018 -0.1716 0.07992 0.001206 -0.0005415 0.05976 0.000909 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6076 0.0183 0.1149 0.3926 0.9747 0.9883 0.6638 0.9047 0.9703 0.5474 ] Network output: [ 0.01988 0.7926 0.9899 -0.0001453 6.521e-05 0.1772 -0.0001095 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0209 0.01351 0.01836 0.02075 0.9864 0.9904 0.02119 0.97 0.9822 0.02271 ] Network output: [ 0.01428 0.01374 0.9176 -0.00157 0.0007048 1.034 -0.001183 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.665 0.508 0.4418 0.51 0.9774 0.9899 0.6665 0.9124 0.974 0.5299 ] Network output: [ -0.05738 0.6169 0.9359 -0.0003095 0.0001389 0.5607 -0.0002332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2664 0.2557 0.1775 0.1804 0.9871 0.9916 0.2665 0.972 0.9829 0.183 ] Network output: [ -0.1051 0.4651 1.016 -0.0005576 0.0002503 0.7264 -0.0004202 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.312 0.3095 0.229 0.229 0.98 0.9873 0.312 0.9471 0.9706 0.2309 ] Network output: [ 0.05518 0.5858 0.05592 0.001329 -0.0005968 1.253 0.001002 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1951 Epoch 3053 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05065 0.7088 1.014 2.316e-05 -1.04e-05 0.1757 1.745e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01633 -0.005092 0.0134 0.0279 0.9462 0.9542 0.02773 0.8917 0.9114 0.06613 ] Network output: [ 1.019 -0.1703 0.07856 0.00121 -0.0005431 0.05945 0.0009116 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6074 0.01832 0.1142 0.3929 0.9747 0.9883 0.6634 0.9048 0.9704 0.5475 ] Network output: [ 0.02021 0.7913 0.9893 -0.000143 6.422e-05 0.1785 -0.0001078 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02081 0.01345 0.01834 0.02074 0.9864 0.9904 0.0211 0.97 0.9822 0.02269 ] Network output: [ 0.0141 0.01296 0.9185 -0.001564 0.0007023 1.034 -0.001179 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6645 0.5077 0.4415 0.5102 0.9774 0.9899 0.6661 0.9124 0.974 0.5301 ] Network output: [ -0.05786 0.6157 0.939 -0.0003155 0.0001417 0.5598 -0.0002378 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2647 0.2541 0.1772 0.1802 0.9871 0.9916 0.2648 0.9719 0.9829 0.1828 ] Network output: [ -0.1053 0.4641 1.018 -0.0005611 0.0002519 0.7261 -0.0004229 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3108 0.3083 0.2289 0.229 0.98 0.9873 0.3108 0.947 0.9706 0.2308 ] Network output: [ 0.05516 0.5894 0.05335 0.001323 -0.0005941 1.252 0.0009973 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1945 Epoch 3054 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05112 0.7075 1.013 2.711e-05 -1.217e-05 0.1772 2.043e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01631 -0.005086 0.01335 0.02792 0.9462 0.9542 0.02769 0.8917 0.9114 0.06611 ] Network output: [ 1.019 -0.1689 0.07719 0.001213 -0.0005444 0.05913 0.000914 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6071 0.01834 0.1135 0.3932 0.9748 0.9883 0.663 0.9048 0.9704 0.5476 ] Network output: [ 0.02053 0.79 0.9886 -0.0001407 6.318e-05 0.1797 -0.0001061 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02073 0.01339 0.01832 0.02073 0.9864 0.9904 0.02102 0.97 0.9822 0.02268 ] Network output: [ 0.01393 0.01215 0.9193 -0.001559 0.0006998 1.034 -0.001175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.664 0.5074 0.4412 0.5104 0.9775 0.9899 0.6656 0.9124 0.974 0.5302 ] Network output: [ -0.05834 0.6145 0.9421 -0.0003215 0.0001443 0.5589 -0.0002423 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2631 0.2525 0.177 0.18 0.9871 0.9916 0.2632 0.9719 0.9829 0.1826 ] Network output: [ -0.1056 0.463 1.02 -0.0005644 0.0002534 0.7258 -0.0004254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3096 0.3071 0.2288 0.2289 0.98 0.9873 0.3096 0.9469 0.9706 0.2307 ] Network output: [ 0.05513 0.5931 0.05082 0.001317 -0.0005914 1.251 0.0009927 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1939 Epoch 3055 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05159 0.7062 1.012 3.108e-05 -1.395e-05 0.1786 2.342e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0163 -0.00508 0.01331 0.02793 0.9463 0.9542 0.02764 0.8917 0.9115 0.06609 ] Network output: [ 1.019 -0.1675 0.07583 0.001215 -0.0005456 0.05879 0.0009159 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6069 0.01836 0.1128 0.3935 0.9748 0.9883 0.6627 0.9048 0.9704 0.5478 ] Network output: [ 0.02084 0.7888 0.9879 -0.0001384 6.211e-05 0.181 -0.0001043 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02065 0.01334 0.0183 0.02073 0.9864 0.9904 0.02093 0.97 0.9822 0.02267 ] Network output: [ 0.01376 0.01131 0.9202 -0.001553 0.0006973 1.035 -0.001171 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6636 0.507 0.441 0.5106 0.9775 0.9899 0.6651 0.9124 0.974 0.5303 ] Network output: [ -0.05883 0.6132 0.9451 -0.0003272 0.0001469 0.558 -0.0002466 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2615 0.251 0.1768 0.1798 0.9871 0.9916 0.2616 0.9719 0.9829 0.1824 ] Network output: [ -0.1059 0.462 1.022 -0.0005676 0.0002548 0.7255 -0.0004277 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3085 0.306 0.2287 0.2289 0.98 0.9873 0.3085 0.9468 0.9705 0.2307 ] Network output: [ 0.05511 0.5967 0.04834 0.001311 -0.0005887 1.25 0.0009882 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1933 Epoch 3056 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05206 0.7051 1.011 3.505e-05 -1.573e-05 0.18 2.641e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01629 -0.005075 0.01326 0.02794 0.9463 0.9543 0.0276 0.8917 0.9115 0.06608 ] Network output: [ 1.019 -0.1661 0.07446 0.001217 -0.0005466 0.05843 0.0009175 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6067 0.01838 0.1121 0.3939 0.9748 0.9883 0.6623 0.9048 0.9704 0.5479 ] Network output: [ 0.02116 0.7877 0.9873 -0.0001359 6.101e-05 0.1822 -0.0001024 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02056 0.01328 0.01828 0.02072 0.9864 0.9904 0.02085 0.97 0.9822 0.02266 ] Network output: [ 0.0136 0.01044 0.921 -0.001548 0.0006948 1.035 -0.001166 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6632 0.5067 0.4408 0.5108 0.9775 0.9899 0.6647 0.9124 0.974 0.5305 ] Network output: [ -0.05931 0.6118 0.9482 -0.0003329 0.0001494 0.5572 -0.0002509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2599 0.2495 0.1767 0.1797 0.9871 0.9916 0.26 0.9719 0.9829 0.1823 ] Network output: [ -0.1061 0.4609 1.024 -0.0005705 0.0002561 0.7253 -0.00043 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3074 0.3049 0.2287 0.2288 0.9799 0.9873 0.3074 0.9467 0.9705 0.2306 ] Network output: [ 0.0551 0.6002 0.04591 0.001305 -0.0005861 1.249 0.0009838 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1927 Epoch 3057 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05253 0.704 1.01 3.902e-05 -1.752e-05 0.1813 2.941e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01628 -0.005069 0.01322 0.02796 0.9463 0.9543 0.02756 0.8917 0.9115 0.06607 ] Network output: [ 1.019 -0.1647 0.0731 0.001219 -0.0005473 0.05805 0.0009188 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6065 0.01841 0.1115 0.3941 0.9748 0.9884 0.662 0.9048 0.9704 0.5481 ] Network output: [ 0.02148 0.7866 0.9866 -0.0001334 5.987e-05 0.1833 -0.0001005 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02049 0.01323 0.01826 0.02072 0.9864 0.9905 0.02077 0.97 0.9822 0.02265 ] Network output: [ 0.01345 0.009534 0.9219 -0.001542 0.0006923 1.035 -0.001162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6627 0.5064 0.4406 0.511 0.9775 0.9899 0.6643 0.9124 0.974 0.5306 ] Network output: [ -0.0598 0.6105 0.9513 -0.0003384 0.0001519 0.5565 -0.000255 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2584 0.248 0.1765 0.1796 0.9871 0.9916 0.2585 0.9719 0.9829 0.1822 ] Network output: [ -0.1064 0.4597 1.026 -0.0005734 0.0002574 0.7251 -0.0004321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3063 0.3038 0.2287 0.2288 0.9799 0.9873 0.3063 0.9466 0.9705 0.2306 ] Network output: [ 0.05509 0.6038 0.04352 0.0013 -0.0005834 1.248 0.0009794 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1921 Epoch 3058 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05299 0.7029 1.009 4.3e-05 -1.93e-05 0.1826 3.241e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01626 -0.005064 0.01318 0.02797 0.9464 0.9543 0.02752 0.8917 0.9115 0.06606 ] Network output: [ 1.019 -0.1632 0.07173 0.00122 -0.0005479 0.05766 0.0009197 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6063 0.01843 0.1108 0.3944 0.9748 0.9884 0.6617 0.9048 0.9704 0.5482 ] Network output: [ 0.0218 0.7855 0.9859 -0.0001308 5.871e-05 0.1845 -9.855e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02041 0.01318 0.01825 0.02072 0.9864 0.9905 0.02069 0.97 0.9822 0.02265 ] Network output: [ 0.0133 0.008599 0.9227 -0.001536 0.0006898 1.036 -0.001158 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6623 0.5062 0.4404 0.5112 0.9775 0.9899 0.6639 0.9124 0.974 0.5308 ] Network output: [ -0.06029 0.6091 0.9543 -0.0003437 0.0001543 0.5558 -0.000259 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2569 0.2466 0.1764 0.1795 0.9871 0.9916 0.257 0.9719 0.9829 0.1821 ] Network output: [ -0.1067 0.4586 1.027 -0.000576 0.0002586 0.7249 -0.0004341 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3052 0.3028 0.2287 0.2288 0.9799 0.9873 0.3053 0.9465 0.9705 0.2306 ] Network output: [ 0.05508 0.6073 0.04117 0.001294 -0.0005809 1.247 0.0009751 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1914 Epoch 3059 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05346 0.702 1.007 4.698e-05 -2.109e-05 0.1838 3.54e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01625 -0.005059 0.01314 0.02799 0.9464 0.9543 0.02748 0.8918 0.9115 0.06606 ] Network output: [ 1.02 -0.1617 0.07038 0.001221 -0.0005482 0.05725 0.0009203 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6061 0.01846 0.1102 0.3947 0.9749 0.9884 0.6614 0.9049 0.9704 0.5484 ] Network output: [ 0.02211 0.7845 0.9852 -0.0001281 5.751e-05 0.1856 -9.655e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02033 0.01313 0.01824 0.02073 0.9864 0.9905 0.02061 0.97 0.9822 0.02265 ] Network output: [ 0.01316 0.007635 0.9235 -0.001531 0.0006872 1.036 -0.001154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6619 0.5059 0.4402 0.5114 0.9775 0.9899 0.6634 0.9124 0.974 0.5309 ] Network output: [ -0.06079 0.6076 0.9573 -0.0003489 0.0001567 0.5552 -0.000263 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2554 0.2452 0.1764 0.1795 0.9871 0.9916 0.2555 0.9719 0.9829 0.182 ] Network output: [ -0.1069 0.4574 1.029 -0.0005786 0.0002597 0.7248 -0.000436 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3042 0.3018 0.2287 0.2289 0.9799 0.9873 0.3042 0.9465 0.9704 0.2307 ] Network output: [ 0.05507 0.6108 0.03887 0.001288 -0.0005783 1.245 0.0009709 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1908 Epoch 3060 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05392 0.701 1.006 5.095e-05 -2.287e-05 0.1851 3.84e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01624 -0.005055 0.0131 0.028 0.9464 0.9544 0.02745 0.8918 0.9115 0.06606 ] Network output: [ 1.02 -0.1602 0.06902 0.001222 -0.0005484 0.05683 0.0009206 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6059 0.01849 0.1097 0.395 0.9749 0.9884 0.6611 0.9049 0.9704 0.5485 ] Network output: [ 0.02243 0.7835 0.9845 -0.0001254 5.629e-05 0.1867 -9.449e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02026 0.01309 0.01823 0.02073 0.9864 0.9905 0.02054 0.97 0.9822 0.02265 ] Network output: [ 0.01303 0.006641 0.9244 -0.001525 0.0006847 1.037 -0.001149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6615 0.5056 0.44 0.5116 0.9776 0.9899 0.663 0.9124 0.974 0.5311 ] Network output: [ -0.06129 0.6061 0.9604 -0.000354 0.0001589 0.5547 -0.0002668 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.254 0.2438 0.1763 0.1795 0.9871 0.9916 0.2541 0.9719 0.9829 0.182 ] Network output: [ -0.1072 0.4562 1.031 -0.0005809 0.0002608 0.7247 -0.0004378 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3032 0.3008 0.2287 0.2289 0.9799 0.9873 0.3033 0.9464 0.9704 0.2307 ] Network output: [ 0.05507 0.6142 0.03661 0.001283 -0.0005758 1.244 0.0009667 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1901 Epoch 3061 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05438 0.7002 1.005 5.492e-05 -2.466e-05 0.1862 4.139e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01624 -0.00505 0.01307 0.02802 0.9465 0.9544 0.02741 0.8918 0.9115 0.06606 ] Network output: [ 1.02 -0.1586 0.06767 0.001221 -0.0005484 0.05639 0.0009205 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6057 0.01851 0.1091 0.3953 0.9749 0.9884 0.6608 0.9049 0.9704 0.5487 ] Network output: [ 0.02274 0.7826 0.9837 -0.0001226 5.504e-05 0.1877 -9.239e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02019 0.01304 0.01822 0.02074 0.9865 0.9905 0.02047 0.97 0.9822 0.02266 ] Network output: [ 0.01291 0.005619 0.9252 -0.001519 0.0006821 1.037 -0.001145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6611 0.5054 0.4398 0.5118 0.9776 0.9899 0.6627 0.9124 0.974 0.5313 ] Network output: [ -0.06179 0.6046 0.9634 -0.000359 0.0001611 0.5541 -0.0002705 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2527 0.2425 0.1763 0.1795 0.9871 0.9916 0.2528 0.9719 0.9829 0.182 ] Network output: [ -0.1074 0.455 1.033 -0.0005831 0.0002618 0.7247 -0.0004394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3023 0.2998 0.2288 0.229 0.9799 0.9873 0.3023 0.9463 0.9704 0.2308 ] Network output: [ 0.05507 0.6176 0.03439 0.001277 -0.0005734 1.243 0.0009626 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1894 Epoch 3062 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05483 0.6993 1.004 5.888e-05 -2.644e-05 0.1873 4.438e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01623 -0.005046 0.01304 0.02803 0.9465 0.9544 0.02738 0.8918 0.9116 0.06606 ] Network output: [ 1.02 -0.157 0.06633 0.001221 -0.0005481 0.05593 0.0009201 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6055 0.01854 0.1086 0.3956 0.9749 0.9884 0.6605 0.9049 0.9704 0.5488 ] Network output: [ 0.02306 0.7817 0.983 -0.0001198 5.376e-05 0.1887 -9.025e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02012 0.013 0.01822 0.02075 0.9865 0.9905 0.0204 0.97 0.9822 0.02266 ] Network output: [ 0.0128 0.004569 0.926 -0.001514 0.0006795 1.038 -0.001141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6608 0.5051 0.4397 0.5121 0.9776 0.9899 0.6623 0.9124 0.974 0.5314 ] Network output: [ -0.06229 0.603 0.9664 -0.0003638 0.0001633 0.5537 -0.0002741 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2514 0.2413 0.1763 0.1795 0.9871 0.9916 0.2515 0.9719 0.9829 0.182 ] Network output: [ -0.1077 0.4537 1.035 -0.0005851 0.0002627 0.7246 -0.000441 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3013 0.2989 0.2288 0.2291 0.9799 0.9873 0.3014 0.9463 0.9704 0.2309 ] Network output: [ 0.05507 0.621 0.03222 0.001272 -0.000571 1.242 0.0009585 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1887 Epoch 3063 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05529 0.6986 1.003 6.284e-05 -2.821e-05 0.1884 4.736e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01622 -0.005042 0.013 0.02805 0.9465 0.9544 0.02735 0.8918 0.9116 0.06607 ] Network output: [ 1.02 -0.1555 0.06499 0.00122 -0.0005477 0.05547 0.0009195 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6054 0.01857 0.1081 0.3958 0.9749 0.9884 0.6602 0.9049 0.9704 0.549 ] Network output: [ 0.02337 0.7809 0.9823 -0.0001169 5.246e-05 0.1897 -8.807e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02006 0.01296 0.01822 0.02076 0.9865 0.9905 0.02033 0.97 0.9822 0.02267 ] Network output: [ 0.0127 0.003492 0.9268 -0.001508 0.0006769 1.038 -0.001136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6604 0.5049 0.4396 0.5123 0.9776 0.9899 0.6619 0.9124 0.974 0.5316 ] Network output: [ -0.0628 0.6015 0.9693 -0.0003684 0.0001654 0.5533 -0.0002777 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2501 0.24 0.1763 0.1796 0.9871 0.9916 0.2502 0.9719 0.9829 0.1821 ] Network output: [ -0.1079 0.4525 1.036 -0.000587 0.0002635 0.7246 -0.0004424 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3004 0.298 0.2289 0.2292 0.9799 0.9873 0.3005 0.9462 0.9704 0.231 ] Network output: [ 0.05508 0.6244 0.03008 0.001267 -0.0005686 1.241 0.0009545 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.188 Epoch 3064 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05574 0.6979 1.001 6.678e-05 -2.998e-05 0.1895 5.033e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01621 -0.005039 0.01297 0.02807 0.9466 0.9545 0.02732 0.8918 0.9116 0.06608 ] Network output: [ 1.02 -0.1538 0.06366 0.001219 -0.0005471 0.05498 0.0009185 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6052 0.01859 0.1076 0.3961 0.975 0.9884 0.66 0.9049 0.9704 0.5492 ] Network output: [ 0.02368 0.7801 0.9815 -0.0001139 5.114e-05 0.1906 -8.585e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01999 0.01291 0.01822 0.02077 0.9865 0.9905 0.02027 0.97 0.9822 0.02268 ] Network output: [ 0.0126 0.002388 0.9276 -0.001502 0.0006743 1.039 -0.001132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.66 0.5046 0.4395 0.5125 0.9776 0.9899 0.6615 0.9124 0.974 0.5318 ] Network output: [ -0.0633 0.5998 0.9723 -0.000373 0.0001674 0.5529 -0.0002811 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2488 0.2389 0.1764 0.1797 0.9872 0.9916 0.2489 0.9719 0.9829 0.1822 ] Network output: [ -0.1082 0.4512 1.038 -0.0005888 0.0002643 0.7247 -0.0004437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2996 0.2972 0.229 0.2293 0.9799 0.9873 0.2996 0.9461 0.9703 0.2311 ] Network output: [ 0.05509 0.6277 0.02799 0.001261 -0.0005662 1.239 0.0009506 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1872 Epoch 3065 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05619 0.6972 1 7.07e-05 -3.174e-05 0.1905 5.329e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01621 -0.005035 0.01295 0.02809 0.9466 0.9545 0.02729 0.8918 0.9116 0.06609 ] Network output: [ 1.02 -0.1522 0.06234 0.001217 -0.0005464 0.05449 0.0009172 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6051 0.01862 0.1072 0.3964 0.975 0.9884 0.6597 0.9049 0.9704 0.5494 ] Network output: [ 0.024 0.7793 0.9807 -0.0001109 4.98e-05 0.1915 -8.36e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01993 0.01287 0.01822 0.02079 0.9865 0.9905 0.0202 0.97 0.9822 0.0227 ] Network output: [ 0.01251 0.001259 0.9283 -0.001496 0.0006717 1.039 -0.001128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6597 0.5044 0.4394 0.5127 0.9776 0.9899 0.6612 0.9124 0.974 0.532 ] Network output: [ -0.06381 0.5982 0.9753 -0.0003774 0.0001694 0.5526 -0.0002844 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2476 0.2377 0.1765 0.1798 0.9872 0.9916 0.2477 0.9719 0.9829 0.1823 ] Network output: [ -0.1084 0.4499 1.04 -0.0005904 0.000265 0.7247 -0.0004449 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2987 0.2963 0.2292 0.2295 0.9799 0.9873 0.2988 0.9461 0.9703 0.2312 ] Network output: [ 0.0551 0.631 0.02593 0.001256 -0.0005639 1.238 0.0009467 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1865 Epoch 3066 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05664 0.6967 0.999 7.461e-05 -3.35e-05 0.1914 5.623e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0162 -0.005032 0.01292 0.02811 0.9466 0.9545 0.02727 0.8918 0.9116 0.06611 ] Network output: [ 1.02 -0.1505 0.06102 0.001215 -0.0005454 0.05398 0.0009156 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.605 0.01865 0.1067 0.3966 0.975 0.9884 0.6595 0.9049 0.9705 0.5495 ] Network output: [ 0.02431 0.7786 0.98 -0.0001079 4.844e-05 0.1924 -8.131e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01987 0.01284 0.01822 0.0208 0.9865 0.9905 0.02014 0.97 0.9822 0.02271 ] Network output: [ 0.01244 0.0001051 0.9291 -0.00149 0.0006691 1.04 -0.001123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6593 0.5042 0.4393 0.5129 0.9777 0.9899 0.6608 0.9125 0.974 0.5321 ] Network output: [ -0.06433 0.5965 0.9782 -0.0003817 0.0001713 0.5524 -0.0002876 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2465 0.2366 0.1766 0.1799 0.9872 0.9916 0.2465 0.9719 0.9829 0.1824 ] Network output: [ -0.1087 0.4485 1.042 -0.0005918 0.0002657 0.7248 -0.000446 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2979 0.2955 0.2293 0.2296 0.9799 0.9873 0.2979 0.946 0.9703 0.2314 ] Network output: [ 0.05511 0.6342 0.02392 0.001251 -0.0005617 1.237 0.0009429 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1857 Epoch 3067 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05709 0.6961 0.9977 7.851e-05 -3.524e-05 0.1923 5.916e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01619 -0.005029 0.0129 0.02813 0.9466 0.9545 0.02724 0.8918 0.9116 0.06613 ] Network output: [ 1.02 -0.1489 0.05972 0.001212 -0.0005443 0.05346 0.0009138 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6048 0.01867 0.1063 0.3969 0.975 0.9884 0.6593 0.905 0.9705 0.5497 ] Network output: [ 0.02462 0.7779 0.9792 -0.0001048 4.706e-05 0.1932 -7.9e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01982 0.0128 0.01823 0.02082 0.9865 0.9905 0.02008 0.97 0.9822 0.02273 ] Network output: [ 0.01237 -0.001073 0.9298 -0.001484 0.0006664 1.04 -0.001119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.659 0.504 0.4392 0.5131 0.9777 0.9899 0.6605 0.9125 0.9741 0.5323 ] Network output: [ -0.06484 0.5948 0.9811 -0.0003858 0.0001732 0.5522 -0.0002908 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2453 0.2355 0.1767 0.1801 0.9872 0.9917 0.2454 0.9719 0.9829 0.1826 ] Network output: [ -0.1089 0.4472 1.043 -0.0005931 0.0002663 0.725 -0.000447 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2971 0.2947 0.2295 0.2298 0.9799 0.9873 0.2971 0.946 0.9703 0.2315 ] Network output: [ 0.05512 0.6375 0.02194 0.001246 -0.0005594 1.235 0.0009391 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1849 Epoch 3068 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05753 0.6956 0.9965 8.238e-05 -3.698e-05 0.1932 6.208e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01619 -0.005026 0.01287 0.02815 0.9467 0.9546 0.02722 0.8918 0.9116 0.06615 ] Network output: [ 1.02 -0.1472 0.05842 0.00121 -0.0005431 0.05293 0.0009116 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6047 0.0187 0.1059 0.3971 0.975 0.9884 0.659 0.905 0.9705 0.5499 ] Network output: [ 0.02492 0.7773 0.9784 -0.0001017 4.566e-05 0.194 -7.665e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01976 0.01276 0.01824 0.02084 0.9865 0.9905 0.02002 0.97 0.9822 0.02275 ] Network output: [ 0.01231 -0.002276 0.9306 -0.001479 0.0006638 1.041 -0.001114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6587 0.5038 0.4392 0.5133 0.9777 0.99 0.6602 0.9125 0.9741 0.5325 ] Network output: [ -0.06535 0.593 0.984 -0.0003899 0.000175 0.5521 -0.0002938 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2442 0.2345 0.1769 0.1803 0.9872 0.9917 0.2443 0.9719 0.9829 0.1827 ] Network output: [ -0.1091 0.4458 1.045 -0.0005942 0.0002668 0.7251 -0.0004478 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2964 0.294 0.2296 0.23 0.9799 0.9873 0.2964 0.9459 0.9703 0.2317 ] Network output: [ 0.05514 0.6407 0.02 0.001241 -0.0005572 1.234 0.0009354 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1841 Epoch 3069 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05797 0.6952 0.9952 8.622e-05 -3.871e-05 0.194 6.498e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01619 -0.005023 0.01285 0.02817 0.9467 0.9546 0.02719 0.8919 0.9116 0.06617 ] Network output: [ 1.02 -0.1455 0.05714 0.001206 -0.0005416 0.05239 0.0009092 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6046 0.01872 0.1056 0.3974 0.975 0.9884 0.6588 0.905 0.9705 0.5501 ] Network output: [ 0.02523 0.7767 0.9776 -9.857e-05 4.425e-05 0.1948 -7.429e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01971 0.01273 0.01825 0.02086 0.9865 0.9905 0.01997 0.97 0.9822 0.02277 ] Network output: [ 0.01226 -0.003501 0.9313 -0.001473 0.0006611 1.042 -0.00111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6584 0.5036 0.4391 0.5135 0.9777 0.99 0.6599 0.9125 0.9741 0.5327 ] Network output: [ -0.06587 0.5913 0.9869 -0.0003938 0.0001768 0.552 -0.0002968 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2432 0.2334 0.1771 0.1805 0.9872 0.9917 0.2433 0.9719 0.9829 0.1829 ] Network output: [ -0.1094 0.4444 1.047 -0.0005953 0.0002672 0.7253 -0.0004486 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2956 0.2932 0.2298 0.2302 0.9799 0.9873 0.2956 0.9459 0.9703 0.2319 ] Network output: [ 0.05516 0.6438 0.0181 0.001236 -0.000555 1.233 0.0009317 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1833 Epoch 3070 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05841 0.6948 0.9939 9.005e-05 -4.043e-05 0.1948 6.786e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01618 -0.005021 0.01283 0.02819 0.9467 0.9546 0.02717 0.8919 0.9116 0.06619 ] Network output: [ 1.02 -0.1438 0.05586 0.001203 -0.00054 0.05184 0.0009066 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6045 0.01875 0.1052 0.3976 0.9751 0.9885 0.6586 0.905 0.9705 0.5502 ] Network output: [ 0.02554 0.7762 0.9769 -9.54e-05 4.283e-05 0.1955 -7.189e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01965 0.0127 0.01826 0.02089 0.9866 0.9905 0.01992 0.97 0.9822 0.02279 ] Network output: [ 0.01222 -0.004748 0.932 -0.001467 0.0006584 1.042 -0.001105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6581 0.5034 0.4391 0.5137 0.9777 0.99 0.6596 0.9125 0.9741 0.5329 ] Network output: [ -0.06639 0.5894 0.9898 -0.0003976 0.0001785 0.5519 -0.0002996 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2422 0.2325 0.1773 0.1807 0.9872 0.9917 0.2422 0.9719 0.9829 0.1832 ] Network output: [ -0.1096 0.443 1.048 -0.0005961 0.0002676 0.7255 -0.0004493 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2949 0.2925 0.23 0.2304 0.9799 0.9873 0.2949 0.9459 0.9703 0.2321 ] Network output: [ 0.05517 0.647 0.01623 0.001232 -0.0005529 1.231 0.0009282 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1825 Epoch 3071 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05885 0.6945 0.9927 9.384e-05 -4.213e-05 0.1955 7.072e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01618 -0.005018 0.01281 0.02821 0.9468 0.9546 0.02715 0.8919 0.9116 0.06622 ] Network output: [ 1.021 -0.142 0.0546 0.001199 -0.0005383 0.05127 0.0009037 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6044 0.01877 0.1049 0.3978 0.9751 0.9885 0.6584 0.905 0.9705 0.5504 ] Network output: [ 0.02584 0.7757 0.9761 -9.219e-05 4.139e-05 0.1962 -6.948e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0196 0.01266 0.01828 0.02091 0.9866 0.9905 0.01987 0.97 0.9822 0.02282 ] Network output: [ 0.01219 -0.006018 0.9327 -0.001461 0.0006557 1.043 -0.001101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6578 0.5032 0.4391 0.5139 0.9777 0.99 0.6593 0.9125 0.9741 0.5331 ] Network output: [ -0.06691 0.5876 0.9927 -0.0004012 0.0001801 0.5519 -0.0003024 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2412 0.2315 0.1775 0.181 0.9872 0.9917 0.2413 0.9719 0.9829 0.1834 ] Network output: [ -0.1098 0.4416 1.05 -0.0005969 0.0002679 0.7257 -0.0004498 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2942 0.2919 0.2303 0.2306 0.9799 0.9873 0.2943 0.9458 0.9703 0.2324 ] Network output: [ 0.05519 0.6501 0.01441 0.001227 -0.0005508 1.23 0.0009246 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1817 Epoch 3072 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05928 0.6942 0.9914 9.761e-05 -4.382e-05 0.1962 7.356e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01618 -0.005016 0.0128 0.02823 0.9468 0.9546 0.02713 0.8919 0.9117 0.06625 ] Network output: [ 1.021 -0.1403 0.05335 0.001195 -0.0005364 0.0507 0.0009005 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6043 0.01879 0.1046 0.398 0.9751 0.9885 0.6582 0.905 0.9705 0.5506 ] Network output: [ 0.02615 0.7752 0.9753 -8.897e-05 3.994e-05 0.1969 -6.705e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01956 0.01263 0.01829 0.02094 0.9866 0.9906 0.01982 0.97 0.9822 0.02284 ] Network output: [ 0.01217 -0.007308 0.9334 -0.001455 0.000653 1.044 -0.001096 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6575 0.5031 0.4391 0.5142 0.9777 0.99 0.659 0.9125 0.9741 0.5332 ] Network output: [ -0.06743 0.5858 0.9955 -0.0004048 0.0001817 0.5519 -0.000305 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2402 0.2306 0.1778 0.1812 0.9872 0.9917 0.2403 0.9719 0.9829 0.1837 ] Network output: [ -0.11 0.4401 1.051 -0.0005974 0.0002682 0.726 -0.0004503 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2936 0.2912 0.2305 0.2309 0.9799 0.9873 0.2936 0.9458 0.9703 0.2326 ] Network output: [ 0.05522 0.6531 0.01261 0.001222 -0.0005487 1.229 0.0009212 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1809 Epoch 3073 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05971 0.694 0.9901 0.0001013 -4.55e-05 0.1969 7.638e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01618 -0.005014 0.01278 0.02826 0.9468 0.9547 0.02712 0.8919 0.9117 0.06628 ] Network output: [ 1.021 -0.1385 0.05211 0.00119 -0.0005344 0.05012 0.0008971 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6042 0.01881 0.1044 0.3983 0.9751 0.9885 0.6581 0.905 0.9705 0.5508 ] Network output: [ 0.02645 0.7748 0.9745 -8.572e-05 3.848e-05 0.1975 -6.46e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01951 0.0126 0.01831 0.02097 0.9866 0.9906 0.01977 0.97 0.9822 0.02287 ] Network output: [ 0.01216 -0.008619 0.9341 -0.001448 0.0006503 1.044 -0.001092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6572 0.5029 0.4391 0.5144 0.9777 0.99 0.6587 0.9125 0.9741 0.5334 ] Network output: [ -0.06795 0.5839 0.9984 -0.0004082 0.0001833 0.552 -0.0003076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2393 0.2297 0.1781 0.1815 0.9872 0.9917 0.2394 0.9719 0.9829 0.184 ] Network output: [ -0.1102 0.4387 1.053 -0.0005979 0.0002684 0.7263 -0.0004506 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2929 0.2906 0.2308 0.2312 0.9799 0.9873 0.293 0.9457 0.9703 0.2329 ] Network output: [ 0.05524 0.6562 0.01086 0.001218 -0.0005467 1.227 0.0009177 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.18 Epoch 3074 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06014 0.6938 0.9889 0.0001051 -4.716e-05 0.1975 7.917e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01618 -0.005013 0.01277 0.02828 0.9468 0.9547 0.0271 0.8919 0.9117 0.06632 ] Network output: [ 1.021 -0.1367 0.05089 0.001186 -0.0005322 0.04953 0.0008935 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6041 0.01883 0.1041 0.3985 0.9751 0.9885 0.6579 0.905 0.9705 0.551 ] Network output: [ 0.02675 0.7744 0.9737 -8.246e-05 3.702e-05 0.1981 -6.214e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01946 0.01257 0.01833 0.021 0.9866 0.9906 0.01972 0.97 0.9822 0.0229 ] Network output: [ 0.01216 -0.009949 0.9347 -0.001442 0.0006475 1.045 -0.001087 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6569 0.5027 0.4391 0.5146 0.9778 0.99 0.6584 0.9125 0.9741 0.5336 ] Network output: [ -0.06847 0.582 1.001 -0.0004115 0.0001847 0.5521 -0.0003101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2384 0.2289 0.1784 0.1818 0.9872 0.9917 0.2385 0.9719 0.9829 0.1843 ] Network output: [ -0.1104 0.4372 1.055 -0.0005982 0.0002686 0.7266 -0.0004508 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2923 0.29 0.231 0.2314 0.9799 0.9873 0.2924 0.9457 0.9703 0.2332 ] Network output: [ 0.05526 0.6592 0.009134 0.001213 -0.0005447 1.226 0.0009144 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1792 Epoch 3075 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06057 0.6936 0.9876 0.0001087 -4.881e-05 0.1981 8.193e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01618 -0.005011 0.01276 0.0283 0.9469 0.9547 0.02708 0.8919 0.9117 0.06635 ] Network output: [ 1.021 -0.1349 0.04967 0.00118 -0.0005299 0.04893 0.0008896 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.604 0.01885 0.1039 0.3987 0.9751 0.9885 0.6577 0.905 0.9705 0.5512 ] Network output: [ 0.02705 0.774 0.9729 -7.917e-05 3.554e-05 0.1987 -5.967e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01942 0.01255 0.01835 0.02103 0.9866 0.9906 0.01968 0.97 0.9822 0.02294 ] Network output: [ 0.01217 -0.0113 0.9353 -0.001436 0.0006448 1.046 -0.001082 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6567 0.5026 0.4392 0.5148 0.9778 0.99 0.6581 0.9125 0.9741 0.5338 ] Network output: [ -0.069 0.58 1.004 -0.0004147 0.0001862 0.5523 -0.0003126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2376 0.2281 0.1787 0.1822 0.9872 0.9917 0.2377 0.9719 0.9829 0.1847 ] Network output: [ -0.1106 0.4357 1.056 -0.0005984 0.0002687 0.727 -0.000451 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2918 0.2894 0.2313 0.2317 0.9799 0.9873 0.2918 0.9457 0.9703 0.2334 ] Network output: [ 0.05529 0.6622 0.007446 0.001209 -0.0005427 1.225 0.0009111 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1783 Epoch 3076 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06099 0.6935 0.9863 0.0001124 -5.044e-05 0.1986 8.467e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01618 -0.00501 0.01275 0.02833 0.9469 0.9547 0.02707 0.8919 0.9117 0.06639 ] Network output: [ 1.021 -0.1331 0.04848 0.001175 -0.0005275 0.04832 0.0008855 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6039 0.01886 0.1037 0.3989 0.9752 0.9885 0.6576 0.905 0.9705 0.5513 ] Network output: [ 0.02735 0.7737 0.9721 -7.588e-05 3.407e-05 0.1992 -5.719e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01938 0.01252 0.01838 0.02107 0.9866 0.9906 0.01964 0.97 0.9822 0.02297 ] Network output: [ 0.01219 -0.01267 0.936 -0.00143 0.000642 1.047 -0.001078 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6564 0.5024 0.4392 0.515 0.9778 0.99 0.6579 0.9125 0.9741 0.534 ] Network output: [ -0.06952 0.5781 1.007 -0.0004178 0.0001876 0.5525 -0.0003149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2368 0.2273 0.179 0.1825 0.9872 0.9917 0.2368 0.9719 0.9829 0.185 ] Network output: [ -0.1108 0.4343 1.058 -0.0005985 0.0002687 0.7273 -0.000451 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2912 0.2888 0.2316 0.232 0.9799 0.9873 0.2912 0.9457 0.9703 0.2337 ] Network output: [ 0.05531 0.6652 0.005792 0.001205 -0.0005408 1.223 0.0009078 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1774 Epoch 3077 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06141 0.6935 0.985 0.0001159 -5.205e-05 0.1991 8.738e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01618 -0.005009 0.01274 0.02835 0.9469 0.9547 0.02706 0.8919 0.9117 0.06643 ] Network output: [ 1.021 -0.1313 0.0473 0.001169 -0.000525 0.0477 0.0008812 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6039 0.01888 0.1035 0.3991 0.9752 0.9885 0.6574 0.905 0.9705 0.5515 ] Network output: [ 0.02764 0.7734 0.9712 -7.257e-05 3.258e-05 0.1997 -5.469e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01934 0.0125 0.0184 0.0211 0.9866 0.9906 0.0196 0.97 0.9822 0.02301 ] Network output: [ 0.01222 -0.01405 0.9365 -0.001424 0.0006392 1.047 -0.001073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6562 0.5023 0.4393 0.5152 0.9778 0.99 0.6576 0.9125 0.9741 0.5342 ] Network output: [ -0.07005 0.5761 1.01 -0.0004208 0.0001889 0.5528 -0.0003171 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.236 0.2266 0.1794 0.1829 0.9872 0.9917 0.2361 0.9719 0.983 0.1854 ] Network output: [ -0.111 0.4327 1.059 -0.0005984 0.0002687 0.7277 -0.000451 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2907 0.2883 0.2319 0.2323 0.9799 0.9874 0.2907 0.9456 0.9703 0.2341 ] Network output: [ 0.05533 0.6681 0.004171 0.0012 -0.0005388 1.222 0.0009046 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1766 Epoch 3078 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06182 0.6935 0.9838 0.0001195 -5.365e-05 0.1996 9.006e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01618 -0.005008 0.01273 0.02837 0.9469 0.9547 0.02705 0.8919 0.9117 0.06647 ] Network output: [ 1.021 -0.1295 0.04613 0.001163 -0.0005223 0.04708 0.0008767 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6038 0.01889 0.1034 0.3993 0.9752 0.9885 0.6573 0.9051 0.9705 0.5517 ] Network output: [ 0.02794 0.7732 0.9704 -6.926e-05 3.109e-05 0.2002 -5.22e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0193 0.01247 0.01843 0.02114 0.9866 0.9906 0.01956 0.97 0.9822 0.02305 ] Network output: [ 0.01226 -0.01546 0.9371 -0.001418 0.0006364 1.048 -0.001068 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6559 0.5021 0.4394 0.5154 0.9778 0.99 0.6574 0.9125 0.9741 0.5344 ] Network output: [ -0.07057 0.5741 1.012 -0.0004237 0.0001902 0.5531 -0.0003193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2352 0.2258 0.1798 0.1833 0.9873 0.9917 0.2353 0.9719 0.983 0.1858 ] Network output: [ -0.1112 0.4312 1.061 -0.0005982 0.0002686 0.7281 -0.0004509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2902 0.2878 0.2322 0.2327 0.9799 0.9874 0.2902 0.9456 0.9703 0.2344 ] Network output: [ 0.05536 0.671 0.002584 0.001196 -0.000537 1.221 0.0009014 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1757 Epoch 3079 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06224 0.6935 0.9825 0.000123 -5.523e-05 0.2 9.271e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01618 -0.005007 0.01273 0.0284 0.9469 0.9548 0.02704 0.8919 0.9117 0.06651 ] Network output: [ 1.02 -0.1277 0.04498 0.001157 -0.0005195 0.04645 0.000872 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6038 0.01891 0.1032 0.3995 0.9752 0.9885 0.6571 0.9051 0.9705 0.5519 ] Network output: [ 0.02823 0.773 0.9696 -6.595e-05 2.961e-05 0.2006 -4.97e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01927 0.01245 0.01846 0.02117 0.9866 0.9906 0.01952 0.97 0.9823 0.02309 ] Network output: [ 0.01232 -0.01688 0.9377 -0.001411 0.0006336 1.049 -0.001064 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6557 0.502 0.4395 0.5156 0.9778 0.99 0.6571 0.9125 0.9741 0.5346 ] Network output: [ -0.0711 0.5721 1.015 -0.0004265 0.0001915 0.5534 -0.0003214 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2345 0.2252 0.1802 0.1837 0.9873 0.9917 0.2346 0.9719 0.983 0.1862 ] Network output: [ -0.1114 0.4297 1.062 -0.0005979 0.0002684 0.7286 -0.0004506 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2897 0.2873 0.2326 0.233 0.9799 0.9874 0.2897 0.9456 0.9703 0.2347 ] Network output: [ 0.05538 0.6739 0.001028 0.001192 -0.0005351 1.219 0.0008983 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1748 Epoch 3080 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06265 0.6936 0.9812 0.0001265 -5.678e-05 0.2004 9.532e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01618 -0.005006 0.01272 0.02842 0.947 0.9548 0.02703 0.8919 0.9117 0.06656 ] Network output: [ 1.02 -0.1259 0.04384 0.001151 -0.0005166 0.04581 0.0008672 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6037 0.01892 0.1031 0.3997 0.9752 0.9885 0.657 0.9051 0.9705 0.5521 ] Network output: [ 0.02852 0.7728 0.9688 -6.263e-05 2.812e-05 0.2011 -4.72e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01923 0.01243 0.01849 0.02121 0.9866 0.9906 0.01949 0.97 0.9823 0.02313 ] Network output: [ 0.01238 -0.01831 0.9382 -0.001405 0.0006308 1.05 -0.001059 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6555 0.5019 0.4395 0.5158 0.9778 0.99 0.6569 0.9125 0.9741 0.5348 ] Network output: [ -0.07162 0.57 1.018 -0.0004291 0.0001926 0.5538 -0.0003234 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2338 0.2245 0.1806 0.1842 0.9873 0.9917 0.2339 0.9719 0.983 0.1867 ] Network output: [ -0.1116 0.4282 1.064 -0.0005975 0.0002682 0.729 -0.0004503 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2892 0.2869 0.2329 0.2334 0.9799 0.9874 0.2892 0.9456 0.9703 0.2351 ] Network output: [ 0.05541 0.6767 -0.0004947 0.001188 -0.0005333 1.218 0.0008952 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1739 Epoch 3081 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06305 0.6937 0.98 0.0001299 -5.832e-05 0.2008 9.791e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01618 -0.005006 0.01272 0.02845 0.947 0.9548 0.02702 0.8919 0.9117 0.06661 ] Network output: [ 1.02 -0.124 0.04272 0.001144 -0.0005135 0.04516 0.0008621 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6036 0.01893 0.103 0.3999 0.9752 0.9885 0.6569 0.9051 0.9705 0.5523 ] Network output: [ 0.02881 0.7727 0.968 -5.931e-05 2.663e-05 0.2014 -4.47e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0192 0.01241 0.01852 0.02126 0.9867 0.9906 0.01945 0.97 0.9823 0.02317 ] Network output: [ 0.01245 -0.01976 0.9388 -0.001399 0.000628 1.05 -0.001054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6552 0.5017 0.4397 0.516 0.9778 0.99 0.6567 0.9125 0.9741 0.535 ] Network output: [ -0.07215 0.5679 1.02 -0.0004317 0.0001938 0.5542 -0.0003253 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2332 0.2239 0.181 0.1846 0.9873 0.9917 0.2333 0.9719 0.983 0.1871 ] Network output: [ -0.1118 0.4266 1.065 -0.0005969 0.000268 0.7295 -0.0004499 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2888 0.2864 0.2333 0.2337 0.9799 0.9874 0.2888 0.9456 0.9703 0.2354 ] Network output: [ 0.05544 0.6795 -0.001986 0.001184 -0.0005315 1.216 0.0008921 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1729 Epoch 3082 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06345 0.6939 0.9787 0.0001333 -5.984e-05 0.2011 0.0001005 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01619 -0.005006 0.01272 0.02848 0.947 0.9548 0.02701 0.8919 0.9117 0.06665 ] Network output: [ 1.02 -0.1222 0.04162 0.001137 -0.0005104 0.04452 0.0008569 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6036 0.01894 0.1029 0.4 0.9752 0.9885 0.6568 0.9051 0.9705 0.5525 ] Network output: [ 0.0291 0.7726 0.9672 -5.599e-05 2.514e-05 0.2018 -4.22e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01917 0.01239 0.01856 0.0213 0.9867 0.9906 0.01942 0.97 0.9823 0.02322 ] Network output: [ 0.01254 -0.02123 0.9393 -0.001392 0.0006251 1.051 -0.001049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.655 0.5016 0.4398 0.5162 0.9779 0.99 0.6565 0.9125 0.9741 0.5351 ] Network output: [ -0.07267 0.5658 1.023 -0.0004341 0.0001949 0.5546 -0.0003272 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2326 0.2233 0.1815 0.1851 0.9873 0.9917 0.2326 0.9719 0.983 0.1876 ] Network output: [ -0.112 0.4251 1.066 -0.0005963 0.0002677 0.73 -0.0004494 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2884 0.286 0.2336 0.2341 0.98 0.9874 0.2884 0.9456 0.9703 0.2358 ] Network output: [ 0.05546 0.6823 -0.003447 0.00118 -0.0005297 1.215 0.0008891 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.172 Epoch 3083 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06385 0.6941 0.9775 0.0001366 -6.134e-05 0.2013 0.000103 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01619 -0.005006 0.01272 0.0285 0.947 0.9548 0.02701 0.8919 0.9117 0.0667 ] Network output: [ 1.02 -0.1203 0.04053 0.00113 -0.0005072 0.04386 0.0008514 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6035 0.01894 0.1028 0.4002 0.9752 0.9885 0.6567 0.9051 0.9705 0.5526 ] Network output: [ 0.02939 0.7725 0.9664 -5.268e-05 2.365e-05 0.2021 -3.97e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01914 0.01237 0.01859 0.02134 0.9867 0.9906 0.01939 0.97 0.9823 0.02326 ] Network output: [ 0.01263 -0.02271 0.9397 -0.001386 0.0006223 1.052 -0.001045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6548 0.5015 0.4399 0.5164 0.9779 0.99 0.6562 0.9125 0.9741 0.5353 ] Network output: [ -0.07319 0.5637 1.026 -0.0004365 0.000196 0.5551 -0.0003289 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.232 0.2227 0.182 0.1856 0.9873 0.9917 0.2321 0.9719 0.983 0.1881 ] Network output: [ -0.1121 0.4235 1.068 -0.0005955 0.0002673 0.7305 -0.0004488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.288 0.2856 0.234 0.2345 0.98 0.9874 0.288 0.9456 0.9703 0.2362 ] Network output: [ 0.05549 0.6851 -0.004876 0.001176 -0.0005279 1.214 0.0008862 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1711 Epoch 3084 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06425 0.6943 0.9762 0.0001399 -6.281e-05 0.2016 0.0001054 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01619 -0.005006 0.01272 0.02853 0.947 0.9548 0.027 0.8919 0.9117 0.06676 ] Network output: [ 1.02 -0.1185 0.03947 0.001122 -0.0005039 0.0432 0.0008459 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6035 0.01895 0.1028 0.4004 0.9753 0.9885 0.6566 0.9051 0.9706 0.5528 ] Network output: [ 0.02967 0.7725 0.9656 -4.938e-05 2.217e-05 0.2024 -3.721e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01911 0.01235 0.01863 0.02139 0.9867 0.9906 0.01936 0.97 0.9823 0.02331 ] Network output: [ 0.01274 -0.0242 0.9402 -0.00138 0.0006194 1.053 -0.00104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6546 0.5014 0.44 0.5166 0.9779 0.99 0.656 0.9125 0.9741 0.5355 ] Network output: [ -0.07372 0.5616 1.028 -0.0004387 0.000197 0.5557 -0.0003306 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2314 0.2222 0.1825 0.1861 0.9873 0.9917 0.2315 0.9719 0.983 0.1886 ] Network output: [ -0.1123 0.4219 1.069 -0.0005946 0.0002669 0.7311 -0.0004481 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2876 0.2853 0.2344 0.2349 0.98 0.9874 0.2876 0.9455 0.9703 0.2366 ] Network output: [ 0.05551 0.6878 -0.006276 0.001172 -0.0005262 1.212 0.0008833 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1701 Epoch 3085 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06464 0.6946 0.975 0.0001431 -6.426e-05 0.2018 0.0001079 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0162 -0.005006 0.01272 0.02855 0.9471 0.9548 0.027 0.8919 0.9117 0.06681 ] Network output: [ 1.02 -0.1166 0.03841 0.001115 -0.0005005 0.04254 0.0008401 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6035 0.01896 0.1028 0.4005 0.9753 0.9885 0.6565 0.9051 0.9706 0.553 ] Network output: [ 0.02995 0.7724 0.9648 -4.609e-05 2.069e-05 0.2027 -3.473e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01909 0.01233 0.01867 0.02143 0.9867 0.9906 0.01934 0.97 0.9823 0.02336 ] Network output: [ 0.01285 -0.02571 0.9406 -0.001373 0.0006165 1.054 -0.001035 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6544 0.5012 0.4402 0.5168 0.9779 0.99 0.6558 0.9125 0.9741 0.5357 ] Network output: [ -0.07424 0.5594 1.031 -0.0004409 0.0001979 0.5562 -0.0003323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2309 0.2217 0.183 0.1866 0.9873 0.9917 0.231 0.9719 0.983 0.1891 ] Network output: [ -0.1125 0.4203 1.071 -0.0005936 0.0002665 0.7316 -0.0004474 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2872 0.2849 0.2348 0.2353 0.98 0.9874 0.2873 0.9455 0.9703 0.237 ] Network output: [ 0.05553 0.6905 -0.007645 0.001168 -0.0005244 1.211 0.0008804 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1692 Epoch 3086 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06502 0.6949 0.9737 0.0001463 -6.569e-05 0.2019 0.0001103 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0162 -0.005007 0.01273 0.02858 0.9471 0.9549 0.02699 0.8919 0.9117 0.06686 ] Network output: [ 1.02 -0.1148 0.03738 0.001107 -0.000497 0.04187 0.0008342 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6034 0.01896 0.1028 0.4007 0.9753 0.9885 0.6564 0.9051 0.9706 0.5532 ] Network output: [ 0.03023 0.7725 0.964 -4.281e-05 1.922e-05 0.2029 -3.226e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01906 0.01232 0.01871 0.02148 0.9867 0.9906 0.01931 0.97 0.9823 0.02341 ] Network output: [ 0.01298 -0.02723 0.9411 -0.001367 0.0006136 1.055 -0.00103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6542 0.5011 0.4403 0.517 0.9779 0.99 0.6556 0.9125 0.9741 0.5359 ] Network output: [ -0.07476 0.5573 1.034 -0.0004429 0.0001989 0.5568 -0.0003338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2304 0.2212 0.1835 0.1872 0.9873 0.9917 0.2305 0.9719 0.983 0.1897 ] Network output: [ -0.1126 0.4188 1.072 -0.0005925 0.000266 0.7322 -0.0004465 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2869 0.2846 0.2352 0.2357 0.98 0.9874 0.2869 0.9455 0.9703 0.2374 ] Network output: [ 0.05556 0.6932 -0.008985 0.001164 -0.0005227 1.209 0.0008775 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1683 Epoch 3087 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06541 0.6952 0.9725 0.0001494 -6.709e-05 0.2021 0.0001126 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01621 -0.005007 0.01273 0.02861 0.9471 0.9549 0.02699 0.8919 0.9117 0.06692 ] Network output: [ 1.02 -0.1129 0.03636 0.001099 -0.0004934 0.0412 0.0008282 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6034 0.01896 0.1028 0.4009 0.9753 0.9886 0.6563 0.9051 0.9706 0.5534 ] Network output: [ 0.03051 0.7725 0.9632 -3.954e-05 1.775e-05 0.2031 -2.98e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01904 0.0123 0.01875 0.02153 0.9867 0.9906 0.01929 0.97 0.9823 0.02346 ] Network output: [ 0.01312 -0.02876 0.9415 -0.00136 0.0006107 1.056 -0.001025 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.654 0.501 0.4405 0.5171 0.9779 0.99 0.6554 0.9125 0.9741 0.5361 ] Network output: [ -0.07528 0.5551 1.036 -0.0004449 0.0001997 0.5575 -0.0003353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2299 0.2208 0.184 0.1877 0.9873 0.9917 0.23 0.9719 0.983 0.1902 ] Network output: [ -0.1128 0.4172 1.073 -0.0005913 0.0002654 0.7328 -0.0004456 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2866 0.2843 0.2356 0.2361 0.98 0.9874 0.2866 0.9455 0.9703 0.2378 ] Network output: [ 0.05558 0.6959 -0.0103 0.001161 -0.000521 1.208 0.0008747 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1673 Epoch 3088 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06579 0.6956 0.9713 0.0001525 -6.847e-05 0.2021 0.0001149 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01622 -0.005008 0.01274 0.02863 0.9471 0.9549 0.02699 0.8919 0.9117 0.06698 ] Network output: [ 1.02 -0.1111 0.03537 0.001091 -0.0004897 0.04052 0.0008221 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6034 0.01896 0.1028 0.401 0.9753 0.9886 0.6562 0.9051 0.9706 0.5535 ] Network output: [ 0.03078 0.7726 0.9624 -3.63e-05 1.63e-05 0.2033 -2.736e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01902 0.01229 0.01879 0.02158 0.9867 0.9906 0.01926 0.97 0.9823 0.02352 ] Network output: [ 0.01326 -0.0303 0.9419 -0.001354 0.0006077 1.056 -0.00102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6538 0.5009 0.4407 0.5173 0.9779 0.99 0.6553 0.9125 0.9741 0.5363 ] Network output: [ -0.0758 0.5529 1.039 -0.0004468 0.0002006 0.5582 -0.0003367 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2295 0.2203 0.1846 0.1883 0.9873 0.9917 0.2296 0.9719 0.983 0.1908 ] Network output: [ -0.1129 0.4156 1.074 -0.0005899 0.0002648 0.7334 -0.0004446 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2863 0.284 0.2361 0.2365 0.98 0.9874 0.2863 0.9455 0.9703 0.2383 ] Network output: [ 0.0556 0.6985 -0.01158 0.001157 -0.0005194 1.207 0.0008719 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1663 Epoch 3089 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06616 0.6961 0.9701 0.0001555 -6.983e-05 0.2022 0.0001172 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01622 -0.005009 0.01275 0.02866 0.9471 0.9549 0.02699 0.8919 0.9117 0.06703 ] Network output: [ 1.02 -0.1092 0.03439 0.001082 -0.000486 0.03984 0.0008158 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6033 0.01896 0.1028 0.4011 0.9753 0.9886 0.6561 0.9051 0.9706 0.5537 ] Network output: [ 0.03105 0.7727 0.9617 -3.307e-05 1.485e-05 0.2034 -2.492e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.019 0.01228 0.01884 0.02163 0.9867 0.9906 0.01924 0.97 0.9823 0.02357 ] Network output: [ 0.01342 -0.03185 0.9422 -0.001347 0.0006048 1.057 -0.001015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6537 0.5008 0.4408 0.5175 0.9779 0.9901 0.6551 0.9125 0.9741 0.5364 ] Network output: [ -0.07632 0.5507 1.041 -0.0004485 0.0002014 0.5589 -0.000338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2291 0.2199 0.1852 0.1889 0.9873 0.9917 0.2291 0.9719 0.983 0.1914 ] Network output: [ -0.1131 0.414 1.076 -0.0005885 0.0002642 0.7341 -0.0004435 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.286 0.2837 0.2365 0.237 0.98 0.9874 0.2861 0.9456 0.9703 0.2387 ] Network output: [ 0.05562 0.7011 -0.01283 0.001153 -0.0005177 1.205 0.0008691 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1654 Epoch 3090 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06653 0.6965 0.9689 0.0001585 -7.116e-05 0.2022 0.0001195 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01623 -0.00501 0.01276 0.02869 0.9472 0.9549 0.02699 0.8919 0.9118 0.06709 ] Network output: [ 1.02 -0.1073 0.03343 0.001074 -0.0004821 0.03916 0.0008094 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6033 0.01896 0.1029 0.4013 0.9753 0.9886 0.656 0.9051 0.9706 0.5539 ] Network output: [ 0.03132 0.7728 0.9609 -2.986e-05 1.341e-05 0.2035 -2.251e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01898 0.01227 0.01888 0.02168 0.9867 0.9906 0.01923 0.97 0.9823 0.02363 ] Network output: [ 0.01359 -0.03341 0.9426 -0.001341 0.0006018 1.058 -0.00101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6535 0.5007 0.441 0.5177 0.9779 0.9901 0.6549 0.9125 0.9741 0.5366 ] Network output: [ -0.07683 0.5485 1.044 -0.0004502 0.0002021 0.5596 -0.0003393 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2287 0.2196 0.1858 0.1895 0.9873 0.9917 0.2288 0.9719 0.983 0.192 ] Network output: [ -0.1132 0.4124 1.077 -0.000587 0.0002635 0.7347 -0.0004424 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2858 0.2835 0.237 0.2374 0.98 0.9874 0.2858 0.9456 0.9703 0.2392 ] Network output: [ 0.05564 0.7037 -0.01406 0.00115 -0.0005161 1.204 0.0008664 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1644 Epoch 3091 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0669 0.697 0.9676 0.0001614 -7.246e-05 0.2022 0.0001216 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01624 -0.005011 0.01277 0.02872 0.9472 0.9549 0.027 0.8919 0.9118 0.06715 ] Network output: [ 1.019 -0.1055 0.03249 0.001065 -0.0004783 0.03848 0.0008028 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6033 0.01895 0.103 0.4014 0.9753 0.9886 0.6559 0.9051 0.9706 0.5541 ] Network output: [ 0.03159 0.773 0.9601 -2.668e-05 1.198e-05 0.2036 -2.011e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01896 0.01225 0.01893 0.02173 0.9867 0.9906 0.01921 0.97 0.9823 0.02369 ] Network output: [ 0.01377 -0.03498 0.9429 -0.001334 0.0005989 1.059 -0.001005 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6533 0.5006 0.4412 0.5179 0.9779 0.9901 0.6547 0.9125 0.9741 0.5368 ] Network output: [ -0.07735 0.5462 1.046 -0.0004518 0.0002028 0.5604 -0.0003405 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2283 0.2192 0.1864 0.1901 0.9873 0.9917 0.2284 0.9719 0.983 0.1926 ] Network output: [ -0.1134 0.4108 1.078 -0.0005854 0.0002628 0.7354 -0.0004412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2856 0.2833 0.2374 0.2379 0.98 0.9874 0.2856 0.9456 0.9704 0.2396 ] Network output: [ 0.05566 0.7062 -0.01526 0.001146 -0.0005145 1.202 0.0008637 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1634 Epoch 3092 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06726 0.6975 0.9665 0.0001643 -7.374e-05 0.2022 0.0001238 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01624 -0.005012 0.01278 0.02874 0.9472 0.9549 0.027 0.8919 0.9118 0.06721 ] Network output: [ 1.019 -0.1036 0.03156 0.001057 -0.0004743 0.03779 0.0007962 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6033 0.01895 0.1031 0.4015 0.9753 0.9886 0.6559 0.9051 0.9706 0.5542 ] Network output: [ 0.03185 0.7732 0.9593 -2.353e-05 1.056e-05 0.2037 -1.773e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01895 0.01224 0.01898 0.02179 0.9867 0.9907 0.01919 0.97 0.9823 0.02375 ] Network output: [ 0.01396 -0.03656 0.9432 -0.001327 0.0005959 1.06 -0.001 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6531 0.5005 0.4414 0.518 0.978 0.9901 0.6546 0.9125 0.9741 0.537 ] Network output: [ -0.07786 0.5439 1.049 -0.0004533 0.0002035 0.5612 -0.0003416 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.228 0.2189 0.187 0.1907 0.9873 0.9918 0.2281 0.9719 0.983 0.1933 ] Network output: [ -0.1135 0.4092 1.079 -0.0005837 0.000262 0.7361 -0.0004399 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2854 0.283 0.2379 0.2384 0.98 0.9874 0.2854 0.9456 0.9704 0.2401 ] Network output: [ 0.05567 0.7088 -0.01643 0.001142 -0.0005129 1.201 0.000861 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1625 Epoch 3093 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06761 0.6981 0.9653 0.000167 -7.499e-05 0.2021 0.0001259 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01625 -0.005014 0.01279 0.02877 0.9472 0.955 0.027 0.8919 0.9118 0.06728 ] Network output: [ 1.019 -0.1018 0.03066 0.001048 -0.0004703 0.0371 0.0007895 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6033 0.01894 0.1032 0.4017 0.9754 0.9886 0.6558 0.9051 0.9706 0.5544 ] Network output: [ 0.03211 0.7734 0.9586 -2.04e-05 9.157e-06 0.2037 -1.537e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01893 0.01224 0.01903 0.02184 0.9867 0.9907 0.01918 0.97 0.9823 0.02381 ] Network output: [ 0.01417 -0.03815 0.9435 -0.001321 0.0005929 1.061 -0.0009953 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.653 0.5004 0.4416 0.5182 0.978 0.9901 0.6544 0.9126 0.9741 0.5371 ] Network output: [ -0.07836 0.5417 1.051 -0.0004547 0.0002041 0.562 -0.0003427 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2277 0.2186 0.1876 0.1914 0.9873 0.9918 0.2278 0.9719 0.983 0.1939 ] Network output: [ -0.1136 0.4075 1.081 -0.0005819 0.0002612 0.7368 -0.0004385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2852 0.2829 0.2383 0.2388 0.9801 0.9875 0.2852 0.9456 0.9704 0.2406 ] Network output: [ 0.05569 0.7113 -0.01757 0.001139 -0.0005113 1.2 0.0008583 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1615 Epoch 3094 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06797 0.6987 0.9641 0.0001698 -7.622e-05 0.202 0.000128 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01626 -0.005015 0.0128 0.0288 0.9472 0.955 0.02701 0.8919 0.9118 0.06734 ] Network output: [ 1.019 -0.09991 0.02978 0.001039 -0.0004662 0.03642 0.0007827 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6033 0.01894 0.1033 0.4018 0.9754 0.9886 0.6557 0.9051 0.9706 0.5546 ] Network output: [ 0.03237 0.7737 0.9578 -1.73e-05 7.765e-06 0.2037 -1.304e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01892 0.01223 0.01908 0.0219 0.9867 0.9907 0.01916 0.97 0.9823 0.02387 ] Network output: [ 0.01438 -0.03975 0.9437 -0.001314 0.0005899 1.062 -0.0009902 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6528 0.5003 0.4418 0.5184 0.978 0.9901 0.6542 0.9126 0.9741 0.5373 ] Network output: [ -0.07887 0.5394 1.054 -0.000456 0.0002047 0.5629 -0.0003436 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2274 0.2184 0.1883 0.192 0.9873 0.9918 0.2275 0.9719 0.983 0.1946 ] Network output: [ -0.1137 0.4059 1.082 -0.00058 0.0002604 0.7375 -0.0004371 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.285 0.2827 0.2388 0.2393 0.9801 0.9875 0.285 0.9456 0.9704 0.241 ] Network output: [ 0.0557 0.7137 -0.01869 0.001135 -0.0005097 1.198 0.0008556 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1605 Epoch 3095 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06831 0.6993 0.9629 0.0001724 -7.742e-05 0.2018 0.00013 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01627 -0.005017 0.01282 0.02883 0.9472 0.955 0.02701 0.8919 0.9118 0.0674 ] Network output: [ 1.019 -0.09806 0.02891 0.001029 -0.0004621 0.03573 0.0007757 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6032 0.01893 0.1035 0.4019 0.9754 0.9886 0.6557 0.9051 0.9706 0.5547 ] Network output: [ 0.03263 0.774 0.9571 -1.423e-05 6.388e-06 0.2037 -1.072e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01891 0.01222 0.01913 0.02196 0.9868 0.9907 0.01915 0.97 0.9823 0.02393 ] Network output: [ 0.0146 -0.04135 0.944 -0.001307 0.0005869 1.063 -0.0009852 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6527 0.5002 0.442 0.5185 0.978 0.9901 0.6541 0.9126 0.9741 0.5375 ] Network output: [ -0.07937 0.5371 1.056 -0.0004572 0.0002053 0.5638 -0.0003446 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2272 0.2182 0.1889 0.1927 0.9873 0.9918 0.2273 0.9719 0.983 0.1953 ] Network output: [ -0.1139 0.4043 1.083 -0.000578 0.0002595 0.7382 -0.0004356 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2848 0.2825 0.2393 0.2398 0.9801 0.9875 0.2849 0.9456 0.9704 0.2415 ] Network output: [ 0.05571 0.7162 -0.01978 0.001132 -0.0005081 1.197 0.000853 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1595 Epoch 3096 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06865 0.7 0.9618 0.0001751 -7.859e-05 0.2017 0.0001319 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01628 -0.005019 0.01283 0.02885 0.9472 0.955 0.02702 0.8919 0.9118 0.06747 ] Network output: [ 1.019 -0.09622 0.02806 0.00102 -0.0004579 0.03504 0.0007687 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6032 0.01892 0.1036 0.402 0.9754 0.9886 0.6556 0.9051 0.9706 0.5549 ] Network output: [ 0.03288 0.7742 0.9563 -1.119e-05 5.025e-06 0.2036 -8.436e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0189 0.01221 0.01918 0.02202 0.9868 0.9907 0.01914 0.97 0.9823 0.02399 ] Network output: [ 0.01483 -0.04296 0.9442 -0.0013 0.0005838 1.064 -0.0009801 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6525 0.5001 0.4423 0.5187 0.978 0.9901 0.6539 0.9126 0.9742 0.5377 ] Network output: [ -0.07987 0.5348 1.058 -0.0004583 0.0002058 0.5648 -0.0003454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.227 0.2179 0.1896 0.1934 0.9874 0.9918 0.227 0.9719 0.983 0.196 ] Network output: [ -0.114 0.4027 1.084 -0.0005759 0.0002586 0.739 -0.000434 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2847 0.2824 0.2398 0.2403 0.9801 0.9875 0.2847 0.9456 0.9704 0.242 ] Network output: [ 0.05572 0.7186 -0.02084 0.001128 -0.0005065 1.195 0.0008503 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1585 Epoch 3097 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06899 0.7006 0.9606 0.0001776 -7.973e-05 0.2015 0.0001338 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01629 -0.005021 0.01285 0.02888 0.9473 0.955 0.02703 0.8919 0.9118 0.06754 ] Network output: [ 1.018 -0.09437 0.02724 0.001011 -0.0004537 0.03435 0.0007617 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6032 0.01891 0.1038 0.4021 0.9754 0.9886 0.6555 0.9051 0.9706 0.555 ] Network output: [ 0.03313 0.7746 0.9556 -8.193e-06 3.678e-06 0.2036 -6.175e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01889 0.01221 0.01924 0.02208 0.9868 0.9907 0.01913 0.97 0.9823 0.02406 ] Network output: [ 0.01508 -0.04458 0.9444 -0.001294 0.0005808 1.065 -0.000975 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6524 0.5 0.4425 0.5188 0.978 0.9901 0.6538 0.9126 0.9742 0.5378 ] Network output: [ -0.08037 0.5325 1.061 -0.0004594 0.0002062 0.5657 -0.0003462 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2268 0.2178 0.1903 0.1941 0.9874 0.9918 0.2268 0.9719 0.983 0.1967 ] Network output: [ -0.1141 0.4011 1.085 -0.0005738 0.0002576 0.7398 -0.0004324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2846 0.2823 0.2403 0.2408 0.9801 0.9875 0.2846 0.9457 0.9705 0.2425 ] Network output: [ 0.05573 0.721 -0.02188 0.001125 -0.000505 1.194 0.0008477 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1575 Epoch 3098 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06932 0.7013 0.9595 0.0001801 -8.084e-05 0.2012 0.0001357 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0163 -0.005023 0.01287 0.02891 0.9473 0.955 0.02703 0.8919 0.9118 0.0676 ] Network output: [ 1.018 -0.09253 0.02643 0.001001 -0.0004495 0.03365 0.0007545 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6032 0.0189 0.1039 0.4022 0.9754 0.9886 0.6555 0.9051 0.9706 0.5552 ] Network output: [ 0.03337 0.7749 0.9549 -5.23e-06 2.348e-06 0.2035 -3.941e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01888 0.0122 0.01929 0.02214 0.9868 0.9907 0.01913 0.97 0.9823 0.02412 ] Network output: [ 0.01533 -0.0462 0.9446 -0.001287 0.0005777 1.066 -0.0009699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6522 0.4999 0.4427 0.519 0.978 0.9901 0.6536 0.9126 0.9742 0.538 ] Network output: [ -0.08087 0.5301 1.063 -0.0004604 0.0002067 0.5667 -0.0003469 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2266 0.2176 0.191 0.1948 0.9874 0.9918 0.2267 0.9719 0.983 0.1974 ] Network output: [ -0.1142 0.3995 1.086 -0.0005715 0.0002566 0.7405 -0.0004307 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2845 0.2822 0.2408 0.2413 0.9801 0.9875 0.2845 0.9457 0.9705 0.243 ] Network output: [ 0.05573 0.7234 -0.0229 0.001121 -0.0005034 1.193 0.0008451 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1565 Epoch 3099 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06965 0.7021 0.9584 0.0001825 -8.193e-05 0.201 0.0001375 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01631 -0.005025 0.01289 0.02894 0.9473 0.955 0.02704 0.8919 0.9118 0.06767 ] Network output: [ 1.018 -0.0907 0.02564 0.0009917 -0.0004452 0.03296 0.0007473 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6032 0.01889 0.1041 0.4023 0.9754 0.9886 0.6554 0.9051 0.9706 0.5554 ] Network output: [ 0.03362 0.7753 0.9541 -2.304e-06 1.034e-06 0.2033 -1.737e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01888 0.0122 0.01935 0.0222 0.9868 0.9907 0.01912 0.97 0.9823 0.02419 ] Network output: [ 0.0156 -0.04783 0.9447 -0.00128 0.0005747 1.067 -0.0009647 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6521 0.4998 0.4429 0.5191 0.978 0.9901 0.6535 0.9126 0.9742 0.5381 ] Network output: [ -0.08136 0.5278 1.065 -0.0004613 0.0002071 0.5677 -0.0003476 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2265 0.2175 0.1917 0.1955 0.9874 0.9918 0.2265 0.9719 0.983 0.1981 ] Network output: [ -0.1143 0.3979 1.087 -0.0005692 0.0002556 0.7413 -0.000429 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2844 0.2821 0.2413 0.2418 0.9801 0.9875 0.2844 0.9457 0.9705 0.2435 ] Network output: [ 0.05574 0.7258 -0.02389 0.001118 -0.0005019 1.191 0.0008425 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1555 Epoch 3100 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06997 0.7028 0.9573 0.0001849 -8.299e-05 0.2007 0.0001393 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01632 -0.005027 0.01291 0.02897 0.9473 0.955 0.02705 0.8919 0.9118 0.06774 ] Network output: [ 1.018 -0.08887 0.02487 0.000982 -0.0004409 0.03227 0.0007401 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6032 0.01888 0.1043 0.4024 0.9754 0.9886 0.6554 0.9051 0.9706 0.5555 ] Network output: [ 0.03386 0.7757 0.9534 5.815e-07 -2.611e-07 0.2032 4.382e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01887 0.0122 0.01941 0.02226 0.9868 0.9907 0.01911 0.97 0.9823 0.02426 ] Network output: [ 0.01587 -0.04947 0.9449 -0.001273 0.0005716 1.068 -0.0009596 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.652 0.4997 0.4432 0.5193 0.978 0.9901 0.6534 0.9126 0.9742 0.5383 ] Network output: [ -0.08184 0.5254 1.068 -0.0004621 0.0002074 0.5688 -0.0003482 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2263 0.2173 0.1924 0.1962 0.9874 0.9918 0.2264 0.9719 0.983 0.1988 ] Network output: [ -0.1144 0.3962 1.088 -0.0005669 0.0002545 0.7421 -0.0004272 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2844 0.2821 0.2418 0.2423 0.9801 0.9875 0.2844 0.9457 0.9705 0.2441 ] Network output: [ 0.05574 0.7281 -0.02485 0.001114 -0.0005003 1.19 0.0008399 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1545 Epoch 3101 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07029 0.7036 0.9562 0.0001871 -8.402e-05 0.2003 0.000141 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01633 -0.005029 0.01293 0.02899 0.9473 0.955 0.02706 0.8919 0.9118 0.06781 ] Network output: [ 1.018 -0.08705 0.02412 0.0009723 -0.0004365 0.03158 0.0007328 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6032 0.01887 0.1046 0.4025 0.9754 0.9886 0.6553 0.9051 0.9706 0.5557 ] Network output: [ 0.03409 0.7761 0.9527 3.426e-06 -1.538e-06 0.203 2.582e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01887 0.0122 0.01947 0.02232 0.9868 0.9907 0.01911 0.9701 0.9823 0.02433 ] Network output: [ 0.01616 -0.0511 0.945 -0.001266 0.0005685 1.069 -0.0009544 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6518 0.4997 0.4434 0.5194 0.978 0.9901 0.6532 0.9126 0.9742 0.5384 ] Network output: [ -0.08233 0.5231 1.07 -0.0004628 0.0002078 0.5699 -0.0003488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2262 0.2173 0.1932 0.1969 0.9874 0.9918 0.2263 0.9719 0.983 0.1996 ] Network output: [ -0.1144 0.3946 1.089 -0.0005644 0.0002534 0.7429 -0.0004254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2843 0.282 0.2424 0.2428 0.9802 0.9875 0.2843 0.9458 0.9705 0.2446 ] Network output: [ 0.05574 0.7304 -0.02579 0.001111 -0.0004988 1.188 0.0008373 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1535 Epoch 3102 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0706 0.7044 0.9551 0.0001894 -8.502e-05 0.2 0.0001427 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01634 -0.005032 0.01295 0.02902 0.9473 0.955 0.02707 0.8919 0.9118 0.06788 ] Network output: [ 1.017 -0.08523 0.02339 0.0009626 -0.0004321 0.0309 0.0007254 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6032 0.01885 0.1048 0.4025 0.9754 0.9886 0.6553 0.9051 0.9706 0.5558 ] Network output: [ 0.03432 0.7765 0.952 6.227e-06 -2.796e-06 0.2028 4.693e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01887 0.01219 0.01952 0.02238 0.9868 0.9907 0.01911 0.9701 0.9823 0.02439 ] Network output: [ 0.01645 -0.05274 0.9451 -0.001259 0.0005654 1.07 -0.0009492 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6517 0.4996 0.4437 0.5196 0.978 0.9901 0.6531 0.9126 0.9742 0.5386 ] Network output: [ -0.08281 0.5207 1.072 -0.0004634 0.0002081 0.571 -0.0003493 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2262 0.2172 0.1939 0.1977 0.9874 0.9918 0.2262 0.9719 0.983 0.2003 ] Network output: [ -0.1145 0.393 1.09 -0.0005619 0.0002523 0.7437 -0.0004235 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2843 0.282 0.2429 0.2433 0.9802 0.9875 0.2843 0.9458 0.9706 0.2451 ] Network output: [ 0.05573 0.7327 -0.02671 0.001108 -0.0004972 1.187 0.0008347 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1525 Epoch 3103 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07091 0.7053 0.9541 0.0001915 -8.599e-05 0.1996 0.0001443 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01635 -0.005034 0.01297 0.02905 0.9473 0.955 0.02708 0.8919 0.9118 0.06794 ] Network output: [ 1.017 -0.08342 0.02268 0.0009528 -0.0004277 0.03021 0.0007181 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6032 0.01884 0.105 0.4026 0.9754 0.9886 0.6552 0.9051 0.9706 0.5559 ] Network output: [ 0.03455 0.777 0.9514 8.985e-06 -4.034e-06 0.2026 6.771e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01886 0.01219 0.01958 0.02245 0.9868 0.9907 0.01911 0.9701 0.9823 0.02446 ] Network output: [ 0.01675 -0.05439 0.9452 -0.001253 0.0005623 1.071 -0.000944 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6516 0.4995 0.4439 0.5197 0.978 0.9901 0.653 0.9126 0.9742 0.5387 ] Network output: [ -0.08328 0.5183 1.074 -0.000464 0.0002083 0.5721 -0.0003497 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2261 0.2171 0.1946 0.1984 0.9874 0.9918 0.2262 0.9719 0.983 0.2011 ] Network output: [ -0.1146 0.3914 1.091 -0.0005593 0.0002511 0.7446 -0.0004215 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2843 0.282 0.2434 0.2439 0.9802 0.9875 0.2843 0.9458 0.9706 0.2456 ] Network output: [ 0.05573 0.735 -0.0276 0.001104 -0.0004957 1.186 0.0008321 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1515 Epoch 3104 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07121 0.7061 0.953 0.0001936 -8.693e-05 0.1992 0.0001459 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01637 -0.005037 0.01299 0.02908 0.9473 0.9551 0.0271 0.8919 0.9118 0.06801 ] Network output: [ 1.017 -0.08162 0.02199 0.000943 -0.0004233 0.02952 0.0007106 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6032 0.01883 0.1053 0.4027 0.9755 0.9886 0.6552 0.9051 0.9706 0.5561 ] Network output: [ 0.03478 0.7775 0.9507 1.17e-05 -5.251e-06 0.2024 8.815e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01887 0.01219 0.01964 0.02251 0.9868 0.9907 0.01911 0.9701 0.9823 0.02454 ] Network output: [ 0.01707 -0.05604 0.9452 -0.001246 0.0005592 1.072 -0.0009387 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6514 0.4994 0.4442 0.5198 0.978 0.9901 0.6528 0.9126 0.9742 0.5389 ] Network output: [ -0.08375 0.516 1.076 -0.0004645 0.0002085 0.5733 -0.0003501 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2261 0.2171 0.1954 0.1992 0.9874 0.9918 0.2262 0.9719 0.983 0.2019 ] Network output: [ -0.1147 0.3898 1.092 -0.0005567 0.0002499 0.7454 -0.0004195 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2843 0.282 0.2439 0.2444 0.9802 0.9876 0.2843 0.9458 0.9706 0.2462 ] Network output: [ 0.05572 0.7372 -0.02847 0.001101 -0.0004941 1.184 0.0008295 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1505 Epoch 3105 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0715 0.707 0.952 0.0001957 -8.784e-05 0.1988 0.0001475 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01638 -0.00504 0.01302 0.0291 0.9474 0.9551 0.02711 0.8919 0.9118 0.06808 ] Network output: [ 1.017 -0.07983 0.02131 0.0009331 -0.0004189 0.02884 0.0007032 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6032 0.01881 0.1055 0.4028 0.9755 0.9886 0.6551 0.9051 0.9706 0.5562 ] Network output: [ 0.035 0.7779 0.95 1.436e-05 -6.448e-06 0.2021 1.082e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01887 0.0122 0.01971 0.02258 0.9868 0.9907 0.01911 0.9701 0.9823 0.02461 ] Network output: [ 0.01739 -0.05769 0.9453 -0.001239 0.0005561 1.073 -0.0009335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6513 0.4993 0.4444 0.52 0.9781 0.9901 0.6527 0.9126 0.9742 0.539 ] Network output: [ -0.08422 0.5136 1.079 -0.0004649 0.0002087 0.5744 -0.0003504 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2261 0.2171 0.1962 0.2 0.9874 0.9918 0.2262 0.9719 0.983 0.2027 ] Network output: [ -0.1147 0.3882 1.093 -0.000554 0.0002487 0.7463 -0.0004175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2843 0.282 0.2445 0.2449 0.9802 0.9876 0.2843 0.9459 0.9706 0.2467 ] Network output: [ 0.05571 0.7395 -0.02931 0.001097 -0.0004926 1.183 0.0008269 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1495 Epoch 3106 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07179 0.7079 0.9509 0.0001976 -8.872e-05 0.1983 0.0001489 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01639 -0.005042 0.01304 0.02913 0.9474 0.9551 0.02712 0.8919 0.9118 0.06816 ] Network output: [ 1.016 -0.07804 0.02066 0.0009232 -0.0004145 0.02816 0.0006958 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6032 0.0188 0.1058 0.4028 0.9755 0.9886 0.6551 0.9051 0.9706 0.5564 ] Network output: [ 0.03522 0.7785 0.9494 1.698e-05 -7.622e-06 0.2018 1.28e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01887 0.0122 0.01977 0.02264 0.9868 0.9907 0.01911 0.9701 0.9823 0.02468 ] Network output: [ 0.01772 -0.05934 0.9453 -0.001232 0.0005529 1.074 -0.0009282 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6512 0.4993 0.4447 0.5201 0.9781 0.9901 0.6526 0.9126 0.9742 0.5392 ] Network output: [ -0.08468 0.5112 1.081 -0.0004653 0.0002089 0.5756 -0.0003506 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2261 0.2171 0.1969 0.2007 0.9874 0.9918 0.2262 0.9719 0.983 0.2034 ] Network output: [ -0.1148 0.3866 1.094 -0.0005512 0.0002475 0.7471 -0.0004154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2843 0.282 0.245 0.2455 0.9802 0.9876 0.2843 0.9459 0.9706 0.2472 ] Network output: [ 0.05569 0.7417 -0.03014 0.001094 -0.000491 1.182 0.0008243 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1485 Epoch 3107 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07208 0.7089 0.9499 0.0001995 -8.957e-05 0.1978 0.0001504 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0164 -0.005045 0.01307 0.02916 0.9474 0.9551 0.02714 0.8919 0.9117 0.06823 ] Network output: [ 1.016 -0.07626 0.02002 0.0009133 -0.00041 0.02748 0.0006883 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6031 0.01879 0.1061 0.4029 0.9755 0.9886 0.6551 0.9051 0.9706 0.5565 ] Network output: [ 0.03543 0.779 0.9487 1.955e-05 -8.775e-06 0.2015 1.473e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01887 0.0122 0.01983 0.02271 0.9868 0.9907 0.01911 0.9701 0.9823 0.02475 ] Network output: [ 0.01807 -0.06099 0.9453 -0.001225 0.0005498 1.075 -0.0009229 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6511 0.4992 0.4449 0.5202 0.9781 0.9901 0.6525 0.9126 0.9742 0.5393 ] Network output: [ -0.08514 0.5088 1.083 -0.0004655 0.000209 0.5769 -0.0003508 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2262 0.2172 0.1977 0.2015 0.9874 0.9918 0.2262 0.9719 0.983 0.2042 ] Network output: [ -0.1148 0.385 1.094 -0.0005484 0.0002462 0.748 -0.0004133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2844 0.2821 0.2456 0.246 0.9802 0.9876 0.2844 0.946 0.9707 0.2478 ] Network output: [ 0.05568 0.7439 -0.03094 0.00109 -0.0004894 1.18 0.0008216 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1475 Epoch 3108 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07236 0.7098 0.9489 0.0002014 -9.039e-05 0.1973 0.0001517 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01641 -0.005048 0.01309 0.02918 0.9474 0.9551 0.02715 0.8919 0.9117 0.0683 ] Network output: [ 1.016 -0.07449 0.0194 0.0009034 -0.0004056 0.0268 0.0006808 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6031 0.01878 0.1063 0.4029 0.9755 0.9886 0.655 0.9051 0.9706 0.5566 ] Network output: [ 0.03564 0.7795 0.9481 2.206e-05 -9.905e-06 0.2012 1.663e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01888 0.0122 0.0199 0.02278 0.9868 0.9907 0.01912 0.9701 0.9823 0.02483 ] Network output: [ 0.01842 -0.06264 0.9453 -0.001218 0.0005466 1.076 -0.0009176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.651 0.4991 0.4452 0.5203 0.9781 0.9901 0.6524 0.9126 0.9742 0.5394 ] Network output: [ -0.08559 0.5064 1.085 -0.0004657 0.0002091 0.5781 -0.000351 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2262 0.2172 0.1985 0.2023 0.9874 0.9918 0.2263 0.9719 0.983 0.205 ] Network output: [ -0.1149 0.3834 1.095 -0.0005456 0.0002449 0.7489 -0.0004111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2844 0.2821 0.2461 0.2465 0.9803 0.9876 0.2844 0.946 0.9707 0.2483 ] Network output: [ 0.05566 0.746 -0.03171 0.001087 -0.0004879 1.179 0.000819 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1465 Epoch 3109 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07263 0.7108 0.948 0.0002031 -9.119e-05 0.1968 0.0001531 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01643 -0.005051 0.01312 0.02921 0.9474 0.9551 0.02716 0.8919 0.9117 0.06837 ] Network output: [ 1.016 -0.07273 0.0188 0.0008935 -0.0004011 0.02613 0.0006734 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6031 0.01876 0.1066 0.4029 0.9755 0.9886 0.655 0.9051 0.9706 0.5567 ] Network output: [ 0.03585 0.7801 0.9475 2.453e-05 -1.101e-05 0.2008 1.849e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01888 0.01221 0.01996 0.02284 0.9868 0.9907 0.01912 0.9701 0.9823 0.0249 ] Network output: [ 0.01878 -0.0643 0.9453 -0.001211 0.0005435 1.077 -0.0009123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6509 0.499 0.4455 0.5204 0.9781 0.9901 0.6522 0.9126 0.9742 0.5396 ] Network output: [ -0.08604 0.504 1.087 -0.0004659 0.0002092 0.5794 -0.0003511 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2263 0.2173 0.1993 0.2031 0.9874 0.9918 0.2264 0.9719 0.983 0.2059 ] Network output: [ -0.1149 0.3819 1.096 -0.0005426 0.0002436 0.7498 -0.000409 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2845 0.2822 0.2467 0.2471 0.9803 0.9876 0.2845 0.946 0.9707 0.2489 ] Network output: [ 0.05563 0.7482 -0.03247 0.001083 -0.0004863 1.177 0.0008164 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1455 Epoch 3110 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0729 0.7118 0.947 0.0002048 -9.195e-05 0.1963 0.0001544 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01644 -0.005054 0.01314 0.02924 0.9474 0.9551 0.02718 0.8919 0.9117 0.06844 ] Network output: [ 1.015 -0.07098 0.01822 0.0008836 -0.0003967 0.02546 0.0006659 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6031 0.01875 0.1069 0.403 0.9755 0.9886 0.6549 0.9051 0.9706 0.5569 ] Network output: [ 0.03605 0.7807 0.9469 2.694e-05 -1.21e-05 0.2005 2.03e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01889 0.01221 0.02002 0.02291 0.9868 0.9907 0.01913 0.9701 0.9823 0.02497 ] Network output: [ 0.01915 -0.06596 0.9452 -0.001204 0.0005403 1.078 -0.000907 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6507 0.4989 0.4457 0.5205 0.9781 0.9901 0.6521 0.9126 0.9742 0.5397 ] Network output: [ -0.08648 0.5016 1.089 -0.0004659 0.0002092 0.5807 -0.0003512 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2264 0.2174 0.2001 0.2039 0.9874 0.9918 0.2265 0.9719 0.983 0.2067 ] Network output: [ -0.115 0.3803 1.097 -0.0005397 0.0002423 0.7506 -0.0004067 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2846 0.2823 0.2472 0.2476 0.9803 0.9876 0.2846 0.9461 0.9707 0.2494 ] Network output: [ 0.05561 0.7503 -0.0332 0.00108 -0.0004847 1.176 0.0008137 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1445 Epoch 3111 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07316 0.7128 0.9461 0.0002065 -9.268e-05 0.1957 0.0001556 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01645 -0.005057 0.01317 0.02926 0.9474 0.9551 0.0272 0.8919 0.9117 0.06851 ] Network output: [ 1.015 -0.06923 0.01765 0.0008737 -0.0003922 0.02479 0.0006585 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6031 0.01874 0.1072 0.403 0.9755 0.9886 0.6549 0.9051 0.9706 0.557 ] Network output: [ 0.03625 0.7813 0.9462 2.93e-05 -1.315e-05 0.2001 2.208e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0189 0.01222 0.02009 0.02298 0.9868 0.9907 0.01914 0.9701 0.9823 0.02505 ] Network output: [ 0.01952 -0.06761 0.9452 -0.001196 0.0005371 1.079 -0.0009017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6506 0.4989 0.446 0.5206 0.9781 0.9901 0.652 0.9126 0.9742 0.5398 ] Network output: [ -0.08691 0.4992 1.091 -0.0004659 0.0002092 0.582 -0.0003512 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2265 0.2176 0.2009 0.2047 0.9874 0.9918 0.2266 0.9719 0.983 0.2075 ] Network output: [ -0.115 0.3787 1.098 -0.0005367 0.0002409 0.7515 -0.0004045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2847 0.2824 0.2478 0.2482 0.9803 0.9876 0.2847 0.9461 0.9708 0.25 ] Network output: [ 0.05558 0.7524 -0.03392 0.001076 -0.0004831 1.175 0.000811 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1436 Epoch 3112 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07341 0.7138 0.9451 0.000208 -9.339e-05 0.1951 0.0001568 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01647 -0.00506 0.0132 0.02929 0.9474 0.9551 0.02721 0.8919 0.9117 0.06858 ] Network output: [ 1.015 -0.0675 0.01711 0.0008639 -0.0003878 0.02413 0.000651 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6031 0.01873 0.1075 0.403 0.9755 0.9886 0.6549 0.9051 0.9706 0.5571 ] Network output: [ 0.03645 0.7819 0.9457 3.161e-05 -1.419e-05 0.1997 2.382e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01891 0.01222 0.02016 0.02305 0.9868 0.9907 0.01915 0.9701 0.9823 0.02513 ] Network output: [ 0.01991 -0.06926 0.9451 -0.001189 0.0005339 1.08 -0.0008963 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6505 0.4988 0.4462 0.5207 0.9781 0.9901 0.6519 0.9126 0.9742 0.5399 ] Network output: [ -0.08734 0.4967 1.093 -0.0004659 0.0002092 0.5833 -0.0003511 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2267 0.2177 0.2017 0.2056 0.9874 0.9918 0.2268 0.9719 0.983 0.2083 ] Network output: [ -0.115 0.3771 1.098 -0.0005336 0.0002396 0.7525 -0.0004022 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2848 0.2825 0.2483 0.2487 0.9803 0.9876 0.2848 0.9462 0.9708 0.2505 ] Network output: [ 0.05555 0.7545 -0.03461 0.001073 -0.0004815 1.173 0.0008083 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1426 Epoch 3113 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07366 0.7149 0.9442 0.0002095 -9.406e-05 0.1945 0.0001579 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01648 -0.005064 0.01323 0.02932 0.9474 0.9551 0.02723 0.8919 0.9117 0.06866 ] Network output: [ 1.015 -0.06577 0.01658 0.000854 -0.0003834 0.02347 0.0006436 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6031 0.01872 0.1078 0.4031 0.9755 0.9886 0.6548 0.9051 0.9706 0.5572 ] Network output: [ 0.03664 0.7825 0.9451 3.386e-05 -1.52e-05 0.1993 2.552e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01892 0.01223 0.02022 0.02311 0.9868 0.9907 0.01916 0.9701 0.9823 0.0252 ] Network output: [ 0.0203 -0.07092 0.945 -0.001182 0.0005307 1.081 -0.0008909 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6504 0.4987 0.4465 0.5208 0.9781 0.9901 0.6518 0.9126 0.9742 0.54 ] Network output: [ -0.08777 0.4943 1.095 -0.0004658 0.0002091 0.5847 -0.000351 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2269 0.2179 0.2026 0.2064 0.9874 0.9918 0.2269 0.9719 0.983 0.2092 ] Network output: [ -0.1151 0.3756 1.099 -0.0005306 0.0002382 0.7534 -0.0003999 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2849 0.2827 0.2489 0.2493 0.9803 0.9876 0.285 0.9462 0.9708 0.2511 ] Network output: [ 0.05551 0.7565 -0.03528 0.001069 -0.0004799 1.172 0.0008056 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1416 Epoch 3114 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07391 0.7159 0.9433 0.000211 -9.471e-05 0.1938 0.000159 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0165 -0.005067 0.01325 0.02934 0.9474 0.9551 0.02725 0.8918 0.9117 0.06873 ] Network output: [ 1.014 -0.06406 0.01606 0.0008443 -0.000379 0.02281 0.0006363 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6031 0.01872 0.1082 0.4031 0.9755 0.9886 0.6548 0.9051 0.9706 0.5573 ] Network output: [ 0.03682 0.7832 0.9445 3.605e-05 -1.618e-05 0.1988 2.717e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01893 0.01224 0.02029 0.02318 0.9868 0.9907 0.01917 0.9701 0.9823 0.02528 ] Network output: [ 0.0207 -0.07257 0.9449 -0.001175 0.0005275 1.082 -0.0008856 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6503 0.4986 0.4468 0.5209 0.9781 0.9901 0.6517 0.9126 0.9742 0.5402 ] Network output: [ -0.08818 0.4919 1.097 -0.0004656 0.000209 0.586 -0.0003509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.227 0.218 0.2034 0.2072 0.9874 0.9918 0.2271 0.9719 0.983 0.21 ] Network output: [ -0.1151 0.374 1.1 -0.0005275 0.0002368 0.7543 -0.0003975 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2851 0.2828 0.2495 0.2498 0.9804 0.9877 0.2851 0.9462 0.9708 0.2517 ] Network output: [ 0.05548 0.7586 -0.03593 0.001065 -0.0004783 1.171 0.0008028 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1406 Epoch 3115 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07415 0.717 0.9424 0.0002123 -9.532e-05 0.1932 0.00016 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01651 -0.00507 0.01328 0.02937 0.9474 0.9551 0.02726 0.8918 0.9117 0.0688 ] Network output: [ 1.014 -0.06235 0.01557 0.0008345 -0.0003746 0.02215 0.0006289 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6031 0.01871 0.1085 0.4031 0.9755 0.9886 0.6547 0.9051 0.9706 0.5574 ] Network output: [ 0.037 0.7838 0.944 3.819e-05 -1.714e-05 0.1984 2.878e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01894 0.01224 0.02036 0.02325 0.9869 0.9907 0.01918 0.9701 0.9823 0.02535 ] Network output: [ 0.02111 -0.07422 0.9447 -0.001168 0.0005243 1.083 -0.0008802 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6502 0.4986 0.447 0.521 0.9781 0.9901 0.6516 0.9126 0.9742 0.5403 ] Network output: [ -0.0886 0.4895 1.098 -0.0004653 0.0002089 0.5874 -0.0003507 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2272 0.2182 0.2042 0.208 0.9874 0.9918 0.2273 0.9719 0.983 0.2108 ] Network output: [ -0.1151 0.3725 1.1 -0.0005243 0.0002354 0.7552 -0.0003951 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2852 0.2829 0.25 0.2504 0.9804 0.9877 0.2852 0.9463 0.9709 0.2522 ] Network output: [ 0.05543 0.7606 -0.03656 0.001062 -0.0004766 1.169 0.0008001 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1396 Epoch 3116 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07438 0.7181 0.9416 0.0002136 -9.591e-05 0.1925 0.000161 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01652 -0.005073 0.01331 0.02939 0.9475 0.9551 0.02728 0.8918 0.9117 0.06887 ] Network output: [ 1.014 -0.06065 0.01509 0.0008248 -0.0003703 0.02151 0.0006216 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6031 0.01871 0.1088 0.4031 0.9755 0.9887 0.6547 0.9051 0.9706 0.5575 ] Network output: [ 0.03718 0.7845 0.9434 4.027e-05 -1.808e-05 0.1979 3.035e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01895 0.01225 0.02042 0.02332 0.9869 0.9907 0.0192 0.9701 0.9824 0.02543 ] Network output: [ 0.02153 -0.07587 0.9446 -0.001161 0.0005211 1.084 -0.0008748 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6501 0.4985 0.4473 0.5211 0.9781 0.9901 0.6515 0.9126 0.9742 0.5404 ] Network output: [ -0.089 0.4871 1.1 -0.000465 0.0002088 0.5888 -0.0003504 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2275 0.2184 0.2051 0.2089 0.9874 0.9918 0.2275 0.9719 0.983 0.2117 ] Network output: [ -0.1151 0.3709 1.101 -0.0005211 0.000234 0.7561 -0.0003928 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2854 0.2831 0.2506 0.2509 0.9804 0.9877 0.2854 0.9463 0.9709 0.2528 ] Network output: [ 0.05539 0.7626 -0.03717 0.001058 -0.0004749 1.168 0.0007973 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1386 Epoch 3117 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0746 0.7192 0.9407 0.0002149 -9.647e-05 0.1918 0.0001619 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01654 -0.005077 0.01334 0.02942 0.9475 0.9551 0.0273 0.8918 0.9117 0.06894 ] Network output: [ 1.013 -0.05897 0.01463 0.0008152 -0.000366 0.02086 0.0006144 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.603 0.0187 0.1091 0.4031 0.9755 0.9887 0.6546 0.9051 0.9706 0.5576 ] Network output: [ 0.03736 0.7852 0.9429 4.229e-05 -1.898e-05 0.1974 3.187e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01897 0.01226 0.02049 0.02339 0.9869 0.9907 0.01921 0.9701 0.9824 0.02551 ] Network output: [ 0.02196 -0.07751 0.9444 -0.001154 0.0005179 1.084 -0.0008693 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.65 0.4984 0.4475 0.5212 0.9781 0.9901 0.6514 0.9126 0.9742 0.5405 ] Network output: [ -0.0894 0.4847 1.102 -0.0004646 0.0002086 0.5903 -0.0003502 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2277 0.2187 0.2059 0.2097 0.9874 0.9918 0.2278 0.9719 0.983 0.2125 ] Network output: [ -0.1151 0.3694 1.102 -0.000518 0.0002325 0.7571 -0.0003903 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2856 0.2833 0.2511 0.2515 0.9804 0.9877 0.2856 0.9464 0.9709 0.2533 ] Network output: [ 0.05534 0.7646 -0.03775 0.001054 -0.0004733 1.167 0.0007945 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1376 Epoch 3118 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07482 0.7203 0.9399 0.0002161 -9.7e-05 0.1911 0.0001628 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01655 -0.00508 0.01337 0.02944 0.9475 0.9551 0.02732 0.8918 0.9117 0.06901 ] Network output: [ 1.013 -0.05729 0.01418 0.0008057 -0.0003617 0.02022 0.0006072 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.603 0.0187 0.1095 0.4031 0.9755 0.9887 0.6546 0.9051 0.9706 0.5577 ] Network output: [ 0.03753 0.7859 0.9424 4.425e-05 -1.986e-05 0.1969 3.335e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01898 0.01227 0.02056 0.02346 0.9869 0.9907 0.01922 0.9701 0.9824 0.02559 ] Network output: [ 0.02239 -0.07916 0.9442 -0.001146 0.0005146 1.085 -0.0008639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6499 0.4984 0.4478 0.5212 0.9781 0.9901 0.6513 0.9126 0.9742 0.5406 ] Network output: [ -0.0898 0.4823 1.104 -0.0004642 0.0002084 0.5917 -0.0003499 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.228 0.2189 0.2067 0.2105 0.9874 0.9918 0.228 0.9719 0.983 0.2134 ] Network output: [ -0.1151 0.3679 1.102 -0.0005147 0.0002311 0.758 -0.0003879 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2858 0.2835 0.2517 0.2521 0.9804 0.9877 0.2858 0.9464 0.971 0.2539 ] Network output: [ 0.05529 0.7666 -0.03832 0.00105 -0.0004716 1.165 0.0007916 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1367 Epoch 3119 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07504 0.7214 0.9391 0.0002172 -9.75e-05 0.1903 0.0001637 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01657 -0.005083 0.0134 0.02946 0.9475 0.9552 0.02734 0.8918 0.9117 0.06908 ] Network output: [ 1.013 -0.05563 0.01375 0.0007962 -0.0003574 0.01958 0.0006 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.603 0.0187 0.1098 0.4031 0.9755 0.9887 0.6545 0.9051 0.9706 0.5578 ] Network output: [ 0.03769 0.7866 0.9419 4.615e-05 -2.072e-05 0.1964 3.478e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.019 0.01228 0.02063 0.02353 0.9869 0.9907 0.01924 0.9701 0.9824 0.02566 ] Network output: [ 0.02283 -0.0808 0.944 -0.001139 0.0005114 1.086 -0.0008585 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6498 0.4983 0.448 0.5213 0.9781 0.9901 0.6511 0.9126 0.9742 0.5407 ] Network output: [ -0.09018 0.4799 1.105 -0.0004638 0.0002082 0.5932 -0.0003495 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2282 0.2192 0.2076 0.2114 0.9874 0.9918 0.2283 0.9719 0.983 0.2142 ] Network output: [ -0.1151 0.3664 1.103 -0.0005115 0.0002296 0.7589 -0.0003855 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2859 0.2837 0.2523 0.2526 0.9804 0.9877 0.286 0.9465 0.971 0.2545 ] Network output: [ 0.05524 0.7685 -0.03888 0.001047 -0.0004699 1.164 0.0007888 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1357 Epoch 3120 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07525 0.7226 0.9383 0.0002182 -9.797e-05 0.1896 0.0001645 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01658 -0.005086 0.01343 0.02949 0.9475 0.9552 0.02736 0.8918 0.9117 0.06915 ] Network output: [ 1.012 -0.05397 0.01333 0.0007868 -0.0003532 0.01895 0.000593 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.603 0.01871 0.1101 0.4031 0.9756 0.9887 0.6545 0.9051 0.9706 0.5579 ] Network output: [ 0.03786 0.7873 0.9414 4.799e-05 -2.155e-05 0.1958 3.617e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01901 0.01229 0.0207 0.0236 0.9869 0.9907 0.01926 0.9701 0.9824 0.02574 ] Network output: [ 0.02328 -0.08243 0.9438 -0.001132 0.0005081 1.087 -0.000853 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6497 0.4982 0.4483 0.5214 0.9781 0.9901 0.651 0.9126 0.9742 0.5407 ] Network output: [ -0.09056 0.4775 1.107 -0.0004632 0.000208 0.5946 -0.0003491 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2285 0.2195 0.2084 0.2122 0.9874 0.9918 0.2286 0.9719 0.983 0.2151 ] Network output: [ -0.1151 0.3648 1.103 -0.0005083 0.0002282 0.7599 -0.000383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2861 0.2839 0.2528 0.2532 0.9805 0.9877 0.2862 0.9465 0.971 0.255 ] Network output: [ 0.05518 0.7705 -0.03941 0.001043 -0.0004681 1.163 0.0007858 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1347 Epoch 3121 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07545 0.7237 0.9375 0.0002192 -9.841e-05 0.1888 0.0001652 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0166 -0.00509 0.01346 0.02951 0.9475 0.9552 0.02738 0.8918 0.9117 0.06922 ] Network output: [ 1.012 -0.05233 0.01293 0.0007775 -0.000349 0.01832 0.0005859 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.603 0.01871 0.1105 0.403 0.9756 0.9887 0.6544 0.9051 0.9706 0.5579 ] Network output: [ 0.03801 0.788 0.9409 4.978e-05 -2.235e-05 0.1953 3.751e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01903 0.0123 0.02076 0.02367 0.9869 0.9907 0.01927 0.9701 0.9824 0.02582 ] Network output: [ 0.02373 -0.08406 0.9436 -0.001125 0.0005049 1.088 -0.0008476 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6496 0.4982 0.4485 0.5214 0.9781 0.9901 0.6509 0.9126 0.9742 0.5408 ] Network output: [ -0.09094 0.4751 1.109 -0.0004627 0.0002077 0.5961 -0.0003487 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2288 0.2198 0.2093 0.2131 0.9874 0.9918 0.2289 0.9719 0.983 0.216 ] Network output: [ -0.1151 0.3633 1.104 -0.000505 0.0002267 0.7608 -0.0003806 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2864 0.2841 0.2534 0.2537 0.9805 0.9877 0.2864 0.9466 0.9711 0.2556 ] Network output: [ 0.05512 0.7724 -0.03992 0.001039 -0.0004664 1.162 0.0007829 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1338 Epoch 3122 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07565 0.7249 0.9367 0.0002201 -9.883e-05 0.188 0.0001659 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01661 -0.005093 0.01349 0.02953 0.9475 0.9552 0.0274 0.8918 0.9117 0.0693 ] Network output: [ 1.012 -0.0507 0.01255 0.0007683 -0.0003449 0.0177 0.000579 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6029 0.01872 0.1108 0.403 0.9756 0.9887 0.6544 0.9051 0.9706 0.558 ] Network output: [ 0.03817 0.7887 0.9404 5.15e-05 -2.312e-05 0.1947 3.881e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01905 0.01232 0.02083 0.02374 0.9869 0.9907 0.01929 0.9701 0.9824 0.0259 ] Network output: [ 0.02419 -0.08569 0.9434 -0.001117 0.0005016 1.089 -0.0008421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6494 0.4981 0.4487 0.5215 0.9781 0.9901 0.6508 0.9126 0.9742 0.5409 ] Network output: [ -0.0913 0.4727 1.11 -0.000462 0.0002074 0.5976 -0.0003482 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2291 0.2201 0.2101 0.2139 0.9874 0.9918 0.2292 0.9718 0.983 0.2168 ] Network output: [ -0.1151 0.3618 1.104 -0.0005017 0.0002252 0.7618 -0.0003781 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2866 0.2843 0.254 0.2543 0.9805 0.9877 0.2866 0.9466 0.9711 0.2561 ] Network output: [ 0.05506 0.7743 -0.04041 0.001035 -0.0004646 1.16 0.0007799 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1328 Epoch 3123 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07584 0.726 0.936 0.000221 -9.922e-05 0.1872 0.0001666 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01663 -0.005096 0.01352 0.02956 0.9475 0.9552 0.02742 0.8918 0.9117 0.06936 ] Network output: [ 1.011 -0.04907 0.01218 0.0007592 -0.0003408 0.01708 0.0005721 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6029 0.01873 0.1111 0.403 0.9756 0.9887 0.6543 0.9051 0.9706 0.5581 ] Network output: [ 0.03832 0.7895 0.94 5.316e-05 -2.386e-05 0.1941 4.006e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01907 0.01233 0.0209 0.02381 0.9869 0.9907 0.01931 0.9701 0.9824 0.02598 ] Network output: [ 0.02466 -0.08731 0.9431 -0.00111 0.0004984 1.09 -0.0008366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6493 0.498 0.449 0.5215 0.9781 0.9901 0.6507 0.9126 0.9742 0.541 ] Network output: [ -0.09166 0.4703 1.112 -0.0004614 0.0002071 0.5991 -0.0003477 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2295 0.2204 0.211 0.2148 0.9874 0.9918 0.2296 0.9718 0.983 0.2177 ] Network output: [ -0.115 0.3603 1.105 -0.0004984 0.0002238 0.7627 -0.0003756 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2868 0.2845 0.2545 0.2548 0.9805 0.9878 0.2868 0.9467 0.9711 0.2567 ] Network output: [ 0.05499 0.7761 -0.04089 0.001031 -0.0004628 1.159 0.0007769 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1318 Epoch 3124 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07602 0.7272 0.9352 0.0002218 -9.958e-05 0.1864 0.0001672 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01664 -0.0051 0.01355 0.02958 0.9475 0.9552 0.02744 0.8918 0.9117 0.06943 ] Network output: [ 1.011 -0.04746 0.01182 0.0007502 -0.0003368 0.01647 0.0005654 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6029 0.01874 0.1115 0.403 0.9756 0.9887 0.6543 0.9051 0.9706 0.5582 ] Network output: [ 0.03846 0.7902 0.9395 5.476e-05 -2.458e-05 0.1936 4.127e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01909 0.01234 0.02097 0.02388 0.9869 0.9907 0.01933 0.9701 0.9824 0.02605 ] Network output: [ 0.02513 -0.08893 0.9429 -0.001103 0.0004951 1.091 -0.0008311 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6492 0.498 0.4492 0.5216 0.9782 0.9901 0.6506 0.9126 0.9742 0.5411 ] Network output: [ -0.09202 0.4679 1.114 -0.0004607 0.0002068 0.6006 -0.0003472 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2298 0.2207 0.2118 0.2156 0.9874 0.9918 0.2299 0.9718 0.983 0.2186 ] Network output: [ -0.115 0.3589 1.105 -0.0004951 0.0002223 0.7637 -0.0003732 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2871 0.2848 0.2551 0.2554 0.9805 0.9878 0.2871 0.9467 0.9711 0.2572 ] Network output: [ 0.05492 0.778 -0.04135 0.001027 -0.000461 1.158 0.0007739 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1309 Epoch 3125 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0762 0.7284 0.9345 0.0002226 -9.991e-05 0.1856 0.0001677 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01666 -0.005103 0.01358 0.0296 0.9475 0.9552 0.02746 0.8918 0.9117 0.0695 ] Network output: [ 1.011 -0.04586 0.01148 0.0007413 -0.0003328 0.01586 0.0005587 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6028 0.01876 0.1118 0.4029 0.9756 0.9887 0.6542 0.9051 0.9706 0.5582 ] Network output: [ 0.0386 0.791 0.9391 5.629e-05 -2.527e-05 0.1929 4.242e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01911 0.01235 0.02104 0.02395 0.9869 0.9907 0.01935 0.9701 0.9824 0.02613 ] Network output: [ 0.02561 -0.09054 0.9426 -0.001096 0.0004918 1.092 -0.0008256 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6491 0.4979 0.4494 0.5216 0.9782 0.9901 0.6505 0.9126 0.9742 0.5411 ] Network output: [ -0.09236 0.4655 1.115 -0.0004599 0.0002065 0.6022 -0.0003466 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2302 0.2211 0.2127 0.2165 0.9874 0.9918 0.2303 0.9718 0.983 0.2194 ] Network output: [ -0.115 0.3574 1.106 -0.0004919 0.0002208 0.7646 -0.0003707 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2873 0.285 0.2556 0.2559 0.9805 0.9878 0.2873 0.9468 0.9712 0.2578 ] Network output: [ 0.05484 0.7798 -0.04179 0.001023 -0.0004591 1.156 0.0007708 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.13 Epoch 3126 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07637 0.7296 0.9338 0.0002232 -0.0001002 0.1847 0.0001682 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01667 -0.005106 0.01361 0.02962 0.9475 0.9552 0.02748 0.8918 0.9117 0.06957 ] Network output: [ 1.01 -0.04427 0.01115 0.0007325 -0.0003289 0.01526 0.0005521 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6028 0.01878 0.1121 0.4029 0.9756 0.9887 0.6542 0.9051 0.9706 0.5583 ] Network output: [ 0.03874 0.7918 0.9386 5.777e-05 -2.593e-05 0.1923 4.353e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01913 0.01237 0.02111 0.02402 0.9869 0.9908 0.01937 0.9701 0.9824 0.02621 ] Network output: [ 0.02609 -0.09215 0.9423 -0.001088 0.0004885 1.093 -0.0008201 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.649 0.4978 0.4497 0.5217 0.9782 0.9901 0.6504 0.9126 0.9742 0.5412 ] Network output: [ -0.0927 0.4631 1.117 -0.0004591 0.0002061 0.6037 -0.000346 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2306 0.2214 0.2135 0.2173 0.9874 0.9918 0.2306 0.9718 0.983 0.2203 ] Network output: [ -0.115 0.3559 1.106 -0.0004886 0.0002193 0.7656 -0.0003682 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2876 0.2853 0.2562 0.2565 0.9806 0.9878 0.2876 0.9469 0.9712 0.2584 ] Network output: [ 0.05477 0.7817 -0.04221 0.001019 -0.0004573 1.155 0.0007676 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.129 Epoch 3127 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07654 0.7308 0.9331 0.0002239 -0.0001005 0.1839 0.0001687 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01669 -0.005109 0.01364 0.02964 0.9475 0.9552 0.0275 0.8918 0.9117 0.06964 ] Network output: [ 1.01 -0.04269 0.01083 0.0007239 -0.000325 0.01466 0.0005456 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6028 0.0188 0.1124 0.4028 0.9756 0.9887 0.6541 0.9051 0.9706 0.5583 ] Network output: [ 0.03887 0.7925 0.9382 5.918e-05 -2.657e-05 0.1917 4.46e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01915 0.01238 0.02118 0.02409 0.9869 0.9908 0.01939 0.9701 0.9824 0.02629 ] Network output: [ 0.02658 -0.09375 0.942 -0.001081 0.0004853 1.094 -0.0008146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6489 0.4978 0.4499 0.5217 0.9782 0.9901 0.6503 0.9126 0.9742 0.5412 ] Network output: [ -0.09303 0.4608 1.118 -0.0004583 0.0002057 0.6053 -0.0003454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.231 0.2218 0.2144 0.2181 0.9874 0.9918 0.231 0.9718 0.983 0.2212 ] Network output: [ -0.1149 0.3545 1.107 -0.0004853 0.0002179 0.7665 -0.0003657 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2878 0.2855 0.2568 0.257 0.9806 0.9878 0.2878 0.9469 0.9712 0.2589 ] Network output: [ 0.05469 0.7835 -0.04262 0.001014 -0.0004554 1.154 0.0007645 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1281 Epoch 3128 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0767 0.732 0.9325 0.0002244 -0.0001008 0.183 0.0001691 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0167 -0.005112 0.01367 0.02966 0.9475 0.9552 0.02752 0.8918 0.9117 0.06971 ] Network output: [ 1.01 -0.04112 0.01053 0.0007154 -0.0003212 0.01407 0.0005391 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6027 0.01883 0.1128 0.4028 0.9756 0.9887 0.654 0.9051 0.9706 0.5584 ] Network output: [ 0.039 0.7933 0.9378 6.053e-05 -2.717e-05 0.1911 4.562e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01917 0.0124 0.02124 0.02416 0.9869 0.9908 0.01942 0.9701 0.9824 0.02637 ] Network output: [ 0.02707 -0.09534 0.9417 -0.001074 0.000482 1.095 -0.0008091 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6488 0.4977 0.4501 0.5217 0.9782 0.9901 0.6501 0.9126 0.9742 0.5413 ] Network output: [ -0.09336 0.4584 1.12 -0.0004574 0.0002054 0.6069 -0.0003447 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2314 0.2222 0.2152 0.219 0.9874 0.9918 0.2314 0.9718 0.983 0.222 ] Network output: [ -0.1149 0.353 1.107 -0.000482 0.0002164 0.7675 -0.0003633 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2881 0.2858 0.2573 0.2576 0.9806 0.9878 0.2881 0.947 0.9713 0.2595 ] Network output: [ 0.0546 0.7853 -0.04301 0.00101 -0.0004535 1.153 0.0007613 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1271 Epoch 3129 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07686 0.7332 0.9318 0.0002249 -0.000101 0.1821 0.0001695 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01672 -0.005115 0.0137 0.02968 0.9475 0.9552 0.02754 0.8918 0.9117 0.06978 ] Network output: [ 1.009 -0.03956 0.01024 0.000707 -0.0003174 0.01348 0.0005328 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6027 0.01885 0.1131 0.4027 0.9756 0.9887 0.654 0.9051 0.9706 0.5584 ] Network output: [ 0.03913 0.7941 0.9375 6.182e-05 -2.775e-05 0.1904 4.659e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0192 0.01241 0.02131 0.02423 0.9869 0.9908 0.01944 0.9701 0.9824 0.02644 ] Network output: [ 0.02757 -0.09693 0.9414 -0.001066 0.0004787 1.096 -0.0008036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6487 0.4976 0.4503 0.5217 0.9782 0.9902 0.65 0.9126 0.9742 0.5414 ] Network output: [ -0.09367 0.456 1.121 -0.0004565 0.000205 0.6084 -0.0003441 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2318 0.2226 0.2161 0.2198 0.9874 0.9918 0.2319 0.9718 0.983 0.2229 ] Network output: [ -0.1148 0.3516 1.108 -0.0004788 0.0002149 0.7685 -0.0003608 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2884 0.2861 0.2579 0.2581 0.9806 0.9878 0.2884 0.947 0.9713 0.26 ] Network output: [ 0.05452 0.7871 -0.04338 0.001006 -0.0004515 1.151 0.000758 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1262 Epoch 3130 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07701 0.7345 0.9312 0.0002254 -0.0001012 0.1812 0.0001699 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01673 -0.005118 0.01373 0.0297 0.9475 0.9552 0.02756 0.8917 0.9117 0.06984 ] Network output: [ 1.009 -0.03801 0.009959 0.0006988 -0.0003137 0.0129 0.0005266 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6026 0.01889 0.1134 0.4026 0.9756 0.9887 0.6539 0.9051 0.9706 0.5585 ] Network output: [ 0.03925 0.7949 0.9371 6.304e-05 -2.83e-05 0.1898 4.751e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01922 0.01243 0.02138 0.02429 0.9869 0.9908 0.01946 0.9701 0.9824 0.02652 ] Network output: [ 0.02808 -0.09851 0.941 -0.001059 0.0004754 1.097 -0.000798 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6485 0.4976 0.4505 0.5218 0.9782 0.9902 0.6499 0.9126 0.9742 0.5414 ] Network output: [ -0.09398 0.4537 1.122 -0.0004556 0.0002045 0.61 -0.0003434 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2322 0.223 0.2169 0.2207 0.9874 0.9918 0.2323 0.9718 0.983 0.2238 ] Network output: [ -0.1148 0.3501 1.108 -0.0004755 0.0002135 0.7694 -0.0003584 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2887 0.2864 0.2584 0.2586 0.9806 0.9878 0.2887 0.9471 0.9713 0.2606 ] Network output: [ 0.05442 0.7888 -0.04374 0.001001 -0.0004496 1.15 0.0007547 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1253 Epoch 3131 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07716 0.7357 0.9306 0.0002258 -0.0001014 0.1803 0.0001702 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01675 -0.005121 0.01376 0.02972 0.9475 0.9552 0.02758 0.8917 0.9117 0.06991 ] Network output: [ 1.009 -0.03647 0.009693 0.0006907 -0.0003101 0.01232 0.0005206 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6026 0.01893 0.1137 0.4026 0.9756 0.9887 0.6538 0.9051 0.9706 0.5585 ] Network output: [ 0.03936 0.7957 0.9367 6.421e-05 -2.882e-05 0.1891 4.839e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01924 0.01245 0.02145 0.02436 0.9869 0.9908 0.01949 0.9701 0.9824 0.0266 ] Network output: [ 0.02858 -0.1001 0.9407 -0.001052 0.0004721 1.098 -0.0007925 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6484 0.4975 0.4507 0.5218 0.9782 0.9902 0.6498 0.9126 0.9742 0.5414 ] Network output: [ -0.09428 0.4513 1.124 -0.0004546 0.0002041 0.6116 -0.0003426 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2326 0.2234 0.2178 0.2215 0.9874 0.9918 0.2327 0.9718 0.983 0.2246 ] Network output: [ -0.1147 0.3487 1.108 -0.0004723 0.000212 0.7704 -0.0003559 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2889 0.2867 0.259 0.2592 0.9807 0.9879 0.289 0.9471 0.9714 0.2611 ] Network output: [ 0.05433 0.7906 -0.04408 0.000997 -0.0004476 1.149 0.0007513 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1244 Epoch 3132 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07729 0.7369 0.93 0.0002261 -0.0001015 0.1794 0.0001704 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01676 -0.005124 0.01379 0.02974 0.9475 0.9552 0.02761 0.8917 0.9117 0.06997 ] Network output: [ 1.008 -0.03494 0.009438 0.0006828 -0.0003065 0.01175 0.0005146 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6025 0.01897 0.114 0.4025 0.9756 0.9887 0.6538 0.9051 0.9706 0.5586 ] Network output: [ 0.03948 0.7965 0.9364 6.531e-05 -2.932e-05 0.1884 4.922e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01927 0.01246 0.02152 0.02443 0.9869 0.9908 0.01951 0.9701 0.9824 0.02668 ] Network output: [ 0.0291 -0.1017 0.9403 -0.001044 0.0004688 1.099 -0.0007869 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6483 0.4974 0.4509 0.5218 0.9782 0.9902 0.6497 0.9125 0.9742 0.5415 ] Network output: [ -0.09458 0.449 1.125 -0.0004537 0.0002037 0.6132 -0.0003419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2331 0.2239 0.2186 0.2224 0.9874 0.9918 0.2332 0.9718 0.983 0.2255 ] Network output: [ -0.1147 0.3473 1.109 -0.000469 0.0002106 0.7713 -0.0003535 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2892 0.287 0.2595 0.2597 0.9807 0.9879 0.2893 0.9472 0.9714 0.2617 ] Network output: [ 0.05423 0.7923 -0.04441 0.0009925 -0.0004456 1.148 0.000748 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1235 Epoch 3133 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07743 0.7382 0.9294 0.0002264 -0.0001017 0.1785 0.0001706 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01678 -0.005127 0.01382 0.02976 0.9475 0.9552 0.02763 0.8917 0.9116 0.07004 ] Network output: [ 1.008 -0.03342 0.009195 0.000675 -0.000303 0.01119 0.0005087 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6025 0.01901 0.1143 0.4024 0.9756 0.9887 0.6537 0.9051 0.9706 0.5586 ] Network output: [ 0.03958 0.7973 0.936 6.634e-05 -2.978e-05 0.1877 5e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01929 0.01248 0.02158 0.0245 0.9869 0.9908 0.01954 0.9701 0.9824 0.02675 ] Network output: [ 0.02961 -0.1032 0.94 -0.001037 0.0004655 1.1 -0.0007814 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6482 0.4974 0.451 0.5218 0.9782 0.9902 0.6495 0.9125 0.9742 0.5415 ] Network output: [ -0.09486 0.4467 1.126 -0.0004526 0.0002032 0.6148 -0.0003411 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2336 0.2243 0.2195 0.2232 0.9874 0.9918 0.2336 0.9718 0.983 0.2263 ] Network output: [ -0.1146 0.3459 1.109 -0.0004658 0.0002091 0.7723 -0.0003511 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2895 0.2873 0.26 0.2603 0.9807 0.9879 0.2896 0.9473 0.9714 0.2622 ] Network output: [ 0.05413 0.794 -0.04472 0.0009879 -0.0004435 1.146 0.0007445 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1226 Epoch 3134 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07756 0.7394 0.9289 0.0002267 -0.0001018 0.1776 0.0001708 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01679 -0.00513 0.01384 0.02978 0.9475 0.9552 0.02765 0.8917 0.9116 0.0701 ] Network output: [ 1.008 -0.03191 0.008963 0.0006674 -0.0002996 0.01063 0.000503 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6024 0.01906 0.1146 0.4023 0.9756 0.9887 0.6536 0.9051 0.9706 0.5586 ] Network output: [ 0.03969 0.7981 0.9357 6.732e-05 -3.022e-05 0.1871 5.074e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01932 0.0125 0.02165 0.02457 0.9869 0.9908 0.01956 0.9701 0.9824 0.02683 ] Network output: [ 0.03014 -0.1048 0.9396 -0.001029 0.0004622 1.101 -0.0007758 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.648 0.4973 0.4512 0.5218 0.9782 0.9902 0.6494 0.9125 0.9742 0.5416 ] Network output: [ -0.09514 0.4444 1.128 -0.0004516 0.0002027 0.6164 -0.0003403 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.234 0.2248 0.2203 0.2241 0.9874 0.9918 0.2341 0.9718 0.983 0.2272 ] Network output: [ -0.1145 0.3445 1.11 -0.0004627 0.0002077 0.7732 -0.0003487 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2898 0.2876 0.2606 0.2608 0.9807 0.9879 0.2899 0.9473 0.9715 0.2627 ] Network output: [ 0.05403 0.7957 -0.04501 0.0009833 -0.0004414 1.145 0.000741 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1217 Epoch 3135 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07768 0.7406 0.9283 0.0002269 -0.0001018 0.1766 0.000171 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01681 -0.005132 0.01387 0.0298 0.9476 0.9552 0.02767 0.8917 0.9116 0.07017 ] Network output: [ 1.007 -0.03042 0.008742 0.00066 -0.0002963 0.01007 0.0004974 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6024 0.01912 0.1149 0.4023 0.9756 0.9887 0.6535 0.9051 0.9706 0.5587 ] Network output: [ 0.03979 0.7989 0.9354 6.824e-05 -3.063e-05 0.1864 5.142e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01934 0.01251 0.02172 0.02463 0.9869 0.9908 0.01959 0.9701 0.9824 0.0269 ] Network output: [ 0.03066 -0.1063 0.9392 -0.001022 0.0004588 1.102 -0.0007703 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6479 0.4972 0.4514 0.5218 0.9782 0.9902 0.6493 0.9125 0.9742 0.5416 ] Network output: [ -0.09541 0.4421 1.129 -0.0004505 0.0002023 0.618 -0.0003395 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2345 0.2252 0.2212 0.2249 0.9874 0.9918 0.2346 0.9718 0.983 0.228 ] Network output: [ -0.1145 0.3431 1.11 -0.0004595 0.0002063 0.7742 -0.0003463 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2902 0.2879 0.2611 0.2613 0.9807 0.9879 0.2902 0.9474 0.9715 0.2633 ] Network output: [ 0.05392 0.7974 -0.04529 0.0009786 -0.0004393 1.144 0.0007375 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1208 Epoch 3136 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0778 0.7419 0.9278 0.000227 -0.0001019 0.1757 0.0001711 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01682 -0.005135 0.0139 0.02981 0.9476 0.9552 0.02769 0.8917 0.9116 0.07023 ] Network output: [ 1.007 -0.02893 0.008531 0.0006527 -0.000293 0.009521 0.0004919 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6023 0.01918 0.1151 0.4022 0.9756 0.9887 0.6534 0.9051 0.9706 0.5587 ] Network output: [ 0.03989 0.7998 0.9351 6.909e-05 -3.102e-05 0.1857 5.207e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01937 0.01253 0.02178 0.0247 0.9869 0.9908 0.01961 0.9701 0.9824 0.02698 ] Network output: [ 0.03119 -0.1078 0.9388 -0.001015 0.0004555 1.103 -0.0007647 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6478 0.4972 0.4515 0.5218 0.9782 0.9902 0.6491 0.9125 0.9742 0.5416 ] Network output: [ -0.09568 0.4398 1.13 -0.0004494 0.0002018 0.6197 -0.0003387 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.235 0.2257 0.222 0.2257 0.9874 0.9918 0.2351 0.9718 0.983 0.2289 ] Network output: [ -0.1144 0.3417 1.11 -0.0004564 0.0002049 0.7751 -0.0003439 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2905 0.2882 0.2617 0.2618 0.9807 0.9879 0.2905 0.9474 0.9715 0.2638 ] Network output: [ 0.05381 0.799 -0.04556 0.0009738 -0.0004372 1.143 0.0007339 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1199 Epoch 3137 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07791 0.7431 0.9273 0.0002271 -0.0001019 0.1747 0.0001711 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01684 -0.005137 0.01393 0.02983 0.9476 0.9552 0.02771 0.8917 0.9116 0.07029 ] Network output: [ 1.006 -0.02745 0.00833 0.0006456 -0.0002898 0.008976 0.0004865 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6022 0.01924 0.1154 0.4021 0.9756 0.9887 0.6533 0.9051 0.9706 0.5587 ] Network output: [ 0.03998 0.8006 0.9348 6.988e-05 -3.137e-05 0.1849 5.266e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0194 0.01255 0.02185 0.02476 0.9869 0.9908 0.01964 0.9701 0.9824 0.02706 ] Network output: [ 0.03172 -0.1094 0.9384 -0.001007 0.0004522 1.103 -0.0007591 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6476 0.4971 0.4517 0.5218 0.9782 0.9902 0.649 0.9125 0.9742 0.5416 ] Network output: [ -0.09593 0.4375 1.131 -0.0004483 0.0002013 0.6213 -0.0003379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2355 0.2262 0.2228 0.2266 0.9874 0.9918 0.2356 0.9718 0.983 0.2298 ] Network output: [ -0.1143 0.3403 1.11 -0.0004533 0.0002035 0.7761 -0.0003416 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2908 0.2885 0.2622 0.2624 0.9808 0.9879 0.2908 0.9475 0.9715 0.2643 ] Network output: [ 0.0537 0.8007 -0.04581 0.000969 -0.000435 1.142 0.0007303 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.119 Epoch 3138 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07802 0.7444 0.9268 0.0002271 -0.000102 0.1738 0.0001712 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01685 -0.005139 0.01395 0.02985 0.9476 0.9552 0.02773 0.8917 0.9116 0.07036 ] Network output: [ 1.006 -0.02598 0.008139 0.0006387 -0.0002867 0.008436 0.0004813 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6021 0.01931 0.1157 0.402 0.9756 0.9887 0.6532 0.905 0.9706 0.5587 ] Network output: [ 0.04007 0.8014 0.9345 7.061e-05 -3.17e-05 0.1842 5.321e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01942 0.01257 0.02191 0.02483 0.9869 0.9908 0.01967 0.97 0.9824 0.02713 ] Network output: [ 0.03225 -0.1109 0.938 -0.0009999 0.0004489 1.104 -0.0007536 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6475 0.4971 0.4518 0.5217 0.9782 0.9902 0.6489 0.9125 0.9742 0.5416 ] Network output: [ -0.09618 0.4352 1.132 -0.0004472 0.0002008 0.6229 -0.000337 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.236 0.2267 0.2237 0.2274 0.9874 0.9918 0.2361 0.9718 0.983 0.2306 ] Network output: [ -0.1143 0.3389 1.111 -0.0004502 0.0002021 0.777 -0.0003393 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2911 0.2888 0.2627 0.2629 0.9808 0.9879 0.2911 0.9476 0.9716 0.2649 ] Network output: [ 0.05358 0.8023 -0.04605 0.0009642 -0.0004328 1.14 0.0007266 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1182 Epoch 3139 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07812 0.7456 0.9263 0.0002271 -0.000102 0.1728 0.0001711 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01686 -0.005142 0.01398 0.02986 0.9476 0.9552 0.02775 0.8917 0.9116 0.07042 ] Network output: [ 1.006 -0.02452 0.007957 0.0006319 -0.0002837 0.007902 0.0004762 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6021 0.01939 0.1159 0.4019 0.9756 0.9887 0.6531 0.905 0.9706 0.5587 ] Network output: [ 0.04015 0.8022 0.9343 7.128e-05 -3.2e-05 0.1835 5.372e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01945 0.01259 0.02198 0.0249 0.9869 0.9908 0.01969 0.97 0.9824 0.0272 ] Network output: [ 0.03279 -0.1124 0.9376 -0.0009925 0.0004456 1.105 -0.000748 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6474 0.497 0.452 0.5217 0.9782 0.9902 0.6487 0.9125 0.9742 0.5417 ] Network output: [ -0.09642 0.4329 1.134 -0.0004461 0.0002003 0.6246 -0.0003362 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2365 0.2272 0.2245 0.2282 0.9874 0.9918 0.2366 0.9718 0.983 0.2314 ] Network output: [ -0.1142 0.3375 1.111 -0.0004471 0.0002007 0.7779 -0.000337 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2914 0.2891 0.2632 0.2634 0.9808 0.9879 0.2914 0.9476 0.9716 0.2654 ] Network output: [ 0.05346 0.8039 -0.04627 0.0009592 -0.0004306 1.139 0.0007229 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1173 Epoch 3140 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07821 0.7469 0.9258 0.000227 -0.0001019 0.1718 0.0001711 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01688 -0.005144 0.014 0.02988 0.9476 0.9552 0.02777 0.8917 0.9116 0.07048 ] Network output: [ 1.005 -0.02307 0.007785 0.0006253 -0.0002807 0.007373 0.0004713 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.602 0.01947 0.1161 0.4017 0.9756 0.9887 0.653 0.905 0.9706 0.5587 ] Network output: [ 0.04023 0.8031 0.934 7.189e-05 -3.227e-05 0.1828 5.418e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01948 0.01261 0.02204 0.02496 0.9869 0.9908 0.01972 0.97 0.9823 0.02728 ] Network output: [ 0.03333 -0.1139 0.9372 -0.0009851 0.0004422 1.106 -0.0007424 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6472 0.4969 0.4521 0.5217 0.9782 0.9902 0.6486 0.9125 0.9742 0.5417 ] Network output: [ -0.09666 0.4307 1.135 -0.0004449 0.0001997 0.6262 -0.0003353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.237 0.2277 0.2253 0.229 0.9874 0.9918 0.2371 0.9718 0.983 0.2323 ] Network output: [ -0.1141 0.3362 1.111 -0.0004441 0.0001994 0.7789 -0.0003347 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2918 0.2895 0.2638 0.2639 0.9808 0.988 0.2918 0.9477 0.9716 0.2659 ] Network output: [ 0.05334 0.8055 -0.04648 0.0009542 -0.0004284 1.138 0.0007191 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1165 Epoch 3141 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0783 0.7481 0.9254 0.0002269 -0.0001019 0.1708 0.000171 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01689 -0.005146 0.01403 0.02989 0.9476 0.9552 0.02779 0.8917 0.9116 0.07054 ] Network output: [ 1.005 -0.02163 0.007622 0.000619 -0.0002779 0.006849 0.0004665 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6019 0.01955 0.1164 0.4016 0.9756 0.9887 0.6529 0.905 0.9706 0.5587 ] Network output: [ 0.04031 0.8039 0.9338 7.244e-05 -3.252e-05 0.182 5.459e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01951 0.01263 0.02211 0.02502 0.9869 0.9908 0.01975 0.97 0.9823 0.02735 ] Network output: [ 0.03387 -0.1154 0.9368 -0.0009777 0.0004389 1.107 -0.0007368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6471 0.4969 0.4522 0.5216 0.9782 0.9902 0.6484 0.9125 0.9742 0.5417 ] Network output: [ -0.09688 0.4284 1.136 -0.0004438 0.0001992 0.6278 -0.0003344 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2376 0.2282 0.2262 0.2299 0.9874 0.9918 0.2376 0.9718 0.983 0.2331 ] Network output: [ -0.114 0.3348 1.112 -0.0004412 0.0001981 0.7798 -0.0003325 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2921 0.2898 0.2643 0.2644 0.9808 0.988 0.2921 0.9477 0.9717 0.2664 ] Network output: [ 0.05322 0.8071 -0.04668 0.0009491 -0.0004261 1.137 0.0007153 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1156 Epoch 3142 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07839 0.7494 0.9249 0.0002268 -0.0001018 0.1698 0.0001709 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01691 -0.005148 0.01405 0.02991 0.9476 0.9552 0.02781 0.8917 0.9116 0.0706 ] Network output: [ 1.004 -0.0202 0.007467 0.0006128 -0.0002751 0.00633 0.0004618 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6018 0.01964 0.1166 0.4015 0.9756 0.9887 0.6528 0.905 0.9706 0.5587 ] Network output: [ 0.04039 0.8047 0.9335 7.293e-05 -3.274e-05 0.1813 5.496e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01953 0.01265 0.02217 0.02509 0.9869 0.9908 0.01978 0.97 0.9823 0.02742 ] Network output: [ 0.03442 -0.1169 0.9363 -0.0009703 0.0004356 1.108 -0.0007312 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6469 0.4968 0.4523 0.5216 0.9782 0.9902 0.6483 0.9125 0.9742 0.5417 ] Network output: [ -0.0971 0.4262 1.137 -0.0004426 0.0001987 0.6295 -0.0003335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2381 0.2287 0.227 0.2307 0.9874 0.9918 0.2382 0.9718 0.983 0.234 ] Network output: [ -0.1139 0.3335 1.112 -0.0004382 0.0001967 0.7808 -0.0003303 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2924 0.2901 0.2648 0.2649 0.9809 0.988 0.2924 0.9478 0.9717 0.2669 ] Network output: [ 0.05309 0.8087 -0.04686 0.000944 -0.0004238 1.136 0.0007114 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1148 Epoch 3143 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07847 0.7506 0.9245 0.0002266 -0.0001017 0.1689 0.0001708 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01692 -0.00515 0.01408 0.02992 0.9476 0.9552 0.02783 0.8917 0.9116 0.07065 ] Network output: [ 1.004 -0.01878 0.007321 0.0006068 -0.0002724 0.005817 0.0004573 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6017 0.01974 0.1168 0.4014 0.9756 0.9887 0.6527 0.905 0.9706 0.5587 ] Network output: [ 0.04046 0.8055 0.9333 7.336e-05 -3.293e-05 0.1806 5.529e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01956 0.01267 0.02223 0.02515 0.9869 0.9908 0.01981 0.97 0.9823 0.0275 ] Network output: [ 0.03497 -0.1183 0.9359 -0.0009629 0.0004323 1.109 -0.0007257 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6468 0.4968 0.4524 0.5216 0.9782 0.9902 0.6481 0.9125 0.9742 0.5417 ] Network output: [ -0.09731 0.424 1.138 -0.0004414 0.0001982 0.6311 -0.0003327 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2386 0.2292 0.2278 0.2315 0.9874 0.9918 0.2387 0.9718 0.983 0.2348 ] Network output: [ -0.1138 0.3322 1.112 -0.0004353 0.0001954 0.7817 -0.0003281 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2928 0.2905 0.2653 0.2654 0.9809 0.988 0.2928 0.9479 0.9717 0.2674 ] Network output: [ 0.05296 0.8103 -0.04704 0.0009388 -0.0004214 1.135 0.0007075 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1139 Epoch 3144 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07855 0.7519 0.9241 0.0002263 -0.0001016 0.1679 0.0001706 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01693 -0.005151 0.0141 0.02993 0.9476 0.9552 0.02785 0.8916 0.9116 0.07071 ] Network output: [ 1.004 -0.01737 0.007183 0.000601 -0.0002698 0.005308 0.0004529 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6016 0.01984 0.117 0.4013 0.9756 0.9887 0.6526 0.905 0.9706 0.5587 ] Network output: [ 0.04052 0.8064 0.9331 7.373e-05 -3.31e-05 0.1798 5.557e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01959 0.01269 0.02229 0.02521 0.9869 0.9908 0.01984 0.97 0.9823 0.02757 ] Network output: [ 0.03551 -0.1198 0.9355 -0.0009555 0.0004289 1.109 -0.0007201 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6466 0.4967 0.4525 0.5215 0.9782 0.9902 0.6479 0.9125 0.9742 0.5417 ] Network output: [ -0.09751 0.4218 1.139 -0.0004402 0.0001976 0.6328 -0.0003318 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2392 0.2298 0.2286 0.2323 0.9874 0.9918 0.2393 0.9718 0.983 0.2356 ] Network output: [ -0.1138 0.3308 1.112 -0.0004325 0.0001942 0.7826 -0.0003259 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2931 0.2908 0.2658 0.2659 0.9809 0.988 0.2931 0.9479 0.9718 0.2679 ] Network output: [ 0.05282 0.8118 -0.04719 0.0009335 -0.0004191 1.134 0.0007035 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1131 Epoch 3145 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07862 0.7531 0.9237 0.0002261 -0.0001015 0.1669 0.0001704 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01695 -0.005153 0.01412 0.02995 0.9476 0.9552 0.02787 0.8916 0.9116 0.07077 ] Network output: [ 1.003 -0.01596 0.007053 0.0005953 -0.0002673 0.004804 0.0004487 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6015 0.01995 0.1172 0.4011 0.9756 0.9887 0.6524 0.905 0.9706 0.5587 ] Network output: [ 0.04059 0.8072 0.9329 7.405e-05 -3.324e-05 0.1791 5.58e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01962 0.01271 0.02235 0.02527 0.9869 0.9908 0.01987 0.97 0.9823 0.02764 ] Network output: [ 0.03607 -0.1213 0.935 -0.000948 0.0004256 1.11 -0.0007145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6464 0.4967 0.4526 0.5215 0.9782 0.9902 0.6478 0.9125 0.9742 0.5416 ] Network output: [ -0.09771 0.4196 1.14 -0.000439 0.0001971 0.6344 -0.0003309 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2397 0.2303 0.2294 0.2331 0.9874 0.9918 0.2398 0.9717 0.983 0.2364 ] Network output: [ -0.1137 0.3295 1.113 -0.0004297 0.0001929 0.7835 -0.0003238 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2934 0.2911 0.2663 0.2664 0.9809 0.988 0.2934 0.948 0.9718 0.2684 ] Network output: [ 0.05269 0.8133 -0.04734 0.0009281 -0.0004167 1.132 0.0006995 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1123 Epoch 3146 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07869 0.7544 0.9233 0.0002257 -0.0001013 0.1659 0.0001701 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01696 -0.005154 0.01414 0.02996 0.9476 0.9552 0.02789 0.8916 0.9116 0.07082 ] Network output: [ 1.003 -0.01457 0.006931 0.0005899 -0.0002648 0.004306 0.0004446 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6014 0.02006 0.1173 0.401 0.9756 0.9887 0.6523 0.905 0.9706 0.5587 ] Network output: [ 0.04065 0.808 0.9327 7.43e-05 -3.336e-05 0.1783 5.6e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01965 0.01273 0.02241 0.02534 0.9869 0.9908 0.01989 0.97 0.9823 0.02771 ] Network output: [ 0.03662 -0.1227 0.9345 -0.0009406 0.0004223 1.111 -0.0007089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6463 0.4966 0.4526 0.5214 0.9782 0.9902 0.6476 0.9125 0.9742 0.5416 ] Network output: [ -0.09789 0.4174 1.141 -0.0004378 0.0001966 0.636 -0.00033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2403 0.2308 0.2302 0.2339 0.9874 0.9918 0.2404 0.9717 0.983 0.2372 ] Network output: [ -0.1136 0.3282 1.113 -0.0004269 0.0001917 0.7845 -0.0003218 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2938 0.2915 0.2668 0.2669 0.9809 0.988 0.2938 0.948 0.9718 0.2689 ] Network output: [ 0.05255 0.8149 -0.04748 0.0009227 -0.0004143 1.131 0.0006954 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1115 Epoch 3147 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07875 0.7556 0.9229 0.0002254 -0.0001012 0.1649 0.0001699 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01697 -0.005155 0.01416 0.02997 0.9476 0.9552 0.02791 0.8916 0.9116 0.07088 ] Network output: [ 1.002 -0.01318 0.006816 0.0005847 -0.0002625 0.003812 0.0004406 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6013 0.02018 0.1175 0.4008 0.9756 0.9887 0.6522 0.905 0.9706 0.5587 ] Network output: [ 0.0407 0.8088 0.9325 7.45e-05 -3.345e-05 0.1776 5.615e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01968 0.01275 0.02247 0.0254 0.9869 0.9908 0.01992 0.97 0.9823 0.02778 ] Network output: [ 0.03717 -0.1241 0.9341 -0.0009332 0.000419 1.112 -0.0007033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6461 0.4965 0.4527 0.5214 0.9782 0.9902 0.6474 0.9125 0.9742 0.5416 ] Network output: [ -0.09807 0.4152 1.141 -0.0004366 0.000196 0.6377 -0.0003291 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2409 0.2314 0.231 0.2347 0.9874 0.9918 0.241 0.9717 0.983 0.2381 ] Network output: [ -0.1135 0.3269 1.113 -0.0004242 0.0001904 0.7854 -0.0003197 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2941 0.2918 0.2673 0.2674 0.9809 0.988 0.2941 0.9481 0.9719 0.2694 ] Network output: [ 0.0524 0.8164 -0.0476 0.0009173 -0.0004118 1.13 0.0006913 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1107 Epoch 3148 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07881 0.7568 0.9226 0.000225 -0.000101 0.1639 0.0001696 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01698 -0.005157 0.01418 0.02998 0.9476 0.9552 0.02793 0.8916 0.9116 0.07093 ] Network output: [ 1.002 -0.01181 0.006709 0.0005797 -0.0002602 0.003323 0.0004369 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6011 0.02031 0.1177 0.4007 0.9756 0.9887 0.652 0.905 0.9706 0.5586 ] Network output: [ 0.04076 0.8097 0.9323 7.465e-05 -3.351e-05 0.1768 5.626e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01971 0.01277 0.02253 0.02545 0.9869 0.9908 0.01995 0.97 0.9823 0.02785 ] Network output: [ 0.03773 -0.1256 0.9336 -0.0009258 0.0004156 1.113 -0.0006977 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6459 0.4965 0.4528 0.5213 0.9782 0.9902 0.6473 0.9125 0.9742 0.5416 ] Network output: [ -0.09825 0.413 1.142 -0.0004354 0.0001955 0.6393 -0.0003282 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2414 0.2319 0.2318 0.2355 0.9874 0.9918 0.2415 0.9717 0.983 0.2389 ] Network output: [ -0.1134 0.3256 1.113 -0.0004216 0.0001893 0.7863 -0.0003177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2944 0.2922 0.2678 0.2679 0.981 0.988 0.2945 0.9482 0.9719 0.2699 ] Network output: [ 0.05226 0.8178 -0.04772 0.0009117 -0.0004093 1.129 0.0006871 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1099 Epoch 3149 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07886 0.7581 0.9222 0.0002245 -0.0001008 0.1629 0.0001692 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.017 -0.005158 0.0142 0.02999 0.9476 0.9552 0.02795 0.8916 0.9116 0.07099 ] Network output: [ 1.002 -0.01044 0.006609 0.0005748 -0.0002581 0.002838 0.0004332 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.601 0.02044 0.1178 0.4005 0.9756 0.9887 0.6519 0.905 0.9706 0.5586 ] Network output: [ 0.04081 0.8105 0.9321 7.474e-05 -3.355e-05 0.176 5.632e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01974 0.01279 0.02259 0.02551 0.9869 0.9908 0.01998 0.97 0.9823 0.02792 ] Network output: [ 0.03828 -0.127 0.9332 -0.0009184 0.0004123 1.114 -0.0006921 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6457 0.4964 0.4528 0.5212 0.9782 0.9902 0.6471 0.9125 0.9742 0.5416 ] Network output: [ -0.09841 0.4109 1.143 -0.0004342 0.0001949 0.641 -0.0003273 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.242 0.2325 0.2325 0.2362 0.9874 0.9918 0.2421 0.9717 0.983 0.2397 ] Network output: [ -0.1132 0.3243 1.113 -0.0004189 0.0001881 0.7872 -0.0003157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2948 0.2925 0.2683 0.2683 0.981 0.9881 0.2948 0.9482 0.9719 0.2704 ] Network output: [ 0.05211 0.8193 -0.04782 0.0009061 -0.0004068 1.128 0.0006829 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1091 Epoch 3150 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07891 0.7593 0.9219 0.0002241 -0.0001006 0.1619 0.0001689 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01701 -0.005158 0.01422 0.03 0.9476 0.9552 0.02797 0.8916 0.9116 0.07104 ] Network output: [ 1.001 -0.009078 0.006515 0.0005702 -0.000256 0.002358 0.0004297 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6009 0.02058 0.1179 0.4004 0.9756 0.9887 0.6517 0.905 0.9706 0.5586 ] Network output: [ 0.04085 0.8113 0.932 7.477e-05 -3.357e-05 0.1753 5.635e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01976 0.01281 0.02264 0.02557 0.9869 0.9908 0.02001 0.97 0.9823 0.02798 ] Network output: [ 0.03884 -0.1284 0.9327 -0.000911 0.000409 1.114 -0.0006865 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6455 0.4964 0.4528 0.5212 0.9782 0.9902 0.6469 0.9125 0.9742 0.5415 ] Network output: [ -0.09857 0.4088 1.144 -0.0004331 0.0001944 0.6426 -0.0003264 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2426 0.2331 0.2333 0.237 0.9874 0.9918 0.2427 0.9717 0.983 0.2404 ] Network output: [ -0.1131 0.323 1.113 -0.0004164 0.0001869 0.7881 -0.0003138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2951 0.2928 0.2688 0.2688 0.981 0.9881 0.2951 0.9483 0.972 0.2709 ] Network output: [ 0.05196 0.8208 -0.04791 0.0009005 -0.0004042 1.127 0.0006786 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1083 Epoch 3151 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07896 0.7605 0.9216 0.0002236 -0.0001004 0.1609 0.0001685 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01702 -0.005159 0.01424 0.03001 0.9476 0.9552 0.02799 0.8916 0.9115 0.07109 ] Network output: [ 1.001 -0.007726 0.006429 0.0005658 -0.000254 0.001883 0.0004264 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6007 0.02073 0.118 0.4002 0.9757 0.9887 0.6516 0.905 0.9706 0.5585 ] Network output: [ 0.0409 0.8121 0.9318 7.475e-05 -3.356e-05 0.1745 5.633e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01979 0.01283 0.0227 0.02563 0.9869 0.9908 0.02004 0.97 0.9823 0.02805 ] Network output: [ 0.03939 -0.1298 0.9322 -0.0009036 0.0004056 1.115 -0.000681 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6454 0.4963 0.4529 0.5211 0.9782 0.9902 0.6467 0.9125 0.9742 0.5415 ] Network output: [ -0.09872 0.4066 1.145 -0.0004319 0.0001939 0.6443 -0.0003255 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2432 0.2336 0.2341 0.2378 0.9874 0.9918 0.2432 0.9717 0.983 0.2412 ] Network output: [ -0.113 0.3217 1.114 -0.0004139 0.0001858 0.789 -0.0003119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2954 0.2932 0.2693 0.2693 0.981 0.9881 0.2955 0.9483 0.972 0.2714 ] Network output: [ 0.05181 0.8222 -0.04799 0.0008947 -0.0004017 1.126 0.0006743 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1075 Epoch 3152 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.079 0.7617 0.9213 0.000223 -0.0001001 0.1599 0.0001681 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01703 -0.00516 0.01425 0.03002 0.9476 0.9552 0.028 0.8916 0.9115 0.07114 ] Network output: [ 1 -0.006383 0.006348 0.0005616 -0.0002521 0.001412 0.0004232 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6006 0.02088 0.1181 0.4001 0.9757 0.9887 0.6514 0.905 0.9706 0.5585 ] Network output: [ 0.04094 0.813 0.9317 7.468e-05 -3.353e-05 0.1738 5.628e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01982 0.01285 0.02276 0.02569 0.9869 0.9908 0.02007 0.97 0.9823 0.02812 ] Network output: [ 0.03995 -0.1311 0.9317 -0.0008962 0.0004023 1.116 -0.0006754 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6452 0.4963 0.4529 0.521 0.9782 0.9902 0.6465 0.9125 0.9742 0.5415 ] Network output: [ -0.09886 0.4045 1.146 -0.0004307 0.0001934 0.6459 -0.0003246 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2437 0.2342 0.2349 0.2385 0.9874 0.9918 0.2438 0.9717 0.983 0.242 ] Network output: [ -0.1129 0.3204 1.114 -0.0004114 0.0001847 0.7899 -0.00031 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2958 0.2935 0.2697 0.2698 0.981 0.9881 0.2958 0.9484 0.972 0.2718 ] Network output: [ 0.05165 0.8237 -0.04806 0.0008889 -0.0003991 1.125 0.0006699 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1068 Epoch 3153 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07903 0.763 0.921 0.0002224 -9.986e-05 0.1589 0.0001676 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01704 -0.00516 0.01427 0.03003 0.9476 0.9552 0.02802 0.8916 0.9115 0.07119 ] Network output: [ 1 -0.005047 0.006275 0.0005575 -0.0002503 0.0009461 0.0004202 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6005 0.02104 0.1182 0.3999 0.9757 0.9887 0.6512 0.905 0.9706 0.5585 ] Network output: [ 0.04098 0.8138 0.9316 7.455e-05 -3.347e-05 0.173 5.618e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01985 0.01288 0.02281 0.02574 0.9869 0.9908 0.0201 0.97 0.9823 0.02818 ] Network output: [ 0.04051 -0.1325 0.9312 -0.0008888 0.000399 1.117 -0.0006698 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.645 0.4962 0.4529 0.5209 0.9782 0.9902 0.6463 0.9125 0.9742 0.5414 ] Network output: [ -0.099 0.4024 1.146 -0.0004295 0.0001928 0.6475 -0.0003237 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2443 0.2348 0.2356 0.2393 0.9874 0.9918 0.2444 0.9717 0.983 0.2428 ] Network output: [ -0.1128 0.3192 1.114 -0.000409 0.0001836 0.7908 -0.0003082 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2961 0.2938 0.2702 0.2702 0.9811 0.9881 0.2961 0.9485 0.972 0.2723 ] Network output: [ 0.0515 0.8251 -0.04812 0.0008831 -0.0003964 1.124 0.0006655 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.106 Epoch 3154 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07906 0.7642 0.9207 0.0002218 -9.958e-05 0.1579 0.0001672 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01705 -0.00516 0.01429 0.03004 0.9476 0.9552 0.02804 0.8916 0.9115 0.07124 ] Network output: [ 0.9996 -0.00372 0.006207 0.0005537 -0.0002486 0.0004841 0.0004173 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6003 0.02121 0.1183 0.3997 0.9757 0.9887 0.6511 0.905 0.9706 0.5584 ] Network output: [ 0.04101 0.8146 0.9315 7.437e-05 -3.339e-05 0.1722 5.605e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01988 0.0129 0.02286 0.0258 0.9869 0.9908 0.02013 0.97 0.9823 0.02825 ] Network output: [ 0.04107 -0.1339 0.9308 -0.0008813 0.0003957 1.117 -0.0006642 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6448 0.4962 0.4529 0.5208 0.9782 0.9902 0.6461 0.9125 0.9742 0.5414 ] Network output: [ -0.09913 0.4004 1.147 -0.0004283 0.0001923 0.6492 -0.0003228 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2449 0.2353 0.2364 0.24 0.9874 0.9918 0.245 0.9717 0.983 0.2436 ] Network output: [ -0.1127 0.3179 1.114 -0.0004066 0.0001825 0.7917 -0.0003064 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2965 0.2942 0.2707 0.2707 0.9811 0.9881 0.2965 0.9485 0.9721 0.2728 ] Network output: [ 0.05134 0.8265 -0.04817 0.0008772 -0.0003938 1.123 0.0006611 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1053 Epoch 3155 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07909 0.7654 0.9205 0.0002212 -9.929e-05 0.1568 0.0001667 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01707 -0.00516 0.0143 0.03005 0.9476 0.9552 0.02805 0.8916 0.9115 0.07129 ] Network output: [ 0.9992 -0.002401 0.006145 0.0005501 -0.000247 2.633e-05 0.0004146 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6001 0.02138 0.1183 0.3996 0.9757 0.9887 0.6509 0.905 0.9706 0.5584 ] Network output: [ 0.04104 0.8154 0.9313 7.415e-05 -3.329e-05 0.1715 5.588e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01991 0.01292 0.02292 0.02585 0.9869 0.9908 0.02016 0.97 0.9823 0.02831 ] Network output: [ 0.04163 -0.1352 0.9303 -0.0008739 0.0003923 1.118 -0.0006586 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6445 0.4961 0.4529 0.5208 0.9782 0.9902 0.6459 0.9125 0.9742 0.5413 ] Network output: [ -0.09925 0.3983 1.148 -0.0004272 0.0001918 0.6508 -0.0003219 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2455 0.2359 0.2371 0.2408 0.9874 0.9918 0.2456 0.9717 0.9829 0.2443 ] Network output: [ -0.1126 0.3167 1.114 -0.0004043 0.0001815 0.7926 -0.0003047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2968 0.2945 0.2711 0.2711 0.9811 0.9881 0.2968 0.9486 0.9721 0.2732 ] Network output: [ 0.05117 0.8279 -0.04822 0.0008712 -0.0003911 1.122 0.0006566 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1045 Epoch 3156 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07912 0.7666 0.9202 0.0002205 -9.899e-05 0.1558 0.0001662 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01708 -0.00516 0.01431 0.03006 0.9476 0.9552 0.02807 0.8916 0.9115 0.07134 ] Network output: [ 0.9988 -0.001089 0.006089 0.0005467 -0.0002454 -0.0004273 0.000412 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.6 0.02156 0.1183 0.3994 0.9757 0.9887 0.6507 0.905 0.9706 0.5583 ] Network output: [ 0.04107 0.8162 0.9312 7.387e-05 -3.316e-05 0.1707 5.567e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01994 0.01294 0.02297 0.02591 0.9869 0.9908 0.02019 0.9699 0.9823 0.02837 ] Network output: [ 0.04218 -0.1365 0.9298 -0.0008665 0.000389 1.119 -0.0006531 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6443 0.4961 0.4528 0.5207 0.9782 0.9902 0.6457 0.9125 0.9742 0.5413 ] Network output: [ -0.09936 0.3962 1.148 -0.0004261 0.0001913 0.6524 -0.0003211 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2461 0.2365 0.2378 0.2415 0.9874 0.9918 0.2462 0.9717 0.9829 0.2451 ] Network output: [ -0.1125 0.3154 1.114 -0.0004021 0.0001805 0.7934 -0.000303 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2971 0.2948 0.2716 0.2716 0.9811 0.9881 0.2971 0.9486 0.9721 0.2737 ] Network output: [ 0.05101 0.8293 -0.04825 0.0008652 -0.0003884 1.12 0.000652 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1038 Epoch 3157 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07914 0.7678 0.92 0.0002198 -9.867e-05 0.1548 0.0001656 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01709 -0.00516 0.01432 0.03006 0.9476 0.9552 0.02809 0.8916 0.9115 0.07138 ] Network output: [ 0.9984 0.0002143 0.006039 0.0005435 -0.000244 -0.0008768 0.0004096 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5998 0.02174 0.1184 0.3992 0.9757 0.9887 0.6505 0.905 0.9706 0.5582 ] Network output: [ 0.0411 0.817 0.9311 7.354e-05 -3.302e-05 0.17 5.542e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01997 0.01296 0.02302 0.02596 0.9869 0.9908 0.02022 0.9699 0.9823 0.02844 ] Network output: [ 0.04274 -0.1379 0.9293 -0.0008591 0.0003857 1.12 -0.0006475 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6441 0.496 0.4528 0.5206 0.9782 0.9902 0.6455 0.9125 0.9742 0.5412 ] Network output: [ -0.09947 0.3942 1.149 -0.0004249 0.0001908 0.654 -0.0003202 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2467 0.237 0.2386 0.2422 0.9874 0.9918 0.2468 0.9716 0.9829 0.2458 ] Network output: [ -0.1123 0.3142 1.115 -0.0003999 0.0001795 0.7943 -0.0003014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2974 0.2952 0.272 0.272 0.9811 0.9881 0.2975 0.9487 0.9722 0.2741 ] Network output: [ 0.05084 0.8306 -0.04827 0.0008591 -0.0003857 1.119 0.0006474 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.103 Epoch 3158 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07915 0.769 0.9198 0.0002191 -9.835e-05 0.1538 0.0001651 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0171 -0.005159 0.01434 0.03007 0.9476 0.9552 0.0281 0.8916 0.9115 0.07143 ] Network output: [ 0.998 0.00151 0.005995 0.0005404 -0.0002426 -0.001322 0.0004073 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5997 0.02193 0.1184 0.399 0.9757 0.9887 0.6503 0.9049 0.9706 0.5582 ] Network output: [ 0.04112 0.8178 0.931 7.317e-05 -3.285e-05 0.1692 5.514e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02 0.01299 0.02307 0.02601 0.9869 0.9908 0.02025 0.9699 0.9823 0.0285 ] Network output: [ 0.0433 -0.1392 0.9288 -0.0008518 0.0003824 1.12 -0.0006419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6439 0.496 0.4527 0.5205 0.9782 0.9902 0.6452 0.9125 0.9742 0.5412 ] Network output: [ -0.09957 0.3922 1.15 -0.0004238 0.0001903 0.6556 -0.0003194 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2473 0.2376 0.2393 0.243 0.9874 0.9918 0.2474 0.9716 0.9829 0.2466 ] Network output: [ -0.1122 0.3129 1.115 -0.0003978 0.0001786 0.7952 -0.0002998 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2978 0.2955 0.2725 0.2724 0.9811 0.9882 0.2978 0.9488 0.9722 0.2746 ] Network output: [ 0.05067 0.832 -0.04829 0.0008529 -0.0003829 1.118 0.0006428 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1023 Epoch 3159 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07917 0.7702 0.9195 0.0002183 -9.8e-05 0.1528 0.0001645 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01711 -0.005159 0.01435 0.03008 0.9476 0.9552 0.02812 0.8915 0.9115 0.07148 ] Network output: [ 0.9976 0.002798 0.005956 0.0005376 -0.0002413 -0.001764 0.0004052 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5995 0.02213 0.1184 0.3989 0.9757 0.9887 0.6502 0.9049 0.9706 0.5581 ] Network output: [ 0.04114 0.8186 0.931 7.274e-05 -3.266e-05 0.1684 5.482e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02003 0.01301 0.02312 0.02606 0.9869 0.9908 0.02028 0.9699 0.9823 0.02856 ] Network output: [ 0.04386 -0.1405 0.9283 -0.0008444 0.0003791 1.121 -0.0006363 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6437 0.4959 0.4527 0.5204 0.9783 0.9902 0.645 0.9125 0.9742 0.5411 ] Network output: [ -0.09966 0.3902 1.15 -0.0004227 0.0001898 0.6572 -0.0003186 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2479 0.2382 0.24 0.2437 0.9874 0.9918 0.2479 0.9716 0.9829 0.2473 ] Network output: [ -0.1121 0.3117 1.115 -0.0003957 0.0001776 0.796 -0.0002982 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2981 0.2958 0.2729 0.2729 0.9812 0.9882 0.2981 0.9488 0.9722 0.275 ] Network output: [ 0.0505 0.8333 -0.04829 0.0008467 -0.0003801 1.117 0.0006381 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1016 Epoch 3160 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07918 0.7713 0.9193 0.0002175 -9.765e-05 0.1519 0.0001639 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01711 -0.005158 0.01435 0.03008 0.9476 0.9552 0.02813 0.8915 0.9115 0.07152 ] Network output: [ 0.9972 0.004078 0.005922 0.000535 -0.0002402 -0.002202 0.0004032 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5993 0.02234 0.1183 0.3987 0.9757 0.9887 0.65 0.9049 0.9705 0.5581 ] Network output: [ 0.04116 0.8194 0.9309 7.227e-05 -3.245e-05 0.1677 5.447e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02006 0.01303 0.02317 0.02611 0.9869 0.9908 0.02031 0.9699 0.9823 0.02862 ] Network output: [ 0.04441 -0.1417 0.9278 -0.000837 0.0003758 1.122 -0.0006308 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6434 0.4959 0.4526 0.5202 0.9783 0.9902 0.6448 0.9125 0.9742 0.541 ] Network output: [ -0.09975 0.3882 1.151 -0.0004216 0.0001893 0.6588 -0.0003177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2484 0.2388 0.2407 0.2444 0.9874 0.9918 0.2485 0.9716 0.9829 0.2481 ] Network output: [ -0.112 0.3105 1.115 -0.0003937 0.0001767 0.7969 -0.0002967 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2984 0.2962 0.2733 0.2733 0.9812 0.9882 0.2984 0.9489 0.9723 0.2755 ] Network output: [ 0.05032 0.8347 -0.04829 0.0008404 -0.0003773 1.116 0.0006334 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1009 Epoch 3161 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07918 0.7725 0.9191 0.0002167 -9.728e-05 0.1509 0.0001633 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01712 -0.005157 0.01436 0.03009 0.9476 0.9552 0.02815 0.8915 0.9115 0.07156 ] Network output: [ 0.9968 0.005351 0.005894 0.0005325 -0.0002391 -0.002636 0.0004013 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5991 0.02255 0.1183 0.3985 0.9757 0.9887 0.6497 0.9049 0.9705 0.558 ] Network output: [ 0.04117 0.8202 0.9308 7.176e-05 -3.222e-05 0.1669 5.408e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02009 0.01305 0.02321 0.02616 0.9869 0.9908 0.02034 0.9699 0.9823 0.02868 ] Network output: [ 0.04497 -0.143 0.9273 -0.0008296 0.0003724 1.122 -0.0006252 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6432 0.4958 0.4525 0.5201 0.9783 0.9902 0.6445 0.9125 0.9742 0.541 ] Network output: [ -0.09983 0.3862 1.151 -0.0004205 0.0001888 0.6604 -0.0003169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.249 0.2393 0.2414 0.2451 0.9874 0.9918 0.2491 0.9716 0.9829 0.2488 ] Network output: [ -0.1118 0.3093 1.115 -0.0003917 0.0001758 0.7977 -0.0002952 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2988 0.2965 0.2738 0.2737 0.9812 0.9882 0.2988 0.9489 0.9723 0.2759 ] Network output: [ 0.05015 0.836 -0.04828 0.0008341 -0.0003745 1.115 0.0006286 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1002 Epoch 3162 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07919 0.7737 0.919 0.0002159 -9.691e-05 0.1499 0.0001627 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01713 -0.005156 0.01437 0.03009 0.9476 0.9552 0.02816 0.8915 0.9115 0.07161 ] Network output: [ 0.9964 0.006616 0.005871 0.0005303 -0.0002381 -0.003066 0.0003996 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5989 0.02277 0.1182 0.3983 0.9757 0.9887 0.6495 0.9049 0.9705 0.5579 ] Network output: [ 0.04119 0.821 0.9308 7.12e-05 -3.196e-05 0.1662 5.366e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02012 0.01308 0.02326 0.02621 0.9869 0.9908 0.02037 0.9699 0.9823 0.02873 ] Network output: [ 0.04552 -0.1443 0.9268 -0.0008222 0.0003691 1.123 -0.0006197 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.643 0.4958 0.4524 0.52 0.9783 0.9902 0.6443 0.9125 0.9742 0.5409 ] Network output: [ -0.09991 0.3842 1.152 -0.0004195 0.0001883 0.662 -0.0003161 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2496 0.2399 0.2421 0.2458 0.9874 0.9918 0.2497 0.9716 0.9829 0.2495 ] Network output: [ -0.1117 0.308 1.115 -0.0003898 0.000175 0.7986 -0.0002938 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2991 0.2968 0.2742 0.2741 0.9812 0.9882 0.2991 0.949 0.9723 0.2763 ] Network output: [ 0.04997 0.8373 -0.04827 0.0008278 -0.0003716 1.114 0.0006238 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09952 Epoch 3163 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07919 0.7748 0.9188 0.000215 -9.652e-05 0.1489 0.000162 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01714 -0.005154 0.01438 0.0301 0.9476 0.9552 0.02817 0.8915 0.9115 0.07165 ] Network output: [ 0.996 0.007873 0.005853 0.0005282 -0.0002371 -0.003493 0.0003981 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5987 0.023 0.1182 0.3981 0.9757 0.9887 0.6493 0.9049 0.9705 0.5578 ] Network output: [ 0.0412 0.8218 0.9307 7.06e-05 -3.169e-05 0.1654 5.32e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02014 0.0131 0.0233 0.02626 0.9869 0.9907 0.0204 0.9699 0.9823 0.02879 ] Network output: [ 0.04608 -0.1455 0.9263 -0.0008149 0.0003658 1.124 -0.0006141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6427 0.4957 0.4523 0.5199 0.9783 0.9902 0.644 0.9125 0.9742 0.5408 ] Network output: [ -0.09997 0.3823 1.152 -0.0004184 0.0001879 0.6636 -0.0003154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2502 0.2405 0.2428 0.2465 0.9874 0.9918 0.2503 0.9716 0.9829 0.2502 ] Network output: [ -0.1116 0.3068 1.115 -0.000388 0.0001742 0.7994 -0.0002924 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2994 0.2971 0.2746 0.2746 0.9812 0.9882 0.2994 0.9491 0.9723 0.2767 ] Network output: [ 0.04979 0.8386 -0.04824 0.0008213 -0.0003687 1.113 0.000619 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09884 Epoch 3164 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07918 0.776 0.9186 0.0002141 -9.612e-05 0.1479 0.0001614 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01715 -0.005153 0.01438 0.0301 0.9476 0.9552 0.02819 0.8915 0.9115 0.07169 ] Network output: [ 0.9955 0.009123 0.005839 0.0005263 -0.0002363 -0.003916 0.0003967 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5985 0.02323 0.1181 0.3979 0.9757 0.9887 0.6491 0.9049 0.9705 0.5578 ] Network output: [ 0.04121 0.8226 0.9306 6.995e-05 -3.14e-05 0.1647 5.272e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02017 0.01312 0.02335 0.02631 0.9869 0.9907 0.02042 0.9699 0.9823 0.02885 ] Network output: [ 0.04663 -0.1468 0.9258 -0.0008075 0.0003625 1.124 -0.0006086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6425 0.4957 0.4522 0.5198 0.9783 0.9902 0.6438 0.9125 0.9742 0.5407 ] Network output: [ -0.1 0.3804 1.153 -0.0004174 0.0001874 0.6652 -0.0003146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2508 0.2411 0.2435 0.2472 0.9874 0.9918 0.2509 0.9716 0.9829 0.2509 ] Network output: [ -0.1114 0.3056 1.115 -0.0003862 0.0001734 0.8003 -0.000291 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2997 0.2974 0.275 0.275 0.9812 0.9882 0.2997 0.9491 0.9724 0.2772 ] Network output: [ 0.04961 0.8399 -0.04821 0.0008149 -0.0003658 1.112 0.0006141 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09816 Epoch 3165 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07917 0.7771 0.9185 0.0002132 -9.571e-05 0.1469 0.0001607 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01716 -0.005151 0.01439 0.03011 0.9476 0.9553 0.0282 0.8915 0.9115 0.07173 ] Network output: [ 0.9951 0.01037 0.005831 0.0005247 -0.0002355 -0.004336 0.0003954 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5983 0.02347 0.118 0.3977 0.9757 0.9887 0.6489 0.9049 0.9705 0.5577 ] Network output: [ 0.04121 0.8233 0.9306 6.927e-05 -3.11e-05 0.1639 5.22e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0202 0.01314 0.02339 0.02635 0.9869 0.9907 0.02045 0.9699 0.9823 0.0289 ] Network output: [ 0.04718 -0.148 0.9253 -0.0008001 0.0003592 1.125 -0.000603 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6422 0.4956 0.4521 0.5197 0.9783 0.9902 0.6435 0.9125 0.9742 0.5407 ] Network output: [ -0.1001 0.3784 1.153 -0.0004164 0.000187 0.6668 -0.0003138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2514 0.2417 0.2441 0.2478 0.9874 0.9918 0.2515 0.9716 0.9829 0.2516 ] Network output: [ -0.1113 0.3044 1.115 -0.0003844 0.0001726 0.8011 -0.0002897 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3 0.2977 0.2755 0.2754 0.9813 0.9882 0.3 0.9492 0.9724 0.2776 ] Network output: [ 0.04942 0.8412 -0.04817 0.0008084 -0.0003629 1.111 0.0006092 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0975 Epoch 3166 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07916 0.7783 0.9183 0.0002123 -9.529e-05 0.146 0.00016 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01716 -0.00515 0.01439 0.03011 0.9476 0.9553 0.02821 0.8915 0.9115 0.07177 ] Network output: [ 0.9947 0.0116 0.005828 0.0005232 -0.0002349 -0.004752 0.0003943 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5981 0.02371 0.1178 0.3975 0.9757 0.9887 0.6486 0.9049 0.9705 0.5576 ] Network output: [ 0.04121 0.8241 0.9306 6.854e-05 -3.077e-05 0.1632 5.165e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02023 0.01317 0.02343 0.0264 0.9869 0.9907 0.02048 0.9699 0.9822 0.02896 ] Network output: [ 0.04773 -0.1492 0.9248 -0.0007928 0.0003559 1.126 -0.0005975 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6419 0.4956 0.452 0.5195 0.9783 0.9902 0.6433 0.9125 0.9742 0.5406 ] Network output: [ -0.1001 0.3765 1.154 -0.0004155 0.0001865 0.6683 -0.0003131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.252 0.2422 0.2448 0.2485 0.9874 0.9918 0.2521 0.9715 0.9829 0.2523 ] Network output: [ -0.1112 0.3033 1.116 -0.0003828 0.0001718 0.8019 -0.0002885 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3003 0.2981 0.2759 0.2758 0.9813 0.9882 0.3003 0.9492 0.9724 0.278 ] Network output: [ 0.04924 0.8424 -0.04813 0.0008018 -0.00036 1.11 0.0006043 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09684 Epoch 3167 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07915 0.7794 0.9182 0.0002113 -9.487e-05 0.145 0.0001593 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01717 -0.005148 0.01439 0.03012 0.9476 0.9553 0.02822 0.8915 0.9115 0.07181 ] Network output: [ 0.9943 0.01283 0.005829 0.0005218 -0.0002343 -0.005165 0.0003933 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5979 0.02397 0.1177 0.3973 0.9757 0.9887 0.6484 0.9049 0.9705 0.5575 ] Network output: [ 0.04122 0.8249 0.9305 6.777e-05 -3.043e-05 0.1624 5.108e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02026 0.01319 0.02347 0.02644 0.9869 0.9907 0.02051 0.9698 0.9822 0.02901 ] Network output: [ 0.04828 -0.1504 0.9243 -0.0007855 0.0003526 1.126 -0.0005919 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6417 0.4955 0.4518 0.5194 0.9783 0.9902 0.643 0.9125 0.9742 0.5405 ] Network output: [ -0.1002 0.3746 1.154 -0.0004145 0.0001861 0.6699 -0.0003124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2526 0.2428 0.2455 0.2492 0.9874 0.9918 0.2526 0.9715 0.9829 0.253 ] Network output: [ -0.111 0.3021 1.116 -0.0003812 0.0001711 0.8028 -0.0002873 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3006 0.2984 0.2763 0.2762 0.9813 0.9883 0.3006 0.9493 0.9724 0.2784 ] Network output: [ 0.04905 0.8437 -0.04808 0.0007952 -0.000357 1.11 0.0005993 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09618 Epoch 3168 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07914 0.7805 0.9181 0.0002103 -9.443e-05 0.144 0.0001585 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01718 -0.005145 0.01439 0.03012 0.9476 0.9553 0.02823 0.8915 0.9115 0.07185 ] Network output: [ 0.9939 0.01405 0.005835 0.0005207 -0.0002338 -0.005575 0.0003924 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5977 0.02423 0.1176 0.3971 0.9757 0.9887 0.6482 0.9049 0.9705 0.5574 ] Network output: [ 0.04121 0.8256 0.9305 6.697e-05 -3.006e-05 0.1617 5.047e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02028 0.01321 0.02351 0.02648 0.9869 0.9907 0.02054 0.9698 0.9822 0.02906 ] Network output: [ 0.04883 -0.1516 0.9238 -0.0007781 0.0003493 1.127 -0.0005864 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6414 0.4955 0.4517 0.5193 0.9783 0.9902 0.6427 0.9125 0.9742 0.5404 ] Network output: [ -0.1002 0.3728 1.155 -0.0004136 0.0001857 0.6714 -0.0003117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2531 0.2434 0.2461 0.2498 0.9874 0.9918 0.2532 0.9715 0.9829 0.2537 ] Network output: [ -0.1109 0.3009 1.116 -0.0003796 0.0001704 0.8036 -0.0002861 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3009 0.2987 0.2767 0.2765 0.9813 0.9883 0.3009 0.9493 0.9725 0.2788 ] Network output: [ 0.04886 0.8449 -0.04802 0.0007886 -0.000354 1.109 0.0005943 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09554 Epoch 3169 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07912 0.7816 0.9179 0.0002094 -9.399e-05 0.1431 0.0001578 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01718 -0.005143 0.01439 0.03012 0.9476 0.9553 0.02824 0.8915 0.9114 0.07188 ] Network output: [ 0.9935 0.01526 0.005846 0.0005197 -0.0002333 -0.005982 0.0003917 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5974 0.02449 0.1174 0.3969 0.9757 0.9887 0.6479 0.9049 0.9705 0.5573 ] Network output: [ 0.04121 0.8264 0.9305 6.613e-05 -2.969e-05 0.161 4.983e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02031 0.01323 0.02355 0.02653 0.9869 0.9907 0.02057 0.9698 0.9822 0.02911 ] Network output: [ 0.04938 -0.1528 0.9233 -0.0007708 0.000346 1.128 -0.0005809 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6411 0.4955 0.4515 0.5191 0.9783 0.9902 0.6425 0.9125 0.9742 0.5403 ] Network output: [ -0.1002 0.3709 1.155 -0.0004126 0.0001853 0.673 -0.000311 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2537 0.2439 0.2467 0.2505 0.9874 0.9918 0.2538 0.9715 0.9829 0.2543 ] Network output: [ -0.1108 0.2997 1.116 -0.0003781 0.0001698 0.8044 -0.000285 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3012 0.299 0.277 0.2769 0.9813 0.9883 0.3012 0.9494 0.9725 0.2792 ] Network output: [ 0.04867 0.8462 -0.04795 0.0007819 -0.000351 1.108 0.0005892 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0949 Epoch 3170 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0791 0.7827 0.9178 0.0002083 -9.353e-05 0.1421 0.000157 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01719 -0.005141 0.01439 0.03012 0.9476 0.9553 0.02825 0.8915 0.9114 0.07192 ] Network output: [ 0.9931 0.01646 0.005861 0.0005189 -0.000233 -0.006386 0.0003911 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5972 0.02477 0.1172 0.3967 0.9757 0.9887 0.6477 0.9049 0.9705 0.5572 ] Network output: [ 0.04121 0.8271 0.9305 6.525e-05 -2.929e-05 0.1602 4.917e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02034 0.01326 0.02359 0.02657 0.9869 0.9907 0.02059 0.9698 0.9822 0.02917 ] Network output: [ 0.04992 -0.154 0.9228 -0.0007635 0.0003427 1.128 -0.0005754 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6408 0.4954 0.4514 0.519 0.9783 0.9902 0.6422 0.9125 0.9742 0.5402 ] Network output: [ -0.1003 0.3691 1.155 -0.0004118 0.0001849 0.6745 -0.0003103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2543 0.2445 0.2474 0.2511 0.9874 0.9918 0.2544 0.9715 0.9829 0.255 ] Network output: [ -0.1106 0.2986 1.116 -0.0003767 0.0001691 0.8052 -0.0002839 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3015 0.2993 0.2774 0.2773 0.9813 0.9883 0.3015 0.9495 0.9725 0.2796 ] Network output: [ 0.04848 0.8474 -0.04788 0.0007751 -0.000348 1.107 0.0005842 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09427 Epoch 3171 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07907 0.7838 0.9177 0.0002073 -9.307e-05 0.1412 0.0001562 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0172 -0.005138 0.01439 0.03012 0.9476 0.9553 0.02826 0.8915 0.9114 0.07195 ] Network output: [ 0.9927 0.01766 0.005881 0.0005183 -0.0002327 -0.006786 0.0003906 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.597 0.02504 0.117 0.3965 0.9757 0.9887 0.6474 0.9049 0.9705 0.5571 ] Network output: [ 0.0412 0.8279 0.9305 6.433e-05 -2.888e-05 0.1595 4.848e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02036 0.01328 0.02362 0.02661 0.9869 0.9907 0.02062 0.9698 0.9822 0.02922 ] Network output: [ 0.05047 -0.1551 0.9223 -0.0007562 0.0003395 1.129 -0.0005699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6406 0.4954 0.4512 0.5189 0.9783 0.9902 0.6419 0.9125 0.9742 0.5401 ] Network output: [ -0.1003 0.3672 1.156 -0.0004109 0.0001845 0.676 -0.0003097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2549 0.2451 0.248 0.2517 0.9874 0.9918 0.255 0.9715 0.9829 0.2556 ] Network output: [ -0.1105 0.2974 1.116 -0.0003753 0.0001685 0.806 -0.0002828 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3018 0.2995 0.2778 0.2777 0.9814 0.9883 0.3018 0.9495 0.9726 0.28 ] Network output: [ 0.04828 0.8486 -0.04781 0.0007684 -0.000345 1.106 0.0005791 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09365 Epoch 3172 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07904 0.7849 0.9176 0.0002063 -9.261e-05 0.1402 0.0001555 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0172 -0.005135 0.01438 0.03013 0.9476 0.9553 0.02827 0.8915 0.9114 0.07199 ] Network output: [ 0.9923 0.01885 0.005905 0.0005178 -0.0002325 -0.007184 0.0003902 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5967 0.02533 0.1168 0.3963 0.9757 0.9887 0.6472 0.9049 0.9705 0.557 ] Network output: [ 0.04119 0.8286 0.9305 6.339e-05 -2.846e-05 0.1588 4.777e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02039 0.0133 0.02366 0.02665 0.9869 0.9907 0.02065 0.9698 0.9822 0.02927 ] Network output: [ 0.05101 -0.1563 0.9218 -0.0007489 0.0003362 1.129 -0.0005644 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6403 0.4953 0.451 0.5187 0.9783 0.9902 0.6416 0.9125 0.9742 0.54 ] Network output: [ -0.1003 0.3654 1.156 -0.00041 0.0001841 0.6775 -0.000309 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2554 0.2456 0.2486 0.2524 0.9874 0.9918 0.2555 0.9715 0.9828 0.2563 ] Network output: [ -0.1103 0.2962 1.116 -0.000374 0.0001679 0.8068 -0.0002819 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3021 0.2998 0.2782 0.278 0.9814 0.9883 0.3021 0.9496 0.9726 0.2803 ] Network output: [ 0.04808 0.8498 -0.04773 0.0007616 -0.0003419 1.105 0.000574 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09304 Epoch 3173 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07901 0.786 0.9175 0.0002052 -9.213e-05 0.1393 0.0001547 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01721 -0.005132 0.01438 0.03013 0.9476 0.9553 0.02828 0.8915 0.9114 0.07202 ] Network output: [ 0.9919 0.02003 0.005934 0.0005175 -0.0002323 -0.007578 0.00039 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5965 0.02562 0.1166 0.396 0.9757 0.9887 0.6469 0.9049 0.9705 0.5569 ] Network output: [ 0.04118 0.8294 0.9305 6.241e-05 -2.802e-05 0.1581 4.703e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02042 0.01332 0.02369 0.02669 0.9869 0.9907 0.02067 0.9698 0.9822 0.02931 ] Network output: [ 0.05155 -0.1574 0.9213 -0.0007416 0.0003329 1.13 -0.0005589 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.64 0.4953 0.4508 0.5186 0.9783 0.9902 0.6413 0.9125 0.9742 0.5399 ] Network output: [ -0.1003 0.3636 1.156 -0.0004092 0.0001837 0.679 -0.0003084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.256 0.2462 0.2492 0.253 0.9874 0.9918 0.2561 0.9715 0.9828 0.2569 ] Network output: [ -0.1102 0.2951 1.116 -0.0003727 0.0001673 0.8076 -0.0002809 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3024 0.3001 0.2786 0.2784 0.9814 0.9883 0.3024 0.9496 0.9726 0.2807 ] Network output: [ 0.04789 0.851 -0.04764 0.0007548 -0.0003388 1.104 0.0005688 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09243 Epoch 3174 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07898 0.7871 0.9175 0.0002042 -9.165e-05 0.1383 0.0001539 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01721 -0.005129 0.01437 0.03013 0.9476 0.9553 0.02829 0.8915 0.9114 0.07206 ] Network output: [ 0.9915 0.0212 0.005967 0.0005173 -0.0002323 -0.00797 0.0003899 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5962 0.02592 0.1163 0.3958 0.9757 0.9887 0.6466 0.9049 0.9705 0.5568 ] Network output: [ 0.04116 0.8301 0.9305 6.139e-05 -2.756e-05 0.1574 4.627e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02044 0.01335 0.02373 0.02673 0.9869 0.9907 0.0207 0.9698 0.9822 0.02936 ] Network output: [ 0.05209 -0.1586 0.9208 -0.0007343 0.0003296 1.131 -0.0005534 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6397 0.4952 0.4506 0.5184 0.9783 0.9902 0.641 0.9125 0.9742 0.5398 ] Network output: [ -0.1003 0.3618 1.157 -0.0004084 0.0001833 0.6805 -0.0003078 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2566 0.2467 0.2498 0.2536 0.9874 0.9918 0.2567 0.9714 0.9828 0.2576 ] Network output: [ -0.1101 0.2939 1.116 -0.0003715 0.0001668 0.8084 -0.00028 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3026 0.3004 0.2789 0.2788 0.9814 0.9883 0.3027 0.9497 0.9726 0.2811 ] Network output: [ 0.04769 0.8521 -0.04755 0.0007479 -0.0003358 1.103 0.0005636 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09183 Epoch 3175 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07895 0.7881 0.9174 0.0002031 -9.116e-05 0.1374 0.000153 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01722 -0.005126 0.01437 0.03013 0.9476 0.9553 0.0283 0.8915 0.9114 0.07209 ] Network output: [ 0.991 0.02237 0.006005 0.0005173 -0.0002322 -0.008359 0.0003899 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.596 0.02622 0.1161 0.3956 0.9757 0.9887 0.6463 0.9049 0.9705 0.5567 ] Network output: [ 0.04115 0.8308 0.9305 6.035e-05 -2.709e-05 0.1567 4.548e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02047 0.01337 0.02376 0.02677 0.9869 0.9907 0.02072 0.9698 0.9822 0.02941 ] Network output: [ 0.05262 -0.1597 0.9203 -0.000727 0.0003264 1.131 -0.0005479 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6394 0.4952 0.4503 0.5183 0.9783 0.9902 0.6407 0.9125 0.9742 0.5397 ] Network output: [ -0.1003 0.3601 1.157 -0.0004076 0.000183 0.682 -0.0003072 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2571 0.2473 0.2504 0.2542 0.9874 0.9918 0.2572 0.9714 0.9828 0.2582 ] Network output: [ -0.1099 0.2928 1.116 -0.0003704 0.0001663 0.8091 -0.0002791 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3029 0.3007 0.2793 0.2791 0.9814 0.9883 0.3029 0.9497 0.9727 0.2814 ] Network output: [ 0.04748 0.8533 -0.04745 0.000741 -0.0003327 1.102 0.0005584 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09124 Epoch 3176 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07891 0.7892 0.9173 0.000202 -9.067e-05 0.1365 0.0001522 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01722 -0.005122 0.01436 0.03013 0.9476 0.9553 0.02831 0.8915 0.9114 0.07212 ] Network output: [ 0.9906 0.02353 0.006047 0.0005175 -0.0002323 -0.008745 0.00039 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5957 0.02653 0.1158 0.3954 0.9757 0.9887 0.6461 0.9049 0.9705 0.5566 ] Network output: [ 0.04113 0.8315 0.9305 5.928e-05 -2.661e-05 0.156 4.467e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02049 0.01339 0.02379 0.0268 0.9868 0.9907 0.02075 0.9697 0.9822 0.02945 ] Network output: [ 0.05316 -0.1608 0.9198 -0.0007197 0.0003231 1.132 -0.0005424 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6391 0.4952 0.4501 0.5181 0.9783 0.9902 0.6404 0.9125 0.9742 0.5396 ] Network output: [ -0.1003 0.3583 1.157 -0.0004069 0.0001827 0.6835 -0.0003066 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2577 0.2479 0.251 0.2548 0.9874 0.9918 0.2578 0.9714 0.9828 0.2588 ] Network output: [ -0.1098 0.2917 1.116 -0.0003693 0.0001658 0.8099 -0.0002783 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3032 0.3009 0.2796 0.2795 0.9814 0.9883 0.3032 0.9498 0.9727 0.2818 ] Network output: [ 0.04728 0.8545 -0.04735 0.0007341 -0.0003296 1.101 0.0005532 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09066 Epoch 3177 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07887 0.7902 0.9173 0.0002009 -9.017e-05 0.1356 0.0001514 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01722 -0.005118 0.01435 0.03013 0.9476 0.9553 0.02831 0.8915 0.9114 0.07215 ] Network output: [ 0.9902 0.02468 0.006093 0.0005178 -0.0002324 -0.009128 0.0003902 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5954 0.02685 0.1155 0.3952 0.9757 0.9887 0.6458 0.9049 0.9705 0.5565 ] Network output: [ 0.04111 0.8323 0.9305 5.818e-05 -2.612e-05 0.1553 4.384e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02052 0.01341 0.02382 0.02684 0.9868 0.9907 0.02077 0.9697 0.9822 0.0295 ] Network output: [ 0.05369 -0.1619 0.9193 -0.0007125 0.0003199 1.132 -0.0005369 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6388 0.4951 0.4499 0.518 0.9783 0.9902 0.6401 0.9125 0.9742 0.5395 ] Network output: [ -0.1003 0.3566 1.157 -0.0004061 0.0001823 0.685 -0.0003061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2582 0.2484 0.2516 0.2554 0.9874 0.9918 0.2583 0.9714 0.9828 0.2594 ] Network output: [ -0.1096 0.2905 1.117 -0.0003682 0.0001653 0.8107 -0.0002775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3034 0.3012 0.28 0.2798 0.9814 0.9883 0.3035 0.9498 0.9727 0.2822 ] Network output: [ 0.04708 0.8556 -0.04724 0.0007271 -0.0003264 1.1 0.000548 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09008 Epoch 3178 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07883 0.7913 0.9172 0.0001997 -8.967e-05 0.1347 0.0001505 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01723 -0.005115 0.01434 0.03013 0.9476 0.9553 0.02832 0.8915 0.9114 0.07218 ] Network output: [ 0.9898 0.02582 0.006144 0.0005182 -0.0002326 -0.009509 0.0003905 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5952 0.02717 0.1152 0.3949 0.9757 0.9887 0.6455 0.9049 0.9705 0.5564 ] Network output: [ 0.04109 0.833 0.9305 5.705e-05 -2.561e-05 0.1546 4.299e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02054 0.01343 0.02385 0.02687 0.9868 0.9907 0.0208 0.9697 0.9822 0.02954 ] Network output: [ 0.05422 -0.1629 0.9188 -0.0007052 0.0003166 1.133 -0.0005315 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6384 0.4951 0.4496 0.5178 0.9783 0.9902 0.6398 0.9125 0.9742 0.5394 ] Network output: [ -0.1003 0.3549 1.158 -0.0004054 0.000182 0.6864 -0.0003055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2588 0.2489 0.2521 0.256 0.9874 0.9918 0.2589 0.9714 0.9828 0.26 ] Network output: [ -0.1095 0.2894 1.117 -0.0003672 0.0001649 0.8115 -0.0002768 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3037 0.3015 0.2803 0.2802 0.9815 0.9884 0.3037 0.9499 0.9727 0.2825 ] Network output: [ 0.04687 0.8568 -0.04713 0.0007202 -0.0003233 1.1 0.0005427 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08951 Epoch 3179 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07879 0.7923 0.9172 0.0001986 -8.916e-05 0.1338 0.0001497 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01723 -0.005111 0.01433 0.03013 0.9476 0.9553 0.02832 0.8914 0.9114 0.07221 ] Network output: [ 0.9894 0.02695 0.006199 0.0005187 -0.0002329 -0.009886 0.0003909 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5949 0.0275 0.1149 0.3947 0.9757 0.9887 0.6452 0.9049 0.9705 0.5562 ] Network output: [ 0.04107 0.8337 0.9305 5.59e-05 -2.509e-05 0.1539 4.212e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02057 0.01345 0.02388 0.02691 0.9868 0.9907 0.02082 0.9697 0.9822 0.02958 ] Network output: [ 0.05475 -0.164 0.9183 -0.000698 0.0003133 1.133 -0.000526 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6381 0.495 0.4494 0.5177 0.9783 0.9902 0.6395 0.9125 0.9742 0.5393 ] Network output: [ -0.1002 0.3532 1.158 -0.0004047 0.0001817 0.6879 -0.000305 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2593 0.2495 0.2527 0.2565 0.9874 0.9918 0.2594 0.9714 0.9828 0.2606 ] Network output: [ -0.1093 0.2883 1.117 -0.0003663 0.0001644 0.8122 -0.000276 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3039 0.3017 0.2807 0.2805 0.9815 0.9884 0.304 0.9499 0.9728 0.2829 ] Network output: [ 0.04666 0.8579 -0.04701 0.0007132 -0.0003202 1.099 0.0005375 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08895 Epoch 3180 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07874 0.7933 0.9171 0.0001975 -8.865e-05 0.1329 0.0001488 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01723 -0.005106 0.01432 0.03013 0.9476 0.9553 0.02833 0.8914 0.9114 0.07224 ] Network output: [ 0.989 0.02808 0.006258 0.0005194 -0.0002332 -0.01026 0.0003915 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5946 0.02783 0.1145 0.3945 0.9757 0.9887 0.6449 0.9049 0.9705 0.5561 ] Network output: [ 0.04105 0.8344 0.9306 5.472e-05 -2.456e-05 0.1532 4.124e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02059 0.01348 0.0239 0.02694 0.9868 0.9907 0.02085 0.9697 0.9821 0.02963 ] Network output: [ 0.05527 -0.1651 0.9178 -0.0006907 0.0003101 1.134 -0.0005206 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6378 0.495 0.4491 0.5175 0.9783 0.9902 0.6391 0.9125 0.9742 0.5392 ] Network output: [ -0.1002 0.3515 1.158 -0.0004041 0.0001814 0.6893 -0.0003045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2599 0.25 0.2533 0.2571 0.9874 0.9918 0.26 0.9714 0.9828 0.2612 ] Network output: [ -0.1092 0.2872 1.117 -0.0003654 0.000164 0.813 -0.0002754 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3042 0.302 0.281 0.2808 0.9815 0.9884 0.3042 0.95 0.9728 0.2832 ] Network output: [ 0.04645 0.859 -0.04689 0.0007061 -0.000317 1.098 0.0005322 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08839 Epoch 3181 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0787 0.7943 0.9171 0.0001963 -8.813e-05 0.132 0.0001479 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01723 -0.005102 0.01431 0.03013 0.9476 0.9553 0.02833 0.8914 0.9114 0.07227 ] Network output: [ 0.9886 0.0292 0.006322 0.0005203 -0.0002336 -0.01063 0.0003921 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5943 0.02817 0.1142 0.3943 0.9757 0.9887 0.6446 0.9049 0.9705 0.556 ] Network output: [ 0.04102 0.8351 0.9306 5.351e-05 -2.402e-05 0.1525 4.033e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02061 0.0135 0.02393 0.02698 0.9868 0.9907 0.02087 0.9697 0.9821 0.02967 ] Network output: [ 0.0558 -0.1661 0.9173 -0.0006835 0.0003069 1.134 -0.0005151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6375 0.495 0.4488 0.5173 0.9783 0.9902 0.6388 0.9125 0.9742 0.5391 ] Network output: [ -0.1002 0.3498 1.158 -0.0004034 0.0001811 0.6908 -0.000304 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2604 0.2505 0.2538 0.2576 0.9874 0.9918 0.2605 0.9714 0.9828 0.2618 ] Network output: [ -0.109 0.2861 1.117 -0.0003645 0.0001637 0.8137 -0.0002747 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3044 0.3022 0.2813 0.2812 0.9815 0.9884 0.3045 0.95 0.9728 0.2835 ] Network output: [ 0.04624 0.8601 -0.04677 0.0006991 -0.0003139 1.097 0.0005269 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08785 Epoch 3182 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07865 0.7953 0.9171 0.0001951 -8.761e-05 0.1311 0.0001471 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01724 -0.005098 0.01429 0.03013 0.9476 0.9553 0.02834 0.8914 0.9114 0.0723 ] Network output: [ 0.9882 0.03031 0.006389 0.0005212 -0.000234 -0.011 0.0003928 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5941 0.02852 0.1138 0.3941 0.9757 0.9887 0.6443 0.9049 0.9705 0.5559 ] Network output: [ 0.041 0.8358 0.9306 5.229e-05 -2.347e-05 0.1518 3.941e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02064 0.01352 0.02395 0.02701 0.9868 0.9907 0.02089 0.9697 0.9821 0.02971 ] Network output: [ 0.05632 -0.1672 0.9168 -0.0006763 0.0003036 1.135 -0.0005097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6371 0.4949 0.4485 0.5172 0.9783 0.9902 0.6385 0.9125 0.9742 0.539 ] Network output: [ -0.1001 0.3481 1.158 -0.0004028 0.0001808 0.6922 -0.0003036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2609 0.2511 0.2543 0.2582 0.9874 0.9918 0.261 0.9713 0.9828 0.2623 ] Network output: [ -0.1089 0.285 1.117 -0.0003638 0.0001633 0.8145 -0.0002741 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3047 0.3025 0.2817 0.2815 0.9815 0.9884 0.3047 0.9501 0.9728 0.2839 ] Network output: [ 0.04603 0.8612 -0.04664 0.0006921 -0.0003107 1.096 0.0005216 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0873 Epoch 3183 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0786 0.7963 0.917 0.000194 -8.708e-05 0.1302 0.0001462 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01724 -0.005093 0.01428 0.03012 0.9476 0.9553 0.02834 0.8914 0.9114 0.07232 ] Network output: [ 0.9878 0.03141 0.006461 0.0005222 -0.0002345 -0.01137 0.0003936 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5938 0.02887 0.1134 0.3938 0.9757 0.9887 0.644 0.9049 0.9705 0.5558 ] Network output: [ 0.04097 0.8364 0.9307 5.104e-05 -2.292e-05 0.1512 3.847e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02066 0.01354 0.02398 0.02704 0.9868 0.9907 0.02092 0.9697 0.9821 0.02975 ] Network output: [ 0.05684 -0.1682 0.9164 -0.0006691 0.0003004 1.135 -0.0005043 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6368 0.4949 0.4482 0.517 0.9783 0.9902 0.6381 0.9125 0.9742 0.5388 ] Network output: [ -0.1001 0.3465 1.158 -0.0004022 0.0001806 0.6936 -0.0003031 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2615 0.2516 0.2549 0.2587 0.9874 0.9918 0.2615 0.9713 0.9828 0.2629 ] Network output: [ -0.1087 0.2839 1.117 -0.000363 0.000163 0.8152 -0.0002736 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3049 0.3027 0.282 0.2818 0.9815 0.9884 0.3049 0.9501 0.9729 0.2842 ] Network output: [ 0.04582 0.8623 -0.04651 0.000685 -0.0003075 1.095 0.0005162 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08677 Epoch 3184 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07855 0.7973 0.917 0.0001928 -8.656e-05 0.1294 0.0001453 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01724 -0.005088 0.01426 0.03012 0.9476 0.9553 0.02835 0.8914 0.9114 0.07235 ] Network output: [ 0.9874 0.0325 0.006538 0.0005234 -0.000235 -0.01174 0.0003945 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5935 0.02922 0.113 0.3936 0.9757 0.9887 0.6437 0.9049 0.9705 0.5556 ] Network output: [ 0.04094 0.8371 0.9307 4.978e-05 -2.235e-05 0.1505 3.751e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02068 0.01356 0.024 0.02707 0.9868 0.9907 0.02094 0.9696 0.9821 0.02979 ] Network output: [ 0.05735 -0.1692 0.9159 -0.0006619 0.0002972 1.136 -0.0004989 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6365 0.4949 0.4479 0.5169 0.9783 0.9902 0.6378 0.9125 0.9742 0.5387 ] Network output: [ -0.1 0.3449 1.159 -0.0004016 0.0001803 0.695 -0.0003027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.262 0.2521 0.2554 0.2593 0.9874 0.9918 0.2621 0.9713 0.9828 0.2635 ] Network output: [ -0.1086 0.2828 1.117 -0.0003623 0.0001627 0.8159 -0.0002731 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3051 0.3029 0.2823 0.2821 0.9815 0.9884 0.3052 0.9502 0.9729 0.2845 ] Network output: [ 0.0456 0.8634 -0.04638 0.0006779 -0.0003043 1.095 0.0005109 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08624 Epoch 3185 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07849 0.7983 0.917 0.0001916 -8.603e-05 0.1285 0.0001444 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01724 -0.005084 0.01425 0.03012 0.9476 0.9553 0.02835 0.8914 0.9114 0.07237 ] Network output: [ 0.987 0.03358 0.006618 0.0005247 -0.0002355 -0.0121 0.0003954 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5932 0.02958 0.1126 0.3934 0.9757 0.9887 0.6434 0.9049 0.9705 0.5555 ] Network output: [ 0.04091 0.8378 0.9307 4.849e-05 -2.177e-05 0.1499 3.655e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0207 0.01358 0.02402 0.0271 0.9868 0.9907 0.02096 0.9696 0.9821 0.02982 ] Network output: [ 0.05787 -0.1702 0.9154 -0.0006548 0.000294 1.136 -0.0004935 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6361 0.4948 0.4476 0.5167 0.9783 0.9902 0.6375 0.9125 0.9742 0.5386 ] Network output: [ -0.09999 0.3433 1.159 -0.0004011 0.0001801 0.6964 -0.0003023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2625 0.2526 0.2559 0.2598 0.9873 0.9918 0.2626 0.9713 0.9827 0.264 ] Network output: [ -0.1084 0.2817 1.117 -0.0003617 0.0001624 0.8166 -0.0002726 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3054 0.3032 0.2826 0.2824 0.9816 0.9884 0.3054 0.9502 0.9729 0.2848 ] Network output: [ 0.04539 0.8645 -0.04624 0.0006708 -0.0003012 1.094 0.0005056 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08572 Epoch 3186 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07844 0.7992 0.917 0.0001904 -8.549e-05 0.1277 0.0001435 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01724 -0.005079 0.01423 0.03012 0.9476 0.9553 0.02835 0.8914 0.9114 0.0724 ] Network output: [ 0.9866 0.03466 0.006703 0.000526 -0.0002362 -0.01246 0.0003964 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5929 0.02994 0.1122 0.3932 0.9757 0.9887 0.6431 0.9048 0.9705 0.5554 ] Network output: [ 0.04088 0.8385 0.9308 4.719e-05 -2.119e-05 0.1492 3.556e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02072 0.0136 0.02404 0.02713 0.9868 0.9907 0.02098 0.9696 0.9821 0.02986 ] Network output: [ 0.05838 -0.1712 0.9149 -0.0006476 0.0002907 1.137 -0.0004881 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6358 0.4948 0.4473 0.5165 0.9783 0.9902 0.6371 0.9125 0.9742 0.5385 ] Network output: [ -0.09994 0.3417 1.159 -0.0004006 0.0001798 0.6977 -0.0003019 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.263 0.2531 0.2564 0.2603 0.9873 0.9918 0.2631 0.9713 0.9827 0.2646 ] Network output: [ -0.1083 0.2806 1.117 -0.0003611 0.0001621 0.8174 -0.0002721 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3056 0.3034 0.2829 0.2827 0.9816 0.9884 0.3056 0.9503 0.9729 0.2851 ] Network output: [ 0.04517 0.8655 -0.0461 0.0006638 -0.000298 1.093 0.0005002 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08521 Epoch 3187 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07838 0.8002 0.917 0.0001892 -8.496e-05 0.1268 0.0001426 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01724 -0.005074 0.01421 0.03012 0.9476 0.9553 0.02835 0.8914 0.9114 0.07242 ] Network output: [ 0.9862 0.03572 0.006791 0.0005275 -0.0002368 -0.01282 0.0003975 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5926 0.03031 0.1118 0.3929 0.9757 0.9887 0.6427 0.9048 0.9705 0.5553 ] Network output: [ 0.04084 0.8391 0.9308 4.587e-05 -2.059e-05 0.1486 3.457e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02074 0.01362 0.02406 0.02716 0.9868 0.9907 0.021 0.9696 0.9821 0.0299 ] Network output: [ 0.05889 -0.1722 0.9144 -0.0006405 0.0002875 1.137 -0.0004827 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6354 0.4948 0.447 0.5164 0.9783 0.9902 0.6368 0.9125 0.9742 0.5384 ] Network output: [ -0.09988 0.3401 1.159 -0.0004 0.0001796 0.6991 -0.0003015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2635 0.2536 0.2569 0.2608 0.9873 0.9918 0.2636 0.9713 0.9827 0.2651 ] Network output: [ -0.1081 0.2796 1.117 -0.0003605 0.0001618 0.8181 -0.0002717 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3058 0.3036 0.2832 0.283 0.9816 0.9884 0.3058 0.9503 0.9729 0.2855 ] Network output: [ 0.04495 0.8666 -0.04595 0.0006567 -0.0002948 1.092 0.0004949 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08471 Epoch 3188 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07832 0.8012 0.917 0.000188 -8.442e-05 0.126 0.0001417 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01724 -0.005068 0.01419 0.03011 0.9476 0.9553 0.02835 0.8914 0.9114 0.07245 ] Network output: [ 0.9858 0.03678 0.006884 0.0005291 -0.0002375 -0.01317 0.0003987 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5923 0.03069 0.1113 0.3927 0.9757 0.9887 0.6424 0.9048 0.9705 0.5551 ] Network output: [ 0.04081 0.8398 0.9309 4.454e-05 -1.999e-05 0.1479 3.356e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02076 0.01364 0.02408 0.02718 0.9868 0.9907 0.02102 0.9696 0.9821 0.02993 ] Network output: [ 0.0594 -0.1731 0.9139 -0.0006333 0.0002843 1.138 -0.0004773 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6351 0.4947 0.4467 0.5162 0.9783 0.9902 0.6364 0.9125 0.9742 0.5382 ] Network output: [ -0.09982 0.3385 1.159 -0.0003996 0.0001794 0.7005 -0.0003011 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.264 0.2541 0.2574 0.2613 0.9873 0.9918 0.2641 0.9713 0.9827 0.2656 ] Network output: [ -0.108 0.2785 1.117 -0.00036 0.0001616 0.8188 -0.0002713 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.306 0.3038 0.2835 0.2833 0.9816 0.9884 0.306 0.9504 0.973 0.2858 ] Network output: [ 0.04473 0.8676 -0.04581 0.0006496 -0.0002916 1.091 0.0004895 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08421 Epoch 3189 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07826 0.8021 0.917 0.0001868 -8.388e-05 0.1251 0.0001408 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01724 -0.005063 0.01417 0.03011 0.9476 0.9553 0.02836 0.8914 0.9114 0.07247 ] Network output: [ 0.9854 0.03783 0.006981 0.0005307 -0.0002382 -0.01352 0.0003999 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.592 0.03107 0.1109 0.3925 0.9757 0.9887 0.6421 0.9048 0.9705 0.555 ] Network output: [ 0.04077 0.8404 0.9309 4.319e-05 -1.939e-05 0.1473 3.255e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02078 0.01366 0.0241 0.02721 0.9868 0.9907 0.02104 0.9696 0.9821 0.02997 ] Network output: [ 0.0599 -0.1741 0.9134 -0.0006262 0.0002811 1.138 -0.0004719 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6347 0.4947 0.4463 0.516 0.9783 0.9902 0.6361 0.9125 0.9742 0.5381 ] Network output: [ -0.09975 0.337 1.159 -0.0003991 0.0001792 0.7018 -0.0003008 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2645 0.2546 0.2578 0.2618 0.9873 0.9918 0.2646 0.9712 0.9827 0.2661 ] Network output: [ -0.1078 0.2774 1.117 -0.0003595 0.0001614 0.8195 -0.0002709 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3062 0.304 0.2838 0.2836 0.9816 0.9884 0.3062 0.9504 0.973 0.2861 ] Network output: [ 0.04451 0.8687 -0.04566 0.0006425 -0.0002884 1.091 0.0004842 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08371 Epoch 3190 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0782 0.803 0.917 0.0001856 -8.334e-05 0.1243 0.0001399 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01724 -0.005057 0.01415 0.03011 0.9476 0.9553 0.02836 0.8914 0.9114 0.07249 ] Network output: [ 0.985 0.03887 0.007082 0.0005324 -0.000239 -0.01387 0.0004012 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5916 0.03145 0.1104 0.3923 0.9757 0.9887 0.6417 0.9048 0.9705 0.5549 ] Network output: [ 0.04074 0.8411 0.931 4.183e-05 -1.878e-05 0.1467 3.152e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0208 0.01368 0.02412 0.02724 0.9868 0.9907 0.02106 0.9696 0.9821 0.03 ] Network output: [ 0.0604 -0.175 0.913 -0.0006191 0.000278 1.139 -0.0004666 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6344 0.4947 0.446 0.5159 0.9783 0.9902 0.6357 0.9125 0.9742 0.538 ] Network output: [ -0.09968 0.3354 1.159 -0.0003986 0.000179 0.7031 -0.0003004 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.265 0.2551 0.2583 0.2623 0.9873 0.9918 0.2651 0.9712 0.9827 0.2667 ] Network output: [ -0.1077 0.2764 1.117 -0.000359 0.0001612 0.8202 -0.0002706 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3064 0.3042 0.2841 0.2839 0.9816 0.9885 0.3064 0.9505 0.973 0.2864 ] Network output: [ 0.04429 0.8697 -0.0455 0.0006354 -0.0002852 1.09 0.0004788 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08323 Epoch 3191 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07814 0.8039 0.917 0.0001844 -8.28e-05 0.1235 0.000139 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01724 -0.005052 0.01413 0.0301 0.9476 0.9553 0.02836 0.8914 0.9114 0.07251 ] Network output: [ 0.9847 0.0399 0.007187 0.0005342 -0.0002398 -0.01422 0.0004026 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5913 0.03183 0.1099 0.392 0.9757 0.9887 0.6414 0.9048 0.9705 0.5548 ] Network output: [ 0.0407 0.8417 0.931 4.046e-05 -1.816e-05 0.146 3.049e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02082 0.0137 0.02413 0.02726 0.9868 0.9907 0.02108 0.9696 0.9821 0.03003 ] Network output: [ 0.0609 -0.176 0.9125 -0.000612 0.0002748 1.139 -0.0004613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.634 0.4946 0.4456 0.5157 0.9783 0.9902 0.6353 0.9125 0.9742 0.5379 ] Network output: [ -0.0996 0.3339 1.159 -0.0003982 0.0001788 0.7044 -0.0003001 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2655 0.2556 0.2588 0.2628 0.9873 0.9918 0.2655 0.9712 0.9827 0.2672 ] Network output: [ -0.1075 0.2753 1.117 -0.0003586 0.000161 0.8209 -0.0002703 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3066 0.3044 0.2844 0.2842 0.9816 0.9885 0.3066 0.9505 0.973 0.2866 ] Network output: [ 0.04407 0.8707 -0.04535 0.0006283 -0.000282 1.089 0.0004735 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08275 Epoch 3192 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07807 0.8049 0.9171 0.0001832 -8.225e-05 0.1227 0.0001381 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01724 -0.005046 0.01411 0.0301 0.9477 0.9553 0.02836 0.8914 0.9114 0.07253 ] Network output: [ 0.9843 0.04092 0.007296 0.0005361 -0.0002407 -0.01456 0.000404 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.591 0.03222 0.1094 0.3918 0.9757 0.9887 0.6411 0.9049 0.9705 0.5546 ] Network output: [ 0.04066 0.8423 0.9311 3.907e-05 -1.754e-05 0.1454 2.945e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02084 0.01372 0.02415 0.02729 0.9868 0.9907 0.0211 0.9695 0.982 0.03007 ] Network output: [ 0.0614 -0.1769 0.912 -0.000605 0.0002716 1.14 -0.0004559 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6337 0.4946 0.4453 0.5155 0.9783 0.9902 0.635 0.9125 0.9742 0.5378 ] Network output: [ -0.09952 0.3324 1.159 -0.0003978 0.0001786 0.7058 -0.0002998 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2659 0.2561 0.2592 0.2633 0.9873 0.9918 0.266 0.9712 0.9827 0.2677 ] Network output: [ -0.1074 0.2743 1.117 -0.0003583 0.0001608 0.8216 -0.00027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3068 0.3046 0.2847 0.2844 0.9816 0.9885 0.3068 0.9505 0.973 0.2869 ] Network output: [ 0.04384 0.8717 -0.04519 0.0006212 -0.0002789 1.088 0.0004681 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08228 Epoch 3193 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07801 0.8058 0.9171 0.000182 -8.171e-05 0.1219 0.0001372 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01723 -0.00504 0.01408 0.0301 0.9477 0.9553 0.02835 0.8914 0.9114 0.07255 ] Network output: [ 0.9839 0.04193 0.00741 0.000538 -0.0002415 -0.0149 0.0004054 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5907 0.03262 0.1089 0.3916 0.9757 0.9887 0.6407 0.9049 0.9705 0.5545 ] Network output: [ 0.04062 0.843 0.9311 3.768e-05 -1.692e-05 0.1448 2.84e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02086 0.01374 0.02416 0.02731 0.9868 0.9907 0.02112 0.9695 0.982 0.0301 ] Network output: [ 0.06189 -0.1778 0.9115 -0.0005979 0.0002684 1.14 -0.0004506 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6333 0.4946 0.4449 0.5154 0.9783 0.9902 0.6346 0.9125 0.9742 0.5376 ] Network output: [ -0.09944 0.3309 1.159 -0.0003974 0.0001784 0.707 -0.0002995 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2664 0.2565 0.2597 0.2637 0.9873 0.9918 0.2665 0.9712 0.9827 0.2681 ] Network output: [ -0.1072 0.2732 1.117 -0.0003579 0.0001607 0.8222 -0.0002698 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.307 0.3048 0.2849 0.2847 0.9817 0.9885 0.307 0.9506 0.9731 0.2872 ] Network output: [ 0.04362 0.8728 -0.04503 0.0006141 -0.0002757 1.088 0.0004628 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08181 Epoch 3194 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07794 0.8066 0.9171 0.0001808 -8.116e-05 0.1211 0.0001363 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01723 -0.005034 0.01406 0.03009 0.9477 0.9553 0.02835 0.8914 0.9114 0.07257 ] Network output: [ 0.9835 0.04293 0.007527 0.0005399 -0.0002424 -0.01524 0.0004069 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5904 0.03302 0.1084 0.3914 0.9757 0.9887 0.6404 0.9049 0.9705 0.5544 ] Network output: [ 0.04058 0.8436 0.9312 3.628e-05 -1.629e-05 0.1442 2.735e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02088 0.01376 0.02417 0.02733 0.9868 0.9907 0.02114 0.9695 0.982 0.03013 ] Network output: [ 0.06238 -0.1787 0.9111 -0.0005909 0.0002653 1.141 -0.0004453 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6329 0.4946 0.4445 0.5152 0.9783 0.9902 0.6343 0.9125 0.9742 0.5375 ] Network output: [ -0.09936 0.3294 1.159 -0.000397 0.0001782 0.7083 -0.0002992 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2669 0.257 0.2601 0.2642 0.9873 0.9918 0.2669 0.9712 0.9827 0.2686 ] Network output: [ -0.107 0.2722 1.118 -0.0003576 0.0001606 0.8229 -0.0002695 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3072 0.305 0.2852 0.285 0.9817 0.9885 0.3072 0.9506 0.9731 0.2875 ] Network output: [ 0.04339 0.8738 -0.04487 0.000607 -0.0002725 1.087 0.0004574 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08135 Epoch 3195 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07787 0.8075 0.9171 0.0001796 -8.062e-05 0.1203 0.0001353 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01723 -0.005028 0.01403 0.03009 0.9477 0.9553 0.02835 0.8914 0.9114 0.07259 ] Network output: [ 0.9831 0.04392 0.007648 0.000542 -0.0002433 -0.01558 0.0004084 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.59 0.03342 0.1079 0.3912 0.9757 0.9887 0.64 0.9049 0.9705 0.5542 ] Network output: [ 0.04054 0.8442 0.9313 3.488e-05 -1.566e-05 0.1436 2.629e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02089 0.01378 0.02419 0.02735 0.9868 0.9907 0.02116 0.9695 0.982 0.03016 ] Network output: [ 0.06287 -0.1796 0.9106 -0.0005839 0.0002621 1.141 -0.00044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6326 0.4945 0.4441 0.515 0.9783 0.9902 0.6339 0.9125 0.9742 0.5374 ] Network output: [ -0.09927 0.328 1.159 -0.0003966 0.000178 0.7096 -0.0002989 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2673 0.2574 0.2605 0.2647 0.9873 0.9917 0.2674 0.9712 0.9827 0.2691 ] Network output: [ -0.1069 0.2712 1.118 -0.0003574 0.0001604 0.8236 -0.0002693 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3073 0.3052 0.2855 0.2852 0.9817 0.9885 0.3073 0.9507 0.9731 0.2878 ] Network output: [ 0.04317 0.8748 -0.0447 0.0005999 -0.0002693 1.086 0.0004521 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0809 Epoch 3196 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0778 0.8084 0.9172 0.0001784 -8.008e-05 0.1195 0.0001344 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01723 -0.005022 0.01401 0.03009 0.9477 0.9553 0.02835 0.8914 0.9114 0.07261 ] Network output: [ 0.9827 0.0449 0.007773 0.000544 -0.0002442 -0.01591 0.00041 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5897 0.03382 0.1074 0.3909 0.9757 0.9887 0.6397 0.9049 0.9705 0.5541 ] Network output: [ 0.04049 0.8448 0.9313 3.347e-05 -1.503e-05 0.143 2.522e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02091 0.0138 0.0242 0.02738 0.9868 0.9907 0.02117 0.9695 0.982 0.03019 ] Network output: [ 0.06336 -0.1805 0.9101 -0.0005769 0.000259 1.141 -0.0004348 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6322 0.4945 0.4438 0.5149 0.9783 0.9902 0.6335 0.9125 0.9742 0.5373 ] Network output: [ -0.09918 0.3265 1.159 -0.0003962 0.0001779 0.7109 -0.0002986 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2678 0.2579 0.261 0.2651 0.9873 0.9917 0.2678 0.9711 0.9827 0.2696 ] Network output: [ -0.1067 0.2702 1.118 -0.0003571 0.0001603 0.8243 -0.0002691 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3075 0.3053 0.2857 0.2855 0.9817 0.9885 0.3075 0.9507 0.9731 0.288 ] Network output: [ 0.04294 0.8757 -0.04453 0.0005929 -0.0002662 1.085 0.0004468 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08046 Epoch 3197 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07773 0.8093 0.9172 0.0001772 -7.953e-05 0.1188 0.0001335 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01722 -0.005016 0.01398 0.03008 0.9477 0.9553 0.02835 0.8914 0.9114 0.07263 ] Network output: [ 0.9823 0.04587 0.007902 0.0005461 -0.0002452 -0.01625 0.0004116 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5894 0.03423 0.1068 0.3907 0.9757 0.9887 0.6394 0.9049 0.9705 0.554 ] Network output: [ 0.04045 0.8454 0.9314 3.205e-05 -1.439e-05 0.1424 2.416e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02093 0.01382 0.02421 0.0274 0.9868 0.9907 0.02119 0.9695 0.982 0.03022 ] Network output: [ 0.06384 -0.1814 0.9096 -0.0005699 0.0002558 1.142 -0.0004295 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6318 0.4945 0.4434 0.5147 0.9783 0.9902 0.6331 0.9125 0.9742 0.5371 ] Network output: [ -0.09908 0.3251 1.159 -0.0003959 0.0001777 0.7121 -0.0002983 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2682 0.2583 0.2614 0.2656 0.9873 0.9917 0.2683 0.9711 0.9826 0.27 ] Network output: [ -0.1066 0.2691 1.118 -0.0003569 0.0001602 0.8249 -0.000269 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3077 0.3055 0.286 0.2857 0.9817 0.9885 0.3077 0.9508 0.9731 0.2883 ] Network output: [ 0.04271 0.8767 -0.04436 0.0005858 -0.000263 1.085 0.0004415 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08002 Epoch 3198 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07766 0.8101 0.9172 0.000176 -7.899e-05 0.118 0.0001326 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01722 -0.00501 0.01395 0.03008 0.9477 0.9553 0.02834 0.8914 0.9114 0.07265 ] Network output: [ 0.982 0.04683 0.008034 0.0005483 -0.0002461 -0.01657 0.0004132 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.589 0.03464 0.1063 0.3905 0.9757 0.9887 0.639 0.9049 0.9705 0.5539 ] Network output: [ 0.0404 0.846 0.9314 3.064e-05 -1.375e-05 0.1419 2.309e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02094 0.01384 0.02422 0.02742 0.9868 0.9907 0.02121 0.9695 0.982 0.03024 ] Network output: [ 0.06432 -0.1823 0.9092 -0.0005629 0.0002527 1.142 -0.0004242 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6315 0.4944 0.443 0.5145 0.9783 0.9902 0.6328 0.9125 0.9742 0.537 ] Network output: [ -0.09899 0.3237 1.159 -0.0003955 0.0001776 0.7133 -0.0002981 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2686 0.2588 0.2618 0.266 0.9873 0.9917 0.2687 0.9711 0.9826 0.2705 ] Network output: [ -0.1064 0.2681 1.118 -0.0003567 0.0001602 0.8256 -0.0002689 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3078 0.3057 0.2862 0.286 0.9817 0.9885 0.3078 0.9508 0.9732 0.2886 ] Network output: [ 0.04248 0.8777 -0.04419 0.0005788 -0.0002598 1.084 0.0004362 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07958 Epoch 3199 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07759 0.811 0.9173 0.0001747 -7.845e-05 0.1173 0.0001317 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01722 -0.005004 0.01393 0.03007 0.9477 0.9553 0.02834 0.8914 0.9114 0.07266 ] Network output: [ 0.9816 0.04778 0.008171 0.0005505 -0.0002471 -0.0169 0.0004148 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5887 0.03505 0.1057 0.3903 0.9757 0.9887 0.6386 0.9049 0.9705 0.5537 ] Network output: [ 0.04036 0.8466 0.9315 2.922e-05 -1.312e-05 0.1413 2.202e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02096 0.01385 0.02423 0.02744 0.9868 0.9907 0.02122 0.9695 0.982 0.03027 ] Network output: [ 0.0648 -0.1831 0.9087 -0.000556 0.0002496 1.143 -0.000419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6311 0.4944 0.4426 0.5144 0.9783 0.9902 0.6324 0.9125 0.9742 0.5369 ] Network output: [ -0.09889 0.3223 1.159 -0.0003952 0.0001774 0.7146 -0.0002978 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2691 0.2592 0.2622 0.2664 0.9873 0.9917 0.2691 0.9711 0.9826 0.2709 ] Network output: [ -0.1063 0.2671 1.118 -0.0003566 0.0001601 0.8262 -0.0002687 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.308 0.3058 0.2865 0.2862 0.9817 0.9885 0.308 0.9508 0.9732 0.2888 ] Network output: [ 0.04225 0.8787 -0.04402 0.0005718 -0.0002567 1.083 0.0004309 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07916 Epoch 3200 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07752 0.8118 0.9173 0.0001735 -7.791e-05 0.1165 0.0001308 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01721 -0.004997 0.0139 0.03007 0.9477 0.9553 0.02834 0.8914 0.9114 0.07268 ] Network output: [ 0.9812 0.04872 0.008311 0.0005527 -0.0002481 -0.01722 0.0004165 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5884 0.03547 0.1052 0.3901 0.9757 0.9887 0.6383 0.9049 0.9705 0.5536 ] Network output: [ 0.04031 0.8472 0.9316 2.78e-05 -1.248e-05 0.1407 2.095e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02097 0.01387 0.02424 0.02746 0.9868 0.9907 0.02124 0.9694 0.982 0.0303 ] Network output: [ 0.06527 -0.184 0.9082 -0.0005491 0.0002465 1.143 -0.0004138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6307 0.4944 0.4422 0.5142 0.9783 0.9902 0.632 0.9125 0.9742 0.5368 ] Network output: [ -0.09879 0.3209 1.159 -0.0003949 0.0001773 0.7158 -0.0002976 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2695 0.2596 0.2626 0.2668 0.9873 0.9917 0.2696 0.9711 0.9826 0.2714 ] Network output: [ -0.1061 0.2661 1.118 -0.0003565 0.00016 0.8269 -0.0002686 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3081 0.306 0.2867 0.2865 0.9817 0.9885 0.3081 0.9509 0.9732 0.2891 ] Network output: [ 0.04202 0.8796 -0.04384 0.0005648 -0.0002536 1.082 0.0004257 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07874 Epoch 3201 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07745 0.8127 0.9174 0.0001723 -7.737e-05 0.1158 0.0001299 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01721 -0.004991 0.01387 0.03006 0.9477 0.9553 0.02833 0.8915 0.9114 0.07269 ] Network output: [ 0.9808 0.04965 0.008455 0.0005549 -0.0002491 -0.01754 0.0004182 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.588 0.03589 0.1046 0.3899 0.9757 0.9887 0.6379 0.9049 0.9705 0.5535 ] Network output: [ 0.04026 0.8478 0.9316 2.638e-05 -1.184e-05 0.1401 1.988e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02099 0.01389 0.02425 0.02748 0.9868 0.9907 0.02125 0.9694 0.982 0.03032 ] Network output: [ 0.06575 -0.1848 0.9078 -0.0005422 0.0002434 1.143 -0.0004086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6303 0.4944 0.4418 0.514 0.9783 0.9902 0.6317 0.9126 0.9742 0.5366 ] Network output: [ -0.09868 0.3195 1.159 -0.0003946 0.0001771 0.717 -0.0002974 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2699 0.2601 0.263 0.2672 0.9873 0.9917 0.27 0.9711 0.9826 0.2718 ] Network output: [ -0.106 0.2652 1.118 -0.0003563 0.00016 0.8275 -0.0002686 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3083 0.3061 0.287 0.2867 0.9817 0.9885 0.3083 0.9509 0.9732 0.2893 ] Network output: [ 0.04179 0.8806 -0.04367 0.0005579 -0.0002504 1.082 0.0004204 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07832 Epoch 3202 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07737 0.8135 0.9174 0.0001711 -7.683e-05 0.115 0.000129 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0172 -0.004984 0.01384 0.03006 0.9477 0.9553 0.02833 0.8915 0.9114 0.07271 ] Network output: [ 0.9805 0.05057 0.008602 0.0005571 -0.0002501 -0.01786 0.0004198 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5877 0.03631 0.104 0.3897 0.9757 0.9887 0.6376 0.9049 0.9705 0.5533 ] Network output: [ 0.04022 0.8484 0.9317 2.497e-05 -1.121e-05 0.1396 1.882e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.021 0.01391 0.02425 0.02749 0.9868 0.9907 0.02127 0.9694 0.982 0.03035 ] Network output: [ 0.06622 -0.1857 0.9073 -0.0005353 0.0002403 1.144 -0.0004034 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.63 0.4944 0.4414 0.5139 0.9783 0.9902 0.6313 0.9126 0.9742 0.5365 ] Network output: [ -0.09857 0.3181 1.159 -0.0003943 0.000177 0.7182 -0.0002971 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2703 0.2605 0.2634 0.2677 0.9873 0.9917 0.2704 0.9711 0.9826 0.2722 ] Network output: [ -0.1058 0.2642 1.118 -0.0003563 0.0001599 0.8281 -0.0002685 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3084 0.3063 0.2872 0.287 0.9818 0.9885 0.3084 0.951 0.9732 0.2896 ] Network output: [ 0.04155 0.8815 -0.04349 0.0005509 -0.0002473 1.081 0.0004152 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07791 Epoch 3203 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0773 0.8143 0.9175 0.00017 -7.63e-05 0.1143 0.0001281 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0172 -0.004977 0.01381 0.03006 0.9477 0.9553 0.02832 0.8915 0.9114 0.07273 ] Network output: [ 0.9801 0.05148 0.008753 0.0005593 -0.0002511 -0.01817 0.0004215 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5874 0.03673 0.1034 0.3895 0.9757 0.9887 0.6372 0.9049 0.9705 0.5532 ] Network output: [ 0.04017 0.8489 0.9318 2.355e-05 -1.057e-05 0.139 1.775e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02102 0.01392 0.02426 0.02751 0.9868 0.9907 0.02128 0.9694 0.982 0.03037 ] Network output: [ 0.06668 -0.1865 0.9068 -0.0005285 0.0002372 1.144 -0.0003983 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6296 0.4943 0.4409 0.5137 0.9783 0.9902 0.6309 0.9126 0.9742 0.5364 ] Network output: [ -0.09846 0.3168 1.159 -0.000394 0.0001769 0.7193 -0.0002969 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2707 0.2609 0.2637 0.2681 0.9873 0.9917 0.2708 0.9711 0.9826 0.2727 ] Network output: [ -0.1057 0.2632 1.118 -0.0003562 0.0001599 0.8287 -0.0002684 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3085 0.3064 0.2874 0.2872 0.9818 0.9885 0.3086 0.951 0.9733 0.2898 ] Network output: [ 0.04132 0.8825 -0.04331 0.000544 -0.0002442 1.08 0.00041 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07751 Epoch 3204 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07722 0.8151 0.9175 0.0001688 -7.577e-05 0.1136 0.0001272 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01719 -0.004971 0.01378 0.03005 0.9477 0.9553 0.02832 0.8915 0.9114 0.07274 ] Network output: [ 0.9797 0.05238 0.008908 0.0005615 -0.0002521 -0.01849 0.0004232 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.587 0.03715 0.1029 0.3892 0.9757 0.9887 0.6369 0.9049 0.9705 0.5531 ] Network output: [ 0.04012 0.8495 0.9318 2.215e-05 -9.943e-06 0.1385 1.669e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02103 0.01394 0.02427 0.02753 0.9868 0.9907 0.0213 0.9694 0.982 0.0304 ] Network output: [ 0.06715 -0.1873 0.9064 -0.0005216 0.0002342 1.144 -0.0003931 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6292 0.4943 0.4405 0.5135 0.9783 0.9902 0.6305 0.9126 0.9742 0.5363 ] Network output: [ -0.09835 0.3155 1.159 -0.0003937 0.0001767 0.7205 -0.0002967 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2711 0.2613 0.2641 0.2684 0.9873 0.9917 0.2712 0.971 0.9826 0.2731 ] Network output: [ -0.1055 0.2622 1.118 -0.0003561 0.0001599 0.8294 -0.0002684 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3087 0.3066 0.2876 0.2874 0.9818 0.9886 0.3087 0.951 0.9733 0.29 ] Network output: [ 0.04108 0.8834 -0.04313 0.0005372 -0.0002411 1.08 0.0004048 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07711 Epoch 3205 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07715 0.8159 0.9176 0.0001676 -7.524e-05 0.1129 0.0001263 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01719 -0.004964 0.01374 0.03005 0.9477 0.9553 0.02831 0.8915 0.9114 0.07275 ] Network output: [ 0.9794 0.05326 0.009066 0.0005637 -0.0002531 -0.01879 0.0004249 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5867 0.03758 0.1023 0.389 0.9757 0.9887 0.6365 0.9049 0.9705 0.5529 ] Network output: [ 0.04007 0.8501 0.9319 2.075e-05 -9.314e-06 0.138 1.563e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02104 0.01396 0.02427 0.02755 0.9868 0.9907 0.02131 0.9694 0.9819 0.03042 ] Network output: [ 0.06761 -0.1881 0.9059 -0.0005148 0.0002311 1.145 -0.000388 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6288 0.4943 0.4401 0.5134 0.9783 0.9902 0.6302 0.9126 0.9742 0.5362 ] Network output: [ -0.09824 0.3141 1.159 -0.0003934 0.0001766 0.7217 -0.0002965 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2715 0.2617 0.2645 0.2688 0.9873 0.9917 0.2716 0.971 0.9826 0.2735 ] Network output: [ -0.1053 0.2613 1.118 -0.0003561 0.0001599 0.83 -0.0002684 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3088 0.3067 0.2879 0.2876 0.9818 0.9886 0.3088 0.9511 0.9733 0.2903 ] Network output: [ 0.04085 0.8844 -0.04295 0.0005303 -0.0002381 1.079 0.0003997 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07672 Epoch 3206 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07707 0.8167 0.9177 0.0001664 -7.471e-05 0.1122 0.0001254 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01718 -0.004957 0.01371 0.03004 0.9477 0.9553 0.02831 0.8915 0.9114 0.07277 ] Network output: [ 0.979 0.05414 0.009227 0.0005659 -0.0002541 -0.0191 0.0004265 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5863 0.038 0.1017 0.3888 0.9757 0.9887 0.6362 0.9049 0.9705 0.5528 ] Network output: [ 0.04002 0.8506 0.932 1.935e-05 -8.688e-06 0.1374 1.458e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02106 0.01397 0.02427 0.02756 0.9868 0.9907 0.02132 0.9694 0.9819 0.03044 ] Network output: [ 0.06806 -0.1889 0.9055 -0.0005081 0.0002281 1.145 -0.0003829 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6285 0.4943 0.4397 0.5132 0.9783 0.9902 0.6298 0.9126 0.9742 0.536 ] Network output: [ -0.09812 0.3128 1.159 -0.0003931 0.0001765 0.7228 -0.0002963 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2718 0.262 0.2648 0.2692 0.9873 0.9917 0.2719 0.971 0.9826 0.2739 ] Network output: [ -0.1052 0.2603 1.118 -0.0003561 0.0001598 0.8306 -0.0002683 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3089 0.3068 0.2881 0.2878 0.9818 0.9886 0.3089 0.9511 0.9733 0.2905 ] Network output: [ 0.04061 0.8853 -0.04276 0.0005235 -0.000235 1.078 0.0003945 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07634 Epoch 3207 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07699 0.8175 0.9177 0.0001653 -7.419e-05 0.1115 0.0001245 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01718 -0.004951 0.01368 0.03004 0.9477 0.9553 0.0283 0.8915 0.9114 0.07278 ] Network output: [ 0.9787 0.055 0.009391 0.0005681 -0.0002551 -0.0194 0.0004282 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.586 0.03843 0.1011 0.3886 0.9757 0.9887 0.6358 0.9049 0.9705 0.5527 ] Network output: [ 0.03996 0.8512 0.932 1.796e-05 -8.065e-06 0.1369 1.354e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02107 0.01399 0.02428 0.02758 0.9868 0.9907 0.02133 0.9694 0.9819 0.03046 ] Network output: [ 0.06852 -0.1897 0.905 -0.0005013 0.0002251 1.146 -0.0003778 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6281 0.4943 0.4392 0.513 0.9783 0.9902 0.6294 0.9126 0.9742 0.5359 ] Network output: [ -0.098 0.3116 1.159 -0.0003928 0.0001764 0.7239 -0.0002961 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2722 0.2624 0.2651 0.2696 0.9873 0.9917 0.2723 0.971 0.9826 0.2743 ] Network output: [ -0.105 0.2594 1.118 -0.000356 0.0001598 0.8312 -0.0002683 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.309 0.3069 0.2883 0.2881 0.9818 0.9886 0.309 0.9512 0.9733 0.2907 ] Network output: [ 0.04038 0.8862 -0.04258 0.0005167 -0.000232 1.078 0.0003894 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07596 Epoch 3208 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07692 0.8182 0.9178 0.0001641 -7.367e-05 0.1108 0.0001237 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01717 -0.004944 0.01364 0.03003 0.9477 0.9553 0.02829 0.8915 0.9114 0.07279 ] Network output: [ 0.9783 0.05586 0.009559 0.0005703 -0.000256 -0.0197 0.0004298 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5856 0.03886 0.1005 0.3884 0.9757 0.9887 0.6354 0.9049 0.9705 0.5526 ] Network output: [ 0.03991 0.8517 0.9321 1.659e-05 -7.447e-06 0.1364 1.25e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02108 0.01401 0.02428 0.02759 0.9867 0.9907 0.02134 0.9693 0.9819 0.03049 ] Network output: [ 0.06897 -0.1904 0.9045 -0.0004946 0.0002221 1.146 -0.0003728 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6277 0.4943 0.4388 0.5129 0.9783 0.9902 0.629 0.9126 0.9742 0.5358 ] Network output: [ -0.09788 0.3103 1.159 -0.0003926 0.0001762 0.725 -0.0002959 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2726 0.2628 0.2655 0.27 0.9873 0.9917 0.2727 0.971 0.9826 0.2747 ] Network output: [ -0.1049 0.2584 1.118 -0.000356 0.0001598 0.8318 -0.0002683 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3091 0.307 0.2885 0.2883 0.9818 0.9886 0.3092 0.9512 0.9733 0.2909 ] Network output: [ 0.04014 0.8871 -0.04239 0.00051 -0.000229 1.077 0.0003843 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07559 Epoch 3209 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07684 0.819 0.9178 0.0001629 -7.315e-05 0.1102 0.0001228 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01717 -0.004937 0.01361 0.03002 0.9477 0.9553 0.02828 0.8915 0.9114 0.0728 ] Network output: [ 0.9779 0.0567 0.00973 0.0005724 -0.000257 -0.02 0.0004314 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5853 0.03929 0.09985 0.3882 0.9757 0.9887 0.6351 0.9049 0.9705 0.5524 ] Network output: [ 0.03986 0.8523 0.9322 1.522e-05 -6.834e-06 0.1359 1.147e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02109 0.01402 0.02428 0.02761 0.9867 0.9906 0.02136 0.9693 0.9819 0.03051 ] Network output: [ 0.06942 -0.1912 0.9041 -0.0004879 0.0002191 1.146 -0.0003677 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6273 0.4942 0.4384 0.5127 0.9783 0.9902 0.6287 0.9126 0.9742 0.5357 ] Network output: [ -0.09776 0.309 1.159 -0.0003923 0.0001761 0.7261 -0.0002956 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2729 0.2632 0.2658 0.2703 0.9873 0.9917 0.273 0.971 0.9826 0.275 ] Network output: [ -0.1047 0.2575 1.118 -0.000356 0.0001598 0.8324 -0.0002683 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3092 0.3072 0.2887 0.2885 0.9818 0.9886 0.3093 0.9512 0.9734 0.2912 ] Network output: [ 0.0399 0.888 -0.0422 0.0005033 -0.000226 1.076 0.0003793 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07522 Epoch 3210 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07676 0.8197 0.9179 0.0001618 -7.264e-05 0.1095 0.0001219 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01716 -0.00493 0.01358 0.03002 0.9477 0.9553 0.02828 0.8915 0.9114 0.07282 ] Network output: [ 0.9776 0.05753 0.009904 0.0005745 -0.0002579 -0.02029 0.000433 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.585 0.03972 0.09923 0.388 0.9757 0.9887 0.6347 0.9049 0.9705 0.5523 ] Network output: [ 0.03981 0.8528 0.9323 1.387e-05 -6.225e-06 0.1354 1.045e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0211 0.01404 0.02429 0.02762 0.9867 0.9906 0.02137 0.9693 0.9819 0.03053 ] Network output: [ 0.06986 -0.192 0.9036 -0.0004813 0.0002161 1.147 -0.0003627 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.627 0.4942 0.438 0.5126 0.9783 0.9902 0.6283 0.9126 0.9742 0.5356 ] Network output: [ -0.09764 0.3078 1.159 -0.000392 0.000176 0.7272 -0.0002954 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2733 0.2635 0.2661 0.2707 0.9873 0.9917 0.2734 0.971 0.9825 0.2754 ] Network output: [ -0.1046 0.2566 1.118 -0.000356 0.0001598 0.833 -0.0002683 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3093 0.3073 0.2889 0.2887 0.9818 0.9886 0.3094 0.9513 0.9734 0.2914 ] Network output: [ 0.03966 0.889 -0.04202 0.0004966 -0.000223 1.076 0.0003743 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07486 Epoch 3211 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07668 0.8205 0.918 0.0001607 -7.213e-05 0.1088 0.0001211 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01715 -0.004923 0.01354 0.03001 0.9477 0.9553 0.02827 0.8915 0.9114 0.07283 ] Network output: [ 0.9773 0.05835 0.01008 0.0005766 -0.0002589 -0.02058 0.0004345 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5846 0.04015 0.09862 0.3878 0.9757 0.9887 0.6344 0.9049 0.9705 0.5522 ] Network output: [ 0.03975 0.8534 0.9323 1.252e-05 -5.623e-06 0.1349 9.439e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02111 0.01405 0.02429 0.02763 0.9867 0.9906 0.02138 0.9693 0.9819 0.03055 ] Network output: [ 0.0703 -0.1927 0.9032 -0.0004747 0.0002131 1.147 -0.0003577 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6266 0.4942 0.4375 0.5124 0.9783 0.9902 0.6279 0.9126 0.9742 0.5354 ] Network output: [ -0.09752 0.3065 1.159 -0.0003917 0.0001759 0.7283 -0.0002952 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2736 0.2639 0.2665 0.271 0.9873 0.9917 0.2737 0.9709 0.9825 0.2758 ] Network output: [ -0.1044 0.2557 1.118 -0.0003561 0.0001599 0.8335 -0.0002683 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3094 0.3074 0.2891 0.2889 0.9818 0.9886 0.3095 0.9513 0.9734 0.2916 ] Network output: [ 0.03942 0.8899 -0.04183 0.00049 -0.00022 1.075 0.0003693 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0745 Epoch 3212 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0766 0.8212 0.918 0.0001595 -7.163e-05 0.1082 0.0001202 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01715 -0.004916 0.01351 0.03001 0.9477 0.9553 0.02826 0.8915 0.9114 0.07284 ] Network output: [ 0.9769 0.05915 0.01026 0.0005786 -0.0002598 -0.02087 0.0004361 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5843 0.04058 0.098 0.3876 0.9757 0.9887 0.634 0.9049 0.9705 0.5521 ] Network output: [ 0.0397 0.8539 0.9324 1.12e-05 -5.027e-06 0.1344 8.438e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02112 0.01407 0.02429 0.02765 0.9867 0.9906 0.02139 0.9693 0.9819 0.03057 ] Network output: [ 0.07074 -0.1935 0.9027 -0.0004681 0.0002101 1.147 -0.0003528 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6262 0.4942 0.4371 0.5123 0.9783 0.9902 0.6275 0.9126 0.9742 0.5353 ] Network output: [ -0.09739 0.3053 1.159 -0.0003914 0.0001757 0.7294 -0.000295 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.274 0.2642 0.2668 0.2714 0.9873 0.9917 0.2741 0.9709 0.9825 0.2762 ] Network output: [ -0.1043 0.2548 1.118 -0.0003561 0.0001599 0.8341 -0.0002684 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3095 0.3075 0.2893 0.2891 0.9819 0.9886 0.3095 0.9513 0.9734 0.2918 ] Network output: [ 0.03918 0.8908 -0.04164 0.0004835 -0.000217 1.074 0.0003644 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07415 Epoch 3213 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07652 0.822 0.9181 0.0001584 -7.113e-05 0.1076 0.0001194 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01714 -0.004909 0.01347 0.03 0.9477 0.9553 0.02825 0.8915 0.9114 0.07285 ] Network output: [ 0.9766 0.05995 0.01044 0.0005806 -0.0002607 -0.02115 0.0004376 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5839 0.04101 0.09738 0.3874 0.9757 0.9888 0.6337 0.9049 0.9705 0.5519 ] Network output: [ 0.03964 0.8544 0.9325 9.884e-06 -4.437e-06 0.1339 7.449e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02113 0.01408 0.02429 0.02766 0.9867 0.9906 0.0214 0.9693 0.9819 0.03059 ] Network output: [ 0.07118 -0.1942 0.9023 -0.0004615 0.0002072 1.148 -0.0003478 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6258 0.4942 0.4366 0.5121 0.9783 0.9902 0.6272 0.9126 0.9742 0.5352 ] Network output: [ -0.09726 0.3041 1.158 -0.0003911 0.0001756 0.7304 -0.0002948 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2743 0.2646 0.2671 0.2717 0.9873 0.9917 0.2744 0.9709 0.9825 0.2765 ] Network output: [ -0.1041 0.2539 1.118 -0.0003561 0.0001599 0.8347 -0.0002684 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3096 0.3076 0.2895 0.2893 0.9819 0.9886 0.3096 0.9514 0.9734 0.292 ] Network output: [ 0.03894 0.8916 -0.04144 0.000477 -0.0002141 1.074 0.0003594 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07381 Epoch 3214 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07644 0.8227 0.9182 0.0001573 -7.063e-05 0.1069 0.0001186 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01713 -0.004902 0.01343 0.03 0.9477 0.9553 0.02824 0.8915 0.9114 0.07286 ] Network output: [ 0.9762 0.06073 0.01063 0.0005825 -0.0002615 -0.02143 0.000439 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5836 0.04144 0.09676 0.3873 0.9757 0.9888 0.6333 0.905 0.9705 0.5518 ] Network output: [ 0.03959 0.8549 0.9325 8.587e-06 -3.855e-06 0.1334 6.471e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02114 0.0141 0.02429 0.02767 0.9867 0.9906 0.02141 0.9693 0.9819 0.0306 ] Network output: [ 0.07161 -0.1949 0.9018 -0.000455 0.0002043 1.148 -0.0003429 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6255 0.4942 0.4362 0.5119 0.9783 0.9902 0.6268 0.9127 0.9742 0.5351 ] Network output: [ -0.09713 0.3029 1.158 -0.0003908 0.0001755 0.7315 -0.0002946 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2746 0.2649 0.2674 0.272 0.9872 0.9917 0.2747 0.9709 0.9825 0.2769 ] Network output: [ -0.104 0.253 1.118 -0.0003561 0.0001599 0.8352 -0.0002684 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3097 0.3076 0.2897 0.2894 0.9819 0.9886 0.3097 0.9514 0.9734 0.2922 ] Network output: [ 0.0387 0.8925 -0.04125 0.0004705 -0.0002112 1.073 0.0003546 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07347 Epoch 3215 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07636 0.8234 0.9182 0.0001562 -7.014e-05 0.1063 0.0001177 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01712 -0.004895 0.0134 0.02999 0.9477 0.9553 0.02823 0.8915 0.9114 0.07287 ] Network output: [ 0.9759 0.06151 0.01082 0.0005844 -0.0002623 -0.02171 0.0004404 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5833 0.04187 0.09614 0.3871 0.9757 0.9888 0.6329 0.905 0.9705 0.5517 ] Network output: [ 0.03953 0.8554 0.9326 7.307e-06 -3.28e-06 0.1329 5.506e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02115 0.01411 0.02429 0.02768 0.9867 0.9906 0.02141 0.9693 0.9819 0.03062 ] Network output: [ 0.07204 -0.1956 0.9014 -0.0004485 0.0002014 1.148 -0.000338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6251 0.4942 0.4358 0.5118 0.9783 0.9902 0.6264 0.9127 0.9742 0.535 ] Network output: [ -0.09701 0.3017 1.158 -0.0003905 0.0001753 0.7325 -0.0002943 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2749 0.2652 0.2677 0.2724 0.9872 0.9917 0.275 0.9709 0.9825 0.2772 ] Network output: [ -0.1038 0.2521 1.118 -0.0003561 0.0001599 0.8358 -0.0002684 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3098 0.3077 0.2898 0.2896 0.9819 0.9886 0.3098 0.9515 0.9735 0.2924 ] Network output: [ 0.03846 0.8934 -0.04106 0.0004641 -0.0002083 1.073 0.0003497 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07313 Epoch 3216 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07628 0.8241 0.9183 0.0001552 -6.966e-05 0.1057 0.0001169 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01712 -0.004889 0.01336 0.02999 0.9477 0.9553 0.02823 0.8915 0.9114 0.07288 ] Network output: [ 0.9755 0.06227 0.01101 0.0005862 -0.0002632 -0.02198 0.0004418 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5829 0.0423 0.09552 0.3869 0.9757 0.9888 0.6326 0.905 0.9705 0.5516 ] Network output: [ 0.03948 0.8559 0.9327 6.044e-06 -2.714e-06 0.1325 4.555e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02116 0.01413 0.02429 0.0277 0.9867 0.9906 0.02142 0.9693 0.9819 0.03064 ] Network output: [ 0.07247 -0.1963 0.9009 -0.0004421 0.0001985 1.149 -0.0003332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6247 0.4942 0.4353 0.5116 0.9783 0.9902 0.6261 0.9127 0.9742 0.5349 ] Network output: [ -0.09687 0.3005 1.158 -0.0003902 0.0001752 0.7335 -0.0002941 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2752 0.2655 0.268 0.2727 0.9872 0.9917 0.2753 0.9709 0.9825 0.2775 ] Network output: [ -0.1036 0.2512 1.118 -0.0003561 0.0001599 0.8363 -0.0002684 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3099 0.3078 0.29 0.2898 0.9819 0.9886 0.3099 0.9515 0.9735 0.2925 ] Network output: [ 0.03822 0.8943 -0.04086 0.0004577 -0.0002055 1.072 0.0003449 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0728 Epoch 3217 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0762 0.8248 0.9184 0.0001541 -6.918e-05 0.1051 0.0001161 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01711 -0.004882 0.01332 0.02998 0.9477 0.9554 0.02822 0.8916 0.9114 0.07289 ] Network output: [ 0.9752 0.06301 0.0112 0.0005879 -0.0002639 -0.02225 0.0004431 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5826 0.04273 0.0949 0.3867 0.9757 0.9888 0.6322 0.905 0.9705 0.5515 ] Network output: [ 0.03942 0.8564 0.9327 4.802e-06 -2.156e-06 0.132 3.619e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02116 0.01414 0.02429 0.02771 0.9867 0.9906 0.02143 0.9692 0.9819 0.03066 ] Network output: [ 0.07289 -0.197 0.9005 -0.0004357 0.0001956 1.149 -0.0003284 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6244 0.4942 0.4349 0.5115 0.9783 0.9902 0.6257 0.9127 0.9742 0.5348 ] Network output: [ -0.09674 0.2993 1.158 -0.0003899 0.000175 0.7345 -0.0002938 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2755 0.2658 0.2682 0.273 0.9872 0.9917 0.2756 0.9709 0.9825 0.2779 ] Network output: [ -0.1035 0.2503 1.118 -0.0003561 0.0001599 0.8369 -0.0002684 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3099 0.3079 0.2902 0.29 0.9819 0.9886 0.3099 0.9515 0.9735 0.2927 ] Network output: [ 0.03798 0.8952 -0.04067 0.0004514 -0.0002026 1.071 0.0003402 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07248 Epoch 3218 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07612 0.8255 0.9184 0.000153 -6.87e-05 0.1045 0.0001153 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0171 -0.004875 0.01329 0.02997 0.9477 0.9554 0.02821 0.8916 0.9114 0.0729 ] Network output: [ 0.9749 0.06375 0.01139 0.0005896 -0.0002647 -0.02252 0.0004443 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5822 0.04316 0.09428 0.3865 0.9757 0.9888 0.6319 0.905 0.9705 0.5513 ] Network output: [ 0.03936 0.8569 0.9328 3.579e-06 -1.607e-06 0.1315 2.697e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02117 0.01415 0.02429 0.02772 0.9867 0.9906 0.02144 0.9692 0.9818 0.03067 ] Network output: [ 0.07331 -0.1977 0.9 -0.0004293 0.0001927 1.149 -0.0003236 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.624 0.4942 0.4345 0.5114 0.9783 0.9902 0.6253 0.9127 0.9742 0.5347 ] Network output: [ -0.09661 0.2982 1.158 -0.0003896 0.0001749 0.7355 -0.0002936 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2758 0.2661 0.2685 0.2733 0.9872 0.9917 0.2759 0.9709 0.9825 0.2782 ] Network output: [ -0.1033 0.2495 1.118 -0.0003561 0.0001599 0.8374 -0.0002684 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.31 0.308 0.2904 0.2902 0.9819 0.9886 0.31 0.9516 0.9735 0.2929 ] Network output: [ 0.03773 0.896 -0.04047 0.0004451 -0.0001998 1.071 0.0003355 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07216 Epoch 3219 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07604 0.8262 0.9185 0.000152 -6.823e-05 0.1039 0.0001145 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01709 -0.004868 0.01325 0.02997 0.9477 0.9554 0.0282 0.8916 0.9114 0.0729 ] Network output: [ 0.9746 0.06447 0.01159 0.0005911 -0.0002654 -0.02278 0.0004455 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5819 0.04358 0.09366 0.3863 0.9757 0.9888 0.6315 0.905 0.9705 0.5512 ] Network output: [ 0.03931 0.8574 0.9329 2.376e-06 -1.067e-06 0.1311 1.791e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02118 0.01417 0.02428 0.02773 0.9867 0.9906 0.02145 0.9692 0.9818 0.03069 ] Network output: [ 0.07372 -0.1984 0.8996 -0.000423 0.0001899 1.15 -0.0003188 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6237 0.4942 0.434 0.5112 0.9783 0.9902 0.625 0.9127 0.9742 0.5345 ] Network output: [ -0.09648 0.297 1.158 -0.0003892 0.0001747 0.7365 -0.0002933 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2761 0.2664 0.2688 0.2736 0.9872 0.9917 0.2762 0.9709 0.9825 0.2785 ] Network output: [ -0.1032 0.2486 1.118 -0.0003561 0.0001599 0.8379 -0.0002684 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.31 0.308 0.2905 0.2903 0.9819 0.9886 0.3101 0.9516 0.9735 0.2931 ] Network output: [ 0.03749 0.8969 -0.04027 0.0004389 -0.0001971 1.07 0.0003308 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07185 Epoch 3220 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07596 0.8268 0.9186 0.000151 -6.777e-05 0.1033 0.0001138 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01708 -0.004861 0.01321 0.02996 0.9477 0.9554 0.02819 0.8916 0.9114 0.07291 ] Network output: [ 0.9742 0.06519 0.01179 0.0005926 -0.0002661 -0.02304 0.0004466 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5816 0.04401 0.09304 0.3861 0.9758 0.9888 0.6312 0.905 0.9706 0.5511 ] Network output: [ 0.03925 0.8579 0.9329 1.196e-06 -5.37e-07 0.1306 9.015e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02118 0.01418 0.02428 0.02774 0.9867 0.9906 0.02145 0.9692 0.9818 0.0307 ] Network output: [ 0.07413 -0.1991 0.8991 -0.0004167 0.0001871 1.15 -0.0003141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6233 0.4942 0.4336 0.5111 0.9783 0.9902 0.6246 0.9127 0.9742 0.5344 ] Network output: [ -0.09634 0.2959 1.158 -0.0003889 0.0001746 0.7375 -0.0002931 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2764 0.2667 0.2691 0.2739 0.9872 0.9917 0.2765 0.9709 0.9825 0.2788 ] Network output: [ -0.103 0.2478 1.118 -0.0003561 0.0001599 0.8385 -0.0002684 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3101 0.3081 0.2907 0.2905 0.9819 0.9887 0.3101 0.9516 0.9735 0.2933 ] Network output: [ 0.03725 0.8978 -0.04007 0.0004328 -0.0001943 1.07 0.0003262 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07154 Epoch 3221 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07588 0.8275 0.9187 0.0001499 -6.732e-05 0.1027 0.000113 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01707 -0.004854 0.01318 0.02996 0.9477 0.9554 0.02817 0.8916 0.9114 0.07292 ] Network output: [ 0.9739 0.06589 0.01199 0.0005941 -0.0002667 -0.0233 0.0004477 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5812 0.04443 0.09243 0.386 0.9758 0.9888 0.6309 0.905 0.9706 0.551 ] Network output: [ 0.03919 0.8584 0.933 3.854e-08 -1.73e-08 0.1302 2.905e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02119 0.01419 0.02428 0.02775 0.9867 0.9906 0.02146 0.9692 0.9818 0.03072 ] Network output: [ 0.07454 -0.1998 0.8987 -0.0004105 0.0001843 1.15 -0.0003094 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.623 0.4942 0.4332 0.5109 0.9783 0.9902 0.6243 0.9127 0.9742 0.5343 ] Network output: [ -0.09621 0.2948 1.158 -0.0003885 0.0001744 0.7384 -0.0002928 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2766 0.267 0.2693 0.2742 0.9872 0.9917 0.2767 0.9708 0.9825 0.2791 ] Network output: [ -0.1029 0.2469 1.118 -0.0003561 0.0001599 0.839 -0.0002684 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3102 0.3081 0.2909 0.2906 0.9819 0.9887 0.3102 0.9517 0.9736 0.2934 ] Network output: [ 0.03701 0.8986 -0.03988 0.0004267 -0.0001916 1.069 0.0003216 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07124 Epoch 3222 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0758 0.8282 0.9187 0.0001489 -6.687e-05 0.1021 0.0001122 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01706 -0.004848 0.01314 0.02995 0.9477 0.9554 0.02816 0.8916 0.9114 0.07293 ] Network output: [ 0.9736 0.06658 0.01219 0.0005954 -0.0002673 -0.02355 0.0004487 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5809 0.04485 0.09181 0.3858 0.9758 0.9888 0.6305 0.905 0.9706 0.5509 ] Network output: [ 0.03913 0.8589 0.9331 -1.096e-06 4.918e-07 0.1298 -8.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0212 0.0142 0.02428 0.02776 0.9867 0.9906 0.02146 0.9692 0.9818 0.03074 ] Network output: [ 0.07495 -0.2004 0.8983 -0.0004043 0.0001815 1.151 -0.0003047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6226 0.4942 0.4327 0.5108 0.9783 0.9902 0.6239 0.9127 0.9742 0.5342 ] Network output: [ -0.09607 0.2937 1.158 -0.0003881 0.0001742 0.7394 -0.0002925 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2769 0.2673 0.2696 0.2745 0.9872 0.9917 0.277 0.9708 0.9825 0.2795 ] Network output: [ -0.1027 0.2461 1.118 -0.000356 0.0001598 0.8395 -0.0002683 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3102 0.3082 0.291 0.2908 0.9819 0.9887 0.3102 0.9517 0.9736 0.2936 ] Network output: [ 0.03676 0.8995 -0.03968 0.0004207 -0.0001889 1.068 0.000317 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07094 Epoch 3223 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07572 0.8288 0.9188 0.000148 -6.642e-05 0.1015 0.0001115 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01706 -0.004841 0.0131 0.02994 0.9477 0.9554 0.02815 0.8916 0.9114 0.07293 ] Network output: [ 0.9733 0.06725 0.01239 0.0005966 -0.0002678 -0.0238 0.0004496 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5806 0.04527 0.0912 0.3856 0.9758 0.9888 0.6302 0.9051 0.9706 0.5508 ] Network output: [ 0.03907 0.8594 0.9331 -2.205e-06 9.901e-07 0.1293 -1.662e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0212 0.01421 0.02427 0.02777 0.9867 0.9906 0.02147 0.9692 0.9818 0.03075 ] Network output: [ 0.07535 -0.2011 0.8978 -0.0003982 0.0001787 1.151 -0.0003001 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6223 0.4942 0.4323 0.5106 0.9783 0.9902 0.6236 0.9128 0.9742 0.5341 ] Network output: [ -0.09594 0.2926 1.157 -0.0003877 0.000174 0.7403 -0.0002922 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2772 0.2676 0.2698 0.2748 0.9872 0.9917 0.2773 0.9708 0.9825 0.2798 ] Network output: [ -0.1026 0.2453 1.118 -0.000356 0.0001598 0.84 -0.0002683 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3103 0.3082 0.2912 0.291 0.9819 0.9887 0.3103 0.9517 0.9736 0.2938 ] Network output: [ 0.03652 0.9003 -0.03947 0.0004147 -0.0001862 1.068 0.0003126 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07064 Epoch 3224 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07564 0.8295 0.9189 0.000147 -6.599e-05 0.101 0.0001108 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01705 -0.004834 0.01306 0.02994 0.9477 0.9554 0.02814 0.8916 0.9114 0.07294 ] Network output: [ 0.973 0.06792 0.0126 0.0005978 -0.0002684 -0.02405 0.0004505 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5802 0.04569 0.0906 0.3854 0.9758 0.9888 0.6298 0.9051 0.9706 0.5507 ] Network output: [ 0.03902 0.8598 0.9332 -3.29e-06 1.477e-06 0.1289 -2.48e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02121 0.01423 0.02427 0.02777 0.9867 0.9906 0.02147 0.9692 0.9818 0.03076 ] Network output: [ 0.07575 -0.2017 0.8974 -0.0003921 0.000176 1.151 -0.0002955 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6219 0.4942 0.4319 0.5105 0.9783 0.9902 0.6232 0.9128 0.9742 0.534 ] Network output: [ -0.0958 0.2915 1.157 -0.0003873 0.0001739 0.7412 -0.0002919 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2774 0.2678 0.2701 0.275 0.9872 0.9917 0.2775 0.9708 0.9825 0.28 ] Network output: [ -0.1024 0.2445 1.118 -0.0003559 0.0001598 0.8405 -0.0002682 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3103 0.3083 0.2913 0.2911 0.982 0.9887 0.3103 0.9517 0.9736 0.2939 ] Network output: [ 0.03628 0.9012 -0.03927 0.0004088 -0.0001835 1.067 0.0003081 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07035 Epoch 3225 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07556 0.8301 0.9189 0.000146 -6.556e-05 0.1004 0.00011 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01704 -0.004828 0.01302 0.02993 0.9477 0.9554 0.02813 0.8916 0.9115 0.07294 ] Network output: [ 0.9727 0.06857 0.0128 0.0005988 -0.0002688 -0.02429 0.0004513 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5799 0.04611 0.09 0.3853 0.9758 0.9888 0.6295 0.9051 0.9706 0.5506 ] Network output: [ 0.03896 0.8603 0.9333 -4.349e-06 1.952e-06 0.1285 -3.278e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02121 0.01424 0.02427 0.02778 0.9867 0.9906 0.02148 0.9692 0.9818 0.03078 ] Network output: [ 0.07614 -0.2023 0.8969 -0.000386 0.0001733 1.152 -0.0002909 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6216 0.4942 0.4315 0.5104 0.9783 0.9902 0.6229 0.9128 0.9742 0.5339 ] Network output: [ -0.09566 0.2904 1.157 -0.0003868 0.0001737 0.7421 -0.0002915 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2777 0.2681 0.2703 0.2753 0.9872 0.9917 0.2778 0.9708 0.9825 0.2803 ] Network output: [ -0.1023 0.2437 1.118 -0.0003558 0.0001597 0.841 -0.0002682 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3103 0.3083 0.2915 0.2913 0.982 0.9887 0.3104 0.9518 0.9736 0.2941 ] Network output: [ 0.03603 0.902 -0.03907 0.000403 -0.0001809 1.067 0.0003037 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07007 Epoch 3226 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07548 0.8307 0.919 0.0001451 -6.513e-05 0.09989 0.0001093 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01703 -0.004821 0.01299 0.02992 0.9478 0.9554 0.02812 0.8917 0.9115 0.07295 ] Network output: [ 0.9724 0.06921 0.01301 0.0005997 -0.0002692 -0.02453 0.000452 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5796 0.04652 0.0894 0.3851 0.9758 0.9888 0.6292 0.9051 0.9706 0.5505 ] Network output: [ 0.0389 0.8607 0.9333 -5.382e-06 2.416e-06 0.1281 -4.056e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02121 0.01425 0.02426 0.02779 0.9867 0.9906 0.02148 0.9692 0.9818 0.03079 ] Network output: [ 0.07653 -0.203 0.8965 -0.00038 0.0001706 1.152 -0.0002864 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6212 0.4942 0.431 0.5102 0.9783 0.9902 0.6225 0.9128 0.9742 0.5338 ] Network output: [ -0.09553 0.2893 1.157 -0.0003864 0.0001735 0.743 -0.0002912 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2779 0.2683 0.2705 0.2756 0.9872 0.9917 0.278 0.9708 0.9824 0.2806 ] Network output: [ -0.1021 0.2429 1.118 -0.0003557 0.0001597 0.8415 -0.0002681 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3104 0.3084 0.2916 0.2914 0.982 0.9887 0.3104 0.9518 0.9736 0.2942 ] Network output: [ 0.03579 0.9029 -0.03887 0.0003973 -0.0001783 1.066 0.0002994 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06979 Epoch 3227 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0754 0.8313 0.9191 0.0001442 -6.472e-05 0.09936 0.0001086 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01702 -0.004814 0.01295 0.02992 0.9478 0.9554 0.02811 0.8917 0.9115 0.07296 ] Network output: [ 0.9721 0.06984 0.01322 0.0006006 -0.0002696 -0.02476 0.0004526 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5793 0.04693 0.0888 0.3849 0.9758 0.9888 0.6288 0.9051 0.9706 0.5504 ] Network output: [ 0.03884 0.8612 0.9334 -6.387e-06 2.867e-06 0.1277 -4.814e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02122 0.01426 0.02426 0.0278 0.9867 0.9906 0.02149 0.9692 0.9818 0.0308 ] Network output: [ 0.07692 -0.2036 0.896 -0.0003741 0.0001679 1.152 -0.0002819 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6209 0.4942 0.4306 0.5101 0.9783 0.9902 0.6222 0.9128 0.9742 0.5337 ] Network output: [ -0.09539 0.2883 1.157 -0.0003859 0.0001732 0.7439 -0.0002908 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2781 0.2686 0.2708 0.2758 0.9872 0.9917 0.2782 0.9708 0.9824 0.2809 ] Network output: [ -0.1019 0.2421 1.118 -0.0003556 0.0001596 0.8419 -0.000268 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3104 0.3084 0.2917 0.2915 0.982 0.9887 0.3104 0.9518 0.9736 0.2944 ] Network output: [ 0.03554 0.9037 -0.03866 0.0003916 -0.0001758 1.065 0.0002951 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06951 Epoch 3228 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07532 0.832 0.9192 0.0001432 -6.431e-05 0.09883 0.000108 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01701 -0.004808 0.01291 0.02991 0.9478 0.9554 0.02809 0.8917 0.9115 0.07296 ] Network output: [ 0.9718 0.07045 0.01343 0.0006013 -0.0002699 -0.02499 0.0004532 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.579 0.04734 0.08822 0.3848 0.9758 0.9888 0.6285 0.9051 0.9706 0.5502 ] Network output: [ 0.03878 0.8617 0.9335 -7.365e-06 3.306e-06 0.1273 -5.55e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02122 0.01427 0.02425 0.02781 0.9867 0.9906 0.02149 0.9692 0.9818 0.03082 ] Network output: [ 0.0773 -0.2042 0.8956 -0.0003682 0.0001653 1.152 -0.0002775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6206 0.4942 0.4302 0.51 0.9784 0.9902 0.6219 0.9128 0.9742 0.5336 ] Network output: [ -0.09525 0.2872 1.157 -0.0003854 0.000173 0.7448 -0.0002905 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2783 0.2688 0.271 0.2761 0.9872 0.9917 0.2784 0.9708 0.9824 0.2812 ] Network output: [ -0.1018 0.2413 1.118 -0.0003554 0.0001596 0.8424 -0.0002679 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3104 0.3084 0.2919 0.2917 0.982 0.9887 0.3104 0.9519 0.9737 0.2945 ] Network output: [ 0.0353 0.9045 -0.03846 0.0003859 -0.0001733 1.065 0.0002909 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06924 Epoch 3229 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07524 0.8326 0.9192 0.0001424 -6.391e-05 0.0983 0.0001073 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.017 -0.004802 0.01287 0.0299 0.9478 0.9554 0.02808 0.8917 0.9115 0.07297 ] Network output: [ 0.9715 0.07106 0.01364 0.0006019 -0.0002702 -0.02522 0.0004536 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5786 0.04775 0.08763 0.3846 0.9758 0.9888 0.6282 0.9051 0.9706 0.5501 ] Network output: [ 0.03872 0.8621 0.9335 -8.314e-06 3.733e-06 0.1269 -6.266e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02122 0.01428 0.02425 0.02781 0.9867 0.9906 0.02149 0.9692 0.9818 0.03083 ] Network output: [ 0.07768 -0.2048 0.8952 -0.0003623 0.0001627 1.153 -0.0002731 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6202 0.4942 0.4298 0.5098 0.9784 0.9902 0.6215 0.9129 0.9742 0.5335 ] Network output: [ -0.09511 0.2862 1.157 -0.0003849 0.0001728 0.7457 -0.0002901 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2786 0.269 0.2712 0.2763 0.9872 0.9917 0.2787 0.9708 0.9824 0.2814 ] Network output: [ -0.1016 0.2405 1.118 -0.0003552 0.0001595 0.8429 -0.0002677 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3104 0.3085 0.292 0.2918 0.982 0.9887 0.3105 0.9519 0.9737 0.2947 ] Network output: [ 0.03505 0.9054 -0.03825 0.0003804 -0.0001708 1.064 0.0002867 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06898 Epoch 3230 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07516 0.8332 0.9193 0.0001415 -6.351e-05 0.09779 0.0001066 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01699 -0.004795 0.01283 0.0299 0.9478 0.9554 0.02807 0.8917 0.9115 0.07297 ] Network output: [ 0.9712 0.07165 0.01385 0.0006024 -0.0002705 -0.02544 0.000454 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5783 0.04815 0.08705 0.3844 0.9758 0.9888 0.6279 0.9052 0.9706 0.55 ] Network output: [ 0.03866 0.8625 0.9336 -9.235e-06 4.146e-06 0.1265 -6.96e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02123 0.01429 0.02424 0.02782 0.9867 0.9906 0.0215 0.9691 0.9818 0.03084 ] Network output: [ 0.07806 -0.2054 0.8947 -0.0003565 0.0001601 1.153 -0.0002687 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6199 0.4942 0.4294 0.5097 0.9784 0.9903 0.6212 0.9129 0.9742 0.5334 ] Network output: [ -0.09498 0.2852 1.157 -0.0003843 0.0001725 0.7465 -0.0002897 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2788 0.2693 0.2714 0.2766 0.9872 0.9917 0.2789 0.9708 0.9824 0.2817 ] Network output: [ -0.1015 0.2398 1.118 -0.000355 0.0001594 0.8433 -0.0002676 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3105 0.3085 0.2921 0.2919 0.982 0.9887 0.3105 0.9519 0.9737 0.2948 ] Network output: [ 0.03481 0.9062 -0.03804 0.0003749 -0.0001683 1.064 0.0002825 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06872 Epoch 3231 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07508 0.8338 0.9194 0.0001406 -6.313e-05 0.09728 0.000106 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01698 -0.004789 0.01279 0.02989 0.9478 0.9554 0.02806 0.8917 0.9115 0.07297 ] Network output: [ 0.9709 0.07223 0.01406 0.0006028 -0.0002706 -0.02566 0.0004543 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.578 0.04855 0.08648 0.3843 0.9758 0.9888 0.6275 0.9052 0.9706 0.5499 ] Network output: [ 0.0386 0.863 0.9337 -1.013e-05 4.546e-06 0.1261 -7.631e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02123 0.0143 0.02424 0.02783 0.9867 0.9906 0.0215 0.9691 0.9818 0.03085 ] Network output: [ 0.07843 -0.206 0.8943 -0.0003508 0.0001575 1.153 -0.0002644 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6196 0.4942 0.429 0.5096 0.9784 0.9903 0.6209 0.9129 0.9742 0.5334 ] Network output: [ -0.09484 0.2842 1.157 -0.0003838 0.0001723 0.7474 -0.0002892 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.279 0.2695 0.2716 0.2768 0.9872 0.9917 0.2791 0.9708 0.9824 0.282 ] Network output: [ -0.1013 0.239 1.118 -0.0003548 0.0001593 0.8438 -0.0002674 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3105 0.3085 0.2923 0.2921 0.982 0.9887 0.3105 0.952 0.9737 0.2949 ] Network output: [ 0.03457 0.907 -0.03784 0.0003695 -0.0001659 1.063 0.0002785 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06846 Epoch 3232 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.075 0.8343 0.9194 0.0001398 -6.275e-05 0.09678 0.0001053 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01697 -0.004783 0.01276 0.02988 0.9478 0.9554 0.02804 0.8917 0.9115 0.07298 ] Network output: [ 0.9706 0.0728 0.01428 0.0006031 -0.0002708 -0.02587 0.0004545 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5777 0.04895 0.08591 0.3841 0.9758 0.9888 0.6272 0.9052 0.9706 0.5498 ] Network output: [ 0.03854 0.8634 0.9337 -1.099e-05 4.933e-06 0.1257 -8.281e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02123 0.01431 0.02424 0.02783 0.9867 0.9906 0.0215 0.9691 0.9818 0.03087 ] Network output: [ 0.0788 -0.2066 0.8939 -0.0003451 0.0001549 1.154 -0.0002601 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6193 0.4942 0.4286 0.5094 0.9784 0.9903 0.6206 0.9129 0.9742 0.5333 ] Network output: [ -0.0947 0.2831 1.157 -0.0003832 0.000172 0.7482 -0.0002888 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2792 0.2697 0.2719 0.2771 0.9872 0.9917 0.2793 0.9708 0.9824 0.2822 ] Network output: [ -0.1012 0.2383 1.118 -0.0003546 0.0001592 0.8442 -0.0002672 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3105 0.3085 0.2924 0.2922 0.982 0.9887 0.3105 0.952 0.9737 0.2951 ] Network output: [ 0.03432 0.9078 -0.03763 0.0003642 -0.0001635 1.063 0.0002745 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06821 Epoch 3233 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07492 0.8349 0.9195 0.0001389 -6.238e-05 0.09628 0.0001047 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01696 -0.004777 0.01272 0.02988 0.9478 0.9554 0.02803 0.8918 0.9115 0.07298 ] Network output: [ 0.9703 0.07336 0.01449 0.0006033 -0.0002708 -0.02609 0.0004546 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5774 0.04935 0.08535 0.384 0.9758 0.9888 0.6269 0.9052 0.9706 0.5497 ] Network output: [ 0.03848 0.8638 0.9338 -1.182e-05 5.306e-06 0.1254 -8.908e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02123 0.01432 0.02423 0.02784 0.9867 0.9906 0.0215 0.9691 0.9818 0.03088 ] Network output: [ 0.07916 -0.2072 0.8934 -0.0003395 0.0001524 1.154 -0.0002559 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6189 0.4942 0.4282 0.5093 0.9784 0.9903 0.6203 0.9129 0.9743 0.5332 ] Network output: [ -0.09456 0.2821 1.156 -0.0003826 0.0001718 0.749 -0.0002883 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2793 0.2699 0.2721 0.2773 0.9872 0.9917 0.2794 0.9707 0.9824 0.2825 ] Network output: [ -0.101 0.2375 1.118 -0.0003543 0.0001591 0.8447 -0.000267 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3105 0.3086 0.2925 0.2923 0.982 0.9887 0.3105 0.952 0.9737 0.2952 ] Network output: [ 0.03408 0.9087 -0.03742 0.0003589 -0.0001611 1.062 0.0002705 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06796 Epoch 3234 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07485 0.8355 0.9196 0.0001381 -6.201e-05 0.09579 0.0001041 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01694 -0.004771 0.01268 0.02987 0.9478 0.9554 0.02802 0.8918 0.9115 0.07298 ] Network output: [ 0.9701 0.0739 0.0147 0.0006033 -0.0002708 -0.02629 0.0004547 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5771 0.04974 0.0848 0.3838 0.9758 0.9888 0.6266 0.9052 0.9706 0.5496 ] Network output: [ 0.03842 0.8643 0.9338 -1.262e-05 5.666e-06 0.125 -9.511e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02123 0.01433 0.02423 0.02785 0.9867 0.9906 0.02151 0.9691 0.9818 0.03089 ] Network output: [ 0.07952 -0.2077 0.893 -0.0003339 0.0001499 1.154 -0.0002517 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6186 0.4942 0.4278 0.5092 0.9784 0.9903 0.6199 0.9129 0.9743 0.5331 ] Network output: [ -0.09442 0.2812 1.156 -0.0003819 0.0001715 0.7498 -0.0002878 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2795 0.2701 0.2723 0.2775 0.9872 0.9917 0.2796 0.9707 0.9824 0.2827 ] Network output: [ -0.1009 0.2368 1.118 -0.000354 0.0001589 0.8451 -0.0002668 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3105 0.3086 0.2926 0.2924 0.982 0.9887 0.3105 0.952 0.9737 0.2953 ] Network output: [ 0.03383 0.9095 -0.03721 0.0003537 -0.0001588 1.062 0.0002666 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06771 Epoch 3235 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07477 0.8361 0.9196 0.0001373 -6.166e-05 0.09531 0.0001035 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01693 -0.004765 0.01264 0.02986 0.9478 0.9554 0.028 0.8918 0.9115 0.07299 ] Network output: [ 0.9698 0.07444 0.01491 0.0006032 -0.0002708 -0.0265 0.0004546 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5768 0.05012 0.08426 0.3837 0.9758 0.9888 0.6263 0.9052 0.9706 0.5495 ] Network output: [ 0.03836 0.8647 0.9339 -1.339e-05 6.012e-06 0.1246 -1.009e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02124 0.01434 0.02422 0.02785 0.9867 0.9906 0.02151 0.9691 0.9818 0.0309 ] Network output: [ 0.07988 -0.2083 0.8925 -0.0003284 0.0001474 1.155 -0.0002475 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6183 0.4943 0.4274 0.5091 0.9784 0.9903 0.6196 0.913 0.9743 0.533 ] Network output: [ -0.09429 0.2802 1.156 -0.0003813 0.0001712 0.7506 -0.0002873 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2797 0.2703 0.2725 0.2778 0.9872 0.9917 0.2798 0.9707 0.9824 0.283 ] Network output: [ -0.1007 0.2361 1.118 -0.0003536 0.0001588 0.8455 -0.0002665 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3105 0.3086 0.2927 0.2925 0.982 0.9887 0.3105 0.9521 0.9738 0.2955 ] Network output: [ 0.03359 0.9103 -0.037 0.0003486 -0.0001565 1.061 0.0002627 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06747 Epoch 3236 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07469 0.8366 0.9197 0.0001366 -6.131e-05 0.09483 0.0001029 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01692 -0.004759 0.0126 0.02986 0.9478 0.9554 0.02799 0.8918 0.9116 0.07299 ] Network output: [ 0.9695 0.07496 0.01513 0.000603 -0.0002707 -0.0267 0.0004544 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5765 0.05051 0.08372 0.3835 0.9758 0.9888 0.626 0.9053 0.9706 0.5494 ] Network output: [ 0.03829 0.8651 0.934 -1.413e-05 6.343e-06 0.1243 -1.065e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02124 0.01435 0.02422 0.02786 0.9867 0.9906 0.02151 0.9691 0.9818 0.03091 ] Network output: [ 0.08023 -0.2088 0.8921 -0.000323 0.000145 1.155 -0.0002434 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.618 0.4943 0.4271 0.509 0.9784 0.9903 0.6193 0.913 0.9743 0.5329 ] Network output: [ -0.09415 0.2792 1.156 -0.0003806 0.0001709 0.7514 -0.0002868 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2799 0.2705 0.2726 0.278 0.9872 0.9917 0.28 0.9707 0.9824 0.2832 ] Network output: [ -0.1006 0.2354 1.118 -0.0003533 0.0001586 0.8459 -0.0002662 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3105 0.3086 0.2928 0.2927 0.982 0.9887 0.3105 0.9521 0.9738 0.2956 ] Network output: [ 0.03335 0.9111 -0.03678 0.0003436 -0.0001542 1.06 0.0002589 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06723 Epoch 3237 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07461 0.8372 0.9198 0.0001358 -6.097e-05 0.09436 0.0001024 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01691 -0.004753 0.01257 0.02985 0.9478 0.9554 0.02798 0.8918 0.9116 0.07299 ] Network output: [ 0.9693 0.07547 0.01534 0.0006027 -0.0002706 -0.02689 0.0004542 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5762 0.05089 0.08319 0.3834 0.9758 0.9888 0.6257 0.9053 0.9706 0.5494 ] Network output: [ 0.03823 0.8655 0.934 -1.484e-05 6.661e-06 0.1239 -1.118e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02124 0.01435 0.02421 0.02786 0.9867 0.9906 0.02151 0.9691 0.9818 0.03092 ] Network output: [ 0.08058 -0.2094 0.8917 -0.0003176 0.0001426 1.155 -0.0002393 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6177 0.4943 0.4267 0.5088 0.9784 0.9903 0.619 0.913 0.9743 0.5328 ] Network output: [ -0.09401 0.2782 1.156 -0.0003799 0.0001705 0.7522 -0.0002863 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.28 0.2707 0.2728 0.2782 0.9872 0.9917 0.2801 0.9707 0.9824 0.2834 ] Network output: [ -0.1004 0.2347 1.118 -0.0003529 0.0001584 0.8463 -0.0002659 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3105 0.3086 0.2929 0.2928 0.9821 0.9887 0.3105 0.9521 0.9738 0.2957 ] Network output: [ 0.0331 0.9119 -0.03657 0.0003386 -0.000152 1.06 0.0002552 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.067 Epoch 3238 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07453 0.8377 0.9198 0.0001351 -6.064e-05 0.0939 0.0001018 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0169 -0.004748 0.01253 0.02984 0.9478 0.9554 0.02796 0.8918 0.9116 0.07299 ] Network output: [ 0.969 0.07597 0.01555 0.0006022 -0.0002704 -0.02709 0.0004539 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5759 0.05127 0.08266 0.3832 0.9758 0.9888 0.6254 0.9053 0.9706 0.5493 ] Network output: [ 0.03817 0.8659 0.9341 -1.551e-05 6.965e-06 0.1236 -1.169e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02124 0.01436 0.02421 0.02787 0.9867 0.9906 0.02151 0.9691 0.9817 0.03093 ] Network output: [ 0.08092 -0.2099 0.8912 -0.0003123 0.0001402 1.156 -0.0002353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6174 0.4943 0.4263 0.5087 0.9784 0.9903 0.6187 0.913 0.9743 0.5327 ] Network output: [ -0.09388 0.2773 1.156 -0.0003791 0.0001702 0.753 -0.0002857 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2802 0.2708 0.273 0.2784 0.9872 0.9917 0.2803 0.9707 0.9824 0.2836 ] Network output: [ -0.1003 0.234 1.118 -0.0003525 0.0001582 0.8467 -0.0002656 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3105 0.3086 0.293 0.2929 0.9821 0.9887 0.3105 0.9522 0.9738 0.2958 ] Network output: [ 0.03286 0.9127 -0.03636 0.0003337 -0.0001498 1.059 0.0002515 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06677 Epoch 3239 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07446 0.8383 0.9199 0.0001344 -6.032e-05 0.09345 0.0001013 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01689 -0.004742 0.01249 0.02984 0.9478 0.9555 0.02795 0.8919 0.9116 0.073 ] Network output: [ 0.9688 0.07646 0.01576 0.0006017 -0.0002701 -0.02728 0.0004534 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5756 0.05164 0.08215 0.3831 0.9758 0.9888 0.6251 0.9053 0.9706 0.5492 ] Network output: [ 0.03811 0.8663 0.9341 -1.616e-05 7.255e-06 0.1232 -1.218e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02124 0.01437 0.0242 0.02788 0.9867 0.9906 0.02151 0.9691 0.9817 0.03094 ] Network output: [ 0.08126 -0.2104 0.8908 -0.000307 0.0001378 1.156 -0.0002314 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6171 0.4943 0.426 0.5086 0.9784 0.9903 0.6184 0.913 0.9743 0.5326 ] Network output: [ -0.09374 0.2763 1.156 -0.0003784 0.0001699 0.7537 -0.0002852 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2803 0.271 0.2732 0.2786 0.9872 0.9917 0.2804 0.9707 0.9824 0.2839 ] Network output: [ -0.1001 0.2333 1.118 -0.000352 0.000158 0.8471 -0.0002653 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3105 0.3086 0.2931 0.293 0.9821 0.9887 0.3105 0.9522 0.9738 0.2959 ] Network output: [ 0.03262 0.9135 -0.03614 0.0003289 -0.0001477 1.059 0.0002479 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06655 Epoch 3240 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07438 0.8388 0.92 0.0001337 -6e-05 0.093 0.0001007 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01688 -0.004737 0.01245 0.02983 0.9478 0.9555 0.02793 0.8919 0.9116 0.073 ] Network output: [ 0.9685 0.07694 0.01598 0.000601 -0.0002698 -0.02746 0.0004529 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5754 0.05201 0.08164 0.3829 0.9758 0.9888 0.6248 0.9053 0.9707 0.5491 ] Network output: [ 0.03805 0.8667 0.9342 -1.677e-05 7.53e-06 0.1229 -1.264e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02124 0.01438 0.02419 0.02788 0.9867 0.9906 0.02151 0.9691 0.9817 0.03095 ] Network output: [ 0.0816 -0.211 0.8904 -0.0003018 0.0001355 1.156 -0.0002274 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6168 0.4944 0.4256 0.5085 0.9784 0.9903 0.6182 0.913 0.9743 0.5326 ] Network output: [ -0.0936 0.2754 1.156 -0.0003776 0.0001695 0.7545 -0.0002846 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2805 0.2712 0.2734 0.2788 0.9872 0.9917 0.2806 0.9707 0.9824 0.2841 ] Network output: [ -0.09994 0.2326 1.118 -0.0003515 0.0001578 0.8475 -0.0002649 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3105 0.3086 0.2932 0.2931 0.9821 0.9888 0.3105 0.9522 0.9738 0.296 ] Network output: [ 0.03238 0.9143 -0.03592 0.0003242 -0.0001455 1.058 0.0002443 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06633 Epoch 3241 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0743 0.8394 0.92 0.000133 -5.97e-05 0.09255 0.0001002 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01687 -0.004731 0.01242 0.02982 0.9478 0.9555 0.02792 0.8919 0.9116 0.073 ] Network output: [ 0.9682 0.0774 0.01619 0.0006001 -0.0002694 -0.02764 0.0004523 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5751 0.05237 0.08114 0.3828 0.9758 0.9888 0.6246 0.9054 0.9707 0.549 ] Network output: [ 0.03799 0.8671 0.9343 -1.735e-05 7.791e-06 0.1226 -1.308e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02124 0.01438 0.02419 0.02789 0.9867 0.9906 0.02151 0.9691 0.9817 0.03096 ] Network output: [ 0.08193 -0.2115 0.8899 -0.0002966 0.0001332 1.156 -0.0002236 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6166 0.4944 0.4252 0.5084 0.9784 0.9903 0.6179 0.9131 0.9743 0.5325 ] Network output: [ -0.09347 0.2744 1.156 -0.0003768 0.0001691 0.7552 -0.0002839 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2806 0.2713 0.2735 0.279 0.9872 0.9917 0.2807 0.9707 0.9824 0.2843 ] Network output: [ -0.09979 0.232 1.118 -0.000351 0.0001576 0.8479 -0.0002645 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3105 0.3086 0.2933 0.2932 0.9821 0.9888 0.3105 0.9522 0.9738 0.2961 ] Network output: [ 0.03213 0.9151 -0.03571 0.0003196 -0.0001435 1.058 0.0002408 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06611 Epoch 3242 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07423 0.8399 0.9201 0.0001323 -5.94e-05 0.09212 9.971e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01686 -0.004726 0.01238 0.02981 0.9478 0.9555 0.02791 0.8919 0.9116 0.073 ] Network output: [ 0.968 0.07786 0.0164 0.0005992 -0.000269 -0.02782 0.0004516 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5748 0.05273 0.08065 0.3826 0.9758 0.9888 0.6243 0.9054 0.9707 0.5489 ] Network output: [ 0.03793 0.8675 0.9343 -1.79e-05 8.038e-06 0.1223 -1.349e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02123 0.01439 0.02418 0.02789 0.9867 0.9906 0.02151 0.9691 0.9817 0.03097 ] Network output: [ 0.08226 -0.212 0.8895 -0.0002916 0.0001309 1.157 -0.0002197 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6163 0.4944 0.4249 0.5082 0.9784 0.9903 0.6176 0.9131 0.9743 0.5324 ] Network output: [ -0.09333 0.2735 1.156 -0.0003759 0.0001688 0.7559 -0.0002833 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2808 0.2715 0.2737 0.2792 0.9872 0.9917 0.2809 0.9707 0.9824 0.2845 ] Network output: [ -0.09963 0.2313 1.118 -0.0003505 0.0001573 0.8483 -0.0002641 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3104 0.3085 0.2934 0.2933 0.9821 0.9888 0.3105 0.9523 0.9738 0.2962 ] Network output: [ 0.03189 0.9159 -0.03549 0.000315 -0.0001414 1.057 0.0002374 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0659 Epoch 3243 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07415 0.8404 0.9202 0.0001317 -5.911e-05 0.09168 9.922e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01684 -0.004721 0.01234 0.02981 0.9478 0.9555 0.02789 0.8919 0.9116 0.073 ] Network output: [ 0.9678 0.07831 0.01661 0.0005981 -0.0002685 -0.02799 0.0004507 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5745 0.05309 0.08017 0.3825 0.9758 0.9888 0.624 0.9054 0.9707 0.5488 ] Network output: [ 0.03787 0.8679 0.9344 -1.842e-05 8.271e-06 0.1219 -1.388e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02123 0.0144 0.02418 0.02789 0.9867 0.9906 0.02151 0.9691 0.9817 0.03098 ] Network output: [ 0.08258 -0.2125 0.8891 -0.0002865 0.0001286 1.157 -0.0002159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.616 0.4944 0.4246 0.5081 0.9784 0.9903 0.6173 0.9131 0.9743 0.5323 ] Network output: [ -0.0932 0.2726 1.156 -0.000375 0.0001684 0.7567 -0.0002826 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2809 0.2716 0.2739 0.2794 0.9872 0.9917 0.281 0.9707 0.9824 0.2847 ] Network output: [ -0.09948 0.2307 1.118 -0.0003499 0.0001571 0.8487 -0.0002637 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3104 0.3085 0.2935 0.2934 0.9821 0.9888 0.3104 0.9523 0.9739 0.2963 ] Network output: [ 0.03165 0.9166 -0.03527 0.0003105 -0.0001394 1.057 0.000234 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06569 Epoch 3244 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07408 0.8409 0.9202 0.000131 -5.883e-05 0.09126 9.875e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01683 -0.004716 0.01231 0.0298 0.9478 0.9555 0.02788 0.892 0.9116 0.073 ] Network output: [ 0.9675 0.07874 0.01681 0.0005969 -0.000268 -0.02817 0.0004498 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5743 0.05344 0.0797 0.3823 0.9758 0.9888 0.6237 0.9054 0.9707 0.5487 ] Network output: [ 0.0378 0.8683 0.9344 -1.891e-05 8.489e-06 0.1216 -1.425e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02123 0.0144 0.02417 0.0279 0.9867 0.9906 0.0215 0.9691 0.9817 0.03099 ] Network output: [ 0.0829 -0.213 0.8886 -0.0002816 0.0001264 1.157 -0.0002122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6157 0.4945 0.4242 0.508 0.9784 0.9903 0.617 0.9131 0.9743 0.5322 ] Network output: [ -0.09306 0.2717 1.156 -0.0003741 0.000168 0.7574 -0.0002819 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.281 0.2717 0.274 0.2796 0.9872 0.9917 0.2811 0.9707 0.9824 0.2849 ] Network output: [ -0.09933 0.23 1.118 -0.0003493 0.0001568 0.849 -0.0002632 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3104 0.3085 0.2936 0.2934 0.9821 0.9888 0.3104 0.9523 0.9739 0.2964 ] Network output: [ 0.03141 0.9174 -0.03505 0.0003061 -0.0001374 1.056 0.0002307 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06548 Epoch 3245 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.074 0.8414 0.9203 0.0001304 -5.855e-05 0.09084 9.829e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01682 -0.004711 0.01227 0.02979 0.9479 0.9555 0.02786 0.892 0.9117 0.073 ] Network output: [ 0.9673 0.07917 0.01702 0.0005956 -0.0002674 -0.02833 0.0004488 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.574 0.05379 0.07923 0.3822 0.9758 0.9888 0.6235 0.9054 0.9707 0.5486 ] Network output: [ 0.03774 0.8687 0.9345 -1.936e-05 8.694e-06 0.1213 -1.459e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02123 0.01441 0.02417 0.0279 0.9867 0.9906 0.0215 0.9691 0.9817 0.031 ] Network output: [ 0.08322 -0.2135 0.8882 -0.0002767 0.0001242 1.158 -0.0002085 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6155 0.4945 0.4239 0.5079 0.9784 0.9903 0.6168 0.9132 0.9743 0.5322 ] Network output: [ -0.09293 0.2708 1.155 -0.0003732 0.0001675 0.7581 -0.0002812 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2811 0.2719 0.2742 0.2798 0.9872 0.9917 0.2812 0.9707 0.9824 0.2851 ] Network output: [ -0.09917 0.2294 1.118 -0.0003486 0.0001565 0.8494 -0.0002627 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3104 0.3085 0.2937 0.2935 0.9821 0.9888 0.3104 0.9523 0.9739 0.2965 ] Network output: [ 0.03117 0.9182 -0.03482 0.0003017 -0.0001355 1.056 0.0002274 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06528 Epoch 3246 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07393 0.8419 0.9203 0.0001298 -5.829e-05 0.09043 9.784e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01681 -0.004706 0.01223 0.02978 0.9479 0.9555 0.02785 0.892 0.9117 0.073 ] Network output: [ 0.9671 0.07958 0.01723 0.0005941 -0.0002667 -0.0285 0.0004478 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5737 0.05414 0.07878 0.3821 0.9758 0.9888 0.6232 0.9055 0.9707 0.5485 ] Network output: [ 0.03768 0.869 0.9345 -1.979e-05 8.884e-06 0.121 -1.491e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02123 0.01442 0.02416 0.02791 0.9867 0.9906 0.0215 0.9691 0.9817 0.031 ] Network output: [ 0.08353 -0.214 0.8878 -0.0002719 0.0001221 1.158 -0.0002049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6152 0.4945 0.4236 0.5078 0.9784 0.9903 0.6165 0.9132 0.9743 0.5321 ] Network output: [ -0.09279 0.2699 1.155 -0.0003722 0.0001671 0.7588 -0.0002805 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2812 0.272 0.2744 0.28 0.9872 0.9917 0.2813 0.9707 0.9824 0.2853 ] Network output: [ -0.09902 0.2288 1.118 -0.0003479 0.0001562 0.8497 -0.0002622 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3103 0.3085 0.2938 0.2936 0.9821 0.9888 0.3104 0.9524 0.9739 0.2966 ] Network output: [ 0.03093 0.919 -0.0346 0.0002975 -0.0001335 1.055 0.0002242 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06508 Epoch 3247 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07385 0.8424 0.9204 0.0001293 -5.803e-05 0.09002 9.741e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0168 -0.004702 0.0122 0.02978 0.9479 0.9555 0.02783 0.892 0.9117 0.073 ] Network output: [ 0.9668 0.07999 0.01743 0.0005926 -0.000266 -0.02865 0.0004466 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5735 0.05448 0.07833 0.3819 0.9758 0.9888 0.623 0.9055 0.9707 0.5485 ] Network output: [ 0.03762 0.8694 0.9346 -2.018e-05 9.06e-06 0.1207 -1.521e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02123 0.01442 0.02416 0.02791 0.9867 0.9906 0.0215 0.9691 0.9817 0.03101 ] Network output: [ 0.08384 -0.2144 0.8873 -0.0002671 0.0001199 1.158 -0.0002013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6149 0.4945 0.4233 0.5077 0.9784 0.9903 0.6163 0.9132 0.9743 0.532 ] Network output: [ -0.09266 0.269 1.155 -0.0003712 0.0001667 0.7595 -0.0002798 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2813 0.2721 0.2745 0.2801 0.9872 0.9917 0.2814 0.9707 0.9824 0.2855 ] Network output: [ -0.09886 0.2282 1.118 -0.0003472 0.0001559 0.8501 -0.0002617 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3103 0.3084 0.2939 0.2937 0.9821 0.9888 0.3103 0.9524 0.9739 0.2967 ] Network output: [ 0.03069 0.9197 -0.03438 0.0002933 -0.0001317 1.054 0.000221 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06488 Epoch 3248 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07378 0.8429 0.9204 0.0001287 -5.778e-05 0.08962 9.699e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01678 -0.004697 0.01216 0.02977 0.9479 0.9555 0.02782 0.892 0.9117 0.073 ] Network output: [ 0.9666 0.08038 0.01763 0.0005909 -0.0002653 -0.02881 0.0004453 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5732 0.05481 0.0779 0.3818 0.9758 0.9888 0.6227 0.9055 0.9707 0.5484 ] Network output: [ 0.03756 0.8698 0.9346 -2.054e-05 9.223e-06 0.1204 -1.548e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02122 0.01443 0.02415 0.02792 0.9867 0.9906 0.0215 0.9691 0.9817 0.03102 ] Network output: [ 0.08414 -0.2149 0.8869 -0.0002624 0.0001178 1.159 -0.0001978 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6147 0.4946 0.423 0.5076 0.9784 0.9903 0.616 0.9132 0.9743 0.5319 ] Network output: [ -0.09253 0.2681 1.155 -0.0003702 0.0001662 0.7601 -0.000279 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2814 0.2722 0.2747 0.2803 0.9872 0.9917 0.2815 0.9707 0.9824 0.2857 ] Network output: [ -0.09871 0.2276 1.118 -0.0003464 0.0001555 0.8504 -0.0002611 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3103 0.3084 0.2939 0.2938 0.9821 0.9888 0.3103 0.9524 0.9739 0.2968 ] Network output: [ 0.03045 0.9205 -0.03415 0.0002892 -0.0001298 1.054 0.0002179 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06469 Epoch 3249 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0737 0.8434 0.9205 0.0001282 -5.754e-05 0.08922 9.659e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01677 -0.004693 0.01213 0.02976 0.9479 0.9555 0.0278 0.8921 0.9117 0.073 ] Network output: [ 0.9664 0.08076 0.01784 0.0005891 -0.0002644 -0.02896 0.0004439 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.573 0.05514 0.07747 0.3816 0.9758 0.9888 0.6224 0.9055 0.9707 0.5483 ] Network output: [ 0.0375 0.8701 0.9347 -2.088e-05 9.372e-06 0.1201 -1.573e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02122 0.01443 0.02415 0.02792 0.9867 0.9906 0.02149 0.9691 0.9817 0.03103 ] Network output: [ 0.08444 -0.2154 0.8865 -0.0002578 0.0001157 1.159 -0.0001943 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6144 0.4946 0.4227 0.5075 0.9784 0.9903 0.6158 0.9132 0.9743 0.5318 ] Network output: [ -0.0924 0.2672 1.155 -0.0003692 0.0001657 0.7608 -0.0002782 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2815 0.2723 0.2748 0.2805 0.9872 0.9917 0.2816 0.9707 0.9824 0.2859 ] Network output: [ -0.09856 0.227 1.118 -0.0003457 0.0001552 0.8507 -0.0002605 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3102 0.3084 0.294 0.2938 0.9821 0.9888 0.3103 0.9524 0.9739 0.2969 ] Network output: [ 0.03021 0.9213 -0.03392 0.0002851 -0.000128 1.053 0.0002149 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0645 Epoch 3250 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07363 0.8439 0.9205 0.0001276 -5.73e-05 0.08884 9.619e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01676 -0.004688 0.01209 0.02975 0.9479 0.9555 0.02779 0.8921 0.9117 0.073 ] Network output: [ 0.9662 0.08114 0.01804 0.0005871 -0.0002636 -0.02911 0.0004425 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5727 0.05547 0.07705 0.3815 0.9758 0.9888 0.6222 0.9056 0.9707 0.5482 ] Network output: [ 0.03744 0.8705 0.9347 -2.118e-05 9.507e-06 0.1198 -1.596e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02122 0.01444 0.02415 0.02792 0.9867 0.9906 0.02149 0.9691 0.9817 0.03104 ] Network output: [ 0.08474 -0.2158 0.886 -0.0002532 0.0001137 1.159 -0.0001908 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6142 0.4946 0.4224 0.5074 0.9784 0.9903 0.6155 0.9133 0.9744 0.5318 ] Network output: [ -0.09226 0.2663 1.155 -0.0003681 0.0001652 0.7614 -0.0002774 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2816 0.2725 0.275 0.2806 0.9872 0.9917 0.2817 0.9707 0.9824 0.2861 ] Network output: [ -0.0984 0.2264 1.118 -0.0003448 0.0001548 0.8511 -0.0002599 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3102 0.3083 0.2941 0.2939 0.9821 0.9888 0.3102 0.9525 0.9739 0.2969 ] Network output: [ 0.02997 0.922 -0.03369 0.0002812 -0.0001262 1.053 0.0002119 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06431 Epoch 3251 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07356 0.8444 0.9206 0.0001271 -5.708e-05 0.08845 9.581e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01675 -0.004684 0.01206 0.02975 0.9479 0.9555 0.02777 0.8921 0.9117 0.07299 ] Network output: [ 0.9659 0.0815 0.01824 0.0005851 -0.0002627 -0.02926 0.0004409 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5725 0.05579 0.07664 0.3814 0.9758 0.9888 0.622 0.9056 0.9707 0.5481 ] Network output: [ 0.03737 0.8709 0.9348 -2.145e-05 9.629e-06 0.1195 -1.616e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02121 0.01444 0.02414 0.02793 0.9867 0.9906 0.02149 0.9691 0.9817 0.03104 ] Network output: [ 0.08503 -0.2163 0.8856 -0.0002487 0.0001117 1.16 -0.0001874 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6139 0.4947 0.4221 0.5073 0.9784 0.9903 0.6153 0.9133 0.9744 0.5317 ] Network output: [ -0.09213 0.2655 1.155 -0.000367 0.0001648 0.7621 -0.0002766 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2817 0.2726 0.2751 0.2808 0.9872 0.9917 0.2818 0.9707 0.9824 0.2863 ] Network output: [ -0.09825 0.2258 1.118 -0.000344 0.0001544 0.8514 -0.0002592 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3102 0.3083 0.2941 0.294 0.9821 0.9888 0.3102 0.9525 0.974 0.297 ] Network output: [ 0.02974 0.9228 -0.03346 0.0002773 -0.0001245 1.052 0.000209 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06413 Epoch 3252 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07349 0.8448 0.9206 0.0001266 -5.686e-05 0.08807 9.545e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01674 -0.00468 0.01202 0.02974 0.9479 0.9555 0.02776 0.8921 0.9118 0.07299 ] Network output: [ 0.9657 0.08186 0.01843 0.0005829 -0.0002617 -0.0294 0.0004393 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5722 0.05611 0.07624 0.3812 0.9758 0.9888 0.6217 0.9056 0.9707 0.548 ] Network output: [ 0.03731 0.8712 0.9348 -2.169e-05 9.737e-06 0.1192 -1.635e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02121 0.01445 0.02414 0.02793 0.9867 0.9906 0.02148 0.9691 0.9817 0.03105 ] Network output: [ 0.08532 -0.2167 0.8852 -0.0002443 0.0001097 1.16 -0.0001841 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6137 0.4947 0.4218 0.5071 0.9784 0.9903 0.615 0.9133 0.9744 0.5316 ] Network output: [ -0.092 0.2646 1.155 -0.0003659 0.0001642 0.7627 -0.0002757 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2818 0.2726 0.2753 0.281 0.9872 0.9917 0.2819 0.9707 0.9824 0.2864 ] Network output: [ -0.09809 0.2252 1.118 -0.0003431 0.000154 0.8517 -0.0002586 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3101 0.3083 0.2942 0.294 0.9821 0.9888 0.3101 0.9525 0.974 0.2971 ] Network output: [ 0.0295 0.9235 -0.03323 0.0002735 -0.0001228 1.052 0.0002061 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06395 Epoch 3253 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07341 0.8453 0.9207 0.0001262 -5.665e-05 0.0877 9.509e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01672 -0.004676 0.01199 0.02973 0.9479 0.9555 0.02774 0.8921 0.9118 0.07299 ] Network output: [ 0.9655 0.08221 0.01863 0.0005806 -0.0002607 -0.02954 0.0004376 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.572 0.05643 0.07585 0.3811 0.9759 0.9888 0.6215 0.9056 0.9708 0.5479 ] Network output: [ 0.03725 0.8716 0.9349 -2.19e-05 9.833e-06 0.119 -1.651e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02121 0.01445 0.02413 0.02793 0.9867 0.9906 0.02148 0.9691 0.9817 0.03106 ] Network output: [ 0.0856 -0.2171 0.8848 -0.0002399 0.0001077 1.16 -0.0001808 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6135 0.4947 0.4215 0.507 0.9784 0.9903 0.6148 0.9133 0.9744 0.5315 ] Network output: [ -0.09187 0.2638 1.155 -0.0003647 0.0001637 0.7634 -0.0002748 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2819 0.2727 0.2754 0.2811 0.9872 0.9917 0.282 0.9707 0.9824 0.2866 ] Network output: [ -0.09794 0.2247 1.118 -0.0003422 0.0001536 0.852 -0.0002579 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3101 0.3082 0.2943 0.2941 0.9822 0.9888 0.3101 0.9525 0.974 0.2972 ] Network output: [ 0.02926 0.9243 -0.033 0.0002698 -0.0001211 1.051 0.0002033 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06377 Epoch 3254 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07334 0.8457 0.9207 0.0001257 -5.644e-05 0.08733 9.475e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01671 -0.004672 0.01195 0.02972 0.9479 0.9555 0.02773 0.8922 0.9118 0.07299 ] Network output: [ 0.9653 0.08254 0.01882 0.0005783 -0.0002596 -0.02968 0.0004358 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5718 0.05674 0.07547 0.381 0.9759 0.9888 0.6213 0.9057 0.9708 0.5479 ] Network output: [ 0.03719 0.8719 0.9349 -2.209e-05 9.915e-06 0.1187 -1.665e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0212 0.01445 0.02413 0.02794 0.9867 0.9906 0.02148 0.9691 0.9817 0.03107 ] Network output: [ 0.08588 -0.2175 0.8843 -0.0002356 0.0001058 1.16 -0.0001775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6132 0.4948 0.4213 0.5069 0.9784 0.9903 0.6146 0.9134 0.9744 0.5315 ] Network output: [ -0.09174 0.2629 1.155 -0.0003635 0.0001632 0.764 -0.000274 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2819 0.2728 0.2755 0.2813 0.9872 0.9917 0.282 0.9707 0.9824 0.2868 ] Network output: [ -0.09779 0.2241 1.118 -0.0003412 0.0001532 0.8523 -0.0002572 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.31 0.3082 0.2943 0.2942 0.9822 0.9888 0.31 0.9526 0.974 0.2972 ] Network output: [ 0.02903 0.925 -0.03277 0.0002661 -0.0001195 1.051 0.0002006 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06359 Epoch 3255 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07327 0.8462 0.9208 0.0001253 -5.625e-05 0.08697 9.442e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0167 -0.004668 0.01192 0.02971 0.9479 0.9556 0.02771 0.8922 0.9118 0.07299 ] Network output: [ 0.9651 0.08287 0.01902 0.0005758 -0.0002585 -0.02981 0.0004339 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5715 0.05704 0.0751 0.3808 0.9759 0.9888 0.621 0.9057 0.9708 0.5478 ] Network output: [ 0.03713 0.8722 0.935 -2.224e-05 9.985e-06 0.1184 -1.676e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0212 0.01446 0.02412 0.02794 0.9867 0.9906 0.02147 0.9691 0.9817 0.03107 ] Network output: [ 0.08616 -0.218 0.8839 -0.0002313 0.0001039 1.161 -0.0001743 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.613 0.4948 0.421 0.5068 0.9784 0.9903 0.6143 0.9134 0.9744 0.5314 ] Network output: [ -0.09162 0.2621 1.155 -0.0003623 0.0001627 0.7646 -0.000273 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.282 0.2729 0.2757 0.2814 0.9872 0.9917 0.2821 0.9707 0.9824 0.2869 ] Network output: [ -0.09763 0.2236 1.118 -0.0003403 0.0001528 0.8526 -0.0002564 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.31 0.3081 0.2944 0.2942 0.9822 0.9888 0.31 0.9526 0.974 0.2973 ] Network output: [ 0.02879 0.9258 -0.03253 0.0002625 -0.0001179 1.05 0.0001979 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06342 Epoch 3256 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0732 0.8467 0.9208 0.0001249 -5.606e-05 0.08661 9.41e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01669 -0.004665 0.01189 0.0297 0.9479 0.9556 0.0277 0.8922 0.9118 0.07298 ] Network output: [ 0.9649 0.08319 0.01921 0.0005732 -0.0002573 -0.02994 0.0004319 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5713 0.05734 0.07474 0.3807 0.9759 0.9888 0.6208 0.9057 0.9708 0.5477 ] Network output: [ 0.03707 0.8726 0.935 -2.237e-05 1.004e-05 0.1182 -1.686e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02119 0.01446 0.02412 0.02794 0.9867 0.9906 0.02147 0.9691 0.9817 0.03108 ] Network output: [ 0.08643 -0.2184 0.8835 -0.0002272 0.000102 1.161 -0.0001712 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6128 0.4948 0.4207 0.5067 0.9784 0.9903 0.6141 0.9134 0.9744 0.5313 ] Network output: [ -0.09149 0.2612 1.155 -0.0003611 0.0001621 0.7652 -0.0002721 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2821 0.273 0.2758 0.2816 0.9872 0.9917 0.2822 0.9707 0.9824 0.2871 ] Network output: [ -0.09748 0.223 1.118 -0.0003392 0.0001523 0.8529 -0.0002557 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3099 0.3081 0.2945 0.2943 0.9822 0.9888 0.3099 0.9526 0.974 0.2974 ] Network output: [ 0.02856 0.9265 -0.0323 0.000259 -0.0001163 1.05 0.0001952 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06325 Epoch 3257 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07313 0.8471 0.9209 0.0001245 -5.588e-05 0.08626 9.38e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01667 -0.004661 0.01185 0.0297 0.9479 0.9556 0.02768 0.8922 0.9118 0.07298 ] Network output: [ 0.9647 0.08351 0.0194 0.0005704 -0.0002561 -0.03007 0.0004299 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5711 0.05764 0.07439 0.3805 0.9759 0.9888 0.6206 0.9057 0.9708 0.5476 ] Network output: [ 0.03701 0.8729 0.9351 -2.247e-05 1.009e-05 0.1179 -1.694e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02119 0.01446 0.02412 0.02794 0.9867 0.9906 0.02146 0.9691 0.9817 0.03109 ] Network output: [ 0.0867 -0.2188 0.883 -0.000223 0.0001001 1.161 -0.0001681 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6126 0.4949 0.4205 0.5066 0.9784 0.9903 0.6139 0.9134 0.9744 0.5312 ] Network output: [ -0.09136 0.2604 1.155 -0.0003598 0.0001615 0.7658 -0.0002712 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2821 0.2731 0.2759 0.2817 0.9872 0.9917 0.2822 0.9707 0.9824 0.2873 ] Network output: [ -0.09732 0.2225 1.118 -0.0003382 0.0001518 0.8532 -0.0002549 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3099 0.308 0.2945 0.2943 0.9822 0.9888 0.3099 0.9526 0.974 0.2974 ] Network output: [ 0.02832 0.9272 -0.03206 0.0002556 -0.0001147 1.049 0.0001926 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06309 Epoch 3258 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07306 0.8475 0.9209 0.0001241 -5.57e-05 0.08591 9.351e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01666 -0.004658 0.01182 0.02969 0.9479 0.9556 0.02767 0.8923 0.9118 0.07298 ] Network output: [ 0.9646 0.08381 0.01958 0.0005676 -0.0002548 -0.03019 0.0004278 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5709 0.05793 0.07405 0.3804 0.9759 0.9888 0.6204 0.9058 0.9708 0.5475 ] Network output: [ 0.03694 0.8732 0.9351 -2.255e-05 1.012e-05 0.1177 -1.699e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02119 0.01447 0.02411 0.02795 0.9867 0.9906 0.02146 0.9691 0.9817 0.03109 ] Network output: [ 0.08696 -0.2192 0.8826 -0.000219 9.831e-05 1.162 -0.000165 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6124 0.4949 0.4202 0.5065 0.9784 0.9903 0.6137 0.9135 0.9744 0.5312 ] Network output: [ -0.09123 0.2596 1.155 -0.0003585 0.000161 0.7664 -0.0002702 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2822 0.2731 0.2761 0.2819 0.9872 0.9917 0.2823 0.9707 0.9824 0.2874 ] Network output: [ -0.09717 0.222 1.118 -0.0003371 0.0001513 0.8535 -0.0002541 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3098 0.308 0.2946 0.2944 0.9822 0.9888 0.3098 0.9527 0.974 0.2975 ] Network output: [ 0.02809 0.928 -0.03182 0.0002522 -0.0001132 1.049 0.0001901 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06292 Epoch 3259 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07299 0.848 0.921 0.0001237 -5.554e-05 0.08557 9.323e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01665 -0.004654 0.01179 0.02968 0.948 0.9556 0.02765 0.8923 0.9119 0.07297 ] Network output: [ 0.9644 0.0841 0.01977 0.0005647 -0.0002535 -0.03031 0.0004256 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5706 0.05822 0.07371 0.3803 0.9759 0.9888 0.6202 0.9058 0.9708 0.5474 ] Network output: [ 0.03688 0.8736 0.9352 -2.259e-05 1.014e-05 0.1174 -1.703e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02118 0.01447 0.02411 0.02795 0.9867 0.9906 0.02145 0.9691 0.9817 0.0311 ] Network output: [ 0.08722 -0.2196 0.8822 -0.000215 9.651e-05 1.162 -0.000162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6122 0.495 0.42 0.5064 0.9784 0.9903 0.6135 0.9135 0.9744 0.5311 ] Network output: [ -0.09111 0.2587 1.155 -0.0003572 0.0001604 0.767 -0.0002692 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2822 0.2732 0.2762 0.282 0.9872 0.9917 0.2823 0.9707 0.9824 0.2876 ] Network output: [ -0.09702 0.2214 1.118 -0.000336 0.0001508 0.8537 -0.0002532 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3098 0.3079 0.2946 0.2944 0.9822 0.9888 0.3098 0.9527 0.974 0.2975 ] Network output: [ 0.02786 0.9287 -0.03158 0.0002489 -0.0001118 1.048 0.0001876 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06276 Epoch 3260 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07292 0.8484 0.921 0.0001233 -5.538e-05 0.08523 9.296e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01664 -0.004651 0.01176 0.02967 0.948 0.9556 0.02764 0.8923 0.9119 0.07297 ] Network output: [ 0.9642 0.08439 0.01995 0.0005617 -0.0002522 -0.03043 0.0004233 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5704 0.0585 0.07339 0.3802 0.9759 0.9889 0.62 0.9058 0.9708 0.5474 ] Network output: [ 0.03682 0.8739 0.9352 -2.262e-05 1.015e-05 0.1172 -1.704e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02118 0.01447 0.0241 0.02795 0.9867 0.9906 0.02145 0.9691 0.9817 0.03111 ] Network output: [ 0.08748 -0.2199 0.8817 -0.0002111 9.475e-05 1.162 -0.0001591 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.612 0.495 0.4198 0.5063 0.9784 0.9903 0.6133 0.9135 0.9744 0.531 ] Network output: [ -0.09098 0.2579 1.155 -0.0003559 0.0001598 0.7676 -0.0002682 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2822 0.2732 0.2763 0.2821 0.9872 0.9917 0.2823 0.9707 0.9824 0.2877 ] Network output: [ -0.09686 0.2209 1.117 -0.0003349 0.0001503 0.854 -0.0002524 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3097 0.3079 0.2947 0.2945 0.9822 0.9888 0.3097 0.9527 0.9741 0.2976 ] Network output: [ 0.02763 0.9294 -0.03134 0.0002457 -0.0001103 1.048 0.0001852 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06261 Epoch 3261 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07285 0.8488 0.921 0.000123 -5.522e-05 0.0849 9.27e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01662 -0.004648 0.01172 0.02966 0.948 0.9556 0.02762 0.8923 0.9119 0.07296 ] Network output: [ 0.964 0.08467 0.02013 0.0005586 -0.0002508 -0.03054 0.000421 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5702 0.05878 0.07308 0.38 0.9759 0.9889 0.6198 0.9058 0.9708 0.5473 ] Network output: [ 0.03676 0.8742 0.9352 -2.261e-05 1.015e-05 0.1169 -1.704e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02117 0.01448 0.0241 0.02795 0.9867 0.9906 0.02144 0.9691 0.9817 0.03111 ] Network output: [ 0.08773 -0.2203 0.8813 -0.0002072 9.301e-05 1.163 -0.0001561 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6118 0.495 0.4196 0.5062 0.9784 0.9903 0.6131 0.9135 0.9744 0.5309 ] Network output: [ -0.09086 0.2571 1.155 -0.0003545 0.0001591 0.7682 -0.0002672 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2823 0.2733 0.2764 0.2823 0.9872 0.9917 0.2824 0.9707 0.9824 0.2879 ] Network output: [ -0.09671 0.2204 1.117 -0.0003337 0.0001498 0.8542 -0.0002515 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3096 0.3078 0.2947 0.2945 0.9822 0.9888 0.3097 0.9527 0.9741 0.2976 ] Network output: [ 0.0274 0.9301 -0.0311 0.0002426 -0.0001089 1.047 0.0001828 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06245 Epoch 3262 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07279 0.8493 0.9211 0.0001227 -5.507e-05 0.08457 9.245e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01661 -0.004645 0.01169 0.02965 0.948 0.9556 0.02761 0.8924 0.9119 0.07296 ] Network output: [ 0.9638 0.08494 0.02031 0.0005554 -0.0002493 -0.03065 0.0004186 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.57 0.05906 0.07277 0.3799 0.9759 0.9889 0.6196 0.9059 0.9708 0.5472 ] Network output: [ 0.0367 0.8745 0.9353 -2.259e-05 1.014e-05 0.1167 -1.702e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02117 0.01448 0.0241 0.02796 0.9867 0.9906 0.02144 0.9691 0.9817 0.03112 ] Network output: [ 0.08798 -0.2207 0.8809 -0.0002034 9.13e-05 1.163 -0.0001533 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6116 0.4951 0.4193 0.5061 0.9784 0.9903 0.6129 0.9136 0.9745 0.5309 ] Network output: [ -0.09073 0.2563 1.155 -0.0003531 0.0001585 0.7687 -0.0002661 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2823 0.2734 0.2766 0.2824 0.9872 0.9917 0.2824 0.9707 0.9824 0.288 ] Network output: [ -0.09655 0.2199 1.117 -0.0003325 0.0001493 0.8545 -0.0002506 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3096 0.3078 0.2948 0.2946 0.9822 0.9888 0.3096 0.9527 0.9741 0.2977 ] Network output: [ 0.02717 0.9308 -0.03085 0.0002395 -0.0001075 1.047 0.0001805 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0623 Epoch 3263 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07272 0.8497 0.9211 0.0001224 -5.493e-05 0.08425 9.222e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0166 -0.004642 0.01166 0.02964 0.948 0.9556 0.02759 0.8924 0.9119 0.07295 ] Network output: [ 0.9637 0.0852 0.02049 0.0005521 -0.0002479 -0.03076 0.0004161 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5698 0.05933 0.07248 0.3798 0.9759 0.9889 0.6194 0.9059 0.9708 0.5471 ] Network output: [ 0.03664 0.8749 0.9353 -2.254e-05 1.012e-05 0.1165 -1.699e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02116 0.01448 0.0241 0.02796 0.9867 0.9906 0.02143 0.9691 0.9817 0.03112 ] Network output: [ 0.08823 -0.2211 0.8805 -0.0001996 8.962e-05 1.163 -0.0001505 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6114 0.4951 0.4191 0.506 0.9785 0.9903 0.6127 0.9136 0.9745 0.5308 ] Network output: [ -0.09061 0.2555 1.155 -0.0003517 0.0001579 0.7693 -0.0002651 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2824 0.2734 0.2767 0.2825 0.9872 0.9917 0.2825 0.9707 0.9824 0.2882 ] Network output: [ -0.0964 0.2194 1.117 -0.0003313 0.0001487 0.8547 -0.0002497 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3095 0.3077 0.2948 0.2946 0.9822 0.9888 0.3095 0.9528 0.9741 0.2977 ] Network output: [ 0.02694 0.9315 -0.03061 0.0002365 -0.0001062 1.046 0.0001782 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06215 Epoch 3264 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07265 0.8501 0.9212 0.0001221 -5.48e-05 0.08393 9.199e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01659 -0.00464 0.01163 0.02963 0.948 0.9556 0.02758 0.8924 0.9119 0.07295 ] Network output: [ 0.9635 0.08546 0.02066 0.0005487 -0.0002463 -0.03087 0.0004135 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5696 0.05959 0.07219 0.3796 0.9759 0.9889 0.6192 0.9059 0.9709 0.547 ] Network output: [ 0.03658 0.8752 0.9354 -2.246e-05 1.009e-05 0.1162 -1.693e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02115 0.01448 0.02409 0.02796 0.9867 0.9906 0.02143 0.9691 0.9817 0.03113 ] Network output: [ 0.08847 -0.2214 0.88 -0.0001959 8.797e-05 1.164 -0.0001477 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6112 0.4952 0.4189 0.5059 0.9785 0.9903 0.6125 0.9136 0.9745 0.5307 ] Network output: [ -0.09049 0.2547 1.155 -0.0003503 0.0001573 0.7698 -0.000264 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2824 0.2734 0.2768 0.2827 0.9872 0.9917 0.2825 0.9707 0.9824 0.2883 ] Network output: [ -0.09625 0.2189 1.117 -0.00033 0.0001482 0.855 -0.0002487 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3094 0.3076 0.2948 0.2947 0.9822 0.9889 0.3095 0.9528 0.9741 0.2978 ] Network output: [ 0.02671 0.9323 -0.03036 0.0002335 -0.0001048 1.046 0.000176 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.062 Epoch 3265 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07258 0.8505 0.9212 0.0001218 -5.467e-05 0.08362 9.178e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01657 -0.004637 0.0116 0.02962 0.948 0.9556 0.02756 0.8924 0.912 0.07294 ] Network output: [ 0.9633 0.08571 0.02083 0.0005453 -0.0002448 -0.03097 0.0004109 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5694 0.05985 0.07191 0.3795 0.9759 0.9889 0.619 0.906 0.9709 0.547 ] Network output: [ 0.03652 0.8755 0.9354 -2.237e-05 1.004e-05 0.116 -1.686e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02115 0.01448 0.02409 0.02796 0.9867 0.9906 0.02142 0.9691 0.9817 0.03114 ] Network output: [ 0.0887 -0.2218 0.8796 -0.0001923 8.634e-05 1.164 -0.0001449 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.611 0.4952 0.4187 0.5058 0.9785 0.9903 0.6123 0.9136 0.9745 0.5306 ] Network output: [ -0.09037 0.2539 1.155 -0.0003488 0.0001566 0.7704 -0.0002629 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2824 0.2735 0.2769 0.2828 0.9872 0.9917 0.2825 0.9707 0.9824 0.2884 ] Network output: [ -0.09609 0.2185 1.117 -0.0003288 0.0001476 0.8552 -0.0002478 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3094 0.3076 0.2949 0.2947 0.9822 0.9889 0.3094 0.9528 0.9741 0.2978 ] Network output: [ 0.02648 0.933 -0.03011 0.0002306 -0.0001035 1.045 0.0001738 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06185 Epoch 3266 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07252 0.8509 0.9212 0.0001215 -5.455e-05 0.08331 9.157e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01656 -0.004635 0.01157 0.02962 0.948 0.9556 0.02755 0.8925 0.912 0.07294 ] Network output: [ 0.9632 0.08595 0.02101 0.0005417 -0.0002432 -0.03107 0.0004083 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5692 0.06011 0.07164 0.3794 0.9759 0.9889 0.6188 0.906 0.9709 0.5469 ] Network output: [ 0.03646 0.8758 0.9354 -2.225e-05 9.99e-06 0.1158 -1.677e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02114 0.01448 0.02409 0.02796 0.9867 0.9906 0.02142 0.9692 0.9817 0.03114 ] Network output: [ 0.08894 -0.2221 0.8792 -0.0001888 8.474e-05 1.164 -0.0001423 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6108 0.4952 0.4185 0.5057 0.9785 0.9903 0.6121 0.9137 0.9745 0.5306 ] Network output: [ -0.09024 0.2531 1.155 -0.0003474 0.0001559 0.7709 -0.0002618 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2824 0.2735 0.277 0.2829 0.9872 0.9917 0.2825 0.9707 0.9824 0.2886 ] Network output: [ -0.09594 0.218 1.117 -0.0003274 0.000147 0.8555 -0.0002468 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3093 0.3075 0.2949 0.2947 0.9822 0.9889 0.3093 0.9528 0.9741 0.2979 ] Network output: [ 0.02625 0.9336 -0.02986 0.0002278 -0.0001023 1.045 0.0001717 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06171 Epoch 3267 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07245 0.8513 0.9213 0.0001213 -5.443e-05 0.08301 9.138e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01655 -0.004632 0.01154 0.02961 0.948 0.9556 0.02753 0.8925 0.912 0.07293 ] Network output: [ 0.963 0.08619 0.02117 0.0005381 -0.0002416 -0.03117 0.0004055 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.569 0.06037 0.07138 0.3792 0.9759 0.9889 0.6186 0.906 0.9709 0.5468 ] Network output: [ 0.0364 0.8761 0.9355 -2.211e-05 9.928e-06 0.1156 -1.667e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02114 0.01449 0.02408 0.02797 0.9867 0.9906 0.02141 0.9692 0.9817 0.03115 ] Network output: [ 0.08917 -0.2224 0.8787 -0.0001853 8.317e-05 1.165 -0.0001396 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6106 0.4953 0.4184 0.5056 0.9785 0.9903 0.612 0.9137 0.9745 0.5305 ] Network output: [ -0.09012 0.2523 1.155 -0.0003459 0.0001553 0.7715 -0.0002607 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2824 0.2736 0.2771 0.283 0.9872 0.9917 0.2825 0.9707 0.9824 0.2887 ] Network output: [ -0.09578 0.2175 1.117 -0.0003261 0.0001464 0.8557 -0.0002458 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3092 0.3074 0.2949 0.2948 0.9822 0.9889 0.3093 0.9529 0.9741 0.2979 ] Network output: [ 0.02603 0.9343 -0.02961 0.000225 -0.000101 1.044 0.0001696 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06157 Epoch 3268 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07239 0.8517 0.9213 0.000121 -5.432e-05 0.08271 9.119e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01654 -0.00463 0.01151 0.0296 0.948 0.9557 0.02751 0.8925 0.912 0.07293 ] Network output: [ 0.9628 0.08642 0.02134 0.0005344 -0.0002399 -0.03127 0.0004027 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5688 0.06062 0.07113 0.3791 0.9759 0.9889 0.6184 0.906 0.9709 0.5467 ] Network output: [ 0.03634 0.8764 0.9355 -2.196e-05 9.857e-06 0.1153 -1.655e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02113 0.01449 0.02408 0.02797 0.9867 0.9906 0.02141 0.9692 0.9817 0.03115 ] Network output: [ 0.0894 -0.2228 0.8783 -0.0001818 8.162e-05 1.165 -0.000137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6104 0.4953 0.4182 0.5055 0.9785 0.9903 0.6118 0.9137 0.9745 0.5304 ] Network output: [ -0.09 0.2515 1.155 -0.0003444 0.0001546 0.772 -0.0002595 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2825 0.2736 0.2772 0.2832 0.9872 0.9917 0.2826 0.9707 0.9824 0.2888 ] Network output: [ -0.09563 0.2171 1.117 -0.0003248 0.0001458 0.8559 -0.0002448 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3092 0.3074 0.295 0.2948 0.9822 0.9889 0.3092 0.9529 0.9742 0.298 ] Network output: [ 0.0258 0.935 -0.02936 0.0002223 -9.981e-05 1.044 0.0001676 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06143 Epoch 3269 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07232 0.8521 0.9213 0.0001208 -5.422e-05 0.08241 9.102e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01652 -0.004628 0.01148 0.02959 0.948 0.9557 0.0275 0.8925 0.912 0.07292 ] Network output: [ 0.9627 0.08664 0.0215 0.0005306 -0.0002382 -0.03136 0.0003999 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5687 0.06086 0.07088 0.379 0.9759 0.9889 0.6183 0.9061 0.9709 0.5466 ] Network output: [ 0.03628 0.8767 0.9355 -2.178e-05 9.777e-06 0.1151 -1.641e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02112 0.01449 0.02408 0.02797 0.9867 0.9906 0.0214 0.9692 0.9817 0.03116 ] Network output: [ 0.08962 -0.2231 0.8779 -0.0001784 8.009e-05 1.165 -0.0001345 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6103 0.4954 0.418 0.5054 0.9785 0.9903 0.6116 0.9138 0.9745 0.5303 ] Network output: [ -0.08988 0.2508 1.155 -0.0003428 0.0001539 0.7725 -0.0002584 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2825 0.2736 0.2774 0.2833 0.9872 0.9917 0.2826 0.9707 0.9824 0.289 ] Network output: [ -0.09548 0.2166 1.117 -0.0003234 0.0001452 0.8561 -0.0002437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3091 0.3073 0.295 0.2948 0.9822 0.9889 0.3091 0.9529 0.9742 0.298 ] Network output: [ 0.02558 0.9357 -0.02911 0.0002197 -9.863e-05 1.043 0.0001656 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06129 Epoch 3270 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07226 0.8525 0.9213 0.0001206 -5.412e-05 0.08212 9.085e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01651 -0.004626 0.01145 0.02958 0.948 0.9557 0.02748 0.8926 0.9121 0.07291 ] Network output: [ 0.9625 0.08686 0.02167 0.0005268 -0.0002365 -0.03145 0.000397 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5685 0.0611 0.07065 0.3788 0.9759 0.9889 0.6181 0.9061 0.9709 0.5465 ] Network output: [ 0.03622 0.877 0.9356 -2.158e-05 9.688e-06 0.1149 -1.626e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02112 0.01449 0.02408 0.02797 0.9867 0.9906 0.02139 0.9692 0.9818 0.03116 ] Network output: [ 0.08984 -0.2234 0.8775 -0.0001751 7.86e-05 1.166 -0.0001319 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6101 0.4954 0.4178 0.5053 0.9785 0.9903 0.6114 0.9138 0.9745 0.5303 ] Network output: [ -0.08976 0.25 1.155 -0.0003413 0.0001532 0.773 -0.0002572 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2825 0.2736 0.2775 0.2834 0.9872 0.9917 0.2826 0.9707 0.9824 0.2891 ] Network output: [ -0.09532 0.2162 1.117 -0.000322 0.0001445 0.8563 -0.0002427 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.309 0.3072 0.295 0.2949 0.9822 0.9889 0.309 0.9529 0.9742 0.298 ] Network output: [ 0.02535 0.9364 -0.02886 0.0002171 -9.747e-05 1.043 0.0001636 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06116 Epoch 3271 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07219 0.8529 0.9214 0.0001203 -5.403e-05 0.08183 9.069e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0165 -0.004624 0.01142 0.02957 0.9481 0.9557 0.02747 0.8926 0.9121 0.07291 ] Network output: [ 0.9624 0.08707 0.02183 0.0005229 -0.0002347 -0.03154 0.0003941 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5683 0.06134 0.07042 0.3787 0.9759 0.9889 0.6179 0.9061 0.9709 0.5465 ] Network output: [ 0.03616 0.8773 0.9356 -2.137e-05 9.592e-06 0.1147 -1.61e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02111 0.01449 0.02407 0.02797 0.9867 0.9906 0.02139 0.9692 0.9818 0.03117 ] Network output: [ 0.09005 -0.2237 0.877 -0.0001718 7.712e-05 1.166 -0.0001295 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6099 0.4955 0.4177 0.5052 0.9785 0.9903 0.6113 0.9138 0.9745 0.5302 ] Network output: [ -0.08964 0.2492 1.155 -0.0003397 0.0001525 0.7735 -0.000256 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2825 0.2737 0.2776 0.2835 0.9872 0.9917 0.2826 0.9707 0.9824 0.2892 ] Network output: [ -0.09517 0.2157 1.117 -0.0003206 0.0001439 0.8565 -0.0002416 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3089 0.3072 0.2951 0.2949 0.9822 0.9889 0.309 0.9529 0.9742 0.2981 ] Network output: [ 0.02513 0.9371 -0.0286 0.0002146 -9.634e-05 1.042 0.0001617 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06103 Epoch 3272 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07213 0.8533 0.9214 0.0001201 -5.394e-05 0.08155 9.055e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01648 -0.004622 0.01139 0.02956 0.9481 0.9557 0.02745 0.8926 0.9121 0.0729 ] Network output: [ 0.9622 0.08727 0.02199 0.0005189 -0.000233 -0.03163 0.0003911 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5681 0.06158 0.0702 0.3786 0.9759 0.9889 0.6177 0.9062 0.9709 0.5464 ] Network output: [ 0.0361 0.8776 0.9357 -2.113e-05 9.488e-06 0.1145 -1.593e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02111 0.01449 0.02407 0.02797 0.9867 0.9906 0.02138 0.9692 0.9818 0.03117 ] Network output: [ 0.09027 -0.224 0.8766 -0.0001686 7.567e-05 1.166 -0.000127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6098 0.4955 0.4175 0.5051 0.9785 0.9903 0.6111 0.9138 0.9745 0.5301 ] Network output: [ -0.08953 0.2485 1.155 -0.0003381 0.0001518 0.774 -0.0002548 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2825 0.2737 0.2777 0.2836 0.9872 0.9917 0.2826 0.9707 0.9824 0.2893 ] Network output: [ -0.09502 0.2153 1.117 -0.0003191 0.0001433 0.8567 -0.0002405 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3089 0.3071 0.2951 0.2949 0.9823 0.9889 0.3089 0.953 0.9742 0.2981 ] Network output: [ 0.02491 0.9377 -0.02835 0.0002121 -9.523e-05 1.042 0.0001599 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0609 Epoch 3273 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07207 0.8537 0.9214 0.00012 -5.385e-05 0.08127 9.04e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01647 -0.00462 0.01136 0.02955 0.9481 0.9557 0.02744 0.8927 0.9121 0.07289 ] Network output: [ 0.9621 0.08747 0.02214 0.0005149 -0.0002311 -0.03171 0.000388 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5679 0.06181 0.06999 0.3784 0.9759 0.9889 0.6176 0.9062 0.9709 0.5463 ] Network output: [ 0.03604 0.8778 0.9357 -2.088e-05 9.376e-06 0.1143 -1.574e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0211 0.01449 0.02407 0.02797 0.9867 0.9906 0.02137 0.9692 0.9818 0.03118 ] Network output: [ 0.09048 -0.2243 0.8762 -0.0001654 7.425e-05 1.167 -0.0001246 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6096 0.4956 0.4174 0.505 0.9785 0.9903 0.6109 0.9139 0.9746 0.53 ] Network output: [ -0.08941 0.2477 1.155 -0.0003365 0.0001511 0.7745 -0.0002536 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2825 0.2737 0.2778 0.2837 0.9872 0.9917 0.2826 0.9707 0.9824 0.2894 ] Network output: [ -0.09486 0.2149 1.117 -0.0003176 0.0001426 0.8569 -0.0002394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3088 0.307 0.2951 0.2949 0.9823 0.9889 0.3088 0.953 0.9742 0.2981 ] Network output: [ 0.02469 0.9384 -0.02809 0.0002097 -9.415e-05 1.041 0.000158 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06077 Epoch 3274 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.072 0.854 0.9215 0.0001198 -5.377e-05 0.08099 9.027e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01646 -0.004619 0.01133 0.02954 0.9481 0.9557 0.02742 0.8927 0.9121 0.07289 ] Network output: [ 0.962 0.08766 0.0223 0.0005108 -0.0002293 -0.0318 0.0003849 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5678 0.06203 0.06979 0.3783 0.9759 0.9889 0.6174 0.9062 0.971 0.5462 ] Network output: [ 0.03598 0.8781 0.9357 -2.062e-05 9.257e-06 0.1141 -1.554e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02109 0.01449 0.02407 0.02797 0.9867 0.9906 0.02137 0.9692 0.9818 0.03118 ] Network output: [ 0.09068 -0.2246 0.8758 -0.0001623 7.285e-05 1.167 -0.0001223 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6095 0.4956 0.4172 0.5049 0.9785 0.9903 0.6108 0.9139 0.9746 0.5299 ] Network output: [ -0.08929 0.247 1.155 -0.0003349 0.0001504 0.775 -0.0002524 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2825 0.2737 0.2779 0.2838 0.9872 0.9917 0.2826 0.9708 0.9824 0.2896 ] Network output: [ -0.09471 0.2145 1.117 -0.0003162 0.0001419 0.8571 -0.0002383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3087 0.3069 0.2951 0.2949 0.9823 0.9889 0.3087 0.953 0.9742 0.2981 ] Network output: [ 0.02447 0.9391 -0.02783 0.0002074 -9.309e-05 1.041 0.0001563 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06064 Epoch 3275 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07194 0.8544 0.9215 0.0001196 -5.37e-05 0.08072 9.015e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01645 -0.004617 0.0113 0.02953 0.9481 0.9557 0.02741 0.8927 0.9122 0.07288 ] Network output: [ 0.9618 0.08784 0.02245 0.0005066 -0.0002274 -0.03188 0.0003818 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5676 0.06226 0.06959 0.3782 0.976 0.9889 0.6173 0.9063 0.971 0.5461 ] Network output: [ 0.03592 0.8784 0.9358 -2.034e-05 9.131e-06 0.1139 -1.533e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02109 0.01449 0.02407 0.02798 0.9867 0.9906 0.02136 0.9692 0.9818 0.03119 ] Network output: [ 0.09089 -0.2249 0.8754 -0.0001592 7.147e-05 1.167 -0.00012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6093 0.4957 0.4171 0.5047 0.9785 0.9904 0.6106 0.9139 0.9746 0.5299 ] Network output: [ -0.08917 0.2462 1.155 -0.0003333 0.0001496 0.7755 -0.0002512 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2825 0.2737 0.278 0.2839 0.9872 0.9917 0.2826 0.9708 0.9824 0.2897 ] Network output: [ -0.09456 0.214 1.116 -0.0003147 0.0001413 0.8573 -0.0002371 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3086 0.3069 0.2952 0.295 0.9823 0.9889 0.3086 0.953 0.9742 0.2982 ] Network output: [ 0.02425 0.9397 -0.02757 0.0002051 -9.206e-05 1.04 0.0001545 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06052 Epoch 3276 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07188 0.8548 0.9215 0.0001195 -5.363e-05 0.08045 9.003e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01643 -0.004616 0.01127 0.02952 0.9481 0.9557 0.02739 0.8927 0.9122 0.07287 ] Network output: [ 0.9617 0.08803 0.0226 0.0005024 -0.0002256 -0.03196 0.0003787 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5674 0.06248 0.06941 0.378 0.976 0.9889 0.6171 0.9063 0.971 0.546 ] Network output: [ 0.03586 0.8787 0.9358 -2.004e-05 8.998e-06 0.1137 -1.51e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02108 0.01449 0.02407 0.02798 0.9867 0.9906 0.02135 0.9692 0.9818 0.03119 ] Network output: [ 0.09108 -0.2252 0.8749 -0.0001562 7.012e-05 1.167 -0.0001177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6092 0.4957 0.4169 0.5046 0.9785 0.9904 0.6105 0.914 0.9746 0.5298 ] Network output: [ -0.08906 0.2455 1.155 -0.0003317 0.0001489 0.776 -0.0002499 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2825 0.2737 0.2781 0.284 0.9872 0.9917 0.2826 0.9708 0.9824 0.2898 ] Network output: [ -0.09441 0.2136 1.116 -0.0003131 0.0001406 0.8575 -0.000236 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3085 0.3068 0.2952 0.295 0.9823 0.9889 0.3085 0.953 0.9742 0.2982 ] Network output: [ 0.02403 0.9404 -0.02731 0.0002028 -9.105e-05 1.04 0.0001528 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06039 Epoch 3277 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07182 0.8551 0.9215 0.0001193 -5.356e-05 0.08019 8.992e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01642 -0.004614 0.01125 0.02951 0.9481 0.9557 0.02738 0.8928 0.9122 0.07286 ] Network output: [ 0.9616 0.0882 0.02275 0.0004982 -0.0002237 -0.03203 0.0003754 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5673 0.06269 0.06923 0.3779 0.976 0.9889 0.617 0.9063 0.971 0.546 ] Network output: [ 0.0358 0.879 0.9358 -1.973e-05 8.859e-06 0.1135 -1.487e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02107 0.01449 0.02406 0.02798 0.9867 0.9906 0.02135 0.9692 0.9818 0.03119 ] Network output: [ 0.09128 -0.2255 0.8745 -0.0001532 6.879e-05 1.168 -0.0001155 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.609 0.4958 0.4168 0.5045 0.9785 0.9904 0.6103 0.914 0.9746 0.5297 ] Network output: [ -0.08894 0.2447 1.155 -0.00033 0.0001482 0.7765 -0.0002487 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2824 0.2737 0.2782 0.2841 0.9872 0.9917 0.2826 0.9708 0.9824 0.2899 ] Network output: [ -0.09425 0.2132 1.116 -0.0003116 0.0001399 0.8577 -0.0002348 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3084 0.3067 0.2952 0.295 0.9823 0.9889 0.3085 0.9531 0.9743 0.2982 ] Network output: [ 0.02381 0.941 -0.02705 0.0002006 -9.006e-05 1.039 0.0001512 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06027 Epoch 3278 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07175 0.8555 0.9215 0.0001192 -5.35e-05 0.07993 8.982e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01641 -0.004613 0.01122 0.0295 0.9481 0.9557 0.02736 0.8928 0.9122 0.07286 ] Network output: [ 0.9614 0.08837 0.02289 0.0004939 -0.0002217 -0.03211 0.0003722 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5671 0.0629 0.06905 0.3778 0.976 0.9889 0.6168 0.9064 0.971 0.5459 ] Network output: [ 0.03574 0.8792 0.9358 -1.941e-05 8.714e-06 0.1134 -1.463e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02106 0.01449 0.02406 0.02798 0.9867 0.9906 0.02134 0.9692 0.9818 0.0312 ] Network output: [ 0.09147 -0.2257 0.8741 -0.0001503 6.748e-05 1.168 -0.0001133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6089 0.4958 0.4167 0.5044 0.9785 0.9904 0.6102 0.914 0.9746 0.5296 ] Network output: [ -0.08883 0.244 1.155 -0.0003283 0.0001474 0.7769 -0.0002474 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2824 0.2737 0.2783 0.2842 0.9872 0.9917 0.2825 0.9708 0.9824 0.29 ] Network output: [ -0.0941 0.2128 1.116 -0.00031 0.0001392 0.8578 -0.0002337 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3084 0.3066 0.2952 0.295 0.9823 0.9889 0.3084 0.9531 0.9743 0.2982 ] Network output: [ 0.0236 0.9417 -0.02679 0.0001985 -8.91e-05 1.039 0.0001496 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06015 Epoch 3279 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07169 0.8559 0.9216 0.000119 -5.345e-05 0.07967 8.972e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01639 -0.004612 0.01119 0.02949 0.9481 0.9557 0.02734 0.8928 0.9122 0.07285 ] Network output: [ 0.9613 0.08854 0.02304 0.0004896 -0.0002198 -0.03218 0.0003689 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5669 0.06311 0.06889 0.3776 0.976 0.9889 0.6167 0.9064 0.971 0.5458 ] Network output: [ 0.03568 0.8795 0.9359 -1.908e-05 8.564e-06 0.1132 -1.438e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02106 0.01449 0.02406 0.02798 0.9867 0.9906 0.02133 0.9692 0.9818 0.0312 ] Network output: [ 0.09166 -0.226 0.8737 -0.0001475 6.62e-05 1.168 -0.0001111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6087 0.4959 0.4166 0.5043 0.9785 0.9904 0.6101 0.9141 0.9746 0.5296 ] Network output: [ -0.08871 0.2432 1.155 -0.0003267 0.0001466 0.7774 -0.0002462 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2824 0.2737 0.2784 0.2843 0.9872 0.9917 0.2825 0.9708 0.9824 0.2901 ] Network output: [ -0.09395 0.2124 1.116 -0.0003085 0.0001385 0.858 -0.0002325 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3083 0.3065 0.2952 0.295 0.9823 0.9889 0.3083 0.9531 0.9743 0.2982 ] Network output: [ 0.02338 0.9423 -0.02652 0.0001964 -8.816e-05 1.038 0.000148 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06004 Epoch 3280 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07163 0.8562 0.9216 0.0001189 -5.339e-05 0.07942 8.963e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01638 -0.004611 0.01116 0.02948 0.9481 0.9558 0.02733 0.8929 0.9123 0.07284 ] Network output: [ 0.9612 0.0887 0.02318 0.0004852 -0.0002178 -0.03225 0.0003656 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5668 0.06332 0.06873 0.3775 0.976 0.9889 0.6165 0.9064 0.971 0.5457 ] Network output: [ 0.03562 0.8798 0.9359 -1.873e-05 8.408e-06 0.113 -1.411e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02105 0.01449 0.02406 0.02798 0.9867 0.9906 0.02133 0.9693 0.9818 0.03121 ] Network output: [ 0.09185 -0.2263 0.8732 -0.0001446 6.493e-05 1.169 -0.000109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6086 0.4959 0.4164 0.5042 0.9785 0.9904 0.6099 0.9141 0.9746 0.5295 ] Network output: [ -0.0886 0.2425 1.156 -0.000325 0.0001459 0.7779 -0.0002449 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2824 0.2737 0.2785 0.2844 0.9872 0.9917 0.2825 0.9708 0.9824 0.2902 ] Network output: [ -0.0938 0.212 1.116 -0.0003069 0.0001378 0.8582 -0.0002313 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3082 0.3064 0.2953 0.295 0.9823 0.9889 0.3082 0.9531 0.9743 0.2983 ] Network output: [ 0.02317 0.9429 -0.02626 0.0001943 -8.723e-05 1.038 0.0001464 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05992 Epoch 3281 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07157 0.8566 0.9216 0.0001188 -5.334e-05 0.07917 8.954e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01637 -0.00461 0.01114 0.02947 0.9481 0.9558 0.02731 0.8929 0.9123 0.07283 ] Network output: [ 0.9611 0.08885 0.02332 0.0004808 -0.0002158 -0.03232 0.0003623 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5666 0.06352 0.06858 0.3774 0.976 0.9889 0.6164 0.9064 0.971 0.5456 ] Network output: [ 0.03556 0.88 0.9359 -1.837e-05 8.247e-06 0.1128 -1.384e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02104 0.01449 0.02406 0.02798 0.9867 0.9906 0.02132 0.9693 0.9818 0.03121 ] Network output: [ 0.09203 -0.2265 0.8728 -0.0001419 6.369e-05 1.169 -0.0001069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6084 0.496 0.4163 0.5041 0.9785 0.9904 0.6098 0.9141 0.9746 0.5294 ] Network output: [ -0.08848 0.2418 1.156 -0.0003233 0.0001451 0.7783 -0.0002436 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2824 0.2737 0.2785 0.2845 0.9872 0.9917 0.2825 0.9708 0.9824 0.2903 ] Network output: [ -0.09365 0.2117 1.116 -0.0003053 0.000137 0.8583 -0.0002301 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3081 0.3064 0.2953 0.295 0.9823 0.9889 0.3081 0.9531 0.9743 0.2983 ] Network output: [ 0.02295 0.9436 -0.02599 0.0001923 -8.633e-05 1.037 0.0001449 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05981 Epoch 3282 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07151 0.8569 0.9216 0.0001187 -5.33e-05 0.07892 8.947e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01636 -0.004609 0.01111 0.02946 0.9482 0.9558 0.0273 0.8929 0.9123 0.07282 ] Network output: [ 0.9609 0.089 0.02346 0.0004763 -0.0002138 -0.03239 0.000359 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5665 0.06372 0.06843 0.3772 0.976 0.9889 0.6162 0.9065 0.9711 0.5455 ] Network output: [ 0.0355 0.8803 0.936 -1.8e-05 8.081e-06 0.1127 -1.357e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02104 0.01449 0.02406 0.02798 0.9867 0.9906 0.02131 0.9693 0.9818 0.03121 ] Network output: [ 0.09222 -0.2268 0.8724 -0.0001391 6.247e-05 1.169 -0.0001049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6083 0.496 0.4162 0.504 0.9785 0.9904 0.6096 0.9141 0.9746 0.5293 ] Network output: [ -0.08837 0.2411 1.156 -0.0003216 0.0001444 0.7788 -0.0002423 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2824 0.2737 0.2786 0.2846 0.9872 0.9917 0.2825 0.9708 0.9824 0.2904 ] Network output: [ -0.0935 0.2113 1.116 -0.0003037 0.0001363 0.8585 -0.0002288 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.308 0.3063 0.2953 0.295 0.9823 0.9889 0.308 0.9532 0.9743 0.2983 ] Network output: [ 0.02274 0.9442 -0.02573 0.0001903 -8.545e-05 1.037 0.0001435 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05969 Epoch 3283 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07145 0.8573 0.9216 0.0001186 -5.325e-05 0.07868 8.939e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01634 -0.004609 0.01108 0.02944 0.9482 0.9558 0.02728 0.893 0.9123 0.07281 ] Network output: [ 0.9608 0.08915 0.0236 0.0004718 -0.0002118 -0.03246 0.0003556 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5663 0.06392 0.06829 0.3771 0.976 0.9889 0.6161 0.9065 0.9711 0.5455 ] Network output: [ 0.03544 0.8806 0.936 -1.762e-05 7.911e-06 0.1125 -1.328e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02103 0.01449 0.02406 0.02798 0.9867 0.9906 0.0213 0.9693 0.9818 0.03122 ] Network output: [ 0.09239 -0.227 0.872 -0.0001365 6.126e-05 1.17 -0.0001028 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6082 0.4961 0.4161 0.5039 0.9785 0.9904 0.6095 0.9142 0.9747 0.5292 ] Network output: [ -0.08826 0.2403 1.156 -0.0003198 0.0001436 0.7792 -0.000241 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2823 0.2737 0.2787 0.2847 0.9872 0.9917 0.2824 0.9708 0.9824 0.2905 ] Network output: [ -0.09334 0.2109 1.116 -0.000302 0.0001356 0.8586 -0.0002276 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3079 0.3062 0.2953 0.295 0.9823 0.9889 0.3079 0.9532 0.9743 0.2983 ] Network output: [ 0.02253 0.9448 -0.02546 0.0001884 -8.459e-05 1.036 0.000142 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05958 Epoch 3284 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07139 0.8576 0.9216 0.0001185 -5.321e-05 0.07844 8.933e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01633 -0.004608 0.01106 0.02943 0.9482 0.9558 0.02727 0.893 0.9123 0.0728 ] Network output: [ 0.9607 0.08929 0.02373 0.0004673 -0.0002098 -0.03253 0.0003522 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5662 0.06411 0.06816 0.377 0.976 0.9889 0.616 0.9065 0.9711 0.5454 ] Network output: [ 0.03538 0.8808 0.936 -1.723e-05 7.737e-06 0.1123 -1.299e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02102 0.01449 0.02406 0.02798 0.9867 0.9906 0.0213 0.9693 0.9818 0.03122 ] Network output: [ 0.09257 -0.2272 0.8716 -0.0001338 6.008e-05 1.17 -0.0001009 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.608 0.4961 0.416 0.5038 0.9785 0.9904 0.6094 0.9142 0.9747 0.5292 ] Network output: [ -0.08815 0.2396 1.156 -0.0003181 0.0001428 0.7797 -0.0002397 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2823 0.2737 0.2788 0.2848 0.9872 0.9917 0.2824 0.9708 0.9824 0.2906 ] Network output: [ -0.09319 0.2105 1.116 -0.0003004 0.0001349 0.8588 -0.0002264 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3078 0.3061 0.2953 0.2951 0.9823 0.9889 0.3078 0.9532 0.9743 0.2983 ] Network output: [ 0.02232 0.9454 -0.02519 0.0001866 -8.375e-05 1.036 0.0001406 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05947 Epoch 3285 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07133 0.858 0.9217 0.0001184 -5.318e-05 0.0782 8.927e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01632 -0.004607 0.01103 0.02942 0.9482 0.9558 0.02725 0.893 0.9124 0.07279 ] Network output: [ 0.9606 0.08943 0.02387 0.0004628 -0.0002077 -0.03259 0.0003487 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.566 0.0643 0.06803 0.3768 0.976 0.9889 0.6158 0.9066 0.9711 0.5453 ] Network output: [ 0.03532 0.8811 0.936 -1.684e-05 7.559e-06 0.1122 -1.269e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02101 0.01449 0.02406 0.02798 0.9867 0.9906 0.02129 0.9693 0.9818 0.03122 ] Network output: [ 0.09274 -0.2275 0.8712 -0.0001312 5.892e-05 1.17 -9.891e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6079 0.4962 0.4159 0.5037 0.9785 0.9904 0.6093 0.9142 0.9747 0.5291 ] Network output: [ -0.08803 0.2389 1.156 -0.0003164 0.000142 0.7801 -0.0002384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2823 0.2737 0.2789 0.2848 0.9872 0.9917 0.2824 0.9708 0.9824 0.2907 ] Network output: [ -0.09304 0.2102 1.116 -0.0002987 0.0001341 0.8589 -0.0002251 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3077 0.306 0.2953 0.2951 0.9823 0.9889 0.3077 0.9532 0.9743 0.2983 ] Network output: [ 0.02211 0.946 -0.02492 0.0001847 -8.293e-05 1.035 0.0001392 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05937 Epoch 3286 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07128 0.8583 0.9217 0.0001184 -5.314e-05 0.07797 8.921e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0163 -0.004607 0.011 0.02941 0.9482 0.9558 0.02724 0.893 0.9124 0.07278 ] Network output: [ 0.9605 0.08956 0.024 0.0004582 -0.0002057 -0.03265 0.0003453 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5659 0.06449 0.06791 0.3767 0.976 0.9889 0.6157 0.9066 0.9711 0.5452 ] Network output: [ 0.03527 0.8813 0.9361 -1.643e-05 7.377e-06 0.112 -1.238e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02101 0.01449 0.02405 0.02798 0.9867 0.9907 0.02128 0.9693 0.9818 0.03123 ] Network output: [ 0.09291 -0.2277 0.8707 -0.0001287 5.778e-05 1.171 -9.699e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6078 0.4962 0.4159 0.5036 0.9786 0.9904 0.6091 0.9143 0.9747 0.529 ] Network output: [ -0.08792 0.2382 1.156 -0.0003146 0.0001413 0.7805 -0.0002371 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2822 0.2736 0.279 0.2849 0.9872 0.9917 0.2824 0.9708 0.9825 0.2908 ] Network output: [ -0.09289 0.2098 1.116 -0.0002971 0.0001334 0.8591 -0.0002239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3076 0.3059 0.2953 0.2951 0.9823 0.9889 0.3076 0.9532 0.9744 0.2983 ] Network output: [ 0.0219 0.9466 -0.02466 0.0001829 -8.213e-05 1.035 0.0001379 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05926 Epoch 3287 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07122 0.8586 0.9217 0.0001183 -5.311e-05 0.07774 8.916e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01629 -0.004607 0.01098 0.0294 0.9482 0.9558 0.02722 0.8931 0.9124 0.07277 ] Network output: [ 0.9604 0.08969 0.02413 0.0004536 -0.0002036 -0.03271 0.0003418 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5658 0.06467 0.0678 0.3766 0.976 0.9889 0.6156 0.9066 0.9711 0.5451 ] Network output: [ 0.03521 0.8816 0.9361 -1.602e-05 7.192e-06 0.1118 -1.207e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.021 0.01449 0.02405 0.02798 0.9867 0.9907 0.02128 0.9693 0.9818 0.03123 ] Network output: [ 0.09308 -0.2279 0.8703 -0.0001262 5.666e-05 1.171 -9.511e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6077 0.4963 0.4158 0.5035 0.9786 0.9904 0.609 0.9143 0.9747 0.5289 ] Network output: [ -0.08781 0.2375 1.156 -0.0003129 0.0001405 0.781 -0.0002358 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2822 0.2736 0.2791 0.285 0.9872 0.9917 0.2823 0.9708 0.9825 0.2909 ] Network output: [ -0.09274 0.2094 1.116 -0.0002954 0.0001326 0.8592 -0.0002226 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3075 0.3058 0.2953 0.2951 0.9823 0.9889 0.3075 0.9533 0.9744 0.2983 ] Network output: [ 0.02169 0.9472 -0.02439 0.0001812 -8.134e-05 1.034 0.0001366 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05916 Epoch 3288 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07116 0.859 0.9217 0.0001182 -5.308e-05 0.07751 8.911e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01628 -0.004606 0.01095 0.02939 0.9482 0.9558 0.02721 0.8931 0.9124 0.07276 ] Network output: [ 0.9603 0.08982 0.02426 0.000449 -0.0002016 -0.03277 0.0003384 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5656 0.06485 0.06769 0.3764 0.976 0.9889 0.6155 0.9067 0.9711 0.545 ] Network output: [ 0.03515 0.8818 0.9361 -1.56e-05 7.003e-06 0.1117 -1.176e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02099 0.01448 0.02405 0.02798 0.9867 0.9907 0.02127 0.9693 0.9818 0.03123 ] Network output: [ 0.09324 -0.2281 0.8699 -0.0001237 5.555e-05 1.171 -9.325e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6075 0.4963 0.4157 0.5034 0.9786 0.9904 0.6089 0.9143 0.9747 0.5288 ] Network output: [ -0.0877 0.2368 1.156 -0.0003111 0.0001397 0.7814 -0.0002345 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2822 0.2736 0.2792 0.2851 0.9872 0.9917 0.2823 0.9708 0.9825 0.291 ] Network output: [ -0.09259 0.2091 1.116 -0.0002937 0.0001319 0.8593 -0.0002214 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3074 0.3057 0.2953 0.2951 0.9823 0.9889 0.3074 0.9533 0.9744 0.2983 ] Network output: [ 0.02149 0.9478 -0.02412 0.0001795 -8.058e-05 1.034 0.0001353 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05905 Epoch 3289 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0711 0.8593 0.9217 0.0001182 -5.306e-05 0.07728 8.907e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01627 -0.004606 0.01092 0.02938 0.9482 0.9558 0.02719 0.8931 0.9124 0.07275 ] Network output: [ 0.9602 0.08994 0.02439 0.0004444 -0.0001995 -0.03283 0.0003349 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5655 0.06503 0.06759 0.3763 0.976 0.9889 0.6153 0.9067 0.9711 0.5449 ] Network output: [ 0.03509 0.8821 0.9361 -1.517e-05 6.812e-06 0.1115 -1.144e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02098 0.01448 0.02405 0.02798 0.9867 0.9907 0.02126 0.9693 0.9818 0.03124 ] Network output: [ 0.0934 -0.2283 0.8695 -0.0001213 5.446e-05 1.172 -9.143e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6074 0.4964 0.4156 0.5033 0.9786 0.9904 0.6088 0.9144 0.9747 0.5288 ] Network output: [ -0.08759 0.2361 1.156 -0.0003094 0.0001389 0.7818 -0.0002332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2821 0.2736 0.2793 0.2852 0.9872 0.9917 0.2823 0.9709 0.9825 0.2911 ] Network output: [ -0.09244 0.2087 1.116 -0.0002921 0.0001311 0.8595 -0.0002201 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3073 0.3056 0.2953 0.2951 0.9823 0.989 0.3074 0.9533 0.9744 0.2984 ] Network output: [ 0.02128 0.9484 -0.02385 0.0001778 -7.982e-05 1.034 0.000134 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05895 Epoch 3290 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07104 0.8596 0.9217 0.0001181 -5.303e-05 0.07706 8.903e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01625 -0.004606 0.0109 0.02937 0.9482 0.9559 0.02718 0.8932 0.9125 0.07274 ] Network output: [ 0.9601 0.09006 0.02451 0.0004397 -0.0001974 -0.03289 0.0003314 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5653 0.0652 0.06749 0.3762 0.976 0.989 0.6152 0.9067 0.9711 0.5448 ] Network output: [ 0.03503 0.8823 0.9362 -1.474e-05 6.618e-06 0.1114 -1.111e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02098 0.01448 0.02405 0.02798 0.9867 0.9907 0.02125 0.9694 0.9818 0.03124 ] Network output: [ 0.09356 -0.2285 0.8691 -0.0001189 5.34e-05 1.172 -8.964e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6073 0.4964 0.4155 0.5031 0.9786 0.9904 0.6087 0.9144 0.9747 0.5287 ] Network output: [ -0.08748 0.2354 1.156 -0.0003076 0.0001381 0.7822 -0.0002318 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2821 0.2736 0.2793 0.2852 0.9872 0.9917 0.2822 0.9709 0.9825 0.2912 ] Network output: [ -0.09229 0.2084 1.115 -0.0002904 0.0001304 0.8596 -0.0002188 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3072 0.3055 0.2953 0.2951 0.9823 0.989 0.3073 0.9533 0.9744 0.2984 ] Network output: [ 0.02108 0.949 -0.02358 0.0001762 -7.909e-05 1.033 0.0001328 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05885 Epoch 3291 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07099 0.8599 0.9217 0.0001181 -5.301e-05 0.07684 8.899e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01624 -0.004606 0.01087 0.02936 0.9482 0.9559 0.02716 0.8932 0.9125 0.07273 ] Network output: [ 0.96 0.09017 0.02464 0.0004351 -0.0001953 -0.03295 0.0003279 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5652 0.06538 0.0674 0.376 0.976 0.989 0.6151 0.9068 0.9712 0.5447 ] Network output: [ 0.03497 0.8826 0.9362 -1.431e-05 6.422e-06 0.1112 -1.078e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02097 0.01448 0.02405 0.02798 0.9867 0.9907 0.02125 0.9694 0.9818 0.03124 ] Network output: [ 0.09372 -0.2287 0.8687 -0.0001166 5.235e-05 1.172 -8.787e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6072 0.4965 0.4155 0.503 0.9786 0.9904 0.6085 0.9144 0.9747 0.5286 ] Network output: [ -0.08737 0.2347 1.156 -0.0003059 0.0001373 0.7826 -0.0002305 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2821 0.2735 0.2794 0.2853 0.9872 0.9917 0.2822 0.9709 0.9825 0.2913 ] Network output: [ -0.09215 0.208 1.115 -0.0002887 0.0001296 0.8597 -0.0002176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3071 0.3054 0.2953 0.2951 0.9823 0.989 0.3072 0.9534 0.9744 0.2984 ] Network output: [ 0.02087 0.9496 -0.0233 0.0001746 -7.837e-05 1.033 0.0001316 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05875 Epoch 3292 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07093 0.8603 0.9217 0.000118 -5.3e-05 0.07663 8.896e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01623 -0.004606 0.01085 0.02934 0.9483 0.9559 0.02715 0.8932 0.9125 0.07272 ] Network output: [ 0.9599 0.09028 0.02476 0.0004304 -0.0001932 -0.033 0.0003244 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5651 0.06555 0.06731 0.3759 0.976 0.989 0.615 0.9068 0.9712 0.5447 ] Network output: [ 0.03491 0.8828 0.9362 -1.386e-05 6.224e-06 0.1111 -1.045e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02096 0.01448 0.02405 0.02798 0.9868 0.9907 0.02124 0.9694 0.9818 0.03124 ] Network output: [ 0.09387 -0.2289 0.8683 -0.0001143 5.131e-05 1.172 -8.614e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6071 0.4965 0.4154 0.5029 0.9786 0.9904 0.6084 0.9145 0.9748 0.5285 ] Network output: [ -0.08726 0.2341 1.156 -0.0003041 0.0001365 0.7831 -0.0002292 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.282 0.2735 0.2795 0.2854 0.9872 0.9917 0.2821 0.9709 0.9825 0.2913 ] Network output: [ -0.092 0.2077 1.115 -0.000287 0.0001288 0.8598 -0.0002163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.307 0.3053 0.2953 0.295 0.9823 0.989 0.3071 0.9534 0.9744 0.2984 ] Network output: [ 0.02067 0.9502 -0.02303 0.000173 -7.767e-05 1.032 0.0001304 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05865 Epoch 3293 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07087 0.8606 0.9217 0.000118 -5.298e-05 0.07641 8.894e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01621 -0.004606 0.01082 0.02933 0.9483 0.9559 0.02713 0.8933 0.9125 0.07271 ] Network output: [ 0.9598 0.09039 0.02488 0.0004257 -0.0001911 -0.03306 0.0003208 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5649 0.06572 0.06723 0.3758 0.976 0.989 0.6149 0.9069 0.9712 0.5446 ] Network output: [ 0.03486 0.883 0.9362 -1.342e-05 6.024e-06 0.111 -1.011e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02095 0.01448 0.02405 0.02797 0.9868 0.9907 0.02123 0.9694 0.9819 0.03125 ] Network output: [ 0.09402 -0.2291 0.8679 -0.000112 5.029e-05 1.173 -8.443e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.607 0.4966 0.4153 0.5028 0.9786 0.9904 0.6083 0.9145 0.9748 0.5284 ] Network output: [ -0.08715 0.2334 1.156 -0.0003023 0.0001357 0.7835 -0.0002278 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.282 0.2735 0.2796 0.2855 0.9872 0.9917 0.2821 0.9709 0.9825 0.2914 ] Network output: [ -0.09185 0.2074 1.115 -0.0002853 0.0001281 0.8599 -0.000215 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3069 0.3052 0.2953 0.295 0.9824 0.989 0.307 0.9534 0.9744 0.2984 ] Network output: [ 0.02047 0.9508 -0.02276 0.0001715 -7.698e-05 1.032 0.0001292 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05856 Epoch 3294 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07082 0.8609 0.9218 0.000118 -5.296e-05 0.0762 8.891e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0162 -0.004606 0.0108 0.02932 0.9483 0.9559 0.02712 0.8933 0.9126 0.0727 ] Network output: [ 0.9597 0.09049 0.025 0.0004211 -0.000189 -0.03311 0.0003173 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5648 0.06588 0.06715 0.3756 0.9761 0.989 0.6148 0.9069 0.9712 0.5445 ] Network output: [ 0.0348 0.8833 0.9362 -1.297e-05 5.823e-06 0.1108 -9.775e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02095 0.01448 0.02405 0.02797 0.9868 0.9907 0.02122 0.9694 0.9819 0.03125 ] Network output: [ 0.09417 -0.2293 0.8675 -0.0001098 4.929e-05 1.173 -8.275e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6069 0.4966 0.4153 0.5027 0.9786 0.9904 0.6082 0.9145 0.9748 0.5283 ] Network output: [ -0.08704 0.2327 1.156 -0.0003006 0.0001349 0.7839 -0.0002265 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.282 0.2735 0.2797 0.2855 0.9872 0.9917 0.2821 0.9709 0.9825 0.2915 ] Network output: [ -0.0917 0.207 1.115 -0.0002836 0.0001273 0.8601 -0.0002137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3068 0.3051 0.2953 0.295 0.9824 0.989 0.3069 0.9534 0.9745 0.2984 ] Network output: [ 0.02027 0.9513 -0.02249 0.00017 -7.63e-05 1.031 0.0001281 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05846 Epoch 3295 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07076 0.8612 0.9218 0.0001179 -5.295e-05 0.07599 8.889e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01619 -0.004607 0.01077 0.02931 0.9483 0.9559 0.0271 0.8933 0.9126 0.07269 ] Network output: [ 0.9596 0.09059 0.02512 0.0004164 -0.0001869 -0.03316 0.0003138 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5647 0.06604 0.06708 0.3755 0.9761 0.989 0.6147 0.9069 0.9712 0.5444 ] Network output: [ 0.03474 0.8835 0.9363 -1.252e-05 5.62e-06 0.1107 -9.434e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02094 0.01447 0.02405 0.02797 0.9868 0.9907 0.02122 0.9694 0.9819 0.03125 ] Network output: [ 0.09431 -0.2295 0.867 -0.0001076 4.831e-05 1.173 -8.11e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6068 0.4967 0.4152 0.5026 0.9786 0.9904 0.6081 0.9146 0.9748 0.5283 ] Network output: [ -0.08693 0.232 1.156 -0.0002988 0.0001341 0.7843 -0.0002252 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2819 0.2734 0.2797 0.2856 0.9872 0.9917 0.282 0.9709 0.9825 0.2916 ] Network output: [ -0.09155 0.2067 1.115 -0.0002818 0.0001265 0.8602 -0.0002124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3067 0.3051 0.2953 0.295 0.9824 0.989 0.3068 0.9534 0.9745 0.2984 ] Network output: [ 0.02007 0.9519 -0.02221 0.0001685 -7.564e-05 1.031 0.000127 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05837 Epoch 3296 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07071 0.8615 0.9218 0.0001179 -5.294e-05 0.07579 8.887e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01618 -0.004607 0.01075 0.0293 0.9483 0.9559 0.02709 0.8934 0.9126 0.07268 ] Network output: [ 0.9595 0.09069 0.02523 0.0004117 -0.0001848 -0.03321 0.0003103 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5646 0.0662 0.06701 0.3753 0.9761 0.989 0.6146 0.907 0.9712 0.5443 ] Network output: [ 0.03468 0.8838 0.9363 -1.206e-05 5.416e-06 0.1105 -9.091e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02093 0.01447 0.02405 0.02797 0.9868 0.9907 0.02121 0.9694 0.9819 0.03125 ] Network output: [ 0.09446 -0.2297 0.8666 -0.0001055 4.734e-05 1.174 -7.947e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6066 0.4967 0.4152 0.5025 0.9786 0.9904 0.608 0.9146 0.9748 0.5282 ] Network output: [ -0.08683 0.2314 1.156 -0.000297 0.0001333 0.7847 -0.0002238 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2819 0.2734 0.2798 0.2857 0.9872 0.9917 0.282 0.9709 0.9825 0.2917 ] Network output: [ -0.09141 0.2064 1.115 -0.0002801 0.0001258 0.8603 -0.0002111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3066 0.305 0.2953 0.295 0.9824 0.989 0.3067 0.9535 0.9745 0.2984 ] Network output: [ 0.01987 0.9524 -0.02194 0.0001671 -7.5e-05 1.03 0.0001259 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05827 Epoch 3297 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07065 0.8618 0.9218 0.0001179 -5.293e-05 0.07558 8.886e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01616 -0.004607 0.01072 0.02929 0.9483 0.9559 0.02707 0.8934 0.9126 0.07266 ] Network output: [ 0.9594 0.09078 0.02535 0.000407 -0.0001827 -0.03327 0.0003067 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5644 0.06636 0.06695 0.3752 0.9761 0.989 0.6144 0.907 0.9712 0.5442 ] Network output: [ 0.03463 0.884 0.9363 -1.161e-05 5.21e-06 0.1104 -8.747e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02092 0.01447 0.02405 0.02797 0.9868 0.9907 0.0212 0.9694 0.9819 0.03126 ] Network output: [ 0.0946 -0.2298 0.8662 -0.0001033 4.639e-05 1.174 -7.787e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6065 0.4968 0.4151 0.5024 0.9786 0.9904 0.6079 0.9146 0.9748 0.5281 ] Network output: [ -0.08672 0.2307 1.156 -0.0002952 0.0001325 0.7851 -0.0002225 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2818 0.2734 0.2799 0.2857 0.9872 0.9917 0.2819 0.9709 0.9825 0.2918 ] Network output: [ -0.09126 0.206 1.115 -0.0002784 0.000125 0.8604 -0.0002098 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3065 0.3049 0.2953 0.295 0.9824 0.989 0.3066 0.9535 0.9745 0.2984 ] Network output: [ 0.01967 0.953 -0.02167 0.0001656 -7.436e-05 1.03 0.0001248 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05818 Epoch 3298 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0706 0.8621 0.9218 0.0001179 -5.292e-05 0.07538 8.884e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01615 -0.004608 0.0107 0.02927 0.9483 0.9559 0.02706 0.8934 0.9127 0.07265 ] Network output: [ 0.9593 0.09088 0.02546 0.0004023 -0.0001806 -0.03332 0.0003032 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5643 0.06652 0.06689 0.3751 0.9761 0.989 0.6143 0.907 0.9713 0.5441 ] Network output: [ 0.03457 0.8842 0.9363 -1.115e-05 5.004e-06 0.1103 -8.401e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02092 0.01447 0.02405 0.02797 0.9868 0.9907 0.02119 0.9694 0.9819 0.03126 ] Network output: [ 0.09474 -0.23 0.8658 -0.0001012 4.545e-05 1.174 -7.63e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6064 0.4968 0.4151 0.5023 0.9786 0.9904 0.6078 0.9147 0.9748 0.528 ] Network output: [ -0.08661 0.23 1.157 -0.0002935 0.0001317 0.7854 -0.0002212 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2818 0.2733 0.28 0.2858 0.9872 0.9917 0.2819 0.9709 0.9825 0.2918 ] Network output: [ -0.09111 0.2057 1.115 -0.0002767 0.0001242 0.8605 -0.0002085 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3064 0.3048 0.2953 0.295 0.9824 0.989 0.3064 0.9535 0.9745 0.2983 ] Network output: [ 0.01948 0.9536 -0.02139 0.0001643 -7.374e-05 1.03 0.0001238 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05809 Epoch 3299 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07054 0.8624 0.9218 0.0001179 -5.292e-05 0.07518 8.883e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01614 -0.004608 0.01067 0.02926 0.9483 0.9559 0.02704 0.8935 0.9127 0.07264 ] Network output: [ 0.9592 0.09097 0.02558 0.0003976 -0.0001785 -0.03336 0.0002997 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5642 0.06667 0.06683 0.3749 0.9761 0.989 0.6142 0.9071 0.9713 0.544 ] Network output: [ 0.03451 0.8844 0.9363 -1.069e-05 4.798e-06 0.1101 -8.054e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02091 0.01447 0.02405 0.02797 0.9868 0.9907 0.02119 0.9695 0.9819 0.03126 ] Network output: [ 0.09487 -0.2302 0.8654 -9.919e-05 4.453e-05 1.175 -7.475e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6063 0.4969 0.415 0.5021 0.9786 0.9904 0.6077 0.9147 0.9748 0.5279 ] Network output: [ -0.0865 0.2294 1.157 -0.0002917 0.0001309 0.7858 -0.0002198 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2817 0.2733 0.28 0.2859 0.9872 0.9917 0.2818 0.9709 0.9825 0.2919 ] Network output: [ -0.09097 0.2054 1.115 -0.000275 0.0001234 0.8606 -0.0002072 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3063 0.3047 0.2953 0.295 0.9824 0.989 0.3063 0.9535 0.9745 0.2983 ] Network output: [ 0.01928 0.9541 -0.02112 0.0001629 -7.314e-05 1.029 0.0001228 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.058 Epoch 3300 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07049 0.8627 0.9218 0.0001179 -5.291e-05 0.07499 8.882e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01613 -0.004609 0.01065 0.02925 0.9483 0.956 0.02703 0.8935 0.9127 0.07263 ] Network output: [ 0.9591 0.09105 0.02569 0.000393 -0.0001764 -0.03341 0.0002962 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5641 0.06682 0.06678 0.3748 0.9761 0.989 0.6141 0.9071 0.9713 0.5439 ] Network output: [ 0.03445 0.8847 0.9364 -1.023e-05 4.591e-06 0.11 -7.707e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0209 0.01447 0.02405 0.02797 0.9868 0.9907 0.02118 0.9695 0.9819 0.03126 ] Network output: [ 0.09501 -0.2303 0.865 -9.716e-05 4.362e-05 1.175 -7.323e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6062 0.497 0.415 0.502 0.9786 0.9904 0.6076 0.9147 0.9748 0.5278 ] Network output: [ -0.0864 0.2287 1.157 -0.0002899 0.0001302 0.7862 -0.0002185 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2817 0.2733 0.2801 0.2859 0.9872 0.9917 0.2818 0.971 0.9825 0.292 ] Network output: [ -0.09082 0.2051 1.115 -0.0002732 0.0001227 0.8607 -0.0002059 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3062 0.3046 0.2953 0.295 0.9824 0.989 0.3062 0.9535 0.9745 0.2983 ] Network output: [ 0.01909 0.9546 -0.02085 0.0001616 -7.254e-05 1.029 0.0001218 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05791 Epoch 3301 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07043 0.863 0.9218 0.0001179 -5.291e-05 0.07479 8.882e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01611 -0.00461 0.01062 0.02924 0.9484 0.956 0.02701 0.8935 0.9127 0.07262 ] Network output: [ 0.9591 0.09113 0.0258 0.0003883 -0.0001743 -0.03346 0.0002926 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.564 0.06697 0.06674 0.3746 0.9761 0.989 0.614 0.9071 0.9713 0.5438 ] Network output: [ 0.0344 0.8849 0.9364 -9.765e-06 4.384e-06 0.1099 -7.359e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02089 0.01446 0.02405 0.02796 0.9868 0.9907 0.02117 0.9695 0.9819 0.03126 ] Network output: [ 0.09514 -0.2305 0.8646 -9.517e-05 4.273e-05 1.175 -7.172e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6061 0.497 0.415 0.5019 0.9786 0.9904 0.6075 0.9148 0.9749 0.5278 ] Network output: [ -0.08629 0.2281 1.157 -0.0002881 0.0001294 0.7866 -0.0002172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2816 0.2732 0.2802 0.286 0.9872 0.9917 0.2817 0.971 0.9825 0.2921 ] Network output: [ -0.09068 0.2048 1.115 -0.0002715 0.0001219 0.8608 -0.0002046 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3061 0.3045 0.2953 0.295 0.9824 0.989 0.3061 0.9536 0.9745 0.2983 ] Network output: [ 0.01889 0.9552 -0.02057 0.0001603 -7.196e-05 1.028 0.0001208 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05783 Epoch 3302 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07038 0.8633 0.9218 0.0001178 -5.291e-05 0.0746 8.881e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0161 -0.00461 0.0106 0.02923 0.9484 0.956 0.027 0.8936 0.9127 0.0726 ] Network output: [ 0.959 0.09121 0.02591 0.0003836 -0.0001722 -0.03351 0.0002891 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5638 0.06712 0.06669 0.3745 0.9761 0.989 0.6139 0.9072 0.9713 0.5437 ] Network output: [ 0.03434 0.8851 0.9364 -9.304e-06 4.177e-06 0.1098 -7.012e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02089 0.01446 0.02405 0.02796 0.9868 0.9907 0.02116 0.9695 0.9819 0.03127 ] Network output: [ 0.09527 -0.2306 0.8642 -9.321e-05 4.185e-05 1.175 -7.025e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6061 0.4971 0.4149 0.5018 0.9786 0.9904 0.6074 0.9148 0.9749 0.5277 ] Network output: [ -0.08618 0.2274 1.157 -0.0002864 0.0001286 0.787 -0.0002158 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2816 0.2732 0.2803 0.286 0.9873 0.9917 0.2817 0.971 0.9825 0.2921 ] Network output: [ -0.09053 0.2045 1.115 -0.0002698 0.0001211 0.8609 -0.0002033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.306 0.3044 0.2953 0.2949 0.9824 0.989 0.306 0.9536 0.9745 0.2983 ] Network output: [ 0.0187 0.9557 -0.0203 0.000159 -7.139e-05 1.028 0.0001198 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05774 Epoch 3303 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07032 0.8636 0.9218 0.0001178 -5.29e-05 0.07441 8.881e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01609 -0.004611 0.01057 0.02921 0.9484 0.956 0.02698 0.8936 0.9128 0.07259 ] Network output: [ 0.9589 0.09129 0.02602 0.000379 -0.0001701 -0.03356 0.0002856 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5637 0.06727 0.06665 0.3744 0.9761 0.989 0.6138 0.9072 0.9713 0.5437 ] Network output: [ 0.03428 0.8853 0.9364 -8.843e-06 3.97e-06 0.1096 -6.664e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02088 0.01446 0.02405 0.02796 0.9868 0.9907 0.02116 0.9695 0.9819 0.03127 ] Network output: [ 0.0954 -0.2308 0.8638 -9.128e-05 4.098e-05 1.176 -6.879e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.606 0.4971 0.4149 0.5017 0.9786 0.9904 0.6073 0.9148 0.9749 0.5276 ] Network output: [ -0.08608 0.2268 1.157 -0.0002846 0.0001278 0.7873 -0.0002145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2815 0.2732 0.2803 0.2861 0.9873 0.9917 0.2817 0.971 0.9825 0.2922 ] Network output: [ -0.09039 0.2041 1.115 -0.0002681 0.0001203 0.861 -0.000202 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3059 0.3042 0.2953 0.2949 0.9824 0.989 0.3059 0.9536 0.9746 0.2983 ] Network output: [ 0.01851 0.9562 -0.02002 0.0001578 -7.082e-05 1.027 0.0001189 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05765 Epoch 3304 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07027 0.8639 0.9218 0.0001178 -5.29e-05 0.07423 8.881e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01607 -0.004612 0.01055 0.0292 0.9484 0.956 0.02697 0.8936 0.9128 0.07258 ] Network output: [ 0.9588 0.09137 0.02612 0.0003744 -0.0001681 -0.0336 0.0002821 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5636 0.06741 0.06662 0.3742 0.9761 0.989 0.6138 0.9072 0.9713 0.5436 ] Network output: [ 0.03422 0.8856 0.9364 -8.382e-06 3.763e-06 0.1095 -6.317e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02087 0.01446 0.02405 0.02796 0.9868 0.9907 0.02115 0.9695 0.9819 0.03127 ] Network output: [ 0.09552 -0.2309 0.8634 -8.938e-05 4.013e-05 1.176 -6.736e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6059 0.4972 0.4149 0.5016 0.9786 0.9904 0.6072 0.9149 0.9749 0.5275 ] Network output: [ -0.08597 0.2261 1.157 -0.0002828 0.000127 0.7877 -0.0002132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2815 0.2731 0.2804 0.2862 0.9873 0.9918 0.2816 0.971 0.9825 0.2923 ] Network output: [ -0.09024 0.2038 1.115 -0.0002664 0.0001196 0.861 -0.0002007 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3058 0.3041 0.2953 0.2949 0.9824 0.989 0.3058 0.9536 0.9746 0.2983 ] Network output: [ 0.01832 0.9568 -0.01975 0.0001565 -7.027e-05 1.027 0.000118 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05757 Epoch 3305 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07022 0.8642 0.9218 0.0001178 -5.29e-05 0.07404 8.881e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01606 -0.004613 0.01052 0.02919 0.9484 0.956 0.02696 0.8937 0.9128 0.07257 ] Network output: [ 0.9587 0.09144 0.02623 0.0003697 -0.000166 -0.03365 0.0002787 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5635 0.06755 0.06659 0.3741 0.9761 0.989 0.6137 0.9073 0.9713 0.5435 ] Network output: [ 0.03417 0.8858 0.9364 -7.923e-06 3.557e-06 0.1094 -5.971e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02086 0.01446 0.02405 0.02796 0.9868 0.9907 0.02114 0.9695 0.9819 0.03127 ] Network output: [ 0.09564 -0.231 0.863 -8.751e-05 3.929e-05 1.176 -6.595e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6058 0.4972 0.4148 0.5015 0.9786 0.9904 0.6071 0.9149 0.9749 0.5274 ] Network output: [ -0.08587 0.2255 1.157 -0.0002811 0.0001262 0.7881 -0.0002118 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2815 0.2731 0.2805 0.2862 0.9873 0.9918 0.2816 0.971 0.9826 0.2923 ] Network output: [ -0.0901 0.2035 1.114 -0.0002646 0.0001188 0.8611 -0.0001994 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3057 0.304 0.2953 0.2949 0.9824 0.989 0.3057 0.9536 0.9746 0.2983 ] Network output: [ 0.01813 0.9573 -0.01948 0.0001553 -6.973e-05 1.027 0.0001171 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05749 Epoch 3306 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07016 0.8645 0.9218 0.0001178 -5.29e-05 0.07386 8.881e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01605 -0.004614 0.0105 0.02918 0.9484 0.956 0.02694 0.8937 0.9128 0.07255 ] Network output: [ 0.9587 0.09151 0.02634 0.0003651 -0.0001639 -0.03369 0.0002752 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5634 0.06769 0.06656 0.3739 0.9761 0.989 0.6136 0.9073 0.9714 0.5434 ] Network output: [ 0.03411 0.886 0.9365 -7.464e-06 3.351e-06 0.1093 -5.625e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02086 0.01445 0.02405 0.02795 0.9868 0.9907 0.02113 0.9695 0.9819 0.03127 ] Network output: [ 0.09577 -0.2312 0.8627 -8.567e-05 3.846e-05 1.177 -6.456e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6057 0.4973 0.4148 0.5013 0.9787 0.9904 0.607 0.9149 0.9749 0.5273 ] Network output: [ -0.08576 0.2249 1.157 -0.0002793 0.0001254 0.7885 -0.0002105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2814 0.2731 0.2806 0.2863 0.9873 0.9918 0.2815 0.971 0.9826 0.2924 ] Network output: [ -0.08996 0.2032 1.114 -0.0002629 0.000118 0.8612 -0.0001982 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3056 0.3039 0.2953 0.2949 0.9824 0.989 0.3056 0.9537 0.9746 0.2983 ] Network output: [ 0.01794 0.9578 -0.0192 0.0001541 -6.92e-05 1.026 0.0001162 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0574 Epoch 3307 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07011 0.8648 0.9218 0.0001178 -5.291e-05 0.07368 8.881e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01604 -0.004615 0.01048 0.02916 0.9484 0.956 0.02693 0.8937 0.9129 0.07254 ] Network output: [ 0.9586 0.09158 0.02644 0.0003606 -0.0001619 -0.03374 0.0002717 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5633 0.06783 0.06653 0.3738 0.9761 0.989 0.6135 0.9074 0.9714 0.5433 ] Network output: [ 0.03405 0.8862 0.9365 -7.008e-06 3.146e-06 0.1092 -5.281e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02085 0.01445 0.02405 0.02795 0.9868 0.9907 0.02113 0.9696 0.9819 0.03127 ] Network output: [ 0.09588 -0.2313 0.8623 -8.385e-05 3.764e-05 1.177 -6.319e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6056 0.4973 0.4148 0.5012 0.9787 0.9905 0.6069 0.915 0.9749 0.5272 ] Network output: [ -0.08566 0.2242 1.157 -0.0002776 0.0001246 0.7888 -0.0002092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2814 0.273 0.2806 0.2863 0.9873 0.9918 0.2815 0.971 0.9826 0.2925 ] Network output: [ -0.08981 0.2029 1.114 -0.0002612 0.0001173 0.8613 -0.0001969 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3055 0.3038 0.2953 0.2949 0.9824 0.989 0.3055 0.9537 0.9746 0.2983 ] Network output: [ 0.01776 0.9583 -0.01893 0.000153 -6.868e-05 1.026 0.0001153 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05732 Epoch 3308 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07006 0.8651 0.9218 0.0001179 -5.291e-05 0.0735 8.882e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01602 -0.004616 0.01045 0.02915 0.9484 0.956 0.02691 0.8938 0.9129 0.07253 ] Network output: [ 0.9585 0.09164 0.02654 0.000356 -0.0001598 -0.03378 0.0002683 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5632 0.06797 0.06651 0.3737 0.9761 0.989 0.6134 0.9074 0.9714 0.5432 ] Network output: [ 0.03399 0.8864 0.9365 -6.554e-06 2.942e-06 0.1091 -4.939e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02084 0.01445 0.02405 0.02795 0.9868 0.9907 0.02112 0.9696 0.982 0.03127 ] Network output: [ 0.096 -0.2314 0.8619 -8.206e-05 3.684e-05 1.177 -6.185e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6055 0.4974 0.4148 0.5011 0.9787 0.9905 0.6069 0.915 0.9749 0.5271 ] Network output: [ -0.08555 0.2236 1.157 -0.0002758 0.0001238 0.7892 -0.0002079 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2813 0.273 0.2807 0.2864 0.9873 0.9918 0.2814 0.971 0.9826 0.2925 ] Network output: [ -0.08967 0.2026 1.114 -0.0002595 0.0001165 0.8614 -0.0001956 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3054 0.3037 0.2953 0.2948 0.9824 0.989 0.3054 0.9537 0.9746 0.2983 ] Network output: [ 0.01757 0.9588 -0.01866 0.0001518 -6.817e-05 1.025 0.0001144 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05724 Epoch 3309 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.07001 0.8653 0.9218 0.0001179 -5.291e-05 0.07332 8.882e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01601 -0.004618 0.01043 0.02914 0.9484 0.956 0.0269 0.8938 0.9129 0.07251 ] Network output: [ 0.9585 0.09171 0.02664 0.0003514 -0.0001578 -0.03383 0.0002648 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5631 0.06811 0.06649 0.3735 0.9761 0.989 0.6133 0.9074 0.9714 0.5431 ] Network output: [ 0.03394 0.8866 0.9365 -6.101e-06 2.739e-06 0.109 -4.598e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02083 0.01445 0.02405 0.02795 0.9868 0.9907 0.02111 0.9696 0.982 0.03127 ] Network output: [ 0.09612 -0.2315 0.8615 -8.03e-05 3.605e-05 1.177 -6.052e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6054 0.4975 0.4148 0.501 0.9787 0.9905 0.6068 0.915 0.975 0.5271 ] Network output: [ -0.08545 0.223 1.157 -0.0002741 0.0001231 0.7895 -0.0002066 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2812 0.2729 0.2808 0.2864 0.9873 0.9918 0.2814 0.971 0.9826 0.2926 ] Network output: [ -0.08953 0.2024 1.114 -0.0002578 0.0001157 0.8614 -0.0001943 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3053 0.3036 0.2953 0.2948 0.9824 0.989 0.3053 0.9537 0.9746 0.2982 ] Network output: [ 0.01739 0.9593 -0.01838 0.0001507 -6.767e-05 1.025 0.0001136 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05716 Epoch 3310 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06995 0.8656 0.9218 0.0001179 -5.291e-05 0.07315 8.883e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.016 -0.004619 0.0104 0.02913 0.9485 0.9561 0.02688 0.8938 0.9129 0.0725 ] Network output: [ 0.9584 0.09177 0.02675 0.0003469 -0.0001557 -0.03387 0.0002614 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5629 0.06824 0.06648 0.3734 0.9762 0.989 0.6132 0.9075 0.9714 0.543 ] Network output: [ 0.03388 0.8868 0.9365 -5.652e-06 2.537e-06 0.1088 -4.259e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02083 0.01445 0.02405 0.02795 0.9868 0.9907 0.0211 0.9696 0.982 0.03127 ] Network output: [ 0.09623 -0.2316 0.8611 -7.857e-05 3.527e-05 1.178 -5.921e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6053 0.4975 0.4147 0.5009 0.9787 0.9905 0.6067 0.9151 0.975 0.527 ] Network output: [ -0.08534 0.2224 1.157 -0.0002724 0.0001223 0.7899 -0.0002053 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2812 0.2729 0.2808 0.2865 0.9873 0.9918 0.2813 0.9711 0.9826 0.2927 ] Network output: [ -0.08939 0.2021 1.114 -0.0002561 0.000115 0.8615 -0.000193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3052 0.3035 0.2952 0.2948 0.9824 0.989 0.3052 0.9537 0.9746 0.2982 ] Network output: [ 0.0172 0.9598 -0.01811 0.0001496 -6.717e-05 1.024 0.0001128 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05708 Epoch 3311 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0699 0.8659 0.9218 0.0001179 -5.292e-05 0.07297 8.883e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01599 -0.00462 0.01038 0.02911 0.9485 0.9561 0.02687 0.8939 0.913 0.07249 ] Network output: [ 0.9583 0.09183 0.02685 0.0003424 -0.0001537 -0.03391 0.000258 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5628 0.06837 0.06646 0.3732 0.9762 0.989 0.6131 0.9075 0.9714 0.5429 ] Network output: [ 0.03382 0.8871 0.9365 -5.205e-06 2.337e-06 0.1087 -3.922e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02082 0.01444 0.02405 0.02794 0.9868 0.9907 0.0211 0.9696 0.982 0.03127 ] Network output: [ 0.09634 -0.2318 0.8607 -7.686e-05 3.45e-05 1.178 -5.792e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6053 0.4976 0.4147 0.5008 0.9787 0.9905 0.6066 0.9151 0.975 0.5269 ] Network output: [ -0.08524 0.2218 1.157 -0.0002706 0.0001215 0.7902 -0.000204 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2811 0.2729 0.2809 0.2865 0.9873 0.9918 0.2813 0.9711 0.9826 0.2927 ] Network output: [ -0.08925 0.2018 1.114 -0.0002544 0.0001142 0.8616 -0.0001917 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3051 0.3034 0.2952 0.2948 0.9824 0.989 0.3051 0.9538 0.9747 0.2982 ] Network output: [ 0.01702 0.9603 -0.01784 0.0001485 -6.669e-05 1.024 0.0001119 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.057 Epoch 3312 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06985 0.8662 0.9218 0.0001179 -5.292e-05 0.0728 8.884e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01597 -0.004621 0.01035 0.0291 0.9485 0.9561 0.02686 0.8939 0.913 0.07247 ] Network output: [ 0.9582 0.09189 0.02694 0.0003379 -0.0001517 -0.03395 0.0002547 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5627 0.0685 0.06645 0.3731 0.9762 0.989 0.613 0.9075 0.9714 0.5428 ] Network output: [ 0.03377 0.8873 0.9366 -4.761e-06 2.137e-06 0.1086 -3.588e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02081 0.01444 0.02405 0.02794 0.9868 0.9907 0.02109 0.9696 0.982 0.03128 ] Network output: [ 0.09645 -0.2319 0.8603 -7.517e-05 3.375e-05 1.178 -5.665e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6052 0.4976 0.4147 0.5006 0.9787 0.9905 0.6065 0.9152 0.975 0.5268 ] Network output: [ -0.08514 0.2212 1.157 -0.0002689 0.0001207 0.7906 -0.0002027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2811 0.2728 0.281 0.2866 0.9873 0.9918 0.2812 0.9711 0.9826 0.2928 ] Network output: [ -0.08911 0.2015 1.114 -0.0002527 0.0001135 0.8617 -0.0001905 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3049 0.3033 0.2952 0.2948 0.9824 0.989 0.305 0.9538 0.9747 0.2982 ] Network output: [ 0.01684 0.9608 -0.01756 0.0001475 -6.621e-05 1.024 0.0001111 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05693 Epoch 3313 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0698 0.8664 0.9218 0.0001179 -5.293e-05 0.07263 8.885e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01596 -0.004623 0.01033 0.02909 0.9485 0.9561 0.02684 0.8939 0.913 0.07246 ] Network output: [ 0.9582 0.09194 0.02704 0.0003334 -0.0001497 -0.034 0.0002513 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5626 0.06863 0.06645 0.3729 0.9762 0.989 0.613 0.9076 0.9715 0.5427 ] Network output: [ 0.03371 0.8875 0.9366 -4.32e-06 1.94e-06 0.1085 -3.256e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02081 0.01444 0.02405 0.02794 0.9868 0.9907 0.02108 0.9696 0.982 0.03128 ] Network output: [ 0.09656 -0.232 0.8599 -7.352e-05 3.3e-05 1.179 -5.54e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6051 0.4977 0.4147 0.5005 0.9787 0.9905 0.6064 0.9152 0.975 0.5267 ] Network output: [ -0.08503 0.2206 1.157 -0.0002672 0.00012 0.7909 -0.0002014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.281 0.2728 0.281 0.2867 0.9873 0.9918 0.2811 0.9711 0.9826 0.2929 ] Network output: [ -0.08897 0.2012 1.114 -0.000251 0.0001127 0.8617 -0.0001892 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3048 0.3032 0.2952 0.2947 0.9825 0.989 0.3049 0.9538 0.9747 0.2982 ] Network output: [ 0.01666 0.9613 -0.01729 0.0001464 -6.574e-05 1.023 0.0001104 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05685 Epoch 3314 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06975 0.8667 0.9218 0.0001179 -5.293e-05 0.07246 8.886e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01595 -0.004624 0.01031 0.02908 0.9485 0.9561 0.02683 0.894 0.913 0.07244 ] Network output: [ 0.9581 0.092 0.02714 0.000329 -0.0001477 -0.03404 0.0002479 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5625 0.06876 0.06644 0.3728 0.9762 0.989 0.6129 0.9076 0.9715 0.5426 ] Network output: [ 0.03365 0.8877 0.9366 -3.884e-06 1.743e-06 0.1084 -2.927e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0208 0.01444 0.02405 0.02793 0.9868 0.9907 0.02108 0.9697 0.982 0.03128 ] Network output: [ 0.09666 -0.2321 0.8596 -7.188e-05 3.227e-05 1.179 -5.417e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.605 0.4977 0.4147 0.5004 0.9787 0.9905 0.6064 0.9152 0.975 0.5266 ] Network output: [ -0.08493 0.22 1.158 -0.0002655 0.0001192 0.7913 -0.0002001 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.281 0.2728 0.2811 0.2867 0.9873 0.9918 0.2811 0.9711 0.9826 0.2929 ] Network output: [ -0.08883 0.2009 1.114 -0.0002494 0.000112 0.8618 -0.0001879 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3047 0.3031 0.2952 0.2947 0.9825 0.989 0.3047 0.9538 0.9747 0.2982 ] Network output: [ 0.01648 0.9618 -0.01702 0.0001454 -6.528e-05 1.023 0.0001096 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05677 Epoch 3315 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0697 0.867 0.9218 0.0001179 -5.294e-05 0.0723 8.887e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01594 -0.004626 0.01028 0.02906 0.9485 0.9561 0.02681 0.894 0.9131 0.07243 ] Network output: [ 0.9581 0.09205 0.02724 0.0003246 -0.0001457 -0.03408 0.0002446 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5624 0.06889 0.06644 0.3727 0.9762 0.9891 0.6128 0.9076 0.9715 0.5425 ] Network output: [ 0.0336 0.8879 0.9366 -3.45e-06 1.549e-06 0.1083 -2.6e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02079 0.01444 0.02405 0.02793 0.9868 0.9907 0.02107 0.9697 0.982 0.03128 ] Network output: [ 0.09677 -0.2322 0.8592 -7.027e-05 3.155e-05 1.179 -5.296e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6049 0.4978 0.4147 0.5003 0.9787 0.9905 0.6063 0.9153 0.975 0.5265 ] Network output: [ -0.08483 0.2194 1.158 -0.0002638 0.0001184 0.7916 -0.0001988 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2809 0.2727 0.2812 0.2867 0.9873 0.9918 0.281 0.9711 0.9826 0.293 ] Network output: [ -0.08869 0.2006 1.114 -0.0002477 0.0001112 0.8619 -0.0001867 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3046 0.303 0.2952 0.2947 0.9825 0.9891 0.3046 0.9538 0.9747 0.2982 ] Network output: [ 0.0163 0.9623 -0.01675 0.0001444 -6.483e-05 1.022 0.0001088 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0567 Epoch 3316 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06965 0.8673 0.9218 0.0001179 -5.294e-05 0.07213 8.888e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01592 -0.004627 0.01026 0.02905 0.9485 0.9561 0.0268 0.894 0.9131 0.07242 ] Network output: [ 0.958 0.0921 0.02733 0.0003202 -0.0001437 -0.03412 0.0002413 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5623 0.06901 0.06644 0.3725 0.9762 0.9891 0.6127 0.9077 0.9715 0.5424 ] Network output: [ 0.03354 0.8881 0.9366 -3.021e-06 1.356e-06 0.1082 -2.277e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02078 0.01443 0.02405 0.02793 0.9868 0.9907 0.02106 0.9697 0.982 0.03128 ] Network output: [ 0.09687 -0.2323 0.8588 -6.868e-05 3.083e-05 1.179 -5.176e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6048 0.4979 0.4147 0.5002 0.9787 0.9905 0.6062 0.9153 0.975 0.5264 ] Network output: [ -0.08472 0.2188 1.158 -0.0002621 0.0001177 0.792 -0.0001975 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2809 0.2727 0.2812 0.2868 0.9873 0.9918 0.281 0.9711 0.9826 0.293 ] Network output: [ -0.08855 0.2004 1.114 -0.000246 0.0001105 0.8619 -0.0001854 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3045 0.3029 0.2952 0.2947 0.9825 0.9891 0.3045 0.9539 0.9747 0.2981 ] Network output: [ 0.01612 0.9627 -0.01648 0.0001434 -6.438e-05 1.022 0.0001081 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05663 Epoch 3317 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0696 0.8675 0.9218 0.0001179 -5.295e-05 0.07197 8.888e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01591 -0.004629 0.01024 0.02904 0.9485 0.9561 0.02678 0.8941 0.9131 0.0724 ] Network output: [ 0.9579 0.09214 0.02743 0.0003158 -0.0001418 -0.03416 0.000238 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5622 0.06913 0.06644 0.3724 0.9762 0.9891 0.6126 0.9077 0.9715 0.5423 ] Network output: [ 0.03348 0.8883 0.9366 -2.596e-06 1.165e-06 0.1081 -1.956e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02078 0.01443 0.02405 0.02793 0.9868 0.9907 0.02106 0.9697 0.982 0.03128 ] Network output: [ 0.09697 -0.2324 0.8584 -6.711e-05 3.013e-05 1.18 -5.058e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6048 0.4979 0.4147 0.5001 0.9787 0.9905 0.6061 0.9153 0.9751 0.5263 ] Network output: [ -0.08462 0.2182 1.158 -0.0002604 0.0001169 0.7923 -0.0001963 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2808 0.2726 0.2813 0.2868 0.9873 0.9918 0.2809 0.9711 0.9826 0.2931 ] Network output: [ -0.08841 0.2001 1.114 -0.0002444 0.0001097 0.862 -0.0001842 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3044 0.3028 0.2952 0.2947 0.9825 0.9891 0.3044 0.9539 0.9747 0.2981 ] Network output: [ 0.01595 0.9632 -0.01621 0.0001424 -6.394e-05 1.022 0.0001073 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05655 Epoch 3318 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06954 0.8678 0.9218 0.000118 -5.295e-05 0.07181 8.889e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0159 -0.004631 0.01021 0.02902 0.9486 0.9561 0.02677 0.8941 0.9131 0.07239 ] Network output: [ 0.9579 0.09219 0.02752 0.0003115 -0.0001398 -0.0342 0.0002347 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5621 0.06926 0.06644 0.3722 0.9762 0.9891 0.6126 0.9078 0.9715 0.5422 ] Network output: [ 0.03343 0.8885 0.9366 -2.175e-06 9.765e-07 0.108 -1.639e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02077 0.01443 0.02405 0.02792 0.9868 0.9907 0.02105 0.9697 0.982 0.03128 ] Network output: [ 0.09707 -0.2324 0.858 -6.557e-05 2.944e-05 1.18 -4.941e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6047 0.498 0.4147 0.4999 0.9787 0.9905 0.6061 0.9154 0.9751 0.5262 ] Network output: [ -0.08452 0.2176 1.158 -0.0002587 0.0001162 0.7926 -0.000195 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2808 0.2726 0.2814 0.2869 0.9873 0.9918 0.2809 0.9711 0.9826 0.2932 ] Network output: [ -0.08827 0.1998 1.114 -0.0002427 0.000109 0.8621 -0.0001829 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3043 0.3027 0.2951 0.2946 0.9825 0.9891 0.3043 0.9539 0.9747 0.2981 ] Network output: [ 0.01577 0.9637 -0.01594 0.0001415 -6.35e-05 1.021 0.0001066 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05648 Epoch 3319 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06949 0.868 0.9218 0.000118 -5.296e-05 0.07165 8.89e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01589 -0.004632 0.01019 0.02901 0.9486 0.9562 0.02676 0.8941 0.9132 0.07237 ] Network output: [ 0.9578 0.09223 0.02762 0.0003072 -0.0001379 -0.03425 0.0002315 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.562 0.06938 0.06645 0.3721 0.9762 0.9891 0.6125 0.9078 0.9715 0.5421 ] Network output: [ 0.03337 0.8887 0.9366 -1.759e-06 7.897e-07 0.1079 -1.326e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02076 0.01443 0.02405 0.02792 0.9868 0.9907 0.02104 0.9697 0.982 0.03128 ] Network output: [ 0.09717 -0.2325 0.8577 -6.404e-05 2.875e-05 1.18 -4.826e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6046 0.498 0.4147 0.4998 0.9787 0.9905 0.606 0.9154 0.9751 0.5262 ] Network output: [ -0.08442 0.217 1.158 -0.0002571 0.0001154 0.793 -0.0001937 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2807 0.2726 0.2814 0.2869 0.9873 0.9918 0.2808 0.9712 0.9826 0.2932 ] Network output: [ -0.08814 0.1995 1.114 -0.0002411 0.0001082 0.8621 -0.0001817 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3042 0.3026 0.2951 0.2946 0.9825 0.9891 0.3042 0.9539 0.9747 0.2981 ] Network output: [ 0.0156 0.9641 -0.01567 0.0001405 -6.308e-05 1.021 0.0001059 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05641 Epoch 3320 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06944 0.8683 0.9218 0.000118 -5.296e-05 0.07149 8.891e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01588 -0.004634 0.01016 0.029 0.9486 0.9562 0.02674 0.8942 0.9132 0.07236 ] Network output: [ 0.9578 0.09228 0.02771 0.0003029 -0.000136 -0.03429 0.0002283 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5619 0.0695 0.06646 0.3719 0.9762 0.9891 0.6124 0.9078 0.9716 0.542 ] Network output: [ 0.03331 0.8889 0.9367 -1.347e-06 6.049e-07 0.1078 -1.015e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02076 0.01443 0.02405 0.02792 0.9868 0.9907 0.02103 0.9697 0.982 0.03128 ] Network output: [ 0.09726 -0.2326 0.8573 -6.254e-05 2.808e-05 1.181 -4.713e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6045 0.4981 0.4147 0.4997 0.9787 0.9905 0.6059 0.9154 0.9751 0.5261 ] Network output: [ -0.08432 0.2164 1.158 -0.0002554 0.0001147 0.7933 -0.0001925 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2807 0.2725 0.2815 0.287 0.9873 0.9918 0.2808 0.9712 0.9826 0.2933 ] Network output: [ -0.088 0.1992 1.114 -0.0002395 0.0001075 0.8622 -0.0001805 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3041 0.3025 0.2951 0.2946 0.9825 0.9891 0.3041 0.9539 0.9748 0.2981 ] Network output: [ 0.01542 0.9646 -0.0154 0.0001396 -6.266e-05 1.021 0.0001052 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05634 Epoch 3321 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06939 0.8686 0.9218 0.000118 -5.297e-05 0.07134 8.892e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01586 -0.004636 0.01014 0.02899 0.9486 0.9562 0.02673 0.8942 0.9132 0.07234 ] Network output: [ 0.9577 0.09232 0.0278 0.0002987 -0.0001341 -0.03433 0.0002251 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5618 0.06962 0.06647 0.3718 0.9762 0.9891 0.6123 0.9079 0.9716 0.5419 ] Network output: [ 0.03326 0.8891 0.9367 -9.407e-07 4.223e-07 0.1077 -7.09e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02075 0.01442 0.02405 0.02791 0.9868 0.9907 0.02103 0.9698 0.982 0.03128 ] Network output: [ 0.09735 -0.2327 0.8569 -6.106e-05 2.741e-05 1.181 -4.602e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6045 0.4982 0.4147 0.4996 0.9787 0.9905 0.6058 0.9155 0.9751 0.526 ] Network output: [ -0.08421 0.2158 1.158 -0.0002537 0.0001139 0.7936 -0.0001912 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2806 0.2725 0.2815 0.287 0.9873 0.9918 0.2807 0.9712 0.9827 0.2933 ] Network output: [ -0.08786 0.199 1.114 -0.0002378 0.0001068 0.8622 -0.0001792 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.304 0.3024 0.2951 0.2946 0.9825 0.9891 0.304 0.954 0.9748 0.298 ] Network output: [ 0.01525 0.9651 -0.01513 0.0001386 -6.224e-05 1.02 0.0001045 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05627 Epoch 3322 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06934 0.8688 0.9218 0.000118 -5.298e-05 0.07118 8.893e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01585 -0.004638 0.01012 0.02897 0.9486 0.9562 0.02672 0.8942 0.9133 0.07233 ] Network output: [ 0.9577 0.09236 0.02789 0.0002944 -0.0001322 -0.03437 0.0002219 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5617 0.06973 0.06648 0.3717 0.9762 0.9891 0.6122 0.9079 0.9716 0.5418 ] Network output: [ 0.0332 0.8893 0.9367 -5.39e-07 2.42e-07 0.1077 -4.062e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02074 0.01442 0.02405 0.02791 0.9869 0.9908 0.02102 0.9698 0.9821 0.03128 ] Network output: [ 0.09745 -0.2328 0.8566 -5.96e-05 2.676e-05 1.181 -4.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6044 0.4982 0.4147 0.4995 0.9787 0.9905 0.6058 0.9155 0.9751 0.5259 ] Network output: [ -0.08411 0.2153 1.158 -0.0002521 0.0001132 0.7939 -0.00019 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2806 0.2724 0.2816 0.2871 0.9873 0.9918 0.2807 0.9712 0.9827 0.2934 ] Network output: [ -0.08773 0.1987 1.114 -0.0002362 0.000106 0.8623 -0.000178 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3039 0.3023 0.2951 0.2945 0.9825 0.9891 0.3039 0.954 0.9748 0.298 ] Network output: [ 0.01508 0.9655 -0.01486 0.0001377 -6.183e-05 1.02 0.0001038 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0562 Epoch 3323 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06929 0.8691 0.9218 0.000118 -5.298e-05 0.07103 8.894e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01584 -0.00464 0.01009 0.02896 0.9486 0.9562 0.0267 0.8943 0.9133 0.07231 ] Network output: [ 0.9576 0.09239 0.02799 0.0002902 -0.0001303 -0.03441 0.0002187 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5616 0.06985 0.06649 0.3715 0.9762 0.9891 0.6122 0.908 0.9716 0.5417 ] Network output: [ 0.03314 0.8895 0.9367 -1.424e-07 6.391e-08 0.1076 -1.073e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02074 0.01442 0.02405 0.02791 0.9869 0.9908 0.02101 0.9698 0.9821 0.03128 ] Network output: [ 0.09754 -0.2328 0.8562 -5.816e-05 2.611e-05 1.181 -4.383e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6043 0.4983 0.4147 0.4993 0.9787 0.9905 0.6057 0.9155 0.9751 0.5258 ] Network output: [ -0.08401 0.2147 1.158 -0.0002505 0.0001124 0.7943 -0.0001888 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2805 0.2724 0.2817 0.2871 0.9873 0.9918 0.2806 0.9712 0.9827 0.2934 ] Network output: [ -0.08759 0.1984 1.113 -0.0002346 0.0001053 0.8623 -0.0001768 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3037 0.3022 0.2951 0.2945 0.9825 0.9891 0.3038 0.954 0.9748 0.298 ] Network output: [ 0.01491 0.966 -0.0146 0.0001368 -6.143e-05 1.019 0.0001031 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05613 Epoch 3324 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06924 0.8693 0.9218 0.000118 -5.299e-05 0.07087 8.895e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01583 -0.004642 0.01007 0.02895 0.9486 0.9562 0.02669 0.8943 0.9133 0.0723 ] Network output: [ 0.9576 0.09243 0.02808 0.0002861 -0.0001284 -0.03445 0.0002156 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5615 0.06997 0.06651 0.3714 0.9762 0.9891 0.6121 0.908 0.9716 0.5416 ] Network output: [ 0.03309 0.8896 0.9367 2.491e-07 -1.118e-07 0.1075 1.877e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02073 0.01442 0.02405 0.0279 0.9869 0.9908 0.02101 0.9698 0.9821 0.03127 ] Network output: [ 0.09762 -0.2329 0.8558 -5.674e-05 2.547e-05 1.182 -4.276e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6043 0.4983 0.4147 0.4992 0.9788 0.9905 0.6056 0.9156 0.9751 0.5257 ] Network output: [ -0.08391 0.2141 1.158 -0.0002488 0.0001117 0.7946 -0.0001875 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2805 0.2724 0.2817 0.2871 0.9873 0.9918 0.2806 0.9712 0.9827 0.2935 ] Network output: [ -0.08746 0.1982 1.113 -0.000233 0.0001046 0.8624 -0.0001756 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3036 0.302 0.295 0.2945 0.9825 0.9891 0.3037 0.954 0.9748 0.298 ] Network output: [ 0.01474 0.9664 -0.01433 0.0001359 -6.103e-05 1.019 0.0001025 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05606 Epoch 3325 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06919 0.8696 0.9218 0.000118 -5.299e-05 0.07072 8.896e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01581 -0.004644 0.01004 0.02893 0.9486 0.9562 0.02667 0.8944 0.9133 0.07228 ] Network output: [ 0.9575 0.09246 0.02817 0.000282 -0.0001266 -0.03449 0.0002125 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5614 0.07008 0.06653 0.3712 0.9763 0.9891 0.612 0.908 0.9716 0.5415 ] Network output: [ 0.03303 0.8898 0.9367 6.351e-07 -2.851e-07 0.1074 4.787e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02072 0.01442 0.02405 0.0279 0.9869 0.9908 0.021 0.9698 0.9821 0.03127 ] Network output: [ 0.09771 -0.233 0.8555 -5.534e-05 2.484e-05 1.182 -4.17e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6042 0.4984 0.4147 0.4991 0.9788 0.9905 0.6056 0.9156 0.9752 0.5256 ] Network output: [ -0.08381 0.2136 1.158 -0.0002472 0.000111 0.7949 -0.0001863 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2804 0.2723 0.2818 0.2872 0.9873 0.9918 0.2805 0.9712 0.9827 0.2935 ] Network output: [ -0.08732 0.1979 1.113 -0.0002314 0.0001039 0.8624 -0.0001744 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3035 0.3019 0.295 0.2945 0.9825 0.9891 0.3035 0.954 0.9748 0.298 ] Network output: [ 0.01457 0.9668 -0.01407 0.0001351 -6.064e-05 1.019 0.0001018 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05599 Epoch 3326 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06915 0.8698 0.9218 0.000118 -5.3e-05 0.07057 8.897e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0158 -0.004646 0.01002 0.02892 0.9487 0.9562 0.02666 0.8944 0.9134 0.07227 ] Network output: [ 0.9575 0.0925 0.02826 0.0002779 -0.0001247 -0.03453 0.0002094 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5613 0.07019 0.06655 0.3711 0.9763 0.9891 0.612 0.9081 0.9717 0.5414 ] Network output: [ 0.03298 0.89 0.9367 1.016e-06 -4.56e-07 0.1073 7.655e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02072 0.01441 0.02405 0.02789 0.9869 0.9908 0.02099 0.9698 0.9821 0.03127 ] Network output: [ 0.0978 -0.233 0.8551 -5.395e-05 2.422e-05 1.182 -4.066e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6041 0.4985 0.4147 0.499 0.9788 0.9905 0.6055 0.9157 0.9752 0.5255 ] Network output: [ -0.08371 0.213 1.158 -0.0002456 0.0001103 0.7952 -0.0001851 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2804 0.2723 0.2818 0.2872 0.9873 0.9918 0.2805 0.9712 0.9827 0.2936 ] Network output: [ -0.08719 0.1976 1.113 -0.0002298 0.0001032 0.8625 -0.0001732 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3034 0.3018 0.295 0.2944 0.9825 0.9891 0.3034 0.9541 0.9748 0.2979 ] Network output: [ 0.0144 0.9673 -0.0138 0.0001342 -6.025e-05 1.018 0.0001011 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05592 Epoch 3327 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0691 0.8701 0.9218 0.0001181 -5.3e-05 0.07043 8.897e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01579 -0.004648 0.009997 0.02891 0.9487 0.9562 0.02665 0.8944 0.9134 0.07225 ] Network output: [ 0.9574 0.09253 0.02834 0.0002738 -0.0001229 -0.03457 0.0002064 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5612 0.07031 0.06657 0.3709 0.9763 0.9891 0.6119 0.9081 0.9717 0.5413 ] Network output: [ 0.03292 0.8902 0.9367 1.391e-06 -6.244e-07 0.1072 1.048e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02071 0.01441 0.02405 0.02789 0.9869 0.9908 0.02099 0.9698 0.9821 0.03127 ] Network output: [ 0.09788 -0.2331 0.8547 -5.259e-05 2.361e-05 1.182 -3.963e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.604 0.4985 0.4147 0.4989 0.9788 0.9905 0.6054 0.9157 0.9752 0.5254 ] Network output: [ -0.08361 0.2125 1.158 -0.000244 0.0001095 0.7955 -0.0001839 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2803 0.2722 0.2819 0.2873 0.9873 0.9918 0.2804 0.9713 0.9827 0.2936 ] Network output: [ -0.08705 0.1974 1.113 -0.0002282 0.0001025 0.8625 -0.000172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3033 0.3017 0.295 0.2944 0.9825 0.9891 0.3033 0.9541 0.9748 0.2979 ] Network output: [ 0.01424 0.9677 -0.01354 0.0001334 -5.987e-05 1.018 0.0001005 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05586 Epoch 3328 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06905 0.8703 0.9218 0.0001181 -5.301e-05 0.07028 8.898e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01578 -0.00465 0.009973 0.02889 0.9487 0.9563 0.02663 0.8945 0.9134 0.07223 ] Network output: [ 0.9574 0.09256 0.02843 0.0002698 -0.0001211 -0.03461 0.0002033 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5611 0.07042 0.06659 0.3708 0.9763 0.9891 0.6118 0.9081 0.9717 0.5412 ] Network output: [ 0.03286 0.8904 0.9367 1.76e-06 -7.902e-07 0.1071 1.327e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0207 0.01441 0.02405 0.02789 0.9869 0.9908 0.02098 0.9699 0.9821 0.03127 ] Network output: [ 0.09796 -0.2332 0.8544 -5.124e-05 2.3e-05 1.183 -3.862e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.604 0.4986 0.4147 0.4987 0.9788 0.9905 0.6054 0.9157 0.9752 0.5253 ] Network output: [ -0.08351 0.2119 1.158 -0.0002424 0.0001088 0.7958 -0.0001827 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2803 0.2722 0.282 0.2873 0.9873 0.9918 0.2804 0.9713 0.9827 0.2937 ] Network output: [ -0.08692 0.1971 1.113 -0.0002267 0.0001018 0.8626 -0.0001708 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3032 0.3016 0.295 0.2944 0.9825 0.9891 0.3032 0.9541 0.9749 0.2979 ] Network output: [ 0.01407 0.9681 -0.01327 0.0001325 -5.949e-05 1.018 9.986e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05579 Epoch 3329 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.069 0.8706 0.9218 0.0001181 -5.301e-05 0.07014 8.899e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01577 -0.004652 0.009949 0.02888 0.9487 0.9563 0.02662 0.8945 0.9134 0.07222 ] Network output: [ 0.9573 0.09259 0.02852 0.0002658 -0.0001193 -0.03465 0.0002003 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5611 0.07053 0.06661 0.3706 0.9763 0.9891 0.6117 0.9082 0.9717 0.5411 ] Network output: [ 0.03281 0.8906 0.9367 2.124e-06 -9.535e-07 0.1071 1.601e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0207 0.01441 0.02405 0.02788 0.9869 0.9908 0.02098 0.9699 0.9821 0.03127 ] Network output: [ 0.09805 -0.2332 0.854 -4.991e-05 2.241e-05 1.183 -3.761e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6039 0.4986 0.4148 0.4986 0.9788 0.9905 0.6053 0.9158 0.9752 0.5252 ] Network output: [ -0.08341 0.2114 1.158 -0.0002408 0.0001081 0.7962 -0.0001815 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2802 0.2722 0.282 0.2873 0.9873 0.9918 0.2803 0.9713 0.9827 0.2937 ] Network output: [ -0.08679 0.1968 1.113 -0.0002251 0.0001011 0.8626 -0.0001697 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3031 0.3015 0.295 0.2943 0.9825 0.9891 0.3031 0.9541 0.9749 0.2979 ] Network output: [ 0.01391 0.9686 -0.01301 0.0001317 -5.911e-05 1.017 9.924e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05573 Epoch 3330 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06895 0.8708 0.9218 0.0001181 -5.301e-05 0.06999 8.899e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01575 -0.004654 0.009925 0.02887 0.9487 0.9563 0.02661 0.8945 0.9135 0.0722 ] Network output: [ 0.9573 0.09261 0.02861 0.0002618 -0.0001175 -0.03469 0.0001973 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.561 0.07064 0.06664 0.3705 0.9763 0.9891 0.6117 0.9082 0.9717 0.541 ] Network output: [ 0.03275 0.8908 0.9368 2.482e-06 -1.114e-06 0.107 1.87e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02069 0.01441 0.02405 0.02788 0.9869 0.9908 0.02097 0.9699 0.9821 0.03127 ] Network output: [ 0.09812 -0.2333 0.8537 -4.86e-05 2.182e-05 1.183 -3.663e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6038 0.4987 0.4148 0.4985 0.9788 0.9905 0.6052 0.9158 0.9752 0.5251 ] Network output: [ -0.08331 0.2108 1.158 -0.0002393 0.0001074 0.7965 -0.0001803 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2802 0.2721 0.2821 0.2874 0.9873 0.9918 0.2803 0.9713 0.9827 0.2938 ] Network output: [ -0.08666 0.1966 1.113 -0.0002236 0.0001004 0.8627 -0.0001685 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.303 0.3014 0.2949 0.2943 0.9825 0.9891 0.303 0.9542 0.9749 0.2979 ] Network output: [ 0.01375 0.969 -0.01275 0.0001309 -5.875e-05 1.017 9.862e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05566 Epoch 3331 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0689 0.8711 0.9218 0.0001181 -5.302e-05 0.06985 8.9e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01574 -0.004656 0.009901 0.02885 0.9487 0.9563 0.02659 0.8946 0.9135 0.07219 ] Network output: [ 0.9572 0.09264 0.02869 0.0002579 -0.0001158 -0.03472 0.0001944 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5609 0.07075 0.06666 0.3703 0.9763 0.9891 0.6116 0.9083 0.9717 0.5409 ] Network output: [ 0.03269 0.891 0.9368 2.834e-06 -1.272e-06 0.1069 2.136e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02068 0.0144 0.02405 0.02787 0.9869 0.9908 0.02096 0.9699 0.9821 0.03127 ] Network output: [ 0.0982 -0.2333 0.8533 -4.731e-05 2.124e-05 1.183 -3.565e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6038 0.4988 0.4148 0.4984 0.9788 0.9905 0.6052 0.9158 0.9752 0.525 ] Network output: [ -0.08321 0.2103 1.158 -0.0002377 0.0001067 0.7968 -0.0001791 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2801 0.2721 0.2821 0.2874 0.9873 0.9918 0.2802 0.9713 0.9827 0.2938 ] Network output: [ -0.08653 0.1963 1.113 -0.000222 9.968e-05 0.8627 -0.0001673 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3029 0.3013 0.2949 0.2943 0.9825 0.9891 0.3029 0.9542 0.9749 0.2978 ] Network output: [ 0.01359 0.9694 -0.01249 0.00013 -5.838e-05 1.016 9.8e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0556 Epoch 3332 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06885 0.8713 0.9218 0.0001181 -5.302e-05 0.06971 8.901e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01573 -0.004659 0.009877 0.02884 0.9487 0.9563 0.02658 0.8946 0.9135 0.07217 ] Network output: [ 0.9572 0.09266 0.02878 0.000254 -0.000114 -0.03476 0.0001914 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5608 0.07086 0.06669 0.3702 0.9763 0.9891 0.6115 0.9083 0.9717 0.5408 ] Network output: [ 0.03264 0.8911 0.9368 3.18e-06 -1.428e-06 0.1068 2.396e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02068 0.0144 0.02405 0.02787 0.9869 0.9908 0.02096 0.9699 0.9821 0.03127 ] Network output: [ 0.09828 -0.2334 0.853 -4.603e-05 2.066e-05 1.184 -3.469e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6037 0.4988 0.4148 0.4983 0.9788 0.9905 0.6051 0.9159 0.9753 0.5249 ] Network output: [ -0.08311 0.2097 1.158 -0.0002362 0.000106 0.7971 -0.000178 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2801 0.272 0.2822 0.2875 0.9873 0.9918 0.2802 0.9713 0.9827 0.2939 ] Network output: [ -0.08639 0.1961 1.113 -0.0002205 9.9e-05 0.8628 -0.0001662 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3028 0.3012 0.2949 0.2943 0.9825 0.9891 0.3028 0.9542 0.9749 0.2978 ] Network output: [ 0.01342 0.9698 -0.01222 0.0001292 -5.802e-05 1.016 9.74e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05553 Epoch 3333 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0688 0.8715 0.9218 0.0001181 -5.302e-05 0.06957 8.901e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01572 -0.004661 0.009854 0.02882 0.9487 0.9563 0.02657 0.8946 0.9135 0.07216 ] Network output: [ 0.9571 0.09269 0.02886 0.0002502 -0.0001123 -0.0348 0.0001885 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5607 0.07096 0.06672 0.3701 0.9763 0.9891 0.6115 0.9083 0.9718 0.5407 ] Network output: [ 0.03258 0.8913 0.9368 3.52e-06 -1.58e-06 0.1067 2.653e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02067 0.0144 0.02405 0.02787 0.9869 0.9908 0.02095 0.9699 0.9821 0.03127 ] Network output: [ 0.09835 -0.2334 0.8526 -4.477e-05 2.01e-05 1.184 -3.374e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6037 0.4989 0.4148 0.4981 0.9788 0.9905 0.605 0.9159 0.9753 0.5249 ] Network output: [ -0.08301 0.2092 1.158 -0.0002346 0.0001053 0.7974 -0.0001768 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.28 0.272 0.2822 0.2875 0.9873 0.9918 0.2801 0.9713 0.9827 0.2939 ] Network output: [ -0.08626 0.1958 1.113 -0.000219 9.831e-05 0.8628 -0.000165 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3027 0.3011 0.2949 0.2942 0.9826 0.9891 0.3027 0.9542 0.9749 0.2978 ] Network output: [ 0.01326 0.9702 -0.01196 0.0001284 -5.766e-05 1.016 9.68e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05547 Epoch 3334 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06876 0.8718 0.9218 0.0001181 -5.303e-05 0.06943 8.902e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0157 -0.004663 0.00983 0.02881 0.9488 0.9563 0.02655 0.8947 0.9136 0.07214 ] Network output: [ 0.9571 0.09271 0.02895 0.0002464 -0.0001106 -0.03484 0.0001857 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5606 0.07107 0.06675 0.3699 0.9763 0.9891 0.6114 0.9084 0.9718 0.5406 ] Network output: [ 0.03252 0.8915 0.9368 3.854e-06 -1.73e-06 0.1067 2.905e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02066 0.0144 0.02405 0.02786 0.9869 0.9908 0.02094 0.9699 0.9822 0.03126 ] Network output: [ 0.09843 -0.2334 0.8522 -4.352e-05 1.954e-05 1.184 -3.28e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6036 0.4989 0.4148 0.498 0.9788 0.9906 0.605 0.916 0.9753 0.5248 ] Network output: [ -0.08291 0.2087 1.159 -0.0002331 0.0001046 0.7977 -0.0001757 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.28 0.272 0.2823 0.2875 0.9873 0.9918 0.2801 0.9713 0.9827 0.294 ] Network output: [ -0.08613 0.1956 1.113 -0.0002175 9.764e-05 0.8628 -0.0001639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3025 0.301 0.2949 0.2942 0.9826 0.9891 0.3026 0.9542 0.9749 0.2978 ] Network output: [ 0.01311 0.9706 -0.0117 0.0001277 -5.731e-05 1.015 9.621e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05541 Epoch 3335 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06871 0.872 0.9218 0.0001181 -5.303e-05 0.06929 8.902e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01569 -0.004666 0.009806 0.0288 0.9488 0.9563 0.02654 0.8947 0.9136 0.07212 ] Network output: [ 0.9571 0.09273 0.02903 0.0002426 -0.0001089 -0.03488 0.0001828 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5605 0.07118 0.06678 0.3698 0.9763 0.9891 0.6113 0.9084 0.9718 0.5405 ] Network output: [ 0.03247 0.8917 0.9368 4.182e-06 -1.878e-06 0.1066 3.152e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02066 0.0144 0.02405 0.02786 0.9869 0.9908 0.02094 0.97 0.9822 0.03126 ] Network output: [ 0.0985 -0.2335 0.8519 -4.229e-05 1.899e-05 1.184 -3.187e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6035 0.499 0.4148 0.4979 0.9788 0.9906 0.6049 0.916 0.9753 0.5247 ] Network output: [ -0.08281 0.2081 1.159 -0.0002316 0.000104 0.798 -0.0001745 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2799 0.2719 0.2824 0.2876 0.9873 0.9918 0.28 0.9713 0.9828 0.294 ] Network output: [ -0.086 0.1953 1.113 -0.000216 9.696e-05 0.8629 -0.0001628 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3024 0.3009 0.2948 0.2942 0.9826 0.9891 0.3025 0.9543 0.975 0.2977 ] Network output: [ 0.01295 0.971 -0.01145 0.0001269 -5.696e-05 1.015 9.562e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05535 Epoch 3336 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06866 0.8723 0.9217 0.0001181 -5.303e-05 0.06915 8.902e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01568 -0.004668 0.009782 0.02878 0.9488 0.9564 0.02653 0.8948 0.9136 0.07211 ] Network output: [ 0.957 0.09275 0.02912 0.0002388 -0.0001072 -0.03492 0.00018 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5604 0.07128 0.06681 0.3696 0.9763 0.9891 0.6113 0.9085 0.9718 0.5404 ] Network output: [ 0.03241 0.8919 0.9368 4.504e-06 -2.022e-06 0.1065 3.394e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02065 0.0144 0.02405 0.02785 0.9869 0.9908 0.02093 0.97 0.9822 0.03126 ] Network output: [ 0.09857 -0.2335 0.8516 -4.108e-05 1.844e-05 1.185 -3.096e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6035 0.4991 0.4149 0.4978 0.9788 0.9906 0.6048 0.916 0.9753 0.5246 ] Network output: [ -0.08271 0.2076 1.159 -0.0002301 0.0001033 0.7983 -0.0001734 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2799 0.2719 0.2824 0.2876 0.9874 0.9918 0.28 0.9714 0.9828 0.2941 ] Network output: [ -0.08588 0.1951 1.113 -0.0002145 9.63e-05 0.8629 -0.0001617 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3023 0.3008 0.2948 0.2941 0.9826 0.9891 0.3023 0.9543 0.975 0.2977 ] Network output: [ 0.01279 0.9714 -0.01119 0.0001261 -5.662e-05 1.015 9.504e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05528 Epoch 3337 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06861 0.8725 0.9217 0.0001181 -5.303e-05 0.06902 8.902e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01567 -0.004671 0.009758 0.02877 0.9488 0.9564 0.02651 0.8948 0.9137 0.07209 ] Network output: [ 0.957 0.09276 0.0292 0.0002351 -0.0001055 -0.03496 0.0001772 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5603 0.07139 0.06685 0.3695 0.9763 0.9892 0.6112 0.9085 0.9718 0.5403 ] Network output: [ 0.03236 0.8921 0.9368 4.82e-06 -2.164e-06 0.1064 3.632e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02065 0.01439 0.02405 0.02785 0.9869 0.9908 0.02093 0.97 0.9822 0.03126 ] Network output: [ 0.09864 -0.2336 0.8512 -3.988e-05 1.79e-05 1.185 -3.005e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6034 0.4991 0.4149 0.4977 0.9788 0.9906 0.6048 0.9161 0.9753 0.5245 ] Network output: [ -0.08261 0.2071 1.159 -0.0002286 0.0001026 0.7986 -0.0001723 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2798 0.2718 0.2825 0.2876 0.9874 0.9918 0.2799 0.9714 0.9828 0.2941 ] Network output: [ -0.08575 0.1948 1.113 -0.000213 9.563e-05 0.863 -0.0001605 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3022 0.3007 0.2948 0.2941 0.9826 0.9891 0.3022 0.9543 0.975 0.2977 ] Network output: [ 0.01264 0.9718 -0.01093 0.0001254 -5.627e-05 1.014 9.447e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05522 Epoch 3338 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06857 0.8727 0.9217 0.0001181 -5.303e-05 0.06888 8.903e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01566 -0.004673 0.009734 0.02876 0.9488 0.9564 0.0265 0.8948 0.9137 0.07207 ] Network output: [ 0.9569 0.09278 0.02928 0.0002314 -0.0001039 -0.035 0.0001744 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5603 0.07149 0.06688 0.3693 0.9763 0.9892 0.6112 0.9085 0.9718 0.5402 ] Network output: [ 0.0323 0.8922 0.9368 5.129e-06 -2.303e-06 0.1064 3.866e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02064 0.01439 0.02405 0.02784 0.9869 0.9908 0.02092 0.97 0.9822 0.03126 ] Network output: [ 0.09871 -0.2336 0.8509 -3.87e-05 1.737e-05 1.185 -2.916e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6033 0.4992 0.4149 0.4975 0.9788 0.9906 0.6047 0.9161 0.9753 0.5244 ] Network output: [ -0.08252 0.2066 1.159 -0.0002271 0.0001019 0.7989 -0.0001711 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2798 0.2718 0.2825 0.2877 0.9874 0.9918 0.2799 0.9714 0.9828 0.2941 ] Network output: [ -0.08562 0.1945 1.113 -0.0002116 9.498e-05 0.863 -0.0001594 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3021 0.3006 0.2948 0.2941 0.9826 0.9891 0.3021 0.9543 0.975 0.2977 ] Network output: [ 0.01248 0.9722 -0.01068 0.0001246 -5.594e-05 1.014 9.39e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05516 Epoch 3339 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06852 0.873 0.9217 0.0001181 -5.303e-05 0.06875 8.903e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01565 -0.004676 0.00971 0.02874 0.9488 0.9564 0.02649 0.8949 0.9137 0.07206 ] Network output: [ 0.9569 0.0928 0.02937 0.0002278 -0.0001023 -0.03504 0.0001717 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5602 0.07159 0.06691 0.3692 0.9764 0.9892 0.6111 0.9086 0.9719 0.5401 ] Network output: [ 0.03224 0.8924 0.9368 5.433e-06 -2.439e-06 0.1063 4.094e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02063 0.01439 0.02405 0.02784 0.9869 0.9908 0.02091 0.97 0.9822 0.03126 ] Network output: [ 0.09878 -0.2336 0.8505 -3.753e-05 1.685e-05 1.185 -2.828e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6033 0.4993 0.4149 0.4974 0.9788 0.9906 0.6047 0.9161 0.9754 0.5243 ] Network output: [ -0.08242 0.2061 1.159 -0.0002256 0.0001013 0.7992 -0.00017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2797 0.2718 0.2826 0.2877 0.9874 0.9918 0.2798 0.9714 0.9828 0.2942 ] Network output: [ -0.08549 0.1943 1.113 -0.0002101 9.432e-05 0.863 -0.0001583 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.302 0.3005 0.2948 0.2941 0.9826 0.9892 0.302 0.9543 0.975 0.2976 ] Network output: [ 0.01233 0.9726 -0.01042 0.0001239 -5.56e-05 1.014 9.334e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0551 Epoch 3340 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06847 0.8732 0.9217 0.0001181 -5.303e-05 0.06862 8.903e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01563 -0.004678 0.009686 0.02873 0.9488 0.9564 0.02647 0.8949 0.9137 0.07204 ] Network output: [ 0.9569 0.09281 0.02945 0.0002242 -0.0001006 -0.03508 0.0001689 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5601 0.07169 0.06695 0.369 0.9764 0.9892 0.611 0.9086 0.9719 0.54 ] Network output: [ 0.03219 0.8926 0.9368 5.73e-06 -2.572e-06 0.1062 4.318e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02063 0.01439 0.02405 0.02783 0.9869 0.9908 0.02091 0.97 0.9822 0.03125 ] Network output: [ 0.09884 -0.2336 0.8502 -3.638e-05 1.633e-05 1.186 -2.741e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6032 0.4993 0.415 0.4973 0.9789 0.9906 0.6046 0.9162 0.9754 0.5242 ] Network output: [ -0.08232 0.2055 1.159 -0.0002241 0.0001006 0.7994 -0.0001689 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2797 0.2717 0.2826 0.2877 0.9874 0.9918 0.2798 0.9714 0.9828 0.2942 ] Network output: [ -0.08536 0.194 1.113 -0.0002087 9.367e-05 0.8631 -0.0001572 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3019 0.3003 0.2947 0.294 0.9826 0.9892 0.3019 0.9544 0.975 0.2976 ] Network output: [ 0.01217 0.973 -0.01017 0.0001231 -5.527e-05 1.013 9.278e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05504 Epoch 3341 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06842 0.8734 0.9217 0.0001181 -5.303e-05 0.06849 8.903e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01562 -0.004681 0.009661 0.02871 0.9488 0.9564 0.02646 0.8949 0.9138 0.07202 ] Network output: [ 0.9568 0.09282 0.02953 0.0002206 -9.904e-05 -0.03512 0.0001663 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.56 0.0718 0.06699 0.3689 0.9764 0.9892 0.611 0.9087 0.9719 0.5399 ] Network output: [ 0.03213 0.8928 0.9369 6.021e-06 -2.703e-06 0.1061 4.538e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02062 0.01439 0.02405 0.02783 0.9869 0.9908 0.0209 0.9701 0.9822 0.03125 ] Network output: [ 0.09891 -0.2337 0.8498 -3.524e-05 1.582e-05 1.186 -2.655e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6032 0.4994 0.415 0.4972 0.9789 0.9906 0.6045 0.9162 0.9754 0.5241 ] Network output: [ -0.08222 0.205 1.159 -0.0002227 9.996e-05 0.7997 -0.0001678 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2796 0.2717 0.2827 0.2878 0.9874 0.9918 0.2797 0.9714 0.9828 0.2943 ] Network output: [ -0.08524 0.1938 1.113 -0.0002072 9.303e-05 0.8631 -0.0001562 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3018 0.3002 0.2947 0.294 0.9826 0.9892 0.3018 0.9544 0.975 0.2976 ] Network output: [ 0.01202 0.9734 -0.009913 0.0001224 -5.494e-05 1.013 9.223e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05498 Epoch 3342 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06838 0.8736 0.9217 0.0001181 -5.303e-05 0.06836 8.903e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01561 -0.004683 0.009637 0.0287 0.9489 0.9564 0.02645 0.895 0.9138 0.07201 ] Network output: [ 0.9568 0.09283 0.02961 0.0002171 -9.745e-05 -0.03516 0.0001636 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5599 0.0719 0.06702 0.3687 0.9764 0.9892 0.6109 0.9087 0.9719 0.5398 ] Network output: [ 0.03207 0.8929 0.9369 6.306e-06 -2.831e-06 0.1061 4.752e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02062 0.01438 0.02405 0.02782 0.9869 0.9908 0.0209 0.9701 0.9822 0.03125 ] Network output: [ 0.09897 -0.2337 0.8495 -3.411e-05 1.531e-05 1.186 -2.571e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6031 0.4994 0.415 0.4971 0.9789 0.9906 0.6045 0.9163 0.9754 0.524 ] Network output: [ -0.08213 0.2045 1.159 -0.0002212 9.932e-05 0.8 -0.0001667 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2796 0.2717 0.2827 0.2878 0.9874 0.9918 0.2797 0.9714 0.9828 0.2943 ] Network output: [ -0.08511 0.1936 1.113 -0.0002058 9.239e-05 0.8631 -0.0001551 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3017 0.3001 0.2947 0.294 0.9826 0.9892 0.3017 0.9544 0.975 0.2976 ] Network output: [ 0.01187 0.9738 -0.009661 0.0001217 -5.462e-05 1.013 9.168e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05492 Epoch 3343 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06833 0.8739 0.9217 0.0001181 -5.303e-05 0.06823 8.902e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0156 -0.004686 0.009613 0.02869 0.9489 0.9564 0.02644 0.895 0.9138 0.07199 ] Network output: [ 0.9568 0.09285 0.02969 0.0002136 -9.588e-05 -0.0352 0.000161 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5598 0.072 0.06706 0.3686 0.9764 0.9892 0.6109 0.9087 0.9719 0.5397 ] Network output: [ 0.03202 0.8931 0.9369 6.584e-06 -2.956e-06 0.106 4.962e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02061 0.01438 0.02405 0.02782 0.9869 0.9908 0.02089 0.9701 0.9822 0.03125 ] Network output: [ 0.09903 -0.2337 0.8492 -3.3e-05 1.481e-05 1.186 -2.487e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.603 0.4995 0.415 0.4969 0.9789 0.9906 0.6044 0.9163 0.9754 0.5239 ] Network output: [ -0.08203 0.204 1.159 -0.0002198 9.867e-05 0.8003 -0.0001656 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2795 0.2716 0.2828 0.2878 0.9874 0.9919 0.2796 0.9715 0.9828 0.2944 ] Network output: [ -0.08499 0.1933 1.113 -0.0002044 9.175e-05 0.8632 -0.000154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3016 0.3 0.2947 0.2939 0.9826 0.9892 0.3016 0.9544 0.9751 0.2975 ] Network output: [ 0.01172 0.9741 -0.009409 0.0001209 -5.429e-05 1.012 9.114e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05487 Epoch 3344 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06828 0.8741 0.9217 0.0001181 -5.303e-05 0.0681 8.902e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01559 -0.004689 0.009589 0.02867 0.9489 0.9565 0.02642 0.8951 0.9139 0.07197 ] Network output: [ 0.9567 0.09285 0.02977 0.0002101 -9.432e-05 -0.03524 0.0001583 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5597 0.0721 0.0671 0.3685 0.9764 0.9892 0.6108 0.9088 0.9719 0.5396 ] Network output: [ 0.03196 0.8933 0.9369 6.857e-06 -3.078e-06 0.1059 5.167e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0206 0.01438 0.02405 0.02781 0.9869 0.9908 0.02089 0.9701 0.9822 0.03124 ] Network output: [ 0.09909 -0.2337 0.8488 -3.19e-05 1.432e-05 1.187 -2.404e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.603 0.4996 0.4151 0.4968 0.9789 0.9906 0.6044 0.9163 0.9754 0.5238 ] Network output: [ -0.08193 0.2035 1.159 -0.0002184 9.803e-05 0.8006 -0.0001646 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2795 0.2716 0.2828 0.2878 0.9874 0.9919 0.2796 0.9715 0.9828 0.2944 ] Network output: [ -0.08486 0.1931 1.113 -0.000203 9.112e-05 0.8632 -0.000153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3015 0.2999 0.2947 0.2939 0.9826 0.9892 0.3015 0.9545 0.9751 0.2975 ] Network output: [ 0.01157 0.9745 -0.009158 0.0001202 -5.397e-05 1.012 9.061e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05481 Epoch 3345 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06823 0.8743 0.9217 0.0001181 -5.303e-05 0.06797 8.902e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01557 -0.004691 0.009565 0.02866 0.9489 0.9565 0.02641 0.8951 0.9139 0.07196 ] Network output: [ 0.9567 0.09286 0.02985 0.0002067 -9.278e-05 -0.03527 0.0001558 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5597 0.0722 0.06714 0.3683 0.9764 0.9892 0.6107 0.9088 0.9719 0.5395 ] Network output: [ 0.03191 0.8935 0.9369 7.123e-06 -3.198e-06 0.1059 5.368e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0206 0.01438 0.02405 0.02781 0.9869 0.9908 0.02088 0.9701 0.9823 0.03124 ] Network output: [ 0.09915 -0.2337 0.8485 -3.082e-05 1.383e-05 1.187 -2.322e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6029 0.4996 0.4151 0.4967 0.9789 0.9906 0.6043 0.9164 0.9754 0.5237 ] Network output: [ -0.08183 0.203 1.159 -0.0002169 9.74e-05 0.8009 -0.0001635 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2794 0.2716 0.2829 0.2879 0.9874 0.9919 0.2795 0.9715 0.9828 0.2944 ] Network output: [ -0.08474 0.1928 1.113 -0.0002016 9.05e-05 0.8632 -0.0001519 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3014 0.2998 0.2946 0.2939 0.9826 0.9892 0.3014 0.9545 0.9751 0.2975 ] Network output: [ 0.01142 0.9749 -0.008909 0.0001195 -5.366e-05 1.012 9.007e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05475 Epoch 3346 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06819 0.8745 0.9217 0.0001181 -5.303e-05 0.06785 8.901e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01556 -0.004694 0.00954 0.02864 0.9489 0.9565 0.0264 0.8951 0.9139 0.07194 ] Network output: [ 0.9567 0.09287 0.02993 0.0002033 -9.126e-05 -0.03531 0.0001532 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5596 0.07229 0.06718 0.3682 0.9764 0.9892 0.6107 0.9089 0.972 0.5394 ] Network output: [ 0.03185 0.8936 0.9369 7.383e-06 -3.314e-06 0.1058 5.564e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02059 0.01438 0.02405 0.0278 0.987 0.9908 0.02088 0.9701 0.9823 0.03124 ] Network output: [ 0.09921 -0.2338 0.8482 -2.975e-05 1.335e-05 1.187 -2.242e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6029 0.4997 0.4151 0.4966 0.9789 0.9906 0.6043 0.9164 0.9754 0.5236 ] Network output: [ -0.08174 0.2025 1.159 -0.0002155 9.677e-05 0.8011 -0.0001624 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2794 0.2715 0.2829 0.2879 0.9874 0.9919 0.2795 0.9715 0.9828 0.2945 ] Network output: [ -0.08461 0.1926 1.113 -0.0002002 8.988e-05 0.8633 -0.0001509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3012 0.2997 0.2946 0.2938 0.9826 0.9892 0.3013 0.9545 0.9751 0.2975 ] Network output: [ 0.01128 0.9752 -0.00866 0.0001188 -5.334e-05 1.011 8.955e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05469 Epoch 3347 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06814 0.8748 0.9217 0.0001181 -5.302e-05 0.06772 8.901e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01555 -0.004697 0.009516 0.02863 0.9489 0.9565 0.02639 0.8952 0.9139 0.07192 ] Network output: [ 0.9566 0.09288 0.03001 0.0001999 -8.975e-05 -0.03535 0.0001507 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5595 0.07239 0.06722 0.368 0.9764 0.9892 0.6106 0.9089 0.972 0.5393 ] Network output: [ 0.03179 0.8938 0.9369 7.636e-06 -3.428e-06 0.1057 5.755e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02059 0.01438 0.02405 0.0278 0.987 0.9908 0.02087 0.9701 0.9823 0.03124 ] Network output: [ 0.09927 -0.2338 0.8478 -2.869e-05 1.288e-05 1.187 -2.162e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6028 0.4998 0.4151 0.4964 0.9789 0.9906 0.6042 0.9165 0.9755 0.5235 ] Network output: [ -0.08164 0.202 1.159 -0.0002142 9.614e-05 0.8014 -0.0001614 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2793 0.2715 0.283 0.2879 0.9874 0.9919 0.2795 0.9715 0.9829 0.2945 ] Network output: [ -0.08449 0.1923 1.113 -0.0001988 8.926e-05 0.8633 -0.0001498 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3011 0.2996 0.2946 0.2938 0.9826 0.9892 0.3012 0.9545 0.9751 0.2974 ] Network output: [ 0.01113 0.9756 -0.008412 0.0001181 -5.303e-05 1.011 8.903e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05464 Epoch 3348 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06809 0.875 0.9217 0.0001181 -5.302e-05 0.0676 8.9e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01554 -0.004699 0.009492 0.02862 0.9489 0.9565 0.02637 0.8952 0.914 0.0719 ] Network output: [ 0.9566 0.09288 0.03009 0.0001966 -8.826e-05 -0.03539 0.0001482 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5594 0.07249 0.06727 0.3679 0.9764 0.9892 0.6106 0.909 0.972 0.5392 ] Network output: [ 0.03174 0.894 0.9369 7.884e-06 -3.539e-06 0.1057 5.942e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02058 0.01438 0.02405 0.02779 0.987 0.9908 0.02086 0.9702 0.9823 0.03123 ] Network output: [ 0.09933 -0.2338 0.8475 -2.764e-05 1.241e-05 1.187 -2.083e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6028 0.4998 0.4152 0.4963 0.9789 0.9906 0.6042 0.9165 0.9755 0.5234 ] Network output: [ -0.08154 0.2016 1.159 -0.0002128 9.552e-05 0.8017 -0.0001604 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2793 0.2715 0.283 0.288 0.9874 0.9919 0.2794 0.9715 0.9829 0.2945 ] Network output: [ -0.08436 0.1921 1.113 -0.0001975 8.865e-05 0.8633 -0.0001488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.301 0.2995 0.2946 0.2938 0.9826 0.9892 0.3011 0.9545 0.9751 0.2974 ] Network output: [ 0.01099 0.976 -0.008165 0.0001174 -5.272e-05 1.011 8.851e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05458 Epoch 3349 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06805 0.8752 0.9217 0.0001181 -5.302e-05 0.06747 8.9e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01553 -0.004702 0.009467 0.0286 0.949 0.9565 0.02636 0.8952 0.914 0.07189 ] Network output: [ 0.9566 0.09288 0.03017 0.0001933 -8.678e-05 -0.03543 0.0001457 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5593 0.07259 0.06731 0.3677 0.9764 0.9892 0.6105 0.909 0.972 0.5391 ] Network output: [ 0.03168 0.8941 0.9369 8.125e-06 -3.648e-06 0.1056 6.124e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02058 0.01437 0.02404 0.02779 0.987 0.9908 0.02086 0.9702 0.9823 0.03123 ] Network output: [ 0.09938 -0.2338 0.8472 -2.661e-05 1.195e-05 1.188 -2.005e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6027 0.4999 0.4152 0.4962 0.9789 0.9906 0.6041 0.9165 0.9755 0.5234 ] Network output: [ -0.08145 0.2011 1.159 -0.0002114 9.491e-05 0.802 -0.0001593 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2793 0.2714 0.2831 0.288 0.9874 0.9919 0.2794 0.9715 0.9829 0.2946 ] Network output: [ -0.08424 0.1918 1.112 -0.0001961 8.804e-05 0.8633 -0.0001478 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3009 0.2994 0.2945 0.2937 0.9826 0.9892 0.3009 0.9546 0.9751 0.2974 ] Network output: [ 0.01084 0.9763 -0.007918 0.0001168 -5.242e-05 1.01 8.799e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05453 Epoch 3350 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.068 0.8754 0.9217 0.0001181 -5.301e-05 0.06735 8.899e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01552 -0.004705 0.009443 0.02859 0.949 0.9565 0.02635 0.8953 0.914 0.07187 ] Network output: [ 0.9566 0.09289 0.03025 0.0001901 -8.532e-05 -0.03547 0.0001432 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5592 0.07268 0.06735 0.3676 0.9764 0.9892 0.6105 0.909 0.972 0.539 ] Network output: [ 0.03163 0.8943 0.9369 8.361e-06 -3.753e-06 0.1056 6.301e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02057 0.01437 0.02404 0.02778 0.987 0.9909 0.02085 0.9702 0.9823 0.03123 ] Network output: [ 0.09944 -0.2338 0.8469 -2.559e-05 1.149e-05 1.188 -1.928e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6027 0.5 0.4152 0.4961 0.9789 0.9906 0.6041 0.9166 0.9755 0.5233 ] Network output: [ -0.08135 0.2006 1.159 -0.00021 9.429e-05 0.8022 -0.0001583 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2792 0.2714 0.2831 0.288 0.9874 0.9919 0.2793 0.9715 0.9829 0.2946 ] Network output: [ -0.08412 0.1916 1.112 -0.0001948 8.744e-05 0.8634 -0.0001468 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3008 0.2993 0.2945 0.2937 0.9826 0.9892 0.3008 0.9546 0.9751 0.2974 ] Network output: [ 0.0107 0.9767 -0.007673 0.0001161 -5.211e-05 1.01 8.748e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05447 Epoch 3351 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06796 0.8756 0.9217 0.0001181 -5.301e-05 0.06723 8.898e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0155 -0.004708 0.009419 0.02857 0.949 0.9565 0.02634 0.8953 0.9141 0.07185 ] Network output: [ 0.9565 0.09289 0.03032 0.0001868 -8.388e-05 -0.03551 0.0001408 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5592 0.07278 0.06739 0.3674 0.9764 0.9892 0.6104 0.9091 0.972 0.5389 ] Network output: [ 0.03157 0.8945 0.9369 8.59e-06 -3.856e-06 0.1055 6.474e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02057 0.01437 0.02404 0.02777 0.987 0.9909 0.02085 0.9702 0.9823 0.03123 ] Network output: [ 0.09949 -0.2338 0.8465 -2.458e-05 1.104e-05 1.188 -1.853e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6026 0.5 0.4152 0.496 0.9789 0.9906 0.604 0.9166 0.9755 0.5232 ] Network output: [ -0.08126 0.2001 1.159 -0.0002087 9.369e-05 0.8025 -0.0001573 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2792 0.2714 0.2832 0.288 0.9874 0.9919 0.2793 0.9716 0.9829 0.2947 ] Network output: [ -0.084 0.1914 1.112 -0.0001934 8.684e-05 0.8634 -0.0001458 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3007 0.2992 0.2945 0.2937 0.9826 0.9892 0.3007 0.9546 0.9752 0.2973 ] Network output: [ 0.01056 0.977 -0.007429 0.0001154 -5.181e-05 1.01 8.698e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05441 Epoch 3352 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06791 0.8758 0.9217 0.0001181 -5.3e-05 0.06711 8.898e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01549 -0.004711 0.009394 0.02856 0.949 0.9566 0.02632 0.8954 0.9141 0.07183 ] Network output: [ 0.9565 0.09289 0.0304 0.0001837 -8.245e-05 -0.03555 0.0001384 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5591 0.07288 0.06744 0.3673 0.9764 0.9892 0.6103 0.9091 0.9721 0.5388 ] Network output: [ 0.03151 0.8946 0.9369 8.813e-06 -3.956e-06 0.1054 6.642e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02056 0.01437 0.02404 0.02777 0.987 0.9909 0.02084 0.9702 0.9823 0.03122 ] Network output: [ 0.09954 -0.2338 0.8462 -2.358e-05 1.059e-05 1.188 -1.777e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6026 0.5001 0.4153 0.4958 0.9789 0.9906 0.6039 0.9166 0.9755 0.5231 ] Network output: [ -0.08116 0.1997 1.159 -0.0002074 9.309e-05 0.8028 -0.0001563 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2791 0.2713 0.2832 0.2881 0.9874 0.9919 0.2792 0.9716 0.9829 0.2947 ] Network output: [ -0.08388 0.1911 1.112 -0.0001921 8.625e-05 0.8634 -0.0001448 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3006 0.2991 0.2945 0.2936 0.9827 0.9892 0.3006 0.9546 0.9752 0.2973 ] Network output: [ 0.01041 0.9774 -0.007186 0.0001147 -5.151e-05 1.009 8.648e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05436 Epoch 3353 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06786 0.8761 0.9217 0.000118 -5.3e-05 0.06699 8.897e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01548 -0.004714 0.00937 0.02855 0.949 0.9566 0.02631 0.8954 0.9141 0.07182 ] Network output: [ 0.9565 0.09289 0.03048 0.0001805 -8.104e-05 -0.03559 0.000136 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.559 0.07297 0.06748 0.3671 0.9765 0.9892 0.6103 0.9092 0.9721 0.5387 ] Network output: [ 0.03146 0.8948 0.9369 9.03e-06 -4.054e-06 0.1054 6.805e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02056 0.01437 0.02404 0.02776 0.987 0.9909 0.02084 0.9702 0.9823 0.03122 ] Network output: [ 0.09959 -0.2338 0.8459 -2.26e-05 1.015e-05 1.189 -1.703e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6025 0.5002 0.4153 0.4957 0.9789 0.9906 0.6039 0.9167 0.9755 0.523 ] Network output: [ -0.08106 0.1992 1.159 -0.000206 9.249e-05 0.803 -0.0001553 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2791 0.2713 0.2833 0.2881 0.9874 0.9919 0.2792 0.9716 0.9829 0.2947 ] Network output: [ -0.08376 0.1909 1.112 -0.0001908 8.566e-05 0.8634 -0.0001438 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3005 0.299 0.2945 0.2936 0.9827 0.9892 0.3005 0.9546 0.9752 0.2973 ] Network output: [ 0.01027 0.9777 -0.006943 0.0001141 -5.122e-05 1.009 8.598e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05431 Epoch 3354 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06782 0.8763 0.9217 0.000118 -5.299e-05 0.06687 8.896e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01547 -0.004717 0.009345 0.02853 0.949 0.9566 0.0263 0.8954 0.9141 0.0718 ] Network output: [ 0.9565 0.09289 0.03056 0.0001774 -7.964e-05 -0.03563 0.0001337 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5589 0.07307 0.06753 0.367 0.9765 0.9892 0.6102 0.9092 0.9721 0.5386 ] Network output: [ 0.0314 0.895 0.937 9.241e-06 -4.149e-06 0.1053 6.964e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02055 0.01437 0.02404 0.02776 0.987 0.9909 0.02083 0.9703 0.9823 0.03122 ] Network output: [ 0.09964 -0.2338 0.8456 -2.163e-05 9.71e-06 1.189 -1.63e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6025 0.5002 0.4153 0.4956 0.9789 0.9906 0.6038 0.9167 0.9756 0.5229 ] Network output: [ -0.08097 0.1987 1.159 -0.0002047 9.19e-05 0.8033 -0.0001543 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.279 0.2713 0.2833 0.2881 0.9874 0.9919 0.2791 0.9716 0.9829 0.2948 ] Network output: [ -0.08364 0.1906 1.112 -0.0001895 8.508e-05 0.8635 -0.0001428 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3004 0.2989 0.2944 0.2936 0.9827 0.9892 0.3004 0.9547 0.9752 0.2973 ] Network output: [ 0.01013 0.9781 -0.006702 0.0001134 -5.092e-05 1.009 8.548e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05425 Epoch 3355 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06777 0.8765 0.9217 0.000118 -5.298e-05 0.06675 8.895e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01546 -0.00472 0.009321 0.02852 0.949 0.9566 0.02629 0.8955 0.9142 0.07178 ] Network output: [ 0.9564 0.09289 0.03063 0.0001743 -7.826e-05 -0.03567 0.0001314 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5588 0.07316 0.06757 0.3668 0.9765 0.9892 0.6102 0.9092 0.9721 0.5385 ] Network output: [ 0.03134 0.8951 0.937 9.446e-06 -4.241e-06 0.1053 7.119e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02055 0.01437 0.02404 0.02775 0.987 0.9909 0.02083 0.9703 0.9823 0.03121 ] Network output: [ 0.09969 -0.2338 0.8453 -2.067e-05 9.278e-06 1.189 -1.558e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6024 0.5003 0.4154 0.4955 0.9789 0.9906 0.6038 0.9168 0.9756 0.5228 ] Network output: [ -0.08087 0.1982 1.159 -0.0002034 9.131e-05 0.8036 -0.0001533 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.279 0.2712 0.2834 0.2881 0.9874 0.9919 0.2791 0.9716 0.9829 0.2948 ] Network output: [ -0.08352 0.1904 1.112 -0.0001882 8.45e-05 0.8635 -0.0001419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3003 0.2988 0.2944 0.2936 0.9827 0.9892 0.3003 0.9547 0.9752 0.2972 ] Network output: [ 0.009995 0.9784 -0.006462 0.0001128 -5.063e-05 1.009 8.499e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0542 Epoch 3356 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06773 0.8767 0.9217 0.000118 -5.298e-05 0.06664 8.893e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01545 -0.004723 0.009296 0.0285 0.9491 0.9566 0.02627 0.8955 0.9142 0.07176 ] Network output: [ 0.9564 0.09288 0.03071 0.0001713 -7.689e-05 -0.0357 0.0001291 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5588 0.07325 0.06762 0.3667 0.9765 0.9892 0.6101 0.9093 0.9721 0.5384 ] Network output: [ 0.03129 0.8953 0.937 9.645e-06 -4.33e-06 0.1052 7.269e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02054 0.01436 0.02404 0.02774 0.987 0.9909 0.02082 0.9703 0.9824 0.03121 ] Network output: [ 0.09973 -0.2338 0.8449 -1.972e-05 8.852e-06 1.189 -1.486e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6023 0.5004 0.4154 0.4954 0.979 0.9906 0.6037 0.9168 0.9756 0.5227 ] Network output: [ -0.08078 0.1978 1.159 -0.0002021 9.073e-05 0.8038 -0.0001523 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.279 0.2712 0.2834 0.2882 0.9874 0.9919 0.2791 0.9716 0.9829 0.2948 ] Network output: [ -0.0834 0.1902 1.112 -0.0001869 8.393e-05 0.8635 -0.0001409 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3002 0.2987 0.2944 0.2935 0.9827 0.9892 0.3002 0.9547 0.9752 0.2972 ] Network output: [ 0.009858 0.9787 -0.006222 0.0001121 -5.034e-05 1.008 8.451e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05415 Epoch 3357 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06768 0.8769 0.9217 0.000118 -5.297e-05 0.06652 8.892e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01543 -0.004726 0.009271 0.02849 0.9491 0.9566 0.02626 0.8956 0.9142 0.07174 ] Network output: [ 0.9564 0.09288 0.03078 0.0001683 -7.554e-05 -0.03574 0.0001268 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5587 0.07335 0.06766 0.3666 0.9765 0.9892 0.6101 0.9093 0.9721 0.5383 ] Network output: [ 0.03123 0.8955 0.937 9.839e-06 -4.417e-06 0.1052 7.415e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02054 0.01436 0.02404 0.02774 0.987 0.9909 0.02082 0.9703 0.9824 0.03121 ] Network output: [ 0.09978 -0.2338 0.8446 -1.878e-05 8.431e-06 1.189 -1.415e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6023 0.5004 0.4154 0.4952 0.979 0.9906 0.6037 0.9168 0.9756 0.5226 ] Network output: [ -0.08069 0.1973 1.159 -0.0002008 9.015e-05 0.8041 -0.0001513 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2789 0.2712 0.2834 0.2882 0.9874 0.9919 0.279 0.9716 0.9829 0.2949 ] Network output: [ -0.08328 0.1899 1.112 -0.0001857 8.336e-05 0.8635 -0.0001399 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3001 0.2986 0.2944 0.2935 0.9827 0.9892 0.3001 0.9547 0.9752 0.2972 ] Network output: [ 0.009721 0.9791 -0.005984 0.0001115 -5.005e-05 1.008 8.402e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05409 Epoch 3358 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06764 0.8771 0.9217 0.000118 -5.296e-05 0.0664 8.891e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01542 -0.004729 0.009247 0.02848 0.9491 0.9566 0.02625 0.8956 0.9143 0.07173 ] Network output: [ 0.9564 0.09287 0.03086 0.0001653 -7.421e-05 -0.03578 0.0001246 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5586 0.07344 0.06771 0.3664 0.9765 0.9893 0.61 0.9094 0.9722 0.5382 ] Network output: [ 0.03118 0.8956 0.937 1.003e-05 -4.501e-06 0.1051 7.556e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02053 0.01436 0.02403 0.02773 0.987 0.9909 0.02081 0.9703 0.9824 0.0312 ] Network output: [ 0.09983 -0.2338 0.8443 -1.785e-05 8.015e-06 1.19 -1.345e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6022 0.5005 0.4155 0.4951 0.979 0.9906 0.6036 0.9169 0.9756 0.5225 ] Network output: [ -0.08059 0.1969 1.159 -0.0001995 8.958e-05 0.8044 -0.0001504 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2789 0.2711 0.2835 0.2882 0.9874 0.9919 0.279 0.9717 0.9829 0.2949 ] Network output: [ -0.08316 0.1897 1.112 -0.0001844 8.279e-05 0.8636 -0.000139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.3 0.2985 0.2943 0.2935 0.9827 0.9892 0.3 0.9548 0.9753 0.2972 ] Network output: [ 0.009585 0.9794 -0.005746 0.0001109 -4.977e-05 1.008 8.354e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05404 Epoch 3359 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06759 0.8773 0.9217 0.000118 -5.295e-05 0.06629 8.89e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01541 -0.004732 0.009222 0.02846 0.9491 0.9566 0.02624 0.8956 0.9143 0.07171 ] Network output: [ 0.9563 0.09287 0.03093 0.0001624 -7.289e-05 -0.03582 0.0001224 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5585 0.07353 0.06776 0.3663 0.9765 0.9893 0.61 0.9094 0.9722 0.5381 ] Network output: [ 0.03112 0.8958 0.937 1.021e-05 -4.583e-06 0.105 7.693e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02053 0.01436 0.02403 0.02773 0.987 0.9909 0.02081 0.9703 0.9824 0.0312 ] Network output: [ 0.09987 -0.2337 0.844 -1.694e-05 7.603e-06 1.19 -1.276e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6022 0.5006 0.4155 0.495 0.979 0.9907 0.6036 0.9169 0.9756 0.5224 ] Network output: [ -0.0805 0.1964 1.159 -0.0001983 8.901e-05 0.8046 -0.0001494 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2788 0.2711 0.2835 0.2882 0.9874 0.9919 0.2789 0.9717 0.983 0.2949 ] Network output: [ -0.08304 0.1895 1.112 -0.0001832 8.223e-05 0.8636 -0.000138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2999 0.2984 0.2943 0.2934 0.9827 0.9892 0.2999 0.9548 0.9753 0.2971 ] Network output: [ 0.00945 0.9797 -0.00551 0.0001102 -4.948e-05 1.007 8.307e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05399 Epoch 3360 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06754 0.8775 0.9217 0.0001179 -5.295e-05 0.06618 8.888e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0154 -0.004735 0.009197 0.02845 0.9491 0.9567 0.02622 0.8957 0.9143 0.07169 ] Network output: [ 0.9563 0.09286 0.03101 0.0001594 -7.158e-05 -0.03586 0.0001202 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5584 0.07363 0.0678 0.3661 0.9765 0.9893 0.6099 0.9094 0.9722 0.538 ] Network output: [ 0.03106 0.8959 0.937 1.038e-05 -4.662e-06 0.105 7.825e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02052 0.01436 0.02403 0.02772 0.987 0.9909 0.0208 0.9704 0.9824 0.0312 ] Network output: [ 0.09991 -0.2337 0.8437 -1.603e-05 7.197e-06 1.19 -1.208e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6021 0.5006 0.4155 0.4949 0.979 0.9907 0.6036 0.917 0.9756 0.5223 ] Network output: [ -0.0804 0.196 1.159 -0.000197 8.845e-05 0.8049 -0.0001485 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2788 0.2711 0.2836 0.2883 0.9874 0.9919 0.2789 0.9717 0.983 0.295 ] Network output: [ -0.08292 0.1892 1.112 -0.0001819 8.168e-05 0.8636 -0.0001371 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2998 0.2983 0.2943 0.2934 0.9827 0.9892 0.2998 0.9548 0.9753 0.2971 ] Network output: [ 0.009316 0.9801 -0.005274 0.0001096 -4.92e-05 1.007 8.259e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05394 Epoch 3361 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0675 0.8777 0.9217 0.0001179 -5.294e-05 0.06606 8.887e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01539 -0.004738 0.009172 0.02843 0.9491 0.9567 0.02621 0.8957 0.9144 0.07167 ] Network output: [ 0.9563 0.09285 0.03108 0.0001566 -7.029e-05 -0.0359 0.000118 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5584 0.07372 0.06785 0.366 0.9765 0.9893 0.6099 0.9095 0.9722 0.5379 ] Network output: [ 0.03101 0.8961 0.937 1.055e-05 -4.738e-06 0.1049 7.954e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02052 0.01436 0.02403 0.02771 0.987 0.9909 0.0208 0.9704 0.9824 0.03119 ] Network output: [ 0.09996 -0.2337 0.8434 -1.514e-05 6.795e-06 1.19 -1.141e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6021 0.5007 0.4156 0.4948 0.979 0.9907 0.6035 0.917 0.9757 0.5222 ] Network output: [ -0.08031 0.1955 1.159 -0.0001958 8.789e-05 0.8051 -0.0001475 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2788 0.2711 0.2836 0.2883 0.9874 0.9919 0.2789 0.9717 0.983 0.295 ] Network output: [ -0.08281 0.189 1.112 -0.0001807 8.113e-05 0.8636 -0.0001362 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2997 0.2982 0.2943 0.2934 0.9827 0.9892 0.2997 0.9548 0.9753 0.2971 ] Network output: [ 0.009182 0.9804 -0.00504 0.000109 -4.892e-05 1.007 8.212e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05389 Epoch 3362 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06745 0.8779 0.9217 0.0001179 -5.293e-05 0.06595 8.885e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01538 -0.004741 0.009148 0.02842 0.9491 0.9567 0.0262 0.8957 0.9144 0.07165 ] Network output: [ 0.9563 0.09284 0.03116 0.0001537 -6.902e-05 -0.03594 0.0001159 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5583 0.07381 0.0679 0.3658 0.9765 0.9893 0.6098 0.9095 0.9722 0.5378 ] Network output: [ 0.03095 0.8963 0.937 1.072e-05 -4.812e-06 0.1049 8.078e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02051 0.01436 0.02403 0.02771 0.987 0.9909 0.02079 0.9704 0.9824 0.03119 ] Network output: [ 0.1 -0.2337 0.8431 -1.425e-05 6.398e-06 1.191 -1.074e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6021 0.5008 0.4156 0.4946 0.979 0.9907 0.6035 0.917 0.9757 0.5221 ] Network output: [ -0.08021 0.1951 1.159 -0.0001945 8.734e-05 0.8054 -0.0001466 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2787 0.271 0.2837 0.2883 0.9874 0.9919 0.2788 0.9717 0.983 0.295 ] Network output: [ -0.08269 0.1888 1.112 -0.0001795 8.058e-05 0.8637 -0.0001353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2996 0.2981 0.2942 0.2933 0.9827 0.9893 0.2996 0.9548 0.9753 0.297 ] Network output: [ 0.00905 0.9807 -0.004806 0.0001084 -4.864e-05 1.006 8.166e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05383 Epoch 3363 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06741 0.8781 0.9217 0.0001179 -5.292e-05 0.06584 8.883e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01537 -0.004744 0.009123 0.0284 0.9492 0.9567 0.02619 0.8958 0.9144 0.07163 ] Network output: [ 0.9563 0.09283 0.03123 0.0001509 -6.775e-05 -0.03598 0.0001137 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5582 0.0739 0.06795 0.3657 0.9765 0.9893 0.6098 0.9096 0.9722 0.5377 ] Network output: [ 0.03089 0.8964 0.937 1.088e-05 -4.883e-06 0.1048 8.197e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02051 0.01436 0.02403 0.0277 0.987 0.9909 0.02079 0.9704 0.9824 0.03118 ] Network output: [ 0.1 -0.2337 0.8428 -1.338e-05 6.005e-06 1.191 -1.008e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.602 0.5008 0.4156 0.4945 0.979 0.9907 0.6034 0.9171 0.9757 0.522 ] Network output: [ -0.08012 0.1946 1.159 -0.0001933 8.679e-05 0.8056 -0.0001457 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2787 0.271 0.2837 0.2883 0.9874 0.9919 0.2788 0.9717 0.983 0.2951 ] Network output: [ -0.08257 0.1885 1.112 -0.0001783 8.004e-05 0.8637 -0.0001344 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2995 0.298 0.2942 0.2933 0.9827 0.9893 0.2995 0.9549 0.9753 0.297 ] Network output: [ 0.008919 0.981 -0.004573 0.0001077 -4.837e-05 1.006 8.119e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05378 Epoch 3364 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06736 0.8783 0.9217 0.0001179 -5.291e-05 0.06573 8.882e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01536 -0.004747 0.009098 0.02839 0.9492 0.9567 0.02618 0.8958 0.9144 0.07161 ] Network output: [ 0.9562 0.09282 0.0313 0.0001481 -6.651e-05 -0.03602 0.0001116 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5581 0.07399 0.068 0.3655 0.9765 0.9893 0.6097 0.9096 0.9723 0.5376 ] Network output: [ 0.03084 0.8966 0.937 1.103e-05 -4.952e-06 0.1048 8.313e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0205 0.01435 0.02403 0.02769 0.987 0.9909 0.02079 0.9704 0.9824 0.03118 ] Network output: [ 0.1001 -0.2337 0.8425 -1.251e-05 5.618e-06 1.191 -9.43e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.602 0.5009 0.4157 0.4944 0.979 0.9907 0.6034 0.9171 0.9757 0.5219 ] Network output: [ -0.08003 0.1942 1.159 -0.0001921 8.624e-05 0.8059 -0.0001448 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2786 0.271 0.2838 0.2883 0.9875 0.9919 0.2788 0.9717 0.983 0.2951 ] Network output: [ -0.08246 0.1883 1.112 -0.0001771 7.95e-05 0.8637 -0.0001335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2993 0.2979 0.2942 0.2933 0.9827 0.9893 0.2994 0.9549 0.9753 0.297 ] Network output: [ 0.008788 0.9813 -0.004342 0.0001071 -4.809e-05 1.006 8.073e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05373 Epoch 3365 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06732 0.8785 0.9217 0.0001178 -5.29e-05 0.06562 8.88e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01534 -0.00475 0.009073 0.02837 0.9492 0.9567 0.02616 0.8959 0.9145 0.0716 ] Network output: [ 0.9562 0.09281 0.03137 0.0001454 -6.528e-05 -0.03606 0.0001096 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5581 0.07408 0.06804 0.3654 0.9765 0.9893 0.6097 0.9097 0.9723 0.5375 ] Network output: [ 0.03078 0.8967 0.937 1.118e-05 -5.018e-06 0.1047 8.424e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0205 0.01435 0.02402 0.02769 0.987 0.9909 0.02078 0.9704 0.9824 0.03118 ] Network output: [ 0.1001 -0.2336 0.8422 -1.166e-05 5.234e-06 1.191 -8.787e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6019 0.501 0.4157 0.4943 0.979 0.9907 0.6033 0.9172 0.9757 0.5219 ] Network output: [ -0.07993 0.1938 1.159 -0.0001909 8.57e-05 0.8061 -0.0001439 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2786 0.2709 0.2838 0.2884 0.9875 0.9919 0.2787 0.9717 0.983 0.2951 ] Network output: [ -0.08234 0.1881 1.112 -0.0001759 7.897e-05 0.8637 -0.0001326 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2992 0.2978 0.2942 0.2932 0.9827 0.9893 0.2993 0.9549 0.9754 0.297 ] Network output: [ 0.008658 0.9816 -0.004111 0.0001065 -4.782e-05 1.006 8.027e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05368 Epoch 3366 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06727 0.8787 0.9217 0.0001178 -5.289e-05 0.06551 8.878e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01533 -0.004753 0.009048 0.02836 0.9492 0.9567 0.02615 0.8959 0.9145 0.07158 ] Network output: [ 0.9562 0.0928 0.03145 0.0001427 -6.406e-05 -0.03609 0.0001075 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.558 0.07417 0.06809 0.3653 0.9766 0.9893 0.6096 0.9097 0.9723 0.5374 ] Network output: [ 0.03073 0.8969 0.937 1.132e-05 -5.082e-06 0.1047 8.532e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02049 0.01435 0.02402 0.02768 0.987 0.9909 0.02078 0.9705 0.9824 0.03117 ] Network output: [ 0.1002 -0.2336 0.8419 -1.082e-05 4.856e-06 1.191 -8.151e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6019 0.501 0.4157 0.4941 0.979 0.9907 0.6033 0.9172 0.9757 0.5218 ] Network output: [ -0.07984 0.1933 1.159 -0.0001897 8.517e-05 0.8064 -0.000143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2786 0.2709 0.2838 0.2884 0.9875 0.9919 0.2787 0.9718 0.983 0.2952 ] Network output: [ -0.08223 0.1878 1.112 -0.0001747 7.845e-05 0.8637 -0.0001317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2991 0.2977 0.2942 0.2932 0.9827 0.9893 0.2992 0.9549 0.9754 0.2969 ] Network output: [ 0.008529 0.982 -0.003881 0.0001059 -4.755e-05 1.005 7.982e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05363 Epoch 3367 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06723 0.8789 0.9217 0.0001178 -5.288e-05 0.0654 8.876e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01532 -0.004757 0.009023 0.02835 0.9492 0.9567 0.02614 0.8959 0.9145 0.07156 ] Network output: [ 0.9562 0.09279 0.03152 0.00014 -6.286e-05 -0.03613 0.0001055 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5579 0.07426 0.06814 0.3651 0.9766 0.9893 0.6096 0.9097 0.9723 0.5373 ] Network output: [ 0.03067 0.897 0.937 1.146e-05 -5.144e-06 0.1046 8.635e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02049 0.01435 0.02402 0.02767 0.9871 0.9909 0.02077 0.9705 0.9825 0.03117 ] Network output: [ 0.1002 -0.2336 0.8416 -9.982e-06 4.481e-06 1.192 -7.522e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6018 0.5011 0.4158 0.494 0.979 0.9907 0.6032 0.9172 0.9758 0.5217 ] Network output: [ -0.07975 0.1929 1.159 -0.0001885 8.464e-05 0.8066 -0.0001421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2785 0.2709 0.2839 0.2884 0.9875 0.9919 0.2786 0.9718 0.983 0.2952 ] Network output: [ -0.08211 0.1876 1.112 -0.0001736 7.792e-05 0.8637 -0.0001308 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.299 0.2976 0.2941 0.2932 0.9827 0.9893 0.2991 0.955 0.9754 0.2969 ] Network output: [ 0.008401 0.9823 -0.003653 0.0001053 -4.728e-05 1.005 7.937e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05358 Epoch 3368 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06719 0.8791 0.9217 0.0001178 -5.286e-05 0.0653 8.874e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01531 -0.00476 0.008998 0.02833 0.9492 0.9568 0.02613 0.896 0.9146 0.07154 ] Network output: [ 0.9562 0.09277 0.03159 0.0001374 -6.167e-05 -0.03617 0.0001035 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5578 0.07435 0.06819 0.365 0.9766 0.9893 0.6095 0.9098 0.9723 0.5372 ] Network output: [ 0.03061 0.8972 0.937 1.159e-05 -5.203e-06 0.1046 8.734e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02048 0.01435 0.02402 0.02767 0.9871 0.9909 0.02077 0.9705 0.9825 0.03116 ] Network output: [ 0.1002 -0.2336 0.8413 -9.157e-06 4.111e-06 1.192 -6.901e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6018 0.5012 0.4158 0.4939 0.979 0.9907 0.6032 0.9173 0.9758 0.5216 ] Network output: [ -0.07966 0.1925 1.159 -0.0001874 8.411e-05 0.8069 -0.0001412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2785 0.2709 0.2839 0.2884 0.9875 0.9919 0.2786 0.9718 0.983 0.2952 ] Network output: [ -0.082 0.1874 1.112 -0.0001724 7.74e-05 0.8638 -0.0001299 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2989 0.2975 0.2941 0.2931 0.9827 0.9893 0.299 0.955 0.9754 0.2969 ] Network output: [ 0.008274 0.9826 -0.003425 0.0001047 -4.701e-05 1.005 7.892e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05354 Epoch 3369 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06714 0.8793 0.9217 0.0001177 -5.285e-05 0.06519 8.872e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0153 -0.004763 0.008973 0.02832 0.9492 0.9568 0.02612 0.896 0.9146 0.07152 ] Network output: [ 0.9562 0.09276 0.03166 0.0001348 -6.049e-05 -0.03621 0.0001016 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5578 0.07444 0.06824 0.3648 0.9766 0.9893 0.6095 0.9098 0.9723 0.5371 ] Network output: [ 0.03056 0.8974 0.9371 1.172e-05 -5.259e-06 0.1045 8.829e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02048 0.01435 0.02402 0.02766 0.9871 0.9909 0.02076 0.9705 0.9825 0.03116 ] Network output: [ 0.1003 -0.2335 0.841 -8.343e-06 3.745e-06 1.192 -6.287e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6017 0.5012 0.4158 0.4938 0.979 0.9907 0.6031 0.9173 0.9758 0.5215 ] Network output: [ -0.07956 0.1921 1.159 -0.0001862 8.359e-05 0.8071 -0.0001403 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2785 0.2708 0.284 0.2884 0.9875 0.9919 0.2786 0.9718 0.983 0.2953 ] Network output: [ -0.08189 0.1872 1.112 -0.0001713 7.689e-05 0.8638 -0.0001291 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2988 0.2974 0.2941 0.2931 0.9828 0.9893 0.2989 0.955 0.9754 0.2969 ] Network output: [ 0.008148 0.9829 -0.003198 0.0001041 -4.675e-05 1.004 7.847e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05349 Epoch 3370 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0671 0.8795 0.9217 0.0001177 -5.284e-05 0.06508 8.87e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01529 -0.004766 0.008948 0.0283 0.9493 0.9568 0.02611 0.8961 0.9146 0.0715 ] Network output: [ 0.9562 0.09274 0.03173 0.0001322 -5.933e-05 -0.03625 9.961e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5577 0.07453 0.06829 0.3647 0.9766 0.9893 0.6094 0.9099 0.9724 0.537 ] Network output: [ 0.0305 0.8975 0.9371 1.184e-05 -5.314e-06 0.1045 8.92e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02047 0.01435 0.02401 0.02765 0.9871 0.9909 0.02076 0.9705 0.9825 0.03115 ] Network output: [ 0.1003 -0.2335 0.8407 -7.537e-06 3.384e-06 1.192 -5.68e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6017 0.5013 0.4159 0.4937 0.979 0.9907 0.6031 0.9174 0.9758 0.5214 ] Network output: [ -0.07947 0.1916 1.159 -0.000185 8.307e-05 0.8074 -0.0001395 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2784 0.2708 0.284 0.2885 0.9875 0.9919 0.2785 0.9718 0.9831 0.2953 ] Network output: [ -0.08177 0.1869 1.112 -0.0001701 7.638e-05 0.8638 -0.0001282 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2987 0.2973 0.2941 0.2931 0.9828 0.9893 0.2988 0.955 0.9754 0.2968 ] Network output: [ 0.008022 0.9832 -0.002973 0.0001035 -4.648e-05 1.004 7.803e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05344 Epoch 3371 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06705 0.8797 0.9217 0.0001177 -5.283e-05 0.06498 8.868e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01528 -0.00477 0.008922 0.02829 0.9493 0.9568 0.02609 0.8961 0.9147 0.07148 ] Network output: [ 0.9561 0.09272 0.0318 0.0001296 -5.819e-05 -0.03629 9.768e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5576 0.07462 0.06834 0.3645 0.9766 0.9893 0.6094 0.9099 0.9724 0.5369 ] Network output: [ 0.03044 0.8977 0.9371 1.195e-05 -5.366e-06 0.1044 9.007e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02047 0.01435 0.02401 0.02764 0.9871 0.9909 0.02075 0.9705 0.9825 0.03115 ] Network output: [ 0.1003 -0.2335 0.8404 -6.741e-06 3.026e-06 1.192 -5.081e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6016 0.5014 0.4159 0.4935 0.9791 0.9907 0.603 0.9174 0.9758 0.5213 ] Network output: [ -0.07938 0.1912 1.159 -0.0001839 8.256e-05 0.8076 -0.0001386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2784 0.2708 0.2841 0.2885 0.9875 0.9919 0.2785 0.9718 0.9831 0.2953 ] Network output: [ -0.08166 0.1867 1.112 -0.000169 7.587e-05 0.8638 -0.0001274 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2986 0.2972 0.294 0.293 0.9828 0.9893 0.2987 0.9551 0.9754 0.2968 ] Network output: [ 0.007897 0.9835 -0.002748 0.000103 -4.622e-05 1.004 7.759e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05339 Epoch 3372 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06701 0.8799 0.9217 0.0001176 -5.281e-05 0.06487 8.866e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01527 -0.004773 0.008897 0.02827 0.9493 0.9568 0.02608 0.8961 0.9147 0.07146 ] Network output: [ 0.9561 0.09271 0.03187 0.0001271 -5.706e-05 -0.03633 9.578e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5575 0.07471 0.06839 0.3644 0.9766 0.9893 0.6093 0.91 0.9724 0.5368 ] Network output: [ 0.03039 0.8978 0.9371 1.206e-05 -5.415e-06 0.1044 9.091e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02047 0.01435 0.02401 0.02764 0.9871 0.9909 0.02075 0.9706 0.9825 0.03115 ] Network output: [ 0.1004 -0.2334 0.8401 -5.955e-06 2.673e-06 1.193 -4.488e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6016 0.5014 0.4159 0.4934 0.9791 0.9907 0.603 0.9174 0.9758 0.5212 ] Network output: [ -0.07929 0.1908 1.159 -0.0001828 8.205e-05 0.8078 -0.0001377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2784 0.2708 0.2841 0.2885 0.9875 0.9919 0.2785 0.9718 0.9831 0.2953 ] Network output: [ -0.08155 0.1865 1.112 -0.0001679 7.537e-05 0.8638 -0.0001265 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2985 0.2971 0.294 0.293 0.9828 0.9893 0.2986 0.9551 0.9755 0.2968 ] Network output: [ 0.007774 0.9838 -0.002524 0.0001024 -4.596e-05 1.004 7.715e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05334 Epoch 3373 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06696 0.8801 0.9217 0.0001176 -5.28e-05 0.06477 8.864e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01525 -0.004776 0.008872 0.02826 0.9493 0.9568 0.02607 0.8962 0.9147 0.07144 ] Network output: [ 0.9561 0.09269 0.03194 0.0001246 -5.594e-05 -0.03637 9.39e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5575 0.07479 0.06844 0.3642 0.9766 0.9893 0.6093 0.91 0.9724 0.5367 ] Network output: [ 0.03033 0.898 0.9371 1.217e-05 -5.463e-06 0.1043 9.17e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02046 0.01435 0.02401 0.02763 0.9871 0.9909 0.02075 0.9706 0.9825 0.03114 ] Network output: [ 0.1004 -0.2334 0.8398 -5.177e-06 2.324e-06 1.193 -3.902e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6015 0.5015 0.416 0.4933 0.9791 0.9907 0.603 0.9175 0.9758 0.5211 ] Network output: [ -0.0792 0.1904 1.159 -0.0001816 8.154e-05 0.8081 -0.0001369 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2783 0.2707 0.2841 0.2885 0.9875 0.9919 0.2784 0.9719 0.9831 0.2954 ] Network output: [ -0.08144 0.1863 1.112 -0.0001668 7.488e-05 0.8638 -0.0001257 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2984 0.297 0.294 0.293 0.9828 0.9893 0.2984 0.9551 0.9755 0.2967 ] Network output: [ 0.007651 0.9841 -0.002301 0.0001018 -4.57e-05 1.003 7.672e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05329 Epoch 3374 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06692 0.8803 0.9217 0.0001176 -5.279e-05 0.06466 8.861e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01524 -0.00478 0.008847 0.02824 0.9493 0.9568 0.02606 0.8962 0.9147 0.07142 ] Network output: [ 0.9561 0.09267 0.03201 0.0001221 -5.483e-05 -0.0364 9.205e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5574 0.07488 0.06849 0.3641 0.9766 0.9893 0.6093 0.91 0.9724 0.5366 ] Network output: [ 0.03028 0.8981 0.9371 1.227e-05 -5.508e-06 0.1043 9.246e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02046 0.01434 0.02401 0.02762 0.9871 0.9909 0.02074 0.9706 0.9825 0.03114 ] Network output: [ 0.1004 -0.2334 0.8396 -4.409e-06 1.979e-06 1.193 -3.323e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6015 0.5016 0.416 0.4932 0.9791 0.9907 0.6029 0.9175 0.9759 0.521 ] Network output: [ -0.07911 0.19 1.159 -0.0001805 8.104e-05 0.8083 -0.000136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2783 0.2707 0.2842 0.2885 0.9875 0.9919 0.2784 0.9719 0.9831 0.2954 ] Network output: [ -0.08132 0.186 1.112 -0.0001657 7.438e-05 0.8639 -0.0001249 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2983 0.2969 0.294 0.2929 0.9828 0.9893 0.2983 0.9551 0.9755 0.2967 ] Network output: [ 0.007528 0.9843 -0.002079 0.0001012 -4.544e-05 1.003 7.628e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05325 Epoch 3375 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06688 0.8805 0.9217 0.0001176 -5.277e-05 0.06456 8.859e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01523 -0.004783 0.008821 0.02823 0.9493 0.9569 0.02605 0.8963 0.9148 0.07141 ] Network output: [ 0.9561 0.09265 0.03208 0.0001197 -5.374e-05 -0.03644 9.021e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5573 0.07497 0.06854 0.364 0.9766 0.9893 0.6092 0.9101 0.9724 0.5365 ] Network output: [ 0.03022 0.8983 0.9371 1.236e-05 -5.551e-06 0.1043 9.318e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02045 0.01434 0.024 0.02762 0.9871 0.9909 0.02074 0.9706 0.9825 0.03113 ] Network output: [ 0.1004 -0.2333 0.8393 -3.649e-06 1.638e-06 1.193 -2.75e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6015 0.5016 0.416 0.4931 0.9791 0.9907 0.6029 0.9176 0.9759 0.5209 ] Network output: [ -0.07901 0.1896 1.159 -0.0001794 8.055e-05 0.8085 -0.0001352 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2783 0.2707 0.2842 0.2885 0.9875 0.9919 0.2784 0.9719 0.9831 0.2954 ] Network output: [ -0.08121 0.1858 1.112 -0.0001646 7.389e-05 0.8639 -0.000124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2982 0.2968 0.2939 0.2929 0.9828 0.9893 0.2982 0.9551 0.9755 0.2967 ] Network output: [ 0.007407 0.9846 -0.001859 0.0001007 -4.519e-05 1.003 7.585e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0532 Epoch 3376 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06683 0.8807 0.9217 0.0001175 -5.276e-05 0.06446 8.857e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01522 -0.004786 0.008796 0.02821 0.9493 0.9569 0.02604 0.8963 0.9148 0.07139 ] Network output: [ 0.9561 0.09263 0.03215 0.0001173 -5.266e-05 -0.03648 8.84e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5572 0.07506 0.06859 0.3638 0.9766 0.9893 0.6092 0.9101 0.9725 0.5364 ] Network output: [ 0.03016 0.8984 0.9371 1.246e-05 -5.592e-06 0.1042 9.387e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02045 0.01434 0.024 0.02761 0.9871 0.991 0.02073 0.9706 0.9825 0.03113 ] Network output: [ 0.1005 -0.2333 0.839 -2.898e-06 1.301e-06 1.193 -2.184e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6014 0.5017 0.4161 0.493 0.9791 0.9907 0.6028 0.9176 0.9759 0.5208 ] Network output: [ -0.07892 0.1892 1.159 -0.0001783 8.006e-05 0.8088 -0.0001344 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2782 0.2707 0.2843 0.2886 0.9875 0.9919 0.2783 0.9719 0.9831 0.2954 ] Network output: [ -0.0811 0.1856 1.112 -0.0001635 7.341e-05 0.8639 -0.0001232 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2981 0.2967 0.2939 0.2929 0.9828 0.9893 0.2981 0.9552 0.9755 0.2967 ] Network output: [ 0.007286 0.9849 -0.001639 0.0001001 -4.493e-05 1.003 7.543e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05315 Epoch 3377 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06679 0.8808 0.9217 0.0001175 -5.274e-05 0.06436 8.854e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01521 -0.00479 0.00877 0.0282 0.9494 0.9569 0.02602 0.8963 0.9148 0.07137 ] Network output: [ 0.9561 0.09261 0.03222 0.0001149 -5.16e-05 -0.03652 8.661e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5572 0.07514 0.06864 0.3637 0.9766 0.9893 0.6091 0.9102 0.9725 0.5363 ] Network output: [ 0.03011 0.8986 0.9371 1.254e-05 -5.63e-06 0.1042 9.451e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02044 0.01434 0.024 0.0276 0.9871 0.991 0.02073 0.9706 0.9826 0.03112 ] Network output: [ 0.1005 -0.2333 0.8387 -2.156e-06 9.681e-07 1.194 -1.625e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6014 0.5018 0.4161 0.4928 0.9791 0.9907 0.6028 0.9176 0.9759 0.5207 ] Network output: [ -0.07883 0.1888 1.159 -0.0001772 7.957e-05 0.809 -0.0001336 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2782 0.2707 0.2843 0.2886 0.9875 0.992 0.2783 0.9719 0.9831 0.2955 ] Network output: [ -0.08099 0.1854 1.112 -0.0001624 7.293e-05 0.8639 -0.0001224 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.298 0.2966 0.2939 0.2928 0.9828 0.9893 0.298 0.9552 0.9755 0.2966 ] Network output: [ 0.007167 0.9852 -0.00142 9.952e-05 -4.468e-05 1.002 7.5e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05311 Epoch 3378 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06674 0.881 0.9217 0.0001175 -5.273e-05 0.06426 8.852e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0152 -0.004793 0.008745 0.02819 0.9494 0.9569 0.02601 0.8964 0.9149 0.07135 ] Network output: [ 0.9561 0.09258 0.03229 0.0001126 -5.054e-05 -0.03656 8.485e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5571 0.07523 0.06869 0.3635 0.9767 0.9893 0.6091 0.9102 0.9725 0.5362 ] Network output: [ 0.03005 0.8987 0.9371 1.262e-05 -5.666e-06 0.1041 9.512e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02044 0.01434 0.024 0.02759 0.9871 0.991 0.02073 0.9707 0.9826 0.03112 ] Network output: [ 0.1005 -0.2332 0.8384 -1.423e-06 6.388e-07 1.194 -1.072e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6013 0.5019 0.4161 0.4927 0.9791 0.9907 0.6028 0.9177 0.9759 0.5206 ] Network output: [ -0.07874 0.1884 1.159 -0.0001762 7.909e-05 0.8092 -0.0001328 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2782 0.2706 0.2843 0.2886 0.9875 0.992 0.2783 0.9719 0.9831 0.2955 ] Network output: [ -0.08088 0.1851 1.112 -0.0001614 7.245e-05 0.8639 -0.0001216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2979 0.2965 0.2939 0.2928 0.9828 0.9893 0.2979 0.9552 0.9755 0.2966 ] Network output: [ 0.007048 0.9855 -0.001202 9.896e-05 -4.443e-05 1.002 7.458e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05306 Epoch 3379 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0667 0.8812 0.9217 0.0001174 -5.271e-05 0.06416 8.849e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01519 -0.004797 0.008719 0.02817 0.9494 0.9569 0.026 0.8964 0.9149 0.07133 ] Network output: [ 0.9561 0.09256 0.03235 0.0001103 -4.95e-05 -0.0366 8.31e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.557 0.07532 0.06874 0.3634 0.9767 0.9894 0.609 0.9103 0.9725 0.5361 ] Network output: [ 0.02999 0.8989 0.9371 1.27e-05 -5.701e-06 0.1041 9.57e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02044 0.01434 0.02399 0.02759 0.9871 0.991 0.02072 0.9707 0.9826 0.03111 ] Network output: [ 0.1006 -0.2332 0.8381 -6.98e-07 3.134e-07 1.194 -5.261e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6013 0.5019 0.4162 0.4926 0.9791 0.9907 0.6027 0.9177 0.9759 0.5206 ] Network output: [ -0.07865 0.188 1.159 -0.0001751 7.861e-05 0.8095 -0.000132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2781 0.2706 0.2844 0.2886 0.9875 0.992 0.2782 0.9719 0.9831 0.2955 ] Network output: [ -0.08077 0.1849 1.112 -0.0001603 7.198e-05 0.8639 -0.0001208 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2978 0.2964 0.2938 0.2928 0.9828 0.9893 0.2978 0.9552 0.9756 0.2966 ] Network output: [ 0.006929 0.9858 -0.0009852 9.84e-05 -4.418e-05 1.002 7.416e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05301 Epoch 3380 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06666 0.8814 0.9217 0.0001174 -5.27e-05 0.06406 8.846e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01518 -0.0048 0.008694 0.02816 0.9494 0.9569 0.02599 0.8965 0.9149 0.07131 ] Network output: [ 0.9561 0.09254 0.03242 0.000108 -4.848e-05 -0.03663 8.138e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.557 0.0754 0.06879 0.3632 0.9767 0.9894 0.609 0.9103 0.9725 0.536 ] Network output: [ 0.02994 0.899 0.9371 1.277e-05 -5.733e-06 0.104 9.624e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02043 0.01434 0.02399 0.02758 0.9871 0.991 0.02072 0.9707 0.9826 0.03111 ] Network output: [ 0.1006 -0.2331 0.8379 1.844e-08 -8.279e-09 1.194 1.39e-08 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6012 0.502 0.4162 0.4925 0.9791 0.9907 0.6027 0.9178 0.9759 0.5205 ] Network output: [ -0.07856 0.1876 1.159 -0.000174 7.813e-05 0.8097 -0.0001312 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2781 0.2706 0.2844 0.2886 0.9875 0.992 0.2782 0.972 0.9831 0.2955 ] Network output: [ -0.08067 0.1847 1.112 -0.0001593 7.151e-05 0.8639 -0.0001201 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2977 0.2963 0.2938 0.2927 0.9828 0.9893 0.2977 0.9553 0.9756 0.2965 ] Network output: [ 0.006812 0.986 -0.0007693 9.785e-05 -4.393e-05 1.002 7.374e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05297 Epoch 3381 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06661 0.8816 0.9217 0.0001173 -5.268e-05 0.06396 8.844e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01517 -0.004803 0.008668 0.02814 0.9494 0.9569 0.02598 0.8965 0.915 0.07129 ] Network output: [ 0.9561 0.09251 0.03249 0.0001057 -4.746e-05 -0.03667 7.968e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5569 0.07549 0.06884 0.3631 0.9767 0.9894 0.6089 0.9103 0.9725 0.5359 ] Network output: [ 0.02988 0.8992 0.9371 1.284e-05 -5.763e-06 0.104 9.674e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02043 0.01434 0.02399 0.02757 0.9871 0.991 0.02071 0.9707 0.9826 0.0311 ] Network output: [ 0.1006 -0.2331 0.8376 7.266e-07 -3.262e-07 1.194 5.476e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6012 0.5021 0.4163 0.4924 0.9791 0.9907 0.6026 0.9178 0.976 0.5204 ] Network output: [ -0.07847 0.1872 1.159 -0.000173 7.766e-05 0.8099 -0.0001304 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2781 0.2706 0.2844 0.2886 0.9875 0.992 0.2782 0.972 0.9832 0.2956 ] Network output: [ -0.08056 0.1845 1.112 -0.0001583 7.105e-05 0.864 -0.0001193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2976 0.2962 0.2938 0.2927 0.9828 0.9893 0.2976 0.9553 0.9756 0.2965 ] Network output: [ 0.006695 0.9863 -0.0005544 9.73e-05 -4.368e-05 1.001 7.333e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05292 Epoch 3382 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06657 0.8818 0.9217 0.0001173 -5.267e-05 0.06386 8.841e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01515 -0.004807 0.008643 0.02813 0.9494 0.957 0.02597 0.8965 0.915 0.07127 ] Network output: [ 0.956 0.09249 0.03255 0.0001035 -4.646e-05 -0.03671 7.799e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5568 0.07557 0.06889 0.363 0.9767 0.9894 0.6089 0.9104 0.9726 0.5358 ] Network output: [ 0.02983 0.8993 0.9371 1.29e-05 -5.791e-06 0.104 9.721e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02042 0.01434 0.02399 0.02756 0.9871 0.991 0.02071 0.9707 0.9826 0.0311 ] Network output: [ 0.1006 -0.2331 0.8373 1.426e-06 -6.404e-07 1.194 1.075e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6012 0.5021 0.4163 0.4922 0.9791 0.9907 0.6026 0.9178 0.976 0.5203 ] Network output: [ -0.07838 0.1868 1.159 -0.000172 7.72e-05 0.8101 -0.0001296 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.278 0.2706 0.2845 0.2887 0.9875 0.992 0.2782 0.972 0.9832 0.2956 ] Network output: [ -0.08045 0.1843 1.112 -0.0001572 7.059e-05 0.864 -0.0001185 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2975 0.2961 0.2938 0.2927 0.9828 0.9893 0.2975 0.9553 0.9756 0.2965 ] Network output: [ 0.006579 0.9866 -0.0003404 9.675e-05 -4.344e-05 1.001 7.292e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05288 Epoch 3383 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06653 0.882 0.9217 0.0001173 -5.265e-05 0.06376 8.838e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01514 -0.00481 0.008617 0.02811 0.9494 0.957 0.02596 0.8966 0.915 0.07125 ] Network output: [ 0.956 0.09246 0.03262 0.0001013 -4.547e-05 -0.03675 7.633e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5567 0.07566 0.06894 0.3628 0.9767 0.9894 0.6089 0.9104 0.9726 0.5357 ] Network output: [ 0.02977 0.8995 0.9371 1.296e-05 -5.817e-06 0.1039 9.765e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02042 0.01434 0.02398 0.02756 0.9871 0.991 0.02071 0.9707 0.9826 0.03109 ] Network output: [ 0.1006 -0.233 0.837 2.118e-06 -9.51e-07 1.195 1.596e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6011 0.5022 0.4163 0.4921 0.9791 0.9908 0.6026 0.9179 0.976 0.5202 ] Network output: [ -0.07829 0.1864 1.159 -0.0001709 7.673e-05 0.8104 -0.0001288 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.278 0.2705 0.2845 0.2887 0.9875 0.992 0.2781 0.972 0.9832 0.2956 ] Network output: [ -0.08034 0.184 1.112 -0.0001562 7.014e-05 0.864 -0.0001177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2974 0.296 0.2938 0.2926 0.9828 0.9894 0.2974 0.9553 0.9756 0.2965 ] Network output: [ 0.006464 0.9868 -0.0001274 9.621e-05 -4.319e-05 1.001 7.251e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05283 Epoch 3384 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06648 0.8821 0.9217 0.0001172 -5.263e-05 0.06367 8.836e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01513 -0.004814 0.008592 0.0281 0.9495 0.957 0.02594 0.8966 0.9151 0.07123 ] Network output: [ 0.956 0.09243 0.03269 9.911e-05 -4.45e-05 -0.03679 7.469e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5567 0.07574 0.06899 0.3627 0.9767 0.9894 0.6088 0.9105 0.9726 0.5356 ] Network output: [ 0.02971 0.8996 0.9371 1.301e-05 -5.841e-06 0.1039 9.806e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02042 0.01434 0.02398 0.02755 0.9871 0.991 0.0207 0.9708 0.9826 0.03108 ] Network output: [ 0.1007 -0.233 0.8368 2.802e-06 -1.258e-06 1.195 2.112e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6011 0.5023 0.4164 0.492 0.9791 0.9908 0.6025 0.9179 0.976 0.5201 ] Network output: [ -0.0782 0.186 1.159 -0.0001699 7.628e-05 0.8106 -0.000128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.278 0.2705 0.2846 0.2887 0.9875 0.992 0.2781 0.972 0.9832 0.2956 ] Network output: [ -0.08024 0.1838 1.112 -0.0001552 6.968e-05 0.864 -0.000117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2973 0.2959 0.2937 0.2926 0.9828 0.9894 0.2974 0.9554 0.9756 0.2964 ] Network output: [ 0.00635 0.9871 8.46e-05 9.567e-05 -4.295e-05 1 7.21e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05279 Epoch 3385 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06644 0.8823 0.9217 0.0001172 -5.262e-05 0.06357 8.833e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01512 -0.004817 0.008566 0.02808 0.9495 0.957 0.02593 0.8967 0.9151 0.07121 ] Network output: [ 0.956 0.09241 0.03275 9.696e-05 -4.353e-05 -0.03682 7.308e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5566 0.07583 0.06904 0.3625 0.9767 0.9894 0.6088 0.9105 0.9726 0.5355 ] Network output: [ 0.02966 0.8998 0.9371 1.306e-05 -5.863e-06 0.1038 9.843e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02041 0.01434 0.02398 0.02754 0.9871 0.991 0.0207 0.9708 0.9826 0.03108 ] Network output: [ 0.1007 -0.2329 0.8365 3.478e-06 -1.561e-06 1.195 2.621e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.601 0.5024 0.4164 0.4919 0.9792 0.9908 0.6025 0.918 0.976 0.52 ] Network output: [ -0.07812 0.1857 1.159 -0.0001689 7.582e-05 0.8108 -0.0001273 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.278 0.2705 0.2846 0.2887 0.9875 0.992 0.2781 0.972 0.9832 0.2957 ] Network output: [ -0.08013 0.1836 1.112 -0.0001542 6.924e-05 0.864 -0.0001162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2972 0.2958 0.2937 0.2926 0.9828 0.9894 0.2973 0.9554 0.9756 0.2964 ] Network output: [ 0.006236 0.9874 0.0002957 9.513e-05 -4.271e-05 1 7.169e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05274 Epoch 3386 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0664 0.8825 0.9217 0.0001172 -5.26e-05 0.06348 8.83e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01511 -0.004821 0.00854 0.02807 0.9495 0.957 0.02592 0.8967 0.9151 0.07119 ] Network output: [ 0.956 0.09238 0.03282 9.484e-05 -4.258e-05 -0.03686 7.148e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5565 0.07591 0.06909 0.3624 0.9767 0.9894 0.6087 0.9106 0.9726 0.5354 ] Network output: [ 0.0296 0.8999 0.9372 1.311e-05 -5.883e-06 0.1038 9.876e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02041 0.01434 0.02398 0.02753 0.9871 0.991 0.0207 0.9708 0.9826 0.03107 ] Network output: [ 0.1007 -0.2329 0.8362 4.146e-06 -1.861e-06 1.195 3.124e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.601 0.5024 0.4164 0.4918 0.9792 0.9908 0.6024 0.918 0.976 0.5199 ] Network output: [ -0.07803 0.1853 1.159 -0.0001679 7.537e-05 0.811 -0.0001265 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2779 0.2705 0.2846 0.2887 0.9875 0.992 0.278 0.972 0.9832 0.2957 ] Network output: [ -0.08002 0.1834 1.112 -0.0001532 6.879e-05 0.864 -0.0001155 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2971 0.2957 0.2937 0.2925 0.9829 0.9894 0.2972 0.9554 0.9757 0.2964 ] Network output: [ 0.006123 0.9876 0.0005058 9.459e-05 -4.247e-05 1 7.129e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0527 Epoch 3387 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06635 0.8827 0.9217 0.0001171 -5.258e-05 0.06338 8.827e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0151 -0.004824 0.008514 0.02805 0.9495 0.957 0.02591 0.8967 0.9151 0.07117 ] Network output: [ 0.956 0.09235 0.03288 9.275e-05 -4.164e-05 -0.0369 6.99e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5565 0.076 0.06914 0.3623 0.9767 0.9894 0.6087 0.9106 0.9726 0.5353 ] Network output: [ 0.02954 0.9 0.9372 1.315e-05 -5.902e-06 0.1038 9.907e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0204 0.01434 0.02397 0.02752 0.9872 0.991 0.02069 0.9708 0.9827 0.03107 ] Network output: [ 0.1007 -0.2328 0.836 4.806e-06 -2.157e-06 1.195 3.622e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.601 0.5025 0.4165 0.4917 0.9792 0.9908 0.6024 0.918 0.9761 0.5198 ] Network output: [ -0.07794 0.1849 1.159 -0.0001669 7.492e-05 0.8112 -0.0001258 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2779 0.2705 0.2847 0.2887 0.9875 0.992 0.278 0.9721 0.9832 0.2957 ] Network output: [ -0.07992 0.1832 1.112 -0.0001523 6.835e-05 0.864 -0.0001147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.297 0.2956 0.2937 0.2925 0.9829 0.9894 0.2971 0.9554 0.9757 0.2963 ] Network output: [ 0.006011 0.9879 0.0007149 9.406e-05 -4.223e-05 0.9997 7.089e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05265 Epoch 3388 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06631 0.8829 0.9217 0.0001171 -5.256e-05 0.06329 8.824e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01509 -0.004828 0.008488 0.02804 0.9495 0.957 0.0259 0.8968 0.9152 0.07115 ] Network output: [ 0.956 0.09232 0.03295 9.068e-05 -4.071e-05 -0.03694 6.834e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5564 0.07608 0.06919 0.3621 0.9767 0.9894 0.6086 0.9106 0.9727 0.5352 ] Network output: [ 0.02949 0.9002 0.9372 1.318e-05 -5.918e-06 0.1037 9.935e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0204 0.01433 0.02397 0.02752 0.9872 0.991 0.02069 0.9708 0.9827 0.03106 ] Network output: [ 0.1007 -0.2328 0.8357 5.458e-06 -2.45e-06 1.196 4.113e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6009 0.5026 0.4165 0.4915 0.9792 0.9908 0.6024 0.9181 0.9761 0.5197 ] Network output: [ -0.07785 0.1845 1.159 -0.0001659 7.448e-05 0.8115 -0.000125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2779 0.2704 0.2847 0.2887 0.9876 0.992 0.278 0.9721 0.9832 0.2957 ] Network output: [ -0.07981 0.1829 1.112 -0.0001513 6.792e-05 0.864 -0.000114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2969 0.2955 0.2936 0.2925 0.9829 0.9894 0.297 0.9554 0.9757 0.2963 ] Network output: [ 0.0059 0.9882 0.0009231 9.353e-05 -4.199e-05 0.9995 7.049e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05261 Epoch 3389 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06627 0.883 0.9217 0.000117 -5.255e-05 0.06319 8.821e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01508 -0.004831 0.008463 0.02802 0.9495 0.957 0.02589 0.8968 0.9152 0.07113 ] Network output: [ 0.956 0.09229 0.03301 8.863e-05 -3.979e-05 -0.03697 6.68e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5563 0.07616 0.06924 0.362 0.9767 0.9894 0.6086 0.9107 0.9727 0.5351 ] Network output: [ 0.02943 0.9003 0.9372 1.321e-05 -5.932e-06 0.1037 9.959e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0204 0.01433 0.02397 0.02751 0.9872 0.991 0.02068 0.9708 0.9827 0.03106 ] Network output: [ 0.1008 -0.2327 0.8354 6.103e-06 -2.74e-06 1.196 4.599e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6009 0.5026 0.4166 0.4914 0.9792 0.9908 0.6023 0.9181 0.9761 0.5196 ] Network output: [ -0.07776 0.1842 1.159 -0.0001649 7.404e-05 0.8117 -0.0001243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2778 0.2704 0.2847 0.2888 0.9876 0.992 0.278 0.9721 0.9832 0.2958 ] Network output: [ -0.07971 0.1827 1.112 -0.0001503 6.748e-05 0.864 -0.0001133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2968 0.2954 0.2936 0.2924 0.9829 0.9894 0.2969 0.9555 0.9757 0.2963 ] Network output: [ 0.005789 0.9884 0.00113 9.301e-05 -4.176e-05 0.9993 7.009e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05257 Epoch 3390 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06622 0.8832 0.9217 0.000117 -5.253e-05 0.0631 8.818e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01507 -0.004835 0.008437 0.02801 0.9495 0.9571 0.02588 0.8969 0.9152 0.07111 ] Network output: [ 0.956 0.09226 0.03307 8.662e-05 -3.889e-05 -0.03701 6.528e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5563 0.07625 0.06929 0.3618 0.9767 0.9894 0.6086 0.9107 0.9727 0.535 ] Network output: [ 0.02938 0.9005 0.9372 1.324e-05 -5.945e-06 0.1036 9.98e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02039 0.01433 0.02396 0.0275 0.9872 0.991 0.02068 0.9709 0.9827 0.03105 ] Network output: [ 0.1008 -0.2326 0.8352 6.74e-06 -3.026e-06 1.196 5.079e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6009 0.5027 0.4166 0.4913 0.9792 0.9908 0.6023 0.9182 0.9761 0.5196 ] Network output: [ -0.07767 0.1838 1.159 -0.000164 7.361e-05 0.8119 -0.0001236 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2778 0.2704 0.2848 0.2888 0.9876 0.992 0.2779 0.9721 0.9832 0.2958 ] Network output: [ -0.0796 0.1825 1.112 -0.0001494 6.705e-05 0.864 -0.0001126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2967 0.2953 0.2936 0.2924 0.9829 0.9894 0.2968 0.9555 0.9757 0.2963 ] Network output: [ 0.005679 0.9887 0.001337 9.249e-05 -4.152e-05 0.999 6.97e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05252 Epoch 3391 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06618 0.8834 0.9217 0.000117 -5.251e-05 0.06301 8.815e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01506 -0.004839 0.008411 0.02799 0.9496 0.9571 0.02587 0.8969 0.9153 0.07109 ] Network output: [ 0.956 0.09223 0.03314 8.463e-05 -3.799e-05 -0.03705 6.378e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5562 0.07633 0.06934 0.3617 0.9768 0.9894 0.6085 0.9108 0.9727 0.5349 ] Network output: [ 0.02932 0.9006 0.9372 1.327e-05 -5.956e-06 0.1036 9.998e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02039 0.01433 0.02396 0.02749 0.9872 0.991 0.02068 0.9709 0.9827 0.03104 ] Network output: [ 0.1008 -0.2326 0.8349 7.369e-06 -3.308e-06 1.196 5.554e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6008 0.5028 0.4166 0.4912 0.9792 0.9908 0.6022 0.9182 0.9761 0.5195 ] Network output: [ -0.07759 0.1834 1.159 -0.000163 7.318e-05 0.8121 -0.0001228 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2778 0.2704 0.2848 0.2888 0.9876 0.992 0.2779 0.9721 0.9832 0.2958 ] Network output: [ -0.0795 0.1823 1.112 -0.0001484 6.663e-05 0.8641 -0.0001119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2966 0.2952 0.2936 0.2924 0.9829 0.9894 0.2967 0.9555 0.9757 0.2962 ] Network output: [ 0.00557 0.9889 0.001542 9.197e-05 -4.129e-05 0.9988 6.931e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05248 Epoch 3392 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06614 0.8836 0.9217 0.0001169 -5.249e-05 0.06292 8.812e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01505 -0.004842 0.008385 0.02798 0.9496 0.9571 0.02586 0.8969 0.9153 0.07107 ] Network output: [ 0.956 0.0922 0.0332 8.266e-05 -3.711e-05 -0.03709 6.229e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5561 0.07642 0.06939 0.3615 0.9768 0.9894 0.6085 0.9108 0.9727 0.5348 ] Network output: [ 0.02926 0.9008 0.9372 1.329e-05 -5.965e-06 0.1036 1.001e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02039 0.01433 0.02396 0.02748 0.9872 0.991 0.02067 0.9709 0.9827 0.03104 ] Network output: [ 0.1008 -0.2325 0.8346 7.992e-06 -3.588e-06 1.196 6.023e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6008 0.5029 0.4167 0.4911 0.9792 0.9908 0.6022 0.9182 0.9761 0.5194 ] Network output: [ -0.0775 0.1831 1.159 -0.000162 7.275e-05 0.8123 -0.0001221 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2778 0.2704 0.2848 0.2888 0.9876 0.992 0.2779 0.9721 0.9833 0.2958 ] Network output: [ -0.07939 0.1821 1.112 -0.0001475 6.621e-05 0.8641 -0.0001111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2965 0.2951 0.2935 0.2923 0.9829 0.9894 0.2966 0.9555 0.9757 0.2962 ] Network output: [ 0.005462 0.9892 0.001746 9.145e-05 -4.106e-05 0.9985 6.892e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05244 Epoch 3393 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0661 0.8837 0.9217 0.0001169 -5.247e-05 0.06282 8.809e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01504 -0.004846 0.008359 0.02796 0.9496 0.9571 0.02584 0.897 0.9153 0.07105 ] Network output: [ 0.956 0.09216 0.03326 8.072e-05 -3.624e-05 -0.03712 6.083e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5561 0.0765 0.06944 0.3614 0.9768 0.9894 0.6084 0.9109 0.9727 0.5347 ] Network output: [ 0.02921 0.9009 0.9372 1.33e-05 -5.972e-06 0.1035 1.003e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02038 0.01433 0.02396 0.02747 0.9872 0.991 0.02067 0.9709 0.9827 0.03103 ] Network output: [ 0.1008 -0.2325 0.8344 8.606e-06 -3.864e-06 1.196 6.486e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6007 0.5029 0.4167 0.4909 0.9792 0.9908 0.6022 0.9183 0.9761 0.5193 ] Network output: [ -0.07741 0.1827 1.159 -0.0001611 7.233e-05 0.8125 -0.0001214 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2777 0.2704 0.2849 0.2888 0.9876 0.992 0.2778 0.9721 0.9833 0.2959 ] Network output: [ -0.07929 0.1819 1.112 -0.0001465 6.579e-05 0.8641 -0.0001104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2965 0.295 0.2935 0.2923 0.9829 0.9894 0.2965 0.9556 0.9758 0.2962 ] Network output: [ 0.005354 0.9894 0.00195 9.094e-05 -4.082e-05 0.9983 6.853e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0524 Epoch 3394 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06605 0.8839 0.9217 0.0001168 -5.246e-05 0.06273 8.806e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01502 -0.004849 0.008333 0.02795 0.9496 0.9571 0.02583 0.897 0.9154 0.07102 ] Network output: [ 0.956 0.09213 0.03332 7.88e-05 -3.538e-05 -0.03716 5.939e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.556 0.07658 0.06949 0.3613 0.9768 0.9894 0.6084 0.9109 0.9728 0.5346 ] Network output: [ 0.02915 0.901 0.9372 1.332e-05 -5.978e-06 0.1035 1.004e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02038 0.01433 0.02395 0.02747 0.9872 0.991 0.02067 0.9709 0.9827 0.03103 ] Network output: [ 0.1008 -0.2324 0.8341 9.214e-06 -4.137e-06 1.197 6.944e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6007 0.503 0.4167 0.4908 0.9792 0.9908 0.6021 0.9183 0.9762 0.5192 ] Network output: [ -0.07732 0.1823 1.159 -0.0001602 7.191e-05 0.8127 -0.0001207 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2777 0.2703 0.2849 0.2888 0.9876 0.992 0.2778 0.9722 0.9833 0.2959 ] Network output: [ -0.07919 0.1817 1.112 -0.0001456 6.538e-05 0.8641 -0.0001097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2964 0.295 0.2935 0.2923 0.9829 0.9894 0.2964 0.9556 0.9758 0.2961 ] Network output: [ 0.005247 0.9897 0.002152 9.043e-05 -4.06e-05 0.9981 6.815e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05235 Epoch 3395 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06601 0.8841 0.9217 0.0001168 -5.244e-05 0.06264 8.803e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01501 -0.004853 0.008307 0.02793 0.9496 0.9571 0.02582 0.8971 0.9154 0.071 ] Network output: [ 0.956 0.0921 0.03338 7.691e-05 -3.453e-05 -0.0372 5.796e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5559 0.07666 0.06954 0.3611 0.9768 0.9894 0.6084 0.9109 0.9728 0.5345 ] Network output: [ 0.02909 0.9012 0.9372 1.332e-05 -5.982e-06 0.1035 1.004e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02038 0.01433 0.02395 0.02746 0.9872 0.991 0.02066 0.971 0.9827 0.03102 ] Network output: [ 0.1009 -0.2324 0.8339 9.815e-06 -4.406e-06 1.197 7.397e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6007 0.5031 0.4168 0.4907 0.9792 0.9908 0.6021 0.9184 0.9762 0.5191 ] Network output: [ -0.07724 0.182 1.159 -0.0001592 7.149e-05 0.8129 -0.00012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2777 0.2703 0.2849 0.2888 0.9876 0.992 0.2778 0.9722 0.9833 0.2959 ] Network output: [ -0.07908 0.1814 1.112 -0.0001447 6.497e-05 0.8641 -0.0001091 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2963 0.2949 0.2935 0.2922 0.9829 0.9894 0.2963 0.9556 0.9758 0.2961 ] Network output: [ 0.005141 0.9899 0.002354 8.992e-05 -4.037e-05 0.9978 6.776e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05231 Epoch 3396 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06597 0.8843 0.9217 0.0001168 -5.242e-05 0.06255 8.799e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.015 -0.004857 0.008281 0.02792 0.9496 0.9571 0.02581 0.8971 0.9154 0.07098 ] Network output: [ 0.956 0.09206 0.03345 7.504e-05 -3.369e-05 -0.03723 5.655e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5559 0.07675 0.06959 0.361 0.9768 0.9894 0.6083 0.911 0.9728 0.5344 ] Network output: [ 0.02904 0.9013 0.9372 1.333e-05 -5.984e-06 0.1034 1.005e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02037 0.01433 0.02395 0.02745 0.9872 0.991 0.02066 0.971 0.9827 0.03101 ] Network output: [ 0.1009 -0.2323 0.8336 1.041e-05 -4.673e-06 1.197 7.844e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6006 0.5031 0.4168 0.4906 0.9792 0.9908 0.6021 0.9184 0.9762 0.519 ] Network output: [ -0.07715 0.1816 1.159 -0.0001583 7.108e-05 0.8132 -0.0001193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2777 0.2703 0.285 0.2888 0.9876 0.992 0.2778 0.9722 0.9833 0.2959 ] Network output: [ -0.07898 0.1812 1.112 -0.0001438 6.456e-05 0.8641 -0.0001084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2962 0.2948 0.2934 0.2922 0.9829 0.9894 0.2962 0.9556 0.9758 0.2961 ] Network output: [ 0.005035 0.9901 0.002554 8.941e-05 -4.014e-05 0.9976 6.738e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05227 Epoch 3397 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06593 0.8844 0.9217 0.0001167 -5.24e-05 0.06246 8.796e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01499 -0.00486 0.008255 0.0279 0.9497 0.9572 0.0258 0.8971 0.9155 0.07096 ] Network output: [ 0.956 0.09203 0.03351 7.319e-05 -3.286e-05 -0.03727 5.516e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5558 0.07683 0.06964 0.3608 0.9768 0.9894 0.6083 0.911 0.9728 0.5343 ] Network output: [ 0.02898 0.9015 0.9372 1.333e-05 -5.985e-06 0.1034 1.005e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02037 0.01433 0.02394 0.02744 0.9872 0.991 0.02066 0.971 0.9828 0.03101 ] Network output: [ 0.1009 -0.2322 0.8334 1.099e-05 -4.936e-06 1.197 8.286e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6006 0.5032 0.4169 0.4905 0.9792 0.9908 0.602 0.9184 0.9762 0.5189 ] Network output: [ -0.07706 0.1813 1.159 -0.0001574 7.067e-05 0.8134 -0.0001186 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2776 0.2703 0.285 0.2888 0.9876 0.992 0.2777 0.9722 0.9833 0.2959 ] Network output: [ -0.07888 0.181 1.112 -0.0001429 6.416e-05 0.8641 -0.0001077 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2961 0.2947 0.2934 0.2922 0.9829 0.9894 0.2961 0.9557 0.9758 0.2961 ] Network output: [ 0.00493 0.9904 0.002754 8.891e-05 -3.992e-05 0.9974 6.701e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05223 Epoch 3398 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06588 0.8846 0.9217 0.0001167 -5.238e-05 0.06238 8.793e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01498 -0.004864 0.008228 0.02789 0.9497 0.9572 0.02579 0.8972 0.9155 0.07094 ] Network output: [ 0.956 0.09199 0.03357 7.137e-05 -3.204e-05 -0.03731 5.379e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5557 0.07691 0.06968 0.3607 0.9768 0.9895 0.6083 0.9111 0.9728 0.5342 ] Network output: [ 0.02893 0.9016 0.9372 1.333e-05 -5.983e-06 0.1034 1.004e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02037 0.01433 0.02394 0.02743 0.9872 0.991 0.02065 0.971 0.9828 0.031 ] Network output: [ 0.1009 -0.2322 0.8331 1.157e-05 -5.196e-06 1.197 8.722e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6006 0.5033 0.4169 0.4904 0.9792 0.9908 0.602 0.9185 0.9762 0.5188 ] Network output: [ -0.07698 0.1809 1.159 -0.0001565 7.026e-05 0.8136 -0.000118 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2776 0.2703 0.285 0.2889 0.9876 0.992 0.2777 0.9722 0.9833 0.296 ] Network output: [ -0.07878 0.1808 1.112 -0.000142 6.376e-05 0.8641 -0.000107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.296 0.2946 0.2934 0.2921 0.9829 0.9894 0.296 0.9557 0.9758 0.296 ] Network output: [ 0.004826 0.9906 0.002952 8.841e-05 -3.969e-05 0.9971 6.663e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05219 Epoch 3399 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06584 0.8848 0.9217 0.0001166 -5.236e-05 0.06229 8.79e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01497 -0.004868 0.008202 0.02787 0.9497 0.9572 0.02578 0.8972 0.9155 0.07092 ] Network output: [ 0.956 0.09196 0.03363 6.957e-05 -3.123e-05 -0.03734 5.243e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5557 0.07699 0.06973 0.3606 0.9768 0.9895 0.6082 0.9111 0.9729 0.5341 ] Network output: [ 0.02887 0.9017 0.9372 1.332e-05 -5.981e-06 0.1033 1.004e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02036 0.01433 0.02394 0.02742 0.9872 0.991 0.02065 0.971 0.9828 0.03099 ] Network output: [ 0.1009 -0.2321 0.8329 1.215e-05 -5.453e-06 1.197 9.154e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6005 0.5034 0.4169 0.4903 0.9793 0.9908 0.602 0.9185 0.9762 0.5187 ] Network output: [ -0.07689 0.1806 1.159 -0.0001556 6.986e-05 0.8138 -0.0001173 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2776 0.2703 0.2851 0.2889 0.9876 0.992 0.2777 0.9722 0.9833 0.296 ] Network output: [ -0.07868 0.1806 1.112 -0.0001411 6.336e-05 0.8641 -0.0001064 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2959 0.2945 0.2934 0.2921 0.9829 0.9894 0.2959 0.9557 0.9758 0.296 ] Network output: [ 0.004723 0.9909 0.00315 8.792e-05 -3.947e-05 0.9969 6.626e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05214 Epoch 3400 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0658 0.8849 0.9217 0.0001166 -5.234e-05 0.0622 8.786e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01496 -0.004871 0.008176 0.02786 0.9497 0.9572 0.02577 0.8973 0.9156 0.0709 ] Network output: [ 0.956 0.09192 0.03369 6.78e-05 -3.044e-05 -0.03738 5.11e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5556 0.07707 0.06978 0.3604 0.9768 0.9895 0.6082 0.9112 0.9729 0.534 ] Network output: [ 0.02881 0.9019 0.9372 1.331e-05 -5.977e-06 0.1033 1.003e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02036 0.01433 0.02393 0.02741 0.9872 0.9911 0.02065 0.971 0.9828 0.03099 ] Network output: [ 0.1009 -0.2321 0.8326 1.271e-05 -5.707e-06 1.198 9.58e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6005 0.5034 0.417 0.4901 0.9793 0.9908 0.6019 0.9186 0.9762 0.5187 ] Network output: [ -0.0768 0.1802 1.159 -0.0001547 6.946e-05 0.814 -0.0001166 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2776 0.2703 0.2851 0.2889 0.9876 0.992 0.2777 0.9723 0.9833 0.296 ] Network output: [ -0.07858 0.1804 1.112 -0.0001403 6.297e-05 0.8641 -0.0001057 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2958 0.2944 0.2933 0.2921 0.9829 0.9894 0.2958 0.9557 0.9759 0.296 ] Network output: [ 0.00462 0.9911 0.003347 8.742e-05 -3.925e-05 0.9967 6.588e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0521 Epoch 3401 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06576 0.8851 0.9217 0.0001165 -5.232e-05 0.06211 8.783e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01495 -0.004875 0.00815 0.02784 0.9497 0.9572 0.02576 0.8973 0.9156 0.07088 ] Network output: [ 0.956 0.09188 0.03375 6.605e-05 -2.965e-05 -0.03742 4.977e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5555 0.07715 0.06983 0.3603 0.9768 0.9895 0.6081 0.9112 0.9729 0.5339 ] Network output: [ 0.02876 0.902 0.9372 1.33e-05 -5.971e-06 0.1033 1.002e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02036 0.01433 0.02393 0.0274 0.9872 0.9911 0.02064 0.9711 0.9828 0.03098 ] Network output: [ 0.1009 -0.232 0.8324 1.327e-05 -5.958e-06 1.198 1e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6004 0.5035 0.417 0.49 0.9793 0.9908 0.6019 0.9186 0.9763 0.5186 ] Network output: [ -0.07672 0.1799 1.159 -0.0001538 6.907e-05 0.8142 -0.0001159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2775 0.2702 0.2851 0.2889 0.9876 0.992 0.2777 0.9723 0.9833 0.296 ] Network output: [ -0.07848 0.1802 1.112 -0.0001394 6.258e-05 0.8641 -0.000105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2957 0.2943 0.2933 0.292 0.9829 0.9894 0.2957 0.9558 0.9759 0.2959 ] Network output: [ 0.004517 0.9913 0.003543 8.693e-05 -3.903e-05 0.9964 6.551e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05206 Epoch 3402 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06572 0.8853 0.9218 0.0001165 -5.23e-05 0.06203 8.78e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01494 -0.004879 0.008124 0.02783 0.9497 0.9572 0.02575 0.8973 0.9156 0.07086 ] Network output: [ 0.956 0.09184 0.03381 6.432e-05 -2.887e-05 -0.03745 4.847e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5555 0.07723 0.06988 0.3601 0.9768 0.9895 0.6081 0.9112 0.9729 0.5338 ] Network output: [ 0.0287 0.9022 0.9373 1.328e-05 -5.963e-06 0.1032 1.001e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02035 0.01433 0.02392 0.0274 0.9872 0.9911 0.02064 0.9711 0.9828 0.03097 ] Network output: [ 0.1009 -0.2319 0.8321 1.382e-05 -6.206e-06 1.198 1.042e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6004 0.5036 0.4171 0.4899 0.9793 0.9908 0.6019 0.9186 0.9763 0.5185 ] Network output: [ -0.07663 0.1795 1.159 -0.000153 6.868e-05 0.8144 -0.0001153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2775 0.2702 0.2852 0.2889 0.9876 0.992 0.2776 0.9723 0.9834 0.296 ] Network output: [ -0.07838 0.18 1.112 -0.0001385 6.219e-05 0.8641 -0.0001044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2956 0.2942 0.2933 0.292 0.983 0.9894 0.2956 0.9558 0.9759 0.2959 ] Network output: [ 0.004416 0.9916 0.003738 8.644e-05 -3.881e-05 0.9962 6.515e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05202 Epoch 3403 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06567 0.8854 0.9218 0.0001165 -5.228e-05 0.06194 8.776e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01493 -0.004882 0.008097 0.02781 0.9497 0.9572 0.02574 0.8974 0.9157 0.07084 ] Network output: [ 0.956 0.09181 0.03386 6.261e-05 -2.811e-05 -0.03749 4.718e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5554 0.07732 0.06993 0.36 0.9769 0.9895 0.6081 0.9113 0.9729 0.5337 ] Network output: [ 0.02865 0.9023 0.9373 1.326e-05 -5.954e-06 0.1032 9.996e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02035 0.01433 0.02392 0.02739 0.9872 0.9911 0.02064 0.9711 0.9828 0.03097 ] Network output: [ 0.1009 -0.2319 0.8319 1.437e-05 -6.451e-06 1.198 1.083e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6004 0.5037 0.4171 0.4898 0.9793 0.9908 0.6018 0.9187 0.9763 0.5184 ] Network output: [ -0.07655 0.1792 1.159 -0.0001521 6.829e-05 0.8146 -0.0001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2775 0.2702 0.2852 0.2889 0.9876 0.992 0.2776 0.9723 0.9834 0.296 ] Network output: [ -0.07828 0.1797 1.112 -0.0001377 6.181e-05 0.8641 -0.0001038 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2955 0.2941 0.2933 0.292 0.983 0.9894 0.2955 0.9558 0.9759 0.2959 ] Network output: [ 0.004315 0.9918 0.003932 8.596e-05 -3.859e-05 0.996 6.478e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05198 Epoch 3404 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06563 0.8856 0.9218 0.0001164 -5.226e-05 0.06186 8.773e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01492 -0.004886 0.008071 0.0278 0.9498 0.9573 0.02572 0.8974 0.9157 0.07082 ] Network output: [ 0.956 0.09177 0.03392 6.092e-05 -2.735e-05 -0.03752 4.591e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5553 0.0774 0.06998 0.3599 0.9769 0.9895 0.608 0.9113 0.9729 0.5336 ] Network output: [ 0.02859 0.9024 0.9373 1.324e-05 -5.944e-06 0.1032 9.978e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02035 0.01433 0.02392 0.02738 0.9872 0.9911 0.02063 0.9711 0.9828 0.03096 ] Network output: [ 0.1009 -0.2318 0.8316 1.491e-05 -6.693e-06 1.198 1.123e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6003 0.5037 0.4171 0.4897 0.9793 0.9908 0.6018 0.9187 0.9763 0.5183 ] Network output: [ -0.07646 0.1789 1.159 -0.0001513 6.79e-05 0.8148 -0.000114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2775 0.2702 0.2852 0.2889 0.9876 0.992 0.2776 0.9723 0.9834 0.2961 ] Network output: [ -0.07818 0.1795 1.112 -0.0001368 6.143e-05 0.8641 -0.0001031 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2954 0.294 0.2932 0.2919 0.983 0.9895 0.2954 0.9558 0.9759 0.2959 ] Network output: [ 0.004215 0.992 0.004125 8.548e-05 -3.837e-05 0.9958 6.442e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05194 Epoch 3405 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06559 0.8858 0.9218 0.0001164 -5.224e-05 0.06177 8.77e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01491 -0.00489 0.008045 0.02778 0.9498 0.9573 0.02571 0.8975 0.9157 0.07079 ] Network output: [ 0.956 0.09173 0.03398 5.926e-05 -2.66e-05 -0.03756 4.466e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5553 0.07748 0.07003 0.3597 0.9769 0.9895 0.608 0.9114 0.973 0.5335 ] Network output: [ 0.02853 0.9026 0.9373 1.321e-05 -5.932e-06 0.1031 9.958e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02034 0.01433 0.02391 0.02737 0.9872 0.9911 0.02063 0.9711 0.9828 0.03095 ] Network output: [ 0.1009 -0.2317 0.8314 1.544e-05 -6.932e-06 1.198 1.164e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6003 0.5038 0.4172 0.4896 0.9793 0.9908 0.6018 0.9188 0.9763 0.5182 ] Network output: [ -0.07638 0.1785 1.159 -0.0001504 6.752e-05 0.815 -0.0001134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2775 0.2702 0.2853 0.2889 0.9876 0.992 0.2776 0.9723 0.9834 0.2961 ] Network output: [ -0.07808 0.1793 1.112 -0.000136 6.105e-05 0.8641 -0.0001025 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2953 0.2939 0.2932 0.2919 0.983 0.9895 0.2953 0.9558 0.9759 0.2958 ] Network output: [ 0.004115 0.9922 0.004318 8.5e-05 -3.816e-05 0.9956 6.406e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0519 Epoch 3406 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06555 0.8859 0.9218 0.0001163 -5.222e-05 0.06169 8.766e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0149 -0.004894 0.008018 0.02777 0.9498 0.9573 0.0257 0.8975 0.9157 0.07077 ] Network output: [ 0.9561 0.09169 0.03404 5.762e-05 -2.587e-05 -0.0376 4.342e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5552 0.07756 0.07008 0.3596 0.9769 0.9895 0.608 0.9114 0.973 0.5334 ] Network output: [ 0.02848 0.9027 0.9373 1.318e-05 -5.919e-06 0.1031 9.936e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02034 0.01433 0.02391 0.02736 0.9873 0.9911 0.02063 0.9711 0.9829 0.03095 ] Network output: [ 0.101 -0.2316 0.8312 1.597e-05 -7.168e-06 1.199 1.203e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6003 0.5039 0.4172 0.4894 0.9793 0.9909 0.6017 0.9188 0.9763 0.5181 ] Network output: [ -0.07629 0.1782 1.159 -0.0001496 6.715e-05 0.8152 -0.0001127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2774 0.2702 0.2853 0.2889 0.9876 0.992 0.2775 0.9723 0.9834 0.2961 ] Network output: [ -0.07798 0.1791 1.112 -0.0001352 6.068e-05 0.8642 -0.0001019 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2952 0.2938 0.2932 0.2919 0.983 0.9895 0.2952 0.9559 0.9759 0.2958 ] Network output: [ 0.004017 0.9925 0.004509 8.452e-05 -3.794e-05 0.9953 6.37e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05186 Epoch 3407 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06551 0.8861 0.9218 0.0001163 -5.22e-05 0.0616 8.763e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01489 -0.004897 0.007992 0.02775 0.9498 0.9573 0.02569 0.8975 0.9158 0.07075 ] Network output: [ 0.9561 0.09165 0.03409 5.599e-05 -2.514e-05 -0.03763 4.22e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5551 0.07764 0.07012 0.3595 0.9769 0.9895 0.6079 0.9115 0.973 0.5333 ] Network output: [ 0.02842 0.9028 0.9373 1.315e-05 -5.904e-06 0.1031 9.912e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02034 0.01433 0.02391 0.02735 0.9873 0.9911 0.02063 0.9712 0.9829 0.03094 ] Network output: [ 0.101 -0.2316 0.8309 1.649e-05 -7.401e-06 1.199 1.242e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6002 0.5039 0.4173 0.4893 0.9793 0.9909 0.6017 0.9189 0.9764 0.518 ] Network output: [ -0.07621 0.1779 1.159 -0.0001487 6.677e-05 0.8153 -0.0001121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2774 0.2702 0.2853 0.2889 0.9876 0.9921 0.2775 0.9724 0.9834 0.2961 ] Network output: [ -0.07788 0.1789 1.112 -0.0001343 6.031e-05 0.8642 -0.0001012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2951 0.2938 0.2932 0.2918 0.983 0.9895 0.2951 0.9559 0.976 0.2958 ] Network output: [ 0.003918 0.9927 0.004699 8.405e-05 -3.773e-05 0.9951 6.334e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05182 Epoch 3408 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06547 0.8862 0.9218 0.0001162 -5.218e-05 0.06152 8.759e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01488 -0.004901 0.007965 0.02774 0.9498 0.9573 0.02568 0.8976 0.9158 0.07073 ] Network output: [ 0.9561 0.09161 0.03415 5.439e-05 -2.442e-05 -0.03767 4.099e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5551 0.07772 0.07017 0.3593 0.9769 0.9895 0.6079 0.9115 0.973 0.5332 ] Network output: [ 0.02837 0.903 0.9373 1.312e-05 -5.888e-06 0.1031 9.885e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02033 0.01433 0.0239 0.02734 0.9873 0.9911 0.02062 0.9712 0.9829 0.03093 ] Network output: [ 0.101 -0.2315 0.8307 1.7e-05 -7.632e-06 1.199 1.281e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6002 0.504 0.4173 0.4892 0.9793 0.9909 0.6017 0.9189 0.9764 0.518 ] Network output: [ -0.07612 0.1775 1.159 -0.0001479 6.64e-05 0.8155 -0.0001115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2774 0.2701 0.2854 0.2889 0.9876 0.9921 0.2775 0.9724 0.9834 0.2961 ] Network output: [ -0.07778 0.1787 1.112 -0.0001335 5.994e-05 0.8642 -0.0001006 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.295 0.2937 0.2931 0.2918 0.983 0.9895 0.2951 0.9559 0.976 0.2957 ] Network output: [ 0.003821 0.9929 0.004889 8.357e-05 -3.752e-05 0.9949 6.298e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05178 Epoch 3409 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06542 0.8864 0.9218 0.0001162 -5.216e-05 0.06144 8.756e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01487 -0.004905 0.007939 0.02772 0.9498 0.9573 0.02567 0.8976 0.9158 0.07071 ] Network output: [ 0.9561 0.09156 0.03421 5.281e-05 -2.371e-05 -0.0377 3.98e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.555 0.0778 0.07022 0.3592 0.9769 0.9895 0.6078 0.9116 0.973 0.5331 ] Network output: [ 0.02831 0.9031 0.9373 1.308e-05 -5.871e-06 0.103 9.855e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02033 0.01433 0.0239 0.02733 0.9873 0.9911 0.02062 0.9712 0.9829 0.03093 ] Network output: [ 0.101 -0.2314 0.8304 1.751e-05 -7.859e-06 1.199 1.319e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6002 0.5041 0.4173 0.4891 0.9793 0.9909 0.6016 0.9189 0.9764 0.5179 ] Network output: [ -0.07604 0.1772 1.159 -0.0001471 6.603e-05 0.8157 -0.0001108 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2774 0.2701 0.2854 0.289 0.9876 0.9921 0.2775 0.9724 0.9834 0.2962 ] Network output: [ -0.07769 0.1785 1.112 -0.0001327 5.958e-05 0.8642 -0.0001 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2949 0.2936 0.2931 0.2918 0.983 0.9895 0.295 0.9559 0.976 0.2957 ] Network output: [ 0.003724 0.9931 0.005078 8.311e-05 -3.731e-05 0.9947 6.263e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05174 Epoch 3410 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06538 0.8866 0.9218 0.0001161 -5.214e-05 0.06135 8.752e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01486 -0.004909 0.007912 0.02771 0.9499 0.9573 0.02566 0.8977 0.9159 0.07069 ] Network output: [ 0.9561 0.09152 0.03426 5.125e-05 -2.301e-05 -0.03774 3.863e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5549 0.07788 0.07027 0.359 0.9769 0.9895 0.6078 0.9116 0.973 0.533 ] Network output: [ 0.02826 0.9032 0.9373 1.304e-05 -5.852e-06 0.103 9.824e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02033 0.01433 0.02389 0.02732 0.9873 0.9911 0.02062 0.9712 0.9829 0.03092 ] Network output: [ 0.101 -0.2314 0.8302 1.801e-05 -8.084e-06 1.199 1.357e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6001 0.5042 0.4174 0.489 0.9793 0.9909 0.6016 0.919 0.9764 0.5178 ] Network output: [ -0.07595 0.1769 1.159 -0.0001463 6.567e-05 0.8159 -0.0001102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2773 0.2701 0.2854 0.289 0.9876 0.9921 0.2775 0.9724 0.9834 0.2962 ] Network output: [ -0.07759 0.1783 1.112 -0.0001319 5.922e-05 0.8642 -9.94e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2949 0.2935 0.2931 0.2917 0.983 0.9895 0.2949 0.956 0.976 0.2957 ] Network output: [ 0.003627 0.9933 0.005265 8.264e-05 -3.71e-05 0.9945 6.228e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0517 Epoch 3411 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06534 0.8867 0.9218 0.0001161 -5.212e-05 0.06127 8.749e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01484 -0.004912 0.007886 0.02769 0.9499 0.9574 0.02565 0.8977 0.9159 0.07067 ] Network output: [ 0.9561 0.09148 0.03432 4.971e-05 -2.232e-05 -0.03777 3.747e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5549 0.07795 0.07032 0.3589 0.9769 0.9895 0.6078 0.9116 0.9731 0.5329 ] Network output: [ 0.0282 0.9034 0.9373 1.299e-05 -5.832e-06 0.103 9.79e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02032 0.01433 0.02389 0.02731 0.9873 0.9911 0.02061 0.9712 0.9829 0.03091 ] Network output: [ 0.101 -0.2313 0.83 1.85e-05 -8.306e-06 1.199 1.394e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6001 0.5042 0.4174 0.4889 0.9793 0.9909 0.6016 0.919 0.9764 0.5177 ] Network output: [ -0.07587 0.1766 1.158 -0.0001455 6.53e-05 0.8161 -0.0001096 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2773 0.2701 0.2854 0.289 0.9877 0.9921 0.2774 0.9724 0.9834 0.2962 ] Network output: [ -0.07749 0.1781 1.112 -0.0001311 5.886e-05 0.8642 -9.88e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2948 0.2934 0.2931 0.2917 0.983 0.9895 0.2948 0.956 0.976 0.2957 ] Network output: [ 0.003532 0.9936 0.005452 8.218e-05 -3.689e-05 0.9943 6.193e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05166 Epoch 3412 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0653 0.8869 0.9218 0.000116 -5.21e-05 0.06119 8.745e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01483 -0.004916 0.007859 0.02768 0.9499 0.9574 0.02564 0.8978 0.9159 0.07064 ] Network output: [ 0.9561 0.09144 0.03437 4.82e-05 -2.164e-05 -0.03781 3.632e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5548 0.07803 0.07037 0.3588 0.9769 0.9895 0.6077 0.9117 0.9731 0.5328 ] Network output: [ 0.02814 0.9035 0.9373 1.294e-05 -5.81e-06 0.1029 9.754e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02032 0.01433 0.02388 0.0273 0.9873 0.9911 0.02061 0.9713 0.9829 0.0309 ] Network output: [ 0.101 -0.2312 0.8297 1.899e-05 -8.526e-06 1.2 1.431e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6001 0.5043 0.4175 0.4888 0.9793 0.9909 0.6015 0.9191 0.9764 0.5176 ] Network output: [ -0.07579 0.1762 1.158 -0.0001447 6.495e-05 0.8163 -0.000109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2773 0.2701 0.2855 0.289 0.9877 0.9921 0.2774 0.9724 0.9834 0.2962 ] Network output: [ -0.07739 0.1779 1.112 -0.0001303 5.85e-05 0.8642 -9.821e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2947 0.2933 0.293 0.2917 0.983 0.9895 0.2947 0.956 0.976 0.2956 ] Network output: [ 0.003437 0.9938 0.005638 8.172e-05 -3.669e-05 0.9941 6.158e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05163 Epoch 3413 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06526 0.887 0.9218 0.000116 -5.207e-05 0.06111 8.742e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01482 -0.00492 0.007833 0.02766 0.9499 0.9574 0.02563 0.8978 0.916 0.07062 ] Network output: [ 0.9561 0.0914 0.03443 4.67e-05 -2.096e-05 -0.03784 3.519e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5547 0.07811 0.07041 0.3586 0.9769 0.9895 0.6077 0.9117 0.9731 0.5327 ] Network output: [ 0.02809 0.9036 0.9373 1.289e-05 -5.788e-06 0.1029 9.716e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02032 0.01433 0.02388 0.02729 0.9873 0.9911 0.02061 0.9713 0.9829 0.0309 ] Network output: [ 0.101 -0.2311 0.8295 1.947e-05 -8.742e-06 1.2 1.468e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6 0.5044 0.4175 0.4887 0.9794 0.9909 0.6015 0.9191 0.9764 0.5175 ] Network output: [ -0.0757 0.1759 1.158 -0.0001439 6.459e-05 0.8165 -0.0001084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2773 0.2701 0.2855 0.289 0.9877 0.9921 0.2774 0.9724 0.9835 0.2962 ] Network output: [ -0.0773 0.1777 1.112 -0.0001295 5.815e-05 0.8642 -9.762e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2946 0.2932 0.293 0.2916 0.983 0.9895 0.2946 0.956 0.9761 0.2956 ] Network output: [ 0.003342 0.994 0.005823 8.126e-05 -3.648e-05 0.9938 6.124e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05159 Epoch 3414 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06522 0.8872 0.9218 0.0001159 -5.205e-05 0.06103 8.738e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01481 -0.004924 0.007806 0.02765 0.9499 0.9574 0.02562 0.8978 0.916 0.0706 ] Network output: [ 0.9561 0.09135 0.03448 4.522e-05 -2.03e-05 -0.03788 3.408e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5547 0.07819 0.07046 0.3585 0.977 0.9895 0.6077 0.9118 0.9731 0.5326 ] Network output: [ 0.02803 0.9038 0.9373 1.284e-05 -5.764e-06 0.1029 9.675e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02031 0.01433 0.02388 0.02728 0.9873 0.9911 0.0206 0.9713 0.9829 0.03089 ] Network output: [ 0.101 -0.2311 0.8293 1.995e-05 -8.957e-06 1.2 1.504e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6 0.5045 0.4175 0.4885 0.9794 0.9909 0.6015 0.9191 0.9765 0.5174 ] Network output: [ -0.07562 0.1756 1.158 -0.0001431 6.424e-05 0.8167 -0.0001078 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2773 0.2701 0.2855 0.289 0.9877 0.9921 0.2774 0.9725 0.9835 0.2962 ] Network output: [ -0.0772 0.1775 1.112 -0.0001288 5.78e-05 0.8642 -9.703e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2945 0.2931 0.293 0.2916 0.983 0.9895 0.2945 0.9561 0.9761 0.2956 ] Network output: [ 0.003248 0.9942 0.006007 8.08e-05 -3.628e-05 0.9936 6.09e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05155 Epoch 3415 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06518 0.8874 0.9218 0.0001159 -5.203e-05 0.06095 8.735e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0148 -0.004928 0.007779 0.02763 0.9499 0.9574 0.02561 0.8979 0.916 0.07058 ] Network output: [ 0.9561 0.09131 0.03453 4.376e-05 -1.964e-05 -0.03791 3.298e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5546 0.07827 0.07051 0.3584 0.977 0.9895 0.6076 0.9118 0.9731 0.5325 ] Network output: [ 0.02798 0.9039 0.9373 1.278e-05 -5.738e-06 0.1029 9.633e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02031 0.01433 0.02387 0.02728 0.9873 0.9911 0.0206 0.9713 0.9829 0.03088 ] Network output: [ 0.101 -0.231 0.829 2.042e-05 -9.168e-06 1.2 1.539e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.6 0.5045 0.4176 0.4884 0.9794 0.9909 0.6014 0.9192 0.9765 0.5173 ] Network output: [ -0.07554 0.1753 1.158 -0.0001423 6.389e-05 0.8169 -0.0001073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2772 0.2701 0.2856 0.289 0.9877 0.9921 0.2774 0.9725 0.9835 0.2962 ] Network output: [ -0.07711 0.1773 1.112 -0.000128 5.746e-05 0.8642 -9.645e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2944 0.293 0.293 0.2916 0.983 0.9895 0.2944 0.9561 0.9761 0.2955 ] Network output: [ 0.003155 0.9944 0.006191 8.035e-05 -3.607e-05 0.9934 6.055e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05151 Epoch 3416 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06514 0.8875 0.9218 0.0001159 -5.201e-05 0.06087 8.731e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01479 -0.004931 0.007753 0.02762 0.95 0.9574 0.0256 0.8979 0.9161 0.07056 ] Network output: [ 0.9561 0.09126 0.03459 4.231e-05 -1.9e-05 -0.03794 3.189e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5546 0.07835 0.07055 0.3582 0.977 0.9895 0.6076 0.9119 0.9732 0.5324 ] Network output: [ 0.02792 0.904 0.9373 1.272e-05 -5.712e-06 0.1028 9.589e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02031 0.01433 0.02387 0.02727 0.9873 0.9911 0.0206 0.9713 0.983 0.03088 ] Network output: [ 0.101 -0.2309 0.8288 2.089e-05 -9.377e-06 1.2 1.574e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5999 0.5046 0.4176 0.4883 0.9794 0.9909 0.6014 0.9192 0.9765 0.5173 ] Network output: [ -0.07545 0.175 1.158 -0.0001415 6.354e-05 0.817 -0.0001067 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2772 0.2701 0.2856 0.289 0.9877 0.9921 0.2773 0.9725 0.9835 0.2963 ] Network output: [ -0.07701 0.1771 1.112 -0.0001272 5.711e-05 0.8642 -9.588e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2943 0.2929 0.2929 0.2915 0.983 0.9895 0.2943 0.9561 0.9761 0.2955 ] Network output: [ 0.003063 0.9946 0.006373 7.99e-05 -3.587e-05 0.9932 6.022e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05147 Epoch 3417 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0651 0.8877 0.9218 0.0001158 -5.199e-05 0.06079 8.728e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01478 -0.004935 0.007726 0.0276 0.95 0.9574 0.02559 0.898 0.9161 0.07053 ] Network output: [ 0.9561 0.09122 0.03464 4.089e-05 -1.836e-05 -0.03798 3.082e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5545 0.07843 0.0706 0.3581 0.977 0.9896 0.6076 0.9119 0.9732 0.5323 ] Network output: [ 0.02787 0.9042 0.9373 1.266e-05 -5.684e-06 0.1028 9.542e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0203 0.01432 0.02386 0.02726 0.9873 0.9911 0.0206 0.9713 0.983 0.03087 ] Network output: [ 0.101 -0.2308 0.8286 2.135e-05 -9.583e-06 1.2 1.609e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5999 0.5047 0.4176 0.4882 0.9794 0.9909 0.6014 0.9193 0.9765 0.5172 ] Network output: [ -0.07537 0.1747 1.158 -0.0001408 6.32e-05 0.8172 -0.0001061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2772 0.2701 0.2856 0.289 0.9877 0.9921 0.2773 0.9725 0.9835 0.2963 ] Network output: [ -0.07692 0.1769 1.112 -0.0001265 5.677e-05 0.8642 -9.531e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2942 0.2929 0.2929 0.2915 0.983 0.9895 0.2942 0.9561 0.9761 0.2955 ] Network output: [ 0.00297 0.9948 0.006555 7.945e-05 -3.567e-05 0.993 5.988e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05144 Epoch 3418 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06506 0.8878 0.9218 0.0001158 -5.197e-05 0.06071 8.724e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01477 -0.004939 0.007699 0.02759 0.95 0.9575 0.02558 0.898 0.9161 0.07051 ] Network output: [ 0.9562 0.09117 0.03469 3.949e-05 -1.773e-05 -0.03801 2.976e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5544 0.0785 0.07065 0.3579 0.977 0.9896 0.6075 0.9119 0.9732 0.5322 ] Network output: [ 0.02781 0.9043 0.9373 1.26e-05 -5.655e-06 0.1028 9.494e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0203 0.01432 0.02386 0.02725 0.9873 0.9911 0.02059 0.9714 0.983 0.03086 ] Network output: [ 0.101 -0.2308 0.8284 2.18e-05 -9.786e-06 1.201 1.643e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5999 0.5048 0.4177 0.4881 0.9794 0.9909 0.6013 0.9193 0.9765 0.5171 ] Network output: [ -0.07529 0.1744 1.158 -0.00014 6.286e-05 0.8174 -0.0001055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2772 0.27 0.2856 0.289 0.9877 0.9921 0.2773 0.9725 0.9835 0.2963 ] Network output: [ -0.07682 0.1767 1.112 -0.0001257 5.644e-05 0.8642 -9.474e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2941 0.2928 0.2929 0.2915 0.9831 0.9895 0.2941 0.9562 0.9761 0.2955 ] Network output: [ 0.002879 0.995 0.006735 7.901e-05 -3.547e-05 0.9928 5.954e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0514 Epoch 3419 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06502 0.888 0.9218 0.0001157 -5.195e-05 0.06064 8.721e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01476 -0.004943 0.007673 0.02757 0.95 0.9575 0.02557 0.898 0.9162 0.07049 ] Network output: [ 0.9562 0.09113 0.03475 3.81e-05 -1.71e-05 -0.03805 2.871e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5544 0.07858 0.0707 0.3578 0.977 0.9896 0.6075 0.912 0.9732 0.5322 ] Network output: [ 0.02776 0.9044 0.9374 1.253e-05 -5.625e-06 0.1028 9.443e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0203 0.01432 0.02385 0.02724 0.9873 0.9911 0.02059 0.9714 0.983 0.03085 ] Network output: [ 0.101 -0.2307 0.8281 2.225e-05 -9.987e-06 1.201 1.677e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5999 0.5048 0.4177 0.488 0.9794 0.9909 0.6013 0.9193 0.9765 0.517 ] Network output: [ -0.07521 0.1741 1.158 -0.0001393 6.252e-05 0.8176 -0.000105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2772 0.27 0.2857 0.289 0.9877 0.9921 0.2773 0.9725 0.9835 0.2963 ] Network output: [ -0.07673 0.1765 1.112 -0.000125 5.61e-05 0.8642 -9.418e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.294 0.2927 0.2929 0.2914 0.9831 0.9895 0.2941 0.9562 0.9761 0.2954 ] Network output: [ 0.002788 0.9952 0.006915 7.857e-05 -3.527e-05 0.9926 5.921e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05136 Epoch 3420 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06497 0.8881 0.9218 0.0001157 -5.193e-05 0.06056 8.717e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01475 -0.004947 0.007646 0.02756 0.95 0.9575 0.02556 0.8981 0.9162 0.07047 ] Network output: [ 0.9562 0.09108 0.0348 3.673e-05 -1.649e-05 -0.03808 2.768e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5543 0.07866 0.07074 0.3577 0.977 0.9896 0.6075 0.912 0.9732 0.5321 ] Network output: [ 0.0277 0.9046 0.9374 1.246e-05 -5.594e-06 0.1027 9.391e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0203 0.01432 0.02385 0.02723 0.9873 0.9911 0.02059 0.9714 0.983 0.03084 ] Network output: [ 0.101 -0.2306 0.8279 2.269e-05 -1.019e-05 1.201 1.71e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5998 0.5049 0.4178 0.4879 0.9794 0.9909 0.6013 0.9194 0.9766 0.5169 ] Network output: [ -0.07513 0.1738 1.158 -0.0001385 6.219e-05 0.8178 -0.0001044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2772 0.27 0.2857 0.289 0.9877 0.9921 0.2773 0.9726 0.9835 0.2963 ] Network output: [ -0.07664 0.1763 1.112 -0.0001242 5.577e-05 0.8642 -9.362e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2939 0.2926 0.2928 0.2914 0.9831 0.9895 0.294 0.9562 0.9762 0.2954 ] Network output: [ 0.002698 0.9954 0.007094 7.813e-05 -3.507e-05 0.9924 5.888e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05132 Epoch 3421 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06493 0.8883 0.9218 0.0001156 -5.191e-05 0.06048 8.713e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01474 -0.004951 0.007619 0.02754 0.95 0.9575 0.02555 0.8981 0.9162 0.07045 ] Network output: [ 0.9562 0.09104 0.03485 3.538e-05 -1.588e-05 -0.03811 2.666e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5542 0.07874 0.07079 0.3575 0.977 0.9896 0.6074 0.9121 0.9732 0.532 ] Network output: [ 0.02764 0.9047 0.9374 1.239e-05 -5.562e-06 0.1027 9.337e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02029 0.01432 0.02385 0.02722 0.9873 0.9911 0.02059 0.9714 0.983 0.03084 ] Network output: [ 0.101 -0.2305 0.8277 2.313e-05 -1.038e-05 1.201 1.743e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5998 0.505 0.4178 0.4878 0.9794 0.9909 0.6013 0.9194 0.9766 0.5168 ] Network output: [ -0.07504 0.1735 1.158 -0.0001378 6.186e-05 0.8179 -0.0001038 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2771 0.27 0.2857 0.289 0.9877 0.9921 0.2772 0.9726 0.9835 0.2963 ] Network output: [ -0.07654 0.1761 1.112 -0.0001235 5.544e-05 0.8642 -9.307e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2939 0.2925 0.2928 0.2914 0.9831 0.9895 0.2939 0.9562 0.9762 0.2954 ] Network output: [ 0.002608 0.9956 0.007272 7.769e-05 -3.488e-05 0.9922 5.855e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05129 Epoch 3422 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06489 0.8884 0.9218 0.0001156 -5.188e-05 0.06041 8.71e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01473 -0.004954 0.007592 0.02753 0.95 0.9575 0.02553 0.8982 0.9163 0.07042 ] Network output: [ 0.9562 0.09099 0.0349 3.405e-05 -1.529e-05 -0.03815 2.566e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5542 0.07881 0.07083 0.3574 0.977 0.9896 0.6074 0.9121 0.9733 0.5319 ] Network output: [ 0.02759 0.9048 0.9374 1.231e-05 -5.529e-06 0.1027 9.281e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02029 0.01432 0.02384 0.02721 0.9873 0.9912 0.02058 0.9714 0.983 0.03083 ] Network output: [ 0.101 -0.2304 0.8275 2.356e-05 -1.058e-05 1.201 1.775e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5998 0.505 0.4178 0.4876 0.9794 0.9909 0.6012 0.9195 0.9766 0.5168 ] Network output: [ -0.07496 0.1732 1.158 -0.000137 6.153e-05 0.8181 -0.0001033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2771 0.27 0.2858 0.289 0.9877 0.9921 0.2772 0.9726 0.9835 0.2964 ] Network output: [ -0.07645 0.1759 1.112 -0.0001228 5.512e-05 0.8642 -9.252e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2938 0.2924 0.2928 0.2913 0.9831 0.9895 0.2938 0.9563 0.9762 0.2953 ] Network output: [ 0.002519 0.9958 0.00745 7.726e-05 -3.468e-05 0.992 5.822e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05125 Epoch 3423 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06485 0.8886 0.9218 0.0001155 -5.186e-05 0.06033 8.706e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01472 -0.004958 0.007565 0.02751 0.9501 0.9575 0.02552 0.8982 0.9163 0.0704 ] Network output: [ 0.9562 0.09094 0.03495 3.273e-05 -1.47e-05 -0.03818 2.467e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5541 0.07889 0.07088 0.3573 0.977 0.9896 0.6074 0.9122 0.9733 0.5318 ] Network output: [ 0.02753 0.9049 0.9374 1.224e-05 -5.494e-06 0.1027 9.223e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02029 0.01432 0.02384 0.0272 0.9873 0.9912 0.02058 0.9714 0.983 0.03082 ] Network output: [ 0.101 -0.2304 0.8273 2.398e-05 -1.077e-05 1.201 1.807e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5997 0.5051 0.4179 0.4875 0.9794 0.9909 0.6012 0.9195 0.9766 0.5167 ] Network output: [ -0.07488 0.1729 1.158 -0.0001363 6.12e-05 0.8183 -0.0001027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2771 0.27 0.2858 0.289 0.9877 0.9921 0.2772 0.9726 0.9836 0.2964 ] Network output: [ -0.07636 0.1757 1.112 -0.000122 5.479e-05 0.8642 -9.198e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2937 0.2923 0.2928 0.2913 0.9831 0.9895 0.2937 0.9563 0.9762 0.2953 ] Network output: [ 0.002431 0.996 0.007626 7.682e-05 -3.449e-05 0.9918 5.79e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05121 Epoch 3424 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06481 0.8887 0.9219 0.0001155 -5.184e-05 0.06025 8.703e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01471 -0.004962 0.007538 0.0275 0.9501 0.9575 0.02551 0.8982 0.9163 0.07038 ] Network output: [ 0.9562 0.09089 0.035 3.144e-05 -1.411e-05 -0.03821 2.369e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5541 0.07897 0.07093 0.3571 0.977 0.9896 0.6073 0.9122 0.9733 0.5317 ] Network output: [ 0.02748 0.9051 0.9374 1.216e-05 -5.459e-06 0.1026 9.163e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02028 0.01432 0.02383 0.02719 0.9874 0.9912 0.02058 0.9715 0.983 0.03081 ] Network output: [ 0.101 -0.2303 0.827 2.44e-05 -1.096e-05 1.201 1.839e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5997 0.5052 0.4179 0.4874 0.9794 0.9909 0.6012 0.9196 0.9766 0.5166 ] Network output: [ -0.0748 0.1726 1.158 -0.0001356 6.088e-05 0.8185 -0.0001022 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2771 0.27 0.2858 0.289 0.9877 0.9921 0.2772 0.9726 0.9836 0.2964 ] Network output: [ -0.07626 0.1755 1.112 -0.0001213 5.447e-05 0.8642 -9.144e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2936 0.2922 0.2927 0.2913 0.9831 0.9895 0.2936 0.9563 0.9762 0.2953 ] Network output: [ 0.002343 0.9962 0.007802 7.639e-05 -3.43e-05 0.9916 5.757e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05118 Epoch 3425 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06477 0.8889 0.9219 0.0001154 -5.182e-05 0.06018 8.699e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0147 -0.004966 0.007512 0.02748 0.9501 0.9576 0.0255 0.8983 0.9164 0.07036 ] Network output: [ 0.9562 0.09085 0.03505 3.016e-05 -1.354e-05 -0.03825 2.273e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.554 0.07904 0.07097 0.357 0.977 0.9896 0.6073 0.9123 0.9733 0.5316 ] Network output: [ 0.02742 0.9052 0.9374 1.208e-05 -5.422e-06 0.1026 9.102e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02028 0.01432 0.02383 0.02718 0.9874 0.9912 0.02057 0.9715 0.9831 0.03081 ] Network output: [ 0.101 -0.2302 0.8268 2.482e-05 -1.114e-05 1.202 1.87e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5997 0.5053 0.418 0.4873 0.9794 0.9909 0.6011 0.9196 0.9766 0.5165 ] Network output: [ -0.07472 0.1723 1.158 -0.0001349 6.056e-05 0.8186 -0.0001017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2771 0.27 0.2858 0.289 0.9877 0.9921 0.2772 0.9726 0.9836 0.2964 ] Network output: [ -0.07617 0.1753 1.112 -0.0001206 5.415e-05 0.8642 -9.091e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2935 0.2922 0.2927 0.2912 0.9831 0.9896 0.2935 0.9563 0.9762 0.2953 ] Network output: [ 0.002255 0.9964 0.007976 7.597e-05 -3.41e-05 0.9914 5.725e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05114 Epoch 3426 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06473 0.889 0.9219 0.0001154 -5.18e-05 0.0601 8.695e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01469 -0.00497 0.007485 0.02746 0.9501 0.9576 0.02549 0.8983 0.9164 0.07033 ] Network output: [ 0.9563 0.0908 0.0351 2.889e-05 -1.297e-05 -0.03828 2.177e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5539 0.07912 0.07102 0.3569 0.9771 0.9896 0.6073 0.9123 0.9733 0.5315 ] Network output: [ 0.02737 0.9053 0.9374 1.199e-05 -5.384e-06 0.1026 9.039e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02028 0.01432 0.02382 0.02717 0.9874 0.9912 0.02057 0.9715 0.9831 0.0308 ] Network output: [ 0.101 -0.2301 0.8266 2.523e-05 -1.133e-05 1.202 1.901e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5996 0.5053 0.418 0.4872 0.9795 0.9909 0.6011 0.9196 0.9767 0.5164 ] Network output: [ -0.07464 0.172 1.158 -0.0001342 6.024e-05 0.8188 -0.0001011 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2771 0.27 0.2859 0.289 0.9877 0.9921 0.2772 0.9726 0.9836 0.2964 ] Network output: [ -0.07608 0.1751 1.112 -0.0001199 5.384e-05 0.8642 -9.038e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2934 0.2921 0.2927 0.2912 0.9831 0.9896 0.2934 0.9563 0.9762 0.2952 ] Network output: [ 0.002168 0.9966 0.00815 7.554e-05 -3.391e-05 0.9912 5.693e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0511 Epoch 3427 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06469 0.8892 0.9219 0.0001153 -5.178e-05 0.06003 8.692e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01468 -0.004974 0.007458 0.02745 0.9501 0.9576 0.02548 0.8984 0.9164 0.07031 ] Network output: [ 0.9563 0.09075 0.03515 2.764e-05 -1.241e-05 -0.03831 2.083e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5539 0.0792 0.07106 0.3567 0.9771 0.9896 0.6072 0.9123 0.9733 0.5314 ] Network output: [ 0.02731 0.9054 0.9374 1.191e-05 -5.346e-06 0.1026 8.974e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02028 0.01432 0.02382 0.02716 0.9874 0.9912 0.02057 0.9715 0.9831 0.03079 ] Network output: [ 0.1009 -0.23 0.8264 2.563e-05 -1.151e-05 1.202 1.932e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5996 0.5054 0.418 0.4871 0.9795 0.9909 0.6011 0.9197 0.9767 0.5163 ] Network output: [ -0.07456 0.1717 1.158 -0.0001335 5.992e-05 0.819 -0.0001006 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.277 0.27 0.2859 0.289 0.9877 0.9921 0.2771 0.9727 0.9836 0.2964 ] Network output: [ -0.07599 0.1749 1.112 -0.0001192 5.352e-05 0.8642 -8.985e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2933 0.292 0.2927 0.2912 0.9831 0.9896 0.2933 0.9564 0.9763 0.2952 ] Network output: [ 0.002082 0.9968 0.008323 7.512e-05 -3.373e-05 0.991 5.662e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05107 Epoch 3428 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06465 0.8893 0.9219 0.0001153 -5.176e-05 0.05996 8.688e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01467 -0.004978 0.007431 0.02743 0.9501 0.9576 0.02547 0.8984 0.9165 0.07029 ] Network output: [ 0.9563 0.0907 0.03519 2.641e-05 -1.186e-05 -0.03835 1.991e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5538 0.07927 0.07111 0.3566 0.9771 0.9896 0.6072 0.9124 0.9734 0.5313 ] Network output: [ 0.02726 0.9056 0.9374 1.182e-05 -5.306e-06 0.1025 8.907e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02027 0.01432 0.02381 0.02715 0.9874 0.9912 0.02057 0.9715 0.9831 0.03078 ] Network output: [ 0.1009 -0.23 0.8262 2.603e-05 -1.169e-05 1.202 1.962e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5996 0.5055 0.4181 0.487 0.9795 0.991 0.6011 0.9197 0.9767 0.5163 ] Network output: [ -0.07448 0.1714 1.158 -0.0001328 5.961e-05 0.8192 -0.0001001 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.277 0.27 0.2859 0.289 0.9877 0.9921 0.2771 0.9727 0.9836 0.2964 ] Network output: [ -0.0759 0.1747 1.112 -0.0001185 5.321e-05 0.8642 -8.933e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2932 0.2919 0.2926 0.2911 0.9831 0.9896 0.2933 0.9564 0.9763 0.2952 ] Network output: [ 0.001996 0.997 0.008495 7.47e-05 -3.354e-05 0.9908 5.63e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05103 Epoch 3429 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06461 0.8895 0.9219 0.0001152 -5.173e-05 0.05988 8.685e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01466 -0.004982 0.007404 0.02742 0.9502 0.9576 0.02546 0.8985 0.9165 0.07027 ] Network output: [ 0.9563 0.09065 0.03524 2.52e-05 -1.131e-05 -0.03838 1.899e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5538 0.07935 0.07115 0.3565 0.9771 0.9896 0.6072 0.9124 0.9734 0.5312 ] Network output: [ 0.0272 0.9057 0.9374 1.173e-05 -5.266e-06 0.1025 8.839e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02027 0.01432 0.02381 0.02714 0.9874 0.9912 0.02056 0.9716 0.9831 0.03077 ] Network output: [ 0.1009 -0.2299 0.826 2.642e-05 -1.186e-05 1.202 1.991e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5995 0.5056 0.4181 0.4869 0.9795 0.991 0.601 0.9198 0.9767 0.5162 ] Network output: [ -0.0744 0.1711 1.158 -0.0001321 5.93e-05 0.8193 -9.955e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.277 0.27 0.2859 0.2891 0.9877 0.9921 0.2771 0.9727 0.9836 0.2964 ] Network output: [ -0.07581 0.1745 1.112 -0.0001178 5.291e-05 0.8642 -8.881e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2931 0.2918 0.2926 0.2911 0.9831 0.9896 0.2932 0.9564 0.9763 0.2951 ] Network output: [ 0.001911 0.9972 0.008667 7.429e-05 -3.335e-05 0.9907 5.599e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.051 Epoch 3430 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06457 0.8896 0.9219 0.0001152 -5.171e-05 0.05981 8.681e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01465 -0.004986 0.007377 0.0274 0.9502 0.9576 0.02545 0.8985 0.9165 0.07024 ] Network output: [ 0.9563 0.0906 0.03529 2.4e-05 -1.077e-05 -0.03841 1.809e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5537 0.07942 0.0712 0.3563 0.9771 0.9896 0.6071 0.9125 0.9734 0.5311 ] Network output: [ 0.02715 0.9058 0.9374 1.164e-05 -5.224e-06 0.1025 8.77e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02027 0.01432 0.0238 0.02713 0.9874 0.9912 0.02056 0.9716 0.9831 0.03076 ] Network output: [ 0.1009 -0.2298 0.8258 2.681e-05 -1.204e-05 1.202 2.021e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5995 0.5056 0.4182 0.4868 0.9795 0.991 0.601 0.9198 0.9767 0.5161 ] Network output: [ -0.07432 0.1708 1.158 -0.0001314 5.899e-05 0.8195 -9.903e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.277 0.2699 0.286 0.2891 0.9877 0.9921 0.2771 0.9727 0.9836 0.2965 ] Network output: [ -0.07571 0.1743 1.112 -0.0001172 5.26e-05 0.8642 -8.83e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2931 0.2917 0.2926 0.2911 0.9831 0.9896 0.2931 0.9564 0.9763 0.2951 ] Network output: [ 0.001827 0.9973 0.008837 7.387e-05 -3.316e-05 0.9905 5.567e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05096 Epoch 3431 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06453 0.8898 0.9219 0.0001151 -5.169e-05 0.05974 8.677e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01464 -0.004989 0.00735 0.02739 0.9502 0.9576 0.02544 0.8985 0.9166 0.07022 ] Network output: [ 0.9563 0.09056 0.03533 2.282e-05 -1.024e-05 -0.03844 1.719e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5536 0.0795 0.07124 0.3562 0.9771 0.9896 0.6071 0.9125 0.9734 0.531 ] Network output: [ 0.02709 0.9059 0.9374 1.154e-05 -5.182e-06 0.1025 8.698e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02026 0.01432 0.0238 0.02712 0.9874 0.9912 0.02056 0.9716 0.9831 0.03076 ] Network output: [ 0.1009 -0.2297 0.8256 2.72e-05 -1.221e-05 1.202 2.05e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5995 0.5057 0.4182 0.4867 0.9795 0.991 0.601 0.9198 0.9767 0.516 ] Network output: [ -0.07424 0.1705 1.158 -0.0001307 5.869e-05 0.8197 -9.852e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.277 0.2699 0.286 0.2891 0.9877 0.9921 0.2771 0.9727 0.9836 0.2965 ] Network output: [ -0.07562 0.1741 1.113 -0.0001165 5.23e-05 0.8642 -8.779e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.293 0.2916 0.2926 0.291 0.9831 0.9896 0.293 0.9565 0.9763 0.2951 ] Network output: [ 0.001742 0.9975 0.009007 7.346e-05 -3.298e-05 0.9903 5.536e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05092 Epoch 3432 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06449 0.8899 0.9219 0.0001151 -5.167e-05 0.05967 8.674e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01463 -0.004993 0.007323 0.02737 0.9502 0.9577 0.02543 0.8986 0.9166 0.0702 ] Network output: [ 0.9563 0.09051 0.03538 2.165e-05 -9.719e-06 -0.03848 1.631e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5536 0.07957 0.07129 0.356 0.9771 0.9896 0.6071 0.9126 0.9734 0.5309 ] Network output: [ 0.02704 0.9061 0.9374 1.145e-05 -5.138e-06 0.1025 8.625e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02026 0.01432 0.02379 0.02711 0.9874 0.9912 0.02056 0.9716 0.9831 0.03075 ] Network output: [ 0.1009 -0.2296 0.8253 2.757e-05 -1.238e-05 1.203 2.078e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5995 0.5058 0.4182 0.4865 0.9795 0.991 0.6009 0.9199 0.9767 0.5159 ] Network output: [ -0.07416 0.1702 1.158 -0.0001301 5.839e-05 0.8198 -9.802e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.277 0.2699 0.286 0.2891 0.9877 0.9921 0.2771 0.9727 0.9837 0.2965 ] Network output: [ -0.07553 0.1739 1.113 -0.0001158 5.2e-05 0.8642 -8.729e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2929 0.2916 0.2926 0.291 0.9831 0.9896 0.2929 0.9565 0.9763 0.2951 ] Network output: [ 0.001659 0.9977 0.009176 7.305e-05 -3.28e-05 0.9901 5.506e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05089 Epoch 3433 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06445 0.8901 0.9219 0.000115 -5.165e-05 0.05959 8.67e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01462 -0.004997 0.007296 0.02736 0.9502 0.9577 0.02542 0.8986 0.9166 0.07017 ] Network output: [ 0.9564 0.09046 0.03543 2.05e-05 -9.201e-06 -0.03851 1.545e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5535 0.07965 0.07133 0.3559 0.9771 0.9896 0.6071 0.9126 0.9735 0.5308 ] Network output: [ 0.02698 0.9062 0.9374 1.135e-05 -5.094e-06 0.1024 8.551e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02026 0.01432 0.02379 0.02709 0.9874 0.9912 0.02055 0.9716 0.9831 0.03074 ] Network output: [ 0.1009 -0.2295 0.8251 2.795e-05 -1.255e-05 1.203 2.106e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5994 0.5059 0.4183 0.4864 0.9795 0.991 0.6009 0.9199 0.9768 0.5158 ] Network output: [ -0.07408 0.17 1.158 -0.0001294 5.809e-05 0.82 -9.751e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2769 0.2699 0.286 0.2891 0.9878 0.9921 0.2771 0.9728 0.9837 0.2965 ] Network output: [ -0.07544 0.1737 1.113 -0.0001152 5.17e-05 0.8642 -8.679e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2928 0.2915 0.2925 0.291 0.9832 0.9896 0.2928 0.9565 0.9764 0.295 ] Network output: [ 0.001576 0.9979 0.009344 7.265e-05 -3.261e-05 0.9899 5.475e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05085 Epoch 3434 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06441 0.8902 0.9219 0.000115 -5.163e-05 0.05952 8.667e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01461 -0.005001 0.007269 0.02734 0.9502 0.9577 0.02541 0.8987 0.9167 0.07015 ] Network output: [ 0.9564 0.09041 0.03547 1.936e-05 -8.691e-06 -0.03854 1.459e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5534 0.07972 0.07138 0.3558 0.9771 0.9896 0.607 0.9126 0.9735 0.5308 ] Network output: [ 0.02693 0.9063 0.9374 1.125e-05 -5.049e-06 0.1024 8.475e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02026 0.01432 0.02378 0.02708 0.9874 0.9912 0.02055 0.9716 0.9832 0.03073 ] Network output: [ 0.1009 -0.2295 0.8249 2.832e-05 -1.271e-05 1.203 2.134e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5994 0.5059 0.4183 0.4863 0.9795 0.991 0.6009 0.92 0.9768 0.5158 ] Network output: [ -0.074 0.1697 1.158 -0.0001287 5.779e-05 0.8202 -9.701e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2769 0.2699 0.2861 0.2891 0.9878 0.9921 0.277 0.9728 0.9837 0.2965 ] Network output: [ -0.07536 0.1735 1.113 -0.0001145 5.14e-05 0.8642 -8.629e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2927 0.2914 0.2925 0.2909 0.9832 0.9896 0.2927 0.9565 0.9764 0.295 ] Network output: [ 0.001493 0.9981 0.009511 7.224e-05 -3.243e-05 0.9897 5.444e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05082 Epoch 3435 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06437 0.8904 0.9219 0.000115 -5.161e-05 0.05945 8.663e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0146 -0.005005 0.007242 0.02732 0.9503 0.9577 0.0254 0.8987 0.9167 0.07013 ] Network output: [ 0.9564 0.09035 0.03552 1.824e-05 -8.187e-06 -0.03857 1.374e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5534 0.0798 0.07142 0.3556 0.9771 0.9896 0.607 0.9127 0.9735 0.5307 ] Network output: [ 0.02687 0.9064 0.9375 1.114e-05 -5.002e-06 0.1024 8.398e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02025 0.01432 0.02378 0.02707 0.9874 0.9912 0.02055 0.9717 0.9832 0.03072 ] Network output: [ 0.1009 -0.2294 0.8247 2.868e-05 -1.288e-05 1.203 2.161e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5994 0.506 0.4184 0.4862 0.9795 0.991 0.6009 0.92 0.9768 0.5157 ] Network output: [ -0.07392 0.1694 1.158 -0.0001281 5.75e-05 0.8203 -9.652e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2769 0.2699 0.2861 0.2891 0.9878 0.9922 0.277 0.9728 0.9837 0.2965 ] Network output: [ -0.07527 0.1733 1.113 -0.0001138 5.111e-05 0.8642 -8.58e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2926 0.2913 0.2925 0.2909 0.9832 0.9896 0.2926 0.9566 0.9764 0.295 ] Network output: [ 0.001411 0.9983 0.009678 7.184e-05 -3.225e-05 0.9895 5.414e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05078 Epoch 3436 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06434 0.8905 0.9219 0.0001149 -5.158e-05 0.05938 8.66e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01459 -0.005009 0.007214 0.02731 0.9503 0.9577 0.02539 0.8987 0.9167 0.07011 ] Network output: [ 0.9564 0.0903 0.03556 1.713e-05 -7.69e-06 -0.0386 1.291e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5533 0.07987 0.07146 0.3555 0.9771 0.9897 0.607 0.9127 0.9735 0.5306 ] Network output: [ 0.02682 0.9066 0.9375 1.104e-05 -4.956e-06 0.1024 8.319e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02025 0.01432 0.02377 0.02706 0.9874 0.9912 0.02055 0.9717 0.9832 0.03071 ] Network output: [ 0.1009 -0.2293 0.8245 2.904e-05 -1.304e-05 1.203 2.188e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5993 0.5061 0.4184 0.4861 0.9795 0.991 0.6008 0.92 0.9768 0.5156 ] Network output: [ -0.07384 0.1691 1.158 -0.0001274 5.72e-05 0.8205 -9.603e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2769 0.2699 0.2861 0.2891 0.9878 0.9922 0.277 0.9728 0.9837 0.2965 ] Network output: [ -0.07518 0.1731 1.113 -0.0001132 5.082e-05 0.8642 -8.531e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2925 0.2912 0.2925 0.2909 0.9832 0.9896 0.2926 0.9566 0.9764 0.2949 ] Network output: [ 0.00133 0.9984 0.009844 7.144e-05 -3.207e-05 0.9894 5.384e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05075 Epoch 3437 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0643 0.8906 0.9219 0.0001149 -5.156e-05 0.05931 8.656e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01458 -0.005013 0.007187 0.02729 0.9503 0.9577 0.02538 0.8988 0.9168 0.07008 ] Network output: [ 0.9564 0.09025 0.03561 1.604e-05 -7.2e-06 -0.03864 1.209e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5533 0.07995 0.07151 0.3554 0.9772 0.9897 0.6069 0.9128 0.9735 0.5305 ] Network output: [ 0.02676 0.9067 0.9375 1.093e-05 -4.908e-06 0.1024 8.239e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02025 0.01432 0.02377 0.02705 0.9874 0.9912 0.02054 0.9717 0.9832 0.0307 ] Network output: [ 0.1009 -0.2292 0.8243 2.939e-05 -1.32e-05 1.203 2.215e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5993 0.5062 0.4184 0.486 0.9795 0.991 0.6008 0.9201 0.9768 0.5155 ] Network output: [ -0.07376 0.1689 1.158 -0.0001268 5.692e-05 0.8206 -9.554e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2769 0.2699 0.2861 0.2891 0.9878 0.9922 0.277 0.9728 0.9837 0.2965 ] Network output: [ -0.07509 0.1729 1.113 -0.0001126 5.053e-05 0.8642 -8.482e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2924 0.2911 0.2924 0.2908 0.9832 0.9896 0.2925 0.9566 0.9764 0.2949 ] Network output: [ 0.001249 0.9986 0.01001 7.105e-05 -3.19e-05 0.9892 5.354e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05072 Epoch 3438 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06426 0.8908 0.9219 0.0001148 -5.154e-05 0.05924 8.652e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01457 -0.005017 0.00716 0.02728 0.9503 0.9577 0.02537 0.8988 0.9168 0.07006 ] Network output: [ 0.9564 0.0902 0.03565 1.496e-05 -6.716e-06 -0.03867 1.127e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5532 0.08002 0.07155 0.3552 0.9772 0.9897 0.6069 0.9128 0.9735 0.5304 ] Network output: [ 0.02671 0.9068 0.9375 1.082e-05 -4.859e-06 0.1023 8.157e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02024 0.01432 0.02376 0.02704 0.9874 0.9912 0.02054 0.9717 0.9832 0.0307 ] Network output: [ 0.1009 -0.2291 0.8241 2.974e-05 -1.335e-05 1.203 2.241e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5993 0.5062 0.4185 0.4859 0.9795 0.991 0.6008 0.9201 0.9768 0.5154 ] Network output: [ -0.07368 0.1686 1.157 -0.0001261 5.663e-05 0.8208 -9.506e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2769 0.2699 0.2861 0.2891 0.9878 0.9922 0.277 0.9728 0.9837 0.2965 ] Network output: [ -0.075 0.1727 1.113 -0.0001119 5.024e-05 0.8642 -8.434e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2924 0.2911 0.2924 0.2908 0.9832 0.9896 0.2924 0.9566 0.9764 0.2949 ] Network output: [ 0.001168 0.9988 0.01017 7.065e-05 -3.172e-05 0.989 5.325e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05068 Epoch 3439 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06422 0.8909 0.9219 0.0001148 -5.152e-05 0.05917 8.649e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01456 -0.005021 0.007133 0.02726 0.9503 0.9578 0.02536 0.8989 0.9168 0.07004 ] Network output: [ 0.9565 0.09015 0.03569 1.389e-05 -6.238e-06 -0.0387 1.047e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5532 0.08009 0.07159 0.3551 0.9772 0.9897 0.6069 0.9129 0.9736 0.5303 ] Network output: [ 0.02665 0.9069 0.9375 1.071e-05 -4.81e-06 0.1023 8.074e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02024 0.01432 0.02375 0.02703 0.9874 0.9912 0.02054 0.9717 0.9832 0.03069 ] Network output: [ 0.1008 -0.229 0.8239 3.009e-05 -1.351e-05 1.204 2.267e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5993 0.5063 0.4185 0.4858 0.9796 0.991 0.6007 0.9202 0.9769 0.5154 ] Network output: [ -0.07361 0.1683 1.157 -0.0001255 5.634e-05 0.821 -9.458e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2768 0.2699 0.2862 0.2891 0.9878 0.9922 0.277 0.9728 0.9837 0.2965 ] Network output: [ -0.07491 0.1725 1.113 -0.0001113 4.996e-05 0.8642 -8.387e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2923 0.291 0.2924 0.2908 0.9832 0.9896 0.2923 0.9567 0.9764 0.2949 ] Network output: [ 0.001088 0.999 0.01034 7.026e-05 -3.154e-05 0.9888 5.295e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05065 Epoch 3440 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06418 0.8911 0.9219 0.0001147 -5.15e-05 0.05911 8.645e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01455 -0.005025 0.007106 0.02725 0.9503 0.9578 0.02535 0.8989 0.9169 0.07001 ] Network output: [ 0.9565 0.0901 0.03574 1.285e-05 -5.767e-06 -0.03873 9.68e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5531 0.08017 0.07164 0.355 0.9772 0.9897 0.6068 0.9129 0.9736 0.5302 ] Network output: [ 0.0266 0.9071 0.9375 1.06e-05 -4.759e-06 0.1023 7.99e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02024 0.01432 0.02375 0.02702 0.9874 0.9912 0.02053 0.9717 0.9832 0.03068 ] Network output: [ 0.1008 -0.2289 0.8237 3.043e-05 -1.366e-05 1.204 2.293e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5992 0.5064 0.4186 0.4857 0.9796 0.991 0.6007 0.9202 0.9769 0.5153 ] Network output: [ -0.07353 0.168 1.157 -0.0001249 5.606e-05 0.8211 -9.411e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2768 0.2699 0.2862 0.2891 0.9878 0.9922 0.2769 0.9729 0.9837 0.2966 ] Network output: [ -0.07483 0.1723 1.113 -0.0001107 4.968e-05 0.8642 -8.339e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2922 0.2909 0.2924 0.2907 0.9832 0.9896 0.2922 0.9567 0.9765 0.2948 ] Network output: [ 0.001009 0.9991 0.0105 6.987e-05 -3.137e-05 0.9886 5.266e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05061 Epoch 3441 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06414 0.8912 0.9219 0.0001147 -5.148e-05 0.05904 8.642e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01454 -0.005029 0.007079 0.02723 0.9504 0.9578 0.02534 0.8989 0.9169 0.06999 ] Network output: [ 0.9565 0.09005 0.03578 1.181e-05 -5.302e-06 -0.03876 8.9e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.553 0.08024 0.07168 0.3548 0.9772 0.9897 0.6068 0.913 0.9736 0.5301 ] Network output: [ 0.02654 0.9072 0.9375 1.049e-05 -4.708e-06 0.1023 7.904e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02024 0.01432 0.02374 0.02701 0.9875 0.9912 0.02053 0.9718 0.9832 0.03067 ] Network output: [ 0.1008 -0.2288 0.8235 3.076e-05 -1.381e-05 1.204 2.318e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5992 0.5064 0.4186 0.4856 0.9796 0.991 0.6007 0.9203 0.9769 0.5152 ] Network output: [ -0.07345 0.1678 1.157 -0.0001243 5.578e-05 0.8213 -9.364e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2768 0.2699 0.2862 0.2891 0.9878 0.9922 0.2769 0.9729 0.9837 0.2966 ] Network output: [ -0.07474 0.1721 1.113 -0.00011 4.94e-05 0.8642 -8.292e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2921 0.2908 0.2923 0.2907 0.9832 0.9896 0.2921 0.9567 0.9765 0.2948 ] Network output: [ 0.0009301 0.9993 0.01066 6.949e-05 -3.119e-05 0.9885 5.237e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05058 Epoch 3442 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0641 0.8913 0.922 0.0001146 -5.146e-05 0.05897 8.638e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01453 -0.005033 0.007052 0.02722 0.9504 0.9578 0.02533 0.899 0.9169 0.06997 ] Network output: [ 0.9565 0.09 0.03582 1.079e-05 -4.843e-06 -0.03879 8.129e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.553 0.08031 0.07172 0.3547 0.9772 0.9897 0.6068 0.913 0.9736 0.53 ] Network output: [ 0.02649 0.9073 0.9375 1.037e-05 -4.657e-06 0.1023 7.817e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02023 0.01432 0.02374 0.027 0.9875 0.9912 0.02053 0.9718 0.9832 0.03066 ] Network output: [ 0.1008 -0.2288 0.8233 3.109e-05 -1.396e-05 1.204 2.343e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5992 0.5065 0.4186 0.4855 0.9796 0.991 0.6007 0.9203 0.9769 0.5151 ] Network output: [ -0.07337 0.1675 1.157 -0.0001236 5.55e-05 0.8214 -9.317e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2768 0.2699 0.2862 0.2891 0.9878 0.9922 0.2769 0.9729 0.9838 0.2966 ] Network output: [ -0.07465 0.1719 1.113 -0.0001094 4.912e-05 0.8642 -8.246e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.292 0.2907 0.2923 0.2907 0.9832 0.9896 0.292 0.9567 0.9765 0.2948 ] Network output: [ 0.0008516 0.9995 0.01082 6.91e-05 -3.102e-05 0.9883 5.208e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05055 Epoch 3443 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06406 0.8915 0.922 0.0001146 -5.144e-05 0.0589 8.635e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01452 -0.005037 0.007024 0.0272 0.9504 0.9578 0.02532 0.899 0.917 0.06994 ] Network output: [ 0.9565 0.08994 0.03586 9.778e-06 -4.39e-06 -0.03882 7.369e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5529 0.08039 0.07177 0.3546 0.9772 0.9897 0.6068 0.913 0.9736 0.5299 ] Network output: [ 0.02644 0.9074 0.9375 1.026e-05 -4.604e-06 0.1023 7.729e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02023 0.01432 0.02373 0.02699 0.9875 0.9912 0.02053 0.9718 0.9833 0.03065 ] Network output: [ 0.1008 -0.2287 0.8231 3.142e-05 -1.41e-05 1.204 2.368e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5991 0.5066 0.4187 0.4854 0.9796 0.991 0.6006 0.9203 0.9769 0.515 ] Network output: [ -0.0733 0.1672 1.157 -0.000123 5.523e-05 0.8216 -9.271e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2768 0.2699 0.2863 0.2891 0.9878 0.9922 0.2769 0.9729 0.9838 0.2966 ] Network output: [ -0.07456 0.1717 1.113 -0.0001088 4.884e-05 0.8642 -8.2e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2919 0.2906 0.2923 0.2906 0.9832 0.9896 0.292 0.9568 0.9765 0.2947 ] Network output: [ 0.0007736 0.9996 0.01098 6.872e-05 -3.085e-05 0.9881 5.179e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05051 Epoch 3444 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06402 0.8916 0.922 0.0001145 -5.142e-05 0.05884 8.631e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01451 -0.00504 0.006997 0.02718 0.9504 0.9578 0.02531 0.8991 0.917 0.06992 ] Network output: [ 0.9565 0.08989 0.0359 8.783e-06 -3.943e-06 -0.03885 6.619e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5529 0.08046 0.07181 0.3544 0.9772 0.9897 0.6067 0.9131 0.9736 0.5299 ] Network output: [ 0.02638 0.9075 0.9375 1.014e-05 -4.551e-06 0.1022 7.639e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02023 0.01432 0.02373 0.02698 0.9875 0.9912 0.02052 0.9718 0.9833 0.03064 ] Network output: [ 0.1008 -0.2286 0.823 3.174e-05 -1.425e-05 1.204 2.392e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5991 0.5067 0.4187 0.4852 0.9796 0.991 0.6006 0.9204 0.9769 0.515 ] Network output: [ -0.07322 0.167 1.157 -0.0001224 5.496e-05 0.8217 -9.225e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2768 0.2699 0.2863 0.2891 0.9878 0.9922 0.2769 0.9729 0.9838 0.2966 ] Network output: [ -0.07448 0.1715 1.113 -0.0001082 4.857e-05 0.8642 -8.154e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2918 0.2906 0.2923 0.2906 0.9832 0.9896 0.2919 0.9568 0.9765 0.2947 ] Network output: [ 0.0006962 0.9998 0.01114 6.834e-05 -3.068e-05 0.9879 5.15e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05048 Epoch 3445 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06398 0.8918 0.922 0.0001145 -5.139e-05 0.05877 8.628e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0145 -0.005044 0.00697 0.02717 0.9504 0.9578 0.0253 0.8991 0.917 0.0699 ] Network output: [ 0.9566 0.08984 0.03594 7.801e-06 -3.502e-06 -0.03888 5.879e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5528 0.08053 0.07185 0.3543 0.9772 0.9897 0.6067 0.9131 0.9737 0.5298 ] Network output: [ 0.02633 0.9076 0.9375 1.002e-05 -4.497e-06 0.1022 7.549e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02023 0.01432 0.02372 0.02697 0.9875 0.9913 0.02052 0.9718 0.9833 0.03063 ] Network output: [ 0.1008 -0.2285 0.8228 3.206e-05 -1.439e-05 1.204 2.416e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5991 0.5067 0.4188 0.4851 0.9796 0.991 0.6006 0.9204 0.9769 0.5149 ] Network output: [ -0.07314 0.1667 1.157 -0.0001218 5.468e-05 0.8219 -9.18e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2768 0.2699 0.2863 0.2891 0.9878 0.9922 0.2769 0.9729 0.9838 0.2966 ] Network output: [ -0.07439 0.1714 1.113 -0.0001076 4.83e-05 0.8642 -8.108e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2918 0.2905 0.2922 0.2906 0.9832 0.9897 0.2918 0.9568 0.9765 0.2947 ] Network output: [ 0.0006192 1 0.0113 6.796e-05 -3.051e-05 0.9878 5.122e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05045 Epoch 3446 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06395 0.8919 0.922 0.0001144 -5.137e-05 0.0587 8.624e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01449 -0.005048 0.006943 0.02715 0.9504 0.9579 0.02529 0.8992 0.9171 0.06987 ] Network output: [ 0.9566 0.08979 0.03598 6.832e-06 -3.067e-06 -0.03891 5.149e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5527 0.0806 0.07189 0.3542 0.9772 0.9897 0.6067 0.9132 0.9737 0.5297 ] Network output: [ 0.02627 0.9078 0.9375 9.895e-06 -4.442e-06 0.1022 7.457e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02022 0.01432 0.02371 0.02695 0.9875 0.9913 0.02052 0.9719 0.9833 0.03062 ] Network output: [ 0.1008 -0.2284 0.8226 3.237e-05 -1.453e-05 1.204 2.439e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5991 0.5068 0.4188 0.485 0.9796 0.991 0.6006 0.9205 0.977 0.5148 ] Network output: [ -0.07306 0.1665 1.157 -0.0001212 5.442e-05 0.8221 -9.135e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2768 0.2699 0.2863 0.2891 0.9878 0.9922 0.2769 0.973 0.9838 0.2966 ] Network output: [ -0.07431 0.1712 1.113 -0.000107 4.803e-05 0.8642 -8.063e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2917 0.2904 0.2922 0.2905 0.9832 0.9897 0.2917 0.9568 0.9766 0.2946 ] Network output: [ 0.0005427 1 0.01146 6.759e-05 -3.034e-05 0.9876 5.094e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05041 Epoch 3447 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06391 0.892 0.922 0.0001144 -5.135e-05 0.05864 8.621e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01448 -0.005052 0.006915 0.02714 0.9505 0.9579 0.02528 0.8992 0.9171 0.06985 ] Network output: [ 0.9566 0.08973 0.03602 5.876e-06 -2.638e-06 -0.03895 4.428e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5527 0.08068 0.07193 0.354 0.9772 0.9897 0.6066 0.9132 0.9737 0.5296 ] Network output: [ 0.02622 0.9079 0.9375 9.771e-06 -4.387e-06 0.1022 7.364e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02022 0.01432 0.02371 0.02694 0.9875 0.9913 0.02052 0.9719 0.9833 0.03062 ] Network output: [ 0.1007 -0.2283 0.8224 3.268e-05 -1.467e-05 1.205 2.463e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.599 0.5069 0.4188 0.4849 0.9796 0.991 0.6005 0.9205 0.977 0.5147 ] Network output: [ -0.07299 0.1662 1.157 -0.0001206 5.415e-05 0.8222 -9.09e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2767 0.2698 0.2863 0.2891 0.9878 0.9922 0.2769 0.973 0.9838 0.2966 ] Network output: [ -0.07422 0.171 1.113 -0.0001064 4.776e-05 0.8642 -8.018e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2916 0.2903 0.2922 0.2905 0.9832 0.9897 0.2916 0.9569 0.9766 0.2946 ] Network output: [ 0.0004666 1 0.01161 6.721e-05 -3.018e-05 0.9874 5.066e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05038 Epoch 3448 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06387 0.8922 0.922 0.0001143 -5.133e-05 0.05857 8.617e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01447 -0.005056 0.006888 0.02712 0.9505 0.9579 0.02527 0.8992 0.9171 0.06982 ] Network output: [ 0.9566 0.08968 0.03606 4.932e-06 -2.214e-06 -0.03898 3.717e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5526 0.08075 0.07197 0.3539 0.9772 0.9897 0.6066 0.9133 0.9737 0.5295 ] Network output: [ 0.02616 0.908 0.9375 9.646e-06 -4.331e-06 0.1022 7.27e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02022 0.01432 0.0237 0.02693 0.9875 0.9913 0.02051 0.9719 0.9833 0.03061 ] Network output: [ 0.1007 -0.2282 0.8222 3.298e-05 -1.481e-05 1.205 2.485e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.599 0.507 0.4189 0.4848 0.9796 0.991 0.6005 0.9205 0.977 0.5146 ] Network output: [ -0.07291 0.1659 1.157 -0.00012 5.388e-05 0.8224 -9.046e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2767 0.2698 0.2864 0.2891 0.9878 0.9922 0.2768 0.973 0.9838 0.2966 ] Network output: [ -0.07413 0.1708 1.113 -0.0001058 4.75e-05 0.8642 -7.974e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2915 0.2902 0.2922 0.2905 0.9833 0.9897 0.2915 0.9569 0.9766 0.2946 ] Network output: [ 0.000391 1 0.01177 6.684e-05 -3.001e-05 0.9872 5.038e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05035 Epoch 3449 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06383 0.8923 0.922 0.0001143 -5.131e-05 0.05851 8.614e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01446 -0.00506 0.006861 0.0271 0.9505 0.9579 0.02526 0.8993 0.9172 0.0698 ] Network output: [ 0.9566 0.08963 0.0361 4.001e-06 -1.796e-06 -0.03901 3.015e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5526 0.08082 0.07202 0.3538 0.9773 0.9897 0.6066 0.9133 0.9737 0.5294 ] Network output: [ 0.02611 0.9081 0.9375 9.52e-06 -4.274e-06 0.1022 7.174e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02022 0.01432 0.0237 0.02692 0.9875 0.9913 0.02051 0.9719 0.9833 0.0306 ] Network output: [ 0.1007 -0.2281 0.822 3.328e-05 -1.494e-05 1.205 2.508e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.599 0.507 0.4189 0.4847 0.9796 0.991 0.6005 0.9206 0.977 0.5146 ] Network output: [ -0.07283 0.1657 1.157 -0.0001194 5.362e-05 0.8225 -9.002e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2767 0.2698 0.2864 0.2891 0.9878 0.9922 0.2768 0.973 0.9838 0.2966 ] Network output: [ -0.07405 0.1706 1.113 -0.0001052 4.724e-05 0.8642 -7.93e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2914 0.2901 0.2921 0.2904 0.9833 0.9897 0.2914 0.9569 0.9766 0.2946 ] Network output: [ 0.0003159 1.001 0.01193 6.648e-05 -2.984e-05 0.9871 5.01e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05032 Epoch 3450 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06379 0.8924 0.922 0.0001142 -5.129e-05 0.05844 8.61e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01445 -0.005064 0.006833 0.02709 0.9505 0.9579 0.02525 0.8993 0.9172 0.06978 ] Network output: [ 0.9567 0.08957 0.03614 3.083e-06 -1.384e-06 -0.03904 2.323e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5525 0.08089 0.07206 0.3536 0.9773 0.9897 0.6065 0.9134 0.9738 0.5293 ] Network output: [ 0.02606 0.9082 0.9376 9.392e-06 -4.216e-06 0.1021 7.078e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02021 0.01432 0.02369 0.02691 0.9875 0.9913 0.02051 0.9719 0.9833 0.03059 ] Network output: [ 0.1007 -0.228 0.8218 3.357e-05 -1.507e-05 1.205 2.53e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.599 0.5071 0.419 0.4846 0.9796 0.9911 0.6004 0.9206 0.977 0.5145 ] Network output: [ -0.07276 0.1654 1.157 -0.0001189 5.336e-05 0.8227 -8.958e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2767 0.2698 0.2864 0.2891 0.9878 0.9922 0.2768 0.973 0.9838 0.2966 ] Network output: [ -0.07396 0.1704 1.113 -0.0001046 4.698e-05 0.8642 -7.886e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2913 0.2901 0.2921 0.2904 0.9833 0.9897 0.2914 0.9569 0.9766 0.2945 ] Network output: [ 0.0002413 1.001 0.01208 6.611e-05 -2.968e-05 0.9869 4.982e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05028 Epoch 3451 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06375 0.8926 0.922 0.0001142 -5.127e-05 0.05838 8.607e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01444 -0.005068 0.006806 0.02707 0.9505 0.9579 0.02524 0.8994 0.9172 0.06975 ] Network output: [ 0.9567 0.08952 0.03618 2.176e-06 -9.77e-07 -0.03907 1.64e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5524 0.08096 0.0721 0.3535 0.9773 0.9897 0.6065 0.9134 0.9738 0.5292 ] Network output: [ 0.026 0.9084 0.9376 9.263e-06 -4.158e-06 0.1021 6.981e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02021 0.01432 0.02369 0.0269 0.9875 0.9913 0.02051 0.9719 0.9833 0.03058 ] Network output: [ 0.1007 -0.2279 0.8216 3.386e-05 -1.52e-05 1.205 2.552e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5989 0.5072 0.419 0.4845 0.9796 0.9911 0.6004 0.9207 0.977 0.5144 ] Network output: [ -0.07268 0.1652 1.157 -0.0001183 5.31e-05 0.8228 -8.915e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2767 0.2698 0.2864 0.2891 0.9878 0.9922 0.2768 0.973 0.9838 0.2967 ] Network output: [ -0.07388 0.1702 1.113 -0.0001041 4.672e-05 0.8642 -7.842e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2913 0.29 0.2921 0.2904 0.9833 0.9897 0.2913 0.9569 0.9766 0.2945 ] Network output: [ 0.0001671 1.001 0.01223 6.575e-05 -2.952e-05 0.9867 4.955e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05025 Epoch 3452 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06371 0.8927 0.922 0.0001142 -5.125e-05 0.05831 8.603e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01443 -0.005072 0.006779 0.02706 0.9505 0.958 0.02523 0.8994 0.9173 0.06973 ] Network output: [ 0.9567 0.08947 0.03622 1.282e-06 -5.756e-07 -0.0391 9.662e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5524 0.08104 0.07214 0.3534 0.9773 0.9897 0.6065 0.9134 0.9738 0.5291 ] Network output: [ 0.02595 0.9085 0.9376 9.132e-06 -4.1e-06 0.1021 6.882e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02021 0.01432 0.02368 0.02689 0.9875 0.9913 0.0205 0.972 0.9834 0.03057 ] Network output: [ 0.1007 -0.2279 0.8214 3.415e-05 -1.533e-05 1.205 2.574e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5989 0.5072 0.419 0.4844 0.9797 0.9911 0.6004 0.9207 0.9771 0.5143 ] Network output: [ -0.07261 0.1649 1.157 -0.0001177 5.285e-05 0.8229 -8.872e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2767 0.2698 0.2864 0.2891 0.9878 0.9922 0.2768 0.973 0.9839 0.2967 ] Network output: [ -0.0738 0.17 1.113 -0.0001035 4.646e-05 0.8641 -7.799e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2912 0.2899 0.2921 0.2903 0.9833 0.9897 0.2912 0.957 0.9766 0.2945 ] Network output: [ 9.341e-05 1.001 0.01239 6.539e-05 -2.936e-05 0.9866 4.928e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05022 Epoch 3453 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06368 0.8928 0.922 0.0001141 -5.123e-05 0.05825 8.6e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01442 -0.005076 0.006751 0.02704 0.9506 0.958 0.02522 0.8994 0.9173 0.0697 ] Network output: [ 0.9567 0.08941 0.03625 3.997e-07 -1.795e-07 -0.03912 3.013e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5523 0.08111 0.07218 0.3533 0.9773 0.9897 0.6065 0.9135 0.9738 0.5291 ] Network output: [ 0.02589 0.9086 0.9376 9e-06 -4.04e-06 0.1021 6.782e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0202 0.01432 0.02367 0.02688 0.9875 0.9913 0.0205 0.972 0.9834 0.03056 ] Network output: [ 0.1006 -0.2278 0.8213 3.443e-05 -1.546e-05 1.205 2.595e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5989 0.5073 0.4191 0.4843 0.9797 0.9911 0.6004 0.9207 0.9771 0.5142 ] Network output: [ -0.07253 0.1647 1.157 -0.0001172 5.26e-05 0.8231 -8.829e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2767 0.2698 0.2865 0.2891 0.9878 0.9922 0.2768 0.9731 0.9839 0.2967 ] Network output: [ -0.07371 0.1698 1.113 -0.0001029 4.62e-05 0.8641 -7.756e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2911 0.2898 0.292 0.2903 0.9833 0.9897 0.2911 0.957 0.9767 0.2944 ] Network output: [ 2.015e-05 1.001 0.01254 6.503e-05 -2.919e-05 0.9864 4.901e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05019 Epoch 3454 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06364 0.893 0.922 0.0001141 -5.121e-05 0.05819 8.596e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01441 -0.00508 0.006724 0.02703 0.9506 0.958 0.02521 0.8995 0.9173 0.06968 ] Network output: [ 0.9568 0.08936 0.03629 -4.708e-07 2.114e-07 -0.03915 -3.548e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5523 0.08118 0.07222 0.3531 0.9773 0.9898 0.6064 0.9135 0.9738 0.529 ] Network output: [ 0.02584 0.9087 0.9376 8.866e-06 -3.98e-06 0.1021 6.682e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0202 0.01432 0.02367 0.02686 0.9875 0.9913 0.0205 0.972 0.9834 0.03055 ] Network output: [ 0.1006 -0.2277 0.8211 3.471e-05 -1.558e-05 1.205 2.616e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5988 0.5074 0.4191 0.4842 0.9797 0.9911 0.6003 0.9208 0.9771 0.5142 ] Network output: [ -0.07246 0.1644 1.157 -0.0001166 5.234e-05 0.8232 -8.787e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2766 0.2698 0.2865 0.2891 0.9879 0.9922 0.2768 0.9731 0.9839 0.2967 ] Network output: [ -0.07363 0.1697 1.113 -0.0001024 4.595e-05 0.8641 -7.714e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.291 0.2897 0.292 0.2903 0.9833 0.9897 0.291 0.957 0.9767 0.2944 ] Network output: [ -5.266e-05 1.001 0.01269 6.467e-05 -2.903e-05 0.9862 4.874e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05016 Epoch 3455 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0636 0.8931 0.922 0.000114 -5.119e-05 0.05813 8.593e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0144 -0.005084 0.006696 0.02701 0.9506 0.958 0.0252 0.8995 0.9174 0.06966 ] Network output: [ 0.9568 0.08931 0.03633 -1.33e-06 5.97e-07 -0.03918 -1.002e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5522 0.08125 0.07226 0.353 0.9773 0.9898 0.6064 0.9136 0.9738 0.5289 ] Network output: [ 0.02579 0.9088 0.9376 8.731e-06 -3.92e-06 0.1021 6.58e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0202 0.01432 0.02366 0.02685 0.9875 0.9913 0.0205 0.972 0.9834 0.03054 ] Network output: [ 0.1006 -0.2276 0.8209 3.499e-05 -1.571e-05 1.206 2.637e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5988 0.5075 0.4191 0.4841 0.9797 0.9911 0.6003 0.9208 0.9771 0.5141 ] Network output: [ -0.07238 0.1642 1.157 -0.000116 5.209e-05 0.8234 -8.745e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2766 0.2698 0.2865 0.2891 0.9879 0.9922 0.2767 0.9731 0.9839 0.2967 ] Network output: [ -0.07354 0.1695 1.113 -0.0001018 4.57e-05 0.8641 -7.672e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2909 0.2897 0.292 0.2902 0.9833 0.9897 0.2909 0.957 0.9767 0.2944 ] Network output: [ -0.000125 1.002 0.01284 6.432e-05 -2.888e-05 0.9861 4.847e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05012 Epoch 3456 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06356 0.8932 0.922 0.000114 -5.117e-05 0.05806 8.59e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01439 -0.005088 0.006669 0.02699 0.9506 0.958 0.02519 0.8996 0.9174 0.06963 ] Network output: [ 0.9568 0.08925 0.03636 -2.177e-06 9.774e-07 -0.03921 -1.641e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5522 0.08132 0.0723 0.3529 0.9773 0.9898 0.6064 0.9136 0.9739 0.5288 ] Network output: [ 0.02573 0.9089 0.9376 8.595e-06 -3.859e-06 0.102 6.478e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0202 0.01432 0.02365 0.02684 0.9875 0.9913 0.02049 0.972 0.9834 0.03053 ] Network output: [ 0.1006 -0.2275 0.8207 3.526e-05 -1.583e-05 1.206 2.657e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5988 0.5075 0.4192 0.484 0.9797 0.9911 0.6003 0.9209 0.9771 0.514 ] Network output: [ -0.07231 0.1639 1.157 -0.0001155 5.185e-05 0.8235 -8.703e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2766 0.2698 0.2865 0.2891 0.9879 0.9922 0.2767 0.9731 0.9839 0.2967 ] Network output: [ -0.07346 0.1693 1.113 -0.0001012 4.545e-05 0.8641 -7.63e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2908 0.2896 0.292 0.2902 0.9833 0.9897 0.2909 0.9571 0.9767 0.2944 ] Network output: [ -0.000197 1.002 0.01299 6.397e-05 -2.872e-05 0.9859 4.821e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05009 Epoch 3457 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06352 0.8934 0.9221 0.0001139 -5.115e-05 0.058 8.586e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01438 -0.005092 0.006642 0.02698 0.9506 0.958 0.02518 0.8996 0.9174 0.06961 ] Network output: [ 0.9568 0.0892 0.0364 -3.013e-06 1.353e-06 -0.03924 -2.271e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5521 0.08139 0.07234 0.3527 0.9773 0.9898 0.6063 0.9137 0.9739 0.5287 ] Network output: [ 0.02568 0.909 0.9376 8.458e-06 -3.797e-06 0.102 6.374e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02019 0.01432 0.02365 0.02683 0.9875 0.9913 0.02049 0.9721 0.9834 0.03052 ] Network output: [ 0.1006 -0.2274 0.8205 3.552e-05 -1.595e-05 1.206 2.677e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5988 0.5076 0.4192 0.4839 0.9797 0.9911 0.6003 0.9209 0.9771 0.5139 ] Network output: [ -0.07223 0.1637 1.157 -0.0001149 5.16e-05 0.8237 -8.662e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2766 0.2698 0.2865 0.2891 0.9879 0.9922 0.2767 0.9731 0.9839 0.2967 ] Network output: [ -0.07338 0.1691 1.113 -0.0001007 4.52e-05 0.8641 -7.588e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2908 0.2895 0.2919 0.2902 0.9833 0.9897 0.2908 0.9571 0.9767 0.2943 ] Network output: [ -0.0002684 1.002 0.01314 6.362e-05 -2.856e-05 0.9857 4.795e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05006 Epoch 3458 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06349 0.8935 0.9221 0.0001139 -5.113e-05 0.05794 8.583e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01437 -0.005096 0.006614 0.02696 0.9506 0.958 0.02517 0.8997 0.9175 0.06958 ] Network output: [ 0.9568 0.08914 0.03643 -3.838e-06 1.723e-06 -0.03927 -2.893e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.552 0.08146 0.07238 0.3526 0.9773 0.9898 0.6063 0.9137 0.9739 0.5286 ] Network output: [ 0.02563 0.9092 0.9376 8.319e-06 -3.735e-06 0.102 6.269e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02019 0.01432 0.02364 0.02682 0.9875 0.9913 0.02049 0.9721 0.9834 0.03051 ] Network output: [ 0.1006 -0.2273 0.8204 3.578e-05 -1.606e-05 1.206 2.697e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5987 0.5077 0.4193 0.4838 0.9797 0.9911 0.6002 0.921 0.9771 0.5139 ] Network output: [ -0.07216 0.1635 1.157 -0.0001144 5.136e-05 0.8238 -8.621e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2766 0.2698 0.2866 0.2891 0.9879 0.9922 0.2767 0.9731 0.9839 0.2967 ] Network output: [ -0.0733 0.1689 1.113 -0.0001001 4.496e-05 0.8641 -7.547e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2907 0.2894 0.2919 0.2901 0.9833 0.9897 0.2907 0.9571 0.9767 0.2943 ] Network output: [ -0.0003395 1.002 0.01329 6.327e-05 -2.841e-05 0.9856 4.768e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05003 Epoch 3459 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06345 0.8936 0.9221 0.0001138 -5.111e-05 0.05788 8.58e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01436 -0.0051 0.006587 0.02695 0.9506 0.9581 0.02516 0.8997 0.9175 0.06956 ] Network output: [ 0.9569 0.08909 0.03647 -4.652e-06 2.089e-06 -0.0393 -3.506e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.552 0.08153 0.07242 0.3525 0.9773 0.9898 0.6063 0.9137 0.9739 0.5285 ] Network output: [ 0.02557 0.9093 0.9376 8.179e-06 -3.672e-06 0.102 6.164e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02019 0.01432 0.02364 0.02681 0.9876 0.9913 0.02049 0.9721 0.9834 0.0305 ] Network output: [ 0.1005 -0.2272 0.8202 3.604e-05 -1.618e-05 1.206 2.716e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5987 0.5077 0.4193 0.4837 0.9797 0.9911 0.6002 0.921 0.9772 0.5138 ] Network output: [ -0.07208 0.1632 1.157 -0.0001139 5.112e-05 0.824 -8.581e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2766 0.2698 0.2866 0.2891 0.9879 0.9922 0.2767 0.9732 0.9839 0.2967 ] Network output: [ -0.07321 0.1687 1.113 -9.96e-05 4.471e-05 0.8641 -7.506e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2906 0.2893 0.2919 0.2901 0.9833 0.9897 0.2906 0.9571 0.9768 0.2943 ] Network output: [ -0.0004101 1.002 0.01344 6.293e-05 -2.825e-05 0.9854 4.742e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05 Epoch 3460 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06341 0.8937 0.9221 0.0001138 -5.109e-05 0.05782 8.576e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01435 -0.005104 0.006559 0.02693 0.9507 0.9581 0.02515 0.8997 0.9175 0.06954 ] Network output: [ 0.9569 0.08904 0.0365 -5.455e-06 2.449e-06 -0.03933 -4.111e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5519 0.0816 0.07246 0.3523 0.9774 0.9898 0.6063 0.9138 0.9739 0.5285 ] Network output: [ 0.02552 0.9094 0.9376 8.038e-06 -3.609e-06 0.102 6.058e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02019 0.01432 0.02363 0.0268 0.9876 0.9913 0.02048 0.9721 0.9834 0.03049 ] Network output: [ 0.1005 -0.2271 0.82 3.63e-05 -1.629e-05 1.206 2.735e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5987 0.5078 0.4193 0.4836 0.9797 0.9911 0.6002 0.921 0.9772 0.5137 ] Network output: [ -0.07201 0.163 1.156 -0.0001133 5.088e-05 0.8241 -8.541e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2766 0.2698 0.2866 0.289 0.9879 0.9922 0.2767 0.9732 0.9839 0.2967 ] Network output: [ -0.07313 0.1685 1.113 -9.906e-05 4.447e-05 0.8641 -7.465e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2905 0.2893 0.2919 0.2901 0.9833 0.9897 0.2905 0.9572 0.9768 0.2942 ] Network output: [ -0.0004803 1.002 0.01359 6.258e-05 -2.81e-05 0.9853 4.717e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04997 Epoch 3461 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06337 0.8939 0.9221 0.0001138 -5.107e-05 0.05776 8.573e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01434 -0.005108 0.006532 0.02691 0.9507 0.9581 0.02514 0.8998 0.9176 0.06951 ] Network output: [ 0.9569 0.08898 0.03653 -6.248e-06 2.805e-06 -0.03936 -4.708e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5519 0.08167 0.0725 0.3522 0.9774 0.9898 0.6062 0.9138 0.9739 0.5284 ] Network output: [ 0.02547 0.9095 0.9376 7.896e-06 -3.545e-06 0.102 5.95e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02018 0.01432 0.02362 0.02678 0.9876 0.9913 0.02048 0.9721 0.9834 0.03048 ] Network output: [ 0.1005 -0.227 0.8198 3.655e-05 -1.641e-05 1.206 2.754e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5987 0.5079 0.4194 0.4835 0.9797 0.9911 0.6002 0.9211 0.9772 0.5136 ] Network output: [ -0.07193 0.1627 1.156 -0.0001128 5.064e-05 0.8242 -8.501e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2766 0.2698 0.2866 0.289 0.9879 0.9922 0.2767 0.9732 0.984 0.2967 ] Network output: [ -0.07305 0.1684 1.113 -9.852e-05 4.423e-05 0.8641 -7.425e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2904 0.2892 0.2918 0.29 0.9833 0.9897 0.2904 0.9572 0.9768 0.2942 ] Network output: [ -0.00055 1.003 0.01373 6.224e-05 -2.794e-05 0.9851 4.691e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04994 Epoch 3462 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06334 0.894 0.9221 0.0001137 -5.105e-05 0.0577 8.57e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01433 -0.005112 0.006504 0.0269 0.9507 0.9581 0.02513 0.8998 0.9176 0.06949 ] Network output: [ 0.9569 0.08893 0.03657 -7.029e-06 3.156e-06 -0.03939 -5.297e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5518 0.08174 0.07253 0.3521 0.9774 0.9898 0.6062 0.9139 0.974 0.5283 ] Network output: [ 0.02541 0.9096 0.9376 7.752e-06 -3.48e-06 0.102 5.842e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02018 0.01432 0.02362 0.02677 0.9876 0.9913 0.02048 0.9721 0.9835 0.03047 ] Network output: [ 0.1005 -0.2269 0.8197 3.679e-05 -1.652e-05 1.206 2.773e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5986 0.508 0.4194 0.4834 0.9797 0.9911 0.6001 0.9211 0.9772 0.5136 ] Network output: [ -0.07186 0.1625 1.156 -0.0001123 5.04e-05 0.8244 -8.461e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2765 0.2698 0.2866 0.289 0.9879 0.9922 0.2767 0.9732 0.984 0.2967 ] Network output: [ -0.07297 0.1682 1.113 -9.799e-05 4.399e-05 0.8641 -7.385e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2903 0.2891 0.2918 0.29 0.9833 0.9897 0.2904 0.9572 0.9768 0.2942 ] Network output: [ -0.0006194 1.003 0.01388 6.191e-05 -2.779e-05 0.985 4.665e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04991 Epoch 3463 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0633 0.8941 0.9221 0.0001137 -5.103e-05 0.05764 8.566e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01432 -0.005116 0.006477 0.02688 0.9507 0.9581 0.02512 0.8999 0.9176 0.06946 ] Network output: [ 0.957 0.08887 0.0366 -7.8e-06 3.502e-06 -0.03942 -5.879e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5518 0.08181 0.07257 0.3519 0.9774 0.9898 0.6062 0.9139 0.974 0.5282 ] Network output: [ 0.02536 0.9097 0.9376 7.608e-06 -3.415e-06 0.102 5.733e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02018 0.01432 0.02361 0.02676 0.9876 0.9913 0.02048 0.9722 0.9835 0.03046 ] Network output: [ 0.1005 -0.2268 0.8195 3.703e-05 -1.663e-05 1.207 2.791e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5986 0.508 0.4195 0.4832 0.9797 0.9911 0.6001 0.9212 0.9772 0.5135 ] Network output: [ -0.07179 0.1623 1.156 -0.0001117 5.017e-05 0.8245 -8.422e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2765 0.2698 0.2867 0.289 0.9879 0.9923 0.2767 0.9732 0.984 0.2967 ] Network output: [ -0.07289 0.168 1.113 -9.747e-05 4.376e-05 0.8641 -7.345e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2903 0.289 0.2918 0.29 0.9834 0.9897 0.2903 0.9572 0.9768 0.2942 ] Network output: [ -0.0006883 1.003 0.01403 6.157e-05 -2.764e-05 0.9848 4.64e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04988 Epoch 3464 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06326 0.8943 0.9221 0.0001136 -5.101e-05 0.05758 8.563e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01431 -0.00512 0.006449 0.02686 0.9507 0.9581 0.02511 0.8999 0.9177 0.06944 ] Network output: [ 0.957 0.08882 0.03663 -8.561e-06 3.843e-06 -0.03944 -6.452e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5517 0.08187 0.07261 0.3518 0.9774 0.9898 0.6062 0.914 0.974 0.5281 ] Network output: [ 0.02531 0.9098 0.9376 7.462e-06 -3.35e-06 0.1019 5.624e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02018 0.01432 0.0236 0.02675 0.9876 0.9913 0.02047 0.9722 0.9835 0.03045 ] Network output: [ 0.1004 -0.2268 0.8193 3.727e-05 -1.673e-05 1.207 2.809e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5986 0.5081 0.4195 0.4831 0.9797 0.9911 0.6001 0.9212 0.9772 0.5134 ] Network output: [ -0.07171 0.162 1.156 -0.0001112 4.994e-05 0.8246 -8.383e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2765 0.2698 0.2867 0.289 0.9879 0.9923 0.2766 0.9732 0.984 0.2967 ] Network output: [ -0.07281 0.1678 1.113 -9.694e-05 4.352e-05 0.8641 -7.306e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2902 0.2889 0.2918 0.2899 0.9834 0.9897 0.2902 0.9573 0.9768 0.2941 ] Network output: [ -0.0007568 1.003 0.01417 6.124e-05 -2.749e-05 0.9846 4.615e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04985 Epoch 3465 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06322 0.8944 0.9221 0.0001136 -5.099e-05 0.05752 8.56e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0143 -0.005124 0.006422 0.02685 0.9507 0.9581 0.0251 0.8999 0.9177 0.06941 ] Network output: [ 0.957 0.08877 0.03666 -9.312e-06 4.18e-06 -0.03947 -7.018e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5516 0.08194 0.07265 0.3517 0.9774 0.9898 0.6061 0.914 0.974 0.528 ] Network output: [ 0.02525 0.9099 0.9377 7.315e-06 -3.284e-06 0.1019 5.513e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02017 0.01432 0.0236 0.02674 0.9876 0.9913 0.02047 0.9722 0.9835 0.03044 ] Network output: [ 0.1004 -0.2267 0.8192 3.751e-05 -1.684e-05 1.207 2.827e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5986 0.5082 0.4195 0.483 0.9798 0.9911 0.6001 0.9212 0.9773 0.5133 ] Network output: [ -0.07164 0.1618 1.156 -0.0001107 4.97e-05 0.8248 -8.344e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2765 0.2698 0.2867 0.289 0.9879 0.9923 0.2766 0.9732 0.984 0.2967 ] Network output: [ -0.07272 0.1676 1.113 -9.642e-05 4.329e-05 0.8641 -7.267e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2901 0.2889 0.2917 0.2899 0.9834 0.9898 0.2901 0.9573 0.9768 0.2941 ] Network output: [ -0.0008249 1.003 0.01431 6.09e-05 -2.734e-05 0.9845 4.59e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04982 Epoch 3466 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06319 0.8945 0.9221 0.0001135 -5.097e-05 0.05746 8.556e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01429 -0.005128 0.006394 0.02683 0.9508 0.9582 0.02509 0.9 0.9177 0.06939 ] Network output: [ 0.957 0.08871 0.03669 -1.005e-05 4.513e-06 -0.0395 -7.576e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5516 0.08201 0.07269 0.3515 0.9774 0.9898 0.6061 0.9141 0.974 0.5279 ] Network output: [ 0.0252 0.9101 0.9377 7.167e-06 -3.218e-06 0.1019 5.402e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02017 0.01432 0.02359 0.02672 0.9876 0.9914 0.02047 0.9722 0.9835 0.03043 ] Network output: [ 0.1004 -0.2266 0.819 3.774e-05 -1.694e-05 1.207 2.844e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5985 0.5082 0.4196 0.4829 0.9798 0.9911 0.6 0.9213 0.9773 0.5133 ] Network output: [ -0.07157 0.1616 1.156 -0.0001102 4.948e-05 0.8249 -8.306e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2765 0.2698 0.2867 0.289 0.9879 0.9923 0.2766 0.9733 0.984 0.2967 ] Network output: [ -0.07264 0.1675 1.113 -9.591e-05 4.306e-05 0.8641 -7.228e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.29 0.2888 0.2917 0.2899 0.9834 0.9898 0.29 0.9573 0.9769 0.2941 ] Network output: [ -0.0008925 1.003 0.01446 6.058e-05 -2.719e-05 0.9843 4.565e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04979 Epoch 3467 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06315 0.8946 0.9221 0.0001135 -5.095e-05 0.0574 8.553e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01428 -0.005132 0.006367 0.02682 0.9508 0.9582 0.02508 0.9 0.9178 0.06936 ] Network output: [ 0.9571 0.08866 0.03672 -1.078e-05 4.841e-06 -0.03953 -8.126e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5515 0.08208 0.07272 0.3514 0.9774 0.9898 0.6061 0.9141 0.9741 0.5279 ] Network output: [ 0.02515 0.9102 0.9377 7.019e-06 -3.151e-06 0.1019 5.289e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02017 0.01432 0.02358 0.02671 0.9876 0.9914 0.02047 0.9722 0.9835 0.03042 ] Network output: [ 0.1004 -0.2265 0.8188 3.797e-05 -1.704e-05 1.207 2.861e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5985 0.5083 0.4196 0.4828 0.9798 0.9911 0.6 0.9213 0.9773 0.5132 ] Network output: [ -0.07149 0.1613 1.156 -0.0001097 4.925e-05 0.825 -8.267e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2765 0.2698 0.2867 0.289 0.9879 0.9923 0.2766 0.9733 0.984 0.2967 ] Network output: [ -0.07256 0.1673 1.113 -9.539e-05 4.283e-05 0.8641 -7.189e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2899 0.2887 0.2917 0.2898 0.9834 0.9898 0.29 0.9573 0.9769 0.294 ] Network output: [ -0.0009598 1.003 0.0146 6.025e-05 -2.705e-05 0.9842 4.541e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04976 Epoch 3468 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06311 0.8948 0.9221 0.0001134 -5.093e-05 0.05734 8.55e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01427 -0.005136 0.006339 0.0268 0.9508 0.9582 0.02507 0.9001 0.9178 0.06934 ] Network output: [ 0.9571 0.0886 0.03675 -1.15e-05 5.164e-06 -0.03956 -8.67e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5515 0.08215 0.07276 0.3513 0.9774 0.9898 0.606 0.9141 0.9741 0.5278 ] Network output: [ 0.02509 0.9103 0.9377 6.869e-06 -3.084e-06 0.1019 5.177e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02016 0.01432 0.02358 0.0267 0.9876 0.9914 0.02046 0.9722 0.9835 0.03041 ] Network output: [ 0.1003 -0.2264 0.8187 3.819e-05 -1.715e-05 1.207 2.878e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5985 0.5084 0.4197 0.4827 0.9798 0.9911 0.6 0.9214 0.9773 0.5131 ] Network output: [ -0.07142 0.1611 1.156 -0.0001092 4.902e-05 0.8252 -8.23e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2765 0.2698 0.2867 0.289 0.9879 0.9923 0.2766 0.9733 0.984 0.2968 ] Network output: [ -0.07248 0.1671 1.113 -9.488e-05 4.26e-05 0.8641 -7.151e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2899 0.2886 0.2917 0.2898 0.9834 0.9898 0.2899 0.9574 0.9769 0.294 ] Network output: [ -0.001027 1.004 0.01474 5.992e-05 -2.69e-05 0.984 4.516e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04973 Epoch 3469 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06308 0.8949 0.9221 0.0001134 -5.091e-05 0.05729 8.547e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01426 -0.00514 0.006312 0.02678 0.9508 0.9582 0.02506 0.9001 0.9178 0.06931 ] Network output: [ 0.9571 0.08855 0.03678 -1.221e-05 5.484e-06 -0.03958 -9.205e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5514 0.08222 0.0728 0.3512 0.9774 0.9898 0.606 0.9142 0.9741 0.5277 ] Network output: [ 0.02504 0.9104 0.9377 6.718e-06 -3.016e-06 0.1019 5.063e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02016 0.01432 0.02357 0.02669 0.9876 0.9914 0.02046 0.9723 0.9835 0.0304 ] Network output: [ 0.1003 -0.2263 0.8185 3.841e-05 -1.724e-05 1.207 2.895e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5984 0.5084 0.4197 0.4826 0.9798 0.9911 0.6 0.9214 0.9773 0.513 ] Network output: [ -0.07135 0.1609 1.156 -0.0001087 4.88e-05 0.8253 -8.192e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2765 0.2698 0.2868 0.289 0.9879 0.9923 0.2766 0.9733 0.984 0.2968 ] Network output: [ -0.0724 0.1669 1.113 -9.438e-05 4.237e-05 0.8641 -7.113e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2898 0.2885 0.2916 0.2898 0.9834 0.9898 0.2898 0.9574 0.9769 0.294 ] Network output: [ -0.001093 1.004 0.01488 5.96e-05 -2.676e-05 0.9839 4.492e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0497 Epoch 3470 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06304 0.895 0.9221 0.0001134 -5.089e-05 0.05723 8.543e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01425 -0.005143 0.006284 0.02677 0.9508 0.9582 0.02505 0.9001 0.9179 0.06929 ] Network output: [ 0.9571 0.08849 0.03681 -1.292e-05 5.799e-06 -0.03961 -9.734e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5514 0.08228 0.07284 0.351 0.9774 0.9898 0.606 0.9142 0.9741 0.5276 ] Network output: [ 0.02499 0.9105 0.9377 6.566e-06 -2.948e-06 0.1019 4.948e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02016 0.01432 0.02356 0.02668 0.9876 0.9914 0.02046 0.9723 0.9835 0.03039 ] Network output: [ 0.1003 -0.2262 0.8183 3.863e-05 -1.734e-05 1.207 2.911e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5984 0.5085 0.4197 0.4825 0.9798 0.9911 0.5999 0.9214 0.9773 0.513 ] Network output: [ -0.07128 0.1607 1.156 -0.0001082 4.858e-05 0.8254 -8.155e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2765 0.2698 0.2868 0.289 0.9879 0.9923 0.2766 0.9733 0.984 0.2968 ] Network output: [ -0.07232 0.1667 1.113 -9.388e-05 4.214e-05 0.864 -7.075e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2897 0.2885 0.2916 0.2897 0.9834 0.9898 0.2897 0.9574 0.9769 0.294 ] Network output: [ -0.001159 1.004 0.01502 5.928e-05 -2.661e-05 0.9837 4.468e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04967 Epoch 3471 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.063 0.8951 0.9221 0.0001133 -5.087e-05 0.05717 8.54e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01424 -0.005147 0.006257 0.02675 0.9508 0.9582 0.02504 0.9002 0.9179 0.06926 ] Network output: [ 0.9572 0.08844 0.03684 -1.361e-05 6.109e-06 -0.03964 -1.026e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5513 0.08235 0.07287 0.3509 0.9775 0.9898 0.606 0.9143 0.9741 0.5275 ] Network output: [ 0.02494 0.9106 0.9377 6.413e-06 -2.879e-06 0.1019 4.833e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02016 0.01432 0.02356 0.02666 0.9876 0.9914 0.02046 0.9723 0.9836 0.03038 ] Network output: [ 0.1003 -0.2261 0.8182 3.884e-05 -1.744e-05 1.208 2.927e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5984 0.5086 0.4198 0.4824 0.9798 0.9911 0.5999 0.9215 0.9774 0.5129 ] Network output: [ -0.0712 0.1604 1.156 -0.0001077 4.836e-05 0.8256 -8.118e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2764 0.2698 0.2868 0.289 0.9879 0.9923 0.2766 0.9733 0.9841 0.2968 ] Network output: [ -0.07224 0.1665 1.114 -9.338e-05 4.192e-05 0.864 -7.037e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2896 0.2884 0.2916 0.2897 0.9834 0.9898 0.2896 0.9574 0.9769 0.2939 ] Network output: [ -0.001225 1.004 0.01516 5.896e-05 -2.647e-05 0.9836 4.444e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04964 Epoch 3472 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06296 0.8953 0.9222 0.0001133 -5.086e-05 0.05712 8.537e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01423 -0.005151 0.006229 0.02674 0.9509 0.9583 0.02503 0.9002 0.9179 0.06924 ] Network output: [ 0.9572 0.08839 0.03687 -1.429e-05 6.416e-06 -0.03967 -1.077e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5512 0.08242 0.07291 0.3508 0.9775 0.9898 0.6059 0.9143 0.9741 0.5274 ] Network output: [ 0.02488 0.9107 0.9377 6.26e-06 -2.81e-06 0.1018 4.717e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02015 0.01432 0.02355 0.02665 0.9876 0.9914 0.02045 0.9723 0.9836 0.03037 ] Network output: [ 0.1003 -0.226 0.818 3.905e-05 -1.753e-05 1.208 2.943e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5984 0.5087 0.4198 0.4823 0.9798 0.9912 0.5999 0.9215 0.9774 0.5128 ] Network output: [ -0.07113 0.1602 1.156 -0.0001072 4.814e-05 0.8257 -8.081e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2764 0.2697 0.2868 0.289 0.9879 0.9923 0.2765 0.9734 0.9841 0.2968 ] Network output: [ -0.07216 0.1664 1.114 -9.288e-05 4.17e-05 0.864 -7e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2895 0.2883 0.2916 0.2897 0.9834 0.9898 0.2896 0.9574 0.977 0.2939 ] Network output: [ -0.00129 1.004 0.0153 5.865e-05 -2.633e-05 0.9834 4.42e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04961 Epoch 3473 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06293 0.8954 0.9222 0.0001132 -5.084e-05 0.05706 8.534e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01422 -0.005155 0.006202 0.02672 0.9509 0.9583 0.02502 0.9003 0.918 0.06921 ] Network output: [ 0.9572 0.08833 0.0369 -1.496e-05 6.718e-06 -0.03969 -1.128e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5512 0.08249 0.07294 0.3506 0.9775 0.9899 0.6059 0.9144 0.9742 0.5274 ] Network output: [ 0.02483 0.9108 0.9377 6.105e-06 -2.741e-06 0.1018 4.601e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02015 0.01432 0.02354 0.02664 0.9876 0.9914 0.02045 0.9723 0.9836 0.03036 ] Network output: [ 0.1002 -0.2259 0.8178 3.925e-05 -1.762e-05 1.208 2.958e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5983 0.5087 0.4199 0.4822 0.9798 0.9912 0.5999 0.9216 0.9774 0.5128 ] Network output: [ -0.07106 0.16 1.156 -0.0001067 4.792e-05 0.8258 -8.045e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2764 0.2697 0.2868 0.289 0.9879 0.9923 0.2765 0.9734 0.9841 0.2968 ] Network output: [ -0.07209 0.1662 1.114 -9.239e-05 4.148e-05 0.864 -6.963e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2895 0.2882 0.2915 0.2896 0.9834 0.9898 0.2895 0.9575 0.977 0.2939 ] Network output: [ -0.001355 1.004 0.01544 5.833e-05 -2.619e-05 0.9833 4.396e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04958 Epoch 3474 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06289 0.8955 0.9222 0.0001132 -5.082e-05 0.057 8.531e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01421 -0.005159 0.006174 0.0267 0.9509 0.9583 0.02501 0.9003 0.918 0.06919 ] Network output: [ 0.9572 0.08828 0.03693 -1.563e-05 7.016e-06 -0.03972 -1.178e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5511 0.08255 0.07298 0.3505 0.9775 0.9899 0.6059 0.9144 0.9742 0.5273 ] Network output: [ 0.02478 0.9109 0.9377 5.949e-06 -2.671e-06 0.1018 4.484e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02015 0.01432 0.02354 0.02663 0.9876 0.9914 0.02045 0.9724 0.9836 0.03035 ] Network output: [ 0.1002 -0.2258 0.8177 3.946e-05 -1.771e-05 1.208 2.974e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5983 0.5088 0.4199 0.4821 0.9798 0.9912 0.5998 0.9216 0.9774 0.5127 ] Network output: [ -0.07099 0.1598 1.156 -0.0001063 4.771e-05 0.826 -8.009e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2764 0.2697 0.2868 0.289 0.9879 0.9923 0.2765 0.9734 0.9841 0.2968 ] Network output: [ -0.07201 0.166 1.114 -9.19e-05 4.126e-05 0.864 -6.926e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2894 0.2881 0.2915 0.2896 0.9834 0.9898 0.2894 0.9575 0.977 0.2938 ] Network output: [ -0.001419 1.004 0.01558 5.802e-05 -2.605e-05 0.9831 4.372e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04955 Epoch 3475 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06285 0.8956 0.9222 0.0001132 -5.08e-05 0.05695 8.528e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0142 -0.005163 0.006147 0.02669 0.9509 0.9583 0.025 0.9004 0.918 0.06916 ] Network output: [ 0.9573 0.08822 0.03695 -1.628e-05 7.31e-06 -0.03975 -1.227e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5511 0.08262 0.07302 0.3504 0.9775 0.9899 0.6059 0.9145 0.9742 0.5272 ] Network output: [ 0.02473 0.911 0.9377 5.793e-06 -2.601e-06 0.1018 4.366e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02015 0.01432 0.02353 0.02662 0.9876 0.9914 0.02045 0.9724 0.9836 0.03034 ] Network output: [ 0.1002 -0.2257 0.8175 3.966e-05 -1.78e-05 1.208 2.989e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5983 0.5089 0.4199 0.482 0.9798 0.9912 0.5998 0.9217 0.9774 0.5126 ] Network output: [ -0.07092 0.1596 1.156 -0.0001058 4.75e-05 0.8261 -7.973e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2764 0.2697 0.2868 0.289 0.988 0.9923 0.2765 0.9734 0.9841 0.2968 ] Network output: [ -0.07193 0.1658 1.114 -9.142e-05 4.104e-05 0.864 -6.89e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2893 0.2881 0.2915 0.2896 0.9834 0.9898 0.2893 0.9575 0.977 0.2938 ] Network output: [ -0.001484 1.005 0.01572 5.771e-05 -2.591e-05 0.983 4.349e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04952 Epoch 3476 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06282 0.8957 0.9222 0.0001131 -5.078e-05 0.05689 8.525e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01419 -0.005167 0.006119 0.02667 0.9509 0.9583 0.02499 0.9004 0.9181 0.06914 ] Network output: [ 0.9573 0.08817 0.03698 -1.693e-05 7.601e-06 -0.03978 -1.276e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.551 0.08269 0.07305 0.3502 0.9775 0.9899 0.6058 0.9145 0.9742 0.5271 ] Network output: [ 0.02467 0.9111 0.9377 5.636e-06 -2.53e-06 0.1018 4.247e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02014 0.01432 0.02352 0.0266 0.9877 0.9914 0.02044 0.9724 0.9836 0.03033 ] Network output: [ 0.1002 -0.2256 0.8174 3.985e-05 -1.789e-05 1.208 3.003e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5983 0.5089 0.42 0.4819 0.9798 0.9912 0.5998 0.9217 0.9774 0.5125 ] Network output: [ -0.07084 0.1593 1.156 -0.0001053 4.728e-05 0.8262 -7.938e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2764 0.2697 0.2869 0.289 0.988 0.9923 0.2765 0.9734 0.9841 0.2968 ] Network output: [ -0.07185 0.1657 1.114 -9.094e-05 4.082e-05 0.864 -6.853e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2892 0.288 0.2915 0.2895 0.9834 0.9898 0.2892 0.9575 0.977 0.2938 ] Network output: [ -0.001547 1.005 0.01585 5.74e-05 -2.577e-05 0.9828 4.326e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04949 Epoch 3477 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06278 0.8959 0.9222 0.0001131 -5.076e-05 0.05684 8.521e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01418 -0.005171 0.006091 0.02665 0.9509 0.9583 0.02498 0.9004 0.9181 0.06911 ] Network output: [ 0.9573 0.08812 0.03701 -1.757e-05 7.887e-06 -0.0398 -1.324e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.551 0.08275 0.07309 0.3501 0.9775 0.9899 0.6058 0.9145 0.9742 0.527 ] Network output: [ 0.02462 0.9112 0.9377 5.478e-06 -2.459e-06 0.1018 4.128e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02014 0.01432 0.02352 0.02659 0.9877 0.9914 0.02044 0.9724 0.9836 0.03032 ] Network output: [ 0.1001 -0.2255 0.8172 4.004e-05 -1.798e-05 1.208 3.018e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5982 0.509 0.42 0.4818 0.9798 0.9912 0.5998 0.9217 0.9774 0.5125 ] Network output: [ -0.07077 0.1591 1.156 -0.0001049 4.707e-05 0.8263 -7.902e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2764 0.2697 0.2869 0.289 0.988 0.9923 0.2765 0.9734 0.9841 0.2968 ] Network output: [ -0.07177 0.1655 1.114 -9.046e-05 4.061e-05 0.864 -6.817e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2891 0.2879 0.2914 0.2895 0.9834 0.9898 0.2892 0.9576 0.977 0.2938 ] Network output: [ -0.001611 1.005 0.01599 5.709e-05 -2.563e-05 0.9827 4.303e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04947 Epoch 3478 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06275 0.896 0.9222 0.000113 -5.074e-05 0.05678 8.518e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01417 -0.005175 0.006064 0.02664 0.951 0.9583 0.02497 0.9005 0.9181 0.06909 ] Network output: [ 0.9573 0.08806 0.03703 -1.82e-05 8.169e-06 -0.03983 -1.371e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5509 0.08282 0.07312 0.35 0.9775 0.9899 0.6058 0.9146 0.9743 0.527 ] Network output: [ 0.02457 0.9114 0.9377 5.319e-06 -2.388e-06 0.1018 4.008e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02014 0.01432 0.02351 0.02658 0.9877 0.9914 0.02044 0.9724 0.9836 0.03031 ] Network output: [ 0.1001 -0.2254 0.817 4.023e-05 -1.806e-05 1.208 3.032e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5982 0.5091 0.4201 0.4817 0.9799 0.9912 0.5997 0.9218 0.9775 0.5124 ] Network output: [ -0.0707 0.1589 1.156 -0.0001044 4.687e-05 0.8265 -7.867e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2764 0.2697 0.2869 0.289 0.988 0.9923 0.2765 0.9735 0.9841 0.2968 ] Network output: [ -0.07169 0.1653 1.114 -8.998e-05 4.04e-05 0.864 -6.781e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2891 0.2878 0.2914 0.2895 0.9835 0.9898 0.2891 0.9576 0.977 0.2937 ] Network output: [ -0.001674 1.005 0.01612 5.679e-05 -2.55e-05 0.9825 4.28e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04944 Epoch 3479 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06271 0.8961 0.9222 0.000113 -5.073e-05 0.05673 8.515e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01416 -0.005179 0.006036 0.02662 0.951 0.9584 0.02496 0.9005 0.9182 0.06906 ] Network output: [ 0.9574 0.08801 0.03706 -1.882e-05 8.448e-06 -0.03986 -1.418e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5508 0.08288 0.07316 0.3499 0.9775 0.9899 0.6058 0.9146 0.9743 0.5269 ] Network output: [ 0.02452 0.9115 0.9377 5.159e-06 -2.316e-06 0.1018 3.888e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02013 0.01432 0.0235 0.02657 0.9877 0.9914 0.02044 0.9724 0.9836 0.0303 ] Network output: [ 0.1001 -0.2253 0.8169 4.042e-05 -1.814e-05 1.208 3.046e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5982 0.5091 0.4201 0.4816 0.9799 0.9912 0.5997 0.9218 0.9775 0.5123 ] Network output: [ -0.07063 0.1587 1.156 -0.0001039 4.666e-05 0.8266 -7.833e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2764 0.2697 0.2869 0.289 0.988 0.9923 0.2765 0.9735 0.9841 0.2968 ] Network output: [ -0.07162 0.1651 1.114 -8.951e-05 4.019e-05 0.864 -6.746e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.289 0.2878 0.2914 0.2894 0.9835 0.9898 0.289 0.9576 0.9771 0.2937 ] Network output: [ -0.001736 1.005 0.01626 5.649e-05 -2.536e-05 0.9824 4.257e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04941 Epoch 3480 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06267 0.8962 0.9222 0.0001129 -5.071e-05 0.05668 8.512e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01415 -0.005183 0.006009 0.0266 0.951 0.9584 0.02495 0.9006 0.9182 0.06904 ] Network output: [ 0.9574 0.08795 0.03708 -1.943e-05 8.723e-06 -0.03989 -1.464e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5508 0.08295 0.07319 0.3497 0.9775 0.9899 0.6057 0.9147 0.9743 0.5268 ] Network output: [ 0.02446 0.9116 0.9378 4.999e-06 -2.244e-06 0.1018 3.767e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02013 0.01432 0.02349 0.02655 0.9877 0.9914 0.02043 0.9725 0.9837 0.03029 ] Network output: [ 0.1001 -0.2253 0.8167 4.06e-05 -1.823e-05 1.209 3.06e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5982 0.5092 0.4201 0.4815 0.9799 0.9912 0.5997 0.9219 0.9775 0.5123 ] Network output: [ -0.07056 0.1585 1.156 -0.0001035 4.645e-05 0.8267 -7.798e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2763 0.2697 0.2869 0.289 0.988 0.9923 0.2765 0.9735 0.9842 0.2968 ] Network output: [ -0.07154 0.1649 1.114 -8.904e-05 3.997e-05 0.864 -6.711e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2889 0.2877 0.2914 0.2894 0.9835 0.9898 0.2889 0.9576 0.9771 0.2937 ] Network output: [ -0.001799 1.005 0.01639 5.619e-05 -2.523e-05 0.9823 4.235e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04938 Epoch 3481 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06264 0.8963 0.9222 0.0001129 -5.069e-05 0.05662 8.509e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01414 -0.005187 0.005981 0.02659 0.951 0.9584 0.02494 0.9006 0.9182 0.06901 ] Network output: [ 0.9574 0.0879 0.03711 -2.003e-05 8.994e-06 -0.03991 -1.51e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5507 0.08301 0.07323 0.3496 0.9775 0.9899 0.6057 0.9147 0.9743 0.5267 ] Network output: [ 0.02441 0.9117 0.9378 4.837e-06 -2.172e-06 0.1018 3.646e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02013 0.01432 0.02349 0.02654 0.9877 0.9914 0.02043 0.9725 0.9837 0.03028 ] Network output: [ 0.1 -0.2252 0.8166 4.078e-05 -1.831e-05 1.209 3.073e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5981 0.5093 0.4202 0.4814 0.9799 0.9912 0.5997 0.9219 0.9775 0.5122 ] Network output: [ -0.07049 0.1583 1.155 -0.000103 4.625e-05 0.8268 -7.764e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2763 0.2697 0.2869 0.2889 0.988 0.9923 0.2764 0.9735 0.9842 0.2968 ] Network output: [ -0.07146 0.1648 1.114 -8.858e-05 3.977e-05 0.864 -6.676e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2888 0.2876 0.2913 0.2894 0.9835 0.9898 0.2888 0.9577 0.9771 0.2936 ] Network output: [ -0.00186 1.005 0.01652 5.589e-05 -2.509e-05 0.9821 4.212e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04935 Epoch 3482 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0626 0.8965 0.9222 0.0001129 -5.067e-05 0.05657 8.506e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01413 -0.005191 0.005953 0.02657 0.951 0.9584 0.02493 0.9006 0.9183 0.06899 ] Network output: [ 0.9574 0.08785 0.03713 -2.063e-05 9.261e-06 -0.03994 -1.555e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5507 0.08308 0.07326 0.3495 0.9776 0.9899 0.6057 0.9148 0.9743 0.5266 ] Network output: [ 0.02436 0.9118 0.9378 4.675e-06 -2.099e-06 0.1018 3.524e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02013 0.01432 0.02348 0.02653 0.9877 0.9914 0.02043 0.9725 0.9837 0.03027 ] Network output: [ 0.1 -0.2251 0.8164 4.095e-05 -1.839e-05 1.209 3.086e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5981 0.5093 0.4202 0.4813 0.9799 0.9912 0.5996 0.9219 0.9775 0.5121 ] Network output: [ -0.07042 0.1581 1.155 -0.0001026 4.605e-05 0.827 -7.73e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2763 0.2697 0.287 0.2889 0.988 0.9923 0.2764 0.9735 0.9842 0.2968 ] Network output: [ -0.07139 0.1646 1.114 -8.812e-05 3.956e-05 0.864 -6.641e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2887 0.2875 0.2913 0.2893 0.9835 0.9898 0.2888 0.9577 0.9771 0.2936 ] Network output: [ -0.001922 1.005 0.01666 5.56e-05 -2.496e-05 0.982 4.19e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04933 Epoch 3483 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06256 0.8966 0.9222 0.0001128 -5.065e-05 0.05652 8.503e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01412 -0.005195 0.005926 0.02655 0.951 0.9584 0.02492 0.9007 0.9183 0.06896 ] Network output: [ 0.9575 0.08779 0.03715 -2.122e-05 9.525e-06 -0.03997 -1.599e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5506 0.08315 0.0733 0.3493 0.9776 0.9899 0.6056 0.9148 0.9743 0.5266 ] Network output: [ 0.02431 0.9119 0.9378 4.513e-06 -2.026e-06 0.1017 3.401e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02012 0.01432 0.02347 0.02652 0.9877 0.9914 0.02043 0.9725 0.9837 0.03025 ] Network output: [ 0.09998 -0.225 0.8163 4.113e-05 -1.846e-05 1.209 3.099e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5981 0.5094 0.4203 0.4812 0.9799 0.9912 0.5996 0.922 0.9775 0.5121 ] Network output: [ -0.07035 0.1578 1.155 -0.0001021 4.585e-05 0.8271 -7.696e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2763 0.2697 0.287 0.2889 0.988 0.9923 0.2764 0.9735 0.9842 0.2968 ] Network output: [ -0.07131 0.1644 1.114 -8.766e-05 3.935e-05 0.864 -6.606e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2887 0.2875 0.2913 0.2893 0.9835 0.9898 0.2887 0.9577 0.9771 0.2936 ] Network output: [ -0.001983 1.006 0.01679 5.53e-05 -2.483e-05 0.9818 4.168e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0493 Epoch 3484 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06253 0.8967 0.9222 0.0001128 -5.064e-05 0.05646 8.5e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01411 -0.005199 0.005898 0.02654 0.9511 0.9584 0.02491 0.9007 0.9183 0.06894 ] Network output: [ 0.9575 0.08774 0.03718 -2.18e-05 9.785e-06 -0.03999 -1.643e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5506 0.08321 0.07333 0.3492 0.9776 0.9899 0.6056 0.9148 0.9744 0.5265 ] Network output: [ 0.02426 0.912 0.9378 4.349e-06 -1.953e-06 0.1017 3.278e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02012 0.01432 0.02346 0.0265 0.9877 0.9914 0.02042 0.9725 0.9837 0.03024 ] Network output: [ 0.09996 -0.2249 0.8161 4.129e-05 -1.854e-05 1.209 3.112e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5981 0.5095 0.4203 0.4811 0.9799 0.9912 0.5996 0.922 0.9776 0.512 ] Network output: [ -0.07028 0.1576 1.155 -0.0001017 4.565e-05 0.8272 -7.663e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2763 0.2697 0.287 0.2889 0.988 0.9923 0.2764 0.9735 0.9842 0.2968 ] Network output: [ -0.07123 0.1642 1.114 -8.72e-05 3.915e-05 0.8639 -6.572e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2886 0.2874 0.2913 0.2893 0.9835 0.9898 0.2886 0.9577 0.9771 0.2936 ] Network output: [ -0.002044 1.006 0.01692 5.501e-05 -2.47e-05 0.9817 4.146e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04927 Epoch 3485 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06249 0.8968 0.9222 0.0001128 -5.062e-05 0.05641 8.497e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0141 -0.005203 0.005871 0.02652 0.9511 0.9584 0.0249 0.9008 0.9184 0.06891 ] Network output: [ 0.9575 0.08769 0.0372 -2.237e-05 1.004e-05 -0.04002 -1.686e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5505 0.08327 0.07336 0.3491 0.9776 0.9899 0.6056 0.9149 0.9744 0.5264 ] Network output: [ 0.0242 0.9121 0.9378 4.185e-06 -1.879e-06 0.1017 3.154e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02012 0.01431 0.02346 0.02649 0.9877 0.9914 0.02042 0.9726 0.9837 0.03023 ] Network output: [ 0.09993 -0.2248 0.816 4.146e-05 -1.861e-05 1.209 3.125e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.598 0.5095 0.4203 0.481 0.9799 0.9912 0.5996 0.9221 0.9776 0.5119 ] Network output: [ -0.07021 0.1574 1.155 -0.0001012 4.545e-05 0.8273 -7.63e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2763 0.2697 0.287 0.2889 0.988 0.9923 0.2764 0.9736 0.9842 0.2968 ] Network output: [ -0.07116 0.1641 1.114 -8.675e-05 3.894e-05 0.8639 -6.537e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2885 0.2873 0.2912 0.2892 0.9835 0.9899 0.2885 0.9578 0.9772 0.2935 ] Network output: [ -0.002104 1.006 0.01705 5.472e-05 -2.457e-05 0.9816 4.124e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04924 Epoch 3486 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06246 0.8969 0.9223 0.0001127 -5.06e-05 0.05636 8.494e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01409 -0.005207 0.005843 0.0265 0.9511 0.9585 0.02489 0.9008 0.9184 0.06888 ] Network output: [ 0.9575 0.08763 0.03722 -2.293e-05 1.029e-05 -0.04005 -1.728e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5505 0.08334 0.0734 0.349 0.9776 0.9899 0.6056 0.9149 0.9744 0.5263 ] Network output: [ 0.02415 0.9122 0.9378 4.02e-06 -1.805e-06 0.1017 3.03e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02011 0.01431 0.02345 0.02648 0.9877 0.9914 0.02042 0.9726 0.9837 0.03022 ] Network output: [ 0.0999 -0.2247 0.8158 4.162e-05 -1.869e-05 1.209 3.137e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.598 0.5096 0.4204 0.4809 0.9799 0.9912 0.5996 0.9221 0.9776 0.5118 ] Network output: [ -0.07014 0.1572 1.155 -0.0001008 4.525e-05 0.8274 -7.597e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2763 0.2697 0.287 0.2889 0.988 0.9923 0.2764 0.9736 0.9842 0.2968 ] Network output: [ -0.07108 0.1639 1.114 -8.63e-05 3.874e-05 0.8639 -6.504e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2884 0.2872 0.2912 0.2892 0.9835 0.9899 0.2884 0.9578 0.9772 0.2935 ] Network output: [ -0.002164 1.006 0.01718 5.443e-05 -2.444e-05 0.9814 4.102e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04922 Epoch 3487 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06242 0.897 0.9223 0.0001127 -5.058e-05 0.05631 8.491e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01408 -0.005211 0.005815 0.02649 0.9511 0.9585 0.02488 0.9009 0.9184 0.06886 ] Network output: [ 0.9576 0.08758 0.03724 -2.349e-05 1.054e-05 -0.04007 -1.77e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5504 0.0834 0.07343 0.3488 0.9776 0.9899 0.6055 0.915 0.9744 0.5262 ] Network output: [ 0.0241 0.9123 0.9378 3.855e-06 -1.731e-06 0.1017 2.905e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02011 0.01431 0.02344 0.02647 0.9877 0.9914 0.02042 0.9726 0.9837 0.03021 ] Network output: [ 0.09988 -0.2246 0.8157 4.178e-05 -1.876e-05 1.209 3.149e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.598 0.5097 0.4204 0.4808 0.9799 0.9912 0.5995 0.9221 0.9776 0.5118 ] Network output: [ -0.07007 0.157 1.155 -0.0001004 4.506e-05 0.8275 -7.564e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2763 0.2697 0.287 0.2889 0.988 0.9923 0.2764 0.9736 0.9842 0.2968 ] Network output: [ -0.071 0.1637 1.114 -8.585e-05 3.854e-05 0.8639 -6.47e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2883 0.2871 0.2912 0.2892 0.9835 0.9899 0.2884 0.9578 0.9772 0.2935 ] Network output: [ -0.002224 1.006 0.01731 5.415e-05 -2.431e-05 0.9813 4.081e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04919 Epoch 3488 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06238 0.8972 0.9223 0.0001126 -5.057e-05 0.05626 8.488e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01407 -0.005215 0.005788 0.02647 0.9511 0.9585 0.02487 0.9009 0.9185 0.06883 ] Network output: [ 0.9576 0.08753 0.03727 -2.404e-05 1.079e-05 -0.0401 -1.811e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5503 0.08347 0.07346 0.3487 0.9776 0.9899 0.6055 0.915 0.9744 0.5262 ] Network output: [ 0.02405 0.9124 0.9378 3.689e-06 -1.656e-06 0.1017 2.78e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02011 0.01431 0.02344 0.02645 0.9877 0.9915 0.02041 0.9726 0.9837 0.0302 ] Network output: [ 0.09985 -0.2245 0.8155 4.194e-05 -1.883e-05 1.209 3.161e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.598 0.5097 0.4205 0.4807 0.9799 0.9912 0.5995 0.9222 0.9776 0.5117 ] Network output: [ -0.07 0.1568 1.155 -9.993e-05 4.486e-05 0.8277 -7.531e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2762 0.2697 0.287 0.2889 0.988 0.9923 0.2764 0.9736 0.9842 0.2968 ] Network output: [ -0.07093 0.1636 1.114 -8.54e-05 3.834e-05 0.8639 -6.436e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2883 0.2871 0.2912 0.2892 0.9835 0.9899 0.2883 0.9578 0.9772 0.2934 ] Network output: [ -0.002284 1.006 0.01744 5.386e-05 -2.418e-05 0.9811 4.059e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04916 Epoch 3489 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06235 0.8973 0.9223 0.0001126 -5.055e-05 0.05621 8.486e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01406 -0.005219 0.00576 0.02645 0.9511 0.9585 0.02486 0.9009 0.9185 0.06881 ] Network output: [ 0.9576 0.08747 0.03729 -2.458e-05 1.103e-05 -0.04012 -1.852e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5503 0.08353 0.0735 0.3486 0.9776 0.9899 0.6055 0.9151 0.9744 0.5261 ] Network output: [ 0.024 0.9125 0.9378 3.522e-06 -1.581e-06 0.1017 2.654e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02011 0.01431 0.02343 0.02644 0.9877 0.9915 0.02041 0.9726 0.9838 0.03019 ] Network output: [ 0.09982 -0.2244 0.8154 4.209e-05 -1.89e-05 1.21 3.172e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5979 0.5098 0.4205 0.4806 0.9799 0.9912 0.5995 0.9222 0.9776 0.5116 ] Network output: [ -0.06993 0.1566 1.155 -9.951e-05 4.467e-05 0.8278 -7.499e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2762 0.2697 0.287 0.2889 0.988 0.9923 0.2764 0.9736 0.9843 0.2968 ] Network output: [ -0.07085 0.1634 1.114 -8.496e-05 3.814e-05 0.8639 -6.403e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2882 0.287 0.2911 0.2891 0.9835 0.9899 0.2882 0.9579 0.9772 0.2934 ] Network output: [ -0.002343 1.006 0.01757 5.358e-05 -2.405e-05 0.981 4.038e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04914 Epoch 3490 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06231 0.8974 0.9223 0.0001126 -5.053e-05 0.05616 8.483e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01405 -0.005223 0.005732 0.02644 0.9512 0.9585 0.02485 0.901 0.9185 0.06878 ] Network output: [ 0.9577 0.08742 0.03731 -2.511e-05 1.127e-05 -0.04015 -1.892e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5502 0.08359 0.07353 0.3484 0.9776 0.9899 0.6055 0.9151 0.9745 0.526 ] Network output: [ 0.02395 0.9126 0.9378 3.355e-06 -1.506e-06 0.1017 2.528e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0201 0.01431 0.02342 0.02643 0.9877 0.9915 0.02041 0.9726 0.9838 0.03018 ] Network output: [ 0.09979 -0.2243 0.8152 4.224e-05 -1.896e-05 1.21 3.184e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5979 0.5099 0.4205 0.4805 0.9799 0.9912 0.5995 0.9223 0.9776 0.5116 ] Network output: [ -0.06986 0.1564 1.155 -9.908e-05 4.448e-05 0.8279 -7.467e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2762 0.2697 0.2871 0.2889 0.988 0.9924 0.2763 0.9736 0.9843 0.2968 ] Network output: [ -0.07078 0.1632 1.114 -8.452e-05 3.795e-05 0.8639 -6.37e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2881 0.2869 0.2911 0.2891 0.9835 0.9899 0.2881 0.9579 0.9772 0.2934 ] Network output: [ -0.002401 1.006 0.01769 5.33e-05 -2.393e-05 0.9809 4.017e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04911 Epoch 3491 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06228 0.8975 0.9223 0.0001125 -5.051e-05 0.05611 8.48e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01404 -0.005227 0.005705 0.02642 0.9512 0.9585 0.02484 0.901 0.9186 0.06876 ] Network output: [ 0.9577 0.08737 0.03733 -2.564e-05 1.151e-05 -0.04018 -1.932e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5502 0.08366 0.07356 0.3483 0.9776 0.99 0.6054 0.9151 0.9745 0.5259 ] Network output: [ 0.0239 0.9127 0.9378 3.187e-06 -1.431e-06 0.1017 2.402e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0201 0.01431 0.02341 0.02641 0.9877 0.9915 0.0204 0.9727 0.9838 0.03017 ] Network output: [ 0.09977 -0.2242 0.8151 4.239e-05 -1.903e-05 1.21 3.195e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5979 0.5099 0.4206 0.4804 0.98 0.9912 0.5994 0.9223 0.9777 0.5115 ] Network output: [ -0.0698 0.1562 1.155 -9.866e-05 4.429e-05 0.828 -7.435e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2762 0.2697 0.2871 0.2889 0.988 0.9924 0.2763 0.9737 0.9843 0.2968 ] Network output: [ -0.0707 0.163 1.114 -8.409e-05 3.775e-05 0.8639 -6.337e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.288 0.2868 0.2911 0.2891 0.9835 0.9899 0.2881 0.9579 0.9772 0.2934 ] Network output: [ -0.00246 1.007 0.01782 5.302e-05 -2.38e-05 0.9807 3.996e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04908 Epoch 3492 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06224 0.8976 0.9223 0.0001125 -5.05e-05 0.05606 8.477e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01403 -0.00523 0.005677 0.0264 0.9512 0.9586 0.02483 0.9011 0.9186 0.06873 ] Network output: [ 0.9577 0.08732 0.03735 -2.615e-05 1.174e-05 -0.0402 -1.971e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5501 0.08372 0.07359 0.3482 0.9776 0.99 0.6054 0.9152 0.9745 0.5259 ] Network output: [ 0.02385 0.9128 0.9378 3.018e-06 -1.355e-06 0.1017 2.275e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0201 0.01431 0.02341 0.0264 0.9878 0.9915 0.0204 0.9727 0.9838 0.03016 ] Network output: [ 0.09974 -0.2241 0.815 4.253e-05 -1.91e-05 1.21 3.206e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5979 0.51 0.4206 0.4803 0.98 0.9912 0.5994 0.9223 0.9777 0.5114 ] Network output: [ -0.06973 0.156 1.155 -9.824e-05 4.41e-05 0.8281 -7.404e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2762 0.2697 0.2871 0.2889 0.988 0.9924 0.2763 0.9737 0.9843 0.2968 ] Network output: [ -0.07063 0.1629 1.114 -8.365e-05 3.756e-05 0.8639 -6.304e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.288 0.2868 0.2911 0.289 0.9835 0.9899 0.288 0.9579 0.9773 0.2933 ] Network output: [ -0.002518 1.007 0.01795 5.275e-05 -2.368e-05 0.9806 3.975e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04906 Epoch 3493 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06221 0.8977 0.9223 0.0001124 -5.048e-05 0.05601 8.474e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01402 -0.005234 0.005649 0.02639 0.9512 0.9586 0.02482 0.9011 0.9186 0.0687 ] Network output: [ 0.9577 0.08726 0.03737 -2.667e-05 1.197e-05 -0.04023 -2.01e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5501 0.08378 0.07362 0.348 0.9777 0.99 0.6054 0.9152 0.9745 0.5258 ] Network output: [ 0.02379 0.9129 0.9378 2.849e-06 -1.279e-06 0.1017 2.147e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02009 0.01431 0.0234 0.02639 0.9878 0.9915 0.0204 0.9727 0.9838 0.03014 ] Network output: [ 0.09971 -0.224 0.8148 4.268e-05 -1.916e-05 1.21 3.216e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5978 0.5101 0.4206 0.4803 0.98 0.9912 0.5994 0.9224 0.9777 0.5114 ] Network output: [ -0.06966 0.1558 1.155 -9.783e-05 4.392e-05 0.8282 -7.373e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2762 0.2697 0.2871 0.2889 0.988 0.9924 0.2763 0.9737 0.9843 0.2968 ] Network output: [ -0.07055 0.1627 1.114 -8.322e-05 3.736e-05 0.8639 -6.272e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2879 0.2867 0.291 0.289 0.9836 0.9899 0.2879 0.9579 0.9773 0.2933 ] Network output: [ -0.002576 1.007 0.01807 5.247e-05 -2.356e-05 0.9805 3.954e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04903 Epoch 3494 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06217 0.8978 0.9223 0.0001124 -5.046e-05 0.05596 8.471e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01401 -0.005238 0.005622 0.02637 0.9512 0.9586 0.02481 0.9011 0.9187 0.06868 ] Network output: [ 0.9578 0.08721 0.03738 -2.717e-05 1.22e-05 -0.04026 -2.048e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.55 0.08385 0.07366 0.3479 0.9777 0.99 0.6054 0.9153 0.9745 0.5257 ] Network output: [ 0.02374 0.913 0.9379 2.679e-06 -1.203e-06 0.1017 2.019e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02009 0.01431 0.02339 0.02638 0.9878 0.9915 0.0204 0.9727 0.9838 0.03013 ] Network output: [ 0.09968 -0.224 0.8147 4.281e-05 -1.922e-05 1.21 3.227e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5978 0.5101 0.4207 0.4802 0.98 0.9913 0.5994 0.9224 0.9777 0.5113 ] Network output: [ -0.06959 0.1556 1.155 -9.741e-05 4.373e-05 0.8283 -7.341e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2762 0.2697 0.2871 0.2889 0.988 0.9924 0.2763 0.9737 0.9843 0.2968 ] Network output: [ -0.07048 0.1625 1.114 -8.28e-05 3.717e-05 0.8639 -6.24e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2878 0.2866 0.291 0.289 0.9836 0.9899 0.2878 0.958 0.9773 0.2933 ] Network output: [ -0.002633 1.007 0.0182 5.22e-05 -2.343e-05 0.9803 3.934e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.049 Epoch 3495 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06214 0.898 0.9223 0.0001124 -5.045e-05 0.05591 8.468e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.014 -0.005242 0.005594 0.02635 0.9512 0.9586 0.0248 0.9012 0.9187 0.06865 ] Network output: [ 0.9578 0.08716 0.0374 -2.767e-05 1.242e-05 -0.04028 -2.085e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.55 0.08391 0.07369 0.3478 0.9777 0.99 0.6053 0.9153 0.9746 0.5256 ] Network output: [ 0.02369 0.9131 0.9379 2.509e-06 -1.126e-06 0.1017 1.891e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02009 0.01431 0.02338 0.02636 0.9878 0.9915 0.02039 0.9727 0.9838 0.03012 ] Network output: [ 0.09965 -0.2239 0.8145 4.295e-05 -1.928e-05 1.21 3.237e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5978 0.5102 0.4207 0.4801 0.98 0.9913 0.5993 0.9225 0.9777 0.5112 ] Network output: [ -0.06952 0.1554 1.155 -9.7e-05 4.355e-05 0.8285 -7.311e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2762 0.2697 0.2871 0.2888 0.9881 0.9924 0.2763 0.9737 0.9843 0.2968 ] Network output: [ -0.07041 0.1624 1.114 -8.237e-05 3.698e-05 0.8638 -6.208e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2877 0.2865 0.291 0.2889 0.9836 0.9899 0.2877 0.958 0.9773 0.2932 ] Network output: [ -0.00269 1.007 0.01832 5.193e-05 -2.331e-05 0.9802 3.914e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04898 Epoch 3496 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0621 0.8981 0.9223 0.0001123 -5.043e-05 0.05586 8.466e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01399 -0.005246 0.005566 0.02634 0.9513 0.9586 0.02479 0.9012 0.9187 0.06863 ] Network output: [ 0.9578 0.08711 0.03742 -2.816e-05 1.264e-05 -0.04031 -2.122e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5499 0.08397 0.07372 0.3477 0.9777 0.99 0.6053 0.9154 0.9746 0.5255 ] Network output: [ 0.02364 0.9132 0.9379 2.338e-06 -1.05e-06 0.1017 1.762e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02009 0.01431 0.02337 0.02635 0.9878 0.9915 0.02039 0.9727 0.9838 0.03011 ] Network output: [ 0.09962 -0.2238 0.8144 4.308e-05 -1.934e-05 1.21 3.247e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5978 0.5103 0.4208 0.48 0.98 0.9913 0.5993 0.9225 0.9777 0.5112 ] Network output: [ -0.06945 0.1552 1.155 -9.66e-05 4.337e-05 0.8286 -7.28e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2762 0.2697 0.2871 0.2888 0.9881 0.9924 0.2763 0.9737 0.9843 0.2968 ] Network output: [ -0.07033 0.1622 1.114 -8.195e-05 3.679e-05 0.8638 -6.176e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2876 0.2865 0.291 0.2889 0.9836 0.9899 0.2877 0.958 0.9773 0.2932 ] Network output: [ -0.002747 1.007 0.01845 5.166e-05 -2.319e-05 0.9801 3.893e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04895 Epoch 3497 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06207 0.8982 0.9223 0.0001123 -5.041e-05 0.05581 8.463e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01399 -0.00525 0.005539 0.02632 0.9513 0.9586 0.02478 0.9013 0.9188 0.0686 ] Network output: [ 0.9579 0.08705 0.03744 -2.864e-05 1.286e-05 -0.04033 -2.159e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5498 0.08403 0.07375 0.3475 0.9777 0.99 0.6053 0.9154 0.9746 0.5255 ] Network output: [ 0.02359 0.9133 0.9379 2.167e-06 -9.729e-07 0.1017 1.633e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02008 0.01431 0.02337 0.02634 0.9878 0.9915 0.02039 0.9728 0.9838 0.0301 ] Network output: [ 0.09959 -0.2237 0.8142 4.321e-05 -1.94e-05 1.21 3.257e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5977 0.5103 0.4208 0.4799 0.98 0.9913 0.5993 0.9225 0.9778 0.5111 ] Network output: [ -0.06939 0.155 1.155 -9.619e-05 4.319e-05 0.8287 -7.25e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2761 0.2697 0.2871 0.2888 0.9881 0.9924 0.2763 0.9737 0.9843 0.2968 ] Network output: [ -0.07026 0.162 1.114 -8.153e-05 3.66e-05 0.8638 -6.144e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2876 0.2864 0.2909 0.2889 0.9836 0.9899 0.2876 0.958 0.9773 0.2932 ] Network output: [ -0.002803 1.007 0.01857 5.139e-05 -2.307e-05 0.9799 3.873e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04893 Epoch 3498 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06203 0.8983 0.9223 0.0001123 -5.04e-05 0.05576 8.46e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01398 -0.005254 0.005511 0.0263 0.9513 0.9586 0.02477 0.9013 0.9188 0.06857 ] Network output: [ 0.9579 0.087 0.03746 -2.912e-05 1.307e-05 -0.04036 -2.195e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5498 0.0841 0.07378 0.3474 0.9777 0.99 0.6053 0.9155 0.9746 0.5254 ] Network output: [ 0.02354 0.9134 0.9379 1.995e-06 -8.958e-07 0.1016 1.504e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02008 0.01431 0.02336 0.02632 0.9878 0.9915 0.02039 0.9728 0.9839 0.03009 ] Network output: [ 0.09956 -0.2236 0.8141 4.334e-05 -1.946e-05 1.211 3.266e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5977 0.5104 0.4208 0.4798 0.98 0.9913 0.5993 0.9226 0.9778 0.5111 ] Network output: [ -0.06932 0.1548 1.155 -9.579e-05 4.301e-05 0.8288 -7.219e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2761 0.2697 0.2872 0.2888 0.9881 0.9924 0.2763 0.9738 0.9843 0.2968 ] Network output: [ -0.07018 0.1619 1.114 -8.111e-05 3.641e-05 0.8638 -6.113e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2875 0.2863 0.2909 0.2888 0.9836 0.9899 0.2875 0.9581 0.9774 0.2932 ] Network output: [ -0.002859 1.007 0.01869 5.113e-05 -2.295e-05 0.9798 3.853e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0489 Epoch 3499 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.062 0.8984 0.9223 0.0001122 -5.038e-05 0.05572 8.457e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01397 -0.005258 0.005483 0.02629 0.9513 0.9587 0.02476 0.9013 0.9188 0.06855 ] Network output: [ 0.9579 0.08695 0.03747 -2.959e-05 1.328e-05 -0.04038 -2.23e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5497 0.08416 0.07381 0.3473 0.9777 0.99 0.6052 0.9155 0.9746 0.5253 ] Network output: [ 0.02349 0.9135 0.9379 1.823e-06 -8.185e-07 0.1016 1.374e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02008 0.01431 0.02335 0.02631 0.9878 0.9915 0.02038 0.9728 0.9839 0.03008 ] Network output: [ 0.09953 -0.2235 0.814 4.346e-05 -1.951e-05 1.211 3.275e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5977 0.5105 0.4209 0.4797 0.98 0.9913 0.5992 0.9226 0.9778 0.511 ] Network output: [ -0.06925 0.1546 1.155 -9.54e-05 4.283e-05 0.8289 -7.189e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2761 0.2697 0.2872 0.2888 0.9881 0.9924 0.2762 0.9738 0.9844 0.2968 ] Network output: [ -0.07011 0.1617 1.114 -8.069e-05 3.623e-05 0.8638 -6.081e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2874 0.2862 0.2909 0.2888 0.9836 0.9899 0.2874 0.9581 0.9774 0.2931 ] Network output: [ -0.002915 1.008 0.01881 5.087e-05 -2.284e-05 0.9797 3.833e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04887 Epoch 3500 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06196 0.8985 0.9224 0.0001122 -5.036e-05 0.05567 8.455e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01396 -0.005262 0.005456 0.02627 0.9513 0.9587 0.02475 0.9014 0.9189 0.06852 ] Network output: [ 0.9579 0.0869 0.03749 -3.006e-05 1.349e-05 -0.04041 -2.265e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5497 0.08422 0.07384 0.3471 0.9777 0.99 0.6052 0.9155 0.9746 0.5252 ] Network output: [ 0.02344 0.9136 0.9379 1.65e-06 -7.409e-07 0.1016 1.244e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02007 0.01431 0.02334 0.0263 0.9878 0.9915 0.02038 0.9728 0.9839 0.03007 ] Network output: [ 0.0995 -0.2234 0.8138 4.358e-05 -1.957e-05 1.211 3.284e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5977 0.5105 0.4209 0.4796 0.98 0.9913 0.5992 0.9227 0.9778 0.5109 ] Network output: [ -0.06918 0.1545 1.155 -9.5e-05 4.265e-05 0.829 -7.16e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2761 0.2697 0.2872 0.2888 0.9881 0.9924 0.2762 0.9738 0.9844 0.2968 ] Network output: [ -0.07004 0.1615 1.114 -8.028e-05 3.604e-05 0.8638 -6.05e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2873 0.2862 0.2909 0.2888 0.9836 0.9899 0.2874 0.9581 0.9774 0.2931 ] Network output: [ -0.00297 1.008 0.01893 5.06e-05 -2.272e-05 0.9795 3.814e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04885 Epoch 3501 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06193 0.8986 0.9224 0.0001121 -5.035e-05 0.05562 8.452e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01395 -0.005266 0.005428 0.02625 0.9513 0.9587 0.02474 0.9014 0.9189 0.06849 ] Network output: [ 0.958 0.08685 0.0375 -3.051e-05 1.37e-05 -0.04044 -2.3e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5496 0.08428 0.07387 0.347 0.9777 0.99 0.6052 0.9156 0.9747 0.5252 ] Network output: [ 0.02339 0.9137 0.9379 1.477e-06 -6.631e-07 0.1016 1.113e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02007 0.01431 0.02334 0.02628 0.9878 0.9915 0.02038 0.9728 0.9839 0.03005 ] Network output: [ 0.09947 -0.2233 0.8137 4.37e-05 -1.962e-05 1.211 3.293e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5976 0.5106 0.421 0.4795 0.98 0.9913 0.5992 0.9227 0.9778 0.5109 ] Network output: [ -0.06912 0.1543 1.154 -9.461e-05 4.247e-05 0.8291 -7.13e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2761 0.2696 0.2872 0.2888 0.9881 0.9924 0.2762 0.9738 0.9844 0.2968 ] Network output: [ -0.06997 0.1613 1.114 -7.987e-05 3.586e-05 0.8638 -6.019e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2873 0.2861 0.2908 0.2887 0.9836 0.9899 0.2873 0.9581 0.9774 0.2931 ] Network output: [ -0.003025 1.008 0.01906 5.035e-05 -2.26e-05 0.9794 3.794e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04882 Epoch 3502 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06189 0.8987 0.9224 0.0001121 -5.033e-05 0.05558 8.449e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01394 -0.00527 0.0054 0.02624 0.9514 0.9587 0.02473 0.9015 0.9189 0.06847 ] Network output: [ 0.958 0.0868 0.03752 -3.096e-05 1.39e-05 -0.04046 -2.334e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5496 0.08434 0.0739 0.3469 0.9777 0.99 0.6051 0.9156 0.9747 0.5251 ] Network output: [ 0.02334 0.9138 0.9379 1.303e-06 -5.852e-07 0.1016 9.823e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02007 0.01431 0.02333 0.02627 0.9878 0.9915 0.02037 0.9729 0.9839 0.03004 ] Network output: [ 0.09944 -0.2232 0.8136 4.382e-05 -1.967e-05 1.211 3.302e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5976 0.5107 0.421 0.4794 0.98 0.9913 0.5992 0.9227 0.9778 0.5108 ] Network output: [ -0.06905 0.1541 1.154 -9.422e-05 4.23e-05 0.8292 -7.101e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2761 0.2696 0.2872 0.2888 0.9881 0.9924 0.2762 0.9738 0.9844 0.2968 ] Network output: [ -0.06989 0.1612 1.115 -7.946e-05 3.567e-05 0.8638 -5.989e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2872 0.286 0.2908 0.2887 0.9836 0.9899 0.2872 0.9582 0.9774 0.293 ] Network output: [ -0.00308 1.008 0.01918 5.009e-05 -2.249e-05 0.9793 3.775e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0488 Epoch 3503 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06186 0.8988 0.9224 0.0001121 -5.032e-05 0.05553 8.447e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01393 -0.005273 0.005373 0.02622 0.9514 0.9587 0.02472 0.9015 0.919 0.06844 ] Network output: [ 0.958 0.08675 0.03753 -3.141e-05 1.41e-05 -0.04049 -2.367e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5495 0.0844 0.07393 0.3468 0.9777 0.99 0.6051 0.9157 0.9747 0.525 ] Network output: [ 0.02329 0.9139 0.9379 1.129e-06 -5.07e-07 0.1016 8.511e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02007 0.01431 0.02332 0.02626 0.9878 0.9915 0.02037 0.9729 0.9839 0.03003 ] Network output: [ 0.09941 -0.2231 0.8134 4.393e-05 -1.972e-05 1.211 3.311e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5976 0.5107 0.421 0.4793 0.98 0.9913 0.5991 0.9228 0.9778 0.5107 ] Network output: [ -0.06898 0.1539 1.154 -9.383e-05 4.212e-05 0.8293 -7.071e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2761 0.2696 0.2872 0.2888 0.9881 0.9924 0.2762 0.9738 0.9844 0.2968 ] Network output: [ -0.06982 0.161 1.115 -7.906e-05 3.549e-05 0.8638 -5.958e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2871 0.2859 0.2908 0.2887 0.9836 0.9899 0.2871 0.9582 0.9774 0.293 ] Network output: [ -0.003135 1.008 0.0193 4.983e-05 -2.237e-05 0.9792 3.756e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04877 Epoch 3504 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06182 0.8989 0.9224 0.000112 -5.03e-05 0.05548 8.444e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01392 -0.005277 0.005345 0.0262 0.9514 0.9587 0.02471 0.9016 0.919 0.06842 ] Network output: [ 0.9581 0.0867 0.03755 -3.185e-05 1.43e-05 -0.04051 -2.4e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5495 0.08446 0.07396 0.3466 0.9778 0.99 0.6051 0.9157 0.9747 0.525 ] Network output: [ 0.02324 0.914 0.9379 9.548e-07 -4.286e-07 0.1016 7.195e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02006 0.01431 0.02331 0.02624 0.9878 0.9915 0.02037 0.9729 0.9839 0.03002 ] Network output: [ 0.09938 -0.223 0.8133 4.404e-05 -1.977e-05 1.211 3.319e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5976 0.5108 0.4211 0.4792 0.9801 0.9913 0.5991 0.9228 0.9779 0.5107 ] Network output: [ -0.06892 0.1537 1.154 -9.345e-05 4.195e-05 0.8294 -7.042e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2761 0.2696 0.2872 0.2888 0.9881 0.9924 0.2762 0.9739 0.9844 0.2968 ] Network output: [ -0.06975 0.1608 1.115 -7.866e-05 3.531e-05 0.8638 -5.928e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.287 0.2859 0.2908 0.2886 0.9836 0.9899 0.2871 0.9582 0.9774 0.293 ] Network output: [ -0.003189 1.008 0.01941 4.958e-05 -2.226e-05 0.979 3.736e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04875 Epoch 3505 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06179 0.8991 0.9224 0.000112 -5.028e-05 0.05544 8.441e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01391 -0.005281 0.005317 0.02618 0.9514 0.9587 0.0247 0.9016 0.919 0.06839 ] Network output: [ 0.9581 0.08665 0.03756 -3.228e-05 1.449e-05 -0.04054 -2.433e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5494 0.08452 0.07399 0.3465 0.9778 0.99 0.6051 0.9158 0.9747 0.5249 ] Network output: [ 0.02319 0.9141 0.9379 7.798e-07 -3.501e-07 0.1016 5.877e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02006 0.01431 0.0233 0.02623 0.9878 0.9915 0.02037 0.9729 0.9839 0.03001 ] Network output: [ 0.09935 -0.2229 0.8132 4.415e-05 -1.982e-05 1.211 3.327e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5975 0.5109 0.4211 0.4791 0.9801 0.9913 0.5991 0.9229 0.9779 0.5106 ] Network output: [ -0.06885 0.1535 1.154 -9.307e-05 4.178e-05 0.8295 -7.014e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2761 0.2696 0.2872 0.2888 0.9881 0.9924 0.2762 0.9739 0.9844 0.2968 ] Network output: [ -0.06968 0.1607 1.115 -7.826e-05 3.513e-05 0.8637 -5.898e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.287 0.2858 0.2907 0.2886 0.9836 0.9899 0.287 0.9582 0.9775 0.293 ] Network output: [ -0.003243 1.008 0.01953 4.933e-05 -2.214e-05 0.9789 3.717e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04872 Epoch 3506 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06175 0.8992 0.9224 0.000112 -5.027e-05 0.05539 8.439e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0139 -0.005285 0.00529 0.02617 0.9514 0.9588 0.02469 0.9016 0.9191 0.06836 ] Network output: [ 0.9581 0.0866 0.03758 -3.271e-05 1.468e-05 -0.04056 -2.465e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5493 0.08458 0.07402 0.3464 0.9778 0.99 0.605 0.9158 0.9747 0.5248 ] Network output: [ 0.02314 0.9142 0.9379 6.044e-07 -2.713e-07 0.1016 4.555e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02006 0.0143 0.02329 0.02622 0.9878 0.9915 0.02036 0.9729 0.9839 0.03 ] Network output: [ 0.09931 -0.2229 0.813 4.425e-05 -1.987e-05 1.211 3.335e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5975 0.5109 0.4212 0.479 0.9801 0.9913 0.5991 0.9229 0.9779 0.5106 ] Network output: [ -0.06878 0.1533 1.154 -9.269e-05 4.161e-05 0.8296 -6.985e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.276 0.2696 0.2872 0.2887 0.9881 0.9924 0.2762 0.9739 0.9844 0.2968 ] Network output: [ -0.0696 0.1605 1.115 -7.786e-05 3.495e-05 0.8637 -5.868e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2869 0.2857 0.2907 0.2886 0.9836 0.99 0.2869 0.9583 0.9775 0.2929 ] Network output: [ -0.003297 1.008 0.01965 4.908e-05 -2.203e-05 0.9788 3.698e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0487 Epoch 3507 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06172 0.8993 0.9224 0.0001119 -5.025e-05 0.05535 8.436e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01389 -0.005289 0.005262 0.02615 0.9515 0.9588 0.02468 0.9017 0.9191 0.06834 ] Network output: [ 0.9582 0.08655 0.03759 -3.313e-05 1.487e-05 -0.04059 -2.497e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5493 0.08464 0.07405 0.3462 0.9778 0.99 0.605 0.9158 0.9748 0.5247 ] Network output: [ 0.02309 0.9143 0.938 4.286e-07 -1.924e-07 0.1016 3.23e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02005 0.0143 0.02329 0.0262 0.9878 0.9915 0.02036 0.9729 0.984 0.02999 ] Network output: [ 0.09928 -0.2228 0.8129 4.435e-05 -1.991e-05 1.211 3.343e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5975 0.511 0.4212 0.4789 0.9801 0.9913 0.599 0.923 0.9779 0.5105 ] Network output: [ -0.06872 0.1531 1.154 -9.231e-05 4.144e-05 0.8297 -6.957e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.276 0.2696 0.2873 0.2887 0.9881 0.9924 0.2762 0.9739 0.9844 0.2968 ] Network output: [ -0.06953 0.1604 1.115 -7.746e-05 3.478e-05 0.8637 -5.838e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2868 0.2856 0.2907 0.2885 0.9836 0.99 0.2868 0.9583 0.9775 0.2929 ] Network output: [ -0.00335 1.008 0.01977 4.883e-05 -2.192e-05 0.9787 3.68e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04868 Epoch 3508 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06168 0.8994 0.9224 0.0001119 -5.024e-05 0.0553 8.434e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01388 -0.005293 0.005234 0.02613 0.9515 0.9588 0.02467 0.9017 0.9191 0.06831 ] Network output: [ 0.9582 0.0865 0.0376 -3.354e-05 1.506e-05 -0.04061 -2.528e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5492 0.0847 0.07408 0.3461 0.9778 0.99 0.605 0.9159 0.9748 0.5247 ] Network output: [ 0.02304 0.9144 0.938 2.525e-07 -1.133e-07 0.1016 1.903e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02005 0.0143 0.02328 0.02619 0.9878 0.9915 0.02036 0.973 0.984 0.02997 ] Network output: [ 0.09925 -0.2227 0.8128 4.445e-05 -1.996e-05 1.212 3.35e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5975 0.511 0.4212 0.4788 0.9801 0.9913 0.599 0.923 0.9779 0.5104 ] Network output: [ -0.06865 0.153 1.154 -9.194e-05 4.127e-05 0.8298 -6.929e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.276 0.2696 0.2873 0.2887 0.9881 0.9924 0.2761 0.9739 0.9845 0.2968 ] Network output: [ -0.06946 0.1602 1.115 -7.707e-05 3.46e-05 0.8637 -5.808e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2867 0.2856 0.2907 0.2885 0.9837 0.99 0.2867 0.9583 0.9775 0.2929 ] Network output: [ -0.003403 1.009 0.01988 4.858e-05 -2.181e-05 0.9785 3.661e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04865 Epoch 3509 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06165 0.8995 0.9224 0.0001119 -5.022e-05 0.05526 8.431e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01387 -0.005297 0.005207 0.02612 0.9515 0.9588 0.02466 0.9018 0.9192 0.06828 ] Network output: [ 0.9582 0.08645 0.03761 -3.395e-05 1.524e-05 -0.04064 -2.559e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5492 0.08476 0.0741 0.346 0.9778 0.99 0.605 0.9159 0.9748 0.5246 ] Network output: [ 0.02299 0.9145 0.938 7.592e-08 -3.408e-08 0.1016 5.722e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02005 0.0143 0.02327 0.02618 0.9879 0.9916 0.02035 0.973 0.984 0.02996 ] Network output: [ 0.09922 -0.2226 0.8127 4.455e-05 -2e-05 1.212 3.357e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5974 0.5111 0.4213 0.4787 0.9801 0.9913 0.599 0.923 0.9779 0.5104 ] Network output: [ -0.06859 0.1528 1.154 -9.157e-05 4.111e-05 0.8299 -6.901e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.276 0.2696 0.2873 0.2887 0.9881 0.9924 0.2761 0.9739 0.9845 0.2968 ] Network output: [ -0.06939 0.16 1.115 -7.668e-05 3.442e-05 0.8637 -5.779e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2866 0.2855 0.2906 0.2885 0.9837 0.99 0.2867 0.9583 0.9775 0.2928 ] Network output: [ -0.003456 1.009 0.02 4.833e-05 -2.17e-05 0.9784 3.643e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04863 Epoch 3510 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06161 0.8996 0.9224 0.0001118 -5.021e-05 0.05521 8.428e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01386 -0.005301 0.005179 0.0261 0.9515 0.9588 0.02465 0.9018 0.9192 0.06826 ] Network output: [ 0.9583 0.0864 0.03763 -3.436e-05 1.542e-05 -0.04066 -2.589e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5491 0.08482 0.07413 0.3459 0.9778 0.9901 0.6049 0.916 0.9748 0.5245 ] Network output: [ 0.02294 0.9146 0.938 -1.01e-07 4.534e-08 0.1016 -7.611e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02004 0.0143 0.02326 0.02616 0.9879 0.9916 0.02035 0.973 0.984 0.02995 ] Network output: [ 0.09919 -0.2225 0.8125 4.464e-05 -2.004e-05 1.212 3.364e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5974 0.5112 0.4213 0.4786 0.9801 0.9913 0.599 0.9231 0.978 0.5103 ] Network output: [ -0.06852 0.1526 1.154 -9.12e-05 4.094e-05 0.83 -6.873e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.276 0.2696 0.2873 0.2887 0.9881 0.9924 0.2761 0.9739 0.9845 0.2968 ] Network output: [ -0.06932 0.1599 1.115 -7.629e-05 3.425e-05 0.8637 -5.749e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2866 0.2854 0.2906 0.2884 0.9837 0.99 0.2866 0.9583 0.9775 0.2928 ] Network output: [ -0.003508 1.009 0.02012 4.809e-05 -2.159e-05 0.9783 3.624e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0486 Epoch 3511 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06158 0.8997 0.9224 0.0001118 -5.019e-05 0.05517 8.426e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01385 -0.005305 0.005151 0.02608 0.9515 0.9588 0.02464 0.9018 0.9192 0.06823 ] Network output: [ 0.9583 0.08635 0.03764 -3.475e-05 1.56e-05 -0.04069 -2.619e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5491 0.08488 0.07416 0.3457 0.9778 0.9901 0.6049 0.916 0.9748 0.5244 ] Network output: [ 0.02289 0.9146 0.938 -2.782e-07 1.249e-07 0.1016 -2.097e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02004 0.0143 0.02325 0.02615 0.9879 0.9916 0.02035 0.973 0.984 0.02994 ] Network output: [ 0.09915 -0.2224 0.8124 4.473e-05 -2.008e-05 1.212 3.371e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5974 0.5112 0.4214 0.4785 0.9801 0.9913 0.5989 0.9231 0.978 0.5102 ] Network output: [ -0.06846 0.1524 1.154 -9.083e-05 4.078e-05 0.8301 -6.845e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.276 0.2696 0.2873 0.2887 0.9881 0.9924 0.2761 0.974 0.9845 0.2968 ] Network output: [ -0.06925 0.1597 1.115 -7.59e-05 3.408e-05 0.8637 -5.72e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2865 0.2853 0.2906 0.2884 0.9837 0.99 0.2865 0.9584 0.9775 0.2928 ] Network output: [ -0.00356 1.009 0.02023 4.785e-05 -2.148e-05 0.9782 3.606e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04858 Epoch 3512 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06155 0.8998 0.9224 0.0001118 -5.018e-05 0.05513 8.423e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01384 -0.005308 0.005124 0.02606 0.9515 0.9589 0.02463 0.9019 0.9193 0.0682 ] Network output: [ 0.9583 0.0863 0.03765 -3.514e-05 1.578e-05 -0.04072 -2.649e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.549 0.08494 0.07419 0.3456 0.9778 0.9901 0.6049 0.9161 0.9749 0.5244 ] Network output: [ 0.02284 0.9147 0.938 -4.559e-07 2.047e-07 0.1016 -3.435e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02004 0.0143 0.02325 0.02614 0.9879 0.9916 0.02034 0.973 0.984 0.02993 ] Network output: [ 0.09912 -0.2223 0.8123 4.482e-05 -2.012e-05 1.212 3.378e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5974 0.5113 0.4214 0.4784 0.9801 0.9913 0.5989 0.9232 0.978 0.5102 ] Network output: [ -0.06839 0.1522 1.154 -9.047e-05 4.061e-05 0.8302 -6.818e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.276 0.2696 0.2873 0.2887 0.9881 0.9924 0.2761 0.974 0.9845 0.2968 ] Network output: [ -0.06918 0.1595 1.115 -7.552e-05 3.39e-05 0.8637 -5.691e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2864 0.2853 0.2905 0.2884 0.9837 0.99 0.2864 0.9584 0.9776 0.2928 ] Network output: [ -0.003612 1.009 0.02035 4.761e-05 -2.137e-05 0.978 3.588e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04856 Epoch 3513 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06151 0.8999 0.9225 0.0001117 -5.016e-05 0.05508 8.421e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01383 -0.005312 0.005096 0.02605 0.9516 0.9589 0.02462 0.9019 0.9193 0.06818 ] Network output: [ 0.9583 0.08625 0.03766 -3.553e-05 1.595e-05 -0.04074 -2.678e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.549 0.085 0.07422 0.3455 0.9778 0.9901 0.6049 0.9161 0.9749 0.5243 ] Network output: [ 0.02279 0.9148 0.938 -6.338e-07 2.845e-07 0.1016 -4.777e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02003 0.0143 0.02324 0.02612 0.9879 0.9916 0.02034 0.973 0.984 0.02991 ] Network output: [ 0.09909 -0.2222 0.8122 4.491e-05 -2.016e-05 1.212 3.384e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5973 0.5113 0.4214 0.4784 0.9801 0.9913 0.5989 0.9232 0.978 0.5101 ] Network output: [ -0.06833 0.1521 1.154 -9.011e-05 4.045e-05 0.8303 -6.791e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.276 0.2696 0.2873 0.2887 0.9881 0.9924 0.2761 0.974 0.9845 0.2968 ] Network output: [ -0.06911 0.1594 1.115 -7.513e-05 3.373e-05 0.8637 -5.662e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2863 0.2852 0.2905 0.2883 0.9837 0.99 0.2864 0.9584 0.9776 0.2927 ] Network output: [ -0.003664 1.009 0.02046 4.737e-05 -2.127e-05 0.9779 3.57e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04853 Epoch 3514 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06148 0.9 0.9225 0.0001117 -5.015e-05 0.05504 8.418e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01382 -0.005316 0.005068 0.02603 0.9516 0.9589 0.02461 0.902 0.9193 0.06815 ] Network output: [ 0.9584 0.0862 0.03767 -3.591e-05 1.612e-05 -0.04077 -2.706e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5489 0.08506 0.07424 0.3454 0.9778 0.9901 0.6048 0.9161 0.9749 0.5242 ] Network output: [ 0.02274 0.9149 0.938 -8.121e-07 3.646e-07 0.1016 -6.12e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02003 0.0143 0.02323 0.02611 0.9879 0.9916 0.02034 0.9731 0.984 0.0299 ] Network output: [ 0.09905 -0.2221 0.812 4.499e-05 -2.02e-05 1.212 3.391e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5973 0.5114 0.4215 0.4783 0.9801 0.9913 0.5989 0.9232 0.978 0.5101 ] Network output: [ -0.06826 0.1519 1.154 -8.975e-05 4.029e-05 0.8304 -6.764e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2759 0.2696 0.2873 0.2887 0.9881 0.9924 0.2761 0.974 0.9845 0.2968 ] Network output: [ -0.06904 0.1592 1.115 -7.475e-05 3.356e-05 0.8636 -5.634e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2863 0.2851 0.2905 0.2883 0.9837 0.99 0.2863 0.9584 0.9776 0.2927 ] Network output: [ -0.003715 1.009 0.02057 4.713e-05 -2.116e-05 0.9778 3.552e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04851 Epoch 3515 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06144 0.9001 0.9225 0.0001117 -5.013e-05 0.055 8.416e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01381 -0.00532 0.005041 0.02601 0.9516 0.9589 0.0246 0.902 0.9194 0.06812 ] Network output: [ 0.9584 0.08616 0.03768 -3.629e-05 1.629e-05 -0.04079 -2.735e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5488 0.08511 0.07427 0.3452 0.9779 0.9901 0.6048 0.9162 0.9749 0.5242 ] Network output: [ 0.02269 0.915 0.938 -9.906e-07 4.447e-07 0.1016 -7.466e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02003 0.0143 0.02322 0.0261 0.9879 0.9916 0.02034 0.9731 0.984 0.02989 ] Network output: [ 0.09902 -0.222 0.8119 4.507e-05 -2.023e-05 1.212 3.397e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5973 0.5115 0.4215 0.4782 0.9801 0.9913 0.5988 0.9233 0.978 0.51 ] Network output: [ -0.0682 0.1517 1.154 -8.939e-05 4.013e-05 0.8305 -6.737e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2759 0.2696 0.2873 0.2886 0.9882 0.9924 0.2761 0.974 0.9845 0.2968 ] Network output: [ -0.06897 0.159 1.115 -7.438e-05 3.339e-05 0.8636 -5.605e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2862 0.285 0.2905 0.2883 0.9837 0.99 0.2862 0.9585 0.9776 0.2927 ] Network output: [ -0.003766 1.009 0.02069 4.69e-05 -2.105e-05 0.9777 3.535e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04848 Epoch 3516 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06141 0.9002 0.9225 0.0001116 -5.012e-05 0.05495 8.413e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0138 -0.005324 0.005013 0.02599 0.9516 0.9589 0.02459 0.902 0.9194 0.0681 ] Network output: [ 0.9584 0.08611 0.03769 -3.666e-05 1.646e-05 -0.04082 -2.763e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5488 0.08517 0.0743 0.3451 0.9779 0.9901 0.6048 0.9162 0.9749 0.5241 ] Network output: [ 0.02264 0.9151 0.938 -1.17e-06 5.25e-07 0.1016 -8.814e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02003 0.0143 0.02321 0.02608 0.9879 0.9916 0.02033 0.9731 0.9841 0.02988 ] Network output: [ 0.09899 -0.222 0.8118 4.515e-05 -2.027e-05 1.212 3.403e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5973 0.5115 0.4216 0.4781 0.9802 0.9914 0.5988 0.9233 0.978 0.51 ] Network output: [ -0.06813 0.1515 1.154 -8.904e-05 3.997e-05 0.8306 -6.71e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2759 0.2696 0.2873 0.2886 0.9882 0.9924 0.276 0.974 0.9845 0.2968 ] Network output: [ -0.0689 0.1589 1.115 -7.4e-05 3.322e-05 0.8636 -5.577e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2861 0.285 0.2904 0.2882 0.9837 0.99 0.2861 0.9585 0.9776 0.2926 ] Network output: [ -0.003817 1.009 0.0208 4.667e-05 -2.095e-05 0.9776 3.517e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04846 Epoch 3517 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06138 0.9003 0.9225 0.0001116 -5.01e-05 0.05491 8.411e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01379 -0.005328 0.004986 0.02598 0.9516 0.9589 0.02458 0.9021 0.9194 0.06807 ] Network output: [ 0.9585 0.08606 0.0377 -3.702e-05 1.662e-05 -0.04084 -2.79e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5487 0.08523 0.07432 0.345 0.9779 0.9901 0.6048 0.9163 0.9749 0.524 ] Network output: [ 0.02259 0.9152 0.938 -1.349e-06 6.055e-07 0.1016 -1.016e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02002 0.0143 0.0232 0.02607 0.9879 0.9916 0.02033 0.9731 0.9841 0.02987 ] Network output: [ 0.09895 -0.2219 0.8117 4.523e-05 -2.03e-05 1.212 3.408e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5972 0.5116 0.4216 0.478 0.9802 0.9914 0.5988 0.9234 0.9781 0.5099 ] Network output: [ -0.06807 0.1514 1.154 -8.869e-05 3.981e-05 0.8307 -6.684e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2759 0.2696 0.2873 0.2886 0.9882 0.9925 0.276 0.9741 0.9846 0.2968 ] Network output: [ -0.06883 0.1587 1.115 -7.363e-05 3.305e-05 0.8636 -5.549e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.286 0.2849 0.2904 0.2882 0.9837 0.99 0.2861 0.9585 0.9776 0.2926 ] Network output: [ -0.003867 1.01 0.02091 4.643e-05 -2.085e-05 0.9774 3.499e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04844 Epoch 3518 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06134 0.9004 0.9225 0.0001116 -5.009e-05 0.05487 8.409e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01378 -0.005331 0.004958 0.02596 0.9516 0.9589 0.02457 0.9021 0.9195 0.06804 ] Network output: [ 0.9585 0.08601 0.0377 -3.738e-05 1.678e-05 -0.04087 -2.817e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5487 0.08529 0.07435 0.3448 0.9779 0.9901 0.6047 0.9163 0.975 0.524 ] Network output: [ 0.02254 0.9153 0.938 -1.528e-06 6.861e-07 0.1016 -1.152e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02002 0.0143 0.0232 0.02605 0.9879 0.9916 0.02033 0.9731 0.9841 0.02985 ] Network output: [ 0.09892 -0.2218 0.8115 4.53e-05 -2.034e-05 1.213 3.414e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5972 0.5117 0.4216 0.4779 0.9802 0.9914 0.5988 0.9234 0.9781 0.5098 ] Network output: [ -0.068 0.1512 1.154 -8.834e-05 3.966e-05 0.8308 -6.657e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2759 0.2696 0.2874 0.2886 0.9882 0.9925 0.276 0.9741 0.9846 0.2968 ] Network output: [ -0.06876 0.1586 1.115 -7.325e-05 3.289e-05 0.8636 -5.521e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.286 0.2848 0.2904 0.2882 0.9837 0.99 0.286 0.9585 0.9777 0.2926 ] Network output: [ -0.003917 1.01 0.02102 4.62e-05 -2.074e-05 0.9773 3.482e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04842 Epoch 3519 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06131 0.9005 0.9225 0.0001115 -5.008e-05 0.05483 8.406e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01377 -0.005335 0.00493 0.02594 0.9517 0.959 0.02457 0.9022 0.9195 0.06801 ] Network output: [ 0.9585 0.08597 0.03771 -3.774e-05 1.694e-05 -0.04089 -2.844e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5486 0.08534 0.07438 0.3447 0.9779 0.9901 0.6047 0.9164 0.975 0.5239 ] Network output: [ 0.0225 0.9154 0.938 -1.708e-06 7.668e-07 0.1016 -1.287e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02002 0.01429 0.02319 0.02604 0.9879 0.9916 0.02032 0.9732 0.9841 0.02984 ] Network output: [ 0.09889 -0.2217 0.8114 4.537e-05 -2.037e-05 1.213 3.419e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5972 0.5117 0.4217 0.4778 0.9802 0.9914 0.5987 0.9234 0.9781 0.5098 ] Network output: [ -0.06794 0.151 1.154 -8.799e-05 3.95e-05 0.8309 -6.631e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2759 0.2696 0.2874 0.2886 0.9882 0.9925 0.276 0.9741 0.9846 0.2968 ] Network output: [ -0.06869 0.1584 1.115 -7.288e-05 3.272e-05 0.8636 -5.493e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2859 0.2847 0.2904 0.2881 0.9837 0.99 0.2859 0.9586 0.9777 0.2926 ] Network output: [ -0.003967 1.01 0.02113 4.598e-05 -2.064e-05 0.9772 3.465e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04839 Epoch 3520 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06128 0.9006 0.9225 0.0001115 -5.006e-05 0.05479 8.404e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01376 -0.005339 0.004903 0.02593 0.9517 0.959 0.02456 0.9022 0.9195 0.06799 ] Network output: [ 0.9586 0.08592 0.03772 -3.808e-05 1.71e-05 -0.04092 -2.87e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5486 0.0854 0.0744 0.3446 0.9779 0.9901 0.6047 0.9164 0.975 0.5238 ] Network output: [ 0.02245 0.9155 0.9381 -1.888e-06 8.476e-07 0.1016 -1.423e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02001 0.01429 0.02318 0.02603 0.9879 0.9916 0.02032 0.9732 0.9841 0.02983 ] Network output: [ 0.09885 -0.2216 0.8113 4.544e-05 -2.04e-05 1.213 3.425e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5971 0.5118 0.4217 0.4777 0.9802 0.9914 0.5987 0.9235 0.9781 0.5097 ] Network output: [ -0.06787 0.1508 1.154 -8.764e-05 3.935e-05 0.831 -6.605e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2759 0.2696 0.2874 0.2886 0.9882 0.9925 0.276 0.9741 0.9846 0.2968 ] Network output: [ -0.06862 0.1582 1.115 -7.252e-05 3.255e-05 0.8636 -5.465e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2858 0.2847 0.2903 0.2881 0.9837 0.99 0.2858 0.9586 0.9777 0.2925 ] Network output: [ -0.004017 1.01 0.02124 4.575e-05 -2.054e-05 0.9771 3.448e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04837 Epoch 3521 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06124 0.9007 0.9225 0.0001115 -5.005e-05 0.05475 8.401e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01375 -0.005343 0.004875 0.02591 0.9517 0.959 0.02455 0.9023 0.9196 0.06796 ] Network output: [ 0.9586 0.08587 0.03773 -3.843e-05 1.725e-05 -0.04094 -2.896e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5485 0.08546 0.07443 0.3445 0.9779 0.9901 0.6047 0.9164 0.975 0.5238 ] Network output: [ 0.0224 0.9156 0.9381 -2.068e-06 9.285e-07 0.1016 -1.559e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02001 0.01429 0.02317 0.02601 0.9879 0.9916 0.02032 0.9732 0.9841 0.02982 ] Network output: [ 0.09882 -0.2215 0.8112 4.551e-05 -2.043e-05 1.213 3.43e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5971 0.5118 0.4218 0.4776 0.9802 0.9914 0.5987 0.9235 0.9781 0.5097 ] Network output: [ -0.06781 0.1507 1.153 -8.73e-05 3.919e-05 0.8311 -6.579e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2759 0.2695 0.2874 0.2886 0.9882 0.9925 0.276 0.9741 0.9846 0.2968 ] Network output: [ -0.06855 0.1581 1.115 -7.215e-05 3.239e-05 0.8636 -5.437e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2857 0.2846 0.2903 0.2881 0.9837 0.99 0.2858 0.9586 0.9777 0.2925 ] Network output: [ -0.004066 1.01 0.02135 4.552e-05 -2.044e-05 0.977 3.431e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04835 Epoch 3522 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06121 0.9008 0.9225 0.0001114 -5.003e-05 0.05471 8.399e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01374 -0.005347 0.004847 0.02589 0.9517 0.959 0.02454 0.9023 0.9196 0.06793 ] Network output: [ 0.9586 0.08583 0.03773 -3.877e-05 1.74e-05 -0.04097 -2.922e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5485 0.08551 0.07445 0.3443 0.9779 0.9901 0.6046 0.9165 0.975 0.5237 ] Network output: [ 0.02235 0.9157 0.9381 -2.249e-06 1.01e-06 0.1016 -1.695e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02001 0.01429 0.02316 0.026 0.9879 0.9916 0.02031 0.9732 0.9841 0.02981 ] Network output: [ 0.09878 -0.2214 0.8111 4.557e-05 -2.046e-05 1.213 3.434e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5971 0.5119 0.4218 0.4775 0.9802 0.9914 0.5987 0.9236 0.9781 0.5096 ] Network output: [ -0.06775 0.1505 1.153 -8.696e-05 3.904e-05 0.8312 -6.554e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2758 0.2695 0.2874 0.2886 0.9882 0.9925 0.276 0.9741 0.9846 0.2968 ] Network output: [ -0.06848 0.1579 1.115 -7.178e-05 3.223e-05 0.8636 -5.41e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2857 0.2845 0.2903 0.288 0.9837 0.99 0.2857 0.9586 0.9777 0.2925 ] Network output: [ -0.004115 1.01 0.02146 4.53e-05 -2.034e-05 0.9768 3.414e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04833 Epoch 3523 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06118 0.9009 0.9225 0.0001114 -5.002e-05 0.05466 8.397e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01373 -0.005351 0.00482 0.02587 0.9517 0.959 0.02453 0.9023 0.9196 0.06791 ] Network output: [ 0.9587 0.08578 0.03774 -3.91e-05 1.755e-05 -0.04099 -2.947e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5484 0.08557 0.07448 0.3442 0.9779 0.9901 0.6046 0.9165 0.975 0.5236 ] Network output: [ 0.0223 0.9157 0.9381 -2.43e-06 1.091e-06 0.1016 -1.831e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02 0.01429 0.02315 0.02598 0.9879 0.9916 0.02031 0.9732 0.9841 0.02979 ] Network output: [ 0.09875 -0.2213 0.811 4.563e-05 -2.049e-05 1.213 3.439e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5971 0.512 0.4218 0.4774 0.9802 0.9914 0.5986 0.9236 0.9782 0.5096 ] Network output: [ -0.06768 0.1503 1.153 -8.662e-05 3.889e-05 0.8313 -6.528e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2758 0.2695 0.2874 0.2885 0.9882 0.9925 0.276 0.9741 0.9846 0.2968 ] Network output: [ -0.06841 0.1578 1.115 -7.142e-05 3.206e-05 0.8635 -5.383e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2856 0.2845 0.2903 0.288 0.9838 0.99 0.2856 0.9586 0.9777 0.2924 ] Network output: [ -0.004164 1.01 0.02157 4.508e-05 -2.024e-05 0.9767 3.397e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0483 Epoch 3524 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06114 0.901 0.9225 0.0001114 -5e-05 0.05462 8.394e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01372 -0.005354 0.004792 0.02586 0.9517 0.959 0.02452 0.9024 0.9197 0.06788 ] Network output: [ 0.9587 0.08573 0.03775 -3.943e-05 1.77e-05 -0.04102 -2.972e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5483 0.08563 0.0745 0.3441 0.9779 0.9901 0.6046 0.9166 0.9751 0.5235 ] Network output: [ 0.02225 0.9158 0.9381 -2.611e-06 1.172e-06 0.1016 -1.967e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02 0.01429 0.02314 0.02597 0.9879 0.9916 0.02031 0.9732 0.9841 0.02978 ] Network output: [ 0.09871 -0.2213 0.8108 4.569e-05 -2.051e-05 1.213 3.444e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.597 0.512 0.4219 0.4774 0.9802 0.9914 0.5986 0.9236 0.9782 0.5095 ] Network output: [ -0.06762 0.1502 1.153 -8.629e-05 3.874e-05 0.8314 -6.503e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2758 0.2695 0.2874 0.2885 0.9882 0.9925 0.2759 0.9742 0.9846 0.2968 ] Network output: [ -0.06834 0.1576 1.115 -7.106e-05 3.19e-05 0.8635 -5.355e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2855 0.2844 0.2902 0.288 0.9838 0.99 0.2855 0.9587 0.9777 0.2924 ] Network output: [ -0.004213 1.01 0.02168 4.486e-05 -2.014e-05 0.9766 3.381e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04828 Epoch 3525 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06111 0.9011 0.9225 0.0001114 -4.999e-05 0.05458 8.392e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01371 -0.005358 0.004764 0.02584 0.9518 0.959 0.02451 0.9024 0.9197 0.06785 ] Network output: [ 0.9587 0.08569 0.03775 -3.976e-05 1.785e-05 -0.04104 -2.996e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5483 0.08568 0.07453 0.3439 0.9779 0.9901 0.6046 0.9166 0.9751 0.5235 ] Network output: [ 0.0222 0.9159 0.9381 -2.792e-06 1.253e-06 0.1016 -2.104e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.02 0.01429 0.02313 0.02596 0.9879 0.9916 0.0203 0.9733 0.9842 0.02977 ] Network output: [ 0.09868 -0.2212 0.8107 4.575e-05 -2.054e-05 1.213 3.448e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.597 0.5121 0.4219 0.4773 0.9802 0.9914 0.5986 0.9237 0.9782 0.5094 ] Network output: [ -0.06756 0.15 1.153 -8.595e-05 3.859e-05 0.8315 -6.478e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2758 0.2695 0.2874 0.2885 0.9882 0.9925 0.2759 0.9742 0.9846 0.2968 ] Network output: [ -0.06828 0.1574 1.115 -7.07e-05 3.174e-05 0.8635 -5.328e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2854 0.2843 0.2902 0.2879 0.9838 0.99 0.2855 0.9587 0.9778 0.2924 ] Network output: [ -0.004261 1.01 0.02179 4.464e-05 -2.004e-05 0.9765 3.364e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04826 Epoch 3526 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06108 0.9012 0.9225 0.0001113 -4.998e-05 0.05455 8.39e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0137 -0.005362 0.004737 0.02582 0.9518 0.9591 0.0245 0.9025 0.9197 0.06782 ] Network output: [ 0.9588 0.08564 0.03776 -4.008e-05 1.799e-05 -0.04107 -3.02e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5482 0.08574 0.07455 0.3438 0.978 0.9901 0.6045 0.9167 0.9751 0.5234 ] Network output: [ 0.02216 0.916 0.9381 -2.973e-06 1.335e-06 0.1016 -2.241e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01999 0.01429 0.02313 0.02594 0.988 0.9916 0.0203 0.9733 0.9842 0.02976 ] Network output: [ 0.09864 -0.2211 0.8106 4.58e-05 -2.056e-05 1.213 3.452e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.597 0.5121 0.422 0.4772 0.9802 0.9914 0.5986 0.9237 0.9782 0.5094 ] Network output: [ -0.06749 0.1498 1.153 -8.562e-05 3.844e-05 0.8316 -6.453e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2758 0.2695 0.2874 0.2885 0.9882 0.9925 0.2759 0.9742 0.9846 0.2968 ] Network output: [ -0.06821 0.1573 1.115 -7.035e-05 3.158e-05 0.8635 -5.301e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2854 0.2842 0.2902 0.2879 0.9838 0.9901 0.2854 0.9587 0.9778 0.2924 ] Network output: [ -0.004309 1.011 0.02189 4.442e-05 -1.994e-05 0.9764 3.348e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04824 Epoch 3527 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06104 0.9013 0.9225 0.0001113 -4.996e-05 0.05451 8.387e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01369 -0.005366 0.004709 0.0258 0.9518 0.9591 0.02449 0.9025 0.9198 0.0678 ] Network output: [ 0.9588 0.0856 0.03776 -4.039e-05 1.813e-05 -0.04109 -3.044e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5482 0.08579 0.07458 0.3437 0.978 0.9901 0.6045 0.9167 0.9751 0.5234 ] Network output: [ 0.02211 0.9161 0.9381 -3.155e-06 1.416e-06 0.1016 -2.378e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01999 0.01429 0.02312 0.02593 0.988 0.9916 0.0203 0.9733 0.9842 0.02975 ] Network output: [ 0.09861 -0.221 0.8105 4.586e-05 -2.059e-05 1.213 3.456e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.597 0.5122 0.422 0.4771 0.9802 0.9914 0.5985 0.9238 0.9782 0.5093 ] Network output: [ -0.06743 0.1497 1.153 -8.529e-05 3.829e-05 0.8317 -6.428e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2758 0.2695 0.2874 0.2885 0.9882 0.9925 0.2759 0.9742 0.9847 0.2968 ] Network output: [ -0.06814 0.1571 1.115 -6.999e-05 3.142e-05 0.8635 -5.275e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2853 0.2842 0.2902 0.2879 0.9838 0.9901 0.2853 0.9587 0.9778 0.2923 ] Network output: [ -0.004357 1.011 0.022 4.421e-05 -1.985e-05 0.9763 3.332e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04822 Epoch 3528 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06101 0.9014 0.9226 0.0001113 -4.995e-05 0.05447 8.385e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01369 -0.00537 0.004681 0.02579 0.9518 0.9591 0.02448 0.9025 0.9198 0.06777 ] Network output: [ 0.9588 0.08555 0.03777 -4.07e-05 1.827e-05 -0.04112 -3.067e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5481 0.08585 0.0746 0.3436 0.978 0.9901 0.6045 0.9167 0.9751 0.5233 ] Network output: [ 0.02206 0.9162 0.9381 -3.337e-06 1.498e-06 0.1016 -2.515e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01999 0.01429 0.02311 0.02591 0.988 0.9916 0.02029 0.9733 0.9842 0.02973 ] Network output: [ 0.09857 -0.2209 0.8104 4.591e-05 -2.061e-05 1.214 3.46e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5969 0.5122 0.422 0.477 0.9802 0.9914 0.5985 0.9238 0.9782 0.5093 ] Network output: [ -0.06737 0.1495 1.153 -8.497e-05 3.814e-05 0.8317 -6.403e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2758 0.2695 0.2874 0.2885 0.9882 0.9925 0.2759 0.9742 0.9847 0.2968 ] Network output: [ -0.06807 0.157 1.115 -6.964e-05 3.126e-05 0.8635 -5.248e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2852 0.2841 0.2901 0.2878 0.9838 0.9901 0.2852 0.9588 0.9778 0.2923 ] Network output: [ -0.004404 1.011 0.02211 4.399e-05 -1.975e-05 0.9762 3.315e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04819 Epoch 3529 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06098 0.9015 0.9226 0.0001112 -4.994e-05 0.05443 8.383e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01368 -0.005373 0.004654 0.02577 0.9518 0.9591 0.02447 0.9026 0.9198 0.06774 ] Network output: [ 0.9588 0.08551 0.03777 -4.101e-05 1.841e-05 -0.04114 -3.091e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5481 0.0859 0.07462 0.3434 0.978 0.9902 0.6045 0.9168 0.9751 0.5232 ] Network output: [ 0.02201 0.9163 0.9381 -3.519e-06 1.58e-06 0.1016 -2.652e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01998 0.01428 0.0231 0.0259 0.988 0.9916 0.02029 0.9733 0.9842 0.02972 ] Network output: [ 0.09854 -0.2208 0.8103 4.596e-05 -2.063e-05 1.214 3.463e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5969 0.5123 0.4221 0.4769 0.9803 0.9914 0.5985 0.9238 0.9782 0.5092 ] Network output: [ -0.06731 0.1493 1.153 -8.464e-05 3.8e-05 0.8318 -6.379e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2757 0.2695 0.2874 0.2885 0.9882 0.9925 0.2759 0.9742 0.9847 0.2968 ] Network output: [ -0.068 0.1568 1.115 -6.929e-05 3.111e-05 0.8635 -5.222e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2851 0.284 0.2901 0.2878 0.9838 0.9901 0.2852 0.9588 0.9778 0.2923 ] Network output: [ -0.004451 1.011 0.02221 4.378e-05 -1.965e-05 0.976 3.299e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04817 Epoch 3530 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06094 0.9016 0.9226 0.0001112 -4.992e-05 0.05439 8.381e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01367 -0.005377 0.004626 0.02575 0.9518 0.9591 0.02446 0.9026 0.9199 0.06771 ] Network output: [ 0.9589 0.08547 0.03777 -4.131e-05 1.855e-05 -0.04117 -3.113e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.548 0.08596 0.07465 0.3433 0.978 0.9902 0.6044 0.9168 0.9752 0.5232 ] Network output: [ 0.02196 0.9164 0.9381 -3.701e-06 1.662e-06 0.1016 -2.789e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01998 0.01428 0.02309 0.02589 0.988 0.9917 0.02029 0.9733 0.9842 0.02971 ] Network output: [ 0.0985 -0.2207 0.8102 4.6e-05 -2.065e-05 1.214 3.467e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5969 0.5124 0.4221 0.4768 0.9803 0.9914 0.5985 0.9239 0.9783 0.5092 ] Network output: [ -0.06724 0.1492 1.153 -8.432e-05 3.785e-05 0.8319 -6.354e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2757 0.2695 0.2874 0.2885 0.9882 0.9925 0.2759 0.9743 0.9847 0.2968 ] Network output: [ -0.06794 0.1567 1.115 -6.894e-05 3.095e-05 0.8635 -5.195e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2851 0.2839 0.2901 0.2878 0.9838 0.9901 0.2851 0.9588 0.9778 0.2922 ] Network output: [ -0.004499 1.011 0.02232 4.357e-05 -1.956e-05 0.9759 3.284e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04815 Epoch 3531 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06091 0.9017 0.9226 0.0001112 -4.991e-05 0.05435 8.378e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01366 -0.005381 0.004599 0.02573 0.9519 0.9591 0.02445 0.9027 0.9199 0.06769 ] Network output: [ 0.9589 0.08542 0.03778 -4.161e-05 1.868e-05 -0.04119 -3.136e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.548 0.08601 0.07467 0.3432 0.978 0.9902 0.6044 0.9169 0.9752 0.5231 ] Network output: [ 0.02192 0.9164 0.9381 -3.884e-06 1.744e-06 0.1016 -2.927e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01997 0.01428 0.02308 0.02587 0.988 0.9917 0.02028 0.9734 0.9842 0.0297 ] Network output: [ 0.09847 -0.2207 0.8101 4.604e-05 -2.067e-05 1.214 3.47e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5969 0.5124 0.4222 0.4767 0.9803 0.9914 0.5984 0.9239 0.9783 0.5091 ] Network output: [ -0.06718 0.149 1.153 -8.4e-05 3.771e-05 0.832 -6.33e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2757 0.2695 0.2875 0.2884 0.9882 0.9925 0.2758 0.9743 0.9847 0.2967 ] Network output: [ -0.06787 0.1565 1.116 -6.859e-05 3.079e-05 0.8634 -5.169e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.285 0.2839 0.2901 0.2878 0.9838 0.9901 0.285 0.9588 0.9779 0.2922 ] Network output: [ -0.004545 1.011 0.02242 4.336e-05 -1.947e-05 0.9758 3.268e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04813 Epoch 3532 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06088 0.9018 0.9226 0.0001111 -4.99e-05 0.05431 8.376e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01365 -0.005385 0.004571 0.02571 0.9519 0.9592 0.02444 0.9027 0.9199 0.06766 ] Network output: [ 0.9589 0.08538 0.03778 -4.19e-05 1.881e-05 -0.04122 -3.158e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5479 0.08607 0.07469 0.343 0.978 0.9902 0.6044 0.9169 0.9752 0.523 ] Network output: [ 0.02187 0.9165 0.9381 -4.066e-06 1.826e-06 0.1016 -3.065e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01997 0.01428 0.02307 0.02586 0.988 0.9917 0.02028 0.9734 0.9842 0.02968 ] Network output: [ 0.09843 -0.2206 0.8099 4.609e-05 -2.069e-05 1.214 3.473e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5968 0.5125 0.4222 0.4766 0.9803 0.9914 0.5984 0.9239 0.9783 0.5091 ] Network output: [ -0.06712 0.1488 1.153 -8.368e-05 3.757e-05 0.8321 -6.306e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2757 0.2695 0.2875 0.2884 0.9882 0.9925 0.2758 0.9743 0.9847 0.2967 ] Network output: [ -0.0678 0.1564 1.116 -6.824e-05 3.064e-05 0.8634 -5.143e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2849 0.2838 0.29 0.2877 0.9838 0.9901 0.2849 0.9589 0.9779 0.2922 ] Network output: [ -0.004592 1.011 0.02253 4.315e-05 -1.937e-05 0.9757 3.252e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04811 Epoch 3533 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06084 0.9019 0.9226 0.0001111 -4.988e-05 0.05428 8.374e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01364 -0.005388 0.004543 0.0257 0.9519 0.9592 0.02443 0.9027 0.92 0.06763 ] Network output: [ 0.959 0.08534 0.03778 -4.219e-05 1.894e-05 -0.04124 -3.179e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5478 0.08612 0.07472 0.3429 0.978 0.9902 0.6044 0.917 0.9752 0.523 ] Network output: [ 0.02182 0.9166 0.9382 -4.249e-06 1.908e-06 0.1016 -3.202e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01997 0.01428 0.02306 0.02584 0.988 0.9917 0.02028 0.9734 0.9842 0.02967 ] Network output: [ 0.09839 -0.2205 0.8098 4.612e-05 -2.071e-05 1.214 3.476e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5968 0.5125 0.4223 0.4765 0.9803 0.9914 0.5984 0.924 0.9783 0.509 ] Network output: [ -0.06706 0.1487 1.153 -8.336e-05 3.742e-05 0.8322 -6.282e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2757 0.2695 0.2875 0.2884 0.9882 0.9925 0.2758 0.9743 0.9847 0.2967 ] Network output: [ -0.06773 0.1562 1.116 -6.79e-05 3.048e-05 0.8634 -5.117e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2848 0.2837 0.29 0.2877 0.9838 0.9901 0.2849 0.9589 0.9779 0.2922 ] Network output: [ -0.004638 1.011 0.02263 4.295e-05 -1.928e-05 0.9756 3.237e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04809 Epoch 3534 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06081 0.902 0.9226 0.0001111 -4.987e-05 0.05424 8.372e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01363 -0.005392 0.004516 0.02568 0.9519 0.9592 0.02442 0.9028 0.92 0.0676 ] Network output: [ 0.959 0.08529 0.03778 -4.247e-05 1.907e-05 -0.04127 -3.201e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5478 0.08618 0.07474 0.3428 0.978 0.9902 0.6043 0.917 0.9752 0.5229 ] Network output: [ 0.02177 0.9167 0.9382 -4.432e-06 1.99e-06 0.1016 -3.34e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01996 0.01428 0.02306 0.02583 0.988 0.9917 0.02027 0.9734 0.9843 0.02966 ] Network output: [ 0.09836 -0.2204 0.8097 4.616e-05 -2.072e-05 1.214 3.479e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5968 0.5126 0.4223 0.4765 0.9803 0.9914 0.5984 0.924 0.9783 0.509 ] Network output: [ -0.067 0.1485 1.153 -8.304e-05 3.728e-05 0.8323 -6.258e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2757 0.2695 0.2875 0.2884 0.9882 0.9925 0.2758 0.9743 0.9847 0.2967 ] Network output: [ -0.06767 0.156 1.116 -6.755e-05 3.033e-05 0.8634 -5.091e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2848 0.2837 0.29 0.2877 0.9838 0.9901 0.2848 0.9589 0.9779 0.2921 ] Network output: [ -0.004684 1.011 0.02273 4.274e-05 -1.919e-05 0.9755 3.221e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04807 Epoch 3535 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06078 0.9021 0.9226 0.0001111 -4.986e-05 0.0542 8.37e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01362 -0.005396 0.004488 0.02566 0.9519 0.9592 0.02441 0.9028 0.9201 0.06758 ] Network output: [ 0.959 0.08525 0.03778 -4.275e-05 1.919e-05 -0.04129 -3.222e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5477 0.08623 0.07476 0.3427 0.978 0.9902 0.6043 0.917 0.9753 0.5228 ] Network output: [ 0.02173 0.9168 0.9382 -4.615e-06 2.072e-06 0.1016 -3.478e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01996 0.01428 0.02305 0.02581 0.988 0.9917 0.02027 0.9734 0.9843 0.02965 ] Network output: [ 0.09832 -0.2203 0.8096 4.62e-05 -2.074e-05 1.214 3.481e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5968 0.5126 0.4223 0.4764 0.9803 0.9914 0.5983 0.9241 0.9783 0.5089 ] Network output: [ -0.06693 0.1484 1.153 -8.273e-05 3.714e-05 0.8324 -6.235e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2757 0.2695 0.2875 0.2884 0.9883 0.9925 0.2758 0.9743 0.9847 0.2967 ] Network output: [ -0.0676 0.1559 1.116 -6.721e-05 3.017e-05 0.8634 -5.065e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2847 0.2836 0.29 0.2876 0.9838 0.9901 0.2847 0.9589 0.9779 0.2921 ] Network output: [ -0.00473 1.011 0.02284 4.254e-05 -1.91e-05 0.9754 3.206e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04805 Epoch 3536 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06075 0.9022 0.9226 0.000111 -4.985e-05 0.05417 8.368e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01361 -0.0054 0.004461 0.02564 0.9519 0.9592 0.0244 0.9029 0.9201 0.06755 ] Network output: [ 0.9591 0.08521 0.03779 -4.303e-05 1.932e-05 -0.04132 -3.243e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5477 0.08628 0.07478 0.3425 0.978 0.9902 0.6043 0.9171 0.9753 0.5228 ] Network output: [ 0.02168 0.9169 0.9382 -4.799e-06 2.154e-06 0.1016 -3.616e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01996 0.01428 0.02304 0.0258 0.988 0.9917 0.02027 0.9734 0.9843 0.02963 ] Network output: [ 0.09828 -0.2202 0.8095 4.623e-05 -2.075e-05 1.214 3.484e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5967 0.5127 0.4224 0.4763 0.9803 0.9914 0.5983 0.9241 0.9784 0.5089 ] Network output: [ -0.06687 0.1482 1.153 -8.242e-05 3.7e-05 0.8324 -6.211e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2756 0.2694 0.2875 0.2884 0.9883 0.9925 0.2758 0.9743 0.9848 0.2967 ] Network output: [ -0.06753 0.1557 1.116 -6.687e-05 3.002e-05 0.8634 -5.04e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2846 0.2835 0.2899 0.2876 0.9838 0.9901 0.2846 0.9589 0.9779 0.2921 ] Network output: [ -0.004776 1.012 0.02294 4.234e-05 -1.901e-05 0.9753 3.191e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04803 Epoch 3537 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06071 0.9023 0.9226 0.000111 -4.983e-05 0.05413 8.365e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0136 -0.005403 0.004433 0.02563 0.952 0.9592 0.02439 0.9029 0.9201 0.06752 ] Network output: [ 0.9591 0.08517 0.03779 -4.33e-05 1.944e-05 -0.04134 -3.263e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5476 0.08634 0.0748 0.3424 0.9781 0.9902 0.6042 0.9171 0.9753 0.5227 ] Network output: [ 0.02163 0.917 0.9382 -4.982e-06 2.237e-06 0.1016 -3.755e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01995 0.01428 0.02303 0.02578 0.988 0.9917 0.02026 0.9735 0.9843 0.02962 ] Network output: [ 0.09825 -0.2202 0.8094 4.626e-05 -2.077e-05 1.214 3.486e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5967 0.5128 0.4224 0.4762 0.9803 0.9914 0.5983 0.9241 0.9784 0.5088 ] Network output: [ -0.06681 0.1481 1.153 -8.211e-05 3.686e-05 0.8325 -6.188e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2756 0.2694 0.2875 0.2884 0.9883 0.9925 0.2758 0.9744 0.9848 0.2967 ] Network output: [ -0.06747 0.1556 1.116 -6.653e-05 2.987e-05 0.8634 -5.014e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2845 0.2834 0.2899 0.2876 0.9838 0.9901 0.2846 0.959 0.9779 0.292 ] Network output: [ -0.004821 1.012 0.02304 4.214e-05 -1.892e-05 0.9752 3.175e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04801 Epoch 3538 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06068 0.9024 0.9226 0.000111 -4.982e-05 0.05409 8.363e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01359 -0.005407 0.004405 0.02561 0.952 0.9592 0.02438 0.903 0.9202 0.06749 ] Network output: [ 0.9591 0.08513 0.03779 -4.357e-05 1.956e-05 -0.04137 -3.283e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5476 0.08639 0.07483 0.3423 0.9781 0.9902 0.6042 0.9172 0.9753 0.5227 ] Network output: [ 0.02159 0.917 0.9382 -5.166e-06 2.319e-06 0.1016 -3.893e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01995 0.01427 0.02302 0.02577 0.988 0.9917 0.02026 0.9735 0.9843 0.02961 ] Network output: [ 0.09821 -0.2201 0.8093 4.629e-05 -2.078e-05 1.215 3.488e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5967 0.5128 0.4225 0.4761 0.9803 0.9915 0.5983 0.9242 0.9784 0.5088 ] Network output: [ -0.06675 0.1479 1.153 -8.18e-05 3.672e-05 0.8326 -6.165e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2756 0.2694 0.2875 0.2883 0.9883 0.9925 0.2757 0.9744 0.9848 0.2967 ] Network output: [ -0.0674 0.1554 1.116 -6.62e-05 2.972e-05 0.8633 -4.989e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2845 0.2834 0.2899 0.2875 0.9839 0.9901 0.2845 0.959 0.978 0.292 ] Network output: [ -0.004866 1.012 0.02314 4.194e-05 -1.883e-05 0.975 3.16e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04798 Epoch 3539 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06065 0.9025 0.9226 0.0001109 -4.981e-05 0.05406 8.361e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01358 -0.005411 0.004378 0.02559 0.952 0.9593 0.02437 0.903 0.9202 0.06747 ] Network output: [ 0.9592 0.08509 0.03779 -4.383e-05 1.968e-05 -0.04139 -3.303e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5475 0.08644 0.07485 0.3421 0.9781 0.9902 0.6042 0.9172 0.9753 0.5226 ] Network output: [ 0.02154 0.9171 0.9382 -5.349e-06 2.401e-06 0.1016 -4.031e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01995 0.01427 0.02301 0.02576 0.988 0.9917 0.02026 0.9735 0.9843 0.0296 ] Network output: [ 0.09817 -0.22 0.8092 4.631e-05 -2.079e-05 1.215 3.49e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5967 0.5129 0.4225 0.476 0.9803 0.9915 0.5982 0.9242 0.9784 0.5087 ] Network output: [ -0.06669 0.1477 1.153 -8.15e-05 3.659e-05 0.8327 -6.142e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2756 0.2694 0.2875 0.2883 0.9883 0.9925 0.2757 0.9744 0.9848 0.2967 ] Network output: [ -0.06734 0.1553 1.116 -6.586e-05 2.957e-05 0.8633 -4.964e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2844 0.2833 0.2899 0.2875 0.9839 0.9901 0.2844 0.959 0.978 0.292 ] Network output: [ -0.004911 1.012 0.02324 4.174e-05 -1.874e-05 0.9749 3.146e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04796 Epoch 3540 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06062 0.9026 0.9226 0.0001109 -4.979e-05 0.05402 8.359e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01357 -0.005415 0.00435 0.02557 0.952 0.9593 0.02435 0.903 0.9202 0.06744 ] Network output: [ 0.9592 0.08505 0.03779 -4.409e-05 1.979e-05 -0.04142 -3.323e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5475 0.08649 0.07487 0.342 0.9781 0.9902 0.6042 0.9173 0.9753 0.5225 ] Network output: [ 0.02149 0.9172 0.9382 -5.533e-06 2.484e-06 0.1016 -4.17e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01994 0.01427 0.023 0.02574 0.988 0.9917 0.02025 0.9735 0.9843 0.02958 ] Network output: [ 0.09814 -0.2199 0.8091 4.634e-05 -2.08e-05 1.215 3.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5966 0.5129 0.4225 0.4759 0.9803 0.9915 0.5982 0.9243 0.9784 0.5087 ] Network output: [ -0.06663 0.1476 1.153 -8.119e-05 3.645e-05 0.8328 -6.119e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2756 0.2694 0.2875 0.2883 0.9883 0.9925 0.2757 0.9744 0.9848 0.2967 ] Network output: [ -0.06727 0.1551 1.116 -6.553e-05 2.942e-05 0.8633 -4.939e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2843 0.2832 0.2898 0.2875 0.9839 0.9901 0.2843 0.959 0.978 0.292 ] Network output: [ -0.004956 1.012 0.02334 4.154e-05 -1.865e-05 0.9748 3.131e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04794 Epoch 3541 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06058 0.9027 0.9226 0.0001109 -4.978e-05 0.05399 8.357e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01356 -0.005418 0.004323 0.02555 0.952 0.9593 0.02434 0.9031 0.9203 0.06741 ] Network output: [ 0.9592 0.085 0.03778 -4.435e-05 1.991e-05 -0.04145 -3.342e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5474 0.08655 0.07489 0.3419 0.9781 0.9902 0.6041 0.9173 0.9754 0.5225 ] Network output: [ 0.02144 0.9173 0.9382 -5.717e-06 2.567e-06 0.1016 -4.309e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01994 0.01427 0.02299 0.02573 0.988 0.9917 0.02025 0.9735 0.9843 0.02957 ] Network output: [ 0.0981 -0.2198 0.809 4.636e-05 -2.081e-05 1.215 3.494e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5966 0.513 0.4226 0.4758 0.9803 0.9915 0.5982 0.9243 0.9784 0.5086 ] Network output: [ -0.06657 0.1474 1.153 -8.089e-05 3.631e-05 0.8328 -6.096e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2756 0.2694 0.2875 0.2883 0.9883 0.9925 0.2757 0.9744 0.9848 0.2967 ] Network output: [ -0.06721 0.155 1.116 -6.52e-05 2.927e-05 0.8633 -4.913e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2842 0.2831 0.2898 0.2874 0.9839 0.9901 0.2843 0.9591 0.978 0.2919 ] Network output: [ -0.005 1.012 0.02344 4.135e-05 -1.856e-05 0.9747 3.116e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04792 Epoch 3542 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06055 0.9027 0.9227 0.0001109 -4.977e-05 0.05395 8.355e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01355 -0.005422 0.004295 0.02554 0.952 0.9593 0.02433 0.9031 0.9203 0.06738 ] Network output: [ 0.9593 0.08496 0.03778 -4.46e-05 2.002e-05 -0.04147 -3.361e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5473 0.0866 0.07491 0.3418 0.9781 0.9902 0.6041 0.9173 0.9754 0.5224 ] Network output: [ 0.0214 0.9174 0.9382 -5.901e-06 2.649e-06 0.1016 -4.447e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01994 0.01427 0.02298 0.02571 0.9881 0.9917 0.02025 0.9735 0.9843 0.02956 ] Network output: [ 0.09806 -0.2198 0.8089 4.638e-05 -2.082e-05 1.215 3.495e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5966 0.513 0.4226 0.4758 0.9804 0.9915 0.5982 0.9243 0.9784 0.5086 ] Network output: [ -0.06651 0.1473 1.152 -8.059e-05 3.618e-05 0.8329 -6.073e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2756 0.2694 0.2875 0.2883 0.9883 0.9925 0.2757 0.9744 0.9848 0.2967 ] Network output: [ -0.06714 0.1548 1.116 -6.487e-05 2.912e-05 0.8633 -4.889e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2842 0.2831 0.2898 0.2874 0.9839 0.9901 0.2842 0.9591 0.978 0.2919 ] Network output: [ -0.005044 1.012 0.02354 4.115e-05 -1.848e-05 0.9746 3.102e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0479 Epoch 3543 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06052 0.9028 0.9227 0.0001108 -4.976e-05 0.05392 8.353e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01354 -0.005426 0.004267 0.02552 0.9521 0.9593 0.02432 0.9032 0.9203 0.06735 ] Network output: [ 0.9593 0.08493 0.03778 -4.485e-05 2.013e-05 -0.0415 -3.38e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5473 0.08665 0.07493 0.3416 0.9781 0.9902 0.6041 0.9174 0.9754 0.5223 ] Network output: [ 0.02135 0.9174 0.9382 -6.085e-06 2.732e-06 0.1016 -4.586e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01993 0.01427 0.02297 0.0257 0.9881 0.9917 0.02024 0.9736 0.9844 0.02955 ] Network output: [ 0.09802 -0.2197 0.8088 4.639e-05 -2.083e-05 1.215 3.496e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5965 0.5131 0.4227 0.4757 0.9804 0.9915 0.5981 0.9244 0.9785 0.5085 ] Network output: [ -0.06645 0.1471 1.152 -8.029e-05 3.605e-05 0.833 -6.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2755 0.2694 0.2875 0.2883 0.9883 0.9926 0.2757 0.9745 0.9848 0.2967 ] Network output: [ -0.06707 0.1547 1.116 -6.454e-05 2.897e-05 0.8633 -4.864e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2841 0.283 0.2898 0.2874 0.9839 0.9901 0.2841 0.9591 0.978 0.2919 ] Network output: [ -0.005088 1.012 0.02364 4.096e-05 -1.839e-05 0.9745 3.087e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04789 Epoch 3544 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06049 0.9029 0.9227 0.0001108 -4.975e-05 0.05388 8.351e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01353 -0.005429 0.00424 0.0255 0.9521 0.9593 0.02431 0.9032 0.9204 0.06733 ] Network output: [ 0.9593 0.08489 0.03778 -4.509e-05 2.024e-05 -0.04152 -3.398e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5472 0.0867 0.07495 0.3415 0.9781 0.9902 0.6041 0.9174 0.9754 0.5223 ] Network output: [ 0.0213 0.9175 0.9382 -6.27e-06 2.815e-06 0.1016 -4.725e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01993 0.01427 0.02296 0.02568 0.9881 0.9917 0.02024 0.9736 0.9844 0.02953 ] Network output: [ 0.09799 -0.2196 0.8087 4.641e-05 -2.084e-05 1.215 3.498e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5965 0.5131 0.4227 0.4756 0.9804 0.9915 0.5981 0.9244 0.9785 0.5085 ] Network output: [ -0.06639 0.147 1.152 -7.999e-05 3.591e-05 0.8331 -6.029e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2755 0.2694 0.2875 0.2883 0.9883 0.9926 0.2756 0.9745 0.9848 0.2967 ] Network output: [ -0.06701 0.1545 1.116 -6.421e-05 2.883e-05 0.8633 -4.839e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.284 0.2829 0.2897 0.2873 0.9839 0.9901 0.284 0.9591 0.978 0.2918 ] Network output: [ -0.005132 1.012 0.02374 4.077e-05 -1.83e-05 0.9744 3.073e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04787 Epoch 3545 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06045 0.903 0.9227 0.0001108 -4.973e-05 0.05385 8.349e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01352 -0.005433 0.004212 0.02548 0.9521 0.9593 0.0243 0.9032 0.9204 0.0673 ] Network output: [ 0.9594 0.08485 0.03778 -4.533e-05 2.035e-05 -0.04155 -3.416e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5472 0.08675 0.07497 0.3414 0.9781 0.9902 0.604 0.9175 0.9754 0.5222 ] Network output: [ 0.02126 0.9176 0.9383 -6.454e-06 2.897e-06 0.1016 -4.864e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01992 0.01427 0.02295 0.02567 0.9881 0.9917 0.02024 0.9736 0.9844 0.02952 ] Network output: [ 0.09795 -0.2195 0.8086 4.642e-05 -2.084e-05 1.215 3.499e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5965 0.5132 0.4227 0.4755 0.9804 0.9915 0.5981 0.9245 0.9785 0.5084 ] Network output: [ -0.06633 0.1468 1.152 -7.97e-05 3.578e-05 0.8332 -6.006e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2755 0.2694 0.2875 0.2882 0.9883 0.9926 0.2756 0.9745 0.9849 0.2967 ] Network output: [ -0.06694 0.1544 1.116 -6.388e-05 2.868e-05 0.8632 -4.814e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2839 0.2829 0.2897 0.2873 0.9839 0.9901 0.284 0.9591 0.9781 0.2918 ] Network output: [ -0.005175 1.012 0.02384 4.058e-05 -1.822e-05 0.9743 3.059e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04785 Epoch 3546 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06042 0.9031 0.9227 0.0001108 -4.972e-05 0.05381 8.347e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01351 -0.005437 0.004185 0.02546 0.9521 0.9594 0.02429 0.9033 0.9204 0.06727 ] Network output: [ 0.9594 0.08481 0.03777 -4.557e-05 2.046e-05 -0.04157 -3.434e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5471 0.0868 0.07499 0.3412 0.9781 0.9902 0.604 0.9175 0.9754 0.5222 ] Network output: [ 0.02121 0.9177 0.9383 -6.638e-06 2.98e-06 0.1016 -5.003e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01992 0.01426 0.02295 0.02565 0.9881 0.9917 0.02023 0.9736 0.9844 0.02951 ] Network output: [ 0.09791 -0.2194 0.8085 4.643e-05 -2.085e-05 1.215 3.499e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5965 0.5132 0.4228 0.4754 0.9804 0.9915 0.5981 0.9245 0.9785 0.5084 ] Network output: [ -0.06627 0.1467 1.152 -7.94e-05 3.565e-05 0.8332 -5.984e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2755 0.2694 0.2875 0.2882 0.9883 0.9926 0.2756 0.9745 0.9849 0.2967 ] Network output: [ -0.06688 0.1542 1.116 -6.356e-05 2.853e-05 0.8632 -4.79e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2839 0.2828 0.2897 0.2873 0.9839 0.9901 0.2839 0.9592 0.9781 0.2918 ] Network output: [ -0.005219 1.012 0.02393 4.04e-05 -1.814e-05 0.9742 3.044e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04783 Epoch 3547 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06039 0.9032 0.9227 0.0001107 -4.971e-05 0.05378 8.345e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0135 -0.005441 0.004157 0.02545 0.9521 0.9594 0.02428 0.9033 0.9205 0.06724 ] Network output: [ 0.9594 0.08477 0.03777 -4.58e-05 2.056e-05 -0.0416 -3.452e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5471 0.08686 0.07501 0.3411 0.9781 0.9902 0.604 0.9176 0.9755 0.5221 ] Network output: [ 0.02117 0.9178 0.9383 -6.823e-06 3.063e-06 0.1016 -5.142e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01992 0.01426 0.02294 0.02564 0.9881 0.9917 0.02023 0.9736 0.9844 0.02949 ] Network output: [ 0.09787 -0.2194 0.8084 4.644e-05 -2.085e-05 1.215 3.5e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5964 0.5133 0.4228 0.4753 0.9804 0.9915 0.598 0.9245 0.9785 0.5083 ] Network output: [ -0.06621 0.1465 1.152 -7.911e-05 3.552e-05 0.8333 -5.962e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2755 0.2694 0.2875 0.2882 0.9883 0.9926 0.2756 0.9745 0.9849 0.2967 ] Network output: [ -0.06681 0.1541 1.116 -6.324e-05 2.839e-05 0.8632 -4.766e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2838 0.2827 0.2897 0.2872 0.9839 0.9902 0.2838 0.9592 0.9781 0.2918 ] Network output: [ -0.005262 1.013 0.02403 4.021e-05 -1.805e-05 0.9741 3.03e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04781 Epoch 3548 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06036 0.9033 0.9227 0.0001107 -4.97e-05 0.05375 8.343e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01349 -0.005444 0.00413 0.02543 0.9521 0.9594 0.02427 0.9034 0.9205 0.06721 ] Network output: [ 0.9595 0.08473 0.03777 -4.603e-05 2.066e-05 -0.04162 -3.469e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.547 0.08691 0.07503 0.341 0.9781 0.9903 0.604 0.9176 0.9755 0.5221 ] Network output: [ 0.02112 0.9179 0.9383 -7.008e-06 3.146e-06 0.1016 -5.281e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01991 0.01426 0.02293 0.02562 0.9881 0.9917 0.02022 0.9737 0.9844 0.02948 ] Network output: [ 0.09783 -0.2193 0.8083 4.645e-05 -2.085e-05 1.215 3.501e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5964 0.5133 0.4229 0.4752 0.9804 0.9915 0.598 0.9246 0.9785 0.5083 ] Network output: [ -0.06615 0.1464 1.152 -7.882e-05 3.539e-05 0.8334 -5.94e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2755 0.2693 0.2875 0.2882 0.9883 0.9926 0.2756 0.9745 0.9849 0.2967 ] Network output: [ -0.06675 0.1539 1.116 -6.291e-05 2.824e-05 0.8632 -4.741e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2837 0.2826 0.2896 0.2872 0.9839 0.9902 0.2838 0.9592 0.9781 0.2917 ] Network output: [ -0.005305 1.013 0.02413 4.002e-05 -1.797e-05 0.974 3.016e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04779 Epoch 3549 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06033 0.9034 0.9227 0.0001107 -4.969e-05 0.05371 8.341e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01348 -0.005448 0.004102 0.02541 0.9522 0.9594 0.02426 0.9034 0.9205 0.06719 ] Network output: [ 0.9595 0.08469 0.03776 -4.625e-05 2.077e-05 -0.04165 -3.486e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5469 0.08696 0.07505 0.3409 0.9782 0.9903 0.6039 0.9176 0.9755 0.522 ] Network output: [ 0.02107 0.9179 0.9383 -7.192e-06 3.229e-06 0.1016 -5.42e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01991 0.01426 0.02292 0.02561 0.9881 0.9917 0.02022 0.9737 0.9844 0.02947 ] Network output: [ 0.0978 -0.2192 0.8082 4.646e-05 -2.086e-05 1.216 3.501e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5964 0.5134 0.4229 0.4752 0.9804 0.9915 0.598 0.9246 0.9785 0.5082 ] Network output: [ -0.06609 0.1462 1.152 -7.853e-05 3.526e-05 0.8335 -5.919e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2754 0.2693 0.2876 0.2882 0.9883 0.9926 0.2756 0.9745 0.9849 0.2967 ] Network output: [ -0.06669 0.1538 1.116 -6.259e-05 2.81e-05 0.8632 -4.717e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2837 0.2826 0.2896 0.2872 0.9839 0.9902 0.2837 0.9592 0.9781 0.2917 ] Network output: [ -0.005347 1.013 0.02422 3.984e-05 -1.789e-05 0.9739 3.003e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04777 Epoch 3550 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0603 0.9035 0.9227 0.0001106 -4.967e-05 0.05368 8.339e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01347 -0.005452 0.004075 0.02539 0.9522 0.9594 0.02425 0.9034 0.9206 0.06716 ] Network output: [ 0.9595 0.08466 0.03776 -4.648e-05 2.086e-05 -0.04168 -3.503e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5469 0.08701 0.07507 0.3407 0.9782 0.9903 0.6039 0.9177 0.9755 0.5219 ] Network output: [ 0.02103 0.918 0.9383 -7.377e-06 3.312e-06 0.1016 -5.56e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0199 0.01426 0.02291 0.02559 0.9881 0.9917 0.02022 0.9737 0.9844 0.02946 ] Network output: [ 0.09776 -0.2191 0.8081 4.646e-05 -2.086e-05 1.216 3.501e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5964 0.5135 0.423 0.4751 0.9804 0.9915 0.598 0.9247 0.9786 0.5082 ] Network output: [ -0.06603 0.1461 1.152 -7.825e-05 3.513e-05 0.8335 -5.897e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2754 0.2693 0.2876 0.2882 0.9883 0.9926 0.2755 0.9746 0.9849 0.2967 ] Network output: [ -0.06662 0.1536 1.116 -6.227e-05 2.796e-05 0.8632 -4.693e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2836 0.2825 0.2896 0.2871 0.9839 0.9902 0.2836 0.9593 0.9781 0.2917 ] Network output: [ -0.00539 1.013 0.02432 3.966e-05 -1.78e-05 0.9738 2.989e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04775 Epoch 3551 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06026 0.9036 0.9227 0.0001106 -4.966e-05 0.05365 8.337e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01346 -0.005455 0.004047 0.02537 0.9522 0.9594 0.02424 0.9035 0.9206 0.06713 ] Network output: [ 0.9596 0.08462 0.03775 -4.67e-05 2.096e-05 -0.0417 -3.519e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5468 0.08706 0.07509 0.3406 0.9782 0.9903 0.6039 0.9177 0.9755 0.5219 ] Network output: [ 0.02098 0.9181 0.9383 -7.562e-06 3.395e-06 0.1016 -5.699e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0199 0.01426 0.0229 0.02558 0.9881 0.9917 0.02021 0.9737 0.9844 0.02944 ] Network output: [ 0.09772 -0.2191 0.808 4.646e-05 -2.086e-05 1.216 3.502e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5963 0.5135 0.423 0.475 0.9804 0.9915 0.5979 0.9247 0.9786 0.5081 ] Network output: [ -0.06597 0.1459 1.152 -7.796e-05 3.5e-05 0.8336 -5.876e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2754 0.2693 0.2876 0.2881 0.9883 0.9926 0.2755 0.9746 0.9849 0.2966 ] Network output: [ -0.06656 0.1535 1.116 -6.196e-05 2.781e-05 0.8632 -4.669e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2835 0.2824 0.2896 0.2871 0.9839 0.9902 0.2835 0.9593 0.9782 0.2917 ] Network output: [ -0.005432 1.013 0.02441 3.948e-05 -1.772e-05 0.9737 2.975e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04773 Epoch 3552 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06023 0.9037 0.9227 0.0001106 -4.965e-05 0.05362 8.335e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01345 -0.005459 0.00402 0.02535 0.9522 0.9595 0.02423 0.9035 0.9206 0.0671 ] Network output: [ 0.9596 0.08458 0.03775 -4.691e-05 2.106e-05 -0.04173 -3.535e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5468 0.08711 0.0751 0.3405 0.9782 0.9903 0.6039 0.9178 0.9755 0.5218 ] Network output: [ 0.02094 0.9182 0.9383 -7.747e-06 3.478e-06 0.1016 -5.838e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0199 0.01425 0.02289 0.02556 0.9881 0.9918 0.02021 0.9737 0.9845 0.02943 ] Network output: [ 0.09768 -0.219 0.8079 4.646e-05 -2.086e-05 1.216 3.501e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5963 0.5136 0.423 0.4749 0.9804 0.9915 0.5979 0.9247 0.9786 0.5081 ] Network output: [ -0.06591 0.1458 1.152 -7.768e-05 3.487e-05 0.8337 -5.854e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2754 0.2693 0.2876 0.2881 0.9883 0.9926 0.2755 0.9746 0.9849 0.2966 ] Network output: [ -0.06649 0.1533 1.116 -6.164e-05 2.767e-05 0.8631 -4.645e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2834 0.2823 0.2895 0.2871 0.9839 0.9902 0.2835 0.9593 0.9782 0.2916 ] Network output: [ -0.005474 1.013 0.02451 3.93e-05 -1.764e-05 0.9736 2.962e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04771 Epoch 3553 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0602 0.9037 0.9227 0.0001106 -4.964e-05 0.05359 8.333e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01344 -0.005462 0.003992 0.02534 0.9522 0.9595 0.02422 0.9036 0.9207 0.06707 ] Network output: [ 0.9596 0.08455 0.03774 -4.712e-05 2.115e-05 -0.04175 -3.551e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5467 0.08716 0.07512 0.3403 0.9782 0.9903 0.6038 0.9178 0.9756 0.5218 ] Network output: [ 0.02089 0.9182 0.9383 -7.932e-06 3.561e-06 0.1016 -5.978e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01989 0.01425 0.02288 0.02555 0.9881 0.9918 0.02021 0.9737 0.9845 0.02942 ] Network output: [ 0.09764 -0.2189 0.8078 4.646e-05 -2.086e-05 1.216 3.501e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5963 0.5136 0.4231 0.4748 0.9804 0.9915 0.5979 0.9248 0.9786 0.5081 ] Network output: [ -0.06585 0.1456 1.152 -7.74e-05 3.475e-05 0.8338 -5.833e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2754 0.2693 0.2876 0.2881 0.9883 0.9926 0.2755 0.9746 0.9849 0.2966 ] Network output: [ -0.06643 0.1532 1.116 -6.132e-05 2.753e-05 0.8631 -4.622e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2834 0.2823 0.2895 0.287 0.984 0.9902 0.2834 0.9593 0.9782 0.2916 ] Network output: [ -0.005516 1.013 0.0246 3.912e-05 -1.756e-05 0.9735 2.948e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0477 Epoch 3554 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06017 0.9038 0.9227 0.0001105 -4.963e-05 0.05355 8.331e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01343 -0.005466 0.003965 0.02532 0.9522 0.9595 0.02421 0.9036 0.9207 0.06704 ] Network output: [ 0.9597 0.08451 0.03773 -4.733e-05 2.125e-05 -0.04178 -3.567e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5467 0.08721 0.07514 0.3402 0.9782 0.9903 0.6038 0.9178 0.9756 0.5217 ] Network output: [ 0.02085 0.9183 0.9383 -8.117e-06 3.644e-06 0.1016 -6.117e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01989 0.01425 0.02287 0.02553 0.9881 0.9918 0.0202 0.9738 0.9845 0.0294 ] Network output: [ 0.0976 -0.2188 0.8077 4.645e-05 -2.086e-05 1.216 3.501e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5962 0.5137 0.4231 0.4747 0.9804 0.9915 0.5979 0.9248 0.9786 0.508 ] Network output: [ -0.06579 0.1455 1.152 -7.712e-05 3.462e-05 0.8338 -5.812e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2754 0.2693 0.2876 0.2881 0.9883 0.9926 0.2755 0.9746 0.9849 0.2966 ] Network output: [ -0.06637 0.153 1.116 -6.101e-05 2.739e-05 0.8631 -4.598e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2833 0.2822 0.2895 0.287 0.984 0.9902 0.2833 0.9593 0.9782 0.2916 ] Network output: [ -0.005557 1.013 0.0247 3.894e-05 -1.748e-05 0.9734 2.935e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04768 Epoch 3555 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06014 0.9039 0.9227 0.0001105 -4.962e-05 0.05352 8.329e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01342 -0.00547 0.003937 0.0253 0.9523 0.9595 0.0242 0.9036 0.9207 0.06702 ] Network output: [ 0.9597 0.08448 0.03773 -4.754e-05 2.134e-05 -0.0418 -3.582e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5466 0.08725 0.07516 0.3401 0.9782 0.9903 0.6038 0.9179 0.9756 0.5217 ] Network output: [ 0.0208 0.9184 0.9383 -8.302e-06 3.727e-06 0.1016 -6.257e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01988 0.01425 0.02286 0.02552 0.9881 0.9918 0.0202 0.9738 0.9845 0.02939 ] Network output: [ 0.09756 -0.2187 0.8077 4.645e-05 -2.085e-05 1.216 3.5e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5962 0.5137 0.4232 0.4747 0.9805 0.9915 0.5978 0.9249 0.9786 0.508 ] Network output: [ -0.06573 0.1454 1.152 -7.684e-05 3.45e-05 0.8339 -5.791e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2753 0.2693 0.2876 0.2881 0.9884 0.9926 0.2755 0.9746 0.985 0.2966 ] Network output: [ -0.0663 0.1529 1.116 -6.07e-05 2.725e-05 0.8631 -4.574e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2832 0.2821 0.2895 0.287 0.984 0.9902 0.2832 0.9594 0.9782 0.2915 ] Network output: [ -0.005599 1.013 0.02479 3.877e-05 -1.74e-05 0.9733 2.922e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04766 Epoch 3556 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06011 0.904 0.9227 0.0001105 -4.961e-05 0.05349 8.327e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01341 -0.005473 0.00391 0.02528 0.9523 0.9595 0.02419 0.9037 0.9208 0.06699 ] Network output: [ 0.9597 0.08444 0.03772 -4.774e-05 2.143e-05 -0.04183 -3.598e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5465 0.0873 0.07517 0.34 0.9782 0.9903 0.6038 0.9179 0.9756 0.5216 ] Network output: [ 0.02075 0.9185 0.9383 -8.487e-06 3.81e-06 0.1016 -6.396e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01988 0.01425 0.02285 0.0255 0.9881 0.9918 0.02019 0.9738 0.9845 0.02938 ] Network output: [ 0.09753 -0.2187 0.8076 4.644e-05 -2.085e-05 1.216 3.5e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5962 0.5138 0.4232 0.4746 0.9805 0.9915 0.5978 0.9249 0.9787 0.5079 ] Network output: [ -0.06567 0.1452 1.152 -7.656e-05 3.437e-05 0.834 -5.77e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2753 0.2693 0.2876 0.2881 0.9884 0.9926 0.2754 0.9746 0.985 0.2966 ] Network output: [ -0.06624 0.1527 1.116 -6.038e-05 2.711e-05 0.8631 -4.551e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2831 0.2821 0.2894 0.2869 0.984 0.9902 0.2832 0.9594 0.9782 0.2915 ] Network output: [ -0.00564 1.013 0.02488 3.859e-05 -1.733e-05 0.9732 2.908e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04764 Epoch 3557 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06007 0.9041 0.9227 0.0001105 -4.96e-05 0.05346 8.326e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0134 -0.005477 0.003882 0.02526 0.9523 0.9595 0.02418 0.9037 0.9208 0.06696 ] Network output: [ 0.9598 0.08441 0.03771 -4.794e-05 2.152e-05 -0.04186 -3.613e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5465 0.08735 0.07519 0.3398 0.9782 0.9903 0.6037 0.918 0.9756 0.5215 ] Network output: [ 0.02071 0.9186 0.9383 -8.673e-06 3.893e-06 0.1016 -6.536e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01988 0.01425 0.02284 0.02549 0.9881 0.9918 0.02019 0.9738 0.9845 0.02936 ] Network output: [ 0.09749 -0.2186 0.8075 4.643e-05 -2.084e-05 1.216 3.499e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5962 0.5138 0.4233 0.4745 0.9805 0.9915 0.5978 0.9249 0.9787 0.5079 ] Network output: [ -0.06562 0.1451 1.152 -7.629e-05 3.425e-05 0.8341 -5.749e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2753 0.2692 0.2876 0.288 0.9884 0.9926 0.2754 0.9747 0.985 0.2966 ] Network output: [ -0.06618 0.1526 1.116 -6.007e-05 2.697e-05 0.8631 -4.527e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2831 0.282 0.2894 0.2869 0.984 0.9902 0.2831 0.9594 0.9782 0.2915 ] Network output: [ -0.005681 1.013 0.02498 3.842e-05 -1.725e-05 0.9731 2.895e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04762 Epoch 3558 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06004 0.9042 0.9228 0.0001104 -4.958e-05 0.05343 8.324e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01339 -0.005481 0.003855 0.02524 0.9523 0.9595 0.02417 0.9038 0.9208 0.06693 ] Network output: [ 0.9598 0.08437 0.03771 -4.813e-05 2.161e-05 -0.04188 -3.627e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5464 0.0874 0.07521 0.3397 0.9782 0.9903 0.6037 0.918 0.9756 0.5215 ] Network output: [ 0.02066 0.9186 0.9384 -8.858e-06 3.977e-06 0.1016 -6.676e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01987 0.01424 0.02283 0.02547 0.9881 0.9918 0.02019 0.9738 0.9845 0.02935 ] Network output: [ 0.09745 -0.2185 0.8074 4.642e-05 -2.084e-05 1.216 3.498e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5961 0.5139 0.4233 0.4744 0.9805 0.9915 0.5977 0.925 0.9787 0.5078 ] Network output: [ -0.06556 0.1449 1.152 -7.601e-05 3.412e-05 0.8341 -5.729e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2753 0.2692 0.2876 0.288 0.9884 0.9926 0.2754 0.9747 0.985 0.2966 ] Network output: [ -0.06611 0.1524 1.116 -5.976e-05 2.683e-05 0.863 -4.504e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.283 0.2819 0.2894 0.2869 0.984 0.9902 0.283 0.9594 0.9783 0.2915 ] Network output: [ -0.005722 1.014 0.02507 3.825e-05 -1.717e-05 0.973 2.882e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04761 Epoch 3559 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06001 0.9043 0.9228 0.0001104 -4.957e-05 0.0534 8.322e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01338 -0.005484 0.003827 0.02523 0.9523 0.9596 0.02416 0.9038 0.9209 0.0669 ] Network output: [ 0.9598 0.08434 0.0377 -4.832e-05 2.169e-05 -0.04191 -3.642e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5464 0.08745 0.07523 0.3396 0.9782 0.9903 0.6037 0.9181 0.9757 0.5214 ] Network output: [ 0.02062 0.9187 0.9384 -9.043e-06 4.06e-06 0.1017 -6.815e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01987 0.01424 0.02282 0.02546 0.9882 0.9918 0.02018 0.9738 0.9845 0.02934 ] Network output: [ 0.09741 -0.2184 0.8073 4.64e-05 -2.083e-05 1.217 3.497e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5961 0.5139 0.4233 0.4743 0.9805 0.9915 0.5977 0.925 0.9787 0.5078 ] Network output: [ -0.0655 0.1448 1.152 -7.574e-05 3.4e-05 0.8342 -5.708e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2753 0.2692 0.2876 0.288 0.9884 0.9926 0.2754 0.9747 0.985 0.2966 ] Network output: [ -0.06605 0.1523 1.117 -5.946e-05 2.669e-05 0.863 -4.481e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2829 0.2818 0.2894 0.2868 0.984 0.9902 0.2829 0.9595 0.9783 0.2914 ] Network output: [ -0.005762 1.014 0.02516 3.808e-05 -1.709e-05 0.9729 2.87e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04759 Epoch 3560 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05998 0.9044 0.9228 0.0001104 -4.956e-05 0.05337 8.32e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01337 -0.005488 0.0038 0.02521 0.9523 0.9596 0.02415 0.9038 0.9209 0.06687 ] Network output: [ 0.9599 0.08431 0.03769 -4.851e-05 2.178e-05 -0.04194 -3.656e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5463 0.0875 0.07524 0.3394 0.9783 0.9903 0.6036 0.9181 0.9757 0.5214 ] Network output: [ 0.02057 0.9188 0.9384 -9.228e-06 4.143e-06 0.1017 -6.955e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01986 0.01424 0.02281 0.02544 0.9882 0.9918 0.02018 0.9739 0.9845 0.02932 ] Network output: [ 0.09737 -0.2184 0.8072 4.639e-05 -2.083e-05 1.217 3.496e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5961 0.514 0.4234 0.4742 0.9805 0.9915 0.5977 0.925 0.9787 0.5078 ] Network output: [ -0.06544 0.1446 1.152 -7.547e-05 3.388e-05 0.8343 -5.688e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2752 0.2692 0.2876 0.288 0.9884 0.9926 0.2754 0.9747 0.985 0.2966 ] Network output: [ -0.06599 0.1522 1.117 -5.915e-05 2.655e-05 0.863 -4.458e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2828 0.2818 0.2893 0.2868 0.984 0.9902 0.2829 0.9595 0.9783 0.2914 ] Network output: [ -0.005802 1.014 0.02525 3.791e-05 -1.702e-05 0.9728 2.857e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04757 Epoch 3561 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05995 0.9044 0.9228 0.0001104 -4.955e-05 0.05334 8.318e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01336 -0.005491 0.003772 0.02519 0.9524 0.9596 0.02414 0.9039 0.9209 0.06684 ] Network output: [ 0.9599 0.08427 0.03768 -4.87e-05 2.186e-05 -0.04196 -3.67e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5463 0.08754 0.07526 0.3393 0.9783 0.9903 0.6036 0.9181 0.9757 0.5213 ] Network output: [ 0.02053 0.9189 0.9384 -9.414e-06 4.226e-06 0.1017 -7.095e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01986 0.01424 0.0228 0.02543 0.9882 0.9918 0.02017 0.9739 0.9846 0.02931 ] Network output: [ 0.09733 -0.2183 0.8071 4.637e-05 -2.082e-05 1.217 3.495e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5961 0.514 0.4234 0.4742 0.9805 0.9916 0.5977 0.9251 0.9787 0.5077 ] Network output: [ -0.06538 0.1445 1.152 -7.52e-05 3.376e-05 0.8343 -5.667e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2752 0.2692 0.2876 0.288 0.9884 0.9926 0.2754 0.9747 0.985 0.2966 ] Network output: [ -0.06593 0.152 1.117 -5.884e-05 2.642e-05 0.863 -4.435e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2828 0.2817 0.2893 0.2868 0.984 0.9902 0.2828 0.9595 0.9783 0.2914 ] Network output: [ -0.005843 1.014 0.02534 3.774e-05 -1.694e-05 0.9727 2.844e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04756 Epoch 3562 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05992 0.9045 0.9228 0.0001104 -4.954e-05 0.05331 8.316e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01335 -0.005495 0.003745 0.02517 0.9524 0.9596 0.02413 0.9039 0.921 0.06682 ] Network output: [ 0.9599 0.08424 0.03767 -4.888e-05 2.194e-05 -0.04199 -3.684e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5462 0.08759 0.07527 0.3392 0.9783 0.9903 0.6036 0.9182 0.9757 0.5213 ] Network output: [ 0.02049 0.9189 0.9384 -9.599e-06 4.309e-06 0.1017 -7.234e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01986 0.01424 0.02279 0.02541 0.9882 0.9918 0.02017 0.9739 0.9846 0.0293 ] Network output: [ 0.09729 -0.2182 0.807 4.635e-05 -2.081e-05 1.217 3.493e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.596 0.5141 0.4235 0.4741 0.9805 0.9916 0.5976 0.9251 0.9787 0.5077 ] Network output: [ -0.06533 0.1444 1.152 -7.493e-05 3.364e-05 0.8344 -5.647e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2752 0.2692 0.2876 0.288 0.9884 0.9926 0.2753 0.9747 0.985 0.2966 ] Network output: [ -0.06586 0.1519 1.117 -5.854e-05 2.628e-05 0.863 -4.412e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2827 0.2816 0.2893 0.2867 0.984 0.9902 0.2827 0.9595 0.9783 0.2913 ] Network output: [ -0.005883 1.014 0.02543 3.757e-05 -1.687e-05 0.9726 2.832e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04754 Epoch 3563 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05989 0.9046 0.9228 0.0001103 -4.953e-05 0.05328 8.315e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01334 -0.005499 0.003717 0.02515 0.9524 0.9596 0.02412 0.904 0.921 0.06679 ] Network output: [ 0.96 0.08421 0.03766 -4.906e-05 2.203e-05 -0.04202 -3.697e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5461 0.08764 0.07529 0.3391 0.9783 0.9903 0.6036 0.9182 0.9757 0.5212 ] Network output: [ 0.02044 0.919 0.9384 -9.785e-06 4.393e-06 0.1017 -7.374e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01985 0.01424 0.02278 0.0254 0.9882 0.9918 0.02017 0.9739 0.9846 0.02929 ] Network output: [ 0.09725 -0.2182 0.8069 4.633e-05 -2.08e-05 1.217 3.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.596 0.5141 0.4235 0.474 0.9805 0.9916 0.5976 0.9252 0.9788 0.5076 ] Network output: [ -0.06527 0.1442 1.152 -7.466e-05 3.352e-05 0.8345 -5.627e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2752 0.2692 0.2876 0.2879 0.9884 0.9926 0.2753 0.9748 0.985 0.2966 ] Network output: [ -0.0658 0.1517 1.117 -5.824e-05 2.614e-05 0.863 -4.389e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2826 0.2816 0.2893 0.2867 0.984 0.9902 0.2826 0.9595 0.9783 0.2913 ] Network output: [ -0.005922 1.014 0.02552 3.741e-05 -1.679e-05 0.9725 2.819e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04752 Epoch 3564 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05986 0.9047 0.9228 0.0001103 -4.952e-05 0.05325 8.313e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01333 -0.005502 0.00369 0.02513 0.9524 0.9596 0.02411 0.904 0.921 0.06676 ] Network output: [ 0.96 0.08418 0.03765 -4.924e-05 2.21e-05 -0.04204 -3.711e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5461 0.08768 0.0753 0.3389 0.9783 0.9903 0.6035 0.9183 0.9757 0.5212 ] Network output: [ 0.0204 0.9191 0.9384 -9.97e-06 4.476e-06 0.1017 -7.514e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01985 0.01423 0.02277 0.02538 0.9882 0.9918 0.02016 0.9739 0.9846 0.02927 ] Network output: [ 0.09721 -0.2181 0.8069 4.631e-05 -2.079e-05 1.217 3.49e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.596 0.5142 0.4236 0.4739 0.9805 0.9916 0.5976 0.9252 0.9788 0.5076 ] Network output: [ -0.06521 0.1441 1.151 -7.44e-05 3.34e-05 0.8345 -5.607e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2752 0.2692 0.2876 0.2879 0.9884 0.9926 0.2753 0.9748 0.9851 0.2966 ] Network output: [ -0.06574 0.1516 1.117 -5.793e-05 2.601e-05 0.8629 -4.366e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2825 0.2815 0.2892 0.2867 0.984 0.9902 0.2826 0.9596 0.9783 0.2913 ] Network output: [ -0.005962 1.014 0.02561 3.724e-05 -1.672e-05 0.9724 2.807e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0475 Epoch 3565 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05983 0.9048 0.9228 0.0001103 -4.951e-05 0.05323 8.311e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01333 -0.005506 0.003662 0.02511 0.9524 0.9596 0.0241 0.904 0.9211 0.06673 ] Network output: [ 0.96 0.08415 0.03764 -4.941e-05 2.218e-05 -0.04207 -3.724e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.546 0.08773 0.07532 0.3388 0.9783 0.9903 0.6035 0.9183 0.9758 0.5211 ] Network output: [ 0.02035 0.9192 0.9384 -1.016e-05 4.559e-06 0.1017 -7.654e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01984 0.01423 0.02276 0.02537 0.9882 0.9918 0.02016 0.9739 0.9846 0.02926 ] Network output: [ 0.09717 -0.218 0.8068 4.628e-05 -2.078e-05 1.217 3.488e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5959 0.5142 0.4236 0.4738 0.9805 0.9916 0.5976 0.9252 0.9788 0.5076 ] Network output: [ -0.06515 0.144 1.151 -7.413e-05 3.328e-05 0.8346 -5.587e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2751 0.2691 0.2876 0.2879 0.9884 0.9926 0.2753 0.9748 0.9851 0.2966 ] Network output: [ -0.06568 0.1514 1.117 -5.763e-05 2.587e-05 0.8629 -4.343e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2825 0.2814 0.2892 0.2866 0.984 0.9902 0.2825 0.9596 0.9784 0.2913 ] Network output: [ -0.006001 1.014 0.0257 3.708e-05 -1.665e-05 0.9723 2.795e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04749 Epoch 3566 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05979 0.9049 0.9228 0.0001103 -4.95e-05 0.0532 8.309e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01332 -0.005509 0.003635 0.0251 0.9524 0.9597 0.02409 0.9041 0.9211 0.0667 ] Network output: [ 0.9601 0.08412 0.03763 -4.958e-05 2.226e-05 -0.0421 -3.737e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.546 0.08778 0.07533 0.3387 0.9783 0.9903 0.6035 0.9183 0.9758 0.5211 ] Network output: [ 0.02031 0.9192 0.9384 -1.034e-05 4.642e-06 0.1017 -7.793e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01984 0.01423 0.02275 0.02535 0.9882 0.9918 0.02015 0.974 0.9846 0.02925 ] Network output: [ 0.09713 -0.2179 0.8067 4.625e-05 -2.077e-05 1.217 3.486e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5959 0.5142 0.4236 0.4737 0.9805 0.9916 0.5975 0.9253 0.9788 0.5075 ] Network output: [ -0.0651 0.1438 1.151 -7.387e-05 3.316e-05 0.8347 -5.567e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2751 0.2691 0.2876 0.2879 0.9884 0.9926 0.2753 0.9748 0.9851 0.2965 ] Network output: [ -0.06562 0.1513 1.117 -5.733e-05 2.574e-05 0.8629 -4.321e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2824 0.2813 0.2892 0.2866 0.984 0.9902 0.2824 0.9596 0.9784 0.2912 ] Network output: [ -0.006041 1.014 0.02579 3.692e-05 -1.657e-05 0.9722 2.782e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04747 Epoch 3567 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05976 0.9049 0.9228 0.0001102 -4.949e-05 0.05317 8.308e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01331 -0.005513 0.003607 0.02508 0.9525 0.9597 0.02408 0.9041 0.9211 0.06667 ] Network output: [ 0.9601 0.08409 0.03762 -4.975e-05 2.233e-05 -0.04212 -3.749e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5459 0.08782 0.07535 0.3385 0.9783 0.9904 0.6035 0.9184 0.9758 0.521 ] Network output: [ 0.02026 0.9193 0.9384 -1.053e-05 4.726e-06 0.1017 -7.933e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01983 0.01423 0.02274 0.02534 0.9882 0.9918 0.02015 0.974 0.9846 0.02923 ] Network output: [ 0.09709 -0.2179 0.8066 4.623e-05 -2.075e-05 1.217 3.484e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5959 0.5143 0.4237 0.4737 0.9805 0.9916 0.5975 0.9253 0.9788 0.5075 ] Network output: [ -0.06504 0.1437 1.151 -7.361e-05 3.305e-05 0.8347 -5.548e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2751 0.2691 0.2876 0.2879 0.9884 0.9926 0.2752 0.9748 0.9851 0.2965 ] Network output: [ -0.06555 0.1512 1.117 -5.703e-05 2.56e-05 0.8629 -4.298e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2823 0.2813 0.2892 0.2866 0.984 0.9902 0.2823 0.9596 0.9784 0.2912 ] Network output: [ -0.00608 1.014 0.02588 3.676e-05 -1.65e-05 0.9721 2.77e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04746 Epoch 3568 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05973 0.905 0.9228 0.0001102 -4.948e-05 0.05314 8.306e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0133 -0.005516 0.00358 0.02506 0.9525 0.9597 0.02407 0.9042 0.9212 0.06664 ] Network output: [ 0.9601 0.08406 0.03761 -4.992e-05 2.241e-05 -0.04215 -3.762e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5459 0.08787 0.07536 0.3384 0.9783 0.9904 0.6034 0.9184 0.9758 0.521 ] Network output: [ 0.02022 0.9194 0.9384 -1.071e-05 4.809e-06 0.1017 -8.073e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01983 0.01423 0.02273 0.02532 0.9882 0.9918 0.02015 0.974 0.9846 0.02922 ] Network output: [ 0.09705 -0.2178 0.8065 4.62e-05 -2.074e-05 1.217 3.481e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5959 0.5143 0.4237 0.4736 0.9806 0.9916 0.5975 0.9254 0.9788 0.5074 ] Network output: [ -0.06498 0.1436 1.151 -7.335e-05 3.293e-05 0.8348 -5.528e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2751 0.2691 0.2876 0.2879 0.9884 0.9926 0.2752 0.9748 0.9851 0.2965 ] Network output: [ -0.06549 0.151 1.117 -5.673e-05 2.547e-05 0.8629 -4.276e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2822 0.2812 0.2891 0.2865 0.984 0.9903 0.2823 0.9597 0.9784 0.2912 ] Network output: [ -0.006119 1.014 0.02597 3.66e-05 -1.643e-05 0.972 2.758e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04744 Epoch 3569 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0597 0.9051 0.9228 0.0001102 -4.947e-05 0.05311 8.304e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01329 -0.00552 0.003553 0.02504 0.9525 0.9597 0.02406 0.9042 0.9212 0.06662 ] Network output: [ 0.9602 0.08403 0.03759 -5.008e-05 2.248e-05 -0.04218 -3.774e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5458 0.08792 0.07538 0.3383 0.9783 0.9904 0.6034 0.9185 0.9758 0.5209 ] Network output: [ 0.02018 0.9194 0.9384 -1.09e-05 4.892e-06 0.1017 -8.213e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01983 0.01422 0.02272 0.02531 0.9882 0.9918 0.02014 0.974 0.9846 0.02921 ] Network output: [ 0.09701 -0.2177 0.8064 4.616e-05 -2.072e-05 1.217 3.479e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5958 0.5144 0.4238 0.4735 0.9806 0.9916 0.5975 0.9254 0.9788 0.5074 ] Network output: [ -0.06493 0.1434 1.151 -7.309e-05 3.281e-05 0.8349 -5.508e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2751 0.2691 0.2876 0.2878 0.9884 0.9926 0.2752 0.9748 0.9851 0.2965 ] Network output: [ -0.06543 0.1509 1.117 -5.644e-05 2.534e-05 0.8629 -4.253e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2822 0.2811 0.2891 0.2865 0.9841 0.9903 0.2822 0.9597 0.9784 0.2911 ] Network output: [ -0.006157 1.015 0.02606 3.644e-05 -1.636e-05 0.9719 2.746e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04742 Epoch 3570 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05967 0.9052 0.9228 0.0001102 -4.946e-05 0.05309 8.302e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01328 -0.005523 0.003525 0.02502 0.9525 0.9597 0.02405 0.9042 0.9212 0.06659 ] Network output: [ 0.9602 0.084 0.03758 -5.024e-05 2.255e-05 -0.0422 -3.786e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5457 0.08796 0.07539 0.3382 0.9783 0.9904 0.6034 0.9185 0.9758 0.5209 ] Network output: [ 0.02013 0.9195 0.9385 -1.108e-05 4.976e-06 0.1017 -8.353e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01982 0.01422 0.02271 0.02529 0.9882 0.9918 0.02014 0.974 0.9846 0.02919 ] Network output: [ 0.09697 -0.2177 0.8064 4.613e-05 -2.071e-05 1.218 3.476e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5958 0.5144 0.4238 0.4734 0.9806 0.9916 0.5974 0.9254 0.9789 0.5074 ] Network output: [ -0.06487 0.1433 1.151 -7.284e-05 3.27e-05 0.8349 -5.489e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.275 0.2691 0.2876 0.2878 0.9884 0.9927 0.2752 0.9749 0.9851 0.2965 ] Network output: [ -0.06537 0.1507 1.117 -5.614e-05 2.52e-05 0.8628 -4.231e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2821 0.281 0.2891 0.2865 0.9841 0.9903 0.2821 0.9597 0.9784 0.2911 ] Network output: [ -0.006196 1.015 0.02614 3.628e-05 -1.629e-05 0.9718 2.734e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04741 Epoch 3571 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05964 0.9053 0.9228 0.0001101 -4.945e-05 0.05306 8.301e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01327 -0.005527 0.003498 0.025 0.9525 0.9597 0.02404 0.9043 0.9213 0.06656 ] Network output: [ 0.9602 0.08397 0.03757 -5.039e-05 2.262e-05 -0.04223 -3.798e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5457 0.08801 0.07541 0.338 0.9784 0.9904 0.6034 0.9186 0.9759 0.5208 ] Network output: [ 0.02009 0.9196 0.9385 -1.127e-05 5.059e-06 0.1017 -8.492e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01982 0.01422 0.0227 0.02527 0.9882 0.9918 0.02013 0.974 0.9847 0.02918 ] Network output: [ 0.09693 -0.2176 0.8063 4.609e-05 -2.069e-05 1.218 3.474e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5958 0.5145 0.4239 0.4733 0.9806 0.9916 0.5974 0.9255 0.9789 0.5073 ] Network output: [ -0.06481 0.1432 1.151 -7.258e-05 3.258e-05 0.835 -5.47e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.275 0.2691 0.2876 0.2878 0.9884 0.9927 0.2752 0.9749 0.9851 0.2965 ] Network output: [ -0.06531 0.1506 1.117 -5.585e-05 2.507e-05 0.8628 -4.209e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.282 0.281 0.2891 0.2864 0.9841 0.9903 0.2821 0.9597 0.9784 0.2911 ] Network output: [ -0.006234 1.015 0.02623 3.613e-05 -1.622e-05 0.9717 2.723e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04739 Epoch 3572 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05961 0.9054 0.9228 0.0001101 -4.944e-05 0.05303 8.299e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01326 -0.00553 0.00347 0.02498 0.9525 0.9598 0.02403 0.9043 0.9213 0.06653 ] Network output: [ 0.9603 0.08394 0.03756 -5.055e-05 2.269e-05 -0.04226 -3.809e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5456 0.08805 0.07542 0.3379 0.9784 0.9904 0.6033 0.9186 0.9759 0.5208 ] Network output: [ 0.02004 0.9197 0.9385 -1.145e-05 5.142e-06 0.1017 -8.632e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01981 0.01422 0.02269 0.02526 0.9882 0.9918 0.02013 0.9741 0.9847 0.02917 ] Network output: [ 0.09689 -0.2175 0.8062 4.606e-05 -2.068e-05 1.218 3.471e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5957 0.5145 0.4239 0.4733 0.9806 0.9916 0.5974 0.9255 0.9789 0.5073 ] Network output: [ -0.06476 0.143 1.151 -7.232e-05 3.247e-05 0.8351 -5.451e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.275 0.269 0.2876 0.2878 0.9884 0.9927 0.2751 0.9749 0.9851 0.2965 ] Network output: [ -0.06525 0.1505 1.117 -5.555e-05 2.494e-05 0.8628 -4.186e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.282 0.2809 0.289 0.2864 0.9841 0.9903 0.282 0.9597 0.9785 0.2911 ] Network output: [ -0.006272 1.015 0.02632 3.597e-05 -1.615e-05 0.9716 2.711e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04738 Epoch 3573 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05958 0.9054 0.9228 0.0001101 -4.943e-05 0.05301 8.297e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01325 -0.005534 0.003443 0.02496 0.9526 0.9598 0.02402 0.9044 0.9213 0.0665 ] Network output: [ 0.9603 0.08391 0.03754 -5.07e-05 2.276e-05 -0.04229 -3.821e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5456 0.0881 0.07543 0.3378 0.9784 0.9904 0.6033 0.9186 0.9759 0.5207 ] Network output: [ 0.02 0.9197 0.9385 -1.164e-05 5.225e-06 0.1017 -8.772e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01981 0.01422 0.02268 0.02524 0.9882 0.9919 0.02012 0.9741 0.9847 0.02915 ] Network output: [ 0.09685 -0.2175 0.8061 4.602e-05 -2.066e-05 1.218 3.468e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5957 0.5146 0.424 0.4732 0.9806 0.9916 0.5973 0.9255 0.9789 0.5073 ] Network output: [ -0.0647 0.1429 1.151 -7.207e-05 3.236e-05 0.8351 -5.432e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.275 0.269 0.2876 0.2878 0.9884 0.9927 0.2751 0.9749 0.9852 0.2965 ] Network output: [ -0.06519 0.1503 1.117 -5.526e-05 2.481e-05 0.8628 -4.164e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2819 0.2808 0.289 0.2864 0.9841 0.9903 0.2819 0.9598 0.9785 0.291 ] Network output: [ -0.00631 1.015 0.0264 3.582e-05 -1.608e-05 0.9715 2.699e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04736 Epoch 3574 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05955 0.9055 0.9228 0.0001101 -4.942e-05 0.05298 8.296e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01324 -0.005537 0.003416 0.02495 0.9526 0.9598 0.02401 0.9044 0.9214 0.06647 ] Network output: [ 0.9603 0.08389 0.03753 -5.085e-05 2.283e-05 -0.04231 -3.832e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5455 0.08814 0.07545 0.3376 0.9784 0.9904 0.6033 0.9187 0.9759 0.5207 ] Network output: [ 0.01996 0.9198 0.9385 -1.183e-05 5.309e-06 0.1017 -8.912e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0198 0.01421 0.02267 0.02523 0.9882 0.9919 0.02012 0.9741 0.9847 0.02914 ] Network output: [ 0.0968 -0.2174 0.806 4.598e-05 -2.064e-05 1.218 3.465e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5957 0.5146 0.424 0.4731 0.9806 0.9916 0.5973 0.9256 0.9789 0.5072 ] Network output: [ -0.06464 0.1428 1.151 -7.182e-05 3.224e-05 0.8352 -5.413e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.275 0.269 0.2876 0.2877 0.9884 0.9927 0.2751 0.9749 0.9852 0.2965 ] Network output: [ -0.06513 0.1502 1.117 -5.497e-05 2.468e-05 0.8628 -4.142e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2818 0.2808 0.289 0.2863 0.9841 0.9903 0.2818 0.9598 0.9785 0.291 ] Network output: [ -0.006348 1.015 0.02649 3.567e-05 -1.601e-05 0.9714 2.688e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04734 Epoch 3575 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05952 0.9056 0.9229 0.0001101 -4.941e-05 0.05296 8.294e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01323 -0.005541 0.003388 0.02493 0.9526 0.9598 0.024 0.9045 0.9214 0.06644 ] Network output: [ 0.9604 0.08386 0.03751 -5.099e-05 2.289e-05 -0.04234 -3.843e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5455 0.08818 0.07546 0.3375 0.9784 0.9904 0.6032 0.9187 0.9759 0.5206 ] Network output: [ 0.01991 0.9199 0.9385 -1.201e-05 5.392e-06 0.1018 -9.052e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0198 0.01421 0.02266 0.02521 0.9882 0.9919 0.02011 0.9741 0.9847 0.02913 ] Network output: [ 0.09676 -0.2173 0.806 4.593e-05 -2.062e-05 1.218 3.462e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5956 0.5147 0.424 0.473 0.9806 0.9916 0.5973 0.9256 0.9789 0.5072 ] Network output: [ -0.06459 0.1426 1.151 -7.157e-05 3.213e-05 0.8352 -5.394e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2749 0.269 0.2876 0.2877 0.9885 0.9927 0.2751 0.9749 0.9852 0.2965 ] Network output: [ -0.06507 0.15 1.117 -5.467e-05 2.454e-05 0.8628 -4.12e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2817 0.2807 0.289 0.2863 0.9841 0.9903 0.2818 0.9598 0.9785 0.291 ] Network output: [ -0.006385 1.015 0.02657 3.552e-05 -1.594e-05 0.9713 2.677e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04733 Epoch 3576 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05949 0.9057 0.9229 0.00011 -4.94e-05 0.05293 8.292e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01322 -0.005544 0.003361 0.02491 0.9526 0.9598 0.02399 0.9045 0.9214 0.06641 ] Network output: [ 0.9604 0.08383 0.0375 -5.114e-05 2.296e-05 -0.04237 -3.854e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5454 0.08823 0.07547 0.3374 0.9784 0.9904 0.6032 0.9188 0.976 0.5206 ] Network output: [ 0.01987 0.9199 0.9385 -1.22e-05 5.475e-06 0.1018 -9.191e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01979 0.01421 0.02265 0.0252 0.9883 0.9919 0.02011 0.9741 0.9847 0.02911 ] Network output: [ 0.09672 -0.2173 0.8059 4.589e-05 -2.06e-05 1.218 3.458e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5956 0.5147 0.4241 0.4729 0.9806 0.9916 0.5973 0.9257 0.9789 0.5071 ] Network output: [ -0.06453 0.1425 1.151 -7.132e-05 3.202e-05 0.8353 -5.375e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2749 0.269 0.2876 0.2877 0.9885 0.9927 0.275 0.9749 0.9852 0.2965 ] Network output: [ -0.065 0.1499 1.117 -5.438e-05 2.441e-05 0.8627 -4.098e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2817 0.2806 0.2889 0.2863 0.9841 0.9903 0.2817 0.9598 0.9785 0.2909 ] Network output: [ -0.006423 1.015 0.02666 3.537e-05 -1.588e-05 0.9712 2.665e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04731 Epoch 3577 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05946 0.9058 0.9229 0.00011 -4.939e-05 0.05291 8.291e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01321 -0.005548 0.003333 0.02489 0.9526 0.9598 0.02398 0.9045 0.9215 0.06638 ] Network output: [ 0.9604 0.08381 0.03748 -5.128e-05 2.302e-05 -0.0424 -3.865e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5453 0.08827 0.07548 0.3372 0.9784 0.9904 0.6032 0.9188 0.976 0.5205 ] Network output: [ 0.01983 0.92 0.9385 -1.238e-05 5.559e-06 0.1018 -9.331e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01979 0.01421 0.02264 0.02518 0.9883 0.9919 0.02011 0.9741 0.9847 0.0291 ] Network output: [ 0.09668 -0.2172 0.8058 4.584e-05 -2.058e-05 1.218 3.455e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5956 0.5148 0.4241 0.4729 0.9806 0.9916 0.5972 0.9257 0.979 0.5071 ] Network output: [ -0.06448 0.1424 1.151 -7.107e-05 3.191e-05 0.8354 -5.356e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2749 0.269 0.2876 0.2877 0.9885 0.9927 0.275 0.975 0.9852 0.2965 ] Network output: [ -0.06494 0.1498 1.117 -5.409e-05 2.428e-05 0.8627 -4.077e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2816 0.2805 0.2889 0.2863 0.9841 0.9903 0.2816 0.9599 0.9785 0.2909 ] Network output: [ -0.00646 1.015 0.02674 3.522e-05 -1.581e-05 0.9711 2.654e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0473 Epoch 3578 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05943 0.9058 0.9229 0.00011 -4.938e-05 0.05288 8.289e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0132 -0.005551 0.003306 0.02487 0.9526 0.9598 0.02397 0.9046 0.9215 0.06635 ] Network output: [ 0.9605 0.08378 0.03747 -5.142e-05 2.308e-05 -0.04242 -3.875e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5453 0.08832 0.07549 0.3371 0.9784 0.9904 0.6032 0.9188 0.976 0.5205 ] Network output: [ 0.01978 0.9201 0.9385 -1.257e-05 5.642e-06 0.1018 -9.471e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01979 0.01421 0.02263 0.02517 0.9883 0.9919 0.0201 0.9742 0.9847 0.02908 ] Network output: [ 0.09664 -0.2171 0.8057 4.579e-05 -2.056e-05 1.218 3.451e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5956 0.5148 0.4242 0.4728 0.9806 0.9916 0.5972 0.9257 0.979 0.5071 ] Network output: [ -0.06442 0.1423 1.151 -7.082e-05 3.18e-05 0.8354 -5.337e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2749 0.269 0.2876 0.2877 0.9885 0.9927 0.275 0.975 0.9852 0.2964 ] Network output: [ -0.06488 0.1496 1.117 -5.38e-05 2.415e-05 0.8627 -4.055e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2815 0.2805 0.2889 0.2862 0.9841 0.9903 0.2815 0.9599 0.9786 0.2909 ] Network output: [ -0.006497 1.015 0.02683 3.507e-05 -1.574e-05 0.971 2.643e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04729 Epoch 3579 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0594 0.9059 0.9229 0.00011 -4.937e-05 0.05286 8.287e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01319 -0.005555 0.003279 0.02485 0.9527 0.9599 0.02396 0.9046 0.9215 0.06633 ] Network output: [ 0.9605 0.08376 0.03745 -5.155e-05 2.314e-05 -0.04245 -3.885e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5452 0.08836 0.07551 0.337 0.9784 0.9904 0.6031 0.9189 0.976 0.5204 ] Network output: [ 0.01974 0.9201 0.9385 -1.275e-05 5.725e-06 0.1018 -9.611e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01978 0.0142 0.02262 0.02515 0.9883 0.9919 0.0201 0.9742 0.9847 0.02907 ] Network output: [ 0.0966 -0.2171 0.8057 4.574e-05 -2.054e-05 1.218 3.447e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5955 0.5148 0.4242 0.4727 0.9806 0.9916 0.5972 0.9258 0.979 0.507 ] Network output: [ -0.06437 0.1421 1.151 -7.058e-05 3.168e-05 0.8355 -5.319e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2749 0.2689 0.2876 0.2876 0.9885 0.9927 0.275 0.975 0.9852 0.2964 ] Network output: [ -0.06482 0.1495 1.117 -5.352e-05 2.403e-05 0.8627 -4.033e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2814 0.2804 0.2889 0.2862 0.9841 0.9903 0.2815 0.9599 0.9786 0.2909 ] Network output: [ -0.006534 1.015 0.02691 3.492e-05 -1.568e-05 0.9709 2.632e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04727 Epoch 3580 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05937 0.906 0.9229 0.0001099 -4.936e-05 0.05283 8.286e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01318 -0.005558 0.003251 0.02483 0.9527 0.9599 0.02395 0.9047 0.9216 0.0663 ] Network output: [ 0.9606 0.08373 0.03744 -5.169e-05 2.32e-05 -0.04248 -3.895e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5452 0.0884 0.07552 0.3369 0.9784 0.9904 0.6031 0.9189 0.976 0.5204 ] Network output: [ 0.0197 0.9202 0.9385 -1.294e-05 5.808e-06 0.1018 -9.75e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01978 0.0142 0.02261 0.02513 0.9883 0.9919 0.02009 0.9742 0.9848 0.02906 ] Network output: [ 0.09656 -0.217 0.8056 4.569e-05 -2.051e-05 1.218 3.444e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5955 0.5149 0.4243 0.4726 0.9806 0.9916 0.5971 0.9258 0.979 0.507 ] Network output: [ -0.06431 0.142 1.151 -7.033e-05 3.157e-05 0.8355 -5.301e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2748 0.2689 0.2876 0.2876 0.9885 0.9927 0.275 0.975 0.9852 0.2964 ] Network output: [ -0.06476 0.1493 1.117 -5.323e-05 2.39e-05 0.8627 -4.012e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2814 0.2803 0.2888 0.2862 0.9841 0.9903 0.2814 0.9599 0.9786 0.2908 ] Network output: [ -0.006571 1.015 0.02699 3.478e-05 -1.561e-05 0.9708 2.621e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04726 Epoch 3581 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05934 0.9061 0.9229 0.0001099 -4.935e-05 0.05281 8.284e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01317 -0.005562 0.003224 0.02481 0.9527 0.9599 0.02393 0.9047 0.9216 0.06627 ] Network output: [ 0.9606 0.08371 0.03742 -5.182e-05 2.326e-05 -0.04251 -3.905e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5451 0.08844 0.07553 0.3367 0.9784 0.9904 0.6031 0.919 0.976 0.5204 ] Network output: [ 0.01966 0.9203 0.9385 -1.312e-05 5.892e-06 0.1018 -9.89e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01977 0.0142 0.0226 0.02512 0.9883 0.9919 0.02009 0.9742 0.9848 0.02904 ] Network output: [ 0.09652 -0.2169 0.8055 4.564e-05 -2.049e-05 1.219 3.44e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5955 0.5149 0.4243 0.4725 0.9807 0.9916 0.5971 0.9259 0.979 0.507 ] Network output: [ -0.06426 0.1419 1.151 -7.009e-05 3.147e-05 0.8356 -5.282e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2748 0.2689 0.2876 0.2876 0.9885 0.9927 0.2749 0.975 0.9852 0.2964 ] Network output: [ -0.0647 0.1492 1.117 -5.294e-05 2.377e-05 0.8627 -3.99e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2813 0.2802 0.2888 0.2861 0.9841 0.9903 0.2813 0.9599 0.9786 0.2908 ] Network output: [ -0.006607 1.016 0.02708 3.463e-05 -1.555e-05 0.9707 2.61e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04724 Epoch 3582 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05931 0.9062 0.9229 0.0001099 -4.934e-05 0.05279 8.283e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01316 -0.005565 0.003197 0.02479 0.9527 0.9599 0.02392 0.9047 0.9216 0.06624 ] Network output: [ 0.9606 0.08369 0.0374 -5.195e-05 2.332e-05 -0.04254 -3.915e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.545 0.08849 0.07554 0.3366 0.9784 0.9904 0.6031 0.919 0.9761 0.5203 ] Network output: [ 0.01961 0.9204 0.9385 -1.331e-05 5.975e-06 0.1018 -1.003e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01977 0.0142 0.02259 0.0251 0.9883 0.9919 0.02008 0.9742 0.9848 0.02903 ] Network output: [ 0.09648 -0.2169 0.8054 4.558e-05 -2.046e-05 1.219 3.435e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5954 0.515 0.4244 0.4725 0.9807 0.9916 0.5971 0.9259 0.979 0.507 ] Network output: [ -0.0642 0.1417 1.151 -6.985e-05 3.136e-05 0.8356 -5.264e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2748 0.2689 0.2876 0.2876 0.9885 0.9927 0.2749 0.975 0.9853 0.2964 ] Network output: [ -0.06465 0.1491 1.117 -5.266e-05 2.364e-05 0.8626 -3.968e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2812 0.2802 0.2888 0.2861 0.9841 0.9903 0.2812 0.96 0.9786 0.2908 ] Network output: [ -0.006644 1.016 0.02716 3.449e-05 -1.548e-05 0.9707 2.599e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04723 Epoch 3583 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05928 0.9062 0.9229 0.0001099 -4.933e-05 0.05276 8.281e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01315 -0.005569 0.003169 0.02478 0.9527 0.9599 0.02391 0.9048 0.9217 0.06621 ] Network output: [ 0.9607 0.08366 0.03739 -5.207e-05 2.338e-05 -0.04257 -3.924e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.545 0.08853 0.07555 0.3365 0.9785 0.9904 0.603 0.9191 0.9761 0.5203 ] Network output: [ 0.01957 0.9204 0.9386 -1.349e-05 6.058e-06 0.1018 -1.017e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01976 0.01419 0.02258 0.02509 0.9883 0.9919 0.02008 0.9742 0.9848 0.02902 ] Network output: [ 0.09643 -0.2168 0.8054 4.553e-05 -2.044e-05 1.219 3.431e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5954 0.515 0.4244 0.4724 0.9807 0.9916 0.5971 0.9259 0.9791 0.5069 ] Network output: [ -0.06415 0.1416 1.151 -6.961e-05 3.125e-05 0.8357 -5.246e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2748 0.2689 0.2876 0.2876 0.9885 0.9927 0.2749 0.9751 0.9853 0.2964 ] Network output: [ -0.06459 0.1489 1.117 -5.237e-05 2.351e-05 0.8626 -3.947e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2811 0.2801 0.2888 0.2861 0.9841 0.9903 0.2812 0.96 0.9786 0.2908 ] Network output: [ -0.00668 1.016 0.02724 3.435e-05 -1.542e-05 0.9706 2.588e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04721 Epoch 3584 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05925 0.9063 0.9229 0.0001099 -4.932e-05 0.05274 8.279e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01314 -0.005572 0.003142 0.02476 0.9527 0.9599 0.0239 0.9048 0.9217 0.06618 ] Network output: [ 0.9607 0.08364 0.03737 -5.219e-05 2.343e-05 -0.04259 -3.934e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5449 0.08857 0.07556 0.3363 0.9785 0.9904 0.603 0.9191 0.9761 0.5202 ] Network output: [ 0.01953 0.9205 0.9386 -1.368e-05 6.141e-06 0.1018 -1.031e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01976 0.01419 0.02257 0.02507 0.9883 0.9919 0.02007 0.9743 0.9848 0.029 ] Network output: [ 0.09639 -0.2168 0.8053 4.547e-05 -2.041e-05 1.219 3.427e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5954 0.5151 0.4245 0.4723 0.9807 0.9917 0.597 0.926 0.9791 0.5069 ] Network output: [ -0.06409 0.1415 1.151 -6.937e-05 3.114e-05 0.8358 -5.228e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2747 0.2689 0.2876 0.2875 0.9885 0.9927 0.2749 0.9751 0.9853 0.2964 ] Network output: [ -0.06453 0.1488 1.117 -5.209e-05 2.338e-05 0.8626 -3.926e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2811 0.28 0.2887 0.286 0.9841 0.9903 0.2811 0.96 0.9786 0.2907 ] Network output: [ -0.006716 1.016 0.02732 3.42e-05 -1.536e-05 0.9705 2.578e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0472 Epoch 3585 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05922 0.9064 0.9229 0.0001098 -4.931e-05 0.05272 8.278e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01313 -0.005575 0.003115 0.02474 0.9528 0.9599 0.02389 0.9049 0.9217 0.06615 ] Network output: [ 0.9607 0.08362 0.03735 -5.232e-05 2.349e-05 -0.04262 -3.943e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5449 0.08861 0.07557 0.3362 0.9785 0.9904 0.603 0.9191 0.9761 0.5202 ] Network output: [ 0.01949 0.9206 0.9386 -1.386e-05 6.224e-06 0.1018 -1.045e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01975 0.01419 0.02256 0.02505 0.9883 0.9919 0.02007 0.9743 0.9848 0.02899 ] Network output: [ 0.09635 -0.2167 0.8052 4.541e-05 -2.039e-05 1.219 3.422e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5954 0.5151 0.4245 0.4722 0.9807 0.9917 0.597 0.926 0.9791 0.5069 ] Network output: [ -0.06404 0.1414 1.151 -6.913e-05 3.103e-05 0.8358 -5.21e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2747 0.2688 0.2876 0.2875 0.9885 0.9927 0.2748 0.9751 0.9853 0.2964 ] Network output: [ -0.06447 0.1487 1.117 -5.181e-05 2.326e-05 0.8626 -3.904e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.281 0.28 0.2887 0.286 0.9842 0.9903 0.281 0.96 0.9787 0.2907 ] Network output: [ -0.006752 1.016 0.0274 3.406e-05 -1.529e-05 0.9704 2.567e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04719 Epoch 3586 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05919 0.9065 0.9229 0.0001098 -4.93e-05 0.05269 8.276e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01312 -0.005579 0.003087 0.02472 0.9528 0.96 0.02388 0.9049 0.9218 0.06612 ] Network output: [ 0.9608 0.0836 0.03733 -5.243e-05 2.354e-05 -0.04265 -3.952e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5448 0.08866 0.07558 0.3361 0.9785 0.9905 0.6029 0.9192 0.9761 0.5201 ] Network output: [ 0.01944 0.9206 0.9386 -1.405e-05 6.308e-06 0.1019 -1.059e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01975 0.01419 0.02255 0.02504 0.9883 0.9919 0.02006 0.9743 0.9848 0.02898 ] Network output: [ 0.09631 -0.2166 0.8051 4.535e-05 -2.036e-05 1.219 3.418e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5953 0.5151 0.4245 0.4722 0.9807 0.9917 0.597 0.926 0.9791 0.5068 ] Network output: [ -0.06398 0.1413 1.151 -6.889e-05 3.093e-05 0.8359 -5.192e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2747 0.2688 0.2876 0.2875 0.9885 0.9927 0.2748 0.9751 0.9853 0.2964 ] Network output: [ -0.06441 0.1485 1.118 -5.152e-05 2.313e-05 0.8626 -3.883e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2809 0.2799 0.2887 0.286 0.9842 0.9903 0.2809 0.96 0.9787 0.2907 ] Network output: [ -0.006788 1.016 0.02748 3.392e-05 -1.523e-05 0.9703 2.557e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04717 Epoch 3587 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05916 0.9065 0.9229 0.0001098 -4.929e-05 0.05267 8.275e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01311 -0.005582 0.00306 0.0247 0.9528 0.96 0.02387 0.9049 0.9218 0.06609 ] Network output: [ 0.9608 0.08358 0.03731 -5.255e-05 2.359e-05 -0.04268 -3.96e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5447 0.0887 0.07559 0.3359 0.9785 0.9905 0.6029 0.9192 0.9761 0.5201 ] Network output: [ 0.0194 0.9207 0.9386 -1.424e-05 6.391e-06 0.1019 -1.073e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01974 0.01418 0.02254 0.02502 0.9883 0.9919 0.02006 0.9743 0.9848 0.02896 ] Network output: [ 0.09627 -0.2166 0.8051 4.529e-05 -2.033e-05 1.219 3.413e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5953 0.5152 0.4246 0.4721 0.9807 0.9917 0.5969 0.9261 0.9791 0.5068 ] Network output: [ -0.06393 0.1411 1.151 -6.865e-05 3.082e-05 0.8359 -5.174e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2747 0.2688 0.2876 0.2875 0.9885 0.9927 0.2748 0.9751 0.9853 0.2964 ] Network output: [ -0.06435 0.1484 1.118 -5.124e-05 2.3e-05 0.8625 -3.862e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2808 0.2798 0.2887 0.2859 0.9842 0.9903 0.2809 0.9601 0.9787 0.2906 ] Network output: [ -0.006824 1.016 0.02757 3.379e-05 -1.517e-05 0.9702 2.546e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04716 Epoch 3588 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05913 0.9066 0.9229 0.0001098 -4.928e-05 0.05265 8.273e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0131 -0.005586 0.003033 0.02468 0.9528 0.96 0.02386 0.905 0.9218 0.06606 ] Network output: [ 0.9608 0.08355 0.03729 -5.267e-05 2.364e-05 -0.04271 -3.969e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5447 0.08874 0.0756 0.3358 0.9785 0.9905 0.6029 0.9193 0.9762 0.5201 ] Network output: [ 0.01936 0.9207 0.9386 -1.442e-05 6.474e-06 0.1019 -1.087e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01974 0.01418 0.02253 0.02501 0.9883 0.9919 0.02006 0.9743 0.9848 0.02895 ] Network output: [ 0.09623 -0.2165 0.805 4.522e-05 -2.03e-05 1.219 3.408e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5953 0.5152 0.4246 0.472 0.9807 0.9917 0.5969 0.9261 0.9791 0.5068 ] Network output: [ -0.06388 0.141 1.15 -6.842e-05 3.071e-05 0.836 -5.156e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2746 0.2688 0.2876 0.2875 0.9885 0.9927 0.2748 0.9751 0.9853 0.2964 ] Network output: [ -0.06429 0.1483 1.118 -5.096e-05 2.288e-05 0.8625 -3.841e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2808 0.2797 0.2886 0.2859 0.9842 0.9903 0.2808 0.9601 0.9787 0.2906 ] Network output: [ -0.006859 1.016 0.02765 3.365e-05 -1.511e-05 0.9701 2.536e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04715 Epoch 3589 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0591 0.9067 0.9229 0.0001098 -4.927e-05 0.05263 8.272e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01309 -0.005589 0.003005 0.02466 0.9528 0.96 0.02385 0.905 0.9219 0.06603 ] Network output: [ 0.9609 0.08353 0.03727 -5.278e-05 2.369e-05 -0.04274 -3.977e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5446 0.08878 0.07561 0.3357 0.9785 0.9905 0.6029 0.9193 0.9762 0.52 ] Network output: [ 0.01932 0.9208 0.9386 -1.461e-05 6.557e-06 0.1019 -1.101e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01973 0.01418 0.02252 0.02499 0.9883 0.9919 0.02005 0.9743 0.9849 0.02893 ] Network output: [ 0.09618 -0.2164 0.8049 4.515e-05 -2.027e-05 1.219 3.403e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5952 0.5153 0.4247 0.4719 0.9807 0.9917 0.5969 0.9262 0.9791 0.5067 ] Network output: [ -0.06382 0.1409 1.15 -6.818e-05 3.061e-05 0.836 -5.138e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2746 0.2688 0.2876 0.2874 0.9885 0.9927 0.2747 0.9751 0.9853 0.2964 ] Network output: [ -0.06423 0.1481 1.118 -5.068e-05 2.275e-05 0.8625 -3.819e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2807 0.2797 0.2886 0.2859 0.9842 0.9904 0.2807 0.9601 0.9787 0.2906 ] Network output: [ -0.006894 1.016 0.02773 3.351e-05 -1.505e-05 0.97 2.526e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04713 Epoch 3590 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05907 0.9068 0.9229 0.0001097 -4.927e-05 0.05261 8.27e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01308 -0.005592 0.002978 0.02464 0.9528 0.96 0.02384 0.9051 0.9219 0.066 ] Network output: [ 0.9609 0.08352 0.03725 -5.289e-05 2.374e-05 -0.04277 -3.986e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5446 0.08882 0.07562 0.3355 0.9785 0.9905 0.6028 0.9193 0.9762 0.52 ] Network output: [ 0.01928 0.9209 0.9386 -1.479e-05 6.64e-06 0.1019 -1.115e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01973 0.01418 0.02251 0.02497 0.9883 0.9919 0.02005 0.9744 0.9849 0.02892 ] Network output: [ 0.09614 -0.2164 0.8049 4.509e-05 -2.024e-05 1.219 3.398e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5952 0.5153 0.4247 0.4719 0.9807 0.9917 0.5969 0.9262 0.9792 0.5067 ] Network output: [ -0.06377 0.1408 1.15 -6.795e-05 3.05e-05 0.8361 -5.121e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2746 0.2687 0.2876 0.2874 0.9885 0.9927 0.2747 0.9752 0.9853 0.2963 ] Network output: [ -0.06417 0.148 1.118 -5.04e-05 2.263e-05 0.8625 -3.798e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2806 0.2796 0.2886 0.2858 0.9842 0.9904 0.2806 0.9601 0.9787 0.2906 ] Network output: [ -0.006929 1.016 0.02781 3.338e-05 -1.498e-05 0.9699 2.515e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04712 Epoch 3591 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05904 0.9068 0.9229 0.0001097 -4.926e-05 0.05259 8.269e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01307 -0.005596 0.002951 0.02462 0.9529 0.96 0.02383 0.9051 0.9219 0.06598 ] Network output: [ 0.9609 0.0835 0.03723 -5.299e-05 2.379e-05 -0.0428 -3.994e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5445 0.08886 0.07563 0.3354 0.9785 0.9905 0.6028 0.9194 0.9762 0.5199 ] Network output: [ 0.01924 0.9209 0.9386 -1.498e-05 6.723e-06 0.1019 -1.129e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01972 0.01417 0.0225 0.02496 0.9883 0.9919 0.02004 0.9744 0.9849 0.02891 ] Network output: [ 0.0961 -0.2163 0.8048 4.502e-05 -2.021e-05 1.22 3.393e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5952 0.5153 0.4248 0.4718 0.9807 0.9917 0.5968 0.9262 0.9792 0.5067 ] Network output: [ -0.06371 0.1407 1.15 -6.771e-05 3.04e-05 0.8361 -5.103e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2746 0.2687 0.2876 0.2874 0.9885 0.9927 0.2747 0.9752 0.9853 0.2963 ] Network output: [ -0.06411 0.1479 1.118 -5.012e-05 2.25e-05 0.8625 -3.777e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2805 0.2795 0.2886 0.2858 0.9842 0.9904 0.2806 0.9602 0.9787 0.2905 ] Network output: [ -0.006964 1.016 0.02788 3.324e-05 -1.492e-05 0.9698 2.505e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04711 Epoch 3592 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05901 0.9069 0.9229 0.0001097 -4.925e-05 0.05256 8.267e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01306 -0.005599 0.002924 0.0246 0.9529 0.96 0.02382 0.9051 0.922 0.06595 ] Network output: [ 0.961 0.08348 0.03721 -5.31e-05 2.384e-05 -0.04283 -4.002e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5445 0.0889 0.07564 0.3353 0.9785 0.9905 0.6028 0.9194 0.9762 0.5199 ] Network output: [ 0.01919 0.921 0.9386 -1.516e-05 6.806e-06 0.1019 -1.143e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01972 0.01417 0.02249 0.02494 0.9883 0.9919 0.02004 0.9744 0.9849 0.02889 ] Network output: [ 0.09606 -0.2163 0.8047 4.494e-05 -2.018e-05 1.22 3.387e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5951 0.5154 0.4248 0.4717 0.9807 0.9917 0.5968 0.9263 0.9792 0.5067 ] Network output: [ -0.06366 0.1405 1.15 -6.748e-05 3.03e-05 0.8362 -5.086e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2745 0.2687 0.2876 0.2874 0.9885 0.9927 0.2747 0.9752 0.9854 0.2963 ] Network output: [ -0.06406 0.1477 1.118 -4.984e-05 2.238e-05 0.8624 -3.756e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2805 0.2794 0.2885 0.2858 0.9842 0.9904 0.2805 0.9602 0.9788 0.2905 ] Network output: [ -0.006999 1.016 0.02796 3.311e-05 -1.486e-05 0.9697 2.495e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04709 Epoch 3593 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05898 0.907 0.9229 0.0001097 -4.924e-05 0.05254 8.266e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01305 -0.005602 0.002896 0.02458 0.9529 0.9601 0.02381 0.9052 0.922 0.06592 ] Network output: [ 0.961 0.08346 0.03719 -5.32e-05 2.388e-05 -0.04286 -4.01e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5444 0.08894 0.07564 0.3351 0.9785 0.9905 0.6028 0.9195 0.9762 0.5199 ] Network output: [ 0.01915 0.9211 0.9386 -1.535e-05 6.889e-06 0.1019 -1.157e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01971 0.01417 0.02248 0.02493 0.9884 0.9919 0.02003 0.9744 0.9849 0.02888 ] Network output: [ 0.09601 -0.2162 0.8047 4.487e-05 -2.014e-05 1.22 3.382e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5951 0.5154 0.4249 0.4716 0.9807 0.9917 0.5968 0.9263 0.9792 0.5066 ] Network output: [ -0.06361 0.1404 1.15 -6.725e-05 3.019e-05 0.8362 -5.068e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2745 0.2687 0.2876 0.2874 0.9885 0.9927 0.2746 0.9752 0.9854 0.2963 ] Network output: [ -0.064 0.1476 1.118 -4.957e-05 2.225e-05 0.8624 -3.736e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2804 0.2794 0.2885 0.2857 0.9842 0.9904 0.2804 0.9602 0.9788 0.2905 ] Network output: [ -0.007034 1.017 0.02804 3.298e-05 -1.481e-05 0.9697 2.485e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04708 Epoch 3594 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05896 0.9071 0.9229 0.0001097 -4.923e-05 0.05252 8.264e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01304 -0.005606 0.002869 0.02456 0.9529 0.9601 0.0238 0.9052 0.922 0.06589 ] Network output: [ 0.961 0.08344 0.03717 -5.33e-05 2.393e-05 -0.04289 -4.017e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5443 0.08898 0.07565 0.335 0.9786 0.9905 0.6027 0.9195 0.9763 0.5198 ] Network output: [ 0.01911 0.9211 0.9386 -1.553e-05 6.972e-06 0.1019 -1.17e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01971 0.01417 0.02247 0.02491 0.9884 0.9919 0.02003 0.9744 0.9849 0.02887 ] Network output: [ 0.09597 -0.2162 0.8046 4.48e-05 -2.011e-05 1.22 3.376e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5951 0.5155 0.4249 0.4715 0.9807 0.9917 0.5967 0.9264 0.9792 0.5066 ] Network output: [ -0.06355 0.1403 1.15 -6.702e-05 3.009e-05 0.8363 -5.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2745 0.2687 0.2876 0.2873 0.9885 0.9927 0.2746 0.9752 0.9854 0.2963 ] Network output: [ -0.06394 0.1475 1.118 -4.929e-05 2.213e-05 0.8624 -3.715e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2803 0.2793 0.2885 0.2857 0.9842 0.9904 0.2803 0.9602 0.9788 0.2904 ] Network output: [ -0.007069 1.017 0.02812 3.285e-05 -1.475e-05 0.9696 2.476e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04707 Epoch 3595 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05893 0.9071 0.923 0.0001096 -4.922e-05 0.0525 8.263e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01303 -0.005609 0.002842 0.02454 0.9529 0.9601 0.02379 0.9053 0.9221 0.06586 ] Network output: [ 0.9611 0.08342 0.03715 -5.34e-05 2.397e-05 -0.04292 -4.025e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5443 0.08902 0.07566 0.3349 0.9786 0.9905 0.6027 0.9195 0.9763 0.5198 ] Network output: [ 0.01907 0.9212 0.9386 -1.572e-05 7.055e-06 0.102 -1.184e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0197 0.01416 0.02246 0.02489 0.9884 0.992 0.02002 0.9744 0.9849 0.02885 ] Network output: [ 0.09593 -0.2161 0.8045 4.472e-05 -2.008e-05 1.22 3.37e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5951 0.5155 0.425 0.4715 0.9808 0.9917 0.5967 0.9264 0.9792 0.5066 ] Network output: [ -0.0635 0.1402 1.15 -6.679e-05 2.999e-05 0.8363 -5.034e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2745 0.2686 0.2876 0.2873 0.9885 0.9927 0.2746 0.9752 0.9854 0.2963 ] Network output: [ -0.06388 0.1473 1.118 -4.901e-05 2.2e-05 0.8624 -3.694e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2802 0.2792 0.2885 0.2857 0.9842 0.9904 0.2803 0.9602 0.9788 0.2904 ] Network output: [ -0.007103 1.017 0.0282 3.272e-05 -1.469e-05 0.9695 2.466e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04706 Epoch 3596 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0589 0.9072 0.923 0.0001096 -4.921e-05 0.05248 8.261e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01302 -0.005612 0.002815 0.02452 0.9529 0.9601 0.02378 0.9053 0.9221 0.06583 ] Network output: [ 0.9611 0.08341 0.03713 -5.35e-05 2.402e-05 -0.04295 -4.032e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5442 0.08906 0.07567 0.3348 0.9786 0.9905 0.6027 0.9196 0.9763 0.5197 ] Network output: [ 0.01903 0.9213 0.9387 -1.59e-05 7.139e-06 0.102 -1.198e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0197 0.01416 0.02245 0.02488 0.9884 0.992 0.02002 0.9745 0.9849 0.02884 ] Network output: [ 0.09589 -0.216 0.8045 4.464e-05 -2.004e-05 1.22 3.364e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.595 0.5155 0.425 0.4714 0.9808 0.9917 0.5967 0.9264 0.9792 0.5066 ] Network output: [ -0.06345 0.1401 1.15 -6.657e-05 2.988e-05 0.8364 -5.017e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2744 0.2686 0.2876 0.2873 0.9886 0.9927 0.2746 0.9752 0.9854 0.2963 ] Network output: [ -0.06382 0.1472 1.118 -4.874e-05 2.188e-05 0.8624 -3.673e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2802 0.2792 0.2884 0.2856 0.9842 0.9904 0.2802 0.9603 0.9788 0.2904 ] Network output: [ -0.007137 1.017 0.02828 3.259e-05 -1.463e-05 0.9694 2.456e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04704 Epoch 3597 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05887 0.9073 0.923 0.0001096 -4.92e-05 0.05247 8.26e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01301 -0.005616 0.002787 0.0245 0.9529 0.9601 0.02377 0.9053 0.9221 0.0658 ] Network output: [ 0.9611 0.08339 0.0371 -5.359e-05 2.406e-05 -0.04298 -4.039e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5442 0.0891 0.07567 0.3346 0.9786 0.9905 0.6026 0.9196 0.9763 0.5197 ] Network output: [ 0.01899 0.9213 0.9387 -1.609e-05 7.222e-06 0.102 -1.212e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01969 0.01416 0.02244 0.02486 0.9884 0.992 0.02001 0.9745 0.9849 0.02882 ] Network output: [ 0.09584 -0.216 0.8044 4.456e-05 -2.001e-05 1.22 3.358e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.595 0.5156 0.4251 0.4713 0.9808 0.9917 0.5966 0.9265 0.9793 0.5065 ] Network output: [ -0.06339 0.1399 1.15 -6.634e-05 2.978e-05 0.8364 -5e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2744 0.2686 0.2876 0.2873 0.9886 0.9928 0.2745 0.9753 0.9854 0.2963 ] Network output: [ -0.06377 0.1471 1.118 -4.846e-05 2.176e-05 0.8623 -3.652e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2801 0.2791 0.2884 0.2856 0.9842 0.9904 0.2801 0.9603 0.9788 0.2904 ] Network output: [ -0.007171 1.017 0.02835 3.246e-05 -1.457e-05 0.9693 2.446e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04703 Epoch 3598 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05884 0.9074 0.923 0.0001096 -4.919e-05 0.05245 8.258e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.013 -0.005619 0.00276 0.02449 0.953 0.9601 0.02376 0.9054 0.9222 0.06577 ] Network output: [ 0.9612 0.08338 0.03708 -5.369e-05 2.41e-05 -0.04301 -4.046e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5441 0.08913 0.07568 0.3345 0.9786 0.9905 0.6026 0.9197 0.9763 0.5197 ] Network output: [ 0.01895 0.9214 0.9387 -1.627e-05 7.304e-06 0.102 -1.226e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01969 0.01416 0.02243 0.02485 0.9884 0.992 0.02001 0.9745 0.985 0.02881 ] Network output: [ 0.0958 -0.2159 0.8043 4.448e-05 -1.997e-05 1.22 3.352e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.595 0.5156 0.4251 0.4713 0.9808 0.9917 0.5966 0.9265 0.9793 0.5065 ] Network output: [ -0.06334 0.1398 1.15 -6.611e-05 2.968e-05 0.8365 -4.983e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2744 0.2686 0.2876 0.2872 0.9886 0.9928 0.2745 0.9753 0.9854 0.2963 ] Network output: [ -0.06371 0.1469 1.118 -4.819e-05 2.163e-05 0.8623 -3.632e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.28 0.279 0.2884 0.2856 0.9842 0.9904 0.28 0.9603 0.9788 0.2903 ] Network output: [ -0.007205 1.017 0.02843 3.233e-05 -1.452e-05 0.9692 2.437e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04702 Epoch 3599 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05881 0.9074 0.923 0.0001096 -4.919e-05 0.05243 8.257e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01299 -0.005622 0.002733 0.02447 0.953 0.9601 0.02374 0.9054 0.9222 0.06574 ] Network output: [ 0.9612 0.08336 0.03706 -5.378e-05 2.414e-05 -0.04304 -4.053e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.544 0.08917 0.07569 0.3344 0.9786 0.9905 0.6026 0.9197 0.9763 0.5196 ] Network output: [ 0.01891 0.9214 0.9387 -1.646e-05 7.387e-06 0.102 -1.24e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01968 0.01415 0.02241 0.02483 0.9884 0.992 0.02 0.9745 0.985 0.0288 ] Network output: [ 0.09576 -0.2159 0.8043 4.44e-05 -1.993e-05 1.22 3.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5949 0.5157 0.4252 0.4712 0.9808 0.9917 0.5966 0.9265 0.9793 0.5065 ] Network output: [ -0.06329 0.1397 1.15 -6.589e-05 2.958e-05 0.8365 -4.966e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2744 0.2686 0.2876 0.2872 0.9886 0.9928 0.2745 0.9753 0.9854 0.2963 ] Network output: [ -0.06365 0.1468 1.118 -4.791e-05 2.151e-05 0.8623 -3.611e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2799 0.2789 0.2884 0.2855 0.9842 0.9904 0.28 0.9603 0.9789 0.2903 ] Network output: [ -0.007239 1.017 0.02851 3.221e-05 -1.446e-05 0.9691 2.427e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04701 Epoch 3600 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05878 0.9075 0.923 0.0001095 -4.918e-05 0.05241 8.255e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01298 -0.005626 0.002706 0.02445 0.953 0.9602 0.02373 0.9055 0.9222 0.06571 ] Network output: [ 0.9612 0.08335 0.03703 -5.387e-05 2.418e-05 -0.04307 -4.06e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.544 0.08921 0.07569 0.3342 0.9786 0.9905 0.6026 0.9197 0.9764 0.5196 ] Network output: [ 0.01887 0.9215 0.9387 -1.664e-05 7.47e-06 0.102 -1.254e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01968 0.01415 0.0224 0.02481 0.9884 0.992 0.02 0.9745 0.985 0.02878 ] Network output: [ 0.09572 -0.2158 0.8042 4.431e-05 -1.989e-05 1.22 3.34e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5949 0.5157 0.4252 0.4711 0.9808 0.9917 0.5966 0.9266 0.9793 0.5065 ] Network output: [ -0.06324 0.1396 1.15 -6.566e-05 2.948e-05 0.8366 -4.949e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2743 0.2685 0.2876 0.2872 0.9886 0.9928 0.2745 0.9753 0.9854 0.2962 ] Network output: [ -0.06359 0.1467 1.118 -4.764e-05 2.139e-05 0.8623 -3.59e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2799 0.2789 0.2883 0.2855 0.9842 0.9904 0.2799 0.9603 0.9789 0.2903 ] Network output: [ -0.007273 1.017 0.02858 3.208e-05 -1.44e-05 0.969 2.418e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.047 Epoch 3601 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05875 0.9076 0.923 0.0001095 -4.917e-05 0.05239 8.254e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01297 -0.005629 0.002678 0.02443 0.953 0.9602 0.02372 0.9055 0.9223 0.06568 ] Network output: [ 0.9613 0.08333 0.03701 -5.395e-05 2.422e-05 -0.0431 -4.066e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5439 0.08925 0.0757 0.3341 0.9786 0.9905 0.6025 0.9198 0.9764 0.5196 ] Network output: [ 0.01883 0.9216 0.9387 -1.682e-05 7.553e-06 0.102 -1.268e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01967 0.01415 0.02239 0.0248 0.9884 0.992 0.01999 0.9745 0.985 0.02877 ] Network output: [ 0.09567 -0.2158 0.8041 4.423e-05 -1.986e-05 1.22 3.333e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5949 0.5157 0.4253 0.471 0.9808 0.9917 0.5965 0.9266 0.9793 0.5064 ] Network output: [ -0.06318 0.1395 1.15 -6.544e-05 2.938e-05 0.8366 -4.932e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2743 0.2685 0.2876 0.2872 0.9886 0.9928 0.2744 0.9753 0.9855 0.2962 ] Network output: [ -0.06354 0.1465 1.118 -4.737e-05 2.127e-05 0.8623 -3.57e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2798 0.2788 0.2883 0.2855 0.9843 0.9904 0.2798 0.9604 0.9789 0.2903 ] Network output: [ -0.007306 1.017 0.02866 3.196e-05 -1.435e-05 0.9689 2.409e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04699 Epoch 3602 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05872 0.9076 0.923 0.0001095 -4.916e-05 0.05237 8.252e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01296 -0.005632 0.002651 0.02441 0.953 0.9602 0.02371 0.9055 0.9223 0.06565 ] Network output: [ 0.9613 0.08332 0.03699 -5.404e-05 2.426e-05 -0.04313 -4.072e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5438 0.08929 0.0757 0.334 0.9786 0.9905 0.6025 0.9198 0.9764 0.5195 ] Network output: [ 0.01879 0.9216 0.9387 -1.701e-05 7.636e-06 0.102 -1.282e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01967 0.01414 0.02238 0.02478 0.9884 0.992 0.01999 0.9746 0.985 0.02875 ] Network output: [ 0.09563 -0.2157 0.8041 4.414e-05 -1.982e-05 1.221 3.327e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5948 0.5158 0.4253 0.471 0.9808 0.9917 0.5965 0.9267 0.9793 0.5064 ] Network output: [ -0.06313 0.1394 1.15 -6.522e-05 2.928e-05 0.8367 -4.915e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2743 0.2685 0.2876 0.2872 0.9886 0.9928 0.2744 0.9753 0.9855 0.2962 ] Network output: [ -0.06348 0.1464 1.118 -4.71e-05 2.114e-05 0.8622 -3.549e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2797 0.2787 0.2883 0.2854 0.9843 0.9904 0.2797 0.9604 0.9789 0.2902 ] Network output: [ -0.00734 1.017 0.02873 3.184e-05 -1.429e-05 0.9689 2.399e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04697 Epoch 3603 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0587 0.9077 0.923 0.0001095 -4.915e-05 0.05236 8.251e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01295 -0.005635 0.002624 0.02439 0.953 0.9602 0.0237 0.9056 0.9223 0.06562 ] Network output: [ 0.9613 0.08331 0.03696 -5.412e-05 2.43e-05 -0.04317 -4.079e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5438 0.08932 0.07571 0.3338 0.9786 0.9905 0.6025 0.9199 0.9764 0.5195 ] Network output: [ 0.01875 0.9217 0.9387 -1.719e-05 7.719e-06 0.1021 -1.296e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01966 0.01414 0.02237 0.02476 0.9884 0.992 0.01998 0.9746 0.985 0.02874 ] Network output: [ 0.09559 -0.2157 0.804 4.405e-05 -1.978e-05 1.221 3.32e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5948 0.5158 0.4254 0.4709 0.9808 0.9917 0.5965 0.9267 0.9793 0.5064 ] Network output: [ -0.06308 0.1393 1.15 -6.5e-05 2.918e-05 0.8367 -4.898e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2742 0.2685 0.2876 0.2871 0.9886 0.9928 0.2744 0.9754 0.9855 0.2962 ] Network output: [ -0.06342 0.1463 1.118 -4.683e-05 2.102e-05 0.8622 -3.529e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2796 0.2786 0.2883 0.2854 0.9843 0.9904 0.2797 0.9604 0.9789 0.2902 ] Network output: [ -0.007373 1.017 0.02881 3.171e-05 -1.424e-05 0.9688 2.39e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04696 Epoch 3604 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05867 0.9078 0.923 0.0001095 -4.914e-05 0.05234 8.25e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01294 -0.005639 0.002597 0.02437 0.9531 0.9602 0.02369 0.9056 0.9224 0.06559 ] Network output: [ 0.9614 0.08329 0.03694 -5.42e-05 2.433e-05 -0.0432 -4.085e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5437 0.08936 0.07572 0.3337 0.9786 0.9905 0.6024 0.9199 0.9764 0.5195 ] Network output: [ 0.01871 0.9217 0.9387 -1.738e-05 7.802e-06 0.1021 -1.31e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01965 0.01414 0.02236 0.02475 0.9884 0.992 0.01997 0.9746 0.985 0.02873 ] Network output: [ 0.09555 -0.2156 0.804 4.396e-05 -1.974e-05 1.221 3.313e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5948 0.5158 0.4254 0.4708 0.9808 0.9917 0.5964 0.9267 0.9794 0.5064 ] Network output: [ -0.06303 0.1392 1.15 -6.478e-05 2.908e-05 0.8368 -4.882e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2742 0.2685 0.2876 0.2871 0.9886 0.9928 0.2743 0.9754 0.9855 0.2962 ] Network output: [ -0.06337 0.1462 1.118 -4.655e-05 2.09e-05 0.8622 -3.508e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2796 0.2786 0.2882 0.2854 0.9843 0.9904 0.2796 0.9604 0.9789 0.2902 ] Network output: [ -0.007406 1.017 0.02888 3.159e-05 -1.418e-05 0.9687 2.381e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04695 Epoch 3605 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05864 0.9078 0.923 0.0001094 -4.913e-05 0.05232 8.248e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01293 -0.005642 0.00257 0.02435 0.9531 0.9602 0.02368 0.9057 0.9224 0.06556 ] Network output: [ 0.9614 0.08328 0.03691 -5.428e-05 2.437e-05 -0.04323 -4.091e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5437 0.0894 0.07572 0.3336 0.9786 0.9905 0.6024 0.92 0.9764 0.5194 ] Network output: [ 0.01866 0.9218 0.9387 -1.756e-05 7.885e-06 0.1021 -1.324e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01965 0.01414 0.02235 0.02473 0.9884 0.992 0.01997 0.9746 0.985 0.02871 ] Network output: [ 0.0955 -0.2156 0.8039 4.387e-05 -1.969e-05 1.221 3.306e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5947 0.5159 0.4255 0.4707 0.9808 0.9917 0.5964 0.9268 0.9794 0.5064 ] Network output: [ -0.06298 0.139 1.15 -6.456e-05 2.898e-05 0.8368 -4.865e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2742 0.2684 0.2876 0.2871 0.9886 0.9928 0.2743 0.9754 0.9855 0.2962 ] Network output: [ -0.06331 0.146 1.118 -4.628e-05 2.078e-05 0.8622 -3.488e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2795 0.2785 0.2882 0.2853 0.9843 0.9904 0.2795 0.9604 0.9789 0.2901 ] Network output: [ -0.007439 1.017 0.02896 3.147e-05 -1.413e-05 0.9686 2.372e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04694 Epoch 3606 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05861 0.9079 0.923 0.0001094 -4.913e-05 0.05231 8.247e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01292 -0.005645 0.002542 0.02433 0.9531 0.9603 0.02367 0.9057 0.9224 0.06553 ] Network output: [ 0.9614 0.08327 0.03688 -5.436e-05 2.44e-05 -0.04326 -4.096e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5436 0.08943 0.07572 0.3334 0.9787 0.9906 0.6024 0.92 0.9765 0.5194 ] Network output: [ 0.01863 0.9219 0.9387 -1.775e-05 7.968e-06 0.1021 -1.338e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01964 0.01413 0.02234 0.02471 0.9884 0.992 0.01996 0.9746 0.985 0.0287 ] Network output: [ 0.09546 -0.2155 0.8038 4.377e-05 -1.965e-05 1.221 3.299e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5947 0.5159 0.4255 0.4707 0.9808 0.9917 0.5964 0.9268 0.9794 0.5063 ] Network output: [ -0.06292 0.1389 1.15 -6.434e-05 2.888e-05 0.8369 -4.849e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2742 0.2684 0.2876 0.2871 0.9886 0.9928 0.2743 0.9754 0.9855 0.2962 ] Network output: [ -0.06325 0.1459 1.118 -4.601e-05 2.066e-05 0.8622 -3.468e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2794 0.2784 0.2882 0.2853 0.9843 0.9904 0.2794 0.9605 0.979 0.2901 ] Network output: [ -0.007472 1.018 0.02903 3.135e-05 -1.408e-05 0.9685 2.363e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04693 Epoch 3607 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05858 0.908 0.923 0.0001094 -4.912e-05 0.05229 8.245e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01291 -0.005648 0.002515 0.02431 0.9531 0.9603 0.02366 0.9057 0.9225 0.0655 ] Network output: [ 0.9615 0.08326 0.03686 -5.443e-05 2.444e-05 -0.04329 -4.102e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5435 0.08947 0.07573 0.3333 0.9787 0.9906 0.6024 0.92 0.9765 0.5194 ] Network output: [ 0.01859 0.9219 0.9387 -1.793e-05 8.05e-06 0.1021 -1.351e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01964 0.01413 0.02233 0.0247 0.9884 0.992 0.01996 0.9746 0.985 0.02868 ] Network output: [ 0.09542 -0.2155 0.8038 4.368e-05 -1.961e-05 1.221 3.292e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5947 0.516 0.4256 0.4706 0.9808 0.9918 0.5963 0.9268 0.9794 0.5063 ] Network output: [ -0.06287 0.1388 1.15 -6.412e-05 2.879e-05 0.8369 -4.832e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2741 0.2684 0.2876 0.287 0.9886 0.9928 0.2743 0.9754 0.9855 0.2962 ] Network output: [ -0.0632 0.1458 1.118 -4.574e-05 2.054e-05 0.8621 -3.447e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2793 0.2783 0.2882 0.2853 0.9843 0.9904 0.2794 0.9605 0.979 0.2901 ] Network output: [ -0.007505 1.018 0.02911 3.123e-05 -1.402e-05 0.9684 2.354e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04692 Epoch 3608 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05855 0.9081 0.923 0.0001094 -4.911e-05 0.05227 8.244e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0129 -0.005652 0.002488 0.02429 0.9531 0.9603 0.02365 0.9058 0.9225 0.06547 ] Network output: [ 0.9615 0.08325 0.03683 -5.451e-05 2.447e-05 -0.04333 -4.108e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5435 0.0895 0.07573 0.3332 0.9787 0.9906 0.6023 0.9201 0.9765 0.5193 ] Network output: [ 0.01855 0.922 0.9387 -1.812e-05 8.133e-06 0.1021 -1.365e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01963 0.01413 0.02232 0.02468 0.9884 0.992 0.01995 0.9747 0.9851 0.02867 ] Network output: [ 0.09537 -0.2154 0.8037 4.358e-05 -1.957e-05 1.221 3.284e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5946 0.516 0.4256 0.4705 0.9809 0.9918 0.5963 0.9269 0.9794 0.5063 ] Network output: [ -0.06282 0.1387 1.15 -6.39e-05 2.869e-05 0.8369 -4.816e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2741 0.2684 0.2876 0.287 0.9886 0.9928 0.2742 0.9754 0.9855 0.2962 ] Network output: [ -0.06314 0.1457 1.118 -4.547e-05 2.042e-05 0.8621 -3.427e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2793 0.2783 0.2881 0.2852 0.9843 0.9904 0.2793 0.9605 0.979 0.2901 ] Network output: [ -0.007537 1.018 0.02918 3.112e-05 -1.397e-05 0.9683 2.345e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04691 Epoch 3609 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05852 0.9081 0.923 0.0001094 -4.91e-05 0.05226 8.243e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01289 -0.005655 0.002461 0.02427 0.9531 0.9603 0.02364 0.9058 0.9225 0.06544 ] Network output: [ 0.9615 0.08324 0.0368 -5.458e-05 2.45e-05 -0.04336 -4.113e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5434 0.08954 0.07574 0.333 0.9787 0.9906 0.6023 0.9201 0.9765 0.5193 ] Network output: [ 0.01851 0.922 0.9388 -1.83e-05 8.216e-06 0.1021 -1.379e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01963 0.01412 0.02231 0.02466 0.9884 0.992 0.01995 0.9747 0.9851 0.02866 ] Network output: [ 0.09533 -0.2154 0.8037 4.348e-05 -1.952e-05 1.221 3.277e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5946 0.516 0.4257 0.4704 0.9809 0.9918 0.5963 0.9269 0.9794 0.5063 ] Network output: [ -0.06277 0.1386 1.15 -6.369e-05 2.859e-05 0.837 -4.8e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2741 0.2683 0.2876 0.287 0.9886 0.9928 0.2742 0.9754 0.9855 0.2962 ] Network output: [ -0.06308 0.1455 1.118 -4.521e-05 2.029e-05 0.8621 -3.407e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2792 0.2782 0.2881 0.2852 0.9843 0.9904 0.2792 0.9605 0.979 0.29 ] Network output: [ -0.00757 1.018 0.02925 3.1e-05 -1.392e-05 0.9683 2.336e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0469 Epoch 3610 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0585 0.9082 0.923 0.0001094 -4.909e-05 0.05224 8.241e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01288 -0.005658 0.002434 0.02425 0.9532 0.9603 0.02363 0.9059 0.9226 0.06541 ] Network output: [ 0.9616 0.08323 0.03678 -5.465e-05 2.453e-05 -0.04339 -4.118e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5434 0.08958 0.07574 0.3329 0.9787 0.9906 0.6023 0.9202 0.9765 0.5193 ] Network output: [ 0.01847 0.9221 0.9388 -1.849e-05 8.299e-06 0.1021 -1.393e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01962 0.01412 0.0223 0.02465 0.9885 0.992 0.01994 0.9747 0.9851 0.02864 ] Network output: [ 0.09529 -0.2153 0.8036 4.338e-05 -1.948e-05 1.221 3.269e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5946 0.5161 0.4257 0.4704 0.9809 0.9918 0.5963 0.927 0.9794 0.5063 ] Network output: [ -0.06272 0.1385 1.15 -6.347e-05 2.85e-05 0.837 -4.784e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.274 0.2683 0.2876 0.287 0.9886 0.9928 0.2742 0.9755 0.9856 0.2961 ] Network output: [ -0.06303 0.1454 1.118 -4.494e-05 2.017e-05 0.8621 -3.387e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2791 0.2781 0.2881 0.2852 0.9843 0.9904 0.2791 0.9606 0.979 0.29 ] Network output: [ -0.007602 1.018 0.02932 3.088e-05 -1.386e-05 0.9682 2.328e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04689 Epoch 3611 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05847 0.9083 0.923 0.0001093 -4.908e-05 0.05223 8.24e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01287 -0.005661 0.002406 0.02423 0.9532 0.9603 0.02362 0.9059 0.9226 0.06538 ] Network output: [ 0.9616 0.08322 0.03675 -5.471e-05 2.456e-05 -0.04342 -4.123e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5433 0.08961 0.07574 0.3328 0.9787 0.9906 0.6022 0.9202 0.9765 0.5192 ] Network output: [ 0.01843 0.9221 0.9388 -1.867e-05 8.381e-06 0.1022 -1.407e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01962 0.01412 0.02229 0.02463 0.9885 0.992 0.01994 0.9747 0.9851 0.02863 ] Network output: [ 0.09524 -0.2153 0.8035 4.328e-05 -1.943e-05 1.221 3.262e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5945 0.5161 0.4258 0.4703 0.9809 0.9918 0.5962 0.927 0.9795 0.5062 ] Network output: [ -0.06267 0.1384 1.15 -6.326e-05 2.84e-05 0.8371 -4.767e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.274 0.2683 0.2876 0.287 0.9886 0.9928 0.2741 0.9755 0.9856 0.2961 ] Network output: [ -0.06297 0.1453 1.118 -4.467e-05 2.005e-05 0.862 -3.366e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.279 0.278 0.2881 0.2851 0.9843 0.9905 0.2791 0.9606 0.979 0.29 ] Network output: [ -0.007634 1.018 0.0294 3.077e-05 -1.381e-05 0.9681 2.319e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04688 Epoch 3612 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05844 0.9083 0.923 0.0001093 -4.908e-05 0.05221 8.239e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01286 -0.005665 0.002379 0.02421 0.9532 0.9603 0.02361 0.9059 0.9226 0.06535 ] Network output: [ 0.9616 0.08322 0.03672 -5.478e-05 2.459e-05 -0.04346 -4.128e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5432 0.08965 0.07575 0.3326 0.9787 0.9906 0.6022 0.9202 0.9765 0.5192 ] Network output: [ 0.01839 0.9222 0.9388 -1.885e-05 8.464e-06 0.1022 -1.421e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01961 0.01411 0.02227 0.02461 0.9885 0.992 0.01993 0.9747 0.9851 0.02861 ] Network output: [ 0.0952 -0.2152 0.8035 4.318e-05 -1.938e-05 1.222 3.254e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5945 0.5161 0.4258 0.4702 0.9809 0.9918 0.5962 0.927 0.9795 0.5062 ] Network output: [ -0.06262 0.1383 1.15 -6.304e-05 2.83e-05 0.8371 -4.751e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.274 0.2683 0.2876 0.2869 0.9886 0.9928 0.2741 0.9755 0.9856 0.2961 ] Network output: [ -0.06292 0.1452 1.118 -4.44e-05 1.993e-05 0.862 -3.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.279 0.278 0.288 0.2851 0.9843 0.9905 0.279 0.9606 0.979 0.29 ] Network output: [ -0.007666 1.018 0.02947 3.066e-05 -1.376e-05 0.968 2.31e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04687 Epoch 3613 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05841 0.9084 0.923 0.0001093 -4.907e-05 0.0522 8.237e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01285 -0.005668 0.002352 0.02419 0.9532 0.9604 0.02359 0.906 0.9227 0.06532 ] Network output: [ 0.9617 0.08321 0.03669 -5.484e-05 2.462e-05 -0.04349 -4.133e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5432 0.08968 0.07575 0.3325 0.9787 0.9906 0.6022 0.9203 0.9766 0.5192 ] Network output: [ 0.01835 0.9222 0.9388 -1.904e-05 8.547e-06 0.1022 -1.435e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0196 0.01411 0.02226 0.0246 0.9885 0.992 0.01993 0.9747 0.9851 0.0286 ] Network output: [ 0.09516 -0.2152 0.8034 4.307e-05 -1.934e-05 1.222 3.246e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5945 0.5162 0.4259 0.4702 0.9809 0.9918 0.5962 0.9271 0.9795 0.5062 ] Network output: [ -0.06257 0.1382 1.15 -6.283e-05 2.821e-05 0.8371 -4.735e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2739 0.2682 0.2876 0.2869 0.9886 0.9928 0.2741 0.9755 0.9856 0.2961 ] Network output: [ -0.06286 0.145 1.119 -4.413e-05 1.981e-05 0.862 -3.326e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2789 0.2779 0.288 0.2851 0.9843 0.9905 0.2789 0.9606 0.9791 0.2899 ] Network output: [ -0.007698 1.018 0.02954 3.054e-05 -1.371e-05 0.9679 2.302e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04686 Epoch 3614 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05838 0.9085 0.923 0.0001093 -4.906e-05 0.05218 8.236e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01284 -0.005671 0.002325 0.02417 0.9532 0.9604 0.02358 0.906 0.9227 0.06529 ] Network output: [ 0.9617 0.0832 0.03666 -5.491e-05 2.465e-05 -0.04352 -4.138e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5431 0.08971 0.07575 0.3324 0.9787 0.9906 0.6022 0.9203 0.9766 0.5192 ] Network output: [ 0.01831 0.9223 0.9388 -1.922e-05 8.63e-06 0.1022 -1.449e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0196 0.01411 0.02225 0.02458 0.9885 0.992 0.01992 0.9748 0.9851 0.02859 ] Network output: [ 0.09511 -0.2151 0.8034 4.297e-05 -1.929e-05 1.222 3.238e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5944 0.5162 0.4259 0.4701 0.9809 0.9918 0.5961 0.9271 0.9795 0.5062 ] Network output: [ -0.06252 0.1381 1.15 -6.262e-05 2.811e-05 0.8372 -4.719e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2739 0.2682 0.2876 0.2869 0.9886 0.9928 0.274 0.9755 0.9856 0.2961 ] Network output: [ -0.0628 0.1449 1.119 -4.387e-05 1.969e-05 0.862 -3.306e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2788 0.2778 0.288 0.285 0.9843 0.9905 0.2788 0.9606 0.9791 0.2899 ] Network output: [ -0.00773 1.018 0.02961 3.043e-05 -1.366e-05 0.9678 2.293e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04685 Epoch 3615 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05836 0.9085 0.923 0.0001093 -4.905e-05 0.05217 8.234e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01282 -0.005674 0.002298 0.02415 0.9532 0.9604 0.02357 0.9061 0.9227 0.06526 ] Network output: [ 0.9618 0.0832 0.03664 -5.497e-05 2.468e-05 -0.04356 -4.143e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.543 0.08975 0.07575 0.3322 0.9787 0.9906 0.6021 0.9204 0.9766 0.5191 ] Network output: [ 0.01827 0.9224 0.9388 -1.941e-05 8.712e-06 0.1022 -1.463e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01959 0.0141 0.02224 0.02456 0.9885 0.992 0.01991 0.9748 0.9851 0.02857 ] Network output: [ 0.09507 -0.2151 0.8033 4.286e-05 -1.924e-05 1.222 3.23e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5944 0.5162 0.426 0.47 0.9809 0.9918 0.5961 0.9271 0.9795 0.5062 ] Network output: [ -0.06246 0.138 1.149 -6.241e-05 2.802e-05 0.8372 -4.703e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2739 0.2682 0.2876 0.2869 0.9886 0.9928 0.274 0.9755 0.9856 0.2961 ] Network output: [ -0.06275 0.1448 1.119 -4.36e-05 1.957e-05 0.862 -3.286e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2787 0.2777 0.288 0.285 0.9843 0.9905 0.2788 0.9607 0.9791 0.2899 ] Network output: [ -0.007762 1.018 0.02968 3.032e-05 -1.361e-05 0.9677 2.285e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04684 Epoch 3616 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05833 0.9086 0.923 0.0001092 -4.904e-05 0.05216 8.233e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01281 -0.005677 0.002271 0.02413 0.9533 0.9604 0.02356 0.9061 0.9228 0.06523 ] Network output: [ 0.9618 0.08319 0.03661 -5.503e-05 2.47e-05 -0.04359 -4.147e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.543 0.08978 0.07576 0.3321 0.9787 0.9906 0.6021 0.9204 0.9766 0.5191 ] Network output: [ 0.01823 0.9224 0.9388 -1.959e-05 8.795e-06 0.1022 -1.476e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01959 0.0141 0.02223 0.02455 0.9885 0.992 0.01991 0.9748 0.9851 0.02856 ] Network output: [ 0.09502 -0.2151 0.8033 4.275e-05 -1.919e-05 1.222 3.222e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5944 0.5163 0.426 0.4699 0.9809 0.9918 0.5961 0.9272 0.9795 0.5062 ] Network output: [ -0.06241 0.1379 1.149 -6.22e-05 2.792e-05 0.8373 -4.687e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2738 0.2682 0.2876 0.2868 0.9887 0.9928 0.274 0.9755 0.9856 0.2961 ] Network output: [ -0.06269 0.1447 1.119 -4.334e-05 1.945e-05 0.8619 -3.266e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2787 0.2777 0.2879 0.285 0.9843 0.9905 0.2787 0.9607 0.9791 0.2898 ] Network output: [ -0.007793 1.018 0.02976 3.021e-05 -1.356e-05 0.9677 2.277e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04683 Epoch 3617 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0583 0.9087 0.923 0.0001092 -4.904e-05 0.05214 8.232e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0128 -0.00568 0.002244 0.02411 0.9533 0.9604 0.02355 0.9061 0.9228 0.0652 ] Network output: [ 0.9618 0.08319 0.03658 -5.508e-05 2.473e-05 -0.04363 -4.151e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5429 0.08982 0.07576 0.332 0.9787 0.9906 0.6021 0.9204 0.9766 0.5191 ] Network output: [ 0.01819 0.9225 0.9388 -1.977e-05 8.877e-06 0.1023 -1.49e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01958 0.0141 0.02222 0.02453 0.9885 0.992 0.0199 0.9748 0.9852 0.02854 ] Network output: [ 0.09498 -0.215 0.8032 4.264e-05 -1.914e-05 1.222 3.213e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5944 0.5163 0.4261 0.4699 0.9809 0.9918 0.596 0.9272 0.9795 0.5062 ] Network output: [ -0.06236 0.1378 1.149 -6.199e-05 2.783e-05 0.8373 -4.672e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2738 0.2681 0.2876 0.2868 0.9887 0.9928 0.2739 0.9756 0.9856 0.2961 ] Network output: [ -0.06264 0.1445 1.119 -4.307e-05 1.934e-05 0.8619 -3.246e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2786 0.2776 0.2879 0.2849 0.9843 0.9905 0.2786 0.9607 0.9791 0.2898 ] Network output: [ -0.007825 1.018 0.02983 3.01e-05 -1.351e-05 0.9676 2.268e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04683 Epoch 3618 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05827 0.9087 0.923 0.0001092 -4.903e-05 0.05213 8.23e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01279 -0.005684 0.002216 0.02409 0.9533 0.9604 0.02354 0.9062 0.9228 0.06517 ] Network output: [ 0.9619 0.08318 0.03655 -5.514e-05 2.475e-05 -0.04366 -4.156e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5429 0.08985 0.07576 0.3318 0.9788 0.9906 0.602 0.9205 0.9766 0.519 ] Network output: [ 0.01815 0.9225 0.9388 -1.996e-05 8.96e-06 0.1023 -1.504e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01958 0.01409 0.02221 0.02451 0.9885 0.9921 0.0199 0.9748 0.9852 0.02853 ] Network output: [ 0.09494 -0.215 0.8032 4.252e-05 -1.909e-05 1.222 3.205e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5943 0.5163 0.4261 0.4698 0.9809 0.9918 0.596 0.9273 0.9796 0.5061 ] Network output: [ -0.06231 0.1377 1.149 -6.178e-05 2.773e-05 0.8373 -4.656e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2738 0.2681 0.2876 0.2868 0.9887 0.9928 0.2739 0.9756 0.9856 0.2961 ] Network output: [ -0.06258 0.1444 1.119 -4.28e-05 1.922e-05 0.8619 -3.226e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2785 0.2775 0.2879 0.2849 0.9844 0.9905 0.2785 0.9607 0.9791 0.2898 ] Network output: [ -0.007856 1.018 0.0299 2.999e-05 -1.346e-05 0.9675 2.26e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04682 Epoch 3619 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05824 0.9088 0.923 0.0001092 -4.902e-05 0.05212 8.229e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01278 -0.005687 0.002189 0.02407 0.9533 0.9604 0.02353 0.9062 0.9229 0.06514 ] Network output: [ 0.9619 0.08318 0.03652 -5.519e-05 2.478e-05 -0.04369 -4.16e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5428 0.08988 0.07576 0.3317 0.9788 0.9906 0.602 0.9205 0.9767 0.519 ] Network output: [ 0.01812 0.9226 0.9388 -2.014e-05 9.043e-06 0.1023 -1.518e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01957 0.01409 0.0222 0.0245 0.9885 0.9921 0.01989 0.9748 0.9852 0.02851 ] Network output: [ 0.09489 -0.2149 0.8031 4.241e-05 -1.904e-05 1.222 3.196e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5943 0.5164 0.4262 0.4697 0.9809 0.9918 0.596 0.9273 0.9796 0.5061 ] Network output: [ -0.06226 0.1376 1.149 -6.157e-05 2.764e-05 0.8374 -4.64e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2737 0.2681 0.2876 0.2868 0.9887 0.9928 0.2739 0.9756 0.9856 0.296 ] Network output: [ -0.06253 0.1443 1.119 -4.254e-05 1.91e-05 0.8619 -3.206e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2784 0.2774 0.2879 0.2849 0.9844 0.9905 0.2785 0.9607 0.9791 0.2898 ] Network output: [ -0.007887 1.019 0.02997 2.988e-05 -1.341e-05 0.9674 2.252e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04681 Epoch 3620 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05822 0.9089 0.9231 0.0001092 -4.901e-05 0.05211 8.228e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01277 -0.00569 0.002162 0.02405 0.9533 0.9605 0.02352 0.9063 0.9229 0.06511 ] Network output: [ 0.9619 0.08317 0.03648 -5.525e-05 2.48e-05 -0.04373 -4.164e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5427 0.08992 0.07576 0.3315 0.9788 0.9906 0.602 0.9206 0.9767 0.519 ] Network output: [ 0.01808 0.9226 0.9388 -2.033e-05 9.125e-06 0.1023 -1.532e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01956 0.01409 0.02219 0.02448 0.9885 0.9921 0.01989 0.9749 0.9852 0.0285 ] Network output: [ 0.09485 -0.2149 0.8031 4.229e-05 -1.899e-05 1.222 3.187e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5943 0.5164 0.4262 0.4697 0.9809 0.9918 0.5959 0.9273 0.9796 0.5061 ] Network output: [ -0.06221 0.1374 1.149 -6.136e-05 2.755e-05 0.8374 -4.624e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2737 0.268 0.2876 0.2867 0.9887 0.9928 0.2738 0.9756 0.9857 0.296 ] Network output: [ -0.06247 0.1442 1.119 -4.227e-05 1.898e-05 0.8618 -3.186e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2784 0.2774 0.2878 0.2848 0.9844 0.9905 0.2784 0.9608 0.9792 0.2897 ] Network output: [ -0.007919 1.019 0.03004 2.977e-05 -1.337e-05 0.9673 2.244e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0468 Epoch 3621 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05819 0.9089 0.9231 0.0001092 -4.9e-05 0.05209 8.226e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01276 -0.005693 0.002135 0.02403 0.9533 0.9605 0.02351 0.9063 0.9229 0.06508 ] Network output: [ 0.962 0.08317 0.03645 -5.53e-05 2.482e-05 -0.04376 -4.167e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5427 0.08995 0.07576 0.3314 0.9788 0.9906 0.602 0.9206 0.9767 0.519 ] Network output: [ 0.01804 0.9227 0.9388 -2.051e-05 9.208e-06 0.1023 -1.546e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01956 0.01408 0.02218 0.02446 0.9885 0.9921 0.01988 0.9749 0.9852 0.02849 ] Network output: [ 0.09481 -0.2148 0.803 4.218e-05 -1.893e-05 1.222 3.179e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5942 0.5164 0.4263 0.4696 0.9809 0.9918 0.5959 0.9274 0.9796 0.5061 ] Network output: [ -0.06216 0.1373 1.149 -6.115e-05 2.745e-05 0.8374 -4.609e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2737 0.268 0.2876 0.2867 0.9887 0.9928 0.2738 0.9756 0.9857 0.296 ] Network output: [ -0.06242 0.144 1.119 -4.201e-05 1.886e-05 0.8618 -3.166e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2783 0.2773 0.2878 0.2848 0.9844 0.9905 0.2783 0.9608 0.9792 0.2897 ] Network output: [ -0.00795 1.019 0.03011 2.967e-05 -1.332e-05 0.9672 2.236e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04679 Epoch 3622 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05816 0.909 0.9231 0.0001091 -4.9e-05 0.05208 8.225e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01275 -0.005696 0.002108 0.02401 0.9534 0.9605 0.0235 0.9063 0.923 0.06505 ] Network output: [ 0.962 0.08317 0.03642 -5.535e-05 2.485e-05 -0.0438 -4.171e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5426 0.08998 0.07576 0.3313 0.9788 0.9906 0.6019 0.9206 0.9767 0.5189 ] Network output: [ 0.018 0.9227 0.9388 -2.069e-05 9.29e-06 0.1023 -1.56e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01955 0.01408 0.02216 0.02445 0.9885 0.9921 0.01987 0.9749 0.9852 0.02847 ] Network output: [ 0.09476 -0.2148 0.803 4.206e-05 -1.888e-05 1.223 3.17e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5942 0.5164 0.4263 0.4695 0.981 0.9918 0.5959 0.9274 0.9796 0.5061 ] Network output: [ -0.06211 0.1372 1.149 -6.095e-05 2.736e-05 0.8375 -4.593e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2736 0.268 0.2876 0.2867 0.9887 0.9928 0.2738 0.9756 0.9857 0.296 ] Network output: [ -0.06236 0.1439 1.119 -4.175e-05 1.874e-05 0.8618 -3.146e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2782 0.2772 0.2878 0.2848 0.9844 0.9905 0.2782 0.9608 0.9792 0.2897 ] Network output: [ -0.00798 1.019 0.03017 2.956e-05 -1.327e-05 0.9672 2.228e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04678 Epoch 3623 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05813 0.909 0.9231 0.0001091 -4.899e-05 0.05207 8.224e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01274 -0.005699 0.002081 0.02399 0.9534 0.9605 0.02349 0.9064 0.923 0.06502 ] Network output: [ 0.962 0.08317 0.03639 -5.539e-05 2.487e-05 -0.04383 -4.175e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5425 0.09001 0.07576 0.3311 0.9788 0.9906 0.6019 0.9207 0.9767 0.5189 ] Network output: [ 0.01796 0.9228 0.9389 -2.088e-05 9.373e-06 0.1024 -1.573e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01955 0.01408 0.02215 0.02443 0.9885 0.9921 0.01987 0.9749 0.9852 0.02846 ] Network output: [ 0.09472 -0.2148 0.8029 4.194e-05 -1.883e-05 1.223 3.16e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5942 0.5165 0.4264 0.4695 0.981 0.9918 0.5958 0.9274 0.9796 0.5061 ] Network output: [ -0.06206 0.1371 1.149 -6.074e-05 2.727e-05 0.8375 -4.578e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2736 0.268 0.2876 0.2867 0.9887 0.9928 0.2737 0.9757 0.9857 0.296 ] Network output: [ -0.06231 0.1438 1.119 -4.148e-05 1.862e-05 0.8618 -3.126e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2781 0.2771 0.2878 0.2847 0.9844 0.9905 0.2781 0.9608 0.9792 0.2896 ] Network output: [ -0.008011 1.019 0.03024 2.946e-05 -1.322e-05 0.9671 2.22e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04678 Epoch 3624 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05811 0.9091 0.9231 0.0001091 -4.898e-05 0.05206 8.223e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01273 -0.005702 0.002054 0.02397 0.9534 0.9605 0.02347 0.9064 0.923 0.06499 ] Network output: [ 0.9621 0.08317 0.03636 -5.544e-05 2.489e-05 -0.04387 -4.178e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5425 0.09004 0.07576 0.331 0.9788 0.9906 0.6019 0.9207 0.9767 0.5189 ] Network output: [ 0.01793 0.9228 0.9389 -2.106e-05 9.455e-06 0.1024 -1.587e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01954 0.01407 0.02214 0.02441 0.9885 0.9921 0.01986 0.9749 0.9852 0.02844 ] Network output: [ 0.09467 -0.2147 0.8029 4.181e-05 -1.877e-05 1.223 3.151e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5941 0.5165 0.4264 0.4694 0.981 0.9918 0.5958 0.9275 0.9796 0.5061 ] Network output: [ -0.06202 0.137 1.149 -6.054e-05 2.718e-05 0.8375 -4.562e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2736 0.2679 0.2876 0.2866 0.9887 0.9928 0.2737 0.9757 0.9857 0.296 ] Network output: [ -0.06225 0.1437 1.119 -4.122e-05 1.85e-05 0.8617 -3.106e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.278 0.2771 0.2877 0.2847 0.9844 0.9905 0.2781 0.9608 0.9792 0.2896 ] Network output: [ -0.008042 1.019 0.03031 2.935e-05 -1.318e-05 0.967 2.212e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04677 Epoch 3625 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05808 0.9092 0.9231 0.0001091 -4.897e-05 0.05205 8.221e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01272 -0.005705 0.002027 0.02395 0.9534 0.9605 0.02346 0.9065 0.9231 0.06496 ] Network output: [ 0.9621 0.08317 0.03632 -5.548e-05 2.491e-05 -0.04391 -4.181e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5424 0.09007 0.07576 0.3309 0.9788 0.9906 0.6018 0.9208 0.9768 0.5189 ] Network output: [ 0.01789 0.9229 0.9389 -2.124e-05 9.538e-06 0.1024 -1.601e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01953 0.01407 0.02213 0.02439 0.9885 0.9921 0.01986 0.9749 0.9852 0.02843 ] Network output: [ 0.09463 -0.2147 0.8028 4.169e-05 -1.872e-05 1.223 3.142e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5941 0.5165 0.4265 0.4693 0.981 0.9918 0.5958 0.9275 0.9797 0.5061 ] Network output: [ -0.06197 0.1369 1.149 -6.033e-05 2.709e-05 0.8376 -4.547e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2735 0.2679 0.2876 0.2866 0.9887 0.9929 0.2737 0.9757 0.9857 0.296 ] Network output: [ -0.0622 0.1436 1.119 -4.095e-05 1.839e-05 0.8617 -3.086e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.278 0.277 0.2877 0.2847 0.9844 0.9905 0.278 0.9609 0.9792 0.2896 ] Network output: [ -0.008072 1.019 0.03038 2.925e-05 -1.313e-05 0.9669 2.204e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04676 Epoch 3626 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05805 0.9092 0.9231 0.0001091 -4.897e-05 0.05204 8.22e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01271 -0.005708 0.002 0.02393 0.9534 0.9605 0.02345 0.9065 0.9231 0.06493 ] Network output: [ 0.9621 0.08317 0.03629 -5.553e-05 2.493e-05 -0.04394 -4.185e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5423 0.09011 0.07576 0.3307 0.9788 0.9907 0.6018 0.9208 0.9768 0.5189 ] Network output: [ 0.01785 0.9229 0.9389 -2.143e-05 9.62e-06 0.1024 -1.615e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01953 0.01407 0.02212 0.02438 0.9885 0.9921 0.01985 0.975 0.9852 0.02842 ] Network output: [ 0.09459 -0.2147 0.8028 4.157e-05 -1.866e-05 1.223 3.132e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5941 0.5166 0.4266 0.4693 0.981 0.9918 0.5957 0.9276 0.9797 0.5061 ] Network output: [ -0.06192 0.1368 1.149 -6.013e-05 2.699e-05 0.8376 -4.531e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2735 0.2679 0.2876 0.2866 0.9887 0.9929 0.2736 0.9757 0.9857 0.296 ] Network output: [ -0.06214 0.1434 1.119 -4.069e-05 1.827e-05 0.8617 -3.067e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2779 0.2769 0.2877 0.2846 0.9844 0.9905 0.2779 0.9609 0.9792 0.2896 ] Network output: [ -0.008103 1.019 0.03045 2.915e-05 -1.309e-05 0.9668 2.197e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04675 Epoch 3627 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05802 0.9093 0.9231 0.0001091 -4.896e-05 0.05203 8.219e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0127 -0.005712 0.001973 0.02391 0.9534 0.9606 0.02344 0.9065 0.9231 0.0649 ] Network output: [ 0.9622 0.08317 0.03626 -5.557e-05 2.495e-05 -0.04398 -4.188e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5423 0.09014 0.07576 0.3306 0.9788 0.9907 0.6018 0.9208 0.9768 0.5188 ] Network output: [ 0.01781 0.923 0.9389 -2.161e-05 9.702e-06 0.1024 -1.629e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01952 0.01406 0.02211 0.02436 0.9886 0.9921 0.01985 0.975 0.9853 0.0284 ] Network output: [ 0.09454 -0.2146 0.8027 4.144e-05 -1.86e-05 1.223 3.123e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.594 0.5166 0.4266 0.4692 0.981 0.9918 0.5957 0.9276 0.9797 0.5061 ] Network output: [ -0.06187 0.1367 1.149 -5.993e-05 2.69e-05 0.8376 -4.516e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2735 0.2678 0.2876 0.2866 0.9887 0.9929 0.2736 0.9757 0.9857 0.2959 ] Network output: [ -0.06209 0.1433 1.119 -4.043e-05 1.815e-05 0.8617 -3.047e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2778 0.2768 0.2877 0.2846 0.9844 0.9905 0.2778 0.9609 0.9793 0.2895 ] Network output: [ -0.008133 1.019 0.03052 2.905e-05 -1.304e-05 0.9668 2.189e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04675 Epoch 3628 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.058 0.9094 0.9231 0.000109 -4.895e-05 0.05202 8.217e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01269 -0.005715 0.001945 0.02389 0.9534 0.9606 0.02343 0.9066 0.9232 0.06487 ] Network output: [ 0.9622 0.08317 0.03622 -5.561e-05 2.496e-05 -0.04401 -4.191e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5422 0.09017 0.07576 0.3305 0.9788 0.9907 0.6018 0.9209 0.9768 0.5188 ] Network output: [ 0.01778 0.923 0.9389 -2.18e-05 9.785e-06 0.1024 -1.643e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01952 0.01406 0.0221 0.02434 0.9886 0.9921 0.01984 0.975 0.9853 0.02839 ] Network output: [ 0.0945 -0.2146 0.8027 4.131e-05 -1.855e-05 1.223 3.113e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.594 0.5166 0.4267 0.4691 0.981 0.9918 0.5957 0.9276 0.9797 0.506 ] Network output: [ -0.06182 0.1366 1.149 -5.972e-05 2.681e-05 0.8377 -4.501e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2734 0.2678 0.2875 0.2865 0.9887 0.9929 0.2736 0.9757 0.9857 0.2959 ] Network output: [ -0.06203 0.1432 1.119 -4.017e-05 1.803e-05 0.8616 -3.027e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2777 0.2768 0.2876 0.2846 0.9844 0.9905 0.2778 0.9609 0.9793 0.2895 ] Network output: [ -0.008163 1.019 0.03058 2.895e-05 -1.3e-05 0.9667 2.182e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04674 Epoch 3629 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05797 0.9094 0.9231 0.000109 -4.894e-05 0.05201 8.216e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01268 -0.005718 0.001918 0.02387 0.9535 0.9606 0.02342 0.9066 0.9232 0.06484 ] Network output: [ 0.9622 0.08317 0.03619 -5.565e-05 2.498e-05 -0.04405 -4.194e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5422 0.0902 0.07576 0.3303 0.9788 0.9907 0.6017 0.9209 0.9768 0.5188 ] Network output: [ 0.01774 0.9231 0.9389 -2.198e-05 9.867e-06 0.1025 -1.656e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01951 0.01405 0.02209 0.02433 0.9886 0.9921 0.01983 0.975 0.9853 0.02837 ] Network output: [ 0.09445 -0.2145 0.8026 4.118e-05 -1.849e-05 1.223 3.103e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5939 0.5167 0.4267 0.469 0.981 0.9918 0.5956 0.9277 0.9797 0.506 ] Network output: [ -0.06177 0.1366 1.149 -5.952e-05 2.672e-05 0.8377 -4.486e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2734 0.2678 0.2875 0.2865 0.9887 0.9929 0.2735 0.9757 0.9858 0.2959 ] Network output: [ -0.06198 0.1431 1.119 -3.99e-05 1.791e-05 0.8616 -3.007e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2777 0.2767 0.2876 0.2845 0.9844 0.9905 0.2777 0.9609 0.9793 0.2895 ] Network output: [ -0.008194 1.019 0.03065 2.885e-05 -1.295e-05 0.9666 2.174e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04673 Epoch 3630 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05794 0.9095 0.9231 0.000109 -4.894e-05 0.052 8.215e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01267 -0.005721 0.001891 0.02385 0.9535 0.9606 0.02341 0.9066 0.9232 0.06481 ] Network output: [ 0.9623 0.08318 0.03615 -5.568e-05 2.5e-05 -0.04409 -4.196e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5421 0.09023 0.07575 0.3302 0.9789 0.9907 0.6017 0.921 0.9768 0.5188 ] Network output: [ 0.0177 0.9231 0.9389 -2.216e-05 9.95e-06 0.1025 -1.67e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0195 0.01405 0.02207 0.02431 0.9886 0.9921 0.01983 0.975 0.9853 0.02836 ] Network output: [ 0.09441 -0.2145 0.8026 4.105e-05 -1.843e-05 1.223 3.094e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5939 0.5167 0.4268 0.469 0.981 0.9918 0.5956 0.9277 0.9797 0.506 ] Network output: [ -0.06172 0.1365 1.149 -5.932e-05 2.663e-05 0.8377 -4.47e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2734 0.2677 0.2875 0.2865 0.9887 0.9929 0.2735 0.9758 0.9858 0.2959 ] Network output: [ -0.06193 0.143 1.119 -3.964e-05 1.78e-05 0.8616 -2.987e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2776 0.2766 0.2876 0.2845 0.9844 0.9905 0.2776 0.961 0.9793 0.2895 ] Network output: [ -0.008224 1.019 0.03072 2.875e-05 -1.291e-05 0.9665 2.167e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04673 Epoch 3631 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05791 0.9095 0.9231 0.000109 -4.893e-05 0.05199 8.213e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01266 -0.005724 0.001864 0.02382 0.9535 0.9606 0.0234 0.9067 0.9233 0.06478 ] Network output: [ 0.9623 0.08318 0.03612 -5.572e-05 2.501e-05 -0.04413 -4.199e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.542 0.09026 0.07575 0.33 0.9789 0.9907 0.6017 0.921 0.9769 0.5188 ] Network output: [ 0.01766 0.9232 0.9389 -2.235e-05 1.003e-05 0.1025 -1.684e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0195 0.01405 0.02206 0.02429 0.9886 0.9921 0.01982 0.975 0.9853 0.02834 ] Network output: [ 0.09436 -0.2145 0.8025 4.092e-05 -1.837e-05 1.223 3.083e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5939 0.5167 0.4268 0.4689 0.981 0.9918 0.5956 0.9277 0.9797 0.506 ] Network output: [ -0.06167 0.1364 1.149 -5.912e-05 2.654e-05 0.8378 -4.455e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2733 0.2677 0.2875 0.2864 0.9887 0.9929 0.2735 0.9758 0.9858 0.2959 ] Network output: [ -0.06187 0.1428 1.119 -3.938e-05 1.768e-05 0.8616 -2.968e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2775 0.2765 0.2876 0.2845 0.9844 0.9905 0.2775 0.961 0.9793 0.2894 ] Network output: [ -0.008253 1.019 0.03078 2.865e-05 -1.286e-05 0.9664 2.159e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04672 Epoch 3632 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05789 0.9096 0.9231 0.000109 -4.892e-05 0.05198 8.212e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01265 -0.005727 0.001837 0.0238 0.9535 0.9606 0.02339 0.9067 0.9233 0.06475 ] Network output: [ 0.9623 0.08318 0.03608 -5.575e-05 2.503e-05 -0.04416 -4.202e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.542 0.09029 0.07575 0.3299 0.9789 0.9907 0.6016 0.921 0.9769 0.5187 ] Network output: [ 0.01763 0.9232 0.9389 -2.253e-05 1.011e-05 0.1025 -1.698e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01949 0.01404 0.02205 0.02427 0.9886 0.9921 0.01981 0.9751 0.9853 0.02833 ] Network output: [ 0.09432 -0.2144 0.8025 4.078e-05 -1.831e-05 1.223 3.073e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5938 0.5167 0.4269 0.4688 0.981 0.9919 0.5956 0.9278 0.9798 0.506 ] Network output: [ -0.06162 0.1363 1.149 -5.892e-05 2.645e-05 0.8378 -4.44e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2733 0.2677 0.2875 0.2864 0.9887 0.9929 0.2734 0.9758 0.9858 0.2959 ] Network output: [ -0.06182 0.1427 1.119 -3.912e-05 1.756e-05 0.8615 -2.948e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2774 0.2765 0.2875 0.2844 0.9844 0.9905 0.2774 0.961 0.9793 0.2894 ] Network output: [ -0.008283 1.019 0.03085 2.855e-05 -1.282e-05 0.9663 2.152e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04671 Epoch 3633 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05786 0.9097 0.9231 0.000109 -4.891e-05 0.05198 8.211e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01264 -0.00573 0.00181 0.02378 0.9535 0.9606 0.02337 0.9068 0.9233 0.06472 ] Network output: [ 0.9624 0.08319 0.03605 -5.579e-05 2.504e-05 -0.0442 -4.204e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5419 0.09032 0.07575 0.3298 0.9789 0.9907 0.6016 0.9211 0.9769 0.5187 ] Network output: [ 0.01759 0.9233 0.9389 -2.271e-05 1.02e-05 0.1025 -1.712e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01948 0.01404 0.02204 0.02426 0.9886 0.9921 0.01981 0.9751 0.9853 0.02831 ] Network output: [ 0.09428 -0.2144 0.8024 4.064e-05 -1.825e-05 1.224 3.063e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5938 0.5168 0.4269 0.4688 0.981 0.9919 0.5955 0.9278 0.9798 0.506 ] Network output: [ -0.06158 0.1362 1.149 -5.872e-05 2.636e-05 0.8378 -4.425e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2732 0.2677 0.2875 0.2864 0.9887 0.9929 0.2734 0.9758 0.9858 0.2959 ] Network output: [ -0.06177 0.1426 1.119 -3.885e-05 1.744e-05 0.8615 -2.928e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2773 0.2764 0.2875 0.2844 0.9844 0.9905 0.2774 0.961 0.9793 0.2894 ] Network output: [ -0.008313 1.02 0.03092 2.846e-05 -1.277e-05 0.9663 2.145e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04671 Epoch 3634 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05783 0.9097 0.9231 0.0001089 -4.89e-05 0.05197 8.21e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01263 -0.005733 0.001783 0.02376 0.9535 0.9607 0.02336 0.9068 0.9234 0.06469 ] Network output: [ 0.9624 0.08319 0.03601 -5.582e-05 2.506e-05 -0.04424 -4.206e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5418 0.09034 0.07574 0.3296 0.9789 0.9907 0.6016 0.9211 0.9769 0.5187 ] Network output: [ 0.01755 0.9233 0.9389 -2.29e-05 1.028e-05 0.1026 -1.726e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01948 0.01404 0.02203 0.02424 0.9886 0.9921 0.0198 0.9751 0.9853 0.0283 ] Network output: [ 0.09423 -0.2144 0.8024 4.051e-05 -1.818e-05 1.224 3.053e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5938 0.5168 0.427 0.4687 0.981 0.9919 0.5955 0.9278 0.9798 0.506 ] Network output: [ -0.06153 0.1361 1.149 -5.852e-05 2.627e-05 0.8379 -4.41e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2732 0.2676 0.2875 0.2864 0.9887 0.9929 0.2733 0.9758 0.9858 0.2959 ] Network output: [ -0.06171 0.1425 1.119 -3.859e-05 1.733e-05 0.8615 -2.908e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2773 0.2763 0.2875 0.2844 0.9844 0.9906 0.2773 0.961 0.9794 0.2893 ] Network output: [ -0.008343 1.02 0.03098 2.836e-05 -1.273e-05 0.9662 2.137e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0467 Epoch 3635 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05781 0.9098 0.9231 0.0001089 -4.89e-05 0.05196 8.208e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01262 -0.005736 0.001756 0.02374 0.9536 0.9607 0.02335 0.9068 0.9234 0.06466 ] Network output: [ 0.9624 0.0832 0.03598 -5.585e-05 2.507e-05 -0.04428 -4.209e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5418 0.09037 0.07574 0.3295 0.9789 0.9907 0.6016 0.9212 0.9769 0.5187 ] Network output: [ 0.01752 0.9234 0.9389 -2.308e-05 1.036e-05 0.1026 -1.739e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01947 0.01403 0.02202 0.02422 0.9886 0.9921 0.0198 0.9751 0.9853 0.02829 ] Network output: [ 0.09419 -0.2143 0.8023 4.037e-05 -1.812e-05 1.224 3.042e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5937 0.5168 0.4271 0.4686 0.981 0.9919 0.5955 0.9279 0.9798 0.506 ] Network output: [ -0.06148 0.136 1.149 -5.832e-05 2.618e-05 0.8379 -4.395e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2732 0.2676 0.2875 0.2863 0.9887 0.9929 0.2733 0.9758 0.9858 0.2959 ] Network output: [ -0.06166 0.1424 1.119 -3.833e-05 1.721e-05 0.8615 -2.889e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2772 0.2762 0.2875 0.2843 0.9845 0.9906 0.2772 0.9611 0.9794 0.2893 ] Network output: [ -0.008372 1.02 0.03105 2.826e-05 -1.269e-05 0.9661 2.13e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0467 Epoch 3636 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05778 0.9098 0.9231 0.0001089 -4.889e-05 0.05195 8.207e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01261 -0.005739 0.001729 0.02372 0.9536 0.9607 0.02334 0.9069 0.9234 0.06463 ] Network output: [ 0.9625 0.08321 0.03594 -5.587e-05 2.508e-05 -0.04432 -4.211e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5417 0.0904 0.07574 0.3294 0.9789 0.9907 0.6015 0.9212 0.9769 0.5187 ] Network output: [ 0.01748 0.9234 0.9389 -2.326e-05 1.044e-05 0.1026 -1.753e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01946 0.01403 0.02201 0.02421 0.9886 0.9921 0.01979 0.9751 0.9854 0.02827 ] Network output: [ 0.09414 -0.2143 0.8023 4.023e-05 -1.806e-05 1.224 3.032e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5937 0.5168 0.4271 0.4686 0.9811 0.9919 0.5954 0.9279 0.9798 0.506 ] Network output: [ -0.06143 0.1359 1.149 -5.812e-05 2.609e-05 0.8379 -4.38e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2731 0.2676 0.2875 0.2863 0.9887 0.9929 0.2733 0.9758 0.9858 0.2958 ] Network output: [ -0.0616 0.1423 1.119 -3.807e-05 1.709e-05 0.8614 -2.869e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2771 0.2761 0.2874 0.2843 0.9845 0.9906 0.2771 0.9611 0.9794 0.2893 ] Network output: [ -0.008401 1.02 0.03111 2.817e-05 -1.265e-05 0.966 2.123e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04669 Epoch 3637 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05775 0.9099 0.9231 0.0001089 -4.888e-05 0.05195 8.206e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0126 -0.005742 0.001702 0.0237 0.9536 0.9607 0.02333 0.9069 0.9235 0.0646 ] Network output: [ 0.9625 0.08321 0.0359 -5.59e-05 2.51e-05 -0.04436 -4.213e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5416 0.09043 0.07573 0.3292 0.9789 0.9907 0.6015 0.9212 0.977 0.5187 ] Network output: [ 0.01744 0.9235 0.939 -2.345e-05 1.053e-05 0.1026 -1.767e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01946 0.01402 0.022 0.02419 0.9886 0.9921 0.01978 0.9751 0.9854 0.02826 ] Network output: [ 0.0941 -0.2143 0.8023 4.008e-05 -1.799e-05 1.224 3.021e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5937 0.5169 0.4272 0.4685 0.9811 0.9919 0.5954 0.928 0.9798 0.506 ] Network output: [ -0.06138 0.1358 1.149 -5.793e-05 2.6e-05 0.8379 -4.365e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2731 0.2675 0.2875 0.2863 0.9888 0.9929 0.2732 0.9759 0.9858 0.2958 ] Network output: [ -0.06155 0.1421 1.119 -3.781e-05 1.697e-05 0.8614 -2.849e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.277 0.2761 0.2874 0.2842 0.9845 0.9906 0.2771 0.9611 0.9794 0.2893 ] Network output: [ -0.008431 1.02 0.03118 2.808e-05 -1.26e-05 0.9659 2.116e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04668 Epoch 3638 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05773 0.91 0.9231 0.0001089 -4.887e-05 0.05194 8.205e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01259 -0.005745 0.001675 0.02368 0.9536 0.9607 0.02332 0.907 0.9235 0.06457 ] Network output: [ 0.9625 0.08322 0.03586 -5.593e-05 2.511e-05 -0.04439 -4.215e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5416 0.09046 0.07573 0.3291 0.9789 0.9907 0.6015 0.9213 0.977 0.5186 ] Network output: [ 0.01741 0.9235 0.939 -2.363e-05 1.061e-05 0.1026 -1.781e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01945 0.01402 0.02198 0.02417 0.9886 0.9921 0.01978 0.9751 0.9854 0.02824 ] Network output: [ 0.09405 -0.2143 0.8022 3.994e-05 -1.793e-05 1.224 3.01e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5936 0.5169 0.4272 0.4684 0.9811 0.9919 0.5954 0.928 0.9798 0.506 ] Network output: [ -0.06134 0.1357 1.149 -5.773e-05 2.592e-05 0.838 -4.351e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.273 0.2675 0.2875 0.2863 0.9888 0.9929 0.2732 0.9759 0.9859 0.2958 ] Network output: [ -0.0615 0.142 1.119 -3.755e-05 1.686e-05 0.8614 -2.83e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.277 0.276 0.2874 0.2842 0.9845 0.9906 0.277 0.9611 0.9794 0.2892 ] Network output: [ -0.00846 1.02 0.03124 2.798e-05 -1.256e-05 0.9659 2.109e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04668 Epoch 3639 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0577 0.91 0.9231 0.0001089 -4.887e-05 0.05193 8.203e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01257 -0.005748 0.001648 0.02366 0.9536 0.9607 0.02331 0.907 0.9235 0.06454 ] Network output: [ 0.9626 0.08323 0.03583 -5.595e-05 2.512e-05 -0.04443 -4.217e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5415 0.09048 0.07572 0.3289 0.9789 0.9907 0.6014 0.9213 0.977 0.5186 ] Network output: [ 0.01737 0.9235 0.939 -2.381e-05 1.069e-05 0.1027 -1.795e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01945 0.01402 0.02197 0.02415 0.9886 0.9921 0.01977 0.9752 0.9854 0.02823 ] Network output: [ 0.09401 -0.2142 0.8022 3.979e-05 -1.786e-05 1.224 2.999e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5936 0.5169 0.4273 0.4684 0.9811 0.9919 0.5953 0.928 0.9799 0.506 ] Network output: [ -0.06129 0.1356 1.149 -5.753e-05 2.583e-05 0.838 -4.336e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.273 0.2675 0.2875 0.2862 0.9888 0.9929 0.2731 0.9759 0.9859 0.2958 ] Network output: [ -0.06145 0.1419 1.119 -3.729e-05 1.674e-05 0.8614 -2.81e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2769 0.2759 0.2874 0.2842 0.9845 0.9906 0.2769 0.9611 0.9794 0.2892 ] Network output: [ -0.008489 1.02 0.03131 2.789e-05 -1.252e-05 0.9658 2.102e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04667 Epoch 3640 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05767 0.9101 0.9231 0.0001088 -4.886e-05 0.05193 8.202e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01256 -0.005751 0.001621 0.02364 0.9536 0.9607 0.0233 0.907 0.9236 0.06451 ] Network output: [ 0.9626 0.08324 0.03579 -5.597e-05 2.513e-05 -0.04447 -4.218e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5414 0.09051 0.07572 0.3288 0.9789 0.9907 0.6014 0.9214 0.977 0.5186 ] Network output: [ 0.01734 0.9236 0.939 -2.4e-05 1.077e-05 0.1027 -1.808e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01944 0.01401 0.02196 0.02414 0.9886 0.9922 0.01976 0.9752 0.9854 0.02821 ] Network output: [ 0.09396 -0.2142 0.8021 3.965e-05 -1.78e-05 1.224 2.988e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5936 0.517 0.4273 0.4683 0.9811 0.9919 0.5953 0.9281 0.9799 0.506 ] Network output: [ -0.06124 0.1355 1.149 -5.734e-05 2.574e-05 0.838 -4.321e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.273 0.2674 0.2875 0.2862 0.9888 0.9929 0.2731 0.9759 0.9859 0.2958 ] Network output: [ -0.06139 0.1418 1.12 -3.702e-05 1.662e-05 0.8613 -2.79e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2768 0.2758 0.2873 0.2841 0.9845 0.9906 0.2768 0.9612 0.9794 0.2892 ] Network output: [ -0.008518 1.02 0.03137 2.78e-05 -1.248e-05 0.9657 2.095e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04667 Epoch 3641 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05765 0.9101 0.9231 0.0001088 -4.885e-05 0.05192 8.201e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01255 -0.005754 0.001594 0.02362 0.9537 0.9607 0.02329 0.9071 0.9236 0.06448 ] Network output: [ 0.9626 0.08325 0.03575 -5.599e-05 2.514e-05 -0.04451 -4.22e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5414 0.09054 0.07571 0.3287 0.9789 0.9907 0.6014 0.9214 0.977 0.5186 ] Network output: [ 0.0173 0.9236 0.939 -2.418e-05 1.086e-05 0.1027 -1.822e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01943 0.01401 0.02195 0.02412 0.9886 0.9922 0.01976 0.9752 0.9854 0.0282 ] Network output: [ 0.09392 -0.2142 0.8021 3.95e-05 -1.773e-05 1.224 2.977e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5935 0.517 0.4274 0.4683 0.9811 0.9919 0.5953 0.9281 0.9799 0.506 ] Network output: [ -0.06119 0.1354 1.149 -5.714e-05 2.565e-05 0.838 -4.306e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2729 0.2674 0.2875 0.2862 0.9888 0.9929 0.2731 0.9759 0.9859 0.2958 ] Network output: [ -0.06134 0.1417 1.12 -3.676e-05 1.65e-05 0.8613 -2.771e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2767 0.2758 0.2873 0.2841 0.9845 0.9906 0.2767 0.9612 0.9795 0.2892 ] Network output: [ -0.008547 1.02 0.03143 2.771e-05 -1.244e-05 0.9656 2.088e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04666 Epoch 3642 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05762 0.9102 0.9231 0.0001088 -4.884e-05 0.05192 8.2e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01254 -0.005756 0.001567 0.0236 0.9537 0.9608 0.02327 0.9071 0.9236 0.06445 ] Network output: [ 0.9627 0.08326 0.03571 -5.601e-05 2.515e-05 -0.04455 -4.221e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5413 0.09057 0.07571 0.3285 0.979 0.9907 0.6013 0.9214 0.977 0.5186 ] Network output: [ 0.01726 0.9237 0.939 -2.436e-05 1.094e-05 0.1027 -1.836e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01943 0.014 0.02194 0.0241 0.9886 0.9922 0.01975 0.9752 0.9854 0.02818 ] Network output: [ 0.09387 -0.2141 0.802 3.935e-05 -1.766e-05 1.225 2.965e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5935 0.517 0.4275 0.4682 0.9811 0.9919 0.5952 0.9281 0.9799 0.506 ] Network output: [ -0.06115 0.1353 1.149 -5.694e-05 2.556e-05 0.8381 -4.292e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2729 0.2673 0.2875 0.2861 0.9888 0.9929 0.273 0.9759 0.9859 0.2958 ] Network output: [ -0.06129 0.1416 1.12 -3.65e-05 1.639e-05 0.8613 -2.751e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2766 0.2757 0.2873 0.2841 0.9845 0.9906 0.2767 0.9612 0.9795 0.2891 ] Network output: [ -0.008576 1.02 0.0315 2.762e-05 -1.24e-05 0.9655 2.081e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04666 Epoch 3643 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05759 0.9102 0.9231 0.0001088 -4.884e-05 0.05191 8.198e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01253 -0.005759 0.00154 0.02358 0.9537 0.9608 0.02326 0.9072 0.9237 0.06442 ] Network output: [ 0.9627 0.08327 0.03567 -5.603e-05 2.515e-05 -0.04459 -4.223e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5412 0.09059 0.0757 0.3284 0.979 0.9907 0.6013 0.9215 0.9771 0.5186 ] Network output: [ 0.01723 0.9237 0.939 -2.455e-05 1.102e-05 0.1027 -1.85e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01942 0.014 0.02193 0.02408 0.9886 0.9922 0.01974 0.9752 0.9854 0.02817 ] Network output: [ 0.09383 -0.2141 0.802 3.919e-05 -1.76e-05 1.225 2.954e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5935 0.517 0.4275 0.4681 0.9811 0.9919 0.5952 0.9282 0.9799 0.506 ] Network output: [ -0.0611 0.1352 1.149 -5.675e-05 2.548e-05 0.8381 -4.277e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2728 0.2673 0.2875 0.2861 0.9888 0.9929 0.273 0.9759 0.9859 0.2958 ] Network output: [ -0.06123 0.1414 1.12 -3.624e-05 1.627e-05 0.8613 -2.731e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2766 0.2756 0.2873 0.284 0.9845 0.9906 0.2766 0.9612 0.9795 0.2891 ] Network output: [ -0.008605 1.02 0.03156 2.753e-05 -1.236e-05 0.9655 2.075e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04666 Epoch 3644 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05757 0.9103 0.9231 0.0001088 -4.883e-05 0.05191 8.197e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01252 -0.005762 0.001513 0.02355 0.9537 0.9608 0.02325 0.9072 0.9237 0.06439 ] Network output: [ 0.9627 0.08329 0.03563 -5.605e-05 2.516e-05 -0.04463 -4.224e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5412 0.09062 0.07569 0.3282 0.979 0.9907 0.6013 0.9215 0.9771 0.5186 ] Network output: [ 0.01719 0.9238 0.939 -2.473e-05 1.11e-05 0.1028 -1.864e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01941 0.01399 0.02192 0.02406 0.9886 0.9922 0.01974 0.9752 0.9854 0.02816 ] Network output: [ 0.09378 -0.2141 0.802 3.904e-05 -1.753e-05 1.225 2.942e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5934 0.5171 0.4276 0.4681 0.9811 0.9919 0.5951 0.9282 0.9799 0.506 ] Network output: [ -0.06105 0.1352 1.149 -5.656e-05 2.539e-05 0.8381 -4.262e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2728 0.2673 0.2875 0.2861 0.9888 0.9929 0.2729 0.976 0.9859 0.2957 ] Network output: [ -0.06118 0.1413 1.12 -3.598e-05 1.615e-05 0.8612 -2.711e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2765 0.2755 0.2872 0.284 0.9845 0.9906 0.2765 0.9612 0.9795 0.2891 ] Network output: [ -0.008633 1.02 0.03162 2.744e-05 -1.232e-05 0.9654 2.068e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04665 Epoch 3645 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05754 0.9104 0.9231 0.0001088 -4.882e-05 0.0519 8.196e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01251 -0.005765 0.001486 0.02353 0.9537 0.9608 0.02324 0.9072 0.9237 0.06436 ] Network output: [ 0.9628 0.0833 0.03559 -5.606e-05 2.517e-05 -0.04468 -4.225e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5411 0.09064 0.07569 0.3281 0.979 0.9907 0.6013 0.9216 0.9771 0.5186 ] Network output: [ 0.01716 0.9238 0.939 -2.491e-05 1.118e-05 0.1028 -1.877e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01941 0.01399 0.0219 0.02405 0.9887 0.9922 0.01973 0.9753 0.9854 0.02814 ] Network output: [ 0.09374 -0.2141 0.8019 3.888e-05 -1.746e-05 1.225 2.93e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5934 0.5171 0.4276 0.468 0.9811 0.9919 0.5951 0.9283 0.9799 0.506 ] Network output: [ -0.06101 0.1351 1.149 -5.636e-05 2.53e-05 0.8381 -4.248e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2728 0.2672 0.2875 0.2861 0.9888 0.9929 0.2729 0.976 0.9859 0.2957 ] Network output: [ -0.06113 0.1412 1.12 -3.572e-05 1.603e-05 0.8612 -2.692e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2764 0.2754 0.2872 0.284 0.9845 0.9906 0.2764 0.9613 0.9795 0.289 ] Network output: [ -0.008662 1.02 0.03169 2.735e-05 -1.228e-05 0.9653 2.061e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04665 Epoch 3646 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05751 0.9104 0.9231 0.0001087 -4.882e-05 0.0519 8.195e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0125 -0.005768 0.001459 0.02351 0.9537 0.9608 0.02323 0.9073 0.9238 0.06433 ] Network output: [ 0.9628 0.08331 0.03555 -5.608e-05 2.518e-05 -0.04472 -4.226e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.541 0.09067 0.07568 0.328 0.979 0.9907 0.6012 0.9216 0.9771 0.5185 ] Network output: [ 0.01712 0.9238 0.939 -2.51e-05 1.127e-05 0.1028 -1.891e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0194 0.01399 0.02189 0.02403 0.9887 0.9922 0.01972 0.9753 0.9855 0.02813 ] Network output: [ 0.09369 -0.214 0.8019 3.873e-05 -1.739e-05 1.225 2.919e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5934 0.5171 0.4277 0.4679 0.9811 0.9919 0.5951 0.9283 0.98 0.506 ] Network output: [ -0.06096 0.135 1.149 -5.617e-05 2.522e-05 0.8382 -4.233e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2727 0.2672 0.2875 0.286 0.9888 0.9929 0.2729 0.976 0.9859 0.2957 ] Network output: [ -0.06108 0.1411 1.12 -3.546e-05 1.592e-05 0.8612 -2.672e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2763 0.2754 0.2872 0.2839 0.9845 0.9906 0.2763 0.9613 0.9795 0.289 ] Network output: [ -0.00869 1.021 0.03175 2.727e-05 -1.224e-05 0.9652 2.055e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04664 Epoch 3647 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05749 0.9105 0.9231 0.0001087 -4.881e-05 0.0519 8.193e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01249 -0.005771 0.001432 0.02349 0.9537 0.9608 0.02322 0.9073 0.9238 0.0643 ] Network output: [ 0.9628 0.08333 0.03551 -5.609e-05 2.518e-05 -0.04476 -4.227e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.541 0.09069 0.07567 0.3278 0.979 0.9908 0.6012 0.9216 0.9771 0.5185 ] Network output: [ 0.01709 0.9239 0.939 -2.528e-05 1.135e-05 0.1028 -1.905e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01939 0.01398 0.02188 0.02401 0.9887 0.9922 0.01972 0.9753 0.9855 0.02811 ] Network output: [ 0.09365 -0.214 0.8018 3.857e-05 -1.732e-05 1.225 2.907e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5933 0.5171 0.4278 0.4679 0.9811 0.9919 0.595 0.9283 0.98 0.506 ] Network output: [ -0.06091 0.1349 1.149 -5.598e-05 2.513e-05 0.8382 -4.219e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2727 0.2672 0.2875 0.286 0.9888 0.9929 0.2728 0.976 0.9859 0.2957 ] Network output: [ -0.06102 0.141 1.12 -3.519e-05 1.58e-05 0.8611 -2.652e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2762 0.2753 0.2872 0.2839 0.9845 0.9906 0.2763 0.9613 0.9795 0.289 ] Network output: [ -0.008719 1.021 0.03181 2.718e-05 -1.22e-05 0.9651 2.048e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04664 Epoch 3648 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05746 0.9105 0.9231 0.0001087 -4.88e-05 0.05189 8.192e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01248 -0.005774 0.001405 0.02347 0.9538 0.9608 0.02321 0.9074 0.9238 0.06427 ] Network output: [ 0.9629 0.08334 0.03547 -5.61e-05 2.519e-05 -0.0448 -4.228e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5409 0.09072 0.07566 0.3277 0.979 0.9908 0.6012 0.9217 0.9771 0.5185 ] Network output: [ 0.01705 0.9239 0.939 -2.546e-05 1.143e-05 0.1029 -1.919e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01938 0.01398 0.02187 0.02399 0.9887 0.9922 0.01971 0.9753 0.9855 0.0281 ] Network output: [ 0.0936 -0.214 0.8018 3.841e-05 -1.724e-05 1.225 2.895e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5933 0.5171 0.4278 0.4678 0.9811 0.9919 0.595 0.9284 0.98 0.506 ] Network output: [ -0.06087 0.1348 1.149 -5.578e-05 2.504e-05 0.8382 -4.204e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2726 0.2671 0.2875 0.286 0.9888 0.9929 0.2728 0.976 0.986 0.2957 ] Network output: [ -0.06097 0.1409 1.12 -3.493e-05 1.568e-05 0.8611 -2.633e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2762 0.2752 0.2871 0.2839 0.9845 0.9906 0.2762 0.9613 0.9795 0.289 ] Network output: [ -0.008747 1.021 0.03187 2.709e-05 -1.216e-05 0.9651 2.042e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04664 Epoch 3649 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05744 0.9106 0.9231 0.0001087 -4.879e-05 0.05189 8.191e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01247 -0.005777 0.001378 0.02345 0.9538 0.9609 0.02319 0.9074 0.9239 0.06424 ] Network output: [ 0.9629 0.08336 0.03543 -5.611e-05 2.519e-05 -0.04484 -4.229e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5408 0.09074 0.07566 0.3275 0.979 0.9908 0.6011 0.9217 0.9771 0.5185 ] Network output: [ 0.01702 0.924 0.939 -2.564e-05 1.151e-05 0.1029 -1.933e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01938 0.01397 0.02186 0.02398 0.9887 0.9922 0.0197 0.9753 0.9855 0.02808 ] Network output: [ 0.09356 -0.214 0.8018 3.825e-05 -1.717e-05 1.225 2.882e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5932 0.5172 0.4279 0.4677 0.9811 0.9919 0.595 0.9284 0.98 0.506 ] Network output: [ -0.06082 0.1347 1.148 -5.559e-05 2.496e-05 0.8382 -4.19e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2726 0.2671 0.2874 0.2859 0.9888 0.9929 0.2727 0.976 0.986 0.2957 ] Network output: [ -0.06092 0.1408 1.12 -3.467e-05 1.557e-05 0.8611 -2.613e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2761 0.2751 0.2871 0.2838 0.9845 0.9906 0.2761 0.9613 0.9796 0.2889 ] Network output: [ -0.008775 1.021 0.03193 2.701e-05 -1.213e-05 0.965 2.035e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04663 Epoch 3650 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05741 0.9106 0.9231 0.0001087 -4.879e-05 0.05189 8.19e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01246 -0.00578 0.001351 0.02343 0.9538 0.9609 0.02318 0.9074 0.9239 0.06421 ] Network output: [ 0.9629 0.08337 0.03539 -5.612e-05 2.52e-05 -0.04489 -4.23e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5408 0.09077 0.07565 0.3274 0.979 0.9908 0.6011 0.9218 0.9772 0.5185 ] Network output: [ 0.01698 0.924 0.939 -2.583e-05 1.16e-05 0.1029 -1.946e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01937 0.01397 0.02185 0.02396 0.9887 0.9922 0.0197 0.9753 0.9855 0.02807 ] Network output: [ 0.09351 -0.214 0.8017 3.808e-05 -1.71e-05 1.225 2.87e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5932 0.5172 0.4279 0.4677 0.9811 0.9919 0.5949 0.9284 0.98 0.5061 ] Network output: [ -0.06077 0.1346 1.148 -5.54e-05 2.487e-05 0.8382 -4.175e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2725 0.267 0.2874 0.2859 0.9888 0.9929 0.2727 0.976 0.986 0.2957 ] Network output: [ -0.06087 0.1407 1.12 -3.441e-05 1.545e-05 0.8611 -2.593e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.276 0.275 0.2871 0.2838 0.9845 0.9906 0.276 0.9614 0.9796 0.2889 ] Network output: [ -0.008803 1.021 0.03199 2.692e-05 -1.209e-05 0.9649 2.029e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04663 Epoch 3651 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05738 0.9107 0.9231 0.0001087 -4.878e-05 0.05189 8.188e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01245 -0.005783 0.001323 0.02341 0.9538 0.9609 0.02317 0.9075 0.9239 0.06418 ] Network output: [ 0.963 0.08339 0.03534 -5.613e-05 2.52e-05 -0.04493 -4.23e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5407 0.09079 0.07564 0.3273 0.979 0.9908 0.6011 0.9218 0.9772 0.5185 ] Network output: [ 0.01695 0.924 0.939 -2.601e-05 1.168e-05 0.1029 -1.96e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01936 0.01396 0.02183 0.02394 0.9887 0.9922 0.01969 0.9754 0.9855 0.02805 ] Network output: [ 0.09347 -0.2139 0.8017 3.792e-05 -1.702e-05 1.225 2.858e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5932 0.5172 0.428 0.4676 0.9812 0.9919 0.5949 0.9285 0.98 0.5061 ] Network output: [ -0.06073 0.1345 1.148 -5.521e-05 2.479e-05 0.8383 -4.161e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2725 0.267 0.2874 0.2859 0.9888 0.9929 0.2726 0.9761 0.986 0.2956 ] Network output: [ -0.06082 0.1405 1.12 -3.415e-05 1.533e-05 0.861 -2.574e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2759 0.275 0.2871 0.2838 0.9845 0.9906 0.2759 0.9614 0.9796 0.2889 ] Network output: [ -0.008832 1.021 0.03206 2.684e-05 -1.205e-05 0.9648 2.023e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04663 Epoch 3652 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05736 0.9107 0.9231 0.0001086 -4.877e-05 0.05189 8.187e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01243 -0.005785 0.001296 0.02339 0.9538 0.9609 0.02316 0.9075 0.924 0.06415 ] Network output: [ 0.963 0.08341 0.0353 -5.614e-05 2.52e-05 -0.04497 -4.231e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5406 0.09082 0.07563 0.3271 0.979 0.9908 0.601 0.9218 0.9772 0.5185 ] Network output: [ 0.01691 0.9241 0.939 -2.619e-05 1.176e-05 0.1029 -1.974e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01936 0.01396 0.02182 0.02392 0.9887 0.9922 0.01968 0.9754 0.9855 0.02804 ] Network output: [ 0.09342 -0.2139 0.8017 3.775e-05 -1.695e-05 1.226 2.845e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5931 0.5172 0.4281 0.4676 0.9812 0.9919 0.5949 0.9285 0.98 0.5061 ] Network output: [ -0.06068 0.1345 1.148 -5.502e-05 2.47e-05 0.8383 -4.146e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2725 0.267 0.2874 0.2859 0.9888 0.993 0.2726 0.9761 0.986 0.2956 ] Network output: [ -0.06076 0.1404 1.12 -3.389e-05 1.521e-05 0.861 -2.554e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2758 0.2749 0.287 0.2837 0.9845 0.9906 0.2759 0.9614 0.9796 0.2889 ] Network output: [ -0.00886 1.021 0.03212 2.676e-05 -1.201e-05 0.9647 2.017e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04663 Epoch 3653 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05733 0.9108 0.9231 0.0001086 -4.876e-05 0.05188 8.186e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01242 -0.005788 0.001269 0.02336 0.9538 0.9609 0.02315 0.9076 0.924 0.06412 ] Network output: [ 0.9631 0.08343 0.03526 -5.614e-05 2.52e-05 -0.04502 -4.231e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5406 0.09084 0.07562 0.327 0.979 0.9908 0.601 0.9219 0.9772 0.5185 ] Network output: [ 0.01688 0.9241 0.939 -2.638e-05 1.184e-05 0.103 -1.988e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01935 0.01395 0.02181 0.0239 0.9887 0.9922 0.01968 0.9754 0.9855 0.02802 ] Network output: [ 0.09338 -0.2139 0.8016 3.758e-05 -1.687e-05 1.226 2.832e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5931 0.5173 0.4281 0.4675 0.9812 0.9919 0.5948 0.9285 0.98 0.5061 ] Network output: [ -0.06063 0.1344 1.148 -5.483e-05 2.461e-05 0.8383 -4.132e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2724 0.2669 0.2874 0.2858 0.9888 0.993 0.2725 0.9761 0.986 0.2956 ] Network output: [ -0.06071 0.1403 1.12 -3.362e-05 1.51e-05 0.861 -2.534e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2758 0.2748 0.287 0.2837 0.9846 0.9906 0.2758 0.9614 0.9796 0.2888 ] Network output: [ -0.008887 1.021 0.03218 2.667e-05 -1.198e-05 0.9647 2.01e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04662 Epoch 3654 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05731 0.9108 0.9231 0.0001086 -4.876e-05 0.05188 8.185e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01241 -0.005791 0.001242 0.02334 0.9539 0.9609 0.02314 0.9076 0.924 0.06409 ] Network output: [ 0.9631 0.08345 0.03521 -5.615e-05 2.521e-05 -0.04506 -4.231e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5405 0.09086 0.07561 0.3268 0.9791 0.9908 0.601 0.9219 0.9772 0.5185 ] Network output: [ 0.01684 0.9242 0.9391 -2.656e-05 1.192e-05 0.103 -2.002e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01934 0.01395 0.0218 0.02389 0.9887 0.9922 0.01967 0.9754 0.9855 0.02801 ] Network output: [ 0.09333 -0.2139 0.8016 3.741e-05 -1.68e-05 1.226 2.82e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5931 0.5173 0.4282 0.4674 0.9812 0.9919 0.5948 0.9286 0.9801 0.5061 ] Network output: [ -0.06059 0.1343 1.148 -5.464e-05 2.453e-05 0.8383 -4.118e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2724 0.2669 0.2874 0.2858 0.9888 0.993 0.2725 0.9761 0.986 0.2956 ] Network output: [ -0.06066 0.1402 1.12 -3.336e-05 1.498e-05 0.8609 -2.514e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2757 0.2747 0.287 0.2837 0.9846 0.9906 0.2757 0.9614 0.9796 0.2888 ] Network output: [ -0.008915 1.021 0.03224 2.659e-05 -1.194e-05 0.9646 2.004e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04662 Epoch 3655 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05728 0.9109 0.9231 0.0001086 -4.875e-05 0.05188 8.184e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0124 -0.005794 0.001215 0.02332 0.9539 0.9609 0.02313 0.9076 0.9241 0.06406 ] Network output: [ 0.9631 0.08347 0.03517 -5.615e-05 2.521e-05 -0.0451 -4.232e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5404 0.09088 0.0756 0.3267 0.9791 0.9908 0.6009 0.922 0.9772 0.5185 ] Network output: [ 0.01681 0.9242 0.9391 -2.674e-05 1.201e-05 0.103 -2.015e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01934 0.01395 0.02179 0.02387 0.9887 0.9922 0.01966 0.9754 0.9856 0.028 ] Network output: [ 0.09329 -0.2139 0.8016 3.724e-05 -1.672e-05 1.226 2.807e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.593 0.5173 0.4283 0.4674 0.9812 0.9919 0.5948 0.9286 0.9801 0.5061 ] Network output: [ -0.06054 0.1342 1.148 -5.445e-05 2.444e-05 0.8383 -4.103e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2723 0.2669 0.2874 0.2858 0.9888 0.993 0.2725 0.9761 0.986 0.2956 ] Network output: [ -0.06061 0.1401 1.12 -3.31e-05 1.486e-05 0.8609 -2.495e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2756 0.2747 0.287 0.2836 0.9846 0.9906 0.2756 0.9615 0.9796 0.2888 ] Network output: [ -0.008943 1.021 0.0323 2.651e-05 -1.19e-05 0.9645 1.998e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04662 Epoch 3656 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05725 0.9109 0.9231 0.0001086 -4.874e-05 0.05188 8.182e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01239 -0.005797 0.001188 0.0233 0.9539 0.961 0.02311 0.9077 0.9241 0.06403 ] Network output: [ 0.9632 0.08349 0.03513 -5.615e-05 2.521e-05 -0.04515 -4.232e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5403 0.09091 0.07559 0.3265 0.9791 0.9908 0.6009 0.922 0.9773 0.5185 ] Network output: [ 0.01678 0.9242 0.9391 -2.693e-05 1.209e-05 0.103 -2.029e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01933 0.01394 0.02178 0.02385 0.9887 0.9922 0.01966 0.9754 0.9856 0.02798 ] Network output: [ 0.09324 -0.2139 0.8015 3.707e-05 -1.664e-05 1.226 2.794e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.593 0.5173 0.4283 0.4673 0.9812 0.992 0.5947 0.9287 0.9801 0.5061 ] Network output: [ -0.0605 0.1341 1.148 -5.426e-05 2.436e-05 0.8383 -4.089e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2723 0.2668 0.2874 0.2857 0.9888 0.993 0.2724 0.9761 0.986 0.2956 ] Network output: [ -0.06056 0.14 1.12 -3.284e-05 1.474e-05 0.8609 -2.475e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2755 0.2746 0.2869 0.2836 0.9846 0.9906 0.2755 0.9615 0.9797 0.2887 ] Network output: [ -0.008971 1.021 0.03236 2.643e-05 -1.187e-05 0.9644 1.992e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04662 Epoch 3657 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05723 0.911 0.9231 0.0001086 -4.873e-05 0.05188 8.181e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01238 -0.005799 0.001161 0.02328 0.9539 0.961 0.0231 0.9077 0.9241 0.064 ] Network output: [ 0.9632 0.08351 0.03508 -5.615e-05 2.521e-05 -0.04519 -4.232e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5403 0.09093 0.07558 0.3264 0.9791 0.9908 0.6009 0.922 0.9773 0.5185 ] Network output: [ 0.01674 0.9243 0.9391 -2.711e-05 1.217e-05 0.1031 -2.043e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01932 0.01394 0.02176 0.02383 0.9887 0.9922 0.01965 0.9755 0.9856 0.02797 ] Network output: [ 0.0932 -0.2138 0.8015 3.689e-05 -1.656e-05 1.226 2.78e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.593 0.5173 0.4284 0.4672 0.9812 0.992 0.5947 0.9287 0.9801 0.5061 ] Network output: [ -0.06045 0.134 1.148 -5.407e-05 2.427e-05 0.8384 -4.075e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2722 0.2668 0.2874 0.2857 0.9889 0.993 0.2724 0.9762 0.9861 0.2956 ] Network output: [ -0.06051 0.1399 1.12 -3.258e-05 1.462e-05 0.8609 -2.455e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2754 0.2745 0.2869 0.2836 0.9846 0.9906 0.2755 0.9615 0.9797 0.2887 ] Network output: [ -0.008998 1.021 0.03242 2.635e-05 -1.183e-05 0.9643 1.986e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04662 Epoch 3658 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0572 0.911 0.9231 0.0001085 -4.873e-05 0.05188 8.18e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01237 -0.005802 0.001134 0.02326 0.9539 0.961 0.02309 0.9078 0.9242 0.06396 ] Network output: [ 0.9632 0.08353 0.03504 -5.615e-05 2.521e-05 -0.04524 -4.232e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5402 0.09095 0.07557 0.3263 0.9791 0.9908 0.6009 0.9221 0.9773 0.5185 ] Network output: [ 0.01671 0.9243 0.9391 -2.729e-05 1.225e-05 0.1031 -2.057e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01931 0.01393 0.02175 0.02382 0.9887 0.9922 0.01964 0.9755 0.9856 0.02795 ] Network output: [ 0.09315 -0.2138 0.8015 3.672e-05 -1.648e-05 1.226 2.767e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5929 0.5174 0.4285 0.4672 0.9812 0.992 0.5947 0.9287 0.9801 0.5061 ] Network output: [ -0.06041 0.134 1.148 -5.388e-05 2.419e-05 0.8384 -4.061e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2722 0.2667 0.2874 0.2857 0.9889 0.993 0.2723 0.9762 0.9861 0.2956 ] Network output: [ -0.06045 0.1398 1.12 -3.231e-05 1.451e-05 0.8608 -2.435e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2753 0.2744 0.2869 0.2835 0.9846 0.9907 0.2754 0.9615 0.9797 0.2887 ] Network output: [ -0.009026 1.021 0.03248 2.627e-05 -1.179e-05 0.9643 1.98e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04661 Epoch 3659 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05718 0.9111 0.9231 0.0001085 -4.872e-05 0.05188 8.179e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01236 -0.005805 0.001107 0.02324 0.9539 0.961 0.02308 0.9078 0.9242 0.06393 ] Network output: [ 0.9633 0.08356 0.03499 -5.615e-05 2.521e-05 -0.04528 -4.232e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5401 0.09097 0.07556 0.3261 0.9791 0.9908 0.6008 0.9221 0.9773 0.5185 ] Network output: [ 0.01667 0.9243 0.9391 -2.748e-05 1.234e-05 0.1031 -2.071e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01931 0.01393 0.02174 0.0238 0.9887 0.9922 0.01963 0.9755 0.9856 0.02794 ] Network output: [ 0.09311 -0.2138 0.8014 3.654e-05 -1.64e-05 1.226 2.754e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5929 0.5174 0.4285 0.4671 0.9812 0.992 0.5946 0.9288 0.9801 0.5061 ] Network output: [ -0.06036 0.1339 1.148 -5.369e-05 2.41e-05 0.8384 -4.046e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2721 0.2667 0.2874 0.2856 0.9889 0.993 0.2723 0.9762 0.9861 0.2955 ] Network output: [ -0.0604 0.1397 1.12 -3.205e-05 1.439e-05 0.8608 -2.415e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2753 0.2743 0.2868 0.2835 0.9846 0.9907 0.2753 0.9615 0.9797 0.2887 ] Network output: [ -0.009053 1.021 0.03254 2.619e-05 -1.176e-05 0.9642 1.974e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04661 Epoch 3660 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05715 0.9111 0.9231 0.0001085 -4.871e-05 0.05189 8.177e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01235 -0.005808 0.00108 0.02322 0.9539 0.961 0.02307 0.9078 0.9242 0.0639 ] Network output: [ 0.9633 0.08358 0.03494 -5.615e-05 2.521e-05 -0.04533 -4.231e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5401 0.09099 0.07555 0.326 0.9791 0.9908 0.6008 0.9221 0.9773 0.5185 ] Network output: [ 0.01664 0.9244 0.9391 -2.766e-05 1.242e-05 0.1031 -2.085e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0193 0.01392 0.02173 0.02378 0.9887 0.9922 0.01963 0.9755 0.9856 0.02792 ] Network output: [ 0.09306 -0.2138 0.8014 3.636e-05 -1.632e-05 1.226 2.74e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5928 0.5174 0.4286 0.4671 0.9812 0.992 0.5946 0.9288 0.9801 0.5062 ] Network output: [ -0.06032 0.1338 1.148 -5.35e-05 2.402e-05 0.8384 -4.032e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2721 0.2666 0.2874 0.2856 0.9889 0.993 0.2722 0.9762 0.9861 0.2955 ] Network output: [ -0.06035 0.1396 1.12 -3.179e-05 1.427e-05 0.8608 -2.396e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2752 0.2742 0.2868 0.2835 0.9846 0.9907 0.2752 0.9616 0.9797 0.2886 ] Network output: [ -0.009081 1.022 0.03259 2.612e-05 -1.172e-05 0.9641 1.968e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04661 Epoch 3661 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05713 0.9112 0.9231 0.0001085 -4.871e-05 0.05189 8.176e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01234 -0.005811 0.001053 0.02319 0.954 0.961 0.02306 0.9079 0.9243 0.06387 ] Network output: [ 0.9633 0.0836 0.0349 -5.614e-05 2.52e-05 -0.04538 -4.231e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.54 0.09101 0.07554 0.3258 0.9791 0.9908 0.6008 0.9222 0.9773 0.5185 ] Network output: [ 0.01661 0.9244 0.9391 -2.784e-05 1.25e-05 0.1032 -2.098e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01929 0.01392 0.02172 0.02376 0.9887 0.9922 0.01962 0.9755 0.9856 0.02791 ] Network output: [ 0.09302 -0.2138 0.8014 3.618e-05 -1.624e-05 1.227 2.726e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5928 0.5174 0.4286 0.467 0.9812 0.992 0.5946 0.9288 0.9802 0.5062 ] Network output: [ -0.06027 0.1337 1.148 -5.332e-05 2.394e-05 0.8384 -4.018e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.272 0.2666 0.2874 0.2856 0.9889 0.993 0.2722 0.9762 0.9861 0.2955 ] Network output: [ -0.0603 0.1394 1.12 -3.152e-05 1.415e-05 0.8607 -2.376e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2751 0.2742 0.2868 0.2834 0.9846 0.9907 0.2751 0.9616 0.9797 0.2886 ] Network output: [ -0.009108 1.022 0.03265 2.604e-05 -1.169e-05 0.964 1.962e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04661 Epoch 3662 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0571 0.9112 0.9231 0.0001085 -4.87e-05 0.05189 8.175e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01233 -0.005813 0.001026 0.02317 0.954 0.961 0.02305 0.9079 0.9243 0.06384 ] Network output: [ 0.9634 0.08363 0.03485 -5.614e-05 2.52e-05 -0.04542 -4.231e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5399 0.09104 0.07552 0.3257 0.9791 0.9908 0.6007 0.9222 0.9774 0.5185 ] Network output: [ 0.01657 0.9244 0.9391 -2.803e-05 1.258e-05 0.1032 -2.112e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01928 0.01391 0.02171 0.02374 0.9887 0.9922 0.01961 0.9755 0.9856 0.02789 ] Network output: [ 0.09297 -0.2138 0.8013 3.599e-05 -1.616e-05 1.227 2.713e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5928 0.5174 0.4287 0.4669 0.9812 0.992 0.5945 0.9289 0.9802 0.5062 ] Network output: [ -0.06023 0.1336 1.148 -5.313e-05 2.385e-05 0.8384 -4.004e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.272 0.2666 0.2874 0.2856 0.9889 0.993 0.2721 0.9762 0.9861 0.2955 ] Network output: [ -0.06025 0.1393 1.12 -3.126e-05 1.403e-05 0.8607 -2.356e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.275 0.2741 0.2868 0.2834 0.9846 0.9907 0.275 0.9616 0.9797 0.2886 ] Network output: [ -0.009135 1.022 0.03271 2.596e-05 -1.166e-05 0.9639 1.957e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04661 Epoch 3663 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05708 0.9113 0.9231 0.0001085 -4.869e-05 0.05189 8.174e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01231 -0.005816 0.0009993 0.02315 0.954 0.9611 0.02303 0.908 0.9243 0.06381 ] Network output: [ 0.9634 0.08366 0.03481 -5.613e-05 2.52e-05 -0.04547 -4.23e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5399 0.09106 0.07551 0.3255 0.9791 0.9908 0.6007 0.9223 0.9774 0.5185 ] Network output: [ 0.01654 0.9245 0.9391 -2.821e-05 1.266e-05 0.1032 -2.126e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01928 0.01391 0.02169 0.02372 0.9888 0.9923 0.0196 0.9755 0.9856 0.02788 ] Network output: [ 0.09292 -0.2138 0.8013 3.581e-05 -1.608e-05 1.227 2.699e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5927 0.5174 0.4288 0.4669 0.9812 0.992 0.5945 0.9289 0.9802 0.5062 ] Network output: [ -0.06018 0.1335 1.148 -5.294e-05 2.377e-05 0.8384 -3.99e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2719 0.2665 0.2874 0.2855 0.9889 0.993 0.2721 0.9762 0.9861 0.2955 ] Network output: [ -0.0602 0.1392 1.12 -3.1e-05 1.392e-05 0.8607 -2.336e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2749 0.274 0.2867 0.2833 0.9846 0.9907 0.275 0.9616 0.9798 0.2886 ] Network output: [ -0.009162 1.022 0.03277 2.589e-05 -1.162e-05 0.9639 1.951e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04661 Epoch 3664 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05705 0.9113 0.9231 0.0001084 -4.868e-05 0.05189 8.173e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0123 -0.005819 0.0009722 0.02313 0.954 0.9611 0.02302 0.908 0.9244 0.06378 ] Network output: [ 0.9634 0.08368 0.03476 -5.612e-05 2.519e-05 -0.04552 -4.229e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5398 0.09108 0.0755 0.3254 0.9791 0.9908 0.6007 0.9223 0.9774 0.5185 ] Network output: [ 0.01651 0.9245 0.9391 -2.839e-05 1.275e-05 0.1033 -2.14e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01927 0.0139 0.02168 0.02371 0.9888 0.9923 0.0196 0.9756 0.9856 0.02786 ] Network output: [ 0.09288 -0.2138 0.8013 3.562e-05 -1.599e-05 1.227 2.685e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5927 0.5175 0.4288 0.4668 0.9812 0.992 0.5944 0.9289 0.9802 0.5062 ] Network output: [ -0.06014 0.1335 1.148 -5.276e-05 2.368e-05 0.8385 -3.976e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2719 0.2665 0.2873 0.2855 0.9889 0.993 0.272 0.9763 0.9861 0.2955 ] Network output: [ -0.06015 0.1391 1.12 -3.073e-05 1.38e-05 0.8606 -2.316e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2749 0.2739 0.2867 0.2833 0.9846 0.9907 0.2749 0.9616 0.9798 0.2885 ] Network output: [ -0.00919 1.022 0.03283 2.581e-05 -1.159e-05 0.9638 1.945e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04661 Epoch 3665 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05702 0.9114 0.9231 0.0001084 -4.868e-05 0.0519 8.171e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01229 -0.005822 0.0009452 0.02311 0.954 0.9611 0.02301 0.908 0.9244 0.06375 ] Network output: [ 0.9635 0.08371 0.03471 -5.611e-05 2.519e-05 -0.04557 -4.229e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5397 0.0911 0.07549 0.3252 0.9791 0.9908 0.6006 0.9223 0.9774 0.5185 ] Network output: [ 0.01647 0.9245 0.9391 -2.858e-05 1.283e-05 0.1033 -2.154e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01926 0.0139 0.02167 0.02369 0.9888 0.9923 0.01959 0.9756 0.9857 0.02785 ] Network output: [ 0.09283 -0.2137 0.8012 3.543e-05 -1.591e-05 1.227 2.67e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5927 0.5175 0.4289 0.4668 0.9813 0.992 0.5944 0.929 0.9802 0.5062 ] Network output: [ -0.06009 0.1334 1.148 -5.257e-05 2.36e-05 0.8385 -3.962e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2718 0.2664 0.2873 0.2855 0.9889 0.993 0.272 0.9763 0.9861 0.2955 ] Network output: [ -0.0601 0.139 1.12 -3.047e-05 1.368e-05 0.8606 -2.296e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2748 0.2738 0.2867 0.2833 0.9846 0.9907 0.2748 0.9617 0.9798 0.2885 ] Network output: [ -0.009217 1.022 0.03289 2.574e-05 -1.155e-05 0.9637 1.94e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04661 Epoch 3666 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.057 0.9114 0.9231 0.0001084 -4.867e-05 0.0519 8.17e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01228 -0.005824 0.0009182 0.02309 0.954 0.9611 0.023 0.9081 0.9244 0.06372 ] Network output: [ 0.9635 0.08374 0.03466 -5.61e-05 2.519e-05 -0.04561 -4.228e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5396 0.09111 0.07547 0.3251 0.9791 0.9908 0.6006 0.9224 0.9774 0.5185 ] Network output: [ 0.01644 0.9246 0.9391 -2.876e-05 1.291e-05 0.1033 -2.167e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01925 0.01389 0.02166 0.02367 0.9888 0.9923 0.01958 0.9756 0.9857 0.02783 ] Network output: [ 0.09279 -0.2137 0.8012 3.524e-05 -1.582e-05 1.227 2.656e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5926 0.5175 0.429 0.4667 0.9813 0.992 0.5944 0.929 0.9802 0.5063 ] Network output: [ -0.06005 0.1333 1.148 -5.238e-05 2.352e-05 0.8385 -3.948e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2718 0.2664 0.2873 0.2854 0.9889 0.993 0.2719 0.9763 0.9862 0.2954 ] Network output: [ -0.06005 0.1389 1.12 -3.02e-05 1.356e-05 0.8606 -2.276e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2747 0.2738 0.2867 0.2832 0.9846 0.9907 0.2747 0.9617 0.9798 0.2885 ] Network output: [ -0.009244 1.022 0.03294 2.566e-05 -1.152e-05 0.9636 1.934e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04661 Epoch 3667 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05697 0.9115 0.9231 0.0001084 -4.866e-05 0.0519 8.169e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01227 -0.005827 0.0008912 0.02306 0.9541 0.9611 0.02299 0.9081 0.9245 0.06369 ] Network output: [ 0.9635 0.08377 0.03461 -5.609e-05 2.518e-05 -0.04566 -4.227e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5396 0.09113 0.07546 0.325 0.9792 0.9908 0.6006 0.9224 0.9774 0.5185 ] Network output: [ 0.01641 0.9246 0.9391 -2.894e-05 1.299e-05 0.1033 -2.181e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01925 0.01389 0.02165 0.02365 0.9888 0.9923 0.01957 0.9756 0.9857 0.02782 ] Network output: [ 0.09274 -0.2137 0.8012 3.505e-05 -1.574e-05 1.227 2.641e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5926 0.5175 0.429 0.4666 0.9813 0.992 0.5943 0.9291 0.9802 0.5063 ] Network output: [ -0.06 0.1332 1.148 -5.22e-05 2.343e-05 0.8385 -3.934e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2717 0.2663 0.2873 0.2854 0.9889 0.993 0.2719 0.9763 0.9862 0.2954 ] Network output: [ -0.06 0.1388 1.121 -2.994e-05 1.344e-05 0.8606 -2.256e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2746 0.2737 0.2866 0.2832 0.9846 0.9907 0.2746 0.9617 0.9798 0.2884 ] Network output: [ -0.00927 1.022 0.033 2.559e-05 -1.149e-05 0.9636 1.928e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04661 Epoch 3668 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05695 0.9115 0.9231 0.0001084 -4.865e-05 0.05191 8.168e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01226 -0.00583 0.0008641 0.02304 0.9541 0.9611 0.02298 0.9082 0.9245 0.06366 ] Network output: [ 0.9636 0.0838 0.03457 -5.608e-05 2.518e-05 -0.04571 -4.226e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5395 0.09115 0.07544 0.3248 0.9792 0.9909 0.6005 0.9225 0.9775 0.5185 ] Network output: [ 0.01638 0.9246 0.9391 -2.913e-05 1.308e-05 0.1034 -2.195e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01924 0.01388 0.02163 0.02363 0.9888 0.9923 0.01957 0.9756 0.9857 0.02781 ] Network output: [ 0.0927 -0.2137 0.8012 3.486e-05 -1.565e-05 1.227 2.627e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5925 0.5175 0.4291 0.4666 0.9813 0.992 0.5943 0.9291 0.9803 0.5063 ] Network output: [ -0.05996 0.1332 1.148 -5.201e-05 2.335e-05 0.8385 -3.92e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2717 0.2663 0.2873 0.2854 0.9889 0.993 0.2718 0.9763 0.9862 0.2954 ] Network output: [ -0.05995 0.1387 1.121 -2.967e-05 1.332e-05 0.8605 -2.236e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2745 0.2736 0.2866 0.2832 0.9846 0.9907 0.2745 0.9617 0.9798 0.2884 ] Network output: [ -0.009297 1.022 0.03306 2.551e-05 -1.145e-05 0.9635 1.923e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04661 Epoch 3669 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05692 0.9116 0.9231 0.0001084 -4.865e-05 0.05191 8.166e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01225 -0.005832 0.0008371 0.02302 0.9541 0.9611 0.02296 0.9082 0.9245 0.06363 ] Network output: [ 0.9636 0.08383 0.03452 -5.606e-05 2.517e-05 -0.04576 -4.225e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5394 0.09117 0.07543 0.3247 0.9792 0.9909 0.6005 0.9225 0.9775 0.5185 ] Network output: [ 0.01634 0.9247 0.9391 -2.931e-05 1.316e-05 0.1034 -2.209e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01923 0.01388 0.02162 0.02361 0.9888 0.9923 0.01956 0.9756 0.9857 0.02779 ] Network output: [ 0.09265 -0.2137 0.8011 3.466e-05 -1.556e-05 1.227 2.612e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5925 0.5175 0.4292 0.4665 0.9813 0.992 0.5943 0.9291 0.9803 0.5063 ] Network output: [ -0.05992 0.1331 1.148 -5.182e-05 2.327e-05 0.8385 -3.906e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2716 0.2662 0.2873 0.2853 0.9889 0.993 0.2718 0.9763 0.9862 0.2954 ] Network output: [ -0.0599 0.1386 1.121 -2.941e-05 1.32e-05 0.8605 -2.216e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2744 0.2735 0.2866 0.2831 0.9846 0.9907 0.2745 0.9617 0.9798 0.2884 ] Network output: [ -0.009324 1.022 0.03311 2.544e-05 -1.142e-05 0.9634 1.917e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04661 Epoch 3670 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0569 0.9116 0.9231 0.0001083 -4.864e-05 0.05192 8.165e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01224 -0.005835 0.0008101 0.023 0.9541 0.9612 0.02295 0.9082 0.9246 0.0636 ] Network output: [ 0.9636 0.08386 0.03447 -5.605e-05 2.516e-05 -0.04581 -4.224e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5394 0.09119 0.07541 0.3245 0.9792 0.9909 0.6005 0.9225 0.9775 0.5185 ] Network output: [ 0.01631 0.9247 0.9391 -2.949e-05 1.324e-05 0.1034 -2.223e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01922 0.01387 0.02161 0.0236 0.9888 0.9923 0.01955 0.9757 0.9857 0.02778 ] Network output: [ 0.0926 -0.2137 0.8011 3.446e-05 -1.547e-05 1.228 2.597e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5925 0.5176 0.4293 0.4665 0.9813 0.992 0.5942 0.9292 0.9803 0.5063 ] Network output: [ -0.05987 0.133 1.148 -5.164e-05 2.318e-05 0.8385 -3.892e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2716 0.2662 0.2873 0.2853 0.9889 0.993 0.2717 0.9763 0.9862 0.2954 ] Network output: [ -0.05985 0.1385 1.121 -2.914e-05 1.308e-05 0.8605 -2.196e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2743 0.2734 0.2866 0.2831 0.9846 0.9907 0.2744 0.9618 0.9798 0.2884 ] Network output: [ -0.009351 1.022 0.03317 2.537e-05 -1.139e-05 0.9633 1.912e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04661 Epoch 3671 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05687 0.9117 0.9231 0.0001083 -4.863e-05 0.05192 8.164e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01222 -0.005838 0.000783 0.02298 0.9541 0.9612 0.02294 0.9083 0.9246 0.06357 ] Network output: [ 0.9637 0.08389 0.03442 -5.603e-05 2.515e-05 -0.04586 -4.223e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5393 0.09121 0.0754 0.3244 0.9792 0.9909 0.6005 0.9226 0.9775 0.5185 ] Network output: [ 0.01628 0.9247 0.9391 -2.968e-05 1.332e-05 0.1035 -2.237e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01922 0.01387 0.0216 0.02358 0.9888 0.9923 0.01954 0.9757 0.9857 0.02776 ] Network output: [ 0.09256 -0.2137 0.8011 3.426e-05 -1.538e-05 1.228 2.582e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5924 0.5176 0.4293 0.4664 0.9813 0.992 0.5942 0.9292 0.9803 0.5064 ] Network output: [ -0.05983 0.1329 1.148 -5.145e-05 2.31e-05 0.8385 -3.878e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2715 0.2661 0.2873 0.2853 0.9889 0.993 0.2717 0.9764 0.9862 0.2954 ] Network output: [ -0.05979 0.1384 1.121 -2.887e-05 1.296e-05 0.8604 -2.176e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2743 0.2733 0.2865 0.2831 0.9847 0.9907 0.2743 0.9618 0.9799 0.2883 ] Network output: [ -0.009378 1.022 0.03323 2.53e-05 -1.136e-05 0.9632 1.907e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04661 Epoch 3672 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05685 0.9117 0.9231 0.0001083 -4.862e-05 0.05193 8.163e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01221 -0.00584 0.000756 0.02295 0.9541 0.9612 0.02293 0.9083 0.9246 0.06354 ] Network output: [ 0.9637 0.08393 0.03437 -5.601e-05 2.515e-05 -0.04591 -4.221e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5392 0.09122 0.07538 0.3242 0.9792 0.9909 0.6004 0.9226 0.9775 0.5185 ] Network output: [ 0.01625 0.9248 0.9391 -2.986e-05 1.341e-05 0.1035 -2.25e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01921 0.01386 0.02159 0.02356 0.9888 0.9923 0.01954 0.9757 0.9857 0.02775 ] Network output: [ 0.09251 -0.2137 0.8011 3.406e-05 -1.529e-05 1.228 2.567e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5924 0.5176 0.4294 0.4663 0.9813 0.992 0.5942 0.9292 0.9803 0.5064 ] Network output: [ -0.05978 0.1329 1.148 -5.127e-05 2.302e-05 0.8385 -3.864e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2715 0.2661 0.2873 0.2852 0.9889 0.993 0.2716 0.9764 0.9862 0.2954 ] Network output: [ -0.05974 0.1383 1.121 -2.861e-05 1.284e-05 0.8604 -2.156e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2742 0.2733 0.2865 0.283 0.9847 0.9907 0.2742 0.9618 0.9799 0.2883 ] Network output: [ -0.009404 1.022 0.03328 2.523e-05 -1.133e-05 0.9632 1.901e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04662 Epoch 3673 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05682 0.9118 0.9231 0.0001083 -4.862e-05 0.05194 8.161e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0122 -0.005843 0.0007289 0.02293 0.9541 0.9612 0.02292 0.9084 0.9247 0.06351 ] Network output: [ 0.9637 0.08396 0.03432 -5.6e-05 2.514e-05 -0.04596 -4.22e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5391 0.09124 0.07537 0.3241 0.9792 0.9909 0.6004 0.9227 0.9775 0.5185 ] Network output: [ 0.01622 0.9248 0.9391 -3.005e-05 1.349e-05 0.1035 -2.264e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0192 0.01386 0.02157 0.02354 0.9888 0.9923 0.01953 0.9757 0.9857 0.02773 ] Network output: [ 0.09247 -0.2137 0.801 3.386e-05 -1.52e-05 1.228 2.552e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5924 0.5176 0.4295 0.4663 0.9813 0.992 0.5941 0.9293 0.9803 0.5064 ] Network output: [ -0.05974 0.1328 1.148 -5.108e-05 2.293e-05 0.8385 -3.85e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2714 0.266 0.2873 0.2852 0.9889 0.993 0.2716 0.9764 0.9862 0.2953 ] Network output: [ -0.05969 0.1382 1.121 -2.834e-05 1.272e-05 0.8604 -2.136e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2741 0.2732 0.2865 0.283 0.9847 0.9907 0.2741 0.9618 0.9799 0.2883 ] Network output: [ -0.009431 1.023 0.03334 2.516e-05 -1.129e-05 0.9631 1.896e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04662 Epoch 3674 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0568 0.9118 0.9231 0.0001083 -4.861e-05 0.05194 8.16e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01219 -0.005846 0.0007019 0.02291 0.9542 0.9612 0.02291 0.9084 0.9247 0.06348 ] Network output: [ 0.9638 0.084 0.03426 -5.598e-05 2.513e-05 -0.04601 -4.219e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5391 0.09126 0.07535 0.3239 0.9792 0.9909 0.6004 0.9227 0.9775 0.5186 ] Network output: [ 0.01618 0.9248 0.9392 -3.023e-05 1.357e-05 0.1035 -2.278e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01919 0.01385 0.02156 0.02352 0.9888 0.9923 0.01952 0.9757 0.9857 0.02772 ] Network output: [ 0.09242 -0.2137 0.801 3.366e-05 -1.511e-05 1.228 2.536e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5923 0.5176 0.4295 0.4662 0.9813 0.992 0.5941 0.9293 0.9803 0.5064 ] Network output: [ -0.0597 0.1327 1.148 -5.09e-05 2.285e-05 0.8385 -3.836e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2714 0.266 0.2873 0.2852 0.9889 0.993 0.2715 0.9764 0.9862 0.2953 ] Network output: [ -0.05964 0.1381 1.121 -2.807e-05 1.26e-05 0.8603 -2.116e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.274 0.2731 0.2865 0.283 0.9847 0.9907 0.274 0.9618 0.9799 0.2883 ] Network output: [ -0.009457 1.023 0.03339 2.509e-05 -1.126e-05 0.963 1.891e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04662 Epoch 3675 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05677 0.9118 0.9231 0.0001083 -4.86e-05 0.05195 8.159e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01218 -0.005848 0.0006748 0.02289 0.9542 0.9612 0.02289 0.9084 0.9247 0.06345 ] Network output: [ 0.9638 0.08403 0.03421 -5.596e-05 2.512e-05 -0.04606 -4.217e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.539 0.09127 0.07533 0.3238 0.9792 0.9909 0.6003 0.9227 0.9776 0.5186 ] Network output: [ 0.01615 0.9248 0.9392 -3.041e-05 1.365e-05 0.1036 -2.292e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01918 0.01384 0.02155 0.0235 0.9888 0.9923 0.01951 0.9757 0.9858 0.0277 ] Network output: [ 0.09238 -0.2137 0.801 3.345e-05 -1.502e-05 1.228 2.521e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5923 0.5176 0.4296 0.4662 0.9813 0.992 0.594 0.9293 0.9804 0.5065 ] Network output: [ -0.05965 0.1326 1.148 -5.071e-05 2.277e-05 0.8386 -3.822e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2713 0.2659 0.2873 0.2851 0.9889 0.993 0.2715 0.9764 0.9863 0.2953 ] Network output: [ -0.05959 0.138 1.121 -2.781e-05 1.248e-05 0.8603 -2.096e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2739 0.273 0.2864 0.2829 0.9847 0.9907 0.274 0.9619 0.9799 0.2882 ] Network output: [ -0.009484 1.023 0.03345 2.502e-05 -1.123e-05 0.9629 1.886e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04662 Epoch 3676 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05675 0.9119 0.9231 0.0001082 -4.86e-05 0.05196 8.158e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01217 -0.005851 0.0006478 0.02287 0.9542 0.9612 0.02288 0.9085 0.9248 0.06342 ] Network output: [ 0.9638 0.08407 0.03416 -5.593e-05 2.511e-05 -0.04612 -4.215e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5389 0.09129 0.07532 0.3236 0.9792 0.9909 0.6003 0.9228 0.9776 0.5186 ] Network output: [ 0.01612 0.9249 0.9392 -3.06e-05 1.374e-05 0.1036 -2.306e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01918 0.01384 0.02154 0.02348 0.9888 0.9923 0.01951 0.9758 0.9858 0.02769 ] Network output: [ 0.09233 -0.2137 0.801 3.324e-05 -1.492e-05 1.228 2.505e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5922 0.5176 0.4297 0.4661 0.9813 0.992 0.594 0.9294 0.9804 0.5065 ] Network output: [ -0.05961 0.1326 1.148 -5.053e-05 2.268e-05 0.8386 -3.808e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2713 0.2659 0.2873 0.2851 0.9889 0.993 0.2714 0.9764 0.9863 0.2953 ] Network output: [ -0.05954 0.1379 1.121 -2.754e-05 1.236e-05 0.8603 -2.075e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2738 0.2729 0.2864 0.2829 0.9847 0.9907 0.2739 0.9619 0.9799 0.2882 ] Network output: [ -0.00951 1.023 0.0335 2.495e-05 -1.12e-05 0.9629 1.88e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04662 Epoch 3677 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05673 0.9119 0.9231 0.0001082 -4.859e-05 0.05196 8.157e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01216 -0.005854 0.0006207 0.02284 0.9542 0.9612 0.02287 0.9085 0.9248 0.06339 ] Network output: [ 0.9639 0.08411 0.03411 -5.591e-05 2.51e-05 -0.04617 -4.214e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5388 0.09131 0.0753 0.3235 0.9792 0.9909 0.6003 0.9228 0.9776 0.5186 ] Network output: [ 0.01609 0.9249 0.9392 -3.078e-05 1.382e-05 0.1036 -2.32e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01917 0.01383 0.02153 0.02347 0.9888 0.9923 0.0195 0.9758 0.9858 0.02767 ] Network output: [ 0.09228 -0.2137 0.8009 3.303e-05 -1.483e-05 1.228 2.489e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5922 0.5177 0.4297 0.4661 0.9813 0.992 0.594 0.9294 0.9804 0.5065 ] Network output: [ -0.05957 0.1325 1.148 -5.035e-05 2.26e-05 0.8386 -3.794e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2712 0.2658 0.2872 0.2851 0.9889 0.993 0.2713 0.9764 0.9863 0.2953 ] Network output: [ -0.0595 0.1378 1.121 -2.727e-05 1.224e-05 0.8602 -2.055e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2738 0.2728 0.2864 0.2828 0.9847 0.9907 0.2738 0.9619 0.9799 0.2882 ] Network output: [ -0.009536 1.023 0.03356 2.488e-05 -1.117e-05 0.9628 1.875e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04663 Epoch 3678 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0567 0.912 0.9231 0.0001082 -4.858e-05 0.05197 8.155e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01215 -0.005856 0.0005936 0.02282 0.9542 0.9613 0.02286 0.9085 0.9248 0.06335 ] Network output: [ 0.9639 0.08415 0.03406 -5.589e-05 2.509e-05 -0.04622 -4.212e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5388 0.09132 0.07528 0.3233 0.9792 0.9909 0.6002 0.9229 0.9776 0.5186 ] Network output: [ 0.01606 0.9249 0.9392 -3.097e-05 1.39e-05 0.1037 -2.334e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01916 0.01383 0.02151 0.02345 0.9888 0.9923 0.01949 0.9758 0.9858 0.02766 ] Network output: [ 0.09224 -0.2137 0.8009 3.282e-05 -1.473e-05 1.228 2.473e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5922 0.5177 0.4298 0.466 0.9813 0.992 0.5939 0.9294 0.9804 0.5066 ] Network output: [ -0.05952 0.1324 1.148 -5.016e-05 2.252e-05 0.8386 -3.78e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2711 0.2658 0.2872 0.285 0.989 0.993 0.2713 0.9765 0.9863 0.2953 ] Network output: [ -0.05945 0.1377 1.121 -2.7e-05 1.212e-05 0.8602 -2.035e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2737 0.2728 0.2864 0.2828 0.9847 0.9907 0.2737 0.9619 0.98 0.2882 ] Network output: [ -0.009563 1.023 0.03361 2.482e-05 -1.114e-05 0.9627 1.87e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04663 Epoch 3679 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05668 0.912 0.9231 0.0001082 -4.857e-05 0.05198 8.154e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01213 -0.005859 0.0005666 0.0228 0.9542 0.9613 0.02285 0.9086 0.9249 0.06332 ] Network output: [ 0.9639 0.08419 0.034 -5.586e-05 2.508e-05 -0.04628 -4.21e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5387 0.09134 0.07526 0.3232 0.9792 0.9909 0.6002 0.9229 0.9776 0.5186 ] Network output: [ 0.01603 0.925 0.9392 -3.115e-05 1.398e-05 0.1037 -2.348e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01915 0.01382 0.0215 0.02343 0.9888 0.9923 0.01948 0.9758 0.9858 0.02764 ] Network output: [ 0.09219 -0.2137 0.8009 3.261e-05 -1.464e-05 1.229 2.457e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5921 0.5177 0.4299 0.4659 0.9813 0.992 0.5939 0.9295 0.9804 0.5066 ] Network output: [ -0.05948 0.1323 1.148 -4.998e-05 2.244e-05 0.8386 -3.766e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2711 0.2657 0.2872 0.285 0.989 0.993 0.2712 0.9765 0.9863 0.2953 ] Network output: [ -0.0594 0.1376 1.121 -2.673e-05 1.2e-05 0.8602 -2.014e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2736 0.2727 0.2863 0.2828 0.9847 0.9907 0.2736 0.9619 0.98 0.2881 ] Network output: [ -0.009589 1.023 0.03367 2.475e-05 -1.111e-05 0.9626 1.865e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04663 Epoch 3680 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05665 0.9121 0.9231 0.0001082 -4.857e-05 0.05199 8.153e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01212 -0.005861 0.0005395 0.02278 0.9542 0.9613 0.02283 0.9086 0.9249 0.06329 ] Network output: [ 0.964 0.08423 0.03395 -5.584e-05 2.507e-05 -0.04633 -4.208e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5386 0.09135 0.07524 0.323 0.9793 0.9909 0.6002 0.9229 0.9776 0.5186 ] Network output: [ 0.016 0.925 0.9392 -3.133e-05 1.407e-05 0.1037 -2.361e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01914 0.01382 0.02149 0.02341 0.9888 0.9923 0.01947 0.9758 0.9858 0.02763 ] Network output: [ 0.09215 -0.2137 0.8009 3.239e-05 -1.454e-05 1.229 2.441e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5921 0.5177 0.43 0.4659 0.9814 0.992 0.5939 0.9295 0.9804 0.5066 ] Network output: [ -0.05944 0.1323 1.148 -4.979e-05 2.235e-05 0.8386 -3.753e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.271 0.2657 0.2872 0.285 0.989 0.9931 0.2712 0.9765 0.9863 0.2952 ] Network output: [ -0.05935 0.1375 1.121 -2.646e-05 1.188e-05 0.8601 -1.994e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2735 0.2726 0.2863 0.2827 0.9847 0.9907 0.2735 0.9619 0.98 0.2881 ] Network output: [ -0.009615 1.023 0.03372 2.468e-05 -1.108e-05 0.9625 1.86e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04664 Epoch 3681 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05663 0.9121 0.9231 0.0001082 -4.856e-05 0.052 8.152e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01211 -0.005864 0.0005124 0.02275 0.9543 0.9613 0.02282 0.9087 0.9249 0.06326 ] Network output: [ 0.964 0.08427 0.0339 -5.581e-05 2.505e-05 -0.04638 -4.206e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5385 0.09137 0.07522 0.3229 0.9793 0.9909 0.6001 0.923 0.9777 0.5187 ] Network output: [ 0.01597 0.925 0.9392 -3.152e-05 1.415e-05 0.1038 -2.375e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01913 0.01381 0.02148 0.02339 0.9889 0.9923 0.01947 0.9758 0.9858 0.02761 ] Network output: [ 0.0921 -0.2137 0.8008 3.217e-05 -1.444e-05 1.229 2.425e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.592 0.5177 0.43 0.4658 0.9814 0.992 0.5938 0.9296 0.9804 0.5066 ] Network output: [ -0.05939 0.1322 1.148 -4.961e-05 2.227e-05 0.8386 -3.739e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.271 0.2656 0.2872 0.2849 0.989 0.9931 0.2711 0.9765 0.9863 0.2952 ] Network output: [ -0.0593 0.1374 1.121 -2.619e-05 1.176e-05 0.8601 -1.974e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2734 0.2725 0.2863 0.2827 0.9847 0.9907 0.2734 0.962 0.98 0.2881 ] Network output: [ -0.009641 1.023 0.03378 2.462e-05 -1.105e-05 0.9625 1.855e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04664 Epoch 3682 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0566 0.9121 0.9231 0.0001081 -4.855e-05 0.05201 8.15e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0121 -0.005867 0.0004853 0.02273 0.9543 0.9613 0.02281 0.9087 0.925 0.06323 ] Network output: [ 0.964 0.08431 0.03384 -5.578e-05 2.504e-05 -0.04644 -4.204e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5385 0.09138 0.0752 0.3227 0.9793 0.9909 0.6001 0.923 0.9777 0.5187 ] Network output: [ 0.01593 0.925 0.9392 -3.17e-05 1.423e-05 0.1038 -2.389e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01913 0.01381 0.02147 0.02337 0.9889 0.9923 0.01946 0.9759 0.9858 0.0276 ] Network output: [ 0.09205 -0.2137 0.8008 3.195e-05 -1.434e-05 1.229 2.408e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.592 0.5177 0.4301 0.4658 0.9814 0.9921 0.5938 0.9296 0.9804 0.5067 ] Network output: [ -0.05935 0.1321 1.148 -4.943e-05 2.219e-05 0.8386 -3.725e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2709 0.2656 0.2872 0.2849 0.989 0.9931 0.2711 0.9765 0.9863 0.2952 ] Network output: [ -0.05925 0.1373 1.121 -2.592e-05 1.163e-05 0.8601 -1.953e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2733 0.2724 0.2863 0.2827 0.9847 0.9907 0.2734 0.962 0.98 0.288 ] Network output: [ -0.009667 1.023 0.03383 2.455e-05 -1.102e-05 0.9624 1.85e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04664 Epoch 3683 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05658 0.9122 0.9231 0.0001081 -4.854e-05 0.05202 8.149e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01209 -0.005869 0.0004582 0.02271 0.9543 0.9613 0.0228 0.9087 0.925 0.0632 ] Network output: [ 0.9641 0.08435 0.03379 -5.575e-05 2.503e-05 -0.04649 -4.202e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5384 0.09139 0.07518 0.3226 0.9793 0.9909 0.6001 0.923 0.9777 0.5187 ] Network output: [ 0.0159 0.9251 0.9392 -3.189e-05 1.432e-05 0.1038 -2.403e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01912 0.0138 0.02145 0.02335 0.9889 0.9923 0.01945 0.9759 0.9858 0.02758 ] Network output: [ 0.09201 -0.2137 0.8008 3.173e-05 -1.425e-05 1.229 2.391e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.592 0.5177 0.4302 0.4657 0.9814 0.9921 0.5937 0.9296 0.9805 0.5067 ] Network output: [ -0.05931 0.1321 1.148 -4.924e-05 2.211e-05 0.8386 -3.711e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2709 0.2655 0.2872 0.2849 0.989 0.9931 0.271 0.9765 0.9863 0.2952 ] Network output: [ -0.0592 0.1372 1.121 -2.564e-05 1.151e-05 0.86 -1.933e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2732 0.2723 0.2862 0.2826 0.9847 0.9908 0.2733 0.962 0.98 0.288 ] Network output: [ -0.009694 1.023 0.03388 2.449e-05 -1.099e-05 0.9623 1.845e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04665 Epoch 3684 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05655 0.9122 0.9231 0.0001081 -4.854e-05 0.05203 8.148e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01208 -0.005872 0.0004311 0.02269 0.9543 0.9613 0.02279 0.9088 0.925 0.06317 ] Network output: [ 0.9641 0.0844 0.03373 -5.572e-05 2.501e-05 -0.04655 -4.199e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5383 0.09141 0.07516 0.3224 0.9793 0.9909 0.6 0.9231 0.9777 0.5187 ] Network output: [ 0.01587 0.9251 0.9392 -3.207e-05 1.44e-05 0.1039 -2.417e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01911 0.01379 0.02144 0.02333 0.9889 0.9923 0.01944 0.9759 0.9859 0.02757 ] Network output: [ 0.09196 -0.2138 0.8008 3.151e-05 -1.415e-05 1.229 2.375e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5919 0.5177 0.4303 0.4657 0.9814 0.9921 0.5937 0.9297 0.9805 0.5067 ] Network output: [ -0.05927 0.132 1.148 -4.906e-05 2.202e-05 0.8386 -3.697e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2708 0.2655 0.2872 0.2848 0.989 0.9931 0.2709 0.9766 0.9863 0.2952 ] Network output: [ -0.05915 0.1371 1.121 -2.537e-05 1.139e-05 0.86 -1.912e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2731 0.2722 0.2862 0.2826 0.9847 0.9908 0.2732 0.962 0.98 0.288 ] Network output: [ -0.00972 1.023 0.03394 2.442e-05 -1.096e-05 0.9622 1.841e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04665 Epoch 3685 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05653 0.9123 0.9231 0.0001081 -4.853e-05 0.05204 8.147e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01206 -0.005874 0.000404 0.02267 0.9543 0.9614 0.02277 0.9088 0.9251 0.06314 ] Network output: [ 0.9641 0.08444 0.03368 -5.569e-05 2.5e-05 -0.04661 -4.197e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5382 0.09142 0.07514 0.3223 0.9793 0.9909 0.6 0.9231 0.9777 0.5187 ] Network output: [ 0.01584 0.9251 0.9392 -3.226e-05 1.448e-05 0.1039 -2.431e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0191 0.01379 0.02143 0.02332 0.9889 0.9923 0.01943 0.9759 0.9859 0.02755 ] Network output: [ 0.09192 -0.2138 0.8008 3.128e-05 -1.404e-05 1.229 2.358e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5919 0.5177 0.4303 0.4656 0.9814 0.9921 0.5937 0.9297 0.9805 0.5068 ] Network output: [ -0.05923 0.1319 1.148 -4.887e-05 2.194e-05 0.8386 -3.683e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2707 0.2654 0.2872 0.2848 0.989 0.9931 0.2709 0.9766 0.9864 0.2952 ] Network output: [ -0.0591 0.137 1.121 -2.51e-05 1.127e-05 0.86 -1.892e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2731 0.2722 0.2862 0.2826 0.9847 0.9908 0.2731 0.962 0.9801 0.288 ] Network output: [ -0.009746 1.023 0.03399 2.436e-05 -1.094e-05 0.9621 1.836e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04666 Epoch 3686 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05651 0.9123 0.9231 0.0001081 -4.852e-05 0.05205 8.145e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01205 -0.005877 0.0003769 0.02264 0.9543 0.9614 0.02276 0.9089 0.9251 0.06311 ] Network output: [ 0.9642 0.08449 0.03362 -5.566e-05 2.499e-05 -0.04666 -4.194e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5382 0.09143 0.07512 0.3221 0.9793 0.9909 0.6 0.9232 0.9777 0.5187 ] Network output: [ 0.01581 0.9251 0.9392 -3.244e-05 1.456e-05 0.1039 -2.445e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01909 0.01378 0.02142 0.0233 0.9889 0.9923 0.01942 0.9759 0.9859 0.02754 ] Network output: [ 0.09187 -0.2138 0.8007 3.106e-05 -1.394e-05 1.229 2.34e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5918 0.5178 0.4304 0.4656 0.9814 0.9921 0.5936 0.9297 0.9805 0.5068 ] Network output: [ -0.05918 0.1319 1.148 -4.869e-05 2.186e-05 0.8386 -3.67e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2707 0.2654 0.2872 0.2848 0.989 0.9931 0.2708 0.9766 0.9864 0.2951 ] Network output: [ -0.05905 0.1369 1.121 -2.483e-05 1.115e-05 0.8599 -1.871e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.273 0.2721 0.2862 0.2825 0.9847 0.9908 0.273 0.9621 0.9801 0.2879 ] Network output: [ -0.009771 1.024 0.03404 2.43e-05 -1.091e-05 0.9621 1.831e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04666 Epoch 3687 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05648 0.9123 0.9231 0.0001081 -4.851e-05 0.05207 8.144e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01204 -0.005879 0.0003498 0.02262 0.9544 0.9614 0.02275 0.9089 0.9251 0.06308 ] Network output: [ 0.9642 0.08454 0.03356 -5.562e-05 2.497e-05 -0.04672 -4.192e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5381 0.09144 0.0751 0.322 0.9793 0.9909 0.6 0.9232 0.9778 0.5188 ] Network output: [ 0.01578 0.9251 0.9392 -3.263e-05 1.465e-05 0.104 -2.459e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01908 0.01378 0.0214 0.02328 0.9889 0.9924 0.01942 0.9759 0.9859 0.02752 ] Network output: [ 0.09182 -0.2138 0.8007 3.083e-05 -1.384e-05 1.23 2.323e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5918 0.5178 0.4305 0.4655 0.9814 0.9921 0.5936 0.9298 0.9805 0.5068 ] Network output: [ -0.05914 0.1318 1.148 -4.851e-05 2.178e-05 0.8386 -3.656e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2706 0.2653 0.2872 0.2847 0.989 0.9931 0.2708 0.9766 0.9864 0.2951 ] Network output: [ -0.059 0.1368 1.121 -2.455e-05 1.102e-05 0.8599 -1.85e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2729 0.272 0.2861 0.2825 0.9847 0.9908 0.2729 0.9621 0.9801 0.2879 ] Network output: [ -0.009797 1.024 0.0341 2.423e-05 -1.088e-05 0.962 1.826e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04667 Epoch 3688 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05646 0.9124 0.9231 0.000108 -4.851e-05 0.05208 8.143e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01203 -0.005882 0.0003226 0.0226 0.9544 0.9614 0.02274 0.9089 0.9252 0.06305 ] Network output: [ 0.9642 0.08458 0.03351 -5.559e-05 2.495e-05 -0.04678 -4.189e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.538 0.09146 0.07508 0.3218 0.9793 0.9909 0.5999 0.9232 0.9778 0.5188 ] Network output: [ 0.01575 0.9252 0.9392 -3.281e-05 1.473e-05 0.104 -2.473e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01908 0.01377 0.02139 0.02326 0.9889 0.9924 0.01941 0.9759 0.9859 0.02751 ] Network output: [ 0.09178 -0.2138 0.8007 3.06e-05 -1.374e-05 1.23 2.306e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5918 0.5178 0.4306 0.4654 0.9814 0.9921 0.5935 0.9298 0.9805 0.5069 ] Network output: [ -0.0591 0.1317 1.148 -4.832e-05 2.169e-05 0.8386 -3.642e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2706 0.2653 0.2871 0.2847 0.989 0.9931 0.2707 0.9766 0.9864 0.2951 ] Network output: [ -0.05895 0.1367 1.121 -2.428e-05 1.09e-05 0.8599 -1.83e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2728 0.2719 0.2861 0.2824 0.9847 0.9908 0.2728 0.9621 0.9801 0.2879 ] Network output: [ -0.009823 1.024 0.03415 2.417e-05 -1.085e-05 0.9619 1.822e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04667 Epoch 3689 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05643 0.9124 0.9231 0.000108 -4.85e-05 0.05209 8.141e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01202 -0.005884 0.0002955 0.02257 0.9544 0.9614 0.02273 0.909 0.9252 0.06302 ] Network output: [ 0.9643 0.08463 0.03345 -5.555e-05 2.494e-05 -0.04684 -4.186e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5379 0.09147 0.07505 0.3217 0.9793 0.9909 0.5999 0.9233 0.9778 0.5188 ] Network output: [ 0.01572 0.9252 0.9392 -3.3e-05 1.481e-05 0.104 -2.487e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01907 0.01376 0.02138 0.02324 0.9889 0.9924 0.0194 0.976 0.9859 0.0275 ] Network output: [ 0.09173 -0.2138 0.8007 3.036e-05 -1.363e-05 1.23 2.288e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5917 0.5178 0.4306 0.4654 0.9814 0.9921 0.5935 0.9298 0.9805 0.5069 ] Network output: [ -0.05906 0.1317 1.148 -4.814e-05 2.161e-05 0.8386 -3.628e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2705 0.2652 0.2871 0.2846 0.989 0.9931 0.2706 0.9766 0.9864 0.2951 ] Network output: [ -0.0589 0.1366 1.121 -2.4e-05 1.077e-05 0.8598 -1.809e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2727 0.2718 0.2861 0.2824 0.9847 0.9908 0.2727 0.9621 0.9801 0.2879 ] Network output: [ -0.009849 1.024 0.0342 2.411e-05 -1.082e-05 0.9618 1.817e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04668 Epoch 3690 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05641 0.9124 0.9231 0.000108 -4.849e-05 0.0521 8.14e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01201 -0.005887 0.0002684 0.02255 0.9544 0.9614 0.02271 0.909 0.9252 0.06299 ] Network output: [ 0.9643 0.08468 0.03339 -5.551e-05 2.492e-05 -0.0469 -4.184e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5379 0.09148 0.07503 0.3215 0.9793 0.991 0.5999 0.9233 0.9778 0.5188 ] Network output: [ 0.01569 0.9252 0.9392 -3.318e-05 1.49e-05 0.1041 -2.501e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01906 0.01376 0.02137 0.02322 0.9889 0.9924 0.01939 0.976 0.9859 0.02748 ] Network output: [ 0.09168 -0.2138 0.8007 3.013e-05 -1.353e-05 1.23 2.27e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5917 0.5178 0.4307 0.4653 0.9814 0.9921 0.5935 0.9299 0.9806 0.507 ] Network output: [ -0.05902 0.1316 1.148 -4.796e-05 2.153e-05 0.8386 -3.614e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2704 0.2652 0.2871 0.2846 0.989 0.9931 0.2706 0.9766 0.9864 0.2951 ] Network output: [ -0.05886 0.1365 1.121 -2.372e-05 1.065e-05 0.8598 -1.788e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2726 0.2717 0.2861 0.2824 0.9847 0.9908 0.2726 0.9621 0.9801 0.2878 ] Network output: [ -0.009875 1.024 0.03425 2.405e-05 -1.08e-05 0.9618 1.812e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04668 Epoch 3691 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05639 0.9125 0.9231 0.000108 -4.848e-05 0.05212 8.139e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01199 -0.005889 0.0002412 0.02253 0.9544 0.9614 0.0227 0.9091 0.9253 0.06296 ] Network output: [ 0.9643 0.08473 0.03334 -5.547e-05 2.49e-05 -0.04695 -4.181e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5378 0.09149 0.07501 0.3213 0.9793 0.991 0.5998 0.9234 0.9778 0.5189 ] Network output: [ 0.01566 0.9252 0.9392 -3.337e-05 1.498e-05 0.1041 -2.515e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01905 0.01375 0.02136 0.0232 0.9889 0.9924 0.01938 0.976 0.9859 0.02747 ] Network output: [ 0.09164 -0.2138 0.8007 2.989e-05 -1.342e-05 1.23 2.253e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5916 0.5178 0.4308 0.4653 0.9814 0.9921 0.5934 0.9299 0.9806 0.507 ] Network output: [ -0.05897 0.1315 1.148 -4.777e-05 2.145e-05 0.8386 -3.6e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2704 0.2651 0.2871 0.2846 0.989 0.9931 0.2705 0.9767 0.9864 0.2951 ] Network output: [ -0.05881 0.1364 1.121 -2.345e-05 1.053e-05 0.8597 -1.767e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2725 0.2716 0.286 0.2823 0.9848 0.9908 0.2726 0.9622 0.9801 0.2878 ] Network output: [ -0.009901 1.024 0.0343 2.399e-05 -1.077e-05 0.9617 1.808e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04669 Epoch 3692 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05636 0.9125 0.9231 0.000108 -4.848e-05 0.05213 8.138e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01198 -0.005892 0.000214 0.02251 0.9544 0.9614 0.02269 0.9091 0.9253 0.06293 ] Network output: [ 0.9644 0.08479 0.03328 -5.543e-05 2.489e-05 -0.04701 -4.178e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5377 0.0915 0.07498 0.3212 0.9793 0.991 0.5998 0.9234 0.9778 0.5189 ] Network output: [ 0.01563 0.9252 0.9392 -3.356e-05 1.506e-05 0.1041 -2.529e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01904 0.01375 0.02134 0.02318 0.9889 0.9924 0.01937 0.976 0.9859 0.02745 ] Network output: [ 0.09159 -0.2139 0.8006 2.965e-05 -1.331e-05 1.23 2.235e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5916 0.5178 0.4309 0.4652 0.9814 0.9921 0.5934 0.93 0.9806 0.507 ] Network output: [ -0.05893 0.1315 1.148 -4.759e-05 2.137e-05 0.8386 -3.587e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2703 0.265 0.2871 0.2845 0.989 0.9931 0.2705 0.9767 0.9864 0.2951 ] Network output: [ -0.05876 0.1363 1.121 -2.317e-05 1.04e-05 0.8597 -1.746e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2724 0.2715 0.286 0.2823 0.9848 0.9908 0.2725 0.9622 0.9801 0.2878 ] Network output: [ -0.009926 1.024 0.03436 2.393e-05 -1.074e-05 0.9616 1.803e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04669 Epoch 3693 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05634 0.9126 0.9231 0.000108 -4.847e-05 0.05215 8.136e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01197 -0.005894 0.0001869 0.02248 0.9544 0.9615 0.02268 0.9091 0.9253 0.0629 ] Network output: [ 0.9644 0.08484 0.03322 -5.539e-05 2.487e-05 -0.04707 -4.175e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5376 0.09151 0.07496 0.321 0.9794 0.991 0.5998 0.9234 0.9778 0.5189 ] Network output: [ 0.0156 0.9253 0.9392 -3.374e-05 1.515e-05 0.1042 -2.543e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01903 0.01374 0.02133 0.02316 0.9889 0.9924 0.01937 0.976 0.9859 0.02744 ] Network output: [ 0.09155 -0.2139 0.8006 2.941e-05 -1.32e-05 1.23 2.216e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5916 0.5178 0.431 0.4652 0.9814 0.9921 0.5934 0.93 0.9806 0.5071 ] Network output: [ -0.05889 0.1314 1.148 -4.741e-05 2.128e-05 0.8386 -3.573e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2703 0.265 0.2871 0.2845 0.989 0.9931 0.2704 0.9767 0.9864 0.295 ] Network output: [ -0.05871 0.1362 1.121 -2.289e-05 1.028e-05 0.8597 -1.725e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2724 0.2715 0.286 0.2823 0.9848 0.9908 0.2724 0.9622 0.9802 0.2877 ] Network output: [ -0.009952 1.024 0.03441 2.387e-05 -1.071e-05 0.9615 1.799e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0467 Epoch 3694 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05631 0.9126 0.9231 0.0001079 -4.846e-05 0.05216 8.135e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01196 -0.005897 0.0001597 0.02246 0.9545 0.9615 0.02267 0.9092 0.9254 0.06287 ] Network output: [ 0.9644 0.08489 0.03316 -5.535e-05 2.485e-05 -0.04713 -4.172e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5375 0.09152 0.07494 0.3209 0.9794 0.991 0.5997 0.9235 0.9779 0.5189 ] Network output: [ 0.01558 0.9253 0.9392 -3.393e-05 1.523e-05 0.1042 -2.557e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01902 0.01373 0.02132 0.02314 0.9889 0.9924 0.01936 0.976 0.986 0.02742 ] Network output: [ 0.0915 -0.2139 0.8006 2.917e-05 -1.309e-05 1.23 2.198e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5915 0.5178 0.431 0.4651 0.9814 0.9921 0.5933 0.93 0.9806 0.5071 ] Network output: [ -0.05885 0.1313 1.148 -4.722e-05 2.12e-05 0.8385 -3.559e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2702 0.2649 0.2871 0.2845 0.989 0.9931 0.2703 0.9767 0.9865 0.295 ] Network output: [ -0.05866 0.1361 1.122 -2.261e-05 1.015e-05 0.8596 -1.704e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2723 0.2714 0.286 0.2822 0.9848 0.9908 0.2723 0.9622 0.9802 0.2877 ] Network output: [ -0.009978 1.024 0.03446 2.381e-05 -1.069e-05 0.9614 1.794e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04671 Epoch 3695 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05629 0.9126 0.9231 0.0001079 -4.845e-05 0.05218 8.134e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01195 -0.005899 0.0001325 0.02244 0.9545 0.9615 0.02265 0.9092 0.9254 0.06284 ] Network output: [ 0.9645 0.08495 0.0331 -5.531e-05 2.483e-05 -0.0472 -4.168e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5375 0.09153 0.07491 0.3207 0.9794 0.991 0.5997 0.9235 0.9779 0.519 ] Network output: [ 0.01555 0.9253 0.9392 -3.411e-05 1.532e-05 0.1042 -2.571e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01902 0.01373 0.02131 0.02312 0.9889 0.9924 0.01935 0.9761 0.986 0.02741 ] Network output: [ 0.09145 -0.2139 0.8006 2.892e-05 -1.298e-05 1.231 2.18e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5915 0.5178 0.4311 0.4651 0.9814 0.9921 0.5933 0.9301 0.9806 0.5072 ] Network output: [ -0.05881 0.1313 1.148 -4.704e-05 2.112e-05 0.8385 -3.545e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2701 0.2649 0.2871 0.2844 0.989 0.9931 0.2703 0.9767 0.9865 0.295 ] Network output: [ -0.05861 0.136 1.122 -2.233e-05 1.003e-05 0.8596 -1.683e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2722 0.2713 0.2859 0.2822 0.9848 0.9908 0.2722 0.9622 0.9802 0.2877 ] Network output: [ -0.01 1.024 0.03451 2.375e-05 -1.066e-05 0.9614 1.79e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04671 Epoch 3696 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05627 0.9127 0.923 0.0001079 -4.844e-05 0.05219 8.132e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01193 -0.005902 0.0001053 0.02242 0.9545 0.9615 0.02264 0.9093 0.9254 0.0628 ] Network output: [ 0.9645 0.085 0.03304 -5.527e-05 2.481e-05 -0.04726 -4.165e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5374 0.09154 0.07489 0.3206 0.9794 0.991 0.5997 0.9236 0.9779 0.519 ] Network output: [ 0.01552 0.9253 0.9392 -3.43e-05 1.54e-05 0.1043 -2.585e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01901 0.01372 0.02129 0.0231 0.9889 0.9924 0.01934 0.9761 0.986 0.02739 ] Network output: [ 0.09141 -0.2139 0.8006 2.867e-05 -1.287e-05 1.231 2.161e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5914 0.5178 0.4312 0.465 0.9815 0.9921 0.5932 0.9301 0.9806 0.5072 ] Network output: [ -0.05877 0.1312 1.148 -4.686e-05 2.104e-05 0.8385 -3.531e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2701 0.2648 0.2871 0.2844 0.989 0.9931 0.2702 0.9767 0.9865 0.295 ] Network output: [ -0.05856 0.1359 1.122 -2.205e-05 9.9e-06 0.8596 -1.662e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2721 0.2712 0.2859 0.2822 0.9848 0.9908 0.2721 0.9622 0.9802 0.2877 ] Network output: [ -0.01003 1.024 0.03456 2.369e-05 -1.063e-05 0.9613 1.785e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04672 Epoch 3697 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05624 0.9127 0.923 0.0001079 -4.844e-05 0.05221 8.131e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01192 -0.005904 7.807e-05 0.02239 0.9545 0.9615 0.02263 0.9093 0.9255 0.06277 ] Network output: [ 0.9645 0.08506 0.03298 -5.522e-05 2.479e-05 -0.04732 -4.162e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5373 0.09155 0.07486 0.3204 0.9794 0.991 0.5996 0.9236 0.9779 0.519 ] Network output: [ 0.01549 0.9253 0.9392 -3.449e-05 1.548e-05 0.1043 -2.599e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.019 0.01372 0.02128 0.02308 0.9889 0.9924 0.01933 0.9761 0.986 0.02738 ] Network output: [ 0.09136 -0.214 0.8006 2.842e-05 -1.276e-05 1.231 2.142e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5914 0.5178 0.4313 0.465 0.9815 0.9921 0.5932 0.9301 0.9807 0.5073 ] Network output: [ -0.05873 0.1311 1.148 -4.667e-05 2.095e-05 0.8385 -3.517e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.27 0.2647 0.2871 0.2843 0.989 0.9931 0.2701 0.9767 0.9865 0.295 ] Network output: [ -0.05852 0.1358 1.122 -2.177e-05 9.774e-06 0.8595 -1.641e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.272 0.2711 0.2859 0.2821 0.9848 0.9908 0.272 0.9623 0.9802 0.2876 ] Network output: [ -0.01005 1.024 0.03461 2.363e-05 -1.061e-05 0.9612 1.781e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04673 Epoch 3698 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05622 0.9127 0.923 0.0001079 -4.843e-05 0.05223 8.13e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01191 -0.005906 5.084e-05 0.02237 0.9545 0.9615 0.02262 0.9093 0.9255 0.06274 ] Network output: [ 0.9646 0.08512 0.03292 -5.517e-05 2.477e-05 -0.04738 -4.158e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5372 0.09156 0.07483 0.3203 0.9794 0.991 0.5996 0.9236 0.9779 0.5191 ] Network output: [ 0.01546 0.9253 0.9392 -3.467e-05 1.557e-05 0.1044 -2.613e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01899 0.01371 0.02127 0.02307 0.9889 0.9924 0.01932 0.9761 0.986 0.02736 ] Network output: [ 0.09131 -0.214 0.8005 2.817e-05 -1.265e-05 1.231 2.123e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5913 0.5178 0.4314 0.4649 0.9815 0.9921 0.5932 0.9302 0.9807 0.5073 ] Network output: [ -0.05869 0.1311 1.148 -4.649e-05 2.087e-05 0.8385 -3.504e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2699 0.2647 0.2871 0.2843 0.989 0.9931 0.2701 0.9768 0.9865 0.295 ] Network output: [ -0.05847 0.1357 1.122 -2.149e-05 9.647e-06 0.8595 -1.619e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2719 0.271 0.2859 0.2821 0.9848 0.9908 0.2719 0.9623 0.9802 0.2876 ] Network output: [ -0.01008 1.024 0.03466 2.357e-05 -1.058e-05 0.9611 1.777e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04674 Epoch 3699 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0562 0.9128 0.923 0.0001079 -4.842e-05 0.05224 8.129e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0119 -0.005909 2.361e-05 0.02235 0.9545 0.9615 0.0226 0.9094 0.9255 0.06271 ] Network output: [ 0.9646 0.08518 0.03286 -5.513e-05 2.475e-05 -0.04745 -4.155e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5371 0.09156 0.07481 0.3201 0.9794 0.991 0.5996 0.9237 0.9779 0.5191 ] Network output: [ 0.01543 0.9254 0.9392 -3.486e-05 1.565e-05 0.1044 -2.627e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01898 0.0137 0.02126 0.02305 0.989 0.9924 0.01931 0.9761 0.986 0.02735 ] Network output: [ 0.09127 -0.214 0.8005 2.792e-05 -1.253e-05 1.231 2.104e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5913 0.5178 0.4314 0.4649 0.9815 0.9921 0.5931 0.9302 0.9807 0.5074 ] Network output: [ -0.05864 0.131 1.148 -4.631e-05 2.079e-05 0.8385 -3.49e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2699 0.2646 0.287 0.2843 0.989 0.9931 0.27 0.9768 0.9865 0.2949 ] Network output: [ -0.05842 0.1356 1.122 -2.121e-05 9.52e-06 0.8594 -1.598e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2718 0.2709 0.2858 0.282 0.9848 0.9908 0.2718 0.9623 0.9802 0.2876 ] Network output: [ -0.01011 1.025 0.03471 2.352e-05 -1.056e-05 0.9611 1.772e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04674 Epoch 3700 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05617 0.9128 0.923 0.0001078 -4.841e-05 0.05226 8.127e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01189 -0.005911 -3.639e-06 0.02232 0.9545 0.9616 0.02259 0.9094 0.9256 0.06268 ] Network output: [ 0.9646 0.08524 0.0328 -5.508e-05 2.473e-05 -0.04751 -4.151e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5371 0.09157 0.07478 0.3199 0.9794 0.991 0.5995 0.9237 0.978 0.5191 ] Network output: [ 0.0154 0.9254 0.9392 -3.505e-05 1.573e-05 0.1044 -2.641e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01897 0.0137 0.02124 0.02303 0.989 0.9924 0.0193 0.9761 0.986 0.02733 ] Network output: [ 0.09122 -0.214 0.8005 2.766e-05 -1.242e-05 1.231 2.085e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5913 0.5178 0.4315 0.4648 0.9815 0.9921 0.5931 0.9302 0.9807 0.5074 ] Network output: [ -0.0586 0.131 1.148 -4.612e-05 2.071e-05 0.8385 -3.476e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2698 0.2646 0.287 0.2842 0.9891 0.9931 0.2699 0.9768 0.9865 0.2949 ] Network output: [ -0.05837 0.1355 1.122 -2.092e-05 9.393e-06 0.8594 -1.577e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2717 0.2708 0.2858 0.282 0.9848 0.9908 0.2718 0.9623 0.9803 0.2876 ] Network output: [ -0.01013 1.025 0.03476 2.346e-05 -1.053e-05 0.961 1.768e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04675 Epoch 3701 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05615 0.9128 0.923 0.0001078 -4.841e-05 0.05228 8.126e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01187 -0.005914 -3.09e-05 0.0223 0.9546 0.9616 0.02258 0.9095 0.9256 0.06265 ] Network output: [ 0.9647 0.0853 0.03273 -5.503e-05 2.47e-05 -0.04758 -4.147e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.537 0.09158 0.07475 0.3198 0.9794 0.991 0.5995 0.9237 0.978 0.5192 ] Network output: [ 0.01538 0.9254 0.9392 -3.524e-05 1.582e-05 0.1045 -2.656e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01896 0.01369 0.02123 0.02301 0.989 0.9924 0.0193 0.9762 0.986 0.02732 ] Network output: [ 0.09117 -0.214 0.8005 2.74e-05 -1.23e-05 1.231 2.065e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5912 0.5179 0.4316 0.4647 0.9815 0.9921 0.593 0.9303 0.9807 0.5075 ] Network output: [ -0.05856 0.1309 1.148 -4.594e-05 2.062e-05 0.8385 -3.462e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2697 0.2645 0.287 0.2842 0.9891 0.9931 0.2699 0.9768 0.9865 0.2949 ] Network output: [ -0.05832 0.1354 1.122 -2.064e-05 9.265e-06 0.8594 -1.555e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2716 0.2707 0.2858 0.282 0.9848 0.9908 0.2717 0.9623 0.9803 0.2875 ] Network output: [ -0.01016 1.025 0.03481 2.34e-05 -1.051e-05 0.9609 1.764e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04676 Epoch 3702 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05613 0.9129 0.923 0.0001078 -4.84e-05 0.0523 8.125e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01186 -0.005916 -5.817e-05 0.02228 0.9546 0.9616 0.02257 0.9095 0.9256 0.06262 ] Network output: [ 0.9647 0.08536 0.03267 -5.498e-05 2.468e-05 -0.04764 -4.143e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5369 0.09158 0.07472 0.3196 0.9794 0.991 0.5995 0.9238 0.978 0.5192 ] Network output: [ 0.01535 0.9254 0.9392 -3.542e-05 1.59e-05 0.1045 -2.67e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01895 0.01368 0.02122 0.02299 0.989 0.9924 0.01929 0.9762 0.986 0.0273 ] Network output: [ 0.09113 -0.2141 0.8005 2.714e-05 -1.219e-05 1.231 2.046e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5912 0.5179 0.4317 0.4647 0.9815 0.9921 0.593 0.9303 0.9807 0.5075 ] Network output: [ -0.05852 0.1308 1.148 -4.575e-05 2.054e-05 0.8385 -3.448e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2697 0.2644 0.287 0.2842 0.9891 0.9931 0.2698 0.9768 0.9865 0.2949 ] Network output: [ -0.05827 0.1353 1.122 -2.035e-05 9.137e-06 0.8593 -1.534e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2715 0.2707 0.2858 0.2819 0.9848 0.9908 0.2716 0.9624 0.9803 0.2875 ] Network output: [ -0.01018 1.025 0.03486 2.335e-05 -1.048e-05 0.9608 1.76e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04677 Epoch 3703 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0561 0.9129 0.923 0.0001078 -4.839e-05 0.05232 8.123e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01185 -0.005918 -8.546e-05 0.02225 0.9546 0.9616 0.02255 0.9095 0.9257 0.06259 ] Network output: [ 0.9647 0.08542 0.03261 -5.493e-05 2.466e-05 -0.04771 -4.139e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5368 0.09159 0.07469 0.3195 0.9794 0.991 0.5994 0.9238 0.978 0.5192 ] Network output: [ 0.01532 0.9254 0.9392 -3.561e-05 1.599e-05 0.1046 -2.684e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01894 0.01368 0.02121 0.02297 0.989 0.9924 0.01928 0.9762 0.986 0.02729 ] Network output: [ 0.09108 -0.2141 0.8005 2.688e-05 -1.207e-05 1.232 2.026e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5911 0.5179 0.4318 0.4646 0.9815 0.9921 0.593 0.9303 0.9807 0.5076 ] Network output: [ -0.05848 0.1308 1.148 -4.557e-05 2.046e-05 0.8385 -3.434e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2696 0.2644 0.287 0.2841 0.9891 0.9931 0.2697 0.9768 0.9866 0.2949 ] Network output: [ -0.05823 0.1352 1.122 -2.007e-05 9.008e-06 0.8593 -1.512e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2714 0.2706 0.2857 0.2819 0.9848 0.9908 0.2715 0.9624 0.9803 0.2875 ] Network output: [ -0.01021 1.025 0.03491 2.329e-05 -1.046e-05 0.9607 1.755e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04678 Epoch 3704 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05608 0.9129 0.923 0.0001078 -4.838e-05 0.05234 8.122e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01184 -0.005921 -0.0001128 0.02223 0.9546 0.9616 0.02254 0.9096 0.9257 0.06256 ] Network output: [ 0.9648 0.08549 0.03254 -5.487e-05 2.463e-05 -0.04777 -4.135e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5367 0.0916 0.07466 0.3193 0.9794 0.991 0.5994 0.9239 0.978 0.5193 ] Network output: [ 0.01529 0.9254 0.9392 -3.58e-05 1.607e-05 0.1046 -2.698e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01893 0.01367 0.02119 0.02295 0.989 0.9924 0.01927 0.9762 0.9861 0.02727 ] Network output: [ 0.09103 -0.2141 0.8005 2.661e-05 -1.195e-05 1.232 2.006e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5911 0.5179 0.4319 0.4646 0.9815 0.9921 0.5929 0.9304 0.9807 0.5076 ] Network output: [ -0.05844 0.1307 1.148 -4.539e-05 2.038e-05 0.8385 -3.42e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2695 0.2643 0.287 0.2841 0.9891 0.9931 0.2697 0.9769 0.9866 0.2949 ] Network output: [ -0.05818 0.1351 1.122 -1.978e-05 8.879e-06 0.8593 -1.491e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2714 0.2705 0.2857 0.2819 0.9848 0.9908 0.2714 0.9624 0.9803 0.2875 ] Network output: [ -0.01023 1.025 0.03496 2.324e-05 -1.043e-05 0.9607 1.751e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04679 Epoch 3705 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05606 0.913 0.923 0.0001078 -4.837e-05 0.05236 8.121e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01183 -0.005923 -0.0001401 0.02221 0.9546 0.9616 0.02253 0.9096 0.9257 0.06253 ] Network output: [ 0.9648 0.08555 0.03248 -5.482e-05 2.461e-05 -0.04784 -4.131e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5366 0.0916 0.07464 0.3191 0.9794 0.991 0.5994 0.9239 0.978 0.5193 ] Network output: [ 0.01526 0.9254 0.9392 -3.599e-05 1.616e-05 0.1046 -2.712e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01892 0.01366 0.02118 0.02293 0.989 0.9924 0.01926 0.9762 0.9861 0.02726 ] Network output: [ 0.09099 -0.2142 0.8005 2.635e-05 -1.183e-05 1.232 1.986e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5911 0.5179 0.432 0.4645 0.9815 0.9921 0.5929 0.9304 0.9808 0.5077 ] Network output: [ -0.0584 0.1307 1.148 -4.52e-05 2.029e-05 0.8384 -3.407e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2695 0.2642 0.287 0.284 0.9891 0.9931 0.2696 0.9769 0.9866 0.2948 ] Network output: [ -0.05813 0.135 1.122 -1.949e-05 8.75e-06 0.8592 -1.469e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2713 0.2704 0.2857 0.2818 0.9848 0.9908 0.2713 0.9624 0.9803 0.2874 ] Network output: [ -0.01026 1.025 0.03501 2.318e-05 -1.041e-05 0.9606 1.747e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0468 Epoch 3706 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05603 0.913 0.923 0.0001077 -4.837e-05 0.05238 8.119e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01181 -0.005925 -0.0001674 0.02218 0.9546 0.9616 0.02252 0.9097 0.9258 0.0625 ] Network output: [ 0.9648 0.08562 0.03242 -5.476e-05 2.459e-05 -0.04791 -4.127e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5366 0.09161 0.07461 0.319 0.9794 0.991 0.5993 0.9239 0.978 0.5193 ] Network output: [ 0.01524 0.9255 0.9392 -3.618e-05 1.624e-05 0.1047 -2.726e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01891 0.01366 0.02117 0.02291 0.989 0.9924 0.01925 0.9762 0.9861 0.02724 ] Network output: [ 0.09094 -0.2142 0.8005 2.608e-05 -1.171e-05 1.232 1.965e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.591 0.5179 0.432 0.4645 0.9815 0.9921 0.5928 0.9304 0.9808 0.5077 ] Network output: [ -0.05836 0.1306 1.148 -4.502e-05 2.021e-05 0.8384 -3.393e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2694 0.2642 0.287 0.284 0.9891 0.9931 0.2695 0.9769 0.9866 0.2948 ] Network output: [ -0.05808 0.1349 1.122 -1.92e-05 8.62e-06 0.8592 -1.447e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2712 0.2703 0.2857 0.2818 0.9848 0.9908 0.2712 0.9624 0.9803 0.2874 ] Network output: [ -0.01028 1.025 0.03505 2.313e-05 -1.038e-05 0.9605 1.743e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04681 Epoch 3707 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05601 0.913 0.923 0.0001077 -4.836e-05 0.0524 8.118e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0118 -0.005928 -0.0001947 0.02216 0.9546 0.9616 0.02251 0.9097 0.9258 0.06247 ] Network output: [ 0.9649 0.08569 0.03235 -5.471e-05 2.456e-05 -0.04798 -4.123e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5365 0.09161 0.07457 0.3188 0.9795 0.991 0.5993 0.924 0.9781 0.5194 ] Network output: [ 0.01521 0.9255 0.9392 -3.636e-05 1.633e-05 0.1047 -2.741e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01891 0.01365 0.02116 0.02289 0.989 0.9924 0.01924 0.9762 0.9861 0.02723 ] Network output: [ 0.09089 -0.2142 0.8004 2.581e-05 -1.159e-05 1.232 1.945e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.591 0.5179 0.4321 0.4644 0.9815 0.9921 0.5928 0.9305 0.9808 0.5078 ] Network output: [ -0.05832 0.1305 1.147 -4.483e-05 2.013e-05 0.8384 -3.379e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2693 0.2641 0.287 0.284 0.9891 0.9931 0.2695 0.9769 0.9866 0.2948 ] Network output: [ -0.05804 0.1348 1.122 -1.891e-05 8.49e-06 0.8591 -1.425e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2711 0.2702 0.2856 0.2817 0.9848 0.9908 0.2711 0.9625 0.9803 0.2874 ] Network output: [ -0.01031 1.025 0.0351 2.307e-05 -1.036e-05 0.9604 1.739e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04682 Epoch 3708 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05599 0.9131 0.923 0.0001077 -4.835e-05 0.05242 8.117e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01179 -0.00593 -0.0002221 0.02214 0.9547 0.9617 0.02249 0.9097 0.9258 0.06244 ] Network output: [ 0.9649 0.08575 0.03229 -5.465e-05 2.453e-05 -0.04804 -4.119e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5364 0.09162 0.07454 0.3187 0.9795 0.991 0.5993 0.924 0.9781 0.5194 ] Network output: [ 0.01518 0.9255 0.9393 -3.655e-05 1.641e-05 0.1048 -2.755e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0189 0.01364 0.02114 0.02287 0.989 0.9924 0.01923 0.9763 0.9861 0.02721 ] Network output: [ 0.09085 -0.2143 0.8004 2.553e-05 -1.146e-05 1.232 1.924e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5909 0.5179 0.4322 0.4644 0.9815 0.9921 0.5928 0.9305 0.9808 0.5078 ] Network output: [ -0.05828 0.1305 1.147 -4.465e-05 2.004e-05 0.8384 -3.365e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2692 0.264 0.2869 0.2839 0.9891 0.9931 0.2694 0.9769 0.9866 0.2948 ] Network output: [ -0.05799 0.1347 1.122 -1.862e-05 8.359e-06 0.8591 -1.403e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.271 0.2701 0.2856 0.2817 0.9848 0.9908 0.271 0.9625 0.9804 0.2873 ] Network output: [ -0.01033 1.025 0.03515 2.302e-05 -1.033e-05 0.9603 1.735e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04683 Epoch 3709 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05596 0.9131 0.923 0.0001077 -4.834e-05 0.05244 8.115e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01178 -0.005932 -0.0002495 0.02211 0.9547 0.9617 0.02248 0.9098 0.9259 0.06241 ] Network output: [ 0.9649 0.08582 0.03222 -5.459e-05 2.451e-05 -0.04811 -4.114e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5363 0.09162 0.07451 0.3185 0.9795 0.991 0.5992 0.9241 0.9781 0.5195 ] Network output: [ 0.01515 0.9255 0.9393 -3.674e-05 1.65e-05 0.1048 -2.769e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01889 0.01364 0.02113 0.02285 0.989 0.9924 0.01922 0.9763 0.9861 0.0272 ] Network output: [ 0.0908 -0.2143 0.8004 2.525e-05 -1.134e-05 1.232 1.903e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5909 0.5179 0.4323 0.4643 0.9815 0.9922 0.5927 0.9306 0.9808 0.5079 ] Network output: [ -0.05824 0.1304 1.147 -4.446e-05 1.996e-05 0.8384 -3.351e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2692 0.264 0.2869 0.2839 0.9891 0.9932 0.2693 0.9769 0.9866 0.2948 ] Network output: [ -0.05794 0.1346 1.122 -1.833e-05 8.228e-06 0.8591 -1.381e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2709 0.27 0.2856 0.2817 0.9848 0.9909 0.2709 0.9625 0.9804 0.2873 ] Network output: [ -0.01036 1.025 0.0352 2.297e-05 -1.031e-05 0.9603 1.731e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04684 Epoch 3710 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05594 0.9131 0.923 0.0001077 -4.833e-05 0.05246 8.114e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01176 -0.005935 -0.0002769 0.02209 0.9547 0.9617 0.02247 0.9098 0.9259 0.06238 ] Network output: [ 0.965 0.08589 0.03216 -5.453e-05 2.448e-05 -0.04818 -4.11e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5362 0.09162 0.07448 0.3183 0.9795 0.991 0.5992 0.9241 0.9781 0.5195 ] Network output: [ 0.01513 0.9255 0.9393 -3.693e-05 1.658e-05 0.1048 -2.783e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01888 0.01363 0.02112 0.02283 0.989 0.9924 0.01921 0.9763 0.9861 0.02718 ] Network output: [ 0.09076 -0.2143 0.8004 2.498e-05 -1.121e-05 1.232 1.882e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5908 0.5179 0.4324 0.4643 0.9815 0.9922 0.5927 0.9306 0.9808 0.508 ] Network output: [ -0.0582 0.1304 1.147 -4.428e-05 1.988e-05 0.8384 -3.337e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2691 0.2639 0.2869 0.2838 0.9891 0.9932 0.2692 0.9769 0.9866 0.2948 ] Network output: [ -0.05789 0.1345 1.122 -1.803e-05 8.096e-06 0.859 -1.359e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2708 0.2699 0.2856 0.2816 0.9848 0.9909 0.2708 0.9625 0.9804 0.2873 ] Network output: [ -0.01039 1.025 0.03525 2.291e-05 -1.029e-05 0.9602 1.727e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04685 Epoch 3711 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05592 0.9131 0.923 0.0001076 -4.833e-05 0.05249 8.112e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01175 -0.005937 -0.0003043 0.02207 0.9547 0.9617 0.02245 0.9099 0.9259 0.06235 ] Network output: [ 0.965 0.08597 0.03209 -5.447e-05 2.445e-05 -0.04825 -4.105e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5361 0.09163 0.07445 0.3182 0.9795 0.991 0.5992 0.9241 0.9781 0.5195 ] Network output: [ 0.0151 0.9255 0.9393 -3.712e-05 1.667e-05 0.1049 -2.798e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01887 0.01362 0.02111 0.02281 0.989 0.9925 0.0192 0.9763 0.9861 0.02717 ] Network output: [ 0.09071 -0.2144 0.8004 2.469e-05 -1.109e-05 1.233 1.861e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5908 0.5179 0.4325 0.4642 0.9815 0.9922 0.5926 0.9306 0.9808 0.508 ] Network output: [ -0.05816 0.1303 1.147 -4.409e-05 1.979e-05 0.8384 -3.323e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.269 0.2638 0.2869 0.2838 0.9891 0.9932 0.2692 0.977 0.9866 0.2947 ] Network output: [ -0.05785 0.1345 1.122 -1.774e-05 7.964e-06 0.859 -1.337e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2707 0.2698 0.2855 0.2816 0.9848 0.9909 0.2707 0.9625 0.9804 0.2873 ] Network output: [ -0.01041 1.026 0.03529 2.286e-05 -1.026e-05 0.9601 1.723e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04686 Epoch 3712 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0559 0.9132 0.923 0.0001076 -4.832e-05 0.05251 8.111e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01174 -0.005939 -0.0003317 0.02204 0.9547 0.9617 0.02244 0.9099 0.926 0.06232 ] Network output: [ 0.965 0.08604 0.03202 -5.441e-05 2.443e-05 -0.04833 -4.1e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5361 0.09163 0.07441 0.318 0.9795 0.9911 0.5991 0.9242 0.9781 0.5196 ] Network output: [ 0.01507 0.9255 0.9393 -3.731e-05 1.675e-05 0.1049 -2.812e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01886 0.01361 0.02109 0.02279 0.989 0.9925 0.01919 0.9763 0.9861 0.02715 ] Network output: [ 0.09066 -0.2144 0.8004 2.441e-05 -1.096e-05 1.233 1.84e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5907 0.5179 0.4326 0.4642 0.9816 0.9922 0.5926 0.9307 0.9809 0.5081 ] Network output: [ -0.05812 0.1303 1.147 -4.391e-05 1.971e-05 0.8383 -3.309e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.269 0.2638 0.2869 0.2838 0.9891 0.9932 0.2691 0.977 0.9867 0.2947 ] Network output: [ -0.0578 0.1344 1.122 -1.744e-05 7.831e-06 0.8589 -1.315e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2706 0.2697 0.2855 0.2815 0.9849 0.9909 0.2706 0.9625 0.9804 0.2872 ] Network output: [ -0.01044 1.026 0.03534 2.281e-05 -1.024e-05 0.96 1.719e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04687 Epoch 3713 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05587 0.9132 0.923 0.0001076 -4.831e-05 0.05253 8.11e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01173 -0.005942 -0.0003591 0.02202 0.9547 0.9617 0.02243 0.9099 0.926 0.06229 ] Network output: [ 0.9651 0.08611 0.03195 -5.434e-05 2.44e-05 -0.0484 -4.096e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.536 0.09163 0.07438 0.3178 0.9795 0.9911 0.5991 0.9242 0.9782 0.5196 ] Network output: [ 0.01505 0.9255 0.9393 -3.75e-05 1.684e-05 0.105 -2.826e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01885 0.01361 0.02108 0.02277 0.989 0.9925 0.01918 0.9763 0.9862 0.02714 ] Network output: [ 0.09062 -0.2144 0.8004 2.412e-05 -1.083e-05 1.233 1.818e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5907 0.5179 0.4327 0.4641 0.9816 0.9922 0.5925 0.9307 0.9809 0.5081 ] Network output: [ -0.05808 0.1302 1.147 -4.372e-05 1.963e-05 0.8383 -3.295e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2689 0.2637 0.2869 0.2837 0.9891 0.9932 0.269 0.977 0.9867 0.2947 ] Network output: [ -0.05775 0.1343 1.122 -1.715e-05 7.698e-06 0.8589 -1.292e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2705 0.2696 0.2855 0.2815 0.9849 0.9909 0.2705 0.9626 0.9804 0.2872 ] Network output: [ -0.01046 1.026 0.03539 2.276e-05 -1.022e-05 0.9599 1.715e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04688 Epoch 3714 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05585 0.9132 0.923 0.0001076 -4.83e-05 0.05256 8.108e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01171 -0.005944 -0.0003866 0.02199 0.9547 0.9617 0.02242 0.91 0.926 0.06226 ] Network output: [ 0.9651 0.08619 0.03189 -5.428e-05 2.437e-05 -0.04847 -4.091e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5359 0.09163 0.07435 0.3177 0.9795 0.9911 0.5991 0.9242 0.9782 0.5197 ] Network output: [ 0.01502 0.9255 0.9393 -3.769e-05 1.692e-05 0.105 -2.841e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01884 0.0136 0.02107 0.02275 0.989 0.9925 0.01917 0.9764 0.9862 0.02712 ] Network output: [ 0.09057 -0.2145 0.8004 2.383e-05 -1.07e-05 1.233 1.796e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5907 0.5179 0.4328 0.4641 0.9816 0.9922 0.5925 0.9307 0.9809 0.5082 ] Network output: [ -0.05805 0.1302 1.147 -4.353e-05 1.954e-05 0.8383 -3.281e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2688 0.2636 0.2869 0.2837 0.9891 0.9932 0.269 0.977 0.9867 0.2947 ] Network output: [ -0.0577 0.1342 1.122 -1.685e-05 7.565e-06 0.8588 -1.27e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2704 0.2695 0.2855 0.2815 0.9849 0.9909 0.2704 0.9626 0.9804 0.2872 ] Network output: [ -0.01049 1.026 0.03544 2.271e-05 -1.019e-05 0.9599 1.711e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0469 Epoch 3715 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05583 0.9132 0.923 0.0001076 -4.829e-05 0.05258 8.107e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0117 -0.005946 -0.000414 0.02197 0.9548 0.9617 0.0224 0.91 0.9261 0.06223 ] Network output: [ 0.9651 0.08627 0.03182 -5.421e-05 2.434e-05 -0.04854 -4.086e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5358 0.09163 0.07431 0.3175 0.9795 0.9911 0.599 0.9243 0.9782 0.5197 ] Network output: [ 0.015 0.9255 0.9393 -3.788e-05 1.701e-05 0.1051 -2.855e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01883 0.01359 0.02106 0.02273 0.989 0.9925 0.01916 0.9764 0.9862 0.02711 ] Network output: [ 0.09052 -0.2145 0.8004 2.354e-05 -1.057e-05 1.233 1.774e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5906 0.5179 0.4328 0.464 0.9816 0.9922 0.5925 0.9308 0.9809 0.5083 ] Network output: [ -0.05801 0.1301 1.147 -4.335e-05 1.946e-05 0.8383 -3.267e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2687 0.2636 0.2869 0.2836 0.9891 0.9932 0.2689 0.977 0.9867 0.2947 ] Network output: [ -0.05766 0.1341 1.122 -1.655e-05 7.43e-06 0.8588 -1.247e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2703 0.2694 0.2854 0.2814 0.9849 0.9909 0.2704 0.9626 0.9805 0.2872 ] Network output: [ -0.01051 1.026 0.03548 2.266e-05 -1.017e-05 0.9598 1.707e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04691 Epoch 3716 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0558 0.9133 0.9229 0.0001076 -4.828e-05 0.05261 8.105e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01169 -0.005948 -0.0004415 0.02195 0.9548 0.9618 0.02239 0.91 0.9261 0.0622 ] Network output: [ 0.9652 0.08634 0.03175 -5.415e-05 2.431e-05 -0.04862 -4.081e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5357 0.09164 0.07428 0.3173 0.9795 0.9911 0.599 0.9243 0.9782 0.5198 ] Network output: [ 0.01497 0.9255 0.9393 -3.807e-05 1.709e-05 0.1051 -2.869e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01882 0.01359 0.02104 0.02271 0.989 0.9925 0.01915 0.9764 0.9862 0.02709 ] Network output: [ 0.09047 -0.2146 0.8004 2.325e-05 -1.044e-05 1.233 1.752e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5906 0.5179 0.4329 0.464 0.9816 0.9922 0.5924 0.9308 0.9809 0.5083 ] Network output: [ -0.05797 0.1301 1.147 -4.316e-05 1.938e-05 0.8383 -3.253e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2687 0.2635 0.2869 0.2836 0.9891 0.9932 0.2688 0.977 0.9867 0.2947 ] Network output: [ -0.05761 0.134 1.122 -1.625e-05 7.296e-06 0.8588 -1.225e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2702 0.2694 0.2854 0.2814 0.9849 0.9909 0.2703 0.9626 0.9805 0.2871 ] Network output: [ -0.01054 1.026 0.03553 2.261e-05 -1.015e-05 0.9597 1.704e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04692 Epoch 3717 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05578 0.9133 0.9229 0.0001075 -4.828e-05 0.05264 8.104e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01168 -0.005951 -0.000469 0.02192 0.9548 0.9618 0.02238 0.9101 0.9261 0.06216 ] Network output: [ 0.9652 0.08642 0.03168 -5.408e-05 2.428e-05 -0.04869 -4.076e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5356 0.09164 0.07424 0.3172 0.9795 0.9911 0.599 0.9244 0.9782 0.5198 ] Network output: [ 0.01494 0.9255 0.9393 -3.827e-05 1.718e-05 0.1052 -2.884e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01881 0.01358 0.02103 0.02269 0.989 0.9925 0.01915 0.9764 0.9862 0.02708 ] Network output: [ 0.09043 -0.2146 0.8004 2.295e-05 -1.03e-05 1.233 1.73e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5905 0.5179 0.433 0.4639 0.9816 0.9922 0.5924 0.9308 0.9809 0.5084 ] Network output: [ -0.05793 0.13 1.147 -4.297e-05 1.929e-05 0.8382 -3.239e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2686 0.2634 0.2868 0.2835 0.9891 0.9932 0.2687 0.977 0.9867 0.2946 ] Network output: [ -0.05756 0.1339 1.122 -1.595e-05 7.161e-06 0.8587 -1.202e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2701 0.2693 0.2854 0.2813 0.9849 0.9909 0.2702 0.9626 0.9805 0.2871 ] Network output: [ -0.01056 1.026 0.03558 2.256e-05 -1.013e-05 0.9596 1.7e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04693 Epoch 3718 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05576 0.9133 0.9229 0.0001075 -4.827e-05 0.05266 8.103e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01166 -0.005953 -0.0004966 0.0219 0.9548 0.9618 0.02237 0.9101 0.9262 0.06213 ] Network output: [ 0.9652 0.0865 0.03161 -5.401e-05 2.425e-05 -0.04877 -4.07e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5355 0.09164 0.0742 0.317 0.9795 0.9911 0.5989 0.9244 0.9782 0.5199 ] Network output: [ 0.01492 0.9255 0.9393 -3.846e-05 1.726e-05 0.1052 -2.898e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0188 0.01357 0.02102 0.02267 0.9891 0.9925 0.01914 0.9764 0.9862 0.02706 ] Network output: [ 0.09038 -0.2147 0.8004 2.265e-05 -1.017e-05 1.234 1.707e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5905 0.5179 0.4331 0.4639 0.9816 0.9922 0.5923 0.9309 0.9809 0.5085 ] Network output: [ -0.05789 0.13 1.147 -4.279e-05 1.921e-05 0.8382 -3.225e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2685 0.2633 0.2868 0.2835 0.9891 0.9932 0.2687 0.9771 0.9867 0.2946 ] Network output: [ -0.05751 0.1338 1.122 -1.565e-05 7.025e-06 0.8587 -1.179e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.27 0.2692 0.2854 0.2813 0.9849 0.9909 0.2701 0.9627 0.9805 0.2871 ] Network output: [ -0.01059 1.026 0.03562 2.251e-05 -1.01e-05 0.9595 1.696e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04695 Epoch 3719 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05574 0.9133 0.9229 0.0001075 -4.826e-05 0.05269 8.101e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01165 -0.005955 -0.0005241 0.02187 0.9548 0.9618 0.02235 0.9102 0.9262 0.0621 ] Network output: [ 0.9652 0.08659 0.03154 -5.394e-05 2.422e-05 -0.04884 -4.065e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5355 0.09164 0.07417 0.3168 0.9795 0.9911 0.5989 0.9244 0.9782 0.5199 ] Network output: [ 0.01489 0.9256 0.9393 -3.865e-05 1.735e-05 0.1052 -2.913e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01879 0.01356 0.021 0.02265 0.9891 0.9925 0.01913 0.9764 0.9862 0.02705 ] Network output: [ 0.09033 -0.2147 0.8004 2.235e-05 -1.003e-05 1.234 1.685e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5904 0.5178 0.4332 0.4638 0.9816 0.9922 0.5923 0.9309 0.981 0.5085 ] Network output: [ -0.05785 0.1299 1.147 -4.26e-05 1.912e-05 0.8382 -3.21e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2684 0.2633 0.2868 0.2835 0.9891 0.9932 0.2686 0.9771 0.9867 0.2946 ] Network output: [ -0.05747 0.1337 1.123 -1.534e-05 6.889e-06 0.8586 -1.156e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2699 0.2691 0.2853 0.2813 0.9849 0.9909 0.27 0.9627 0.9805 0.2871 ] Network output: [ -0.01061 1.026 0.03567 2.246e-05 -1.008e-05 0.9595 1.692e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04696 Epoch 3720 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05572 0.9134 0.9229 0.0001075 -4.825e-05 0.05272 8.1e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01164 -0.005957 -0.0005517 0.02185 0.9548 0.9618 0.02234 0.9102 0.9262 0.06207 ] Network output: [ 0.9653 0.08667 0.03147 -5.387e-05 2.418e-05 -0.04892 -4.06e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5354 0.09163 0.07413 0.3167 0.9796 0.9911 0.5989 0.9245 0.9783 0.52 ] Network output: [ 0.01487 0.9256 0.9393 -3.884e-05 1.744e-05 0.1053 -2.927e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01878 0.01356 0.02099 0.02263 0.9891 0.9925 0.01912 0.9764 0.9862 0.02703 ] Network output: [ 0.09029 -0.2147 0.8004 2.205e-05 -9.898e-06 1.234 1.662e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5904 0.5178 0.4333 0.4638 0.9816 0.9922 0.5922 0.9309 0.981 0.5086 ] Network output: [ -0.05781 0.1299 1.147 -4.241e-05 1.904e-05 0.8382 -3.196e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2684 0.2632 0.2868 0.2834 0.9891 0.9932 0.2685 0.9771 0.9867 0.2946 ] Network output: [ -0.05742 0.1336 1.123 -1.504e-05 6.752e-06 0.8586 -1.133e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2698 0.269 0.2853 0.2812 0.9849 0.9909 0.2699 0.9627 0.9805 0.287 ] Network output: [ -0.01064 1.026 0.03571 2.241e-05 -1.006e-05 0.9594 1.689e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04697 Epoch 3721 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05569 0.9134 0.9229 0.0001075 -4.824e-05 0.05275 8.098e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01163 -0.005959 -0.0005793 0.02183 0.9548 0.9618 0.02233 0.9102 0.9263 0.06204 ] Network output: [ 0.9653 0.08675 0.0314 -5.379e-05 2.415e-05 -0.049 -4.054e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5353 0.09163 0.07409 0.3165 0.9796 0.9911 0.5988 0.9245 0.9783 0.52 ] Network output: [ 0.01484 0.9256 0.9393 -3.903e-05 1.752e-05 0.1053 -2.942e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01877 0.01355 0.02098 0.02261 0.9891 0.9925 0.01911 0.9765 0.9862 0.02702 ] Network output: [ 0.09024 -0.2148 0.8004 2.174e-05 -9.76e-06 1.234 1.638e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5903 0.5178 0.4334 0.4637 0.9816 0.9922 0.5922 0.931 0.981 0.5087 ] Network output: [ -0.05777 0.1298 1.147 -4.222e-05 1.896e-05 0.8382 -3.182e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2683 0.2631 0.2868 0.2834 0.9892 0.9932 0.2684 0.9771 0.9868 0.2946 ] Network output: [ -0.05737 0.1336 1.123 -1.473e-05 6.614e-06 0.8585 -1.11e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2697 0.2689 0.2853 0.2812 0.9849 0.9909 0.2698 0.9627 0.9805 0.287 ] Network output: [ -0.01066 1.026 0.03576 2.236e-05 -1.004e-05 0.9593 1.685e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04699 Epoch 3722 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05567 0.9134 0.9229 0.0001074 -4.823e-05 0.05277 8.097e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01161 -0.005962 -0.0006069 0.0218 0.9549 0.9618 0.02232 0.9103 0.9263 0.06201 ] Network output: [ 0.9653 0.08684 0.03133 -5.372e-05 2.412e-05 -0.04908 -4.048e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5352 0.09163 0.07405 0.3163 0.9796 0.9911 0.5988 0.9246 0.9783 0.5201 ] Network output: [ 0.01482 0.9256 0.9393 -3.923e-05 1.761e-05 0.1054 -2.956e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01876 0.01354 0.02097 0.02259 0.9891 0.9925 0.0191 0.9765 0.9862 0.027 ] Network output: [ 0.09019 -0.2148 0.8004 2.143e-05 -9.621e-06 1.234 1.615e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5903 0.5178 0.4335 0.4637 0.9816 0.9922 0.5922 0.931 0.981 0.5088 ] Network output: [ -0.05774 0.1298 1.147 -4.203e-05 1.887e-05 0.8381 -3.168e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2682 0.2631 0.2868 0.2833 0.9892 0.9932 0.2683 0.9771 0.9868 0.2946 ] Network output: [ -0.05733 0.1335 1.123 -1.443e-05 6.476e-06 0.8585 -1.087e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2696 0.2688 0.2853 0.2812 0.9849 0.9909 0.2697 0.9627 0.9805 0.287 ] Network output: [ -0.01069 1.026 0.0358 2.231e-05 -1.002e-05 0.9592 1.681e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.047 Epoch 3723 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05565 0.9134 0.9229 0.0001074 -4.822e-05 0.0528 8.095e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0116 -0.005964 -0.0006345 0.02178 0.9549 0.9618 0.0223 0.9103 0.9263 0.06198 ] Network output: [ 0.9654 0.08692 0.03126 -5.364e-05 2.408e-05 -0.04915 -4.043e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5351 0.09163 0.07402 0.3162 0.9796 0.9911 0.5988 0.9246 0.9783 0.5202 ] Network output: [ 0.01479 0.9256 0.9393 -3.942e-05 1.77e-05 0.1054 -2.971e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01875 0.01353 0.02095 0.02257 0.9891 0.9925 0.01909 0.9765 0.9863 0.02699 ] Network output: [ 0.09015 -0.2149 0.8004 2.112e-05 -9.481e-06 1.234 1.592e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5903 0.5178 0.4336 0.4636 0.9816 0.9922 0.5921 0.9311 0.981 0.5088 ] Network output: [ -0.0577 0.1297 1.147 -4.185e-05 1.879e-05 0.8381 -3.154e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2681 0.263 0.2868 0.2833 0.9892 0.9932 0.2683 0.9771 0.9868 0.2945 ] Network output: [ -0.05728 0.1334 1.123 -1.412e-05 6.338e-06 0.8585 -1.064e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2695 0.2687 0.2852 0.2811 0.9849 0.9909 0.2696 0.9627 0.9806 0.2869 ] Network output: [ -0.01071 1.027 0.03585 2.226e-05 -9.994e-06 0.9591 1.678e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04702 Epoch 3724 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05563 0.9135 0.9229 0.0001074 -4.821e-05 0.05283 8.094e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01159 -0.005966 -0.0006622 0.02175 0.9549 0.9619 0.02229 0.9104 0.9264 0.06195 ] Network output: [ 0.9654 0.08701 0.03118 -5.357e-05 2.405e-05 -0.04923 -4.037e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.535 0.09163 0.07398 0.316 0.9796 0.9911 0.5987 0.9246 0.9783 0.5202 ] Network output: [ 0.01477 0.9256 0.9393 -3.961e-05 1.778e-05 0.1055 -2.985e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01874 0.01353 0.02094 0.02255 0.9891 0.9925 0.01908 0.9765 0.9863 0.02698 ] Network output: [ 0.0901 -0.215 0.8004 2.08e-05 -9.339e-06 1.234 1.568e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5902 0.5178 0.4337 0.4636 0.9816 0.9922 0.5921 0.9311 0.981 0.5089 ] Network output: [ -0.05766 0.1297 1.147 -4.166e-05 1.87e-05 0.8381 -3.139e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.268 0.2629 0.2868 0.2832 0.9892 0.9932 0.2682 0.9772 0.9868 0.2945 ] Network output: [ -0.05723 0.1333 1.123 -1.381e-05 6.199e-06 0.8584 -1.041e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2694 0.2686 0.2852 0.2811 0.9849 0.9909 0.2695 0.9628 0.9806 0.2869 ] Network output: [ -0.01074 1.027 0.03589 2.221e-05 -9.972e-06 0.9591 1.674e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04703 Epoch 3725 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0556 0.9135 0.9229 0.0001074 -4.821e-05 0.05286 8.092e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01157 -0.005968 -0.0006899 0.02173 0.9549 0.9619 0.02228 0.9104 0.9264 0.06192 ] Network output: [ 0.9654 0.0871 0.03111 -5.349e-05 2.401e-05 -0.04932 -4.031e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5349 0.09162 0.07394 0.3158 0.9796 0.9911 0.5987 0.9247 0.9783 0.5203 ] Network output: [ 0.01474 0.9256 0.9393 -3.981e-05 1.787e-05 0.1055 -3e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01873 0.01352 0.02093 0.02252 0.9891 0.9925 0.01907 0.9765 0.9863 0.02696 ] Network output: [ 0.09005 -0.215 0.8004 2.048e-05 -9.196e-06 1.235 1.544e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5902 0.5178 0.4338 0.4636 0.9816 0.9922 0.592 0.9311 0.981 0.509 ] Network output: [ -0.05762 0.1296 1.147 -4.147e-05 1.862e-05 0.838 -3.125e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.268 0.2628 0.2867 0.2832 0.9892 0.9932 0.2681 0.9772 0.9868 0.2945 ] Network output: [ -0.05719 0.1332 1.123 -1.35e-05 6.059e-06 0.8584 -1.017e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2694 0.2685 0.2852 0.281 0.9849 0.9909 0.2694 0.9628 0.9806 0.2869 ] Network output: [ -0.01077 1.027 0.03594 2.216e-05 -9.951e-06 0.959 1.67e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04705 Epoch 3726 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05558 0.9135 0.9229 0.0001074 -4.82e-05 0.0529 8.091e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01156 -0.00597 -0.0007176 0.0217 0.9549 0.9619 0.02226 0.9104 0.9264 0.06189 ] Network output: [ 0.9655 0.08719 0.03104 -5.341e-05 2.398e-05 -0.0494 -4.025e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5348 0.09162 0.0739 0.3156 0.9796 0.9911 0.5987 0.9247 0.9784 0.5203 ] Network output: [ 0.01472 0.9256 0.9393 -4e-05 1.796e-05 0.1056 -3.015e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01872 0.01351 0.02091 0.0225 0.9891 0.9925 0.01906 0.9765 0.9863 0.02695 ] Network output: [ 0.09001 -0.2151 0.8004 2.016e-05 -9.052e-06 1.235 1.52e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5901 0.5178 0.4339 0.4635 0.9816 0.9922 0.592 0.9312 0.981 0.5091 ] Network output: [ -0.05758 0.1296 1.147 -4.128e-05 1.853e-05 0.838 -3.111e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2679 0.2627 0.2867 0.2831 0.9892 0.9932 0.268 0.9772 0.9868 0.2945 ] Network output: [ -0.05714 0.1331 1.123 -1.318e-05 5.918e-06 0.8583 -9.935e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2693 0.2684 0.2852 0.281 0.9849 0.9909 0.2693 0.9628 0.9806 0.2869 ] Network output: [ -0.01079 1.027 0.03598 2.212e-05 -9.929e-06 0.9589 1.667e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04707 Epoch 3727 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05556 0.9135 0.9229 0.0001073 -4.819e-05 0.05293 8.089e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01155 -0.005972 -0.0007453 0.02168 0.9549 0.9619 0.02225 0.9105 0.9265 0.06186 ] Network output: [ 0.9655 0.08728 0.03096 -5.333e-05 2.394e-05 -0.04948 -4.019e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5347 0.09162 0.07385 0.3155 0.9796 0.9911 0.5986 0.9247 0.9784 0.5204 ] Network output: [ 0.01469 0.9256 0.9393 -4.02e-05 1.805e-05 0.1056 -3.029e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01871 0.0135 0.0209 0.02248 0.9891 0.9925 0.01904 0.9766 0.9863 0.02693 ] Network output: [ 0.08996 -0.2151 0.8004 1.984e-05 -8.907e-06 1.235 1.495e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5901 0.5178 0.434 0.4635 0.9816 0.9922 0.5919 0.9312 0.9811 0.5091 ] Network output: [ -0.05755 0.1295 1.147 -4.109e-05 1.844e-05 0.838 -3.096e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2678 0.2627 0.2867 0.2831 0.9892 0.9932 0.2679 0.9772 0.9868 0.2945 ] Network output: [ -0.05709 0.133 1.123 -1.287e-05 5.777e-06 0.8583 -9.698e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2692 0.2683 0.2851 0.281 0.9849 0.9909 0.2692 0.9628 0.9806 0.2868 ] Network output: [ -0.01082 1.027 0.03603 2.207e-05 -9.908e-06 0.9588 1.663e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04708 Epoch 3728 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05554 0.9135 0.9229 0.0001073 -4.818e-05 0.05296 8.088e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01153 -0.005975 -0.0007731 0.02166 0.9549 0.9619 0.02224 0.9105 0.9265 0.06183 ] Network output: [ 0.9655 0.08738 0.03089 -5.325e-05 2.39e-05 -0.04956 -4.013e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5346 0.09161 0.07381 0.3153 0.9796 0.9911 0.5986 0.9248 0.9784 0.5205 ] Network output: [ 0.01467 0.9256 0.9393 -4.039e-05 1.813e-05 0.1057 -3.044e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0187 0.0135 0.02089 0.02246 0.9891 0.9925 0.01903 0.9766 0.9863 0.02692 ] Network output: [ 0.08991 -0.2152 0.8004 1.951e-05 -8.76e-06 1.235 1.471e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.59 0.5178 0.4341 0.4634 0.9816 0.9922 0.5919 0.9312 0.9811 0.5092 ] Network output: [ -0.05751 0.1295 1.147 -4.089e-05 1.836e-05 0.838 -3.082e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2677 0.2626 0.2867 0.2831 0.9892 0.9932 0.2679 0.9772 0.9868 0.2945 ] Network output: [ -0.05705 0.1329 1.123 -1.255e-05 5.635e-06 0.8582 -9.46e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2691 0.2682 0.2851 0.2809 0.9849 0.9909 0.2691 0.9628 0.9806 0.2868 ] Network output: [ -0.01084 1.027 0.03607 2.202e-05 -9.887e-06 0.9587 1.66e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0471 Epoch 3729 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05552 0.9136 0.9229 0.0001073 -4.817e-05 0.05299 8.086e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01152 -0.005977 -0.0008009 0.02163 0.955 0.9619 0.02223 0.9106 0.9265 0.0618 ] Network output: [ 0.9656 0.08747 0.03081 -5.316e-05 2.387e-05 -0.04965 -4.006e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5346 0.09161 0.07377 0.3151 0.9796 0.9911 0.5986 0.9248 0.9784 0.5205 ] Network output: [ 0.01464 0.9256 0.9393 -4.059e-05 1.822e-05 0.1057 -3.059e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01869 0.01349 0.02088 0.02244 0.9891 0.9925 0.01902 0.9766 0.9863 0.0269 ] Network output: [ 0.08987 -0.2152 0.8004 1.918e-05 -8.612e-06 1.235 1.446e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.59 0.5178 0.4342 0.4634 0.9817 0.9922 0.5919 0.9313 0.9811 0.5093 ] Network output: [ -0.05747 0.1294 1.147 -4.07e-05 1.827e-05 0.8379 -3.068e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2676 0.2625 0.2867 0.283 0.9892 0.9932 0.2678 0.9772 0.9868 0.2944 ] Network output: [ -0.057 0.1328 1.123 -1.224e-05 5.493e-06 0.8582 -9.221e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.269 0.2681 0.2851 0.2809 0.9849 0.9909 0.269 0.9628 0.9806 0.2868 ] Network output: [ -0.01087 1.027 0.03612 2.198e-05 -9.866e-06 0.9586 1.656e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04711 Epoch 3730 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05549 0.9136 0.9229 0.0001073 -4.816e-05 0.05302 8.085e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01151 -0.005979 -0.0008287 0.02161 0.955 0.9619 0.02221 0.9106 0.9266 0.06177 ] Network output: [ 0.9656 0.08757 0.03074 -5.308e-05 2.383e-05 -0.04973 -4e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5345 0.0916 0.07373 0.3149 0.9796 0.9911 0.5985 0.9249 0.9784 0.5206 ] Network output: [ 0.01462 0.9255 0.9393 -4.078e-05 1.831e-05 0.1058 -3.074e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01867 0.01348 0.02086 0.02242 0.9891 0.9925 0.01901 0.9766 0.9863 0.02689 ] Network output: [ 0.08982 -0.2153 0.8004 1.885e-05 -8.463e-06 1.235 1.421e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5899 0.5178 0.4343 0.4633 0.9817 0.9922 0.5918 0.9313 0.9811 0.5094 ] Network output: [ -0.05743 0.1294 1.147 -4.051e-05 1.819e-05 0.8379 -3.053e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2675 0.2624 0.2867 0.283 0.9892 0.9932 0.2677 0.9772 0.9869 0.2944 ] Network output: [ -0.05695 0.1328 1.123 -1.192e-05 5.35e-06 0.8581 -8.981e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2689 0.268 0.2851 0.2808 0.9849 0.9909 0.2689 0.9629 0.9806 0.2868 ] Network output: [ -0.01089 1.027 0.03616 2.193e-05 -9.845e-06 0.9586 1.653e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04713 Epoch 3731 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05547 0.9136 0.9228 0.0001073 -4.815e-05 0.05306 8.083e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01149 -0.005981 -0.0008565 0.02158 0.955 0.962 0.0222 0.9106 0.9266 0.06174 ] Network output: [ 0.9656 0.08767 0.03066 -5.299e-05 2.379e-05 -0.04982 -3.994e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5344 0.09159 0.07368 0.3148 0.9796 0.9911 0.5985 0.9249 0.9784 0.5207 ] Network output: [ 0.0146 0.9255 0.9393 -4.098e-05 1.84e-05 0.1058 -3.088e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01866 0.01347 0.02085 0.0224 0.9891 0.9925 0.019 0.9766 0.9863 0.02687 ] Network output: [ 0.08977 -0.2154 0.8004 1.852e-05 -8.312e-06 1.236 1.395e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5899 0.5178 0.4344 0.4633 0.9817 0.9922 0.5918 0.9313 0.9811 0.5095 ] Network output: [ -0.05739 0.1293 1.147 -4.032e-05 1.81e-05 0.8379 -3.039e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2675 0.2624 0.2867 0.2829 0.9892 0.9932 0.2676 0.9773 0.9869 0.2944 ] Network output: [ -0.05691 0.1327 1.123 -1.16e-05 5.206e-06 0.8581 -8.74e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2688 0.2679 0.285 0.2808 0.9849 0.9909 0.2688 0.9629 0.9807 0.2867 ] Network output: [ -0.01092 1.027 0.0362 2.188e-05 -9.824e-06 0.9585 1.649e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04715 Epoch 3732 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05545 0.9136 0.9228 0.0001072 -4.814e-05 0.05309 8.082e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01148 -0.005983 -0.0008844 0.02156 0.955 0.962 0.02219 0.9107 0.9266 0.06171 ] Network output: [ 0.9657 0.08777 0.03059 -5.29e-05 2.375e-05 -0.0499 -3.987e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5343 0.09159 0.07364 0.3146 0.9796 0.9911 0.5985 0.9249 0.9784 0.5207 ] Network output: [ 0.01457 0.9255 0.9393 -4.118e-05 1.849e-05 0.1059 -3.103e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01865 0.01346 0.02084 0.02238 0.9891 0.9925 0.01899 0.9766 0.9863 0.02686 ] Network output: [ 0.08973 -0.2154 0.8004 1.818e-05 -8.16e-06 1.236 1.37e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5898 0.5178 0.4345 0.4632 0.9817 0.9922 0.5917 0.9314 0.9811 0.5096 ] Network output: [ -0.05736 0.1293 1.147 -4.013e-05 1.801e-05 0.8378 -3.024e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2674 0.2623 0.2867 0.2829 0.9892 0.9932 0.2675 0.9773 0.9869 0.2944 ] Network output: [ -0.05686 0.1326 1.123 -1.127e-05 5.062e-06 0.8581 -8.497e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2687 0.2678 0.285 0.2807 0.9849 0.9909 0.2687 0.9629 0.9807 0.2867 ] Network output: [ -0.01094 1.027 0.03625 2.184e-05 -9.803e-06 0.9584 1.646e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04717 Epoch 3733 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05543 0.9136 0.9228 0.0001072 -4.813e-05 0.05313 8.08e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01147 -0.005985 -0.0009123 0.02153 0.955 0.962 0.02217 0.9107 0.9267 0.06168 ] Network output: [ 0.9657 0.08787 0.03051 -5.281e-05 2.371e-05 -0.04999 -3.98e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5342 0.09158 0.07359 0.3144 0.9796 0.9911 0.5984 0.925 0.9785 0.5208 ] Network output: [ 0.01455 0.9255 0.9393 -4.137e-05 1.857e-05 0.1059 -3.118e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01864 0.01346 0.02082 0.02236 0.9891 0.9925 0.01898 0.9767 0.9864 0.02684 ] Network output: [ 0.08968 -0.2155 0.8004 1.784e-05 -8.007e-06 1.236 1.344e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5898 0.5177 0.4346 0.4632 0.9817 0.9922 0.5917 0.9314 0.9811 0.5096 ] Network output: [ -0.05732 0.1293 1.147 -3.993e-05 1.793e-05 0.8378 -3.009e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2673 0.2622 0.2866 0.2828 0.9892 0.9932 0.2674 0.9773 0.9869 0.2944 ] Network output: [ -0.05681 0.1325 1.123 -1.095e-05 4.917e-06 0.858 -8.253e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2685 0.2677 0.285 0.2807 0.9849 0.9909 0.2686 0.9629 0.9807 0.2867 ] Network output: [ -0.01097 1.027 0.03629 2.179e-05 -9.783e-06 0.9583 1.642e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04719 Epoch 3734 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05541 0.9136 0.9228 0.0001072 -4.812e-05 0.05316 8.079e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01145 -0.005987 -0.0009402 0.02151 0.955 0.962 0.02216 0.9108 0.9267 0.06165 ] Network output: [ 0.9657 0.08797 0.03043 -5.272e-05 2.367e-05 -0.05008 -3.973e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5341 0.09157 0.07355 0.3142 0.9796 0.9911 0.5984 0.925 0.9785 0.5209 ] Network output: [ 0.01453 0.9255 0.9393 -4.157e-05 1.866e-05 0.106 -3.133e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01863 0.01345 0.02081 0.02234 0.9891 0.9925 0.01897 0.9767 0.9864 0.02683 ] Network output: [ 0.08963 -0.2156 0.8004 1.749e-05 -7.852e-06 1.236 1.318e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5897 0.5177 0.4347 0.4631 0.9817 0.9922 0.5916 0.9314 0.9812 0.5097 ] Network output: [ -0.05728 0.1292 1.147 -3.974e-05 1.784e-05 0.8378 -2.995e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2672 0.2621 0.2866 0.2828 0.9892 0.9932 0.2673 0.9773 0.9869 0.2943 ] Network output: [ -0.05677 0.1324 1.123 -1.063e-05 4.771e-06 0.858 -8.008e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2684 0.2676 0.285 0.2807 0.985 0.9909 0.2685 0.9629 0.9807 0.2867 ] Network output: [ -0.011 1.028 0.03633 2.175e-05 -9.762e-06 0.9582 1.639e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0472 Epoch 3735 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05539 0.9137 0.9228 0.0001072 -4.811e-05 0.0532 8.077e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01144 -0.005989 -0.0009681 0.02148 0.955 0.962 0.02215 0.9108 0.9267 0.06162 ] Network output: [ 0.9658 0.08807 0.03035 -5.263e-05 2.363e-05 -0.05016 -3.966e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.534 0.09156 0.0735 0.3141 0.9797 0.9912 0.5984 0.9251 0.9785 0.5209 ] Network output: [ 0.0145 0.9255 0.9393 -4.177e-05 1.875e-05 0.1061 -3.148e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01862 0.01344 0.0208 0.02232 0.9891 0.9926 0.01896 0.9767 0.9864 0.02681 ] Network output: [ 0.08958 -0.2156 0.8004 1.714e-05 -7.696e-06 1.236 1.292e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5897 0.5177 0.4348 0.4631 0.9817 0.9922 0.5916 0.9315 0.9812 0.5098 ] Network output: [ -0.05725 0.1292 1.147 -3.954e-05 1.775e-05 0.8377 -2.98e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2671 0.262 0.2866 0.2827 0.9892 0.9932 0.2673 0.9773 0.9869 0.2943 ] Network output: [ -0.05672 0.1323 1.123 -1.03e-05 4.624e-06 0.8579 -7.762e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2683 0.2675 0.2849 0.2806 0.985 0.9909 0.2684 0.963 0.9807 0.2866 ] Network output: [ -0.01102 1.028 0.03637 2.17e-05 -9.742e-06 0.9581 1.635e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04722 Epoch 3736 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05536 0.9137 0.9228 0.0001072 -4.81e-05 0.05324 8.075e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01143 -0.005991 -0.0009961 0.02146 0.955 0.962 0.02213 0.9108 0.9268 0.06159 ] Network output: [ 0.9658 0.08818 0.03028 -5.254e-05 2.359e-05 -0.05025 -3.959e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5339 0.09156 0.07345 0.3139 0.9797 0.9912 0.5983 0.9251 0.9785 0.521 ] Network output: [ 0.01448 0.9255 0.9393 -4.197e-05 1.884e-05 0.1061 -3.163e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01861 0.01343 0.02078 0.02229 0.9891 0.9926 0.01895 0.9767 0.9864 0.0268 ] Network output: [ 0.08954 -0.2157 0.8004 1.679e-05 -7.538e-06 1.236 1.265e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5896 0.5177 0.4349 0.463 0.9817 0.9922 0.5915 0.9315 0.9812 0.5099 ] Network output: [ -0.05721 0.1291 1.147 -3.935e-05 1.767e-05 0.8377 -2.966e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.267 0.2619 0.2866 0.2827 0.9892 0.9932 0.2672 0.9773 0.9869 0.2943 ] Network output: [ -0.05667 0.1322 1.123 -9.971e-06 4.476e-06 0.8579 -7.515e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2682 0.2674 0.2849 0.2806 0.985 0.9909 0.2683 0.963 0.9807 0.2866 ] Network output: [ -0.01105 1.028 0.03642 2.165e-05 -9.722e-06 0.9581 1.632e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04724 Epoch 3737 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05534 0.9137 0.9228 0.0001071 -4.81e-05 0.05327 8.074e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01141 -0.005993 -0.001024 0.02143 0.9551 0.962 0.02212 0.9109 0.9268 0.06156 ] Network output: [ 0.9658 0.08828 0.0302 -5.244e-05 2.354e-05 -0.05035 -3.952e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5338 0.09155 0.07341 0.3137 0.9797 0.9912 0.5983 0.9251 0.9785 0.5211 ] Network output: [ 0.01446 0.9255 0.9392 -4.217e-05 1.893e-05 0.1062 -3.178e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0186 0.01342 0.02077 0.02227 0.9892 0.9926 0.01894 0.9767 0.9864 0.02678 ] Network output: [ 0.08949 -0.2158 0.8004 1.644e-05 -7.379e-06 1.236 1.239e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5896 0.5177 0.4351 0.463 0.9817 0.9923 0.5915 0.9315 0.9812 0.51 ] Network output: [ -0.05717 0.1291 1.147 -3.915e-05 1.758e-05 0.8377 -2.951e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2669 0.2619 0.2866 0.2826 0.9892 0.9933 0.2671 0.9773 0.9869 0.2943 ] Network output: [ -0.05663 0.1322 1.123 -9.641e-06 4.328e-06 0.8578 -7.266e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2681 0.2673 0.2849 0.2805 0.985 0.991 0.2682 0.963 0.9807 0.2866 ] Network output: [ -0.01107 1.028 0.03646 2.161e-05 -9.701e-06 0.958 1.629e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04726 Epoch 3738 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05532 0.9137 0.9228 0.0001071 -4.809e-05 0.05331 8.072e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0114 -0.005995 -0.001052 0.02141 0.9551 0.962 0.02211 0.9109 0.9268 0.06153 ] Network output: [ 0.9659 0.08839 0.03012 -5.235e-05 2.35e-05 -0.05044 -3.945e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5337 0.09154 0.07336 0.3135 0.9797 0.9912 0.5983 0.9252 0.9785 0.5212 ] Network output: [ 0.01443 0.9255 0.9392 -4.237e-05 1.902e-05 0.1062 -3.193e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01859 0.01341 0.02076 0.02225 0.9892 0.9926 0.01893 0.9767 0.9864 0.02677 ] Network output: [ 0.08944 -0.2158 0.8004 1.608e-05 -7.219e-06 1.237 1.212e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5896 0.5177 0.4352 0.4629 0.9817 0.9923 0.5914 0.9316 0.9812 0.5101 ] Network output: [ -0.05714 0.1291 1.147 -3.896e-05 1.749e-05 0.8376 -2.936e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2668 0.2618 0.2866 0.2826 0.9892 0.9933 0.267 0.9774 0.9869 0.2943 ] Network output: [ -0.05658 0.1321 1.123 -9.309e-06 4.179e-06 0.8578 -7.016e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.268 0.2672 0.2849 0.2805 0.985 0.991 0.2681 0.963 0.9808 0.2865 ] Network output: [ -0.0111 1.028 0.0365 2.156e-05 -9.681e-06 0.9579 1.625e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04728 Epoch 3739 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0553 0.9137 0.9228 0.0001071 -4.808e-05 0.05335 8.071e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01139 -0.005997 -0.00108 0.02138 0.9551 0.962 0.02209 0.911 0.9269 0.0615 ] Network output: [ 0.9659 0.0885 0.03004 -5.225e-05 2.346e-05 -0.05053 -3.938e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5336 0.09153 0.07331 0.3133 0.9797 0.9912 0.5982 0.9252 0.9786 0.5212 ] Network output: [ 0.01441 0.9255 0.9392 -4.257e-05 1.911e-05 0.1063 -3.208e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01858 0.01341 0.02075 0.02223 0.9892 0.9926 0.01892 0.9767 0.9864 0.02675 ] Network output: [ 0.0894 -0.2159 0.8004 1.572e-05 -7.057e-06 1.237 1.185e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5895 0.5177 0.4353 0.4629 0.9817 0.9923 0.5914 0.9316 0.9812 0.5102 ] Network output: [ -0.0571 0.129 1.147 -3.876e-05 1.74e-05 0.8376 -2.921e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2668 0.2617 0.2866 0.2825 0.9892 0.9933 0.2669 0.9774 0.987 0.2943 ] Network output: [ -0.05654 0.132 1.123 -8.976e-06 4.03e-06 0.8577 -6.764e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2679 0.2671 0.2848 0.2805 0.985 0.991 0.268 0.963 0.9808 0.2865 ] Network output: [ -0.01112 1.028 0.03654 2.152e-05 -9.661e-06 0.9578 1.622e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0473 Epoch 3740 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05528 0.9137 0.9228 0.0001071 -4.807e-05 0.05339 8.069e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01137 -0.005999 -0.001108 0.02136 0.9551 0.9621 0.02208 0.911 0.9269 0.06147 ] Network output: [ 0.9659 0.08861 0.02996 -5.215e-05 2.341e-05 -0.05062 -3.93e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5335 0.09152 0.07326 0.3131 0.9797 0.9912 0.5982 0.9252 0.9786 0.5213 ] Network output: [ 0.01439 0.9255 0.9392 -4.277e-05 1.92e-05 0.1063 -3.223e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01856 0.0134 0.02073 0.02221 0.9892 0.9926 0.01891 0.9768 0.9864 0.02674 ] Network output: [ 0.08935 -0.216 0.8004 1.535e-05 -6.893e-06 1.237 1.157e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5895 0.5177 0.4354 0.4629 0.9817 0.9923 0.5914 0.9317 0.9812 0.5103 ] Network output: [ -0.05706 0.129 1.147 -3.856e-05 1.731e-05 0.8376 -2.906e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2667 0.2616 0.2865 0.2825 0.9892 0.9933 0.2668 0.9774 0.987 0.2942 ] Network output: [ -0.05649 0.1319 1.123 -8.64e-06 3.879e-06 0.8577 -6.512e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2678 0.267 0.2848 0.2804 0.985 0.991 0.2679 0.963 0.9808 0.2865 ] Network output: [ -0.01115 1.028 0.03658 2.147e-05 -9.641e-06 0.9577 1.618e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04732 Epoch 3741 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05526 0.9137 0.9228 0.000107 -4.806e-05 0.05343 8.067e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01136 -0.006001 -0.001136 0.02133 0.9551 0.9621 0.02207 0.911 0.927 0.06144 ] Network output: [ 0.966 0.08872 0.02988 -5.205e-05 2.337e-05 -0.05072 -3.923e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5334 0.09151 0.07321 0.313 0.9797 0.9912 0.5982 0.9253 0.9786 0.5214 ] Network output: [ 0.01437 0.9255 0.9392 -4.297e-05 1.929e-05 0.1064 -3.238e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01855 0.01339 0.02072 0.02219 0.9892 0.9926 0.0189 0.9768 0.9864 0.02672 ] Network output: [ 0.0893 -0.2161 0.8004 1.499e-05 -6.728e-06 1.237 1.129e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5894 0.5176 0.4355 0.4628 0.9817 0.9923 0.5913 0.9317 0.9812 0.5104 ] Network output: [ -0.05703 0.1289 1.147 -3.837e-05 1.722e-05 0.8375 -2.891e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2666 0.2615 0.2865 0.2824 0.9892 0.9933 0.2667 0.9774 0.987 0.2942 ] Network output: [ -0.05644 0.1318 1.123 -8.303e-06 3.728e-06 0.8576 -6.257e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2677 0.2669 0.2848 0.2804 0.985 0.991 0.2678 0.9631 0.9808 0.2865 ] Network output: [ -0.01118 1.028 0.03663 2.143e-05 -9.621e-06 0.9576 1.615e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04734 Epoch 3742 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05524 0.9137 0.9227 0.000107 -4.805e-05 0.05347 8.066e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01135 -0.006003 -0.001165 0.02131 0.9551 0.9621 0.02205 0.9111 0.927 0.06141 ] Network output: [ 0.966 0.08884 0.02979 -5.195e-05 2.332e-05 -0.05081 -3.915e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5333 0.09149 0.07316 0.3128 0.9797 0.9912 0.5981 0.9253 0.9786 0.5215 ] Network output: [ 0.01434 0.9254 0.9392 -4.317e-05 1.938e-05 0.1064 -3.254e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01854 0.01338 0.02071 0.02217 0.9892 0.9926 0.01888 0.9768 0.9864 0.02671 ] Network output: [ 0.08926 -0.2161 0.8004 1.462e-05 -6.562e-06 1.237 1.101e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5894 0.5176 0.4356 0.4628 0.9817 0.9923 0.5913 0.9317 0.9813 0.5105 ] Network output: [ -0.05699 0.1289 1.147 -3.817e-05 1.713e-05 0.8375 -2.876e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2665 0.2614 0.2865 0.2824 0.9892 0.9933 0.2666 0.9774 0.987 0.2942 ] Network output: [ -0.0564 0.1317 1.123 -7.964e-06 3.575e-06 0.8576 -6.002e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2676 0.2668 0.2848 0.2803 0.985 0.991 0.2676 0.9631 0.9808 0.2864 ] Network output: [ -0.0112 1.028 0.03667 2.139e-05 -9.601e-06 0.9576 1.612e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04737 Epoch 3743 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05522 0.9138 0.9227 0.000107 -4.804e-05 0.05351 8.064e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01133 -0.006005 -0.001193 0.02128 0.9551 0.9621 0.02204 0.9111 0.927 0.06138 ] Network output: [ 0.966 0.08895 0.02971 -5.184e-05 2.327e-05 -0.05091 -3.907e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5332 0.09148 0.07311 0.3126 0.9797 0.9912 0.5981 0.9254 0.9786 0.5216 ] Network output: [ 0.01432 0.9254 0.9392 -4.337e-05 1.947e-05 0.1065 -3.269e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01853 0.01337 0.02069 0.02214 0.9892 0.9926 0.01887 0.9768 0.9865 0.02669 ] Network output: [ 0.08921 -0.2162 0.8004 1.424e-05 -6.393e-06 1.237 1.073e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5893 0.5176 0.4357 0.4627 0.9817 0.9923 0.5912 0.9318 0.9813 0.5106 ] Network output: [ -0.05695 0.1289 1.147 -3.797e-05 1.705e-05 0.8375 -2.861e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2664 0.2613 0.2865 0.2823 0.9893 0.9933 0.2665 0.9774 0.987 0.2942 ] Network output: [ -0.05635 0.1317 1.123 -7.623e-06 3.422e-06 0.8575 -5.745e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2675 0.2667 0.2847 0.2803 0.985 0.991 0.2675 0.9631 0.9808 0.2864 ] Network output: [ -0.01123 1.028 0.03671 2.134e-05 -9.581e-06 0.9575 1.608e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04739 Epoch 3744 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05519 0.9138 0.9227 0.000107 -4.803e-05 0.05355 8.062e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01132 -0.006007 -0.001221 0.02125 0.9552 0.9621 0.02203 0.9112 0.9271 0.06135 ] Network output: [ 0.966 0.08907 0.02963 -5.174e-05 2.323e-05 -0.05101 -3.899e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5331 0.09147 0.07305 0.3124 0.9797 0.9912 0.5981 0.9254 0.9786 0.5217 ] Network output: [ 0.0143 0.9254 0.9392 -4.358e-05 1.956e-05 0.1066 -3.284e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01852 0.01336 0.02068 0.02212 0.9892 0.9926 0.01886 0.9768 0.9865 0.02668 ] Network output: [ 0.08916 -0.2163 0.8004 1.386e-05 -6.224e-06 1.238 1.045e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5893 0.5176 0.4358 0.4627 0.9817 0.9923 0.5912 0.9318 0.9813 0.5107 ] Network output: [ -0.05692 0.1288 1.147 -3.777e-05 1.696e-05 0.8374 -2.846e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2663 0.2612 0.2865 0.2823 0.9893 0.9933 0.2664 0.9775 0.987 0.2942 ] Network output: [ -0.0563 0.1316 1.124 -7.28e-06 3.268e-06 0.8575 -5.486e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2674 0.2666 0.2847 0.2802 0.985 0.991 0.2674 0.9631 0.9808 0.2864 ] Network output: [ -0.01125 1.028 0.03675 2.13e-05 -9.561e-06 0.9574 1.605e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04741 Epoch 3745 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05517 0.9138 0.9227 0.000107 -4.802e-05 0.0536 8.061e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0113 -0.006009 -0.001249 0.02123 0.9552 0.9621 0.02201 0.9112 0.9271 0.06132 ] Network output: [ 0.9661 0.08919 0.02955 -5.163e-05 2.318e-05 -0.0511 -3.891e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.533 0.09145 0.073 0.3122 0.9797 0.9912 0.598 0.9254 0.9786 0.5217 ] Network output: [ 0.01428 0.9254 0.9392 -4.378e-05 1.965e-05 0.1066 -3.299e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01851 0.01335 0.02067 0.0221 0.9892 0.9926 0.01885 0.9768 0.9865 0.02666 ] Network output: [ 0.08911 -0.2164 0.8005 1.348e-05 -6.052e-06 1.238 1.016e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5892 0.5176 0.4359 0.4626 0.9817 0.9923 0.5911 0.9318 0.9813 0.5108 ] Network output: [ -0.05688 0.1288 1.147 -3.757e-05 1.687e-05 0.8374 -2.831e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2662 0.2612 0.2865 0.2822 0.9893 0.9933 0.2663 0.9775 0.987 0.2941 ] Network output: [ -0.05626 0.1315 1.124 -6.935e-06 3.113e-06 0.8574 -5.226e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2673 0.2665 0.2847 0.2802 0.985 0.991 0.2673 0.9631 0.9808 0.2864 ] Network output: [ -0.01128 1.029 0.03679 2.125e-05 -9.541e-06 0.9573 1.602e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04743 Epoch 3746 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05515 0.9138 0.9227 0.0001069 -4.801e-05 0.05364 8.059e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01129 -0.006011 -0.001278 0.0212 0.9552 0.9621 0.022 0.9112 0.9271 0.06129 ] Network output: [ 0.9661 0.08931 0.02946 -5.152e-05 2.313e-05 -0.0512 -3.883e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5329 0.09144 0.07295 0.312 0.9797 0.9912 0.598 0.9255 0.9787 0.5218 ] Network output: [ 0.01426 0.9254 0.9392 -4.398e-05 1.975e-05 0.1067 -3.315e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0185 0.01334 0.02065 0.02208 0.9892 0.9926 0.01884 0.9769 0.9865 0.02665 ] Network output: [ 0.08907 -0.2165 0.8005 1.31e-05 -5.879e-06 1.238 9.87e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5892 0.5176 0.4361 0.4626 0.9818 0.9923 0.5911 0.9319 0.9813 0.5109 ] Network output: [ -0.05685 0.1288 1.147 -3.737e-05 1.678e-05 0.8373 -2.816e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2661 0.2611 0.2865 0.2822 0.9893 0.9933 0.2663 0.9775 0.987 0.2941 ] Network output: [ -0.05621 0.1314 1.124 -6.588e-06 2.958e-06 0.8574 -4.965e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2672 0.2663 0.2847 0.2802 0.985 0.991 0.2672 0.9631 0.9809 0.2863 ] Network output: [ -0.01131 1.029 0.03683 2.121e-05 -9.522e-06 0.9572 1.598e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04746 Epoch 3747 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05513 0.9138 0.9227 0.0001069 -4.8e-05 0.05368 8.057e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01128 -0.006013 -0.001306 0.02118 0.9552 0.9621 0.02199 0.9113 0.9272 0.06126 ] Network output: [ 0.9661 0.08943 0.02938 -5.141e-05 2.308e-05 -0.0513 -3.874e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5328 0.09143 0.07289 0.3118 0.9797 0.9912 0.5979 0.9255 0.9787 0.5219 ] Network output: [ 0.01424 0.9254 0.9392 -4.419e-05 1.984e-05 0.1067 -3.33e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01848 0.01334 0.02064 0.02206 0.9892 0.9926 0.01883 0.9769 0.9865 0.02663 ] Network output: [ 0.08902 -0.2166 0.8005 1.271e-05 -5.705e-06 1.238 9.576e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5891 0.5175 0.4362 0.4625 0.9818 0.9923 0.591 0.9319 0.9813 0.511 ] Network output: [ -0.05681 0.1287 1.147 -3.716e-05 1.668e-05 0.8373 -2.801e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.266 0.261 0.2864 0.2821 0.9893 0.9933 0.2662 0.9775 0.987 0.2941 ] Network output: [ -0.05617 0.1313 1.124 -6.239e-06 2.801e-06 0.8573 -4.702e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2671 0.2662 0.2846 0.2801 0.985 0.991 0.2671 0.9632 0.9809 0.2863 ] Network output: [ -0.01133 1.029 0.03687 2.117e-05 -9.502e-06 0.9571 1.595e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04748 Epoch 3748 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05511 0.9138 0.9227 0.0001069 -4.799e-05 0.05373 8.055e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01126 -0.006015 -0.001334 0.02115 0.9552 0.9622 0.02197 0.9113 0.9272 0.06123 ] Network output: [ 0.9662 0.08956 0.02929 -5.13e-05 2.303e-05 -0.0514 -3.866e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5327 0.09141 0.07284 0.3117 0.9797 0.9912 0.5979 0.9256 0.9787 0.522 ] Network output: [ 0.01422 0.9254 0.9392 -4.439e-05 1.993e-05 0.1068 -3.346e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01847 0.01333 0.02063 0.02204 0.9892 0.9926 0.01882 0.9769 0.9865 0.02662 ] Network output: [ 0.08897 -0.2166 0.8005 1.231e-05 -5.528e-06 1.238 9.28e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5891 0.5175 0.4363 0.4625 0.9818 0.9923 0.591 0.9319 0.9813 0.5111 ] Network output: [ -0.05677 0.1287 1.147 -3.696e-05 1.659e-05 0.8372 -2.786e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2659 0.2609 0.2864 0.2821 0.9893 0.9933 0.2661 0.9775 0.9871 0.2941 ] Network output: [ -0.05612 0.1312 1.124 -5.888e-06 2.643e-06 0.8573 -4.437e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.267 0.2661 0.2846 0.2801 0.985 0.991 0.267 0.9632 0.9809 0.2863 ] Network output: [ -0.01136 1.029 0.03691 2.112e-05 -9.482e-06 0.957 1.592e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0475 Epoch 3749 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05509 0.9138 0.9227 0.0001069 -4.797e-05 0.05377 8.054e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01125 -0.006017 -0.001363 0.02113 0.9552 0.9622 0.02196 0.9114 0.9272 0.0612 ] Network output: [ 0.9662 0.08968 0.02921 -5.118e-05 2.298e-05 -0.05151 -3.857e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5326 0.0914 0.07278 0.3115 0.9797 0.9912 0.5979 0.9256 0.9787 0.5221 ] Network output: [ 0.01419 0.9253 0.9392 -4.46e-05 2.002e-05 0.1069 -3.361e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01846 0.01332 0.02061 0.02201 0.9892 0.9926 0.0188 0.9769 0.9865 0.0266 ] Network output: [ 0.08893 -0.2167 0.8005 1.192e-05 -5.35e-06 1.238 8.982e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.589 0.5175 0.4364 0.4624 0.9818 0.9923 0.5909 0.932 0.9814 0.5112 ] Network output: [ -0.05674 0.1287 1.147 -3.676e-05 1.65e-05 0.8372 -2.77e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2658 0.2608 0.2864 0.282 0.9893 0.9933 0.266 0.9775 0.9871 0.2941 ] Network output: [ -0.05607 0.1312 1.124 -5.535e-06 2.485e-06 0.8572 -4.171e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2669 0.266 0.2846 0.28 0.985 0.991 0.2669 0.9632 0.9809 0.2863 ] Network output: [ -0.01139 1.029 0.03695 2.108e-05 -9.463e-06 0.957 1.588e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04753 Epoch 3750 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05507 0.9138 0.9227 0.0001068 -4.796e-05 0.05382 8.052e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01123 -0.006019 -0.001391 0.0211 0.9552 0.9622 0.02195 0.9114 0.9273 0.06117 ] Network output: [ 0.9662 0.08981 0.02912 -5.107e-05 2.293e-05 -0.05161 -3.848e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5325 0.09138 0.07272 0.3113 0.9798 0.9912 0.5978 0.9256 0.9787 0.5222 ] Network output: [ 0.01417 0.9253 0.9392 -4.481e-05 2.012e-05 0.1069 -3.377e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01845 0.01331 0.0206 0.02199 0.9892 0.9926 0.01879 0.9769 0.9865 0.02659 ] Network output: [ 0.08888 -0.2168 0.8005 1.152e-05 -5.171e-06 1.239 8.68e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.589 0.5175 0.4365 0.4624 0.9818 0.9923 0.5909 0.932 0.9814 0.5113 ] Network output: [ -0.0567 0.1286 1.147 -3.655e-05 1.641e-05 0.8372 -2.755e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2657 0.2607 0.2864 0.282 0.9893 0.9933 0.2659 0.9776 0.9871 0.2941 ] Network output: [ -0.05603 0.1311 1.124 -5.179e-06 2.325e-06 0.8572 -3.903e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2668 0.2659 0.2846 0.28 0.985 0.991 0.2668 0.9632 0.9809 0.2862 ] Network output: [ -0.01141 1.029 0.03699 2.103e-05 -9.443e-06 0.9569 1.585e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04755 Epoch 3751 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05505 0.9138 0.9227 0.0001068 -4.795e-05 0.05386 8.05e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01122 -0.006021 -0.00142 0.02107 0.9552 0.9622 0.02193 0.9114 0.9273 0.06114 ] Network output: [ 0.9663 0.08994 0.02904 -5.095e-05 2.287e-05 -0.05171 -3.84e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5324 0.09136 0.07266 0.3111 0.9798 0.9912 0.5978 0.9257 0.9787 0.5223 ] Network output: [ 0.01415 0.9253 0.9392 -4.501e-05 2.021e-05 0.107 -3.392e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01844 0.0133 0.02059 0.02197 0.9892 0.9926 0.01878 0.9769 0.9865 0.02657 ] Network output: [ 0.08883 -0.2169 0.8005 1.111e-05 -4.989e-06 1.239 8.375e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5889 0.5175 0.4367 0.4624 0.9818 0.9923 0.5908 0.932 0.9814 0.5115 ] Network output: [ -0.05667 0.1286 1.147 -3.635e-05 1.632e-05 0.8371 -2.739e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2656 0.2606 0.2864 0.2819 0.9893 0.9933 0.2658 0.9776 0.9871 0.294 ] Network output: [ -0.05598 0.131 1.124 -4.822e-06 2.165e-06 0.8571 -3.634e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2667 0.2658 0.2845 0.2799 0.985 0.991 0.2667 0.9632 0.9809 0.2862 ] Network output: [ -0.01144 1.029 0.03703 2.099e-05 -9.423e-06 0.9568 1.582e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04758 Epoch 3752 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05503 0.9138 0.9226 0.0001068 -4.794e-05 0.05391 8.048e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0112 -0.006023 -0.001449 0.02105 0.9553 0.9622 0.02192 0.9115 0.9273 0.06111 ] Network output: [ 0.9663 0.09007 0.02895 -5.083e-05 2.282e-05 -0.05182 -3.831e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5323 0.09134 0.07261 0.3109 0.9798 0.9912 0.5978 0.9257 0.9788 0.5224 ] Network output: [ 0.01413 0.9253 0.9392 -4.522e-05 2.03e-05 0.1071 -3.408e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01843 0.01329 0.02057 0.02195 0.9892 0.9926 0.01877 0.977 0.9866 0.02656 ] Network output: [ 0.08879 -0.217 0.8005 1.071e-05 -4.806e-06 1.239 8.068e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5889 0.5174 0.4368 0.4623 0.9818 0.9923 0.5908 0.9321 0.9814 0.5116 ] Network output: [ -0.05663 0.1286 1.147 -3.614e-05 1.623e-05 0.8371 -2.724e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2655 0.2605 0.2864 0.2819 0.9893 0.9933 0.2657 0.9776 0.9871 0.294 ] Network output: [ -0.05594 0.1309 1.124 -4.462e-06 2.003e-06 0.8571 -3.363e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2666 0.2657 0.2845 0.2799 0.985 0.991 0.2666 0.9633 0.9809 0.2862 ] Network output: [ -0.01146 1.029 0.03707 2.095e-05 -9.404e-06 0.9567 1.579e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0476 Epoch 3753 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05501 0.9138 0.9226 0.0001068 -4.793e-05 0.05396 8.046e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01119 -0.006025 -0.001477 0.02102 0.9553 0.9622 0.02191 0.9115 0.9274 0.06108 ] Network output: [ 0.9663 0.0902 0.02886 -5.071e-05 2.276e-05 -0.05193 -3.821e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5322 0.09133 0.07255 0.3107 0.9798 0.9912 0.5977 0.9257 0.9788 0.5225 ] Network output: [ 0.01411 0.9253 0.9392 -4.543e-05 2.04e-05 0.1071 -3.424e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01841 0.01328 0.02056 0.02192 0.9892 0.9926 0.01876 0.977 0.9866 0.02654 ] Network output: [ 0.08874 -0.2171 0.8006 1.029e-05 -4.621e-06 1.239 7.757e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5888 0.5174 0.4369 0.4623 0.9818 0.9923 0.5907 0.9321 0.9814 0.5117 ] Network output: [ -0.0566 0.1285 1.147 -3.594e-05 1.613e-05 0.837 -2.708e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2654 0.2604 0.2864 0.2818 0.9893 0.9933 0.2656 0.9776 0.9871 0.294 ] Network output: [ -0.05589 0.1308 1.124 -4.1e-06 1.841e-06 0.857 -3.09e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2664 0.2656 0.2845 0.2799 0.985 0.991 0.2665 0.9633 0.9809 0.2862 ] Network output: [ -0.01149 1.029 0.0371 2.09e-05 -9.384e-06 0.9566 1.575e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04763 Epoch 3754 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05499 0.9138 0.9226 0.0001067 -4.792e-05 0.05401 8.045e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01118 -0.006027 -0.001506 0.02099 0.9553 0.9622 0.02189 0.9116 0.9274 0.06105 ] Network output: [ 0.9664 0.09034 0.02877 -5.058e-05 2.271e-05 -0.05203 -3.812e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5321 0.09131 0.07249 0.3105 0.9798 0.9912 0.5977 0.9258 0.9788 0.5226 ] Network output: [ 0.01409 0.9252 0.9392 -4.564e-05 2.049e-05 0.1072 -3.44e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0184 0.01327 0.02055 0.0219 0.9892 0.9926 0.01875 0.977 0.9866 0.02653 ] Network output: [ 0.08869 -0.2172 0.8006 9.877e-06 -4.434e-06 1.239 7.443e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5888 0.5174 0.437 0.4622 0.9818 0.9923 0.5907 0.9321 0.9814 0.5118 ] Network output: [ -0.05656 0.1285 1.147 -3.573e-05 1.604e-05 0.837 -2.693e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2653 0.2603 0.2863 0.2817 0.9893 0.9933 0.2655 0.9776 0.9871 0.294 ] Network output: [ -0.05585 0.1307 1.124 -3.736e-06 1.677e-06 0.857 -2.815e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2663 0.2655 0.2845 0.2798 0.985 0.991 0.2664 0.9633 0.981 0.2861 ] Network output: [ -0.01152 1.029 0.03714 2.086e-05 -9.364e-06 0.9565 1.572e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04765 Epoch 3755 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05497 0.9138 0.9226 0.0001067 -4.791e-05 0.05406 8.043e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01116 -0.006029 -0.001534 0.02097 0.9553 0.9622 0.02188 0.9116 0.9274 0.06102 ] Network output: [ 0.9664 0.09047 0.02869 -5.046e-05 2.265e-05 -0.05214 -3.803e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.532 0.09129 0.07242 0.3103 0.9798 0.9912 0.5977 0.9258 0.9788 0.5227 ] Network output: [ 0.01407 0.9252 0.9392 -4.585e-05 2.058e-05 0.1073 -3.455e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01839 0.01326 0.02053 0.02188 0.9892 0.9926 0.01873 0.977 0.9866 0.02651 ] Network output: [ 0.08864 -0.2173 0.8006 9.456e-06 -4.245e-06 1.239 7.127e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5887 0.5174 0.4371 0.4622 0.9818 0.9923 0.5906 0.9322 0.9814 0.5119 ] Network output: [ -0.05653 0.1285 1.148 -3.552e-05 1.595e-05 0.8369 -2.677e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2652 0.2602 0.2863 0.2817 0.9893 0.9933 0.2654 0.9776 0.9871 0.294 ] Network output: [ -0.0558 0.1307 1.124 -3.369e-06 1.513e-06 0.8569 -2.539e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2662 0.2654 0.2844 0.2798 0.985 0.991 0.2663 0.9633 0.981 0.2861 ] Network output: [ -0.01155 1.03 0.03718 2.082e-05 -9.345e-06 0.9564 1.569e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04768 Epoch 3756 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05495 0.9138 0.9226 0.0001067 -4.79e-05 0.05411 8.041e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01115 -0.00603 -0.001563 0.02094 0.9553 0.9623 0.02186 0.9117 0.9275 0.06099 ] Network output: [ 0.9664 0.09061 0.0286 -5.033e-05 2.259e-05 -0.05225 -3.793e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5319 0.09127 0.07236 0.3101 0.9798 0.9912 0.5976 0.9259 0.9788 0.5228 ] Network output: [ 0.01405 0.9252 0.9392 -4.606e-05 2.068e-05 0.1073 -3.471e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01838 0.01325 0.02052 0.02186 0.9893 0.9926 0.01872 0.977 0.9866 0.0265 ] Network output: [ 0.0886 -0.2174 0.8006 9.032e-06 -4.055e-06 1.24 6.807e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5886 0.5173 0.4373 0.4621 0.9818 0.9923 0.5906 0.9322 0.9814 0.5121 ] Network output: [ -0.05649 0.1285 1.148 -3.531e-05 1.585e-05 0.8369 -2.661e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2651 0.2601 0.2863 0.2816 0.9893 0.9933 0.2653 0.9776 0.9872 0.2939 ] Network output: [ -0.05575 0.1306 1.124 -3e-06 1.347e-06 0.8569 -2.261e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2661 0.2653 0.2844 0.2797 0.985 0.991 0.2661 0.9633 0.981 0.2861 ] Network output: [ -0.01157 1.03 0.03722 2.077e-05 -9.325e-06 0.9563 1.565e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04771 Epoch 3757 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05493 0.9138 0.9226 0.0001067 -4.789e-05 0.05416 8.039e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01113 -0.006032 -0.001592 0.02092 0.9553 0.9623 0.02185 0.9117 0.9275 0.06096 ] Network output: [ 0.9665 0.09075 0.02851 -5.02e-05 2.254e-05 -0.05236 -3.783e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5317 0.09125 0.0723 0.3099 0.9798 0.9912 0.5976 0.9259 0.9788 0.5229 ] Network output: [ 0.01403 0.9252 0.9392 -4.627e-05 2.077e-05 0.1074 -3.487e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01836 0.01324 0.02051 0.02184 0.9893 0.9926 0.01871 0.977 0.9866 0.02648 ] Network output: [ 0.08855 -0.2175 0.8006 8.603e-06 -3.862e-06 1.24 6.484e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5886 0.5173 0.4374 0.4621 0.9818 0.9923 0.5905 0.9323 0.9815 0.5122 ] Network output: [ -0.05646 0.1284 1.148 -3.51e-05 1.576e-05 0.8368 -2.645e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.265 0.26 0.2863 0.2816 0.9893 0.9933 0.2652 0.9777 0.9872 0.2939 ] Network output: [ -0.05571 0.1305 1.124 -2.629e-06 1.18e-06 0.8568 -1.981e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.266 0.2652 0.2844 0.2797 0.985 0.991 0.266 0.9633 0.981 0.286 ] Network output: [ -0.0116 1.03 0.03726 2.073e-05 -9.306e-06 0.9562 1.562e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04774 Epoch 3758 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0549 0.9138 0.9226 0.0001066 -4.788e-05 0.05421 8.037e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01112 -0.006034 -0.001621 0.02089 0.9553 0.9623 0.02184 0.9117 0.9275 0.06093 ] Network output: [ 0.9665 0.09089 0.02842 -5.007e-05 2.248e-05 -0.05248 -3.773e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5316 0.09123 0.07224 0.3097 0.9798 0.9912 0.5976 0.9259 0.9788 0.523 ] Network output: [ 0.01401 0.9251 0.9392 -4.648e-05 2.087e-05 0.1075 -3.503e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01835 0.01323 0.02049 0.02181 0.9893 0.9926 0.0187 0.977 0.9866 0.02647 ] Network output: [ 0.0885 -0.2176 0.8006 8.17e-06 -3.668e-06 1.24 6.157e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5885 0.5173 0.4375 0.462 0.9818 0.9923 0.5905 0.9323 0.9815 0.5123 ] Network output: [ -0.05642 0.1284 1.148 -3.489e-05 1.566e-05 0.8368 -2.629e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2649 0.2599 0.2863 0.2815 0.9893 0.9933 0.2651 0.9777 0.9872 0.2939 ] Network output: [ -0.05566 0.1304 1.124 -2.255e-06 1.012e-06 0.8568 -1.699e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2659 0.265 0.2843 0.2796 0.985 0.991 0.2659 0.9634 0.981 0.286 ] Network output: [ -0.01163 1.03 0.03729 2.068e-05 -9.286e-06 0.9562 1.559e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04776 Epoch 3759 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05488 0.9138 0.9226 0.0001066 -4.787e-05 0.05426 8.035e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0111 -0.006036 -0.00165 0.02086 0.9554 0.9623 0.02182 0.9118 0.9276 0.0609 ] Network output: [ 0.9665 0.09104 0.02833 -4.994e-05 2.242e-05 -0.05259 -3.763e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5315 0.0912 0.07217 0.3095 0.9798 0.9912 0.5975 0.926 0.9789 0.5231 ] Network output: [ 0.014 0.9251 0.9392 -4.67e-05 2.096e-05 0.1075 -3.519e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01834 0.01322 0.02048 0.02179 0.9893 0.9926 0.01869 0.9771 0.9866 0.02646 ] Network output: [ 0.08846 -0.2177 0.8007 7.733e-06 -3.471e-06 1.24 5.828e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5885 0.5173 0.4377 0.462 0.9818 0.9923 0.5904 0.9323 0.9815 0.5124 ] Network output: [ -0.05639 0.1284 1.148 -3.468e-05 1.557e-05 0.8367 -2.613e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2648 0.2598 0.2863 0.2815 0.9893 0.9933 0.265 0.9777 0.9872 0.2939 ] Network output: [ -0.05562 0.1303 1.124 -1.878e-06 8.432e-07 0.8567 -1.415e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2658 0.2649 0.2843 0.2796 0.985 0.991 0.2658 0.9634 0.981 0.286 ] Network output: [ -0.01165 1.03 0.03733 2.064e-05 -9.266e-06 0.9561 1.555e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04779 Epoch 3760 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05486 0.9138 0.9226 0.0001066 -4.785e-05 0.05432 8.033e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01109 -0.006038 -0.001679 0.02084 0.9554 0.9623 0.02181 0.9118 0.9276 0.06087 ] Network output: [ 0.9665 0.09118 0.02823 -4.98e-05 2.236e-05 -0.0527 -3.753e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5314 0.09118 0.07211 0.3093 0.9798 0.9913 0.5975 0.926 0.9789 0.5232 ] Network output: [ 0.01398 0.9251 0.9392 -4.691e-05 2.106e-05 0.1076 -3.535e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01833 0.01321 0.02047 0.02177 0.9893 0.9927 0.01867 0.9771 0.9866 0.02644 ] Network output: [ 0.08841 -0.2178 0.8007 7.291e-06 -3.273e-06 1.24 5.495e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5884 0.5172 0.4378 0.462 0.9818 0.9923 0.5904 0.9324 0.9815 0.5126 ] Network output: [ -0.05635 0.1284 1.148 -3.446e-05 1.547e-05 0.8367 -2.597e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2647 0.2597 0.2862 0.2814 0.9893 0.9933 0.2648 0.9777 0.9872 0.2939 ] Network output: [ -0.05557 0.1303 1.124 -1.499e-06 6.73e-07 0.8567 -1.13e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2657 0.2648 0.2843 0.2795 0.9851 0.991 0.2657 0.9634 0.981 0.286 ] Network output: [ -0.01168 1.03 0.03737 2.06e-05 -9.246e-06 0.956 1.552e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04782 Epoch 3761 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05484 0.9138 0.9225 0.0001066 -4.784e-05 0.05437 8.031e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01107 -0.00604 -0.001708 0.02081 0.9554 0.9623 0.02179 0.9119 0.9276 0.06084 ] Network output: [ 0.9666 0.09133 0.02814 -4.966e-05 2.23e-05 -0.05282 -3.743e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5313 0.09116 0.07204 0.3091 0.9798 0.9913 0.5974 0.9261 0.9789 0.5233 ] Network output: [ 0.01396 0.9251 0.9392 -4.713e-05 2.116e-05 0.1077 -3.551e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01831 0.0132 0.02045 0.02174 0.9893 0.9927 0.01866 0.9771 0.9866 0.02643 ] Network output: [ 0.08836 -0.2179 0.8007 6.844e-06 -3.073e-06 1.241 5.158e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5884 0.5172 0.4379 0.4619 0.9818 0.9923 0.5903 0.9324 0.9815 0.5127 ] Network output: [ -0.05632 0.1283 1.148 -3.425e-05 1.538e-05 0.8366 -2.581e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2646 0.2596 0.2862 0.2814 0.9893 0.9933 0.2647 0.9777 0.9872 0.2938 ] Network output: [ -0.05553 0.1302 1.124 -1.117e-06 5.017e-07 0.8566 -8.422e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2656 0.2647 0.2843 0.2795 0.9851 0.991 0.2656 0.9634 0.9811 0.2859 ] Network output: [ -0.01171 1.03 0.03741 2.055e-05 -9.226e-06 0.9559 1.549e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04785 Epoch 3762 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05482 0.9138 0.9225 0.0001065 -4.783e-05 0.05442 8.029e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01106 -0.006041 -0.001737 0.02078 0.9554 0.9623 0.02178 0.9119 0.9277 0.06081 ] Network output: [ 0.9666 0.09148 0.02805 -4.952e-05 2.223e-05 -0.05294 -3.732e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5312 0.09113 0.07197 0.3089 0.9798 0.9913 0.5974 0.9261 0.9789 0.5234 ] Network output: [ 0.01394 0.925 0.9392 -4.734e-05 2.125e-05 0.1077 -3.568e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0183 0.01319 0.02044 0.02172 0.9893 0.9927 0.01865 0.9771 0.9867 0.02641 ] Network output: [ 0.08832 -0.218 0.8007 6.394e-06 -2.87e-06 1.241 4.818e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5883 0.5172 0.438 0.4619 0.9818 0.9923 0.5903 0.9324 0.9815 0.5128 ] Network output: [ -0.05628 0.1283 1.148 -3.403e-05 1.528e-05 0.8365 -2.565e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2645 0.2595 0.2862 0.2813 0.9893 0.9933 0.2646 0.9777 0.9872 0.2938 ] Network output: [ -0.05548 0.1301 1.124 -7.332e-07 3.291e-07 0.8565 -5.525e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2654 0.2646 0.2842 0.2794 0.9851 0.991 0.2655 0.9634 0.9811 0.2859 ] Network output: [ -0.01174 1.03 0.03744 2.051e-05 -9.207e-06 0.9558 1.546e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04788 Epoch 3763 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0548 0.9138 0.9225 0.0001065 -4.782e-05 0.05448 8.027e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01104 -0.006043 -0.001766 0.02075 0.9554 0.9623 0.02177 0.9119 0.9277 0.06078 ] Network output: [ 0.9666 0.09163 0.02796 -4.938e-05 2.217e-05 -0.05306 -3.722e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5311 0.09111 0.0719 0.3087 0.9798 0.9913 0.5974 0.9261 0.9789 0.5236 ] Network output: [ 0.01392 0.925 0.9392 -4.756e-05 2.135e-05 0.1078 -3.584e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01829 0.01318 0.02042 0.0217 0.9893 0.9927 0.01864 0.9771 0.9867 0.0264 ] Network output: [ 0.08827 -0.2182 0.8007 5.938e-06 -2.666e-06 1.241 4.475e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5883 0.5172 0.4382 0.4618 0.9818 0.9923 0.5902 0.9325 0.9815 0.513 ] Network output: [ -0.05625 0.1283 1.148 -3.382e-05 1.518e-05 0.8365 -2.549e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2644 0.2594 0.2862 0.2812 0.9893 0.9933 0.2645 0.9778 0.9872 0.2938 ] Network output: [ -0.05543 0.13 1.124 -3.462e-07 1.554e-07 0.8565 -2.609e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2653 0.2645 0.2842 0.2794 0.9851 0.991 0.2654 0.9634 0.9811 0.2859 ] Network output: [ -0.01176 1.03 0.03748 2.046e-05 -9.187e-06 0.9557 1.542e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04791 Epoch 3764 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05478 0.9138 0.9225 0.0001065 -4.781e-05 0.05453 8.025e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01103 -0.006045 -0.001795 0.02073 0.9554 0.9624 0.02175 0.912 0.9277 0.06076 ] Network output: [ 0.9667 0.09179 0.02786 -4.924e-05 2.21e-05 -0.05318 -3.711e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.531 0.09108 0.07183 0.3085 0.9798 0.9913 0.5973 0.9262 0.9789 0.5237 ] Network output: [ 0.0139 0.925 0.9392 -4.777e-05 2.145e-05 0.1079 -3.6e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01828 0.01317 0.02041 0.02168 0.9893 0.9927 0.01862 0.9771 0.9867 0.02638 ] Network output: [ 0.08822 -0.2183 0.8008 5.478e-06 -2.459e-06 1.241 4.128e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5882 0.5171 0.4383 0.4618 0.9818 0.9923 0.5902 0.9325 0.9816 0.5131 ] Network output: [ -0.05621 0.1283 1.148 -3.36e-05 1.508e-05 0.8364 -2.532e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2643 0.2593 0.2862 0.2812 0.9893 0.9933 0.2644 0.9778 0.9872 0.2938 ] Network output: [ -0.05539 0.1299 1.124 4.354e-08 -1.955e-08 0.8564 3.282e-08 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2652 0.2644 0.2842 0.2794 0.9851 0.991 0.2652 0.9635 0.9811 0.2859 ] Network output: [ -0.01179 1.031 0.03751 2.042e-05 -9.167e-06 0.9556 1.539e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04794 Epoch 3765 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05476 0.9138 0.9225 0.0001065 -4.78e-05 0.05459 8.023e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01101 -0.006047 -0.001824 0.0207 0.9554 0.9624 0.02174 0.912 0.9278 0.06073 ] Network output: [ 0.9667 0.09194 0.02777 -4.909e-05 2.204e-05 -0.0533 -3.7e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5308 0.09106 0.07176 0.3083 0.9799 0.9913 0.5973 0.9262 0.979 0.5238 ] Network output: [ 0.01389 0.9249 0.9392 -4.799e-05 2.155e-05 0.108 -3.617e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01826 0.01316 0.0204 0.02165 0.9893 0.9927 0.01861 0.9772 0.9867 0.02637 ] Network output: [ 0.08818 -0.2184 0.8008 5.013e-06 -2.25e-06 1.241 3.778e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5882 0.5171 0.4384 0.4617 0.9819 0.9923 0.5901 0.9325 0.9816 0.5132 ] Network output: [ -0.05618 0.1282 1.148 -3.338e-05 1.499e-05 0.8364 -2.516e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2642 0.2592 0.2862 0.2811 0.9894 0.9934 0.2643 0.9778 0.9873 0.2938 ] Network output: [ -0.05534 0.1299 1.124 4.361e-07 -1.958e-07 0.8564 3.286e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2651 0.2643 0.2842 0.2793 0.9851 0.991 0.2651 0.9635 0.9811 0.2858 ] Network output: [ -0.01182 1.031 0.03755 2.037e-05 -9.146e-06 0.9555 1.535e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04797 Epoch 3766 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05474 0.9138 0.9225 0.0001064 -4.778e-05 0.05465 8.021e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.011 -0.006048 -0.001853 0.02067 0.9554 0.9624 0.02172 0.9121 0.9278 0.0607 ] Network output: [ 0.9667 0.0921 0.02767 -4.894e-05 2.197e-05 -0.05342 -3.688e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5307 0.09103 0.07169 0.3081 0.9799 0.9913 0.5973 0.9263 0.979 0.5239 ] Network output: [ 0.01387 0.9249 0.9392 -4.821e-05 2.164e-05 0.108 -3.633e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01825 0.01315 0.02038 0.02163 0.9893 0.9927 0.0186 0.9772 0.9867 0.02635 ] Network output: [ 0.08813 -0.2185 0.8008 4.543e-06 -2.04e-06 1.241 3.424e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5881 0.5171 0.4386 0.4617 0.9819 0.9924 0.5901 0.9326 0.9816 0.5134 ] Network output: [ -0.05615 0.1282 1.148 -3.316e-05 1.489e-05 0.8363 -2.499e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.264 0.2591 0.2861 0.2811 0.9894 0.9934 0.2642 0.9778 0.9873 0.2937 ] Network output: [ -0.0553 0.1298 1.124 8.314e-07 -3.733e-07 0.8563 6.266e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.265 0.2641 0.2841 0.2793 0.9851 0.991 0.265 0.9635 0.9811 0.2858 ] Network output: [ -0.01185 1.031 0.03759 2.033e-05 -9.126e-06 0.9554 1.532e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04801 Epoch 3767 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05472 0.9138 0.9225 0.0001064 -4.777e-05 0.05471 8.019e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01098 -0.00605 -0.001883 0.02065 0.9555 0.9624 0.02171 0.9121 0.9278 0.06067 ] Network output: [ 0.9668 0.09226 0.02758 -4.879e-05 2.19e-05 -0.05354 -3.677e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5306 0.091 0.07162 0.3079 0.9799 0.9913 0.5972 0.9263 0.979 0.524 ] Network output: [ 0.01385 0.9249 0.9391 -4.843e-05 2.174e-05 0.1081 -3.65e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01824 0.01314 0.02037 0.02161 0.9893 0.9927 0.01859 0.9772 0.9867 0.02634 ] Network output: [ 0.08808 -0.2186 0.8008 4.068e-06 -1.826e-06 1.242 3.066e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5881 0.517 0.4387 0.4616 0.9819 0.9924 0.59 0.9326 0.9816 0.5135 ] Network output: [ -0.05611 0.1282 1.148 -3.294e-05 1.479e-05 0.8363 -2.483e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2639 0.259 0.2861 0.281 0.9894 0.9934 0.2641 0.9778 0.9873 0.2937 ] Network output: [ -0.05525 0.1297 1.125 1.23e-06 -5.521e-07 0.8563 9.267e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2649 0.264 0.2841 0.2792 0.9851 0.991 0.2649 0.9635 0.9811 0.2858 ] Network output: [ -0.01187 1.031 0.03762 2.028e-05 -9.106e-06 0.9553 1.529e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04804 Epoch 3768 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05471 0.9138 0.9225 0.0001064 -4.776e-05 0.05477 8.017e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01097 -0.006052 -0.001912 0.02062 0.9555 0.9624 0.0217 0.9121 0.9279 0.06064 ] Network output: [ 0.9668 0.09242 0.02748 -4.864e-05 2.183e-05 -0.05367 -3.665e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5305 0.09097 0.07155 0.3077 0.9799 0.9913 0.5972 0.9263 0.979 0.5242 ] Network output: [ 0.01383 0.9248 0.9391 -4.865e-05 2.184e-05 0.1082 -3.667e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01822 0.01313 0.02036 0.02158 0.9893 0.9927 0.01857 0.9772 0.9867 0.02632 ] Network output: [ 0.08804 -0.2188 0.8008 3.588e-06 -1.611e-06 1.242 2.704e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.588 0.517 0.4389 0.4616 0.9819 0.9924 0.59 0.9326 0.9816 0.5137 ] Network output: [ -0.05608 0.1282 1.148 -3.272e-05 1.469e-05 0.8362 -2.466e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2638 0.2589 0.2861 0.2809 0.9894 0.9934 0.264 0.9778 0.9873 0.2937 ] Network output: [ -0.05521 0.1296 1.125 1.631e-06 -7.322e-07 0.8562 1.229e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2647 0.2639 0.2841 0.2792 0.9851 0.9911 0.2648 0.9635 0.9811 0.2858 ] Network output: [ -0.0119 1.031 0.03766 2.024e-05 -9.086e-06 0.9552 1.525e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04807 Epoch 3769 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05469 0.9138 0.9224 0.0001064 -4.775e-05 0.05483 8.015e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01095 -0.006054 -0.001942 0.02059 0.9555 0.9624 0.02168 0.9122 0.9279 0.06061 ] Network output: [ 0.9668 0.09259 0.02739 -4.848e-05 2.176e-05 -0.0538 -3.654e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5304 0.09094 0.07147 0.3075 0.9799 0.9913 0.5971 0.9264 0.979 0.5243 ] Network output: [ 0.01382 0.9248 0.9391 -4.887e-05 2.194e-05 0.1082 -3.683e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01821 0.01312 0.02034 0.02156 0.9893 0.9927 0.01856 0.9772 0.9867 0.02631 ] Network output: [ 0.08799 -0.2189 0.8009 3.104e-06 -1.393e-06 1.242 2.339e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5879 0.517 0.439 0.4615 0.9819 0.9924 0.5899 0.9327 0.9816 0.5138 ] Network output: [ -0.05604 0.1282 1.148 -3.25e-05 1.459e-05 0.8361 -2.449e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2637 0.2588 0.2861 0.2809 0.9894 0.9934 0.2639 0.9778 0.9873 0.2937 ] Network output: [ -0.05516 0.1295 1.125 2.035e-06 -9.136e-07 0.8562 1.534e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2646 0.2638 0.2841 0.2791 0.9851 0.9911 0.2647 0.9635 0.9812 0.2857 ] Network output: [ -0.01193 1.031 0.03769 2.019e-05 -9.065e-06 0.9552 1.522e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04811 Epoch 3770 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05467 0.9138 0.9224 0.0001063 -4.773e-05 0.05489 8.013e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01094 -0.006055 -0.001971 0.02056 0.9555 0.9624 0.02167 0.9122 0.9279 0.06058 ] Network output: [ 0.9668 0.09276 0.02729 -4.832e-05 2.169e-05 -0.05393 -3.642e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5303 0.09091 0.0714 0.3072 0.9799 0.9913 0.5971 0.9264 0.979 0.5244 ] Network output: [ 0.0138 0.9248 0.9391 -4.91e-05 2.204e-05 0.1083 -3.7e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0182 0.01311 0.02033 0.02154 0.9893 0.9927 0.01855 0.9772 0.9867 0.02629 ] Network output: [ 0.08794 -0.219 0.8009 2.614e-06 -1.173e-06 1.242 1.97e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5879 0.5169 0.4391 0.4615 0.9819 0.9924 0.5899 0.9327 0.9816 0.514 ] Network output: [ -0.05601 0.1281 1.148 -3.227e-05 1.449e-05 0.8361 -2.432e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2636 0.2586 0.2861 0.2808 0.9894 0.9934 0.2637 0.9779 0.9873 0.2937 ] Network output: [ -0.05511 0.1295 1.125 2.442e-06 -1.096e-06 0.8561 1.841e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2645 0.2637 0.284 0.2791 0.9851 0.9911 0.2645 0.9636 0.9812 0.2857 ] Network output: [ -0.01196 1.031 0.03772 2.015e-05 -9.044e-06 0.9551 1.518e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04814 Epoch 3771 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05465 0.9138 0.9224 0.0001063 -4.772e-05 0.05495 8.011e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01092 -0.006057 -0.002001 0.02054 0.9555 0.9624 0.02165 0.9123 0.928 0.06055 ] Network output: [ 0.9669 0.09293 0.02719 -4.816e-05 2.162e-05 -0.05405 -3.629e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5301 0.09088 0.07132 0.307 0.9799 0.9913 0.5971 0.9264 0.979 0.5246 ] Network output: [ 0.01378 0.9247 0.9391 -4.932e-05 2.214e-05 0.1084 -3.717e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01818 0.0131 0.02031 0.02151 0.9893 0.9927 0.01853 0.9773 0.9867 0.02628 ] Network output: [ 0.0879 -0.2191 0.8009 2.118e-06 -9.509e-07 1.242 1.596e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5878 0.5169 0.4393 0.4615 0.9819 0.9924 0.5898 0.9327 0.9816 0.5141 ] Network output: [ -0.05598 0.1281 1.148 -3.205e-05 1.439e-05 0.836 -2.415e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2635 0.2585 0.286 0.2807 0.9894 0.9934 0.2636 0.9779 0.9873 0.2936 ] Network output: [ -0.05507 0.1294 1.125 2.853e-06 -1.281e-06 0.8561 2.15e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2644 0.2636 0.284 0.279 0.9851 0.9911 0.2644 0.9636 0.9812 0.2857 ] Network output: [ -0.01199 1.031 0.03776 2.01e-05 -9.024e-06 0.955 1.515e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04817 Epoch 3772 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05463 0.9138 0.9224 0.0001063 -4.771e-05 0.05501 8.009e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0109 -0.006059 -0.00203 0.02051 0.9555 0.9624 0.02164 0.9123 0.928 0.06052 ] Network output: [ 0.9669 0.0931 0.02709 -4.8e-05 2.155e-05 -0.05419 -3.617e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.53 0.09085 0.07124 0.3068 0.9799 0.9913 0.597 0.9265 0.9791 0.5247 ] Network output: [ 0.01377 0.9247 0.9391 -4.954e-05 2.224e-05 0.1085 -3.734e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01817 0.01309 0.0203 0.02149 0.9893 0.9927 0.01852 0.9773 0.9868 0.02626 ] Network output: [ 0.08785 -0.2193 0.8009 1.618e-06 -7.262e-07 1.243 1.219e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5878 0.5169 0.4394 0.4614 0.9819 0.9924 0.5898 0.9328 0.9817 0.5143 ] Network output: [ -0.05594 0.1281 1.148 -3.182e-05 1.428e-05 0.8359 -2.398e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2634 0.2584 0.286 0.2807 0.9894 0.9934 0.2635 0.9779 0.9873 0.2936 ] Network output: [ -0.05502 0.1293 1.125 3.266e-06 -1.466e-06 0.856 2.462e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2643 0.2635 0.284 0.279 0.9851 0.9911 0.2643 0.9636 0.9812 0.2857 ] Network output: [ -0.01202 1.031 0.03779 2.005e-05 -9.003e-06 0.9549 1.511e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04821 Epoch 3773 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05461 0.9138 0.9224 0.0001062 -4.769e-05 0.05508 8.007e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01089 -0.00606 -0.00206 0.02048 0.9555 0.9625 0.02162 0.9123 0.9281 0.06049 ] Network output: [ 0.9669 0.09327 0.02699 -4.783e-05 2.147e-05 -0.05432 -3.605e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5299 0.09082 0.07116 0.3066 0.9799 0.9913 0.597 0.9265 0.9791 0.5248 ] Network output: [ 0.01375 0.9246 0.9391 -4.977e-05 2.234e-05 0.1086 -3.751e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01816 0.01308 0.02029 0.02146 0.9893 0.9927 0.01851 0.9773 0.9868 0.02625 ] Network output: [ 0.0878 -0.2194 0.801 1.112e-06 -4.99e-07 1.243 8.377e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5877 0.5168 0.4396 0.4614 0.9819 0.9924 0.5897 0.9328 0.9817 0.5144 ] Network output: [ -0.05591 0.1281 1.148 -3.159e-05 1.418e-05 0.8359 -2.381e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2632 0.2583 0.286 0.2806 0.9894 0.9934 0.2634 0.9779 0.9874 0.2936 ] Network output: [ -0.05498 0.1292 1.125 3.683e-06 -1.653e-06 0.8559 2.776e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2642 0.2633 0.284 0.2789 0.9851 0.9911 0.2642 0.9636 0.9812 0.2856 ] Network output: [ -0.01205 1.032 0.03783 2.001e-05 -8.982e-06 0.9548 1.508e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04824 Epoch 3774 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05459 0.9137 0.9224 0.0001062 -4.768e-05 0.05514 8.004e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01087 -0.006062 -0.00209 0.02045 0.9555 0.9625 0.02161 0.9124 0.9281 0.06046 ] Network output: [ 0.967 0.09345 0.02689 -4.766e-05 2.14e-05 -0.05445 -3.592e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5298 0.09079 0.07108 0.3064 0.9799 0.9913 0.5969 0.9266 0.9791 0.525 ] Network output: [ 0.01373 0.9246 0.9391 -5e-05 2.245e-05 0.1086 -3.768e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01814 0.01307 0.02027 0.02144 0.9893 0.9927 0.01849 0.9773 0.9868 0.02623 ] Network output: [ 0.08776 -0.2195 0.801 5.999e-07 -2.693e-07 1.243 4.521e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5877 0.5168 0.4397 0.4613 0.9819 0.9924 0.5897 0.9329 0.9817 0.5146 ] Network output: [ -0.05588 0.1281 1.148 -3.136e-05 1.408e-05 0.8358 -2.363e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2631 0.2582 0.286 0.2806 0.9894 0.9934 0.2633 0.9779 0.9874 0.2936 ] Network output: [ -0.05493 0.1291 1.125 4.103e-06 -1.842e-06 0.8559 3.092e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.264 0.2632 0.2839 0.2789 0.9851 0.9911 0.2641 0.9636 0.9812 0.2856 ] Network output: [ -0.01207 1.032 0.03786 1.996e-05 -8.96e-06 0.9547 1.504e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04828 Epoch 3775 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05457 0.9137 0.9224 0.0001062 -4.767e-05 0.05521 8.002e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01086 -0.006064 -0.00212 0.02042 0.9556 0.9625 0.0216 0.9124 0.9281 0.06043 ] Network output: [ 0.967 0.09363 0.02679 -4.749e-05 2.132e-05 -0.05459 -3.579e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5297 0.09075 0.071 0.3062 0.9799 0.9913 0.5969 0.9266 0.9791 0.5251 ] Network output: [ 0.01372 0.9245 0.9391 -5.022e-05 2.255e-05 0.1087 -3.785e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01813 0.01306 0.02026 0.02142 0.9893 0.9927 0.01848 0.9773 0.9868 0.02622 ] Network output: [ 0.08771 -0.2197 0.801 8.274e-08 -3.715e-08 1.243 6.236e-08 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5876 0.5168 0.4399 0.4613 0.9819 0.9924 0.5896 0.9329 0.9817 0.5147 ] Network output: [ -0.05584 0.1281 1.148 -3.113e-05 1.398e-05 0.8357 -2.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.263 0.2581 0.286 0.2805 0.9894 0.9934 0.2632 0.9779 0.9874 0.2936 ] Network output: [ -0.05489 0.1291 1.125 4.526e-06 -2.032e-06 0.8558 3.411e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2639 0.2631 0.2839 0.2788 0.9851 0.9911 0.264 0.9636 0.9812 0.2856 ] Network output: [ -0.0121 1.032 0.03789 1.991e-05 -8.939e-06 0.9546 1.501e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04832 Epoch 3776 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05455 0.9137 0.9223 0.0001062 -4.766e-05 0.05527 8e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01084 -0.006066 -0.00215 0.0204 0.9556 0.9625 0.02158 0.9125 0.9282 0.0604 ] Network output: [ 0.967 0.09381 0.02669 -4.731e-05 2.124e-05 -0.05473 -3.566e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5295 0.09072 0.07092 0.3059 0.9799 0.9913 0.5969 0.9266 0.9791 0.5252 ] Network output: [ 0.0137 0.9245 0.9391 -5.045e-05 2.265e-05 0.1088 -3.802e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01812 0.01305 0.02024 0.02139 0.9894 0.9927 0.01847 0.9773 0.9868 0.0262 ] Network output: [ 0.08766 -0.2198 0.8011 -4.402e-07 1.976e-07 1.243 -3.317e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5875 0.5167 0.44 0.4612 0.9819 0.9924 0.5896 0.9329 0.9817 0.5149 ] Network output: [ -0.05581 0.1281 1.148 -3.09e-05 1.387e-05 0.8357 -2.329e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2629 0.258 0.286 0.2804 0.9894 0.9934 0.263 0.978 0.9874 0.2935 ] Network output: [ -0.05484 0.129 1.125 4.953e-06 -2.224e-06 0.8558 3.733e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2638 0.263 0.2839 0.2788 0.9851 0.9911 0.2638 0.9637 0.9812 0.2855 ] Network output: [ -0.01213 1.032 0.03792 1.986e-05 -8.917e-06 0.9545 1.497e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04835 Epoch 3777 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05453 0.9137 0.9223 0.0001061 -4.764e-05 0.05534 7.998e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01082 -0.006067 -0.00218 0.02037 0.9556 0.9625 0.02157 0.9125 0.9282 0.06038 ] Network output: [ 0.967 0.09399 0.02659 -4.713e-05 2.116e-05 -0.05486 -3.552e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5294 0.09068 0.07084 0.3057 0.9799 0.9913 0.5968 0.9267 0.9791 0.5254 ] Network output: [ 0.01369 0.9245 0.9391 -5.068e-05 2.275e-05 0.1089 -3.82e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0181 0.01303 0.02023 0.02137 0.9894 0.9927 0.01845 0.9773 0.9868 0.02619 ] Network output: [ 0.08762 -0.22 0.8011 -9.689e-07 4.35e-07 1.244 -7.302e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5875 0.5167 0.4401 0.4612 0.9819 0.9924 0.5895 0.933 0.9817 0.5151 ] Network output: [ -0.05578 0.128 1.148 -3.066e-05 1.377e-05 0.8356 -2.311e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2628 0.2579 0.2859 0.2804 0.9894 0.9934 0.2629 0.978 0.9874 0.2935 ] Network output: [ -0.05479 0.1289 1.125 5.383e-06 -2.417e-06 0.8557 4.057e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2637 0.2629 0.2839 0.2787 0.9851 0.9911 0.2637 0.9637 0.9813 0.2855 ] Network output: [ -0.01216 1.032 0.03796 1.981e-05 -8.896e-06 0.9544 1.493e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04839 Epoch 3778 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05451 0.9137 0.9223 0.0001061 -4.763e-05 0.05541 7.995e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01081 -0.006069 -0.00221 0.02034 0.9556 0.9625 0.02155 0.9125 0.9282 0.06035 ] Network output: [ 0.9671 0.09418 0.02649 -4.695e-05 2.108e-05 -0.055 -3.539e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5293 0.09065 0.07075 0.3055 0.9799 0.9913 0.5968 0.9267 0.9792 0.5255 ] Network output: [ 0.01367 0.9244 0.9391 -5.092e-05 2.286e-05 0.109 -3.837e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01809 0.01302 0.02022 0.02134 0.9894 0.9927 0.01844 0.9774 0.9868 0.02617 ] Network output: [ 0.08757 -0.2201 0.8011 -1.503e-06 6.75e-07 1.244 -1.133e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5874 0.5166 0.4403 0.4611 0.9819 0.9924 0.5894 0.933 0.9817 0.5152 ] Network output: [ -0.05574 0.128 1.148 -3.043e-05 1.366e-05 0.8355 -2.293e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2626 0.2577 0.2859 0.2803 0.9894 0.9934 0.2628 0.978 0.9874 0.2935 ] Network output: [ -0.05475 0.1288 1.125 5.817e-06 -2.611e-06 0.8556 4.384e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2636 0.2627 0.2838 0.2787 0.9851 0.9911 0.2636 0.9637 0.9813 0.2855 ] Network output: [ -0.01219 1.032 0.03799 1.977e-05 -8.874e-06 0.9543 1.49e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04843 Epoch 3779 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05449 0.9137 0.9223 0.0001061 -4.761e-05 0.05548 7.993e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01079 -0.00607 -0.00224 0.02031 0.9556 0.9625 0.02154 0.9126 0.9283 0.06032 ] Network output: [ 0.9671 0.09436 0.02638 -4.677e-05 2.1e-05 -0.05515 -3.525e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5291 0.09061 0.07067 0.3053 0.9799 0.9913 0.5968 0.9268 0.9792 0.5257 ] Network output: [ 0.01365 0.9244 0.9391 -5.115e-05 2.296e-05 0.109 -3.855e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01807 0.01301 0.0202 0.02132 0.9894 0.9927 0.01843 0.9774 0.9868 0.02616 ] Network output: [ 0.08752 -0.2203 0.8011 -2.044e-06 9.176e-07 1.244 -1.54e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5874 0.5166 0.4405 0.4611 0.9819 0.9924 0.5894 0.933 0.9818 0.5154 ] Network output: [ -0.05571 0.128 1.148 -3.019e-05 1.355e-05 0.8355 -2.275e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2625 0.2576 0.2859 0.2802 0.9894 0.9934 0.2627 0.978 0.9874 0.2935 ] Network output: [ -0.0547 0.1287 1.125 6.254e-06 -2.808e-06 0.8556 4.713e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2634 0.2626 0.2838 0.2786 0.9851 0.9911 0.2635 0.9637 0.9813 0.2855 ] Network output: [ -0.01222 1.032 0.03802 1.972e-05 -8.851e-06 0.9542 1.486e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04847 Epoch 3780 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05447 0.9137 0.9223 0.000106 -4.76e-05 0.05555 7.991e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01078 -0.006072 -0.00227 0.02028 0.9556 0.9625 0.02152 0.9126 0.9283 0.06029 ] Network output: [ 0.9671 0.09455 0.02628 -4.658e-05 2.091e-05 -0.05529 -3.511e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.529 0.09057 0.07058 0.305 0.9799 0.9913 0.5967 0.9268 0.9792 0.5258 ] Network output: [ 0.01364 0.9243 0.9391 -5.138e-05 2.307e-05 0.1091 -3.872e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01806 0.013 0.02019 0.0213 0.9894 0.9927 0.01841 0.9774 0.9868 0.02615 ] Network output: [ 0.08748 -0.2204 0.8012 -2.591e-06 1.163e-06 1.244 -1.952e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5873 0.5166 0.4406 0.4611 0.9819 0.9924 0.5893 0.9331 0.9818 0.5156 ] Network output: [ -0.05568 0.128 1.148 -2.995e-05 1.345e-05 0.8354 -2.257e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2624 0.2575 0.2859 0.2802 0.9894 0.9934 0.2626 0.978 0.9874 0.2935 ] Network output: [ -0.05466 0.1287 1.125 6.695e-06 -3.006e-06 0.8555 5.046e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2633 0.2625 0.2838 0.2786 0.9851 0.9911 0.2633 0.9637 0.9813 0.2854 ] Network output: [ -0.01225 1.032 0.03805 1.967e-05 -8.829e-06 0.9541 1.482e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04851 Epoch 3781 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05445 0.9136 0.9223 0.000106 -4.759e-05 0.05562 7.988e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01076 -0.006074 -0.0023 0.02025 0.9556 0.9626 0.02151 0.9127 0.9283 0.06026 ] Network output: [ 0.9672 0.09475 0.02618 -4.639e-05 2.083e-05 -0.05544 -3.496e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5289 0.09053 0.07049 0.3048 0.9799 0.9913 0.5967 0.9268 0.9792 0.526 ] Network output: [ 0.01363 0.9243 0.9391 -5.162e-05 2.317e-05 0.1092 -3.89e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01805 0.01299 0.02017 0.02127 0.9894 0.9927 0.0184 0.9774 0.9869 0.02613 ] Network output: [ 0.08743 -0.2206 0.8012 -3.144e-06 1.411e-06 1.244 -2.369e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5872 0.5165 0.4408 0.461 0.9819 0.9924 0.5893 0.9331 0.9818 0.5157 ] Network output: [ -0.05564 0.128 1.148 -2.971e-05 1.334e-05 0.8353 -2.239e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2623 0.2574 0.2859 0.2801 0.9894 0.9934 0.2624 0.978 0.9874 0.2934 ] Network output: [ -0.05461 0.1286 1.125 7.14e-06 -3.205e-06 0.8555 5.381e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2632 0.2624 0.2838 0.2785 0.9851 0.9911 0.2632 0.9637 0.9813 0.2854 ] Network output: [ -0.01228 1.033 0.03808 1.962e-05 -8.806e-06 0.954 1.478e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04855 Epoch 3782 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05444 0.9136 0.9223 0.000106 -4.757e-05 0.05569 7.986e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01074 -0.006075 -0.002331 0.02023 0.9556 0.9626 0.02149 0.9127 0.9284 0.06023 ] Network output: [ 0.9672 0.09494 0.02607 -4.62e-05 2.074e-05 -0.05558 -3.482e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5288 0.09049 0.07041 0.3046 0.98 0.9913 0.5966 0.9269 0.9792 0.5261 ] Network output: [ 0.01361 0.9242 0.939 -5.185e-05 2.328e-05 0.1093 -3.908e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01803 0.01298 0.02016 0.02125 0.9894 0.9927 0.01838 0.9774 0.9869 0.02612 ] Network output: [ 0.08738 -0.2207 0.8012 -3.703e-06 1.662e-06 1.245 -2.791e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5872 0.5165 0.4409 0.461 0.9819 0.9924 0.5892 0.9331 0.9818 0.5159 ] Network output: [ -0.05561 0.128 1.148 -2.947e-05 1.323e-05 0.8352 -2.221e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2622 0.2573 0.2858 0.28 0.9894 0.9934 0.2623 0.9781 0.9875 0.2934 ] Network output: [ -0.05457 0.1285 1.125 7.588e-06 -3.407e-06 0.8554 5.719e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2631 0.2622 0.2837 0.2785 0.9851 0.9911 0.2631 0.9638 0.9813 0.2854 ] Network output: [ -0.01231 1.033 0.03811 1.956e-05 -8.783e-06 0.9539 1.474e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04859 Epoch 3783 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05442 0.9136 0.9222 0.0001059 -4.756e-05 0.05576 7.984e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01073 -0.006077 -0.002361 0.0202 0.9557 0.9626 0.02148 0.9128 0.9284 0.0602 ] Network output: [ 0.9672 0.09514 0.02597 -4.6e-05 2.065e-05 -0.05573 -3.467e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5286 0.09045 0.07032 0.3044 0.98 0.9913 0.5966 0.9269 0.9792 0.5263 ] Network output: [ 0.0136 0.9242 0.939 -5.209e-05 2.339e-05 0.1094 -3.926e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01802 0.01297 0.02015 0.02122 0.9894 0.9927 0.01837 0.9774 0.9869 0.0261 ] Network output: [ 0.08734 -0.2209 0.8013 -4.269e-06 1.916e-06 1.245 -3.217e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5871 0.5164 0.4411 0.4609 0.9819 0.9924 0.5892 0.9332 0.9818 0.5161 ] Network output: [ -0.05558 0.128 1.148 -2.923e-05 1.312e-05 0.8352 -2.203e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.262 0.2571 0.2858 0.2799 0.9894 0.9934 0.2622 0.9781 0.9875 0.2934 ] Network output: [ -0.05452 0.1284 1.125 8.041e-06 -3.61e-06 0.8554 6.06e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2629 0.2621 0.2837 0.2784 0.9851 0.9911 0.263 0.9638 0.9813 0.2854 ] Network output: [ -0.01234 1.033 0.03814 1.951e-05 -8.76e-06 0.9538 1.471e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04863 Epoch 3784 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0544 0.9136 0.9222 0.0001059 -4.754e-05 0.05583 7.981e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01071 -0.006078 -0.002391 0.02017 0.9557 0.9626 0.02146 0.9128 0.9284 0.06017 ] Network output: [ 0.9672 0.09534 0.02586 -4.58e-05 2.056e-05 -0.05588 -3.452e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5285 0.09041 0.07022 0.3041 0.98 0.9913 0.5966 0.9269 0.9793 0.5264 ] Network output: [ 0.01358 0.9241 0.939 -5.233e-05 2.349e-05 0.1095 -3.944e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.018 0.01295 0.02013 0.0212 0.9894 0.9927 0.01836 0.9775 0.9869 0.02609 ] Network output: [ 0.08729 -0.221 0.8013 -4.841e-06 2.173e-06 1.245 -3.648e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5871 0.5164 0.4412 0.4609 0.9819 0.9924 0.5891 0.9332 0.9818 0.5163 ] Network output: [ -0.05555 0.128 1.148 -2.899e-05 1.301e-05 0.8351 -2.184e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2619 0.257 0.2858 0.2799 0.9894 0.9934 0.2621 0.9781 0.9875 0.2934 ] Network output: [ -0.05447 0.1283 1.125 8.497e-06 -3.815e-06 0.8553 6.404e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2628 0.262 0.2837 0.2784 0.9851 0.9911 0.2629 0.9638 0.9813 0.2853 ] Network output: [ -0.01237 1.033 0.03817 1.946e-05 -8.737e-06 0.9537 1.467e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04867 Epoch 3785 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05438 0.9136 0.9222 0.0001059 -4.753e-05 0.05591 7.979e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01069 -0.00608 -0.002422 0.02014 0.9557 0.9626 0.02145 0.9128 0.9285 0.06015 ] Network output: [ 0.9673 0.09555 0.02575 -4.559e-05 2.047e-05 -0.05603 -3.436e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5284 0.09037 0.07013 0.3039 0.98 0.9914 0.5965 0.927 0.9793 0.5266 ] Network output: [ 0.01357 0.9241 0.939 -5.257e-05 2.36e-05 0.1096 -3.962e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01799 0.01294 0.02012 0.02117 0.9894 0.9928 0.01834 0.9775 0.9869 0.02607 ] Network output: [ 0.08725 -0.2212 0.8013 -5.42e-06 2.433e-06 1.245 -4.084e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.587 0.5163 0.4414 0.4608 0.982 0.9924 0.589 0.9332 0.9818 0.5164 ] Network output: [ -0.05551 0.128 1.148 -2.874e-05 1.29e-05 0.835 -2.166e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2618 0.2569 0.2858 0.2798 0.9894 0.9934 0.2619 0.9781 0.9875 0.2933 ] Network output: [ -0.05443 0.1283 1.125 8.957e-06 -4.021e-06 0.8552 6.75e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2627 0.2619 0.2837 0.2783 0.9851 0.9911 0.2627 0.9638 0.9814 0.2853 ] Network output: [ -0.0124 1.033 0.0382 1.941e-05 -8.713e-06 0.9536 1.463e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04871 Epoch 3786 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05436 0.9135 0.9222 0.0001058 -4.751e-05 0.05598 7.976e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01068 -0.006082 -0.002453 0.02011 0.9557 0.9626 0.02143 0.9129 0.9285 0.06012 ] Network output: [ 0.9673 0.09575 0.02564 -4.539e-05 2.038e-05 -0.05619 -3.421e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5282 0.09032 0.07004 0.3037 0.98 0.9914 0.5965 0.927 0.9793 0.5268 ] Network output: [ 0.01356 0.924 0.939 -5.281e-05 2.371e-05 0.1097 -3.98e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01797 0.01293 0.0201 0.02115 0.9894 0.9928 0.01833 0.9775 0.9869 0.02606 ] Network output: [ 0.0872 -0.2213 0.8014 -6.005e-06 2.696e-06 1.246 -4.526e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5869 0.5163 0.4415 0.4608 0.982 0.9924 0.589 0.9333 0.9819 0.5166 ] Network output: [ -0.05548 0.128 1.148 -2.849e-05 1.279e-05 0.8349 -2.147e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2616 0.2568 0.2858 0.2797 0.9894 0.9934 0.2618 0.9781 0.9875 0.2933 ] Network output: [ -0.05438 0.1282 1.125 9.421e-06 -4.23e-06 0.8552 7.1e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2626 0.2617 0.2836 0.2783 0.9851 0.9911 0.2626 0.9638 0.9814 0.2853 ] Network output: [ -0.01243 1.033 0.03823 1.935e-05 -8.689e-06 0.9535 1.459e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04876 Epoch 3787 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05434 0.9135 0.9222 0.0001058 -4.75e-05 0.05606 7.974e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01066 -0.006083 -0.002484 0.02008 0.9557 0.9626 0.02142 0.9129 0.9285 0.06009 ] Network output: [ 0.9673 0.09596 0.02554 -4.518e-05 2.028e-05 -0.05634 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5281 0.09028 0.06994 0.3034 0.98 0.9914 0.5964 0.9271 0.9793 0.5269 ] Network output: [ 0.01354 0.9239 0.939 -5.305e-05 2.382e-05 0.1097 -3.998e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01796 0.01292 0.02009 0.02112 0.9894 0.9928 0.01831 0.9775 0.9869 0.02604 ] Network output: [ 0.08715 -0.2215 0.8014 -6.598e-06 2.962e-06 1.246 -4.972e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5869 0.5162 0.4417 0.4607 0.982 0.9924 0.5889 0.9333 0.9819 0.5168 ] Network output: [ -0.05545 0.128 1.148 -2.824e-05 1.268e-05 0.8348 -2.128e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2615 0.2566 0.2857 0.2797 0.9895 0.9934 0.2617 0.9781 0.9875 0.2933 ] Network output: [ -0.05434 0.1281 1.125 9.89e-06 -4.44e-06 0.8551 7.453e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2624 0.2616 0.2836 0.2782 0.9851 0.9911 0.2625 0.9638 0.9814 0.2853 ] Network output: [ -0.01247 1.033 0.03826 1.93e-05 -8.664e-06 0.9534 1.455e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0488 Epoch 3788 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05432 0.9135 0.9222 0.0001058 -4.748e-05 0.05614 7.971e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01064 -0.006085 -0.002514 0.02005 0.9557 0.9626 0.0214 0.913 0.9286 0.06006 ] Network output: [ 0.9674 0.09618 0.02543 -4.496e-05 2.018e-05 -0.0565 -3.388e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5279 0.09023 0.06984 0.3032 0.98 0.9914 0.5964 0.9271 0.9793 0.5271 ] Network output: [ 0.01353 0.9239 0.939 -5.33e-05 2.393e-05 0.1098 -4.017e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01794 0.01291 0.02007 0.0211 0.9894 0.9928 0.0183 0.9775 0.9869 0.02603 ] Network output: [ 0.08711 -0.2217 0.8014 -7.198e-06 3.231e-06 1.246 -5.424e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5868 0.5162 0.4419 0.4607 0.982 0.9924 0.5889 0.9334 0.9819 0.517 ] Network output: [ -0.05542 0.128 1.148 -2.799e-05 1.257e-05 0.8348 -2.109e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2614 0.2565 0.2857 0.2796 0.9895 0.9934 0.2615 0.9782 0.9875 0.2933 ] Network output: [ -0.05429 0.128 1.126 1.036e-05 -4.652e-06 0.8551 7.81e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2623 0.2615 0.2836 0.2782 0.9851 0.9911 0.2623 0.9639 0.9814 0.2852 ] Network output: [ -0.0125 1.033 0.03829 1.924e-05 -8.64e-06 0.9533 1.45e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04884 Epoch 3789 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05431 0.9135 0.9221 0.0001057 -4.747e-05 0.05622 7.969e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01063 -0.006086 -0.002545 0.02002 0.9557 0.9626 0.02139 0.913 0.9286 0.06003 ] Network output: [ 0.9674 0.09639 0.02532 -4.474e-05 2.009e-05 -0.05666 -3.372e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5278 0.09019 0.06974 0.3029 0.98 0.9914 0.5963 0.9271 0.9793 0.5273 ] Network output: [ 0.01352 0.9238 0.939 -5.355e-05 2.404e-05 0.1099 -4.035e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01793 0.01289 0.02006 0.02107 0.9894 0.9928 0.01828 0.9775 0.9869 0.02601 ] Network output: [ 0.08706 -0.2218 0.8015 -7.804e-06 3.504e-06 1.246 -5.882e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5867 0.5161 0.442 0.4606 0.982 0.9924 0.5888 0.9334 0.9819 0.5172 ] Network output: [ -0.05538 0.128 1.148 -2.774e-05 1.245e-05 0.8347 -2.09e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2613 0.2564 0.2857 0.2795 0.9895 0.9934 0.2614 0.9782 0.9875 0.2933 ] Network output: [ -0.05424 0.128 1.126 1.084e-05 -4.866e-06 0.855 8.169e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2622 0.2614 0.2836 0.2781 0.9852 0.9911 0.2622 0.9639 0.9814 0.2852 ] Network output: [ -0.01253 1.034 0.03832 1.919e-05 -8.615e-06 0.9532 1.446e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04889 Epoch 3790 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05429 0.9134 0.9221 0.0001057 -4.745e-05 0.0563 7.966e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01061 -0.006088 -0.002576 0.01999 0.9557 0.9627 0.02137 0.913 0.9286 0.06 ] Network output: [ 0.9674 0.09661 0.02521 -4.452e-05 1.999e-05 -0.05682 -3.355e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5277 0.09014 0.06964 0.3027 0.98 0.9914 0.5963 0.9272 0.9793 0.5274 ] Network output: [ 0.0135 0.9238 0.939 -5.379e-05 2.415e-05 0.11 -4.054e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01791 0.01288 0.02005 0.02105 0.9894 0.9928 0.01827 0.9776 0.9869 0.026 ] Network output: [ 0.08702 -0.222 0.8015 -8.418e-06 3.779e-06 1.246 -6.344e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5867 0.5161 0.4422 0.4606 0.982 0.9924 0.5887 0.9334 0.9819 0.5174 ] Network output: [ -0.05535 0.128 1.148 -2.748e-05 1.234e-05 0.8346 -2.071e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2611 0.2562 0.2857 0.2794 0.9895 0.9934 0.2613 0.9782 0.9876 0.2932 ] Network output: [ -0.0542 0.1279 1.126 1.132e-05 -5.082e-06 0.8549 8.532e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2621 0.2612 0.2835 0.2781 0.9852 0.9911 0.2621 0.9639 0.9814 0.2852 ] Network output: [ -0.01256 1.034 0.03835 1.913e-05 -8.589e-06 0.9531 1.442e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04894 Epoch 3791 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05427 0.9134 0.9221 0.0001057 -4.744e-05 0.05638 7.963e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01059 -0.006089 -0.002608 0.01996 0.9558 0.9627 0.02136 0.9131 0.9287 0.05998 ] Network output: [ 0.9674 0.09683 0.0251 -4.429e-05 1.988e-05 -0.05699 -3.338e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5275 0.09009 0.06954 0.3025 0.98 0.9914 0.5963 0.9272 0.9794 0.5276 ] Network output: [ 0.01349 0.9237 0.939 -5.404e-05 2.426e-05 0.1101 -4.073e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0179 0.01287 0.02003 0.02102 0.9894 0.9928 0.01825 0.9776 0.987 0.02598 ] Network output: [ 0.08697 -0.2222 0.8016 -9.04e-06 4.058e-06 1.247 -6.813e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5866 0.516 0.4424 0.4605 0.982 0.9924 0.5887 0.9335 0.9819 0.5176 ] Network output: [ -0.05532 0.128 1.148 -2.723e-05 1.222e-05 0.8345 -2.052e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.261 0.2561 0.2857 0.2794 0.9895 0.9934 0.2611 0.9782 0.9876 0.2932 ] Network output: [ -0.05415 0.1278 1.126 1.181e-05 -5.3e-06 0.8549 8.898e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2619 0.2611 0.2835 0.278 0.9852 0.9911 0.262 0.9639 0.9814 0.2852 ] Network output: [ -0.01259 1.034 0.03837 1.907e-05 -8.563e-06 0.953 1.438e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04898 Epoch 3792 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05425 0.9134 0.9221 0.0001056 -4.742e-05 0.05646 7.961e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01057 -0.006091 -0.002639 0.01993 0.9558 0.9627 0.02134 0.9131 0.9287 0.05995 ] Network output: [ 0.9675 0.09706 0.02499 -4.406e-05 1.978e-05 -0.05715 -3.32e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5274 0.09004 0.06944 0.3022 0.98 0.9914 0.5962 0.9273 0.9794 0.5278 ] Network output: [ 0.01348 0.9236 0.939 -5.429e-05 2.437e-05 0.1102 -4.092e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01788 0.01286 0.02002 0.02099 0.9894 0.9928 0.01824 0.9776 0.987 0.02597 ] Network output: [ 0.08692 -0.2224 0.8016 -9.669e-06 4.341e-06 1.247 -7.287e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5865 0.516 0.4425 0.4605 0.982 0.9924 0.5886 0.9335 0.9819 0.5178 ] Network output: [ -0.05529 0.128 1.148 -2.697e-05 1.211e-05 0.8344 -2.032e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2609 0.256 0.2856 0.2793 0.9895 0.9934 0.261 0.9782 0.9876 0.2932 ] Network output: [ -0.05411 0.1277 1.126 1.23e-05 -5.521e-06 0.8548 9.267e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2618 0.261 0.2835 0.278 0.9852 0.9911 0.2618 0.9639 0.9815 0.2851 ] Network output: [ -0.01263 1.034 0.0384 1.902e-05 -8.537e-06 0.9529 1.433e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04903 Epoch 3793 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05423 0.9133 0.9221 0.0001056 -4.741e-05 0.05654 7.958e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01056 -0.006092 -0.00267 0.0199 0.9558 0.9627 0.02133 0.9132 0.9288 0.05992 ] Network output: [ 0.9675 0.09728 0.02487 -4.382e-05 1.967e-05 -0.05732 -3.303e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5272 0.08999 0.06933 0.302 0.98 0.9914 0.5962 0.9273 0.9794 0.528 ] Network output: [ 0.01347 0.9236 0.939 -5.454e-05 2.449e-05 0.1103 -4.111e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01787 0.01284 0.02 0.02097 0.9894 0.9928 0.01822 0.9776 0.987 0.02595 ] Network output: [ 0.08688 -0.2226 0.8016 -1.031e-05 4.627e-06 1.247 -7.767e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5865 0.5159 0.4427 0.4604 0.982 0.9924 0.5886 0.9335 0.9819 0.518 ] Network output: [ -0.05525 0.128 1.148 -2.671e-05 1.199e-05 0.8343 -2.013e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2607 0.2559 0.2856 0.2792 0.9895 0.9935 0.2609 0.9782 0.9876 0.2932 ] Network output: [ -0.05406 0.1276 1.126 1.279e-05 -5.743e-06 0.8548 9.64e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2617 0.2609 0.2835 0.2779 0.9852 0.9911 0.2617 0.9639 0.9815 0.2851 ] Network output: [ -0.01266 1.034 0.03843 1.896e-05 -8.51e-06 0.9528 1.429e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04908 Epoch 3794 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05422 0.9133 0.9221 0.0001056 -4.739e-05 0.05662 7.955e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01054 -0.006094 -0.002702 0.01987 0.9558 0.9627 0.02131 0.9132 0.9288 0.05989 ] Network output: [ 0.9675 0.09751 0.02476 -4.358e-05 1.957e-05 -0.05749 -3.285e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5271 0.08994 0.06923 0.3017 0.98 0.9914 0.5961 0.9273 0.9794 0.5282 ] Network output: [ 0.01346 0.9235 0.9389 -5.48e-05 2.46e-05 0.1104 -4.13e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01785 0.01283 0.01999 0.02094 0.9894 0.9928 0.01821 0.9776 0.987 0.02594 ] Network output: [ 0.08683 -0.2227 0.8017 -1.095e-05 4.916e-06 1.247 -8.252e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5864 0.5159 0.4429 0.4604 0.982 0.9924 0.5885 0.9336 0.982 0.5182 ] Network output: [ -0.05522 0.128 1.148 -2.644e-05 1.187e-05 0.8343 -1.993e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2606 0.2557 0.2856 0.2791 0.9895 0.9935 0.2607 0.9783 0.9876 0.2931 ] Network output: [ -0.05401 0.1275 1.126 1.329e-05 -5.967e-06 0.8547 1.002e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2615 0.2607 0.2834 0.2779 0.9852 0.9911 0.2616 0.964 0.9815 0.2851 ] Network output: [ -0.01269 1.034 0.03845 1.89e-05 -8.483e-06 0.9527 1.424e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04912 Epoch 3795 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0542 0.9133 0.922 0.0001055 -4.737e-05 0.05671 7.952e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01052 -0.006095 -0.002733 0.01984 0.9558 0.9627 0.0213 0.9133 0.9288 0.05986 ] Network output: [ 0.9675 0.09775 0.02465 -4.334e-05 1.946e-05 -0.05766 -3.266e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5269 0.08989 0.06912 0.3015 0.98 0.9914 0.5961 0.9274 0.9794 0.5283 ] Network output: [ 0.01344 0.9234 0.9389 -5.505e-05 2.472e-05 0.1105 -4.149e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01784 0.01282 0.01997 0.02092 0.9894 0.9928 0.01819 0.9776 0.987 0.02593 ] Network output: [ 0.08679 -0.2229 0.8017 -1.16e-05 5.209e-06 1.248 -8.744e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5863 0.5158 0.4431 0.4603 0.982 0.9924 0.5884 0.9336 0.982 0.5184 ] Network output: [ -0.05519 0.128 1.148 -2.618e-05 1.175e-05 0.8342 -1.973e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2604 0.2556 0.2856 0.2791 0.9895 0.9935 0.2606 0.9783 0.9876 0.2931 ] Network output: [ -0.05397 0.1275 1.126 1.38e-05 -6.194e-06 0.8546 1.04e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2614 0.2606 0.2834 0.2778 0.9852 0.9911 0.2614 0.964 0.9815 0.2851 ] Network output: [ -0.01272 1.034 0.03848 1.884e-05 -8.456e-06 0.9526 1.419e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04917 Epoch 3796 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05418 0.9132 0.922 0.0001055 -4.736e-05 0.0568 7.95e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0105 -0.006097 -0.002765 0.01981 0.9558 0.9627 0.02128 0.9133 0.9289 0.05983 ] Network output: [ 0.9676 0.09798 0.02453 -4.309e-05 1.935e-05 -0.05784 -3.247e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5268 0.08983 0.06901 0.3012 0.98 0.9914 0.596 0.9274 0.9794 0.5285 ] Network output: [ 0.01343 0.9234 0.9389 -5.531e-05 2.483e-05 0.1106 -4.168e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01782 0.0128 0.01996 0.02089 0.9895 0.9928 0.01818 0.9777 0.987 0.02591 ] Network output: [ 0.08674 -0.2231 0.8018 -1.226e-05 5.506e-06 1.248 -9.242e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5863 0.5157 0.4432 0.4603 0.982 0.9924 0.5884 0.9336 0.982 0.5186 ] Network output: [ -0.05516 0.128 1.148 -2.591e-05 1.163e-05 0.8341 -1.953e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2603 0.2554 0.2856 0.279 0.9895 0.9935 0.2605 0.9783 0.9876 0.2931 ] Network output: [ -0.05392 0.1274 1.126 1.431e-05 -6.422e-06 0.8546 1.078e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2613 0.2605 0.2834 0.2777 0.9852 0.9911 0.2613 0.964 0.9815 0.285 ] Network output: [ -0.01276 1.035 0.0385 1.877e-05 -8.428e-06 0.9524 1.415e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04922 Epoch 3797 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05416 0.9132 0.922 0.0001054 -4.734e-05 0.05688 7.947e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01048 -0.006098 -0.002797 0.01978 0.9558 0.9627 0.02127 0.9133 0.9289 0.05981 ] Network output: [ 0.9676 0.09822 0.02442 -4.284e-05 1.923e-05 -0.05801 -3.228e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5266 0.08978 0.0689 0.301 0.98 0.9914 0.596 0.9275 0.9795 0.5287 ] Network output: [ 0.01342 0.9233 0.9389 -5.557e-05 2.495e-05 0.1107 -4.188e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01781 0.01279 0.01994 0.02086 0.9895 0.9928 0.01816 0.9777 0.987 0.0259 ] Network output: [ 0.0867 -0.2233 0.8018 -1.293e-05 5.806e-06 1.248 -9.747e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5862 0.5157 0.4434 0.4602 0.982 0.9925 0.5883 0.9337 0.982 0.5188 ] Network output: [ -0.05513 0.128 1.148 -2.564e-05 1.151e-05 0.834 -1.932e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2602 0.2553 0.2855 0.2789 0.9895 0.9935 0.2603 0.9783 0.9876 0.2931 ] Network output: [ -0.05387 0.1273 1.126 1.482e-05 -6.653e-06 0.8545 1.117e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2611 0.2603 0.2834 0.2777 0.9852 0.9911 0.2612 0.964 0.9815 0.285 ] Network output: [ -0.01279 1.035 0.03853 1.871e-05 -8.399e-06 0.9523 1.41e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04927 Epoch 3798 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05414 0.9132 0.922 0.0001054 -4.732e-05 0.05697 7.944e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01047 -0.006099 -0.002828 0.01975 0.9558 0.9627 0.02125 0.9134 0.9289 0.05978 ] Network output: [ 0.9676 0.09847 0.0243 -4.258e-05 1.912e-05 -0.05819 -3.209e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5265 0.08972 0.06879 0.3007 0.98 0.9914 0.5959 0.9275 0.9795 0.5289 ] Network output: [ 0.01341 0.9232 0.9389 -5.583e-05 2.506e-05 0.1108 -4.208e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01779 0.01278 0.01993 0.02084 0.9895 0.9928 0.01815 0.9777 0.987 0.02588 ] Network output: [ 0.08665 -0.2235 0.8018 -1.361e-05 6.11e-06 1.248 -1.026e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5861 0.5156 0.4436 0.4602 0.982 0.9925 0.5882 0.9337 0.982 0.5191 ] Network output: [ -0.0551 0.128 1.148 -2.537e-05 1.139e-05 0.8339 -1.912e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.26 0.2552 0.2855 0.2788 0.9895 0.9935 0.2602 0.9783 0.9877 0.2931 ] Network output: [ -0.05383 0.1272 1.126 1.534e-05 -6.886e-06 0.8544 1.156e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.261 0.2602 0.2833 0.2776 0.9852 0.9911 0.261 0.964 0.9815 0.285 ] Network output: [ -0.01282 1.035 0.03855 1.864e-05 -8.37e-06 0.9522 1.405e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04932 Epoch 3799 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05413 0.9131 0.922 0.0001054 -4.731e-05 0.05706 7.941e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01045 -0.006101 -0.00286 0.01972 0.9558 0.9628 0.02124 0.9134 0.929 0.05975 ] Network output: [ 0.9676 0.09871 0.02419 -4.232e-05 1.9e-05 -0.05837 -3.189e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5263 0.08967 0.06867 0.3004 0.9801 0.9914 0.5959 0.9275 0.9795 0.5291 ] Network output: [ 0.0134 0.9232 0.9389 -5.609e-05 2.518e-05 0.1109 -4.227e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01777 0.01277 0.01991 0.02081 0.9895 0.9928 0.01813 0.9777 0.987 0.02587 ] Network output: [ 0.08661 -0.2237 0.8019 -1.43e-05 6.418e-06 1.249 -1.077e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5861 0.5156 0.4438 0.4601 0.982 0.9925 0.5882 0.9337 0.982 0.5193 ] Network output: [ -0.05506 0.128 1.148 -2.51e-05 1.127e-05 0.8338 -1.891e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2599 0.255 0.2855 0.2787 0.9895 0.9935 0.26 0.9783 0.9877 0.293 ] Network output: [ -0.05378 0.1271 1.126 1.586e-05 -7.122e-06 0.8544 1.196e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2609 0.2601 0.2833 0.2776 0.9852 0.9911 0.2609 0.964 0.9815 0.2849 ] Network output: [ -0.01286 1.035 0.03858 1.858e-05 -8.341e-06 0.9521 1.4e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04938 Epoch 3800 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05411 0.9131 0.922 0.0001053 -4.729e-05 0.05715 7.938e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01043 -0.006102 -0.002892 0.01969 0.9559 0.9628 0.02122 0.9135 0.929 0.05972 ] Network output: [ 0.9677 0.09896 0.02407 -4.205e-05 1.888e-05 -0.05856 -3.169e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5262 0.08961 0.06856 0.3002 0.9801 0.9914 0.5959 0.9276 0.9795 0.5293 ] Network output: [ 0.01339 0.9231 0.9389 -5.636e-05 2.53e-05 0.111 -4.247e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01776 0.01275 0.0199 0.02078 0.9895 0.9928 0.01812 0.9777 0.9871 0.02585 ] Network output: [ 0.08656 -0.2239 0.8019 -1.499e-05 6.73e-06 1.249 -1.13e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.586 0.5155 0.444 0.4601 0.982 0.9925 0.5881 0.9338 0.982 0.5195 ] Network output: [ -0.05503 0.128 1.148 -2.482e-05 1.114e-05 0.8337 -1.871e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2597 0.2549 0.2855 0.2787 0.9895 0.9935 0.2599 0.9784 0.9877 0.293 ] Network output: [ -0.05373 0.1271 1.126 1.639e-05 -7.36e-06 0.8543 1.235e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2607 0.2599 0.2833 0.2775 0.9852 0.9911 0.2608 0.964 0.9816 0.2849 ] Network output: [ -0.01289 1.035 0.0386 1.851e-05 -8.311e-06 0.952 1.395e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04943 Epoch 3801 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05409 0.9131 0.9219 0.0001053 -4.727e-05 0.05724 7.935e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01041 -0.006104 -0.002925 0.01966 0.9559 0.9628 0.0212 0.9135 0.929 0.0597 ] Network output: [ 0.9677 0.09922 0.02395 -4.177e-05 1.875e-05 -0.05874 -3.148e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.526 0.08955 0.06844 0.2999 0.9801 0.9914 0.5958 0.9276 0.9795 0.5295 ] Network output: [ 0.01338 0.923 0.9389 -5.663e-05 2.542e-05 0.1111 -4.267e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01774 0.01274 0.01988 0.02076 0.9895 0.9928 0.0181 0.9777 0.9871 0.02584 ] Network output: [ 0.08652 -0.2241 0.802 -1.57e-05 7.046e-06 1.249 -1.183e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5859 0.5154 0.4441 0.46 0.982 0.9925 0.588 0.9338 0.9821 0.5197 ] Network output: [ -0.055 0.128 1.148 -2.454e-05 1.102e-05 0.8336 -1.85e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2596 0.2547 0.2854 0.2786 0.9895 0.9935 0.2598 0.9784 0.9877 0.293 ] Network output: [ -0.05369 0.127 1.126 1.693e-05 -7.6e-06 0.8543 1.276e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2606 0.2598 0.2833 0.2775 0.9852 0.9911 0.2606 0.9641 0.9816 0.2849 ] Network output: [ -0.01293 1.035 0.03863 1.844e-05 -8.28e-06 0.9519 1.39e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04948 Epoch 3802 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05407 0.913 0.9219 0.0001053 -4.725e-05 0.05734 7.932e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01039 -0.006105 -0.002957 0.01963 0.9559 0.9628 0.02119 0.9136 0.9291 0.05967 ] Network output: [ 0.9677 0.09947 0.02384 -4.149e-05 1.863e-05 -0.05893 -3.127e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5259 0.08949 0.06832 0.2997 0.9801 0.9914 0.5958 0.9277 0.9795 0.5297 ] Network output: [ 0.01337 0.9229 0.9389 -5.689e-05 2.554e-05 0.1112 -4.288e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01773 0.01272 0.01987 0.02073 0.9895 0.9928 0.01809 0.9777 0.9871 0.02582 ] Network output: [ 0.08647 -0.2243 0.802 -1.641e-05 7.367e-06 1.249 -1.237e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5859 0.5154 0.4443 0.46 0.982 0.9925 0.588 0.9339 0.9821 0.5199 ] Network output: [ -0.05497 0.128 1.148 -2.426e-05 1.089e-05 0.8335 -1.829e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2594 0.2546 0.2854 0.2785 0.9895 0.9935 0.2596 0.9784 0.9877 0.293 ] Network output: [ -0.05364 0.1269 1.126 1.747e-05 -7.843e-06 0.8542 1.317e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2605 0.2597 0.2832 0.2774 0.9852 0.9912 0.2605 0.9641 0.9816 0.2849 ] Network output: [ -0.01296 1.036 0.03865 1.837e-05 -8.249e-06 0.9518 1.385e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04954 Epoch 3803 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05406 0.913 0.9219 0.0001052 -4.723e-05 0.05743 7.929e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01037 -0.006106 -0.002989 0.0196 0.9559 0.9628 0.02117 0.9136 0.9291 0.05964 ] Network output: [ 0.9678 0.09973 0.02372 -4.121e-05 1.85e-05 -0.05912 -3.106e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5257 0.08943 0.0682 0.2994 0.9801 0.9914 0.5957 0.9277 0.9795 0.5299 ] Network output: [ 0.01336 0.9228 0.9389 -5.716e-05 2.566e-05 0.1113 -4.308e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01771 0.01271 0.01985 0.0207 0.9895 0.9928 0.01807 0.9778 0.9871 0.02581 ] Network output: [ 0.08643 -0.2245 0.8021 -1.713e-05 7.691e-06 1.25 -1.291e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5858 0.5153 0.4445 0.4599 0.982 0.9925 0.5879 0.9339 0.9821 0.5202 ] Network output: [ -0.05494 0.128 1.148 -2.398e-05 1.077e-05 0.8334 -1.807e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2593 0.2545 0.2854 0.2784 0.9895 0.9935 0.2595 0.9784 0.9877 0.2929 ] Network output: [ -0.05359 0.1268 1.126 1.802e-05 -8.088e-06 0.8541 1.358e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2603 0.2595 0.2832 0.2773 0.9852 0.9912 0.2604 0.9641 0.9816 0.2848 ] Network output: [ -0.013 1.036 0.03867 1.83e-05 -8.217e-06 0.9517 1.379e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04959 Epoch 3804 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05404 0.9129 0.9219 0.0001052 -4.722e-05 0.05753 7.926e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01036 -0.006108 -0.003022 0.01956 0.9559 0.9628 0.02116 0.9136 0.9292 0.05961 ] Network output: [ 0.9678 0.1 0.0236 -4.092e-05 1.837e-05 -0.05931 -3.084e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5256 0.08936 0.06807 0.2991 0.9801 0.9914 0.5957 0.9277 0.9796 0.5301 ] Network output: [ 0.01336 0.9228 0.9388 -5.744e-05 2.579e-05 0.1115 -4.329e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01769 0.0127 0.01984 0.02067 0.9895 0.9928 0.01805 0.9778 0.9871 0.02579 ] Network output: [ 0.08638 -0.2247 0.8021 -1.786e-05 8.02e-06 1.25 -1.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5857 0.5152 0.4447 0.4599 0.982 0.9925 0.5878 0.9339 0.9821 0.5204 ] Network output: [ -0.05491 0.128 1.148 -2.369e-05 1.064e-05 0.8333 -1.786e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2591 0.2543 0.2854 0.2783 0.9895 0.9935 0.2593 0.9784 0.9877 0.2929 ] Network output: [ -0.05355 0.1267 1.126 1.857e-05 -8.336e-06 0.8541 1.399e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2602 0.2594 0.2832 0.2773 0.9852 0.9912 0.2602 0.9641 0.9816 0.2848 ] Network output: [ -0.01303 1.036 0.03869 1.823e-05 -8.184e-06 0.9516 1.374e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04965 Epoch 3805 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05402 0.9129 0.9219 0.0001051 -4.72e-05 0.05762 7.923e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01034 -0.006109 -0.003055 0.01953 0.9559 0.9628 0.02114 0.9137 0.9292 0.05959 ] Network output: [ 0.9678 0.1003 0.02348 -4.062e-05 1.824e-05 -0.05951 -3.062e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5254 0.0893 0.06795 0.2988 0.9801 0.9914 0.5956 0.9278 0.9796 0.5303 ] Network output: [ 0.01335 0.9227 0.9388 -5.771e-05 2.591e-05 0.1116 -4.349e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01768 0.01268 0.01982 0.02065 0.9895 0.9928 0.01804 0.9778 0.9871 0.02578 ] Network output: [ 0.08634 -0.2249 0.8022 -1.861e-05 8.353e-06 1.25 -1.402e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5856 0.5151 0.4449 0.4598 0.982 0.9925 0.5878 0.934 0.9821 0.5207 ] Network output: [ -0.05487 0.1281 1.148 -2.341e-05 1.051e-05 0.8332 -1.764e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.259 0.2542 0.2853 0.2782 0.9895 0.9935 0.2592 0.9784 0.9877 0.2929 ] Network output: [ -0.0535 0.1266 1.126 1.913e-05 -8.587e-06 0.854 1.441e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2601 0.2593 0.2831 0.2772 0.9852 0.9912 0.2601 0.9641 0.9816 0.2848 ] Network output: [ -0.01307 1.036 0.03871 1.816e-05 -8.151e-06 0.9515 1.368e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0497 Epoch 3806 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05401 0.9128 0.9218 0.0001051 -4.718e-05 0.05772 7.92e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01032 -0.006111 -0.003088 0.0195 0.9559 0.9628 0.02113 0.9137 0.9292 0.05956 ] Network output: [ 0.9678 0.1005 0.02336 -4.032e-05 1.81e-05 -0.05971 -3.039e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5252 0.08923 0.06782 0.2986 0.9801 0.9914 0.5956 0.9278 0.9796 0.5306 ] Network output: [ 0.01334 0.9226 0.9388 -5.799e-05 2.603e-05 0.1117 -4.37e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01766 0.01267 0.01981 0.02062 0.9895 0.9928 0.01802 0.9778 0.9871 0.02577 ] Network output: [ 0.08629 -0.2252 0.8022 -1.936e-05 8.69e-06 1.25 -1.459e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5856 0.5151 0.4451 0.4598 0.9821 0.9925 0.5877 0.934 0.9821 0.5209 ] Network output: [ -0.05484 0.1281 1.148 -2.312e-05 1.038e-05 0.8331 -1.742e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2589 0.254 0.2853 0.2781 0.9895 0.9935 0.259 0.9785 0.9878 0.2929 ] Network output: [ -0.05345 0.1266 1.126 1.969e-05 -8.84e-06 0.8539 1.484e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2599 0.2591 0.2831 0.2772 0.9852 0.9912 0.26 0.9641 0.9816 0.2848 ] Network output: [ -0.01311 1.036 0.03873 1.808e-05 -8.117e-06 0.9513 1.363e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04976 Epoch 3807 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05399 0.9128 0.9218 0.000105 -4.716e-05 0.05782 7.917e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0103 -0.006112 -0.00312 0.01947 0.9559 0.9628 0.02111 0.9138 0.9293 0.05953 ] Network output: [ 0.9678 0.1008 0.02324 -4.001e-05 1.796e-05 -0.05991 -3.016e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5251 0.08917 0.06769 0.2983 0.9801 0.9914 0.5955 0.9278 0.9796 0.5308 ] Network output: [ 0.01333 0.9225 0.9388 -5.827e-05 2.616e-05 0.1118 -4.391e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01764 0.01265 0.01979 0.02059 0.9895 0.9928 0.01801 0.9778 0.9871 0.02575 ] Network output: [ 0.08625 -0.2254 0.8023 -2.012e-05 9.033e-06 1.251 -1.516e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5855 0.515 0.4453 0.4597 0.9821 0.9925 0.5876 0.934 0.9821 0.5211 ] Network output: [ -0.05481 0.1281 1.148 -2.282e-05 1.025e-05 0.833 -1.72e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2587 0.2539 0.2853 0.2781 0.9895 0.9935 0.2589 0.9785 0.9878 0.2928 ] Network output: [ -0.05341 0.1265 1.127 2.026e-05 -9.096e-06 0.8539 1.527e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2598 0.259 0.2831 0.2771 0.9852 0.9912 0.2598 0.9642 0.9816 0.2847 ] Network output: [ -0.01314 1.036 0.03875 1.8e-05 -8.082e-06 0.9512 1.357e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04982 Epoch 3808 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05397 0.9128 0.9218 0.000105 -4.714e-05 0.05792 7.913e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01028 -0.006113 -0.003154 0.01944 0.956 0.9629 0.02109 0.9138 0.9293 0.0595 ] Network output: [ 0.9679 0.1011 0.02311 -3.97e-05 1.782e-05 -0.06011 -2.992e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5249 0.0891 0.06756 0.298 0.9801 0.9914 0.5955 0.9279 0.9796 0.531 ] Network output: [ 0.01332 0.9224 0.9388 -5.855e-05 2.628e-05 0.1119 -4.412e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01763 0.01264 0.01978 0.02056 0.9895 0.9928 0.01799 0.9778 0.9871 0.02574 ] Network output: [ 0.0862 -0.2256 0.8023 -2.089e-05 9.379e-06 1.251 -1.575e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5854 0.5149 0.4455 0.4596 0.9821 0.9925 0.5876 0.9341 0.9822 0.5214 ] Network output: [ -0.05478 0.1281 1.148 -2.253e-05 1.011e-05 0.8329 -1.698e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2585 0.2537 0.2853 0.278 0.9895 0.9935 0.2587 0.9785 0.9878 0.2928 ] Network output: [ -0.05336 0.1264 1.127 2.084e-05 -9.354e-06 0.8538 1.57e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2596 0.2588 0.2831 0.277 0.9852 0.9912 0.2597 0.9642 0.9817 0.2847 ] Network output: [ -0.01318 1.037 0.03877 1.792e-05 -8.047e-06 0.9511 1.351e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04988 Epoch 3809 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05396 0.9127 0.9218 0.000105 -4.712e-05 0.05802 7.91e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01026 -0.006115 -0.003187 0.01941 0.956 0.9629 0.02108 0.9139 0.9293 0.05948 ] Network output: [ 0.9679 0.1014 0.02299 -3.938e-05 1.768e-05 -0.06031 -2.968e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5247 0.08903 0.06743 0.2977 0.9801 0.9914 0.5954 0.9279 0.9796 0.5312 ] Network output: [ 0.01332 0.9223 0.9388 -5.883e-05 2.641e-05 0.112 -4.434e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01761 0.01263 0.01976 0.02054 0.9895 0.9928 0.01797 0.9779 0.9871 0.02572 ] Network output: [ 0.08616 -0.2258 0.8024 -2.168e-05 9.731e-06 1.251 -1.634e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5853 0.5148 0.4457 0.4596 0.9821 0.9925 0.5875 0.9341 0.9822 0.5216 ] Network output: [ -0.05475 0.1281 1.148 -2.223e-05 9.979e-06 0.8328 -1.675e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2584 0.2536 0.2852 0.2779 0.9896 0.9935 0.2586 0.9785 0.9878 0.2928 ] Network output: [ -0.05331 0.1263 1.127 2.142e-05 -9.616e-06 0.8538 1.614e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2595 0.2587 0.283 0.277 0.9852 0.9912 0.2595 0.9642 0.9817 0.2847 ] Network output: [ -0.01322 1.037 0.03879 1.784e-05 -8.011e-06 0.951 1.345e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04994 Epoch 3810 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05394 0.9127 0.9218 0.0001049 -4.71e-05 0.05813 7.907e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01024 -0.006116 -0.00322 0.01937 0.956 0.9629 0.02106 0.9139 0.9294 0.05945 ] Network output: [ 0.9679 0.1017 0.02287 -3.905e-05 1.753e-05 -0.06052 -2.943e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5246 0.08896 0.06729 0.2975 0.9801 0.9914 0.5954 0.928 0.9797 0.5315 ] Network output: [ 0.01331 0.9222 0.9388 -5.912e-05 2.654e-05 0.1121 -4.455e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01759 0.01261 0.01975 0.02051 0.9895 0.9929 0.01796 0.9779 0.9872 0.02571 ] Network output: [ 0.08612 -0.2261 0.8024 -2.247e-05 1.009e-05 1.251 -1.693e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5853 0.5148 0.4459 0.4595 0.9821 0.9925 0.5874 0.9341 0.9822 0.5219 ] Network output: [ -0.05472 0.1281 1.149 -2.193e-05 9.844e-06 0.8327 -1.653e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2582 0.2534 0.2852 0.2778 0.9896 0.9935 0.2584 0.9785 0.9878 0.2928 ] Network output: [ -0.05327 0.1262 1.127 2.201e-05 -9.88e-06 0.8537 1.659e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2594 0.2586 0.283 0.2769 0.9852 0.9912 0.2594 0.9642 0.9817 0.2847 ] Network output: [ -0.01325 1.037 0.03881 1.776e-05 -7.974e-06 0.9509 1.339e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05 Epoch 3811 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05392 0.9126 0.9218 0.0001049 -4.708e-05 0.05823 7.904e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01022 -0.006117 -0.003254 0.01934 0.956 0.9629 0.02104 0.9139 0.9294 0.05942 ] Network output: [ 0.9679 0.102 0.02274 -3.872e-05 1.738e-05 -0.06073 -2.918e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5244 0.08888 0.06716 0.2972 0.9801 0.9915 0.5953 0.928 0.9797 0.5317 ] Network output: [ 0.0133 0.9221 0.9388 -5.941e-05 2.667e-05 0.1123 -4.477e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01757 0.0126 0.01973 0.02048 0.9895 0.9929 0.01794 0.9779 0.9872 0.02569 ] Network output: [ 0.08607 -0.2263 0.8025 -2.327e-05 1.045e-05 1.252 -1.754e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5852 0.5147 0.4461 0.4595 0.9821 0.9925 0.5873 0.9342 0.9822 0.5222 ] Network output: [ -0.05469 0.1282 1.149 -2.162e-05 9.707e-06 0.8326 -1.63e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2581 0.2533 0.2852 0.2777 0.9896 0.9935 0.2582 0.9785 0.9878 0.2927 ] Network output: [ -0.05322 0.1261 1.127 2.26e-05 -1.015e-05 0.8536 1.703e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2592 0.2584 0.283 0.2769 0.9852 0.9912 0.2593 0.9642 0.9817 0.2846 ] Network output: [ -0.01329 1.037 0.03883 1.768e-05 -7.936e-06 0.9508 1.332e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05006 Epoch 3812 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05391 0.9125 0.9217 0.0001048 -4.706e-05 0.05834 7.9e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0102 -0.006118 -0.003287 0.01931 0.956 0.9629 0.02103 0.914 0.9294 0.0594 ] Network output: [ 0.968 0.1022 0.02262 -3.838e-05 1.723e-05 -0.06095 -2.892e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5242 0.08881 0.06702 0.2969 0.9801 0.9915 0.5953 0.928 0.9797 0.5319 ] Network output: [ 0.0133 0.922 0.9387 -5.97e-05 2.68e-05 0.1124 -4.499e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01756 0.01258 0.01972 0.02045 0.9895 0.9929 0.01792 0.9779 0.9872 0.02568 ] Network output: [ 0.08603 -0.2265 0.8025 -2.409e-05 1.081e-05 1.252 -1.816e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5851 0.5146 0.4463 0.4594 0.9821 0.9925 0.5873 0.9342 0.9822 0.5224 ] Network output: [ -0.05466 0.1282 1.149 -2.132e-05 9.57e-06 0.8325 -1.606e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2579 0.2531 0.2852 0.2776 0.9896 0.9935 0.2581 0.9786 0.9878 0.2927 ] Network output: [ -0.05317 0.126 1.127 2.321e-05 -1.042e-05 0.8536 1.749e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2591 0.2583 0.283 0.2768 0.9852 0.9912 0.2591 0.9642 0.9817 0.2846 ] Network output: [ -0.01333 1.037 0.03885 1.759e-05 -7.897e-06 0.9506 1.326e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05012 Epoch 3813 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05389 0.9125 0.9217 0.0001048 -4.704e-05 0.05844 7.897e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01018 -0.00612 -0.003321 0.01928 0.956 0.9629 0.02101 0.914 0.9295 0.05937 ] Network output: [ 0.968 0.1025 0.02249 -3.803e-05 1.707e-05 -0.06116 -2.866e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.524 0.08874 0.06687 0.2966 0.9801 0.9915 0.5952 0.9281 0.9797 0.5322 ] Network output: [ 0.01329 0.922 0.9387 -5.999e-05 2.693e-05 0.1125 -4.521e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01754 0.01257 0.0197 0.02042 0.9895 0.9929 0.0179 0.9779 0.9872 0.02566 ] Network output: [ 0.08599 -0.2268 0.8026 -2.492e-05 1.119e-05 1.252 -1.878e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.585 0.5145 0.4465 0.4593 0.9821 0.9925 0.5872 0.9343 0.9822 0.5227 ] Network output: [ -0.05463 0.1282 1.149 -2.101e-05 9.43e-06 0.8324 -1.583e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2578 0.253 0.2851 0.2775 0.9896 0.9935 0.2579 0.9786 0.9878 0.2927 ] Network output: [ -0.05312 0.126 1.127 2.381e-05 -1.069e-05 0.8535 1.795e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2589 0.2581 0.2829 0.2767 0.9852 0.9912 0.259 0.9643 0.9817 0.2846 ] Network output: [ -0.01337 1.037 0.03886 1.75e-05 -7.857e-06 0.9505 1.319e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05018 Epoch 3814 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05387 0.9124 0.9217 0.0001047 -4.702e-05 0.05855 7.893e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01016 -0.006121 -0.003355 0.01924 0.956 0.9629 0.021 0.9141 0.9295 0.05934 ] Network output: [ 0.968 0.1029 0.02237 -3.767e-05 1.691e-05 -0.06138 -2.839e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5238 0.08866 0.06673 0.2963 0.9801 0.9915 0.5952 0.9281 0.9797 0.5324 ] Network output: [ 0.01328 0.9219 0.9387 -6.029e-05 2.706e-05 0.1126 -4.543e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01752 0.01255 0.01969 0.02039 0.9895 0.9929 0.01789 0.9779 0.9872 0.02565 ] Network output: [ 0.08594 -0.227 0.8027 -2.576e-05 1.156e-05 1.252 -1.941e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5849 0.5144 0.4467 0.4593 0.9821 0.9925 0.5871 0.9343 0.9822 0.523 ] Network output: [ -0.05459 0.1282 1.149 -2.069e-05 9.29e-06 0.8323 -1.56e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2576 0.2528 0.2851 0.2774 0.9896 0.9935 0.2578 0.9786 0.9879 0.2927 ] Network output: [ -0.05308 0.1259 1.127 2.443e-05 -1.097e-05 0.8534 1.841e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2588 0.258 0.2829 0.2767 0.9852 0.9912 0.2588 0.9643 0.9817 0.2846 ] Network output: [ -0.01341 1.038 0.03888 1.741e-05 -7.817e-06 0.9504 1.312e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05025 Epoch 3815 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05386 0.9124 0.9217 0.0001047 -4.7e-05 0.05866 7.89e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01014 -0.006122 -0.003389 0.01921 0.956 0.9629 0.02098 0.9141 0.9296 0.05931 ] Network output: [ 0.968 0.1032 0.02224 -3.731e-05 1.675e-05 -0.06161 -2.812e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5237 0.08858 0.06658 0.296 0.9801 0.9915 0.5951 0.9282 0.9797 0.5327 ] Network output: [ 0.01328 0.9218 0.9387 -6.059e-05 2.72e-05 0.1127 -4.566e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0175 0.01254 0.01967 0.02036 0.9895 0.9929 0.01787 0.978 0.9872 0.02563 ] Network output: [ 0.0859 -0.2272 0.8027 -2.661e-05 1.195e-05 1.253 -2.005e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5849 0.5143 0.4469 0.4592 0.9821 0.9925 0.587 0.9343 0.9822 0.5232 ] Network output: [ -0.05456 0.1282 1.149 -2.038e-05 9.148e-06 0.8321 -1.536e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2574 0.2526 0.2851 0.2773 0.9896 0.9935 0.2576 0.9786 0.9879 0.2926 ] Network output: [ -0.05303 0.1258 1.127 2.505e-05 -1.125e-05 0.8534 1.888e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2587 0.2578 0.2829 0.2766 0.9852 0.9912 0.2587 0.9643 0.9817 0.2845 ] Network output: [ -0.01345 1.038 0.0389 1.732e-05 -7.775e-06 0.9503 1.305e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05031 Epoch 3816 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05384 0.9123 0.9217 0.0001046 -4.698e-05 0.05877 7.886e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01012 -0.006123 -0.003423 0.01918 0.956 0.9629 0.02096 0.9142 0.9296 0.05929 ] Network output: [ 0.968 0.1035 0.02212 -3.694e-05 1.658e-05 -0.06183 -2.784e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5235 0.0885 0.06643 0.2957 0.9801 0.9915 0.5951 0.9282 0.9798 0.5329 ] Network output: [ 0.01327 0.9216 0.9387 -6.089e-05 2.733e-05 0.1129 -4.589e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01749 0.01252 0.01966 0.02033 0.9896 0.9929 0.01785 0.978 0.9872 0.02562 ] Network output: [ 0.08586 -0.2275 0.8028 -2.747e-05 1.233e-05 1.253 -2.07e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5848 0.5143 0.4471 0.4591 0.9821 0.9925 0.587 0.9344 0.9823 0.5235 ] Network output: [ -0.05453 0.1283 1.149 -2.006e-05 9.005e-06 0.832 -1.512e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2573 0.2525 0.2851 0.2772 0.9896 0.9935 0.2574 0.9786 0.9879 0.2926 ] Network output: [ -0.05298 0.1257 1.127 2.568e-05 -1.153e-05 0.8533 1.936e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2585 0.2577 0.2829 0.2766 0.9852 0.9912 0.2585 0.9643 0.9818 0.2845 ] Network output: [ -0.01349 1.038 0.03891 1.722e-05 -7.733e-06 0.9502 1.298e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05038 Epoch 3817 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05382 0.9123 0.9216 0.0001046 -4.695e-05 0.05889 7.882e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0101 -0.006125 -0.003458 0.01914 0.9561 0.963 0.02095 0.9142 0.9296 0.05926 ] Network output: [ 0.9681 0.1038 0.02199 -3.656e-05 1.641e-05 -0.06206 -2.755e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5233 0.08842 0.06628 0.2954 0.9801 0.9915 0.595 0.9282 0.9798 0.5332 ] Network output: [ 0.01327 0.9215 0.9387 -6.119e-05 2.747e-05 0.113 -4.612e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01747 0.01251 0.01964 0.02031 0.9896 0.9929 0.01783 0.978 0.9872 0.02561 ] Network output: [ 0.08582 -0.2277 0.8028 -2.835e-05 1.273e-05 1.253 -2.136e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5847 0.5142 0.4473 0.4591 0.9821 0.9925 0.5869 0.9344 0.9823 0.5238 ] Network output: [ -0.0545 0.1283 1.149 -1.974e-05 8.86e-06 0.8319 -1.487e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2571 0.2523 0.285 0.2771 0.9896 0.9935 0.2573 0.9787 0.9879 0.2926 ] Network output: [ -0.05293 0.1256 1.127 2.632e-05 -1.182e-05 0.8532 1.984e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2584 0.2576 0.2828 0.2765 0.9852 0.9912 0.2584 0.9643 0.9818 0.2845 ] Network output: [ -0.01353 1.038 0.03892 1.713e-05 -7.689e-06 0.95 1.291e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05045 Epoch 3818 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05381 0.9122 0.9216 0.0001045 -4.693e-05 0.059 7.879e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01008 -0.006126 -0.003492 0.01911 0.9561 0.963 0.02093 0.9142 0.9297 0.05924 ] Network output: [ 0.9681 0.1041 0.02186 -3.617e-05 1.624e-05 -0.06229 -2.726e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5231 0.08834 0.06613 0.2951 0.9802 0.9915 0.595 0.9283 0.9798 0.5334 ] Network output: [ 0.01326 0.9214 0.9387 -6.15e-05 2.761e-05 0.1131 -4.635e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01745 0.01249 0.01962 0.02028 0.9896 0.9929 0.01782 0.978 0.9872 0.02559 ] Network output: [ 0.08577 -0.228 0.8029 -2.924e-05 1.313e-05 1.253 -2.203e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5846 0.5141 0.4475 0.459 0.9821 0.9925 0.5868 0.9344 0.9823 0.5241 ] Network output: [ -0.05447 0.1283 1.149 -1.941e-05 8.714e-06 0.8318 -1.463e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2569 0.2522 0.285 0.277 0.9896 0.9935 0.2571 0.9787 0.9879 0.2926 ] Network output: [ -0.05288 0.1255 1.127 2.697e-05 -1.211e-05 0.8532 2.032e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2582 0.2574 0.2828 0.2764 0.9852 0.9912 0.2582 0.9643 0.9818 0.2845 ] Network output: [ -0.01357 1.038 0.03894 1.703e-05 -7.645e-06 0.9499 1.283e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05051 Epoch 3819 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05379 0.9121 0.9216 0.0001045 -4.691e-05 0.05912 7.875e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01006 -0.006127 -0.003527 0.01908 0.9561 0.963 0.02091 0.9143 0.9297 0.05921 ] Network output: [ 0.9681 0.1044 0.02173 -3.577e-05 1.606e-05 -0.06253 -2.696e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5229 0.08825 0.06597 0.2948 0.9802 0.9915 0.5949 0.9283 0.9798 0.5337 ] Network output: [ 0.01326 0.9213 0.9387 -6.181e-05 2.775e-05 0.1132 -4.658e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01743 0.01247 0.01961 0.02025 0.9896 0.9929 0.0178 0.978 0.9873 0.02558 ] Network output: [ 0.08573 -0.2283 0.803 -3.014e-05 1.353e-05 1.254 -2.271e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5845 0.514 0.4478 0.4589 0.9821 0.9925 0.5867 0.9345 0.9823 0.5244 ] Network output: [ -0.05444 0.1284 1.149 -1.908e-05 8.566e-06 0.8317 -1.438e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2568 0.252 0.285 0.2769 0.9896 0.9935 0.257 0.9787 0.9879 0.2925 ] Network output: [ -0.05283 0.1254 1.127 2.762e-05 -1.24e-05 0.8531 2.081e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2581 0.2573 0.2828 0.2764 0.9852 0.9912 0.2581 0.9644 0.9818 0.2844 ] Network output: [ -0.01361 1.039 0.03895 1.693e-05 -7.599e-06 0.9498 1.276e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05058 Epoch 3820 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05378 0.9121 0.9216 0.0001044 -4.689e-05 0.05923 7.871e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01004 -0.006128 -0.003562 0.01904 0.9561 0.963 0.02089 0.9143 0.9297 0.05918 ] Network output: [ 0.9681 0.1048 0.0216 -3.537e-05 1.588e-05 -0.06277 -2.666e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5227 0.08817 0.06581 0.2945 0.9802 0.9915 0.5948 0.9284 0.9798 0.534 ] Network output: [ 0.01326 0.9212 0.9386 -6.212e-05 2.789e-05 0.1134 -4.682e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01741 0.01246 0.01959 0.02022 0.9896 0.9929 0.01778 0.978 0.9873 0.02556 ] Network output: [ 0.08569 -0.2285 0.803 -3.106e-05 1.394e-05 1.254 -2.34e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5844 0.5139 0.448 0.4589 0.9821 0.9925 0.5867 0.9345 0.9823 0.5247 ] Network output: [ -0.05441 0.1284 1.149 -1.875e-05 8.417e-06 0.8316 -1.413e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2566 0.2518 0.285 0.2768 0.9896 0.9935 0.2568 0.9787 0.9879 0.2925 ] Network output: [ -0.05279 0.1253 1.127 2.828e-05 -1.27e-05 0.8531 2.131e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2579 0.2571 0.2828 0.2763 0.9852 0.9912 0.2579 0.9644 0.9818 0.2844 ] Network output: [ -0.01365 1.039 0.03896 1.682e-05 -7.552e-06 0.9497 1.268e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05065 Epoch 3821 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05376 0.912 0.9216 0.0001044 -4.686e-05 0.05935 7.867e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.01002 -0.006129 -0.003597 0.01901 0.9561 0.963 0.02088 0.9144 0.9298 0.05916 ] Network output: [ 0.9681 0.1051 0.02147 -3.496e-05 1.569e-05 -0.06301 -2.634e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5225 0.08808 0.06565 0.2942 0.9802 0.9915 0.5948 0.9284 0.9798 0.5342 ] Network output: [ 0.01325 0.9211 0.9386 -6.244e-05 2.803e-05 0.1135 -4.705e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01739 0.01244 0.01958 0.02019 0.9896 0.9929 0.01776 0.9781 0.9873 0.02555 ] Network output: [ 0.08565 -0.2288 0.8031 -3.199e-05 1.436e-05 1.254 -2.41e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5843 0.5138 0.4482 0.4588 0.9821 0.9925 0.5866 0.9345 0.9823 0.525 ] Network output: [ -0.05438 0.1284 1.149 -1.841e-05 8.266e-06 0.8314 -1.388e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2564 0.2517 0.2849 0.2767 0.9896 0.9936 0.2566 0.9787 0.988 0.2925 ] Network output: [ -0.05274 0.1252 1.127 2.895e-05 -1.3e-05 0.853 2.182e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2578 0.257 0.2827 0.2762 0.9852 0.9912 0.2578 0.9644 0.9818 0.2844 ] Network output: [ -0.01369 1.039 0.03897 1.671e-05 -7.504e-06 0.9495 1.26e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05072 Epoch 3822 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05375 0.9119 0.9215 0.0001043 -4.684e-05 0.05947 7.863e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009997 -0.006131 -0.003633 0.01897 0.9561 0.963 0.02086 0.9144 0.9298 0.05913 ] Network output: [ 0.9682 0.1054 0.02134 -3.453e-05 1.55e-05 -0.06325 -2.602e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5223 0.08799 0.06549 0.2938 0.9802 0.9915 0.5947 0.9284 0.9798 0.5345 ] Network output: [ 0.01325 0.921 0.9386 -6.276e-05 2.817e-05 0.1136 -4.729e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01738 0.01243 0.01956 0.02016 0.9896 0.9929 0.01774 0.9781 0.9873 0.02553 ] Network output: [ 0.08561 -0.2291 0.8031 -3.293e-05 1.478e-05 1.255 -2.482e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5843 0.5137 0.4484 0.4587 0.9821 0.9925 0.5865 0.9346 0.9823 0.5253 ] Network output: [ -0.05435 0.1285 1.149 -1.807e-05 8.113e-06 0.8313 -1.362e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2563 0.2515 0.2849 0.2766 0.9896 0.9936 0.2564 0.9787 0.988 0.2925 ] Network output: [ -0.05269 0.1251 1.127 2.963e-05 -1.33e-05 0.8529 2.233e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2576 0.2568 0.2827 0.2762 0.9852 0.9912 0.2577 0.9644 0.9818 0.2844 ] Network output: [ -0.01374 1.039 0.03899 1.66e-05 -7.454e-06 0.9494 1.251e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05079 Epoch 3823 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05373 0.9118 0.9215 0.0001043 -4.682e-05 0.0596 7.859e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009976 -0.006132 -0.003668 0.01894 0.9561 0.963 0.02084 0.9145 0.9299 0.0591 ] Network output: [ 0.9682 0.1058 0.02121 -3.41e-05 1.531e-05 -0.0635 -2.57e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5221 0.0879 0.06532 0.2935 0.9802 0.9915 0.5947 0.9285 0.9799 0.5348 ] Network output: [ 0.01325 0.9209 0.9386 -6.308e-05 2.832e-05 0.1138 -4.754e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01736 0.01241 0.01954 0.02012 0.9896 0.9929 0.01773 0.9781 0.9873 0.02552 ] Network output: [ 0.08556 -0.2293 0.8032 -3.389e-05 1.521e-05 1.255 -2.554e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5842 0.5136 0.4486 0.4587 0.9821 0.9925 0.5864 0.9346 0.9824 0.5256 ] Network output: [ -0.05432 0.1285 1.149 -1.773e-05 7.959e-06 0.8312 -1.336e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2561 0.2513 0.2849 0.2765 0.9896 0.9936 0.2563 0.9788 0.988 0.2924 ] Network output: [ -0.05264 0.125 1.127 3.031e-05 -1.361e-05 0.8529 2.284e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2575 0.2567 0.2827 0.2761 0.9852 0.9912 0.2575 0.9644 0.9819 0.2843 ] Network output: [ -0.01378 1.039 0.039 1.649e-05 -7.404e-06 0.9493 1.243e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05087 Epoch 3824 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05372 0.9118 0.9215 0.0001042 -4.679e-05 0.05972 7.855e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009955 -0.006133 -0.003704 0.0189 0.9561 0.963 0.02083 0.9145 0.9299 0.05908 ] Network output: [ 0.9682 0.1061 0.02108 -3.366e-05 1.511e-05 -0.06376 -2.537e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5219 0.0878 0.06515 0.2932 0.9802 0.9915 0.5946 0.9285 0.9799 0.5351 ] Network output: [ 0.01325 0.9208 0.9386 -6.34e-05 2.846e-05 0.1139 -4.778e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01734 0.01239 0.01953 0.02009 0.9896 0.9929 0.01771 0.9781 0.9873 0.02551 ] Network output: [ 0.08552 -0.2296 0.8033 -3.486e-05 1.565e-05 1.255 -2.627e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5841 0.5135 0.4489 0.4586 0.9821 0.9925 0.5863 0.9347 0.9824 0.5259 ] Network output: [ -0.05429 0.1285 1.149 -1.738e-05 7.803e-06 0.8311 -1.31e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2559 0.2511 0.2848 0.2764 0.9896 0.9936 0.2561 0.9788 0.988 0.2924 ] Network output: [ -0.05259 0.1249 1.128 3.1e-05 -1.392e-05 0.8528 2.337e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2573 0.2565 0.2827 0.276 0.9852 0.9912 0.2573 0.9644 0.9819 0.2843 ] Network output: [ -0.01382 1.04 0.039 1.638e-05 -7.352e-06 0.9492 1.234e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05094 Epoch 3825 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0537 0.9117 0.9215 0.0001042 -4.677e-05 0.05985 7.851e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009933 -0.006134 -0.00374 0.01887 0.9561 0.963 0.02081 0.9146 0.9299 0.05905 ] Network output: [ 0.9682 0.1065 0.02095 -3.32e-05 1.491e-05 -0.06401 -2.502e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5217 0.08771 0.06498 0.2929 0.9802 0.9915 0.5945 0.9286 0.9799 0.5354 ] Network output: [ 0.01324 0.9206 0.9386 -6.373e-05 2.861e-05 0.1141 -4.803e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01732 0.01238 0.01951 0.02006 0.9896 0.9929 0.01769 0.9781 0.9873 0.02549 ] Network output: [ 0.08548 -0.2299 0.8033 -3.585e-05 1.609e-05 1.255 -2.701e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.584 0.5134 0.4491 0.4585 0.9821 0.9925 0.5862 0.9347 0.9824 0.5262 ] Network output: [ -0.05426 0.1286 1.149 -1.703e-05 7.646e-06 0.8309 -1.283e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2558 0.251 0.2848 0.2763 0.9896 0.9936 0.2559 0.9788 0.988 0.2924 ] Network output: [ -0.05254 0.1248 1.128 3.171e-05 -1.423e-05 0.8527 2.39e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2572 0.2564 0.2826 0.276 0.9852 0.9912 0.2572 0.9644 0.9819 0.2843 ] Network output: [ -0.01387 1.04 0.03901 1.626e-05 -7.298e-06 0.949 1.225e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05102 Epoch 3826 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05369 0.9116 0.9215 0.0001041 -4.674e-05 0.05997 7.847e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009911 -0.006135 -0.003776 0.01883 0.9562 0.9631 0.02079 0.9146 0.93 0.05903 ] Network output: [ 0.9682 0.1068 0.02082 -3.274e-05 1.47e-05 -0.06427 -2.467e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5215 0.08761 0.0648 0.2926 0.9802 0.9915 0.5945 0.9286 0.9799 0.5357 ] Network output: [ 0.01324 0.9205 0.9386 -6.406e-05 2.876e-05 0.1142 -4.828e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0173 0.01236 0.0195 0.02003 0.9896 0.9929 0.01767 0.9781 0.9873 0.02548 ] Network output: [ 0.08544 -0.2302 0.8034 -3.685e-05 1.654e-05 1.256 -2.777e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5839 0.5132 0.4493 0.4584 0.9821 0.9925 0.5861 0.9347 0.9824 0.5265 ] Network output: [ -0.05422 0.1286 1.149 -1.668e-05 7.486e-06 0.8308 -1.257e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2556 0.2508 0.2848 0.2762 0.9896 0.9936 0.2558 0.9788 0.988 0.2924 ] Network output: [ -0.05249 0.1247 1.128 3.242e-05 -1.455e-05 0.8527 2.443e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.257 0.2562 0.2826 0.2759 0.9852 0.9912 0.257 0.9645 0.9819 0.2843 ] Network output: [ -0.01391 1.04 0.03902 1.613e-05 -7.244e-06 0.9489 1.216e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05109 Epoch 3827 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05367 0.9115 0.9214 0.0001041 -4.672e-05 0.0601 7.843e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009889 -0.006136 -0.003812 0.0188 0.9562 0.9631 0.02077 0.9146 0.93 0.059 ] Network output: [ 0.9683 0.1072 0.02068 -3.227e-05 1.449e-05 -0.06453 -2.432e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5213 0.08751 0.06462 0.2922 0.9802 0.9915 0.5944 0.9286 0.9799 0.536 ] Network output: [ 0.01324 0.9204 0.9385 -6.44e-05 2.891e-05 0.1143 -4.853e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01728 0.01234 0.01948 0.02 0.9896 0.9929 0.01765 0.9782 0.9873 0.02546 ] Network output: [ 0.0854 -0.2305 0.8035 -3.787e-05 1.7e-05 1.256 -2.854e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5838 0.5131 0.4496 0.4584 0.9821 0.9925 0.5861 0.9348 0.9824 0.5268 ] Network output: [ -0.05419 0.1287 1.149 -1.632e-05 7.325e-06 0.8307 -1.23e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2554 0.2506 0.2848 0.2761 0.9896 0.9936 0.2556 0.9788 0.988 0.2923 ] Network output: [ -0.05244 0.1246 1.128 3.314e-05 -1.488e-05 0.8526 2.497e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2569 0.2561 0.2826 0.2758 0.9852 0.9912 0.2569 0.9645 0.9819 0.2842 ] Network output: [ -0.01396 1.04 0.03903 1.601e-05 -7.187e-06 0.9488 1.207e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05117 Epoch 3828 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05366 0.9115 0.9214 0.000104 -4.669e-05 0.06023 7.838e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009867 -0.006137 -0.003848 0.01876 0.9562 0.9631 0.02076 0.9147 0.9301 0.05898 ] Network output: [ 0.9683 0.1076 0.02055 -3.178e-05 1.427e-05 -0.0648 -2.395e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5211 0.08741 0.06444 0.2919 0.9802 0.9915 0.5944 0.9287 0.9799 0.5363 ] Network output: [ 0.01324 0.9203 0.9385 -6.474e-05 2.906e-05 0.1145 -4.879e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01726 0.01232 0.01946 0.01997 0.9896 0.9929 0.01763 0.9782 0.9874 0.02545 ] Network output: [ 0.08536 -0.2307 0.8035 -3.89e-05 1.746e-05 1.256 -2.932e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5837 0.513 0.4498 0.4583 0.9821 0.9925 0.586 0.9348 0.9824 0.5272 ] Network output: [ -0.05416 0.1287 1.149 -1.595e-05 7.162e-06 0.8305 -1.202e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2552 0.2504 0.2847 0.2759 0.9896 0.9936 0.2554 0.9788 0.988 0.2923 ] Network output: [ -0.05239 0.1245 1.128 3.387e-05 -1.521e-05 0.8526 2.552e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2567 0.2559 0.2826 0.2757 0.9852 0.9912 0.2567 0.9645 0.9819 0.2842 ] Network output: [ -0.014 1.04 0.03903 1.588e-05 -7.13e-06 0.9486 1.197e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05125 Epoch 3829 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05364 0.9114 0.9214 0.000104 -4.667e-05 0.06037 7.834e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009845 -0.006138 -0.003885 0.01873 0.9562 0.9631 0.02074 0.9147 0.9301 0.05895 ] Network output: [ 0.9683 0.108 0.02042 -3.129e-05 1.405e-05 -0.06507 -2.358e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5209 0.08731 0.06425 0.2915 0.9802 0.9915 0.5943 0.9287 0.98 0.5366 ] Network output: [ 0.01324 0.9201 0.9385 -6.508e-05 2.922e-05 0.1146 -4.905e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01724 0.01231 0.01945 0.01994 0.9896 0.9929 0.01761 0.9782 0.9874 0.02543 ] Network output: [ 0.08532 -0.231 0.8036 -3.995e-05 1.794e-05 1.257 -3.011e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5836 0.5129 0.45 0.4582 0.9821 0.9925 0.5859 0.9348 0.9824 0.5275 ] Network output: [ -0.05413 0.1287 1.149 -1.559e-05 6.998e-06 0.8304 -1.175e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.255 0.2503 0.2847 0.2758 0.9896 0.9936 0.2552 0.9789 0.9881 0.2923 ] Network output: [ -0.05234 0.1244 1.128 3.461e-05 -1.554e-05 0.8525 2.608e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2565 0.2557 0.2825 0.2757 0.9852 0.9912 0.2566 0.9645 0.9819 0.2842 ] Network output: [ -0.01405 1.041 0.03904 1.575e-05 -7.07e-06 0.9485 1.187e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05133 Epoch 3830 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05363 0.9113 0.9214 0.0001039 -4.664e-05 0.0605 7.83e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009822 -0.006139 -0.003922 0.01869 0.9562 0.9631 0.02072 0.9148 0.9301 0.05892 ] Network output: [ 0.9683 0.1083 0.02028 -3.078e-05 1.382e-05 -0.06535 -2.319e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5206 0.0872 0.06407 0.2912 0.9802 0.9915 0.5942 0.9288 0.98 0.5369 ] Network output: [ 0.01324 0.92 0.9385 -6.543e-05 2.937e-05 0.1148 -4.931e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01722 0.01229 0.01943 0.0199 0.9896 0.9929 0.01759 0.9782 0.9874 0.02542 ] Network output: [ 0.08528 -0.2313 0.8037 -4.102e-05 1.842e-05 1.257 -3.092e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5835 0.5128 0.4503 0.4581 0.9821 0.9925 0.5858 0.9349 0.9825 0.5278 ] Network output: [ -0.0541 0.1288 1.149 -1.522e-05 6.831e-06 0.8303 -1.147e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2549 0.2501 0.2847 0.2757 0.9897 0.9936 0.255 0.9789 0.9881 0.2922 ] Network output: [ -0.05229 0.1243 1.128 3.536e-05 -1.587e-05 0.8524 2.665e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2564 0.2556 0.2825 0.2756 0.9852 0.9912 0.2564 0.9645 0.9819 0.2842 ] Network output: [ -0.0141 1.041 0.03904 1.561e-05 -7.009e-06 0.9484 1.177e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05141 Epoch 3831 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05361 0.9112 0.9214 0.0001038 -4.661e-05 0.06064 7.825e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0098 -0.00614 -0.003959 0.01866 0.9562 0.9631 0.0207 0.9148 0.9302 0.0589 ] Network output: [ 0.9683 0.1087 0.02015 -3.026e-05 1.358e-05 -0.06562 -2.28e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5204 0.0871 0.06387 0.2909 0.9802 0.9915 0.5942 0.9288 0.98 0.5372 ] Network output: [ 0.01324 0.9198 0.9385 -6.578e-05 2.953e-05 0.1149 -4.958e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0172 0.01227 0.01941 0.01987 0.9896 0.9929 0.01757 0.9782 0.9874 0.02541 ] Network output: [ 0.08525 -0.2316 0.8037 -4.211e-05 1.89e-05 1.257 -3.173e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5834 0.5127 0.4505 0.458 0.9822 0.9926 0.5857 0.9349 0.9825 0.5282 ] Network output: [ -0.05407 0.1288 1.149 -1.484e-05 6.662e-06 0.8301 -1.118e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2547 0.2499 0.2846 0.2756 0.9897 0.9936 0.2548 0.9789 0.9881 0.2922 ] Network output: [ -0.05224 0.1242 1.128 3.612e-05 -1.621e-05 0.8524 2.722e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2562 0.2554 0.2825 0.2755 0.9853 0.9912 0.2563 0.9645 0.982 0.2841 ] Network output: [ -0.01415 1.041 0.03905 1.547e-05 -6.946e-06 0.9482 1.166e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05149 Epoch 3832 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0536 0.9111 0.9213 0.0001038 -4.659e-05 0.06077 7.821e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009777 -0.006141 -0.003996 0.01862 0.9562 0.9631 0.02068 0.9149 0.9302 0.05887 ] Network output: [ 0.9683 0.1091 0.02001 -2.972e-05 1.334e-05 -0.06591 -2.24e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5202 0.08699 0.06368 0.2905 0.9802 0.9915 0.5941 0.9289 0.98 0.5375 ] Network output: [ 0.01325 0.9197 0.9385 -6.614e-05 2.969e-05 0.1151 -4.984e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01718 0.01225 0.0194 0.01984 0.9896 0.9929 0.01755 0.9782 0.9874 0.02539 ] Network output: [ 0.08521 -0.2319 0.8038 -4.321e-05 1.94e-05 1.258 -3.257e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5833 0.5125 0.4508 0.4579 0.9822 0.9926 0.5856 0.935 0.9825 0.5285 ] Network output: [ -0.05404 0.1289 1.149 -1.446e-05 6.491e-06 0.83 -1.09e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2545 0.2497 0.2846 0.2755 0.9897 0.9936 0.2547 0.9789 0.9881 0.2922 ] Network output: [ -0.05219 0.1241 1.128 3.689e-05 -1.656e-05 0.8523 2.78e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2561 0.2553 0.2825 0.2754 0.9853 0.9912 0.2561 0.9646 0.982 0.2841 ] Network output: [ -0.01419 1.041 0.03905 1.533e-05 -6.882e-06 0.9481 1.155e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05157 Epoch 3833 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05358 0.911 0.9213 0.0001037 -4.656e-05 0.06091 7.816e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009754 -0.006142 -0.004034 0.01858 0.9562 0.9631 0.02067 0.9149 0.9302 0.05885 ] Network output: [ 0.9683 0.1095 0.01988 -2.918e-05 1.31e-05 -0.06619 -2.199e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.52 0.08688 0.06348 0.2902 0.9802 0.9915 0.594 0.9289 0.98 0.5378 ] Network output: [ 0.01325 0.9196 0.9384 -6.65e-05 2.985e-05 0.1152 -5.011e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01716 0.01223 0.01938 0.01981 0.9896 0.9929 0.01753 0.9782 0.9874 0.02538 ] Network output: [ 0.08517 -0.2323 0.8039 -4.433e-05 1.99e-05 1.258 -3.341e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5832 0.5124 0.451 0.4579 0.9822 0.9926 0.5855 0.935 0.9825 0.5289 ] Network output: [ -0.05401 0.1289 1.149 -1.408e-05 6.319e-06 0.8299 -1.061e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2543 0.2495 0.2846 0.2753 0.9897 0.9936 0.2545 0.9789 0.9881 0.2922 ] Network output: [ -0.05214 0.124 1.128 3.767e-05 -1.691e-05 0.8523 2.839e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2559 0.2551 0.2824 0.2754 0.9853 0.9912 0.2559 0.9646 0.982 0.2841 ] Network output: [ -0.01424 1.042 0.03905 1.518e-05 -6.816e-06 0.9479 1.144e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05165 Epoch 3834 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05357 0.9109 0.9213 0.0001036 -4.653e-05 0.06106 7.811e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009731 -0.006143 -0.004072 0.01855 0.9562 0.9631 0.02065 0.915 0.9303 0.05882 ] Network output: [ 0.9684 0.1099 0.01974 -2.862e-05 1.285e-05 -0.06648 -2.157e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5197 0.08677 0.06328 0.2898 0.9802 0.9915 0.5939 0.9289 0.98 0.5382 ] Network output: [ 0.01325 0.9194 0.9384 -6.686e-05 3.002e-05 0.1154 -5.039e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01714 0.01222 0.01936 0.01977 0.9896 0.9929 0.01751 0.9783 0.9874 0.02536 ] Network output: [ 0.08513 -0.2326 0.804 -4.547e-05 2.041e-05 1.258 -3.427e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5831 0.5123 0.4513 0.4578 0.9822 0.9926 0.5854 0.935 0.9825 0.5292 ] Network output: [ -0.05398 0.129 1.149 -1.369e-05 6.144e-06 0.8297 -1.031e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2541 0.2493 0.2845 0.2752 0.9897 0.9936 0.2543 0.979 0.9881 0.2921 ] Network output: [ -0.05209 0.1239 1.128 3.846e-05 -1.726e-05 0.8522 2.898e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2557 0.2549 0.2824 0.2753 0.9853 0.9912 0.2558 0.9646 0.982 0.2841 ] Network output: [ -0.01429 1.042 0.03905 1.503e-05 -6.748e-06 0.9478 1.133e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05174 Epoch 3835 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05356 0.9108 0.9213 0.0001036 -4.65e-05 0.0612 7.806e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009707 -0.006144 -0.00411 0.01851 0.9562 0.9631 0.02063 0.915 0.9303 0.0588 ] Network output: [ 0.9684 0.1103 0.01961 -2.805e-05 1.259e-05 -0.06678 -2.114e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5195 0.08665 0.06307 0.2894 0.9802 0.9915 0.5939 0.929 0.9801 0.5385 ] Network output: [ 0.01325 0.9193 0.9384 -6.723e-05 3.018e-05 0.1155 -5.066e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01712 0.0122 0.01935 0.01974 0.9896 0.9929 0.01749 0.9783 0.9874 0.02535 ] Network output: [ 0.08509 -0.2329 0.804 -4.663e-05 2.093e-05 1.258 -3.514e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.583 0.5121 0.4515 0.4577 0.9822 0.9926 0.5853 0.9351 0.9825 0.5296 ] Network output: [ -0.05395 0.129 1.149 -1.329e-05 5.967e-06 0.8296 -1.002e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2539 0.2491 0.2845 0.2751 0.9897 0.9936 0.2541 0.979 0.9881 0.2921 ] Network output: [ -0.05204 0.1238 1.128 3.926e-05 -1.762e-05 0.8521 2.958e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2556 0.2548 0.2824 0.2752 0.9853 0.9912 0.2556 0.9646 0.982 0.284 ] Network output: [ -0.01434 1.042 0.03905 1.487e-05 -6.678e-06 0.9477 1.121e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05182 Epoch 3836 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05354 0.9107 0.9213 0.0001035 -4.647e-05 0.06134 7.802e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009684 -0.006145 -0.004148 0.01847 0.9563 0.9632 0.02061 0.9151 0.9304 0.05877 ] Network output: [ 0.9684 0.1108 0.01947 -2.746e-05 1.233e-05 -0.06708 -2.069e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5193 0.08653 0.06286 0.2891 0.9802 0.9915 0.5938 0.929 0.9801 0.5388 ] Network output: [ 0.01326 0.9191 0.9384 -6.76e-05 3.035e-05 0.1157 -5.095e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0171 0.01218 0.01933 0.01971 0.9896 0.993 0.01747 0.9783 0.9874 0.02533 ] Network output: [ 0.08506 -0.2332 0.8041 -4.781e-05 2.146e-05 1.259 -3.603e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5829 0.512 0.4518 0.4576 0.9822 0.9926 0.5852 0.9351 0.9825 0.53 ] Network output: [ -0.05392 0.1291 1.149 -1.289e-05 5.788e-06 0.8294 -9.716e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2537 0.249 0.2845 0.275 0.9897 0.9936 0.2539 0.979 0.9882 0.2921 ] Network output: [ -0.05199 0.1237 1.128 4.007e-05 -1.799e-05 0.8521 3.02e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2554 0.2546 0.2824 0.2751 0.9853 0.9912 0.2555 0.9646 0.982 0.284 ] Network output: [ -0.01439 1.042 0.03905 1.471e-05 -6.606e-06 0.9475 1.109e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05191 Epoch 3837 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05353 0.9106 0.9212 0.0001035 -4.644e-05 0.06149 7.797e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00966 -0.006146 -0.004186 0.01843 0.9563 0.9632 0.02059 0.9151 0.9304 0.05875 ] Network output: [ 0.9684 0.1112 0.01933 -2.686e-05 1.206e-05 -0.06738 -2.024e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.519 0.08641 0.06265 0.2887 0.9802 0.9915 0.5937 0.9291 0.9801 0.5392 ] Network output: [ 0.01326 0.919 0.9384 -6.798e-05 3.052e-05 0.1159 -5.123e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01708 0.01216 0.01931 0.01967 0.9897 0.993 0.01745 0.9783 0.9874 0.02532 ] Network output: [ 0.08502 -0.2335 0.8042 -4.901e-05 2.2e-05 1.259 -3.693e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5828 0.5119 0.4521 0.4575 0.9822 0.9926 0.5851 0.9351 0.9826 0.5303 ] Network output: [ -0.05388 0.1291 1.149 -1.249e-05 5.607e-06 0.8293 -9.412e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2535 0.2488 0.2844 0.2748 0.9897 0.9936 0.2537 0.979 0.9882 0.2921 ] Network output: [ -0.05194 0.1236 1.128 4.089e-05 -1.836e-05 0.852 3.082e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2553 0.2545 0.2823 0.2751 0.9853 0.9912 0.2553 0.9646 0.982 0.284 ] Network output: [ -0.01445 1.043 0.03904 1.455e-05 -6.532e-06 0.9474 1.096e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.052 Epoch 3838 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05352 0.9105 0.9212 0.0001034 -4.641e-05 0.06164 7.792e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009636 -0.006147 -0.004225 0.0184 0.9563 0.9632 0.02058 0.9151 0.9304 0.05873 ] Network output: [ 0.9684 0.1116 0.0192 -2.624e-05 1.178e-05 -0.06769 -1.978e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5188 0.08629 0.06243 0.2883 0.9802 0.9915 0.5936 0.9291 0.9801 0.5395 ] Network output: [ 0.01326 0.9188 0.9384 -6.836e-05 3.069e-05 0.116 -5.152e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01705 0.01214 0.01929 0.01964 0.9897 0.993 0.01743 0.9783 0.9875 0.02531 ] Network output: [ 0.08498 -0.2339 0.8043 -5.023e-05 2.255e-05 1.259 -3.785e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5827 0.5117 0.4523 0.4574 0.9822 0.9926 0.585 0.9352 0.9826 0.5307 ] Network output: [ -0.05385 0.1292 1.149 -1.208e-05 5.423e-06 0.8291 -9.104e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2533 0.2486 0.2844 0.2747 0.9897 0.9936 0.2535 0.979 0.9882 0.292 ] Network output: [ -0.05189 0.1234 1.129 4.173e-05 -1.873e-05 0.852 3.145e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2551 0.2543 0.2823 0.275 0.9853 0.9912 0.2551 0.9646 0.9821 0.284 ] Network output: [ -0.0145 1.043 0.03904 1.438e-05 -6.456e-06 0.9472 1.084e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05209 Epoch 3839 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0535 0.9104 0.9212 0.0001033 -4.638e-05 0.06179 7.786e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009612 -0.006148 -0.004264 0.01836 0.9563 0.9632 0.02056 0.9152 0.9305 0.0587 ] Network output: [ 0.9684 0.112 0.01906 -2.561e-05 1.15e-05 -0.06801 -1.93e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5185 0.08617 0.06221 0.2879 0.9803 0.9916 0.5936 0.9291 0.9801 0.5399 ] Network output: [ 0.01327 0.9187 0.9383 -6.874e-05 3.086e-05 0.1162 -5.181e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01703 0.01212 0.01928 0.0196 0.9897 0.993 0.01741 0.9783 0.9875 0.02529 ] Network output: [ 0.08495 -0.2342 0.8043 -5.147e-05 2.311e-05 1.26 -3.879e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5825 0.5116 0.4526 0.4573 0.9822 0.9926 0.5849 0.9352 0.9826 0.5311 ] Network output: [ -0.05382 0.1293 1.149 -1.167e-05 5.237e-06 0.829 -8.792e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2531 0.2484 0.2844 0.2746 0.9897 0.9936 0.2533 0.979 0.9882 0.292 ] Network output: [ -0.05183 0.1233 1.129 4.257e-05 -1.911e-05 0.8519 3.208e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2549 0.2541 0.2823 0.2749 0.9853 0.9912 0.255 0.9647 0.9821 0.2839 ] Network output: [ -0.01455 1.043 0.03904 1.42e-05 -6.377e-06 0.9471 1.071e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05218 Epoch 3840 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05349 0.9103 0.9212 0.0001032 -4.635e-05 0.06195 7.781e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009587 -0.006149 -0.004303 0.01832 0.9563 0.9632 0.02054 0.9152 0.9305 0.05868 ] Network output: [ 0.9684 0.1125 0.01892 -2.496e-05 1.12e-05 -0.06832 -1.881e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5183 0.08604 0.06199 0.2876 0.9803 0.9916 0.5935 0.9292 0.9801 0.5402 ] Network output: [ 0.01327 0.9185 0.9383 -6.914e-05 3.104e-05 0.1163 -5.21e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01701 0.0121 0.01926 0.01957 0.9897 0.993 0.01739 0.9784 0.9875 0.02528 ] Network output: [ 0.08491 -0.2345 0.8044 -5.273e-05 2.367e-05 1.26 -3.974e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5824 0.5114 0.4528 0.4572 0.9822 0.9926 0.5848 0.9353 0.9826 0.5315 ] Network output: [ -0.05379 0.1293 1.149 -1.125e-05 5.049e-06 0.8288 -8.476e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2529 0.2482 0.2843 0.2744 0.9897 0.9936 0.2531 0.9791 0.9882 0.292 ] Network output: [ -0.05178 0.1232 1.129 4.343e-05 -1.95e-05 0.8519 3.273e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2548 0.254 0.2823 0.2748 0.9853 0.9912 0.2548 0.9647 0.9821 0.2839 ] Network output: [ -0.01461 1.043 0.03903 1.403e-05 -6.296e-06 0.9469 1.057e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05227 Epoch 3841 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05348 0.9102 0.9212 0.0001032 -4.632e-05 0.0621 7.776e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009563 -0.00615 -0.004343 0.01828 0.9563 0.9632 0.02052 0.9153 0.9306 0.05865 ] Network output: [ 0.9684 0.1129 0.01879 -2.429e-05 1.091e-05 -0.06865 -1.831e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.518 0.08591 0.06176 0.2872 0.9803 0.9916 0.5934 0.9292 0.9802 0.5406 ] Network output: [ 0.01328 0.9183 0.9383 -6.953e-05 3.122e-05 0.1165 -5.24e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01699 0.01208 0.01924 0.01953 0.9897 0.993 0.01737 0.9784 0.9875 0.02526 ] Network output: [ 0.08488 -0.2349 0.8045 -5.401e-05 2.425e-05 1.26 -4.071e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5823 0.5113 0.4531 0.4571 0.9822 0.9926 0.5847 0.9353 0.9826 0.5319 ] Network output: [ -0.05376 0.1294 1.149 -1.082e-05 4.858e-06 0.8287 -8.155e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2527 0.248 0.2843 0.2743 0.9897 0.9936 0.2529 0.9791 0.9882 0.2919 ] Network output: [ -0.05173 0.1231 1.129 4.43e-05 -1.989e-05 0.8518 3.339e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2546 0.2538 0.2822 0.2747 0.9853 0.9912 0.2546 0.9647 0.9821 0.2839 ] Network output: [ -0.01466 1.044 0.03902 1.384e-05 -6.213e-06 0.9468 1.043e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05237 Epoch 3842 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05346 0.9101 0.9211 0.0001031 -4.629e-05 0.06226 7.771e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009538 -0.006151 -0.004383 0.01824 0.9563 0.9632 0.0205 0.9153 0.9306 0.05863 ] Network output: [ 0.9684 0.1134 0.01865 -2.361e-05 1.06e-05 -0.06897 -1.779e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5177 0.08578 0.06152 0.2868 0.9803 0.9916 0.5933 0.9293 0.9802 0.541 ] Network output: [ 0.01329 0.9182 0.9383 -6.993e-05 3.14e-05 0.1167 -5.27e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01697 0.01206 0.01922 0.0195 0.9897 0.993 0.01735 0.9784 0.9875 0.02525 ] Network output: [ 0.08484 -0.2352 0.8046 -5.532e-05 2.484e-05 1.261 -4.169e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5822 0.5111 0.4534 0.457 0.9822 0.9926 0.5846 0.9353 0.9826 0.5323 ] Network output: [ -0.05373 0.1295 1.149 -1.039e-05 4.665e-06 0.8285 -7.831e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2525 0.2478 0.2843 0.2742 0.9897 0.9936 0.2527 0.9791 0.9882 0.2919 ] Network output: [ -0.05168 0.123 1.129 4.519e-05 -2.029e-05 0.8518 3.405e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2544 0.2536 0.2822 0.2747 0.9853 0.9912 0.2545 0.9647 0.9821 0.2839 ] Network output: [ -0.01472 1.044 0.03901 1.365e-05 -6.128e-06 0.9467 1.029e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05246 Epoch 3843 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05345 0.91 0.9211 0.000103 -4.626e-05 0.06242 7.765e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009513 -0.006152 -0.004423 0.0182 0.9563 0.9632 0.02048 0.9154 0.9306 0.05861 ] Network output: [ 0.9684 0.1138 0.01851 -2.291e-05 1.029e-05 -0.06931 -1.727e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5175 0.08564 0.06129 0.2864 0.9803 0.9916 0.5932 0.9293 0.9802 0.5414 ] Network output: [ 0.01329 0.918 0.9383 -7.034e-05 3.158e-05 0.1169 -5.301e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01694 0.01204 0.01921 0.01946 0.9897 0.993 0.01733 0.9784 0.9875 0.02523 ] Network output: [ 0.08481 -0.2356 0.8047 -5.665e-05 2.543e-05 1.261 -4.269e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5821 0.511 0.4537 0.4568 0.9822 0.9926 0.5845 0.9354 0.9826 0.5327 ] Network output: [ -0.0537 0.1295 1.149 -9.955e-06 4.469e-06 0.8284 -7.503e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2523 0.2476 0.2842 0.274 0.9897 0.9936 0.2525 0.9791 0.9883 0.2919 ] Network output: [ -0.05162 0.1228 1.129 4.608e-05 -2.069e-05 0.8517 3.473e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2543 0.2535 0.2822 0.2746 0.9853 0.9912 0.2543 0.9647 0.9821 0.2839 ] Network output: [ -0.01477 1.044 0.039 1.345e-05 -6.04e-06 0.9465 1.014e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05256 Epoch 3844 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05344 0.9099 0.9211 0.000103 -4.622e-05 0.06258 7.759e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009488 -0.006152 -0.004463 0.01817 0.9563 0.9632 0.02046 0.9154 0.9307 0.05858 ] Network output: [ 0.9684 0.1143 0.01838 -2.219e-05 9.963e-06 -0.06965 -1.673e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5172 0.08551 0.06104 0.286 0.9803 0.9916 0.5932 0.9293 0.9802 0.5417 ] Network output: [ 0.0133 0.9178 0.9382 -7.075e-05 3.176e-05 0.117 -5.332e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01692 0.01202 0.01919 0.01943 0.9897 0.993 0.0173 0.9784 0.9875 0.02522 ] Network output: [ 0.08478 -0.2359 0.8047 -5.8e-05 2.604e-05 1.261 -4.371e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5819 0.5108 0.4539 0.4567 0.9822 0.9926 0.5843 0.9354 0.9827 0.5331 ] Network output: [ -0.05366 0.1296 1.149 -9.513e-06 4.271e-06 0.8282 -7.17e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2521 0.2474 0.2842 0.2739 0.9897 0.9936 0.2523 0.9791 0.9883 0.2918 ] Network output: [ -0.05157 0.1227 1.129 4.699e-05 -2.11e-05 0.8517 3.541e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2541 0.2533 0.2822 0.2745 0.9853 0.9913 0.2541 0.9647 0.9821 0.2838 ] Network output: [ -0.01483 1.044 0.03899 1.325e-05 -5.95e-06 0.9464 9.988e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05266 Epoch 3845 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05343 0.9097 0.9211 0.0001029 -4.619e-05 0.06275 7.754e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009462 -0.006153 -0.004504 0.01813 0.9564 0.9632 0.02044 0.9155 0.9307 0.05856 ] Network output: [ 0.9684 0.1148 0.01824 -2.146e-05 9.632e-06 -0.06999 -1.617e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5169 0.08537 0.0608 0.2856 0.9803 0.9916 0.5931 0.9294 0.9802 0.5421 ] Network output: [ 0.01331 0.9176 0.9382 -7.117e-05 3.195e-05 0.1172 -5.364e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0169 0.012 0.01917 0.01939 0.9897 0.993 0.01728 0.9784 0.9875 0.02521 ] Network output: [ 0.08474 -0.2363 0.8048 -5.938e-05 2.666e-05 1.262 -4.475e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5818 0.5106 0.4542 0.4566 0.9822 0.9926 0.5842 0.9354 0.9827 0.5335 ] Network output: [ -0.05363 0.1297 1.15 -9.066e-06 4.07e-06 0.8281 -6.832e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2519 0.2472 0.2841 0.2737 0.9897 0.9936 0.2521 0.9792 0.9883 0.2918 ] Network output: [ -0.05152 0.1226 1.129 4.791e-05 -2.151e-05 0.8516 3.611e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2539 0.2531 0.2821 0.2744 0.9853 0.9913 0.254 0.9648 0.9821 0.2838 ] Network output: [ -0.01489 1.045 0.03898 1.305e-05 -5.857e-06 0.9462 9.831e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05276 Epoch 3846 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05342 0.9096 0.9211 0.0001028 -4.615e-05 0.06291 7.748e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009436 -0.006154 -0.004545 0.01809 0.9564 0.9633 0.02042 0.9155 0.9308 0.05854 ] Network output: [ 0.9684 0.1153 0.01811 -2.07e-05 9.293e-06 -0.07034 -1.56e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5167 0.08522 0.06055 0.2852 0.9803 0.9916 0.593 0.9294 0.9802 0.5425 ] Network output: [ 0.01332 0.9175 0.9382 -7.159e-05 3.214e-05 0.1174 -5.396e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01688 0.01198 0.01915 0.01935 0.9897 0.993 0.01726 0.9785 0.9875 0.02519 ] Network output: [ 0.08471 -0.2367 0.8049 -6.078e-05 2.729e-05 1.262 -4.581e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5817 0.5105 0.4545 0.4565 0.9822 0.9926 0.5841 0.9355 0.9827 0.534 ] Network output: [ -0.0536 0.1297 1.15 -8.612e-06 3.866e-06 0.8279 -6.49e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2517 0.247 0.2841 0.2736 0.9897 0.9936 0.2519 0.9792 0.9883 0.2918 ] Network output: [ -0.05146 0.1224 1.129 4.885e-05 -2.193e-05 0.8516 3.682e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2537 0.2529 0.2821 0.2743 0.9853 0.9913 0.2538 0.9648 0.9822 0.2838 ] Network output: [ -0.01495 1.045 0.03897 1.283e-05 -5.761e-06 0.9461 9.67e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05286 Epoch 3847 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0534 0.9095 0.921 0.0001027 -4.612e-05 0.06308 7.742e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00941 -0.006155 -0.004586 0.01805 0.9564 0.9633 0.0204 0.9156 0.9308 0.05851 ] Network output: [ 0.9684 0.1158 0.01797 -1.992e-05 8.944e-06 -0.07069 -1.501e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5164 0.08508 0.06029 0.2847 0.9803 0.9916 0.5929 0.9295 0.9803 0.5429 ] Network output: [ 0.01333 0.9173 0.9382 -7.202e-05 3.233e-05 0.1176 -5.428e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01685 0.01196 0.01913 0.01932 0.9897 0.993 0.01724 0.9785 0.9876 0.02518 ] Network output: [ 0.08468 -0.237 0.805 -6.221e-05 2.793e-05 1.262 -4.689e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5816 0.5103 0.4548 0.4564 0.9822 0.9926 0.584 0.9355 0.9827 0.5344 ] Network output: [ -0.05357 0.1298 1.15 -8.152e-06 3.66e-06 0.8277 -6.143e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2515 0.2467 0.2841 0.2734 0.9897 0.9937 0.2517 0.9792 0.9883 0.2917 ] Network output: [ -0.05141 0.1223 1.129 4.98e-05 -2.236e-05 0.8515 3.753e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2536 0.2528 0.2821 0.2742 0.9853 0.9913 0.2536 0.9648 0.9822 0.2838 ] Network output: [ -0.01501 1.045 0.03895 1.261e-05 -5.662e-06 0.9459 9.505e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05296 Epoch 3848 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05339 0.9094 0.921 0.0001026 -4.608e-05 0.06325 7.736e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009384 -0.006156 -0.004628 0.01801 0.9564 0.9633 0.02038 0.9156 0.9308 0.05849 ] Network output: [ 0.9684 0.1163 0.01783 -1.913e-05 8.586e-06 -0.07105 -1.441e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5161 0.08493 0.06003 0.2843 0.9803 0.9916 0.5928 0.9295 0.9803 0.5433 ] Network output: [ 0.01334 0.9171 0.9382 -7.246e-05 3.253e-05 0.1178 -5.461e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01683 0.01194 0.01912 0.01928 0.9897 0.993 0.01722 0.9785 0.9876 0.02516 ] Network output: [ 0.08465 -0.2374 0.8051 -6.367e-05 2.858e-05 1.263 -4.798e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5814 0.5101 0.4551 0.4562 0.9822 0.9926 0.5839 0.9356 0.9827 0.5348 ] Network output: [ -0.05354 0.1299 1.15 -7.686e-06 3.45e-06 0.8276 -5.792e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2513 0.2465 0.284 0.2733 0.9897 0.9937 0.2515 0.9792 0.9883 0.2917 ] Network output: [ -0.05136 0.1222 1.129 5.077e-05 -2.279e-05 0.8515 3.826e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2534 0.2526 0.2821 0.2741 0.9853 0.9913 0.2534 0.9648 0.9822 0.2837 ] Network output: [ -0.01507 1.046 0.03894 1.239e-05 -5.56e-06 0.9457 9.334e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05306 Epoch 3849 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05338 0.9092 0.921 0.0001026 -4.605e-05 0.06343 7.73e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009358 -0.006156 -0.00467 0.01797 0.9564 0.9633 0.02036 0.9157 0.9309 0.05847 ] Network output: [ 0.9684 0.1168 0.0177 -1.831e-05 8.218e-06 -0.07141 -1.38e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5158 0.08478 0.05976 0.2839 0.9803 0.9916 0.5927 0.9296 0.9803 0.5437 ] Network output: [ 0.01335 0.9169 0.9381 -7.29e-05 3.273e-05 0.1179 -5.494e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01681 0.01192 0.0191 0.01924 0.9897 0.993 0.01719 0.9785 0.9876 0.02515 ] Network output: [ 0.08462 -0.2378 0.8052 -6.515e-05 2.925e-05 1.263 -4.91e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5813 0.5099 0.4554 0.4561 0.9822 0.9926 0.5837 0.9356 0.9827 0.5353 ] Network output: [ -0.0535 0.13 1.15 -7.213e-06 3.238e-06 0.8274 -5.436e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2511 0.2463 0.284 0.2731 0.9897 0.9937 0.2513 0.9792 0.9883 0.2917 ] Network output: [ -0.0513 0.122 1.129 5.175e-05 -2.323e-05 0.8514 3.9e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2532 0.2524 0.282 0.2741 0.9853 0.9913 0.2533 0.9648 0.9822 0.2837 ] Network output: [ -0.01513 1.046 0.03892 1.215e-05 -5.456e-06 0.9456 9.158e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05317 Epoch 3850 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05337 0.9091 0.921 0.0001025 -4.601e-05 0.0636 7.723e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009331 -0.006157 -0.004712 0.01792 0.9564 0.9633 0.02034 0.9157 0.9309 0.05844 ] Network output: [ 0.9684 0.1173 0.01756 -1.747e-05 7.841e-06 -0.07178 -1.316e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5155 0.08462 0.05949 0.2835 0.9803 0.9916 0.5926 0.9296 0.9803 0.5442 ] Network output: [ 0.01336 0.9167 0.9381 -7.335e-05 3.293e-05 0.1181 -5.528e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01678 0.01189 0.01908 0.0192 0.9897 0.993 0.01717 0.9785 0.9876 0.02514 ] Network output: [ 0.08459 -0.2381 0.8053 -6.666e-05 2.993e-05 1.263 -5.024e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5812 0.5097 0.4557 0.456 0.9822 0.9926 0.5836 0.9356 0.9827 0.5357 ] Network output: [ -0.05347 0.13 1.15 -6.733e-06 3.023e-06 0.8272 -5.074e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2509 0.2461 0.2839 0.273 0.9897 0.9937 0.2511 0.9793 0.9884 0.2916 ] Network output: [ -0.05125 0.1219 1.129 5.274e-05 -2.368e-05 0.8514 3.975e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.253 0.2522 0.282 0.274 0.9853 0.9913 0.2531 0.9648 0.9822 0.2837 ] Network output: [ -0.01519 1.046 0.0389 1.191e-05 -5.348e-06 0.9454 8.977e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05327 Epoch 3851 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05336 0.909 0.9209 0.0001024 -4.597e-05 0.06378 7.717e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009304 -0.006158 -0.004755 0.01788 0.9564 0.9633 0.02032 0.9158 0.931 0.05842 ] Network output: [ 0.9684 0.1178 0.01743 -1.66e-05 7.453e-06 -0.07216 -1.251e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5152 0.08447 0.05921 0.283 0.9803 0.9916 0.5925 0.9296 0.9803 0.5446 ] Network output: [ 0.01338 0.9165 0.9381 -7.381e-05 3.313e-05 0.1183 -5.562e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01676 0.01187 0.01906 0.01916 0.9897 0.993 0.01715 0.9785 0.9876 0.02512 ] Network output: [ 0.08456 -0.2385 0.8053 -6.82e-05 3.062e-05 1.264 -5.14e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.581 0.5095 0.456 0.4558 0.9822 0.9926 0.5835 0.9357 0.9828 0.5362 ] Network output: [ -0.05344 0.1301 1.15 -6.247e-06 2.805e-06 0.8271 -4.708e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2506 0.2459 0.2839 0.2728 0.9898 0.9937 0.2508 0.9793 0.9884 0.2916 ] Network output: [ -0.05119 0.1217 1.13 5.375e-05 -2.413e-05 0.8513 4.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2529 0.2521 0.282 0.2739 0.9853 0.9913 0.2529 0.9648 0.9822 0.2837 ] Network output: [ -0.01526 1.046 0.03888 1.166e-05 -5.237e-06 0.9453 8.791e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05338 Epoch 3852 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05335 0.9088 0.9209 0.0001023 -4.593e-05 0.06396 7.711e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009277 -0.006159 -0.004798 0.01784 0.9564 0.9633 0.0203 0.9158 0.931 0.0584 ] Network output: [ 0.9684 0.1183 0.01729 -1.571e-05 7.054e-06 -0.07254 -1.184e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5149 0.08431 0.05893 0.2826 0.9803 0.9916 0.5924 0.9297 0.9803 0.545 ] Network output: [ 0.01339 0.9163 0.9381 -7.427e-05 3.334e-05 0.1185 -5.597e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01673 0.01185 0.01904 0.01913 0.9897 0.993 0.01712 0.9786 0.9876 0.02511 ] Network output: [ 0.08453 -0.2389 0.8054 -6.977e-05 3.132e-05 1.264 -5.258e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5809 0.5093 0.4563 0.4557 0.9822 0.9926 0.5834 0.9357 0.9828 0.5366 ] Network output: [ -0.05341 0.1302 1.15 -5.754e-06 2.583e-06 0.8269 -4.337e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2504 0.2457 0.2839 0.2727 0.9898 0.9937 0.2506 0.9793 0.9884 0.2916 ] Network output: [ -0.05114 0.1216 1.13 5.478e-05 -2.459e-05 0.8513 4.128e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2527 0.2519 0.282 0.2738 0.9853 0.9913 0.2527 0.9649 0.9822 0.2836 ] Network output: [ -0.01532 1.047 0.03886 1.141e-05 -5.122e-06 0.9451 8.599e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05349 Epoch 3853 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05334 0.9087 0.9209 0.0001022 -4.589e-05 0.06415 7.704e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009249 -0.006159 -0.004841 0.0178 0.9564 0.9633 0.02028 0.9159 0.9311 0.05838 ] Network output: [ 0.9684 0.1189 0.01716 -1.48e-05 6.645e-06 -0.07293 -1.115e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5146 0.08414 0.05864 0.2821 0.9803 0.9916 0.5923 0.9297 0.9804 0.5455 ] Network output: [ 0.0134 0.9161 0.9381 -7.474e-05 3.355e-05 0.1187 -5.632e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01671 0.01183 0.01902 0.01909 0.9897 0.993 0.0171 0.9786 0.9876 0.02509 ] Network output: [ 0.0845 -0.2393 0.8055 -7.137e-05 3.204e-05 1.264 -5.379e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5807 0.5091 0.4566 0.4555 0.9822 0.9926 0.5832 0.9358 0.9828 0.5371 ] Network output: [ -0.05337 0.1303 1.15 -5.254e-06 2.359e-06 0.8267 -3.96e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2502 0.2454 0.2838 0.2725 0.9898 0.9937 0.2504 0.9793 0.9884 0.2915 ] Network output: [ -0.05108 0.1214 1.13 5.582e-05 -2.506e-05 0.8512 4.207e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2525 0.2517 0.2819 0.2737 0.9853 0.9913 0.2525 0.9649 0.9823 0.2836 ] Network output: [ -0.01539 1.047 0.03883 1.115e-05 -5.005e-06 0.9449 8.401e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0536 Epoch 3854 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05333 0.9085 0.9209 0.0001021 -4.585e-05 0.06433 7.697e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009222 -0.00616 -0.004885 0.01776 0.9564 0.9633 0.02026 0.9159 0.9311 0.05835 ] Network output: [ 0.9684 0.1194 0.01703 -1.386e-05 6.224e-06 -0.07333 -1.045e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5142 0.08398 0.05835 0.2817 0.9803 0.9916 0.5922 0.9298 0.9804 0.5459 ] Network output: [ 0.01342 0.9159 0.938 -7.521e-05 3.377e-05 0.1189 -5.668e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01668 0.0118 0.019 0.01905 0.9897 0.993 0.01707 0.9786 0.9876 0.02508 ] Network output: [ 0.08448 -0.2397 0.8056 -7.3e-05 3.277e-05 1.265 -5.502e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5806 0.5089 0.4569 0.4554 0.9822 0.9926 0.5831 0.9358 0.9828 0.5376 ] Network output: [ -0.05334 0.1304 1.15 -4.747e-06 2.131e-06 0.8265 -3.577e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.25 0.2452 0.2838 0.2723 0.9898 0.9937 0.2502 0.9793 0.9884 0.2915 ] Network output: [ -0.05102 0.1213 1.13 5.688e-05 -2.554e-05 0.8512 4.287e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2523 0.2515 0.2819 0.2736 0.9853 0.9913 0.2524 0.9649 0.9823 0.2836 ] Network output: [ -0.01546 1.047 0.03881 1.088e-05 -4.884e-06 0.9448 8.198e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05371 Epoch 3855 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05332 0.9084 0.9209 0.000102 -4.581e-05 0.06452 7.69e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009194 -0.00616 -0.004929 0.01772 0.9565 0.9633 0.02024 0.916 0.9311 0.05833 ] Network output: [ 0.9684 0.12 0.0169 -1.29e-05 5.791e-06 -0.07373 -9.722e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5139 0.08381 0.05805 0.2812 0.9803 0.9916 0.5921 0.9298 0.9804 0.5464 ] Network output: [ 0.01343 0.9157 0.938 -7.57e-05 3.398e-05 0.1191 -5.705e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01666 0.01178 0.01898 0.01901 0.9897 0.993 0.01705 0.9786 0.9876 0.02507 ] Network output: [ 0.08445 -0.2401 0.8057 -7.466e-05 3.352e-05 1.265 -5.627e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5804 0.5087 0.4572 0.4552 0.9822 0.9926 0.5829 0.9358 0.9828 0.5381 ] Network output: [ -0.05331 0.1305 1.15 -4.232e-06 1.9e-06 0.8264 -3.189e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2498 0.245 0.2837 0.2722 0.9898 0.9937 0.2499 0.9794 0.9884 0.2915 ] Network output: [ -0.05097 0.1211 1.13 5.796e-05 -2.602e-05 0.8512 4.368e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2521 0.2513 0.2819 0.2735 0.9853 0.9913 0.2522 0.9649 0.9823 0.2836 ] Network output: [ -0.01552 1.048 0.03878 1.06e-05 -4.759e-06 0.9446 7.988e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05383 Epoch 3856 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05331 0.9082 0.9208 0.0001019 -4.577e-05 0.06472 7.683e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009165 -0.006161 -0.004974 0.01767 0.9565 0.9634 0.02022 0.916 0.9312 0.05831 ] Network output: [ 0.9684 0.1205 0.01677 -1.191e-05 5.347e-06 -0.07413 -8.976e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5136 0.08363 0.05774 0.2807 0.9803 0.9916 0.592 0.9299 0.9804 0.5468 ] Network output: [ 0.01345 0.9155 0.938 -7.619e-05 3.42e-05 0.1193 -5.742e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01663 0.01176 0.01896 0.01897 0.9897 0.993 0.01703 0.9786 0.9877 0.02505 ] Network output: [ 0.08443 -0.2405 0.8058 -7.636e-05 3.428e-05 1.266 -5.755e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5803 0.5085 0.4575 0.4551 0.9822 0.9926 0.5828 0.9359 0.9828 0.5386 ] Network output: [ -0.05327 0.1306 1.15 -3.709e-06 1.665e-06 0.8262 -2.795e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2495 0.2448 0.2837 0.272 0.9898 0.9937 0.2497 0.9794 0.9884 0.2914 ] Network output: [ -0.05091 0.1209 1.13 5.905e-05 -2.651e-05 0.8511 4.45e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.252 0.2512 0.2819 0.2734 0.9853 0.9913 0.252 0.9649 0.9823 0.2836 ] Network output: [ -0.01559 1.048 0.03875 1.031e-05 -4.63e-06 0.9445 7.773e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05394 Epoch 3857 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0533 0.9081 0.9208 0.0001018 -4.572e-05 0.06491 7.676e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009137 -0.006162 -0.005019 0.01763 0.9565 0.9634 0.0202 0.9161 0.9312 0.05829 ] Network output: [ 0.9684 0.1211 0.01664 -1.089e-05 4.889e-06 -0.07455 -8.208e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5133 0.08345 0.05743 0.2803 0.9803 0.9916 0.5919 0.9299 0.9804 0.5473 ] Network output: [ 0.01347 0.9152 0.938 -7.669e-05 3.443e-05 0.1195 -5.78e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01661 0.01173 0.01894 0.01893 0.9898 0.993 0.017 0.9786 0.9877 0.02504 ] Network output: [ 0.0844 -0.2409 0.8059 -7.809e-05 3.506e-05 1.266 -5.885e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5801 0.5083 0.4578 0.4549 0.9822 0.9926 0.5827 0.9359 0.9829 0.5391 ] Network output: [ -0.05324 0.1307 1.15 -3.179e-06 1.427e-06 0.826 -2.396e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2493 0.2445 0.2836 0.2718 0.9898 0.9937 0.2495 0.9794 0.9885 0.2914 ] Network output: [ -0.05085 0.1208 1.13 6.016e-05 -2.701e-05 0.8511 4.534e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2518 0.251 0.2818 0.2733 0.9853 0.9913 0.2518 0.9649 0.9823 0.2835 ] Network output: [ -0.01566 1.048 0.03872 1.002e-05 -4.498e-06 0.9443 7.55e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05406 Epoch 3858 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05329 0.9079 0.9208 0.0001018 -4.568e-05 0.06511 7.668e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009108 -0.006162 -0.005064 0.01759 0.9565 0.9634 0.02018 0.9161 0.9313 0.05827 ] Network output: [ 0.9684 0.1217 0.01651 -9.844e-06 4.419e-06 -0.07497 -7.419e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5129 0.08327 0.05711 0.2798 0.9803 0.9916 0.5917 0.9299 0.9804 0.5478 ] Network output: [ 0.01348 0.915 0.938 -7.72e-05 3.466e-05 0.1198 -5.818e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01658 0.01171 0.01892 0.01888 0.9898 0.993 0.01698 0.9787 0.9877 0.02502 ] Network output: [ 0.08438 -0.2413 0.806 -7.985e-05 3.585e-05 1.266 -6.018e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.58 0.5081 0.4581 0.4547 0.9822 0.9926 0.5825 0.936 0.9829 0.5396 ] Network output: [ -0.05321 0.1308 1.15 -2.641e-06 1.186e-06 0.8258 -1.99e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2491 0.2443 0.2836 0.2716 0.9898 0.9937 0.2493 0.9794 0.9885 0.2914 ] Network output: [ -0.05079 0.1206 1.13 6.129e-05 -2.752e-05 0.8511 4.619e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2516 0.2508 0.2818 0.2732 0.9853 0.9913 0.2516 0.965 0.9823 0.2835 ] Network output: [ -0.01574 1.049 0.03869 9.715e-06 -4.361e-06 0.9441 7.321e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05418 Epoch 3859 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05329 0.9077 0.9208 0.0001017 -4.563e-05 0.06531 7.661e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.009079 -0.006163 -0.00511 0.01754 0.9565 0.9634 0.02016 0.9162 0.9313 0.05824 ] Network output: [ 0.9684 0.1223 0.01638 -8.766e-06 3.936e-06 -0.0754 -6.607e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5126 0.08309 0.05679 0.2793 0.9803 0.9916 0.5916 0.93 0.9805 0.5483 ] Network output: [ 0.0135 0.9148 0.9379 -7.771e-05 3.489e-05 0.12 -5.857e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01656 0.01169 0.0189 0.01884 0.9898 0.993 0.01695 0.9787 0.9877 0.02501 ] Network output: [ 0.08436 -0.2417 0.806 -8.165e-05 3.666e-05 1.267 -6.154e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5798 0.5078 0.4584 0.4546 0.9822 0.9926 0.5824 0.936 0.9829 0.5401 ] Network output: [ -0.05317 0.1309 1.15 -2.095e-06 9.405e-07 0.8256 -1.579e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2488 0.2441 0.2835 0.2715 0.9898 0.9937 0.249 0.9794 0.9885 0.2913 ] Network output: [ -0.05074 0.1204 1.13 6.244e-05 -2.803e-05 0.851 4.706e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2514 0.2506 0.2818 0.2731 0.9853 0.9913 0.2514 0.965 0.9823 0.2835 ] Network output: [ -0.01581 1.049 0.03865 9.402e-06 -4.221e-06 0.9439 7.086e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0543 Epoch 3860 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05328 0.9076 0.9208 0.0001015 -4.559e-05 0.06551 7.653e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00905 -0.006163 -0.005156 0.0175 0.9565 0.9634 0.02013 0.9162 0.9313 0.05822 ] Network output: [ 0.9683 0.1229 0.01625 -7.658e-06 3.438e-06 -0.07583 -5.772e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5122 0.0829 0.05646 0.2788 0.9803 0.9916 0.5915 0.93 0.9805 0.5488 ] Network output: [ 0.01352 0.9146 0.9379 -7.824e-05 3.512e-05 0.1202 -5.896e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01653 0.01166 0.01888 0.0188 0.9898 0.993 0.01693 0.9787 0.9877 0.025 ] Network output: [ 0.08434 -0.2422 0.8061 -8.349e-05 3.748e-05 1.267 -6.292e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5796 0.5076 0.4588 0.4544 0.9822 0.9926 0.5822 0.936 0.9829 0.5407 ] Network output: [ -0.05314 0.131 1.15 -1.54e-06 6.915e-07 0.8254 -1.161e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2486 0.2438 0.2835 0.2713 0.9898 0.9937 0.2488 0.9795 0.9885 0.2913 ] Network output: [ -0.05068 0.1202 1.13 6.361e-05 -2.856e-05 0.851 4.794e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2512 0.2504 0.2818 0.273 0.9853 0.9913 0.2512 0.965 0.9823 0.2835 ] Network output: [ -0.01588 1.049 0.03862 9.08e-06 -4.076e-06 0.9438 6.843e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05442 Epoch 3861 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05327 0.9074 0.9207 0.0001014 -4.554e-05 0.06572 7.645e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00902 -0.006164 -0.005202 0.01745 0.9565 0.9634 0.02011 0.9163 0.9314 0.0582 ] Network output: [ 0.9683 0.1235 0.01613 -6.518e-06 2.926e-06 -0.07627 -4.912e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5118 0.08271 0.05612 0.2783 0.9803 0.9916 0.5914 0.9301 0.9805 0.5493 ] Network output: [ 0.01354 0.9143 0.9379 -7.877e-05 3.536e-05 0.1204 -5.937e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0165 0.01164 0.01886 0.01876 0.9898 0.9931 0.0169 0.9787 0.9877 0.02498 ] Network output: [ 0.08432 -0.2426 0.8062 -8.536e-05 3.832e-05 1.267 -6.433e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5795 0.5073 0.4591 0.4542 0.9822 0.9926 0.582 0.9361 0.9829 0.5412 ] Network output: [ -0.0531 0.1311 1.15 -9.773e-07 4.387e-07 0.8252 -7.365e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2484 0.2436 0.2835 0.2711 0.9898 0.9937 0.2485 0.9795 0.9885 0.2913 ] Network output: [ -0.05062 0.12 1.13 6.48e-05 -2.909e-05 0.851 4.883e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.251 0.2502 0.2817 0.2729 0.9853 0.9913 0.2511 0.965 0.9824 0.2834 ] Network output: [ -0.01596 1.05 0.03858 8.748e-06 -3.927e-06 0.9436 6.593e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05455 Epoch 3862 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05326 0.9072 0.9207 0.0001013 -4.549e-05 0.06593 7.637e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00899 -0.006164 -0.005249 0.01741 0.9565 0.9634 0.02009 0.9163 0.9314 0.05818 ] Network output: [ 0.9683 0.1241 0.01601 -5.345e-06 2.4e-06 -0.07672 -4.028e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5115 0.08251 0.05577 0.2778 0.9803 0.9916 0.5912 0.9301 0.9805 0.5498 ] Network output: [ 0.01356 0.9141 0.9379 -7.932e-05 3.561e-05 0.1206 -5.977e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01648 0.01161 0.01884 0.01872 0.9898 0.9931 0.01688 0.9787 0.9877 0.02497 ] Network output: [ 0.0843 -0.243 0.8063 -8.727e-05 3.918e-05 1.268 -6.577e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5793 0.5071 0.4594 0.454 0.9822 0.9926 0.5819 0.9361 0.9829 0.5417 ] Network output: [ -0.05307 0.1312 1.15 -4.056e-07 1.821e-07 0.8251 -3.057e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2481 0.2433 0.2834 0.2709 0.9898 0.9937 0.2483 0.9795 0.9885 0.2912 ] Network output: [ -0.05056 0.1199 1.131 6.6e-05 -2.963e-05 0.8509 4.974e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2508 0.25 0.2817 0.2728 0.9853 0.9913 0.2509 0.965 0.9824 0.2834 ] Network output: [ -0.01604 1.05 0.03854 8.407e-06 -3.774e-06 0.9434 6.336e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05467 Epoch 3863 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05325 0.9071 0.9207 0.0001012 -4.544e-05 0.06614 7.629e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00896 -0.006165 -0.005297 0.01736 0.9565 0.9634 0.02007 0.9164 0.9315 0.05816 ] Network output: [ 0.9683 0.1247 0.01588 -4.138e-06 1.858e-06 -0.07717 -3.118e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5111 0.08231 0.05542 0.2773 0.9804 0.9916 0.5911 0.9302 0.9805 0.5503 ] Network output: [ 0.01359 0.9138 0.9378 -7.987e-05 3.586e-05 0.1209 -6.019e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01645 0.01158 0.01882 0.01867 0.9898 0.9931 0.01685 0.9787 0.9877 0.02495 ] Network output: [ 0.08428 -0.2435 0.8064 -8.923e-05 4.006e-05 1.268 -6.724e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5791 0.5068 0.4598 0.4538 0.9823 0.9926 0.5817 0.9361 0.9829 0.5423 ] Network output: [ -0.05303 0.1313 1.15 1.75e-07 -7.858e-08 0.8249 1.319e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2479 0.2431 0.2834 0.2707 0.9898 0.9937 0.2481 0.9795 0.9885 0.2912 ] Network output: [ -0.0505 0.1197 1.131 6.723e-05 -3.018e-05 0.8509 5.067e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2506 0.2498 0.2817 0.2727 0.9853 0.9913 0.2507 0.965 0.9824 0.2834 ] Network output: [ -0.01611 1.051 0.0385 8.055e-06 -3.616e-06 0.9433 6.071e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0548 Epoch 3864 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05325 0.9069 0.9207 0.0001011 -4.539e-05 0.06635 7.62e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008929 -0.006165 -0.005344 0.01732 0.9565 0.9634 0.02005 0.9164 0.9315 0.05814 ] Network output: [ 0.9682 0.1254 0.01576 -2.895e-06 1.3e-06 -0.07764 -2.182e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5107 0.08211 0.05506 0.2768 0.9804 0.9916 0.591 0.9302 0.9806 0.5508 ] Network output: [ 0.01361 0.9136 0.9378 -8.043e-05 3.611e-05 0.1211 -6.061e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01642 0.01156 0.0188 0.01863 0.9898 0.9931 0.01682 0.9788 0.9877 0.02494 ] Network output: [ 0.08426 -0.2439 0.8065 -9.122e-05 4.095e-05 1.268 -6.875e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5789 0.5066 0.4601 0.4536 0.9823 0.9926 0.5815 0.9362 0.983 0.5428 ] Network output: [ -0.053 0.1314 1.15 7.648e-07 -3.433e-07 0.8247 5.763e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2476 0.2428 0.2833 0.2705 0.9898 0.9937 0.2478 0.9795 0.9886 0.2912 ] Network output: [ -0.05044 0.1195 1.131 6.848e-05 -3.074e-05 0.8509 5.161e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2504 0.2496 0.2817 0.2726 0.9853 0.9913 0.2505 0.965 0.9824 0.2834 ] Network output: [ -0.01619 1.051 0.03846 7.693e-06 -3.454e-06 0.9431 5.798e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05493 Epoch 3865 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05324 0.9067 0.9207 0.000101 -4.534e-05 0.06657 7.611e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008898 -0.006165 -0.005393 0.01727 0.9565 0.9634 0.02002 0.9165 0.9316 0.05812 ] Network output: [ 0.9682 0.126 0.01565 -1.616e-06 7.256e-07 -0.07811 -1.218e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5103 0.0819 0.05469 0.2762 0.9804 0.9916 0.5908 0.9303 0.9806 0.5513 ] Network output: [ 0.01363 0.9133 0.9378 -8.1e-05 3.636e-05 0.1213 -6.105e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01639 0.01153 0.01878 0.01858 0.9898 0.9931 0.0168 0.9788 0.9878 0.02493 ] Network output: [ 0.08424 -0.2443 0.8066 -9.325e-05 4.187e-05 1.269 -7.028e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5788 0.5063 0.4604 0.4534 0.9823 0.9926 0.5814 0.9362 0.983 0.5434 ] Network output: [ -0.05296 0.1315 1.15 1.364e-06 -6.123e-07 0.8245 1.028e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2474 0.2426 0.2832 0.2703 0.9898 0.9937 0.2476 0.9796 0.9886 0.2911 ] Network output: [ -0.05038 0.1193 1.131 6.975e-05 -3.131e-05 0.8509 5.257e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2502 0.2494 0.2816 0.2725 0.9853 0.9913 0.2503 0.9651 0.9824 0.2834 ] Network output: [ -0.01628 1.051 0.03841 7.321e-06 -3.286e-06 0.9429 5.517e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05506 Epoch 3866 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05324 0.9065 0.9207 0.0001009 -4.529e-05 0.0668 7.603e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008867 -0.006166 -0.005442 0.01723 0.9566 0.9635 0.02 0.9165 0.9316 0.0581 ] Network output: [ 0.9682 0.1267 0.01553 -2.996e-07 1.345e-07 -0.07858 -2.258e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5099 0.08169 0.05432 0.2757 0.9804 0.9916 0.5907 0.9303 0.9806 0.5519 ] Network output: [ 0.01366 0.913 0.9378 -8.158e-05 3.663e-05 0.1216 -6.148e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01637 0.01151 0.01876 0.01854 0.9898 0.9931 0.01677 0.9788 0.9878 0.02491 ] Network output: [ 0.08423 -0.2448 0.8067 -9.533e-05 4.28e-05 1.269 -7.185e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5786 0.506 0.4608 0.4532 0.9823 0.9926 0.5812 0.9363 0.983 0.544 ] Network output: [ -0.05292 0.1317 1.15 1.972e-06 -8.855e-07 0.8243 1.486e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2471 0.2423 0.2832 0.2701 0.9898 0.9937 0.2473 0.9796 0.9886 0.2911 ] Network output: [ -0.05032 0.1191 1.131 7.104e-05 -3.189e-05 0.8509 5.354e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.25 0.2492 0.2816 0.2723 0.9853 0.9913 0.2501 0.9651 0.9824 0.2833 ] Network output: [ -0.01636 1.052 0.03836 6.937e-06 -3.114e-06 0.9427 5.228e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0552 Epoch 3867 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05323 0.9063 0.9206 0.0001008 -4.523e-05 0.06702 7.593e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008835 -0.006166 -0.005491 0.01718 0.9566 0.9635 0.01998 0.9166 0.9317 0.05808 ] Network output: [ 0.9682 0.1273 0.01542 1.056e-06 -4.741e-07 -0.07907 7.96e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5095 0.08147 0.05393 0.2752 0.9804 0.9916 0.5905 0.9303 0.9806 0.5524 ] Network output: [ 0.01368 0.9128 0.9377 -8.218e-05 3.689e-05 0.1218 -6.193e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01634 0.01148 0.01874 0.01849 0.9898 0.9931 0.01674 0.9788 0.9878 0.0249 ] Network output: [ 0.08422 -0.2452 0.8068 -9.746e-05 4.375e-05 1.27 -7.345e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5784 0.5057 0.4611 0.453 0.9823 0.9927 0.581 0.9363 0.983 0.5446 ] Network output: [ -0.05289 0.1318 1.15 2.591e-06 -1.163e-06 0.8241 1.952e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2469 0.2421 0.2831 0.2699 0.9898 0.9937 0.2471 0.9796 0.9886 0.291 ] Network output: [ -0.05025 0.1188 1.131 7.235e-05 -3.248e-05 0.8509 5.453e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2498 0.249 0.2816 0.2722 0.9853 0.9913 0.2499 0.9651 0.9824 0.2833 ] Network output: [ -0.01644 1.052 0.03831 6.542e-06 -2.937e-06 0.9425 4.931e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05533 Epoch 3868 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05323 0.9061 0.9206 0.0001006 -4.518e-05 0.06725 7.584e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008803 -0.006166 -0.005541 0.01713 0.9566 0.9635 0.01995 0.9166 0.9317 0.05806 ] Network output: [ 0.9681 0.128 0.01531 2.452e-06 -1.101e-06 -0.07956 1.848e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5091 0.08125 0.05354 0.2746 0.9804 0.9916 0.5904 0.9304 0.9806 0.553 ] Network output: [ 0.01371 0.9125 0.9377 -8.278e-05 3.716e-05 0.122 -6.239e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01631 0.01145 0.01872 0.01845 0.9898 0.9931 0.01671 0.9788 0.9878 0.02489 ] Network output: [ 0.0842 -0.2457 0.8069 -9.962e-05 4.473e-05 1.27 -7.508e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5782 0.5054 0.4615 0.4527 0.9823 0.9927 0.5808 0.9364 0.983 0.5452 ] Network output: [ -0.05285 0.1319 1.15 3.219e-06 -1.445e-06 0.8239 2.426e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2466 0.2418 0.2831 0.2697 0.9898 0.9937 0.2468 0.9796 0.9886 0.291 ] Network output: [ -0.05019 0.1186 1.131 7.369e-05 -3.308e-05 0.8508 5.554e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2496 0.2488 0.2816 0.2721 0.9853 0.9913 0.2497 0.9651 0.9825 0.2833 ] Network output: [ -0.01653 1.052 0.03826 6.136e-06 -2.755e-06 0.9424 4.624e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05547 Epoch 3869 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05322 0.9059 0.9206 0.0001005 -4.512e-05 0.06748 7.574e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008771 -0.006167 -0.005591 0.01709 0.9566 0.9635 0.01993 0.9167 0.9317 0.05804 ] Network output: [ 0.9681 0.1287 0.0152 3.89e-06 -1.746e-06 -0.08006 2.932e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5087 0.08103 0.05314 0.274 0.9804 0.9917 0.5902 0.9304 0.9806 0.5536 ] Network output: [ 0.01374 0.9122 0.9377 -8.34e-05 3.744e-05 0.1223 -6.285e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01628 0.01142 0.01869 0.0184 0.9898 0.9931 0.01669 0.9788 0.9878 0.02487 ] Network output: [ 0.08419 -0.2461 0.8069 -0.0001018 4.572e-05 1.27 -7.675e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.578 0.5051 0.4618 0.4525 0.9823 0.9927 0.5806 0.9364 0.983 0.5458 ] Network output: [ -0.05281 0.132 1.15 3.857e-06 -1.732e-06 0.8237 2.907e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2464 0.2416 0.283 0.2695 0.9898 0.9937 0.2466 0.9796 0.9886 0.291 ] Network output: [ -0.05013 0.1184 1.131 7.505e-05 -3.369e-05 0.8508 5.656e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2494 0.2486 0.2815 0.272 0.9853 0.9913 0.2495 0.9651 0.9825 0.2833 ] Network output: [ -0.01662 1.053 0.03821 5.718e-06 -2.567e-06 0.9422 4.309e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05561 Epoch 3870 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05322 0.9057 0.9206 0.0001004 -4.506e-05 0.06772 7.564e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008738 -0.006167 -0.005642 0.01704 0.9566 0.9635 0.01991 0.9167 0.9318 0.05802 ] Network output: [ 0.9681 0.1294 0.01509 5.371e-06 -2.411e-06 -0.08057 4.048e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5083 0.08079 0.05273 0.2735 0.9804 0.9917 0.5901 0.9305 0.9807 0.5542 ] Network output: [ 0.01377 0.9119 0.9376 -8.402e-05 3.772e-05 0.1225 -6.332e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01625 0.0114 0.01867 0.01835 0.9898 0.9931 0.01666 0.9789 0.9878 0.02486 ] Network output: [ 0.08418 -0.2466 0.807 -0.0001041 4.674e-05 1.271 -7.846e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5778 0.5048 0.4622 0.4523 0.9823 0.9927 0.5804 0.9364 0.9831 0.5464 ] Network output: [ -0.05278 0.1322 1.15 4.506e-06 -2.023e-06 0.8234 3.396e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2461 0.2413 0.283 0.2692 0.9898 0.9937 0.2463 0.9797 0.9887 0.2909 ] Network output: [ -0.05006 0.1182 1.131 7.644e-05 -3.432e-05 0.8508 5.761e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2492 0.2484 0.2815 0.2719 0.9853 0.9913 0.2493 0.9651 0.9825 0.2833 ] Network output: [ -0.01671 1.053 0.03815 5.288e-06 -2.374e-06 0.942 3.985e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05575 Epoch 3871 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05321 0.9055 0.9206 0.0001002 -4.5e-05 0.06796 7.554e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008705 -0.006167 -0.005693 0.01699 0.9566 0.9635 0.01988 0.9168 0.9318 0.058 ] Network output: [ 0.968 0.1301 0.01499 6.897e-06 -3.096e-06 -0.08109 5.197e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5078 0.08056 0.05231 0.2729 0.9804 0.9917 0.5899 0.9305 0.9807 0.5548 ] Network output: [ 0.0138 0.9117 0.9376 -8.466e-05 3.801e-05 0.1228 -6.38e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01622 0.01137 0.01865 0.01831 0.9898 0.9931 0.01663 0.9789 0.9878 0.02484 ] Network output: [ 0.08417 -0.247 0.8071 -0.0001064 4.778e-05 1.271 -8.02e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5775 0.5045 0.4626 0.452 0.9823 0.9927 0.5802 0.9365 0.9831 0.547 ] Network output: [ -0.05274 0.1323 1.15 5.166e-06 -2.319e-06 0.8232 3.893e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2458 0.241 0.2829 0.269 0.9899 0.9938 0.246 0.9797 0.9887 0.2909 ] Network output: [ -0.05 0.1179 1.132 7.785e-05 -3.495e-05 0.8508 5.867e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.249 0.2482 0.2815 0.2718 0.9853 0.9913 0.2491 0.9651 0.9825 0.2832 ] Network output: [ -0.0168 1.054 0.03809 4.846e-06 -2.175e-06 0.9418 3.652e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05589 Epoch 3872 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05321 0.9052 0.9205 0.0001001 -4.494e-05 0.0682 7.544e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008672 -0.006167 -0.005745 0.01694 0.9566 0.9635 0.01986 0.9168 0.9319 0.05798 ] Network output: [ 0.968 0.1308 0.01489 8.468e-06 -3.802e-06 -0.08162 6.382e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5074 0.08032 0.05189 0.2723 0.9804 0.9917 0.5897 0.9306 0.9807 0.5554 ] Network output: [ 0.01383 0.9114 0.9376 -8.531e-05 3.83e-05 0.123 -6.429e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01619 0.01134 0.01863 0.01826 0.9898 0.9931 0.0166 0.9789 0.9878 0.02483 ] Network output: [ 0.08417 -0.2475 0.8072 -0.0001088 4.884e-05 1.272 -8.199e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5773 0.5042 0.4629 0.4518 0.9823 0.9927 0.58 0.9365 0.9831 0.5477 ] Network output: [ -0.0527 0.1324 1.15 5.837e-06 -2.62e-06 0.823 4.399e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2456 0.2408 0.2829 0.2688 0.9899 0.9938 0.2458 0.9797 0.9887 0.2908 ] Network output: [ -0.04994 0.1177 1.132 7.928e-05 -3.559e-05 0.8508 5.975e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2488 0.248 0.2815 0.2716 0.9853 0.9913 0.2489 0.9652 0.9825 0.2832 ] Network output: [ -0.01689 1.054 0.03803 4.391e-06 -1.971e-06 0.9416 3.309e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05603 Epoch 3873 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05321 0.905 0.9205 9.996e-05 -4.488e-05 0.06845 7.533e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008638 -0.006167 -0.005798 0.01689 0.9566 0.9635 0.01984 0.9169 0.9319 0.05796 ] Network output: [ 0.9679 0.1316 0.01479 1.009e-05 -4.529e-06 -0.08215 7.602e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5069 0.08008 0.05145 0.2717 0.9804 0.9917 0.5895 0.9306 0.9807 0.556 ] Network output: [ 0.01387 0.9111 0.9376 -8.598e-05 3.86e-05 0.1233 -6.48e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01616 0.01131 0.0186 0.01821 0.9898 0.9931 0.01657 0.9789 0.9878 0.02482 ] Network output: [ 0.08416 -0.248 0.8073 -0.0001112 4.992e-05 1.272 -8.381e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5771 0.5039 0.4633 0.4515 0.9823 0.9927 0.5798 0.9366 0.9831 0.5483 ] Network output: [ -0.05266 0.1326 1.15 6.518e-06 -2.926e-06 0.8228 4.912e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2453 0.2405 0.2828 0.2686 0.9899 0.9938 0.2455 0.9797 0.9887 0.2908 ] Network output: [ -0.04987 0.1174 1.132 8.074e-05 -3.625e-05 0.8508 6.085e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2486 0.2478 0.2814 0.2715 0.9853 0.9913 0.2487 0.9652 0.9825 0.2832 ] Network output: [ -0.01698 1.055 0.03797 3.923e-06 -1.761e-06 0.9414 2.956e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05618 Epoch 3874 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05321 0.9048 0.9205 9.981e-05 -4.481e-05 0.0687 7.522e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008604 -0.006168 -0.005851 0.01684 0.9566 0.9635 0.01981 0.9169 0.932 0.05795 ] Network output: [ 0.9679 0.1323 0.0147 1.176e-05 -5.278e-06 -0.0827 8.86e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5065 0.07983 0.051 0.2711 0.9804 0.9917 0.5894 0.9307 0.9807 0.5566 ] Network output: [ 0.0139 0.9108 0.9375 -8.665e-05 3.89e-05 0.1236 -6.531e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01613 0.01128 0.01858 0.01816 0.9898 0.9931 0.01654 0.9789 0.9879 0.0248 ] Network output: [ 0.08416 -0.2484 0.8074 -0.0001137 5.103e-05 1.272 -8.567e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5769 0.5035 0.4637 0.4512 0.9823 0.9927 0.5796 0.9366 0.9831 0.549 ] Network output: [ -0.05262 0.1327 1.15 7.212e-06 -3.238e-06 0.8226 5.435e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.245 0.2402 0.2827 0.2683 0.9899 0.9938 0.2452 0.9797 0.9887 0.2907 ] Network output: [ -0.04981 0.1172 1.132 8.223e-05 -3.692e-05 0.8509 6.197e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2484 0.2476 0.2814 0.2714 0.9853 0.9913 0.2485 0.9652 0.9825 0.2832 ] Network output: [ -0.01708 1.055 0.03791 3.442e-06 -1.545e-06 0.9413 2.594e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05633 Epoch 3875 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0532 0.9046 0.9205 9.966e-05 -4.474e-05 0.06895 7.511e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00857 -0.006168 -0.005905 0.01679 0.9566 0.9635 0.01979 0.917 0.932 0.05793 ] Network output: [ 0.9678 0.133 0.01461 1.348e-05 -6.05e-06 -0.08325 1.016e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.506 0.07957 0.05055 0.2705 0.9804 0.9917 0.5892 0.9307 0.9808 0.5572 ] Network output: [ 0.01394 0.9104 0.9375 -8.735e-05 3.921e-05 0.1238 -6.583e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0161 0.01125 0.01856 0.01811 0.9898 0.9931 0.01651 0.9789 0.9879 0.02479 ] Network output: [ 0.08416 -0.2489 0.8075 -0.0001162 5.217e-05 1.273 -8.758e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5766 0.5032 0.464 0.4509 0.9823 0.9927 0.5794 0.9366 0.9831 0.5497 ] Network output: [ -0.05258 0.1329 1.15 7.917e-06 -3.554e-06 0.8224 5.966e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2448 0.2399 0.2827 0.2681 0.9899 0.9938 0.245 0.9798 0.9887 0.2907 ] Network output: [ -0.04974 0.1169 1.132 8.374e-05 -3.76e-05 0.8509 6.311e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2482 0.2474 0.2814 0.2713 0.9853 0.9913 0.2482 0.9652 0.9826 0.2832 ] Network output: [ -0.01718 1.055 0.03784 2.948e-06 -1.323e-06 0.9411 2.221e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05648 Epoch 3876 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0532 0.9043 0.9205 9.951e-05 -4.467e-05 0.06921 7.499e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008535 -0.006168 -0.005959 0.01674 0.9567 0.9636 0.01976 0.917 0.9321 0.05791 ] Network output: [ 0.9678 0.1338 0.01452 1.525e-05 -6.845e-06 -0.08381 1.149e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5055 0.07931 0.05008 0.2699 0.9804 0.9917 0.589 0.9308 0.9808 0.5579 ] Network output: [ 0.01397 0.9101 0.9375 -8.805e-05 3.953e-05 0.1241 -6.636e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01607 0.01122 0.01853 0.01806 0.9898 0.9931 0.01648 0.979 0.9879 0.02478 ] Network output: [ 0.08416 -0.2494 0.8075 -0.0001188 5.333e-05 1.273 -8.953e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5764 0.5028 0.4644 0.4506 0.9823 0.9927 0.5792 0.9367 0.9832 0.5503 ] Network output: [ -0.05254 0.133 1.15 8.634e-06 -3.876e-06 0.8221 6.507e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2445 0.2397 0.2826 0.2678 0.9899 0.9938 0.2447 0.9798 0.9888 0.2906 ] Network output: [ -0.04967 0.1166 1.132 8.529e-05 -3.829e-05 0.8509 6.427e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.248 0.2472 0.2814 0.2711 0.9853 0.9913 0.248 0.9652 0.9826 0.2831 ] Network output: [ -0.01728 1.056 0.03777 2.44e-06 -1.095e-06 0.9409 1.839e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05663 Epoch 3877 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0532 0.9041 0.9205 9.935e-05 -4.46e-05 0.06947 7.487e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0085 -0.006168 -0.006014 0.01669 0.9567 0.9636 0.01974 0.9171 0.9321 0.05789 ] Network output: [ 0.9677 0.1346 0.01444 1.707e-05 -7.665e-06 -0.08438 1.287e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.505 0.07904 0.0496 0.2693 0.9804 0.9917 0.5888 0.9308 0.9808 0.5585 ] Network output: [ 0.01401 0.9098 0.9374 -8.877e-05 3.985e-05 0.1244 -6.69e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01604 0.01119 0.01851 0.01801 0.9898 0.9931 0.01645 0.979 0.9879 0.02476 ] Network output: [ 0.08416 -0.2498 0.8076 -0.0001214 5.452e-05 1.273 -9.152e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5762 0.5025 0.4648 0.4503 0.9823 0.9927 0.5789 0.9367 0.9832 0.551 ] Network output: [ -0.0525 0.1332 1.15 9.363e-06 -4.203e-06 0.8219 7.056e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2442 0.2394 0.2825 0.2676 0.9899 0.9938 0.2444 0.9798 0.9888 0.2906 ] Network output: [ -0.0496 0.1163 1.132 8.686e-05 -3.899e-05 0.8509 6.546e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2478 0.247 0.2813 0.271 0.9853 0.9913 0.2478 0.9652 0.9826 0.2831 ] Network output: [ -0.01738 1.056 0.0377 1.918e-06 -8.61e-07 0.9407 1.445e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05678 Epoch 3878 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0532 0.9038 0.9204 9.919e-05 -4.453e-05 0.06973 7.475e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008465 -0.006167 -0.006069 0.01664 0.9567 0.9636 0.01971 0.9171 0.9322 0.05788 ] Network output: [ 0.9676 0.1354 0.01437 1.896e-05 -8.51e-06 -0.08496 1.429e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5045 0.07877 0.04912 0.2686 0.9804 0.9917 0.5886 0.9309 0.9808 0.5592 ] Network output: [ 0.01405 0.9095 0.9374 -8.95e-05 4.018e-05 0.1247 -6.745e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01601 0.01116 0.01848 0.01796 0.9899 0.9931 0.01642 0.979 0.9879 0.02475 ] Network output: [ 0.08416 -0.2503 0.8077 -0.0001242 5.574e-05 1.274 -9.356e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5759 0.5021 0.4652 0.45 0.9823 0.9927 0.5787 0.9368 0.9832 0.5517 ] Network output: [ -0.05246 0.1333 1.15 1.01e-05 -4.536e-06 0.8217 7.615e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2439 0.2391 0.2825 0.2673 0.9899 0.9938 0.2441 0.9798 0.9888 0.2906 ] Network output: [ -0.04954 0.1161 1.132 8.846e-05 -3.971e-05 0.8509 6.666e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2475 0.2467 0.2813 0.2709 0.9853 0.9913 0.2476 0.9653 0.9826 0.2831 ] Network output: [ -0.01748 1.057 0.03762 1.382e-06 -6.204e-07 0.9405 1.042e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05694 Epoch 3879 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0532 0.9036 0.9204 9.902e-05 -4.445e-05 0.07 7.462e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008429 -0.006167 -0.006125 0.01659 0.9567 0.9636 0.01969 0.9172 0.9322 0.05786 ] Network output: [ 0.9676 0.1362 0.01429 2.09e-05 -9.382e-06 -0.08555 1.575e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.504 0.0785 0.04862 0.268 0.9804 0.9917 0.5884 0.9309 0.9808 0.5599 ] Network output: [ 0.01409 0.9091 0.9374 -9.025e-05 4.052e-05 0.125 -6.802e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01597 0.01113 0.01846 0.0179 0.9899 0.9931 0.01639 0.979 0.9879 0.02474 ] Network output: [ 0.08417 -0.2508 0.8078 -0.0001269 5.698e-05 1.274 -9.565e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5756 0.5017 0.4656 0.4497 0.9823 0.9927 0.5785 0.9368 0.9832 0.5525 ] Network output: [ -0.05241 0.1335 1.15 1.086e-05 -4.875e-06 0.8215 8.184e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2436 0.2388 0.2824 0.2671 0.9899 0.9938 0.2439 0.9798 0.9888 0.2905 ] Network output: [ -0.04947 0.1158 1.133 9.009e-05 -4.044e-05 0.851 6.789e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2473 0.2465 0.2813 0.2707 0.9853 0.9913 0.2474 0.9653 0.9826 0.2831 ] Network output: [ -0.01759 1.057 0.03755 8.318e-07 -3.734e-07 0.9403 6.269e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0571 Epoch 3880 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0532 0.9033 0.9204 9.884e-05 -4.437e-05 0.07028 7.449e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008392 -0.006167 -0.006182 0.01654 0.9567 0.9636 0.01966 0.9173 0.9323 0.05784 ] Network output: [ 0.9675 0.137 0.01423 2.29e-05 -1.028e-05 -0.08615 1.726e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5035 0.07821 0.04811 0.2673 0.9804 0.9917 0.5882 0.9309 0.9809 0.5606 ] Network output: [ 0.01414 0.9088 0.9373 -9.102e-05 4.086e-05 0.1252 -6.86e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01594 0.01109 0.01844 0.01785 0.9899 0.9931 0.01636 0.979 0.9879 0.02472 ] Network output: [ 0.08417 -0.2512 0.8079 -0.0001298 5.825e-05 1.275 -9.779e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5754 0.5013 0.4659 0.4494 0.9823 0.9927 0.5782 0.9369 0.9832 0.5532 ] Network output: [ -0.05237 0.1337 1.15 1.163e-05 -5.22e-06 0.8212 8.762e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2434 0.2385 0.2823 0.2668 0.9899 0.9938 0.2436 0.9799 0.9888 0.2905 ] Network output: [ -0.0494 0.1154 1.133 9.175e-05 -4.119e-05 0.851 6.914e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2471 0.2463 0.2813 0.2706 0.9853 0.9913 0.2472 0.9653 0.9826 0.2831 ] Network output: [ -0.01769 1.058 0.03747 2.671e-07 -1.199e-07 0.9401 2.013e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05725 Epoch 3881 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05321 0.903 0.9204 9.866e-05 -4.429e-05 0.07056 7.436e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008356 -0.006167 -0.00624 0.01648 0.9567 0.9636 0.01963 0.9173 0.9323 0.05782 ] Network output: [ 0.9674 0.1378 0.01417 2.496e-05 -1.121e-05 -0.08676 1.881e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5029 0.07793 0.04759 0.2666 0.9804 0.9917 0.5879 0.931 0.9809 0.5613 ] Network output: [ 0.01418 0.9084 0.9373 -9.18e-05 4.121e-05 0.1255 -6.919e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01591 0.01106 0.01841 0.0178 0.9899 0.9931 0.01633 0.979 0.9879 0.02471 ] Network output: [ 0.08418 -0.2517 0.8079 -0.0001327 5.956e-05 1.275 -9.998e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5751 0.5009 0.4663 0.449 0.9823 0.9927 0.578 0.9369 0.9832 0.5539 ] Network output: [ -0.05233 0.1339 1.15 1.241e-05 -5.57e-06 0.821 9.351e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2431 0.2382 0.2823 0.2665 0.9899 0.9938 0.2433 0.9799 0.9888 0.2904 ] Network output: [ -0.04933 0.1151 1.133 9.344e-05 -4.195e-05 0.851 7.042e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2469 0.2461 0.2812 0.2704 0.9853 0.9913 0.2469 0.9653 0.9826 0.283 ] Network output: [ -0.0178 1.058 0.03738 -3.124e-07 1.402e-07 0.9399 -2.354e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05742 Epoch 3882 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05321 0.9028 0.9204 9.848e-05 -4.421e-05 0.07084 7.422e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008318 -0.006167 -0.006298 0.01643 0.9567 0.9636 0.01961 0.9174 0.9324 0.05781 ] Network output: [ 0.9674 0.1386 0.01411 2.709e-05 -1.216e-05 -0.08737 2.041e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5024 0.07763 0.04706 0.266 0.9804 0.9917 0.5877 0.931 0.9809 0.562 ] Network output: [ 0.01423 0.9081 0.9373 -9.26e-05 4.157e-05 0.1258 -6.979e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01588 0.01103 0.01838 0.01774 0.9899 0.9931 0.0163 0.979 0.988 0.0247 ] Network output: [ 0.08419 -0.2522 0.808 -0.0001356 6.089e-05 1.275 -0.0001022 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5748 0.5005 0.4667 0.4487 0.9823 0.9927 0.5777 0.9369 0.9833 0.5547 ] Network output: [ -0.05228 0.134 1.15 1.32e-05 -5.927e-06 0.8208 9.95e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2428 0.2379 0.2822 0.2663 0.9899 0.9938 0.243 0.9799 0.9888 0.2904 ] Network output: [ -0.04925 0.1148 1.133 9.516e-05 -4.272e-05 0.8511 7.172e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2467 0.2458 0.2812 0.2703 0.9853 0.9913 0.2467 0.9653 0.9827 0.283 ] Network output: [ -0.01792 1.059 0.0373 -9.067e-07 4.07e-07 0.9397 -6.833e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05758 Epoch 3883 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05321 0.9025 0.9204 9.829e-05 -4.412e-05 0.07113 7.407e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008281 -0.006166 -0.006357 0.01638 0.9567 0.9636 0.01958 0.9174 0.9324 0.05779 ] Network output: [ 0.9673 0.1395 0.01406 2.928e-05 -1.315e-05 -0.088 2.207e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5018 0.07733 0.04651 0.2653 0.9804 0.9917 0.5875 0.9311 0.9809 0.5627 ] Network output: [ 0.01428 0.9077 0.9372 -9.342e-05 4.194e-05 0.1261 -7.04e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01584 0.01099 0.01836 0.01769 0.9899 0.9931 0.01627 0.9791 0.988 0.02468 ] Network output: [ 0.0842 -0.2527 0.8081 -0.0001387 6.225e-05 1.276 -0.0001045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5745 0.5 0.4671 0.4483 0.9823 0.9927 0.5774 0.937 0.9833 0.5555 ] Network output: [ -0.05224 0.1342 1.15 1.401e-05 -6.291e-06 0.8205 1.056e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2425 0.2376 0.2821 0.266 0.9899 0.9938 0.2427 0.9799 0.9889 0.2903 ] Network output: [ -0.04918 0.1145 1.133 9.692e-05 -4.351e-05 0.8511 7.304e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2464 0.2456 0.2812 0.2701 0.9853 0.9913 0.2465 0.9653 0.9827 0.283 ] Network output: [ -0.01803 1.059 0.03721 -1.516e-06 6.806e-07 0.9395 -1.143e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05775 Epoch 3884 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05322 0.9022 0.9203 9.809e-05 -4.404e-05 0.07142 7.392e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008243 -0.006166 -0.006416 0.01632 0.9567 0.9636 0.01955 0.9175 0.9325 0.05778 ] Network output: [ 0.9672 0.1404 0.01402 3.154e-05 -1.416e-05 -0.08864 2.377e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5012 0.07702 0.04595 0.2646 0.9804 0.9917 0.5872 0.9311 0.9809 0.5634 ] Network output: [ 0.01433 0.9074 0.9372 -9.425e-05 4.231e-05 0.1264 -7.103e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01581 0.01096 0.01833 0.01763 0.9899 0.9931 0.01623 0.9791 0.988 0.02467 ] Network output: [ 0.08422 -0.2532 0.8081 -0.0001418 6.365e-05 1.276 -0.0001069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5743 0.4996 0.4675 0.4479 0.9823 0.9927 0.5772 0.937 0.9833 0.5562 ] Network output: [ -0.05219 0.1344 1.15 1.484e-05 -6.66e-06 0.8203 1.118e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2422 0.2373 0.282 0.2657 0.9899 0.9938 0.2424 0.9799 0.9889 0.2902 ] Network output: [ -0.04911 0.1141 1.133 9.871e-05 -4.431e-05 0.8512 7.439e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2462 0.2454 0.2812 0.27 0.9853 0.9913 0.2463 0.9653 0.9827 0.283 ] Network output: [ -0.01815 1.06 0.03712 -2.14e-06 9.609e-07 0.9393 -1.613e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05791 Epoch 3885 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05322 0.9019 0.9203 9.789e-05 -4.394e-05 0.07171 7.377e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008205 -0.006166 -0.006477 0.01627 0.9567 0.9636 0.01953 0.9175 0.9325 0.05776 ] Network output: [ 0.9671 0.1412 0.01398 3.387e-05 -1.521e-05 -0.08929 2.553e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5006 0.07671 0.04538 0.2639 0.9804 0.9917 0.587 0.9312 0.981 0.5642 ] Network output: [ 0.01438 0.907 0.9371 -9.511e-05 4.27e-05 0.1268 -7.168e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01577 0.01093 0.01831 0.01758 0.9899 0.9931 0.0162 0.9791 0.988 0.02465 ] Network output: [ 0.08424 -0.2536 0.8082 -0.000145 6.508e-05 1.276 -0.0001093 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.574 0.4992 0.4679 0.4475 0.9823 0.9927 0.5769 0.9371 0.9833 0.557 ] Network output: [ -0.05214 0.1346 1.15 1.567e-05 -7.037e-06 0.82 1.181e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2419 0.237 0.282 0.2654 0.9899 0.9938 0.2421 0.98 0.9889 0.2902 ] Network output: [ -0.04904 0.1138 1.133 0.0001005 -4.513e-05 0.8513 7.576e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.246 0.2452 0.2811 0.2698 0.9853 0.9913 0.246 0.9654 0.9827 0.283 ] Network output: [ -0.01826 1.06 0.03703 -2.78e-06 1.248e-06 0.9391 -2.095e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05808 Epoch 3886 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05323 0.9016 0.9203 9.767e-05 -4.385e-05 0.07201 7.361e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008166 -0.006165 -0.006538 0.01621 0.9567 0.9637 0.0195 0.9176 0.9326 0.05774 ] Network output: [ 0.967 0.1421 0.01395 3.628e-05 -1.629e-05 -0.08994 2.734e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.5 0.07639 0.0448 0.2631 0.9804 0.9917 0.5867 0.9312 0.981 0.5649 ] Network output: [ 0.01443 0.9066 0.9371 -9.598e-05 4.309e-05 0.1271 -7.233e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01574 0.01089 0.01828 0.01752 0.9899 0.9931 0.01617 0.9791 0.988 0.02464 ] Network output: [ 0.08426 -0.2541 0.8083 -0.0001482 6.655e-05 1.277 -0.0001117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5736 0.4987 0.4683 0.4471 0.9823 0.9927 0.5766 0.9371 0.9833 0.5578 ] Network output: [ -0.0521 0.1348 1.15 1.653e-05 -7.42e-06 0.8198 1.246e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2416 0.2367 0.2819 0.2651 0.9899 0.9938 0.2418 0.98 0.9889 0.2901 ] Network output: [ -0.04896 0.1134 1.134 0.0001024 -4.597e-05 0.8513 7.716e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2457 0.2449 0.2811 0.2697 0.9853 0.9913 0.2458 0.9654 0.9827 0.283 ] Network output: [ -0.01838 1.061 0.03694 -3.435e-06 1.542e-06 0.9389 -2.589e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05825 Epoch 3887 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05324 0.9013 0.9203 9.746e-05 -4.375e-05 0.07232 7.345e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008127 -0.006165 -0.0066 0.01616 0.9568 0.9637 0.01947 0.9177 0.9326 0.05773 ] Network output: [ 0.9669 0.143 0.01392 3.875e-05 -1.74e-05 -0.09061 2.921e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4994 0.07607 0.0442 0.2624 0.9804 0.9917 0.5864 0.9313 0.981 0.5657 ] Network output: [ 0.01448 0.9062 0.9371 -9.687e-05 4.349e-05 0.1274 -7.301e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0157 0.01085 0.01825 0.01746 0.9899 0.9932 0.01613 0.9791 0.988 0.02463 ] Network output: [ 0.08428 -0.2546 0.8083 -0.0001516 6.805e-05 1.277 -0.0001142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5733 0.4982 0.4688 0.4467 0.9823 0.9927 0.5763 0.9372 0.9833 0.5586 ] Network output: [ -0.05205 0.135 1.15 1.74e-05 -7.81e-06 0.8196 1.311e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2413 0.2364 0.2818 0.2648 0.9899 0.9938 0.2415 0.98 0.9889 0.2901 ] Network output: [ -0.04889 0.113 1.134 0.0001043 -4.682e-05 0.8514 7.859e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2455 0.2447 0.2811 0.2695 0.9853 0.9913 0.2456 0.9654 0.9827 0.2829 ] Network output: [ -0.01851 1.061 0.03684 -4.105e-06 1.843e-06 0.9387 -3.094e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05843 Epoch 3888 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05324 0.901 0.9203 9.723e-05 -4.365e-05 0.07263 7.328e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008087 -0.006164 -0.006662 0.0161 0.9568 0.9637 0.01944 0.9177 0.9327 0.05771 ] Network output: [ 0.9668 0.1439 0.01391 4.131e-05 -1.855e-05 -0.09129 3.113e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4988 0.07573 0.04359 0.2617 0.9804 0.9917 0.5861 0.9313 0.981 0.5665 ] Network output: [ 0.01454 0.9058 0.937 -9.779e-05 4.39e-05 0.1277 -7.37e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01567 0.01082 0.01823 0.0174 0.9899 0.9932 0.0161 0.9791 0.988 0.02462 ] Network output: [ 0.0843 -0.255 0.8084 -0.000155 6.959e-05 1.277 -0.0001168 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.573 0.4977 0.4692 0.4463 0.9823 0.9927 0.576 0.9372 0.9834 0.5595 ] Network output: [ -0.052 0.1352 1.15 1.828e-05 -8.207e-06 0.8193 1.378e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.241 0.2361 0.2817 0.2645 0.9899 0.9938 0.2412 0.98 0.9889 0.29 ] Network output: [ -0.04881 0.1127 1.134 0.0001062 -4.768e-05 0.8515 8.005e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2453 0.2445 0.2811 0.2694 0.9853 0.9913 0.2453 0.9654 0.9828 0.2829 ] Network output: [ -0.01863 1.062 0.03674 -4.791e-06 2.151e-06 0.9385 -3.61e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0586 Epoch 3889 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05325 0.9007 0.9203 9.7e-05 -4.355e-05 0.07294 7.31e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008047 -0.006164 -0.006726 0.01605 0.9568 0.9637 0.01941 0.9178 0.9327 0.0577 ] Network output: [ 0.9667 0.1448 0.0139 4.395e-05 -1.973e-05 -0.09198 3.312e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4982 0.07539 0.04297 0.2609 0.9804 0.9917 0.5859 0.9314 0.981 0.5673 ] Network output: [ 0.0146 0.9054 0.937 -9.872e-05 4.432e-05 0.128 -7.44e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01563 0.01078 0.0182 0.01734 0.9899 0.9932 0.01606 0.9792 0.988 0.0246 ] Network output: [ 0.08433 -0.2555 0.8084 -0.0001585 7.116e-05 1.278 -0.0001195 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5727 0.4972 0.4696 0.4459 0.9823 0.9927 0.5757 0.9372 0.9834 0.5603 ] Network output: [ -0.05195 0.1354 1.15 1.918e-05 -8.611e-06 0.8191 1.446e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2407 0.2358 0.2816 0.2642 0.9899 0.9938 0.2409 0.9801 0.989 0.29 ] Network output: [ -0.04873 0.1123 1.134 0.0001082 -4.857e-05 0.8516 8.153e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.245 0.2442 0.281 0.2692 0.9853 0.9913 0.2451 0.9654 0.9828 0.2829 ] Network output: [ -0.01876 1.063 0.03664 -5.491e-06 2.465e-06 0.9383 -4.139e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05878 Epoch 3890 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05326 0.9003 0.9203 9.675e-05 -4.344e-05 0.07326 7.292e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.008006 -0.006163 -0.00679 0.01599 0.9568 0.9637 0.01938 0.9178 0.9328 0.05769 ] Network output: [ 0.9666 0.1458 0.0139 4.666e-05 -2.095e-05 -0.09268 3.517e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4975 0.07505 0.04233 0.2601 0.9804 0.9917 0.5856 0.9314 0.9811 0.5681 ] Network output: [ 0.01466 0.905 0.9369 -9.968e-05 4.475e-05 0.1284 -7.512e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01559 0.01074 0.01817 0.01728 0.9899 0.9932 0.01603 0.9792 0.988 0.02459 ] Network output: [ 0.08436 -0.256 0.8085 -0.0001621 7.278e-05 1.278 -0.0001222 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5723 0.4967 0.47 0.4454 0.9823 0.9927 0.5753 0.9373 0.9834 0.5612 ] Network output: [ -0.0519 0.1356 1.149 2.01e-05 -9.023e-06 0.8188 1.515e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2403 0.2354 0.2815 0.2638 0.99 0.9938 0.2406 0.9801 0.989 0.2899 ] Network output: [ -0.04866 0.1119 1.134 0.0001102 -4.947e-05 0.8517 8.304e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2448 0.244 0.281 0.269 0.9853 0.9913 0.2448 0.9654 0.9828 0.2829 ] Network output: [ -0.01889 1.063 0.03654 -6.207e-06 2.787e-06 0.9381 -4.678e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05896 Epoch 3891 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05327 0.9 0.9202 9.65e-05 -4.332e-05 0.07359 7.273e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.007966 -0.006162 -0.006855 0.01593 0.9568 0.9637 0.01935 0.9179 0.9328 0.05767 ] Network output: [ 0.9665 0.1467 0.01391 4.946e-05 -2.221e-05 -0.09339 3.728e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4968 0.0747 0.04167 0.2593 0.9805 0.9917 0.5852 0.9315 0.9811 0.569 ] Network output: [ 0.01472 0.9046 0.9369 -0.0001007 4.519e-05 0.1287 -7.586e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01556 0.01071 0.01814 0.01722 0.9899 0.9932 0.01599 0.9792 0.9881 0.02458 ] Network output: [ 0.08439 -0.2564 0.8085 -0.0001658 7.443e-05 1.278 -0.0001249 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.572 0.4962 0.4704 0.4449 0.9823 0.9927 0.575 0.9373 0.9834 0.5621 ] Network output: [ -0.05185 0.1358 1.149 2.103e-05 -9.442e-06 0.8185 1.585e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.24 0.2351 0.2814 0.2635 0.99 0.9938 0.2403 0.9801 0.989 0.2898 ] Network output: [ -0.04858 0.1114 1.134 0.0001122 -5.039e-05 0.8518 8.459e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2446 0.2437 0.281 0.2689 0.9853 0.9913 0.2446 0.9655 0.9828 0.2829 ] Network output: [ -0.01902 1.064 0.03643 -6.938e-06 3.115e-06 0.9379 -5.229e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05914 Epoch 3892 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05328 0.8997 0.9202 9.624e-05 -4.321e-05 0.07392 7.253e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.007924 -0.006161 -0.006921 0.01587 0.9568 0.9637 0.01932 0.9179 0.9329 0.05766 ] Network output: [ 0.9663 0.1477 0.01393 5.235e-05 -2.35e-05 -0.09411 3.945e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4962 0.07433 0.041 0.2586 0.9805 0.9917 0.5849 0.9315 0.9811 0.5698 ] Network output: [ 0.01479 0.9041 0.9368 -0.0001017 4.564e-05 0.1291 -7.662e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01552 0.01067 0.01811 0.01716 0.9899 0.9932 0.01596 0.9792 0.9881 0.02456 ] Network output: [ 0.08442 -0.2569 0.8086 -0.0001696 7.613e-05 1.279 -0.0001278 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5716 0.4956 0.4709 0.4444 0.9823 0.9927 0.5747 0.9374 0.9834 0.563 ] Network output: [ -0.05179 0.136 1.149 2.198e-05 -9.869e-06 0.8183 1.657e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2397 0.2348 0.2814 0.2632 0.99 0.9938 0.2399 0.9801 0.989 0.2898 ] Network output: [ -0.0485 0.111 1.135 0.0001143 -5.132e-05 0.8519 8.616e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2443 0.2435 0.281 0.2687 0.9853 0.9913 0.2444 0.9655 0.9828 0.2829 ] Network output: [ -0.01915 1.064 0.03632 -7.684e-06 3.449e-06 0.9377 -5.791e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05932 Epoch 3893 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0533 0.8993 0.9202 9.597e-05 -4.309e-05 0.07425 7.233e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.007882 -0.00616 -0.006987 0.01581 0.9568 0.9637 0.01929 0.918 0.9329 0.05764 ] Network output: [ 0.9662 0.1487 0.01395 5.533e-05 -2.484e-05 -0.09484 4.17e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4955 0.07397 0.04032 0.2577 0.9805 0.9917 0.5846 0.9316 0.9811 0.5707 ] Network output: [ 0.01485 0.9037 0.9368 -0.0001027 4.61e-05 0.1294 -7.739e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01548 0.01063 0.01808 0.0171 0.9899 0.9932 0.01592 0.9792 0.9881 0.02455 ] Network output: [ 0.08446 -0.2574 0.8086 -0.0001734 7.786e-05 1.279 -0.0001307 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5712 0.495 0.4713 0.4439 0.9823 0.9927 0.5743 0.9374 0.9834 0.5639 ] Network output: [ -0.05174 0.1363 1.149 2.295e-05 -1.03e-05 0.818 1.73e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2394 0.2344 0.2813 0.2628 0.99 0.9939 0.2396 0.9801 0.989 0.2897 ] Network output: [ -0.04842 0.1106 1.135 0.0001165 -5.228e-05 0.852 8.776e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2441 0.2432 0.281 0.2685 0.9853 0.9913 0.2441 0.9655 0.9828 0.2828 ] Network output: [ -0.01929 1.065 0.03621 -8.444e-06 3.791e-06 0.9375 -6.363e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0595 Epoch 3894 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05331 0.899 0.9202 9.569e-05 -4.296e-05 0.07459 7.212e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00784 -0.00616 -0.007055 0.01576 0.9568 0.9637 0.01926 0.9181 0.933 0.05763 ] Network output: [ 0.9661 0.1497 0.01399 5.84e-05 -2.622e-05 -0.09559 4.401e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4947 0.07359 0.03962 0.2569 0.9805 0.9917 0.5843 0.9316 0.9811 0.5716 ] Network output: [ 0.01492 0.9032 0.9367 -0.0001037 4.657e-05 0.1298 -7.818e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01544 0.01059 0.01805 0.01703 0.9899 0.9932 0.01588 0.9792 0.9881 0.02454 ] Network output: [ 0.0845 -0.2578 0.8086 -0.0001774 7.964e-05 1.279 -0.0001337 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5709 0.4945 0.4717 0.4434 0.9823 0.9927 0.574 0.9375 0.9835 0.5648 ] Network output: [ -0.05168 0.1365 1.149 2.394e-05 -1.075e-05 0.8178 1.804e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2391 0.2341 0.2812 0.2625 0.99 0.9939 0.2393 0.9802 0.9891 0.2897 ] Network output: [ -0.04834 0.1101 1.135 0.0001186 -5.325e-05 0.8521 8.94e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2438 0.243 0.2809 0.2683 0.9853 0.9913 0.2439 0.9655 0.9828 0.2828 ] Network output: [ -0.01943 1.065 0.0361 -9.218e-06 4.138e-06 0.9373 -6.947e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05969 Epoch 3895 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05333 0.8986 0.9202 9.54e-05 -4.283e-05 0.07494 7.19e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.007797 -0.006159 -0.007124 0.0157 0.9568 0.9638 0.01923 0.9181 0.933 0.05762 ] Network output: [ 0.9659 0.1507 0.01404 6.156e-05 -2.764e-05 -0.09634 4.639e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.494 0.07321 0.0389 0.2561 0.9805 0.9917 0.5839 0.9317 0.9812 0.5725 ] Network output: [ 0.01499 0.9028 0.9367 -0.0001048 4.706e-05 0.1301 -7.9e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01541 0.01055 0.01802 0.01697 0.9899 0.9932 0.01585 0.9792 0.9881 0.02452 ] Network output: [ 0.08454 -0.2582 0.8086 -0.0001815 8.147e-05 1.28 -0.0001368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5705 0.4939 0.4721 0.4429 0.9823 0.9927 0.5736 0.9375 0.9835 0.5657 ] Network output: [ -0.05163 0.1367 1.149 2.494e-05 -1.12e-05 0.8175 1.88e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2387 0.2338 0.2811 0.2621 0.99 0.9939 0.239 0.9802 0.9891 0.2896 ] Network output: [ -0.04826 0.1096 1.135 0.0001208 -5.425e-05 0.8522 9.107e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2436 0.2427 0.2809 0.2681 0.9853 0.9913 0.2436 0.9655 0.9829 0.2828 ] Network output: [ -0.01957 1.066 0.03598 -1.001e-05 4.492e-06 0.9371 -7.541e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05988 Epoch 3896 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05334 0.8982 0.9202 9.51e-05 -4.269e-05 0.07529 7.167e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.007754 -0.006157 -0.007193 0.01564 0.9568 0.9638 0.0192 0.9182 0.9331 0.05761 ] Network output: [ 0.9658 0.1517 0.0141 6.482e-05 -2.91e-05 -0.0971 4.885e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4933 0.07282 0.03817 0.2553 0.9805 0.9917 0.5836 0.9317 0.9812 0.5734 ] Network output: [ 0.01507 0.9023 0.9366 -0.0001059 4.755e-05 0.1305 -7.983e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01537 0.01051 0.01799 0.0169 0.9899 0.9932 0.01581 0.9793 0.9881 0.02451 ] Network output: [ 0.08459 -0.2587 0.8087 -0.0001856 8.334e-05 1.28 -0.0001399 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5701 0.4933 0.4726 0.4423 0.9823 0.9927 0.5732 0.9376 0.9835 0.5667 ] Network output: [ -0.05157 0.137 1.149 2.597e-05 -1.166e-05 0.8172 1.957e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2384 0.2334 0.281 0.2617 0.99 0.9939 0.2386 0.9802 0.9891 0.2895 ] Network output: [ -0.04817 0.1091 1.135 0.0001231 -5.526e-05 0.8524 9.277e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2433 0.2425 0.2809 0.268 0.9853 0.9913 0.2434 0.9655 0.9829 0.2828 ] Network output: [ -0.01972 1.067 0.03587 -1.081e-05 4.852e-06 0.937 -8.144e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06007 Epoch 3897 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05336 0.8979 0.9202 9.478e-05 -4.255e-05 0.07564 7.143e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.007711 -0.006156 -0.007263 0.01557 0.9569 0.9638 0.01917 0.9183 0.9331 0.05759 ] Network output: [ 0.9656 0.1527 0.01417 6.818e-05 -3.061e-05 -0.09788 5.138e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4925 0.07242 0.03742 0.2544 0.9805 0.9917 0.5832 0.9318 0.9812 0.5743 ] Network output: [ 0.01514 0.9019 0.9366 -0.0001071 4.806e-05 0.1309 -8.068e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01533 0.01047 0.01796 0.01684 0.9899 0.9932 0.01577 0.9793 0.9881 0.0245 ] Network output: [ 0.08464 -0.2591 0.8087 -0.0001899 8.525e-05 1.28 -0.0001431 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5696 0.4926 0.473 0.4418 0.9823 0.9927 0.5728 0.9376 0.9835 0.5677 ] Network output: [ -0.05151 0.1372 1.149 2.701e-05 -1.213e-05 0.817 2.036e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.238 0.2331 0.2809 0.2614 0.99 0.9939 0.2383 0.9802 0.9891 0.2895 ] Network output: [ -0.04809 0.1086 1.136 0.0001254 -5.629e-05 0.8525 9.45e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.243 0.2422 0.2809 0.2678 0.9853 0.9913 0.2431 0.9655 0.9829 0.2828 ] Network output: [ -0.01986 1.067 0.03575 -1.162e-05 5.217e-06 0.9368 -8.758e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06026 Epoch 3898 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05338 0.8975 0.9201 9.445e-05 -4.24e-05 0.07601 7.118e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.007667 -0.006155 -0.007335 0.01551 0.9569 0.9638 0.01914 0.9183 0.9332 0.05758 ] Network output: [ 0.9655 0.1538 0.01425 7.164e-05 -3.216e-05 -0.09866 5.399e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4917 0.07202 0.03665 0.2535 0.9805 0.9918 0.5828 0.9319 0.9812 0.5752 ] Network output: [ 0.01522 0.9014 0.9365 -0.0001082 4.858e-05 0.1312 -8.156e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01529 0.01043 0.01793 0.01677 0.9899 0.9932 0.01573 0.9793 0.9881 0.02449 ] Network output: [ 0.08469 -0.2595 0.8087 -0.0001943 8.722e-05 1.281 -0.0001464 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5692 0.492 0.4734 0.4412 0.9823 0.9927 0.5724 0.9377 0.9835 0.5686 ] Network output: [ -0.05145 0.1375 1.149 2.808e-05 -1.26e-05 0.8167 2.116e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2377 0.2327 0.2807 0.261 0.99 0.9939 0.238 0.9802 0.9891 0.2894 ] Network output: [ -0.048 0.1081 1.136 0.0001277 -5.735e-05 0.8527 9.627e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2428 0.2419 0.2808 0.2676 0.9853 0.9913 0.2428 0.9656 0.9829 0.2828 ] Network output: [ -0.02001 1.068 0.03563 -1.245e-05 5.588e-06 0.9366 -9.38e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06045 Epoch 3899 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0534 0.8971 0.9201 9.411e-05 -4.225e-05 0.07637 7.092e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.007622 -0.006154 -0.007407 0.01545 0.9569 0.9638 0.01911 0.9184 0.9332 0.05757 ] Network output: [ 0.9653 0.1548 0.01434 7.52e-05 -3.376e-05 -0.09946 5.668e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4909 0.0716 0.03587 0.2526 0.9805 0.9918 0.5824 0.9319 0.9812 0.5762 ] Network output: [ 0.0153 0.9009 0.9365 -0.0001094 4.912e-05 0.1316 -8.245e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01525 0.01038 0.0179 0.0167 0.99 0.9932 0.01569 0.9793 0.9881 0.02447 ] Network output: [ 0.08474 -0.2599 0.8087 -0.0001988 8.923e-05 1.281 -0.0001498 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5688 0.4913 0.4739 0.4406 0.9823 0.9927 0.572 0.9377 0.9836 0.5696 ] Network output: [ -0.05139 0.1377 1.149 2.916e-05 -1.309e-05 0.8164 2.198e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2374 0.2324 0.2806 0.2606 0.99 0.9939 0.2376 0.9803 0.9891 0.2893 ] Network output: [ -0.04792 0.1076 1.136 0.0001301 -5.842e-05 0.8528 9.807e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2425 0.2417 0.2808 0.2674 0.9853 0.9913 0.2426 0.9656 0.9829 0.2828 ] Network output: [ -0.02016 1.068 0.03551 -1.328e-05 5.963e-06 0.9364 -1.001e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06064 Epoch 3900 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05342 0.8967 0.9201 9.375e-05 -4.209e-05 0.07674 7.066e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.007577 -0.006152 -0.00748 0.01539 0.9569 0.9638 0.01907 0.9184 0.9333 0.05756 ] Network output: [ 0.9651 0.1559 0.01445 7.888e-05 -3.541e-05 -0.1003 5.944e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4901 0.07118 0.03506 0.2518 0.9805 0.9918 0.582 0.932 0.9813 0.5772 ] Network output: [ 0.01538 0.9004 0.9364 -0.0001106 4.967e-05 0.132 -8.337e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0152 0.01034 0.01787 0.01663 0.99 0.9932 0.01565 0.9793 0.9882 0.02446 ] Network output: [ 0.0848 -0.2603 0.8087 -0.0002034 9.13e-05 1.281 -0.0001533 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5683 0.4906 0.4743 0.4399 0.9823 0.9927 0.5716 0.9378 0.9836 0.5707 ] Network output: [ -0.05133 0.138 1.149 3.027e-05 -1.359e-05 0.8161 2.281e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.237 0.232 0.2805 0.2602 0.99 0.9939 0.2373 0.9803 0.9892 0.2892 ] Network output: [ -0.04783 0.1071 1.136 0.0001326 -5.952e-05 0.853 9.991e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2423 0.2414 0.2808 0.2672 0.9853 0.9913 0.2423 0.9656 0.9829 0.2827 ] Network output: [ -0.02032 1.069 0.03539 -1.413e-05 6.344e-06 0.9362 -1.065e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06084 Epoch 3901 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05344 0.8963 0.9201 9.338e-05 -4.192e-05 0.07712 7.038e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.007532 -0.006151 -0.007554 0.01533 0.9569 0.9638 0.01904 0.9185 0.9334 0.05755 ] Network output: [ 0.9649 0.157 0.01457 8.266e-05 -3.711e-05 -0.1011 6.229e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4893 0.07075 0.03424 0.2508 0.9805 0.9918 0.5816 0.932 0.9813 0.5782 ] Network output: [ 0.01547 0.8999 0.9363 -0.0001119 5.023e-05 0.1324 -8.432e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01516 0.0103 0.01783 0.01656 0.99 0.9932 0.01561 0.9793 0.9882 0.02445 ] Network output: [ 0.08486 -0.2607 0.8086 -0.0002081 9.341e-05 1.282 -0.0001568 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5679 0.4899 0.4748 0.4393 0.9823 0.9927 0.5711 0.9378 0.9836 0.5717 ] Network output: [ -0.05126 0.1383 1.149 3.139e-05 -1.409e-05 0.8158 2.366e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2367 0.2316 0.2804 0.2598 0.99 0.9939 0.2369 0.9803 0.9892 0.2892 ] Network output: [ -0.04774 0.1065 1.136 0.0001351 -6.063e-05 0.8532 0.0001018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.242 0.2411 0.2808 0.267 0.9853 0.9913 0.242 0.9656 0.983 0.2827 ] Network output: [ -0.02047 1.07 0.03526 -1.499e-05 6.728e-06 0.936 -1.129e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06103 Epoch 3902 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05347 0.8959 0.9201 9.3e-05 -4.175e-05 0.0775 7.009e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.007486 -0.006149 -0.007629 0.01526 0.9569 0.9638 0.01901 0.9186 0.9334 0.05754 ] Network output: [ 0.9648 0.158 0.0147 8.655e-05 -3.886e-05 -0.1019 6.523e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4884 0.07031 0.0334 0.2499 0.9805 0.9918 0.5811 0.9321 0.9813 0.5792 ] Network output: [ 0.01556 0.8994 0.9363 -0.0001132 5.08e-05 0.1328 -8.528e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01512 0.01025 0.0178 0.01649 0.99 0.9932 0.01557 0.9793 0.9882 0.02444 ] Network output: [ 0.08493 -0.2611 0.8086 -0.0002129 9.558e-05 1.282 -0.0001605 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5674 0.4892 0.4752 0.4386 0.9823 0.9927 0.5707 0.9379 0.9836 0.5728 ] Network output: [ -0.0512 0.1386 1.148 3.254e-05 -1.461e-05 0.8156 2.452e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2363 0.2313 0.2803 0.2594 0.99 0.9939 0.2366 0.9803 0.9892 0.2891 ] Network output: [ -0.04765 0.1059 1.137 0.0001376 -6.177e-05 0.8534 0.0001037 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2417 0.2409 0.2807 0.2668 0.9853 0.9913 0.2418 0.9656 0.983 0.2827 ] Network output: [ -0.02063 1.07 0.03514 -1.585e-05 7.117e-06 0.9358 -1.195e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06123 Epoch 3903 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05349 0.8954 0.9201 9.26e-05 -4.157e-05 0.07789 6.978e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00744 -0.006148 -0.007705 0.0152 0.9569 0.9638 0.01897 0.9186 0.9335 0.05753 ] Network output: [ 0.9646 0.1592 0.01485 9.056e-05 -4.066e-05 -0.1028 6.825e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4875 0.06987 0.03254 0.249 0.9805 0.9918 0.5807 0.9321 0.9813 0.5802 ] Network output: [ 0.01565 0.8988 0.9362 -0.0001145 5.139e-05 0.1332 -8.628e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01508 0.01021 0.01777 0.01642 0.99 0.9932 0.01553 0.9794 0.9882 0.02442 ] Network output: [ 0.08499 -0.2615 0.8086 -0.0002179 9.78e-05 1.282 -0.0001642 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5669 0.4885 0.4756 0.4379 0.9823 0.9927 0.5702 0.9379 0.9836 0.5738 ] Network output: [ -0.05113 0.1389 1.148 3.371e-05 -1.513e-05 0.8153 2.54e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.236 0.2309 0.2802 0.2589 0.99 0.9939 0.2362 0.9803 0.9892 0.289 ] Network output: [ -0.04756 0.1053 1.137 0.0001402 -6.294e-05 0.8536 0.0001057 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2414 0.2406 0.2807 0.2666 0.9853 0.9913 0.2415 0.9656 0.983 0.2827 ] Network output: [ -0.02079 1.071 0.03501 -1.672e-05 7.508e-06 0.9357 -1.26e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06143 Epoch 3904 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05352 0.895 0.9201 9.218e-05 -4.138e-05 0.07829 6.947e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.007393 -0.006146 -0.007782 0.01514 0.9569 0.9638 0.01894 0.9187 0.9335 0.05752 ] Network output: [ 0.9644 0.1603 0.01501 9.469e-05 -4.251e-05 -0.1036 7.136e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4866 0.06941 0.03166 0.248 0.9805 0.9918 0.5802 0.9322 0.9814 0.5813 ] Network output: [ 0.01574 0.8983 0.9361 -0.0001158 5.2e-05 0.1336 -8.729e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01504 0.01016 0.01773 0.01635 0.99 0.9932 0.01549 0.9794 0.9882 0.02441 ] Network output: [ 0.08507 -0.2618 0.8086 -0.0002229 0.0001001 1.282 -0.000168 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5664 0.4877 0.4761 0.4372 0.9823 0.9927 0.5697 0.938 0.9837 0.5749 ] Network output: [ -0.05106 0.1392 1.148 3.49e-05 -1.567e-05 0.815 2.63e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2356 0.2305 0.28 0.2585 0.99 0.9939 0.2359 0.9804 0.9892 0.2889 ] Network output: [ -0.04747 0.1047 1.137 0.0001428 -6.412e-05 0.8538 0.0001076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2412 0.2403 0.2807 0.2663 0.9853 0.9913 0.2412 0.9657 0.983 0.2827 ] Network output: [ -0.02096 1.071 0.03488 -1.76e-05 7.902e-06 0.9355 -1.327e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06163 Epoch 3905 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05355 0.8945 0.92 9.174e-05 -4.118e-05 0.07869 6.914e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.007346 -0.006144 -0.007861 0.01507 0.9569 0.9639 0.0189 0.9188 0.9336 0.05751 ] Network output: [ 0.9641 0.1614 0.01519 9.893e-05 -4.441e-05 -0.1045 7.456e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4857 0.06895 0.03076 0.2471 0.9805 0.9918 0.5797 0.9322 0.9814 0.5823 ] Network output: [ 0.01584 0.8978 0.9361 -0.0001172 5.262e-05 0.134 -8.834e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01499 0.01012 0.0177 0.01627 0.99 0.9932 0.01545 0.9794 0.9882 0.0244 ] Network output: [ 0.08514 -0.2622 0.8085 -0.0002281 0.0001024 1.282 -0.0001719 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5659 0.4869 0.4765 0.4365 0.9823 0.9927 0.5692 0.938 0.9837 0.576 ] Network output: [ -0.05099 0.1395 1.148 3.611e-05 -1.621e-05 0.8147 2.722e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2352 0.2302 0.2799 0.2581 0.99 0.9939 0.2355 0.9804 0.9892 0.2888 ] Network output: [ -0.04738 0.1041 1.137 0.0001455 -6.533e-05 0.854 0.0001097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2409 0.24 0.2807 0.2661 0.9853 0.9913 0.241 0.9657 0.983 0.2827 ] Network output: [ -0.02112 1.072 0.03476 -1.848e-05 8.298e-06 0.9353 -1.393e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06183 Epoch 3906 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05358 0.8941 0.92 9.128e-05 -4.098e-05 0.0791 6.879e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.007299 -0.006142 -0.00794 0.015 0.9569 0.9639 0.01887 0.9188 0.9336 0.0575 ] Network output: [ 0.9639 0.1625 0.01538 0.0001033 -4.637e-05 -0.1053 7.785e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4848 0.06848 0.02984 0.2461 0.9805 0.9918 0.5792 0.9323 0.9814 0.5834 ] Network output: [ 0.01593 0.8972 0.936 -0.0001186 5.326e-05 0.1344 -8.941e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01495 0.01007 0.01766 0.0162 0.99 0.9932 0.01541 0.9794 0.9882 0.02439 ] Network output: [ 0.08522 -0.2625 0.8085 -0.0002335 0.0001048 1.283 -0.000176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5653 0.4861 0.477 0.4357 0.9823 0.9928 0.5687 0.9381 0.9837 0.5772 ] Network output: [ -0.05092 0.1398 1.148 3.735e-05 -1.677e-05 0.8144 2.815e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2349 0.2298 0.2798 0.2576 0.99 0.9939 0.2351 0.9804 0.9893 0.2888 ] Network output: [ -0.04729 0.1035 1.137 0.0001483 -6.656e-05 0.8542 0.0001117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2406 0.2397 0.2806 0.2659 0.9853 0.9913 0.2407 0.9657 0.983 0.2827 ] Network output: [ -0.02129 1.073 0.03463 -1.937e-05 8.695e-06 0.9351 -1.46e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06203 Epoch 3907 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05361 0.8936 0.92 9.08e-05 -4.077e-05 0.07951 6.843e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.007251 -0.00614 -0.00802 0.01494 0.957 0.9639 0.01883 0.9189 0.9337 0.05749 ] Network output: [ 0.9637 0.1637 0.01559 0.0001078 -4.839e-05 -0.1062 8.123e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4838 0.068 0.0289 0.2451 0.9805 0.9918 0.5787 0.9324 0.9814 0.5845 ] Network output: [ 0.01604 0.8967 0.9359 -0.0001201 5.392e-05 0.1349 -9.051e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0149 0.01002 0.01763 0.01612 0.99 0.9932 0.01537 0.9794 0.9882 0.02438 ] Network output: [ 0.0853 -0.2628 0.8084 -0.0002389 0.0001073 1.283 -0.0001801 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5648 0.4853 0.4774 0.4349 0.9824 0.9928 0.5682 0.9381 0.9837 0.5783 ] Network output: [ -0.05085 0.1401 1.148 3.862e-05 -1.734e-05 0.8141 2.91e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2345 0.2294 0.2797 0.2572 0.99 0.9939 0.2348 0.9804 0.9893 0.2887 ] Network output: [ -0.04719 0.1028 1.138 0.0001511 -6.782e-05 0.8545 0.0001139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2403 0.2395 0.2806 0.2657 0.9853 0.9913 0.2404 0.9657 0.9831 0.2826 ] Network output: [ -0.02146 1.073 0.0345 -2.025e-05 9.093e-06 0.935 -1.526e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06223 Epoch 3908 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05364 0.8932 0.92 9.031e-05 -4.054e-05 0.07992 6.806e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.007203 -0.006138 -0.008101 0.01487 0.957 0.9639 0.01879 0.919 0.9338 0.05748 ] Network output: [ 0.9635 0.1649 0.01581 0.0001124 -5.046e-05 -0.1071 8.471e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4829 0.06751 0.02794 0.2441 0.9805 0.9918 0.5782 0.9324 0.9815 0.5856 ] Network output: [ 0.01614 0.8961 0.9358 -0.0001216 5.459e-05 0.1353 -9.164e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01486 0.009974 0.01759 0.01605 0.99 0.9932 0.01532 0.9794 0.9882 0.02436 ] Network output: [ 0.08538 -0.2631 0.8083 -0.0002446 0.0001098 1.283 -0.0001843 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5642 0.4844 0.4779 0.4341 0.9824 0.9928 0.5677 0.9382 0.9837 0.5795 ] Network output: [ -0.05077 0.1404 1.147 3.991e-05 -1.791e-05 0.8138 3.007e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2341 0.229 0.2795 0.2567 0.9901 0.9939 0.2344 0.9805 0.9893 0.2886 ] Network output: [ -0.0471 0.1021 1.138 0.0001539 -6.91e-05 0.8547 0.000116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.24 0.2392 0.2806 0.2654 0.9853 0.9913 0.2401 0.9657 0.9831 0.2826 ] Network output: [ -0.02163 1.074 0.03438 -2.114e-05 9.49e-06 0.9348 -1.593e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06243 Epoch 3909 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05368 0.8927 0.92 8.979e-05 -4.031e-05 0.08035 6.767e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.007154 -0.006135 -0.008184 0.01481 0.957 0.9639 0.01876 0.9191 0.9338 0.05747 ] Network output: [ 0.9632 0.166 0.01605 0.0001171 -5.259e-05 -0.108 8.828e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4819 0.06701 0.02696 0.2431 0.9805 0.9918 0.5776 0.9325 0.9815 0.5868 ] Network output: [ 0.01625 0.8955 0.9358 -0.0001231 5.528e-05 0.1357 -9.28e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01481 0.009925 0.01756 0.01597 0.99 0.9932 0.01528 0.9794 0.9883 0.02435 ] Network output: [ 0.08547 -0.2633 0.8082 -0.0002503 0.0001124 1.283 -0.0001886 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5636 0.4836 0.4783 0.4333 0.9824 0.9928 0.5671 0.9382 0.9838 0.5807 ] Network output: [ -0.0507 0.1408 1.147 4.122e-05 -1.85e-05 0.8135 3.106e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2338 0.2286 0.2794 0.2562 0.9901 0.9939 0.234 0.9805 0.9893 0.2885 ] Network output: [ -0.047 0.1014 1.138 0.0001568 -7.041e-05 0.855 0.0001182 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2398 0.2389 0.2805 0.2652 0.9853 0.9913 0.2398 0.9657 0.9831 0.2826 ] Network output: [ -0.02181 1.075 0.03425 -2.202e-05 9.886e-06 0.9347 -1.659e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06263 Epoch 3910 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05371 0.8922 0.92 8.924e-05 -4.006e-05 0.08078 6.726e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.007105 -0.006133 -0.008267 0.01474 0.957 0.9639 0.01872 0.9191 0.9339 0.05747 ] Network output: [ 0.9629 0.1672 0.01631 0.000122 -5.478e-05 -0.1089 9.195e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4808 0.0665 0.02596 0.2421 0.9805 0.9918 0.577 0.9325 0.9815 0.5879 ] Network output: [ 0.01636 0.8949 0.9357 -0.0001247 5.599e-05 0.1362 -9.399e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01477 0.009876 0.01752 0.01589 0.99 0.9932 0.01524 0.9795 0.9883 0.02434 ] Network output: [ 0.08556 -0.2636 0.8081 -0.0002562 0.000115 1.283 -0.0001931 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.563 0.4827 0.4788 0.4324 0.9824 0.9928 0.5665 0.9383 0.9838 0.5819 ] Network output: [ -0.05062 0.1411 1.147 4.256e-05 -1.911e-05 0.8132 3.207e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2334 0.2282 0.2792 0.2557 0.9901 0.9939 0.2337 0.9805 0.9893 0.2884 ] Network output: [ -0.04691 0.1007 1.138 0.0001598 -7.175e-05 0.8553 0.0001204 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2395 0.2386 0.2805 0.265 0.9853 0.9913 0.2395 0.9658 0.9831 0.2826 ] Network output: [ -0.02198 1.075 0.03413 -2.29e-05 1.028e-05 0.9345 -1.725e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06283 Epoch 3911 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05375 0.8917 0.92 8.867e-05 -3.981e-05 0.08121 6.683e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.007056 -0.006131 -0.008352 0.01467 0.957 0.9639 0.01868 0.9192 0.934 0.05746 ] Network output: [ 0.9627 0.1684 0.01659 0.000127 -5.702e-05 -0.1098 9.572e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4798 0.06599 0.02493 0.241 0.9805 0.9918 0.5765 0.9326 0.9815 0.5891 ] Network output: [ 0.01647 0.8943 0.9356 -0.0001263 5.672e-05 0.1366 -9.521e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01472 0.009826 0.01748 0.01581 0.99 0.9933 0.01519 0.9795 0.9883 0.02433 ] Network output: [ 0.08565 -0.2638 0.808 -0.0002622 0.0001177 1.283 -0.0001976 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5624 0.4817 0.4792 0.4316 0.9824 0.9928 0.5659 0.9383 0.9838 0.5831 ] Network output: [ -0.05054 0.1415 1.147 4.392e-05 -1.972e-05 0.8129 3.31e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.233 0.2278 0.2791 0.2552 0.9901 0.9939 0.2333 0.9805 0.9894 0.2883 ] Network output: [ -0.04681 0.1 1.139 0.0001628 -7.311e-05 0.8556 0.0001227 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2392 0.2383 0.2805 0.2647 0.9853 0.9913 0.2392 0.9658 0.9831 0.2826 ] Network output: [ -0.02216 1.076 0.034 -2.376e-05 1.067e-05 0.9344 -1.791e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06303 Epoch 3912 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05379 0.8912 0.9199 8.808e-05 -3.954e-05 0.08165 6.638e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.007006 -0.006128 -0.008438 0.0146 0.957 0.9639 0.01864 0.9193 0.934 0.05745 ] Network output: [ 0.9624 0.1696 0.01689 0.0001321 -5.933e-05 -0.1108 9.959e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4788 0.06546 0.02389 0.24 0.9805 0.9918 0.5759 0.9327 0.9815 0.5903 ] Network output: [ 0.01658 0.8937 0.9355 -0.000128 5.746e-05 0.1371 -9.646e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01467 0.009775 0.01744 0.01573 0.99 0.9933 0.01515 0.9795 0.9883 0.02432 ] Network output: [ 0.08575 -0.264 0.8079 -0.0002684 0.0001205 1.284 -0.0002023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5618 0.4808 0.4797 0.4307 0.9824 0.9928 0.5653 0.9384 0.9838 0.5844 ] Network output: [ -0.05046 0.1419 1.147 4.532e-05 -2.034e-05 0.8126 3.415e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2326 0.2274 0.2789 0.2547 0.9901 0.9939 0.2329 0.9805 0.9894 0.2882 ] Network output: [ -0.04671 0.09926 1.139 0.0001659 -7.449e-05 0.8559 0.0001251 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2389 0.238 0.2805 0.2645 0.9852 0.9913 0.2389 0.9658 0.9831 0.2826 ] Network output: [ -0.02234 1.076 0.03388 -2.462e-05 1.105e-05 0.9343 -1.855e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06324 Epoch 3913 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05384 0.8907 0.9199 8.746e-05 -3.926e-05 0.0821 6.591e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006956 -0.006125 -0.008524 0.01453 0.957 0.964 0.01861 0.9193 0.9341 0.05745 ] Network output: [ 0.9621 0.1708 0.0172 0.0001374 -6.169e-05 -0.1117 0.0001036 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4777 0.06493 0.02282 0.2389 0.9805 0.9918 0.5752 0.9327 0.9816 0.5915 ] Network output: [ 0.0167 0.8931 0.9354 -0.0001297 5.823e-05 0.1375 -9.775e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01463 0.009723 0.0174 0.01565 0.99 0.9933 0.0151 0.9795 0.9883 0.02431 ] Network output: [ 0.08585 -0.2642 0.8078 -0.0002747 0.0001233 1.284 -0.000207 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5611 0.4798 0.4801 0.4297 0.9824 0.9928 0.5647 0.9384 0.9838 0.5856 ] Network output: [ -0.05037 0.1422 1.146 4.674e-05 -2.098e-05 0.8123 3.522e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2322 0.227 0.2788 0.2542 0.9901 0.994 0.2325 0.9806 0.9894 0.2881 ] Network output: [ -0.04661 0.0985 1.139 0.0001691 -7.591e-05 0.8562 0.0001274 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2386 0.2377 0.2804 0.2642 0.9852 0.9913 0.2386 0.9658 0.9832 0.2826 ] Network output: [ -0.02252 1.077 0.03376 -2.546e-05 1.143e-05 0.9341 -1.919e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06344 Epoch 3914 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05388 0.8901 0.9199 8.681e-05 -3.897e-05 0.08255 6.542e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006906 -0.006122 -0.008612 0.01446 0.957 0.964 0.01857 0.9194 0.9341 0.05744 ] Network output: [ 0.9618 0.172 0.01754 0.0001428 -6.411e-05 -0.1126 0.0001076 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4766 0.06439 0.02173 0.2378 0.9805 0.9918 0.5746 0.9328 0.9816 0.5928 ] Network output: [ 0.01682 0.8925 0.9353 -0.0001315 5.902e-05 0.138 -9.907e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01458 0.009671 0.01737 0.01557 0.99 0.9933 0.01506 0.9795 0.9883 0.0243 ] Network output: [ 0.08595 -0.2644 0.8076 -0.0002812 0.0001262 1.284 -0.0002119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5605 0.4788 0.4805 0.4288 0.9824 0.9928 0.5641 0.9385 0.9839 0.5869 ] Network output: [ -0.05029 0.1426 1.146 4.819e-05 -2.163e-05 0.812 3.632e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2319 0.2266 0.2786 0.2537 0.9901 0.994 0.2321 0.9806 0.9894 0.288 ] Network output: [ -0.0465 0.09772 1.139 0.0001723 -7.735e-05 0.8565 0.0001298 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2383 0.2374 0.2804 0.2639 0.9852 0.9914 0.2383 0.9658 0.9832 0.2825 ] Network output: [ -0.0227 1.078 0.03364 -2.629e-05 1.18e-05 0.934 -1.981e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06364 Epoch 3915 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05393 0.8896 0.9199 8.613e-05 -3.867e-05 0.08301 6.491e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006855 -0.006119 -0.008701 0.01439 0.957 0.964 0.01853 0.9195 0.9342 0.05743 ] Network output: [ 0.9615 0.1733 0.01789 0.0001483 -6.66e-05 -0.1136 0.0001118 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4754 0.06384 0.02062 0.2367 0.9805 0.9918 0.5739 0.9328 0.9816 0.594 ] Network output: [ 0.01695 0.8919 0.9352 -0.0001333 5.982e-05 0.1385 -0.0001004 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01453 0.009618 0.01733 0.01549 0.99 0.9933 0.01501 0.9795 0.9883 0.02429 ] Network output: [ 0.08606 -0.2645 0.8075 -0.0002879 0.0001292 1.284 -0.0002169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5598 0.4778 0.481 0.4278 0.9824 0.9928 0.5634 0.9385 0.9839 0.5882 ] Network output: [ -0.0502 0.143 1.146 4.967e-05 -2.23e-05 0.8116 3.743e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2315 0.2262 0.2784 0.2531 0.9901 0.994 0.2318 0.9806 0.9894 0.2879 ] Network output: [ -0.0464 0.09691 1.14 0.0001756 -7.882e-05 0.8568 0.0001323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.238 0.2371 0.2804 0.2637 0.9852 0.9914 0.238 0.9658 0.9832 0.2825 ] Network output: [ -0.02288 1.078 0.03352 -2.71e-05 1.216e-05 0.9339 -2.042e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06384 Epoch 3916 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05397 0.8891 0.9199 8.541e-05 -3.835e-05 0.08347 6.437e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006804 -0.006116 -0.008791 0.01432 0.9571 0.964 0.01849 0.9196 0.9343 0.05743 ] Network output: [ 0.9612 0.1745 0.01826 0.000154 -6.914e-05 -0.1146 0.0001161 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4743 0.06328 0.01948 0.2356 0.9805 0.9918 0.5732 0.9329 0.9816 0.5953 ] Network output: [ 0.01708 0.8912 0.9351 -0.0001351 6.065e-05 0.1389 -0.0001018 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01448 0.009565 0.01729 0.01541 0.99 0.9933 0.01497 0.9795 0.9883 0.02428 ] Network output: [ 0.08617 -0.2646 0.8073 -0.0002947 0.0001323 1.284 -0.0002221 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5591 0.4768 0.4814 0.4268 0.9824 0.9928 0.5628 0.9386 0.9839 0.5896 ] Network output: [ -0.05011 0.1434 1.146 5.117e-05 -2.297e-05 0.8113 3.857e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2311 0.2258 0.2783 0.2526 0.9901 0.994 0.2314 0.9806 0.9894 0.2878 ] Network output: [ -0.0463 0.09609 1.14 0.0001789 -8.031e-05 0.8572 0.0001348 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2377 0.2368 0.2803 0.2634 0.9852 0.9914 0.2377 0.9659 0.9832 0.2825 ] Network output: [ -0.02307 1.079 0.03341 -2.788e-05 1.252e-05 0.9338 -2.101e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06404 Epoch 3917 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05402 0.8885 0.9198 8.467e-05 -3.801e-05 0.08394 6.381e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006753 -0.006113 -0.008882 0.01425 0.9571 0.964 0.01845 0.9196 0.9343 0.05742 ] Network output: [ 0.9609 0.1757 0.01866 0.0001598 -7.175e-05 -0.1155 0.0001204 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4731 0.06272 0.01832 0.2345 0.9806 0.9918 0.5725 0.933 0.9817 0.5966 ] Network output: [ 0.01721 0.8906 0.935 -0.000137 6.15e-05 0.1394 -0.0001032 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01443 0.009511 0.01725 0.01532 0.99 0.9933 0.01492 0.9795 0.9883 0.02427 ] Network output: [ 0.08628 -0.2647 0.8072 -0.0003016 0.0001354 1.284 -0.0002273 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5584 0.4757 0.4819 0.4257 0.9824 0.9928 0.5621 0.9387 0.9839 0.5909 ] Network output: [ -0.05001 0.1438 1.145 5.271e-05 -2.366e-05 0.811 3.973e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2307 0.2254 0.2781 0.252 0.9901 0.994 0.231 0.9806 0.9895 0.2877 ] Network output: [ -0.04619 0.09526 1.14 0.0001823 -8.184e-05 0.8576 0.0001374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2374 0.2365 0.2803 0.2631 0.9852 0.9914 0.2374 0.9659 0.9832 0.2825 ] Network output: [ -0.02325 1.079 0.0333 -2.864e-05 1.286e-05 0.9337 -2.158e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06423 Epoch 3918 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05408 0.8879 0.9198 8.389e-05 -3.766e-05 0.08442 6.322e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006702 -0.00611 -0.008975 0.01418 0.9571 0.964 0.01841 0.9197 0.9344 0.05742 ] Network output: [ 0.9606 0.177 0.01908 0.0001658 -7.442e-05 -0.1165 0.0001249 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4719 0.06214 0.01714 0.2333 0.9806 0.9918 0.5718 0.933 0.9817 0.5979 ] Network output: [ 0.01734 0.8899 0.9349 -0.0001389 6.238e-05 0.1399 -0.0001047 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01439 0.009456 0.0172 0.01524 0.9901 0.9933 0.01487 0.9796 0.9884 0.02426 ] Network output: [ 0.08639 -0.2647 0.807 -0.0003087 0.0001386 1.284 -0.0002327 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5576 0.4746 0.4823 0.4246 0.9824 0.9928 0.5614 0.9387 0.9839 0.5923 ] Network output: [ -0.04992 0.1443 1.145 5.428e-05 -2.437e-05 0.8107 4.091e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2303 0.225 0.2779 0.2515 0.9901 0.994 0.2306 0.9807 0.9895 0.2876 ] Network output: [ -0.04609 0.0944 1.141 0.0001858 -8.339e-05 0.8579 0.00014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2371 0.2361 0.2803 0.2629 0.9852 0.9914 0.2371 0.9659 0.9832 0.2825 ] Network output: [ -0.02343 1.08 0.0332 -2.936e-05 1.318e-05 0.9336 -2.213e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06443 Epoch 3919 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05413 0.8874 0.9198 8.308e-05 -3.73e-05 0.08489 6.261e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00665 -0.006107 -0.009068 0.01411 0.9571 0.964 0.01837 0.9198 0.9345 0.05742 ] Network output: [ 0.9602 0.1782 0.01951 0.0001718 -7.714e-05 -0.1175 0.0001295 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4707 0.06156 0.01594 0.2322 0.9806 0.9918 0.5711 0.9331 0.9817 0.5993 ] Network output: [ 0.01748 0.8893 0.9348 -0.0001409 6.327e-05 0.1404 -0.0001062 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01434 0.0094 0.01716 0.01515 0.9901 0.9933 0.01483 0.9796 0.9884 0.02425 ] Network output: [ 0.08651 -0.2647 0.8068 -0.000316 0.0001419 1.284 -0.0002382 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5569 0.4735 0.4827 0.4235 0.9824 0.9928 0.5606 0.9388 0.984 0.5937 ] Network output: [ -0.04982 0.1447 1.145 5.588e-05 -2.509e-05 0.8104 4.212e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2299 0.2245 0.2777 0.2509 0.9901 0.994 0.2302 0.9807 0.9895 0.2874 ] Network output: [ -0.04598 0.09353 1.141 0.0001893 -8.498e-05 0.8583 0.0001427 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2367 0.2358 0.2802 0.2626 0.9852 0.9914 0.2368 0.9659 0.9833 0.2825 ] Network output: [ -0.02362 1.08 0.0331 -3.005e-05 1.349e-05 0.9336 -2.265e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06462 Epoch 3920 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05419 0.8868 0.9198 8.223e-05 -3.691e-05 0.08538 6.197e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006598 -0.006103 -0.009163 0.01404 0.9571 0.964 0.01832 0.9199 0.9345 0.05741 ] Network output: [ 0.9599 0.1795 0.01997 0.000178 -7.993e-05 -0.1185 0.0001342 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4695 0.06097 0.01471 0.231 0.9806 0.9918 0.5703 0.9332 0.9818 0.6007 ] Network output: [ 0.01761 0.8886 0.9347 -0.000143 6.419e-05 0.1409 -0.0001078 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01429 0.009344 0.01712 0.01506 0.9901 0.9933 0.01478 0.9796 0.9884 0.02424 ] Network output: [ 0.08662 -0.2647 0.8066 -0.0003235 0.0001452 1.284 -0.0002438 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5561 0.4723 0.4831 0.4224 0.9824 0.9928 0.5599 0.9388 0.984 0.5951 ] Network output: [ -0.04972 0.1452 1.144 5.752e-05 -2.582e-05 0.81 4.335e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2295 0.2241 0.2775 0.2503 0.9901 0.994 0.2298 0.9807 0.9895 0.2873 ] Network output: [ -0.04587 0.09263 1.141 0.0001929 -8.659e-05 0.8587 0.0001454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2364 0.2355 0.2802 0.2623 0.9852 0.9914 0.2365 0.9659 0.9833 0.2825 ] Network output: [ -0.0238 1.081 0.033 -3.07e-05 1.378e-05 0.9335 -2.314e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06482 Epoch 3921 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05425 0.8862 0.9198 8.133e-05 -3.651e-05 0.08587 6.13e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006547 -0.006099 -0.009258 0.01397 0.9571 0.9641 0.01828 0.92 0.9346 0.05741 ] Network output: [ 0.9595 0.1807 0.02045 0.0001844 -8.278e-05 -0.1194 0.000139 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4682 0.06037 0.01346 0.2298 0.9806 0.9918 0.5695 0.9332 0.9818 0.602 ] Network output: [ 0.01775 0.8879 0.9346 -0.0001451 6.514e-05 0.1414 -0.0001093 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01424 0.009288 0.01708 0.01497 0.9901 0.9933 0.01473 0.9796 0.9884 0.02423 ] Network output: [ 0.08674 -0.2647 0.8063 -0.0003311 0.0001486 1.284 -0.0002495 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5553 0.4711 0.4836 0.4213 0.9824 0.9928 0.5591 0.9389 0.984 0.5965 ] Network output: [ -0.04962 0.1456 1.144 5.919e-05 -2.657e-05 0.8097 4.461e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2291 0.2237 0.2774 0.2497 0.9901 0.994 0.2294 0.9807 0.9895 0.2872 ] Network output: [ -0.04576 0.09172 1.141 0.0001966 -8.824e-05 0.8591 0.0001481 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2361 0.2352 0.2802 0.262 0.9852 0.9914 0.2362 0.966 0.9833 0.2824 ] Network output: [ -0.02399 1.081 0.03291 -3.131e-05 1.406e-05 0.9335 -2.36e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06501 Epoch 3922 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05431 0.8856 0.9197 8.04e-05 -3.61e-05 0.08636 6.059e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006495 -0.006095 -0.009355 0.0139 0.9571 0.9641 0.01824 0.92 0.9347 0.05741 ] Network output: [ 0.9591 0.182 0.02095 0.0001909 -8.568e-05 -0.1204 0.0001438 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4669 0.05976 0.01219 0.2287 0.9806 0.9918 0.5687 0.9333 0.9818 0.6035 ] Network output: [ 0.0179 0.8872 0.9345 -0.0001472 6.61e-05 0.1419 -0.000111 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01419 0.009231 0.01703 0.01489 0.9901 0.9933 0.01468 0.9796 0.9884 0.02423 ] Network output: [ 0.08686 -0.2646 0.8061 -0.0003389 0.0001521 1.283 -0.0002554 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5545 0.4699 0.484 0.4201 0.9824 0.9928 0.5583 0.9389 0.984 0.598 ] Network output: [ -0.04952 0.1461 1.144 6.089e-05 -2.734e-05 0.8094 4.589e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2287 0.2233 0.2772 0.2491 0.9901 0.994 0.229 0.9807 0.9895 0.2871 ] Network output: [ -0.04565 0.0908 1.142 0.0002003 -8.991e-05 0.8596 0.0001509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2358 0.2349 0.2801 0.2617 0.9852 0.9914 0.2359 0.966 0.9833 0.2824 ] Network output: [ -0.02417 1.082 0.03282 -3.187e-05 1.431e-05 0.9335 -2.402e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06519 Epoch 3923 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05437 0.885 0.9197 7.943e-05 -3.566e-05 0.08686 5.986e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006443 -0.006092 -0.009452 0.01382 0.9572 0.9641 0.0182 0.9201 0.9347 0.0574 ] Network output: [ 0.9588 0.1833 0.02147 0.0001975 -8.865e-05 -0.1214 0.0001488 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4656 0.05915 0.01089 0.2275 0.9806 0.9918 0.5678 0.9334 0.9818 0.6049 ] Network output: [ 0.01805 0.8865 0.9344 -0.0001495 6.71e-05 0.1424 -0.0001126 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01413 0.009173 0.01699 0.0148 0.9901 0.9933 0.01463 0.9796 0.9884 0.02422 ] Network output: [ 0.08699 -0.2645 0.8059 -0.0003469 0.0001557 1.283 -0.0002614 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5536 0.4687 0.4844 0.4188 0.9824 0.9928 0.5575 0.939 0.9841 0.5995 ] Network output: [ -0.04941 0.1466 1.143 6.263e-05 -2.811e-05 0.809 4.72e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2283 0.2228 0.277 0.2485 0.9901 0.994 0.2286 0.9808 0.9896 0.287 ] Network output: [ -0.04554 0.08985 1.142 0.0002041 -9.162e-05 0.86 0.0001538 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2355 0.2345 0.2801 0.2614 0.9852 0.9914 0.2356 0.966 0.9833 0.2824 ] Network output: [ -0.02436 1.082 0.03274 -3.237e-05 1.453e-05 0.9334 -2.44e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06538 Epoch 3924 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05443 0.8844 0.9197 7.841e-05 -3.52e-05 0.08736 5.909e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006391 -0.006087 -0.009551 0.01375 0.9572 0.9641 0.01815 0.9202 0.9348 0.0574 ] Network output: [ 0.9584 0.1845 0.02202 0.0002042 -9.167e-05 -0.1224 0.0001539 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4643 0.05852 0.009572 0.2263 0.9806 0.9919 0.567 0.9334 0.9819 0.6063 ] Network output: [ 0.01819 0.8858 0.9343 -0.0001517 6.812e-05 0.1429 -0.0001143 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01408 0.009115 0.01695 0.01471 0.9901 0.9933 0.01458 0.9796 0.9884 0.02421 ] Network output: [ 0.08711 -0.2643 0.8056 -0.000355 0.0001594 1.283 -0.0002675 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5528 0.4674 0.4848 0.4176 0.9824 0.9928 0.5567 0.9391 0.9841 0.601 ] Network output: [ -0.0493 0.1471 1.143 6.44e-05 -2.891e-05 0.8087 4.853e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2279 0.2224 0.2767 0.2479 0.9902 0.994 0.2282 0.9808 0.9896 0.2868 ] Network output: [ -0.04543 0.08889 1.142 0.000208 -9.336e-05 0.8605 0.0001567 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2352 0.2342 0.2801 0.2611 0.9852 0.9914 0.2352 0.966 0.9834 0.2824 ] Network output: [ -0.02454 1.083 0.03266 -3.282e-05 1.474e-05 0.9334 -2.474e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06556 Epoch 3925 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0545 0.8838 0.9197 7.734e-05 -3.472e-05 0.08787 5.829e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006338 -0.006083 -0.009651 0.01368 0.9572 0.9641 0.01811 0.9203 0.9349 0.0574 ] Network output: [ 0.958 0.1858 0.02258 0.000211 -9.474e-05 -0.1234 0.000159 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.463 0.0579 0.008232 0.2251 0.9806 0.9919 0.5661 0.9335 0.9819 0.6078 ] Network output: [ 0.01835 0.8851 0.9342 -0.0001541 6.916e-05 0.1434 -0.0001161 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01403 0.009056 0.0169 0.01462 0.9901 0.9933 0.01453 0.9797 0.9884 0.0242 ] Network output: [ 0.08723 -0.2641 0.8053 -0.0003633 0.0001631 1.283 -0.0002738 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5519 0.4661 0.4852 0.4163 0.9824 0.9928 0.5558 0.9391 0.9841 0.6025 ] Network output: [ -0.04919 0.1476 1.143 6.621e-05 -2.972e-05 0.8083 4.99e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2275 0.222 0.2765 0.2472 0.9902 0.994 0.2278 0.9808 0.9896 0.2867 ] Network output: [ -0.04532 0.08792 1.143 0.0002119 -9.513e-05 0.8609 0.0001597 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2348 0.2339 0.28 0.2608 0.9852 0.9914 0.2349 0.966 0.9834 0.2824 ] Network output: [ -0.02472 1.083 0.03259 -3.321e-05 1.491e-05 0.9335 -2.502e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06574 Epoch 3926 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05457 0.8831 0.9196 7.622e-05 -3.422e-05 0.08838 5.745e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006286 -0.006079 -0.009752 0.01361 0.9572 0.9641 0.01807 0.9204 0.935 0.0574 ] Network output: [ 0.9576 0.187 0.02317 0.000218 -9.787e-05 -0.1244 0.0001643 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4616 0.05726 0.006869 0.2238 0.9806 0.9919 0.5652 0.9336 0.9819 0.6093 ] Network output: [ 0.0185 0.8844 0.934 -0.0001564 7.023e-05 0.1439 -0.0001179 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01398 0.008996 0.01686 0.01453 0.9901 0.9933 0.01448 0.9797 0.9884 0.0242 ] Network output: [ 0.08735 -0.2639 0.805 -0.0003718 0.0001669 1.283 -0.0002802 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.551 0.4648 0.4856 0.415 0.9824 0.9928 0.555 0.9392 0.9841 0.6041 ] Network output: [ -0.04907 0.1482 1.142 6.805e-05 -3.055e-05 0.808 5.129e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2271 0.2215 0.2763 0.2466 0.9902 0.994 0.2274 0.9808 0.9896 0.2866 ] Network output: [ -0.0452 0.08693 1.143 0.0002159 -9.693e-05 0.8614 0.0001627 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2345 0.2336 0.28 0.2605 0.9852 0.9914 0.2346 0.9661 0.9834 0.2823 ] Network output: [ -0.02489 1.084 0.03253 -3.352e-05 1.505e-05 0.9335 -2.526e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06591 Epoch 3927 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05464 0.8825 0.9196 7.506e-05 -3.37e-05 0.0889 5.657e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006234 -0.006075 -0.009853 0.01353 0.9572 0.9641 0.01802 0.9205 0.935 0.0574 ] Network output: [ 0.9571 0.1883 0.02378 0.0002251 -0.000101 -0.1254 0.0001696 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4602 0.05662 0.005485 0.2226 0.9806 0.9919 0.5642 0.9337 0.9819 0.6108 ] Network output: [ 0.01865 0.8837 0.9339 -0.0001589 7.133e-05 0.1444 -0.0001197 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01393 0.008937 0.01681 0.01443 0.9901 0.9933 0.01444 0.9797 0.9885 0.02419 ] Network output: [ 0.08748 -0.2636 0.8047 -0.0003805 0.0001708 1.282 -0.0002867 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5501 0.4634 0.486 0.4137 0.9824 0.9928 0.5541 0.9393 0.9842 0.6056 ] Network output: [ -0.04896 0.1487 1.142 6.994e-05 -3.14e-05 0.8077 5.271e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2267 0.2211 0.2761 0.2459 0.9902 0.994 0.227 0.9808 0.9896 0.2864 ] Network output: [ -0.04509 0.08592 1.143 0.00022 -9.876e-05 0.8619 0.0001658 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2342 0.2332 0.2799 0.2602 0.9852 0.9914 0.2343 0.9661 0.9834 0.2823 ] Network output: [ -0.02507 1.084 0.03248 -3.376e-05 1.516e-05 0.9335 -2.544e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06609 Epoch 3928 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05471 0.8819 0.9196 7.384e-05 -3.315e-05 0.08941 5.565e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006182 -0.00607 -0.009956 0.01346 0.9572 0.9642 0.01798 0.9205 0.9351 0.0574 ] Network output: [ 0.9567 0.1895 0.02442 0.0002323 -0.0001043 -0.1264 0.0001751 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4588 0.05597 0.00408 0.2214 0.9806 0.9919 0.5633 0.9337 0.982 0.6124 ] Network output: [ 0.01881 0.883 0.9338 -0.0001614 7.245e-05 0.145 -0.0001216 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01388 0.008876 0.01676 0.01434 0.9901 0.9933 0.01439 0.9797 0.9885 0.02419 ] Network output: [ 0.0876 -0.2633 0.8044 -0.0003893 0.0001748 1.282 -0.0002934 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5491 0.462 0.4864 0.4123 0.9824 0.9928 0.5532 0.9393 0.9842 0.6072 ] Network output: [ -0.04884 0.1493 1.141 7.186e-05 -3.226e-05 0.8073 5.416e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2263 0.2207 0.2759 0.2452 0.9902 0.994 0.2266 0.9809 0.9897 0.2863 ] Network output: [ -0.04497 0.0849 1.144 0.0002241 -0.0001006 0.8624 0.0001689 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2339 0.2329 0.2799 0.2598 0.9852 0.9914 0.234 0.9661 0.9834 0.2823 ] Network output: [ -0.02524 1.084 0.03243 -3.392e-05 1.523e-05 0.9336 -2.556e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06625 Epoch 3929 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05478 0.8812 0.9196 7.257e-05 -3.258e-05 0.08993 5.469e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006131 -0.006065 -0.01006 0.01339 0.9572 0.9642 0.01793 0.9206 0.9352 0.0574 ] Network output: [ 0.9563 0.1908 0.02507 0.0002396 -0.0001076 -0.1274 0.0001806 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4574 0.05531 0.002654 0.2201 0.9806 0.9919 0.5623 0.9338 0.982 0.6139 ] Network output: [ 0.01897 0.8822 0.9337 -0.0001639 7.36e-05 0.1455 -0.0001236 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01382 0.008816 0.01672 0.01425 0.9901 0.9933 0.01434 0.9797 0.9885 0.02418 ] Network output: [ 0.08772 -0.2629 0.8041 -0.0003983 0.0001788 1.282 -0.0003002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5481 0.4606 0.4868 0.4109 0.9824 0.9928 0.5522 0.9394 0.9842 0.6088 ] Network output: [ -0.04871 0.1498 1.141 7.383e-05 -3.314e-05 0.807 5.564e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2259 0.2203 0.2756 0.2446 0.9902 0.994 0.2262 0.9809 0.9897 0.2861 ] Network output: [ -0.04485 0.08387 1.144 0.0002284 -0.0001025 0.8629 0.0001721 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2336 0.2326 0.2798 0.2595 0.9852 0.9914 0.2336 0.9661 0.9834 0.2823 ] Network output: [ -0.02541 1.085 0.03239 -3.4e-05 1.526e-05 0.9337 -2.562e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06641 Epoch 3930 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05486 0.8806 0.9195 7.124e-05 -3.198e-05 0.09046 5.369e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006079 -0.006061 -0.01016 0.01331 0.9573 0.9642 0.01789 0.9207 0.9353 0.0574 ] Network output: [ 0.9559 0.192 0.02574 0.000247 -0.0001109 -0.1284 0.0001861 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4559 0.05465 0.001208 0.2189 0.9806 0.9919 0.5613 0.9339 0.982 0.6155 ] Network output: [ 0.01913 0.8815 0.9335 -0.0001666 7.478e-05 0.146 -0.0001255 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01377 0.008755 0.01667 0.01415 0.9901 0.9933 0.01428 0.9797 0.9885 0.02418 ] Network output: [ 0.08783 -0.2625 0.8038 -0.0004075 0.0001829 1.281 -0.0003071 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5472 0.4592 0.4872 0.4095 0.9824 0.9928 0.5513 0.9395 0.9842 0.6105 ] Network output: [ -0.04859 0.1504 1.14 7.583e-05 -3.404e-05 0.8066 5.715e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2255 0.2198 0.2754 0.2439 0.9902 0.994 0.2258 0.9809 0.9897 0.286 ] Network output: [ -0.04473 0.08283 1.144 0.0002327 -0.0001044 0.8634 0.0001753 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2332 0.2323 0.2798 0.2592 0.9852 0.9914 0.2333 0.9662 0.9835 0.2823 ] Network output: [ -0.02558 1.085 0.03236 -3.398e-05 1.526e-05 0.9338 -2.561e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06657 Epoch 3931 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05494 0.8799 0.9195 6.986e-05 -3.136e-05 0.09099 5.265e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006028 -0.006056 -0.01027 0.01324 0.9573 0.9642 0.01784 0.9208 0.9353 0.0574 ] Network output: [ 0.9554 0.1932 0.02644 0.0002545 -0.0001142 -0.1294 0.0001918 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4545 0.05398 -0.0002585 0.2176 0.9806 0.9919 0.5602 0.934 0.9821 0.6171 ] Network output: [ 0.01929 0.8808 0.9334 -0.0001693 7.599e-05 0.1465 -0.0001276 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01372 0.008693 0.01662 0.01406 0.9901 0.9933 0.01423 0.9797 0.9885 0.02417 ] Network output: [ 0.08795 -0.262 0.8035 -0.0004168 0.0001871 1.281 -0.0003141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5461 0.4577 0.4876 0.408 0.9824 0.9928 0.5503 0.9395 0.9843 0.6121 ] Network output: [ -0.04846 0.151 1.14 7.788e-05 -3.496e-05 0.8062 5.869e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2251 0.2194 0.2752 0.2432 0.9902 0.994 0.2254 0.9809 0.9897 0.2858 ] Network output: [ -0.04462 0.08177 1.144 0.000237 -0.0001064 0.864 0.0001786 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2329 0.2319 0.2797 0.2588 0.9852 0.9914 0.233 0.9662 0.9835 0.2822 ] Network output: [ -0.02574 1.085 0.03233 -3.387e-05 1.52e-05 0.9339 -2.552e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06672 Epoch 3932 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05502 0.8792 0.9195 6.841e-05 -3.071e-05 0.09151 5.156e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005977 -0.006051 -0.01038 0.01317 0.9573 0.9642 0.01779 0.9209 0.9354 0.0574 ] Network output: [ 0.955 0.1944 0.02715 0.0002621 -0.0001176 -0.1304 0.0001975 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.453 0.05331 -0.001744 0.2164 0.9806 0.9919 0.5592 0.934 0.9821 0.6187 ] Network output: [ 0.01946 0.88 0.9333 -0.000172 7.722e-05 0.1471 -0.0001296 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01367 0.008631 0.01658 0.01397 0.9901 0.9933 0.01418 0.9797 0.9885 0.02417 ] Network output: [ 0.08806 -0.2615 0.8031 -0.0004263 0.0001914 1.281 -0.0003213 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5451 0.4562 0.4879 0.4065 0.9824 0.9928 0.5493 0.9396 0.9843 0.6138 ] Network output: [ -0.04833 0.1517 1.139 7.996e-05 -3.59e-05 0.8059 6.026e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2247 0.219 0.2749 0.2425 0.9902 0.9941 0.2251 0.9809 0.9897 0.2857 ] Network output: [ -0.04449 0.08071 1.145 0.0002415 -0.0001084 0.8645 0.000182 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2326 0.2316 0.2797 0.2585 0.9852 0.9914 0.2327 0.9662 0.9835 0.2822 ] Network output: [ -0.02589 1.085 0.03231 -3.365e-05 1.511e-05 0.934 -2.536e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06687 Epoch 3933 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0551 0.8786 0.9195 6.691e-05 -3.004e-05 0.09204 5.042e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005926 -0.006045 -0.01048 0.01309 0.9573 0.9642 0.01775 0.921 0.9355 0.0574 ] Network output: [ 0.9545 0.1956 0.02789 0.0002697 -0.0001211 -0.1314 0.0002033 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4515 0.05264 -0.003248 0.2151 0.9806 0.9919 0.5581 0.9341 0.9821 0.6204 ] Network output: [ 0.01962 0.8793 0.9332 -0.0001748 7.848e-05 0.1476 -0.0001318 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01361 0.008569 0.01653 0.01387 0.9901 0.9934 0.01413 0.9798 0.9885 0.02416 ] Network output: [ 0.08816 -0.261 0.8028 -0.000436 0.0001957 1.28 -0.0003286 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5441 0.4547 0.4883 0.405 0.9824 0.9928 0.5483 0.9397 0.9843 0.6155 ] Network output: [ -0.0482 0.1523 1.139 8.21e-05 -3.686e-05 0.8055 6.187e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2243 0.2186 0.2747 0.2418 0.9902 0.9941 0.2247 0.981 0.9897 0.2855 ] Network output: [ -0.04437 0.07964 1.145 0.000246 -0.0001104 0.8651 0.0001854 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2323 0.2313 0.2796 0.2582 0.9852 0.9914 0.2324 0.9662 0.9835 0.2822 ] Network output: [ -0.02604 1.085 0.0323 -3.333e-05 1.496e-05 0.9342 -2.512e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06701 Epoch 3934 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05518 0.8779 0.9194 6.534e-05 -2.933e-05 0.09258 4.924e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005876 -0.00604 -0.01059 0.01302 0.9573 0.9642 0.0177 0.9211 0.9356 0.0574 ] Network output: [ 0.954 0.1968 0.02864 0.0002775 -0.0001246 -0.1324 0.0002091 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.45 0.05196 -0.00477 0.2138 0.9806 0.9919 0.557 0.9342 0.9822 0.622 ] Network output: [ 0.01979 0.8786 0.933 -0.0001777 7.978e-05 0.1481 -0.0001339 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01356 0.008507 0.01648 0.01378 0.9901 0.9934 0.01408 0.9798 0.9885 0.02416 ] Network output: [ 0.08827 -0.2604 0.8024 -0.0004459 0.0002002 1.28 -0.000336 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.543 0.4531 0.4887 0.4034 0.9824 0.9928 0.5472 0.9397 0.9843 0.6172 ] Network output: [ -0.04806 0.153 1.138 8.427e-05 -3.783e-05 0.8052 6.351e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2239 0.2181 0.2744 0.2411 0.9902 0.9941 0.2243 0.981 0.9898 0.2854 ] Network output: [ -0.04425 0.07856 1.145 0.0002506 -0.0001125 0.8656 0.0001888 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.232 0.231 0.2796 0.2578 0.9852 0.9914 0.232 0.9663 0.9835 0.2821 ] Network output: [ -0.02619 1.086 0.0323 -3.29e-05 1.477e-05 0.9344 -2.479e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06715 Epoch 3935 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05526 0.8772 0.9194 6.37e-05 -2.86e-05 0.09311 4.801e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005826 -0.006035 -0.0107 0.01295 0.9573 0.9643 0.01766 0.9212 0.9356 0.05741 ] Network output: [ 0.9536 0.1979 0.02941 0.0002853 -0.0001281 -0.1333 0.000215 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4484 0.05127 -0.006309 0.2126 0.9807 0.9919 0.5558 0.9343 0.9822 0.6237 ] Network output: [ 0.01995 0.8778 0.9329 -0.0001806 8.109e-05 0.1486 -0.0001361 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01351 0.008444 0.01643 0.01368 0.9901 0.9934 0.01403 0.9798 0.9886 0.02416 ] Network output: [ 0.08836 -0.2598 0.8021 -0.0004559 0.0002047 1.279 -0.0003436 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5419 0.4515 0.489 0.4019 0.9824 0.9928 0.5462 0.9398 0.9844 0.6189 ] Network output: [ -0.04792 0.1536 1.138 8.649e-05 -3.883e-05 0.8048 6.518e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2236 0.2177 0.2742 0.2404 0.9902 0.9941 0.2239 0.981 0.9898 0.2852 ] Network output: [ -0.04413 0.07748 1.146 0.0002552 -0.0001146 0.8662 0.0001923 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2317 0.2306 0.2795 0.2575 0.9852 0.9914 0.2317 0.9663 0.9836 0.2821 ] Network output: [ -0.02633 1.086 0.03231 -3.234e-05 1.452e-05 0.9346 -2.437e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06728 Epoch 3936 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05535 0.8765 0.9194 6.2e-05 -2.783e-05 0.09364 4.673e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005776 -0.006029 -0.01081 0.01288 0.9574 0.9643 0.01761 0.9213 0.9357 0.05741 ] Network output: [ 0.9531 0.199 0.0302 0.0002931 -0.0001316 -0.1343 0.0002209 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4469 0.05058 -0.007865 0.2113 0.9807 0.9919 0.5547 0.9344 0.9822 0.6254 ] Network output: [ 0.02012 0.8771 0.9328 -0.0001836 8.244e-05 0.1492 -0.0001384 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01346 0.008381 0.01638 0.01358 0.9902 0.9934 0.01398 0.9798 0.9886 0.02416 ] Network output: [ 0.08845 -0.2591 0.8017 -0.000466 0.0002092 1.279 -0.0003512 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5408 0.4499 0.4894 0.4003 0.9824 0.9929 0.5451 0.9399 0.9844 0.6207 ] Network output: [ -0.04778 0.1543 1.137 8.875e-05 -3.985e-05 0.8044 6.689e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2232 0.2173 0.2739 0.2396 0.9902 0.9941 0.2235 0.981 0.9898 0.285 ] Network output: [ -0.044 0.07639 1.146 0.0002599 -0.0001167 0.8668 0.0001959 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2313 0.2303 0.2795 0.2571 0.9852 0.9914 0.2314 0.9663 0.9836 0.2821 ] Network output: [ -0.02646 1.086 0.03232 -3.166e-05 1.421e-05 0.9348 -2.386e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0674 Epoch 3937 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05543 0.8758 0.9194 6.023e-05 -2.704e-05 0.09418 4.539e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005727 -0.006024 -0.01092 0.0128 0.9574 0.9643 0.01756 0.9214 0.9358 0.05741 ] Network output: [ 0.9527 0.2002 0.03101 0.000301 -0.0001351 -0.1352 0.0002269 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4453 0.04989 -0.009436 0.21 0.9807 0.9919 0.5535 0.9344 0.9822 0.6271 ] Network output: [ 0.02028 0.8763 0.9326 -0.0001867 8.382e-05 0.1497 -0.0001407 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0134 0.008318 0.01634 0.01349 0.9902 0.9934 0.01393 0.9798 0.9886 0.02416 ] Network output: [ 0.08853 -0.2583 0.8013 -0.0004764 0.0002139 1.278 -0.000359 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5396 0.4483 0.4897 0.3986 0.9824 0.9929 0.544 0.94 0.9844 0.6224 ] Network output: [ -0.04763 0.155 1.137 9.107e-05 -4.088e-05 0.8041 6.863e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2228 0.2169 0.2736 0.2389 0.9902 0.9941 0.2232 0.981 0.9898 0.2848 ] Network output: [ -0.04388 0.0753 1.146 0.0002647 -0.0001189 0.8673 0.0001995 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.231 0.23 0.2794 0.2567 0.9852 0.9914 0.2311 0.9663 0.9836 0.282 ] Network output: [ -0.02658 1.086 0.03234 -3.086e-05 1.385e-05 0.935 -2.325e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06752 Epoch 3938 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05552 0.8751 0.9193 5.839e-05 -2.621e-05 0.09471 4.401e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005679 -0.006018 -0.01103 0.01273 0.9574 0.9643 0.01752 0.9215 0.9359 0.05742 ] Network output: [ 0.9522 0.2012 0.03183 0.000309 -0.0001387 -0.1362 0.0002329 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4438 0.0492 -0.01102 0.2087 0.9807 0.9919 0.5523 0.9345 0.9823 0.6288 ] Network output: [ 0.02045 0.8756 0.9325 -0.0001898 8.522e-05 0.1502 -0.0001431 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01335 0.008255 0.01629 0.01339 0.9902 0.9934 0.01388 0.9798 0.9886 0.02416 ] Network output: [ 0.08861 -0.2576 0.801 -0.0004868 0.0002186 1.277 -0.0003669 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5385 0.4466 0.49 0.397 0.9825 0.9929 0.5428 0.94 0.9844 0.6242 ] Network output: [ -0.04749 0.1558 1.136 9.342e-05 -4.194e-05 0.8037 7.041e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2225 0.2165 0.2734 0.2382 0.9902 0.9941 0.2228 0.9811 0.9898 0.2847 ] Network output: [ -0.04375 0.07421 1.146 0.0002696 -0.000121 0.8679 0.0002032 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2307 0.2297 0.2793 0.2564 0.9852 0.9914 0.2308 0.9664 0.9836 0.282 ] Network output: [ -0.0267 1.086 0.03237 -2.992e-05 1.343e-05 0.9353 -2.255e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06762 Epoch 3939 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05561 0.8744 0.9193 5.648e-05 -2.536e-05 0.09524 4.257e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005631 -0.006013 -0.01114 0.01266 0.9574 0.9643 0.01747 0.9216 0.936 0.05742 ] Network output: [ 0.9517 0.2023 0.03266 0.000317 -0.0001423 -0.1371 0.0002389 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4422 0.04851 -0.01262 0.2075 0.9807 0.9919 0.5511 0.9346 0.9823 0.6306 ] Network output: [ 0.02061 0.8749 0.9324 -0.000193 8.665e-05 0.1507 -0.0001455 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0133 0.008192 0.01624 0.0133 0.9902 0.9934 0.01383 0.9798 0.9886 0.02416 ] Network output: [ 0.08867 -0.2567 0.8006 -0.0004974 0.0002233 1.277 -0.0003749 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5373 0.445 0.4904 0.3953 0.9825 0.9929 0.5417 0.9401 0.9845 0.626 ] Network output: [ -0.04734 0.1565 1.135 9.583e-05 -4.302e-05 0.8033 7.222e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2221 0.2161 0.2731 0.2374 0.9903 0.9941 0.2225 0.9811 0.9899 0.2845 ] Network output: [ -0.04362 0.07312 1.147 0.0002746 -0.0001233 0.8685 0.0002069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2304 0.2294 0.2792 0.256 0.9852 0.9914 0.2305 0.9664 0.9837 0.2819 ] Network output: [ -0.02681 1.085 0.03241 -2.884e-05 1.295e-05 0.9356 -2.173e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06772 Epoch 3940 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0557 0.8737 0.9193 5.45e-05 -2.447e-05 0.09577 4.107e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005584 -0.006007 -0.01125 0.01259 0.9574 0.9644 0.01742 0.9217 0.936 0.05743 ] Network output: [ 0.9512 0.2033 0.03351 0.000325 -0.0001459 -0.138 0.0002449 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4406 0.04781 -0.01423 0.2062 0.9807 0.9919 0.5498 0.9347 0.9823 0.6323 ] Network output: [ 0.02078 0.8741 0.9323 -0.0001963 8.811e-05 0.1513 -0.0001479 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01325 0.008129 0.01619 0.0132 0.9902 0.9934 0.01378 0.9799 0.9886 0.02416 ] Network output: [ 0.08873 -0.2559 0.8002 -0.0005082 0.0002281 1.276 -0.000383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5361 0.4432 0.4907 0.3936 0.9825 0.9929 0.5405 0.9402 0.9845 0.6278 ] Network output: [ -0.04718 0.1572 1.135 9.828e-05 -4.412e-05 0.8029 7.407e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2217 0.2157 0.2728 0.2367 0.9903 0.9941 0.2221 0.9811 0.9899 0.2843 ] Network output: [ -0.04349 0.07204 1.147 0.0002796 -0.0001255 0.8691 0.0002107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2301 0.2291 0.2792 0.2556 0.9852 0.9914 0.2302 0.9664 0.9837 0.2819 ] Network output: [ -0.0269 1.085 0.03245 -2.762e-05 1.24e-05 0.9359 -2.081e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06782 Epoch 3941 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05578 0.8731 0.9193 5.244e-05 -2.354e-05 0.0963 3.952e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005538 -0.006001 -0.01136 0.01252 0.9575 0.9644 0.01738 0.9218 0.9361 0.05743 ] Network output: [ 0.9508 0.2044 0.03438 0.000333 -0.0001495 -0.1389 0.0002509 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.439 0.04711 -0.01586 0.2049 0.9807 0.9919 0.5485 0.9348 0.9824 0.6341 ] Network output: [ 0.02094 0.8734 0.9321 -0.0001996 8.96e-05 0.1518 -0.0001504 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0132 0.008066 0.01614 0.01311 0.9902 0.9934 0.01373 0.9799 0.9886 0.02416 ] Network output: [ 0.08877 -0.255 0.7999 -0.0005191 0.000233 1.275 -0.0003912 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5349 0.4415 0.491 0.3919 0.9825 0.9929 0.5393 0.9403 0.9845 0.6297 ] Network output: [ -0.04703 0.158 1.134 0.0001008 -4.524e-05 0.8026 7.595e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2214 0.2153 0.2725 0.2359 0.9903 0.9941 0.2218 0.9811 0.9899 0.2841 ] Network output: [ -0.04336 0.07096 1.147 0.0002846 -0.0001278 0.8697 0.0002145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2298 0.2288 0.2791 0.2553 0.9852 0.9914 0.2299 0.9665 0.9837 0.2818 ] Network output: [ -0.02699 1.085 0.0325 -2.626e-05 1.179e-05 0.9363 -1.979e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0679 Epoch 3942 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05587 0.8724 0.9193 5.031e-05 -2.259e-05 0.09683 3.791e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005492 -0.005995 -0.01147 0.01245 0.9575 0.9644 0.01733 0.9219 0.9362 0.05744 ] Network output: [ 0.9503 0.2053 0.03525 0.000341 -0.0001531 -0.1398 0.000257 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4374 0.04641 -0.01749 0.2037 0.9807 0.9919 0.5472 0.9349 0.9824 0.6359 ] Network output: [ 0.0211 0.8727 0.932 -0.0002029 9.111e-05 0.1523 -0.0001529 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01315 0.008003 0.01609 0.01301 0.9902 0.9934 0.01368 0.9799 0.9886 0.02416 ] Network output: [ 0.08881 -0.254 0.7995 -0.0005301 0.000238 1.275 -0.0003995 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5336 0.4398 0.4913 0.3901 0.9825 0.9929 0.5381 0.9403 0.9845 0.6315 ] Network output: [ -0.04687 0.1588 1.133 0.0001033 -4.639e-05 0.8022 7.787e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.221 0.2149 0.2722 0.2351 0.9903 0.9941 0.2214 0.9811 0.9899 0.284 ] Network output: [ -0.04323 0.06988 1.147 0.0002898 -0.0001301 0.8703 0.0002184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2295 0.2285 0.279 0.2549 0.9852 0.9914 0.2296 0.9665 0.9837 0.2818 ] Network output: [ -0.02707 1.085 0.03255 -2.474e-05 1.111e-05 0.9366 -1.865e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06798 Epoch 3943 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05596 0.8717 0.9193 4.81e-05 -2.159e-05 0.09736 3.625e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005447 -0.005989 -0.01158 0.01238 0.9575 0.9644 0.01728 0.922 0.9363 0.05744 ] Network output: [ 0.9498 0.2063 0.03613 0.000349 -0.0001567 -0.1407 0.000263 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4358 0.04572 -0.01913 0.2024 0.9807 0.992 0.5459 0.935 0.9824 0.6377 ] Network output: [ 0.02125 0.8719 0.9319 -0.0002064 9.265e-05 0.1528 -0.0001555 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0131 0.00794 0.01604 0.01292 0.9902 0.9934 0.01363 0.9799 0.9887 0.02417 ] Network output: [ 0.08883 -0.253 0.7992 -0.0005412 0.000243 1.274 -0.0004078 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5324 0.438 0.4916 0.3884 0.9825 0.9929 0.5369 0.9404 0.9846 0.6334 ] Network output: [ -0.04671 0.1596 1.132 0.0001059 -4.756e-05 0.8018 7.983e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2207 0.2145 0.2719 0.2344 0.9903 0.9941 0.2211 0.9812 0.9899 0.2838 ] Network output: [ -0.0431 0.06881 1.148 0.000295 -0.0001324 0.871 0.0002223 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2293 0.2282 0.2789 0.2545 0.9852 0.9914 0.2294 0.9665 0.9837 0.2817 ] Network output: [ -0.02714 1.085 0.03261 -2.308e-05 1.036e-05 0.937 -1.74e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06805 Epoch 3944 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05605 0.871 0.9193 4.582e-05 -2.057e-05 0.09788 3.453e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005403 -0.005983 -0.0117 0.01231 0.9575 0.9644 0.01724 0.9221 0.9364 0.05745 ] Network output: [ 0.9494 0.2072 0.03702 0.0003569 -0.0001602 -0.1415 0.000269 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4342 0.04502 -0.02079 0.2012 0.9807 0.992 0.5446 0.9351 0.9825 0.6395 ] Network output: [ 0.02141 0.8712 0.9318 -0.0002099 9.421e-05 0.1533 -0.0001582 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01304 0.007877 0.01599 0.01282 0.9902 0.9934 0.01358 0.9799 0.9887 0.02417 ] Network output: [ 0.08883 -0.2519 0.7988 -0.0005524 0.000248 1.273 -0.0004163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5311 0.4362 0.4919 0.3866 0.9825 0.9929 0.5356 0.9405 0.9846 0.6352 ] Network output: [ -0.04654 0.1604 1.132 0.0001086 -4.874e-05 0.8014 8.183e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2204 0.2142 0.2716 0.2336 0.9903 0.9941 0.2208 0.9812 0.9899 0.2836 ] Network output: [ -0.04297 0.06776 1.148 0.0003003 -0.0001348 0.8716 0.0002263 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.229 0.2279 0.2788 0.2541 0.9852 0.9914 0.2291 0.9665 0.9838 0.2817 ] Network output: [ -0.0272 1.084 0.03267 -2.127e-05 9.549e-06 0.9374 -1.603e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06811 Epoch 3945 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05614 0.8703 0.9192 4.346e-05 -1.951e-05 0.0984 3.275e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00536 -0.005977 -0.01181 0.01224 0.9575 0.9644 0.01719 0.9222 0.9365 0.05745 ] Network output: [ 0.9489 0.2081 0.03792 0.0003649 -0.0001638 -0.1424 0.000275 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4326 0.04432 -0.02245 0.1999 0.9807 0.992 0.5432 0.9352 0.9825 0.6413 ] Network output: [ 0.02156 0.8705 0.9317 -0.0002134 9.58e-05 0.1538 -0.0001608 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01299 0.007814 0.01594 0.01273 0.9902 0.9934 0.01354 0.9799 0.9887 0.02417 ] Network output: [ 0.08882 -0.2509 0.7985 -0.0005638 0.0002531 1.272 -0.0004249 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5298 0.4344 0.4922 0.3848 0.9825 0.9929 0.5344 0.9406 0.9846 0.6371 ] Network output: [ -0.04638 0.1613 1.131 0.0001113 -4.996e-05 0.801 8.386e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2201 0.2138 0.2713 0.2328 0.9903 0.9941 0.2205 0.9812 0.99 0.2834 ] Network output: [ -0.04283 0.06671 1.148 0.0003056 -0.0001372 0.8722 0.0002303 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2287 0.2276 0.2787 0.2537 0.9852 0.9914 0.2288 0.9666 0.9838 0.2816 ] Network output: [ -0.02724 1.084 0.03273 -1.93e-05 8.666e-06 0.9378 -1.455e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06816 Epoch 3946 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05622 0.8696 0.9192 4.103e-05 -1.842e-05 0.09891 3.092e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005318 -0.005971 -0.01192 0.01217 0.9576 0.9645 0.01714 0.9223 0.9365 0.05746 ] Network output: [ 0.9485 0.2089 0.03883 0.0003728 -0.0001674 -0.1432 0.0002809 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4309 0.04362 -0.02411 0.1987 0.9807 0.992 0.5418 0.9353 0.9825 0.6432 ] Network output: [ 0.0217 0.8698 0.9316 -0.000217 9.742e-05 0.1543 -0.0001635 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01295 0.007752 0.01589 0.01263 0.9902 0.9934 0.01349 0.98 0.9887 0.02418 ] Network output: [ 0.0888 -0.2497 0.7982 -0.0005752 0.0002582 1.272 -0.0004335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5285 0.4326 0.4925 0.383 0.9825 0.9929 0.5331 0.9407 0.9847 0.639 ] Network output: [ -0.04621 0.1621 1.13 0.000114 -5.119e-05 0.8006 8.593e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2198 0.2135 0.271 0.2321 0.9903 0.9941 0.2202 0.9812 0.99 0.2832 ] Network output: [ -0.0427 0.06568 1.148 0.000311 -0.0001396 0.8728 0.0002344 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2285 0.2274 0.2786 0.2533 0.9852 0.9914 0.2286 0.9666 0.9838 0.2815 ] Network output: [ -0.02728 1.083 0.03279 -1.718e-05 7.713e-06 0.9383 -1.295e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0682 Epoch 3947 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05631 0.8689 0.9192 3.852e-05 -1.729e-05 0.09942 2.903e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005277 -0.005965 -0.01203 0.01211 0.9576 0.9645 0.0171 0.9224 0.9366 0.05747 ] Network output: [ 0.9481 0.2097 0.03974 0.0003806 -0.0001709 -0.144 0.0002868 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4293 0.04293 -0.02578 0.1975 0.9808 0.992 0.5404 0.9353 0.9826 0.645 ] Network output: [ 0.02185 0.8691 0.9315 -0.0002206 9.905e-05 0.1548 -0.0001663 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0129 0.00769 0.01584 0.01254 0.9902 0.9934 0.01344 0.98 0.9887 0.02418 ] Network output: [ 0.08876 -0.2486 0.7979 -0.0005867 0.0002634 1.271 -0.0004422 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5272 0.4307 0.4927 0.3812 0.9825 0.9929 0.5318 0.9407 0.9847 0.6409 ] Network output: [ -0.04603 0.163 1.129 0.0001168 -5.244e-05 0.8002 8.804e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2195 0.2131 0.2707 0.2313 0.9903 0.9941 0.2199 0.9812 0.99 0.283 ] Network output: [ -0.04256 0.06466 1.148 0.0003164 -0.0001421 0.8734 0.0002385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2282 0.2271 0.2785 0.2529 0.9852 0.9914 0.2283 0.9667 0.9838 0.2815 ] Network output: [ -0.0273 1.083 0.03286 -1.491e-05 6.692e-06 0.9388 -1.123e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06823 Epoch 3948 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05639 0.8682 0.9193 3.593e-05 -1.613e-05 0.09993 2.708e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005236 -0.005958 -0.01215 0.01204 0.9576 0.9645 0.01705 0.9225 0.9367 0.05747 ] Network output: [ 0.9476 0.2104 0.04065 0.0003884 -0.0001744 -0.1447 0.0002927 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4277 0.04224 -0.02746 0.1962 0.9808 0.992 0.539 0.9354 0.9826 0.6469 ] Network output: [ 0.02199 0.8684 0.9314 -0.0002243 0.0001007 0.1553 -0.0001691 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01285 0.007628 0.01579 0.01244 0.9902 0.9934 0.01339 0.98 0.9887 0.02419 ] Network output: [ 0.0887 -0.2473 0.7976 -0.0005983 0.0002686 1.27 -0.0004509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5258 0.4289 0.493 0.3793 0.9825 0.9929 0.5305 0.9408 0.9847 0.6428 ] Network output: [ -0.04586 0.1639 1.128 0.0001197 -5.372e-05 0.7999 9.018e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2192 0.2128 0.2704 0.2305 0.9903 0.9941 0.2196 0.9813 0.99 0.2828 ] Network output: [ -0.04243 0.06366 1.149 0.000322 -0.0001445 0.874 0.0002426 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.228 0.2269 0.2784 0.2525 0.9852 0.9914 0.228 0.9667 0.9838 0.2814 ] Network output: [ -0.02731 1.082 0.03292 -1.248e-05 5.601e-06 0.9392 -9.402e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06826 Epoch 3949 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05648 0.8675 0.9193 3.327e-05 -1.494e-05 0.1004 2.507e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005197 -0.005952 -0.01226 0.01198 0.9576 0.9645 0.017 0.9226 0.9368 0.05748 ] Network output: [ 0.9472 0.2111 0.04156 0.0003961 -0.0001778 -0.1455 0.0002985 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4261 0.04155 -0.02914 0.195 0.9808 0.992 0.5375 0.9355 0.9826 0.6487 ] Network output: [ 0.02212 0.8677 0.9313 -0.0002281 0.0001024 0.1558 -0.0001719 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0128 0.007566 0.01574 0.01235 0.9902 0.9934 0.01335 0.98 0.9887 0.02419 ] Network output: [ 0.08863 -0.2461 0.7974 -0.00061 0.0002739 1.269 -0.0004597 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5245 0.427 0.4933 0.3775 0.9825 0.9929 0.5291 0.9409 0.9847 0.6447 ] Network output: [ -0.04568 0.1648 1.128 0.0001226 -5.502e-05 0.7995 9.237e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2189 0.2125 0.2701 0.2297 0.9903 0.9942 0.2193 0.9813 0.99 0.2826 ] Network output: [ -0.04229 0.06268 1.149 0.0003275 -0.000147 0.8746 0.0002468 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2277 0.2266 0.2783 0.2521 0.9852 0.9914 0.2278 0.9667 0.9839 0.2813 ] Network output: [ -0.0273 1.082 0.03299 -9.894e-06 4.442e-06 0.9398 -7.456e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06827 Epoch 3950 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05656 0.8668 0.9193 3.053e-05 -1.371e-05 0.1009 2.301e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005159 -0.005946 -0.01237 0.01191 0.9576 0.9645 0.01696 0.9227 0.9369 0.05749 ] Network output: [ 0.9468 0.2118 0.04247 0.0004037 -0.0001812 -0.1462 0.0003042 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4245 0.04086 -0.03082 0.1938 0.9808 0.992 0.5361 0.9356 0.9827 0.6506 ] Network output: [ 0.02225 0.8671 0.9312 -0.0002319 0.0001041 0.1563 -0.0001747 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01275 0.007505 0.01569 0.01226 0.9902 0.9934 0.0133 0.98 0.9887 0.0242 ] Network output: [ 0.08853 -0.2448 0.7971 -0.0006217 0.0002791 1.268 -0.0004686 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5231 0.4251 0.4935 0.3756 0.9825 0.9929 0.5278 0.941 0.9848 0.6466 ] Network output: [ -0.0455 0.1657 1.127 0.0001255 -5.635e-05 0.7991 9.459e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2186 0.2121 0.2698 0.229 0.9903 0.9942 0.219 0.9813 0.99 0.2824 ] Network output: [ -0.04215 0.06172 1.149 0.0003332 -0.0001496 0.8752 0.0002511 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2275 0.2264 0.2782 0.2517 0.9852 0.9914 0.2276 0.9668 0.9839 0.2812 ] Network output: [ -0.02728 1.081 0.03305 -7.161e-06 3.215e-06 0.9403 -5.397e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06827 Epoch 3951 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05664 0.8661 0.9193 2.772e-05 -1.245e-05 0.1014 2.089e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005122 -0.00594 -0.01248 0.01185 0.9577 0.9646 0.01691 0.9229 0.937 0.05749 ] Network output: [ 0.9464 0.2124 0.04338 0.0004112 -0.0001846 -0.1469 0.0003099 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4229 0.04018 -0.0325 0.1927 0.9808 0.992 0.5346 0.9357 0.9827 0.6525 ] Network output: [ 0.02237 0.8664 0.9312 -0.0002357 0.0001058 0.1567 -0.0001776 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01271 0.007445 0.01565 0.01217 0.9903 0.9934 0.01325 0.98 0.9888 0.02421 ] Network output: [ 0.08842 -0.2435 0.7969 -0.0006335 0.0002844 1.267 -0.0004775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5217 0.4232 0.4938 0.3737 0.9825 0.9929 0.5264 0.9411 0.9848 0.6486 ] Network output: [ -0.04532 0.1666 1.126 0.0001285 -5.769e-05 0.7987 9.685e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2184 0.2118 0.2695 0.2282 0.9903 0.9942 0.2188 0.9813 0.9901 0.2822 ] Network output: [ -0.04201 0.06078 1.149 0.0003388 -0.0001521 0.8757 0.0002554 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2273 0.2261 0.2781 0.2513 0.9852 0.9914 0.2274 0.9668 0.9839 0.2811 ] Network output: [ -0.02725 1.081 0.03311 -4.279e-06 1.921e-06 0.9409 -3.225e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06827 Epoch 3952 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05671 0.8655 0.9193 2.484e-05 -1.115e-05 0.1019 1.872e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005086 -0.005933 -0.0126 0.01178 0.9577 0.9646 0.01687 0.923 0.9371 0.0575 ] Network output: [ 0.946 0.213 0.04429 0.0004186 -0.0001879 -0.1476 0.0003155 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4213 0.0395 -0.03418 0.1915 0.9808 0.992 0.5331 0.9358 0.9827 0.6543 ] Network output: [ 0.02249 0.8658 0.9311 -0.0002396 0.0001076 0.1572 -0.0001806 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01266 0.007385 0.0156 0.01208 0.9903 0.9935 0.01321 0.9801 0.9888 0.02422 ] Network output: [ 0.08829 -0.2422 0.7967 -0.0006454 0.0002897 1.266 -0.0004864 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5203 0.4213 0.494 0.3718 0.9825 0.9929 0.525 0.9412 0.9848 0.6505 ] Network output: [ -0.04514 0.1676 1.125 0.0001315 -5.906e-05 0.7983 9.914e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2181 0.2115 0.2691 0.2274 0.9903 0.9942 0.2185 0.9813 0.9901 0.282 ] Network output: [ -0.04186 0.05987 1.149 0.0003446 -0.0001547 0.8763 0.0002597 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.227 0.2259 0.2779 0.2509 0.9852 0.9914 0.2271 0.9668 0.9839 0.281 ] Network output: [ -0.02721 1.08 0.03317 -1.253e-06 5.623e-07 0.9415 -9.44e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06825 Epoch 3953 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05679 0.8648 0.9194 2.189e-05 -9.826e-06 0.1024 1.649e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005051 -0.005927 -0.01271 0.01172 0.9577 0.9646 0.01682 0.9231 0.9372 0.05751 ] Network output: [ 0.9457 0.2135 0.0452 0.0004259 -0.0001912 -0.1483 0.000321 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4197 0.03883 -0.03586 0.1903 0.9808 0.992 0.5316 0.9359 0.9828 0.6562 ] Network output: [ 0.0226 0.8651 0.9311 -0.0002435 0.0001093 0.1576 -0.0001835 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01261 0.007325 0.01555 0.01199 0.9903 0.9935 0.01316 0.9801 0.9888 0.02422 ] Network output: [ 0.08813 -0.2408 0.7965 -0.0006573 0.0002951 1.265 -0.0004953 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5189 0.4194 0.4942 0.3699 0.9826 0.9929 0.5236 0.9413 0.9849 0.6524 ] Network output: [ -0.04495 0.1685 1.124 0.0001346 -6.044e-05 0.7979 0.0001015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2179 0.2112 0.2688 0.2266 0.9904 0.9942 0.2183 0.9814 0.9901 0.2818 ] Network output: [ -0.04172 0.05898 1.149 0.0003503 -0.0001573 0.8769 0.000264 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2268 0.2257 0.2778 0.2505 0.9853 0.9914 0.2269 0.9669 0.984 0.2809 ] Network output: [ -0.02715 1.079 0.03322 1.916e-06 -8.599e-07 0.9421 1.444e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06823 Epoch 3954 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05686 0.8641 0.9194 1.886e-05 -8.469e-06 0.1028 1.422e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005017 -0.005921 -0.01282 0.01166 0.9577 0.9646 0.01678 0.9232 0.9373 0.05751 ] Network output: [ 0.9453 0.2139 0.04609 0.000433 -0.0001944 -0.1489 0.0003263 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4181 0.03815 -0.03753 0.1892 0.9808 0.992 0.5301 0.936 0.9828 0.6581 ] Network output: [ 0.02271 0.8645 0.931 -0.0002474 0.0001111 0.1581 -0.0001865 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01257 0.007266 0.0155 0.0119 0.9903 0.9935 0.01312 0.9801 0.9888 0.02423 ] Network output: [ 0.08796 -0.2394 0.7964 -0.0006692 0.0003004 1.264 -0.0005043 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5175 0.4175 0.4945 0.368 0.9826 0.9929 0.5222 0.9413 0.9849 0.6543 ] Network output: [ -0.04477 0.1695 1.123 0.0001378 -6.185e-05 0.7975 0.0001038 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2177 0.211 0.2685 0.2259 0.9904 0.9942 0.2181 0.9814 0.9901 0.2815 ] Network output: [ -0.04158 0.05811 1.149 0.0003562 -0.0001599 0.8774 0.0002684 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2266 0.2255 0.2777 0.2501 0.9853 0.9914 0.2267 0.9669 0.984 0.2808 ] Network output: [ -0.02707 1.078 0.03326 5.221e-06 -2.344e-06 0.9427 3.934e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06819 Epoch 3955 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05693 0.8635 0.9194 1.578e-05 -7.082e-06 0.1033 1.189e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004985 -0.005915 -0.01293 0.0116 0.9578 0.9646 0.01674 0.9233 0.9373 0.05752 ] Network output: [ 0.945 0.2144 0.04698 0.0004401 -0.0001976 -0.1495 0.0003316 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4166 0.03749 -0.03921 0.1881 0.9808 0.992 0.5286 0.9361 0.9828 0.66 ] Network output: [ 0.0228 0.8639 0.931 -0.0002514 0.0001129 0.1585 -0.0001895 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01252 0.007208 0.01545 0.01181 0.9903 0.9935 0.01307 0.9801 0.9888 0.02424 ] Network output: [ 0.08776 -0.2379 0.7963 -0.0006811 0.0003058 1.263 -0.0005133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5161 0.4156 0.4947 0.3661 0.9826 0.9929 0.5208 0.9414 0.9849 0.6563 ] Network output: [ -0.04458 0.1705 1.122 0.000141 -6.328e-05 0.7971 0.0001062 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2175 0.2107 0.2682 0.2251 0.9904 0.9942 0.2179 0.9814 0.9901 0.2813 ] Network output: [ -0.04143 0.05728 1.149 0.000362 -0.0001625 0.878 0.0002728 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2265 0.2253 0.2775 0.2497 0.9853 0.9914 0.2266 0.967 0.984 0.2807 ] Network output: [ -0.02698 1.077 0.0333 8.658e-06 -3.887e-06 0.9433 6.525e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06814 Epoch 3956 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05699 0.8628 0.9195 1.262e-05 -5.667e-06 0.1038 9.513e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004954 -0.005909 -0.01304 0.01154 0.9578 0.9647 0.01669 0.9234 0.9374 0.05753 ] Network output: [ 0.9447 0.2147 0.04786 0.0004469 -0.0002007 -0.1501 0.0003368 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.415 0.03683 -0.04088 0.1869 0.9809 0.992 0.527 0.9362 0.9829 0.6618 ] Network output: [ 0.02289 0.8633 0.931 -0.0002554 0.0001147 0.1589 -0.0001925 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01248 0.00715 0.0154 0.01172 0.9903 0.9935 0.01303 0.9801 0.9888 0.02425 ] Network output: [ 0.08754 -0.2365 0.7962 -0.000693 0.0003111 1.262 -0.0005223 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5147 0.4136 0.4949 0.3642 0.9826 0.9929 0.5194 0.9415 0.9849 0.6582 ] Network output: [ -0.04439 0.1715 1.121 0.0001442 -6.474e-05 0.7967 0.0001087 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2173 0.2104 0.2678 0.2243 0.9904 0.9942 0.2177 0.9814 0.9901 0.2811 ] Network output: [ -0.04128 0.05647 1.149 0.0003679 -0.0001652 0.8785 0.0002773 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2263 0.2251 0.2774 0.2492 0.9853 0.9914 0.2264 0.967 0.984 0.2806 ] Network output: [ -0.02688 1.076 0.03332 1.222e-05 -5.486e-06 0.944 9.21e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06809 Epoch 3957 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05705 0.8622 0.9196 9.407e-06 -4.223e-06 0.1042 7.09e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004923 -0.005902 -0.01315 0.01148 0.9578 0.9647 0.01665 0.9235 0.9375 0.05753 ] Network output: [ 0.9444 0.215 0.04873 0.0004537 -0.0002037 -0.1507 0.0003419 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4135 0.03617 -0.04254 0.1858 0.9809 0.992 0.5255 0.9363 0.9829 0.6637 ] Network output: [ 0.02298 0.8627 0.931 -0.0002594 0.0001165 0.1593 -0.0001955 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01244 0.007092 0.01536 0.01163 0.9903 0.9935 0.01299 0.9802 0.9888 0.02426 ] Network output: [ 0.0873 -0.235 0.7961 -0.000705 0.0003165 1.261 -0.0005313 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5132 0.4117 0.4951 0.3623 0.9826 0.9929 0.518 0.9416 0.985 0.6601 ] Network output: [ -0.04419 0.1725 1.12 0.0001475 -6.621e-05 0.7963 0.0001111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2171 0.2102 0.2675 0.2235 0.9904 0.9942 0.2175 0.9814 0.9902 0.2809 ] Network output: [ -0.04113 0.0557 1.149 0.0003738 -0.0001678 0.879 0.0002817 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2261 0.2249 0.2772 0.2488 0.9853 0.9914 0.2262 0.967 0.9841 0.2804 ] Network output: [ -0.02676 1.076 0.03334 1.59e-05 -7.14e-06 0.9447 1.199e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06802 Epoch 3958 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05711 0.8615 0.9196 6.132e-06 -2.753e-06 0.1046 4.621e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004895 -0.005896 -0.01326 0.01142 0.9578 0.9647 0.01661 0.9237 0.9376 0.05754 ] Network output: [ 0.9441 0.2153 0.04959 0.0004603 -0.0002066 -0.1513 0.0003469 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.412 0.03552 -0.0442 0.1848 0.9809 0.992 0.5239 0.9364 0.9829 0.6656 ] Network output: [ 0.02305 0.8621 0.931 -0.0002635 0.0001183 0.1597 -0.0001986 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0124 0.007036 0.01531 0.01155 0.9903 0.9935 0.01295 0.9802 0.9889 0.02427 ] Network output: [ 0.08703 -0.2335 0.7961 -0.0007169 0.0003218 1.26 -0.0005403 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5118 0.4098 0.4953 0.3604 0.9826 0.993 0.5165 0.9417 0.985 0.662 ] Network output: [ -0.044 0.1735 1.119 0.0001508 -6.77e-05 0.7959 0.0001136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2169 0.21 0.2672 0.2228 0.9904 0.9942 0.2173 0.9815 0.9902 0.2807 ] Network output: [ -0.04098 0.05495 1.149 0.0003798 -0.0001705 0.8796 0.0002862 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.226 0.2248 0.277 0.2484 0.9853 0.9914 0.2261 0.9671 0.9841 0.2803 ] Network output: [ -0.02663 1.075 0.03335 1.97e-05 -8.845e-06 0.9454 1.485e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06794 Epoch 3959 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05717 0.8609 0.9197 2.801e-06 -1.257e-06 0.1051 2.111e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004867 -0.00589 -0.01336 0.01137 0.9579 0.9647 0.01657 0.9238 0.9377 0.05755 ] Network output: [ 0.9439 0.2155 0.05043 0.0004667 -0.0002095 -0.1518 0.0003517 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4104 0.03487 -0.04585 0.1837 0.9809 0.9921 0.5224 0.9366 0.983 0.6674 ] Network output: [ 0.02312 0.8616 0.931 -0.0002675 0.0001201 0.1601 -0.0002016 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01235 0.00698 0.01526 0.01146 0.9903 0.9935 0.0129 0.9802 0.9889 0.02428 ] Network output: [ 0.08674 -0.2319 0.7961 -0.0007288 0.0003272 1.259 -0.0005492 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5103 0.4078 0.4956 0.3585 0.9826 0.993 0.5151 0.9418 0.985 0.6639 ] Network output: [ -0.04381 0.1745 1.118 0.0001542 -6.921e-05 0.7955 0.0001162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2167 0.2098 0.2668 0.222 0.9904 0.9942 0.2171 0.9815 0.9902 0.2804 ] Network output: [ -0.04083 0.05424 1.149 0.0003858 -0.0001732 0.8801 0.0002908 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2258 0.2246 0.2769 0.248 0.9853 0.9914 0.2259 0.9671 0.9841 0.2802 ] Network output: [ -0.02648 1.074 0.03334 2.361e-05 -1.06e-05 0.9461 1.779e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06786 Epoch 3960 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05721 0.8603 0.9198 -5.841e-07 2.622e-07 0.1055 -4.402e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004841 -0.005884 -0.01347 0.01131 0.9579 0.9647 0.01653 0.9239 0.9378 0.05755 ] Network output: [ 0.9437 0.2156 0.05126 0.0004729 -0.0002123 -0.1523 0.0003564 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4089 0.03423 -0.0475 0.1826 0.9809 0.9921 0.5208 0.9367 0.983 0.6693 ] Network output: [ 0.02317 0.8611 0.931 -0.0002716 0.0001219 0.1605 -0.0002047 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01231 0.006925 0.01521 0.01138 0.9903 0.9935 0.01286 0.9802 0.9889 0.02429 ] Network output: [ 0.08643 -0.2304 0.7962 -0.0007407 0.0003325 1.258 -0.0005582 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5088 0.4059 0.4958 0.3566 0.9826 0.993 0.5136 0.9419 0.9851 0.6659 ] Network output: [ -0.04361 0.1755 1.117 0.0001576 -7.074e-05 0.7951 0.0001187 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2166 0.2095 0.2665 0.2213 0.9904 0.9942 0.217 0.9815 0.9902 0.2802 ] Network output: [ -0.04068 0.05355 1.149 0.0003919 -0.0001759 0.8805 0.0002953 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2257 0.2245 0.2767 0.2475 0.9853 0.9914 0.2258 0.9672 0.9841 0.28 ] Network output: [ -0.02632 1.073 0.03333 2.761e-05 -1.239e-05 0.9469 2.081e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06776 Epoch 3961 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05726 0.8597 0.9199 -4.019e-06 1.804e-06 0.1059 -3.029e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004815 -0.005878 -0.01358 0.01126 0.9579 0.9648 0.01648 0.924 0.9379 0.05756 ] Network output: [ 0.9434 0.2157 0.05208 0.000479 -0.000215 -0.1527 0.000361 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4075 0.0336 -0.04914 0.1816 0.9809 0.9921 0.5192 0.9368 0.983 0.6711 ] Network output: [ 0.02322 0.8605 0.931 -0.0002757 0.0001238 0.1609 -0.0002077 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01227 0.00687 0.01517 0.01129 0.9903 0.9935 0.01282 0.9802 0.9889 0.0243 ] Network output: [ 0.0861 -0.2288 0.7963 -0.0007525 0.0003378 1.257 -0.0005671 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5074 0.404 0.4959 0.3547 0.9826 0.993 0.5121 0.942 0.9851 0.6677 ] Network output: [ -0.04341 0.1766 1.116 0.000161 -7.229e-05 0.7947 0.0001213 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2164 0.2093 0.2662 0.2205 0.9904 0.9942 0.2168 0.9815 0.9902 0.28 ] Network output: [ -0.04053 0.0529 1.149 0.0003979 -0.0001786 0.881 0.0002999 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2255 0.2243 0.2765 0.2471 0.9853 0.9914 0.2256 0.9672 0.9842 0.2799 ] Network output: [ -0.02615 1.072 0.0333 3.17e-05 -1.423e-05 0.9476 2.389e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06765 Epoch 3962 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0573 0.8591 0.92 -7.501e-06 3.368e-06 0.1063 -5.653e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004791 -0.005872 -0.01368 0.0112 0.9579 0.9648 0.01644 0.9241 0.938 0.05756 ] Network output: [ 0.9433 0.2158 0.05288 0.0004849 -0.0002177 -0.1532 0.0003654 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.406 0.03297 -0.05077 0.1806 0.9809 0.9921 0.5176 0.9369 0.9831 0.673 ] Network output: [ 0.02326 0.86 0.9311 -0.0002797 0.0001256 0.1612 -0.0002108 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01223 0.006816 0.01512 0.01121 0.9903 0.9935 0.01278 0.9803 0.9889 0.02431 ] Network output: [ 0.08574 -0.2272 0.7964 -0.0007643 0.0003431 1.256 -0.000576 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5059 0.402 0.4961 0.3528 0.9826 0.993 0.5107 0.9421 0.9851 0.6696 ] Network output: [ -0.04321 0.1776 1.115 0.0001645 -7.385e-05 0.7943 0.000124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2163 0.2092 0.2658 0.2198 0.9904 0.9942 0.2167 0.9815 0.9902 0.2798 ] Network output: [ -0.04038 0.05229 1.149 0.000404 -0.0001814 0.8815 0.0003045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2254 0.2242 0.2763 0.2467 0.9853 0.9914 0.2255 0.9673 0.9842 0.2797 ] Network output: [ -0.02596 1.07 0.03325 3.588e-05 -1.611e-05 0.9484 2.704e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06754 Epoch 3963 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05733 0.8585 0.9201 -1.103e-05 4.95e-06 0.1067 -8.31e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004769 -0.005866 -0.01379 0.01115 0.958 0.9648 0.0164 0.9243 0.9381 0.05757 ] Network output: [ 0.9431 0.2157 0.05366 0.0004906 -0.0002203 -0.1536 0.0003697 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4046 0.03235 -0.05239 0.1796 0.9809 0.9921 0.516 0.937 0.9831 0.6748 ] Network output: [ 0.02329 0.8595 0.9311 -0.0002838 0.0001274 0.1616 -0.0002139 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0122 0.006763 0.01507 0.01113 0.9903 0.9935 0.01274 0.9803 0.9889 0.02432 ] Network output: [ 0.08536 -0.2256 0.7966 -0.000776 0.0003484 1.255 -0.0005848 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5044 0.4001 0.4963 0.3509 0.9826 0.993 0.5092 0.9422 0.9851 0.6715 ] Network output: [ -0.04302 0.1787 1.114 0.000168 -7.543e-05 0.7939 0.0001266 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2162 0.209 0.2655 0.219 0.9904 0.9942 0.2166 0.9816 0.9902 0.2795 ] Network output: [ -0.04022 0.0517 1.148 0.0004101 -0.0001841 0.8819 0.0003091 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2253 0.2241 0.2761 0.2462 0.9853 0.9914 0.2254 0.9673 0.9842 0.2795 ] Network output: [ -0.02575 1.069 0.03319 4.014e-05 -1.802e-05 0.9492 3.025e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06741 Epoch 3964 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05736 0.8579 0.9202 -1.459e-05 6.551e-06 0.1071 -1.1e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004747 -0.00586 -0.01389 0.0111 0.958 0.9648 0.01636 0.9244 0.9382 0.05757 ] Network output: [ 0.943 0.2157 0.05442 0.0004961 -0.0002227 -0.154 0.0003739 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4031 0.03173 -0.054 0.1786 0.981 0.9921 0.5145 0.9371 0.9831 0.6766 ] Network output: [ 0.02331 0.8591 0.9312 -0.0002879 0.0001293 0.1619 -0.000217 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01216 0.00671 0.01503 0.01105 0.9904 0.9935 0.01271 0.9803 0.989 0.02434 ] Network output: [ 0.08495 -0.224 0.7968 -0.0007877 0.0003536 1.254 -0.0005936 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.503 0.3982 0.4965 0.3491 0.9827 0.993 0.5077 0.9423 0.9852 0.6734 ] Network output: [ -0.04282 0.1797 1.113 0.0001716 -7.703e-05 0.7935 0.0001293 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2161 0.2088 0.2651 0.2183 0.9904 0.9942 0.2165 0.9816 0.9903 0.2793 ] Network output: [ -0.04006 0.05115 1.148 0.0004162 -0.0001869 0.8824 0.0003137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2252 0.224 0.2759 0.2458 0.9853 0.9915 0.2253 0.9674 0.9842 0.2794 ] Network output: [ -0.02554 1.068 0.03312 4.446e-05 -1.996e-05 0.95 3.35e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06727 Epoch 3965 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05738 0.8573 0.9204 -1.819e-05 8.168e-06 0.1075 -1.371e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004727 -0.005855 -0.01399 0.01105 0.958 0.9649 0.01633 0.9245 0.9382 0.05758 ] Network output: [ 0.9428 0.2155 0.05517 0.0005015 -0.0002251 -0.1544 0.0003779 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4017 0.03113 -0.0556 0.1777 0.981 0.9921 0.5129 0.9372 0.9832 0.6784 ] Network output: [ 0.02332 0.8586 0.9313 -0.000292 0.0001311 0.1622 -0.00022 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01212 0.006659 0.01498 0.01097 0.9904 0.9935 0.01267 0.9803 0.989 0.02435 ] Network output: [ 0.08453 -0.2224 0.797 -0.0007993 0.0003588 1.253 -0.0006024 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5015 0.3963 0.4967 0.3472 0.9827 0.993 0.5062 0.9424 0.9852 0.6752 ] Network output: [ -0.04261 0.1808 1.112 0.0001752 -7.865e-05 0.7931 0.000132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.216 0.2087 0.2648 0.2176 0.9904 0.9942 0.2164 0.9816 0.9903 0.2791 ] Network output: [ -0.03991 0.05064 1.148 0.0004224 -0.0001896 0.8828 0.0003183 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2251 0.2239 0.2757 0.2454 0.9853 0.9915 0.2252 0.9674 0.9843 0.2792 ] Network output: [ -0.02531 1.067 0.03303 4.884e-05 -2.192e-05 0.9508 3.68e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06713 Epoch 3966 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0574 0.8568 0.9205 -2.183e-05 9.8e-06 0.1078 -1.645e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004707 -0.005849 -0.0141 0.011 0.958 0.9649 0.01629 0.9246 0.9383 0.05758 ] Network output: [ 0.9428 0.2154 0.05589 0.0005066 -0.0002274 -0.1547 0.0003818 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.4003 0.03052 -0.05719 0.1767 0.981 0.9921 0.5113 0.9373 0.9832 0.6802 ] Network output: [ 0.02333 0.8582 0.9314 -0.000296 0.0001329 0.1626 -0.0002231 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01208 0.006608 0.01494 0.01089 0.9904 0.9935 0.01263 0.9804 0.989 0.02436 ] Network output: [ 0.08408 -0.2207 0.7973 -0.0008109 0.000364 1.252 -0.0006111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.5 0.3944 0.4968 0.3453 0.9827 0.993 0.5047 0.9425 0.9852 0.6771 ] Network output: [ -0.04241 0.1818 1.111 0.0001788 -8.028e-05 0.7927 0.0001348 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2159 0.2085 0.2645 0.2168 0.9904 0.9943 0.2163 0.9816 0.9903 0.2788 ] Network output: [ -0.03975 0.05015 1.148 0.0004285 -0.0001924 0.8832 0.0003229 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2251 0.2238 0.2755 0.2449 0.9853 0.9915 0.2252 0.9675 0.9843 0.279 ] Network output: [ -0.02507 1.066 0.03292 5.326e-05 -2.391e-05 0.9517 4.014e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06697 Epoch 3967 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05741 0.8562 0.9207 -2.549e-05 1.144e-05 0.1082 -1.921e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004689 -0.005843 -0.0142 0.01095 0.9581 0.9649 0.01625 0.9247 0.9384 0.05758 ] Network output: [ 0.9427 0.2151 0.0566 0.0005116 -0.0002297 -0.155 0.0003855 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.399 0.02992 -0.05877 0.1758 0.981 0.9921 0.5097 0.9374 0.9833 0.682 ] Network output: [ 0.02332 0.8578 0.9315 -0.0003001 0.0001347 0.1629 -0.0002262 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01205 0.006557 0.01489 0.01081 0.9904 0.9935 0.01259 0.9804 0.989 0.02437 ] Network output: [ 0.08361 -0.2191 0.7976 -0.0008223 0.0003692 1.251 -0.0006197 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4985 0.3924 0.497 0.3435 0.9827 0.993 0.5033 0.9426 0.9853 0.6789 ] Network output: [ -0.04221 0.1829 1.11 0.0001825 -8.192e-05 0.7923 0.0001375 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2158 0.2084 0.2641 0.2161 0.9905 0.9943 0.2162 0.9816 0.9903 0.2786 ] Network output: [ -0.03959 0.0497 1.148 0.0004347 -0.0001951 0.8836 0.0003276 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.225 0.2237 0.2753 0.2445 0.9853 0.9915 0.2251 0.9675 0.9843 0.2788 ] Network output: [ -0.02481 1.065 0.0328 5.773e-05 -2.592e-05 0.9525 4.351e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06681 Epoch 3968 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05742 0.8557 0.9208 -2.918e-05 1.31e-05 0.1085 -2.199e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004673 -0.005838 -0.0143 0.0109 0.9581 0.9649 0.01621 0.9249 0.9385 0.05759 ] Network output: [ 0.9426 0.2149 0.05728 0.0005163 -0.0002318 -0.1553 0.0003891 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3976 0.02933 -0.06034 0.1749 0.981 0.9921 0.5081 0.9375 0.9833 0.6838 ] Network output: [ 0.0233 0.8573 0.9317 -0.0003041 0.0001365 0.1632 -0.0002292 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01201 0.006508 0.01485 0.01073 0.9904 0.9935 0.01256 0.9804 0.989 0.02438 ] Network output: [ 0.08312 -0.2174 0.798 -0.0008337 0.0003743 1.25 -0.0006283 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.497 0.3906 0.4972 0.3416 0.9827 0.993 0.5018 0.9426 0.9853 0.6807 ] Network output: [ -0.04201 0.1839 1.109 0.0001862 -8.358e-05 0.7919 0.0001403 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2157 0.2083 0.2638 0.2154 0.9905 0.9943 0.2162 0.9816 0.9903 0.2784 ] Network output: [ -0.03943 0.04929 1.147 0.0004409 -0.0001979 0.8839 0.0003322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2249 0.2237 0.2751 0.244 0.9853 0.9915 0.2251 0.9676 0.9843 0.2786 ] Network output: [ -0.02455 1.063 0.03266 6.223e-05 -2.794e-05 0.9534 4.69e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06663 Epoch 3969 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05742 0.8551 0.921 -3.289e-05 1.476e-05 0.1089 -2.479e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004657 -0.005832 -0.0144 0.01085 0.9581 0.9649 0.01618 0.925 0.9386 0.05759 ] Network output: [ 0.9426 0.2145 0.05795 0.0005209 -0.0002339 -0.1556 0.0003926 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3963 0.02875 -0.0619 0.174 0.981 0.9921 0.5065 0.9376 0.9833 0.6855 ] Network output: [ 0.02327 0.857 0.9318 -0.0003081 0.0001383 0.1634 -0.0002322 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01198 0.006459 0.0148 0.01066 0.9904 0.9936 0.01252 0.9804 0.989 0.02439 ] Network output: [ 0.08261 -0.2158 0.7984 -0.0008449 0.0003793 1.249 -0.0006368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4955 0.3887 0.4973 0.3398 0.9827 0.993 0.5003 0.9427 0.9853 0.6825 ] Network output: [ -0.04181 0.185 1.108 0.0001899 -8.526e-05 0.7915 0.0001431 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2157 0.2081 0.2634 0.2147 0.9905 0.9943 0.2161 0.9817 0.9903 0.2781 ] Network output: [ -0.03927 0.0489 1.147 0.000447 -0.0002007 0.8843 0.0003369 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2249 0.2236 0.2748 0.2436 0.9853 0.9915 0.225 0.9676 0.9843 0.2784 ] Network output: [ -0.02427 1.062 0.0325 6.676e-05 -2.997e-05 0.9543 5.031e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06645 Epoch 3970 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05742 0.8546 0.9212 -3.661e-05 1.644e-05 0.1092 -2.759e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004642 -0.005827 -0.01449 0.01081 0.9581 0.965 0.01614 0.9251 0.9387 0.05759 ] Network output: [ 0.9426 0.2141 0.05859 0.0005253 -0.0002358 -0.1558 0.0003959 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.395 0.02817 -0.06344 0.1731 0.981 0.9921 0.5049 0.9377 0.9834 0.6872 ] Network output: [ 0.02324 0.8566 0.932 -0.0003121 0.0001401 0.1637 -0.0002352 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01195 0.006411 0.01476 0.01058 0.9904 0.9936 0.01249 0.9805 0.989 0.0244 ] Network output: [ 0.08208 -0.2141 0.7989 -0.0008561 0.0003843 1.248 -0.0006452 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4941 0.3868 0.4975 0.3379 0.9827 0.993 0.4988 0.9428 0.9853 0.6843 ] Network output: [ -0.0416 0.1861 1.107 0.0001937 -8.694e-05 0.7911 0.0001459 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2156 0.208 0.2631 0.214 0.9905 0.9943 0.2161 0.9817 0.9903 0.2779 ] Network output: [ -0.03911 0.04855 1.147 0.0004532 -0.0002035 0.8846 0.0003416 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2249 0.2236 0.2746 0.2432 0.9853 0.9915 0.225 0.9677 0.9844 0.2782 ] Network output: [ -0.02398 1.061 0.03232 7.129e-05 -3.201e-05 0.9552 5.373e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06626 Epoch 3971 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0574 0.8541 0.9214 -4.035e-05 1.812e-05 0.1095 -3.041e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004629 -0.005821 -0.01459 0.01076 0.9582 0.965 0.0161 0.9252 0.9388 0.05759 ] Network output: [ 0.9426 0.2137 0.05921 0.0005294 -0.0002377 -0.1561 0.000399 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3937 0.0276 -0.06497 0.1723 0.981 0.9921 0.5033 0.9378 0.9834 0.689 ] Network output: [ 0.02319 0.8562 0.9321 -0.0003161 0.0001419 0.164 -0.0002382 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01191 0.006364 0.01471 0.01051 0.9904 0.9936 0.01245 0.9805 0.9891 0.02441 ] Network output: [ 0.08154 -0.2124 0.7993 -0.0008671 0.0003893 1.247 -0.0006535 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4926 0.3849 0.4976 0.3361 0.9827 0.993 0.4973 0.9429 0.9854 0.686 ] Network output: [ -0.0414 0.1871 1.106 0.0001974 -8.864e-05 0.7908 0.0001488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2156 0.208 0.2628 0.2133 0.9905 0.9943 0.216 0.9817 0.9904 0.2776 ] Network output: [ -0.03894 0.04823 1.147 0.0004594 -0.0002062 0.885 0.0003462 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2248 0.2235 0.2744 0.2427 0.9853 0.9915 0.225 0.9677 0.9844 0.278 ] Network output: [ -0.02368 1.059 0.03213 7.584e-05 -3.405e-05 0.9561 5.715e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06605 Epoch 3972 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05739 0.8536 0.9216 -4.41e-05 1.98e-05 0.1099 -3.324e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004616 -0.005816 -0.01468 0.01072 0.9582 0.965 0.01607 0.9253 0.9389 0.05759 ] Network output: [ 0.9427 0.2132 0.05981 0.0005334 -0.0002395 -0.1562 0.000402 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3924 0.02704 -0.06649 0.1714 0.9811 0.9921 0.5017 0.9379 0.9834 0.6907 ] Network output: [ 0.02313 0.8559 0.9323 -0.00032 0.0001437 0.1642 -0.0002411 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01188 0.006317 0.01467 0.01044 0.9904 0.9936 0.01242 0.9805 0.9891 0.02442 ] Network output: [ 0.08097 -0.2108 0.7999 -0.0008781 0.0003942 1.245 -0.0006617 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4911 0.383 0.4977 0.3343 0.9827 0.993 0.4958 0.943 0.9854 0.6878 ] Network output: [ -0.0412 0.1882 1.105 0.0002012 -9.035e-05 0.7904 0.0001517 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2156 0.2079 0.2624 0.2126 0.9905 0.9943 0.216 0.9817 0.9904 0.2774 ] Network output: [ -0.03878 0.04794 1.146 0.0004656 -0.000209 0.8853 0.0003509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2248 0.2235 0.2741 0.2423 0.9854 0.9915 0.2249 0.9678 0.9844 0.2778 ] Network output: [ -0.02337 1.058 0.03192 8.038e-05 -3.609e-05 0.957 6.058e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06585 Epoch 3973 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05736 0.8532 0.9218 -4.786e-05 2.149e-05 0.1102 -3.607e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004605 -0.005811 -0.01478 0.01067 0.9582 0.965 0.01603 0.9255 0.939 0.05759 ] Network output: [ 0.9428 0.2127 0.06039 0.0005372 -0.0002412 -0.1564 0.0004048 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3912 0.02648 -0.068 0.1706 0.9811 0.9922 0.5001 0.938 0.9835 0.6923 ] Network output: [ 0.02307 0.8556 0.9325 -0.0003239 0.0001454 0.1644 -0.0002441 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01185 0.006271 0.01463 0.01037 0.9904 0.9936 0.01239 0.9805 0.9891 0.02443 ] Network output: [ 0.08038 -0.2091 0.8004 -0.0008889 0.0003991 1.244 -0.0006699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4896 0.3812 0.4979 0.3325 0.9827 0.993 0.4943 0.9431 0.9854 0.6895 ] Network output: [ -0.041 0.1892 1.104 0.0002051 -9.207e-05 0.79 0.0001546 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2156 0.2078 0.2621 0.2119 0.9905 0.9943 0.216 0.9817 0.9904 0.2772 ] Network output: [ -0.03861 0.04768 1.146 0.0004718 -0.0002118 0.8856 0.0003555 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2248 0.2235 0.2739 0.2418 0.9854 0.9915 0.2249 0.9678 0.9844 0.2776 ] Network output: [ -0.02305 1.057 0.03169 8.491e-05 -3.812e-05 0.958 6.399e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06563 Epoch 3974 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05733 0.8527 0.922 -5.162e-05 2.317e-05 0.1105 -3.89e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004595 -0.005806 -0.01487 0.01063 0.9583 0.9651 0.016 0.9256 0.9391 0.05759 ] Network output: [ 0.9428 0.2121 0.06094 0.0005407 -0.0002428 -0.1566 0.0004075 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.39 0.02592 -0.06949 0.1698 0.9811 0.9922 0.4985 0.9382 0.9835 0.694 ] Network output: [ 0.02299 0.8553 0.9327 -0.0003277 0.0001471 0.1647 -0.000247 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01182 0.006226 0.01458 0.01029 0.9904 0.9936 0.01236 0.9806 0.9891 0.02445 ] Network output: [ 0.07978 -0.2074 0.801 -0.0008996 0.0004039 1.243 -0.000678 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4882 0.3794 0.498 0.3307 0.9828 0.993 0.4928 0.9432 0.9855 0.6912 ] Network output: [ -0.04079 0.1903 1.103 0.0002089 -9.38e-05 0.7896 0.0001575 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2156 0.2077 0.2617 0.2112 0.9905 0.9943 0.216 0.9818 0.9904 0.2769 ] Network output: [ -0.03845 0.04745 1.145 0.0004779 -0.0002146 0.8859 0.0003602 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2248 0.2235 0.2736 0.2414 0.9854 0.9915 0.2249 0.9679 0.9845 0.2774 ] Network output: [ -0.02272 1.055 0.03144 8.942e-05 -4.014e-05 0.9589 6.739e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0654 Epoch 3975 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05729 0.8522 0.9222 -5.537e-05 2.486e-05 0.1107 -4.173e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004585 -0.0058 -0.01496 0.01059 0.9583 0.9651 0.01597 0.9257 0.9391 0.05759 ] Network output: [ 0.943 0.2115 0.06147 0.0005441 -0.0002443 -0.1567 0.0004101 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3888 0.02538 -0.07097 0.169 0.9811 0.9922 0.497 0.9383 0.9835 0.6956 ] Network output: [ 0.02291 0.855 0.9329 -0.0003315 0.0001488 0.1649 -0.0002498 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01179 0.006182 0.01454 0.01022 0.9904 0.9936 0.01232 0.9806 0.9891 0.02446 ] Network output: [ 0.07916 -0.2057 0.8016 -0.0009101 0.0004086 1.242 -0.0006859 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4867 0.3775 0.4981 0.329 0.9828 0.993 0.4913 0.9433 0.9855 0.6929 ] Network output: [ -0.04059 0.1913 1.101 0.0002128 -9.554e-05 0.7893 0.0001604 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2156 0.2077 0.2614 0.2105 0.9905 0.9943 0.216 0.9818 0.9904 0.2767 ] Network output: [ -0.03828 0.04725 1.145 0.0004841 -0.0002173 0.8862 0.0003648 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2248 0.2235 0.2734 0.2409 0.9854 0.9915 0.225 0.9679 0.9845 0.2771 ] Network output: [ -0.02238 1.054 0.03118 9.391e-05 -4.216e-05 0.9599 7.077e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06517 Epoch 3976 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05725 0.8518 0.9224 -5.913e-05 2.654e-05 0.111 -4.456e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004577 -0.005796 -0.01505 0.01055 0.9583 0.9651 0.01593 0.9258 0.9392 0.05759 ] Network output: [ 0.9431 0.2109 0.06198 0.0005473 -0.0002457 -0.1568 0.0004125 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3876 0.02484 -0.07243 0.1683 0.9811 0.9922 0.4954 0.9384 0.9836 0.6973 ] Network output: [ 0.02282 0.8548 0.9332 -0.0003353 0.0001505 0.1651 -0.0002527 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01176 0.006138 0.0145 0.01016 0.9905 0.9936 0.01229 0.9806 0.9891 0.02447 ] Network output: [ 0.07853 -0.204 0.8023 -0.0009206 0.0004133 1.241 -0.0006938 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4853 0.3757 0.4982 0.3272 0.9828 0.993 0.4899 0.9434 0.9855 0.6945 ] Network output: [ -0.04039 0.1924 1.1 0.0002167 -9.728e-05 0.7889 0.0001633 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2156 0.2076 0.2611 0.2099 0.9905 0.9943 0.216 0.9818 0.9904 0.2764 ] Network output: [ -0.03811 0.04708 1.145 0.0004902 -0.0002201 0.8864 0.0003695 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2249 0.2235 0.2731 0.2405 0.9854 0.9915 0.225 0.968 0.9845 0.2769 ] Network output: [ -0.02204 1.053 0.0309 9.836e-05 -4.416e-05 0.9609 7.412e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06493 Epoch 3977 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0572 0.8514 0.9227 -6.287e-05 2.822e-05 0.1113 -4.738e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004569 -0.005791 -0.01514 0.01051 0.9583 0.9651 0.0159 0.9259 0.9393 0.05759 ] Network output: [ 0.9433 0.2102 0.06247 0.0005503 -0.000247 -0.1569 0.0004147 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3864 0.0243 -0.07389 0.1675 0.9811 0.9922 0.4938 0.9385 0.9836 0.6989 ] Network output: [ 0.02271 0.8545 0.9334 -0.000339 0.0001522 0.1653 -0.0002555 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01173 0.006095 0.01446 0.01009 0.9905 0.9936 0.01226 0.9806 0.9892 0.02448 ] Network output: [ 0.07788 -0.2024 0.803 -0.0009308 0.0004179 1.24 -0.0007015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4838 0.3739 0.4983 0.3255 0.9828 0.993 0.4884 0.9435 0.9855 0.6962 ] Network output: [ -0.04019 0.1934 1.099 0.0002206 -9.904e-05 0.7886 0.0001663 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2156 0.2076 0.2607 0.2092 0.9905 0.9943 0.216 0.9818 0.9904 0.2762 ] Network output: [ -0.03795 0.04694 1.144 0.0004964 -0.0002228 0.8867 0.0003741 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2249 0.2235 0.2729 0.24 0.9854 0.9915 0.225 0.968 0.9845 0.2767 ] Network output: [ -0.02168 1.051 0.0306 0.0001028 -4.613e-05 0.9618 7.745e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06468 Epoch 3978 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05715 0.851 0.9229 -6.66e-05 2.99e-05 0.1115 -5.019e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004563 -0.005786 -0.01523 0.01047 0.9584 0.9651 0.01587 0.9261 0.9394 0.05759 ] Network output: [ 0.9434 0.2094 0.06294 0.0005531 -0.0002483 -0.1569 0.0004168 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3853 0.02378 -0.07532 0.1668 0.9811 0.9922 0.4923 0.9386 0.9836 0.7005 ] Network output: [ 0.0226 0.8543 0.9337 -0.0003426 0.0001538 0.1654 -0.0002582 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0117 0.006053 0.01441 0.01002 0.9905 0.9936 0.01223 0.9807 0.9892 0.02449 ] Network output: [ 0.07722 -0.2007 0.8038 -0.000941 0.0004224 1.239 -0.0007092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4824 0.3721 0.4984 0.3237 0.9828 0.9931 0.4869 0.9436 0.9856 0.6978 ] Network output: [ -0.03998 0.1944 1.098 0.0002245 -0.0001008 0.7882 0.0001692 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2156 0.2076 0.2604 0.2086 0.9905 0.9943 0.2161 0.9818 0.9905 0.2759 ] Network output: [ -0.03778 0.04682 1.144 0.0005025 -0.0002256 0.8869 0.0003787 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2249 0.2235 0.2726 0.2396 0.9854 0.9915 0.2251 0.9681 0.9846 0.2764 ] Network output: [ -0.02132 1.05 0.03028 0.0001071 -4.809e-05 0.9628 8.073e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06442 Epoch 3979 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05708 0.8506 0.9232 -7.032e-05 3.157e-05 0.1118 -5.3e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004557 -0.005781 -0.01531 0.01043 0.9584 0.9652 0.01584 0.9262 0.9395 0.05758 ] Network output: [ 0.9436 0.2086 0.06338 0.0005557 -0.0002495 -0.157 0.0004188 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3842 0.02325 -0.07675 0.1661 0.9812 0.9922 0.4907 0.9387 0.9836 0.702 ] Network output: [ 0.02248 0.8541 0.9339 -0.0003462 0.0001554 0.1656 -0.0002609 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01168 0.006012 0.01437 0.009954 0.9905 0.9936 0.0122 0.9807 0.9892 0.02449 ] Network output: [ 0.07655 -0.199 0.8045 -0.000951 0.0004269 1.238 -0.0007167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4809 0.3704 0.4985 0.322 0.9828 0.9931 0.4855 0.9437 0.9856 0.6994 ] Network output: [ -0.03978 0.1955 1.097 0.0002285 -0.0001026 0.7879 0.0001722 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2157 0.2076 0.2601 0.2079 0.9905 0.9943 0.2161 0.9818 0.9905 0.2757 ] Network output: [ -0.03761 0.04673 1.143 0.0005086 -0.0002283 0.8872 0.0003833 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.225 0.2236 0.2723 0.2391 0.9854 0.9915 0.2251 0.9681 0.9846 0.2762 ] Network output: [ -0.02095 1.049 0.02995 0.0001114 -5.002e-05 0.9639 8.398e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06416 Epoch 3980 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05702 0.8502 0.9235 -7.402e-05 3.323e-05 0.112 -5.578e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004553 -0.005777 -0.0154 0.01039 0.9584 0.9652 0.01581 0.9263 0.9396 0.05758 ] Network output: [ 0.9438 0.2078 0.06381 0.0005581 -0.0002506 -0.157 0.0004206 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3831 0.02274 -0.07815 0.1654 0.9812 0.9922 0.4892 0.9388 0.9837 0.7036 ] Network output: [ 0.02235 0.8539 0.9342 -0.0003498 0.000157 0.1658 -0.0002636 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01165 0.005971 0.01433 0.009889 0.9905 0.9936 0.01217 0.9807 0.9892 0.0245 ] Network output: [ 0.07586 -0.1973 0.8054 -0.0009608 0.0004313 1.236 -0.0007241 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4795 0.3686 0.4986 0.3203 0.9828 0.9931 0.484 0.9438 0.9856 0.701 ] Network output: [ -0.03958 0.1965 1.096 0.0002324 -0.0001044 0.7875 0.0001752 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2157 0.2075 0.2597 0.2073 0.9905 0.9943 0.2161 0.9819 0.9905 0.2755 ] Network output: [ -0.03744 0.04666 1.143 0.0005147 -0.0002311 0.8874 0.0003879 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2251 0.2236 0.272 0.2387 0.9854 0.9915 0.2252 0.9682 0.9846 0.2759 ] Network output: [ -0.02058 1.047 0.0296 0.0001157 -5.193e-05 0.9649 8.717e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06389 Epoch 3981 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05694 0.8498 0.9237 -7.77e-05 3.488e-05 0.1122 -5.856e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004549 -0.005772 -0.01548 0.01036 0.9584 0.9652 0.01578 0.9264 0.9397 0.05758 ] Network output: [ 0.9441 0.2069 0.06421 0.0005603 -0.0002516 -0.157 0.0004223 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.382 0.02223 -0.07955 0.1647 0.9812 0.9922 0.4877 0.9389 0.9837 0.7051 ] Network output: [ 0.02221 0.8537 0.9345 -0.0003533 0.0001586 0.1659 -0.0002662 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01162 0.005931 0.01429 0.009825 0.9905 0.9936 0.01215 0.9807 0.9892 0.02451 ] Network output: [ 0.07516 -0.1957 0.8062 -0.0009705 0.0004357 1.235 -0.0007314 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4781 0.3669 0.4987 0.3186 0.9828 0.9931 0.4826 0.9439 0.9856 0.7025 ] Network output: [ -0.03938 0.1975 1.095 0.0002364 -0.0001061 0.7872 0.0001782 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2158 0.2075 0.2594 0.2066 0.9905 0.9943 0.2162 0.9819 0.9905 0.2752 ] Network output: [ -0.03726 0.04661 1.142 0.0005208 -0.0002338 0.8876 0.0003925 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2251 0.2237 0.2718 0.2382 0.9854 0.9915 0.2252 0.9682 0.9846 0.2757 ] Network output: [ -0.0202 1.046 0.02924 0.0001198 -5.38e-05 0.9659 9.032e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06362 Epoch 3982 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05686 0.8495 0.924 -8.136e-05 3.652e-05 0.1125 -6.131e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004545 -0.005768 -0.01556 0.01032 0.9585 0.9652 0.01575 0.9265 0.9397 0.05757 ] Network output: [ 0.9443 0.206 0.06459 0.0005624 -0.0002525 -0.157 0.0004238 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3809 0.02173 -0.08093 0.164 0.9812 0.9922 0.4861 0.939 0.9837 0.7066 ] Network output: [ 0.02207 0.8536 0.9348 -0.0003567 0.0001601 0.166 -0.0002688 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0116 0.005891 0.01425 0.009762 0.9905 0.9936 0.01212 0.9808 0.9892 0.02452 ] Network output: [ 0.07445 -0.194 0.8071 -0.00098 0.00044 1.234 -0.0007386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4767 0.3651 0.4988 0.317 0.9828 0.9931 0.4811 0.944 0.9857 0.704 ] Network output: [ -0.03918 0.1985 1.094 0.0002404 -0.0001079 0.7868 0.0001812 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2159 0.2075 0.2591 0.206 0.9905 0.9943 0.2163 0.9819 0.9905 0.275 ] Network output: [ -0.03709 0.04659 1.142 0.0005269 -0.0002365 0.8878 0.0003971 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2252 0.2238 0.2715 0.2378 0.9854 0.9915 0.2253 0.9683 0.9847 0.2754 ] Network output: [ -0.01982 1.044 0.02886 0.0001239 -5.564e-05 0.967 9.341e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06334 Epoch 3983 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05677 0.8491 0.9243 -8.499e-05 3.815e-05 0.1127 -6.405e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004543 -0.005764 -0.01564 0.01029 0.9585 0.9652 0.01572 0.9267 0.9398 0.05757 ] Network output: [ 0.9446 0.2051 0.06495 0.0005642 -0.0002533 -0.1569 0.0004252 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3799 0.02123 -0.08229 0.1634 0.9812 0.9922 0.4846 0.9391 0.9838 0.7081 ] Network output: [ 0.02191 0.8534 0.9351 -0.0003601 0.0001617 0.1661 -0.0002714 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01157 0.005853 0.01421 0.009699 0.9905 0.9936 0.01209 0.9808 0.9892 0.02453 ] Network output: [ 0.07373 -0.1923 0.808 -0.0009894 0.0004442 1.233 -0.0007456 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4753 0.3634 0.4989 0.3153 0.9829 0.9931 0.4797 0.9441 0.9857 0.7056 ] Network output: [ -0.03898 0.1995 1.093 0.0002444 -0.0001097 0.7865 0.0001842 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2159 0.2076 0.2587 0.2054 0.9906 0.9943 0.2163 0.9819 0.9905 0.2748 ] Network output: [ -0.03692 0.04659 1.141 0.0005329 -0.0002393 0.888 0.0004016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2253 0.2238 0.2712 0.2374 0.9854 0.9915 0.2254 0.9683 0.9847 0.2752 ] Network output: [ -0.01943 1.043 0.02847 0.000128 -5.745e-05 0.968 9.643e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06305 Epoch 3984 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05668 0.8488 0.9246 -8.859e-05 3.977e-05 0.1129 -6.676e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004542 -0.005759 -0.01572 0.01025 0.9585 0.9653 0.01569 0.9268 0.9399 0.05756 ] Network output: [ 0.9449 0.2041 0.06529 0.0005659 -0.0002541 -0.1568 0.0004265 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3789 0.02074 -0.08364 0.1627 0.9812 0.9922 0.4831 0.9392 0.9838 0.7095 ] Network output: [ 0.02175 0.8533 0.9355 -0.0003634 0.0001631 0.1662 -0.0002739 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01155 0.005815 0.01417 0.009638 0.9905 0.9936 0.01207 0.9808 0.9893 0.02454 ] Network output: [ 0.07301 -0.1907 0.8089 -0.0009986 0.0004483 1.232 -0.0007526 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4739 0.3617 0.499 0.3137 0.9829 0.9931 0.4783 0.9442 0.9857 0.707 ] Network output: [ -0.03878 0.2004 1.092 0.0002484 -0.0001115 0.7862 0.0001872 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.216 0.2076 0.2584 0.2048 0.9906 0.9943 0.2164 0.9819 0.9905 0.2745 ] Network output: [ -0.03675 0.04662 1.141 0.000539 -0.000242 0.8882 0.0004062 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2254 0.2239 0.2709 0.2369 0.9854 0.9915 0.2255 0.9684 0.9847 0.2749 ] Network output: [ -0.01904 1.041 0.02806 0.0001319 -5.921e-05 0.9691 9.94e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06276 Epoch 3985 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05658 0.8485 0.9249 -9.216e-05 4.137e-05 0.113 -6.945e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004541 -0.005755 -0.0158 0.01022 0.9585 0.9653 0.01566 0.9269 0.94 0.05755 ] Network output: [ 0.9452 0.2031 0.0656 0.0005674 -0.0002547 -0.1568 0.0004276 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3779 0.02025 -0.08498 0.1621 0.9812 0.9922 0.4817 0.9393 0.9838 0.711 ] Network output: [ 0.02158 0.8532 0.9358 -0.0003666 0.0001646 0.1663 -0.0002763 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01153 0.005777 0.01413 0.009577 0.9905 0.9936 0.01204 0.9809 0.9893 0.02455 ] Network output: [ 0.07227 -0.189 0.8098 -0.001008 0.0004524 1.231 -0.0007594 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4725 0.36 0.499 0.312 0.9829 0.9931 0.4769 0.9442 0.9857 0.7085 ] Network output: [ -0.03858 0.2014 1.091 0.0002524 -0.0001133 0.7859 0.0001902 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2161 0.2076 0.2581 0.2042 0.9906 0.9943 0.2165 0.9819 0.9905 0.2743 ] Network output: [ -0.03657 0.04666 1.14 0.000545 -0.0002447 0.8883 0.0004107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2255 0.224 0.2706 0.2365 0.9854 0.9915 0.2256 0.9684 0.9847 0.2746 ] Network output: [ -0.01864 1.04 0.02764 0.0001357 -6.094e-05 0.9702 0.0001023 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06246 Epoch 3986 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05648 0.8482 0.9252 -9.569e-05 4.296e-05 0.1132 -7.212e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004541 -0.005751 -0.01588 0.01018 0.9586 0.9653 0.01564 0.927 0.9401 0.05755 ] Network output: [ 0.9455 0.2021 0.0659 0.0005687 -0.0002553 -0.1566 0.0004286 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3769 0.01977 -0.0863 0.1615 0.9812 0.9922 0.4802 0.9394 0.9839 0.7124 ] Network output: [ 0.0214 0.8531 0.9361 -0.0003698 0.000166 0.1664 -0.0002787 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0115 0.00574 0.01409 0.009518 0.9905 0.9936 0.01201 0.9809 0.9893 0.02455 ] Network output: [ 0.07153 -0.1874 0.8108 -0.001016 0.0004563 1.229 -0.0007661 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4711 0.3584 0.4991 0.3104 0.9829 0.9931 0.4755 0.9443 0.9858 0.71 ] Network output: [ -0.03838 0.2024 1.09 0.0002564 -0.0001151 0.7856 0.0001932 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2162 0.2077 0.2578 0.2036 0.9906 0.9943 0.2166 0.982 0.9905 0.274 ] Network output: [ -0.0364 0.04672 1.14 0.000551 -0.0002473 0.8885 0.0004152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2256 0.2241 0.2704 0.236 0.9855 0.9915 0.2257 0.9685 0.9847 0.2744 ] Network output: [ -0.01824 1.039 0.0272 0.0001395 -6.262e-05 0.9712 0.0001051 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06216 Epoch 3987 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05637 0.8479 0.9256 -9.919e-05 4.453e-05 0.1134 -7.475e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004541 -0.005747 -0.01595 0.01015 0.9586 0.9653 0.01561 0.9271 0.9401 0.05754 ] Network output: [ 0.9458 0.201 0.06618 0.0005698 -0.0002558 -0.1565 0.0004295 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3759 0.0193 -0.0876 0.1609 0.9813 0.9922 0.4787 0.9395 0.9839 0.7138 ] Network output: [ 0.02122 0.8531 0.9365 -0.0003729 0.0001674 0.1665 -0.000281 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01148 0.005704 0.01405 0.009459 0.9905 0.9937 0.01199 0.9809 0.9893 0.02456 ] Network output: [ 0.07078 -0.1857 0.8118 -0.001025 0.0004602 1.228 -0.0007726 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4697 0.3567 0.4992 0.3088 0.9829 0.9931 0.4741 0.9444 0.9858 0.7114 ] Network output: [ -0.03818 0.2033 1.089 0.0002604 -0.0001169 0.7853 0.0001963 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2163 0.2077 0.2575 0.203 0.9906 0.9944 0.2167 0.982 0.9906 0.2738 ] Network output: [ -0.03622 0.0468 1.139 0.0005569 -0.00025 0.8887 0.0004197 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2257 0.2242 0.2701 0.2356 0.9855 0.9915 0.2258 0.9685 0.9848 0.2741 ] Network output: [ -0.01784 1.037 0.02676 0.0001431 -6.426e-05 0.9723 0.0001079 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06185 Epoch 3988 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05625 0.8476 0.9259 -0.0001027 4.609e-05 0.1135 -7.736e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004542 -0.005744 -0.01603 0.01012 0.9586 0.9653 0.01558 0.9272 0.9402 0.05753 ] Network output: [ 0.9462 0.1999 0.06644 0.0005708 -0.0002563 -0.1564 0.0004302 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.375 0.01883 -0.08889 0.1603 0.9813 0.9923 0.4773 0.9396 0.9839 0.7152 ] Network output: [ 0.02103 0.853 0.9369 -0.000376 0.0001688 0.1665 -0.0002833 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01146 0.005669 0.01401 0.009401 0.9905 0.9937 0.01196 0.9809 0.9893 0.02457 ] Network output: [ 0.07002 -0.1841 0.8129 -0.001034 0.0004641 1.227 -0.0007791 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4684 0.3551 0.4992 0.3073 0.9829 0.9931 0.4727 0.9445 0.9858 0.7128 ] Network output: [ -0.03799 0.2042 1.088 0.0002645 -0.0001187 0.785 0.0001993 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2164 0.2078 0.2572 0.2024 0.9906 0.9944 0.2168 0.982 0.9906 0.2736 ] Network output: [ -0.03605 0.04689 1.139 0.0005628 -0.0002527 0.8888 0.0004242 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2259 0.2243 0.2698 0.2351 0.9855 0.9916 0.226 0.9686 0.9848 0.2738 ] Network output: [ -0.01744 1.036 0.0263 0.0001467 -6.585e-05 0.9734 0.0001105 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06154 Epoch 3989 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05613 0.8474 0.9262 -0.0001061 4.762e-05 0.1137 -7.994e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004544 -0.00574 -0.0161 0.01009 0.9586 0.9654 0.01556 0.9273 0.9403 0.05752 ] Network output: [ 0.9465 0.1988 0.06667 0.0005716 -0.0002566 -0.1562 0.0004308 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.374 0.01837 -0.09016 0.1598 0.9813 0.9923 0.4758 0.9397 0.984 0.7165 ] Network output: [ 0.02083 0.853 0.9372 -0.0003789 0.0001701 0.1666 -0.0002856 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01144 0.005634 0.01397 0.009344 0.9906 0.9937 0.01194 0.981 0.9893 0.02458 ] Network output: [ 0.06926 -0.1825 0.8139 -0.001042 0.0004678 1.226 -0.0007854 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4671 0.3535 0.4993 0.3057 0.9829 0.9931 0.4713 0.9446 0.9858 0.7142 ] Network output: [ -0.03779 0.2052 1.087 0.0002685 -0.0001205 0.7847 0.0002024 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2166 0.2078 0.2569 0.2019 0.9906 0.9944 0.217 0.982 0.9906 0.2734 ] Network output: [ -0.03587 0.047 1.138 0.0005688 -0.0002553 0.889 0.0004286 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.226 0.2245 0.2695 0.2347 0.9855 0.9916 0.2261 0.9686 0.9848 0.2736 ] Network output: [ -0.01704 1.034 0.02583 0.0001501 -6.739e-05 0.9745 0.0001131 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06122 Epoch 3990 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05601 0.8471 0.9266 -0.0001095 4.914e-05 0.1138 -8.249e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004547 -0.005736 -0.01617 0.01006 0.9587 0.9654 0.01553 0.9275 0.9404 0.05752 ] Network output: [ 0.9469 0.1977 0.06689 0.0005722 -0.0002569 -0.156 0.0004313 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3731 0.01791 -0.09142 0.1592 0.9813 0.9923 0.4744 0.9398 0.984 0.7179 ] Network output: [ 0.02063 0.8529 0.9376 -0.0003818 0.0001714 0.1666 -0.0002878 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01142 0.005599 0.01394 0.009288 0.9906 0.9937 0.01192 0.981 0.9893 0.02458 ] Network output: [ 0.0685 -0.1809 0.815 -0.00105 0.0004715 1.225 -0.0007915 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4657 0.3519 0.4993 0.3042 0.9829 0.9931 0.47 0.9447 0.9859 0.7155 ] Network output: [ -0.03759 0.2061 1.086 0.0002725 -0.0001224 0.7844 0.0002054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2167 0.2079 0.2565 0.2013 0.9906 0.9944 0.2171 0.982 0.9906 0.2731 ] Network output: [ -0.03569 0.04713 1.137 0.0005746 -0.000258 0.8891 0.0004331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2261 0.2246 0.2692 0.2343 0.9855 0.9916 0.2262 0.9687 0.9848 0.2733 ] Network output: [ -0.01663 1.033 0.02535 0.0001534 -6.889e-05 0.9756 0.0001156 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0609 Epoch 3991 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05588 0.8469 0.9269 -0.0001128 5.064e-05 0.114 -8.5e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00455 -0.005733 -0.01624 0.01003 0.9587 0.9654 0.01551 0.9276 0.9405 0.05751 ] Network output: [ 0.9473 0.1965 0.06709 0.0005727 -0.0002571 -0.1558 0.0004316 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3722 0.01746 -0.09266 0.1587 0.9813 0.9923 0.473 0.9399 0.984 0.7192 ] Network output: [ 0.02042 0.8529 0.938 -0.0003846 0.0001727 0.1666 -0.0002899 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0114 0.005566 0.0139 0.009233 0.9906 0.9937 0.0119 0.981 0.9894 0.02459 ] Network output: [ 0.06773 -0.1793 0.8161 -0.001058 0.0004751 1.223 -0.0007976 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4644 0.3503 0.4994 0.3027 0.9829 0.9931 0.4686 0.9448 0.9859 0.7169 ] Network output: [ -0.03739 0.207 1.085 0.0002766 -0.0001242 0.7841 0.0002084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2168 0.208 0.2562 0.2007 0.9906 0.9944 0.2172 0.982 0.9906 0.2729 ] Network output: [ -0.03551 0.04727 1.137 0.0005805 -0.0002606 0.8892 0.0004375 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2263 0.2247 0.2689 0.2338 0.9855 0.9916 0.2264 0.9687 0.9849 0.273 ] Network output: [ -0.01622 1.031 0.02486 0.0001567 -7.033e-05 0.9767 0.0001181 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06058 Epoch 3992 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05574 0.8467 0.9273 -0.0001161 5.211e-05 0.1141 -8.748e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004553 -0.00573 -0.01631 0.01 0.9587 0.9654 0.01548 0.9277 0.9405 0.0575 ] Network output: [ 0.9477 0.1953 0.06727 0.000573 -0.0002572 -0.1556 0.0004318 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3714 0.01701 -0.09389 0.1581 0.9813 0.9923 0.4716 0.94 0.984 0.7205 ] Network output: [ 0.0202 0.853 0.9384 -0.0003874 0.0001739 0.1667 -0.0002919 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01138 0.005533 0.01386 0.009179 0.9906 0.9937 0.01187 0.981 0.9894 0.0246 ] Network output: [ 0.06696 -0.1777 0.8173 -0.001066 0.0004786 1.222 -0.0008035 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4631 0.3488 0.4994 0.3011 0.983 0.9931 0.4673 0.9449 0.9859 0.7182 ] Network output: [ -0.0372 0.2078 1.084 0.0002806 -0.000126 0.7839 0.0002115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.217 0.208 0.2559 0.2002 0.9906 0.9944 0.2174 0.982 0.9906 0.2727 ] Network output: [ -0.03533 0.04742 1.136 0.0005863 -0.0002632 0.8894 0.0004419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2265 0.2249 0.2686 0.2334 0.9855 0.9916 0.2266 0.9688 0.9849 0.2728 ] Network output: [ -0.01582 1.03 0.02436 0.0001598 -7.172e-05 0.9779 0.0001204 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06026 Epoch 3993 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0556 0.8465 0.9276 -0.0001193 5.357e-05 0.1142 -8.992e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004557 -0.005726 -0.01637 0.009974 0.9587 0.9654 0.01546 0.9278 0.9406 0.05749 ] Network output: [ 0.9481 0.1941 0.06744 0.0005731 -0.0002573 -0.1553 0.0004319 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3705 0.01657 -0.0951 0.1576 0.9813 0.9923 0.4702 0.9401 0.9841 0.7217 ] Network output: [ 0.01998 0.853 0.9388 -0.0003901 0.0001751 0.1667 -0.000294 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01136 0.0055 0.01383 0.009125 0.9906 0.9937 0.01185 0.9811 0.9894 0.0246 ] Network output: [ 0.06618 -0.1761 0.8184 -0.001074 0.0004821 1.221 -0.0008093 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4618 0.3472 0.4995 0.2997 0.983 0.9931 0.466 0.9449 0.9859 0.7195 ] Network output: [ -0.037 0.2087 1.083 0.0002847 -0.0001278 0.7836 0.0002145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2171 0.2081 0.2556 0.1996 0.9906 0.9944 0.2175 0.9821 0.9906 0.2725 ] Network output: [ -0.03515 0.04759 1.136 0.0005921 -0.0002658 0.8895 0.0004462 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2266 0.2251 0.2683 0.233 0.9855 0.9916 0.2267 0.9688 0.9849 0.2725 ] Network output: [ -0.01541 1.029 0.02385 0.0001627 -7.306e-05 0.979 0.0001226 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05993 Epoch 3994 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05546 0.8463 0.928 -0.0001225 5.5e-05 0.1143 -9.233e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004562 -0.005723 -0.01644 0.009946 0.9588 0.9655 0.01544 0.9279 0.9407 0.05748 ] Network output: [ 0.9485 0.1928 0.06758 0.0005731 -0.0002573 -0.1551 0.0004319 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3697 0.01613 -0.09629 0.1571 0.9814 0.9923 0.4689 0.9402 0.9841 0.723 ] Network output: [ 0.01975 0.853 0.9392 -0.0003926 0.0001763 0.1666 -0.0002959 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01134 0.005468 0.01379 0.009072 0.9906 0.9937 0.01183 0.9811 0.9894 0.02461 ] Network output: [ 0.0654 -0.1745 0.8196 -0.001081 0.0004854 1.22 -0.0008149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4605 0.3457 0.4995 0.2982 0.983 0.9931 0.4647 0.945 0.986 0.7207 ] Network output: [ -0.03681 0.2096 1.082 0.0002887 -0.0001296 0.7834 0.0002176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2173 0.2082 0.2554 0.1991 0.9906 0.9944 0.2177 0.9821 0.9906 0.2722 ] Network output: [ -0.03497 0.04777 1.135 0.0005979 -0.0002684 0.8896 0.0004506 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2268 0.2252 0.268 0.2326 0.9855 0.9916 0.2269 0.9689 0.9849 0.2722 ] Network output: [ -0.01501 1.027 0.02333 0.0001656 -7.434e-05 0.9801 0.0001248 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05959 Epoch 3995 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05531 0.8461 0.9284 -0.0001257 5.641e-05 0.1144 -9.47e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004567 -0.00572 -0.0165 0.009919 0.9588 0.9655 0.01541 0.928 0.9408 0.05747 ] Network output: [ 0.9489 0.1916 0.06771 0.0005729 -0.0002572 -0.1548 0.0004318 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3689 0.0157 -0.09747 0.1566 0.9814 0.9923 0.4675 0.9403 0.9841 0.7242 ] Network output: [ 0.01952 0.8531 0.9396 -0.0003952 0.0001774 0.1666 -0.0002978 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01132 0.005437 0.01375 0.00902 0.9906 0.9937 0.01181 0.9811 0.9894 0.02461 ] Network output: [ 0.06463 -0.1729 0.8207 -0.001089 0.0004887 1.219 -0.0008204 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4593 0.3442 0.4995 0.2967 0.983 0.9931 0.4634 0.9451 0.986 0.722 ] Network output: [ -0.03661 0.2104 1.081 0.0002928 -0.0001314 0.7831 0.0002206 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2174 0.2083 0.2551 0.1986 0.9906 0.9944 0.2178 0.9821 0.9906 0.272 ] Network output: [ -0.03479 0.04795 1.134 0.0006036 -0.000271 0.8897 0.0004549 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.227 0.2254 0.2677 0.2321 0.9855 0.9916 0.2271 0.9689 0.9849 0.2719 ] Network output: [ -0.0146 1.026 0.02281 0.0001683 -7.557e-05 0.9813 0.0001269 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05926 Epoch 3996 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05515 0.846 0.9287 -0.0001287 5.78e-05 0.1145 -9.703e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004572 -0.005717 -0.01657 0.009893 0.9588 0.9655 0.01539 0.9281 0.9408 0.05746 ] Network output: [ 0.9494 0.1903 0.06782 0.0005726 -0.000257 -0.1545 0.0004315 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3681 0.01528 -0.09863 0.1562 0.9814 0.9923 0.4662 0.9404 0.9841 0.7255 ] Network output: [ 0.01928 0.8532 0.9401 -0.0003976 0.0001785 0.1666 -0.0002997 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0113 0.005406 0.01372 0.008969 0.9906 0.9937 0.01179 0.9811 0.9894 0.02462 ] Network output: [ 0.06385 -0.1714 0.8219 -0.001096 0.0004919 1.217 -0.0008258 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.458 0.3427 0.4996 0.2953 0.983 0.9931 0.4621 0.9452 0.986 0.7232 ] Network output: [ -0.03642 0.2112 1.08 0.0002968 -0.0001332 0.7829 0.0002237 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2176 0.2084 0.2548 0.198 0.9906 0.9944 0.218 0.9821 0.9906 0.2718 ] Network output: [ -0.03461 0.04815 1.134 0.0006093 -0.0002736 0.8898 0.0004592 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2272 0.2256 0.2674 0.2317 0.9855 0.9916 0.2273 0.969 0.985 0.2717 ] Network output: [ -0.0142 1.024 0.02228 0.0001709 -7.674e-05 0.9824 0.0001288 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05892 Epoch 3997 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05499 0.8458 0.9291 -0.0001318 5.916e-05 0.1145 -9.932e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004578 -0.005714 -0.01663 0.009868 0.9588 0.9655 0.01537 0.9282 0.9409 0.05745 ] Network output: [ 0.9498 0.189 0.06791 0.0005721 -0.0002568 -0.1542 0.0004311 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3673 0.01486 -0.09978 0.1557 0.9814 0.9923 0.4649 0.9405 0.9842 0.7266 ] Network output: [ 0.01904 0.8533 0.9405 -0.0004 0.0001796 0.1665 -0.0003014 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01129 0.005376 0.01368 0.008919 0.9906 0.9937 0.01177 0.9812 0.9894 0.02463 ] Network output: [ 0.06307 -0.1698 0.8232 -0.001103 0.0004951 1.216 -0.0008311 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4568 0.3412 0.4996 0.2938 0.983 0.9931 0.4608 0.9453 0.986 0.7244 ] Network output: [ -0.03622 0.212 1.079 0.0003009 -0.0001351 0.7827 0.0002267 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2178 0.2085 0.2545 0.1975 0.9906 0.9944 0.2182 0.9821 0.9907 0.2716 ] Network output: [ -0.03443 0.04835 1.133 0.000615 -0.0002761 0.8899 0.0004635 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2274 0.2258 0.2672 0.2313 0.9856 0.9916 0.2275 0.969 0.985 0.2714 ] Network output: [ -0.0138 1.023 0.02174 0.0001734 -7.786e-05 0.9835 0.0001307 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05858 Epoch 3998 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05483 0.8457 0.9295 -0.0001348 6.05e-05 0.1146 -0.0001016 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004585 -0.005712 -0.01669 0.009842 0.9589 0.9655 0.01535 0.9283 0.941 0.05743 ] Network output: [ 0.9502 0.1877 0.06799 0.0005714 -0.0002565 -0.1539 0.0004306 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3665 0.01444 -0.1009 0.1552 0.9814 0.9923 0.4636 0.9406 0.9842 0.7278 ] Network output: [ 0.01879 0.8534 0.9409 -0.0004023 0.0001806 0.1665 -0.0003032 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01127 0.005346 0.01365 0.008869 0.9906 0.9937 0.01175 0.9812 0.9895 0.02463 ] Network output: [ 0.06229 -0.1683 0.8244 -0.00111 0.0004981 1.215 -0.0008362 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4556 0.3398 0.4996 0.2924 0.983 0.9932 0.4596 0.9454 0.986 0.7256 ] Network output: [ -0.03603 0.2128 1.078 0.0003049 -0.0001369 0.7824 0.0002298 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.218 0.2087 0.2542 0.197 0.9906 0.9944 0.2183 0.9821 0.9907 0.2714 ] Network output: [ -0.03424 0.04857 1.132 0.0006207 -0.0002786 0.8901 0.0004678 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2276 0.226 0.2669 0.2309 0.9856 0.9916 0.2277 0.9691 0.985 0.2711 ] Network output: [ -0.0134 1.022 0.02119 0.0001758 -7.891e-05 0.9847 0.0001325 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05824 Epoch 3999 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05466 0.8456 0.9299 -0.0001377 6.182e-05 0.1146 -0.0001038 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004591 -0.005709 -0.01675 0.009818 0.9589 0.9656 0.01533 0.9284 0.9411 0.05742 ] Network output: [ 0.9507 0.1864 0.06805 0.0005706 -0.0002562 -0.1535 0.00043 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3657 0.01403 -0.102 0.1548 0.9814 0.9923 0.4623 0.9407 0.9842 0.729 ] Network output: [ 0.01854 0.8535 0.9414 -0.0004045 0.0001816 0.1664 -0.0003048 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01125 0.005317 0.01362 0.008821 0.9906 0.9937 0.01173 0.9812 0.9895 0.02464 ] Network output: [ 0.06151 -0.1668 0.8256 -0.001116 0.0005011 1.214 -0.0008412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4544 0.3383 0.4996 0.291 0.983 0.9932 0.4584 0.9454 0.9861 0.7268 ] Network output: [ -0.03583 0.2136 1.077 0.0003089 -0.0001387 0.7822 0.0002328 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2181 0.2088 0.254 0.1965 0.9906 0.9944 0.2185 0.9821 0.9907 0.2712 ] Network output: [ -0.03406 0.04879 1.132 0.0006263 -0.0002812 0.8902 0.000472 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2278 0.2262 0.2666 0.2305 0.9856 0.9916 0.2279 0.9691 0.985 0.2709 ] Network output: [ -0.013 1.02 0.02065 0.000178 -7.991e-05 0.9858 0.0001341 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0579 Epoch 4000 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05449 0.8455 0.9302 -0.0001406 6.311e-05 0.1147 -0.0001059 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004599 -0.005707 -0.01681 0.009794 0.9589 0.9656 0.01531 0.9285 0.9411 0.05741 ] Network output: [ 0.9512 0.1851 0.06809 0.0005697 -0.0002557 -0.1532 0.0004293 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.365 0.01363 -0.1031 0.1544 0.9814 0.9923 0.461 0.9408 0.9842 0.7301 ] Network output: [ 0.01829 0.8536 0.9418 -0.0004066 0.0001825 0.1663 -0.0003064 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01124 0.005288 0.01358 0.008773 0.9906 0.9937 0.01171 0.9812 0.9895 0.02464 ] Network output: [ 0.06073 -0.1652 0.8269 -0.001123 0.000504 1.212 -0.0008461 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4532 0.3369 0.4997 0.2897 0.983 0.9932 0.4571 0.9455 0.9861 0.728 ] Network output: [ -0.03564 0.2144 1.076 0.000313 -0.0001405 0.782 0.0002359 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2183 0.2089 0.2537 0.196 0.9906 0.9944 0.2187 0.9822 0.9907 0.271 ] Network output: [ -0.03387 0.04901 1.131 0.0006319 -0.0002837 0.8903 0.0004762 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.228 0.2264 0.2663 0.2301 0.9856 0.9916 0.2281 0.9692 0.985 0.2706 ] Network output: [ -0.01261 1.019 0.02009 0.0001801 -8.085e-05 0.987 0.0001357 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05755 Epoch 4001 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05432 0.8454 0.9306 -0.0001434 6.437e-05 0.1147 -0.0001081 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004606 -0.005704 -0.01686 0.00977 0.9589 0.9656 0.01529 0.9286 0.9412 0.0574 ] Network output: [ 0.9516 0.1837 0.06812 0.0005686 -0.0002553 -0.1528 0.0004285 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3643 0.01323 -0.1042 0.154 0.9814 0.9923 0.4598 0.9408 0.9843 0.7312 ] Network output: [ 0.01803 0.8537 0.9423 -0.0004086 0.0001835 0.1662 -0.000308 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01122 0.00526 0.01355 0.008726 0.9906 0.9937 0.0117 0.9813 0.9895 0.02465 ] Network output: [ 0.05996 -0.1637 0.8282 -0.001129 0.0005068 1.211 -0.0008508 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.452 0.3355 0.4997 0.2883 0.983 0.9932 0.4559 0.9456 0.9861 0.7291 ] Network output: [ -0.03545 0.2151 1.075 0.000317 -0.0001423 0.7818 0.0002389 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2185 0.2091 0.2534 0.1955 0.9906 0.9944 0.2189 0.9822 0.9907 0.2708 ] Network output: [ -0.03368 0.04924 1.13 0.0006374 -0.0002862 0.8904 0.0004804 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2283 0.2266 0.266 0.2297 0.9856 0.9916 0.2284 0.9692 0.9851 0.2703 ] Network output: [ -0.01221 1.018 0.01954 0.0001821 -8.174e-05 0.9881 0.0001372 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0572 Epoch 4002 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05414 0.8453 0.931 -0.0001461 6.561e-05 0.1147 -0.0001101 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004614 -0.005702 -0.01692 0.009747 0.959 0.9656 0.01527 0.9287 0.9413 0.05739 ] Network output: [ 0.9521 0.1824 0.06813 0.0005673 -0.0002547 -0.1524 0.0004276 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3636 0.01283 -0.1053 0.1535 0.9815 0.9923 0.4586 0.9409 0.9843 0.7323 ] Network output: [ 0.01777 0.8539 0.9428 -0.0004106 0.0001843 0.1661 -0.0003095 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01121 0.005232 0.01352 0.008679 0.9906 0.9937 0.01168 0.9813 0.9895 0.02465 ] Network output: [ 0.05918 -0.1623 0.8294 -0.001135 0.0005096 1.21 -0.0008555 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4508 0.3341 0.4997 0.287 0.9831 0.9932 0.4547 0.9457 0.9861 0.7302 ] Network output: [ -0.03526 0.2159 1.074 0.000321 -0.0001441 0.7816 0.0002419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2188 0.2092 0.2532 0.1951 0.9906 0.9944 0.2191 0.9822 0.9907 0.2706 ] Network output: [ -0.0335 0.04948 1.13 0.000643 -0.0002887 0.8905 0.0004846 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2285 0.2268 0.2657 0.2293 0.9856 0.9916 0.2286 0.9693 0.9851 0.2701 ] Network output: [ -0.01183 1.016 0.01898 0.0001839 -8.256e-05 0.9892 0.0001386 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05686 Epoch 4003 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05396 0.8453 0.9314 -0.0001489 6.682e-05 0.1148 -0.0001122 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004622 -0.0057 -0.01697 0.009725 0.959 0.9656 0.01525 0.9288 0.9413 0.05737 ] Network output: [ 0.9526 0.181 0.06813 0.000566 -0.0002541 -0.152 0.0004265 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3629 0.01244 -0.1063 0.1531 0.9815 0.9923 0.4574 0.941 0.9843 0.7334 ] Network output: [ 0.0175 0.8541 0.9432 -0.0004125 0.0001852 0.166 -0.0003109 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01119 0.005205 0.01349 0.008633 0.9907 0.9937 0.01166 0.9813 0.9895 0.02466 ] Network output: [ 0.05841 -0.1608 0.8307 -0.001141 0.0005123 1.209 -0.0008599 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4497 0.3328 0.4997 0.2856 0.9831 0.9932 0.4536 0.9458 0.9861 0.7313 ] Network output: [ -0.03506 0.2166 1.073 0.0003251 -0.0001459 0.7815 0.000245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.219 0.2094 0.2529 0.1946 0.9907 0.9944 0.2193 0.9822 0.9907 0.2704 ] Network output: [ -0.03331 0.04972 1.129 0.0006485 -0.0002911 0.8906 0.0004887 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2287 0.227 0.2655 0.2289 0.9856 0.9916 0.2288 0.9693 0.9851 0.2698 ] Network output: [ -0.01144 1.015 0.01841 0.0001856 -8.333e-05 0.9904 0.0001399 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05651 Epoch 4004 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05378 0.8452 0.9318 -0.0001515 6.801e-05 0.1148 -0.0001142 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00463 -0.005698 -0.01702 0.009703 0.959 0.9657 0.01523 0.9289 0.9414 0.05736 ] Network output: [ 0.9531 0.1796 0.06811 0.0005644 -0.0002534 -0.1516 0.0004254 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3622 0.01206 -0.1074 0.1527 0.9815 0.9923 0.4562 0.9411 0.9843 0.7345 ] Network output: [ 0.01724 0.8543 0.9437 -0.0004143 0.000186 0.1659 -0.0003122 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01118 0.005178 0.01346 0.008588 0.9907 0.9937 0.01164 0.9813 0.9895 0.02466 ] Network output: [ 0.05764 -0.1593 0.832 -0.001147 0.0005149 1.207 -0.0008643 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4486 0.3314 0.4997 0.2843 0.9831 0.9932 0.4524 0.9458 0.9862 0.7324 ] Network output: [ -0.03487 0.2173 1.073 0.0003291 -0.0001477 0.7813 0.000248 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2192 0.2095 0.2527 0.1941 0.9907 0.9944 0.2196 0.9822 0.9907 0.2702 ] Network output: [ -0.03312 0.04996 1.128 0.000654 -0.0002936 0.8907 0.0004928 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.229 0.2273 0.2652 0.2285 0.9856 0.9916 0.2291 0.9694 0.9851 0.2695 ] Network output: [ -0.01106 1.013 0.01785 0.0001872 -8.404e-05 0.9915 0.0001411 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05616 Epoch 4005 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05359 0.8452 0.9322 -0.0001541 6.917e-05 0.1148 -0.0001161 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004639 -0.005696 -0.01708 0.009682 0.959 0.9657 0.01521 0.929 0.9415 0.05735 ] Network output: [ 0.9536 0.1783 0.06807 0.0005628 -0.0002527 -0.1512 0.0004241 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3615 0.01168 -0.1084 0.1524 0.9815 0.9924 0.455 0.9412 0.9844 0.7355 ] Network output: [ 0.01696 0.8544 0.9442 -0.0004161 0.0001868 0.1658 -0.0003136 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01117 0.005152 0.01342 0.008544 0.9907 0.9937 0.01163 0.9814 0.9895 0.02467 ] Network output: [ 0.05688 -0.1578 0.8333 -0.001153 0.0005174 1.206 -0.0008686 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4474 0.3301 0.4997 0.283 0.9831 0.9932 0.4512 0.9459 0.9862 0.7335 ] Network output: [ -0.03468 0.218 1.072 0.0003331 -0.0001495 0.7811 0.000251 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2194 0.2097 0.2524 0.1937 0.9907 0.9944 0.2198 0.9822 0.9907 0.2701 ] Network output: [ -0.03293 0.0502 1.128 0.0006594 -0.000296 0.8908 0.0004969 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2292 0.2275 0.2649 0.2281 0.9856 0.9916 0.2293 0.9694 0.9851 0.2693 ] Network output: [ -0.01068 1.012 0.01728 0.0001886 -8.469e-05 0.9927 0.0001422 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05581 Epoch 4006 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0534 0.8452 0.9326 -0.0001566 7.031e-05 0.1148 -0.000118 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004648 -0.005694 -0.01713 0.009661 0.9591 0.9657 0.0152 0.9291 0.9415 0.05734 ] Network output: [ 0.9541 0.1769 0.06802 0.000561 -0.0002519 -0.1508 0.0004228 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3609 0.0113 -0.1094 0.152 0.9815 0.9924 0.4538 0.9413 0.9844 0.7366 ] Network output: [ 0.01669 0.8547 0.9446 -0.0004177 0.0001875 0.1656 -0.0003148 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01115 0.005126 0.01339 0.008501 0.9907 0.9937 0.01161 0.9814 0.9896 0.02467 ] Network output: [ 0.05611 -0.1564 0.8347 -0.001158 0.0005199 1.205 -0.0008727 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4463 0.3288 0.4998 0.2817 0.9831 0.9932 0.4501 0.946 0.9862 0.7345 ] Network output: [ -0.03449 0.2186 1.071 0.0003371 -0.0001513 0.781 0.0002541 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2196 0.2098 0.2522 0.1932 0.9907 0.9944 0.22 0.9822 0.9907 0.2699 ] Network output: [ -0.03274 0.05045 1.127 0.0006648 -0.0002985 0.8909 0.000501 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2295 0.2278 0.2646 0.2277 0.9856 0.9916 0.2296 0.9694 0.9851 0.269 ] Network output: [ -0.01031 1.011 0.01672 0.00019 -8.528e-05 0.9938 0.0001432 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05546 Epoch 4007 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05321 0.8452 0.933 -0.0001591 7.141e-05 0.1147 -0.0001199 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004657 -0.005692 -0.01718 0.00964 0.9591 0.9657 0.01518 0.9292 0.9416 0.05732 ] Network output: [ 0.9546 0.1755 0.06796 0.0005591 -0.000251 -0.1503 0.0004214 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3603 0.01093 -0.1104 0.1516 0.9815 0.9924 0.4527 0.9414 0.9844 0.7376 ] Network output: [ 0.01641 0.8549 0.9451 -0.0004193 0.0001882 0.1655 -0.000316 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01114 0.005101 0.01337 0.008458 0.9907 0.9937 0.0116 0.9814 0.9896 0.02468 ] Network output: [ 0.05536 -0.155 0.836 -0.001163 0.0005222 1.204 -0.0008767 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4453 0.3275 0.4998 0.2804 0.9831 0.9932 0.449 0.9461 0.9862 0.7355 ] Network output: [ -0.0343 0.2193 1.07 0.0003411 -0.0001531 0.7808 0.0002571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2199 0.21 0.252 0.1928 0.9907 0.9944 0.2202 0.9822 0.9907 0.2697 ] Network output: [ -0.03255 0.05069 1.126 0.0006702 -0.0003009 0.891 0.0005051 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2298 0.228 0.2644 0.2273 0.9856 0.9916 0.2299 0.9695 0.9852 0.2688 ] Network output: [ -0.009942 1.01 0.01615 0.0001911 -8.581e-05 0.9949 0.0001441 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05511 Epoch 4008 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05302 0.8452 0.9334 -0.0001615 7.249e-05 0.1147 -0.0001217 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004667 -0.00569 -0.01722 0.00962 0.9591 0.9657 0.01516 0.9293 0.9416 0.05731 ] Network output: [ 0.9551 0.1741 0.06789 0.0005571 -0.0002501 -0.1499 0.0004198 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3596 0.01056 -0.1113 0.1513 0.9815 0.9924 0.4515 0.9414 0.9844 0.7386 ] Network output: [ 0.01614 0.8551 0.9456 -0.0004208 0.0001889 0.1653 -0.0003171 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01113 0.005076 0.01334 0.008416 0.9907 0.9937 0.01158 0.9814 0.9896 0.02468 ] Network output: [ 0.0546 -0.1536 0.8373 -0.001168 0.0005246 1.202 -0.0008806 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4442 0.3262 0.4998 0.2792 0.9831 0.9932 0.4479 0.9461 0.9862 0.7365 ] Network output: [ -0.03411 0.2199 1.069 0.0003451 -0.0001549 0.7807 0.0002601 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2201 0.2102 0.2517 0.1923 0.9907 0.9944 0.2205 0.9823 0.9907 0.2695 ] Network output: [ -0.03236 0.05094 1.125 0.0006755 -0.0003033 0.8911 0.0005091 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2301 0.2283 0.2641 0.2269 0.9856 0.9916 0.2302 0.9695 0.9852 0.2685 ] Network output: [ -0.009579 1.008 0.01558 0.0001922 -8.629e-05 0.9961 0.0001449 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05475 Epoch 4009 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05282 0.8452 0.9338 -0.0001638 7.355e-05 0.1147 -0.0001235 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004676 -0.005689 -0.01727 0.009601 0.9591 0.9657 0.01515 0.9294 0.9417 0.0573 ] Network output: [ 0.9556 0.1727 0.06779 0.0005549 -0.0002491 -0.1494 0.0004182 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.359 0.0102 -0.1123 0.1509 0.9815 0.9924 0.4504 0.9415 0.9845 0.7395 ] Network output: [ 0.01586 0.8553 0.9461 -0.0004222 0.0001896 0.1651 -0.0003182 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01112 0.005052 0.01331 0.008374 0.9907 0.9938 0.01157 0.9814 0.9896 0.02468 ] Network output: [ 0.05385 -0.1521 0.8387 -0.001173 0.0005268 1.201 -0.0008844 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4431 0.325 0.4998 0.278 0.9831 0.9932 0.4468 0.9462 0.9863 0.7375 ] Network output: [ -0.03391 0.2205 1.068 0.0003491 -0.0001567 0.7806 0.0002631 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2204 0.2104 0.2515 0.1919 0.9907 0.9944 0.2207 0.9823 0.9907 0.2694 ] Network output: [ -0.03216 0.05118 1.125 0.0006809 -0.0003057 0.8912 0.0005131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2303 0.2286 0.2638 0.2266 0.9857 0.9916 0.2304 0.9696 0.9852 0.2683 ] Network output: [ -0.00922 1.007 0.01501 0.0001931 -8.671e-05 0.9972 0.0001456 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0544 Epoch 4010 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05262 0.8452 0.9342 -0.0001661 7.458e-05 0.1146 -0.0001252 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004686 -0.005687 -0.01732 0.009582 0.9591 0.9658 0.01513 0.9295 0.9418 0.05729 ] Network output: [ 0.9561 0.1713 0.06769 0.0005526 -0.0002481 -0.1489 0.0004164 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3584 0.009842 -0.1132 0.1506 0.9816 0.9924 0.4493 0.9416 0.9845 0.7405 ] Network output: [ 0.01557 0.8556 0.9466 -0.0004236 0.0001902 0.165 -0.0003192 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01111 0.005028 0.01328 0.008333 0.9907 0.9938 0.01155 0.9815 0.9896 0.02469 ] Network output: [ 0.05311 -0.1508 0.84 -0.001178 0.000529 1.2 -0.000888 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4421 0.3237 0.4998 0.2767 0.9831 0.9932 0.4457 0.9463 0.9863 0.7385 ] Network output: [ -0.03372 0.2211 1.067 0.0003531 -0.0001585 0.7805 0.0002661 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2206 0.2106 0.2513 0.1915 0.9907 0.9944 0.221 0.9823 0.9908 0.2692 ] Network output: [ -0.03197 0.05143 1.124 0.0006862 -0.000308 0.8913 0.0005171 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2306 0.2289 0.2636 0.2262 0.9857 0.9917 0.2307 0.9696 0.9852 0.268 ] Network output: [ -0.008866 1.006 0.01445 0.0001939 -8.707e-05 0.9983 0.0001462 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05405 Epoch 4011 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05242 0.8453 0.9346 -0.0001683 7.558e-05 0.1146 -0.0001269 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004696 -0.005686 -0.01736 0.009563 0.9592 0.9658 0.01511 0.9295 0.9418 0.05727 ] Network output: [ 0.9566 0.1699 0.06757 0.0005502 -0.000247 -0.1484 0.0004146 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3578 0.009488 -0.1142 0.1503 0.9816 0.9924 0.4483 0.9417 0.9845 0.7415 ] Network output: [ 0.01529 0.8559 0.9471 -0.0004249 0.0001907 0.1648 -0.0003202 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01109 0.005004 0.01325 0.008293 0.9907 0.9938 0.01154 0.9815 0.9896 0.02469 ] Network output: [ 0.05236 -0.1494 0.8413 -0.001183 0.0005311 1.198 -0.0008916 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4411 0.3225 0.4998 0.2755 0.9831 0.9932 0.4447 0.9463 0.9863 0.7395 ] Network output: [ -0.03353 0.2217 1.066 0.0003571 -0.0001603 0.7804 0.0002691 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2209 0.2108 0.2511 0.1911 0.9907 0.9944 0.2212 0.9823 0.9908 0.2691 ] Network output: [ -0.03177 0.05167 1.123 0.0006914 -0.0003104 0.8915 0.0005211 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2309 0.2291 0.2633 0.2258 0.9857 0.9917 0.231 0.9697 0.9852 0.2678 ] Network output: [ -0.008517 1.004 0.01388 0.0001946 -8.738e-05 0.9994 0.0001467 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0537 Epoch 4012 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05221 0.8453 0.9351 -0.0001705 7.655e-05 0.1145 -0.0001285 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004707 -0.005685 -0.01741 0.009545 0.9592 0.9658 0.0151 0.9296 0.9419 0.05726 ] Network output: [ 0.9571 0.1685 0.06744 0.0005476 -0.0002458 -0.1479 0.0004127 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3573 0.009138 -0.1151 0.1499 0.9816 0.9924 0.4472 0.9418 0.9845 0.7424 ] Network output: [ 0.015 0.8561 0.9476 -0.0004261 0.0001913 0.1646 -0.0003211 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01108 0.004981 0.01322 0.008254 0.9907 0.9938 0.01153 0.9815 0.9896 0.0247 ] Network output: [ 0.05163 -0.148 0.8427 -0.001188 0.0005331 1.197 -0.000895 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4401 0.3213 0.4999 0.2743 0.9831 0.9932 0.4437 0.9464 0.9863 0.7404 ] Network output: [ -0.03334 0.2223 1.066 0.0003611 -0.0001621 0.7803 0.0002721 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2211 0.211 0.2509 0.1907 0.9907 0.9944 0.2215 0.9823 0.9908 0.2689 ] Network output: [ -0.03157 0.05191 1.122 0.0006967 -0.0003128 0.8916 0.000525 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2312 0.2294 0.2631 0.2255 0.9857 0.9917 0.2313 0.9697 0.9853 0.2675 ] Network output: [ -0.008174 1.003 0.01332 0.0001952 -8.763e-05 1.001 0.0001471 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05335 Epoch 4013 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05201 0.8454 0.9355 -0.0001726 7.75e-05 0.1144 -0.0001301 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004717 -0.005683 -0.01745 0.009527 0.9592 0.9658 0.01509 0.9297 0.9419 0.05725 ] Network output: [ 0.9576 0.1671 0.0673 0.000545 -0.0002447 -0.1474 0.0004107 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3567 0.008792 -0.116 0.1496 0.9816 0.9924 0.4462 0.9418 0.9845 0.7433 ] Network output: [ 0.01471 0.8564 0.9481 -0.0004272 0.0001918 0.1644 -0.000322 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01107 0.004958 0.0132 0.008215 0.9907 0.9938 0.01152 0.9815 0.9896 0.0247 ] Network output: [ 0.0509 -0.1466 0.844 -0.001192 0.0005351 1.196 -0.0008983 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4391 0.3201 0.4999 0.2731 0.9832 0.9932 0.4426 0.9465 0.9863 0.7413 ] Network output: [ -0.03315 0.2228 1.065 0.0003651 -0.0001639 0.7802 0.0002751 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2214 0.2112 0.2507 0.1903 0.9907 0.9944 0.2218 0.9823 0.9908 0.2688 ] Network output: [ -0.03138 0.05214 1.122 0.0007019 -0.0003151 0.8917 0.000529 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2316 0.2297 0.2628 0.2251 0.9857 0.9917 0.2316 0.9698 0.9853 0.2673 ] Network output: [ -0.007836 1.002 0.01276 0.0001956 -8.782e-05 1.002 0.0001474 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05299 Epoch 4014 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0518 0.8455 0.9359 -0.0001747 7.842e-05 0.1143 -0.0001316 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004728 -0.005682 -0.01749 0.009509 0.9592 0.9658 0.01507 0.9298 0.942 0.05724 ] Network output: [ 0.9581 0.1657 0.06715 0.0005422 -0.0002434 -0.1469 0.0004086 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3562 0.008451 -0.1168 0.1493 0.9816 0.9924 0.4451 0.9419 0.9846 0.7442 ] Network output: [ 0.01443 0.8567 0.9486 -0.0004283 0.0001923 0.1641 -0.0003228 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01106 0.004936 0.01317 0.008176 0.9907 0.9938 0.0115 0.9816 0.9896 0.02471 ] Network output: [ 0.05017 -0.1453 0.8454 -0.001196 0.000537 1.195 -0.0009015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4381 0.319 0.4999 0.272 0.9832 0.9932 0.4416 0.9466 0.9863 0.7422 ] Network output: [ -0.03296 0.2233 1.064 0.000369 -0.0001657 0.7801 0.0002781 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2217 0.2114 0.2505 0.1899 0.9907 0.9944 0.222 0.9823 0.9908 0.2686 ] Network output: [ -0.03118 0.05237 1.121 0.0007071 -0.0003174 0.8918 0.0005329 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2319 0.23 0.2626 0.2247 0.9857 0.9917 0.232 0.9698 0.9853 0.2671 ] Network output: [ -0.007503 1.001 0.0122 0.0001959 -8.796e-05 1.003 0.0001477 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05264 Epoch 4015 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05159 0.8456 0.9363 -0.0001767 7.931e-05 0.1142 -0.0001331 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004738 -0.005681 -0.01753 0.009492 0.9592 0.9658 0.01506 0.9299 0.9421 0.05722 ] Network output: [ 0.9586 0.1643 0.06698 0.0005393 -0.0002421 -0.1463 0.0004064 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3556 0.008113 -0.1177 0.149 0.9816 0.9924 0.4441 0.942 0.9846 0.7451 ] Network output: [ 0.01414 0.857 0.9491 -0.0004293 0.0001927 0.1639 -0.0003235 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01105 0.004914 0.01315 0.008139 0.9907 0.9938 0.01149 0.9816 0.9896 0.02471 ] Network output: [ 0.04945 -0.144 0.8468 -0.0012 0.0005389 1.193 -0.0009046 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4371 0.3178 0.4999 0.2708 0.9832 0.9932 0.4406 0.9466 0.9864 0.7431 ] Network output: [ -0.03277 0.2238 1.063 0.000373 -0.0001674 0.78 0.0002811 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.222 0.2116 0.2503 0.1895 0.9907 0.9944 0.2223 0.9823 0.9908 0.2685 ] Network output: [ -0.03098 0.0526 1.12 0.0007122 -0.0003198 0.892 0.0005368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2322 0.2304 0.2623 0.2244 0.9857 0.9917 0.2323 0.9699 0.9853 0.2669 ] Network output: [ -0.007176 0.9996 0.01164 0.0001961 -8.805e-05 1.004 0.0001478 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05229 Epoch 4016 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05138 0.8457 0.9367 -0.0001786 8.018e-05 0.1141 -0.0001346 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004749 -0.005681 -0.01757 0.009476 0.9593 0.9659 0.01505 0.93 0.9421 0.05721 ] Network output: [ 0.9591 0.1629 0.0668 0.0005363 -0.0002408 -0.1458 0.0004042 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3551 0.007779 -0.1185 0.1487 0.9816 0.9924 0.4431 0.9421 0.9846 0.746 ] Network output: [ 0.01385 0.8573 0.9496 -0.0004302 0.0001931 0.1637 -0.0003242 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01104 0.004892 0.01312 0.008102 0.9907 0.9938 0.01148 0.9816 0.9897 0.02472 ] Network output: [ 0.04874 -0.1427 0.8481 -0.001204 0.0005407 1.192 -0.0009076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4362 0.3167 0.5 0.2697 0.9832 0.9932 0.4397 0.9467 0.9864 0.744 ] Network output: [ -0.03258 0.2243 1.062 0.0003769 -0.0001692 0.78 0.0002841 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2222 0.2118 0.2501 0.1891 0.9907 0.9944 0.2226 0.9824 0.9908 0.2684 ] Network output: [ -0.03078 0.05282 1.12 0.0007174 -0.0003221 0.8921 0.0005406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2325 0.2307 0.2621 0.2241 0.9857 0.9917 0.2326 0.9699 0.9853 0.2666 ] Network output: [ -0.006855 0.9985 0.01109 0.0001962 -8.809e-05 1.005 0.0001479 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05194 Epoch 4017 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05117 0.8458 0.9371 -0.0001805 8.102e-05 0.114 -0.000136 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00476 -0.00568 -0.01761 0.009459 0.9593 0.9659 0.01503 0.9301 0.9422 0.0572 ] Network output: [ 0.9596 0.1615 0.06661 0.0005332 -0.0002394 -0.1453 0.0004018 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3546 0.007448 -0.1194 0.1484 0.9816 0.9924 0.4422 0.9421 0.9846 0.7468 ] Network output: [ 0.01355 0.8576 0.9501 -0.000431 0.0001935 0.1634 -0.0003248 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01104 0.004871 0.0131 0.008065 0.9907 0.9938 0.01147 0.9816 0.9897 0.02472 ] Network output: [ 0.04803 -0.1414 0.8495 -0.001208 0.0005424 1.191 -0.0009105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4352 0.3156 0.5 0.2685 0.9832 0.9932 0.4387 0.9467 0.9864 0.7449 ] Network output: [ -0.03239 0.2248 1.062 0.0003809 -0.000171 0.7799 0.000287 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2225 0.2121 0.2499 0.1887 0.9907 0.9945 0.2229 0.9824 0.9908 0.2683 ] Network output: [ -0.03058 0.05304 1.119 0.0007225 -0.0003243 0.8922 0.0005445 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2329 0.231 0.2619 0.2237 0.9857 0.9917 0.2329 0.97 0.9853 0.2664 ] Network output: [ -0.00654 0.9973 0.01055 0.0001962 -8.807e-05 1.006 0.0001478 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05159 Epoch 4018 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05096 0.8459 0.9375 -0.0001823 8.183e-05 0.1139 -0.0001374 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004771 -0.005679 -0.01765 0.009444 0.9593 0.9659 0.01502 0.9301 0.9422 0.05719 ] Network output: [ 0.9602 0.1601 0.06641 0.00053 -0.0002379 -0.1447 0.0003994 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3541 0.007122 -0.1202 0.1481 0.9816 0.9924 0.4412 0.9422 0.9846 0.7477 ] Network output: [ 0.01326 0.858 0.9506 -0.0004318 0.0001938 0.1632 -0.0003254 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01103 0.00485 0.01307 0.00803 0.9907 0.9938 0.01146 0.9816 0.9897 0.02473 ] Network output: [ 0.04733 -0.1401 0.8508 -0.001212 0.0005441 1.19 -0.0009133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4343 0.3144 0.5 0.2674 0.9832 0.9932 0.4378 0.9468 0.9864 0.7457 ] Network output: [ -0.0322 0.2253 1.061 0.0003848 -0.0001728 0.7799 0.00029 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2228 0.2123 0.2498 0.1884 0.9907 0.9945 0.2232 0.9824 0.9908 0.2681 ] Network output: [ -0.03038 0.05325 1.118 0.0007276 -0.0003266 0.8924 0.0005483 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2332 0.2313 0.2616 0.2234 0.9857 0.9917 0.2333 0.97 0.9854 0.2662 ] Network output: [ -0.00623 0.9962 0.01 0.000196 -8.8e-05 1.007 0.0001477 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05124 Epoch 4019 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05074 0.846 0.9379 -0.000184 8.262e-05 0.1138 -0.0001387 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004782 -0.005679 -0.01768 0.009428 0.9593 0.9659 0.01501 0.9302 0.9423 0.05718 ] Network output: [ 0.9607 0.1587 0.0662 0.0005266 -0.0002364 -0.1441 0.0003969 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3536 0.006799 -0.121 0.1479 0.9817 0.9924 0.4403 0.9423 0.9847 0.7485 ] Network output: [ 0.01297 0.8583 0.9511 -0.0004325 0.0001942 0.1629 -0.0003259 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01102 0.00483 0.01305 0.007994 0.9907 0.9938 0.01145 0.9817 0.9897 0.02473 ] Network output: [ 0.04664 -0.1388 0.8522 -0.001215 0.0005457 1.188 -0.000916 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4334 0.3134 0.5 0.2663 0.9832 0.9932 0.4368 0.9469 0.9864 0.7466 ] Network output: [ -0.03201 0.2257 1.06 0.0003888 -0.0001745 0.7799 0.000293 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2231 0.2125 0.2496 0.188 0.9907 0.9945 0.2235 0.9824 0.9908 0.268 ] Network output: [ -0.03017 0.05346 1.117 0.0007326 -0.0003289 0.8925 0.0005521 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2335 0.2317 0.2614 0.2231 0.9857 0.9917 0.2336 0.97 0.9854 0.266 ] Network output: [ -0.005926 0.995 0.009462 0.0001958 -8.789e-05 1.008 0.0001475 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05089 Epoch 4020 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05053 0.8462 0.9384 -0.0001857 8.338e-05 0.1136 -0.00014 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004794 -0.005678 -0.01772 0.009413 0.9593 0.9659 0.015 0.9303 0.9423 0.05716 ] Network output: [ 0.9612 0.1574 0.06597 0.0005232 -0.0002349 -0.1436 0.0003943 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3532 0.00648 -0.1218 0.1476 0.9817 0.9924 0.4394 0.9424 0.9847 0.7493 ] Network output: [ 0.01268 0.8587 0.9516 -0.0004331 0.0001944 0.1626 -0.0003264 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01101 0.00481 0.01303 0.00796 0.9908 0.9938 0.01144 0.9817 0.9897 0.02474 ] Network output: [ 0.04595 -0.1375 0.8536 -0.001219 0.0005472 1.187 -0.0009186 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4325 0.3123 0.5001 0.2652 0.9832 0.9932 0.4359 0.9469 0.9864 0.7474 ] Network output: [ -0.03182 0.2261 1.059 0.0003927 -0.0001763 0.7799 0.0002959 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2234 0.2128 0.2494 0.1877 0.9907 0.9945 0.2238 0.9824 0.9908 0.2679 ] Network output: [ -0.02997 0.05366 1.117 0.0007376 -0.0003312 0.8927 0.0005559 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2339 0.232 0.2612 0.2227 0.9858 0.9917 0.234 0.9701 0.9854 0.2658 ] Network output: [ -0.005629 0.9939 0.008928 0.0001954 -8.772e-05 1.009 0.0001473 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05054 Epoch 4021 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05031 0.8463 0.9388 -0.0001874 8.412e-05 0.1135 -0.0001412 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004805 -0.005678 -0.01775 0.009398 0.9594 0.9659 0.01499 0.9304 0.9424 0.05715 ] Network output: [ 0.9617 0.156 0.06574 0.0005197 -0.0002333 -0.143 0.0003916 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3527 0.006164 -0.1225 0.1473 0.9817 0.9924 0.4384 0.9424 0.9847 0.7501 ] Network output: [ 0.01238 0.859 0.9521 -0.0004337 0.0001947 0.1624 -0.0003268 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.011 0.00479 0.01301 0.007926 0.9908 0.9938 0.01143 0.9817 0.9897 0.02474 ] Network output: [ 0.04527 -0.1363 0.8549 -0.001222 0.0005487 1.186 -0.0009211 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4317 0.3112 0.5001 0.2642 0.9832 0.9932 0.435 0.947 0.9865 0.7482 ] Network output: [ -0.03163 0.2265 1.059 0.0003966 -0.0001781 0.7799 0.0002989 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2237 0.213 0.2493 0.1873 0.9907 0.9945 0.2241 0.9824 0.9908 0.2678 ] Network output: [ -0.02976 0.05385 1.116 0.0007426 -0.0003334 0.8929 0.0005597 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2343 0.2324 0.261 0.2224 0.9858 0.9917 0.2343 0.9701 0.9854 0.2656 ] Network output: [ -0.005337 0.9928 0.008398 0.0001949 -8.75e-05 1.01 0.0001469 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05019 Epoch 4022 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0501 0.8465 0.9392 -0.000189 8.483e-05 0.1133 -0.0001424 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004816 -0.005677 -0.01779 0.009384 0.9594 0.9659 0.01497 0.9304 0.9424 0.05714 ] Network output: [ 0.9622 0.1546 0.06549 0.000516 -0.0002317 -0.1424 0.0003889 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3523 0.005851 -0.1233 0.1471 0.9817 0.9924 0.4376 0.9425 0.9847 0.7509 ] Network output: [ 0.01209 0.8594 0.9526 -0.0004342 0.0001949 0.1621 -0.0003272 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.011 0.004771 0.01299 0.007892 0.9908 0.9938 0.01142 0.9817 0.9897 0.02475 ] Network output: [ 0.04459 -0.135 0.8563 -0.001225 0.0005502 1.185 -0.0009235 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4308 0.3102 0.5001 0.2631 0.9832 0.9932 0.4341 0.9471 0.9865 0.749 ] Network output: [ -0.03144 0.2269 1.058 0.0004005 -0.0001798 0.7799 0.0003019 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.224 0.2133 0.2491 0.187 0.9907 0.9945 0.2244 0.9824 0.9908 0.2677 ] Network output: [ -0.02956 0.05404 1.115 0.0007476 -0.0003356 0.893 0.0005634 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2346 0.2327 0.2608 0.2221 0.9858 0.9917 0.2347 0.9702 0.9854 0.2654 ] Network output: [ -0.005052 0.9917 0.007873 0.0001943 -8.724e-05 1.011 0.0001465 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04985 Epoch 4023 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04988 0.8467 0.9396 -0.0001905 8.552e-05 0.1132 -0.0001436 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004828 -0.005677 -0.01782 0.009369 0.9594 0.966 0.01496 0.9305 0.9425 0.05713 ] Network output: [ 0.9627 0.1533 0.06524 0.0005123 -0.00023 -0.1418 0.0003861 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3518 0.005542 -0.124 0.1468 0.9817 0.9924 0.4367 0.9426 0.9847 0.7517 ] Network output: [ 0.0118 0.8598 0.9531 -0.0004346 0.0001951 0.1618 -0.0003276 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01099 0.004752 0.01297 0.007859 0.9908 0.9938 0.01141 0.9817 0.9897 0.02475 ] Network output: [ 0.04392 -0.1338 0.8576 -0.001229 0.0005515 1.183 -0.0009259 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.43 0.3092 0.5002 0.262 0.9832 0.9932 0.4333 0.9471 0.9865 0.7498 ] Network output: [ -0.03126 0.2272 1.057 0.0004044 -0.0001816 0.7799 0.0003048 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2244 0.2135 0.249 0.1866 0.9907 0.9945 0.2247 0.9824 0.9908 0.2676 ] Network output: [ -0.02935 0.05421 1.114 0.0007526 -0.0003379 0.8932 0.0005672 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.235 0.2331 0.2606 0.2218 0.9858 0.9917 0.2351 0.9702 0.9854 0.2652 ] Network output: [ -0.004773 0.9907 0.007354 0.0001936 -8.693e-05 1.012 0.0001459 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0495 Epoch 4024 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04966 0.8469 0.94 -0.000192 8.618e-05 0.113 -0.0001447 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004839 -0.005677 -0.01785 0.009356 0.9594 0.966 0.01495 0.9306 0.9425 0.05712 ] Network output: [ 0.9632 0.1519 0.06497 0.0005085 -0.0002283 -0.1412 0.0003832 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3514 0.005236 -0.1247 0.1466 0.9817 0.9924 0.4358 0.9426 0.9847 0.7525 ] Network output: [ 0.0115 0.8601 0.9536 -0.000435 0.0001953 0.1615 -0.0003278 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01098 0.004733 0.01295 0.007827 0.9908 0.9938 0.0114 0.9818 0.9897 0.02476 ] Network output: [ 0.04326 -0.1326 0.859 -0.001232 0.0005529 1.182 -0.0009281 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4291 0.3082 0.5002 0.261 0.9833 0.9933 0.4324 0.9472 0.9865 0.7506 ] Network output: [ -0.03107 0.2276 1.056 0.0004083 -0.0001833 0.7799 0.0003077 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2247 0.2138 0.2489 0.1863 0.9907 0.9945 0.225 0.9824 0.9908 0.2675 ] Network output: [ -0.02914 0.05438 1.114 0.0007575 -0.0003401 0.8934 0.0005709 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2354 0.2334 0.2604 0.2215 0.9858 0.9917 0.2355 0.9703 0.9855 0.265 ] Network output: [ -0.004499 0.9896 0.006841 0.0001929 -8.658e-05 1.013 0.0001453 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04915 Epoch 4025 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04944 0.8471 0.9404 -0.0001934 8.682e-05 0.1128 -0.0001457 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004851 -0.005677 -0.01788 0.009342 0.9594 0.966 0.01494 0.9307 0.9426 0.05711 ] Network output: [ 0.9637 0.1506 0.0647 0.0005046 -0.0002265 -0.1406 0.0003803 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.351 0.004934 -0.1254 0.1463 0.9817 0.9925 0.435 0.9427 0.9848 0.7532 ] Network output: [ 0.01121 0.8605 0.9541 -0.0004353 0.0001954 0.1612 -0.0003281 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01098 0.004714 0.01293 0.007795 0.9908 0.9938 0.01139 0.9818 0.9897 0.02476 ] Network output: [ 0.0426 -0.1314 0.8603 -0.001234 0.0005542 1.181 -0.0009303 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4283 0.3072 0.5003 0.26 0.9833 0.9933 0.4316 0.9472 0.9865 0.7514 ] Network output: [ -0.03088 0.2279 1.056 0.0004122 -0.0001851 0.78 0.0003107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.225 0.214 0.2487 0.186 0.9907 0.9945 0.2253 0.9825 0.9908 0.2675 ] Network output: [ -0.02893 0.05454 1.113 0.0007624 -0.0003423 0.8936 0.0005746 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2358 0.2338 0.2602 0.2212 0.9858 0.9917 0.2358 0.9703 0.9855 0.2648 ] Network output: [ -0.004232 0.9886 0.006334 0.000192 -8.618e-05 1.014 0.0001447 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04881 Epoch 4026 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04922 0.8473 0.9408 -0.0001948 8.743e-05 0.1127 -0.0001468 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004862 -0.005677 -0.01791 0.009329 0.9594 0.966 0.01494 0.9307 0.9426 0.0571 ] Network output: [ 0.9642 0.1492 0.06442 0.0005006 -0.0002247 -0.14 0.0003772 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3506 0.004634 -0.1261 0.1461 0.9817 0.9925 0.4342 0.9428 0.9848 0.754 ] Network output: [ 0.01091 0.8609 0.9546 -0.0004356 0.0001955 0.1609 -0.0003283 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01097 0.004696 0.01291 0.007763 0.9908 0.9938 0.01138 0.9818 0.9898 0.02477 ] Network output: [ 0.04195 -0.1302 0.8617 -0.001237 0.0005554 1.18 -0.0009323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4275 0.3062 0.5003 0.259 0.9833 0.9933 0.4308 0.9473 0.9865 0.7521 ] Network output: [ -0.03069 0.2282 1.055 0.0004161 -0.0001868 0.78 0.0003136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2254 0.2143 0.2486 0.1857 0.9907 0.9945 0.2257 0.9825 0.9908 0.2674 ] Network output: [ -0.02872 0.05469 1.112 0.0007673 -0.0003445 0.8938 0.0005783 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2361 0.2342 0.26 0.2209 0.9858 0.9917 0.2362 0.9703 0.9855 0.2646 ] Network output: [ -0.003972 0.9876 0.005832 0.000191 -8.574e-05 1.015 0.0001439 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04846 Epoch 4027 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.049 0.8475 0.9412 -0.0001961 8.802e-05 0.1125 -0.0001478 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004874 -0.005677 -0.01794 0.009316 0.9595 0.966 0.01493 0.9308 0.9427 0.05709 ] Network output: [ 0.9647 0.1479 0.06412 0.0004965 -0.0002229 -0.1394 0.0003741 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3502 0.004337 -0.1268 0.1458 0.9817 0.9925 0.4334 0.9428 0.9848 0.7547 ] Network output: [ 0.01062 0.8613 0.9551 -0.0004358 0.0001956 0.1605 -0.0003284 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01097 0.004679 0.01289 0.007733 0.9908 0.9938 0.01138 0.9818 0.9898 0.02477 ] Network output: [ 0.04131 -0.129 0.863 -0.00124 0.0005566 1.178 -0.0009343 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4267 0.3052 0.5004 0.2579 0.9833 0.9933 0.4299 0.9474 0.9865 0.7529 ] Network output: [ -0.0305 0.2284 1.054 0.00042 -0.0001886 0.7801 0.0003165 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2257 0.2146 0.2485 0.1854 0.9907 0.9945 0.226 0.9825 0.9908 0.2673 ] Network output: [ -0.02851 0.05483 1.111 0.0007722 -0.0003467 0.894 0.0005819 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2365 0.2346 0.2598 0.2206 0.9858 0.9917 0.2366 0.9704 0.9855 0.2645 ] Network output: [ -0.003717 0.9866 0.005337 0.0001899 -8.525e-05 1.016 0.0001431 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04812 Epoch 4028 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04878 0.8477 0.9416 -0.0001973 8.859e-05 0.1123 -0.0001487 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004886 -0.005678 -0.01797 0.009303 0.9595 0.966 0.01492 0.9309 0.9427 0.05708 ] Network output: [ 0.9652 0.1466 0.06382 0.0004923 -0.000221 -0.1388 0.000371 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3498 0.004044 -0.1275 0.1456 0.9817 0.9925 0.4326 0.9429 0.9848 0.7554 ] Network output: [ 0.01033 0.8617 0.9556 -0.0004359 0.0001957 0.1602 -0.0003285 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01096 0.004661 0.01287 0.007702 0.9908 0.9938 0.01137 0.9818 0.9898 0.02478 ] Network output: [ 0.04068 -0.1279 0.8644 -0.001242 0.0005577 1.177 -0.0009362 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.426 0.3043 0.5004 0.257 0.9833 0.9933 0.4292 0.9474 0.9866 0.7536 ] Network output: [ -0.03031 0.2287 1.053 0.0004239 -0.0001903 0.7802 0.0003195 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.226 0.2149 0.2484 0.1851 0.9907 0.9945 0.2264 0.9825 0.9909 0.2673 ] Network output: [ -0.02829 0.05496 1.111 0.000777 -0.0003488 0.8942 0.0005856 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2369 0.235 0.2596 0.2203 0.9858 0.9917 0.237 0.9704 0.9855 0.2643 ] Network output: [ -0.003468 0.9856 0.004849 0.0001887 -8.473e-05 1.017 0.0001422 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04778 Epoch 4029 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04856 0.848 0.9421 -0.0001985 8.913e-05 0.1121 -0.0001496 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004898 -0.005678 -0.018 0.009291 0.9595 0.966 0.01491 0.931 0.9428 0.05707 ] Network output: [ 0.9657 0.1453 0.06351 0.000488 -0.0002191 -0.1382 0.0003678 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3494 0.003753 -0.1281 0.1454 0.9818 0.9925 0.4318 0.943 0.9848 0.7562 ] Network output: [ 0.01004 0.8622 0.9561 -0.000436 0.0001957 0.1599 -0.0003286 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01096 0.004644 0.01285 0.007672 0.9908 0.9938 0.01136 0.9819 0.9898 0.02479 ] Network output: [ 0.04005 -0.1267 0.8657 -0.001245 0.0005588 1.176 -0.0009381 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4252 0.3034 0.5005 0.256 0.9833 0.9933 0.4284 0.9475 0.9866 0.7543 ] Network output: [ -0.03012 0.2289 1.053 0.0004278 -0.000192 0.7803 0.0003224 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2264 0.2152 0.2483 0.1848 0.9907 0.9945 0.2267 0.9825 0.9909 0.2672 ] Network output: [ -0.02808 0.05508 1.11 0.0007818 -0.000351 0.8944 0.0005892 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2374 0.2354 0.2595 0.22 0.9858 0.9917 0.2374 0.9705 0.9855 0.2641 ] Network output: [ -0.003226 0.9846 0.004367 0.0001875 -8.416e-05 1.018 0.0001413 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04744 Epoch 4030 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04833 0.8482 0.9425 -0.0001997 8.965e-05 0.1118 -0.0001505 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004909 -0.005679 -0.01803 0.009279 0.9595 0.966 0.0149 0.931 0.9428 0.05706 ] Network output: [ 0.9662 0.144 0.0632 0.0004836 -0.0002171 -0.1376 0.0003645 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.349 0.003465 -0.1287 0.1451 0.9818 0.9925 0.431 0.943 0.9848 0.7569 ] Network output: [ 0.009744 0.8626 0.9566 -0.000436 0.0001957 0.1595 -0.0003286 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01095 0.004627 0.01284 0.007643 0.9908 0.9938 0.01136 0.9819 0.9898 0.02479 ] Network output: [ 0.03943 -0.1256 0.867 -0.001247 0.0005599 1.175 -0.0009398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4245 0.3024 0.5006 0.255 0.9833 0.9933 0.4276 0.9475 0.9866 0.755 ] Network output: [ -0.02993 0.2292 1.052 0.0004316 -0.0001938 0.7804 0.0003253 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2267 0.2155 0.2482 0.1845 0.9908 0.9945 0.2271 0.9825 0.9909 0.2672 ] Network output: [ -0.02786 0.05519 1.109 0.0007866 -0.0003531 0.8946 0.0005928 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2378 0.2358 0.2593 0.2197 0.9858 0.9917 0.2378 0.9705 0.9855 0.264 ] Network output: [ -0.00299 0.9837 0.003891 0.0001861 -8.355e-05 1.019 0.0001403 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04709 Epoch 4031 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04811 0.8485 0.9429 -0.0002008 9.015e-05 0.1116 -0.0001513 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004921 -0.005679 -0.01805 0.009267 0.9595 0.9661 0.01489 0.9311 0.9428 0.05705 ] Network output: [ 0.9667 0.1427 0.06287 0.0004792 -0.0002151 -0.137 0.0003611 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3486 0.00318 -0.1294 0.1449 0.9818 0.9925 0.4303 0.9431 0.9849 0.7575 ] Network output: [ 0.009452 0.863 0.9572 -0.0004359 0.0001957 0.1592 -0.0003285 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01095 0.00461 0.01282 0.007614 0.9908 0.9938 0.01135 0.9819 0.9898 0.0248 ] Network output: [ 0.03881 -0.1245 0.8683 -0.001249 0.0005609 1.173 -0.0009415 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4237 0.3015 0.5006 0.254 0.9833 0.9933 0.4269 0.9476 0.9866 0.7557 ] Network output: [ -0.02974 0.2294 1.051 0.0004355 -0.0001955 0.7805 0.0003282 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2271 0.2158 0.2481 0.1842 0.9908 0.9945 0.2274 0.9825 0.9909 0.2671 ] Network output: [ -0.02765 0.05529 1.108 0.0007914 -0.0003553 0.8949 0.0005964 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2382 0.2362 0.2591 0.2195 0.9859 0.9917 0.2383 0.9706 0.9856 0.2638 ] Network output: [ -0.00276 0.9828 0.003423 0.0001847 -8.291e-05 1.02 0.0001392 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04675 Epoch 4032 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04789 0.8487 0.9433 -0.0002019 9.062e-05 0.1114 -0.0001521 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004933 -0.00568 -0.01808 0.009256 0.9595 0.9661 0.01489 0.9312 0.9429 0.05704 ] Network output: [ 0.9672 0.1414 0.06254 0.0004747 -0.0002131 -0.1363 0.0003577 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3483 0.002897 -0.13 0.1447 0.9818 0.9925 0.4296 0.9431 0.9849 0.7582 ] Network output: [ 0.009162 0.8634 0.9577 -0.0004358 0.0001957 0.1588 -0.0003285 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01094 0.004594 0.01281 0.007586 0.9908 0.9938 0.01134 0.9819 0.9898 0.02481 ] Network output: [ 0.0382 -0.1233 0.8697 -0.001251 0.0005618 1.172 -0.0009432 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.423 0.3006 0.5007 0.2531 0.9833 0.9933 0.4261 0.9476 0.9866 0.7564 ] Network output: [ -0.02955 0.2295 1.051 0.0004394 -0.0001972 0.7806 0.0003311 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2275 0.2161 0.2481 0.1839 0.9908 0.9945 0.2278 0.9825 0.9909 0.2671 ] Network output: [ -0.02743 0.05538 1.108 0.0007961 -0.0003574 0.8951 0.0006 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2386 0.2366 0.259 0.2192 0.9859 0.9918 0.2387 0.9706 0.9856 0.2637 ] Network output: [ -0.002536 0.9818 0.002961 0.0001832 -8.223e-05 1.021 0.000138 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04641 Epoch 4033 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04767 0.849 0.9437 -0.0002029 9.107e-05 0.1111 -0.0001529 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004944 -0.00568 -0.0181 0.009245 0.9596 0.9661 0.01488 0.9312 0.9429 0.05704 ] Network output: [ 0.9676 0.1401 0.0622 0.0004701 -0.000211 -0.1357 0.0003543 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.348 0.002617 -0.1306 0.1445 0.9818 0.9925 0.4288 0.9432 0.9849 0.7589 ] Network output: [ 0.008872 0.8639 0.9582 -0.0004357 0.0001956 0.1584 -0.0003283 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01094 0.004578 0.01279 0.007558 0.9908 0.9938 0.01134 0.9819 0.9898 0.02481 ] Network output: [ 0.0376 -0.1222 0.871 -0.001254 0.0005628 1.171 -0.0009447 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4223 0.2998 0.5008 0.2521 0.9833 0.9933 0.4254 0.9477 0.9866 0.7571 ] Network output: [ -0.02936 0.2297 1.05 0.0004432 -0.000199 0.7807 0.000334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2278 0.2164 0.248 0.1837 0.9908 0.9945 0.2282 0.9825 0.9909 0.2671 ] Network output: [ -0.02721 0.05546 1.107 0.0008009 -0.0003595 0.8954 0.0006036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.239 0.237 0.2588 0.2189 0.9859 0.9918 0.2391 0.9706 0.9856 0.2635 ] Network output: [ -0.002318 0.981 0.002507 0.0001816 -8.151e-05 1.022 0.0001368 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04607 Epoch 4034 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04744 0.8493 0.9441 -0.0002038 9.15e-05 0.1109 -0.0001536 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004956 -0.005681 -0.01813 0.009234 0.9596 0.9661 0.01487 0.9313 0.943 0.05703 ] Network output: [ 0.9681 0.1389 0.06185 0.0004654 -0.000209 -0.1351 0.0003508 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3476 0.002339 -0.1311 0.1443 0.9818 0.9925 0.4281 0.9433 0.9849 0.7596 ] Network output: [ 0.008582 0.8643 0.9587 -0.0004355 0.0001955 0.1581 -0.0003282 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01093 0.004562 0.01278 0.007531 0.9908 0.9938 0.01133 0.9819 0.9898 0.02482 ] Network output: [ 0.03701 -0.1212 0.8723 -0.001256 0.0005636 1.17 -0.0009462 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4216 0.2989 0.5009 0.2512 0.9833 0.9933 0.4247 0.9477 0.9866 0.7578 ] Network output: [ -0.02917 0.2298 1.049 0.0004471 -0.0002007 0.7809 0.0003369 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2282 0.2167 0.2479 0.1834 0.9908 0.9945 0.2285 0.9825 0.9909 0.267 ] Network output: [ -0.02699 0.05552 1.106 0.0008056 -0.0003617 0.8956 0.0006071 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2395 0.2374 0.2587 0.2187 0.9859 0.9918 0.2395 0.9707 0.9856 0.2634 ] Network output: [ -0.002106 0.9801 0.002059 0.0001799 -8.076e-05 1.023 0.0001356 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04573 Epoch 4035 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04722 0.8496 0.9445 -0.0002047 9.191e-05 0.1106 -0.0001543 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004968 -0.005682 -0.01815 0.009223 0.9596 0.9661 0.01487 0.9314 0.943 0.05702 ] Network output: [ 0.9686 0.1376 0.06149 0.0004607 -0.0002068 -0.1344 0.0003472 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3473 0.002064 -0.1317 0.1441 0.9818 0.9925 0.4275 0.9433 0.9849 0.7602 ] Network output: [ 0.008294 0.8648 0.9592 -0.0004352 0.0001954 0.1577 -0.000328 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01093 0.004547 0.01276 0.007504 0.9908 0.9938 0.01133 0.982 0.9898 0.02483 ] Network output: [ 0.03642 -0.1201 0.8736 -0.001257 0.0005645 1.169 -0.0009476 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4209 0.298 0.501 0.2503 0.9833 0.9933 0.424 0.9478 0.9866 0.7584 ] Network output: [ -0.02898 0.23 1.049 0.0004509 -0.0002024 0.781 0.0003398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2286 0.217 0.2479 0.1832 0.9908 0.9945 0.2289 0.9826 0.9909 0.267 ] Network output: [ -0.02677 0.05558 1.105 0.0008103 -0.0003638 0.8959 0.0006107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2399 0.2378 0.2586 0.2184 0.9859 0.9918 0.24 0.9707 0.9856 0.2633 ] Network output: [ -0.001899 0.9792 0.001619 0.0001781 -7.997e-05 1.024 0.0001342 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04539 Epoch 4036 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.047 0.8499 0.9449 -0.0002056 9.23e-05 0.1104 -0.0001549 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00498 -0.005683 -0.01817 0.009213 0.9596 0.9661 0.01486 0.9314 0.9431 0.05702 ] Network output: [ 0.9691 0.1364 0.06113 0.0004559 -0.0002047 -0.1338 0.0003436 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.347 0.001791 -0.1322 0.1439 0.9818 0.9925 0.4268 0.9434 0.9849 0.7609 ] Network output: [ 0.008006 0.8653 0.9597 -0.0004349 0.0001953 0.1573 -0.0003278 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01093 0.004532 0.01275 0.007477 0.9908 0.9938 0.01132 0.982 0.9898 0.02484 ] Network output: [ 0.03584 -0.119 0.8749 -0.001259 0.0005653 1.167 -0.000949 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4203 0.2972 0.501 0.2494 0.9833 0.9933 0.4233 0.9478 0.9867 0.7591 ] Network output: [ -0.02879 0.2301 1.048 0.0004547 -0.0002041 0.7812 0.0003427 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.229 0.2173 0.2478 0.1829 0.9908 0.9945 0.2293 0.9826 0.9909 0.267 ] Network output: [ -0.02655 0.05562 1.105 0.000815 -0.0003659 0.8962 0.0006142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2404 0.2383 0.2584 0.2182 0.9859 0.9918 0.2404 0.9708 0.9856 0.2631 ] Network output: [ -0.001699 0.9784 0.001186 0.0001763 -7.915e-05 1.025 0.0001329 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04506 Epoch 4037 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04678 0.8502 0.9453 -0.0002064 9.267e-05 0.1101 -0.0001556 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004991 -0.005684 -0.0182 0.009203 0.9596 0.9661 0.01486 0.9315 0.9431 0.05701 ] Network output: [ 0.9695 0.1352 0.06076 0.000451 -0.0002025 -0.1332 0.0003399 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3467 0.00152 -0.1328 0.1437 0.9818 0.9925 0.4261 0.9434 0.9849 0.7615 ] Network output: [ 0.007719 0.8657 0.9602 -0.0004346 0.0001951 0.1569 -0.0003275 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01093 0.004517 0.01274 0.007451 0.9908 0.9939 0.01132 0.982 0.9898 0.02485 ] Network output: [ 0.03527 -0.118 0.8762 -0.001261 0.0005661 1.166 -0.0009503 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4196 0.2964 0.5011 0.2485 0.9834 0.9933 0.4226 0.9479 0.9867 0.7597 ] Network output: [ -0.0286 0.2302 1.048 0.0004586 -0.0002059 0.7814 0.0003456 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2294 0.2176 0.2478 0.1827 0.9908 0.9945 0.2297 0.9826 0.9909 0.267 ] Network output: [ -0.02633 0.05565 1.104 0.0008196 -0.000368 0.8964 0.0006177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2408 0.2387 0.2583 0.2179 0.9859 0.9918 0.2409 0.9708 0.9856 0.263 ] Network output: [ -0.001505 0.9775 0.0007603 0.0001744 -7.83e-05 1.025 0.0001314 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04472 Epoch 4038 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04655 0.8505 0.9457 -0.0002072 9.301e-05 0.1098 -0.0001561 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005003 -0.005686 -0.01822 0.009193 0.9596 0.9661 0.01485 0.9315 0.9431 0.057 ] Network output: [ 0.97 0.134 0.06039 0.0004461 -0.0002003 -0.1325 0.0003362 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3464 0.001252 -0.1333 0.1435 0.9818 0.9925 0.4255 0.9435 0.985 0.7621 ] Network output: [ 0.007434 0.8662 0.9607 -0.0004342 0.0001949 0.1565 -0.0003272 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01092 0.004502 0.01273 0.007425 0.9908 0.9939 0.01131 0.982 0.9899 0.02485 ] Network output: [ 0.0347 -0.1169 0.8775 -0.001263 0.0005668 1.165 -0.0009515 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.419 0.2956 0.5012 0.2476 0.9834 0.9933 0.422 0.9479 0.9867 0.7604 ] Network output: [ -0.02841 0.2302 1.047 0.0004624 -0.0002076 0.7816 0.0003485 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2298 0.218 0.2478 0.1824 0.9908 0.9945 0.2301 0.9826 0.9909 0.267 ] Network output: [ -0.02611 0.05567 1.103 0.0008243 -0.0003701 0.8967 0.0006212 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2413 0.2392 0.2582 0.2177 0.9859 0.9918 0.2413 0.9708 0.9857 0.2629 ] Network output: [ -0.001317 0.9767 0.000342 0.0001724 -7.741e-05 1.026 0.00013 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04438 Epoch 4039 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04633 0.8509 0.9461 -0.0002079 9.334e-05 0.1096 -0.0001567 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005015 -0.005687 -0.01824 0.009183 0.9597 0.9662 0.01484 0.9316 0.9432 0.057 ] Network output: [ 0.9705 0.1328 0.06001 0.000441 -0.000198 -0.1319 0.0003324 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3461 0.0009854 -0.1338 0.1433 0.9818 0.9925 0.4249 0.9435 0.985 0.7628 ] Network output: [ 0.007149 0.8667 0.9612 -0.0004337 0.0001947 0.1561 -0.0003269 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01092 0.004488 0.01272 0.0074 0.9909 0.9939 0.01131 0.982 0.9899 0.02486 ] Network output: [ 0.03414 -0.1159 0.8787 -0.001264 0.0005675 1.164 -0.0009527 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4184 0.2948 0.5014 0.2467 0.9834 0.9933 0.4213 0.948 0.9867 0.761 ] Network output: [ -0.02822 0.2303 1.046 0.0004662 -0.0002093 0.7818 0.0003513 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2302 0.2183 0.2477 0.1822 0.9908 0.9945 0.2305 0.9826 0.9909 0.267 ] Network output: [ -0.02588 0.05568 1.102 0.0008289 -0.0003721 0.897 0.0006247 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2417 0.2396 0.2581 0.2175 0.9859 0.9918 0.2418 0.9709 0.9857 0.2628 ] Network output: [ -0.001134 0.976 -6.905e-05 0.0001704 -7.65e-05 1.027 0.0001284 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04405 Epoch 4040 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04611 0.8512 0.9465 -0.0002086 9.365e-05 0.1093 -0.0001572 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005026 -0.005688 -0.01826 0.009174 0.9597 0.9662 0.01484 0.9317 0.9432 0.05699 ] Network output: [ 0.9709 0.1316 0.05962 0.000436 -0.0001957 -0.1313 0.0003286 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3458 0.0007212 -0.1343 0.1431 0.9819 0.9925 0.4242 0.9436 0.985 0.7634 ] Network output: [ 0.006865 0.8672 0.9617 -0.0004332 0.0001945 0.1557 -0.0003265 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01092 0.004473 0.01271 0.007376 0.9909 0.9939 0.01131 0.9821 0.9899 0.02487 ] Network output: [ 0.03359 -0.1149 0.88 -0.001266 0.0005682 1.163 -0.0009538 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4178 0.294 0.5015 0.2459 0.9834 0.9933 0.4207 0.948 0.9867 0.7616 ] Network output: [ -0.02803 0.2303 1.046 0.00047 -0.000211 0.782 0.0003542 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2306 0.2186 0.2477 0.182 0.9908 0.9945 0.2309 0.9826 0.9909 0.267 ] Network output: [ -0.02566 0.05567 1.102 0.0008335 -0.0003742 0.8974 0.0006282 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2422 0.2401 0.258 0.2173 0.9859 0.9918 0.2423 0.9709 0.9857 0.2627 ] Network output: [ -0.0009568 0.9752 -0.0004727 0.0001683 -7.555e-05 1.028 0.0001268 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04371 Epoch 4041 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04589 0.8516 0.9469 -0.0002092 9.394e-05 0.109 -0.0001577 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005038 -0.00569 -0.01828 0.009165 0.9597 0.9662 0.01484 0.9317 0.9432 0.05699 ] Network output: [ 0.9714 0.1304 0.05923 0.0004308 -0.0001934 -0.1306 0.0003247 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3455 0.0004589 -0.1348 0.1429 0.9819 0.9925 0.4236 0.9436 0.985 0.764 ] Network output: [ 0.006582 0.8677 0.9622 -0.0004327 0.0001942 0.1552 -0.0003261 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01092 0.004459 0.0127 0.007351 0.9909 0.9939 0.0113 0.9821 0.9899 0.02488 ] Network output: [ 0.03304 -0.1139 0.8813 -0.001267 0.0005688 1.161 -0.0009549 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4172 0.2932 0.5016 0.245 0.9834 0.9933 0.4201 0.9481 0.9867 0.7622 ] Network output: [ -0.02783 0.2303 1.045 0.0004738 -0.0002127 0.7823 0.0003571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.231 0.219 0.2477 0.1818 0.9908 0.9945 0.2313 0.9826 0.9909 0.2671 ] Network output: [ -0.02543 0.05565 1.101 0.0008381 -0.0003763 0.8977 0.0006316 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2427 0.2405 0.2579 0.217 0.986 0.9918 0.2428 0.971 0.9857 0.2626 ] Network output: [ -0.0007854 0.9744 -0.0008689 0.0001661 -7.458e-05 1.029 0.0001252 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04338 Epoch 4042 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04567 0.8519 0.9473 -0.0002098 9.421e-05 0.1086 -0.0001581 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00505 -0.005691 -0.0183 0.009156 0.9597 0.9662 0.01483 0.9318 0.9433 0.05698 ] Network output: [ 0.9718 0.1293 0.05883 0.0004256 -0.0001911 -0.13 0.0003208 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3453 0.0001985 -0.1352 0.1427 0.9819 0.9925 0.423 0.9437 0.985 0.7646 ] Network output: [ 0.0063 0.8682 0.9627 -0.0004321 0.000194 0.1548 -0.0003256 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01092 0.004446 0.01269 0.007327 0.9909 0.9939 0.0113 0.9821 0.9899 0.02489 ] Network output: [ 0.0325 -0.1129 0.8825 -0.001268 0.0005694 1.16 -0.0009559 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4166 0.2924 0.5017 0.2441 0.9834 0.9933 0.4195 0.9481 0.9867 0.7628 ] Network output: [ -0.02764 0.2303 1.044 0.0004776 -0.0002144 0.7825 0.00036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2314 0.2193 0.2477 0.1815 0.9908 0.9945 0.2317 0.9826 0.9909 0.2671 ] Network output: [ -0.0252 0.05562 1.1 0.0008427 -0.0003783 0.898 0.0006351 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2432 0.241 0.2578 0.2168 0.986 0.9918 0.2432 0.971 0.9857 0.2625 ] Network output: [ -0.0006195 0.9737 -0.001258 0.0001639 -7.358e-05 1.029 0.0001235 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04304 Epoch 4043 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04544 0.8523 0.9476 -0.0002104 9.446e-05 0.1083 -0.0001586 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005061 -0.005693 -0.01832 0.009147 0.9597 0.9662 0.01483 0.9318 0.9433 0.05698 ] Network output: [ 0.9723 0.1281 0.05843 0.0004204 -0.0001887 -0.1294 0.0003168 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.345 -6.011e-05 -0.1357 0.1425 0.9819 0.9925 0.4225 0.9437 0.985 0.7652 ] Network output: [ 0.006019 0.8687 0.9632 -0.0004315 0.0001937 0.1544 -0.0003252 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01092 0.004432 0.01268 0.007304 0.9909 0.9939 0.0113 0.9821 0.9899 0.0249 ] Network output: [ 0.03197 -0.1119 0.8838 -0.00127 0.00057 1.159 -0.0009569 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.416 0.2917 0.5018 0.2433 0.9834 0.9933 0.4189 0.9482 0.9867 0.7634 ] Network output: [ -0.02745 0.2302 1.044 0.0004814 -0.0002161 0.7828 0.0003628 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2318 0.2197 0.2477 0.1813 0.9908 0.9945 0.2321 0.9826 0.9909 0.2671 ] Network output: [ -0.02497 0.05557 1.099 0.0008473 -0.0003804 0.8983 0.0006385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2437 0.2415 0.2577 0.2166 0.986 0.9918 0.2437 0.971 0.9857 0.2624 ] Network output: [ -0.0004591 0.973 -0.001639 0.0001616 -7.256e-05 1.03 0.0001218 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04271 Epoch 4044 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04522 0.8526 0.948 -0.0002109 9.469e-05 0.108 -0.000159 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005073 -0.005695 -0.01834 0.009139 0.9597 0.9662 0.01482 0.9319 0.9433 0.05698 ] Network output: [ 0.9727 0.127 0.05802 0.000415 -0.0001863 -0.1287 0.0003128 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3447 -0.0003169 -0.1361 0.1423 0.9819 0.9925 0.4219 0.9438 0.985 0.7657 ] Network output: [ 0.005739 0.8692 0.9637 -0.0004308 0.0001934 0.1539 -0.0003247 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01091 0.004419 0.01267 0.007281 0.9909 0.9939 0.0113 0.9821 0.9899 0.02491 ] Network output: [ 0.03144 -0.1109 0.885 -0.001271 0.0005706 1.158 -0.0009579 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4154 0.2909 0.502 0.2425 0.9834 0.9933 0.4183 0.9482 0.9867 0.764 ] Network output: [ -0.02726 0.2302 1.043 0.0004852 -0.0002178 0.783 0.0003657 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2322 0.22 0.2477 0.1811 0.9908 0.9945 0.2325 0.9826 0.9909 0.2672 ] Network output: [ -0.02474 0.05552 1.099 0.0008518 -0.0003824 0.8987 0.0006419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2441 0.242 0.2576 0.2164 0.986 0.9918 0.2442 0.9711 0.9857 0.2623 ] Network output: [ -0.0003042 0.9723 -0.002013 0.0001593 -7.151e-05 1.031 0.00012 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04237 Epoch 4045 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.045 0.853 0.9484 -0.0002114 9.49e-05 0.1077 -0.0001593 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005084 -0.005696 -0.01835 0.00913 0.9597 0.9662 0.01482 0.9319 0.9434 0.05698 ] Network output: [ 0.9731 0.1259 0.05761 0.0004097 -0.0001839 -0.1281 0.0003087 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3445 -0.000572 -0.1366 0.1421 0.9819 0.9925 0.4214 0.9438 0.985 0.7663 ] Network output: [ 0.005461 0.8697 0.9642 -0.0004301 0.0001931 0.1535 -0.0003241 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01091 0.004406 0.01266 0.007258 0.9909 0.9939 0.01129 0.9821 0.9899 0.02492 ] Network output: [ 0.03092 -0.11 0.8863 -0.001272 0.0005711 1.157 -0.0009588 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4149 0.2902 0.5021 0.2416 0.9834 0.9933 0.4178 0.9483 0.9867 0.7646 ] Network output: [ -0.02707 0.2301 1.043 0.000489 -0.0002195 0.7833 0.0003685 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2327 0.2204 0.2477 0.1809 0.9908 0.9945 0.233 0.9826 0.9909 0.2672 ] Network output: [ -0.02451 0.05544 1.098 0.0008563 -0.0003844 0.8991 0.0006454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2446 0.2424 0.2575 0.2162 0.986 0.9918 0.2447 0.9711 0.9858 0.2623 ] Network output: [ -0.0001546 0.9716 -0.00238 0.0001569 -7.043e-05 1.032 0.0001182 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04204 Epoch 4046 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04478 0.8534 0.9488 -0.0002118 9.51e-05 0.1073 -0.0001596 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005096 -0.005698 -0.01837 0.009122 0.9598 0.9662 0.01482 0.932 0.9434 0.05697 ] Network output: [ 0.9736 0.1248 0.0572 0.0004042 -0.0001815 -0.1275 0.0003047 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3443 -0.0008254 -0.137 0.142 0.9819 0.9925 0.4208 0.9439 0.9851 0.7669 ] Network output: [ 0.005183 0.8702 0.9646 -0.0004293 0.0001927 0.153 -0.0003235 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01091 0.004393 0.01266 0.007236 0.9909 0.9939 0.01129 0.9822 0.9899 0.02493 ] Network output: [ 0.03041 -0.109 0.8875 -0.001273 0.0005716 1.156 -0.0009596 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4144 0.2895 0.5023 0.2408 0.9834 0.9933 0.4172 0.9483 0.9868 0.7651 ] Network output: [ -0.02688 0.23 1.042 0.0004928 -0.0002212 0.7836 0.0003714 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2331 0.2208 0.2478 0.1808 0.9908 0.9945 0.2334 0.9827 0.9909 0.2673 ] Network output: [ -0.02428 0.05536 1.097 0.0008608 -0.0003865 0.8994 0.0006488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2451 0.2429 0.2575 0.216 0.986 0.9918 0.2452 0.9712 0.9858 0.2622 ] Network output: [ -1.025e-05 0.9709 -0.002739 0.0001544 -6.933e-05 1.032 0.0001164 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04171 Epoch 4047 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04456 0.8538 0.9492 -0.0002122 9.528e-05 0.107 -0.0001599 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005107 -0.0057 -0.01839 0.009115 0.9598 0.9662 0.01482 0.932 0.9434 0.05697 ] Network output: [ 0.974 0.1237 0.05678 0.0003988 -0.000179 -0.1268 0.0003005 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.344 -0.001077 -0.1374 0.1418 0.9819 0.9925 0.4203 0.9439 0.9851 0.7674 ] Network output: [ 0.004907 0.8707 0.9651 -0.0004285 0.0001924 0.1526 -0.000323 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01091 0.00438 0.01265 0.007214 0.9909 0.9939 0.01129 0.9822 0.9899 0.02494 ] Network output: [ 0.0299 -0.1081 0.8887 -0.001274 0.0005721 1.154 -0.0009604 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4138 0.2888 0.5024 0.24 0.9834 0.9933 0.4167 0.9483 0.9868 0.7657 ] Network output: [ -0.02669 0.2299 1.042 0.0004966 -0.0002229 0.784 0.0003742 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2335 0.2212 0.2478 0.1806 0.9908 0.9945 0.2338 0.9827 0.9909 0.2674 ] Network output: [ -0.02405 0.05526 1.097 0.0008653 -0.0003885 0.8998 0.0006522 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2457 0.2434 0.2574 0.2158 0.986 0.9918 0.2457 0.9712 0.9858 0.2622 ] Network output: [ 0.0001289 0.9703 -0.003091 0.0001519 -6.821e-05 1.033 0.0001145 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04137 Epoch 4048 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04434 0.8542 0.9496 -0.0002126 9.544e-05 0.1066 -0.0001602 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005119 -0.005703 -0.0184 0.009107 0.9598 0.9662 0.01481 0.9321 0.9435 0.05697 ] Network output: [ 0.9744 0.1226 0.05635 0.0003932 -0.0001765 -0.1262 0.0002964 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3438 -0.001328 -0.1378 0.1416 0.9819 0.9925 0.4198 0.944 0.9851 0.768 ] Network output: [ 0.004632 0.8713 0.9656 -0.0004277 0.000192 0.1521 -0.0003223 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01091 0.004368 0.01265 0.007192 0.9909 0.9939 0.01129 0.9822 0.9899 0.02496 ] Network output: [ 0.0294 -0.1071 0.8899 -0.001275 0.0005726 1.153 -0.0009612 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4133 0.2881 0.5026 0.2392 0.9834 0.9933 0.4161 0.9484 0.9868 0.7663 ] Network output: [ -0.02649 0.2298 1.041 0.0005004 -0.0002246 0.7843 0.0003771 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.234 0.2215 0.2478 0.1804 0.9908 0.9945 0.2343 0.9827 0.9909 0.2674 ] Network output: [ -0.02381 0.05515 1.096 0.0008698 -0.0003905 0.9002 0.0006555 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2462 0.2439 0.2573 0.2157 0.986 0.9918 0.2462 0.9713 0.9858 0.2621 ] Network output: [ 0.0002629 0.9696 -0.003435 0.0001494 -6.706e-05 1.034 0.0001126 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04104 Epoch 4049 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04412 0.8546 0.95 -0.0002129 9.559e-05 0.1063 -0.0001605 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00513 -0.005705 -0.01842 0.0091 0.9598 0.9663 0.01481 0.9321 0.9435 0.05697 ] Network output: [ 0.9749 0.1215 0.05592 0.0003877 -0.000174 -0.1256 0.0002922 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3436 -0.001577 -0.1381 0.1414 0.9819 0.9925 0.4193 0.944 0.9851 0.7685 ] Network output: [ 0.004357 0.8718 0.9661 -0.0004268 0.0001916 0.1516 -0.0003217 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01091 0.004356 0.01264 0.007171 0.9909 0.9939 0.01129 0.9822 0.9899 0.02497 ] Network output: [ 0.0289 -0.1062 0.8911 -0.001276 0.000573 1.152 -0.0009619 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4128 0.2874 0.5028 0.2384 0.9834 0.9933 0.4156 0.9484 0.9868 0.7668 ] Network output: [ -0.0263 0.2296 1.04 0.0005041 -0.0002263 0.7846 0.0003799 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2344 0.2219 0.2479 0.1802 0.9908 0.9945 0.2347 0.9827 0.9909 0.2675 ] Network output: [ -0.02358 0.05502 1.095 0.0008743 -0.0003925 0.9006 0.0006589 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2467 0.2444 0.2573 0.2155 0.986 0.9918 0.2468 0.9713 0.9858 0.2621 ] Network output: [ 0.0003918 0.969 -0.003772 0.0001468 -6.59e-05 1.035 0.0001106 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04071 Epoch 4050 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0439 0.8551 0.9503 -0.0002132 9.572e-05 0.1059 -0.0001607 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005142 -0.005707 -0.01843 0.009093 0.9598 0.9663 0.01481 0.9322 0.9435 0.05697 ] Network output: [ 0.9753 0.1205 0.05549 0.000382 -0.0001715 -0.1249 0.0002879 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3434 -0.001824 -0.1385 0.1413 0.9819 0.9925 0.4188 0.9441 0.9851 0.7691 ] Network output: [ 0.004085 0.8723 0.9666 -0.0004259 0.0001912 0.1512 -0.000321 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01092 0.004344 0.01264 0.007151 0.9909 0.9939 0.01129 0.9822 0.9899 0.02498 ] Network output: [ 0.02841 -0.1053 0.8923 -0.001277 0.0005734 1.151 -0.0009626 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4123 0.2867 0.5029 0.2377 0.9834 0.9933 0.4151 0.9485 0.9868 0.7674 ] Network output: [ -0.02611 0.2294 1.04 0.0005079 -0.000228 0.785 0.0003828 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2349 0.2223 0.2479 0.1801 0.9908 0.9945 0.2352 0.9827 0.9909 0.2676 ] Network output: [ -0.02334 0.05488 1.094 0.0008788 -0.0003945 0.901 0.0006623 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2472 0.245 0.2573 0.2153 0.986 0.9918 0.2473 0.9713 0.9858 0.262 ] Network output: [ 0.0005159 0.9684 -0.004102 0.0001441 -6.471e-05 1.035 0.0001086 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04038 Epoch 4051 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04368 0.8555 0.9507 -0.0002135 9.583e-05 0.1055 -0.0001609 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005153 -0.005709 -0.01845 0.009086 0.9598 0.9663 0.01481 0.9322 0.9436 0.05697 ] Network output: [ 0.9757 0.1194 0.05505 0.0003764 -0.000169 -0.1243 0.0002836 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3432 -0.002071 -0.1389 0.1411 0.9819 0.9925 0.4183 0.9441 0.9851 0.7696 ] Network output: [ 0.003813 0.8729 0.9671 -0.000425 0.0001908 0.1507 -0.0003203 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01092 0.004332 0.01263 0.00713 0.9909 0.9939 0.01129 0.9822 0.9899 0.02499 ] Network output: [ 0.02793 -0.1044 0.8935 -0.001278 0.0005738 1.15 -0.0009633 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4118 0.286 0.5031 0.2369 0.9834 0.9933 0.4146 0.9485 0.9868 0.7679 ] Network output: [ -0.02592 0.2292 1.039 0.0005117 -0.0002297 0.7853 0.0003856 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2353 0.2227 0.248 0.1799 0.9908 0.9945 0.2356 0.9827 0.9909 0.2677 ] Network output: [ -0.0231 0.05473 1.094 0.0008832 -0.0003965 0.9014 0.0006656 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2477 0.2455 0.2572 0.2151 0.9861 0.9918 0.2478 0.9714 0.9858 0.262 ] Network output: [ 0.0006349 0.9679 -0.004425 0.0001415 -6.351e-05 1.036 0.0001066 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04004 Epoch 4052 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04346 0.8559 0.9511 -0.0002137 9.593e-05 0.1052 -0.000161 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005164 -0.005712 -0.01846 0.009079 0.9598 0.9663 0.01481 0.9323 0.9436 0.05697 ] Network output: [ 0.9761 0.1184 0.05461 0.0003706 -0.0001664 -0.1237 0.0002793 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.343 -0.002315 -0.1392 0.1409 0.9819 0.9925 0.4179 0.9442 0.9851 0.7701 ] Network output: [ 0.003542 0.8734 0.9676 -0.000424 0.0001904 0.1502 -0.0003196 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01092 0.00432 0.01263 0.00711 0.9909 0.9939 0.01129 0.9822 0.9899 0.02501 ] Network output: [ 0.02745 -0.1035 0.8947 -0.001279 0.0005742 1.149 -0.000964 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4114 0.2854 0.5033 0.2361 0.9834 0.9933 0.4141 0.9486 0.9868 0.7685 ] Network output: [ -0.02572 0.229 1.039 0.0005154 -0.0002314 0.7857 0.0003885 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2358 0.2231 0.2481 0.1798 0.9908 0.9945 0.2361 0.9827 0.9909 0.2678 ] Network output: [ -0.02286 0.05456 1.093 0.0008876 -0.0003985 0.9019 0.000669 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2483 0.246 0.2572 0.215 0.9861 0.9919 0.2484 0.9714 0.9859 0.262 ] Network output: [ 0.0007495 0.9673 -0.00474 0.0001387 -6.228e-05 1.037 0.0001046 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03971 Epoch 4053 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04325 0.8564 0.9515 -0.0002139 9.601e-05 0.1048 -0.0001612 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005175 -0.005714 -0.01847 0.009072 0.9598 0.9663 0.01481 0.9323 0.9436 0.05697 ] Network output: [ 0.9765 0.1174 0.05417 0.0003649 -0.0001638 -0.1231 0.000275 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3428 -0.00256 -0.1395 0.1408 0.982 0.9925 0.4174 0.9442 0.9851 0.7707 ] Network output: [ 0.003272 0.874 0.968 -0.000423 0.0001899 0.1497 -0.0003188 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01092 0.004309 0.01263 0.007091 0.9909 0.9939 0.01129 0.9823 0.99 0.02502 ] Network output: [ 0.02699 -0.1027 0.8959 -0.00128 0.0005746 1.148 -0.0009645 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4109 0.2847 0.5035 0.2354 0.9835 0.9933 0.4137 0.9486 0.9868 0.769 ] Network output: [ -0.02553 0.2288 1.038 0.0005192 -0.0002331 0.7861 0.0003913 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2363 0.2235 0.2482 0.1796 0.9908 0.9945 0.2366 0.9827 0.991 0.2679 ] Network output: [ -0.02263 0.05438 1.092 0.0008921 -0.0004005 0.9023 0.0006723 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2488 0.2465 0.2572 0.2148 0.9861 0.9919 0.2489 0.9715 0.9859 0.2619 ] Network output: [ 0.0008588 0.9668 -0.00505 0.000136 -6.104e-05 1.037 0.0001025 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03938 Epoch 4054 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04303 0.8569 0.9519 -0.000214 9.609e-05 0.1044 -0.0001613 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005187 -0.005717 -0.01849 0.009066 0.9599 0.9663 0.01481 0.9324 0.9437 0.05698 ] Network output: [ 0.9769 0.1164 0.05373 0.000359 -0.0001612 -0.1225 0.0002706 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3426 -0.002801 -0.1398 0.1406 0.982 0.9926 0.417 0.9443 0.9851 0.7712 ] Network output: [ 0.003004 0.8746 0.9685 -0.000422 0.0001894 0.1492 -0.000318 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01092 0.004297 0.01263 0.007071 0.9909 0.9939 0.01129 0.9823 0.99 0.02503 ] Network output: [ 0.02651 -0.1017 0.897 -0.001281 0.000575 1.146 -0.0009652 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4105 0.2841 0.5037 0.2346 0.9835 0.9933 0.4132 0.9486 0.9868 0.7695 ] Network output: [ -0.02534 0.2285 1.038 0.000523 -0.0002348 0.7865 0.0003941 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2368 0.2239 0.2483 0.1795 0.9908 0.9945 0.2371 0.9827 0.991 0.268 ] Network output: [ -0.02238 0.05419 1.091 0.0008965 -0.0004025 0.9028 0.0006756 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2494 0.2471 0.2571 0.2147 0.9861 0.9919 0.2494 0.9715 0.9859 0.2619 ] Network output: [ 0.0009642 0.9662 -0.00535 0.0001332 -5.978e-05 1.038 0.0001004 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03905 Epoch 4055 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04281 0.8573 0.9522 -0.0002141 9.613e-05 0.104 -0.0001614 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005198 -0.00572 -0.0185 0.00906 0.9599 0.9663 0.01481 0.9324 0.9437 0.05698 ] Network output: [ 0.9773 0.1153 0.05328 0.0003533 -0.0001586 -0.1218 0.0002663 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3424 -0.003045 -0.1401 0.1404 0.982 0.9926 0.4165 0.9443 0.9851 0.7717 ] Network output: [ 0.002737 0.8751 0.969 -0.0004209 0.000189 0.1487 -0.0003172 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01092 0.004286 0.01263 0.007053 0.9909 0.9939 0.01129 0.9823 0.99 0.02505 ] Network output: [ 0.02606 -0.101 0.8982 -0.001281 0.0005752 1.145 -0.0009656 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.41 0.2835 0.5039 0.2339 0.9835 0.9933 0.4127 0.9487 0.9868 0.77 ] Network output: [ -0.02514 0.2283 1.037 0.0005267 -0.0002365 0.787 0.000397 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2373 0.2243 0.2484 0.1794 0.9908 0.9945 0.2375 0.9827 0.991 0.2682 ] Network output: [ -0.02214 0.05398 1.091 0.0009009 -0.0004044 0.9032 0.0006789 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2499 0.2476 0.2571 0.2145 0.9861 0.9919 0.25 0.9715 0.9859 0.2619 ] Network output: [ 0.001064 0.9657 -0.005646 0.0001303 -5.851e-05 1.038 9.822e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03872 Epoch 4056 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04259 0.8578 0.9526 -0.0002142 9.618e-05 0.1035 -0.0001615 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005209 -0.005723 -0.01851 0.009054 0.9599 0.9663 0.01481 0.9325 0.9437 0.05698 ] Network output: [ 0.9777 0.1144 0.05282 0.0003472 -0.0001559 -0.1212 0.0002617 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3423 -0.003282 -0.1404 0.1403 0.982 0.9926 0.4161 0.9443 0.9852 0.7722 ] Network output: [ 0.002471 0.8757 0.9695 -0.0004198 0.0001885 0.1482 -0.0003164 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01093 0.004276 0.01263 0.007034 0.9909 0.9939 0.01129 0.9823 0.99 0.02506 ] Network output: [ 0.02559 -0.09999 0.8993 -0.001282 0.0005756 1.144 -0.0009663 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4096 0.2829 0.5041 0.2331 0.9835 0.9933 0.4123 0.9487 0.9868 0.7705 ] Network output: [ -0.02495 0.228 1.037 0.0005305 -0.0002381 0.7874 0.0003998 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2377 0.2248 0.2485 0.1792 0.9908 0.9945 0.238 0.9827 0.991 0.2683 ] Network output: [ -0.0219 0.05375 1.09 0.0009053 -0.0004064 0.9037 0.0006822 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2505 0.2482 0.2571 0.2144 0.9861 0.9919 0.2506 0.9716 0.9859 0.2619 ] Network output: [ 0.001161 0.9652 -0.005931 0.0001275 -5.722e-05 1.039 9.605e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03838 Epoch 4057 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04238 0.8583 0.953 -0.0002143 9.619e-05 0.1031 -0.0001615 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00522 -0.005726 -0.01852 0.009049 0.9599 0.9663 0.01481 0.9325 0.9437 0.05699 ] Network output: [ 0.9781 0.1134 0.05238 0.0003416 -0.0001534 -0.1206 0.0002575 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3421 -0.003528 -0.1407 0.1401 0.982 0.9926 0.4157 0.9444 0.9852 0.7727 ] Network output: [ 0.002206 0.8763 0.97 -0.0004187 0.000188 0.1477 -0.0003155 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01093 0.004265 0.01262 0.007016 0.9909 0.9939 0.01129 0.9823 0.99 0.02508 ] Network output: [ 0.02517 -0.09934 0.9006 -0.001282 0.0005757 1.143 -0.0009665 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4092 0.2822 0.5043 0.2324 0.9835 0.9933 0.4119 0.9487 0.9868 0.771 ] Network output: [ -0.02476 0.2277 1.036 0.0005342 -0.0002398 0.7878 0.0004026 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2382 0.2252 0.2486 0.1791 0.9908 0.9945 0.2385 0.9828 0.991 0.2684 ] Network output: [ -0.02166 0.05353 1.089 0.0009096 -0.0004084 0.9042 0.0006855 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2511 0.2487 0.2571 0.2143 0.9861 0.9919 0.2511 0.9716 0.9859 0.2619 ] Network output: [ 0.001251 0.9648 -0.006217 0.0001245 -5.591e-05 1.039 9.385e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03805 Epoch 4058 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04216 0.8588 0.9533 -0.0002143 9.623e-05 0.1027 -0.0001615 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005231 -0.005729 -0.01853 0.009042 0.9599 0.9663 0.01481 0.9326 0.9438 0.05699 ] Network output: [ 0.9785 0.1126 0.05191 0.0003351 -0.0001504 -0.12 0.0002526 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.342 -0.003758 -0.1409 0.1399 0.982 0.9926 0.4153 0.9444 0.9852 0.7732 ] Network output: [ 0.001943 0.8769 0.9704 -0.0004175 0.0001874 0.1471 -0.0003147 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01093 0.004254 0.01263 0.006997 0.9909 0.9939 0.0113 0.9823 0.99 0.02509 ] Network output: [ 0.02468 -0.09821 0.9016 -0.001284 0.0005764 1.142 -0.0009675 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4088 0.2817 0.5046 0.2317 0.9835 0.9933 0.4115 0.9488 0.9869 0.7715 ] Network output: [ -0.02456 0.2273 1.036 0.0005379 -0.0002415 0.7883 0.0004054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2387 0.2256 0.2487 0.179 0.9908 0.9945 0.239 0.9828 0.991 0.2686 ] Network output: [ -0.02141 0.05325 1.089 0.000914 -0.0004103 0.9047 0.0006888 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2516 0.2493 0.2571 0.2141 0.9861 0.9919 0.2517 0.9717 0.9859 0.2619 ] Network output: [ 0.001341 0.9643 -0.006482 0.0001216 -5.46e-05 1.04 9.165e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03772 Epoch 4059 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04195 0.8592 0.9537 -0.0002142 9.618e-05 0.1023 -0.0001615 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005242 -0.005732 -0.01855 0.009038 0.9599 0.9663 0.01481 0.9326 0.9438 0.057 ] Network output: [ 0.9789 0.1114 0.05148 0.00033 -0.0001481 -0.1194 0.0002487 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3418 -0.00401 -0.1412 0.1398 0.982 0.9926 0.4149 0.9445 0.9852 0.7737 ] Network output: [ 0.001679 0.8774 0.9709 -0.0004163 0.0001869 0.1466 -0.0003138 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01093 0.004244 0.01263 0.006982 0.9909 0.9939 0.0113 0.9824 0.99 0.02511 ] Network output: [ 0.02432 -0.09787 0.9029 -0.001283 0.0005761 1.141 -0.0009671 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4084 0.2811 0.5048 0.231 0.9835 0.9933 0.411 0.9488 0.9869 0.772 ] Network output: [ -0.02437 0.227 1.035 0.0005417 -0.0002432 0.7888 0.0004083 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2392 0.2261 0.2488 0.1789 0.9908 0.9945 0.2395 0.9828 0.991 0.2687 ] Network output: [ -0.02117 0.05303 1.088 0.0009183 -0.0004123 0.9052 0.0006921 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2522 0.2498 0.2572 0.214 0.9861 0.9919 0.2523 0.9717 0.9859 0.2619 ] Network output: [ 0.001419 0.9639 -0.006763 0.0001186 -5.325e-05 1.041 8.938e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03739 Epoch 4060 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04172 0.8598 0.9541 -0.0002144 9.623e-05 0.1018 -0.0001615 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005253 -0.005735 -0.01855 0.009031 0.9599 0.9664 0.01481 0.9327 0.9438 0.057 ] Network output: [ 0.9792 0.1108 0.05097 0.0003226 -0.0001448 -0.1188 0.0002431 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3417 -0.004225 -0.1414 0.1396 0.982 0.9926 0.4146 0.9445 0.9852 0.7742 ] Network output: [ 0.00142 0.878 0.9714 -0.0004151 0.0001864 0.1461 -0.0003129 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01094 0.004234 0.01263 0.006962 0.9909 0.9939 0.0113 0.9824 0.99 0.02513 ] Network output: [ 0.02376 -0.0963 0.9037 -0.001286 0.0005772 1.14 -0.000969 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.408 0.2805 0.5051 0.2302 0.9835 0.9933 0.4107 0.9489 0.9869 0.7725 ] Network output: [ -0.02417 0.2266 1.035 0.0005454 -0.0002449 0.7892 0.000411 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2397 0.2265 0.249 0.1788 0.9908 0.9945 0.24 0.9828 0.991 0.2689 ] Network output: [ -0.02091 0.05268 1.087 0.0009228 -0.0004143 0.9057 0.0006954 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2528 0.2504 0.2572 0.2139 0.9861 0.9919 0.2529 0.9717 0.986 0.262 ] Network output: [ 0.001507 0.9634 -0.007001 0.0001157 -5.193e-05 1.041 8.717e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03705 Epoch 4061 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04153 0.8602 0.9545 -0.000214 9.609e-05 0.1014 -0.0001613 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005264 -0.005739 -0.01857 0.009029 0.9599 0.9664 0.01481 0.9327 0.9438 0.05701 ] Network output: [ 0.9798 0.1093 0.05059 0.0003187 -0.0001431 -0.1182 0.0002402 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3415 -0.004497 -0.1418 0.1396 0.982 0.9926 0.4142 0.9445 0.9852 0.7746 ] Network output: [ 0.001155 0.8786 0.9718 -0.0004139 0.0001858 0.1455 -0.0003119 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01094 0.004223 0.01263 0.006949 0.991 0.9939 0.0113 0.9824 0.99 0.02514 ] Network output: [ 0.02354 -0.09671 0.9053 -0.001283 0.0005762 1.139 -0.0009672 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4076 0.2799 0.5053 0.2297 0.9835 0.9933 0.4103 0.9489 0.9869 0.773 ] Network output: [ -0.02398 0.2262 1.034 0.0005492 -0.0002466 0.7898 0.0004139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2403 0.2269 0.2491 0.1787 0.9908 0.9945 0.2406 0.9828 0.991 0.2691 ] Network output: [ -0.02069 0.05249 1.086 0.0009269 -0.0004161 0.9062 0.0006985 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2534 0.251 0.2572 0.2138 0.9862 0.9919 0.2535 0.9718 0.986 0.262 ] Network output: [ 0.001567 0.963 -0.007293 0.0001125 -5.052e-05 1.042 8.481e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03673 Epoch 4062 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04128 0.8609 0.9548 -0.0002143 9.623e-05 0.1009 -0.0001615 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005275 -0.005741 -0.01857 0.009019 0.9599 0.9664 0.01481 0.9327 0.9439 0.05701 ] Network output: [ 0.9797 0.1094 0.05 0.0003091 -0.0001388 -0.1176 0.000233 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3414 -0.004676 -0.1417 0.1392 0.982 0.9926 0.4139 0.9446 0.9852 0.7751 ] Network output: [ 0.0009032 0.8793 0.9723 -0.0004126 0.0001852 0.145 -0.000311 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01095 0.004215 0.01264 0.006926 0.991 0.9939 0.01131 0.9824 0.99 0.02516 ] Network output: [ 0.02279 -0.09396 0.9056 -0.001289 0.0005785 1.137 -0.0009711 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4073 0.2794 0.5056 0.2287 0.9835 0.9934 0.4099 0.9489 0.9869 0.7735 ] Network output: [ -0.02378 0.2259 1.034 0.0005529 -0.0002482 0.7902 0.0004167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2408 0.2274 0.2493 0.1786 0.9909 0.9945 0.2411 0.9828 0.991 0.2692 ] Network output: [ -0.0204 0.05202 1.086 0.0009316 -0.0004182 0.9068 0.0007021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.254 0.2516 0.2572 0.2137 0.9862 0.9919 0.254 0.9718 0.986 0.262 ] Network output: [ 0.001662 0.9626 -0.007476 0.0001097 -4.923e-05 1.042 8.265e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03638 Epoch 4063 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04112 0.8612 0.9552 -0.0002136 9.588e-05 0.1005 -0.000161 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005285 -0.005746 -0.01859 0.009023 0.96 0.9664 0.01481 0.9328 0.9439 0.05702 ] Network output: [ 0.9808 0.1069 0.04975 0.0003086 -0.0001385 -0.1171 0.0002326 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3412 -0.005003 -0.1423 0.1394 0.982 0.9926 0.4135 0.9446 0.9852 0.7756 ] Network output: [ 0.0006333 0.8798 0.9728 -0.0004113 0.0001846 0.1445 -0.0003099 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01095 0.004202 0.01263 0.006921 0.991 0.9939 0.01131 0.9824 0.99 0.02517 ] Network output: [ 0.02289 -0.09631 0.908 -0.001282 0.0005754 1.137 -0.0009659 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4069 0.2787 0.5057 0.2286 0.9835 0.9934 0.4095 0.949 0.9869 0.774 ] Network output: [ -0.0236 0.2253 1.033 0.0005567 -0.0002499 0.7909 0.0004196 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2413 0.2279 0.2494 0.1786 0.9909 0.9945 0.2416 0.9828 0.991 0.2694 ] Network output: [ -0.02021 0.05197 1.085 0.0009353 -0.0004199 0.9073 0.0007049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2546 0.2522 0.2573 0.2136 0.9862 0.9919 0.2547 0.9719 0.986 0.2621 ] Network output: [ 0.001689 0.9623 -0.007825 0.0001063 -4.77e-05 1.043 8.008e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03608 Epoch 4064 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04081 0.8621 0.9555 -0.0002145 9.628e-05 0.09994 -0.0001616 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005297 -0.005748 -0.01858 0.009005 0.96 0.9664 0.01482 0.9328 0.9439 0.05703 ] Network output: [ 0.98 0.1086 0.04895 0.0002934 -0.0001317 -0.1164 0.0002211 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3412 -0.005091 -0.1419 0.1388 0.982 0.9926 0.4132 0.9447 0.9852 0.7761 ] Network output: [ 0.0003954 0.8805 0.9732 -0.0004101 0.0001841 0.1438 -0.000309 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01096 0.004197 0.01266 0.006886 0.991 0.9939 0.01131 0.9824 0.99 0.0252 ] Network output: [ 0.02164 -0.09048 0.9071 -0.001294 0.000581 1.135 -0.0009753 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4067 0.2783 0.5063 0.2269 0.9835 0.9934 0.4093 0.949 0.9869 0.7745 ] Network output: [ -0.02338 0.2251 1.033 0.0005602 -0.0002515 0.7912 0.0004222 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2418 0.2283 0.2496 0.1783 0.9909 0.9946 0.2421 0.9828 0.991 0.2696 ] Network output: [ -0.01987 0.05119 1.085 0.0009406 -0.0004223 0.9079 0.0007089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2551 0.2527 0.2573 0.2135 0.9862 0.9919 0.2552 0.9719 0.986 0.2621 ] Network output: [ 0.001819 0.9617 -0.007879 0.0001037 -4.657e-05 1.043 7.818e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0357 Epoch 4065 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04075 0.8621 0.956 -0.0002126 9.545e-05 0.09959 -0.0001602 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005306 -0.005754 -0.01862 0.009022 0.96 0.9664 0.01482 0.9329 0.9439 0.05704 ] Network output: [ 0.9823 0.1036 0.04902 0.0003018 -0.0001355 -0.1159 0.0002274 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.341 -0.00556 -0.1431 0.1393 0.982 0.9926 0.4128 0.9447 0.9852 0.7765 ] Network output: [ 0.0001079 0.881 0.9737 -0.0004085 0.0001834 0.1434 -0.0003079 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01095 0.004181 0.01263 0.006902 0.991 0.9939 0.01131 0.9824 0.99 0.0252 ] Network output: [ 0.02258 -0.09787 0.9114 -0.001275 0.0005725 1.136 -0.0009611 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4061 0.2776 0.5061 0.2278 0.9835 0.9934 0.4087 0.949 0.9869 0.7749 ] Network output: [ -0.02322 0.2243 1.032 0.0005643 -0.0002533 0.7922 0.0004253 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2425 0.2288 0.2498 0.1786 0.9909 0.9946 0.2427 0.9828 0.991 0.2699 ] Network output: [ -0.01977 0.05157 1.083 0.0009433 -0.0004235 0.9084 0.0007109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2559 0.2534 0.2574 0.2135 0.9862 0.9919 0.2559 0.9719 0.986 0.2622 ] Network output: [ 0.001768 0.9618 -0.008409 9.962e-05 -4.472e-05 1.043 7.508e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03546 Epoch 4066 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04029 0.8635 0.9561 -0.0002151 9.657e-05 0.09891 -0.0001621 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005319 -0.005754 -0.01858 0.008984 0.96 0.9664 0.01482 0.9329 0.944 0.05705 ] Network output: [ 0.9796 0.1095 0.04769 0.0002721 -0.0001222 -0.1152 0.0002051 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.341 -0.005416 -0.1416 0.138 0.982 0.9926 0.4126 0.9447 0.9852 0.777 ] Network output: [ -9.525e-05 0.8819 0.974 -0.0004075 0.0001829 0.1426 -0.0003071 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01097 0.004183 0.01269 0.006836 0.991 0.9939 0.01133 0.9825 0.99 0.02525 ] Network output: [ 0.02001 -0.08388 0.9072 -0.001307 0.0005868 1.131 -0.000985 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4061 0.2774 0.5071 0.2246 0.9835 0.9934 0.4087 0.9491 0.9869 0.7754 ] Network output: [ -0.02297 0.2244 1.032 0.0005675 -0.0002548 0.7921 0.0004277 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2428 0.2292 0.2499 0.178 0.9909 0.9946 0.2431 0.9828 0.991 0.27 ] Network output: [ -0.01926 0.05001 1.083 0.0009502 -0.0004266 0.909 0.0007161 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2563 0.2538 0.2574 0.2132 0.9862 0.9919 0.2563 0.972 0.986 0.2622 ] Network output: [ 0.002009 0.9607 -0.008119 9.82e-05 -4.408e-05 1.044 7.4e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03499 Epoch 4067 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04048 0.8626 0.9568 -0.0002105 9.452e-05 0.09873 -0.0001587 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005327 -0.005763 -0.01866 0.009035 0.96 0.9664 0.01482 0.9329 0.944 0.05706 ] Network output: [ 0.9848 0.0977 0.04863 0.0003038 -0.0001364 -0.1148 0.0002289 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3407 -0.006261 -0.1444 0.1398 0.982 0.9926 0.4121 0.9448 0.9853 0.7774 ] Network output: [ -0.0004346 0.8821 0.9747 -0.0004055 0.0001821 0.1424 -0.0003056 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01096 0.004155 0.0126 0.006904 0.991 0.9939 0.01131 0.9825 0.99 0.02521 ] Network output: [ 0.02311 -0.1046 0.9168 -0.001257 0.0005643 1.137 -0.0009472 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4053 0.2763 0.5063 0.2281 0.9835 0.9934 0.4078 0.9491 0.9869 0.7758 ] Network output: [ -0.02286 0.223 1.031 0.0005721 -0.0002568 0.7937 0.0004312 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2437 0.2299 0.2502 0.1787 0.9909 0.9946 0.244 0.9828 0.991 0.2703 ] Network output: [ -0.01942 0.05161 1.081 0.0009501 -0.0004265 0.9096 0.000716 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2573 0.2548 0.2576 0.2134 0.9862 0.9919 0.2573 0.972 0.9861 0.2623 ] Network output: [ 0.001756 0.9617 -0.009189 9.213e-05 -4.136e-05 1.044 6.943e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03492 Epoch 4068 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03961 0.8656 0.9566 -0.0002173 9.755e-05 0.09772 -0.0001638 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005342 -0.005759 -0.01856 0.008942 0.96 0.9664 0.01483 0.933 0.944 0.05707 ] Network output: [ 0.9772 0.1147 0.04588 0.000236 -0.000106 -0.1141 0.0001779 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3409 -0.005503 -0.1403 0.1365 0.982 0.9926 0.4122 0.9448 0.9853 0.7779 ] Network output: [ -0.0005467 0.8834 0.9748 -0.0004051 0.0001818 0.1413 -0.0003053 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01099 0.004177 0.01276 0.006755 0.991 0.9939 0.01135 0.9825 0.99 0.02533 ] Network output: [ 0.01705 -0.06883 0.904 -0.00134 0.0006014 1.125 -0.00101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4058 0.2767 0.5084 0.2207 0.9835 0.9934 0.4084 0.9491 0.9869 0.7764 ] Network output: [ -0.02252 0.2239 1.031 0.0005744 -0.0002579 0.7926 0.0004329 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2438 0.23 0.2503 0.1774 0.9909 0.9946 0.244 0.9829 0.991 0.2703 ] Network output: [ -0.01849 0.04796 1.083 0.0009616 -0.0004317 0.9102 0.0007247 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2573 0.2548 0.2575 0.2129 0.9862 0.9919 0.2574 0.972 0.9861 0.2623 ] Network output: [ 0.002325 0.9591 -0.007933 9.41e-05 -4.225e-05 1.045 7.092e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03427 Epoch 4069 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04047 0.8623 0.9579 -0.0002056 9.232e-05 0.09806 -0.000155 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005345 -0.005777 -0.01874 0.009085 0.96 0.9664 0.01482 0.933 0.944 0.05708 ] Network output: [ 0.9905 0.0847 0.04918 0.0003298 -0.000148 -0.1136 0.0002485 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3403 -0.007347 -0.1474 0.1415 0.982 0.9926 0.4114 0.9448 0.9853 0.7783 ] Network output: [ -0.001033 0.883 0.9759 -0.0004021 0.0001805 0.1415 -0.0003031 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01095 0.004118 0.01252 0.00696 0.991 0.9939 0.0113 0.9825 0.99 0.02519 ] Network output: [ 0.02588 -0.1253 0.9276 -0.001206 0.0005414 1.141 -0.0009089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4042 0.2746 0.5058 0.2311 0.9835 0.9934 0.4067 0.9491 0.9869 0.7766 ] Network output: [ -0.02257 0.2211 1.03 0.0005804 -0.0002606 0.796 0.0004374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2451 0.2311 0.2507 0.1794 0.9909 0.9946 0.2454 0.9829 0.991 0.271 ] Network output: [ -0.01934 0.05296 1.079 0.000954 -0.0004283 0.9108 0.0007189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2589 0.2564 0.2578 0.2135 0.9863 0.9919 0.259 0.9721 0.9861 0.2625 ] Network output: [ 0.00153 0.9627 -0.01054 8.247e-05 -3.702e-05 1.045 6.215e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03475 Epoch 4070 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03851 0.8692 0.9567 -0.0002239 0.0001005 0.09616 -0.0001687 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005368 -0.005759 -0.01846 0.008842 0.96 0.9664 0.01485 0.933 0.944 0.05709 ] Network output: [ 0.9697 0.1315 0.04264 0.0001604 -7.199e-05 -0.1129 0.0001208 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.341 -0.004955 -0.1361 0.133 0.9821 0.9926 0.412 0.9449 0.9853 0.7788 ] Network output: [ -0.0008985 0.8853 0.9753 -0.0004031 0.000181 0.1396 -0.0003038 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01103 0.004191 0.01293 0.006584 0.991 0.9939 0.01139 0.9825 0.9901 0.02547 ] Network output: [ 0.01046 -0.03074 0.8917 -0.001425 0.0006399 1.112 -0.001074 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4061 0.2767 0.5108 0.2123 0.9835 0.9934 0.4087 0.9492 0.9869 0.7775 ] Network output: [ -0.02194 0.2243 1.03 0.0005805 -0.0002606 0.792 0.0004375 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2444 0.2305 0.2505 0.176 0.9909 0.9946 0.2447 0.9829 0.991 0.2705 ] Network output: [ -0.01726 0.04364 1.083 0.0009779 -0.000439 0.9114 0.000737 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.258 0.2555 0.2576 0.2124 0.9862 0.9919 0.258 0.9721 0.9861 0.2623 ] Network output: [ 0.003064 0.9553 -0.006494 9.488e-05 -4.26e-05 1.045 7.151e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03389 Epoch 4071 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04114 0.8594 0.9596 -0.0001935 8.685e-05 0.09793 -0.0001458 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005358 -0.0058 -0.01893 0.009235 0.96 0.9664 0.01481 0.9331 0.9441 0.0571 ] Network output: [ 1.005 0.05206 0.05238 0.0004208 -0.0001889 -0.1121 0.0003171 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3397 -0.009476 -0.1548 0.1466 0.9821 0.9926 0.4103 0.9449 0.9853 0.7791 ] Network output: [ -0.001799 0.8834 0.9774 -0.0003979 0.0001786 0.1412 -0.0002999 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01091 0.00405 0.01229 0.007164 0.991 0.9939 0.01126 0.9825 0.99 0.02506 ] Network output: [ 0.03468 -0.1838 0.9531 -0.001067 0.0004791 1.157 -0.0008043 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4022 0.2721 0.5035 0.2414 0.9835 0.9934 0.4047 0.9492 0.9869 0.7772 ] Network output: [ -0.02241 0.2178 1.029 0.00059 -0.0002649 0.8001 0.0004447 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.247 0.2328 0.2514 0.1815 0.9909 0.9946 0.2473 0.9829 0.991 0.2718 ] Network output: [ -0.01995 0.05811 1.074 0.0009497 -0.0004264 0.9118 0.0007157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2611 0.2586 0.2581 0.214 0.9863 0.992 0.2612 0.9722 0.9861 0.2628 ] Network output: [ 0.000819 0.966 -0.01337 6.796e-05 -3.051e-05 1.046 5.122e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03655 Epoch 4072 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03623 0.8772 0.9557 -0.0002427 0.000109 0.09362 -0.0001829 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005401 -0.005743 -0.01816 0.008587 0.96 0.9664 0.01488 0.9331 0.9441 0.0571 ] Network output: [ 0.948 0.1798 0.03581 -2.243e-05 1.007e-05 -0.1116 -1.69e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3416 -0.002687 -0.1243 0.1241 0.9821 0.9926 0.4123 0.9449 0.9853 0.7798 ] Network output: [ -0.0009932 0.8885 0.9749 -0.000403 0.0001809 0.1369 -0.0003037 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01112 0.00426 0.01336 0.006165 0.991 0.994 0.01148 0.9826 0.9901 0.02578 ] Network output: [ -0.005942 0.07068 0.8544 -0.001657 0.0007437 1.08 -0.001249 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4079 0.2784 0.516 0.1918 0.9836 0.9934 0.4104 0.9493 0.987 0.7786 ] Network output: [ -0.02094 0.2275 1.029 0.0005846 -0.0002624 0.788 0.0004405 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2441 0.2303 0.2502 0.1724 0.9909 0.9946 0.2444 0.9829 0.991 0.27 ] Network output: [ -0.01471 0.03329 1.088 0.001008 -0.0004524 0.9126 0.0007595 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2576 0.255 0.2572 0.2112 0.9862 0.9919 0.2576 0.9721 0.9861 0.262 ] Network output: [ 0.005273 0.9436 -0.001 0.0001134 -5.089e-05 1.047 8.543e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0372 Epoch 4073 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04361 0.8499 0.9629 -0.0001622 7.283e-05 0.09936 -0.0001223 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005359 -0.005848 -0.01941 0.009666 0.9601 0.9665 0.01479 0.9332 0.9441 0.05711 ] Network output: [ 1.042 -0.03572 0.06371 0.0006886 -0.0003091 -0.1084 0.000519 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3383 -0.01444 -0.1738 0.1615 0.9821 0.9926 0.4084 0.9449 0.9853 0.7797 ] Network output: [ -0.003071 0.8826 0.9799 -0.0003922 0.0001761 0.1421 -0.0002956 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0108 0.0039 0.01164 0.007757 0.991 0.9939 0.01114 0.9824 0.99 0.02465 ] Network output: [ 0.05989 -0.3443 1.018 -0.0006901 0.0003098 1.203 -0.0005201 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3978 0.2668 0.4961 0.2716 0.9835 0.9934 0.4003 0.9492 0.9869 0.7772 ] Network output: [ -0.02246 0.2116 1.027 0.0006035 -0.0002709 0.8083 0.0004548 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2503 0.2356 0.2522 0.187 0.9908 0.9946 0.2506 0.9828 0.991 0.273 ] Network output: [ -0.02233 0.07406 1.063 0.0009237 -0.0004147 0.9118 0.0006962 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.265 0.2623 0.2582 0.2156 0.9863 0.992 0.265 0.9724 0.9862 0.263 ] Network output: [ -0.0004281 0.9711 -0.01845 5.115e-05 -2.296e-05 1.048 3.855e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05096 Epoch 4074 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0306 0.8973 0.9518 -0.0002965 0.0001331 0.08848 -0.0002235 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005457 -0.005687 -0.01731 0.007934 0.96 0.9664 0.01495 0.9331 0.944 0.05708 ] Network output: [ 0.8873 0.3096 0.02158 -0.0004923 0.000221 -0.1077 -0.000371 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3435 0.004234 -0.09082 0.1014 0.9821 0.9926 0.414 0.945 0.9853 0.7805 ] Network output: [ -0.0005079 0.8961 0.9719 -0.0004098 0.000184 0.1313 -0.0003088 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01136 0.004484 0.01452 0.005046 0.9911 0.994 0.01172 0.9828 0.9902 0.02654 ] Network output: [ -0.04873 0.3446 0.7488 -0.002278 0.001023 0.9947 -0.001717 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4138 0.2847 0.5286 0.1384 0.9836 0.9934 0.4163 0.9493 0.987 0.7797 ] Network output: [ -0.01835 0.2404 1.026 0.0005828 -0.0002616 0.773 0.0004392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2414 0.2279 0.2478 0.1624 0.991 0.9946 0.2417 0.983 0.991 0.2669 ] Network output: [ -0.008267 0.009098 1.1 0.001073 -0.0004819 0.9117 0.0008089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.254 0.2516 0.2552 0.2079 0.9862 0.9919 0.2541 0.9719 0.986 0.26 ] Network output: [ 0.01309 0.9028 0.01816 0.0001997 -8.967e-05 1.054 0.0001505 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07093 Epoch 4075 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05053 0.8229 0.9704 -8.366e-05 3.756e-05 0.1052 -6.305e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005327 -0.005962 -0.02058 0.0109 0.9601 0.9665 0.01469 0.9332 0.9442 0.05702 ] Network output: [ 1.137 -0.2779 0.1025 0.001421 -0.000638 -0.09244 0.001071 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3348 -0.02697 -0.2214 0.204 0.9821 0.9926 0.4041 0.9449 0.9853 0.7791 ] Network output: [ -0.005831 0.8807 0.9838 -0.0003863 0.0001734 0.1456 -0.0002912 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01046 0.003537 0.009925 0.009277 0.9909 0.9939 0.0108 0.9821 0.9899 0.02344 ] Network output: [ 0.129 -0.7595 1.18 0.0002976 -0.0001336 1.323 0.0002243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3869 0.2545 0.4727 0.3516 0.9835 0.9934 0.3893 0.949 0.9869 0.7739 ] Network output: [ -0.02171 0.2023 1.021 0.0006286 -0.0002822 0.8224 0.0004737 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.256 0.2405 0.2506 0.201 0.9907 0.9945 0.2563 0.9826 0.991 0.2725 ] Network output: [ -0.02817 0.1174 1.035 0.000849 -0.0003811 0.9071 0.0006398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2716 0.2689 0.256 0.2199 0.9864 0.992 0.2717 0.9725 0.9862 0.2606 ] Network output: [ 0.003518 0.9362 -0.01161 0.0001293 -5.805e-05 1.069 9.744e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1476 Epoch 4076 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01519 0.9518 0.9393 -0.0004519 0.0002029 0.07667 -0.0003405 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005578 -0.005534 -0.01484 0.006528 0.96 0.9664 0.01515 0.9329 0.9438 0.05678 ] Network output: [ 0.7278 0.6048 0.009342 -0.001636 0.0007346 -0.07644 -0.001233 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3486 0.02293 0.002226 0.05297 0.9821 0.9926 0.4194 0.9448 0.9852 0.7794 ] Network output: [ 0.0002664 0.9231 0.9588 -0.0004511 0.0002025 0.1157 -0.0003399 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01198 0.005195 0.01762 0.00227 0.9912 0.9941 0.01236 0.9831 0.9904 0.02829 ] Network output: [ -0.1553 1.012 0.4888 -0.003781 0.001698 0.7946 -0.00285 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4303 0.3047 0.5613 0.01157 0.9836 0.9934 0.4329 0.9491 0.9869 0.7772 ] Network output: [ -0.01006 0.3016 1.002 0.0005456 -0.000245 0.7187 0.0004112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2337 0.2212 0.2366 0.1352 0.9911 0.9946 0.234 0.9829 0.9908 0.253 ] Network output: [ 0.008768 -0.0113 1.112 0.00118 -0.00053 0.8861 0.0008896 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2431 0.2408 0.2433 0.1947 0.986 0.9918 0.2432 0.9709 0.9856 0.248 ] Network output: [ 0.03862 0.808 0.05991 0.0004492 -0.0002017 1.057 0.0003385 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.296 Epoch 4077 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06529 0.7578 0.9889 8.514e-05 -3.822e-05 0.1231 6.417e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005183 -0.006161 -0.02245 0.01391 0.9601 0.9665 0.01434 0.9329 0.944 0.05592 ] Network output: [ 1.324 -0.8526 0.2333 0.002994 -0.001344 -0.0164 0.002256 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3237 -0.05535 -0.3066 0.3078 0.9821 0.9926 0.391 0.9442 0.9852 0.7705 ] Network output: [ -0.01013 0.8806 0.9869 -0.0003839 0.0001724 0.1512 -0.0002893 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009608 0.002668 0.006398 0.01186 0.9907 0.9938 0.009918 0.9812 0.9895 0.02052 ] Network output: [ 0.2881 -1.533 1.456 0.002237 -0.001004 1.509 0.001686 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3614 0.2228 0.4017 0.503 0.9834 0.9933 0.3636 0.9478 0.9866 0.7545 ] Network output: [ -0.01509 0.2299 0.9934 0.000635 -0.0002851 0.8094 0.0004786 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2564 0.239 0.2292 0.2177 0.9904 0.9944 0.2567 0.9816 0.9907 0.2532 ] Network output: [ -0.03742 0.2093 0.9907 0.0007017 -0.000315 0.8777 0.0005288 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2718 0.2688 0.238 0.2235 0.9862 0.9919 0.2719 0.9716 0.9857 0.2426 ] Network output: [ 0.04402 0.6288 0.1024 0.0008196 -0.000368 1.184 0.0006177 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6051 Epoch 4078 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01994 1.06 0.913 -0.0007881 0.0003538 0.06338 -0.0005939 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00579 -0.005234 -0.00853 0.006571 0.96 0.9664 0.01545 0.9317 0.9428 0.05447 ] Network output: [ 0.4559 0.6763 0.2067 -0.002902 0.001303 0.1935 -0.002187 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3552 0.0571 0.2014 0.06226 0.982 0.9926 0.4258 0.9435 0.9848 0.7635 ] Network output: [ -0.004279 1.019 0.9111 -0.0006202 0.0002784 0.07625 -0.0004674 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01356 0.007524 0.02365 0.001622 0.9912 0.9942 0.01397 0.9835 0.9907 0.03023 ] Network output: [ -0.2949 1.537 0.2952 -0.004938 0.002217 0.7377 -0.003721 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.47 0.3673 0.6416 -0.06624 0.9837 0.9934 0.4727 0.9474 0.9866 0.7467 ] Network output: [ 0.01272 0.5274 0.8627 0.0003703 -0.0001663 0.5859 0.0002791 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2412 0.2319 0.2119 0.0986 0.9912 0.9947 0.2415 0.9829 0.9907 0.2182 ] Network output: [ 0.03825 0.2204 0.9793 0.0009847 -0.0004421 0.7279 0.0007421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2398 0.2381 0.203 0.1511 0.9857 0.9916 0.2399 0.9691 0.9846 0.2046 ] Network output: [ 0.08 0.9905 -0.05352 0.0003629 -0.0001629 0.9045 0.0002735 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.6413 Epoch 4079 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06773 0.7191 1.009 0.0001109 -4.979e-05 0.1374 8.359e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004952 -0.00595 -0.0185 0.01514 0.9601 0.9665 0.01339 0.931 0.9426 0.05067 ] Network output: [ 1.219 -1.188 0.5196 0.002851 -0.00128 0.2418 0.002149 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3056 -0.06544 -0.1911 0.3709 0.9819 0.9926 0.3674 0.9416 0.9847 0.7284 ] Network output: [ 0.00574 0.8733 0.9762 -0.00033 0.0001481 0.1376 -0.0002487 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009407 0.002345 0.009286 0.01363 0.9904 0.9937 0.009703 0.9802 0.9893 0.01964 ] Network output: [ 0.2796 -1.761 1.571 0.002049 -0.00092 1.639 0.001544 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3664 0.2104 0.4832 0.5422 0.9834 0.9933 0.3686 0.9453 0.9862 0.7026 ] Network output: [ 0.01436 0.2954 0.9379 0.0006905 -0.00031 0.7409 0.0005204 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2538 0.2343 0.2162 0.2107 0.9902 0.9943 0.2541 0.9808 0.9905 0.2298 ] Network output: [ -0.005325 0.3535 0.8916 0.0007102 -0.0003188 0.7684 0.0005352 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2699 0.2664 0.2067 0.2002 0.986 0.9918 0.2699 0.9698 0.985 0.2092 ] Network output: [ 0.109 0.5365 0.09072 0.00135 -0.000606 1.16 0.001017 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.9211 Epoch 4080 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.01286 1.07 0.9013 -0.0007613 0.0003418 0.05151 -0.0005737 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005912 -0.005481 -0.01018 0.007886 0.9601 0.9664 0.01521 0.9297 0.9414 0.05037 ] Network output: [ 0.6096 0.373 0.2109 -0.002032 0.0009121 0.1886 -0.001531 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3613 0.03725 0.1379 0.1246 0.982 0.9925 0.4296 0.9408 0.9843 0.7225 ] Network output: [ -0.009384 1.041 0.9062 -0.0006277 0.0002818 0.06901 -0.0004731 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.013 0.00693 0.0193 0.005928 0.991 0.994 0.01338 0.9823 0.9902 0.0255 ] Network output: [ -0.1521 0.6466 0.6218 -0.002834 0.001272 1.024 -0.002136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4672 0.3605 0.6055 0.1181 0.9837 0.9934 0.4698 0.9451 0.9861 0.7071 ] Network output: [ 0.004007 0.5287 0.8526 0.0003711 -0.0001666 0.6123 0.0002796 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2543 0.2444 0.2149 0.1333 0.991 0.9946 0.2546 0.9823 0.9906 0.2211 ] Network output: [ 0.01815 0.3418 0.9177 0.0007569 -0.0003398 0.7072 0.0005704 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2549 0.2531 0.2034 0.1626 0.9859 0.9917 0.2549 0.969 0.9846 0.2049 ] Network output: [ 0.07001 1.055 -0.1021 0.0002591 -0.0001163 0.9083 0.0001952 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2396 Epoch 4081 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04322 0.8483 0.9709 -0.0001952 8.763e-05 0.09353 -0.0001471 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005396 -0.005913 -0.01714 0.01173 0.9602 0.9666 0.01391 0.9294 0.9415 0.05002 ] Network output: [ 1.076 -0.5095 0.2704 0.0009275 -0.0004164 0.08999 0.000699 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3358 -0.04241 -0.1164 0.2602 0.982 0.9926 0.3994 0.9398 0.9843 0.7119 ] Network output: [ -0.003259 0.9184 0.97 -0.0004021 0.0001805 0.1165 -0.000303 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01015 0.003304 0.01134 0.01101 0.9906 0.9937 0.01045 0.9804 0.9893 0.02075 ] Network output: [ 0.1533 -1.026 1.283 0.0005625 -0.0002525 1.439 0.0004239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4004 0.2546 0.5134 0.415 0.9835 0.9933 0.4026 0.9443 0.986 0.7003 ] Network output: [ -0.01161 0.2847 0.9946 0.0006157 -0.0002764 0.7464 0.000464 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2447 0.2289 0.2314 0.2012 0.9904 0.9944 0.245 0.981 0.9904 0.244 ] Network output: [ -0.01561 0.2853 0.9561 0.0007395 -0.000332 0.7928 0.0005573 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2635 0.2606 0.2227 0.2026 0.986 0.9918 0.2635 0.9693 0.9849 0.2253 ] Network output: [ 0.06584 0.7861 0.008135 0.0007269 -0.0003263 1.077 0.0005478 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2968 Epoch 4082 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.007848 1.017 0.9155 -0.000567 0.0002545 0.05003 -0.0004273 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005984 -0.005777 -0.01495 0.005914 0.9602 0.9665 0.01504 0.9289 0.9409 0.05134 ] Network output: [ 0.792 0.6778 -0.1162 -0.002046 0.0009186 -0.1538 -0.001542 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3714 0.009227 -0.002476 0.04751 0.982 0.9926 0.4392 0.9396 0.9841 0.7192 ] Network output: [ -0.009892 0.9634 0.9547 -0.000465 0.0002088 0.09979 -0.0003505 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0114 0.004617 0.01467 0.003058 0.991 0.9939 0.01172 0.9812 0.9894 0.02316 ] Network output: [ -0.09214 0.6797 0.607 -0.003004 0.001349 0.8853 -0.002264 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4354 0.2998 0.549 0.07647 0.9836 0.9934 0.4377 0.9442 0.986 0.7155 ] Network output: [ -0.0282 0.3318 1.03 0.000498 -0.0002236 0.6963 0.0003753 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2186 0.2062 0.2271 0.1455 0.9908 0.9944 0.2188 0.9811 0.99 0.2388 ] Network output: [ -0.01064 0.1069 1.086 0.0009983 -0.0004482 0.8323 0.0007524 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2335 0.2311 0.2279 0.1904 0.9857 0.9916 0.2336 0.9678 0.9843 0.2308 ] Network output: [ 0.05227 0.7717 0.05962 0.0006502 -0.0002919 1.067 0.00049 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2165 Epoch 4083 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04389 0.8327 0.9703 -8.775e-05 3.94e-05 0.1089 -6.613e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005544 -0.006135 -0.01872 0.01229 0.9603 0.9666 0.01422 0.929 0.9412 0.05128 ] Network output: [ 1.157 -0.5542 0.1968 0.00121 -0.0005431 0.04916 0.0009118 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3453 -0.05153 -0.1588 0.2692 0.982 0.9926 0.4101 0.9392 0.9841 0.7157 ] Network output: [ -0.01209 0.9102 0.9778 -0.0003374 0.0001515 0.1348 -0.0002543 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01006 0.003109 0.01022 0.01131 0.9906 0.9938 0.01035 0.9802 0.9891 0.02038 ] Network output: [ 0.1687 -1.14 1.324 0.0009177 -0.000412 1.483 0.0006916 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3971 0.2493 0.4972 0.4423 0.9835 0.9933 0.3993 0.9436 0.9858 0.7037 ] Network output: [ -0.02752 0.247 1.024 0.0006654 -0.0002987 0.7866 0.0005015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2439 0.2278 0.2374 0.213 0.9904 0.9944 0.2442 0.9808 0.9903 0.2517 ] Network output: [ -0.03101 0.2676 0.9726 0.0007581 -0.0003403 0.8249 0.0005713 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2647 0.2617 0.2287 0.2117 0.9861 0.9919 0.2647 0.9691 0.9849 0.2316 ] Network output: [ 0.05647 0.7305 0.0313 0.0008286 -0.000372 1.129 0.0006245 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3449 Epoch 4084 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.001086 1.032 0.9122 -0.0005727 0.0002571 0.05141 -0.0004316 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006113 -0.005812 -0.01444 0.006006 0.9602 0.9665 0.01531 0.9283 0.9405 0.05121 ] Network output: [ 0.7638 0.6947 -0.1094 -0.002163 0.0009711 -0.1218 -0.00163 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3778 0.01651 0.01912 0.04941 0.982 0.9925 0.4464 0.9388 0.9839 0.7146 ] Network output: [ -0.01372 0.9765 0.9508 -0.0004569 0.0002051 0.09827 -0.0003443 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01181 0.005113 0.01529 0.003149 0.991 0.9939 0.01213 0.9812 0.9894 0.02335 ] Network output: [ -0.1158 0.7427 0.5847 -0.003053 0.001371 0.8918 -0.002301 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4449 0.3151 0.5565 0.07307 0.9836 0.9934 0.4473 0.9435 0.9858 0.7097 ] Network output: [ -0.0318 0.3658 1.013 0.0004726 -0.0002122 0.6868 0.0003561 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2222 0.2105 0.2262 0.1432 0.9908 0.9944 0.2224 0.981 0.99 0.2368 ] Network output: [ -0.01257 0.1457 1.067 0.0009581 -0.0004301 0.816 0.0007221 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2354 0.2332 0.2244 0.1865 0.9857 0.9916 0.2354 0.9675 0.9842 0.2271 ] Network output: [ 0.04889 0.8233 0.0323 0.0005685 -0.0002552 1.049 0.0004284 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2384 Epoch 4085 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04435 0.8252 0.9754 -5.949e-05 2.671e-05 0.1104 -4.484e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005542 -0.006158 -0.01867 0.01242 0.9603 0.9666 0.01418 0.9284 0.9407 0.05066 ] Network output: [ 1.159 -0.5683 0.2019 0.001224 -0.0005496 0.05284 0.0009227 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.345 -0.05363 -0.1588 0.2737 0.982 0.9926 0.4096 0.9383 0.9839 0.7069 ] Network output: [ -0.01168 0.9055 0.9815 -0.0003123 0.0001402 0.1351 -0.0002353 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01004 0.003049 0.009936 0.01134 0.9906 0.9937 0.01033 0.9798 0.9889 0.01994 ] Network output: [ 0.1756 -1.142 1.321 0.0009581 -0.0004301 1.474 0.000722 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3967 0.2473 0.4892 0.4471 0.9835 0.9933 0.3989 0.9428 0.9856 0.6938 ] Network output: [ -0.02941 0.2561 1.025 0.0006538 -0.0002935 0.7804 0.0004927 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2423 0.2261 0.2343 0.2128 0.9903 0.9943 0.2426 0.9804 0.9901 0.2484 ] Network output: [ -0.03378 0.2795 0.9728 0.0007361 -0.0003305 0.8182 0.0005547 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2628 0.2598 0.2257 0.2108 0.9861 0.9918 0.2628 0.9686 0.9846 0.2286 ] Network output: [ 0.05749 0.7103 0.03937 0.0008619 -0.0003869 1.139 0.0006496 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.352 Epoch 4086 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.001009 1.025 0.9162 -0.0005516 0.0002476 0.05432 -0.0004157 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006127 -0.005826 -0.01426 0.006497 0.9602 0.9665 0.0153 0.9277 0.94 0.0505 ] Network output: [ 0.7692 0.6114 -0.07262 -0.002004 0.0008999 -0.08536 -0.001511 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3782 0.01674 0.0226 0.06778 0.982 0.9925 0.4466 0.938 0.9837 0.7052 ] Network output: [ -0.01409 0.9777 0.9511 -0.0004477 0.000201 0.09757 -0.0003374 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0119 0.005305 0.01518 0.003947 0.9909 0.9939 0.01223 0.9809 0.9893 0.02296 ] Network output: [ -0.1071 0.6342 0.6294 -0.002761 0.001239 0.9394 -0.002081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4479 0.3215 0.5545 0.1015 0.9836 0.9934 0.4502 0.9427 0.9856 0.6998 ] Network output: [ -0.03354 0.3835 1.003 0.0004458 -0.0002001 0.6829 0.000336 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2253 0.214 0.2256 0.1465 0.9908 0.9944 0.2256 0.9808 0.9899 0.2356 ] Network output: [ -0.0153 0.1877 1.047 0.000884 -0.0003969 0.7996 0.0006662 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2378 0.2357 0.2217 0.1846 0.9858 0.9917 0.2378 0.9672 0.984 0.2242 ] Network output: [ 0.04486 0.8789 0.002814 0.0004512 -0.0002026 1.03 0.0003401 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1945 Epoch 4087 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04288 0.832 0.975 -7.666e-05 3.442e-05 0.1069 -5.778e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005576 -0.006118 -0.01832 0.01184 0.9603 0.9666 0.01418 0.9278 0.9403 0.05009 ] Network output: [ 1.127 -0.4585 0.174 0.0009268 -0.0004161 0.03501 0.0006985 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3473 -0.04871 -0.1445 0.2547 0.982 0.9925 0.4118 0.9375 0.9837 0.6986 ] Network output: [ -0.009658 0.9007 0.9839 -0.0002966 0.0001332 0.1335 -0.0002236 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01013 0.003177 0.01013 0.01076 0.9906 0.9937 0.01042 0.9795 0.9888 0.0199 ] Network output: [ 0.1568 -0.9957 1.262 0.0006754 -0.0003032 1.422 0.000509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4008 0.2529 0.4886 0.4209 0.9835 0.9933 0.4029 0.9421 0.9855 0.687 ] Network output: [ -0.03238 0.2584 1.035 0.0006296 -0.0002826 0.7744 0.0004745 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2385 0.2228 0.2338 0.2085 0.9903 0.9943 0.2387 0.9801 0.99 0.2478 ] Network output: [ -0.03495 0.2678 0.987 0.0007313 -0.0003283 0.8181 0.0005512 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2588 0.256 0.2261 0.2092 0.986 0.9918 0.2589 0.9681 0.9844 0.229 ] Network output: [ 0.05115 0.7453 0.02867 0.0007484 -0.000336 1.127 0.000564 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2717 Epoch 4088 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.007121 1 0.9233 -0.0004831 0.0002169 0.06004 -0.0003641 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006099 -0.00586 -0.01491 0.006595 0.9602 0.9666 0.01518 0.9272 0.9397 0.05021 ] Network output: [ 0.8042 0.5779 -0.08665 -0.001831 0.0008222 -0.1072 -0.00138 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3772 0.01121 0.001004 0.07086 0.982 0.9925 0.4452 0.9372 0.9836 0.6992 ] Network output: [ -0.01194 0.958 0.9596 -0.0004019 0.0001804 0.1046 -0.0003029 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01165 0.005011 0.01435 0.004102 0.9909 0.9939 0.01197 0.9805 0.989 0.02238 ] Network output: [ -0.08706 0.5517 0.662 -0.002564 0.001151 0.9501 -0.001932 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4424 0.3136 0.5424 0.1151 0.9836 0.9934 0.4447 0.9421 0.9855 0.6948 ] Network output: [ -0.03762 0.3596 1.023 0.0004559 -0.0002047 0.6947 0.0003436 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2216 0.2101 0.2268 0.1504 0.9907 0.9944 0.2218 0.9804 0.9897 0.2375 ] Network output: [ -0.02045 0.1739 1.06 0.0008766 -0.0003935 0.8101 0.0006606 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2354 0.2333 0.2238 0.1876 0.9857 0.9917 0.2355 0.9668 0.9839 0.2265 ] Network output: [ 0.03925 0.8716 0.008501 0.0004223 -0.0001896 1.043 0.0003182 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1656 Epoch 4089 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0432 0.8297 0.9745 -6.27e-05 2.815e-05 0.1091 -4.725e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005603 -0.006097 -0.01827 0.01149 0.9603 0.9666 0.01419 0.9273 0.9399 0.04991 ] Network output: [ 1.11 -0.3892 0.1499 0.0007756 -0.0003482 0.02241 0.0005845 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3489 -0.04534 -0.1393 0.2419 0.982 0.9925 0.4133 0.9369 0.9836 0.6942 ] Network output: [ -0.007861 0.8921 0.9859 -0.0002692 0.0001209 0.1366 -0.0002029 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01018 0.003257 0.01018 0.01035 0.9905 0.9937 0.01047 0.9793 0.9886 0.0199 ] Network output: [ 0.1437 -0.8984 1.223 0.0005052 -0.0002268 1.39 0.0003808 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4025 0.2562 0.4864 0.4028 0.9835 0.9933 0.4047 0.9415 0.9854 0.6837 ] Network output: [ -0.03522 0.2534 1.044 0.0006217 -0.0002791 0.7759 0.0004685 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2355 0.2203 0.2343 0.206 0.9903 0.9943 0.2357 0.9799 0.9898 0.2483 ] Network output: [ -0.03653 0.2535 0.9991 0.0007352 -0.0003301 0.8234 0.0005541 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2559 0.2531 0.2273 0.2089 0.986 0.9918 0.2559 0.9677 0.9843 0.2302 ] Network output: [ 0.045 0.7684 0.02135 0.0006627 -0.0002975 1.123 0.0004994 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2251 Epoch 4090 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01196 0.9801 0.9282 -0.0004247 0.0001907 0.06605 -0.0003201 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006076 -0.005876 -0.01533 0.006673 0.9602 0.9666 0.0151 0.9269 0.9394 0.05004 ] Network output: [ 0.8249 0.554 -0.09322 -0.001704 0.0007648 -0.1175 -0.001284 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.376 0.008106 -0.01255 0.07296 0.982 0.9925 0.4436 0.9367 0.9834 0.6954 ] Network output: [ -0.00962 0.9419 0.965 -0.0003618 0.0001624 0.1109 -0.0002727 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0115 0.004839 0.01383 0.004176 0.9908 0.9938 0.01182 0.9802 0.9889 0.02206 ] Network output: [ -0.07556 0.5073 0.6795 -0.002445 0.001098 0.9543 -0.001843 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4385 0.3086 0.534 0.122 0.9836 0.9934 0.4408 0.9416 0.9854 0.6915 ] Network output: [ -0.03989 0.3439 1.035 0.0004643 -0.0002084 0.7028 0.0003499 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.219 0.2074 0.2274 0.1524 0.9906 0.9944 0.2192 0.9801 0.9896 0.2386 ] Network output: [ -0.0235 0.1638 1.069 0.0008734 -0.0003921 0.8175 0.0006582 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2335 0.2313 0.2251 0.1891 0.9857 0.9916 0.2336 0.9665 0.9838 0.2278 ] Network output: [ 0.03484 0.8697 0.01067 0.0003907 -0.0001754 1.052 0.0002944 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1501 Epoch 4091 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04449 0.8239 0.9748 -4.135e-05 1.856e-05 0.1122 -3.116e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005609 -0.006077 -0.01827 0.0113 0.9603 0.9666 0.01417 0.927 0.9397 0.04975 ] Network output: [ 1.1 -0.3512 0.1378 0.0007098 -0.0003187 0.01705 0.000535 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.349 -0.04306 -0.1373 0.2347 0.9819 0.9925 0.4132 0.9364 0.9835 0.691 ] Network output: [ -0.005741 0.8835 0.9873 -0.0002438 0.0001095 0.1397 -0.0001838 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01019 0.003299 0.01015 0.01009 0.9905 0.9937 0.01048 0.9791 0.9885 0.01988 ] Network output: [ 0.1364 -0.8374 1.198 0.0004078 -0.0001831 1.368 0.0003073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4027 0.2577 0.4833 0.3914 0.9835 0.9933 0.4049 0.9411 0.9853 0.6809 ] Network output: [ -0.03659 0.2502 1.049 0.0006159 -0.0002765 0.7767 0.0004641 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2333 0.2184 0.234 0.2042 0.9903 0.9943 0.2335 0.9797 0.9897 0.2483 ] Network output: [ -0.03742 0.244 1.007 0.0007355 -0.0003302 0.8269 0.0005543 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2535 0.2508 0.2277 0.2084 0.986 0.9918 0.2536 0.9674 0.9842 0.2307 ] Network output: [ 0.04072 0.7822 0.01691 0.0006013 -0.00027 1.122 0.0004532 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1997 Epoch 4092 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01575 0.9643 0.9315 -0.0003803 0.0001707 0.07111 -0.0002866 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006053 -0.005877 -0.01559 0.006774 0.9603 0.9666 0.01503 0.9266 0.9392 0.04988 ] Network output: [ 0.8366 0.53 -0.09118 -0.001597 0.0007171 -0.1186 -0.001204 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3743 0.006435 -0.0207 0.07601 0.982 0.9925 0.4416 0.9362 0.9833 0.6924 ] Network output: [ -0.007355 0.93 0.968 -0.0003323 0.0001492 0.1153 -0.0002504 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01141 0.004748 0.01351 0.00427 0.9908 0.9938 0.01172 0.9799 0.9888 0.02185 ] Network output: [ -0.06821 0.4759 0.6924 -0.002355 0.001057 0.9584 -0.001775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4357 0.3057 0.5281 0.1274 0.9836 0.9934 0.438 0.9411 0.9853 0.6887 ] Network output: [ -0.04089 0.3355 1.041 0.0004663 -0.0002094 0.7072 0.0003514 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2175 0.2058 0.2274 0.1535 0.9906 0.9943 0.2177 0.9799 0.9895 0.239 ] Network output: [ -0.02519 0.16 1.073 0.0008647 -0.0003882 0.8209 0.0006516 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2322 0.23 0.2255 0.1897 0.9857 0.9916 0.2322 0.9663 0.9837 0.2283 ] Network output: [ 0.03145 0.8742 0.009083 0.0003524 -0.0001582 1.055 0.0002656 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.139 Epoch 4093 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04598 0.8182 0.975 -2.343e-05 1.052e-05 0.1147 -1.766e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005606 -0.006053 -0.01828 0.01116 0.9603 0.9666 0.01414 0.9267 0.9394 0.04958 ] Network output: [ 1.091 -0.3246 0.1307 0.0006729 -0.0003021 0.01417 0.0005071 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3483 -0.04117 -0.1362 0.2296 0.9819 0.9925 0.4123 0.9359 0.9834 0.6883 ] Network output: [ -0.003589 0.8761 0.9881 -0.0002243 0.0001007 0.1421 -0.0001691 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01019 0.003327 0.01009 0.009891 0.9905 0.9937 0.01048 0.9789 0.9884 0.01984 ] Network output: [ 0.1314 -0.7912 1.179 0.0003375 -0.0001515 1.351 0.0002544 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4023 0.2584 0.48 0.3827 0.9835 0.9933 0.4044 0.9407 0.9852 0.6785 ] Network output: [ -0.03724 0.2489 1.052 0.000608 -0.000273 0.7764 0.0004582 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2314 0.2167 0.2335 0.2025 0.9903 0.9943 0.2317 0.9795 0.9896 0.2479 ] Network output: [ -0.03792 0.2373 1.013 0.0007319 -0.0003286 0.8289 0.0005516 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2514 0.2487 0.2277 0.2078 0.9859 0.9918 0.2515 0.9672 0.984 0.2308 ] Network output: [ 0.03736 0.7927 0.01372 0.0005491 -0.0002465 1.121 0.0004138 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1823 Epoch 4094 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01898 0.9513 0.934 -0.0003454 0.0001551 0.07533 -0.0002603 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006028 -0.005871 -0.01577 0.006878 0.9603 0.9666 0.01496 0.9263 0.939 0.04971 ] Network output: [ 0.8448 0.5073 -0.08621 -0.001503 0.0006746 -0.1167 -0.001132 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3725 0.005315 -0.02689 0.07929 0.982 0.9925 0.4394 0.9358 0.9833 0.6898 ] Network output: [ -0.005227 0.9205 0.9699 -0.0003101 0.0001392 0.1188 -0.0002337 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01133 0.004683 0.01326 0.004367 0.9908 0.9938 0.01164 0.9797 0.9886 0.02169 ] Network output: [ -0.06244 0.4493 0.7038 -0.002277 0.001022 0.9625 -0.001716 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4332 0.3034 0.5231 0.1323 0.9836 0.9934 0.4355 0.9408 0.9852 0.6863 ] Network output: [ -0.04137 0.3302 1.044 0.0004649 -0.0002087 0.7101 0.0003504 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2163 0.2047 0.2273 0.1543 0.9906 0.9943 0.2165 0.9797 0.9894 0.2392 ] Network output: [ -0.02633 0.1586 1.075 0.0008533 -0.0003831 0.8227 0.000643 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2311 0.2289 0.2255 0.1899 0.9857 0.9916 0.2311 0.9661 0.9836 0.2285 ] Network output: [ 0.02861 0.8803 0.006576 0.0003137 -0.0001408 1.057 0.0002364 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.13 Epoch 4095 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04744 0.8135 0.9749 -9.644e-06 4.329e-06 0.1167 -7.268e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0056 -0.006029 -0.01829 0.01105 0.9603 0.9667 0.01411 0.9265 0.9392 0.04943 ] Network output: [ 1.084 -0.3027 0.1254 0.0006477 -0.0002908 0.01196 0.0004881 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3475 -0.03944 -0.1358 0.2256 0.9819 0.9925 0.4112 0.9356 0.9833 0.686 ] Network output: [ -0.00158 0.87 0.9884 -0.0002097 9.412e-05 0.1439 -0.000158 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01019 0.003348 0.01002 0.009718 0.9905 0.9937 0.01047 0.9788 0.9884 0.0198 ] Network output: [ 0.1274 -0.7524 1.163 0.0002809 -0.0001261 1.336 0.0002117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4015 0.2589 0.4767 0.3753 0.9835 0.9933 0.4037 0.9404 0.9851 0.6764 ] Network output: [ -0.03762 0.2485 1.054 0.0005988 -0.0002688 0.7755 0.0004513 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2297 0.2152 0.2327 0.2009 0.9903 0.9942 0.2299 0.9793 0.9895 0.2473 ] Network output: [ -0.03826 0.232 1.017 0.0007262 -0.000326 0.8302 0.0005473 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2494 0.2467 0.2276 0.2072 0.9859 0.9918 0.2494 0.9669 0.9839 0.2308 ] Network output: [ 0.03445 0.8017 0.01112 0.0005017 -0.0002252 1.12 0.0003781 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1687 Epoch 4096 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02184 0.9403 0.9358 -0.000317 0.0001423 0.07894 -0.0002389 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.006004 -0.00586 -0.01593 0.006973 0.9603 0.9666 0.01489 0.9261 0.9388 0.04955 ] Network output: [ 0.8512 0.487 -0.0808 -0.001417 0.0006361 -0.1143 -0.001068 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3706 0.004469 -0.03226 0.08239 0.982 0.9925 0.4371 0.9354 0.9832 0.6877 ] Network output: [ -0.003267 0.9126 0.9712 -0.0002928 0.0001314 0.1216 -0.0002206 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01126 0.004627 0.01304 0.004455 0.9907 0.9938 0.01157 0.9795 0.9886 0.02156 ] Network output: [ -0.05754 0.4261 0.714 -0.002208 0.0009912 0.966 -0.001664 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4308 0.3014 0.5187 0.1366 0.9836 0.9934 0.4331 0.9404 0.9852 0.6843 ] Network output: [ -0.04163 0.3261 1.047 0.0004619 -0.0002073 0.7122 0.0003481 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2152 0.2036 0.227 0.1549 0.9905 0.9943 0.2154 0.9795 0.9893 0.2391 ] Network output: [ -0.0272 0.1578 1.076 0.0008414 -0.0003777 0.824 0.0006341 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.23 0.2278 0.2255 0.19 0.9857 0.9916 0.23 0.9659 0.9835 0.2285 ] Network output: [ 0.02613 0.8861 0.004158 0.0002776 -0.0001246 1.059 0.0002092 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1224 Epoch 4097 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04881 0.8094 0.9745 1.002e-06 -4.5e-07 0.1185 7.553e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005593 -0.006004 -0.01832 0.01096 0.9604 0.9667 0.01407 0.9263 0.9391 0.04928 ] Network output: [ 1.078 -0.2841 0.1209 0.0006305 -0.0002831 0.01015 0.0004752 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3466 -0.03785 -0.1359 0.2222 0.9819 0.9925 0.41 0.9352 0.9832 0.6842 ] Network output: [ 0.0002446 0.8648 0.9885 -0.0001985 8.911e-05 0.1454 -0.0001496 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01017 0.003366 0.009949 0.009566 0.9905 0.9936 0.01046 0.9786 0.9883 0.01976 ] Network output: [ 0.124 -0.7195 1.15 0.000235 -0.0001055 1.323 0.0001771 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4006 0.2591 0.4735 0.369 0.9835 0.9933 0.4028 0.9401 0.9851 0.6748 ] Network output: [ -0.03787 0.2485 1.055 0.0005891 -0.0002645 0.7744 0.000444 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.228 0.2137 0.232 0.1993 0.9903 0.9942 0.2283 0.9791 0.9894 0.2468 ] Network output: [ -0.03852 0.2276 1.021 0.0007195 -0.000323 0.8311 0.0005422 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2475 0.2448 0.2275 0.2066 0.9859 0.9918 0.2475 0.9667 0.9838 0.2307 ] Network output: [ 0.03186 0.8098 0.008886 0.0004585 -0.0002058 1.119 0.0003456 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1578 Epoch 4098 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02437 0.9308 0.9372 -0.0002937 0.0001318 0.08209 -0.0002213 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00598 -0.005845 -0.01608 0.007058 0.9603 0.9666 0.01482 0.9259 0.9386 0.04941 ] Network output: [ 0.8564 0.4693 -0.07566 -0.00134 0.0006016 -0.1119 -0.00101 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3687 0.003824 -0.03706 0.08515 0.982 0.9925 0.4348 0.9351 0.9831 0.686 ] Network output: [ -0.001486 0.9058 0.972 -0.0002791 0.0001253 0.1241 -0.0002103 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01119 0.004577 0.01284 0.00453 0.9907 0.9938 0.0115 0.9794 0.9885 0.02145 ] Network output: [ -0.05336 0.406 0.723 -0.002147 0.0009637 0.969 -0.001618 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4285 0.2995 0.5147 0.1402 0.9836 0.9934 0.4307 0.9401 0.9851 0.6826 ] Network output: [ -0.04176 0.3228 1.049 0.000458 -0.0002056 0.7139 0.0003452 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2141 0.2025 0.2267 0.1553 0.9905 0.9943 0.2143 0.9794 0.9892 0.2391 ] Network output: [ -0.0279 0.1572 1.077 0.0008298 -0.0003725 0.825 0.0006253 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2289 0.2267 0.2255 0.19 0.9857 0.9916 0.229 0.9657 0.9834 0.2285 ] Network output: [ 0.02395 0.8914 0.002005 0.0002447 -0.0001099 1.06 0.0001844 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1163 Epoch 4099 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05008 0.806 0.9739 9.264e-06 -4.159e-06 0.12 6.981e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005586 -0.005979 -0.01835 0.01088 0.9604 0.9667 0.01403 0.9261 0.9389 0.04914 ] Network output: [ 1.073 -0.2686 0.1172 0.0006201 -0.0002784 0.008782 0.0004673 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3456 -0.03641 -0.1363 0.2194 0.9819 0.9925 0.4086 0.9349 0.9832 0.6827 ] Network output: [ 0.001882 0.8604 0.9883 -0.00019 8.531e-05 0.1467 -0.0001432 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01015 0.003379 0.009869 0.009432 0.9905 0.9936 0.01044 0.9785 0.9882 0.01972 ] Network output: [ 0.1211 -0.6919 1.139 0.000199 -8.932e-05 1.311 0.0001499 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3996 0.2592 0.4705 0.3636 0.9835 0.9933 0.4017 0.9398 0.985 0.6734 ] Network output: [ -0.03802 0.2489 1.056 0.0005794 -0.0002601 0.7733 0.0004367 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2265 0.2123 0.2312 0.1979 0.9902 0.9942 0.2267 0.979 0.9894 0.2461 ] Network output: [ -0.03873 0.224 1.025 0.0007122 -0.0003197 0.8318 0.0005368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2457 0.243 0.2273 0.2059 0.9859 0.9917 0.2457 0.9665 0.9837 0.2305 ] Network output: [ 0.02957 0.817 0.006925 0.0004197 -0.0001884 1.119 0.0003163 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1492 Epoch 4100 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02659 0.9227 0.9381 -0.0002746 0.0001233 0.08485 -0.000207 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005957 -0.005829 -0.0162 0.00713 0.9603 0.9666 0.01475 0.9258 0.9385 0.04927 ] Network output: [ 0.8606 0.4542 -0.07097 -0.001272 0.000571 -0.1096 -0.0009586 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3669 0.003352 -0.04136 0.08756 0.982 0.9925 0.4325 0.9348 0.983 0.6845 ] Network output: [ 0.0001162 0.9 0.9725 -0.0002684 0.0001205 0.1262 -0.0002023 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01112 0.004532 0.01266 0.00459 0.9907 0.9937 0.01143 0.9792 0.9884 0.02135 ] Network output: [ -0.04985 0.389 0.7308 -0.002093 0.0009395 0.9714 -0.001577 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4263 0.2978 0.511 0.1432 0.9836 0.9934 0.4285 0.9399 0.985 0.6812 ] Network output: [ -0.04182 0.3201 1.05 0.0004535 -0.0002036 0.7152 0.0003418 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.213 0.2015 0.2263 0.1555 0.9905 0.9943 0.2132 0.9792 0.9892 0.2389 ] Network output: [ -0.02846 0.1568 1.078 0.0008186 -0.0003675 0.8258 0.0006169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2279 0.2257 0.2253 0.1899 0.9857 0.9916 0.2279 0.9655 0.9834 0.2284 ] Network output: [ 0.02204 0.8961 9.406e-05 0.0002151 -9.655e-05 1.061 0.0001621 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1113 Epoch 4101 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05125 0.803 0.9732 1.561e-05 -7.006e-06 0.1214 1.176e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005578 -0.005954 -0.01838 0.01081 0.9604 0.9667 0.01399 0.926 0.9388 0.04901 ] Network output: [ 1.068 -0.256 0.1141 0.0006152 -0.0002762 0.007872 0.0004636 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3446 -0.03511 -0.137 0.2172 0.9819 0.9925 0.4073 0.9347 0.9831 0.6815 ] Network output: [ 0.003344 0.8567 0.988 -0.0001838 8.249e-05 0.1479 -0.0001385 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01013 0.003389 0.009785 0.009317 0.9905 0.9936 0.01041 0.9784 0.9881 0.01968 ] Network output: [ 0.1188 -0.6691 1.13 0.0001718 -7.711e-05 1.302 0.0001294 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3985 0.2592 0.4676 0.3591 0.9835 0.9933 0.4006 0.9395 0.985 0.6723 ] Network output: [ -0.03811 0.2495 1.057 0.0005699 -0.0002559 0.7721 0.0004295 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.225 0.211 0.2304 0.1966 0.9902 0.9942 0.2252 0.9788 0.9893 0.2455 ] Network output: [ -0.03891 0.221 1.027 0.0007046 -0.0003163 0.8323 0.000531 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2439 0.2414 0.227 0.2053 0.9859 0.9917 0.244 0.9663 0.9837 0.2303 ] Network output: [ 0.02757 0.8234 0.005176 0.000385 -0.0001728 1.118 0.0002901 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1424 Epoch 4102 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02853 0.9159 0.9388 -0.0002593 0.0001164 0.08727 -0.0001954 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005936 -0.005811 -0.01632 0.007191 0.9604 0.9667 0.01468 0.9256 0.9384 0.04913 ] Network output: [ 0.8639 0.4415 -0.06678 -0.001212 0.0005441 -0.1074 -0.0009133 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3652 0.003026 -0.0452 0.08962 0.982 0.9925 0.4304 0.9346 0.983 0.6833 ] Network output: [ 0.001546 0.895 0.9728 -0.0002603 0.0001168 0.128 -0.0001961 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01106 0.004492 0.0125 0.004636 0.9907 0.9937 0.01136 0.9791 0.9883 0.02126 ] Network output: [ -0.04695 0.3749 0.7375 -0.002046 0.0009184 0.9732 -0.001542 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4242 0.2962 0.5076 0.1457 0.9836 0.9934 0.4264 0.9396 0.985 0.6801 ] Network output: [ -0.04182 0.3181 1.051 0.0004486 -0.0002014 0.7161 0.0003381 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.212 0.2005 0.2258 0.1556 0.9905 0.9943 0.2122 0.9791 0.9891 0.2386 ] Network output: [ -0.02892 0.1564 1.078 0.0008079 -0.0003627 0.8265 0.0006089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2268 0.2246 0.2251 0.1897 0.9857 0.9916 0.2268 0.9654 0.9833 0.2283 ] Network output: [ 0.02038 0.9004 -0.001612 0.0001885 -8.462e-05 1.061 0.000142 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1073 Epoch 4103 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05231 0.8003 0.9725 2.036e-05 -9.142e-06 0.1227 1.535e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00557 -0.005929 -0.01842 0.01076 0.9604 0.9667 0.01394 0.9258 0.9387 0.04889 ] Network output: [ 1.065 -0.2459 0.1117 0.0006146 -0.0002759 0.007411 0.0004631 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3435 -0.03394 -0.138 0.2155 0.9819 0.9925 0.4059 0.9344 0.983 0.6804 ] Network output: [ 0.004641 0.8534 0.9876 -0.0001792 8.046e-05 0.1489 -0.0001351 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0101 0.003396 0.009699 0.009218 0.9904 0.9936 0.01038 0.9783 0.9881 0.01963 ] Network output: [ 0.1169 -0.6505 1.123 0.0001523 -6.836e-05 1.295 0.0001148 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3973 0.2591 0.4648 0.3554 0.9835 0.9933 0.3994 0.9393 0.9849 0.6713 ] Network output: [ -0.03814 0.2503 1.057 0.0005607 -0.0002517 0.7708 0.0004226 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2235 0.2097 0.2295 0.1953 0.9902 0.9942 0.2237 0.9787 0.9892 0.2448 ] Network output: [ -0.03905 0.2187 1.03 0.0006967 -0.0003128 0.8325 0.0005251 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2423 0.2398 0.2266 0.2046 0.9858 0.9917 0.2424 0.9661 0.9836 0.23 ] Network output: [ 0.02584 0.8291 0.003598 0.0003542 -0.000159 1.117 0.000267 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1371 Epoch 4104 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03021 0.91 0.9392 -0.0002472 0.000111 0.08939 -0.0001863 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005917 -0.005792 -0.01642 0.007241 0.9604 0.9667 0.01461 0.9255 0.9383 0.049 ] Network output: [ 0.8665 0.431 -0.06314 -0.001159 0.0005205 -0.1055 -0.0008738 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3636 0.00282 -0.04863 0.09136 0.982 0.9925 0.4284 0.9343 0.9829 0.6822 ] Network output: [ 0.002815 0.8908 0.9729 -0.0002541 0.0001141 0.1296 -0.0001915 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.011 0.004456 0.01234 0.004669 0.9907 0.9937 0.0113 0.979 0.9882 0.02117 ] Network output: [ -0.04457 0.3632 0.7432 -0.002005 0.0008999 0.9746 -0.001511 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4223 0.2948 0.5046 0.1476 0.9836 0.9934 0.4244 0.9394 0.9849 0.6791 ] Network output: [ -0.04179 0.3165 1.052 0.0004434 -0.0001991 0.7166 0.0003342 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2109 0.1995 0.2253 0.1555 0.9904 0.9943 0.2111 0.979 0.989 0.2383 ] Network output: [ -0.0293 0.1562 1.079 0.0007977 -0.0003581 0.8269 0.0006012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2257 0.2236 0.2249 0.1895 0.9857 0.9916 0.2258 0.9652 0.9832 0.2281 ] Network output: [ 0.01895 0.9042 -0.003133 0.0001648 -7.397e-05 1.062 0.0001242 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1042 Epoch 4105 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05328 0.7979 0.9718 2.381e-05 -1.069e-05 0.1239 1.794e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005561 -0.005904 -0.01845 0.01072 0.9604 0.9667 0.0139 0.9257 0.9385 0.04876 ] Network output: [ 1.062 -0.2379 0.1099 0.0006172 -0.0002771 0.007362 0.0004651 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3425 -0.0329 -0.139 0.2142 0.9819 0.9925 0.4046 0.9342 0.983 0.6795 ] Network output: [ 0.005789 0.8507 0.9872 -0.0001761 7.906e-05 0.1499 -0.0001327 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01007 0.003401 0.009611 0.009133 0.9904 0.9936 0.01035 0.9782 0.988 0.01959 ] Network output: [ 0.1154 -0.6356 1.117 0.0001393 -6.255e-05 1.289 0.000105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3962 0.259 0.4622 0.3523 0.9835 0.9934 0.3982 0.9391 0.9849 0.6704 ] Network output: [ -0.03814 0.2513 1.058 0.0005518 -0.0002477 0.7696 0.0004159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2222 0.2085 0.2286 0.1942 0.9902 0.9942 0.2224 0.9786 0.9892 0.2441 ] Network output: [ -0.03916 0.2169 1.032 0.0006888 -0.0003092 0.8326 0.0005191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2408 0.2382 0.2262 0.204 0.9858 0.9917 0.2408 0.9659 0.9835 0.2297 ] Network output: [ 0.02436 0.8342 0.002159 0.0003271 -0.0001468 1.116 0.0002465 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1331 Epoch 4106 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03167 0.9051 0.9394 -0.0002378 0.0001067 0.09126 -0.0001792 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005898 -0.005773 -0.01651 0.00728 0.9604 0.9667 0.01455 0.9254 0.9382 0.04886 ] Network output: [ 0.8685 0.4224 -0.06005 -0.001114 0.0005001 -0.1039 -0.0008396 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3621 0.00271 -0.05169 0.0928 0.982 0.9925 0.4265 0.9341 0.9829 0.6813 ] Network output: [ 0.003934 0.8872 0.9729 -0.0002496 0.0001121 0.131 -0.0001881 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01094 0.004423 0.0122 0.004691 0.9907 0.9937 0.01124 0.9789 0.9882 0.02109 ] Network output: [ -0.04264 0.3536 0.7481 -0.001969 0.0008838 0.9755 -0.001484 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4205 0.2935 0.5018 0.149 0.9836 0.9934 0.4226 0.9392 0.9849 0.6782 ] Network output: [ -0.04175 0.3154 1.053 0.0004381 -0.0001967 0.7169 0.0003301 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2098 0.1985 0.2247 0.1553 0.9904 0.9942 0.21 0.9788 0.989 0.2379 ] Network output: [ -0.02961 0.156 1.079 0.000788 -0.0003538 0.8272 0.0005939 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2246 0.2225 0.2245 0.1892 0.9856 0.9916 0.2247 0.9651 0.9832 0.2278 ] Network output: [ 0.01772 0.9077 -0.004482 0.0001437 -6.453e-05 1.062 0.0001083 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1017 Epoch 4107 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05415 0.7957 0.9711 2.618e-05 -1.175e-05 0.125 1.973e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005552 -0.00588 -0.01849 0.01069 0.9604 0.9667 0.01385 0.9256 0.9384 0.04863 ] Network output: [ 1.059 -0.2319 0.1084 0.0006221 -0.0002793 0.007682 0.0004689 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3415 -0.03197 -0.1401 0.2133 0.982 0.9925 0.4033 0.934 0.9829 0.6787 ] Network output: [ 0.006801 0.8482 0.9867 -0.0001741 7.815e-05 0.1508 -0.0001312 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01004 0.003403 0.009524 0.009059 0.9904 0.9936 0.01032 0.9781 0.988 0.01954 ] Network output: [ 0.1141 -0.6239 1.112 0.0001318 -5.919e-05 1.284 9.936e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.395 0.2588 0.4597 0.3498 0.9835 0.9934 0.397 0.9389 0.9848 0.6697 ] Network output: [ -0.03812 0.2523 1.058 0.0005434 -0.000244 0.7683 0.0004095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2208 0.2073 0.2277 0.1931 0.9902 0.9942 0.221 0.9785 0.9891 0.2434 ] Network output: [ -0.03926 0.2157 1.033 0.0006808 -0.0003056 0.8325 0.0005131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2393 0.2368 0.2258 0.2033 0.9858 0.9917 0.2393 0.9658 0.9834 0.2293 ] Network output: [ 0.02311 0.8386 0.0008359 0.0003032 -0.0001361 1.116 0.0002285 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1301 Epoch 4108 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03292 0.9009 0.9395 -0.0002305 0.0001035 0.09291 -0.0001737 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005882 -0.005753 -0.01659 0.00731 0.9604 0.9667 0.01448 0.9253 0.9381 0.04872 ] Network output: [ 0.8701 0.4154 -0.05749 -0.001075 0.0004826 -0.1025 -0.0008101 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3607 0.002673 -0.05442 0.09396 0.982 0.9925 0.4247 0.9339 0.9828 0.6804 ] Network output: [ 0.004917 0.884 0.9729 -0.0002465 0.0001106 0.1323 -0.0001857 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01088 0.004393 0.01207 0.004703 0.9906 0.9937 0.01117 0.9787 0.9881 0.02101 ] Network output: [ -0.0411 0.346 0.7522 -0.001937 0.0008698 0.9761 -0.00146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4187 0.2924 0.4992 0.1501 0.9836 0.9934 0.4209 0.939 0.9848 0.6774 ] Network output: [ -0.0417 0.3146 1.054 0.0004326 -0.0001942 0.717 0.000326 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2087 0.1974 0.2241 0.1551 0.9904 0.9942 0.2089 0.9787 0.9889 0.2374 ] Network output: [ -0.02987 0.1559 1.08 0.0007788 -0.0003496 0.8274 0.0005869 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2236 0.2214 0.2242 0.1888 0.9856 0.9916 0.2236 0.9649 0.9831 0.2275 ] Network output: [ 0.01667 0.9108 -0.005665 0.0001252 -5.622e-05 1.062 9.437e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09982 Epoch 4109 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05492 0.7938 0.9704 2.767e-05 -1.242e-05 0.1261 2.085e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005543 -0.005857 -0.01852 0.01066 0.9605 0.9668 0.0138 0.9255 0.9383 0.0485 ] Network output: [ 1.057 -0.2274 0.1074 0.0006286 -0.0002822 0.008322 0.0004738 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3406 -0.03115 -0.1413 0.2126 0.982 0.9925 0.402 0.9338 0.9829 0.678 ] Network output: [ 0.007692 0.8461 0.9863 -0.0001729 7.763e-05 0.1516 -0.0001303 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01 0.003404 0.009438 0.008996 0.9904 0.9936 0.01028 0.978 0.9879 0.01948 ] Network output: [ 0.1132 -0.6148 1.109 0.0001288 -5.783e-05 1.28 9.708e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3938 0.2586 0.4574 0.3479 0.9835 0.9934 0.3959 0.9387 0.9848 0.6689 ] Network output: [ -0.03809 0.2534 1.058 0.0005354 -0.0002404 0.767 0.0004035 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2195 0.2062 0.2269 0.192 0.9902 0.9942 0.2198 0.9784 0.989 0.2427 ] Network output: [ -0.03934 0.2148 1.034 0.0006729 -0.0003021 0.8322 0.0005071 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2379 0.2354 0.2253 0.2026 0.9858 0.9917 0.2379 0.9656 0.9833 0.2288 ] Network output: [ 0.02206 0.8426 -0.0003892 0.0002824 -0.0001268 1.115 0.0002128 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.128 Epoch 4110 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.034 0.8973 0.9394 -0.0002252 0.0001011 0.09436 -0.0001697 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005866 -0.005733 -0.01666 0.007332 0.9604 0.9667 0.01442 0.9252 0.938 0.04858 ] Network output: [ 0.8713 0.41 -0.05545 -0.001042 0.0004676 -0.1015 -0.000785 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3595 0.00269 -0.05687 0.09487 0.982 0.9925 0.4231 0.9337 0.9828 0.6797 ] Network output: [ 0.005778 0.8813 0.9727 -0.0002443 0.0001097 0.1334 -0.0001841 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01082 0.004366 0.01195 0.004706 0.9906 0.9937 0.01112 0.9786 0.9881 0.02093 ] Network output: [ -0.03989 0.3399 0.7557 -0.00191 0.0008575 0.9764 -0.001439 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4172 0.2913 0.4969 0.1508 0.9836 0.9934 0.4193 0.9388 0.9848 0.6766 ] Network output: [ -0.04167 0.3141 1.054 0.0004272 -0.0001918 0.7168 0.0003219 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2076 0.1964 0.2234 0.1547 0.9904 0.9942 0.2078 0.9786 0.9889 0.2369 ] Network output: [ -0.0301 0.1559 1.08 0.00077 -0.0003457 0.8274 0.0005803 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2225 0.2204 0.2238 0.1883 0.9856 0.9916 0.2225 0.9648 0.983 0.2272 ] Network output: [ 0.01578 0.9135 -0.006689 0.000109 -4.894e-05 1.062 8.215e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09841 Epoch 4111 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05562 0.792 0.9698 2.846e-05 -1.278e-05 0.1272 2.145e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005534 -0.005834 -0.01855 0.01064 0.9605 0.9668 0.01375 0.9254 0.9382 0.04837 ] Network output: [ 1.055 -0.2242 0.1067 0.000636 -0.0002855 0.009239 0.0004793 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3397 -0.03041 -0.1424 0.2122 0.982 0.9926 0.4008 0.9336 0.9829 0.6773 ] Network output: [ 0.008473 0.8442 0.9858 -0.0001724 7.739e-05 0.1523 -0.0001299 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009964 0.003403 0.009353 0.008942 0.9904 0.9936 0.01024 0.9779 0.9879 0.01943 ] Network output: [ 0.1124 -0.6079 1.106 0.0001294 -5.808e-05 1.278 9.749e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3927 0.2583 0.4552 0.3463 0.9835 0.9934 0.3948 0.9385 0.9847 0.6682 ] Network output: [ -0.03805 0.2545 1.058 0.0005278 -0.000237 0.7657 0.0003978 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2183 0.205 0.226 0.1911 0.9902 0.9942 0.2185 0.9783 0.989 0.2419 ] Network output: [ -0.03941 0.2143 1.035 0.0006652 -0.0002986 0.8318 0.0005013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2366 0.2341 0.2248 0.2019 0.9858 0.9917 0.2366 0.9654 0.9833 0.2284 ] Network output: [ 0.02119 0.8462 -0.001529 0.0002642 -0.0001186 1.114 0.0001991 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1264 Epoch 4112 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03493 0.8942 0.9393 -0.0002213 9.936e-05 0.09566 -0.0001668 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005852 -0.005713 -0.01672 0.007347 0.9605 0.9667 0.01436 0.9251 0.9379 0.04844 ] Network output: [ 0.8723 0.4058 -0.05388 -0.001013 0.0004549 -0.1006 -0.0007636 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3583 0.002744 -0.05908 0.09557 0.982 0.9925 0.4216 0.9335 0.9827 0.6789 ] Network output: [ 0.00653 0.879 0.9726 -0.000243 0.0001091 0.1344 -0.0001831 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01077 0.00434 0.01183 0.004702 0.9906 0.9937 0.01106 0.9786 0.988 0.02085 ] Network output: [ -0.03893 0.3351 0.7586 -0.001886 0.0008467 0.9765 -0.001421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4157 0.2904 0.4947 0.1512 0.9836 0.9934 0.4178 0.9386 0.9848 0.6759 ] Network output: [ -0.04164 0.3138 1.055 0.0004218 -0.0001894 0.7165 0.0003179 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2065 0.1953 0.2227 0.1543 0.9904 0.9942 0.2067 0.9785 0.9888 0.2364 ] Network output: [ -0.0303 0.1559 1.08 0.0007618 -0.000342 0.8274 0.0005741 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2214 0.2193 0.2233 0.1879 0.9856 0.9916 0.2214 0.9646 0.9829 0.2268 ] Network output: [ 0.01503 0.9159 -0.00756 9.491e-05 -4.261e-05 1.062 7.152e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09738 Epoch 4113 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05623 0.7903 0.9692 2.87e-05 -1.288e-05 0.1282 2.163e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005526 -0.005812 -0.01858 0.01062 0.9605 0.9668 0.01371 0.9253 0.9382 0.04823 ] Network output: [ 1.054 -0.222 0.1062 0.0006438 -0.000289 0.01039 0.0004852 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3389 -0.02977 -0.1434 0.212 0.982 0.9926 0.3996 0.9334 0.9828 0.6766 ] Network output: [ 0.009158 0.8426 0.9854 -0.0001724 7.738e-05 0.153 -0.0001299 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009927 0.003401 0.009271 0.008895 0.9904 0.9936 0.0102 0.9778 0.9878 0.01937 ] Network output: [ 0.1118 -0.6029 1.104 0.0001327 -5.956e-05 1.276 9.999e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3917 0.2581 0.4532 0.3451 0.9835 0.9934 0.3937 0.9383 0.9847 0.6675 ] Network output: [ -0.03802 0.2557 1.058 0.0005207 -0.0002337 0.7645 0.0003924 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2171 0.2039 0.2251 0.1902 0.9902 0.9942 0.2173 0.9782 0.9889 0.2411 ] Network output: [ -0.03947 0.2141 1.036 0.0006576 -0.0002952 0.8313 0.0004956 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2353 0.2328 0.2242 0.2012 0.9858 0.9917 0.2353 0.9653 0.9832 0.2278 ] Network output: [ 0.02048 0.8493 -0.002595 0.0002485 -0.0001116 1.113 0.0001873 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1255 Epoch 4114 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03572 0.8917 0.9392 -0.0002187 9.817e-05 0.09681 -0.0001648 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005838 -0.005694 -0.01677 0.007355 0.9605 0.9668 0.01431 0.925 0.9378 0.0483 ] Network output: [ 0.8731 0.4027 -0.05275 -0.0009892 0.0004441 -0.1001 -0.0007455 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3573 0.002822 -0.06107 0.09607 0.982 0.9925 0.4202 0.9333 0.9827 0.6782 ] Network output: [ 0.007185 0.8769 0.9725 -0.0002423 0.0001088 0.1353 -0.0001826 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01071 0.004315 0.01172 0.004691 0.9906 0.9937 0.011 0.9785 0.988 0.02077 ] Network output: [ -0.03819 0.3314 0.761 -0.001865 0.0008372 0.9763 -0.001405 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4143 0.2895 0.4927 0.1514 0.9836 0.9934 0.4164 0.9384 0.9847 0.6752 ] Network output: [ -0.04163 0.3137 1.055 0.0004166 -0.000187 0.7161 0.000314 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2054 0.1943 0.222 0.1538 0.9904 0.9942 0.2056 0.9784 0.9888 0.2358 ] Network output: [ -0.03049 0.156 1.081 0.000754 -0.0003385 0.8273 0.0005683 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2203 0.2182 0.2229 0.1874 0.9856 0.9916 0.2204 0.9645 0.9829 0.2263 ] Network output: [ 0.0144 0.9179 -0.008285 8.274e-05 -3.714e-05 1.062 6.235e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09666 Epoch 4115 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05678 0.7887 0.9687 2.848e-05 -1.279e-05 0.1291 2.147e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005517 -0.005791 -0.0186 0.01061 0.9605 0.9668 0.01366 0.9252 0.9381 0.04809 ] Network output: [ 1.053 -0.2207 0.1058 0.0006516 -0.0002925 0.01172 0.0004911 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3381 -0.02919 -0.1444 0.212 0.982 0.9926 0.3985 0.9332 0.9828 0.676 ] Network output: [ 0.009758 0.8411 0.985 -0.0001727 7.751e-05 0.1536 -0.0001301 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009888 0.003398 0.009191 0.008854 0.9904 0.9936 0.01016 0.9777 0.9878 0.01931 ] Network output: [ 0.1113 -0.5994 1.103 0.000138 -6.197e-05 1.275 0.000104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3906 0.2579 0.4513 0.3441 0.9835 0.9934 0.3927 0.9382 0.9847 0.6668 ] Network output: [ -0.03799 0.2568 1.058 0.000514 -0.0002307 0.7632 0.0003873 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2159 0.2029 0.2242 0.1893 0.9902 0.9942 0.2161 0.9781 0.9889 0.2403 ] Network output: [ -0.03953 0.2142 1.037 0.0006502 -0.0002919 0.8307 0.00049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.234 0.2316 0.2236 0.2005 0.9858 0.9917 0.2341 0.9651 0.9831 0.2273 ] Network output: [ 0.0199 0.8521 -0.003596 0.0002349 -0.0001054 1.113 0.000177 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1249 Epoch 4116 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03641 0.8895 0.939 -0.0002169 9.74e-05 0.09784 -0.0001635 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005826 -0.005675 -0.01682 0.007358 0.9605 0.9668 0.01425 0.925 0.9377 0.04815 ] Network output: [ 0.8737 0.4005 -0.05201 -0.0009689 0.000435 -0.09975 -0.0007302 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3563 0.002912 -0.06289 0.09642 0.982 0.9926 0.4189 0.9331 0.9827 0.6774 ] Network output: [ 0.007757 0.8751 0.9723 -0.000242 0.0001086 0.1361 -0.0001824 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01066 0.004291 0.01162 0.004674 0.9906 0.9937 0.01095 0.9784 0.9879 0.02069 ] Network output: [ -0.03761 0.3286 0.7631 -0.001846 0.0008287 0.976 -0.001391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.413 0.2887 0.4908 0.1514 0.9836 0.9934 0.4151 0.9383 0.9847 0.6745 ] Network output: [ -0.04163 0.3137 1.056 0.0004116 -0.0001848 0.7156 0.0003102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2043 0.1932 0.2213 0.1533 0.9904 0.9942 0.2045 0.9783 0.9887 0.2352 ] Network output: [ -0.03067 0.1561 1.081 0.0007467 -0.0003352 0.8271 0.0005627 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2192 0.2172 0.2224 0.1868 0.9856 0.9916 0.2193 0.9643 0.9828 0.2259 ] Network output: [ 0.01387 0.9197 -0.008874 7.231e-05 -3.246e-05 1.062 5.449e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09618 Epoch 4117 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05726 0.7873 0.9682 2.793e-05 -1.254e-05 0.1301 2.105e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005508 -0.00577 -0.01862 0.0106 0.9606 0.9668 0.01361 0.9252 0.938 0.04795 ] Network output: [ 1.052 -0.2201 0.1057 0.0006591 -0.0002959 0.01321 0.0004967 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3373 -0.02868 -0.1454 0.2121 0.982 0.9926 0.3974 0.933 0.9827 0.6753 ] Network output: [ 0.01028 0.8398 0.9847 -0.0001732 7.775e-05 0.1543 -0.0001305 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009849 0.003394 0.009115 0.008818 0.9904 0.9936 0.01012 0.9777 0.9877 0.01924 ] Network output: [ 0.1109 -0.597 1.102 0.0001448 -6.502e-05 1.274 0.0001091 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3897 0.2576 0.4495 0.3434 0.9835 0.9934 0.3917 0.938 0.9846 0.6661 ] Network output: [ -0.03797 0.258 1.058 0.0005076 -0.0002279 0.762 0.0003826 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2147 0.2018 0.2233 0.1885 0.9901 0.9942 0.2149 0.978 0.9888 0.2395 ] Network output: [ -0.03959 0.2145 1.037 0.0006431 -0.0002887 0.83 0.0004847 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2328 0.2305 0.223 0.1998 0.9857 0.9917 0.2329 0.965 0.9831 0.2267 ] Network output: [ 0.01943 0.8547 -0.00454 0.0002231 -0.0001001 1.112 0.0001681 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1247 Epoch 4118 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03701 0.8876 0.9388 -0.000216 9.695e-05 0.09877 -0.0001627 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005814 -0.005656 -0.01686 0.007357 0.9605 0.9668 0.0142 0.9249 0.9376 0.048 ] Network output: [ 0.8742 0.399 -0.05161 -0.0009517 0.0004273 -0.09965 -0.0007173 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3554 0.003003 -0.06455 0.09663 0.982 0.9926 0.4177 0.933 0.9826 0.6767 ] Network output: [ 0.008256 0.8735 0.9722 -0.000242 0.0001086 0.1368 -0.0001824 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01061 0.004268 0.01151 0.004654 0.9906 0.9937 0.01089 0.9783 0.9879 0.02061 ] Network output: [ -0.03714 0.3264 0.7648 -0.001829 0.000821 0.9756 -0.001378 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4118 0.2879 0.4891 0.1512 0.9836 0.9934 0.4139 0.9381 0.9846 0.6738 ] Network output: [ -0.04166 0.3138 1.056 0.0004067 -0.0001826 0.715 0.0003065 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2031 0.1922 0.2206 0.1527 0.9904 0.9942 0.2033 0.9782 0.9887 0.2345 ] Network output: [ -0.03084 0.1563 1.081 0.0007397 -0.0003321 0.8269 0.0005575 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2182 0.2161 0.2219 0.1863 0.9856 0.9916 0.2182 0.9642 0.9828 0.2254 ] Network output: [ 0.01342 0.9211 -0.009333 6.343e-05 -2.847e-05 1.062 4.78e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0959 Epoch 4119 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05768 0.786 0.9677 2.71e-05 -1.217e-05 0.131 2.042e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0055 -0.00575 -0.01864 0.01059 0.9606 0.9668 0.01357 0.9251 0.9379 0.04781 ] Network output: [ 1.051 -0.2199 0.1056 0.0006659 -0.0002989 0.0148 0.0005018 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3366 -0.02823 -0.1462 0.2122 0.982 0.9926 0.3964 0.9328 0.9827 0.6747 ] Network output: [ 0.01075 0.8386 0.9844 -0.0001739 7.805e-05 0.1548 -0.000131 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009811 0.00339 0.009042 0.008785 0.9904 0.9936 0.01008 0.9776 0.9877 0.01918 ] Network output: [ 0.1105 -0.5954 1.101 0.0001525 -6.846e-05 1.274 0.0001149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3888 0.2574 0.4479 0.3428 0.9836 0.9934 0.3907 0.9378 0.9846 0.6654 ] Network output: [ -0.03797 0.2592 1.058 0.0005017 -0.0002252 0.7608 0.0003781 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2136 0.2008 0.2224 0.1877 0.9901 0.9942 0.2138 0.9779 0.9888 0.2387 ] Network output: [ -0.03964 0.2149 1.038 0.0006362 -0.0002856 0.8293 0.0004795 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2317 0.2293 0.2224 0.1991 0.9857 0.9917 0.2317 0.9648 0.983 0.2261 ] Network output: [ 0.01907 0.857 -0.005433 0.0002129 -9.557e-05 1.111 0.0001604 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1247 Epoch 4120 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03752 0.8859 0.9386 -0.0002155 9.674e-05 0.09961 -0.0001624 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005803 -0.005638 -0.0169 0.007351 0.9606 0.9668 0.01414 0.9248 0.9375 0.04785 ] Network output: [ 0.8747 0.398 -0.0515 -0.0009372 0.0004207 -0.09975 -0.0007063 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3547 0.003088 -0.0661 0.09674 0.982 0.9926 0.4166 0.9328 0.9826 0.6759 ] Network output: [ 0.008691 0.8721 0.9721 -0.0002422 0.0001087 0.1375 -0.0001825 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01056 0.004245 0.01141 0.00463 0.9906 0.9937 0.01084 0.9782 0.9878 0.02052 ] Network output: [ -0.03676 0.3246 0.7663 -0.001813 0.0008139 0.9752 -0.001366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4107 0.2871 0.4874 0.1509 0.9836 0.9934 0.4127 0.9379 0.9846 0.6731 ] Network output: [ -0.04171 0.314 1.057 0.0004021 -0.0001805 0.7144 0.000303 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.202 0.1911 0.2198 0.1522 0.9904 0.9942 0.2022 0.9781 0.9886 0.2339 ] Network output: [ -0.03102 0.1565 1.082 0.0007331 -0.0003291 0.8267 0.0005525 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2171 0.2151 0.2213 0.1858 0.9856 0.9916 0.2172 0.964 0.9827 0.2249 ] Network output: [ 0.01305 0.9224 -0.009673 5.592e-05 -2.51e-05 1.061 4.214e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09576 Epoch 4121 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05805 0.7848 0.9673 2.607e-05 -1.17e-05 0.1319 1.965e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005491 -0.005731 -0.01865 0.01058 0.9606 0.9669 0.01352 0.925 0.9378 0.04767 ] Network output: [ 1.05 -0.2201 0.1055 0.0006719 -0.0003016 0.01647 0.0005063 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.336 -0.02783 -0.1469 0.2124 0.982 0.9926 0.3955 0.9327 0.9827 0.674 ] Network output: [ 0.01115 0.8375 0.9841 -0.0001746 7.839e-05 0.1554 -0.0001316 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009772 0.003385 0.008972 0.008755 0.9904 0.9936 0.01004 0.9775 0.9876 0.01911 ] Network output: [ 0.1102 -0.5945 1.1 0.0001606 -7.208e-05 1.275 0.000121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3879 0.2571 0.4464 0.3424 0.9836 0.9934 0.3899 0.9376 0.9846 0.6647 ] Network output: [ -0.03797 0.2603 1.058 0.0004961 -0.0002227 0.7596 0.0003739 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2125 0.1998 0.2216 0.1869 0.9901 0.9941 0.2127 0.9778 0.9887 0.2379 ] Network output: [ -0.03969 0.2155 1.038 0.0006297 -0.0002827 0.8285 0.0004745 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2306 0.2282 0.2217 0.1984 0.9857 0.9917 0.2306 0.9647 0.9829 0.2255 ] Network output: [ 0.01878 0.8591 -0.006283 0.000204 -9.16e-05 1.11 0.0001538 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1249 Epoch 4122 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03798 0.8845 0.9383 -0.0002154 9.669e-05 0.1004 -0.0001623 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005793 -0.005621 -0.01694 0.007343 0.9606 0.9668 0.01409 0.9247 0.9375 0.0477 ] Network output: [ 0.8752 0.3976 -0.05163 -0.0009247 0.0004151 -0.1 -0.0006969 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3539 0.00316 -0.06755 0.09677 0.982 0.9926 0.4156 0.9326 0.9825 0.6752 ] Network output: [ 0.009074 0.8707 0.972 -0.0002425 0.0001089 0.1381 -0.0001828 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01051 0.004223 0.01131 0.004605 0.9906 0.9937 0.01079 0.9781 0.9878 0.02044 ] Network output: [ -0.03642 0.3232 0.7676 -0.001798 0.0008073 0.9747 -0.001355 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4096 0.2864 0.4858 0.1506 0.9836 0.9934 0.4116 0.9378 0.9846 0.6723 ] Network output: [ -0.04177 0.3142 1.057 0.0003977 -0.0001785 0.7138 0.0002997 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2009 0.1901 0.2191 0.1516 0.9903 0.9942 0.2011 0.978 0.9886 0.2332 ] Network output: [ -0.0312 0.1566 1.082 0.0007269 -0.0003263 0.8264 0.0005478 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2161 0.214 0.2208 0.1852 0.9856 0.9916 0.2161 0.9639 0.9826 0.2244 ] Network output: [ 0.01273 0.9233 -0.009903 4.961e-05 -2.227e-05 1.061 3.739e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09572 Epoch 4123 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05837 0.7837 0.9669 2.489e-05 -1.117e-05 0.1327 1.876e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005483 -0.005712 -0.01866 0.01057 0.9606 0.9669 0.01347 0.9249 0.9377 0.04752 ] Network output: [ 1.05 -0.2204 0.1055 0.0006769 -0.0003039 0.01818 0.0005101 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3354 -0.02746 -0.1475 0.2127 0.982 0.9926 0.3946 0.9325 0.9826 0.6733 ] Network output: [ 0.01151 0.8365 0.9838 -0.0001754 7.873e-05 0.1559 -0.0001322 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009734 0.003379 0.008906 0.008726 0.9904 0.9936 0.009999 0.9774 0.9876 0.01904 ] Network output: [ 0.1098 -0.594 1.1 0.0001687 -7.571e-05 1.275 0.0001271 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3871 0.2569 0.445 0.342 0.9836 0.9934 0.389 0.9375 0.9845 0.6639 ] Network output: [ -0.03799 0.2614 1.058 0.0004908 -0.0002203 0.7585 0.0003699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2114 0.1988 0.2208 0.1862 0.9901 0.9941 0.2116 0.9777 0.9887 0.2371 ] Network output: [ -0.03974 0.2162 1.038 0.0006234 -0.0002798 0.8277 0.0004698 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2295 0.2271 0.2211 0.1977 0.9857 0.9916 0.2295 0.9645 0.9829 0.2249 ] Network output: [ 0.01855 0.8611 -0.007094 0.0001963 -8.813e-05 1.11 0.000148 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1252 Epoch 4124 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03838 0.8832 0.9381 -0.0002155 9.675e-05 0.1011 -0.0001624 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005782 -0.005604 -0.01697 0.007333 0.9606 0.9669 0.01404 0.9246 0.9374 0.04754 ] Network output: [ 0.8757 0.3974 -0.05196 -0.0009139 0.0004103 -0.1005 -0.0006888 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3533 0.003217 -0.06892 0.09673 0.982 0.9926 0.4147 0.9324 0.9825 0.6744 ] Network output: [ 0.009412 0.8695 0.9719 -0.0002428 0.000109 0.1387 -0.000183 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01046 0.0042 0.01122 0.004578 0.9906 0.9937 0.01074 0.9781 0.9877 0.02035 ] Network output: [ -0.0361 0.3219 0.7688 -0.001784 0.0008011 0.9742 -0.001345 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4086 0.2858 0.4842 0.1502 0.9836 0.9934 0.4106 0.9376 0.9845 0.6716 ] Network output: [ -0.04185 0.3144 1.058 0.0003935 -0.0001767 0.7131 0.0002966 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1998 0.189 0.2184 0.1511 0.9903 0.9942 0.2 0.9779 0.9885 0.2325 ] Network output: [ -0.03138 0.1569 1.083 0.0007209 -0.0003236 0.8262 0.0005433 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2151 0.213 0.2203 0.1847 0.9855 0.9916 0.2151 0.9638 0.9826 0.2239 ] Network output: [ 0.01246 0.9241 -0.01003 4.435e-05 -1.991e-05 1.061 3.342e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09574 Epoch 4125 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05865 0.7827 0.9666 2.36e-05 -1.059e-05 0.1336 1.778e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005475 -0.005694 -0.01867 0.01057 0.9607 0.9669 0.01343 0.9248 0.9377 0.04737 ] Network output: [ 1.049 -0.2209 0.1055 0.0006809 -0.0003057 0.01991 0.0005131 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3348 -0.02714 -0.1481 0.2129 0.982 0.9926 0.3938 0.9323 0.9826 0.6726 ] Network output: [ 0.01183 0.8356 0.9836 -0.0001761 7.907e-05 0.1564 -0.0001327 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009697 0.003374 0.008844 0.008699 0.9904 0.9936 0.00996 0.9774 0.9876 0.01898 ] Network output: [ 0.1095 -0.5938 1.1 0.0001765 -7.923e-05 1.276 0.000133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3863 0.2567 0.4437 0.3417 0.9836 0.9934 0.3882 0.9373 0.9845 0.6631 ] Network output: [ -0.03803 0.2626 1.058 0.0004858 -0.0002181 0.7574 0.0003661 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2103 0.1978 0.22 0.1855 0.9901 0.9941 0.2105 0.9776 0.9887 0.2364 ] Network output: [ -0.03979 0.217 1.038 0.0006173 -0.0002771 0.8268 0.0004652 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2284 0.2261 0.2204 0.197 0.9857 0.9916 0.2285 0.9644 0.9828 0.2242 ] Network output: [ 0.01838 0.8629 -0.007872 0.0001895 -8.509e-05 1.109 0.0001428 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1255 Epoch 4126 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03875 0.882 0.9379 -0.0002158 9.687e-05 0.1018 -0.0001626 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005772 -0.005588 -0.017 0.007322 0.9606 0.9669 0.014 0.9245 0.9373 0.04738 ] Network output: [ 0.8762 0.3974 -0.05246 -0.0009044 0.000406 -0.101 -0.0006816 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3527 0.003254 -0.07023 0.09664 0.982 0.9926 0.4138 0.9322 0.9825 0.6736 ] Network output: [ 0.009713 0.8684 0.9719 -0.0002432 0.0001092 0.1393 -0.0001832 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01041 0.004177 0.01112 0.00455 0.9906 0.9937 0.01069 0.978 0.9877 0.02026 ] Network output: [ -0.03578 0.3208 0.7699 -0.001771 0.000795 0.9737 -0.001335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4076 0.2851 0.4827 0.1499 0.9836 0.9934 0.4096 0.9374 0.9845 0.6708 ] Network output: [ -0.04195 0.3146 1.058 0.0003896 -0.0001749 0.7125 0.0002936 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1987 0.188 0.2176 0.1505 0.9903 0.9942 0.1989 0.9778 0.9885 0.2319 ] Network output: [ -0.03157 0.1571 1.083 0.0007152 -0.0003211 0.8259 0.000539 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.214 0.212 0.2197 0.1841 0.9855 0.9915 0.2141 0.9636 0.9825 0.2234 ] Network output: [ 0.01223 0.9247 -0.01007 3.998e-05 -1.795e-05 1.061 3.013e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09581 Epoch 4127 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05889 0.7817 0.9663 2.224e-05 -9.984e-06 0.1343 1.676e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005467 -0.005676 -0.01867 0.01056 0.9607 0.9669 0.01339 0.9248 0.9376 0.04722 ] Network output: [ 1.048 -0.2213 0.1055 0.0006839 -0.000307 0.02163 0.0005154 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3343 -0.02684 -0.1485 0.2132 0.982 0.9926 0.393 0.9321 0.9825 0.6718 ] Network output: [ 0.01212 0.8348 0.9833 -0.0001768 7.938e-05 0.1569 -0.0001333 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00966 0.003367 0.008785 0.008673 0.9904 0.9936 0.009921 0.9773 0.9875 0.01891 ] Network output: [ 0.1092 -0.5936 1.099 0.0001838 -8.253e-05 1.277 0.0001385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3856 0.2565 0.4425 0.3414 0.9836 0.9934 0.3875 0.9371 0.9844 0.6623 ] Network output: [ -0.03808 0.2636 1.058 0.000481 -0.000216 0.7563 0.0003625 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2092 0.1968 0.2192 0.1847 0.9901 0.9941 0.2094 0.9776 0.9886 0.2356 ] Network output: [ -0.03984 0.2178 1.038 0.0006116 -0.0002746 0.826 0.0004609 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2274 0.2251 0.2198 0.1963 0.9857 0.9916 0.2274 0.9643 0.9827 0.2236 ] Network output: [ 0.01824 0.8647 -0.00862 0.0001835 -8.239e-05 1.108 0.0001383 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1259 Epoch 4128 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03908 0.8809 0.9377 -0.0002161 9.701e-05 0.1024 -0.0001629 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005762 -0.005572 -0.01702 0.007309 0.9607 0.9669 0.01395 0.9245 0.9372 0.04723 ] Network output: [ 0.8767 0.3976 -0.05307 -0.0008958 0.0004022 -0.1017 -0.0006751 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3521 0.003271 -0.0715 0.09653 0.982 0.9926 0.4129 0.932 0.9824 0.6728 ] Network output: [ 0.009984 0.8673 0.9719 -0.0002434 0.0001093 0.1399 -0.0001834 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01037 0.004154 0.01103 0.004521 0.9906 0.9937 0.01064 0.9779 0.9876 0.02018 ] Network output: [ -0.03545 0.3196 0.7709 -0.001758 0.0007891 0.9732 -0.001325 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4066 0.2844 0.4813 0.1495 0.9836 0.9934 0.4086 0.9372 0.9845 0.67 ] Network output: [ -0.04206 0.3148 1.059 0.0003859 -0.0001732 0.7119 0.0002908 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1976 0.187 0.2169 0.1499 0.9903 0.9942 0.1978 0.9778 0.9884 0.2312 ] Network output: [ -0.03177 0.1573 1.083 0.0007098 -0.0003187 0.8257 0.000535 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.213 0.211 0.2192 0.1836 0.9855 0.9915 0.2131 0.9635 0.9824 0.2229 ] Network output: [ 0.01202 0.9251 -0.01003 3.636e-05 -1.632e-05 1.061 2.74e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09589 Epoch 4129 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0591 0.7808 0.9659 2.084e-05 -9.355e-06 0.1351 1.57e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005459 -0.005659 -0.01867 0.01055 0.9607 0.9669 0.01334 0.9247 0.9375 0.04707 ] Network output: [ 1.048 -0.2217 0.1055 0.0006859 -0.0003079 0.02332 0.0005169 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3338 -0.02656 -0.1489 0.2134 0.982 0.9926 0.3923 0.9319 0.9825 0.6711 ] Network output: [ 0.01237 0.834 0.9831 -0.0001775 7.967e-05 0.1574 -0.0001337 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009623 0.003361 0.008729 0.008647 0.9904 0.9936 0.009883 0.9772 0.9875 0.01884 ] Network output: [ 0.1088 -0.5935 1.099 0.0001906 -8.556e-05 1.278 0.0001436 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3849 0.2563 0.4415 0.3411 0.9836 0.9934 0.3868 0.9369 0.9844 0.6615 ] Network output: [ -0.03815 0.2647 1.058 0.0004765 -0.0002139 0.7552 0.0003591 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2082 0.1959 0.2184 0.1841 0.9901 0.9941 0.2084 0.9775 0.9886 0.2349 ] Network output: [ -0.03989 0.2186 1.038 0.0006061 -0.0002721 0.8251 0.0004568 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2264 0.2241 0.2192 0.1957 0.9857 0.9916 0.2264 0.9641 0.9826 0.223 ] Network output: [ 0.01813 0.8664 -0.009339 0.0001781 -7.996e-05 1.107 0.0001342 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1263 Epoch 4130 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03939 0.8799 0.9375 -0.0002164 9.715e-05 0.103 -0.0001631 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005753 -0.005557 -0.01704 0.007296 0.9607 0.9669 0.0139 0.9244 0.9371 0.04707 ] Network output: [ 0.8773 0.3979 -0.05378 -0.000888 0.0003986 -0.1024 -0.0006692 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3515 0.003269 -0.07273 0.0964 0.982 0.9926 0.4121 0.9319 0.9824 0.672 ] Network output: [ 0.01023 0.8663 0.9719 -0.0002436 0.0001093 0.1404 -0.0001836 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01032 0.004131 0.01094 0.004493 0.9906 0.9937 0.01059 0.9778 0.9876 0.02009 ] Network output: [ -0.0351 0.3184 0.7719 -0.001745 0.0007833 0.9727 -0.001315 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4057 0.2838 0.4799 0.1491 0.9836 0.9934 0.4077 0.9371 0.9844 0.6692 ] Network output: [ -0.04218 0.315 1.06 0.0003824 -0.0001717 0.7113 0.0002882 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1966 0.186 0.2162 0.1494 0.9903 0.9942 0.1968 0.9777 0.9884 0.2305 ] Network output: [ -0.03198 0.1575 1.084 0.0007047 -0.0003164 0.8255 0.0005311 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2121 0.21 0.2187 0.183 0.9855 0.9915 0.2121 0.9633 0.9824 0.2224 ] Network output: [ 0.01183 0.9254 -0.00992 3.338e-05 -1.499e-05 1.061 2.516e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09598 Epoch 4131 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05928 0.78 0.9656 1.942e-05 -8.719e-06 0.1358 1.464e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005451 -0.005643 -0.01866 0.01054 0.9607 0.967 0.0133 0.9246 0.9374 0.04692 ] Network output: [ 1.047 -0.2221 0.1054 0.0006869 -0.0003084 0.02498 0.0005177 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3333 -0.02631 -0.1492 0.2136 0.982 0.9926 0.3916 0.9318 0.9825 0.6704 ] Network output: [ 0.01261 0.8333 0.983 -0.000178 7.992e-05 0.1578 -0.0001342 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009588 0.003355 0.008676 0.008621 0.9904 0.9936 0.009845 0.9772 0.9874 0.01878 ] Network output: [ 0.1084 -0.5933 1.099 0.0001966 -8.828e-05 1.279 0.0001482 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3842 0.2561 0.4404 0.3407 0.9836 0.9934 0.3862 0.9368 0.9844 0.6607 ] Network output: [ -0.03822 0.2657 1.058 0.0004723 -0.000212 0.7542 0.0003559 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2071 0.1949 0.2177 0.1834 0.9901 0.9941 0.2073 0.9774 0.9885 0.2341 ] Network output: [ -0.03994 0.2195 1.039 0.0006008 -0.0002697 0.8243 0.0004528 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2254 0.2231 0.2185 0.195 0.9857 0.9916 0.2254 0.964 0.9826 0.2224 ] Network output: [ 0.01805 0.868 -0.01003 0.0001732 -7.776e-05 1.107 0.0001305 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1266 Epoch 4132 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03968 0.8789 0.9373 -0.0002166 9.726e-05 0.1035 -0.0001633 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005743 -0.005542 -0.01707 0.007282 0.9607 0.9669 0.01385 0.9243 0.937 0.04691 ] Network output: [ 0.878 0.3982 -0.05455 -0.0008806 0.0003954 -0.1033 -0.0006637 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.351 0.003246 -0.07393 0.09625 0.982 0.9926 0.4114 0.9317 0.9823 0.6711 ] Network output: [ 0.01046 0.8653 0.9719 -0.0002436 0.0001094 0.1409 -0.0001836 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01027 0.004108 0.01085 0.004465 0.9906 0.9937 0.01054 0.9778 0.9876 0.02 ] Network output: [ -0.03472 0.3172 0.7729 -0.001732 0.0007776 0.9723 -0.001305 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4048 0.2831 0.4785 0.1487 0.9836 0.9934 0.4068 0.9369 0.9844 0.6684 ] Network output: [ -0.04231 0.3152 1.06 0.0003791 -0.0001702 0.7108 0.0002857 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1955 0.185 0.2155 0.1489 0.9903 0.9942 0.1957 0.9776 0.9884 0.2299 ] Network output: [ -0.0322 0.1577 1.084 0.0006997 -0.0003141 0.8253 0.0005273 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2111 0.2091 0.2182 0.1825 0.9855 0.9915 0.2111 0.9632 0.9823 0.2219 ] Network output: [ 0.01166 0.9256 -0.009748 3.094e-05 -1.389e-05 1.061 2.332e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09607 Epoch 4133 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05944 0.7793 0.9654 1.801e-05 -8.086e-06 0.1365 1.357e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005444 -0.005626 -0.01866 0.01053 0.9608 0.967 0.01326 0.9245 0.9373 0.04678 ] Network output: [ 1.047 -0.2223 0.1053 0.0006871 -0.0003085 0.02659 0.0005178 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3328 -0.02607 -0.1494 0.2138 0.982 0.9926 0.3909 0.9316 0.9824 0.6696 ] Network output: [ 0.01283 0.8326 0.9828 -0.0001785 8.013e-05 0.1583 -0.0001345 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009553 0.003348 0.008626 0.008595 0.9904 0.9936 0.009809 0.9771 0.9874 0.01871 ] Network output: [ 0.1079 -0.593 1.098 0.000202 -9.068e-05 1.28 0.0001522 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3836 0.2559 0.4395 0.3404 0.9836 0.9934 0.3855 0.9366 0.9843 0.6599 ] Network output: [ -0.03831 0.2667 1.059 0.0004682 -0.0002102 0.7532 0.0003528 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2061 0.194 0.2169 0.1827 0.9901 0.9941 0.2063 0.9773 0.9885 0.2334 ] Network output: [ -0.04 0.2203 1.039 0.0005958 -0.0002675 0.8235 0.000449 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2244 0.2221 0.2179 0.1943 0.9856 0.9916 0.2245 0.9638 0.9825 0.2218 ] Network output: [ 0.01797 0.8696 -0.0107 0.0001687 -7.573e-05 1.106 0.0001271 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1269 Epoch 4134 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03996 0.8779 0.9372 -0.0002168 9.734e-05 0.1041 -0.0001634 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005733 -0.005528 -0.01709 0.007269 0.9607 0.9669 0.01381 0.9242 0.937 0.04676 ] Network output: [ 0.8787 0.3985 -0.05535 -0.0008737 0.0003922 -0.1041 -0.0006584 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3505 0.003207 -0.0751 0.0961 0.982 0.9926 0.4106 0.9315 0.9823 0.6703 ] Network output: [ 0.01067 0.8644 0.9719 -0.0002436 0.0001094 0.1414 -0.0001836 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01023 0.004085 0.01076 0.004438 0.9906 0.9937 0.0105 0.9777 0.9875 0.01991 ] Network output: [ -0.03432 0.3159 0.7738 -0.001719 0.0007719 0.972 -0.001296 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.404 0.2825 0.4771 0.1483 0.9837 0.9934 0.4059 0.9367 0.9843 0.6676 ] Network output: [ -0.04245 0.3153 1.061 0.000376 -0.0001688 0.7102 0.0002834 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1945 0.184 0.2149 0.1484 0.9903 0.9942 0.1947 0.9775 0.9883 0.2293 ] Network output: [ -0.03241 0.1578 1.085 0.000695 -0.000312 0.8251 0.0005238 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2102 0.2082 0.2177 0.182 0.9855 0.9915 0.2102 0.963 0.9822 0.2214 ] Network output: [ 0.01151 0.9256 -0.009524 2.893e-05 -1.299e-05 1.061 2.18e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09615 Epoch 4135 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05958 0.7786 0.9651 1.662e-05 -7.462e-06 0.1372 1.253e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005436 -0.005611 -0.01865 0.01052 0.9608 0.967 0.01322 0.9244 0.9372 0.04663 ] Network output: [ 1.046 -0.2225 0.1052 0.0006866 -0.0003082 0.02814 0.0005174 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3324 -0.02584 -0.1495 0.2139 0.982 0.9926 0.3902 0.9314 0.9824 0.6689 ] Network output: [ 0.01303 0.8319 0.9826 -0.0001789 8.031e-05 0.1587 -0.0001348 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009519 0.003341 0.008579 0.008569 0.9904 0.9936 0.009773 0.977 0.9874 0.01865 ] Network output: [ 0.1075 -0.5926 1.098 0.0002066 -9.277e-05 1.281 0.0001557 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.383 0.2558 0.4386 0.34 0.9836 0.9934 0.3849 0.9364 0.9843 0.6591 ] Network output: [ -0.0384 0.2676 1.059 0.0004642 -0.0002084 0.7523 0.0003499 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2051 0.1931 0.2162 0.1821 0.9901 0.9941 0.2053 0.9772 0.9884 0.2327 ] Network output: [ -0.04005 0.2211 1.039 0.0005911 -0.0002653 0.8226 0.0004454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2234 0.2212 0.2173 0.1937 0.9856 0.9916 0.2235 0.9637 0.9824 0.2212 ] Network output: [ 0.0179 0.8712 -0.01135 0.0001645 -7.385e-05 1.105 0.000124 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1272 Epoch 4136 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04023 0.877 0.937 -0.0002169 9.737e-05 0.1046 -0.0001635 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005724 -0.005514 -0.01711 0.007255 0.9608 0.967 0.01376 0.9241 0.9369 0.0466 ] Network output: [ 0.8794 0.3988 -0.05617 -0.000867 0.0003892 -0.1051 -0.0006534 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.35 0.003152 -0.07625 0.09595 0.9821 0.9926 0.4099 0.9313 0.9822 0.6694 ] Network output: [ 0.01087 0.8634 0.9719 -0.0002434 0.0001093 0.1419 -0.0001835 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01018 0.004061 0.01067 0.004411 0.9906 0.9937 0.01045 0.9776 0.9875 0.01982 ] Network output: [ -0.03389 0.3145 0.7747 -0.001707 0.0007663 0.9716 -0.001286 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4031 0.2818 0.4758 0.148 0.9837 0.9934 0.4051 0.9365 0.9843 0.6668 ] Network output: [ -0.04259 0.3154 1.062 0.0003731 -0.0001675 0.7098 0.0002811 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1935 0.183 0.2142 0.1479 0.9903 0.9942 0.1937 0.9774 0.9883 0.2287 ] Network output: [ -0.03263 0.158 1.085 0.0006904 -0.0003099 0.825 0.0005203 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2092 0.2072 0.2172 0.1815 0.9855 0.9915 0.2093 0.9629 0.9822 0.2209 ] Network output: [ 0.01136 0.9256 -0.009256 2.729e-05 -1.225e-05 1.061 2.057e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09621 Epoch 4137 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05971 0.7779 0.9648 1.526e-05 -6.852e-06 0.1379 1.15e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005428 -0.005595 -0.01864 0.0105 0.9608 0.967 0.01318 0.9244 0.9372 0.04648 ] Network output: [ 1.045 -0.2225 0.105 0.0006854 -0.0003077 0.02964 0.0005166 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.332 -0.02563 -0.1496 0.2141 0.982 0.9926 0.3896 0.9312 0.9823 0.6681 ] Network output: [ 0.01322 0.8313 0.9825 -0.0001792 8.045e-05 0.1591 -0.0001351 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009485 0.003334 0.008533 0.008542 0.9904 0.9936 0.009738 0.977 0.9873 0.01859 ] Network output: [ 0.107 -0.592 1.097 0.0002106 -9.457e-05 1.282 0.0001587 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3824 0.2556 0.4378 0.3396 0.9836 0.9934 0.3843 0.9362 0.9843 0.6582 ] Network output: [ -0.0385 0.2685 1.059 0.0004605 -0.0002067 0.7514 0.000347 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2041 0.1922 0.2156 0.1815 0.9901 0.9941 0.2043 0.9772 0.9884 0.232 ] Network output: [ -0.04011 0.2219 1.039 0.0005865 -0.0002633 0.8219 0.000442 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2225 0.2203 0.2168 0.193 0.9856 0.9916 0.2226 0.9635 0.9824 0.2206 ] Network output: [ 0.01784 0.8727 -0.01197 0.0001606 -7.209e-05 1.104 0.000121 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1274 Epoch 4138 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04049 0.8761 0.9369 -0.0002169 9.737e-05 0.1051 -0.0001635 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005714 -0.005501 -0.01713 0.007242 0.9608 0.967 0.01372 0.9241 0.9368 0.04645 ] Network output: [ 0.8802 0.3991 -0.05699 -0.0008606 0.0003863 -0.106 -0.0006485 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3496 0.003083 -0.07737 0.0958 0.9821 0.9926 0.4092 0.9311 0.9822 0.6686 ] Network output: [ 0.01106 0.8625 0.972 -0.0002432 0.0001092 0.1424 -0.0001833 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01014 0.004038 0.01059 0.004385 0.9906 0.9936 0.0104 0.9775 0.9874 0.01973 ] Network output: [ -0.03344 0.313 0.7756 -0.001695 0.0007607 0.9713 -0.001277 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4023 0.2812 0.4745 0.1477 0.9837 0.9934 0.4043 0.9363 0.9843 0.666 ] Network output: [ -0.04274 0.3154 1.062 0.0003703 -0.0001662 0.7093 0.0002791 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1925 0.1821 0.2136 0.1474 0.9903 0.9942 0.1927 0.9773 0.9882 0.2281 ] Network output: [ -0.03286 0.1582 1.086 0.000686 -0.000308 0.8248 0.000517 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2083 0.2063 0.2167 0.1811 0.9855 0.9915 0.2084 0.9628 0.9821 0.2205 ] Network output: [ 0.01121 0.9255 -0.008953 2.595e-05 -1.165e-05 1.061 1.955e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09626 Epoch 4139 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05983 0.7773 0.9646 1.395e-05 -6.261e-06 0.1385 1.051e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00542 -0.00558 -0.01863 0.01049 0.9608 0.967 0.01314 0.9243 0.9371 0.04633 ] Network output: [ 1.045 -0.2225 0.1049 0.0006838 -0.000307 0.03109 0.0005153 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3316 -0.02543 -0.1497 0.2142 0.9821 0.9926 0.389 0.931 0.9823 0.6674 ] Network output: [ 0.0134 0.8307 0.9823 -0.0001794 8.056e-05 0.1594 -0.0001352 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009452 0.003327 0.00849 0.008516 0.9904 0.9936 0.009703 0.9769 0.9873 0.01853 ] Network output: [ 0.1065 -0.5914 1.097 0.0002141 -9.61e-05 1.283 0.0001613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3819 0.2554 0.437 0.3392 0.9836 0.9934 0.3838 0.9361 0.9842 0.6574 ] Network output: [ -0.03861 0.2693 1.059 0.0004568 -0.0002051 0.7505 0.0003443 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2032 0.1913 0.2149 0.1808 0.9901 0.9941 0.2034 0.9771 0.9884 0.2314 ] Network output: [ -0.04016 0.2226 1.039 0.0005821 -0.0002613 0.8211 0.0004387 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2216 0.2194 0.2162 0.1924 0.9856 0.9916 0.2216 0.9634 0.9823 0.2201 ] Network output: [ 0.01778 0.8742 -0.01256 0.0001569 -7.043e-05 1.103 0.0001182 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1276 Epoch 4140 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04074 0.8753 0.9368 -0.0002168 9.732e-05 0.1056 -0.0001634 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005704 -0.005488 -0.01714 0.007228 0.9608 0.967 0.01368 0.924 0.9367 0.0463 ] Network output: [ 0.881 0.3993 -0.05779 -0.0008542 0.0003835 -0.1069 -0.0006438 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3491 0.003003 -0.07848 0.09566 0.9821 0.9926 0.4086 0.9309 0.9822 0.6678 ] Network output: [ 0.01124 0.8617 0.972 -0.0002429 0.000109 0.1429 -0.000183 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0101 0.004014 0.0105 0.004359 0.9906 0.9936 0.01036 0.9775 0.9874 0.01965 ] Network output: [ -0.03298 0.3116 0.7765 -0.001682 0.0007552 0.9711 -0.001268 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4015 0.2805 0.4732 0.1474 0.9837 0.9934 0.4034 0.9362 0.9842 0.6651 ] Network output: [ -0.04289 0.3155 1.063 0.0003676 -0.000165 0.7089 0.0002771 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1915 0.1812 0.2129 0.147 0.9903 0.9942 0.1917 0.9772 0.9882 0.2275 ] Network output: [ -0.03308 0.1583 1.086 0.0006818 -0.0003061 0.8247 0.0005138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2074 0.2055 0.2162 0.1806 0.9855 0.9915 0.2075 0.9626 0.982 0.22 ] Network output: [ 0.01107 0.9254 -0.00862 2.485e-05 -1.115e-05 1.061 1.872e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09629 Epoch 4141 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05993 0.7768 0.9643 1.268e-05 -5.691e-06 0.1391 9.553e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005412 -0.005565 -0.01861 0.01048 0.9608 0.967 0.0131 0.9242 0.937 0.04619 ] Network output: [ 1.044 -0.2224 0.1047 0.0006817 -0.000306 0.03248 0.0005138 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3312 -0.02524 -0.1497 0.2142 0.9821 0.9926 0.3884 0.9308 0.9822 0.6666 ] Network output: [ 0.01357 0.8302 0.9822 -0.0001796 8.063e-05 0.1598 -0.0001354 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00942 0.00332 0.008448 0.008489 0.9904 0.9936 0.009669 0.9768 0.9873 0.01846 ] Network output: [ 0.106 -0.5907 1.096 0.0002169 -9.739e-05 1.283 0.0001635 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3813 0.2552 0.4362 0.3387 0.9836 0.9934 0.3832 0.9359 0.9842 0.6566 ] Network output: [ -0.03872 0.2701 1.06 0.0004533 -0.0002035 0.7497 0.0003417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2022 0.1904 0.2143 0.1802 0.9901 0.9941 0.2024 0.977 0.9883 0.2307 ] Network output: [ -0.04022 0.2233 1.039 0.0005778 -0.0002594 0.8204 0.0004355 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2207 0.2185 0.2156 0.1918 0.9856 0.9916 0.2207 0.9633 0.9822 0.2195 ] Network output: [ 0.01773 0.8757 -0.01314 0.0001534 -6.885e-05 1.103 0.0001156 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1278 Epoch 4142 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04098 0.8744 0.9367 -0.0002166 9.724e-05 0.1061 -0.0001632 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005694 -0.005475 -0.01716 0.007216 0.9608 0.967 0.01363 0.9239 0.9366 0.04615 ] Network output: [ 0.8818 0.3995 -0.05857 -0.0008481 0.0003807 -0.1079 -0.0006391 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3486 0.002914 -0.07955 0.09552 0.9821 0.9926 0.4079 0.9307 0.9821 0.6669 ] Network output: [ 0.01142 0.8608 0.972 -0.0002425 0.0001089 0.1433 -0.0001827 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01005 0.003991 0.01042 0.004334 0.9906 0.9936 0.01032 0.9774 0.9873 0.01956 ] Network output: [ -0.0325 0.3101 0.7773 -0.00167 0.0007498 0.9708 -0.001259 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.4007 0.2799 0.4719 0.1471 0.9837 0.9934 0.4026 0.936 0.9842 0.6643 ] Network output: [ -0.04303 0.3155 1.064 0.0003651 -0.0001639 0.7085 0.0002752 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1906 0.1802 0.2123 0.1465 0.9903 0.9942 0.1907 0.9771 0.9881 0.2269 ] Network output: [ -0.0333 0.1584 1.086 0.0006776 -0.0003042 0.8246 0.0005107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2066 0.2046 0.2158 0.1802 0.9854 0.9915 0.2066 0.9625 0.982 0.2196 ] Network output: [ 0.01094 0.9252 -0.008265 2.395e-05 -1.075e-05 1.061 1.805e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09631 Epoch 4143 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06003 0.7762 0.9641 1.145e-05 -5.142e-06 0.1397 8.632e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005405 -0.005551 -0.0186 0.01047 0.9609 0.9671 0.01306 0.9241 0.9369 0.04605 ] Network output: [ 1.043 -0.2222 0.1044 0.0006793 -0.000305 0.03381 0.000512 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3309 -0.02507 -0.1498 0.2143 0.9821 0.9926 0.3879 0.9307 0.9822 0.6659 ] Network output: [ 0.01374 0.8296 0.982 -0.0001797 8.068e-05 0.1602 -0.0001354 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009388 0.003313 0.008408 0.008462 0.9904 0.9936 0.009636 0.9768 0.9872 0.01841 ] Network output: [ 0.1055 -0.5899 1.095 0.0002194 -9.849e-05 1.284 0.0001653 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3808 0.255 0.4355 0.3382 0.9836 0.9934 0.3827 0.9357 0.9841 0.6558 ] Network output: [ -0.03883 0.2709 1.06 0.00045 -0.000202 0.7489 0.0003391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2013 0.1895 0.2137 0.1797 0.9901 0.9941 0.2015 0.9769 0.9883 0.2301 ] Network output: [ -0.04028 0.224 1.039 0.0005738 -0.0002576 0.8197 0.0004324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2198 0.2176 0.2151 0.1912 0.9856 0.9916 0.2198 0.9631 0.9822 0.219 ] Network output: [ 0.01767 0.8772 -0.01368 0.00015 -6.735e-05 1.102 0.0001131 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1279 Epoch 4144 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04122 0.8736 0.9365 -0.0002163 9.712e-05 0.1065 -0.000163 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005684 -0.005462 -0.01717 0.007203 0.9609 0.967 0.01359 0.9238 0.9365 0.046 ] Network output: [ 0.8825 0.3997 -0.05933 -0.000842 0.000378 -0.1089 -0.0006345 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3482 0.002818 -0.08061 0.09539 0.9821 0.9926 0.4072 0.9305 0.9821 0.6661 ] Network output: [ 0.01159 0.86 0.9721 -0.000242 0.0001086 0.1438 -0.0001824 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.01001 0.003968 0.01034 0.00431 0.9906 0.9936 0.01027 0.9773 0.9873 0.01948 ] Network output: [ -0.03201 0.3085 0.7781 -0.001658 0.0007445 0.9706 -0.00125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3999 0.2793 0.4707 0.1468 0.9837 0.9934 0.4019 0.9358 0.9842 0.6635 ] Network output: [ -0.04318 0.3155 1.064 0.0003627 -0.0001628 0.7081 0.0002734 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1896 0.1794 0.2118 0.1461 0.9903 0.9942 0.1898 0.9771 0.9881 0.2264 ] Network output: [ -0.03352 0.1585 1.087 0.0006737 -0.0003024 0.8246 0.0005077 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2057 0.2038 0.2153 0.1797 0.9854 0.9915 0.2058 0.9623 0.9819 0.2192 ] Network output: [ 0.01081 0.925 -0.007891 2.321e-05 -1.042e-05 1.061 1.749e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09632 Epoch 4145 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06012 0.7757 0.9638 1.028e-05 -4.616e-06 0.1403 7.749e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005397 -0.005537 -0.01859 0.01045 0.9609 0.9671 0.01303 0.924 0.9368 0.04591 ] Network output: [ 1.043 -0.222 0.1042 0.0006767 -0.0003038 0.0351 0.00051 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3305 -0.0249 -0.1497 0.2144 0.9821 0.9926 0.3873 0.9305 0.9822 0.6651 ] Network output: [ 0.0139 0.8291 0.9819 -0.0001798 8.07e-05 0.1605 -0.0001355 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009357 0.003305 0.008369 0.008436 0.9904 0.9936 0.009603 0.9767 0.9872 0.01835 ] Network output: [ 0.105 -0.589 1.095 0.0002214 -9.94e-05 1.285 0.0001669 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3803 0.2548 0.4348 0.3378 0.9836 0.9934 0.3822 0.9355 0.9841 0.6551 ] Network output: [ -0.03895 0.2716 1.06 0.0004467 -0.0002005 0.7481 0.0003367 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.2004 0.1887 0.2131 0.1791 0.9901 0.9941 0.2005 0.9769 0.9882 0.2295 ] Network output: [ -0.04033 0.2247 1.039 0.0005698 -0.0002558 0.819 0.0004294 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2189 0.2167 0.2146 0.1906 0.9856 0.9916 0.219 0.963 0.9821 0.2185 ] Network output: [ 0.01761 0.8786 -0.01421 0.0001468 -6.591e-05 1.101 0.0001106 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.128 Epoch 4146 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04146 0.8728 0.9364 -0.000216 9.698e-05 0.107 -0.0001628 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005674 -0.00545 -0.01718 0.007191 0.9609 0.9671 0.01355 0.9237 0.9364 0.04585 ] Network output: [ 0.8833 0.3998 -0.06005 -0.000836 0.0003753 -0.1098 -0.00063 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3477 0.002716 -0.08163 0.09527 0.9821 0.9926 0.4066 0.9304 0.982 0.6653 ] Network output: [ 0.01176 0.8592 0.9721 -0.0002415 0.0001084 0.1442 -0.000182 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009971 0.003946 0.01026 0.004287 0.9906 0.9936 0.01023 0.9772 0.9872 0.0194 ] Network output: [ -0.03153 0.307 0.779 -0.001647 0.0007392 0.9704 -0.001241 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3992 0.2786 0.4695 0.1465 0.9837 0.9934 0.4011 0.9356 0.9841 0.6628 ] Network output: [ -0.04333 0.3155 1.065 0.0003604 -0.0001618 0.7078 0.0002716 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1887 0.1785 0.2112 0.1457 0.9903 0.9941 0.1889 0.977 0.988 0.2258 ] Network output: [ -0.03373 0.1586 1.087 0.0006698 -0.0003007 0.8245 0.0005048 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2049 0.2029 0.2149 0.1793 0.9854 0.9915 0.2049 0.9622 0.9818 0.2187 ] Network output: [ 0.01068 0.9248 -0.007505 2.261e-05 -1.015e-05 1.061 1.704e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09633 Epoch 4147 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06021 0.7752 0.9636 9.158e-06 -4.111e-06 0.1408 6.902e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005389 -0.005523 -0.01857 0.01044 0.9609 0.9671 0.01299 0.924 0.9367 0.04577 ] Network output: [ 1.042 -0.2217 0.104 0.0006739 -0.0003026 0.03634 0.0005079 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3301 -0.02473 -0.1497 0.2144 0.9821 0.9926 0.3868 0.9303 0.9821 0.6644 ] Network output: [ 0.01406 0.8286 0.9817 -0.0001798 8.07e-05 0.1608 -0.0001355 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009326 0.003298 0.008332 0.00841 0.9904 0.9936 0.009571 0.9766 0.9871 0.01829 ] Network output: [ 0.1044 -0.5881 1.094 0.0002231 -0.0001002 1.286 0.0001681 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3798 0.2546 0.4342 0.3373 0.9837 0.9934 0.3816 0.9354 0.9841 0.6543 ] Network output: [ -0.03907 0.2723 1.06 0.0004435 -0.0001991 0.7474 0.0003343 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1995 0.1878 0.2125 0.1785 0.9901 0.9941 0.1996 0.9768 0.9882 0.2289 ] Network output: [ -0.04039 0.2253 1.039 0.000566 -0.0002541 0.8184 0.0004266 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2181 0.2159 0.2141 0.1901 0.9856 0.9916 0.2181 0.9628 0.9821 0.218 ] Network output: [ 0.01756 0.8801 -0.01471 0.0001438 -6.454e-05 1.1 0.0001083 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1281 Epoch 4148 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04169 0.872 0.9363 -0.0002156 9.681e-05 0.1074 -0.0001625 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005665 -0.005438 -0.0172 0.007179 0.9609 0.9671 0.01351 0.9237 0.9364 0.04571 ] Network output: [ 0.8841 0.3999 -0.06073 -0.0008301 0.0003727 -0.1108 -0.0006256 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3473 0.00261 -0.08264 0.09515 0.9821 0.9926 0.406 0.9302 0.982 0.6645 ] Network output: [ 0.01193 0.8584 0.9722 -0.0002409 0.0001082 0.1446 -0.0001816 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009931 0.003923 0.01018 0.004264 0.9906 0.9936 0.01019 0.9772 0.9872 0.01931 ] Network output: [ -0.03104 0.3055 0.7797 -0.001635 0.000734 0.9702 -0.001232 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3984 0.278 0.4683 0.1462 0.9837 0.9934 0.4003 0.9355 0.9841 0.662 ] Network output: [ -0.04347 0.3155 1.065 0.0003582 -0.0001608 0.7075 0.0002699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1879 0.1776 0.2106 0.1453 0.9903 0.9941 0.188 0.9769 0.988 0.2253 ] Network output: [ -0.03394 0.1587 1.087 0.000666 -0.000299 0.8245 0.0005019 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2041 0.2021 0.2145 0.1789 0.9854 0.9915 0.2041 0.9621 0.9818 0.2183 ] Network output: [ 0.01056 0.9246 -0.00711 2.213e-05 -9.933e-06 1.062 1.667e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09632 Epoch 4149 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06029 0.7748 0.9633 8.082e-06 -3.628e-06 0.1413 6.091e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005381 -0.00551 -0.01856 0.01043 0.9609 0.9671 0.01295 0.9239 0.9367 0.04563 ] Network output: [ 1.041 -0.2214 0.1038 0.000671 -0.0003012 0.03754 0.0005057 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3298 -0.02458 -0.1497 0.2144 0.9821 0.9926 0.3862 0.9301 0.9821 0.6637 ] Network output: [ 0.01421 0.8281 0.9816 -0.0001797 8.068e-05 0.1611 -0.0001354 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009296 0.00329 0.008295 0.008384 0.9904 0.9936 0.00954 0.9766 0.9871 0.01823 ] Network output: [ 0.1039 -0.5871 1.093 0.0002245 -0.0001008 1.287 0.0001692 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3793 0.2545 0.4336 0.3368 0.9837 0.9934 0.3811 0.9352 0.984 0.6535 ] Network output: [ -0.03918 0.2729 1.061 0.0004405 -0.0001977 0.7467 0.000332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1986 0.187 0.212 0.178 0.9901 0.9941 0.1988 0.9767 0.9881 0.2284 ] Network output: [ -0.04044 0.2259 1.04 0.0005623 -0.0002524 0.8178 0.0004238 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2172 0.215 0.2136 0.1895 0.9856 0.9916 0.2173 0.9627 0.982 0.2175 ] Network output: [ 0.01751 0.8814 -0.01519 0.0001408 -6.322e-05 1.099 0.0001061 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1281 Epoch 4150 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04191 0.8713 0.9363 -0.0002152 9.662e-05 0.1078 -0.0001622 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005655 -0.005426 -0.01721 0.007168 0.9609 0.9671 0.01347 0.9236 0.9363 0.04556 ] Network output: [ 0.8849 0.3999 -0.06137 -0.0008244 0.0003701 -0.1117 -0.0006213 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3469 0.002501 -0.08361 0.09504 0.9821 0.9926 0.4053 0.93 0.9819 0.6637 ] Network output: [ 0.01209 0.8576 0.9722 -0.0002403 0.0001079 0.145 -0.0001811 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009892 0.003901 0.01011 0.004241 0.9905 0.9936 0.01015 0.9771 0.9872 0.01924 ] Network output: [ -0.03055 0.304 0.7805 -0.001624 0.000729 0.97 -0.001224 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3977 0.2774 0.4671 0.146 0.9837 0.9934 0.3996 0.9353 0.984 0.6612 ] Network output: [ -0.04361 0.3154 1.066 0.000356 -0.0001598 0.7072 0.0002683 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.187 0.1768 0.2101 0.1449 0.9902 0.9941 0.1871 0.9768 0.9879 0.2248 ] Network output: [ -0.03415 0.1588 1.088 0.0006623 -0.0002974 0.8245 0.0004992 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2033 0.2013 0.2141 0.1785 0.9854 0.9915 0.2033 0.9619 0.9817 0.218 ] Network output: [ 0.01044 0.9243 -0.006708 2.174e-05 -9.758e-06 1.062 1.638e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09631 Epoch 4151 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06037 0.7744 0.9631 7.05e-06 -3.165e-06 0.1418 5.313e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005373 -0.005496 -0.01854 0.01041 0.961 0.9671 0.01292 0.9238 0.9366 0.04549 ] Network output: [ 1.041 -0.2211 0.1036 0.000668 -0.0002999 0.0387 0.0005034 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3294 -0.02443 -0.1497 0.2144 0.9821 0.9926 0.3857 0.9299 0.982 0.663 ] Network output: [ 0.01436 0.8276 0.9815 -0.0001796 8.065e-05 0.1614 -0.0001354 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009266 0.003283 0.00826 0.008358 0.9904 0.9936 0.009509 0.9765 0.9871 0.01818 ] Network output: [ 0.1034 -0.5861 1.093 0.0002257 -0.0001013 1.288 0.0001701 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3788 0.2543 0.4329 0.3363 0.9837 0.9934 0.3806 0.935 0.984 0.6528 ] Network output: [ -0.0393 0.2735 1.061 0.0004375 -0.0001964 0.746 0.0003297 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1977 0.1862 0.2115 0.1774 0.9901 0.9941 0.1979 0.9766 0.9881 0.2278 ] Network output: [ -0.0405 0.2264 1.04 0.0005587 -0.0002508 0.8172 0.0004211 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2164 0.2142 0.2131 0.189 0.9856 0.9916 0.2164 0.9626 0.9819 0.217 ] Network output: [ 0.01745 0.8828 -0.01564 0.000138 -6.195e-05 1.099 0.000104 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1282 Epoch 4152 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04213 0.8705 0.9362 -0.0002148 9.641e-05 0.1082 -0.0001618 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005645 -0.005414 -0.01722 0.007157 0.961 0.9671 0.01343 0.9235 0.9362 0.04542 ] Network output: [ 0.8856 0.3999 -0.06197 -0.0008187 0.0003675 -0.1126 -0.000617 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3464 0.002391 -0.08456 0.09493 0.9821 0.9926 0.4047 0.9298 0.9819 0.663 ] Network output: [ 0.01225 0.8569 0.9723 -0.0002397 0.0001076 0.1454 -0.0001806 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009853 0.00388 0.01003 0.00422 0.9905 0.9936 0.01011 0.977 0.9871 0.01916 ] Network output: [ -0.03007 0.3025 0.7812 -0.001613 0.000724 0.9698 -0.001215 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3969 0.2768 0.466 0.1457 0.9837 0.9934 0.3988 0.9351 0.984 0.6604 ] Network output: [ -0.04374 0.3154 1.067 0.0003539 -0.0001589 0.7069 0.0002667 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1861 0.176 0.2096 0.1445 0.9902 0.9941 0.1863 0.9767 0.9879 0.2243 ] Network output: [ -0.03435 0.1589 1.088 0.0006588 -0.0002957 0.8245 0.0004965 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2025 0.2006 0.2137 0.1781 0.9854 0.9915 0.2026 0.9618 0.9816 0.2176 ] Network output: [ 0.01033 0.924 -0.006303 2.142e-05 -9.618e-06 1.062 1.615e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09629 Epoch 4153 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06044 0.7739 0.9629 6.061e-06 -2.721e-06 0.1423 4.568e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005366 -0.005483 -0.01852 0.0104 0.961 0.9672 0.01288 0.9237 0.9365 0.04536 ] Network output: [ 1.04 -0.2208 0.1033 0.0006649 -0.0002985 0.03982 0.0005011 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3291 -0.02429 -0.1496 0.2145 0.9821 0.9926 0.3852 0.9297 0.982 0.6623 ] Network output: [ 0.0145 0.8272 0.9813 -0.0001795 8.06e-05 0.1617 -0.0001353 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009237 0.003275 0.008225 0.008332 0.9904 0.9936 0.009478 0.9765 0.987 0.01813 ] Network output: [ 0.1029 -0.5851 1.092 0.0002266 -0.0001017 1.288 0.0001708 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3783 0.2541 0.4323 0.3358 0.9837 0.9934 0.3801 0.9348 0.984 0.652 ] Network output: [ -0.03941 0.2741 1.061 0.0004346 -0.0001951 0.7454 0.0003275 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1969 0.1854 0.211 0.1769 0.9901 0.9941 0.197 0.9766 0.9881 0.2273 ] Network output: [ -0.04055 0.227 1.04 0.0005552 -0.0002493 0.8166 0.0004184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2156 0.2134 0.2126 0.1885 0.9856 0.9916 0.2156 0.9624 0.9819 0.2165 ] Network output: [ 0.0174 0.8841 -0.01608 0.0001353 -6.073e-05 1.098 0.0001019 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1282 Epoch 4154 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04234 0.8698 0.9361 -0.0002143 9.619e-05 0.1086 -0.0001615 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005635 -0.005403 -0.01722 0.007146 0.961 0.9671 0.01339 0.9234 0.9361 0.04528 ] Network output: [ 0.8864 0.4 -0.06253 -0.0008131 0.000365 -0.1135 -0.0006128 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.346 0.002278 -0.08548 0.09483 0.9821 0.9926 0.4041 0.9296 0.9819 0.6622 ] Network output: [ 0.0124 0.8562 0.9723 -0.000239 0.0001073 0.1458 -0.0001801 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009815 0.003858 0.009962 0.004199 0.9905 0.9936 0.01007 0.9769 0.9871 0.01908 ] Network output: [ -0.02959 0.3011 0.782 -0.001602 0.0007192 0.9696 -0.001207 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3962 0.2761 0.4649 0.1454 0.9837 0.9934 0.3981 0.9349 0.984 0.6597 ] Network output: [ -0.04387 0.3154 1.067 0.0003519 -0.000158 0.7067 0.0002652 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1853 0.1752 0.2091 0.1441 0.9902 0.9941 0.1855 0.9766 0.9878 0.2238 ] Network output: [ -0.03455 0.1589 1.088 0.0006553 -0.0002942 0.8245 0.0004938 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2018 0.1998 0.2133 0.1777 0.9854 0.9915 0.2018 0.9617 0.9816 0.2172 ] Network output: [ 0.01021 0.9238 -0.005896 2.118e-05 -9.51e-06 1.062 1.596e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09627 Epoch 4155 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06052 0.7735 0.9627 5.111e-06 -2.295e-06 0.1428 3.852e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005358 -0.00547 -0.01851 0.01039 0.961 0.9672 0.01285 0.9237 0.9364 0.04522 ] Network output: [ 1.04 -0.2204 0.1031 0.0006618 -0.0002971 0.0409 0.0004988 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3287 -0.02416 -0.1495 0.2145 0.9821 0.9926 0.3847 0.9295 0.982 0.6616 ] Network output: [ 0.01464 0.8268 0.9812 -0.0001794 8.053e-05 0.162 -0.0001352 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009208 0.003268 0.008191 0.008307 0.9904 0.9936 0.009449 0.9764 0.987 0.01807 ] Network output: [ 0.1024 -0.5841 1.091 0.0002274 -0.0001021 1.289 0.0001714 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3778 0.2538 0.4318 0.3353 0.9837 0.9934 0.3796 0.9347 0.9839 0.6513 ] Network output: [ -0.03953 0.2747 1.061 0.0004317 -0.0001938 0.7448 0.0003254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.196 0.1847 0.2105 0.1764 0.9901 0.9941 0.1962 0.9765 0.988 0.2268 ] Network output: [ -0.0406 0.2275 1.04 0.0005518 -0.0002477 0.8161 0.0004159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2148 0.2127 0.2122 0.1879 0.9856 0.9916 0.2148 0.9623 0.9818 0.2161 ] Network output: [ 0.01735 0.8854 -0.01649 0.0001327 -5.956e-05 1.097 9.998e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1282 Epoch 4156 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04255 0.8691 0.936 -0.0002137 9.596e-05 0.1089 -0.0001611 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005625 -0.005391 -0.01723 0.007136 0.961 0.9671 0.01335 0.9233 0.936 0.04514 ] Network output: [ 0.8871 0.3999 -0.06305 -0.0008076 0.0003626 -0.1144 -0.0006086 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3456 0.002166 -0.08638 0.09474 0.9821 0.9926 0.4035 0.9294 0.9818 0.6615 ] Network output: [ 0.01256 0.8555 0.9723 -0.0002383 0.000107 0.1461 -0.0001796 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009778 0.003838 0.009892 0.004178 0.9905 0.9936 0.01003 0.9769 0.987 0.01901 ] Network output: [ -0.02912 0.2997 0.7827 -0.001591 0.0007144 0.9695 -0.001199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3955 0.2755 0.4638 0.1452 0.9837 0.9934 0.3974 0.9347 0.9839 0.659 ] Network output: [ -0.044 0.3153 1.068 0.0003499 -0.0001571 0.7065 0.0002637 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1845 0.1744 0.2086 0.1438 0.9902 0.9941 0.1847 0.9765 0.9878 0.2234 ] Network output: [ -0.03474 0.159 1.089 0.0006518 -0.0002926 0.8245 0.0004912 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.201 0.1991 0.2129 0.1773 0.9854 0.9915 0.2011 0.9615 0.9815 0.2168 ] Network output: [ 0.0101 0.9235 -0.005489 2.1e-05 -9.428e-06 1.062 1.583e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09624 Epoch 4157 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06059 0.7732 0.9625 4.199e-06 -1.885e-06 0.1432 3.164e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00535 -0.005458 -0.01849 0.01037 0.961 0.9672 0.01282 0.9236 0.9363 0.04509 ] Network output: [ 1.039 -0.2201 0.1029 0.0006587 -0.0002957 0.04195 0.0004964 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3284 -0.02403 -0.1495 0.2145 0.9821 0.9926 0.3842 0.9294 0.9819 0.661 ] Network output: [ 0.01478 0.8264 0.9811 -0.0001792 8.046e-05 0.1623 -0.0001351 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00918 0.00326 0.008158 0.008282 0.9904 0.9936 0.009419 0.9763 0.987 0.01802 ] Network output: [ 0.1019 -0.5831 1.09 0.000228 -0.0001024 1.29 0.0001718 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3773 0.2536 0.4312 0.3347 0.9837 0.9934 0.3791 0.9345 0.9839 0.6506 ] Network output: [ -0.03964 0.2752 1.062 0.000429 -0.0001926 0.7442 0.0003233 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1952 0.1839 0.21 0.1759 0.99 0.9941 0.1954 0.9764 0.988 0.2263 ] Network output: [ -0.04065 0.228 1.04 0.0005485 -0.0002462 0.8156 0.0004134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.214 0.2119 0.2118 0.1874 0.9855 0.9916 0.2141 0.9622 0.9817 0.2157 ] Network output: [ 0.0173 0.8867 -0.01688 0.0001302 -5.843e-05 1.096 9.809e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1282 Epoch 4158 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04276 0.8684 0.9359 -0.0002132 9.571e-05 0.1093 -0.0001607 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005615 -0.00538 -0.01724 0.007126 0.961 0.9672 0.01332 0.9233 0.9359 0.04501 ] Network output: [ 0.8878 0.3999 -0.06353 -0.0008022 0.0003601 -0.1153 -0.0006045 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3451 0.002053 -0.08725 0.09466 0.9821 0.9926 0.4029 0.9292 0.9818 0.6607 ] Network output: [ 0.01271 0.8548 0.9724 -0.0002377 0.0001067 0.1465 -0.0001791 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009742 0.003817 0.009823 0.004158 0.9905 0.9936 0.009992 0.9768 0.987 0.01894 ] Network output: [ -0.02866 0.2983 0.7833 -0.001581 0.0007098 0.9693 -0.001191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3948 0.275 0.4627 0.1449 0.9837 0.9934 0.3967 0.9346 0.9839 0.6582 ] Network output: [ -0.04413 0.3153 1.068 0.0003479 -0.0001562 0.7062 0.0002622 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1837 0.1737 0.2081 0.1434 0.9902 0.9941 0.1839 0.9765 0.9878 0.2229 ] Network output: [ -0.03493 0.159 1.089 0.0006485 -0.0002911 0.8245 0.0004887 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2003 0.1983 0.2125 0.177 0.9854 0.9915 0.2003 0.9614 0.9815 0.2165 ] Network output: [ 0.009998 0.9232 -0.005083 2.087e-05 -9.369e-06 1.062 1.573e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09622 Epoch 4159 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06066 0.7728 0.9623 3.321e-06 -1.491e-06 0.1436 2.502e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005342 -0.005445 -0.01847 0.01036 0.9611 0.9672 0.01278 0.9235 0.9363 0.04496 ] Network output: [ 1.038 -0.2198 0.1027 0.0006556 -0.0002943 0.04296 0.0004941 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.328 -0.02391 -0.1494 0.2145 0.9821 0.9926 0.3837 0.9292 0.9819 0.6603 ] Network output: [ 0.01492 0.826 0.981 -0.000179 8.037e-05 0.1625 -0.0001349 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009152 0.003253 0.008126 0.008257 0.9904 0.9936 0.00939 0.9763 0.9869 0.01797 ] Network output: [ 0.1015 -0.5821 1.089 0.0002284 -0.0001026 1.291 0.0001722 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3769 0.2534 0.4306 0.3342 0.9837 0.9934 0.3787 0.9343 0.9838 0.6499 ] Network output: [ -0.03976 0.2757 1.062 0.0004263 -0.0001914 0.7437 0.0003212 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1944 0.1832 0.2095 0.1754 0.99 0.9941 0.1946 0.9763 0.9879 0.2258 ] Network output: [ -0.0407 0.2285 1.04 0.0005453 -0.0002448 0.8151 0.0004109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2133 0.2111 0.2113 0.187 0.9855 0.9916 0.2133 0.9621 0.9817 0.2153 ] Network output: [ 0.01725 0.8879 -0.01726 0.0001277 -5.734e-05 1.095 9.626e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1282 Epoch 4160 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04296 0.8677 0.9359 -0.0002126 9.546e-05 0.1096 -0.0001603 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005605 -0.005369 -0.01724 0.007116 0.961 0.9672 0.01328 0.9232 0.9359 0.04487 ] Network output: [ 0.8885 0.3998 -0.06397 -0.0007968 0.0003577 -0.1161 -0.0006005 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3447 0.00194 -0.0881 0.09458 0.9821 0.9926 0.4023 0.929 0.9817 0.66 ] Network output: [ 0.01285 0.8541 0.9724 -0.000237 0.0001064 0.1468 -0.0001786 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009707 0.003797 0.009757 0.004138 0.9905 0.9936 0.009955 0.9767 0.9869 0.01887 ] Network output: [ -0.0282 0.2969 0.784 -0.001571 0.0007052 0.9691 -0.001184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3941 0.2744 0.4616 0.1447 0.9837 0.9934 0.396 0.9344 0.9838 0.6575 ] Network output: [ -0.04425 0.3152 1.069 0.000346 -0.0001553 0.706 0.0002608 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1829 0.1729 0.2076 0.1431 0.9902 0.9941 0.1831 0.9764 0.9877 0.2225 ] Network output: [ -0.03511 0.1591 1.089 0.0006452 -0.0002896 0.8245 0.0004862 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1996 0.1976 0.2122 0.1766 0.9854 0.9915 0.1996 0.9613 0.9814 0.2161 ] Network output: [ 0.009895 0.923 -0.004679 2.079e-05 -9.331e-06 1.062 1.566e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09618 Epoch 4161 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06072 0.7725 0.962 2.474e-06 -1.111e-06 0.144 1.865e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005334 -0.005433 -0.01846 0.01035 0.9611 0.9672 0.01275 0.9234 0.9362 0.04483 ] Network output: [ 1.038 -0.2194 0.1025 0.0006524 -0.0002929 0.04394 0.0004917 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3277 -0.02379 -0.1494 0.2145 0.9821 0.9926 0.3832 0.929 0.9818 0.6596 ] Network output: [ 0.01505 0.8256 0.9808 -0.0001788 8.028e-05 0.1627 -0.0001348 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009125 0.003245 0.008094 0.008233 0.9904 0.9936 0.009362 0.9762 0.9869 0.01792 ] Network output: [ 0.101 -0.5811 1.089 0.0002287 -0.0001027 1.291 0.0001724 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3764 0.2532 0.4301 0.3337 0.9837 0.9934 0.3782 0.9341 0.9838 0.6492 ] Network output: [ -0.03987 0.2762 1.062 0.0004236 -0.0001902 0.7432 0.0003193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1936 0.1824 0.2091 0.175 0.99 0.9941 0.1938 0.9763 0.9879 0.2254 ] Network output: [ -0.04075 0.2289 1.04 0.0005421 -0.0002434 0.8146 0.0004085 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2125 0.2104 0.2109 0.1865 0.9855 0.9916 0.2126 0.9619 0.9816 0.2148 ] Network output: [ 0.0172 0.8891 -0.01761 0.0001254 -5.63e-05 1.095 9.451e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1282 Epoch 4162 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04315 0.8671 0.9358 -0.0002121 9.52e-05 0.1099 -0.0001598 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005596 -0.005358 -0.01725 0.007106 0.9611 0.9672 0.01324 0.9231 0.9358 0.04474 ] Network output: [ 0.8892 0.3997 -0.06437 -0.0007915 0.0003554 -0.117 -0.0005965 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3443 0.001828 -0.08893 0.09451 0.9821 0.9926 0.4017 0.9289 0.9817 0.6593 ] Network output: [ 0.013 0.8535 0.9724 -0.0002362 0.0001061 0.1471 -0.000178 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009672 0.003777 0.009691 0.004119 0.9905 0.9936 0.009919 0.9767 0.9869 0.0188 ] Network output: [ -0.02775 0.2956 0.7846 -0.001561 0.0007007 0.969 -0.001176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3935 0.2738 0.4606 0.1445 0.9837 0.9935 0.3953 0.9342 0.9838 0.6568 ] Network output: [ -0.04437 0.3151 1.069 0.0003442 -0.0001545 0.7059 0.0002594 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1822 0.1722 0.2071 0.1427 0.9902 0.9941 0.1824 0.9763 0.9877 0.222 ] Network output: [ -0.03528 0.1592 1.089 0.0006419 -0.0002882 0.8246 0.0004838 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1989 0.1969 0.2118 0.1763 0.9854 0.9915 0.1989 0.9611 0.9813 0.2158 ] Network output: [ 0.009795 0.9227 -0.004279 2.074e-05 -9.311e-06 1.062 1.563e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09615 Epoch 4163 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06079 0.7721 0.9618 1.658e-06 -7.444e-07 0.1444 1.25e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005327 -0.005421 -0.01844 0.01033 0.9611 0.9672 0.01272 0.9234 0.9361 0.04471 ] Network output: [ 1.037 -0.2191 0.1023 0.0006493 -0.0002915 0.04489 0.0004893 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3273 -0.02368 -0.1493 0.2145 0.9821 0.9926 0.3827 0.9288 0.9818 0.659 ] Network output: [ 0.01518 0.8252 0.9807 -0.0001786 8.018e-05 0.163 -0.0001346 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009098 0.003238 0.008063 0.008209 0.9904 0.9936 0.009334 0.9761 0.9868 0.01787 ] Network output: [ 0.1005 -0.5801 1.088 0.0002289 -0.0001028 1.292 0.0001725 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3759 0.253 0.4296 0.3332 0.9837 0.9934 0.3777 0.934 0.9838 0.6485 ] Network output: [ -0.03998 0.2766 1.062 0.000421 -0.000189 0.7427 0.0003173 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1929 0.1817 0.2086 0.1745 0.99 0.9941 0.193 0.9762 0.9879 0.2249 ] Network output: [ -0.0408 0.2294 1.04 0.000539 -0.000242 0.8142 0.0004062 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2118 0.2097 0.2105 0.186 0.9855 0.9916 0.2118 0.9618 0.9816 0.2144 ] Network output: [ 0.01715 0.8903 -0.01795 0.0001232 -5.529e-05 1.094 9.281e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1282 Epoch 4164 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04334 0.8665 0.9357 -0.0002115 9.494e-05 0.1102 -0.0001594 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005586 -0.005347 -0.01725 0.007097 0.9611 0.9672 0.01321 0.923 0.9357 0.0446 ] Network output: [ 0.8898 0.3996 -0.06474 -0.0007863 0.000353 -0.1178 -0.0005926 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3438 0.001716 -0.08974 0.09444 0.9821 0.9926 0.4011 0.9287 0.9817 0.6586 ] Network output: [ 0.01314 0.8529 0.9724 -0.0002355 0.0001057 0.1474 -0.0001775 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009638 0.003758 0.009627 0.004101 0.9905 0.9936 0.009883 0.9766 0.9869 0.01873 ] Network output: [ -0.02731 0.2943 0.7852 -0.001551 0.0006963 0.9688 -0.001169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3928 0.2732 0.4596 0.1442 0.9837 0.9935 0.3946 0.934 0.9838 0.6561 ] Network output: [ -0.04449 0.3151 1.07 0.0003424 -0.0001537 0.7057 0.000258 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1815 0.1715 0.2067 0.1424 0.9902 0.9941 0.1816 0.9762 0.9876 0.2216 ] Network output: [ -0.03546 0.1592 1.09 0.0006387 -0.0002868 0.8246 0.0004814 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1982 0.1963 0.2115 0.176 0.9854 0.9915 0.1982 0.961 0.9813 0.2155 ] Network output: [ 0.009698 0.9224 -0.003882 2.073e-05 -9.308e-06 1.062 1.562e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09611 Epoch 4165 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06085 0.7718 0.9616 8.7e-07 -3.906e-07 0.1448 6.556e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005319 -0.00541 -0.01842 0.01032 0.9611 0.9673 0.01269 0.9233 0.936 0.04458 ] Network output: [ 1.037 -0.2188 0.1021 0.0006462 -0.0002901 0.04581 0.000487 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.327 -0.02357 -0.1492 0.2145 0.9821 0.9926 0.3822 0.9286 0.9817 0.6584 ] Network output: [ 0.01531 0.8249 0.9806 -0.0001784 8.007e-05 0.1632 -0.0001344 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009072 0.00323 0.008033 0.008185 0.9904 0.9936 0.009306 0.9761 0.9868 0.01782 ] Network output: [ 0.1001 -0.579 1.087 0.000229 -0.0001028 1.293 0.0001726 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3755 0.2528 0.429 0.3328 0.9837 0.9934 0.3772 0.9338 0.9837 0.6478 ] Network output: [ -0.04009 0.2771 1.063 0.0004185 -0.0001879 0.7422 0.0003154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1921 0.181 0.2082 0.1741 0.99 0.9941 0.1923 0.9761 0.9878 0.2245 ] Network output: [ -0.04085 0.2298 1.04 0.0005359 -0.0002406 0.8137 0.0004039 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2111 0.209 0.2101 0.1856 0.9855 0.9916 0.2111 0.9617 0.9815 0.2141 ] Network output: [ 0.01711 0.8915 -0.01828 0.000121 -5.432e-05 1.093 9.118e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1282 Epoch 4166 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04353 0.8659 0.9357 -0.0002109 9.467e-05 0.1105 -0.0001589 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005576 -0.005337 -0.01725 0.007089 0.9611 0.9672 0.01317 0.923 0.9356 0.04447 ] Network output: [ 0.8905 0.3995 -0.06508 -0.0007812 0.0003507 -0.1186 -0.0005887 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3434 0.001605 -0.09052 0.09438 0.9821 0.9926 0.4005 0.9285 0.9816 0.6579 ] Network output: [ 0.01328 0.8523 0.9724 -0.0002348 0.0001054 0.1477 -0.0001769 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009604 0.003739 0.009565 0.004083 0.9905 0.9936 0.009848 0.9765 0.9868 0.01866 ] Network output: [ -0.02688 0.293 0.7858 -0.001542 0.000692 0.9687 -0.001162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3921 0.2727 0.4586 0.144 0.9838 0.9935 0.394 0.9339 0.9837 0.6554 ] Network output: [ -0.0446 0.315 1.07 0.0003406 -0.0001529 0.7055 0.0002567 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1807 0.1708 0.2063 0.1421 0.9902 0.9941 0.1809 0.9761 0.9876 0.2212 ] Network output: [ -0.03562 0.1593 1.09 0.0006356 -0.0002853 0.8247 0.000479 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1975 0.1956 0.2112 0.1756 0.9854 0.9915 0.1976 0.9609 0.9812 0.2152 ] Network output: [ 0.009605 0.9222 -0.00349 2.076e-05 -9.319e-06 1.062 1.564e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09606 Epoch 4167 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06091 0.7715 0.9615 1.081e-07 -4.855e-08 0.1452 8.149e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005311 -0.005398 -0.0184 0.01031 0.9612 0.9673 0.01265 0.9232 0.9359 0.04445 ] Network output: [ 1.036 -0.2184 0.1019 0.000643 -0.0002887 0.0467 0.0004846 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3267 -0.02347 -0.1492 0.2144 0.9821 0.9926 0.3817 0.9284 0.9817 0.6577 ] Network output: [ 0.01543 0.8245 0.9805 -0.0001781 7.996e-05 0.1634 -0.0001342 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009045 0.003223 0.008003 0.008161 0.9904 0.9936 0.009279 0.976 0.9868 0.01777 ] Network output: [ 0.09961 -0.578 1.086 0.0002289 -0.0001028 1.294 0.0001725 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.375 0.2526 0.4285 0.3323 0.9837 0.9934 0.3768 0.9336 0.9837 0.6472 ] Network output: [ -0.04019 0.2775 1.063 0.000416 -0.0001868 0.7418 0.0003135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1914 0.1803 0.2078 0.1737 0.99 0.9941 0.1915 0.976 0.9878 0.2241 ] Network output: [ -0.0409 0.2302 1.04 0.0005329 -0.0002392 0.8133 0.0004016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2104 0.2083 0.2098 0.1851 0.9855 0.9916 0.2104 0.9615 0.9814 0.2137 ] Network output: [ 0.01706 0.8926 -0.01858 0.0001189 -5.338e-05 1.092 8.96e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1281 Epoch 4168 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04371 0.8653 0.9356 -0.0002103 9.44e-05 0.1108 -0.0001585 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005567 -0.005326 -0.01726 0.00708 0.9611 0.9673 0.01313 0.9229 0.9355 0.04434 ] Network output: [ 0.8911 0.3994 -0.06538 -0.0007761 0.0003484 -0.1194 -0.0005849 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.343 0.001496 -0.09129 0.09432 0.9822 0.9926 0.4 0.9283 0.9816 0.6572 ] Network output: [ 0.01342 0.8517 0.9725 -0.0002341 0.0001051 0.148 -0.0001764 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009572 0.003721 0.009504 0.004065 0.9905 0.9936 0.009814 0.9764 0.9868 0.0186 ] Network output: [ -0.02645 0.2917 0.7864 -0.001532 0.0006878 0.9685 -0.001155 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3915 0.2721 0.4576 0.1437 0.9838 0.9935 0.3933 0.9337 0.9837 0.6548 ] Network output: [ -0.04471 0.3149 1.07 0.0003388 -0.0001521 0.7054 0.0002554 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.18 0.1701 0.2058 0.1418 0.9902 0.9941 0.1802 0.9761 0.9875 0.2208 ] Network output: [ -0.03579 0.1593 1.09 0.0006325 -0.000284 0.8247 0.0004767 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1969 0.1949 0.2109 0.1753 0.9854 0.9915 0.1969 0.9608 0.9812 0.2149 ] Network output: [ 0.009514 0.9219 -0.003102 2.081e-05 -9.343e-06 1.062 1.568e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09602 Epoch 4169 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06097 0.7713 0.9613 -6.289e-07 2.823e-07 0.1455 -4.739e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005304 -0.005387 -0.01838 0.01029 0.9612 0.9673 0.01262 0.9231 0.9359 0.04433 ] Network output: [ 1.036 -0.2181 0.1017 0.0006399 -0.0002873 0.04756 0.0004823 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3263 -0.02337 -0.1491 0.2144 0.9821 0.9926 0.3812 0.9283 0.9817 0.6571 ] Network output: [ 0.01555 0.8242 0.9804 -0.0001779 7.984e-05 0.1636 -0.000134 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00902 0.003215 0.007974 0.008138 0.9904 0.9936 0.009252 0.976 0.9867 0.01773 ] Network output: [ 0.09917 -0.577 1.085 0.0002288 -0.0001027 1.294 0.0001724 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3745 0.2523 0.428 0.3318 0.9837 0.9934 0.3763 0.9334 0.9837 0.6465 ] Network output: [ -0.0403 0.2779 1.063 0.0004136 -0.0001857 0.7413 0.0003117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1907 0.1796 0.2074 0.1732 0.99 0.9941 0.1908 0.976 0.9877 0.2237 ] Network output: [ -0.04095 0.2306 1.041 0.00053 -0.0002379 0.8129 0.0003994 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2097 0.2076 0.2094 0.1847 0.9855 0.9915 0.2097 0.9614 0.9814 0.2133 ] Network output: [ 0.01702 0.8937 -0.01888 0.0001169 -5.247e-05 1.092 8.808e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1281 Epoch 4170 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04389 0.8647 0.9355 -0.0002096 9.412e-05 0.1111 -0.000158 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005557 -0.005316 -0.01726 0.007072 0.9612 0.9673 0.0131 0.9228 0.9354 0.04422 ] Network output: [ 0.8917 0.3992 -0.06564 -0.0007711 0.0003462 -0.1201 -0.0005811 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3426 0.001387 -0.09203 0.09427 0.9822 0.9926 0.3994 0.9281 0.9815 0.6565 ] Network output: [ 0.01356 0.8512 0.9725 -0.0002333 0.0001047 0.1483 -0.0001758 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009539 0.003702 0.009444 0.004048 0.9905 0.9936 0.009781 0.9764 0.9867 0.01853 ] Network output: [ -0.02604 0.2905 0.787 -0.001523 0.0006837 0.9684 -0.001148 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3908 0.2716 0.4566 0.1435 0.9838 0.9935 0.3927 0.9335 0.9837 0.6541 ] Network output: [ -0.04482 0.3149 1.071 0.0003371 -0.0001513 0.7053 0.0002541 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1794 0.1695 0.2054 0.1415 0.9902 0.9941 0.1795 0.976 0.9875 0.2204 ] Network output: [ -0.03594 0.1593 1.09 0.0006295 -0.0002826 0.8248 0.0004744 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1962 0.1943 0.2105 0.175 0.9854 0.9915 0.1963 0.9606 0.9811 0.2146 ] Network output: [ 0.009426 0.9217 -0.002719 2.089e-05 -9.379e-06 1.062 1.574e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09597 Epoch 4171 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06103 0.771 0.9611 -1.342e-06 6.027e-07 0.1459 -1.012e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005296 -0.005375 -0.01837 0.01028 0.9612 0.9673 0.01259 0.9231 0.9358 0.04421 ] Network output: [ 1.035 -0.2177 0.1015 0.0006368 -0.0002859 0.04839 0.0004799 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.326 -0.02327 -0.1491 0.2144 0.9822 0.9926 0.3808 0.9281 0.9816 0.6565 ] Network output: [ 0.01567 0.8239 0.9803 -0.0001776 7.972e-05 0.1638 -0.0001338 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008995 0.003208 0.007945 0.008115 0.9904 0.9936 0.009226 0.9759 0.9867 0.01768 ] Network output: [ 0.09873 -0.576 1.085 0.0002286 -0.0001026 1.295 0.0001723 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3741 0.2521 0.4275 0.3313 0.9837 0.9935 0.3758 0.9333 0.9836 0.6459 ] Network output: [ -0.04041 0.2782 1.063 0.0004112 -0.0001846 0.7409 0.0003099 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1899 0.179 0.207 0.1728 0.99 0.9941 0.1901 0.9759 0.9877 0.2233 ] Network output: [ -0.04099 0.2309 1.041 0.0005271 -0.0002366 0.8125 0.0003973 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.209 0.2069 0.209 0.1843 0.9855 0.9915 0.209 0.9613 0.9813 0.213 ] Network output: [ 0.01698 0.8948 -0.01915 0.0001149 -5.159e-05 1.091 8.661e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.128 Epoch 4172 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04406 0.8642 0.9355 -0.000209 9.384e-05 0.1114 -0.0001575 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005548 -0.005306 -0.01726 0.007064 0.9612 0.9673 0.01306 0.9227 0.9354 0.04409 ] Network output: [ 0.8923 0.399 -0.06588 -0.0007662 0.000344 -0.1209 -0.0005774 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3422 0.00128 -0.09276 0.09423 0.9822 0.9926 0.3988 0.9279 0.9815 0.6558 ] Network output: [ 0.01369 0.8507 0.9725 -0.0002326 0.0001044 0.1485 -0.0001753 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009508 0.003685 0.009385 0.004031 0.9905 0.9936 0.009748 0.9763 0.9867 0.01847 ] Network output: [ -0.02563 0.2893 0.7876 -0.001514 0.0006796 0.9683 -0.001141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3902 0.271 0.4557 0.1433 0.9838 0.9935 0.392 0.9333 0.9836 0.6534 ] Network output: [ -0.04492 0.3148 1.071 0.0003354 -0.0001506 0.7052 0.0002528 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1787 0.1688 0.205 0.1412 0.9902 0.9941 0.1788 0.9759 0.9875 0.2201 ] Network output: [ -0.0361 0.1594 1.09 0.0006265 -0.0002813 0.8249 0.0004722 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1956 0.1937 0.2102 0.1747 0.9853 0.9915 0.1956 0.9605 0.981 0.2143 ] Network output: [ 0.009341 0.9214 -0.002341 2.1e-05 -9.427e-06 1.062 1.582e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09591 Epoch 4173 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06109 0.7707 0.9609 -2.034e-06 9.13e-07 0.1462 -1.533e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005288 -0.005364 -0.01835 0.01027 0.9612 0.9673 0.01256 0.923 0.9357 0.04409 ] Network output: [ 1.035 -0.2174 0.1013 0.0006338 -0.0002845 0.0492 0.0004776 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3257 -0.02318 -0.149 0.2144 0.9822 0.9926 0.3803 0.9279 0.9816 0.6559 ] Network output: [ 0.01579 0.8236 0.9801 -0.0001773 7.959e-05 0.164 -0.0001336 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00897 0.0032 0.007916 0.008092 0.9904 0.9936 0.0092 0.9758 0.9867 0.01763 ] Network output: [ 0.09831 -0.5749 1.084 0.0002283 -0.0001025 1.295 0.000172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3736 0.2519 0.4271 0.3308 0.9837 0.9935 0.3754 0.9331 0.9836 0.6452 ] Network output: [ -0.04051 0.2786 1.064 0.0004088 -0.0001835 0.7405 0.0003081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1892 0.1783 0.2066 0.1724 0.99 0.9941 0.1894 0.9758 0.9877 0.2229 ] Network output: [ -0.04104 0.2313 1.041 0.0005243 -0.0002354 0.8121 0.0003951 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2083 0.2062 0.2087 0.1838 0.9855 0.9915 0.2084 0.9612 0.9813 0.2126 ] Network output: [ 0.01693 0.8958 -0.01942 0.000113 -5.074e-05 1.09 8.518e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.128 Epoch 4174 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04423 0.8637 0.9354 -0.0002084 9.355e-05 0.1116 -0.000157 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005538 -0.005296 -0.01726 0.007056 0.9612 0.9673 0.01303 0.9227 0.9353 0.04396 ] Network output: [ 0.8929 0.3989 -0.06609 -0.0007613 0.0003418 -0.1216 -0.0005737 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3418 0.001173 -0.09347 0.09419 0.9822 0.9926 0.3983 0.9277 0.9814 0.6552 ] Network output: [ 0.01382 0.8501 0.9725 -0.0002318 0.0001041 0.1488 -0.0001747 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009477 0.003667 0.009328 0.004014 0.9905 0.9936 0.009716 0.9762 0.9866 0.01841 ] Network output: [ -0.02523 0.2881 0.7881 -0.001505 0.0006756 0.9681 -0.001134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3896 0.2705 0.4548 0.1431 0.9838 0.9935 0.3914 0.9331 0.9836 0.6528 ] Network output: [ -0.04502 0.3147 1.072 0.0003338 -0.0001498 0.7051 0.0002515 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.178 0.1682 0.2046 0.1409 0.9902 0.9941 0.1782 0.9758 0.9874 0.2197 ] Network output: [ -0.03625 0.1594 1.091 0.0006236 -0.00028 0.8249 0.00047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.195 0.1931 0.2099 0.1744 0.9853 0.9915 0.195 0.9604 0.981 0.214 ] Network output: [ 0.009258 0.9212 -0.001969 2.113e-05 -9.484e-06 1.062 1.592e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09586 Epoch 4175 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06114 0.7705 0.9607 -2.704e-06 1.214e-06 0.1465 -2.038e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005281 -0.005353 -0.01833 0.01026 0.9612 0.9673 0.01253 0.9229 0.9356 0.04397 ] Network output: [ 1.034 -0.217 0.1011 0.0006307 -0.0002832 0.04998 0.0004753 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3254 -0.02309 -0.1489 0.2144 0.9822 0.9926 0.3798 0.9277 0.9815 0.6553 ] Network output: [ 0.0159 0.8233 0.98 -0.000177 7.946e-05 0.1641 -0.0001334 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008945 0.003193 0.007888 0.00807 0.9904 0.9936 0.009174 0.9758 0.9866 0.01759 ] Network output: [ 0.09789 -0.5739 1.083 0.0002279 -0.0001023 1.296 0.0001717 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3732 0.2517 0.4266 0.3303 0.9837 0.9935 0.3749 0.9329 0.9835 0.6446 ] Network output: [ -0.04061 0.2789 1.064 0.0004065 -0.0001825 0.7402 0.0003064 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1886 0.1777 0.2063 0.172 0.99 0.9941 0.1887 0.9757 0.9876 0.2225 ] Network output: [ -0.04108 0.2316 1.041 0.0005215 -0.0002341 0.8118 0.000393 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2077 0.2056 0.2083 0.1834 0.9855 0.9915 0.2077 0.9611 0.9812 0.2123 ] Network output: [ 0.01689 0.8968 -0.01967 0.0001112 -4.992e-05 1.089 8.38e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1279 Epoch 4176 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04439 0.8631 0.9354 -0.0002077 9.326e-05 0.1118 -0.0001566 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005529 -0.005286 -0.01726 0.007049 0.9612 0.9673 0.013 0.9226 0.9352 0.04384 ] Network output: [ 0.8935 0.3987 -0.06627 -0.0007564 0.0003396 -0.1224 -0.0005701 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3413 0.001069 -0.09416 0.09415 0.9822 0.9926 0.3977 0.9275 0.9814 0.6545 ] Network output: [ 0.01395 0.8496 0.9725 -0.0002311 0.0001037 0.149 -0.0001741 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009446 0.00365 0.009272 0.003998 0.9905 0.9936 0.009685 0.9762 0.9866 0.01835 ] Network output: [ -0.02484 0.287 0.7886 -0.001496 0.0006717 0.968 -0.001128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.389 0.27 0.4538 0.1428 0.9838 0.9935 0.3908 0.933 0.9835 0.6521 ] Network output: [ -0.04512 0.3147 1.072 0.0003321 -0.0001491 0.705 0.0002503 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1774 0.1676 0.2043 0.1406 0.9902 0.9941 0.1775 0.9757 0.9874 0.2194 ] Network output: [ -0.03639 0.1594 1.091 0.0006207 -0.0002787 0.825 0.0004678 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1944 0.1925 0.2096 0.1741 0.9853 0.9915 0.1944 0.9603 0.9809 0.2137 ] Network output: [ 0.009178 0.9209 -0.001602 2.127e-05 -9.551e-06 1.062 1.603e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0958 Epoch 4177 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0612 0.7703 0.9605 -3.354e-06 1.506e-06 0.1468 -2.527e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005274 -0.005343 -0.01831 0.01025 0.9613 0.9674 0.0125 0.9228 0.9355 0.04385 ] Network output: [ 1.034 -0.2167 0.1008 0.0006277 -0.0002818 0.05074 0.000473 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.325 -0.023 -0.1489 0.2144 0.9822 0.9926 0.3794 0.9275 0.9815 0.6548 ] Network output: [ 0.01601 0.823 0.9799 -0.0001767 7.932e-05 0.1643 -0.0001332 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008921 0.003186 0.007861 0.008048 0.9904 0.9936 0.009149 0.9757 0.9866 0.01754 ] Network output: [ 0.09747 -0.5729 1.082 0.0002274 -0.0001021 1.297 0.0001714 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3727 0.2514 0.4261 0.3298 0.9838 0.9935 0.3745 0.9327 0.9835 0.644 ] Network output: [ -0.04071 0.2792 1.064 0.0004043 -0.0001815 0.7398 0.0003047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1879 0.1771 0.2059 0.1716 0.99 0.9941 0.188 0.9757 0.9876 0.2221 ] Network output: [ -0.04112 0.2319 1.041 0.0005188 -0.0002329 0.8115 0.000391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.207 0.2049 0.208 0.183 0.9855 0.9915 0.207 0.9609 0.9811 0.212 ] Network output: [ 0.01685 0.8979 -0.01991 0.0001094 -4.913e-05 1.089 8.247e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1279 Epoch 4178 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04456 0.8626 0.9353 -0.0002071 9.297e-05 0.1121 -0.0001561 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00552 -0.005276 -0.01726 0.007042 0.9613 0.9673 0.01296 0.9225 0.9351 0.04372 ] Network output: [ 0.894 0.3984 -0.06642 -0.0007517 0.0003374 -0.1231 -0.0005665 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3409 0.0009652 -0.09483 0.09412 0.9822 0.9926 0.3971 0.9274 0.9814 0.6539 ] Network output: [ 0.01408 0.8492 0.9725 -0.0002303 0.0001034 0.1493 -0.0001736 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009417 0.003633 0.009217 0.003983 0.9905 0.9936 0.009654 0.9761 0.9866 0.01829 ] Network output: [ -0.02445 0.2858 0.7892 -0.001488 0.0006679 0.9679 -0.001121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3884 0.2694 0.4529 0.1426 0.9838 0.9935 0.3902 0.9328 0.9835 0.6515 ] Network output: [ -0.04522 0.3146 1.072 0.0003305 -0.0001484 0.7049 0.0002491 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1768 0.167 0.2039 0.1403 0.9902 0.9941 0.1769 0.9757 0.9873 0.219 ] Network output: [ -0.03653 0.1595 1.091 0.0006179 -0.0002774 0.8251 0.0004656 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1938 0.1919 0.2094 0.1738 0.9853 0.9915 0.1938 0.9602 0.9809 0.2134 ] Network output: [ 0.009101 0.9207 -0.00124 2.144e-05 -9.627e-06 1.062 1.616e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09573 Epoch 4179 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06125 0.77 0.9604 -3.984e-06 1.789e-06 0.1471 -3.003e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005266 -0.005332 -0.01829 0.01023 0.9613 0.9674 0.01247 0.9228 0.9354 0.04373 ] Network output: [ 1.033 -0.2164 0.1006 0.0006247 -0.0002804 0.05147 0.0004708 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3247 -0.02292 -0.1488 0.2144 0.9822 0.9927 0.3789 0.9273 0.9815 0.6542 ] Network output: [ 0.01612 0.8228 0.9798 -0.0001764 7.919e-05 0.1644 -0.0001329 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008897 0.003179 0.007834 0.008026 0.9904 0.9936 0.009124 0.9757 0.9865 0.0175 ] Network output: [ 0.09707 -0.5719 1.081 0.0002268 -0.0001018 1.297 0.0001709 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3723 0.2512 0.4257 0.3293 0.9838 0.9935 0.374 0.9325 0.9835 0.6434 ] Network output: [ -0.04082 0.2795 1.064 0.000402 -0.0001805 0.7395 0.000303 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1872 0.1765 0.2056 0.1713 0.99 0.9941 0.1874 0.9756 0.9875 0.2218 ] Network output: [ -0.04117 0.2322 1.041 0.0005161 -0.0002317 0.8111 0.000389 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2064 0.2043 0.2077 0.1827 0.9855 0.9915 0.2064 0.9608 0.9811 0.2116 ] Network output: [ 0.01681 0.8988 -0.02014 0.0001077 -4.835e-05 1.088 8.117e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1278 Epoch 4180 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04471 0.8621 0.9353 -0.0002064 9.268e-05 0.1123 -0.0001556 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00551 -0.005266 -0.01725 0.007035 0.9613 0.9674 0.01293 0.9224 0.935 0.0436 ] Network output: [ 0.8945 0.3982 -0.06655 -0.0007469 0.0003353 -0.1238 -0.0005629 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3405 0.0008632 -0.09549 0.0941 0.9822 0.9926 0.3966 0.9272 0.9813 0.6533 ] Network output: [ 0.0142 0.8487 0.9725 -0.0002296 0.0001031 0.1495 -0.000173 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009387 0.003617 0.009164 0.003967 0.9905 0.9936 0.009623 0.976 0.9865 0.01823 ] Network output: [ -0.02407 0.2847 0.7897 -0.001479 0.0006641 0.9677 -0.001115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3878 0.2689 0.4521 0.1424 0.9838 0.9935 0.3896 0.9326 0.9835 0.6509 ] Network output: [ -0.04531 0.3145 1.073 0.000329 -0.0001477 0.7048 0.0002479 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1761 0.1664 0.2035 0.1401 0.9902 0.9941 0.1763 0.9756 0.9873 0.2187 ] Network output: [ -0.03667 0.1595 1.091 0.0006151 -0.0002761 0.8252 0.0004635 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1932 0.1913 0.2091 0.1735 0.9853 0.9915 0.1933 0.96 0.9808 0.2132 ] Network output: [ 0.009026 0.9204 -0.0008847 2.163e-05 -9.711e-06 1.062 1.63e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09567 Epoch 4181 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0613 0.7698 0.9602 -4.596e-06 2.063e-06 0.1474 -3.464e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005259 -0.005322 -0.01827 0.01022 0.9613 0.9674 0.01245 0.9227 0.9354 0.04362 ] Network output: [ 1.033 -0.216 0.1004 0.0006217 -0.0002791 0.05218 0.0004685 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3244 -0.02284 -0.1488 0.2143 0.9822 0.9927 0.3785 0.9271 0.9814 0.6536 ] Network output: [ 0.01623 0.8225 0.9797 -0.0001761 7.904e-05 0.1646 -0.0001327 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008874 0.003171 0.007807 0.008004 0.9904 0.9936 0.0091 0.9756 0.9865 0.01746 ] Network output: [ 0.09667 -0.5709 1.081 0.0002262 -0.0001016 1.298 0.0001705 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3718 0.251 0.4252 0.3289 0.9838 0.9935 0.3736 0.9324 0.9834 0.6428 ] Network output: [ -0.04091 0.2798 1.064 0.0003999 -0.0001795 0.7392 0.0003013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1866 0.1758 0.2052 0.1709 0.99 0.9941 0.1867 0.9755 0.9875 0.2214 ] Network output: [ -0.04121 0.2325 1.041 0.0005135 -0.0002305 0.8108 0.000387 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2057 0.2037 0.2074 0.1823 0.9855 0.9915 0.2058 0.9607 0.981 0.2113 ] Network output: [ 0.01677 0.8998 -0.02036 0.000106 -4.761e-05 1.087 7.992e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1277 Epoch 4182 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04487 0.8617 0.9352 -0.0002058 9.239e-05 0.1125 -0.0001551 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005501 -0.005257 -0.01725 0.007028 0.9613 0.9674 0.0129 0.9224 0.935 0.04347 ] Network output: [ 0.8951 0.398 -0.06665 -0.0007423 0.0003332 -0.1245 -0.0005594 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3401 0.0007627 -0.09613 0.09408 0.9822 0.9926 0.396 0.927 0.9813 0.6526 ] Network output: [ 0.01432 0.8482 0.9725 -0.0002288 0.0001027 0.1497 -0.0001725 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009359 0.003601 0.009111 0.003952 0.9905 0.9936 0.009594 0.976 0.9865 0.01817 ] Network output: [ -0.02371 0.2836 0.7902 -0.001471 0.0006604 0.9676 -0.001109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3872 0.2684 0.4512 0.1422 0.9838 0.9935 0.389 0.9324 0.9834 0.6503 ] Network output: [ -0.0454 0.3144 1.073 0.0003274 -0.000147 0.7048 0.0002468 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1755 0.1658 0.2032 0.1398 0.9902 0.9941 0.1757 0.9755 0.9872 0.2184 ] Network output: [ -0.0368 0.1595 1.091 0.0006123 -0.0002749 0.8253 0.0004615 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1926 0.1907 0.2088 0.1732 0.9853 0.9915 0.1927 0.9599 0.9808 0.2129 ] Network output: [ 0.008953 0.9202 -0.0005347 2.183e-05 -9.802e-06 1.063 1.645e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0956 Epoch 4183 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06135 0.7696 0.96 -5.191e-06 2.33e-06 0.1476 -3.912e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005252 -0.005311 -0.01825 0.01021 0.9613 0.9674 0.01242 0.9226 0.9353 0.0435 ] Network output: [ 1.033 -0.2157 0.1002 0.0006187 -0.0002778 0.05287 0.0004663 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3241 -0.02276 -0.1487 0.2143 0.9822 0.9927 0.378 0.927 0.9814 0.6531 ] Network output: [ 0.01633 0.8223 0.9796 -0.0001757 7.89e-05 0.1647 -0.0001324 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008851 0.003164 0.007781 0.007982 0.9904 0.9936 0.009076 0.9755 0.9865 0.01742 ] Network output: [ 0.09627 -0.5699 1.08 0.0002255 -0.0001013 1.298 0.00017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3714 0.2508 0.4248 0.3284 0.9838 0.9935 0.3731 0.9322 0.9834 0.6422 ] Network output: [ -0.04101 0.28 1.065 0.0003977 -0.0001785 0.7389 0.0002997 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1859 0.1753 0.2049 0.1705 0.99 0.9941 0.1861 0.9754 0.9875 0.2211 ] Network output: [ -0.04125 0.2328 1.041 0.0005109 -0.0002294 0.8105 0.0003851 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2051 0.2031 0.2071 0.1819 0.9855 0.9915 0.2052 0.9606 0.981 0.211 ] Network output: [ 0.01673 0.9008 -0.02056 0.0001044 -4.689e-05 1.087 7.871e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1276 Epoch 4184 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04502 0.8612 0.9352 -0.0002051 9.209e-05 0.1127 -0.0001546 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005492 -0.005247 -0.01725 0.007022 0.9613 0.9674 0.01287 0.9223 0.9349 0.04336 ] Network output: [ 0.8956 0.3977 -0.06673 -0.0007376 0.0003311 -0.1251 -0.0005559 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3397 0.0006637 -0.09676 0.09406 0.9822 0.9927 0.3955 0.9268 0.9812 0.652 ] Network output: [ 0.01444 0.8478 0.9725 -0.0002281 0.0001024 0.1499 -0.0001719 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00933 0.003585 0.009059 0.003938 0.9905 0.9936 0.009564 0.9759 0.9864 0.01812 ] Network output: [ -0.02334 0.2826 0.7907 -0.001463 0.0006567 0.9675 -0.001102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3866 0.2679 0.4503 0.142 0.9838 0.9935 0.3884 0.9323 0.9834 0.6497 ] Network output: [ -0.04549 0.3143 1.073 0.0003259 -0.0001463 0.7047 0.0002456 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.175 0.1652 0.2028 0.1395 0.9902 0.9941 0.1751 0.9754 0.9872 0.218 ] Network output: [ -0.03693 0.1595 1.091 0.0006096 -0.0002737 0.8254 0.0004594 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1921 0.1902 0.2086 0.173 0.9853 0.9915 0.1921 0.9598 0.9807 0.2127 ] Network output: [ 0.008883 0.92 -0.0001906 2.205e-05 -9.9e-06 1.063 1.662e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09553 Epoch 4185 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0614 0.7695 0.9598 -5.768e-06 2.589e-06 0.1479 -4.347e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005245 -0.005301 -0.01823 0.0102 0.9614 0.9674 0.01239 0.9226 0.9352 0.04339 ] Network output: [ 1.032 -0.2153 0.1 0.0006157 -0.0002764 0.05354 0.000464 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3238 -0.02268 -0.1487 0.2143 0.9822 0.9927 0.3776 0.9268 0.9813 0.6525 ] Network output: [ 0.01644 0.8221 0.9795 -0.0001754 7.875e-05 0.1649 -0.0001322 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008828 0.003157 0.007755 0.007961 0.9904 0.9936 0.009052 0.9755 0.9864 0.01737 ] Network output: [ 0.09588 -0.5689 1.079 0.0002248 -0.0001009 1.299 0.0001694 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.371 0.2505 0.4243 0.3279 0.9838 0.9935 0.3727 0.932 0.9834 0.6416 ] Network output: [ -0.04111 0.2803 1.065 0.0003956 -0.0001776 0.7387 0.0002981 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1853 0.1747 0.2046 0.1702 0.99 0.9941 0.1855 0.9754 0.9874 0.2208 ] Network output: [ -0.04129 0.233 1.041 0.0005084 -0.0002282 0.8102 0.0003831 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2045 0.2025 0.2068 0.1815 0.9855 0.9915 0.2046 0.9605 0.9809 0.2107 ] Network output: [ 0.01669 0.9017 -0.02076 0.0001029 -4.619e-05 1.086 7.753e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1276 Epoch 4186 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04517 0.8608 0.9352 -0.0002045 9.179e-05 0.1129 -0.0001541 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005483 -0.005238 -0.01724 0.007016 0.9613 0.9674 0.01284 0.9222 0.9348 0.04324 ] Network output: [ 0.8961 0.3974 -0.06678 -0.0007331 0.0003291 -0.1258 -0.0005525 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3393 0.0005663 -0.09737 0.09405 0.9822 0.9927 0.3949 0.9266 0.9812 0.6514 ] Network output: [ 0.01456 0.8474 0.9725 -0.0002273 0.0001021 0.1501 -0.0001713 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009303 0.003569 0.009009 0.003923 0.9905 0.9936 0.009535 0.9758 0.9864 0.01806 ] Network output: [ -0.02299 0.2815 0.7912 -0.001455 0.0006531 0.9674 -0.001096 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.386 0.2674 0.4495 0.1418 0.9838 0.9935 0.3878 0.9321 0.9834 0.6491 ] Network output: [ -0.04557 0.3142 1.074 0.0003244 -0.0001456 0.7047 0.0002445 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1744 0.1647 0.2025 0.1393 0.9902 0.9941 0.1745 0.9753 0.9872 0.2177 ] Network output: [ -0.03706 0.1596 1.091 0.0006069 -0.0002725 0.8255 0.0004574 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1915 0.1896 0.2083 0.1727 0.9853 0.9914 0.1916 0.9597 0.9807 0.2124 ] Network output: [ 0.008815 0.9197 0.0001477 2.229e-05 -1.001e-05 1.063 1.68e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09546 Epoch 4187 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06144 0.7693 0.9597 -6.329e-06 2.841e-06 0.1481 -4.77e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005238 -0.005291 -0.01821 0.01019 0.9614 0.9675 0.01236 0.9225 0.9351 0.04327 ] Network output: [ 1.032 -0.215 0.09982 0.0006128 -0.0002751 0.05418 0.0004618 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3234 -0.02261 -0.1486 0.2143 0.9822 0.9927 0.3771 0.9266 0.9813 0.652 ] Network output: [ 0.01654 0.8218 0.9794 -0.0001751 7.86e-05 0.165 -0.0001319 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008805 0.00315 0.007729 0.00794 0.9904 0.9936 0.009029 0.9754 0.9864 0.01733 ] Network output: [ 0.0955 -0.5679 1.078 0.000224 -0.0001006 1.3 0.0001688 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3705 0.2503 0.4239 0.3274 0.9838 0.9935 0.3722 0.9318 0.9833 0.641 ] Network output: [ -0.0412 0.2805 1.065 0.0003935 -0.0001767 0.7384 0.0002966 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1847 0.1741 0.2043 0.1699 0.99 0.9941 0.1849 0.9753 0.9874 0.2205 ] Network output: [ -0.04133 0.2333 1.041 0.0005059 -0.0002271 0.81 0.0003813 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2039 0.2019 0.2065 0.1812 0.9855 0.9915 0.204 0.9603 0.9809 0.2105 ] Network output: [ 0.01665 0.9026 -0.02094 0.0001014 -4.551e-05 1.085 7.639e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1275 Epoch 4188 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04531 0.8603 0.9351 -0.0002038 9.149e-05 0.1131 -0.0001536 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005475 -0.005228 -0.01724 0.007009 0.9614 0.9674 0.01281 0.9221 0.9347 0.04312 ] Network output: [ 0.8966 0.3972 -0.06682 -0.0007285 0.0003271 -0.1264 -0.000549 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3389 0.0004703 -0.09797 0.09404 0.9822 0.9927 0.3944 0.9264 0.9811 0.6508 ] Network output: [ 0.01468 0.847 0.9725 -0.0002266 0.0001017 0.1503 -0.0001708 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009275 0.003554 0.008959 0.003909 0.9905 0.9936 0.009507 0.9758 0.9864 0.01801 ] Network output: [ -0.02264 0.2805 0.7917 -0.001447 0.0006496 0.9672 -0.001091 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3854 0.267 0.4487 0.1416 0.9838 0.9935 0.3872 0.9319 0.9833 0.6485 ] Network output: [ -0.04566 0.3142 1.074 0.0003229 -0.000145 0.7046 0.0002434 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1738 0.1641 0.2022 0.1391 0.9902 0.9941 0.174 0.9753 0.9871 0.2174 ] Network output: [ -0.03718 0.1596 1.092 0.0006042 -0.0002713 0.8257 0.0004554 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.191 0.1891 0.208 0.1724 0.9853 0.9914 0.191 0.9596 0.9806 0.2122 ] Network output: [ 0.008749 0.9195 0.0004802 2.253e-05 -1.012e-05 1.063 1.698e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09538 Epoch 4189 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06149 0.7691 0.9595 -6.874e-06 3.086e-06 0.1484 -5.18e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005231 -0.005281 -0.01819 0.01017 0.9614 0.9675 0.01233 0.9224 0.9351 0.04316 ] Network output: [ 1.031 -0.2146 0.09961 0.0006099 -0.0002738 0.05481 0.0004596 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3231 -0.02254 -0.1486 0.2143 0.9822 0.9927 0.3767 0.9264 0.9813 0.6514 ] Network output: [ 0.01663 0.8216 0.9793 -0.0001747 7.844e-05 0.1651 -0.0001317 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008783 0.003144 0.007704 0.007919 0.9904 0.9936 0.009006 0.9754 0.9864 0.01729 ] Network output: [ 0.09513 -0.5669 1.077 0.0002232 -0.0001002 1.3 0.0001682 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3701 0.2501 0.4234 0.327 0.9838 0.9935 0.3718 0.9317 0.9833 0.6404 ] Network output: [ -0.0413 0.2807 1.065 0.0003915 -0.0001757 0.7382 0.000295 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1841 0.1735 0.204 0.1695 0.99 0.9941 0.1843 0.9752 0.9873 0.2202 ] Network output: [ -0.04137 0.2335 1.042 0.0005034 -0.000226 0.8097 0.0003794 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2033 0.2013 0.2062 0.1808 0.9855 0.9915 0.2034 0.9602 0.9808 0.2102 ] Network output: [ 0.01662 0.9035 -0.02112 9.99e-05 -4.485e-05 1.085 7.529e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1274 Epoch 4190 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04545 0.8599 0.9351 -0.0002031 9.12e-05 0.1133 -0.0001531 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005466 -0.005219 -0.01723 0.007004 0.9614 0.9675 0.01277 0.9221 0.9346 0.043 ] Network output: [ 0.897 0.3969 -0.06683 -0.0007241 0.0003251 -0.1271 -0.0005457 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3385 0.0003759 -0.09855 0.09403 0.9822 0.9927 0.3939 0.9262 0.9811 0.6502 ] Network output: [ 0.01479 0.8466 0.9725 -0.0002258 0.0001014 0.1504 -0.0001702 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009248 0.003539 0.00891 0.003895 0.9905 0.9936 0.009479 0.9757 0.9863 0.01795 ] Network output: [ -0.0223 0.2795 0.7921 -0.001439 0.0006461 0.9671 -0.001085 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3849 0.2665 0.4479 0.1414 0.9838 0.9935 0.3866 0.9317 0.9833 0.6479 ] Network output: [ -0.04574 0.3141 1.074 0.0003215 -0.0001443 0.7046 0.0002423 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1732 0.1636 0.2019 0.1388 0.9901 0.9941 0.1734 0.9752 0.9871 0.2171 ] Network output: [ -0.0373 0.1596 1.092 0.0006016 -0.0002701 0.8258 0.0004534 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1905 0.1886 0.2078 0.1722 0.9853 0.9914 0.1905 0.9595 0.9805 0.2119 ] Network output: [ 0.008686 0.9193 0.0008068 2.28e-05 -1.023e-05 1.063 1.718e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0953 Epoch 4191 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06153 0.769 0.9594 -7.404e-06 3.324e-06 0.1486 -5.58e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005224 -0.005272 -0.01817 0.01016 0.9614 0.9675 0.01231 0.9223 0.935 0.04305 ] Network output: [ 1.031 -0.2143 0.0994 0.000607 -0.0002725 0.05541 0.0004575 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3228 -0.02247 -0.1485 0.2142 0.9822 0.9927 0.3763 0.9262 0.9812 0.6509 ] Network output: [ 0.01673 0.8214 0.9792 -0.0001744 7.829e-05 0.1652 -0.0001314 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008761 0.003137 0.007679 0.007898 0.9904 0.9936 0.008983 0.9753 0.9863 0.01725 ] Network output: [ 0.09476 -0.5659 1.077 0.0002223 -9.981e-05 1.301 0.0001675 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3697 0.2499 0.423 0.3265 0.9838 0.9935 0.3714 0.9315 0.9832 0.6399 ] Network output: [ -0.04139 0.2809 1.066 0.0003895 -0.0001748 0.7379 0.0002935 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1835 0.173 0.2037 0.1692 0.99 0.9941 0.1837 0.9752 0.9873 0.2199 ] Network output: [ -0.04141 0.2337 1.042 0.000501 -0.0002249 0.8095 0.0003776 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2028 0.2007 0.2059 0.1805 0.9855 0.9915 0.2028 0.9601 0.9807 0.2099 ] Network output: [ 0.01658 0.9044 -0.02129 9.848e-05 -4.421e-05 1.084 7.421e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1273 Epoch 4192 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04559 0.8595 0.9351 -0.0002025 9.09e-05 0.1135 -0.0001526 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005457 -0.00521 -0.01723 0.006998 0.9614 0.9675 0.01274 0.922 0.9346 0.04289 ] Network output: [ 0.8975 0.3966 -0.06683 -0.0007196 0.0003231 -0.1277 -0.0005423 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3381 0.000283 -0.09912 0.09403 0.9822 0.9927 0.3933 0.926 0.9811 0.6497 ] Network output: [ 0.0149 0.8462 0.9725 -0.0002251 0.0001011 0.1506 -0.0001696 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009222 0.003525 0.008863 0.003882 0.9905 0.9936 0.009452 0.9756 0.9863 0.0179 ] Network output: [ -0.02197 0.2785 0.7926 -0.001432 0.0006427 0.967 -0.001079 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3843 0.266 0.4471 0.1412 0.9838 0.9935 0.386 0.9315 0.9832 0.6473 ] Network output: [ -0.04582 0.314 1.074 0.00032 -0.0001437 0.7046 0.0002412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1727 0.1631 0.2016 0.1386 0.9901 0.9941 0.1728 0.9751 0.987 0.2169 ] Network output: [ -0.03741 0.1596 1.092 0.000599 -0.0002689 0.8259 0.0004515 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.19 0.1881 0.2076 0.1719 0.9853 0.9914 0.19 0.9593 0.9805 0.2117 ] Network output: [ 0.008624 0.9191 0.001128 2.307e-05 -1.036e-05 1.063 1.738e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09522 Epoch 4193 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06158 0.7688 0.9592 -7.919e-06 3.555e-06 0.1488 -5.968e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005217 -0.005262 -0.01815 0.01015 0.9614 0.9675 0.01228 0.9223 0.9349 0.04294 ] Network output: [ 1.031 -0.2139 0.09918 0.0006042 -0.0002712 0.056 0.0004553 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3225 -0.0224 -0.1485 0.2142 0.9822 0.9927 0.3758 0.926 0.9812 0.6504 ] Network output: [ 0.01682 0.8212 0.9791 -0.000174 7.813e-05 0.1653 -0.0001312 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00874 0.00313 0.007655 0.007878 0.9904 0.9936 0.008961 0.9752 0.9863 0.01721 ] Network output: [ 0.0944 -0.5649 1.076 0.0002214 -9.939e-05 1.301 0.0001669 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3692 0.2496 0.4226 0.3261 0.9838 0.9935 0.3709 0.9313 0.9832 0.6393 ] Network output: [ -0.04148 0.2811 1.066 0.0003875 -0.000174 0.7377 0.000292 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1829 0.1724 0.2034 0.1689 0.99 0.9941 0.1831 0.9751 0.9873 0.2196 ] Network output: [ -0.04144 0.2339 1.042 0.0004986 -0.0002238 0.8092 0.0003758 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2022 0.2002 0.2056 0.1802 0.9855 0.9915 0.2022 0.96 0.9807 0.2096 ] Network output: [ 0.01654 0.9052 -0.02145 9.71e-05 -4.359e-05 1.084 7.317e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1272 Epoch 4194 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04572 0.8591 0.935 -0.0002018 9.06e-05 0.1136 -0.0001521 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005449 -0.005201 -0.01722 0.006992 0.9614 0.9675 0.01271 0.9219 0.9345 0.04278 ] Network output: [ 0.8979 0.3963 -0.06681 -0.0007153 0.0003211 -0.1283 -0.000539 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3377 0.0001917 -0.09967 0.09403 0.9822 0.9927 0.3928 0.9259 0.981 0.6491 ] Network output: [ 0.01501 0.8458 0.9725 -0.0002243 0.0001007 0.1508 -0.0001691 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009196 0.00351 0.008816 0.003869 0.9905 0.9936 0.009425 0.9756 0.9862 0.01785 ] Network output: [ -0.02165 0.2776 0.7931 -0.001424 0.0006394 0.9669 -0.001073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3837 0.2655 0.4463 0.1409 0.9838 0.9935 0.3855 0.9314 0.9832 0.6468 ] Network output: [ -0.04589 0.3139 1.075 0.0003186 -0.000143 0.7046 0.0002401 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1722 0.1626 0.2013 0.1384 0.9901 0.9941 0.1723 0.975 0.987 0.2166 ] Network output: [ -0.03752 0.1596 1.092 0.0005965 -0.0002678 0.826 0.0004495 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1894 0.1876 0.2073 0.1717 0.9853 0.9914 0.1895 0.9592 0.9804 0.2115 ] Network output: [ 0.008565 0.9189 0.001443 2.335e-05 -1.048e-05 1.063 1.76e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09514 Epoch 4195 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06162 0.7687 0.9591 -8.419e-06 3.78e-06 0.149 -6.345e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00521 -0.005252 -0.01813 0.01014 0.9615 0.9675 0.01226 0.9222 0.9348 0.04283 ] Network output: [ 1.03 -0.2136 0.09897 0.0006013 -0.00027 0.05657 0.0004532 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3222 -0.02233 -0.1484 0.2142 0.9822 0.9927 0.3754 0.9258 0.9811 0.6499 ] Network output: [ 0.01691 0.821 0.979 -0.0001737 7.797e-05 0.1654 -0.0001309 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008719 0.003123 0.007631 0.007858 0.9904 0.9936 0.008939 0.9752 0.9863 0.01717 ] Network output: [ 0.09404 -0.5639 1.075 0.0002204 -9.896e-05 1.302 0.0001661 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3688 0.2494 0.4222 0.3256 0.9838 0.9935 0.3705 0.9311 0.9832 0.6388 ] Network output: [ -0.04157 0.2812 1.066 0.0003855 -0.0001731 0.7375 0.0002906 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1823 0.1719 0.2031 0.1686 0.99 0.9941 0.1825 0.975 0.9872 0.2193 ] Network output: [ -0.04148 0.2341 1.042 0.0004963 -0.0002228 0.809 0.000374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2016 0.1996 0.2054 0.1798 0.9855 0.9915 0.2017 0.9599 0.9806 0.2094 ] Network output: [ 0.01651 0.9061 -0.0216 9.576e-05 -4.299e-05 1.083 7.216e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1271 Epoch 4196 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04585 0.8587 0.935 -0.0002011 9.029e-05 0.1138 -0.0001516 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00544 -0.005192 -0.01721 0.006987 0.9615 0.9675 0.01269 0.9219 0.9344 0.04266 ] Network output: [ 0.8984 0.396 -0.06677 -0.0007109 0.0003192 -0.1289 -0.0005358 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3373 0.0001019 -0.1002 0.09404 0.9822 0.9927 0.3923 0.9257 0.981 0.6485 ] Network output: [ 0.01512 0.8454 0.9725 -0.0002236 0.0001004 0.1509 -0.0001685 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009171 0.003496 0.00877 0.003856 0.9905 0.9936 0.009399 0.9755 0.9862 0.0178 ] Network output: [ -0.02133 0.2766 0.7935 -0.001417 0.0006361 0.9668 -0.001068 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3832 0.2651 0.4455 0.1407 0.9838 0.9935 0.3849 0.9312 0.9832 0.6462 ] Network output: [ -0.04597 0.3138 1.075 0.0003172 -0.0001424 0.7046 0.0002391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1716 0.1621 0.201 0.1381 0.9901 0.9941 0.1718 0.9749 0.9869 0.2163 ] Network output: [ -0.03763 0.1596 1.092 0.000594 -0.0002667 0.8262 0.0004476 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1889 0.1871 0.2071 0.1715 0.9853 0.9914 0.189 0.9591 0.9804 0.2113 ] Network output: [ 0.008507 0.9187 0.001752 2.365e-05 -1.062e-05 1.063 1.782e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09506 Epoch 4197 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06166 0.7685 0.9589 -8.906e-06 3.998e-06 0.1492 -6.712e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005203 -0.005243 -0.01811 0.01013 0.9615 0.9675 0.01223 0.9221 0.9347 0.04272 ] Network output: [ 1.03 -0.2133 0.09875 0.0005985 -0.0002687 0.05713 0.000451 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3219 -0.02227 -0.1484 0.2142 0.9822 0.9927 0.375 0.9256 0.9811 0.6493 ] Network output: [ 0.017 0.8209 0.9789 -0.0001733 7.78e-05 0.1655 -0.0001306 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008698 0.003117 0.007607 0.007837 0.9904 0.9936 0.008917 0.9751 0.9862 0.01713 ] Network output: [ 0.09369 -0.5629 1.074 0.0002194 -9.851e-05 1.302 0.0001654 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3684 0.2492 0.4218 0.3251 0.9838 0.9935 0.3701 0.931 0.9831 0.6382 ] Network output: [ -0.04166 0.2814 1.066 0.0003836 -0.0001722 0.7373 0.0002891 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1818 0.1714 0.2029 0.1683 0.99 0.9941 0.1819 0.9749 0.9872 0.219 ] Network output: [ -0.04152 0.2343 1.042 0.0004939 -0.0002218 0.8088 0.0003723 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2011 0.1991 0.2051 0.1795 0.9855 0.9915 0.2011 0.9598 0.9806 0.2091 ] Network output: [ 0.01647 0.9069 -0.02174 9.446e-05 -4.24e-05 1.082 7.119e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.127 Epoch 4198 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04598 0.8583 0.935 -0.0002005 8.999e-05 0.1139 -0.0001511 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005432 -0.005183 -0.01721 0.006982 0.9615 0.9675 0.01266 0.9218 0.9343 0.04255 ] Network output: [ 0.8988 0.3956 -0.06671 -0.0007067 0.0003173 -0.1294 -0.0005326 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3369 1.363e-05 -0.1007 0.09404 0.9822 0.9927 0.3918 0.9255 0.9809 0.648 ] Network output: [ 0.01523 0.8451 0.9725 -0.0002229 0.0001 0.1511 -0.0001679 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009146 0.003482 0.008725 0.003843 0.9905 0.9936 0.009373 0.9754 0.9862 0.01775 ] Network output: [ -0.02102 0.2757 0.794 -0.00141 0.0006328 0.9666 -0.001062 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3826 0.2646 0.4447 0.1405 0.9839 0.9935 0.3844 0.931 0.9831 0.6457 ] Network output: [ -0.04604 0.3137 1.075 0.0003158 -0.0001418 0.7046 0.000238 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1711 0.1616 0.2007 0.1379 0.9901 0.9941 0.1713 0.9749 0.9869 0.216 ] Network output: [ -0.03774 0.1596 1.092 0.0005915 -0.0002655 0.8263 0.0004458 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1884 0.1866 0.2069 0.1712 0.9853 0.9914 0.1885 0.959 0.9803 0.2111 ] Network output: [ 0.008452 0.9185 0.002056 2.395e-05 -1.075e-05 1.063 1.805e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09497 Epoch 4199 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0617 0.7684 0.9587 -9.38e-06 4.211e-06 0.1494 -7.069e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005196 -0.005234 -0.01809 0.01012 0.9615 0.9676 0.0122 0.9221 0.9347 0.04261 ] Network output: [ 1.03 -0.2129 0.09854 0.0005957 -0.0002674 0.05766 0.0004489 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3216 -0.02221 -0.1483 0.2141 0.9822 0.9927 0.3745 0.9255 0.981 0.6488 ] Network output: [ 0.01709 0.8207 0.9788 -0.0001729 7.764e-05 0.1656 -0.0001303 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008677 0.00311 0.007583 0.007817 0.9904 0.9936 0.008895 0.9751 0.9862 0.01709 ] Network output: [ 0.09334 -0.5619 1.074 0.0002184 -9.805e-05 1.303 0.0001646 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3679 0.2489 0.4214 0.3247 0.9838 0.9935 0.3696 0.9308 0.9831 0.6377 ] Network output: [ -0.04175 0.2815 1.066 0.0003817 -0.0001714 0.7371 0.0002877 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1812 0.1708 0.2026 0.168 0.99 0.9941 0.1814 0.9749 0.9872 0.2188 ] Network output: [ -0.04155 0.2345 1.042 0.0004917 -0.0002207 0.8086 0.0003705 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2005 0.1985 0.2049 0.1792 0.9855 0.9915 0.2006 0.9596 0.9805 0.2089 ] Network output: [ 0.01644 0.9077 -0.02188 9.319e-05 -4.184e-05 1.082 7.023e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1269 Epoch 4200 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0461 0.858 0.9349 -0.0001998 8.969e-05 0.1141 -0.0001506 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005423 -0.005174 -0.0172 0.006977 0.9615 0.9675 0.01263 0.9217 0.9342 0.04244 ] Network output: [ 0.8992 0.3953 -0.06663 -0.0007024 0.0003154 -0.13 -0.0005294 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3366 -7.314e-05 -0.1013 0.09405 0.9823 0.9927 0.3913 0.9253 0.9809 0.6474 ] Network output: [ 0.01533 0.8448 0.9724 -0.0002221 9.971e-05 0.1512 -0.0001674 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009121 0.003469 0.008681 0.003831 0.9905 0.9936 0.009347 0.9754 0.9861 0.0177 ] Network output: [ -0.02071 0.2748 0.7944 -0.001402 0.0006296 0.9665 -0.001057 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3821 0.2642 0.444 0.1403 0.9839 0.9935 0.3838 0.9308 0.9831 0.6451 ] Network output: [ -0.04611 0.3136 1.075 0.0003145 -0.0001412 0.7046 0.000237 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1706 0.1611 0.2004 0.1377 0.9901 0.9941 0.1708 0.9748 0.9869 0.2158 ] Network output: [ -0.03784 0.1596 1.092 0.000589 -0.0002644 0.8264 0.0004439 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.188 0.1861 0.2066 0.171 0.9853 0.9914 0.188 0.9589 0.9803 0.2108 ] Network output: [ 0.008398 0.9183 0.002354 2.426e-05 -1.089e-05 1.063 1.829e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09488 Epoch 4201 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06174 0.7683 0.9586 -9.84e-06 4.418e-06 0.1496 -7.416e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00519 -0.005225 -0.01807 0.01011 0.9615 0.9676 0.01218 0.922 0.9346 0.04251 ] Network output: [ 1.029 -0.2126 0.09832 0.0005929 -0.0002662 0.05819 0.0004469 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3213 -0.02214 -0.1483 0.2141 0.9823 0.9927 0.3741 0.9253 0.981 0.6483 ] Network output: [ 0.01718 0.8205 0.9787 -0.0001726 7.747e-05 0.1657 -0.0001301 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008656 0.003103 0.00756 0.007798 0.9904 0.9936 0.008874 0.975 0.9861 0.01706 ] Network output: [ 0.093 -0.561 1.073 0.0002173 -9.757e-05 1.303 0.0001638 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3675 0.2487 0.421 0.3242 0.9838 0.9935 0.3692 0.9306 0.9831 0.6372 ] Network output: [ -0.04183 0.2817 1.067 0.0003799 -0.0001705 0.737 0.0002863 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1807 0.1703 0.2023 0.1677 0.99 0.9941 0.1808 0.9748 0.9871 0.2185 ] Network output: [ -0.04158 0.2346 1.042 0.0004894 -0.0002197 0.8084 0.0003688 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2 0.198 0.2046 0.1789 0.9855 0.9915 0.2 0.9595 0.9805 0.2086 ] Network output: [ 0.0164 0.9085 -0.02201 9.197e-05 -4.129e-05 1.081 6.931e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1267 Epoch 4202 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04622 0.8576 0.9349 -0.0001991 8.939e-05 0.1142 -0.0001501 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005415 -0.005166 -0.01719 0.006972 0.9615 0.9676 0.0126 0.9216 0.9342 0.04233 ] Network output: [ 0.8996 0.395 -0.06654 -0.0006983 0.0003135 -0.1306 -0.0005262 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3362 -0.0001584 -0.1018 0.09407 0.9823 0.9927 0.3907 0.9251 0.9809 0.6469 ] Network output: [ 0.01543 0.8444 0.9724 -0.0002214 9.938e-05 0.1514 -0.0001668 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009097 0.003456 0.008637 0.003819 0.9905 0.9936 0.009322 0.9753 0.9861 0.01765 ] Network output: [ -0.02041 0.2739 0.7948 -0.001395 0.0006265 0.9664 -0.001052 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3816 0.2637 0.4432 0.1402 0.9839 0.9935 0.3833 0.9306 0.983 0.6446 ] Network output: [ -0.04618 0.3135 1.076 0.0003132 -0.0001406 0.7047 0.000236 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1701 0.1606 0.2001 0.1375 0.9901 0.9941 0.1703 0.9747 0.9868 0.2155 ] Network output: [ -0.03793 0.1596 1.092 0.0005866 -0.0002634 0.8266 0.0004421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1875 0.1856 0.2064 0.1708 0.9853 0.9914 0.1875 0.9588 0.9802 0.2106 ] Network output: [ 0.008346 0.9181 0.002647 2.459e-05 -1.104e-05 1.063 1.853e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09479 Epoch 4203 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06177 0.7682 0.9585 -1.029e-05 4.619e-06 0.1498 -7.754e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005183 -0.005216 -0.01805 0.01009 0.9616 0.9676 0.01215 0.9219 0.9345 0.0424 ] Network output: [ 1.029 -0.2122 0.0981 0.0005902 -0.000265 0.05869 0.0004448 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.321 -0.02209 -0.1483 0.2141 0.9823 0.9927 0.3737 0.9251 0.981 0.6478 ] Network output: [ 0.01726 0.8204 0.9786 -0.0001722 7.73e-05 0.1657 -0.0001298 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008636 0.003097 0.007537 0.007778 0.9904 0.9936 0.008853 0.975 0.9861 0.01702 ] Network output: [ 0.09267 -0.56 1.072 0.0002162 -9.707e-05 1.303 0.000163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3671 0.2485 0.4206 0.3238 0.9838 0.9935 0.3688 0.9304 0.983 0.6366 ] Network output: [ -0.04192 0.2818 1.067 0.000378 -0.0001697 0.7368 0.0002849 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1801 0.1698 0.2021 0.1674 0.99 0.9941 0.1803 0.9747 0.9871 0.2182 ] Network output: [ -0.04162 0.2348 1.042 0.0004872 -0.0002187 0.8082 0.0003672 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1995 0.1975 0.2044 0.1786 0.9855 0.9915 0.1995 0.9594 0.9804 0.2084 ] Network output: [ 0.01637 0.9093 -0.02213 9.078e-05 -4.075e-05 1.081 6.841e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1266 Epoch 4204 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04634 0.8573 0.9349 -0.0001984 8.909e-05 0.1144 -0.0001496 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005407 -0.005157 -0.01718 0.006967 0.9615 0.9676 0.01257 0.9216 0.9341 0.04222 ] Network output: [ 0.9 0.3946 -0.06644 -0.0006941 0.0003116 -0.1311 -0.0005231 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3358 -0.0002422 -0.1023 0.09408 0.9823 0.9927 0.3902 0.9249 0.9808 0.6463 ] Network output: [ 0.01553 0.8441 0.9724 -0.0002206 9.905e-05 0.1515 -0.0001663 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009073 0.003442 0.008594 0.003807 0.9905 0.9936 0.009297 0.9752 0.986 0.0176 ] Network output: [ -0.02012 0.2731 0.7953 -0.001389 0.0006234 0.9663 -0.001046 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.381 0.2633 0.4425 0.14 0.9839 0.9935 0.3828 0.9305 0.983 0.644 ] Network output: [ -0.04624 0.3133 1.076 0.0003118 -0.00014 0.7047 0.000235 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1696 0.1601 0.1998 0.1373 0.9901 0.9941 0.1698 0.9746 0.9868 0.2153 ] Network output: [ -0.03803 0.1596 1.092 0.0005842 -0.0002623 0.8267 0.0004403 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.187 0.1851 0.2062 0.1706 0.9853 0.9914 0.187 0.9587 0.9802 0.2104 ] Network output: [ 0.008296 0.9179 0.002934 2.492e-05 -1.119e-05 1.063 1.878e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0947 Epoch 4205 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06181 0.7681 0.9583 -1.072e-05 4.814e-06 0.1499 -8.082e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005177 -0.005207 -0.01803 0.01008 0.9616 0.9676 0.01213 0.9218 0.9344 0.04229 ] Network output: [ 1.029 -0.2119 0.09787 0.0005874 -0.0002637 0.05919 0.0004427 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3207 -0.02203 -0.1482 0.214 0.9823 0.9927 0.3733 0.9249 0.9809 0.6474 ] Network output: [ 0.01734 0.8202 0.9786 -0.0001718 7.713e-05 0.1658 -0.0001295 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008616 0.00309 0.007514 0.007759 0.9904 0.9936 0.008832 0.9749 0.9861 0.01698 ] Network output: [ 0.09234 -0.5591 1.071 0.0002151 -9.656e-05 1.304 0.0001621 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3667 0.2482 0.4202 0.3233 0.9838 0.9935 0.3684 0.9302 0.983 0.6361 ] Network output: [ -0.042 0.2819 1.067 0.0003762 -0.0001689 0.7367 0.0002835 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1796 0.1693 0.2018 0.1671 0.99 0.9941 0.1798 0.9747 0.987 0.218 ] Network output: [ -0.04165 0.2349 1.042 0.000485 -0.0002177 0.808 0.0003655 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1989 0.197 0.2042 0.1783 0.9855 0.9915 0.199 0.9593 0.9804 0.2082 ] Network output: [ 0.01633 0.91 -0.02225 8.962e-05 -4.023e-05 1.08 6.754e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1265 Epoch 4206 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04646 0.8569 0.9349 -0.0001978 8.879e-05 0.1145 -0.000149 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005399 -0.005149 -0.01717 0.006963 0.9616 0.9676 0.01254 0.9215 0.934 0.04211 ] Network output: [ 0.9004 0.3943 -0.06632 -0.00069 0.0003098 -0.1316 -0.00052 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3354 -0.0003245 -0.1028 0.0941 0.9823 0.9927 0.3897 0.9247 0.9808 0.6458 ] Network output: [ 0.01563 0.8438 0.9724 -0.0002199 9.871e-05 0.1516 -0.0001657 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009049 0.00343 0.008552 0.003795 0.9905 0.9936 0.009272 0.9752 0.986 0.01755 ] Network output: [ -0.01984 0.2722 0.7957 -0.001382 0.0006203 0.9662 -0.001041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3805 0.2629 0.4418 0.1398 0.9839 0.9935 0.3822 0.9303 0.983 0.6435 ] Network output: [ -0.04631 0.3132 1.076 0.0003105 -0.0001394 0.7047 0.000234 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1692 0.1597 0.1996 0.1371 0.9901 0.9941 0.1693 0.9746 0.9867 0.2151 ] Network output: [ -0.03812 0.1596 1.092 0.0005819 -0.0002612 0.8268 0.0004385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1865 0.1847 0.206 0.1703 0.9853 0.9914 0.1866 0.9585 0.9801 0.2102 ] Network output: [ 0.008247 0.9177 0.003215 2.526e-05 -1.134e-05 1.063 1.903e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09461 Epoch 4207 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06184 0.768 0.9582 -1.115e-05 5.005e-06 0.1501 -8.401e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00517 -0.005198 -0.01801 0.01007 0.9616 0.9676 0.0121 0.9218 0.9343 0.04219 ] Network output: [ 1.028 -0.2116 0.09765 0.0005847 -0.0002625 0.05966 0.0004407 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3204 -0.02197 -0.1482 0.214 0.9823 0.9927 0.3729 0.9247 0.9809 0.6469 ] Network output: [ 0.01742 0.8201 0.9785 -0.0001714 7.696e-05 0.1659 -0.0001292 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008597 0.003084 0.007491 0.00774 0.9904 0.9936 0.008812 0.9748 0.986 0.01694 ] Network output: [ 0.09202 -0.5581 1.071 0.0002139 -9.605e-05 1.304 0.0001612 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3663 0.248 0.4198 0.3229 0.9839 0.9935 0.3679 0.9301 0.9829 0.6356 ] Network output: [ -0.04209 0.282 1.067 0.0003744 -0.0001681 0.7366 0.0002822 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1791 0.1688 0.2016 0.1668 0.99 0.9941 0.1792 0.9746 0.987 0.2178 ] Network output: [ -0.04168 0.2351 1.042 0.0004829 -0.0002168 0.8079 0.0003639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1984 0.1964 0.2039 0.178 0.9855 0.9915 0.1985 0.9592 0.9803 0.2079 ] Network output: [ 0.0163 0.9108 -0.02236 8.85e-05 -3.973e-05 1.079 6.669e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1264 Epoch 4208 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04657 0.8566 0.9348 -0.0001971 8.849e-05 0.1146 -0.0001485 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005391 -0.00514 -0.01716 0.006958 0.9616 0.9676 0.01251 0.9214 0.9339 0.042 ] Network output: [ 0.9008 0.394 -0.06619 -0.000686 0.000308 -0.1322 -0.000517 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.335 -0.0004054 -0.1032 0.09412 0.9823 0.9927 0.3892 0.9245 0.9807 0.6453 ] Network output: [ 0.01573 0.8435 0.9724 -0.0002191 9.838e-05 0.1517 -0.0001652 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009026 0.003417 0.008511 0.003783 0.9905 0.9936 0.009248 0.9751 0.986 0.01751 ] Network output: [ -0.01956 0.2714 0.7961 -0.001375 0.0006173 0.966 -0.001036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.38 0.2624 0.4411 0.1396 0.9839 0.9935 0.3817 0.9301 0.9829 0.643 ] Network output: [ -0.04637 0.3131 1.076 0.0003093 -0.0001388 0.7048 0.0002331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1687 0.1592 0.1993 0.1369 0.9901 0.9941 0.1688 0.9745 0.9867 0.2148 ] Network output: [ -0.03821 0.1596 1.092 0.0005795 -0.0002602 0.827 0.0004368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1861 0.1842 0.2058 0.1701 0.9853 0.9914 0.1861 0.9584 0.98 0.21 ] Network output: [ 0.0082 0.9175 0.003492 2.56e-05 -1.149e-05 1.063 1.929e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09452 Epoch 4209 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06188 0.7679 0.958 -1.156e-05 5.19e-06 0.1503 -8.712e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005164 -0.005189 -0.01798 0.01006 0.9616 0.9676 0.01208 0.9217 0.9343 0.04209 ] Network output: [ 1.028 -0.2113 0.09742 0.000582 -0.0002613 0.06013 0.0004386 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3201 -0.02192 -0.1481 0.214 0.9823 0.9927 0.3725 0.9245 0.9808 0.6464 ] Network output: [ 0.0175 0.82 0.9784 -0.000171 7.679e-05 0.1659 -0.0001289 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008577 0.003078 0.007469 0.00772 0.9904 0.9936 0.008792 0.9748 0.986 0.01691 ] Network output: [ 0.0917 -0.5572 1.07 0.0002128 -9.552e-05 1.305 0.0001603 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3659 0.2478 0.4194 0.3224 0.9839 0.9935 0.3675 0.9299 0.9829 0.6351 ] Network output: [ -0.04217 0.2821 1.067 0.0003727 -0.0001673 0.7364 0.0002809 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1786 0.1684 0.2014 0.1666 0.99 0.9941 0.1787 0.9745 0.987 0.2175 ] Network output: [ -0.04171 0.2352 1.042 0.0004807 -0.0002158 0.8077 0.0003623 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1979 0.1959 0.2037 0.1777 0.9855 0.9915 0.1979 0.9591 0.9803 0.2077 ] Network output: [ 0.01626 0.9115 -0.02246 8.74e-05 -3.924e-05 1.079 6.587e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1263 Epoch 4210 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04668 0.8563 0.9348 -0.0001964 8.818e-05 0.1147 -0.000148 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005383 -0.005132 -0.01716 0.006954 0.9616 0.9676 0.01249 0.9214 0.9338 0.0419 ] Network output: [ 0.9012 0.3936 -0.06605 -0.000682 0.0003062 -0.1327 -0.000514 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3347 -0.0004848 -0.1037 0.09414 0.9823 0.9927 0.3887 0.9243 0.9807 0.6447 ] Network output: [ 0.01583 0.8432 0.9724 -0.0002184 9.805e-05 0.1519 -0.0001646 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.009003 0.003405 0.008471 0.003772 0.9905 0.9936 0.009225 0.975 0.9859 0.01746 ] Network output: [ -0.01928 0.2706 0.7965 -0.001368 0.0006143 0.9659 -0.001031 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3795 0.262 0.4404 0.1394 0.9839 0.9935 0.3812 0.9299 0.9829 0.6425 ] Network output: [ -0.04643 0.313 1.076 0.000308 -0.0001383 0.7048 0.0002321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1682 0.1588 0.1991 0.1367 0.9901 0.9941 0.1684 0.9744 0.9867 0.2146 ] Network output: [ -0.03829 0.1595 1.092 0.0005772 -0.0002591 0.8271 0.000435 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1856 0.1838 0.2056 0.1699 0.9853 0.9914 0.1857 0.9583 0.98 0.2099 ] Network output: [ 0.008155 0.9173 0.003763 2.595e-05 -1.165e-05 1.063 1.956e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09442 Epoch 4211 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06191 0.7678 0.9579 -1.196e-05 5.37e-06 0.1504 -9.014e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005158 -0.00518 -0.01796 0.01005 0.9616 0.9677 0.01206 0.9216 0.9342 0.04198 ] Network output: [ 1.028 -0.2109 0.09719 0.0005794 -0.0002601 0.06058 0.0004366 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3198 -0.02187 -0.1481 0.214 0.9823 0.9927 0.3721 0.9243 0.9808 0.6459 ] Network output: [ 0.01758 0.8198 0.9783 -0.0001707 7.661e-05 0.166 -0.0001286 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008558 0.003072 0.007447 0.007702 0.9904 0.9936 0.008772 0.9747 0.986 0.01687 ] Network output: [ 0.09138 -0.5562 1.069 0.0002116 -9.497e-05 1.305 0.0001594 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3654 0.2475 0.419 0.322 0.9839 0.9935 0.3671 0.9297 0.9829 0.6346 ] Network output: [ -0.04225 0.2822 1.067 0.000371 -0.0001665 0.7363 0.0002796 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1781 0.1679 0.2011 0.1663 0.99 0.9941 0.1782 0.9744 0.9869 0.2173 ] Network output: [ -0.04174 0.2353 1.043 0.0004786 -0.0002149 0.8076 0.0003607 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1974 0.1954 0.2035 0.1774 0.9855 0.9915 0.1974 0.959 0.9802 0.2075 ] Network output: [ 0.01623 0.9122 -0.02256 8.634e-05 -3.876e-05 1.078 6.507e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1261 Epoch 4212 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04679 0.856 0.9348 -0.0001958 8.788e-05 0.1149 -0.0001475 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005375 -0.005124 -0.01714 0.00695 0.9616 0.9676 0.01246 0.9213 0.9338 0.04179 ] Network output: [ 0.9015 0.3932 -0.06589 -0.000678 0.0003044 -0.1332 -0.000511 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3343 -0.0005629 -0.1041 0.09417 0.9823 0.9927 0.3882 0.9242 0.9806 0.6442 ] Network output: [ 0.01592 0.8429 0.9724 -0.0002177 9.772e-05 0.152 -0.000164 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00898 0.003393 0.008431 0.003761 0.9905 0.9936 0.009201 0.975 0.9859 0.01742 ] Network output: [ -0.01902 0.2698 0.7969 -0.001362 0.0006114 0.9658 -0.001026 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.379 0.2616 0.4397 0.1392 0.9839 0.9935 0.3807 0.9297 0.9829 0.6419 ] Network output: [ -0.04648 0.3129 1.076 0.0003068 -0.0001377 0.7048 0.0002312 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1678 0.1583 0.1988 0.1365 0.9901 0.9941 0.1679 0.9743 0.9866 0.2144 ] Network output: [ -0.03838 0.1595 1.092 0.000575 -0.0002581 0.8273 0.0004333 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1852 0.1833 0.2054 0.1697 0.9853 0.9914 0.1852 0.9582 0.9799 0.2097 ] Network output: [ 0.008111 0.9171 0.004028 2.631e-05 -1.181e-05 1.063 1.983e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09433 Epoch 4213 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06194 0.7678 0.9578 -1.235e-05 5.545e-06 0.1505 -9.308e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005152 -0.005172 -0.01794 0.01004 0.9617 0.9677 0.01203 0.9216 0.9341 0.04188 ] Network output: [ 1.027 -0.2106 0.09696 0.0005767 -0.0002589 0.06102 0.0004346 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3195 -0.02181 -0.1481 0.2139 0.9823 0.9927 0.3717 0.9241 0.9808 0.6455 ] Network output: [ 0.01765 0.8197 0.9782 -0.0001703 7.644e-05 0.1661 -0.0001283 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008539 0.003065 0.007425 0.007683 0.9904 0.9936 0.008752 0.9747 0.9859 0.01684 ] Network output: [ 0.09108 -0.5553 1.068 0.0002103 -9.442e-05 1.306 0.0001585 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.365 0.2473 0.4187 0.3216 0.9839 0.9935 0.3667 0.9295 0.9828 0.6341 ] Network output: [ -0.04233 0.2823 1.068 0.0003693 -0.0001658 0.7362 0.0002783 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1776 0.1674 0.2009 0.166 0.99 0.9941 0.1777 0.9744 0.9869 0.2171 ] Network output: [ -0.04177 0.2354 1.043 0.0004766 -0.0002139 0.8074 0.0003592 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1969 0.195 0.2033 0.1772 0.9855 0.9915 0.1969 0.9588 0.9801 0.2073 ] Network output: [ 0.01619 0.913 -0.02266 8.53e-05 -3.83e-05 1.078 6.429e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.126 Epoch 4214 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04689 0.8557 0.9348 -0.0001951 8.758e-05 0.115 -0.000147 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005367 -0.005115 -0.01713 0.006946 0.9617 0.9677 0.01243 0.9212 0.9337 0.04169 ] Network output: [ 0.9019 0.3929 -0.06573 -0.0006741 0.0003026 -0.1337 -0.000508 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3339 -0.0006395 -0.1046 0.0942 0.9823 0.9927 0.3877 0.924 0.9806 0.6437 ] Network output: [ 0.01601 0.8427 0.9723 -0.0002169 9.739e-05 0.1521 -0.0001635 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008958 0.003381 0.008392 0.00375 0.9905 0.9936 0.009178 0.9749 0.9858 0.01737 ] Network output: [ -0.01876 0.269 0.7973 -0.001355 0.0006085 0.9657 -0.001022 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3785 0.2612 0.439 0.139 0.9839 0.9935 0.3802 0.9296 0.9828 0.6414 ] Network output: [ -0.04654 0.3128 1.077 0.0003055 -0.0001372 0.7049 0.0002303 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1673 0.1579 0.1986 0.1363 0.9901 0.9941 0.1675 0.9743 0.9866 0.2141 ] Network output: [ -0.03846 0.1595 1.092 0.0005727 -0.0002571 0.8274 0.0004316 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1848 0.1829 0.2052 0.1695 0.9853 0.9914 0.1848 0.9581 0.9799 0.2095 ] Network output: [ 0.008068 0.9169 0.004289 2.667e-05 -1.197e-05 1.063 2.01e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09423 Epoch 4215 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06197 0.7677 0.9576 -1.273e-05 5.715e-06 0.1507 -9.594e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005146 -0.005163 -0.01792 0.01003 0.9617 0.9677 0.01201 0.9215 0.934 0.04178 ] Network output: [ 1.027 -0.2103 0.09673 0.0005741 -0.0002577 0.06144 0.0004327 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3192 -0.02176 -0.148 0.2139 0.9823 0.9927 0.3713 0.9239 0.9807 0.645 ] Network output: [ 0.01772 0.8196 0.9781 -0.0001699 7.626e-05 0.1661 -0.000128 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00852 0.003059 0.007404 0.007664 0.9904 0.9936 0.008732 0.9746 0.9859 0.0168 ] Network output: [ 0.09077 -0.5544 1.068 0.0002091 -9.386e-05 1.306 0.0001576 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3646 0.2471 0.4183 0.3211 0.9839 0.9935 0.3663 0.9293 0.9828 0.6336 ] Network output: [ -0.04241 0.2823 1.068 0.0003676 -0.000165 0.7361 0.000277 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1771 0.167 0.2007 0.1658 0.99 0.994 0.1772 0.9743 0.9868 0.2169 ] Network output: [ -0.0418 0.2355 1.043 0.0004745 -0.000213 0.8073 0.0003576 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1964 0.1945 0.2031 0.1769 0.9854 0.9915 0.1965 0.9587 0.9801 0.2071 ] Network output: [ 0.01616 0.9137 -0.02274 8.429e-05 -3.784e-05 1.077 6.353e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1259 Epoch 4216 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04699 0.8554 0.9348 -0.0001944 8.728e-05 0.1151 -0.0001465 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00536 -0.005107 -0.01712 0.006942 0.9617 0.9677 0.01241 0.9212 0.9336 0.04158 ] Network output: [ 0.9022 0.3925 -0.06555 -0.0006703 0.0003009 -0.1341 -0.0005051 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3335 -0.0007148 -0.105 0.09423 0.9823 0.9927 0.3872 0.9238 0.9806 0.6432 ] Network output: [ 0.0161 0.8424 0.9723 -0.0002162 9.706e-05 0.1522 -0.0001629 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008936 0.003369 0.008353 0.003739 0.9905 0.9936 0.009156 0.9749 0.9858 0.01733 ] Network output: [ -0.0185 0.2682 0.7977 -0.001349 0.0006057 0.9656 -0.001017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.378 0.2608 0.4383 0.1388 0.9839 0.9935 0.3797 0.9294 0.9828 0.6409 ] Network output: [ -0.0466 0.3126 1.077 0.0003043 -0.0001366 0.705 0.0002294 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1669 0.1575 0.1983 0.1362 0.9901 0.9941 0.167 0.9742 0.9865 0.2139 ] Network output: [ -0.03853 0.1595 1.092 0.0005705 -0.0002561 0.8276 0.0004299 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1843 0.1825 0.205 0.1693 0.9853 0.9914 0.1844 0.958 0.9798 0.2093 ] Network output: [ 0.008027 0.9168 0.004545 2.704e-05 -1.214e-05 1.063 2.038e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09413 Epoch 4217 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.062 0.7676 0.9575 -1.31e-05 5.881e-06 0.1508 -9.872e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005139 -0.005155 -0.0179 0.01002 0.9617 0.9677 0.01199 0.9214 0.9339 0.04168 ] Network output: [ 1.027 -0.21 0.0965 0.0005715 -0.0002566 0.06186 0.0004307 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3189 -0.02171 -0.148 0.2139 0.9823 0.9927 0.3709 0.9238 0.9807 0.6445 ] Network output: [ 0.01779 0.8195 0.9781 -0.0001695 7.608e-05 0.1662 -0.0001277 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008502 0.003053 0.007382 0.007646 0.9904 0.9936 0.008713 0.9745 0.9859 0.01677 ] Network output: [ 0.09047 -0.5534 1.067 0.0002078 -9.329e-05 1.306 0.0001566 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3642 0.2469 0.4179 0.3207 0.9839 0.9935 0.3659 0.9292 0.9827 0.6331 ] Network output: [ -0.04248 0.2824 1.068 0.0003659 -0.0001643 0.7361 0.0002758 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1766 0.1665 0.2005 0.1655 0.99 0.994 0.1767 0.9742 0.9868 0.2166 ] Network output: [ -0.04183 0.2356 1.043 0.0004725 -0.0002121 0.8072 0.0003561 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1959 0.194 0.2028 0.1766 0.9854 0.9915 0.196 0.9586 0.98 0.2069 ] Network output: [ 0.01613 0.9144 -0.02283 8.331e-05 -3.74e-05 1.077 6.279e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1258 Epoch 4218 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04709 0.8551 0.9348 -0.0001937 8.698e-05 0.1152 -0.000146 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005352 -0.005099 -0.01711 0.006938 0.9617 0.9677 0.01238 0.9211 0.9335 0.04148 ] Network output: [ 0.9026 0.3921 -0.06536 -0.0006664 0.0002992 -0.1346 -0.0005022 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3332 -0.0007887 -0.1054 0.09426 0.9823 0.9927 0.3867 0.9236 0.9805 0.6427 ] Network output: [ 0.01619 0.8422 0.9723 -0.0002155 9.673e-05 0.1523 -0.0001624 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008915 0.003357 0.008315 0.003729 0.9905 0.9936 0.009133 0.9748 0.9858 0.01728 ] Network output: [ -0.01825 0.2675 0.7981 -0.001343 0.0006029 0.9654 -0.001012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3775 0.2604 0.4377 0.1386 0.9839 0.9935 0.3791 0.9292 0.9827 0.6404 ] Network output: [ -0.04665 0.3125 1.077 0.0003031 -0.0001361 0.705 0.0002285 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1665 0.1571 0.1981 0.136 0.9901 0.9941 0.1666 0.9741 0.9865 0.2137 ] Network output: [ -0.03861 0.1594 1.092 0.0005683 -0.0002551 0.8278 0.0004283 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1839 0.1821 0.2048 0.1691 0.9853 0.9914 0.1839 0.9579 0.9798 0.2091 ] Network output: [ 0.007987 0.9166 0.004796 2.741e-05 -1.231e-05 1.063 2.066e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09403 Epoch 4219 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06203 0.7676 0.9574 -1.346e-05 6.042e-06 0.1509 -1.014e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005133 -0.005147 -0.01788 0.01001 0.9617 0.9677 0.01196 0.9214 0.9339 0.04158 ] Network output: [ 1.027 -0.2096 0.09626 0.0005689 -0.0002554 0.06226 0.0004287 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3186 -0.02167 -0.148 0.2138 0.9823 0.9927 0.3705 0.9236 0.9806 0.6441 ] Network output: [ 0.01786 0.8194 0.978 -0.0001691 7.59e-05 0.1662 -0.0001274 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008483 0.003047 0.007361 0.007628 0.9904 0.9936 0.008694 0.9745 0.9858 0.01673 ] Network output: [ 0.09018 -0.5525 1.066 0.0002065 -9.271e-05 1.307 0.0001556 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3638 0.2466 0.4176 0.3203 0.9839 0.9935 0.3655 0.929 0.9827 0.6326 ] Network output: [ -0.04256 0.2824 1.068 0.0003643 -0.0001635 0.736 0.0002745 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1761 0.1661 0.2003 0.1653 0.99 0.994 0.1762 0.9742 0.9868 0.2164 ] Network output: [ -0.04186 0.2357 1.043 0.0004705 -0.0002112 0.807 0.0003546 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1955 0.1935 0.2026 0.1764 0.9854 0.9915 0.1955 0.9585 0.98 0.2067 ] Network output: [ 0.01609 0.915 -0.02291 8.236e-05 -3.697e-05 1.076 6.207e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1256 Epoch 4220 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04719 0.8548 0.9347 -0.0001931 8.668e-05 0.1153 -0.0001455 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005345 -0.005091 -0.0171 0.006934 0.9617 0.9677 0.01235 0.921 0.9334 0.04137 ] Network output: [ 0.9029 0.3918 -0.06516 -0.0006627 0.0002975 -0.1351 -0.0004994 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3328 -0.0008612 -0.1059 0.09429 0.9823 0.9927 0.3863 0.9234 0.9805 0.6422 ] Network output: [ 0.01628 0.8419 0.9723 -0.0002147 9.64e-05 0.1524 -0.0001618 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008893 0.003346 0.008278 0.003718 0.9905 0.9936 0.009111 0.9747 0.9857 0.01724 ] Network output: [ -0.01801 0.2667 0.7985 -0.001337 0.0006001 0.9653 -0.001007 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.377 0.26 0.437 0.1384 0.9839 0.9935 0.3787 0.929 0.9827 0.64 ] Network output: [ -0.0467 0.3124 1.077 0.000302 -0.0001356 0.7051 0.0002276 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.166 0.1567 0.1979 0.1358 0.9901 0.9941 0.1662 0.974 0.9864 0.2135 ] Network output: [ -0.03868 0.1594 1.092 0.0005661 -0.0002541 0.8279 0.0004266 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1835 0.1816 0.2047 0.1689 0.9853 0.9914 0.1835 0.9578 0.9797 0.209 ] Network output: [ 0.007948 0.9164 0.005042 2.779e-05 -1.248e-05 1.063 2.095e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09393 Epoch 4221 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06206 0.7675 0.9572 -1.381e-05 6.199e-06 0.1511 -1.041e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005127 -0.005138 -0.01786 0.009999 0.9617 0.9677 0.01194 0.9213 0.9338 0.04148 ] Network output: [ 1.026 -0.2093 0.09602 0.0005663 -0.0002542 0.06266 0.0004268 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3184 -0.02162 -0.1479 0.2138 0.9823 0.9927 0.3701 0.9234 0.9806 0.6436 ] Network output: [ 0.01793 0.8193 0.9779 -0.0001687 7.572e-05 0.1662 -0.0001271 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008465 0.003041 0.00734 0.007609 0.9904 0.9936 0.008675 0.9744 0.9858 0.0167 ] Network output: [ 0.08989 -0.5516 1.066 0.0002052 -9.213e-05 1.307 0.0001547 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3634 0.2464 0.4172 0.3198 0.9839 0.9935 0.3651 0.9288 0.9827 0.6322 ] Network output: [ -0.04263 0.2825 1.068 0.0003627 -0.0001628 0.7359 0.0002733 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1756 0.1656 0.2001 0.165 0.99 0.994 0.1758 0.9741 0.9867 0.2162 ] Network output: [ -0.04188 0.2358 1.043 0.0004685 -0.0002103 0.8069 0.0003531 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.195 0.1931 0.2024 0.1761 0.9854 0.9915 0.195 0.9584 0.9799 0.2065 ] Network output: [ 0.01606 0.9157 -0.02298 8.143e-05 -3.655e-05 1.075 6.136e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1255 Epoch 4222 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04728 0.8546 0.9347 -0.0001924 8.638e-05 0.1154 -0.000145 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005337 -0.005083 -0.01709 0.00693 0.9617 0.9677 0.01233 0.9209 0.9334 0.04127 ] Network output: [ 0.9032 0.3914 -0.06495 -0.0006589 0.0002958 -0.1355 -0.0004966 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3324 -0.0009324 -0.1063 0.09433 0.9823 0.9927 0.3858 0.9232 0.9804 0.6417 ] Network output: [ 0.01636 0.8417 0.9723 -0.000214 9.607e-05 0.1524 -0.0001613 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008872 0.003335 0.008241 0.003708 0.9905 0.9936 0.009089 0.9747 0.9857 0.0172 ] Network output: [ -0.01777 0.266 0.7989 -0.001331 0.0005974 0.9652 -0.001003 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3765 0.2596 0.4364 0.1382 0.9839 0.9935 0.3782 0.9288 0.9827 0.6395 ] Network output: [ -0.04675 0.3123 1.077 0.0003008 -0.000135 0.7052 0.0002267 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1656 0.1563 0.1976 0.1356 0.9901 0.9941 0.1657 0.974 0.9864 0.2133 ] Network output: [ -0.03875 0.1594 1.092 0.000564 -0.0002532 0.8281 0.000425 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1831 0.1812 0.2045 0.1687 0.9853 0.9914 0.1831 0.9577 0.9797 0.2088 ] Network output: [ 0.00791 0.9162 0.005283 2.817e-05 -1.265e-05 1.063 2.123e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09383 Epoch 4223 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06208 0.7675 0.9571 -1.415e-05 6.352e-06 0.1512 -1.066e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005122 -0.00513 -0.01783 0.009988 0.9618 0.9678 0.01192 0.9212 0.9337 0.04138 ] Network output: [ 1.026 -0.209 0.09578 0.0005638 -0.0002531 0.06304 0.0004249 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3181 -0.02157 -0.1479 0.2138 0.9823 0.9927 0.3697 0.9232 0.9805 0.6432 ] Network output: [ 0.018 0.8192 0.9778 -0.0001682 7.553e-05 0.1663 -0.0001268 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008447 0.003035 0.007319 0.007591 0.9904 0.9936 0.008656 0.9744 0.9857 0.01666 ] Network output: [ 0.08961 -0.5507 1.065 0.0002039 -9.154e-05 1.308 0.0001537 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.363 0.2462 0.4169 0.3194 0.9839 0.9935 0.3647 0.9286 0.9826 0.6317 ] Network output: [ -0.04271 0.2825 1.069 0.0003611 -0.0001621 0.7359 0.0002721 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1752 0.1652 0.1999 0.1648 0.99 0.994 0.1753 0.974 0.9867 0.216 ] Network output: [ -0.04191 0.2359 1.043 0.0004666 -0.0002095 0.8068 0.0003516 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1945 0.1926 0.2023 0.1759 0.9854 0.9915 0.1946 0.9583 0.9799 0.2063 ] Network output: [ 0.01602 0.9164 -0.02305 8.052e-05 -3.615e-05 1.075 6.068e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1253 Epoch 4224 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04738 0.8543 0.9347 -0.0001917 8.608e-05 0.1155 -0.0001445 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00533 -0.005075 -0.01708 0.006927 0.9618 0.9677 0.0123 0.9209 0.9333 0.04117 ] Network output: [ 0.9035 0.391 -0.06474 -0.0006552 0.0002941 -0.136 -0.0004938 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3321 -0.001002 -0.1067 0.09436 0.9823 0.9927 0.3853 0.923 0.9804 0.6412 ] Network output: [ 0.01645 0.8415 0.9722 -0.0002133 9.575e-05 0.1525 -0.0001607 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008852 0.003324 0.008205 0.003698 0.9905 0.9936 0.009068 0.9746 0.9856 0.01716 ] Network output: [ -0.01753 0.2653 0.7993 -0.001325 0.0005947 0.9651 -0.0009984 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.376 0.2592 0.4357 0.1381 0.9839 0.9935 0.3777 0.9287 0.9826 0.639 ] Network output: [ -0.04679 0.3121 1.077 0.0002996 -0.0001345 0.7052 0.0002258 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1652 0.1559 0.1974 0.1355 0.9901 0.9941 0.1653 0.9739 0.9864 0.2131 ] Network output: [ -0.03881 0.1593 1.092 0.0005618 -0.0002522 0.8282 0.0004234 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1827 0.1808 0.2043 0.1686 0.9853 0.9914 0.1827 0.9576 0.9796 0.2086 ] Network output: [ 0.007874 0.9161 0.005519 2.856e-05 -1.282e-05 1.063 2.152e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09372 Epoch 4225 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06211 0.7674 0.957 -1.448e-05 6.5e-06 0.1513 -1.091e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005116 -0.005122 -0.01781 0.009978 0.9618 0.9678 0.0119 0.9212 0.9336 0.04128 ] Network output: [ 1.026 -0.2087 0.09553 0.0005612 -0.0002519 0.06341 0.0004229 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3178 -0.02153 -0.1479 0.2137 0.9823 0.9927 0.3693 0.923 0.9805 0.6428 ] Network output: [ 0.01806 0.8191 0.9777 -0.0001678 7.535e-05 0.1663 -0.0001265 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00843 0.003029 0.007299 0.007574 0.9904 0.9936 0.008638 0.9743 0.9857 0.01663 ] Network output: [ 0.08933 -0.5498 1.064 0.0002026 -9.094e-05 1.308 0.0001527 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3626 0.2459 0.4165 0.319 0.9839 0.9935 0.3642 0.9284 0.9826 0.6312 ] Network output: [ -0.04278 0.2825 1.069 0.0003595 -0.0001614 0.7358 0.0002709 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1747 0.1647 0.1997 0.1646 0.99 0.994 0.1748 0.9739 0.9866 0.2158 ] Network output: [ -0.04193 0.2359 1.043 0.0004647 -0.0002086 0.8067 0.0003502 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1941 0.1921 0.2021 0.1756 0.9854 0.9915 0.1941 0.9582 0.9798 0.2061 ] Network output: [ 0.01599 0.917 -0.02312 7.963e-05 -3.575e-05 1.074 6.001e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1252 Epoch 4226 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04747 0.854 0.9347 -0.0001911 8.578e-05 0.1155 -0.000144 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005323 -0.005067 -0.01706 0.006923 0.9618 0.9678 0.01228 0.9208 0.9332 0.04107 ] Network output: [ 0.9038 0.3906 -0.06451 -0.0006515 0.0002925 -0.1364 -0.000491 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3317 -0.001071 -0.107 0.0944 0.9823 0.9927 0.3848 0.9228 0.9803 0.6408 ] Network output: [ 0.01653 0.8412 0.9722 -0.0002125 9.542e-05 0.1526 -0.0001602 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008831 0.003313 0.00817 0.003688 0.9905 0.9936 0.009047 0.9745 0.9856 0.01712 ] Network output: [ -0.0173 0.2646 0.7997 -0.001319 0.0005921 0.9649 -0.0009939 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3755 0.2588 0.4351 0.1379 0.9839 0.9935 0.3772 0.9285 0.9826 0.6385 ] Network output: [ -0.04684 0.312 1.078 0.0002985 -0.000134 0.7053 0.000225 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1648 0.1555 0.1972 0.1353 0.9901 0.9941 0.1649 0.9738 0.9863 0.2129 ] Network output: [ -0.03888 0.1593 1.092 0.0005597 -0.0002513 0.8284 0.0004218 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1823 0.1804 0.2041 0.1684 0.9853 0.9914 0.1823 0.9574 0.9796 0.2085 ] Network output: [ 0.007838 0.9159 0.005751 2.895e-05 -1.3e-05 1.063 2.182e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09362 Epoch 4227 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06213 0.7674 0.9569 -1.48e-05 6.645e-06 0.1514 -1.115e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00511 -0.005114 -0.01779 0.009968 0.9618 0.9678 0.01187 0.9211 0.9336 0.04118 ] Network output: [ 1.026 -0.2084 0.09529 0.0005587 -0.0002508 0.06377 0.000421 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3175 -0.02149 -0.1479 0.2137 0.9823 0.9927 0.3689 0.9228 0.9805 0.6423 ] Network output: [ 0.01813 0.8191 0.9777 -0.0001674 7.516e-05 0.1663 -0.0001262 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008412 0.003023 0.007279 0.007556 0.9904 0.9936 0.00862 0.9742 0.9857 0.0166 ] Network output: [ 0.08905 -0.5489 1.063 0.0002012 -9.033e-05 1.308 0.0001516 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3622 0.2457 0.4162 0.3186 0.9839 0.9935 0.3638 0.9283 0.9826 0.6308 ] Network output: [ -0.04285 0.2825 1.069 0.0003579 -0.0001607 0.7358 0.0002697 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1742 0.1643 0.1995 0.1643 0.99 0.994 0.1744 0.9739 0.9866 0.2157 ] Network output: [ -0.04196 0.236 1.043 0.0004628 -0.0002078 0.8066 0.0003488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1936 0.1917 0.2019 0.1754 0.9854 0.9915 0.1936 0.9581 0.9798 0.2059 ] Network output: [ 0.01595 0.9177 -0.02318 7.877e-05 -3.536e-05 1.074 5.936e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1251 Epoch 4228 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04755 0.8538 0.9347 -0.0001904 8.548e-05 0.1156 -0.0001435 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005315 -0.005059 -0.01705 0.00692 0.9618 0.9678 0.01225 0.9207 0.9331 0.04097 ] Network output: [ 0.9041 0.3902 -0.06428 -0.0006479 0.0002909 -0.1368 -0.0004883 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3314 -0.001138 -0.1074 0.09444 0.9823 0.9927 0.3843 0.9226 0.9803 0.6403 ] Network output: [ 0.01661 0.841 0.9722 -0.0002118 9.509e-05 0.1527 -0.0001596 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008811 0.003302 0.008135 0.003678 0.9905 0.9936 0.009026 0.9745 0.9856 0.01707 ] Network output: [ -0.01708 0.2639 0.8001 -0.001313 0.0005895 0.9648 -0.0009895 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.375 0.2584 0.4345 0.1377 0.9839 0.9935 0.3767 0.9283 0.9825 0.6381 ] Network output: [ -0.04688 0.3119 1.078 0.0002974 -0.0001335 0.7054 0.0002241 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1644 0.1551 0.197 0.1351 0.9901 0.994 0.1645 0.9737 0.9863 0.2127 ] Network output: [ -0.03894 0.1593 1.092 0.0005576 -0.0002503 0.8286 0.0004203 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1819 0.18 0.204 0.1682 0.9853 0.9914 0.1819 0.9573 0.9795 0.2083 ] Network output: [ 0.007804 0.9157 0.005979 2.934e-05 -1.317e-05 1.063 2.211e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09351 Epoch 4229 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06216 0.7674 0.9567 -1.511e-05 6.785e-06 0.1515 -1.139e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005104 -0.005106 -0.01777 0.009958 0.9618 0.9678 0.01185 0.921 0.9335 0.04109 ] Network output: [ 1.026 -0.2081 0.09504 0.0005562 -0.0002497 0.06412 0.0004192 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3172 -0.02144 -0.1478 0.2137 0.9823 0.9927 0.3685 0.9226 0.9804 0.6419 ] Network output: [ 0.01819 0.819 0.9776 -0.000167 7.498e-05 0.1664 -0.0001259 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008395 0.003018 0.007259 0.007538 0.9904 0.9936 0.008602 0.9742 0.9856 0.01656 ] Network output: [ 0.08878 -0.548 1.063 0.0001999 -8.972e-05 1.309 0.0001506 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3618 0.2455 0.4158 0.3181 0.9839 0.9935 0.3634 0.9281 0.9825 0.6303 ] Network output: [ -0.04292 0.2825 1.069 0.0003564 -0.00016 0.7357 0.0002686 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1738 0.1639 0.1993 0.1641 0.99 0.994 0.1739 0.9738 0.9866 0.2155 ] Network output: [ -0.04198 0.236 1.043 0.0004609 -0.0002069 0.8065 0.0003474 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1932 0.1912 0.2017 0.1751 0.9854 0.9915 0.1932 0.958 0.9797 0.2058 ] Network output: [ 0.01592 0.9183 -0.02323 7.792e-05 -3.498e-05 1.073 5.872e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1249 Epoch 4230 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04764 0.8536 0.9347 -0.0001897 8.518e-05 0.1157 -0.000143 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005308 -0.005052 -0.01704 0.006916 0.9618 0.9678 0.01223 0.9207 0.933 0.04087 ] Network output: [ 0.9044 0.3898 -0.06403 -0.0006443 0.0002893 -0.1372 -0.0004856 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.331 -0.001204 -0.1078 0.09448 0.9823 0.9927 0.3839 0.9224 0.9803 0.6398 ] Network output: [ 0.01669 0.8408 0.9722 -0.0002111 9.477e-05 0.1527 -0.0001591 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008791 0.003292 0.0081 0.003669 0.9905 0.9936 0.009005 0.9744 0.9855 0.01703 ] Network output: [ -0.01686 0.2632 0.8005 -0.001307 0.0005869 0.9647 -0.0009852 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3746 0.2581 0.4339 0.1375 0.984 0.9936 0.3762 0.9281 0.9825 0.6376 ] Network output: [ -0.04693 0.3117 1.078 0.0002963 -0.000133 0.7055 0.0002233 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.164 0.1547 0.1968 0.135 0.9901 0.994 0.1641 0.9737 0.9862 0.2126 ] Network output: [ -0.039 0.1592 1.092 0.0005556 -0.0002494 0.8287 0.0004187 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1815 0.1797 0.2038 0.168 0.9853 0.9914 0.1815 0.9572 0.9795 0.2082 ] Network output: [ 0.007771 0.9156 0.006202 2.974e-05 -1.335e-05 1.063 2.241e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09341 Epoch 4231 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06218 0.7674 0.9566 -1.542e-05 6.922e-06 0.1516 -1.162e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005099 -0.005099 -0.01775 0.009948 0.9619 0.9678 0.01183 0.921 0.9334 0.04099 ] Network output: [ 1.025 -0.2078 0.09479 0.0005537 -0.0002486 0.06446 0.0004173 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.317 -0.0214 -0.1478 0.2136 0.9823 0.9927 0.3681 0.9224 0.9804 0.6415 ] Network output: [ 0.01825 0.8189 0.9775 -0.0001666 7.479e-05 0.1664 -0.0001255 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008377 0.003012 0.007239 0.007521 0.9904 0.9936 0.008584 0.9741 0.9856 0.01653 ] Network output: [ 0.08851 -0.5471 1.062 0.0001985 -8.91e-05 1.309 0.0001496 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3614 0.2453 0.4155 0.3177 0.9839 0.9935 0.363 0.9279 0.9825 0.6299 ] Network output: [ -0.04299 0.2825 1.069 0.0003549 -0.0001593 0.7357 0.0002674 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1733 0.1635 0.1991 0.1639 0.99 0.994 0.1735 0.9737 0.9865 0.2153 ] Network output: [ -0.042 0.2361 1.043 0.0004591 -0.0002061 0.8064 0.000346 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1927 0.1908 0.2015 0.1749 0.9854 0.9915 0.1927 0.9579 0.9797 0.2056 ] Network output: [ 0.01588 0.9189 -0.02329 7.71e-05 -3.461e-05 1.073 5.81e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1248 Epoch 4232 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04772 0.8533 0.9347 -0.0001891 8.488e-05 0.1158 -0.0001425 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005301 -0.005044 -0.01702 0.006913 0.9619 0.9678 0.0122 0.9206 0.933 0.04077 ] Network output: [ 0.9047 0.3895 -0.06378 -0.0006408 0.0002877 -0.1376 -0.0004829 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3306 -0.001269 -0.1082 0.09453 0.9824 0.9927 0.3834 0.9222 0.9802 0.6394 ] Network output: [ 0.01677 0.8406 0.9722 -0.0002104 9.444e-05 0.1528 -0.0001585 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008771 0.003282 0.008066 0.003659 0.9905 0.9936 0.008984 0.9743 0.9855 0.017 ] Network output: [ -0.01665 0.2626 0.8009 -0.001302 0.0005843 0.9646 -0.0009809 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3741 0.2577 0.4333 0.1373 0.984 0.9936 0.3758 0.9279 0.9825 0.6371 ] Network output: [ -0.04697 0.3116 1.078 0.0002952 -0.0001325 0.7056 0.0002225 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1636 0.1543 0.1966 0.1348 0.9901 0.994 0.1637 0.9736 0.9862 0.2124 ] Network output: [ -0.03905 0.1592 1.092 0.0005536 -0.0002485 0.8289 0.0004172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1811 0.1793 0.2036 0.1678 0.9853 0.9914 0.1811 0.9571 0.9794 0.208 ] Network output: [ 0.007738 0.9154 0.006421 3.013e-05 -1.353e-05 1.063 2.271e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0933 Epoch 4233 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0622 0.7673 0.9565 -1.572e-05 7.055e-06 0.1517 -1.184e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005093 -0.005091 -0.01773 0.009938 0.9619 0.9678 0.01181 0.9209 0.9333 0.04089 ] Network output: [ 1.025 -0.2075 0.09454 0.0005512 -0.0002475 0.0648 0.0004154 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3167 -0.02136 -0.1478 0.2136 0.9824 0.9927 0.3678 0.9222 0.9803 0.641 ] Network output: [ 0.01831 0.8189 0.9775 -0.0001662 7.46e-05 0.1664 -0.0001252 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00836 0.003006 0.007219 0.007503 0.9904 0.9936 0.008566 0.9741 0.9856 0.0165 ] Network output: [ 0.08825 -0.5463 1.061 0.0001971 -8.848e-05 1.309 0.0001485 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.361 0.245 0.4151 0.3173 0.9839 0.9935 0.3626 0.9277 0.9824 0.6294 ] Network output: [ -0.04306 0.2825 1.069 0.0003534 -0.0001586 0.7357 0.0002663 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1729 0.1631 0.199 0.1637 0.99 0.994 0.173 0.9736 0.9865 0.2151 ] Network output: [ -0.04203 0.2361 1.043 0.0004572 -0.0002053 0.8063 0.0003446 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1923 0.1904 0.2013 0.1747 0.9854 0.9915 0.1923 0.9577 0.9796 0.2054 ] Network output: [ 0.01585 0.9195 -0.02334 7.629e-05 -3.425e-05 1.072 5.749e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1246 Epoch 4234 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0478 0.8531 0.9347 -0.0001884 8.459e-05 0.1159 -0.000142 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005294 -0.005036 -0.01701 0.00691 0.9619 0.9678 0.01218 0.9205 0.9329 0.04067 ] Network output: [ 0.905 0.3891 -0.06353 -0.0006372 0.0002861 -0.138 -0.0004802 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3303 -0.001333 -0.1085 0.09457 0.9824 0.9927 0.3829 0.922 0.9802 0.6389 ] Network output: [ 0.01685 0.8404 0.9721 -0.0002096 9.412e-05 0.1529 -0.000158 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008752 0.003271 0.008033 0.00365 0.9905 0.9936 0.008964 0.9743 0.9854 0.01696 ] Network output: [ -0.01644 0.2619 0.8012 -0.001296 0.0005818 0.9644 -0.0009767 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3736 0.2573 0.4327 0.1371 0.984 0.9936 0.3753 0.9277 0.9824 0.6367 ] Network output: [ -0.04701 0.3114 1.078 0.0002941 -0.000132 0.7057 0.0002217 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1632 0.154 0.1964 0.1347 0.9901 0.994 0.1633 0.9735 0.9862 0.2122 ] Network output: [ -0.03911 0.1591 1.092 0.0005515 -0.0002476 0.8291 0.0004157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1807 0.1789 0.2035 0.1677 0.9853 0.9914 0.1807 0.957 0.9794 0.2078 ] Network output: [ 0.007707 0.9153 0.006635 3.054e-05 -1.371e-05 1.063 2.301e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09319 Epoch 4235 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06222 0.7673 0.9564 -1.6e-05 7.185e-06 0.1518 -1.206e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005087 -0.005083 -0.0177 0.009928 0.9619 0.9679 0.01179 0.9208 0.9332 0.0408 ] Network output: [ 1.025 -0.2071 0.09428 0.0005488 -0.0002464 0.06512 0.0004136 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3164 -0.02132 -0.1478 0.2136 0.9824 0.9927 0.3674 0.922 0.9803 0.6406 ] Network output: [ 0.01837 0.8188 0.9774 -0.0001657 7.441e-05 0.1664 -0.0001249 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008343 0.003001 0.007199 0.007486 0.9904 0.9936 0.008548 0.974 0.9855 0.01647 ] Network output: [ 0.08799 -0.5454 1.061 0.0001957 -8.786e-05 1.31 0.0001475 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3606 0.2448 0.4148 0.3169 0.984 0.9936 0.3622 0.9275 0.9824 0.629 ] Network output: [ -0.04313 0.2825 1.07 0.0003519 -0.000158 0.7357 0.0002652 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1725 0.1627 0.1988 0.1635 0.99 0.994 0.1726 0.9736 0.9864 0.2149 ] Network output: [ -0.04205 0.2361 1.044 0.0004554 -0.0002045 0.8063 0.0003432 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1918 0.1899 0.2012 0.1744 0.9854 0.9915 0.1919 0.9576 0.9795 0.2053 ] Network output: [ 0.01581 0.9202 -0.02339 7.55e-05 -3.39e-05 1.072 5.69e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1245 Epoch 4236 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04788 0.8529 0.9347 -0.0001878 8.429e-05 0.1159 -0.0001415 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005287 -0.005029 -0.017 0.006907 0.9619 0.9678 0.01215 0.9205 0.9328 0.04058 ] Network output: [ 0.9052 0.3887 -0.06327 -0.0006338 0.0002845 -0.1384 -0.0004776 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3299 -0.001396 -0.1089 0.09462 0.9824 0.9927 0.3824 0.9218 0.9801 0.6385 ] Network output: [ 0.01693 0.8403 0.9721 -0.0002089 9.38e-05 0.1529 -0.0001575 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008733 0.003261 0.008 0.003641 0.9905 0.9936 0.008944 0.9742 0.9854 0.01692 ] Network output: [ -0.01623 0.2613 0.8016 -0.001291 0.0005794 0.9643 -0.0009726 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3732 0.2569 0.4321 0.137 0.984 0.9936 0.3748 0.9275 0.9824 0.6362 ] Network output: [ -0.04704 0.3113 1.078 0.0002931 -0.0001316 0.7058 0.0002209 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1628 0.1536 0.1962 0.1345 0.9901 0.994 0.163 0.9734 0.9861 0.212 ] Network output: [ -0.03916 0.1591 1.092 0.0005495 -0.0002467 0.8292 0.0004142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1803 0.1785 0.2033 0.1675 0.9853 0.9914 0.1804 0.9569 0.9793 0.2077 ] Network output: [ 0.007676 0.9151 0.006846 3.094e-05 -1.389e-05 1.063 2.332e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09308 Epoch 4237 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06225 0.7673 0.9563 -1.628e-05 7.311e-06 0.1519 -1.227e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005082 -0.005076 -0.01768 0.009918 0.9619 0.9679 0.01177 0.9208 0.9332 0.04071 ] Network output: [ 1.025 -0.2068 0.09402 0.0005463 -0.0002453 0.06544 0.0004117 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3161 -0.02128 -0.1478 0.2135 0.9824 0.9927 0.367 0.9218 0.9802 0.6402 ] Network output: [ 0.01842 0.8187 0.9773 -0.0001653 7.422e-05 0.1664 -0.0001246 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008327 0.002995 0.00718 0.007469 0.9904 0.9936 0.008531 0.974 0.9855 0.01643 ] Network output: [ 0.08773 -0.5445 1.06 0.0001943 -8.723e-05 1.31 0.0001464 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3602 0.2446 0.4145 0.3165 0.984 0.9936 0.3618 0.9273 0.9824 0.6285 ] Network output: [ -0.0432 0.2825 1.07 0.0003504 -0.0001573 0.7357 0.0002641 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.172 0.1623 0.1986 0.1633 0.99 0.994 0.1722 0.9735 0.9864 0.2148 ] Network output: [ -0.04207 0.2362 1.044 0.0004537 -0.0002037 0.8062 0.0003419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1914 0.1895 0.201 0.1742 0.9854 0.9915 0.1914 0.9575 0.9795 0.2051 ] Network output: [ 0.01577 0.9208 -0.02343 7.473e-05 -3.355e-05 1.071 5.632e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1243 Epoch 4238 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04796 0.8527 0.9347 -0.0001871 8.399e-05 0.116 -0.000141 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00528 -0.005021 -0.01698 0.006904 0.9619 0.9679 0.01213 0.9204 0.9327 0.04048 ] Network output: [ 0.9055 0.3883 -0.063 -0.0006303 0.000283 -0.1388 -0.000475 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3296 -0.001457 -0.1092 0.09466 0.9824 0.9927 0.382 0.9216 0.9801 0.638 ] Network output: [ 0.017 0.8401 0.9721 -0.0002082 9.347e-05 0.153 -0.0001569 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008714 0.003252 0.007968 0.003632 0.9905 0.9936 0.008925 0.9742 0.9854 0.01688 ] Network output: [ -0.01603 0.2607 0.802 -0.001285 0.0005769 0.9642 -0.0009685 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3727 0.2566 0.4315 0.1368 0.984 0.9936 0.3744 0.9274 0.9823 0.6358 ] Network output: [ -0.04708 0.3112 1.078 0.000292 -0.0001311 0.7059 0.0002201 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1625 0.1532 0.196 0.1344 0.9901 0.994 0.1626 0.9734 0.9861 0.2118 ] Network output: [ -0.03921 0.159 1.092 0.0005476 -0.0002458 0.8294 0.0004127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.18 0.1781 0.2032 0.1673 0.9853 0.9914 0.18 0.9568 0.9792 0.2076 ] Network output: [ 0.007647 0.915 0.007052 3.134e-05 -1.407e-05 1.063 2.362e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09297 Epoch 4239 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06227 0.7673 0.9561 -1.656e-05 7.434e-06 0.152 -1.248e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005077 -0.005068 -0.01766 0.009908 0.9619 0.9679 0.01174 0.9207 0.9331 0.04061 ] Network output: [ 1.025 -0.2065 0.09376 0.0005439 -0.0002442 0.06575 0.0004099 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3159 -0.02125 -0.1477 0.2135 0.9824 0.9927 0.3666 0.9216 0.9802 0.6398 ] Network output: [ 0.01848 0.8187 0.9772 -0.0001649 7.403e-05 0.1664 -0.0001243 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00831 0.002989 0.007161 0.007452 0.9904 0.9936 0.008514 0.9739 0.9855 0.0164 ] Network output: [ 0.08748 -0.5437 1.059 0.0001929 -8.659e-05 1.31 0.0001454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3599 0.2444 0.4141 0.316 0.984 0.9936 0.3615 0.9271 0.9823 0.6281 ] Network output: [ -0.04326 0.2824 1.07 0.000349 -0.0001567 0.7357 0.000263 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1716 0.1619 0.1984 0.1631 0.99 0.994 0.1718 0.9734 0.9864 0.2146 ] Network output: [ -0.04209 0.2362 1.044 0.0004519 -0.0002029 0.8061 0.0003406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.191 0.1891 0.2008 0.174 0.9854 0.9915 0.191 0.9574 0.9794 0.2049 ] Network output: [ 0.01574 0.9214 -0.02347 7.397e-05 -3.321e-05 1.071 5.575e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1242 Epoch 4240 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04804 0.8525 0.9346 -0.0001864 8.37e-05 0.1161 -0.0001405 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005273 -0.005014 -0.01697 0.006901 0.9619 0.9679 0.01211 0.9203 0.9326 0.04038 ] Network output: [ 0.9057 0.3879 -0.06272 -0.0006269 0.0002814 -0.1392 -0.0004725 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3292 -0.001517 -0.1095 0.09471 0.9824 0.9927 0.3815 0.9214 0.98 0.6376 ] Network output: [ 0.01708 0.8399 0.9721 -0.0002075 9.315e-05 0.153 -0.0001564 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008695 0.003242 0.007936 0.003623 0.9905 0.9936 0.008905 0.9741 0.9853 0.01684 ] Network output: [ -0.01584 0.2601 0.8024 -0.00128 0.0005745 0.964 -0.0009644 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3723 0.2562 0.4309 0.1366 0.984 0.9936 0.3739 0.9272 0.9823 0.6353 ] Network output: [ -0.04712 0.311 1.078 0.000291 -0.0001306 0.706 0.0002193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1621 0.1529 0.1958 0.1342 0.9901 0.994 0.1622 0.9733 0.986 0.2117 ] Network output: [ -0.03926 0.1589 1.092 0.0005456 -0.0002449 0.8296 0.0004112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1796 0.1778 0.203 0.1672 0.9853 0.9915 0.1796 0.9567 0.9792 0.2074 ] Network output: [ 0.007618 0.9148 0.007255 3.175e-05 -1.425e-05 1.063 2.393e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09286 Epoch 4241 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06228 0.7673 0.956 -1.682e-05 7.553e-06 0.152 -1.268e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005071 -0.00506 -0.01764 0.009898 0.962 0.9679 0.01172 0.9206 0.933 0.04052 ] Network output: [ 1.024 -0.2062 0.0935 0.0005415 -0.0002431 0.06605 0.0004081 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3156 -0.02121 -0.1477 0.2135 0.9824 0.9927 0.3663 0.9214 0.9802 0.6394 ] Network output: [ 0.01853 0.8186 0.9772 -0.0001645 7.384e-05 0.1664 -0.0001239 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008294 0.002984 0.007142 0.007435 0.9904 0.9936 0.008497 0.9738 0.9854 0.01637 ] Network output: [ 0.08724 -0.5428 1.059 0.0001915 -8.596e-05 1.31 0.0001443 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3595 0.2441 0.4138 0.3156 0.984 0.9936 0.3611 0.927 0.9823 0.6277 ] Network output: [ -0.04333 0.2824 1.07 0.0003476 -0.000156 0.7357 0.0002619 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1712 0.1615 0.1983 0.1629 0.9899 0.994 0.1713 0.9734 0.9863 0.2145 ] Network output: [ -0.04211 0.2362 1.044 0.0004501 -0.0002021 0.8061 0.0003392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1906 0.1887 0.2007 0.1738 0.9854 0.9915 0.1906 0.9573 0.9794 0.2048 ] Network output: [ 0.0157 0.9219 -0.02351 7.324e-05 -3.288e-05 1.07 5.519e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.124 Epoch 4242 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04811 0.8523 0.9346 -0.0001858 8.34e-05 0.1161 -0.00014 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005267 -0.005006 -0.01695 0.006898 0.962 0.9679 0.01208 0.9203 0.9326 0.04029 ] Network output: [ 0.906 0.3875 -0.06244 -0.0006235 0.0002799 -0.1395 -0.0004699 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3289 -0.001576 -0.1099 0.09476 0.9824 0.9927 0.3811 0.9213 0.98 0.6371 ] Network output: [ 0.01715 0.8397 0.972 -0.0002068 9.283e-05 0.1531 -0.0001558 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008676 0.003232 0.007904 0.003614 0.9905 0.9936 0.008886 0.974 0.9853 0.0168 ] Network output: [ -0.01565 0.2595 0.8027 -0.001274 0.0005721 0.9639 -0.0009604 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3718 0.2559 0.4304 0.1364 0.984 0.9936 0.3734 0.927 0.9823 0.6349 ] Network output: [ -0.04715 0.3109 1.079 0.00029 -0.0001302 0.7061 0.0002185 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1617 0.1525 0.1956 0.1341 0.9901 0.994 0.1618 0.9732 0.986 0.2115 ] Network output: [ -0.0393 0.1589 1.092 0.0005437 -0.0002441 0.8297 0.0004097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1792 0.1774 0.2029 0.167 0.9853 0.9915 0.1793 0.9566 0.9791 0.2073 ] Network output: [ 0.00759 0.9147 0.007453 3.216e-05 -1.444e-05 1.063 2.424e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09275 Epoch 4243 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0623 0.7673 0.9559 -1.708e-05 7.67e-06 0.1521 -1.287e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005066 -0.005053 -0.01762 0.009888 0.962 0.9679 0.0117 0.9206 0.9329 0.04042 ] Network output: [ 1.024 -0.2059 0.09324 0.0005391 -0.000242 0.06634 0.0004063 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3153 -0.02117 -0.1477 0.2134 0.9824 0.9927 0.3659 0.9213 0.9801 0.639 ] Network output: [ 0.01859 0.8186 0.9771 -0.000164 7.364e-05 0.1664 -0.0001236 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008278 0.002978 0.007123 0.007418 0.9904 0.9936 0.00848 0.9738 0.9854 0.01634 ] Network output: [ 0.08699 -0.542 1.058 0.00019 -8.532e-05 1.311 0.0001432 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3591 0.2439 0.4135 0.3152 0.984 0.9936 0.3607 0.9268 0.9822 0.6272 ] Network output: [ -0.04339 0.2824 1.07 0.0003462 -0.0001554 0.7357 0.0002609 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1708 0.1611 0.1981 0.1627 0.9899 0.994 0.1709 0.9733 0.9863 0.2143 ] Network output: [ -0.04213 0.2362 1.044 0.0004484 -0.0002013 0.806 0.0003379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1901 0.1883 0.2005 0.1735 0.9854 0.9915 0.1902 0.9572 0.9793 0.2046 ] Network output: [ 0.01566 0.9225 -0.02355 7.251e-05 -3.255e-05 1.07 5.465e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1239 Epoch 4244 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04818 0.8521 0.9346 -0.0001851 8.311e-05 0.1162 -0.0001395 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00526 -0.004999 -0.01694 0.006896 0.962 0.9679 0.01206 0.9202 0.9325 0.04019 ] Network output: [ 0.9062 0.3871 -0.06215 -0.0006202 0.0002784 -0.1399 -0.0004674 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3285 -0.001634 -0.1102 0.09481 0.9824 0.9927 0.3806 0.9211 0.9799 0.6367 ] Network output: [ 0.01722 0.8396 0.972 -0.0002061 9.251e-05 0.1531 -0.0001553 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008658 0.003223 0.007873 0.003606 0.9905 0.9936 0.008867 0.974 0.9852 0.01677 ] Network output: [ -0.01546 0.2589 0.8031 -0.001269 0.0005697 0.9638 -0.0009564 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3714 0.2555 0.4298 0.1362 0.984 0.9936 0.373 0.9268 0.9822 0.6345 ] Network output: [ -0.04718 0.3107 1.079 0.000289 -0.0001297 0.7062 0.0002178 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1614 0.1522 0.1954 0.134 0.9901 0.994 0.1615 0.9731 0.9859 0.2114 ] Network output: [ -0.03934 0.1588 1.092 0.0005418 -0.0002432 0.8299 0.0004083 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1789 0.1771 0.2027 0.1669 0.9853 0.9915 0.1789 0.9565 0.9791 0.2071 ] Network output: [ 0.007563 0.9145 0.007648 3.257e-05 -1.462e-05 1.063 2.454e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09264 Epoch 4245 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06232 0.7673 0.9558 -1.734e-05 7.783e-06 0.1522 -1.307e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00506 -0.005046 -0.01759 0.009879 0.962 0.9679 0.01168 0.9205 0.9328 0.04033 ] Network output: [ 1.024 -0.2056 0.09297 0.0005368 -0.000241 0.06662 0.0004045 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3151 -0.02114 -0.1477 0.2134 0.9824 0.9928 0.3655 0.9211 0.9801 0.6386 ] Network output: [ 0.01864 0.8186 0.977 -0.0001636 7.345e-05 0.1664 -0.0001233 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008262 0.002973 0.007104 0.007402 0.9904 0.9936 0.008463 0.9737 0.9853 0.01631 ] Network output: [ 0.08675 -0.5411 1.057 0.0001886 -8.467e-05 1.311 0.0001421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3587 0.2437 0.4131 0.3148 0.984 0.9936 0.3603 0.9266 0.9822 0.6268 ] Network output: [ -0.04345 0.2823 1.07 0.0003448 -0.0001548 0.7357 0.0002598 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1704 0.1607 0.198 0.1625 0.9899 0.994 0.1705 0.9732 0.9862 0.2141 ] Network output: [ -0.04214 0.2362 1.044 0.0004467 -0.0002005 0.806 0.0003367 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1897 0.1878 0.2004 0.1733 0.9854 0.9915 0.1897 0.9571 0.9793 0.2045 ] Network output: [ 0.01563 0.9231 -0.02358 7.18e-05 -3.223e-05 1.07 5.411e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1237 Epoch 4246 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04825 0.8519 0.9346 -0.0001845 8.281e-05 0.1162 -0.000139 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005253 -0.004992 -0.01692 0.006893 0.962 0.9679 0.01204 0.9201 0.9324 0.0401 ] Network output: [ 0.9065 0.3867 -0.06186 -0.0006169 0.0002769 -0.1402 -0.0004649 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3282 -0.001691 -0.1105 0.09487 0.9824 0.9927 0.3802 0.9209 0.9799 0.6362 ] Network output: [ 0.01729 0.8394 0.972 -0.0002053 9.219e-05 0.1532 -0.0001548 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00864 0.003214 0.007843 0.003597 0.9905 0.9936 0.008848 0.9739 0.9852 0.01673 ] Network output: [ -0.01528 0.2583 0.8035 -0.001264 0.0005674 0.9637 -0.0009525 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3709 0.2552 0.4293 0.1361 0.984 0.9936 0.3725 0.9266 0.9822 0.634 ] Network output: [ -0.04721 0.3105 1.079 0.000288 -0.0001293 0.7063 0.000217 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.161 0.1518 0.1953 0.1338 0.9901 0.994 0.1611 0.973 0.9859 0.2112 ] Network output: [ -0.03939 0.1587 1.092 0.0005399 -0.0002424 0.8301 0.0004069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1785 0.1767 0.2026 0.1667 0.9853 0.9915 0.1785 0.9564 0.979 0.207 ] Network output: [ 0.007537 0.9144 0.007839 3.298e-05 -1.481e-05 1.063 2.486e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09252 Epoch 4247 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06234 0.7673 0.9557 -1.758e-05 7.893e-06 0.1522 -1.325e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005055 -0.005038 -0.01757 0.009869 0.962 0.968 0.01166 0.9204 0.9328 0.04024 ] Network output: [ 1.024 -0.2053 0.0927 0.0005344 -0.0002399 0.0669 0.0004027 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3148 -0.0211 -0.1477 0.2133 0.9824 0.9928 0.3651 0.9209 0.98 0.6382 ] Network output: [ 0.01869 0.8185 0.977 -0.0001632 7.326e-05 0.1664 -0.000123 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008246 0.002968 0.007086 0.007385 0.9904 0.9936 0.008447 0.9737 0.9853 0.01628 ] Network output: [ 0.08652 -0.5403 1.057 0.0001872 -8.403e-05 1.311 0.0001411 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3583 0.2435 0.4128 0.3144 0.984 0.9936 0.3599 0.9264 0.9822 0.6264 ] Network output: [ -0.04352 0.2822 1.07 0.0003434 -0.0001542 0.7357 0.0002588 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.17 0.1603 0.1978 0.1623 0.9899 0.994 0.1701 0.9731 0.9862 0.214 ] Network output: [ -0.04216 0.2362 1.044 0.000445 -0.0001998 0.8059 0.0003354 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1893 0.1874 0.2002 0.1731 0.9854 0.9915 0.1893 0.957 0.9792 0.2043 ] Network output: [ 0.01559 0.9237 -0.02361 7.111e-05 -3.192e-05 1.069 5.359e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1236 Epoch 4248 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04832 0.8517 0.9346 -0.0001838 8.252e-05 0.1163 -0.0001385 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005247 -0.004984 -0.01691 0.00689 0.962 0.968 0.01201 0.9201 0.9323 0.04 ] Network output: [ 0.9067 0.3863 -0.06156 -0.0006136 0.0002755 -0.1406 -0.0004624 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3279 -0.001746 -0.1108 0.09492 0.9824 0.9927 0.3797 0.9207 0.9799 0.6358 ] Network output: [ 0.01736 0.8393 0.972 -0.0002046 9.187e-05 0.1532 -0.0001542 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008622 0.003205 0.007812 0.003589 0.9905 0.9936 0.008829 0.9738 0.9852 0.01669 ] Network output: [ -0.0151 0.2577 0.8038 -0.001259 0.0005651 0.9635 -0.0009486 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3705 0.2548 0.4287 0.1359 0.984 0.9936 0.3721 0.9264 0.9821 0.6336 ] Network output: [ -0.04724 0.3104 1.079 0.000287 -0.0001288 0.7064 0.0002163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1606 0.1515 0.1951 0.1337 0.9901 0.994 0.1608 0.973 0.9859 0.211 ] Network output: [ -0.03942 0.1587 1.092 0.000538 -0.0002415 0.8303 0.0004055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1782 0.1764 0.2024 0.1665 0.9853 0.9915 0.1782 0.9563 0.979 0.2069 ] Network output: [ 0.007511 0.9143 0.008027 3.339e-05 -1.499e-05 1.063 2.517e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09241 Epoch 4249 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06235 0.7673 0.9556 -1.782e-05 8.001e-06 0.1523 -1.343e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00505 -0.005031 -0.01755 0.009859 0.962 0.968 0.01164 0.9204 0.9327 0.04015 ] Network output: [ 1.024 -0.205 0.09243 0.0005321 -0.0002389 0.06717 0.000401 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3145 -0.02107 -0.1476 0.2133 0.9824 0.9928 0.3648 0.9207 0.98 0.6378 ] Network output: [ 0.01874 0.8185 0.9769 -0.0001627 7.306e-05 0.1664 -0.0001226 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00823 0.002962 0.007068 0.007369 0.9904 0.9936 0.00843 0.9736 0.9853 0.01625 ] Network output: [ 0.08628 -0.5394 1.056 0.0001857 -8.338e-05 1.312 0.00014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3579 0.2432 0.4125 0.314 0.984 0.9936 0.3595 0.9262 0.9821 0.626 ] Network output: [ -0.04358 0.2822 1.071 0.000342 -0.0001536 0.7357 0.0002578 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1696 0.1599 0.1977 0.1621 0.9899 0.994 0.1697 0.9731 0.9862 0.2138 ] Network output: [ -0.04218 0.2362 1.044 0.0004434 -0.000199 0.8059 0.0003341 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1889 0.187 0.2001 0.1729 0.9854 0.9915 0.1889 0.9569 0.9792 0.2042 ] Network output: [ 0.01555 0.9242 -0.02364 7.042e-05 -3.162e-05 1.069 5.307e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1234 Epoch 4250 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04839 0.8515 0.9346 -0.0001832 8.222e-05 0.1163 -0.000138 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00524 -0.004977 -0.01689 0.006888 0.962 0.968 0.01199 0.92 0.9322 0.03991 ] Network output: [ 0.9069 0.3859 -0.06126 -0.0006103 0.000274 -0.1409 -0.00046 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3275 -0.001801 -0.1111 0.09497 0.9824 0.9928 0.3792 0.9205 0.9798 0.6354 ] Network output: [ 0.01743 0.8391 0.9719 -0.0002039 9.155e-05 0.1532 -0.0001537 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008604 0.003196 0.007783 0.003581 0.9905 0.9936 0.008811 0.9738 0.9851 0.01666 ] Network output: [ -0.01492 0.2572 0.8042 -0.001254 0.0005628 0.9634 -0.0009448 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.37 0.2545 0.4282 0.1357 0.984 0.9936 0.3716 0.9262 0.9821 0.6332 ] Network output: [ -0.04727 0.3102 1.079 0.000286 -0.0001284 0.7065 0.0002155 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1603 0.1512 0.1949 0.1336 0.9901 0.994 0.1604 0.9729 0.9858 0.2109 ] Network output: [ -0.03946 0.1586 1.092 0.0005362 -0.0002407 0.8304 0.0004041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1778 0.176 0.2023 0.1664 0.9853 0.9915 0.1778 0.9562 0.9789 0.2067 ] Network output: [ 0.007486 0.9141 0.008211 3.381e-05 -1.518e-05 1.063 2.548e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09229 Epoch 4251 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06237 0.7673 0.9555 -1.805e-05 8.105e-06 0.1524 -1.361e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005045 -0.005024 -0.01753 0.00985 0.9621 0.968 0.01162 0.9203 0.9326 0.04006 ] Network output: [ 1.024 -0.2048 0.09216 0.0005297 -0.0002378 0.06743 0.0003992 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3143 -0.02104 -0.1476 0.2133 0.9824 0.9928 0.3644 0.9205 0.9799 0.6374 ] Network output: [ 0.01879 0.8185 0.9769 -0.0001623 7.287e-05 0.1664 -0.0001223 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008214 0.002957 0.007049 0.007352 0.9904 0.9936 0.008414 0.9736 0.9852 0.01622 ] Network output: [ 0.08605 -0.5386 1.055 0.0001843 -8.273e-05 1.312 0.0001389 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3575 0.243 0.4122 0.3136 0.984 0.9936 0.3591 0.926 0.9821 0.6256 ] Network output: [ -0.04364 0.2821 1.071 0.0003407 -0.000153 0.7357 0.0002568 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1692 0.1596 0.1975 0.1619 0.9899 0.994 0.1693 0.973 0.9861 0.2137 ] Network output: [ -0.04219 0.2362 1.044 0.0004417 -0.0001983 0.8058 0.0003329 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1885 0.1866 0.1999 0.1727 0.9854 0.9915 0.1885 0.9568 0.9791 0.204 ] Network output: [ 0.01551 0.9248 -0.02366 6.975e-05 -3.131e-05 1.068 5.257e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1232 Epoch 4252 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04845 0.8514 0.9346 -0.0001825 8.193e-05 0.1164 -0.0001375 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005234 -0.00497 -0.01688 0.006885 0.9621 0.968 0.01197 0.9199 0.9322 0.03982 ] Network output: [ 0.9071 0.3855 -0.06095 -0.0006071 0.0002726 -0.1413 -0.0004576 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3272 -0.001854 -0.1114 0.09503 0.9824 0.9928 0.3788 0.9203 0.9798 0.635 ] Network output: [ 0.0175 0.839 0.9719 -0.0002032 9.123e-05 0.1533 -0.0001532 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008586 0.003187 0.007753 0.003572 0.9905 0.9936 0.008793 0.9737 0.9851 0.01662 ] Network output: [ -0.01475 0.2566 0.8045 -0.001249 0.0005606 0.9633 -0.000941 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3696 0.2541 0.4277 0.1355 0.984 0.9936 0.3712 0.9261 0.9821 0.6328 ] Network output: [ -0.0473 0.3101 1.079 0.0002851 -0.000128 0.7067 0.0002148 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1599 0.1508 0.1947 0.1334 0.9901 0.994 0.1601 0.9728 0.9858 0.2107 ] Network output: [ -0.0395 0.1585 1.092 0.0005343 -0.0002399 0.8306 0.0004027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1775 0.1757 0.2022 0.1662 0.9853 0.9915 0.1775 0.956 0.9789 0.2066 ] Network output: [ 0.007462 0.914 0.008392 3.422e-05 -1.536e-05 1.063 2.579e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09218 Epoch 4253 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06238 0.7674 0.9554 -1.828e-05 8.207e-06 0.1524 -1.378e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00504 -0.005017 -0.0175 0.00984 0.9621 0.968 0.0116 0.9202 0.9325 0.03997 ] Network output: [ 1.024 -0.2045 0.09188 0.0005274 -0.0002368 0.06769 0.0003975 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.314 -0.021 -0.1476 0.2132 0.9824 0.9928 0.3641 0.9203 0.9799 0.6371 ] Network output: [ 0.01884 0.8184 0.9768 -0.0001619 7.267e-05 0.1664 -0.000122 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008199 0.002952 0.007031 0.007336 0.9904 0.9936 0.008398 0.9735 0.9852 0.01619 ] Network output: [ 0.08583 -0.5378 1.055 0.0001828 -8.208e-05 1.312 0.0001378 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3572 0.2428 0.4119 0.3132 0.984 0.9936 0.3587 0.9258 0.982 0.6252 ] Network output: [ -0.0437 0.2821 1.071 0.0003394 -0.0001524 0.7358 0.0002558 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1688 0.1592 0.1974 0.1617 0.9899 0.994 0.1689 0.9729 0.9861 0.2136 ] Network output: [ -0.04221 0.2361 1.044 0.0004401 -0.0001976 0.8058 0.0003316 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1881 0.1862 0.1998 0.1725 0.9854 0.9915 0.1881 0.9567 0.9791 0.2039 ] Network output: [ 0.01547 0.9254 -0.02369 6.909e-05 -3.102e-05 1.068 5.207e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1231 Epoch 4254 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04851 0.8512 0.9346 -0.0001819 8.164e-05 0.1164 -0.0001371 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005227 -0.004963 -0.01686 0.006883 0.9621 0.968 0.01194 0.9199 0.9321 0.03972 ] Network output: [ 0.9073 0.3851 -0.06064 -0.0006039 0.0002711 -0.1416 -0.0004552 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3269 -0.001907 -0.1117 0.09508 0.9824 0.9928 0.3784 0.9201 0.9797 0.6346 ] Network output: [ 0.01756 0.8389 0.9719 -0.0002025 9.092e-05 0.1533 -0.0001526 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008569 0.003178 0.007725 0.003564 0.9905 0.9936 0.008774 0.9737 0.985 0.01659 ] Network output: [ -0.01458 0.2561 0.8049 -0.001244 0.0005584 0.9631 -0.0009373 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3692 0.2538 0.4271 0.1354 0.984 0.9936 0.3708 0.9259 0.982 0.6324 ] Network output: [ -0.04732 0.3099 1.079 0.0002841 -0.0001275 0.7068 0.0002141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1596 0.1505 0.1946 0.1333 0.9901 0.994 0.1597 0.9727 0.9857 0.2106 ] Network output: [ -0.03953 0.1584 1.092 0.0005325 -0.0002391 0.8308 0.0004013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1771 0.1753 0.202 0.1661 0.9853 0.9915 0.1772 0.9559 0.9788 0.2065 ] Network output: [ 0.007438 0.9139 0.008569 3.464e-05 -1.555e-05 1.063 2.611e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09206 Epoch 4255 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0624 0.7674 0.9552 -1.85e-05 8.307e-06 0.1525 -1.394e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005035 -0.00501 -0.01748 0.009831 0.9621 0.968 0.01158 0.9202 0.9324 0.03988 ] Network output: [ 1.023 -0.2042 0.0916 0.0005251 -0.0002358 0.06794 0.0003958 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3138 -0.02097 -0.1476 0.2132 0.9824 0.9928 0.3637 0.9201 0.9799 0.6367 ] Network output: [ 0.01888 0.8184 0.9767 -0.0001614 7.248e-05 0.1664 -0.0001217 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008183 0.002946 0.007013 0.00732 0.9904 0.9936 0.008382 0.9734 0.9852 0.01616 ] Network output: [ 0.08561 -0.537 1.054 0.0001814 -8.142e-05 1.312 0.0001367 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3568 0.2426 0.4116 0.3128 0.984 0.9936 0.3584 0.9257 0.982 0.6248 ] Network output: [ -0.04375 0.282 1.071 0.000338 -0.0001518 0.7358 0.0002548 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1684 0.1588 0.1972 0.1615 0.9899 0.994 0.1685 0.9729 0.986 0.2134 ] Network output: [ -0.04222 0.2361 1.044 0.0004385 -0.0001968 0.8058 0.0003304 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1877 0.1858 0.1996 0.1723 0.9855 0.9915 0.1877 0.9566 0.979 0.2038 ] Network output: [ 0.01543 0.9259 -0.02371 6.845e-05 -3.073e-05 1.067 5.158e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1229 Epoch 4256 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04857 0.851 0.9346 -0.0001812 8.135e-05 0.1165 -0.0001366 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005221 -0.004955 -0.01684 0.00688 0.9621 0.968 0.01192 0.9198 0.932 0.03963 ] Network output: [ 0.9076 0.3847 -0.06032 -0.0006008 0.0002697 -0.1419 -0.0004528 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3265 -0.001958 -0.112 0.09514 0.9824 0.9928 0.3779 0.9199 0.9797 0.6342 ] Network output: [ 0.01763 0.8387 0.9719 -0.0002018 9.06e-05 0.1533 -0.0001521 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008552 0.003169 0.007696 0.003556 0.9905 0.9936 0.008757 0.9736 0.985 0.01655 ] Network output: [ -0.01442 0.2556 0.8053 -0.001239 0.0005562 0.963 -0.0009336 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3687 0.2535 0.4266 0.1352 0.984 0.9936 0.3703 0.9257 0.982 0.632 ] Network output: [ -0.04734 0.3097 1.079 0.0002832 -0.0001271 0.7069 0.0002134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1593 0.1502 0.1944 0.1332 0.9901 0.994 0.1594 0.9727 0.9857 0.2104 ] Network output: [ -0.03956 0.1584 1.092 0.0005307 -0.0002383 0.831 0.0004 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1768 0.175 0.2019 0.166 0.9853 0.9915 0.1768 0.9558 0.9788 0.2063 ] Network output: [ 0.007415 0.9137 0.008743 3.506e-05 -1.574e-05 1.063 2.642e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09195 Epoch 4257 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06241 0.7674 0.9551 -1.872e-05 8.404e-06 0.1525 -1.411e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00503 -0.005003 -0.01746 0.009821 0.9621 0.968 0.01156 0.9201 0.9324 0.03979 ] Network output: [ 1.023 -0.2039 0.09132 0.0005229 -0.0002347 0.06818 0.000394 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3135 -0.02094 -0.1476 0.2132 0.9824 0.9928 0.3633 0.9199 0.9798 0.6363 ] Network output: [ 0.01893 0.8184 0.9767 -0.000161 7.228e-05 0.1664 -0.0001213 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008168 0.002941 0.006996 0.007304 0.9904 0.9936 0.008366 0.9734 0.9851 0.01613 ] Network output: [ 0.08539 -0.5361 1.054 0.0001799 -8.077e-05 1.313 0.0001356 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3564 0.2423 0.4113 0.3124 0.984 0.9936 0.358 0.9255 0.982 0.6244 ] Network output: [ -0.04381 0.2819 1.071 0.0003367 -0.0001512 0.7358 0.0002538 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.168 0.1585 0.1971 0.1613 0.9899 0.994 0.1681 0.9728 0.986 0.2133 ] Network output: [ -0.04224 0.2361 1.044 0.0004369 -0.0001961 0.8057 0.0003292 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1873 0.1854 0.1995 0.1721 0.9855 0.9915 0.1873 0.9564 0.979 0.2036 ] Network output: [ 0.01539 0.9265 -0.02373 6.781e-05 -3.044e-05 1.067 5.11e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1228 Epoch 4258 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04863 0.8509 0.9346 -0.0001806 8.106e-05 0.1165 -0.0001361 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005214 -0.004948 -0.01683 0.006878 0.9621 0.968 0.0119 0.9197 0.9319 0.03954 ] Network output: [ 0.9078 0.3842 -0.06001 -0.0005977 0.0002683 -0.1422 -0.0004504 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3262 -0.002009 -0.1123 0.0952 0.9824 0.9928 0.3775 0.9197 0.9796 0.6337 ] Network output: [ 0.01769 0.8386 0.9718 -0.0002011 9.029e-05 0.1533 -0.0001516 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008535 0.003161 0.007668 0.003549 0.9905 0.9936 0.008739 0.9735 0.9849 0.01652 ] Network output: [ -0.01426 0.255 0.8056 -0.001234 0.000554 0.9628 -0.00093 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3683 0.2532 0.4261 0.135 0.984 0.9936 0.3699 0.9255 0.9819 0.6316 ] Network output: [ -0.04737 0.3096 1.079 0.0002822 -0.0001267 0.707 0.0002127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1589 0.1499 0.1942 0.133 0.9901 0.994 0.1591 0.9726 0.9857 0.2103 ] Network output: [ -0.03959 0.1583 1.092 0.0005289 -0.0002375 0.8312 0.0003986 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1764 0.1746 0.2018 0.1658 0.9853 0.9915 0.1765 0.9557 0.9787 0.2062 ] Network output: [ 0.007393 0.9136 0.008914 3.548e-05 -1.593e-05 1.063 2.674e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09183 Epoch 4259 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06242 0.7675 0.955 -1.893e-05 8.498e-06 0.1526 -1.427e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005025 -0.004995 -0.01744 0.009812 0.9622 0.9681 0.01154 0.92 0.9323 0.0397 ] Network output: [ 1.023 -0.2036 0.09104 0.0005206 -0.0002337 0.06842 0.0003923 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3132 -0.02091 -0.1476 0.2131 0.9824 0.9928 0.363 0.9197 0.9798 0.6359 ] Network output: [ 0.01897 0.8184 0.9766 -0.0001606 7.208e-05 0.1664 -0.000121 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008153 0.002936 0.006978 0.007288 0.9904 0.9936 0.00835 0.9733 0.9851 0.0161 ] Network output: [ 0.08517 -0.5353 1.053 0.0001784 -8.011e-05 1.313 0.0001345 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.356 0.2421 0.411 0.312 0.984 0.9936 0.3576 0.9253 0.9819 0.624 ] Network output: [ -0.04387 0.2818 1.071 0.0003355 -0.0001506 0.7359 0.0002528 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1676 0.1581 0.1969 0.1612 0.9899 0.994 0.1677 0.9727 0.986 0.2132 ] Network output: [ -0.04225 0.236 1.045 0.0004353 -0.0001954 0.8057 0.000328 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1869 0.1851 0.1994 0.1719 0.9855 0.9915 0.1869 0.9563 0.9789 0.2035 ] Network output: [ 0.01534 0.927 -0.02374 6.718e-05 -3.016e-05 1.066 5.063e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1226 Epoch 4260 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04869 0.8507 0.9346 -0.0001799 8.077e-05 0.1166 -0.0001356 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005208 -0.004941 -0.01681 0.006875 0.9621 0.9681 0.01188 0.9197 0.9318 0.03945 ] Network output: [ 0.908 0.3838 -0.05968 -0.0005946 0.0002669 -0.1425 -0.0004481 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3259 -0.002058 -0.1125 0.09526 0.9824 0.9928 0.377 0.9195 0.9796 0.6333 ] Network output: [ 0.01776 0.8385 0.9718 -0.0002004 8.997e-05 0.1534 -0.000151 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008518 0.003152 0.00764 0.003541 0.9905 0.9936 0.008721 0.9735 0.9849 0.01649 ] Network output: [ -0.01411 0.2545 0.806 -0.001229 0.0005518 0.9627 -0.0009264 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3679 0.2528 0.4256 0.1348 0.9841 0.9936 0.3695 0.9253 0.9819 0.6312 ] Network output: [ -0.04739 0.3094 1.079 0.0002813 -0.0001263 0.7072 0.000212 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1586 0.1496 0.1941 0.1329 0.9901 0.994 0.1587 0.9725 0.9856 0.2102 ] Network output: [ -0.03961 0.1582 1.092 0.0005272 -0.0002367 0.8313 0.0003973 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1761 0.1743 0.2016 0.1657 0.9853 0.9915 0.1761 0.9556 0.9787 0.2061 ] Network output: [ 0.007372 0.9135 0.009082 3.59e-05 -1.612e-05 1.063 2.705e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09171 Epoch 4261 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06243 0.7675 0.9549 -1.913e-05 8.59e-06 0.1526 -1.442e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00502 -0.004989 -0.01741 0.009802 0.9622 0.9681 0.01152 0.92 0.9322 0.03961 ] Network output: [ 1.023 -0.2033 0.09076 0.0005183 -0.0002327 0.06865 0.0003906 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.313 -0.02088 -0.1476 0.2131 0.9824 0.9928 0.3626 0.9195 0.9797 0.6356 ] Network output: [ 0.01902 0.8184 0.9765 -0.0001601 7.189e-05 0.1664 -0.0001207 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008138 0.002931 0.006961 0.007272 0.9904 0.9936 0.008334 0.9733 0.985 0.01607 ] Network output: [ 0.08496 -0.5345 1.052 0.000177 -7.945e-05 1.313 0.0001334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3557 0.2419 0.4107 0.3116 0.984 0.9936 0.3572 0.9251 0.9819 0.6236 ] Network output: [ -0.04392 0.2817 1.072 0.0003342 -0.00015 0.7359 0.0002519 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1672 0.1578 0.1968 0.161 0.9899 0.994 0.1674 0.9726 0.9859 0.213 ] Network output: [ -0.04226 0.236 1.045 0.0004337 -0.0001947 0.8057 0.0003269 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1865 0.1847 0.1992 0.1717 0.9855 0.9915 0.1866 0.9562 0.9788 0.2034 ] Network output: [ 0.0153 0.9275 -0.02376 6.656e-05 -2.988e-05 1.066 5.016e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1224 Epoch 4262 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04875 0.8506 0.9346 -0.0001793 8.048e-05 0.1166 -0.0001351 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005202 -0.004934 -0.01679 0.006873 0.9622 0.9681 0.01186 0.9196 0.9318 0.03936 ] Network output: [ 0.9081 0.3834 -0.05936 -0.0005915 0.0002655 -0.1428 -0.0004458 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3255 -0.002107 -0.1128 0.09532 0.9824 0.9928 0.3766 0.9193 0.9795 0.6329 ] Network output: [ 0.01782 0.8384 0.9718 -0.0001997 8.966e-05 0.1534 -0.0001505 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008501 0.003144 0.007613 0.003533 0.9905 0.9936 0.008704 0.9734 0.9849 0.01645 ] Network output: [ -0.01395 0.254 0.8063 -0.001224 0.0005497 0.9626 -0.0009228 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3674 0.2525 0.4251 0.1347 0.9841 0.9936 0.369 0.9251 0.9819 0.6308 ] Network output: [ -0.04741 0.3092 1.079 0.0002804 -0.0001259 0.7073 0.0002113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1583 0.1492 0.1939 0.1328 0.9901 0.994 0.1584 0.9724 0.9856 0.21 ] Network output: [ -0.03964 0.1581 1.092 0.0005254 -0.0002359 0.8315 0.000396 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1758 0.174 0.2015 0.1655 0.9853 0.9915 0.1758 0.9555 0.9786 0.206 ] Network output: [ 0.007351 0.9134 0.009247 3.632e-05 -1.63e-05 1.063 2.737e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0916 Epoch 4263 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06244 0.7675 0.9548 -1.933e-05 8.68e-06 0.1527 -1.457e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005015 -0.004982 -0.01739 0.009793 0.9622 0.9681 0.0115 0.9199 0.9321 0.03952 ] Network output: [ 1.023 -0.203 0.09047 0.0005161 -0.0002317 0.06888 0.000389 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3127 -0.02085 -0.1476 0.213 0.9824 0.9928 0.3623 0.9193 0.9797 0.6352 ] Network output: [ 0.01906 0.8184 0.9765 -0.0001597 7.169e-05 0.1664 -0.0001203 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008123 0.002926 0.006943 0.007256 0.9904 0.9936 0.008319 0.9732 0.985 0.01604 ] Network output: [ 0.08475 -0.5337 1.052 0.0001755 -7.879e-05 1.313 0.0001323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3553 0.2417 0.4104 0.3112 0.984 0.9936 0.3568 0.9249 0.9818 0.6232 ] Network output: [ -0.04398 0.2816 1.072 0.0003329 -0.0001495 0.736 0.0002509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1669 0.1574 0.1967 0.1608 0.9899 0.994 0.167 0.9726 0.9859 0.2129 ] Network output: [ -0.04228 0.2359 1.045 0.0004321 -0.000194 0.8057 0.0003257 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1861 0.1843 0.1991 0.1715 0.9855 0.9915 0.1862 0.9561 0.9788 0.2032 ] Network output: [ 0.01526 0.9281 -0.02377 6.596e-05 -2.961e-05 1.065 4.971e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1223 Epoch 4264 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0488 0.8504 0.9346 -0.0001786 8.02e-05 0.1166 -0.0001346 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005196 -0.004927 -0.01678 0.006871 0.9622 0.9681 0.01183 0.9195 0.9317 0.03927 ] Network output: [ 0.9083 0.383 -0.05903 -0.0005885 0.0002642 -0.1431 -0.0004435 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3252 -0.002155 -0.1131 0.09538 0.9825 0.9928 0.3762 0.9191 0.9795 0.6326 ] Network output: [ 0.01788 0.8383 0.9718 -0.000199 8.934e-05 0.1534 -0.00015 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008484 0.003136 0.007586 0.003526 0.9905 0.9936 0.008687 0.9733 0.9848 0.01642 ] Network output: [ -0.0138 0.2535 0.8067 -0.00122 0.0005476 0.9624 -0.0009193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.367 0.2522 0.4246 0.1345 0.9841 0.9936 0.3686 0.9249 0.9818 0.6304 ] Network output: [ -0.04743 0.309 1.08 0.0002795 -0.0001255 0.7074 0.0002107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1579 0.1489 0.1938 0.1327 0.9901 0.994 0.1581 0.9724 0.9855 0.2099 ] Network output: [ -0.03966 0.158 1.092 0.0005237 -0.0002351 0.8317 0.0003947 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1754 0.1737 0.2014 0.1654 0.9853 0.9915 0.1755 0.9554 0.9786 0.2059 ] Network output: [ 0.00733 0.9132 0.009409 3.674e-05 -1.649e-05 1.063 2.769e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09148 Epoch 4265 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06246 0.7676 0.9547 -1.953e-05 8.768e-06 0.1527 -1.472e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00501 -0.004975 -0.01737 0.009784 0.9622 0.9681 0.01148 0.9198 0.932 0.03943 ] Network output: [ 1.023 -0.2027 0.09018 0.0005139 -0.0002307 0.0691 0.0003873 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3125 -0.02082 -0.1475 0.213 0.9825 0.9928 0.3619 0.9191 0.9796 0.6348 ] Network output: [ 0.0191 0.8184 0.9764 -0.0001593 7.149e-05 0.1664 -0.00012 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008108 0.002921 0.006926 0.007241 0.9904 0.9936 0.008303 0.9731 0.985 0.01601 ] Network output: [ 0.08455 -0.5329 1.051 0.000174 -7.813e-05 1.313 0.0001312 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3549 0.2414 0.4101 0.3108 0.9841 0.9936 0.3565 0.9247 0.9818 0.6228 ] Network output: [ -0.04403 0.2815 1.072 0.0003317 -0.0001489 0.736 0.00025 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1665 0.157 0.1966 0.1606 0.9899 0.994 0.1666 0.9725 0.9858 0.2128 ] Network output: [ -0.04229 0.2359 1.045 0.0004306 -0.0001933 0.8057 0.0003245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1858 0.1839 0.199 0.1714 0.9855 0.9915 0.1858 0.956 0.9787 0.2031 ] Network output: [ 0.01521 0.9286 -0.02378 6.536e-05 -2.934e-05 1.065 4.925e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1221 Epoch 4266 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04886 0.8503 0.9346 -0.000178 7.991e-05 0.1167 -0.0001341 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00519 -0.00492 -0.01676 0.006869 0.9622 0.9681 0.01181 0.9195 0.9316 0.03918 ] Network output: [ 0.9085 0.3826 -0.05869 -0.0005855 0.0002628 -0.1433 -0.0004412 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3249 -0.002201 -0.1133 0.09544 0.9825 0.9928 0.3757 0.9189 0.9795 0.6322 ] Network output: [ 0.01794 0.8382 0.9717 -0.0001983 8.903e-05 0.1534 -0.0001495 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008468 0.003128 0.007559 0.003518 0.9905 0.9936 0.00867 0.9733 0.9848 0.01639 ] Network output: [ -0.01366 0.253 0.807 -0.001215 0.0005455 0.9623 -0.0009158 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3666 0.2519 0.4241 0.1343 0.9841 0.9936 0.3682 0.9247 0.9818 0.63 ] Network output: [ -0.04744 0.3089 1.08 0.0002787 -0.0001251 0.7076 0.00021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1576 0.1486 0.1936 0.1326 0.9901 0.994 0.1577 0.9723 0.9855 0.2098 ] Network output: [ -0.03968 0.1579 1.092 0.000522 -0.0002343 0.8319 0.0003934 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1751 0.1733 0.2012 0.1653 0.9853 0.9915 0.1752 0.9553 0.9785 0.2057 ] Network output: [ 0.00731 0.9131 0.009568 3.716e-05 -1.668e-05 1.063 2.801e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09136 Epoch 4267 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06247 0.7676 0.9546 -1.972e-05 8.853e-06 0.1528 -1.486e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005005 -0.004968 -0.01734 0.009775 0.9622 0.9681 0.01147 0.9198 0.932 0.03935 ] Network output: [ 1.023 -0.2024 0.08989 0.0005117 -0.0002297 0.06931 0.0003856 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3122 -0.02079 -0.1475 0.213 0.9825 0.9928 0.3616 0.9189 0.9796 0.6345 ] Network output: [ 0.01914 0.8184 0.9764 -0.0001588 7.13e-05 0.1663 -0.0001197 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008093 0.002916 0.006909 0.007225 0.9904 0.9936 0.008288 0.9731 0.9849 0.01598 ] Network output: [ 0.08434 -0.5321 1.05 0.0001726 -7.747e-05 1.314 0.0001301 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3545 0.2412 0.4098 0.3104 0.9841 0.9936 0.3561 0.9245 0.9818 0.6224 ] Network output: [ -0.04409 0.2814 1.072 0.0003304 -0.0001483 0.7361 0.000249 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1661 0.1567 0.1964 0.1605 0.9899 0.994 0.1662 0.9724 0.9858 0.2127 ] Network output: [ -0.0423 0.2358 1.045 0.0004291 -0.0001926 0.8057 0.0003234 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1854 0.1835 0.1988 0.1712 0.9855 0.9915 0.1854 0.9559 0.9787 0.203 ] Network output: [ 0.01517 0.9291 -0.02379 6.476e-05 -2.908e-05 1.065 4.881e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1219 Epoch 4268 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04891 0.8501 0.9346 -0.0001774 7.963e-05 0.1167 -0.0001337 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005183 -0.004913 -0.01674 0.006867 0.9622 0.9681 0.01179 0.9194 0.9315 0.03909 ] Network output: [ 0.9087 0.3822 -0.05836 -0.0005825 0.0002615 -0.1436 -0.000439 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3245 -0.002247 -0.1136 0.0955 0.9825 0.9928 0.3753 0.9187 0.9794 0.6318 ] Network output: [ 0.018 0.8381 0.9717 -0.0001976 8.872e-05 0.1534 -0.0001489 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008452 0.00312 0.007533 0.003511 0.9905 0.9936 0.008653 0.9732 0.9847 0.01635 ] Network output: [ -0.01352 0.2526 0.8074 -0.001211 0.0005435 0.9622 -0.0009123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3662 0.2516 0.4236 0.1341 0.9841 0.9936 0.3678 0.9245 0.9817 0.6296 ] Network output: [ -0.04746 0.3087 1.08 0.0002778 -0.0001247 0.7077 0.0002094 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1573 0.1483 0.1935 0.1324 0.99 0.994 0.1574 0.9722 0.9854 0.2096 ] Network output: [ -0.0397 0.1578 1.092 0.0005203 -0.0002336 0.8321 0.0003921 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1748 0.173 0.2011 0.1651 0.9853 0.9915 0.1748 0.9552 0.9785 0.2056 ] Network output: [ 0.007291 0.913 0.009724 3.759e-05 -1.687e-05 1.063 2.833e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09124 Epoch 4269 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06247 0.7677 0.9545 -1.991e-05 8.937e-06 0.1528 -1.5e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005 -0.004961 -0.01732 0.009765 0.9623 0.9681 0.01145 0.9197 0.9319 0.03926 ] Network output: [ 1.023 -0.2022 0.0896 0.0005095 -0.0002287 0.06952 0.000384 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.312 -0.02076 -0.1475 0.2129 0.9825 0.9928 0.3612 0.9187 0.9795 0.6341 ] Network output: [ 0.01918 0.8184 0.9763 -0.0001584 7.11e-05 0.1663 -0.0001194 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008079 0.002911 0.006892 0.00721 0.9904 0.9936 0.008273 0.973 0.9849 0.01596 ] Network output: [ 0.08414 -0.5314 1.05 0.0001711 -7.681e-05 1.314 0.0001289 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3542 0.241 0.4095 0.31 0.9841 0.9936 0.3557 0.9243 0.9817 0.6221 ] Network output: [ -0.04414 0.2813 1.072 0.0003292 -0.0001478 0.7362 0.0002481 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1657 0.1564 0.1963 0.1603 0.9899 0.994 0.1659 0.9724 0.9858 0.2125 ] Network output: [ -0.04231 0.2358 1.045 0.0004276 -0.000192 0.8057 0.0003222 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.185 0.1832 0.1987 0.171 0.9855 0.9915 0.185 0.9558 0.9786 0.2029 ] Network output: [ 0.01512 0.9297 -0.0238 6.418e-05 -2.881e-05 1.064 4.837e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1218 Epoch 4270 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04896 0.85 0.9346 -0.0001767 7.934e-05 0.1167 -0.0001332 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005177 -0.004906 -0.01672 0.006865 0.9623 0.9681 0.01177 0.9193 0.9314 0.039 ] Network output: [ 0.9089 0.3818 -0.05802 -0.0005795 0.0002602 -0.1439 -0.0004367 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3242 -0.002292 -0.1138 0.09556 0.9825 0.9928 0.3749 0.9185 0.9794 0.6314 ] Network output: [ 0.01806 0.838 0.9717 -0.0001969 8.841e-05 0.1535 -0.0001484 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008436 0.003112 0.007507 0.003503 0.9905 0.9936 0.008636 0.9732 0.9847 0.01632 ] Network output: [ -0.01338 0.2521 0.8077 -0.001206 0.0005414 0.962 -0.0009089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3658 0.2512 0.4232 0.134 0.9841 0.9936 0.3673 0.9243 0.9817 0.6292 ] Network output: [ -0.04748 0.3085 1.08 0.0002769 -0.0001243 0.7078 0.0002087 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.157 0.148 0.1933 0.1323 0.99 0.994 0.1571 0.9721 0.9854 0.2095 ] Network output: [ -0.03972 0.1577 1.092 0.0005186 -0.0002328 0.8322 0.0003908 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1745 0.1727 0.201 0.165 0.9853 0.9915 0.1745 0.9551 0.9784 0.2055 ] Network output: [ 0.007272 0.9129 0.009877 3.801e-05 -1.706e-05 1.063 2.865e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09112 Epoch 4271 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06248 0.7677 0.9544 -2.009e-05 9.018e-06 0.1528 -1.514e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004995 -0.004954 -0.0173 0.009756 0.9623 0.9682 0.01143 0.9196 0.9318 0.03917 ] Network output: [ 1.022 -0.2019 0.0893 0.0005073 -0.0002278 0.06972 0.0003823 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3117 -0.02073 -0.1475 0.2129 0.9825 0.9928 0.3609 0.9185 0.9795 0.6338 ] Network output: [ 0.01922 0.8184 0.9762 -0.0001579 7.09e-05 0.1663 -0.000119 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008064 0.002906 0.006875 0.007194 0.9904 0.9936 0.008258 0.973 0.9849 0.01593 ] Network output: [ 0.08395 -0.5306 1.049 0.0001696 -7.615e-05 1.314 0.0001278 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3538 0.2408 0.4092 0.3096 0.9841 0.9936 0.3553 0.9241 0.9817 0.6217 ] Network output: [ -0.04419 0.2812 1.072 0.000328 -0.0001473 0.7362 0.0002472 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1654 0.156 0.1962 0.1601 0.9899 0.994 0.1655 0.9723 0.9857 0.2124 ] Network output: [ -0.04232 0.2357 1.045 0.0004261 -0.0001913 0.8057 0.0003211 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1846 0.1828 0.1986 0.1708 0.9855 0.9915 0.1847 0.9557 0.9786 0.2027 ] Network output: [ 0.01508 0.9302 -0.02381 6.36e-05 -2.855e-05 1.064 4.793e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1216 Epoch 4272 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04901 0.8499 0.9346 -0.0001761 7.906e-05 0.1168 -0.0001327 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005171 -0.004899 -0.01671 0.006863 0.9623 0.9681 0.01175 0.9193 0.9314 0.03891 ] Network output: [ 0.909 0.3814 -0.05767 -0.0005766 0.0002589 -0.1441 -0.0004345 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3239 -0.002336 -0.114 0.09563 0.9825 0.9928 0.3744 0.9183 0.9793 0.631 ] Network output: [ 0.01812 0.8379 0.9716 -0.0001963 8.81e-05 0.1535 -0.0001479 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00842 0.003104 0.007481 0.003496 0.9905 0.9936 0.008619 0.9731 0.9847 0.01629 ] Network output: [ -0.01324 0.2516 0.8081 -0.001202 0.0005394 0.9619 -0.0009055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3654 0.2509 0.4227 0.1338 0.9841 0.9936 0.3669 0.9242 0.9817 0.6288 ] Network output: [ -0.04749 0.3083 1.08 0.0002761 -0.0001239 0.708 0.0002081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1567 0.1477 0.1932 0.1322 0.99 0.994 0.1568 0.9721 0.9854 0.2094 ] Network output: [ -0.03973 0.1576 1.092 0.000517 -0.0002321 0.8324 0.0003896 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1742 0.1724 0.2009 0.1649 0.9853 0.9915 0.1742 0.955 0.9783 0.2054 ] Network output: [ 0.007254 0.9127 0.01003 3.844e-05 -1.726e-05 1.063 2.897e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.091 Epoch 4273 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06249 0.7678 0.9543 -2.027e-05 9.098e-06 0.1529 -1.527e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004991 -0.004948 -0.01728 0.009747 0.9623 0.9682 0.01141 0.9196 0.9317 0.03909 ] Network output: [ 1.022 -0.2016 0.089 0.0005051 -0.0002268 0.06992 0.0003807 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3115 -0.02071 -0.1475 0.2128 0.9825 0.9928 0.3605 0.9183 0.9795 0.6334 ] Network output: [ 0.01926 0.8184 0.9762 -0.0001575 7.071e-05 0.1663 -0.0001187 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00805 0.002901 0.006858 0.007179 0.9904 0.9936 0.008243 0.9729 0.9848 0.0159 ] Network output: [ 0.08375 -0.5298 1.049 0.0001682 -7.549e-05 1.314 0.0001267 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3534 0.2406 0.4089 0.3092 0.9841 0.9936 0.355 0.9239 0.9816 0.6213 ] Network output: [ -0.04424 0.2811 1.072 0.0003268 -0.0001467 0.7363 0.0002463 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.165 0.1557 0.1961 0.16 0.9899 0.994 0.1652 0.9722 0.9857 0.2123 ] Network output: [ -0.04233 0.2356 1.045 0.0004246 -0.0001906 0.8057 0.00032 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1843 0.1824 0.1985 0.1706 0.9855 0.9915 0.1843 0.9556 0.9785 0.2026 ] Network output: [ 0.01503 0.9307 -0.02382 6.303e-05 -2.83e-05 1.063 4.75e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1214 Epoch 4274 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04906 0.8498 0.9346 -0.0001755 7.877e-05 0.1168 -0.0001322 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005165 -0.004893 -0.01669 0.006861 0.9623 0.9682 0.01173 0.9192 0.9313 0.03882 ] Network output: [ 0.9092 0.381 -0.05733 -0.0005737 0.0002575 -0.1444 -0.0004323 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3236 -0.002379 -0.1143 0.09569 0.9825 0.9928 0.374 0.9181 0.9793 0.6306 ] Network output: [ 0.01817 0.8378 0.9716 -0.0001956 8.78e-05 0.1535 -0.0001474 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008404 0.003096 0.007456 0.003489 0.9905 0.9936 0.008603 0.973 0.9846 0.01626 ] Network output: [ -0.01311 0.2512 0.8084 -0.001197 0.0005374 0.9617 -0.0009022 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3649 0.2506 0.4222 0.1336 0.9841 0.9936 0.3665 0.924 0.9816 0.6285 ] Network output: [ -0.0475 0.3081 1.08 0.0002752 -0.0001236 0.7081 0.0002074 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1564 0.1474 0.193 0.1321 0.99 0.994 0.1565 0.972 0.9853 0.2093 ] Network output: [ -0.03975 0.1575 1.092 0.0005153 -0.0002313 0.8326 0.0003884 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1738 0.1721 0.2008 0.1647 0.9853 0.9915 0.1739 0.9549 0.9783 0.2053 ] Network output: [ 0.007237 0.9126 0.01018 3.886e-05 -1.745e-05 1.063 2.929e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09088 Epoch 4275 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0625 0.7678 0.9542 -2.044e-05 9.176e-06 0.1529 -1.54e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004986 -0.004941 -0.01725 0.009738 0.9623 0.9682 0.01139 0.9195 0.9317 0.039 ] Network output: [ 1.022 -0.2013 0.0887 0.000503 -0.0002258 0.07011 0.0003791 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3112 -0.02068 -0.1475 0.2128 0.9825 0.9928 0.3602 0.9181 0.9794 0.6331 ] Network output: [ 0.01929 0.8184 0.9761 -0.0001571 7.051e-05 0.1662 -0.0001184 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008036 0.002896 0.006842 0.007164 0.9904 0.9936 0.008228 0.9729 0.9848 0.01587 ] Network output: [ 0.08356 -0.529 1.048 0.0001667 -7.483e-05 1.315 0.0001256 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3531 0.2403 0.4086 0.3088 0.9841 0.9936 0.3546 0.9237 0.9816 0.621 ] Network output: [ -0.04429 0.281 1.073 0.0003256 -0.0001462 0.7364 0.0002454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1647 0.1553 0.1959 0.1598 0.9899 0.994 0.1648 0.9721 0.9856 0.2122 ] Network output: [ -0.04234 0.2355 1.045 0.0004232 -0.00019 0.8057 0.0003189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1839 0.1821 0.1983 0.1705 0.9855 0.9915 0.1839 0.9555 0.9785 0.2025 ] Network output: [ 0.01498 0.9312 -0.02382 6.247e-05 -2.805e-05 1.063 4.708e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1213 Epoch 4276 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0491 0.8496 0.9346 -0.0001748 7.849e-05 0.1168 -0.0001318 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005159 -0.004886 -0.01667 0.006859 0.9623 0.9682 0.01171 0.9191 0.9312 0.03874 ] Network output: [ 0.9094 0.3806 -0.05698 -0.0005708 0.0002563 -0.1446 -0.0004302 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3232 -0.002421 -0.1145 0.09576 0.9825 0.9928 0.3736 0.9179 0.9792 0.6303 ] Network output: [ 0.01823 0.8377 0.9716 -0.0001949 8.749e-05 0.1535 -0.0001469 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008388 0.003089 0.007431 0.003482 0.9905 0.9936 0.008586 0.973 0.9846 0.01623 ] Network output: [ -0.01298 0.2507 0.8088 -0.001193 0.0005354 0.9616 -0.0008989 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3645 0.2503 0.4218 0.1334 0.9841 0.9936 0.3661 0.9238 0.9816 0.6281 ] Network output: [ -0.04751 0.3079 1.08 0.0002744 -0.0001232 0.7082 0.0002068 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.156 0.1471 0.1929 0.132 0.99 0.994 0.1562 0.9719 0.9853 0.2091 ] Network output: [ -0.03976 0.1574 1.091 0.0005137 -0.0002306 0.8328 0.0003871 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1735 0.1718 0.2006 0.1646 0.9853 0.9915 0.1736 0.9548 0.9782 0.2052 ] Network output: [ 0.00722 0.9125 0.01032 3.929e-05 -1.764e-05 1.063 2.961e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09076 Epoch 4277 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06251 0.7679 0.9541 -2.061e-05 9.252e-06 0.1529 -1.553e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004981 -0.004934 -0.01723 0.009729 0.9623 0.9682 0.01137 0.9194 0.9316 0.03892 ] Network output: [ 1.022 -0.201 0.0884 0.0005009 -0.0002249 0.07029 0.0003775 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.311 -0.02065 -0.1475 0.2128 0.9825 0.9928 0.3599 0.9179 0.9794 0.6327 ] Network output: [ 0.01933 0.8184 0.9761 -0.0001566 7.031e-05 0.1662 -0.000118 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008021 0.002891 0.006825 0.007148 0.9904 0.9936 0.008213 0.9728 0.9847 0.01585 ] Network output: [ 0.08337 -0.5282 1.047 0.0001652 -7.417e-05 1.315 0.0001245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3527 0.2401 0.4083 0.3084 0.9841 0.9936 0.3542 0.9236 0.9816 0.6206 ] Network output: [ -0.04434 0.2808 1.073 0.0003245 -0.0001457 0.7364 0.0002445 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1643 0.155 0.1958 0.1597 0.9899 0.994 0.1644 0.9721 0.9856 0.2121 ] Network output: [ -0.04235 0.2355 1.045 0.0004217 -0.0001893 0.8057 0.0003178 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1835 0.1817 0.1982 0.1703 0.9855 0.9915 0.1835 0.9554 0.9784 0.2024 ] Network output: [ 0.01494 0.9318 -0.02383 6.191e-05 -2.78e-05 1.062 4.666e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1211 Epoch 4278 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04915 0.8495 0.9346 -0.0001742 7.821e-05 0.1169 -0.0001313 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005153 -0.004879 -0.01665 0.006857 0.9623 0.9682 0.01168 0.9191 0.9311 0.03865 ] Network output: [ 0.9095 0.3802 -0.05663 -0.000568 0.000255 -0.1449 -0.000428 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3229 -0.002463 -0.1147 0.09582 0.9825 0.9928 0.3732 0.9177 0.9792 0.6299 ] Network output: [ 0.01829 0.8376 0.9715 -0.0001942 8.718e-05 0.1535 -0.0001464 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008372 0.003081 0.007406 0.003475 0.9905 0.9936 0.00857 0.9729 0.9845 0.0162 ] Network output: [ -0.01285 0.2503 0.8091 -0.001188 0.0005335 0.9615 -0.0008956 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3641 0.25 0.4213 0.1333 0.9841 0.9936 0.3657 0.9236 0.9815 0.6277 ] Network output: [ -0.04752 0.3077 1.08 0.0002736 -0.0001228 0.7084 0.0002062 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1557 0.1468 0.1928 0.1319 0.99 0.994 0.1559 0.9718 0.9852 0.209 ] Network output: [ -0.03977 0.1572 1.091 0.0005121 -0.0002299 0.833 0.0003859 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1732 0.1714 0.2005 0.1645 0.9853 0.9915 0.1733 0.9547 0.9782 0.2051 ] Network output: [ 0.007203 0.9124 0.01047 3.972e-05 -1.783e-05 1.063 2.993e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09064 Epoch 4279 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06251 0.7679 0.954 -2.077e-05 9.326e-06 0.153 -1.566e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004977 -0.004928 -0.01721 0.00972 0.9624 0.9682 0.01135 0.9194 0.9315 0.03883 ] Network output: [ 1.022 -0.2007 0.08809 0.0004987 -0.0002239 0.07048 0.0003759 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3107 -0.02063 -0.1475 0.2127 0.9825 0.9928 0.3595 0.9177 0.9793 0.6324 ] Network output: [ 0.01936 0.8184 0.976 -0.0001562 7.012e-05 0.1662 -0.0001177 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008007 0.002886 0.006809 0.007133 0.9904 0.9936 0.008199 0.9727 0.9847 0.01582 ] Network output: [ 0.08319 -0.5275 1.047 0.0001637 -7.351e-05 1.315 0.0001234 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3523 0.2399 0.4081 0.3081 0.9841 0.9936 0.3538 0.9234 0.9815 0.6203 ] Network output: [ -0.04439 0.2807 1.073 0.0003233 -0.0001451 0.7365 0.0002437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.164 0.1547 0.1957 0.1595 0.9899 0.994 0.1641 0.972 0.9855 0.212 ] Network output: [ -0.04235 0.2354 1.045 0.0004203 -0.0001887 0.8057 0.0003167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1832 0.1813 0.1981 0.1701 0.9855 0.9916 0.1832 0.9553 0.9784 0.2023 ] Network output: [ 0.01489 0.9323 -0.02383 6.136e-05 -2.755e-05 1.062 4.624e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1209 Epoch 4280 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04919 0.8494 0.9346 -0.0001736 7.793e-05 0.1169 -0.0001308 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005148 -0.004872 -0.01664 0.006855 0.9624 0.9682 0.01166 0.919 0.931 0.03856 ] Network output: [ 0.9097 0.3798 -0.05628 -0.0005651 0.0002537 -0.1451 -0.0004259 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3226 -0.002503 -0.115 0.09589 0.9825 0.9928 0.3727 0.9175 0.9791 0.6295 ] Network output: [ 0.01834 0.8375 0.9715 -0.0001935 8.688e-05 0.1535 -0.0001458 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008357 0.003074 0.007381 0.003468 0.9905 0.9936 0.008554 0.9728 0.9845 0.01617 ] Network output: [ -0.01273 0.2498 0.8095 -0.001184 0.0005316 0.9613 -0.0008923 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3637 0.2497 0.4209 0.1331 0.9841 0.9936 0.3653 0.9234 0.9815 0.6274 ] Network output: [ -0.04753 0.3076 1.08 0.0002728 -0.0001225 0.7085 0.0002056 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1554 0.1465 0.1926 0.1318 0.99 0.994 0.1556 0.9718 0.9852 0.2089 ] Network output: [ -0.03978 0.1571 1.091 0.0005105 -0.0002292 0.8332 0.0003847 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1729 0.1711 0.2004 0.1643 0.9853 0.9915 0.1729 0.9545 0.9781 0.205 ] Network output: [ 0.007187 0.9123 0.01061 4.015e-05 -1.802e-05 1.063 3.026e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09052 Epoch 4281 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06252 0.768 0.9539 -2.094e-05 9.399e-06 0.153 -1.578e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004972 -0.004921 -0.01719 0.009711 0.9624 0.9682 0.01134 0.9193 0.9314 0.03875 ] Network output: [ 1.022 -0.2005 0.08779 0.0004966 -0.000223 0.07065 0.0003743 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3105 -0.0206 -0.1475 0.2127 0.9825 0.9928 0.3592 0.9175 0.9793 0.6321 ] Network output: [ 0.0194 0.8185 0.976 -0.0001557 6.992e-05 0.1661 -0.0001174 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007993 0.002881 0.006793 0.007118 0.9904 0.9936 0.008184 0.9727 0.9847 0.01579 ] Network output: [ 0.083 -0.5267 1.046 0.0001623 -7.285e-05 1.315 0.0001223 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.352 0.2397 0.4078 0.3077 0.9841 0.9936 0.3535 0.9232 0.9815 0.6199 ] Network output: [ -0.04443 0.2806 1.073 0.0003222 -0.0001446 0.7366 0.0002428 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1636 0.1544 0.1956 0.1593 0.9899 0.994 0.1637 0.9719 0.9855 0.2119 ] Network output: [ -0.04236 0.2353 1.045 0.0004189 -0.000188 0.8057 0.0003157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1828 0.181 0.198 0.1699 0.9855 0.9916 0.1828 0.9552 0.9783 0.2022 ] Network output: [ 0.01484 0.9328 -0.02383 6.082e-05 -2.73e-05 1.062 4.583e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1208 Epoch 4282 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04924 0.8493 0.9346 -0.000173 7.765e-05 0.1169 -0.0001304 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005142 -0.004865 -0.01662 0.006853 0.9624 0.9682 0.01164 0.9189 0.9309 0.03848 ] Network output: [ 0.9098 0.3794 -0.05593 -0.0005623 0.0002524 -0.1454 -0.0004238 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3223 -0.002543 -0.1152 0.09595 0.9825 0.9928 0.3723 0.9172 0.9791 0.6292 ] Network output: [ 0.01839 0.8375 0.9715 -0.0001928 8.658e-05 0.1535 -0.0001453 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008341 0.003066 0.007357 0.003461 0.9905 0.9936 0.008538 0.9728 0.9845 0.01614 ] Network output: [ -0.01261 0.2494 0.8098 -0.00118 0.0005296 0.9612 -0.0008891 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3633 0.2494 0.4204 0.1329 0.9841 0.9936 0.3649 0.9232 0.9815 0.627 ] Network output: [ -0.04754 0.3074 1.08 0.000272 -0.0001221 0.7087 0.000205 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1551 0.1463 0.1925 0.1317 0.99 0.994 0.1553 0.9717 0.9851 0.2088 ] Network output: [ -0.03979 0.157 1.091 0.0005089 -0.0002285 0.8334 0.0003835 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1726 0.1708 0.2003 0.1642 0.9853 0.9915 0.1726 0.9544 0.9781 0.2049 ] Network output: [ 0.007172 0.9122 0.01075 4.058e-05 -1.822e-05 1.063 3.058e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0904 Epoch 4283 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06252 0.7681 0.9538 -2.11e-05 9.47e-06 0.153 -1.59e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004967 -0.004915 -0.01716 0.009702 0.9624 0.9683 0.01132 0.9192 0.9313 0.03866 ] Network output: [ 1.022 -0.2002 0.08748 0.0004945 -0.000222 0.07082 0.0003727 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3102 -0.02058 -0.1475 0.2126 0.9825 0.9928 0.3589 0.9172 0.9792 0.6317 ] Network output: [ 0.01943 0.8185 0.9759 -0.0001553 6.972e-05 0.1661 -0.000117 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007979 0.002876 0.006777 0.007103 0.9904 0.9936 0.008169 0.9726 0.9846 0.01577 ] Network output: [ 0.08282 -0.526 1.046 0.0001608 -7.219e-05 1.315 0.0001212 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3516 0.2395 0.4075 0.3073 0.9841 0.9936 0.3531 0.923 0.9814 0.6196 ] Network output: [ -0.04448 0.2805 1.073 0.000321 -0.0001441 0.7367 0.0002419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1633 0.154 0.1955 0.1592 0.9899 0.994 0.1634 0.9718 0.9855 0.2118 ] Network output: [ -0.04237 0.2352 1.046 0.0004174 -0.0001874 0.8057 0.0003146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1824 0.1806 0.1979 0.1698 0.9855 0.9916 0.1825 0.955 0.9783 0.2021 ] Network output: [ 0.01479 0.9333 -0.02383 6.027e-05 -2.706e-05 1.061 4.542e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1206 Epoch 4284 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04928 0.8492 0.9346 -0.0001724 7.738e-05 0.1169 -0.0001299 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005136 -0.004859 -0.0166 0.006851 0.9624 0.9682 0.01162 0.9189 0.9309 0.03839 ] Network output: [ 0.91 0.379 -0.05557 -0.0005595 0.0002512 -0.1456 -0.0004217 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.322 -0.002582 -0.1154 0.09602 0.9825 0.9928 0.3719 0.917 0.979 0.6288 ] Network output: [ 0.01845 0.8374 0.9715 -0.0001922 8.627e-05 0.1535 -0.0001448 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008326 0.003059 0.007333 0.003454 0.9905 0.9936 0.008522 0.9727 0.9844 0.01611 ] Network output: [ -0.01249 0.249 0.8102 -0.001176 0.0005278 0.961 -0.0008859 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3629 0.2491 0.42 0.1327 0.9841 0.9936 0.3645 0.923 0.9814 0.6267 ] Network output: [ -0.04755 0.3072 1.08 0.0002712 -0.0001218 0.7088 0.0002044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1548 0.146 0.1923 0.1316 0.99 0.994 0.155 0.9716 0.9851 0.2087 ] Network output: [ -0.03979 0.1569 1.091 0.0005073 -0.0002277 0.8335 0.0003823 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1723 0.1705 0.2002 0.1641 0.9853 0.9915 0.1723 0.9543 0.978 0.2048 ] Network output: [ 0.007157 0.9121 0.01088 4.101e-05 -1.841e-05 1.063 3.091e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09027 Epoch 4285 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06253 0.7681 0.9537 -2.125e-05 9.54e-06 0.153 -1.602e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004963 -0.004908 -0.01714 0.009693 0.9624 0.9683 0.0113 0.9192 0.9313 0.03858 ] Network output: [ 1.022 -0.1999 0.08717 0.0004924 -0.0002211 0.07099 0.0003711 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.31 -0.02055 -0.1475 0.2126 0.9825 0.9928 0.3585 0.917 0.9792 0.6314 ] Network output: [ 0.01946 0.8185 0.9759 -0.0001549 6.953e-05 0.1661 -0.0001167 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007965 0.002872 0.006761 0.007088 0.9904 0.9936 0.008155 0.9726 0.9846 0.01574 ] Network output: [ 0.08264 -0.5252 1.045 0.0001593 -7.153e-05 1.315 0.0001201 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3512 0.2393 0.4072 0.3069 0.9841 0.9936 0.3527 0.9228 0.9814 0.6192 ] Network output: [ -0.04453 0.2803 1.073 0.0003199 -0.0001436 0.7368 0.0002411 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1629 0.1537 0.1954 0.159 0.9899 0.994 0.163 0.9718 0.9854 0.2117 ] Network output: [ -0.04238 0.2351 1.046 0.000416 -0.0001868 0.8057 0.0003135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1821 0.1803 0.1978 0.1696 0.9855 0.9916 0.1821 0.9549 0.9782 0.202 ] Network output: [ 0.01473 0.9338 -0.02383 5.974e-05 -2.682e-05 1.061 4.502e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1204 Epoch 4286 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04932 0.8491 0.9346 -0.0001717 7.71e-05 0.117 -0.0001294 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00513 -0.004852 -0.01658 0.00685 0.9624 0.9683 0.0116 0.9188 0.9308 0.03831 ] Network output: [ 0.9101 0.3786 -0.05521 -0.0005568 0.00025 -0.1458 -0.0004196 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3217 -0.002621 -0.1156 0.09609 0.9825 0.9928 0.3715 0.9168 0.979 0.6285 ] Network output: [ 0.0185 0.8373 0.9714 -0.0001915 8.597e-05 0.1535 -0.0001443 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008311 0.003052 0.00731 0.003448 0.9905 0.9936 0.008507 0.9727 0.9844 0.01608 ] Network output: [ -0.01237 0.2486 0.8105 -0.001171 0.0005259 0.9609 -0.0008828 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3625 0.2488 0.4196 0.1326 0.9841 0.9936 0.3641 0.9228 0.9814 0.6263 ] Network output: [ -0.04756 0.307 1.08 0.0002704 -0.0001214 0.709 0.0002038 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1545 0.1457 0.1922 0.1315 0.99 0.994 0.1547 0.9715 0.9851 0.2085 ] Network output: [ -0.0398 0.1567 1.091 0.0005058 -0.0002271 0.8337 0.0003812 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.172 0.1702 0.2001 0.164 0.9853 0.9915 0.172 0.9542 0.978 0.2047 ] Network output: [ 0.007142 0.9119 0.01102 4.145e-05 -1.861e-05 1.063 3.124e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09015 Epoch 4287 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06253 0.7682 0.9536 -2.14e-05 9.608e-06 0.1531 -1.613e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004958 -0.004902 -0.01712 0.009684 0.9624 0.9683 0.01128 0.9191 0.9312 0.0385 ] Network output: [ 1.022 -0.1996 0.08686 0.0004904 -0.0002201 0.07115 0.0003696 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3098 -0.02053 -0.1475 0.2126 0.9825 0.9928 0.3582 0.9168 0.9791 0.6311 ] Network output: [ 0.0195 0.8185 0.9758 -0.0001544 6.933e-05 0.166 -0.0001164 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007952 0.002867 0.006745 0.007073 0.9904 0.9936 0.008141 0.9725 0.9845 0.01571 ] Network output: [ 0.08247 -0.5245 1.045 0.0001579 -7.087e-05 1.316 0.000119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3509 0.239 0.407 0.3065 0.9841 0.9936 0.3524 0.9226 0.9814 0.6189 ] Network output: [ -0.04457 0.2802 1.073 0.0003188 -0.0001431 0.7369 0.0002402 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1626 0.1534 0.1953 0.1589 0.9899 0.994 0.1627 0.9717 0.9854 0.2116 ] Network output: [ -0.04238 0.235 1.046 0.0004147 -0.0001862 0.8057 0.0003125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1817 0.1799 0.1977 0.1694 0.9855 0.9916 0.1818 0.9548 0.9781 0.2019 ] Network output: [ 0.01468 0.9343 -0.02383 5.92e-05 -2.658e-05 1.06 4.462e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1202 Epoch 4288 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04936 0.849 0.9346 -0.0001711 7.682e-05 0.117 -0.000129 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005124 -0.004845 -0.01656 0.006848 0.9624 0.9683 0.01158 0.9187 0.9307 0.03822 ] Network output: [ 0.9102 0.3782 -0.05485 -0.000554 0.0002487 -0.146 -0.0004175 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3213 -0.002658 -0.1158 0.09615 0.9825 0.9928 0.3711 0.9166 0.979 0.6281 ] Network output: [ 0.01855 0.8372 0.9714 -0.0001908 8.567e-05 0.1535 -0.0001438 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008296 0.003045 0.007287 0.003441 0.9905 0.9936 0.008491 0.9726 0.9843 0.01605 ] Network output: [ -0.01226 0.2482 0.8108 -0.001167 0.000524 0.9608 -0.0008797 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3621 0.2485 0.4191 0.1324 0.9841 0.9936 0.3637 0.9226 0.9813 0.626 ] Network output: [ -0.04756 0.3068 1.08 0.0002697 -0.0001211 0.7091 0.0002032 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1542 0.1454 0.1921 0.1314 0.99 0.994 0.1544 0.9715 0.985 0.2084 ] Network output: [ -0.0398 0.1566 1.091 0.0005042 -0.0002264 0.8339 0.00038 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1717 0.1699 0.2 0.1639 0.9853 0.9915 0.1717 0.9541 0.9779 0.2046 ] Network output: [ 0.007128 0.9118 0.01115 4.188e-05 -1.88e-05 1.063 3.156e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09003 Epoch 4289 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06254 0.7683 0.9535 -2.155e-05 9.675e-06 0.1531 -1.624e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004954 -0.004895 -0.01709 0.009675 0.9625 0.9683 0.01126 0.919 0.9311 0.03841 ] Network output: [ 1.022 -0.1993 0.08654 0.0004883 -0.0002192 0.07131 0.000368 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3095 -0.0205 -0.1475 0.2125 0.9825 0.9928 0.3578 0.9166 0.9791 0.6308 ] Network output: [ 0.01953 0.8186 0.9757 -0.000154 6.914e-05 0.166 -0.0001161 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007938 0.002862 0.006729 0.007059 0.9904 0.9936 0.008127 0.9724 0.9845 0.01569 ] Network output: [ 0.08229 -0.5237 1.044 0.0001564 -7.02e-05 1.316 0.0001179 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3505 0.2388 0.4067 0.3061 0.9841 0.9936 0.352 0.9224 0.9813 0.6185 ] Network output: [ -0.04462 0.28 1.074 0.0003177 -0.0001426 0.737 0.0002394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1622 0.1531 0.1952 0.1587 0.9899 0.994 0.1624 0.9716 0.9853 0.2115 ] Network output: [ -0.04239 0.2349 1.046 0.0004133 -0.0001855 0.8058 0.0003115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1814 0.1796 0.1976 0.1693 0.9855 0.9916 0.1814 0.9547 0.9781 0.2018 ] Network output: [ 0.01463 0.9348 -0.02382 5.867e-05 -2.634e-05 1.06 4.422e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1201 Epoch 4290 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04939 0.8489 0.9346 -0.0001705 7.655e-05 0.117 -0.0001285 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005119 -0.004839 -0.01654 0.006846 0.9625 0.9683 0.01156 0.9187 0.9306 0.03814 ] Network output: [ 0.9104 0.3778 -0.05449 -0.0005513 0.0002475 -0.1462 -0.0004155 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.321 -0.002695 -0.116 0.09622 0.9825 0.9928 0.3706 0.9164 0.9789 0.6278 ] Network output: [ 0.0186 0.8372 0.9714 -0.0001902 8.537e-05 0.1535 -0.0001433 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008281 0.003038 0.007264 0.003435 0.9905 0.9936 0.008475 0.9725 0.9843 0.01602 ] Network output: [ -0.01215 0.2478 0.8112 -0.001163 0.0005222 0.9606 -0.0008766 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3617 0.2482 0.4187 0.1322 0.9841 0.9936 0.3633 0.9224 0.9813 0.6256 ] Network output: [ -0.04756 0.3065 1.08 0.0002689 -0.0001207 0.7093 0.0002026 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.154 0.1451 0.192 0.1313 0.99 0.994 0.1541 0.9714 0.985 0.2083 ] Network output: [ -0.0398 0.1565 1.091 0.0005027 -0.0002257 0.8341 0.0003788 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1714 0.1696 0.1999 0.1637 0.9853 0.9915 0.1714 0.954 0.9779 0.2045 ] Network output: [ 0.007115 0.9117 0.01128 4.232e-05 -1.9e-05 1.063 3.189e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0899 Epoch 4291 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06254 0.7684 0.9534 -2.17e-05 9.741e-06 0.1531 -1.635e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004949 -0.004889 -0.01707 0.009666 0.9625 0.9683 0.01125 0.919 0.931 0.03833 ] Network output: [ 1.022 -0.1991 0.08622 0.0004863 -0.0002183 0.07146 0.0003665 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3093 -0.02048 -0.1475 0.2125 0.9825 0.9928 0.3575 0.9164 0.979 0.6305 ] Network output: [ 0.01956 0.8186 0.9757 -0.0001536 6.894e-05 0.166 -0.0001157 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007924 0.002857 0.006713 0.007044 0.9904 0.9936 0.008112 0.9724 0.9845 0.01566 ] Network output: [ 0.08212 -0.523 1.043 0.0001549 -6.954e-05 1.316 0.0001167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3502 0.2386 0.4064 0.3057 0.9841 0.9936 0.3517 0.9222 0.9813 0.6182 ] Network output: [ -0.04466 0.2799 1.074 0.0003166 -0.0001421 0.7371 0.0002386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1619 0.1527 0.1951 0.1586 0.9899 0.994 0.162 0.9715 0.9853 0.2114 ] Network output: [ -0.04239 0.2348 1.046 0.0004119 -0.0001849 0.8058 0.0003105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.181 0.1792 0.1974 0.1691 0.9855 0.9916 0.181 0.9546 0.978 0.2017 ] Network output: [ 0.01458 0.9353 -0.02382 5.815e-05 -2.61e-05 1.06 4.382e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1199 Epoch 4292 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04943 0.8488 0.9346 -0.0001699 7.628e-05 0.117 -0.000128 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005113 -0.004832 -0.01652 0.006844 0.9625 0.9683 0.01154 0.9186 0.9305 0.03806 ] Network output: [ 0.9105 0.3774 -0.05413 -0.0005486 0.0002463 -0.1464 -0.0004135 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3207 -0.002731 -0.1162 0.09629 0.9825 0.9928 0.3702 0.9162 0.9789 0.6274 ] Network output: [ 0.01865 0.8371 0.9713 -0.0001895 8.507e-05 0.1535 -0.0001428 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008266 0.003031 0.007241 0.003428 0.9905 0.9936 0.00846 0.9725 0.9843 0.01599 ] Network output: [ -0.01205 0.2474 0.8115 -0.001159 0.0005204 0.9605 -0.0008735 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3614 0.248 0.4183 0.1321 0.9842 0.9936 0.3629 0.9222 0.9812 0.6253 ] Network output: [ -0.04757 0.3063 1.08 0.0002681 -0.0001204 0.7094 0.0002021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1537 0.1449 0.1918 0.1312 0.99 0.994 0.1538 0.9713 0.9849 0.2082 ] Network output: [ -0.0398 0.1563 1.091 0.0005012 -0.000225 0.8343 0.0003777 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1711 0.1693 0.1998 0.1636 0.9853 0.9915 0.1711 0.9539 0.9778 0.2044 ] Network output: [ 0.007102 0.9116 0.01141 4.276e-05 -1.919e-05 1.063 3.222e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08978 Epoch 4293 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06254 0.7684 0.9533 -2.184e-05 9.805e-06 0.1531 -1.646e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004945 -0.004883 -0.01705 0.009657 0.9625 0.9683 0.01123 0.9189 0.9309 0.03825 ] Network output: [ 1.022 -0.1988 0.0859 0.0004842 -0.0002174 0.07161 0.0003649 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.309 -0.02046 -0.1475 0.2124 0.9825 0.9928 0.3572 0.9162 0.979 0.6301 ] Network output: [ 0.01959 0.8186 0.9756 -0.0001531 6.875e-05 0.1659 -0.0001154 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007911 0.002852 0.006698 0.007029 0.9904 0.9936 0.008098 0.9723 0.9844 0.01563 ] Network output: [ 0.08195 -0.5222 1.043 0.0001534 -6.889e-05 1.316 0.0001156 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3498 0.2384 0.4062 0.3053 0.9841 0.9936 0.3513 0.922 0.9812 0.6179 ] Network output: [ -0.0447 0.2797 1.074 0.0003155 -0.0001416 0.7372 0.0002378 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1616 0.1524 0.195 0.1584 0.9899 0.994 0.1617 0.9715 0.9853 0.2113 ] Network output: [ -0.0424 0.2347 1.046 0.0004106 -0.0001843 0.8058 0.0003094 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1807 0.1789 0.1973 0.169 0.9855 0.9916 0.1807 0.9545 0.978 0.2016 ] Network output: [ 0.01452 0.9358 -0.02382 5.762e-05 -2.587e-05 1.059 4.343e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1197 Epoch 4294 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04947 0.8487 0.9346 -0.0001693 7.6e-05 0.117 -0.0001276 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005107 -0.004826 -0.01651 0.006843 0.9625 0.9683 0.01152 0.9185 0.9305 0.03797 ] Network output: [ 0.9106 0.377 -0.05376 -0.000546 0.0002451 -0.1466 -0.0004115 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3204 -0.002767 -0.1164 0.09636 0.9825 0.9928 0.3698 0.916 0.9788 0.6271 ] Network output: [ 0.0187 0.8371 0.9713 -0.0001888 8.477e-05 0.1535 -0.0001423 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008252 0.003024 0.007218 0.003422 0.9905 0.9936 0.008445 0.9724 0.9842 0.01596 ] Network output: [ -0.01194 0.247 0.8119 -0.001155 0.0005186 0.9603 -0.0008705 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.361 0.2477 0.4179 0.1319 0.9842 0.9936 0.3625 0.922 0.9812 0.625 ] Network output: [ -0.04757 0.3061 1.081 0.0002674 -0.00012 0.7096 0.0002015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1534 0.1446 0.1917 0.1311 0.99 0.994 0.1535 0.9712 0.9849 0.2081 ] Network output: [ -0.0398 0.1562 1.091 0.0004997 -0.0002243 0.8345 0.0003766 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1708 0.1691 0.1997 0.1635 0.9853 0.9915 0.1708 0.9538 0.9778 0.2043 ] Network output: [ 0.00709 0.9115 0.01153 4.319e-05 -1.939e-05 1.063 3.255e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08965 Epoch 4295 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06254 0.7685 0.9532 -2.198e-05 9.869e-06 0.1531 -1.657e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00494 -0.004876 -0.01703 0.009648 0.9625 0.9684 0.01121 0.9188 0.9309 0.03817 ] Network output: [ 1.022 -0.1985 0.08558 0.0004822 -0.0002165 0.07175 0.0003634 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3088 -0.02043 -0.1475 0.2124 0.9826 0.9928 0.3569 0.916 0.979 0.6298 ] Network output: [ 0.01962 0.8187 0.9756 -0.0001527 6.855e-05 0.1659 -0.0001151 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007898 0.002848 0.006682 0.007015 0.9904 0.9936 0.008084 0.9723 0.9844 0.01561 ] Network output: [ 0.08179 -0.5215 1.042 0.000152 -6.823e-05 1.316 0.0001145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3494 0.2382 0.4059 0.305 0.9841 0.9936 0.3509 0.9218 0.9812 0.6175 ] Network output: [ -0.04475 0.2796 1.074 0.0003144 -0.0001411 0.7373 0.0002369 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1612 0.1521 0.1949 0.1583 0.9899 0.994 0.1613 0.9714 0.9852 0.2112 ] Network output: [ -0.0424 0.2345 1.046 0.0004093 -0.0001837 0.8059 0.0003084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1803 0.1785 0.1972 0.1688 0.9855 0.9916 0.1804 0.9544 0.9779 0.2015 ] Network output: [ 0.01446 0.9364 -0.02381 5.71e-05 -2.564e-05 1.059 4.304e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1195 Epoch 4296 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0495 0.8486 0.9346 -0.0001687 7.573e-05 0.1171 -0.0001271 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005102 -0.004819 -0.01649 0.006841 0.9625 0.9683 0.0115 0.9185 0.9304 0.03789 ] Network output: [ 0.9107 0.3765 -0.05339 -0.0005433 0.0002439 -0.1468 -0.0004095 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3201 -0.002802 -0.1166 0.09643 0.9826 0.9928 0.3694 0.9158 0.9788 0.6268 ] Network output: [ 0.01875 0.837 0.9713 -0.0001882 8.448e-05 0.1535 -0.0001418 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008237 0.003017 0.007196 0.003415 0.9905 0.9936 0.00843 0.9723 0.9842 0.01593 ] Network output: [ -0.01184 0.2466 0.8122 -0.001151 0.0005168 0.9602 -0.0008675 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3606 0.2474 0.4175 0.1317 0.9842 0.9936 0.3621 0.9218 0.9812 0.6246 ] Network output: [ -0.04757 0.3059 1.081 0.0002667 -0.0001197 0.7097 0.000201 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1531 0.1443 0.1916 0.131 0.99 0.994 0.1532 0.9712 0.9848 0.208 ] Network output: [ -0.0398 0.156 1.091 0.0004982 -0.0002237 0.8347 0.0003755 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1705 0.1688 0.1996 0.1634 0.9853 0.9915 0.1705 0.9537 0.9777 0.2042 ] Network output: [ 0.007078 0.9114 0.01166 4.364e-05 -1.959e-05 1.063 3.289e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08953 Epoch 4297 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06255 0.7686 0.9531 -2.212e-05 9.931e-06 0.1531 -1.667e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004936 -0.00487 -0.017 0.00964 0.9625 0.9684 0.01119 0.9188 0.9308 0.03809 ] Network output: [ 1.022 -0.1982 0.08526 0.0004802 -0.0002156 0.07189 0.0003619 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3086 -0.02041 -0.1475 0.2123 0.9826 0.9928 0.3565 0.9158 0.9789 0.6295 ] Network output: [ 0.01964 0.8187 0.9755 -0.0001523 6.836e-05 0.1659 -0.0001148 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007884 0.002843 0.006667 0.007 0.9904 0.9936 0.00807 0.9722 0.9844 0.01558 ] Network output: [ 0.08162 -0.5207 1.042 0.0001505 -6.757e-05 1.316 0.0001134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3491 0.238 0.4056 0.3046 0.9842 0.9936 0.3506 0.9216 0.9811 0.6172 ] Network output: [ -0.04479 0.2794 1.074 0.0003133 -0.0001407 0.7374 0.0002361 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1609 0.1518 0.1948 0.1581 0.9899 0.994 0.161 0.9713 0.9852 0.2111 ] Network output: [ -0.0424 0.2344 1.046 0.0004079 -0.0001831 0.8059 0.0003074 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.18 0.1782 0.1971 0.1687 0.9855 0.9916 0.18 0.9543 0.9779 0.2014 ] Network output: [ 0.01441 0.9369 -0.0238 5.659e-05 -2.54e-05 1.058 4.264e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1194 Epoch 4298 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04954 0.8486 0.9346 -0.0001681 7.546e-05 0.1171 -0.0001267 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005096 -0.004812 -0.01647 0.00684 0.9625 0.9684 0.01148 0.9184 0.9303 0.03781 ] Network output: [ 0.9109 0.3761 -0.05302 -0.0005407 0.0002427 -0.147 -0.0004075 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3198 -0.002836 -0.1168 0.0965 0.9826 0.9928 0.369 0.9156 0.9787 0.6264 ] Network output: [ 0.0188 0.837 0.9712 -0.0001875 8.418e-05 0.1534 -0.0001413 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008222 0.00301 0.007174 0.003409 0.9905 0.9936 0.008414 0.9723 0.9841 0.0159 ] Network output: [ -0.01174 0.2462 0.8126 -0.001147 0.000515 0.96 -0.0008645 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3602 0.2471 0.4171 0.1315 0.9842 0.9937 0.3617 0.9216 0.9811 0.6243 ] Network output: [ -0.04757 0.3057 1.081 0.0002659 -0.0001194 0.7099 0.0002004 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1528 0.144 0.1915 0.1309 0.99 0.994 0.1529 0.9711 0.9848 0.2079 ] Network output: [ -0.03979 0.1559 1.091 0.0004967 -0.000223 0.8349 0.0003744 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1702 0.1685 0.1995 0.1633 0.9853 0.9915 0.1702 0.9536 0.9777 0.2041 ] Network output: [ 0.007067 0.9113 0.01178 4.408e-05 -1.979e-05 1.063 3.322e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08941 Epoch 4299 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06255 0.7687 0.953 -2.226e-05 9.992e-06 0.1531 -1.677e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004931 -0.004864 -0.01698 0.009631 0.9626 0.9684 0.01118 0.9187 0.9307 0.03801 ] Network output: [ 1.021 -0.1979 0.08493 0.0004782 -0.0002147 0.07202 0.0003604 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3083 -0.02039 -0.1475 0.2123 0.9826 0.9928 0.3562 0.9156 0.9789 0.6292 ] Network output: [ 0.01967 0.8188 0.9755 -0.0001518 6.817e-05 0.1658 -0.0001144 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007871 0.002838 0.006651 0.006986 0.9904 0.9936 0.008057 0.9721 0.9843 0.01556 ] Network output: [ 0.08146 -0.52 1.041 0.000149 -6.691e-05 1.316 0.0001123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3487 0.2377 0.4054 0.3042 0.9842 0.9936 0.3502 0.9214 0.9811 0.6169 ] Network output: [ -0.04483 0.2792 1.074 0.0003123 -0.0001402 0.7375 0.0002353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1606 0.1515 0.1947 0.158 0.9899 0.994 0.1607 0.9713 0.9851 0.211 ] Network output: [ -0.04241 0.2343 1.046 0.0004066 -0.0001826 0.8059 0.0003065 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1796 0.1779 0.197 0.1685 0.9855 0.9916 0.1797 0.9542 0.9778 0.2013 ] Network output: [ 0.01435 0.9374 -0.0238 5.607e-05 -2.517e-05 1.058 4.226e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1192 Epoch 4300 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04957 0.8485 0.9346 -0.0001675 7.519e-05 0.1171 -0.0001262 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005091 -0.004806 -0.01645 0.006838 0.9626 0.9684 0.01146 0.9183 0.9302 0.03772 ] Network output: [ 0.911 0.3757 -0.05266 -0.0005381 0.0002416 -0.1472 -0.0004055 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3195 -0.002869 -0.117 0.09657 0.9826 0.9928 0.3686 0.9154 0.9787 0.6261 ] Network output: [ 0.01884 0.8369 0.9712 -0.0001869 8.389e-05 0.1534 -0.0001408 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008208 0.003003 0.007152 0.003403 0.9905 0.9936 0.008399 0.9722 0.9841 0.01588 ] Network output: [ -0.01164 0.2458 0.8129 -0.001143 0.0005132 0.9599 -0.0008616 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3598 0.2468 0.4167 0.1314 0.9842 0.9937 0.3613 0.9214 0.9811 0.624 ] Network output: [ -0.04757 0.3055 1.081 0.0002652 -0.0001191 0.7101 0.0001999 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1525 0.1438 0.1913 0.1308 0.99 0.994 0.1526 0.971 0.9848 0.2078 ] Network output: [ -0.03979 0.1557 1.091 0.0004953 -0.0002224 0.8351 0.0003733 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1699 0.1682 0.1994 0.1632 0.9854 0.9915 0.17 0.9535 0.9776 0.204 ] Network output: [ 0.007057 0.9112 0.0119 4.452e-05 -1.999e-05 1.063 3.355e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08928 Epoch 4301 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06255 0.7688 0.9529 -2.239e-05 1.005e-05 0.1531 -1.687e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004927 -0.004857 -0.01696 0.009622 0.9626 0.9684 0.01116 0.9186 0.9306 0.03793 ] Network output: [ 1.021 -0.1977 0.0846 0.0004762 -0.0002138 0.07215 0.0003589 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3081 -0.02037 -0.1475 0.2123 0.9826 0.9928 0.3559 0.9154 0.9788 0.6289 ] Network output: [ 0.0197 0.8188 0.9754 -0.0001514 6.797e-05 0.1658 -0.0001141 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007858 0.002834 0.006636 0.006971 0.9904 0.9936 0.008043 0.9721 0.9843 0.01553 ] Network output: [ 0.0813 -0.5193 1.041 0.0001476 -6.625e-05 1.317 0.0001112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3484 0.2375 0.4051 0.3038 0.9842 0.9937 0.3499 0.9212 0.9811 0.6166 ] Network output: [ -0.04487 0.2791 1.074 0.0003112 -0.0001397 0.7376 0.0002346 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1602 0.1512 0.1946 0.1579 0.9899 0.994 0.1604 0.9712 0.9851 0.2109 ] Network output: [ -0.04241 0.2342 1.046 0.0004053 -0.000182 0.806 0.0003055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1793 0.1775 0.1969 0.1683 0.9855 0.9916 0.1793 0.9541 0.9778 0.2012 ] Network output: [ 0.01429 0.9379 -0.02379 5.556e-05 -2.494e-05 1.058 4.187e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.119 Epoch 4302 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0496 0.8484 0.9346 -0.0001669 7.492e-05 0.1171 -0.0001258 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005085 -0.004799 -0.01643 0.006837 0.9626 0.9684 0.01144 0.9183 0.9301 0.03764 ] Network output: [ 0.9111 0.3753 -0.05228 -0.0005355 0.0002404 -0.1474 -0.0004036 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3192 -0.002902 -0.1172 0.09664 0.9826 0.9928 0.3682 0.9152 0.9786 0.6258 ] Network output: [ 0.01889 0.8369 0.9712 -0.0001862 8.36e-05 0.1534 -0.0001403 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008194 0.002997 0.007131 0.003397 0.9905 0.9936 0.008385 0.9722 0.9841 0.01585 ] Network output: [ -0.01155 0.2455 0.8132 -0.001139 0.0005115 0.9598 -0.0008587 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3594 0.2465 0.4163 0.1312 0.9842 0.9937 0.3609 0.9212 0.981 0.6237 ] Network output: [ -0.04756 0.3053 1.081 0.0002645 -0.0001188 0.7102 0.0001994 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1522 0.1435 0.1912 0.1307 0.99 0.994 0.1523 0.9709 0.9847 0.2077 ] Network output: [ -0.03978 0.1556 1.091 0.0004938 -0.0002217 0.8353 0.0003722 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1696 0.1679 0.1993 0.1631 0.9854 0.9915 0.1697 0.9534 0.9775 0.2039 ] Network output: [ 0.007047 0.911 0.01202 4.497e-05 -2.019e-05 1.063 3.389e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08915 Epoch 4303 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06255 0.7689 0.9528 -2.252e-05 1.011e-05 0.1532 -1.697e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004923 -0.004851 -0.01693 0.009614 0.9626 0.9684 0.01114 0.9186 0.9305 0.03785 ] Network output: [ 1.021 -0.1974 0.08427 0.0004743 -0.0002129 0.07228 0.0003574 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3078 -0.02035 -0.1476 0.2122 0.9826 0.9928 0.3556 0.9152 0.9788 0.6286 ] Network output: [ 0.01972 0.8188 0.9754 -0.000151 6.778e-05 0.1657 -0.0001138 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007844 0.002829 0.006621 0.006957 0.9904 0.9936 0.008029 0.972 0.9842 0.01551 ] Network output: [ 0.08115 -0.5186 1.04 0.0001461 -6.559e-05 1.317 0.0001101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.348 0.2373 0.4049 0.3034 0.9842 0.9937 0.3495 0.921 0.981 0.6163 ] Network output: [ -0.04491 0.2789 1.074 0.0003102 -0.0001393 0.7377 0.0002338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1599 0.1509 0.1945 0.1577 0.9899 0.994 0.16 0.9711 0.985 0.2108 ] Network output: [ -0.04241 0.234 1.046 0.0004041 -0.0001814 0.806 0.0003045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.179 0.1772 0.1969 0.1682 0.9855 0.9916 0.179 0.954 0.9777 0.2011 ] Network output: [ 0.01423 0.9384 -0.02378 5.504e-05 -2.471e-05 1.057 4.148e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1188 Epoch 4304 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04963 0.8483 0.9346 -0.0001663 7.466e-05 0.1171 -0.0001253 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005079 -0.004793 -0.01641 0.006835 0.9626 0.9684 0.01142 0.9182 0.9301 0.03756 ] Network output: [ 0.9112 0.3749 -0.05191 -0.0005329 0.0002392 -0.1476 -0.0004016 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3189 -0.002935 -0.1174 0.09671 0.9826 0.9928 0.3678 0.915 0.9786 0.6255 ] Network output: [ 0.01894 0.8368 0.9711 -0.0001856 8.33e-05 0.1534 -0.0001398 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008179 0.00299 0.00711 0.00339 0.9905 0.9936 0.00837 0.9721 0.984 0.01582 ] Network output: [ -0.01146 0.2451 0.8136 -0.001136 0.0005098 0.9596 -0.0008558 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.359 0.2463 0.4159 0.131 0.9842 0.9937 0.3605 0.921 0.981 0.6234 ] Network output: [ -0.04756 0.305 1.081 0.0002638 -0.0001184 0.7104 0.0001988 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.152 0.1432 0.1911 0.1306 0.99 0.994 0.1521 0.9709 0.9847 0.2076 ] Network output: [ -0.03977 0.1554 1.091 0.0004924 -0.0002211 0.8355 0.0003711 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1694 0.1676 0.1992 0.1629 0.9854 0.9915 0.1694 0.9533 0.9775 0.2038 ] Network output: [ 0.007037 0.9109 0.01214 4.542e-05 -2.039e-05 1.063 3.423e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08903 Epoch 4305 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06255 0.769 0.9527 -2.265e-05 1.017e-05 0.1532 -1.707e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004918 -0.004845 -0.01691 0.009605 0.9626 0.9684 0.01112 0.9185 0.9305 0.03777 ] Network output: [ 1.021 -0.1971 0.08394 0.0004723 -0.000212 0.07239 0.0003559 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3076 -0.02032 -0.1476 0.2122 0.9826 0.9928 0.3552 0.915 0.9787 0.6284 ] Network output: [ 0.01975 0.8189 0.9753 -0.0001506 6.759e-05 0.1657 -0.0001135 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007831 0.002824 0.006606 0.006943 0.9904 0.9936 0.008016 0.972 0.9842 0.01548 ] Network output: [ 0.08099 -0.5178 1.04 0.0001446 -6.494e-05 1.317 0.000109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3477 0.2371 0.4046 0.3031 0.9842 0.9937 0.3491 0.9208 0.981 0.616 ] Network output: [ -0.04495 0.2787 1.075 0.0003092 -0.0001388 0.7378 0.000233 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1596 0.1506 0.1944 0.1576 0.9899 0.994 0.1597 0.971 0.985 0.2107 ] Network output: [ -0.04241 0.2339 1.047 0.0004028 -0.0001808 0.8061 0.0003035 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1786 0.1769 0.1968 0.168 0.9855 0.9916 0.1786 0.9539 0.9777 0.201 ] Network output: [ 0.01417 0.9389 -0.02378 5.453e-05 -2.448e-05 1.057 4.11e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1187 Epoch 4306 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04966 0.8483 0.9346 -0.0001657 7.439e-05 0.1171 -0.0001249 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005074 -0.004786 -0.01639 0.006834 0.9626 0.9684 0.0114 0.9181 0.93 0.03748 ] Network output: [ 0.9113 0.3745 -0.05154 -0.0005304 0.0002381 -0.1478 -0.0003997 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3186 -0.002966 -0.1175 0.09678 0.9826 0.9928 0.3674 0.9147 0.9785 0.6252 ] Network output: [ 0.01898 0.8368 0.9711 -0.0001849 8.301e-05 0.1534 -0.0001394 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008165 0.002984 0.007089 0.003384 0.9905 0.9936 0.008355 0.972 0.984 0.01579 ] Network output: [ -0.01137 0.2448 0.8139 -0.001132 0.0005081 0.9595 -0.0008529 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3587 0.246 0.4155 0.1308 0.9842 0.9937 0.3602 0.9208 0.981 0.6231 ] Network output: [ -0.04756 0.3048 1.081 0.0002631 -0.0001181 0.7105 0.0001983 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1517 0.143 0.191 0.1305 0.99 0.994 0.1518 0.9708 0.9846 0.2075 ] Network output: [ -0.03976 0.1552 1.091 0.000491 -0.0002204 0.8357 0.00037 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1691 0.1673 0.1991 0.1628 0.9854 0.9915 0.1691 0.9532 0.9774 0.2037 ] Network output: [ 0.007028 0.9108 0.01226 4.587e-05 -2.059e-05 1.063 3.457e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0889 Epoch 4307 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06255 0.769 0.9526 -2.278e-05 1.023e-05 0.1532 -1.717e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004914 -0.004839 -0.01689 0.009596 0.9626 0.9685 0.01111 0.9184 0.9304 0.03769 ] Network output: [ 1.021 -0.1968 0.08361 0.0004703 -0.0002112 0.07251 0.0003545 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3074 -0.0203 -0.1476 0.2121 0.9826 0.9928 0.3549 0.9147 0.9787 0.6281 ] Network output: [ 0.01977 0.8189 0.9753 -0.0001501 6.74e-05 0.1656 -0.0001131 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007818 0.00282 0.006591 0.006928 0.9904 0.9936 0.008002 0.9719 0.9842 0.01546 ] Network output: [ 0.08084 -0.5171 1.039 0.0001432 -6.428e-05 1.317 0.0001079 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3473 0.2369 0.4044 0.3027 0.9842 0.9937 0.3488 0.9206 0.9809 0.6157 ] Network output: [ -0.04498 0.2786 1.075 0.0003081 -0.0001383 0.7379 0.0002322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1593 0.1503 0.1943 0.1575 0.9899 0.994 0.1594 0.971 0.985 0.2106 ] Network output: [ -0.04241 0.2337 1.047 0.0004015 -0.0001803 0.8061 0.0003026 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1783 0.1765 0.1967 0.1679 0.9855 0.9916 0.1783 0.9538 0.9776 0.2009 ] Network output: [ 0.01411 0.9394 -0.02377 5.402e-05 -2.425e-05 1.056 4.071e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1185 Epoch 4308 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04969 0.8482 0.9346 -0.0001651 7.412e-05 0.1171 -0.0001244 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005068 -0.00478 -0.01637 0.006832 0.9626 0.9684 0.01138 0.9181 0.9299 0.0374 ] Network output: [ 0.9114 0.3742 -0.05116 -0.0005278 0.000237 -0.1479 -0.0003978 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3183 -0.002998 -0.1177 0.09685 0.9826 0.9928 0.367 0.9145 0.9785 0.6249 ] Network output: [ 0.01903 0.8368 0.9711 -0.0001843 8.272e-05 0.1534 -0.0001389 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008151 0.002977 0.007068 0.003378 0.9905 0.9936 0.00834 0.972 0.9839 0.01577 ] Network output: [ -0.01128 0.2444 0.8142 -0.001128 0.0005064 0.9593 -0.0008501 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3583 0.2457 0.4151 0.1307 0.9842 0.9937 0.3598 0.9206 0.9809 0.6227 ] Network output: [ -0.04755 0.3046 1.081 0.0002625 -0.0001178 0.7107 0.0001978 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1514 0.1427 0.1909 0.1304 0.99 0.994 0.1515 0.9707 0.9846 0.2074 ] Network output: [ -0.03975 0.1551 1.091 0.0004896 -0.0002198 0.8359 0.000369 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1688 0.1671 0.199 0.1627 0.9854 0.9915 0.1688 0.953 0.9774 0.2036 ] Network output: [ 0.00702 0.9107 0.01237 4.632e-05 -2.08e-05 1.063 3.491e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08877 Epoch 4309 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06255 0.7691 0.9525 -2.29e-05 1.028e-05 0.1532 -1.726e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00491 -0.004833 -0.01687 0.009588 0.9627 0.9685 0.01109 0.9184 0.9303 0.03761 ] Network output: [ 1.021 -0.1966 0.08327 0.0004684 -0.0002103 0.07262 0.000353 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3071 -0.02028 -0.1476 0.2121 0.9826 0.9928 0.3546 0.9145 0.9786 0.6278 ] Network output: [ 0.0198 0.819 0.9752 -0.0001497 6.721e-05 0.1656 -0.0001128 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007805 0.002815 0.006576 0.006914 0.9904 0.9936 0.007989 0.9719 0.9841 0.01543 ] Network output: [ 0.08068 -0.5164 1.039 0.0001417 -6.362e-05 1.317 0.0001068 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.347 0.2367 0.4041 0.3023 0.9842 0.9937 0.3484 0.9204 0.9809 0.6154 ] Network output: [ -0.04502 0.2784 1.075 0.0003071 -0.0001379 0.738 0.0002315 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1589 0.15 0.1942 0.1573 0.9899 0.994 0.1591 0.9709 0.9849 0.2106 ] Network output: [ -0.04241 0.2336 1.047 0.0004003 -0.0001797 0.8062 0.0003017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1779 0.1762 0.1966 0.1678 0.9855 0.9916 0.178 0.9536 0.9775 0.2008 ] Network output: [ 0.01405 0.9399 -0.02376 5.351e-05 -2.402e-05 1.056 4.033e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1183 Epoch 4310 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04971 0.8481 0.9346 -0.0001645 7.386e-05 0.1171 -0.000124 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005063 -0.004774 -0.01635 0.006831 0.9627 0.9685 0.01136 0.918 0.9298 0.03732 ] Network output: [ 0.9115 0.3738 -0.05079 -0.0005253 0.0002358 -0.1481 -0.0003959 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.318 -0.003028 -0.1179 0.09692 0.9826 0.9928 0.3666 0.9143 0.9784 0.6246 ] Network output: [ 0.01907 0.8367 0.971 -0.0001836 8.244e-05 0.1534 -0.0001384 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008137 0.002971 0.007047 0.003372 0.9905 0.9936 0.008326 0.9719 0.9839 0.01574 ] Network output: [ -0.0112 0.2441 0.8146 -0.001124 0.0005047 0.9592 -0.0008473 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3579 0.2454 0.4147 0.1305 0.9842 0.9937 0.3594 0.9204 0.9809 0.6224 ] Network output: [ -0.04754 0.3043 1.081 0.0002618 -0.0001175 0.7109 0.0001973 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1511 0.1424 0.1908 0.1303 0.99 0.994 0.1512 0.9706 0.9845 0.2073 ] Network output: [ -0.03973 0.1549 1.09 0.0004882 -0.0002192 0.8361 0.0003679 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1685 0.1668 0.1989 0.1626 0.9854 0.9915 0.1685 0.9529 0.9773 0.2035 ] Network output: [ 0.007012 0.9106 0.01248 4.678e-05 -2.1e-05 1.063 3.525e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08865 Epoch 4311 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06254 0.7692 0.9524 -2.303e-05 1.034e-05 0.1532 -1.735e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004905 -0.004827 -0.01684 0.009579 0.9627 0.9685 0.01107 0.9183 0.9302 0.03753 ] Network output: [ 1.021 -0.1963 0.08293 0.0004665 -0.0002094 0.07273 0.0003516 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3069 -0.02026 -0.1476 0.212 0.9826 0.9929 0.3543 0.9143 0.9786 0.6275 ] Network output: [ 0.01982 0.8191 0.9752 -0.0001493 6.702e-05 0.1655 -0.0001125 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007793 0.002811 0.006562 0.0069 0.9904 0.9936 0.007975 0.9718 0.9841 0.01541 ] Network output: [ 0.08053 -0.5157 1.038 0.0001403 -6.297e-05 1.317 0.0001057 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3466 0.2364 0.4039 0.3019 0.9842 0.9937 0.3481 0.9202 0.9808 0.6151 ] Network output: [ -0.04506 0.2782 1.075 0.0003061 -0.0001374 0.7381 0.0002307 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1586 0.1497 0.1941 0.1572 0.9899 0.994 0.1587 0.9708 0.9849 0.2105 ] Network output: [ -0.04241 0.2334 1.047 0.000399 -0.0001791 0.8062 0.0003007 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1776 0.1759 0.1965 0.1676 0.9855 0.9916 0.1776 0.9535 0.9775 0.2008 ] Network output: [ 0.01398 0.9404 -0.02375 5.3e-05 -2.379e-05 1.056 3.994e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1181 Epoch 4312 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04974 0.8481 0.9346 -0.0001639 7.36e-05 0.1172 -0.0001235 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005058 -0.004767 -0.01633 0.006829 0.9627 0.9685 0.01134 0.9179 0.9297 0.03724 ] Network output: [ 0.9116 0.3734 -0.05041 -0.0005228 0.0002347 -0.1483 -0.000394 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3177 -0.003058 -0.1181 0.09699 0.9826 0.9928 0.3662 0.9141 0.9784 0.6243 ] Network output: [ 0.01911 0.8367 0.971 -0.000183 8.215e-05 0.1533 -0.0001379 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008123 0.002964 0.007027 0.003366 0.9905 0.9936 0.008311 0.9719 0.9839 0.01571 ] Network output: [ -0.01111 0.2437 0.8149 -0.001121 0.0005031 0.959 -0.0008445 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3575 0.2452 0.4144 0.1303 0.9842 0.9937 0.359 0.9202 0.9808 0.6221 ] Network output: [ -0.04754 0.3041 1.081 0.0002611 -0.0001172 0.711 0.0001968 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1509 0.1422 0.1906 0.1302 0.99 0.994 0.151 0.9706 0.9845 0.2072 ] Network output: [ -0.03972 0.1547 1.09 0.0004869 -0.0002186 0.8363 0.0003669 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1682 0.1665 0.1988 0.1625 0.9854 0.9915 0.1683 0.9528 0.9773 0.2035 ] Network output: [ 0.007005 0.9105 0.0126 4.724e-05 -2.121e-05 1.063 3.56e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08852 Epoch 4313 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06254 0.7693 0.9523 -2.315e-05 1.039e-05 0.1532 -1.744e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004901 -0.00482 -0.01682 0.00957 0.9627 0.9685 0.01106 0.9182 0.9301 0.03745 ] Network output: [ 1.021 -0.196 0.08259 0.0004646 -0.0002086 0.07283 0.0003501 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3067 -0.02024 -0.1476 0.212 0.9826 0.9929 0.3539 0.9141 0.9785 0.6272 ] Network output: [ 0.01984 0.8191 0.9751 -0.0001489 6.683e-05 0.1655 -0.0001122 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00778 0.002806 0.006547 0.006886 0.9904 0.9936 0.007962 0.9717 0.984 0.01538 ] Network output: [ 0.08039 -0.515 1.037 0.0001388 -6.231e-05 1.317 0.0001046 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3463 0.2362 0.4036 0.3015 0.9842 0.9937 0.3477 0.92 0.9808 0.6148 ] Network output: [ -0.0451 0.278 1.075 0.0003051 -0.000137 0.7383 0.00023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1583 0.1494 0.194 0.157 0.9899 0.994 0.1584 0.9707 0.9848 0.2104 ] Network output: [ -0.04241 0.2333 1.047 0.0003978 -0.0001786 0.8063 0.0002998 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1773 0.1755 0.1964 0.1675 0.9855 0.9916 0.1773 0.9534 0.9774 0.2007 ] Network output: [ 0.01392 0.9409 -0.02374 5.249e-05 -2.356e-05 1.055 3.956e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1179 Epoch 4314 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04977 0.848 0.9346 -0.0001634 7.334e-05 0.1172 -0.0001231 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005052 -0.004761 -0.01631 0.006828 0.9627 0.9685 0.01133 0.9179 0.9296 0.03716 ] Network output: [ 0.9117 0.373 -0.05003 -0.0005203 0.0002336 -0.1484 -0.0003921 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3174 -0.003088 -0.1182 0.09706 0.9826 0.9928 0.3658 0.9139 0.9783 0.624 ] Network output: [ 0.01916 0.8367 0.971 -0.0001823 8.186e-05 0.1533 -0.0001374 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008109 0.002958 0.007007 0.003361 0.9905 0.9936 0.008297 0.9718 0.9838 0.01569 ] Network output: [ -0.01103 0.2434 0.8153 -0.001117 0.0005014 0.9589 -0.0008417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3571 0.2449 0.414 0.1301 0.9842 0.9937 0.3586 0.92 0.9808 0.6219 ] Network output: [ -0.04753 0.3039 1.081 0.0002605 -0.0001169 0.7112 0.0001963 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1506 0.1419 0.1905 0.1301 0.99 0.994 0.1507 0.9705 0.9845 0.2071 ] Network output: [ -0.0397 0.1545 1.09 0.0004855 -0.000218 0.8365 0.0003659 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1679 0.1662 0.1987 0.1624 0.9854 0.9915 0.168 0.9527 0.9772 0.2034 ] Network output: [ 0.006999 0.9104 0.01271 4.77e-05 -2.141e-05 1.063 3.594e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08839 Epoch 4315 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06254 0.7695 0.9522 -2.327e-05 1.045e-05 0.1532 -1.753e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004897 -0.004814 -0.0168 0.009562 0.9627 0.9685 0.01104 0.9182 0.93 0.03737 ] Network output: [ 1.021 -0.1957 0.08225 0.0004627 -0.0002077 0.07293 0.0003487 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3065 -0.02022 -0.1476 0.2119 0.9826 0.9929 0.3536 0.9139 0.9785 0.627 ] Network output: [ 0.01986 0.8192 0.9751 -0.0001484 6.664e-05 0.1654 -0.0001119 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007767 0.002802 0.006532 0.006872 0.9904 0.9936 0.007948 0.9717 0.984 0.01536 ] Network output: [ 0.08024 -0.5143 1.037 0.0001373 -6.165e-05 1.317 0.0001035 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3459 0.236 0.4034 0.3011 0.9842 0.9937 0.3474 0.9198 0.9808 0.6145 ] Network output: [ -0.04513 0.2779 1.075 0.0003042 -0.0001365 0.7384 0.0002292 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.158 0.1491 0.1939 0.1569 0.9899 0.994 0.1581 0.9707 0.9848 0.2103 ] Network output: [ -0.04241 0.2331 1.047 0.0003966 -0.000178 0.8063 0.0002989 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.177 0.1752 0.1963 0.1673 0.9855 0.9916 0.177 0.9533 0.9774 0.2006 ] Network output: [ 0.01385 0.9415 -0.02373 5.198e-05 -2.334e-05 1.055 3.917e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1177 Epoch 4316 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04979 0.8479 0.9346 -0.0001628 7.307e-05 0.1172 -0.0001227 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005047 -0.004754 -0.01629 0.006827 0.9627 0.9685 0.01131 0.9178 0.9296 0.03708 ] Network output: [ 0.9118 0.3726 -0.04965 -0.0005179 0.0002325 -0.1486 -0.0003903 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3171 -0.003117 -0.1184 0.09714 0.9826 0.9928 0.3654 0.9137 0.9783 0.6237 ] Network output: [ 0.0192 0.8366 0.9709 -0.0001817 8.158e-05 0.1533 -0.0001369 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008096 0.002952 0.006987 0.003355 0.9905 0.9936 0.008283 0.9717 0.9838 0.01566 ] Network output: [ -0.01095 0.243 0.8156 -0.001113 0.0004998 0.9587 -0.000839 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3568 0.2446 0.4136 0.13 0.9842 0.9937 0.3583 0.9198 0.9807 0.6216 ] Network output: [ -0.04752 0.3036 1.081 0.0002598 -0.0001166 0.7114 0.0001958 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1503 0.1417 0.1904 0.1301 0.99 0.994 0.1504 0.9704 0.9844 0.207 ] Network output: [ -0.03969 0.1544 1.09 0.0004842 -0.0002174 0.8367 0.0003649 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1677 0.1659 0.1986 0.1623 0.9854 0.9915 0.1677 0.9526 0.9772 0.2033 ] Network output: [ 0.006993 0.9102 0.01282 4.816e-05 -2.162e-05 1.063 3.629e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08826 Epoch 4317 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06254 0.7696 0.9521 -2.339e-05 1.05e-05 0.1532 -1.762e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004892 -0.004808 -0.01678 0.009553 0.9627 0.9685 0.01102 0.9181 0.93 0.0373 ] Network output: [ 1.021 -0.1954 0.0819 0.0004608 -0.0002069 0.07302 0.0003473 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3062 -0.02021 -0.1477 0.2119 0.9826 0.9929 0.3533 0.9137 0.9784 0.6267 ] Network output: [ 0.01989 0.8192 0.975 -0.000148 6.646e-05 0.1654 -0.0001116 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007754 0.002797 0.006518 0.006858 0.9905 0.9936 0.007935 0.9716 0.984 0.01534 ] Network output: [ 0.0801 -0.5136 1.036 0.0001359 -6.1e-05 1.317 0.0001024 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3456 0.2358 0.4031 0.3008 0.9842 0.9937 0.347 0.9196 0.9807 0.6142 ] Network output: [ -0.04517 0.2777 1.075 0.0003032 -0.0001361 0.7385 0.0002285 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1577 0.1488 0.1938 0.1568 0.9899 0.994 0.1578 0.9706 0.9847 0.2103 ] Network output: [ -0.04241 0.233 1.047 0.0003954 -0.0001775 0.8064 0.000298 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1766 0.1749 0.1962 0.1672 0.9855 0.9916 0.1767 0.9532 0.9773 0.2005 ] Network output: [ 0.01379 0.942 -0.02372 5.147e-05 -2.311e-05 1.054 3.879e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1175 Epoch 4318 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04982 0.8479 0.9347 -0.0001622 7.281e-05 0.1172 -0.0001222 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005041 -0.004748 -0.01627 0.006825 0.9627 0.9685 0.01129 0.9177 0.9295 0.037 ] Network output: [ 0.9119 0.3722 -0.04927 -0.0005154 0.0002314 -0.1487 -0.0003884 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3168 -0.003146 -0.1186 0.09721 0.9826 0.9929 0.365 0.9135 0.9782 0.6234 ] Network output: [ 0.01924 0.8366 0.9709 -0.0001811 8.13e-05 0.1533 -0.0001365 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008082 0.002945 0.006967 0.003349 0.9905 0.9936 0.008269 0.9717 0.9837 0.01564 ] Network output: [ -0.01088 0.2427 0.8159 -0.00111 0.0004982 0.9586 -0.0008363 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3564 0.2444 0.4133 0.1298 0.9842 0.9937 0.3579 0.9196 0.9807 0.6213 ] Network output: [ -0.04751 0.3034 1.081 0.0002592 -0.0001164 0.7115 0.0001953 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.15 0.1414 0.1903 0.13 0.99 0.994 0.1502 0.9703 0.9844 0.207 ] Network output: [ -0.03967 0.1542 1.09 0.0004828 -0.0002168 0.8369 0.0003639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1674 0.1657 0.1985 0.1622 0.9854 0.9915 0.1674 0.9525 0.9771 0.2032 ] Network output: [ 0.006987 0.9101 0.01292 4.862e-05 -2.183e-05 1.063 3.665e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08814 Epoch 4319 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06253 0.7697 0.952 -2.35e-05 1.055e-05 0.1531 -1.771e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004888 -0.004802 -0.01675 0.009545 0.9628 0.9685 0.01101 0.9181 0.9299 0.03722 ] Network output: [ 1.021 -0.1951 0.08156 0.0004589 -0.000206 0.07311 0.0003458 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.306 -0.02019 -0.1477 0.2118 0.9826 0.9929 0.353 0.9135 0.9784 0.6264 ] Network output: [ 0.01991 0.8193 0.975 -0.0001476 6.627e-05 0.1653 -0.0001112 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007742 0.002792 0.006503 0.006844 0.9905 0.9936 0.007922 0.9716 0.9839 0.01531 ] Network output: [ 0.07995 -0.5129 1.036 0.0001344 -6.034e-05 1.318 0.0001013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3452 0.2356 0.4029 0.3004 0.9842 0.9937 0.3467 0.9194 0.9807 0.6139 ] Network output: [ -0.0452 0.2775 1.076 0.0003022 -0.0001357 0.7386 0.0002278 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1574 0.1485 0.1938 0.1566 0.9899 0.994 0.1575 0.9705 0.9847 0.2102 ] Network output: [ -0.04241 0.2328 1.047 0.0003942 -0.000177 0.8065 0.0002971 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1763 0.1746 0.1961 0.167 0.9855 0.9916 0.1763 0.9531 0.9773 0.2004 ] Network output: [ 0.01372 0.9425 -0.02371 5.096e-05 -2.288e-05 1.054 3.84e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1174 Epoch 4320 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04984 0.8478 0.9347 -0.0001616 7.256e-05 0.1172 -0.0001218 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005036 -0.004742 -0.01625 0.006824 0.9628 0.9685 0.01127 0.9177 0.9294 0.03693 ] Network output: [ 0.9119 0.3718 -0.04889 -0.000513 0.0002303 -0.1489 -0.0003866 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3165 -0.003174 -0.1187 0.09728 0.9826 0.9929 0.3646 0.9133 0.9782 0.6231 ] Network output: [ 0.01928 0.8366 0.9709 -0.0001805 8.101e-05 0.1532 -0.000136 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008068 0.002939 0.006948 0.003343 0.9905 0.9936 0.008254 0.9716 0.9837 0.01561 ] Network output: [ -0.0108 0.2424 0.8163 -0.001106 0.0004966 0.9585 -0.0008336 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.356 0.2441 0.4129 0.1296 0.9842 0.9937 0.3575 0.9194 0.9807 0.621 ] Network output: [ -0.0475 0.3031 1.081 0.0002586 -0.0001161 0.7117 0.0001949 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1498 0.1412 0.1902 0.1299 0.99 0.994 0.1499 0.9703 0.9843 0.2069 ] Network output: [ -0.03965 0.154 1.09 0.0004815 -0.0002162 0.8371 0.0003629 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1671 0.1654 0.1984 0.1621 0.9854 0.9915 0.1671 0.9524 0.9771 0.2031 ] Network output: [ 0.006983 0.91 0.01303 4.909e-05 -2.204e-05 1.063 3.7e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08801 Epoch 4321 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06253 0.7698 0.9519 -2.362e-05 1.06e-05 0.1531 -1.78e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004884 -0.004796 -0.01673 0.009536 0.9628 0.9686 0.01099 0.918 0.9298 0.03714 ] Network output: [ 1.021 -0.1949 0.08121 0.000457 -0.0002052 0.07319 0.0003444 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3058 -0.02017 -0.1477 0.2118 0.9826 0.9929 0.3527 0.9133 0.9783 0.6262 ] Network output: [ 0.01993 0.8194 0.9749 -0.0001472 6.609e-05 0.1653 -0.0001109 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007729 0.002788 0.006489 0.00683 0.9905 0.9936 0.007909 0.9715 0.9839 0.01529 ] Network output: [ 0.07981 -0.5122 1.035 0.000133 -5.969e-05 1.318 0.0001002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3449 0.2354 0.4027 0.3 0.9842 0.9937 0.3463 0.9192 0.9806 0.6137 ] Network output: [ -0.04524 0.2773 1.076 0.0003013 -0.0001352 0.7388 0.000227 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.157 0.1482 0.1937 0.1565 0.9899 0.994 0.1572 0.9704 0.9847 0.2101 ] Network output: [ -0.04241 0.2326 1.047 0.000393 -0.0001764 0.8065 0.0002962 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.176 0.1742 0.1961 0.1669 0.9855 0.9916 0.176 0.953 0.9772 0.2004 ] Network output: [ 0.01365 0.943 -0.0237 5.044e-05 -2.265e-05 1.054 3.801e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1172 Epoch 4322 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04986 0.8478 0.9347 -0.000161 7.23e-05 0.1172 -0.0001214 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005031 -0.004735 -0.01623 0.006823 0.9628 0.9685 0.01125 0.9176 0.9293 0.03685 ] Network output: [ 0.912 0.3714 -0.04851 -0.0005106 0.0002292 -0.149 -0.0003848 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3162 -0.003202 -0.1189 0.09735 0.9826 0.9929 0.3642 0.913 0.9782 0.6228 ] Network output: [ 0.01932 0.8366 0.9708 -0.0001798 8.073e-05 0.1532 -0.0001355 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008055 0.002933 0.006928 0.003338 0.9905 0.9936 0.00824 0.9715 0.9836 0.01559 ] Network output: [ -0.01073 0.2421 0.8166 -0.001103 0.000495 0.9583 -0.0008309 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3557 0.2438 0.4126 0.1294 0.9842 0.9937 0.3571 0.9192 0.9806 0.6207 ] Network output: [ -0.04748 0.3029 1.081 0.000258 -0.0001158 0.7119 0.0001944 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1495 0.1409 0.1901 0.1298 0.99 0.994 0.1496 0.9702 0.9843 0.2068 ] Network output: [ -0.03963 0.1538 1.09 0.0004802 -0.0002156 0.8373 0.0003619 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1668 0.1651 0.1983 0.162 0.9854 0.9915 0.1669 0.9523 0.977 0.2031 ] Network output: [ 0.006979 0.9099 0.01313 4.957e-05 -2.225e-05 1.063 3.735e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08788 Epoch 4323 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06252 0.7699 0.9518 -2.373e-05 1.065e-05 0.1531 -1.789e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00488 -0.00479 -0.01671 0.009528 0.9628 0.9686 0.01097 0.9179 0.9297 0.03707 ] Network output: [ 1.021 -0.1946 0.08086 0.0004552 -0.0002043 0.07327 0.000343 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3055 -0.02015 -0.1477 0.2117 0.9826 0.9929 0.3524 0.913 0.9783 0.6259 ] Network output: [ 0.01994 0.8194 0.9749 -0.0001468 6.59e-05 0.1652 -0.0001106 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007716 0.002783 0.006475 0.006816 0.9905 0.9936 0.007896 0.9714 0.9838 0.01527 ] Network output: [ 0.07967 -0.5115 1.035 0.0001315 -5.903e-05 1.318 9.91e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3445 0.2352 0.4024 0.2996 0.9842 0.9937 0.346 0.919 0.9806 0.6134 ] Network output: [ -0.04527 0.2771 1.076 0.0003003 -0.0001348 0.7389 0.0002263 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1567 0.1479 0.1936 0.1564 0.9899 0.994 0.1569 0.9704 0.9846 0.21 ] Network output: [ -0.04241 0.2325 1.047 0.0003918 -0.0001759 0.8066 0.0002953 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1757 0.1739 0.196 0.1668 0.9855 0.9916 0.1757 0.9529 0.9772 0.2003 ] Network output: [ 0.01358 0.9435 -0.02369 4.993e-05 -2.241e-05 1.053 3.763e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.117 Epoch 4324 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04988 0.8477 0.9347 -0.0001605 7.204e-05 0.1172 -0.0001209 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005025 -0.004729 -0.01621 0.006822 0.9628 0.9686 0.01123 0.9175 0.9292 0.03677 ] Network output: [ 0.9121 0.371 -0.04812 -0.0005082 0.0002281 -0.1491 -0.000383 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3159 -0.003229 -0.119 0.09743 0.9826 0.9929 0.3638 0.9128 0.9781 0.6225 ] Network output: [ 0.01936 0.8366 0.9708 -0.0001792 8.045e-05 0.1532 -0.0001351 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008041 0.002927 0.006909 0.003332 0.9905 0.9936 0.008226 0.9715 0.9836 0.01556 ] Network output: [ -0.01066 0.2417 0.8169 -0.001099 0.0004934 0.9582 -0.0008283 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3553 0.2436 0.4122 0.1293 0.9843 0.9937 0.3568 0.919 0.9806 0.6205 ] Network output: [ -0.04747 0.3027 1.081 0.0002573 -0.0001155 0.7121 0.0001939 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1492 0.1407 0.19 0.1297 0.99 0.994 0.1494 0.9701 0.9842 0.2067 ] Network output: [ -0.0396 0.1536 1.09 0.0004789 -0.000215 0.8375 0.0003609 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1666 0.1649 0.1982 0.1619 0.9854 0.9915 0.1666 0.9522 0.9769 0.203 ] Network output: [ 0.006976 0.9098 0.01324 5.004e-05 -2.247e-05 1.063 3.771e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08775 Epoch 4325 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06252 0.77 0.9517 -2.385e-05 1.071e-05 0.1531 -1.797e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004876 -0.004784 -0.01669 0.00952 0.9628 0.9686 0.01096 0.9179 0.9296 0.03699 ] Network output: [ 1.021 -0.1943 0.0805 0.0004533 -0.0002035 0.07335 0.0003416 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3053 -0.02013 -0.1477 0.2117 0.9826 0.9929 0.3521 0.9128 0.9783 0.6257 ] Network output: [ 0.01996 0.8195 0.9748 -0.0001464 6.572e-05 0.1652 -0.0001103 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007704 0.002779 0.006461 0.006802 0.9905 0.9936 0.007883 0.9714 0.9838 0.01524 ] Network output: [ 0.07953 -0.5108 1.034 0.00013 -5.838e-05 1.318 9.799e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3442 0.2349 0.4022 0.2992 0.9842 0.9937 0.3456 0.9187 0.9805 0.6131 ] Network output: [ -0.0453 0.2769 1.076 0.0002994 -0.0001344 0.739 0.0002256 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1564 0.1476 0.1935 0.1563 0.9899 0.994 0.1565 0.9703 0.9846 0.21 ] Network output: [ -0.04241 0.2323 1.047 0.0003907 -0.0001754 0.8067 0.0002944 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1753 0.1736 0.1959 0.1666 0.9855 0.9916 0.1754 0.9528 0.9771 0.2002 ] Network output: [ 0.01351 0.9441 -0.02368 4.941e-05 -2.218e-05 1.053 3.724e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1168 Epoch 4326 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0499 0.8477 0.9347 -0.0001599 7.179e-05 0.1172 -0.0001205 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00502 -0.004723 -0.01619 0.00682 0.9628 0.9686 0.01121 0.9175 0.9292 0.03669 ] Network output: [ 0.9122 0.3706 -0.04774 -0.0005058 0.0002271 -0.1493 -0.0003812 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3156 -0.003256 -0.1192 0.0975 0.9826 0.9929 0.3634 0.9126 0.9781 0.6223 ] Network output: [ 0.0194 0.8365 0.9708 -0.0001786 8.017e-05 0.1532 -0.0001346 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008028 0.002921 0.00689 0.003326 0.9905 0.9936 0.008212 0.9714 0.9836 0.01554 ] Network output: [ -0.01059 0.2414 0.8173 -0.001096 0.0004919 0.958 -0.0008257 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3549 0.2433 0.4119 0.1291 0.9843 0.9937 0.3564 0.9188 0.9805 0.6202 ] Network output: [ -0.04745 0.3024 1.081 0.0002567 -0.0001153 0.7122 0.0001935 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.149 0.1404 0.1899 0.1296 0.99 0.994 0.1491 0.97 0.9842 0.2066 ] Network output: [ -0.03958 0.1534 1.09 0.0004776 -0.0002144 0.8377 0.00036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1663 0.1646 0.1982 0.1618 0.9854 0.9915 0.1663 0.9521 0.9769 0.2029 ] Network output: [ 0.006973 0.9096 0.01334 5.052e-05 -2.268e-05 1.063 3.807e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08762 Epoch 4327 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06251 0.7701 0.9516 -2.396e-05 1.076e-05 0.1531 -1.806e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004871 -0.004779 -0.01666 0.009511 0.9628 0.9686 0.01094 0.9178 0.9296 0.03692 ] Network output: [ 1.021 -0.194 0.08015 0.0004515 -0.0002027 0.07342 0.0003403 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3051 -0.02011 -0.1478 0.2116 0.9826 0.9929 0.3517 0.9126 0.9782 0.6254 ] Network output: [ 0.01998 0.8196 0.9748 -0.000146 6.553e-05 0.1651 -0.00011 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007692 0.002774 0.006446 0.006789 0.9905 0.9936 0.00787 0.9713 0.9838 0.01522 ] Network output: [ 0.0794 -0.5101 1.034 0.0001286 -5.772e-05 1.318 9.689e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3438 0.2347 0.402 0.2989 0.9843 0.9937 0.3453 0.9185 0.9805 0.6129 ] Network output: [ -0.04533 0.2767 1.076 0.0002984 -0.000134 0.7391 0.0002249 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1561 0.1473 0.1934 0.1561 0.9899 0.994 0.1562 0.9702 0.9845 0.2099 ] Network output: [ -0.0424 0.2321 1.048 0.0003895 -0.0001749 0.8067 0.0002935 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.175 0.1733 0.1958 0.1665 0.9855 0.9916 0.175 0.9527 0.977 0.2001 ] Network output: [ 0.01343 0.9446 -0.02366 4.889e-05 -2.195e-05 1.052 3.685e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1166 Epoch 4328 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04992 0.8476 0.9347 -0.0001593 7.153e-05 0.1172 -0.0001201 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005015 -0.004716 -0.01617 0.006819 0.9628 0.9686 0.01119 0.9174 0.9291 0.03662 ] Network output: [ 0.9122 0.3702 -0.04735 -0.0005034 0.000226 -0.1494 -0.0003794 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3153 -0.003283 -0.1193 0.09757 0.9826 0.9929 0.363 0.9124 0.978 0.622 ] Network output: [ 0.01944 0.8365 0.9707 -0.000178 7.99e-05 0.1531 -0.0001341 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008014 0.002915 0.006872 0.003321 0.9905 0.9936 0.008199 0.9714 0.9835 0.01551 ] Network output: [ -0.01053 0.2411 0.8176 -0.001092 0.0004903 0.9579 -0.0008231 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3546 0.243 0.4115 0.1289 0.9843 0.9937 0.356 0.9186 0.9805 0.6199 ] Network output: [ -0.04744 0.3021 1.081 0.0002562 -0.000115 0.7124 0.000193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1487 0.1402 0.1898 0.1296 0.99 0.994 0.1488 0.97 0.9842 0.2065 ] Network output: [ -0.03955 0.1532 1.09 0.0004764 -0.0002139 0.8379 0.000359 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.166 0.1643 0.1981 0.1617 0.9854 0.9915 0.1661 0.952 0.9768 0.2028 ] Network output: [ 0.006971 0.9095 0.01344 5.1e-05 -2.29e-05 1.063 3.844e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08749 Epoch 4329 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06251 0.7702 0.9515 -2.407e-05 1.081e-05 0.1531 -1.814e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004867 -0.004773 -0.01664 0.009503 0.9629 0.9686 0.01092 0.9177 0.9295 0.03684 ] Network output: [ 1.021 -0.1937 0.07979 0.0004497 -0.0002019 0.07348 0.0003389 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3049 -0.0201 -0.1478 0.2116 0.9827 0.9929 0.3514 0.9124 0.9782 0.6252 ] Network output: [ 0.02 0.8197 0.9747 -0.0001456 6.535e-05 0.165 -0.0001097 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007679 0.00277 0.006432 0.006775 0.9905 0.9936 0.007857 0.9713 0.9837 0.0152 ] Network output: [ 0.07926 -0.5094 1.033 0.0001271 -5.706e-05 1.318 9.579e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3435 0.2345 0.4017 0.2985 0.9843 0.9937 0.3449 0.9183 0.9805 0.6126 ] Network output: [ -0.04537 0.2765 1.076 0.0002975 -0.0001336 0.7393 0.0002242 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1558 0.147 0.1934 0.156 0.9899 0.994 0.1559 0.9701 0.9845 0.2098 ] Network output: [ -0.0424 0.2319 1.048 0.0003884 -0.0001743 0.8068 0.0002927 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1747 0.173 0.1958 0.1664 0.9855 0.9916 0.1747 0.9526 0.977 0.2001 ] Network output: [ 0.01336 0.9451 -0.02365 4.837e-05 -2.172e-05 1.052 3.645e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1164 Epoch 4330 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04994 0.8476 0.9347 -0.0001588 7.128e-05 0.1172 -0.0001197 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005009 -0.00471 -0.01615 0.006818 0.9629 0.9686 0.01118 0.9174 0.929 0.03654 ] Network output: [ 0.9123 0.3698 -0.04696 -0.0005011 0.000225 -0.1495 -0.0003776 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.315 -0.003309 -0.1195 0.09765 0.9827 0.9929 0.3626 0.9122 0.978 0.6217 ] Network output: [ 0.01948 0.8365 0.9707 -0.0001773 7.962e-05 0.1531 -0.0001337 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008001 0.002909 0.006853 0.003315 0.9905 0.9936 0.008185 0.9713 0.9835 0.01549 ] Network output: [ -0.01046 0.2408 0.818 -0.001089 0.0004888 0.9577 -0.0008205 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3542 0.2428 0.4112 0.1287 0.9843 0.9937 0.3556 0.9184 0.9804 0.6197 ] Network output: [ -0.04742 0.3019 1.081 0.0002556 -0.0001147 0.7126 0.0001926 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1485 0.1399 0.1897 0.1295 0.99 0.994 0.1486 0.9699 0.9841 0.2064 ] Network output: [ -0.03953 0.153 1.09 0.0004751 -0.0002133 0.8381 0.0003581 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1658 0.1641 0.198 0.1616 0.9854 0.9915 0.1658 0.9519 0.9768 0.2028 ] Network output: [ 0.00697 0.9094 0.01354 5.149e-05 -2.312e-05 1.063 3.88e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08736 Epoch 4331 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0625 0.7704 0.9515 -2.418e-05 1.086e-05 0.1531 -1.822e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004863 -0.004767 -0.01662 0.009494 0.9629 0.9686 0.01091 0.9177 0.9294 0.03677 ] Network output: [ 1.021 -0.1934 0.07943 0.0004478 -0.0002011 0.07355 0.0003375 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3046 -0.02008 -0.1478 0.2115 0.9827 0.9929 0.3511 0.9122 0.9781 0.6249 ] Network output: [ 0.02001 0.8197 0.9747 -0.0001452 6.517e-05 0.165 -0.0001094 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007667 0.002765 0.006418 0.006761 0.9905 0.9936 0.007844 0.9712 0.9837 0.01517 ] Network output: [ 0.07913 -0.5087 1.033 0.0001256 -5.641e-05 1.318 9.469e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3431 0.2343 0.4015 0.2981 0.9843 0.9937 0.3446 0.9181 0.9804 0.6124 ] Network output: [ -0.0454 0.2763 1.076 0.0002966 -0.0001331 0.7394 0.0002235 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1555 0.1468 0.1933 0.1559 0.9899 0.994 0.1556 0.9701 0.9844 0.2098 ] Network output: [ -0.0424 0.2317 1.048 0.0003872 -0.0001738 0.8069 0.0002918 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1744 0.1727 0.1957 0.1662 0.9855 0.9916 0.1744 0.9524 0.9769 0.2 ] Network output: [ 0.01328 0.9457 -0.02364 4.785e-05 -2.148e-05 1.052 3.606e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1162 Epoch 4332 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04996 0.8476 0.9347 -0.0001582 7.103e-05 0.1172 -0.0001192 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.005004 -0.004704 -0.01613 0.006817 0.9629 0.9686 0.01116 0.9173 0.9289 0.03646 ] Network output: [ 0.9124 0.3694 -0.04658 -0.0004987 0.0002239 -0.1496 -0.0003759 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3147 -0.003335 -0.1196 0.09772 0.9827 0.9929 0.3622 0.912 0.9779 0.6215 ] Network output: [ 0.01952 0.8365 0.9707 -0.0001767 7.934e-05 0.1531 -0.0001332 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007988 0.002903 0.006835 0.00331 0.9905 0.9936 0.008171 0.9712 0.9834 0.01546 ] Network output: [ -0.0104 0.2405 0.8183 -0.001085 0.0004873 0.9576 -0.000818 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3538 0.2425 0.4109 0.1286 0.9843 0.9937 0.3553 0.9181 0.9804 0.6194 ] Network output: [ -0.0474 0.3016 1.081 0.000255 -0.0001145 0.7128 0.0001922 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1482 0.1397 0.1896 0.1294 0.99 0.994 0.1483 0.9698 0.9841 0.2064 ] Network output: [ -0.0395 0.1528 1.09 0.0004739 -0.0002127 0.8383 0.0003571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1655 0.1638 0.1979 0.1615 0.9854 0.9915 0.1655 0.9517 0.9767 0.2027 ] Network output: [ 0.006969 0.9092 0.01364 5.198e-05 -2.334e-05 1.063 3.917e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08722 Epoch 4333 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06249 0.7705 0.9514 -2.429e-05 1.091e-05 0.1531 -1.831e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004859 -0.004761 -0.0166 0.009486 0.9629 0.9687 0.01089 0.9176 0.9293 0.03669 ] Network output: [ 1.021 -0.1931 0.07907 0.000446 -0.0002002 0.0736 0.0003361 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3044 -0.02006 -0.1479 0.2115 0.9827 0.9929 0.3508 0.912 0.9781 0.6247 ] Network output: [ 0.02003 0.8198 0.9746 -0.0001448 6.499e-05 0.1649 -0.0001091 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007655 0.002761 0.006405 0.006747 0.9905 0.9936 0.007832 0.9712 0.9836 0.01515 ] Network output: [ 0.079 -0.508 1.032 0.0001242 -5.575e-05 1.318 9.359e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3428 0.2341 0.4013 0.2977 0.9843 0.9937 0.3442 0.9179 0.9804 0.6121 ] Network output: [ -0.04543 0.2761 1.076 0.0002957 -0.0001327 0.7396 0.0002228 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1552 0.1465 0.1932 0.1558 0.9899 0.994 0.1553 0.97 0.9844 0.2097 ] Network output: [ -0.04239 0.2315 1.048 0.0003861 -0.0001733 0.807 0.000291 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1741 0.1723 0.1956 0.1661 0.9855 0.9916 0.1741 0.9523 0.9769 0.1999 ] Network output: [ 0.01321 0.9462 -0.02363 4.732e-05 -2.124e-05 1.051 3.566e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.116 Epoch 4334 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04997 0.8475 0.9347 -0.0001576 7.077e-05 0.1172 -0.0001188 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004999 -0.004698 -0.01611 0.006816 0.9629 0.9686 0.01114 0.9172 0.9288 0.03639 ] Network output: [ 0.9125 0.369 -0.04619 -0.0004964 0.0002229 -0.1498 -0.0003741 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3144 -0.003361 -0.1198 0.09779 0.9827 0.9929 0.3619 0.9117 0.9779 0.6212 ] Network output: [ 0.01956 0.8365 0.9706 -0.0001761 7.907e-05 0.1531 -0.0001327 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007975 0.002897 0.006817 0.003304 0.9905 0.9936 0.008157 0.9712 0.9834 0.01544 ] Network output: [ -0.01034 0.2402 0.8186 -0.001082 0.0004858 0.9574 -0.0008154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3535 0.2422 0.4106 0.1284 0.9843 0.9937 0.3549 0.9179 0.9803 0.6192 ] Network output: [ -0.04739 0.3014 1.081 0.0002544 -0.0001142 0.7129 0.0001917 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1479 0.1394 0.1895 0.1293 0.99 0.994 0.148 0.9697 0.984 0.2063 ] Network output: [ -0.03947 0.1525 1.09 0.0004727 -0.0002122 0.8386 0.0003562 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1652 0.1635 0.1978 0.1615 0.9854 0.9915 0.1653 0.9516 0.9767 0.2026 ] Network output: [ 0.00697 0.9091 0.01374 5.247e-05 -2.356e-05 1.063 3.955e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08709 Epoch 4335 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06249 0.7706 0.9513 -2.44e-05 1.095e-05 0.153 -1.839e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004855 -0.004755 -0.01657 0.009478 0.9629 0.9687 0.01088 0.9175 0.9292 0.03662 ] Network output: [ 1.021 -0.1928 0.0787 0.0004442 -0.0001994 0.07365 0.0003348 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3042 -0.02005 -0.1479 0.2114 0.9827 0.9929 0.3505 0.9117 0.978 0.6245 ] Network output: [ 0.02004 0.8199 0.9746 -0.0001444 6.481e-05 0.1649 -0.0001088 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007642 0.002756 0.006391 0.006734 0.9905 0.9936 0.007819 0.9711 0.9836 0.01513 ] Network output: [ 0.07887 -0.5073 1.032 0.0001227 -5.509e-05 1.318 9.248e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3424 0.2339 0.4011 0.2973 0.9843 0.9937 0.3439 0.9177 0.9803 0.6119 ] Network output: [ -0.04546 0.2759 1.077 0.0002948 -0.0001323 0.7397 0.0002221 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1549 0.1462 0.1931 0.1556 0.9899 0.994 0.155 0.9699 0.9844 0.2096 ] Network output: [ -0.04239 0.2313 1.048 0.000385 -0.0001728 0.8071 0.0002901 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1737 0.172 0.1955 0.166 0.9855 0.9916 0.1738 0.9522 0.9768 0.1999 ] Network output: [ 0.01313 0.9467 -0.02362 4.679e-05 -2.101e-05 1.051 3.526e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1158 Epoch 4336 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04999 0.8475 0.9347 -0.0001571 7.052e-05 0.1172 -0.0001184 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004993 -0.004692 -0.01609 0.006815 0.9629 0.9687 0.01112 0.9172 0.9287 0.03631 ] Network output: [ 0.9125 0.3686 -0.0458 -0.0004941 0.0002218 -0.1499 -0.0003724 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3141 -0.003386 -0.1199 0.09787 0.9827 0.9929 0.3615 0.9115 0.9778 0.621 ] Network output: [ 0.01959 0.8365 0.9706 -0.0001755 7.88e-05 0.153 -0.0001323 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007962 0.002891 0.006799 0.003299 0.9905 0.9936 0.008144 0.9711 0.9834 0.01542 ] Network output: [ -0.01028 0.2399 0.819 -0.001079 0.0004843 0.9573 -0.0008129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3531 0.242 0.4102 0.1282 0.9843 0.9937 0.3545 0.9177 0.9803 0.6189 ] Network output: [ -0.04737 0.3011 1.082 0.0002539 -0.000114 0.7131 0.0001913 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1477 0.1392 0.1894 0.1293 0.99 0.994 0.1478 0.9696 0.984 0.2062 ] Network output: [ -0.03944 0.1523 1.09 0.0004715 -0.0002117 0.8388 0.0003553 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.165 0.1633 0.1978 0.1614 0.9854 0.9915 0.165 0.9515 0.9766 0.2025 ] Network output: [ 0.006971 0.909 0.01384 5.297e-05 -2.378e-05 1.063 3.992e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08696 Epoch 4337 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06248 0.7707 0.9512 -2.451e-05 1.1e-05 0.153 -1.847e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00485 -0.004749 -0.01655 0.009469 0.9629 0.9687 0.01086 0.9175 0.9292 0.03654 ] Network output: [ 1.021 -0.1925 0.07833 0.0004424 -0.0001986 0.0737 0.0003334 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3039 -0.02003 -0.1479 0.2114 0.9827 0.9929 0.3502 0.9115 0.978 0.6242 ] Network output: [ 0.02006 0.82 0.9745 -0.000144 6.463e-05 0.1648 -0.0001085 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00763 0.002752 0.006377 0.00672 0.9905 0.9936 0.007806 0.971 0.9836 0.01511 ] Network output: [ 0.07874 -0.5066 1.031 0.0001212 -5.443e-05 1.318 9.137e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3421 0.2336 0.4009 0.2969 0.9843 0.9937 0.3435 0.9175 0.9803 0.6116 ] Network output: [ -0.04549 0.2757 1.077 0.0002939 -0.0001319 0.7398 0.0002215 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1546 0.1459 0.1931 0.1555 0.9899 0.994 0.1547 0.9698 0.9843 0.2096 ] Network output: [ -0.04239 0.2311 1.048 0.0003839 -0.0001723 0.8072 0.0002893 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1734 0.1717 0.1955 0.1658 0.9855 0.9916 0.1735 0.9521 0.9768 0.1998 ] Network output: [ 0.01305 0.9473 -0.02361 4.626e-05 -2.077e-05 1.05 3.486e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1156 Epoch 4338 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05001 0.8474 0.9347 -0.0001565 7.027e-05 0.1172 -0.000118 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004988 -0.004685 -0.01607 0.006814 0.9629 0.9687 0.0111 0.9171 0.9287 0.03624 ] Network output: [ 0.9126 0.3682 -0.04541 -0.0004918 0.0002208 -0.15 -0.0003706 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3138 -0.003412 -0.1201 0.09794 0.9827 0.9929 0.3611 0.9113 0.9778 0.6207 ] Network output: [ 0.01963 0.8365 0.9705 -0.0001749 7.852e-05 0.153 -0.0001318 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007949 0.002885 0.006781 0.003294 0.9905 0.9936 0.00813 0.971 0.9833 0.01539 ] Network output: [ -0.01022 0.2396 0.8193 -0.001075 0.0004828 0.9572 -0.0008104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3527 0.2417 0.4099 0.128 0.9843 0.9937 0.3542 0.9175 0.9803 0.6187 ] Network output: [ -0.04734 0.3008 1.082 0.0002533 -0.0001137 0.7133 0.0001909 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1474 0.1389 0.1893 0.1292 0.99 0.994 0.1475 0.9696 0.9839 0.2061 ] Network output: [ -0.03941 0.1521 1.09 0.0004703 -0.0002111 0.839 0.0003544 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1647 0.163 0.1977 0.1613 0.9854 0.9915 0.1647 0.9514 0.9766 0.2025 ] Network output: [ 0.006973 0.9088 0.01394 5.348e-05 -2.401e-05 1.063 4.03e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08683 Epoch 4339 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06247 0.7709 0.9511 -2.462e-05 1.105e-05 0.153 -1.855e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004846 -0.004744 -0.01653 0.009461 0.963 0.9687 0.01084 0.9174 0.9291 0.03647 ] Network output: [ 1.021 -0.1922 0.07796 0.0004407 -0.0001978 0.07374 0.0003321 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3037 -0.02002 -0.1479 0.2113 0.9827 0.9929 0.3499 0.9113 0.9779 0.624 ] Network output: [ 0.02007 0.8201 0.9745 -0.0001436 6.446e-05 0.1647 -0.0001082 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007618 0.002747 0.006363 0.006707 0.9905 0.9936 0.007794 0.971 0.9835 0.01509 ] Network output: [ 0.07861 -0.5059 1.031 0.0001198 -5.377e-05 1.318 9.027e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3418 0.2334 0.4006 0.2966 0.9843 0.9937 0.3432 0.9173 0.9802 0.6114 ] Network output: [ -0.04552 0.2754 1.077 0.000293 -0.0001315 0.74 0.0002208 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1543 0.1456 0.193 0.1554 0.9899 0.994 0.1544 0.9698 0.9843 0.2095 ] Network output: [ -0.04238 0.2309 1.048 0.0003828 -0.0001718 0.8072 0.0002885 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1731 0.1714 0.1954 0.1657 0.9855 0.9916 0.1731 0.952 0.9767 0.1997 ] Network output: [ 0.01297 0.9478 -0.0236 4.572e-05 -2.053e-05 1.05 3.446e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1154 Epoch 4340 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05002 0.8474 0.9347 -0.000156 7.002e-05 0.1172 -0.0001176 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004983 -0.004679 -0.01605 0.006813 0.963 0.9687 0.01108 0.917 0.9286 0.03617 ] Network output: [ 0.9126 0.3678 -0.04502 -0.0004895 0.0002198 -0.1501 -0.0003689 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3135 -0.003437 -0.1202 0.09802 0.9827 0.9929 0.3607 0.9111 0.9777 0.6205 ] Network output: [ 0.01967 0.8365 0.9705 -0.0001743 7.825e-05 0.153 -0.0001314 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007936 0.002879 0.006764 0.003288 0.9905 0.9936 0.008117 0.971 0.9833 0.01537 ] Network output: [ -0.01017 0.2393 0.8196 -0.001072 0.0004813 0.957 -0.000808 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3524 0.2415 0.4096 0.1279 0.9843 0.9937 0.3538 0.9173 0.9802 0.6184 ] Network output: [ -0.04732 0.3006 1.082 0.0002528 -0.0001135 0.7135 0.0001905 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1472 0.1387 0.1892 0.1291 0.99 0.994 0.1473 0.9695 0.9839 0.2061 ] Network output: [ -0.03937 0.1519 1.09 0.0004691 -0.0002106 0.8392 0.0003535 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1644 0.1627 0.1976 0.1612 0.9854 0.9915 0.1645 0.9513 0.9765 0.2024 ] Network output: [ 0.006976 0.9087 0.01403 5.398e-05 -2.423e-05 1.064 4.068e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08669 Epoch 4341 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06246 0.771 0.951 -2.472e-05 1.11e-05 0.153 -1.863e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004842 -0.004738 -0.01651 0.009453 0.963 0.9687 0.01083 0.9174 0.929 0.0364 ] Network output: [ 1.021 -0.1919 0.07759 0.0004389 -0.000197 0.07378 0.0003308 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3035 -0.02 -0.148 0.2113 0.9827 0.9929 0.3496 0.9111 0.9779 0.6238 ] Network output: [ 0.02009 0.8201 0.9744 -0.0001432 6.428e-05 0.1647 -0.0001079 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007606 0.002743 0.00635 0.006693 0.9905 0.9936 0.007781 0.9709 0.9835 0.01506 ] Network output: [ 0.07848 -0.5052 1.03 0.0001183 -5.311e-05 1.318 8.916e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3414 0.2332 0.4004 0.2962 0.9843 0.9937 0.3428 0.9171 0.9802 0.6112 ] Network output: [ -0.04555 0.2752 1.077 0.0002921 -0.0001311 0.7401 0.0002201 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.154 0.1453 0.1929 0.1553 0.9899 0.994 0.1541 0.9697 0.9842 0.2095 ] Network output: [ -0.04238 0.2307 1.048 0.0003817 -0.0001714 0.8073 0.0002877 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1728 0.1711 0.1953 0.1656 0.9855 0.9916 0.1728 0.9519 0.9766 0.1997 ] Network output: [ 0.01289 0.9484 -0.02358 4.518e-05 -2.028e-05 1.05 3.405e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1152 Epoch 4342 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05003 0.8474 0.9347 -0.0001554 6.978e-05 0.1172 -0.0001171 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004978 -0.004673 -0.01603 0.006812 0.963 0.9687 0.01107 0.917 0.9285 0.03609 ] Network output: [ 0.9127 0.3674 -0.04462 -0.0004872 0.0002187 -0.1502 -0.0003672 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3132 -0.003461 -0.1204 0.09809 0.9827 0.9929 0.3603 0.9109 0.9777 0.6203 ] Network output: [ 0.0197 0.8365 0.9705 -0.0001737 7.798e-05 0.1529 -0.0001309 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007923 0.002873 0.006746 0.003283 0.9905 0.9936 0.008103 0.9709 0.9832 0.01535 ] Network output: [ -0.01011 0.239 0.82 -0.001069 0.0004799 0.9569 -0.0008055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.352 0.2412 0.4093 0.1277 0.9843 0.9937 0.3535 0.9171 0.9802 0.6182 ] Network output: [ -0.0473 0.3003 1.082 0.0002523 -0.0001132 0.7137 0.0001901 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1469 0.1384 0.1891 0.129 0.99 0.994 0.147 0.9694 0.9838 0.206 ] Network output: [ -0.03934 0.1516 1.09 0.0004679 -0.0002101 0.8394 0.0003526 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1642 0.1625 0.1975 0.1611 0.9854 0.9915 0.1642 0.9512 0.9764 0.2023 ] Network output: [ 0.006979 0.9086 0.01413 5.449e-05 -2.446e-05 1.064 4.107e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08656 Epoch 4343 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06245 0.7711 0.9509 -2.483e-05 1.115e-05 0.153 -1.871e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004838 -0.004732 -0.01649 0.009444 0.963 0.9687 0.01081 0.9173 0.9289 0.03632 ] Network output: [ 1.021 -0.1916 0.07722 0.0004371 -0.0001962 0.07381 0.0003294 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3033 -0.01999 -0.148 0.2112 0.9827 0.9929 0.3493 0.9109 0.9778 0.6236 ] Network output: [ 0.0201 0.8202 0.9744 -0.0001428 6.41e-05 0.1646 -0.0001076 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007594 0.002738 0.006336 0.00668 0.9905 0.9936 0.007769 0.9709 0.9834 0.01504 ] Network output: [ 0.07835 -0.5045 1.03 0.0001168 -5.245e-05 1.318 8.805e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3411 0.233 0.4002 0.2958 0.9843 0.9937 0.3425 0.9169 0.9801 0.611 ] Network output: [ -0.04557 0.275 1.077 0.0002912 -0.0001307 0.7403 0.0002195 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1537 0.1451 0.1928 0.1551 0.9899 0.994 0.1538 0.9696 0.9842 0.2094 ] Network output: [ -0.04237 0.2305 1.048 0.0003806 -0.0001709 0.8074 0.0002869 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1725 0.1708 0.1953 0.1655 0.9855 0.9916 0.1725 0.9518 0.9766 0.1996 ] Network output: [ 0.01281 0.9489 -0.02357 4.463e-05 -2.004e-05 1.049 3.364e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.115 Epoch 4344 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05005 0.8473 0.9347 -0.0001549 6.953e-05 0.1172 -0.0001167 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004972 -0.004667 -0.01601 0.006811 0.963 0.9687 0.01105 0.9169 0.9284 0.03602 ] Network output: [ 0.9127 0.3671 -0.04423 -0.000485 0.0002177 -0.1503 -0.0003655 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3129 -0.003486 -0.1205 0.09817 0.9827 0.9929 0.3599 0.9106 0.9776 0.62 ] Network output: [ 0.01974 0.8365 0.9704 -0.0001731 7.772e-05 0.1529 -0.0001305 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00791 0.002867 0.006729 0.003278 0.9905 0.9936 0.00809 0.9709 0.9832 0.01532 ] Network output: [ -0.01006 0.2387 0.8203 -0.001066 0.0004784 0.9567 -0.0008031 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3517 0.2409 0.409 0.1275 0.9843 0.9937 0.3531 0.9169 0.9801 0.618 ] Network output: [ -0.04728 0.3 1.082 0.0002517 -0.000113 0.7139 0.0001897 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1467 0.1382 0.189 0.129 0.99 0.994 0.1468 0.9693 0.9838 0.2059 ] Network output: [ -0.0393 0.1514 1.089 0.0004667 -0.0002095 0.8397 0.0003518 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1639 0.1622 0.1975 0.161 0.9854 0.9915 0.1639 0.9511 0.9764 0.2023 ] Network output: [ 0.006983 0.9084 0.01422 5.501e-05 -2.47e-05 1.064 4.146e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08642 Epoch 4345 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06244 0.7713 0.9508 -2.494e-05 1.12e-05 0.1529 -1.879e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004834 -0.004726 -0.01646 0.009436 0.963 0.9687 0.0108 0.9172 0.9288 0.03625 ] Network output: [ 1.021 -0.1913 0.07684 0.0004354 -0.0001955 0.07384 0.0003281 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3031 -0.01997 -0.1481 0.2112 0.9827 0.9929 0.349 0.9106 0.9778 0.6234 ] Network output: [ 0.02011 0.8203 0.9743 -0.0001424 6.393e-05 0.1646 -0.0001073 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007582 0.002734 0.006323 0.006666 0.9905 0.9936 0.007756 0.9708 0.9834 0.01502 ] Network output: [ 0.07823 -0.5038 1.029 0.0001154 -5.179e-05 1.318 8.693e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3407 0.2328 0.4 0.2954 0.9843 0.9937 0.3421 0.9167 0.9801 0.6107 ] Network output: [ -0.0456 0.2748 1.077 0.0002904 -0.0001304 0.7404 0.0002188 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1534 0.1448 0.1928 0.155 0.9899 0.994 0.1535 0.9695 0.9841 0.2093 ] Network output: [ -0.04237 0.2303 1.048 0.0003796 -0.0001704 0.8075 0.000286 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1722 0.1705 0.1952 0.1653 0.9855 0.9916 0.1722 0.9517 0.9765 0.1996 ] Network output: [ 0.01272 0.9495 -0.02356 4.408e-05 -1.979e-05 1.049 3.322e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1148 Epoch 4346 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05006 0.8473 0.9347 -0.0001543 6.928e-05 0.1172 -0.0001163 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004967 -0.004661 -0.01599 0.00681 0.963 0.9687 0.01103 0.9168 0.9283 0.03594 ] Network output: [ 0.9128 0.3667 -0.04384 -0.0004827 0.0002167 -0.1504 -0.0003638 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3126 -0.00351 -0.1206 0.09824 0.9827 0.9929 0.3595 0.9104 0.9776 0.6198 ] Network output: [ 0.01977 0.8365 0.9704 -0.0001725 7.745e-05 0.1529 -0.00013 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007897 0.002861 0.006712 0.003272 0.9905 0.9936 0.008077 0.9708 0.9831 0.0153 ] Network output: [ -0.01001 0.2385 0.8207 -0.001062 0.000477 0.9566 -0.0008007 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3513 0.2407 0.4087 0.1273 0.9843 0.9937 0.3527 0.9167 0.9801 0.6178 ] Network output: [ -0.04725 0.2997 1.082 0.0002512 -0.0001128 0.7141 0.0001893 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1464 0.138 0.1889 0.1289 0.99 0.994 0.1465 0.9693 0.9838 0.2058 ] Network output: [ -0.03927 0.1512 1.089 0.0004656 -0.000209 0.8399 0.0003509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1637 0.162 0.1974 0.1609 0.9854 0.9915 0.1637 0.951 0.9763 0.2022 ] Network output: [ 0.006989 0.9083 0.01432 5.553e-05 -2.493e-05 1.064 4.185e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08629 Epoch 4347 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06243 0.7714 0.9507 -2.504e-05 1.124e-05 0.1529 -1.887e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00483 -0.004721 -0.01644 0.009428 0.963 0.9688 0.01078 0.9172 0.9288 0.03618 ] Network output: [ 1.021 -0.191 0.07646 0.0004336 -0.0001947 0.07386 0.0003268 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3028 -0.01996 -0.1481 0.2111 0.9827 0.9929 0.3487 0.9104 0.9777 0.6232 ] Network output: [ 0.02012 0.8204 0.9743 -0.000142 6.376e-05 0.1645 -0.000107 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00757 0.002729 0.006309 0.006653 0.9905 0.9936 0.007744 0.9707 0.9834 0.015 ] Network output: [ 0.07811 -0.5031 1.029 0.0001139 -5.112e-05 1.318 8.582e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3404 0.2326 0.3998 0.295 0.9843 0.9937 0.3418 0.9165 0.9801 0.6105 ] Network output: [ -0.04563 0.2746 1.077 0.0002895 -0.00013 0.7406 0.0002182 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1531 0.1445 0.1927 0.1549 0.9899 0.994 0.1533 0.9695 0.9841 0.2093 ] Network output: [ -0.04236 0.2301 1.049 0.0003785 -0.0001699 0.8076 0.0002853 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1719 0.1702 0.1951 0.1652 0.9855 0.9916 0.1719 0.9516 0.9765 0.1995 ] Network output: [ 0.01264 0.9501 -0.02355 4.353e-05 -1.954e-05 1.048 3.28e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1146 Epoch 4348 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05007 0.8473 0.9347 -0.0001538 6.904e-05 0.1172 -0.0001159 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004962 -0.004655 -0.01597 0.006809 0.963 0.9688 0.01101 0.9168 0.9283 0.03587 ] Network output: [ 0.9128 0.3663 -0.04344 -0.0004805 0.0002157 -0.1505 -0.0003621 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3123 -0.003534 -0.1208 0.09831 0.9827 0.9929 0.3591 0.9102 0.9775 0.6196 ] Network output: [ 0.01981 0.8365 0.9704 -0.0001719 7.718e-05 0.1528 -0.0001296 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007884 0.002856 0.006695 0.003267 0.9905 0.9936 0.008063 0.9707 0.9831 0.01528 ] Network output: [ -0.009964 0.2382 0.821 -0.001059 0.0004756 0.9564 -0.0007983 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3509 0.2404 0.4084 0.1271 0.9843 0.9937 0.3524 0.9165 0.98 0.6175 ] Network output: [ -0.04722 0.2994 1.082 0.0002507 -0.0001126 0.7143 0.0001889 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1462 0.1377 0.1889 0.1288 0.99 0.994 0.1463 0.9692 0.9837 0.2058 ] Network output: [ -0.03923 0.1509 1.089 0.0004645 -0.0002085 0.8401 0.00035 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1634 0.1617 0.1973 0.1608 0.9854 0.9915 0.1634 0.9509 0.9763 0.2021 ] Network output: [ 0.006995 0.9081 0.01441 5.606e-05 -2.517e-05 1.064 4.225e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08615 Epoch 4349 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06242 0.7716 0.9506 -2.515e-05 1.129e-05 0.1529 -1.895e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004826 -0.004715 -0.01642 0.00942 0.9631 0.9688 0.01076 0.9171 0.9287 0.03611 ] Network output: [ 1.021 -0.1907 0.07608 0.0004319 -0.0001939 0.07387 0.0003255 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3026 -0.01994 -0.1481 0.211 0.9827 0.9929 0.3484 0.9102 0.9777 0.623 ] Network output: [ 0.02013 0.8205 0.9742 -0.0001416 6.358e-05 0.1644 -0.0001067 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007558 0.002725 0.006296 0.006639 0.9905 0.9936 0.007731 0.9707 0.9833 0.01498 ] Network output: [ 0.07798 -0.5024 1.028 0.0001124 -5.046e-05 1.319 8.47e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.34 0.2323 0.3996 0.2946 0.9843 0.9937 0.3414 0.9162 0.98 0.6103 ] Network output: [ -0.04566 0.2743 1.077 0.0002887 -0.0001296 0.7407 0.0002175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1528 0.1442 0.1926 0.1548 0.9899 0.994 0.153 0.9694 0.9841 0.2092 ] Network output: [ -0.04235 0.2299 1.049 0.0003775 -0.0001695 0.8078 0.0002845 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1716 0.1699 0.1951 0.1651 0.9855 0.9916 0.1716 0.9515 0.9764 0.1994 ] Network output: [ 0.01255 0.9506 -0.02354 4.297e-05 -1.929e-05 1.048 3.238e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1144 Epoch 4350 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05008 0.8473 0.9347 -0.0001532 6.88e-05 0.1172 -0.0001155 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004957 -0.004649 -0.01595 0.006808 0.9631 0.9688 0.01099 0.9167 0.9282 0.0358 ] Network output: [ 0.9129 0.3659 -0.04305 -0.0004783 0.0002147 -0.1506 -0.0003604 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.312 -0.003558 -0.1209 0.09839 0.9827 0.9929 0.3588 0.91 0.9775 0.6194 ] Network output: [ 0.01984 0.8365 0.9703 -0.0001713 7.692e-05 0.1528 -0.0001291 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007872 0.00285 0.006678 0.003262 0.9905 0.9936 0.00805 0.9707 0.9831 0.01526 ] Network output: [ -0.009918 0.2379 0.8213 -0.001056 0.0004742 0.9563 -0.000796 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3506 0.2402 0.4081 0.1269 0.9843 0.9937 0.352 0.9163 0.98 0.6173 ] Network output: [ -0.0472 0.2991 1.082 0.0002502 -0.0001123 0.7145 0.0001886 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1459 0.1375 0.1888 0.1287 0.99 0.994 0.146 0.9691 0.9837 0.2057 ] Network output: [ -0.03919 0.1507 1.089 0.0004634 -0.000208 0.8403 0.0003492 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1631 0.1615 0.1972 0.1607 0.9854 0.9915 0.1632 0.9508 0.9762 0.2021 ] Network output: [ 0.007002 0.9079 0.0145 5.659e-05 -2.541e-05 1.064 4.265e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08602 Epoch 4351 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06241 0.7717 0.9505 -2.526e-05 1.134e-05 0.1529 -1.903e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004822 -0.004709 -0.0164 0.009411 0.9631 0.9688 0.01075 0.917 0.9286 0.03604 ] Network output: [ 1.021 -0.1904 0.07569 0.0004302 -0.0001931 0.07389 0.0003242 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3024 -0.01993 -0.1482 0.211 0.9827 0.9929 0.3481 0.91 0.9776 0.6228 ] Network output: [ 0.02014 0.8206 0.9742 -0.0001413 6.341e-05 0.1644 -0.0001065 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007546 0.00272 0.006283 0.006626 0.9905 0.9936 0.007719 0.9706 0.9833 0.01496 ] Network output: [ 0.07786 -0.5018 1.028 0.0001109 -4.979e-05 1.319 8.358e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3397 0.2321 0.3994 0.2942 0.9843 0.9937 0.3411 0.916 0.98 0.6101 ] Network output: [ -0.04568 0.2741 1.078 0.0002878 -0.0001292 0.7409 0.0002169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1526 0.144 0.1926 0.1547 0.9899 0.994 0.1527 0.9693 0.984 0.2092 ] Network output: [ -0.04235 0.2297 1.049 0.0003764 -0.000169 0.8079 0.0002837 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1713 0.1696 0.195 0.165 0.9855 0.9916 0.1713 0.9513 0.9764 0.1994 ] Network output: [ 0.01246 0.9512 -0.02353 4.24e-05 -1.903e-05 1.048 3.195e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1142 Epoch 4352 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05009 0.8472 0.9347 -0.0001527 6.855e-05 0.1172 -0.0001151 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004951 -0.004643 -0.01593 0.006807 0.9631 0.9688 0.01098 0.9167 0.9281 0.03573 ] Network output: [ 0.9129 0.3655 -0.04265 -0.000476 0.0002137 -0.1507 -0.0003588 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3117 -0.003582 -0.121 0.09847 0.9827 0.9929 0.3584 0.9098 0.9774 0.6191 ] Network output: [ 0.01987 0.8365 0.9703 -0.0001707 7.666e-05 0.1527 -0.0001287 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007859 0.002844 0.006662 0.003257 0.9905 0.9936 0.008037 0.9706 0.983 0.01524 ] Network output: [ -0.009873 0.2376 0.8217 -0.001053 0.0004728 0.9562 -0.0007936 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3502 0.2399 0.4078 0.1268 0.9843 0.9937 0.3516 0.916 0.9799 0.6171 ] Network output: [ -0.04717 0.2989 1.082 0.0002497 -0.0001121 0.7147 0.0001882 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1457 0.1373 0.1887 0.1287 0.99 0.994 0.1458 0.969 0.9836 0.2056 ] Network output: [ -0.03915 0.1504 1.089 0.0004623 -0.0002075 0.8406 0.0003484 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1629 0.1612 0.1972 0.1607 0.9854 0.9915 0.1629 0.9506 0.9762 0.202 ] Network output: [ 0.00701 0.9078 0.0146 5.713e-05 -2.565e-05 1.064 4.306e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08588 Epoch 4353 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0624 0.7718 0.9504 -2.536e-05 1.139e-05 0.1528 -1.911e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004817 -0.004704 -0.01638 0.009403 0.9631 0.9688 0.01073 0.917 0.9285 0.03597 ] Network output: [ 1.021 -0.1901 0.0753 0.0004285 -0.0001923 0.07389 0.0003229 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3022 -0.01992 -0.1482 0.2109 0.9827 0.9929 0.3478 0.9097 0.9776 0.6226 ] Network output: [ 0.02015 0.8207 0.9741 -0.0001409 6.324e-05 0.1643 -0.0001062 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007535 0.002716 0.00627 0.006612 0.9905 0.9936 0.007707 0.9706 0.9832 0.01494 ] Network output: [ 0.07774 -0.5011 1.027 0.0001094 -4.912e-05 1.319 8.245e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3394 0.2319 0.3992 0.2938 0.9843 0.9937 0.3408 0.9158 0.9799 0.6099 ] Network output: [ -0.04571 0.2739 1.078 0.000287 -0.0001288 0.741 0.0002163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1523 0.1437 0.1925 0.1545 0.9899 0.994 0.1524 0.9692 0.984 0.2091 ] Network output: [ -0.04234 0.2294 1.049 0.0003754 -0.0001685 0.808 0.0002829 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.171 0.1693 0.195 0.1648 0.9855 0.9916 0.171 0.9512 0.9763 0.1993 ] Network output: [ 0.01237 0.9518 -0.02352 4.183e-05 -1.878e-05 1.047 3.152e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.114 Epoch 4354 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0501 0.8472 0.9347 -0.0001522 6.831e-05 0.1172 -0.0001147 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004946 -0.004636 -0.01591 0.006806 0.9631 0.9688 0.01096 0.9166 0.928 0.03566 ] Network output: [ 0.913 0.3651 -0.04225 -0.0004738 0.0002127 -0.1507 -0.0003571 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3114 -0.003606 -0.1212 0.09854 0.9827 0.9929 0.358 0.9095 0.9774 0.6189 ] Network output: [ 0.0199 0.8365 0.9703 -0.0001702 7.639e-05 0.1527 -0.0001282 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007846 0.002838 0.006645 0.003252 0.9905 0.9936 0.008024 0.9706 0.983 0.01521 ] Network output: [ -0.00983 0.2373 0.822 -0.00105 0.0004714 0.956 -0.0007913 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3499 0.2397 0.4075 0.1266 0.9843 0.9937 0.3513 0.9158 0.9799 0.6169 ] Network output: [ -0.04714 0.2986 1.082 0.0002493 -0.0001119 0.7148 0.0001878 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1454 0.137 0.1886 0.1286 0.99 0.994 0.1455 0.969 0.9836 0.2056 ] Network output: [ -0.03911 0.1501 1.089 0.0004612 -0.000207 0.8408 0.0003475 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1626 0.161 0.1971 0.1606 0.9854 0.9915 0.1627 0.9505 0.9761 0.2019 ] Network output: [ 0.007019 0.9076 0.01469 5.768e-05 -2.589e-05 1.064 4.347e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08574 Epoch 4355 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06239 0.772 0.9503 -2.547e-05 1.143e-05 0.1528 -1.919e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004813 -0.004698 -0.01635 0.009395 0.9631 0.9688 0.01072 0.9169 0.9284 0.0359 ] Network output: [ 1.021 -0.1898 0.07491 0.0004267 -0.0001916 0.07389 0.0003216 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3019 -0.01991 -0.1483 0.2109 0.9827 0.9929 0.3475 0.9095 0.9775 0.6224 ] Network output: [ 0.02016 0.8208 0.9741 -0.0001405 6.307e-05 0.1642 -0.0001059 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007523 0.002711 0.006256 0.006599 0.9905 0.9936 0.007695 0.9705 0.9832 0.01491 ] Network output: [ 0.07762 -0.5004 1.027 0.0001079 -4.844e-05 1.319 8.132e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.339 0.2317 0.399 0.2935 0.9843 0.9937 0.3404 0.9156 0.9799 0.6097 ] Network output: [ -0.04573 0.2737 1.078 0.0002861 -0.0001285 0.7412 0.0002156 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.152 0.1434 0.1925 0.1544 0.9899 0.994 0.1521 0.9692 0.9839 0.2091 ] Network output: [ -0.04234 0.2292 1.049 0.0003744 -0.0001681 0.8081 0.0002822 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1707 0.169 0.1949 0.1647 0.9855 0.9916 0.1707 0.9511 0.9763 0.1993 ] Network output: [ 0.01228 0.9524 -0.02351 4.125e-05 -1.852e-05 1.047 3.109e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1138 Epoch 4356 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05011 0.8472 0.9347 -0.0001516 6.807e-05 0.1172 -0.0001143 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004941 -0.00463 -0.01589 0.006805 0.9631 0.9688 0.01094 0.9165 0.9279 0.03558 ] Network output: [ 0.913 0.3647 -0.04185 -0.0004716 0.0002117 -0.1508 -0.0003554 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3111 -0.003629 -0.1213 0.09862 0.9827 0.9929 0.3576 0.9093 0.9773 0.6187 ] Network output: [ 0.01994 0.8365 0.9702 -0.0001696 7.613e-05 0.1527 -0.0001278 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007834 0.002833 0.006629 0.003247 0.9905 0.9936 0.008011 0.9705 0.9829 0.01519 ] Network output: [ -0.009788 0.2371 0.8224 -0.001047 0.00047 0.9559 -0.000789 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3495 0.2394 0.4073 0.1264 0.9844 0.9937 0.3509 0.9156 0.9799 0.6167 ] Network output: [ -0.04711 0.2983 1.082 0.0002488 -0.0001117 0.715 0.0001875 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1452 0.1368 0.1885 0.1285 0.99 0.994 0.1453 0.9689 0.9835 0.2055 ] Network output: [ -0.03906 0.1499 1.089 0.0004601 -0.0002066 0.841 0.0003467 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1624 0.1607 0.197 0.1605 0.9854 0.9915 0.1624 0.9504 0.9761 0.2019 ] Network output: [ 0.007029 0.9075 0.01478 5.823e-05 -2.614e-05 1.064 4.388e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0856 Epoch 4357 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06237 0.7721 0.9502 -2.558e-05 1.148e-05 0.1528 -1.927e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004809 -0.004692 -0.01633 0.009387 0.9631 0.9688 0.0107 0.9168 0.9284 0.03583 ] Network output: [ 1.021 -0.1894 0.07452 0.000425 -0.0001908 0.07388 0.0003203 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3017 -0.01989 -0.1483 0.2108 0.9827 0.9929 0.3472 0.9093 0.9775 0.6222 ] Network output: [ 0.02017 0.8209 0.9741 -0.0001401 6.291e-05 0.1641 -0.0001056 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007511 0.002706 0.006243 0.006585 0.9905 0.9936 0.007682 0.9704 0.9832 0.01489 ] Network output: [ 0.0775 -0.4997 1.027 0.0001064 -4.777e-05 1.319 8.019e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3387 0.2315 0.3988 0.2931 0.9843 0.9937 0.3401 0.9154 0.9798 0.6095 ] Network output: [ -0.04576 0.2734 1.078 0.0002853 -0.0001281 0.7414 0.000215 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1517 0.1432 0.1924 0.1543 0.9899 0.994 0.1518 0.9691 0.9839 0.209 ] Network output: [ -0.04233 0.229 1.049 0.0003734 -0.0001676 0.8082 0.0002814 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1704 0.1687 0.1948 0.1646 0.9855 0.9916 0.1704 0.951 0.9762 0.1992 ] Network output: [ 0.01218 0.9529 -0.0235 4.066e-05 -1.825e-05 1.046 3.064e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1136 Epoch 4358 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05012 0.8472 0.9347 -0.0001511 6.783e-05 0.1172 -0.0001139 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004936 -0.004624 -0.01587 0.006804 0.9631 0.9688 0.01092 0.9165 0.9278 0.03551 ] Network output: [ 0.9131 0.3643 -0.04145 -0.0004694 0.0002108 -0.1509 -0.0003538 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3108 -0.003653 -0.1214 0.09869 0.9827 0.9929 0.3572 0.9091 0.9773 0.6185 ] Network output: [ 0.01997 0.8366 0.9702 -0.000169 7.587e-05 0.1526 -0.0001274 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007821 0.002827 0.006613 0.003242 0.9905 0.9936 0.007998 0.9704 0.9829 0.01517 ] Network output: [ -0.009748 0.2368 0.8227 -0.001044 0.0004687 0.9557 -0.0007867 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3492 0.2392 0.407 0.1262 0.9844 0.9937 0.3506 0.9154 0.9798 0.6165 ] Network output: [ -0.04708 0.298 1.082 0.0002483 -0.0001115 0.7153 0.0001871 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1449 0.1366 0.1884 0.1285 0.99 0.994 0.145 0.9688 0.9835 0.2054 ] Network output: [ -0.03902 0.1496 1.089 0.000459 -0.0002061 0.8413 0.0003459 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1621 0.1605 0.197 0.1604 0.9854 0.9915 0.1622 0.9503 0.976 0.2018 ] Network output: [ 0.007041 0.9073 0.01487 5.878e-05 -2.639e-05 1.064 4.43e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08546 Epoch 4359 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06236 0.7723 0.9502 -2.568e-05 1.153e-05 0.1527 -1.936e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004805 -0.004687 -0.01631 0.009379 0.9632 0.9689 0.01069 0.9168 0.9283 0.03576 ] Network output: [ 1.021 -0.1891 0.07412 0.0004233 -0.0001901 0.07387 0.000319 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3015 -0.01988 -0.1484 0.2107 0.9827 0.9929 0.3469 0.9091 0.9774 0.622 ] Network output: [ 0.02017 0.821 0.974 -0.0001398 6.274e-05 0.1641 -0.0001053 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007499 0.002702 0.00623 0.006572 0.9905 0.9936 0.00767 0.9704 0.9831 0.01487 ] Network output: [ 0.07738 -0.499 1.026 0.0001049 -4.709e-05 1.319 7.905e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3383 0.2312 0.3986 0.2927 0.9844 0.9937 0.3397 0.9152 0.9798 0.6094 ] Network output: [ -0.04578 0.2732 1.078 0.0002845 -0.0001277 0.7415 0.0002144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1514 0.1429 0.1923 0.1542 0.9899 0.994 0.1515 0.969 0.9838 0.209 ] Network output: [ -0.04232 0.2287 1.049 0.0003724 -0.0001672 0.8083 0.0002807 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1701 0.1684 0.1948 0.1645 0.9855 0.9916 0.1701 0.9509 0.9761 0.1992 ] Network output: [ 0.01209 0.9535 -0.02349 4.007e-05 -1.799e-05 1.046 3.02e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1134 Epoch 4360 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05013 0.8472 0.9348 -0.0001505 6.759e-05 0.1172 -0.0001135 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004931 -0.004618 -0.01585 0.006803 0.9632 0.9688 0.0109 0.9164 0.9278 0.03544 ] Network output: [ 0.9131 0.3639 -0.04105 -0.0004673 0.0002098 -0.151 -0.0003521 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3105 -0.003677 -0.1216 0.09877 0.9827 0.9929 0.3568 0.9089 0.9772 0.6183 ] Network output: [ 0.02 0.8366 0.9702 -0.0001684 7.561e-05 0.1526 -0.0001269 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007809 0.002821 0.006597 0.003237 0.9905 0.9936 0.007985 0.9704 0.9829 0.01515 ] Network output: [ -0.009709 0.2365 0.8231 -0.001041 0.0004673 0.9556 -0.0007845 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3488 0.2389 0.4067 0.126 0.9844 0.9937 0.3502 0.9152 0.9798 0.6163 ] Network output: [ -0.04704 0.2977 1.082 0.0002479 -0.0001113 0.7155 0.0001868 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1447 0.1363 0.1883 0.1284 0.99 0.994 0.1448 0.9687 0.9835 0.2054 ] Network output: [ -0.03897 0.1493 1.089 0.000458 -0.0002056 0.8415 0.0003451 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1619 0.1602 0.1969 0.1603 0.9854 0.9915 0.1619 0.9502 0.9759 0.2018 ] Network output: [ 0.007053 0.9071 0.01495 5.935e-05 -2.664e-05 1.064 4.472e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08532 Epoch 4361 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06235 0.7725 0.9501 -2.579e-05 1.158e-05 0.1527 -1.944e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004801 -0.004681 -0.01629 0.00937 0.9632 0.9689 0.01067 0.9167 0.9282 0.03569 ] Network output: [ 1.021 -0.1888 0.07372 0.0004217 -0.0001893 0.07385 0.0003178 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3013 -0.01987 -0.1484 0.2107 0.9828 0.9929 0.3466 0.9088 0.9774 0.6219 ] Network output: [ 0.02018 0.8211 0.974 -0.0001394 6.258e-05 0.164 -0.000105 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007488 0.002697 0.006217 0.006558 0.9905 0.9936 0.007658 0.9703 0.9831 0.01485 ] Network output: [ 0.07726 -0.4983 1.026 0.0001034 -4.641e-05 1.319 7.791e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.338 0.231 0.3984 0.2923 0.9844 0.9937 0.3394 0.915 0.9797 0.6092 ] Network output: [ -0.04581 0.273 1.078 0.0002837 -0.0001274 0.7417 0.0002138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1511 0.1426 0.1923 0.1541 0.9899 0.994 0.1512 0.9689 0.9838 0.2089 ] Network output: [ -0.04231 0.2285 1.049 0.0003714 -0.0001667 0.8085 0.0002799 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1698 0.1681 0.1947 0.1644 0.9855 0.9916 0.1698 0.9508 0.9761 0.1991 ] Network output: [ 0.01199 0.9541 -0.02348 3.947e-05 -1.772e-05 1.046 2.975e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1132 Epoch 4362 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05013 0.8472 0.9348 -0.00015 6.735e-05 0.1172 -0.0001131 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004926 -0.004612 -0.01583 0.006802 0.9632 0.9689 0.01089 0.9163 0.9277 0.03537 ] Network output: [ 0.9131 0.3635 -0.04064 -0.0004651 0.0002088 -0.151 -0.0003505 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3102 -0.0037 -0.1217 0.09885 0.9827 0.9929 0.3565 0.9086 0.9772 0.6182 ] Network output: [ 0.02003 0.8366 0.9701 -0.0001679 7.536e-05 0.1525 -0.0001265 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007796 0.002816 0.006581 0.003232 0.9905 0.9936 0.007972 0.9703 0.9828 0.01513 ] Network output: [ -0.009672 0.2363 0.8234 -0.001038 0.000466 0.9555 -0.0007822 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3485 0.2386 0.4064 0.1258 0.9844 0.9937 0.3499 0.915 0.9797 0.6162 ] Network output: [ -0.04701 0.2973 1.082 0.0002474 -0.0001111 0.7157 0.0001865 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1444 0.1361 0.1883 0.1283 0.99 0.994 0.1445 0.9687 0.9834 0.2053 ] Network output: [ -0.03892 0.1491 1.089 0.0004569 -0.0002051 0.8418 0.0003444 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1616 0.16 0.1968 0.1603 0.9854 0.9915 0.1617 0.9501 0.9759 0.2017 ] Network output: [ 0.007066 0.9069 0.01504 5.992e-05 -2.69e-05 1.064 4.515e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08518 Epoch 4363 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06233 0.7726 0.95 -2.59e-05 1.163e-05 0.1526 -1.952e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004797 -0.004676 -0.01627 0.009362 0.9632 0.9689 0.01066 0.9167 0.9281 0.03562 ] Network output: [ 1.021 -0.1884 0.07332 0.00042 -0.0001885 0.07383 0.0003165 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3011 -0.01986 -0.1485 0.2106 0.9828 0.9929 0.3463 0.9086 0.9773 0.6217 ] Network output: [ 0.02019 0.8212 0.9739 -0.000139 6.241e-05 0.1639 -0.0001048 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007476 0.002693 0.006204 0.006545 0.9905 0.9936 0.007646 0.9703 0.983 0.01483 ] Network output: [ 0.07714 -0.4976 1.025 0.0001019 -4.573e-05 1.319 7.676e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3377 0.2308 0.3982 0.2919 0.9844 0.9937 0.339 0.9147 0.9797 0.609 ] Network output: [ -0.04583 0.2727 1.078 0.0002829 -0.000127 0.7419 0.0002132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1508 0.1424 0.1922 0.154 0.9899 0.994 0.151 0.9689 0.9837 0.2089 ] Network output: [ -0.04231 0.2282 1.049 0.0003704 -0.0001663 0.8086 0.0002792 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1695 0.1678 0.1947 0.1642 0.9855 0.9916 0.1695 0.9507 0.976 0.1991 ] Network output: [ 0.01189 0.9547 -0.02347 3.886e-05 -1.745e-05 1.045 2.929e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.113 Epoch 4364 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05014 0.8471 0.9348 -0.0001495 6.711e-05 0.1172 -0.0001127 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00492 -0.004606 -0.01581 0.006802 0.9632 0.9689 0.01087 0.9163 0.9276 0.0353 ] Network output: [ 0.9132 0.3631 -0.04024 -0.0004629 0.0002078 -0.1511 -0.0003489 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.31 -0.003724 -0.1218 0.09892 0.9828 0.9929 0.3561 0.9084 0.9771 0.618 ] Network output: [ 0.02006 0.8366 0.9701 -0.0001673 7.51e-05 0.1525 -0.0001261 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007784 0.00281 0.006566 0.003227 0.9906 0.9936 0.007959 0.9703 0.9828 0.01511 ] Network output: [ -0.009637 0.236 0.8238 -0.001035 0.0004647 0.9553 -0.00078 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3481 0.2384 0.4062 0.1257 0.9844 0.9937 0.3495 0.9148 0.9797 0.616 ] Network output: [ -0.04698 0.297 1.082 0.000247 -0.0001109 0.7159 0.0001862 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1442 0.1359 0.1882 0.1283 0.99 0.994 0.1443 0.9686 0.9834 0.2052 ] Network output: [ -0.03887 0.1488 1.089 0.0004559 -0.0002047 0.842 0.0003436 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1614 0.1597 0.1968 0.1602 0.9854 0.9915 0.1614 0.95 0.9758 0.2016 ] Network output: [ 0.00708 0.9067 0.01513 6.049e-05 -2.716e-05 1.064 4.559e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08504 Epoch 4365 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06232 0.7728 0.9499 -2.601e-05 1.167e-05 0.1526 -1.96e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004793 -0.00467 -0.01625 0.009354 0.9632 0.9689 0.01064 0.9166 0.928 0.03555 ] Network output: [ 1.022 -0.1881 0.07291 0.0004183 -0.0001878 0.0738 0.0003152 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3008 -0.01985 -0.1486 0.2106 0.9828 0.9929 0.346 0.9084 0.9773 0.6215 ] Network output: [ 0.02019 0.8213 0.9739 -0.0001387 6.225e-05 0.1639 -0.0001045 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007465 0.002688 0.006192 0.006532 0.9905 0.9936 0.007634 0.9702 0.983 0.01481 ] Network output: [ 0.07702 -0.4969 1.025 0.0001003 -4.504e-05 1.319 7.561e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3373 0.2306 0.398 0.2915 0.9844 0.9937 0.3387 0.9145 0.9797 0.6088 ] Network output: [ -0.04585 0.2725 1.078 0.0002821 -0.0001266 0.742 0.0002126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1506 0.1421 0.1922 0.1538 0.9899 0.994 0.1507 0.9688 0.9837 0.2089 ] Network output: [ -0.0423 0.228 1.049 0.0003695 -0.0001659 0.8087 0.0002785 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1692 0.1675 0.1946 0.1641 0.9855 0.9916 0.1692 0.9506 0.976 0.1991 ] Network output: [ 0.01179 0.9553 -0.02346 3.825e-05 -1.717e-05 1.045 2.882e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1127 Epoch 4366 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05014 0.8471 0.9348 -0.000149 6.687e-05 0.1172 -0.0001123 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004915 -0.0046 -0.01579 0.006801 0.9632 0.9689 0.01085 0.9162 0.9275 0.03523 ] Network output: [ 0.9132 0.3627 -0.03983 -0.0004608 0.0002069 -0.1512 -0.0003473 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3097 -0.003748 -0.1219 0.099 0.9828 0.9929 0.3557 0.9082 0.9771 0.6178 ] Network output: [ 0.02009 0.8366 0.97 -0.0001667 7.484e-05 0.1525 -0.0001256 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007772 0.002804 0.00655 0.003222 0.9906 0.9936 0.007946 0.9702 0.9827 0.01509 ] Network output: [ -0.009603 0.2357 0.8241 -0.001032 0.0004633 0.9552 -0.0007778 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3477 0.2381 0.4059 0.1255 0.9844 0.9937 0.3491 0.9146 0.9796 0.6158 ] Network output: [ -0.04694 0.2967 1.082 0.0002466 -0.0001107 0.7161 0.0001858 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.144 0.1356 0.1881 0.1282 0.99 0.994 0.1441 0.9685 0.9833 0.2052 ] Network output: [ -0.03882 0.1485 1.089 0.0004549 -0.0002042 0.8423 0.0003428 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1611 0.1595 0.1967 0.1601 0.9854 0.9915 0.1612 0.9499 0.9758 0.2016 ] Network output: [ 0.007096 0.9066 0.01522 6.108e-05 -2.742e-05 1.064 4.603e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0849 Epoch 4367 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0623 0.7729 0.9498 -2.611e-05 1.172e-05 0.1526 -1.968e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004789 -0.004665 -0.01623 0.009346 0.9632 0.9689 0.01063 0.9165 0.928 0.03548 ] Network output: [ 1.022 -0.1877 0.07251 0.0004166 -0.000187 0.07376 0.000314 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3006 -0.01984 -0.1486 0.2105 0.9828 0.9929 0.3457 0.9082 0.9772 0.6214 ] Network output: [ 0.0202 0.8214 0.9738 -0.0001383 6.209e-05 0.1638 -0.0001042 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007453 0.002684 0.006179 0.006518 0.9905 0.9936 0.007622 0.9702 0.983 0.01479 ] Network output: [ 0.0769 -0.4962 1.024 9.879e-05 -4.435e-05 1.319 7.445e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.337 0.2303 0.3978 0.2911 0.9844 0.9937 0.3384 0.9143 0.9796 0.6087 ] Network output: [ -0.04588 0.2722 1.078 0.0002813 -0.0001263 0.7422 0.000212 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1503 0.1418 0.1921 0.1537 0.9899 0.994 0.1504 0.9687 0.9837 0.2088 ] Network output: [ -0.04229 0.2277 1.05 0.0003685 -0.0001654 0.8089 0.0002777 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1689 0.1672 0.1946 0.164 0.9855 0.9916 0.1689 0.9505 0.9759 0.199 ] Network output: [ 0.01169 0.956 -0.02346 3.762e-05 -1.689e-05 1.044 2.835e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1125 Epoch 4368 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05015 0.8471 0.9348 -0.0001484 6.663e-05 0.1172 -0.0001119 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00491 -0.004594 -0.01577 0.0068 0.9632 0.9689 0.01083 0.9162 0.9274 0.03516 ] Network output: [ 0.9132 0.3623 -0.03943 -0.0004586 0.0002059 -0.1512 -0.0003456 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3094 -0.003772 -0.1221 0.09908 0.9828 0.9929 0.3553 0.908 0.977 0.6176 ] Network output: [ 0.02012 0.8366 0.97 -0.0001661 7.459e-05 0.1524 -0.0001252 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007759 0.002799 0.006535 0.003217 0.9906 0.9936 0.007934 0.9701 0.9827 0.01507 ] Network output: [ -0.00957 0.2354 0.8245 -0.001029 0.000462 0.955 -0.0007756 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3474 0.2379 0.4057 0.1253 0.9844 0.9937 0.3488 0.9143 0.9796 0.6157 ] Network output: [ -0.0469 0.2964 1.082 0.0002462 -0.0001105 0.7163 0.0001855 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1437 0.1354 0.188 0.1281 0.99 0.994 0.1438 0.9684 0.9833 0.2051 ] Network output: [ -0.03877 0.1482 1.089 0.0004539 -0.0002038 0.8425 0.0003421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1609 0.1592 0.1966 0.16 0.9854 0.9915 0.1609 0.9498 0.9757 0.2015 ] Network output: [ 0.007113 0.9064 0.0153 6.167e-05 -2.768e-05 1.064 4.647e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08475 Epoch 4369 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06229 0.7731 0.9497 -2.622e-05 1.177e-05 0.1525 -1.976e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004785 -0.004659 -0.01621 0.009338 0.9633 0.9689 0.01061 0.9165 0.9279 0.03541 ] Network output: [ 1.022 -0.1874 0.07209 0.000415 -0.0001863 0.07371 0.0003127 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3004 -0.01983 -0.1487 0.2104 0.9828 0.9929 0.3454 0.9079 0.9772 0.6212 ] Network output: [ 0.0202 0.8215 0.9738 -0.0001379 6.193e-05 0.1637 -0.000104 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007442 0.002679 0.006166 0.006505 0.9905 0.9936 0.00761 0.9701 0.9829 0.01478 ] Network output: [ 0.07679 -0.4955 1.024 9.725e-05 -4.366e-05 1.319 7.329e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3367 0.2301 0.3977 0.2907 0.9844 0.9937 0.338 0.9141 0.9796 0.6085 ] Network output: [ -0.0459 0.272 1.079 0.0002805 -0.0001259 0.7424 0.0002114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.15 0.1416 0.1921 0.1536 0.9899 0.994 0.1501 0.9686 0.9836 0.2088 ] Network output: [ -0.04228 0.2275 1.05 0.0003676 -0.000165 0.809 0.000277 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1686 0.1669 0.1945 0.1639 0.9855 0.9916 0.1686 0.9504 0.9759 0.199 ] Network output: [ 0.01158 0.9566 -0.02345 3.699e-05 -1.661e-05 1.044 2.788e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1123 Epoch 4370 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05015 0.8471 0.9348 -0.0001479 6.64e-05 0.1172 -0.0001115 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004905 -0.004588 -0.01576 0.006799 0.9632 0.9689 0.01082 0.9161 0.9274 0.0351 ] Network output: [ 0.9133 0.3619 -0.03902 -0.0004565 0.0002049 -0.1513 -0.000344 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3091 -0.003795 -0.1222 0.09915 0.9828 0.9929 0.3549 0.9078 0.977 0.6175 ] Network output: [ 0.02015 0.8367 0.97 -0.0001656 7.434e-05 0.1524 -0.0001248 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007747 0.002793 0.00652 0.003212 0.9906 0.9936 0.007921 0.9701 0.9826 0.01505 ] Network output: [ -0.009539 0.2352 0.8248 -0.001026 0.0004607 0.9549 -0.0007734 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.347 0.2376 0.4054 0.1251 0.9844 0.9938 0.3484 0.9141 0.9795 0.6155 ] Network output: [ -0.04686 0.2961 1.082 0.0002458 -0.0001103 0.7165 0.0001852 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1435 0.1352 0.188 0.1281 0.99 0.994 0.1436 0.9683 0.9832 0.2051 ] Network output: [ -0.03872 0.1479 1.089 0.000453 -0.0002033 0.8428 0.0003414 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1606 0.159 0.1966 0.16 0.9854 0.9915 0.1607 0.9497 0.9757 0.2015 ] Network output: [ 0.007131 0.9062 0.01539 6.227e-05 -2.795e-05 1.064 4.693e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08461 Epoch 4371 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06227 0.7733 0.9496 -2.633e-05 1.182e-05 0.1525 -1.985e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004781 -0.004654 -0.01619 0.009329 0.9633 0.9689 0.0106 0.9164 0.9278 0.03535 ] Network output: [ 1.022 -0.187 0.07168 0.0004133 -0.0001855 0.07366 0.0003115 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3002 -0.01982 -0.1487 0.2103 0.9828 0.9929 0.3451 0.9077 0.9771 0.6211 ] Network output: [ 0.0202 0.8216 0.9738 -0.0001376 6.177e-05 0.1636 -0.0001037 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00743 0.002674 0.006153 0.006491 0.9905 0.9936 0.007598 0.97 0.9829 0.01476 ] Network output: [ 0.07667 -0.4947 1.023 9.569e-05 -4.296e-05 1.319 7.211e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3363 0.2299 0.3975 0.2903 0.9844 0.9937 0.3377 0.9139 0.9795 0.6084 ] Network output: [ -0.04592 0.2717 1.079 0.0002798 -0.0001256 0.7425 0.0002108 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1497 0.1413 0.192 0.1535 0.9899 0.994 0.1498 0.9686 0.9836 0.2087 ] Network output: [ -0.04227 0.2272 1.05 0.0003667 -0.0001646 0.8091 0.0002763 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1683 0.1666 0.1945 0.1638 0.9856 0.9916 0.1683 0.9502 0.9758 0.1989 ] Network output: [ 0.01148 0.9572 -0.02344 3.635e-05 -1.632e-05 1.043 2.739e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1121 Epoch 4372 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05016 0.8471 0.9348 -0.0001474 6.616e-05 0.1172 -0.0001111 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0049 -0.004583 -0.01574 0.006799 0.9633 0.9689 0.0108 0.916 0.9273 0.03503 ] Network output: [ 0.9133 0.3615 -0.03861 -0.0004543 0.000204 -0.1513 -0.0003424 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3088 -0.00382 -0.1223 0.09923 0.9828 0.9929 0.3545 0.9075 0.9769 0.6173 ] Network output: [ 0.02018 0.8367 0.9699 -0.000165 7.408e-05 0.1523 -0.0001244 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007735 0.002787 0.006505 0.003208 0.9906 0.9936 0.007908 0.97 0.9826 0.01503 ] Network output: [ -0.009509 0.2349 0.8252 -0.001023 0.0004595 0.9548 -0.0007713 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3467 0.2374 0.4052 0.1249 0.9844 0.9938 0.3481 0.9139 0.9795 0.6153 ] Network output: [ -0.04682 0.2957 1.082 0.0002454 -0.0001102 0.7167 0.0001849 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1432 0.135 0.1879 0.128 0.99 0.994 0.1433 0.9683 0.9832 0.205 ] Network output: [ -0.03867 0.1476 1.089 0.000452 -0.0002029 0.843 0.0003406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1604 0.1587 0.1965 0.1599 0.9854 0.9915 0.1604 0.9495 0.9756 0.2014 ] Network output: [ 0.00715 0.906 0.01548 6.287e-05 -2.823e-05 1.065 4.738e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08446 Epoch 4373 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06226 0.7734 0.9495 -2.644e-05 1.187e-05 0.1524 -1.993e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004777 -0.004649 -0.01616 0.009321 0.9633 0.969 0.01058 0.9164 0.9277 0.03528 ] Network output: [ 1.022 -0.1867 0.07126 0.0004117 -0.0001848 0.0736 0.0003102 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3 -0.01981 -0.1488 0.2103 0.9828 0.9929 0.3448 0.9075 0.9771 0.621 ] Network output: [ 0.02021 0.8218 0.9737 -0.0001372 6.161e-05 0.1636 -0.0001034 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007419 0.00267 0.006141 0.006478 0.9905 0.9936 0.007586 0.97 0.9828 0.01474 ] Network output: [ 0.07655 -0.494 1.023 9.412e-05 -4.226e-05 1.319 7.093e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.336 0.2297 0.3973 0.2898 0.9844 0.9938 0.3373 0.9137 0.9795 0.6082 ] Network output: [ -0.04594 0.2715 1.079 0.000279 -0.0001253 0.7427 0.0002103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1495 0.141 0.1919 0.1534 0.9899 0.994 0.1496 0.9685 0.9835 0.2087 ] Network output: [ -0.04227 0.2269 1.05 0.0003657 -0.0001642 0.8093 0.0002756 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.168 0.1664 0.1944 0.1637 0.9856 0.9916 0.168 0.9501 0.9757 0.1989 ] Network output: [ 0.01137 0.9579 -0.02344 3.57e-05 -1.603e-05 1.043 2.69e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1119 Epoch 4374 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05016 0.8471 0.9348 -0.0001468 6.593e-05 0.1172 -0.0001107 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004894 -0.004577 -0.01572 0.006798 0.9633 0.9689 0.01078 0.916 0.9272 0.03496 ] Network output: [ 0.9133 0.3611 -0.03819 -0.0004522 0.000203 -0.1514 -0.0003408 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3085 -0.003844 -0.1224 0.09931 0.9828 0.9929 0.3542 0.9073 0.9769 0.6172 ] Network output: [ 0.02021 0.8367 0.9699 -0.0001645 7.383e-05 0.1523 -0.0001239 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007723 0.002782 0.00649 0.003203 0.9906 0.9936 0.007896 0.9699 0.9826 0.01501 ] Network output: [ -0.009481 0.2346 0.8255 -0.001021 0.0004582 0.9546 -0.0007692 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3463 0.2371 0.4049 0.1247 0.9844 0.9938 0.3477 0.9137 0.9795 0.6152 ] Network output: [ -0.04678 0.2954 1.082 0.000245 -0.00011 0.7169 0.0001846 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.143 0.1347 0.1878 0.1279 0.99 0.994 0.1431 0.9682 0.9831 0.205 ] Network output: [ -0.03861 0.1473 1.088 0.000451 -0.0002025 0.8433 0.0003399 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1602 0.1585 0.1965 0.1598 0.9854 0.9916 0.1602 0.9494 0.9755 0.2014 ] Network output: [ 0.007171 0.9057 0.01556 6.349e-05 -2.85e-05 1.065 4.785e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08432 Epoch 4375 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06224 0.7736 0.9494 -2.655e-05 1.192e-05 0.1524 -2.001e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004773 -0.004643 -0.01614 0.009313 0.9633 0.969 0.01057 0.9163 0.9276 0.03521 ] Network output: [ 1.022 -0.1863 0.07083 0.00041 -0.0001841 0.07354 0.000309 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2997 -0.0198 -0.1489 0.2102 0.9828 0.9929 0.3445 0.9073 0.977 0.6208 ] Network output: [ 0.02021 0.8219 0.9737 -0.0001369 6.146e-05 0.1635 -0.0001032 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007407 0.002665 0.006128 0.006465 0.9905 0.9936 0.007575 0.9699 0.9828 0.01472 ] Network output: [ 0.07644 -0.4933 1.022 9.255e-05 -4.155e-05 1.318 6.975e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3356 0.2294 0.3971 0.2894 0.9844 0.9938 0.337 0.9134 0.9794 0.6081 ] Network output: [ -0.04596 0.2712 1.079 0.0002783 -0.0001249 0.7429 0.0002097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1492 0.1408 0.1919 0.1533 0.9899 0.994 0.1493 0.9684 0.9835 0.2087 ] Network output: [ -0.04226 0.2266 1.05 0.0003648 -0.0001638 0.8094 0.0002749 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1677 0.1661 0.1944 0.1636 0.9856 0.9916 0.1678 0.95 0.9757 0.1989 ] Network output: [ 0.01126 0.9585 -0.02343 3.503e-05 -1.573e-05 1.043 2.64e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1116 Epoch 4376 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05016 0.8471 0.9348 -0.0001463 6.569e-05 0.1172 -0.0001103 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004889 -0.004571 -0.0157 0.006797 0.9633 0.969 0.01077 0.9159 0.9271 0.03489 ] Network output: [ 0.9134 0.3607 -0.03778 -0.0004501 0.0002021 -0.1514 -0.0003392 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3082 -0.003868 -0.1226 0.09939 0.9828 0.9929 0.3538 0.9071 0.9768 0.617 ] Network output: [ 0.02024 0.8367 0.9699 -0.0001639 7.358e-05 0.1522 -0.0001235 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00771 0.002776 0.006475 0.003198 0.9906 0.9936 0.007883 0.9699 0.9825 0.01499 ] Network output: [ -0.009454 0.2344 0.8259 -0.001018 0.0004569 0.9545 -0.000767 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.346 0.2368 0.4047 0.1245 0.9844 0.9938 0.3474 0.9135 0.9794 0.6151 ] Network output: [ -0.04674 0.2951 1.082 0.0002446 -0.0001098 0.7172 0.0001844 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1428 0.1345 0.1877 0.1279 0.99 0.994 0.1429 0.9681 0.9831 0.2049 ] Network output: [ -0.03855 0.147 1.088 0.0004501 -0.0002021 0.8436 0.0003392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1599 0.1583 0.1964 0.1597 0.9854 0.9916 0.16 0.9493 0.9755 0.2013 ] Network output: [ 0.007193 0.9055 0.01565 6.411e-05 -2.878e-05 1.065 4.832e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08417 Epoch 4377 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06222 0.7738 0.9493 -2.667e-05 1.197e-05 0.1523 -2.01e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004769 -0.004638 -0.01612 0.009305 0.9633 0.969 0.01055 0.9162 0.9276 0.03515 ] Network output: [ 1.022 -0.1859 0.07041 0.0004084 -0.0001833 0.07346 0.0003078 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2995 -0.0198 -0.149 0.2101 0.9828 0.9929 0.3442 0.907 0.977 0.6207 ] Network output: [ 0.02021 0.822 0.9736 -0.0001365 6.13e-05 0.1634 -0.0001029 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007396 0.00266 0.006116 0.006451 0.9905 0.9936 0.007563 0.9699 0.9828 0.0147 ] Network output: [ 0.07632 -0.4926 1.022 9.096e-05 -4.083e-05 1.318 6.855e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3353 0.2292 0.397 0.289 0.9844 0.9938 0.3366 0.9132 0.9794 0.608 ] Network output: [ -0.04599 0.271 1.079 0.0002775 -0.0001246 0.7431 0.0002091 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1489 0.1405 0.1918 0.1532 0.9899 0.994 0.149 0.9683 0.9834 0.2086 ] Network output: [ -0.04225 0.2264 1.05 0.0003639 -0.0001634 0.8096 0.0002743 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1674 0.1658 0.1944 0.1634 0.9856 0.9916 0.1675 0.9499 0.9756 0.1988 ] Network output: [ 0.01114 0.9591 -0.02342 3.436e-05 -1.543e-05 1.042 2.59e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1114 Epoch 4378 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05017 0.8471 0.9348 -0.0001458 6.546e-05 0.1172 -0.0001099 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004884 -0.004565 -0.01568 0.006797 0.9633 0.969 0.01075 0.9159 0.927 0.03483 ] Network output: [ 0.9134 0.3603 -0.03737 -0.0004479 0.0002011 -0.1515 -0.0003376 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3079 -0.003893 -0.1227 0.09946 0.9828 0.9929 0.3534 0.9068 0.9768 0.6169 ] Network output: [ 0.02026 0.8368 0.9698 -0.0001633 7.333e-05 0.1522 -0.0001231 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007698 0.00277 0.006461 0.003193 0.9906 0.9936 0.00787 0.9698 0.9825 0.01497 ] Network output: [ -0.009429 0.2341 0.8262 -0.001015 0.0004557 0.9544 -0.0007649 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3456 0.2366 0.4045 0.1243 0.9844 0.9938 0.347 0.9133 0.9794 0.6149 ] Network output: [ -0.0467 0.2947 1.082 0.0002443 -0.0001097 0.7174 0.0001841 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1425 0.1343 0.1877 0.1278 0.99 0.994 0.1426 0.968 0.9831 0.2049 ] Network output: [ -0.03849 0.1467 1.088 0.0004492 -0.0002017 0.8438 0.0003385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1597 0.158 0.1963 0.1597 0.9854 0.9916 0.1597 0.9492 0.9754 0.2013 ] Network output: [ 0.007216 0.9053 0.01573 6.474e-05 -2.906e-05 1.065 4.879e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08402 Epoch 4379 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0622 0.774 0.9493 -2.678e-05 1.202e-05 0.1523 -2.018e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004765 -0.004632 -0.0161 0.009297 0.9633 0.969 0.01054 0.9162 0.9275 0.03508 ] Network output: [ 1.022 -0.1856 0.06997 0.0004067 -0.0001826 0.07338 0.0003065 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2993 -0.01979 -0.149 0.21 0.9828 0.9929 0.3439 0.9068 0.9769 0.6206 ] Network output: [ 0.02021 0.8221 0.9736 -0.0001362 6.115e-05 0.1633 -0.0001026 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007385 0.002656 0.006103 0.006438 0.9905 0.9936 0.007551 0.9698 0.9827 0.01468 ] Network output: [ 0.0762 -0.4918 1.021 8.936e-05 -4.012e-05 1.318 6.734e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.335 0.229 0.3968 0.2886 0.9844 0.9938 0.3363 0.913 0.9793 0.6079 ] Network output: [ -0.04601 0.2707 1.079 0.0002768 -0.0001243 0.7433 0.0002086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1486 0.1403 0.1918 0.153 0.9899 0.994 0.1487 0.9683 0.9834 0.2086 ] Network output: [ -0.04224 0.2261 1.05 0.000363 -0.000163 0.8098 0.0002736 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1672 0.1655 0.1943 0.1633 0.9856 0.9916 0.1672 0.9498 0.9756 0.1988 ] Network output: [ 0.01103 0.9598 -0.02342 3.368e-05 -1.512e-05 1.042 2.538e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1112 Epoch 4380 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05017 0.8471 0.9348 -0.0001453 6.522e-05 0.1172 -0.0001095 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004879 -0.004559 -0.01566 0.006796 0.9633 0.969 0.01073 0.9158 0.927 0.03476 ] Network output: [ 0.9134 0.3598 -0.03695 -0.0004458 0.0002001 -0.1515 -0.000336 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3076 -0.003918 -0.1228 0.09954 0.9828 0.9929 0.353 0.9066 0.9767 0.6168 ] Network output: [ 0.02029 0.8368 0.9698 -0.0001628 7.308e-05 0.1522 -0.0001227 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007686 0.002765 0.006447 0.003189 0.9906 0.9936 0.007858 0.9698 0.9824 0.01496 ] Network output: [ -0.009404 0.2338 0.8266 -0.001012 0.0004544 0.9542 -0.0007629 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3453 0.2363 0.4042 0.1241 0.9844 0.9938 0.3467 0.913 0.9793 0.6148 ] Network output: [ -0.04665 0.2944 1.082 0.0002439 -0.0001095 0.7176 0.0001838 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1423 0.1341 0.1876 0.1278 0.99 0.994 0.1424 0.968 0.983 0.2048 ] Network output: [ -0.03843 0.1463 1.088 0.0004483 -0.0002013 0.8441 0.0003378 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1595 0.1578 0.1963 0.1596 0.9854 0.9916 0.1595 0.9491 0.9754 0.2012 ] Network output: [ 0.007241 0.9051 0.01581 6.538e-05 -2.935e-05 1.065 4.927e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08387 Epoch 4381 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06219 0.7741 0.9492 -2.69e-05 1.207e-05 0.1522 -2.027e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004761 -0.004627 -0.01608 0.009289 0.9634 0.969 0.01052 0.9161 0.9274 0.03502 ] Network output: [ 1.022 -0.1852 0.06954 0.0004051 -0.0001819 0.07329 0.0003053 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2991 -0.01978 -0.1491 0.21 0.9828 0.9929 0.3436 0.9066 0.9769 0.6205 ] Network output: [ 0.02021 0.8222 0.9735 -0.0001359 6.099e-05 0.1632 -0.0001024 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007373 0.002651 0.006091 0.006424 0.9905 0.9936 0.007539 0.9698 0.9827 0.01466 ] Network output: [ 0.07609 -0.4911 1.021 8.774e-05 -3.939e-05 1.318 6.613e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3346 0.2287 0.3966 0.2882 0.9844 0.9938 0.336 0.9128 0.9793 0.6078 ] Network output: [ -0.04603 0.2705 1.079 0.000276 -0.0001239 0.7434 0.000208 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1484 0.14 0.1918 0.1529 0.9899 0.994 0.1485 0.9682 0.9833 0.2086 ] Network output: [ -0.04223 0.2258 1.05 0.0003622 -0.0001626 0.8099 0.0002729 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1669 0.1652 0.1943 0.1632 0.9856 0.9916 0.1669 0.9497 0.9755 0.1988 ] Network output: [ 0.01091 0.9605 -0.02341 3.298e-05 -1.481e-05 1.041 2.486e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1109 Epoch 4382 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05017 0.8471 0.9349 -0.0001448 6.499e-05 0.1171 -0.0001091 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004874 -0.004553 -0.01564 0.006796 0.9634 0.969 0.01071 0.9157 0.9269 0.03469 ] Network output: [ 0.9134 0.3594 -0.03653 -0.0004437 0.0001992 -0.1515 -0.0003344 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3073 -0.003943 -0.1229 0.09962 0.9828 0.9929 0.3526 0.9064 0.9767 0.6166 ] Network output: [ 0.02032 0.8368 0.9698 -0.0001622 7.284e-05 0.1521 -0.0001223 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007674 0.002759 0.006432 0.003184 0.9906 0.9936 0.007845 0.9697 0.9824 0.01494 ] Network output: [ -0.009382 0.2336 0.827 -0.00101 0.0004532 0.9541 -0.0007608 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3449 0.2361 0.404 0.1239 0.9844 0.9938 0.3463 0.9128 0.9793 0.6147 ] Network output: [ -0.04661 0.294 1.082 0.0002436 -0.0001094 0.7179 0.0001836 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1421 0.1339 0.1875 0.1277 0.99 0.994 0.1422 0.9679 0.983 0.2048 ] Network output: [ -0.03837 0.146 1.088 0.0004474 -0.0002009 0.8444 0.0003372 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1592 0.1576 0.1962 0.1595 0.9854 0.9916 0.1592 0.949 0.9753 0.2012 ] Network output: [ 0.007267 0.9048 0.0159 6.603e-05 -2.964e-05 1.065 4.976e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08372 Epoch 4383 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06217 0.7743 0.9491 -2.701e-05 1.213e-05 0.1522 -2.036e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004757 -0.004622 -0.01606 0.00928 0.9634 0.969 0.01051 0.9161 0.9273 0.03495 ] Network output: [ 1.022 -0.1848 0.0691 0.0004035 -0.0001811 0.07319 0.0003041 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2989 -0.01978 -0.1492 0.2099 0.9828 0.993 0.3433 0.9064 0.9768 0.6204 ] Network output: [ 0.02021 0.8224 0.9735 -0.0001355 6.084e-05 0.1632 -0.0001021 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007362 0.002646 0.006078 0.006411 0.9905 0.9936 0.007528 0.9697 0.9826 0.01464 ] Network output: [ 0.07597 -0.4904 1.021 8.611e-05 -3.866e-05 1.318 6.49e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3343 0.2285 0.3965 0.2878 0.9844 0.9938 0.3356 0.9126 0.9792 0.6077 ] Network output: [ -0.04605 0.2702 1.079 0.0002753 -0.0001236 0.7436 0.0002075 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1481 0.1398 0.1917 0.1528 0.9899 0.994 0.1482 0.9681 0.9833 0.2085 ] Network output: [ -0.04222 0.2255 1.05 0.0003613 -0.0001622 0.8101 0.0002723 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1666 0.1649 0.1942 0.1631 0.9856 0.9916 0.1666 0.9496 0.9755 0.1987 ] Network output: [ 0.01079 0.9612 -0.02341 3.228e-05 -1.449e-05 1.041 2.432e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1107 Epoch 4384 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05017 0.8471 0.9349 -0.0001442 6.476e-05 0.1171 -0.0001087 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004869 -0.004547 -0.01562 0.006795 0.9634 0.969 0.0107 0.9157 0.9268 0.03463 ] Network output: [ 0.9134 0.359 -0.03611 -0.0004416 0.0001982 -0.1516 -0.0003328 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.307 -0.003969 -0.1231 0.0997 0.9828 0.9929 0.3523 0.9062 0.9766 0.6165 ] Network output: [ 0.02034 0.8369 0.9697 -0.0001617 7.259e-05 0.1521 -0.0001219 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007662 0.002753 0.006418 0.003179 0.9906 0.9936 0.007833 0.9697 0.9824 0.01492 ] Network output: [ -0.00936 0.2333 0.8273 -0.001007 0.000452 0.954 -0.0007588 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3446 0.2358 0.4038 0.1237 0.9844 0.9938 0.3459 0.9126 0.9792 0.6146 ] Network output: [ -0.04656 0.2937 1.082 0.0002433 -0.0001092 0.7181 0.0001833 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1419 0.1336 0.1874 0.1276 0.99 0.994 0.142 0.9678 0.9829 0.2047 ] Network output: [ -0.03831 0.1457 1.088 0.0004465 -0.0002005 0.8446 0.0003365 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.159 0.1573 0.1962 0.1595 0.9855 0.9916 0.159 0.9489 0.9753 0.2012 ] Network output: [ 0.007295 0.9046 0.01598 6.669e-05 -2.994e-05 1.065 5.026e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08356 Epoch 4385 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06215 0.7745 0.949 -2.713e-05 1.218e-05 0.1521 -2.045e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004753 -0.004617 -0.01604 0.009272 0.9634 0.969 0.01049 0.916 0.9272 0.03489 ] Network output: [ 1.022 -0.1844 0.06865 0.0004018 -0.0001804 0.07309 0.0003028 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2986 -0.01977 -0.1493 0.2098 0.9828 0.993 0.343 0.9061 0.9768 0.6203 ] Network output: [ 0.02021 0.8225 0.9735 -0.0001352 6.069e-05 0.1631 -0.0001019 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007351 0.002641 0.006066 0.006397 0.9905 0.9936 0.007516 0.9696 0.9826 0.01463 ] Network output: [ 0.07585 -0.4896 1.02 8.447e-05 -3.792e-05 1.318 6.366e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3339 0.2283 0.3963 0.2873 0.9844 0.9938 0.3353 0.9124 0.9792 0.6076 ] Network output: [ -0.04607 0.2699 1.079 0.0002746 -0.0001233 0.7438 0.0002069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1478 0.1395 0.1917 0.1527 0.9899 0.994 0.1479 0.968 0.9833 0.2085 ] Network output: [ -0.04221 0.2252 1.05 0.0003604 -0.0001618 0.8103 0.0002716 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1663 0.1647 0.1942 0.163 0.9856 0.9916 0.1663 0.9495 0.9754 0.1987 ] Network output: [ 0.01067 0.9618 -0.02341 3.156e-05 -1.417e-05 1.04 2.378e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1104 Epoch 4386 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05017 0.8471 0.9349 -0.0001437 6.452e-05 0.1171 -0.0001083 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004863 -0.004541 -0.0156 0.006795 0.9634 0.969 0.01068 0.9156 0.9267 0.03456 ] Network output: [ 0.9135 0.3586 -0.03569 -0.0004395 0.0001973 -0.1516 -0.0003312 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3067 -0.003995 -0.1232 0.09978 0.9828 0.9929 0.3519 0.9059 0.9766 0.6164 ] Network output: [ 0.02037 0.8369 0.9697 -0.0001611 7.235e-05 0.152 -0.0001214 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00765 0.002748 0.006404 0.003175 0.9906 0.9936 0.007821 0.9696 0.9823 0.0149 ] Network output: [ -0.00934 0.233 0.8277 -0.001004 0.0004508 0.9539 -0.0007567 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3442 0.2355 0.4036 0.1236 0.9844 0.9938 0.3456 0.9124 0.9792 0.6145 ] Network output: [ -0.04651 0.2933 1.082 0.000243 -0.0001091 0.7183 0.0001831 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1416 0.1334 0.1874 0.1276 0.99 0.994 0.1417 0.9677 0.9829 0.2047 ] Network output: [ -0.03825 0.1453 1.088 0.0004457 -0.0002001 0.8449 0.0003359 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1588 0.1571 0.1961 0.1594 0.9855 0.9916 0.1588 0.9488 0.9752 0.2011 ] Network output: [ 0.007324 0.9043 0.01606 6.736e-05 -3.024e-05 1.065 5.076e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08341 Epoch 4387 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06213 0.7747 0.9489 -2.725e-05 1.223e-05 0.152 -2.053e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004749 -0.004611 -0.01602 0.009264 0.9634 0.9691 0.01048 0.9159 0.9272 0.03482 ] Network output: [ 1.022 -0.184 0.06821 0.0004002 -0.0001797 0.07297 0.0003016 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2984 -0.01977 -0.1494 0.2097 0.9828 0.993 0.3427 0.9059 0.9767 0.6202 ] Network output: [ 0.0202 0.8226 0.9734 -0.0001349 6.055e-05 0.163 -0.0001016 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00734 0.002637 0.006054 0.006384 0.9905 0.9936 0.007505 0.9696 0.9825 0.01461 ] Network output: [ 0.07573 -0.4889 1.02 8.281e-05 -3.718e-05 1.318 6.241e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3336 0.228 0.3961 0.2869 0.9844 0.9938 0.3349 0.9121 0.9792 0.6075 ] Network output: [ -0.04608 0.2697 1.08 0.0002739 -0.000123 0.744 0.0002064 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1476 0.1393 0.1916 0.1526 0.9899 0.994 0.1477 0.968 0.9832 0.2085 ] Network output: [ -0.0422 0.2249 1.051 0.0003596 -0.0001614 0.8104 0.000271 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.166 0.1644 0.1942 0.1629 0.9856 0.9916 0.1661 0.9494 0.9753 0.1987 ] Network output: [ 0.01054 0.9625 -0.0234 3.082e-05 -1.384e-05 1.04 2.323e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1102 Epoch 4388 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05017 0.8471 0.9349 -0.0001432 6.429e-05 0.1171 -0.0001079 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004858 -0.004536 -0.01558 0.006794 0.9634 0.969 0.01066 0.9156 0.9266 0.0345 ] Network output: [ 0.9135 0.3581 -0.03526 -0.0004374 0.0001964 -0.1516 -0.0003296 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3064 -0.004021 -0.1233 0.09986 0.9828 0.9929 0.3515 0.9057 0.9765 0.6163 ] Network output: [ 0.0204 0.8369 0.9697 -0.0001606 7.21e-05 0.152 -0.000121 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007638 0.002742 0.00639 0.00317 0.9906 0.9936 0.007808 0.9695 0.9823 0.01488 ] Network output: [ -0.009321 0.2327 0.8281 -0.001001 0.0004496 0.9537 -0.0007547 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3439 0.2353 0.4034 0.1234 0.9844 0.9938 0.3452 0.9122 0.9791 0.6144 ] Network output: [ -0.04646 0.293 1.082 0.0002427 -0.0001089 0.7186 0.0001829 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1414 0.1332 0.1873 0.1275 0.99 0.994 0.1415 0.9677 0.9828 0.2046 ] Network output: [ -0.03818 0.145 1.088 0.0004448 -0.0001997 0.8452 0.0003353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1585 0.1569 0.1961 0.1593 0.9855 0.9916 0.1585 0.9487 0.9752 0.2011 ] Network output: [ 0.007355 0.9041 0.01614 6.804e-05 -3.055e-05 1.065 5.128e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08325 Epoch 4389 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06211 0.7749 0.9488 -2.737e-05 1.229e-05 0.152 -2.063e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004745 -0.004606 -0.016 0.009256 0.9634 0.9691 0.01046 0.9159 0.9271 0.03476 ] Network output: [ 1.022 -0.1835 0.06775 0.0003986 -0.0001789 0.07284 0.0003004 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2982 -0.01976 -0.1495 0.2096 0.9828 0.993 0.3424 0.9057 0.9767 0.6201 ] Network output: [ 0.0202 0.8228 0.9734 -0.0001345 6.04e-05 0.1629 -0.0001014 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007329 0.002632 0.006042 0.00637 0.9905 0.9936 0.007493 0.9695 0.9825 0.01459 ] Network output: [ 0.07561 -0.4881 1.019 8.113e-05 -3.642e-05 1.318 6.114e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3333 0.2278 0.396 0.2865 0.9844 0.9938 0.3346 0.9119 0.9791 0.6074 ] Network output: [ -0.0461 0.2694 1.08 0.0002732 -0.0001226 0.7442 0.0002059 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1473 0.139 0.1916 0.1525 0.9899 0.994 0.1474 0.9679 0.9832 0.2085 ] Network output: [ -0.04219 0.2246 1.051 0.0003588 -0.0001611 0.8106 0.0002704 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1657 0.1641 0.1942 0.1628 0.9856 0.9916 0.1658 0.9493 0.9753 0.1987 ] Network output: [ 0.01042 0.9632 -0.0234 3.008e-05 -1.35e-05 1.039 2.267e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1099 Epoch 4390 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05017 0.8471 0.9349 -0.0001427 6.406e-05 0.1171 -0.0001075 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004853 -0.00453 -0.01556 0.006794 0.9634 0.9691 0.01065 0.9155 0.9266 0.03443 ] Network output: [ 0.9135 0.3577 -0.03484 -0.0004353 0.0001954 -0.1516 -0.000328 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3061 -0.004048 -0.1234 0.09994 0.9828 0.993 0.3511 0.9055 0.9765 0.6162 ] Network output: [ 0.02042 0.837 0.9696 -0.0001601 7.186e-05 0.1519 -0.0001206 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007626 0.002736 0.006377 0.003165 0.9906 0.9936 0.007796 0.9695 0.9822 0.01487 ] Network output: [ -0.009303 0.2324 0.8285 -0.0009988 0.0004484 0.9536 -0.0007527 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3435 0.235 0.4031 0.1232 0.9845 0.9938 0.3449 0.912 0.9791 0.6143 ] Network output: [ -0.04641 0.2926 1.082 0.0002424 -0.0001088 0.7188 0.0001827 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1412 0.133 0.1873 0.1275 0.99 0.994 0.1413 0.9676 0.9828 0.2046 ] Network output: [ -0.03811 0.1446 1.088 0.000444 -0.0001993 0.8455 0.0003346 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1583 0.1566 0.196 0.1593 0.9855 0.9916 0.1583 0.9486 0.9751 0.201 ] Network output: [ 0.007388 0.9038 0.01622 6.873e-05 -3.086e-05 1.065 5.18e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08309 Epoch 4391 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06208 0.7751 0.9487 -2.749e-05 1.234e-05 0.1519 -2.072e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004741 -0.004601 -0.01598 0.009248 0.9635 0.9691 0.01045 0.9158 0.927 0.0347 ] Network output: [ 1.022 -0.1831 0.06729 0.000397 -0.0001782 0.07271 0.0002992 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.298 -0.01976 -0.1496 0.2095 0.9828 0.993 0.3421 0.9054 0.9766 0.62 ] Network output: [ 0.02019 0.8229 0.9734 -0.0001342 6.025e-05 0.1628 -0.0001011 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007318 0.002627 0.006029 0.006356 0.9905 0.9936 0.007482 0.9695 0.9825 0.01457 ] Network output: [ 0.0755 -0.4873 1.019 7.944e-05 -3.566e-05 1.318 5.987e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3329 0.2276 0.3958 0.286 0.9844 0.9938 0.3342 0.9117 0.9791 0.6073 ] Network output: [ -0.04612 0.2691 1.08 0.0002725 -0.0001223 0.7444 0.0002054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.147 0.1388 0.1915 0.1524 0.9899 0.994 0.1471 0.9678 0.9831 0.2085 ] Network output: [ -0.04218 0.2243 1.051 0.000358 -0.0001607 0.8108 0.0002698 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1655 0.1638 0.1941 0.1627 0.9856 0.9916 0.1655 0.9491 0.9752 0.1986 ] Network output: [ 0.01029 0.964 -0.0234 2.932e-05 -1.316e-05 1.039 2.209e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1097 Epoch 4392 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05016 0.8471 0.9349 -0.0001422 6.383e-05 0.1171 -0.0001071 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004848 -0.004524 -0.01554 0.006794 0.9635 0.9691 0.01063 0.9155 0.9265 0.03437 ] Network output: [ 0.9135 0.3573 -0.03441 -0.0004331 0.0001945 -0.1516 -0.0003264 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3058 -0.004075 -0.1236 0.1 0.9828 0.993 0.3507 0.9053 0.9764 0.6161 ] Network output: [ 0.02045 0.837 0.9696 -0.0001595 7.162e-05 0.1519 -0.0001202 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007615 0.002731 0.006363 0.003161 0.9906 0.9936 0.007784 0.9694 0.9822 0.01485 ] Network output: [ -0.009286 0.2322 0.8288 -0.0009961 0.0004472 0.9535 -0.0007507 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3432 0.2347 0.4029 0.123 0.9845 0.9938 0.3445 0.9117 0.979 0.6142 ] Network output: [ -0.04636 0.2922 1.082 0.0002421 -0.0001087 0.7191 0.0001825 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.141 0.1328 0.1872 0.1274 0.99 0.994 0.1411 0.9675 0.9827 0.2045 ] Network output: [ -0.03804 0.1443 1.088 0.0004432 -0.000199 0.8458 0.000334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1581 0.1564 0.196 0.1592 0.9855 0.9916 0.1581 0.9484 0.975 0.201 ] Network output: [ 0.007422 0.9036 0.0163 6.943e-05 -3.117e-05 1.066 5.232e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08293 Epoch 4393 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06206 0.7753 0.9486 -2.761e-05 1.24e-05 0.1518 -2.081e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004737 -0.004596 -0.01596 0.009239 0.9635 0.9691 0.01043 0.9158 0.9269 0.03463 ] Network output: [ 1.022 -0.1827 0.06683 0.0003953 -0.0001775 0.07256 0.0002979 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2978 -0.01976 -0.1497 0.2095 0.9828 0.993 0.3418 0.9052 0.9766 0.6199 ] Network output: [ 0.02019 0.823 0.9733 -0.0001339 6.011e-05 0.1627 -0.0001009 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007307 0.002622 0.006017 0.006343 0.9905 0.9936 0.00747 0.9694 0.9824 0.01456 ] Network output: [ 0.07538 -0.4865 1.018 7.772e-05 -3.489e-05 1.318 5.857e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3326 0.2273 0.3957 0.2856 0.9845 0.9938 0.3339 0.9115 0.979 0.6072 ] Network output: [ -0.04614 0.2689 1.08 0.0002718 -0.000122 0.7446 0.0002048 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1468 0.1385 0.1915 0.1522 0.9899 0.994 0.1469 0.9677 0.9831 0.2084 ] Network output: [ -0.04217 0.2239 1.051 0.0003571 -0.0001603 0.811 0.0002692 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1652 0.1636 0.1941 0.1626 0.9856 0.9916 0.1652 0.949 0.9752 0.1986 ] Network output: [ 0.01015 0.9647 -0.0234 2.854e-05 -1.281e-05 1.039 2.151e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1094 Epoch 4394 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05016 0.8471 0.9349 -0.0001417 6.359e-05 0.1171 -0.0001068 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004843 -0.004518 -0.01552 0.006793 0.9635 0.9691 0.01061 0.9154 0.9264 0.0343 ] Network output: [ 0.9135 0.3568 -0.03398 -0.000431 0.0001935 -0.1516 -0.0003248 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3055 -0.004103 -0.1237 0.1001 0.9828 0.993 0.3503 0.905 0.9764 0.616 ] Network output: [ 0.02047 0.837 0.9696 -0.000159 7.137e-05 0.1518 -0.0001198 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007603 0.002725 0.00635 0.003156 0.9906 0.9936 0.007771 0.9694 0.9821 0.01483 ] Network output: [ -0.009271 0.2319 0.8292 -0.0009935 0.000446 0.9534 -0.0007488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3428 0.2345 0.4028 0.1228 0.9845 0.9938 0.3442 0.9115 0.979 0.6141 ] Network output: [ -0.0463 0.2919 1.082 0.0002418 -0.0001086 0.7193 0.0001823 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1407 0.1326 0.1871 0.1274 0.99 0.994 0.1408 0.9674 0.9827 0.2045 ] Network output: [ -0.03797 0.1439 1.088 0.0004424 -0.0001986 0.8461 0.0003334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1578 0.1562 0.1959 0.1591 0.9855 0.9916 0.1579 0.9483 0.975 0.201 ] Network output: [ 0.007458 0.9033 0.01638 7.014e-05 -3.149e-05 1.066 5.286e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08277 Epoch 4395 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06204 0.7755 0.9485 -2.774e-05 1.245e-05 0.1518 -2.091e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004733 -0.004591 -0.01595 0.009231 0.9635 0.9691 0.01042 0.9157 0.9268 0.03457 ] Network output: [ 1.023 -0.1822 0.06636 0.0003937 -0.0001768 0.0724 0.0002967 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2975 -0.01975 -0.1498 0.2094 0.9829 0.993 0.3415 0.905 0.9765 0.6199 ] Network output: [ 0.02018 0.8232 0.9733 -0.0001336 5.997e-05 0.1626 -0.0001007 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007296 0.002617 0.006005 0.006329 0.9905 0.9936 0.007459 0.9694 0.9824 0.01454 ] Network output: [ 0.07526 -0.4857 1.018 7.598e-05 -3.411e-05 1.318 5.726e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3322 0.2271 0.3955 0.2851 0.9845 0.9938 0.3336 0.9113 0.979 0.6072 ] Network output: [ -0.04616 0.2686 1.08 0.0002711 -0.0001217 0.7448 0.0002043 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1465 0.1383 0.1915 0.1521 0.9899 0.994 0.1466 0.9677 0.983 0.2084 ] Network output: [ -0.04216 0.2236 1.051 0.0003564 -0.00016 0.8112 0.0002686 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1649 0.1633 0.1941 0.1625 0.9856 0.9916 0.1649 0.9489 0.9751 0.1986 ] Network output: [ 0.01002 0.9654 -0.0234 2.775e-05 -1.246e-05 1.038 2.091e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1092 Epoch 4396 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05016 0.8471 0.935 -0.0001411 6.336e-05 0.1171 -0.0001064 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004838 -0.004513 -0.01551 0.006793 0.9635 0.9691 0.0106 0.9153 0.9263 0.03424 ] Network output: [ 0.9135 0.3564 -0.03354 -0.0004289 0.0001926 -0.1516 -0.0003232 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3052 -0.004132 -0.1238 0.1002 0.9828 0.993 0.3499 0.9048 0.9763 0.616 ] Network output: [ 0.02049 0.8371 0.9695 -0.0001584 7.113e-05 0.1518 -0.0001194 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007591 0.002719 0.006336 0.003152 0.9906 0.9936 0.007759 0.9693 0.9821 0.01482 ] Network output: [ -0.009257 0.2316 0.8296 -0.0009909 0.0004449 0.9533 -0.0007468 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3425 0.2342 0.4026 0.1226 0.9845 0.9938 0.3438 0.9113 0.979 0.614 ] Network output: [ -0.04625 0.2915 1.082 0.0002416 -0.0001085 0.7196 0.0001821 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1405 0.1324 0.1871 0.1273 0.99 0.994 0.1406 0.9674 0.9827 0.2045 ] Network output: [ -0.0379 0.1435 1.088 0.0004417 -0.0001983 0.8464 0.0003329 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1576 0.156 0.1959 0.1591 0.9855 0.9916 0.1576 0.9482 0.9749 0.2009 ] Network output: [ 0.007496 0.903 0.01646 7.086e-05 -3.181e-05 1.066 5.341e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0826 Epoch 4397 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06202 0.7757 0.9485 -2.787e-05 1.251e-05 0.1517 -2.1e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004729 -0.004586 -0.01593 0.009223 0.9635 0.9691 0.01041 0.9157 0.9268 0.03451 ] Network output: [ 1.023 -0.1817 0.06588 0.0003921 -0.000176 0.07223 0.0002955 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2973 -0.01975 -0.1499 0.2093 0.9829 0.993 0.3412 0.9047 0.9765 0.6198 ] Network output: [ 0.02018 0.8233 0.9733 -0.0001333 5.983e-05 0.1625 -0.0001004 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007285 0.002612 0.005993 0.006315 0.9905 0.9936 0.007448 0.9693 0.9823 0.01452 ] Network output: [ 0.07513 -0.4849 1.017 7.422e-05 -3.332e-05 1.318 5.594e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3319 0.2268 0.3954 0.2847 0.9845 0.9938 0.3332 0.911 0.9789 0.6071 ] Network output: [ -0.04618 0.2683 1.08 0.0002704 -0.0001214 0.745 0.0002038 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1462 0.138 0.1914 0.152 0.9899 0.994 0.1464 0.9676 0.983 0.2084 ] Network output: [ -0.04215 0.2233 1.051 0.0003556 -0.0001596 0.8114 0.000268 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1647 0.163 0.1941 0.1624 0.9856 0.9916 0.1647 0.9488 0.9751 0.1986 ] Network output: [ 0.009881 0.9662 -0.0234 2.694e-05 -1.21e-05 1.038 2.031e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1089 Epoch 4398 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05016 0.8471 0.935 -0.0001406 6.313e-05 0.1171 -0.000106 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004832 -0.004507 -0.01549 0.006793 0.9635 0.9691 0.01058 0.9153 0.9262 0.03418 ] Network output: [ 0.9135 0.3559 -0.03311 -0.0004268 0.0001916 -0.1516 -0.0003216 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3049 -0.004161 -0.1239 0.1003 0.9829 0.993 0.3496 0.9046 0.9763 0.6159 ] Network output: [ 0.02052 0.8371 0.9695 -0.0001579 7.089e-05 0.1517 -0.000119 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007579 0.002713 0.006323 0.003147 0.9906 0.9936 0.007747 0.9692 0.9821 0.0148 ] Network output: [ -0.009244 0.2313 0.83 -0.0009884 0.0004437 0.9532 -0.0007449 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3421 0.2339 0.4024 0.1224 0.9845 0.9938 0.3435 0.9111 0.9789 0.614 ] Network output: [ -0.04619 0.2911 1.082 0.0002413 -0.0001083 0.7198 0.0001819 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1403 0.1321 0.187 0.1272 0.99 0.994 0.1404 0.9673 0.9826 0.2044 ] Network output: [ -0.03783 0.1432 1.088 0.0004409 -0.0001979 0.8467 0.0003323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1574 0.1558 0.1959 0.159 0.9855 0.9916 0.1574 0.9481 0.9749 0.2009 ] Network output: [ 0.007536 0.9027 0.01654 7.16e-05 -3.214e-05 1.066 5.396e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08244 Epoch 4399 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06199 0.7759 0.9484 -2.8e-05 1.257e-05 0.1516 -2.11e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004725 -0.00458 -0.01591 0.009215 0.9635 0.9692 0.01039 0.9156 0.9267 0.03445 ] Network output: [ 1.023 -0.1813 0.0654 0.0003905 -0.0001753 0.07205 0.0002943 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2971 -0.01975 -0.15 0.2092 0.9829 0.993 0.3409 0.9045 0.9764 0.6198 ] Network output: [ 0.02017 0.8234 0.9732 -0.000133 5.969e-05 0.1625 -0.0001002 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007274 0.002607 0.005981 0.006301 0.9905 0.9936 0.007436 0.9692 0.9823 0.01451 ] Network output: [ 0.07501 -0.4841 1.017 7.244e-05 -3.252e-05 1.317 5.459e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3316 0.2266 0.3953 0.2842 0.9845 0.9938 0.3329 0.9108 0.9789 0.6071 ] Network output: [ -0.04619 0.268 1.08 0.0002698 -0.0001211 0.7452 0.0002033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.146 0.1378 0.1914 0.1519 0.9899 0.994 0.1461 0.9675 0.983 0.2084 ] Network output: [ -0.04214 0.2229 1.051 0.0003548 -0.0001593 0.8116 0.0002674 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1644 0.1628 0.194 0.1623 0.9856 0.9916 0.1644 0.9487 0.975 0.1986 ] Network output: [ 0.009739 0.9669 -0.0234 2.612e-05 -1.173e-05 1.037 1.968e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1086 Epoch 4400 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05015 0.8471 0.935 -0.0001401 6.29e-05 0.1171 -0.0001056 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004827 -0.004501 -0.01547 0.006792 0.9635 0.9691 0.01056 0.9152 0.9262 0.03412 ] Network output: [ 0.9136 0.3555 -0.03267 -0.0004247 0.0001907 -0.1516 -0.00032 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3046 -0.00419 -0.1241 0.1004 0.9829 0.993 0.3492 0.9043 0.9762 0.6158 ] Network output: [ 0.02054 0.8371 0.9695 -0.0001574 7.065e-05 0.1517 -0.0001186 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007568 0.002708 0.00631 0.003143 0.9906 0.9936 0.007735 0.9692 0.982 0.01478 ] Network output: [ -0.009232 0.231 0.8304 -0.0009858 0.0004426 0.9531 -0.0007429 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3417 0.2337 0.4022 0.1222 0.9845 0.9938 0.3431 0.9109 0.9789 0.6139 ] Network output: [ -0.04613 0.2907 1.082 0.0002411 -0.0001082 0.7201 0.0001817 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1401 0.1319 0.187 0.1272 0.99 0.994 0.1402 0.9672 0.9826 0.2044 ] Network output: [ -0.03775 0.1428 1.087 0.0004402 -0.0001976 0.847 0.0003317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1572 0.1555 0.1958 0.159 0.9855 0.9916 0.1572 0.948 0.9748 0.2009 ] Network output: [ 0.007578 0.9024 0.01662 7.235e-05 -3.248e-05 1.066 5.452e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08227 Epoch 4401 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06197 0.7761 0.9483 -2.813e-05 1.263e-05 0.1515 -2.12e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004721 -0.004575 -0.01589 0.009206 0.9636 0.9692 0.01038 0.9155 0.9266 0.03439 ] Network output: [ 1.023 -0.1808 0.06492 0.0003889 -0.0001746 0.07186 0.0002931 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2969 -0.01975 -0.1501 0.2091 0.9829 0.993 0.3407 0.9043 0.9764 0.6197 ] Network output: [ 0.02016 0.8236 0.9732 -0.0001327 5.955e-05 0.1624 -9.997e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007263 0.002602 0.005969 0.006287 0.9905 0.9937 0.007425 0.9692 0.9823 0.01449 ] Network output: [ 0.07489 -0.4833 1.017 7.063e-05 -3.171e-05 1.317 5.323e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3312 0.2263 0.3951 0.2838 0.9845 0.9938 0.3325 0.9106 0.9788 0.6071 ] Network output: [ -0.04621 0.2677 1.08 0.0002691 -0.0001208 0.7455 0.0002028 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1457 0.1375 0.1914 0.1518 0.9899 0.994 0.1458 0.9674 0.9829 0.2084 ] Network output: [ -0.04213 0.2226 1.051 0.0003541 -0.0001589 0.8119 0.0002668 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1641 0.1625 0.194 0.1622 0.9856 0.9916 0.1641 0.9486 0.9749 0.1986 ] Network output: [ 0.009594 0.9677 -0.0234 2.528e-05 -1.135e-05 1.037 1.905e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1083 Epoch 4402 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05015 0.8471 0.935 -0.0001396 6.266e-05 0.1171 -0.0001052 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004822 -0.004496 -0.01545 0.006792 0.9635 0.9692 0.01055 0.9152 0.9261 0.03406 ] Network output: [ 0.9136 0.355 -0.03222 -0.0004225 0.0001897 -0.1516 -0.0003184 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3043 -0.004221 -0.1242 0.1004 0.9829 0.993 0.3488 0.9041 0.9762 0.6158 ] Network output: [ 0.02056 0.8372 0.9695 -0.0001569 7.042e-05 0.1516 -0.0001182 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007556 0.002702 0.006298 0.003138 0.9906 0.9936 0.007723 0.9691 0.982 0.01477 ] Network output: [ -0.009221 0.2307 0.8308 -0.0009833 0.0004414 0.953 -0.000741 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3414 0.2334 0.402 0.122 0.9845 0.9938 0.3427 0.9106 0.9788 0.6139 ] Network output: [ -0.04607 0.2903 1.082 0.0002409 -0.0001082 0.7204 0.0001816 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1399 0.1317 0.1869 0.1271 0.99 0.994 0.14 0.9671 0.9825 0.2044 ] Network output: [ -0.03767 0.1424 1.087 0.0004395 -0.0001973 0.8473 0.0003312 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.157 0.1553 0.1958 0.1589 0.9855 0.9916 0.157 0.9479 0.9748 0.2008 ] Network output: [ 0.007621 0.9021 0.0167 7.311e-05 -3.282e-05 1.066 5.51e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0821 Epoch 4403 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06194 0.7763 0.9482 -2.826e-05 1.269e-05 0.1515 -2.13e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004717 -0.00457 -0.01587 0.009198 0.9636 0.9692 0.01036 0.9155 0.9265 0.03433 ] Network output: [ 1.023 -0.1803 0.06442 0.0003872 -0.0001738 0.07165 0.0002918 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2967 -0.01975 -0.1502 0.2089 0.9829 0.993 0.3404 0.9041 0.9763 0.6197 ] Network output: [ 0.02015 0.8237 0.9732 -0.0001324 5.942e-05 0.1623 -9.974e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007252 0.002597 0.005957 0.006273 0.9905 0.9937 0.007414 0.9691 0.9822 0.01447 ] Network output: [ 0.07476 -0.4825 1.016 6.879e-05 -3.088e-05 1.317 5.185e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3309 0.2261 0.395 0.2833 0.9845 0.9938 0.3322 0.9104 0.9788 0.607 ] Network output: [ -0.04623 0.2674 1.08 0.0002685 -0.0001205 0.7457 0.0002023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1455 0.1373 0.1913 0.1517 0.9899 0.994 0.1456 0.9674 0.9829 0.2084 ] Network output: [ -0.04212 0.2222 1.051 0.0003533 -0.0001586 0.8121 0.0002663 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1638 0.1622 0.194 0.1621 0.9856 0.9916 0.1639 0.9485 0.9749 0.1986 ] Network output: [ 0.009446 0.9685 -0.02341 2.442e-05 -1.096e-05 1.036 1.84e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1081 Epoch 4404 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05014 0.8471 0.935 -0.0001391 6.243e-05 0.1171 -0.0001048 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004817 -0.00449 -0.01543 0.006792 0.9636 0.9692 0.01053 0.9151 0.926 0.03399 ] Network output: [ 0.9136 0.3545 -0.03178 -0.0004204 0.0001887 -0.1516 -0.0003168 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.304 -0.004252 -0.1243 0.1005 0.9829 0.993 0.3484 0.9039 0.9761 0.6157 ] Network output: [ 0.02059 0.8372 0.9694 -0.0001563 7.018e-05 0.1515 -0.0001178 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007544 0.002696 0.006285 0.003134 0.9906 0.9936 0.007711 0.9691 0.9819 0.01475 ] Network output: [ -0.009211 0.2304 0.8312 -0.0009808 0.0004403 0.9529 -0.0007391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.341 0.2331 0.4018 0.1218 0.9845 0.9938 0.3424 0.9104 0.9788 0.6139 ] Network output: [ -0.04601 0.2899 1.082 0.0002407 -0.0001081 0.7206 0.0001814 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1396 0.1315 0.1869 0.1271 0.99 0.994 0.1397 0.9671 0.9825 0.2043 ] Network output: [ -0.03759 0.142 1.087 0.0004388 -0.000197 0.8477 0.0003307 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1567 0.1551 0.1957 0.1589 0.9855 0.9916 0.1568 0.9478 0.9747 0.2008 ] Network output: [ 0.007667 0.9018 0.01677 7.388e-05 -3.317e-05 1.066 5.568e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08192 Epoch 4405 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06192 0.7766 0.9481 -2.84e-05 1.275e-05 0.1514 -2.14e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004713 -0.004565 -0.01585 0.009189 0.9636 0.9692 0.01035 0.9154 0.9265 0.03427 ] Network output: [ 1.023 -0.1798 0.06392 0.0003856 -0.0001731 0.07144 0.0002906 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2964 -0.01975 -0.1503 0.2088 0.9829 0.993 0.3401 0.9038 0.9763 0.6197 ] Network output: [ 0.02014 0.8239 0.9731 -0.0001321 5.928e-05 0.1622 -9.952e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007242 0.002592 0.005945 0.006259 0.9905 0.9937 0.007403 0.9691 0.9822 0.01446 ] Network output: [ 0.07464 -0.4816 1.016 6.693e-05 -3.005e-05 1.317 5.044e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3305 0.2258 0.3949 0.2828 0.9845 0.9938 0.3318 0.9102 0.9787 0.607 ] Network output: [ -0.04624 0.2671 1.081 0.0002678 -0.0001202 0.7459 0.0002019 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1452 0.137 0.1913 0.1516 0.9899 0.994 0.1453 0.9673 0.9828 0.2084 ] Network output: [ -0.04211 0.2218 1.051 0.0003526 -0.0001583 0.8123 0.0002657 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1636 0.162 0.194 0.162 0.9856 0.9916 0.1636 0.9484 0.9748 0.1986 ] Network output: [ 0.009295 0.9693 -0.02341 2.354e-05 -1.057e-05 1.036 1.774e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1078 Epoch 4406 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05014 0.8471 0.9351 -0.0001385 6.22e-05 0.117 -0.0001044 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004812 -0.004484 -0.01541 0.006792 0.9636 0.9692 0.01051 0.9151 0.9259 0.03393 ] Network output: [ 0.9136 0.354 -0.03133 -0.0004183 0.0001878 -0.1516 -0.0003152 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3037 -0.004284 -0.1244 0.1006 0.9829 0.993 0.348 0.9037 0.9761 0.6157 ] Network output: [ 0.02061 0.8373 0.9694 -0.0001558 6.994e-05 0.1515 -0.0001174 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007533 0.00269 0.006272 0.00313 0.9906 0.9936 0.007699 0.969 0.9819 0.01474 ] Network output: [ -0.009202 0.23 0.8316 -0.0009783 0.0004392 0.9528 -0.0007373 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3407 0.2328 0.4017 0.1216 0.9845 0.9938 0.342 0.9102 0.9787 0.6138 ] Network output: [ -0.04594 0.2894 1.083 0.0002405 -0.000108 0.7209 0.0001813 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1394 0.1313 0.1868 0.127 0.99 0.994 0.1395 0.967 0.9824 0.2043 ] Network output: [ -0.03751 0.1416 1.087 0.0004381 -0.0001967 0.848 0.0003302 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1565 0.1549 0.1957 0.1588 0.9855 0.9916 0.1565 0.9477 0.9746 0.2008 ] Network output: [ 0.007715 0.9014 0.01685 7.466e-05 -3.352e-05 1.067 5.627e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08174 Epoch 4407 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06189 0.7768 0.948 -2.854e-05 1.281e-05 0.1513 -2.151e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004709 -0.00456 -0.01583 0.009181 0.9636 0.9692 0.01034 0.9154 0.9264 0.03421 ] Network output: [ 1.023 -0.1792 0.06341 0.000384 -0.0001724 0.0712 0.0002894 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2962 -0.01975 -0.1505 0.2087 0.9829 0.993 0.3398 0.9036 0.9762 0.6197 ] Network output: [ 0.02013 0.824 0.9731 -0.0001318 5.915e-05 0.1621 -9.93e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007231 0.002587 0.005934 0.006245 0.9905 0.9937 0.007391 0.969 0.9821 0.01444 ] Network output: [ 0.07451 -0.4807 1.015 6.503e-05 -2.92e-05 1.317 4.901e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3302 0.2256 0.3947 0.2824 0.9845 0.9938 0.3315 0.91 0.9787 0.607 ] Network output: [ -0.04626 0.2669 1.081 0.0002672 -0.00012 0.7461 0.0002014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.145 0.1368 0.1913 0.1514 0.9899 0.994 0.1451 0.9672 0.9828 0.2084 ] Network output: [ -0.0421 0.2215 1.052 0.0003519 -0.000158 0.8126 0.0002652 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1633 0.1617 0.194 0.1619 0.9856 0.9916 0.1633 0.9483 0.9748 0.1986 ] Network output: [ 0.009139 0.9701 -0.02342 2.264e-05 -1.016e-05 1.035 1.706e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1075 Epoch 4408 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05013 0.8471 0.9351 -0.000138 6.196e-05 0.117 -0.000104 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004806 -0.004479 -0.0154 0.006792 0.9636 0.9692 0.0105 0.915 0.9259 0.03387 ] Network output: [ 0.9136 0.3535 -0.03088 -0.0004161 0.0001868 -0.1516 -0.0003136 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3034 -0.004317 -0.1246 0.1007 0.9829 0.993 0.3476 0.9034 0.976 0.6157 ] Network output: [ 0.02063 0.8373 0.9694 -0.0001553 6.971e-05 0.1514 -0.000117 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007521 0.002684 0.00626 0.003125 0.9906 0.9936 0.007687 0.969 0.9819 0.01472 ] Network output: [ -0.009194 0.2297 0.832 -0.0009758 0.0004381 0.9527 -0.0007354 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3403 0.2326 0.4015 0.1214 0.9845 0.9938 0.3417 0.91 0.9787 0.6138 ] Network output: [ -0.04587 0.289 1.083 0.0002404 -0.0001079 0.7212 0.0001811 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1392 0.1311 0.1868 0.127 0.99 0.994 0.1393 0.9669 0.9824 0.2043 ] Network output: [ -0.03743 0.1411 1.087 0.0004375 -0.0001964 0.8483 0.0003297 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1563 0.1547 0.1957 0.1587 0.9855 0.9916 0.1563 0.9476 0.9746 0.2008 ] Network output: [ 0.007765 0.9011 0.01693 7.546e-05 -3.388e-05 1.067 5.687e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08156 Epoch 4409 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06186 0.777 0.9479 -2.868e-05 1.288e-05 0.1512 -2.162e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004705 -0.004556 -0.01582 0.009173 0.9636 0.9692 0.01032 0.9153 0.9263 0.03415 ] Network output: [ 1.023 -0.1787 0.0629 0.0003823 -0.0001716 0.07095 0.0002881 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.296 -0.01975 -0.1506 0.2086 0.9829 0.993 0.3395 0.9034 0.9762 0.6197 ] Network output: [ 0.02012 0.8242 0.9731 -0.0001315 5.902e-05 0.162 -9.908e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00722 0.002582 0.005922 0.006231 0.9905 0.9937 0.00738 0.969 0.9821 0.01443 ] Network output: [ 0.07438 -0.4799 1.015 6.31e-05 -2.833e-05 1.317 4.756e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3298 0.2253 0.3946 0.2819 0.9845 0.9938 0.3311 0.9097 0.9787 0.607 ] Network output: [ -0.04628 0.2666 1.081 0.0002666 -0.0001197 0.7463 0.0002009 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1447 0.1366 0.1913 0.1513 0.9899 0.994 0.1448 0.9671 0.9827 0.2084 ] Network output: [ -0.04209 0.2211 1.052 0.0003512 -0.0001577 0.8128 0.0002647 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1631 0.1614 0.194 0.1618 0.9856 0.9917 0.1631 0.9482 0.9747 0.1986 ] Network output: [ 0.00898 0.9709 -0.02342 2.172e-05 -9.751e-06 1.035 1.637e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1072 Epoch 4410 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05013 0.8471 0.9351 -0.0001375 6.173e-05 0.117 -0.0001036 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004801 -0.004473 -0.01538 0.006792 0.9636 0.9692 0.01048 0.915 0.9258 0.03381 ] Network output: [ 0.9136 0.353 -0.03042 -0.000414 0.0001858 -0.1515 -0.000312 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.303 -0.004351 -0.1247 0.1008 0.9829 0.993 0.3472 0.9032 0.976 0.6157 ] Network output: [ 0.02065 0.8373 0.9693 -0.0001547 6.947e-05 0.1514 -0.0001166 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007509 0.002678 0.006248 0.003121 0.9906 0.9936 0.007675 0.9689 0.9818 0.01471 ] Network output: [ -0.009187 0.2294 0.8324 -0.0009733 0.000437 0.9526 -0.0007335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.34 0.2323 0.4014 0.1211 0.9845 0.9938 0.3413 0.9098 0.9786 0.6138 ] Network output: [ -0.04581 0.2886 1.083 0.0002402 -0.0001078 0.7215 0.000181 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.139 0.1309 0.1867 0.127 0.99 0.994 0.1391 0.9668 0.9824 0.2043 ] Network output: [ -0.03734 0.1407 1.087 0.0004368 -0.0001961 0.8487 0.0003292 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1561 0.1545 0.1956 0.1587 0.9855 0.9916 0.1561 0.9475 0.9745 0.2007 ] Network output: [ 0.007817 0.9007 0.017 7.628e-05 -3.424e-05 1.067 5.749e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08138 Epoch 4411 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06184 0.7772 0.9479 -2.883e-05 1.294e-05 0.1511 -2.173e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004701 -0.004551 -0.0158 0.009164 0.9636 0.9692 0.01031 0.9153 0.9262 0.03409 ] Network output: [ 1.023 -0.1781 0.06237 0.0003807 -0.0001709 0.07069 0.0002869 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2958 -0.01976 -0.1507 0.2085 0.9829 0.993 0.3392 0.9031 0.9761 0.6197 ] Network output: [ 0.0201 0.8244 0.973 -0.0001312 5.889e-05 0.1619 -9.886e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007209 0.002577 0.00591 0.006217 0.9905 0.9937 0.007369 0.9689 0.9821 0.01441 ] Network output: [ 0.07424 -0.479 1.014 6.114e-05 -2.745e-05 1.316 4.607e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3295 0.225 0.3945 0.2814 0.9845 0.9938 0.3308 0.9095 0.9786 0.6071 ] Network output: [ -0.04629 0.2662 1.081 0.000266 -0.0001194 0.7466 0.0002005 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1445 0.1363 0.1913 0.1512 0.9899 0.994 0.1446 0.9671 0.9827 0.2084 ] Network output: [ -0.04208 0.2207 1.052 0.0003505 -0.0001574 0.8131 0.0002642 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1628 0.1612 0.194 0.1617 0.9856 0.9917 0.1628 0.9481 0.9747 0.1986 ] Network output: [ 0.008818 0.9718 -0.02343 2.078e-05 -9.329e-06 1.034 1.566e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1069 Epoch 4412 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05012 0.8471 0.9351 -0.000137 6.149e-05 0.117 -0.0001032 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004796 -0.004468 -0.01536 0.006792 0.9636 0.9692 0.01046 0.9149 0.9257 0.03375 ] Network output: [ 0.9136 0.3525 -0.02996 -0.0004118 0.0001849 -0.1515 -0.0003103 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3027 -0.004385 -0.1248 0.1009 0.9829 0.993 0.3468 0.903 0.9759 0.6157 ] Network output: [ 0.02067 0.8374 0.9693 -0.0001542 6.924e-05 0.1513 -0.0001162 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007498 0.002672 0.006236 0.003117 0.9906 0.9936 0.007663 0.9688 0.9818 0.0147 ] Network output: [ -0.009181 0.229 0.8329 -0.0009709 0.0004359 0.9525 -0.0007317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3396 0.232 0.4012 0.1209 0.9845 0.9938 0.3409 0.9096 0.9786 0.6138 ] Network output: [ -0.04574 0.2882 1.083 0.0002401 -0.0001078 0.7218 0.0001809 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1388 0.1307 0.1867 0.1269 0.99 0.994 0.1389 0.9668 0.9823 0.2042 ] Network output: [ -0.03726 0.1403 1.087 0.0004362 -0.0001958 0.849 0.0003287 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1559 0.1543 0.1956 0.1586 0.9855 0.9916 0.1559 0.9474 0.9745 0.2007 ] Network output: [ 0.007872 0.9004 0.01707 7.711e-05 -3.462e-05 1.067 5.811e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08119 Epoch 4413 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06181 0.7775 0.9478 -2.898e-05 1.301e-05 0.151 -2.184e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004697 -0.004546 -0.01578 0.009155 0.9637 0.9692 0.01029 0.9152 0.9262 0.03403 ] Network output: [ 1.023 -0.1775 0.06184 0.0003791 -0.0001702 0.07041 0.0002857 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2955 -0.01976 -0.1509 0.2084 0.9829 0.993 0.3389 0.9029 0.9761 0.6197 ] Network output: [ 0.02009 0.8245 0.973 -0.0001309 5.876e-05 0.1618 -9.865e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007199 0.002571 0.005898 0.006202 0.9906 0.9937 0.007358 0.9689 0.982 0.0144 ] Network output: [ 0.07411 -0.478 1.014 5.913e-05 -2.655e-05 1.316 4.456e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3291 0.2248 0.3944 0.2809 0.9845 0.9938 0.3304 0.9093 0.9786 0.6071 ] Network output: [ -0.04631 0.2659 1.081 0.0002654 -0.0001191 0.7468 0.0002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1442 0.1361 0.1912 0.1511 0.9899 0.994 0.1443 0.967 0.9826 0.2084 ] Network output: [ -0.04207 0.2203 1.052 0.0003498 -0.0001571 0.8133 0.0002637 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1625 0.1609 0.194 0.1616 0.9856 0.9917 0.1626 0.948 0.9746 0.1986 ] Network output: [ 0.008651 0.9726 -0.02343 1.982e-05 -8.896e-06 1.034 1.493e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1066 Epoch 4414 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05012 0.8471 0.9351 -0.0001365 6.126e-05 0.117 -0.0001028 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004791 -0.004462 -0.01534 0.006792 0.9637 0.9692 0.01045 0.9149 0.9256 0.0337 ] Network output: [ 0.9136 0.352 -0.0295 -0.0004096 0.0001839 -0.1514 -0.0003087 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3024 -0.004421 -0.125 0.101 0.9829 0.993 0.3464 0.9028 0.9759 0.6157 ] Network output: [ 0.02069 0.8374 0.9693 -0.0001537 6.9e-05 0.1513 -0.0001158 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007486 0.002666 0.006224 0.003113 0.9906 0.9936 0.007651 0.9688 0.9817 0.01468 ] Network output: [ -0.009175 0.2287 0.8333 -0.0009685 0.0004348 0.9524 -0.0007299 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3392 0.2317 0.4011 0.1207 0.9845 0.9938 0.3406 0.9093 0.9786 0.6138 ] Network output: [ -0.04567 0.2877 1.083 0.00024 -0.0001077 0.7221 0.0001808 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1386 0.1305 0.1866 0.1269 0.99 0.994 0.1387 0.9667 0.9823 0.2042 ] Network output: [ -0.03717 0.1398 1.087 0.0004356 -0.0001956 0.8494 0.0003283 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1557 0.1541 0.1956 0.1586 0.9855 0.9916 0.1557 0.9473 0.9744 0.2007 ] Network output: [ 0.007929 0.9 0.01715 7.795e-05 -3.499e-05 1.067 5.874e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.081 Epoch 4415 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06178 0.7777 0.9477 -2.913e-05 1.308e-05 0.1509 -2.195e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004694 -0.004541 -0.01576 0.009147 0.9637 0.9693 0.01028 0.9152 0.9261 0.03398 ] Network output: [ 1.024 -0.1769 0.0613 0.0003774 -0.0001694 0.07011 0.0002844 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2953 -0.01977 -0.151 0.2082 0.9829 0.993 0.3386 0.9027 0.976 0.6197 ] Network output: [ 0.02007 0.8247 0.973 -0.0001306 5.864e-05 0.1617 -9.844e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007188 0.002566 0.005887 0.006187 0.9906 0.9937 0.007347 0.9688 0.982 0.01438 ] Network output: [ 0.07397 -0.4771 1.013 5.709e-05 -2.563e-05 1.316 4.302e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3288 0.2245 0.3943 0.2804 0.9845 0.9938 0.3301 0.9091 0.9785 0.6071 ] Network output: [ -0.04632 0.2656 1.081 0.0002648 -0.0001189 0.7471 0.0001996 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.144 0.1358 0.1912 0.151 0.9899 0.994 0.1441 0.9669 0.9826 0.2084 ] Network output: [ -0.04206 0.2199 1.052 0.0003492 -0.0001568 0.8136 0.0002632 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1623 0.1607 0.194 0.1615 0.9856 0.9917 0.1623 0.9479 0.9745 0.1986 ] Network output: [ 0.00848 0.9735 -0.02344 1.883e-05 -8.453e-06 1.033 1.419e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1062 Epoch 4416 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05011 0.8471 0.9352 -0.0001359 6.102e-05 0.117 -0.0001024 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004786 -0.004456 -0.01533 0.006793 0.9637 0.9692 0.01043 0.9148 0.9256 0.03364 ] Network output: [ 0.9137 0.3515 -0.02903 -0.0004074 0.0001829 -0.1514 -0.000307 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3021 -0.004458 -0.1251 0.1011 0.9829 0.993 0.346 0.9025 0.9758 0.6157 ] Network output: [ 0.02071 0.8375 0.9693 -0.0001532 6.877e-05 0.1512 -0.0001154 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007475 0.00266 0.006212 0.003108 0.9906 0.9936 0.007639 0.9687 0.9817 0.01467 ] Network output: [ -0.009171 0.2283 0.8338 -0.0009661 0.0004337 0.9524 -0.000728 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3389 0.2314 0.4009 0.1205 0.9845 0.9938 0.3402 0.9091 0.9785 0.6139 ] Network output: [ -0.04559 0.2873 1.083 0.0002399 -0.0001077 0.7224 0.0001808 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1384 0.1303 0.1866 0.1268 0.99 0.994 0.1385 0.9666 0.9822 0.2042 ] Network output: [ -0.03708 0.1394 1.087 0.0004351 -0.0001953 0.8498 0.0003279 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1555 0.1539 0.1956 0.1585 0.9855 0.9916 0.1555 0.9472 0.9744 0.2007 ] Network output: [ 0.007989 0.8996 0.01722 7.881e-05 -3.538e-05 1.068 5.939e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08081 Epoch 4417 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06175 0.778 0.9476 -2.928e-05 1.315e-05 0.1508 -2.207e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00469 -0.004536 -0.01575 0.009138 0.9637 0.9693 0.01027 0.9151 0.926 0.03392 ] Network output: [ 1.024 -0.1763 0.06075 0.0003757 -0.0001687 0.06979 0.0002832 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2951 -0.01977 -0.1512 0.2081 0.9829 0.993 0.3383 0.9025 0.976 0.6198 ] Network output: [ 0.02006 0.8248 0.9729 -0.0001303 5.852e-05 0.1616 -9.823e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007178 0.002561 0.005875 0.006173 0.9906 0.9937 0.007336 0.9688 0.9819 0.01437 ] Network output: [ 0.07383 -0.4761 1.013 5.5e-05 -2.469e-05 1.316 4.145e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3285 0.2242 0.3942 0.2799 0.9845 0.9938 0.3297 0.9089 0.9785 0.6072 ] Network output: [ -0.04634 0.2653 1.081 0.0002642 -0.0001186 0.7473 0.0001991 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1437 0.1356 0.1912 0.1509 0.9899 0.994 0.1438 0.9669 0.9826 0.2084 ] Network output: [ -0.04205 0.2195 1.052 0.0003486 -0.0001565 0.8139 0.0002627 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.162 0.1604 0.194 0.1614 0.9856 0.9917 0.1621 0.9478 0.9745 0.1986 ] Network output: [ 0.008304 0.9744 -0.02345 1.782e-05 -7.998e-06 1.033 1.343e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1059 Epoch 4418 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0501 0.8471 0.9352 -0.0001354 6.078e-05 0.117 -0.000102 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00478 -0.004451 -0.01531 0.006793 0.9637 0.9693 0.01041 0.9148 0.9255 0.03358 ] Network output: [ 0.9137 0.3509 -0.02856 -0.0004052 0.0001819 -0.1513 -0.0003054 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3018 -0.004497 -0.1253 0.1012 0.9829 0.993 0.3456 0.9023 0.9758 0.6157 ] Network output: [ 0.02073 0.8375 0.9692 -0.0001527 6.854e-05 0.1512 -0.0001151 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007464 0.002654 0.0062 0.003104 0.9906 0.9936 0.007627 0.9687 0.9817 0.01465 ] Network output: [ -0.009167 0.2279 0.8342 -0.0009637 0.0004326 0.9523 -0.0007262 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3385 0.2311 0.4008 0.1203 0.9845 0.9938 0.3398 0.9089 0.9785 0.6139 ] Network output: [ -0.04552 0.2868 1.083 0.0002398 -0.0001076 0.7227 0.0001807 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1382 0.1301 0.1865 0.1268 0.99 0.994 0.1383 0.9665 0.9822 0.2042 ] Network output: [ -0.03698 0.1389 1.087 0.0004345 -0.0001951 0.8501 0.0003275 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1553 0.1537 0.1955 0.1585 0.9855 0.9916 0.1553 0.947 0.9743 0.2007 ] Network output: [ 0.008051 0.8992 0.01729 7.968e-05 -3.577e-05 1.068 6.005e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08061 Epoch 4419 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06172 0.7782 0.9475 -2.944e-05 1.322e-05 0.1507 -2.219e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004686 -0.004531 -0.01573 0.009129 0.9637 0.9693 0.01025 0.9151 0.9259 0.03386 ] Network output: [ 1.024 -0.1756 0.06019 0.0003741 -0.0001679 0.06945 0.0002819 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2949 -0.01978 -0.1513 0.2079 0.9829 0.993 0.338 0.9022 0.976 0.6198 ] Network output: [ 0.02004 0.825 0.9729 -0.0001301 5.839e-05 0.1615 -9.803e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007167 0.002555 0.005863 0.006158 0.9906 0.9937 0.007325 0.9687 0.9819 0.01436 ] Network output: [ 0.07369 -0.4751 1.013 5.286e-05 -2.373e-05 1.315 3.984e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3281 0.224 0.3941 0.2793 0.9845 0.9938 0.3294 0.9086 0.9784 0.6073 ] Network output: [ -0.04635 0.265 1.081 0.0002636 -0.0001184 0.7476 0.0001987 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1435 0.1354 0.1912 0.1507 0.9899 0.994 0.1436 0.9668 0.9825 0.2084 ] Network output: [ -0.04204 0.219 1.052 0.000348 -0.0001562 0.8142 0.0002622 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1618 0.1602 0.194 0.1613 0.9856 0.9917 0.1618 0.9476 0.9744 0.1986 ] Network output: [ 0.008124 0.9753 -0.02346 1.678e-05 -7.532e-06 1.032 1.264e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1056 Epoch 4420 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0501 0.847 0.9352 -0.0001349 6.055e-05 0.117 -0.0001016 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004775 -0.004446 -0.01529 0.006793 0.9637 0.9693 0.0104 0.9147 0.9254 0.03352 ] Network output: [ 0.9137 0.3503 -0.02808 -0.000403 0.0001809 -0.1513 -0.0003037 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3015 -0.004536 -0.1254 0.1013 0.9829 0.993 0.3452 0.9021 0.9757 0.6158 ] Network output: [ 0.02075 0.8376 0.9692 -0.0001521 6.831e-05 0.1511 -0.0001147 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007452 0.002648 0.006189 0.0031 0.9906 0.9936 0.007615 0.9686 0.9816 0.01464 ] Network output: [ -0.009164 0.2275 0.8347 -0.0009613 0.0004315 0.9523 -0.0007244 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3382 0.2308 0.4007 0.1201 0.9845 0.9938 0.3395 0.9087 0.9784 0.6139 ] Network output: [ -0.04544 0.2863 1.083 0.0002397 -0.0001076 0.723 0.0001807 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.138 0.13 0.1865 0.1267 0.99 0.994 0.1381 0.9665 0.9821 0.2042 ] Network output: [ -0.03689 0.1384 1.087 0.000434 -0.0001948 0.8505 0.0003271 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1551 0.1535 0.1955 0.1585 0.9855 0.9916 0.1551 0.9469 0.9743 0.2007 ] Network output: [ 0.008115 0.8988 0.01736 8.057e-05 -3.617e-05 1.068 6.072e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08041 Epoch 4421 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06169 0.7785 0.9474 -2.961e-05 1.329e-05 0.1506 -2.231e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004682 -0.004527 -0.01571 0.009121 0.9637 0.9693 0.01024 0.915 0.9259 0.03381 ] Network output: [ 1.024 -0.175 0.05962 0.0003724 -0.0001672 0.0691 0.0002806 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2947 -0.01979 -0.1515 0.2078 0.9829 0.993 0.3377 0.902 0.9759 0.6199 ] Network output: [ 0.02002 0.8252 0.9729 -0.0001298 5.828e-05 0.1614 -9.783e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007157 0.00255 0.005852 0.006143 0.9906 0.9937 0.007315 0.9686 0.9819 0.01434 ] Network output: [ 0.07354 -0.4741 1.012 5.068e-05 -2.275e-05 1.315 3.819e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3278 0.2237 0.394 0.2788 0.9845 0.9938 0.329 0.9084 0.9784 0.6073 ] Network output: [ -0.04637 0.2647 1.081 0.0002631 -0.0001181 0.7478 0.0001983 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1433 0.1352 0.1912 0.1506 0.9899 0.994 0.1434 0.9667 0.9825 0.2084 ] Network output: [ -0.04203 0.2186 1.052 0.0003474 -0.0001559 0.8145 0.0002618 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1615 0.1599 0.194 0.1612 0.9856 0.9917 0.1616 0.9475 0.9744 0.1986 ] Network output: [ 0.007939 0.9762 -0.02347 1.571e-05 -7.054e-06 1.031 1.184e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1052 Epoch 4422 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05009 0.847 0.9353 -0.0001343 6.03e-05 0.117 -0.0001012 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00477 -0.00444 -0.01527 0.006794 0.9637 0.9693 0.01038 0.9147 0.9253 0.03347 ] Network output: [ 0.9137 0.3498 -0.0276 -0.0004007 0.0001799 -0.1512 -0.000302 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3011 -0.004577 -0.1255 0.1014 0.9829 0.993 0.3448 0.9019 0.9757 0.6158 ] Network output: [ 0.02077 0.8376 0.9692 -0.0001516 6.807e-05 0.151 -0.0001143 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007441 0.002642 0.006177 0.003096 0.9906 0.9936 0.007604 0.9686 0.9816 0.01463 ] Network output: [ -0.009161 0.2271 0.8351 -0.0009589 0.0004305 0.9522 -0.0007227 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3378 0.2305 0.4006 0.1199 0.9845 0.9938 0.3391 0.9085 0.9784 0.614 ] Network output: [ -0.04536 0.2858 1.083 0.0002397 -0.0001076 0.7234 0.0001806 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1378 0.1298 0.1865 0.1267 0.99 0.994 0.1379 0.9664 0.9821 0.2042 ] Network output: [ -0.03679 0.138 1.086 0.0004335 -0.0001946 0.8509 0.0003267 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1549 0.1533 0.1955 0.1584 0.9855 0.9916 0.1549 0.9468 0.9742 0.2006 ] Network output: [ 0.008183 0.8984 0.01743 8.148e-05 -3.658e-05 1.068 6.14e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0802 Epoch 4423 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06165 0.7788 0.9473 -2.978e-05 1.337e-05 0.1505 -2.244e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004678 -0.004522 -0.0157 0.009112 0.9637 0.9693 0.01023 0.915 0.9258 0.03375 ] Network output: [ 1.024 -0.1743 0.05904 0.0003707 -0.0001664 0.06872 0.0002794 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2944 -0.01979 -0.1517 0.2076 0.9829 0.993 0.3374 0.9018 0.9759 0.62 ] Network output: [ 0.02 0.8253 0.9729 -0.0001295 5.816e-05 0.1613 -9.763e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007146 0.002544 0.00584 0.006128 0.9906 0.9937 0.007304 0.9686 0.9818 0.01433 ] Network output: [ 0.07339 -0.4731 1.012 4.844e-05 -2.175e-05 1.315 3.651e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3274 0.2234 0.3939 0.2782 0.9845 0.9938 0.3287 0.9082 0.9783 0.6074 ] Network output: [ -0.04638 0.2643 1.081 0.0002625 -0.0001179 0.7481 0.0001978 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.143 0.1349 0.1912 0.1505 0.9899 0.994 0.1431 0.9666 0.9824 0.2085 ] Network output: [ -0.04202 0.2181 1.053 0.0003468 -0.0001557 0.8148 0.0002613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1613 0.1597 0.194 0.1611 0.9856 0.9917 0.1613 0.9474 0.9743 0.1987 ] Network output: [ 0.007749 0.9772 -0.02348 1.462e-05 -6.563e-06 1.031 1.102e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1049 Epoch 4424 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05008 0.847 0.9353 -0.0001338 6.006e-05 0.117 -0.0001008 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004765 -0.004435 -0.01526 0.006794 0.9637 0.9693 0.01037 0.9146 0.9253 0.03341 ] Network output: [ 0.9137 0.3492 -0.02711 -0.0003984 0.0001789 -0.1511 -0.0003003 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3008 -0.00462 -0.1257 0.1015 0.9829 0.993 0.3444 0.9016 0.9756 0.6159 ] Network output: [ 0.02079 0.8377 0.9692 -0.0001511 6.784e-05 0.151 -0.0001139 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007429 0.002635 0.006166 0.003092 0.9906 0.9937 0.007592 0.9685 0.9815 0.01462 ] Network output: [ -0.009159 0.2266 0.8356 -0.0009565 0.0004294 0.9522 -0.0007209 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3374 0.2302 0.4005 0.1197 0.9846 0.9938 0.3387 0.9083 0.9783 0.6141 ] Network output: [ -0.04527 0.2853 1.082 0.0002396 -0.0001076 0.7237 0.0001806 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1376 0.1296 0.1864 0.1267 0.99 0.994 0.1377 0.9663 0.9821 0.2042 ] Network output: [ -0.03669 0.1375 1.086 0.000433 -0.0001944 0.8513 0.0003263 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1547 0.1531 0.1955 0.1584 0.9855 0.9916 0.1547 0.9467 0.9742 0.2006 ] Network output: [ 0.008253 0.898 0.0175 8.24e-05 -3.699e-05 1.068 6.21e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07998 Epoch 4425 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06162 0.779 0.9472 -2.995e-05 1.345e-05 0.1504 -2.257e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004674 -0.004517 -0.01568 0.009103 0.9638 0.9693 0.01021 0.9149 0.9257 0.0337 ] Network output: [ 1.024 -0.1735 0.05845 0.000369 -0.0001656 0.06831 0.0002781 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2942 -0.0198 -0.1519 0.2075 0.9829 0.993 0.3371 0.9016 0.9758 0.62 ] Network output: [ 0.01998 0.8255 0.9728 -0.0001293 5.804e-05 0.1611 -9.744e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007136 0.002539 0.005829 0.006112 0.9906 0.9937 0.007293 0.9685 0.9818 0.01432 ] Network output: [ 0.07324 -0.472 1.011 4.614e-05 -2.072e-05 1.314 3.478e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3271 0.2231 0.3938 0.2777 0.9845 0.9938 0.3283 0.908 0.9783 0.6075 ] Network output: [ -0.04639 0.264 1.082 0.000262 -0.0001176 0.7483 0.0001974 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1428 0.1347 0.1912 0.1504 0.9899 0.994 0.1429 0.9666 0.9824 0.2085 ] Network output: [ -0.04201 0.2177 1.053 0.0003462 -0.0001554 0.8151 0.0002609 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.161 0.1594 0.194 0.161 0.9856 0.9917 0.1611 0.9473 0.9743 0.1987 ] Network output: [ 0.007554 0.9781 -0.02349 1.35e-05 -6.059e-06 1.03 1.017e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1045 Epoch 4426 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05007 0.847 0.9353 -0.0001332 5.982e-05 0.117 -0.0001004 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004759 -0.004429 -0.01524 0.006795 0.9638 0.9693 0.01035 0.9146 0.9252 0.03336 ] Network output: [ 0.9137 0.3486 -0.02662 -0.0003961 0.0001778 -0.151 -0.0002985 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3005 -0.004664 -0.1258 0.1016 0.9829 0.993 0.344 0.9014 0.9756 0.616 ] Network output: [ 0.02081 0.8377 0.9691 -0.0001506 6.761e-05 0.1509 -0.0001135 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007418 0.002629 0.006155 0.003088 0.9906 0.9937 0.00758 0.9685 0.9815 0.01461 ] Network output: [ -0.009157 0.2262 0.8361 -0.0009542 0.0004284 0.9522 -0.0007191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3371 0.2299 0.4004 0.1195 0.9846 0.9938 0.3384 0.9081 0.9783 0.6142 ] Network output: [ -0.04519 0.2848 1.082 0.0002396 -0.0001076 0.724 0.0001806 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1374 0.1294 0.1864 0.1266 0.99 0.994 0.1375 0.9663 0.982 0.2042 ] Network output: [ -0.03659 0.1369 1.086 0.0004326 -0.0001942 0.8517 0.000326 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1545 0.1529 0.1955 0.1583 0.9855 0.9916 0.1545 0.9466 0.9741 0.2006 ] Network output: [ 0.008326 0.8975 0.01757 8.334e-05 -3.742e-05 1.069 6.281e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07977 Epoch 4427 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06159 0.7793 0.9472 -3.013e-05 1.353e-05 0.1502 -2.27e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00467 -0.004513 -0.01567 0.009094 0.9638 0.9693 0.0102 0.9149 0.9256 0.03365 ] Network output: [ 1.024 -0.1728 0.05784 0.0003672 -0.0001649 0.06788 0.0002768 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.294 -0.01981 -0.152 0.2073 0.9829 0.993 0.3368 0.9013 0.9758 0.6202 ] Network output: [ 0.01996 0.8257 0.9728 -0.000129 5.793e-05 0.161 -9.725e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007126 0.002533 0.005817 0.006097 0.9906 0.9937 0.007282 0.9685 0.9818 0.01431 ] Network output: [ 0.07308 -0.4709 1.011 4.379e-05 -1.966e-05 1.314 3.3e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3267 0.2229 0.3937 0.2771 0.9846 0.9938 0.328 0.9078 0.9783 0.6077 ] Network output: [ -0.04641 0.2637 1.082 0.0002615 -0.0001174 0.7486 0.000197 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1426 0.1345 0.1912 0.1503 0.9899 0.994 0.1427 0.9665 0.9823 0.2085 ] Network output: [ -0.042 0.2172 1.053 0.0003457 -0.0001552 0.8154 0.0002605 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1608 0.1592 0.194 0.1609 0.9856 0.9917 0.1608 0.9472 0.9742 0.1987 ] Network output: [ 0.007353 0.9791 -0.0235 1.234e-05 -5.541e-06 1.03 9.302e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1041 Epoch 4428 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05007 0.847 0.9354 -0.0001327 5.957e-05 0.117 -0.0001 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004754 -0.004424 -0.01523 0.006795 0.9638 0.9693 0.01033 0.9145 0.9251 0.0333 ] Network output: [ 0.9137 0.3479 -0.02612 -0.0003938 0.0001768 -0.1509 -0.0002968 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.3002 -0.004709 -0.126 0.1017 0.9829 0.993 0.3436 0.9012 0.9755 0.6161 ] Network output: [ 0.02083 0.8378 0.9691 -0.0001501 6.738e-05 0.1509 -0.0001131 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007407 0.002623 0.006144 0.003084 0.9906 0.9937 0.007568 0.9684 0.9815 0.0146 ] Network output: [ -0.009155 0.2257 0.8366 -0.0009518 0.0004273 0.9522 -0.0007173 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3367 0.2296 0.4003 0.1193 0.9846 0.9938 0.338 0.9078 0.9782 0.6143 ] Network output: [ -0.0451 0.2843 1.082 0.0002396 -0.0001076 0.7244 0.0001806 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1372 0.1292 0.1864 0.1266 0.99 0.994 0.1373 0.9662 0.982 0.2042 ] Network output: [ -0.03648 0.1364 1.086 0.0004322 -0.000194 0.8521 0.0003257 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1543 0.1527 0.1955 0.1583 0.9855 0.9916 0.1544 0.9465 0.9741 0.2006 ] Network output: [ 0.008402 0.8971 0.01763 8.43e-05 -3.785e-05 1.069 6.353e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07954 Epoch 4429 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06155 0.7796 0.9471 -3.031e-05 1.361e-05 0.1501 -2.284e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004667 -0.004508 -0.01565 0.009084 0.9638 0.9694 0.01019 0.9148 0.9256 0.03359 ] Network output: [ 1.024 -0.172 0.05722 0.0003655 -0.0001641 0.06743 0.0002754 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2938 -0.01983 -0.1522 0.2071 0.9829 0.993 0.3366 0.9011 0.9757 0.6203 ] Network output: [ 0.01993 0.8259 0.9728 -0.0001288 5.782e-05 0.1609 -9.706e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007115 0.002527 0.005806 0.006081 0.9906 0.9937 0.007272 0.9684 0.9817 0.01429 ] Network output: [ 0.07292 -0.4698 1.01 4.136e-05 -1.857e-05 1.314 3.117e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3263 0.2226 0.3936 0.2765 0.9846 0.9938 0.3276 0.9076 0.9782 0.6078 ] Network output: [ -0.04642 0.2633 1.082 0.0002609 -0.0001171 0.7489 0.0001967 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1423 0.1343 0.1912 0.1501 0.9899 0.994 0.1424 0.9664 0.9823 0.2085 ] Network output: [ -0.04199 0.2167 1.053 0.0003452 -0.000155 0.8158 0.0002601 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1606 0.159 0.1941 0.1608 0.9856 0.9917 0.1606 0.9471 0.9742 0.1988 ] Network output: [ 0.007146 0.9801 -0.02351 1.116e-05 -5.009e-06 1.029 8.409e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1037 Epoch 4430 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05006 0.847 0.9354 -0.0001321 5.932e-05 0.1169 -9.959e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004749 -0.004419 -0.01521 0.006796 0.9638 0.9693 0.01032 0.9145 0.925 0.03325 ] Network output: [ 0.9138 0.3473 -0.02561 -0.0003915 0.0001757 -0.1508 -0.000295 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2998 -0.004757 -0.1261 0.1018 0.9829 0.993 0.3432 0.901 0.9755 0.6162 ] Network output: [ 0.02085 0.8378 0.9691 -0.0001496 6.715e-05 0.1508 -0.0001127 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007395 0.002616 0.006133 0.00308 0.9906 0.9937 0.007557 0.9684 0.9814 0.01458 ] Network output: [ -0.009154 0.2252 0.8371 -0.0009495 0.0004263 0.9522 -0.0007156 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3363 0.2293 0.4002 0.1191 0.9846 0.9938 0.3376 0.9076 0.9782 0.6144 ] Network output: [ -0.04501 0.2838 1.082 0.0002397 -0.0001076 0.7248 0.0001806 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1371 0.129 0.1864 0.1266 0.99 0.994 0.1372 0.9661 0.9819 0.2042 ] Network output: [ -0.03637 0.1359 1.086 0.0004318 -0.0001938 0.8525 0.0003254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1542 0.1525 0.1954 0.1583 0.9855 0.9916 0.1542 0.9464 0.974 0.2006 ] Network output: [ 0.008481 0.8966 0.01769 8.528e-05 -3.828e-05 1.069 6.427e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07931 Epoch 4431 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06151 0.7799 0.947 -3.05e-05 1.369e-05 0.15 -2.298e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004663 -0.004504 -0.01564 0.009075 0.9638 0.9694 0.01017 0.9148 0.9255 0.03354 ] Network output: [ 1.025 -0.1711 0.05659 0.0003637 -0.0001633 0.06695 0.0002741 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2935 -0.01984 -0.1524 0.2069 0.983 0.993 0.3363 0.9009 0.9757 0.6204 ] Network output: [ 0.01991 0.8261 0.9728 -0.0001285 5.771e-05 0.1608 -9.688e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007105 0.002522 0.005794 0.006065 0.9906 0.9937 0.007261 0.9684 0.9817 0.01428 ] Network output: [ 0.07275 -0.4686 1.01 3.887e-05 -1.745e-05 1.313 2.929e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.326 0.2223 0.3936 0.2759 0.9846 0.9938 0.3273 0.9074 0.9782 0.608 ] Network output: [ -0.04644 0.263 1.082 0.0002604 -0.0001169 0.7492 0.0001963 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1421 0.134 0.1912 0.15 0.9899 0.994 0.1422 0.9664 0.9823 0.2086 ] Network output: [ -0.04197 0.2162 1.053 0.0003447 -0.0001547 0.8162 0.0002598 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1603 0.1587 0.1941 0.1607 0.9856 0.9917 0.1604 0.947 0.9741 0.1988 ] Network output: [ 0.006933 0.9812 -0.02353 9.938e-06 -4.461e-06 1.029 7.489e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1033 Epoch 4432 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05005 0.847 0.9354 -0.0001316 5.907e-05 0.1169 -9.917e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004744 -0.004413 -0.01519 0.006797 0.9638 0.9694 0.0103 0.9144 0.925 0.0332 ] Network output: [ 0.9138 0.3466 -0.0251 -0.0003891 0.0001747 -0.1507 -0.0002932 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2995 -0.004806 -0.1263 0.1019 0.9829 0.993 0.3428 0.9008 0.9754 0.6163 ] Network output: [ 0.02086 0.8379 0.9691 -0.0001491 6.692e-05 0.1507 -0.0001123 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007384 0.00261 0.006123 0.003077 0.9906 0.9937 0.007545 0.9683 0.9814 0.01457 ] Network output: [ -0.009153 0.2247 0.8376 -0.0009472 0.0004252 0.9522 -0.0007138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3359 0.229 0.4001 0.1189 0.9846 0.9938 0.3372 0.9074 0.9782 0.6145 ] Network output: [ -0.04492 0.2832 1.082 0.0002398 -0.0001076 0.7251 0.0001807 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1369 0.1289 0.1863 0.1265 0.99 0.994 0.137 0.966 0.9819 0.2042 ] Network output: [ -0.03626 0.1354 1.086 0.0004314 -0.0001937 0.853 0.0003251 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.154 0.1524 0.1954 0.1582 0.9855 0.9916 0.154 0.9463 0.9739 0.2006 ] Network output: [ 0.008563 0.8962 0.01776 8.627e-05 -3.873e-05 1.069 6.502e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07907 Epoch 4433 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06148 0.7802 0.9469 -3.069e-05 1.378e-05 0.1498 -2.313e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004659 -0.004499 -0.01562 0.009066 0.9638 0.9694 0.01016 0.9147 0.9254 0.03349 ] Network output: [ 1.025 -0.1702 0.05594 0.0003619 -0.0001625 0.06644 0.0002727 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2933 -0.01985 -0.1526 0.2067 0.983 0.993 0.336 0.9007 0.9756 0.6205 ] Network output: [ 0.01988 0.8263 0.9728 -0.0001283 5.76e-05 0.1607 -9.67e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007095 0.002516 0.005783 0.006049 0.9906 0.9937 0.007251 0.9683 0.9816 0.01427 ] Network output: [ 0.07258 -0.4674 1.009 3.63e-05 -1.63e-05 1.313 2.736e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3256 0.222 0.3935 0.2753 0.9846 0.9938 0.3269 0.9072 0.9781 0.6081 ] Network output: [ -0.04645 0.2626 1.082 0.0002599 -0.0001167 0.7495 0.0001959 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1419 0.1338 0.1912 0.1499 0.9899 0.994 0.142 0.9663 0.9822 0.2086 ] Network output: [ -0.04196 0.2157 1.053 0.0003442 -0.0001545 0.8165 0.0002594 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1601 0.1585 0.1941 0.1606 0.9856 0.9917 0.1601 0.9469 0.9741 0.1989 ] Network output: [ 0.006714 0.9822 -0.02354 8.683e-06 -3.898e-06 1.028 6.544e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1029 Epoch 4434 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05004 0.847 0.9355 -0.000131 5.882e-05 0.1169 -9.874e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004738 -0.004408 -0.01518 0.006798 0.9638 0.9694 0.01029 0.9144 0.9249 0.03314 ] Network output: [ 0.9138 0.3459 -0.02458 -0.0003866 0.0001736 -0.1505 -0.0002914 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2992 -0.004858 -0.1265 0.102 0.983 0.993 0.3423 0.9005 0.9754 0.6165 ] Network output: [ 0.02088 0.8379 0.9691 -0.0001485 6.669e-05 0.1507 -0.000112 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007373 0.002603 0.006112 0.003073 0.9906 0.9937 0.007533 0.9683 0.9814 0.01456 ] Network output: [ -0.009151 0.2241 0.8381 -0.0009448 0.0004242 0.9522 -0.0007121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3356 0.2286 0.4 0.1187 0.9846 0.9938 0.3368 0.9072 0.9781 0.6147 ] Network output: [ -0.04482 0.2827 1.082 0.0002398 -0.0001077 0.7255 0.0001807 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1367 0.1287 0.1863 0.1265 0.99 0.994 0.1368 0.966 0.9818 0.2042 ] Network output: [ -0.03615 0.1348 1.086 0.0004311 -0.0001935 0.8534 0.0003249 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1538 0.1522 0.1954 0.1582 0.9855 0.9916 0.1538 0.9462 0.9739 0.2007 ] Network output: [ 0.008648 0.8957 0.01782 8.729e-05 -3.919e-05 1.07 6.578e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07882 Epoch 4435 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06144 0.7805 0.9468 -3.089e-05 1.387e-05 0.1497 -2.328e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004655 -0.004495 -0.01561 0.009056 0.9639 0.9694 0.01015 0.9147 0.9254 0.03344 ] Network output: [ 1.025 -0.1693 0.05528 0.0003601 -0.0001617 0.06589 0.0002714 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2931 -0.01987 -0.1529 0.2065 0.983 0.993 0.3357 0.9005 0.9756 0.6207 ] Network output: [ 0.01986 0.8265 0.9728 -0.0001281 5.75e-05 0.1606 -9.652e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007085 0.00251 0.005772 0.006032 0.9906 0.9937 0.00724 0.9683 0.9816 0.01426 ] Network output: [ 0.0724 -0.4661 1.009 3.365e-05 -1.511e-05 1.312 2.536e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3253 0.2217 0.3934 0.2747 0.9846 0.9938 0.3265 0.9069 0.9781 0.6083 ] Network output: [ -0.04646 0.2622 1.082 0.0002595 -0.0001165 0.7498 0.0001955 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1417 0.1336 0.1912 0.1498 0.9899 0.994 0.1418 0.9662 0.9822 0.2087 ] Network output: [ -0.04195 0.2151 1.053 0.0003438 -0.0001543 0.8169 0.0002591 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1599 0.1583 0.1942 0.1605 0.9856 0.9917 0.1599 0.9468 0.974 0.1989 ] Network output: [ 0.006488 0.9833 -0.02355 7.391e-06 -3.318e-06 1.027 5.57e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1025 Epoch 4436 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05004 0.847 0.9355 -0.0001305 5.856e-05 0.1169 -9.831e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004733 -0.004403 -0.01516 0.006799 0.9638 0.9694 0.01027 0.9144 0.9248 0.03309 ] Network output: [ 0.9139 0.3452 -0.02405 -0.0003842 0.0001725 -0.1504 -0.0002895 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2988 -0.004912 -0.1266 0.1021 0.983 0.993 0.3419 0.9003 0.9754 0.6166 ] Network output: [ 0.0209 0.8379 0.969 -0.000148 6.646e-05 0.1506 -0.0001116 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007362 0.002596 0.006102 0.003069 0.9906 0.9937 0.007522 0.9682 0.9813 0.01456 ] Network output: [ -0.009149 0.2235 0.8387 -0.0009425 0.0004231 0.9523 -0.0007103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3352 0.2283 0.4 0.1185 0.9846 0.9938 0.3365 0.907 0.9781 0.6148 ] Network output: [ -0.04473 0.2821 1.082 0.00024 -0.0001077 0.7259 0.0001808 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1365 0.1285 0.1863 0.1265 0.99 0.994 0.1366 0.9659 0.9818 0.2042 ] Network output: [ -0.03603 0.1342 1.086 0.0004308 -0.0001934 0.8539 0.0003246 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1536 0.152 0.1954 0.1582 0.9855 0.9916 0.1537 0.9461 0.9738 0.2007 ] Network output: [ 0.008737 0.8952 0.01787 8.832e-05 -3.965e-05 1.07 6.656e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07857 Epoch 4437 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0614 0.7808 0.9467 -3.11e-05 1.396e-05 0.1495 -2.344e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004652 -0.004491 -0.01559 0.009046 0.9639 0.9694 0.01014 0.9147 0.9253 0.03339 ] Network output: [ 1.025 -0.1684 0.0546 0.0003582 -0.0001608 0.06531 0.00027 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2928 -0.01988 -0.1531 0.2063 0.983 0.993 0.3354 0.9002 0.9755 0.6209 ] Network output: [ 0.01983 0.8266 0.9727 -0.0001278 5.74e-05 0.1604 -9.635e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007075 0.002504 0.00576 0.006015 0.9906 0.9937 0.00723 0.9683 0.9816 0.01425 ] Network output: [ 0.07222 -0.4648 1.009 3.091e-05 -1.387e-05 1.312 2.329e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3249 0.2214 0.3934 0.274 0.9846 0.9938 0.3262 0.9067 0.978 0.6085 ] Network output: [ -0.04647 0.2619 1.082 0.000259 -0.0001163 0.7501 0.0001952 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1414 0.1334 0.1912 0.1496 0.9899 0.994 0.1415 0.9662 0.9821 0.2087 ] Network output: [ -0.04194 0.2146 1.053 0.0003434 -0.0001542 0.8173 0.0002588 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1596 0.158 0.1942 0.1605 0.9856 0.9917 0.1597 0.9467 0.974 0.199 ] Network output: [ 0.006256 0.9844 -0.02357 6.06e-06 -2.721e-06 1.027 4.567e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.102 Epoch 4438 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05003 0.8469 0.9355 -0.0001299 5.831e-05 0.1169 -9.788e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004728 -0.004398 -0.01515 0.006801 0.9639 0.9694 0.01026 0.9143 0.9248 0.03304 ] Network output: [ 0.9139 0.3444 -0.02352 -0.0003817 0.0001713 -0.1502 -0.0002876 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2985 -0.004968 -0.1268 0.1022 0.983 0.993 0.3415 0.9001 0.9753 0.6168 ] Network output: [ 0.02091 0.838 0.969 -0.0001475 6.623e-05 0.1506 -0.0001112 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00735 0.00259 0.006092 0.003066 0.9906 0.9937 0.00751 0.9682 0.9813 0.01455 ] Network output: [ -0.009147 0.2229 0.8393 -0.0009402 0.0004221 0.9523 -0.0007086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3348 0.228 0.3999 0.1183 0.9846 0.9938 0.3361 0.9068 0.978 0.615 ] Network output: [ -0.04462 0.2815 1.082 0.0002401 -0.0001078 0.7263 0.0001809 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1364 0.1283 0.1863 0.1265 0.99 0.994 0.1365 0.9658 0.9818 0.2042 ] Network output: [ -0.03592 0.1336 1.086 0.0004305 -0.0001933 0.8543 0.0003244 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1535 0.1519 0.1955 0.1582 0.9855 0.9916 0.1535 0.946 0.9738 0.2007 ] Network output: [ 0.008828 0.8947 0.01793 8.937e-05 -4.012e-05 1.07 6.736e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07831 Epoch 4439 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06136 0.7811 0.9466 -3.131e-05 1.406e-05 0.1494 -2.36e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004648 -0.004486 -0.01558 0.009037 0.9639 0.9694 0.01012 0.9146 0.9252 0.03334 ] Network output: [ 1.025 -0.1674 0.05391 0.0003564 -0.00016 0.0647 0.0002686 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2926 -0.0199 -0.1533 0.2061 0.983 0.993 0.3351 0.9 0.9755 0.6211 ] Network output: [ 0.0198 0.8268 0.9727 -0.0001276 5.73e-05 0.1603 -9.618e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007065 0.002497 0.005749 0.005998 0.9906 0.9937 0.00722 0.9682 0.9815 0.01424 ] Network output: [ 0.07203 -0.4634 1.008 2.807e-05 -1.26e-05 1.311 2.115e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3245 0.221 0.3933 0.2734 0.9846 0.9938 0.3258 0.9065 0.978 0.6088 ] Network output: [ -0.04649 0.2615 1.082 0.0002585 -0.0001161 0.7504 0.0001948 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1412 0.1332 0.1912 0.1495 0.9899 0.994 0.1413 0.9661 0.9821 0.2088 ] Network output: [ -0.04193 0.214 1.054 0.000343 -0.000154 0.8178 0.0002585 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1594 0.1578 0.1942 0.1604 0.9856 0.9917 0.1594 0.9466 0.9739 0.199 ] Network output: [ 0.006015 0.9855 -0.02358 4.689e-06 -2.105e-06 1.026 3.534e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1016 Epoch 4440 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05002 0.8469 0.9356 -0.0001293 5.804e-05 0.1169 -9.744e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004722 -0.004392 -0.01513 0.006802 0.9639 0.9694 0.01024 0.9143 0.9247 0.03299 ] Network output: [ 0.9139 0.3436 -0.02298 -0.0003791 0.0001702 -0.1501 -0.0002857 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2981 -0.005026 -0.127 0.1024 0.983 0.993 0.3411 0.8999 0.9753 0.617 ] Network output: [ 0.02093 0.838 0.969 -0.000147 6.6e-05 0.1505 -0.0001108 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007339 0.002583 0.006082 0.003063 0.9906 0.9937 0.007499 0.9681 0.9812 0.01454 ] Network output: [ -0.009144 0.2222 0.8398 -0.0009379 0.000421 0.9524 -0.0007068 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3344 0.2276 0.3999 0.1181 0.9846 0.9938 0.3357 0.9066 0.978 0.6152 ] Network output: [ -0.04452 0.2809 1.082 0.0002403 -0.0001079 0.7267 0.0001811 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1362 0.1282 0.1863 0.1264 0.99 0.994 0.1363 0.9658 0.9817 0.2043 ] Network output: [ -0.03579 0.133 1.085 0.0004303 -0.0001932 0.8548 0.0003243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1533 0.1517 0.1955 0.1581 0.9855 0.9916 0.1533 0.946 0.9737 0.2007 ] Network output: [ 0.008924 0.8941 0.01798 9.045e-05 -4.06e-05 1.07 6.816e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07803 Epoch 4441 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06132 0.7815 0.9466 -3.154e-05 1.416e-05 0.1492 -2.377e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004644 -0.004482 -0.01557 0.009026 0.9639 0.9694 0.01011 0.9146 0.9252 0.03329 ] Network output: [ 1.025 -0.1663 0.05319 0.0003544 -0.0001591 0.06404 0.0002671 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2924 -0.01991 -0.1536 0.2059 0.983 0.993 0.3348 0.8998 0.9754 0.6213 ] Network output: [ 0.01976 0.827 0.9727 -0.0001274 5.72e-05 0.1602 -9.602e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007055 0.002491 0.005738 0.005981 0.9906 0.9937 0.007209 0.9682 0.9815 0.01423 ] Network output: [ 0.07183 -0.462 1.008 2.513e-05 -1.128e-05 1.311 1.894e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3242 0.2207 0.3933 0.2727 0.9846 0.9938 0.3254 0.9063 0.978 0.609 ] Network output: [ -0.0465 0.2611 1.082 0.0002581 -0.0001159 0.7507 0.0001945 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.141 0.133 0.1912 0.1494 0.9899 0.994 0.1411 0.966 0.9821 0.2088 ] Network output: [ -0.04192 0.2134 1.054 0.0003426 -0.0001538 0.8182 0.0002582 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1592 0.1576 0.1943 0.1603 0.9856 0.9917 0.1592 0.9466 0.9739 0.1991 ] Network output: [ 0.005767 0.9867 -0.02359 3.276e-06 -1.471e-06 1.025 2.469e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1011 Epoch 4442 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05002 0.8469 0.9356 -0.0001287 5.777e-05 0.1169 -9.699e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004717 -0.004387 -0.01512 0.006804 0.9639 0.9694 0.01022 0.9142 0.9247 0.03294 ] Network output: [ 0.914 0.3428 -0.02243 -0.0003765 0.000169 -0.1499 -0.0002837 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2978 -0.005087 -0.1272 0.1025 0.983 0.993 0.3406 0.8997 0.9752 0.6172 ] Network output: [ 0.02094 0.8381 0.969 -0.0001465 6.577e-05 0.1504 -0.0001104 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007328 0.002576 0.006073 0.003059 0.9906 0.9937 0.007487 0.9681 0.9812 0.01453 ] Network output: [ -0.00914 0.2215 0.8404 -0.0009355 0.00042 0.9525 -0.000705 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.334 0.2273 0.3999 0.1179 0.9846 0.9938 0.3353 0.9064 0.9779 0.6154 ] Network output: [ -0.04441 0.2803 1.082 0.0002405 -0.0001079 0.7272 0.0001812 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.136 0.128 0.1863 0.1264 0.99 0.994 0.1361 0.9657 0.9817 0.2043 ] Network output: [ -0.03567 0.1324 1.085 0.0004301 -0.0001931 0.8553 0.0003241 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1531 0.1515 0.1955 0.1581 0.9855 0.9916 0.1532 0.9459 0.9737 0.2007 ] Network output: [ 0.009022 0.8936 0.01803 9.154e-05 -4.109e-05 1.071 6.899e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07775 Epoch 4443 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06127 0.7818 0.9465 -3.176e-05 1.426e-05 0.149 -2.394e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004641 -0.004478 -0.01555 0.009016 0.9639 0.9695 0.0101 0.9145 0.9251 0.03324 ] Network output: [ 1.025 -0.1652 0.05246 0.0003525 -0.0001582 0.06335 0.0002656 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2921 -0.01993 -0.1538 0.2056 0.983 0.993 0.3345 0.8996 0.9754 0.6215 ] Network output: [ 0.01973 0.8273 0.9727 -0.0001272 5.71e-05 0.1601 -9.586e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007045 0.002485 0.005726 0.005963 0.9906 0.9937 0.007199 0.9681 0.9815 0.01422 ] Network output: [ 0.07162 -0.4605 1.007 2.208e-05 -9.912e-06 1.31 1.664e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3238 0.2204 0.3933 0.272 0.9846 0.9938 0.3251 0.9061 0.9779 0.6093 ] Network output: [ -0.04651 0.2607 1.082 0.0002577 -0.0001157 0.7511 0.0001942 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1408 0.1328 0.1913 0.1493 0.9899 0.994 0.1409 0.966 0.982 0.2089 ] Network output: [ -0.04191 0.2127 1.054 0.0003423 -0.0001537 0.8187 0.000258 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.159 0.1574 0.1944 0.1602 0.9856 0.9917 0.159 0.9465 0.9738 0.1992 ] Network output: [ 0.005511 0.9879 -0.02361 1.819e-06 -8.165e-07 1.025 1.371e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1006 Epoch 4444 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05001 0.8468 0.9357 -0.0001281 5.75e-05 0.1169 -9.653e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004712 -0.004382 -0.01511 0.006805 0.9639 0.9694 0.01021 0.9142 0.9246 0.0329 ] Network output: [ 0.914 0.3419 -0.02186 -0.0003738 0.0001678 -0.1496 -0.0002817 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2974 -0.005151 -0.1274 0.1026 0.983 0.993 0.3402 0.8995 0.9752 0.6174 ] Network output: [ 0.02096 0.8381 0.969 -0.000146 6.554e-05 0.1504 -0.00011 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007317 0.002569 0.006063 0.003056 0.9906 0.9937 0.007475 0.968 0.9812 0.01452 ] Network output: [ -0.009135 0.2208 0.8411 -0.0009332 0.0004189 0.9527 -0.0007033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3336 0.2269 0.3998 0.1177 0.9846 0.9938 0.3349 0.9062 0.9779 0.6157 ] Network output: [ -0.0443 0.2797 1.082 0.0002407 -0.0001081 0.7276 0.0001814 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1359 0.1279 0.1863 0.1264 0.99 0.994 0.136 0.9657 0.9817 0.2043 ] Network output: [ -0.03554 0.1318 1.085 0.0004299 -0.000193 0.8558 0.000324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.153 0.1514 0.1955 0.1581 0.9855 0.9916 0.153 0.9458 0.9736 0.2008 ] Network output: [ 0.009125 0.893 0.01808 9.265e-05 -4.159e-05 1.071 6.982e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07746 Epoch 4445 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06123 0.7822 0.9464 -3.2e-05 1.437e-05 0.1489 -2.412e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004637 -0.004474 -0.01554 0.009006 0.9639 0.9695 0.01009 0.9145 0.925 0.0332 ] Network output: [ 1.026 -0.164 0.0517 0.0003505 -0.0001573 0.06261 0.0002641 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2919 -0.01995 -0.1541 0.2054 0.983 0.993 0.3342 0.8994 0.9753 0.6218 ] Network output: [ 0.0197 0.8275 0.9727 -0.000127 5.701e-05 0.1599 -9.571e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007035 0.002478 0.005715 0.005945 0.9906 0.9937 0.007189 0.9681 0.9814 0.01421 ] Network output: [ 0.0714 -0.4589 1.007 1.891e-05 -8.488e-06 1.31 1.425e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3234 0.2201 0.3932 0.2713 0.9846 0.9938 0.3247 0.9059 0.9779 0.6096 ] Network output: [ -0.04652 0.2603 1.082 0.0002573 -0.0001155 0.7514 0.0001939 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1406 0.1325 0.1913 0.1491 0.9899 0.994 0.1407 0.9659 0.982 0.209 ] Network output: [ -0.0419 0.2121 1.054 0.000342 -0.0001535 0.8191 0.0002578 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1588 0.1572 0.1944 0.1601 0.9857 0.9917 0.1588 0.9464 0.9738 0.1992 ] Network output: [ 0.005247 0.9891 -0.02362 3.156e-07 -1.417e-07 1.024 2.379e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1001 Epoch 4446 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05001 0.8468 0.9357 -0.0001275 5.723e-05 0.1169 -9.607e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004706 -0.004377 -0.01509 0.006807 0.9639 0.9695 0.01019 0.9142 0.9245 0.03285 ] Network output: [ 0.9141 0.341 -0.02129 -0.000371 0.0001666 -0.1494 -0.0002796 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2971 -0.005219 -0.1276 0.1028 0.983 0.993 0.3398 0.8993 0.9751 0.6177 ] Network output: [ 0.02097 0.8382 0.969 -0.0001455 6.53e-05 0.1503 -0.0001096 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007306 0.002561 0.006054 0.003053 0.9906 0.9937 0.007464 0.968 0.9811 0.01452 ] Network output: [ -0.009129 0.22 0.8417 -0.0009308 0.0004179 0.9528 -0.0007015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3332 0.2266 0.3998 0.1175 0.9846 0.9939 0.3345 0.906 0.9779 0.6159 ] Network output: [ -0.04419 0.279 1.082 0.0002409 -0.0001082 0.7281 0.0001816 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1357 0.1277 0.1863 0.1264 0.99 0.994 0.1358 0.9656 0.9816 0.2044 ] Network output: [ -0.03541 0.1311 1.085 0.0004298 -0.0001929 0.8564 0.0003239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1528 0.1512 0.1955 0.1581 0.9855 0.9916 0.1529 0.9457 0.9736 0.2008 ] Network output: [ 0.00923 0.8925 0.01812 9.378e-05 -4.21e-05 1.071 7.067e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07715 Epoch 4447 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06118 0.7825 0.9463 -3.225e-05 1.448e-05 0.1487 -2.431e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004633 -0.00447 -0.01553 0.008995 0.964 0.9695 0.01008 0.9145 0.925 0.03315 ] Network output: [ 1.026 -0.1627 0.05092 0.0003484 -0.0001564 0.06182 0.0002626 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2917 -0.01997 -0.1544 0.2051 0.983 0.993 0.3339 0.8992 0.9753 0.622 ] Network output: [ 0.01966 0.8277 0.9727 -0.0001268 5.692e-05 0.1598 -9.556e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007025 0.002471 0.005704 0.005926 0.9906 0.9937 0.007179 0.968 0.9814 0.01421 ] Network output: [ 0.07118 -0.4573 1.006 1.56e-05 -7.005e-06 1.309 1.176e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3231 0.2197 0.3932 0.2705 0.9846 0.9938 0.3243 0.9057 0.9778 0.6099 ] Network output: [ -0.04653 0.2599 1.082 0.0002569 -0.0001153 0.7518 0.0001936 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1404 0.1323 0.1913 0.149 0.9899 0.994 0.1405 0.9659 0.9819 0.209 ] Network output: [ -0.04189 0.2114 1.054 0.0003418 -0.0001534 0.8197 0.0002576 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1585 0.157 0.1945 0.16 0.9857 0.9917 0.1586 0.9463 0.9737 0.1993 ] Network output: [ 0.004973 0.9903 -0.02363 -1.235e-06 5.546e-07 1.023 -9.31e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09954 Epoch 4448 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05 0.8468 0.9358 -0.0001268 5.695e-05 0.1169 -9.559e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004701 -0.004372 -0.01508 0.00681 0.9639 0.9695 0.01018 0.9141 0.9245 0.0328 ] Network output: [ 0.9141 0.3401 -0.02071 -0.0003682 0.0001653 -0.1492 -0.0002775 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2967 -0.005289 -0.1278 0.1029 0.983 0.993 0.3393 0.8991 0.9751 0.618 ] Network output: [ 0.02099 0.8382 0.9689 -0.0001449 6.507e-05 0.1503 -0.0001092 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007295 0.002554 0.006045 0.00305 0.9906 0.9937 0.007452 0.9679 0.9811 0.01451 ] Network output: [ -0.009121 0.2191 0.8424 -0.0009284 0.0004168 0.953 -0.0006997 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3328 0.2262 0.3998 0.1173 0.9846 0.9939 0.3341 0.9058 0.9778 0.6162 ] Network output: [ -0.04407 0.2783 1.082 0.0002412 -0.0001083 0.7286 0.0001818 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1356 0.1275 0.1863 0.1264 0.99 0.994 0.1357 0.9655 0.9816 0.2044 ] Network output: [ -0.03527 0.1305 1.085 0.0004297 -0.0001929 0.8569 0.0003238 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1527 0.1511 0.1955 0.1581 0.9856 0.9916 0.1527 0.9456 0.9736 0.2008 ] Network output: [ 0.00934 0.8919 0.01816 9.493e-05 -4.262e-05 1.072 7.154e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07683 Epoch 4449 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06114 0.7829 0.9462 -3.251e-05 1.459e-05 0.1485 -2.45e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00463 -0.004466 -0.01552 0.008984 0.964 0.9695 0.01006 0.9144 0.9249 0.03311 ] Network output: [ 1.026 -0.1614 0.05012 0.0003463 -0.0001555 0.06098 0.000261 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2914 -0.02 -0.1546 0.2048 0.983 0.993 0.3336 0.899 0.9753 0.6223 ] Network output: [ 0.01962 0.8279 0.9727 -0.0001266 5.684e-05 0.1596 -9.541e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007016 0.002465 0.005693 0.005907 0.9906 0.9937 0.007169 0.968 0.9814 0.0142 ] Network output: [ 0.07094 -0.4556 1.006 1.216e-05 -5.459e-06 1.308 9.165e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3227 0.2194 0.3932 0.2697 0.9846 0.9939 0.3239 0.9055 0.9778 0.6103 ] Network output: [ -0.04655 0.2594 1.083 0.0002565 -0.0001152 0.7521 0.0001933 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1402 0.1321 0.1914 0.1488 0.9899 0.994 0.1403 0.9658 0.9819 0.2091 ] Network output: [ -0.04188 0.2107 1.054 0.0003416 -0.0001533 0.8202 0.0002574 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1583 0.1567 0.1946 0.16 0.9857 0.9917 0.1584 0.9462 0.9737 0.1994 ] Network output: [ 0.004689 0.9916 -0.02365 -2.836e-06 1.273e-06 1.023 -2.137e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09898 Epoch 4450 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05 0.8467 0.9358 -0.0001262 5.666e-05 0.1169 -9.511e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004695 -0.004367 -0.01507 0.006812 0.964 0.9695 0.01016 0.9141 0.9244 0.03276 ] Network output: [ 0.9142 0.3391 -0.02012 -0.0003653 0.000164 -0.1489 -0.0002753 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2963 -0.005363 -0.128 0.1031 0.983 0.993 0.3389 0.8989 0.975 0.6183 ] Network output: [ 0.021 0.8383 0.9689 -0.0001444 6.484e-05 0.1502 -0.0001088 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007283 0.002547 0.006036 0.003048 0.9906 0.9937 0.007441 0.9679 0.9811 0.01451 ] Network output: [ -0.009111 0.2182 0.843 -0.000926 0.0004157 0.9532 -0.0006979 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3324 0.2258 0.3998 0.1171 0.9846 0.9939 0.3337 0.9056 0.9778 0.6165 ] Network output: [ -0.04395 0.2776 1.082 0.0002416 -0.0001084 0.7291 0.000182 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1354 0.1274 0.1863 0.1264 0.99 0.994 0.1355 0.9655 0.9815 0.2045 ] Network output: [ -0.03513 0.1298 1.085 0.0004296 -0.0001929 0.8575 0.0003238 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1526 0.151 0.1956 0.1581 0.9856 0.9916 0.1526 0.9455 0.9735 0.2009 ] Network output: [ 0.009452 0.8913 0.01819 9.609e-05 -4.314e-05 1.072 7.242e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0765 Epoch 4451 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06109 0.7833 0.9461 -3.278e-05 1.472e-05 0.1483 -2.47e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004626 -0.004462 -0.01551 0.008973 0.964 0.9695 0.01005 0.9144 0.9249 0.03307 ] Network output: [ 1.026 -0.16 0.04929 0.0003441 -0.0001545 0.06008 0.0002594 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2912 -0.02002 -0.1549 0.2045 0.983 0.993 0.3333 0.8988 0.9752 0.6227 ] Network output: [ 0.01958 0.8281 0.9727 -0.0001264 5.675e-05 0.1595 -9.527e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007006 0.002458 0.005682 0.005887 0.9906 0.9937 0.007159 0.968 0.9813 0.01419 ] Network output: [ 0.07069 -0.4538 1.005 8.564e-06 -3.845e-06 1.307 6.454e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3223 0.219 0.3932 0.2689 0.9846 0.9939 0.3236 0.9053 0.9778 0.6107 ] Network output: [ -0.04656 0.259 1.083 0.0002562 -0.000115 0.7525 0.000193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.14 0.1319 0.1914 0.1487 0.9899 0.994 0.1401 0.9657 0.9819 0.2092 ] Network output: [ -0.04187 0.2099 1.054 0.0003414 -0.0001533 0.8207 0.0002573 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1581 0.1565 0.1947 0.1599 0.9857 0.9917 0.1582 0.9461 0.9736 0.1995 ] Network output: [ 0.004396 0.9929 -0.02366 -4.489e-06 2.015e-06 1.022 -3.383e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09839 Epoch 4452 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04999 0.8467 0.9359 -0.0001256 5.636e-05 0.1169 -9.462e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00469 -0.004362 -0.01506 0.006815 0.964 0.9695 0.01015 0.9141 0.9244 0.03272 ] Network output: [ 0.9143 0.338 -0.01952 -0.0003623 0.0001627 -0.1486 -0.0002731 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2959 -0.005441 -0.1282 0.1033 0.983 0.993 0.3384 0.8987 0.975 0.6186 ] Network output: [ 0.02102 0.8383 0.9689 -0.0001439 6.461e-05 0.1501 -0.0001085 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007272 0.002539 0.006027 0.003045 0.9906 0.9937 0.007429 0.9679 0.981 0.0145 ] Network output: [ -0.009098 0.2172 0.8438 -0.0009236 0.0004146 0.9535 -0.000696 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.332 0.2255 0.3999 0.117 0.9846 0.9939 0.3333 0.9054 0.9778 0.6168 ] Network output: [ -0.04382 0.2769 1.082 0.0002419 -0.0001086 0.7296 0.0001823 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1353 0.1273 0.1864 0.1264 0.99 0.994 0.1354 0.9654 0.9815 0.2045 ] Network output: [ -0.03499 0.1291 1.085 0.0004296 -0.0001929 0.8581 0.0003238 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1524 0.1508 0.1956 0.1581 0.9856 0.9916 0.1525 0.9454 0.9735 0.2009 ] Network output: [ 0.009569 0.8907 0.01822 9.728e-05 -4.367e-05 1.072 7.331e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07615 Epoch 4453 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06104 0.7837 0.946 -3.306e-05 1.484e-05 0.148 -2.491e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004622 -0.004458 -0.0155 0.008961 0.964 0.9695 0.01004 0.9144 0.9248 0.03302 ] Network output: [ 1.026 -0.1585 0.04843 0.0003419 -0.0001535 0.05912 0.0002577 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.291 -0.02004 -0.1552 0.2041 0.983 0.993 0.333 0.8986 0.9752 0.623 ] Network output: [ 0.01954 0.8284 0.9727 -0.0001262 5.667e-05 0.1594 -9.514e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006997 0.002451 0.00567 0.005867 0.9906 0.9937 0.007149 0.9679 0.9813 0.01419 ] Network output: [ 0.07043 -0.4519 1.005 4.802e-06 -2.156e-06 1.306 3.619e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3219 0.2187 0.3932 0.2681 0.9846 0.9939 0.3232 0.9052 0.9777 0.6111 ] Network output: [ -0.04657 0.2585 1.083 0.0002558 -0.0001148 0.7529 0.0001928 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1398 0.1317 0.1915 0.1486 0.9899 0.994 0.1399 0.9657 0.9818 0.2093 ] Network output: [ -0.04186 0.2092 1.055 0.0003412 -0.0001532 0.8213 0.0002572 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1579 0.1563 0.1948 0.1598 0.9857 0.9917 0.158 0.946 0.9736 0.1996 ] Network output: [ 0.004093 0.9942 -0.02367 -6.195e-06 2.781e-06 1.021 -4.669e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09777 Epoch 4454 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04999 0.8466 0.936 -0.0001249 5.606e-05 0.1169 -9.411e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004684 -0.004358 -0.01505 0.006817 0.964 0.9695 0.01013 0.9141 0.9243 0.03267 ] Network output: [ 0.9144 0.3369 -0.0189 -0.0003592 0.0001613 -0.1483 -0.0002707 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2956 -0.005523 -0.1284 0.1035 0.983 0.993 0.338 0.8985 0.975 0.619 ] Network output: [ 0.02103 0.8384 0.9689 -0.0001434 6.437e-05 0.1501 -0.0001081 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007261 0.002531 0.006019 0.003043 0.9906 0.9937 0.007418 0.9678 0.981 0.0145 ] Network output: [ -0.009082 0.2161 0.8445 -0.0009211 0.0004135 0.9538 -0.0006942 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3316 0.2251 0.3999 0.1168 0.9846 0.9939 0.3329 0.9052 0.9777 0.6172 ] Network output: [ -0.04369 0.2762 1.082 0.0002423 -0.0001088 0.7301 0.0001826 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1351 0.1271 0.1864 0.1264 0.99 0.994 0.1352 0.9654 0.9815 0.2046 ] Network output: [ -0.03484 0.1283 1.084 0.0004297 -0.0001929 0.8586 0.0003238 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1523 0.1507 0.1957 0.1581 0.9856 0.9916 0.1523 0.9453 0.9734 0.201 ] Network output: [ 0.009689 0.8901 0.01824 9.848e-05 -4.421e-05 1.073 7.422e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07578 Epoch 4455 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06099 0.7842 0.9459 -3.335e-05 1.497e-05 0.1478 -2.513e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004619 -0.004454 -0.01549 0.008949 0.964 0.9695 0.01003 0.9144 0.9248 0.03298 ] Network output: [ 1.026 -0.1569 0.04754 0.0003396 -0.0001525 0.05809 0.0002559 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2907 -0.02007 -0.1556 0.2038 0.983 0.993 0.3327 0.8984 0.9751 0.6234 ] Network output: [ 0.0195 0.8286 0.9727 -0.0001261 5.659e-05 0.1592 -9.501e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006987 0.002444 0.005659 0.005846 0.9906 0.9937 0.007139 0.9679 0.9813 0.01418 ] Network output: [ 0.07016 -0.4498 1.004 8.593e-07 -3.858e-07 1.305 6.476e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3216 0.2183 0.3932 0.2672 0.9846 0.9939 0.3228 0.905 0.9777 0.6115 ] Network output: [ -0.04658 0.2581 1.083 0.0002555 -0.0001147 0.7533 0.0001926 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1396 0.1315 0.1915 0.1484 0.9899 0.994 0.1397 0.9656 0.9818 0.2094 ] Network output: [ -0.04185 0.2084 1.055 0.0003412 -0.0001532 0.8219 0.0002571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1577 0.1561 0.1949 0.1597 0.9857 0.9917 0.1578 0.9459 0.9735 0.1998 ] Network output: [ 0.003778 0.9956 -0.02367 -7.956e-06 3.572e-06 1.02 -5.996e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09713 Epoch 4456 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04999 0.8465 0.936 -0.0001242 5.576e-05 0.117 -9.36e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004679 -0.004353 -0.01504 0.006821 0.964 0.9695 0.01012 0.914 0.9242 0.03263 ] Network output: [ 0.9145 0.3357 -0.01827 -0.000356 0.0001598 -0.1479 -0.0002683 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2952 -0.005609 -0.1287 0.1036 0.983 0.993 0.3375 0.8983 0.9749 0.6193 ] Network output: [ 0.02104 0.8384 0.9689 -0.0001429 6.414e-05 0.15 -0.0001077 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00725 0.002523 0.006011 0.003041 0.9906 0.9937 0.007406 0.9678 0.981 0.01449 ] Network output: [ -0.009062 0.215 0.8453 -0.0009186 0.0004124 0.9541 -0.0006923 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3312 0.2247 0.4 0.1166 0.9846 0.9939 0.3324 0.905 0.9777 0.6176 ] Network output: [ -0.04356 0.2754 1.082 0.0002428 -0.000109 0.7307 0.000183 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.135 0.127 0.1864 0.1264 0.99 0.994 0.1351 0.9653 0.9814 0.2047 ] Network output: [ -0.03469 0.1276 1.084 0.0004297 -0.0001929 0.8593 0.0003239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1522 0.1506 0.1957 0.1581 0.9856 0.9916 0.1522 0.9453 0.9734 0.2011 ] Network output: [ 0.009812 0.8895 0.01826 9.97e-05 -4.476e-05 1.073 7.514e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07539 Epoch 4457 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06093 0.7846 0.9458 -3.366e-05 1.511e-05 0.1476 -2.536e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004615 -0.00445 -0.01548 0.008937 0.964 0.9695 0.01002 0.9143 0.9247 0.03294 ] Network output: [ 1.026 -0.1552 0.04662 0.0003372 -0.0001514 0.057 0.0002541 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2905 -0.02009 -0.1559 0.2034 0.983 0.9931 0.3324 0.8982 0.9751 0.6238 ] Network output: [ 0.01946 0.8288 0.9727 -0.0001259 5.652e-05 0.1591 -9.488e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006978 0.002436 0.005648 0.005824 0.9906 0.9937 0.007129 0.9678 0.9812 0.01418 ] Network output: [ 0.06987 -0.4477 1.004 -3.279e-06 1.472e-06 1.304 -2.471e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3212 0.2179 0.3933 0.2663 0.9846 0.9939 0.3224 0.9048 0.9776 0.612 ] Network output: [ -0.04659 0.2576 1.083 0.0002552 -0.0001146 0.7537 0.0001923 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1394 0.1314 0.1916 0.1483 0.9899 0.994 0.1395 0.9656 0.9818 0.2095 ] Network output: [ -0.04184 0.2075 1.055 0.0003411 -0.0001531 0.8226 0.0002571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1575 0.156 0.195 0.1597 0.9857 0.9917 0.1576 0.9459 0.9735 0.1999 ] Network output: [ 0.003452 0.997 -0.02368 -9.775e-06 4.388e-06 1.02 -7.367e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09645 Epoch 4458 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04999 0.8464 0.9361 -0.0001235 5.544e-05 0.117 -9.307e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004673 -0.004348 -0.01503 0.006824 0.964 0.9695 0.0101 0.914 0.9242 0.03259 ] Network output: [ 0.9146 0.3345 -0.01763 -0.0003527 0.0001584 -0.1476 -0.0002658 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2948 -0.0057 -0.1289 0.1038 0.983 0.993 0.337 0.8981 0.9749 0.6197 ] Network output: [ 0.02105 0.8385 0.9689 -0.0001423 6.39e-05 0.1499 -0.0001073 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007239 0.002515 0.006003 0.003039 0.9906 0.9937 0.007395 0.9677 0.981 0.01449 ] Network output: [ -0.009039 0.2137 0.8461 -0.000916 0.0004112 0.9545 -0.0006903 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3307 0.2243 0.4 0.1165 0.9846 0.9939 0.332 0.9049 0.9776 0.618 ] Network output: [ -0.04342 0.2746 1.082 0.0002433 -0.0001092 0.7313 0.0001833 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1349 0.1269 0.1865 0.1264 0.99 0.994 0.135 0.9653 0.9814 0.2048 ] Network output: [ -0.03453 0.1268 1.084 0.0004299 -0.000193 0.8599 0.000324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1521 0.1505 0.1958 0.1581 0.9856 0.9916 0.1521 0.9452 0.9733 0.2011 ] Network output: [ 0.009939 0.8889 0.01827 0.0001009 -4.531e-05 1.073 7.606e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07499 Epoch 4459 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06088 0.7851 0.9457 -3.398e-05 1.525e-05 0.1473 -2.561e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004612 -0.004446 -0.01548 0.008924 0.9641 0.9696 0.01001 0.9143 0.9246 0.03291 ] Network output: [ 1.027 -0.1534 0.04567 0.0003347 -0.0001503 0.05583 0.0002523 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2902 -0.02012 -0.1563 0.203 0.983 0.9931 0.3321 0.898 0.9751 0.6242 ] Network output: [ 0.01941 0.8291 0.9727 -0.0001257 5.645e-05 0.1589 -9.476e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006968 0.002429 0.005637 0.005802 0.9906 0.9937 0.00712 0.9678 0.9812 0.01417 ] Network output: [ 0.06956 -0.4455 1.003 -7.63e-06 3.426e-06 1.303 -5.75e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3208 0.2175 0.3933 0.2653 0.9846 0.9939 0.322 0.9046 0.9776 0.6125 ] Network output: [ -0.0466 0.2571 1.083 0.0002549 -0.0001145 0.7541 0.0001921 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1392 0.1312 0.1917 0.1481 0.9899 0.994 0.1393 0.9655 0.9817 0.2097 ] Network output: [ -0.04182 0.2066 1.055 0.0003411 -0.0001532 0.8233 0.0002571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1574 0.1558 0.1951 0.1596 0.9857 0.9917 0.1574 0.9458 0.9734 0.2 ] Network output: [ 0.003114 0.9984 -0.02368 -1.165e-05 5.231e-06 1.019 -8.781e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09573 Epoch 4460 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04999 0.8463 0.9362 -0.0001228 5.511e-05 0.117 -9.252e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004668 -0.004344 -0.01502 0.006828 0.964 0.9695 0.01009 0.914 0.9242 0.03255 ] Network output: [ 0.9148 0.3331 -0.01697 -0.0003493 0.0001568 -0.1472 -0.0002632 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2944 -0.005796 -0.1292 0.1041 0.983 0.993 0.3365 0.8979 0.9748 0.6202 ] Network output: [ 0.02106 0.8385 0.9689 -0.0001418 6.366e-05 0.1499 -0.0001069 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007228 0.002507 0.005995 0.003038 0.9907 0.9937 0.007383 0.9677 0.9809 0.01449 ] Network output: [ -0.00901 0.2124 0.847 -0.0009134 0.0004101 0.955 -0.0006884 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3303 0.2238 0.4001 0.1164 0.9847 0.9939 0.3316 0.9047 0.9776 0.6184 ] Network output: [ -0.04328 0.2737 1.082 0.0002438 -0.0001095 0.7319 0.0001837 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1348 0.1267 0.1866 0.1264 0.99 0.994 0.1348 0.9652 0.9814 0.2049 ] Network output: [ -0.03437 0.126 1.084 0.00043 -0.0001931 0.8606 0.0003241 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.152 0.1504 0.1958 0.1581 0.9856 0.9916 0.152 0.9451 0.9733 0.2012 ] Network output: [ 0.01007 0.8883 0.01827 0.0001022 -4.587e-05 1.074 7.7e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07456 Epoch 4461 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06082 0.7855 0.9456 -3.431e-05 1.54e-05 0.1471 -2.586e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004609 -0.004443 -0.01547 0.008911 0.9641 0.9696 0.009996 0.9143 0.9246 0.03287 ] Network output: [ 1.027 -0.1515 0.04468 0.0003322 -0.0001491 0.05458 0.0002503 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.29 -0.02015 -0.1566 0.2026 0.983 0.9931 0.3318 0.8978 0.975 0.6247 ] Network output: [ 0.01936 0.8293 0.9727 -0.0001256 5.638e-05 0.1587 -9.465e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006959 0.002421 0.005626 0.005779 0.9906 0.9937 0.00711 0.9678 0.9812 0.01417 ] Network output: [ 0.06924 -0.4431 1.002 -1.221e-05 5.483e-06 1.302 -9.204e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3204 0.2171 0.3934 0.2643 0.9846 0.9939 0.3216 0.9044 0.9776 0.613 ] Network output: [ -0.04661 0.2566 1.083 0.0002547 -0.0001143 0.7546 0.000192 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.139 0.131 0.1918 0.148 0.9899 0.994 0.1391 0.9655 0.9817 0.2098 ] Network output: [ -0.04181 0.2057 1.055 0.0003412 -0.0001532 0.824 0.0002571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1572 0.1556 0.1952 0.1595 0.9857 0.9917 0.1572 0.9457 0.9734 0.2002 ] Network output: [ 0.002763 0.9999 -0.02368 -1.359e-05 6.1e-06 1.018 -1.024e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09498 Epoch 4462 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05 0.8462 0.9362 -0.000122 5.478e-05 0.117 -9.196e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004662 -0.004339 -0.01501 0.006832 0.9641 0.9696 0.01007 0.914 0.9241 0.03252 ] Network output: [ 0.9149 0.3317 -0.01629 -0.0003457 0.0001552 -0.1467 -0.0002605 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.294 -0.005898 -0.1295 0.1043 0.983 0.993 0.3361 0.8978 0.9748 0.6207 ] Network output: [ 0.02107 0.8385 0.9689 -0.0001413 6.342e-05 0.1498 -0.0001065 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007216 0.002499 0.005988 0.003036 0.9907 0.9937 0.007372 0.9677 0.9809 0.01449 ] Network output: [ -0.008977 0.2109 0.8478 -0.0009107 0.0004088 0.9555 -0.0006863 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3299 0.2234 0.4002 0.1162 0.9847 0.9939 0.3311 0.9045 0.9776 0.6189 ] Network output: [ -0.04313 0.2729 1.082 0.0002444 -0.0001097 0.7325 0.0001842 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1346 0.1266 0.1866 0.1265 0.99 0.994 0.1347 0.9652 0.9814 0.205 ] Network output: [ -0.03421 0.1252 1.084 0.0004303 -0.0001932 0.8613 0.0003243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1519 0.1503 0.1959 0.1581 0.9856 0.9916 0.1519 0.945 0.9733 0.2013 ] Network output: [ 0.0102 0.8877 0.01826 0.0001034 -4.643e-05 1.074 7.794e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07411 Epoch 4463 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06076 0.786 0.9455 -3.467e-05 1.556e-05 0.1468 -2.612e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004605 -0.004439 -0.01546 0.008898 0.9641 0.9696 0.009986 0.9143 0.9246 0.03283 ] Network output: [ 1.027 -0.1494 0.04366 0.0003295 -0.0001479 0.05323 0.0002483 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2897 -0.02018 -0.157 0.2021 0.983 0.9931 0.3315 0.8976 0.975 0.6252 ] Network output: [ 0.01931 0.8296 0.9727 -0.0001255 5.632e-05 0.1586 -9.454e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00695 0.002413 0.005615 0.005755 0.9906 0.9937 0.007101 0.9678 0.9811 0.01417 ] Network output: [ 0.06889 -0.4405 1.002 -1.705e-05 7.653e-06 1.301 -1.285e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.32 0.2167 0.3934 0.2633 0.9846 0.9939 0.3212 0.9043 0.9775 0.6136 ] Network output: [ -0.04661 0.256 1.083 0.0002545 -0.0001143 0.7551 0.0001918 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1388 0.1308 0.1918 0.1478 0.9899 0.994 0.1389 0.9654 0.9817 0.2099 ] Network output: [ -0.0418 0.2047 1.056 0.0003413 -0.0001532 0.8247 0.0002573 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.157 0.1554 0.1954 0.1595 0.9857 0.9917 0.157 0.9457 0.9734 0.2003 ] Network output: [ 0.002399 1.001 -0.02367 -1.558e-05 6.997e-06 1.017 -1.175e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0942 Epoch 4464 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05 0.8461 0.9363 -0.0001212 5.443e-05 0.117 -9.138e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004657 -0.004334 -0.015 0.006837 0.9641 0.9696 0.01006 0.914 0.9241 0.03248 ] Network output: [ 0.9151 0.3302 -0.0156 -0.000342 0.0001535 -0.1462 -0.0002577 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2936 -0.006006 -0.1297 0.1045 0.983 0.9931 0.3356 0.8976 0.9748 0.6212 ] Network output: [ 0.02108 0.8386 0.9689 -0.0001407 6.318e-05 0.1498 -0.0001061 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007205 0.00249 0.00598 0.003036 0.9907 0.9937 0.00736 0.9676 0.9809 0.01449 ] Network output: [ -0.008936 0.2093 0.8488 -0.0009079 0.0004076 0.9561 -0.0006843 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3294 0.223 0.4003 0.1161 0.9847 0.9939 0.3307 0.9044 0.9775 0.6194 ] Network output: [ -0.04298 0.272 1.082 0.000245 -0.00011 0.7332 0.0001847 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1345 0.1265 0.1867 0.1265 0.99 0.994 0.1346 0.9651 0.9813 0.2051 ] Network output: [ -0.03404 0.1243 1.084 0.0004305 -0.0001933 0.862 0.0003245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1518 0.1502 0.196 0.1581 0.9856 0.9916 0.1518 0.945 0.9732 0.2014 ] Network output: [ 0.01034 0.8871 0.01824 0.0001047 -4.699e-05 1.074 7.889e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07364 Epoch 4465 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0607 0.7866 0.9454 -3.504e-05 1.573e-05 0.1465 -2.64e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004602 -0.004436 -0.01546 0.008884 0.9641 0.9696 0.009975 0.9143 0.9245 0.0328 ] Network output: [ 1.027 -0.1472 0.04259 0.0003267 -0.0001467 0.05179 0.0002462 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2895 -0.02021 -0.1574 0.2016 0.9831 0.9931 0.3311 0.8975 0.9749 0.6257 ] Network output: [ 0.01926 0.8299 0.9727 -0.0001253 5.626e-05 0.1584 -9.444e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006941 0.002405 0.005604 0.005731 0.9906 0.9937 0.007091 0.9677 0.9811 0.01417 ] Network output: [ 0.06853 -0.4378 1.001 -2.215e-05 9.945e-06 1.3 -1.67e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3196 0.2163 0.3935 0.2622 0.9847 0.9939 0.3208 0.9041 0.9775 0.6142 ] Network output: [ -0.04662 0.2555 1.083 0.0002543 -0.0001142 0.7555 0.0001917 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1387 0.1306 0.1919 0.1476 0.9899 0.994 0.1388 0.9654 0.9816 0.2101 ] Network output: [ -0.04178 0.2036 1.056 0.0003416 -0.0001533 0.8255 0.0002574 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1568 0.1552 0.1955 0.1594 0.9857 0.9917 0.1568 0.9456 0.9733 0.2005 ] Network output: [ 0.002022 1.003 -0.02365 -1.764e-05 7.92e-06 1.017 -1.33e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09336 Epoch 4466 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05001 0.846 0.9364 -0.0001205 5.408e-05 0.1171 -9.078e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004651 -0.00433 -0.01499 0.006842 0.9641 0.9696 0.01004 0.914 0.924 0.03245 ] Network output: [ 0.9153 0.3286 -0.01489 -0.0003381 0.0001518 -0.1457 -0.0002548 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2932 -0.006119 -0.13 0.1048 0.983 0.9931 0.3351 0.8974 0.9747 0.6217 ] Network output: [ 0.02109 0.8386 0.9689 -0.0001402 6.294e-05 0.1497 -0.0001057 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007194 0.002481 0.005973 0.003035 0.9907 0.9937 0.007349 0.9676 0.9809 0.01449 ] Network output: [ -0.008889 0.2076 0.8498 -0.0009051 0.0004063 0.9567 -0.0006821 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.329 0.2225 0.4005 0.116 0.9847 0.9939 0.3302 0.9042 0.9775 0.62 ] Network output: [ -0.04282 0.271 1.082 0.0002457 -0.0001103 0.7339 0.0001852 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1344 0.1264 0.1868 0.1265 0.99 0.994 0.1345 0.9651 0.9813 0.2052 ] Network output: [ -0.03387 0.1234 1.083 0.0004309 -0.0001934 0.8627 0.0003247 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1517 0.1501 0.1961 0.1581 0.9856 0.9917 0.1517 0.9449 0.9732 0.2015 ] Network output: [ 0.01048 0.8864 0.01821 0.0001059 -4.756e-05 1.075 7.983e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07313 Epoch 4467 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06064 0.7871 0.9453 -3.543e-05 1.59e-05 0.1462 -2.67e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004599 -0.004433 -0.01545 0.008869 0.9641 0.9696 0.009965 0.9143 0.9245 0.03277 ] Network output: [ 1.027 -0.1449 0.04147 0.0003237 -0.0001453 0.05025 0.000244 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2892 -0.02024 -0.1578 0.2011 0.9831 0.9931 0.3308 0.8973 0.9749 0.6263 ] Network output: [ 0.0192 0.8301 0.9727 -0.0001252 5.62e-05 0.1582 -9.435e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006932 0.002396 0.005593 0.005705 0.9906 0.9937 0.007082 0.9677 0.9811 0.01417 ] Network output: [ 0.06814 -0.4349 1 -2.756e-05 1.237e-05 1.298 -2.077e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3192 0.2159 0.3936 0.261 0.9847 0.9939 0.3204 0.9039 0.9775 0.6149 ] Network output: [ -0.04663 0.2549 1.083 0.0002542 -0.0001141 0.756 0.0001915 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1385 0.1304 0.192 0.1475 0.99 0.994 0.1386 0.9653 0.9816 0.2103 ] Network output: [ -0.04177 0.2025 1.056 0.0003418 -0.0001535 0.8264 0.0002576 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1567 0.1551 0.1957 0.1593 0.9857 0.9917 0.1567 0.9455 0.9733 0.2007 ] Network output: [ 0.001631 1.005 -0.02363 -1.976e-05 8.87e-06 1.016 -1.489e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09248 Epoch 4468 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05002 0.8459 0.9365 -0.0001196 5.371e-05 0.1171 -9.016e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004646 -0.004326 -0.01498 0.006848 0.9641 0.9696 0.01003 0.914 0.924 0.03241 ] Network output: [ 0.9156 0.3268 -0.01417 -0.000334 0.0001499 -0.1451 -0.0002517 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2927 -0.00624 -0.1304 0.1051 0.983 0.9931 0.3345 0.8973 0.9747 0.6223 ] Network output: [ 0.0211 0.8387 0.9689 -0.0001397 6.27e-05 0.1496 -0.0001052 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007183 0.002472 0.005967 0.003035 0.9907 0.9937 0.007337 0.9676 0.9808 0.01449 ] Network output: [ -0.008833 0.2058 0.8508 -0.0009021 0.000405 0.9574 -0.0006799 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3285 0.222 0.4006 0.116 0.9847 0.9939 0.3298 0.9041 0.9775 0.6206 ] Network output: [ -0.04266 0.27 1.082 0.0002465 -0.0001106 0.7346 0.0001857 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1343 0.1263 0.1869 0.1266 0.99 0.994 0.1344 0.965 0.9813 0.2053 ] Network output: [ -0.03369 0.1225 1.083 0.0004313 -0.0001936 0.8635 0.000325 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1516 0.15 0.1962 0.1582 0.9856 0.9917 0.1517 0.9449 0.9731 0.2016 ] Network output: [ 0.01062 0.8858 0.01817 0.0001072 -4.812e-05 1.075 8.078e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0726 Epoch 4469 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06057 0.7877 0.9452 -3.584e-05 1.609e-05 0.1458 -2.701e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004595 -0.004429 -0.01545 0.008854 0.9641 0.9696 0.009955 0.9143 0.9244 0.03274 ] Network output: [ 1.027 -0.1423 0.04032 0.0003207 -0.000144 0.0486 0.0002417 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.289 -0.02027 -0.1583 0.2005 0.9831 0.9931 0.3305 0.8971 0.9749 0.6269 ] Network output: [ 0.01915 0.8304 0.9727 -0.0001251 5.615e-05 0.1581 -9.426e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006923 0.002388 0.005582 0.005678 0.9907 0.9937 0.007073 0.9677 0.9811 0.01417 ] Network output: [ 0.06772 -0.4318 0.9998 -3.328e-05 1.494e-05 1.296 -2.508e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3188 0.2154 0.3937 0.2598 0.9847 0.9939 0.32 0.9038 0.9774 0.6156 ] Network output: [ -0.04663 0.2543 1.083 0.000254 -0.000114 0.7566 0.0001914 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1383 0.1303 0.1921 0.1473 0.99 0.994 0.1384 0.9653 0.9816 0.2104 ] Network output: [ -0.04175 0.2013 1.056 0.0003422 -0.0001536 0.8273 0.0002579 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1565 0.1549 0.1959 0.1593 0.9857 0.9917 0.1565 0.9455 0.9732 0.2009 ] Network output: [ 0.001226 1.006 -0.02361 -2.193e-05 9.847e-06 1.015 -1.653e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09156 Epoch 4470 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05004 0.8457 0.9366 -0.0001188 5.332e-05 0.1172 -8.951e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00464 -0.004322 -0.01498 0.006854 0.9641 0.9696 0.01001 0.914 0.924 0.03238 ] Network output: [ 0.9158 0.3249 -0.01342 -0.0003297 0.000148 -0.1445 -0.0002485 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2923 -0.006369 -0.1307 0.1054 0.9831 0.9931 0.334 0.8971 0.9747 0.623 ] Network output: [ 0.02111 0.8387 0.9689 -0.0001391 6.245e-05 0.1496 -0.0001048 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007172 0.002463 0.00596 0.003036 0.9907 0.9937 0.007326 0.9676 0.9808 0.0145 ] Network output: [ -0.008768 0.2037 0.8519 -0.000899 0.0004036 0.9583 -0.0006776 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.328 0.2216 0.4008 0.1159 0.9847 0.9939 0.3293 0.9039 0.9774 0.6212 ] Network output: [ -0.04249 0.269 1.082 0.0002473 -0.000111 0.7354 0.0001863 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1343 0.1262 0.187 0.1266 0.99 0.994 0.1344 0.965 0.9812 0.2055 ] Network output: [ -0.03351 0.1216 1.083 0.0004317 -0.0001938 0.8643 0.0003254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1516 0.15 0.1963 0.1582 0.9856 0.9917 0.1516 0.9448 0.9731 0.2018 ] Network output: [ 0.01076 0.8853 0.01811 0.0001084 -4.868e-05 1.076 8.172e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07203 Epoch 4471 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0605 0.7883 0.9451 -3.627e-05 1.628e-05 0.1455 -2.734e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004592 -0.004426 -0.01545 0.008838 0.9641 0.9696 0.009945 0.9143 0.9244 0.03271 ] Network output: [ 1.027 -0.1396 0.03911 0.0003174 -0.0001425 0.04683 0.0002392 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2887 -0.02031 -0.1587 0.1999 0.9831 0.9931 0.3302 0.897 0.9748 0.6275 ] Network output: [ 0.01909 0.8307 0.9728 -0.000125 5.611e-05 0.1579 -9.418e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006914 0.002379 0.005571 0.00565 0.9907 0.9937 0.007064 0.9677 0.981 0.01417 ] Network output: [ 0.06728 -0.4285 0.9991 -3.935e-05 1.767e-05 1.295 -2.966e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3184 0.215 0.3938 0.2585 0.9847 0.9939 0.3196 0.9037 0.9774 0.6164 ] Network output: [ -0.04663 0.2536 1.084 0.0002539 -0.000114 0.7571 0.0001914 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1382 0.1301 0.1923 0.1471 0.99 0.994 0.1383 0.9653 0.9815 0.2106 ] Network output: [ -0.04173 0.2001 1.057 0.0003426 -0.0001538 0.8282 0.0002582 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1563 0.1547 0.1961 0.1592 0.9857 0.9917 0.1564 0.9454 0.9732 0.2011 ] Network output: [ 0.0008068 1.008 -0.02357 -2.416e-05 1.085e-05 1.014 -1.821e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09058 Epoch 4472 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05005 0.8456 0.9367 -0.0001179 5.293e-05 0.1172 -8.885e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004634 -0.004317 -0.01497 0.006861 0.9641 0.9696 0.009998 0.914 0.9239 0.03235 ] Network output: [ 0.9161 0.3229 -0.01266 -0.0003252 0.000146 -0.1438 -0.0002451 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2918 -0.006505 -0.1311 0.1057 0.9831 0.9931 0.3335 0.897 0.9747 0.6237 ] Network output: [ 0.02111 0.8387 0.969 -0.0001386 6.221e-05 0.1495 -0.0001044 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00716 0.002453 0.005954 0.003037 0.9907 0.9937 0.007314 0.9676 0.9808 0.0145 ] Network output: [ -0.008692 0.2015 0.8531 -0.0008958 0.0004022 0.9592 -0.0006751 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3275 0.2211 0.401 0.1159 0.9847 0.9939 0.3288 0.9038 0.9774 0.6219 ] Network output: [ -0.04232 0.2679 1.081 0.0002481 -0.0001114 0.7362 0.000187 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1342 0.1261 0.1871 0.1267 0.9901 0.994 0.1343 0.965 0.9812 0.2057 ] Network output: [ -0.03332 0.1206 1.083 0.0004322 -0.000194 0.8651 0.0003257 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1515 0.1499 0.1964 0.1583 0.9856 0.9917 0.1516 0.9448 0.9731 0.2019 ] Network output: [ 0.0109 0.8847 0.01803 0.0001097 -4.923e-05 1.076 8.264e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07143 Epoch 4473 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06043 0.7889 0.945 -3.673e-05 1.649e-05 0.1451 -2.768e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004589 -0.004423 -0.01544 0.008821 0.9642 0.9696 0.009936 0.9143 0.9244 0.03268 ] Network output: [ 1.028 -0.1366 0.03785 0.000314 -0.000141 0.04493 0.0002366 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2885 -0.02034 -0.1592 0.1993 0.9831 0.9931 0.3299 0.8968 0.9748 0.6282 ] Network output: [ 0.01903 0.831 0.9728 -0.0001249 5.606e-05 0.1577 -9.411e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006906 0.00237 0.005561 0.005621 0.9907 0.9937 0.007055 0.9677 0.981 0.01417 ] Network output: [ 0.06681 -0.4249 0.9984 -4.581e-05 2.056e-05 1.293 -3.452e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3179 0.2145 0.394 0.2572 0.9847 0.9939 0.3191 0.9035 0.9774 0.6172 ] Network output: [ -0.04663 0.253 1.084 0.0002539 -0.000114 0.7577 0.0001913 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.138 0.1299 0.1924 0.1469 0.99 0.994 0.1381 0.9652 0.9815 0.2108 ] Network output: [ -0.04171 0.1988 1.057 0.0003431 -0.0001541 0.8293 0.0002586 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1562 0.1546 0.1963 0.1592 0.9857 0.9917 0.1562 0.9454 0.9732 0.2013 ] Network output: [ 0.000373 1.009 -0.02352 -2.645e-05 1.187e-05 1.013 -1.993e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08954 Epoch 4474 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05007 0.8454 0.9368 -0.000117 5.251e-05 0.1172 -8.816e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004629 -0.004313 -0.01497 0.006868 0.9642 0.9696 0.009984 0.914 0.9239 0.03233 ] Network output: [ 0.9164 0.3208 -0.01188 -0.0003204 0.0001439 -0.1431 -0.0002415 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2914 -0.00665 -0.1314 0.1061 0.9831 0.9931 0.333 0.8968 0.9746 0.6244 ] Network output: [ 0.02112 0.8388 0.969 -0.000138 6.196e-05 0.1495 -0.000104 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007149 0.002444 0.005949 0.003039 0.9907 0.9937 0.007303 0.9675 0.9808 0.01451 ] Network output: [ -0.008605 0.1991 0.8543 -0.0008925 0.0004007 0.9602 -0.0006726 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3271 0.2206 0.4013 0.1159 0.9847 0.9939 0.3283 0.9036 0.9774 0.6226 ] Network output: [ -0.04214 0.2668 1.081 0.0002491 -0.0001118 0.7371 0.0001877 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1341 0.126 0.1872 0.1268 0.9901 0.994 0.1342 0.9649 0.9812 0.2059 ] Network output: [ -0.03312 0.1196 1.082 0.0004328 -0.0001943 0.866 0.0003262 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1515 0.1499 0.1966 0.1583 0.9856 0.9917 0.1515 0.9447 0.9731 0.202 ] Network output: [ 0.01104 0.8842 0.01794 0.0001109 -4.977e-05 1.076 8.355e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07079 Epoch 4475 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06035 0.7895 0.9449 -3.722e-05 1.671e-05 0.1447 -2.805e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004586 -0.00442 -0.01544 0.008803 0.9642 0.9697 0.009927 0.9143 0.9243 0.03266 ] Network output: [ 1.028 -0.1335 0.03653 0.0003104 -0.0001393 0.04289 0.0002339 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2882 -0.02037 -0.1597 0.1986 0.9831 0.9931 0.3295 0.8967 0.9748 0.629 ] Network output: [ 0.01896 0.8313 0.9728 -0.0001248 5.603e-05 0.1575 -9.405e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006897 0.00236 0.00555 0.00559 0.9907 0.9937 0.007046 0.9676 0.981 0.01418 ] Network output: [ 0.0663 -0.4211 0.9976 -5.266e-05 2.364e-05 1.291 -3.969e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3175 0.214 0.3941 0.2558 0.9847 0.9939 0.3187 0.9034 0.9774 0.6181 ] Network output: [ -0.04663 0.2523 1.084 0.0002539 -0.000114 0.7583 0.0001913 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1379 0.1298 0.1925 0.1467 0.99 0.994 0.138 0.9652 0.9815 0.211 ] Network output: [ -0.04169 0.1973 1.057 0.0003438 -0.0001543 0.8303 0.0002591 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.156 0.1544 0.1965 0.1591 0.9857 0.9917 0.1561 0.9453 0.9732 0.2016 ] Network output: [ -7.495e-05 1.011 -0.02346 -2.877e-05 1.292e-05 1.012 -2.168e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08844 Epoch 4476 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0501 0.8452 0.9369 -0.000116 5.209e-05 0.1173 -8.744e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004623 -0.00431 -0.01497 0.006876 0.9642 0.9696 0.00997 0.914 0.9239 0.0323 ] Network output: [ 0.9168 0.3184 -0.01107 -0.0003154 0.0001416 -0.1423 -0.0002377 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2909 -0.006804 -0.1318 0.1064 0.9831 0.9931 0.3324 0.8967 0.9746 0.6252 ] Network output: [ 0.02112 0.8388 0.969 -0.0001375 6.171e-05 0.1494 -0.0001036 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007138 0.002433 0.005943 0.003041 0.9907 0.9937 0.007291 0.9675 0.9808 0.01452 ] Network output: [ -0.008504 0.1965 0.8556 -0.000889 0.0003991 0.9613 -0.00067 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3266 0.22 0.4015 0.1159 0.9847 0.9939 0.3278 0.9035 0.9774 0.6234 ] Network output: [ -0.04195 0.2657 1.081 0.00025 -0.0001123 0.738 0.0001884 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1341 0.126 0.1874 0.1268 0.9901 0.994 0.1342 0.9649 0.9812 0.2061 ] Network output: [ -0.03293 0.1186 1.082 0.0004334 -0.0001946 0.8669 0.0003267 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1515 0.1498 0.1967 0.1584 0.9856 0.9917 0.1515 0.9447 0.973 0.2022 ] Network output: [ 0.01118 0.8837 0.01783 0.000112 -5.03e-05 1.077 8.443e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0701 Epoch 4477 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06027 0.7902 0.9448 -3.774e-05 1.694e-05 0.1443 -2.844e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004583 -0.004417 -0.01544 0.008785 0.9642 0.9697 0.009918 0.9143 0.9243 0.03263 ] Network output: [ 1.028 -0.1301 0.03516 0.0003066 -0.0001376 0.0407 0.0002311 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2879 -0.02041 -0.1603 0.1978 0.9831 0.9931 0.3292 0.8966 0.9748 0.6298 ] Network output: [ 0.0189 0.8316 0.9728 -0.0001247 5.599e-05 0.1573 -9.4e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006889 0.002351 0.005539 0.005558 0.9907 0.9937 0.007038 0.9676 0.981 0.01418 ] Network output: [ 0.06576 -0.417 0.9968 -5.996e-05 2.692e-05 1.288 -4.519e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3171 0.2135 0.3943 0.2543 0.9847 0.9939 0.3183 0.9033 0.9773 0.619 ] Network output: [ -0.04662 0.2516 1.084 0.0002539 -0.000114 0.7589 0.0001914 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1377 0.1296 0.1927 0.1465 0.99 0.994 0.1378 0.9652 0.9815 0.2113 ] Network output: [ -0.04166 0.1958 1.057 0.0003445 -0.0001547 0.8315 0.0002596 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1559 0.1543 0.1967 0.1591 0.9857 0.9917 0.1559 0.9453 0.9731 0.2019 ] Network output: [ -0.0005368 1.013 -0.02339 -3.114e-05 1.398e-05 1.011 -2.347e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08728 Epoch 4478 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05012 0.845 0.937 -0.000115 5.164e-05 0.1174 -8.669e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004617 -0.004306 -0.01497 0.006885 0.9642 0.9697 0.009956 0.914 0.9239 0.03228 ] Network output: [ 0.9172 0.3159 -0.01025 -0.0003101 0.0001392 -0.1414 -0.0002337 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2905 -0.006968 -0.1323 0.1068 0.9831 0.9931 0.3319 0.8966 0.9746 0.626 ] Network output: [ 0.02112 0.8389 0.969 -0.0001369 6.146e-05 0.1493 -0.0001032 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007126 0.002423 0.005938 0.003044 0.9907 0.9937 0.007279 0.9675 0.9807 0.01452 ] Network output: [ -0.008388 0.1936 0.857 -0.0008853 0.0003974 0.9626 -0.0006672 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.326 0.2195 0.4018 0.116 0.9847 0.9939 0.3273 0.9034 0.9774 0.6243 ] Network output: [ -0.04176 0.2644 1.081 0.0002511 -0.0001127 0.7389 0.0001892 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.134 0.1259 0.1875 0.1269 0.9901 0.994 0.1341 0.9649 0.9812 0.2063 ] Network output: [ -0.03272 0.1175 1.082 0.0004341 -0.0001949 0.8678 0.0003272 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1515 0.1498 0.1969 0.1584 0.9856 0.9917 0.1515 0.9447 0.973 0.2024 ] Network output: [ 0.01132 0.8832 0.01769 0.0001132 -5.08e-05 1.077 8.528e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06938 Epoch 4479 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06018 0.791 0.9446 -3.829e-05 1.719e-05 0.1439 -2.885e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00458 -0.004415 -0.01545 0.008766 0.9642 0.9697 0.009909 0.9143 0.9243 0.03261 ] Network output: [ 1.028 -0.1265 0.03374 0.0003026 -0.0001358 0.03836 0.000228 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2877 -0.02044 -0.1608 0.197 0.9831 0.9931 0.3289 0.8965 0.9747 0.6307 ] Network output: [ 0.01883 0.8319 0.9729 -0.0001247 5.597e-05 0.1571 -9.395e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006881 0.00234 0.005529 0.005524 0.9907 0.9937 0.007029 0.9676 0.981 0.01419 ] Network output: [ 0.06518 -0.4126 0.9959 -6.773e-05 3.041e-05 1.286 -5.104e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3166 0.213 0.3945 0.2527 0.9847 0.9939 0.3179 0.9032 0.9773 0.62 ] Network output: [ -0.04661 0.2508 1.084 0.000254 -0.000114 0.7596 0.0001914 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1376 0.1295 0.1929 0.1463 0.99 0.994 0.1377 0.9651 0.9815 0.2115 ] Network output: [ -0.04163 0.1942 1.058 0.0003454 -0.000155 0.8327 0.0002603 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1558 0.1542 0.197 0.1591 0.9857 0.9917 0.1558 0.9453 0.9731 0.2021 ] Network output: [ -0.001012 1.015 -0.02329 -3.354e-05 1.506e-05 1.011 -2.527e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08606 Epoch 4480 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05015 0.8447 0.9371 -0.000114 5.118e-05 0.1174 -8.592e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004612 -0.004302 -0.01497 0.006895 0.9642 0.9697 0.009942 0.9141 0.9238 0.03226 ] Network output: [ 0.9177 0.3132 -0.009405 -0.0003045 0.0001367 -0.1405 -0.0002295 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.29 -0.007143 -0.1327 0.1073 0.9831 0.9931 0.3313 0.8965 0.9746 0.6269 ] Network output: [ 0.02112 0.8389 0.969 -0.0001363 6.121e-05 0.1493 -0.0001027 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007115 0.002412 0.005933 0.003048 0.9907 0.9937 0.007268 0.9675 0.9807 0.01454 ] Network output: [ -0.008256 0.1905 0.8584 -0.0008814 0.0003957 0.964 -0.0006642 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3255 0.2189 0.4021 0.1161 0.9847 0.9939 0.3267 0.9033 0.9773 0.6252 ] Network output: [ -0.04157 0.2632 1.081 0.0002522 -0.0001132 0.7399 0.0001901 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.134 0.1259 0.1877 0.127 0.9901 0.994 0.1341 0.9649 0.9812 0.2065 ] Network output: [ -0.03252 0.1164 1.082 0.0004349 -0.0001952 0.8688 0.0003278 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1515 0.1498 0.1971 0.1585 0.9856 0.9917 0.1515 0.9446 0.973 0.2026 ] Network output: [ 0.01145 0.8828 0.01753 0.0001142 -5.129e-05 1.077 8.609e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06861 Epoch 4481 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06009 0.7917 0.9445 -3.887e-05 1.745e-05 0.1434 -2.929e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004577 -0.004412 -0.01545 0.008746 0.9642 0.9697 0.009901 0.9143 0.9243 0.0326 ] Network output: [ 1.028 -0.1226 0.03225 0.0002983 -0.0001339 0.03585 0.0002248 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2874 -0.02047 -0.1614 0.1961 0.9831 0.9931 0.3286 0.8964 0.9747 0.6316 ] Network output: [ 0.01876 0.8322 0.9729 -0.0001246 5.594e-05 0.1568 -9.391e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006873 0.00233 0.005519 0.005489 0.9907 0.9937 0.007021 0.9676 0.9809 0.0142 ] Network output: [ 0.06456 -0.4079 0.995 -7.6e-05 3.412e-05 1.283 -5.728e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3162 0.2125 0.3948 0.251 0.9847 0.9939 0.3174 0.9031 0.9773 0.6211 ] Network output: [ -0.0466 0.25 1.084 0.0002542 -0.0001141 0.7602 0.0001916 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1375 0.1293 0.193 0.1461 0.99 0.9941 0.1376 0.9651 0.9814 0.2118 ] Network output: [ -0.04159 0.1925 1.058 0.0003463 -0.0001555 0.834 0.000261 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1557 0.1541 0.1973 0.159 0.9857 0.9917 0.1557 0.9452 0.9731 0.2024 ] Network output: [ -0.001499 1.016 -0.02318 -3.595e-05 1.614e-05 1.01 -2.709e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08477 Epoch 4482 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05019 0.8445 0.9372 -0.0001129 5.07e-05 0.1175 -8.512e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004606 -0.004299 -0.01497 0.006906 0.9642 0.9697 0.009928 0.9141 0.9238 0.03225 ] Network output: [ 0.9182 0.3103 -0.008542 -0.0002986 0.0001341 -0.1395 -0.0002251 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2895 -0.00733 -0.1332 0.1078 0.9831 0.9931 0.3307 0.8964 0.9745 0.6279 ] Network output: [ 0.02111 0.8389 0.9691 -0.0001358 6.096e-05 0.1492 -0.0001023 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007104 0.002401 0.005929 0.003053 0.9907 0.9937 0.007256 0.9675 0.9807 0.01455 ] Network output: [ -0.008106 0.1871 0.86 -0.0008773 0.0003938 0.9656 -0.0006611 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.325 0.2183 0.4024 0.1163 0.9847 0.9939 0.3262 0.9032 0.9773 0.6261 ] Network output: [ -0.04137 0.2618 1.081 0.0002535 -0.0001138 0.7409 0.000191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.134 0.1258 0.1879 0.1272 0.9901 0.9941 0.1341 0.9648 0.9811 0.2068 ] Network output: [ -0.0323 0.1153 1.081 0.0004357 -0.0001956 0.8698 0.0003284 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1515 0.1498 0.1973 0.1586 0.9856 0.9917 0.1515 0.9446 0.973 0.2028 ] Network output: [ 0.01158 0.8825 0.01734 0.0001152 -5.174e-05 1.077 8.685e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06779 Epoch 4483 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.06 0.7926 0.9444 -3.948e-05 1.773e-05 0.1429 -2.976e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004574 -0.004409 -0.01545 0.008724 0.9642 0.9697 0.009893 0.9144 0.9243 0.03258 ] Network output: [ 1.028 -0.1184 0.0307 0.0002937 -0.0001319 0.03317 0.0002214 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2871 -0.02051 -0.162 0.1952 0.9831 0.9931 0.3282 0.8963 0.9747 0.6325 ] Network output: [ 0.01868 0.8326 0.9729 -0.0001246 5.593e-05 0.1566 -9.389e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006865 0.002319 0.005509 0.005452 0.9907 0.9937 0.007013 0.9676 0.9809 0.01421 ] Network output: [ 0.0639 -0.4029 0.994 -8.481e-05 3.807e-05 1.281 -6.391e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3158 0.2119 0.395 0.2492 0.9847 0.9939 0.317 0.903 0.9773 0.6222 ] Network output: [ -0.04658 0.2492 1.084 0.0002544 -0.0001142 0.761 0.0001917 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1373 0.1292 0.1932 0.1458 0.99 0.9941 0.1374 0.9651 0.9814 0.2121 ] Network output: [ -0.04155 0.1907 1.058 0.0003475 -0.000156 0.8354 0.0002619 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1556 0.1539 0.1976 0.159 0.9857 0.9917 0.1556 0.9452 0.9731 0.2028 ] Network output: [ -0.001997 1.018 -0.02305 -3.836e-05 1.722e-05 1.009 -2.891e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0834 Epoch 4484 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05022 0.8442 0.9373 -0.0001118 5.021e-05 0.1176 -8.428e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0046 -0.004296 -0.01497 0.006918 0.9642 0.9697 0.009915 0.9141 0.9238 0.03223 ] Network output: [ 0.9188 0.3072 -0.007661 -0.0002924 0.0001313 -0.1384 -0.0002204 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.289 -0.007528 -0.1337 0.1083 0.9831 0.9931 0.3301 0.8963 0.9745 0.629 ] Network output: [ 0.0211 0.839 0.9691 -0.0001352 6.071e-05 0.1492 -0.0001019 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007092 0.00239 0.005925 0.003059 0.9907 0.9937 0.007244 0.9675 0.9807 0.01456 ] Network output: [ -0.007936 0.1834 0.8616 -0.0008729 0.0003919 0.9673 -0.0006579 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3244 0.2177 0.4028 0.1165 0.9847 0.9939 0.3256 0.9031 0.9773 0.6271 ] Network output: [ -0.04116 0.2604 1.081 0.0002547 -0.0001144 0.742 0.000192 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.134 0.1258 0.1881 0.1273 0.9901 0.9941 0.1341 0.9648 0.9811 0.207 ] Network output: [ -0.03209 0.1141 1.081 0.0004366 -0.000196 0.8708 0.0003291 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1515 0.1499 0.1975 0.1587 0.9856 0.9917 0.1515 0.9446 0.973 0.203 ] Network output: [ 0.01171 0.8822 0.01712 0.0001162 -5.215e-05 1.078 8.754e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06692 Epoch 4485 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05989 0.7934 0.9442 -4.014e-05 1.802e-05 0.1424 -3.025e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004571 -0.004407 -0.01546 0.008702 0.9643 0.9697 0.009885 0.9144 0.9243 0.03257 ] Network output: [ 1.028 -0.1139 0.02909 0.0002889 -0.0001297 0.03031 0.0002177 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2868 -0.02054 -0.1626 0.1942 0.9831 0.9931 0.3279 0.8962 0.9747 0.6336 ] Network output: [ 0.01861 0.8329 0.973 -0.0001246 5.592e-05 0.1564 -9.388e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006857 0.002308 0.005499 0.005413 0.9907 0.9937 0.007005 0.9676 0.9809 0.01422 ] Network output: [ 0.06319 -0.3974 0.993 -9.418e-05 4.228e-05 1.278 -7.098e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3153 0.2114 0.3953 0.2473 0.9847 0.9939 0.3165 0.9029 0.9773 0.6235 ] Network output: [ -0.04656 0.2483 1.084 0.0002547 -0.0001143 0.7617 0.0001919 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1372 0.1291 0.1934 0.1456 0.99 0.9941 0.1373 0.9651 0.9814 0.2124 ] Network output: [ -0.0415 0.1888 1.059 0.0003487 -0.0001566 0.8368 0.0002628 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1555 0.1538 0.1979 0.159 0.9857 0.9917 0.1555 0.9452 0.973 0.2031 ] Network output: [ -0.002503 1.02 -0.0229 -4.075e-05 1.829e-05 1.008 -3.071e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08197 Epoch 4486 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05027 0.8439 0.9374 -0.0001107 4.969e-05 0.1177 -8.342e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004594 -0.004293 -0.01498 0.00693 0.9642 0.9697 0.009902 0.9142 0.9238 0.03222 ] Network output: [ 0.9195 0.3038 -0.006762 -0.0002858 0.0001283 -0.1372 -0.0002154 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2885 -0.00774 -0.1343 0.1089 0.9831 0.9931 0.3295 0.8962 0.9745 0.6301 ] Network output: [ 0.02109 0.839 0.9691 -0.0001347 6.046e-05 0.1491 -0.0001015 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007081 0.002378 0.005921 0.003066 0.9907 0.9937 0.007232 0.9675 0.9807 0.01458 ] Network output: [ -0.007745 0.1794 0.8634 -0.0008683 0.0003898 0.9692 -0.0006544 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3239 0.2171 0.4032 0.1167 0.9847 0.9939 0.3251 0.903 0.9773 0.6282 ] Network output: [ -0.04095 0.259 1.081 0.0002561 -0.000115 0.7432 0.000193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.134 0.1258 0.1883 0.1274 0.9901 0.9941 0.1341 0.9648 0.9811 0.2073 ] Network output: [ -0.03187 0.113 1.081 0.0004376 -0.0001965 0.8719 0.0003298 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1516 0.1499 0.1977 0.1588 0.9857 0.9917 0.1516 0.9446 0.973 0.2033 ] Network output: [ 0.01182 0.8821 0.01687 0.000117 -5.252e-05 1.078 8.816e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.066 Epoch 4487 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05979 0.7943 0.9441 -4.084e-05 1.833e-05 0.1418 -3.078e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004569 -0.004405 -0.01547 0.008679 0.9643 0.9697 0.009877 0.9145 0.9242 0.03256 ] Network output: [ 1.028 -0.109 0.02742 0.0002838 -0.0001274 0.02726 0.0002139 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2865 -0.02057 -0.1632 0.1932 0.9831 0.9931 0.3275 0.8961 0.9747 0.6347 ] Network output: [ 0.01853 0.8333 0.973 -0.0001246 5.592e-05 0.1561 -9.387e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00685 0.002297 0.005489 0.005372 0.9907 0.9938 0.006997 0.9677 0.9809 0.01424 ] Network output: [ 0.06243 -0.3916 0.992 -0.0001041 4.675e-05 1.274 -7.848e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3149 0.2108 0.3956 0.2453 0.9847 0.9939 0.3161 0.9028 0.9773 0.6248 ] Network output: [ -0.04652 0.2474 1.084 0.000255 -0.0001145 0.7625 0.0001922 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1371 0.1289 0.1936 0.1454 0.99 0.9941 0.1372 0.9651 0.9814 0.2127 ] Network output: [ -0.04144 0.1867 1.059 0.0003501 -0.0001572 0.8384 0.0002639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1554 0.1538 0.1982 0.159 0.9858 0.9918 0.1554 0.9452 0.973 0.2035 ] Network output: [ -0.003016 1.021 -0.02272 -4.31e-05 1.935e-05 1.007 -3.248e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08047 Epoch 4488 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05031 0.8436 0.9376 -0.0001095 4.916e-05 0.1178 -8.253e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004589 -0.00429 -0.01498 0.006944 0.9643 0.9697 0.009889 0.9142 0.9239 0.03222 ] Network output: [ 0.9202 0.3002 -0.005848 -0.0002788 0.0001252 -0.1359 -0.0002101 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2879 -0.007966 -0.1349 0.1095 0.9831 0.9931 0.3289 0.8962 0.9745 0.6312 ] Network output: [ 0.02106 0.8391 0.9692 -0.0001341 6.021e-05 0.149 -0.0001011 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007069 0.002366 0.005918 0.003073 0.9907 0.9937 0.007221 0.9676 0.9807 0.01459 ] Network output: [ -0.007531 0.175 0.8652 -0.0008634 0.0003876 0.9713 -0.0006507 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3233 0.2164 0.4036 0.117 0.9847 0.9939 0.3245 0.903 0.9773 0.6294 ] Network output: [ -0.04073 0.2575 1.081 0.0002575 -0.0001156 0.7444 0.0001941 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.134 0.1258 0.1886 0.1276 0.9901 0.9941 0.1341 0.9648 0.9811 0.2077 ] Network output: [ -0.03164 0.1117 1.08 0.0004386 -0.0001969 0.873 0.0003306 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1516 0.15 0.198 0.1589 0.9857 0.9917 0.1517 0.9446 0.9729 0.2036 ] Network output: [ 0.01192 0.882 0.01658 0.0001177 -5.283e-05 1.078 8.869e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06502 Epoch 4489 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05967 0.7953 0.944 -4.158e-05 1.867e-05 0.1413 -3.134e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004566 -0.004403 -0.01547 0.008654 0.9643 0.9697 0.00987 0.9145 0.9243 0.03255 ] Network output: [ 1.028 -0.1039 0.02569 0.0002784 -0.000125 0.02403 0.0002098 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2862 -0.02061 -0.1639 0.192 0.9831 0.9931 0.3272 0.8961 0.9746 0.6359 ] Network output: [ 0.01844 0.8337 0.973 -0.0001246 5.593e-05 0.1559 -9.388e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006843 0.002285 0.00548 0.00533 0.9907 0.9938 0.00699 0.9677 0.9809 0.01425 ] Network output: [ 0.06161 -0.3855 0.9909 -0.0001147 5.15e-05 1.271 -8.645e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3144 0.2102 0.3959 0.2432 0.9847 0.9939 0.3156 0.9028 0.9772 0.6261 ] Network output: [ -0.04648 0.2464 1.084 0.0002554 -0.0001147 0.7633 0.0001925 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.137 0.1288 0.1939 0.1451 0.99 0.9941 0.1371 0.9651 0.9814 0.213 ] Network output: [ -0.04137 0.1846 1.06 0.0003517 -0.0001579 0.84 0.0002651 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1553 0.1537 0.1986 0.159 0.9858 0.9918 0.1553 0.9452 0.973 0.2039 ] Network output: [ -0.003531 1.023 -0.02251 -4.539e-05 2.038e-05 1.007 -3.421e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07889 Epoch 4490 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05036 0.8433 0.9377 -0.0001083 4.862e-05 0.1179 -8.161e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004583 -0.004288 -0.01499 0.006959 0.9643 0.9697 0.009876 0.9143 0.9239 0.03221 ] Network output: [ 0.921 0.2963 -0.004922 -0.0002714 0.0001219 -0.1345 -0.0002046 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2874 -0.008205 -0.1355 0.1101 0.9831 0.9931 0.3283 0.8961 0.9745 0.6325 ] Network output: [ 0.02104 0.8392 0.9692 -0.0001336 5.997e-05 0.1489 -0.0001007 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007058 0.002353 0.005915 0.003082 0.9907 0.9937 0.007209 0.9676 0.9807 0.01461 ] Network output: [ -0.007294 0.1703 0.8672 -0.0008583 0.0003853 0.9736 -0.0006468 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3227 0.2157 0.4041 0.1173 0.9847 0.9939 0.3239 0.9029 0.9773 0.6306 ] Network output: [ -0.04051 0.2559 1.081 0.0002591 -0.0001163 0.7457 0.0001952 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.134 0.1258 0.1889 0.1278 0.9901 0.9941 0.1341 0.9649 0.9811 0.208 ] Network output: [ -0.03142 0.1105 1.08 0.0004397 -0.0001974 0.8742 0.0003314 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1517 0.1501 0.1982 0.159 0.9857 0.9917 0.1518 0.9447 0.9729 0.2039 ] Network output: [ 0.01201 0.8821 0.01625 0.0001182 -5.309e-05 1.078 8.912e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.064 Epoch 4491 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05954 0.7963 0.9438 -4.237e-05 1.902e-05 0.1406 -3.193e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004563 -0.004401 -0.01548 0.008628 0.9643 0.9698 0.009864 0.9146 0.9243 0.03255 ] Network output: [ 1.028 -0.09844 0.02391 0.0002727 -0.0001224 0.02061 0.0002055 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2859 -0.02064 -0.1646 0.1908 0.9831 0.9931 0.3268 0.896 0.9746 0.6371 ] Network output: [ 0.01836 0.8341 0.9731 -0.0001246 5.594e-05 0.1556 -9.391e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006836 0.002273 0.005471 0.005285 0.9907 0.9938 0.006983 0.9677 0.9809 0.01427 ] Network output: [ 0.06075 -0.3789 0.9897 -0.0001259 5.653e-05 1.267 -9.49e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3139 0.2095 0.3963 0.2409 0.9847 0.9939 0.3151 0.9027 0.9772 0.6276 ] Network output: [ -0.04643 0.2454 1.084 0.0002559 -0.0001149 0.7641 0.0001929 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.137 0.1287 0.1941 0.1448 0.99 0.9941 0.1371 0.9651 0.9814 0.2134 ] Network output: [ -0.04129 0.1822 1.06 0.0003535 -0.0001587 0.8418 0.0002664 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1552 0.1536 0.1989 0.159 0.9858 0.9918 0.1553 0.9452 0.973 0.2043 ] Network output: [ -0.004046 1.024 -0.02226 -4.758e-05 2.136e-05 1.006 -3.586e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07725 Epoch 4492 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05041 0.843 0.9378 -0.000107 4.805e-05 0.1179 -8.067e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004577 -0.004285 -0.015 0.006976 0.9643 0.9697 0.009864 0.9144 0.9239 0.03221 ] Network output: [ 0.9219 0.2921 -0.003988 -0.0002637 0.0001184 -0.1331 -0.0001987 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2869 -0.00846 -0.1362 0.1108 0.9831 0.9931 0.3277 0.8961 0.9745 0.6338 ] Network output: [ 0.021 0.8393 0.9693 -0.0001331 5.973e-05 0.1489 -0.0001003 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007047 0.00234 0.005913 0.003092 0.9907 0.9937 0.007197 0.9676 0.9807 0.01463 ] Network output: [ -0.007031 0.1653 0.8692 -0.0008529 0.0003829 0.9761 -0.0006427 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3221 0.215 0.4046 0.1177 0.9847 0.9939 0.3233 0.9029 0.9773 0.632 ] Network output: [ -0.04029 0.2543 1.08 0.0002607 -0.000117 0.747 0.0001964 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1341 0.1258 0.1892 0.1279 0.9901 0.9941 0.1342 0.9649 0.9811 0.2084 ] Network output: [ -0.03119 0.1093 1.08 0.0004409 -0.0001979 0.8754 0.0003323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1518 0.1502 0.1985 0.1591 0.9857 0.9917 0.1519 0.9447 0.9729 0.2042 ] Network output: [ 0.01209 0.8823 0.01587 0.0001187 -5.327e-05 1.078 8.942e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06292 Epoch 4493 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05941 0.7973 0.9437 -4.32e-05 1.94e-05 0.14 -3.256e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004561 -0.004399 -0.0155 0.008602 0.9643 0.9698 0.009857 0.9146 0.9243 0.03255 ] Network output: [ 1.027 -0.09265 0.02208 0.0002666 -0.0001197 0.017 0.000201 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2856 -0.02067 -0.1653 0.1895 0.9831 0.9931 0.3264 0.896 0.9746 0.6385 ] Network output: [ 0.01827 0.8345 0.9732 -0.0001247 5.596e-05 0.1553 -9.395e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00683 0.00226 0.005463 0.005238 0.9907 0.9938 0.006976 0.9677 0.9809 0.01429 ] Network output: [ 0.05982 -0.3718 0.9885 -0.0001378 6.185e-05 1.263 -0.0001038 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3135 0.2089 0.3967 0.2386 0.9847 0.9939 0.3147 0.9027 0.9772 0.6291 ] Network output: [ -0.04637 0.2443 1.084 0.0002565 -0.0001152 0.765 0.0001933 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1369 0.1286 0.1944 0.1446 0.99 0.9941 0.137 0.9651 0.9814 0.2137 ] Network output: [ -0.0412 0.1798 1.06 0.0003554 -0.0001596 0.8436 0.0002679 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1552 0.1536 0.1993 0.159 0.9858 0.9918 0.1552 0.9452 0.973 0.2047 ] Network output: [ -0.004556 1.026 -0.02199 -4.964e-05 2.228e-05 1.005 -3.741e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07554 Epoch 4494 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05046 0.8426 0.938 -0.0001058 4.748e-05 0.118 -7.971e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004572 -0.004284 -0.01501 0.006993 0.9643 0.9697 0.009852 0.9145 0.9239 0.03221 ] Network output: [ 0.9229 0.2877 -0.003048 -0.0002556 0.0001147 -0.1315 -0.0001926 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2863 -0.00873 -0.1369 0.1116 0.9831 0.9931 0.3271 0.8961 0.9745 0.6353 ] Network output: [ 0.02096 0.8394 0.9694 -0.0001325 5.951e-05 0.1488 -9.989e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007035 0.002327 0.00591 0.003104 0.9907 0.9937 0.007186 0.9676 0.9807 0.01466 ] Network output: [ -0.006743 0.1599 0.8714 -0.0008471 0.0003803 0.9788 -0.0006384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3215 0.2143 0.4052 0.1182 0.9847 0.9939 0.3227 0.9029 0.9773 0.6334 ] Network output: [ -0.04006 0.2526 1.08 0.0002623 -0.0001178 0.7484 0.0001977 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1342 0.1258 0.1895 0.1282 0.9901 0.9941 0.1343 0.9649 0.9811 0.2088 ] Network output: [ -0.03096 0.108 1.079 0.0004421 -0.0001985 0.8766 0.0003332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.152 0.1503 0.1988 0.1593 0.9857 0.9917 0.152 0.9447 0.973 0.2045 ] Network output: [ 0.01215 0.8827 0.01545 0.0001189 -5.338e-05 1.078 8.96e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06179 Epoch 4495 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05927 0.7984 0.9436 -4.409e-05 1.979e-05 0.1393 -3.323e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004558 -0.004397 -0.01551 0.008574 0.9643 0.9698 0.009851 0.9147 0.9243 0.03255 ] Network output: [ 1.027 -0.08654 0.02022 0.0002603 -0.0001169 0.01321 0.0001962 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2853 -0.0207 -0.166 0.1882 0.9831 0.9931 0.3261 0.896 0.9746 0.6399 ] Network output: [ 0.01817 0.8349 0.9732 -0.0001247 5.6e-05 0.155 -9.4e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006823 0.002247 0.005455 0.00519 0.9908 0.9938 0.00697 0.9678 0.9809 0.01432 ] Network output: [ 0.05884 -0.3644 0.9872 -0.0001502 6.745e-05 1.259 -0.0001132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.313 0.2082 0.3972 0.2361 0.9847 0.9939 0.3142 0.9027 0.9772 0.6308 ] Network output: [ -0.04629 0.2432 1.084 0.0002572 -0.0001155 0.7659 0.0001939 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1368 0.1285 0.1946 0.1443 0.99 0.9941 0.1369 0.9651 0.9814 0.2141 ] Network output: [ -0.04109 0.1772 1.061 0.0003576 -0.0001605 0.8456 0.0002695 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1552 0.1535 0.1998 0.1591 0.9858 0.9918 0.1552 0.9453 0.973 0.2052 ] Network output: [ -0.005056 1.027 -0.02168 -5.154e-05 2.314e-05 1.005 -3.884e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07378 Epoch 4496 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05051 0.8423 0.9382 -0.0001045 4.69e-05 0.1181 -7.873e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004566 -0.004282 -0.01502 0.007012 0.9643 0.9698 0.009841 0.9145 0.924 0.03222 ] Network output: [ 0.924 0.283 -0.00211 -0.0002471 0.0001109 -0.1299 -0.0001862 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2858 -0.009016 -0.1377 0.1124 0.9831 0.9931 0.3264 0.8961 0.9745 0.6368 ] Network output: [ 0.02091 0.8395 0.9694 -0.0001321 5.929e-05 0.1487 -9.953e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007024 0.002313 0.005909 0.003116 0.9908 0.9938 0.007174 0.9677 0.9807 0.01468 ] Network output: [ -0.006429 0.1541 0.8737 -0.0008411 0.0003776 0.9816 -0.0006339 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3208 0.2136 0.4058 0.1187 0.9847 0.9939 0.322 0.9029 0.9773 0.6348 ] Network output: [ -0.03983 0.2509 1.08 0.0002641 -0.0001185 0.7498 0.000199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1343 0.1259 0.1898 0.1284 0.9901 0.9941 0.1344 0.9649 0.9811 0.2092 ] Network output: [ -0.03072 0.1067 1.079 0.0004434 -0.000199 0.8779 0.0003341 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1521 0.1504 0.1992 0.1594 0.9857 0.9917 0.1521 0.9448 0.973 0.2049 ] Network output: [ 0.01219 0.8833 0.01499 0.0001189 -5.34e-05 1.078 8.964e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06061 Epoch 4497 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05911 0.7996 0.9434 -4.503e-05 2.021e-05 0.1386 -3.393e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004556 -0.004395 -0.01552 0.008545 0.9643 0.9698 0.009846 0.9148 0.9243 0.03256 ] Network output: [ 1.027 -0.08013 0.01832 0.0002536 -0.0001139 0.009256 0.0001911 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2849 -0.02073 -0.1667 0.1868 0.9831 0.9931 0.3257 0.896 0.9746 0.6413 ] Network output: [ 0.01807 0.8354 0.9733 -0.0001248 5.604e-05 0.1547 -9.407e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006817 0.002233 0.005448 0.005139 0.9908 0.9938 0.006964 0.9678 0.9809 0.01434 ] Network output: [ 0.05781 -0.3566 0.9859 -0.0001634 7.333e-05 1.254 -0.0001231 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3125 0.2075 0.3977 0.2335 0.9847 0.9939 0.3137 0.9027 0.9772 0.6325 ] Network output: [ -0.0462 0.242 1.085 0.000258 -0.0001158 0.7669 0.0001945 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1368 0.1284 0.1949 0.144 0.9901 0.9941 0.1369 0.9652 0.9814 0.2145 ] Network output: [ -0.04097 0.1745 1.061 0.0003599 -0.0001616 0.8476 0.0002713 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1552 0.1535 0.2002 0.1591 0.9858 0.9918 0.1552 0.9453 0.973 0.2057 ] Network output: [ -0.00554 1.028 -0.02134 -5.324e-05 2.39e-05 1.004 -4.012e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07196 Epoch 4498 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05056 0.8419 0.9383 -0.0001032 4.631e-05 0.1182 -7.774e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004561 -0.004281 -0.01504 0.007033 0.9643 0.9698 0.00983 0.9146 0.924 0.03223 ] Network output: [ 0.9252 0.278 -0.001178 -0.0002382 0.0001069 -0.1282 -0.0001795 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2852 -0.009318 -0.1385 0.1133 0.9831 0.9931 0.3258 0.8961 0.9745 0.6383 ] Network output: [ 0.02084 0.8397 0.9695 -0.0001316 5.908e-05 0.1486 -9.918e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007013 0.002299 0.005907 0.00313 0.9908 0.9938 0.007163 0.9677 0.9807 0.01471 ] Network output: [ -0.00609 0.148 0.8761 -0.0008349 0.0003748 0.9847 -0.0006292 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3202 0.2128 0.4064 0.1193 0.9848 0.9939 0.3214 0.9029 0.9773 0.6364 ] Network output: [ -0.0396 0.2491 1.08 0.0002659 -0.0001194 0.7513 0.0002004 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1344 0.126 0.1902 0.1286 0.9901 0.9941 0.1345 0.965 0.9812 0.2097 ] Network output: [ -0.03049 0.1054 1.078 0.0004447 -0.0001996 0.8792 0.0003351 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1523 0.1506 0.1995 0.1595 0.9857 0.9917 0.1523 0.9448 0.973 0.2052 ] Network output: [ 0.0122 0.884 0.01447 0.0001188 -5.333e-05 1.078 8.952e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0594 Epoch 4499 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05894 0.8008 0.9433 -4.602e-05 2.066e-05 0.1378 -3.468e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004554 -0.004394 -0.01554 0.008516 0.9643 0.9698 0.009841 0.9149 0.9244 0.03256 ] Network output: [ 1.026 -0.07344 0.0164 0.0002466 -0.0001107 0.005151 0.0001859 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2846 -0.02076 -0.1675 0.1853 0.9832 0.9931 0.3253 0.896 0.9746 0.6429 ] Network output: [ 0.01796 0.8359 0.9733 -0.0001249 5.609e-05 0.1543 -9.416e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006812 0.002219 0.005441 0.005087 0.9908 0.9938 0.006958 0.9678 0.9809 0.01437 ] Network output: [ 0.05671 -0.3484 0.9845 -0.000177 7.948e-05 1.25 -0.0001334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.312 0.2068 0.3982 0.2308 0.9847 0.9939 0.3132 0.9028 0.9772 0.6343 ] Network output: [ -0.04609 0.2408 1.085 0.0002589 -0.0001162 0.7679 0.0001951 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1368 0.1284 0.1952 0.1437 0.9901 0.9941 0.1369 0.9652 0.9814 0.2149 ] Network output: [ -0.04082 0.1716 1.062 0.0003625 -0.0001627 0.8497 0.0002732 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1552 0.1535 0.2007 0.1592 0.9858 0.9918 0.1552 0.9454 0.9731 0.2062 ] Network output: [ -0.006002 1.029 -0.02096 -5.47e-05 2.456e-05 1.004 -4.122e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07011 Epoch 4500 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05061 0.8416 0.9385 -0.0001018 4.572e-05 0.1183 -7.676e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004556 -0.00428 -0.01506 0.007054 0.9643 0.9698 0.00982 0.9147 0.9241 0.03224 ] Network output: [ 0.9265 0.2728 -0.0002577 -0.0002291 0.0001028 -0.1264 -0.0001726 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2846 -0.009636 -0.1394 0.1142 0.9831 0.9931 0.3251 0.8961 0.9745 0.64 ] Network output: [ 0.02076 0.8399 0.9696 -0.0001312 5.889e-05 0.1484 -9.886e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007002 0.002285 0.005906 0.003144 0.9908 0.9938 0.007152 0.9678 0.9808 0.01474 ] Network output: [ -0.005728 0.1415 0.8786 -0.0008284 0.0003719 0.988 -0.0006243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3195 0.212 0.4071 0.1199 0.9848 0.9939 0.3207 0.9029 0.9773 0.638 ] Network output: [ -0.03936 0.2473 1.08 0.0002677 -0.0001202 0.7528 0.0002018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1345 0.1261 0.1906 0.1289 0.9901 0.9941 0.1346 0.965 0.9812 0.2102 ] Network output: [ -0.03026 0.1041 1.078 0.0004461 -0.0002003 0.8805 0.0003362 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1525 0.1508 0.1999 0.1597 0.9857 0.9917 0.1525 0.9449 0.973 0.2056 ] Network output: [ 0.01219 0.885 0.01391 0.0001184 -5.316e-05 1.077 8.924e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05814 Epoch 4501 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05876 0.8021 0.9432 -4.706e-05 2.113e-05 0.137 -3.546e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004551 -0.004393 -0.01555 0.008485 0.9644 0.9698 0.009836 0.915 0.9244 0.03258 ] Network output: [ 1.026 -0.0665 0.01448 0.0002394 -0.0001075 0.0009182 0.0001804 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2843 -0.0208 -0.1682 0.1838 0.9832 0.9931 0.3249 0.896 0.9746 0.6445 ] Network output: [ 0.01784 0.8364 0.9734 -0.0001251 5.616e-05 0.154 -9.428e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006807 0.002205 0.005436 0.005033 0.9908 0.9938 0.006953 0.9679 0.9809 0.0144 ] Network output: [ 0.05557 -0.3399 0.9831 -0.0001913 8.588e-05 1.245 -0.0001442 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3116 0.2061 0.3988 0.2281 0.9848 0.9939 0.3127 0.9028 0.9772 0.6362 ] Network output: [ -0.04596 0.2396 1.084 0.0002599 -0.0001167 0.7689 0.0001959 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1368 0.1283 0.1955 0.1434 0.9901 0.9941 0.1369 0.9652 0.9814 0.2154 ] Network output: [ -0.04066 0.1686 1.062 0.0003652 -0.000164 0.8519 0.0002752 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1552 0.1535 0.2011 0.1593 0.9858 0.9918 0.1552 0.9454 0.9731 0.2067 ] Network output: [ -0.006438 1.03 -0.02055 -5.59e-05 2.51e-05 1.004 -4.213e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06823 Epoch 4502 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05066 0.8412 0.9387 -0.0001005 4.514e-05 0.1184 -7.578e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004551 -0.004279 -0.01508 0.007077 0.9643 0.9698 0.00981 0.9149 0.9241 0.03226 ] Network output: [ 0.9278 0.2673 0.0006429 -0.0002196 9.859e-05 -0.1245 -0.0001655 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2841 -0.00997 -0.1403 0.1151 0.9832 0.9931 0.3245 0.8962 0.9745 0.6418 ] Network output: [ 0.02066 0.8401 0.9697 -0.0001308 5.872e-05 0.1483 -9.857e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006992 0.00227 0.005906 0.00316 0.9908 0.9938 0.007141 0.9679 0.9808 0.01477 ] Network output: [ -0.005343 0.1348 0.8812 -0.0008217 0.0003689 0.9914 -0.0006192 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3189 0.2112 0.4078 0.1206 0.9848 0.9939 0.3201 0.903 0.9773 0.6398 ] Network output: [ -0.03912 0.2455 1.079 0.0002697 -0.0001211 0.7544 0.0002032 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1347 0.1262 0.191 0.1291 0.9901 0.9941 0.1348 0.9651 0.9812 0.2107 ] Network output: [ -0.03002 0.1028 1.077 0.0004475 -0.0002009 0.8819 0.0003373 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1527 0.151 0.2003 0.1598 0.9857 0.9917 0.1527 0.945 0.973 0.2061 ] Network output: [ 0.01216 0.8861 0.01329 0.0001178 -5.289e-05 1.077 8.879e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05686 Epoch 4503 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05856 0.8034 0.9431 -4.815e-05 2.162e-05 0.1362 -3.629e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004549 -0.004392 -0.01557 0.008455 0.9644 0.9698 0.009832 0.9151 0.9245 0.03259 ] Network output: [ 1.026 -0.05935 0.01257 0.0002319 -0.0001041 -0.003416 0.0001748 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2839 -0.02083 -0.169 0.1822 0.9832 0.9931 0.3245 0.8961 0.9746 0.6462 ] Network output: [ 0.01772 0.8369 0.9735 -0.0001253 5.624e-05 0.1536 -9.441e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006802 0.002191 0.005431 0.004978 0.9908 0.9938 0.006948 0.968 0.9809 0.01443 ] Network output: [ 0.05437 -0.3311 0.9817 -0.0002061 9.25e-05 1.24 -0.0001553 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3111 0.2054 0.3994 0.2252 0.9848 0.9939 0.3123 0.9028 0.9773 0.6381 ] Network output: [ -0.04581 0.2383 1.084 0.0002611 -0.0001172 0.7699 0.0001968 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1368 0.1283 0.1958 0.1431 0.9901 0.9941 0.1369 0.9653 0.9814 0.2158 ] Network output: [ -0.04047 0.1655 1.063 0.0003682 -0.0001653 0.8542 0.0002775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1552 0.1536 0.2016 0.1593 0.9858 0.9918 0.1553 0.9455 0.9731 0.2072 ] Network output: [ -0.00684 1.03 -0.02012 -5.68e-05 2.55e-05 1.003 -4.281e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06633 Epoch 4504 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05069 0.8409 0.9389 -9.927e-05 4.457e-05 0.1184 -7.481e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004546 -0.004279 -0.0151 0.0071 0.9644 0.9698 0.009801 0.915 0.9242 0.03228 ] Network output: [ 0.9293 0.2616 0.001518 -0.0002099 9.424e-05 -0.1226 -0.0001582 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2835 -0.01032 -0.1413 0.1161 0.9832 0.9931 0.3238 0.8962 0.9745 0.6436 ] Network output: [ 0.02055 0.8404 0.9699 -0.0001305 5.857e-05 0.1481 -9.832e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006981 0.002255 0.005906 0.003176 0.9908 0.9938 0.00713 0.9679 0.9808 0.0148 ] Network output: [ -0.00494 0.1278 0.8838 -0.0008148 0.0003658 0.9949 -0.0006141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3182 0.2103 0.4085 0.1213 0.9848 0.9939 0.3194 0.903 0.9773 0.6416 ] Network output: [ -0.03888 0.2436 1.079 0.0002716 -0.0001219 0.756 0.0002047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1349 0.1263 0.1915 0.1294 0.9902 0.9941 0.1349 0.9651 0.9812 0.2112 ] Network output: [ -0.02979 0.1016 1.077 0.000449 -0.0002016 0.8833 0.0003384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.153 0.1512 0.2007 0.16 0.9858 0.9918 0.153 0.9451 0.973 0.2065 ] Network output: [ 0.0121 0.8874 0.01264 0.000117 -5.252e-05 1.076 8.817e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05556 Epoch 4505 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05835 0.8048 0.943 -4.93e-05 2.213e-05 0.1353 -3.715e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004547 -0.004391 -0.01559 0.008423 0.9644 0.9698 0.009828 0.9152 0.9245 0.03261 ] Network output: [ 1.025 -0.05206 0.01068 0.0002242 -0.0001007 -0.007819 0.000169 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2835 -0.02087 -0.1697 0.1806 0.9832 0.9931 0.3241 0.8962 0.9747 0.648 ] Network output: [ 0.01759 0.8375 0.9736 -0.0001255 5.633e-05 0.1532 -9.457e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006798 0.002176 0.005428 0.004922 0.9908 0.9938 0.006944 0.968 0.981 0.01447 ] Network output: [ 0.05313 -0.322 0.9803 -0.0002212 9.932e-05 1.235 -0.0001667 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3106 0.2046 0.4 0.2222 0.9848 0.9939 0.3118 0.9029 0.9773 0.6402 ] Network output: [ -0.04564 0.237 1.084 0.0002623 -0.0001178 0.771 0.0001977 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1368 0.1283 0.1962 0.1428 0.9901 0.9941 0.1369 0.9653 0.9814 0.2163 ] Network output: [ -0.04025 0.1624 1.063 0.0003713 -0.0001667 0.8566 0.0002799 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1553 0.1536 0.2021 0.1594 0.9858 0.9918 0.1553 0.9456 0.9731 0.2078 ] Network output: [ -0.007203 1.031 -0.01965 -5.738e-05 2.576e-05 1.003 -4.324e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06443 Epoch 4506 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05072 0.8406 0.9391 -9.803e-05 4.401e-05 0.1184 -7.388e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004541 -0.00428 -0.01512 0.007125 0.9644 0.9698 0.009793 0.9151 0.9242 0.03231 ] Network output: [ 0.9309 0.2558 0.002359 -0.0002001 8.982e-05 -0.1207 -0.0001508 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2829 -0.01068 -0.1422 0.1171 0.9832 0.9931 0.3232 0.8963 0.9746 0.6455 ] Network output: [ 0.02042 0.8407 0.97 -0.0001302 5.844e-05 0.148 -9.81e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006971 0.00224 0.005906 0.003193 0.9908 0.9938 0.00712 0.968 0.9808 0.01484 ] Network output: [ -0.004522 0.1205 0.8866 -0.0008078 0.0003627 0.9986 -0.0006088 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3175 0.2095 0.4093 0.1221 0.9848 0.9939 0.3187 0.9031 0.9773 0.6434 ] Network output: [ -0.03864 0.2417 1.079 0.0002737 -0.0001229 0.7577 0.0002062 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1351 0.1265 0.1919 0.1297 0.9902 0.9941 0.1351 0.9652 0.9812 0.2117 ] Network output: [ -0.02955 0.1003 1.076 0.0004505 -0.0002023 0.8847 0.0003395 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1532 0.1515 0.2011 0.1602 0.9858 0.9918 0.1533 0.9452 0.9731 0.2069 ] Network output: [ 0.01202 0.889 0.01193 0.000116 -5.206e-05 1.076 8.739e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05424 Epoch 4507 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05812 0.8062 0.9429 -5.049e-05 2.267e-05 0.1344 -3.805e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004545 -0.00439 -0.01561 0.008392 0.9644 0.9698 0.009825 0.9153 0.9246 0.03263 ] Network output: [ 1.024 -0.04467 0.008842 0.0002164 -9.713e-05 -0.01226 0.0001631 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2832 -0.02091 -0.1705 0.1789 0.9832 0.9931 0.3236 0.8963 0.9747 0.6498 ] Network output: [ 0.01744 0.8381 0.9737 -0.0001257 5.644e-05 0.1528 -9.475e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006794 0.002161 0.005425 0.004865 0.9908 0.9938 0.00694 0.9681 0.981 0.0145 ] Network output: [ 0.05185 -0.3127 0.9789 -0.0002367 0.0001063 1.229 -0.0001784 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3101 0.2038 0.4008 0.2192 0.9848 0.9939 0.3113 0.903 0.9773 0.6423 ] Network output: [ -0.04544 0.2356 1.084 0.0002637 -0.0001184 0.7721 0.0001987 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1369 0.1283 0.1965 0.1425 0.9901 0.9941 0.137 0.9654 0.9814 0.2168 ] Network output: [ -0.04001 0.1591 1.063 0.0003747 -0.0001682 0.859 0.0002824 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1554 0.1537 0.2026 0.1595 0.9858 0.9918 0.1554 0.9457 0.9731 0.2083 ] Network output: [ -0.007524 1.031 -0.01916 -5.762e-05 2.587e-05 1.003 -4.342e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06255 Epoch 4508 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05073 0.8404 0.9393 -9.684e-05 4.347e-05 0.1184 -7.298e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004537 -0.004281 -0.01514 0.00715 0.9644 0.9698 0.009786 0.9153 0.9243 0.03234 ] Network output: [ 0.9325 0.2498 0.003162 -0.0001901 8.535e-05 -0.1187 -0.0001433 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2824 -0.01106 -0.1433 0.1182 0.9832 0.9931 0.3226 0.8964 0.9746 0.6475 ] Network output: [ 0.02027 0.841 0.9701 -0.0001299 5.834e-05 0.1478 -9.793e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006962 0.002224 0.005906 0.00321 0.9908 0.9938 0.007111 0.9681 0.9809 0.01487 ] Network output: [ -0.004094 0.1132 0.8894 -0.0008008 0.0003595 1.002 -0.0006035 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3168 0.2086 0.4101 0.1228 0.9848 0.9939 0.318 0.9032 0.9773 0.6454 ] Network output: [ -0.03839 0.2399 1.079 0.0002757 -0.0001238 0.7594 0.0002078 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1353 0.1266 0.1924 0.1299 0.9902 0.9941 0.1354 0.9653 0.9813 0.2123 ] Network output: [ -0.02931 0.09904 1.075 0.0004521 -0.000203 0.8861 0.0003407 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1535 0.1518 0.2016 0.1603 0.9858 0.9918 0.1536 0.9453 0.9731 0.2074 ] Network output: [ 0.01192 0.8907 0.01119 0.0001147 -5.15e-05 1.075 8.645e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05293 Epoch 4509 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05787 0.8077 0.9428 -5.172e-05 2.322e-05 0.1335 -3.898e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004543 -0.00439 -0.01563 0.008361 0.9644 0.9698 0.009822 0.9155 0.9246 0.03265 ] Network output: [ 1.024 -0.03725 0.007061 0.0002084 -9.355e-05 -0.01669 0.000157 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2828 -0.02095 -0.1712 0.1773 0.9832 0.9931 0.3232 0.8964 0.9747 0.6518 ] Network output: [ 0.01729 0.8388 0.9738 -0.000126 5.657e-05 0.1523 -9.496e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006791 0.002146 0.005424 0.004808 0.9908 0.9938 0.006937 0.9682 0.981 0.01454 ] Network output: [ 0.05054 -0.3033 0.9775 -0.0002525 0.0001134 1.224 -0.0001903 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3097 0.203 0.4015 0.2162 0.9848 0.9939 0.3108 0.9031 0.9773 0.6444 ] Network output: [ -0.04523 0.2343 1.084 0.0002652 -0.0001191 0.7732 0.0001999 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.137 0.1283 0.1969 0.1422 0.9901 0.9941 0.137 0.9655 0.9815 0.2172 ] Network output: [ -0.03974 0.1558 1.064 0.0003782 -0.0001698 0.8615 0.000285 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1556 0.1538 0.2032 0.1597 0.9858 0.9918 0.1556 0.9458 0.9732 0.2089 ] Network output: [ -0.007797 1.031 -0.01865 -5.751e-05 2.582e-05 1.003 -4.334e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0607 Epoch 4510 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05073 0.8402 0.9395 -9.571e-05 4.297e-05 0.1184 -7.213e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004533 -0.004282 -0.01517 0.007176 0.9644 0.9698 0.00978 0.9154 0.9244 0.03237 ] Network output: [ 0.9342 0.2437 0.00392 -0.0001802 8.088e-05 -0.1167 -0.0001358 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2818 -0.01144 -0.1443 0.1193 0.9832 0.9931 0.3219 0.8965 0.9746 0.6495 ] Network output: [ 0.02009 0.8415 0.9703 -0.0001298 5.826e-05 0.1475 -9.781e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006953 0.002209 0.005907 0.003228 0.9908 0.9938 0.007101 0.9682 0.9809 0.01491 ] Network output: [ -0.00366 0.1057 0.8922 -0.0007938 0.0003563 1.006 -0.0005982 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3161 0.2077 0.411 0.1236 0.9848 0.9939 0.3173 0.9033 0.9773 0.6474 ] Network output: [ -0.03814 0.238 1.078 0.0002778 -0.0001247 0.7611 0.0002094 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1355 0.1268 0.1929 0.1302 0.9902 0.9941 0.1356 0.9654 0.9813 0.2129 ] Network output: [ -0.02908 0.09781 1.075 0.0004537 -0.0002037 0.8875 0.0003419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1539 0.1521 0.2021 0.1605 0.9858 0.9918 0.1539 0.9454 0.9732 0.2079 ] Network output: [ 0.01179 0.8926 0.01042 0.0001133 -5.086e-05 1.074 8.538e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05163 Epoch 4511 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0576 0.8092 0.9428 -5.3e-05 2.379e-05 0.1326 -3.994e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004541 -0.00439 -0.01565 0.008331 0.9644 0.9699 0.00982 0.9156 0.9247 0.03268 ] Network output: [ 1.023 -0.02987 0.005354 0.0002004 -8.995e-05 -0.02108 0.000151 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2824 -0.021 -0.172 0.1756 0.9832 0.9931 0.3228 0.8965 0.9747 0.6537 ] Network output: [ 0.01712 0.8394 0.9739 -0.0001263 5.671e-05 0.1519 -9.519e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006789 0.00213 0.005425 0.004751 0.9908 0.9938 0.006934 0.9683 0.981 0.01458 ] Network output: [ 0.04919 -0.2938 0.9761 -0.0002684 0.0001205 1.218 -0.0002023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3092 0.2023 0.4024 0.2131 0.9848 0.9939 0.3104 0.9032 0.9773 0.6467 ] Network output: [ -0.04498 0.2329 1.084 0.0002668 -0.0001198 0.7743 0.0002011 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1371 0.1283 0.1972 0.1419 0.9901 0.9941 0.1372 0.9655 0.9815 0.2177 ] Network output: [ -0.03945 0.1524 1.064 0.0003819 -0.0001714 0.864 0.0002878 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1557 0.154 0.2037 0.1598 0.9859 0.9918 0.1557 0.9459 0.9732 0.2094 ] Network output: [ -0.00802 1.03 -0.01814 -5.705e-05 2.561e-05 1.004 -4.3e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05888 Epoch 4512 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05072 0.8401 0.9398 -9.466e-05 4.25e-05 0.1183 -7.134e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004529 -0.004284 -0.01519 0.007203 0.9644 0.9698 0.009774 0.9156 0.9245 0.0324 ] Network output: [ 0.936 0.2375 0.004629 -0.0001703 7.644e-05 -0.1148 -0.0001283 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2813 -0.01184 -0.1454 0.1203 0.9832 0.9931 0.3213 0.8967 0.9746 0.6516 ] Network output: [ 0.0199 0.8419 0.9705 -0.0001297 5.822e-05 0.1473 -9.773e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006945 0.002193 0.005909 0.003246 0.9908 0.9938 0.007093 0.9682 0.981 0.01495 ] Network output: [ -0.003227 0.09812 0.895 -0.0007868 0.0003532 1.01 -0.000593 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3155 0.2068 0.4118 0.1244 0.9848 0.9939 0.3166 0.9034 0.9774 0.6495 ] Network output: [ -0.03789 0.2361 1.078 0.0002799 -0.0001257 0.7628 0.000211 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1358 0.127 0.1934 0.1305 0.9902 0.9941 0.1359 0.9654 0.9813 0.2135 ] Network output: [ -0.02884 0.0966 1.074 0.0004554 -0.0002044 0.8889 0.0003432 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1542 0.1524 0.2025 0.1607 0.9858 0.9918 0.1542 0.9456 0.9732 0.2084 ] Network output: [ 0.01164 0.8946 0.009627 0.0001117 -5.014e-05 1.073 8.418e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05034 Epoch 4513 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05732 0.8108 0.9427 -5.432e-05 2.438e-05 0.1316 -4.093e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00454 -0.00439 -0.01567 0.008301 0.9644 0.9699 0.009818 0.9158 0.9248 0.03271 ] Network output: [ 1.023 -0.0226 0.003736 0.0001923 -8.634e-05 -0.02539 0.0001449 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.282 -0.02106 -0.1727 0.174 0.9832 0.9931 0.3224 0.8966 0.9748 0.6557 ] Network output: [ 0.01693 0.8402 0.9741 -0.0001267 5.686e-05 0.1514 -9.545e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006787 0.002115 0.005426 0.004694 0.9909 0.9938 0.006932 0.9684 0.9811 0.01463 ] Network output: [ 0.04783 -0.2844 0.9748 -0.0002844 0.0001277 1.213 -0.0002143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3087 0.2015 0.4032 0.21 0.9848 0.9939 0.3099 0.9034 0.9774 0.6489 ] Network output: [ -0.04472 0.2315 1.084 0.0002685 -0.0001206 0.7754 0.0002024 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1372 0.1284 0.1976 0.1417 0.9902 0.9942 0.1373 0.9656 0.9815 0.2182 ] Network output: [ -0.03913 0.149 1.064 0.0003857 -0.0001731 0.8665 0.0002907 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1559 0.1541 0.2042 0.1599 0.9859 0.9918 0.1559 0.946 0.9733 0.21 ] Network output: [ -0.008193 1.03 -0.01761 -5.626e-05 2.526e-05 1.004 -4.24e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05712 Epoch 4514 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05068 0.84 0.94 -9.369e-05 4.206e-05 0.1182 -7.061e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004525 -0.004286 -0.01522 0.00723 0.9644 0.9698 0.00977 0.9157 0.9246 0.03244 ] Network output: [ 0.9378 0.2313 0.005286 -0.0001605 7.207e-05 -0.1128 -0.000121 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2807 -0.01225 -0.1466 0.1214 0.9832 0.9931 0.3207 0.8968 0.9747 0.6538 ] Network output: [ 0.01968 0.8425 0.9707 -0.0001296 5.82e-05 0.147 -9.77e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006937 0.002177 0.005911 0.003263 0.9909 0.9938 0.007085 0.9683 0.981 0.01499 ] Network output: [ -0.0028 0.09063 0.8979 -0.0007801 0.0003502 1.014 -0.0005879 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3148 0.2059 0.4128 0.1252 0.9848 0.9939 0.316 0.9036 0.9774 0.6516 ] Network output: [ -0.03764 0.2343 1.078 0.0002821 -0.0001266 0.7645 0.0002126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1361 0.1273 0.1939 0.1308 0.9902 0.9942 0.1362 0.9655 0.9814 0.2141 ] Network output: [ -0.0286 0.09541 1.073 0.0004571 -0.0002052 0.8904 0.0003445 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1546 0.1528 0.203 0.1608 0.9858 0.9918 0.1546 0.9457 0.9732 0.2089 ] Network output: [ 0.01148 0.8968 0.008814 0.00011 -4.937e-05 1.072 8.288e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04908 Epoch 4515 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05701 0.8123 0.9427 -5.566e-05 2.499e-05 0.1307 -4.195e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004538 -0.00439 -0.01569 0.008272 0.9644 0.9699 0.009817 0.9159 0.9249 0.03274 ] Network output: [ 1.022 -0.0155 0.002217 0.0001843 -8.276e-05 -0.02958 0.0001389 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2816 -0.02112 -0.1734 0.1723 0.9832 0.9931 0.3219 0.8968 0.9748 0.6578 ] Network output: [ 0.01673 0.8409 0.9742 -0.000127 5.703e-05 0.1508 -9.574e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006785 0.002099 0.005429 0.004638 0.9909 0.9938 0.006931 0.9684 0.9811 0.01467 ] Network output: [ 0.04647 -0.275 0.9736 -0.0003003 0.0001348 1.207 -0.0002263 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3083 0.2007 0.4041 0.207 0.9848 0.9939 0.3095 0.9035 0.9774 0.6512 ] Network output: [ -0.04443 0.2301 1.083 0.0002704 -0.0001214 0.7766 0.0002038 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1374 0.1285 0.198 0.1414 0.9902 0.9942 0.1375 0.9657 0.9815 0.2187 ] Network output: [ -0.03878 0.1457 1.064 0.0003896 -0.0001749 0.869 0.0002936 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1561 0.1544 0.2048 0.1601 0.9859 0.9918 0.1561 0.9461 0.9733 0.2106 ] Network output: [ -0.008315 1.029 -0.0171 -5.515e-05 2.476e-05 1.004 -4.156e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05542 Epoch 4516 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05062 0.84 0.9403 -9.283e-05 4.167e-05 0.1181 -6.996e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004522 -0.004288 -0.01525 0.007256 0.9644 0.9699 0.009766 0.9159 0.9247 0.03248 ] Network output: [ 0.9396 0.2252 0.005889 -0.000151 6.781e-05 -0.111 -0.0001138 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2802 -0.01266 -0.1477 0.1225 0.9832 0.9931 0.3202 0.897 0.9747 0.656 ] Network output: [ 0.01944 0.8431 0.9709 -0.0001297 5.821e-05 0.1466 -9.772e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00693 0.002161 0.005913 0.00328 0.9909 0.9938 0.007078 0.9684 0.981 0.01503 ] Network output: [ -0.002387 0.08325 0.9007 -0.0007736 0.0003473 1.018 -0.000583 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3141 0.205 0.4137 0.126 0.9848 0.9939 0.3153 0.9037 0.9774 0.6537 ] Network output: [ -0.03738 0.2325 1.077 0.0002843 -0.0001276 0.7661 0.0002142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1364 0.1275 0.1944 0.1311 0.9902 0.9942 0.1365 0.9656 0.9814 0.2147 ] Network output: [ -0.02836 0.09424 1.073 0.0004588 -0.000206 0.8918 0.0003458 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.155 0.1532 0.2035 0.161 0.9858 0.9918 0.155 0.9459 0.9733 0.2094 ] Network output: [ 0.0113 0.8991 0.007992 0.0001082 -4.856e-05 1.071 8.152e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04786 Epoch 4517 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05668 0.8139 0.9428 -5.703e-05 2.56e-05 0.1297 -4.298e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004537 -0.004391 -0.01571 0.008245 0.9644 0.9699 0.009816 0.9161 0.925 0.03277 ] Network output: [ 1.021 -0.008637 0.0008074 0.0001765 -7.922e-05 -0.03362 0.000133 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2812 -0.0212 -0.1741 0.1708 0.9832 0.9931 0.3215 0.8969 0.9748 0.6599 ] Network output: [ 0.01651 0.8418 0.9744 -0.0001274 5.722e-05 0.1503 -9.605e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006785 0.002084 0.005434 0.004583 0.9909 0.9939 0.00693 0.9685 0.9811 0.01472 ] Network output: [ 0.0451 -0.2658 0.9724 -0.000316 0.0001419 1.202 -0.0002381 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3079 0.1999 0.4051 0.2039 0.9848 0.9939 0.309 0.9037 0.9774 0.6536 ] Network output: [ -0.04411 0.2287 1.083 0.0002723 -0.0001223 0.7777 0.0002052 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1376 0.1286 0.1983 0.1411 0.9902 0.9942 0.1377 0.9658 0.9816 0.2192 ] Network output: [ -0.03841 0.1423 1.065 0.0003937 -0.0001767 0.8715 0.0002967 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1564 0.1546 0.2053 0.1602 0.9859 0.9918 0.1564 0.9463 0.9734 0.2112 ] Network output: [ -0.008388 1.028 -0.01658 -5.375e-05 2.413e-05 1.005 -4.051e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05378 Epoch 4518 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05054 0.84 0.9406 -9.206e-05 4.133e-05 0.1179 -6.938e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004519 -0.004292 -0.01528 0.007283 0.9644 0.9699 0.009763 0.9161 0.9248 0.03252 ] Network output: [ 0.9415 0.2191 0.006437 -0.0001419 6.368e-05 -0.1091 -0.0001069 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2797 -0.01308 -0.1488 0.1235 0.9832 0.9931 0.3196 0.8971 0.9748 0.6582 ] Network output: [ 0.01917 0.8437 0.9711 -0.0001298 5.825e-05 0.1463 -9.779e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006923 0.002145 0.005916 0.003296 0.9909 0.9938 0.007071 0.9686 0.9811 0.01508 ] Network output: [ -0.001991 0.07604 0.9035 -0.0007674 0.0003445 1.021 -0.0005783 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3135 0.2041 0.4147 0.1267 0.9848 0.9939 0.3147 0.9039 0.9775 0.6559 ] Network output: [ -0.03712 0.2308 1.077 0.0002865 -0.0001286 0.7678 0.0002159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1368 0.1278 0.195 0.1314 0.9902 0.9942 0.1369 0.9657 0.9815 0.2154 ] Network output: [ -0.02811 0.09309 1.072 0.0004607 -0.0002068 0.8933 0.0003472 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1554 0.1536 0.204 0.1612 0.9858 0.9918 0.1554 0.946 0.9733 0.21 ] Network output: [ 0.01111 0.9014 0.007171 0.0001063 -4.772e-05 1.07 8.011e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04668 Epoch 4519 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05634 0.8155 0.9429 -5.842e-05 2.623e-05 0.1287 -4.402e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004535 -0.004392 -0.01573 0.008219 0.9645 0.9699 0.009816 0.9163 0.9251 0.03281 ] Network output: [ 1.02 -0.002065 -0.0004889 0.0001687 -7.576e-05 -0.03749 0.0001272 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2809 -0.02128 -0.1747 0.1693 0.9832 0.9931 0.321 0.8971 0.9748 0.6621 ] Network output: [ 0.01628 0.8426 0.9746 -0.0001279 5.742e-05 0.1497 -9.638e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006784 0.002068 0.00544 0.00453 0.9909 0.9939 0.00693 0.9687 0.9812 0.01476 ] Network output: [ 0.04373 -0.2569 0.9713 -0.0003315 0.0001488 1.197 -0.0002498 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3074 0.1991 0.4061 0.201 0.9848 0.9939 0.3086 0.9038 0.9774 0.6559 ] Network output: [ -0.04378 0.2273 1.083 0.0002743 -0.0001232 0.7789 0.0002068 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1378 0.1288 0.1987 0.1409 0.9902 0.9942 0.1379 0.9659 0.9816 0.2197 ] Network output: [ -0.03802 0.139 1.065 0.0003978 -0.0001786 0.874 0.0002998 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1567 0.1549 0.2058 0.1604 0.9859 0.9918 0.1567 0.9464 0.9734 0.2117 ] Network output: [ -0.008416 1.027 -0.01609 -5.21e-05 2.339e-05 1.005 -3.926e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05223 Epoch 4520 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05043 0.8402 0.9409 -9.141e-05 4.104e-05 0.1177 -6.889e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004517 -0.004295 -0.01531 0.007309 0.9644 0.9699 0.009761 0.9163 0.9249 0.03257 ] Network output: [ 0.9433 0.2132 0.006933 -0.0001331 5.973e-05 -0.1073 -0.0001003 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2792 -0.0135 -0.15 0.1246 0.9832 0.9931 0.319 0.8973 0.9748 0.6605 ] Network output: [ 0.01888 0.8445 0.9714 -0.0001299 5.832e-05 0.1458 -9.79e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006918 0.002129 0.005919 0.003311 0.9909 0.9939 0.007066 0.9687 0.9811 0.01512 ] Network output: [ -0.00162 0.06908 0.9062 -0.0007616 0.0003419 1.025 -0.0005739 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3129 0.2032 0.4157 0.1273 0.9848 0.9939 0.314 0.904 0.9775 0.6582 ] Network output: [ -0.03685 0.2291 1.076 0.0002887 -0.0001296 0.7695 0.0002176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1371 0.1281 0.1955 0.1317 0.9903 0.9942 0.1372 0.9658 0.9815 0.216 ] Network output: [ -0.02785 0.09195 1.071 0.0004626 -0.0002077 0.8947 0.0003486 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1559 0.154 0.2045 0.1613 0.9859 0.9918 0.1559 0.9462 0.9734 0.2105 ] Network output: [ 0.01092 0.9037 0.006359 0.0001044 -4.687e-05 1.069 7.868e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04554 Epoch 4521 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05598 0.8171 0.943 -5.981e-05 2.685e-05 0.1277 -4.508e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004534 -0.004393 -0.01575 0.008194 0.9645 0.9699 0.009816 0.9165 0.9252 0.03285 ] Network output: [ 1.02 0.00417 -0.001669 0.0001612 -7.238e-05 -0.04115 0.0001215 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2805 -0.02138 -0.1754 0.1678 0.9832 0.9932 0.3206 0.8973 0.9749 0.6642 ] Network output: [ 0.01603 0.8435 0.9748 -0.0001284 5.763e-05 0.1491 -9.674e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006785 0.002053 0.005447 0.004478 0.9909 0.9939 0.00693 0.9688 0.9812 0.01481 ] Network output: [ 0.04239 -0.2482 0.9704 -0.0003466 0.0001556 1.192 -0.0002612 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.307 0.1983 0.4072 0.1981 0.9848 0.9939 0.3082 0.904 0.9775 0.6583 ] Network output: [ -0.04343 0.2259 1.082 0.0002765 -0.0001241 0.78 0.0002084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1381 0.129 0.1991 0.1406 0.9902 0.9942 0.1381 0.966 0.9816 0.2202 ] Network output: [ -0.0376 0.1357 1.065 0.000402 -0.0001805 0.8765 0.0003029 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.157 0.1552 0.2064 0.1606 0.9859 0.9919 0.157 0.9466 0.9735 0.2123 ] Network output: [ -0.008402 1.026 -0.01561 -5.023e-05 2.255e-05 1.006 -3.785e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05074 Epoch 4522 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0503 0.8404 0.9412 -9.087e-05 4.079e-05 0.1174 -6.848e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004514 -0.004299 -0.01534 0.007335 0.9645 0.9699 0.00976 0.9164 0.925 0.03261 ] Network output: [ 0.9452 0.2074 0.007379 -0.0001247 5.599e-05 -0.1056 -9.398e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2787 -0.01392 -0.1511 0.1255 0.9832 0.9931 0.3185 0.8975 0.9748 0.6628 ] Network output: [ 0.01857 0.8453 0.9717 -0.0001301 5.841e-05 0.1454 -9.806e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006913 0.002113 0.005924 0.003325 0.9909 0.9939 0.007061 0.9688 0.9812 0.01516 ] Network output: [ -0.001278 0.0624 0.9089 -0.0007562 0.0003395 1.028 -0.0005699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3122 0.2023 0.4167 0.1279 0.9848 0.994 0.3134 0.9042 0.9775 0.6604 ] Network output: [ -0.03658 0.2274 1.076 0.000291 -0.0001306 0.7711 0.0002193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1375 0.1284 0.196 0.1319 0.9903 0.9942 0.1376 0.966 0.9816 0.2167 ] Network output: [ -0.02759 0.09081 1.07 0.0004645 -0.0002085 0.8961 0.0003501 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1564 0.1545 0.2051 0.1615 0.9859 0.9918 0.1564 0.9464 0.9735 0.211 ] Network output: [ 0.01073 0.9061 0.005563 0.0001025 -4.603e-05 1.067 7.727e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04444 Epoch 4523 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05559 0.8188 0.9431 -6.122e-05 2.748e-05 0.1267 -4.613e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004533 -0.004395 -0.01577 0.008171 0.9645 0.9699 0.009817 0.9166 0.9253 0.03288 ] Network output: [ 1.019 0.01003 -0.002734 0.0001539 -6.91e-05 -0.0446 0.000116 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2801 -0.02149 -0.176 0.1664 0.9832 0.9932 0.3202 0.8975 0.9749 0.6664 ] Network output: [ 0.01575 0.8445 0.975 -0.0001289 5.785e-05 0.1485 -9.711e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006786 0.002038 0.005455 0.004428 0.9909 0.9939 0.006931 0.9689 0.9813 0.01486 ] Network output: [ 0.04107 -0.2398 0.9695 -0.0003613 0.0001622 1.187 -0.0002723 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3066 0.1975 0.4082 0.1952 0.9848 0.9939 0.3078 0.9042 0.9775 0.6607 ] Network output: [ -0.04306 0.2245 1.082 0.0002787 -0.0001251 0.7811 0.00021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1383 0.1292 0.1995 0.1404 0.9902 0.9942 0.1384 0.9661 0.9817 0.2208 ] Network output: [ -0.03717 0.1326 1.064 0.0004062 -0.0001824 0.8789 0.0003061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1573 0.1555 0.2069 0.1607 0.9859 0.9919 0.1574 0.9467 0.9735 0.2129 ] Network output: [ -0.008352 1.025 -0.01515 -4.818e-05 2.163e-05 1.007 -3.631e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04934 Epoch 4524 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05014 0.8407 0.9416 -9.043e-05 4.06e-05 0.1171 -6.815e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004512 -0.004303 -0.01537 0.007361 0.9645 0.9699 0.009761 0.9166 0.9252 0.03266 ] Network output: [ 0.947 0.2018 0.007779 -0.0001169 5.246e-05 -0.104 -8.807e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2782 -0.01435 -0.1522 0.1265 0.9832 0.9932 0.318 0.8977 0.9749 0.665 ] Network output: [ 0.01824 0.8461 0.9719 -0.0001304 5.853e-05 0.1449 -9.825e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006909 0.002097 0.005928 0.003338 0.9909 0.9939 0.007057 0.9689 0.9812 0.01521 ] Network output: [ -0.0009693 0.05607 0.9115 -0.0007513 0.0003373 1.031 -0.0005662 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3116 0.2014 0.4177 0.1284 0.9848 0.994 0.3128 0.9044 0.9776 0.6627 ] Network output: [ -0.03629 0.2258 1.075 0.0002932 -0.0001316 0.7727 0.000221 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1379 0.1287 0.1966 0.1322 0.9903 0.9942 0.138 0.9661 0.9816 0.2173 ] Network output: [ -0.02732 0.08968 1.069 0.0004666 -0.0002095 0.8976 0.0003516 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1568 0.155 0.2056 0.1617 0.9859 0.9918 0.1569 0.9465 0.9735 0.2116 ] Network output: [ 0.01054 0.9083 0.004791 0.0001007 -4.52e-05 1.066 7.589e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0434 Epoch 4525 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05519 0.8204 0.9432 -6.261e-05 2.811e-05 0.1257 -4.719e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004532 -0.004397 -0.01579 0.008151 0.9645 0.9699 0.009819 0.9168 0.9254 0.03292 ] Network output: [ 1.018 0.01549 -0.003685 0.0001469 -6.593e-05 -0.04782 0.0001107 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2797 -0.02161 -0.1766 0.1651 0.9832 0.9932 0.3198 0.8977 0.975 0.6686 ] Network output: [ 0.01546 0.8455 0.9752 -0.0001294 5.808e-05 0.1478 -9.75e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006787 0.002023 0.005465 0.00438 0.9909 0.9939 0.006933 0.969 0.9813 0.01491 ] Network output: [ 0.03978 -0.2318 0.9687 -0.0003755 0.0001686 1.182 -0.000283 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3062 0.1967 0.4094 0.1925 0.9848 0.994 0.3074 0.9044 0.9776 0.6631 ] Network output: [ -0.04267 0.2232 1.081 0.000281 -0.0001261 0.7823 0.0002117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1387 0.1294 0.1999 0.1402 0.9902 0.9942 0.1387 0.9662 0.9817 0.2213 ] Network output: [ -0.03672 0.1295 1.064 0.0004105 -0.0001843 0.8813 0.0003094 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1577 0.1559 0.2074 0.1609 0.9859 0.9919 0.1577 0.9469 0.9736 0.2134 ] Network output: [ -0.00827 1.024 -0.01471 -4.601e-05 2.066e-05 1.007 -3.467e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04801 Epoch 4526 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04995 0.8411 0.9419 -9.01e-05 4.045e-05 0.1167 -6.79e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004511 -0.004308 -0.01539 0.007385 0.9645 0.9699 0.009761 0.9168 0.9253 0.03271 ] Network output: [ 0.9488 0.1964 0.008139 -0.0001095 4.917e-05 -0.1025 -8.255e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2778 -0.01477 -0.1533 0.1274 0.9832 0.9932 0.3175 0.8979 0.9749 0.6673 ] Network output: [ 0.01788 0.847 0.9723 -0.0001307 5.866e-05 0.1444 -9.848e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006906 0.002081 0.005934 0.003349 0.9909 0.9939 0.007054 0.969 0.9813 0.01525 ] Network output: [ -0.0006974 0.05012 0.9139 -0.0007469 0.0003353 1.034 -0.0005629 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3111 0.2005 0.4188 0.1289 0.9848 0.994 0.3122 0.9046 0.9776 0.665 ] Network output: [ -0.036 0.2242 1.075 0.0002955 -0.0001327 0.7743 0.0002227 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1384 0.1291 0.1971 0.1325 0.9903 0.9942 0.1385 0.9662 0.9817 0.2179 ] Network output: [ -0.02705 0.08855 1.068 0.0004687 -0.0002104 0.899 0.0003533 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1574 0.1555 0.2061 0.1618 0.9859 0.9918 0.1574 0.9467 0.9736 0.2121 ] Network output: [ 0.01036 0.9106 0.004047 9.893e-05 -4.441e-05 1.065 7.456e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0424 Epoch 4527 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05478 0.822 0.9434 -6.4e-05 2.873e-05 0.1247 -4.823e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004532 -0.004399 -0.01581 0.008132 0.9645 0.9699 0.009821 0.917 0.9255 0.03296 ] Network output: [ 1.018 0.02052 -0.00453 0.0001401 -6.288e-05 -0.0508 0.0001056 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2793 -0.02174 -0.1772 0.1639 0.9833 0.9932 0.3193 0.8979 0.975 0.6708 ] Network output: [ 0.01516 0.8465 0.9755 -0.0001299 5.831e-05 0.1471 -9.789e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00679 0.002008 0.005476 0.004334 0.991 0.9939 0.006935 0.9691 0.9813 0.01496 ] Network output: [ 0.03852 -0.2242 0.968 -0.0003893 0.0001748 1.178 -0.0002934 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3058 0.1959 0.4105 0.1898 0.9848 0.994 0.307 0.9046 0.9776 0.6655 ] Network output: [ -0.04227 0.2219 1.08 0.0002833 -0.0001272 0.7834 0.0002135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.139 0.1297 0.2003 0.1401 0.9903 0.9942 0.1391 0.9663 0.9818 0.2218 ] Network output: [ -0.03626 0.1265 1.064 0.0004148 -0.0001862 0.8837 0.0003126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1581 0.1563 0.208 0.1611 0.986 0.9919 0.1581 0.947 0.9736 0.214 ] Network output: [ -0.008162 1.022 -0.0143 -4.374e-05 1.964e-05 1.008 -3.297e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04675 Epoch 4528 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04974 0.8416 0.9423 -8.987e-05 4.035e-05 0.1163 -6.773e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00451 -0.004313 -0.01542 0.007409 0.9645 0.9699 0.009763 0.917 0.9254 0.03275 ] Network output: [ 0.9505 0.1912 0.008465 -0.0001028 4.614e-05 -0.101 -7.746e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2774 -0.01519 -0.1544 0.1283 0.9833 0.9932 0.3171 0.8981 0.975 0.6696 ] Network output: [ 0.01751 0.848 0.9726 -0.000131 5.881e-05 0.1439 -9.873e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006904 0.002066 0.005941 0.003359 0.9909 0.9939 0.007052 0.9691 0.9813 0.0153 ] Network output: [ -0.0004649 0.04458 0.9163 -0.000743 0.0003335 1.037 -0.0005599 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3105 0.1996 0.4199 0.1293 0.9848 0.994 0.3117 0.9048 0.9776 0.6672 ] Network output: [ -0.0357 0.2227 1.074 0.0002978 -0.0001337 0.7758 0.0002245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1388 0.1295 0.1977 0.1327 0.9903 0.9942 0.1389 0.9663 0.9817 0.2186 ] Network output: [ -0.02676 0.08741 1.068 0.000471 -0.0002114 0.9005 0.0003549 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1579 0.156 0.2066 0.162 0.9859 0.9919 0.1579 0.9469 0.9736 0.2127 ] Network output: [ 0.01018 0.9127 0.003338 9.727e-05 -4.367e-05 1.064 7.33e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04145 Epoch 4529 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05435 0.8237 0.9437 -6.537e-05 2.935e-05 0.1237 -4.927e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004531 -0.004402 -0.01583 0.008115 0.9645 0.9699 0.009824 0.9172 0.9256 0.033 ] Network output: [ 1.017 0.02513 -0.005272 0.0001335 -5.995e-05 -0.05355 0.0001006 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2789 -0.02189 -0.1777 0.1628 0.9833 0.9932 0.3189 0.8981 0.9751 0.673 ] Network output: [ 0.01483 0.8476 0.9758 -0.0001304 5.855e-05 0.1464 -9.83e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006792 0.001993 0.005488 0.004291 0.991 0.9939 0.006938 0.9692 0.9814 0.01501 ] Network output: [ 0.03731 -0.217 0.9675 -0.0004025 0.0001807 1.173 -0.0003033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3055 0.1951 0.4117 0.1873 0.9848 0.994 0.3066 0.9048 0.9776 0.6678 ] Network output: [ -0.04186 0.2205 1.08 0.0002857 -0.0001283 0.7846 0.0002153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1394 0.13 0.2008 0.1399 0.9903 0.9942 0.1395 0.9664 0.9818 0.2223 ] Network output: [ -0.03579 0.1235 1.064 0.000419 -0.0001881 0.886 0.0003158 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1586 0.1567 0.2085 0.1613 0.986 0.9919 0.1586 0.9472 0.9737 0.2145 ] Network output: [ -0.008034 1.021 -0.01392 -4.143e-05 1.86e-05 1.009 -3.122e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04556 Epoch 4530 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0495 0.8421 0.9427 -8.974e-05 4.029e-05 0.1158 -6.763e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004509 -0.004318 -0.01545 0.007432 0.9645 0.9699 0.009766 0.9172 0.9255 0.0328 ] Network output: [ 0.9522 0.1862 0.008762 -9.659e-05 4.336e-05 -0.09967 -7.28e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.277 -0.01561 -0.1554 0.1291 0.9833 0.9932 0.3166 0.8983 0.975 0.6719 ] Network output: [ 0.01712 0.849 0.9729 -0.0001314 5.898e-05 0.1433 -9.9e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006903 0.00205 0.005948 0.003368 0.991 0.9939 0.007051 0.9693 0.9814 0.01535 ] Network output: [ -0.0002737 0.03946 0.9186 -0.0007396 0.000332 1.039 -0.0005574 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.31 0.1988 0.421 0.1295 0.9848 0.994 0.3112 0.905 0.9777 0.6695 ] Network output: [ -0.0354 0.2212 1.073 0.0003002 -0.0001348 0.7774 0.0002262 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1393 0.1298 0.1982 0.1329 0.9903 0.9942 0.1394 0.9664 0.9818 0.2192 ] Network output: [ -0.02646 0.08626 1.067 0.0004733 -0.0002125 0.902 0.0003567 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1585 0.1565 0.2071 0.1622 0.9859 0.9919 0.1585 0.9471 0.9737 0.2132 ] Network output: [ 0.01002 0.9148 0.002665 9.571e-05 -4.297e-05 1.063 7.213e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04054 Epoch 4531 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05391 0.8253 0.9439 -6.672e-05 2.995e-05 0.1227 -5.028e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004531 -0.004405 -0.01585 0.0081 0.9645 0.9699 0.009827 0.9174 0.9257 0.03305 ] Network output: [ 1.017 0.0293 -0.005921 0.0001273 -5.715e-05 -0.05606 9.594e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2786 -0.02205 -0.1783 0.1617 0.9833 0.9932 0.3186 0.8983 0.9751 0.6752 ] Network output: [ 0.01449 0.8488 0.9761 -0.000131 5.88e-05 0.1457 -9.87e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006796 0.001978 0.005501 0.004249 0.991 0.9939 0.006941 0.9693 0.9814 0.01507 ] Network output: [ 0.03614 -0.2101 0.967 -0.0004151 0.0001863 1.169 -0.0003128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3051 0.1943 0.4129 0.1849 0.9848 0.994 0.3063 0.905 0.9777 0.6702 ] Network output: [ -0.04145 0.2192 1.079 0.0002882 -0.0001294 0.7857 0.0002172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1398 0.1303 0.2012 0.1398 0.9903 0.9942 0.1399 0.9665 0.9819 0.2228 ] Network output: [ -0.03532 0.1207 1.063 0.0004233 -0.00019 0.8882 0.000319 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.159 0.1571 0.209 0.1614 0.986 0.9919 0.1591 0.9474 0.9737 0.2151 ] Network output: [ -0.007891 1.02 -0.01357 -3.911e-05 1.756e-05 1.009 -2.947e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04443 Epoch 4532 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04924 0.8428 0.9431 -8.969e-05 4.027e-05 0.1153 -6.759e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004508 -0.004323 -0.01548 0.007455 0.9645 0.9699 0.009769 0.9174 0.9257 0.03285 ] Network output: [ 0.9538 0.1814 0.009038 -9.099e-05 4.085e-05 -0.09836 -6.857e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2766 -0.01603 -0.1564 0.1299 0.9833 0.9932 0.3162 0.8985 0.9751 0.6742 ] Network output: [ 0.01672 0.8501 0.9733 -0.0001318 5.915e-05 0.1426 -9.929e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006903 0.002035 0.005957 0.003375 0.991 0.9939 0.007051 0.9694 0.9814 0.01539 ] Network output: [ -0.0001249 0.03478 0.9208 -0.0007368 0.0003308 1.042 -0.0005553 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3095 0.1979 0.4221 0.1297 0.9848 0.994 0.3107 0.9052 0.9777 0.6718 ] Network output: [ -0.03508 0.2197 1.073 0.0003026 -0.0001358 0.7789 0.000228 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1398 0.1302 0.1988 0.1332 0.9903 0.9942 0.1399 0.9665 0.9818 0.2198 ] Network output: [ -0.02615 0.0851 1.066 0.0004757 -0.0002135 0.9034 0.0003585 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.159 0.1571 0.2077 0.1623 0.986 0.9919 0.1591 0.9473 0.9738 0.2137 ] Network output: [ 0.009872 0.9167 0.002032 9.428e-05 -4.233e-05 1.062 7.105e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03967 Epoch 4533 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05345 0.8269 0.9442 -6.804e-05 3.055e-05 0.1217 -5.128e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004531 -0.004408 -0.01587 0.008087 0.9645 0.9699 0.009831 0.9176 0.9259 0.03309 ] Network output: [ 1.016 0.03304 -0.006484 0.0001213 -5.448e-05 -0.05834 9.145e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2782 -0.02223 -0.1788 0.1607 0.9833 0.9932 0.3182 0.8985 0.9751 0.6773 ] Network output: [ 0.01413 0.8499 0.9764 -0.0001315 5.904e-05 0.1449 -9.91e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0068 0.001963 0.005514 0.00421 0.991 0.9939 0.006946 0.9695 0.9815 0.01512 ] Network output: [ 0.03502 -0.2037 0.9666 -0.0004271 0.0001917 1.165 -0.0003219 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3048 0.1935 0.4141 0.1825 0.9848 0.994 0.3059 0.9052 0.9777 0.6725 ] Network output: [ -0.04102 0.2179 1.078 0.0002907 -0.0001305 0.7869 0.0002191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1402 0.1306 0.2016 0.1397 0.9903 0.9942 0.1403 0.9667 0.9819 0.2234 ] Network output: [ -0.03483 0.118 1.063 0.0004276 -0.000192 0.8904 0.0003222 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1595 0.1576 0.2095 0.1616 0.986 0.9919 0.1595 0.9476 0.9738 0.2156 ] Network output: [ -0.007739 1.019 -0.01324 -3.681e-05 1.652e-05 1.01 -2.774e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04336 Epoch 4534 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04895 0.8435 0.9435 -8.972e-05 4.028e-05 0.1148 -6.762e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004508 -0.004329 -0.01551 0.007476 0.9645 0.9699 0.009773 0.9176 0.9258 0.0329 ] Network output: [ 0.9553 0.1769 0.009298 -8.596e-05 3.859e-05 -0.09713 -6.479e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2762 -0.01644 -0.1574 0.1306 0.9833 0.9932 0.3158 0.8987 0.9751 0.6764 ] Network output: [ 0.0163 0.8512 0.9736 -0.0001322 5.933e-05 0.142 -9.959e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006903 0.00202 0.005966 0.003381 0.991 0.9939 0.007051 0.9695 0.9815 0.01544 ] Network output: [ -1.893e-05 0.03053 0.9229 -0.0007346 0.0003298 1.044 -0.0005536 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.309 0.1971 0.4233 0.1298 0.9849 0.994 0.3102 0.9054 0.9778 0.674 ] Network output: [ -0.03475 0.2183 1.072 0.000305 -0.0001369 0.7803 0.0002299 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1403 0.1307 0.1993 0.1334 0.9904 0.9942 0.1404 0.9667 0.9819 0.2205 ] Network output: [ -0.02582 0.08392 1.065 0.0004782 -0.0002147 0.9049 0.0003604 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1596 0.1577 0.2082 0.1625 0.986 0.9919 0.1596 0.9474 0.9738 0.2143 ] Network output: [ 0.009738 0.9185 0.001438 9.298e-05 -4.174e-05 1.061 7.008e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03884 Epoch 4535 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05299 0.8285 0.9445 -6.933e-05 3.112e-05 0.1207 -5.225e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004531 -0.004412 -0.01589 0.008076 0.9645 0.9699 0.009836 0.9178 0.926 0.03313 ] Network output: [ 1.016 0.03636 -0.006971 0.0001157 -5.192e-05 -0.0604 8.716e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2779 -0.02242 -0.1793 0.1598 0.9833 0.9932 0.3178 0.8987 0.9752 0.6795 ] Network output: [ 0.01375 0.8511 0.9767 -0.000132 5.927e-05 0.1441 -9.95e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006804 0.001949 0.005529 0.004173 0.991 0.9939 0.00695 0.9696 0.9815 0.01517 ] Network output: [ 0.03395 -0.1977 0.9664 -0.0004386 0.0001969 1.162 -0.0003305 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3045 0.1928 0.4153 0.1803 0.9848 0.994 0.3056 0.9054 0.9778 0.6748 ] Network output: [ -0.04059 0.2167 1.078 0.0002933 -0.0001317 0.788 0.000221 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1406 0.131 0.2021 0.1396 0.9903 0.9943 0.1407 0.9668 0.9819 0.2239 ] Network output: [ -0.03435 0.1153 1.063 0.0004318 -0.0001939 0.8926 0.0003254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.16 0.1581 0.21 0.1618 0.986 0.9919 0.1601 0.9477 0.9739 0.2162 ] Network output: [ -0.007582 1.017 -0.01294 -3.455e-05 1.551e-05 1.01 -2.604e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04235 Epoch 4536 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04864 0.8442 0.9439 -8.982e-05 4.032e-05 0.1142 -6.769e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004508 -0.004335 -0.01553 0.007497 0.9645 0.9699 0.009778 0.9178 0.9259 0.03295 ] Network output: [ 0.9568 0.1726 0.009549 -8.151e-05 3.659e-05 -0.09595 -6.143e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2759 -0.01685 -0.1583 0.1313 0.9833 0.9932 0.3154 0.8989 0.9752 0.6787 ] Network output: [ 0.01587 0.8524 0.974 -0.0001326 5.951e-05 0.1413 -9.99e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006905 0.002005 0.005977 0.003386 0.991 0.9939 0.007053 0.9696 0.9816 0.01549 ] Network output: [ 4.442e-05 0.02671 0.9249 -0.0007328 0.000329 1.045 -0.0005523 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3086 0.1962 0.4244 0.1299 0.9849 0.994 0.3097 0.9056 0.9778 0.6762 ] Network output: [ -0.03441 0.2169 1.071 0.0003075 -0.000138 0.7818 0.0002317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1408 0.1311 0.1999 0.1336 0.9904 0.9943 0.1409 0.9668 0.9819 0.2211 ] Network output: [ -0.02548 0.08272 1.064 0.0004808 -0.0002158 0.9064 0.0003623 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1602 0.1583 0.2087 0.1627 0.986 0.9919 0.1603 0.9476 0.9739 0.2148 ] Network output: [ 0.009619 0.9201 0.0008844 9.182e-05 -4.122e-05 1.06 6.92e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03803 Epoch 4537 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05252 0.8302 0.9448 -7.058e-05 3.168e-05 0.1197 -5.319e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004532 -0.004417 -0.01591 0.008066 0.9645 0.9699 0.009841 0.918 0.9261 0.03318 ] Network output: [ 1.015 0.03928 -0.007389 0.0001102 -4.948e-05 -0.06224 8.306e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2776 -0.02263 -0.1798 0.159 0.9833 0.9932 0.3175 0.899 0.9752 0.6817 ] Network output: [ 0.01336 0.8524 0.977 -0.0001325 5.95e-05 0.1433 -9.989e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006809 0.001935 0.005544 0.004139 0.991 0.9939 0.006955 0.9697 0.9816 0.01522 ] Network output: [ 0.03293 -0.1921 0.9662 -0.0004495 0.0002018 1.158 -0.0003387 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3042 0.192 0.4165 0.1782 0.9849 0.994 0.3053 0.9056 0.9778 0.6771 ] Network output: [ -0.04015 0.2154 1.077 0.0002959 -0.0001328 0.7892 0.000223 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1411 0.1313 0.2025 0.1395 0.9903 0.9943 0.1412 0.9669 0.982 0.2244 ] Network output: [ -0.03386 0.1128 1.062 0.000436 -0.0001957 0.8947 0.0003286 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1606 0.1586 0.2106 0.162 0.986 0.9919 0.1606 0.9479 0.9739 0.2167 ] Network output: [ -0.007425 1.016 -0.01267 -3.238e-05 1.454e-05 1.011 -2.44e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04138 Epoch 4538 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04831 0.8451 0.9443 -8.998e-05 4.04e-05 0.1136 -6.781e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004508 -0.004342 -0.01556 0.007517 0.9645 0.9699 0.009783 0.918 0.926 0.033 ] Network output: [ 0.9581 0.1685 0.009795 -7.761e-05 3.484e-05 -0.09483 -5.849e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2755 -0.01726 -0.1592 0.1319 0.9833 0.9932 0.3151 0.8992 0.9752 0.6809 ] Network output: [ 0.01543 0.8536 0.9744 -0.000133 5.969e-05 0.1406 -0.0001002 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006907 0.001991 0.005988 0.003389 0.991 0.9939 0.007055 0.9697 0.9816 0.01554 ] Network output: [ 6.586e-05 0.02332 0.9268 -0.0007316 0.0003284 1.047 -0.0005513 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3081 0.1954 0.4256 0.1298 0.9849 0.994 0.3093 0.9058 0.9778 0.6784 ] Network output: [ -0.03406 0.2156 1.071 0.00031 -0.0001392 0.7832 0.0002336 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1414 0.1316 0.2004 0.1338 0.9904 0.9943 0.1415 0.9669 0.982 0.2217 ] Network output: [ -0.02513 0.0815 1.063 0.0004835 -0.000217 0.9079 0.0003644 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1609 0.1589 0.2092 0.1629 0.986 0.9919 0.1609 0.9478 0.974 0.2153 ] Network output: [ 0.009517 0.9216 0.0003707 9.079e-05 -4.076e-05 1.059 6.842e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03726 Epoch 4539 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05204 0.8318 0.9452 -7.178e-05 3.223e-05 0.1187 -5.41e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004532 -0.004421 -0.01592 0.008059 0.9645 0.97 0.009847 0.9181 0.9262 0.03322 ] Network output: [ 1.015 0.04182 -0.007746 0.000105 -4.715e-05 -0.06389 7.915e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2773 -0.02284 -0.1803 0.1583 0.9833 0.9932 0.3171 0.8992 0.9753 0.6838 ] Network output: [ 0.01296 0.8537 0.9774 -0.000133 5.973e-05 0.1425 -0.0001003 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006814 0.001921 0.005561 0.004106 0.991 0.994 0.006961 0.9698 0.9816 0.01527 ] Network output: [ 0.03196 -0.1869 0.9661 -0.0004598 0.0002064 1.155 -0.0003465 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3039 0.1913 0.4178 0.1762 0.9849 0.994 0.305 0.9058 0.9778 0.6793 ] Network output: [ -0.03972 0.2141 1.076 0.0002985 -0.000134 0.7903 0.000225 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1416 0.1317 0.203 0.1394 0.9904 0.9943 0.1417 0.967 0.982 0.225 ] Network output: [ -0.03336 0.1103 1.061 0.0004402 -0.0001976 0.8968 0.0003317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1611 0.1592 0.2111 0.1622 0.986 0.9919 0.1612 0.9481 0.974 0.2172 ] Network output: [ -0.00727 1.015 -0.01241 -3.03e-05 1.36e-05 1.012 -2.284e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04046 Epoch 4540 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04796 0.846 0.9448 -9.019e-05 4.049e-05 0.1129 -6.797e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004509 -0.004348 -0.01558 0.007536 0.9645 0.9699 0.009789 0.9182 0.9262 0.03305 ] Network output: [ 0.9594 0.1645 0.01004 -7.424e-05 3.333e-05 -0.09375 -5.595e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2752 -0.01766 -0.16 0.1325 0.9833 0.9932 0.3147 0.8994 0.9753 0.6831 ] Network output: [ 0.01498 0.8549 0.9748 -0.0001334 5.987e-05 0.1398 -0.0001005 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00691 0.001976 0.006001 0.003392 0.991 0.9939 0.007058 0.9699 0.9817 0.01559 ] Network output: [ 4.649e-05 0.02034 0.9285 -0.0007308 0.0003281 1.048 -0.0005507 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3077 0.1946 0.4268 0.1297 0.9849 0.994 0.3089 0.906 0.9779 0.6806 ] Network output: [ -0.0337 0.2142 1.07 0.0003125 -0.0001403 0.7846 0.0002355 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1419 0.132 0.2009 0.134 0.9904 0.9943 0.142 0.967 0.982 0.2223 ] Network output: [ -0.02477 0.08025 1.062 0.0004862 -0.0002183 0.9094 0.0003664 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1615 0.1595 0.2097 0.163 0.986 0.9919 0.1615 0.948 0.974 0.2159 ] Network output: [ 0.009432 0.923 -0.0001045 8.989e-05 -4.035e-05 1.059 6.774e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03651 Epoch 4541 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05155 0.8334 0.9456 -7.295e-05 3.275e-05 0.1176 -5.498e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004533 -0.004426 -0.01594 0.008053 0.9645 0.97 0.009853 0.9183 0.9263 0.03327 ] Network output: [ 1.015 0.04399 -0.008052 0.0001 -4.492e-05 -0.06534 7.54e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.277 -0.02307 -0.1807 0.1576 0.9833 0.9932 0.3168 0.8994 0.9753 0.6859 ] Network output: [ 0.01255 0.855 0.9777 -0.0001335 5.994e-05 0.1417 -0.0001006 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00682 0.001907 0.005578 0.004076 0.991 0.994 0.006967 0.9699 0.9817 0.01533 ] Network output: [ 0.03104 -0.182 0.9661 -0.0004696 0.0002108 1.152 -0.0003539 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3036 0.1905 0.419 0.1743 0.9849 0.994 0.3047 0.906 0.9779 0.6816 ] Network output: [ -0.03928 0.2129 1.075 0.0003012 -0.0001352 0.7915 0.000227 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1421 0.1321 0.2035 0.1394 0.9904 0.9943 0.1422 0.9671 0.9821 0.2255 ] Network output: [ -0.03287 0.1079 1.061 0.0004443 -0.0001995 0.8988 0.0003349 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1617 0.1597 0.2116 0.1624 0.986 0.9919 0.1618 0.9483 0.9741 0.2178 ] Network output: [ -0.007121 1.014 -0.01219 -2.834e-05 1.272e-05 1.012 -2.136e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03958 Epoch 4542 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04759 0.847 0.9453 -9.044e-05 4.06e-05 0.1122 -6.816e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004509 -0.004355 -0.0156 0.007555 0.9645 0.97 0.009796 0.9184 0.9263 0.0331 ] Network output: [ 0.9606 0.1608 0.0103 -7.139e-05 3.205e-05 -0.09271 -5.38e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2749 -0.01806 -0.1608 0.133 0.9833 0.9932 0.3144 0.8996 0.9753 0.6852 ] Network output: [ 0.01453 0.8562 0.9752 -0.0001337 6.004e-05 0.139 -0.0001008 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006913 0.001962 0.006016 0.003393 0.991 0.994 0.007062 0.97 0.9817 0.01564 ] Network output: [ -1.226e-05 0.01775 0.9302 -0.0007305 0.0003279 1.049 -0.0005505 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3074 0.1938 0.428 0.1295 0.9849 0.994 0.3085 0.9062 0.9779 0.6828 ] Network output: [ -0.03333 0.2129 1.069 0.0003151 -0.0001415 0.786 0.0002375 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1425 0.1325 0.2015 0.1342 0.9904 0.9943 0.1426 0.9672 0.9821 0.223 ] Network output: [ -0.02439 0.07898 1.061 0.0004891 -0.0002196 0.9109 0.0003686 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1622 0.1601 0.2102 0.1632 0.986 0.9919 0.1622 0.9482 0.9741 0.2164 ] Network output: [ 0.009363 0.9243 -0.0005426 8.911e-05 -4.001e-05 1.058 6.716e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03579 Epoch 4543 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05106 0.835 0.946 -7.407e-05 3.325e-05 0.1166 -5.582e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004534 -0.004432 -0.01596 0.008049 0.9646 0.97 0.00986 0.9185 0.9265 0.03332 ] Network output: [ 1.015 0.04582 -0.008314 9.528e-05 -4.277e-05 -0.06663 7.18e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2767 -0.02332 -0.1812 0.1571 0.9833 0.9932 0.3165 0.8996 0.9754 0.688 ] Network output: [ 0.01212 0.8563 0.9781 -0.000134 6.014e-05 0.1408 -0.000101 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006827 0.001893 0.005595 0.004047 0.9911 0.994 0.006974 0.9701 0.9817 0.01538 ] Network output: [ 0.03017 -0.1775 0.9661 -0.0004788 0.000215 1.149 -0.0003609 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3033 0.1898 0.4203 0.1724 0.9849 0.994 0.3045 0.9062 0.9779 0.6837 ] Network output: [ -0.03884 0.2116 1.075 0.0003039 -0.0001364 0.7927 0.000229 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1426 0.1326 0.2039 0.1394 0.9904 0.9943 0.1427 0.9672 0.9821 0.2261 ] Network output: [ -0.03238 0.1056 1.06 0.0004484 -0.0002013 0.9008 0.000338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1623 0.1603 0.2121 0.1626 0.9861 0.9919 0.1624 0.9484 0.9741 0.2183 ] Network output: [ -0.00698 1.013 -0.01198 -2.651e-05 1.19e-05 1.012 -1.998e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03873 Epoch 4544 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04721 0.848 0.9457 -9.073e-05 4.073e-05 0.1115 -6.838e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00451 -0.004362 -0.01563 0.007573 0.9646 0.97 0.009803 0.9186 0.9264 0.03315 ] Network output: [ 0.9618 0.1573 0.01056 -6.902e-05 3.099e-05 -0.0917 -5.202e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2746 -0.01845 -0.1615 0.1335 0.9833 0.9932 0.3141 0.8998 0.9754 0.6874 ] Network output: [ 0.01406 0.8575 0.9756 -0.0001341 6.02e-05 0.1382 -0.0001011 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006918 0.001949 0.006031 0.003393 0.9911 0.994 0.007067 0.9701 0.9818 0.01569 ] Network output: [ -0.0001087 0.01553 0.9318 -0.0007306 0.000328 1.05 -0.0005506 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.307 0.1931 0.4292 0.1292 0.9849 0.994 0.3082 0.9064 0.978 0.6849 ] Network output: [ -0.03295 0.2116 1.068 0.0003177 -0.0001426 0.7874 0.0002394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1431 0.133 0.202 0.1344 0.9904 0.9943 0.1432 0.9673 0.9821 0.2236 ] Network output: [ -0.02401 0.07769 1.06 0.000492 -0.0002209 0.9124 0.0003708 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1628 0.1608 0.2108 0.1634 0.986 0.9919 0.1629 0.9484 0.9742 0.2169 ] Network output: [ 0.009311 0.9254 -0.0009457 8.846e-05 -3.971e-05 1.057 6.666e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03508 Epoch 4545 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05057 0.8366 0.9464 -7.514e-05 3.373e-05 0.1156 -5.663e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004535 -0.004437 -0.01598 0.008046 0.9646 0.97 0.009867 0.9187 0.9266 0.03336 ] Network output: [ 1.015 0.04733 -0.008538 9.069e-05 -4.071e-05 -0.06774 6.835e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2764 -0.02357 -0.1816 0.1565 0.9833 0.9932 0.3162 0.8998 0.9754 0.6901 ] Network output: [ 0.01169 0.8577 0.9785 -0.0001344 6.033e-05 0.1399 -0.0001013 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006834 0.001879 0.005613 0.00402 0.9911 0.994 0.006981 0.9702 0.9818 0.01543 ] Network output: [ 0.02935 -0.1733 0.9663 -0.0004876 0.0002189 1.146 -0.0003675 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3031 0.1891 0.4215 0.1707 0.9849 0.994 0.3042 0.9064 0.978 0.6859 ] Network output: [ -0.03839 0.2104 1.074 0.0003067 -0.0001377 0.7939 0.0002311 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1432 0.133 0.2044 0.1394 0.9904 0.9943 0.1433 0.9674 0.9822 0.2266 ] Network output: [ -0.0319 0.1034 1.059 0.0004525 -0.0002031 0.9028 0.000341 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.163 0.1609 0.2126 0.1628 0.9861 0.9919 0.163 0.9486 0.9742 0.2188 ] Network output: [ -0.006849 1.013 -0.01179 -2.481e-05 1.114e-05 1.013 -1.87e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03791 Epoch 4546 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04681 0.8491 0.9462 -9.104e-05 4.087e-05 0.1107 -6.861e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004512 -0.004369 -0.01565 0.007591 0.9646 0.97 0.009811 0.9188 0.9265 0.03321 ] Network output: [ 0.9628 0.1539 0.01083 -6.713e-05 3.014e-05 -0.09071 -5.059e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2743 -0.01884 -0.1621 0.134 0.9833 0.9932 0.3138 0.9 0.9754 0.6895 ] Network output: [ 0.0136 0.8589 0.976 -0.0001344 6.036e-05 0.1374 -0.0001013 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006923 0.001935 0.006048 0.003392 0.9911 0.994 0.007072 0.9702 0.9818 0.01574 ] Network output: [ -0.0002411 0.01367 0.9334 -0.000731 0.0003282 1.05 -0.0005509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3067 0.1923 0.4304 0.1288 0.9849 0.994 0.3079 0.9066 0.978 0.687 ] Network output: [ -0.03256 0.2103 1.067 0.0003204 -0.0001438 0.7888 0.0002414 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1437 0.1335 0.2026 0.1345 0.9904 0.9943 0.1438 0.9674 0.9822 0.2242 ] Network output: [ -0.0236 0.07637 1.059 0.0004951 -0.0002223 0.9139 0.0003731 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1635 0.1615 0.2113 0.1636 0.9861 0.9919 0.1635 0.9486 0.9742 0.2174 ] Network output: [ 0.009275 0.9264 -0.001316 8.792e-05 -3.947e-05 1.057 6.626e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03439 Epoch 4547 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05007 0.8382 0.9468 -7.616e-05 3.419e-05 0.1146 -5.74e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004537 -0.004443 -0.016 0.008045 0.9646 0.97 0.009875 0.9189 0.9267 0.03341 ] Network output: [ 1.015 0.04854 -0.008732 8.627e-05 -3.873e-05 -0.06871 6.501e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2761 -0.02384 -0.182 0.1561 0.9833 0.9932 0.3159 0.9 0.9755 0.6921 ] Network output: [ 0.01125 0.8591 0.9789 -0.0001348 6.051e-05 0.139 -0.0001016 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006841 0.001866 0.005632 0.003996 0.9911 0.994 0.006988 0.9703 0.9819 0.01548 ] Network output: [ 0.02859 -0.1695 0.9665 -0.0004959 0.0002226 1.144 -0.0003737 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3028 0.1884 0.4228 0.169 0.9849 0.994 0.304 0.9066 0.978 0.688 ] Network output: [ -0.03795 0.2091 1.073 0.0003094 -0.0001389 0.7951 0.0002332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1438 0.1335 0.2049 0.1394 0.9904 0.9943 0.1439 0.9675 0.9822 0.2272 ] Network output: [ -0.03141 0.1012 1.059 0.0004565 -0.000205 0.9047 0.0003441 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1636 0.1615 0.2132 0.163 0.9861 0.992 0.1636 0.9488 0.9743 0.2194 ] Network output: [ -0.006731 1.012 -0.01162 -2.326e-05 1.044e-05 1.013 -1.753e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03712 Epoch 4548 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0464 0.8502 0.9467 -9.138e-05 4.102e-05 0.1099 -6.886e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004513 -0.004376 -0.01567 0.007608 0.9646 0.97 0.009819 0.9189 0.9266 0.03326 ] Network output: [ 0.9638 0.1507 0.01112 -6.568e-05 2.949e-05 -0.08974 -4.95e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.274 -0.01922 -0.1627 0.1344 0.9833 0.9932 0.3135 0.9002 0.9754 0.6915 ] Network output: [ 0.01312 0.8603 0.9764 -0.0001348 6.05e-05 0.1365 -0.0001016 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006929 0.001922 0.006066 0.003391 0.9911 0.994 0.007078 0.9703 0.9819 0.01579 ] Network output: [ -0.0004074 0.01213 0.9348 -0.0007319 0.0003286 1.051 -0.0005516 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3064 0.1916 0.4317 0.1284 0.9849 0.994 0.3076 0.9068 0.978 0.6891 ] Network output: [ -0.03216 0.209 1.066 0.0003231 -0.000145 0.7901 0.0002435 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1443 0.134 0.2032 0.1347 0.9904 0.9943 0.1444 0.9675 0.9822 0.2248 ] Network output: [ -0.02319 0.07502 1.058 0.0004982 -0.0002236 0.9154 0.0003754 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1642 0.1621 0.2118 0.1638 0.9861 0.9919 0.1642 0.9488 0.9743 0.218 ] Network output: [ 0.009255 0.9272 -0.001655 8.748e-05 -3.927e-05 1.056 6.593e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03371 Epoch 4549 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04958 0.8398 0.9472 -7.714e-05 3.463e-05 0.1135 -5.813e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004539 -0.00445 -0.01601 0.008045 0.9646 0.97 0.009884 0.9191 0.9268 0.03346 ] Network output: [ 1.015 0.04948 -0.008901 8.198e-05 -3.68e-05 -0.06954 6.178e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2759 -0.02412 -0.1824 0.1557 0.9833 0.9932 0.3157 0.9003 0.9755 0.6941 ] Network output: [ 0.0108 0.8605 0.9793 -0.0001351 6.066e-05 0.1381 -0.0001018 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006849 0.001852 0.005652 0.003972 0.9911 0.994 0.006996 0.9704 0.9819 0.01554 ] Network output: [ 0.02786 -0.1659 0.9667 -0.0005038 0.0002262 1.141 -0.0003796 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3026 0.1877 0.4241 0.1675 0.9849 0.994 0.3037 0.9068 0.978 0.6901 ] Network output: [ -0.03751 0.2079 1.072 0.0003122 -0.0001402 0.7963 0.0002353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1444 0.134 0.2054 0.1394 0.9904 0.9943 0.1445 0.9676 0.9823 0.2278 ] Network output: [ -0.03093 0.09909 1.058 0.0004605 -0.0002068 0.9066 0.0003471 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1643 0.1622 0.2137 0.1632 0.9861 0.992 0.1643 0.949 0.9743 0.2199 ] Network output: [ -0.006625 1.011 -0.01146 -2.186e-05 9.815e-06 1.013 -1.648e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03636 Epoch 4550 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04598 0.8514 0.9472 -9.172e-05 4.118e-05 0.1091 -6.913e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004515 -0.004383 -0.01569 0.007624 0.9646 0.97 0.009828 0.9191 0.9268 0.03331 ] Network output: [ 0.9647 0.1476 0.01142 -6.465e-05 2.902e-05 -0.08878 -4.872e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2738 -0.0196 -0.1633 0.1348 0.9833 0.9932 0.3133 0.9005 0.9755 0.6936 ] Network output: [ 0.01265 0.8617 0.9769 -0.000135 6.063e-05 0.1356 -0.0001018 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006936 0.001909 0.006085 0.003389 0.9911 0.994 0.007085 0.9705 0.9819 0.01584 ] Network output: [ -0.0006057 0.0109 0.9362 -0.0007331 0.0003291 1.051 -0.0005525 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3061 0.1909 0.433 0.128 0.9849 0.994 0.3073 0.907 0.9781 0.6911 ] Network output: [ -0.03175 0.2077 1.066 0.0003258 -0.0001463 0.7915 0.0002455 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.145 0.1346 0.2037 0.1349 0.9905 0.9943 0.1451 0.9676 0.9823 0.2255 ] Network output: [ -0.02276 0.07364 1.057 0.0005013 -0.0002251 0.917 0.0003778 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1649 0.1628 0.2123 0.164 0.9861 0.992 0.165 0.9489 0.9744 0.2185 ] Network output: [ 0.00925 0.928 -0.001965 8.714e-05 -3.912e-05 1.056 6.567e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03305 Epoch 4551 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04908 0.8414 0.9476 -7.806e-05 3.504e-05 0.1125 -5.883e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004541 -0.004456 -0.01603 0.008047 0.9646 0.97 0.009893 0.9192 0.9269 0.03351 ] Network output: [ 1.015 0.05017 -0.009051 7.782e-05 -3.493e-05 -0.07024 5.865e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2757 -0.0244 -0.1828 0.1553 0.9833 0.9932 0.3155 0.9005 0.9755 0.6961 ] Network output: [ 0.01034 0.862 0.9797 -0.0001354 6.081e-05 0.1371 -0.0001021 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006857 0.001839 0.005672 0.003951 0.9911 0.994 0.007005 0.9705 0.982 0.01559 ] Network output: [ 0.02719 -0.1626 0.967 -0.0005112 0.0002295 1.139 -0.0003852 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3024 0.1869 0.4254 0.166 0.9849 0.994 0.3035 0.907 0.9781 0.6922 ] Network output: [ -0.03708 0.2066 1.071 0.000315 -0.0001414 0.7975 0.0002374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.145 0.1344 0.206 0.1394 0.9904 0.9943 0.1451 0.9677 0.9823 0.2283 ] Network output: [ -0.03045 0.09703 1.057 0.0004645 -0.0002085 0.9085 0.0003501 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1649 0.1628 0.2142 0.1635 0.9861 0.992 0.165 0.9492 0.9744 0.2205 ] Network output: [ -0.006534 1.011 -0.01132 -2.061e-05 9.254e-06 1.014 -1.554e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03562 Epoch 4552 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04555 0.8526 0.9477 -9.208e-05 4.134e-05 0.1083 -6.939e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004517 -0.004391 -0.01571 0.00764 0.9646 0.97 0.009837 0.9193 0.9269 0.03336 ] Network output: [ 0.9656 0.1447 0.01174 -6.401e-05 2.874e-05 -0.08783 -4.824e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2736 -0.01998 -0.1638 0.1352 0.9833 0.9932 0.313 0.9007 0.9755 0.6955 ] Network output: [ 0.01217 0.8631 0.9773 -0.0001353 6.074e-05 0.1347 -0.000102 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006943 0.001896 0.006105 0.003386 0.9911 0.994 0.007092 0.9706 0.982 0.0159 ] Network output: [ -0.0008338 0.009964 0.9375 -0.0007346 0.0003298 1.051 -0.0005536 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3058 0.1901 0.4342 0.1275 0.9849 0.994 0.307 0.9072 0.9781 0.6931 ] Network output: [ -0.03133 0.2064 1.065 0.0003286 -0.0001475 0.7929 0.0002476 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1456 0.1351 0.2043 0.1351 0.9905 0.9943 0.1457 0.9677 0.9823 0.2261 ] Network output: [ -0.02232 0.07224 1.056 0.0005046 -0.0002265 0.9186 0.0003803 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1657 0.1635 0.2128 0.1642 0.9861 0.992 0.1657 0.9491 0.9744 0.219 ] Network output: [ 0.00926 0.9286 -0.002249 8.69e-05 -3.901e-05 1.055 6.549e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0324 Epoch 4553 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04858 0.843 0.9481 -7.893e-05 3.544e-05 0.1114 -5.949e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004543 -0.004464 -0.01605 0.008049 0.9646 0.97 0.009903 0.9194 0.927 0.03356 ] Network output: [ 1.015 0.05063 -0.009187 7.375e-05 -3.311e-05 -0.07083 5.558e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2755 -0.0247 -0.1832 0.155 0.9833 0.9932 0.3152 0.9007 0.9756 0.698 ] Network output: [ 0.009877 0.8634 0.9801 -0.0001357 6.093e-05 0.1361 -0.0001023 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006866 0.001826 0.005692 0.00393 0.9911 0.994 0.007014 0.9706 0.982 0.01564 ] Network output: [ 0.02655 -0.1596 0.9674 -0.0005182 0.0002326 1.137 -0.0003905 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3022 0.1863 0.4267 0.1645 0.9849 0.994 0.3033 0.9072 0.9781 0.6942 ] Network output: [ -0.03664 0.2053 1.07 0.0003178 -0.0001427 0.7988 0.0002395 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1456 0.135 0.2065 0.1395 0.9905 0.9943 0.1457 0.9678 0.9824 0.2289 ] Network output: [ -0.02998 0.09502 1.056 0.0004684 -0.0002103 0.9103 0.000353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1656 0.1635 0.2147 0.1637 0.9861 0.992 0.1656 0.9494 0.9745 0.221 ] Network output: [ -0.006457 1.01 -0.0112 -1.952e-05 8.761e-06 1.014 -1.471e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03489 Epoch 4554 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04511 0.8539 0.9482 -9.243e-05 4.15e-05 0.1074 -6.966e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004519 -0.004399 -0.01573 0.007656 0.9646 0.97 0.009846 0.9195 0.927 0.03341 ] Network output: [ 0.9664 0.1418 0.01208 -6.375e-05 2.862e-05 -0.08689 -4.804e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2733 -0.02035 -0.1642 0.1356 0.9833 0.9932 0.3128 0.9009 0.9756 0.6975 ] Network output: [ 0.01169 0.8646 0.9777 -0.0001355 6.084e-05 0.1337 -0.0001021 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006951 0.001883 0.006127 0.003383 0.9911 0.994 0.0071 0.9707 0.982 0.01595 ] Network output: [ -0.00109 0.009291 0.9388 -0.0007363 0.0003306 1.051 -0.0005549 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3056 0.1895 0.4355 0.1269 0.9849 0.994 0.3068 0.9074 0.9782 0.6951 ] Network output: [ -0.03091 0.2051 1.064 0.0003314 -0.0001488 0.7942 0.0002497 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1463 0.1357 0.2049 0.1353 0.9905 0.9943 0.1464 0.9679 0.9824 0.2267 ] Network output: [ -0.02187 0.07082 1.055 0.0005079 -0.000228 0.9201 0.0003828 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1664 0.1643 0.2134 0.1644 0.9861 0.992 0.1664 0.9493 0.9745 0.2196 ] Network output: [ 0.009284 0.9291 -0.002509 8.673e-05 -3.894e-05 1.055 6.536e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03176 Epoch 4555 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04808 0.8446 0.9485 -7.976e-05 3.581e-05 0.1103 -6.011e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004545 -0.004471 -0.01606 0.008052 0.9646 0.97 0.009913 0.9196 0.9271 0.0336 ] Network output: [ 1.015 0.05088 -0.009314 6.977e-05 -3.132e-05 -0.07131 5.258e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2753 -0.02501 -0.1835 0.1547 0.9833 0.9932 0.315 0.9009 0.9756 0.6999 ] Network output: [ 0.00941 0.865 0.9805 -0.000136 6.104e-05 0.1352 -0.0001025 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006875 0.001813 0.005714 0.003912 0.9911 0.994 0.007023 0.9707 0.982 0.01569 ] Network output: [ 0.02596 -0.1568 0.9677 -0.0005248 0.0002356 1.135 -0.0003955 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.302 0.1856 0.428 0.1632 0.9849 0.994 0.3031 0.9074 0.9781 0.6962 ] Network output: [ -0.03621 0.204 1.07 0.0003207 -0.000144 0.8001 0.0002417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1462 0.1355 0.207 0.1396 0.9905 0.9943 0.1463 0.9679 0.9824 0.2295 ] Network output: [ -0.02951 0.09306 1.056 0.0004723 -0.0002121 0.9122 0.000356 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1663 0.1642 0.2153 0.1639 0.9862 0.992 0.1663 0.9495 0.9745 0.2215 ] Network output: [ -0.006396 1.01 -0.01108 -1.857e-05 8.336e-06 1.014 -1.399e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03419 Epoch 4556 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04466 0.8552 0.9487 -9.278e-05 4.165e-05 0.1065 -6.992e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004521 -0.004407 -0.01574 0.007672 0.9646 0.97 0.009856 0.9196 0.9271 0.03346 ] Network output: [ 0.9671 0.1391 0.01243 -6.384e-05 2.866e-05 -0.08594 -4.811e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2731 -0.02072 -0.1646 0.1359 0.9833 0.9932 0.3126 0.9011 0.9756 0.6994 ] Network output: [ 0.01121 0.8661 0.9781 -0.0001357 6.092e-05 0.1328 -0.0001023 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006959 0.001871 0.00615 0.003379 0.9911 0.994 0.007109 0.9708 0.9821 0.016 ] Network output: [ -0.001372 0.008863 0.94 -0.0007383 0.0003315 1.051 -0.0005564 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3054 0.1888 0.4369 0.1263 0.9849 0.994 0.3066 0.9076 0.9782 0.697 ] Network output: [ -0.03047 0.2038 1.063 0.0003342 -0.00015 0.7956 0.0002519 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.147 0.1362 0.2055 0.1354 0.9905 0.9943 0.1471 0.968 0.9824 0.2273 ] Network output: [ -0.02141 0.06937 1.054 0.0005112 -0.0002295 0.9217 0.0003853 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1672 0.165 0.2139 0.1647 0.9861 0.992 0.1672 0.9495 0.9745 0.2201 ] Network output: [ 0.009322 0.9296 -0.002746 8.664e-05 -3.89e-05 1.055 6.53e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03113 Epoch 4557 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04758 0.8463 0.949 -8.053e-05 3.615e-05 0.1092 -6.069e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004548 -0.004478 -0.01608 0.008057 0.9646 0.97 0.009923 0.9198 0.9272 0.03365 ] Network output: [ 1.015 0.05095 -0.009434 6.585e-05 -2.956e-05 -0.0717 4.962e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2751 -0.02532 -0.1839 0.1545 0.9833 0.9932 0.3149 0.9011 0.9757 0.7018 ] Network output: [ 0.008939 0.8665 0.9809 -0.0001362 6.112e-05 0.1341 -0.0001026 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006884 0.0018 0.005736 0.003894 0.9911 0.994 0.007033 0.9709 0.9821 0.01575 ] Network output: [ 0.0254 -0.1543 0.9682 -0.0005311 0.0002384 1.133 -0.0004003 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3018 0.1849 0.4292 0.1619 0.9849 0.994 0.303 0.9076 0.9782 0.6981 ] Network output: [ -0.03578 0.2027 1.069 0.0003235 -0.0001453 0.8014 0.0002438 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1469 0.136 0.2076 0.1397 0.9905 0.9943 0.1469 0.968 0.9824 0.2302 ] Network output: [ -0.02904 0.09113 1.055 0.0004762 -0.0002138 0.914 0.0003589 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.167 0.1648 0.2158 0.1641 0.9862 0.992 0.1671 0.9497 0.9746 0.2221 ] Network output: [ -0.006349 1.01 -0.01098 -1.777e-05 7.977e-06 1.014 -1.339e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0335 Epoch 4558 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0442 0.8565 0.9492 -9.312e-05 4.181e-05 0.1055 -7.018e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004524 -0.004415 -0.01576 0.007687 0.9646 0.97 0.009867 0.9198 0.9272 0.03351 ] Network output: [ 0.9677 0.1365 0.01281 -6.427e-05 2.885e-05 -0.085 -4.843e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2729 -0.02109 -0.165 0.1362 0.9833 0.9932 0.3124 0.9013 0.9757 0.7013 ] Network output: [ 0.01074 0.8677 0.9786 -0.0001358 6.098e-05 0.1318 -0.0001024 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006968 0.001859 0.006175 0.003375 0.9911 0.994 0.007119 0.9709 0.9821 0.01605 ] Network output: [ -0.001677 0.00866 0.9411 -0.0007405 0.0003325 1.051 -0.0005581 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3052 0.1881 0.4382 0.1257 0.9849 0.994 0.3064 0.9078 0.9782 0.6989 ] Network output: [ -0.03003 0.2025 1.062 0.0003371 -0.0001513 0.7969 0.000254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1477 0.1368 0.206 0.1356 0.9905 0.9944 0.1478 0.9681 0.9824 0.228 ] Network output: [ -0.02094 0.0679 1.053 0.0005147 -0.000231 0.9233 0.0003879 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1679 0.1657 0.2144 0.1649 0.9862 0.992 0.1679 0.9497 0.9746 0.2206 ] Network output: [ 0.009373 0.9299 -0.002963 8.662e-05 -3.889e-05 1.055 6.528e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03051 Epoch 4559 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04708 0.8479 0.9495 -8.125e-05 3.648e-05 0.1082 -6.123e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004551 -0.004486 -0.01609 0.008062 0.9646 0.97 0.009934 0.9199 0.9273 0.0337 ] Network output: [ 1.015 0.05085 -0.009552 6.197e-05 -2.782e-05 -0.07201 4.67e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2749 -0.02565 -0.1842 0.1543 0.9834 0.9932 0.3147 0.9013 0.9757 0.7037 ] Network output: [ 0.008464 0.868 0.9814 -0.0001363 6.119e-05 0.1331 -0.0001027 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006894 0.001787 0.005758 0.003878 0.9912 0.994 0.007043 0.971 0.9821 0.0158 ] Network output: [ 0.02489 -0.152 0.9687 -0.0005371 0.0002411 1.131 -0.0004048 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3016 0.1842 0.4305 0.1606 0.9849 0.994 0.3028 0.9078 0.9782 0.7 ] Network output: [ -0.03535 0.2014 1.068 0.0003264 -0.0001465 0.8027 0.000246 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1475 0.1365 0.2082 0.1398 0.9905 0.9944 0.1476 0.9681 0.9825 0.2308 ] Network output: [ -0.02858 0.08924 1.054 0.0004801 -0.0002155 0.9158 0.0003618 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1678 0.1655 0.2164 0.1644 0.9862 0.992 0.1678 0.9499 0.9746 0.2227 ] Network output: [ -0.006317 1.01 -0.01088 -1.711e-05 7.682e-06 1.014 -1.29e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03283 Epoch 4560 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04374 0.8579 0.9497 -9.345e-05 4.196e-05 0.1046 -7.043e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004526 -0.004423 -0.01578 0.007702 0.9646 0.97 0.009877 0.92 0.9273 0.03356 ] Network output: [ 0.9683 0.134 0.0132 -6.5e-05 2.918e-05 -0.08404 -4.899e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2728 -0.02146 -0.1652 0.1365 0.9834 0.9932 0.3122 0.9015 0.9757 0.7032 ] Network output: [ 0.01026 0.8692 0.979 -0.0001359 6.102e-05 0.1307 -0.0001024 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006978 0.001847 0.006201 0.003371 0.9912 0.994 0.007129 0.971 0.9822 0.01611 ] Network output: [ -0.002005 0.008663 0.9423 -0.000743 0.0003335 1.05 -0.0005599 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.305 0.1875 0.4395 0.125 0.9849 0.994 0.3062 0.908 0.9783 0.7008 ] Network output: [ -0.02959 0.2011 1.061 0.00034 -0.0001526 0.7983 0.0002562 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1484 0.1374 0.2066 0.1358 0.9905 0.9944 0.1485 0.9682 0.9825 0.2286 ] Network output: [ -0.02046 0.0664 1.052 0.0005181 -0.0002326 0.9249 0.0003905 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1687 0.1665 0.215 0.1651 0.9862 0.992 0.1687 0.9499 0.9747 0.2212 ] Network output: [ 0.009436 0.9302 -0.003162 8.665e-05 -3.89e-05 1.054 6.531e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02989 Epoch 4561 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04658 0.8495 0.9499 -8.192e-05 3.678e-05 0.107 -6.174e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004554 -0.004495 -0.01611 0.008068 0.9646 0.97 0.009946 0.9201 0.9274 0.03375 ] Network output: [ 1.016 0.05061 -0.009671 5.811e-05 -2.609e-05 -0.07225 4.38e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2748 -0.02598 -0.1845 0.1541 0.9834 0.9932 0.3146 0.9015 0.9758 0.7055 ] Network output: [ 0.007987 0.8696 0.9818 -0.0001364 6.123e-05 0.1321 -0.0001028 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006904 0.001774 0.005781 0.003862 0.9912 0.994 0.007053 0.9711 0.9822 0.01585 ] Network output: [ 0.02441 -0.1498 0.9692 -0.0005427 0.0002437 1.13 -0.000409 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3015 0.1835 0.4319 0.1594 0.9849 0.994 0.3026 0.908 0.9783 0.7019 ] Network output: [ -0.03492 0.2 1.067 0.0003293 -0.0001479 0.804 0.0002482 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1482 0.1371 0.2088 0.1399 0.9905 0.9944 0.1483 0.9683 0.9825 0.2314 ] Network output: [ -0.02812 0.08738 1.053 0.0004839 -0.0002172 0.9176 0.0003647 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1685 0.1663 0.2169 0.1646 0.9862 0.992 0.1685 0.9501 0.9747 0.2232 ] Network output: [ -0.0063 1.01 -0.01079 -1.659e-05 7.449e-06 1.014 -1.25e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03217 Epoch 4562 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04328 0.8593 0.9502 -9.377e-05 4.21e-05 0.1036 -7.067e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004529 -0.004431 -0.01579 0.007717 0.9646 0.97 0.009888 0.9202 0.9274 0.03361 ] Network output: [ 0.9688 0.1316 0.01361 -6.604e-05 2.965e-05 -0.08309 -4.977e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2726 -0.02182 -0.1655 0.1368 0.9834 0.9932 0.312 0.9017 0.9757 0.705 ] Network output: [ 0.00978 0.8708 0.9794 -0.000136 6.105e-05 0.1297 -0.0001025 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006988 0.001835 0.006228 0.003366 0.9912 0.994 0.007139 0.9711 0.9822 0.01616 ] Network output: [ -0.002352 0.008852 0.9433 -0.0007456 0.0003347 1.049 -0.0005619 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3049 0.1868 0.4409 0.1243 0.9849 0.994 0.306 0.9082 0.9783 0.7026 ] Network output: [ -0.02913 0.1998 1.06 0.000343 -0.000154 0.7997 0.0002585 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1491 0.138 0.2073 0.136 0.9905 0.9944 0.1492 0.9683 0.9825 0.2293 ] Network output: [ -0.01997 0.06488 1.051 0.0005216 -0.0002342 0.9265 0.0003931 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1695 0.1672 0.2155 0.1654 0.9862 0.992 0.1695 0.95 0.9747 0.2217 ] Network output: [ 0.009511 0.9304 -0.003344 8.674e-05 -3.894e-05 1.054 6.537e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02929 Epoch 4563 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04609 0.8512 0.9504 -8.254e-05 3.706e-05 0.1059 -6.22e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004558 -0.004503 -0.01613 0.008075 0.9646 0.97 0.009958 0.9203 0.9276 0.0338 ] Network output: [ 1.016 0.05023 -0.009793 5.427e-05 -2.436e-05 -0.07242 4.09e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2746 -0.02632 -0.1847 0.154 0.9834 0.9932 0.3144 0.9017 0.9758 0.7073 ] Network output: [ 0.007507 0.8712 0.9823 -0.0001365 6.126e-05 0.131 -0.0001028 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006915 0.001761 0.005805 0.003848 0.9912 0.9941 0.007064 0.9712 0.9822 0.0159 ] Network output: [ 0.02396 -0.1478 0.9697 -0.0005481 0.0002461 1.128 -0.0004131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3014 0.1829 0.4332 0.1583 0.9849 0.994 0.3025 0.9082 0.9783 0.7037 ] Network output: [ -0.0345 0.1986 1.066 0.0003323 -0.0001492 0.8054 0.0002504 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1489 0.1376 0.2094 0.14 0.9905 0.9944 0.149 0.9684 0.9826 0.2321 ] Network output: [ -0.02767 0.08554 1.052 0.0004877 -0.0002189 0.9194 0.0003675 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1692 0.167 0.2175 0.1649 0.9862 0.992 0.1693 0.9502 0.9748 0.2238 ] Network output: [ -0.006298 1.01 -0.01071 -1.621e-05 7.276e-06 1.014 -1.221e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03152 Epoch 4564 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04281 0.8608 0.9507 -9.406e-05 4.223e-05 0.1026 -7.089e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004533 -0.00444 -0.0158 0.007731 0.9646 0.97 0.0099 0.9203 0.9275 0.03366 ] Network output: [ 0.9693 0.1292 0.01403 -6.735e-05 3.023e-05 -0.08212 -5.076e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2724 -0.02218 -0.1656 0.137 0.9834 0.9932 0.3119 0.9018 0.9758 0.7068 ] Network output: [ 0.009304 0.8724 0.9798 -0.000136 6.105e-05 0.1286 -0.0001025 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006999 0.001824 0.006256 0.003362 0.9912 0.9941 0.00715 0.9712 0.9823 0.01622 ] Network output: [ -0.002717 0.00921 0.9444 -0.0007483 0.000336 1.049 -0.000564 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3048 0.1862 0.4423 0.1235 0.9849 0.994 0.3059 0.9083 0.9783 0.7044 ] Network output: [ -0.02867 0.1984 1.059 0.0003459 -0.0001553 0.8011 0.0002607 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1498 0.1386 0.2079 0.1362 0.9906 0.9944 0.1499 0.9684 0.9826 0.2299 ] Network output: [ -0.01947 0.06334 1.05 0.0005252 -0.0002358 0.9281 0.0003958 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1703 0.168 0.216 0.1656 0.9862 0.992 0.1703 0.9502 0.9748 0.2222 ] Network output: [ 0.009596 0.9306 -0.003511 8.688e-05 -3.9e-05 1.054 6.548e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02868 Epoch 4565 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04559 0.8528 0.9509 -8.311e-05 3.731e-05 0.1048 -6.263e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004561 -0.004512 -0.01614 0.008083 0.9646 0.97 0.00997 0.9204 0.9276 0.03386 ] Network output: [ 1.016 0.04975 -0.009919 5.041e-05 -2.263e-05 -0.07253 3.799e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2745 -0.02666 -0.185 0.1539 0.9834 0.9932 0.3143 0.9018 0.9758 0.709 ] Network output: [ 0.007026 0.8728 0.9827 -0.0001365 6.126e-05 0.1299 -0.0001028 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006926 0.001749 0.005829 0.003835 0.9912 0.9941 0.007075 0.9713 0.9823 0.01596 ] Network output: [ 0.02354 -0.146 0.9703 -0.0005533 0.0002484 1.126 -0.000417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3012 0.1822 0.4345 0.1572 0.9849 0.994 0.3024 0.9083 0.9783 0.7055 ] Network output: [ -0.03408 0.1972 1.066 0.0003352 -0.0001505 0.8068 0.0002526 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1496 0.1382 0.21 0.1402 0.9905 0.9944 0.1497 0.9685 0.9826 0.2327 ] Network output: [ -0.02722 0.08373 1.052 0.0004915 -0.0002206 0.9212 0.0003704 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.17 0.1677 0.2181 0.1651 0.9862 0.992 0.17 0.9504 0.9748 0.2244 ] Network output: [ -0.006309 1.01 -0.01064 -1.595e-05 7.159e-06 1.014 -1.202e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03088 Epoch 4566 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04233 0.8622 0.9512 -9.433e-05 4.235e-05 0.1015 -7.109e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004536 -0.004448 -0.01582 0.007746 0.9646 0.97 0.009912 0.9205 0.9276 0.03371 ] Network output: [ 0.9697 0.1269 0.01447 -6.892e-05 3.094e-05 -0.08115 -5.194e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2723 -0.02254 -0.1658 0.1373 0.9834 0.9932 0.3117 0.902 0.9758 0.7085 ] Network output: [ 0.008829 0.874 0.9803 -0.0001359 6.103e-05 0.1275 -0.0001024 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00701 0.001812 0.006286 0.003357 0.9912 0.9941 0.007162 0.9713 0.9823 0.01627 ] Network output: [ -0.003098 0.009718 0.9453 -0.0007512 0.0003373 1.048 -0.0005662 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3046 0.1856 0.4437 0.1228 0.9849 0.994 0.3058 0.9085 0.9784 0.7062 ] Network output: [ -0.02821 0.197 1.058 0.000349 -0.0001567 0.8025 0.000263 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1506 0.1392 0.2085 0.1364 0.9906 0.9944 0.1507 0.9685 0.9826 0.2306 ] Network output: [ -0.01897 0.06178 1.049 0.0005288 -0.0002374 0.9297 0.0003985 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1711 0.1688 0.2166 0.1659 0.9862 0.992 0.1711 0.9504 0.9749 0.2228 ] Network output: [ 0.009691 0.9306 -0.003664 8.705e-05 -3.908e-05 1.054 6.561e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02809 Epoch 4567 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0451 0.8545 0.9514 -8.363e-05 3.755e-05 0.1036 -6.303e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004565 -0.004521 -0.01616 0.008091 0.9646 0.97 0.009983 0.9206 0.9277 0.03391 ] Network output: [ 1.017 0.04917 -0.01005 4.654e-05 -2.089e-05 -0.0726 3.507e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2744 -0.02702 -0.1852 0.1538 0.9834 0.9932 0.3142 0.902 0.9759 0.7107 ] Network output: [ 0.006543 0.8744 0.9832 -0.0001364 6.124e-05 0.1288 -0.0001028 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006937 0.001736 0.005854 0.003823 0.9912 0.9941 0.007087 0.9714 0.9823 0.01601 ] Network output: [ 0.02315 -0.1444 0.9708 -0.0005582 0.0002506 1.125 -0.0004206 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3011 0.1816 0.4358 0.1562 0.9849 0.994 0.3023 0.9085 0.9784 0.7073 ] Network output: [ -0.03366 0.1958 1.065 0.0003382 -0.0001518 0.8082 0.0002548 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1503 0.1388 0.2106 0.1403 0.9905 0.9944 0.1504 0.9686 0.9827 0.2334 ] Network output: [ -0.02678 0.08194 1.051 0.0004952 -0.0002223 0.9229 0.0003732 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1708 0.1685 0.2186 0.1654 0.9862 0.992 0.1708 0.9506 0.9749 0.2249 ] Network output: [ -0.006333 1.01 -0.01057 -1.58e-05 7.094e-06 1.013 -1.191e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03025 Epoch 4568 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04185 0.8638 0.9517 -9.458e-05 4.246e-05 0.1005 -7.128e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004539 -0.004457 -0.01583 0.00776 0.9646 0.97 0.009924 0.9206 0.9277 0.03376 ] Network output: [ 0.9701 0.1247 0.01493 -7.073e-05 3.175e-05 -0.08017 -5.331e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2722 -0.0229 -0.1658 0.1375 0.9834 0.9932 0.3116 0.9022 0.9759 0.7102 ] Network output: [ 0.008356 0.8756 0.9807 -0.0001358 6.099e-05 0.1264 -0.0001024 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007022 0.001801 0.006317 0.003352 0.9912 0.9941 0.007174 0.9715 0.9824 0.01633 ] Network output: [ -0.003492 0.01036 0.9463 -0.0007543 0.0003386 1.047 -0.0005684 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3045 0.185 0.4451 0.122 0.9849 0.994 0.3057 0.9087 0.9784 0.7079 ] Network output: [ -0.02774 0.1956 1.057 0.000352 -0.000158 0.8039 0.0002653 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1513 0.1398 0.2091 0.1366 0.9906 0.9944 0.1514 0.9686 0.9827 0.2312 ] Network output: [ -0.01845 0.0602 1.048 0.0005324 -0.000239 0.9314 0.0004012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1719 0.1696 0.2171 0.1661 0.9862 0.992 0.1719 0.9506 0.9749 0.2233 ] Network output: [ 0.009794 0.9307 -0.003806 8.726e-05 -3.918e-05 1.054 6.576e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0275 Epoch 4569 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04461 0.8561 0.9518 -8.41e-05 3.776e-05 0.1025 -6.338e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004569 -0.00453 -0.01617 0.008099 0.9646 0.97 0.009997 0.9207 0.9278 0.03396 ] Network output: [ 1.017 0.0485 -0.01019 4.262e-05 -1.914e-05 -0.07262 3.212e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2743 -0.02738 -0.1854 0.1537 0.9834 0.9932 0.3142 0.9022 0.9759 0.7124 ] Network output: [ 0.00606 0.876 0.9836 -0.0001363 6.12e-05 0.1277 -0.0001027 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006949 0.001724 0.00588 0.003811 0.9912 0.9941 0.007099 0.9715 0.9824 0.01606 ] Network output: [ 0.02278 -0.1429 0.9714 -0.0005628 0.0002527 1.124 -0.0004242 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.301 0.1809 0.4371 0.1551 0.9849 0.994 0.3022 0.9087 0.9784 0.709 ] Network output: [ -0.03325 0.1943 1.064 0.0003411 -0.0001531 0.8096 0.0002571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.151 0.1394 0.2112 0.1405 0.9906 0.9944 0.1511 0.9687 0.9827 0.2341 ] Network output: [ -0.02633 0.08017 1.05 0.0004989 -0.000224 0.9247 0.000376 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1715 0.1692 0.2192 0.1657 0.9863 0.9921 0.1716 0.9507 0.9749 0.2255 ] Network output: [ -0.00637 1.01 -0.01051 -1.577e-05 7.079e-06 1.013 -1.188e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02964 Epoch 4570 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04137 0.8653 0.9522 -9.48e-05 4.256e-05 0.09939 -7.144e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004543 -0.004466 -0.01584 0.007774 0.9646 0.97 0.009937 0.9208 0.9278 0.03381 ] Network output: [ 0.9705 0.1226 0.0154 -7.278e-05 3.267e-05 -0.07918 -5.485e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.272 -0.02327 -0.1659 0.1377 0.9834 0.9932 0.3115 0.9024 0.9759 0.7119 ] Network output: [ 0.007885 0.8773 0.9811 -0.0001357 6.092e-05 0.1253 -0.0001023 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007034 0.00179 0.006349 0.003347 0.9912 0.9941 0.007186 0.9716 0.9824 0.01639 ] Network output: [ -0.003898 0.01112 0.9473 -0.0007574 0.00034 1.046 -0.0005708 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3045 0.1844 0.4466 0.1212 0.9849 0.994 0.3056 0.9088 0.9784 0.7096 ] Network output: [ -0.02726 0.1942 1.056 0.0003551 -0.0001594 0.8053 0.0002676 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1521 0.1405 0.2098 0.1368 0.9906 0.9944 0.1522 0.9687 0.9827 0.2319 ] Network output: [ -0.01793 0.0586 1.046 0.0005361 -0.0002407 0.933 0.000404 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1727 0.1704 0.2177 0.1664 0.9863 0.992 0.1728 0.9507 0.975 0.2239 ] Network output: [ 0.009906 0.9306 -0.003937 8.75e-05 -3.928e-05 1.054 6.594e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02692 Epoch 4571 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04413 0.8578 0.9523 -8.453e-05 3.795e-05 0.1013 -6.37e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004573 -0.004539 -0.01619 0.008108 0.9646 0.97 0.01001 0.9209 0.9279 0.03401 ] Network output: [ 1.018 0.04777 -0.01034 3.866e-05 -1.736e-05 -0.07261 2.914e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2743 -0.02774 -0.1856 0.1536 0.9834 0.9932 0.3141 0.9024 0.9759 0.7141 ] Network output: [ 0.005577 0.8777 0.9841 -0.0001362 6.113e-05 0.1266 -0.0001026 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006961 0.001711 0.005906 0.003801 0.9912 0.9941 0.007111 0.9716 0.9824 0.01611 ] Network output: [ 0.02244 -0.1415 0.9721 -0.0005673 0.0002547 1.122 -0.0004276 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.301 0.1803 0.4385 0.1542 0.9849 0.994 0.3021 0.9088 0.9784 0.7107 ] Network output: [ -0.03284 0.1929 1.063 0.0003441 -0.0001545 0.8111 0.0002593 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1517 0.14 0.2119 0.1407 0.9906 0.9944 0.1518 0.9688 0.9827 0.2348 ] Network output: [ -0.0259 0.07841 1.049 0.0005027 -0.0002257 0.9264 0.0003788 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1723 0.17 0.2198 0.1659 0.9863 0.9921 0.1723 0.9509 0.975 0.2261 ] Network output: [ -0.006417 1.01 -0.01045 -1.584e-05 7.109e-06 1.013 -1.193e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02903 Epoch 4572 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04089 0.8668 0.9527 -9.498e-05 4.264e-05 0.0983 -7.158e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004547 -0.004475 -0.01585 0.007788 0.9646 0.97 0.00995 0.9209 0.9279 0.03386 ] Network output: [ 0.9708 0.1205 0.01587 -7.503e-05 3.369e-05 -0.07819 -5.655e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.272 -0.02363 -0.1658 0.1379 0.9834 0.9932 0.3114 0.9026 0.9759 0.7135 ] Network output: [ 0.007417 0.8789 0.9815 -0.0001355 6.084e-05 0.1242 -0.0001021 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007047 0.001779 0.006382 0.003342 0.9912 0.9941 0.0072 0.9717 0.9825 0.01644 ] Network output: [ -0.004313 0.01197 0.9482 -0.0007606 0.0003414 1.045 -0.0005732 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3044 0.1838 0.448 0.1204 0.9849 0.994 0.3056 0.909 0.9785 0.7113 ] Network output: [ -0.02678 0.1927 1.056 0.0003582 -0.0001608 0.8068 0.0002699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1529 0.1411 0.2104 0.137 0.9906 0.9944 0.153 0.9688 0.9827 0.2326 ] Network output: [ -0.0174 0.05699 1.045 0.0005398 -0.0002423 0.9347 0.0004068 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1736 0.1712 0.2182 0.1667 0.9863 0.9921 0.1736 0.9509 0.975 0.2244 ] Network output: [ 0.01002 0.9306 -0.004059 8.775e-05 -3.939e-05 1.054 6.613e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02634 Epoch 4573 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04364 0.8595 0.9528 -8.49e-05 3.812e-05 0.1001 -6.399e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004578 -0.004549 -0.0162 0.008118 0.9646 0.97 0.01002 0.921 0.928 0.03406 ] Network output: [ 1.018 0.04699 -0.0105 3.463e-05 -1.555e-05 -0.07257 2.61e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2742 -0.02811 -0.1857 0.1535 0.9834 0.9932 0.3141 0.9025 0.976 0.7157 ] Network output: [ 0.005094 0.8793 0.9845 -0.000136 6.105e-05 0.1254 -0.0001025 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006973 0.001699 0.005934 0.003791 0.9912 0.9941 0.007124 0.9717 0.9825 0.01617 ] Network output: [ 0.02213 -0.1402 0.9727 -0.0005717 0.0002566 1.121 -0.0004308 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3009 0.1797 0.4398 0.1532 0.9849 0.994 0.3021 0.909 0.9784 0.7124 ] Network output: [ -0.03243 0.1914 1.062 0.0003471 -0.0001558 0.8125 0.0002616 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1524 0.1406 0.2126 0.1409 0.9906 0.9944 0.1525 0.9689 0.9828 0.2355 ] Network output: [ -0.02546 0.07666 1.048 0.0005064 -0.0002273 0.9282 0.0003816 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1731 0.1707 0.2204 0.1662 0.9863 0.9921 0.1731 0.9511 0.9751 0.2267 ] Network output: [ -0.006475 1.011 -0.0104 -1.599e-05 7.18e-06 1.013 -1.205e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02843 Epoch 4574 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04041 0.8684 0.9532 -9.513e-05 4.271e-05 0.09718 -7.169e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004551 -0.004484 -0.01586 0.007802 0.9646 0.97 0.009963 0.9211 0.928 0.03391 ] Network output: [ 0.971 0.1184 0.01636 -7.749e-05 3.479e-05 -0.07718 -5.84e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2719 -0.02399 -0.1658 0.1381 0.9834 0.9932 0.3114 0.9027 0.976 0.7152 ] Network output: [ 0.00695 0.8806 0.9819 -0.0001353 6.073e-05 0.123 -0.0001019 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00706 0.001768 0.006417 0.003337 0.9912 0.9941 0.007213 0.9717 0.9825 0.0165 ] Network output: [ -0.004736 0.01291 0.9491 -0.0007638 0.0003429 1.044 -0.0005756 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3044 0.1833 0.4495 0.1196 0.9849 0.994 0.3056 0.9092 0.9785 0.7129 ] Network output: [ -0.0263 0.1912 1.055 0.0003613 -0.0001622 0.8083 0.0002723 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1536 0.1418 0.2111 0.1372 0.9906 0.9944 0.1537 0.9689 0.9828 0.2333 ] Network output: [ -0.01687 0.05536 1.044 0.0005435 -0.000244 0.9363 0.0004096 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1744 0.172 0.2188 0.1669 0.9863 0.9921 0.1744 0.9511 0.9751 0.225 ] Network output: [ 0.01015 0.9305 -0.004172 8.801e-05 -3.951e-05 1.054 6.633e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02577 Epoch 4575 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04316 0.8612 0.9533 -8.523e-05 3.826e-05 0.09886 -6.423e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004582 -0.004559 -0.01621 0.008128 0.9646 0.97 0.01004 0.9212 0.9281 0.03411 ] Network output: [ 1.019 0.04617 -0.01066 3.052e-05 -1.37e-05 -0.07251 2.3e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2742 -0.02849 -0.1859 0.1535 0.9834 0.9932 0.314 0.9027 0.976 0.7173 ] Network output: [ 0.004611 0.881 0.985 -0.0001357 6.093e-05 0.1243 -0.0001023 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006986 0.001686 0.005962 0.003781 0.9912 0.9941 0.007137 0.9718 0.9825 0.01622 ] Network output: [ 0.02183 -0.1391 0.9734 -0.0005759 0.0002585 1.12 -0.000434 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3009 0.1791 0.4412 0.1523 0.9849 0.994 0.302 0.9092 0.9785 0.714 ] Network output: [ -0.03203 0.1898 1.062 0.0003502 -0.0001572 0.814 0.0002639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1532 0.1412 0.2132 0.1411 0.9906 0.9944 0.1533 0.969 0.9828 0.2362 ] Network output: [ -0.02503 0.07492 1.047 0.0005101 -0.000229 0.9299 0.0003844 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1739 0.1715 0.221 0.1665 0.9863 0.9921 0.1739 0.9513 0.9751 0.2273 ] Network output: [ -0.006542 1.011 -0.01035 -1.623e-05 7.288e-06 1.012 -1.223e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02784 Epoch 4576 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03993 0.87 0.9537 -9.525e-05 4.276e-05 0.09604 -7.178e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004555 -0.004494 -0.01587 0.007816 0.9646 0.97 0.009977 0.9212 0.9281 0.03396 ] Network output: [ 0.9713 0.1164 0.01685 -8.013e-05 3.597e-05 -0.07617 -6.039e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2718 -0.02435 -0.1657 0.1383 0.9834 0.9932 0.3113 0.9029 0.976 0.7167 ] Network output: [ 0.006486 0.8823 0.9823 -0.000135 6.06e-05 0.1218 -0.0001017 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007074 0.001758 0.006453 0.003332 0.9912 0.9941 0.007227 0.9718 0.9825 0.01656 ] Network output: [ -0.005163 0.01391 0.95 -0.0007671 0.0003444 1.043 -0.0005781 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3044 0.1827 0.451 0.1187 0.9849 0.994 0.3055 0.9093 0.9785 0.7145 ] Network output: [ -0.02582 0.1897 1.054 0.0003644 -0.0001636 0.8097 0.0002747 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1544 0.1424 0.2118 0.1375 0.9906 0.9944 0.1545 0.969 0.9828 0.234 ] Network output: [ -0.01633 0.05373 1.043 0.0005472 -0.0002457 0.938 0.0004124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1753 0.1728 0.2194 0.1672 0.9863 0.9921 0.1753 0.9512 0.9752 0.2255 ] Network output: [ 0.01028 0.9304 -0.004278 8.828e-05 -3.963e-05 1.054 6.653e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0252 Epoch 4577 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04268 0.8629 0.9537 -8.551e-05 3.839e-05 0.09764 -6.444e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004587 -0.004569 -0.01623 0.008137 0.9646 0.97 0.01005 0.9213 0.9282 0.03416 ] Network output: [ 1.019 0.04532 -0.01084 2.632e-05 -1.182e-05 -0.07242 1.984e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2742 -0.02888 -0.186 0.1535 0.9834 0.9932 0.314 0.9029 0.976 0.7189 ] Network output: [ 0.00413 0.8827 0.9854 -0.0001354 6.08e-05 0.1231 -0.0001021 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006999 0.001674 0.00599 0.003773 0.9913 0.9941 0.007151 0.9719 0.9825 0.01627 ] Network output: [ 0.02154 -0.1379 0.974 -0.00058 0.0002604 1.118 -0.0004371 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3008 0.1784 0.4426 0.1514 0.9849 0.994 0.302 0.9093 0.9785 0.7157 ] Network output: [ -0.03163 0.1883 1.061 0.0003532 -0.0001586 0.8156 0.0002662 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1539 0.1418 0.2139 0.1413 0.9906 0.9944 0.154 0.9691 0.9828 0.2369 ] Network output: [ -0.02459 0.07318 1.046 0.0005138 -0.0002307 0.9317 0.0003872 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1747 0.1723 0.2216 0.1668 0.9863 0.9921 0.1748 0.9514 0.9752 0.2279 ] Network output: [ -0.006617 1.011 -0.01029 -1.654e-05 7.427e-06 1.012 -1.247e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02725 Epoch 4578 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03945 0.8716 0.9542 -9.532e-05 4.279e-05 0.09489 -7.184e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00456 -0.004503 -0.01588 0.00783 0.9646 0.97 0.009991 0.9213 0.9282 0.03401 ] Network output: [ 0.9715 0.1144 0.01735 -8.294e-05 3.724e-05 -0.07515 -6.251e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2717 -0.02471 -0.1655 0.1385 0.9834 0.9932 0.3113 0.9031 0.976 0.7183 ] Network output: [ 0.006024 0.884 0.9828 -0.0001346 6.044e-05 0.1206 -0.0001015 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007088 0.001747 0.00649 0.003327 0.9913 0.9941 0.007242 0.9719 0.9826 0.01661 ] Network output: [ -0.005593 0.01496 0.9508 -0.0007704 0.0003459 1.042 -0.0005806 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3044 0.1822 0.4525 0.1179 0.9849 0.994 0.3055 0.9095 0.9785 0.716 ] Network output: [ -0.02533 0.1882 1.053 0.0003676 -0.000165 0.8112 0.000277 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1552 0.1431 0.2125 0.1377 0.9906 0.9944 0.1553 0.9691 0.9829 0.2346 ] Network output: [ -0.01579 0.05208 1.042 0.000551 -0.0002473 0.9396 0.0004152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1761 0.1737 0.2199 0.1675 0.9863 0.9921 0.1761 0.9514 0.9752 0.2261 ] Network output: [ 0.01041 0.9302 -0.004378 8.854e-05 -3.975e-05 1.054 6.673e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02464 Epoch 4579 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0422 0.8647 0.9542 -8.574e-05 3.849e-05 0.09639 -6.462e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004592 -0.004579 -0.01624 0.008148 0.9646 0.97 0.01007 0.9214 0.9283 0.03422 ] Network output: [ 1.019 0.04445 -0.01101 2.202e-05 -9.885e-06 -0.07232 1.659e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2741 -0.02926 -0.186 0.1534 0.9834 0.9932 0.3141 0.903 0.9761 0.7204 ] Network output: [ 0.00365 0.8844 0.9859 -0.0001351 6.064e-05 0.1219 -0.0001018 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007013 0.001662 0.00602 0.003764 0.9913 0.9941 0.007165 0.972 0.9826 0.01633 ] Network output: [ 0.02127 -0.1369 0.9747 -0.000584 0.0002622 1.117 -0.0004401 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3008 0.1778 0.444 0.1505 0.9849 0.994 0.302 0.9095 0.9785 0.7172 ] Network output: [ -0.03123 0.1867 1.06 0.0003563 -0.0001599 0.8171 0.0002685 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1547 0.1424 0.2146 0.1415 0.9906 0.9944 0.1548 0.9691 0.9829 0.2376 ] Network output: [ -0.02416 0.07145 1.046 0.0005175 -0.0002323 0.9335 0.00039 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1756 0.1731 0.2222 0.1671 0.9863 0.9921 0.1756 0.9516 0.9752 0.2285 ] Network output: [ -0.006698 1.012 -0.01025 -1.691e-05 7.592e-06 1.012 -1.274e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02667 Epoch 4580 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03896 0.8733 0.9547 -9.535e-05 4.281e-05 0.09372 -7.186e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004564 -0.004513 -0.01589 0.007843 0.9646 0.97 0.01001 0.9215 0.9282 0.03407 ] Network output: [ 0.9717 0.1125 0.01784 -8.591e-05 3.857e-05 -0.07412 -6.474e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2717 -0.02508 -0.1653 0.1386 0.9834 0.9932 0.3112 0.9032 0.9761 0.7198 ] Network output: [ 0.005564 0.8857 0.9832 -0.0001342 6.026e-05 0.1194 -0.0001012 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007103 0.001737 0.006528 0.003323 0.9913 0.9941 0.007257 0.972 0.9826 0.01667 ] Network output: [ -0.006022 0.01605 0.9517 -0.0007737 0.0003474 1.041 -0.0005831 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3044 0.1817 0.454 0.1171 0.9849 0.994 0.3056 0.9096 0.9786 0.7176 ] Network output: [ -0.02484 0.1866 1.052 0.0003708 -0.0001665 0.8128 0.0002794 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.156 0.1438 0.2132 0.1379 0.9906 0.9944 0.1562 0.9692 0.9829 0.2354 ] Network output: [ -0.01525 0.05042 1.041 0.0005547 -0.000249 0.9413 0.0004181 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.177 0.1745 0.2205 0.1678 0.9863 0.9921 0.177 0.9516 0.9753 0.2267 ] Network output: [ 0.01054 0.9301 -0.004473 8.879e-05 -3.986e-05 1.054 6.692e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02409 Epoch 4581 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04172 0.8664 0.9547 -8.593e-05 3.858e-05 0.09513 -6.476e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004597 -0.00459 -0.01625 0.008158 0.9646 0.97 0.01009 0.9216 0.9284 0.03427 ] Network output: [ 1.02 0.04358 -0.0112 1.76e-05 -7.902e-06 -0.07221 1.327e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2742 -0.02966 -0.1861 0.1534 0.9834 0.9932 0.3141 0.9032 0.9761 0.7219 ] Network output: [ 0.003171 0.8861 0.9863 -0.0001347 6.045e-05 0.1207 -0.0001015 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007027 0.001649 0.006051 0.003756 0.9913 0.9941 0.007179 0.972 0.9826 0.01638 ] Network output: [ 0.02101 -0.1359 0.9754 -0.0005879 0.0002639 1.116 -0.000443 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3008 0.1772 0.4454 0.1496 0.9849 0.994 0.302 0.9096 0.9786 0.7188 ] Network output: [ -0.03083 0.185 1.059 0.0003594 -0.0001613 0.8187 0.0002708 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1555 0.1431 0.2154 0.1418 0.9906 0.9944 0.1556 0.9692 0.9829 0.2383 ] Network output: [ -0.02373 0.06972 1.045 0.0005212 -0.000234 0.9352 0.0003928 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1764 0.1739 0.2229 0.1674 0.9864 0.9921 0.1764 0.9517 0.9753 0.2291 ] Network output: [ -0.006784 1.012 -0.0102 -1.733e-05 7.779e-06 1.011 -1.306e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0261 Epoch 4582 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03848 0.8749 0.9552 -9.535e-05 4.28e-05 0.09253 -7.186e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004569 -0.004523 -0.0159 0.007857 0.9646 0.97 0.01002 0.9216 0.9283 0.03412 ] Network output: [ 0.9719 0.1106 0.01834 -8.902e-05 3.997e-05 -0.07309 -6.709e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2717 -0.02545 -0.1651 0.1388 0.9834 0.9932 0.3112 0.9034 0.9761 0.7213 ] Network output: [ 0.005106 0.8875 0.9836 -0.0001338 6.005e-05 0.1182 -0.0001008 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007118 0.001727 0.006567 0.003318 0.9913 0.9941 0.007272 0.9721 0.9827 0.01673 ] Network output: [ -0.006448 0.01714 0.9525 -0.000777 0.0003488 1.04 -0.0005856 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3044 0.1811 0.4556 0.1162 0.9849 0.994 0.3056 0.9098 0.9786 0.7191 ] Network output: [ -0.02436 0.1851 1.051 0.000374 -0.0001679 0.8143 0.0002819 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1569 0.1445 0.2139 0.1382 0.9907 0.9944 0.157 0.9693 0.9829 0.2361 ] Network output: [ -0.0147 0.04875 1.04 0.0005585 -0.0002507 0.943 0.0004209 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1779 0.1754 0.2211 0.1681 0.9864 0.9921 0.1779 0.9517 0.9753 0.2272 ] Network output: [ 0.01067 0.93 -0.004563 8.902e-05 -3.997e-05 1.054 6.709e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02354 Epoch 4583 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04125 0.8682 0.9551 -8.607e-05 3.864e-05 0.09386 -6.486e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004603 -0.004601 -0.01627 0.008168 0.9646 0.97 0.0101 0.9217 0.9284 0.03432 ] Network output: [ 1.02 0.04271 -0.01138 1.306e-05 -5.863e-06 -0.07209 9.842e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2742 -0.03006 -0.186 0.1533 0.9834 0.9932 0.3141 0.9033 0.9761 0.7234 ] Network output: [ 0.002695 0.8878 0.9868 -0.0001342 6.024e-05 0.1195 -0.0001011 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007041 0.001637 0.006083 0.003749 0.9913 0.9941 0.007194 0.9721 0.9827 0.01643 ] Network output: [ 0.02076 -0.135 0.9761 -0.0005918 0.0002657 1.115 -0.000446 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3008 0.1766 0.4468 0.1488 0.9849 0.994 0.302 0.9097 0.9786 0.7203 ] Network output: [ -0.03043 0.1834 1.059 0.0003625 -0.0001627 0.8203 0.0002732 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1562 0.1437 0.2161 0.142 0.9906 0.9944 0.1563 0.9693 0.983 0.2391 ] Network output: [ -0.0233 0.06798 1.044 0.0005249 -0.0002356 0.937 0.0003956 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1772 0.1747 0.2235 0.1677 0.9864 0.9921 0.1772 0.9519 0.9753 0.2297 ] Network output: [ -0.006872 1.013 -0.01015 -1.778e-05 7.981e-06 1.011 -1.34e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02553 Epoch 4584 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.038 0.8766 0.9557 -9.529e-05 4.278e-05 0.09133 -7.181e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004574 -0.004533 -0.01591 0.007871 0.9646 0.97 0.01004 0.9217 0.9284 0.03417 ] Network output: [ 0.9721 0.1087 0.01882 -9.226e-05 4.142e-05 -0.07206 -6.953e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2717 -0.02582 -0.1648 0.1389 0.9834 0.9932 0.3112 0.9035 0.9761 0.7227 ] Network output: [ 0.00465 0.8892 0.984 -0.0001333 5.982e-05 0.1169 -0.0001004 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007133 0.001716 0.006607 0.003314 0.9913 0.9941 0.007288 0.9722 0.9827 0.01679 ] Network output: [ -0.006869 0.01824 0.9534 -0.0007803 0.0003503 1.039 -0.0005881 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3045 0.1806 0.4571 0.1154 0.9849 0.994 0.3056 0.9099 0.9786 0.7205 ] Network output: [ -0.02387 0.1834 1.05 0.0003772 -0.0001694 0.8159 0.0002843 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1577 0.1452 0.2146 0.1384 0.9907 0.9945 0.1578 0.9694 0.983 0.2368 ] Network output: [ -0.01416 0.04708 1.039 0.0005623 -0.0002524 0.9447 0.0004237 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1788 0.1762 0.2217 0.1684 0.9864 0.9921 0.1788 0.9519 0.9754 0.2278 ] Network output: [ 0.0108 0.9298 -0.00465 8.922e-05 -4.005e-05 1.054 6.724e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02299 Epoch 4585 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04078 0.8699 0.9556 -8.616e-05 3.868e-05 0.09257 -6.493e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004608 -0.004612 -0.01628 0.008179 0.9647 0.97 0.01012 0.9218 0.9285 0.03437 ] Network output: [ 1.021 0.04186 -0.01157 8.383e-06 -3.763e-06 -0.07196 6.317e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2742 -0.03046 -0.186 0.1533 0.9834 0.9932 0.3142 0.9035 0.9762 0.7248 ] Network output: [ 0.002221 0.8895 0.9873 -0.0001337 6e-05 0.1183 -0.0001007 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007056 0.001625 0.006115 0.003742 0.9913 0.9941 0.007209 0.9722 0.9827 0.01648 ] Network output: [ 0.02051 -0.134 0.9768 -0.0005956 0.0002674 1.114 -0.0004489 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3009 0.1761 0.4483 0.1479 0.9849 0.994 0.302 0.9099 0.9786 0.7217 ] Network output: [ -0.03003 0.1817 1.058 0.0003656 -0.0001641 0.822 0.0002755 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.157 0.1443 0.2168 0.1423 0.9906 0.9945 0.1571 0.9694 0.983 0.2399 ] Network output: [ -0.02287 0.06624 1.043 0.0005286 -0.0002373 0.9387 0.0003984 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1781 0.1755 0.2241 0.168 0.9864 0.9921 0.1781 0.9521 0.9754 0.2304 ] Network output: [ -0.006962 1.013 -0.0101 -1.825e-05 8.192e-06 1.01 -1.375e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02497 Epoch 4586 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03753 0.8783 0.9562 -9.519e-05 4.273e-05 0.09011 -7.174e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004579 -0.004543 -0.01591 0.007884 0.9646 0.97 0.01005 0.9219 0.9285 0.03422 ] Network output: [ 0.9723 0.1068 0.0193 -9.562e-05 4.293e-05 -0.07102 -7.206e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2717 -0.02619 -0.1646 0.1391 0.9834 0.9932 0.3113 0.9037 0.9761 0.7241 ] Network output: [ 0.004196 0.8909 0.9844 -0.0001327 5.957e-05 0.1157 -0.0001 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007149 0.001706 0.006648 0.00331 0.9913 0.9941 0.007304 0.9723 0.9827 0.01685 ] Network output: [ -0.007281 0.01931 0.9542 -0.0007835 0.0003518 1.038 -0.0005905 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3046 0.1801 0.4587 0.1146 0.9849 0.994 0.3057 0.91 0.9786 0.722 ] Network output: [ -0.02339 0.1818 1.049 0.0003805 -0.0001708 0.8175 0.0002868 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1585 0.1459 0.2153 0.1387 0.9907 0.9945 0.1586 0.9695 0.983 0.2375 ] Network output: [ -0.01361 0.0454 1.038 0.000566 -0.0002541 0.9464 0.0004266 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1797 0.1771 0.2223 0.1687 0.9864 0.9921 0.1797 0.952 0.9754 0.2284 ] Network output: [ 0.01093 0.9297 -0.004734 8.938e-05 -4.013e-05 1.054 6.736e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02245 Epoch 4587 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0403 0.8717 0.9561 -8.621e-05 3.87e-05 0.09127 -6.497e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004614 -0.004623 -0.01629 0.008189 0.9647 0.97 0.01013 0.9219 0.9286 0.03443 ] Network output: [ 1.021 0.04103 -0.01175 3.56e-06 -1.598e-06 -0.07183 2.683e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2743 -0.03086 -0.1859 0.1532 0.9834 0.9932 0.3143 0.9036 0.9762 0.7262 ] Network output: [ 0.001749 0.8912 0.9877 -0.0001331 5.974e-05 0.117 -0.0001003 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007071 0.001613 0.006149 0.003735 0.9913 0.9941 0.007225 0.9723 0.9827 0.01654 ] Network output: [ 0.02026 -0.1331 0.9774 -0.0005995 0.0002691 1.113 -0.0004518 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3009 0.1755 0.4498 0.1471 0.9849 0.994 0.3021 0.91 0.9786 0.7232 ] Network output: [ -0.02964 0.1799 1.057 0.0003688 -0.0001656 0.8236 0.0002779 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1578 0.145 0.2176 0.1425 0.9907 0.9945 0.1579 0.9695 0.983 0.2406 ] Network output: [ -0.02244 0.0645 1.042 0.0005323 -0.000239 0.9405 0.0004012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1789 0.1763 0.2248 0.1683 0.9864 0.9921 0.1789 0.9522 0.9755 0.231 ] Network output: [ -0.007051 1.014 -0.01005 -1.873e-05 8.408e-06 1.01 -1.411e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02441 Epoch 4588 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03705 0.88 0.9567 -9.504e-05 4.267e-05 0.08888 -7.163e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004584 -0.004554 -0.01592 0.007898 0.9646 0.97 0.01007 0.922 0.9286 0.03427 ] Network output: [ 0.9725 0.1049 0.01976 -9.908e-05 4.448e-05 -0.06999 -7.467e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2717 -0.02658 -0.1642 0.1392 0.9834 0.9932 0.3113 0.9038 0.9762 0.7255 ] Network output: [ 0.003744 0.8927 0.9848 -0.0001321 5.929e-05 0.1144 -9.953e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007165 0.001696 0.006689 0.003307 0.9913 0.9941 0.007321 0.9724 0.9828 0.0169 ] Network output: [ -0.007681 0.02035 0.955 -0.0007867 0.0003532 1.037 -0.0005929 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3046 0.1796 0.4603 0.1138 0.9849 0.994 0.3058 0.9102 0.9787 0.7234 ] Network output: [ -0.0229 0.1801 1.048 0.0003838 -0.0001723 0.8191 0.0002892 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1594 0.1466 0.2161 0.139 0.9907 0.9945 0.1595 0.9696 0.983 0.2383 ] Network output: [ -0.01307 0.04373 1.037 0.0005698 -0.0002558 0.9481 0.0004294 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1806 0.178 0.2229 0.169 0.9864 0.9921 0.1806 0.9522 0.9755 0.229 ] Network output: [ 0.01104 0.9296 -0.004815 8.948e-05 -4.017e-05 1.053 6.744e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02192 Epoch 4589 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03983 0.8735 0.9565 -8.621e-05 3.87e-05 0.08996 -6.497e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00462 -0.004634 -0.0163 0.008199 0.9647 0.9701 0.01015 0.9221 0.9287 0.03448 ] Network output: [ 1.022 0.04024 -0.01193 -1.416e-06 6.357e-07 -0.0717 -1.067e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2743 -0.03127 -0.1858 0.1532 0.9834 0.9932 0.3144 0.9038 0.9762 0.7276 ] Network output: [ 0.001281 0.893 0.9882 -0.0001324 5.946e-05 0.1158 -9.981e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007087 0.0016 0.006184 0.003728 0.9913 0.9941 0.007241 0.9724 0.9828 0.01659 ] Network output: [ 0.02001 -0.1321 0.9781 -0.0006034 0.0002709 1.111 -0.0004547 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.301 0.1749 0.4513 0.1463 0.9849 0.994 0.3021 0.9102 0.9787 0.7246 ] Network output: [ -0.02924 0.1782 1.057 0.0003719 -0.000167 0.8253 0.0002803 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1586 0.1457 0.2184 0.1428 0.9907 0.9945 0.1587 0.9696 0.9831 0.2414 ] Network output: [ -0.02201 0.06274 1.041 0.000536 -0.0002406 0.9423 0.000404 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1798 0.1772 0.2254 0.1686 0.9864 0.9921 0.1798 0.9524 0.9755 0.2316 ] Network output: [ -0.007137 1.015 -0.01001 -1.92e-05 8.621e-06 1.01 -1.447e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02385 Epoch 4590 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03658 0.8817 0.9571 -9.484e-05 4.258e-05 0.08764 -7.148e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00459 -0.004565 -0.01593 0.007912 0.9646 0.97 0.01008 0.9221 0.9286 0.03432 ] Network output: [ 0.9727 0.103 0.0202 -0.0001026 4.608e-05 -0.06896 -7.735e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2717 -0.02696 -0.1639 0.1394 0.9834 0.9932 0.3114 0.9039 0.9762 0.7269 ] Network output: [ 0.003294 0.8945 0.9853 -0.0001314 5.899e-05 0.1132 -9.903e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007181 0.001686 0.006732 0.003304 0.9913 0.9941 0.007337 0.9725 0.9828 0.01696 ] Network output: [ -0.008067 0.02133 0.9559 -0.0007898 0.0003545 1.036 -0.0005952 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3047 0.1791 0.4619 0.113 0.985 0.994 0.3059 0.9103 0.9787 0.7248 ] Network output: [ -0.02242 0.1784 1.047 0.000387 -0.0001738 0.8207 0.0002917 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1602 0.1473 0.2168 0.1392 0.9907 0.9945 0.1603 0.9697 0.9831 0.239 ] Network output: [ -0.01253 0.04206 1.036 0.0005736 -0.0002575 0.9498 0.0004323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1815 0.1789 0.2235 0.1694 0.9864 0.9921 0.1815 0.9524 0.9755 0.2296 ] Network output: [ 0.01116 0.9296 -0.004895 8.953e-05 -4.019e-05 1.053 6.747e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02138 Epoch 4591 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03936 0.8753 0.957 -8.617e-05 3.868e-05 0.08863 -6.494e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004626 -0.004646 -0.01631 0.008209 0.9647 0.9701 0.01017 0.9222 0.9287 0.03453 ] Network output: [ 1.022 0.03948 -0.0121 -6.554e-06 2.943e-06 -0.07156 -4.94e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2744 -0.03169 -0.1856 0.1531 0.9834 0.9932 0.3145 0.9039 0.9762 0.7289 ] Network output: [ 0.0008156 0.8947 0.9886 -0.0001317 5.915e-05 0.1145 -9.929e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007103 0.001588 0.006221 0.003721 0.9913 0.9941 0.007257 0.9725 0.9828 0.01665 ] Network output: [ 0.01975 -0.1311 0.9788 -0.0006073 0.0002726 1.11 -0.0004577 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3011 0.1743 0.4528 0.1454 0.9849 0.994 0.3022 0.9103 0.9787 0.726 ] Network output: [ -0.02884 0.1764 1.056 0.0003751 -0.0001684 0.827 0.0002827 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1594 0.1463 0.2191 0.1431 0.9907 0.9945 0.1595 0.9697 0.9831 0.2422 ] Network output: [ -0.02157 0.06098 1.04 0.0005398 -0.0002423 0.9441 0.0004068 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1806 0.178 0.2261 0.169 0.9864 0.9922 0.1807 0.9525 0.9756 0.2323 ] Network output: [ -0.007219 1.015 -0.009955 -1.966e-05 8.827e-06 1.009 -1.482e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02329 Epoch 4592 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03611 0.8834 0.9576 -9.459e-05 4.247e-05 0.08638 -7.129e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004596 -0.004576 -0.01593 0.007926 0.9646 0.97 0.0101 0.9222 0.9287 0.03437 ] Network output: [ 0.9729 0.1011 0.02063 -0.0001063 4.771e-05 -0.06793 -8.009e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2718 -0.02735 -0.1635 0.1395 0.9834 0.9932 0.3115 0.9041 0.9762 0.7282 ] Network output: [ 0.002845 0.8962 0.9857 -0.0001307 5.866e-05 0.1119 -9.847e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007198 0.001675 0.006776 0.003301 0.9913 0.9941 0.007355 0.9725 0.9829 0.01702 ] Network output: [ -0.008435 0.02223 0.9567 -0.0007927 0.0003559 1.035 -0.0005974 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3049 0.1786 0.4635 0.1122 0.985 0.994 0.306 0.9104 0.9787 0.7261 ] Network output: [ -0.02195 0.1767 1.046 0.0003903 -0.0001752 0.8224 0.0002942 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1611 0.148 0.2176 0.1395 0.9907 0.9945 0.1612 0.9697 0.9831 0.2398 ] Network output: [ -0.01199 0.04039 1.034 0.0005773 -0.0002592 0.9515 0.0004351 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1824 0.1797 0.2241 0.1697 0.9864 0.9922 0.1824 0.9525 0.9756 0.2302 ] Network output: [ 0.01126 0.9296 -0.004973 8.95e-05 -4.018e-05 1.053 6.745e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02085 Epoch 4593 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0389 0.8771 0.9575 -8.608e-05 3.865e-05 0.08729 -6.487e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004632 -0.004657 -0.01632 0.008219 0.9647 0.9701 0.01019 0.9223 0.9288 0.03458 ] Network output: [ 1.022 0.03878 -0.01226 -1.186e-05 5.325e-06 -0.07143 -8.94e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2745 -0.0321 -0.1854 0.153 0.9834 0.9933 0.3146 0.904 0.9763 0.7303 ] Network output: [ 0.0003542 0.8964 0.9891 -0.000131 5.881e-05 0.1132 -9.872e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007119 0.001576 0.006258 0.003715 0.9913 0.9942 0.007274 0.9726 0.9828 0.0167 ] Network output: [ 0.01948 -0.13 0.9795 -0.0006113 0.0002744 1.109 -0.0004607 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3012 0.1738 0.4543 0.1446 0.9849 0.994 0.3023 0.9104 0.9787 0.7274 ] Network output: [ -0.02844 0.1746 1.055 0.0003784 -0.0001699 0.8287 0.0002852 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1602 0.147 0.2199 0.1434 0.9907 0.9945 0.1603 0.9698 0.9831 0.243 ] Network output: [ -0.02112 0.0592 1.039 0.0005435 -0.000244 0.9459 0.0004096 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1815 0.1789 0.2267 0.1693 0.9865 0.9922 0.1815 0.9527 0.9756 0.2329 ] Network output: [ -0.007293 1.016 -0.009904 -2.009e-05 9.017e-06 1.009 -1.514e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02273 Epoch 4594 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03565 0.8851 0.9581 -9.429e-05 4.233e-05 0.08511 -7.106e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004602 -0.004587 -0.01594 0.00794 0.9646 0.97 0.01012 0.9223 0.9288 0.03442 ] Network output: [ 0.9731 0.09923 0.02102 -0.00011 4.937e-05 -0.06691 -8.288e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2718 -0.02775 -0.1631 0.1396 0.9834 0.9932 0.3116 0.9042 0.9762 0.7295 ] Network output: [ 0.002397 0.898 0.9861 -0.0001299 5.831e-05 0.1106 -9.788e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007215 0.001665 0.00682 0.003299 0.9913 0.9942 0.007372 0.9726 0.9829 0.01708 ] Network output: [ -0.008782 0.02305 0.9576 -0.0007956 0.0003572 1.034 -0.0005996 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.305 0.1781 0.4651 0.1115 0.985 0.994 0.3062 0.9106 0.9787 0.7274 ] Network output: [ -0.02148 0.175 1.046 0.0003936 -0.0001767 0.8241 0.0002966 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.162 0.1487 0.2184 0.1398 0.9907 0.9945 0.1621 0.9698 0.9831 0.2405 ] Network output: [ -0.01146 0.03872 1.033 0.000581 -0.0002608 0.9532 0.0004379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1833 0.1806 0.2247 0.17 0.9865 0.9922 0.1833 0.9527 0.9756 0.2307 ] Network output: [ 0.01134 0.9297 -0.00505 8.938e-05 -4.013e-05 1.053 6.736e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02033 Epoch 4595 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03843 0.879 0.9579 -8.595e-05 3.859e-05 0.08593 -6.478e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004639 -0.004669 -0.01633 0.008229 0.9647 0.9701 0.0102 0.9224 0.9289 0.03464 ] Network output: [ 1.023 0.03812 -0.01239 -1.735e-05 7.787e-06 -0.07129 -1.307e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2746 -0.03252 -0.1851 0.1529 0.9834 0.9933 0.3148 0.9042 0.9763 0.7316 ] Network output: [ -0.0001031 0.8982 0.9895 -0.0001302 5.845e-05 0.112 -9.812e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007136 0.001564 0.006297 0.003708 0.9913 0.9942 0.007291 0.9726 0.9829 0.01676 ] Network output: [ 0.0192 -0.1289 0.9801 -0.0006154 0.0002763 1.108 -0.0004638 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3013 0.1732 0.4559 0.1437 0.9849 0.994 0.3025 0.9105 0.9787 0.7287 ] Network output: [ -0.02804 0.1727 1.054 0.0003816 -0.0001713 0.8305 0.0002876 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1611 0.1477 0.2208 0.1437 0.9907 0.9945 0.1612 0.9698 0.9832 0.2438 ] Network output: [ -0.02067 0.0574 1.038 0.0005473 -0.0002457 0.9477 0.0004125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1824 0.1797 0.2274 0.1696 0.9865 0.9922 0.1824 0.9528 0.9757 0.2335 ] Network output: [ -0.007358 1.016 -0.00985 -2.046e-05 9.187e-06 1.008 -1.542e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02218 Epoch 4596 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03519 0.8868 0.9586 -9.393e-05 4.217e-05 0.08383 -7.079e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004608 -0.004598 -0.01595 0.007954 0.9647 0.97 0.01013 0.9224 0.9288 0.03447 ] Network output: [ 0.9734 0.09732 0.02138 -0.0001137 5.106e-05 -0.06591 -8.572e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2719 -0.02816 -0.1626 0.1398 0.9834 0.9933 0.3117 0.9043 0.9763 0.7308 ] Network output: [ 0.001951 0.8998 0.9865 -0.000129 5.793e-05 0.1093 -9.725e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007233 0.001655 0.006865 0.003297 0.9913 0.9942 0.00739 0.9727 0.9829 0.01714 ] Network output: [ -0.009105 0.02376 0.9585 -0.0007983 0.0003584 1.033 -0.0006016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3051 0.1776 0.4668 0.1107 0.985 0.994 0.3063 0.9107 0.9787 0.7287 ] Network output: [ -0.02101 0.1732 1.045 0.0003969 -0.0001782 0.8258 0.0002991 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1628 0.1495 0.2192 0.1401 0.9907 0.9945 0.1629 0.9699 0.9832 0.2413 ] Network output: [ -0.01094 0.03707 1.032 0.0005847 -0.0002625 0.9549 0.0004407 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1842 0.1815 0.2254 0.1704 0.9865 0.9922 0.1842 0.9528 0.9757 0.2314 ] Network output: [ 0.01142 0.9298 -0.005127 8.917e-05 -4.003e-05 1.053 6.72e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0198 Epoch 4597 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03796 0.8808 0.9584 -8.578e-05 3.851e-05 0.08457 -6.464e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004645 -0.004681 -0.01634 0.008239 0.9647 0.9701 0.01022 0.9225 0.9289 0.03469 ] Network output: [ 1.023 0.03753 -0.01251 -2.301e-05 1.033e-05 -0.07115 -1.734e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2747 -0.03294 -0.1848 0.1528 0.9834 0.9933 0.3149 0.9043 0.9763 0.7328 ] Network output: [ -0.0005562 0.8999 0.99 -0.0001293 5.806e-05 0.1107 -9.746e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007153 0.001552 0.006338 0.003702 0.9914 0.9942 0.007308 0.9727 0.9829 0.01681 ] Network output: [ 0.01891 -0.1277 0.9808 -0.0006196 0.0002782 1.107 -0.000467 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3014 0.1727 0.4575 0.1428 0.9849 0.994 0.3026 0.9106 0.9787 0.73 ] Network output: [ -0.02763 0.1708 1.054 0.0003849 -0.0001728 0.8323 0.0002901 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1619 0.1483 0.2216 0.144 0.9907 0.9945 0.162 0.9699 0.9832 0.2446 ] Network output: [ -0.02022 0.05559 1.038 0.0005511 -0.0002474 0.9495 0.0004154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1833 0.1806 0.228 0.17 0.9865 0.9922 0.1833 0.953 0.9757 0.2342 ] Network output: [ -0.007411 1.017 -0.009795 -2.078e-05 9.33e-06 1.008 -1.566e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02163 Epoch 4598 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03474 0.8886 0.9591 -9.353e-05 4.199e-05 0.08254 -7.048e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004614 -0.00461 -0.01595 0.007968 0.9647 0.97 0.01015 0.9225 0.9289 0.03452 ] Network output: [ 0.9737 0.0954 0.0217 -0.0001176 5.277e-05 -0.06492 -8.859e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.272 -0.02858 -0.1622 0.1399 0.9834 0.9933 0.3118 0.9044 0.9763 0.7321 ] Network output: [ 0.001505 0.9015 0.9869 -0.0001281 5.753e-05 0.108 -9.657e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007251 0.001644 0.006911 0.003296 0.9913 0.9942 0.007408 0.9728 0.983 0.01719 ] Network output: [ -0.009401 0.02434 0.9593 -0.0008009 0.0003595 1.032 -0.0006036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3053 0.1772 0.4684 0.11 0.985 0.994 0.3065 0.9108 0.9788 0.73 ] Network output: [ -0.02055 0.1714 1.044 0.0004002 -0.0001797 0.8275 0.0003016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1637 0.1502 0.22 0.1404 0.9907 0.9945 0.1638 0.97 0.9832 0.2421 ] Network output: [ -0.01042 0.03542 1.031 0.0005884 -0.0002642 0.9566 0.0004434 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1852 0.1824 0.226 0.1707 0.9865 0.9922 0.1852 0.953 0.9758 0.232 ] Network output: [ 0.01147 0.93 -0.005204 8.886e-05 -3.989e-05 1.053 6.697e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01928 Epoch 4599 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03749 0.8827 0.9588 -8.556e-05 3.841e-05 0.08319 -6.448e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004652 -0.004693 -0.01635 0.008248 0.9647 0.9701 0.01024 0.9226 0.929 0.03474 ] Network output: [ 1.023 0.037 -0.0126 -2.886e-05 1.296e-05 -0.071 -2.175e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2749 -0.03337 -0.1844 0.1526 0.9834 0.9933 0.3151 0.9044 0.9763 0.7341 ] Network output: [ -0.001005 0.9017 0.9904 -0.0001284 5.765e-05 0.1094 -9.677e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00717 0.00154 0.00638 0.003695 0.9914 0.9942 0.007326 0.9728 0.9829 0.01687 ] Network output: [ 0.01859 -0.1264 0.9814 -0.000624 0.0002801 1.105 -0.0004702 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3016 0.1722 0.4592 0.1419 0.985 0.994 0.3028 0.9108 0.9788 0.7313 ] Network output: [ -0.02723 0.1689 1.053 0.0003882 -0.0001743 0.8341 0.0002926 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1627 0.149 0.2224 0.1443 0.9907 0.9945 0.1629 0.97 0.9832 0.2454 ] Network output: [ -0.01976 0.05376 1.037 0.000555 -0.0002492 0.9513 0.0004183 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1842 0.1814 0.2287 0.1703 0.9865 0.9922 0.1842 0.9531 0.9758 0.2348 ] Network output: [ -0.00745 1.017 -0.009738 -2.103e-05 9.44e-06 1.008 -1.585e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02107 Epoch 4600 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03429 0.8903 0.9595 -9.306e-05 4.178e-05 0.08124 -7.013e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00462 -0.004622 -0.01596 0.007982 0.9647 0.97 0.01017 0.9226 0.9289 0.03458 ] Network output: [ 0.974 0.09346 0.02199 -0.0001214 5.45e-05 -0.06395 -9.15e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2721 -0.029 -0.1617 0.1401 0.9834 0.9933 0.312 0.9046 0.9763 0.7333 ] Network output: [ 0.001059 0.9033 0.9873 -0.0001272 5.71e-05 0.1067 -9.585e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007269 0.001634 0.006957 0.003295 0.9914 0.9942 0.007427 0.9729 0.983 0.01725 ] Network output: [ -0.009668 0.02479 0.9602 -0.0008033 0.0003606 1.031 -0.0006054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3055 0.1767 0.4701 0.1094 0.985 0.994 0.3066 0.9109 0.9788 0.7312 ] Network output: [ -0.0201 0.1695 1.043 0.0004035 -0.0001811 0.8293 0.0003041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1646 0.1509 0.2208 0.1408 0.9907 0.9945 0.1647 0.9701 0.9832 0.2429 ] Network output: [ -0.009911 0.03379 1.03 0.0005921 -0.0002658 0.9583 0.0004462 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1861 0.1833 0.2266 0.171 0.9865 0.9922 0.1861 0.9531 0.9758 0.2326 ] Network output: [ 0.01151 0.9303 -0.005282 8.843e-05 -3.97e-05 1.052 6.664e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01876 Epoch 4601 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03703 0.8845 0.9593 -8.53e-05 3.829e-05 0.0818 -6.428e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004659 -0.004705 -0.01635 0.008257 0.9647 0.9701 0.01026 0.9227 0.929 0.03479 ] Network output: [ 1.023 0.03654 -0.01266 -3.489e-05 1.567e-05 -0.07085 -2.63e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.275 -0.03379 -0.1839 0.1525 0.9834 0.9933 0.3153 0.9045 0.9763 0.7353 ] Network output: [ -0.001448 0.9035 0.9908 -0.0001274 5.721e-05 0.1081 -9.603e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007188 0.001527 0.006424 0.003689 0.9914 0.9942 0.007345 0.9729 0.983 0.01692 ] Network output: [ 0.01825 -0.125 0.982 -0.0006285 0.0002821 1.104 -0.0004736 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3018 0.1716 0.4608 0.141 0.985 0.994 0.303 0.9109 0.9788 0.7325 ] Network output: [ -0.02681 0.1669 1.052 0.0003916 -0.0001758 0.8359 0.0002951 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1636 0.1497 0.2232 0.1446 0.9907 0.9945 0.1637 0.9701 0.9832 0.2462 ] Network output: [ -0.01928 0.05191 1.036 0.0005589 -0.0002509 0.9531 0.0004212 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1851 0.1823 0.2294 0.1707 0.9865 0.9922 0.1851 0.9533 0.9758 0.2355 ] Network output: [ -0.007474 1.017 -0.009678 -2.119e-05 9.511e-06 1.007 -1.597e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02051 Epoch 4602 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03384 0.892 0.96 -9.254e-05 4.155e-05 0.07993 -6.974e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004627 -0.004634 -0.01596 0.007997 0.9647 0.9701 0.01019 0.9227 0.929 0.03463 ] Network output: [ 0.9744 0.0915 0.02222 -0.0001253 5.625e-05 -0.063 -9.443e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2723 -0.02943 -0.1612 0.1402 0.9834 0.9933 0.3122 0.9047 0.9763 0.7345 ] Network output: [ 0.0006138 0.9051 0.9878 -0.0001262 5.665e-05 0.1054 -9.509e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007287 0.001623 0.007003 0.003295 0.9914 0.9942 0.007446 0.9729 0.983 0.01731 ] Network output: [ -0.009902 0.02509 0.9612 -0.0008055 0.0003616 1.03 -0.000607 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3057 0.1762 0.4718 0.1088 0.985 0.994 0.3068 0.911 0.9788 0.7325 ] Network output: [ -0.01966 0.1676 1.042 0.0004068 -0.0001826 0.8311 0.0003066 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1655 0.1516 0.2217 0.1411 0.9908 0.9945 0.1656 0.9702 0.9833 0.2437 ] Network output: [ -0.009413 0.03217 1.029 0.0005957 -0.0002674 0.96 0.0004489 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.187 0.1843 0.2273 0.1714 0.9865 0.9922 0.1871 0.9533 0.9759 0.2332 ] Network output: [ 0.01152 0.9307 -0.005359 8.787e-05 -3.945e-05 1.052 6.622e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01825 Epoch 4603 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03656 0.8864 0.9597 -8.499e-05 3.816e-05 0.08041 -6.405e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004666 -0.004718 -0.01636 0.008266 0.9647 0.9701 0.01028 0.9228 0.9291 0.03485 ] Network output: [ 1.024 0.03614 -0.01269 -4.112e-05 1.846e-05 -0.07069 -3.099e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2752 -0.03422 -0.1834 0.1523 0.9834 0.9933 0.3155 0.9046 0.9764 0.7365 ] Network output: [ -0.001887 0.9052 0.9913 -0.0001264 5.674e-05 0.1068 -9.525e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007207 0.001515 0.00647 0.003682 0.9914 0.9942 0.007364 0.9729 0.983 0.01698 ] Network output: [ 0.01789 -0.1234 0.9826 -0.0006331 0.0002842 1.102 -0.0004771 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.302 0.1711 0.4625 0.14 0.985 0.994 0.3032 0.911 0.9788 0.7338 ] Network output: [ -0.02639 0.165 1.052 0.0003949 -0.0001773 0.8377 0.0002976 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1645 0.1504 0.2241 0.1449 0.9907 0.9945 0.1646 0.9702 0.9833 0.247 ] Network output: [ -0.0188 0.05005 1.035 0.0005628 -0.0002527 0.955 0.0004241 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.186 0.1832 0.23 0.171 0.9866 0.9922 0.186 0.9534 0.9759 0.2361 ] Network output: [ -0.007479 1.018 -0.009616 -2.125e-05 9.539e-06 1.007 -1.601e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01995 Epoch 4604 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03341 0.8938 0.9604 -9.197e-05 4.129e-05 0.07861 -6.931e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004634 -0.004647 -0.01597 0.008011 0.9647 0.9701 0.01021 0.9228 0.9291 0.03468 ] Network output: [ 0.9748 0.08952 0.02241 -0.0001292 5.801e-05 -0.06207 -9.739e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2724 -0.02988 -0.1608 0.1403 0.9834 0.9933 0.3124 0.9048 0.9764 0.7357 ] Network output: [ 0.0001687 0.9069 0.9882 -0.0001251 5.617e-05 0.1041 -9.429e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007306 0.001612 0.007051 0.003295 0.9914 0.9942 0.007465 0.973 0.9831 0.01737 ] Network output: [ -0.0101 0.02522 0.9621 -0.0008075 0.0003625 1.03 -0.0006086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3058 0.1757 0.4735 0.1082 0.985 0.994 0.307 0.9111 0.9788 0.7337 ] Network output: [ -0.01923 0.1657 1.041 0.0004101 -0.0001841 0.8329 0.0003091 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1664 0.1524 0.2225 0.1414 0.9908 0.9945 0.1665 0.9702 0.9833 0.2445 ] Network output: [ -0.008927 0.03057 1.028 0.0005992 -0.000269 0.9617 0.0004516 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.188 0.1852 0.2279 0.1717 0.9866 0.9922 0.188 0.9534 0.9759 0.2338 ] Network output: [ 0.01151 0.9312 -0.005438 8.718e-05 -3.914e-05 1.052 6.57e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01773 Epoch 4605 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03609 0.8883 0.9602 -8.465e-05 3.8e-05 0.079 -6.379e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004673 -0.00473 -0.01636 0.008274 0.9647 0.9701 0.0103 0.9229 0.9291 0.0349 ] Network output: [ 1.024 0.03581 -0.01268 -4.753e-05 2.134e-05 -0.07052 -3.582e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2753 -0.03464 -0.1828 0.1521 0.9834 0.9933 0.3157 0.9048 0.9764 0.7376 ] Network output: [ -0.00232 0.907 0.9917 -0.0001253 5.625e-05 0.1054 -9.443e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007226 0.001503 0.006517 0.003675 0.9914 0.9942 0.007383 0.973 0.983 0.01704 ] Network output: [ 0.01749 -0.1217 0.9832 -0.0006379 0.0002864 1.101 -0.0004808 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3022 0.1706 0.4643 0.139 0.985 0.994 0.3034 0.9111 0.9788 0.735 ] Network output: [ -0.02597 0.1629 1.051 0.0003983 -0.0001788 0.8396 0.0003002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1653 0.1511 0.225 0.1452 0.9907 0.9945 0.1654 0.9702 0.9833 0.2479 ] Network output: [ -0.01831 0.04816 1.034 0.0005668 -0.0002544 0.9568 0.0004271 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1869 0.1841 0.2307 0.1714 0.9866 0.9922 0.187 0.9536 0.9759 0.2368 ] Network output: [ -0.007465 1.018 -0.009551 -2.12e-05 9.518e-06 1.007 -1.598e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01939 Epoch 4606 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03297 0.8955 0.9609 -9.134e-05 4.101e-05 0.07728 -6.884e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004641 -0.00466 -0.01597 0.008026 0.9647 0.9701 0.01023 0.9229 0.9291 0.03473 ] Network output: [ 0.9753 0.08751 0.02254 -0.0001332 5.979e-05 -0.06117 -0.0001004 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2726 -0.03033 -0.1603 0.1405 0.9834 0.9933 0.3126 0.9049 0.9764 0.7368 ] Network output: [ -0.0002764 0.9087 0.9886 -0.000124 5.567e-05 0.1028 -9.345e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007324 0.001601 0.007098 0.003296 0.9914 0.9942 0.007484 0.9731 0.9831 0.01742 ] Network output: [ -0.01026 0.02518 0.963 -0.0008094 0.0003634 1.029 -0.00061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3061 0.1752 0.4752 0.1076 0.985 0.994 0.3072 0.9112 0.9788 0.7348 ] Network output: [ -0.01881 0.1638 1.041 0.0004134 -0.0001856 0.8348 0.0003116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1673 0.1531 0.2234 0.1418 0.9908 0.9945 0.1674 0.9703 0.9833 0.2454 ] Network output: [ -0.008454 0.02898 1.027 0.0006028 -0.0002706 0.9634 0.0004543 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1889 0.1861 0.2286 0.1721 0.9866 0.9922 0.189 0.9536 0.976 0.2345 ] Network output: [ 0.01147 0.9319 -0.005517 8.635e-05 -3.877e-05 1.051 6.508e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01722 Epoch 4607 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03562 0.8902 0.9606 -8.426e-05 3.783e-05 0.07758 -6.35e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00468 -0.004743 -0.01637 0.008282 0.9647 0.9701 0.01032 0.923 0.9292 0.03495 ] Network output: [ 1.024 0.03555 -0.01262 -5.412e-05 2.43e-05 -0.07033 -4.079e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2755 -0.03507 -0.1822 0.1519 0.9834 0.9933 0.3159 0.9049 0.9764 0.7388 ] Network output: [ -0.002748 0.9088 0.9921 -0.0001242 5.574e-05 0.1041 -9.357e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007245 0.001492 0.006567 0.003668 0.9914 0.9942 0.007403 0.9731 0.9831 0.01709 ] Network output: [ 0.01707 -0.1199 0.9838 -0.0006429 0.0002886 1.099 -0.0004845 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3025 0.1701 0.4661 0.138 0.985 0.994 0.3036 0.9112 0.9788 0.7361 ] Network output: [ -0.02553 0.1609 1.05 0.0004018 -0.0001804 0.8414 0.0003028 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1662 0.1518 0.2258 0.1455 0.9908 0.9945 0.1663 0.9703 0.9833 0.2487 ] Network output: [ -0.0178 0.04624 1.033 0.0005708 -0.0002562 0.9587 0.0004301 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1879 0.185 0.2314 0.1718 0.9866 0.9922 0.1879 0.9537 0.976 0.2374 ] Network output: [ -0.007428 1.018 -0.009485 -2.104e-05 9.445e-06 1.006 -1.585e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01883 Epoch 4608 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03255 0.8972 0.9614 -9.066e-05 4.07e-05 0.07594 -6.832e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004649 -0.004673 -0.01598 0.00804 0.9647 0.9701 0.01025 0.923 0.9292 0.03478 ] Network output: [ 0.9758 0.08548 0.02261 -0.0001372 6.157e-05 -0.06031 -0.0001034 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2728 -0.0308 -0.1597 0.1406 0.9834 0.9933 0.3128 0.905 0.9764 0.738 ] Network output: [ -0.0007219 0.9105 0.989 -0.0001228 5.515e-05 0.1014 -9.257e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007343 0.001589 0.007146 0.003298 0.9914 0.9942 0.007503 0.9732 0.9831 0.01748 ] Network output: [ -0.01039 0.02495 0.964 -0.000811 0.0003641 1.029 -0.0006112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3063 0.1747 0.4769 0.1071 0.985 0.994 0.3075 0.9114 0.9789 0.736 ] Network output: [ -0.0184 0.1618 1.04 0.0004167 -0.0001871 0.8367 0.000314 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1682 0.1539 0.2243 0.1422 0.9908 0.9945 0.1683 0.9704 0.9834 0.2462 ] Network output: [ -0.007994 0.02742 1.026 0.0006063 -0.0002722 0.9651 0.0004569 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1899 0.187 0.2293 0.1725 0.9866 0.9922 0.1899 0.9537 0.976 0.2351 ] Network output: [ 0.01141 0.9326 -0.005596 8.537e-05 -3.833e-05 1.051 6.434e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01671 Epoch 4609 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03515 0.8922 0.961 -8.383e-05 3.763e-05 0.07616 -6.317e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004687 -0.004756 -0.01637 0.008289 0.9647 0.9701 0.01034 0.9231 0.9293 0.035 ] Network output: [ 1.024 0.03536 -0.01252 -6.09e-05 2.734e-05 -0.07013 -4.59e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2757 -0.0355 -0.1815 0.1516 0.9834 0.9933 0.3162 0.905 0.9764 0.7399 ] Network output: [ -0.003171 0.9105 0.9925 -0.000123 5.52e-05 0.1028 -9.267e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007265 0.00148 0.006618 0.003661 0.9914 0.9942 0.007423 0.9732 0.9831 0.01715 ] Network output: [ 0.01661 -0.1179 0.9843 -0.0006482 0.000291 1.098 -0.0004885 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3027 0.1696 0.4679 0.137 0.985 0.994 0.3039 0.9113 0.9788 0.7373 ] Network output: [ -0.02509 0.1588 1.05 0.0004052 -0.0001819 0.8433 0.0003054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1671 0.1525 0.2267 0.1458 0.9908 0.9945 0.1672 0.9704 0.9834 0.2495 ] Network output: [ -0.01729 0.04431 1.032 0.0005748 -0.000258 0.9605 0.0004332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1888 0.1859 0.2321 0.1722 0.9866 0.9922 0.1888 0.9538 0.976 0.2381 ] Network output: [ -0.007368 1.018 -0.009416 -2.075e-05 9.314e-06 1.006 -1.564e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01827 Epoch 4610 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03213 0.899 0.9618 -8.992e-05 4.037e-05 0.0746 -6.777e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004656 -0.004686 -0.01598 0.008055 0.9647 0.9701 0.01027 0.9231 0.9292 0.03484 ] Network output: [ 0.9764 0.08342 0.02263 -0.0001412 6.337e-05 -0.05948 -0.0001064 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.273 -0.03128 -0.1592 0.1408 0.9834 0.9933 0.3131 0.9051 0.9764 0.7391 ] Network output: [ -0.001168 0.9122 0.9895 -0.0001216 5.46e-05 0.1001 -9.165e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007363 0.001578 0.007194 0.0033 0.9914 0.9942 0.007523 0.9732 0.9832 0.01754 ] Network output: [ -0.01047 0.02454 0.965 -0.0008125 0.0003648 1.028 -0.0006123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3065 0.1742 0.4786 0.1066 0.985 0.994 0.3077 0.9115 0.9789 0.7371 ] Network output: [ -0.018 0.1598 1.039 0.00042 -0.0001885 0.8386 0.0003165 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1691 0.1546 0.2252 0.1425 0.9908 0.9945 0.1692 0.9705 0.9834 0.2471 ] Network output: [ -0.007549 0.02587 1.025 0.0006097 -0.0002737 0.9668 0.0004595 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1908 0.1879 0.2299 0.1728 0.9866 0.9922 0.1909 0.9539 0.9761 0.2358 ] Network output: [ 0.01132 0.9335 -0.005676 8.424e-05 -3.782e-05 1.05 6.349e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0162 Epoch 4611 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03468 0.8941 0.9615 -8.335e-05 3.742e-05 0.07473 -6.282e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004694 -0.004768 -0.01637 0.008297 0.9647 0.9701 0.01036 0.9232 0.9293 0.03505 ] Network output: [ 1.023 0.03523 -0.01236 -6.786e-05 3.046e-05 -0.06991 -5.114e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2759 -0.03593 -0.1807 0.1514 0.9834 0.9933 0.3164 0.9051 0.9764 0.741 ] Network output: [ -0.003587 0.9123 0.9929 -0.0001217 5.464e-05 0.1014 -9.172e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007285 0.001468 0.006671 0.003653 0.9914 0.9942 0.007444 0.9732 0.9831 0.01721 ] Network output: [ 0.01612 -0.1157 0.9849 -0.0006536 0.0002934 1.096 -0.0004925 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.303 0.1691 0.4698 0.1359 0.985 0.994 0.3042 0.9114 0.9789 0.7384 ] Network output: [ -0.02465 0.1567 1.049 0.0004087 -0.0001835 0.8452 0.000308 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.168 0.1533 0.2276 0.1461 0.9908 0.9945 0.1681 0.9705 0.9834 0.2504 ] Network output: [ -0.01676 0.04236 1.031 0.0005789 -0.0002599 0.9624 0.0004363 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1898 0.1868 0.2328 0.1725 0.9866 0.9923 0.1898 0.954 0.9761 0.2387 ] Network output: [ -0.007283 1.018 -0.009345 -2.032e-05 9.124e-06 1.006 -1.532e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0177 Epoch 4612 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03171 0.9007 0.9622 -8.913e-05 4.002e-05 0.07324 -6.717e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004664 -0.0047 -0.01599 0.00807 0.9647 0.9701 0.01029 0.9232 0.9293 0.03489 ] Network output: [ 0.9771 0.08135 0.02258 -0.0001452 6.518e-05 -0.05869 -0.0001094 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2733 -0.03177 -0.1587 0.1409 0.9834 0.9933 0.3134 0.9052 0.9764 0.7402 ] Network output: [ -0.001614 0.914 0.9899 -0.0001203 5.403e-05 0.09879 -9.069e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007382 0.001566 0.007243 0.003303 0.9914 0.9942 0.007543 0.9733 0.9832 0.01759 ] Network output: [ -0.01051 0.02393 0.966 -0.0008137 0.0003653 1.028 -0.0006133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3068 0.1737 0.4803 0.1062 0.985 0.994 0.308 0.9116 0.9789 0.7382 ] Network output: [ -0.01762 0.1578 1.039 0.0004233 -0.00019 0.8405 0.000319 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.17 0.1553 0.2261 0.1429 0.9908 0.9945 0.1701 0.9705 0.9834 0.2479 ] Network output: [ -0.007119 0.02434 1.024 0.0006131 -0.0002752 0.9685 0.000462 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1918 0.1889 0.2306 0.1732 0.9866 0.9923 0.1918 0.954 0.9761 0.2364 ] Network output: [ 0.01119 0.9345 -0.005758 8.296e-05 -3.724e-05 1.049 6.252e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01569 Epoch 4613 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03421 0.896 0.9619 -8.283e-05 3.719e-05 0.07329 -6.243e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004702 -0.004781 -0.01637 0.008303 0.9647 0.9701 0.01038 0.9233 0.9294 0.0351 ] Network output: [ 1.023 0.03516 -0.01215 -7.498e-05 3.366e-05 -0.06967 -5.651e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2761 -0.03636 -0.1798 0.1511 0.9834 0.9933 0.3167 0.9052 0.9765 0.742 ] Network output: [ -0.003998 0.9141 0.9933 -0.0001204 5.405e-05 0.1001 -9.073e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007306 0.001456 0.006727 0.003646 0.9914 0.9942 0.007465 0.9733 0.9831 0.01727 ] Network output: [ 0.01559 -0.1134 0.9854 -0.0006591 0.0002959 1.094 -0.0004968 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3033 0.1687 0.4717 0.1348 0.985 0.994 0.3045 0.9115 0.9789 0.7395 ] Network output: [ -0.02419 0.1546 1.048 0.0004123 -0.0001851 0.8471 0.0003107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1689 0.154 0.2285 0.1465 0.9908 0.9945 0.169 0.9705 0.9834 0.2512 ] Network output: [ -0.01621 0.04039 1.03 0.000583 -0.0002617 0.9643 0.0004394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1907 0.1877 0.2334 0.1729 0.9866 0.9923 0.1907 0.9541 0.9761 0.2394 ] Network output: [ -0.007172 1.018 -0.009272 -1.976e-05 8.871e-06 1.006 -1.489e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01714 Epoch 4614 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03131 0.9025 0.9627 -8.829e-05 3.964e-05 0.07188 -6.654e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004672 -0.004714 -0.01599 0.008085 0.9647 0.9701 0.01031 0.9233 0.9293 0.03494 ] Network output: [ 0.9778 0.07925 0.02246 -0.0001492 6.7e-05 -0.05793 -0.0001125 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2735 -0.03227 -0.1582 0.141 0.9834 0.9933 0.3137 0.9053 0.9765 0.7413 ] Network output: [ -0.002061 0.9158 0.9904 -0.000119 5.343e-05 0.09747 -8.969e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007402 0.001554 0.007292 0.003306 0.9914 0.9942 0.007563 0.9734 0.9832 0.01765 ] Network output: [ -0.01051 0.02313 0.967 -0.0008148 0.0003658 1.028 -0.0006141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.307 0.1732 0.4821 0.1058 0.985 0.994 0.3082 0.9116 0.9789 0.7393 ] Network output: [ -0.01724 0.1557 1.038 0.0004265 -0.0001915 0.8425 0.0003215 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1709 0.1561 0.227 0.1433 0.9908 0.9945 0.171 0.9706 0.9834 0.2488 ] Network output: [ -0.006704 0.02284 1.023 0.0006164 -0.0002767 0.9701 0.0004646 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1927 0.1898 0.2313 0.1736 0.9866 0.9923 0.1928 0.9541 0.9762 0.2371 ] Network output: [ 0.01104 0.9356 -0.005839 8.152e-05 -3.66e-05 1.048 6.144e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01519 Epoch 4615 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03373 0.898 0.9624 -8.227e-05 3.694e-05 0.07185 -6.2e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004709 -0.004794 -0.01637 0.00831 0.9647 0.9701 0.0104 0.9233 0.9294 0.03515 ] Network output: [ 1.023 0.03514 -0.01189 -8.227e-05 3.693e-05 -0.0694 -6.2e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2763 -0.03679 -0.1789 0.1508 0.9834 0.9933 0.317 0.9053 0.9765 0.7431 ] Network output: [ -0.004403 0.9159 0.9937 -0.000119 5.344e-05 0.09872 -8.971e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007327 0.001444 0.006784 0.003638 0.9914 0.9942 0.007487 0.9734 0.9832 0.01733 ] Network output: [ 0.01503 -0.1109 0.9859 -0.0006649 0.0002985 1.092 -0.0005011 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3037 0.1682 0.4737 0.1337 0.985 0.994 0.3049 0.9116 0.9789 0.7406 ] Network output: [ -0.02373 0.1525 1.048 0.0004158 -0.0001867 0.8491 0.0003134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1698 0.1547 0.2294 0.1468 0.9908 0.9945 0.1699 0.9706 0.9835 0.2521 ] Network output: [ -0.01566 0.03839 1.029 0.0005871 -0.0002636 0.9662 0.0004425 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1917 0.1887 0.2341 0.1733 0.9867 0.9923 0.1917 0.9543 0.9762 0.24 ] Network output: [ -0.007034 1.017 -0.009197 -1.906e-05 8.555e-06 1.006 -1.436e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01657 Epoch 4616 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03091 0.9042 0.9631 -8.74e-05 3.924e-05 0.07052 -6.587e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00468 -0.004729 -0.016 0.0081 0.9647 0.9701 0.01033 0.9234 0.9294 0.035 ] Network output: [ 0.9786 0.07714 0.02228 -0.0001533 6.884e-05 -0.05723 -0.0001156 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2738 -0.03279 -0.1576 0.1412 0.9834 0.9933 0.314 0.9054 0.9765 0.7424 ] Network output: [ -0.002509 0.9176 0.9908 -0.0001176 5.281e-05 0.09614 -8.866e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007422 0.001541 0.007341 0.00331 0.9914 0.9942 0.007584 0.9734 0.9832 0.0177 ] Network output: [ -0.01047 0.02214 0.968 -0.0008157 0.0003662 1.027 -0.0006147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3073 0.1727 0.4838 0.1054 0.985 0.994 0.3085 0.9117 0.9789 0.7404 ] Network output: [ -0.01688 0.1536 1.037 0.0004298 -0.000193 0.8445 0.0003239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1718 0.1568 0.2279 0.1437 0.9908 0.9946 0.1719 0.9707 0.9835 0.2497 ] Network output: [ -0.006305 0.02136 1.022 0.0006197 -0.0002782 0.9718 0.000467 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1937 0.1907 0.232 0.174 0.9867 0.9923 0.1937 0.9543 0.9762 0.2377 ] Network output: [ 0.01086 0.9369 -0.00592 7.994e-05 -3.589e-05 1.048 6.025e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01469 Epoch 4617 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03326 0.9 0.9628 -8.167e-05 3.667e-05 0.0704 -6.155e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004717 -0.004807 -0.01637 0.008316 0.9647 0.9701 0.01042 0.9234 0.9294 0.0352 ] Network output: [ 1.023 0.03518 -0.01157 -8.971e-05 4.027e-05 -0.0691 -6.761e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2766 -0.03722 -0.1779 0.1505 0.9834 0.9933 0.3172 0.9054 0.9765 0.7441 ] Network output: [ -0.004803 0.9176 0.9941 -0.0001176 5.28e-05 0.09736 -8.864e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007349 0.001433 0.006844 0.00363 0.9914 0.9942 0.007509 0.9734 0.9832 0.01739 ] Network output: [ 0.01444 -0.1082 0.9863 -0.0006709 0.0003012 1.09 -0.0005056 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.304 0.1678 0.4757 0.1325 0.985 0.994 0.3052 0.9117 0.9789 0.7417 ] Network output: [ -0.02326 0.1503 1.047 0.0004194 -0.0001883 0.851 0.0003161 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1707 0.1555 0.2303 0.1471 0.9908 0.9946 0.1708 0.9707 0.9835 0.2529 ] Network output: [ -0.01509 0.03638 1.028 0.0005913 -0.0002655 0.9681 0.0004456 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1927 0.1896 0.2348 0.1737 0.9867 0.9923 0.1927 0.9544 0.9762 0.2406 ] Network output: [ -0.006869 1.017 -0.009121 -1.821e-05 8.174e-06 1.006 -1.372e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.016 Epoch 4618 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03051 0.9059 0.9635 -8.646e-05 3.882e-05 0.06914 -6.516e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004688 -0.004744 -0.016 0.008115 0.9647 0.9701 0.01036 0.9235 0.9294 0.03505 ] Network output: [ 0.9794 0.07502 0.02203 -0.0001575 7.069e-05 -0.05657 -0.0001187 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2741 -0.03332 -0.1571 0.1413 0.9834 0.9933 0.3144 0.9055 0.9765 0.7434 ] Network output: [ -0.002957 0.9194 0.9913 -0.0001162 5.217e-05 0.09482 -8.758e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007442 0.001529 0.007391 0.003314 0.9914 0.9942 0.007604 0.9735 0.9833 0.01776 ] Network output: [ -0.01039 0.02096 0.9691 -0.0008163 0.0003665 1.027 -0.0006152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3076 0.1722 0.4856 0.1051 0.985 0.994 0.3088 0.9118 0.9789 0.7415 ] Network output: [ -0.01654 0.1515 1.037 0.000433 -0.0001944 0.8465 0.0003264 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1727 0.1575 0.2289 0.1442 0.9908 0.9946 0.1728 0.9708 0.9835 0.2506 ] Network output: [ -0.005921 0.0199 1.021 0.000623 -0.0002797 0.9735 0.0004695 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1947 0.1916 0.2327 0.1743 0.9867 0.9923 0.1947 0.9544 0.9763 0.2384 ] Network output: [ 0.01065 0.9383 -0.006001 7.821e-05 -3.511e-05 1.047 5.894e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01419 Epoch 4619 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03278 0.9019 0.9632 -8.103e-05 3.638e-05 0.06895 -6.106e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004725 -0.00482 -0.01636 0.008321 0.9647 0.9701 0.01044 0.9235 0.9295 0.03525 ] Network output: [ 1.022 0.03526 -0.0112 -9.728e-05 4.367e-05 -0.06876 -7.332e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2768 -0.03765 -0.1768 0.1501 0.9834 0.9933 0.3175 0.9055 0.9765 0.7451 ] Network output: [ -0.005196 0.9194 0.9945 -0.0001161 5.214e-05 0.09599 -8.753e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007371 0.001421 0.006906 0.003622 0.9914 0.9942 0.007532 0.9735 0.9832 0.01746 ] Network output: [ 0.01381 -0.1054 0.9868 -0.000677 0.0003039 1.088 -0.0005102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3044 0.1673 0.4777 0.1313 0.985 0.994 0.3056 0.9118 0.9789 0.7427 ] Network output: [ -0.02278 0.1481 1.046 0.000423 -0.0001899 0.8529 0.0003188 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1716 0.1562 0.2313 0.1475 0.9908 0.9946 0.1717 0.9708 0.9835 0.2538 ] Network output: [ -0.0145 0.03436 1.027 0.0005955 -0.0002674 0.9699 0.0004488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1936 0.1906 0.2355 0.1741 0.9867 0.9923 0.1937 0.9545 0.9763 0.2413 ] Network output: [ -0.006678 1.016 -0.009044 -1.722e-05 7.733e-06 1.006 -1.298e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01544 Epoch 4620 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03012 0.9077 0.964 -8.547e-05 3.837e-05 0.06776 -6.441e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004697 -0.004759 -0.01601 0.00813 0.9647 0.9701 0.01038 0.9235 0.9295 0.0351 ] Network output: [ 0.9803 0.07288 0.02173 -0.0001616 7.257e-05 -0.05595 -0.0001218 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2744 -0.03386 -0.1565 0.1414 0.9834 0.9933 0.3148 0.9056 0.9765 0.7444 ] Network output: [ -0.003406 0.9211 0.9917 -0.0001147 5.151e-05 0.09349 -8.647e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007462 0.001515 0.007441 0.003319 0.9914 0.9942 0.007625 0.9736 0.9833 0.01781 ] Network output: [ -0.01027 0.0196 0.9702 -0.0008169 0.0003667 1.027 -0.0006156 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3079 0.1717 0.4874 0.1048 0.985 0.994 0.3091 0.9119 0.9789 0.7425 ] Network output: [ -0.0162 0.1494 1.036 0.0004363 -0.0001959 0.8485 0.0003288 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1736 0.1583 0.2298 0.1446 0.9908 0.9946 0.1737 0.9708 0.9835 0.2515 ] Network output: [ -0.005554 0.01846 1.02 0.0006262 -0.0002811 0.9752 0.0004719 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1956 0.1926 0.2334 0.1747 0.9867 0.9923 0.1957 0.9546 0.9763 0.2391 ] Network output: [ 0.0104 0.9399 -0.006084 7.632e-05 -3.426e-05 1.046 5.752e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0137 Epoch 4621 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03231 0.9039 0.9637 -8.034e-05 3.607e-05 0.06749 -6.054e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004733 -0.004834 -0.01636 0.008327 0.9647 0.9701 0.01046 0.9236 0.9295 0.0353 ] Network output: [ 1.022 0.03535 -0.01076 -0.000105 4.713e-05 -0.06838 -7.912e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2771 -0.03808 -0.1757 0.1498 0.9835 0.9933 0.3179 0.9056 0.9765 0.7461 ] Network output: [ -0.005584 0.9212 0.9949 -0.0001146 5.146e-05 0.09462 -8.639e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007393 0.00141 0.00697 0.003614 0.9915 0.9942 0.007555 0.9736 0.9833 0.01752 ] Network output: [ 0.01315 -0.1024 0.9872 -0.0006833 0.0003068 1.086 -0.000515 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3048 0.1669 0.4798 0.13 0.985 0.994 0.306 0.9119 0.9789 0.7438 ] Network output: [ -0.02229 0.1459 1.045 0.0004266 -0.0001915 0.8549 0.0003215 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1725 0.157 0.2322 0.1478 0.9908 0.9946 0.1727 0.9708 0.9835 0.2546 ] Network output: [ -0.01391 0.03233 1.026 0.0005998 -0.0002693 0.9718 0.000452 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1946 0.1915 0.2361 0.1745 0.9867 0.9923 0.1947 0.9547 0.9763 0.2419 ] Network output: [ -0.006461 1.016 -0.008969 -1.611e-05 7.233e-06 1.006 -1.214e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01488 Epoch 4622 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02974 0.9094 0.9644 -8.444e-05 3.791e-05 0.06638 -6.364e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004706 -0.004774 -0.01601 0.008145 0.9647 0.9701 0.0104 0.9236 0.9295 0.03516 ] Network output: [ 0.9813 0.07074 0.02136 -0.0001659 7.446e-05 -0.05537 -0.000125 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2747 -0.03442 -0.156 0.1416 0.9834 0.9933 0.3152 0.9057 0.9765 0.7454 ] Network output: [ -0.003854 0.9229 0.9922 -0.0001132 5.083e-05 0.09217 -8.532e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007483 0.001502 0.007492 0.003324 0.9914 0.9942 0.007646 0.9736 0.9833 0.01786 ] Network output: [ -0.01012 0.01807 0.9713 -0.0008172 0.0003669 1.028 -0.0006159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3082 0.1712 0.4892 0.1046 0.985 0.994 0.3094 0.912 0.979 0.7435 ] Network output: [ -0.01588 0.1472 1.036 0.0004395 -0.0001973 0.8506 0.0003312 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1745 0.159 0.2308 0.145 0.9908 0.9946 0.1746 0.9709 0.9836 0.2524 ] Network output: [ -0.005202 0.01706 1.019 0.0006293 -0.0002825 0.9768 0.0004743 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1966 0.1935 0.2342 0.1751 0.9867 0.9923 0.1966 0.9547 0.9764 0.2398 ] Network output: [ 0.01013 0.9416 -0.006168 7.431e-05 -3.336e-05 1.045 5.6e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01321 Epoch 4623 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03183 0.9059 0.9641 -7.96e-05 3.574e-05 0.06603 -5.999e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004741 -0.004847 -0.01635 0.008332 0.9647 0.9701 0.01048 0.9237 0.9296 0.03535 ] Network output: [ 1.021 0.03548 -0.01026 -0.0001128 5.064e-05 -0.06797 -8.501e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2773 -0.03852 -0.1744 0.1494 0.9835 0.9933 0.3182 0.9057 0.9765 0.747 ] Network output: [ -0.005966 0.923 0.9952 -0.0001131 5.076e-05 0.09324 -8.521e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007416 0.001398 0.007036 0.003605 0.9915 0.9942 0.007579 0.9736 0.9833 0.01758 ] Network output: [ 0.01245 -0.09923 0.9876 -0.0006898 0.0003097 1.084 -0.0005198 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3052 0.1665 0.4819 0.1288 0.985 0.994 0.3064 0.912 0.9789 0.7448 ] Network output: [ -0.02179 0.1437 1.045 0.0004303 -0.0001932 0.8568 0.0003243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1735 0.1577 0.2331 0.1481 0.9908 0.9946 0.1736 0.9709 0.9836 0.2555 ] Network output: [ -0.0133 0.03028 1.025 0.0006041 -0.0002712 0.9737 0.0004552 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1956 0.1925 0.2368 0.1749 0.9867 0.9923 0.1956 0.9548 0.9764 0.2426 ] Network output: [ -0.006214 1.015 -0.00889 -1.484e-05 6.664e-06 1.006 -1.119e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01432 Epoch 4624 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02937 0.9111 0.9648 -8.336e-05 3.743e-05 0.06498 -6.283e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004715 -0.00479 -0.01602 0.00816 0.9647 0.9701 0.01043 0.9237 0.9296 0.03521 ] Network output: [ 0.9823 0.06863 0.02093 -0.0001702 7.64e-05 -0.05486 -0.0001283 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2751 -0.03499 -0.1554 0.1417 0.9834 0.9933 0.3156 0.9058 0.9765 0.7464 ] Network output: [ -0.004303 0.9247 0.9926 -0.0001116 5.012e-05 0.09085 -8.414e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007503 0.001488 0.007543 0.00333 0.9915 0.9942 0.007667 0.9737 0.9834 0.01792 ] Network output: [ -0.009924 0.01639 0.9724 -0.0008175 0.000367 1.028 -0.0006161 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3085 0.1706 0.491 0.1043 0.985 0.994 0.3097 0.9121 0.979 0.7445 ] Network output: [ -0.01557 0.145 1.035 0.0004427 -0.0001988 0.8527 0.0003337 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1754 0.1597 0.2318 0.1455 0.9909 0.9946 0.1756 0.971 0.9836 0.2533 ] Network output: [ -0.004864 0.01565 1.018 0.0006324 -0.0002839 0.9785 0.0004766 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1976 0.1944 0.2349 0.1755 0.9867 0.9923 0.1976 0.9548 0.9764 0.2405 ] Network output: [ 0.009839 0.9433 -0.006241 7.222e-05 -3.242e-05 1.044 5.442e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01273 Epoch 4625 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03136 0.9079 0.9645 -7.883e-05 3.539e-05 0.06457 -5.941e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004749 -0.00486 -0.01634 0.008336 0.9647 0.9701 0.01051 0.9237 0.9296 0.0354 ] Network output: [ 1.021 0.03569 -0.009726 -0.0001208 5.422e-05 -0.06755 -9.102e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2776 -0.03896 -0.1732 0.149 0.9835 0.9933 0.3185 0.9057 0.9766 0.748 ] Network output: [ -0.006343 0.9248 0.9956 -0.0001114 5.003e-05 0.09186 -8.399e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00744 0.001387 0.007105 0.003596 0.9915 0.9942 0.007602 0.9737 0.9833 0.01765 ] Network output: [ 0.01174 -0.09594 0.988 -0.0006963 0.0003126 1.082 -0.0005248 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3057 0.166 0.4841 0.1274 0.985 0.994 0.3069 0.9121 0.979 0.7457 ] Network output: [ -0.02129 0.1415 1.044 0.000434 -0.0001948 0.8588 0.000327 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1744 0.1585 0.2341 0.1485 0.9908 0.9946 0.1745 0.971 0.9836 0.2563 ] Network output: [ -0.01268 0.02821 1.024 0.0006084 -0.0002731 0.9756 0.0004585 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1966 0.1934 0.2375 0.1753 0.9867 0.9923 0.1967 0.955 0.9764 0.2432 ] Network output: [ -0.005941 1.014 -0.008797 -1.341e-05 6.018e-06 1.007 -1.01e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01377 Epoch 4626 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02899 0.9129 0.9652 -8.225e-05 3.692e-05 0.06359 -6.199e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004724 -0.004806 -0.01602 0.008175 0.9647 0.9701 0.01045 0.9238 0.9296 0.03527 ] Network output: [ 0.9834 0.06658 0.02042 -0.0001746 7.839e-05 -0.05443 -0.0001316 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2755 -0.03557 -0.1548 0.1418 0.9835 0.9933 0.3161 0.9059 0.9765 0.7474 ] Network output: [ -0.004753 0.9265 0.9931 -0.00011 4.939e-05 0.08954 -8.291e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007524 0.001474 0.007594 0.003336 0.9915 0.9943 0.007689 0.9738 0.9834 0.01797 ] Network output: [ -0.009694 0.01461 0.9735 -0.0008176 0.0003671 1.028 -0.0006162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3088 0.1701 0.4928 0.1041 0.985 0.994 0.31 0.9122 0.979 0.7455 ] Network output: [ -0.01528 0.1428 1.035 0.000446 -0.0002002 0.8548 0.0003361 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1764 0.1605 0.2328 0.146 0.9909 0.9946 0.1765 0.9711 0.9836 0.2543 ] Network output: [ -0.004543 0.01426 1.017 0.0006355 -0.0002853 0.9802 0.0004789 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1985 0.1954 0.2356 0.1759 0.9867 0.9923 0.1986 0.955 0.9765 0.2412 ] Network output: [ 0.009518 0.9452 -0.006303 7.004e-05 -3.144e-05 1.042 5.278e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01226 Epoch 4627 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03088 0.9099 0.9649 -7.801e-05 3.502e-05 0.06311 -5.879e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004757 -0.004874 -0.01634 0.008341 0.9647 0.9701 0.01053 0.9238 0.9297 0.03545 ] Network output: [ 1.02 0.03588 -0.00914 -0.0001288 5.783e-05 -0.06709 -9.708e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2779 -0.0394 -0.1718 0.1486 0.9835 0.9933 0.3189 0.9058 0.9766 0.7489 ] Network output: [ -0.006716 0.9265 0.996 -0.0001098 4.928e-05 0.09048 -8.273e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007464 0.001375 0.007175 0.003588 0.9915 0.9943 0.007627 0.9738 0.9834 0.01771 ] Network output: [ 0.011 -0.09254 0.9884 -0.000703 0.0003156 1.079 -0.0005298 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3062 0.1656 0.4863 0.1261 0.985 0.994 0.3073 0.9122 0.979 0.7467 ] Network output: [ -0.02078 0.1392 1.043 0.0004377 -0.0001965 0.8608 0.0003298 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1754 0.1592 0.235 0.1488 0.9909 0.9946 0.1755 0.9711 0.9836 0.2572 ] Network output: [ -0.01205 0.02614 1.023 0.0006127 -0.0002751 0.9775 0.0004617 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1976 0.1944 0.2381 0.1758 0.9868 0.9923 0.1977 0.9551 0.9765 0.2438 ] Network output: [ -0.005655 1.013 -0.008712 -1.191e-05 5.349e-06 1.007 -8.979e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01322 Epoch 4628 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02863 0.9146 0.9656 -8.109e-05 3.64e-05 0.06219 -6.111e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004733 -0.004822 -0.01603 0.008189 0.9647 0.9701 0.01047 0.9238 0.9296 0.03532 ] Network output: [ 0.9845 0.06443 0.0199 -0.000179 8.038e-05 -0.05397 -0.0001349 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2758 -0.03617 -0.1542 0.1419 0.9835 0.9933 0.3165 0.906 0.9766 0.7484 ] Network output: [ -0.005203 0.9282 0.9935 -0.0001084 4.864e-05 0.08822 -8.166e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007545 0.00146 0.007647 0.003342 0.9915 0.9943 0.00771 0.9738 0.9834 0.01802 ] Network output: [ -0.009446 0.01265 0.9746 -0.0008176 0.0003671 1.028 -0.0006162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3092 0.1695 0.4947 0.104 0.985 0.994 0.3104 0.9123 0.979 0.7465 ] Network output: [ -0.015 0.1406 1.034 0.0004491 -0.0002016 0.857 0.0003385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1773 0.1612 0.2338 0.1464 0.9909 0.9946 0.1774 0.9711 0.9836 0.2552 ] Network output: [ -0.004236 0.01293 1.016 0.0006385 -0.0002867 0.9818 0.0004812 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1995 0.1963 0.2364 0.1763 0.9868 0.9923 0.1995 0.9551 0.9765 0.2419 ] Network output: [ 0.00916 0.9473 -0.006399 6.761e-05 -3.035e-05 1.041 5.095e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01179 Epoch 4629 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03041 0.9119 0.9653 -7.713e-05 3.463e-05 0.06166 -5.813e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004765 -0.004888 -0.01633 0.008345 0.9647 0.9701 0.01055 0.9239 0.9297 0.0355 ] Network output: [ 1.019 0.03585 -0.008438 -0.0001367 6.139e-05 -0.06643 -0.000103 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2782 -0.03984 -0.1703 0.1483 0.9835 0.9933 0.3192 0.9059 0.9766 0.7498 ] Network output: [ -0.007083 0.9283 0.9963 -0.0001081 4.852e-05 0.08909 -8.144e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007488 0.001364 0.007248 0.00358 0.9915 0.9943 0.007652 0.9738 0.9834 0.01777 ] Network output: [ 0.01022 -0.08916 0.9888 -0.0007096 0.0003186 1.077 -0.0005348 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3066 0.1652 0.4886 0.1248 0.985 0.994 0.3078 0.9122 0.979 0.7476 ] Network output: [ -0.02027 0.137 1.043 0.0004413 -0.0001981 0.8628 0.0003326 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1763 0.16 0.236 0.1492 0.9909 0.9946 0.1764 0.9711 0.9837 0.258 ] Network output: [ -0.01141 0.02418 1.022 0.0006169 -0.000277 0.9793 0.0004649 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1986 0.1954 0.2388 0.1762 0.9868 0.9923 0.1987 0.9552 0.9765 0.2444 ] Network output: [ -0.005364 1.012 -0.008685 -1.056e-05 4.739e-06 1.007 -7.955e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01269 Epoch 4630 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02827 0.9163 0.966 -7.987e-05 3.586e-05 0.06079 -6.019e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004743 -0.004838 -0.01603 0.008205 0.9647 0.9701 0.0105 0.9239 0.9297 0.03537 ] Network output: [ 0.9856 0.06202 0.01942 -0.0001833 8.23e-05 -0.05336 -0.0001382 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2762 -0.03678 -0.1535 0.142 0.9835 0.9933 0.317 0.906 0.9766 0.7493 ] Network output: [ -0.005648 0.93 0.994 -0.0001066 4.788e-05 0.08689 -8.037e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007566 0.001446 0.007701 0.00335 0.9915 0.9943 0.007732 0.9739 0.9834 0.01807 ] Network output: [ -0.009197 0.01044 0.9758 -0.0008174 0.0003669 1.029 -0.000616 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3095 0.169 0.4966 0.1039 0.985 0.994 0.3107 0.9124 0.979 0.7474 ] Network output: [ -0.01472 0.1384 1.034 0.0004523 -0.000203 0.8591 0.0003408 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1782 0.1619 0.2348 0.1469 0.9909 0.9946 0.1783 0.9712 0.9837 0.2562 ] Network output: [ -0.003933 0.01175 1.015 0.0006414 -0.0002879 0.9833 0.0004834 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2005 0.1972 0.2371 0.1767 0.9868 0.9923 0.2005 0.9552 0.9765 0.2426 ] Network output: [ 0.00878 0.9497 -0.006552 6.49e-05 -2.914e-05 1.04 4.891e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01133 Epoch 4631 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02994 0.9138 0.9658 -7.621e-05 3.421e-05 0.06021 -5.743e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004773 -0.004901 -0.01631 0.00835 0.9647 0.9701 0.01057 0.9239 0.9297 0.03554 ] Network output: [ 1.018 0.0357 -0.007634 -0.0001446 6.493e-05 -0.06563 -0.000109 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2785 -0.04029 -0.1688 0.1479 0.9835 0.9933 0.3196 0.906 0.9766 0.7507 ] Network output: [ -0.007437 0.9301 0.9966 -0.0001063 4.773e-05 0.08769 -8.012e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007512 0.001353 0.007323 0.003573 0.9915 0.9943 0.007677 0.9739 0.9834 0.01784 ] Network output: [ 0.009405 -0.08574 0.9892 -0.0007162 0.0003215 1.075 -0.0005398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3072 0.1649 0.4909 0.1235 0.985 0.994 0.3083 0.9123 0.979 0.7485 ] Network output: [ -0.01974 0.1348 1.042 0.000445 -0.0001998 0.8647 0.0003354 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1773 0.1608 0.2369 0.1495 0.9909 0.9946 0.1774 0.9712 0.9837 0.2589 ] Network output: [ -0.01075 0.02225 1.021 0.0006211 -0.0002789 0.9811 0.0004681 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1997 0.1964 0.2395 0.1766 0.9868 0.9924 0.1997 0.9554 0.9766 0.245 ] Network output: [ -0.005022 1.012 -0.008667 -9.015e-06 4.047e-06 1.007 -6.794e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01217 Epoch 4632 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02792 0.9181 0.9664 -7.865e-05 3.531e-05 0.05938 -5.927e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004752 -0.004855 -0.01603 0.008218 0.9647 0.9701 0.01052 0.924 0.9297 0.03543 ] Network output: [ 0.9867 0.05996 0.01883 -0.000188 8.439e-05 -0.05298 -0.0001417 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2767 -0.03739 -0.1529 0.1421 0.9835 0.9933 0.3175 0.9061 0.9766 0.7502 ] Network output: [ -0.006085 0.9317 0.9944 -0.0001049 4.709e-05 0.08558 -7.905e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007587 0.001431 0.007754 0.003357 0.9915 0.9943 0.007754 0.974 0.9835 0.01812 ] Network output: [ -0.008897 0.00828 0.977 -0.0008172 0.0003669 1.029 -0.0006158 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3099 0.1684 0.4985 0.1037 0.985 0.994 0.3111 0.9124 0.979 0.7483 ] Network output: [ -0.01444 0.1361 1.033 0.0004555 -0.0002045 0.8612 0.0003433 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1791 0.1626 0.2358 0.1474 0.9909 0.9946 0.1792 0.9713 0.9837 0.2571 ] Network output: [ -0.00363 0.01041 1.015 0.0006444 -0.0002893 0.9849 0.0004856 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2014 0.1981 0.2378 0.1771 0.9868 0.9924 0.2015 0.9554 0.9766 0.2433 ] Network output: [ 0.008448 0.9516 -0.0066 6.269e-05 -2.814e-05 1.038 4.725e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01089 Epoch 4633 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02947 0.9159 0.9662 -7.531e-05 3.381e-05 0.05873 -5.676e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004781 -0.004915 -0.0163 0.008351 0.9647 0.9701 0.01059 0.924 0.9298 0.03559 ] Network output: [ 1.018 0.03644 -0.007034 -0.0001534 6.886e-05 -0.0654 -0.0001156 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2788 -0.04073 -0.1673 0.1474 0.9835 0.9933 0.32 0.9061 0.9766 0.7515 ] Network output: [ -0.007788 0.9319 0.997 -0.0001045 4.691e-05 0.08632 -7.876e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007537 0.001341 0.007398 0.003562 0.9915 0.9943 0.007702 0.974 0.9834 0.0179 ] Network output: [ 0.008661 -0.0818 0.9894 -0.0007233 0.0003247 1.072 -0.0005451 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3077 0.1645 0.4932 0.122 0.985 0.994 0.3089 0.9124 0.979 0.7494 ] Network output: [ -0.01919 0.1324 1.041 0.0004488 -0.0002015 0.8667 0.0003383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1782 0.1615 0.2379 0.1499 0.9909 0.9946 0.1784 0.9713 0.9837 0.2597 ] Network output: [ -0.01008 0.01991 1.02 0.0006257 -0.0002809 0.9832 0.0004715 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2007 0.1974 0.2401 0.177 0.9868 0.9924 0.2007 0.9555 0.9766 0.2457 ] Network output: [ -0.004572 1.009 -0.008404 -6.311e-06 2.833e-06 1.008 -4.756e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01164 Epoch 4634 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02755 0.9199 0.9668 -7.748e-05 3.478e-05 0.05792 -5.839e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004762 -0.004872 -0.01604 0.008228 0.9647 0.9701 0.01055 0.924 0.9298 0.03548 ] Network output: [ 0.9879 0.05937 0.01778 -0.000194 8.711e-05 -0.05365 -0.0001462 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2771 -0.03803 -0.1523 0.1419 0.9835 0.9933 0.3181 0.9062 0.9766 0.7511 ] Network output: [ -0.006535 0.9334 0.9949 -0.0001031 4.627e-05 0.08431 -7.768e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007609 0.001414 0.007805 0.003358 0.9915 0.9943 0.007776 0.974 0.9835 0.01817 ] Network output: [ -0.008448 0.006788 0.9778 -0.0008179 0.0003672 1.029 -0.0006164 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3103 0.1678 0.5003 0.1035 0.985 0.994 0.3115 0.9125 0.979 0.7493 ] Network output: [ -0.01418 0.1334 1.033 0.0004589 -0.000206 0.8636 0.0003458 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.18 0.1633 0.2369 0.1479 0.9909 0.9946 0.1801 0.9713 0.9837 0.2581 ] Network output: [ -0.003364 0.00848 1.014 0.0006478 -0.0002908 0.987 0.0004882 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2024 0.1991 0.2386 0.1776 0.9868 0.9924 0.2024 0.9555 0.9766 0.244 ] Network output: [ 0.008166 0.9522 -0.006303 6.172e-05 -2.771e-05 1.038 4.651e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01051 Epoch 4635 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02897 0.918 0.9665 -7.442e-05 3.341e-05 0.0572 -5.609e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00479 -0.00493 -0.01629 0.00835 0.9647 0.9701 0.01062 0.9241 0.9298 0.03564 ] Network output: [ 1.017 0.03835 -0.006833 -0.0001632 7.326e-05 -0.06604 -0.000123 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2792 -0.04122 -0.1658 0.1466 0.9835 0.9933 0.3205 0.9062 0.9766 0.7524 ] Network output: [ -0.008167 0.9336 0.9973 -0.0001026 4.607e-05 0.08499 -7.734e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007563 0.001326 0.007471 0.003548 0.9915 0.9943 0.007729 0.974 0.9835 0.01797 ] Network output: [ 0.008052 -0.07727 0.9893 -0.0007312 0.0003283 1.069 -0.0005511 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3082 0.164 0.4954 0.1205 0.985 0.994 0.3094 0.9125 0.979 0.7504 ] Network output: [ -0.0187 0.1298 1.04 0.0004528 -0.0002033 0.869 0.0003412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1792 0.1623 0.2389 0.1503 0.9909 0.9946 0.1793 0.9714 0.9837 0.2606 ] Network output: [ -0.009471 0.01712 1.019 0.0006306 -0.0002831 0.9856 0.0004752 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2017 0.1983 0.2408 0.1776 0.9868 0.9924 0.2017 0.9556 0.9767 0.2463 ] Network output: [ -0.00418 1.007 -0.007912 -3.096e-06 1.39e-06 1.01 -2.333e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01112 Epoch 4636 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02716 0.9218 0.9671 -7.622e-05 3.422e-05 0.05645 -5.744e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004773 -0.00489 -0.01604 0.00824 0.9647 0.9701 0.01058 0.9241 0.9298 0.03555 ] Network output: [ 0.9891 0.05853 0.01671 -0.0002 8.977e-05 -0.05424 -0.0001507 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2776 -0.03871 -0.1517 0.1417 0.9835 0.9933 0.3186 0.9063 0.9766 0.7521 ] Network output: [ -0.007019 0.9352 0.9954 -0.0001012 4.543e-05 0.08304 -7.626e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007632 0.001395 0.007859 0.003362 0.9915 0.9943 0.0078 0.9741 0.9835 0.01823 ] Network output: [ -0.008017 0.00527 0.9787 -0.0008188 0.0003676 1.029 -0.0006171 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3107 0.1671 0.5022 0.1032 0.985 0.994 0.3119 0.9126 0.979 0.7503 ] Network output: [ -0.014 0.1309 1.033 0.0004621 -0.0002075 0.866 0.0003483 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1809 0.164 0.238 0.1484 0.9909 0.9946 0.181 0.9714 0.9838 0.2591 ] Network output: [ -0.003178 0.006812 1.013 0.0006509 -0.0002922 0.989 0.0004906 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2034 0.2 0.2395 0.178 0.9868 0.9924 0.2034 0.9556 0.9767 0.2448 ] Network output: [ 0.007627 0.9542 -0.006196 5.928e-05 -2.661e-05 1.037 4.468e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0101 Epoch 4637 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02849 0.92 0.967 -7.321e-05 3.287e-05 0.0558 -5.518e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004799 -0.004945 -0.01628 0.008361 0.9647 0.9701 0.01064 0.9241 0.9298 0.03569 ] Network output: [ 1.016 0.03699 -0.005838 -0.0001701 7.637e-05 -0.06458 -0.0001282 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2795 -0.04173 -0.1642 0.1465 0.9835 0.9933 0.3209 0.9063 0.9766 0.7533 ] Network output: [ -0.008563 0.9355 0.9976 -0.0001007 4.522e-05 0.08356 -7.591e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007589 0.001315 0.007554 0.003546 0.9915 0.9943 0.007755 0.9741 0.9835 0.01804 ] Network output: [ 0.007175 -0.07435 0.99 -0.0007374 0.000331 1.067 -0.0005557 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3088 0.1636 0.4979 0.1193 0.985 0.994 0.31 0.9126 0.979 0.7513 ] Network output: [ -0.01828 0.1279 1.04 0.0004561 -0.0002048 0.8708 0.0003437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1802 0.1631 0.2399 0.1506 0.9909 0.9946 0.1803 0.9714 0.9838 0.2615 ] Network output: [ -0.008921 0.01595 1.018 0.0006341 -0.0002847 0.987 0.0004779 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2028 0.1993 0.2415 0.1779 0.9869 0.9924 0.2028 0.9557 0.9767 0.247 ] Network output: [ -0.004252 1.009 -0.008351 -4.408e-06 1.979e-06 1.008 -3.322e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01066 Epoch 4638 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02684 0.9232 0.9676 -7.446e-05 3.343e-05 0.05524 -5.612e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004782 -0.004908 -0.01604 0.008271 0.9647 0.9701 0.0106 0.9242 0.9298 0.03559 ] Network output: [ 0.9904 0.05092 0.01758 -0.0002 8.98e-05 -0.05019 -0.0001507 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.278 -0.03936 -0.1505 0.1428 0.9835 0.9933 0.3191 0.9064 0.9766 0.7529 ] Network output: [ -0.007466 0.9372 0.9958 -9.938e-05 4.462e-05 0.08157 -7.49e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007653 0.001386 0.007934 0.00339 0.9915 0.9943 0.007821 0.9741 0.9836 0.01828 ] Network output: [ -0.008215 0.0004406 0.981 -0.0008158 0.0003662 1.032 -0.0006148 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3111 0.1668 0.5046 0.1037 0.985 0.994 0.3123 0.9127 0.9791 0.7511 ] Network output: [ -0.01383 0.13 1.032 0.0004642 -0.0002084 0.8674 0.0003498 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1819 0.1648 0.2391 0.1488 0.9909 0.9946 0.182 0.9715 0.9838 0.2601 ] Network output: [ -0.002933 0.008202 1.012 0.0006517 -0.0002926 0.9888 0.0004912 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2044 0.201 0.2401 0.178 0.9869 0.9924 0.2044 0.9558 0.9767 0.2454 ] Network output: [ 0.006593 0.964 -0.007801 4.98e-05 -2.236e-05 1.031 3.753e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00953 Epoch 4639 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02814 0.9213 0.9675 -7.15e-05 3.21e-05 0.0547 -5.389e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004806 -0.004957 -0.01624 0.008389 0.9647 0.9701 0.01066 0.9242 0.9299 0.03571 ] Network output: [ 1.016 0.02736 -0.002177 -0.0001699 7.63e-05 -0.05715 -0.0001281 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2798 -0.04213 -0.1616 0.1478 0.9835 0.9933 0.3212 0.9063 0.9766 0.7539 ] Network output: [ -0.008828 0.9376 0.9978 -9.896e-05 4.443e-05 0.08188 -7.458e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007612 0.001317 0.007663 0.003574 0.9915 0.9943 0.007778 0.9741 0.9835 0.01811 ] Network output: [ 0.005436 -0.07539 0.9925 -0.0007382 0.0003314 1.069 -0.0005563 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3093 0.1637 0.5011 0.119 0.985 0.994 0.3105 0.9127 0.9791 0.7518 ] Network output: [ -0.01771 0.1278 1.038 0.0004583 -0.0002057 0.8713 0.0003454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1812 0.164 0.2409 0.1507 0.9909 0.9946 0.1814 0.9715 0.9838 0.2623 ] Network output: [ -0.00811 0.01826 1.015 0.0006351 -0.0002851 0.9856 0.0004786 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2038 0.2004 0.2419 0.1777 0.9869 0.9924 0.2039 0.9559 0.9768 0.2472 ] Network output: [ -0.004394 1.018 -0.01063 -1.164e-05 5.224e-06 1.002 -8.769e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01035 Epoch 4640 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02669 0.9241 0.968 -7.26e-05 3.259e-05 0.0542 -5.472e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004789 -0.00492 -0.016 0.008304 0.9647 0.9701 0.01062 0.9242 0.9299 0.0356 ] Network output: [ 0.9914 0.03892 0.02013 -0.0001965 8.82e-05 -0.04268 -0.0001481 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2784 -0.03986 -0.1487 0.1446 0.9835 0.9933 0.3196 0.9064 0.9766 0.7533 ] Network output: [ -0.007711 0.9391 0.996 -9.768e-05 4.385e-05 0.07994 -7.362e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00767 0.001388 0.008017 0.003433 0.9915 0.9943 0.007838 0.9742 0.9836 0.01832 ] Network output: [ -0.008805 -0.007161 0.9844 -0.0008082 0.0003628 1.037 -0.0006091 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3115 0.1668 0.5074 0.1046 0.985 0.994 0.3127 0.9127 0.9791 0.7514 ] Network output: [ -0.01336 0.1296 1.031 0.0004661 -0.0002093 0.868 0.0003513 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1828 0.1656 0.24 0.149 0.9909 0.9946 0.1829 0.9716 0.9838 0.2607 ] Network output: [ -0.002297 0.01102 1.009 0.0006515 -0.0002925 0.9871 0.000491 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2053 0.2019 0.2404 0.1778 0.9869 0.9924 0.2054 0.9559 0.9768 0.2456 ] Network output: [ 0.006312 0.9736 -0.009949 4.121e-05 -1.85e-05 1.024 3.106e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009046 Epoch 4641 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02786 0.9228 0.9678 -7.064e-05 3.171e-05 0.05333 -5.323e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004812 -0.004965 -0.01619 0.008381 0.9647 0.9701 0.01067 0.9242 0.9299 0.0357 ] Network output: [ 1.014 0.02595 0.0001277 -0.0001774 7.962e-05 -0.05441 -0.0001337 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2801 -0.04235 -0.1592 0.1475 0.9835 0.9933 0.3216 0.9064 0.9767 0.7542 ] Network output: [ -0.008881 0.9391 0.9979 -9.712e-05 4.36e-05 0.0804 -7.319e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007634 0.001317 0.00775 0.003567 0.9915 0.9943 0.007801 0.9742 0.9836 0.01814 ] Network output: [ 0.004263 -0.07225 0.993 -0.0007423 0.0003333 1.068 -0.0005594 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.31 0.1638 0.5036 0.1175 0.985 0.994 0.3112 0.9127 0.9791 0.7521 ] Network output: [ -0.01665 0.1253 1.037 0.0004627 -0.0002077 0.8729 0.0003487 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1821 0.1647 0.2415 0.151 0.9909 0.9946 0.1822 0.9716 0.9838 0.2627 ] Network output: [ -0.006806 0.01595 1.013 0.0006398 -0.0002872 0.9869 0.0004822 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2048 0.2013 0.2421 0.178 0.9869 0.9924 0.2048 0.956 0.9768 0.2474 ] Network output: [ -0.002358 1.01 -0.01009 -2.389e-06 1.072e-06 1.005 -1.8e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00985 Epoch 4642 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02636 0.9268 0.9681 -7.285e-05 3.271e-05 0.0521 -5.491e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0048 -0.004936 -0.01602 0.008247 0.9647 0.9701 0.01065 0.9243 0.9299 0.03564 ] Network output: [ 0.9915 0.0559 0.01483 -0.0002183 9.802e-05 -0.05461 -0.0001645 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.279 -0.04039 -0.1489 0.1411 0.9835 0.9933 0.3203 0.9065 0.9767 0.754 ] Network output: [ -0.007946 0.9398 0.9966 -9.553e-05 4.288e-05 0.07917 -7.199e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007693 0.001355 0.008015 0.003367 0.9915 0.9943 0.007862 0.9742 0.9836 0.01833 ] Network output: [ -0.006708 -0.001288 0.9814 -0.0008163 0.0003665 1.03 -0.0006152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3121 0.1658 0.5078 0.1025 0.985 0.994 0.3133 0.9128 0.9791 0.7522 ] Network output: [ -0.01257 0.1222 1.032 0.0004734 -0.0002125 0.8728 0.0003568 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1834 0.166 0.2406 0.15 0.9909 0.9946 0.1835 0.9716 0.9838 0.2614 ] Network output: [ -0.001627 0.000434 1.011 0.0006617 -0.000297 0.9946 0.0004986 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.206 0.2026 0.2413 0.1795 0.9869 0.9924 0.2061 0.956 0.9768 0.2465 ] Network output: [ 0.009019 0.946 -0.004409 6.692e-05 -3.004e-05 1.041 5.043e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009226 Epoch 4643 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02698 0.9278 0.9677 -7.267e-05 3.262e-05 0.05022 -5.476e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004827 -0.004986 -0.01627 0.00825 0.9647 0.9701 0.01071 0.9243 0.9299 0.03583 ] Network output: [ 1.012 0.07168 -0.0115 -0.0002256 0.0001013 -0.08451 -0.00017 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.281 -0.04297 -0.1606 0.1387 0.9835 0.9933 0.3225 0.9065 0.9767 0.7555 ] Network output: [ -0.009346 0.9391 0.9988 -9.436e-05 4.236e-05 0.08037 -7.111e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007673 0.001247 0.007703 0.003391 0.9915 0.9943 0.007841 0.9742 0.9836 0.0182 ] Network output: [ 0.007888 -0.04702 0.9831 -0.0007746 0.0003477 1.045 -0.0005837 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3109 0.1614 0.5023 0.1114 0.985 0.994 0.3121 0.9128 0.9791 0.754 ] Network output: [ -0.01584 0.1124 1.04 0.0004744 -0.000213 0.8815 0.0003575 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1826 0.1648 0.2423 0.1525 0.9909 0.9946 0.1828 0.9716 0.9838 0.2637 ] Network output: [ -0.006352 -0.006761 1.019 0.0006601 -0.0002963 1.003 0.0004975 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2055 0.2019 0.2439 0.1817 0.9869 0.9924 0.2055 0.9561 0.9769 0.2493 ] Network output: [ 0.001983 0.9533 0.001699 4.981e-05 -2.236e-05 1.041 3.754e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009961 Epoch 4644 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02507 0.9334 0.9679 -7.526e-05 3.379e-05 0.04827 -5.672e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004825 -0.004975 -0.01619 0.008099 0.9647 0.9701 0.01071 0.9243 0.9299 0.03591 ] Network output: [ 0.9924 0.1195 -0.00522 -0.0002807 0.000126 -0.1001 -0.0002115 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2803 -0.04171 -0.1534 0.1292 0.9835 0.9933 0.3218 0.9066 0.9767 0.7566 ] Network output: [ -0.0091 0.9397 0.9982 -9.218e-05 4.138e-05 0.07988 -6.947e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007744 0.001237 0.007899 0.003139 0.9915 0.9943 0.007914 0.9743 0.9836 0.01843 ] Network output: [ 0.0002173 0.02806 0.9679 -0.0008579 0.0003851 1 -0.0006465 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3129 0.1615 0.5047 0.09585 0.985 0.994 0.3141 0.9129 0.9791 0.7556 ] Network output: [ -0.01292 0.1064 1.037 0.0004857 -0.000218 0.8847 0.000366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1839 0.1657 0.242 0.152 0.991 0.9946 0.1841 0.9716 0.9838 0.2635 ] Network output: [ -0.002863 -0.02774 1.02 0.0006851 -0.0003076 1.017 0.0005163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2068 0.2032 0.2444 0.1844 0.9869 0.9924 0.2068 0.9562 0.9769 0.2498 ] Network output: [ 0.01094 0.8863 0.01044 0.0001194 -5.362e-05 1.082 9e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01317 Epoch 4645 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02523 0.9344 0.9679 -7.315e-05 3.284e-05 0.04699 -5.513e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004856 -0.005037 -0.01645 0.008189 0.9647 0.9701 0.01079 0.9244 0.93 0.03618 ] Network output: [ 1.014 0.1176 -0.02771 -0.0002729 0.0001225 -0.1192 -0.0002057 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2821 -0.04476 -0.1645 0.1303 0.9835 0.9933 0.3239 0.9066 0.9767 0.759 ] Network output: [ -0.01119 0.9409 1.001 -9.108e-05 4.089e-05 0.08056 -6.864e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007732 0.001131 0.007657 0.003236 0.9916 0.9943 0.007902 0.9743 0.9836 0.01838 ] Network output: [ 0.01315 -0.02244 0.9729 -0.0008142 0.0003655 1.02 -0.0006136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3115 0.157 0.501 0.1065 0.985 0.994 0.3127 0.9129 0.9791 0.758 ] Network output: [ -0.01746 0.1031 1.043 0.0004809 -0.0002159 0.8907 0.0003624 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1837 0.165 0.2445 0.1541 0.991 0.9946 0.1839 0.9717 0.9838 0.2665 ] Network output: [ -0.008866 -0.02421 1.025 0.0006752 -0.0003031 1.02 0.0005089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2067 0.203 0.2473 0.1853 0.9869 0.9924 0.2067 0.9563 0.977 0.2528 ] Network output: [ -0.001637 0.9352 0.009735 6.288e-05 -2.823e-05 1.059 4.739e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01189 Epoch 4646 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02372 0.9351 0.9691 -6.967e-05 3.128e-05 0.04803 -5.251e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004848 -0.005023 -0.01626 0.008305 0.9647 0.9701 0.01078 0.9245 0.93 0.0362 ] Network output: [ 0.9983 0.08374 -0.0005222 -0.0002562 0.000115 -0.08096 -0.0001931 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2808 -0.04361 -0.1522 0.1358 0.9835 0.9933 0.3224 0.9067 0.9767 0.7598 ] Network output: [ -0.01123 0.9452 0.9991 -9.025e-05 4.052e-05 0.07775 -6.801e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007789 0.001213 0.008103 0.003287 0.9916 0.9943 0.00796 0.9744 0.9836 0.01869 ] Network output: [ -0.00293 0.01785 0.9757 -0.0008574 0.0003849 1.009 -0.0006461 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3129 0.1602 0.5103 0.09907 0.985 0.994 0.3142 0.913 0.9791 0.7585 ] Network output: [ -0.01595 0.1167 1.034 0.0004766 -0.0002139 0.8826 0.0003591 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1861 0.1674 0.2454 0.1518 0.991 0.9947 0.1862 0.9718 0.9839 0.2666 ] Network output: [ -0.005879 -0.007586 1.015 0.0006707 -0.0003011 1.008 0.0005055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2089 0.2051 0.2463 0.1824 0.987 0.9924 0.2089 0.9564 0.977 0.2516 ] Network output: [ -0.002584 0.9794 -0.003497 2.926e-05 -1.314e-05 1.029 2.205e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.008898 Epoch 4647 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02569 0.9255 0.9706 -5.793e-05 2.601e-05 0.05226 -4.366e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004853 -0.005054 -0.01619 0.0088 0.9647 0.9701 0.0108 0.9245 0.9301 0.03612 ] Network output: [ 1.024 -0.06613 0.01783 -0.0001196 5.369e-05 0.0005776 -9.013e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.281 -0.04589 -0.1525 0.1637 0.9835 0.9933 0.3228 0.9067 0.9767 0.7594 ] Network output: [ -0.01237 0.951 0.9992 -9.171e-05 4.117e-05 0.07416 -6.912e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00772 0.00131 0.008266 0.003937 0.9916 0.9943 0.00789 0.9745 0.9837 0.01859 ] Network output: [ -0.004199 -0.1076 1.015 -0.000722 0.0003241 1.098 -0.0005441 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3106 0.1628 0.5185 0.1256 0.985 0.994 0.3118 0.913 0.9791 0.7565 ] Network output: [ -0.02094 0.1479 1.03 0.0004462 -0.0002003 0.8661 0.0003362 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.187 0.169 0.2481 0.15 0.991 0.9947 0.1871 0.9719 0.984 0.2684 ] Network output: [ -0.0107 0.06521 0.998 0.0006065 -0.0002723 0.9607 0.0004571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2095 0.2059 0.2447 0.172 0.987 0.9925 0.2095 0.9565 0.977 0.2496 ] Network output: [ -0.02531 1.196 -0.04085 -0.0001776 7.972e-05 0.8952 -0.0001338 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02044 Epoch 4648 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0272 0.9117 0.9735 -4.52e-05 2.029e-05 0.06028 -3.406e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004792 -0.00496 -0.01536 0.009278 0.9647 0.9701 0.01065 0.9245 0.9301 0.03528 ] Network output: [ 1.004 -0.2822 0.104 5.15e-05 -2.312e-05 0.171 3.881e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2773 -0.04208 -0.122 0.2017 0.9835 0.9933 0.3185 0.9067 0.9767 0.7529 ] Network output: [ -0.009182 0.9587 0.9936 -9.41e-05 4.225e-05 0.06572 -7.092e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007688 0.001691 0.009115 0.004724 0.9915 0.9943 0.007857 0.9746 0.9839 0.01853 ] Network output: [ -0.02862 -0.1901 1.057 -0.0006042 0.0002713 1.188 -0.0004554 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3117 0.1766 0.5382 0.1432 0.985 0.994 0.3129 0.9129 0.979 0.7464 ] Network output: [ -0.01534 0.2066 1.003 0.000415 -0.0001863 0.8229 0.0003128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1897 0.1735 0.2448 0.1427 0.9909 0.9946 0.1898 0.9719 0.984 0.262 ] Network output: [ -0.002668 0.1684 0.9576 0.0005362 -0.0002407 0.8815 0.0004041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2114 0.2082 0.2341 0.1549 0.987 0.9924 0.2114 0.9563 0.9768 0.2383 ] Network output: [ -0.02316 1.369 -0.09186 -0.0003145 0.0001412 0.7678 -0.000237 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07599 Epoch 4649 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03092 0.8965 0.9752 -4.019e-05 1.804e-05 0.06627 -3.029e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004737 -0.004863 -0.01454 0.009449 0.9646 0.97 0.01051 0.9242 0.93 0.03392 ] Network output: [ 0.9984 -0.4509 0.1598 0.0001883 -8.453e-05 0.295 0.0001419 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2755 -0.03871 -0.1033 0.2311 0.9834 0.9933 0.3163 0.9064 0.9767 0.7404 ] Network output: [ -0.004585 0.9598 0.9893 -9.757e-05 4.38e-05 0.05967 -7.353e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007582 0.001987 0.009194 0.005385 0.9915 0.9943 0.007749 0.9745 0.9839 0.01767 ] Network output: [ -0.02216 -0.3511 1.092 -0.0003892 0.0001747 1.302 -0.0002933 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3117 0.1874 0.5416 0.1752 0.985 0.994 0.3129 0.9124 0.9789 0.7294 ] Network output: [ -0.005541 0.2463 0.9822 0.0004001 -0.0001796 0.7843 0.0003015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1888 0.1745 0.2348 0.1392 0.9908 0.9946 0.1889 0.9717 0.984 0.2498 ] Network output: [ 0.006569 0.2422 0.9288 0.0004838 -0.0002172 0.8178 0.0003646 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.21 0.2072 0.2207 0.1434 0.9868 0.9924 0.21 0.9556 0.9764 0.2243 ] Network output: [ -0.001992 1.393 -0.1166 -0.0003094 0.0001389 0.7262 -0.0002332 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1441 Epoch 4650 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02919 0.9083 0.9742 -6.935e-05 3.113e-05 0.05881 -5.226e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004691 -0.00473 -0.01406 0.008418 0.9646 0.97 0.01036 0.9237 0.9296 0.03276 ] Network output: [ 0.9496 -0.2346 0.1457 -1.639e-05 7.356e-06 0.1895 -1.235e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2749 -0.03176 -0.09621 0.1898 0.9834 0.9933 0.3154 0.9058 0.9766 0.7277 ] Network output: [ 0.0001122 0.9491 0.9882 -0.0001021 4.584e-05 0.06202 -7.695e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007623 0.002025 0.00871 0.004258 0.9914 0.9942 0.00779 0.974 0.9837 0.017 ] Network output: [ -0.02706 -0.1384 1.03 -0.0005707 0.0002562 1.16 -0.0004301 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3163 0.1915 0.5258 0.1286 0.985 0.994 0.3175 0.912 0.9789 0.7197 ] Network output: [ 0.01036 0.2002 0.9835 0.0004513 -0.0002026 0.7974 0.0003401 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1856 0.1715 0.2227 0.1395 0.9908 0.9946 0.1857 0.9713 0.9837 0.2379 ] Network output: [ 0.02413 0.1241 0.9488 0.0005872 -0.0002636 0.8812 0.0004425 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2071 0.2044 0.2151 0.1579 0.9867 0.9923 0.2072 0.9548 0.9762 0.219 ] Network output: [ 0.05887 0.9546 -0.05078 0.0001134 -5.093e-05 0.9789 8.549e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04343 Epoch 4651 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02542 0.9408 0.9688 -0.0001145 5.139e-05 0.03913 -8.627e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004766 -0.004808 -0.01543 0.006858 0.9647 0.97 0.0105 0.9234 0.9293 0.03382 ] Network output: [ 0.9585 0.3261 -0.0335 -0.0004837 0.0002172 -0.2116 -0.0003646 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2821 -0.03464 -0.1524 0.08889 0.9834 0.9933 0.3234 0.9055 0.9765 0.7352 ] Network output: [ -0.003021 0.9237 1 -0.0001003 4.505e-05 0.08187 -7.562e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00769 0.001306 0.006921 0.002248 0.9914 0.9942 0.007857 0.9735 0.9831 0.01675 ] Network output: [ 0.02005 0.1068 0.92 -0.0008692 0.0003902 0.9295 -0.0006551 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3178 0.1679 0.4806 0.0726 0.985 0.994 0.319 0.9118 0.979 0.735 ] Network output: [ 0.006241 0.06613 1.037 0.0005285 -0.0002372 0.8863 0.0003983 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1787 0.1615 0.2235 0.1518 0.9908 0.9946 0.1788 0.9709 0.9835 0.2444 ] Network output: [ 0.01524 -0.1116 1.033 0.0007464 -0.0003351 1.051 0.0005625 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2017 0.1983 0.2315 0.1918 0.9867 0.9923 0.2018 0.9548 0.9765 0.2369 ] Network output: [ 0.09509 0.3372 0.09171 0.0006443 -0.0002892 1.384 0.0004856 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1193 Epoch 4652 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0149 0.9894 0.9635 -0.000155 6.959e-05 0.01671 -0.0001168 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004896 -0.004989 -0.01684 0.005388 0.9647 0.9701 0.0108 0.9235 0.9292 0.03536 ] Network output: [ 0.9628 0.8836 -0.2068 -0.0009663 0.0004338 -0.6063 -0.0007283 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2897 -0.04099 -0.2025 -0.01055 0.9835 0.9933 0.3321 0.9054 0.9763 0.7488 ] Network output: [ -0.01119 0.9129 1.011 -9.335e-05 4.191e-05 0.09834 -7.036e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007897 0.0003519 0.005029 0.0005637 0.9915 0.9942 0.008068 0.9729 0.9826 0.01643 ] Network output: [ 0.1094 0.3155 0.7791 -0.00114 0.000512 0.6819 -0.0008595 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.32 0.132 0.4196 0.03215 0.985 0.994 0.3212 0.9109 0.9789 0.7492 ] Network output: [ -0.001968 0.01577 1.067 0.0005561 -0.0002496 0.9238 0.0004191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1735 0.151 0.2166 0.1569 0.9909 0.9945 0.1737 0.97 0.983 0.2438 ] Network output: [ -0.007625 -0.2554 1.103 0.000843 -0.0003784 1.171 0.0006353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1966 0.192 0.2436 0.2157 0.9866 0.9922 0.1966 0.954 0.9762 0.2507 ] Network output: [ 0.109 -0.1274 0.2102 0.001054 -0.0004731 1.703 0.0007943 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4873 Epoch 4653 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.001148 1.002 0.9733 -0.0001547 6.945e-05 0.02614 -0.0001166 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004986 -0.00514 -0.01613 0.007759 0.9647 0.9701 0.01104 0.9233 0.9289 0.03544 ] Network output: [ 0.9971 0.2845 -0.0696 -0.0004859 0.0002182 -0.2111 -0.0003662 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2909 -0.04475 -0.1658 0.1087 0.9835 0.9933 0.3339 0.9042 0.9761 0.7511 ] Network output: [ -0.03231 0.987 1.008 -0.0001416 6.359e-05 0.06909 -0.0001068 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00807 0.001131 0.007109 0.002917 0.9916 0.9943 0.008246 0.9737 0.9831 0.0176 ] Network output: [ 0.01729 0.09253 0.9233 -0.0009025 0.0004052 0.9459 -0.0006801 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3217 0.1596 0.4843 0.1041 0.985 0.994 0.323 0.9105 0.9786 0.7513 ] Network output: [ -0.02921 0.1514 1.034 0.0004424 -0.0001986 0.8744 0.0003334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1883 0.1687 0.236 0.159 0.991 0.9946 0.1884 0.9709 0.9834 0.2586 ] Network output: [ -0.0219 0.002161 1.025 0.0006487 -0.0002912 1.019 0.0004889 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2106 0.2067 0.2413 0.1942 0.9869 0.9924 0.2106 0.955 0.9764 0.247 ] Network output: [ 0.01477 0.7374 0.04203 0.000261 -0.0001172 1.192 0.0001967 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04365 Epoch 4654 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00506 0.9731 0.9799 -0.0001179 5.291e-05 0.03639 -8.882e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004965 -0.005185 -0.01631 0.00842 0.9647 0.9701 0.01103 0.9234 0.929 0.03571 ] Network output: [ 1.036 0.1064 -0.05049 -0.0002995 0.0001345 -0.1286 -0.0002257 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2885 -0.04985 -0.1747 0.1401 0.9835 0.9933 0.3313 0.9041 0.9761 0.7541 ] Network output: [ -0.03118 0.979 1.013 -0.0001275 5.722e-05 0.07015 -9.605e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007913 0.001087 0.007147 0.003556 0.9916 0.9943 0.008087 0.9737 0.9831 0.01769 ] Network output: [ 0.02134 -0.03881 0.969 -0.0007769 0.0003488 1.024 -0.0005855 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.315 0.1554 0.4894 0.1308 0.985 0.994 0.3162 0.9105 0.9786 0.7527 ] Network output: [ -0.03507 0.1664 1.04 0.0004183 -0.0001878 0.8656 0.0003153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1876 0.1681 0.241 0.1587 0.9909 0.9946 0.1878 0.9708 0.9834 0.2634 ] Network output: [ -0.02815 0.05361 1.02 0.0005981 -0.0002685 0.985 0.0004507 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2097 0.2058 0.2428 0.1869 0.9869 0.9924 0.2097 0.9551 0.9763 0.2483 ] Network output: [ -0.01846 0.9909 0.007688 7.979e-06 -3.582e-06 1.038 6.013e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01414 Epoch 4655 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009212 0.9471 0.9839 -8.626e-05 3.873e-05 0.05025 -6.501e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004873 -0.00507 -0.01555 0.008951 0.9647 0.9701 0.01084 0.9235 0.9291 0.03524 ] Network output: [ 1.002 -0.1012 0.05075 -0.0001287 5.779e-05 0.04574 -9.702e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2814 -0.0458 -0.1397 0.175 0.9835 0.9933 0.3233 0.9043 0.9761 0.7513 ] Network output: [ -0.02464 0.976 1.007 -0.0001156 5.192e-05 0.0661 -8.715e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007894 0.001432 0.008328 0.004157 0.9915 0.9943 0.008068 0.9739 0.9834 0.01815 ] Network output: [ -0.02002 -0.04498 1.01 -0.0007606 0.0003415 1.072 -0.0005732 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3152 0.1675 0.5173 0.1315 0.985 0.994 0.3164 0.9107 0.9785 0.7473 ] Network output: [ -0.028 0.2138 1.014 0.0003957 -0.0001776 0.8298 0.0002982 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1911 0.1731 0.2413 0.1501 0.9909 0.9946 0.1912 0.971 0.9835 0.2606 ] Network output: [ -0.01689 0.1373 0.9814 0.0005487 -0.0002463 0.9174 0.0004135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2123 0.2087 0.235 0.1705 0.987 0.9924 0.2123 0.9551 0.9762 0.2398 ] Network output: [ -0.02446 1.215 -0.05022 -0.0001814 8.145e-05 0.883 -0.0001367 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02845 Epoch 4656 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01892 0.9087 0.9861 -5.071e-05 2.277e-05 0.06716 -3.822e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004799 -0.005 -0.01484 0.009749 0.9647 0.9701 0.01067 0.9235 0.9292 0.03421 ] Network output: [ 1.026 -0.4356 0.1267 0.0001686 -7.57e-05 0.2575 0.0001271 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2779 -0.04604 -0.1234 0.2341 0.9834 0.9933 0.3191 0.9042 0.9762 0.7424 ] Network output: [ -0.01832 0.9723 1.002 -0.0001076 4.832e-05 0.06209 -8.112e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007663 0.001754 0.00873 0.00555 0.9915 0.9943 0.007832 0.9739 0.9835 0.01751 ] Network output: [ -0.01558 -0.3579 1.095 -0.0004106 0.0001843 1.293 -0.0003095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3104 0.1777 0.5336 0.194 0.985 0.994 0.3117 0.9103 0.9785 0.732 ] Network output: [ -0.02438 0.2748 0.9922 0.0003542 -0.000159 0.7832 0.000267 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1909 0.1752 0.2383 0.1455 0.9908 0.9946 0.1911 0.9709 0.9835 0.2542 ] Network output: [ -0.0136 0.2847 0.9367 0.0004319 -0.0001939 0.8076 0.0003255 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2112 0.2082 0.2228 0.1474 0.9869 0.9924 0.2113 0.9547 0.9759 0.2266 ] Network output: [ -0.03476 1.509 -0.1191 -0.0004347 0.0001952 0.6777 -0.0003276 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1626 Epoch 4657 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0222 0.8954 0.9866 -5.012e-05 2.25e-05 0.07344 -3.777e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004663 -0.004736 -0.01323 0.009252 0.9647 0.9701 0.01031 0.923 0.9288 0.03204 ] Network output: [ 0.9426 -0.4843 0.2302 0.0001685 -7.566e-05 0.3697 0.000127 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2713 -0.03407 -0.07148 0.2379 0.9834 0.9933 0.3113 0.9037 0.9761 0.7213 ] Network output: [ -0.005482 0.9633 0.9906 -0.0001017 4.565e-05 0.05668 -7.664e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007657 0.002215 0.009243 0.005466 0.9913 0.9942 0.007825 0.9735 0.9835 0.01673 ] Network output: [ -0.03166 -0.2972 1.079 -0.0003951 0.0001774 1.28 -0.0002977 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3162 0.1965 0.5376 0.1796 0.985 0.994 0.3175 0.9097 0.9784 0.7065 ] Network output: [ 0.00289 0.3072 0.9544 0.0003717 -0.0001668 0.7341 0.0002801 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1899 0.1764 0.2186 0.1363 0.9907 0.9945 0.1901 0.9705 0.9833 0.2313 ] Network output: [ 0.01516 0.297 0.9053 0.0004515 -0.0002027 0.7692 0.0003403 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2094 0.2068 0.203 0.1406 0.9867 0.9923 0.2094 0.9534 0.9753 0.2061 ] Network output: [ 0.02921 1.304 -0.1231 -0.0001924 8.636e-05 0.7601 -0.000145 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1581 Epoch 4658 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02424 0.9027 0.983 -8.001e-05 3.592e-05 0.06549 -6.03e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004669 -0.004685 -0.01351 0.008194 0.9647 0.9701 0.01027 0.9224 0.9285 0.03155 ] Network output: [ 0.941 -0.2015 0.1443 -6.909e-05 3.102e-05 0.1749 -5.207e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2755 -0.03157 -0.09573 0.1847 0.9834 0.9932 0.3158 0.903 0.9759 0.7131 ] Network output: [ -0.002585 0.9429 0.9948 -0.0001053 4.728e-05 0.06704 -7.938e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007601 0.002019 0.008139 0.004072 0.9913 0.9942 0.007766 0.973 0.9831 0.01603 ] Network output: [ -0.02364 -0.08169 1.01 -0.0005982 0.0002686 1.117 -0.0004509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.318 0.1922 0.5108 0.1295 0.985 0.994 0.3192 0.9093 0.9783 0.7045 ] Network output: [ 0.007753 0.2338 0.9816 0.0004148 -0.0001862 0.7708 0.0003126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1823 0.1684 0.2118 0.1383 0.9907 0.9945 0.1824 0.97 0.9831 0.2267 ] Network output: [ 0.0204 0.1513 0.9503 0.000548 -0.000246 0.8598 0.000413 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2032 0.2004 0.2054 0.1575 0.9866 0.9922 0.2032 0.9528 0.9753 0.2091 ] Network output: [ 0.06788 0.8859 -0.0385 0.0001657 -7.44e-05 1.017 0.0001249 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04447 Epoch 4659 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02185 0.9258 0.9773 -0.000111 4.985e-05 0.05275 -8.369e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004746 -0.004775 -0.01489 0.007083 0.9647 0.9701 0.01041 0.9222 0.9282 0.03278 ] Network output: [ 0.9655 0.2361 -0.01629 -0.0004191 0.0001882 -0.1525 -0.0003159 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2828 -0.03545 -0.1496 0.1054 0.9834 0.9932 0.324 0.9028 0.9758 0.7231 ] Network output: [ -0.006758 0.9216 1.005 -0.0001027 4.612e-05 0.08628 -7.741e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007603 0.001389 0.006659 0.002449 0.9914 0.9942 0.007767 0.9726 0.9826 0.01601 ] Network output: [ 0.01172 0.1435 0.9174 -0.0008628 0.0003874 0.9121 -0.0006503 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3177 0.1708 0.4714 0.07782 0.985 0.994 0.3189 0.9092 0.9784 0.7218 ] Network output: [ -0.001212 0.1221 1.033 0.00047 -0.000211 0.8492 0.0003542 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.175 0.1586 0.2149 0.1476 0.9908 0.9945 0.1751 0.9696 0.9828 0.2353 ] Network output: [ 0.007557 -0.04448 1.027 0.0006696 -0.0003006 1.005 0.0005047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1973 0.194 0.222 0.185 0.9866 0.9922 0.1974 0.9527 0.9755 0.2274 ] Network output: [ 0.08207 0.4309 0.07889 0.0005301 -0.000238 1.328 0.0003995 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08926 Epoch 4660 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01535 0.9551 0.9726 -0.0001295 5.815e-05 0.04117 -9.762e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004854 -0.004946 -0.0161 0.006561 0.9647 0.9701 0.01066 0.9222 0.9281 0.0342 ] Network output: [ 0.9983 0.5137 -0.1375 -0.0006397 0.0002872 -0.3755 -0.0004821 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2891 -0.04268 -0.1919 0.05772 0.9834 0.9933 0.3312 0.9026 0.9757 0.7362 ] Network output: [ -0.01557 0.9213 1.013 -0.0001008 4.524e-05 0.09683 -7.594e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007671 0.000788 0.005501 0.001715 0.9915 0.9942 0.007837 0.9724 0.9823 0.0161 ] Network output: [ 0.06583 0.2073 0.8516 -0.0009629 0.0004323 0.8055 -0.0007257 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3162 0.1473 0.4363 0.06733 0.985 0.994 0.3175 0.9088 0.9783 0.7368 ] Network output: [ -0.01495 0.07527 1.064 0.0004873 -0.0002187 0.8926 0.0003672 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1715 0.152 0.218 0.156 0.9908 0.9945 0.1716 0.9693 0.9826 0.2432 ] Network output: [ -0.01389 -0.1377 1.078 0.0007216 -0.0003239 1.091 0.0005438 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1941 0.1902 0.2358 0.2036 0.9866 0.9922 0.1942 0.9526 0.9755 0.2424 ] Network output: [ 0.06945 0.2332 0.1458 0.0006792 -0.0003049 1.485 0.0005119 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.208 Epoch 4661 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.005855 0.974 0.9741 -0.0001318 5.918e-05 0.03962 -9.934e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004917 -0.005055 -0.01617 0.007246 0.9647 0.9701 0.01084 0.9222 0.928 0.03487 ] Network output: [ 1.003 0.3973 -0.1099 -0.0005535 0.0002485 -0.2959 -0.0004172 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2896 -0.04576 -0.1831 0.08358 0.9834 0.9933 0.3321 0.9022 0.9756 0.7435 ] Network output: [ -0.02566 0.9515 1.012 -0.0001139 5.114e-05 0.08751 -8.585e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007828 0.0008487 0.006127 0.002271 0.9915 0.9942 0.007999 0.9727 0.9825 0.01683 ] Network output: [ 0.04466 0.1854 0.8767 -0.0009627 0.0004322 0.8447 -0.0007255 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3173 0.1483 0.4532 0.08318 0.985 0.994 0.3185 0.9085 0.9782 0.7441 ] Network output: [ -0.02743 0.1175 1.055 0.0004524 -0.0002031 0.8837 0.000341 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1773 0.1572 0.2264 0.1587 0.9909 0.9946 0.1774 0.9695 0.9827 0.2512 ] Network output: [ -0.02448 -0.06901 1.06 0.0006742 -0.0003027 1.06 0.0005081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1996 0.1955 0.2394 0.2015 0.9867 0.9923 0.1996 0.953 0.9755 0.2458 ] Network output: [ 0.03149 0.4968 0.09894 0.0004417 -0.0001983 1.343 0.0003329 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1103 Epoch 4662 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.005996 0.9638 0.9779 -0.0001082 4.856e-05 0.04595 -8.153e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004933 -0.005141 -0.01623 0.008142 0.9647 0.9701 0.01092 0.9223 0.928 0.03524 ] Network output: [ 1.036 0.1806 -0.07703 -0.0003508 0.0001575 -0.1761 -0.0002644 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2879 -0.05079 -0.1834 0.1256 0.9834 0.9933 0.3304 0.9019 0.9755 0.7479 ] Network output: [ -0.02975 0.9646 1.013 -0.0001136 5.098e-05 0.08168 -8.558e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007798 0.0009115 0.006549 0.003179 0.9915 0.9943 0.007968 0.9728 0.9826 0.01718 ] Network output: [ 0.03488 0.0424 0.9329 -0.0008306 0.0003729 0.9516 -0.000626 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3129 0.1482 0.4692 0.1186 0.985 0.994 0.3141 0.9084 0.9781 0.747 ] Network output: [ -0.0373 0.1547 1.051 0.0004158 -0.0001867 0.8707 0.0003134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1806 0.1607 0.2347 0.1602 0.9909 0.9946 0.1807 0.9696 0.9827 0.2586 ] Network output: [ -0.03279 0.0168 1.041 0.0006059 -0.000272 1.011 0.0004566 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2024 0.1984 0.2411 0.1937 0.9868 0.9924 0.2024 0.9534 0.9755 0.2471 ] Network output: [ -0.01149 0.8415 0.04172 0.0001207 -5.421e-05 1.14 9.099e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02686 Epoch 4663 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008853 0.944 0.9815 -7.987e-05 3.586e-05 0.05642 -6.019e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004857 -0.005062 -0.0156 0.008735 0.9647 0.9701 0.01077 0.9224 0.9281 0.03494 ] Network output: [ 1.009 -0.02025 0.01558 -0.000182 8.173e-05 -0.01368 -0.0001372 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.281 -0.04832 -0.1532 0.161 0.9834 0.9933 0.3226 0.902 0.9755 0.7467 ] Network output: [ -0.02511 0.9669 1.007 -0.0001051 4.717e-05 0.0756 -7.919e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007797 0.001191 0.007653 0.003817 0.9915 0.9943 0.007968 0.973 0.9828 0.0177 ] Network output: [ -0.003831 0.01943 0.9776 -0.000809 0.0003632 1.007 -0.0006097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3122 0.1577 0.4976 0.1245 0.985 0.994 0.3134 0.9085 0.978 0.7435 ] Network output: [ -0.03306 0.2031 1.027 0.0003895 -0.0001749 0.8375 0.0002936 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1846 0.1659 0.237 0.1534 0.9909 0.9946 0.1848 0.9698 0.9828 0.258 ] Network output: [ -0.02449 0.1046 1.003 0.0005537 -0.0002486 0.9436 0.0004173 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2056 0.2018 0.2351 0.1786 0.9869 0.9924 0.2056 0.9535 0.9755 0.2403 ] Network output: [ -0.02595 1.108 -0.02068 -0.0001057 4.744e-05 0.9637 -7.964e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01635 Epoch 4664 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01782 0.9069 0.9843 -4.28e-05 1.922e-05 0.07304 -3.226e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004772 -0.004991 -0.0149 0.009624 0.9647 0.9701 0.01059 0.9225 0.9282 0.03406 ] Network output: [ 1.028 -0.3646 0.1007 0.0001237 -5.552e-05 0.2084 9.32e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2758 -0.04887 -0.1347 0.2228 0.9834 0.9933 0.3166 0.902 0.9756 0.7395 ] Network output: [ -0.01919 0.9655 1.002 -9.752e-05 4.378e-05 0.07035 -7.35e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007583 0.001511 0.008271 0.005257 0.9914 0.9943 0.00775 0.973 0.9829 0.01731 ] Network output: [ -0.008179 -0.2815 1.069 -0.0004866 0.0002185 1.227 -0.0003667 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.307 0.1677 0.52 0.1857 0.985 0.994 0.3082 0.9083 0.9779 0.7302 ] Network output: [ -0.03081 0.2651 1.005 0.0003463 -0.0001555 0.7931 0.000261 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1859 0.1693 0.2371 0.149 0.9908 0.9946 0.186 0.9698 0.9829 0.2543 ] Network output: [ -0.02111 0.2559 0.9557 0.000438 -0.0001966 0.8323 0.0003301 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2058 0.2025 0.2243 0.1553 0.9868 0.9924 0.2058 0.9533 0.9752 0.2284 ] Network output: [ -0.04406 1.46 -0.09876 -0.0004089 0.0001836 0.7248 -0.0003082 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1219 Epoch 4665 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02269 0.8868 0.9854 -3.298e-05 1.481e-05 0.0823 -2.486e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00462 -0.004721 -0.01318 0.009434 0.9647 0.9701 0.01021 0.9221 0.9279 0.03195 ] Network output: [ 0.9456 -0.504 0.2314 0.0001975 -8.867e-05 0.3823 0.0001488 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2677 -0.03729 -0.07482 0.2434 0.9834 0.9932 0.3071 0.9018 0.9755 0.7193 ] Network output: [ -0.005315 0.9587 0.9892 -9.008e-05 4.044e-05 0.06235 -6.789e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00755 0.002049 0.009154 0.005645 0.9913 0.9942 0.007716 0.9728 0.983 0.01669 ] Network output: [ -0.02787 -0.3061 1.081 -0.0003911 0.0001756 1.279 -0.0002947 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.312 0.189 0.5339 0.1895 0.985 0.994 0.3132 0.9078 0.9779 0.7038 ] Network output: [ -0.004484 0.3196 0.9597 0.0003471 -0.0001558 0.7311 0.0002616 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1864 0.1725 0.2196 0.1384 0.9907 0.9945 0.1865 0.9696 0.9828 0.2326 ] Network output: [ 0.006903 0.3206 0.9088 0.000418 -0.0001876 0.7585 0.000315 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.2051 0.2024 0.2029 0.1408 0.9866 0.9922 0.2051 0.9522 0.9746 0.206 ] Network output: [ 0.009394 1.391 -0.1308 -0.000287 0.0001288 0.7202 -0.0002163 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1793 Epoch 4666 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02691 0.8846 0.9827 -5.339e-05 2.397e-05 0.07863 -4.023e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004592 -0.004616 -0.0129 0.008603 0.9647 0.9701 0.01008 0.9215 0.9276 0.03083 ] Network output: [ 0.9326 -0.3481 0.1964 5.693e-05 -2.556e-05 0.2867 4.29e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.27 -0.03267 -0.0798 0.2122 0.9834 0.9932 0.3093 0.9011 0.9754 0.705 ] Network output: [ 0.0008683 0.9415 0.9891 -9.243e-05 4.15e-05 0.06724 -6.966e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007465 0.00206 0.008377 0.004723 0.9912 0.9942 0.007627 0.9723 0.9827 0.01576 ] Network output: [ -0.02144 -0.1765 1.035 -0.0004807 0.0002158 1.183 -0.0003622 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3142 0.192 0.5142 0.1581 0.985 0.994 0.3154 0.9072 0.9778 0.6921 ] Network output: [ 0.006061 0.2854 0.9681 0.000373 -0.0001675 0.7358 0.0002811 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1796 0.1663 0.2081 0.1362 0.9906 0.9944 0.1797 0.969 0.9825 0.2215 ] Network output: [ 0.01763 0.2376 0.9295 0.0004739 -0.0002128 0.7995 0.0003572 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1992 0.1966 0.1977 0.1481 0.9865 0.9921 0.1992 0.9512 0.9744 0.201 ] Network output: [ 0.05141 1.077 -0.07899 -7.126e-06 3.199e-06 0.8989 -5.37e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08866 Epoch 4667 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02599 0.8999 0.9784 -8.366e-05 3.756e-05 0.06939 -6.305e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004638 -0.004636 -0.01378 0.007647 0.9647 0.9701 0.01014 0.9211 0.9273 0.03139 ] Network output: [ 0.9454 -0.007993 0.07721 -0.0002164 9.714e-05 0.03912 -0.0001631 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2761 -0.03313 -0.1182 0.1496 0.9834 0.9932 0.316 0.9006 0.9753 0.7081 ] Network output: [ -0.000989 0.9231 0.9966 -9.546e-05 4.285e-05 0.08185 -7.194e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007437 0.001691 0.007271 0.003248 0.9913 0.9941 0.007598 0.9719 0.9824 0.01563 ] Network output: [ -0.01182 0.07548 0.9564 -0.0007439 0.000334 0.9887 -0.0005606 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3151 0.1808 0.4851 0.1006 0.985 0.994 0.3163 0.9071 0.9778 0.703 ] Network output: [ 0.001301 0.1979 1.008 0.0004144 -0.0001861 0.793 0.0003123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1723 0.1578 0.2082 0.1403 0.9906 0.9944 0.1724 0.9686 0.9823 0.2255 ] Network output: [ 0.01227 0.07465 0.989 0.0005734 -0.0002574 0.9142 0.0004321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1934 0.1905 0.2084 0.1683 0.9864 0.9921 0.1934 0.9509 0.9745 0.2129 ] Network output: [ 0.06883 0.6935 0.01543 0.0002951 -0.0001325 1.155 0.0002224 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03787 Epoch 4668 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02355 0.9171 0.9736 -9.856e-05 4.425e-05 0.06179 -7.428e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004731 -0.004782 -0.01512 0.007199 0.9647 0.9701 0.01034 0.9211 0.9272 0.03274 ] Network output: [ 0.9966 0.2406 -0.05554 -0.000395 0.0001773 -0.18 -0.0002977 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2832 -0.04031 -0.1694 0.1065 0.9834 0.9932 0.3241 0.9005 0.9753 0.7206 ] Network output: [ -0.008324 0.9147 1.006 -9.395e-05 4.218e-05 0.09589 -7.08e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007401 0.001152 0.006029 0.002513 0.9913 0.9941 0.00756 0.9717 0.982 0.01567 ] Network output: [ 0.03356 0.1229 0.9052 -0.0008186 0.0003675 0.9014 -0.0006169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3121 0.1601 0.4519 0.08921 0.985 0.994 0.3133 0.907 0.9778 0.7193 ] Network output: [ -0.01334 0.1234 1.051 0.0004428 -0.0001988 0.854 0.0003337 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.167 0.1502 0.2141 0.1506 0.9907 0.9945 0.1671 0.9684 0.9822 0.2363 ] Network output: [ -0.007397 -0.04455 1.048 0.000635 -0.0002851 1.014 0.0004786 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1889 0.1855 0.2242 0.1886 0.9865 0.9922 0.1889 0.951 0.9747 0.2301 ] Network output: [ 0.05643 0.4722 0.09124 0.0004473 -0.0002008 1.325 0.0003371 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08524 Epoch 4669 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01626 0.9435 0.9705 -0.0001109 4.978e-05 0.05303 -8.356e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004795 -0.004874 -0.01569 0.006963 0.9647 0.9701 0.01049 0.9211 0.9271 0.03371 ] Network output: [ 0.9936 0.4016 -0.1024 -0.0005309 0.0002383 -0.2885 -0.0004001 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2853 -0.04297 -0.1817 0.07935 0.9834 0.9932 0.3267 0.9004 0.9751 0.7304 ] Network output: [ -0.01466 0.9236 1.007 -9.471e-05 4.252e-05 0.09794 -7.138e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007531 0.0008639 0.005757 0.002031 0.9914 0.9942 0.007693 0.9717 0.9819 0.01611 ] Network output: [ 0.04724 0.2318 0.8617 -0.0009517 0.0004272 0.8081 -0.0007172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3131 0.1486 0.4397 0.07065 0.985 0.994 0.3143 0.9068 0.9778 0.7307 ] Network output: [ -0.02074 0.1109 1.06 0.0004491 -0.0002016 0.8723 0.0003385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1673 0.1486 0.2168 0.1537 0.9907 0.9945 0.1674 0.9682 0.982 0.2415 ] Network output: [ -0.01815 -0.08736 1.069 0.0006662 -0.0002991 1.058 0.0005021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1892 0.1854 0.2321 0.1983 0.9865 0.9922 0.1892 0.951 0.9747 0.2387 ] Network output: [ 0.04686 0.3955 0.1168 0.000509 -0.0002285 1.396 0.0003836 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1395 Epoch 4670 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0125 0.9464 0.9724 -0.0001 4.492e-05 0.05584 -7.54e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004847 -0.005001 -0.0159 0.007909 0.9648 0.9701 0.01065 0.9212 0.927 0.03428 ] Network output: [ 1.035 0.1932 -0.08538 -0.0003417 0.0001534 -0.1797 -0.0002575 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2859 -0.04909 -0.1865 0.1224 0.9834 0.9933 0.3276 0.9 0.9751 0.7367 ] Network output: [ -0.02328 0.9475 1.008 -0.0001043 4.682e-05 0.09036 -7.859e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007538 0.0009103 0.006096 0.003057 0.9914 0.9942 0.007701 0.9719 0.982 0.01648 ] Network output: [ 0.04389 0.04745 0.9201 -0.0007827 0.0003514 0.9415 -0.0005899 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3095 0.148 0.4544 0.1174 0.985 0.994 0.3107 0.9066 0.9776 0.7355 ] Network output: [ -0.03569 0.1441 1.06 0.0004121 -0.000185 0.8689 0.0003105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1712 0.1525 0.2275 0.1593 0.9908 0.9945 0.1713 0.9685 0.9821 0.2516 ] Network output: [ -0.03163 -0.002291 1.053 0.0005958 -0.0002675 1.015 0.000449 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1928 0.1891 0.2363 0.1942 0.9867 0.9923 0.1929 0.9516 0.9747 0.2425 ] Network output: [ -0.0007858 0.7374 0.06177 0.0001943 -8.724e-05 1.203 0.0001465 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03877 Epoch 4671 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0119 0.9437 0.9744 -8.692e-05 3.902e-05 0.05772 -6.551e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004802 -0.004945 -0.01546 0.008084 0.9647 0.9701 0.01057 0.9213 0.927 0.03422 ] Network output: [ 0.9939 0.1404 -0.0219 -0.0003074 0.000138 -0.1076 -0.0002316 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2804 -0.04605 -0.1604 0.1311 0.9834 0.9933 0.3214 0.9001 0.975 0.7377 ] Network output: [ -0.01998 0.9511 1.004 -9.76e-05 4.381e-05 0.08469 -7.355e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007635 0.001039 0.006933 0.003101 0.9914 0.9942 0.007801 0.9721 0.9821 0.01707 ] Network output: [ 0.01132 0.1403 0.9255 -0.0008851 0.0003973 0.9079 -0.000667 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3111 0.1526 0.4731 0.09978 0.985 0.994 0.3122 0.9067 0.9776 0.7361 ] Network output: [ -0.03005 0.1744 1.041 0.0004028 -0.0001808 0.8465 0.0003036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1747 0.1561 0.2278 0.1536 0.9908 0.9945 0.1749 0.9685 0.9821 0.2504 ] Network output: [ -0.02356 0.03277 1.03 0.0005878 -0.0002639 0.9863 0.000443 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1958 0.192 0.2329 0.1874 0.9867 0.9923 0.1958 0.9516 0.9746 0.2388 ] Network output: [ -0.002925 0.8477 0.02941 0.000114 -5.116e-05 1.129 8.589e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02614 Epoch 4672 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01844 0.9158 0.9769 -5.534e-05 2.484e-05 0.07019 -4.17e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004777 -0.004985 -0.0154 0.009066 0.9648 0.9701 0.01054 0.9214 0.9271 0.03408 ] Network output: [ 1.047 -0.159 0.01318 -2.343e-05 1.052e-05 0.05187 -1.766e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.278 -0.05145 -0.1655 0.1873 0.9834 0.9933 0.3188 0.9 0.975 0.737 ] Network output: [ -0.01998 0.9546 1.004 -9.488e-05 4.26e-05 0.08139 -7.151e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007435 0.001151 0.007123 0.004425 0.9914 0.9942 0.007597 0.9721 0.9822 0.01684 ] Network output: [ 0.019 -0.1659 1.011 -0.0005806 0.0002607 1.114 -0.0004376 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3034 0.154 0.4876 0.1641 0.985 0.994 0.3046 0.9065 0.9775 0.7313 ] Network output: [ -0.03772 0.2118 1.037 0.0003626 -0.0001628 0.8283 0.0002733 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1757 0.1581 0.2343 0.1551 0.9907 0.9945 0.1758 0.9687 0.9822 0.2549 ] Network output: [ -0.03067 0.1493 1.004 0.0004886 -0.0002194 0.9101 0.0003682 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1962 0.1927 0.2306 0.1729 0.9867 0.9923 0.1962 0.9518 0.9746 0.2357 ] Network output: [ -0.04229 1.226 -0.0356 -0.0002311 0.0001037 0.8937 -0.0001742 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03816 Epoch 4673 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02068 0.9024 0.9786 -4.363e-05 1.959e-05 0.07751 -3.288e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004638 -0.004748 -0.01395 0.00903 0.9647 0.9701 0.01021 0.9213 0.927 0.03276 ] Network output: [ 0.9548 -0.2728 0.1479 3.022e-05 -1.357e-05 0.2154 2.278e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2687 -0.04048 -0.1041 0.2041 0.9834 0.9932 0.308 0.9001 0.975 0.7257 ] Network output: [ -0.008398 0.9524 0.9913 -8.74e-05 3.924e-05 0.07266 -6.587e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007494 0.001641 0.008547 0.004668 0.9913 0.9942 0.007657 0.9722 0.9824 0.01708 ] Network output: [ -0.03035 -0.07827 1.024 -0.000635 0.0002851 1.112 -0.0004785 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3089 0.1737 0.5159 0.1453 0.985 0.994 0.3101 0.9065 0.9775 0.7166 ] Network output: [ -0.01761 0.267 0.9946 0.0003548 -0.0001593 0.775 0.0002674 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.179 0.1635 0.2253 0.1427 0.9907 0.9945 0.1791 0.9687 0.9822 0.2416 ] Network output: [ -0.005859 0.2163 0.956 0.0004692 -0.0002106 0.8413 0.0003536 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1984 0.1954 0.2152 0.1568 0.9866 0.9923 0.1985 0.9513 0.9743 0.2193 ] Network output: [ -0.01054 1.274 -0.08214 -0.0002265 0.0001017 0.8278 -0.0001707 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07101 Epoch 4674 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03008 0.8753 0.9781 -2.935e-05 1.318e-05 0.08631 -2.212e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004603 -0.004711 -0.01364 0.009265 0.9647 0.9701 0.0101 0.921 0.927 0.03179 ] Network output: [ 0.9931 -0.4362 0.1554 0.0001796 -8.062e-05 0.2954 0.0001353 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2692 -0.0419 -0.1093 0.2326 0.9834 0.9932 0.3084 0.8997 0.975 0.7148 ] Network output: [ -0.003171 0.9424 0.9902 -8.44e-05 3.789e-05 0.07341 -6.361e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007265 0.001734 0.008093 0.005356 0.9913 0.9942 0.007423 0.9719 0.9823 0.01604 ] Network output: [ 0.0008374 -0.3436 1.069 -0.0003423 0.0001537 1.271 -0.000258 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3048 0.1768 0.5111 0.1967 0.985 0.994 0.306 0.906 0.9774 0.7013 ] Network output: [ -0.01298 0.2757 0.9921 0.0003472 -0.0001559 0.7596 0.0002617 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1754 0.1612 0.2198 0.1445 0.9906 0.9944 0.1755 0.9684 0.9822 0.2347 ] Network output: [ -0.003297 0.2659 0.9439 0.0004221 -0.0001895 0.7985 0.0003181 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1949 0.1922 0.2073 0.1499 0.9865 0.9922 0.195 0.9507 0.9739 0.211 ] Network output: [ -0.0016 1.307 -0.09498 -0.0002499 0.0001122 0.7901 -0.0001883 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1325 Epoch 4675 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02792 0.8925 0.975 -5.95e-05 2.671e-05 0.07641 -4.484e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004549 -0.004533 -0.01306 0.008101 0.9647 0.9701 0.009928 0.9206 0.9266 0.03092 ] Network output: [ 0.9098 -0.1568 0.1595 -9.149e-05 4.107e-05 0.1773 -6.895e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2679 -0.03132 -0.08663 0.1779 0.9834 0.9932 0.3066 0.8994 0.9749 0.7042 ] Network output: [ 0.004116 0.9311 0.9856 -8.523e-05 3.826e-05 0.07471 -6.423e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007369 0.001846 0.008122 0.003867 0.9912 0.9941 0.007527 0.9715 0.9821 0.01594 ] Network output: [ -0.03277 0.04599 0.9829 -0.0006904 0.00031 1.034 -0.0005203 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3127 0.1847 0.5019 0.113 0.985 0.994 0.3139 0.9058 0.9774 0.6955 ] Network output: [ 0.003287 0.2523 0.9862 0.0003807 -0.0001709 0.7565 0.0002869 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.172 0.1582 0.2076 0.1357 0.9906 0.9944 0.1721 0.968 0.9819 0.2227 ] Network output: [ 0.01563 0.1603 0.9584 0.0005138 -0.0002307 0.8522 0.0003872 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1918 0.1891 0.2021 0.1571 0.9864 0.9921 0.1918 0.9498 0.9738 0.206 ] Network output: [ 0.05258 0.9346 -0.0429 9.61e-05 -4.314e-05 1.004 7.242e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0436 Epoch 4676 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03164 0.8874 0.9723 -6.481e-05 2.91e-05 0.07675 -4.885e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00463 -0.004676 -0.01427 0.008078 0.9648 0.9701 0.01009 0.9204 0.9265 0.03174 ] Network output: [ 1.001 -0.0814 0.0359 -0.0001113 4.996e-05 0.04303 -8.387e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2761 -0.04017 -0.1443 0.1663 0.9834 0.9932 0.3158 0.899 0.9749 0.7106 ] Network output: [ -0.001832 0.9191 0.996 -8.82e-05 3.96e-05 0.08813 -6.647e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007183 0.001446 0.00676 0.003749 0.9912 0.9941 0.007337 0.9713 0.9819 0.01549 ] Network output: [ 0.0177 -0.08805 0.9799 -0.0005735 0.0002575 1.07 -0.0004322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3066 0.169 0.4739 0.137 0.985 0.994 0.3077 0.9056 0.9774 0.7047 ] Network output: [ -0.0126 0.1829 1.032 0.0003952 -0.0001774 0.8122 0.0002978 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1654 0.1507 0.2144 0.1475 0.9906 0.9944 0.1655 0.9679 0.9819 0.2332 ] Network output: [ -0.003771 0.08278 1.007 0.0005326 -0.0002391 0.9197 0.0004013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1864 0.1835 0.2148 0.1716 0.9864 0.9921 0.1865 0.95 0.9739 0.2197 ] Network output: [ 0.03146 0.831 0.01206 0.0001326 -5.952e-05 1.095 9.991e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02568 Epoch 4677 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02508 0.9196 0.9673 -9.129e-05 4.098e-05 0.0625 -6.88e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004656 -0.004659 -0.01472 0.006998 0.9648 0.9701 0.01013 0.9203 0.9263 0.03244 ] Network output: [ 0.9497 0.3046 -0.02577 -0.0004459 0.0002002 -0.18 -0.000336 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2775 -0.03664 -0.1505 0.09453 0.9834 0.9932 0.3173 0.8991 0.9748 0.7175 ] Network output: [ -0.002152 0.9115 0.9968 -8.305e-05 3.729e-05 0.09568 -6.259e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007355 0.00114 0.006388 0.00214 0.9913 0.9941 0.007512 0.9711 0.9816 0.01596 ] Network output: [ 0.01276 0.2816 0.8785 -0.0009528 0.0004277 0.8105 -0.0007181 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3116 0.1593 0.4543 0.05847 0.985 0.994 0.3128 0.9056 0.9774 0.7168 ] Network output: [ -0.008897 0.1368 1.042 0.0004352 -0.0001954 0.8403 0.000328 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1633 0.1467 0.2107 0.1453 0.9907 0.9944 0.1634 0.9675 0.9816 0.2327 ] Network output: [ -0.002226 -0.04907 1.044 0.0006372 -0.0002861 1.012 0.0004802 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1847 0.1813 0.2214 0.1869 0.9864 0.9921 0.1847 0.9497 0.9739 0.2274 ] Network output: [ 0.05991 0.4639 0.0839 0.0004561 -0.0002048 1.334 0.0003438 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1068 Epoch 4678 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02405 0.9179 0.9675 -8.169e-05 3.668e-05 0.06619 -6.157e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004759 -0.004883 -0.01565 0.007981 0.9648 0.9701 0.01038 0.9203 0.9263 0.0334 ] Network output: [ 1.058 0.1108 -0.08681 -0.0002407 0.0001081 -0.1408 -0.0001814 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2832 -0.04931 -0.1934 0.1368 0.9834 0.9932 0.3241 0.8986 0.9747 0.7267 ] Network output: [ -0.01479 0.9289 1.004 -9.386e-05 4.214e-05 0.09607 -7.074e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007212 0.0009372 0.005712 0.003343 0.9913 0.9941 0.007366 0.9712 0.9816 0.01578 ] Network output: [ 0.0598 -0.07233 0.94 -0.0006148 0.000276 1.01 -0.0004633 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.303 0.1479 0.4453 0.1383 0.9849 0.994 0.3041 0.9052 0.9773 0.7242 ] Network output: [ -0.03352 0.1328 1.069 0.0004062 -0.0001824 0.8669 0.0003061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1628 0.1455 0.2237 0.1591 0.9906 0.9944 0.1629 0.9677 0.9817 0.2475 ] Network output: [ -0.0298 0.001513 1.057 0.0005676 -0.0002548 1.003 0.0004278 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1842 0.1807 0.2323 0.1907 0.9865 0.9922 0.1842 0.9503 0.9741 0.2385 ] Network output: [ -0.0002005 0.7497 0.06217 0.0001646 -7.391e-05 1.189 0.0001241 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03293 Epoch 4679 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01615 0.9444 0.9668 -9.636e-05 4.326e-05 0.05608 -7.262e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00471 -0.004751 -0.01505 0.00719 0.9648 0.9701 0.01028 0.9204 0.9261 0.03346 ] Network output: [ 0.9374 0.3518 -0.02712 -0.0004891 0.0002196 -0.2015 -0.0003686 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.277 -0.03905 -0.1484 0.08944 0.9834 0.9932 0.3169 0.8989 0.9746 0.7289 ] Network output: [ -0.009565 0.9307 0.9966 -8.707e-05 3.909e-05 0.09146 -6.562e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007531 0.0009873 0.006715 0.002047 0.9914 0.9942 0.007693 0.9713 0.9816 0.01682 ] Network output: [ 0.005848 0.3682 0.8616 -0.001066 0.0004784 0.7541 -0.000803 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3126 0.1521 0.4579 0.04758 0.985 0.994 0.3137 0.9054 0.9773 0.7291 ] Network output: [ -0.01732 0.1512 1.041 0.0004244 -0.0001905 0.8444 0.0003198 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1667 0.1484 0.2163 0.147 0.9907 0.9945 0.1668 0.9675 0.9815 0.2397 ] Network output: [ -0.01166 -0.04408 1.046 0.000639 -0.0002869 1.024 0.0004816 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1877 0.184 0.2278 0.191 0.9865 0.9922 0.1877 0.9498 0.9739 0.2342 ] Network output: [ 0.03667 0.5517 0.0731 0.0003795 -0.0001704 1.303 0.000286 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1084 Epoch 4680 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02334 0.9092 0.9707 -5.737e-05 2.575e-05 0.07318 -4.323e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004786 -0.005011 -0.01588 0.009185 0.9648 0.9702 0.01049 0.9204 0.9263 0.03393 ] Network output: [ 1.113 -0.175 -0.05848 2.888e-05 -1.296e-05 0.007953 2.176e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2812 -0.05735 -0.2016 0.194 0.9834 0.9933 0.322 0.8981 0.9746 0.7332 ] Network output: [ -0.02196 0.9498 1.005 -9.885e-05 4.438e-05 0.08829 -7.449e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007137 0.0009456 0.005995 0.004664 0.9914 0.9942 0.007291 0.9713 0.9816 0.016 ] Network output: [ 0.06482 -0.3442 1.019 -0.0003649 0.0001638 1.194 -0.000275 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2954 0.1444 0.4609 0.2018 0.9849 0.994 0.2965 0.9047 0.9771 0.7266 ] Network output: [ -0.04827 0.1867 1.063 0.0003534 -0.0001586 0.8484 0.0002663 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1666 0.1493 0.2336 0.1625 0.9906 0.9945 0.1668 0.9677 0.9816 0.2562 ] Network output: [ -0.04498 0.1356 1.03 0.0004601 -0.0002066 0.9263 0.0003467 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1873 0.1839 0.2333 0.1782 0.9866 0.9923 0.1873 0.9506 0.9739 0.2389 ] Network output: [ -0.05693 1.206 -0.01342 -0.0002413 0.0001083 0.9205 -0.0001819 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05374 Epoch 4681 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01588 0.927 0.9727 -7.226e-05 3.244e-05 0.0683 -5.446e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004577 -0.004581 -0.01356 0.008117 0.9647 0.9701 0.01001 0.9203 0.9259 0.03238 ] Network output: [ 0.8717 -0.0001544 0.1522 -0.0002234 0.0001003 0.1038 -0.0001684 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2651 -0.03236 -0.08337 0.154 0.9834 0.9932 0.3035 0.8985 0.9745 0.7204 ] Network output: [ -0.003948 0.9491 0.9847 -8.981e-05 4.032e-05 0.07376 -6.768e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007557 0.00162 0.008713 0.003296 0.9913 0.9942 0.00772 0.9714 0.9819 0.0174 ] Network output: [ -0.06222 0.3016 0.9406 -0.0009728 0.0004367 0.8782 -0.0007331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3136 0.1751 0.5062 0.0677 0.985 0.994 0.3148 0.905 0.9771 0.715 ] Network output: [ -0.008476 0.2521 0.9924 0.000372 -0.000167 0.7741 0.0002804 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1736 0.1581 0.2159 0.1359 0.9907 0.9945 0.1738 0.9676 0.9816 0.2332 ] Network output: [ 0.00509 0.1265 0.9732 0.0005385 -0.0002418 0.8924 0.0004058 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.193 0.1898 0.2123 0.1648 0.9865 0.9922 0.193 0.9496 0.9735 0.2169 ] Network output: [ 0.02536 0.9802 -0.03988 4.62e-05 -2.074e-05 1.009 3.482e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04695 Epoch 4682 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03458 0.8656 0.9741 -2.377e-05 1.067e-05 0.09108 -1.791e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004646 -0.004846 -0.0147 0.009831 0.9648 0.9702 0.01017 0.9202 0.9262 0.03237 ] Network output: [ 1.101 -0.5126 0.05889 0.0003043 -0.0001366 0.2537 0.0002293 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2729 -0.05408 -0.1661 0.2529 0.9834 0.9932 0.3125 0.8976 0.9745 0.718 ] Network output: [ -0.01021 0.9416 0.997 -9.082e-05 4.077e-05 0.08151 -6.845e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006936 0.001329 0.006769 0.005882 0.9912 0.9941 0.007086 0.971 0.9817 0.01539 ] Network output: [ 0.05881 -0.6227 1.102 -6.938e-05 3.115e-05 1.403 -5.228e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2921 0.158 0.4852 0.256 0.9849 0.994 0.2932 0.9039 0.9769 0.703 ] Network output: [ -0.03279 0.2474 1.028 0.0003268 -0.0001467 0.7912 0.0002463 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1672 0.1523 0.2255 0.1566 0.9905 0.9944 0.1674 0.9673 0.9816 0.243 ] Network output: [ -0.02799 0.2734 0.9736 0.0003713 -0.0001667 0.8105 0.0002798 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.187 0.1841 0.2145 0.1557 0.9865 0.9922 0.187 0.9496 0.9733 0.2187 ] Network output: [ -0.03959 1.393 -0.07971 -0.0003648 0.0001638 0.7644 -0.0002749 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1971 Epoch 4683 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02088 0.9173 0.9714 -8.112e-05 3.642e-05 0.06915 -6.113e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004458 -0.00436 -0.0124 0.007628 0.9647 0.9701 0.009687 0.9195 0.9253 0.03048 ] Network output: [ 0.8149 -0.005571 0.2153 -0.0002451 0.00011 0.1595 -0.0001847 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2603 -0.02354 -0.05094 0.1516 0.9833 0.9932 0.2976 0.8975 0.9742 0.6981 ] Network output: [ 0.007496 0.9396 0.9762 -9.092e-05 4.082e-05 0.06886 -6.852e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007431 0.001937 0.008795 0.003085 0.9911 0.9941 0.00759 0.9707 0.9817 0.01644 ] Network output: [ -0.07529 0.3539 0.9311 -0.0009577 0.00043 0.8617 -0.0007218 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3155 0.1887 0.5047 0.05409 0.985 0.994 0.3167 0.9038 0.9769 0.69 ] Network output: [ 0.01029 0.2857 0.9676 0.0003647 -0.0001637 0.7276 0.0002748 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1698 0.1562 0.1997 0.1254 0.9906 0.9944 0.1699 0.9668 0.9812 0.2142 ] Network output: [ 0.02482 0.1571 0.9495 0.000523 -0.0002348 0.8459 0.0003941 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1885 0.1858 0.1951 0.1535 0.9863 0.992 0.1885 0.9479 0.9728 0.1991 ] Network output: [ 0.07058 0.8685 -0.04834 0.000176 -7.901e-05 1.039 0.0001326 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07393 Epoch 4684 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03765 0.8586 0.9727 -4.119e-05 1.849e-05 0.09323 -3.104e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00459 -0.004713 -0.01426 0.009287 0.9648 0.9702 0.009977 0.9193 0.9256 0.03127 ] Network output: [ 1.08 -0.4413 0.0563 0.0002267 -0.0001018 0.2251 0.0001709 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2734 -0.04902 -0.1603 0.2383 0.9834 0.9932 0.3127 0.8963 0.9743 0.7027 ] Network output: [ -0.004514 0.9273 0.9953 -9.595e-05 4.307e-05 0.08604 -7.231e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006844 0.001436 0.006404 0.005451 0.9912 0.9941 0.006991 0.9704 0.9814 0.01471 ] Network output: [ 0.06589 -0.5678 1.072 -7.793e-05 3.499e-05 1.364 -5.873e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2946 0.1643 0.4699 0.2418 0.9849 0.994 0.2957 0.9027 0.9767 0.6881 ] Network output: [ -0.02419 0.2303 1.031 0.0003376 -0.0001516 0.7885 0.0002544 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1617 0.1478 0.2152 0.154 0.9904 0.9943 0.1618 0.9666 0.9812 0.2326 ] Network output: [ -0.01891 0.2246 0.9846 0.0003974 -0.0001784 0.8303 0.0002995 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1816 0.1789 0.2081 0.159 0.9864 0.9921 0.1817 0.9482 0.9728 0.2125 ] Network output: [ -0.006055 1.16 -0.04315 -0.0001637 7.348e-05 0.895 -0.0001233 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.14 Epoch 4685 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02062 0.9329 0.9662 -0.0001125 5.051e-05 0.0592 -8.479e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004492 -0.004354 -0.01314 0.006521 0.9647 0.9701 0.009702 0.9187 0.9247 0.03063 ] Network output: [ 0.8205 0.3543 0.09877 -0.0005298 0.0002378 -0.09616 -0.0003993 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2662 -0.0228 -0.08701 0.08485 0.9833 0.9932 0.304 0.8963 0.974 0.6951 ] Network output: [ 0.007498 0.9191 0.9822 -9.239e-05 4.148e-05 0.0834 -6.963e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007383 0.001564 0.007431 0.001539 0.9911 0.994 0.007539 0.9699 0.9811 0.01592 ] Network output: [ -0.05253 0.5829 0.8459 -0.001174 0.000527 0.6715 -0.0008847 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3165 0.1773 0.468 0.0005917 0.985 0.994 0.3177 0.9027 0.9768 0.6932 ] Network output: [ 0.009214 0.2251 0.996 0.0003927 -0.0001763 0.7621 0.0002959 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1618 0.1471 0.193 0.1252 0.9905 0.9943 0.1619 0.9658 0.9807 0.2112 ] Network output: [ 0.02031 0.02739 0.9989 0.0005973 -0.0002682 0.9355 0.0004501 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1815 0.1785 0.1996 0.1673 0.9862 0.992 0.1815 0.9465 0.9725 0.2048 ] Network output: [ 0.09466 0.4809 0.04009 0.0004856 -0.000218 1.292 0.000366 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1646 Epoch 4686 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03398 0.8672 0.9715 -5.508e-05 2.473e-05 0.0931 -4.151e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004656 -0.004822 -0.0151 0.009424 0.9648 0.9702 0.0101 0.9185 0.925 0.03179 ] Network output: [ 1.145 -0.4005 -0.03085 0.0002228 -0.0001 0.1416 0.0001679 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2795 -0.05583 -0.2017 0.2368 0.9834 0.9932 0.3195 0.8946 0.974 0.7045 ] Network output: [ -0.01455 0.9318 1.003 -0.0001081 4.851e-05 0.09376 -8.144e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006719 0.001206 0.00535 0.00545 0.9912 0.9941 0.006862 0.9697 0.981 0.01431 ] Network output: [ 0.1032 -0.6563 1.062 3.176e-05 -1.426e-05 1.388 2.393e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2897 0.1545 0.4408 0.2648 0.9849 0.994 0.2908 0.9011 0.9764 0.6907 ] Network output: [ -0.03985 0.2025 1.059 0.000334 -0.0001499 0.8191 0.0002517 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1569 0.1426 0.2167 0.1625 0.9903 0.9943 0.157 0.9658 0.9809 0.2371 ] Network output: [ -0.03757 0.1903 1.018 0.0003936 -0.0001767 0.8689 0.0002966 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1769 0.1741 0.2141 0.169 0.9863 0.9921 0.177 0.9474 0.9725 0.2192 ] Network output: [ -0.0214 1.076 -0.003451 -0.0001223 5.489e-05 0.9695 -9.214e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1402 Epoch 4687 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01125 0.963 0.9649 -0.0001418 6.365e-05 0.04902 -0.0001069 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004494 -0.004316 -0.01322 0.005856 0.9647 0.9701 0.009688 0.9179 0.9239 0.03083 ] Network output: [ 0.7692 0.5973 0.08044 -0.0007473 0.0003355 -0.2191 -0.0005632 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2656 -0.01926 -0.08238 0.0416 0.9833 0.9932 0.3033 0.8947 0.9735 0.6945 ] Network output: [ 0.004555 0.9237 0.9819 -9.986e-05 4.483e-05 0.08485 -7.526e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007497 0.001418 0.007249 0.0004407 0.9911 0.994 0.007655 0.9692 0.9806 0.01613 ] Network output: [ -0.06294 0.8525 0.7777 -0.001427 0.0006408 0.4899 -0.001076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3196 0.1727 0.4532 -0.05284 0.9849 0.994 0.3208 0.9009 0.9764 0.6939 ] Network output: [ 0.009347 0.2467 0.9899 0.0003804 -0.0001708 0.7462 0.0002867 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1599 0.1442 0.1845 0.1169 0.9905 0.9943 0.16 0.9645 0.98 0.2043 ] Network output: [ 0.01848 0.007459 1.007 0.0006142 -0.0002757 0.9509 0.0004629 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.179 0.1757 0.1957 0.1668 0.9861 0.9919 0.179 0.9446 0.9717 0.2017 ] Network output: [ 0.1035 0.3637 0.06107 0.000594 -0.0002667 1.371 0.0004477 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3155 Epoch 4688 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03281 0.8445 0.9779 -3.669e-05 1.647e-05 0.1118 -2.765e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004645 -0.004915 -0.01516 0.01073 0.9648 0.9702 0.01009 0.9174 0.9242 0.03146 ] Network output: [ 1.226 -0.7517 -0.01373 0.0005432 -0.0002439 0.316 0.0004094 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2783 -0.066 -0.2207 0.3087 0.9833 0.9932 0.3183 0.8917 0.9735 0.697 ] Network output: [ -0.02388 0.9465 1.007 -0.000121 5.433e-05 0.09329 -9.121e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006466 0.001176 0.004847 0.006925 0.9911 0.994 0.006605 0.9686 0.9805 0.01336 ] Network output: [ 0.1549 -1.114 1.144 0.0005024 -0.0002256 1.663 0.0003786 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2787 0.149 0.4286 0.3703 0.9849 0.994 0.2798 0.8976 0.9757 0.6701 ] Network output: [ -0.0434 0.2469 1.048 0.0003084 -0.0001384 0.7936 0.0002324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1561 0.1422 0.2076 0.1732 0.9901 0.9942 0.1562 0.9641 0.9801 0.2262 ] Network output: [ -0.04376 0.3009 0.9914 0.0003157 -0.0001417 0.7965 0.0002379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.175 0.1723 0.1989 0.1659 0.9862 0.992 0.175 0.9449 0.9712 0.2032 ] Network output: [ 0.005695 0.9872 -0.01054 1.963e-06 -8.812e-07 1.012 1.479e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3907 Epoch 4689 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.00715 1.025 0.9656 -0.0002158 9.687e-05 0.02249 -0.0001626 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004444 -0.004155 -0.01207 0.004992 0.9647 0.9701 0.009551 0.9157 0.9217 0.02959 ] Network output: [ 0.6413 0.8469 0.1446 -0.001029 0.0004621 -0.2783 -0.0007757 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2609 -0.007865 -0.03462 -0.0001261 0.9832 0.9932 0.2978 0.8905 0.9724 0.6728 ] Network output: [ 0.001732 0.9579 0.974 -0.0001399 6.279e-05 0.06413 -0.0001054 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007715 0.00164 0.007649 -0.0009222 0.9909 0.9939 0.007878 0.9673 0.9796 0.01582 ] Network output: [ -0.1148 1.281 0.699 -0.00179 0.0008037 0.2425 -0.001349 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3266 0.183 0.4473 -0.1304 0.9849 0.994 0.3279 0.8959 0.9754 0.6692 ] Network output: [ 0.0173 0.3953 0.9387 0.0002907 -0.0001305 0.6326 0.0002191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1589 0.1435 0.1591 0.08921 0.9904 0.9942 0.159 0.9612 0.9783 0.1766 ] Network output: [ 0.02676 0.1389 0.9642 0.000534 -0.0002397 0.8455 0.0004024 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1752 0.1719 0.168 0.1403 0.9857 0.9917 0.1752 0.9392 0.9694 0.1736 ] Network output: [ 0.1186 0.4986 0.008172 0.0005282 -0.0002371 1.258 0.0003981 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.5634 Epoch 4690 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03255 0.7775 0.9972 -1.578e-06 7.084e-07 0.1601 -1.189e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004452 -0.004847 -0.01394 0.01221 0.9648 0.9702 0.00969 0.9143 0.9221 0.02909 ] Network output: [ 1.262 -1.245 0.1014 0.0009355 -0.00042 0.6231 0.0007051 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2664 -0.0768 -0.2094 0.4035 0.9832 0.9932 0.3048 0.8841 0.9722 0.6572 ] Network output: [ -0.02606 0.9372 1.011 -0.000126 5.657e-05 0.1031 -9.497e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006066 0.001037 0.004235 0.008262 0.9907 0.9939 0.006197 0.9654 0.9792 0.01114 ] Network output: [ 0.24 -1.594 1.2 0.001003 -0.0004501 1.918 0.0007556 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2645 0.1366 0.3937 0.4824 0.9848 0.9939 0.2655 0.8886 0.9741 0.5957 ] Network output: [ -0.02464 0.3807 0.9912 0.0002534 -0.0001138 0.6784 0.000191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1501 0.1362 0.1653 0.1658 0.9894 0.9939 0.1502 0.9593 0.9783 0.1783 ] Network output: [ -0.03095 0.4603 0.9386 0.0002324 -0.0001043 0.6639 0.0001751 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1656 0.1629 0.1531 0.1497 0.9856 0.9916 0.1656 0.9379 0.9681 0.1559 ] Network output: [ 0.1553 0.2522 0.04181 0.00079 -0.0003547 1.399 0.0005954 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.9873 Epoch 4691 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.03844 1.103 0.9736 -0.0003381 0.0001518 -0.0007867 -0.0002548 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004413 -0.004048 -0.009152 0.007158 0.9647 0.97 0.009431 0.9111 0.918 0.02569 ] Network output: [ 0.61 0.1431 0.3739 -0.0005505 0.0002471 0.2608 -0.0004148 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2585 0.0002439 0.04239 0.1648 0.9831 0.9931 0.2947 0.8806 0.9702 0.5976 ] Network output: [ -0.02165 1.09 0.9575 -0.0002787 0.0001251 -0.005057 -0.0002101 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.008 0.003184 0.008665 0.003871 0.9905 0.9937 0.008169 0.9642 0.9782 0.01362 ] Network output: [ -0.08084 0.4279 0.8575 -0.0007833 0.0003517 0.8731 -0.0005903 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3375 0.2372 0.4588 0.1084 0.9848 0.994 0.3387 0.8861 0.9732 0.5727 ] Network output: [ -0.007854 0.6174 0.8822 9.376e-05 -4.209e-05 0.5165 7.066e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1729 0.1635 0.1585 0.1059 0.9899 0.9939 0.173 0.9593 0.9772 0.1664 ] Network output: [ 0.003312 0.5231 0.8748 0.0001936 -8.69e-05 0.5963 0.0001459 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.181 0.1793 0.1474 0.1188 0.9857 0.9917 0.181 0.9367 0.9675 0.1495 ] Network output: [ 0.07835 1.127 -0.1362 -1.508e-05 6.768e-06 0.8522 -1.136e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2534 Epoch 4692 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01096 0.8907 0.9939 -0.0001587 7.124e-05 0.09288 -0.0001196 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00434 -0.004325 -0.01089 0.00935 0.9648 0.9701 0.009274 0.9105 0.9186 0.02559 ] Network output: [ 0.9328 -0.6139 0.2671 0.0002072 -9.3e-05 0.482 0.0001561 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2617 -0.03601 -0.07189 0.2913 0.9831 0.9931 0.2984 0.8777 0.9705 0.5883 ] Network output: [ -0.009019 0.9701 0.9884 -0.0001871 8.401e-05 0.05874 -0.000141 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006959 0.002163 0.006033 0.00687 0.9904 0.9936 0.007107 0.963 0.9779 0.01149 ] Network output: [ 0.1383 -0.8172 1.029 0.0003663 -0.0001645 1.513 0.0002761 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3072 0.1955 0.4071 0.3473 0.9848 0.9939 0.3083 0.8837 0.973 0.5484 ] Network output: [ -0.01015 0.4517 0.9516 0.0001861 -8.354e-05 0.6178 0.0001402 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1612 0.1506 0.1622 0.1456 0.9895 0.9938 0.1613 0.9579 0.9772 0.1723 ] Network output: [ -0.01125 0.4666 0.9178 0.0001998 -8.968e-05 0.6389 0.0001505 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1738 0.1718 0.1523 0.1399 0.9857 0.9917 0.1738 0.9359 0.9673 0.1548 ] Network output: [ 0.1132 0.6615 -0.03527 0.0003643 -0.0001636 1.149 0.0002746 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3599 Epoch 4693 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.006557 1.004 0.9795 -0.0002682 0.0001204 0.02893 -0.0002021 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004532 -0.004206 -0.01134 0.006481 0.9648 0.9701 0.009565 0.9091 0.9169 0.02602 ] Network output: [ 0.7986 0.3312 0.1158 -0.0005901 0.0002649 -0.04644 -0.0004447 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2777 -0.009979 -0.07501 0.1179 0.9831 0.9931 0.3159 0.8761 0.9696 0.5881 ] Network output: [ -0.01208 0.9841 0.9891 -0.0002138 9.597e-05 0.05006 -0.0001611 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007539 0.002452 0.005759 0.002553 0.9905 0.9936 0.007695 0.9624 0.977 0.01243 ] Network output: [ -0.009631 0.4591 0.8019 -0.0007911 0.0003552 0.7551 -0.0005962 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3294 0.2155 0.3931 0.08183 0.9848 0.9939 0.3306 0.8832 0.9728 0.5826 ] Network output: [ -0.02391 0.4509 0.983 0.0001559 -7e-05 0.6145 0.0001175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1524 0.1424 0.1612 0.1129 0.9898 0.9939 0.1525 0.9573 0.9764 0.1764 ] Network output: [ -0.01719 0.3035 0.986 0.0002868 -0.0001287 0.7461 0.0002161 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1652 0.1633 0.1654 0.1399 0.9856 0.9916 0.1652 0.9345 0.9671 0.1699 ] Network output: [ 0.04568 0.8195 0.003647 0.0001192 -5.35e-05 1.086 8.98e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1475 Epoch 4694 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01906 0.856 0.9939 -0.0001245 5.588e-05 0.1115 -9.38e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004444 -0.004385 -0.01237 0.00931 0.9649 0.9702 0.009411 0.9093 0.9176 0.02692 ] Network output: [ 1.017 -0.4851 0.1277 0.0001844 -8.28e-05 0.3249 0.000139 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2738 -0.03688 -0.1308 0.2677 0.9832 0.9931 0.3116 0.8749 0.9699 0.5996 ] Network output: [ -0.01184 0.9284 1.004 -0.000157 7.049e-05 0.09086 -0.0001183 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006813 0.002067 0.005047 0.006301 0.9905 0.9936 0.006954 0.9623 0.9773 0.01179 ] Network output: [ 0.134 -0.7054 1.004 0.0003072 -0.0001379 1.435 0.0002315 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3055 0.1946 0.3814 0.3229 0.9848 0.9939 0.3066 0.8817 0.9726 0.571 ] Network output: [ -0.03322 0.3512 1.018 0.0002141 -9.61e-05 0.6976 0.0001613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1517 0.1417 0.1746 0.1533 0.9896 0.9938 0.1518 0.9573 0.9767 0.1897 ] Network output: [ -0.03289 0.3464 0.9853 0.000238 -0.0001068 0.735 0.0001793 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1663 0.1644 0.1707 0.1536 0.9858 0.9917 0.1663 0.9352 0.9671 0.1747 ] Network output: [ 0.05133 0.719 0.01599 0.0002109 -9.468e-05 1.163 0.0001589 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2322 Epoch 4695 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.002454 0.9695 0.9772 -0.0002254 0.0001012 0.04748 -0.0001699 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004575 -0.004203 -0.01208 0.006171 0.9648 0.9702 0.009596 0.9082 0.9159 0.02686 ] Network output: [ 0.8196 0.4604 0.04835 -0.0006551 0.0002941 -0.1506 -0.0004937 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.283 -0.008194 -0.09684 0.08809 0.9831 0.9931 0.3215 0.8742 0.9691 0.5959 ] Network output: [ -0.006999 0.9422 0.9945 -0.0001716 7.703e-05 0.07661 -0.0001293 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007448 0.002334 0.00534 0.001804 0.9905 0.9936 0.0076 0.9618 0.9764 0.01273 ] Network output: [ -0.01972 0.6202 0.7679 -0.0009222 0.000414 0.6476 -0.000695 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3287 0.213 0.3798 0.0413 0.9848 0.994 0.3298 0.8814 0.9724 0.5942 ] Network output: [ -0.029 0.3945 1.013 0.0001882 -8.448e-05 0.651 0.0001418 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.145 0.1351 0.1634 0.1115 0.9899 0.9939 0.1451 0.9563 0.9757 0.1815 ] Network output: [ -0.02192 0.2142 1.023 0.0003391 -0.0001522 0.8075 0.0002556 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1586 0.1567 0.1727 0.1463 0.9856 0.9916 0.1587 0.9328 0.9664 0.1783 ] Network output: [ 0.02927 0.7563 0.03981 0.0001383 -6.211e-05 1.146 0.0001043 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1845 Epoch 4696 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02825 0.8179 0.992 -7.847e-05 3.523e-05 0.1333 -5.913e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004449 -0.004406 -0.0128 0.009724 0.9649 0.9702 0.009391 0.9083 0.9167 0.02728 ] Network output: [ 1.063 -0.5601 0.09613 0.0002939 -0.0001319 0.3394 0.0002215 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.275 -0.04013 -0.1505 0.2812 0.9832 0.9931 0.3128 0.8722 0.9693 0.6003 ] Network output: [ -0.008756 0.9022 1.006 -0.0001272 5.71e-05 0.109 -9.586e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006663 0.001992 0.004648 0.006587 0.9905 0.9936 0.0068 0.9613 0.9767 0.01167 ] Network output: [ 0.1533 -0.8022 1.013 0.0004307 -0.0001933 1.485 0.0003246 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3004 0.1909 0.368 0.3445 0.9848 0.9939 0.3015 0.8789 0.972 0.5686 ] Network output: [ -0.03647 0.3198 1.033 0.0002331 -0.0001046 0.7206 0.0001756 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1478 0.138 0.1756 0.1585 0.9895 0.9938 0.1479 0.956 0.976 0.1917 ] Network output: [ -0.0374 0.3242 0.999 0.0002461 -0.0001105 0.7526 0.0001855 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1624 0.1605 0.1726 0.1575 0.9857 0.9917 0.1624 0.9337 0.9663 0.1767 ] Network output: [ 0.04897 0.6689 0.02876 0.0002484 -0.0001115 1.205 0.0001872 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2748 Epoch 4697 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.003804 0.9667 0.9742 -0.0002194 9.85e-05 0.05058 -0.0001653 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004588 -0.004163 -0.01188 0.006176 0.9649 0.9702 0.009583 0.9069 0.9146 0.02671 ] Network output: [ 0.8015 0.4734 0.05962 -0.0006671 0.0002995 -0.1387 -0.0005027 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2838 -0.004044 -0.08627 0.08671 0.9831 0.9931 0.3221 0.8712 0.9683 0.5891 ] Network output: [ -0.004299 0.9389 0.9907 -0.0001651 7.411e-05 0.07824 -0.0001244 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007495 0.002503 0.005546 0.00178 0.9905 0.9936 0.007648 0.9608 0.9758 0.01277 ] Network output: [ -0.03534 0.6682 0.764 -0.0009427 0.0004232 0.6347 -0.0007104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3308 0.2198 0.3818 0.03589 0.9848 0.9939 0.332 0.8784 0.9717 0.5869 ] Network output: [ -0.03025 0.4088 1.012 0.0001754 -7.875e-05 0.6408 0.0001322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1432 0.134 0.1627 0.1088 0.9898 0.9938 0.1433 0.955 0.975 0.1801 ] Network output: [ -0.02174 0.2317 1.02 0.0003226 -0.0001448 0.7926 0.0002431 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1562 0.1543 0.1705 0.1429 0.9855 0.9916 0.1562 0.9309 0.9654 0.176 ] Network output: [ 0.02156 0.8398 0.02122 6.824e-05 -3.064e-05 1.096 5.143e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1954 Epoch 4698 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0333 0.7974 0.9916 -5.975e-05 2.683e-05 0.1442 -4.503e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004407 -0.00435 -0.01252 0.009916 0.9649 0.9703 0.00927 0.9068 0.9155 0.02684 ] Network output: [ 1.058 -0.6152 0.1181 0.0003457 -0.0001552 0.3832 0.0002605 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2724 -0.03922 -0.1435 0.2919 0.9831 0.9931 0.3096 0.8688 0.9686 0.5889 ] Network output: [ -0.003466 0.8892 1.003 -0.0001149 5.161e-05 0.1142 -8.663e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00661 0.002043 0.004641 0.006859 0.9904 0.9936 0.006746 0.96 0.976 0.0114 ] Network output: [ 0.1628 -0.851 1.014 0.0005004 -0.0002247 1.513 0.0003771 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2993 0.1927 0.3617 0.359 0.9848 0.9939 0.3004 0.8754 0.9713 0.5526 ] Network output: [ -0.03238 0.3265 1.028 0.000231 -0.0001037 0.7108 0.0001741 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1461 0.1367 0.1713 0.1585 0.9894 0.9937 0.1462 0.9544 0.9752 0.1866 ] Network output: [ -0.03409 0.3388 0.9935 0.0002353 -0.0001056 0.7368 0.0001773 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1599 0.1581 0.1675 0.1556 0.9856 0.9917 0.1599 0.9315 0.9652 0.1715 ] Network output: [ 0.06376 0.6151 0.02514 0.0003111 -0.0001397 1.233 0.0002344 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3186 Epoch 4699 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.005309 0.9624 0.9732 -0.0002207 9.907e-05 0.05289 -0.0001663 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004594 -0.004134 -0.01149 0.006517 0.9649 0.9702 0.009554 0.9053 0.9131 0.02616 ] Network output: [ 0.8027 0.3762 0.0837 -0.0005951 0.0002671 -0.06771 -0.0004485 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.285 -0.001632 -0.07608 0.1091 0.9831 0.9931 0.3232 0.8675 0.9674 0.5748 ] Network output: [ -0.003123 0.9439 0.9876 -0.0001712 7.686e-05 0.07402 -0.000129 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007524 0.002755 0.005705 0.002446 0.9904 0.9935 0.007676 0.9597 0.9751 0.0125 ] Network output: [ -0.03001 0.5414 0.7864 -0.0007884 0.0003539 0.729 -0.0005941 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3331 0.2294 0.3823 0.07249 0.9848 0.9939 0.3343 0.8748 0.9708 0.5702 ] Network output: [ -0.03347 0.4257 1.011 0.0001524 -6.841e-05 0.6313 0.0001148 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1433 0.135 0.1639 0.1122 0.9897 0.9938 0.1434 0.9539 0.9743 0.1799 ] Network output: [ -0.02447 0.2754 1.013 0.0002774 -0.0001245 0.7619 0.000209 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1553 0.1537 0.1687 0.1404 0.9855 0.9916 0.1553 0.9293 0.9645 0.1736 ] Network output: [ 0.01233 0.9469 -0.002563 -2.692e-05 1.208e-05 1.031 -2.028e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1535 Epoch 4700 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03477 0.8018 0.9894 -6.923e-05 3.108e-05 0.139 -5.218e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004395 -0.004257 -0.0121 0.009595 0.965 0.9703 0.009189 0.9054 0.9141 0.0263 ] Network output: [ 1.018 -0.5344 0.1351 0.0002616 -0.0001174 0.3643 0.0001972 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2728 -0.03218 -0.1262 0.2776 0.9831 0.9931 0.3097 0.8656 0.9677 0.5752 ] Network output: [ 0.00204 0.8848 0.9991 -0.0001143 5.132e-05 0.1115 -8.615e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006696 0.002251 0.004843 0.006666 0.9903 0.9935 0.006833 0.9589 0.9752 0.01136 ] Network output: [ 0.1462 -0.7389 0.9916 0.000422 -0.0001894 1.457 0.000318 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3049 0.2029 0.3609 0.341 0.9848 0.9939 0.306 0.8724 0.9706 0.5412 ] Network output: [ -0.03043 0.3331 1.028 0.0002197 -9.864e-05 0.701 0.0001656 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.145 0.1363 0.1701 0.1553 0.9893 0.9937 0.1451 0.9533 0.9745 0.1848 ] Network output: [ -0.03114 0.337 0.995 0.0002275 -0.0001021 0.7312 0.0001715 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1582 0.1565 0.1666 0.1537 0.9856 0.9916 0.1582 0.9298 0.9643 0.1705 ] Network output: [ 0.05825 0.6731 0.01417 0.0002516 -0.0001129 1.197 0.0001896 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2624 Epoch 4701 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01285 0.9388 0.9728 -0.0002023 9.08e-05 0.06187 -0.0001524 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004596 -0.004121 -0.01155 0.00674 0.9649 0.9702 0.009512 0.904 0.912 0.02596 ] Network output: [ 0.8318 0.3106 0.07007 -0.0005237 0.0002351 -0.04635 -0.0003947 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2874 -0.001709 -0.08418 0.1219 0.9831 0.9931 0.3257 0.8646 0.9667 0.5666 ] Network output: [ 0.0006127 0.9288 0.9889 -0.0001604 7.2e-05 0.08044 -0.0001209 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007428 0.002815 0.005462 0.002846 0.9904 0.9935 0.007576 0.9587 0.9745 0.01223 ] Network output: [ -0.01252 0.4279 0.7997 -0.0006506 0.0002921 0.7948 -0.0004903 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3323 0.2323 0.3735 0.09958 0.9848 0.9939 0.3334 0.8722 0.9702 0.5615 ] Network output: [ -0.03628 0.4057 1.024 0.0001531 -6.871e-05 0.6433 0.0001153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1405 0.1327 0.1658 0.117 0.9896 0.9937 0.1405 0.953 0.9738 0.1821 ] Network output: [ -0.02822 0.2681 1.024 0.0002644 -0.0001187 0.7656 0.0001993 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1524 0.1509 0.1709 0.1428 0.9855 0.9916 0.1524 0.9281 0.9638 0.1759 ] Network output: [ 0.003193 0.9676 0.002234 -6.372e-05 2.861e-05 1.024 -4.802e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1201 Epoch 4702 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03756 0.8032 0.9858 -7.204e-05 3.234e-05 0.1356 -5.429e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004406 -0.004192 -0.01204 0.009281 0.965 0.9703 0.009156 0.9043 0.913 0.02618 ] Network output: [ 0.9993 -0.4367 0.1225 0.0001848 -8.297e-05 0.3163 0.0001393 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2754 -0.02616 -0.122 0.2597 0.9831 0.9931 0.3123 0.8633 0.9671 0.5694 ] Network output: [ 0.006216 0.8765 0.9972 -0.0001092 4.904e-05 0.1134 -8.232e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006734 0.002401 0.004868 0.006335 0.9903 0.9935 0.00687 0.9581 0.9747 0.01145 ] Network output: [ 0.1289 -0.6163 0.9689 0.0003331 -0.0001495 1.391 0.000251 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3089 0.2108 0.3582 0.3178 0.9848 0.9939 0.31 0.8704 0.9701 0.5398 ] Network output: [ -0.03239 0.3208 1.038 0.0002158 -9.69e-05 0.7066 0.0001627 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1421 0.1341 0.1719 0.1532 0.9893 0.9937 0.1422 0.9526 0.9739 0.1872 ] Network output: [ -0.03201 0.3119 1.008 0.0002303 -0.0001034 0.7449 0.0001736 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1553 0.1538 0.1699 0.1544 0.9856 0.9916 0.1553 0.9287 0.9638 0.1741 ] Network output: [ 0.04126 0.7494 0.01197 0.0001605 -7.206e-05 1.157 0.000121 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1982 Epoch 4703 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0211 0.9132 0.971 -0.0001773 7.961e-05 0.0729 -0.0001336 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004586 -0.004096 -0.01174 0.00684 0.965 0.9703 0.009449 0.9032 0.9113 0.02601 ] Network output: [ 0.8558 0.2838 0.05057 -0.0004743 0.0002129 -0.0478 -0.0003574 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2888 -0.001662 -0.09415 0.1256 0.9831 0.9931 0.3269 0.8626 0.9663 0.5645 ] Network output: [ 0.005363 0.9082 0.9898 -0.0001426 6.403e-05 0.09077 -0.0001075 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007296 0.0028 0.005232 0.002968 0.9903 0.9935 0.007441 0.9581 0.974 0.01215 ] Network output: [ -0.002486 0.3763 0.8054 -0.0005814 0.000261 0.8209 -0.0004381 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3301 0.2324 0.3662 0.1093 0.9848 0.9939 0.3312 0.8704 0.9698 0.5597 ] Network output: [ -0.03781 0.3759 1.04 0.0001635 -7.34e-05 0.6607 0.0001232 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1365 0.1291 0.1678 0.1199 0.9896 0.9937 0.1366 0.9523 0.9733 0.185 ] Network output: [ -0.03037 0.2406 1.039 0.0002698 -0.0001211 0.7826 0.0002033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1487 0.1472 0.1741 0.1458 0.9855 0.9916 0.1487 0.9272 0.9633 0.1794 ] Network output: [ -0.004193 0.9648 0.01257 -7.914e-05 3.553e-05 1.031 -5.964e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1026 Epoch 4704 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0419 0.7971 0.9814 -6.457e-05 2.899e-05 0.1374 -4.867e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004408 -0.004142 -0.01212 0.009115 0.965 0.9703 0.009115 0.9035 0.9122 0.02622 ] Network output: [ 0.9957 -0.3788 0.1057 0.0001547 -6.943e-05 0.2824 0.0001166 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2769 -0.02251 -0.1239 0.2487 0.9831 0.9931 0.3137 0.8617 0.9667 0.5678 ] Network output: [ 0.01021 0.8655 0.9952 -9.982e-05 4.481e-05 0.1184 -7.523e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006705 0.002468 0.004813 0.00611 0.9903 0.9935 0.006839 0.9576 0.9743 0.01154 ] Network output: [ 0.1193 -0.5447 0.9557 0.0002849 -0.0001279 1.352 0.0002147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3099 0.2146 0.3551 0.303 0.9848 0.9939 0.3109 0.869 0.9697 0.5411 ] Network output: [ -0.03399 0.303 1.049 0.000218 -9.789e-05 0.7165 0.0001643 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1387 0.1312 0.1737 0.1524 0.9893 0.9936 0.1388 0.952 0.9735 0.1899 ] Network output: [ -0.03302 0.2853 1.021 0.0002368 -0.0001063 0.7609 0.0001785 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.152 0.1506 0.1732 0.1556 0.9856 0.9916 0.152 0.9279 0.9633 0.1776 ] Network output: [ 0.02865 0.7974 0.01291 0.0001006 -4.517e-05 1.133 7.582e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.163 Epoch 4705 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02764 0.8945 0.9679 -0.0001575 7.069e-05 0.08168 -0.0001187 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004567 -0.004061 -0.01188 0.006908 0.965 0.9703 0.009376 0.9027 0.9107 0.02608 ] Network output: [ 0.8692 0.2675 0.03913 -0.0004356 0.0001956 -0.04685 -0.0003283 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.289 -0.001085 -0.1 0.1276 0.9831 0.9931 0.3269 0.8612 0.9659 0.5641 ] Network output: [ 0.009655 0.8928 0.9886 -0.0001286 5.773e-05 0.09881 -9.691e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007181 0.002791 0.005111 0.003039 0.9903 0.9935 0.007323 0.9576 0.9737 0.01214 ] Network output: [ 0.002037 0.3461 0.81 -0.0005387 0.0002418 0.8376 -0.000406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.328 0.2324 0.3622 0.1143 0.9848 0.9939 0.3291 0.8691 0.9695 0.5593 ] Network output: [ -0.03835 0.353 1.05 0.0001717 -7.706e-05 0.6741 0.0001294 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1331 0.126 0.1695 0.1218 0.9896 0.9937 0.1332 0.9517 0.973 0.1873 ] Network output: [ -0.03115 0.2189 1.049 0.0002748 -0.0001234 0.7958 0.0002071 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1455 0.1441 0.1766 0.1478 0.9855 0.9916 0.1455 0.9264 0.9629 0.1821 ] Network output: [ -0.008906 0.9681 0.01744 -9.214e-05 4.136e-05 1.032 -6.944e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09227 Epoch 4706 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04597 0.7913 0.9769 -5.708e-05 2.563e-05 0.1396 -4.302e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004399 -0.004097 -0.01218 0.009021 0.9651 0.9703 0.009061 0.903 0.9116 0.02624 ] Network output: [ 0.9938 -0.3448 0.09485 0.0001448 -6.5e-05 0.263 0.0001091 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2774 -0.01983 -0.1255 0.2423 0.9831 0.9931 0.314 0.8604 0.9663 0.5671 ] Network output: [ 0.01386 0.8569 0.9925 -9.232e-05 4.145e-05 0.1225 -6.958e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006657 0.002509 0.004775 0.005979 0.9903 0.9935 0.006789 0.9572 0.9739 0.0116 ] Network output: [ 0.1133 -0.5037 0.9484 0.0002594 -0.0001165 1.33 0.0001955 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3097 0.2168 0.353 0.294 0.9848 0.9939 0.3108 0.8678 0.9694 0.5421 ] Network output: [ -0.03451 0.289 1.057 0.0002199 -9.87e-05 0.7239 0.0001657 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1358 0.1286 0.175 0.1519 0.9893 0.9936 0.1358 0.9515 0.9732 0.1917 ] Network output: [ -0.03318 0.266 1.029 0.000241 -0.0001082 0.772 0.0001816 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1491 0.1477 0.1754 0.1564 0.9856 0.9916 0.1491 0.9271 0.9629 0.1801 ] Network output: [ 0.02149 0.8285 0.01233 6.378e-05 -2.863e-05 1.116 4.806e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1441 Epoch 4707 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03261 0.8821 0.9645 -0.0001442 6.473e-05 0.08758 -0.0001087 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004547 -0.004024 -0.01195 0.006965 0.9651 0.9703 0.009304 0.9022 0.9103 0.0261 ] Network output: [ 0.8773 0.2542 0.0328 -0.0004036 0.0001812 -0.04331 -0.0003041 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2886 -0.0003192 -0.1035 0.1298 0.9831 0.9931 0.3262 0.8601 0.9656 0.5638 ] Network output: [ 0.01314 0.8828 0.9865 -0.0001198 5.376e-05 0.104 -9.025e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007085 0.00279 0.005045 0.003112 0.9903 0.9934 0.007224 0.9572 0.9734 0.01214 ] Network output: [ 0.004707 0.3203 0.8147 -0.000504 0.0002263 0.8535 -0.0003799 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3261 0.2325 0.3599 0.1188 0.9848 0.9939 0.3273 0.868 0.9692 0.559 ] Network output: [ -0.0385 0.3366 1.058 0.000176 -7.903e-05 0.6835 0.0001327 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1303 0.1235 0.1708 0.1232 0.9896 0.9937 0.1304 0.9513 0.9727 0.1891 ] Network output: [ -0.0314 0.2044 1.055 0.0002762 -0.000124 0.8042 0.0002082 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1429 0.1416 0.1784 0.1491 0.9855 0.9916 0.1429 0.9258 0.9626 0.1841 ] Network output: [ -0.01159 0.9747 0.01878 -0.0001029 4.618e-05 1.029 -7.752e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0849 Epoch 4708 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04921 0.788 0.9727 -5.282e-05 2.371e-05 0.1407 -3.981e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004388 -0.004054 -0.01222 0.008952 0.9651 0.9704 0.009007 0.9025 0.9111 0.02624 ] Network output: [ 0.9914 -0.3197 0.08766 0.0001396 -6.265e-05 0.2497 0.0001052 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2775 -0.01755 -0.1262 0.2378 0.9831 0.9931 0.3139 0.8593 0.966 0.5666 ] Network output: [ 0.01685 0.8515 0.9896 -8.808e-05 3.954e-05 0.1249 -6.638e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00661 0.002542 0.004757 0.00589 0.9903 0.9934 0.00674 0.9568 0.9737 0.01165 ] Network output: [ 0.1085 -0.4756 0.9438 0.0002421 -0.0001087 1.316 0.0001824 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3094 0.2185 0.3518 0.2877 0.9848 0.9939 0.3105 0.8669 0.9691 0.5428 ] Network output: [ -0.03466 0.279 1.062 0.0002198 -9.866e-05 0.7289 0.0001656 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1332 0.1263 0.1759 0.1515 0.9894 0.9936 0.1333 0.9512 0.9729 0.193 ] Network output: [ -0.03302 0.2522 1.035 0.0002425 -0.0001088 0.7794 0.0001827 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1466 0.1453 0.1769 0.1569 0.9856 0.9916 0.1466 0.9265 0.9626 0.1818 ] Network output: [ 0.01723 0.8519 0.01075 3.846e-05 -1.727e-05 1.103 2.898e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1319 Epoch 4709 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03646 0.8739 0.9612 -0.0001356 6.087e-05 0.09142 -0.0001022 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004528 -0.00399 -0.012 0.007015 0.9651 0.9703 0.009237 0.9018 0.9099 0.02609 ] Network output: [ 0.8833 0.2429 0.0284 -0.0003763 0.0001689 -0.0395 -0.0002836 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2882 0.0004428 -0.106 0.1321 0.9831 0.9931 0.3256 0.8591 0.9654 0.5635 ] Network output: [ 0.01586 0.8762 0.9842 -0.0001146 5.144e-05 0.1073 -8.636e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007001 0.002793 0.004998 0.003188 0.9903 0.9934 0.007138 0.9569 0.9732 0.01213 ] Network output: [ 0.006821 0.2953 0.8196 -0.0004727 0.0002122 0.8696 -0.0003562 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3246 0.2328 0.3584 0.1236 0.9848 0.9939 0.3257 0.8671 0.969 0.5587 ] Network output: [ -0.03863 0.3241 1.063 0.0001781 -7.994e-05 0.6906 0.0001342 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.128 0.1215 0.1719 0.1245 0.9896 0.9937 0.1281 0.9509 0.9725 0.1905 ] Network output: [ -0.03157 0.1941 1.06 0.0002754 -0.0001237 0.81 0.0002076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1407 0.1394 0.1797 0.1501 0.9855 0.9916 0.1407 0.9252 0.9623 0.1856 ] Network output: [ -0.01307 0.9807 0.01889 -0.0001103 4.953e-05 1.026 -8.315e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07891 Epoch 4710 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05168 0.7871 0.9688 -5.14e-05 2.307e-05 0.1406 -3.873e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004378 -0.004015 -0.01224 0.008888 0.9651 0.9704 0.008957 0.9021 0.9106 0.02622 ] Network output: [ 0.9891 -0.2968 0.08149 0.0001336 -6e-05 0.2377 0.0001007 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2776 -0.01551 -0.1266 0.2341 0.9831 0.9931 0.3138 0.8585 0.9657 0.5664 ] Network output: [ 0.01917 0.8483 0.9869 -8.628e-05 3.873e-05 0.1261 -6.502e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006566 0.00257 0.004746 0.005814 0.9903 0.9934 0.006695 0.9566 0.9734 0.01168 ] Network output: [ 0.104 -0.4521 0.9404 0.0002264 -0.0001016 1.305 0.0001706 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3091 0.22 0.3512 0.2825 0.9848 0.9939 0.3102 0.8661 0.9689 0.5435 ] Network output: [ -0.03487 0.2713 1.067 0.0002183 -9.799e-05 0.7325 0.0001645 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.131 0.1244 0.1766 0.1511 0.9894 0.9936 0.1311 0.9509 0.9727 0.194 ] Network output: [ -0.03291 0.2415 1.04 0.0002425 -0.0001088 0.7851 0.0001827 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1444 0.1431 0.1781 0.1572 0.9856 0.9916 0.1444 0.926 0.9623 0.1831 ] Network output: [ 0.01415 0.8714 0.009049 1.874e-05 -8.413e-06 1.091 1.412e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.122 Epoch 4711 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03953 0.8681 0.9582 -0.0001297 5.822e-05 0.09403 -9.774e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004511 -0.003959 -0.01204 0.007059 0.9651 0.9704 0.009176 0.9015 0.9095 0.02608 ] Network output: [ 0.8887 0.2332 0.02452 -0.0003524 0.0001582 -0.03646 -0.0002655 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2878 0.00112 -0.1082 0.1343 0.9831 0.9931 0.325 0.8583 0.9652 0.5635 ] Network output: [ 0.01798 0.8717 0.9822 -0.0001115 5.006e-05 0.1097 -8.404e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006925 0.002794 0.004957 0.003263 0.9903 0.9934 0.00706 0.9566 0.973 0.01213 ] Network output: [ 0.008711 0.2709 0.8245 -0.0004439 0.0001993 0.8854 -0.0003345 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3232 0.233 0.3573 0.1283 0.9849 0.9939 0.3242 0.8663 0.9688 0.5586 ] Network output: [ -0.03884 0.3134 1.068 0.000179 -8.037e-05 0.6969 0.0001349 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.126 0.1197 0.1729 0.1257 0.9896 0.9937 0.1261 0.9507 0.9723 0.1917 ] Network output: [ -0.0318 0.1856 1.064 0.0002738 -0.0001229 0.8148 0.0002063 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1388 0.1375 0.181 0.1511 0.9855 0.9916 0.1388 0.9248 0.962 0.1868 ] Network output: [ -0.01385 0.9849 0.01879 -0.0001146 5.144e-05 1.024 -8.635e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07375 Epoch 4712 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05356 0.7877 0.9652 -5.172e-05 2.322e-05 0.1397 -3.898e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004369 -0.003981 -0.01226 0.008822 0.9651 0.9704 0.008914 0.9018 0.9103 0.02621 ] Network output: [ 0.9872 -0.2741 0.07523 0.0001263 -5.671e-05 0.2251 9.52e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2778 -0.0137 -0.1272 0.2304 0.9831 0.9931 0.3138 0.8577 0.9655 0.5665 ] Network output: [ 0.02093 0.8465 0.9845 -8.588e-05 3.856e-05 0.1267 -6.473e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006526 0.002593 0.004735 0.005737 0.9903 0.9934 0.006653 0.9564 0.9732 0.01172 ] Network output: [ 0.09962 -0.4302 0.9374 0.0002103 -9.442e-05 1.294 0.0001585 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3089 0.2214 0.3508 0.2776 0.9848 0.9939 0.3099 0.8654 0.9687 0.5446 ] Network output: [ -0.03526 0.265 1.071 0.0002161 -9.7e-05 0.7356 0.0001628 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.129 0.1226 0.1773 0.1507 0.9894 0.9936 0.129 0.9506 0.9725 0.195 ] Network output: [ -0.03297 0.232 1.045 0.0002419 -0.0001086 0.7902 0.0001823 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1425 0.1412 0.1793 0.1575 0.9856 0.9916 0.1425 0.9255 0.962 0.1844 ] Network output: [ 0.01151 0.8887 0.007615 2.079e-06 -9.334e-07 1.081 1.567e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1132 Epoch 4713 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04206 0.8638 0.9556 -0.0001253 5.623e-05 0.09595 -9.439e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004494 -0.003931 -0.01208 0.007097 0.9651 0.9704 0.00912 0.9013 0.9092 0.02607 ] Network output: [ 0.8937 0.2248 0.02079 -0.0003307 0.0001484 -0.03432 -0.0002492 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2874 0.001682 -0.1103 0.1363 0.9831 0.9931 0.3244 0.8576 0.965 0.5638 ] Network output: [ 0.01965 0.8684 0.9804 -0.0001095 4.916e-05 0.1114 -8.253e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006855 0.002793 0.004919 0.003331 0.9903 0.9934 0.006987 0.9564 0.9728 0.01212 ] Network output: [ 0.01042 0.2474 0.8294 -0.0004176 0.0001875 0.9006 -0.0003147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3218 0.2331 0.3564 0.1327 0.9849 0.9939 0.3229 0.8657 0.9686 0.5588 ] Network output: [ -0.03912 0.3037 1.073 0.0001796 -8.063e-05 0.7026 0.0001353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1242 0.1181 0.1739 0.1269 0.9896 0.9937 0.1243 0.9504 0.9721 0.1929 ] Network output: [ -0.03208 0.1781 1.068 0.000272 -0.0001221 0.8193 0.000205 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1371 0.1359 0.1821 0.1519 0.9855 0.9916 0.1371 0.9245 0.9618 0.188 ] Network output: [ -0.01418 0.9873 0.01874 -0.0001161 5.214e-05 1.022 -8.753e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06921 Epoch 4714 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05502 0.7893 0.962 -5.294e-05 2.377e-05 0.1384 -3.99e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004362 -0.003951 -0.01229 0.008755 0.9652 0.9704 0.008876 0.9016 0.91 0.0262 ] Network output: [ 0.9857 -0.2515 0.06866 0.0001182 -5.305e-05 0.2119 8.906e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.278 -0.0121 -0.128 0.2268 0.9831 0.9931 0.3139 0.8571 0.9653 0.5669 ] Network output: [ 0.02228 0.8456 0.9825 -8.623e-05 3.871e-05 0.127 -6.499e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006488 0.002611 0.004721 0.005657 0.9903 0.9934 0.006614 0.9562 0.9731 0.01175 ] Network output: [ 0.09539 -0.4093 0.9348 0.0001935 -8.689e-05 1.284 0.0001459 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3087 0.2224 0.3507 0.2728 0.9849 0.9939 0.3097 0.8649 0.9686 0.5461 ] Network output: [ -0.03579 0.2593 1.075 0.0002136 -9.59e-05 0.7385 0.000161 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1271 0.1209 0.178 0.1503 0.9894 0.9936 0.1272 0.9504 0.9723 0.196 ] Network output: [ -0.03319 0.2233 1.049 0.0002412 -0.0001083 0.7951 0.0001818 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1407 0.1395 0.1805 0.1578 0.9856 0.9916 0.1407 0.9252 0.9618 0.1857 ] Network output: [ 0.009068 0.9044 0.00649 -1.248e-05 5.603e-06 1.071 -9.406e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1051 Epoch 4715 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04419 0.8604 0.9532 -0.0001216 5.459e-05 0.09751 -9.163e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004478 -0.003905 -0.01212 0.007131 0.9652 0.9704 0.009067 0.9011 0.909 0.02606 ] Network output: [ 0.8985 0.2174 0.01723 -0.0003105 0.0001394 -0.03287 -0.000234 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.287 0.002125 -0.1125 0.1382 0.9831 0.9931 0.3238 0.8571 0.9648 0.5644 ] Network output: [ 0.02101 0.8658 0.9789 -0.0001081 4.852e-05 0.1129 -8.145e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006788 0.002787 0.004883 0.003392 0.9903 0.9934 0.006919 0.9563 0.9727 0.01212 ] Network output: [ 0.01195 0.2251 0.8342 -0.000394 0.0001769 0.9152 -0.0002969 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3205 0.233 0.3559 0.1368 0.9849 0.994 0.3216 0.8652 0.9685 0.5594 ] Network output: [ -0.03942 0.2948 1.077 0.0001801 -8.085e-05 0.7082 0.0001357 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1226 0.1166 0.1748 0.128 0.9896 0.9937 0.1227 0.9503 0.972 0.194 ] Network output: [ -0.03238 0.1711 1.071 0.0002705 -0.0001214 0.8236 0.0002039 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1356 0.1344 0.1831 0.1528 0.9855 0.9916 0.1356 0.9242 0.9616 0.1892 ] Network output: [ -0.0142 0.9882 0.01873 -0.0001157 5.194e-05 1.021 -8.72e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0652 Epoch 4716 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05619 0.7914 0.9591 -5.458e-05 2.45e-05 0.1369 -4.113e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004356 -0.003925 -0.01232 0.008687 0.9652 0.9704 0.008842 0.9014 0.9097 0.02619 ] Network output: [ 0.9848 -0.2292 0.06194 0.0001098 -4.931e-05 0.1982 8.278e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2782 -0.0107 -0.129 0.2232 0.9832 0.9931 0.314 0.8567 0.9652 0.5677 ] Network output: [ 0.02331 0.8451 0.9807 -8.695e-05 3.903e-05 0.1272 -6.553e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006452 0.002622 0.004704 0.005574 0.9903 0.9935 0.006577 0.9561 0.973 0.01179 ] Network output: [ 0.0913 -0.3893 0.9326 0.0001762 -7.912e-05 1.275 0.0001328 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3084 0.2232 0.3507 0.2679 0.9849 0.994 0.3094 0.8645 0.9685 0.5479 ] Network output: [ -0.03641 0.254 1.078 0.0002111 -9.477e-05 0.7414 0.0001591 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1254 0.1193 0.1787 0.1498 0.9894 0.9937 0.1255 0.9503 0.9722 0.1969 ] Network output: [ -0.0335 0.215 1.053 0.0002407 -0.000108 0.8 0.0001814 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.139 0.1379 0.1816 0.1581 0.9856 0.9916 0.139 0.9249 0.9617 0.1869 ] Network output: [ 0.006791 0.9187 0.005614 -2.528e-05 1.135e-05 1.062 -1.905e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09766 Epoch 4717 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04602 0.8576 0.951 -0.0001184 5.315e-05 0.09883 -8.922e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004462 -0.003882 -0.01217 0.007161 0.9652 0.9704 0.009017 0.901 0.9088 0.02606 ] Network output: [ 0.903 0.2108 0.01394 -0.0002915 0.0001309 -0.03192 -0.0002197 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2866 0.002452 -0.1147 0.1399 0.9832 0.9931 0.3231 0.8567 0.9647 0.5654 ] Network output: [ 0.02211 0.8636 0.9775 -0.000107 4.803e-05 0.1143 -8.062e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006725 0.002777 0.00485 0.003446 0.9904 0.9935 0.006854 0.9562 0.9726 0.01212 ] Network output: [ 0.01329 0.204 0.839 -0.0003729 0.0001674 0.9289 -0.0002811 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3193 0.2327 0.3556 0.1405 0.9849 0.994 0.3203 0.8649 0.9684 0.5604 ] Network output: [ -0.03972 0.2864 1.08 0.0001806 -8.108e-05 0.7135 0.0001361 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1211 0.1152 0.1756 0.1291 0.9896 0.9937 0.1212 0.9502 0.9719 0.1951 ] Network output: [ -0.03265 0.1645 1.074 0.0002692 -0.0001209 0.8278 0.0002029 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1342 0.1331 0.1841 0.1536 0.9855 0.9916 0.1342 0.924 0.9615 0.1902 ] Network output: [ -0.01397 0.9883 0.0187 -0.0001138 5.108e-05 1.021 -8.575e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06164 Epoch 4718 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05712 0.7938 0.9564 -5.639e-05 2.531e-05 0.1353 -4.249e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00435 -0.003901 -0.01236 0.008619 0.9652 0.9705 0.00881 0.9013 0.9095 0.02619 ] Network output: [ 0.9841 -0.2075 0.05523 0.0001017 -4.567e-05 0.1845 7.666e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2784 -0.009498 -0.1303 0.2197 0.9832 0.9931 0.314 0.8564 0.9651 0.5688 ] Network output: [ 0.02411 0.845 0.9791 -8.785e-05 3.944e-05 0.1273 -6.621e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006416 0.002628 0.004685 0.005489 0.9903 0.9935 0.00654 0.956 0.9729 0.01182 ] Network output: [ 0.08736 -0.3704 0.9306 0.0001586 -7.119e-05 1.266 0.0001195 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.308 0.2237 0.3509 0.2632 0.9849 0.994 0.309 0.8642 0.9684 0.5501 ] Network output: [ -0.03706 0.2492 1.082 0.0002087 -9.368e-05 0.7441 0.0001573 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1238 0.1179 0.1793 0.1494 0.9894 0.9937 0.1239 0.9502 0.9721 0.1978 ] Network output: [ -0.03386 0.207 1.057 0.0002403 -0.0001079 0.8049 0.0001811 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1375 0.1364 0.1826 0.1583 0.9856 0.9916 0.1375 0.9247 0.9615 0.1881 ] Network output: [ 0.004689 0.9317 0.004923 -3.648e-05 1.638e-05 1.054 -2.749e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0909 Epoch 4719 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04759 0.8553 0.9491 -0.0001155 5.185e-05 0.09999 -8.705e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004446 -0.00386 -0.01221 0.007186 0.9652 0.9705 0.00897 0.9009 0.9087 0.02606 ] Network output: [ 0.9073 0.2047 0.01094 -0.0002734 0.0001228 -0.03132 -0.0002061 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2861 0.00267 -0.1169 0.1414 0.9832 0.9931 0.3224 0.8564 0.9647 0.5667 ] Network output: [ 0.02303 0.8618 0.9762 -0.0001061 4.763e-05 0.1155 -7.996e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006665 0.002764 0.004818 0.003494 0.9904 0.9935 0.006792 0.9561 0.9725 0.01213 ] Network output: [ 0.01445 0.1839 0.8438 -0.0003543 0.000159 0.942 -0.000267 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.318 0.2323 0.3555 0.1438 0.9849 0.994 0.319 0.8646 0.9683 0.5617 ] Network output: [ -0.03998 0.2785 1.084 0.0001811 -8.131e-05 0.7186 0.0001365 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1198 0.114 0.1764 0.1301 0.9896 0.9937 0.1198 0.9501 0.9718 0.1961 ] Network output: [ -0.03289 0.1582 1.077 0.0002682 -0.0001204 0.8319 0.0002021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.133 0.1319 0.185 0.1543 0.9855 0.9916 0.133 0.9239 0.9614 0.1912 ] Network output: [ -0.01356 0.9876 0.0186 -0.0001108 4.973e-05 1.02 -8.349e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05849 Epoch 4720 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05787 0.7964 0.954 -5.824e-05 2.615e-05 0.1337 -4.389e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004344 -0.00388 -0.0124 0.008551 0.9653 0.9705 0.008781 0.9012 0.9094 0.0262 ] Network output: [ 0.9838 -0.1865 0.04866 9.4e-05 -4.22e-05 0.1707 7.084e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2785 -0.008466 -0.1317 0.2163 0.9832 0.9931 0.314 0.8561 0.965 0.5702 ] Network output: [ 0.02473 0.8452 0.9777 -8.886e-05 3.989e-05 0.1273 -6.697e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006382 0.002628 0.004664 0.005402 0.9903 0.9935 0.006504 0.956 0.9728 0.01185 ] Network output: [ 0.08358 -0.3524 0.929 0.0001407 -6.315e-05 1.257 0.000106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3075 0.224 0.3512 0.2584 0.9849 0.994 0.3085 0.8641 0.9683 0.5525 ] Network output: [ -0.03771 0.2447 1.085 0.0002063 -9.263e-05 0.7468 0.0001555 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1223 0.1165 0.1799 0.1489 0.9894 0.9937 0.1224 0.9502 0.972 0.1988 ] Network output: [ -0.03423 0.1994 1.06 0.00024 -0.0001078 0.8098 0.0001809 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1361 0.135 0.1837 0.1585 0.9856 0.9916 0.1362 0.9245 0.9614 0.1893 ] Network output: [ 0.00277 0.9435 0.004378 -4.618e-05 2.073e-05 1.046 -3.48e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08479 Epoch 4721 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04895 0.8533 0.9473 -0.0001129 5.067e-05 0.101 -8.506e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004431 -0.00384 -0.01226 0.007209 0.9653 0.9705 0.008924 0.9009 0.9086 0.02607 ] Network output: [ 0.9113 0.1991 0.00824 -0.0002561 0.000115 -0.03098 -0.000193 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2854 0.002784 -0.1191 0.1428 0.9832 0.9931 0.3216 0.8562 0.9646 0.5683 ] Network output: [ 0.02379 0.8603 0.975 -0.0001054 4.732e-05 0.1166 -7.944e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006607 0.002747 0.004789 0.003535 0.9904 0.9935 0.006732 0.9561 0.9725 0.01214 ] Network output: [ 0.01546 0.1649 0.8485 -0.0003378 0.0001516 0.9543 -0.0002546 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3166 0.2316 0.3555 0.1467 0.9849 0.994 0.3177 0.8645 0.9683 0.5633 ] Network output: [ -0.04021 0.2712 1.086 0.0001817 -8.155e-05 0.7235 0.0001369 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1185 0.1129 0.1772 0.131 0.9896 0.9937 0.1186 0.9501 0.9718 0.197 ] Network output: [ -0.03309 0.1523 1.079 0.0002674 -0.0001201 0.836 0.0002015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1319 0.1308 0.1859 0.155 0.9855 0.9916 0.1319 0.9238 0.9613 0.1922 ] Network output: [ -0.01299 0.9866 0.01843 -0.000107 4.804e-05 1.021 -8.065e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05569 Epoch 4722 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05847 0.799 0.9517 -6.009e-05 2.697e-05 0.1321 -4.528e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004337 -0.003861 -0.01244 0.008485 0.9653 0.9705 0.008754 0.9012 0.9093 0.02621 ] Network output: [ 0.9836 -0.1663 0.04231 8.675e-05 -3.895e-05 0.1572 6.538e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2785 -0.007596 -0.1333 0.2129 0.9832 0.9931 0.314 0.856 0.9649 0.5719 ] Network output: [ 0.0252 0.8454 0.9764 -8.992e-05 4.037e-05 0.1274 -6.777e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006347 0.002623 0.004642 0.005314 0.9903 0.9935 0.006468 0.956 0.9727 0.01189 ] Network output: [ 0.07995 -0.3353 0.9276 0.0001226 -5.503e-05 1.248 9.238e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3069 0.224 0.3516 0.2537 0.9849 0.994 0.3079 0.864 0.9683 0.5551 ] Network output: [ -0.03834 0.2406 1.087 0.0002041 -9.163e-05 0.7495 0.0001538 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1209 0.1152 0.1805 0.1484 0.9895 0.9937 0.121 0.9502 0.972 0.1996 ] Network output: [ -0.03459 0.1921 1.063 0.00024 -0.0001077 0.8146 0.0001808 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1349 0.1337 0.1847 0.1587 0.9856 0.9916 0.1349 0.9245 0.9613 0.1904 ] Network output: [ 0.00103 0.9541 0.003954 -5.447e-05 2.445e-05 1.04 -4.105e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07928 Epoch 4723 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05013 0.8516 0.9457 -0.0001104 4.958e-05 0.102 -8.322e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004415 -0.003822 -0.01231 0.007229 0.9653 0.9705 0.00888 0.9009 0.9086 0.02608 ] Network output: [ 0.9151 0.1938 0.005831 -0.0002394 0.0001075 -0.03085 -0.0001804 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2848 0.002802 -0.1212 0.1442 0.9832 0.9931 0.3207 0.8562 0.9646 0.5702 ] Network output: [ 0.02441 0.8591 0.974 -0.0001048 4.707e-05 0.1177 -7.901e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006551 0.002727 0.004761 0.003571 0.9904 0.9935 0.006675 0.9561 0.9725 0.01215 ] Network output: [ 0.01634 0.1469 0.8531 -0.0003233 0.0001451 0.966 -0.0002436 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3153 0.2308 0.3558 0.1493 0.9849 0.994 0.3163 0.8644 0.9682 0.5652 ] Network output: [ -0.04039 0.2643 1.089 0.0001822 -8.179e-05 0.7282 0.0001373 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1174 0.1118 0.1779 0.1318 0.9896 0.9937 0.1175 0.9501 0.9718 0.198 ] Network output: [ -0.03324 0.1468 1.081 0.0002668 -0.0001198 0.8399 0.0002011 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.131 0.1298 0.1867 0.1556 0.9855 0.9916 0.131 0.9238 0.9613 0.1931 ] Network output: [ -0.01233 0.9852 0.01816 -0.0001027 4.61e-05 1.021 -7.739e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05321 Epoch 4724 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05893 0.8017 0.9496 -6.189e-05 2.778e-05 0.1305 -4.664e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00433 -0.003844 -0.01248 0.008419 0.9653 0.9705 0.008728 0.9013 0.9092 0.02622 ] Network output: [ 0.9835 -0.1467 0.03622 7.998e-05 -3.591e-05 0.1438 6.028e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2785 -0.006872 -0.1349 0.2096 0.9832 0.9931 0.3138 0.856 0.9649 0.5738 ] Network output: [ 0.02555 0.8458 0.9753 -9.1e-05 4.086e-05 0.1274 -6.858e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006313 0.002614 0.004619 0.005226 0.9904 0.9935 0.006433 0.9561 0.9727 0.01192 ] Network output: [ 0.07645 -0.319 0.9265 0.0001043 -4.684e-05 1.24 7.863e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3063 0.2238 0.3521 0.249 0.9849 0.994 0.3073 0.864 0.9682 0.558 ] Network output: [ -0.03893 0.2367 1.09 0.000202 -9.068e-05 0.752 0.0001522 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1197 0.114 0.1811 0.1479 0.9895 0.9937 0.1197 0.9502 0.972 0.2005 ] Network output: [ -0.03494 0.185 1.066 0.0002401 -0.0001078 0.8193 0.0001809 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1337 0.1326 0.1857 0.1589 0.9856 0.9916 0.1337 0.9244 0.9613 0.1915 ] Network output: [ -0.000536 0.9635 0.003639 -6.146e-05 2.759e-05 1.034 -4.632e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07431 Epoch 4725 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05115 0.8502 0.9442 -0.0001082 4.856e-05 0.1028 -8.151e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004399 -0.003806 -0.01235 0.007248 0.9653 0.9705 0.008839 0.901 0.9086 0.02609 ] Network output: [ 0.9188 0.1888 0.003687 -0.0002233 0.0001002 -0.0309 -0.0001683 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.284 0.002729 -0.1234 0.1454 0.9832 0.9931 0.3198 0.8562 0.9646 0.5722 ] Network output: [ 0.02492 0.858 0.973 -0.0001044 4.687e-05 0.1187 -7.868e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006498 0.002704 0.004734 0.003602 0.9904 0.9935 0.006621 0.9562 0.9724 0.01217 ] Network output: [ 0.01711 0.1298 0.8577 -0.0003106 0.0001394 0.9771 -0.0002341 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3139 0.2299 0.3562 0.1516 0.9849 0.994 0.3149 0.8644 0.9682 0.5673 ] Network output: [ -0.04055 0.2579 1.091 0.0001827 -8.202e-05 0.7327 0.0001377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1164 0.1108 0.1785 0.1325 0.9896 0.9937 0.1165 0.9502 0.9718 0.1988 ] Network output: [ -0.03336 0.1416 1.083 0.0002664 -0.0001196 0.8436 0.0002007 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1301 0.129 0.1875 0.1562 0.9855 0.9916 0.1301 0.9238 0.9613 0.1939 ] Network output: [ -0.0116 0.9838 0.01782 -9.802e-05 4.401e-05 1.021 -7.387e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.051 Epoch 4726 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05929 0.8044 0.9477 -6.363e-05 2.857e-05 0.129 -4.795e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004323 -0.003829 -0.01253 0.008354 0.9653 0.9705 0.008703 0.9013 0.9092 0.02624 ] Network output: [ 0.9836 -0.1279 0.03043 7.369e-05 -3.308e-05 0.1306 5.554e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2783 -0.006282 -0.1367 0.2064 0.9832 0.9931 0.3135 0.856 0.9649 0.5759 ] Network output: [ 0.0258 0.8463 0.9742 -9.209e-05 4.134e-05 0.1275 -6.94e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00628 0.002601 0.004595 0.005137 0.9904 0.9935 0.006399 0.9561 0.9727 0.01196 ] Network output: [ 0.0731 -0.3033 0.9255 8.595e-05 -3.859e-05 1.232 6.477e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3056 0.2234 0.3526 0.2444 0.9849 0.994 0.3066 0.8641 0.9682 0.561 ] Network output: [ -0.03948 0.2331 1.092 0.0002 -8.98e-05 0.7545 0.0001507 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1185 0.1129 0.1816 0.1474 0.9895 0.9937 0.1185 0.9503 0.9719 0.2013 ] Network output: [ -0.03527 0.1783 1.069 0.0002404 -0.0001079 0.824 0.0001811 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1326 0.1315 0.1866 0.159 0.9856 0.9917 0.1326 0.9244 0.9613 0.1926 ] Network output: [ -0.001938 0.9717 0.003419 -6.722e-05 3.018e-05 1.028 -5.066e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06982 Epoch 4727 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05203 0.849 0.9429 -0.000106 4.761e-05 0.1036 -7.992e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004383 -0.00379 -0.0124 0.007264 0.9653 0.9705 0.008799 0.9011 0.9087 0.02611 ] Network output: [ 0.9222 0.184 0.001785 -0.0002076 9.321e-05 -0.03109 -0.0001565 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2832 0.002572 -0.1257 0.1465 0.9832 0.9931 0.3188 0.8563 0.9646 0.5745 ] Network output: [ 0.02532 0.8572 0.9722 -0.0001041 4.672e-05 0.1196 -7.843e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006446 0.002679 0.004709 0.003628 0.9904 0.9935 0.006568 0.9562 0.9725 0.01219 ] Network output: [ 0.0178 0.1135 0.8622 -0.0002995 0.0001344 0.9876 -0.0002257 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3125 0.2288 0.3566 0.1537 0.9849 0.994 0.3135 0.8645 0.9682 0.5696 ] Network output: [ -0.04066 0.2519 1.093 0.0001832 -8.225e-05 0.7369 0.0001381 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1155 0.11 0.1792 0.1332 0.9896 0.9937 0.1155 0.9502 0.9718 0.1997 ] Network output: [ -0.03345 0.1367 1.084 0.000266 -0.0001194 0.8473 0.0002005 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1293 0.1282 0.1882 0.1567 0.9856 0.9916 0.1293 0.9239 0.9613 0.1947 ] Network output: [ -0.01084 0.9822 0.01739 -9.316e-05 4.182e-05 1.022 -7.021e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04904 Epoch 4728 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05954 0.8071 0.946 -6.53e-05 2.931e-05 0.1276 -4.921e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004316 -0.003815 -0.01258 0.008291 0.9654 0.9706 0.008679 0.9014 0.9092 0.02626 ] Network output: [ 0.9837 -0.1097 0.02496 6.786e-05 -3.046e-05 0.1177 5.114e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2781 -0.005815 -0.1386 0.2033 0.9832 0.9931 0.3132 0.8561 0.9649 0.5782 ] Network output: [ 0.02596 0.8469 0.9733 -9.317e-05 4.183e-05 0.1275 -7.022e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006247 0.002585 0.004571 0.005048 0.9904 0.9935 0.006365 0.9562 0.9727 0.01199 ] Network output: [ 0.06987 -0.2883 0.9247 6.747e-05 -3.029e-05 1.224 5.084e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3048 0.2228 0.3533 0.2398 0.9849 0.994 0.3057 0.8642 0.9683 0.5642 ] Network output: [ -0.03997 0.2297 1.094 0.0001982 -8.898e-05 0.7569 0.0001494 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1174 0.1118 0.1821 0.1468 0.9895 0.9937 0.1175 0.9504 0.972 0.202 ] Network output: [ -0.03556 0.1718 1.072 0.0002408 -0.0001081 0.8287 0.0001815 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1316 0.1305 0.1874 0.1592 0.9856 0.9917 0.1316 0.9245 0.9613 0.1935 ] Network output: [ -0.003181 0.9789 0.003281 -7.185e-05 3.226e-05 1.024 -5.415e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06577 Epoch 4729 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05279 0.848 0.9417 -0.0001041 4.671e-05 0.1044 -7.842e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004367 -0.003777 -0.01245 0.007279 0.9654 0.9706 0.00876 0.9013 0.9087 0.02613 ] Network output: [ 0.9256 0.1794 0.0001033 -0.0001924 8.639e-05 -0.03139 -0.000145 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2823 0.002335 -0.1279 0.1476 0.9832 0.9931 0.3177 0.8564 0.9646 0.577 ] Network output: [ 0.02564 0.8565 0.9714 -0.0001038 4.661e-05 0.1204 -7.824e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006397 0.002651 0.004684 0.003651 0.9904 0.9935 0.006518 0.9563 0.9725 0.01221 ] Network output: [ 0.01842 0.09789 0.8666 -0.0002898 0.0001301 0.9975 -0.0002184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.311 0.2276 0.3572 0.1556 0.9849 0.994 0.312 0.8647 0.9683 0.5721 ] Network output: [ -0.04075 0.2464 1.095 0.0001837 -8.246e-05 0.741 0.0001384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1146 0.1091 0.1798 0.1337 0.9896 0.9937 0.1147 0.9503 0.9718 0.2004 ] Network output: [ -0.03351 0.1321 1.085 0.0002658 -0.0001193 0.8508 0.0002003 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1286 0.1275 0.1889 0.1572 0.9856 0.9916 0.1286 0.924 0.9613 0.1955 ] Network output: [ -0.01007 0.9808 0.01689 -8.823e-05 3.961e-05 1.022 -6.649e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04729 Epoch 4730 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0597 0.8097 0.9444 -6.689e-05 3.003e-05 0.1262 -5.041e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004308 -0.003802 -0.01263 0.008229 0.9654 0.9706 0.008655 0.9016 0.9092 0.02628 ] Network output: [ 0.9838 -0.09225 0.01985 6.248e-05 -2.805e-05 0.1051 4.709e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2777 -0.005459 -0.1405 0.2002 0.9832 0.9931 0.3128 0.8563 0.9649 0.5807 ] Network output: [ 0.02605 0.8474 0.9725 -9.424e-05 4.231e-05 0.1276 -7.102e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006215 0.002565 0.004546 0.00496 0.9904 0.9935 0.006333 0.9564 0.9727 0.01203 ] Network output: [ 0.06678 -0.2739 0.9241 4.893e-05 -2.197e-05 1.216 3.688e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3039 0.222 0.354 0.2352 0.9849 0.994 0.3049 0.8645 0.9683 0.5675 ] Network output: [ -0.04042 0.2266 1.096 0.0001965 -8.822e-05 0.7591 0.0001481 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1164 0.1109 0.1825 0.1463 0.9895 0.9937 0.1164 0.9505 0.972 0.2028 ] Network output: [ -0.03581 0.1655 1.074 0.0002414 -0.0001084 0.8332 0.0001819 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1307 0.1296 0.1883 0.1593 0.9856 0.9917 0.1307 0.9246 0.9613 0.1945 ] Network output: [ -0.004272 0.9851 0.003208 -7.541e-05 3.385e-05 1.02 -5.683e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06211 Epoch 4731 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05344 0.8471 0.9406 -0.0001022 4.587e-05 0.105 -7.701e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004352 -0.003764 -0.0125 0.007293 0.9654 0.9706 0.008724 0.9015 0.9088 0.02616 ] Network output: [ 0.9288 0.1748 -0.001378 -0.0001776 7.975e-05 -0.03178 -0.0001339 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2813 0.002028 -0.1302 0.1486 0.9832 0.9931 0.3166 0.8566 0.9647 0.5797 ] Network output: [ 0.02587 0.8559 0.9707 -0.0001037 4.654e-05 0.1212 -7.812e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00635 0.002622 0.004659 0.00367 0.9905 0.9935 0.006469 0.9564 0.9725 0.01223 ] Network output: [ 0.01897 0.08305 0.8709 -0.0002814 0.0001264 1.007 -0.0002121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3095 0.2262 0.3579 0.1572 0.985 0.994 0.3105 0.8649 0.9683 0.5748 ] Network output: [ -0.04081 0.2412 1.096 0.0001841 -8.267e-05 0.7448 0.0001388 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1139 0.1084 0.1803 0.1343 0.9896 0.9937 0.1139 0.9505 0.9719 0.2012 ] Network output: [ -0.03355 0.1279 1.086 0.0002657 -0.0001193 0.8542 0.0002003 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1279 0.1269 0.1895 0.1576 0.9856 0.9916 0.128 0.9241 0.9613 0.1962 ] Network output: [ -0.009323 0.9795 0.01632 -8.333e-05 3.741e-05 1.023 -6.28e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04573 Epoch 4732 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05979 0.8123 0.943 -6.839e-05 3.07e-05 0.1248 -5.154e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0043 -0.00379 -0.01268 0.008169 0.9654 0.9706 0.008632 0.9018 0.9093 0.0263 ] Network output: [ 0.9839 -0.07547 0.01509 5.755e-05 -2.583e-05 0.09288 4.337e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2773 -0.005204 -0.1424 0.1973 0.9833 0.9931 0.3122 0.8566 0.9649 0.5833 ] Network output: [ 0.02607 0.8481 0.9717 -9.528e-05 4.277e-05 0.1277 -7.181e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006184 0.002542 0.004521 0.004873 0.9904 0.9935 0.0063 0.9565 0.9727 0.01207 ] Network output: [ 0.0638 -0.26 0.9236 3.041e-05 -1.365e-05 1.209 2.292e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3029 0.221 0.3547 0.2307 0.985 0.994 0.3039 0.8647 0.9683 0.5709 ] Network output: [ -0.0408 0.2237 1.097 0.000195 -8.753e-05 0.7613 0.0001469 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1154 0.1099 0.1829 0.1457 0.9896 0.9937 0.1155 0.9507 0.972 0.2034 ] Network output: [ -0.03603 0.1594 1.076 0.0002422 -0.0001087 0.8377 0.0001825 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1299 0.1288 0.1891 0.1594 0.9856 0.9917 0.1299 0.9247 0.9613 0.1954 ] Network output: [ -0.005213 0.9903 0.003186 -7.796e-05 3.5e-05 1.017 -5.875e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05883 Epoch 4733 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05398 0.8463 0.9396 -0.0001004 4.509e-05 0.1057 -7.569e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004336 -0.003753 -0.01255 0.007306 0.9654 0.9706 0.008689 0.9017 0.909 0.02619 ] Network output: [ 0.9319 0.1704 -0.002681 -0.0001633 7.33e-05 -0.03224 -0.000123 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2803 0.001656 -0.1325 0.1496 0.9833 0.9931 0.3154 0.8569 0.9648 0.5825 ] Network output: [ 0.02602 0.8555 0.97 -0.0001036 4.65e-05 0.122 -7.806e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006304 0.002591 0.004636 0.003686 0.9905 0.9935 0.006423 0.9566 0.9726 0.01226 ] Network output: [ 0.01948 0.06889 0.8751 -0.0002743 0.0001231 1.016 -0.0002067 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.308 0.2248 0.3586 0.1587 0.985 0.994 0.309 0.8652 0.9684 0.5776 ] Network output: [ -0.04084 0.2365 1.097 0.0001846 -8.286e-05 0.7485 0.0001391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1077 0.1808 0.1347 0.9896 0.9938 0.1132 0.9506 0.9719 0.2019 ] Network output: [ -0.03357 0.1239 1.087 0.0002657 -0.0001193 0.8575 0.0002002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1274 0.1263 0.1901 0.158 0.9856 0.9916 0.1274 0.9243 0.9614 0.1969 ] Network output: [ -0.008604 0.9784 0.01567 -7.856e-05 3.527e-05 1.023 -5.921e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04434 Epoch 4734 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05981 0.8148 0.9417 -6.982e-05 3.134e-05 0.1235 -5.262e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004291 -0.00378 -0.01273 0.008111 0.9654 0.9706 0.00861 0.902 0.9094 0.02633 ] Network output: [ 0.9839 -0.05944 0.01072 5.306e-05 -2.382e-05 0.08105 3.998e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2768 -0.00504 -0.1444 0.1944 0.9833 0.9931 0.3116 0.8569 0.965 0.5861 ] Network output: [ 0.02605 0.8487 0.9711 -9.63e-05 4.323e-05 0.1278 -7.257e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006153 0.002518 0.004497 0.004787 0.9905 0.9935 0.006269 0.9566 0.9728 0.01211 ] Network output: [ 0.06094 -0.2466 0.9233 1.198e-05 -5.377e-06 1.202 9.027e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3019 0.22 0.3555 0.2263 0.985 0.994 0.3029 0.8651 0.9684 0.5744 ] Network output: [ -0.04113 0.221 1.099 0.0001936 -8.691e-05 0.7635 0.0001459 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1146 0.1091 0.1833 0.1452 0.9896 0.9938 0.1146 0.9508 0.9721 0.2041 ] Network output: [ -0.03619 0.1536 1.078 0.0002431 -0.0001091 0.8421 0.0001832 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1291 0.128 0.1898 0.1595 0.9856 0.9917 0.1291 0.9248 0.9614 0.1963 ] Network output: [ -0.006007 0.9946 0.003199 -7.959e-05 3.573e-05 1.014 -5.998e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05587 Epoch 4735 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05444 0.8457 0.9388 -9.88e-05 4.435e-05 0.1062 -7.446e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004321 -0.003743 -0.0126 0.007318 0.9654 0.9706 0.008656 0.9019 0.9091 0.02622 ] Network output: [ 0.9349 0.1661 -0.003826 -0.0001493 6.704e-05 -0.03277 -0.0001125 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2793 0.001229 -0.1348 0.1505 0.9833 0.9931 0.3142 0.8572 0.9648 0.5854 ] Network output: [ 0.02611 0.8552 0.9694 -0.0001036 4.65e-05 0.1227 -7.805e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006261 0.002559 0.004612 0.003699 0.9905 0.9935 0.006378 0.9567 0.9727 0.01229 ] Network output: [ 0.01995 0.05543 0.8792 -0.0002682 0.0001204 1.024 -0.0002021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3065 0.2233 0.3594 0.1599 0.985 0.994 0.3075 0.8655 0.9684 0.5806 ] Network output: [ -0.04086 0.2321 1.098 0.0001849 -8.303e-05 0.7519 0.0001394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.1071 0.1813 0.1352 0.9897 0.9938 0.1126 0.9508 0.972 0.2026 ] Network output: [ -0.03357 0.1202 1.087 0.0002657 -0.0001193 0.8606 0.0002002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1269 0.1258 0.1907 0.1583 0.9856 0.9917 0.1269 0.9245 0.9614 0.1975 ] Network output: [ -0.007927 0.9775 0.01496 -7.396e-05 3.32e-05 1.023 -5.574e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04311 Epoch 4736 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05977 0.8173 0.9406 -7.115e-05 3.194e-05 0.1223 -5.362e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004282 -0.00377 -0.01278 0.008056 0.9654 0.9706 0.008589 0.9022 0.9095 0.02636 ] Network output: [ 0.984 -0.04417 0.006744 4.901e-05 -2.2e-05 0.06968 3.694e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2762 -0.004958 -0.1464 0.1916 0.9833 0.9932 0.3109 0.8572 0.965 0.589 ] Network output: [ 0.02598 0.8493 0.9705 -9.729e-05 4.368e-05 0.1279 -7.332e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006123 0.002491 0.004473 0.004703 0.9905 0.9936 0.006238 0.9568 0.9728 0.01215 ] Network output: [ 0.05819 -0.2339 0.923 -6.281e-06 2.82e-06 1.194 -4.734e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3009 0.2188 0.3563 0.222 0.985 0.994 0.3019 0.8654 0.9685 0.578 ] Network output: [ -0.0414 0.2185 1.1 0.0001923 -8.635e-05 0.7655 0.000145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1138 0.1083 0.1836 0.1446 0.9896 0.9938 0.1138 0.951 0.9722 0.2047 ] Network output: [ -0.03631 0.148 1.079 0.0002441 -0.0001096 0.8463 0.000184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1284 0.1273 0.1905 0.1595 0.9856 0.9917 0.1284 0.925 0.9615 0.1971 ] Network output: [ -0.00666 0.998 0.00323 -8.035e-05 3.607e-05 1.012 -6.055e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05324 Epoch 4737 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05482 0.8452 0.938 -9.728e-05 4.367e-05 0.1068 -7.332e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004306 -0.003734 -0.01266 0.00733 0.9654 0.9706 0.008624 0.9022 0.9093 0.02625 ] Network output: [ 0.9379 0.1619 -0.004832 -0.0001358 6.097e-05 -0.03337 -0.0001024 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2782 0.000753 -0.1371 0.1514 0.9833 0.9931 0.313 0.8576 0.9649 0.5885 ] Network output: [ 0.02614 0.855 0.9689 -0.0001036 4.653e-05 0.1234 -7.81e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006219 0.002525 0.004589 0.003709 0.9905 0.9936 0.006336 0.9569 0.9727 0.01232 ] Network output: [ 0.02037 0.04266 0.8832 -0.0002632 0.0001181 1.032 -0.0001983 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.305 0.2217 0.3602 0.161 0.985 0.994 0.306 0.8658 0.9685 0.5836 ] Network output: [ -0.04085 0.2281 1.099 0.0001853 -8.318e-05 0.7552 0.0001396 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.1065 0.1818 0.1355 0.9897 0.9938 0.112 0.951 0.9721 0.2033 ] Network output: [ -0.03356 0.1167 1.088 0.0002658 -0.0001193 0.8636 0.0002003 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1264 0.1253 0.1913 0.1586 0.9856 0.9917 0.1264 0.9247 0.9615 0.1981 ] Network output: [ -0.007301 0.9768 0.0142 -6.957e-05 3.123e-05 1.023 -5.243e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04202 Epoch 4738 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05968 0.8196 0.9395 -7.241e-05 3.251e-05 0.1212 -5.457e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004273 -0.003761 -0.01283 0.008002 0.9655 0.9707 0.008568 0.9024 0.9097 0.02639 ] Network output: [ 0.984 -0.02973 0.003157 4.541e-05 -2.039e-05 0.05882 3.422e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2756 -0.004949 -0.1483 0.189 0.9833 0.9932 0.3101 0.8576 0.9651 0.592 ] Network output: [ 0.02587 0.85 0.9699 -9.825e-05 4.411e-05 0.128 -7.405e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006094 0.002463 0.00445 0.004621 0.9905 0.9936 0.006208 0.957 0.9729 0.01219 ] Network output: [ 0.05557 -0.2217 0.9229 -2.428e-05 1.09e-05 1.188 -1.829e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2998 0.2175 0.3572 0.2177 0.985 0.994 0.3008 0.8658 0.9685 0.5817 ] Network output: [ -0.0416 0.2162 1.1 0.0001912 -8.586e-05 0.7674 0.0001441 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1076 0.1839 0.1441 0.9896 0.9938 0.1131 0.9512 0.9722 0.2053 ] Network output: [ -0.03638 0.1426 1.081 0.0002452 -0.0001101 0.8505 0.0001848 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1278 0.1267 0.1912 0.1596 0.9856 0.9917 0.1278 0.9252 0.9615 0.1978 ] Network output: [ -0.007176 1.001 0.003266 -8.032e-05 3.606e-05 1.01 -6.053e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05089 Epoch 4739 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05512 0.8448 0.9373 -9.588e-05 4.305e-05 0.1073 -7.226e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004292 -0.003726 -0.01271 0.007341 0.9655 0.9707 0.008594 0.9024 0.9095 0.02629 ] Network output: [ 0.9407 0.1578 -0.00572 -0.0001228 5.512e-05 -0.03405 -9.253e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2771 0.0002374 -0.1394 0.1522 0.9833 0.9932 0.3118 0.858 0.965 0.5916 ] Network output: [ 0.02611 0.8549 0.9685 -0.0001038 4.659e-05 0.124 -7.82e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00618 0.002491 0.004567 0.003717 0.9905 0.9936 0.006295 0.9571 0.9728 0.01235 ] Network output: [ 0.02074 0.03062 0.8871 -0.0002591 0.0001163 1.04 -0.0001953 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3035 0.22 0.361 0.162 0.985 0.994 0.3045 0.8662 0.9686 0.5868 ] Network output: [ -0.04082 0.2244 1.1 0.0001856 -8.333e-05 0.7582 0.0001399 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.1059 0.1822 0.1358 0.9897 0.9938 0.1115 0.9512 0.9722 0.2039 ] Network output: [ -0.03354 0.1135 1.088 0.0002659 -0.0001194 0.8665 0.0002004 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.126 0.1249 0.1918 0.1588 0.9856 0.9917 0.1261 0.925 0.9616 0.1987 ] Network output: [ -0.006728 0.9764 0.01339 -6.541e-05 2.937e-05 1.023 -4.93e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04105 Epoch 4740 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05954 0.8219 0.9386 -7.358e-05 3.303e-05 0.1201 -5.545e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004263 -0.003753 -0.01287 0.007952 0.9655 0.9707 0.008547 0.9027 0.9098 0.02642 ] Network output: [ 0.9839 -0.01615 -4.035e-05 4.227e-05 -1.898e-05 0.0485 3.185e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2748 -0.005006 -0.1503 0.1864 0.9833 0.9932 0.3093 0.858 0.9652 0.595 ] Network output: [ 0.02572 0.8506 0.9695 -9.919e-05 4.453e-05 0.1281 -7.475e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006066 0.002433 0.004428 0.004542 0.9905 0.9936 0.00618 0.9572 0.973 0.01223 ] Network output: [ 0.05306 -0.2102 0.9229 -4.191e-05 1.882e-05 1.181 -3.159e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2987 0.2161 0.3581 0.2136 0.985 0.994 0.2997 0.8662 0.9686 0.5853 ] Network output: [ -0.04175 0.214 1.101 0.0001903 -8.543e-05 0.7692 0.0001434 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.1069 0.1842 0.1435 0.9897 0.9938 0.1125 0.9514 0.9723 0.2058 ] Network output: [ -0.0364 0.1375 1.082 0.0002465 -0.0001107 0.8545 0.0001857 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1272 0.1261 0.1918 0.1597 0.9856 0.9917 0.1272 0.9254 0.9616 0.1986 ] Network output: [ -0.007564 1.003 0.003294 -7.958e-05 3.573e-05 1.009 -5.998e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0488 Epoch 4741 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05535 0.8445 0.9367 -9.461e-05 4.247e-05 0.1077 -7.13e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004277 -0.003719 -0.01276 0.007351 0.9655 0.9707 0.008566 0.9027 0.9097 0.02633 ] Network output: [ 0.9435 0.1539 -0.006504 -0.0001103 4.951e-05 -0.0348 -8.311e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.276 -0.0003104 -0.1417 0.153 0.9833 0.9932 0.3105 0.8584 0.9651 0.5948 ] Network output: [ 0.02603 0.8549 0.9681 -0.000104 4.667e-05 0.1245 -7.835e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006142 0.002456 0.004545 0.003722 0.9905 0.9936 0.006256 0.9573 0.9729 0.01238 ] Network output: [ 0.02107 0.01932 0.8908 -0.0002559 0.0001149 1.047 -0.0001929 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.302 0.2183 0.3619 0.1628 0.985 0.994 0.303 0.8666 0.9687 0.59 ] Network output: [ -0.04078 0.2211 1.1 0.0001859 -8.345e-05 0.7611 0.0001401 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.1054 0.1826 0.1361 0.9897 0.9938 0.111 0.9514 0.9723 0.2045 ] Network output: [ -0.03351 0.1106 1.088 0.000266 -0.0001194 0.8693 0.0002005 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1257 0.1246 0.1923 0.159 0.9856 0.9917 0.1257 0.9252 0.9617 0.1993 ] Network output: [ -0.006211 0.9763 0.01255 -6.148e-05 2.76e-05 1.023 -4.634e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04019 Epoch 4742 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05936 0.824 0.9378 -7.467e-05 3.352e-05 0.1191 -5.628e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004253 -0.003746 -0.01292 0.007905 0.9655 0.9707 0.008527 0.903 0.91 0.02645 ] Network output: [ 0.9839 -0.00346 -0.002858 3.958e-05 -1.777e-05 0.03875 2.983e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2741 -0.005122 -0.1522 0.1841 0.9833 0.9932 0.3084 0.8585 0.9653 0.5981 ] Network output: [ 0.02555 0.8513 0.9691 -0.0001001 4.494e-05 0.1282 -7.544e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006039 0.002403 0.004407 0.004465 0.9905 0.9936 0.006152 0.9574 0.9731 0.01227 ] Network output: [ 0.05067 -0.1993 0.9231 -5.912e-05 2.654e-05 1.175 -4.455e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2976 0.2147 0.359 0.2096 0.985 0.994 0.2985 0.8667 0.9687 0.589 ] Network output: [ -0.04184 0.212 1.101 0.0001895 -8.507e-05 0.7709 0.0001428 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.1063 0.1844 0.143 0.9897 0.9938 0.1119 0.9516 0.9724 0.2063 ] Network output: [ -0.03638 0.1326 1.083 0.0002478 -0.0001112 0.8583 0.0001867 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1267 0.1256 0.1924 0.1597 0.9857 0.9917 0.1268 0.9257 0.9617 0.1992 ] Network output: [ -0.007832 1.004 0.003305 -7.822e-05 3.511e-05 1.008 -5.895e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04696 Epoch 4743 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05551 0.8443 0.9362 -9.345e-05 4.195e-05 0.1081 -7.043e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004263 -0.003713 -0.01281 0.007361 0.9655 0.9707 0.008539 0.9031 0.9099 0.02637 ] Network output: [ 0.9461 0.1502 -0.0072 -9.833e-05 4.414e-05 -0.03564 -7.41e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2749 -0.0008833 -0.144 0.1538 0.9833 0.9932 0.3093 0.8589 0.9652 0.5981 ] Network output: [ 0.0259 0.855 0.9678 -0.0001042 4.679e-05 0.125 -7.854e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006106 0.002422 0.004523 0.003725 0.9906 0.9936 0.00622 0.9575 0.973 0.01241 ] Network output: [ 0.02134 0.008814 0.8944 -0.0002537 0.0001139 1.053 -0.0001912 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.3005 0.2166 0.3628 0.1635 0.985 0.994 0.3015 0.8671 0.9688 0.5933 ] Network output: [ -0.04072 0.218 1.1 0.0001861 -8.356e-05 0.7638 0.0001403 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.105 0.183 0.1363 0.9897 0.9938 0.1106 0.9516 0.9724 0.2051 ] Network output: [ -0.03347 0.1078 1.088 0.0002662 -0.0001195 0.8719 0.0002006 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1254 0.1243 0.1927 0.1592 0.9856 0.9917 0.1254 0.9255 0.9618 0.1998 ] Network output: [ -0.005748 0.9764 0.01169 -5.778e-05 2.594e-05 1.023 -4.354e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03943 Epoch 4744 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05913 0.8261 0.9371 -7.568e-05 3.398e-05 0.1182 -5.704e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004243 -0.003739 -0.01296 0.00786 0.9655 0.9707 0.008507 0.9033 0.9102 0.02649 ] Network output: [ 0.9838 0.0083 -0.005311 3.735e-05 -1.677e-05 0.02961 2.814e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2732 -0.00529 -0.1541 0.1818 0.9833 0.9932 0.3075 0.8589 0.9654 0.6013 ] Network output: [ 0.02535 0.8519 0.9687 -0.000101 4.533e-05 0.1282 -7.61e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006012 0.002371 0.004387 0.004392 0.9906 0.9936 0.006125 0.9576 0.9732 0.01231 ] Network output: [ 0.0484 -0.1892 0.9233 -7.58e-05 3.403e-05 1.169 -5.713e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2964 0.2132 0.36 0.2058 0.985 0.994 0.2974 0.8671 0.9688 0.5927 ] Network output: [ -0.04188 0.2102 1.102 0.0001888 -8.476e-05 0.7726 0.0001423 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.1057 0.1846 0.1425 0.9897 0.9938 0.1113 0.9518 0.9725 0.2068 ] Network output: [ -0.03632 0.1281 1.084 0.0002492 -0.0001119 0.862 0.0001878 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1263 0.1252 0.1929 0.1598 0.9857 0.9917 0.1263 0.9259 0.9618 0.1999 ] Network output: [ -0.007992 1.005 0.003291 -7.631e-05 3.426e-05 1.007 -5.751e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04533 Epoch 4745 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05561 0.8442 0.9358 -9.241e-05 4.149e-05 0.1084 -6.965e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00425 -0.003708 -0.01286 0.00737 0.9655 0.9707 0.008514 0.9034 0.9101 0.02641 ] Network output: [ 0.9487 0.1467 -0.007818 -8.698e-05 3.905e-05 -0.03657 -6.555e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2737 -0.001475 -0.1463 0.1544 0.9833 0.9932 0.308 0.8593 0.9653 0.6014 ] Network output: [ 0.02573 0.8551 0.9675 -0.0001045 4.693e-05 0.1255 -7.878e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006071 0.002386 0.004502 0.003725 0.9906 0.9936 0.006185 0.9577 0.9731 0.01245 ] Network output: [ 0.02156 -0.0008872 0.8978 -0.0002523 0.0001133 1.059 -0.0001902 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.299 0.2148 0.3637 0.1639 0.985 0.994 0.3 0.8675 0.9689 0.5967 ] Network output: [ -0.04064 0.2153 1.101 0.0001864 -8.366e-05 0.7663 0.0001404 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.1046 0.1833 0.1365 0.9898 0.9938 0.1102 0.9518 0.9725 0.2056 ] Network output: [ -0.03341 0.1053 1.088 0.0002665 -0.0001196 0.8745 0.0002008 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1252 0.124 0.1932 0.1594 0.9856 0.9917 0.1252 0.9258 0.9619 0.2003 ] Network output: [ -0.005335 0.9766 0.01083 -5.427e-05 2.436e-05 1.023 -4.09e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03876 Epoch 4746 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05888 0.828 0.9366 -7.661e-05 3.439e-05 0.1174 -5.774e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004233 -0.003733 -0.01301 0.007819 0.9655 0.9707 0.008488 0.9036 0.9104 0.02652 ] Network output: [ 0.9837 0.01912 -0.00742 3.556e-05 -1.597e-05 0.02108 2.68e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2724 -0.005505 -0.156 0.1798 0.9834 0.9932 0.3065 0.8594 0.9655 0.6045 ] Network output: [ 0.02512 0.8526 0.9684 -0.0001018 4.571e-05 0.1283 -7.674e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005987 0.002339 0.004368 0.004323 0.9906 0.9936 0.006099 0.9578 0.9733 0.01235 ] Network output: [ 0.04624 -0.1797 0.9236 -9.191e-05 4.126e-05 1.163 -6.927e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2953 0.2116 0.361 0.2022 0.985 0.994 0.2962 0.8676 0.9689 0.5964 ] Network output: [ -0.04187 0.2085 1.102 0.0001883 -8.451e-05 0.7742 0.0001419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.1052 0.1848 0.142 0.9897 0.9938 0.1109 0.952 0.9726 0.2072 ] Network output: [ -0.03622 0.1237 1.084 0.0002506 -0.0001125 0.8656 0.0001889 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1259 0.1248 0.1934 0.1598 0.9857 0.9917 0.1259 0.9262 0.9619 0.2005 ] Network output: [ -0.008057 1.006 0.003248 -7.394e-05 3.32e-05 1.007 -5.573e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0439 Epoch 4747 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05566 0.8442 0.9355 -9.149e-05 4.107e-05 0.1087 -6.895e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004237 -0.003703 -0.01291 0.007378 0.9655 0.9707 0.00849 0.9037 0.9103 0.02645 ] Network output: [ 0.9511 0.1435 -0.008366 -7.625e-05 3.423e-05 -0.0376 -5.746e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2726 -0.002078 -0.1486 0.1551 0.9834 0.9932 0.3068 0.8598 0.9655 0.6047 ] Network output: [ 0.02552 0.8553 0.9673 -0.0001049 4.708e-05 0.1259 -7.904e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006039 0.002351 0.004482 0.003723 0.9906 0.9936 0.006152 0.9579 0.9732 0.01248 ] Network output: [ 0.02172 -0.009762 0.9011 -0.0002518 0.000113 1.064 -0.0001897 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2976 0.213 0.3647 0.1643 0.985 0.994 0.2985 0.868 0.969 0.6 ] Network output: [ -0.04055 0.2128 1.101 0.0001866 -8.376e-05 0.7686 0.0001406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.1042 0.1836 0.1366 0.9898 0.9939 0.1099 0.952 0.9726 0.2061 ] Network output: [ -0.03334 0.1029 1.088 0.0002668 -0.0001198 0.8769 0.0002011 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1249 0.1238 0.1936 0.1595 0.9857 0.9917 0.125 0.9261 0.962 0.2008 ] Network output: [ -0.004968 0.977 0.009964 -5.094e-05 2.287e-05 1.023 -3.839e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03816 Epoch 4748 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0586 0.8298 0.9361 -7.747e-05 3.478e-05 0.1166 -5.838e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004223 -0.003728 -0.01305 0.007781 0.9656 0.9707 0.00847 0.904 0.9106 0.02655 ] Network output: [ 0.9836 0.029 -0.00921 3.422e-05 -1.536e-05 0.01316 2.579e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2715 -0.00576 -0.1578 0.1778 0.9834 0.9932 0.3055 0.8599 0.9656 0.6077 ] Network output: [ 0.02487 0.8532 0.9682 -0.0001026 4.608e-05 0.1284 -7.736e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005963 0.002307 0.004351 0.004257 0.9906 0.9936 0.006074 0.9581 0.9734 0.01239 ] Network output: [ 0.04421 -0.171 0.9241 -0.0001074 4.822e-05 1.158 -8.094e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2942 0.2101 0.362 0.1987 0.985 0.994 0.2951 0.8681 0.969 0.6 ] Network output: [ -0.04182 0.2069 1.102 0.0001878 -8.432e-05 0.7756 0.0001415 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.1047 0.185 0.1415 0.9898 0.9939 0.1104 0.9522 0.9727 0.2076 ] Network output: [ -0.03608 0.1197 1.085 0.0002521 -0.0001132 0.8689 0.00019 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1256 0.1245 0.1939 0.1598 0.9857 0.9917 0.1256 0.9264 0.9621 0.201 ] Network output: [ -0.008039 1.006 0.003173 -7.121e-05 3.197e-05 1.007 -5.366e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04264 Epoch 4749 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05565 0.8442 0.9352 -9.068e-05 4.071e-05 0.1089 -6.834e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004224 -0.0037 -0.01296 0.007385 0.9656 0.9707 0.008468 0.9041 0.9106 0.02649 ] Network output: [ 0.9534 0.1405 -0.008852 -6.616e-05 2.97e-05 -0.03871 -4.986e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2715 -0.002689 -0.1508 0.1556 0.9834 0.9932 0.3055 0.8603 0.9656 0.608 ] Network output: [ 0.02528 0.8556 0.9671 -0.0001053 4.726e-05 0.1263 -7.933e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006008 0.002317 0.004462 0.003719 0.9906 0.9936 0.00612 0.9582 0.9733 0.01252 ] Network output: [ 0.02182 -0.0178 0.9041 -0.000252 0.0001131 1.069 -0.0001899 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2962 0.2113 0.3656 0.1644 0.985 0.994 0.2971 0.8685 0.9691 0.6034 ] Network output: [ -0.04045 0.2105 1.1 0.0001868 -8.385e-05 0.7707 0.0001408 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.1039 0.1839 0.1367 0.9898 0.9939 0.1096 0.9523 0.9727 0.2066 ] Network output: [ -0.03327 0.1006 1.088 0.0002671 -0.0001199 0.8793 0.0002013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1248 0.1236 0.194 0.1596 0.9857 0.9917 0.1248 0.9264 0.9621 0.2013 ] Network output: [ -0.004642 0.9775 0.009117 -4.776e-05 2.144e-05 1.023 -3.6e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03762 Epoch 4750 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05829 0.8315 0.9357 -7.825e-05 3.513e-05 0.1159 -5.897e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004213 -0.003723 -0.01309 0.007747 0.9656 0.9707 0.008452 0.9043 0.9108 0.02659 ] Network output: [ 0.9835 0.03795 -0.01071 3.33e-05 -1.495e-05 0.00585 2.509e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2706 -0.00605 -0.1596 0.1761 0.9834 0.9932 0.3045 0.8604 0.9657 0.6109 ] Network output: [ 0.0246 0.8539 0.968 -0.0001034 4.644e-05 0.1285 -7.795e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005939 0.002275 0.004335 0.004194 0.9906 0.9936 0.00605 0.9583 0.9735 0.01243 ] Network output: [ 0.0423 -0.163 0.9246 -0.0001222 5.488e-05 1.153 -9.212e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.293 0.2085 0.363 0.1955 0.985 0.994 0.294 0.8686 0.9691 0.6037 ] Network output: [ -0.04172 0.2055 1.102 0.0001875 -8.418e-05 0.777 0.0001413 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.1043 0.1852 0.1411 0.9898 0.9939 0.1101 0.9524 0.9728 0.208 ] Network output: [ -0.03591 0.1158 1.085 0.0002535 -0.0001138 0.8722 0.0001911 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1253 0.1242 0.1943 0.1599 0.9857 0.9917 0.1253 0.9267 0.9622 0.2016 ] Network output: [ -0.007953 1.006 0.003066 -6.818e-05 3.061e-05 1.007 -5.138e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04152 Epoch 4751 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0556 0.8444 0.935 -8.998e-05 4.04e-05 0.1091 -6.781e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004212 -0.003696 -0.013 0.007392 0.9656 0.9707 0.008447 0.9044 0.9108 0.02653 ] Network output: [ 0.9556 0.1377 -0.009277 -5.67e-05 2.546e-05 -0.03992 -4.273e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2704 -0.003303 -0.153 0.1561 0.9834 0.9932 0.3043 0.8608 0.9657 0.6114 ] Network output: [ 0.02501 0.856 0.967 -0.0001057 4.745e-05 0.1266 -7.965e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005979 0.002282 0.004443 0.003713 0.9906 0.9936 0.006091 0.9584 0.9735 0.01255 ] Network output: [ 0.02186 -0.02501 0.907 -0.0002529 0.0001135 1.073 -0.0001906 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2948 0.2095 0.3666 0.1645 0.9851 0.994 0.2957 0.869 0.9692 0.6068 ] Network output: [ -0.04032 0.2085 1.1 0.000187 -8.394e-05 0.7728 0.0001409 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1093 0.1036 0.1842 0.1368 0.9898 0.9939 0.1093 0.9525 0.9728 0.2071 ] Network output: [ -0.03317 0.0985 1.087 0.0002676 -0.0001201 0.8815 0.0002017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1246 0.1235 0.1944 0.1597 0.9857 0.9917 0.1246 0.9267 0.9622 0.2017 ] Network output: [ -0.004351 0.978 0.008294 -4.471e-05 2.007e-05 1.022 -3.369e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03714 Epoch 4752 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05797 0.8332 0.9354 -7.897e-05 3.545e-05 0.1152 -5.951e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004203 -0.003719 -0.01313 0.007716 0.9656 0.9707 0.008435 0.9046 0.9111 0.02662 ] Network output: [ 0.9835 0.04599 -0.01194 3.278e-05 -1.472e-05 -0.0008722 2.47e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2696 -0.006372 -0.1614 0.1745 0.9834 0.9932 0.3035 0.861 0.9658 0.6142 ] Network output: [ 0.02431 0.8546 0.9678 -0.0001042 4.678e-05 0.1285 -7.853e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005917 0.002243 0.00432 0.004136 0.9907 0.9937 0.006027 0.9585 0.9736 0.01247 ] Network output: [ 0.0405 -0.1557 0.9253 -0.0001364 6.124e-05 1.149 -0.0001028 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2919 0.2069 0.3641 0.1923 0.9851 0.994 0.2928 0.8691 0.9692 0.6073 ] Network output: [ -0.0416 0.2042 1.101 0.0001873 -8.408e-05 0.7784 0.0001412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.1039 0.1853 0.1406 0.9898 0.9939 0.1097 0.9527 0.9729 0.2084 ] Network output: [ -0.03572 0.1123 1.085 0.000255 -0.0001145 0.8752 0.0001922 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1251 0.1239 0.1947 0.1599 0.9857 0.9917 0.1251 0.927 0.9623 0.2021 ] Network output: [ -0.007811 1.005 0.002928 -6.493e-05 2.915e-05 1.007 -4.893e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04054 Epoch 4753 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0555 0.8446 0.9348 -8.938e-05 4.012e-05 0.1092 -6.736e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004201 -0.003693 -0.01305 0.007397 0.9656 0.9707 0.008427 0.9048 0.9111 0.02657 ] Network output: [ 0.9577 0.1352 -0.009644 -4.789e-05 2.15e-05 -0.04119 -3.609e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2694 -0.003915 -0.1551 0.1566 0.9834 0.9932 0.3031 0.8613 0.9658 0.6147 ] Network output: [ 0.02471 0.8564 0.9669 -0.0001061 4.765e-05 0.1269 -7.999e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005952 0.002248 0.004425 0.003704 0.9907 0.9937 0.006063 0.9586 0.9736 0.01259 ] Network output: [ 0.02184 -0.03141 0.9097 -0.0002546 0.0001143 1.077 -0.0001918 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2934 0.2077 0.3675 0.1644 0.9851 0.994 0.2944 0.8695 0.9693 0.6102 ] Network output: [ -0.04019 0.2066 1.1 0.0001872 -8.403e-05 0.7746 0.0001411 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.1033 0.1844 0.1368 0.9898 0.9939 0.1091 0.9527 0.9729 0.2075 ] Network output: [ -0.03307 0.09649 1.087 0.0002681 -0.0001204 0.8837 0.000202 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1245 0.1233 0.1947 0.1597 0.9857 0.9917 0.1245 0.927 0.9624 0.2022 ] Network output: [ -0.004089 0.9785 0.007503 -4.176e-05 1.875e-05 1.022 -3.147e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03669 Epoch 4754 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05763 0.8347 0.9352 -7.962e-05 3.574e-05 0.1146 -6e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004193 -0.003715 -0.01317 0.007687 0.9656 0.9708 0.008418 0.905 0.9113 0.02666 ] Network output: [ 0.9835 0.05316 -0.01294 3.264e-05 -1.465e-05 -0.007027 2.46e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2687 -0.00672 -0.1631 0.173 0.9834 0.9932 0.3024 0.8615 0.9659 0.6174 ] Network output: [ 0.02401 0.8552 0.9677 -0.0001049 4.711e-05 0.1286 -7.908e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005895 0.002211 0.004306 0.00408 0.9907 0.9937 0.006005 0.9587 0.9737 0.01251 ] Network output: [ 0.03882 -0.1491 0.926 -0.0001499 6.729e-05 1.145 -0.000113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2908 0.2053 0.3651 0.1894 0.9851 0.994 0.2917 0.8696 0.9693 0.6108 ] Network output: [ -0.04144 0.203 1.101 0.0001872 -8.403e-05 0.7797 0.0001411 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.1036 0.1855 0.1402 0.9898 0.9939 0.1094 0.9529 0.973 0.2087 ] Network output: [ -0.03551 0.1089 1.085 0.0002566 -0.0001152 0.8781 0.0001934 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1249 0.1237 0.1951 0.1599 0.9857 0.9917 0.1249 0.9273 0.9624 0.2025 ] Network output: [ -0.007626 1.005 0.002763 -6.153e-05 2.762e-05 1.008 -4.637e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03966 Epoch 4755 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05536 0.8449 0.9347 -8.887e-05 3.99e-05 0.1093 -6.697e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00419 -0.003691 -0.01309 0.007403 0.9656 0.9708 0.008409 0.9051 0.9113 0.02661 ] Network output: [ 0.9597 0.133 -0.009953 -3.968e-05 1.782e-05 -0.04253 -2.991e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2683 -0.004524 -0.1572 0.1569 0.9834 0.9932 0.302 0.8618 0.966 0.618 ] Network output: [ 0.02439 0.8568 0.9668 -0.0001066 4.786e-05 0.1271 -8.034e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005926 0.002214 0.004407 0.003693 0.9907 0.9937 0.006037 0.9589 0.9737 0.01262 ] Network output: [ 0.02176 -0.03703 0.9123 -0.0002568 0.0001153 1.08 -0.0001935 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2921 0.206 0.3685 0.1641 0.9851 0.994 0.2931 0.87 0.9694 0.6135 ] Network output: [ -0.04004 0.205 1.099 0.0001874 -8.414e-05 0.7764 0.0001412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.103 0.1846 0.1368 0.9899 0.9939 0.1089 0.953 0.973 0.2079 ] Network output: [ -0.03295 0.09457 1.087 0.0002687 -0.0001206 0.8858 0.0002025 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1244 0.1232 0.1951 0.1598 0.9857 0.9917 0.1244 0.9273 0.9625 0.2026 ] Network output: [ -0.00385 0.9791 0.006746 -3.889e-05 1.746e-05 1.022 -2.931e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03628 Epoch 4756 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05727 0.8361 0.935 -8.021e-05 3.601e-05 0.114 -6.045e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004183 -0.003711 -0.01321 0.007662 0.9656 0.9708 0.008402 0.9053 0.9116 0.0267 ] Network output: [ 0.9835 0.05949 -0.01372 3.284e-05 -1.474e-05 -0.01264 2.475e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2677 -0.007091 -0.1648 0.1717 0.9834 0.9932 0.3014 0.862 0.9661 0.6206 ] Network output: [ 0.02368 0.8559 0.9676 -0.0001056 4.743e-05 0.1286 -7.961e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005874 0.00218 0.004292 0.004029 0.9907 0.9937 0.005984 0.959 0.9738 0.01255 ] Network output: [ 0.03724 -0.1431 0.9267 -0.0001627 7.305e-05 1.141 -0.0001226 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2897 0.2037 0.3662 0.1867 0.9851 0.994 0.2906 0.8701 0.9694 0.6143 ] Network output: [ -0.04126 0.2018 1.101 0.0001872 -8.402e-05 0.7809 0.000141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.1033 0.1856 0.1399 0.9898 0.9939 0.1092 0.9531 0.9731 0.2091 ] Network output: [ -0.03527 0.1058 1.085 0.0002581 -0.0001159 0.8809 0.0001945 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1248 0.1236 0.1955 0.1599 0.9857 0.9917 0.1248 0.9276 0.9625 0.203 ] Network output: [ -0.007409 1.004 0.002573 -5.805e-05 2.606e-05 1.008 -4.374e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03887 Epoch 4757 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05519 0.8452 0.9347 -8.844e-05 3.97e-05 0.1094 -6.665e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004179 -0.003689 -0.01313 0.007407 0.9656 0.9708 0.008391 0.9055 0.9116 0.02665 ] Network output: [ 0.9615 0.1309 -0.0102 -3.207e-05 1.44e-05 -0.0439 -2.417e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2673 -0.005126 -0.1592 0.1573 0.9834 0.9932 0.3008 0.8624 0.9661 0.6213 ] Network output: [ 0.02405 0.8573 0.9668 -0.0001071 4.808e-05 0.1273 -8.071e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005901 0.002181 0.004391 0.003681 0.9907 0.9937 0.006012 0.9591 0.9738 0.01266 ] Network output: [ 0.02163 -0.0419 0.9147 -0.0002596 0.0001166 1.083 -0.0001957 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2909 0.2043 0.3694 0.1638 0.9851 0.9941 0.2918 0.8705 0.9695 0.6169 ] Network output: [ -0.03987 0.2035 1.099 0.0001877 -8.424e-05 0.778 0.0001414 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.1028 0.1849 0.1368 0.9899 0.9939 0.1087 0.9532 0.9732 0.2083 ] Network output: [ -0.03282 0.09273 1.086 0.0002693 -0.0001209 0.8879 0.000203 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1244 0.1232 0.1954 0.1598 0.9857 0.9917 0.1244 0.9276 0.9626 0.203 ] Network output: [ -0.003631 0.9796 0.006029 -3.609e-05 1.62e-05 1.021 -2.72e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03588 Epoch 4758 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0569 0.8375 0.9349 -8.075e-05 3.625e-05 0.1135 -6.086e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004174 -0.003708 -0.01325 0.00764 0.9656 0.9708 0.008387 0.9057 0.9118 0.02673 ] Network output: [ 0.9836 0.06505 -0.01433 3.336e-05 -1.497e-05 -0.01775 2.514e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2668 -0.00748 -0.1665 0.1705 0.9834 0.9932 0.3003 0.8625 0.9662 0.6238 ] Network output: [ 0.02335 0.8566 0.9676 -0.0001063 4.773e-05 0.1287 -8.013e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005854 0.002148 0.00428 0.00398 0.9907 0.9937 0.005964 0.9592 0.9739 0.01259 ] Network output: [ 0.03577 -0.1376 0.9275 -0.0001749 7.851e-05 1.138 -0.0001318 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2886 0.2021 0.3672 0.184 0.9851 0.9941 0.2895 0.8706 0.9696 0.6178 ] Network output: [ -0.04105 0.2008 1.1 0.0001872 -8.404e-05 0.782 0.0001411 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.103 0.1857 0.1395 0.9899 0.9939 0.109 0.9534 0.9732 0.2094 ] Network output: [ -0.03503 0.1028 1.085 0.0002596 -0.0001165 0.8836 0.0001956 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1247 0.1235 0.1958 0.1599 0.9857 0.9917 0.1247 0.9279 0.9627 0.2034 ] Network output: [ -0.007169 1.003 0.002362 -5.451e-05 2.447e-05 1.009 -4.108e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03816 Epoch 4759 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05498 0.8456 0.9347 -8.809e-05 3.955e-05 0.1094 -6.639e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004168 -0.003688 -0.01318 0.007411 0.9656 0.9708 0.008375 0.9058 0.9118 0.02669 ] Network output: [ 0.9633 0.1291 -0.01039 -2.502e-05 1.123e-05 -0.0453 -1.886e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2662 -0.00572 -0.1612 0.1575 0.9834 0.9932 0.2997 0.8629 0.9662 0.6246 ] Network output: [ 0.02369 0.8579 0.9668 -0.0001076 4.83e-05 0.1275 -8.108e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005878 0.002149 0.004375 0.003668 0.9907 0.9937 0.005988 0.9593 0.9739 0.01269 ] Network output: [ 0.02144 -0.04608 0.9169 -0.0002629 0.000118 1.085 -0.0001981 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2896 0.2026 0.3704 0.1633 0.9851 0.9941 0.2906 0.8709 0.9696 0.6202 ] Network output: [ -0.03969 0.2021 1.099 0.0001879 -8.436e-05 0.7795 0.0001416 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.1026 0.185 0.1368 0.9899 0.9939 0.1086 0.9534 0.9733 0.2087 ] Network output: [ -0.03267 0.09096 1.086 0.00027 -0.0001212 0.8899 0.0002035 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1243 0.1231 0.1957 0.1599 0.9857 0.9917 0.1243 0.9279 0.9627 0.2034 ] Network output: [ -0.003427 0.9801 0.005351 -3.334e-05 1.497e-05 1.021 -2.512e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03551 Epoch 4760 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05652 0.8387 0.9349 -8.124e-05 3.647e-05 0.113 -6.123e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004164 -0.003706 -0.01328 0.007621 0.9656 0.9708 0.008372 0.906 0.912 0.02677 ] Network output: [ 0.9837 0.06987 -0.01477 3.415e-05 -1.533e-05 -0.02238 2.574e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2658 -0.007885 -0.1681 0.1695 0.9834 0.9932 0.2993 0.8631 0.9663 0.627 ] Network output: [ 0.023 0.8573 0.9676 -0.000107 4.803e-05 0.1287 -8.063e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005835 0.002118 0.004269 0.003935 0.9907 0.9937 0.005945 0.9595 0.974 0.01263 ] Network output: [ 0.0344 -0.1327 0.9284 -0.0001864 8.369e-05 1.135 -0.0001405 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2875 0.2005 0.3683 0.1816 0.9851 0.9941 0.2884 0.8711 0.9697 0.6212 ] Network output: [ -0.04083 0.1998 1.099 0.0001873 -8.41e-05 0.7831 0.0001412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.1028 0.1858 0.1392 0.9899 0.9939 0.1088 0.9536 0.9733 0.2097 ] Network output: [ -0.03477 0.1001 1.084 0.0002611 -0.0001172 0.8861 0.0001968 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1246 0.1234 0.1961 0.16 0.9857 0.9918 0.1246 0.9282 0.9628 0.2038 ] Network output: [ -0.006915 1.002 0.002134 -5.099e-05 2.289e-05 1.009 -3.842e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03751 Epoch 4761 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05475 0.8461 0.9347 -8.782e-05 3.942e-05 0.1094 -6.618e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004158 -0.003686 -0.01322 0.007415 0.9656 0.9708 0.00836 0.9062 0.9121 0.02673 ] Network output: [ 0.9649 0.1274 -0.01053 -1.849e-05 8.303e-06 -0.04669 -1.394e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2653 -0.006303 -0.1632 0.1578 0.9834 0.9932 0.2986 0.8634 0.9663 0.6278 ] Network output: [ 0.02332 0.8584 0.9669 -0.0001081 4.853e-05 0.1276 -8.146e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005857 0.002117 0.004361 0.003653 0.9907 0.9937 0.005966 0.9596 0.9741 0.01273 ] Network output: [ 0.02121 -0.0496 0.919 -0.0002667 0.0001197 1.087 -0.000201 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2884 0.2009 0.3713 0.1627 0.9851 0.9941 0.2893 0.8714 0.9697 0.6235 ] Network output: [ -0.0395 0.2009 1.098 0.0001882 -8.45e-05 0.7809 0.0001418 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.1024 0.1852 0.1367 0.9899 0.994 0.1085 0.9537 0.9734 0.2091 ] Network output: [ -0.0325 0.08924 1.085 0.0002708 -0.0001216 0.8919 0.0002041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1243 0.1231 0.1961 0.1599 0.9857 0.9918 0.1243 0.9282 0.9629 0.2037 ] Network output: [ -0.003233 0.9804 0.004714 -3.064e-05 1.375e-05 1.021 -2.309e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03514 Epoch 4762 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05613 0.8399 0.9349 -8.169e-05 3.668e-05 0.1126 -6.157e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004155 -0.003704 -0.01332 0.007604 0.9656 0.9708 0.008358 0.9064 0.9123 0.0268 ] Network output: [ 0.9839 0.07401 -0.01508 3.519e-05 -1.58e-05 -0.02656 2.652e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2649 -0.008301 -0.1697 0.1686 0.9835 0.9932 0.2982 0.8636 0.9664 0.6301 ] Network output: [ 0.02264 0.858 0.9676 -0.0001076 4.832e-05 0.1286 -8.112e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005817 0.002087 0.004258 0.003892 0.9907 0.9937 0.005926 0.9597 0.9741 0.01267 ] Network output: [ 0.03313 -0.1282 0.9293 -0.0001974 8.86e-05 1.132 -0.0001487 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2864 0.199 0.3693 0.1793 0.9851 0.9941 0.2874 0.8716 0.9698 0.6246 ] Network output: [ -0.04059 0.1989 1.099 0.0001875 -8.419e-05 0.7842 0.0001413 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.1025 0.1859 0.1389 0.9899 0.994 0.1086 0.9538 0.9735 0.21 ] Network output: [ -0.0345 0.09751 1.084 0.0002626 -0.0001179 0.8885 0.0001979 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1245 0.1233 0.1965 0.16 0.9857 0.9918 0.1245 0.9285 0.9629 0.2041 ] Network output: [ -0.006654 1.002 0.001894 -4.749e-05 2.132e-05 1.01 -3.579e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03692 Epoch 4763 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05448 0.8466 0.9348 -8.761e-05 3.933e-05 0.1093 -6.602e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004149 -0.003686 -0.01325 0.007418 0.9657 0.9708 0.008345 0.9065 0.9123 0.02677 ] Network output: [ 0.9664 0.1259 -0.0106 -1.246e-05 5.593e-06 -0.04808 -9.388e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2643 -0.006875 -0.165 0.158 0.9835 0.9932 0.2975 0.8639 0.9665 0.631 ] Network output: [ 0.02294 0.8591 0.967 -0.0001086 4.876e-05 0.1277 -8.185e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005836 0.002086 0.004347 0.003637 0.9908 0.9937 0.005946 0.9598 0.9742 0.01276 ] Network output: [ 0.02094 -0.05253 0.921 -0.0002708 0.0001216 1.089 -0.0002041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2873 0.1993 0.3723 0.162 0.9851 0.9941 0.2882 0.8719 0.9698 0.6267 ] Network output: [ -0.03929 0.1998 1.097 0.0001885 -8.464e-05 0.7822 0.0001421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.1023 0.1854 0.1366 0.9899 0.994 0.1084 0.9539 0.9735 0.2094 ] Network output: [ -0.03233 0.08758 1.084 0.0002716 -0.000122 0.8938 0.0002047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1243 0.1231 0.1964 0.1599 0.9857 0.9918 0.1243 0.9285 0.963 0.2041 ] Network output: [ -0.003047 0.9808 0.004116 -2.798e-05 1.256e-05 1.021 -2.108e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03479 Epoch 4764 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05573 0.8411 0.9349 -8.211e-05 3.686e-05 0.1122 -6.188e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004146 -0.003702 -0.01335 0.00759 0.9657 0.9708 0.008345 0.9068 0.9125 0.02684 ] Network output: [ 0.9841 0.07752 -0.01526 3.645e-05 -1.636e-05 -0.03033 2.747e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.264 -0.008726 -0.1712 0.1678 0.9835 0.9932 0.2972 0.8641 0.9666 0.6332 ] Network output: [ 0.02227 0.8588 0.9677 -0.0001083 4.86e-05 0.1286 -8.159e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0058 0.002058 0.004248 0.003853 0.9908 0.9937 0.005909 0.9599 0.9743 0.01271 ] Network output: [ 0.03194 -0.1242 0.9302 -0.0002077 9.325e-05 1.129 -0.0001565 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2854 0.1974 0.3703 0.1771 0.9851 0.9941 0.2863 0.8721 0.9699 0.6279 ] Network output: [ -0.04034 0.198 1.098 0.0001878 -8.43e-05 0.7852 0.0001415 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.1024 0.186 0.1386 0.9899 0.994 0.1085 0.9541 0.9736 0.2102 ] Network output: [ -0.03423 0.09509 1.084 0.0002641 -0.0001185 0.8908 0.000199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1245 0.1233 0.1968 0.16 0.9857 0.9918 0.1245 0.9288 0.963 0.2045 ] Network output: [ -0.00639 1.001 0.001644 -4.406e-05 1.978e-05 1.01 -3.321e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03636 Epoch 4765 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0542 0.8471 0.9349 -8.746e-05 3.926e-05 0.1093 -6.591e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004139 -0.003685 -0.01329 0.007421 0.9657 0.9708 0.008332 0.9069 0.9126 0.02681 ] Network output: [ 0.9678 0.1244 -0.0106 -6.874e-06 3.086e-06 -0.04942 -5.18e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2633 -0.007435 -0.1669 0.1581 0.9835 0.9932 0.2965 0.8644 0.9666 0.6342 ] Network output: [ 0.02255 0.8597 0.967 -0.0001091 4.899e-05 0.1277 -8.224e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005817 0.002056 0.004334 0.003621 0.9908 0.9937 0.005926 0.96 0.9743 0.0128 ] Network output: [ 0.02062 -0.05491 0.9228 -0.0002752 0.0001235 1.09 -0.0002074 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2861 0.1976 0.3732 0.1613 0.9851 0.9941 0.2871 0.8724 0.97 0.63 ] Network output: [ -0.03906 0.1988 1.097 0.0001889 -8.48e-05 0.7835 0.0001424 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.1021 0.1855 0.1366 0.9899 0.994 0.1083 0.9542 0.9736 0.2097 ] Network output: [ -0.03213 0.08596 1.084 0.0002725 -0.0001224 0.8957 0.0002054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1244 0.1231 0.1966 0.16 0.9857 0.9918 0.1244 0.9288 0.9631 0.2044 ] Network output: [ -0.002866 0.981 0.003556 -2.536e-05 1.138e-05 1.021 -1.911e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03443 Epoch 4766 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05533 0.8422 0.935 -8.25e-05 3.704e-05 0.1118 -6.218e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004137 -0.0037 -0.01338 0.007578 0.9657 0.9708 0.008332 0.9071 0.9128 0.02688 ] Network output: [ 0.9844 0.08045 -0.01535 3.788e-05 -1.701e-05 -0.03371 2.855e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.263 -0.009157 -0.1728 0.1671 0.9835 0.9932 0.2962 0.8646 0.9667 0.6363 ] Network output: [ 0.02189 0.8595 0.9677 -0.0001089 4.888e-05 0.1285 -8.205e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005783 0.002028 0.004239 0.003815 0.9908 0.9938 0.005892 0.9602 0.9744 0.01275 ] Network output: [ 0.03084 -0.1206 0.9312 -0.0002175 9.765e-05 1.127 -0.0001639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2844 0.1959 0.3714 0.1751 0.9851 0.9941 0.2853 0.8726 0.97 0.6311 ] Network output: [ -0.04007 0.1973 1.097 0.0001881 -8.444e-05 0.7862 0.0001417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.1022 0.1861 0.1383 0.9899 0.994 0.1084 0.9543 0.9737 0.2105 ] Network output: [ -0.03394 0.09282 1.083 0.0002655 -0.0001192 0.893 0.0002001 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1245 0.1233 0.197 0.16 0.9858 0.9918 0.1245 0.9291 0.9632 0.2049 ] Network output: [ -0.006129 1 0.001388 -4.072e-05 1.828e-05 1.011 -3.069e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03584 Epoch 4767 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05389 0.8477 0.935 -8.736e-05 3.922e-05 0.1092 -6.584e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00413 -0.003684 -0.01333 0.007424 0.9657 0.9708 0.008319 0.9073 0.9129 0.02685 ] Network output: [ 0.9691 0.1231 -0.01055 -1.709e-06 7.67e-07 -0.05072 -1.288e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2624 -0.007982 -0.1686 0.1583 0.9835 0.9932 0.2955 0.8649 0.9667 0.6373 ] Network output: [ 0.02215 0.8604 0.9672 -0.0001096 4.922e-05 0.1277 -8.263e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005799 0.002026 0.004322 0.003603 0.9908 0.9938 0.005908 0.9603 0.9744 0.01284 ] Network output: [ 0.02027 -0.0568 0.9246 -0.0002798 0.0001256 1.091 -0.0002109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.285 0.1961 0.3741 0.1605 0.9851 0.9941 0.286 0.8729 0.9701 0.6331 ] Network output: [ -0.03883 0.1979 1.096 0.0001893 -8.497e-05 0.7846 0.0001426 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.102 0.1857 0.1365 0.99 0.994 0.1083 0.9544 0.9737 0.21 ] Network output: [ -0.03193 0.08439 1.083 0.0002735 -0.0001228 0.8975 0.0002061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1244 0.1231 0.1969 0.16 0.9858 0.9918 0.1244 0.9291 0.9632 0.2048 ] Network output: [ -0.002689 0.9812 0.003032 -2.278e-05 1.023e-05 1.021 -1.717e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03408 Epoch 4768 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05492 0.8433 0.9351 -8.287e-05 3.72e-05 0.1114 -6.245e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004128 -0.003699 -0.01341 0.007568 0.9657 0.9708 0.00832 0.9075 0.913 0.02691 ] Network output: [ 0.9847 0.08285 -0.01534 3.947e-05 -1.772e-05 -0.03674 2.975e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2621 -0.009591 -0.1743 0.1665 0.9835 0.9933 0.2952 0.8651 0.9668 0.6394 ] Network output: [ 0.0215 0.8603 0.9678 -0.0001095 4.914e-05 0.1285 -8.25e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005768 0.002 0.00423 0.003781 0.9908 0.9938 0.005876 0.9604 0.9745 0.01279 ] Network output: [ 0.02981 -0.1173 0.9321 -0.0002268 0.0001018 1.125 -0.0001709 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2834 0.1944 0.3723 0.1732 0.9851 0.9941 0.2843 0.8731 0.9701 0.6343 ] Network output: [ -0.03979 0.1965 1.097 0.0001884 -8.46e-05 0.7871 0.000142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.1021 0.1862 0.1381 0.99 0.994 0.1084 0.9545 0.9738 0.2108 ] Network output: [ -0.03366 0.09068 1.083 0.000267 -0.0001199 0.8952 0.0002012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1245 0.1233 0.1973 0.16 0.9858 0.9918 0.1246 0.9294 0.9633 0.2052 ] Network output: [ -0.005874 0.9992 0.001129 -3.748e-05 1.683e-05 1.011 -2.825e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03534 Epoch 4769 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05357 0.8483 0.9352 -8.732e-05 3.92e-05 0.109 -6.58e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004122 -0.003684 -0.01336 0.007427 0.9657 0.9708 0.008307 0.9076 0.9131 0.02689 ] Network output: [ 0.9703 0.1218 -0.01044 3.07e-06 -1.378e-06 -0.05195 2.314e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2615 -0.008514 -0.1703 0.1584 0.9835 0.9933 0.2945 0.8654 0.9668 0.6403 ] Network output: [ 0.02174 0.8611 0.9673 -0.0001102 4.946e-05 0.1277 -8.302e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005782 0.001997 0.004311 0.003586 0.9908 0.9938 0.00589 0.9605 0.9745 0.01287 ] Network output: [ 0.01989 -0.05824 0.9262 -0.0002847 0.0001278 1.091 -0.0002145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.284 0.1945 0.3751 0.1596 0.9851 0.9941 0.2849 0.8734 0.9702 0.6362 ] Network output: [ -0.03858 0.197 1.095 0.0001897 -8.516e-05 0.7857 0.000143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.1019 0.1858 0.1364 0.99 0.994 0.1082 0.9546 0.9738 0.2103 ] Network output: [ -0.03171 0.08286 1.082 0.0002745 -0.0001232 0.8993 0.0002069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1245 0.1232 0.1972 0.16 0.9858 0.9918 0.1245 0.9294 0.9634 0.2051 ] Network output: [ -0.002513 0.9813 0.002542 -2.024e-05 9.086e-06 1.021 -1.525e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03373 Epoch 4770 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05451 0.8443 0.9353 -8.322e-05 3.736e-05 0.1111 -6.272e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00412 -0.003698 -0.01345 0.00756 0.9657 0.9708 0.008309 0.9078 0.9133 0.02695 ] Network output: [ 0.9851 0.08476 -0.01527 4.118e-05 -1.849e-05 -0.03944 3.104e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2613 -0.01003 -0.1758 0.166 0.9835 0.9933 0.2943 0.8656 0.9669 0.6424 ] Network output: [ 0.0211 0.8611 0.9679 -0.0001101 4.941e-05 0.1283 -8.294e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005753 0.001972 0.004222 0.003748 0.9908 0.9938 0.005861 0.9606 0.9746 0.01283 ] Network output: [ 0.02885 -0.1143 0.9331 -0.0002355 0.0001057 1.123 -0.0001775 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2824 0.1929 0.3733 0.1713 0.9851 0.9941 0.2833 0.8736 0.9702 0.6374 ] Network output: [ -0.03951 0.1958 1.096 0.0001888 -8.477e-05 0.788 0.0001423 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.102 0.1863 0.1378 0.99 0.994 0.1083 0.9547 0.9739 0.211 ] Network output: [ -0.03337 0.08866 1.082 0.0002684 -0.0001205 0.8972 0.0002023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1246 0.1233 0.1976 0.16 0.9858 0.9918 0.1246 0.9297 0.9634 0.2055 ] Network output: [ -0.005627 0.9984 0.0008704 -3.435e-05 1.542e-05 1.012 -2.589e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03487 Epoch 4771 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05323 0.849 0.9354 -8.732e-05 3.92e-05 0.1089 -6.581e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004113 -0.003684 -0.01339 0.007431 0.9657 0.9708 0.008295 0.908 0.9134 0.02693 ] Network output: [ 0.9714 0.1206 -0.01027 7.492e-06 -3.364e-06 -0.05311 5.646e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2606 -0.009033 -0.172 0.1586 0.9835 0.9933 0.2935 0.8659 0.967 0.6434 ] Network output: [ 0.02133 0.8619 0.9674 -0.0001107 4.969e-05 0.1276 -8.342e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005766 0.001969 0.004301 0.003568 0.9908 0.9938 0.005874 0.9607 0.9747 0.01291 ] Network output: [ 0.01948 -0.05926 0.9277 -0.0002896 0.00013 1.091 -0.0002183 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.283 0.193 0.376 0.1587 0.9851 0.9941 0.2839 0.8739 0.9703 0.6393 ] Network output: [ -0.03831 0.1963 1.094 0.0001901 -8.536e-05 0.7867 0.0001433 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.1018 0.186 0.1363 0.99 0.994 0.1082 0.9548 0.974 0.2106 ] Network output: [ -0.03148 0.08136 1.082 0.0002756 -0.0001237 0.901 0.0002077 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1246 0.1233 0.1975 0.16 0.9858 0.9918 0.1246 0.9297 0.9635 0.2054 ] Network output: [ -0.002336 0.9813 0.002085 -1.774e-05 7.965e-06 1.021 -1.337e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03337 Epoch 4772 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0541 0.8453 0.9355 -8.356e-05 3.751e-05 0.1107 -6.297e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004112 -0.003698 -0.01348 0.007554 0.9657 0.9709 0.008298 0.9081 0.9135 0.02699 ] Network output: [ 0.9855 0.08623 -0.01513 4.3e-05 -1.93e-05 -0.04183 3.241e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2604 -0.01046 -0.1773 0.1656 0.9835 0.9933 0.2933 0.8661 0.967 0.6453 ] Network output: [ 0.0207 0.8619 0.9681 -0.0001106 4.967e-05 0.1282 -8.338e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005738 0.001945 0.004214 0.003718 0.9908 0.9938 0.005846 0.9608 0.9747 0.01287 ] Network output: [ 0.02796 -0.1116 0.9341 -0.0002438 0.0001095 1.121 -0.0001838 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2815 0.1915 0.3743 0.1696 0.9851 0.9941 0.2824 0.874 0.9703 0.6405 ] Network output: [ -0.03922 0.1952 1.095 0.0001893 -8.497e-05 0.7889 0.0001426 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.1019 0.1865 0.1376 0.99 0.994 0.1083 0.955 0.974 0.2113 ] Network output: [ -0.03308 0.08674 1.081 0.0002698 -0.0001211 0.8992 0.0002034 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1247 0.1234 0.1979 0.16 0.9858 0.9918 0.1247 0.93 0.9635 0.2059 ] Network output: [ -0.00539 0.9977 0.0006137 -3.136e-05 1.408e-05 1.012 -2.363e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03441 Epoch 4773 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05287 0.8497 0.9356 -8.737e-05 3.922e-05 0.1087 -6.584e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004105 -0.003684 -0.01343 0.007434 0.9657 0.9709 0.008284 0.9083 0.9136 0.02697 ] Network output: [ 0.9724 0.1194 -0.01005 1.158e-05 -5.2e-06 -0.05418 8.73e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2597 -0.009536 -0.1736 0.1587 0.9835 0.9933 0.2925 0.8664 0.9671 0.6463 ] Network output: [ 0.02092 0.8626 0.9676 -0.0001112 4.993e-05 0.1275 -8.381e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00575 0.001942 0.004292 0.00355 0.9908 0.9938 0.005858 0.961 0.9748 0.01295 ] Network output: [ 0.01905 -0.05992 0.9291 -0.0002947 0.0001323 1.091 -0.0002221 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.282 0.1915 0.3769 0.1577 0.9851 0.9941 0.2829 0.8743 0.9704 0.6423 ] Network output: [ -0.03804 0.1955 1.094 0.0001906 -8.558e-05 0.7877 0.0001437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.1018 0.1861 0.1361 0.99 0.994 0.1082 0.9551 0.9741 0.2109 ] Network output: [ -0.03123 0.07989 1.081 0.0002767 -0.0001242 0.9028 0.0002085 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1247 0.1234 0.1977 0.16 0.9858 0.9918 0.1247 0.93 0.9636 0.2058 ] Network output: [ -0.002159 0.9812 0.001657 -1.529e-05 6.863e-06 1.021 -1.152e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03301 Epoch 4774 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05369 0.8463 0.9357 -8.389e-05 3.766e-05 0.1103 -6.322e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004104 -0.003697 -0.01351 0.00755 0.9657 0.9709 0.008287 0.9085 0.9138 0.02702 ] Network output: [ 0.9859 0.08729 -0.01493 4.49e-05 -2.016e-05 -0.04394 3.384e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2595 -0.0109 -0.1787 0.1653 0.9835 0.9933 0.2924 0.8666 0.9672 0.6482 ] Network output: [ 0.02029 0.8627 0.9682 -0.0001112 4.992e-05 0.128 -8.381e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005725 0.001918 0.004207 0.003689 0.9909 0.9938 0.005832 0.9611 0.9748 0.01291 ] Network output: [ 0.02714 -0.1092 0.935 -0.0002516 0.000113 1.119 -0.0001896 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2806 0.1901 0.3752 0.168 0.9851 0.9941 0.2815 0.8745 0.9704 0.6435 ] Network output: [ -0.03892 0.1946 1.094 0.0001897 -8.517e-05 0.7898 0.000143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.1018 0.1866 0.1374 0.99 0.994 0.1083 0.9552 0.9741 0.2116 ] Network output: [ -0.03279 0.08493 1.081 0.0002712 -0.0001218 0.901 0.0002044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1248 0.1234 0.1981 0.1601 0.9858 0.9918 0.1248 0.9303 0.9637 0.2062 ] Network output: [ -0.005166 0.9971 0.0003612 -2.849e-05 1.279e-05 1.013 -2.147e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03396 Epoch 4775 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0525 0.8504 0.9358 -8.746e-05 3.926e-05 0.1085 -6.591e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004097 -0.003684 -0.01346 0.007438 0.9657 0.9709 0.008274 0.9086 0.9139 0.027 ] Network output: [ 0.9734 0.1183 -0.009768 1.537e-05 -6.9e-06 -0.05516 1.158e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2588 -0.01002 -0.1751 0.1588 0.9835 0.9933 0.2916 0.8669 0.9672 0.6492 ] Network output: [ 0.0205 0.8635 0.9677 -0.0001117 5.016e-05 0.1273 -8.421e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005736 0.001916 0.004284 0.003532 0.9909 0.9938 0.005844 0.9612 0.9749 0.01298 ] Network output: [ 0.0186 -0.06025 0.9305 -0.0002999 0.0001346 1.091 -0.000226 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.281 0.1901 0.3778 0.1567 0.9851 0.9941 0.2819 0.8748 0.9705 0.6453 ] Network output: [ -0.03775 0.1949 1.093 0.0001911 -8.58e-05 0.7886 0.000144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.1017 0.1862 0.136 0.99 0.994 0.1083 0.9553 0.9742 0.2112 ] Network output: [ -0.03098 0.07845 1.08 0.0002778 -0.0001247 0.9044 0.0002094 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1248 0.1235 0.198 0.1601 0.9858 0.9918 0.1248 0.9303 0.9637 0.2061 ] Network output: [ -0.00198 0.9811 0.001257 -1.288e-05 5.783e-06 1.022 -9.708e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03265 Epoch 4776 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05327 0.8472 0.9359 -8.422e-05 3.781e-05 0.11 -6.347e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004097 -0.003697 -0.01353 0.007547 0.9657 0.9709 0.008277 0.9088 0.914 0.02706 ] Network output: [ 0.9864 0.08798 -0.0147 4.686e-05 -2.104e-05 -0.04579 3.531e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2587 -0.01133 -0.1801 0.165 0.9835 0.9933 0.2915 0.8671 0.9673 0.6511 ] Network output: [ 0.01988 0.8636 0.9684 -0.0001118 5.018e-05 0.1278 -8.423e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005712 0.001893 0.0042 0.003662 0.9909 0.9938 0.005819 0.9613 0.9749 0.01294 ] Network output: [ 0.02637 -0.107 0.936 -0.000259 0.0001163 1.117 -0.0001952 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2796 0.1887 0.3761 0.1665 0.9852 0.9941 0.2805 0.875 0.9705 0.6465 ] Network output: [ -0.03861 0.194 1.093 0.0001902 -8.539e-05 0.7906 0.0001434 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.1017 0.1867 0.1372 0.99 0.994 0.1083 0.9554 0.9742 0.2118 ] Network output: [ -0.0325 0.08321 1.08 0.0002726 -0.0001224 0.9029 0.0002054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1249 0.1235 0.1984 0.1601 0.9858 0.9918 0.1249 0.9305 0.9638 0.2065 ] Network output: [ -0.004955 0.9965 0.0001147 -2.577e-05 1.157e-05 1.013 -1.942e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03353 Epoch 4777 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05212 0.8511 0.936 -8.759e-05 3.932e-05 0.1082 -6.601e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00409 -0.003684 -0.01349 0.007442 0.9657 0.9709 0.008264 0.909 0.9141 0.02704 ] Network output: [ 0.9742 0.1171 -0.009435 1.887e-05 -8.47e-06 -0.05605 1.422e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.258 -0.0105 -0.1766 0.1589 0.9835 0.9933 0.2907 0.8674 0.9673 0.6521 ] Network output: [ 0.02008 0.8643 0.9679 -0.0001123 5.04e-05 0.1272 -8.461e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005722 0.00189 0.004276 0.003514 0.9909 0.9938 0.00583 0.9614 0.975 0.01302 ] Network output: [ 0.01813 -0.06027 0.9318 -0.0003051 0.000137 1.091 -0.0002299 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2801 0.1887 0.3787 0.1557 0.9852 0.9941 0.281 0.8752 0.9706 0.6482 ] Network output: [ -0.03746 0.1943 1.092 0.0001917 -8.604e-05 0.7895 0.0001444 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.1017 0.1864 0.1359 0.9901 0.9941 0.1083 0.9555 0.9743 0.2115 ] Network output: [ -0.03071 0.07704 1.079 0.000279 -0.0001252 0.9061 0.0002102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1249 0.1236 0.1983 0.1601 0.9858 0.9918 0.1249 0.9306 0.9638 0.2064 ] Network output: [ -0.001798 0.9809 0.0008823 -1.052e-05 4.724e-06 1.022 -7.93e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03228 Epoch 4778 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05285 0.8482 0.9361 -8.455e-05 3.796e-05 0.1096 -6.372e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00409 -0.003697 -0.01356 0.007546 0.9657 0.9709 0.008268 0.9092 0.9143 0.0271 ] Network output: [ 0.9869 0.08833 -0.01443 4.887e-05 -2.194e-05 -0.0474 3.683e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2579 -0.01175 -0.1816 0.1648 0.9835 0.9933 0.2906 0.8676 0.9674 0.6539 ] Network output: [ 0.01946 0.8645 0.9685 -0.0001123 5.043e-05 0.1276 -8.466e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005699 0.001868 0.004193 0.003637 0.9909 0.9938 0.005806 0.9615 0.9751 0.01298 ] Network output: [ 0.02566 -0.105 0.9369 -0.000266 0.0001194 1.116 -0.0002004 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2788 0.1873 0.377 0.165 0.9852 0.9941 0.2797 0.8754 0.9706 0.6494 ] Network output: [ -0.03831 0.1935 1.093 0.0001907 -8.562e-05 0.7913 0.0001437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.1017 0.1868 0.137 0.9901 0.9941 0.1083 0.9556 0.9743 0.2121 ] Network output: [ -0.03221 0.08157 1.079 0.0002739 -0.000123 0.9046 0.0002064 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.125 0.1236 0.1986 0.1601 0.9858 0.9918 0.125 0.9308 0.9639 0.2068 ] Network output: [ -0.004758 0.9959 -0.0001246 -2.319e-05 1.041e-05 1.014 -1.748e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03311 Epoch 4779 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05173 0.8519 0.9363 -8.776e-05 3.94e-05 0.108 -6.614e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004082 -0.003684 -0.01352 0.007446 0.9657 0.9709 0.008255 0.9093 0.9144 0.02708 ] Network output: [ 0.975 0.1159 -0.009052 2.209e-05 -9.918e-06 -0.05684 1.665e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2571 -0.01096 -0.178 0.159 0.9835 0.9933 0.2898 0.8679 0.9674 0.6549 ] Network output: [ 0.01966 0.8652 0.9681 -0.0001128 5.064e-05 0.1269 -8.501e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00571 0.001865 0.00427 0.003496 0.9909 0.9938 0.005817 0.9616 0.9751 0.01306 ] Network output: [ 0.01763 -0.06001 0.933 -0.0003103 0.0001393 1.09 -0.0002339 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2792 0.1874 0.3795 0.1546 0.9852 0.9941 0.2801 0.8757 0.9707 0.6511 ] Network output: [ -0.03715 0.1937 1.091 0.0001922 -8.629e-05 0.7904 0.0001449 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.1017 0.1865 0.1358 0.9901 0.9941 0.1083 0.9557 0.9744 0.2117 ] Network output: [ -0.03043 0.07565 1.079 0.0002802 -0.0001258 0.9077 0.0002111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.125 0.1237 0.1985 0.1601 0.9858 0.9918 0.1251 0.9309 0.964 0.2067 ] Network output: [ -0.001612 0.9807 0.0005315 -8.211e-06 3.686e-06 1.022 -6.188e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0319 Epoch 4780 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05243 0.8491 0.9364 -8.487e-05 3.81e-05 0.1093 -6.396e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004083 -0.003697 -0.01359 0.007546 0.9657 0.9709 0.008259 0.9095 0.9145 0.02713 ] Network output: [ 0.9874 0.08836 -0.01414 5.092e-05 -2.286e-05 -0.04879 3.837e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2571 -0.01217 -0.1829 0.1647 0.9835 0.9933 0.2897 0.868 0.9675 0.6567 ] Network output: [ 0.01904 0.8654 0.9687 -0.0001129 5.068e-05 0.1274 -8.507e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005687 0.001843 0.004187 0.003614 0.9909 0.9938 0.005794 0.9617 0.9752 0.01302 ] Network output: [ 0.02501 -0.1032 0.9378 -0.0002725 0.0001223 1.114 -0.0002053 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2779 0.186 0.3779 0.1636 0.9852 0.9941 0.2788 0.8759 0.9707 0.6523 ] Network output: [ -0.038 0.193 1.092 0.0001913 -8.586e-05 0.7921 0.0001441 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.1017 0.1869 0.1369 0.9901 0.9941 0.1084 0.9558 0.9744 0.2123 ] Network output: [ -0.03192 0.08001 1.079 0.0002753 -0.0001236 0.9063 0.0002074 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1251 0.1237 0.1989 0.1601 0.9858 0.9918 0.1251 0.9311 0.964 0.2071 ] Network output: [ -0.004576 0.9954 -0.0003554 -2.076e-05 9.322e-06 1.014 -1.565e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03269 Epoch 4781 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05134 0.8527 0.9366 -8.796e-05 3.949e-05 0.1077 -6.629e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004075 -0.003685 -0.01354 0.00745 0.9657 0.9709 0.008246 0.9096 0.9146 0.02712 ] Network output: [ 0.9757 0.1148 -0.008618 2.506e-05 -1.125e-05 -0.05752 1.889e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2563 -0.0114 -0.1794 0.1591 0.9836 0.9933 0.2889 0.8683 0.9676 0.6577 ] Network output: [ 0.01923 0.8661 0.9683 -0.0001133 5.088e-05 0.1267 -8.541e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005698 0.001841 0.004264 0.003478 0.9909 0.9938 0.005805 0.9618 0.9752 0.01309 ] Network output: [ 0.01713 -0.05949 0.9341 -0.0003155 0.0001417 1.09 -0.0002378 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2783 0.186 0.3804 0.1536 0.9852 0.9941 0.2792 0.8761 0.9708 0.6539 ] Network output: [ -0.03684 0.1931 1.09 0.0001928 -8.655e-05 0.7912 0.0001453 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.1016 0.1866 0.1357 0.9901 0.9941 0.1084 0.9559 0.9745 0.212 ] Network output: [ -0.03015 0.07428 1.078 0.0002814 -0.0001263 0.9093 0.000212 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1252 0.1238 0.1988 0.1602 0.9858 0.9918 0.1252 0.9312 0.9641 0.207 ] Network output: [ -0.001421 0.9804 0.0002025 -5.949e-06 2.671e-06 1.022 -4.483e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03153 Epoch 4782 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05201 0.8501 0.9367 -8.52e-05 3.825e-05 0.1089 -6.421e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004076 -0.003697 -0.01362 0.007547 0.9658 0.9709 0.008251 0.9098 0.9147 0.02717 ] Network output: [ 0.988 0.0881 -0.01383 5.299e-05 -2.379e-05 -0.04996 3.994e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2563 -0.01258 -0.1843 0.1647 0.9836 0.9933 0.2888 0.8685 0.9676 0.6594 ] Network output: [ 0.01861 0.8664 0.9689 -0.0001134 5.093e-05 0.1271 -8.549e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005676 0.00182 0.004181 0.003592 0.9909 0.9938 0.005783 0.9619 0.9753 0.01305 ] Network output: [ 0.02441 -0.1015 0.9387 -0.0002786 0.0001251 1.113 -0.0002099 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.277 0.1847 0.3787 0.1624 0.9852 0.9941 0.2779 0.8763 0.9708 0.6551 ] Network output: [ -0.03769 0.1925 1.091 0.0001918 -8.611e-05 0.7928 0.0001446 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.1017 0.187 0.1368 0.9901 0.9941 0.1084 0.956 0.9745 0.2126 ] Network output: [ -0.03163 0.07852 1.078 0.0002766 -0.0001242 0.908 0.0002084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1253 0.1239 0.1992 0.1602 0.9858 0.9918 0.1253 0.9314 0.9641 0.2074 ] Network output: [ -0.004411 0.9949 -0.0005769 -1.849e-05 8.299e-06 1.014 -1.393e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03228 Epoch 4783 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05093 0.8536 0.9369 -8.82e-05 3.96e-05 0.1073 -6.647e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004069 -0.003685 -0.01357 0.007455 0.9658 0.9709 0.008238 0.91 0.9148 0.02715 ] Network output: [ 0.9764 0.1136 -0.008136 2.778e-05 -1.247e-05 -0.05811 2.094e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2555 -0.01182 -0.1807 0.1593 0.9836 0.9933 0.288 0.8688 0.9677 0.6604 ] Network output: [ 0.01881 0.8671 0.9684 -0.0001139 5.112e-05 0.1264 -8.581e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005686 0.001818 0.004259 0.00346 0.9909 0.9938 0.005794 0.962 0.9753 0.01313 ] Network output: [ 0.0166 -0.05872 0.9352 -0.0003207 0.000144 1.089 -0.0002417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2775 0.1847 0.3813 0.1525 0.9852 0.9941 0.2784 0.8766 0.9709 0.6566 ] Network output: [ -0.03652 0.1926 1.089 0.0001934 -8.681e-05 0.7919 0.0001457 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.1016 0.1867 0.1356 0.9901 0.9941 0.1085 0.9561 0.9746 0.2122 ] Network output: [ -0.02985 0.07293 1.077 0.0002826 -0.0001269 0.9108 0.000213 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1254 0.124 0.199 0.1602 0.9858 0.9918 0.1254 0.9315 0.9642 0.2073 ] Network output: [ -0.001224 0.98 -0.0001065 -3.733e-06 1.676e-06 1.023 -2.813e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03114 Epoch 4784 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0516 0.851 0.9369 -8.553e-05 3.84e-05 0.1085 -6.446e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004069 -0.003698 -0.01364 0.00755 0.9658 0.9709 0.008243 0.9101 0.915 0.0272 ] Network output: [ 0.9886 0.08757 -0.0135 5.509e-05 -2.473e-05 -0.05095 4.152e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2555 -0.01299 -0.1857 0.1647 0.9836 0.9933 0.288 0.8689 0.9677 0.6621 ] Network output: [ 0.01818 0.8673 0.9691 -0.000114 5.117e-05 0.1268 -8.591e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005665 0.001797 0.004175 0.003572 0.9909 0.9939 0.005772 0.9621 0.9754 0.01309 ] Network output: [ 0.02387 -0.1001 0.9396 -0.0002843 0.0001276 1.112 -0.0002142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2762 0.1834 0.3795 0.1611 0.9852 0.9941 0.2771 0.8767 0.9709 0.6578 ] Network output: [ -0.03738 0.192 1.09 0.0001924 -8.636e-05 0.7936 0.000145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.1017 0.1871 0.1366 0.9901 0.9941 0.1085 0.9563 0.9746 0.2128 ] Network output: [ -0.03135 0.07709 1.077 0.0002778 -0.0001247 0.9096 0.0002094 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1254 0.124 0.1994 0.1602 0.9858 0.9918 0.1254 0.9317 0.9642 0.2077 ] Network output: [ -0.004264 0.9945 -0.0007883 -1.636e-05 7.347e-06 1.015 -1.233e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03187 Epoch 4785 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05052 0.8544 0.9372 -8.848e-05 3.972e-05 0.107 -6.668e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004062 -0.003685 -0.01359 0.00746 0.9658 0.9709 0.00823 0.9103 0.9151 0.02719 ] Network output: [ 0.977 0.1124 -0.007605 3.026e-05 -1.358e-05 -0.05861 2.28e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2548 -0.01223 -0.182 0.1594 0.9836 0.9933 0.2872 0.8692 0.9678 0.663 ] Network output: [ 0.01839 0.868 0.9686 -0.0001144 5.135e-05 0.1261 -8.621e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005676 0.001796 0.004255 0.003443 0.9909 0.9939 0.005783 0.9622 0.9754 0.01317 ] Network output: [ 0.01605 -0.05771 0.9362 -0.0003259 0.0001463 1.088 -0.0002456 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2766 0.1835 0.3821 0.1514 0.9852 0.9941 0.2775 0.877 0.971 0.6593 ] Network output: [ -0.03619 0.1921 1.088 0.000194 -8.709e-05 0.7927 0.0001462 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.1017 0.1868 0.1355 0.9901 0.9941 0.1086 0.9563 0.9747 0.2125 ] Network output: [ -0.02954 0.0716 1.076 0.0002838 -0.0001274 0.9124 0.0002139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1255 0.1241 0.1993 0.1602 0.9858 0.9918 0.1255 0.9317 0.9643 0.2076 ] Network output: [ -0.001021 0.9795 -0.0003971 -1.562e-06 7.01e-07 1.023 -1.177e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03076 Epoch 4786 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05118 0.852 0.9372 -8.587e-05 3.855e-05 0.1081 -6.471e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004063 -0.003698 -0.01367 0.007554 0.9658 0.9709 0.008236 0.9105 0.9152 0.02724 ] Network output: [ 0.9892 0.08678 -0.01318 5.72e-05 -2.568e-05 -0.05175 4.311e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2548 -0.01339 -0.187 0.1647 0.9836 0.9933 0.2872 0.8694 0.9678 0.6647 ] Network output: [ 0.01775 0.8683 0.9693 -0.0001145 5.142e-05 0.1264 -8.632e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005655 0.001775 0.004169 0.003553 0.991 0.9939 0.005762 0.9623 0.9755 0.01313 ] Network output: [ 0.02337 -0.09882 0.9405 -0.0002895 0.00013 1.11 -0.0002182 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2754 0.1822 0.3803 0.16 0.9852 0.9941 0.2763 0.8771 0.971 0.6605 ] Network output: [ -0.03707 0.1916 1.089 0.0001929 -8.662e-05 0.7943 0.0001454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.1017 0.1872 0.1365 0.9901 0.9941 0.1086 0.9565 0.9747 0.2131 ] Network output: [ -0.03107 0.07572 1.076 0.0002791 -0.0001253 0.9111 0.0002103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1256 0.1242 0.1997 0.1603 0.9858 0.9918 0.1256 0.9319 0.9644 0.208 ] Network output: [ -0.004135 0.9942 -0.0009891 -1.44e-05 6.465e-06 1.015 -1.085e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03148 Epoch 4787 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0501 0.8553 0.9375 -8.878e-05 3.986e-05 0.1066 -6.691e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004056 -0.003686 -0.01362 0.007466 0.9658 0.9709 0.008222 0.9106 0.9153 0.02722 ] Network output: [ 0.9775 0.1112 -0.007026 3.25e-05 -1.459e-05 -0.05901 2.449e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.254 -0.01263 -0.1832 0.1595 0.9836 0.9933 0.2864 0.8696 0.9679 0.6656 ] Network output: [ 0.01797 0.869 0.9688 -0.0001149 5.159e-05 0.1257 -8.661e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005666 0.001774 0.004252 0.003426 0.991 0.9939 0.005773 0.9624 0.9755 0.0132 ] Network output: [ 0.01549 -0.05648 0.9372 -0.0003311 0.0001486 1.087 -0.0002495 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2758 0.1823 0.3829 0.1503 0.9852 0.9941 0.2767 0.8774 0.9711 0.6619 ] Network output: [ -0.03585 0.1917 1.087 0.0001946 -8.737e-05 0.7934 0.0001467 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.1017 0.187 0.1354 0.9901 0.9941 0.1087 0.9565 0.9748 0.2128 ] Network output: [ -0.02922 0.07027 1.075 0.0002851 -0.000128 0.9139 0.0002149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1257 0.1243 0.1995 0.1603 0.9858 0.9918 0.1257 0.932 0.9644 0.2079 ] Network output: [ -0.0008092 0.979 -0.0006712 5.697e-07 -2.557e-07 1.023 4.293e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03037 Epoch 4788 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05077 0.8529 0.9375 -8.621e-05 3.87e-05 0.1077 -6.497e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004057 -0.003699 -0.01369 0.007559 0.9658 0.9709 0.008229 0.9108 0.9154 0.02727 ] Network output: [ 0.9899 0.08576 -0.01285 5.934e-05 -2.664e-05 -0.05239 4.472e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.254 -0.01378 -0.1883 0.1649 0.9836 0.9933 0.2864 0.8698 0.9679 0.6673 ] Network output: [ 0.01732 0.8693 0.9695 -0.0001151 5.166e-05 0.126 -8.673e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005645 0.001754 0.004163 0.003535 0.991 0.9939 0.005752 0.9625 0.9756 0.01316 ] Network output: [ 0.02294 -0.09775 0.9414 -0.0002944 0.0001322 1.109 -0.0002219 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2746 0.181 0.381 0.1589 0.9852 0.9941 0.2755 0.8775 0.9711 0.6631 ] Network output: [ -0.03676 0.1912 1.088 0.0001935 -8.687e-05 0.795 0.0001458 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.1017 0.1873 0.1364 0.9901 0.9941 0.1087 0.9566 0.9748 0.2133 ] Network output: [ -0.03079 0.0744 1.076 0.0002803 -0.0001258 0.9127 0.0002112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1257 0.1243 0.1999 0.1603 0.9859 0.9918 0.1258 0.9322 0.9645 0.2083 ] Network output: [ -0.004026 0.994 -0.001179 -1.26e-05 5.657e-06 1.015 -9.497e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03108 Epoch 4789 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04967 0.8563 0.9378 -8.912e-05 4.001e-05 0.1062 -6.716e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00405 -0.003686 -0.01364 0.007471 0.9658 0.9709 0.008215 0.9109 0.9155 0.02726 ] Network output: [ 0.9779 0.1101 -0.006398 3.45e-05 -1.549e-05 -0.05932 2.6e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2533 -0.013 -0.1844 0.1597 0.9836 0.9933 0.2856 0.8701 0.968 0.6682 ] Network output: [ 0.01755 0.8701 0.969 -0.0001154 5.183e-05 0.1253 -8.7e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005657 0.001754 0.00425 0.003409 0.991 0.9939 0.005764 0.9626 0.9756 0.01324 ] Network output: [ 0.0149 -0.05501 0.9381 -0.0003362 0.0001509 1.086 -0.0002534 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2751 0.1811 0.3838 0.1491 0.9852 0.9941 0.276 0.8778 0.9712 0.6645 ] Network output: [ -0.0355 0.1912 1.087 0.0001953 -8.767e-05 0.7941 0.0001472 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.1017 0.1871 0.1353 0.9902 0.9941 0.1088 0.9567 0.9749 0.213 ] Network output: [ -0.02889 0.06896 1.075 0.0002864 -0.0001286 0.9153 0.0002158 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1259 0.1245 0.1997 0.1603 0.9859 0.9918 0.1259 0.9323 0.9645 0.2081 ] Network output: [ -0.0005872 0.9785 -0.0009304 2.667e-06 -1.197e-06 1.024 2.01e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02997 Epoch 4790 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05036 0.8538 0.9378 -8.655e-05 3.886e-05 0.1073 -6.523e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004051 -0.0037 -0.01372 0.007565 0.9658 0.9709 0.008223 0.9111 0.9157 0.02731 ] Network output: [ 0.9906 0.0845 -0.01254 6.149e-05 -2.761e-05 -0.05287 4.634e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2533 -0.01417 -0.1896 0.165 0.9836 0.9933 0.2856 0.8702 0.968 0.6698 ] Network output: [ 0.01689 0.8704 0.9697 -0.0001156 5.19e-05 0.1256 -8.713e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005636 0.001733 0.004158 0.003519 0.991 0.9939 0.005742 0.9627 0.9757 0.0132 ] Network output: [ 0.02256 -0.09687 0.9422 -0.0002988 0.0001342 1.108 -0.0002252 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2738 0.1798 0.3817 0.1579 0.9852 0.9941 0.2747 0.878 0.9712 0.6656 ] Network output: [ -0.03645 0.1907 1.087 0.0001941 -8.714e-05 0.7956 0.0001463 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.1017 0.1874 0.1363 0.9902 0.9941 0.1088 0.9568 0.9749 0.2136 ] Network output: [ -0.03052 0.07314 1.075 0.0002814 -0.0001263 0.9141 0.0002121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1259 0.1245 0.2002 0.1604 0.9859 0.9918 0.1259 0.9325 0.9646 0.2086 ] Network output: [ -0.00394 0.9938 -0.001357 -1.097e-05 4.925e-06 1.015 -8.267e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0307 Epoch 4791 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04924 0.8572 0.9381 -8.949e-05 4.018e-05 0.1058 -6.744e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004044 -0.003686 -0.01366 0.007477 0.9658 0.9709 0.008208 0.9112 0.9157 0.02729 ] Network output: [ 0.9782 0.109 -0.005718 3.626e-05 -1.628e-05 -0.05954 2.732e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2525 -0.01336 -0.1855 0.1598 0.9836 0.9933 0.2848 0.8705 0.9681 0.6707 ] Network output: [ 0.01714 0.8711 0.9692 -0.000116 5.206e-05 0.1249 -8.74e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005648 0.001734 0.004249 0.003392 0.991 0.9939 0.005755 0.9628 0.9757 0.01328 ] Network output: [ 0.01428 -0.0533 0.939 -0.0003413 0.0001532 1.084 -0.0002572 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2743 0.18 0.3846 0.148 0.9852 0.9941 0.2752 0.8782 0.9713 0.6671 ] Network output: [ -0.03514 0.1908 1.086 0.0001959 -8.797e-05 0.7947 0.0001477 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1088 0.1018 0.1872 0.1352 0.9902 0.9941 0.1089 0.9569 0.975 0.2133 ] Network output: [ -0.02854 0.06764 1.074 0.0002877 -0.0001292 0.9168 0.0002168 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1261 0.1246 0.2 0.1604 0.9859 0.9918 0.1261 0.9325 0.9646 0.2084 ] Network output: [ -0.0003527 0.9778 -0.001176 4.737e-06 -2.127e-06 1.024 3.57e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02958 Epoch 4792 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04995 0.8548 0.9381 -8.689e-05 3.901e-05 0.1069 -6.549e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004046 -0.003701 -0.01374 0.007573 0.9658 0.9709 0.008216 0.9114 0.9159 0.02735 ] Network output: [ 0.9913 0.08302 -0.01225 6.367e-05 -2.859e-05 -0.0532 4.799e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2526 -0.01455 -0.1909 0.1653 0.9836 0.9933 0.2849 0.8706 0.9681 0.6723 ] Network output: [ 0.01645 0.8714 0.97 -0.0001162 5.215e-05 0.1252 -8.754e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005627 0.001713 0.004152 0.003505 0.991 0.9939 0.005733 0.9629 0.9758 0.01323 ] Network output: [ 0.02224 -0.09622 0.9431 -0.0003029 0.000136 1.107 -0.0002282 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2731 0.1786 0.3824 0.157 0.9852 0.9941 0.274 0.8783 0.9713 0.6682 ] Network output: [ -0.03614 0.1903 1.086 0.0001947 -8.74e-05 0.7963 0.0001467 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.1018 0.1876 0.1362 0.9902 0.9941 0.1089 0.957 0.975 0.2138 ] Network output: [ -0.03026 0.07193 1.074 0.0002825 -0.0001268 0.9156 0.0002129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1261 0.1247 0.2004 0.1604 0.9859 0.9919 0.1261 0.9327 0.9647 0.2089 ] Network output: [ -0.003877 0.9937 -0.001523 -9.513e-06 4.271e-06 1.016 -7.169e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03032 Epoch 4793 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04881 0.8582 0.9384 -8.989e-05 4.035e-05 0.1054 -6.774e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004038 -0.003687 -0.01369 0.007482 0.9658 0.9709 0.008202 0.9115 0.9159 0.02733 ] Network output: [ 0.9785 0.1079 -0.004984 3.776e-05 -1.695e-05 -0.05968 2.846e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2518 -0.0137 -0.1865 0.1599 0.9836 0.9933 0.284 0.8709 0.9682 0.6731 ] Network output: [ 0.01673 0.8722 0.9694 -0.0001165 5.23e-05 0.1244 -8.78e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00564 0.001714 0.004249 0.003376 0.991 0.9939 0.005746 0.963 0.9758 0.01332 ] Network output: [ 0.01364 -0.05134 0.9398 -0.0003464 0.0001555 1.083 -0.0002611 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2736 0.1789 0.3854 0.1468 0.9852 0.9941 0.2745 0.8786 0.9714 0.6695 ] Network output: [ -0.03478 0.1903 1.085 0.0001966 -8.828e-05 0.7954 0.0001482 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.1018 0.1873 0.1351 0.9902 0.9941 0.109 0.9571 0.9751 0.2135 ] Network output: [ -0.02819 0.06633 1.073 0.000289 -0.0001298 0.9183 0.0002178 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1263 0.1248 0.2002 0.1605 0.9859 0.9919 0.1263 0.9328 0.9647 0.2087 ] Network output: [ -0.0001029 0.9771 -0.001411 6.793e-06 -3.049e-06 1.025 5.119e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02918 Epoch 4794 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04955 0.8557 0.9384 -8.724e-05 3.916e-05 0.1064 -6.575e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004041 -0.003702 -0.01377 0.007581 0.9658 0.9709 0.008211 0.9117 0.9161 0.02738 ] Network output: [ 0.9922 0.08132 -0.01199 6.589e-05 -2.958e-05 -0.0534 4.966e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.252 -0.01492 -0.1922 0.1655 0.9836 0.9933 0.2842 0.871 0.9682 0.6747 ] Network output: [ 0.01602 0.8725 0.9702 -0.0001167 5.238e-05 0.1248 -8.794e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005618 0.001693 0.004146 0.003491 0.991 0.9939 0.005725 0.9631 0.9759 0.01326 ] Network output: [ 0.02199 -0.0958 0.9439 -0.0003064 0.0001376 1.107 -0.0002309 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2723 0.1775 0.383 0.1562 0.9852 0.9941 0.2732 0.8787 0.9714 0.6706 ] Network output: [ -0.03585 0.1899 1.086 0.0001953 -8.766e-05 0.797 0.0001472 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.1018 0.1877 0.1362 0.9902 0.9941 0.109 0.9572 0.9751 0.2141 ] Network output: [ -0.03001 0.07078 1.073 0.0002836 -0.0001273 0.917 0.0002137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1263 0.1248 0.2007 0.1605 0.9859 0.9919 0.1263 0.933 0.9648 0.2092 ] Network output: [ -0.003842 0.9938 -0.001676 -8.239e-06 3.699e-06 1.016 -6.209e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02994 Epoch 4795 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04836 0.8592 0.9388 -9.032e-05 4.055e-05 0.1049 -6.807e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004032 -0.003687 -0.01371 0.007488 0.9658 0.9709 0.008195 0.9118 0.9161 0.02736 ] Network output: [ 0.9786 0.1069 -0.004189 3.901e-05 -1.751e-05 -0.05974 2.94e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2511 -0.01403 -0.1875 0.1601 0.9836 0.9933 0.2832 0.8713 0.9683 0.6755 ] Network output: [ 0.01632 0.8733 0.9696 -0.000117 5.253e-05 0.1239 -8.819e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005632 0.001696 0.004249 0.003359 0.991 0.9939 0.005739 0.9632 0.9759 0.01336 ] Network output: [ 0.01296 -0.04911 0.9406 -0.0003516 0.0001578 1.081 -0.0002649 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2729 0.1778 0.3862 0.1456 0.9852 0.9941 0.2738 0.879 0.9714 0.6719 ] Network output: [ -0.0344 0.1899 1.084 0.0001973 -8.859e-05 0.796 0.0001487 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.1019 0.1874 0.135 0.9902 0.9942 0.1092 0.9573 0.9751 0.2137 ] Network output: [ -0.02782 0.065 1.072 0.0002904 -0.0001304 0.9197 0.0002188 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1265 0.125 0.2004 0.1605 0.9859 0.9919 0.1265 0.933 0.9648 0.209 ] Network output: [ 0.0001659 0.9763 -0.001636 8.845e-06 -3.971e-06 1.025 6.666e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02877 Epoch 4796 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04916 0.8566 0.9388 -8.758e-05 3.932e-05 0.106 -6.6e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004036 -0.003703 -0.01379 0.00759 0.9658 0.9709 0.008206 0.9119 0.9163 0.02742 ] Network output: [ 0.9931 0.0794 -0.01177 6.816e-05 -3.06e-05 -0.05346 5.137e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2513 -0.01529 -0.1935 0.1659 0.9836 0.9933 0.2835 0.8714 0.9683 0.6771 ] Network output: [ 0.01558 0.8736 0.9704 -0.0001172 5.262e-05 0.1243 -8.833e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00561 0.001674 0.004139 0.00348 0.991 0.9939 0.005716 0.9633 0.976 0.0133 ] Network output: [ 0.02181 -0.09565 0.9447 -0.0003095 0.0001389 1.106 -0.0002333 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2716 0.1764 0.3836 0.1555 0.9852 0.9941 0.2725 0.8791 0.9715 0.673 ] Network output: [ -0.03555 0.1895 1.085 0.0001958 -8.792e-05 0.7976 0.0001476 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.1019 0.1878 0.1361 0.9902 0.9942 0.1092 0.9574 0.9752 0.2144 ] Network output: [ -0.02977 0.06967 1.073 0.0002846 -0.0001278 0.9183 0.0002145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1265 0.125 0.2009 0.1605 0.9859 0.9919 0.1265 0.9332 0.9649 0.2095 ] Network output: [ -0.003837 0.9939 -0.001816 -7.16e-06 3.214e-06 1.016 -5.396e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02958 Epoch 4797 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04792 0.8603 0.9391 -9.078e-05 4.076e-05 0.1044 -6.842e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004027 -0.003687 -0.01372 0.007494 0.9658 0.9709 0.008189 0.9121 0.9163 0.02739 ] Network output: [ 0.9786 0.1059 -0.003328 3.998e-05 -1.795e-05 -0.05974 3.013e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2505 -0.01433 -0.1884 0.1602 0.9836 0.9933 0.2825 0.8717 0.9684 0.6778 ] Network output: [ 0.01592 0.8744 0.9698 -0.0001175 5.276e-05 0.1234 -8.858e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005625 0.001678 0.004251 0.003342 0.991 0.9939 0.005732 0.9634 0.976 0.01339 ] Network output: [ 0.01223 -0.04658 0.9413 -0.0003568 0.0001602 1.079 -0.0002689 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2723 0.1768 0.387 0.1444 0.9852 0.9941 0.2732 0.8793 0.9715 0.6743 ] Network output: [ -0.03401 0.1895 1.083 0.0001981 -8.892e-05 0.7966 0.0001493 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.102 0.1876 0.1349 0.9902 0.9942 0.1093 0.9575 0.9752 0.214 ] Network output: [ -0.02743 0.06367 1.071 0.0002917 -0.000131 0.9211 0.0002199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1268 0.1253 0.2007 0.1606 0.9859 0.9919 0.1268 0.9333 0.9649 0.2093 ] Network output: [ 0.000458 0.9754 -0.001854 1.091e-05 -4.899e-06 1.026 8.224e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02837 Epoch 4798 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04877 0.8575 0.9391 -8.792e-05 3.947e-05 0.1055 -6.626e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004031 -0.003704 -0.01382 0.007601 0.9658 0.971 0.008201 0.9122 0.9165 0.02745 ] Network output: [ 0.994 0.07725 -0.01161 7.049e-05 -3.165e-05 -0.05341 5.313e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2507 -0.01565 -0.1949 0.1663 0.9836 0.9933 0.2828 0.8718 0.9684 0.6794 ] Network output: [ 0.01514 0.8748 0.9707 -0.0001177 5.285e-05 0.1238 -8.873e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005602 0.001656 0.004132 0.003469 0.991 0.9939 0.005708 0.9635 0.9761 0.01333 ] Network output: [ 0.02171 -0.09582 0.9455 -0.0003121 0.0001401 1.106 -0.0002352 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2709 0.1753 0.3842 0.1548 0.9852 0.9941 0.2718 0.8795 0.9716 0.6753 ] Network output: [ -0.03527 0.1891 1.084 0.0001964 -8.817e-05 0.7983 0.000148 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.102 0.1879 0.1361 0.9902 0.9942 0.1093 0.9576 0.9753 0.2146 ] Network output: [ -0.02955 0.06862 1.072 0.0002856 -0.0001282 0.9197 0.0002152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1267 0.1252 0.2012 0.1606 0.9859 0.9919 0.1267 0.9335 0.965 0.2098 ] Network output: [ -0.003867 0.9942 -0.001943 -6.288e-06 2.823e-06 1.015 -4.739e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02923 Epoch 4799 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04746 0.8614 0.9394 -9.128e-05 4.098e-05 0.1039 -6.879e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004022 -0.003687 -0.01374 0.007499 0.9658 0.971 0.008183 0.9124 0.9165 0.02743 ] Network output: [ 0.9786 0.1051 -0.00239 4.065e-05 -1.825e-05 -0.05967 3.063e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2498 -0.01462 -0.1893 0.1603 0.9836 0.9933 0.2818 0.8721 0.9685 0.6801 ] Network output: [ 0.01552 0.8756 0.97 -0.000118 5.299e-05 0.1229 -8.896e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005618 0.001662 0.004255 0.003325 0.991 0.9939 0.005725 0.9636 0.9761 0.01343 ] Network output: [ 0.01144 -0.04371 0.942 -0.0003621 0.0001625 1.077 -0.0002729 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2716 0.1758 0.3878 0.1431 0.9852 0.9941 0.2725 0.8797 0.9716 0.6766 ] Network output: [ -0.03362 0.1891 1.082 0.0001988 -8.925e-05 0.7972 0.0001498 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.1021 0.1877 0.1349 0.9902 0.9942 0.1095 0.9577 0.9753 0.2142 ] Network output: [ -0.02702 0.06232 1.07 0.0002931 -0.0001316 0.9225 0.0002209 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.127 0.1255 0.2009 0.1607 0.9859 0.9919 0.127 0.9335 0.965 0.2095 ] Network output: [ 0.0007791 0.9744 -0.002067 1.302e-05 -5.843e-06 1.026 9.809e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02796 Epoch 4800 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04839 0.8585 0.9394 -8.825e-05 3.962e-05 0.105 -6.651e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004026 -0.003705 -0.01384 0.007612 0.9658 0.971 0.008196 0.9125 0.9167 0.02749 ] Network output: [ 0.9951 0.07486 -0.01151 7.292e-05 -3.274e-05 -0.05325 5.496e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2501 -0.01601 -0.1962 0.1667 0.9836 0.9933 0.2821 0.8722 0.9685 0.6816 ] Network output: [ 0.0147 0.8759 0.9709 -0.0001182 5.308e-05 0.1233 -8.911e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005594 0.001639 0.004125 0.003461 0.991 0.9939 0.005701 0.9637 0.9762 0.01336 ] Network output: [ 0.0217 -0.09636 0.9463 -0.0003141 0.000141 1.105 -0.0002367 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2702 0.1743 0.3847 0.1543 0.9852 0.9941 0.2711 0.8798 0.9716 0.6776 ] Network output: [ -0.035 0.1887 1.083 0.000197 -8.842e-05 0.799 0.0001484 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.102 0.1881 0.1361 0.9902 0.9942 0.1094 0.9577 0.9753 0.2149 ] Network output: [ -0.02934 0.06762 1.071 0.0002865 -0.0001286 0.921 0.0002159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1269 0.1254 0.2015 0.1607 0.9859 0.9919 0.1269 0.9337 0.9651 0.2101 ] Network output: [ -0.003937 0.9946 -0.002055 -5.64e-06 2.532e-06 1.015 -4.251e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0289 Epoch 4801 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.047 0.8625 0.9398 -9.18e-05 4.121e-05 0.1033 -6.918e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004017 -0.003687 -0.01376 0.007504 0.9658 0.971 0.008178 0.9126 0.9167 0.02746 ] Network output: [ 0.9783 0.1044 -0.001363 4.099e-05 -1.84e-05 -0.05954 3.089e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2491 -0.01488 -0.19 0.1603 0.9836 0.9933 0.2811 0.8725 0.9686 0.6823 ] Network output: [ 0.01514 0.8767 0.9702 -0.0001185 5.322e-05 0.1223 -8.934e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005613 0.001646 0.004259 0.003308 0.9911 0.9939 0.005719 0.9638 0.9762 0.01347 ] Network output: [ 0.01059 -0.04045 0.9426 -0.0003675 0.000165 1.075 -0.000277 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.271 0.1749 0.3887 0.1417 0.9852 0.9941 0.2719 0.8801 0.9717 0.6789 ] Network output: [ -0.03321 0.1887 1.081 0.0001996 -8.959e-05 0.7977 0.0001504 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.1022 0.1878 0.1348 0.9902 0.9942 0.1096 0.9578 0.9754 0.2145 ] Network output: [ -0.0266 0.06095 1.07 0.0002946 -0.0001322 0.9239 0.000222 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1272 0.1257 0.2011 0.1607 0.9859 0.9919 0.1272 0.9338 0.9651 0.2098 ] Network output: [ 0.001136 0.9732 -0.002278 1.518e-05 -6.816e-06 1.027 1.144e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02756 Epoch 4802 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04802 0.8594 0.9397 -8.856e-05 3.976e-05 0.1045 -6.674e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004022 -0.003707 -0.01387 0.007625 0.9658 0.971 0.008192 0.9128 0.9169 0.02752 ] Network output: [ 0.9963 0.07221 -0.0115 7.547e-05 -3.388e-05 -0.05298 5.688e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2495 -0.01637 -0.1976 0.1672 0.9836 0.9933 0.2815 0.8726 0.9686 0.6839 ] Network output: [ 0.01425 0.8771 0.9712 -0.0001188 5.331e-05 0.1227 -8.949e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005587 0.001621 0.004116 0.003453 0.9911 0.9939 0.005693 0.9638 0.9762 0.01339 ] Network output: [ 0.0218 -0.09732 0.9471 -0.0003156 0.0001417 1.105 -0.0002378 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2695 0.1733 0.3851 0.1539 0.9852 0.9941 0.2704 0.8802 0.9717 0.6798 ] Network output: [ -0.03474 0.1883 1.082 0.0001975 -8.866e-05 0.7996 0.0001488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.1021 0.1882 0.1361 0.9902 0.9942 0.1096 0.9579 0.9754 0.2152 ] Network output: [ -0.02915 0.06668 1.071 0.0002873 -0.000129 0.9222 0.0002165 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1271 0.1256 0.2018 0.1608 0.9859 0.9919 0.1272 0.9339 0.9652 0.2105 ] Network output: [ -0.004054 0.9952 -0.002151 -5.239e-06 2.352e-06 1.015 -3.948e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02858 Epoch 4803 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04653 0.8637 0.9401 -9.236e-05 4.147e-05 0.1027 -6.961e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004012 -0.003687 -0.01377 0.007509 0.9658 0.971 0.008172 0.9129 0.9169 0.02749 ] Network output: [ 0.9779 0.1039 -0.0002293 4.096e-05 -1.839e-05 -0.05936 3.087e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2485 -0.01512 -0.1907 0.1604 0.9836 0.9933 0.2803 0.8728 0.9686 0.6844 ] Network output: [ 0.01476 0.8779 0.9704 -0.000119 5.344e-05 0.1217 -8.971e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005607 0.00163 0.004266 0.003291 0.9911 0.9939 0.005714 0.9639 0.9763 0.01351 ] Network output: [ 0.009648 -0.03673 0.9432 -0.0003732 0.0001676 1.073 -0.0002813 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2704 0.174 0.3895 0.1403 0.9852 0.9941 0.2713 0.8804 0.9718 0.681 ] Network output: [ -0.03278 0.1882 1.08 0.0002004 -8.995e-05 0.7983 0.000151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.1023 0.1879 0.1347 0.9903 0.9942 0.1098 0.958 0.9755 0.2147 ] Network output: [ -0.02614 0.05955 1.069 0.0002961 -0.0001329 0.9253 0.0002231 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1275 0.1259 0.2013 0.1608 0.9859 0.9919 0.1275 0.934 0.9652 0.2101 ] Network output: [ 0.001538 0.9718 -0.00249 1.744e-05 -7.831e-06 1.028 1.315e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02716 Epoch 4804 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04767 0.8603 0.94 -8.886e-05 3.989e-05 0.104 -6.697e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004018 -0.003709 -0.01389 0.007639 0.9658 0.971 0.008189 0.913 0.9171 0.02756 ] Network output: [ 0.9976 0.06926 -0.01159 7.818e-05 -3.51e-05 -0.0526 5.892e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2489 -0.01673 -0.199 0.1678 0.9836 0.9933 0.2809 0.8729 0.9687 0.686 ] Network output: [ 0.01381 0.8783 0.9715 -0.0001192 5.353e-05 0.1222 -8.987e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00558 0.001605 0.004107 0.003448 0.9911 0.994 0.005686 0.964 0.9763 0.01342 ] Network output: [ 0.02202 -0.0988 0.9479 -0.0003163 0.000142 1.106 -0.0002384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2688 0.1723 0.3855 0.1536 0.9852 0.9941 0.2697 0.8805 0.9718 0.682 ] Network output: [ -0.03449 0.1879 1.082 0.000198 -8.89e-05 0.8003 0.0001492 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.1022 0.1884 0.1361 0.9902 0.9942 0.1097 0.9581 0.9755 0.2155 ] Network output: [ -0.02899 0.06581 1.07 0.0002881 -0.0001293 0.9234 0.0002171 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1274 0.1258 0.202 0.1608 0.9859 0.9919 0.1274 0.9342 0.9652 0.2108 ] Network output: [ -0.004226 0.996 -0.00223 -5.112e-06 2.295e-06 1.015 -3.853e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02829 Epoch 4805 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04605 0.8649 0.9405 -9.296e-05 4.173e-05 0.1021 -7.006e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004007 -0.003687 -0.01379 0.007513 0.9658 0.971 0.008166 0.9131 0.9171 0.02752 ] Network output: [ 0.9773 0.1036 0.001033 4.052e-05 -1.819e-05 -0.05913 3.054e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2478 -0.01534 -0.1913 0.1603 0.9837 0.9933 0.2796 0.8732 0.9687 0.6865 ] Network output: [ 0.01439 0.8791 0.9706 -0.0001195 5.366e-05 0.121 -9.007e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005603 0.001616 0.004274 0.003272 0.9911 0.994 0.005709 0.9641 0.9764 0.01355 ] Network output: [ 0.0086 -0.03246 0.9437 -0.0003792 0.0001703 1.07 -0.0002858 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2699 0.1731 0.3904 0.1388 0.9852 0.9941 0.2708 0.8808 0.9718 0.6832 ] Network output: [ -0.03233 0.1878 1.079 0.0002012 -9.032e-05 0.7988 0.0001516 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.1024 0.1881 0.1346 0.9903 0.9942 0.11 0.9582 0.9755 0.2149 ] Network output: [ -0.02566 0.0581 1.068 0.0002976 -0.0001336 0.9267 0.0002243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1277 0.1262 0.2016 0.1609 0.9859 0.9919 0.1277 0.9342 0.9653 0.2103 ] Network output: [ 0.001997 0.9702 -0.002706 1.984e-05 -8.908e-06 1.029 1.495e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02676 Epoch 4806 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04733 0.8611 0.9404 -8.913e-05 4.002e-05 0.1035 -6.717e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004014 -0.003711 -0.01392 0.007655 0.9658 0.971 0.008186 0.9133 0.9173 0.02759 ] Network output: [ 0.9991 0.06599 -0.01181 8.111e-05 -3.641e-05 -0.05213 6.113e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2484 -0.01709 -0.2004 0.1685 0.9837 0.9933 0.2803 0.8733 0.9688 0.6881 ] Network output: [ 0.01335 0.8794 0.9718 -0.0001197 5.375e-05 0.1216 -9.023e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005573 0.001589 0.004096 0.003445 0.9911 0.994 0.005679 0.9642 0.9764 0.01345 ] Network output: [ 0.02238 -0.1009 0.9487 -0.0003163 0.000142 1.106 -0.0002384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2681 0.1713 0.3858 0.1535 0.9852 0.9941 0.269 0.8808 0.9718 0.6841 ] Network output: [ -0.03427 0.1875 1.081 0.0001985 -8.912e-05 0.801 0.0001496 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.1023 0.1885 0.1362 0.9903 0.9942 0.1099 0.9582 0.9756 0.2158 ] Network output: [ -0.02886 0.06503 1.069 0.0002887 -0.0001296 0.9246 0.0002176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1276 0.126 0.2023 0.1609 0.9859 0.9919 0.1276 0.9344 0.9653 0.2111 ] Network output: [ -0.004464 0.9971 -0.00229 -5.295e-06 2.377e-06 1.014 -3.99e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02802 Epoch 4807 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04555 0.8662 0.9408 -9.36e-05 4.202e-05 0.1015 -7.054e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004002 -0.003686 -0.0138 0.007516 0.9658 0.971 0.008161 0.9134 0.9173 0.02755 ] Network output: [ 0.9765 0.1036 0.002452 3.96e-05 -1.778e-05 -0.05886 2.984e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2472 -0.01553 -0.1917 0.1603 0.9837 0.9933 0.279 0.8736 0.9688 0.6885 ] Network output: [ 0.01404 0.8804 0.9707 -0.00012 5.387e-05 0.1203 -9.043e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005599 0.001603 0.004285 0.003253 0.9911 0.994 0.005705 0.9643 0.9765 0.0136 ] Network output: [ 0.007418 -0.02753 0.9442 -0.0003857 0.0001732 1.067 -0.0002907 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2694 0.1723 0.3913 0.1371 0.9852 0.9941 0.2702 0.8811 0.9719 0.6853 ] Network output: [ -0.03186 0.1874 1.078 0.000202 -9.07e-05 0.7993 0.0001523 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.1025 0.1882 0.1345 0.9903 0.9942 0.1102 0.9583 0.9756 0.2152 ] Network output: [ -0.02514 0.05661 1.067 0.0002992 -0.0001343 0.9281 0.0002255 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.128 0.1264 0.2018 0.161 0.9859 0.9919 0.128 0.9344 0.9654 0.2106 ] Network output: [ 0.002527 0.9684 -0.002932 2.243e-05 -1.007e-05 1.03 1.691e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02637 Epoch 4808 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.047 0.862 0.9407 -8.937e-05 4.012e-05 0.103 -6.736e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004011 -0.003713 -0.01394 0.007672 0.9659 0.971 0.008183 0.9135 0.9174 0.02763 ] Network output: [ 1.001 0.06234 -0.0122 8.432e-05 -3.785e-05 -0.05155 6.354e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2479 -0.01746 -0.202 0.1692 0.9837 0.9933 0.2797 0.8736 0.9689 0.6902 ] Network output: [ 0.01289 0.8806 0.9721 -0.0001202 5.397e-05 0.121 -9.059e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005566 0.001573 0.004083 0.003444 0.9911 0.994 0.005672 0.9643 0.9765 0.01347 ] Network output: [ 0.02293 -0.1038 0.9495 -0.0003155 0.0001416 1.107 -0.0002377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2674 0.1703 0.3861 0.1535 0.9852 0.9941 0.2683 0.8812 0.9719 0.6861 ] Network output: [ -0.03407 0.1871 1.08 0.000199 -8.932e-05 0.8017 0.0001499 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.1024 0.1887 0.1362 0.9903 0.9942 0.1101 0.9584 0.9756 0.2161 ] Network output: [ -0.02877 0.06433 1.069 0.0002892 -0.0001298 0.9257 0.000218 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1278 0.1262 0.2026 0.161 0.9859 0.9919 0.1278 0.9346 0.9654 0.2114 ] Network output: [ -0.004779 0.9984 -0.002327 -5.831e-06 2.618e-06 1.013 -4.394e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0278 Epoch 4809 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04504 0.8675 0.9412 -9.43e-05 4.233e-05 0.1008 -7.106e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003997 -0.003685 -0.01381 0.007518 0.9659 0.971 0.008155 0.9136 0.9175 0.02757 ] Network output: [ 0.9753 0.104 0.004066 3.813e-05 -1.712e-05 -0.05854 2.874e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2466 -0.0157 -0.192 0.1601 0.9837 0.9933 0.2783 0.8739 0.9689 0.6905 ] Network output: [ 0.01369 0.8816 0.9709 -0.0001204 5.407e-05 0.1196 -9.077e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005595 0.00159 0.004299 0.003232 0.9911 0.994 0.005702 0.9644 0.9765 0.01364 ] Network output: [ 0.006068 -0.0218 0.9446 -0.0003927 0.0001763 1.063 -0.000296 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2689 0.1715 0.3923 0.1353 0.9852 0.9941 0.2698 0.8814 0.972 0.6873 ] Network output: [ -0.03136 0.1869 1.077 0.0002029 -9.111e-05 0.7998 0.0001529 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.1027 0.1883 0.1344 0.9903 0.9942 0.1104 0.9585 0.9757 0.2154 ] Network output: [ -0.02457 0.05507 1.066 0.0003009 -0.0001351 0.9295 0.0002268 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1283 0.1267 0.202 0.1611 0.9859 0.9919 0.1283 0.9347 0.9655 0.2108 ] Network output: [ 0.003145 0.9662 -0.003174 2.528e-05 -1.135e-05 1.031 1.905e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.026 Epoch 4810 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0467 0.8628 0.941 -8.957e-05 4.021e-05 0.1025 -6.751e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004008 -0.003716 -0.01397 0.007691 0.9659 0.971 0.008181 0.9138 0.9176 0.02766 ] Network output: [ 1.003 0.05823 -0.01278 8.788e-05 -3.945e-05 -0.05088 6.623e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2474 -0.01784 -0.2036 0.1701 0.9837 0.9934 0.2792 0.8739 0.9689 0.6922 ] Network output: [ 0.01243 0.8818 0.9724 -0.0001207 5.417e-05 0.1204 -9.094e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005559 0.001557 0.004068 0.003446 0.9911 0.994 0.005665 0.9645 0.9766 0.0135 ] Network output: [ 0.02369 -0.1075 0.9503 -0.0003136 0.0001408 1.109 -0.0002363 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2668 0.1694 0.3862 0.1538 0.9852 0.9941 0.2676 0.8815 0.972 0.6881 ] Network output: [ -0.0339 0.1867 1.08 0.0001994 -8.95e-05 0.8024 0.0001502 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.1025 0.1889 0.1363 0.9903 0.9942 0.1102 0.9586 0.9757 0.2164 ] Network output: [ -0.02873 0.06376 1.068 0.0002896 -0.00013 0.9268 0.0002183 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.128 0.1264 0.2029 0.1611 0.986 0.9919 0.128 0.9349 0.9655 0.2118 ] Network output: [ -0.00519 1 -0.00234 -6.776e-06 3.042e-06 1.013 -5.107e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02762 Epoch 4811 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04451 0.8689 0.9416 -9.504e-05 4.267e-05 0.1001 -7.163e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003993 -0.003684 -0.01382 0.007518 0.9659 0.971 0.008149 0.9139 0.9176 0.0276 ] Network output: [ 0.9738 0.1047 0.005923 3.601e-05 -1.617e-05 -0.05818 2.714e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2459 -0.01583 -0.1921 0.1599 0.9837 0.9934 0.2776 0.8742 0.969 0.6924 ] Network output: [ 0.01337 0.8829 0.971 -0.0001209 5.427e-05 0.1189 -9.111e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005593 0.001578 0.004316 0.00321 0.9911 0.994 0.005699 0.9646 0.9766 0.01369 ] Network output: [ 0.004507 -0.0151 0.945 -0.0004004 0.0001798 1.059 -0.0003018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2684 0.1708 0.3933 0.1333 0.9852 0.9941 0.2693 0.8817 0.972 0.6893 ] Network output: [ -0.03083 0.1865 1.076 0.0002039 -9.153e-05 0.8002 0.0001537 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.1028 0.1884 0.1342 0.9903 0.9942 0.1106 0.9586 0.9757 0.2156 ] Network output: [ -0.02394 0.05345 1.065 0.0003028 -0.0001359 0.9309 0.0002282 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1285 0.1269 0.2021 0.1612 0.9859 0.9919 0.1286 0.9349 0.9656 0.211 ] Network output: [ 0.003877 0.9636 -0.00344 2.846e-05 -1.278e-05 1.032 2.145e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02565 Epoch 4812 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04643 0.8635 0.9413 -8.972e-05 4.028e-05 0.102 -6.762e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004005 -0.003719 -0.014 0.007712 0.9659 0.971 0.00818 0.914 0.9178 0.0277 ] Network output: [ 1.005 0.05358 -0.01361 9.19e-05 -4.126e-05 -0.05009 6.926e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2469 -0.01823 -0.2053 0.1711 0.9837 0.9934 0.2787 0.8743 0.969 0.6942 ] Network output: [ 0.01195 0.8831 0.9728 -0.0001211 5.437e-05 0.1198 -9.128e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005552 0.001542 0.004049 0.003451 0.9911 0.994 0.005657 0.9646 0.9766 0.01352 ] Network output: [ 0.0247 -0.1124 0.9512 -0.0003105 0.0001394 1.111 -0.000234 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2661 0.1684 0.3863 0.1544 0.9852 0.9941 0.2669 0.8818 0.972 0.69 ] Network output: [ -0.03377 0.1864 1.079 0.0001997 -8.966e-05 0.8031 0.0001505 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.1026 0.189 0.1364 0.9903 0.9942 0.1104 0.9587 0.9758 0.2167 ] Network output: [ -0.02874 0.06333 1.068 0.0002898 -0.0001301 0.9278 0.0002184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1282 0.1266 0.2032 0.1612 0.986 0.9919 0.1282 0.9351 0.9656 0.2121 ] Network output: [ -0.005714 1.002 -0.002323 -8.199e-06 3.681e-06 1.011 -6.179e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02752 Epoch 4813 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04396 0.8704 0.942 -9.585e-05 4.303e-05 0.09936 -7.224e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003988 -0.003683 -0.01382 0.007517 0.9659 0.971 0.008143 0.9141 0.9178 0.02762 ] Network output: [ 0.9719 0.106 0.008087 3.311e-05 -1.487e-05 -0.05778 2.495e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2453 -0.01592 -0.1919 0.1596 0.9837 0.9934 0.2769 0.8746 0.969 0.6942 ] Network output: [ 0.01307 0.8841 0.9711 -0.0001213 5.446e-05 0.1181 -9.143e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005591 0.001567 0.004338 0.003185 0.9911 0.994 0.005697 0.9647 0.9767 0.01373 ] Network output: [ 0.002683 -0.007175 0.9453 -0.0004091 0.0001837 1.055 -0.0003083 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.268 0.1702 0.3945 0.1311 0.9853 0.9941 0.2689 0.882 0.9721 0.6912 ] Network output: [ -0.03025 0.1861 1.075 0.0002049 -9.198e-05 0.8006 0.0001544 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.103 0.1885 0.1341 0.9903 0.9942 0.1108 0.9588 0.9758 0.2158 ] Network output: [ -0.02324 0.05174 1.064 0.0003047 -0.0001368 0.9322 0.0002297 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1288 0.1272 0.2023 0.1612 0.986 0.9919 0.1289 0.9351 0.9656 0.2112 ] Network output: [ 0.004752 0.9605 -0.00374 3.209e-05 -1.441e-05 1.034 2.419e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02533 Epoch 4814 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04618 0.8642 0.9416 -8.98e-05 4.031e-05 0.1015 -6.768e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004003 -0.003722 -0.01403 0.007735 0.9659 0.971 0.008179 0.9142 0.9179 0.02773 ] Network output: [ 1.008 0.04827 -0.01474 9.651e-05 -4.333e-05 -0.04919 7.273e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2465 -0.01865 -0.2072 0.1722 0.9837 0.9934 0.2783 0.8746 0.9691 0.6961 ] Network output: [ 0.01146 0.8843 0.9731 -0.0001216 5.457e-05 0.1192 -9.16e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005544 0.001526 0.004027 0.003459 0.9911 0.994 0.00565 0.9648 0.9767 0.01354 ] Network output: [ 0.02604 -0.1187 0.9521 -0.000306 0.0001374 1.113 -0.0002306 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2654 0.1675 0.3862 0.1553 0.9852 0.9941 0.2662 0.8821 0.9721 0.6919 ] Network output: [ -0.03369 0.186 1.078 0.0002 -8.978e-05 0.8038 0.0001507 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.1027 0.1892 0.1366 0.9903 0.9942 0.1105 0.9588 0.9758 0.2171 ] Network output: [ -0.02883 0.06308 1.067 0.0002898 -0.0001301 0.9287 0.0002184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1284 0.1268 0.2035 0.1613 0.986 0.9919 0.1284 0.9353 0.9656 0.2125 ] Network output: [ -0.006377 1.005 -0.002271 -1.019e-05 4.574e-06 1.01 -7.678e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02751 Epoch 4815 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04337 0.8719 0.9424 -9.675e-05 4.343e-05 0.09857 -7.291e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003983 -0.00368 -0.01382 0.007512 0.9659 0.971 0.008137 0.9143 0.9179 0.02765 ] Network output: [ 0.9694 0.1079 0.01064 2.929e-05 -1.315e-05 -0.05731 2.207e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2446 -0.01597 -0.1915 0.1591 0.9837 0.9934 0.2762 0.8749 0.9691 0.696 ] Network output: [ 0.01279 0.8854 0.9712 -0.0001217 5.465e-05 0.1173 -9.174e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00559 0.001558 0.004365 0.003157 0.9911 0.994 0.005697 0.9649 0.9768 0.01378 ] Network output: [ 0.000526 0.002244 0.9455 -0.0004191 0.0001881 1.05 -0.0003158 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2677 0.1696 0.3957 0.1285 0.9853 0.9941 0.2686 0.8823 0.9722 0.693 ] Network output: [ -0.02961 0.1857 1.073 0.000206 -9.247e-05 0.801 0.0001552 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.1032 0.1887 0.1339 0.9903 0.9942 0.111 0.9589 0.9759 0.216 ] Network output: [ -0.02245 0.04995 1.063 0.0003069 -0.0001378 0.9336 0.0002313 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1292 0.1275 0.2024 0.1613 0.986 0.9919 0.1292 0.9353 0.9657 0.2114 ] Network output: [ 0.00581 0.9568 -0.004086 3.63e-05 -1.63e-05 1.036 2.736e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02507 Epoch 4816 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04597 0.8648 0.9419 -8.979e-05 4.031e-05 0.101 -6.767e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004001 -0.003726 -0.01406 0.007762 0.9659 0.971 0.008179 0.9144 0.9181 0.02777 ] Network output: [ 1.011 0.04214 -0.01624 0.0001019 -4.573e-05 -0.04815 7.676e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2461 -0.0191 -0.2093 0.1736 0.9837 0.9934 0.2779 0.8748 0.9692 0.698 ] Network output: [ 0.01095 0.8855 0.9735 -0.000122 5.475e-05 0.1186 -9.192e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005537 0.001511 0.004 0.003472 0.9911 0.994 0.005642 0.9649 0.9768 0.01355 ] Network output: [ 0.02776 -0.1267 0.953 -0.0002998 0.0001346 1.117 -0.0002259 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2646 0.1665 0.3859 0.1565 0.9853 0.9941 0.2655 0.8823 0.9722 0.6937 ] Network output: [ -0.03366 0.1857 1.078 0.0002002 -8.986e-05 0.8046 0.0001509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.1028 0.1894 0.1368 0.9903 0.9942 0.1107 0.959 0.9759 0.2174 ] Network output: [ -0.02902 0.06306 1.067 0.0002895 -0.00013 0.9295 0.0002182 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1286 0.127 0.2038 0.1614 0.986 0.9919 0.1286 0.9355 0.9657 0.2128 ] Network output: [ -0.007208 1.009 -0.00218 -1.285e-05 5.767e-06 1.008 -9.682e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02763 Epoch 4817 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04274 0.8736 0.9428 -9.774e-05 4.388e-05 0.09774 -7.366e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003978 -0.003678 -0.01381 0.007505 0.9659 0.971 0.00813 0.9145 0.9181 0.02766 ] Network output: [ 0.9663 0.1106 0.01369 2.433e-05 -1.092e-05 -0.05678 1.834e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.244 -0.01597 -0.1908 0.1584 0.9837 0.9934 0.2755 0.8752 0.9691 0.6977 ] Network output: [ 0.01254 0.8868 0.9713 -0.0001221 5.482e-05 0.1164 -9.203e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00559 0.00155 0.004398 0.003125 0.9911 0.994 0.005697 0.965 0.9768 0.01384 ] Network output: [ -0.002048 0.01353 0.9456 -0.0004306 0.0001933 1.043 -0.0003245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2674 0.1691 0.3971 0.1256 0.9853 0.9941 0.2683 0.8826 0.9722 0.6948 ] Network output: [ -0.0289 0.1853 1.072 0.0002071 -9.299e-05 0.8012 0.0001561 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.1034 0.1887 0.1337 0.9903 0.9942 0.1113 0.959 0.9759 0.2161 ] Network output: [ -0.02155 0.04804 1.061 0.0003092 -0.0001388 0.935 0.0002331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1295 0.1278 0.2025 0.1614 0.986 0.9919 0.1295 0.9354 0.9658 0.2115 ] Network output: [ 0.007102 0.9522 -0.004497 4.127e-05 -1.853e-05 1.038 3.11e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02489 Epoch 4818 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04581 0.8653 0.9421 -8.968e-05 4.026e-05 0.1005 -6.759e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004 -0.003731 -0.0141 0.007792 0.9659 0.971 0.00818 0.9147 0.9182 0.0278 ] Network output: [ 1.015 0.03498 -0.01819 0.0001081 -4.855e-05 -0.04694 8.149e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2458 -0.01959 -0.2117 0.1751 0.9837 0.9934 0.2775 0.8751 0.9692 0.6998 ] Network output: [ 0.01041 0.8867 0.974 -0.0001224 5.493e-05 0.118 -9.222e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005529 0.001495 0.003967 0.003491 0.9911 0.994 0.005634 0.965 0.9768 0.01356 ] Network output: [ 0.02998 -0.1368 0.954 -0.0002914 0.0001308 1.122 -0.0002196 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2639 0.1655 0.3854 0.1583 0.9853 0.9941 0.2648 0.8826 0.9722 0.6954 ] Network output: [ -0.03371 0.1854 1.078 0.0002002 -8.989e-05 0.8053 0.0001509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.1029 0.1896 0.137 0.9903 0.9942 0.1109 0.9591 0.9759 0.2177 ] Network output: [ -0.02931 0.06334 1.066 0.0002889 -0.0001297 0.9302 0.0002177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1288 0.1272 0.2042 0.1614 0.986 0.9919 0.1288 0.9357 0.9658 0.2132 ] Network output: [ -0.008243 1.013 -0.002042 -1.63e-05 7.32e-06 1.006 -1.229e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02794 Epoch 4819 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04206 0.8754 0.9432 -9.884e-05 4.437e-05 0.09685 -7.449e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003973 -0.003674 -0.0138 0.007494 0.9659 0.971 0.008122 0.9147 0.9182 0.02768 ] Network output: [ 0.9623 0.1143 0.01737 1.8e-05 -8.083e-06 -0.05614 1.357e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2433 -0.01591 -0.1896 0.1576 0.9837 0.9934 0.2747 0.8755 0.9692 0.6992 ] Network output: [ 0.01233 0.8881 0.9713 -0.0001225 5.498e-05 0.1155 -9.23e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005592 0.001543 0.004441 0.003089 0.9911 0.994 0.005698 0.9651 0.9769 0.01389 ] Network output: [ -0.005145 0.02714 0.9456 -0.0004441 0.0001994 1.036 -0.0003347 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2672 0.1686 0.3987 0.1222 0.9853 0.9941 0.2681 0.8829 0.9723 0.6964 ] Network output: [ -0.02811 0.185 1.071 0.0002084 -9.356e-05 0.8013 0.0001571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.1036 0.1888 0.1334 0.9904 0.9943 0.1116 0.9592 0.976 0.2162 ] Network output: [ -0.0205 0.04602 1.06 0.0003119 -0.00014 0.9363 0.0002351 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1298 0.1282 0.2026 0.1615 0.986 0.9919 0.1298 0.9356 0.9658 0.2116 ] Network output: [ 0.008692 0.9467 -0.004996 4.72e-05 -2.119e-05 1.041 3.557e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02485 Epoch 4820 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0457 0.8657 0.9424 -8.945e-05 4.016e-05 0.1001 -6.741e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003999 -0.003737 -0.01414 0.007827 0.9659 0.971 0.008182 0.9149 0.9184 0.02784 ] Network output: [ 1.02 0.02653 -0.02069 0.0001156 -5.189e-05 -0.04551 8.711e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2455 -0.02014 -0.2143 0.177 0.9837 0.9934 0.2772 0.8753 0.9693 0.7015 ] Network output: [ 0.009845 0.888 0.9745 -0.0001227 5.51e-05 0.1174 -9.25e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00552 0.00148 0.003927 0.003515 0.9912 0.994 0.005625 0.9651 0.9769 0.01356 ] Network output: [ 0.03279 -0.1496 0.9551 -0.0002806 0.000126 1.128 -0.0002115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2631 0.1645 0.3847 0.1606 0.9853 0.9941 0.264 0.8828 0.9723 0.697 ] Network output: [ -0.03384 0.1853 1.077 0.0002001 -8.984e-05 0.806 0.0001508 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.103 0.1898 0.1373 0.9903 0.9942 0.111 0.9592 0.976 0.2181 ] Network output: [ -0.02974 0.06402 1.066 0.0002878 -0.0001292 0.9306 0.0002169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.129 0.1273 0.2045 0.1615 0.986 0.9919 0.129 0.9359 0.9658 0.2136 ] Network output: [ -0.009521 1.018 -0.001852 -2.071e-05 9.298e-06 1.003 -1.561e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02851 Epoch 4821 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04131 0.8774 0.9437 -0.0001001 4.494e-05 0.09592 -7.544e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003967 -0.003669 -0.01378 0.007477 0.9659 0.971 0.008114 0.9149 0.9183 0.02769 ] Network output: [ 0.9572 0.1191 0.02186 9.991e-06 -4.486e-06 -0.05534 7.53e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2426 -0.01577 -0.188 0.1564 0.9837 0.9934 0.2739 0.8757 0.9693 0.7007 ] Network output: [ 0.01216 0.8895 0.9712 -0.0001228 5.514e-05 0.1145 -9.256e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005595 0.001537 0.004493 0.003046 0.9912 0.994 0.005702 0.9653 0.977 0.01396 ] Network output: [ -0.008896 0.04363 0.9455 -0.0004601 0.0002065 1.027 -0.0003467 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2671 0.1683 0.4006 0.1182 0.9853 0.9942 0.268 0.8831 0.9723 0.698 ] Network output: [ -0.0272 0.1847 1.069 0.0002098 -9.418e-05 0.8013 0.0001581 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.1038 0.1889 0.133 0.9904 0.9943 0.1118 0.9593 0.976 0.2162 ] Network output: [ -0.01928 0.04388 1.058 0.0003149 -0.0001414 0.9375 0.0002373 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1302 0.1285 0.2026 0.1615 0.986 0.9919 0.1302 0.9358 0.9659 0.2116 ] Network output: [ 0.01067 0.9398 -0.005612 5.439e-05 -2.442e-05 1.045 4.099e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02502 Epoch 4822 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04566 0.8659 0.9427 -8.905e-05 3.998e-05 0.09976 -6.711e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003999 -0.003744 -0.01418 0.007868 0.9659 0.971 0.008186 0.915 0.9185 0.02787 ] Network output: [ 1.026 0.01644 -0.02384 0.0001245 -5.589e-05 -0.04377 9.382e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2453 -0.02076 -0.2173 0.1792 0.9837 0.9934 0.277 0.8756 0.9693 0.7032 ] Network output: [ 0.009241 0.8892 0.975 -0.0001231 5.526e-05 0.1168 -9.276e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00551 0.001463 0.003878 0.003548 0.9912 0.994 0.005615 0.9652 0.9769 0.01355 ] Network output: [ 0.03635 -0.1656 0.9562 -0.0002666 0.0001197 1.136 -0.0002009 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2623 0.1635 0.3837 0.1637 0.9853 0.9941 0.2631 0.883 0.9723 0.6984 ] Network output: [ -0.03407 0.1853 1.077 0.0001998 -8.97e-05 0.8067 0.0001506 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.103 0.1899 0.1376 0.9903 0.9942 0.1111 0.9593 0.976 0.2184 ] Network output: [ -0.03034 0.06523 1.066 0.0002863 -0.0001285 0.9308 0.0002157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1291 0.1275 0.2048 0.1615 0.986 0.9919 0.1291 0.936 0.9659 0.2139 ] Network output: [ -0.01109 1.025 -0.001605 -2.624e-05 1.178e-05 0.9992 -1.978e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02946 Epoch 4823 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04047 0.8795 0.9442 -0.0001015 4.558e-05 0.09492 -7.652e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003961 -0.003663 -0.01375 0.007455 0.9659 0.971 0.008104 0.9151 0.9184 0.02769 ] Network output: [ 0.9509 0.1252 0.02737 -8.489e-08 3.811e-08 -0.05433 -6.398e-08 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2419 -0.01555 -0.1856 0.155 0.9837 0.9934 0.2731 0.876 0.9693 0.702 ] Network output: [ 0.01205 0.8909 0.9711 -0.0001231 5.527e-05 0.1134 -9.279e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0056 0.001534 0.004559 0.002996 0.9912 0.994 0.005707 0.9654 0.977 0.01402 ] Network output: [ -0.01346 0.06369 0.9453 -0.0004792 0.0002151 1.016 -0.0003611 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2671 0.1681 0.4028 0.1134 0.9853 0.9942 0.268 0.8834 0.9724 0.6994 ] Network output: [ -0.02615 0.1847 1.067 0.0002113 -9.487e-05 0.801 0.0001593 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.104 0.1888 0.1326 0.9904 0.9943 0.1121 0.9594 0.9761 0.2162 ] Network output: [ -0.01783 0.04164 1.057 0.0003183 -0.0001429 0.9387 0.0002399 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1306 0.1289 0.2025 0.1615 0.986 0.9919 0.1306 0.9359 0.966 0.2115 ] Network output: [ 0.01313 0.9313 -0.006385 6.316e-05 -2.835e-05 1.049 4.76e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02551 Epoch 4824 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0457 0.8658 0.9429 -8.845e-05 3.971e-05 0.09947 -6.666e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004 -0.003753 -0.01423 0.007915 0.9659 0.971 0.00819 0.9152 0.9186 0.02791 ] Network output: [ 1.033 0.004268 -0.02775 0.0001352 -6.068e-05 -0.04158 0.0001019 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2452 -0.02147 -0.2208 0.1818 0.9837 0.9934 0.2769 0.8757 0.9694 0.7047 ] Network output: [ 0.008591 0.8904 0.9756 -0.0001234 5.54e-05 0.1163 -9.3e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005499 0.001446 0.003818 0.00359 0.9912 0.994 0.005604 0.9653 0.977 0.01353 ] Network output: [ 0.04081 -0.1856 0.9575 -0.000249 0.0001118 1.146 -0.0001876 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2614 0.1624 0.3823 0.1676 0.9853 0.9941 0.2623 0.8832 0.9723 0.6997 ] Network output: [ -0.03443 0.1855 1.077 0.0001993 -8.945e-05 0.8073 0.0001502 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.1031 0.19 0.138 0.9903 0.9943 0.1112 0.9594 0.976 0.2187 ] Network output: [ -0.03114 0.06712 1.066 0.0002841 -0.0001275 0.9306 0.0002141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1292 0.1276 0.2051 0.1615 0.986 0.9919 0.1292 0.9362 0.9659 0.2143 ] Network output: [ -0.01299 1.032 -0.001302 -3.306e-05 1.484e-05 0.9947 -2.492e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03095 Epoch 4825 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03952 0.8819 0.9447 -0.0001032 4.633e-05 0.09386 -7.778e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003954 -0.003655 -0.01371 0.007424 0.9659 0.971 0.008092 0.9153 0.9185 0.02768 ] Network output: [ 0.9429 0.1331 0.03418 -1.268e-05 5.692e-06 -0.053 -9.556e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2411 -0.01523 -0.1824 0.1531 0.9837 0.9934 0.2723 0.8762 0.9693 0.7031 ] Network output: [ 0.012 0.8923 0.9709 -0.0001234 5.54e-05 0.1122 -9.299e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005607 0.001533 0.004641 0.002936 0.9912 0.994 0.005714 0.9655 0.9771 0.0141 ] Network output: [ -0.019 0.08811 0.9449 -0.0005021 0.0002254 1.003 -0.0003784 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2672 0.168 0.4054 0.1076 0.9853 0.9942 0.2681 0.8835 0.9724 0.7007 ] Network output: [ -0.02492 0.1848 1.065 0.000213 -9.562e-05 0.8005 0.0001605 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.1043 0.1888 0.132 0.9904 0.9943 0.1125 0.9595 0.9761 0.216 ] Network output: [ -0.0161 0.03935 1.055 0.0003221 -0.0001446 0.9396 0.0002427 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.131 0.1293 0.2022 0.1614 0.986 0.9919 0.131 0.936 0.966 0.2113 ] Network output: [ 0.01621 0.9207 -0.007364 7.394e-05 -3.32e-05 1.054 5.573e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02651 Epoch 4826 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04583 0.8655 0.9432 -8.763e-05 3.934e-05 0.09929 -6.604e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004001 -0.003763 -0.01428 0.00797 0.9659 0.971 0.008197 0.9154 0.9188 0.02794 ] Network output: [ 1.041 -0.01054 -0.0325 0.000148 -6.643e-05 -0.03874 0.0001115 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2451 -0.0223 -0.2247 0.1851 0.9837 0.9934 0.2768 0.8758 0.9694 0.7061 ] Network output: [ 0.007883 0.8916 0.9763 -0.0001237 5.553e-05 0.1158 -9.322e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005486 0.001428 0.003745 0.003644 0.9912 0.994 0.005591 0.9654 0.977 0.0135 ] Network output: [ 0.04634 -0.2106 0.9589 -0.0002268 0.0001018 1.158 -0.0001709 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2604 0.1612 0.3805 0.1726 0.9853 0.9941 0.2613 0.8833 0.9723 0.7008 ] Network output: [ -0.03493 0.1861 1.077 0.0001984 -8.906e-05 0.8077 0.0001495 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.1031 0.1901 0.1384 0.9903 0.9943 0.1113 0.9595 0.9761 0.219 ] Network output: [ -0.03218 0.06995 1.066 0.0002811 -0.0001262 0.9299 0.0002118 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1293 0.1276 0.2054 0.1614 0.986 0.9919 0.1293 0.9363 0.9659 0.2145 ] Network output: [ -0.01525 1.042 -0.0009571 -4.134e-05 1.856e-05 0.9892 -3.116e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03323 Epoch 4827 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03844 0.8846 0.9454 -0.0001051 4.72e-05 0.09273 -7.924e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003946 -0.003645 -0.01366 0.007383 0.9659 0.971 0.008078 0.9154 0.9185 0.02766 ] Network output: [ 0.9329 0.1428 0.04259 -2.832e-05 1.271e-05 -0.05124 -2.134e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2403 -0.01479 -0.1782 0.1508 0.9837 0.9934 0.2713 0.8764 0.9694 0.7039 ] Network output: [ 0.01203 0.8939 0.9706 -0.0001236 5.55e-05 0.111 -9.317e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005616 0.001535 0.004742 0.002864 0.9912 0.994 0.005723 0.9656 0.9771 0.01418 ] Network output: [ -0.02571 0.1178 0.9442 -0.0005296 0.0002378 0.9873 -0.0003991 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2675 0.1681 0.4084 0.1008 0.9853 0.9942 0.2684 0.8837 0.9724 0.7016 ] Network output: [ -0.02347 0.1853 1.063 0.0002148 -9.644e-05 0.7994 0.0001619 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1046 0.1886 0.1313 0.9904 0.9943 0.1128 0.9595 0.9761 0.2157 ] Network output: [ -0.01403 0.03709 1.052 0.0003265 -0.0001466 0.9403 0.000246 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1315 0.1297 0.2018 0.1613 0.986 0.9919 0.1315 0.936 0.966 0.2108 ] Network output: [ 0.02006 0.9077 -0.008605 8.722e-05 -3.915e-05 1.061 6.573e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02826 Epoch 4828 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04605 0.8649 0.9434 -8.653e-05 3.885e-05 0.09925 -6.522e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004004 -0.003775 -0.01433 0.008035 0.9659 0.971 0.008205 0.9155 0.9188 0.02796 ] Network output: [ 1.051 -0.02866 -0.03811 0.0001632 -7.329e-05 -0.03492 0.000123 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2452 -0.02326 -0.2291 0.189 0.9837 0.9934 0.2769 0.8759 0.9695 0.7073 ] Network output: [ 0.007111 0.8929 0.9771 -0.0001239 5.564e-05 0.1153 -9.34e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005472 0.001409 0.003659 0.003711 0.9912 0.994 0.005576 0.9655 0.977 0.01345 ] Network output: [ 0.05311 -0.2412 0.9606 -0.0001995 8.957e-05 1.174 -0.0001504 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2594 0.16 0.3783 0.1789 0.9853 0.9942 0.2603 0.8833 0.9724 0.7015 ] Network output: [ -0.03558 0.1871 1.077 0.0001971 -8.848e-05 0.8078 0.0001485 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.1031 0.1901 0.1389 0.9903 0.9943 0.1114 0.9595 0.9761 0.2192 ] Network output: [ -0.03347 0.07401 1.066 0.0002771 -0.0001244 0.9283 0.0002089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1293 0.1277 0.2055 0.1612 0.986 0.9919 0.1294 0.9364 0.9659 0.2147 ] Network output: [ -0.01787 1.054 -0.0006008 -5.114e-05 2.296e-05 0.9825 -3.854e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03661 Epoch 4829 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03719 0.8876 0.9461 -0.0001074 4.822e-05 0.09152 -8.094e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003938 -0.003633 -0.01358 0.007331 0.9659 0.971 0.00806 0.9155 0.9186 0.02762 ] Network output: [ 0.9205 0.1548 0.05291 -4.756e-05 2.135e-05 -0.04894 -3.584e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2394 -0.01422 -0.1728 0.1479 0.9837 0.9934 0.2703 0.8765 0.9694 0.7044 ] Network output: [ 0.01215 0.8954 0.9702 -0.0001238 5.558e-05 0.1096 -9.33e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005628 0.00154 0.004866 0.002778 0.9912 0.994 0.005735 0.9656 0.9772 0.01427 ] Network output: [ -0.03373 0.1534 0.9432 -0.0005623 0.0002524 0.9686 -0.0004237 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2679 0.1684 0.4119 0.09267 0.9853 0.9942 0.2688 0.8837 0.9724 0.7023 ] Network output: [ -0.02176 0.1864 1.06 0.0002168 -9.732e-05 0.7977 0.0001634 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1049 0.1883 0.1304 0.9904 0.9943 0.1132 0.9595 0.9761 0.2152 ] Network output: [ -0.01158 0.03503 1.049 0.0003314 -0.0001488 0.9404 0.0002497 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1319 0.1302 0.2012 0.161 0.986 0.9919 0.132 0.936 0.966 0.2101 ] Network output: [ 0.02486 0.8918 -0.01017 0.0001035 -4.646e-05 1.069 7.799e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03114 Epoch 4830 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04638 0.8638 0.9437 -8.513e-05 3.822e-05 0.0994 -6.416e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004007 -0.003789 -0.01439 0.00811 0.9659 0.971 0.008214 0.9155 0.9189 0.02797 ] Network output: [ 1.063 -0.05083 -0.0445 0.0001812 -8.134e-05 -0.02965 0.0001365 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2454 -0.0244 -0.234 0.1937 0.9837 0.9934 0.2771 0.8758 0.9695 0.7081 ] Network output: [ 0.006269 0.8941 0.9779 -0.0001241 5.572e-05 0.1149 -9.354e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005455 0.001389 0.00356 0.003794 0.9912 0.994 0.00556 0.9655 0.977 0.01337 ] Network output: [ 0.06123 -0.2784 0.9625 -0.0001665 7.477e-05 1.193 -0.0001255 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2583 0.1587 0.3756 0.1866 0.9853 0.9942 0.2592 0.8832 0.9723 0.7018 ] Network output: [ -0.03639 0.1889 1.077 0.0001954 -8.77e-05 0.8075 0.0001472 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.1031 0.1901 0.1395 0.9903 0.9943 0.1114 0.9595 0.976 0.2192 ] Network output: [ -0.03502 0.07967 1.066 0.0002721 -0.0001221 0.9258 0.000205 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1293 0.1276 0.2056 0.1609 0.986 0.9919 0.1293 0.9364 0.9659 0.2147 ] Network output: [ -0.02081 1.067 -0.0002983 -6.236e-05 2.799e-05 0.9744 -4.699e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04156 Epoch 4831 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03574 0.891 0.9468 -0.00011 4.94e-05 0.09023 -8.293e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003927 -0.003618 -0.01348 0.007264 0.9659 0.971 0.00804 0.9155 0.9185 0.02756 ] Network output: [ 0.9056 0.1692 0.06536 -7.092e-05 3.184e-05 -0.04605 -5.345e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2384 -0.01351 -0.166 0.1445 0.9837 0.9934 0.2692 0.8764 0.9693 0.7043 ] Network output: [ 0.01237 0.8971 0.9696 -0.0001239 5.563e-05 0.1081 -9.339e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005643 0.001548 0.005013 0.002676 0.9912 0.994 0.005751 0.9657 0.9772 0.01436 ] Network output: [ -0.0431 0.1956 0.9418 -0.0006004 0.0002696 0.9464 -0.0004525 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2686 0.1689 0.4159 0.08315 0.9853 0.9942 0.2695 0.8837 0.9724 0.7024 ] Network output: [ -0.01978 0.1883 1.057 0.0002188 -9.824e-05 0.7952 0.0001649 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1135 0.1053 0.1877 0.1292 0.9904 0.9943 0.1136 0.9595 0.9761 0.2143 ] Network output: [ -0.008705 0.03341 1.046 0.0003367 -0.0001512 0.9399 0.0002538 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1324 0.1307 0.2002 0.1606 0.986 0.9919 0.1324 0.9359 0.966 0.2091 ] Network output: [ 0.03072 0.8724 -0.01208 0.0001232 -5.531e-05 1.079 9.284e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03561 Epoch 4832 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0468 0.8623 0.944 -8.342e-05 3.745e-05 0.09981 -6.287e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00401 -0.003805 -0.01444 0.008196 0.9659 0.971 0.008224 0.9155 0.9189 0.02797 ] Network output: [ 1.076 -0.07773 -0.05135 0.0002017 -9.053e-05 -0.02231 0.000152 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2457 -0.02571 -0.2391 0.1994 0.9837 0.9934 0.2775 0.8756 0.9695 0.7085 ] Network output: [ 0.00536 0.8954 0.9789 -0.0001242 5.576e-05 0.1145 -9.36e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005436 0.001368 0.00345 0.003895 0.9912 0.994 0.005541 0.9654 0.977 0.01328 ] Network output: [ 0.07066 -0.3224 0.9647 -0.0001279 5.741e-05 1.216 -9.637e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2572 0.1574 0.3724 0.1958 0.9853 0.9942 0.258 0.883 0.9723 0.7015 ] Network output: [ -0.03733 0.1915 1.077 0.0001931 -8.671e-05 0.8066 0.0001456 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.1031 0.1898 0.1401 0.9903 0.9942 0.1114 0.9594 0.976 0.2191 ] Network output: [ -0.03678 0.08734 1.065 0.0002658 -0.0001193 0.9219 0.0002003 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1293 0.1275 0.2054 0.1604 0.986 0.9919 0.1293 0.9363 0.9658 0.2145 ] Network output: [ -0.02391 1.082 -0.0001594 -7.454e-05 3.347e-05 0.9652 -5.618e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04856 Epoch 4833 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03409 0.8947 0.9478 -0.0001131 5.076e-05 0.08886 -8.521e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003915 -0.0036 -0.01335 0.007181 0.9659 0.971 0.008015 0.9155 0.9185 0.02747 ] Network output: [ 0.888 0.1864 0.0799 -9.875e-05 4.433e-05 -0.04275 -7.442e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2374 -0.01268 -0.1577 0.1403 0.9837 0.9934 0.268 0.8763 0.9693 0.7037 ] Network output: [ 0.01268 0.8988 0.9689 -0.0001239 5.564e-05 0.1065 -9.34e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00566 0.001559 0.005182 0.002558 0.9912 0.994 0.005769 0.9656 0.9772 0.01444 ] Network output: [ -0.05362 0.2441 0.9398 -0.0006437 0.000289 0.9207 -0.0004851 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2695 0.1696 0.4203 0.07229 0.9853 0.9942 0.2704 0.8835 0.9724 0.7019 ] Network output: [ -0.01752 0.1912 1.053 0.0002208 -9.913e-05 0.7915 0.0001664 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1139 0.1057 0.1869 0.1277 0.9904 0.9943 0.114 0.9594 0.9761 0.2131 ] Network output: [ -0.005443 0.03256 1.041 0.0003423 -0.0001537 0.9383 0.000258 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1329 0.1312 0.1988 0.1599 0.986 0.9919 0.1329 0.9357 0.9659 0.2077 ] Network output: [ 0.03772 0.8494 -0.01433 0.0001465 -6.579e-05 1.09 0.0001104 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04223 Epoch 4834 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04726 0.8602 0.9444 -8.14e-05 3.655e-05 0.1005 -6.135e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004015 -0.003823 -0.01449 0.008291 0.9659 0.971 0.008235 0.9155 0.9189 0.02794 ] Network output: [ 1.09 -0.1097 -0.05805 0.0002239 -0.0001005 -0.01221 0.0001688 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2461 -0.02721 -0.244 0.2061 0.9837 0.9934 0.278 0.8752 0.9694 0.7083 ] Network output: [ 0.004402 0.8967 0.9799 -0.0001241 5.573e-05 0.1141 -9.356e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005416 0.001347 0.003337 0.004012 0.9912 0.994 0.00552 0.9653 0.9769 0.01316 ] Network output: [ 0.08112 -0.3726 0.9674 -8.446e-05 3.792e-05 1.243 -6.365e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2561 0.1561 0.3691 0.2064 0.9853 0.9941 0.2569 0.8825 0.9722 0.7004 ] Network output: [ -0.03834 0.1953 1.077 0.0001905 -8.552e-05 0.8049 0.0001436 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.103 0.1894 0.1406 0.9903 0.9942 0.1114 0.9593 0.9759 0.2186 ] Network output: [ -0.03866 0.09731 1.065 0.0002585 -0.000116 0.9162 0.0001948 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1291 0.1274 0.2048 0.1596 0.986 0.9919 0.1292 0.9361 0.9657 0.2138 ] Network output: [ -0.02687 1.099 -0.000338 -8.675e-05 3.894e-05 0.955 -6.537e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05807 Epoch 4835 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03223 0.8989 0.9488 -0.0001164 5.227e-05 0.08741 -8.775e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003903 -0.003581 -0.0132 0.007081 0.9659 0.971 0.007988 0.9154 0.9183 0.02734 ] Network output: [ 0.8683 0.2065 0.09599 -0.000131 5.881e-05 -0.03966 -9.872e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2363 -0.01177 -0.1483 0.1355 0.9837 0.9934 0.2669 0.8759 0.9692 0.7022 ] Network output: [ 0.01305 0.9005 0.9681 -0.0001238 5.557e-05 0.1048 -9.329e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00568 0.001571 0.00536 0.002423 0.9912 0.994 0.005789 0.9655 0.9771 0.01452 ] Network output: [ -0.06468 0.2974 0.9371 -0.0006904 0.00031 0.892 -0.0005203 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2706 0.1705 0.4246 0.06042 0.9853 0.9942 0.2715 0.8831 0.9723 0.7005 ] Network output: [ -0.01507 0.1954 1.049 0.0002226 -9.995e-05 0.7867 0.0001678 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1143 0.106 0.1857 0.1259 0.9904 0.9943 0.1144 0.9592 0.976 0.2114 ] Network output: [ -0.001933 0.0328 1.037 0.0003479 -0.0001562 0.9357 0.0002622 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1334 0.1316 0.1971 0.1589 0.986 0.9919 0.1334 0.9353 0.9658 0.2058 ] Network output: [ 0.0457 0.823 -0.01675 0.0001732 -7.777e-05 1.103 0.0001306 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05141 Epoch 4836 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0477 0.8577 0.9449 -7.918e-05 3.555e-05 0.1016 -5.967e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004019 -0.003841 -0.01451 0.008391 0.9659 0.9711 0.008244 0.9153 0.9187 0.02788 ] Network output: [ 1.105 -0.146 -0.06371 0.0002464 -0.0001106 0.00112 0.0001857 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2466 -0.02886 -0.2482 0.2135 0.9837 0.9934 0.2786 0.8745 0.9693 0.7072 ] Network output: [ 0.003429 0.8979 0.9809 -0.0001238 5.56e-05 0.1138 -9.333e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005396 0.001327 0.003231 0.004141 0.9912 0.994 0.0055 0.9652 0.9768 0.01301 ] Network output: [ 0.092 -0.4271 0.9705 -3.865e-05 1.735e-05 1.272 -2.913e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2551 0.1548 0.3658 0.2178 0.9853 0.9941 0.2559 0.8819 0.9721 0.6982 ] Network output: [ -0.03933 0.2003 1.077 0.0001876 -8.422e-05 0.8023 0.0001414 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.1029 0.1887 0.1412 0.9903 0.9942 0.1114 0.959 0.9758 0.2178 ] Network output: [ -0.04048 0.1096 1.064 0.0002504 -0.0001124 0.9087 0.0001887 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.129 0.1273 0.2038 0.1586 0.986 0.9919 0.129 0.9358 0.9655 0.2128 ] Network output: [ -0.02927 1.114 -0.0009923 -9.741e-05 4.373e-05 0.9448 -7.341e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07024 Epoch 4837 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03022 0.9033 0.9499 -0.00012 5.388e-05 0.08588 -9.045e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00389 -0.00356 -0.01303 0.006963 0.9659 0.971 0.007959 0.9151 0.918 0.02717 ] Network output: [ 0.8476 0.23 0.1123 -0.000167 7.498e-05 -0.03808 -0.0001259 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2354 -0.01086 -0.1383 0.1301 0.9837 0.9934 0.2659 0.8753 0.969 0.6997 ] Network output: [ 0.01342 0.9021 0.9674 -0.0001234 5.54e-05 0.1031 -9.299e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005702 0.001582 0.00553 0.002275 0.9911 0.994 0.005811 0.9653 0.977 0.01456 ] Network output: [ -0.07521 0.3525 0.9333 -0.0007375 0.0003311 0.8616 -0.0005558 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2719 0.1715 0.4285 0.04822 0.9853 0.9942 0.2728 0.8824 0.9722 0.6981 ] Network output: [ -0.01261 0.2009 1.044 0.0002241 -0.0001006 0.7809 0.0001689 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1146 0.1063 0.1842 0.1239 0.9904 0.9943 0.1147 0.9589 0.9758 0.2092 ] Network output: [ 0.001535 0.03428 1.032 0.0003531 -0.0001585 0.932 0.0002661 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1338 0.132 0.1949 0.1578 0.986 0.9919 0.1338 0.9348 0.9656 0.2034 ] Network output: [ 0.05421 0.7938 -0.01896 0.0002022 -9.079e-05 1.118 0.0001524 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06318 Epoch 4838 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04798 0.855 0.9457 -7.688e-05 3.452e-05 0.1031 -5.794e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004022 -0.00386 -0.0145 0.008487 0.9659 0.9711 0.008251 0.915 0.9185 0.02777 ] Network output: [ 1.118 -0.1846 -0.06733 0.0002662 -0.0001195 0.01749 0.0002006 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2472 -0.0306 -0.251 0.2211 0.9837 0.9934 0.2793 0.8736 0.9691 0.7052 ] Network output: [ 0.002485 0.8991 0.9819 -0.0001232 5.53e-05 0.1135 -9.282e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005379 0.001308 0.003147 0.004276 0.9912 0.994 0.005482 0.9649 0.9767 0.01285 ] Network output: [ 0.1024 -0.4821 0.974 5.68e-06 -2.55e-06 1.303 4.28e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2543 0.1537 0.3632 0.2293 0.9852 0.9941 0.2552 0.8809 0.9719 0.6949 ] Network output: [ -0.04019 0.2061 1.076 0.0001849 -8.299e-05 0.7989 0.0001393 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.1029 0.1878 0.1416 0.9902 0.9942 0.1114 0.9586 0.9756 0.2167 ] Network output: [ -0.04203 0.1238 1.062 0.0002423 -0.0001088 0.8997 0.0001826 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1289 0.1272 0.2024 0.1573 0.986 0.9919 0.1289 0.9353 0.9652 0.2112 ] Network output: [ -0.03068 1.127 -0.002193 -0.0001046 4.697e-05 0.9357 -7.885e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08449 Epoch 4839 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02816 0.9077 0.9511 -0.0001235 5.545e-05 0.0843 -9.308e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003879 -0.003542 -0.01286 0.006832 0.9659 0.971 0.007932 0.9147 0.9177 0.02696 ] Network output: [ 0.8277 0.2568 0.1268 -0.0002053 9.218e-05 -0.03984 -0.0001547 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2348 -0.01007 -0.1289 0.1241 0.9837 0.9934 0.2651 0.8743 0.9688 0.6962 ] Network output: [ 0.01372 0.9034 0.9669 -0.0001226 5.504e-05 0.1018 -9.24e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005725 0.001589 0.005664 0.002122 0.9911 0.994 0.005834 0.965 0.9768 0.01456 ] Network output: [ -0.08376 0.4046 0.9283 -0.0007805 0.0003504 0.8315 -0.0005882 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2733 0.1723 0.4312 0.03673 0.9853 0.9942 0.2742 0.8813 0.972 0.6946 ] Network output: [ -0.01044 0.2072 1.04 0.0002251 -0.000101 0.7745 0.0001696 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1148 0.1064 0.1824 0.1218 0.9904 0.9943 0.1149 0.9584 0.9756 0.2067 ] Network output: [ 0.004531 0.03683 1.028 0.0003573 -0.0001604 0.9278 0.0002693 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.134 0.1322 0.1925 0.1565 0.9859 0.9919 0.134 0.934 0.9653 0.2008 ] Network output: [ 0.06244 0.7631 -0.02039 0.0002316 -0.000104 1.133 0.0001746 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07691 Epoch 4840 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.048 0.8522 0.9467 -7.466e-05 3.352e-05 0.1048 -5.627e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004025 -0.003876 -0.01446 0.008569 0.9659 0.9711 0.008254 0.9145 0.9182 0.02762 ] Network output: [ 1.128 -0.2221 -0.06821 0.0002803 -0.0001258 0.03572 0.0002112 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2478 -0.03232 -0.2517 0.2284 0.9837 0.9934 0.28 0.8722 0.9689 0.702 ] Network output: [ 0.00161 0.9001 0.9829 -0.000122 5.476e-05 0.1133 -9.193e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005367 0.001291 0.003095 0.004405 0.9911 0.994 0.00547 0.9645 0.9765 0.01269 ] Network output: [ 0.1113 -0.5328 0.9775 4.398e-05 -1.975e-05 1.333 3.315e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.254 0.1528 0.3614 0.2398 0.9852 0.9941 0.2548 0.8795 0.9717 0.6903 ] Network output: [ -0.04088 0.2124 1.075 0.0001827 -8.202e-05 0.795 0.0001377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.1029 0.1868 0.142 0.9902 0.9942 0.1115 0.9581 0.9753 0.2152 ] Network output: [ -0.04319 0.1386 1.059 0.0002351 -0.0001056 0.8897 0.0001772 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1289 0.1272 0.2006 0.156 0.986 0.9919 0.1289 0.9345 0.9648 0.2092 ] Network output: [ -0.03087 1.136 -0.003846 -0.000107 4.803e-05 0.9293 -8.062e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0994 Epoch 4841 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0262 0.912 0.9523 -0.0001265 5.679e-05 0.08272 -9.533e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00387 -0.003529 -0.0127 0.006694 0.9659 0.971 0.00791 0.9142 0.9172 0.02673 ] Network output: [ 0.8109 0.286 0.1374 -0.0002436 0.0001093 -0.04627 -0.0001836 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2345 -0.009522 -0.1212 0.1179 0.9837 0.9934 0.2648 0.873 0.9685 0.6917 ] Network output: [ 0.01384 0.9043 0.9667 -0.0001213 5.444e-05 0.1009 -9.138e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005748 0.001589 0.005738 0.001975 0.9911 0.994 0.005857 0.9645 0.9766 0.0145 ] Network output: [ -0.08903 0.4488 0.922 -0.0008152 0.000366 0.804 -0.0006143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2747 0.1728 0.4321 0.02705 0.9853 0.9942 0.2756 0.88 0.9717 0.6903 ] Network output: [ -0.008865 0.2137 1.037 0.0002255 -0.0001013 0.7682 0.00017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1149 0.1064 0.1803 0.1199 0.9904 0.9942 0.115 0.9577 0.9753 0.2041 ] Network output: [ 0.006611 0.04005 1.025 0.0003602 -0.0001617 0.9236 0.0002715 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.134 0.1322 0.1901 0.1552 0.9859 0.9919 0.134 0.9331 0.9649 0.1982 ] Network output: [ 0.06937 0.7335 -0.02052 0.0002586 -0.0001161 1.149 0.0001949 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09114 Epoch 4842 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04767 0.8497 0.9479 -7.257e-05 3.258e-05 0.1067 -5.469e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004026 -0.003891 -0.01439 0.00863 0.966 0.9711 0.008255 0.9139 0.9177 0.02744 ] Network output: [ 1.134 -0.2546 -0.06621 0.0002863 -0.0001285 0.05406 0.0002158 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2485 -0.03392 -0.2502 0.2345 0.9837 0.9934 0.2807 0.8705 0.9686 0.6979 ] Network output: [ 0.0008391 0.9008 0.9838 -0.0001201 5.393e-05 0.1133 -9.052e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005364 0.001277 0.003079 0.004518 0.9911 0.994 0.005467 0.964 0.9762 0.01253 ] Network output: [ 0.1181 -0.5746 0.9806 7.262e-05 -3.26e-05 1.358 5.473e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2541 0.1522 0.3608 0.2485 0.9852 0.9941 0.255 0.8778 0.9714 0.6849 ] Network output: [ -0.0414 0.2183 1.074 0.0001815 -8.147e-05 0.7914 0.0001368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.103 0.1857 0.1422 0.9901 0.9942 0.1116 0.9575 0.975 0.2137 ] Network output: [ -0.04391 0.1528 1.056 0.0002296 -0.0001031 0.8799 0.000173 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.129 0.1272 0.1985 0.1547 0.9859 0.9919 0.129 0.9336 0.9644 0.2069 ] Network output: [ -0.02998 1.139 -0.005756 -0.0001044 4.689e-05 0.9261 -7.871e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1131 Epoch 4843 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02448 0.9157 0.9535 -0.0001285 5.767e-05 0.08129 -9.682e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003867 -0.003522 -0.01257 0.006562 0.9659 0.971 0.007896 0.9135 0.9166 0.0265 ] Network output: [ 0.7989 0.3151 0.1429 -0.0002786 0.0001251 -0.05695 -0.0002099 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2348 -0.009284 -0.1158 0.112 0.9837 0.9933 0.265 0.8713 0.9681 0.6866 ] Network output: [ 0.01374 0.9046 0.967 -0.0001192 5.352e-05 0.1005 -8.984e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00577 0.00158 0.005743 0.00185 0.991 0.994 0.00588 0.964 0.9763 0.01438 ] Network output: [ -0.09046 0.481 0.915 -0.0008382 0.0003763 0.7816 -0.0006317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.276 0.1731 0.4311 0.0201 0.9853 0.9942 0.277 0.8782 0.9714 0.6852 ] Network output: [ -0.008126 0.2198 1.034 0.0002256 -0.0001013 0.7629 0.00017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1149 0.1064 0.1784 0.1183 0.9903 0.9942 0.115 0.957 0.9749 0.2017 ] Network output: [ 0.007501 0.0437 1.023 0.0003616 -0.0001623 0.92 0.0002725 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1339 0.1321 0.1879 0.1541 0.9859 0.9918 0.1339 0.932 0.9645 0.1959 ] Network output: [ 0.07408 0.7083 -0.01926 0.0002801 -0.0001257 1.164 0.0002111 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1039 Epoch 4844 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04704 0.8476 0.9494 -7.052e-05 3.166e-05 0.1086 -5.315e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004027 -0.003903 -0.01429 0.008665 0.966 0.9711 0.008253 0.9132 0.9171 0.02723 ] Network output: [ 1.136 -0.2798 -0.06168 0.0002837 -0.0001273 0.0709 0.0002138 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2492 -0.0353 -0.2467 0.239 0.9837 0.9934 0.2815 0.8685 0.9682 0.6931 ] Network output: [ 0.0002277 0.9011 0.9845 -0.0001175 5.274e-05 0.1134 -8.854e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00537 0.001265 0.0031 0.004609 0.9911 0.994 0.005474 0.9634 0.9759 0.01239 ] Network output: [ 0.1223 -0.6046 0.9831 8.993e-05 -4.037e-05 1.377 6.777e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2548 0.1519 0.3612 0.2548 0.9852 0.9941 0.2556 0.8759 0.971 0.6789 ] Network output: [ -0.04179 0.2233 1.073 0.0001813 -8.14e-05 0.7883 0.0001366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.1032 0.1848 0.1424 0.9901 0.9941 0.1119 0.9567 0.9746 0.2122 ] Network output: [ -0.04424 0.1653 1.053 0.000226 -0.0001014 0.8711 0.0001703 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1292 0.1274 0.1965 0.1535 0.9859 0.9919 0.1292 0.9326 0.9639 0.2047 ] Network output: [ -0.02844 1.138 -0.007743 -9.841e-05 4.418e-05 0.9257 -7.417e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1239 Epoch 4845 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02317 0.9184 0.9546 -0.000129 5.791e-05 0.08017 -9.721e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003868 -0.003523 -0.01247 0.006451 0.9659 0.971 0.007892 0.9127 0.9159 0.02627 ] Network output: [ 0.7924 0.3401 0.1435 -0.0003072 0.0001379 -0.06971 -0.0002315 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2355 -0.009356 -0.113 0.1071 0.9836 0.9933 0.2658 0.8693 0.9677 0.681 ] Network output: [ 0.01348 0.9043 0.9677 -0.0001164 5.226e-05 0.1006 -8.773e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005793 0.001567 0.005688 0.00176 0.991 0.9939 0.005903 0.9633 0.9759 0.01423 ] Network output: [ -0.08833 0.4989 0.908 -0.0008481 0.0003807 0.7663 -0.0006391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2773 0.1731 0.4287 0.01638 0.9853 0.9941 0.2782 0.8762 0.971 0.6798 ] Network output: [ -0.008265 0.2251 1.034 0.0002252 -0.0001011 0.7588 0.0001697 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1148 0.1062 0.1767 0.1172 0.9903 0.9942 0.1149 0.9561 0.9745 0.1996 ] Network output: [ 0.007198 0.04769 1.022 0.0003614 -0.0001622 0.917 0.0002724 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1337 0.1319 0.1862 0.1533 0.9858 0.9918 0.1337 0.9308 0.964 0.194 ] Network output: [ 0.07611 0.6907 -0.01692 0.0002936 -0.0001318 1.175 0.0002212 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1132 Epoch 4846 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04623 0.846 0.9509 -6.84e-05 3.071e-05 0.1104 -5.155e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004028 -0.003911 -0.01417 0.008673 0.966 0.9711 0.00825 0.9124 0.9164 0.02702 ] Network output: [ 1.134 -0.2958 -0.05534 0.000273 -0.0001225 0.08474 0.0002057 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2498 -0.03634 -0.2415 0.2415 0.9837 0.9934 0.2822 0.8663 0.9678 0.6879 ] Network output: [ -0.0001416 0.9009 0.9852 -0.0001141 5.12e-05 0.1137 -8.595e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005386 0.001259 0.003151 0.004672 0.991 0.9939 0.005489 0.9628 0.9756 0.01228 ] Network output: [ 0.1239 -0.6212 0.9845 9.579e-05 -4.3e-05 1.389 7.219e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2559 0.152 0.3624 0.2583 0.9852 0.9941 0.2567 0.8737 0.9706 0.6728 ] Network output: [ -0.04207 0.227 1.072 0.0001822 -8.181e-05 0.7862 0.0001373 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.1035 0.1842 0.1426 0.9901 0.9941 0.1122 0.9559 0.9742 0.2109 ] Network output: [ -0.04424 0.1753 1.05 0.0002244 -0.0001007 0.864 0.0001691 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1295 0.1277 0.1947 0.1526 0.9859 0.9919 0.1295 0.9315 0.9634 0.2027 ] Network output: [ -0.02671 1.135 -0.009563 -9.066e-05 4.07e-05 0.9276 -6.833e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1306 Epoch 4847 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0224 0.9196 0.9556 -0.0001278 5.738e-05 0.07952 -9.633e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003874 -0.00353 -0.01241 0.00637 0.9659 0.971 0.007897 0.9119 0.9151 0.02606 ] Network output: [ 0.7915 0.3582 0.1399 -0.0003274 0.000147 -0.08231 -0.0002468 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2366 -0.00969 -0.1123 0.1038 0.9836 0.9933 0.267 0.8671 0.9672 0.6754 ] Network output: [ 0.01313 0.9034 0.9687 -0.0001128 5.066e-05 0.1012 -8.505e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005813 0.001553 0.005592 0.001714 0.991 0.9939 0.005924 0.9626 0.9755 0.01406 ] Network output: [ -0.08338 0.5024 0.9018 -0.0008451 0.0003794 0.7591 -0.0006369 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2784 0.173 0.4252 0.01592 0.9853 0.9941 0.2793 0.874 0.9706 0.6744 ] Network output: [ -0.00917 0.229 1.034 0.0002247 -0.0001009 0.7563 0.0001693 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1147 0.106 0.1754 0.1166 0.9903 0.9941 0.1148 0.9552 0.9741 0.1981 ] Network output: [ 0.0059 0.05169 1.023 0.0003598 -0.0001615 0.9147 0.0002712 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1334 0.1315 0.1849 0.1527 0.9858 0.9918 0.1334 0.9295 0.9635 0.1926 ] Network output: [ 0.0755 0.6819 -0.01385 0.0002983 -0.0001339 1.182 0.0002248 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1179 Epoch 4848 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04541 0.8448 0.9523 -6.614e-05 2.969e-05 0.1118 -4.984e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00403 -0.003916 -0.01404 0.008652 0.966 0.9711 0.008246 0.9115 0.9157 0.02682 ] Network output: [ 1.128 -0.3011 -0.04836 0.0002552 -0.0001146 0.09379 0.0001923 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2505 -0.03697 -0.2354 0.2419 0.9836 0.9933 0.2829 0.864 0.9674 0.6827 ] Network output: [ -0.0002014 0.9 0.9857 -0.0001099 4.933e-05 0.1142 -8.281e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005409 0.001258 0.003221 0.004702 0.991 0.9939 0.005513 0.9621 0.9752 0.01221 ] Network output: [ 0.1231 -0.6235 0.9846 9.073e-05 -4.073e-05 1.393 6.838e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2574 0.1525 0.3642 0.2589 0.9852 0.9941 0.2582 0.8715 0.9702 0.6671 ] Network output: [ -0.04225 0.2289 1.071 0.0001842 -8.269e-05 0.7853 0.0001388 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.1038 0.1838 0.1427 0.99 0.9941 0.1126 0.9551 0.9738 0.21 ] Network output: [ -0.044 0.1815 1.048 0.000225 -0.000101 0.8593 0.0001695 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1298 0.128 0.1933 0.152 0.9859 0.9919 0.1298 0.9303 0.9629 0.2011 ] Network output: [ -0.02522 1.13 -0.01081 -8.278e-05 3.716e-05 0.9311 -6.239e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1323 Epoch 4849 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02226 0.9193 0.9563 -0.0001249 5.607e-05 0.07941 -9.412e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003884 -0.003542 -0.01237 0.006324 0.9659 0.971 0.00791 0.911 0.9144 0.02588 ] Network output: [ 0.7954 0.3678 0.1331 -0.0003386 0.000152 -0.09314 -0.0002552 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.238 -0.01023 -0.1135 0.1022 0.9836 0.9933 0.2686 0.8648 0.9668 0.6702 ] Network output: [ 0.01282 0.9019 0.9699 -0.0001086 4.875e-05 0.1021 -8.183e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005832 0.001541 0.005472 0.001712 0.9909 0.9939 0.005943 0.9619 0.975 0.01388 ] Network output: [ -0.07652 0.4928 0.8971 -0.0008308 0.000373 0.7598 -0.0006261 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2794 0.1729 0.4212 0.0184 0.9852 0.9941 0.2803 0.8718 0.9702 0.6694 ] Network output: [ -0.01066 0.231 1.036 0.0002243 -0.0001007 0.7556 0.0001691 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1145 0.1058 0.1747 0.1166 0.9902 0.9941 0.1146 0.9543 0.9736 0.1973 ] Network output: [ 0.003881 0.05507 1.025 0.0003574 -0.0001605 0.9134 0.0002694 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1331 0.1312 0.1843 0.1524 0.9858 0.9918 0.1331 0.9283 0.963 0.1919 ] Network output: [ 0.0726 0.6814 -0.01038 0.0002948 -0.0001324 1.185 0.0002222 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1176 Epoch 4850 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04473 0.8441 0.9535 -6.372e-05 2.861e-05 0.1127 -4.802e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004031 -0.003917 -0.01392 0.008605 0.966 0.9711 0.008242 0.9106 0.915 0.02664 ] Network output: [ 1.121 -0.295 -0.04197 0.0002317 -0.000104 0.09664 0.0001746 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2512 -0.03719 -0.229 0.24 0.9836 0.9933 0.2836 0.8616 0.9669 0.6778 ] Network output: [ 7.687e-05 0.8985 0.9861 -0.0001051 4.718e-05 0.1148 -7.92e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005437 0.001262 0.003298 0.004696 0.991 0.9939 0.005542 0.9614 0.9748 0.01216 ] Network output: [ 0.1204 -0.6115 0.983 7.575e-05 -3.401e-05 1.388 5.709e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.259 0.1533 0.366 0.2566 0.9852 0.9941 0.2599 0.8692 0.9698 0.6622 ] Network output: [ -0.04239 0.2285 1.071 0.000187 -8.395e-05 0.7858 0.0001409 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1041 0.1837 0.1427 0.99 0.9941 0.113 0.9543 0.9734 0.2095 ] Network output: [ -0.04362 0.1834 1.047 0.0002275 -0.0001021 0.8578 0.0001715 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1301 0.1283 0.1925 0.1518 0.9859 0.9918 0.1301 0.9292 0.9624 0.2002 ] Network output: [ -0.02438 1.124 -0.01116 -7.625e-05 3.423e-05 0.9357 -5.747e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1286 Epoch 4851 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02279 0.9173 0.9568 -0.0001202 5.398e-05 0.07991 -9.061e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003897 -0.003557 -0.01237 0.006316 0.9659 0.971 0.007928 0.9102 0.9137 0.02575 ] Network output: [ 0.8035 0.3681 0.1242 -0.0003405 0.0001529 -0.1007 -0.0002566 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2396 -0.01093 -0.1159 0.1025 0.9836 0.9933 0.2703 0.8625 0.9663 0.6658 ] Network output: [ 0.01263 0.8999 0.9711 -0.0001037 4.656e-05 0.1033 -7.816e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005846 0.001531 0.005348 0.001754 0.9909 0.9938 0.005957 0.9612 0.9746 0.01372 ] Network output: [ -0.06864 0.4714 0.8942 -0.0008073 0.0003624 0.7684 -0.0006084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2801 0.1729 0.4174 0.02339 0.9852 0.9941 0.2811 0.8696 0.9698 0.6651 ] Network output: [ -0.01252 0.2307 1.038 0.0002244 -0.0001007 0.757 0.0001691 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1144 0.1056 0.1746 0.1172 0.9902 0.9941 0.1144 0.9535 0.9732 0.1972 ] Network output: [ 0.001437 0.05748 1.028 0.0003546 -0.0001592 0.9132 0.0002672 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1327 0.1309 0.1843 0.1525 0.9858 0.9918 0.1328 0.9272 0.9625 0.1919 ] Network output: [ 0.06785 0.6891 -0.006974 0.0002839 -0.0001275 1.183 0.000214 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1127 Epoch 4852 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04432 0.8439 0.9542 -6.112e-05 2.744e-05 0.113 -4.606e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004034 -0.003915 -0.01382 0.008535 0.966 0.9711 0.008238 0.9098 0.9143 0.0265 ] Network output: [ 1.111 -0.2781 -0.03689 0.0002041 -9.163e-05 0.09293 0.0001538 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2518 -0.03699 -0.223 0.2361 0.9836 0.9933 0.2843 0.8595 0.9665 0.6737 ] Network output: [ 0.0007102 0.8965 0.9862 -9.985e-05 4.483e-05 0.1155 -7.525e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005468 0.001269 0.003376 0.004652 0.991 0.9939 0.005573 0.9608 0.9745 0.01216 ] Network output: [ 0.1159 -0.5865 0.98 5.213e-05 -2.34e-05 1.375 3.929e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2608 0.1543 0.3679 0.2515 0.9852 0.9941 0.2617 0.8672 0.9694 0.6585 ] Network output: [ -0.04247 0.2259 1.072 0.0001906 -8.556e-05 0.7879 0.0001436 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1043 0.1841 0.1427 0.99 0.994 0.1132 0.9536 0.9731 0.2096 ] Network output: [ -0.04315 0.1808 1.047 0.0002319 -0.0001041 0.8594 0.0001748 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1304 0.1286 0.1923 0.152 0.9859 0.9918 0.1304 0.9282 0.962 0.2 ] Network output: [ -0.02434 1.118 -0.01058 -7.198e-05 3.231e-05 0.9406 -5.425e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1202 Epoch 4853 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02396 0.9137 0.9569 -0.000114 5.118e-05 0.08099 -8.592e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00391 -0.003575 -0.0124 0.006347 0.9659 0.9711 0.007948 0.9094 0.913 0.02567 ] Network output: [ 0.8147 0.3586 0.1144 -0.0003333 0.0001496 -0.1038 -0.0002512 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2412 -0.01176 -0.1193 0.1046 0.9836 0.9933 0.2721 0.8604 0.9659 0.6625 ] Network output: [ 0.01264 0.8975 0.9722 -9.838e-05 4.416e-05 0.1047 -7.414e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005855 0.001525 0.005234 0.001836 0.9909 0.9938 0.005966 0.9606 0.9743 0.01359 ] Network output: [ -0.06044 0.4396 0.8937 -0.0007762 0.0003485 0.7844 -0.000585 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2806 0.1729 0.4141 0.03051 0.9852 0.9941 0.2815 0.8676 0.9694 0.6618 ] Network output: [ -0.01457 0.2281 1.042 0.000225 -0.000101 0.7603 0.0001696 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1142 0.1055 0.1751 0.1183 0.9901 0.9941 0.1143 0.9528 0.9729 0.1978 ] Network output: [ -0.001172 0.05894 1.031 0.0003516 -0.0001578 0.9139 0.000265 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1325 0.1306 0.1849 0.153 0.9858 0.9918 0.1325 0.9263 0.9621 0.1924 ] Network output: [ 0.06174 0.7046 -0.004079 0.0002665 -0.0001196 1.177 0.0002008 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1037 Epoch 4854 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04423 0.8442 0.9545 -5.838e-05 2.621e-05 0.1126 -4.4e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004036 -0.00391 -0.01374 0.008444 0.966 0.9711 0.008235 0.9091 0.9136 0.0264 ] Network output: [ 1.101 -0.2516 -0.03336 0.0001739 -7.806e-05 0.08303 0.000131 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2524 -0.03642 -0.2175 0.2304 0.9836 0.9933 0.285 0.8575 0.966 0.6706 ] Network output: [ 0.001698 0.8939 0.9861 -9.434e-05 4.235e-05 0.1162 -7.11e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005499 0.001278 0.003452 0.004572 0.9909 0.9939 0.005605 0.9603 0.9742 0.01219 ] Network output: [ 0.1099 -0.55 0.9759 2.099e-05 -9.421e-06 1.354 1.582e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2625 0.1554 0.3699 0.2438 0.9852 0.9941 0.2634 0.8653 0.969 0.6562 ] Network output: [ -0.04242 0.2212 1.073 0.0001948 -8.744e-05 0.7913 0.0001468 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1045 0.1847 0.1425 0.9899 0.994 0.1134 0.9529 0.9728 0.2101 ] Network output: [ -0.04251 0.1738 1.048 0.0002379 -0.0001068 0.8639 0.0001793 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1306 0.1288 0.1928 0.1526 0.9859 0.9918 0.1306 0.9274 0.9617 0.2004 ] Network output: [ -0.02489 1.113 -0.009197 -6.965e-05 3.127e-05 0.9457 -5.249e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1082 Epoch 4855 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02571 0.909 0.9566 -0.0001066 4.784e-05 0.08254 -8.03e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003923 -0.003594 -0.01245 0.006412 0.966 0.9711 0.007969 0.9088 0.9125 0.02564 ] Network output: [ 0.8284 0.3407 0.1041 -0.0003181 0.0001428 -0.1029 -0.0002397 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2427 -0.0127 -0.1233 0.1083 0.9836 0.9933 0.2737 0.8585 0.9655 0.6605 ] Network output: [ 0.01285 0.8948 0.973 -9.278e-05 4.165e-05 0.1061 -6.992e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005856 0.00152 0.005133 0.001951 0.9909 0.9938 0.005968 0.9601 0.974 0.0135 ] Network output: [ -0.0522 0.399 0.8956 -0.0007395 0.000332 0.8069 -0.0005573 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2807 0.1728 0.4116 0.03933 0.9852 0.9941 0.2817 0.8658 0.9691 0.6597 ] Network output: [ -0.01665 0.2234 1.045 0.0002263 -0.0001016 0.7654 0.0001705 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1141 0.1054 0.1763 0.1198 0.9901 0.9941 0.1142 0.9522 0.9726 0.1992 ] Network output: [ -0.003775 0.05935 1.034 0.0003486 -0.0001565 0.9156 0.0002627 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1322 0.1304 0.186 0.1536 0.9858 0.9918 0.1323 0.9255 0.9617 0.1936 ] Network output: [ 0.05475 0.7264 -0.001858 0.000244 -0.0001096 1.167 0.0001839 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09181 Epoch 4856 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04445 0.8451 0.9542 -5.566e-05 2.499e-05 0.1116 -4.195e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004039 -0.003902 -0.01369 0.008337 0.966 0.9711 0.008232 0.9086 0.9131 0.02634 ] Network output: [ 1.091 -0.2169 -0.03136 0.0001421 -6.381e-05 0.06769 0.0001071 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2529 -0.03554 -0.2128 0.2232 0.9836 0.9933 0.2855 0.8559 0.9657 0.6687 ] Network output: [ 0.002982 0.8912 0.9856 -8.879e-05 3.986e-05 0.1168 -6.692e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005529 0.001287 0.003524 0.004458 0.9909 0.9939 0.005634 0.9599 0.9739 0.01225 ] Network output: [ 0.1026 -0.5043 0.9711 -1.64e-05 7.361e-06 1.328 -1.236e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2641 0.1565 0.3719 0.2339 0.9852 0.9941 0.2649 0.8639 0.9687 0.6554 ] Network output: [ -0.04219 0.2146 1.075 0.0001995 -8.957e-05 0.7958 0.0001504 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1134 0.1045 0.1857 0.1423 0.9899 0.994 0.1134 0.9524 0.9725 0.2111 ] Network output: [ -0.04165 0.1631 1.05 0.0002455 -0.0001102 0.8711 0.000185 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1307 0.1288 0.1937 0.1534 0.9859 0.9918 0.1307 0.9267 0.9614 0.2015 ] Network output: [ -0.02567 1.107 -0.007142 -6.806e-05 3.055e-05 0.9512 -5.129e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0939 Epoch 4857 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02792 0.9034 0.956 -9.834e-05 4.415e-05 0.08435 -7.411e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003936 -0.003613 -0.01253 0.006503 0.966 0.9711 0.00799 0.9083 0.9121 0.02566 ] Network output: [ 0.844 0.3167 0.09336 -0.0002967 0.0001332 -0.09925 -0.0002236 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.244 -0.01375 -0.128 0.1131 0.9836 0.9933 0.2752 0.857 0.9653 0.6599 ] Network output: [ 0.01323 0.8919 0.9737 -8.718e-05 3.914e-05 0.1075 -6.57e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005851 0.001515 0.005044 0.002089 0.9909 0.9938 0.005962 0.9597 0.9737 0.01343 ] Network output: [ -0.04409 0.3518 0.8993 -0.0006992 0.0003139 0.8342 -0.0005269 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2805 0.1725 0.4098 0.04928 0.9852 0.9941 0.2814 0.8644 0.9688 0.6589 ] Network output: [ -0.01867 0.2167 1.05 0.000228 -0.0001024 0.772 0.0001719 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.114 0.1053 0.178 0.1217 0.9901 0.9941 0.1141 0.9519 0.9724 0.2011 ] Network output: [ -0.006291 0.05867 1.037 0.0003459 -0.0001553 0.9181 0.0002607 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1321 0.1302 0.1876 0.1545 0.9858 0.9918 0.1321 0.9251 0.9615 0.1952 ] Network output: [ 0.0473 0.7527 -0.0002875 0.0002183 -9.802e-05 1.154 0.0001645 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07861 Epoch 4858 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04488 0.8465 0.9536 -5.308e-05 2.383e-05 0.11 -4.001e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004041 -0.003892 -0.01366 0.00822 0.966 0.9711 0.008229 0.9082 0.9127 0.02633 ] Network output: [ 1.08 -0.1768 -0.03054 0.0001104 -4.957e-05 0.04856 8.321e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2533 -0.03443 -0.2088 0.2149 0.9836 0.9933 0.2858 0.8547 0.9654 0.6679 ] Network output: [ 0.004451 0.8885 0.985 -8.347e-05 3.747e-05 0.1173 -6.291e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005555 0.001296 0.003593 0.00432 0.9909 0.9938 0.005661 0.9596 0.9737 0.01234 ] Network output: [ 0.09436 -0.4527 0.9661 -5.785e-05 2.597e-05 1.298 -4.359e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2655 0.1575 0.3742 0.2226 0.9852 0.9941 0.2663 0.8628 0.9685 0.656 ] Network output: [ -0.04176 0.2068 1.077 0.0002046 -9.187e-05 0.801 0.0001542 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1045 0.1868 0.142 0.99 0.994 0.1134 0.9521 0.9723 0.2124 ] Network output: [ -0.04056 0.1496 1.052 0.0002542 -0.0001141 0.8801 0.0001915 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1307 0.1289 0.1951 0.1545 0.9859 0.9919 0.1307 0.9262 0.9612 0.2029 ] Network output: [ -0.02628 1.1 -0.004734 -6.605e-05 2.965e-05 0.9574 -4.978e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07919 Epoch 4859 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03037 0.8976 0.9551 -8.994e-05 4.038e-05 0.08616 -6.778e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003948 -0.003633 -0.01262 0.006611 0.966 0.9711 0.00801 0.908 0.9119 0.02572 ] Network output: [ 0.8605 0.2892 0.08245 -0.0002713 0.0001218 -0.09384 -0.0002045 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2452 -0.01488 -0.1332 0.1186 0.9836 0.9933 0.2765 0.8559 0.9651 0.6605 ] Network output: [ 0.01369 0.8893 0.9741 -8.189e-05 3.676e-05 0.1088 -6.171e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005839 0.001507 0.004967 0.002239 0.9909 0.9938 0.00595 0.9595 0.9736 0.0134 ] Network output: [ -0.03633 0.301 0.9046 -0.0006577 0.0002953 0.8644 -0.0004957 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.28 0.172 0.4087 0.05979 0.9852 0.9941 0.2809 0.8634 0.9686 0.6593 ] Network output: [ -0.02058 0.2089 1.054 0.0002301 -0.0001033 0.7795 0.0001734 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1139 0.1052 0.1801 0.1238 0.9901 0.9941 0.114 0.9516 0.9723 0.2034 ] Network output: [ -0.008661 0.05736 1.04 0.0003434 -0.0001542 0.9211 0.0002588 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.132 0.1302 0.1895 0.1555 0.9858 0.9918 0.132 0.9248 0.9613 0.1972 ] Network output: [ 0.03974 0.7823 0.0005637 0.0001907 -8.56e-05 1.138 0.0001437 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06555 Epoch 4860 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04542 0.8484 0.9526 -5.075e-05 2.278e-05 0.108 -3.825e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004042 -0.003881 -0.01365 0.008104 0.966 0.9711 0.008225 0.9079 0.9124 0.02634 ] Network output: [ 1.069 -0.1357 -0.0301 8.077e-05 -3.626e-05 0.02853 6.087e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2535 -0.0332 -0.2054 0.2066 0.9836 0.9933 0.286 0.8539 0.9652 0.6681 ] Network output: [ 0.005984 0.8861 0.9841 -7.863e-05 3.53e-05 0.1175 -5.926e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005577 0.001304 0.003665 0.004173 0.9909 0.9938 0.005684 0.9594 0.9736 0.01245 ] Network output: [ 0.08551 -0.4001 0.9617 -0.0001003 4.501e-05 1.267 -7.556e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2666 0.1584 0.3769 0.2108 0.9852 0.9941 0.2675 0.8621 0.9684 0.6576 ] Network output: [ -0.04109 0.1986 1.078 0.0002098 -9.417e-05 0.8062 0.0001581 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1044 0.188 0.1416 0.99 0.994 0.1133 0.9519 0.9722 0.2138 ] Network output: [ -0.0392 0.1351 1.055 0.0002632 -0.0001182 0.8897 0.0001984 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1307 0.1289 0.1966 0.1556 0.9859 0.9919 0.1307 0.9259 0.9611 0.2045 ] Network output: [ -0.02637 1.092 -0.002568 -6.321e-05 2.838e-05 0.9635 -4.764e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06588 Epoch 4861 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03286 0.8922 0.954 -8.194e-05 3.678e-05 0.08779 -6.175e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003958 -0.003652 -0.01273 0.00673 0.966 0.9711 0.008028 0.9079 0.9117 0.02582 ] Network output: [ 0.8772 0.2597 0.07195 -0.0002436 0.0001094 -0.08709 -0.0001836 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2461 -0.01603 -0.1385 0.1245 0.9836 0.9933 0.2775 0.8551 0.9649 0.6621 ] Network output: [ 0.01415 0.8872 0.9744 -7.723e-05 3.467e-05 0.1099 -5.821e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005823 0.001497 0.004901 0.002393 0.9909 0.9938 0.005933 0.9594 0.9735 0.01339 ] Network output: [ -0.02917 0.2491 0.9109 -0.0006168 0.0002769 0.8958 -0.0004648 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2792 0.1714 0.4083 0.07036 0.9852 0.9941 0.2802 0.8628 0.9685 0.6606 ] Network output: [ -0.02229 0.201 1.058 0.0002321 -0.0001042 0.787 0.0001749 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1139 0.1052 0.1823 0.1259 0.9901 0.9941 0.1139 0.9516 0.9722 0.2059 ] Network output: [ -0.0108 0.05628 1.043 0.0003407 -0.000153 0.924 0.0002568 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.132 0.1301 0.1916 0.1565 0.9858 0.9918 0.132 0.9248 0.9613 0.1993 ] Network output: [ 0.03245 0.8142 0.0005609 0.0001622 -7.284e-05 1.121 0.0001223 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05369 Epoch 4862 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04597 0.8506 0.9515 -4.879e-05 2.19e-05 0.1058 -3.677e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004042 -0.003868 -0.01364 0.007998 0.966 0.9711 0.008218 0.9078 0.9122 0.02637 ] Network output: [ 1.058 -0.09765 -0.02908 5.494e-05 -2.466e-05 0.01053 4.14e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2535 -0.03191 -0.2022 0.1988 0.9836 0.9933 0.2859 0.8535 0.9651 0.6691 ] Network output: [ 0.007486 0.8843 0.983 -7.453e-05 3.346e-05 0.1174 -5.617e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005595 0.001314 0.003743 0.004031 0.9909 0.9938 0.005702 0.9594 0.9735 0.01256 ] Network output: [ 0.07638 -0.3504 0.9585 -0.0001407 6.318e-05 1.239 -0.0001061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2675 0.1593 0.3801 0.1994 0.9852 0.9941 0.2684 0.8617 0.9683 0.66 ] Network output: [ -0.04018 0.1911 1.079 0.0002145 -9.632e-05 0.8109 0.0001617 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1044 0.1892 0.1413 0.99 0.994 0.1133 0.9519 0.9722 0.2151 ] Network output: [ -0.03754 0.1216 1.056 0.0002719 -0.0001221 0.8983 0.0002049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1308 0.1289 0.198 0.1566 0.9859 0.9919 0.1308 0.9258 0.9611 0.206 ] Network output: [ -0.0257 1.084 -0.00118 -5.944e-05 2.669e-05 0.9687 -4.48e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05502 Epoch 4863 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03522 0.8874 0.9528 -7.484e-05 3.36e-05 0.08907 -5.64e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003966 -0.003669 -0.01284 0.006847 0.966 0.9711 0.008044 0.9079 0.9117 0.02592 ] Network output: [ 0.8932 0.2303 0.06217 -0.0002156 9.681e-05 -0.07979 -0.0001625 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2468 -0.01712 -0.1437 0.1304 0.9836 0.9933 0.2783 0.8546 0.9649 0.6643 ] Network output: [ 0.01455 0.8857 0.9745 -7.345e-05 3.297e-05 0.1105 -5.535e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005804 0.001488 0.004844 0.002541 0.9909 0.9938 0.005914 0.9594 0.9735 0.01339 ] Network output: [ -0.02273 0.1986 0.9176 -0.0005778 0.0002594 0.9269 -0.0004354 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2783 0.1707 0.4083 0.08051 0.9852 0.9941 0.2792 0.8624 0.9684 0.6625 ] Network output: [ -0.02374 0.1937 1.061 0.0002337 -0.0001049 0.7939 0.0001762 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1138 0.1051 0.1844 0.1279 0.9901 0.9941 0.1139 0.9516 0.9722 0.2083 ] Network output: [ -0.01264 0.05586 1.045 0.0003379 -0.0001517 0.9261 0.0002547 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.132 0.1302 0.1935 0.1573 0.9859 0.9918 0.132 0.9249 0.9612 0.2013 ] Network output: [ 0.02589 0.8461 -0.0001249 0.000135 -6.059e-05 1.103 0.0001017 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04385 Epoch 4864 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04643 0.853 0.9504 -4.74e-05 2.128e-05 0.1036 -3.572e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00404 -0.003855 -0.01364 0.007903 0.9661 0.9711 0.00821 0.9079 0.9122 0.0264 ] Network output: [ 1.048 -0.06414 -0.02725 3.308e-05 -1.485e-05 -0.004468 2.493e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2532 -0.03062 -0.1992 0.192 0.9836 0.9933 0.2857 0.8534 0.965 0.6706 ] Network output: [ 0.008861 0.8832 0.9819 -7.133e-05 3.202e-05 0.1169 -5.376e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005609 0.001325 0.003825 0.0039 0.9909 0.9939 0.005715 0.9594 0.9735 0.01268 ] Network output: [ 0.06735 -0.3054 0.9564 -0.0001777 7.977e-05 1.214 -0.0001339 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2682 0.1601 0.3836 0.1889 0.9852 0.9941 0.2691 0.8616 0.9683 0.6626 ] Network output: [ -0.03905 0.1846 1.08 0.0002188 -9.822e-05 0.8146 0.0001649 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1043 0.1903 0.1408 0.99 0.9941 0.1132 0.9519 0.9722 0.2163 ] Network output: [ -0.03565 0.1097 1.057 0.0002797 -0.0001256 0.9057 0.0002108 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1308 0.129 0.1992 0.1574 0.9859 0.9919 0.1309 0.9258 0.9611 0.2073 ] Network output: [ -0.02415 1.075 -0.0005204 -5.417e-05 2.432e-05 0.9735 -4.082e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04656 Epoch 4865 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03731 0.8836 0.9516 -6.903e-05 3.099e-05 0.08991 -5.202e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003973 -0.003683 -0.01295 0.006953 0.966 0.9711 0.008058 0.9079 0.9118 0.02602 ] Network output: [ 0.908 0.2044 0.05277 -0.0001899 8.526e-05 -0.07397 -0.0001431 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2474 -0.01809 -0.1488 0.1357 0.9836 0.9933 0.279 0.8544 0.9649 0.6669 ] Network output: [ 0.01483 0.8847 0.9745 -7.061e-05 3.17e-05 0.1108 -5.322e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005783 0.001478 0.004787 0.00267 0.9909 0.9938 0.005893 0.9595 0.9735 0.0134 ] Network output: [ -0.01696 0.1527 0.9239 -0.0005425 0.0002436 0.9551 -0.0004089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2774 0.17 0.4083 0.08964 0.9852 0.9941 0.2783 0.8623 0.9684 0.6647 ] Network output: [ -0.02492 0.1871 1.064 0.0002351 -0.0001055 0.8 0.0001772 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1138 0.1051 0.1863 0.1297 0.9901 0.9941 0.1138 0.9518 0.9722 0.2105 ] Network output: [ -0.01418 0.0554 1.046 0.0003352 -0.0001505 0.9281 0.0002526 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.132 0.1302 0.1952 0.158 0.9859 0.9919 0.132 0.9251 0.9613 0.2031 ] Network output: [ 0.02044 0.8738 -0.0008231 0.0001115 -5.006e-05 1.087 8.403e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03654 Epoch 4866 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04671 0.8556 0.9494 -4.677e-05 2.099e-05 0.1014 -3.524e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004037 -0.003841 -0.01363 0.007817 0.9661 0.9711 0.0082 0.908 0.9122 0.02644 ] Network output: [ 1.039 -0.03398 -0.02543 1.411e-05 -6.336e-06 -0.01783 1.064e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.253 -0.02941 -0.1966 0.1859 0.9836 0.9933 0.2853 0.8535 0.965 0.6724 ] Network output: [ 0.009985 0.8826 0.9809 -6.908e-05 3.101e-05 0.1162 -5.206e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005619 0.001335 0.003901 0.003779 0.991 0.9939 0.005725 0.9595 0.9735 0.01279 ] Network output: [ 0.05897 -0.265 0.9548 -0.000211 9.473e-05 1.191 -0.000159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2688 0.1608 0.3869 0.1794 0.9852 0.9941 0.2697 0.8617 0.9683 0.6654 ] Network output: [ -0.03787 0.1789 1.08 0.0002225 -9.989e-05 0.8178 0.0001677 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1043 0.1912 0.1405 0.99 0.9941 0.1132 0.952 0.9722 0.2173 ] Network output: [ -0.03374 0.09871 1.057 0.0002869 -0.0001288 0.9125 0.0002162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.131 0.1291 0.2003 0.1582 0.986 0.9919 0.131 0.9259 0.9612 0.2084 ] Network output: [ -0.02193 1.064 3.816e-05 -4.673e-05 2.098e-05 0.9792 -3.522e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03993 Epoch 4867 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03901 0.8809 0.9505 -6.464e-05 2.902e-05 0.09026 -4.871e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003979 -0.003697 -0.01305 0.00704 0.966 0.9711 0.008071 0.9081 0.9119 0.02612 ] Network output: [ 0.9214 0.1848 0.0433 -0.0001686 7.567e-05 -0.07159 -0.000127 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2479 -0.01897 -0.154 0.1398 0.9836 0.9933 0.2796 0.8544 0.9649 0.6696 ] Network output: [ 0.01492 0.8841 0.9747 -6.867e-05 3.083e-05 0.1111 -5.175e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005763 0.001465 0.004725 0.00277 0.9909 0.9939 0.005872 0.9597 0.9736 0.0134 ] Network output: [ -0.0118 0.114 0.9291 -0.0005132 0.0002304 0.9784 -0.0003868 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2764 0.1692 0.4083 0.09722 0.9852 0.9941 0.2773 0.8623 0.9684 0.6673 ] Network output: [ -0.02591 0.1809 1.066 0.0002363 -0.0001061 0.8058 0.0001781 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1137 0.1051 0.1879 0.1313 0.9901 0.9941 0.1138 0.9519 0.9723 0.2124 ] Network output: [ -0.01554 0.05386 1.048 0.0003333 -0.0001496 0.9307 0.0002512 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1321 0.1302 0.1969 0.1587 0.9859 0.9919 0.1321 0.9254 0.9613 0.2048 ] Network output: [ 0.01608 0.8946 -0.0009076 9.338e-05 -4.192e-05 1.074 7.037e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03164 Epoch 4868 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04675 0.8585 0.9486 -4.678e-05 2.1e-05 0.09923 -3.526e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004033 -0.003829 -0.01364 0.007743 0.9661 0.9712 0.008193 0.9082 0.9123 0.02648 ] Network output: [ 1.031 -0.006611 -0.02438 -2.313e-06 1.038e-06 -0.03058 -1.743e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2527 -0.0284 -0.1948 0.1804 0.9836 0.9933 0.285 0.8537 0.9651 0.6746 ] Network output: [ 0.01075 0.8826 0.9801 -6.766e-05 3.038e-05 0.1155 -5.099e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005626 0.001341 0.003961 0.003668 0.991 0.9939 0.005733 0.9597 0.9736 0.01289 ] Network output: [ 0.05175 -0.2293 0.9534 -0.0002406 0.000108 1.171 -0.0001813 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2692 0.1613 0.3898 0.171 0.9852 0.9941 0.2701 0.8619 0.9684 0.6684 ] Network output: [ -0.03684 0.1737 1.08 0.0002257 -0.0001013 0.8209 0.0001701 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1043 0.192 0.1402 0.9901 0.9941 0.1131 0.9522 0.9723 0.2182 ] Network output: [ -0.0321 0.08839 1.058 0.0002934 -0.0001317 0.919 0.0002211 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1311 0.1293 0.2013 0.1591 0.986 0.9919 0.1311 0.9261 0.9613 0.2095 ] Network output: [ -0.01963 1.052 0.0008272 -3.82e-05 1.715e-05 0.986 -2.879e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03482 Epoch 4869 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04025 0.8793 0.9497 -6.147e-05 2.76e-05 0.09026 -4.633e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003984 -0.003709 -0.01316 0.007112 0.9661 0.9711 0.008082 0.9083 0.9121 0.02623 ] Network output: [ 0.9331 0.1703 0.03443 -0.000151 6.78e-05 -0.07145 -0.0001138 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2483 -0.01979 -0.159 0.143 0.9836 0.9933 0.28 0.8546 0.965 0.6727 ] Network output: [ 0.01478 0.8841 0.9749 -6.75e-05 3.03e-05 0.1111 -5.087e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005745 0.001449 0.004665 0.002845 0.991 0.9939 0.005854 0.9598 0.9736 0.01342 ] Network output: [ -0.007416 0.08271 0.9333 -0.00049 0.00022 0.9969 -0.0003693 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2755 0.1682 0.4081 0.1033 0.9852 0.9941 0.2764 0.8625 0.9685 0.6702 ] Network output: [ -0.02679 0.1753 1.068 0.0002373 -0.0001065 0.8109 0.0001788 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1137 0.105 0.1893 0.1327 0.9901 0.9941 0.1137 0.9522 0.9724 0.2142 ] Network output: [ -0.01677 0.05176 1.049 0.0003318 -0.000149 0.9338 0.0002501 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1321 0.1303 0.1984 0.1595 0.9859 0.9919 0.1321 0.9257 0.9614 0.2064 ] Network output: [ 0.01244 0.9111 -0.000712 7.886e-05 -3.54e-05 1.065 5.943e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02837 Epoch 4870 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04661 0.8613 0.948 -4.709e-05 2.114e-05 0.0973 -3.549e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00403 -0.00382 -0.01366 0.007689 0.9661 0.9712 0.008186 0.9084 0.9124 0.02654 ] Network output: [ 1.024 0.0148 -0.02338 -1.447e-05 6.498e-06 -0.04047 -1.091e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2523 -0.02763 -0.1937 0.1762 0.9836 0.9933 0.2845 0.854 0.9651 0.6771 ] Network output: [ 0.0112 0.8831 0.9795 -6.692e-05 3.004e-05 0.1147 -5.044e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00563 0.001342 0.004013 0.003578 0.991 0.9939 0.005737 0.9599 0.9737 0.01299 ] Network output: [ 0.04563 -0.2004 0.9528 -0.0002652 0.0001191 1.155 -0.0001999 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2694 0.1615 0.3924 0.1641 0.9852 0.9941 0.2703 0.8623 0.9684 0.6715 ] Network output: [ -0.036 0.1696 1.08 0.0002281 -0.0001024 0.8234 0.0001719 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1043 0.1927 0.14 0.9901 0.9941 0.1132 0.9524 0.9724 0.219 ] Network output: [ -0.03074 0.08013 1.058 0.0002985 -0.000134 0.9244 0.000225 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1312 0.1294 0.2022 0.1597 0.986 0.9919 0.1312 0.9263 0.9614 0.2104 ] Network output: [ -0.01771 1.043 0.00114 -3.157e-05 1.417e-05 0.9907 -2.379e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03127 Epoch 4871 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04113 0.8783 0.949 -5.912e-05 2.654e-05 0.09015 -4.456e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003987 -0.003719 -0.01324 0.007182 0.9661 0.9712 0.008091 0.9086 0.9123 0.02633 ] Network output: [ 0.9427 0.1555 0.02779 -0.0001345 6.036e-05 -0.06932 -0.0001013 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2485 -0.0205 -0.1631 0.1462 0.9836 0.9933 0.2802 0.8548 0.9651 0.6757 ] Network output: [ 0.01452 0.8847 0.9751 -6.698e-05 3.007e-05 0.1109 -5.048e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005729 0.001435 0.004623 0.002914 0.991 0.9939 0.005837 0.96 0.9737 0.01344 ] Network output: [ -0.004161 0.05577 0.9375 -0.0004707 0.0002113 1.013 -0.0003547 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2746 0.1673 0.4084 0.1085 0.9852 0.9941 0.2755 0.8628 0.9685 0.673 ] Network output: [ -0.0275 0.1715 1.07 0.0002376 -0.0001067 0.8148 0.0001791 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1137 0.105 0.1906 0.1338 0.9902 0.9941 0.1138 0.9524 0.9725 0.2157 ] Network output: [ -0.01774 0.05124 1.05 0.00033 -0.0001481 0.9355 0.0002487 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1322 0.1304 0.1996 0.16 0.986 0.9919 0.1322 0.926 0.9615 0.2077 ] Network output: [ 0.009309 0.9284 -0.001334 6.494e-05 -2.916e-05 1.055 4.894e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02586 Epoch 4872 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04639 0.8637 0.9476 -4.746e-05 2.131e-05 0.09576 -3.577e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004026 -0.003811 -0.01366 0.007658 0.9661 0.9712 0.008177 0.9087 0.9126 0.02658 ] Network output: [ 1.019 0.02716 -0.02097 -2.106e-05 9.456e-06 -0.04453 -1.587e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2518 -0.02699 -0.1923 0.1736 0.9836 0.9933 0.2839 0.8544 0.9652 0.6794 ] Network output: [ 0.01149 0.884 0.979 -6.674e-05 2.996e-05 0.1137 -5.03e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005632 0.001346 0.004071 0.003517 0.991 0.9939 0.005739 0.9601 0.9738 0.01308 ] Network output: [ 0.04009 -0.1795 0.9535 -0.0002839 0.0001274 1.145 -0.0002139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2694 0.1617 0.3952 0.159 0.9852 0.9941 0.2703 0.8626 0.9685 0.6742 ] Network output: [ -0.03515 0.1673 1.079 0.0002296 -0.0001031 0.8247 0.000173 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1044 0.1933 0.1397 0.9901 0.9941 0.1133 0.9526 0.9725 0.2196 ] Network output: [ -0.02941 0.07543 1.057 0.0003018 -0.0001355 0.9273 0.0002275 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1315 0.1296 0.2027 0.16 0.986 0.9919 0.1315 0.9265 0.9615 0.211 ] Network output: [ -0.01593 1.04 0.0002059 -2.781e-05 1.248e-05 0.9914 -2.096e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02905 Epoch 4873 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04177 0.8777 0.9486 -5.746e-05 2.58e-05 0.09001 -4.33e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003988 -0.003726 -0.0133 0.007245 0.9661 0.9712 0.008095 0.9089 0.9125 0.02639 ] Network output: [ 0.9504 0.1394 0.02365 -0.0001183 5.311e-05 -0.06432 -8.916e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2484 -0.02103 -0.1662 0.1495 0.9836 0.9933 0.2802 0.8551 0.9652 0.6783 ] Network output: [ 0.01426 0.8857 0.9752 -6.701e-05 3.009e-05 0.1103 -5.05e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005713 0.001428 0.004599 0.002978 0.991 0.9939 0.005821 0.9602 0.9738 0.01346 ] Network output: [ -0.001944 0.03179 0.9418 -0.0004534 0.0002035 1.028 -0.0003417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2737 0.1667 0.4092 0.113 0.9852 0.9941 0.2746 0.8631 0.9686 0.6755 ] Network output: [ -0.02785 0.1694 1.07 0.0002374 -0.0001066 0.817 0.0001789 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1138 0.1051 0.1915 0.1346 0.9902 0.9941 0.1138 0.9526 0.9726 0.2168 ] Network output: [ -0.01827 0.05257 1.05 0.0003276 -0.0001471 0.9354 0.0002469 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1324 0.1305 0.2005 0.1601 0.986 0.9919 0.1324 0.9263 0.9617 0.2086 ] Network output: [ 0.007151 0.9449 -0.002736 5.283e-05 -2.372e-05 1.044 3.981e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02399 Epoch 4874 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04612 0.8658 0.9474 -4.818e-05 2.163e-05 0.0944 -3.631e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00402 -0.003802 -0.01365 0.00763 0.9661 0.9712 0.008167 0.909 0.9128 0.02659 ] Network output: [ 1.014 0.0358 -0.01802 -2.556e-05 1.147e-05 -0.04621 -1.926e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2512 -0.02638 -0.191 0.1718 0.9836 0.9933 0.2833 0.8548 0.9653 0.6815 ] Network output: [ 0.01171 0.8852 0.9785 -6.702e-05 3.009e-05 0.1126 -5.051e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005631 0.001353 0.004124 0.003466 0.991 0.9939 0.005738 0.9603 0.9739 0.01314 ] Network output: [ 0.03513 -0.1624 0.9544 -0.0002989 0.0001342 1.137 -0.0002253 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2694 0.162 0.3978 0.1546 0.9852 0.9941 0.2703 0.863 0.9686 0.6766 ] Network output: [ -0.03416 0.1658 1.078 0.0002307 -0.0001036 0.8252 0.0001738 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1045 0.1936 0.1394 0.9901 0.9941 0.1134 0.9528 0.9726 0.2199 ] Network output: [ -0.02797 0.07195 1.056 0.0003044 -0.0001367 0.929 0.0002294 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1317 0.1298 0.203 0.1602 0.986 0.9919 0.1317 0.9268 0.9617 0.2113 ] Network output: [ -0.01356 1.035 -0.0007992 -2.24e-05 1.005e-05 0.9929 -1.688e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02736 Epoch 4875 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04217 0.8776 0.9482 -5.679e-05 2.55e-05 0.08958 -4.28e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003988 -0.00373 -0.01336 0.007283 0.9661 0.9712 0.008097 0.9091 0.9127 0.02644 ] Network output: [ 0.9567 0.1308 0.01904 -0.0001074 4.822e-05 -0.06372 -8.095e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2484 -0.02142 -0.1693 0.1515 0.9836 0.9933 0.2802 0.8555 0.9653 0.6807 ] Network output: [ 0.01396 0.8865 0.9754 -6.741e-05 3.026e-05 0.1098 -5.081e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005698 0.001419 0.004561 0.003012 0.991 0.9939 0.005806 0.9605 0.974 0.01346 ] Network output: [ 0.0001417 0.01506 0.9444 -0.0004409 0.0001979 1.038 -0.0003323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.273 0.1661 0.4093 0.116 0.9853 0.9941 0.2739 0.8635 0.9687 0.6778 ] Network output: [ -0.02798 0.1669 1.071 0.0002375 -0.0001066 0.8193 0.000179 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1138 0.105 0.1921 0.1352 0.9902 0.9941 0.1138 0.9529 0.9727 0.2176 ] Network output: [ -0.01859 0.05152 1.05 0.0003266 -0.0001466 0.9369 0.0002462 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1324 0.1306 0.2012 0.1604 0.986 0.9919 0.1325 0.9266 0.9618 0.2094 ] Network output: [ 0.006329 0.9503 -0.002698 4.827e-05 -2.167e-05 1.04 3.638e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02304 Epoch 4876 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04567 0.8684 0.9472 -4.962e-05 2.227e-05 0.09285 -3.739e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004016 -0.003795 -0.01367 0.007588 0.9661 0.9712 0.00816 0.9093 0.913 0.02663 ] Network output: [ 1.01 0.05172 -0.01813 -3.392e-05 1.523e-05 -0.05453 -2.556e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2508 -0.02593 -0.191 0.1688 0.9836 0.9933 0.2829 0.8553 0.9654 0.6837 ] Network output: [ 0.01167 0.886 0.9784 -6.758e-05 3.034e-05 0.112 -5.093e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00563 0.001348 0.004139 0.003394 0.991 0.9939 0.005737 0.9605 0.974 0.0132 ] Network output: [ 0.0317 -0.1426 0.9534 -0.0003152 0.0001415 1.125 -0.0002375 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2693 0.1618 0.399 0.1498 0.9852 0.9941 0.2702 0.8634 0.9687 0.6793 ] Network output: [ -0.03338 0.1627 1.078 0.0002323 -0.0001043 0.8271 0.0001751 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1045 0.1938 0.1393 0.9902 0.9941 0.1133 0.953 0.9727 0.2203 ] Network output: [ -0.02693 0.06472 1.056 0.0003084 -0.0001385 0.934 0.0002324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1318 0.1299 0.2035 0.1609 0.986 0.9919 0.1318 0.927 0.9618 0.2119 ] Network output: [ -0.01095 1.019 0.0005604 -1.119e-05 5.026e-06 1.002 -8.437e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02551 Epoch 4877 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04221 0.8787 0.948 -5.702e-05 2.56e-05 0.0887 -4.297e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00399 -0.003738 -0.01344 0.007296 0.9661 0.9712 0.008105 0.9094 0.913 0.02653 ] Network output: [ 0.9627 0.1354 0.01157 -0.0001045 4.691e-05 -0.07278 -7.875e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2485 -0.02193 -0.1736 0.151 0.9836 0.9933 0.2803 0.8558 0.9654 0.6837 ] Network output: [ 0.01337 0.8871 0.976 -6.795e-05 3.051e-05 0.1099 -5.121e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005687 0.001394 0.004492 0.003001 0.991 0.9939 0.005795 0.9607 0.9741 0.01349 ] Network output: [ 0.002786 0.008678 0.9444 -0.0004366 0.000196 1.04 -0.000329 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2723 0.1648 0.4083 0.117 0.9853 0.9941 0.2731 0.8639 0.9688 0.681 ] Network output: [ -0.02835 0.1622 1.072 0.0002386 -0.0001071 0.8233 0.0001798 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1137 0.1049 0.1927 0.136 0.9902 0.9941 0.1137 0.9531 0.9728 0.2186 ] Network output: [ -0.01928 0.04522 1.052 0.0003281 -0.0001473 0.9426 0.0002472 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1324 0.1306 0.2023 0.1615 0.986 0.9919 0.1324 0.9269 0.9619 0.2106 ] Network output: [ 0.005659 0.9436 -6.031e-05 5.04e-05 -2.262e-05 1.045 3.798e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02282 Epoch 4878 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04498 0.8714 0.9471 -5.111e-05 2.295e-05 0.09126 -3.852e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004015 -0.003796 -0.01372 0.007557 0.9661 0.9712 0.008162 0.9096 0.9132 0.02672 ] Network output: [ 1.009 0.07067 -0.02121 -4.302e-05 1.931e-05 -0.06738 -3.242e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2505 -0.02589 -0.1927 0.1654 0.9837 0.9933 0.2826 0.8557 0.9655 0.6867 ] Network output: [ 0.01119 0.887 0.9787 -6.823e-05 3.063e-05 0.1117 -5.142e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00563 0.001327 0.004123 0.003318 0.9911 0.9939 0.005737 0.9607 0.9741 0.01328 ] Network output: [ 0.02995 -0.1233 0.9513 -0.0003319 0.000149 1.111 -0.0002501 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.269 0.1608 0.3992 0.1454 0.9853 0.9941 0.2699 0.8638 0.9688 0.6828 ] Network output: [ -0.03326 0.1585 1.079 0.000234 -0.000105 0.8304 0.0001763 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1044 0.1943 0.1395 0.9902 0.9941 0.1133 0.9532 0.9728 0.2211 ] Network output: [ -0.02677 0.05562 1.058 0.0003127 -0.0001404 0.9413 0.0002357 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1319 0.13 0.2046 0.1621 0.986 0.9919 0.1319 0.9273 0.9619 0.2131 ] Network output: [ -0.01002 1.005 0.003106 -2.331e-06 1.046e-06 1.012 -1.756e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02405 Epoch 4879 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04196 0.8799 0.9479 -5.697e-05 2.557e-05 0.08803 -4.293e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003992 -0.003748 -0.01353 0.007333 0.9661 0.9712 0.008114 0.9097 0.9132 0.02665 ] Network output: [ 0.9681 0.1344 0.006229 -9.878e-05 4.435e-05 -0.07731 -7.444e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2483 -0.02261 -0.1772 0.1516 0.9837 0.9933 0.2801 0.8562 0.9655 0.6871 ] Network output: [ 0.01255 0.8885 0.9765 -6.861e-05 3.08e-05 0.1096 -5.171e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005679 0.001366 0.004451 0.003006 0.9911 0.9939 0.005786 0.9609 0.9742 0.01354 ] Network output: [ 0.004577 0.002352 0.9453 -0.0004343 0.000195 1.041 -0.0003273 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2714 0.1633 0.4081 0.118 0.9853 0.9942 0.2723 0.8642 0.9689 0.6845 ] Network output: [ -0.02905 0.1593 1.073 0.0002388 -0.0001072 0.8267 0.00018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1137 0.1048 0.1935 0.1367 0.9902 0.9942 0.1138 0.9533 0.9729 0.2198 ] Network output: [ -0.02026 0.04182 1.053 0.0003283 -0.0001474 0.9468 0.0002474 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1326 0.1307 0.2035 0.1622 0.986 0.9919 0.1326 0.9272 0.962 0.2119 ] Network output: [ 0.003289 0.9501 0.0007692 4.428e-05 -1.988e-05 1.043 3.337e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0222 Epoch 4880 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04442 0.8731 0.9473 -5.129e-05 2.303e-05 0.09058 -3.866e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004011 -0.003797 -0.01375 0.00759 0.9661 0.9712 0.00816 0.9099 0.9134 0.02679 ] Network output: [ 1.008 0.06581 -0.01901 -3.809e-05 1.71e-05 -0.06319 -2.871e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2499 -0.02605 -0.1928 0.1663 0.9837 0.9934 0.2819 0.8561 0.9656 0.6896 ] Network output: [ 0.01069 0.8891 0.9786 -6.904e-05 3.099e-05 0.1106 -5.203e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005628 0.001318 0.004163 0.003318 0.9911 0.9939 0.005735 0.961 0.9742 0.01336 ] Network output: [ 0.02724 -0.1192 0.9538 -0.0003389 0.0001521 1.11 -0.0002554 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2685 0.1601 0.4014 0.1441 0.9853 0.9942 0.2694 0.8642 0.9689 0.6857 ] Network output: [ -0.03325 0.1592 1.078 0.0002333 -0.0001047 0.8306 0.0001758 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1135 0.1046 0.1949 0.1394 0.9902 0.9942 0.1136 0.9534 0.9729 0.2218 ] Network output: [ -0.02661 0.0569 1.057 0.0003122 -0.0001402 0.941 0.0002353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1322 0.1303 0.2051 0.1619 0.986 0.992 0.1322 0.9276 0.9621 0.2137 ] Network output: [ -0.01131 1.019 0.000617 -1.088e-05 4.886e-06 1.003 -8.202e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02348 Epoch 4881 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04195 0.8793 0.9481 -5.569e-05 2.5e-05 0.08846 -4.197e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003986 -0.003748 -0.01352 0.00743 0.9661 0.9712 0.008107 0.9101 0.9135 0.02667 ] Network output: [ 0.9711 0.1018 0.01137 -7.615e-05 3.419e-05 -0.05572 -5.739e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2476 -0.02298 -0.1768 0.1577 0.9837 0.9934 0.2793 0.8566 0.9656 0.6893 ] Network output: [ 0.01217 0.8912 0.9763 -6.965e-05 3.127e-05 0.1079 -5.249e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005667 0.001375 0.004515 0.003105 0.9911 0.9939 0.005775 0.9611 0.9743 0.01358 ] Network output: [ 0.003098 -0.01927 0.9524 -0.0004209 0.000189 1.059 -0.0003172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2707 0.1632 0.4113 0.1222 0.9853 0.9942 0.2716 0.8646 0.969 0.6861 ] Network output: [ -0.02905 0.164 1.071 0.0002359 -0.0001059 0.824 0.0001778 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1142 0.1053 0.1941 0.1366 0.9902 0.9942 0.1142 0.9535 0.973 0.2202 ] Network output: [ -0.02011 0.05301 1.05 0.0003224 -0.0001447 0.939 0.0002429 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1331 0.1312 0.2034 0.1608 0.986 0.9919 0.1331 0.9275 0.9621 0.2117 ] Network output: [ 0.0005203 0.9883 -0.005626 2.138e-05 -9.598e-06 1.016 1.611e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02074 Epoch 4882 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04439 0.8724 0.9476 -5.065e-05 2.274e-05 0.09097 -3.817e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003998 -0.003783 -0.01366 0.007653 0.9661 0.9712 0.008135 0.9102 0.9137 0.02672 ] Network output: [ 1.005 0.03221 -0.006767 -1.832e-05 8.223e-06 -0.03456 -1.38e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2487 -0.02559 -0.1888 0.1722 0.9837 0.9934 0.2806 0.8566 0.9657 0.6905 ] Network output: [ 0.011 0.8918 0.9777 -7.024e-05 3.153e-05 0.1082 -5.293e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005619 0.001353 0.004292 0.003398 0.9911 0.9939 0.005726 0.9612 0.9743 0.01338 ] Network output: [ 0.02177 -0.1313 0.9617 -0.0003321 0.0001491 1.125 -0.0002503 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2683 0.1614 0.4065 0.1459 0.9853 0.9942 0.2692 0.8647 0.969 0.6858 ] Network output: [ -0.03188 0.1666 1.074 0.0002304 -0.0001034 0.8245 0.0001736 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.114 0.1051 0.1949 0.1386 0.9902 0.9942 0.1141 0.9537 0.973 0.2212 ] Network output: [ -0.02464 0.07121 1.05 0.0003064 -0.0001375 0.929 0.0002309 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1328 0.1309 0.2039 0.1596 0.986 0.992 0.1328 0.9278 0.9621 0.2122 ] Network output: [ -0.01033 1.051 -0.007809 -2.576e-05 1.156e-05 0.9774 -1.941e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02455 Epoch 4883 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04237 0.8777 0.9484 -5.535e-05 2.485e-05 0.08893 -4.172e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003973 -0.003734 -0.01343 0.007479 0.9661 0.9712 0.008082 0.9103 0.9137 0.02654 ] Network output: [ 0.9709 0.06764 0.02 -5.46e-05 2.451e-05 -0.02969 -4.115e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2468 -0.02254 -0.1743 0.1638 0.9837 0.9934 0.2784 0.857 0.9657 0.6893 ] Network output: [ 0.01259 0.893 0.9757 -7.107e-05 3.19e-05 0.1058 -5.356e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00565 0.001413 0.004585 0.003194 0.9911 0.9939 0.005757 0.9613 0.9744 0.01353 ] Network output: [ 0.0009644 -0.04191 0.9594 -0.0004021 0.0001805 1.079 -0.0003031 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2703 0.1647 0.4144 0.1263 0.9853 0.9942 0.2712 0.865 0.9691 0.6852 ] Network output: [ -0.02744 0.17 1.068 0.0002334 -0.0001048 0.8182 0.0001759 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1144 0.1057 0.1936 0.136 0.9902 0.9942 0.1145 0.9538 0.9731 0.2192 ] Network output: [ -0.01817 0.06557 1.044 0.0003165 -0.0001421 0.928 0.0002385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1333 0.1315 0.2018 0.1589 0.986 0.9919 0.1334 0.9278 0.9622 0.21 ] Network output: [ 0.003111 1.006 -0.01175 1.47e-05 -6.6e-06 0.9997 1.108e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02089 Epoch 4884 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04428 0.8735 0.9478 -5.31e-05 2.384e-05 0.08992 -4.002e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003986 -0.003762 -0.01359 0.007583 0.9661 0.9712 0.00811 0.9104 0.9139 0.02659 ] Network output: [ 0.9987 0.03987 -0.002791 -2.378e-05 1.067e-05 -0.03464 -1.792e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2483 -0.02455 -0.187 0.1704 0.9837 0.9934 0.2801 0.857 0.9658 0.6903 ] Network output: [ 0.01157 0.892 0.9774 -7.16e-05 3.214e-05 0.1072 -5.396e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00561 0.001376 0.004306 0.003339 0.9911 0.9939 0.005717 0.9613 0.9744 0.01334 ] Network output: [ 0.01883 -0.1184 0.961 -0.000338 0.0001517 1.118 -0.0002547 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2685 0.1626 0.4072 0.1424 0.9853 0.9942 0.2694 0.8651 0.9691 0.6857 ] Network output: [ -0.02955 0.1657 1.071 0.0002318 -0.0001041 0.8228 0.0001747 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1139 0.1051 0.1938 0.138 0.9902 0.9942 0.1139 0.9539 0.9732 0.22 ] Network output: [ -0.02193 0.06681 1.048 0.0003095 -0.0001389 0.9298 0.0002332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1327 0.1309 0.2029 0.1597 0.986 0.9919 0.1327 0.928 0.9622 0.2112 ] Network output: [ -0.002574 1.015 -0.006347 8.856e-07 -3.976e-07 0.9962 6.674e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02324 Epoch 4885 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04202 0.8812 0.9481 -5.951e-05 2.671e-05 0.08636 -4.485e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003976 -0.003734 -0.01353 0.007336 0.9661 0.9712 0.008086 0.9105 0.9139 0.02658 ] Network output: [ 0.973 0.1216 0.004217 -8.244e-05 3.701e-05 -0.07214 -6.213e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2475 -0.02239 -0.1809 0.1543 0.9837 0.9934 0.2792 0.8574 0.9658 0.6908 ] Network output: [ 0.01221 0.891 0.9768 -7.2e-05 3.232e-05 0.1075 -5.426e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005641 0.00137 0.004387 0.003011 0.9911 0.9939 0.005748 0.9614 0.9745 0.01349 ] Network output: [ 0.006055 -0.01517 0.9488 -0.0004163 0.0001869 1.053 -0.0003137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.27 0.163 0.4084 0.1202 0.9853 0.9942 0.2709 0.8654 0.9692 0.6881 ] Network output: [ -0.02666 0.157 1.071 0.0002391 -0.0001073 0.8266 0.0001802 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1137 0.1049 0.1928 0.1369 0.9903 0.9942 0.1137 0.9539 0.9732 0.2192 ] Network output: [ -0.01797 0.03871 1.051 0.0003279 -0.0001472 0.9478 0.0002472 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1327 0.1308 0.2031 0.1625 0.986 0.9919 0.1327 0.928 0.9624 0.2116 ] Network output: [ 0.01022 0.9223 0.002017 6.47e-05 -2.905e-05 1.056 4.876e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0219 Epoch 4886 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04267 0.8816 0.9474 -5.935e-05 2.664e-05 0.08545 -4.473e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004001 -0.003781 -0.01384 0.007368 0.9661 0.9712 0.008144 0.9107 0.914 0.02684 ] Network output: [ 1.001 0.1454 -0.03123 -8.112e-05 3.642e-05 -0.1163 -6.114e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2494 -0.02507 -0.1985 0.1518 0.9837 0.9934 0.2814 0.8574 0.9659 0.6948 ] Network output: [ 0.01003 0.8892 0.9797 -7.206e-05 3.235e-05 0.1107 -5.43e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005617 0.001266 0.003997 0.003008 0.9911 0.994 0.005723 0.9614 0.9744 0.01341 ] Network output: [ 0.02701 -0.05579 0.9402 -0.0003814 0.0001712 1.06 -0.0002874 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.268 0.1581 0.3969 0.1294 0.9853 0.9942 0.2689 0.8654 0.9692 0.6928 ] Network output: [ -0.03044 0.1428 1.079 0.0002414 -0.0001084 0.8403 0.0001819 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1038 0.1938 0.14 0.9903 0.9942 0.113 0.9539 0.9732 0.2218 ] Network output: [ -0.02402 0.01832 1.063 0.0003306 -0.0001484 0.9683 0.0002491 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1318 0.1299 0.2068 0.1666 0.9861 0.992 0.1318 0.9282 0.9625 0.2157 ] Network output: [ 0.002609 0.8967 0.01693 6.757e-05 -3.033e-05 1.081 5.092e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02487 Epoch 4887 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03978 0.8906 0.9478 -6.437e-05 2.89e-05 0.08186 -4.851e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004004 -0.003781 -0.01393 0.007195 0.9661 0.9712 0.008155 0.9108 0.914 0.02706 ] Network output: [ 0.9823 0.2292 -0.03177 -0.000138 6.195e-05 -0.1626 -0.000104 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2488 -0.02443 -0.1968 0.1358 0.9837 0.9934 0.2807 0.8577 0.9659 0.6988 ] Network output: [ 0.009278 0.8894 0.98 -7.188e-05 3.227e-05 0.1117 -5.417e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005657 0.001203 0.004026 0.002706 0.9911 0.994 0.005765 0.9616 0.9745 0.01365 ] Network output: [ 0.01906 0.03866 0.927 -0.0004599 0.0002065 0.9943 -0.0003466 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2689 0.1557 0.3967 0.1097 0.9853 0.9942 0.2697 0.8656 0.9693 0.6984 ] Network output: [ -0.03001 0.1332 1.08 0.0002479 -0.0001113 0.8479 0.0001868 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1035 0.1942 0.1397 0.9903 0.9942 0.1131 0.9539 0.9732 0.223 ] Network output: [ -0.02331 -0.01009 1.068 0.0003479 -0.0001562 0.9904 0.0002622 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1321 0.1301 0.2089 0.1702 0.9861 0.992 0.1321 0.9283 0.9626 0.2182 ] Network output: [ 0.007513 0.8317 0.02528 0.0001095 -4.918e-05 1.128 8.256e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03352 Epoch 4888 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03995 0.8891 0.9478 -6.054e-05 2.718e-05 0.08298 -4.562e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004028 -0.003838 -0.01418 0.007437 0.9661 0.9712 0.008216 0.911 0.9142 0.02737 ] Network output: [ 1.012 0.1855 -0.05141 -9.869e-05 4.43e-05 -0.1577 -7.437e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2496 -0.02791 -0.2093 0.1457 0.9837 0.9934 0.2817 0.8576 0.9659 0.7039 ] Network output: [ 0.006327 0.8927 0.9822 -7.258e-05 3.258e-05 0.1121 -5.47e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005641 0.001128 0.00384 0.002919 0.9912 0.994 0.005748 0.9617 0.9745 0.01367 ] Network output: [ 0.03446 -0.03308 0.9314 -0.0004107 0.0001844 1.031 -0.0003095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2664 0.1513 0.3923 0.126 0.9853 0.9942 0.2673 0.8656 0.9693 0.7029 ] Network output: [ -0.03535 0.1344 1.084 0.0002423 -0.0001088 0.8528 0.0001826 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1034 0.1967 0.142 0.9903 0.9942 0.1133 0.9541 0.9732 0.2263 ] Network output: [ -0.03042 0.0002782 1.072 0.0003364 -0.000151 0.9899 0.0002535 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1322 0.1302 0.2119 0.1706 0.9861 0.992 0.1322 0.9286 0.9626 0.2213 ] Network output: [ -0.0109 0.9155 0.02372 4.594e-05 -2.062e-05 1.083 3.462e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02671 Epoch 4889 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03816 0.8896 0.9493 -5.835e-05 2.62e-05 0.0846 -4.397e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004007 -0.003815 -0.01398 0.007596 0.9661 0.9712 0.008181 0.9113 0.9143 0.02737 ] Network output: [ 0.99 0.1243 -0.01267 -7.461e-05 3.35e-05 -0.09189 -5.623e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2468 -0.02686 -0.1931 0.1556 0.9837 0.9934 0.2785 0.8579 0.9659 0.7056 ] Network output: [ 0.006584 0.8994 0.9799 -7.355e-05 3.302e-05 0.1073 -5.543e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005671 0.001201 0.004337 0.003038 0.9912 0.994 0.005779 0.962 0.9747 0.01397 ] Network output: [ 0.009852 -0.005527 0.9487 -0.0004484 0.0002013 1.035 -0.0003379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2672 0.1542 0.4088 0.1199 0.9853 0.9942 0.2681 0.8659 0.9693 0.7034 ] Network output: [ -0.03433 0.154 1.076 0.0002352 -0.0001056 0.8394 0.0001772 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1149 0.1052 0.1983 0.1391 0.9903 0.9942 0.115 0.9543 0.9733 0.2265 ] Network output: [ -0.02704 0.03448 1.058 0.000326 -0.0001463 0.9633 0.0002456 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1341 0.132 0.21 0.1651 0.9861 0.992 0.1341 0.9287 0.9625 0.219 ] Network output: [ -0.01622 1.027 -0.0001082 -1.403e-05 6.297e-06 1.005 -1.057e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01968 Epoch 4890 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04151 0.8743 0.95 -4.609e-05 2.069e-05 0.09244 -3.473e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003985 -0.003813 -0.01374 0.008135 0.9661 0.9712 0.008144 0.9115 0.9146 0.02711 ] Network output: [ 1.014 -0.09708 0.01722 6.51e-05 -2.923e-05 0.0531 4.906e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.245 -0.0284 -0.1866 0.1966 0.9837 0.9934 0.2766 0.8579 0.9661 0.7035 ] Network output: [ 0.006995 0.907 0.9779 -7.515e-05 3.374e-05 0.1007 -5.664e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00561 0.001336 0.004649 0.003783 0.9911 0.994 0.005717 0.9621 0.9748 0.01376 ] Network output: [ 0.01023 -0.1904 0.9909 -0.0003154 0.0001416 1.178 -0.0002377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2649 0.1584 0.4225 0.1582 0.9853 0.9942 0.2658 0.8659 0.9692 0.6961 ] Network output: [ -0.03568 0.1955 1.066 0.0002127 -9.547e-05 0.811 0.0001603 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1165 0.1074 0.1994 0.1371 0.9903 0.9942 0.1166 0.9545 0.9734 0.2251 ] Network output: [ -0.02814 0.1309 1.035 0.0002759 -0.0001238 0.8919 0.0002079 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1355 0.1336 0.2047 0.1521 0.9861 0.992 0.1355 0.9288 0.9623 0.2127 ] Network output: [ -0.03519 1.282 -0.03977 -0.0001665 7.474e-05 0.8279 -0.0001255 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0501 Epoch 4891 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04272 0.8628 0.9522 -4.153e-05 1.864e-05 0.09935 -3.13e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003904 -0.003691 -0.01274 0.008296 0.9661 0.9712 0.007967 0.9114 0.9147 0.02602 ] Network output: [ 0.9687 -0.2956 0.1132 0.0001524 -6.841e-05 0.2457 0.0001148 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2398 -0.02294 -0.1439 0.2288 0.9837 0.9934 0.2707 0.8582 0.9662 0.6905 ] Network output: [ 0.01289 0.9146 0.9703 -7.733e-05 3.472e-05 0.08906 -5.828e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005595 0.001658 0.005513 0.004353 0.9911 0.994 0.005702 0.9621 0.9751 0.01344 ] Network output: [ -0.01398 -0.2884 1.03 -0.0002388 0.0001072 1.286 -0.00018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2678 0.1722 0.4479 0.177 0.9853 0.9942 0.2686 0.8657 0.9691 0.6755 ] Network output: [ -0.02351 0.2575 1.036 0.0001956 -8.779e-05 0.7548 0.0001474 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.119 0.111 0.193 0.1299 0.9902 0.9942 0.119 0.9545 0.9734 0.2135 ] Network output: [ -0.01282 0.2383 0.9911 0.0002381 -0.0001069 0.7972 0.0001794 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1378 0.1362 0.1898 0.1356 0.9861 0.9919 0.1378 0.9282 0.9619 0.1962 ] Network output: [ -0.01066 1.399 -0.09271 -0.000194 8.71e-05 0.7145 -0.0001462 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1217 Epoch 4892 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04504 0.8551 0.9535 -4.679e-05 2.1e-05 0.1012 -3.526e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003859 -0.003597 -0.01196 0.008011 0.9661 0.9712 0.007851 0.9109 0.9145 0.02471 ] Network output: [ 0.9473 -0.3477 0.1474 0.0001632 -7.328e-05 0.3064 0.000123 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2395 -0.01824 -0.1252 0.2354 0.9837 0.9934 0.2702 0.8577 0.9662 0.671 ] Network output: [ 0.01815 0.9116 0.967 -8.006e-05 3.594e-05 0.08469 -6.034e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005566 0.001855 0.00547 0.004424 0.991 0.9939 0.005672 0.9616 0.975 0.01257 ] Network output: [ -0.003224 -0.3583 1.028 -0.0001515 6.801e-05 1.336 -0.0001142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2706 0.1817 0.4441 0.1912 0.9853 0.9942 0.2715 0.8649 0.9691 0.6518 ] Network output: [ -0.009445 0.285 1.016 0.0001951 -8.76e-05 0.7186 0.0001471 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1185 0.1113 0.1808 0.1266 0.9901 0.9941 0.1186 0.9539 0.9733 0.1986 ] Network output: [ 0.001337 0.2664 0.972 0.0002338 -0.000105 0.7598 0.0001762 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1371 0.1356 0.1765 0.1313 0.9859 0.9918 0.1371 0.9269 0.9615 0.1821 ] Network output: [ 0.0362 1.247 -0.1012 -6.266e-05 2.813e-05 0.7812 -4.722e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1349 Epoch 4893 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04083 0.8742 0.9547 -7.504e-05 3.369e-05 0.08916 -5.655e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003867 -0.003545 -0.012 0.007049 0.9661 0.9712 0.007839 0.9103 0.914 0.02431 ] Network output: [ 0.917 -0.02819 0.09916 -3.633e-05 1.631e-05 0.0948 -2.738e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2436 -0.01326 -0.1374 0.1766 0.9837 0.9934 0.2746 0.8571 0.966 0.6622 ] Network output: [ 0.0181 0.8981 0.972 -8.592e-05 3.857e-05 0.09336 -6.475e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005607 0.001795 0.004841 0.003213 0.9909 0.9939 0.005713 0.961 0.9746 0.01234 ] Network output: [ -0.0147 -0.05357 0.9664 -0.0003431 0.000154 1.115 -0.0002586 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.275 0.1826 0.4207 0.1277 0.9853 0.9942 0.2759 0.8648 0.9692 0.6535 ] Network output: [ -0.005739 0.2344 1.031 0.0002162 -9.706e-05 0.7471 0.0001629 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1146 0.1074 0.1751 0.1254 0.9901 0.9941 0.1147 0.9534 0.9732 0.1955 ] Network output: [ 0.005976 0.1398 1.003 0.0002915 -0.0001309 0.8464 0.0002197 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1334 0.1318 0.1791 0.1448 0.9858 0.9918 0.1334 0.9264 0.9618 0.1857 ] Network output: [ 0.07182 0.8497 -0.0379 0.0001701 -7.635e-05 1.045 0.0001282 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04086 Epoch 4894 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03893 0.8862 0.9535 -8.938e-05 4.013e-05 0.08208 -6.736e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00395 -0.003651 -0.0132 0.006547 0.9662 0.9712 0.008001 0.9102 0.9139 0.02538 ] Network output: [ 0.971 0.286 -0.03679 -0.0001843 8.273e-05 -0.1919 -0.0001389 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2517 -0.0178 -0.1965 0.1241 0.9837 0.9934 0.2836 0.8568 0.966 0.6756 ] Network output: [ 0.01108 0.8789 0.9843 -8.574e-05 3.849e-05 0.1143 -6.462e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005581 0.001407 0.003497 0.002393 0.991 0.9939 0.005686 0.9608 0.9742 0.01238 ] Network output: [ 0.03104 0.0537 0.903 -0.0004043 0.0001815 0.9796 -0.0003047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2725 0.1673 0.3761 0.1066 0.9853 0.9942 0.2734 0.8647 0.9694 0.6755 ] Network output: [ -0.01796 0.1441 1.071 0.0002425 -0.0001089 0.822 0.0001828 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.1017 0.1783 0.1351 0.9902 0.9941 0.1101 0.9532 0.9731 0.2056 ] Network output: [ -0.01159 -0.01737 1.064 0.0003438 -0.0001544 0.9782 0.0002591 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1287 0.127 0.195 0.1676 0.9859 0.9918 0.1287 0.9267 0.9623 0.204 ] Network output: [ 0.06547 0.5084 0.05385 0.0003261 -0.0001464 1.308 0.0002457 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08464 Epoch 4895 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03129 0.9154 0.9523 -0.0001053 4.727e-05 0.06931 -7.935e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004037 -0.003766 -0.01416 0.005974 0.9662 0.9712 0.008188 0.9103 0.9137 0.02655 ] Network output: [ 0.982 0.6279 -0.1326 -0.000376 0.0001688 -0.4608 -0.0002834 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2567 -0.02194 -0.2336 0.06471 0.9837 0.9934 0.2894 0.8565 0.9657 0.6921 ] Network output: [ 0.003405 0.8723 0.9924 -8.331e-05 3.74e-05 0.1282 -6.279e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005658 0.0009671 0.002525 0.00155 0.9911 0.9939 0.005765 0.9605 0.9738 0.01263 ] Network output: [ 0.07449 0.2153 0.8286 -0.0005234 0.000235 0.805 -0.0003945 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2718 0.1487 0.3372 0.07733 0.9853 0.9942 0.2727 0.8636 0.9693 0.6962 ] Network output: [ -0.02753 0.09634 1.093 0.0002618 -0.0001175 0.8668 0.0001973 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1078 0.09775 0.1783 0.1402 0.9903 0.9941 0.1079 0.9524 0.9727 0.2114 ] Network output: [ -0.02796 -0.1323 1.109 0.0003902 -0.0001752 1.081 0.0002941 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1263 0.1241 0.2076 0.1856 0.9859 0.9919 0.1263 0.9262 0.9623 0.2184 ] Network output: [ 0.0625 0.2388 0.1175 0.000467 -0.0002096 1.521 0.0003519 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2476 Epoch 4896 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02285 0.9272 0.956 -0.0001057 4.745e-05 0.07061 -7.965e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004115 -0.003915 -0.01439 0.006801 0.9662 0.9713 0.008383 0.9102 0.9134 0.02721 ] Network output: [ 1.019 0.4358 -0.1254 -0.0002767 0.0001242 -0.3501 -0.0002086 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2585 -0.02914 -0.2341 0.1039 0.9837 0.9934 0.2917 0.8548 0.9653 0.7031 ] Network output: [ -0.008974 0.9033 0.9958 -9.502e-05 4.266e-05 0.1185 -7.161e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005757 0.0009439 0.002908 0.002276 0.9912 0.994 0.005866 0.9608 0.9739 0.01311 ] Network output: [ 0.06681 0.0889 0.8666 -0.0004766 0.0002139 0.909 -0.0003592 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2712 0.1456 0.3559 0.1116 0.9853 0.9942 0.2721 0.8625 0.9689 0.7057 ] Network output: [ -0.04415 0.1261 1.096 0.0002383 -0.000107 0.8669 0.0001796 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.1013 0.1886 0.1458 0.9903 0.9942 0.112 0.9526 0.9726 0.2214 ] Network output: [ -0.04323 -0.06139 1.1 0.0003522 -0.0001581 1.049 0.0002654 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1307 0.1285 0.2129 0.1842 0.986 0.992 0.1307 0.9264 0.9619 0.2234 ] Network output: [ 0.01143 0.579 0.07649 0.0002507 -0.0001125 1.323 0.0001889 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1076 Epoch 4897 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0207 0.9245 0.9598 -9.282e-05 4.167e-05 0.0739 -6.995e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004106 -0.003937 -0.01429 0.007269 0.9662 0.9713 0.008392 0.9104 0.9133 0.02752 ] Network output: [ 1.008 0.3067 -0.07749 -0.0002071 9.296e-05 -0.2454 -0.0001561 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2544 -0.03086 -0.2179 0.1265 0.9837 0.9934 0.2872 0.8544 0.9651 0.7095 ] Network output: [ -0.01042 0.9146 0.9949 -9.202e-05 4.131e-05 0.1109 -6.935e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005813 0.0009762 0.003542 0.00259 0.9912 0.994 0.005924 0.9611 0.974 0.01366 ] Network output: [ 0.03669 0.09512 0.8989 -0.0005171 0.0002321 0.9305 -0.0003897 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2706 0.1456 0.3798 0.1117 0.9853 0.9942 0.2715 0.8623 0.9687 0.7105 ] Network output: [ -0.04689 0.1528 1.089 0.000228 -0.0001023 0.8525 0.0001718 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1148 0.1039 0.1939 0.1442 0.9904 0.9942 0.1149 0.9527 0.9725 0.2255 ] Network output: [ -0.04326 -0.00762 1.084 0.0003351 -0.0001504 1.012 0.0002525 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1339 0.1316 0.213 0.1781 0.9861 0.992 0.1339 0.9265 0.9617 0.2231 ] Network output: [ -0.0139 0.8263 0.03866 0.0001047 -4.699e-05 1.163 7.889e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04881 Epoch 4898 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02587 0.8984 0.9619 -6.586e-05 2.957e-05 0.08766 -4.964e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004063 -0.00394 -0.01402 0.008176 0.9662 0.9713 0.008326 0.9106 0.9136 0.02741 ] Network output: [ 1.036 -0.02619 -0.0229 7.499e-06 -3.367e-06 -0.02297 5.652e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2492 -0.03408 -0.2064 0.1877 0.9837 0.9934 0.2815 0.8541 0.9652 0.7103 ] Network output: [ -0.009304 0.924 0.9921 -8.866e-05 3.98e-05 0.1021 -6.681e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005731 0.001141 0.004185 0.003641 0.9912 0.994 0.005841 0.9614 0.9742 0.01372 ] Network output: [ 0.02182 -0.1311 0.9693 -0.0003724 0.0001672 1.117 -0.0002806 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2658 0.1498 0.4077 0.1588 0.9853 0.9942 0.2667 0.8622 0.9685 0.7049 ] Network output: [ -0.05039 0.2047 1.077 0.0001995 -8.959e-05 0.8194 0.0001504 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1177 0.1074 0.1995 0.142 0.9903 0.9942 0.1177 0.9531 0.9725 0.2276 ] Network output: [ -0.04463 0.1231 1.052 0.0002713 -0.0001218 0.9154 0.0002044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1365 0.1344 0.2081 0.1606 0.9862 0.992 0.1365 0.9269 0.9613 0.2169 ] Network output: [ -0.0535 1.263 -0.02329 -0.0001617 7.258e-05 0.8666 -0.0001218 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03888 Epoch 4899 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03137 0.8697 0.964 -4.436e-05 1.992e-05 0.1034 -3.343e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003926 -0.003765 -0.01265 0.008664 0.9662 0.9713 0.008042 0.9107 0.9138 0.02608 ] Network output: [ 0.9782 -0.3807 0.1265 0.0001852 -8.313e-05 0.2985 0.0001395 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2392 -0.02772 -0.1438 0.2467 0.9837 0.9934 0.2702 0.8545 0.9654 0.6954 ] Network output: [ 0.001569 0.9307 0.9794 -8.407e-05 3.774e-05 0.08644 -6.336e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00568 0.001598 0.00564 0.004734 0.9911 0.994 0.005789 0.9615 0.9746 0.01347 ] Network output: [ -0.01726 -0.3358 1.047 -0.0002342 0.0001051 1.322 -0.0001765 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2675 0.1684 0.4543 0.1982 0.9853 0.9942 0.2683 0.862 0.9683 0.6778 ] Network output: [ -0.03426 0.2976 1.033 0.0001723 -7.737e-05 0.7381 0.0001299 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.122 0.1135 0.1944 0.1317 0.9902 0.9942 0.1221 0.9532 0.9726 0.2143 ] Network output: [ -0.02373 0.3061 0.983 0.0002038 -9.148e-05 0.7591 0.0001536 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1406 0.1388 0.1878 0.1321 0.9861 0.992 0.1406 0.9261 0.9606 0.1939 ] Network output: [ -0.0351 1.573 -0.115 -0.0002926 0.0001314 0.6111 -0.0002205 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1883 Epoch 4900 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03868 0.8441 0.9649 -3.746e-05 1.682e-05 0.1135 -2.823e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003815 -0.003584 -0.01104 0.008415 0.9662 0.9712 0.007783 0.9101 0.9136 0.02377 ] Network output: [ 0.9283 -0.5801 0.2277 0.00026 -0.0001167 0.4968 0.0001959 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2349 -0.02002 -0.09378 0.276 0.9837 0.9934 0.2651 0.8538 0.9654 0.6619 ] Network output: [ 0.01499 0.9248 0.9684 -7.97e-05 3.578e-05 0.07646 -6.006e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005611 0.001978 0.005923 0.00526 0.9909 0.9939 0.005718 0.9606 0.9745 0.01202 ] Network output: [ 0.008137 -0.5669 1.062 -1.207e-05 5.419e-06 1.489 -9.097e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2713 0.1853 0.4573 0.2456 0.9853 0.9942 0.2722 0.8603 0.9681 0.6302 ] Network output: [ -0.004114 0.3677 0.9852 0.0001721 -7.727e-05 0.656 0.0001297 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.123 0.116 0.172 0.1254 0.9899 0.9941 0.1231 0.952 0.9722 0.1857 ] Network output: [ 0.005858 0.3975 0.9323 0.0001864 -8.368e-05 0.6593 0.0001405 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1407 0.1393 0.1607 0.1199 0.9858 0.9918 0.1407 0.9236 0.9595 0.1649 ] Network output: [ 0.05406 1.378 -0.1575 -8.563e-05 3.844e-05 0.6707 -6.453e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2955 Epoch 4901 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03251 0.8747 0.9665 -8.282e-05 3.718e-05 0.09344 -6.241e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003815 -0.003466 -0.0107 0.006977 0.9662 0.9712 0.00773 0.9088 0.9127 0.02273 ] Network output: [ 0.8591 -0.1406 0.1907 -3.36e-05 1.509e-05 0.2316 -2.532e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2402 -0.00971 -0.0935 0.1934 0.9836 0.9933 0.2708 0.8524 0.965 0.6401 ] Network output: [ 0.01742 0.9105 0.972 -9.145e-05 4.105e-05 0.08227 -6.892e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005707 0.002034 0.005317 0.003444 0.9908 0.9938 0.005815 0.9595 0.9739 0.01162 ] Network output: [ -0.03257 -0.03365 0.9727 -0.0003446 0.0001547 1.125 -0.0002597 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2805 0.1931 0.431 0.1359 0.9853 0.9942 0.2814 0.8596 0.9682 0.6244 ] Network output: [ 0.002491 0.3335 0.9973 0.000182 -8.17e-05 0.6649 0.0001371 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.117 0.1102 0.1611 0.1151 0.9899 0.994 0.117 0.9509 0.972 0.1768 ] Network output: [ 0.01504 0.2646 0.964 0.0002437 -0.0001094 0.7423 0.0001836 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1348 0.1334 0.1595 0.1284 0.9857 0.9917 0.1348 0.922 0.9598 0.1646 ] Network output: [ 0.09198 0.9809 -0.09175 0.000137 -6.151e-05 0.9275 0.0001033 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08207 Epoch 4902 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03571 0.8562 0.9673 -8.236e-05 3.697e-05 0.1048 -6.207e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003879 -0.003571 -0.01185 0.007096 0.9662 0.9713 0.007845 0.9085 0.9127 0.02364 ] Network output: [ 0.9601 -0.07941 0.06521 -1.748e-05 7.849e-06 0.09399 -1.318e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2488 -0.0165 -0.1562 0.1867 0.9837 0.9934 0.2804 0.8514 0.9651 0.6509 ] Network output: [ 0.009723 0.8884 0.9859 -9.334e-05 4.19e-05 0.1059 -7.035e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005564 0.001764 0.004171 0.003365 0.9909 0.9938 0.005669 0.9593 0.9737 0.01151 ] Network output: [ 0.01554 -0.1363 0.9549 -0.0002607 0.000117 1.149 -0.0001965 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2756 0.1823 0.397 0.1554 0.9853 0.9942 0.2765 0.8593 0.9683 0.6405 ] Network output: [ -0.01436 0.252 1.041 0.0001927 -8.649e-05 0.7368 0.0001452 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.1049 0.1676 0.1267 0.99 0.994 0.1121 0.951 0.9721 0.1886 ] Network output: [ -0.005463 0.1632 1.015 0.0002586 -0.0001161 0.8339 0.0001949 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1302 0.1288 0.1733 0.1445 0.9857 0.9917 0.1302 0.9226 0.9603 0.1802 ] Network output: [ 0.0622 0.8198 -0.01675 0.0001645 -7.387e-05 1.073 0.000124 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05045 Epoch 4903 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02994 0.8849 0.9642 -0.0001005 4.512e-05 0.09055 -7.575e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003932 -0.003602 -0.01273 0.006196 0.9662 0.9713 0.007941 0.9084 0.9123 0.02458 ] Network output: [ 0.946 0.3621 -0.03029 -0.0002595 0.0001165 -0.2249 -0.0001956 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2538 -0.01552 -0.1906 0.1074 0.9837 0.9934 0.2858 0.8515 0.9649 0.6637 ] Network output: [ 0.006043 0.8721 0.9923 -8.839e-05 3.968e-05 0.1231 -6.661e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005614 0.001433 0.00329 0.001954 0.991 0.9938 0.00572 0.9591 0.9733 0.0119 ] Network output: [ 0.01665 0.2378 0.8714 -0.0005137 0.0002306 0.8554 -0.0003871 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2768 0.1704 0.3621 0.07788 0.9853 0.9942 0.2776 0.8594 0.9685 0.6645 ] Network output: [ -0.01954 0.179 1.068 0.0002235 -0.0001003 0.7932 0.0001685 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1074 0.09929 0.1672 0.1279 0.9901 0.9941 0.1075 0.9503 0.9718 0.1945 ] Network output: [ -0.01352 -0.002043 1.066 0.0003302 -0.0001483 0.9644 0.0002489 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1257 0.1239 0.186 0.1637 0.9857 0.9918 0.1257 0.9222 0.9606 0.1953 ] Network output: [ 0.06957 0.4432 0.06664 0.0003579 -0.0001607 1.352 0.0002697 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1234 Epoch 4904 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02852 0.884 0.963 -9.25e-05 4.152e-05 0.09558 -6.971e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004013 -0.003771 -0.01362 0.006901 0.9663 0.9713 0.008128 0.9083 0.9123 0.02565 ] Network output: [ 1.042 0.2468 -0.1055 -0.0001502 6.742e-05 -0.2253 -0.0001132 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2583 -0.02599 -0.2328 0.1361 0.9837 0.9934 0.2911 0.8503 0.9647 0.6796 ] Network output: [ -0.005519 0.8832 0.9995 -9.264e-05 4.159e-05 0.1279 -6.981e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005546 0.001217 0.002668 0.00264 0.991 0.9939 0.00565 0.9591 0.9731 0.01201 ] Network output: [ 0.07257 -0.01009 0.8778 -0.0003319 0.000149 0.9858 -0.0002501 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2698 0.1581 0.3416 0.1364 0.9853 0.9942 0.2707 0.8584 0.9682 0.6788 ] Network output: [ -0.0395 0.1554 1.092 0.0002173 -9.756e-05 0.8324 0.0001638 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.107 0.09827 0.1764 0.1408 0.9901 0.9941 0.1071 0.9505 0.9717 0.2074 ] Network output: [ -0.0384 -0.01044 1.092 0.0003108 -0.0001395 0.9962 0.0002342 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1249 0.1231 0.1984 0.1739 0.9858 0.9918 0.1249 0.923 0.9606 0.2085 ] Network output: [ 0.02108 0.5592 0.08476 0.0002461 -0.0001105 1.315 0.0001854 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0819 Epoch 4905 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02008 0.9157 0.9624 -0.0001048 4.705e-05 0.08125 -7.899e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004016 -0.003749 -0.01367 0.006325 0.9662 0.9713 0.008146 0.9084 0.9118 0.02613 ] Network output: [ 0.962 0.5061 -0.08255 -0.0003297 0.000148 -0.3488 -0.0002484 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2551 -0.02217 -0.2144 0.08621 0.9837 0.9934 0.2876 0.8505 0.9643 0.6884 ] Network output: [ -0.005074 0.8874 0.9963 -8.882e-05 3.987e-05 0.1261 -6.693e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00571 0.001071 0.002956 0.001709 0.9911 0.9939 0.005818 0.9591 0.9729 0.01274 ] Network output: [ 0.03451 0.3293 0.8394 -0.0006088 0.0002733 0.7599 -0.0004588 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.274 0.1528 0.3477 0.06638 0.9853 0.9942 0.2748 0.858 0.968 0.6918 ] Network output: [ -0.03526 0.1547 1.086 0.00023 -0.0001032 0.8313 0.0001733 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1075 0.09774 0.1749 0.1352 0.9902 0.9941 0.1076 0.9497 0.9713 0.2074 ] Network output: [ -0.03319 -0.05841 1.095 0.0003511 -0.0001576 1.031 0.0002646 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1257 0.1236 0.2005 0.178 0.9858 0.9918 0.1258 0.9217 0.9602 0.2116 ] Network output: [ 0.03364 0.4517 0.08889 0.0003269 -0.0001467 1.393 0.0002463 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1662 Epoch 4906 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02445 0.8888 0.9638 -7.693e-05 3.454e-05 0.09825 -5.798e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.004056 -0.003915 -0.01403 0.007966 0.9663 0.9713 0.008266 0.9083 0.912 0.0266 ] Network output: [ 1.091 0.003676 -0.096 1.484e-05 -6.66e-06 -0.0902 1.118e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2552 -0.03541 -0.2409 0.1863 0.9837 0.9934 0.288 0.8483 0.9642 0.6961 ] Network output: [ -0.01524 0.913 1 -9.695e-05 4.352e-05 0.117 -7.306e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005564 0.001114 0.002967 0.003598 0.9911 0.9939 0.00567 0.9592 0.973 0.01254 ] Network output: [ 0.07772 -0.2151 0.9272 -0.0002296 0.0001031 1.132 -0.000173 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2632 0.1491 0.3586 0.1872 0.9853 0.9942 0.264 0.8567 0.9675 0.6904 ] Network output: [ -0.05605 0.1917 1.093 0.0001913 -8.587e-05 0.828 0.0001442 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.1013 0.188 0.1471 0.9902 0.9941 0.1108 0.9502 0.9711 0.2188 ] Network output: [ -0.05531 0.08283 1.081 0.0002614 -0.0001174 0.9481 0.000197 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1285 0.1265 0.204 0.1699 0.986 0.9919 0.1285 0.9229 0.9597 0.2137 ] Network output: [ -0.04049 0.989 0.0339 -2.394e-05 1.075e-05 1.058 -1.804e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03565 Epoch 4907 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01916 0.9064 0.9656 -8.399e-05 3.77e-05 0.0894 -6.329e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003932 -0.003687 -0.01286 0.007224 0.9662 0.9713 0.008006 0.9085 0.9115 0.02592 ] Network output: [ 0.9133 0.1674 0.05651 -0.0001547 6.944e-05 -0.05121 -0.0001166 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2435 -0.02124 -0.1607 0.1467 0.9837 0.9934 0.2748 0.8495 0.964 0.6897 ] Network output: [ -0.002035 0.9128 0.9861 -8.852e-05 3.974e-05 0.1049 -6.671e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005757 0.001362 0.004609 0.002657 0.991 0.9939 0.005867 0.9594 0.9731 0.01352 ] Network output: [ -0.03562 0.2564 0.9261 -0.0006118 0.0002747 0.8863 -0.0004611 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2731 0.1626 0.4068 0.0838 0.9853 0.9942 0.274 0.8574 0.9675 0.6872 ] Network output: [ -0.03298 0.225 1.059 0.0002027 -9.1e-05 0.7828 0.0001528 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1037 0.1826 0.1304 0.9902 0.9941 0.1129 0.9499 0.9711 0.2094 ] Network output: [ -0.02286 0.08814 1.042 0.0003 -0.0001347 0.9165 0.0002261 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1313 0.1294 0.1931 0.1593 0.9859 0.9919 0.1313 0.9215 0.9595 0.2022 ] Network output: [ 0.004233 0.9539 -0.008906 5.292e-05 -2.376e-05 1.047 3.989e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0411 Epoch 4908 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03661 0.8486 0.9626 -4.083e-05 1.833e-05 0.1154 -3.077e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003935 -0.003817 -0.01328 0.008666 0.9663 0.9713 0.008032 0.9084 0.9121 0.0258 ] Network output: [ 1.09 -0.3495 -0.00342 0.0002393 -0.0001074 0.174 0.0001804 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2448 -0.03536 -0.2128 0.2481 0.9837 0.9934 0.2763 0.8477 0.9642 0.6877 ] Network output: [ -0.004082 0.9106 0.9901 -8.52e-05 3.825e-05 0.1071 -6.421e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00542 0.00131 0.003729 0.004635 0.991 0.9939 0.005524 0.959 0.973 0.01224 ] Network output: [ 0.07073 -0.4962 1.002 -4.826e-05 2.167e-05 1.352 -3.637e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2572 0.1543 0.39 0.2413 0.9853 0.9942 0.2581 0.8556 0.9671 0.6707 ] Network output: [ -0.04431 0.2537 1.064 0.0001719 -7.718e-05 0.7717 0.0001296 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.1041 0.1859 0.1423 0.99 0.9941 0.1128 0.9498 0.9708 0.2108 ] Network output: [ -0.04165 0.2283 1.031 0.0002049 -9.2e-05 0.825 0.0001544 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1301 0.1283 0.1896 0.1486 0.9859 0.9919 0.1301 0.9219 0.9587 0.1972 ] Network output: [ -0.03977 1.302 -0.03894 -0.0001774 7.964e-05 0.8156 -0.0001337 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.133 Epoch 4909 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02582 0.8845 0.9648 -7.295e-05 3.275e-05 0.09872 -5.498e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003788 -0.00349 -0.01146 0.007338 0.9662 0.9713 0.007694 0.908 0.9112 0.02433 ] Network output: [ 0.8459 -0.06716 0.1868 -5.38e-05 2.415e-05 0.1884 -4.055e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.234 -0.014 -0.1027 0.1839 0.9836 0.9933 0.2639 0.8487 0.9638 0.6673 ] Network output: [ 0.01131 0.9141 0.9721 -8.547e-05 3.837e-05 0.09076 -6.442e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005668 0.001757 0.005682 0.003294 0.9909 0.9939 0.005776 0.9589 0.9731 0.01305 ] Network output: [ -0.06812 0.1524 0.9748 -0.0005258 0.000236 1.007 -0.0003962 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.274 0.179 0.4378 0.1028 0.9853 0.9942 0.2749 0.8561 0.9672 0.6559 ] Network output: [ -0.01473 0.298 1.022 0.0001813 -8.138e-05 0.7106 0.0001366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.114 0.1064 0.1745 0.1197 0.9901 0.9941 0.1141 0.9493 0.9708 0.1941 ] Network output: [ -0.0004432 0.216 0.9889 0.0002544 -0.0001142 0.797 0.0001917 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1319 0.1303 0.1742 0.1371 0.9858 0.9918 0.1319 0.92 0.9586 0.1807 ] Network output: [ 0.03171 1.145 -0.07829 -1.413e-05 6.343e-06 0.8698 -1.065e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06786 Epoch 4910 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04439 0.8261 0.9609 -3.803e-05 1.707e-05 0.1241 -2.866e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00382 -0.003625 -0.01204 0.008291 0.9663 0.9713 0.007759 0.9075 0.9116 0.0241 ] Network output: [ 1.026 -0.4349 0.08382 0.0002435 -0.0001093 0.2998 0.0001835 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2403 -0.02682 -0.1664 0.2563 0.9836 0.9933 0.271 0.8466 0.9641 0.6613 ] Network output: [ 0.01042 0.8965 0.979 -7.976e-05 3.581e-05 0.1033 -6.011e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005369 0.001612 0.004301 0.00475 0.9908 0.9938 0.005471 0.9581 0.9729 0.01155 ] Network output: [ 0.05841 -0.5433 1.015 3.078e-06 -1.382e-06 1.412 2.32e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.262 0.1693 0.4045 0.2459 0.9853 0.9941 0.2628 0.8541 0.967 0.638 ] Network output: [ -0.02143 0.2866 1.034 0.0001734 -7.783e-05 0.7232 0.0001306 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.1048 0.1734 0.1347 0.9898 0.994 0.1122 0.9489 0.9706 0.1931 ] Network output: [ -0.01594 0.2751 0.995 0.0002 -8.977e-05 0.7626 0.0001507 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1293 0.1278 0.172 0.1378 0.9857 0.9918 0.1293 0.92 0.9581 0.1782 ] Network output: [ 0.02045 1.172 -0.0667 -4.788e-05 2.149e-05 0.8532 -3.608e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1679 Epoch 4911 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02974 0.8869 0.9613 -8.958e-05 4.021e-05 0.09189 -6.751e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003789 -0.003432 -0.01145 0.00649 0.9662 0.9713 0.007652 0.9068 0.9105 0.02356 ] Network output: [ 0.8473 0.1731 0.1235 -0.0001901 8.534e-05 0.008132 -0.0001433 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2397 -0.009971 -0.1225 0.1388 0.9836 0.9933 0.2701 0.8469 0.9636 0.6506 ] Network output: [ 0.01574 0.8929 0.9741 -8.557e-05 3.842e-05 0.1011 -6.449e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005612 0.001707 0.004722 0.002354 0.9908 0.9938 0.005718 0.9577 0.9725 0.01224 ] Network output: [ -0.05082 0.3148 0.9175 -0.00059 0.0002649 0.8669 -0.0004446 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2764 0.1801 0.4051 0.06695 0.9853 0.9941 0.2773 0.8545 0.9671 0.6458 ] Network output: [ -0.007636 0.2678 1.029 0.000194 -8.707e-05 0.7188 0.0001462 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.1011 0.1636 0.1164 0.99 0.994 0.1085 0.9479 0.9704 0.1851 ] Network output: [ 0.004353 0.1324 1.012 0.0002859 -0.0001283 0.8484 0.0002154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1261 0.1246 0.171 0.1435 0.9856 0.9917 0.1261 0.918 0.9584 0.1784 ] Network output: [ 0.06829 0.7908 -0.02275 0.0001896 -8.511e-05 1.096 0.0001429 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07023 Epoch 4912 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04206 0.8351 0.96 -5.563e-05 2.497e-05 0.1205 -4.192e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00386 -0.003647 -0.01265 0.007898 0.9663 0.9713 0.007811 0.9066 0.911 0.02445 ] Network output: [ 1.057 -0.2304 -0.003716 0.0001414 -6.35e-05 0.1199 0.0001066 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2472 -0.02735 -0.2019 0.2225 0.9836 0.9933 0.2786 0.8446 0.9637 0.6617 ] Network output: [ 0.005236 0.8853 0.9868 -8.59e-05 3.857e-05 0.1171 -6.474e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005323 0.001471 0.003401 0.004101 0.9909 0.9938 0.005424 0.9574 0.9724 0.01148 ] Network output: [ 0.07816 -0.3967 0.9603 -5.959e-05 2.675e-05 1.28 -4.491e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2621 0.1651 0.3682 0.2179 0.9852 0.9941 0.263 0.8527 0.9669 0.647 ] Network output: [ -0.03123 0.23 1.064 0.0001818 -8.161e-05 0.7696 0.000137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1069 0.09945 0.1734 0.1382 0.9899 0.994 0.107 0.9481 0.9703 0.1982 ] Network output: [ -0.02835 0.1639 1.04 0.0002281 -0.0001024 0.8535 0.0001719 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.124 0.1224 0.1816 0.1518 0.9857 0.9918 0.124 0.9193 0.9582 0.1895 ] Network output: [ 0.009597 0.9449 0.004534 3.648e-05 -1.638e-05 1.032 2.749e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07888 Epoch 4913 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02667 0.901 0.9589 -0.0001003 4.504e-05 0.08634 -7.561e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003837 -0.003482 -0.01232 0.006012 0.9663 0.9713 0.007736 0.9063 0.9098 0.02442 ] Network output: [ 0.8668 0.4513 0.03249 -0.0003266 0.0001466 -0.2188 -0.0002462 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2447 -0.01169 -0.1604 0.09051 0.9836 0.9933 0.2756 0.8456 0.9632 0.6612 ] Network output: [ 0.01102 0.8791 0.981 -8.35e-05 3.749e-05 0.1175 -6.293e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005597 0.001403 0.003798 0.001504 0.9909 0.9938 0.005702 0.957 0.9719 0.01244 ] Network output: [ -0.02804 0.4935 0.858 -0.0007008 0.0003146 0.7018 -0.0005281 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2752 0.1682 0.3707 0.0301 0.9853 0.9941 0.2761 0.853 0.9669 0.6631 ] Network output: [ -0.01542 0.2111 1.055 0.0002119 -9.513e-05 0.766 0.0001597 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1041 0.09596 0.1633 0.1198 0.9901 0.994 0.1042 0.9467 0.9699 0.1907 ] Network output: [ -0.007502 0.01479 1.055 0.0003273 -0.0001469 0.9467 0.0002467 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1217 0.12 0.1813 0.1587 0.9856 0.9917 0.1217 0.9168 0.9582 0.191 ] Network output: [ 0.06321 0.5281 0.04533 0.000309 -0.0001387 1.301 0.0002328 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1572 Epoch 4914 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0386 0.8425 0.9591 -5.621e-05 2.524e-05 0.121 -4.236e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003915 -0.003759 -0.01338 0.008282 0.9663 0.9713 0.007934 0.9059 0.9103 0.02525 ] Network output: [ 1.121 -0.2402 -0.07101 0.0001813 -8.139e-05 0.06977 0.0001366 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2505 -0.03447 -0.2378 0.2313 0.9836 0.9933 0.2824 0.8419 0.9632 0.6722 ] Network output: [ -0.004678 0.894 0.993 -9.126e-05 4.097e-05 0.122 -6.878e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005272 0.001277 0.002747 0.004315 0.9909 0.9938 0.005372 0.9567 0.9718 0.01152 ] Network output: [ 0.1177 -0.4887 0.9452 2.384e-05 -1.07e-05 1.308 1.797e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2567 0.1547 0.343 0.2442 0.9852 0.9941 0.2575 0.8501 0.9663 0.6574 ] Network output: [ -0.04625 0.2152 1.082 0.0001761 -7.906e-05 0.7961 0.0001327 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1054 0.09746 0.1769 0.1456 0.9899 0.994 0.1055 0.9471 0.9696 0.2051 ] Network output: [ -0.04752 0.1439 1.065 0.0002193 -9.845e-05 0.8872 0.0001653 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.122 0.1203 0.1894 0.16 0.9858 0.9918 0.122 0.9184 0.9576 0.1982 ] Network output: [ -0.01951 0.9538 0.0291 1.975e-06 -8.865e-07 1.056 1.488e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09212 Epoch 4915 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01885 0.921 0.9604 -0.0001097 4.925e-05 0.08043 -8.267e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003828 -0.003469 -0.01221 0.005988 0.9663 0.9713 0.007724 0.9055 0.9088 0.02466 ] Network output: [ 0.8203 0.5023 0.07033 -0.0003819 0.0001715 -0.2148 -0.0002878 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2415 -0.01045 -0.1409 0.0816 0.9836 0.9933 0.272 0.8432 0.9624 0.6647 ] Network output: [ 0.008357 0.8929 0.9786 -8.807e-05 3.954e-05 0.1114 -6.637e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005679 0.001391 0.00425 0.001272 0.9909 0.9938 0.005786 0.9564 0.9714 0.01298 ] Network output: [ -0.0579 0.6333 0.8555 -0.0008197 0.000368 0.6236 -0.0006177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2766 0.1674 0.3813 0.004247 0.9853 0.9941 0.2775 0.8503 0.9663 0.6668 ] Network output: [ -0.01706 0.2353 1.048 0.0002032 -9.121e-05 0.7517 0.0001531 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1047 0.09627 0.1632 0.1159 0.9901 0.994 0.1048 0.9452 0.9691 0.1905 ] Network output: [ -0.007272 0.03408 1.047 0.0003249 -0.0001458 0.9344 0.0002448 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1223 0.1204 0.1797 0.1561 0.9856 0.9917 0.1223 0.9144 0.9571 0.1897 ] Network output: [ 0.05646 0.6076 0.02622 0.0002714 -0.0001218 1.254 0.0002045 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1845 Epoch 4916 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04096 0.8241 0.9606 -3.952e-05 1.774e-05 0.1332 -2.978e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003889 -0.003799 -0.01332 0.009041 0.9663 0.9714 0.007898 0.9047 0.9095 0.02515 ] Network output: [ 1.165 -0.4908 -0.054 0.0003426 -0.0001538 0.2166 0.0002582 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2468 -0.04091 -0.2417 0.2804 0.9836 0.9933 0.2784 0.8376 0.9625 0.6696 ] Network output: [ -0.006976 0.9021 0.9929 -9.173e-05 4.118e-05 0.1186 -6.913e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005177 0.001238 0.002752 0.005195 0.9908 0.9938 0.005276 0.9554 0.9711 0.01117 ] Network output: [ 0.1516 -0.7808 0.9819 0.0002222 -9.976e-05 1.497 0.0001675 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.25 0.1497 0.3435 0.3087 0.9852 0.9941 0.2509 0.8452 0.9653 0.6439 ] Network output: [ -0.0475 0.2552 1.071 0.0001603 -7.195e-05 0.7699 0.0001208 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1066 0.09855 0.1741 0.1489 0.9897 0.9939 0.1067 0.9449 0.9685 0.1998 ] Network output: [ -0.05014 0.2339 1.044 0.0001797 -8.07e-05 0.8229 0.0001355 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1225 0.1208 0.181 0.1533 0.9857 0.9917 0.1225 0.9155 0.9557 0.1887 ] Network output: [ -0.01375 1.038 -0.002176 -1.957e-05 8.784e-06 0.9921 -1.475e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2129 Epoch 4917 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01133 0.94 0.964 -0.0001263 5.672e-05 0.07284 -9.522e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003773 -0.003376 -0.01128 0.005925 0.9662 0.9713 0.007606 0.9036 0.9068 0.02385 ] Network output: [ 0.7396 0.468 0.166 -0.0004221 0.0001895 -0.1149 -0.0003181 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2362 -0.005376 -0.09241 0.08649 0.9836 0.9933 0.2661 0.8384 0.9611 0.6491 ] Network output: [ 0.009806 0.9135 0.9719 -9.888e-05 4.439e-05 0.09456 -7.452e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005772 0.001615 0.00517 0.001229 0.9907 0.9937 0.005881 0.9547 0.9705 0.01306 ] Network output: [ -0.1064 0.7534 0.8708 -0.0009118 0.0004093 0.5848 -0.0006872 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2808 0.1774 0.4046 -0.01657 0.9852 0.9941 0.2817 0.8449 0.9651 0.6481 ] Network output: [ -0.01172 0.3112 1.023 0.000174 -7.812e-05 0.6902 0.0001311 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1061 0.09818 0.157 0.1045 0.9899 0.9939 0.1062 0.9424 0.9677 0.1797 ] Network output: [ 0.002616 0.1323 1.013 0.0002907 -0.0001305 0.8506 0.0002191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1231 0.1214 0.1662 0.14 0.9854 0.9916 0.1231 0.9098 0.955 0.1746 ] Network output: [ 0.06329 0.7961 -0.02807 0.0001938 -8.702e-05 1.106 0.0001461 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.202 Epoch 4918 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.045 0.7923 0.965 -2.476e-05 1.112e-05 0.1526 -1.866e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003793 -0.003727 -0.01238 0.009447 0.9663 0.9714 0.007697 0.9023 0.9078 0.02396 ] Network output: [ 1.147 -0.748 0.03106 0.0004492 -0.0002017 0.4246 0.0003385 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2406 -0.04241 -0.2074 0.3239 0.9836 0.9933 0.2714 0.8305 0.9613 0.6463 ] Network output: [ -0.000705 0.8974 0.9878 -8.696e-05 3.904e-05 0.1159 -6.553e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005117 0.001327 0.00325 0.006015 0.9906 0.9937 0.005214 0.9528 0.9701 0.01045 ] Network output: [ 0.1725 -1.059 1.023 0.000395 -0.0001773 1.693 0.0002977 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2491 0.1524 0.3573 0.3662 0.9851 0.9941 0.25 0.837 0.9639 0.6037 ] Network output: [ -0.03157 0.311 1.038 0.0001551 -6.963e-05 0.715 0.0001169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.1007 0.1617 0.1478 0.9894 0.9937 0.1086 0.9413 0.9669 0.1812 ] Network output: [ -0.03245 0.3358 0.9999 0.0001569 -7.044e-05 0.7298 0.0001182 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1237 0.1221 0.1597 0.1429 0.9855 0.9916 0.1237 0.91 0.9532 0.1653 ] Network output: [ 0.06258 0.8336 -0.04071 0.0001877 -8.428e-05 1.083 0.0001415 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4139 Epoch 4919 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.003275 0.974 0.9662 -0.000162 7.274e-05 0.05255 -0.0001221 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003804 -0.003356 -0.01065 0.005512 0.9662 0.9713 0.007637 0.9 0.9036 0.02267 ] Network output: [ 0.7126 0.5683 0.1679 -0.0005215 0.0002341 -0.1636 -0.000393 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.241 -0.001211 -0.08111 0.07245 0.9835 0.9933 0.2714 0.8293 0.9589 0.619 ] Network output: [ 0.006317 0.9343 0.9724 -0.000119 5.344e-05 0.08024 -8.971e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005914 0.001769 0.004896 0.0009742 0.9905 0.9936 0.006025 0.9513 0.9685 0.01219 ] Network output: [ -0.1027 0.8464 0.837 -0.0009327 0.0004187 0.5182 -0.0007029 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2874 0.1859 0.3887 -0.02514 0.9852 0.9941 0.2884 0.8354 0.9631 0.6181 ] Network output: [ -0.01319 0.384 1.01 0.0001371 -6.153e-05 0.6329 0.0001033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1055 0.09798 0.1451 0.09499 0.9897 0.9938 0.1056 0.9377 0.9653 0.1657 ] Network output: [ 0.0001597 0.2051 1.003 0.0002498 -0.0001121 0.7922 0.0001882 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.121 0.1194 0.1537 0.129 0.9852 0.9914 0.121 0.9029 0.9521 0.1612 ] Network output: [ 0.06464 0.8528 -0.03864 0.0001672 -7.504e-05 1.057 0.000126 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2658 Epoch 4920 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04018 0.7789 0.9745 -3.101e-05 1.392e-05 0.166 -2.337e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003722 -0.003651 -0.01135 0.009526 0.9663 0.9714 0.007531 0.8984 0.9048 0.02268 ] Network output: [ 1.099 -0.8608 0.1118 0.0004377 -0.0001965 0.5522 0.0003299 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2378 -0.04176 -0.1672 0.3418 0.9835 0.9933 0.2681 0.8194 0.9593 0.6139 ] Network output: [ 0.0009028 0.8953 0.9872 -8.951e-05 4.019e-05 0.1153 -6.746e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005168 0.001432 0.003621 0.006465 0.9904 0.9935 0.005267 0.9488 0.9683 0.009798 ] Network output: [ 0.1874 -1.153 1.021 0.0004627 -0.0002077 1.759 0.0003487 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2542 0.1585 0.3558 0.3929 0.9851 0.9941 0.2551 0.8251 0.9618 0.5577 ] Network output: [ -0.02257 0.3678 1.014 0.0001417 -6.361e-05 0.6644 0.0001068 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.1026 0.1483 0.1435 0.989 0.9935 0.1103 0.9361 0.9647 0.1635 ] Network output: [ -0.02306 0.4071 0.9737 0.0001371 -6.156e-05 0.6659 0.0001033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1242 0.1227 0.1423 0.1348 0.9852 0.9914 0.1242 0.9027 0.9502 0.1466 ] Network output: [ 0.1284 0.5731 -0.05453 0.0004063 -0.0001824 1.226 0.0003062 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.556 Epoch 4921 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.005142 0.9942 0.9725 -0.0001907 8.561e-05 0.04284 -0.0001437 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003879 -0.003399 -0.009891 0.005945 0.9663 0.9713 0.007751 0.8953 0.8997 0.02132 ] Network output: [ 0.7449 0.3755 0.1733 -0.0004478 0.000201 -0.04049 -0.0003375 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2505 0.0003461 -0.07259 0.1187 0.9834 0.9932 0.2818 0.8164 0.9563 0.5795 ] Network output: [ -0.003674 0.9696 0.9766 -0.0001473 6.615e-05 0.06048 -0.000111 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006122 0.002188 0.00477 0.002212 0.9903 0.9934 0.006238 0.9473 0.9662 0.01111 ] Network output: [ -0.06 0.5412 0.8436 -0.0006552 0.0002941 0.7325 -0.0004938 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2972 0.2049 0.3762 0.06097 0.9851 0.9941 0.2982 0.8232 0.9606 0.574 ] Network output: [ -0.02739 0.4446 1.01 9.055e-05 -4.065e-05 0.6008 6.824e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.1029 0.1447 0.1014 0.9894 0.9936 0.1094 0.9344 0.9632 0.1621 ] Network output: [ -0.01564 0.3156 0.9965 0.0001725 -7.744e-05 0.7199 0.00013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1229 0.1215 0.1487 0.1243 0.9852 0.9914 0.1229 0.8985 0.9494 0.1548 ] Network output: [ 0.03755 1.033 -0.0543 4.249e-05 -1.908e-05 0.9464 3.202e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1689 Epoch 4922 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03021 0.8145 0.981 -6.797e-05 3.052e-05 0.1438 -5.123e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00374 -0.00354 -0.01037 0.008671 0.9663 0.9714 0.007506 0.8947 0.9013 0.02154 ] Network output: [ 0.9939 -0.6316 0.1658 0.000216 -9.698e-05 0.4789 0.0001628 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2431 -0.0291 -0.1204 0.2989 0.9834 0.9932 0.2737 0.8105 0.957 0.5806 ] Network output: [ 0.003354 0.9032 0.9867 -9.871e-05 4.431e-05 0.103 -7.439e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005479 0.001818 0.004109 0.006026 0.9902 0.9934 0.005583 0.9458 0.9664 0.009805 ] Network output: [ 0.1464 -0.8577 0.9748 0.0003013 -0.0001353 1.591 0.0002271 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2727 0.182 0.3565 0.3449 0.9851 0.9941 0.2736 0.817 0.96 0.5341 ] Network output: [ -0.02294 0.3813 1.014 0.0001291 -5.794e-05 0.651 9.726e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.1059 0.1479 0.1383 0.9889 0.9934 0.1127 0.9334 0.963 0.1623 ] Network output: [ -0.02128 0.4014 0.9776 0.0001316 -5.906e-05 0.6641 9.914e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1259 0.1246 0.1426 0.1328 0.9852 0.9914 0.1259 0.8989 0.9485 0.147 ] Network output: [ 0.1025 0.7016 -0.05613 0.0002982 -0.0001339 1.151 0.0002248 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3583 Epoch 4923 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.005324 0.9552 0.9755 -0.0001676 7.524e-05 0.05792 -0.0001263 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00394 -0.003447 -0.01015 0.006102 0.9663 0.9713 0.007829 0.8926 0.8978 0.02111 ] Network output: [ 0.8215 0.314 0.1044 -0.0003916 0.0001758 -0.06292 -0.0002951 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2611 -0.0009451 -0.1006 0.1303 0.9833 0.9932 0.2934 0.8087 0.9549 0.5651 ] Network output: [ -0.001908 0.9404 0.9863 -0.0001304 5.855e-05 0.07655 -9.829e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006089 0.002249 0.004069 0.002572 0.9902 0.9933 0.006202 0.945 0.9648 0.01053 ] Network output: [ -0.01382 0.3776 0.8319 -0.0004889 0.0002195 0.8162 -0.0003684 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2998 0.2097 0.3504 0.09929 0.9851 0.9941 0.3008 0.8165 0.9593 0.5608 ] Network output: [ -0.03417 0.4022 1.035 9.923e-05 -4.455e-05 0.6315 7.478e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.107 0.101 0.1464 0.109 0.9893 0.9935 0.107 0.9324 0.9621 0.1656 ] Network output: [ -0.02581 0.2781 1.024 0.0001704 -7.65e-05 0.7498 0.0001284 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1201 0.1189 0.1535 0.131 0.9852 0.9914 0.1201 0.8962 0.9483 0.1603 ] Network output: [ 0.01728 0.9744 -0.01205 3.392e-05 -1.523e-05 1.003 2.556e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1154 Epoch 4924 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03023 0.8218 0.9813 -7.278e-05 3.268e-05 0.1362 -5.485e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003804 -0.003514 -0.01055 0.008183 0.9664 0.9714 0.007584 0.8926 0.8992 0.02162 ] Network output: [ 0.9836 -0.4239 0.1205 9.697e-05 -4.353e-05 0.3365 7.308e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2522 -0.02179 -0.1275 0.2617 0.9834 0.9932 0.2836 0.8054 0.9556 0.5738 ] Network output: [ 0.003982 0.8899 0.9916 -9.143e-05 4.105e-05 0.1102 -6.891e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005595 0.001992 0.003906 0.005419 0.9902 0.9933 0.0057 0.9444 0.9653 0.01003 ] Network output: [ 0.1194 -0.6178 0.9388 0.0001795 -8.059e-05 1.441 0.0001353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2813 0.1937 0.3457 0.3002 0.9851 0.994 0.2822 0.813 0.9591 0.5406 ] Network output: [ -0.03212 0.3377 1.042 0.0001335 -5.993e-05 0.6851 0.0001006 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.1036 0.1537 0.138 0.989 0.9934 0.1098 0.9323 0.9621 0.1709 ] Network output: [ -0.02957 0.3316 1.01 0.0001448 -6.502e-05 0.7181 0.0001092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1232 0.122 0.1525 0.1382 0.9853 0.9915 0.1232 0.8973 0.9479 0.1581 ] Network output: [ 0.05028 0.8245 -0.02318 0.000157 -7.046e-05 1.099 0.0001183 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.204 Epoch 4925 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01653 0.915 0.9741 -0.000134 6.017e-05 0.07737 -0.000101 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003952 -0.003448 -0.01056 0.006159 0.9664 0.9714 0.007823 0.8913 0.8968 0.02146 ] Network output: [ 0.8636 0.3117 0.05813 -0.0003587 0.000161 -0.09854 -0.0002703 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2657 -0.001299 -0.12 0.1273 0.9833 0.9932 0.2984 0.8051 0.9542 0.5672 ] Network output: [ 0.003475 0.9046 0.9903 -0.0001038 4.662e-05 0.09773 -7.826e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00598 0.002206 0.003718 0.002539 0.9902 0.9933 0.00609 0.9441 0.9641 0.01055 ] Network output: [ -0.0004153 0.3507 0.8268 -0.0004471 0.0002007 0.8215 -0.0003369 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2985 0.2093 0.3384 0.1011 0.9851 0.9941 0.2995 0.8135 0.9587 0.5646 ] Network output: [ -0.0366 0.3462 1.057 0.0001207 -5.419e-05 0.6704 9.097e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1031 0.09735 0.1499 0.1131 0.9893 0.9935 0.1031 0.9312 0.9614 0.1713 ] Network output: [ -0.02958 0.2143 1.049 0.0001912 -8.583e-05 0.7965 0.0001441 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1164 0.1153 0.1599 0.1374 0.9852 0.9914 0.1164 0.8948 0.9477 0.1676 ] Network output: [ 0.001859 0.9254 0.02088 2.995e-05 -1.345e-05 1.05 2.257e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09698 Epoch 4926 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03609 0.8098 0.9769 -5.737e-05 2.575e-05 0.1409 -4.323e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003827 -0.0035 -0.01086 0.008022 0.9664 0.9714 0.007602 0.8914 0.898 0.0219 ] Network output: [ 0.9958 -0.3328 0.0812 6.868e-05 -3.083e-05 0.2603 5.176e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2563 -0.01918 -0.1395 0.2442 0.9834 0.9932 0.288 0.8023 0.9548 0.5756 ] Network output: [ 0.007592 0.8707 0.9919 -7.543e-05 3.386e-05 0.1219 -5.684e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005574 0.002012 0.00369 0.005095 0.9902 0.9933 0.005678 0.9436 0.9645 0.01021 ] Network output: [ 0.1105 -0.5107 0.9218 0.0001291 -5.797e-05 1.368 9.732e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2823 0.196 0.3377 0.2772 0.9851 0.994 0.2832 0.8106 0.9584 0.5486 ] Network output: [ -0.03618 0.2963 1.061 0.0001454 -6.528e-05 0.7158 0.0001096 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1062 0.1004 0.158 0.1389 0.9891 0.9934 0.1063 0.9315 0.9615 0.1775 ] Network output: [ -0.03384 0.2746 1.032 0.0001612 -7.237e-05 0.7614 0.0001215 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1198 0.1187 0.1598 0.1427 0.9853 0.9915 0.1199 0.8963 0.9473 0.1663 ] Network output: [ 0.02203 0.8752 -0.001376 8.824e-05 -3.961e-05 1.083 6.65e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1458 Epoch 4927 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02472 0.8899 0.9697 -0.0001091 4.897e-05 0.09051 -8.22e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003938 -0.003428 -0.01078 0.006239 0.9664 0.9714 0.007775 0.8906 0.896 0.02173 ] Network output: [ 0.8812 0.2993 0.04056 -0.0003252 0.000146 -0.1036 -0.0002451 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2661 -0.001208 -0.1278 0.1275 0.9834 0.9932 0.2987 0.8028 0.9536 0.5706 ] Network output: [ 0.008736 0.8833 0.9886 -8.515e-05 3.823e-05 0.1103 -6.417e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005885 0.002176 0.003623 0.002563 0.9902 0.9933 0.005994 0.9434 0.9636 0.01067 ] Network output: [ 0.003501 0.3312 0.8288 -0.0004238 0.0001903 0.8313 -0.0003194 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2963 0.2082 0.335 0.1022 0.9851 0.9941 0.2972 0.8113 0.9582 0.5683 ] Network output: [ -0.03677 0.3112 1.068 0.0001348 -6.054e-05 0.6949 0.0001016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1004 0.09482 0.153 0.1157 0.9893 0.9935 0.1004 0.9304 0.9609 0.1757 ] Network output: [ -0.03021 0.1775 1.061 0.0002041 -9.162e-05 0.823 0.0001538 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1138 0.1127 0.1642 0.1409 0.9852 0.9914 0.1138 0.8937 0.9471 0.1725 ] Network output: [ -0.006976 0.9227 0.03227 1.545e-05 -6.936e-06 1.059 1.164e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08647 Epoch 4928 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04157 0.8006 0.9714 -4.427e-05 1.987e-05 0.1447 -3.336e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003822 -0.003472 -0.01099 0.007961 0.9664 0.9714 0.007571 0.8907 0.8971 0.02206 ] Network output: [ 0.9993 -0.2927 0.06503 6.649e-05 -2.985e-05 0.2294 5.011e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2569 -0.01741 -0.1442 0.2362 0.9834 0.9932 0.2885 0.8002 0.9542 0.5774 ] Network output: [ 0.01193 0.8587 0.9888 -6.388e-05 2.868e-05 0.1284 -4.814e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005532 0.002016 0.00362 0.004955 0.9902 0.9933 0.005634 0.943 0.9639 0.01035 ] Network output: [ 0.1054 -0.4605 0.915 0.0001059 -4.754e-05 1.335 7.98e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2815 0.1965 0.3349 0.2656 0.9851 0.994 0.2824 0.8087 0.9579 0.5529 ] Network output: [ -0.03665 0.2718 1.069 0.0001531 -6.875e-05 0.7327 0.0001154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1037 0.09806 0.1605 0.1395 0.9891 0.9934 0.1037 0.9307 0.9609 0.1813 ] Network output: [ -0.03445 0.243 1.043 0.0001707 -7.662e-05 0.7839 0.0001286 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1173 0.1162 0.1638 0.145 0.9853 0.9915 0.1173 0.8953 0.9467 0.1707 ] Network output: [ 0.009505 0.9091 0.005391 5.076e-05 -2.279e-05 1.067 3.826e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1226 Epoch 4929 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03073 0.8752 0.965 -9.341e-05 4.194e-05 0.09799 -7.04e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003914 -0.003399 -0.01086 0.006334 0.9664 0.9714 0.007713 0.89 0.8954 0.02185 ] Network output: [ 0.8889 0.2755 0.03692 -0.0002923 0.0001312 -0.09144 -0.0002203 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.265 -0.0009254 -0.1298 0.131 0.9834 0.9932 0.2973 0.801 0.9531 0.5726 ] Network output: [ 0.01316 0.8723 0.9851 -7.454e-05 3.346e-05 0.116 -5.617e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005808 0.002168 0.003634 0.00266 0.9902 0.9933 0.005914 0.9429 0.9632 0.01077 ] Network output: [ 0.005667 0.3004 0.8344 -0.0003983 0.0001788 0.8523 -0.0003002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2941 0.2076 0.3352 0.107 0.9851 0.9941 0.295 0.8096 0.9577 0.5698 ] Network output: [ -0.03611 0.2908 1.073 0.0001418 -6.364e-05 0.7088 0.0001068 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09851 0.09316 0.1555 0.1177 0.9893 0.9935 0.09857 0.9298 0.9604 0.1788 ] Network output: [ -0.02967 0.1603 1.065 0.0002085 -9.36e-05 0.8346 0.0001571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.112 0.1109 0.1669 0.1427 0.9852 0.9914 0.112 0.893 0.9466 0.1753 ] Network output: [ -0.01121 0.9392 0.03297 -1.393e-06 6.254e-07 1.05 -1.05e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07667 Epoch 4930 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04572 0.7966 0.9662 -3.706e-05 1.664e-05 0.1455 -2.793e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003808 -0.003439 -0.01103 0.007914 0.9665 0.9714 0.007527 0.8901 0.8964 0.02212 ] Network output: [ 0.9964 -0.267 0.0599 6.612e-05 -2.968e-05 0.2146 4.983e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2565 -0.01557 -0.1445 0.2313 0.9834 0.9932 0.2879 0.7986 0.9537 0.5784 ] Network output: [ 0.01581 0.8526 0.9849 -5.76e-05 2.586e-05 0.1307 -4.341e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005493 0.002031 0.003631 0.004879 0.9902 0.9933 0.005595 0.9425 0.9635 0.01045 ] Network output: [ 0.09988 -0.4281 0.9126 8.899e-05 -3.995e-05 1.316 6.706e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2808 0.1973 0.335 0.258 0.9851 0.994 0.2817 0.8072 0.9575 0.5553 ] Network output: [ -0.03594 0.2575 1.074 0.0001566 -7.031e-05 0.7414 0.000118 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1017 0.09633 0.1623 0.1396 0.9891 0.9934 0.1018 0.9301 0.9604 0.1836 ] Network output: [ -0.03358 0.2252 1.047 0.0001751 -7.861e-05 0.7952 0.000132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1153 0.1143 0.1661 0.1461 0.9853 0.9915 0.1154 0.8945 0.9462 0.1733 ] Network output: [ 0.003962 0.9366 0.005664 2.637e-05 -1.184e-05 1.05 1.987e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1099 Epoch 4931 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03538 0.8662 0.9607 -8.376e-05 3.76e-05 0.102 -6.312e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00389 -0.003371 -0.01089 0.006425 0.9665 0.9714 0.007654 0.8896 0.8949 0.0219 ] Network output: [ 0.8943 0.2497 0.03644 -0.0002627 0.0001179 -0.0759 -0.000198 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2639 -0.0005481 -0.1303 0.1357 0.9834 0.9932 0.296 0.7996 0.9528 0.5737 ] Network output: [ 0.01666 0.8665 0.9815 -6.88e-05 3.089e-05 0.1184 -5.185e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005741 0.002171 0.003667 0.002785 0.9902 0.9933 0.005846 0.9425 0.9628 0.01083 ] Network output: [ 0.007892 0.2627 0.841 -0.0003701 0.0001662 0.879 -0.0002789 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2923 0.2076 0.3364 0.1138 0.9851 0.9941 0.2932 0.8083 0.9573 0.5704 ] Network output: [ -0.03542 0.2768 1.076 0.0001451 -6.512e-05 0.7182 0.0001093 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0971 0.09192 0.1576 0.1197 0.9893 0.9935 0.09716 0.9294 0.96 0.1811 ] Network output: [ -0.02897 0.1513 1.067 0.0002088 -9.375e-05 0.8401 0.0001574 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1106 0.1095 0.1687 0.1439 0.9852 0.9914 0.1106 0.8924 0.9461 0.1772 ] Network output: [ -0.01275 0.9553 0.03078 -1.405e-05 6.306e-06 1.039 -1.059e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06783 Epoch 4932 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0487 0.7969 0.9617 -3.436e-05 1.542e-05 0.1438 -2.589e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003796 -0.003406 -0.01105 0.007851 0.9665 0.9715 0.007488 0.8897 0.8959 0.02214 ] Network output: [ 0.9918 -0.2392 0.05634 6.037e-05 -2.71e-05 0.1996 4.55e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2563 -0.01363 -0.1439 0.2264 0.9834 0.9932 0.2876 0.7975 0.9533 0.5791 ] Network output: [ 0.01883 0.8499 0.9814 -5.474e-05 2.457e-05 0.1308 -4.125e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005462 0.002052 0.003659 0.004799 0.9902 0.9933 0.005562 0.9422 0.9632 0.01053 ] Network output: [ 0.09344 -0.396 0.9109 7.031e-05 -3.156e-05 1.299 5.299e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2806 0.1985 0.336 0.2506 0.9851 0.994 0.2815 0.8062 0.9571 0.5574 ] Network output: [ -0.03533 0.2473 1.077 0.0001578 -7.084e-05 0.7473 0.0001189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1001 0.09485 0.1637 0.1396 0.9891 0.9934 0.1001 0.9298 0.9601 0.1855 ] Network output: [ -0.03263 0.2115 1.051 0.0001776 -7.972e-05 0.8034 0.0001338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1137 0.1127 0.168 0.1468 0.9853 0.9915 0.1137 0.8939 0.9458 0.1754 ] Network output: [ 0.0006902 0.9581 0.005125 8.492e-06 -3.812e-06 1.035 6.4e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09874 Epoch 4933 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03915 0.8603 0.957 -7.728e-05 3.469e-05 0.1041 -5.824e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003871 -0.003346 -0.01092 0.006504 0.9665 0.9715 0.007602 0.8893 0.8945 0.02194 ] Network output: [ 0.9004 0.2275 0.03455 -0.000237 0.0001064 -0.0638 -0.0001786 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2631 -0.0002146 -0.1314 0.1401 0.9834 0.9932 0.295 0.7986 0.9525 0.575 ] Network output: [ 0.01932 0.863 0.9786 -6.546e-05 2.939e-05 0.1194 -4.934e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005679 0.002174 0.003687 0.002904 0.9902 0.9933 0.005782 0.9423 0.9626 0.01087 ] Network output: [ 0.01042 0.2244 0.8474 -0.0003422 0.0001536 0.906 -0.0002579 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2908 0.2076 0.3375 0.121 0.9851 0.9941 0.2917 0.8073 0.9571 0.5711 ] Network output: [ -0.03502 0.2645 1.08 0.0001472 -6.607e-05 0.7265 0.0001109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09587 0.09084 0.1596 0.1217 0.9893 0.9935 0.09593 0.9291 0.9598 0.1832 ] Network output: [ -0.02855 0.1439 1.069 0.0002082 -9.347e-05 0.8447 0.0001569 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1094 0.1083 0.1704 0.1451 0.9853 0.9914 0.1094 0.8921 0.9458 0.179 ] Network output: [ -0.01301 0.9643 0.02905 -2.093e-05 9.395e-06 1.033 -1.577e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06046 Epoch 4934 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05082 0.7998 0.9578 -3.42e-05 1.535e-05 0.1407 -2.578e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003788 -0.003379 -0.01107 0.007773 0.9665 0.9715 0.007458 0.8895 0.8955 0.02216 ] Network output: [ 0.9881 -0.2059 0.05059 5.006e-05 -2.248e-05 0.1794 3.773e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2565 -0.01179 -0.144 0.2207 0.9834 0.9932 0.2877 0.7968 0.953 0.5802 ] Network output: [ 0.02099 0.8492 0.9786 -5.37e-05 2.411e-05 0.1301 -4.047e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005434 0.00207 0.003672 0.004698 0.9902 0.9933 0.005533 0.9421 0.963 0.01061 ] Network output: [ 0.08664 -0.3605 0.9088 4.844e-05 -2.175e-05 1.279 3.651e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2805 0.1996 0.337 0.2426 0.9851 0.9941 0.2814 0.8056 0.9569 0.5603 ] Network output: [ -0.03525 0.238 1.08 0.0001581 -7.096e-05 0.7529 0.0001191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0985 0.09344 0.1651 0.1395 0.9891 0.9934 0.09855 0.9296 0.9599 0.1873 ] Network output: [ -0.03216 0.1977 1.055 0.0001798 -8.07e-05 0.8122 0.0001355 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1122 0.1112 0.1699 0.1477 0.9853 0.9915 0.1122 0.8935 0.9456 0.1775 ] Network output: [ -0.002289 0.974 0.005842 -5.628e-06 2.527e-06 1.025 -4.242e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08724 Epoch 4935 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04225 0.8561 0.9537 -7.237e-05 3.249e-05 0.1054 -5.454e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003854 -0.003325 -0.01096 0.006572 0.9665 0.9715 0.007557 0.8892 0.8943 0.02198 ] Network output: [ 0.9074 0.2095 0.0308 -0.0002141 9.611e-05 -0.05598 -0.0001613 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2625 -3.564e-05 -0.1333 0.1438 0.9834 0.9932 0.2942 0.7979 0.9523 0.5768 ] Network output: [ 0.0213 0.8607 0.9763 -6.327e-05 2.84e-05 0.1201 -4.768e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005619 0.002171 0.003689 0.003008 0.9902 0.9933 0.005721 0.9422 0.9625 0.0109 ] Network output: [ 0.01304 0.1884 0.8533 -0.0003168 0.0001422 0.931 -0.0002388 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2894 0.2074 0.3383 0.1277 0.9851 0.9941 0.2903 0.8067 0.9569 0.5725 ] Network output: [ -0.03496 0.2525 1.083 0.0001491 -6.692e-05 0.735 0.0001123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09472 0.08982 0.1614 0.1237 0.9893 0.9935 0.09477 0.9291 0.9597 0.1853 ] Network output: [ -0.02849 0.1359 1.072 0.0002078 -9.33e-05 0.8502 0.0001566 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1083 0.1073 0.1721 0.1465 0.9853 0.9915 0.1083 0.892 0.9456 0.1808 ] Network output: [ -0.01285 0.9676 0.02829 -2.37e-05 1.064e-05 1.03 -1.786e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05442 Epoch 4936 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05235 0.804 0.9543 -3.521e-05 1.581e-05 0.1369 -2.653e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003783 -0.003356 -0.01112 0.007689 0.9665 0.9715 0.007434 0.8894 0.8952 0.02221 ] Network output: [ 0.9858 -0.1695 0.04277 3.818e-05 -1.714e-05 0.1553 2.877e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2569 -0.01022 -0.1454 0.2147 0.9834 0.9932 0.288 0.7965 0.9528 0.5819 ] Network output: [ 0.02247 0.8494 0.9763 -5.359e-05 2.406e-05 0.1292 -4.038e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005406 0.002079 0.003663 0.004581 0.9902 0.9933 0.005504 0.9421 0.9628 0.01069 ] Network output: [ 0.08003 -0.3237 0.9066 2.47e-05 -1.109e-05 1.257 1.861e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2803 0.2004 0.3377 0.2342 0.9851 0.9941 0.2812 0.8054 0.9568 0.5639 ] Network output: [ -0.03561 0.2291 1.084 0.0001581 -7.096e-05 0.7587 0.0001191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09697 0.09204 0.1665 0.1393 0.9891 0.9935 0.09702 0.9296 0.9598 0.1892 ] Network output: [ -0.03219 0.1833 1.06 0.0001821 -8.176e-05 0.822 0.0001373 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1107 0.1097 0.172 0.1488 0.9853 0.9915 0.1107 0.8934 0.9455 0.1798 ] Network output: [ -0.005286 0.9855 0.00769 -1.692e-05 7.595e-06 1.017 -1.275e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07611 Epoch 4937 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04481 0.8531 0.9508 -6.833e-05 3.068e-05 0.1062 -5.15e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003837 -0.003308 -0.01103 0.006636 0.9665 0.9715 0.007516 0.8892 0.8942 0.02205 ] Network output: [ 0.9147 0.1945 0.02622 -0.0001926 8.647e-05 -0.05085 -0.0001452 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2619 -5.305e-05 -0.1359 0.1471 0.9834 0.9932 0.2935 0.7976 0.9523 0.5793 ] Network output: [ 0.02276 0.8592 0.9743 -6.176e-05 2.773e-05 0.1207 -4.655e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00556 0.002159 0.00368 0.003098 0.9902 0.9933 0.005661 0.9422 0.9624 0.01095 ] Network output: [ 0.01554 0.1549 0.859 -0.0002946 0.0001322 0.9538 -0.000222 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2878 0.2068 0.3391 0.1336 0.9851 0.9941 0.2887 0.8065 0.9568 0.5748 ] Network output: [ -0.03515 0.2409 1.086 0.0001509 -6.772e-05 0.7435 0.0001137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09363 0.08882 0.1632 0.1256 0.9893 0.9935 0.09368 0.9291 0.9596 0.1875 ] Network output: [ -0.0287 0.1276 1.074 0.0002077 -9.327e-05 0.8564 0.0001566 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1073 0.1063 0.1739 0.148 0.9853 0.9915 0.1073 0.8921 0.9456 0.1827 ] Network output: [ -0.01261 0.9685 0.02793 -2.455e-05 1.102e-05 1.029 -1.85e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04943 Epoch 4938 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05347 0.8088 0.9511 -3.666e-05 1.646e-05 0.133 -2.763e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003778 -0.003337 -0.01119 0.007609 0.9665 0.9715 0.007413 0.8894 0.895 0.02226 ] Network output: [ 0.9844 -0.1334 0.03432 2.701e-05 -1.212e-05 0.1303 2.035e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2571 -0.008942 -0.1474 0.2087 0.9834 0.9932 0.2882 0.7964 0.9527 0.5843 ] Network output: [ 0.02349 0.8501 0.9745 -5.398e-05 2.423e-05 0.1282 -4.068e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005376 0.002078 0.003643 0.00446 0.9902 0.9933 0.005474 0.9422 0.9627 0.01077 ] Network output: [ 0.07386 -0.2883 0.9047 7.846e-07 -3.523e-07 1.236 5.913e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2799 0.2006 0.3383 0.2259 0.9851 0.9941 0.2807 0.8055 0.9568 0.5682 ] Network output: [ -0.03618 0.2206 1.088 0.0001579 -7.09e-05 0.7645 0.000119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09551 0.09068 0.1678 0.1392 0.9892 0.9935 0.09556 0.9296 0.9597 0.1911 ] Network output: [ -0.03245 0.169 1.065 0.0001846 -8.286e-05 0.8322 0.0001391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1093 0.1083 0.174 0.1499 0.9854 0.9915 0.1093 0.8934 0.9454 0.1822 ] Network output: [ -0.007986 0.9939 0.009845 -2.563e-05 1.15e-05 1.012 -1.931e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06636 Epoch 4939 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0469 0.851 0.9481 -6.498e-05 2.917e-05 0.1068 -4.897e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003821 -0.003293 -0.0111 0.006697 0.9665 0.9715 0.007477 0.8893 0.8943 0.02213 ] Network output: [ 0.9217 0.1809 0.02181 -0.0001719 7.718e-05 -0.04677 -0.0001296 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2611 -0.0002466 -0.1389 0.1503 0.9834 0.9932 0.2925 0.7976 0.9523 0.5823 ] Network output: [ 0.02383 0.8584 0.9726 -6.084e-05 2.731e-05 0.1211 -4.585e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005503 0.00214 0.003668 0.003178 0.9903 0.9933 0.005602 0.9423 0.9625 0.011 ] Network output: [ 0.01779 0.1237 0.8646 -0.0002752 0.0001236 0.9749 -0.0002074 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2861 0.2058 0.34 0.139 0.9851 0.9941 0.287 0.8066 0.9568 0.5777 ] Network output: [ -0.03547 0.2303 1.09 0.0001524 -6.84e-05 0.7517 0.0001148 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09262 0.08789 0.1649 0.1275 0.9893 0.9935 0.09268 0.9293 0.9596 0.1895 ] Network output: [ -0.02904 0.1199 1.077 0.0002077 -9.323e-05 0.8624 0.0001565 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1064 0.1054 0.1756 0.1494 0.9853 0.9915 0.1064 0.8924 0.9456 0.1845 ] Network output: [ -0.01235 0.9697 0.02734 -2.483e-05 1.115e-05 1.028 -1.871e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04531 Epoch 4940 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05429 0.8138 0.9482 -3.831e-05 1.72e-05 0.1292 -2.887e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003772 -0.00332 -0.01126 0.007536 0.9666 0.9715 0.007393 0.8896 0.895 0.02233 ] Network output: [ 0.9834 -0.09986 0.02647 1.755e-05 -7.881e-06 0.1066 1.323e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2571 -0.007943 -0.1498 0.2033 0.9834 0.9932 0.2881 0.7967 0.9526 0.5872 ] Network output: [ 0.02422 0.8513 0.9728 -5.472e-05 2.457e-05 0.1272 -4.124e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005344 0.002069 0.00362 0.004344 0.9903 0.9933 0.005441 0.9423 0.9627 0.01085 ] Network output: [ 0.06817 -0.2563 0.9034 -2.215e-05 9.943e-06 1.216 -1.669e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2792 0.2003 0.339 0.2182 0.9851 0.9941 0.28 0.8058 0.9568 0.5727 ] Network output: [ -0.03675 0.2131 1.091 0.0001577 -7.078e-05 0.7698 0.0001188 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09416 0.08941 0.169 0.1391 0.9892 0.9935 0.09422 0.9298 0.9597 0.1929 ] Network output: [ -0.03275 0.1558 1.069 0.0001869 -8.389e-05 0.8417 0.0001408 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.108 0.1071 0.1759 0.151 0.9854 0.9915 0.1081 0.8935 0.9455 0.1843 ] Network output: [ -0.01008 1 0.01166 -3.192e-05 1.433e-05 1.008 -2.406e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05843 Epoch 4941 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04861 0.8497 0.9458 -6.229e-05 2.796e-05 0.1071 -4.694e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003804 -0.003279 -0.01117 0.006755 0.9666 0.9715 0.007438 0.8896 0.8944 0.02221 ] Network output: [ 0.9281 0.1681 0.01801 -0.000152 6.824e-05 -0.04297 -0.0001146 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2601 -0.0005664 -0.1418 0.1533 0.9834 0.9932 0.2914 0.7978 0.9523 0.5858 ] Network output: [ 0.02463 0.8582 0.9711 -6.045e-05 2.714e-05 0.1212 -4.556e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005447 0.002117 0.003656 0.00325 0.9903 0.9933 0.005545 0.9425 0.9625 0.01105 ] Network output: [ 0.01974 0.09455 0.8703 -0.0002584 0.000116 0.9946 -0.0001948 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2843 0.2045 0.3411 0.1439 0.9851 0.9941 0.2852 0.8068 0.9568 0.5809 ] Network output: [ -0.0358 0.221 1.092 0.0001535 -6.89e-05 0.759 0.0001157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09171 0.08703 0.1665 0.1291 0.9893 0.9935 0.09176 0.9296 0.9597 0.1915 ] Network output: [ -0.02939 0.1132 1.079 0.0002074 -9.309e-05 0.8678 0.0001563 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1056 0.1046 0.1772 0.1507 0.9853 0.9915 0.1056 0.8927 0.9456 0.1862 ] Network output: [ -0.012 0.9719 0.02627 -2.503e-05 1.124e-05 1.026 -1.886e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04195 Epoch 4942 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05486 0.8188 0.9456 -4.007e-05 1.799e-05 0.1257 -3.02e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003765 -0.003304 -0.01132 0.00747 0.9666 0.9715 0.007371 0.8898 0.895 0.02239 ] Network output: [ 0.9824 -0.06992 0.01976 9.952e-06 -4.468e-06 0.0854 7.5e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2569 -0.007169 -0.1521 0.1985 0.9834 0.9932 0.2878 0.7971 0.9527 0.5903 ] Network output: [ 0.02474 0.8528 0.9712 -5.577e-05 2.504e-05 0.1262 -4.203e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005312 0.002054 0.003599 0.004237 0.9903 0.9934 0.005408 0.9426 0.9628 0.01092 ] Network output: [ 0.06291 -0.2283 0.9028 -4.355e-05 1.955e-05 1.2 -3.282e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2783 0.1997 0.3398 0.2112 0.9851 0.9941 0.2792 0.8063 0.9568 0.5774 ] Network output: [ -0.03722 0.2067 1.094 0.0001573 -7.06e-05 0.7744 0.0001185 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09294 0.08825 0.17 0.139 0.9892 0.9935 0.093 0.9301 0.9598 0.1945 ] Network output: [ -0.03297 0.1442 1.072 0.0001889 -8.48e-05 0.8503 0.0001423 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1069 0.106 0.1776 0.1519 0.9854 0.9915 0.1069 0.8938 0.9455 0.1862 ] Network output: [ -0.01147 1.005 0.01288 -3.602e-05 1.617e-05 1.005 -2.715e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05222 Epoch 4943 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04997 0.849 0.9436 -6.023e-05 2.704e-05 0.1072 -4.539e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003788 -0.003267 -0.01124 0.006809 0.9666 0.9715 0.007402 0.8899 0.8945 0.02228 ] Network output: [ 0.934 0.1561 0.0148 -0.0001332 5.979e-05 -0.03943 -0.0001004 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2591 -0.000963 -0.1447 0.1561 0.9834 0.9932 0.2901 0.7982 0.9524 0.5894 ] Network output: [ 0.02519 0.8585 0.9697 -6.054e-05 2.718e-05 0.1212 -4.563e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005394 0.002091 0.003645 0.003314 0.9903 0.9934 0.005491 0.9428 0.9626 0.0111 ] Network output: [ 0.02137 0.06761 0.8759 -0.0002441 0.0001096 1.013 -0.000184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2825 0.2031 0.3423 0.1483 0.9852 0.9941 0.2834 0.8073 0.9569 0.5844 ] Network output: [ -0.03611 0.213 1.094 0.0001542 -6.921e-05 0.7655 0.0001162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09089 0.08625 0.1679 0.1306 0.9893 0.9935 0.09094 0.9299 0.9598 0.1932 ] Network output: [ -0.02972 0.1078 1.08 0.0002068 -9.286e-05 0.8725 0.0001559 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1048 0.1039 0.1786 0.1517 0.9854 0.9915 0.1048 0.8931 0.9457 0.1878 ] Network output: [ -0.01154 0.9749 0.02479 -2.511e-05 1.127e-05 1.023 -1.893e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03929 Epoch 4944 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05523 0.8236 0.9434 -4.192e-05 1.882e-05 0.1225 -3.159e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003756 -0.00329 -0.01138 0.007412 0.9666 0.9716 0.007348 0.8901 0.8951 0.02245 ] Network output: [ 0.9814 -0.04353 0.01419 3.961e-06 -1.778e-06 0.06665 2.985e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2564 -0.006578 -0.1544 0.1943 0.9834 0.9932 0.2872 0.7977 0.9527 0.5937 ] Network output: [ 0.0251 0.8545 0.9699 -5.704e-05 2.561e-05 0.1252 -4.298e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00528 0.002036 0.00358 0.00414 0.9903 0.9934 0.005375 0.9429 0.9628 0.01099 ] Network output: [ 0.05807 -0.2042 0.9028 -6.329e-05 2.841e-05 1.185 -4.769e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2773 0.1989 0.3409 0.2049 0.9852 0.9941 0.2782 0.8069 0.9569 0.582 ] Network output: [ -0.03759 0.2013 1.096 0.0001567 -7.036e-05 0.7784 0.0001181 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09185 0.08719 0.1709 0.1388 0.9893 0.9935 0.0919 0.9304 0.9599 0.1959 ] Network output: [ -0.03309 0.1341 1.075 0.0001906 -8.557e-05 0.8578 0.0001436 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1059 0.105 0.179 0.1527 0.9854 0.9915 0.1059 0.8941 0.9457 0.1879 ] Network output: [ -0.01221 1.008 0.01356 -3.818e-05 1.714e-05 1.003 -2.877e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04743 Epoch 4945 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05105 0.8488 0.9418 -5.875e-05 2.638e-05 0.1071 -4.428e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003772 -0.003256 -0.01131 0.006857 0.9666 0.9716 0.007368 0.8902 0.8948 0.02236 ] Network output: [ 0.9393 0.1455 0.01196 -0.0001158 5.199e-05 -0.03653 -8.728e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2579 -0.001403 -0.1476 0.1587 0.9834 0.9932 0.2889 0.7988 0.9525 0.5931 ] Network output: [ 0.02555 0.8592 0.9684 -6.102e-05 2.739e-05 0.121 -4.598e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005344 0.002063 0.003633 0.003368 0.9903 0.9934 0.00544 0.9431 0.9627 0.01115 ] Network output: [ 0.02268 0.04336 0.8811 -0.0002322 0.0001042 1.029 -0.000175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2807 0.2015 0.3435 0.1521 0.9852 0.9941 0.2816 0.8079 0.957 0.588 ] Network output: [ -0.03639 0.2062 1.096 0.0001545 -6.937e-05 0.771 0.0001164 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09014 0.08552 0.1691 0.1318 0.9894 0.9935 0.09019 0.9303 0.9599 0.1947 ] Network output: [ -0.03 0.1032 1.081 0.0002062 -9.256e-05 0.8765 0.0001554 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1042 0.1033 0.1798 0.1526 0.9854 0.9915 0.1042 0.8936 0.9458 0.1891 ] Network output: [ -0.01099 0.9782 0.02312 -2.491e-05 1.118e-05 1.021 -1.877e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03723 Epoch 4946 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05543 0.8281 0.9414 -4.379e-05 1.966e-05 0.1195 -3.3e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003747 -0.003277 -0.01144 0.00736 0.9666 0.9716 0.007325 0.8905 0.8953 0.0225 ] Network output: [ 0.9804 -0.02037 0.009557 -6.656e-07 2.988e-07 0.05007 -5.016e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2558 -0.006145 -0.1565 0.1906 0.9835 0.9932 0.2865 0.7984 0.9528 0.5971 ] Network output: [ 0.02531 0.8562 0.9687 -5.846e-05 2.624e-05 0.1242 -4.406e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005247 0.002016 0.003563 0.004052 0.9903 0.9934 0.005342 0.9432 0.963 0.01106 ] Network output: [ 0.05366 -0.1835 0.9033 -8.137e-05 3.653e-05 1.173 -6.132e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2763 0.1979 0.342 0.1993 0.9852 0.9941 0.2771 0.8077 0.957 0.5865 ] Network output: [ -0.03786 0.1967 1.098 0.0001561 -7.009e-05 0.7818 0.0001177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09087 0.08623 0.1716 0.1387 0.9893 0.9935 0.09092 0.9307 0.96 0.1971 ] Network output: [ -0.03313 0.1253 1.077 0.0001921 -8.623e-05 0.8644 0.0001448 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.105 0.1041 0.1803 0.1534 0.9854 0.9916 0.105 0.8945 0.9458 0.1894 ] Network output: [ -0.01245 1.01 0.0139 -3.872e-05 1.738e-05 1.001 -2.918e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04372 Epoch 4947 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05187 0.849 0.9402 -5.776e-05 2.593e-05 0.1069 -4.353e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003757 -0.003246 -0.01138 0.006898 0.9666 0.9716 0.007336 0.8906 0.895 0.02242 ] Network output: [ 0.9442 0.1365 0.009318 -0.0001001 4.494e-05 -0.03457 -7.544e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2568 -0.00187 -0.1505 0.161 0.9835 0.9932 0.2876 0.7994 0.9527 0.5969 ] Network output: [ 0.02574 0.8601 0.9674 -6.177e-05 2.773e-05 0.1207 -4.655e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005298 0.002035 0.003618 0.003411 0.9904 0.9934 0.005393 0.9434 0.9629 0.01119 ] Network output: [ 0.0237 0.02214 0.886 -0.0002227 9.997e-05 1.044 -0.0001678 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.279 0.1999 0.3447 0.1552 0.9852 0.9941 0.2798 0.8086 0.9572 0.5916 ] Network output: [ -0.03665 0.2005 1.098 0.0001546 -6.94e-05 0.7758 0.0001165 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08945 0.08485 0.1702 0.1329 0.9894 0.9936 0.0895 0.9307 0.9601 0.1961 ] Network output: [ -0.03027 0.09932 1.082 0.0002055 -9.224e-05 0.8801 0.0001548 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1036 0.1027 0.1808 0.1534 0.9854 0.9915 0.1036 0.8941 0.946 0.1903 ] Network output: [ -0.0104 0.9812 0.02146 -2.434e-05 1.093e-05 1.018 -1.834e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03568 Epoch 4948 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05549 0.8322 0.9398 -4.562e-05 2.048e-05 0.1168 -3.438e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003736 -0.003265 -0.0115 0.007314 0.9666 0.9716 0.007303 0.8909 0.8955 0.02255 ] Network output: [ 0.9795 -0.0001253 0.005641 -4.061e-06 1.823e-06 0.03539 -3.06e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2551 -0.005858 -0.1587 0.1874 0.9835 0.9932 0.2857 0.7993 0.9529 0.6005 ] Network output: [ 0.02539 0.858 0.9677 -5.996e-05 2.692e-05 0.1233 -4.519e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005215 0.001993 0.003546 0.003972 0.9904 0.9934 0.005309 0.9436 0.9631 0.01112 ] Network output: [ 0.04968 -0.1659 0.9041 -9.785e-05 4.393e-05 1.162 -7.374e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2751 0.1968 0.3432 0.1943 0.9852 0.9941 0.276 0.8085 0.9572 0.5909 ] Network output: [ -0.03807 0.1928 1.099 0.0001555 -6.981e-05 0.7848 0.0001172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08998 0.08536 0.1723 0.1385 0.9894 0.9936 0.09003 0.9311 0.9602 0.1981 ] Network output: [ -0.03313 0.1176 1.079 0.0001934 -8.681e-05 0.8703 0.0001457 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1042 0.1033 0.1814 0.154 0.9854 0.9916 0.1042 0.8949 0.946 0.1906 ] Network output: [ -0.01236 1.011 0.01401 -3.802e-05 1.707e-05 1 -2.865e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04084 Epoch 4949 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05246 0.8495 0.9388 -5.715e-05 2.566e-05 0.1066 -4.307e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003742 -0.003238 -0.01144 0.006932 0.9666 0.9716 0.007306 0.8911 0.8953 0.02249 ] Network output: [ 0.9486 0.1293 0.006798 -8.603e-05 3.862e-05 -0.03362 -6.484e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2557 -0.002357 -0.1533 0.1629 0.9835 0.9932 0.2863 0.8002 0.9529 0.6007 ] Network output: [ 0.02578 0.8612 0.9665 -6.271e-05 2.815e-05 0.1205 -4.726e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005254 0.002005 0.003601 0.003442 0.9904 0.9934 0.005348 0.9438 0.9631 0.01124 ] Network output: [ 0.02446 0.00403 0.8905 -0.0002155 9.676e-05 1.056 -0.0001624 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2773 0.1983 0.3458 0.1577 0.9852 0.9941 0.2781 0.8093 0.9573 0.5954 ] Network output: [ -0.03689 0.1957 1.099 0.0001545 -6.935e-05 0.78 0.0001164 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08881 0.08421 0.1711 0.1337 0.9894 0.9936 0.08886 0.9311 0.9603 0.1973 ] Network output: [ -0.03052 0.09581 1.083 0.0002048 -9.193e-05 0.8834 0.0001543 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1031 0.1021 0.1818 0.154 0.9854 0.9916 0.1031 0.8947 0.9461 0.1913 ] Network output: [ -0.00983 0.9838 0.01992 -2.346e-05 1.053e-05 1.016 -1.768e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03452 Epoch 4950 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05544 0.836 0.9384 -4.736e-05 2.126e-05 0.1145 -3.57e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003726 -0.003254 -0.01155 0.007275 0.9667 0.9716 0.00728 0.8913 0.8957 0.0226 ] Network output: [ 0.9789 0.01737 0.002335 -6.272e-06 2.816e-06 0.02246 -4.727e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2543 -0.005703 -0.1608 0.1846 0.9835 0.9932 0.2848 0.8001 0.9531 0.604 ] Network output: [ 0.02537 0.8597 0.9668 -6.149e-05 2.76e-05 0.1226 -4.634e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005184 0.001968 0.003529 0.003899 0.9904 0.9934 0.005277 0.944 0.9633 0.01117 ] Network output: [ 0.04614 -0.1509 0.9052 -0.0001128 5.064e-05 1.153 -8.501e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.274 0.1955 0.3443 0.1899 0.9852 0.9941 0.2748 0.8094 0.9574 0.5952 ] Network output: [ -0.03822 0.1894 1.1 0.0001549 -6.952e-05 0.7875 0.0001167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08918 0.08457 0.1728 0.1383 0.9894 0.9936 0.08922 0.9315 0.9604 0.199 ] Network output: [ -0.0331 0.1108 1.081 0.0001945 -8.731e-05 0.8756 0.0001466 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1035 0.1026 0.1823 0.1545 0.9854 0.9916 0.1035 0.8953 0.9462 0.1917 ] Network output: [ -0.01205 1.01 0.01397 -3.645e-05 1.636e-05 0.9996 -2.747e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03861 Epoch 4951 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05287 0.8502 0.9376 -5.683e-05 2.551e-05 0.1062 -4.283e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003727 -0.00323 -0.0115 0.00696 0.9667 0.9716 0.007279 0.8915 0.8957 0.02255 ] Network output: [ 0.9526 0.1236 0.00444 -7.344e-05 3.297e-05 -0.0335 -5.535e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2546 -0.002859 -0.1561 0.1644 0.9835 0.9932 0.285 0.801 0.9531 0.6044 ] Network output: [ 0.02571 0.8623 0.9658 -6.378e-05 2.863e-05 0.1202 -4.807e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005212 0.001975 0.003582 0.003464 0.9904 0.9934 0.005306 0.9442 0.9633 0.01128 ] Network output: [ 0.02499 -0.01117 0.8945 -0.0002105 9.45e-05 1.066 -0.0001586 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2756 0.1966 0.3468 0.1596 0.9852 0.9941 0.2765 0.8101 0.9575 0.5991 ] Network output: [ -0.03711 0.1916 1.1 0.0001542 -6.924e-05 0.7836 0.0001162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08821 0.08361 0.1718 0.1344 0.9894 0.9936 0.08825 0.9315 0.9604 0.1983 ] Network output: [ -0.03076 0.09262 1.083 0.0002042 -9.165e-05 0.8865 0.0001539 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1026 0.1016 0.1826 0.1546 0.9854 0.9916 0.1026 0.8952 0.9463 0.1923 ] Network output: [ -0.009305 0.9861 0.01851 -2.235e-05 1.004e-05 1.014 -1.685e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03364 Epoch 4952 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0553 0.8395 0.9372 -4.897e-05 2.198e-05 0.1126 -3.691e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003714 -0.003244 -0.0116 0.007241 0.9667 0.9716 0.007257 0.8918 0.896 0.02265 ] Network output: [ 0.9785 0.03224 -0.0004076 -7.347e-06 3.298e-06 0.01121 -5.537e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2535 -0.005666 -0.163 0.1823 0.9835 0.9933 0.2838 0.801 0.9533 0.6075 ] Network output: [ 0.02525 0.8613 0.9661 -6.3e-05 2.828e-05 0.1219 -4.748e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005153 0.001942 0.003512 0.003834 0.9904 0.9934 0.005245 0.9444 0.9634 0.01122 ] Network output: [ 0.04302 -0.1385 0.9064 -0.0001263 5.669e-05 1.146 -9.517e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2728 0.1942 0.3454 0.186 0.9852 0.9941 0.2736 0.8102 0.9575 0.5994 ] Network output: [ -0.03833 0.1866 1.101 0.0001542 -6.924e-05 0.7898 0.0001162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08845 0.08384 0.1732 0.1381 0.9894 0.9936 0.0885 0.9319 0.9606 0.1998 ] Network output: [ -0.03306 0.1048 1.082 0.0001954 -8.774e-05 0.8803 0.0001473 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1029 0.1019 0.1831 0.155 0.9855 0.9916 0.1029 0.8958 0.9464 0.1927 ] Network output: [ -0.01162 1.01 0.01379 -3.434e-05 1.542e-05 0.9996 -2.588e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03687 Epoch 4953 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05312 0.8511 0.9366 -5.673e-05 2.547e-05 0.1058 -4.276e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003714 -0.003223 -0.01156 0.006983 0.9667 0.9716 0.007252 0.892 0.896 0.02261 ] Network output: [ 0.9562 0.1191 0.002305 -6.215e-05 2.79e-05 -0.03399 -4.684e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2535 -0.003369 -0.1589 0.1657 0.9835 0.9933 0.2838 0.8018 0.9533 0.6082 ] Network output: [ 0.02554 0.8634 0.9652 -6.492e-05 2.915e-05 0.12 -4.893e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005173 0.001945 0.003561 0.003477 0.9904 0.9934 0.005266 0.9446 0.9635 0.01131 ] Network output: [ 0.0253 -0.02376 0.8982 -0.0002073 9.306e-05 1.074 -0.0001562 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.274 0.1949 0.3478 0.161 0.9852 0.9941 0.2748 0.811 0.9577 0.6029 ] Network output: [ -0.03729 0.1881 1.1 0.0001539 -6.908e-05 0.7868 0.000116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08764 0.08304 0.1724 0.1349 0.9895 0.9936 0.08769 0.932 0.9606 0.1992 ] Network output: [ -0.03096 0.08973 1.084 0.0002036 -9.141e-05 0.8893 0.0001534 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1021 0.1012 0.1833 0.155 0.9854 0.9916 0.1021 0.8957 0.9465 0.1931 ] Network output: [ -0.008825 0.9881 0.01722 -2.109e-05 9.47e-06 1.012 -1.59e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03296 Epoch 4954 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0551 0.8425 0.9362 -5.043e-05 2.264e-05 0.1109 -3.8e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003703 -0.003235 -0.01165 0.007213 0.9667 0.9716 0.007235 0.8923 0.8963 0.02269 ] Network output: [ 0.9782 0.04467 -0.002629 -7.385e-06 3.316e-06 0.001559 -5.566e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2525 -0.005728 -0.1651 0.1803 0.9835 0.9933 0.2828 0.8019 0.9535 0.611 ] Network output: [ 0.02507 0.8628 0.9655 -6.447e-05 2.894e-05 0.1213 -4.859e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005123 0.001914 0.003494 0.003776 0.9904 0.9935 0.005214 0.9447 0.9636 0.01127 ] Network output: [ 0.04029 -0.1283 0.9078 -0.0001384 6.214e-05 1.139 -0.0001043 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2715 0.1927 0.3465 0.1826 0.9852 0.9941 0.2724 0.8111 0.9577 0.6035 ] Network output: [ -0.03839 0.1842 1.101 0.0001536 -6.897e-05 0.7918 0.0001158 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08779 0.08317 0.1735 0.1379 0.9894 0.9936 0.08783 0.9323 0.9607 0.2004 ] Network output: [ -0.033 0.09961 1.083 0.0001962 -8.81e-05 0.8845 0.0001479 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1023 0.1014 0.1838 0.1553 0.9855 0.9916 0.1023 0.8963 0.9466 0.1935 ] Network output: [ -0.0111 1.009 0.01349 -3.19e-05 1.432e-05 0.9997 -2.404e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03552 Epoch 4955 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05325 0.852 0.9358 -5.68e-05 2.55e-05 0.1054 -4.28e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0037 -0.003216 -0.01161 0.007002 0.9667 0.9716 0.007227 0.8925 0.8964 0.02266 ] Network output: [ 0.9593 0.1156 0.0004257 -5.199e-05 2.334e-05 -0.0349 -3.918e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2523 -0.003883 -0.1616 0.1668 0.9835 0.9933 0.2825 0.8026 0.9535 0.6118 ] Network output: [ 0.02531 0.8646 0.9647 -6.609e-05 2.967e-05 0.1198 -4.981e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005137 0.001914 0.00354 0.003484 0.9905 0.9935 0.005228 0.945 0.9637 0.01135 ] Network output: [ 0.02542 -0.03407 0.9015 -0.0002056 9.232e-05 1.081 -0.000155 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2724 0.1932 0.3487 0.1619 0.9852 0.9941 0.2733 0.8118 0.9578 0.6066 ] Network output: [ -0.03743 0.1853 1.101 0.0001535 -6.889e-05 0.7894 0.0001156 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08712 0.0825 0.1729 0.1353 0.9895 0.9936 0.08717 0.9324 0.9608 0.1999 ] Network output: [ -0.03113 0.0871 1.084 0.0002031 -9.119e-05 0.892 0.0001531 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1017 0.1007 0.1839 0.1554 0.9855 0.9916 0.1017 0.8963 0.9467 0.1938 ] Network output: [ -0.00838 0.9898 0.01603 -1.97e-05 8.846e-06 1.011 -1.485e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03242 Epoch 4956 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05486 0.8452 0.9354 -5.174e-05 2.323e-05 0.1095 -3.899e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003691 -0.003226 -0.01169 0.00719 0.9667 0.9716 0.007213 0.8927 0.8967 0.02273 ] Network output: [ 0.9781 0.05494 -0.0044 -6.538e-06 2.935e-06 -0.006675 -4.927e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2515 -0.005871 -0.1672 0.1788 0.9835 0.9933 0.2817 0.8028 0.9537 0.6145 ] Network output: [ 0.02484 0.8642 0.965 -6.589e-05 2.958e-05 0.1209 -4.966e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005093 0.001886 0.003477 0.003725 0.9905 0.9935 0.005184 0.9451 0.9638 0.01131 ] Network output: [ 0.03788 -0.1199 0.9093 -0.0001494 6.705e-05 1.134 -0.0001126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2703 0.1912 0.3475 0.1796 0.9852 0.9941 0.2711 0.812 0.9579 0.6075 ] Network output: [ -0.03841 0.1821 1.102 0.000153 -6.87e-05 0.7936 0.0001153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08718 0.08255 0.1738 0.1378 0.9895 0.9936 0.08723 0.9327 0.9609 0.201 ] Network output: [ -0.03291 0.09509 1.083 0.0001969 -8.841e-05 0.8883 0.0001484 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1018 0.1008 0.1844 0.1556 0.9855 0.9916 0.1018 0.8968 0.9468 0.1942 ] Network output: [ -0.01054 1.008 0.0131 -2.927e-05 1.314e-05 1 -2.206e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03445 Epoch 4957 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05328 0.853 0.9351 -5.698e-05 2.558e-05 0.1051 -4.294e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003687 -0.00321 -0.01166 0.007017 0.9667 0.9716 0.007203 0.893 0.8967 0.02271 ] Network output: [ 0.9621 0.1129 -0.001206 -4.282e-05 1.922e-05 -0.03611 -3.227e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2512 -0.004394 -0.1642 0.1676 0.9835 0.9933 0.2813 0.8035 0.9537 0.6154 ] Network output: [ 0.02504 0.8657 0.9643 -6.728e-05 3.02e-05 0.1197 -5.07e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005102 0.001883 0.003519 0.003484 0.9905 0.9935 0.005193 0.9453 0.9639 0.01139 ] Network output: [ 0.02539 -0.04238 0.9045 -0.0002053 9.217e-05 1.086 -0.0001547 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2709 0.1915 0.3495 0.1624 0.9852 0.9941 0.2717 0.8127 0.958 0.6103 ] Network output: [ -0.03752 0.183 1.101 0.000153 -6.868e-05 0.7917 0.0001153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08662 0.08199 0.1732 0.1356 0.9895 0.9937 0.08667 0.9328 0.961 0.2006 ] Network output: [ -0.03126 0.08468 1.084 0.0002027 -9.102e-05 0.8945 0.0001528 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1013 0.1003 0.1845 0.1556 0.9855 0.9916 0.1013 0.8968 0.9469 0.1945 ] Network output: [ -0.007961 0.9912 0.01495 -1.818e-05 8.16e-06 1.01 -1.37e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03197 Epoch 4958 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05458 0.8475 0.9348 -5.292e-05 2.376e-05 0.1083 -3.988e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003679 -0.003219 -0.01174 0.007171 0.9667 0.9717 0.00719 0.8932 0.897 0.02277 ] Network output: [ 0.9781 0.06338 -0.00581 -4.968e-06 2.23e-06 -0.01371 -3.744e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2505 -0.006079 -0.1692 0.1775 0.9835 0.9933 0.2805 0.8037 0.9539 0.6179 ] Network output: [ 0.02458 0.8655 0.9646 -6.726e-05 3.02e-05 0.1205 -5.069e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005064 0.001858 0.00346 0.003678 0.9905 0.9935 0.005154 0.9455 0.964 0.01135 ] Network output: [ 0.03577 -0.1129 0.9108 -0.0001593 7.151e-05 1.13 -0.00012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.269 0.1897 0.3484 0.1769 0.9852 0.9941 0.2698 0.8129 0.9581 0.6113 ] Network output: [ -0.03838 0.1804 1.102 0.0001525 -6.845e-05 0.7951 0.0001149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08663 0.08198 0.174 0.1376 0.9895 0.9937 0.08668 0.9331 0.9611 0.2015 ] Network output: [ -0.03282 0.09112 1.084 0.0001975 -8.868e-05 0.8916 0.0001489 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1013 0.1004 0.1848 0.1558 0.9855 0.9916 0.1014 0.8973 0.947 0.1948 ] Network output: [ -0.009974 1.007 0.01265 -2.655e-05 1.192e-05 1 -2.001e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03359 Epoch 4959 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05323 0.854 0.9345 -5.725e-05 2.57e-05 0.1048 -4.314e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003674 -0.003204 -0.01171 0.007029 0.9667 0.9717 0.007179 0.8935 0.8971 0.02275 ] Network output: [ 0.9646 0.1109 -0.002616 -3.45e-05 1.549e-05 -0.03754 -2.6e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2501 -0.004901 -0.1668 0.1683 0.9836 0.9933 0.28 0.8044 0.9539 0.619 ] Network output: [ 0.02473 0.8667 0.964 -6.846e-05 3.073e-05 0.1195 -5.159e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005069 0.001853 0.003497 0.003479 0.9905 0.9935 0.005159 0.9457 0.9641 0.01142 ] Network output: [ 0.02524 -0.04896 0.9072 -0.0002061 9.25e-05 1.09 -0.0001553 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2694 0.1897 0.3503 0.1626 0.9852 0.9941 0.2702 0.8135 0.9582 0.6139 ] Network output: [ -0.03757 0.181 1.101 0.0001525 -6.847e-05 0.7936 0.0001149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08615 0.0815 0.1735 0.1358 0.9895 0.9937 0.0862 0.9332 0.9612 0.2011 ] Network output: [ -0.03136 0.08242 1.084 0.0002024 -9.088e-05 0.8968 0.0001526 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09996 0.1849 0.1559 0.9855 0.9916 0.1009 0.8973 0.9471 0.195 ] Network output: [ -0.007569 0.9923 0.01398 -1.653e-05 7.419e-06 1.009 -1.245e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03159 Epoch 4960 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05428 0.8496 0.9342 -5.399e-05 2.424e-05 0.1074 -4.069e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003666 -0.003212 -0.01178 0.007156 0.9667 0.9717 0.007169 0.8938 0.8974 0.02281 ] Network output: [ 0.9782 0.07027 -0.006933 -2.811e-06 1.262e-06 -0.01974 -2.119e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2494 -0.00634 -0.1713 0.1764 0.9836 0.9933 0.2793 0.8046 0.9541 0.6213 ] Network output: [ 0.02429 0.8667 0.9642 -6.857e-05 3.078e-05 0.1203 -5.168e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005035 0.001829 0.003442 0.003635 0.9905 0.9935 0.005125 0.9459 0.9642 0.01139 ] Network output: [ 0.03392 -0.1072 0.9124 -0.0001683 7.557e-05 1.126 -0.0001269 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2677 0.1881 0.3493 0.1744 0.9853 0.9941 0.2685 0.8138 0.9583 0.6151 ] Network output: [ -0.03833 0.179 1.102 0.0001519 -6.821e-05 0.7964 0.0001145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08612 0.08145 0.1741 0.1374 0.9895 0.9937 0.08617 0.9335 0.9613 0.2019 ] Network output: [ -0.03271 0.08759 1.084 0.000198 -8.891e-05 0.8947 0.0001493 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09995 0.1853 0.156 0.9855 0.9916 0.1009 0.8977 0.9472 0.1954 ] Network output: [ -0.009418 1.006 0.01218 -2.379e-05 1.068e-05 1.001 -1.793e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03287 Epoch 4961 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05312 0.855 0.9341 -5.758e-05 2.585e-05 0.1045 -4.339e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003661 -0.003199 -0.01176 0.007039 0.9667 0.9717 0.007157 0.894 0.8975 0.0228 ] Network output: [ 0.9667 0.1093 -0.003819 -2.689e-05 1.207e-05 -0.03911 -2.027e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2489 -0.005404 -0.1692 0.1689 0.9836 0.9933 0.2787 0.8052 0.9541 0.6225 ] Network output: [ 0.0244 0.8678 0.9637 -6.962e-05 3.126e-05 0.1194 -5.247e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005037 0.001822 0.003475 0.00347 0.9905 0.9935 0.005127 0.9461 0.9643 0.01145 ] Network output: [ 0.025 -0.05406 0.9096 -0.0002077 9.323e-05 1.094 -0.0001565 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2679 0.188 0.351 0.1625 0.9853 0.9941 0.2688 0.8144 0.9584 0.6174 ] Network output: [ -0.03759 0.1794 1.101 0.000152 -6.825e-05 0.7953 0.0001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08571 0.08103 0.1737 0.1359 0.9895 0.9937 0.08576 0.9336 0.9614 0.2016 ] Network output: [ -0.03142 0.08027 1.084 0.0002022 -9.078e-05 0.8991 0.0001524 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.0996 0.1853 0.1561 0.9855 0.9916 0.1006 0.8978 0.9474 0.1955 ] Network output: [ -0.007206 0.993 0.01312 -1.479e-05 6.638e-06 1.008 -1.114e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03124 Epoch 4962 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05396 0.8514 0.9338 -5.495e-05 2.467e-05 0.1066 -4.141e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003654 -0.003205 -0.01182 0.007144 0.9667 0.9717 0.007147 0.8943 0.8977 0.02285 ] Network output: [ 0.9784 0.07584 -0.007813 -1.728e-07 7.757e-08 -0.0249 -1.302e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2483 -0.006642 -0.1733 0.1756 0.9836 0.9933 0.2781 0.8055 0.9543 0.6247 ] Network output: [ 0.02397 0.8678 0.9639 -6.983e-05 3.135e-05 0.12 -5.263e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005008 0.0018 0.003424 0.003596 0.9905 0.9935 0.005097 0.9463 0.9644 0.01143 ] Network output: [ 0.03228 -0.1023 0.9139 -0.0001766 7.93e-05 1.123 -0.0001331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2664 0.1865 0.3502 0.1722 0.9853 0.9941 0.2672 0.8147 0.9585 0.6187 ] Network output: [ -0.03824 0.1778 1.102 0.0001514 -6.799e-05 0.7976 0.0001141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08565 0.08095 0.1742 0.1372 0.9895 0.9937 0.08569 0.9339 0.9615 0.2022 ] Network output: [ -0.0326 0.08444 1.084 0.0001985 -8.912e-05 0.8975 0.0001496 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1005 0.09956 0.1856 0.1562 0.9855 0.9916 0.1005 0.8982 0.9474 0.1959 ] Network output: [ -0.008891 1.005 0.01171 -2.108e-05 9.461e-06 1.001 -1.588e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03227 Epoch 4963 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05295 0.8559 0.9337 -5.796e-05 2.602e-05 0.1043 -4.368e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003648 -0.003194 -0.0118 0.007047 0.9668 0.9717 0.007135 0.8945 0.8979 0.02284 ] Network output: [ 0.9687 0.1081 -0.004818 -1.987e-05 8.919e-06 -0.04072 -1.497e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2478 -0.005899 -0.1716 0.1693 0.9836 0.9933 0.2775 0.8061 0.9544 0.6259 ] Network output: [ 0.02405 0.8688 0.9635 -7.077e-05 3.177e-05 0.1194 -5.333e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005007 0.001792 0.003453 0.003458 0.9906 0.9935 0.005097 0.9465 0.9645 0.01148 ] Network output: [ 0.02468 -0.05796 0.9119 -0.00021 9.426e-05 1.096 -0.0001582 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2665 0.1863 0.3517 0.1622 0.9853 0.9941 0.2673 0.8152 0.9586 0.621 ] Network output: [ -0.03756 0.1781 1.101 0.0001516 -6.804e-05 0.7967 0.0001142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08529 0.08058 0.1738 0.1359 0.9896 0.9937 0.08534 0.9341 0.9616 0.202 ] Network output: [ -0.03145 0.07825 1.084 0.0002021 -9.072e-05 0.9012 0.0001523 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1002 0.09926 0.1857 0.1562 0.9855 0.9916 0.1003 0.8983 0.9476 0.196 ] Network output: [ -0.006871 0.9936 0.01234 -1.3e-05 5.836e-06 1.008 -9.797e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03092 Epoch 4964 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05363 0.853 0.9335 -5.583e-05 2.506e-05 0.106 -4.207e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003642 -0.003199 -0.01186 0.007135 0.9668 0.9717 0.007126 0.8948 0.8981 0.02288 ] Network output: [ 0.9788 0.08026 -0.008477 2.863e-06 -1.285e-06 -0.02929 2.158e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2472 -0.006976 -0.1754 0.175 0.9836 0.9933 0.2769 0.8064 0.9545 0.628 ] Network output: [ 0.02364 0.8689 0.9637 -7.105e-05 3.19e-05 0.1199 -5.355e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004981 0.001771 0.003406 0.003561 0.9906 0.9935 0.00507 0.9467 0.9646 0.01147 ] Network output: [ 0.03084 -0.09825 0.9154 -0.0001843 8.273e-05 1.12 -0.0001389 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2651 0.1849 0.3509 0.1702 0.9853 0.9941 0.266 0.8155 0.9587 0.6223 ] Network output: [ -0.03813 0.1768 1.101 0.000151 -6.777e-05 0.7987 0.0001138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08521 0.08048 0.1742 0.137 0.9896 0.9937 0.08525 0.9343 0.9617 0.2025 ] Network output: [ -0.03247 0.08161 1.084 0.0001989 -8.93e-05 0.9 0.0001499 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1002 0.09919 0.186 0.1563 0.9855 0.9916 0.1002 0.8987 0.9477 0.1963 ] Network output: [ -0.008399 1.004 0.01123 -1.844e-05 8.279e-06 1.002 -1.39e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03174 Epoch 4965 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05274 0.8568 0.9334 -5.837e-05 2.621e-05 0.104 -4.399e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003636 -0.003189 -0.01184 0.007054 0.9668 0.9717 0.007114 0.895 0.8982 0.02288 ] Network output: [ 0.9704 0.1071 -0.005613 -1.331e-05 5.975e-06 -0.04226 -1.003e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2467 -0.006387 -0.1739 0.1697 0.9836 0.9933 0.2762 0.807 0.9546 0.6293 ] Network output: [ 0.02368 0.8697 0.9633 -7.19e-05 3.228e-05 0.1193 -5.419e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004978 0.001763 0.003431 0.003444 0.9906 0.9936 0.005067 0.9469 0.9647 0.01151 ] Network output: [ 0.02431 -0.06088 0.9139 -0.0002127 9.551e-05 1.097 -0.0001603 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2651 0.1846 0.3524 0.1617 0.9853 0.9941 0.2659 0.8161 0.9588 0.6244 ] Network output: [ -0.03751 0.177 1.101 0.0001511 -6.783e-05 0.7979 0.0001139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0849 0.08015 0.1739 0.1359 0.9896 0.9937 0.08494 0.9345 0.9618 0.2023 ] Network output: [ -0.03144 0.07634 1.084 0.000202 -9.067e-05 0.9032 0.0001522 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09993 0.09894 0.186 0.1563 0.9855 0.9916 0.09994 0.8988 0.9478 0.1964 ] Network output: [ -0.006559 0.9939 0.01163 -1.12e-05 5.026e-06 1.008 -8.438e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03061 Epoch 4966 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05329 0.8545 0.9332 -5.664e-05 2.543e-05 0.1055 -4.269e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00363 -0.003193 -0.01189 0.007128 0.9668 0.9717 0.007105 0.8953 0.8985 0.02292 ] Network output: [ 0.9791 0.08367 -0.008947 6.213e-06 -2.789e-06 -0.03298 4.682e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2461 -0.00733 -0.1773 0.1745 0.9836 0.9933 0.2757 0.8073 0.9547 0.6313 ] Network output: [ 0.02329 0.8699 0.9635 -7.224e-05 3.243e-05 0.1197 -5.444e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004954 0.001743 0.003388 0.003528 0.9906 0.9936 0.005043 0.9471 0.9648 0.0115 ] Network output: [ 0.02957 -0.09478 0.9169 -0.0001913 8.59e-05 1.118 -0.0001442 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2639 0.1833 0.3516 0.1684 0.9853 0.9941 0.2647 0.8164 0.9589 0.6258 ] Network output: [ -0.03799 0.1759 1.101 0.0001505 -6.757e-05 0.7995 0.0001134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0848 0.08004 0.1742 0.1368 0.9896 0.9937 0.08484 0.9347 0.9619 0.2028 ] Network output: [ -0.03234 0.07908 1.084 0.0001993 -8.946e-05 0.9024 0.0001502 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09985 0.09886 0.1862 0.1564 0.9855 0.9916 0.09986 0.8992 0.9479 0.1967 ] Network output: [ -0.007941 1.003 0.01074 -1.591e-05 7.143e-06 1.002 -1.199e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03127 Epoch 4967 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0525 0.8577 0.9332 -5.882e-05 2.641e-05 0.1039 -4.433e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003623 -0.003184 -0.01188 0.007061 0.9668 0.9717 0.007093 0.8955 0.8986 0.02291 ] Network output: [ 0.9719 0.1061 -0.006219 -7.145e-06 3.207e-06 -0.04369 -5.384e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2455 -0.006863 -0.1762 0.1701 0.9836 0.9933 0.275 0.8078 0.9548 0.6326 ] Network output: [ 0.02332 0.8706 0.9632 -7.302e-05 3.278e-05 0.1192 -5.503e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004951 0.001734 0.00341 0.003428 0.9906 0.9936 0.005039 0.9473 0.9649 0.01154 ] Network output: [ 0.0239 -0.06301 0.9158 -0.0002159 9.692e-05 1.098 -0.0001627 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2638 0.183 0.3529 0.1611 0.9853 0.9941 0.2646 0.8169 0.959 0.6278 ] Network output: [ -0.03742 0.1761 1.1 0.0001506 -6.763e-05 0.7989 0.0001135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08452 0.07974 0.174 0.1359 0.9896 0.9937 0.08456 0.9348 0.962 0.2026 ] Network output: [ -0.03142 0.07455 1.084 0.0002019 -9.065e-05 0.9052 0.0001522 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09963 0.09863 0.1862 0.1564 0.9855 0.9916 0.09964 0.8993 0.948 0.1968 ] Network output: [ -0.006262 0.9941 0.01097 -9.386e-06 4.214e-06 1.007 -7.074e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03031 Epoch 4968 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05295 0.8558 0.933 -5.741e-05 2.577e-05 0.1051 -4.327e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003618 -0.003187 -0.01193 0.007124 0.9668 0.9717 0.007085 0.8958 0.8988 0.02295 ] Network output: [ 0.9796 0.08625 -0.009255 9.794e-06 -4.397e-06 -0.03608 7.381e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.245 -0.007698 -0.1793 0.1742 0.9836 0.9933 0.2744 0.8081 0.9549 0.6345 ] Network output: [ 0.02294 0.8709 0.9634 -7.339e-05 3.295e-05 0.1196 -5.531e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004929 0.001715 0.003369 0.003498 0.9906 0.9936 0.005017 0.9475 0.965 0.01153 ] Network output: [ 0.02842 -0.09178 0.9184 -0.0001979 8.884e-05 1.116 -0.0001491 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2626 0.1817 0.3523 0.1667 0.9853 0.9941 0.2634 0.8172 0.9591 0.6292 ] Network output: [ -0.03783 0.1753 1.101 0.0001501 -6.737e-05 0.8003 0.0001131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08441 0.07962 0.1742 0.1366 0.9896 0.9937 0.08445 0.9351 0.9621 0.203 ] Network output: [ -0.0322 0.07679 1.084 0.0001996 -8.96e-05 0.9045 0.0001504 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09955 0.09854 0.1865 0.1565 0.9855 0.9916 0.09956 0.8996 0.9481 0.197 ] Network output: [ -0.007515 1.002 0.01027 -1.348e-05 6.052e-06 1.003 -1.016e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03085 Epoch 4969 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05224 0.8586 0.933 -5.93e-05 2.662e-05 0.1037 -4.469e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003611 -0.003179 -0.01192 0.007067 0.9668 0.9717 0.007073 0.896 0.899 0.02295 ] Network output: [ 0.9733 0.1051 -0.00666 -1.32e-06 5.925e-07 -0.045 -9.947e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2444 -0.007326 -0.1784 0.1705 0.9836 0.9933 0.2737 0.8086 0.955 0.6358 ] Network output: [ 0.02294 0.8715 0.9631 -7.412e-05 3.327e-05 0.1192 -5.586e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004924 0.001706 0.003389 0.003412 0.9906 0.9936 0.005012 0.9476 0.9651 0.01157 ] Network output: [ 0.02346 -0.06447 0.9176 -0.0002193 9.843e-05 1.099 -0.0001652 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2624 0.1813 0.3535 0.1603 0.9853 0.9942 0.2632 0.8177 0.9592 0.6311 ] Network output: [ -0.0373 0.1754 1.1 0.0001502 -6.743e-05 0.7998 0.0001132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08416 0.07935 0.174 0.1358 0.9896 0.9938 0.08421 0.9352 0.9622 0.2029 ] Network output: [ -0.03136 0.07285 1.084 0.0002019 -9.064e-05 0.907 0.0001522 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09935 0.09834 0.1865 0.1565 0.9855 0.9916 0.09936 0.8998 0.9482 0.1971 ] Network output: [ -0.005979 0.9942 0.01036 -7.577e-06 3.402e-06 1.007 -5.71e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03002 Epoch 4970 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0526 0.857 0.9329 -5.814e-05 2.61e-05 0.1047 -4.382e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003606 -0.003182 -0.01196 0.007121 0.9668 0.9717 0.007065 0.8963 0.8992 0.02298 ] Network output: [ 0.98 0.08812 -0.009431 1.354e-05 -6.077e-06 -0.03869 1.02e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2439 -0.008072 -0.1813 0.174 0.9836 0.9933 0.2732 0.8089 0.9552 0.6377 ] Network output: [ 0.02258 0.8718 0.9633 -7.453e-05 3.346e-05 0.1195 -5.617e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004904 0.001688 0.003351 0.003469 0.9906 0.9936 0.004992 0.9478 0.9652 0.01157 ] Network output: [ 0.0274 -0.08912 0.9198 -0.000204 9.157e-05 1.114 -0.0001537 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2613 0.1801 0.3529 0.1651 0.9853 0.9942 0.2621 0.818 0.9593 0.6325 ] Network output: [ -0.03766 0.1747 1.1 0.0001496 -6.718e-05 0.801 0.0001128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08405 0.07922 0.1742 0.1364 0.9896 0.9938 0.08409 0.9354 0.9623 0.2032 ] Network output: [ -0.03206 0.07469 1.084 0.0001999 -8.973e-05 0.9066 0.0001506 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09926 0.09825 0.1867 0.1565 0.9855 0.9916 0.09927 0.9001 0.9483 0.1974 ] Network output: [ -0.007119 1.001 0.009802 -1.115e-05 5.008e-06 1.004 -8.406e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03045 Epoch 4971 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05195 0.8594 0.9329 -5.981e-05 2.685e-05 0.1036 -4.507e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003599 -0.003174 -0.01196 0.007073 0.9668 0.9717 0.007053 0.8965 0.8994 0.02299 ] Network output: [ 0.9745 0.1042 -0.00696 4.21e-06 -1.89e-06 -0.04617 3.173e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2433 -0.007776 -0.1805 0.1708 0.9836 0.9933 0.2725 0.8094 0.9552 0.639 ] Network output: [ 0.02257 0.8724 0.963 -7.521e-05 3.377e-05 0.1191 -5.668e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004899 0.001678 0.003369 0.003394 0.9906 0.9936 0.004986 0.948 0.9653 0.0116 ] Network output: [ 0.02301 -0.06538 0.9192 -0.0002228 0.0001 1.099 -0.0001679 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2611 0.1797 0.354 0.1595 0.9853 0.9942 0.2619 0.8185 0.9594 0.6344 ] Network output: [ -0.03715 0.1749 1.1 0.0001498 -6.723e-05 0.8005 0.0001129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08382 0.07898 0.1739 0.1357 0.9897 0.9938 0.08387 0.9356 0.9624 0.2031 ] Network output: [ -0.03129 0.07124 1.083 0.0002019 -9.065e-05 0.9087 0.0001522 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09909 0.09807 0.1867 0.1565 0.9855 0.9917 0.0991 0.9003 0.9484 0.1974 ] Network output: [ -0.00571 0.9941 0.009803 -5.783e-06 2.596e-06 1.007 -4.358e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02973 Epoch 4972 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05224 0.8581 0.9328 -5.886e-05 2.642e-05 0.1044 -4.436e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003594 -0.003176 -0.01199 0.00712 0.9668 0.9717 0.007045 0.8967 0.8996 0.02302 ] Network output: [ 0.9805 0.08941 -0.009497 1.739e-05 -7.808e-06 -0.04087 1.311e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2428 -0.008448 -0.1832 0.1738 0.9836 0.9933 0.272 0.8098 0.9554 0.6408 ] Network output: [ 0.02222 0.8727 0.9632 -7.565e-05 3.396e-05 0.1194 -5.701e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00488 0.001661 0.003333 0.003443 0.9906 0.9936 0.004967 0.9482 0.9654 0.0116 ] Network output: [ 0.02647 -0.08673 0.9211 -0.0002096 9.411e-05 1.112 -0.000158 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2601 0.1786 0.3534 0.1636 0.9853 0.9942 0.2609 0.8188 0.9595 0.6358 ] Network output: [ -0.03746 0.1743 1.1 0.0001492 -6.699e-05 0.8015 0.0001125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0837 0.07885 0.1741 0.1362 0.9897 0.9938 0.08375 0.9358 0.9625 0.2034 ] Network output: [ -0.03191 0.07276 1.083 0.0002001 -8.985e-05 0.9085 0.0001508 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09899 0.09797 0.1869 0.1566 0.9855 0.9917 0.099 0.9005 0.9485 0.1977 ] Network output: [ -0.006755 1 0.009358 -8.938e-06 4.012e-06 1.004 -6.736e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03008 Epoch 4973 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05165 0.8602 0.9328 -6.034e-05 2.709e-05 0.1034 -4.548e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003587 -0.003169 -0.01199 0.007079 0.9668 0.9717 0.007034 0.897 0.8997 0.02302 ] Network output: [ 0.9756 0.1032 -0.007132 9.491e-06 -4.261e-06 -0.04718 7.152e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2421 -0.00821 -0.1825 0.1711 0.9837 0.9933 0.2713 0.8103 0.9555 0.6422 ] Network output: [ 0.02219 0.8733 0.9629 -7.63e-05 3.426e-05 0.119 -5.751e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004874 0.001651 0.003349 0.003377 0.9907 0.9936 0.004961 0.9484 0.9655 0.01163 ] Network output: [ 0.02255 -0.06585 0.9207 -0.0002264 0.0001016 1.099 -0.0001706 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2598 0.1781 0.3544 0.1587 0.9853 0.9942 0.2606 0.8193 0.9596 0.6376 ] Network output: [ -0.03698 0.1744 1.099 0.0001493 -6.705e-05 0.8011 0.0001126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0835 0.07863 0.1739 0.1355 0.9897 0.9938 0.08354 0.936 0.9626 0.2033 ] Network output: [ -0.0312 0.0697 1.083 0.0002019 -9.066e-05 0.9104 0.0001522 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09884 0.09781 0.1868 0.1566 0.9855 0.9917 0.09885 0.9007 0.9486 0.1977 ] Network output: [ -0.005455 0.9939 0.009286 -4.024e-06 1.806e-06 1.008 -3.032e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02944 Epoch 4974 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05189 0.8591 0.9327 -5.956e-05 2.674e-05 0.1042 -4.488e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003582 -0.003171 -0.01203 0.007121 0.9668 0.9717 0.007026 0.8972 0.8999 0.02305 ] Network output: [ 0.981 0.09017 -0.009463 2.133e-05 -9.575e-06 -0.04265 1.607e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2417 -0.008822 -0.1851 0.1738 0.9837 0.9933 0.2707 0.8106 0.9556 0.6439 ] Network output: [ 0.02185 0.8736 0.9631 -7.676e-05 3.446e-05 0.1192 -5.785e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004856 0.001635 0.003315 0.003418 0.9907 0.9936 0.004943 0.9485 0.9656 0.01163 ] Network output: [ 0.02564 -0.08456 0.9223 -0.0002149 9.648e-05 1.11 -0.000162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2589 0.1771 0.3539 0.1622 0.9853 0.9942 0.2597 0.8196 0.9597 0.639 ] Network output: [ -0.03725 0.174 1.099 0.0001488 -6.681e-05 0.802 0.0001122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08338 0.07849 0.174 0.1359 0.9897 0.9938 0.08342 0.9362 0.9627 0.2035 ] Network output: [ -0.03175 0.07099 1.083 0.0002004 -8.995e-05 0.9103 0.000151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09874 0.09771 0.187 0.1566 0.9855 0.9917 0.09875 0.901 0.9487 0.1979 ] Network output: [ -0.006421 0.9992 0.008928 -6.842e-06 3.072e-06 1.005 -5.156e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02973 Epoch 4975 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05134 0.861 0.9328 -6.091e-05 2.734e-05 0.1033 -4.59e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003576 -0.003165 -0.01202 0.007085 0.9668 0.9717 0.007015 0.8975 0.9001 0.02305 ] Network output: [ 0.9766 0.1021 -0.007182 1.456e-05 -6.535e-06 -0.04801 1.097e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.241 -0.008628 -0.1845 0.1715 0.9837 0.9933 0.27 0.811 0.9557 0.6452 ] Network output: [ 0.02181 0.8742 0.9629 -7.739e-05 3.474e-05 0.1189 -5.833e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00485 0.001625 0.00333 0.003359 0.9907 0.9936 0.004937 0.9487 0.9657 0.01166 ] Network output: [ 0.0221 -0.06596 0.9221 -0.0002299 0.0001032 1.099 -0.0001733 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2586 0.1766 0.3548 0.1578 0.9853 0.9942 0.2594 0.82 0.9598 0.6407 ] Network output: [ -0.0368 0.1741 1.098 0.0001489 -6.686e-05 0.8017 0.0001122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08319 0.07828 0.1738 0.1354 0.9897 0.9938 0.08324 0.9363 0.9628 0.2034 ] Network output: [ -0.03109 0.06825 1.083 0.000202 -9.068e-05 0.912 0.0001522 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0986 0.09756 0.187 0.1566 0.9855 0.9917 0.09861 0.9012 0.9488 0.198 ] Network output: [ -0.005213 0.9937 0.0088 -2.317e-06 1.04e-06 1.008 -1.746e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02915 Epoch 4976 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05153 0.8601 0.9327 -6.026e-05 2.705e-05 0.1039 -4.541e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003571 -0.003166 -0.01206 0.007123 0.9668 0.9717 0.007007 0.8977 0.9003 0.02308 ] Network output: [ 0.9815 0.09047 -0.009338 2.531e-05 -1.136e-05 -0.04407 1.908e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2406 -0.009188 -0.187 0.1739 0.9837 0.9933 0.2695 0.8114 0.9558 0.6469 ] Network output: [ 0.02149 0.8746 0.9631 -7.786e-05 3.496e-05 0.1191 -5.868e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004833 0.00161 0.003297 0.003395 0.9907 0.9936 0.00492 0.9489 0.9658 0.01166 ] Network output: [ 0.02488 -0.08257 0.9236 -0.0002198 9.868e-05 1.108 -0.0001657 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2577 0.1755 0.3543 0.1609 0.9853 0.9942 0.2584 0.8204 0.9599 0.6421 ] Network output: [ -0.03703 0.1738 1.098 0.0001484 -6.664e-05 0.8024 0.0001119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08307 0.07815 0.1739 0.1357 0.9897 0.9938 0.08312 0.9365 0.9629 0.2037 ] Network output: [ -0.03159 0.06935 1.083 0.0002006 -9.003e-05 0.9119 0.0001511 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0985 0.09746 0.1872 0.1566 0.9856 0.9917 0.09851 0.9014 0.9489 0.1982 ] Network output: [ -0.006113 0.9984 0.008511 -4.867e-06 2.185e-06 1.005 -3.668e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02939 Epoch 4977 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05101 0.8618 0.9328 -6.15e-05 2.761e-05 0.1032 -4.635e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003564 -0.00316 -0.01205 0.007091 0.9668 0.9718 0.006996 0.8979 0.9005 0.02308 ] Network output: [ 0.9775 0.101 -0.007123 1.943e-05 -8.723e-06 -0.04866 1.464e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2399 -0.009027 -0.1865 0.1718 0.9837 0.9933 0.2688 0.8118 0.9559 0.6482 ] Network output: [ 0.02143 0.8751 0.9629 -7.848e-05 3.523e-05 0.1188 -5.915e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004827 0.0016 0.003311 0.003342 0.9907 0.9936 0.004913 0.9491 0.9659 0.01169 ] Network output: [ 0.02164 -0.06578 0.9234 -0.0002335 0.0001048 1.098 -0.0001759 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2573 0.175 0.3552 0.1569 0.9853 0.9942 0.2581 0.8208 0.96 0.6437 ] Network output: [ -0.03659 0.1739 1.098 0.0001485 -6.668e-05 0.8021 0.0001119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0829 0.07796 0.1737 0.1352 0.9897 0.9938 0.08295 0.9367 0.963 0.2036 ] Network output: [ -0.03096 0.06687 1.082 0.000202 -9.07e-05 0.9135 0.0001523 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09837 0.09733 0.1871 0.1566 0.9856 0.9917 0.09838 0.9016 0.949 0.1982 ] Network output: [ -0.004979 0.9934 0.008339 -6.655e-07 2.988e-07 1.008 -5.016e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02886 Epoch 4978 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05117 0.861 0.9327 -6.096e-05 2.737e-05 0.1037 -4.594e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003559 -0.003161 -0.01209 0.007126 0.9668 0.9718 0.006989 0.8982 0.9007 0.02311 ] Network output: [ 0.982 0.09037 -0.009137 2.931e-05 -1.316e-05 -0.04517 2.209e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2395 -0.009545 -0.1888 0.1741 0.9837 0.9933 0.2683 0.8121 0.956 0.6498 ] Network output: [ 0.02112 0.8755 0.963 -7.897e-05 3.545e-05 0.1189 -5.951e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004811 0.001586 0.00328 0.003373 0.9907 0.9936 0.004897 0.9492 0.966 0.01169 ] Network output: [ 0.02419 -0.08072 0.9247 -0.0002244 0.0001007 1.107 -0.0001691 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2565 0.1741 0.3547 0.1597 0.9853 0.9942 0.2572 0.8211 0.9601 0.6451 ] Network output: [ -0.0368 0.1737 1.098 0.000148 -6.646e-05 0.8027 0.0001116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08278 0.07782 0.1738 0.1355 0.9897 0.9938 0.08283 0.9369 0.9631 0.2038 ] Network output: [ -0.03142 0.06782 1.082 0.0002007 -9.011e-05 0.9135 0.0001513 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09827 0.09722 0.1873 0.1566 0.9856 0.9917 0.09828 0.9018 0.9491 0.1984 ] Network output: [ -0.005825 0.9977 0.00811 -3.002e-06 1.348e-06 1.006 -2.263e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02906 Epoch 4979 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05068 0.8626 0.9328 -6.213e-05 2.789e-05 0.103 -4.682e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003553 -0.003155 -0.01208 0.007098 0.9668 0.9718 0.006978 0.8984 0.9008 0.02311 ] Network output: [ 0.9783 0.09968 -0.006972 2.412e-05 -1.083e-05 -0.04915 1.818e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2388 -0.009408 -0.1884 0.1722 0.9837 0.9934 0.2676 0.8126 0.9561 0.6511 ] Network output: [ 0.02106 0.876 0.9629 -7.957e-05 3.572e-05 0.1187 -5.997e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004804 0.001576 0.003292 0.003325 0.9907 0.9937 0.004891 0.9494 0.9661 0.01172 ] Network output: [ 0.02118 -0.06535 0.9247 -0.0002369 0.0001063 1.097 -0.0001785 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2561 0.1736 0.3556 0.156 0.9853 0.9942 0.2569 0.8215 0.9601 0.6467 ] Network output: [ -0.03637 0.1738 1.097 0.0001481 -6.65e-05 0.8024 0.0001116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08262 0.07765 0.1736 0.1351 0.9897 0.9938 0.08267 0.937 0.9631 0.2037 ] Network output: [ -0.03083 0.06555 1.082 0.0002021 -9.072e-05 0.915 0.0001523 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09816 0.09711 0.1873 0.1566 0.9856 0.9917 0.09817 0.902 0.9492 0.1985 ] Network output: [ -0.004751 0.9931 0.007907 9.344e-07 -4.195e-07 1.009 7.042e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02857 Epoch 4980 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05081 0.8619 0.9327 -6.168e-05 2.769e-05 0.1035 -4.648e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003548 -0.003156 -0.01212 0.00713 0.9668 0.9718 0.006971 0.8986 0.901 0.02314 ] Network output: [ 0.9825 0.08995 -0.008876 3.33e-05 -1.495e-05 -0.04601 2.509e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2384 -0.00989 -0.1906 0.1743 0.9837 0.9934 0.2671 0.8129 0.9562 0.6527 ] Network output: [ 0.02075 0.8764 0.963 -8.007e-05 3.595e-05 0.1188 -6.034e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004789 0.001562 0.003262 0.003353 0.9907 0.9937 0.004875 0.9496 0.9662 0.01171 ] Network output: [ 0.02356 -0.07898 0.9258 -0.0002286 0.0001026 1.105 -0.0001723 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2553 0.1726 0.355 0.1585 0.9853 0.9942 0.2561 0.8219 0.9602 0.6481 ] Network output: [ -0.03656 0.1736 1.097 0.0001477 -6.629e-05 0.803 0.0001113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0825 0.07751 0.1736 0.1354 0.9897 0.9938 0.08255 0.9372 0.9632 0.2039 ] Network output: [ -0.03125 0.06639 1.082 0.0002009 -9.017e-05 0.915 0.0001514 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09806 0.097 0.1874 0.1567 0.9856 0.9917 0.09807 0.9023 0.9493 0.1986 ] Network output: [ -0.005558 0.997 0.00773 -1.241e-06 5.572e-07 1.006 -9.353e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02874 Epoch 4981 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05033 0.8634 0.9328 -6.278e-05 2.819e-05 0.1029 -4.732e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003542 -0.003151 -0.01211 0.007105 0.9669 0.9718 0.00696 0.8989 0.9012 0.02314 ] Network output: [ 0.979 0.09833 -0.006747 2.863e-05 -1.285e-05 -0.04949 2.158e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2378 -0.00977 -0.1903 0.1726 0.9837 0.9934 0.2664 0.8133 0.9563 0.654 ] Network output: [ 0.02068 0.877 0.9629 -8.067e-05 3.622e-05 0.1185 -6.079e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004783 0.001553 0.003274 0.003308 0.9907 0.9937 0.004869 0.9497 0.9662 0.01174 ] Network output: [ 0.02074 -0.06471 0.9258 -0.0002402 0.0001078 1.096 -0.000181 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.255 0.1721 0.3558 0.1551 0.9854 0.9942 0.2557 0.8223 0.9603 0.6496 ] Network output: [ -0.03614 0.1737 1.096 0.0001477 -6.633e-05 0.8027 0.0001113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08236 0.07735 0.1735 0.1349 0.9898 0.9938 0.0824 0.9373 0.9633 0.2038 ] Network output: [ -0.03068 0.06429 1.081 0.0002021 -9.074e-05 0.9164 0.0001523 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09796 0.09689 0.1874 0.1566 0.9856 0.9917 0.09797 0.9024 0.9493 0.1987 ] Network output: [ -0.004532 0.9927 0.007503 2.479e-06 -1.113e-06 1.009 1.868e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02828 Epoch 4982 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05044 0.8628 0.9328 -6.241e-05 2.802e-05 0.1033 -4.704e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003152 -0.01214 0.007135 0.9669 0.9718 0.006953 0.8991 0.9013 0.02317 ] Network output: [ 0.983 0.08925 -0.00857 3.725e-05 -1.672e-05 -0.04661 2.807e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2373 -0.01022 -0.1924 0.1746 0.9837 0.9934 0.266 0.8137 0.9564 0.6556 ] Network output: [ 0.02038 0.8773 0.963 -8.117e-05 3.644e-05 0.1186 -6.118e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004768 0.00154 0.003244 0.003334 0.9907 0.9937 0.004854 0.9499 0.9663 0.01174 ] Network output: [ 0.02299 -0.07732 0.9268 -0.0002324 0.0001044 1.104 -0.0001752 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2541 0.1712 0.3553 0.1574 0.9854 0.9942 0.2549 0.8226 0.9604 0.651 ] Network output: [ -0.03631 0.1736 1.096 0.0001473 -6.611e-05 0.8032 0.000111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08224 0.07722 0.1735 0.1352 0.9898 0.9938 0.08229 0.9375 0.9634 0.204 ] Network output: [ -0.03108 0.06503 1.081 0.000201 -9.022e-05 0.9165 0.0001515 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09786 0.09679 0.1875 0.1567 0.9856 0.9917 0.09787 0.9027 0.9494 0.1989 ] Network output: [ -0.005311 0.9964 0.007371 4.13e-07 -1.854e-07 1.007 3.113e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02842 Epoch 4983 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04999 0.8641 0.9329 -6.347e-05 2.85e-05 0.1028 -4.784e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.003146 -0.01214 0.007113 0.9669 0.9718 0.006942 0.8993 0.9015 0.02317 ] Network output: [ 0.9797 0.0969 -0.006455 3.298e-05 -1.48e-05 -0.04968 2.485e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2367 -0.01011 -0.1921 0.173 0.9837 0.9934 0.2653 0.8141 0.9565 0.6568 ] Network output: [ 0.02031 0.8779 0.9629 -8.177e-05 3.671e-05 0.1183 -6.162e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004762 0.001531 0.003256 0.003292 0.9907 0.9937 0.004847 0.9501 0.9664 0.01177 ] Network output: [ 0.02031 -0.0639 0.9269 -0.0002434 0.0001093 1.095 -0.0001834 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2538 0.1708 0.3561 0.1541 0.9854 0.9942 0.2546 0.823 0.9605 0.6525 ] Network output: [ -0.0359 0.1737 1.096 0.0001474 -6.615e-05 0.8029 0.0001111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0821 0.07706 0.1733 0.1348 0.9898 0.9938 0.08215 0.9377 0.9635 0.2039 ] Network output: [ -0.03052 0.06307 1.081 0.0002022 -9.075e-05 0.9178 0.0001523 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09776 0.09669 0.1875 0.1567 0.9856 0.9917 0.09777 0.9028 0.9495 0.1989 ] Network output: [ -0.004322 0.9922 0.007123 3.953e-06 -1.775e-06 1.009 2.979e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02798 Epoch 4984 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05008 0.8636 0.9328 -6.316e-05 2.836e-05 0.1031 -4.76e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003147 -0.01217 0.007141 0.9669 0.9718 0.006936 0.8995 0.9017 0.0232 ] Network output: [ 0.9835 0.08829 -0.008219 4.117e-05 -1.848e-05 -0.04699 3.103e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2363 -0.01054 -0.1942 0.1749 0.9837 0.9934 0.2648 0.8144 0.9566 0.6583 ] Network output: [ 0.02001 0.8783 0.963 -8.228e-05 3.694e-05 0.1183 -6.201e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004748 0.001518 0.003227 0.003316 0.9908 0.9937 0.004833 0.9502 0.9665 0.01177 ] Network output: [ 0.02246 -0.07575 0.9278 -0.000236 0.0001059 1.102 -0.0001779 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.253 0.1699 0.3556 0.1564 0.9854 0.9942 0.2538 0.8233 0.9606 0.6538 ] Network output: [ -0.03605 0.1736 1.096 0.0001469 -6.594e-05 0.8034 0.0001107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08199 0.07694 0.1733 0.135 0.9898 0.9939 0.08203 0.9378 0.9635 0.204 ] Network output: [ -0.03091 0.06376 1.081 0.000201 -9.026e-05 0.9179 0.0001515 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09766 0.09659 0.1876 0.1567 0.9856 0.9917 0.09767 0.9031 0.9496 0.1991 ] Network output: [ -0.005085 0.9958 0.007029 1.95e-06 -8.755e-07 1.007 1.47e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02811 Epoch 4985 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04964 0.8649 0.933 -6.419e-05 2.882e-05 0.1026 -4.838e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.003141 -0.01217 0.00712 0.9669 0.9718 0.006925 0.8997 0.9018 0.0232 ] Network output: [ 0.9803 0.09536 -0.006095 3.717e-05 -1.669e-05 -0.04971 2.801e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2357 -0.01043 -0.1938 0.1735 0.9837 0.9934 0.2641 0.8148 0.9566 0.6596 ] Network output: [ 0.01994 0.8788 0.9629 -8.287e-05 3.72e-05 0.1181 -6.246e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004742 0.00151 0.003238 0.003277 0.9908 0.9937 0.004827 0.9504 0.9666 0.0118 ] Network output: [ 0.01988 -0.06294 0.9279 -0.0002464 0.0001106 1.094 -0.0001857 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2527 0.1694 0.3563 0.1533 0.9854 0.9942 0.2534 0.8237 0.9606 0.6552 ] Network output: [ -0.03564 0.1737 1.095 0.000147 -6.598e-05 0.8031 0.0001108 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08186 0.07679 0.1732 0.1346 0.9898 0.9939 0.0819 0.938 0.9636 0.204 ] Network output: [ -0.03036 0.0619 1.081 0.0002022 -9.076e-05 0.9191 0.0001524 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09758 0.0965 0.1876 0.1567 0.9856 0.9917 0.09759 0.9032 0.9497 0.1991 ] Network output: [ -0.004119 0.9918 0.00676 5.349e-06 -2.402e-06 1.01 4.031e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02769 Epoch 4986 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04972 0.8644 0.9329 -6.393e-05 2.87e-05 0.1029 -4.818e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003516 -0.003142 -0.0122 0.007148 0.9669 0.9718 0.006919 0.9 0.902 0.02322 ] Network output: [ 0.984 0.08709 -0.007826 4.504e-05 -2.022e-05 -0.04715 3.395e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2352 -0.01084 -0.1959 0.1753 0.9837 0.9934 0.2637 0.8151 0.9567 0.661 ] Network output: [ 0.01965 0.8792 0.963 -8.339e-05 3.744e-05 0.1181 -6.285e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004728 0.001497 0.00321 0.003299 0.9908 0.9937 0.004813 0.9505 0.9667 0.0118 ] Network output: [ 0.02198 -0.07427 0.9287 -0.0002392 0.0001074 1.101 -0.0001803 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2519 0.1686 0.3558 0.1554 0.9854 0.9942 0.2527 0.824 0.9607 0.6565 ] Network output: [ -0.03579 0.1737 1.095 0.0001465 -6.576e-05 0.8035 0.0001104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08175 0.07667 0.1732 0.1348 0.9898 0.9939 0.08179 0.9381 0.9637 0.2041 ] Network output: [ -0.03073 0.06255 1.081 0.0002011 -9.028e-05 0.9192 0.0001516 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09748 0.0964 0.1877 0.1567 0.9856 0.9917 0.09749 0.9035 0.9498 0.1993 ] Network output: [ -0.004876 0.9952 0.0067 3.372e-06 -1.514e-06 1.008 2.541e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02781 Epoch 4987 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04928 0.8657 0.933 -6.494e-05 2.916e-05 0.1024 -4.894e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00351 -0.003137 -0.0122 0.007129 0.9669 0.9718 0.006908 0.9002 0.9022 0.02323 ] Network output: [ 0.9809 0.09373 -0.005674 4.122e-05 -1.85e-05 -0.0496 3.106e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2346 -0.01073 -0.1956 0.1739 0.9837 0.9934 0.263 0.8155 0.9568 0.6622 ] Network output: [ 0.01958 0.8798 0.9629 -8.398e-05 3.77e-05 0.1178 -6.329e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004722 0.001489 0.003221 0.003262 0.9908 0.9937 0.004807 0.9507 0.9668 0.01183 ] Network output: [ 0.01946 -0.06188 0.9288 -0.0002492 0.0001119 1.093 -0.0001878 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2515 0.1681 0.3565 0.1524 0.9854 0.9942 0.2523 0.8244 0.9608 0.6579 ] Network output: [ -0.03538 0.1738 1.094 0.0001466 -6.58e-05 0.8032 0.0001105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08163 0.07653 0.173 0.1344 0.9898 0.9939 0.08167 0.9383 0.9638 0.204 ] Network output: [ -0.03019 0.06077 1.08 0.0002022 -9.077e-05 0.9203 0.0001524 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0974 0.09631 0.1877 0.1567 0.9856 0.9917 0.09741 0.9036 0.9499 0.1993 ] Network output: [ -0.00392 0.9913 0.006411 6.674e-06 -2.996e-06 1.01 5.03e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0274 Epoch 4988 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04936 0.8653 0.933 -6.471e-05 2.905e-05 0.1027 -4.877e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003506 -0.003137 -0.01222 0.007156 0.9669 0.9718 0.006902 0.9004 0.9023 0.02325 ] Network output: [ 0.9845 0.08569 -0.007402 4.886e-05 -2.193e-05 -0.04713 3.682e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2342 -0.01112 -0.1976 0.1757 0.9837 0.9934 0.2625 0.8158 0.9569 0.6637 ] Network output: [ 0.01928 0.8802 0.9631 -8.451e-05 3.794e-05 0.1178 -6.369e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004709 0.001478 0.003193 0.003283 0.9908 0.9937 0.004794 0.9508 0.9668 0.01182 ] Network output: [ 0.02155 -0.07286 0.9296 -0.0002421 0.0001087 1.099 -0.0001825 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2508 0.1673 0.3559 0.1544 0.9854 0.9942 0.2516 0.8247 0.9609 0.6592 ] Network output: [ -0.03552 0.1738 1.094 0.0001461 -6.559e-05 0.8036 0.0001101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08152 0.07641 0.173 0.1346 0.9898 0.9939 0.08156 0.9384 0.9639 0.2042 ] Network output: [ -0.03056 0.0614 1.08 0.0002011 -9.029e-05 0.9204 0.0001516 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0973 0.09621 0.1878 0.1567 0.9856 0.9917 0.09731 0.9038 0.9499 0.1994 ] Network output: [ -0.00468 0.9947 0.006387 4.694e-06 -2.107e-06 1.008 3.537e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02751 Epoch 4989 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04893 0.8665 0.9332 -6.573e-05 2.951e-05 0.1022 -4.953e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0035 -0.003132 -0.01222 0.007137 0.9669 0.9718 0.006892 0.9006 0.9025 0.02326 ] Network output: [ 0.9813 0.09206 -0.005208 4.51e-05 -2.025e-05 -0.04936 3.399e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2336 -0.01101 -0.1973 0.1744 0.9838 0.9934 0.2619 0.8162 0.957 0.6648 ] Network output: [ 0.01921 0.8808 0.963 -8.51e-05 3.82e-05 0.1175 -6.413e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004703 0.00147 0.003205 0.003248 0.9908 0.9937 0.004787 0.951 0.9669 0.01185 ] Network output: [ 0.01905 -0.06069 0.9297 -0.0002518 0.0001131 1.092 -0.0001898 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2505 0.1669 0.3566 0.1515 0.9854 0.9942 0.2512 0.825 0.961 0.6606 ] Network output: [ -0.03511 0.1739 1.094 0.0001462 -6.563e-05 0.8033 0.0001102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0814 0.07628 0.1729 0.1343 0.9898 0.9939 0.08145 0.9386 0.9639 0.2041 ] Network output: [ -0.03001 0.05968 1.08 0.0002022 -9.077e-05 0.9215 0.0001524 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09723 0.09613 0.1877 0.1567 0.9856 0.9917 0.09724 0.904 0.95 0.1995 ] Network output: [ -0.003724 0.9909 0.006082 7.941e-06 -3.565e-06 1.011 5.984e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0271 Epoch 4990 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.049 0.8661 0.9332 -6.552e-05 2.942e-05 0.1025 -4.938e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003496 -0.003132 -0.01225 0.007164 0.9669 0.9718 0.006886 0.9008 0.9026 0.02328 ] Network output: [ 0.985 0.08415 -0.006967 5.259e-05 -2.361e-05 -0.04697 3.964e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2332 -0.01139 -0.1993 0.1762 0.9838 0.9934 0.2614 0.8165 0.9571 0.6662 ] Network output: [ 0.01892 0.8812 0.9631 -8.563e-05 3.844e-05 0.1175 -6.453e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00469 0.001459 0.003175 0.003269 0.9908 0.9937 0.004774 0.9511 0.967 0.01185 ] Network output: [ 0.02116 -0.07151 0.9304 -0.0002448 0.0001099 1.098 -0.0001845 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2497 0.1661 0.356 0.1536 0.9854 0.9942 0.2505 0.8253 0.961 0.6618 ] Network output: [ -0.03525 0.1739 1.094 0.0001457 -6.541e-05 0.8037 0.0001098 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0813 0.07616 0.1729 0.1345 0.9898 0.9939 0.08134 0.9387 0.964 0.2042 ] Network output: [ -0.03038 0.06029 1.08 0.0002011 -9.029e-05 0.9216 0.0001516 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09713 0.09604 0.1878 0.1567 0.9856 0.9917 0.09714 0.9042 0.9501 0.1996 ] Network output: [ -0.004499 0.9942 0.006097 5.924e-06 -2.659e-06 1.009 4.464e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02721 Epoch 4991 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04857 0.8673 0.9333 -6.654e-05 2.987e-05 0.102 -5.015e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00349 -0.003127 -0.01224 0.007146 0.9669 0.9718 0.006875 0.901 0.9028 0.02328 ] Network output: [ 0.9818 0.09036 -0.004708 4.882e-05 -2.192e-05 -0.04905 3.679e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2326 -0.01127 -0.1989 0.1749 0.9838 0.9934 0.2608 0.8168 0.9572 0.6674 ] Network output: [ 0.01885 0.8817 0.963 -8.622e-05 3.871e-05 0.1172 -6.497e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004684 0.001452 0.003188 0.003234 0.9908 0.9937 0.004769 0.9513 0.9671 0.01188 ] Network output: [ 0.01866 -0.05939 0.9305 -0.0002543 0.0001142 1.091 -0.0001917 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2494 0.1657 0.3567 0.1506 0.9854 0.9942 0.2502 0.8257 0.9611 0.6632 ] Network output: [ -0.03483 0.174 1.093 0.0001458 -6.546e-05 0.8034 0.0001099 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08119 0.07604 0.1727 0.1341 0.9898 0.9939 0.08124 0.9389 0.9641 0.2042 ] Network output: [ -0.02982 0.0586 1.079 0.0002022 -9.077e-05 0.9227 0.0001524 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09707 0.09596 0.1878 0.1567 0.9856 0.9917 0.09708 0.9044 0.9502 0.1996 ] Network output: [ -0.003532 0.9903 0.005777 9.149e-06 -4.107e-06 1.011 6.895e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02681 Epoch 4992 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04865 0.8669 0.9333 -6.635e-05 2.979e-05 0.1023 -5e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003486 -0.003128 -0.01227 0.007173 0.9669 0.9718 0.00687 0.9012 0.9029 0.0233 ] Network output: [ 0.9855 0.08247 -0.006527 5.625e-05 -2.525e-05 -0.04668 4.24e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2322 -0.01163 -0.201 0.1767 0.9838 0.9934 0.2603 0.8171 0.9573 0.6688 ] Network output: [ 0.01856 0.8822 0.9632 -8.675e-05 3.895e-05 0.1172 -6.538e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004672 0.001441 0.003158 0.003255 0.9908 0.9937 0.004756 0.9514 0.9672 0.01188 ] Network output: [ 0.02081 -0.07023 0.9311 -0.0002471 0.0001109 1.096 -0.0001862 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2486 0.1649 0.3561 0.1527 0.9854 0.9942 0.2494 0.826 0.9612 0.6643 ] Network output: [ -0.03498 0.174 1.093 0.0001453 -6.523e-05 0.8037 0.0001095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08109 0.07593 0.1727 0.1343 0.9898 0.9939 0.08113 0.939 0.9641 0.2043 ] Network output: [ -0.03021 0.05922 1.079 0.0002011 -9.028e-05 0.9228 0.0001515 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09697 0.09587 0.1879 0.1567 0.9856 0.9917 0.09698 0.9046 0.9502 0.1998 ] Network output: [ -0.004337 0.9937 0.00583 7.049e-06 -3.165e-06 1.009 5.313e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02692 Epoch 4993 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04821 0.8681 0.9334 -6.738e-05 3.025e-05 0.1018 -5.078e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00348 -0.003122 -0.01227 0.007155 0.9669 0.9718 0.00686 0.9014 0.9031 0.02331 ] Network output: [ 0.9822 0.08863 -0.004171 5.238e-05 -2.352e-05 -0.04863 3.948e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2316 -0.01151 -0.2005 0.1754 0.9838 0.9934 0.2597 0.8175 0.9573 0.6699 ] Network output: [ 0.0185 0.8827 0.9631 -8.734e-05 3.921e-05 0.1169 -6.582e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004666 0.001434 0.003172 0.00322 0.9908 0.9937 0.00475 0.9516 0.9672 0.01191 ] Network output: [ 0.01826 -0.05799 0.9313 -0.0002566 0.0001152 1.089 -0.0001934 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2483 0.1645 0.3568 0.1498 0.9854 0.9942 0.2491 0.8263 0.9613 0.6657 ] Network output: [ -0.03455 0.1741 1.092 0.0001454 -6.528e-05 0.8034 0.0001096 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08099 0.07581 0.1725 0.134 0.9899 0.9939 0.08103 0.9391 0.9642 0.2042 ] Network output: [ -0.02963 0.05754 1.079 0.0002022 -9.076e-05 0.9239 0.0001524 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09691 0.0958 0.1879 0.1567 0.9856 0.9917 0.09692 0.9047 0.9503 0.1998 ] Network output: [ -0.003347 0.9898 0.005486 1.028e-05 -4.615e-06 1.011 7.747e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02652 Epoch 4994 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0483 0.8677 0.9334 -6.72e-05 3.017e-05 0.102 -5.064e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003477 -0.003123 -0.0123 0.007183 0.9669 0.9718 0.006854 0.9016 0.9032 0.02333 ] Network output: [ 0.986 0.08064 -0.006076 5.986e-05 -2.687e-05 -0.04625 4.511e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2312 -0.01187 -0.2026 0.1772 0.9838 0.9934 0.2593 0.8178 0.9574 0.6712 ] Network output: [ 0.0182 0.8832 0.9632 -8.788e-05 3.945e-05 0.1168 -6.623e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004654 0.001424 0.003141 0.003242 0.9908 0.9937 0.004738 0.9517 0.9673 0.0119 ] Network output: [ 0.02051 -0.06905 0.9318 -0.0002491 0.0001118 1.095 -0.0001877 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2476 0.1637 0.3561 0.152 0.9854 0.9942 0.2484 0.8266 0.9613 0.6668 ] Network output: [ -0.03471 0.1742 1.092 0.0001449 -6.505e-05 0.8038 0.0001092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08089 0.0757 0.1725 0.1341 0.9899 0.9939 0.08093 0.9393 0.9643 0.2044 ] Network output: [ -0.03003 0.05819 1.079 0.000201 -9.025e-05 0.924 0.0001515 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09682 0.0957 0.188 0.1567 0.9856 0.9917 0.09683 0.9049 0.9504 0.2 ] Network output: [ -0.004194 0.9932 0.005574 8.05e-06 -3.614e-06 1.01 6.066e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02663 Epoch 4995 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04785 0.869 0.9336 -6.825e-05 3.064e-05 0.1015 -5.143e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003471 -0.003117 -0.01229 0.007165 0.9669 0.9718 0.006844 0.9018 0.9034 0.02333 ] Network output: [ 0.9825 0.08684 -0.003585 5.58e-05 -2.505e-05 -0.04809 4.206e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2306 -0.01172 -0.2021 0.176 0.9838 0.9934 0.2586 0.8181 0.9575 0.6723 ] Network output: [ 0.01815 0.8837 0.9631 -8.846e-05 3.971e-05 0.1165 -6.667e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004649 0.001418 0.003156 0.003208 0.9908 0.9937 0.004733 0.9518 0.9674 0.01193 ] Network output: [ 0.01787 -0.05653 0.932 -0.0002587 0.0001161 1.088 -0.000195 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2473 0.1634 0.3569 0.149 0.9854 0.9942 0.2481 0.8269 0.9614 0.6681 ] Network output: [ -0.03427 0.1743 1.091 0.000145 -6.511e-05 0.8035 0.0001093 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08079 0.07559 0.1724 0.1338 0.9899 0.9939 0.08084 0.9394 0.9643 0.2043 ] Network output: [ -0.02943 0.05651 1.078 0.0002021 -9.074e-05 0.925 0.0001523 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09676 0.09565 0.1879 0.1567 0.9856 0.9917 0.09677 0.9051 0.9505 0.2 ] Network output: [ -0.003165 0.9893 0.005197 1.132e-05 -5.084e-06 1.012 8.534e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02622 Epoch 4996 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04795 0.8685 0.9336 -6.806e-05 3.055e-05 0.1018 -5.129e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003467 -0.003118 -0.01232 0.007193 0.9669 0.9718 0.006839 0.902 0.9035 0.02336 ] Network output: [ 0.9864 0.07863 -0.00561 6.34e-05 -2.846e-05 -0.04567 4.778e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2303 -0.01208 -0.2042 0.1778 0.9838 0.9934 0.2582 0.8184 0.9576 0.6736 ] Network output: [ 0.01784 0.8842 0.9633 -8.9e-05 3.996e-05 0.1165 -6.707e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004637 0.001408 0.003124 0.003231 0.9908 0.9937 0.00472 0.952 0.9675 0.01193 ] Network output: [ 0.02024 -0.068 0.9325 -0.0002507 0.0001126 1.094 -0.000189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2466 0.1626 0.3561 0.1513 0.9854 0.9942 0.2473 0.8272 0.9615 0.6692 ] Network output: [ -0.03444 0.1744 1.091 0.0001445 -6.486e-05 0.8038 0.0001089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0807 0.07549 0.1723 0.134 0.9899 0.9939 0.08074 0.9396 0.9644 0.2044 ] Network output: [ -0.02986 0.05721 1.078 0.0002009 -9.021e-05 0.9251 0.0001514 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09667 0.09555 0.1881 0.1567 0.9856 0.9917 0.09668 0.9053 0.9505 0.2002 ] Network output: [ -0.004065 0.9929 0.005325 8.933e-06 -4.01e-06 1.01 6.732e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02635 Epoch 4997 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04749 0.8698 0.9337 -6.914e-05 3.104e-05 0.1012 -5.21e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003461 -0.003112 -0.01231 0.007174 0.9669 0.9718 0.006829 0.9022 0.9037 0.02336 ] Network output: [ 0.9828 0.08503 -0.002953 5.906e-05 -2.652e-05 -0.04745 4.451e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2297 -0.01192 -0.2036 0.1765 0.9838 0.9934 0.2576 0.8188 0.9576 0.6747 ] Network output: [ 0.0178 0.8848 0.9632 -8.958e-05 4.022e-05 0.1161 -6.751e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004632 0.001403 0.003141 0.003196 0.9909 0.9938 0.004716 0.9521 0.9675 0.01196 ] Network output: [ 0.01747 -0.05498 0.9327 -0.0002607 0.000117 1.086 -0.0001964 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2463 0.1623 0.3569 0.1482 0.9854 0.9942 0.2471 0.8275 0.9615 0.6705 ] Network output: [ -0.03397 0.1745 1.091 0.0001446 -6.494e-05 0.8035 0.000109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08061 0.07538 0.1722 0.1337 0.9899 0.9939 0.08065 0.9397 0.9645 0.2044 ] Network output: [ -0.02923 0.05551 1.078 0.0002021 -9.071e-05 0.9261 0.0001523 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09662 0.0955 0.188 0.1567 0.9856 0.9917 0.09663 0.9054 0.9506 0.2002 ] Network output: [ -0.002977 0.9888 0.004914 1.232e-05 -5.529e-06 1.012 9.282e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02593 Epoch 4998 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0476 0.8693 0.9337 -6.893e-05 3.095e-05 0.1015 -5.195e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003458 -0.003113 -0.01234 0.007203 0.9669 0.9718 0.006824 0.9024 0.9038 0.02338 ] Network output: [ 0.9869 0.07654 -0.005155 6.687e-05 -3.002e-05 -0.04499 5.039e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2293 -0.01227 -0.2058 0.1784 0.9838 0.9934 0.2572 0.819 0.9577 0.676 ] Network output: [ 0.01749 0.8852 0.9634 -9.012e-05 4.046e-05 0.116 -6.792e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00462 0.001393 0.003107 0.00322 0.9909 0.9938 0.004703 0.9522 0.9676 0.01195 ] Network output: [ 0.02001 -0.06705 0.9331 -0.0002521 0.0001132 1.093 -0.00019 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2456 0.1616 0.356 0.1506 0.9854 0.9942 0.2463 0.8278 0.9616 0.6716 ] Network output: [ -0.03417 0.1745 1.091 0.0001441 -6.468e-05 0.8038 0.0001086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08051 0.07528 0.1722 0.1339 0.9899 0.9939 0.08055 0.9398 0.9645 0.2045 ] Network output: [ -0.02969 0.05626 1.078 0.0002008 -9.015e-05 0.9261 0.0001513 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09652 0.0954 0.1881 0.1567 0.9856 0.9917 0.09653 0.9056 0.9507 0.2003 ] Network output: [ -0.003946 0.9926 0.005096 9.736e-06 -4.371e-06 1.01 7.337e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02607 Epoch 4999 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04713 0.8706 0.9339 -7.006e-05 3.145e-05 0.1009 -5.28e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003452 -0.003107 -0.01233 0.007184 0.9669 0.9718 0.006813 0.9025 0.9039 0.02338 ] Network output: [ 0.983 0.08329 -0.002302 6.212e-05 -2.789e-05 -0.04678 4.682e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2288 -0.01209 -0.2051 0.177 0.9838 0.9934 0.2565 0.8194 0.9578 0.677 ] Network output: [ 0.01746 0.8858 0.9632 -9.07e-05 4.072e-05 0.1157 -6.836e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004616 0.001388 0.003126 0.003184 0.9909 0.9938 0.004699 0.9524 0.9677 0.01198 ] Network output: [ 0.01707 -0.05329 0.9333 -0.0002625 0.0001178 1.085 -0.0001978 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2453 0.1613 0.3569 0.1474 0.9854 0.9942 0.2461 0.8281 0.9617 0.6728 ] Network output: [ -0.03368 0.1746 1.09 0.0001443 -6.477e-05 0.8035 0.0001087 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08043 0.07518 0.172 0.1335 0.9899 0.9939 0.08047 0.9399 0.9646 0.2044 ] Network output: [ -0.02901 0.0545 1.077 0.000202 -9.069e-05 0.9271 0.0001522 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09648 0.09535 0.1881 0.1567 0.9856 0.9917 0.09649 0.9058 0.9507 0.2003 ] Network output: [ -0.002781 0.9882 0.004654 1.329e-05 -5.968e-06 1.013 1.002e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02564 Epoch 5000 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04727 0.8701 0.9339 -6.983e-05 3.135e-05 0.1012 -5.262e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003449 -0.003109 -0.01236 0.007215 0.9669 0.9718 0.006809 0.9027 0.9041 0.0234 ] Network output: [ 0.9874 0.07441 -0.004744 7.023e-05 -3.153e-05 -0.04428 5.293e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2284 -0.01245 -0.2075 0.179 0.9838 0.9934 0.2562 0.8196 0.9579 0.6782 ] Network output: [ 0.01714 0.8862 0.9635 -9.124e-05 4.096e-05 0.1156 -6.876e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004604 0.001378 0.00309 0.00321 0.9909 0.9938 0.004687 0.9525 0.9677 0.01198 ] Network output: [ 0.01984 -0.06619 0.9337 -0.0002532 0.0001136 1.092 -0.0001908 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2446 0.1605 0.3559 0.15 0.9854 0.9942 0.2453 0.8284 0.9617 0.6739 ] Network output: [ -0.0339 0.1747 1.09 0.0001437 -6.449e-05 0.8038 0.0001083 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08033 0.07508 0.172 0.1338 0.9899 0.9939 0.08038 0.9401 0.9646 0.2045 ] Network output: [ -0.02952 0.0553 1.077 0.0002007 -9.009e-05 0.9272 0.0001512 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09639 0.09526 0.1882 0.1568 0.9856 0.9917 0.0964 0.906 0.9508 0.2005 ] Network output: [ -0.003845 0.9922 0.004905 1.047e-05 -4.699e-06 1.011 7.888e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0258 Epoch 5001 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04677 0.8715 0.9341 -7.101e-05 3.188e-05 0.1006 -5.351e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003443 -0.003102 -0.01236 0.007194 0.9669 0.9718 0.006799 0.9029 0.9042 0.02341 ] Network output: [ 0.9832 0.08166 -0.001641 6.497e-05 -2.917e-05 -0.04613 4.896e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2278 -0.01224 -0.2065 0.1776 0.9838 0.9934 0.2555 0.82 0.9579 0.6792 ] Network output: [ 0.01713 0.8868 0.9633 -9.182e-05 4.122e-05 0.1152 -6.92e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0046 0.001374 0.003112 0.003172 0.9909 0.9938 0.004683 0.9526 0.9678 0.01201 ] Network output: [ 0.01667 -0.05145 0.9339 -0.0002642 0.0001186 1.083 -0.0001991 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2444 0.1603 0.3568 0.1466 0.9854 0.9942 0.2451 0.8287 0.9618 0.6751 ] Network output: [ -0.03338 0.1747 1.089 0.0001439 -6.46e-05 0.8035 0.0001084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08025 0.07499 0.1719 0.1334 0.9899 0.9939 0.0803 0.9402 0.9647 0.2045 ] Network output: [ -0.0288 0.05345 1.077 0.000202 -9.067e-05 0.9282 0.0001522 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09635 0.09522 0.1881 0.1568 0.9856 0.9917 0.09636 0.9061 0.9509 0.2005 ] Network output: [ -0.002588 0.9875 0.004419 1.424e-05 -6.392e-06 1.013 1.073e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02535 Epoch 5002 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04693 0.8709 0.934 -7.073e-05 3.175e-05 0.1009 -5.33e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003441 -0.003104 -0.01239 0.007226 0.9669 0.9718 0.006795 0.9031 0.9043 0.02343 ] Network output: [ 0.988 0.0722 -0.004374 7.354e-05 -3.302e-05 -0.04352 5.542e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2275 -0.01262 -0.2091 0.1797 0.9838 0.9934 0.2552 0.8202 0.958 0.6805 ] Network output: [ 0.01679 0.8873 0.9636 -9.236e-05 4.146e-05 0.1152 -6.961e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004588 0.001365 0.003072 0.003202 0.9909 0.9938 0.00467 0.9527 0.9679 0.012 ] Network output: [ 0.01973 -0.06547 0.9342 -0.0002539 0.000114 1.091 -0.0001913 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2436 0.1595 0.3557 0.1495 0.9854 0.9942 0.2444 0.8289 0.9618 0.6761 ] Network output: [ -0.03364 0.1748 1.089 0.0001432 -6.43e-05 0.8038 0.0001079 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08016 0.07489 0.1718 0.1336 0.9899 0.9939 0.0802 0.9403 0.9647 0.2046 ] Network output: [ -0.02937 0.05435 1.077 0.0002005 -9.001e-05 0.9283 0.0001511 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09625 0.09512 0.1883 0.1568 0.9856 0.9917 0.09626 0.9063 0.9509 0.2007 ] Network output: [ -0.003778 0.9919 0.004737 1.106e-05 -4.966e-06 1.011 8.336e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02553 Epoch 5003 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04641 0.8724 0.9342 -7.197e-05 3.231e-05 0.1002 -5.424e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003435 -0.003097 -0.01238 0.007203 0.9669 0.9718 0.006784 0.9033 0.9045 0.02343 ] Network output: [ 0.9833 0.08004 -0.0009298 6.764e-05 -3.037e-05 -0.04539 5.098e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2269 -0.01237 -0.2079 0.1781 0.9838 0.9934 0.2545 0.8206 0.9581 0.6814 ] Network output: [ 0.0168 0.8879 0.9634 -9.293e-05 4.172e-05 0.1147 -7.004e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004584 0.001361 0.003098 0.003161 0.9909 0.9938 0.004667 0.9529 0.9679 0.01204 ] Network output: [ 0.01624 -0.0495 0.9345 -0.0002658 0.0001193 1.081 -0.0002003 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2435 0.1593 0.3568 0.1458 0.9854 0.9942 0.2442 0.8293 0.9619 0.6773 ] Network output: [ -0.03307 0.1749 1.088 0.0001435 -6.443e-05 0.8034 0.0001082 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08009 0.07481 0.1717 0.1333 0.9899 0.994 0.08013 0.9404 0.9648 0.2045 ] Network output: [ -0.02857 0.05242 1.076 0.0002019 -9.064e-05 0.9293 0.0001522 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09623 0.09509 0.1882 0.1568 0.9856 0.9917 0.09624 0.9064 0.951 0.2007 ] Network output: [ -0.002402 0.9869 0.004175 1.508e-05 -6.772e-06 1.014 1.137e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02506 Epoch 5004 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04661 0.8717 0.9342 -7.163e-05 3.216e-05 0.1005 -5.398e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003432 -0.003099 -0.01241 0.007239 0.9669 0.9718 0.006781 0.9034 0.9046 0.02345 ] Network output: [ 0.9886 0.06975 -0.004006 7.687e-05 -3.451e-05 -0.04258 5.793e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2266 -0.01277 -0.2106 0.1804 0.9838 0.9934 0.2542 0.8208 0.9581 0.6826 ] Network output: [ 0.01645 0.8883 0.9637 -9.347e-05 4.196e-05 0.1147 -7.044e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004572 0.001352 0.003055 0.003194 0.9909 0.9938 0.004654 0.953 0.968 0.01202 ] Network output: [ 0.01967 -0.06504 0.9347 -0.0002543 0.0001141 1.09 -0.0001916 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2427 0.1586 0.3555 0.149 0.9854 0.9942 0.2434 0.8295 0.962 0.6782 ] Network output: [ -0.03338 0.175 1.089 0.0001428 -6.411e-05 0.8038 0.0001076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08 0.07471 0.1717 0.1335 0.9899 0.994 0.08004 0.9406 0.9648 0.2047 ] Network output: [ -0.02922 0.05349 1.077 0.0002003 -8.991e-05 0.9293 0.0001509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09612 0.09498 0.1883 0.1568 0.9856 0.9917 0.09613 0.9066 0.951 0.2009 ] Network output: [ -0.003742 0.9919 0.004557 1.146e-05 -5.146e-06 1.011 8.638e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02527 Epoch 5005 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04605 0.8733 0.9344 -7.296e-05 3.275e-05 0.09985 -5.498e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003426 -0.003092 -0.0124 0.007213 0.9669 0.9718 0.00677 0.9036 0.9047 0.02345 ] Network output: [ 0.9833 0.07834 -0.0001185 7.017e-05 -3.15e-05 -0.04448 5.289e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.226 -0.01248 -0.2093 0.1787 0.9838 0.9934 0.2535 0.8211 0.9582 0.6835 ] Network output: [ 0.01648 0.889 0.9635 -9.404e-05 4.222e-05 0.1142 -7.087e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004569 0.001349 0.003085 0.003151 0.9909 0.9938 0.004652 0.9531 0.968 0.01206 ] Network output: [ 0.01577 -0.04747 0.935 -0.0002672 0.00012 1.08 -0.0002014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2425 0.1584 0.3567 0.145 0.9854 0.9942 0.2433 0.8298 0.962 0.6794 ] Network output: [ -0.03276 0.175 1.088 0.0001431 -6.427e-05 0.8034 0.0001079 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07993 0.07464 0.1715 0.1331 0.9899 0.994 0.07998 0.9407 0.9649 0.2046 ] Network output: [ -0.02833 0.05146 1.076 0.0002018 -9.06e-05 0.9303 0.0001521 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09611 0.09497 0.1882 0.1568 0.9856 0.9917 0.09612 0.9067 0.9511 0.2008 ] Network output: [ -0.0022 0.9865 0.003894 1.584e-05 -7.112e-06 1.014 1.194e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02477 Epoch 5006 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04629 0.8725 0.9344 -7.253e-05 3.256e-05 0.1002 -5.466e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003424 -0.003094 -0.01243 0.007252 0.967 0.9719 0.006767 0.9038 0.9048 0.02348 ] Network output: [ 0.9892 0.06707 -0.003643 8.021e-05 -3.601e-05 -0.04147 6.045e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2258 -0.01291 -0.2122 0.1812 0.9838 0.9934 0.2532 0.8213 0.9583 0.6847 ] Network output: [ 0.01611 0.8894 0.9638 -9.458e-05 4.246e-05 0.1142 -7.127e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004557 0.00134 0.003037 0.003188 0.9909 0.9938 0.004639 0.9532 0.9681 0.01204 ] Network output: [ 0.01965 -0.0649 0.9352 -0.0002542 0.0001141 1.089 -0.0001916 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2417 0.1577 0.3553 0.1486 0.9854 0.9942 0.2425 0.83 0.9621 0.6803 ] Network output: [ -0.03312 0.1752 1.088 0.0001423 -6.391e-05 0.8038 0.0001073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07984 0.07454 0.1715 0.1335 0.9899 0.994 0.07988 0.9408 0.9649 0.2047 ] Network output: [ -0.02907 0.05271 1.076 0.0002 -8.978e-05 0.9302 0.0001507 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.096 0.09486 0.1884 0.1568 0.9856 0.9917 0.09601 0.9069 0.9511 0.2011 ] Network output: [ -0.003715 0.992 0.004368 1.173e-05 -5.268e-06 1.011 8.843e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02503 Epoch 5007 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04569 0.8742 0.9346 -7.396e-05 3.321e-05 0.09945 -5.574e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003418 -0.003087 -0.01241 0.007223 0.967 0.9719 0.006755 0.9039 0.9049 0.02347 ] Network output: [ 0.9831 0.07679 0.0007618 7.244e-05 -3.252e-05 -0.04355 5.46e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2252 -0.01256 -0.2105 0.1792 0.9838 0.9934 0.2525 0.8217 0.9583 0.6856 ] Network output: [ 0.01617 0.89 0.9635 -9.514e-05 4.271e-05 0.1137 -7.17e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004555 0.001338 0.003073 0.003141 0.9909 0.9938 0.004637 0.9533 0.9682 0.01209 ] Network output: [ 0.01525 -0.04522 0.9356 -0.0002687 0.0001206 1.078 -0.0002025 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2417 0.1576 0.3567 0.1442 0.9854 0.9942 0.2424 0.8303 0.9621 0.6815 ] Network output: [ -0.03243 0.1752 1.087 0.0001428 -6.41e-05 0.8033 0.0001076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07978 0.07448 0.1714 0.133 0.9899 0.994 0.07983 0.9409 0.965 0.2046 ] Network output: [ -0.02807 0.05048 1.075 0.0002017 -9.057e-05 0.9312 0.000152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.096 0.09485 0.1883 0.1568 0.9856 0.9917 0.09601 0.907 0.9512 0.201 ] Network output: [ -0.001953 0.9858 0.003613 1.667e-05 -7.483e-06 1.015 1.256e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02449 Epoch 5008 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04599 0.8733 0.9346 -7.344e-05 3.297e-05 0.09985 -5.535e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003416 -0.00309 -0.01245 0.007265 0.967 0.9719 0.006753 0.9041 0.9051 0.0235 ] Network output: [ 0.9899 0.06444 -0.003391 8.346e-05 -3.747e-05 -0.04043 6.29e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2249 -0.01303 -0.2138 0.182 0.9838 0.9934 0.2523 0.8219 0.9584 0.6868 ] Network output: [ 0.01577 0.8905 0.9639 -9.567e-05 4.295e-05 0.1137 -7.21e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004542 0.001329 0.003018 0.003183 0.9909 0.9938 0.004624 0.9535 0.9682 0.01207 ] Network output: [ 0.01974 -0.06495 0.9356 -0.0002538 0.0001139 1.089 -0.0001913 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2408 0.1568 0.3549 0.1483 0.9854 0.9942 0.2415 0.8306 0.9622 0.6824 ] Network output: [ -0.03287 0.1753 1.087 0.0001419 -6.371e-05 0.8038 0.0001069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07969 0.07438 0.1713 0.1334 0.9899 0.994 0.07973 0.941 0.965 0.2048 ] Network output: [ -0.02893 0.05187 1.076 0.0001997 -8.965e-05 0.9311 0.0001505 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09588 0.09473 0.1885 0.1569 0.9856 0.9917 0.09589 0.9072 0.9512 0.2012 ] Network output: [ -0.003693 0.9919 0.004248 1.202e-05 -5.398e-06 1.011 9.062e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0248 Epoch 5009 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04532 0.8752 0.9348 -7.501e-05 3.368e-05 0.09901 -5.653e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00341 -0.003081 -0.01243 0.007231 0.967 0.9719 0.006742 0.9043 0.9052 0.0235 ] Network output: [ 0.9829 0.07574 0.001616 7.43e-05 -3.335e-05 -0.04289 5.599e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2243 -0.01263 -0.2117 0.1796 0.9839 0.9934 0.2516 0.8222 0.9584 0.6876 ] Network output: [ 0.01587 0.8911 0.9636 -9.622e-05 4.32e-05 0.1132 -7.252e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004541 0.001328 0.003061 0.003129 0.9909 0.9938 0.004623 0.9536 0.9683 0.01212 ] Network output: [ 0.0147 -0.04248 0.936 -0.0002703 0.0001213 1.076 -0.0002037 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2408 0.1568 0.3566 0.1433 0.9854 0.9942 0.2415 0.8309 0.9622 0.6835 ] Network output: [ -0.0321 0.1752 1.086 0.0001425 -6.396e-05 0.8033 0.0001074 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07964 0.07432 0.1712 0.1329 0.99 0.994 0.07968 0.9411 0.9651 0.2047 ] Network output: [ -0.0278 0.04933 1.075 0.0002017 -9.057e-05 0.9323 0.000152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09589 0.09474 0.1883 0.1569 0.9856 0.9917 0.0959 0.9073 0.9513 0.2012 ] Network output: [ -0.001671 0.9847 0.003407 1.768e-05 -7.939e-06 1.015 1.333e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0242 Epoch 5010 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04569 0.8741 0.9347 -7.436e-05 3.338e-05 0.09947 -5.604e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003408 -0.003085 -0.01248 0.007279 0.967 0.9719 0.006741 0.9044 0.9053 0.02352 ] Network output: [ 0.9907 0.062 -0.003351 8.659e-05 -3.888e-05 -0.03965 6.526e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2241 -0.01315 -0.2155 0.1827 0.9839 0.9934 0.2514 0.8224 0.9585 0.6888 ] Network output: [ 0.01543 0.8915 0.964 -9.675e-05 4.343e-05 0.1132 -7.291e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004527 0.001318 0.002998 0.003178 0.9909 0.9938 0.004609 0.9537 0.9683 0.01209 ] Network output: [ 0.01997 -0.06516 0.9359 -0.0002531 0.0001136 1.088 -0.0001907 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2399 0.156 0.3545 0.1481 0.9854 0.9942 0.2406 0.8311 0.9623 0.6844 ] Network output: [ -0.03264 0.1754 1.087 0.0001415 -6.351e-05 0.8039 0.0001066 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07954 0.07422 0.1712 0.1333 0.99 0.994 0.07958 0.9413 0.9651 0.2049 ] Network output: [ -0.02884 0.05089 1.075 0.0001994 -8.952e-05 0.9322 0.0001503 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09576 0.09461 0.1885 0.1569 0.9856 0.9917 0.09577 0.9075 0.9513 0.2014 ] Network output: [ -0.003728 0.9917 0.004242 1.228e-05 -5.511e-06 1.012 9.252e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02457 Epoch 5011 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04494 0.8762 0.935 -7.609e-05 3.416e-05 0.09853 -5.734e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003402 -0.003076 -0.01245 0.007239 0.967 0.9719 0.006728 0.9046 0.9054 0.02352 ] Network output: [ 0.9826 0.07509 0.002474 7.578e-05 -3.402e-05 -0.04242 5.711e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2234 -0.01267 -0.2129 0.18 0.9839 0.9934 0.2506 0.8228 0.9586 0.6896 ] Network output: [ 0.01556 0.8922 0.9637 -9.73e-05 4.368e-05 0.1126 -7.333e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004528 0.001318 0.00305 0.003117 0.9909 0.9938 0.004609 0.9538 0.9684 0.01214 ] Network output: [ 0.0141 -0.03928 0.9364 -0.000272 0.0001221 1.074 -0.000205 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.24 0.156 0.3564 0.1423 0.9855 0.9942 0.2407 0.8314 0.9623 0.6855 ] Network output: [ -0.03177 0.1752 1.086 0.0001422 -6.383e-05 0.8034 0.0001071 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0795 0.07417 0.171 0.1328 0.99 0.994 0.07954 0.9413 0.9652 0.2048 ] Network output: [ -0.02753 0.04804 1.074 0.0002018 -9.059e-05 0.9335 0.0001521 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09578 0.09463 0.1884 0.1569 0.9856 0.9917 0.09579 0.9076 0.9514 0.2013 ] Network output: [ -0.001405 0.9835 0.003235 1.868e-05 -8.387e-06 1.016 1.408e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0239 Epoch 5012 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04541 0.8749 0.9349 -7.525e-05 3.378e-05 0.09908 -5.671e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003401 -0.003081 -0.0125 0.007294 0.967 0.9719 0.006728 0.9047 0.9055 0.02355 ] Network output: [ 0.9917 0.05917 -0.003421 8.992e-05 -4.037e-05 -0.03871 6.777e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2233 -0.01326 -0.2172 0.1836 0.9839 0.9934 0.2505 0.8229 0.9586 0.6908 ] Network output: [ 0.01507 0.8926 0.9642 -9.782e-05 4.392e-05 0.1127 -7.372e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004513 0.001307 0.002977 0.003175 0.9909 0.9938 0.004594 0.9539 0.9684 0.01211 ] Network output: [ 0.02031 -0.06597 0.9362 -0.0002518 0.000113 1.088 -0.0001897 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.239 0.1551 0.354 0.148 0.9855 0.9942 0.2397 0.8316 0.9624 0.6863 ] Network output: [ -0.03244 0.1754 1.086 0.000141 -6.331e-05 0.804 0.0001063 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0794 0.07406 0.1711 0.1333 0.99 0.994 0.07944 0.9415 0.9652 0.205 ] Network output: [ -0.02878 0.05002 1.075 0.000199 -8.936e-05 0.9332 0.00015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09564 0.09449 0.1886 0.157 0.9856 0.9917 0.09565 0.9078 0.9514 0.2017 ] Network output: [ -0.003884 0.992 0.004229 1.21e-05 -5.43e-06 1.012 9.116e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02436 Epoch 5013 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04456 0.8773 0.9353 -7.717e-05 3.464e-05 0.09804 -5.816e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003394 -0.00307 -0.01246 0.007248 0.967 0.9719 0.006715 0.9049 0.9056 0.02354 ] Network output: [ 0.982 0.0741 0.003607 7.719e-05 -3.465e-05 -0.04149 5.817e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2226 -0.0127 -0.2139 0.1804 0.9839 0.9934 0.2497 0.8233 0.9587 0.6915 ] Network output: [ 0.01527 0.8933 0.9638 -9.836e-05 4.416e-05 0.112 -7.413e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004515 0.001309 0.003042 0.003107 0.9909 0.9938 0.004596 0.954 0.9685 0.01217 ] Network output: [ 0.01332 -0.03596 0.9369 -0.0002738 0.0001229 1.071 -0.0002064 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2391 0.1553 0.3564 0.1414 0.9855 0.9942 0.2399 0.8319 0.9624 0.6875 ] Network output: [ -0.03144 0.1753 1.085 0.0001419 -6.368e-05 0.8034 0.0001069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07937 0.07403 0.1709 0.1326 0.99 0.994 0.07941 0.9416 0.9653 0.2049 ] Network output: [ -0.02723 0.04699 1.074 0.0002018 -9.059e-05 0.9345 0.0001521 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0957 0.09454 0.1884 0.157 0.9856 0.9917 0.09571 0.9079 0.9515 0.2015 ] Network output: [ -0.001156 0.983 0.002898 1.932e-05 -8.675e-06 1.016 1.456e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0236 Epoch 5014 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04515 0.8756 0.9351 -7.607e-05 3.415e-05 0.09872 -5.733e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003394 -0.003076 -0.01252 0.007312 0.967 0.9719 0.006716 0.905 0.9057 0.02357 ] Network output: [ 0.9928 0.05514 -0.003375 9.378e-05 -4.21e-05 -0.03694 7.067e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2225 -0.01337 -0.2188 0.1847 0.9839 0.9934 0.2497 0.8234 0.9587 0.6927 ] Network output: [ 0.01474 0.8937 0.9643 -9.888e-05 4.439e-05 0.1121 -7.452e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004499 0.001298 0.002957 0.003176 0.991 0.9938 0.00458 0.9541 0.9685 0.01213 ] Network output: [ 0.02072 -0.06795 0.9367 -0.0002496 0.0001121 1.089 -0.0001881 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2381 0.1543 0.3536 0.1482 0.9855 0.9942 0.2388 0.8321 0.9625 0.6882 ] Network output: [ -0.03225 0.1756 1.085 0.0001405 -6.306e-05 0.804 0.0001059 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07927 0.07393 0.1709 0.1333 0.99 0.994 0.07931 0.9417 0.9653 0.2051 ] Network output: [ -0.02872 0.04965 1.075 0.0001984 -8.909e-05 0.9339 0.0001496 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09554 0.09438 0.1887 0.157 0.9856 0.9917 0.09555 0.9081 0.9515 0.2018 ] Network output: [ -0.004113 0.9936 0.004038 1.129e-05 -5.068e-06 1.011 8.507e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02419 Epoch 5015 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04418 0.8783 0.9355 -7.825e-05 3.513e-05 0.09754 -5.897e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003386 -0.003064 -0.01248 0.007256 0.967 0.9719 0.0067 0.9052 0.9058 0.02356 ] Network output: [ 0.9811 0.07263 0.005205 7.849e-05 -3.523e-05 -0.03981 5.915e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2218 -0.01268 -0.2147 0.1809 0.9839 0.9934 0.2488 0.8238 0.9588 0.6933 ] Network output: [ 0.01502 0.8944 0.9638 -9.941e-05 4.463e-05 0.1113 -7.492e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004502 0.001302 0.003039 0.003097 0.991 0.9938 0.004584 0.9542 0.9686 0.0122 ] Network output: [ 0.01227 -0.03249 0.9376 -0.0002758 0.0001238 1.069 -0.0002078 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2384 0.1546 0.3566 0.1403 0.9855 0.9942 0.2391 0.8324 0.9625 0.6893 ] Network output: [ -0.03104 0.1755 1.084 0.0001415 -6.353e-05 0.8031 0.0001066 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07926 0.07391 0.1707 0.1325 0.99 0.994 0.0793 0.9418 0.9654 0.2049 ] Network output: [ -0.02683 0.04632 1.073 0.0002017 -9.055e-05 0.9351 0.000152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09562 0.09446 0.1884 0.1569 0.9856 0.9917 0.09563 0.9081 0.9516 0.2016 ] Network output: [ -0.0007711 0.983 0.002327 1.989e-05 -8.93e-06 1.016 1.499e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02332 Epoch 5016 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04494 0.8762 0.9352 -7.686e-05 3.45e-05 0.09838 -5.792e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003386 -0.003072 -0.01254 0.00733 0.967 0.9719 0.006703 0.9053 0.906 0.02359 ] Network output: [ 0.994 0.05054 -0.003363 9.788e-05 -4.394e-05 -0.0348 7.376e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2218 -0.01346 -0.2205 0.1859 0.9839 0.9934 0.2488 0.8239 0.9589 0.6945 ] Network output: [ 0.01442 0.8947 0.9645 -9.991e-05 4.485e-05 0.1115 -7.529e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004485 0.001291 0.002936 0.003179 0.991 0.9938 0.004566 0.9543 0.9686 0.01214 ] Network output: [ 0.02129 -0.07083 0.9371 -0.0002466 0.0001107 1.09 -0.0001859 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2372 0.1536 0.3531 0.1486 0.9855 0.9942 0.2379 0.8325 0.9626 0.6899 ] Network output: [ -0.03202 0.176 1.085 0.0001399 -6.28e-05 0.8038 0.0001054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07914 0.0738 0.1708 0.1332 0.99 0.994 0.07919 0.9419 0.9654 0.2052 ] Network output: [ -0.02863 0.04949 1.074 0.0001978 -8.878e-05 0.9343 0.000149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09543 0.09428 0.1887 0.157 0.9857 0.9917 0.09545 0.9084 0.9516 0.202 ] Network output: [ -0.00426 0.995 0.003835 1.056e-05 -4.74e-06 1.01 7.957e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02407 Epoch 5017 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04379 0.8794 0.9357 -7.941e-05 3.565e-05 0.097 -5.985e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003378 -0.003057 -0.01248 0.00726 0.967 0.9719 0.006686 0.9055 0.906 0.02357 ] Network output: [ 0.9799 0.07252 0.006871 7.879e-05 -3.537e-05 -0.03882 5.938e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2209 -0.01261 -0.2153 0.1811 0.9839 0.9935 0.2478 0.8243 0.9589 0.695 ] Network output: [ 0.0148 0.8955 0.9639 -0.0001004 4.508e-05 0.1107 -7.568e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004491 0.001295 0.003036 0.003083 0.991 0.9938 0.004572 0.9544 0.9687 0.01223 ] Network output: [ 0.01109 -0.02764 0.9381 -0.0002783 0.0001249 1.066 -0.0002097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2376 0.154 0.3567 0.1391 0.9855 0.9942 0.2384 0.8329 0.9626 0.691 ] Network output: [ -0.03057 0.1756 1.083 0.0001413 -6.343e-05 0.803 0.0001065 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07914 0.07379 0.1706 0.1323 0.99 0.994 0.07918 0.942 0.9654 0.2049 ] Network output: [ -0.02636 0.0452 1.072 0.0002018 -9.061e-05 0.9359 0.0001521 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09554 0.09438 0.1884 0.1569 0.9857 0.9917 0.09555 0.9084 0.9517 0.2017 ] Network output: [ -9.949e-05 0.9811 0.001912 2.153e-05 -9.664e-06 1.017 1.622e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02304 Epoch 5018 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04473 0.8768 0.9354 -7.769e-05 3.488e-05 0.09798 -5.855e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00338 -0.003068 -0.01257 0.007345 0.967 0.9719 0.006692 0.9056 0.9062 0.02361 ] Network output: [ 0.9956 0.04756 -0.004146 0.0001014 -4.55e-05 -0.03426 7.639e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2211 -0.01355 -0.2225 0.1869 0.9839 0.9935 0.248 0.8244 0.959 0.6962 ] Network output: [ 0.01408 0.8957 0.9647 -0.0001009 4.53e-05 0.1111 -7.605e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004471 0.001282 0.002907 0.00318 0.991 0.9938 0.004552 0.9545 0.9687 0.01215 ] Network output: [ 0.02233 -0.07351 0.937 -0.0002432 0.0001092 1.091 -0.0001833 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2363 0.1529 0.3522 0.149 0.9855 0.9942 0.237 0.833 0.9627 0.6917 ] Network output: [ -0.03184 0.1758 1.084 0.0001394 -6.259e-05 0.804 0.0001051 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.079 0.07365 0.1706 0.1332 0.99 0.994 0.07905 0.9421 0.9655 0.2052 ] Network output: [ -0.02864 0.04842 1.074 0.0001973 -8.857e-05 0.9355 0.0001487 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09531 0.09415 0.1888 0.1571 0.9857 0.9917 0.09532 0.9086 0.9517 0.2022 ] Network output: [ -0.004364 0.994 0.004175 1.08e-05 -4.851e-06 1.011 8.143e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02395 Epoch 5019 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04334 0.8807 0.936 -8.074e-05 3.625e-05 0.0963 -6.085e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003371 -0.003051 -0.0125 0.007258 0.967 0.9719 0.006674 0.9058 0.9062 0.02359 ] Network output: [ 0.9784 0.07578 0.008001 7.727e-05 -3.469e-05 -0.04025 5.823e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2202 -0.01253 -0.2162 0.1808 0.9839 0.9935 0.247 0.8248 0.959 0.6967 ] Network output: [ 0.01454 0.8964 0.964 -0.0001014 4.553e-05 0.1101 -7.643e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00448 0.001288 0.003028 0.003061 0.991 0.9938 0.004561 0.9546 0.9688 0.01226 ] Network output: [ 0.009927 -0.02007 0.938 -0.0002823 0.0001267 1.061 -0.0002128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2369 0.1534 0.3566 0.1372 0.9855 0.9942 0.2377 0.8333 0.9627 0.6929 ] Network output: [ -0.03015 0.175 1.083 0.0001413 -6.344e-05 0.8034 0.0001065 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07901 0.07365 0.1704 0.1322 0.99 0.994 0.07906 0.9421 0.9655 0.205 ] Network output: [ -0.02594 0.04265 1.072 0.0002025 -9.092e-05 0.9379 0.0001526 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09545 0.09429 0.1885 0.1571 0.9857 0.9917 0.09546 0.9086 0.9518 0.2019 ] Network output: [ 0.0006699 0.9759 0.002068 2.453e-05 -1.101e-05 1.021 1.849e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02277 Epoch 5020 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0445 0.8776 0.9355 -7.854e-05 3.526e-05 0.0975 -5.919e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003375 -0.003065 -0.0126 0.007363 0.967 0.9719 0.006683 0.9059 0.9063 0.02365 ] Network output: [ 0.998 0.04596 -0.006088 0.0001046 -4.696e-05 -0.03555 7.883e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2205 -0.01371 -0.225 0.1876 0.9839 0.9935 0.2473 0.8248 0.9591 0.6982 ] Network output: [ 0.01364 0.8967 0.965 -0.0001019 4.576e-05 0.1107 -7.681e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004457 0.001271 0.002866 0.003179 0.991 0.9938 0.004538 0.9547 0.9688 0.01217 ] Network output: [ 0.02403 -0.07647 0.9363 -0.0002392 0.0001074 1.091 -0.0001803 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2354 0.152 0.3509 0.1496 0.9855 0.9942 0.2361 0.8334 0.9627 0.6936 ] Network output: [ -0.03184 0.1752 1.084 0.0001391 -6.243e-05 0.8048 0.0001048 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07886 0.07349 0.1705 0.1334 0.99 0.994 0.0789 0.9423 0.9655 0.2055 ] Network output: [ -0.0289 0.04642 1.075 0.000197 -8.843e-05 0.9377 0.0001484 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09517 0.09401 0.1891 0.1574 0.9857 0.9917 0.09518 0.9089 0.9518 0.2026 ] Network output: [ -0.004899 0.9923 0.005091 1.081e-05 -4.852e-06 1.012 8.144e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02382 Epoch 5021 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0428 0.8823 0.9362 -8.211e-05 3.686e-05 0.09552 -6.188e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003364 -0.003046 -0.01252 0.00726 0.967 0.9719 0.006662 0.906 0.9064 0.02362 ] Network output: [ 0.9766 0.07956 0.009348 7.517e-05 -3.375e-05 -0.04182 5.665e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2194 -0.01247 -0.2169 0.1803 0.9839 0.9935 0.2461 0.8253 0.9591 0.6986 ] Network output: [ 0.01423 0.8976 0.9641 -0.0001024 4.598e-05 0.1095 -7.719e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00447 0.001279 0.003025 0.003038 0.991 0.9939 0.004551 0.9548 0.9689 0.01231 ] Network output: [ 0.008366 -0.01108 0.9381 -0.0002876 0.0001291 1.055 -0.0002168 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2363 0.1527 0.3566 0.1352 0.9855 0.9942 0.237 0.8338 0.9628 0.695 ] Network output: [ -0.02981 0.1743 1.082 0.0001414 -6.346e-05 0.8039 0.0001065 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07891 0.07354 0.1703 0.1321 0.99 0.994 0.07895 0.9423 0.9656 0.2052 ] Network output: [ -0.02558 0.04002 1.072 0.0002033 -9.125e-05 0.9401 0.0001532 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09539 0.09422 0.1886 0.1574 0.9857 0.9917 0.0954 0.9089 0.9519 0.2023 ] Network output: [ 0.001086 0.9726 0.002061 2.644e-05 -1.187e-05 1.023 1.992e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02248 Epoch 5022 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04433 0.8782 0.9357 -7.91e-05 3.551e-05 0.09715 -5.961e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003369 -0.003064 -0.01264 0.007395 0.967 0.9719 0.006675 0.9061 0.9065 0.02369 ] Network output: [ 1.001 0.03941 -0.007751 0.0001105 -4.961e-05 -0.03371 8.328e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2198 -0.01394 -0.2276 0.1894 0.9839 0.9935 0.2466 0.8252 0.9591 0.7002 ] Network output: [ 0.01316 0.8979 0.9653 -0.0001029 4.621e-05 0.1101 -7.758e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004444 0.001261 0.002832 0.003195 0.991 0.9939 0.004524 0.9549 0.9689 0.01218 ] Network output: [ 0.02589 -0.08386 0.9365 -0.0002328 0.0001045 1.095 -0.0001754 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2344 0.1511 0.3499 0.1511 0.9855 0.9942 0.2351 0.8338 0.9628 0.6955 ] Network output: [ -0.03199 0.1754 1.084 0.0001382 -6.206e-05 0.8052 0.0001042 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07876 0.07338 0.1704 0.1335 0.99 0.994 0.0788 0.9424 0.9656 0.2057 ] Network output: [ -0.02931 0.04643 1.075 0.0001957 -8.788e-05 0.9384 0.0001475 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09508 0.09391 0.1894 0.1575 0.9857 0.9918 0.09509 0.9092 0.9518 0.203 ] Network output: [ -0.006234 0.9978 0.005223 7.051e-06 -3.166e-06 1.009 5.314e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02387 Epoch 5023 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0423 0.8836 0.9366 -8.326e-05 3.738e-05 0.09493 -6.275e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003356 -0.003039 -0.01251 0.00727 0.967 0.9719 0.006648 0.9063 0.9065 0.02364 ] Network output: [ 0.9737 0.07676 0.01338 7.524e-05 -3.378e-05 -0.03714 5.67e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2184 -0.01234 -0.2163 0.1809 0.9839 0.9935 0.2451 0.8258 0.9591 0.7003 ] Network output: [ 0.01405 0.8991 0.964 -0.0001034 4.643e-05 0.1085 -7.795e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004461 0.001277 0.003054 0.00303 0.991 0.9939 0.004542 0.955 0.969 0.01235 ] Network output: [ 0.005366 -0.004202 0.94 -0.0002928 0.0001315 1.052 -0.0002207 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2357 0.1524 0.3579 0.1334 0.9855 0.9942 0.2364 0.8342 0.9629 0.6966 ] Network output: [ -0.02932 0.1752 1.081 0.0001409 -6.325e-05 0.8031 0.0001062 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07888 0.07351 0.1703 0.1319 0.99 0.994 0.07893 0.9425 0.9657 0.2053 ] Network output: [ -0.0249 0.04092 1.07 0.000203 -9.112e-05 0.9393 0.000153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09542 0.09425 0.1885 0.1571 0.9857 0.9918 0.09543 0.9091 0.9519 0.2022 ] Network output: [ 0.001381 0.9783 0.0001584 2.467e-05 -1.108e-05 1.019 1.859e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02214 Epoch 5024 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04439 0.8779 0.9358 -7.918e-05 3.555e-05 0.0972 -5.967e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003362 -0.00306 -0.01265 0.007439 0.967 0.9719 0.006663 0.9064 0.9067 0.02369 ] Network output: [ 1.004 0.02285 -0.007307 0.000121 -5.433e-05 -0.02403 9.12e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.219 -0.01409 -0.2291 0.1928 0.9839 0.9935 0.2458 0.8256 0.9592 0.7016 ] Network output: [ 0.01289 0.8993 0.9654 -0.0001039 4.664e-05 0.1092 -7.83e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004429 0.00126 0.002819 0.003236 0.991 0.9939 0.004509 0.955 0.969 0.01218 ] Network output: [ 0.02739 -0.099 0.9384 -0.000222 9.965e-05 1.105 -0.0001673 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2334 0.1506 0.3498 0.154 0.9855 0.9942 0.2342 0.8342 0.9629 0.6965 ] Network output: [ -0.03191 0.1777 1.083 0.0001367 -6.137e-05 0.8036 0.000103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07871 0.07335 0.1703 0.1335 0.99 0.994 0.07875 0.9426 0.9656 0.2057 ] Network output: [ -0.0294 0.05114 1.073 0.0001929 -8.661e-05 0.9352 0.0001454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09504 0.09388 0.1891 0.1569 0.9857 0.9918 0.09505 0.9094 0.9519 0.2027 ] Network output: [ -0.007488 1.012 0.003459 -2.553e-07 1.146e-07 0.9991 -1.924e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02453 Epoch 5025 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04191 0.8843 0.9369 -8.436e-05 3.787e-05 0.09456 -6.358e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003345 -0.003026 -0.01247 0.00727 0.967 0.9719 0.006626 0.9065 0.9067 0.0236 ] Network output: [ 0.9686 0.07002 0.02045 7.566e-05 -3.397e-05 -0.0273 5.702e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2174 -0.01195 -0.214 0.182 0.9839 0.9935 0.2439 0.8262 0.9592 0.7011 ] Network output: [ 0.01421 0.9004 0.9636 -0.0001043 4.684e-05 0.1071 -7.863e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004451 0.001285 0.00311 0.003027 0.991 0.9939 0.004532 0.9552 0.9691 0.01238 ] Network output: [ 0.0009188 0.002463 0.943 -0.0002981 0.0001338 1.051 -0.0002246 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2352 0.1525 0.3603 0.1315 0.9855 0.9942 0.2359 0.8347 0.9629 0.6971 ] Network output: [ -0.02827 0.1776 1.079 0.0001402 -6.294e-05 0.8006 0.0001057 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07887 0.07352 0.17 0.1313 0.99 0.994 0.07891 0.9427 0.9657 0.2049 ] Network output: [ -0.02347 0.04455 1.068 0.0002022 -9.078e-05 0.9355 0.0001524 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09548 0.09431 0.1878 0.1563 0.9857 0.9918 0.09549 0.9093 0.952 0.2015 ] Network output: [ 0.003177 0.9842 -0.003165 2.44e-05 -1.095e-05 1.013 1.839e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.022 Epoch 5026 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04464 0.8771 0.9359 -7.934e-05 3.562e-05 0.09734 -5.979e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003354 -0.003053 -0.01265 0.007463 0.967 0.9719 0.006647 0.9066 0.9069 0.02366 ] Network output: [ 1.008 0.009055 -0.007515 0.0001301 -5.839e-05 -0.01632 9.801e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2184 -0.01409 -0.2308 0.1958 0.9839 0.9935 0.2451 0.826 0.9593 0.7023 ] Network output: [ 0.01283 0.9 0.9654 -0.0001047 4.7e-05 0.1085 -7.89e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004411 0.001262 0.00279 0.003267 0.991 0.9939 0.004491 0.9552 0.969 0.01214 ] Network output: [ 0.02985 -0.1144 0.9392 -0.0002092 9.392e-05 1.115 -0.0001577 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2326 0.1504 0.3488 0.1569 0.9855 0.9942 0.2333 0.8346 0.9629 0.6968 ] Network output: [ -0.03145 0.1796 1.082 0.0001355 -6.085e-05 0.8019 0.0001021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07858 0.07326 0.1699 0.1333 0.99 0.994 0.07862 0.9428 0.9657 0.2052 ] Network output: [ -0.02914 0.05451 1.072 0.0001907 -8.561e-05 0.9324 0.0001437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09491 0.09375 0.1887 0.1564 0.9857 0.9918 0.09492 0.9096 0.9519 0.2023 ] Network output: [ -0.006964 1.015 0.002707 -1.242e-06 5.576e-07 0.9958 -9.361e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02546 Epoch 5027 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04135 0.8861 0.9373 -8.638e-05 3.878e-05 0.0936 -6.51e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003336 -0.003014 -0.01245 0.007224 0.967 0.9719 0.006607 0.9068 0.9068 0.02356 ] Network output: [ 0.9623 0.08322 0.02432 6.596e-05 -2.961e-05 -0.03187 4.971e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2167 -0.01143 -0.2128 0.1795 0.9839 0.9935 0.2431 0.8267 0.9593 0.7017 ] Network output: [ 0.01438 0.9007 0.9635 -0.0001051 4.717e-05 0.1067 -7.919e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004443 0.001285 0.003121 0.002966 0.991 0.9939 0.004523 0.9553 0.9691 0.0124 ] Network output: [ -0.002509 0.02289 0.9422 -0.0003093 0.0001389 1.039 -0.0002331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2349 0.1525 0.3605 0.1268 0.9855 0.9943 0.2357 0.8351 0.963 0.6981 ] Network output: [ -0.02696 0.1764 1.078 0.0001412 -6.339e-05 0.8004 0.0001064 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0787 0.07336 0.1694 0.1309 0.9901 0.994 0.07874 0.9428 0.9658 0.2045 ] Network output: [ -0.0219 0.03957 1.067 0.0002048 -9.194e-05 0.9382 0.0001543 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09535 0.09419 0.1874 0.1565 0.9857 0.9918 0.09536 0.9094 0.9521 0.2013 ] Network output: [ 0.00746 0.9614 -0.002676 3.807e-05 -1.709e-05 1.027 2.869e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02225 Epoch 5028 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04457 0.8781 0.9359 -8.054e-05 3.616e-05 0.09653 -6.07e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003353 -0.003054 -0.01272 0.007453 0.967 0.9719 0.006645 0.9068 0.907 0.0237 ] Network output: [ 1.014 0.02229 -0.01671 0.0001281 -5.752e-05 -0.03248 9.655e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2183 -0.01434 -0.2366 0.1942 0.9839 0.9935 0.245 0.8263 0.9594 0.704 ] Network output: [ 0.01221 0.8997 0.9663 -0.0001054 4.734e-05 0.1092 -7.947e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004397 0.001241 0.002659 0.003235 0.991 0.9939 0.004477 0.9553 0.9691 0.01212 ] Network output: [ 0.03668 -0.1167 0.9332 -0.0002003 8.993e-05 1.109 -0.000151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2316 0.1491 0.3434 0.1578 0.9855 0.9942 0.2323 0.8349 0.963 0.6989 ] Network output: [ -0.03163 0.1751 1.083 0.0001365 -6.126e-05 0.8056 0.0001028 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07824 0.07289 0.1694 0.134 0.99 0.994 0.07828 0.9429 0.9657 0.2054 ] Network output: [ -0.03006 0.04391 1.075 0.0001927 -8.65e-05 0.9416 0.0001452 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09451 0.09334 0.1894 0.158 0.9857 0.9918 0.09452 0.9099 0.952 0.2033 ] Network output: [ -0.006121 0.9852 0.008873 1.106e-05 -4.964e-06 1.018 8.334e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02512 Epoch 5029 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04004 0.8907 0.9376 -8.976e-05 4.03e-05 0.09122 -6.765e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003335 -0.003012 -0.01253 0.007147 0.967 0.9719 0.006608 0.907 0.9069 0.02367 ] Network output: [ 0.9571 0.13 0.01993 4.1e-05 -1.841e-05 -0.06385 3.09e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2164 -0.01127 -0.2155 0.1713 0.9839 0.9935 0.2428 0.8271 0.9593 0.7044 ] Network output: [ 0.01373 0.9005 0.9642 -0.0001058 4.75e-05 0.1075 -7.974e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004444 0.001253 0.003051 0.002826 0.991 0.9939 0.004524 0.9555 0.9692 0.0125 ] Network output: [ -0.003634 0.06571 0.9352 -0.0003331 0.0001495 1.005 -0.000251 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2347 0.151 0.3572 0.1182 0.9855 0.9943 0.2354 0.8355 0.9631 0.7021 ] Network output: [ -0.02661 0.1678 1.079 0.0001447 -6.497e-05 0.807 0.0001091 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07841 0.07298 0.1692 0.1313 0.9901 0.9941 0.07845 0.9429 0.9658 0.2053 ] Network output: [ -0.02176 0.01919 1.071 0.0002125 -9.54e-05 0.9545 0.0001601 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0951 0.09391 0.1888 0.1592 0.9857 0.9918 0.09511 0.9096 0.9522 0.2031 ] Network output: [ 0.0108 0.9106 0.004165 6.204e-05 -2.785e-05 1.064 4.675e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0247 Epoch 5030 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.044 0.8808 0.9359 -8.175e-05 3.67e-05 0.09498 -6.161e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003363 -0.003073 -0.01292 0.007494 0.967 0.9719 0.006668 0.907 0.9072 0.02392 ] Network output: [ 1.027 0.04114 -0.0343 0.0001273 -5.713e-05 -0.05995 9.591e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2186 -0.01547 -0.247 0.1925 0.9839 0.9935 0.2453 0.8265 0.9594 0.7082 ] Network output: [ 0.01035 0.9005 0.968 -0.0001066 4.784e-05 0.1103 -8.031e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00439 0.001193 0.002459 0.003215 0.991 0.9939 0.004469 0.9554 0.9691 0.01217 ] Network output: [ 0.04812 -0.1229 0.9243 -0.0001887 8.471e-05 1.102 -0.0001422 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2302 0.1464 0.3352 0.1603 0.9855 0.9943 0.2309 0.8351 0.9631 0.7039 ] Network output: [ -0.03388 0.1679 1.087 0.0001371 -6.156e-05 0.8137 0.0001033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07797 0.0725 0.1698 0.1355 0.99 0.994 0.07801 0.943 0.9657 0.2072 ] Network output: [ -0.03369 0.02848 1.082 0.0001946 -8.737e-05 0.9575 0.0001467 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09414 0.09295 0.192 0.1609 0.9857 0.9918 0.09415 0.9101 0.9521 0.2063 ] Network output: [ -0.01132 0.9668 0.01796 1.243e-05 -5.581e-06 1.038 9.368e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02545 Epoch 5031 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03814 0.8959 0.9384 -9.238e-05 4.147e-05 0.08906 -6.962e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003336 -0.003017 -0.0126 0.00716 0.967 0.9719 0.006613 0.9073 0.907 0.02385 ] Network output: [ 0.9494 0.1499 0.02431 2.541e-05 -1.141e-05 -0.07287 1.915e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2155 -0.01145 -0.2142 0.1678 0.9839 0.9935 0.2419 0.8275 0.9593 0.7086 ] Network output: [ 0.01263 0.9035 0.9645 -0.000107 4.804e-05 0.1063 -8.064e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004456 0.001229 0.003121 0.002759 0.991 0.9939 0.004536 0.9557 0.9692 0.01271 ] Network output: [ -0.01131 0.1038 0.936 -0.0003631 0.000163 0.9814 -0.0002737 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2344 0.1497 0.3598 0.1105 0.9855 0.9943 0.2351 0.8358 0.9631 0.7069 ] Network output: [ -0.0272 0.1652 1.079 0.0001457 -6.54e-05 0.8105 0.0001098 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0786 0.07306 0.1702 0.1314 0.9901 0.9941 0.07865 0.943 0.9658 0.207 ] Network output: [ -0.02196 0.01247 1.071 0.0002158 -9.688e-05 0.9612 0.0001626 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09542 0.0942 0.1902 0.1602 0.9857 0.9918 0.09543 0.9097 0.9522 0.2049 ] Network output: [ 0.008046 0.9202 0.002845 5.664e-05 -2.543e-05 1.061 4.268e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02571 Epoch 5032 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04448 0.8782 0.9361 -7.874e-05 3.535e-05 0.09636 -5.934e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003363 -0.003089 -0.013 0.00772 0.967 0.9719 0.006675 0.9073 0.9073 0.02403 ] Network output: [ 1.044 -0.02948 -0.03699 0.0001699 -7.629e-05 -0.02173 0.0001281 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2179 -0.01697 -0.2516 0.2067 0.9839 0.9935 0.2445 0.8265 0.9595 0.7115 ] Network output: [ 0.008872 0.9054 0.9686 -0.000108 4.849e-05 0.1078 -8.141e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004372 0.00119 0.002458 0.003435 0.991 0.9939 0.004451 0.9556 0.9691 0.01217 ] Network output: [ 0.05462 -0.1921 0.9331 -0.0001486 6.67e-05 1.149 -0.000112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2283 0.1451 0.3367 0.1751 0.9855 0.9943 0.229 0.835 0.963 0.7052 ] Network output: [ -0.03662 0.179 1.086 0.0001302 -5.844e-05 0.8085 9.811e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07836 0.07288 0.1711 0.1358 0.99 0.994 0.07841 0.9432 0.9656 0.2081 ] Network output: [ -0.03705 0.05487 1.079 0.0001811 -8.132e-05 0.9407 0.0001365 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09451 0.09333 0.1921 0.1582 0.9857 0.9918 0.09452 0.9103 0.9519 0.2061 ] Network output: [ -0.02486 1.069 0.009562 -4.201e-05 1.886e-05 0.971 -3.166e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03159 Epoch 5033 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03756 0.8932 0.9398 -9.081e-05 4.077e-05 0.09145 -6.844e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003305 -0.002983 -0.01227 0.007317 0.967 0.9719 0.006554 0.9075 0.9071 0.02362 ] Network output: [ 0.9288 0.04523 0.06917 5.797e-05 -2.603e-05 0.02816 4.369e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.0104 -0.1936 0.1846 0.9839 0.9935 0.2384 0.8277 0.9594 0.7069 ] Network output: [ 0.01398 0.911 0.9616 -0.0001086 4.875e-05 0.099 -8.183e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004449 0.001319 0.003652 0.002988 0.991 0.9939 0.00453 0.956 0.9694 0.01279 ] Network output: [ -0.0376 0.08499 0.965 -0.0003721 0.000167 1.024 -0.0002804 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2347 0.1534 0.3811 0.1123 0.9855 0.9943 0.2354 0.8359 0.963 0.7022 ] Network output: [ -0.02516 0.1922 1.07 0.0001364 -6.124e-05 0.7886 0.0001028 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07962 0.07429 0.1711 0.128 0.9901 0.9941 0.07966 0.9431 0.9659 0.2053 ] Network output: [ -0.0171 0.06819 1.053 0.0001994 -8.953e-05 0.9141 0.0001503 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09676 0.09559 0.1855 0.1516 0.9857 0.9918 0.09677 0.9096 0.9519 0.1993 ] Network output: [ 0.00442 1.072 -0.02732 -5.284e-06 2.372e-06 0.9466 -3.982e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02548 Epoch 5034 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04806 0.8636 0.9363 -7.072e-05 3.175e-05 0.1037 -5.329e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003322 -0.003047 -0.01255 0.007956 0.967 0.9719 0.006591 0.9073 0.9076 0.02346 ] Network output: [ 1.047 -0.2151 0.0006362 0.0002549 -0.0001145 0.1219 0.0001921 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2151 -0.01625 -0.2358 0.2386 0.9839 0.9935 0.2414 0.8264 0.9596 0.7041 ] Network output: [ 0.01199 0.9097 0.965 -0.000108 4.847e-05 0.1008 -8.136e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004323 0.001313 0.00284 0.003905 0.991 0.9939 0.004401 0.9555 0.9693 0.01181 ] Network output: [ 0.04965 -0.3466 0.9676 -6.681e-05 2.999e-05 1.279 -5.035e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2274 0.1499 0.3549 0.2034 0.9855 0.9943 0.2281 0.8346 0.9629 0.6913 ] Network output: [ -0.03264 0.221 1.072 0.000116 -5.21e-05 0.7723 8.746e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0793 0.07422 0.1691 0.1321 0.99 0.994 0.07935 0.9431 0.9656 0.2021 ] Network output: [ -0.03147 0.1456 1.054 0.0001504 -6.751e-05 0.8644 0.0001133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09559 0.0945 0.1828 0.1452 0.9857 0.9917 0.0956 0.9099 0.9514 0.1953 ] Network output: [ -0.02248 1.237 -0.02747 -0.0001039 4.665e-05 0.8354 -7.831e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07931 Epoch 5035 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03875 0.8853 0.9412 -9.089e-05 4.08e-05 0.09558 -6.85e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003246 -0.002894 -0.01149 0.007192 0.967 0.9719 0.006424 0.9072 0.907 0.0226 ] Network output: [ 0.892 -0.05939 0.1301 7.815e-05 -3.509e-05 0.1457 5.89e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2095 -0.006676 -0.1623 0.1991 0.9839 0.9935 0.235 0.8275 0.9594 0.6914 ] Network output: [ 0.01927 0.9129 0.9564 -0.0001088 4.886e-05 0.09171 -8.203e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004416 0.001485 0.004134 0.003167 0.9909 0.9939 0.004495 0.9556 0.9694 0.01232 ] Network output: [ -0.05675 0.04366 0.9877 -0.000349 0.0001567 1.081 -0.0002631 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2366 0.1615 0.3983 0.117 0.9855 0.9943 0.2373 0.8354 0.963 0.6821 ] Network output: [ -0.01562 0.2333 1.052 0.000127 -5.702e-05 0.7468 9.572e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08005 0.07524 0.1654 0.1215 0.99 0.994 0.08009 0.9428 0.9658 0.1947 ] Network output: [ -0.004634 0.1372 1.025 0.0001823 -8.186e-05 0.8474 0.0001374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09732 0.09627 0.1736 0.1398 0.9856 0.9917 0.09733 0.9087 0.9516 0.1856 ] Network output: [ 0.03089 1.111 -0.0605 8.539e-06 -3.834e-06 0.8875 6.436e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04351 Epoch 5036 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04956 0.8576 0.9371 -7.44e-05 3.34e-05 0.106 -5.607e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003287 -0.002978 -0.01202 0.007565 0.967 0.9719 0.006503 0.9068 0.9073 0.02255 ] Network output: [ 1.022 -0.1904 0.02048 0.0002195 -9.853e-05 0.1275 0.0001654 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2154 -0.01276 -0.2219 0.2309 0.9839 0.9935 0.2416 0.8258 0.9597 0.6889 ] Network output: [ 0.01663 0.9008 0.9631 -0.0001061 4.764e-05 0.1024 -7.997e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004303 0.001396 0.002853 0.00372 0.9909 0.9938 0.00438 0.955 0.9692 0.01131 ] Network output: [ 0.04919 -0.3248 0.9588 -6.381e-05 2.864e-05 1.267 -4.809e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.23 0.1553 0.3524 0.1963 0.9855 0.9942 0.2307 0.834 0.963 0.6758 ] Network output: [ -0.02257 0.2261 1.063 0.0001211 -5.438e-05 0.7568 9.129e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07849 0.07374 0.1617 0.1289 0.9899 0.994 0.07853 0.9426 0.9656 0.1929 ] Network output: [ -0.02008 0.1409 1.045 0.0001597 -7.171e-05 0.8554 0.0001204 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09474 0.09371 0.1749 0.1437 0.9856 0.9917 0.09475 0.909 0.9514 0.1869 ] Network output: [ 0.01717 1.058 -0.02635 7.938e-06 -3.564e-06 0.9343 5.982e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06584 Epoch 5037 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03708 0.8964 0.9412 -0.000104 4.669e-05 0.08781 -7.839e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003268 -0.002891 -0.01181 0.006444 0.967 0.9719 0.006451 0.9066 0.9066 0.02252 ] Network output: [ 0.8904 0.2549 0.06285 -6.637e-05 2.98e-05 -0.0988 -5.002e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2144 -0.005088 -0.1908 0.1438 0.9839 0.9935 0.2403 0.827 0.9593 0.6875 ] Network output: [ 0.01873 0.8923 0.9622 -0.0001061 4.763e-05 0.1076 -7.996e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004421 0.001342 0.003173 0.002256 0.9909 0.9938 0.004501 0.9549 0.969 0.01204 ] Network output: [ -0.03006 0.2296 0.9232 -0.0004157 0.0001866 0.9056 -0.0003133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2381 0.1574 0.3583 0.08023 0.9855 0.9943 0.2388 0.8349 0.9632 0.6874 ] Network output: [ -0.01196 0.177 1.063 0.0001512 -6.786e-05 0.7841 0.0001139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07714 0.07206 0.1585 0.1239 0.99 0.994 0.07718 0.942 0.9658 0.1927 ] Network output: [ -0.004191 0.006935 1.056 0.0002291 -0.0001028 0.9462 0.0001726 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09396 0.09284 0.1776 0.1555 0.9855 0.9917 0.09397 0.9079 0.9521 0.1916 ] Network output: [ 0.06836 0.6513 0.003918 0.0002245 -0.0001008 1.209 0.0001692 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0665 Epoch 5038 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04289 0.8834 0.9376 -9.283e-05 4.168e-05 0.09284 -6.996e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003375 -0.003077 -0.0131 0.007048 0.9671 0.9719 0.006675 0.9064 0.9068 0.02358 ] Network output: [ 1.058 0.1973 -0.1027 5.72e-05 -2.568e-05 -0.211 4.311e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2235 -0.01588 -0.2791 0.168 0.9839 0.9935 0.2507 0.8247 0.9593 0.7025 ] Network output: [ 0.007198 0.8872 0.9754 -0.0001077 4.834e-05 0.1226 -8.115e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004343 0.001095 0.001463 0.002859 0.991 0.9938 0.004421 0.9546 0.9686 0.01153 ] Network output: [ 0.1103 -0.1278 0.8602 -0.0001181 5.302e-05 1.046 -8.9e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2295 0.1428 0.2861 0.1654 0.9855 0.9942 0.2302 0.8333 0.9631 0.7018 ] Network output: [ -0.03335 0.1384 1.095 0.0001467 -6.585e-05 0.8339 0.0001105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07536 0.06983 0.1611 0.1384 0.99 0.994 0.0754 0.9421 0.9654 0.2016 ] Network output: [ -0.03853 -0.0526 1.107 0.0002142 -9.618e-05 1.023 0.0001615 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09079 0.08959 0.193 0.1722 0.9856 0.9917 0.0908 0.9093 0.9521 0.2081 ] Network output: [ 0.02029 0.5813 0.07395 0.000193 -8.663e-05 1.305 0.0001454 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07875 Epoch 5039 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02517 0.9379 0.9429 -0.0001252 5.621e-05 0.06841 -9.436e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003392 -0.003038 -0.01301 0.005931 0.967 0.9719 0.00671 0.9067 0.9058 0.02416 ] Network output: [ 0.8928 0.6957 -0.03359 -0.0002852 0.000128 -0.4489 -0.0002149 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2215 -0.009311 -0.2319 0.06873 0.9839 0.9935 0.2485 0.826 0.9585 0.713 ] Network output: [ 0.007666 0.8878 0.9732 -0.0001062 4.767e-05 0.1232 -8.002e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004575 0.0009227 0.002166 0.001291 0.9911 0.9939 0.004657 0.9546 0.9684 0.01276 ] Network output: [ 0.01436 0.4611 0.838 -0.0005515 0.0002476 0.6699 -0.0004156 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.24 0.1395 0.313 0.04086 0.9855 0.9943 0.2407 0.8325 0.9628 0.7202 ] Network output: [ -0.02337 0.1123 1.087 0.0001754 -7.873e-05 0.8477 0.0001322 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07647 0.06981 0.1599 0.1317 0.9902 0.9941 0.07651 0.9405 0.9649 0.2033 ] Network output: [ -0.02288 -0.1473 1.106 0.0002806 -0.000126 1.088 0.0002114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09314 0.09164 0.1951 0.18 0.9856 0.9917 0.09315 0.9063 0.9517 0.2128 ] Network output: [ 0.06283 0.3173 0.0695 0.0003591 -0.0001612 1.489 0.0002707 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2695 Epoch 5040 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03322 0.9 0.9431 -9.345e-05 4.195e-05 0.09012 -7.042e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003513 -0.003315 -0.01391 0.008193 0.9671 0.972 0.006989 0.9059 0.9061 0.02491 ] Network output: [ 1.157 -0.03592 -0.1545 0.0001752 -7.863e-05 -0.1227 0.000132 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2289 -0.02815 -0.3079 0.2184 0.9839 0.9935 0.2571 0.8199 0.9583 0.7243 ] Network output: [ -0.01285 0.9273 0.9867 -0.0001217 5.461e-05 0.1112 -9.168e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004441 0.0009673 0.001393 0.003776 0.9911 0.9939 0.004522 0.9544 0.9681 0.01188 ] Network output: [ 0.1372 -0.3805 0.8895 -5.113e-06 2.296e-06 1.217 -3.854e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2262 0.1338 0.2922 0.2285 0.9855 0.9942 0.2269 0.8286 0.9618 0.7177 ] Network output: [ -0.0623 0.18 1.11 0.0001149 -5.159e-05 0.8354 8.661e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07939 0.07307 0.1736 0.1462 0.99 0.994 0.07943 0.9412 0.9641 0.2142 ] Network output: [ -0.06981 0.04613 1.113 0.0001607 -7.215e-05 0.9814 0.0001211 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09496 0.09361 0.2018 0.1697 0.9857 0.9918 0.09497 0.9079 0.9501 0.216 ] Network output: [ -0.06491 1.077 0.0404 -7.466e-05 3.352e-05 1.012 -5.627e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06221 Epoch 5041 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01692 0.932 0.9544 -0.0001133 5.086e-05 0.07933 -8.539e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003336 -0.003002 -0.01167 0.007193 0.967 0.9719 0.006624 0.9062 0.905 0.02375 ] Network output: [ 0.8167 0.1113 0.176 -0.0001022 4.59e-05 0.07898 -7.705e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.008841 -0.1303 0.1661 0.9839 0.9935 0.2375 0.8226 0.9577 0.7115 ] Network output: [ 0.005742 0.9403 0.9646 -0.0001149 5.159e-05 0.08315 -8.66e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00471 0.001384 0.005157 0.00263 0.9911 0.9939 0.004796 0.9552 0.9687 0.01387 ] Network output: [ -0.1335 0.4064 0.991 -0.0006379 0.0002864 0.8671 -0.0004807 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2459 0.1594 0.4284 0.05042 0.9855 0.9943 0.2467 0.83 0.9615 0.7067 ] Network output: [ -0.02646 0.2384 1.057 0.000131 -5.879e-05 0.7582 9.87e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08383 0.07799 0.173 0.1204 0.9902 0.9941 0.08388 0.9405 0.9644 0.204 ] Network output: [ -0.009393 0.1213 1.026 0.0002051 -9.208e-05 0.8718 0.0001546 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1023 0.101 0.18 0.1444 0.9857 0.9918 0.1023 0.9044 0.9496 0.193 ] Network output: [ 0.009619 1.212 -0.08007 -2.982e-05 1.339e-05 0.8482 -2.247e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07641 Epoch 5042 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05141 0.8263 0.9449 -4.175e-05 1.874e-05 0.1258 -3.146e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003346 -0.003179 -0.01213 0.009173 0.9671 0.972 0.006663 0.9051 0.9061 0.02284 ] Network output: [ 1.154 -0.7649 0.0111 0.0005032 -0.0002259 0.4476 0.0003793 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2161 -0.02851 -0.2371 0.3395 0.9839 0.9935 0.2427 0.8162 0.9582 0.6959 ] Network output: [ 0.004357 0.9301 0.9718 -0.0001043 4.684e-05 0.089 -7.864e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004285 0.001325 0.003047 0.00556 0.9909 0.9938 0.004363 0.9529 0.9679 0.01057 ] Network output: [ 0.1224 -1.052 1.041 0.0003085 -0.0001385 1.767 0.0002325 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2223 0.1465 0.3761 0.3503 0.9854 0.9942 0.223 0.8225 0.9605 0.6567 ] Network output: [ -0.03357 0.3284 1.045 9.15e-05 -4.108e-05 0.6943 6.896e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08484 0.07959 0.1619 0.1373 0.9896 0.9939 0.08489 0.9387 0.9628 0.1849 ] Network output: [ -0.03209 0.3743 1.001 8.318e-05 -3.734e-05 0.6893 6.269e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.1 0.1596 0.1277 0.9855 0.9916 0.1011 0.9024 0.9464 0.1672 ] Network output: [ 0.01702 1.39 -0.1148 -8.639e-05 3.878e-05 0.6902 -6.51e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.47 Epoch 5043 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01646 0.9347 0.9594 -0.000127 5.702e-05 0.07244 -9.572e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003213 -0.00279 -0.009251 0.006458 0.967 0.9719 0.006344 0.9031 0.9028 0.02044 ] Network output: [ 0.6986 -0.02187 0.3298 -0.0001403 6.298e-05 0.2942 -0.0001057 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2072 0.001549 -0.04325 0.1798 0.9838 0.9934 0.2324 0.816 0.9565 0.6505 ] Network output: [ 0.01982 0.9528 0.9513 -0.0001177 5.282e-05 0.05583 -8.868e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004724 0.001829 0.005859 0.002823 0.9907 0.9937 0.004809 0.9517 0.9674 0.01189 ] Network output: [ -0.1468 0.3541 0.9992 -0.0005614 0.0002521 0.9381 -0.0004231 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2551 0.1826 0.4443 0.06237 0.9855 0.9942 0.2559 0.8215 0.9601 0.6326 ] Network output: [ 0.0004244 0.3853 1.001 9.793e-05 -4.397e-05 0.6131 7.381e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08527 0.08073 0.1483 0.09935 0.9897 0.9938 0.08532 0.9363 0.9627 0.1656 ] Network output: [ 0.02033 0.3137 0.9601 0.0001553 -6.971e-05 0.6861 0.000117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1028 0.1018 0.1442 0.1127 0.9853 0.9915 0.1028 0.8975 0.9468 0.151 ] Network output: [ 0.09266 1.237 -0.1591 5.003e-05 -2.246e-05 0.7365 3.77e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1591 Epoch 5044 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04889 0.8124 0.9539 -5.839e-05 2.621e-05 0.1357 -4.401e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003252 -0.002983 -0.01003 0.007968 0.9671 0.972 0.006424 0.9015 0.9037 0.02006 ] Network output: [ 1.002 -0.7033 0.1567 0.0003147 -0.0001413 0.543 0.0002372 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2166 -0.01761 -0.1426 0.3096 0.9838 0.9934 0.2429 0.8084 0.957 0.6371 ] Network output: [ 0.01974 0.9078 0.9632 -9.635e-05 4.326e-05 0.08905 -7.261e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004414 0.001636 0.003862 0.005164 0.9906 0.9936 0.004494 0.9496 0.9669 0.009549 ] Network output: [ 0.09974 -0.9091 1.014 0.0002364 -0.0001061 1.696 0.0001781 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2401 0.1686 0.3885 0.3175 0.9854 0.9942 0.2408 0.8145 0.9596 0.5912 ] Network output: [ -0.004054 0.3769 1.007 9.862e-05 -4.427e-05 0.6246 7.432e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08603 0.08149 0.1415 0.1235 0.9893 0.9937 0.08608 0.9348 0.962 0.1576 ] Network output: [ 0.002019 0.4028 0.9625 0.0001019 -4.576e-05 0.631 7.682e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1022 0.1013 0.1355 0.1173 0.9852 0.9914 0.1022 0.8962 0.9453 0.1412 ] Network output: [ 0.1261 1.003 -0.1425 0.0001819 -8.167e-05 0.8879 0.0001371 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.4062 Epoch 5045 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01716 0.9366 0.9619 -0.0001437 6.45e-05 0.06658 -0.0001083 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003323 -0.002857 -0.009882 0.005318 0.9671 0.9719 0.006513 0.8994 0.9005 0.01965 ] Network output: [ 0.7809 0.4283 0.1415 -0.0003177 0.0001426 -0.1328 -0.0002394 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2262 0.002307 -0.125 0.1043 0.9838 0.9934 0.2534 0.8071 0.9552 0.6201 ] Network output: [ 0.01401 0.9115 0.9702 -0.0001136 5.098e-05 0.08985 -8.558e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004792 0.001633 0.00351 0.001497 0.9905 0.9935 0.004878 0.9479 0.9654 0.01041 ] Network output: [ -0.07052 0.5441 0.8821 -0.0005812 0.0002609 0.7125 -0.000438 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2615 0.1794 0.3609 0.02845 0.9854 0.9942 0.2623 0.8139 0.9596 0.6202 ] Network output: [ -0.005217 0.3273 1.03 0.0001134 -5.091e-05 0.6537 8.547e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07992 0.07508 0.1337 0.09911 0.9896 0.9937 0.07997 0.9318 0.9614 0.1577 ] Network output: [ 0.006999 0.1677 1.016 0.0001913 -8.59e-05 0.8031 0.0001442 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09616 0.09509 0.1454 0.1277 0.9851 0.9914 0.09617 0.892 0.9464 0.1552 ] Network output: [ 0.1087 0.6818 -0.0403 0.0002692 -0.0001208 1.142 0.0002029 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1606 Epoch 5046 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03584 0.8254 0.9641 -7.783e-05 3.494e-05 0.1385 -5.866e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003354 -0.003082 -0.01103 0.007839 0.9671 0.972 0.006601 0.8985 0.9017 0.02085 ] Network output: [ 1.074 -0.467 0.02491 0.0002192 -9.842e-05 0.2949 0.0001652 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2306 -0.02096 -0.2027 0.2769 0.9838 0.9934 0.2585 0.7999 0.9557 0.6395 ] Network output: [ 0.000308 0.8979 0.9858 -0.0001054 4.732e-05 0.1152 -7.943e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004454 0.001506 0.00273 0.004656 0.9906 0.9936 0.004535 0.9473 0.9655 0.009693 ] Network output: [ 0.1238 -0.7299 0.9512 0.0001837 -8.245e-05 1.532 0.0001384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2435 0.1659 0.3363 0.2926 0.9854 0.9942 0.2443 0.8076 0.9588 0.6093 ] Network output: [ -0.03286 0.2995 1.059 0.0001003 -4.503e-05 0.7078 7.559e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08232 0.07758 0.1473 0.1315 0.9894 0.9937 0.08237 0.9327 0.9611 0.1713 ] Network output: [ -0.03035 0.2759 1.029 0.000115 -5.162e-05 0.7566 8.665e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09789 0.09689 0.1521 0.1346 0.9853 0.9915 0.0979 0.8941 0.9452 0.1608 ] Network output: [ 0.05442 0.8726 -0.03187 0.0001408 -6.323e-05 1.051 0.0001061 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2229 Epoch 5047 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01051 0.9416 0.966 -0.0001433 6.433e-05 0.07073 -0.000108 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003395 -0.002927 -0.01066 0.005024 0.9671 0.972 0.006642 0.8972 0.8984 0.02051 ] Network output: [ 0.8052 0.6648 0.06157 -0.0004201 0.0001886 -0.3385 -0.0003166 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2345 0.0007729 -0.1592 0.06395 0.9837 0.9934 0.2626 0.8007 0.9537 0.629 ] Network output: [ 0.005321 0.8936 0.9821 -0.0001045 4.692e-05 0.1132 -7.877e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004846 0.001432 0.002788 0.0008948 0.9906 0.9935 0.004932 0.9459 0.9639 0.01066 ] Network output: [ -0.05349 0.7085 0.8331 -0.0006607 0.0002966 0.5627 -0.0004979 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2639 0.1725 0.3286 -0.002248 0.9854 0.9942 0.2647 0.8068 0.9584 0.6348 ] Network output: [ -0.01791 0.2798 1.055 0.0001282 -5.754e-05 0.7014 9.659e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07755 0.07218 0.134 0.1018 0.9896 0.9937 0.07759 0.9282 0.9599 0.164 ] Network output: [ -0.008914 0.0718 1.055 0.0002192 -9.841e-05 0.8922 0.0001652 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09342 0.09222 0.1542 0.1397 0.9851 0.9914 0.09343 0.8871 0.9449 0.1669 ] Network output: [ 0.08105 0.5106 0.02537 0.0003067 -0.0001377 1.303 0.0002311 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2694 Epoch 5048 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03239 0.819 0.9673 -6.671e-05 2.995e-05 0.1487 -5.027e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003408 -0.003188 -0.01147 0.008272 0.9671 0.972 0.006712 0.8956 0.8994 0.02134 ] Network output: [ 1.137 -0.5224 -0.03035 0.0002556 -0.0001147 0.2791 0.0001926 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2354 -0.02858 -0.2288 0.2919 0.9837 0.9934 0.264 0.7896 0.9538 0.6422 ] Network output: [ -0.009468 0.8987 0.9944 -9.959e-05 4.471e-05 0.1255 -7.505e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004459 0.001378 0.002284 0.004965 0.9906 0.9936 0.00454 0.9445 0.9638 0.009621 ] Network output: [ 0.1637 -0.8391 0.9346 0.0002649 -0.0001189 1.578 0.0001996 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2411 0.159 0.3136 0.3239 0.9853 0.9942 0.2419 0.7973 0.9568 0.609 ] Network output: [ -0.04612 0.2905 1.073 0.0001024 -4.597e-05 0.7294 7.717e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08231 0.07721 0.1485 0.1382 0.9892 0.9936 0.08236 0.9287 0.9588 0.1744 ] Network output: [ -0.04639 0.2695 1.046 0.0001122 -5.037e-05 0.7778 8.456e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09719 0.09613 0.1553 0.1407 0.9852 0.9915 0.0972 0.889 0.9425 0.1643 ] Network output: [ 0.03927 0.8113 -0.009326 0.0001579 -7.091e-05 1.12 0.000119 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2666 Epoch 5049 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00112 0.9614 0.971 -0.0001489 6.686e-05 0.06483 -0.0001122 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003428 -0.002949 -0.0104 0.005042 0.9671 0.972 0.006705 0.8939 0.8949 0.02046 ] Network output: [ 0.7709 0.6819 0.09303 -0.0004663 0.0002094 -0.3186 -0.0003515 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2362 0.001658 -0.138 0.0613 0.9837 0.9933 0.2646 0.7904 0.951 0.6223 ] Network output: [ 0.0001514 0.9103 0.9833 -0.0001056 4.742e-05 0.1057 -7.961e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004995 0.00149 0.003157 0.0008371 0.9905 0.9934 0.005085 0.9428 0.9618 0.01087 ] Network output: [ -0.07819 0.8052 0.834 -0.0007281 0.0003269 0.5142 -0.0005487 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2692 0.1761 0.3374 -0.01565 0.9853 0.9942 0.27 0.7958 0.956 0.6274 ] Network output: [ -0.02403 0.3175 1.052 0.0001168 -5.244e-05 0.6787 8.803e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07869 0.07321 0.1334 0.09784 0.9895 0.9936 0.07873 0.923 0.9571 0.1624 ] Network output: [ -0.01318 0.1181 1.048 0.0002067 -9.281e-05 0.8606 0.0001558 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09431 0.09308 0.1508 0.1344 0.985 0.9913 0.09432 0.8791 0.9413 0.1633 ] Network output: [ 0.05968 0.6772 0.005884 0.0002274 -0.0001021 1.199 0.0001714 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2803 Epoch 5050 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03331 0.7971 0.9717 -4.927e-05 2.212e-05 0.1643 -3.713e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003376 -0.003199 -0.011 0.008622 0.9672 0.972 0.006654 0.8916 0.8962 0.02085 ] Network output: [ 1.135 -0.6863 0.01168 0.0003039 -0.0001364 0.4051 0.000229 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2324 -0.03329 -0.2093 0.3189 0.9837 0.9933 0.2607 0.7757 0.9513 0.6269 ] Network output: [ -0.00792 0.8947 0.9938 -8.832e-05 3.965e-05 0.127 -6.656e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004481 0.001341 0.002533 0.005508 0.9903 0.9934 0.004563 0.9399 0.9614 0.009281 ] Network output: [ 0.1862 -1.007 0.9475 0.0003453 -0.000155 1.688 0.0002602 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2406 0.1563 0.3174 0.3616 0.9853 0.9941 0.2414 0.7822 0.954 0.5813 ] Network output: [ -0.04202 0.3262 1.058 0.0001023 -4.593e-05 0.6998 7.71e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08427 0.07891 0.143 0.1385 0.9889 0.9934 0.08432 0.9224 0.9556 0.1652 ] Network output: [ -0.04198 0.3351 1.026 0.0001028 -4.616e-05 0.7231 7.749e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09847 0.09737 0.1446 0.1353 0.9851 0.9913 0.09849 0.8803 0.9383 0.1521 ] Network output: [ 0.07934 0.708 -0.03824 0.0002557 -0.0001148 1.173 0.0001927 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3987 Epoch 5051 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ -0.005267 0.9752 0.976 -0.0001567 7.034e-05 0.0586 -0.0001181 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00346 -0.002965 -0.009655 0.005345 0.9671 0.972 0.006757 0.8889 0.8904 0.01965 ] Network output: [ 0.7591 0.5238 0.1375 -0.0004351 0.0001953 -0.1812 -0.0003279 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2394 0.002851 -0.1092 0.09323 0.9836 0.9933 0.268 0.775 0.9474 0.597 ] Network output: [ -0.004086 0.9378 0.9829 -0.0001141 5.123e-05 0.08696 -8.6e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00518 0.001751 0.003626 0.001505 0.9902 0.9933 0.005274 0.9382 0.9589 0.01055 ] Network output: [ -0.07672 0.6789 0.8466 -0.0006409 0.0002877 0.6253 -0.000483 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2766 0.1886 0.3472 0.02283 0.9853 0.9941 0.2774 0.7808 0.9525 0.598 ] Network output: [ -0.0313 0.3757 1.046 9.088e-05 -4.08e-05 0.6408 6.849e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08176 0.0767 0.1342 0.09759 0.9893 0.9934 0.0818 0.9177 0.9538 0.159 ] Network output: [ -0.01911 0.2195 1.034 0.0001649 -7.403e-05 0.7856 0.0001243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09651 0.0954 0.1451 0.1257 0.9849 0.9912 0.09652 0.8716 0.9371 0.1555 ] Network output: [ 0.0357 0.9285 -0.02643 0.0001063 -4.774e-05 1.027 8.015e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2012 Epoch 5052 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03161 0.7975 0.9764 -5.006e-05 2.248e-05 0.1626 -3.773e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003345 -0.00313 -0.01002 0.008273 0.9672 0.972 0.006575 0.8871 0.8922 0.01984 ] Network output: [ 1.052 -0.6554 0.09558 0.0002146 -9.636e-05 0.457 0.0001618 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2315 -0.02857 -0.1588 0.3067 0.9836 0.9933 0.2596 0.7621 0.9482 0.5983 ] Network output: [ -0.0001082 0.8904 0.9888 -7.897e-05 3.545e-05 0.1207 -5.951e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004652 0.001497 0.003157 0.005565 0.9901 0.9933 0.004737 0.9354 0.9588 0.009156 ] Network output: [ 0.165 -0.921 0.9432 0.0002862 -0.0001285 1.649 0.0002157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2507 0.1669 0.331 0.3479 0.9852 0.9941 0.2515 0.7684 0.9511 0.5505 ] Network output: [ -0.03307 0.3489 1.045 0.0001049 -4.71e-05 0.6731 7.906e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08696 0.08172 0.1399 0.1346 0.9887 0.9932 0.08701 0.9169 0.9528 0.1589 ] Network output: [ -0.03112 0.3679 1.009 0.0001041 -4.674e-05 0.6861 7.847e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09964 0.1377 0.1295 0.985 0.9913 0.1007 0.8728 0.9349 0.1443 ] Network output: [ 0.09672 0.7206 -0.06246 0.0002711 -0.0001217 1.15 0.0002043 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.3751 Epoch 5053 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0009732 0.9546 0.9771 -0.0001431 6.427e-05 0.06575 -0.0001079 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003506 -0.003005 -0.009373 0.005678 0.9671 0.972 0.006825 0.8847 0.887 0.01907 ] Network output: [ 0.8107 0.3593 0.1166 -0.0003612 0.0001622 -0.09879 -0.0002722 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2466 0.002026 -0.1122 0.1256 0.9835 0.9932 0.276 0.7614 0.9448 0.5751 ] Network output: [ -0.003269 0.9346 0.9856 -0.0001071 4.808e-05 0.08593 -8.072e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005263 0.001934 0.003467 0.00228 0.9901 0.9932 0.005358 0.9343 0.9565 0.009989 ] Network output: [ -0.03007 0.4288 0.8422 -0.000454 0.0002038 0.7873 -0.0003421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2812 0.1977 0.3356 0.08386 0.9852 0.9941 0.2821 0.7684 0.9499 0.5729 ] Network output: [ -0.03652 0.3774 1.053 8.562e-05 -3.844e-05 0.6427 6.452e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08348 0.07883 0.1364 0.1045 0.9891 0.9933 0.08352 0.9145 0.9516 0.16 ] Network output: [ -0.02691 0.2523 1.038 0.0001426 -6.4e-05 0.7637 0.0001074 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09726 0.09627 0.1456 0.1263 0.9849 0.9912 0.09727 0.8678 0.9345 0.1553 ] Network output: [ 0.01583 0.9993 -0.01851 5.131e-05 -2.304e-05 0.9877 3.867e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1227 Epoch 5054 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02963 0.8146 0.9778 -5.862e-05 2.632e-05 0.1481 -4.418e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003385 -0.00308 -0.009648 0.007706 0.9672 0.972 0.006615 0.884 0.8889 0.01945 ] Network output: [ 0.9928 -0.4566 0.1114 8.328e-05 -3.739e-05 0.3599 6.277e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2378 -0.01947 -0.1369 0.2688 0.9835 0.9933 0.2664 0.7542 0.9458 0.5821 ] Network output: [ 0.004162 0.8858 0.9881 -7.31e-05 3.282e-05 0.1175 -5.509e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004844 0.001726 0.003386 0.005115 0.99 0.9932 0.004932 0.9329 0.957 0.009353 ] Network output: [ 0.1265 -0.6655 0.919 0.0001642 -7.373e-05 1.494 0.0001238 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.263 0.1823 0.3324 0.3016 0.9852 0.9941 0.2638 0.7615 0.9494 0.5456 ] Network output: [ -0.03368 0.3285 1.054 0.0001092 -4.903e-05 0.6853 8.231e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0872 0.08242 0.1433 0.1324 0.9887 0.9932 0.08725 0.9147 0.9513 0.1633 ] Network output: [ -0.03035 0.3295 1.02 0.0001142 -5.127e-05 0.7113 8.606e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09988 0.1427 0.1311 0.985 0.9913 0.1009 0.8697 0.9335 0.1501 ] Network output: [ 0.05898 0.8499 -0.04462 0.0001661 -7.456e-05 1.077 0.0001252 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2262 Epoch 5055 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01364 0.915 0.9745 -0.0001145 5.138e-05 0.08276 -8.626e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003023 -0.009595 0.00583 0.9672 0.972 0.00685 0.8827 0.8856 0.01917 ] Network output: [ 0.8632 0.3025 0.07257 -0.0003128 0.0001404 -0.1028 -0.0002357 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2523 0.00129 -0.1287 0.1338 0.9835 0.9932 0.2823 0.7549 0.9437 0.5719 ] Network output: [ 0.002632 0.9043 0.988 -8.566e-05 3.846e-05 0.102 -6.456e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005214 0.001957 0.003174 0.002503 0.99 0.9931 0.005308 0.9326 0.9555 0.009838 ] Network output: [ -0.002453 0.3187 0.8371 -0.0003638 0.0001633 0.8476 -0.0002742 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2815 0.1999 0.3238 0.1056 0.9852 0.9941 0.2824 0.7628 0.9489 0.5696 ] Network output: [ -0.03709 0.3319 1.068 0.0001016 -4.561e-05 0.6748 7.656e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08204 0.07763 0.1396 0.1101 0.989 0.9933 0.08209 0.9131 0.9507 0.1647 ] Network output: [ -0.0297 0.2107 1.055 0.0001525 -6.845e-05 0.7947 0.0001149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09558 0.09465 0.1505 0.1317 0.9849 0.9912 0.09559 0.8662 0.9336 0.1607 ] Network output: [ 0.002549 0.9574 0.01043 4.372e-05 -1.963e-05 1.027 3.295e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08957 Epoch 5056 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03367 0.8137 0.9741 -5.178e-05 2.325e-05 0.1447 -3.903e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003424 -0.003064 -0.009827 0.007414 0.9672 0.972 0.006664 0.8825 0.8871 0.01962 ] Network output: [ 0.9887 -0.3113 0.08006 2.334e-05 -1.048e-05 0.2539 1.759e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2437 -0.01454 -0.143 0.242 0.9835 0.9932 0.2728 0.7502 0.9445 0.5814 ] Network output: [ 0.007815 0.8711 0.9882 -6.142e-05 2.757e-05 0.1248 -4.629e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004893 0.001809 0.003266 0.004689 0.99 0.9932 0.004982 0.9319 0.956 0.009585 ] Network output: [ 0.105 -0.4901 0.9015 8.546e-05 -3.837e-05 1.379 6.441e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2675 0.1886 0.327 0.2665 0.9852 0.9941 0.2684 0.7585 0.9485 0.5542 ] Network output: [ -0.03651 0.2876 1.07 0.0001196 -5.368e-05 0.7157 9.011e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08499 0.08051 0.1477 0.1325 0.9888 0.9932 0.08504 0.9139 0.9506 0.1704 ] Network output: [ -0.03312 0.2677 1.041 0.00013 -5.838e-05 0.7584 9.801e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09866 0.09775 0.1504 0.1358 0.985 0.9913 0.09867 0.8686 0.933 0.1591 ] Network output: [ 0.02351 0.9163 -0.01432 8.884e-05 -3.988e-05 1.051 6.695e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1393 Epoch 5057 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02387 0.8852 0.9695 -8.918e-05 4.004e-05 0.09717 -6.721e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.003017 -0.009843 0.005956 0.9672 0.972 0.006827 0.8818 0.8848 0.01946 ] Network output: [ 0.8926 0.2723 0.04678 -0.0002732 0.0001226 -0.1055 -0.0002059 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2539 0.0009183 -0.1392 0.1372 0.9835 0.9932 0.284 0.7519 0.943 0.5761 ] Network output: [ 0.008512 0.8805 0.9867 -6.712e-05 3.013e-05 0.1156 -5.059e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005134 0.001937 0.003058 0.002603 0.9901 0.9931 0.005225 0.932 0.9549 0.009935 ] Network output: [ 0.007389 0.2655 0.84 -0.0003236 0.0001453 0.8784 -0.0002439 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2797 0.1993 0.3201 0.1132 0.9852 0.9941 0.2805 0.7603 0.9483 0.5738 ] Network output: [ -0.03653 0.2893 1.078 0.0001172 -5.261e-05 0.7058 8.832e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08022 0.07596 0.1435 0.1144 0.989 0.9933 0.08027 0.9125 0.9502 0.1702 ] Network output: [ -0.03013 0.1689 1.066 0.0001654 -7.423e-05 0.8259 0.0001246 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09376 0.09287 0.1556 0.1364 0.985 0.9913 0.09377 0.8656 0.9332 0.1665 ] Network output: [ -0.007095 0.9374 0.02826 3.362e-05 -1.509e-05 1.049 2.534e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07299 Epoch 5058 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03908 0.8093 0.9681 -4.151e-05 1.864e-05 0.1443 -3.129e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003436 -0.003048 -0.01003 0.007268 0.9672 0.9721 0.006672 0.8817 0.8861 0.01984 ] Network output: [ 0.9921 -0.2249 0.05455 1.728e-06 -7.757e-07 0.186 1.302e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2461 -0.01193 -0.1505 0.2261 0.9835 0.9932 0.2754 0.7482 0.9437 0.5848 ] Network output: [ 0.01201 0.859 0.9856 -5.058e-05 2.271e-05 0.1312 -3.812e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004878 0.001821 0.003156 0.00441 0.99 0.9932 0.004966 0.9315 0.9553 0.009782 ] Network output: [ 0.09299 -0.3869 0.8924 3.78e-05 -1.697e-05 1.309 2.848e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2679 0.19 0.3236 0.244 0.9852 0.9941 0.2688 0.757 0.948 0.5631 ] Network output: [ -0.03724 0.2549 1.081 0.0001291 -5.796e-05 0.7396 9.73e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08268 0.07837 0.1511 0.1331 0.9888 0.9932 0.08272 0.9134 0.9501 0.176 ] Network output: [ -0.03411 0.22 1.054 0.0001434 -6.437e-05 0.7945 0.0001081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09633 0.09544 0.1563 0.1396 0.9851 0.9913 0.09634 0.868 0.9326 0.1659 ] Network output: [ 0.00353 0.9491 0.004418 4.54e-05 -2.038e-05 1.04 3.421e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09861 Epoch 5059 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03143 0.8669 0.9637 -7.126e-05 3.199e-05 0.1062 -5.37e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003506 -0.003 -0.01001 0.006081 0.9672 0.9721 0.00678 0.8815 0.8844 0.0197 ] Network output: [ 0.9087 0.2429 0.03521 -0.0002358 0.0001059 -0.09634 -0.0001777 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.253 0.0005078 -0.1449 0.1415 0.9835 0.9932 0.2829 0.7504 0.9426 0.5814 ] Network output: [ 0.01341 0.8668 0.983 -5.504e-05 2.471e-05 0.1231 -4.148e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005052 0.001908 0.003038 0.002712 0.9901 0.9931 0.005142 0.9317 0.9546 0.01008 ] Network output: [ 0.01233 0.2209 0.8462 -0.0002963 0.000133 0.9071 -0.0002233 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2769 0.1976 0.3203 0.1196 0.9852 0.9941 0.2777 0.759 0.9479 0.5786 ] Network output: [ -0.03558 0.2595 1.084 0.0001275 -5.722e-05 0.7278 9.606e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07873 0.07457 0.1471 0.1178 0.989 0.9933 0.07877 0.9122 0.9498 0.1748 ] Network output: [ -0.02969 0.1419 1.072 0.0001733 -7.779e-05 0.8462 0.0001306 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09227 0.0914 0.1596 0.1398 0.985 0.9913 0.09228 0.8655 0.9328 0.1709 ] Network output: [ -0.01273 0.9402 0.03497 2.087e-05 -9.371e-06 1.05 1.573e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06156 Epoch 5060 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04355 0.8079 0.9619 -3.424e-05 1.537e-05 0.1429 -2.58e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003432 -0.003026 -0.01017 0.007186 0.9672 0.9721 0.006651 0.8815 0.8855 0.02001 ] Network output: [ 0.992 -0.1711 0.04084 -5.241e-06 2.353e-06 0.1463 -3.95e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2463 -0.01017 -0.1548 0.2164 0.9835 0.9932 0.2755 0.7473 0.9432 0.5887 ] Network output: [ 0.01581 0.8527 0.9816 -4.38e-05 1.966e-05 0.1339 -3.301e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004844 0.001816 0.003112 0.004239 0.9901 0.9932 0.00493 0.9314 0.9549 0.00995 ] Network output: [ 0.08415 -0.3233 0.889 5.527e-06 -2.481e-06 1.266 4.166e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2669 0.1898 0.3229 0.2294 0.9852 0.9941 0.2678 0.7564 0.9476 0.5702 ] Network output: [ -0.03681 0.2333 1.086 0.000135 -6.06e-05 0.7548 0.0001017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08079 0.07659 0.1537 0.1335 0.9889 0.9932 0.08083 0.9131 0.9497 0.18 ] Network output: [ -0.03377 0.1889 1.062 0.0001519 -6.818e-05 0.8174 0.0001144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0944 0.09353 0.1604 0.1421 0.9851 0.9913 0.09441 0.8676 0.9323 0.1707 ] Network output: [ -0.006701 0.9731 0.0129 1.843e-05 -8.276e-06 1.028 1.389e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07813 Epoch 5061 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03696 0.8565 0.9583 -5.966e-05 2.678e-05 0.1111 -4.496e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003481 -0.002979 -0.01012 0.006202 0.9672 0.9721 0.006726 0.8814 0.8842 0.01988 ] Network output: [ 0.918 0.2118 0.03136 -0.0002016 9.052e-05 -0.08003 -0.0001519 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2512 0.0001419 -0.1476 0.1469 0.9835 0.9932 0.2808 0.7496 0.9424 0.5861 ] Network output: [ 0.01725 0.8602 0.9789 -4.826e-05 2.166e-05 0.1262 -3.637e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004978 0.001884 0.003065 0.00284 0.9901 0.9931 0.005067 0.9316 0.9544 0.01021 ] Network output: [ 0.0156 0.176 0.8538 -0.0002724 0.0001223 0.938 -0.0002053 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.274 0.1958 0.3223 0.1268 0.9852 0.9941 0.2749 0.7583 0.9476 0.5825 ] Network output: [ -0.03461 0.2395 1.087 0.0001332 -5.98e-05 0.7428 0.0001004 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07758 0.0735 0.1501 0.1207 0.989 0.9933 0.07762 0.9122 0.9495 0.1785 ] Network output: [ -0.02895 0.1268 1.074 0.0001766 -7.929e-05 0.8575 0.0001331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09112 0.09027 0.1625 0.142 0.985 0.9913 0.09113 0.8656 0.9325 0.1742 ] Network output: [ -0.0152 0.9533 0.03519 8.925e-06 -4.007e-06 1.042 6.726e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05259 Epoch 5062 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04683 0.8098 0.9564 -3.055e-05 1.371e-05 0.14 -2.302e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003421 -0.003001 -0.01026 0.007126 0.9673 0.9721 0.006622 0.8815 0.8851 0.02013 ] Network output: [ 0.9891 -0.1321 0.03386 -8.604e-06 3.863e-06 0.1201 -6.484e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2457 -0.008683 -0.1569 0.2098 0.9835 0.9932 0.2747 0.7472 0.9429 0.5924 ] Network output: [ 0.01889 0.8506 0.9776 -4.05e-05 1.818e-05 0.1338 -3.052e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004806 0.00181 0.00311 0.004121 0.9901 0.9932 0.004892 0.9314 0.9547 0.01009 ] Network output: [ 0.07621 -0.2786 0.8886 -1.97e-05 8.842e-06 1.238 -1.484e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2657 0.1894 0.324 0.2189 0.9852 0.9941 0.2665 0.7564 0.9474 0.5759 ] Network output: [ -0.03601 0.2191 1.089 0.000138 -6.196e-05 0.7645 0.000104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07925 0.07515 0.1557 0.1338 0.9889 0.9933 0.0793 0.9131 0.9495 0.183 ] Network output: [ -0.03286 0.1682 1.066 0.0001569 -7.043e-05 0.8321 0.0001182 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09285 0.092 0.1633 0.1438 0.9851 0.9914 0.09286 0.8676 0.9321 0.174 ] Network output: [ -0.01153 0.9916 0.01597 1.127e-06 -5.058e-07 1.015 8.492e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06594 Epoch 5063 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04109 0.8509 0.9536 -5.244e-05 2.354e-05 0.1132 -3.952e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003457 -0.002959 -0.01019 0.006306 0.9673 0.9721 0.006674 0.8815 0.8841 0.02 ] Network output: [ 0.9249 0.1838 0.02968 -0.0001721 7.727e-05 -0.06395 -0.0001297 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2493 -0.000133 -0.1495 0.1523 0.9835 0.9932 0.2786 0.7494 0.9423 0.5903 ] Network output: [ 0.02013 0.8576 0.9751 -4.486e-05 2.014e-05 0.1268 -3.38e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004912 0.001864 0.003101 0.002964 0.9901 0.9932 0.004999 0.9317 0.9543 0.01031 ] Network output: [ 0.01811 0.133 0.8614 -0.0002509 0.0001126 0.9684 -0.0001891 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2715 0.1943 0.3248 0.134 0.9852 0.9941 0.2723 0.7582 0.9475 0.586 ] Network output: [ -0.03382 0.2251 1.089 0.0001363 -6.119e-05 0.7537 0.0001027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07663 0.07263 0.1527 0.1232 0.989 0.9933 0.07667 0.9124 0.9494 0.1816 ] Network output: [ -0.02826 0.1176 1.075 0.0001775 -7.969e-05 0.8643 0.0001338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0902 0.08936 0.1648 0.1437 0.9851 0.9913 0.09021 0.866 0.9323 0.1767 ] Network output: [ -0.01558 0.966 0.033 4.415e-07 -1.982e-07 1.032 3.328e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04594 Epoch 5064 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04911 0.814 0.9518 -2.952e-05 1.325e-05 0.1358 -2.224e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003409 -0.002979 -0.01032 0.007069 0.9673 0.9721 0.006592 0.8816 0.8849 0.02022 ] Network output: [ 0.9856 -0.09797 0.02884 -1.238e-05 5.559e-06 0.09787 -9.332e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.245 -0.007372 -0.1583 0.2043 0.9835 0.9932 0.2739 0.7475 0.9428 0.5958 ] Network output: [ 0.02115 0.851 0.9742 -3.947e-05 1.772e-05 0.1324 -2.974e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004771 0.001804 0.003117 0.00402 0.9901 0.9932 0.004855 0.9317 0.9546 0.01021 ] Network output: [ 0.06876 -0.2423 0.8894 -4.178e-05 1.875e-05 1.215 -3.148e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2646 0.189 0.3257 0.2102 0.9852 0.9941 0.2654 0.7568 0.9473 0.5811 ] Network output: [ -0.03535 0.2087 1.091 0.0001394 -6.258e-05 0.7715 0.0001051 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07793 0.07392 0.1574 0.134 0.9889 0.9933 0.07798 0.9132 0.9494 0.1854 ] Network output: [ -0.03197 0.1524 1.069 0.0001602 -7.191e-05 0.8432 0.0001207 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09155 0.09072 0.1656 0.1452 0.9851 0.9914 0.09156 0.8677 0.9321 0.1767 ] Network output: [ -0.01365 1.003 0.01727 -9.324e-06 4.186e-06 1.007 -7.027e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05718 Epoch 5065 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04416 0.8483 0.9496 -4.806e-05 2.158e-05 0.1136 -3.622e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003436 -0.002941 -0.01026 0.006391 0.9673 0.9721 0.006629 0.8818 0.8842 0.02011 ] Network output: [ 0.9313 0.1624 0.0272 -0.0001477 6.631e-05 -0.0527 -0.0001113 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2477 -0.0003718 -0.1516 0.1568 0.9835 0.9932 0.2768 0.7496 0.9423 0.5943 ] Network output: [ 0.02212 0.8572 0.972 -4.346e-05 1.951e-05 0.1264 -3.275e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004852 0.001846 0.003124 0.003066 0.9901 0.9932 0.004938 0.932 0.9544 0.0104 ] Network output: [ 0.02019 0.09544 0.8681 -0.0002328 0.0001045 0.9951 -0.0001755 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2692 0.193 0.327 0.1403 0.9852 0.9941 0.27 0.7585 0.9474 0.5895 ] Network output: [ -0.0334 0.2134 1.091 0.000138 -6.197e-05 0.7627 0.000104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07577 0.07184 0.1549 0.1254 0.989 0.9933 0.07581 0.9128 0.9495 0.1842 ] Network output: [ -0.02787 0.1102 1.076 0.0001775 -7.968e-05 0.8698 0.0001338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08939 0.08856 0.1668 0.1453 0.9851 0.9913 0.0894 0.8666 0.9323 0.1788 ] Network output: [ -0.015 0.974 0.03079 -4.228e-06 1.898e-06 1.025 -3.186e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0413 Epoch 5066 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0506 0.8194 0.948 -3.007e-05 1.35e-05 0.1313 -2.266e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003399 -0.00296 -0.01039 0.007015 0.9673 0.9721 0.006567 0.8819 0.8849 0.0203 ] Network output: [ 0.983 -0.06544 0.02351 -1.677e-05 7.53e-06 0.07577 -1.264e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2444 -0.006297 -0.1602 0.1992 0.9835 0.9933 0.2732 0.7482 0.9427 0.5992 ] Network output: [ 0.02263 0.8526 0.9714 -3.977e-05 1.785e-05 0.1306 -2.997e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004736 0.001795 0.003115 0.003925 0.9901 0.9932 0.00482 0.932 0.9546 0.01031 ] Network output: [ 0.06209 -0.2105 0.8903 -6.195e-05 2.781e-05 1.196 -4.668e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2635 0.1885 0.3272 0.2026 0.9852 0.9941 0.2643 0.7576 0.9474 0.5862 ] Network output: [ -0.03505 0.1999 1.093 0.00014 -6.284e-05 0.7776 0.0001055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07674 0.0728 0.1588 0.1343 0.989 0.9933 0.07678 0.9136 0.9495 0.1875 ] Network output: [ -0.03142 0.1383 1.072 0.0001627 -7.304e-05 0.8533 0.0001226 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08957 0.1676 0.1465 0.9851 0.9914 0.0904 0.8681 0.9321 0.1791 ] Network output: [ -0.01461 1.009 0.0185 -1.502e-05 6.744e-06 1.002 -1.132e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0502 Epoch 5067 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04638 0.8478 0.9463 -4.547e-05 2.041e-05 0.113 -3.427e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003418 -0.002927 -0.01034 0.00646 0.9673 0.9721 0.006591 0.8821 0.8844 0.02021 ] Network output: [ 0.9376 0.1477 0.0234 -0.0001275 5.726e-05 -0.04688 -9.612e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2464 -0.0006543 -0.1544 0.1603 0.9835 0.9933 0.2753 0.7501 0.9424 0.5984 ] Network output: [ 0.02337 0.8579 0.9697 -4.325e-05 1.941e-05 0.1256 -3.259e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004798 0.001824 0.003129 0.003145 0.9902 0.9932 0.004882 0.9323 0.9545 0.01047 ] Network output: [ 0.02195 0.06455 0.8739 -0.0002187 9.817e-05 1.017 -0.0001648 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2671 0.1915 0.3287 0.1455 0.9852 0.9941 0.2679 0.7591 0.9475 0.5933 ] Network output: [ -0.03335 0.2032 1.093 0.0001391 -6.244e-05 0.7708 0.0001048 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07496 0.07108 0.1569 0.1273 0.9891 0.9933 0.075 0.9133 0.9496 0.1866 ] Network output: [ -0.02787 0.1031 1.078 0.0001773 -7.959e-05 0.8756 0.0001336 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08863 0.08781 0.1687 0.1468 0.9851 0.9914 0.08864 0.8672 0.9324 0.1808 ] Network output: [ -0.01427 0.978 0.02926 -6.398e-06 2.872e-06 1.021 -4.822e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03804 Epoch 5068 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05151 0.8252 0.9449 -3.14e-05 1.41e-05 0.1267 -2.366e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00339 -0.002944 -0.01046 0.006968 0.9673 0.9721 0.006543 0.8823 0.885 0.02038 ] Network output: [ 0.9816 -0.03523 0.0178 -2.059e-05 9.244e-06 0.05416 -1.552e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2439 -0.005515 -0.1627 0.1947 0.9835 0.9933 0.2725 0.7491 0.9428 0.6029 ] Network output: [ 0.02348 0.8548 0.9693 -4.081e-05 1.832e-05 0.1287 -3.076e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004702 0.001781 0.003101 0.003836 0.9902 0.9932 0.004785 0.9324 0.9547 0.0104 ] Network output: [ 0.05639 -0.1831 0.8913 -8.014e-05 3.598e-05 1.179 -6.04e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2624 0.1877 0.3283 0.1959 0.9852 0.9941 0.2631 0.7585 0.9475 0.5914 ] Network output: [ -0.03508 0.1921 1.095 0.0001402 -6.292e-05 0.7833 0.0001056 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07565 0.07176 0.1601 0.1346 0.989 0.9933 0.07569 0.914 0.9496 0.1895 ] Network output: [ -0.03124 0.1254 1.075 0.0001648 -7.398e-05 0.863 0.0001242 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08935 0.08853 0.1695 0.1479 0.9852 0.9914 0.08936 0.8686 0.9323 0.1814 ] Network output: [ -0.01513 1.01 0.01983 -1.784e-05 8.008e-06 1 -1.344e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04466 Epoch 5069 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04795 0.8484 0.9436 -4.401e-05 1.976e-05 0.1119 -3.316e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003402 -0.002915 -0.01042 0.006518 0.9673 0.9721 0.006558 0.8826 0.8846 0.02031 ] Network output: [ 0.9437 0.1376 0.0191 -0.0001103 4.951e-05 -0.04452 -8.31e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2452 -0.001017 -0.1577 0.1631 0.9835 0.9933 0.2739 0.7508 0.9426 0.6027 ] Network output: [ 0.02407 0.8593 0.9678 -4.378e-05 1.965e-05 0.1246 -3.299e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004747 0.001799 0.003122 0.003204 0.9902 0.9932 0.004831 0.9327 0.9547 0.01054 ] Network output: [ 0.02338 0.03929 0.8789 -0.000208 9.338e-05 1.034 -0.0001568 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2651 0.1899 0.3301 0.1495 0.9852 0.9941 0.2659 0.7599 0.9476 0.5975 ] Network output: [ -0.03357 0.1945 1.095 0.0001396 -6.268e-05 0.7781 0.0001052 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07419 0.07035 0.1585 0.129 0.9891 0.9933 0.07423 0.9138 0.9497 0.1887 ] Network output: [ -0.02816 0.09632 1.079 0.000177 -7.944e-05 0.8815 0.0001334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08792 0.08711 0.1704 0.1482 0.9851 0.9914 0.08793 0.868 0.9326 0.1828 ] Network output: [ -0.0137 0.9807 0.02809 -7.559e-06 3.394e-06 1.019 -5.697e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03567 Epoch 5070 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05203 0.831 0.9423 -3.301e-05 1.482e-05 0.1226 -2.488e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00338 -0.00293 -0.01054 0.006931 0.9673 0.9722 0.006521 0.8828 0.8852 0.02046 ] Network output: [ 0.9808 -0.009171 0.01259 -2.286e-05 1.026e-05 0.03489 -1.723e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2431 -0.005012 -0.1654 0.191 0.9835 0.9933 0.2717 0.7501 0.9429 0.6069 ] Network output: [ 0.02393 0.8574 0.9677 -4.226e-05 1.897e-05 0.1269 -3.185e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004667 0.001761 0.003083 0.003759 0.9902 0.9932 0.00475 0.9329 0.9549 0.01049 ] Network output: [ 0.05159 -0.1607 0.8926 -9.605e-05 4.312e-05 1.165 -7.238e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2611 0.1866 0.3294 0.1903 0.9852 0.9941 0.2618 0.7596 0.9477 0.5966 ] Network output: [ -0.03526 0.1855 1.097 0.0001401 -6.288e-05 0.7885 0.0001056 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07467 0.07081 0.1612 0.1349 0.9891 0.9933 0.07471 0.9144 0.9498 0.1912 ] Network output: [ -0.03125 0.1142 1.077 0.0001664 -7.471e-05 0.8717 0.0001254 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08841 0.08761 0.1713 0.1492 0.9852 0.9914 0.08842 0.8692 0.9325 0.1834 ] Network output: [ -0.01533 1.01 0.02078 -1.91e-05 8.576e-06 0.9996 -1.44e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04052 Epoch 5071 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04903 0.8498 0.9413 -4.327e-05 1.943e-05 0.1106 -3.261e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003387 -0.002905 -0.0105 0.006569 0.9673 0.9722 0.006527 0.8831 0.8849 0.02041 ] Network output: [ 0.949 0.1298 0.01523 -9.491e-05 4.261e-05 -0.04342 -7.153e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2439 -0.001441 -0.1609 0.1655 0.9835 0.9933 0.2724 0.7517 0.9428 0.607 ] Network output: [ 0.02442 0.8612 0.9663 -4.478e-05 2.011e-05 0.1235 -3.375e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0047 0.001772 0.003111 0.003251 0.9902 0.9932 0.004782 0.9332 0.9549 0.01061 ] Network output: [ 0.02441 0.0181 0.8835 -0.0002001 8.982e-05 1.049 -0.0001508 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2631 0.1882 0.3313 0.1529 0.9853 0.9941 0.2639 0.7608 0.9478 0.6018 ] Network output: [ -0.0339 0.1872 1.097 0.0001397 -6.274e-05 0.7844 0.0001053 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07349 0.06966 0.16 0.1305 0.9891 0.9934 0.07353 0.9143 0.9499 0.1907 ] Network output: [ -0.02854 0.09053 1.081 0.0001764 -7.921e-05 0.8867 0.000133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08727 0.08646 0.1719 0.1495 0.9852 0.9914 0.08728 0.8687 0.9328 0.1846 ] Network output: [ -0.01323 0.9839 0.02678 -8.472e-06 3.804e-06 1.016 -6.385e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03388 Epoch 5072 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05229 0.8363 0.9401 -3.468e-05 1.557e-05 0.1189 -2.614e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003369 -0.002918 -0.01061 0.006905 0.9674 0.9722 0.006498 0.8833 0.8854 0.02054 ] Network output: [ 0.9802 0.01181 0.008522 -2.338e-05 1.05e-05 0.01916 -1.762e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2422 -0.00472 -0.168 0.1881 0.9836 0.9933 0.2706 0.7512 0.9431 0.6108 ] Network output: [ 0.02415 0.8601 0.9662 -4.392e-05 1.972e-05 0.1251 -3.31e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004633 0.001739 0.003068 0.003696 0.9902 0.9933 0.004715 0.9334 0.9551 0.01056 ] Network output: [ 0.04748 -0.1436 0.8943 -0.0001096 4.922e-05 1.154 -8.262e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2597 0.1853 0.3305 0.1858 0.9853 0.9941 0.2605 0.7607 0.9479 0.6016 ] Network output: [ -0.03545 0.1801 1.099 0.0001397 -6.271e-05 0.7928 0.0001053 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07381 0.06997 0.1622 0.1353 0.9891 0.9934 0.07385 0.9149 0.95 0.1928 ] Network output: [ -0.0313 0.105 1.079 0.0001675 -7.52e-05 0.8791 0.0001262 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0876 0.0868 0.1728 0.1502 0.9852 0.9914 0.08761 0.8698 0.9328 0.1852 ] Network output: [ -0.01517 1.011 0.021 -1.946e-05 8.737e-06 0.9986 -1.467e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03756 Epoch 5073 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04978 0.8516 0.9394 -4.305e-05 1.933e-05 0.1093 -3.244e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003372 -0.002896 -0.01057 0.006614 0.9674 0.9722 0.006498 0.8836 0.8853 0.0205 ] Network output: [ 0.9535 0.1231 0.01215 -8.11e-05 3.641e-05 -0.04251 -6.112e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2426 -0.001883 -0.164 0.1676 0.9836 0.9933 0.2709 0.7526 0.943 0.6112 ] Network output: [ 0.02456 0.8633 0.9651 -4.609e-05 2.069e-05 0.1223 -3.473e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004656 0.001745 0.003101 0.003288 0.9902 0.9933 0.004738 0.9337 0.9551 0.01067 ] Network output: [ 0.02504 7.92e-05 0.8878 -0.0001942 8.72e-05 1.061 -0.0001464 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2612 0.1864 0.3326 0.1555 0.9853 0.9941 0.262 0.7618 0.948 0.606 ] Network output: [ -0.0342 0.1814 1.098 0.0001395 -6.263e-05 0.7895 0.0001051 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07285 0.06903 0.1612 0.1317 0.9891 0.9934 0.07289 0.9149 0.9501 0.1924 ] Network output: [ -0.02889 0.08597 1.082 0.0001757 -7.89e-05 0.891 0.0001324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08669 0.08588 0.1732 0.1505 0.9852 0.9914 0.0867 0.8695 0.933 0.1861 ] Network output: [ -0.01268 0.9878 0.02517 -9.146e-06 4.106e-06 1.012 -6.893e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03253 Epoch 5074 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05239 0.8411 0.9384 -3.634e-05 1.631e-05 0.1156 -2.739e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003358 -0.002907 -0.01067 0.006885 0.9674 0.9722 0.006474 0.8838 0.8857 0.02061 ] Network output: [ 0.9796 0.02827 0.005588 -2.257e-05 1.013e-05 0.006756 -1.701e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2412 -0.00457 -0.1703 0.1859 0.9836 0.9933 0.2695 0.7524 0.9433 0.6146 ] Network output: [ 0.02423 0.8628 0.965 -4.568e-05 2.051e-05 0.1235 -3.443e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0046 0.001715 0.003056 0.003644 0.9902 0.9933 0.004681 0.9339 0.9553 0.01063 ] Network output: [ 0.04389 -0.1306 0.8964 -0.0001212 5.442e-05 1.146 -9.136e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2583 0.184 0.3318 0.1821 0.9853 0.9941 0.2591 0.7619 0.9481 0.6063 ] Network output: [ -0.03558 0.1759 1.1 0.0001391 -6.247e-05 0.7963 0.0001049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07305 0.06922 0.163 0.1355 0.9891 0.9934 0.07308 0.9155 0.9502 0.1941 ] Network output: [ -0.03129 0.09765 1.081 0.0001682 -7.552e-05 0.8849 0.0001268 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0869 0.08609 0.174 0.1511 0.9852 0.9914 0.08691 0.8704 0.933 0.1866 ] Network output: [ -0.01464 1.011 0.02059 -1.905e-05 8.552e-06 0.9974 -1.436e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03543 Epoch 5075 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05028 0.8536 0.9378 -4.319e-05 1.939e-05 0.1079 -3.255e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003357 -0.002888 -0.01064 0.006651 0.9674 0.9722 0.00647 0.8842 0.8857 0.02058 ] Network output: [ 0.9572 0.1175 0.009668 -6.884e-05 3.091e-05 -0.04195 -5.188e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2413 -0.002315 -0.1668 0.1695 0.9836 0.9933 0.2695 0.7537 0.9432 0.6153 ] Network output: [ 0.02457 0.8655 0.964 -4.756e-05 2.135e-05 0.1211 -3.585e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004615 0.001718 0.00309 0.003316 0.9903 0.9933 0.004697 0.9342 0.9553 0.01073 ] Network output: [ 0.02531 -0.01492 0.8918 -0.0001902 8.541e-05 1.072 -0.0001434 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2594 0.1847 0.3338 0.1576 0.9853 0.9941 0.2602 0.7629 0.9482 0.61 ] Network output: [ -0.03445 0.1769 1.099 0.000139 -6.242e-05 0.7936 0.0001048 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07226 0.06845 0.1622 0.1327 0.9892 0.9934 0.0723 0.9155 0.9503 0.1938 ] Network output: [ -0.02916 0.08233 1.082 0.0001749 -7.854e-05 0.8945 0.0001318 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08617 0.08536 0.1743 0.1513 0.9852 0.9914 0.08618 0.8703 0.9332 0.1873 ] Network output: [ -0.01199 0.9913 0.02344 -9.287e-06 4.169e-06 1.009 -6.999e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03152 Epoch 5076 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05236 0.8453 0.9369 -3.796e-05 1.704e-05 0.1129 -2.861e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003346 -0.002897 -0.01073 0.00687 0.9674 0.9722 0.006451 0.8844 0.8861 0.02067 ] Network output: [ 0.9792 0.04152 0.00334 -2.095e-05 9.403e-06 -0.003395 -1.579e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2402 -0.004523 -0.1725 0.1843 0.9836 0.9933 0.2683 0.7536 0.9435 0.6184 ] Network output: [ 0.0242 0.8654 0.964 -4.748e-05 2.131e-05 0.122 -3.578e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004568 0.001692 0.003044 0.003599 0.9903 0.9933 0.004648 0.9344 0.9555 0.01069 ] Network output: [ 0.04072 -0.1205 0.8986 -0.0001313 5.897e-05 1.14 -9.899e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2569 0.1826 0.333 0.179 0.9853 0.9941 0.2577 0.7631 0.9483 0.6107 ] Network output: [ -0.03565 0.1725 1.1 0.0001385 -6.217e-05 0.7991 0.0001044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07236 0.06855 0.1636 0.1357 0.9892 0.9934 0.0724 0.916 0.9504 0.1952 ] Network output: [ -0.03123 0.09162 1.082 0.0001687 -7.572e-05 0.8899 0.0001271 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08628 0.08547 0.175 0.1518 0.9852 0.9915 0.08629 0.8711 0.9333 0.1879 ] Network output: [ -0.01386 1.011 0.01991 -1.788e-05 8.026e-06 0.9964 -1.347e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03383 Epoch 5077 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05058 0.8555 0.9365 -4.359e-05 1.957e-05 0.1067 -3.285e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003344 -0.00288 -0.0107 0.006681 0.9674 0.9722 0.006444 0.8847 0.8861 0.02065 ] Network output: [ 0.9605 0.1136 0.007416 -5.814e-05 2.61e-05 -0.04224 -4.381e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.24 -0.002735 -0.1695 0.1709 0.9836 0.9933 0.2681 0.7548 0.9435 0.6192 ] Network output: [ 0.02446 0.8676 0.9632 -4.912e-05 2.205e-05 0.1201 -3.702e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004578 0.001691 0.003077 0.003333 0.9903 0.9933 0.004658 0.9347 0.9556 0.01078 ] Network output: [ 0.02533 -0.02681 0.8954 -0.0001879 8.436e-05 1.08 -0.0001416 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2577 0.183 0.3349 0.1591 0.9853 0.9941 0.2584 0.764 0.9485 0.614 ] Network output: [ -0.03464 0.1732 1.1 0.0001384 -6.216e-05 0.797 0.0001043 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07171 0.0679 0.163 0.1334 0.9892 0.9934 0.07175 0.9161 0.9506 0.1949 ] Network output: [ -0.02938 0.07905 1.083 0.0001742 -7.821e-05 0.8977 0.0001313 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08568 0.08487 0.1752 0.152 0.9852 0.9915 0.08569 0.871 0.9335 0.1884 ] Network output: [ -0.01124 0.9938 0.02188 -8.765e-06 3.935e-06 1.007 -6.606e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03074 Epoch 5078 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05225 0.8491 0.9357 -3.952e-05 1.774e-05 0.1105 -2.979e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003334 -0.002887 -0.01078 0.006858 0.9674 0.9722 0.006428 0.885 0.8865 0.02073 ] Network output: [ 0.9791 0.05266 0.001365 -1.88e-05 8.439e-06 -0.01224 -1.417e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2391 -0.004566 -0.1748 0.1829 0.9836 0.9933 0.2671 0.7548 0.9437 0.622 ] Network output: [ 0.02406 0.8677 0.9632 -4.926e-05 2.211e-05 0.1208 -3.712e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004537 0.001668 0.003031 0.003558 0.9903 0.9933 0.004617 0.9349 0.9558 0.01075 ] Network output: [ 0.038 -0.1122 0.9008 -0.0001404 6.303e-05 1.135 -0.0001058 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2555 0.1811 0.334 0.1763 0.9853 0.9941 0.2563 0.7642 0.9486 0.6149 ] Network output: [ -0.0357 0.1696 1.101 0.0001378 -6.187e-05 0.8015 0.0001039 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07173 0.06792 0.1641 0.1359 0.9892 0.9934 0.07177 0.9165 0.9507 0.1962 ] Network output: [ -0.03119 0.0863 1.082 0.000169 -7.586e-05 0.8943 0.0001273 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08571 0.0849 0.1758 0.1524 0.9853 0.9915 0.08572 0.8718 0.9336 0.1889 ] Network output: [ -0.01302 1.011 0.01924 -1.612e-05 7.235e-06 0.9961 -1.215e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03256 Epoch 5079 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05073 0.8575 0.9354 -4.417e-05 1.983e-05 0.1055 -3.329e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00333 -0.002873 -0.01076 0.006704 0.9674 0.9722 0.006419 0.8853 0.8865 0.02071 ] Network output: [ 0.9635 0.1111 0.005228 -4.873e-05 2.188e-05 -0.0435 -3.673e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2388 -0.003158 -0.1723 0.172 0.9836 0.9933 0.2667 0.7559 0.9438 0.623 ] Network output: [ 0.02425 0.8696 0.9625 -5.07e-05 2.276e-05 0.1192 -3.821e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004542 0.001664 0.00306 0.003341 0.9903 0.9933 0.004622 0.9352 0.9558 0.01082 ] Network output: [ 0.02521 -0.03582 0.8985 -0.0001871 8.399e-05 1.086 -0.000141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.256 0.1813 0.3358 0.1599 0.9853 0.9941 0.2568 0.7651 0.9487 0.6179 ] Network output: [ -0.03482 0.17 1.1 0.0001378 -6.187e-05 0.8 0.0001039 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07118 0.06737 0.1636 0.134 0.9892 0.9935 0.07122 0.9167 0.9509 0.1959 ] Network output: [ -0.02959 0.07584 1.083 0.0001736 -7.794e-05 0.9008 0.0001308 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08521 0.0844 0.176 0.1526 0.9852 0.9915 0.08522 0.8718 0.9338 0.1894 ] Network output: [ -0.01054 0.9952 0.02063 -7.754e-06 3.481e-06 1.005 -5.844e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03011 Epoch 5080 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05205 0.8525 0.9348 -4.099e-05 1.84e-05 0.1085 -3.089e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003322 -0.002879 -0.01083 0.006849 0.9674 0.9722 0.006406 0.8856 0.8869 0.02078 ] Network output: [ 0.9792 0.06204 -0.0004482 -1.614e-05 7.244e-06 -0.02007 -1.216e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.238 -0.004697 -0.1771 0.1818 0.9836 0.9933 0.2659 0.756 0.944 0.6256 ] Network output: [ 0.02383 0.8698 0.9626 -5.099e-05 2.289e-05 0.1197 -3.843e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004507 0.001643 0.003014 0.00352 0.9903 0.9933 0.004586 0.9355 0.956 0.0108 ] Network output: [ 0.03568 -0.1052 0.9028 -0.0001486 6.671e-05 1.13 -0.000112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2541 0.1796 0.3349 0.1739 0.9853 0.9942 0.2549 0.7654 0.9488 0.619 ] Network output: [ -0.03575 0.1671 1.101 0.0001371 -6.157e-05 0.8037 0.0001034 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07115 0.06733 0.1645 0.136 0.9892 0.9935 0.07119 0.9171 0.951 0.197 ] Network output: [ -0.03118 0.08147 1.083 0.0001692 -7.597e-05 0.8984 0.0001275 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08519 0.08438 0.1765 0.153 0.9853 0.9915 0.0852 0.8724 0.9339 0.1899 ] Network output: [ -0.01223 1.009 0.01867 -1.409e-05 6.323e-06 0.9964 -1.062e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03152 Epoch 5081 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05074 0.8594 0.9345 -4.485e-05 2.013e-05 0.1044 -3.38e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003317 -0.002866 -0.01081 0.006724 0.9674 0.9722 0.006396 0.8859 0.887 0.02077 ] Network output: [ 0.9661 0.1097 0.003208 -4.024e-05 1.806e-05 -0.04527 -3.032e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2376 -0.003592 -0.1751 0.1729 0.9836 0.9933 0.2654 0.757 0.9441 0.6268 ] Network output: [ 0.02397 0.8714 0.962 -5.227e-05 2.347e-05 0.1184 -3.939e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004508 0.001637 0.003039 0.003342 0.9903 0.9933 0.004587 0.9358 0.9561 0.01086 ] Network output: [ 0.02499 -0.04257 0.9012 -0.0001874 8.413e-05 1.091 -0.0001412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2544 0.1796 0.3365 0.1604 0.9853 0.9942 0.2552 0.7663 0.949 0.6217 ] Network output: [ -0.03497 0.1674 1.101 0.0001372 -6.158e-05 0.8026 0.0001034 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07068 0.06686 0.1641 0.1345 0.9893 0.9935 0.07071 0.9172 0.9511 0.1968 ] Network output: [ -0.0298 0.07274 1.084 0.0001731 -7.771e-05 0.9039 0.0001304 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08477 0.08395 0.1767 0.1532 0.9853 0.9915 0.08478 0.8725 0.9341 0.1902 ] Network output: [ -0.009945 0.996 0.0196 -6.553e-06 2.942e-06 1.004 -4.939e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02957 Epoch 5082 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0518 0.8554 0.934 -4.234e-05 1.901e-05 0.1068 -3.191e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003309 -0.002871 -0.01088 0.006843 0.9674 0.9722 0.006384 0.8862 0.8873 0.02083 ] Network output: [ 0.9795 0.06955 -0.001979 -1.287e-05 5.779e-06 -0.02669 -9.701e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2369 -0.004901 -0.1795 0.1809 0.9836 0.9933 0.2646 0.7572 0.9443 0.6292 ] Network output: [ 0.02355 0.8717 0.9621 -5.268e-05 2.365e-05 0.1189 -3.97e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004477 0.001617 0.002995 0.003486 0.9904 0.9933 0.004555 0.936 0.9563 0.01085 ] Network output: [ 0.03371 -0.09948 0.9047 -0.000156 7.002e-05 1.127 -0.0001175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2527 0.1781 0.3357 0.1718 0.9853 0.9942 0.2535 0.7666 0.9491 0.623 ] Network output: [ -0.03579 0.1651 1.101 0.0001365 -6.126e-05 0.8057 0.0001028 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07061 0.06678 0.1648 0.1361 0.9893 0.9935 0.07065 0.9176 0.9512 0.1977 ] Network output: [ -0.03119 0.07721 1.084 0.0001694 -7.604e-05 0.9021 0.0001277 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08471 0.08389 0.1772 0.1535 0.9853 0.9915 0.08471 0.8731 0.9342 0.1907 ] Network output: [ -0.01152 1.008 0.0181 -1.207e-05 5.419e-06 0.9968 -9.097e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03067 Epoch 5083 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05067 0.8612 0.9338 -4.557e-05 2.046e-05 0.1035 -3.434e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003304 -0.00286 -0.01087 0.006742 0.9674 0.9722 0.006373 0.8865 0.8874 0.02083 ] Network output: [ 0.9684 0.1084 0.00154 -3.231e-05 1.451e-05 -0.04696 -2.435e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2363 -0.004032 -0.1778 0.1737 0.9837 0.9933 0.264 0.7581 0.9444 0.6304 ] Network output: [ 0.02364 0.8732 0.9615 -5.383e-05 2.417e-05 0.1178 -4.057e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004476 0.00161 0.003019 0.003339 0.9904 0.9934 0.004554 0.9363 0.9564 0.0109 ] Network output: [ 0.02468 -0.04774 0.9038 -0.0001885 8.463e-05 1.094 -0.0001421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2528 0.1779 0.3371 0.1605 0.9853 0.9942 0.2536 0.7674 0.9492 0.6255 ] Network output: [ -0.03508 0.1652 1.101 0.0001365 -6.127e-05 0.8047 0.0001029 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0702 0.06637 0.1645 0.1348 0.9893 0.9935 0.07024 0.9178 0.9514 0.1976 ] Network output: [ -0.02998 0.06995 1.084 0.0001726 -7.749e-05 0.9067 0.0001301 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08435 0.08353 0.1773 0.1536 0.9853 0.9915 0.08435 0.8732 0.9343 0.191 ] Network output: [ -0.00943 0.9967 0.01864 -5.35e-06 2.402e-06 1.003 -4.032e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0291 Epoch 5084 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05152 0.858 0.9333 -4.358e-05 1.956e-05 0.1054 -3.284e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003297 -0.002864 -0.01092 0.006842 0.9674 0.9722 0.006362 0.8868 0.8877 0.02088 ] Network output: [ 0.9799 0.07517 -0.003113 -9.036e-06 4.057e-06 -0.03193 -6.81e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2357 -0.005157 -0.1818 0.1804 0.9837 0.9933 0.2633 0.7583 0.9446 0.6327 ] Network output: [ 0.02323 0.8736 0.9617 -5.432e-05 2.438e-05 0.1181 -4.094e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004448 0.001591 0.002977 0.003456 0.9904 0.9934 0.004526 0.9365 0.9565 0.01089 ] Network output: [ 0.032 -0.09496 0.9066 -0.0001626 7.298e-05 1.124 -0.0001225 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2513 0.1765 0.3364 0.17 0.9853 0.9942 0.252 0.7677 0.9494 0.6268 ] Network output: [ -0.03578 0.1634 1.101 0.0001358 -6.094e-05 0.8072 0.0001023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07011 0.06627 0.165 0.1361 0.9893 0.9935 0.07015 0.9182 0.9515 0.1983 ] Network output: [ -0.03118 0.0736 1.084 0.0001694 -7.606e-05 0.9053 0.0001277 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08426 0.08344 0.1777 0.1539 0.9853 0.9915 0.08427 0.8738 0.9345 0.1914 ] Network output: [ -0.01087 1.007 0.01745 -1.016e-05 4.563e-06 0.997 -7.66e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02997 Epoch 5085 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05053 0.8628 0.9332 -4.632e-05 2.08e-05 0.1027 -3.491e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003291 -0.002853 -0.01092 0.006758 0.9674 0.9722 0.006351 0.8871 0.8879 0.02088 ] Network output: [ 0.9704 0.1071 0.0002849 -2.481e-05 1.114e-05 -0.0483 -1.87e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2351 -0.004466 -0.1804 0.1744 0.9837 0.9933 0.2626 0.7592 0.9447 0.634 ] Network output: [ 0.02329 0.8748 0.9612 -5.538e-05 2.486e-05 0.1172 -4.173e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004445 0.001583 0.002998 0.003333 0.9904 0.9934 0.004523 0.9368 0.9567 0.01094 ] Network output: [ 0.02428 -0.05175 0.9061 -0.0001901 8.536e-05 1.096 -0.0001433 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2513 0.1762 0.3377 0.1605 0.9853 0.9942 0.252 0.7685 0.9495 0.6291 ] Network output: [ -0.03513 0.1636 1.101 0.0001358 -6.095e-05 0.8064 0.0001023 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06975 0.0659 0.1647 0.1351 0.9893 0.9935 0.06979 0.9183 0.9517 0.1982 ] Network output: [ -0.0301 0.06754 1.084 0.0001721 -7.727e-05 0.9092 0.0001297 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08395 0.08313 0.1778 0.154 0.9853 0.9915 0.08396 0.8739 0.9346 0.1917 ] Network output: [ -0.008935 0.9974 0.01768 -4.134e-06 1.856e-06 1.003 -3.116e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02868 Epoch 5086 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05122 0.8603 0.9328 -4.474e-05 2.008e-05 0.1043 -3.372e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003284 -0.002856 -0.01097 0.006842 0.9674 0.9722 0.00634 0.8874 0.8882 0.02093 ] Network output: [ 0.9803 0.07921 -0.003891 -4.792e-06 2.151e-06 -0.03599 -3.611e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2345 -0.005441 -0.184 0.1801 0.9837 0.9933 0.2619 0.7595 0.9449 0.6362 ] Network output: [ 0.0229 0.8752 0.9613 -5.591e-05 2.51e-05 0.1174 -4.214e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00442 0.001565 0.002959 0.003429 0.9904 0.9934 0.004497 0.937 0.9568 0.01093 ] Network output: [ 0.0305 -0.09132 0.9085 -0.0001685 7.565e-05 1.121 -0.000127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2499 0.1749 0.337 0.1683 0.9854 0.9942 0.2506 0.7689 0.9496 0.6305 ] Network output: [ -0.03573 0.1622 1.101 0.000135 -6.062e-05 0.8085 0.0001018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06964 0.06579 0.1652 0.1361 0.9893 0.9935 0.06968 0.9187 0.9518 0.1988 ] Network output: [ -0.03114 0.07054 1.084 0.0001694 -7.603e-05 0.9081 0.0001276 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08385 0.08302 0.1781 0.1542 0.9853 0.9915 0.08385 0.8744 0.9347 0.1921 ] Network output: [ -0.01024 1.007 0.01674 -8.298e-06 3.725e-06 0.9972 -6.254e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02938 Epoch 5087 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05034 0.8643 0.9327 -4.711e-05 2.115e-05 0.102 -3.55e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003278 -0.002847 -0.01096 0.006773 0.9675 0.9722 0.006328 0.8877 0.8883 0.02093 ] Network output: [ 0.9721 0.1058 -0.0006566 -1.775e-05 7.966e-06 -0.0494 -1.337e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2338 -0.004887 -0.1828 0.1751 0.9837 0.9933 0.2612 0.7603 0.945 0.6375 ] Network output: [ 0.02294 0.8763 0.9609 -5.69e-05 2.554e-05 0.1167 -4.288e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004415 0.001557 0.002978 0.003325 0.9904 0.9934 0.004493 0.9373 0.9569 0.01098 ] Network output: [ 0.02383 -0.0547 0.9082 -0.0001922 8.627e-05 1.098 -0.0001448 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2498 0.1745 0.3382 0.1602 0.9854 0.9942 0.2505 0.7696 0.9498 0.6327 ] Network output: [ -0.03511 0.1624 1.101 0.000135 -6.061e-05 0.8077 0.0001017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06932 0.06546 0.1649 0.1352 0.9893 0.9935 0.06936 0.9189 0.9519 0.1987 ] Network output: [ -0.03016 0.0654 1.084 0.0001717 -7.706e-05 0.9114 0.0001294 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08357 0.08274 0.1782 0.1543 0.9853 0.9915 0.08358 0.8746 0.9349 0.1923 ] Network output: [ -0.008438 0.9979 0.01678 -2.817e-06 1.265e-06 1.002 -2.123e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02829 Epoch 5088 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0509 0.8623 0.9324 -4.585e-05 2.058e-05 0.1033 -3.455e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003272 -0.00285 -0.01101 0.006845 0.9675 0.9723 0.006318 0.888 0.8886 0.02097 ] Network output: [ 0.9808 0.08219 -0.004458 -3.291e-07 1.478e-07 -0.03927 -2.48e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2332 -0.00574 -0.1862 0.1799 0.9837 0.9933 0.2606 0.7606 0.9451 0.6395 ] Network output: [ 0.02256 0.8768 0.961 -5.748e-05 2.58e-05 0.1169 -4.332e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004392 0.00154 0.00294 0.003404 0.9904 0.9934 0.004469 0.9375 0.9571 0.01097 ] Network output: [ 0.02919 -0.08818 0.9102 -0.0001739 7.808e-05 1.119 -0.0001311 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2485 0.1733 0.3375 0.1668 0.9854 0.9942 0.2492 0.77 0.9499 0.6341 ] Network output: [ -0.03563 0.1613 1.101 0.0001343 -6.028e-05 0.8095 0.0001012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0692 0.06533 0.1652 0.1361 0.9893 0.9935 0.06924 0.9192 0.952 0.1992 ] Network output: [ -0.03109 0.06784 1.084 0.0001692 -7.598e-05 0.9107 0.0001275 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08346 0.08263 0.1785 0.1544 0.9853 0.9915 0.08347 0.875 0.935 0.1926 ] Network output: [ -0.009619 1.006 0.01606 -6.405e-06 2.875e-06 0.9975 -4.827e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02886 Epoch 5089 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05012 0.8657 0.9324 -4.793e-05 2.152e-05 0.1015 -3.612e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003266 -0.002841 -0.011 0.006786 0.9675 0.9723 0.006307 0.8883 0.8888 0.02097 ] Network output: [ 0.9736 0.1047 -0.001419 -1.11e-05 4.981e-06 -0.05046 -8.362e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2326 -0.005293 -0.1852 0.1757 0.9837 0.9934 0.2599 0.7613 0.9453 0.6408 ] Network output: [ 0.02257 0.8778 0.9607 -5.841e-05 2.622e-05 0.1162 -4.402e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004387 0.001531 0.002956 0.003314 0.9904 0.9934 0.004464 0.9377 0.9572 0.01101 ] Network output: [ 0.02337 -0.05664 0.9102 -0.0001945 8.733e-05 1.099 -0.0001466 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2483 0.1729 0.3386 0.1598 0.9854 0.9942 0.249 0.7706 0.95 0.6361 ] Network output: [ -0.03506 0.1614 1.1 0.0001343 -6.028e-05 0.8088 0.0001012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06891 0.06504 0.165 0.1353 0.9894 0.9935 0.06895 0.9194 0.9522 0.1991 ] Network output: [ -0.0302 0.06336 1.084 0.0001712 -7.687e-05 0.9135 0.000129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08321 0.08238 0.1785 0.1545 0.9853 0.9915 0.08322 0.8752 0.9352 0.1928 ] Network output: [ -0.007962 0.9979 0.016 -1.381e-06 6.201e-07 1.002 -1.041e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02792 Epoch 5090 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05057 0.864 0.9321 -4.692e-05 2.107e-05 0.1025 -3.536e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00326 -0.002843 -0.01105 0.006848 0.9675 0.9723 0.006297 0.8886 0.8891 0.02101 ] Network output: [ 0.9813 0.08443 -0.004919 4.261e-06 -1.913e-06 -0.04204 3.212e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.232 -0.006051 -0.1884 0.1799 0.9837 0.9934 0.2592 0.7617 0.9454 0.6428 ] Network output: [ 0.02219 0.8782 0.9608 -5.902e-05 2.65e-05 0.1163 -4.448e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004365 0.001515 0.00292 0.00338 0.9905 0.9934 0.004442 0.938 0.9574 0.011 ] Network output: [ 0.02806 -0.08531 0.9118 -0.0001789 8.033e-05 1.117 -0.0001348 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2471 0.1717 0.3379 0.1654 0.9854 0.9942 0.2478 0.771 0.9502 0.6375 ] Network output: [ -0.03552 0.1605 1.101 0.0001335 -5.995e-05 0.8103 0.0001006 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06878 0.0649 0.1652 0.1361 0.9894 0.9936 0.06882 0.9197 0.9523 0.1996 ] Network output: [ -0.03102 0.06535 1.084 0.0001691 -7.591e-05 0.913 0.0001274 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08308 0.08225 0.1788 0.1547 0.9853 0.9915 0.08309 0.8757 0.9353 0.1931 ] Network output: [ -0.009059 1.005 0.01546 -4.52e-06 2.029e-06 0.998 -3.406e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02839 Epoch 5091 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04985 0.8671 0.9321 -4.878e-05 2.19e-05 0.1009 -3.676e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003253 -0.002835 -0.01105 0.006799 0.9675 0.9723 0.006285 0.8889 0.8893 0.02101 ] Network output: [ 0.9749 0.1037 -0.002044 -4.777e-06 2.145e-06 -0.05148 -3.6e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2314 -0.005688 -0.1876 0.1763 0.9837 0.9934 0.2585 0.7624 0.9455 0.6441 ] Network output: [ 0.02218 0.8792 0.9605 -5.991e-05 2.689e-05 0.1158 -4.515e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004359 0.001506 0.002934 0.003301 0.9905 0.9934 0.004436 0.9382 0.9575 0.01104 ] Network output: [ 0.02291 -0.05775 0.9119 -0.0001971 8.847e-05 1.099 -0.0001485 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2468 0.1713 0.3389 0.1592 0.9854 0.9942 0.2476 0.7717 0.9503 0.6395 ] Network output: [ -0.03498 0.1606 1.1 0.0001335 -5.994e-05 0.8097 0.0001006 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06852 0.06462 0.165 0.1354 0.9894 0.9936 0.06855 0.9199 0.9524 0.1995 ] Network output: [ -0.03022 0.0614 1.084 0.0001708 -7.669e-05 0.9155 0.0001287 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08286 0.08202 0.1788 0.1547 0.9853 0.9915 0.08287 0.8758 0.9355 0.1932 ] Network output: [ -0.007537 0.9976 0.01533 7.886e-08 -3.54e-08 1.002 5.943e-08 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02757 Epoch 5092 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05022 0.8657 0.9318 -4.798e-05 2.154e-05 0.1019 -3.616e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003247 -0.002837 -0.01109 0.006853 0.9675 0.9723 0.006276 0.8892 0.8895 0.02105 ] Network output: [ 0.9818 0.08593 -0.005264 8.994e-06 -4.038e-06 -0.04429 6.779e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2308 -0.00637 -0.1906 0.18 0.9837 0.9934 0.2578 0.7628 0.9457 0.646 ] Network output: [ 0.02182 0.8796 0.9606 -6.054e-05 2.718e-05 0.1159 -4.563e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004339 0.001491 0.002899 0.003358 0.9905 0.9934 0.004415 0.9384 0.9576 0.01104 ] Network output: [ 0.02708 -0.0827 0.9133 -0.0001835 8.239e-05 1.114 -0.0001383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2457 0.1701 0.3382 0.1641 0.9854 0.9942 0.2464 0.7721 0.9504 0.6409 ] Network output: [ -0.03538 0.16 1.1 0.0001328 -5.962e-05 0.811 0.0001001 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06838 0.06448 0.1652 0.136 0.9894 0.9936 0.06842 0.9202 0.9525 0.1999 ] Network output: [ -0.03097 0.06306 1.084 0.0001689 -7.583e-05 0.9152 0.0001273 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08273 0.08189 0.1791 0.1549 0.9853 0.9915 0.08274 0.8763 0.9356 0.1935 ] Network output: [ -0.008574 1.004 0.01493 -2.749e-06 1.234e-06 0.9986 -2.072e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02795 Epoch 5093 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04956 0.8683 0.9318 -4.966e-05 2.229e-05 0.1005 -3.743e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003241 -0.002829 -0.01109 0.006811 0.9675 0.9723 0.006265 0.8895 0.8897 0.02106 ] Network output: [ 0.9761 0.1025 -0.00248 1.335e-06 -5.995e-07 -0.05224 1.006e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2301 -0.006069 -0.1899 0.1769 0.9837 0.9934 0.2571 0.7634 0.9458 0.6474 ] Network output: [ 0.02179 0.8805 0.9603 -6.14e-05 2.756e-05 0.1154 -4.627e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004333 0.001482 0.002912 0.003289 0.9905 0.9934 0.004409 0.9387 0.9577 0.01108 ] Network output: [ 0.02246 -0.05832 0.9135 -0.0001997 8.965e-05 1.099 -0.0001505 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2454 0.1697 0.3391 0.1586 0.9854 0.9942 0.2461 0.7727 0.9506 0.6428 ] Network output: [ -0.03487 0.1601 1.1 0.0001328 -5.96e-05 0.8104 0.0001001 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06814 0.06423 0.1649 0.1354 0.9894 0.9936 0.06818 0.9203 0.9527 0.1998 ] Network output: [ -0.03022 0.05958 1.084 0.0001704 -7.651e-05 0.9175 0.0001284 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08253 0.08168 0.1791 0.1549 0.9853 0.9915 0.08254 0.8765 0.9358 0.1937 ] Network output: [ -0.007165 0.9973 0.01471 1.455e-06 -6.531e-07 1.002 1.096e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02722 Epoch 5094 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04987 0.8671 0.9316 -4.901e-05 2.2e-05 0.1013 -3.693e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003235 -0.00283 -0.01113 0.00686 0.9675 0.9723 0.006256 0.8898 0.89 0.02109 ] Network output: [ 0.9824 0.08662 -0.00543 1.389e-05 -6.236e-06 -0.04589 1.047e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2295 -0.006689 -0.1927 0.1802 0.9837 0.9934 0.2565 0.7638 0.946 0.6492 ] Network output: [ 0.02144 0.8809 0.9605 -6.205e-05 2.786e-05 0.1155 -4.677e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004314 0.001468 0.002878 0.003338 0.9905 0.9935 0.00439 0.9389 0.9579 0.01107 ] Network output: [ 0.02622 -0.08042 0.9147 -0.0001877 8.425e-05 1.113 -0.0001414 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2443 0.1686 0.3384 0.1629 0.9854 0.9942 0.245 0.7731 0.9507 0.6442 ] Network output: [ -0.03523 0.1596 1.1 0.000132 -5.928e-05 0.8115 9.951e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.068 0.06408 0.1651 0.1359 0.9894 0.9936 0.06804 0.9206 0.9528 0.2001 ] Network output: [ -0.0309 0.06105 1.084 0.0001687 -7.572e-05 0.9172 0.0001271 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08239 0.08155 0.1793 0.155 0.9853 0.9915 0.0824 0.8768 0.9359 0.1939 ] Network output: [ -0.008146 1.003 0.01438 -1.153e-06 5.175e-07 0.9992 -8.687e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02754 Epoch 5095 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04925 0.8695 0.9317 -5.056e-05 2.27e-05 0.1001 -3.81e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003228 -0.002823 -0.01112 0.006824 0.9675 0.9723 0.006244 0.8901 0.8902 0.0211 ] Network output: [ 0.9771 0.101 -0.002685 7.303e-06 -3.279e-06 -0.0526 5.504e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2289 -0.006431 -0.1921 0.1775 0.9838 0.9934 0.2557 0.7644 0.9461 0.6505 ] Network output: [ 0.0214 0.8818 0.9602 -6.289e-05 2.823e-05 0.115 -4.74e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004307 0.001459 0.00289 0.003276 0.9905 0.9935 0.004382 0.9391 0.958 0.01111 ] Network output: [ 0.022 -0.05852 0.915 -0.0002023 9.081e-05 1.099 -0.0001524 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.244 0.1681 0.3392 0.158 0.9854 0.9942 0.2447 0.7737 0.9508 0.646 ] Network output: [ -0.03473 0.1598 1.099 0.000132 -5.925e-05 0.8109 9.946e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06778 0.06385 0.1649 0.1354 0.9894 0.9936 0.06782 0.9208 0.9529 0.2001 ] Network output: [ -0.03018 0.05798 1.084 0.00017 -7.632e-05 0.9192 0.0001281 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08221 0.08136 0.1793 0.1551 0.9853 0.9915 0.08222 0.877 0.936 0.194 ] Network output: [ -0.006817 0.9971 0.01409 2.731e-06 -1.226e-06 1.002 2.058e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02689 Epoch 5096 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04951 0.8684 0.9315 -5.003e-05 2.246e-05 0.1008 -3.77e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003223 -0.002824 -0.01116 0.006869 0.9675 0.9723 0.006235 0.8903 0.8904 0.02113 ] Network output: [ 0.9829 0.08656 -0.005413 1.89e-05 -8.484e-06 -0.04687 1.424e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2283 -0.006996 -0.1948 0.1805 0.9838 0.9934 0.2551 0.7648 0.9463 0.6523 ] Network output: [ 0.02106 0.8822 0.9603 -6.356e-05 2.853e-05 0.1151 -4.79e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004289 0.001445 0.002857 0.003319 0.9905 0.9935 0.004364 0.9394 0.9581 0.0111 ] Network output: [ 0.02545 -0.07842 0.916 -0.0001914 8.592e-05 1.111 -0.0001442 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2429 0.1671 0.3385 0.1618 0.9854 0.9942 0.2437 0.7741 0.9509 0.6473 ] Network output: [ -0.03504 0.1594 1.099 0.0001313 -5.893e-05 0.8118 9.893e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06764 0.06371 0.165 0.1358 0.9894 0.9936 0.06768 0.9211 0.953 0.2003 ] Network output: [ -0.03081 0.05931 1.084 0.0001683 -7.557e-05 0.919 0.0001269 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08207 0.08122 0.1795 0.1552 0.9854 0.9915 0.08208 0.8774 0.9361 0.1943 ] Network output: [ -0.007743 1.002 0.01383 3.146e-07 -1.412e-07 0.9996 2.371e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02717 Epoch 5097 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04893 0.8706 0.9316 -5.148e-05 2.311e-05 0.09975 -3.88e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003216 -0.002817 -0.01116 0.006836 0.9675 0.9723 0.006223 0.8906 0.8906 0.02113 ] Network output: [ 0.978 0.09935 -0.0027 1.309e-05 -5.879e-06 -0.05264 9.868e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2276 -0.006768 -0.1943 0.1781 0.9838 0.9934 0.2544 0.7654 0.9464 0.6536 ] Network output: [ 0.02102 0.883 0.9601 -6.438e-05 2.89e-05 0.1146 -4.852e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004282 0.001437 0.002869 0.003263 0.9905 0.9935 0.004357 0.9396 0.9583 0.01114 ] Network output: [ 0.02155 -0.05839 0.9164 -0.0002048 9.193e-05 1.098 -0.0001543 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2426 0.1666 0.3393 0.1573 0.9854 0.9942 0.2433 0.7747 0.951 0.6491 ] Network output: [ -0.03455 0.1597 1.099 0.0001312 -5.889e-05 0.8113 9.887e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06743 0.06349 0.1647 0.1353 0.9894 0.9936 0.06747 0.9213 0.9532 0.2003 ] Network output: [ -0.03012 0.05655 1.084 0.0001696 -7.612e-05 0.9207 0.0001278 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0819 0.08105 0.1794 0.1552 0.9854 0.9915 0.08191 0.8776 0.9363 0.1944 ] Network output: [ -0.006468 0.9968 0.01348 3.976e-06 -1.785e-06 1.003 2.997e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02656 Epoch 5098 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04916 0.8697 0.9314 -5.106e-05 2.292e-05 0.1004 -3.848e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003211 -0.002818 -0.01119 0.006878 0.9675 0.9723 0.006215 0.8909 0.8908 0.02116 ] Network output: [ 0.9834 0.08603 -0.005288 2.391e-05 -1.073e-05 -0.04745 1.802e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2271 -0.007288 -0.1969 0.181 0.9838 0.9934 0.2537 0.7658 0.9465 0.6553 ] Network output: [ 0.02068 0.8835 0.9602 -6.506e-05 2.921e-05 0.1146 -4.903e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004265 0.001424 0.002836 0.003302 0.9905 0.9935 0.00434 0.9398 0.9584 0.01113 ] Network output: [ 0.02477 -0.07656 0.9173 -0.0001947 8.742e-05 1.109 -0.0001467 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2416 0.1656 0.3386 0.1608 0.9854 0.9942 0.2423 0.7751 0.9512 0.6504 ] Network output: [ -0.03483 0.1594 1.099 0.0001305 -5.857e-05 0.812 9.833e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0673 0.06335 0.1648 0.1357 0.9895 0.9936 0.06734 0.9215 0.9533 0.2005 ] Network output: [ -0.0307 0.05774 1.084 0.000168 -7.542e-05 0.9206 0.0001266 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08177 0.08091 0.1796 0.1553 0.9854 0.9916 0.08177 0.878 0.9364 0.1946 ] Network output: [ -0.007354 1.001 0.0133 1.731e-06 -7.771e-07 1 1.305e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02681 Epoch 5099 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04859 0.8717 0.9315 -5.245e-05 2.355e-05 0.09943 -3.953e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003204 -0.002811 -0.01119 0.006848 0.9675 0.9723 0.006203 0.8912 0.891 0.02117 ] Network output: [ 0.9788 0.09765 -0.002618 1.866e-05 -8.376e-06 -0.05256 1.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2264 -0.007079 -0.1964 0.1788 0.9838 0.9934 0.253 0.7664 0.9466 0.6565 ] Network output: [ 0.02064 0.8842 0.96 -6.587e-05 2.957e-05 0.1142 -4.965e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004258 0.001416 0.002847 0.00325 0.9905 0.9935 0.004333 0.94 0.9585 0.01117 ] Network output: [ 0.02111 -0.05788 0.9176 -0.0002072 9.3e-05 1.097 -0.0001561 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2413 0.1652 0.3393 0.1566 0.9854 0.9942 0.242 0.7756 0.9513 0.6521 ] Network output: [ -0.03435 0.1597 1.098 0.0001304 -5.854e-05 0.8115 9.827e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0671 0.06314 0.1646 0.1352 0.9895 0.9936 0.06714 0.9217 0.9534 0.2004 ] Network output: [ -0.03004 0.05516 1.083 0.0001691 -7.592e-05 0.9222 0.0001274 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0816 0.08074 0.1795 0.1553 0.9854 0.9916 0.08161 0.8782 0.9365 0.1947 ] Network output: [ -0.006125 0.9963 0.01294 5.238e-06 -2.352e-06 1.003 3.948e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02624 Epoch 5100 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04879 0.8708 0.9314 -5.21e-05 2.339e-05 0.1 -3.927e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003199 -0.002811 -0.01123 0.006888 0.9675 0.9723 0.006195 0.8914 0.8913 0.0212 ] Network output: [ 0.9839 0.08523 -0.005134 2.887e-05 -1.296e-05 -0.04781 2.176e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2258 -0.007563 -0.1989 0.1814 0.9838 0.9934 0.2524 0.7668 0.9468 0.6582 ] Network output: [ 0.0203 0.8847 0.9602 -6.657e-05 2.989e-05 0.1142 -5.017e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004242 0.001403 0.002815 0.003286 0.9906 0.9935 0.004316 0.9402 0.9586 0.01116 ] Network output: [ 0.02419 -0.07473 0.9184 -0.0001977 8.876e-05 1.107 -0.000149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2403 0.1642 0.3385 0.1599 0.9854 0.9942 0.241 0.776 0.9514 0.6534 ] Network output: [ -0.03461 0.1595 1.098 0.0001297 -5.822e-05 0.8121 9.773e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06697 0.063 0.1646 0.1356 0.9895 0.9936 0.067 0.9219 0.9535 0.2007 ] Network output: [ -0.0306 0.05622 1.084 0.0001676 -7.525e-05 0.9221 0.0001263 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08147 0.08061 0.1797 0.1554 0.9854 0.9916 0.08148 0.8785 0.9366 0.1949 ] Network output: [ -0.006999 1 0.01284 3.102e-06 -1.393e-06 1.001 2.338e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02646 Epoch 5101 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04825 0.8727 0.9315 -5.345e-05 2.4e-05 0.09911 -4.028e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003193 -0.002805 -0.01123 0.006861 0.9675 0.9723 0.006184 0.8917 0.8914 0.0212 ] Network output: [ 0.9795 0.09597 -0.002482 2.4e-05 -1.078e-05 -0.0524 1.809e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2252 -0.007367 -0.1984 0.1794 0.9838 0.9934 0.2517 0.7674 0.9469 0.6594 ] Network output: [ 0.02025 0.8854 0.96 -6.738e-05 3.025e-05 0.1138 -5.078e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004235 0.001396 0.002825 0.003237 0.9906 0.9935 0.004309 0.9404 0.9587 0.01119 ] Network output: [ 0.0207 -0.05701 0.9187 -0.0002095 9.404e-05 1.096 -0.0001579 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2399 0.1638 0.3393 0.1559 0.9854 0.9942 0.2406 0.7766 0.9515 0.655 ] Network output: [ -0.03414 0.1597 1.097 0.0001296 -5.818e-05 0.8116 9.767e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06678 0.06281 0.1644 0.1351 0.9895 0.9936 0.06682 0.9221 0.9536 0.2006 ] Network output: [ -0.02995 0.05379 1.083 0.0001687 -7.572e-05 0.9237 0.0001271 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08132 0.08045 0.1796 0.1554 0.9854 0.9916 0.08132 0.8787 0.9368 0.1949 ] Network output: [ -0.005813 0.9956 0.01247 6.468e-06 -2.904e-06 1.004 4.875e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02592 Epoch 5102 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04843 0.8719 0.9314 -5.317e-05 2.387e-05 0.09966 -4.007e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003188 -0.002805 -0.01126 0.006899 0.9675 0.9723 0.006176 0.892 0.8917 0.02123 ] Network output: [ 0.9844 0.08414 -0.004949 3.38e-05 -1.517e-05 -0.04794 2.547e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2246 -0.007823 -0.2009 0.182 0.9838 0.9934 0.2511 0.7677 0.947 0.661 ] Network output: [ 0.01992 0.8859 0.9602 -6.808e-05 3.056e-05 0.1138 -5.131e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004219 0.001384 0.002793 0.003271 0.9906 0.9935 0.004293 0.9406 0.9589 0.01119 ] Network output: [ 0.02369 -0.07298 0.9194 -0.0002004 8.996e-05 1.105 -0.000151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.239 0.1628 0.3384 0.159 0.9854 0.9942 0.2397 0.777 0.9516 0.6563 ] Network output: [ -0.03439 0.1596 1.098 0.0001289 -5.786e-05 0.8122 9.712e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06665 0.06267 0.1644 0.1355 0.9895 0.9936 0.06669 0.9223 0.9537 0.2008 ] Network output: [ -0.0305 0.05479 1.083 0.0001672 -7.506e-05 0.9236 0.000126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08118 0.08031 0.1798 0.1555 0.9854 0.9916 0.08119 0.879 0.9369 0.1951 ] Network output: [ -0.006697 0.9997 0.01243 4.351e-06 -1.953e-06 1.001 3.279e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02612 Epoch 5103 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04789 0.8737 0.9315 -5.449e-05 2.446e-05 0.09881 -4.107e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003181 -0.002799 -0.01126 0.006873 0.9675 0.9723 0.006164 0.8922 0.8918 0.02124 ] Network output: [ 0.9801 0.09417 -0.00225 2.92e-05 -1.311e-05 -0.05206 2.201e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.224 -0.007632 -0.2005 0.1801 0.9838 0.9934 0.2503 0.7683 0.9472 0.6622 ] Network output: [ 0.01987 0.8866 0.96 -6.888e-05 3.092e-05 0.1134 -5.191e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004212 0.001377 0.002803 0.003225 0.9906 0.9935 0.004286 0.9408 0.959 0.01122 ] Network output: [ 0.02031 -0.05595 0.9198 -0.0002116 9.5e-05 1.095 -0.0001595 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2386 0.1624 0.3391 0.1551 0.9854 0.9942 0.2393 0.7775 0.9517 0.6578 ] Network output: [ -0.03391 0.1598 1.097 0.0001288 -5.782e-05 0.8116 9.706e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06647 0.06249 0.1642 0.135 0.9895 0.9937 0.06651 0.9225 0.9538 0.2007 ] Network output: [ -0.02986 0.0525 1.083 0.0001682 -7.551e-05 0.925 0.0001268 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08104 0.08017 0.1797 0.1555 0.9854 0.9916 0.08105 0.8792 0.937 0.1952 ] Network output: [ -0.005537 0.995 0.01202 7.58e-06 -3.403e-06 1.004 5.713e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02561 Epoch 5104 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04806 0.873 0.9314 -5.425e-05 2.435e-05 0.09932 -4.088e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002799 -0.01129 0.00691 0.9675 0.9723 0.006157 0.8925 0.8921 0.02126 ] Network output: [ 0.985 0.08262 -0.004678 3.874e-05 -1.739e-05 -0.0477 2.92e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2234 -0.008065 -0.2029 0.1826 0.9838 0.9934 0.2498 0.7686 0.9473 0.6638 ] Network output: [ 0.01953 0.8871 0.9601 -6.96e-05 3.125e-05 0.1134 -5.245e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004197 0.001365 0.002771 0.003257 0.9906 0.9935 0.004271 0.941 0.9591 0.01122 ] Network output: [ 0.02326 -0.07141 0.9203 -0.0002027 9.098e-05 1.104 -0.0001527 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2377 0.1615 0.3383 0.1582 0.9854 0.9942 0.2384 0.7779 0.9518 0.6591 ] Network output: [ -0.03415 0.1598 1.097 0.000128 -5.749e-05 0.8121 9.65e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06635 0.06235 0.1642 0.1353 0.9895 0.9937 0.06638 0.9227 0.9539 0.2009 ] Network output: [ -0.03039 0.0535 1.083 0.0001667 -7.485e-05 0.925 0.0001257 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0809 0.08003 0.1799 0.1556 0.9854 0.9916 0.08091 0.8795 0.9371 0.1954 ] Network output: [ -0.006438 0.999 0.01202 5.42e-06 -2.433e-06 1.002 4.085e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0258 Epoch 5105 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04753 0.8747 0.9315 -5.556e-05 2.494e-05 0.09851 -4.187e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.002792 -0.01129 0.006887 0.9675 0.9723 0.006145 0.8927 0.8922 0.02127 ] Network output: [ 0.9806 0.09211 -0.001876 3.429e-05 -1.539e-05 -0.05139 2.584e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2228 -0.00787 -0.2024 0.1808 0.9838 0.9934 0.249 0.7692 0.9474 0.665 ] Network output: [ 0.01949 0.8878 0.96 -7.039e-05 3.16e-05 0.113 -5.305e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00419 0.001359 0.002783 0.003213 0.9906 0.9935 0.004264 0.9412 0.9592 0.01125 ] Network output: [ 0.01991 -0.05481 0.9207 -0.0002135 9.586e-05 1.093 -0.0001609 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2373 0.1611 0.339 0.1544 0.9855 0.9942 0.238 0.7784 0.952 0.6606 ] Network output: [ -0.03366 0.1601 1.096 0.000128 -5.745e-05 0.8116 9.644e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06618 0.06218 0.1639 0.1349 0.9895 0.9937 0.06621 0.9229 0.954 0.2008 ] Network output: [ -0.02974 0.05135 1.083 0.0001677 -7.528e-05 0.9263 0.0001264 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08077 0.07989 0.1798 0.1556 0.9854 0.9916 0.08078 0.8797 0.9372 0.1954 ] Network output: [ -0.005275 0.9946 0.01156 8.564e-06 -3.845e-06 1.004 6.454e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0253 Epoch 5106 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0477 0.874 0.9314 -5.534e-05 2.485e-05 0.09901 -4.171e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003165 -0.002792 -0.01132 0.006923 0.9675 0.9723 0.006138 0.893 0.8925 0.02129 ] Network output: [ 0.9854 0.08068 -0.004306 4.369e-05 -1.961e-05 -0.04707 3.292e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2223 -0.008283 -0.2049 0.1833 0.9838 0.9934 0.2485 0.7695 0.9475 0.6665 ] Network output: [ 0.01916 0.8883 0.9601 -7.112e-05 3.193e-05 0.113 -5.36e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004175 0.001348 0.00275 0.003245 0.9906 0.9935 0.004249 0.9414 0.9593 0.01125 ] Network output: [ 0.02288 -0.07006 0.9212 -0.0002045 9.182e-05 1.102 -0.0001541 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2364 0.1602 0.3381 0.1575 0.9855 0.9942 0.2371 0.7787 0.9521 0.6618 ] Network output: [ -0.0339 0.1601 1.096 0.0001272 -5.711e-05 0.812 9.587e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06605 0.06205 0.1639 0.1352 0.9895 0.9937 0.06609 0.9231 0.9541 0.201 ] Network output: [ -0.03028 0.05238 1.083 0.0001662 -7.462e-05 0.9262 0.0001253 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08064 0.07976 0.1799 0.1556 0.9854 0.9916 0.08065 0.88 0.9373 0.1956 ] Network output: [ -0.006191 0.9986 0.01159 6.355e-06 -2.853e-06 1.002 4.79e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02549 Epoch 5107 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04717 0.8757 0.9316 -5.665e-05 2.543e-05 0.09821 -4.27e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003159 -0.002786 -0.01132 0.0069 0.9675 0.9723 0.006127 0.8932 0.8926 0.0213 ] Network output: [ 0.9811 0.08993 -0.001387 3.92e-05 -1.76e-05 -0.0505 2.954e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2216 -0.008078 -0.2043 0.1815 0.9838 0.9934 0.2477 0.7701 0.9476 0.6676 ] Network output: [ 0.01912 0.8889 0.96 -7.191e-05 3.228e-05 0.1126 -5.419e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004169 0.001342 0.002762 0.003202 0.9906 0.9935 0.004242 0.9416 0.9594 0.01128 ] Network output: [ 0.01951 -0.05353 0.9217 -0.0002153 9.664e-05 1.092 -0.0001622 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2361 0.1598 0.3388 0.1537 0.9855 0.9942 0.2368 0.7792 0.9522 0.6633 ] Network output: [ -0.03339 0.1604 1.095 0.0001271 -5.707e-05 0.8114 9.581e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06589 0.06188 0.1637 0.1348 0.9895 0.9937 0.06593 0.9233 0.9542 0.2009 ] Network output: [ -0.0296 0.0503 1.082 0.0001672 -7.505e-05 0.9274 0.000126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08051 0.07963 0.1798 0.1556 0.9854 0.9916 0.08052 0.8802 0.9374 0.1957 ] Network output: [ -0.004999 0.9941 0.0111 9.507e-06 -4.268e-06 1.005 7.165e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.025 Epoch 5108 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04734 0.8749 0.9315 -5.646e-05 2.535e-05 0.0987 -4.255e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003154 -0.002786 -0.01135 0.006936 0.9676 0.9723 0.006119 0.8935 0.8928 0.02132 ] Network output: [ 0.9859 0.07855 -0.003905 4.854e-05 -2.179e-05 -0.04626 3.658e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2211 -0.008476 -0.2068 0.184 0.9838 0.9934 0.2472 0.7704 0.9478 0.6691 ] Network output: [ 0.01879 0.8894 0.9601 -7.264e-05 3.261e-05 0.1125 -5.474e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004154 0.001332 0.002728 0.003234 0.9906 0.9935 0.004227 0.9418 0.9595 0.01127 ] Network output: [ 0.02256 -0.06879 0.922 -0.0002061 9.251e-05 1.101 -0.0001553 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2352 0.159 0.3378 0.1568 0.9855 0.9942 0.2359 0.7796 0.9523 0.6644 ] Network output: [ -0.03364 0.1605 1.095 0.0001264 -5.673e-05 0.8118 9.523e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06577 0.06176 0.1637 0.1351 0.9895 0.9937 0.06581 0.9235 0.9543 0.2011 ] Network output: [ -0.03015 0.05132 1.082 0.0001657 -7.438e-05 0.9273 0.0001249 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08038 0.0795 0.1799 0.1557 0.9854 0.9916 0.08039 0.8805 0.9375 0.1958 ] Network output: [ -0.005947 0.998 0.0112 7.252e-06 -3.256e-06 1.003 5.466e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02519 Epoch 5109 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04681 0.8766 0.9316 -5.779e-05 2.595e-05 0.0979 -4.355e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003148 -0.002779 -0.01134 0.006913 0.9676 0.9723 0.006108 0.8937 0.893 0.02133 ] Network output: [ 0.9814 0.08787 -0.0008712 4.387e-05 -1.969e-05 -0.04962 3.306e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2204 -0.008256 -0.2062 0.1823 0.9838 0.9934 0.2464 0.7709 0.9479 0.6702 ] Network output: [ 0.01876 0.8901 0.96 -7.343e-05 3.297e-05 0.1121 -5.534e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004148 0.001326 0.002741 0.003191 0.9906 0.9936 0.004221 0.942 0.9596 0.0113 ] Network output: [ 0.01913 -0.05201 0.9225 -0.0002168 9.735e-05 1.09 -0.0001634 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2349 0.1586 0.3385 0.153 0.9855 0.9942 0.2356 0.7801 0.9524 0.6658 ] Network output: [ -0.03311 0.1608 1.095 0.0001263 -5.67e-05 0.8112 9.519e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06562 0.0616 0.1634 0.1347 0.9896 0.9937 0.06565 0.9236 0.9544 0.201 ] Network output: [ -0.02945 0.04922 1.082 0.0001666 -7.481e-05 0.9286 0.0001256 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08026 0.07938 0.1798 0.1557 0.9854 0.9916 0.08027 0.8807 0.9376 0.1959 ] Network output: [ -0.004716 0.9934 0.01069 1.048e-05 -4.704e-06 1.005 7.897e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0247 Epoch 5110 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04699 0.8758 0.9316 -5.761e-05 2.586e-05 0.09839 -4.341e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003144 -0.002779 -0.01138 0.00695 0.9676 0.9723 0.006101 0.8939 0.8932 0.02135 ] Network output: [ 0.9864 0.07643 -0.003557 5.325e-05 -2.391e-05 -0.04546 4.013e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.22 -0.008647 -0.2087 0.1848 0.9839 0.9934 0.2459 0.7712 0.948 0.6717 ] Network output: [ 0.01842 0.8906 0.9602 -7.417e-05 3.33e-05 0.1121 -5.589e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004134 0.001317 0.002706 0.003224 0.9906 0.9936 0.004207 0.9422 0.9597 0.0113 ] Network output: [ 0.02232 -0.06754 0.9227 -0.0002073 9.306e-05 1.099 -0.0001562 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.234 0.1578 0.3374 0.1562 0.9855 0.9942 0.2347 0.7804 0.9525 0.667 ] Network output: [ -0.03337 0.1608 1.095 0.0001255 -5.634e-05 0.8117 9.459e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0655 0.06148 0.1634 0.135 0.9896 0.9937 0.06553 0.9238 0.9544 0.2011 ] Network output: [ -0.03003 0.05023 1.082 0.0001651 -7.413e-05 0.9285 0.0001244 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08013 0.07924 0.18 0.1558 0.9854 0.9916 0.08013 0.881 0.9377 0.1961 ] Network output: [ -0.005732 0.9973 0.01088 8.117e-06 -3.644e-06 1.003 6.117e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0249 Epoch 5111 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04644 0.8776 0.9317 -5.897e-05 2.647e-05 0.09758 -4.444e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003137 -0.002773 -0.01137 0.006926 0.9676 0.9723 0.00609 0.8942 0.8933 0.02136 ] Network output: [ 0.9817 0.08598 -0.0003622 4.829e-05 -2.168e-05 -0.04881 3.639e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2193 -0.008409 -0.208 0.183 0.9839 0.9934 0.2452 0.7718 0.9481 0.6727 ] Network output: [ 0.0184 0.8913 0.96 -7.495e-05 3.365e-05 0.1116 -5.649e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004128 0.001312 0.002721 0.00318 0.9906 0.9936 0.004201 0.9423 0.9598 0.01133 ] Network output: [ 0.01876 -0.05022 0.9232 -0.0002183 9.8e-05 1.089 -0.0001645 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2337 0.1575 0.3381 0.1523 0.9855 0.9942 0.2344 0.7809 0.9526 0.6684 ] Network output: [ -0.03283 0.1611 1.094 0.0001255 -5.634e-05 0.8111 9.457e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06535 0.06133 0.1632 0.1345 0.9896 0.9937 0.06539 0.924 0.9545 0.201 ] Network output: [ -0.0293 0.04808 1.081 0.0001661 -7.458e-05 0.9297 0.0001252 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08001 0.07913 0.1799 0.1558 0.9854 0.9916 0.08002 0.8811 0.9378 0.1961 ] Network output: [ -0.004459 0.9925 0.01035 1.142e-05 -5.126e-06 1.006 8.604e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0244 Epoch 5112 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04663 0.8768 0.9317 -5.877e-05 2.638e-05 0.09806 -4.429e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003133 -0.002773 -0.01141 0.006964 0.9676 0.9723 0.006083 0.8944 0.8935 0.02138 ] Network output: [ 0.9869 0.07419 -0.003248 5.79e-05 -2.599e-05 -0.04457 4.363e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2188 -0.0088 -0.2107 0.1856 0.9839 0.9934 0.2447 0.772 0.9482 0.6741 ] Network output: [ 0.01804 0.8918 0.9602 -7.569e-05 3.398e-05 0.1116 -5.705e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004114 0.001302 0.002683 0.003215 0.9907 0.9936 0.004187 0.9425 0.9599 0.01132 ] Network output: [ 0.02216 -0.06641 0.9233 -0.0002082 9.345e-05 1.098 -0.0001569 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2328 0.1567 0.337 0.1556 0.9855 0.9942 0.2335 0.7812 0.9526 0.6695 ] Network output: [ -0.03312 0.1612 1.094 0.0001247 -5.596e-05 0.8115 9.394e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06524 0.06121 0.1631 0.1349 0.9896 0.9937 0.06527 0.9242 0.9546 0.2012 ] Network output: [ -0.02993 0.04914 1.082 0.0001645 -7.386e-05 0.9297 0.000124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07988 0.079 0.18 0.1559 0.9854 0.9916 0.07989 0.8814 0.9379 0.1963 ] Network output: [ -0.005573 0.9967 0.01061 8.833e-06 -3.965e-06 1.004 6.657e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02462 Epoch 5113 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04606 0.8785 0.9319 -6.018e-05 2.702e-05 0.09723 -4.535e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003127 -0.002766 -0.0114 0.006939 0.9676 0.9723 0.006072 0.8946 0.8937 0.02139 ] Network output: [ 0.9819 0.08403 0.0002174 5.255e-05 -2.359e-05 -0.04785 3.96e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2182 -0.008539 -0.2098 0.1837 0.9839 0.9934 0.2439 0.7726 0.9483 0.6752 ] Network output: [ 0.01803 0.8924 0.9601 -7.648e-05 3.433e-05 0.1111 -5.764e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004109 0.001298 0.0027 0.00317 0.9907 0.9936 0.004181 0.9427 0.96 0.01136 ] Network output: [ 0.01839 -0.04834 0.9239 -0.0002196 9.858e-05 1.087 -0.0001655 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2325 0.1564 0.3378 0.1516 0.9855 0.9942 0.2332 0.7817 0.9527 0.6708 ] Network output: [ -0.03254 0.1614 1.093 0.0001247 -5.596e-05 0.8109 9.395e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06509 0.06106 0.1629 0.1344 0.9896 0.9937 0.06513 0.9243 0.9547 0.2011 ] Network output: [ -0.02915 0.04698 1.081 0.0001656 -7.434e-05 0.9309 0.0001248 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07977 0.07889 0.1799 0.1559 0.9854 0.9916 0.07978 0.8816 0.938 0.1963 ] Network output: [ -0.004233 0.9918 0.01001 1.221e-05 -5.48e-06 1.007 9.2e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02411 Epoch 5114 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04628 0.8777 0.9318 -5.994e-05 2.691e-05 0.09774 -4.517e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003123 -0.002767 -0.01143 0.006979 0.9676 0.9724 0.006066 0.8949 0.8939 0.02141 ] Network output: [ 0.9875 0.07156 -0.002888 6.256e-05 -2.809e-05 -0.04336 4.715e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2177 -0.008934 -0.2125 0.1865 0.9839 0.9934 0.2435 0.7728 0.9484 0.6766 ] Network output: [ 0.01767 0.8929 0.9603 -7.722e-05 3.467e-05 0.1111 -5.82e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004095 0.001289 0.00266 0.003207 0.9907 0.9936 0.004167 0.9428 0.9601 0.01135 ] Network output: [ 0.02204 -0.0656 0.9239 -0.0002086 9.367e-05 1.097 -0.0001572 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2316 0.1556 0.3365 0.1552 0.9855 0.9942 0.2323 0.782 0.9528 0.6719 ] Network output: [ -0.03286 0.1615 1.093 0.0001238 -5.557e-05 0.8113 9.328e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06498 0.06095 0.1629 0.1348 0.9896 0.9937 0.06502 0.9245 0.9548 0.2013 ] Network output: [ -0.02982 0.04821 1.081 0.0001639 -7.357e-05 0.9307 0.0001235 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07964 0.07876 0.18 0.1559 0.9854 0.9916 0.07965 0.8818 0.938 0.1965 ] Network output: [ -0.005458 0.9965 0.01031 9.321e-06 -4.184e-06 1.004 7.024e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02435 Epoch 5115 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04569 0.8795 0.932 -6.14e-05 2.757e-05 0.09687 -4.627e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003117 -0.00276 -0.01142 0.006953 0.9676 0.9724 0.006055 0.8951 0.894 0.02141 ] Network output: [ 0.982 0.08187 0.0009508 5.667e-05 -2.544e-05 -0.04658 4.271e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2171 -0.008639 -0.2114 0.1845 0.9839 0.9934 0.2427 0.7734 0.9485 0.6776 ] Network output: [ 0.01769 0.8936 0.9601 -7.8e-05 3.502e-05 0.1106 -5.878e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00409 0.001285 0.002681 0.003161 0.9907 0.9936 0.004162 0.943 0.9602 0.01139 ] Network output: [ 0.01797 -0.04644 0.9245 -0.0002207 9.907e-05 1.085 -0.0001663 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2313 0.1554 0.3374 0.151 0.9855 0.9942 0.232 0.7825 0.9529 0.6732 ] Network output: [ -0.03223 0.1618 1.093 0.0001238 -5.559e-05 0.8106 9.332e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06485 0.06081 0.1626 0.1343 0.9896 0.9937 0.06488 0.9247 0.9548 0.2012 ] Network output: [ -0.02897 0.04604 1.081 0.000165 -7.408e-05 0.9318 0.0001244 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07955 0.07866 0.1799 0.1559 0.9854 0.9916 0.07956 0.882 0.9381 0.1965 ] Network output: [ -0.004 0.9914 0.009602 1.286e-05 -5.774e-06 1.007 9.693e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02382 Epoch 5116 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04595 0.8785 0.9319 -6.111e-05 2.744e-05 0.09742 -4.606e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003113 -0.00276 -0.01146 0.006995 0.9676 0.9724 0.006049 0.8953 0.8942 0.02144 ] Network output: [ 0.988 0.06854 -0.002466 6.723e-05 -3.018e-05 -0.04181 5.067e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2167 -0.009042 -0.2144 0.1874 0.9839 0.9934 0.2423 0.7736 0.9486 0.6789 ] Network output: [ 0.01732 0.8941 0.9604 -7.874e-05 3.535e-05 0.1106 -5.934e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004076 0.001277 0.002639 0.003201 0.9907 0.9936 0.004148 0.9432 0.9603 0.01137 ] Network output: [ 0.02197 -0.06515 0.9245 -0.0002087 9.368e-05 1.096 -0.0001573 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2304 0.1546 0.336 0.1549 0.9855 0.9942 0.2311 0.7828 0.953 0.6742 ] Network output: [ -0.03259 0.162 1.093 0.0001229 -5.517e-05 0.811 9.261e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06474 0.06071 0.1626 0.1347 0.9896 0.9937 0.06478 0.9249 0.9549 0.2013 ] Network output: [ -0.02971 0.04743 1.081 0.0001632 -7.325e-05 0.9316 0.000123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07942 0.07853 0.18 0.156 0.9854 0.9916 0.07942 0.8823 0.9382 0.1967 ] Network output: [ -0.005342 0.9965 0.009978 9.678e-06 -4.345e-06 1.004 7.294e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0241 Epoch 5117 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04532 0.8805 0.9321 -6.266e-05 2.813e-05 0.0965 -4.722e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003107 -0.002753 -0.01145 0.006966 0.9676 0.9724 0.006037 0.8955 0.8943 0.02144 ] Network output: [ 0.982 0.07976 0.001788 6.054e-05 -2.718e-05 -0.04521 4.563e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.216 -0.008705 -0.213 0.1852 0.9839 0.9934 0.2415 0.7741 0.9487 0.6798 ] Network output: [ 0.01736 0.8948 0.9601 -7.951e-05 3.57e-05 0.11 -5.992e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004072 0.001274 0.002664 0.003153 0.9907 0.9936 0.004144 0.9434 0.9603 0.01141 ] Network output: [ 0.01751 -0.04437 0.9252 -0.0002217 9.951e-05 1.083 -0.000167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2302 0.1544 0.337 0.1503 0.9855 0.9942 0.2309 0.7832 0.9531 0.6755 ] Network output: [ -0.0319 0.1622 1.092 0.000123 -5.522e-05 0.8102 9.269e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06461 0.06058 0.1623 0.1342 0.9896 0.9937 0.06465 0.925 0.955 0.2012 ] Network output: [ -0.02876 0.04514 1.08 0.0001644 -7.383e-05 0.9327 0.0001239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07933 0.07844 0.1798 0.156 0.9854 0.9916 0.07934 0.8824 0.9383 0.1967 ] Network output: [ -0.003717 0.9909 0.009188 1.356e-05 -6.086e-06 1.007 1.022e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02353 Epoch 5118 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04562 0.8794 0.932 -6.23e-05 2.797e-05 0.09709 -4.695e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003103 -0.002754 -0.01149 0.007011 0.9676 0.9724 0.006032 0.8957 0.8945 0.02147 ] Network output: [ 0.9886 0.06555 -0.002118 7.177e-05 -3.222e-05 -0.0403 5.409e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2156 -0.009127 -0.2162 0.1884 0.9839 0.9935 0.2411 0.7744 0.9488 0.6812 ] Network output: [ 0.01697 0.8952 0.9605 -8.025e-05 3.603e-05 0.11 -6.048e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004058 0.001266 0.002616 0.003196 0.9907 0.9936 0.004129 0.9435 0.9604 0.01139 ] Network output: [ 0.022 -0.06485 0.925 -0.0002083 9.353e-05 1.095 -0.000157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2293 0.1536 0.3354 0.1546 0.9855 0.9942 0.23 0.7835 0.9531 0.6764 ] Network output: [ -0.03231 0.1624 1.092 0.000122 -5.477e-05 0.8107 9.194e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06451 0.06047 0.1623 0.1346 0.9896 0.9937 0.06454 0.9252 0.955 0.2014 ] Network output: [ -0.02959 0.04662 1.081 0.0001624 -7.293e-05 0.9325 0.0001224 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07919 0.0783 0.18 0.156 0.9854 0.9916 0.0792 0.8827 0.9383 0.1969 ] Network output: [ -0.005218 0.9962 0.009713 1.007e-05 -4.522e-06 1.005 7.591e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02386 Epoch 5119 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04494 0.8815 0.9323 -6.395e-05 2.871e-05 0.09609 -4.82e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003097 -0.002746 -0.01147 0.006978 0.9676 0.9724 0.00602 0.8959 0.8946 0.02146 ] Network output: [ 0.9818 0.07818 0.00259 6.4e-05 -2.873e-05 -0.04415 4.823e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2149 -0.008742 -0.2146 0.1859 0.9839 0.9935 0.2403 0.7749 0.9488 0.682 ] Network output: [ 0.01703 0.8959 0.9602 -8.102e-05 3.637e-05 0.1095 -6.106e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004054 0.001264 0.002645 0.003143 0.9907 0.9936 0.004126 0.9437 0.9605 0.01144 ] Network output: [ 0.01704 -0.04181 0.9257 -0.0002226 9.995e-05 1.081 -0.0001678 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2291 0.1535 0.3366 0.1496 0.9855 0.9943 0.2298 0.784 0.9532 0.6777 ] Network output: [ -0.03156 0.1626 1.091 0.0001222 -5.486e-05 0.8099 9.209e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06438 0.06034 0.162 0.1341 0.9896 0.9937 0.06441 0.9253 0.9551 0.2013 ] Network output: [ -0.02854 0.04406 1.08 0.0001639 -7.359e-05 0.9338 0.0001235 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07911 0.07822 0.1798 0.156 0.9854 0.9916 0.07912 0.8828 0.9384 0.1968 ] Network output: [ -0.003405 0.9898 0.008864 1.441e-05 -6.469e-06 1.008 1.086e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02325 Epoch 5120 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0453 0.8802 0.9322 -6.351e-05 2.851e-05 0.09674 -4.786e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003094 -0.002747 -0.01151 0.007027 0.9676 0.9724 0.006016 0.8961 0.8948 0.02149 ] Network output: [ 0.9893 0.0628 -0.001974 7.614e-05 -3.418e-05 -0.03906 5.738e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2146 -0.009198 -0.2181 0.1893 0.9839 0.9935 0.24 0.7751 0.9489 0.6834 ] Network output: [ 0.01661 0.8964 0.9606 -8.176e-05 3.67e-05 0.1095 -6.161e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00404 0.001256 0.002592 0.003191 0.9907 0.9936 0.004111 0.9438 0.9606 0.01142 ] Network output: [ 0.02216 -0.06463 0.9253 -0.0002077 9.323e-05 1.094 -0.0001565 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2282 0.1527 0.3347 0.1544 0.9855 0.9943 0.2289 0.7842 0.9533 0.6786 ] Network output: [ -0.03206 0.1627 1.091 0.0001211 -5.438e-05 0.8105 9.128e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06428 0.06024 0.162 0.1345 0.9896 0.9937 0.06431 0.9255 0.9552 0.2015 ] Network output: [ -0.02951 0.04564 1.081 0.0001617 -7.261e-05 0.9335 0.0001219 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07897 0.07808 0.18 0.1561 0.9854 0.9916 0.07898 0.8831 0.9385 0.1971 ] Network output: [ -0.005149 0.9956 0.009581 1.046e-05 -4.697e-06 1.005 7.885e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02363 Epoch 5121 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04456 0.8825 0.9325 -6.529e-05 2.931e-05 0.09563 -4.921e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003088 -0.002739 -0.01149 0.006989 0.9676 0.9724 0.006004 0.8963 0.8949 0.02149 ] Network output: [ 0.9816 0.07707 0.003355 6.708e-05 -3.011e-05 -0.04337 5.055e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2139 -0.008759 -0.2161 0.1865 0.9839 0.9935 0.2392 0.7756 0.949 0.6842 ] Network output: [ 0.0167 0.8971 0.9603 -8.252e-05 3.705e-05 0.1089 -6.219e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004037 0.001254 0.002626 0.003133 0.9907 0.9936 0.004109 0.944 0.9607 0.01147 ] Network output: [ 0.01654 -0.03879 0.9261 -0.0002237 0.0001004 1.079 -0.0001686 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2281 0.1526 0.3361 0.1488 0.9855 0.9943 0.2288 0.7847 0.9534 0.6799 ] Network output: [ -0.03123 0.1628 1.09 0.0001214 -5.451e-05 0.8097 9.151e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06416 0.06012 0.1617 0.134 0.9896 0.9937 0.06419 0.9256 0.9553 0.2013 ] Network output: [ -0.02833 0.0428 1.08 0.0001635 -7.338e-05 0.9349 0.0001232 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0789 0.07801 0.1798 0.1561 0.9854 0.9916 0.07891 0.8832 0.9386 0.197 ] Network output: [ -0.003126 0.9885 0.008611 1.525e-05 -6.847e-06 1.009 1.149e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02297 Epoch 5122 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04499 0.881 0.9323 -6.471e-05 2.905e-05 0.09637 -4.877e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003085 -0.002741 -0.01154 0.007045 0.9676 0.9724 0.006 0.8965 0.8951 0.02152 ] Network output: [ 0.9901 0.0598 -0.001957 8.057e-05 -3.617e-05 -0.03776 6.072e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2136 -0.009262 -0.2201 0.1903 0.9839 0.9935 0.2388 0.7758 0.9491 0.6855 ] Network output: [ 0.01623 0.8975 0.9607 -8.326e-05 3.738e-05 0.109 -6.274e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004023 0.001246 0.002566 0.003189 0.9907 0.9936 0.004093 0.9441 0.9607 0.01144 ] Network output: [ 0.02245 -0.06486 0.9256 -0.0002066 9.273e-05 1.094 -0.0001557 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2271 0.1518 0.3339 0.1543 0.9855 0.9943 0.2277 0.7849 0.9534 0.6808 ] Network output: [ -0.03184 0.163 1.091 0.0001202 -5.398e-05 0.8104 9.062e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06405 0.06002 0.1618 0.1345 0.9896 0.9938 0.06409 0.9258 0.9553 0.2015 ] Network output: [ -0.02947 0.04468 1.08 0.000161 -7.227e-05 0.9346 0.0001213 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07876 0.07787 0.1801 0.1562 0.9854 0.9916 0.07876 0.8835 0.9386 0.1973 ] Network output: [ -0.005196 0.9954 0.009488 1.055e-05 -4.737e-06 1.006 7.952e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02341 Epoch 5123 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04416 0.8836 0.9327 -6.664e-05 2.992e-05 0.09515 -5.022e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003079 -0.002732 -0.01152 0.007002 0.9676 0.9724 0.005988 0.8967 0.8952 0.02152 ] Network output: [ 0.9812 0.07584 0.00431 6.997e-05 -3.141e-05 -0.04231 5.273e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.008753 -0.2176 0.1871 0.9839 0.9935 0.238 0.7763 0.9492 0.6863 ] Network output: [ 0.01638 0.8983 0.9604 -8.4e-05 3.771e-05 0.1083 -6.331e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004021 0.001245 0.00261 0.003123 0.9907 0.9936 0.004092 0.9443 0.9608 0.01149 ] Network output: [ 0.01593 -0.03561 0.9266 -0.0002248 0.0001009 1.076 -0.0001694 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2271 0.1518 0.3357 0.148 0.9855 0.9943 0.2277 0.7854 0.9535 0.682 ] Network output: [ -0.0309 0.1631 1.09 0.0001207 -5.417e-05 0.8095 9.093e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06394 0.05991 0.1615 0.1339 0.9897 0.9938 0.06398 0.9259 0.9554 0.2014 ] Network output: [ -0.0281 0.04168 1.079 0.000163 -7.316e-05 0.936 0.0001228 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0787 0.07781 0.1798 0.1562 0.9854 0.9916 0.07871 0.8835 0.9387 0.1972 ] Network output: [ -0.002878 0.9878 0.008262 1.583e-05 -7.108e-06 1.01 1.193e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02268 Epoch 5124 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0447 0.8818 0.9325 -6.587e-05 2.957e-05 0.09602 -4.964e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003076 -0.002735 -0.01157 0.007064 0.9676 0.9724 0.005985 0.8969 0.8954 0.02155 ] Network output: [ 0.9911 0.05587 -0.001864 8.529e-05 -3.829e-05 -0.03578 6.428e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.009314 -0.222 0.1915 0.9839 0.9935 0.2377 0.7765 0.9493 0.6877 ] Network output: [ 0.01587 0.8987 0.9608 -8.474e-05 3.804e-05 0.1084 -6.386e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004006 0.001237 0.002542 0.003189 0.9907 0.9936 0.004076 0.9444 0.9609 0.01146 ] Network output: [ 0.02281 -0.06606 0.9259 -0.0002048 9.193e-05 1.094 -0.0001543 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.226 0.1509 0.3332 0.1545 0.9855 0.9943 0.2266 0.7856 0.9536 0.6828 ] Network output: [ -0.03162 0.1634 1.09 0.0001193 -5.356e-05 0.8101 8.991e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06385 0.05982 0.1615 0.1345 0.9897 0.9938 0.06388 0.926 0.9554 0.2016 ] Network output: [ -0.02943 0.04409 1.08 0.0001601 -7.187e-05 0.9354 0.0001206 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07855 0.07766 0.1801 0.1563 0.9854 0.9916 0.07856 0.8839 0.9387 0.1975 ] Network output: [ -0.005312 0.9962 0.009276 1.02e-05 -4.579e-06 1.005 7.686e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02323 Epoch 5125 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04377 0.8846 0.9329 -6.8e-05 3.053e-05 0.09465 -5.125e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003069 -0.002724 -0.01154 0.007013 0.9676 0.9724 0.005971 0.8971 0.8955 0.02154 ] Network output: [ 0.9805 0.07427 0.005639 7.267e-05 -3.262e-05 -0.04068 5.476e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.008709 -0.2188 0.1878 0.9839 0.9935 0.2369 0.7771 0.9493 0.6883 ] Network output: [ 0.01609 0.8995 0.9604 -8.547e-05 3.837e-05 0.1076 -6.441e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004005 0.001238 0.002598 0.003115 0.9907 0.9936 0.004076 0.9446 0.961 0.01152 ] Network output: [ 0.01511 -0.0323 0.9272 -0.0002259 0.0001014 1.074 -0.0001703 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2261 0.151 0.3354 0.1471 0.9855 0.9943 0.2267 0.7861 0.9537 0.684 ] Network output: [ -0.03051 0.1635 1.089 0.0001199 -5.382e-05 0.8091 9.034e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06374 0.05971 0.1612 0.1337 0.9897 0.9938 0.06378 0.9261 0.9555 0.2015 ] Network output: [ -0.0278 0.04088 1.079 0.0001624 -7.291e-05 0.9367 0.0001224 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07852 0.07762 0.1797 0.1562 0.9854 0.9916 0.07852 0.8839 0.9388 0.1974 ] Network output: [ -0.002543 0.9877 0.007723 1.63e-05 -7.316e-06 1.01 1.228e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0224 Epoch 5126 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04444 0.8825 0.9327 -6.699e-05 3.008e-05 0.09569 -5.049e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003068 -0.002729 -0.01159 0.007085 0.9676 0.9724 0.005969 0.8973 0.8956 0.02158 ] Network output: [ 0.9921 0.05126 -0.001753 9.02e-05 -4.05e-05 -0.0333 6.798e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.009343 -0.2238 0.1928 0.9839 0.9935 0.2367 0.7772 0.9494 0.6896 ] Network output: [ 0.01554 0.8999 0.9609 -8.619e-05 3.869e-05 0.1078 -6.496e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003989 0.00123 0.002519 0.003192 0.9907 0.9936 0.004059 0.9447 0.961 0.01148 ] Network output: [ 0.0233 -0.06814 0.9262 -0.0002023 9.08e-05 1.094 -0.0001524 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2249 0.1501 0.3324 0.1548 0.9855 0.9943 0.2256 0.7862 0.9537 0.6847 ] Network output: [ -0.03139 0.1639 1.09 0.0001183 -5.313e-05 0.8098 8.918e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06365 0.05962 0.1612 0.1344 0.9897 0.9938 0.06368 0.9263 0.9555 0.2017 ] Network output: [ -0.02937 0.04378 1.08 0.0001591 -7.142e-05 0.9359 0.0001199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07836 0.07747 0.18 0.1563 0.9854 0.9916 0.07837 0.8842 0.9389 0.1977 ] Network output: [ -0.005388 0.9973 0.009011 9.759e-06 -4.381e-06 1.005 7.355e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0231 Epoch 5127 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04338 0.8857 0.9331 -6.94e-05 3.116e-05 0.09412 -5.23e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00306 -0.002717 -0.01155 0.007022 0.9676 0.9724 0.005955 0.8975 0.8957 0.02156 ] Network output: [ 0.9795 0.07341 0.007148 7.473e-05 -3.355e-05 -0.03927 5.632e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2108 -0.008618 -0.2198 0.1883 0.9839 0.9935 0.2358 0.7777 0.9495 0.6902 ] Network output: [ 0.01583 0.9006 0.9605 -8.69e-05 3.901e-05 0.1069 -6.549e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00399 0.001232 0.002587 0.003105 0.9907 0.9936 0.004061 0.9448 0.9611 0.01155 ] Network output: [ 0.01413 -0.02811 0.9278 -0.0002273 0.0001021 1.071 -0.0001713 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2251 0.1503 0.3351 0.1461 0.9855 0.9943 0.2258 0.7867 0.9538 0.6859 ] Network output: [ -0.03007 0.1639 1.088 0.0001191 -5.349e-05 0.8086 8.979e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06354 0.05952 0.1609 0.1336 0.9897 0.9938 0.06358 0.9264 0.9556 0.2015 ] Network output: [ -0.02742 0.03995 1.078 0.000162 -7.271e-05 0.9374 0.0001221 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07834 0.07744 0.1796 0.1563 0.9854 0.9916 0.07834 0.8842 0.939 0.1975 ] Network output: [ -0.002019 0.9866 0.007191 1.721e-05 -7.727e-06 1.01 1.297e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02213 Epoch 5128 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04421 0.8832 0.9328 -6.812e-05 3.058e-05 0.09535 -5.134e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003059 -0.002722 -0.01162 0.007104 0.9676 0.9724 0.005954 0.8976 0.8959 0.0216 ] Network output: [ 0.9934 0.04712 -0.002063 9.495e-05 -4.263e-05 -0.03144 7.156e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2107 -0.00936 -0.2258 0.1941 0.9839 0.9935 0.2357 0.7778 0.9496 0.6916 ] Network output: [ 0.01519 0.9009 0.9611 -8.762e-05 3.934e-05 0.1072 -6.604e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003972 0.001223 0.00249 0.003195 0.9908 0.9936 0.004042 0.9449 0.9611 0.01149 ] Network output: [ 0.0241 -0.07055 0.9263 -0.0001992 8.942e-05 1.095 -0.0001501 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2238 0.1494 0.3314 0.1553 0.9855 0.9943 0.2245 0.7869 0.9538 0.6866 ] Network output: [ -0.03117 0.1642 1.089 0.0001174 -5.271e-05 0.8096 8.848e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06344 0.05943 0.161 0.1344 0.9897 0.9938 0.06348 0.9266 0.9556 0.2018 ] Network output: [ -0.02936 0.04313 1.08 0.0001581 -7.1e-05 0.9367 0.0001192 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07815 0.07727 0.18 0.1564 0.9854 0.9916 0.07816 0.8846 0.939 0.1979 ] Network output: [ -0.00545 0.9972 0.009002 9.654e-06 -4.334e-06 1.005 7.275e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02299 Epoch 5129 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04295 0.887 0.9333 -7.09e-05 3.183e-05 0.0935 -5.343e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003052 -0.002709 -0.01157 0.007028 0.9676 0.9724 0.005939 0.8978 0.8959 0.02158 ] Network output: [ 0.9783 0.07448 0.008494 7.573e-05 -3.4e-05 -0.03917 5.707e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2099 -0.0085 -0.2209 0.1884 0.9839 0.9935 0.2347 0.7784 0.9496 0.6921 ] Network output: [ 0.01557 0.9017 0.9605 -8.832e-05 3.965e-05 0.1063 -6.656e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003976 0.001226 0.002575 0.00309 0.9908 0.9937 0.004046 0.9451 0.9612 0.01158 ] Network output: [ 0.01305 -0.0222 0.9282 -0.0002295 0.000103 1.067 -0.0001729 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2242 0.1497 0.3346 0.1448 0.9855 0.9943 0.2249 0.7874 0.9539 0.6879 ] Network output: [ -0.02963 0.164 1.087 0.0001186 -5.322e-05 0.8084 8.934e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06335 0.05933 0.1606 0.1335 0.9897 0.9938 0.06338 0.9266 0.9557 0.2015 ] Network output: [ -0.02704 0.03829 1.078 0.0001618 -7.263e-05 0.9387 0.0001219 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07815 0.07726 0.1796 0.1564 0.9854 0.9916 0.07816 0.8846 0.9391 0.1977 ] Network output: [ -0.001403 0.9838 0.006918 1.878e-05 -8.429e-06 1.012 1.415e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02187 Epoch 5130 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04397 0.8838 0.933 -6.924e-05 3.109e-05 0.09497 -5.218e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003052 -0.002717 -0.01165 0.007126 0.9676 0.9724 0.005941 0.898 0.8961 0.02163 ] Network output: [ 0.9952 0.04355 -0.003086 9.966e-05 -4.474e-05 -0.03054 7.511e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2098 -0.009399 -0.2282 0.1953 0.984 0.9935 0.2347 0.7784 0.9497 0.6935 ] Network output: [ 0.0148 0.902 0.9613 -8.905e-05 3.998e-05 0.1067 -6.711e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003956 0.001215 0.002456 0.003198 0.9908 0.9937 0.004026 0.9452 0.9613 0.01151 ] Network output: [ 0.02535 -0.07349 0.926 -0.0001954 8.773e-05 1.096 -0.0001473 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2228 0.1486 0.3301 0.1559 0.9855 0.9943 0.2234 0.7875 0.954 0.6885 ] Network output: [ -0.03105 0.1642 1.089 0.0001165 -5.231e-05 0.8097 8.781e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06324 0.05923 0.1607 0.1345 0.9897 0.9938 0.06328 0.9268 0.9557 0.2019 ] Network output: [ -0.0295 0.04195 1.08 0.0001572 -7.059e-05 0.938 0.0001185 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07795 0.07706 0.1801 0.1566 0.9854 0.9916 0.07796 0.885 0.9391 0.1981 ] Network output: [ -0.005741 0.9965 0.009359 9.472e-06 -4.252e-06 1.006 7.138e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02289 Epoch 5131 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04247 0.8884 0.9336 -7.247e-05 3.253e-05 0.09277 -5.461e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003044 -0.002702 -0.01159 0.007033 0.9676 0.9724 0.005925 0.8982 0.8962 0.02161 ] Network output: [ 0.9767 0.0767 0.009926 7.592e-05 -3.408e-05 -0.0397 5.722e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2089 -0.00837 -0.2219 0.1884 0.984 0.9935 0.2337 0.7791 0.9497 0.694 ] Network output: [ 0.01528 0.9029 0.9606 -8.973e-05 4.028e-05 0.1056 -6.762e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003963 0.001219 0.002564 0.003073 0.9908 0.9937 0.004032 0.9454 0.9613 0.01162 ] Network output: [ 0.01171 -0.01479 0.9285 -0.0002325 0.0001044 1.062 -0.0001752 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2233 0.149 0.3343 0.1432 0.9855 0.9943 0.224 0.788 0.954 0.6899 ] Network output: [ -0.02922 0.1638 1.087 0.000118 -5.299e-05 0.8084 8.896e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06316 0.05915 0.1603 0.1333 0.9897 0.9938 0.06319 0.9269 0.9558 0.2017 ] Network output: [ -0.02667 0.03628 1.077 0.0001617 -7.26e-05 0.9403 0.0001219 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07798 0.07709 0.1796 0.1566 0.9854 0.9916 0.07799 0.8849 0.9392 0.1979 ] Network output: [ -0.0008821 0.981 0.006677 2.022e-05 -9.078e-06 1.014 1.524e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02159 Epoch 5132 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04377 0.8844 0.9331 -7.025e-05 3.154e-05 0.09463 -5.295e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003045 -0.002712 -0.01169 0.007153 0.9676 0.9724 0.005929 0.8983 0.8964 0.02166 ] Network output: [ 0.9977 0.03818 -0.004387 0.0001053 -4.727e-05 -0.02877 7.936e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.209 -0.009473 -0.2307 0.1969 0.984 0.9935 0.2338 0.779 0.9498 0.6955 ] Network output: [ 0.01436 0.9032 0.9616 -9.046e-05 4.061e-05 0.1061 -6.817e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00394 0.001208 0.00242 0.003208 0.9908 0.9937 0.00401 0.9454 0.9614 0.01152 ] Network output: [ 0.02695 -0.0787 0.9258 -0.0001904 8.546e-05 1.098 -0.0001435 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2217 0.1478 0.3288 0.157 0.9855 0.9943 0.2223 0.7881 0.9541 0.6904 ] Network output: [ -0.03104 0.1643 1.088 0.0001155 -5.186e-05 0.8099 8.706e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06306 0.05906 0.1605 0.1346 0.9897 0.9938 0.06309 0.9271 0.9558 0.2021 ] Network output: [ -0.02975 0.0413 1.08 0.0001561 -7.006e-05 0.9391 0.0001176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07776 0.07687 0.1803 0.1568 0.9855 0.9916 0.07776 0.8853 0.9392 0.1985 ] Network output: [ -0.006424 0.9982 0.009628 8.082e-06 -3.629e-06 1.005 6.091e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02288 Epoch 5133 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04196 0.8898 0.9339 -7.402e-05 3.323e-05 0.09204 -5.578e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003035 -0.002694 -0.0116 0.007038 0.9676 0.9724 0.005909 0.8985 0.8964 0.02163 ] Network output: [ 0.9744 0.07772 0.01241 7.595e-05 -3.41e-05 -0.03854 5.724e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.208 -0.0082 -0.2223 0.1885 0.984 0.9935 0.2326 0.7797 0.9498 0.6958 ] Network output: [ 0.01504 0.9042 0.9606 -9.11e-05 4.09e-05 0.1047 -6.866e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00395 0.001215 0.002565 0.003059 0.9908 0.9937 0.00402 0.9456 0.9615 0.01166 ] Network output: [ 0.009665 -0.006925 0.9294 -0.0002362 0.0001061 1.057 -0.000178 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2225 0.1485 0.3344 0.1415 0.9856 0.9943 0.2232 0.7886 0.9541 0.6918 ] Network output: [ -0.02873 0.1641 1.086 0.0001175 -5.273e-05 0.808 8.852e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.063 0.05899 0.1601 0.1332 0.9897 0.9938 0.06303 0.9271 0.9559 0.2018 ] Network output: [ -0.02616 0.03522 1.077 0.0001615 -7.252e-05 0.941 0.0001217 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07784 0.07695 0.1795 0.1566 0.9854 0.9916 0.07785 0.8852 0.9393 0.1981 ] Network output: [ -0.0003395 0.9809 0.005816 2.081e-05 -9.344e-06 1.014 1.569e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02132 Epoch 5134 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04368 0.8847 0.9332 -7.105e-05 3.19e-05 0.09444 -5.355e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003037 -0.002707 -0.01172 0.007187 0.9676 0.9724 0.005915 0.8986 0.8966 0.02169 ] Network output: [ 1.001 0.02885 -0.00534 0.0001126 -5.056e-05 -0.02417 8.488e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2081 -0.009546 -0.233 0.1993 0.984 0.9935 0.2328 0.7795 0.95 0.6974 ] Network output: [ 0.01398 0.9044 0.9618 -9.182e-05 4.122e-05 0.1055 -6.92e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003924 0.001204 0.00239 0.00323 0.9908 0.9937 0.003993 0.9457 0.9615 0.01153 ] Network output: [ 0.02879 -0.08785 0.926 -0.0001832 8.223e-05 1.104 -0.000138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2206 0.1471 0.3277 0.159 0.9856 0.9943 0.2212 0.7886 0.9542 0.692 ] Network output: [ -0.03101 0.1651 1.088 0.0001143 -5.13e-05 0.8095 8.612e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0629 0.05891 0.1603 0.1347 0.9897 0.9938 0.06294 0.9273 0.9558 0.2022 ] Network output: [ -0.02999 0.0423 1.08 0.0001543 -6.929e-05 0.9388 0.0001163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07759 0.07671 0.1803 0.1567 0.9855 0.9916 0.0776 0.8857 0.9392 0.1986 ] Network output: [ -0.007226 1.003 0.009359 5.374e-06 -2.413e-06 1.002 4.05e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02312 Epoch 5135 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04146 0.8912 0.9342 -7.559e-05 3.394e-05 0.09132 -5.697e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003026 -0.002684 -0.0116 0.007039 0.9676 0.9724 0.005892 0.8988 0.8966 0.02163 ] Network output: [ 0.9707 0.07798 0.01638 7.532e-05 -3.381e-05 -0.03553 5.676e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2069 -0.007926 -0.2218 0.1886 0.984 0.9935 0.2314 0.7804 0.95 0.6973 ] Network output: [ 0.01495 0.9054 0.9605 -9.24e-05 4.148e-05 0.1038 -6.964e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003938 0.001215 0.002581 0.003043 0.9908 0.9937 0.004008 0.9459 0.9616 0.01169 ] Network output: [ 0.006702 0.002186 0.9309 -0.000241 0.0001082 1.053 -0.0001816 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2218 0.1482 0.3351 0.1394 0.9856 0.9943 0.2225 0.7893 0.9542 0.6933 ] Network output: [ -0.02802 0.1649 1.085 0.0001169 -5.246e-05 0.8069 8.807e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06285 0.05886 0.1597 0.1329 0.9897 0.9938 0.06289 0.9273 0.9559 0.2017 ] Network output: [ -0.02531 0.03521 1.076 0.0001613 -7.241e-05 0.9405 0.0001216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07773 0.07684 0.1792 0.1564 0.9855 0.9916 0.07774 0.8854 0.9393 0.198 ] Network output: [ 0.0007713 0.9816 0.004253 2.174e-05 -9.762e-06 1.013 1.639e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02112 Epoch 5136 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0437 0.8846 0.9333 -7.171e-05 3.219e-05 0.0944 -5.404e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00303 -0.002702 -0.01174 0.00722 0.9676 0.9724 0.005901 0.8989 0.8968 0.0217 ] Network output: [ 1.004 0.01795 -0.006615 0.0001208 -5.421e-05 -0.01879 9.101e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2073 -0.009593 -0.2355 0.2018 0.984 0.9935 0.2319 0.78 0.9501 0.6989 ] Network output: [ 0.01367 0.9054 0.962 -9.31e-05 4.18e-05 0.1048 -7.017e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003907 0.001201 0.002354 0.003256 0.9908 0.9937 0.003976 0.9459 0.9616 0.01153 ] Network output: [ 0.03132 -0.09986 0.926 -0.0001738 7.803e-05 1.111 -0.000131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2195 0.1464 0.3263 0.1615 0.9856 0.9943 0.2201 0.7891 0.9542 0.6933 ] Network output: [ -0.03092 0.1662 1.087 0.000113 -5.071e-05 0.8088 8.513e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06274 0.05877 0.16 0.1348 0.9897 0.9938 0.06277 0.9276 0.9559 0.2021 ] Network output: [ -0.03019 0.0438 1.079 0.0001524 -6.842e-05 0.938 0.0001149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07741 0.07653 0.1801 0.1566 0.9855 0.9916 0.07742 0.886 0.9393 0.1986 ] Network output: [ -0.007724 1.007 0.009168 3.3e-06 -1.481e-06 0.9989 2.487e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02364 Epoch 5137 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04089 0.8929 0.9346 -7.741e-05 3.475e-05 0.09045 -5.834e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003017 -0.002674 -0.01159 0.007026 0.9676 0.9724 0.005874 0.8991 0.8967 0.02163 ] Network output: [ 0.9659 0.08321 0.02064 7.176e-05 -3.222e-05 -0.03527 5.408e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.206 -0.007548 -0.2211 0.1878 0.984 0.9935 0.2304 0.781 0.9501 0.6985 ] Network output: [ 0.01492 0.9065 0.9604 -9.364e-05 4.204e-05 0.1029 -7.057e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003927 0.001214 0.002596 0.003013 0.9908 0.9937 0.003996 0.9461 0.9617 0.01173 ] Network output: [ 0.003183 0.01647 0.9319 -0.0002482 0.0001114 1.044 -0.000187 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2212 0.1479 0.3356 0.1363 0.9856 0.9943 0.2219 0.7898 0.9543 0.6947 ] Network output: [ -0.02712 0.1652 1.083 0.0001166 -5.235e-05 0.806 8.788e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06269 0.05871 0.1593 0.1325 0.9897 0.9938 0.06272 0.9275 0.956 0.2016 ] Network output: [ -0.02423 0.03369 1.074 0.0001617 -7.261e-05 0.941 0.0001219 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0776 0.07672 0.1789 0.1563 0.9855 0.9916 0.07761 0.8856 0.9394 0.1979 ] Network output: [ 0.002764 0.9759 0.003081 2.561e-05 -1.15e-05 1.016 1.93e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02108 Epoch 5138 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04375 0.8845 0.9334 -7.242e-05 3.251e-05 0.09429 -5.457e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003024 -0.002698 -0.01178 0.007251 0.9676 0.9724 0.005891 0.8992 0.897 0.02173 ] Network output: [ 1.009 0.01046 -0.01027 0.0001283 -5.758e-05 -0.0176 9.666e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2067 -0.009699 -0.239 0.204 0.984 0.9935 0.2312 0.7804 0.9502 0.7005 ] Network output: [ 0.01322 0.9062 0.9624 -9.436e-05 4.236e-05 0.1045 -7.111e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003891 0.001195 0.002293 0.003276 0.9908 0.9937 0.003959 0.9461 0.9617 0.01152 ] Network output: [ 0.03546 -0.1124 0.9244 -0.0001628 7.309e-05 1.116 -0.0001227 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2184 0.1457 0.3237 0.1643 0.9856 0.9943 0.219 0.7895 0.9543 0.6947 ] Network output: [ -0.03103 0.1661 1.087 0.0001119 -5.022e-05 0.8091 8.43e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06254 0.05859 0.1597 0.135 0.9897 0.9938 0.06258 0.9278 0.9559 0.2022 ] Network output: [ -0.03075 0.04297 1.08 0.0001509 -6.773e-05 0.9393 0.0001137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07717 0.0763 0.1803 0.1569 0.9855 0.9916 0.07718 0.8863 0.9393 0.1989 ] Network output: [ -0.008309 1.005 0.01043 3.124e-06 -1.403e-06 1.001 2.355e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02419 Epoch 5139 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04009 0.8953 0.935 -7.965e-05 3.576e-05 0.08918 -6.003e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003009 -0.002664 -0.01159 0.006999 0.9676 0.9724 0.00586 0.8994 0.8969 0.02165 ] Network output: [ 0.96 0.09735 0.02414 6.378e-05 -2.863e-05 -0.0413 4.807e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2051 -0.007157 -0.2206 0.1853 0.984 0.9935 0.2294 0.7816 0.9501 0.7 ] Network output: [ 0.01477 0.9074 0.9604 -9.485e-05 4.258e-05 0.1023 -7.148e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003919 0.00121 0.002603 0.00296 0.9908 0.9937 0.003988 0.9463 0.9618 0.01178 ] Network output: [ -0.0007959 0.03905 0.9319 -0.0002597 0.0001166 1.03 -0.0001957 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2207 0.1475 0.3357 0.1315 0.9856 0.9943 0.2214 0.7904 0.9544 0.6966 ] Network output: [ -0.02628 0.164 1.083 0.0001169 -5.25e-05 0.8064 8.813e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0625 0.05853 0.1589 0.1323 0.9898 0.9938 0.06254 0.9276 0.9561 0.2016 ] Network output: [ -0.02322 0.02872 1.074 0.0001634 -7.335e-05 0.9444 0.0001231 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07745 0.07656 0.1788 0.1567 0.9855 0.9916 0.07746 0.8858 0.9395 0.1982 ] Network output: [ 0.004996 0.9628 0.002996 3.221e-05 -1.446e-05 1.024 2.427e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0214 Epoch 5140 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04378 0.8846 0.9334 -7.301e-05 3.277e-05 0.09413 -5.502e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00302 -0.002698 -0.01185 0.007296 0.9677 0.9724 0.005885 0.8994 0.8972 0.02179 ] Network output: [ 1.017 0.003013 -0.01686 0.000137 -6.153e-05 -0.01912 0.0001033 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2062 -0.01002 -0.2442 0.2064 0.984 0.9935 0.2306 0.7807 0.9503 0.7026 ] Network output: [ 0.01246 0.9071 0.9631 -9.567e-05 4.295e-05 0.1045 -7.21e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003875 0.001185 0.002206 0.003304 0.9908 0.9937 0.003943 0.9462 0.9617 0.01151 ] Network output: [ 0.04163 -0.1289 0.9217 -0.0001488 6.682e-05 1.123 -0.0001122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2171 0.1447 0.32 0.1681 0.9856 0.9943 0.2178 0.7899 0.9544 0.6966 ] Network output: [ -0.03168 0.1653 1.088 0.0001107 -4.969e-05 0.8107 8.342e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06235 0.0584 0.1595 0.1356 0.9897 0.9938 0.06238 0.928 0.956 0.2025 ] Network output: [ -0.03205 0.0408 1.081 0.0001492 -6.696e-05 0.9425 0.0001124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07692 0.07605 0.1808 0.1575 0.9855 0.9916 0.07693 0.8866 0.9394 0.1996 ] Network output: [ -0.0103 1.004 0.0129 1.25e-06 -5.611e-07 1.004 9.42e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02501 Epoch 5141 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03902 0.8984 0.9355 -8.207e-05 3.684e-05 0.08765 -6.185e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003003 -0.002656 -0.0116 0.006976 0.9676 0.9724 0.005847 0.8997 0.897 0.02168 ] Network output: [ 0.9522 0.1123 0.02966 5.371e-05 -2.411e-05 -0.04618 4.048e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2042 -0.00677 -0.219 0.1826 0.984 0.9935 0.2284 0.7821 0.9502 0.7018 ] Network output: [ 0.01456 0.9088 0.9604 -9.607e-05 4.313e-05 0.1013 -7.24e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003915 0.001206 0.002637 0.002903 0.9908 0.9937 0.003984 0.9465 0.9618 0.01187 ] Network output: [ -0.006852 0.06691 0.9331 -0.0002758 0.0001238 1.013 -0.0002078 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2204 0.1472 0.3369 0.1257 0.9856 0.9943 0.221 0.7908 0.9545 0.6988 ] Network output: [ -0.02547 0.1631 1.082 0.0001173 -5.267e-05 0.8066 8.842e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06238 0.0584 0.1587 0.1319 0.9898 0.9938 0.06242 0.9277 0.9561 0.2019 ] Network output: [ -0.02209 0.02455 1.073 0.0001652 -7.415e-05 0.9471 0.0001245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07739 0.0765 0.1788 0.157 0.9855 0.9916 0.0774 0.8859 0.9396 0.1985 ] Network output: [ 0.006747 0.9559 0.001822 3.656e-05 -1.641e-05 1.029 2.755e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02207 Epoch 5142 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04405 0.8837 0.9335 -7.281e-05 3.269e-05 0.09447 -5.487e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003017 -0.0027 -0.01191 0.007376 0.9677 0.9724 0.00588 0.8996 0.8974 0.02184 ] Network output: [ 1.027 -0.01964 -0.02319 0.000153 -6.868e-05 -0.01096 0.0001153 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2057 -0.01054 -0.2493 0.2117 0.984 0.9935 0.23 0.7809 0.9504 0.7046 ] Network output: [ 0.01164 0.9086 0.9638 -9.695e-05 4.352e-05 0.1039 -7.306e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003857 0.001178 0.002132 0.003376 0.9908 0.9937 0.003925 0.9463 0.9617 0.01149 ] Network output: [ 0.04905 -0.1607 0.9205 -0.0001275 5.724e-05 1.142 -9.609e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2157 0.1437 0.317 0.1751 0.9856 0.9943 0.2164 0.79 0.9544 0.698 ] Network output: [ -0.03267 0.1669 1.088 0.0001085 -4.87e-05 0.8106 8.175e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06225 0.05831 0.1596 0.1361 0.9897 0.9938 0.06229 0.9281 0.9559 0.2028 ] Network output: [ -0.03373 0.04468 1.082 0.0001453 -6.523e-05 0.9412 0.0001095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07677 0.0759 0.1811 0.1574 0.9855 0.9916 0.07678 0.8869 0.9393 0.1999 ] Network output: [ -0.01397 1.02 0.01356 -7.922e-06 3.556e-06 0.9946 -5.97e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0275 Epoch 5143 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0379 0.9012 0.9363 -8.431e-05 3.785e-05 0.08642 -6.353e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002992 -0.002642 -0.01153 0.006954 0.9677 0.9724 0.005826 0.8999 0.897 0.02166 ] Network output: [ 0.9394 0.1156 0.04277 4.452e-05 -1.999e-05 -0.03697 3.355e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.203 -0.006151 -0.2134 0.1814 0.984 0.9935 0.227 0.7826 0.9502 0.7026 ] Network output: [ 0.0148 0.9109 0.9598 -9.717e-05 4.363e-05 0.09932 -7.323e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003912 0.001214 0.002755 0.002866 0.9908 0.9937 0.003981 0.9467 0.9619 0.01195 ] Network output: [ -0.01746 0.0954 0.9385 -0.0002955 0.0001327 0.9999 -0.0002227 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2203 0.1475 0.3416 0.1193 0.9856 0.9943 0.2209 0.7912 0.9545 0.6997 ] Network output: [ -0.02403 0.1663 1.079 0.0001169 -5.25e-05 0.8031 8.813e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06239 0.05843 0.1584 0.131 0.9898 0.9938 0.06242 0.9278 0.9561 0.2016 ] Network output: [ -0.0198 0.02842 1.069 0.0001656 -7.434e-05 0.9424 0.0001248 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07751 0.07662 0.1778 0.156 0.9855 0.9916 0.07752 0.8858 0.9395 0.1977 ] Network output: [ 0.009363 0.9662 -0.003832 3.64e-05 -1.634e-05 1.019 2.743e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02286 Epoch 5144 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04495 0.8804 0.9334 -7.147e-05 3.209e-05 0.09597 -5.386e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00301 -0.002698 -0.01192 0.007474 0.9677 0.9724 0.005866 0.8998 0.8975 0.02182 ] Network output: [ 1.039 -0.06381 -0.0264 0.0001774 -7.965e-05 0.01365 0.0001337 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2049 -0.011 -0.2527 0.2205 0.984 0.9935 0.2292 0.7808 0.9504 0.7052 ] Network output: [ 0.01133 0.9101 0.964 -9.79e-05 4.395e-05 0.1028 -7.378e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003836 0.001183 0.00209 0.003499 0.9908 0.9937 0.003903 0.9464 0.9618 0.0114 ] Network output: [ 0.05739 -0.2129 0.9219 -9.585e-05 4.303e-05 1.176 -7.224e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2143 0.1432 0.3154 0.1859 0.9856 0.9943 0.215 0.7899 0.9544 0.6971 ] Network output: [ -0.03317 0.1733 1.087 0.000105 -4.713e-05 0.8062 7.912e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06224 0.05834 0.1592 0.1362 0.9897 0.9938 0.06227 0.9282 0.9558 0.2021 ] Network output: [ -0.03477 0.0586 1.08 0.0001386 -6.225e-05 0.9311 0.0001045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0767 0.07584 0.1802 0.156 0.9855 0.9916 0.07671 0.887 0.9391 0.1988 ] Network output: [ -0.0166 1.05 0.01083 -2.045e-05 9.181e-06 0.9727 -1.541e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03399 Epoch 5145 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03679 0.9035 0.9372 -8.686e-05 3.899e-05 0.08541 -6.546e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002976 -0.002618 -0.01139 0.00689 0.9677 0.9724 0.005793 0.9 0.897 0.02153 ] Network output: [ 0.92 0.119 0.06266 3.045e-05 -1.367e-05 -0.02154 2.295e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2016 -0.005042 -0.2037 0.1794 0.984 0.9935 0.2254 0.7829 0.9502 0.7014 ] Network output: [ 0.01577 0.9125 0.9586 -9.796e-05 4.398e-05 0.09695 -7.382e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003909 0.001237 0.002927 0.002809 0.9908 0.9937 0.003977 0.9468 0.9619 0.012 ] Network output: [ -0.03169 0.1322 0.9454 -0.0003202 0.0001437 0.9845 -0.0002413 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2207 0.1488 0.348 0.1106 0.9856 0.9943 0.2213 0.7914 0.9545 0.6983 ] Network output: [ -0.02135 0.1725 1.075 0.0001165 -5.228e-05 0.7957 8.777e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0624 0.05848 0.1574 0.1292 0.9898 0.9938 0.06243 0.9277 0.9561 0.2002 ] Network output: [ -0.0158 0.03643 1.063 0.0001661 -7.455e-05 0.9325 0.0001251 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07765 0.07677 0.1755 0.154 0.9854 0.9916 0.07766 0.8855 0.9394 0.1955 ] Network output: [ 0.01622 0.9695 -0.01209 4.282e-05 -1.923e-05 1.01 3.227e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02526 Epoch 5146 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0462 0.8761 0.9333 -7.007e-05 3.146e-05 0.098 -5.28e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003002 -0.002693 -0.01191 0.00754 0.9677 0.9724 0.00585 0.8998 0.8976 0.02173 ] Network output: [ 1.051 -0.1037 -0.03178 0.0001995 -8.954e-05 0.03402 0.0001503 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2046 -0.01134 -0.2566 0.2285 0.984 0.9935 0.2288 0.7804 0.9505 0.7043 ] Network output: [ 0.0113 0.91 0.9644 -9.841e-05 4.418e-05 0.1027 -7.417e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003812 0.001191 0.002009 0.003606 0.9908 0.9937 0.003879 0.9463 0.9617 0.01126 ] Network output: [ 0.06917 -0.2686 0.92 -5.902e-05 2.65e-05 1.21 -4.448e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2131 0.143 0.3116 0.1973 0.9856 0.9943 0.2138 0.7895 0.9543 0.6947 ] Network output: [ -0.03313 0.1793 1.086 0.000102 -4.579e-05 0.8013 7.687e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06212 0.05827 0.1581 0.1362 0.9897 0.9938 0.06215 0.9281 0.9557 0.2006 ] Network output: [ -0.03543 0.07016 1.079 0.0001329 -5.966e-05 0.9222 0.0001001 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07648 0.07563 0.1788 0.1547 0.9854 0.9916 0.07648 0.8869 0.9389 0.1971 ] Network output: [ -0.01565 1.054 0.01003 -2.125e-05 9.541e-06 0.9673 -1.602e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0425 Epoch 5147 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0351 0.9079 0.9383 -9.108e-05 4.089e-05 0.08323 -6.864e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002964 -0.002596 -0.01127 0.006737 0.9677 0.9724 0.005767 0.9 0.8968 0.0214 ] Network output: [ 0.8975 0.1631 0.07776 -2.557e-06 1.148e-06 -0.03586 -1.927e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2008 -0.003734 -0.1957 0.1702 0.984 0.9935 0.2246 0.783 0.9501 0.6997 ] Network output: [ 0.01659 0.9121 0.958 -9.839e-05 4.417e-05 0.09633 -7.415e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003912 0.001244 0.003018 0.002638 0.9908 0.9937 0.003981 0.9467 0.9619 0.01205 ] Network output: [ -0.04476 0.1996 0.9455 -0.0003571 0.0001603 0.943 -0.0002691 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2215 0.1496 0.3505 0.09607 0.9856 0.9943 0.2222 0.7911 0.9545 0.6975 ] Network output: [ -0.01828 0.1733 1.072 0.0001184 -5.315e-05 0.792 8.923e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06218 0.05829 0.1557 0.1274 0.9898 0.9938 0.06222 0.9273 0.9561 0.1985 ] Network output: [ -0.01165 0.0298 1.06 0.0001715 -7.699e-05 0.934 0.0001292 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07752 0.07665 0.1737 0.1537 0.9854 0.9916 0.07753 0.8848 0.9394 0.194 ] Network output: [ 0.02743 0.9213 -0.01457 7.024e-05 -3.153e-05 1.039 5.293e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03264 Epoch 5148 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04685 0.8735 0.9333 -6.967e-05 3.128e-05 0.09924 -5.25e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003005 -0.002703 -0.01201 0.007595 0.9677 0.9724 0.005857 0.8996 0.8976 0.02176 ] Network output: [ 1.071 -0.1161 -0.05035 0.0002123 -9.529e-05 0.02522 0.00016 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2053 -0.01232 -0.2669 0.2325 0.984 0.9935 0.2296 0.7793 0.9504 0.7047 ] Network output: [ 0.009994 0.909 0.9661 -9.904e-05 4.446e-05 0.1045 -7.464e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003794 0.001172 0.001791 0.003662 0.9908 0.9937 0.003861 0.946 0.9616 0.01114 ] Network output: [ 0.08861 -0.3137 0.9087 -2.176e-05 9.768e-06 1.228 -1.64e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2119 0.1417 0.3009 0.2078 0.9856 0.9943 0.2125 0.7884 0.9542 0.6947 ] Network output: [ -0.03476 0.1777 1.088 0.0001007 -4.519e-05 0.804 7.586e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06183 0.05798 0.1571 0.1372 0.9897 0.9938 0.06186 0.9278 0.9555 0.2001 ] Network output: [ -0.0386 0.06491 1.084 0.0001306 -5.863e-05 0.9292 9.842e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07598 0.07514 0.1794 0.1561 0.9854 0.9916 0.07599 0.8866 0.9387 0.1975 ] Network output: [ -0.01645 1.021 0.01827 -1.171e-05 5.258e-06 0.9937 -8.827e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0484 Epoch 5149 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03205 0.9165 0.9398 -9.691e-05 4.351e-05 0.07916 -7.303e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002964 -0.002588 -0.01125 0.006553 0.9677 0.9724 0.005765 0.8998 0.8964 0.02143 ] Network output: [ 0.8731 0.2468 0.08735 -5.461e-05 2.451e-05 -0.08064 -4.115e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2008 -0.002838 -0.1901 0.1541 0.984 0.9935 0.2245 0.7824 0.9497 0.7002 ] Network output: [ 0.01629 0.9118 0.9584 -9.871e-05 4.432e-05 0.09688 -7.439e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003933 0.001221 0.003062 0.002371 0.9908 0.9937 0.004002 0.9465 0.9617 0.0122 ] Network output: [ -0.05833 0.3 0.9407 -0.0004127 0.0001853 0.8743 -0.000311 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2228 0.1493 0.3507 0.07544 0.9856 0.9943 0.2235 0.7901 0.9543 0.7001 ] Network output: [ -0.01633 0.1682 1.071 0.0001226 -5.502e-05 0.7944 9.236e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06198 0.05799 0.1543 0.1262 0.9898 0.9938 0.06201 0.9264 0.9558 0.1981 ] Network output: [ -0.00886 0.009477 1.061 0.0001815 -8.147e-05 0.9483 0.0001368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07742 0.07651 0.1734 0.1554 0.9854 0.9916 0.07743 0.8835 0.9391 0.1945 ] Network output: [ 0.03685 0.8521 -0.01249 0.0001036 -4.652e-05 1.087 7.809e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0506 Epoch 5150 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04719 0.8706 0.9336 -6.788e-05 3.047e-05 0.1011 -5.116e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00302 -0.002736 -0.0122 0.00778 0.9677 0.9725 0.005887 0.8991 0.8973 0.02192 ] Network output: [ 1.103 -0.1589 -0.07562 0.0002396 -0.0001075 0.02867 0.0001805 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2064 -0.01473 -0.2807 0.2429 0.984 0.9935 0.2309 0.7768 0.9501 0.7072 ] Network output: [ 0.007024 0.9109 0.969 -9.999e-05 4.489e-05 0.1057 -7.535e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003783 0.001137 0.001551 0.003828 0.9908 0.9937 0.00385 0.9455 0.9612 0.01103 ] Network output: [ 0.1147 -0.3955 0.8981 3.26e-05 -1.464e-05 1.268 2.457e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2102 0.1392 0.2893 0.2268 0.9856 0.9943 0.2108 0.7859 0.9538 0.6956 ] Network output: [ -0.03929 0.1802 1.092 9.685e-05 -4.348e-05 0.8069 7.299e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06185 0.05793 0.1571 0.1391 0.9896 0.9938 0.06189 0.9271 0.9549 0.2004 ] Network output: [ -0.04516 0.06972 1.089 0.0001235 -5.545e-05 0.9316 9.309e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07577 0.07492 0.1807 0.1571 0.9854 0.9916 0.07578 0.8857 0.938 0.1985 ] Network output: [ -0.02556 1.033 0.02504 -2.342e-05 1.051e-05 0.9935 -1.765e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06426 Epoch 5151 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02795 0.9254 0.9425 -0.0001021 4.583e-05 0.07572 -7.694e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002958 -0.002575 -0.01105 0.006447 0.9677 0.9724 0.005752 0.8993 0.8956 0.02137 ] Network output: [ 0.8347 0.278 0.1225 -0.0001002 4.499e-05 -0.07022 -7.553e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1997 -0.001908 -0.1706 0.1457 0.984 0.9935 0.2233 0.7806 0.9491 0.6994 ] Network output: [ 0.01633 0.9167 0.9574 -9.916e-05 4.451e-05 0.09282 -7.473e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003972 0.001233 0.00343 0.002208 0.9908 0.9937 0.004041 0.946 0.9613 0.01245 ] Network output: [ -0.08702 0.3977 0.9509 -0.0004811 0.000216 0.8235 -0.0003625 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2252 0.1508 0.3634 0.0545 0.9856 0.9943 0.2258 0.7877 0.9537 0.6993 ] Network output: [ -0.01424 0.1799 1.065 0.0001224 -5.496e-05 0.7836 9.226e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06245 0.05839 0.1537 0.123 0.9898 0.9938 0.06248 0.925 0.9552 0.1967 ] Network output: [ -0.0042 0.02188 1.052 0.0001845 -8.281e-05 0.935 0.000139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07819 0.07726 0.1707 0.1524 0.9854 0.9916 0.0782 0.8811 0.9382 0.1918 ] Network output: [ 0.04273 0.8785 -0.02721 0.0001048 -4.703e-05 1.064 7.895e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06705 Epoch 5152 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04949 0.8582 0.9342 -6.05e-05 2.716e-05 0.1083 -4.559e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003016 -0.002755 -0.01212 0.008089 0.9677 0.9725 0.005883 0.8981 0.8967 0.02178 ] Network output: [ 1.133 -0.3103 -0.07628 0.0003015 -0.0001353 0.1218 0.0002272 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2062 -0.01747 -0.2813 0.2706 0.984 0.9935 0.2307 0.7724 0.9496 0.7043 ] Network output: [ 0.006183 0.9144 0.9696 -9.893e-05 4.441e-05 0.1032 -7.456e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003763 0.001147 0.001601 0.004247 0.9908 0.9937 0.00383 0.9443 0.9606 0.01075 ] Network output: [ 0.1361 -0.5708 0.9087 0.0001184 -5.316e-05 1.39 8.924e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2086 0.1386 0.2922 0.2621 0.9855 0.9943 0.2092 0.7811 0.9529 0.6862 ] Network output: [ -0.04117 0.2076 1.088 8.815e-05 -3.957e-05 0.7874 6.643e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0627 0.05879 0.1561 0.1389 0.9895 0.9937 0.06273 0.9255 0.9537 0.1968 ] Network output: [ -0.04739 0.1309 1.079 0.0001042 -4.679e-05 0.8848 7.855e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07654 0.07569 0.1757 0.1506 0.9854 0.9916 0.07655 0.8833 0.9362 0.1921 ] Network output: [ -0.02809 1.13 0.006976 -5.328e-05 2.392e-05 0.9185 -4.015e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1226 Epoch 5153 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02445 0.9298 0.9462 -0.000106 4.759e-05 0.07473 -7.988e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002929 -0.002531 -0.0104 0.006321 0.9677 0.9724 0.005691 0.898 0.8943 0.02076 ] Network output: [ 0.78 0.2308 0.1897 -0.000131 5.879e-05 0.01894 -9.87e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1978 0.0001831 -0.1326 0.1487 0.9839 0.9935 0.2211 0.7766 0.9482 0.6877 ] Network output: [ 0.01884 0.9246 0.9538 -9.91e-05 4.449e-05 0.08355 -7.469e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004009 0.001329 0.004088 0.002208 0.9907 0.9936 0.004079 0.9447 0.9607 0.01239 ] Network output: [ -0.1229 0.4537 0.9731 -0.0005293 0.0002376 0.8169 -0.0003989 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.229 0.1567 0.3858 0.04063 0.9856 0.9943 0.2297 0.7829 0.9527 0.6846 ] Network output: [ -0.009812 0.2209 1.053 0.0001141 -5.123e-05 0.7464 8.6e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06343 0.05951 0.1512 0.1158 0.9897 0.9938 0.06346 0.9227 0.9541 0.1892 ] Network output: [ 0.004327 0.08527 1.031 0.0001741 -7.817e-05 0.8761 0.0001312 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07947 0.07857 0.1621 0.1415 0.9853 0.9915 0.07948 0.8771 0.9365 0.1809 ] Network output: [ 0.0555 0.9759 -0.06094 9.009e-05 -4.045e-05 0.9745 6.79e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08142 Epoch 5154 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.05277 0.8396 0.9357 -5.247e-05 2.356e-05 0.119 -3.954e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002986 -0.00273 -0.0115 0.008131 0.9677 0.9725 0.005819 0.8962 0.8956 0.02098 ] Network output: [ 1.123 -0.4651 -0.03469 0.0003288 -0.0001476 0.2555 0.0002478 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2055 -0.01796 -0.2555 0.2933 0.9839 0.9935 0.2299 0.7658 0.9487 0.6883 ] Network output: [ 0.01069 0.9114 0.9664 -9.308e-05 4.179e-05 0.1004 -7.015e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003764 0.001218 0.002012 0.004611 0.9906 0.9936 0.003831 0.9422 0.9598 0.01026 ] Network output: [ 0.1396 -0.7386 0.9326 0.0001817 -8.156e-05 1.527 0.0001369 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2108 0.1427 0.3109 0.2911 0.9855 0.9942 0.2114 0.7737 0.9518 0.6614 ] Network output: [ -0.03356 0.2449 1.072 8.528e-05 -3.829e-05 0.7506 6.427e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0641 0.06031 0.1515 0.1361 0.9893 0.9936 0.06414 0.9228 0.9525 0.1867 ] Network output: [ -0.03704 0.2118 1.053 9.171e-05 -4.117e-05 0.8101 6.912e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07814 0.07732 0.1631 0.1401 0.9853 0.9915 0.07815 0.8789 0.9342 0.1773 ] Network output: [ 0.00528 1.139 -0.0321 -1.523e-05 6.836e-06 0.8826 -1.148e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2085 Epoch 5155 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02242 0.9353 0.949 -0.0001117 5.014e-05 0.07036 -8.416e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002929 -0.002508 -0.009948 0.005897 0.9677 0.9724 0.005676 0.8955 0.8924 0.01992 ] Network output: [ 0.7573 0.3083 0.1984 -0.0001914 8.592e-05 -0.022 -0.0001442 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2008 0.002232 -0.1226 0.1327 0.9839 0.9934 0.2244 0.7695 0.9468 0.6674 ] Network output: [ 0.02022 0.9213 0.9546 -9.694e-05 4.352e-05 0.08325 -7.306e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00406 0.001371 0.003928 0.001945 0.9906 0.9935 0.004131 0.9419 0.9593 0.01178 ] Network output: [ -0.1174 0.5018 0.9547 -0.0005374 0.0002413 0.7761 -0.000405 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2337 0.1609 0.3777 0.03121 0.9855 0.9943 0.2344 0.7752 0.9516 0.6652 ] Network output: [ -0.006538 0.2452 1.047 0.0001109 -4.979e-05 0.7219 8.358e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06349 0.05963 0.1444 0.1104 0.9896 0.9937 0.06352 0.9191 0.9528 0.1795 ] Network output: [ 0.00773 0.1041 1.026 0.0001722 -7.732e-05 0.8557 0.0001298 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07923 0.07835 0.155 0.1366 0.9851 0.9914 0.07924 0.8719 0.9349 0.1721 ] Network output: [ 0.0765 0.8843 -0.06146 0.0001419 -6.369e-05 1.025 0.0001069 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.105 Epoch 5156 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0501 0.8363 0.9401 -5.513e-05 2.475e-05 0.1231 -4.155e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002994 -0.002729 -0.01115 0.007897 0.9677 0.9725 0.005821 0.8935 0.8936 0.02047 ] Network output: [ 1.104 -0.4399 -0.01976 0.0002713 -0.0001218 0.2532 0.0002045 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2092 -0.01793 -0.2399 0.2851 0.9839 0.9935 0.2339 0.7572 0.9473 0.6737 ] Network output: [ 0.01069 0.9051 0.9686 -8.891e-05 3.992e-05 0.1046 -6.701e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003822 0.001243 0.002109 0.004556 0.9905 0.9935 0.00389 0.9396 0.9586 0.01004 ] Network output: [ 0.1396 -0.7207 0.9247 0.0001684 -7.56e-05 1.518 0.0001269 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2162 0.1465 0.311 0.287 0.9855 0.9942 0.2169 0.7653 0.9505 0.6466 ] Network output: [ -0.03171 0.2442 1.071 9.065e-05 -4.07e-05 0.7489 6.832e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0647 0.06088 0.1488 0.1354 0.9892 0.9935 0.06473 0.9197 0.9513 0.1829 ] Network output: [ -0.03405 0.2109 1.05 9.841e-05 -4.418e-05 0.8075 7.417e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07869 0.07787 0.1592 0.1397 0.9852 0.9914 0.0787 0.8746 0.9329 0.1732 ] Network output: [ 0.02219 1.034 -0.03022 3.922e-05 -1.761e-05 0.9518 2.956e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1956 Epoch 5157 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02048 0.9404 0.9513 -0.0001139 5.114e-05 0.06681 -8.585e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002976 -0.002545 -0.01013 0.005533 0.9677 0.9724 0.005755 0.8928 0.8901 0.01977 ] Network output: [ 0.7784 0.4704 0.144 -0.0002572 0.0001155 -0.1723 -0.0001939 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2078 0.001923 -0.1452 0.106 0.9838 0.9934 0.2322 0.761 0.9451 0.6581 ] Network output: [ 0.017 0.9101 0.9614 -9.192e-05 4.127e-05 0.09417 -6.928e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004128 0.001299 0.003243 0.001569 0.9905 0.9934 0.004201 0.939 0.9576 0.01138 ] Network output: [ -0.08725 0.5582 0.9131 -0.0005395 0.0002422 0.7011 -0.0004066 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2373 0.1596 0.3502 0.02262 0.9855 0.9942 0.238 0.7664 0.9503 0.6608 ] Network output: [ -0.01051 0.2293 1.056 0.0001174 -5.272e-05 0.7366 8.851e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06292 0.05885 0.1407 0.1107 0.9895 0.9936 0.06295 0.9151 0.9513 0.178 ] Network output: [ 0.0007771 0.05895 1.044 0.0001837 -8.248e-05 0.8967 0.0001385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07815 0.07722 0.1566 0.1418 0.9851 0.9914 0.07816 0.8666 0.9336 0.1744 ] Network output: [ 0.07647 0.7156 -0.02578 0.0001972 -8.853e-05 1.158 0.0001486 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1457 Epoch 5158 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04487 0.8414 0.945 -5.747e-05 2.58e-05 0.1237 -4.331e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003038 -0.002781 -0.01127 0.007861 0.9677 0.9725 0.005903 0.8907 0.8913 0.02059 ] Network output: [ 1.116 -0.3817 -0.04361 0.0002226 -9.994e-05 0.1947 0.0001678 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2147 -0.02036 -0.2457 0.2757 0.9839 0.9934 0.2401 0.7473 0.9455 0.6706 ] Network output: [ 0.00479 0.9039 0.9753 -8.619e-05 3.869e-05 0.1109 -6.496e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003892 0.001199 0.001933 0.004505 0.9905 0.9934 0.003961 0.9371 0.957 0.01009 ] Network output: [ 0.149 -0.6838 0.9064 0.0001538 -6.903e-05 1.48 0.0001159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2198 0.1461 0.3 0.2835 0.9854 0.9942 0.2205 0.7558 0.9489 0.6463 ] Network output: [ -0.03931 0.2257 1.082 9.629e-05 -4.323e-05 0.7712 7.257e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06508 0.06106 0.1503 0.1385 0.9891 0.9935 0.06512 0.9165 0.9497 0.1864 ] Network output: [ -0.04258 0.1819 1.065 0.0001059 -4.754e-05 0.8391 7.98e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07887 0.078 0.163 0.1449 0.9852 0.9914 0.07888 0.8703 0.9312 0.1777 ] Network output: [ 0.008549 0.9803 -0.007746 4.721e-05 -2.119e-05 1.011 3.558e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1675 Epoch 5159 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01715 0.9459 0.9544 -0.0001121 5.032e-05 0.06496 -8.447e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003021 -0.002592 -0.01026 0.005454 0.9677 0.9724 0.00584 0.8902 0.8876 0.0199 ] Network output: [ 0.7891 0.536 0.1199 -0.0002938 0.0001319 -0.2354 -0.0002214 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 0.0007469 -0.1527 0.09536 0.9838 0.9934 0.2375 0.7518 0.9431 0.6562 ] Network output: [ 0.01302 0.9087 0.9664 -8.677e-05 3.895e-05 0.09855 -6.539e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004213 0.001255 0.003043 0.001459 0.9904 0.9934 0.004287 0.9364 0.9559 0.01139 ] Network output: [ -0.07619 0.5895 0.8956 -0.000551 0.0002474 0.6651 -0.0004152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2407 0.1588 0.3407 0.01901 0.9854 0.9942 0.2414 0.7566 0.9485 0.6606 ] Network output: [ -0.0166 0.223 1.063 0.0001213 -5.444e-05 0.7474 9.139e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06332 0.05901 0.1412 0.1117 0.9894 0.9935 0.06335 0.9111 0.9493 0.1798 ] Network output: [ -0.006451 0.04313 1.054 0.0001889 -8.478e-05 0.9162 0.0001423 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07836 0.07738 0.1591 0.1447 0.985 0.9913 0.07837 0.8609 0.9313 0.1774 ] Network output: [ 0.06173 0.704 -0.01016 0.0001908 -8.565e-05 1.183 0.0001438 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.166 Epoch 5160 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04275 0.8389 0.9483 -5.147e-05 2.311e-05 0.127 -3.879e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003063 -0.002824 -0.01121 0.007986 0.9677 0.9725 0.005952 0.8877 0.8889 0.0206 ] Network output: [ 1.122 -0.4174 -0.04196 0.0002124 -9.537e-05 0.2157 0.0001601 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2172 -0.02332 -0.2401 0.2811 0.9838 0.9934 0.2429 0.7361 0.9434 0.6672 ] Network output: [ 0.002243 0.905 0.9782 -7.976e-05 3.581e-05 0.1121 -6.011e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003954 0.001173 0.00204 0.004698 0.9904 0.9934 0.004025 0.9341 0.9552 0.01011 ] Network output: [ 0.1539 -0.7292 0.9094 0.0001633 -7.333e-05 1.513 0.0001231 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2222 0.1455 0.3035 0.2942 0.9854 0.9942 0.2228 0.7445 0.9468 0.6409 ] Network output: [ -0.04316 0.2269 1.085 0.0001002 -4.499e-05 0.7751 7.552e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06638 0.06215 0.152 0.1408 0.989 0.9934 0.06642 0.9125 0.9475 0.1874 ] Network output: [ -0.04607 0.1978 1.064 0.000106 -4.76e-05 0.8306 7.99e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08013 0.07922 0.1627 0.1448 0.9851 0.9914 0.08014 0.8647 0.9284 0.177 ] Network output: [ 0.003389 1.008 -0.01272 4.074e-05 -1.829e-05 0.9981 3.07e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1883 Epoch 5161 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01533 0.9472 0.9572 -0.0001072 4.814e-05 0.06445 -8.081e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003045 -0.002616 -0.01002 0.005536 0.9677 0.9725 0.005883 0.8871 0.8849 0.01969 ] Network output: [ 0.7874 0.4843 0.1321 -0.0002932 0.0001316 -0.1923 -0.000221 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2151 0.0002671 -0.1413 0.1039 0.9837 0.9934 0.2404 0.741 0.9407 0.6486 ] Network output: [ 0.01166 0.9147 0.9676 -8.213e-05 3.687e-05 0.09401 -6.19e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004301 0.001308 0.003243 0.001645 0.9903 0.9933 0.004377 0.9334 0.954 0.01133 ] Network output: [ -0.07639 0.5593 0.8983 -0.0005371 0.0002411 0.6929 -0.0004048 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2445 0.1623 0.3459 0.02772 0.9854 0.9942 0.2453 0.7458 0.9462 0.6515 ] Network output: [ -0.02018 0.2408 1.063 0.0001183 -5.31e-05 0.7366 8.914e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06462 0.0603 0.1422 0.1112 0.9893 0.9934 0.06465 0.907 0.947 0.1792 ] Network output: [ -0.009373 0.07774 1.051 0.000181 -8.127e-05 0.891 0.0001364 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07956 0.07858 0.1576 0.1412 0.985 0.9913 0.07957 0.8549 0.9285 0.175 ] Network output: [ 0.04802 0.8136 -0.01914 0.0001461 -6.558e-05 1.11 0.0001101 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1408 Epoch 5162 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04302 0.8319 0.9509 -4.38e-05 1.966e-05 0.1309 -3.301e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00306 -0.00282 -0.01078 0.007963 0.9678 0.9725 0.00594 0.8847 0.8863 0.02021 ] Network output: [ 1.097 -0.4639 -0.004657 0.0001941 -8.715e-05 0.2758 0.0001463 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2176 -0.02343 -0.2156 0.2847 0.9837 0.9934 0.2434 0.7251 0.9412 0.6567 ] Network output: [ 0.005322 0.9023 0.9766 -7.059e-05 3.169e-05 0.1101 -5.32e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004024 0.001216 0.002431 0.00488 0.9903 0.9933 0.004096 0.9309 0.9534 0.01005 ] Network output: [ 0.1446 -0.7607 0.9223 0.0001596 -7.166e-05 1.55 0.0001203 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2264 0.149 0.318 0.2995 0.9853 0.9942 0.227 0.7332 0.9446 0.6268 ] Network output: [ -0.03955 0.2381 1.078 0.0001053 -4.727e-05 0.7631 7.935e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06807 0.06379 0.1518 0.1403 0.9889 0.9933 0.06811 0.9084 0.9453 0.1846 ] Network output: [ -0.0404 0.2308 1.051 0.0001071 -4.81e-05 0.799 8.074e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08193 0.08101 0.158 0.1409 0.9851 0.9914 0.08194 0.8586 0.9256 0.1715 ] Network output: [ 0.01558 1.029 -0.03325 5.147e-05 -2.311e-05 0.9729 3.879e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2146 Epoch 5163 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01702 0.9422 0.9583 -0.0001005 4.51e-05 0.06508 -7.571e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00307 -0.002635 -0.009778 0.005585 0.9677 0.9725 0.005923 0.8839 0.8823 0.01931 ] Network output: [ 0.8021 0.4275 0.1272 -0.0002802 0.0001258 -0.16 -0.0002112 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.219 0.0002494 -0.1375 0.1141 0.9837 0.9933 0.2447 0.73 0.9385 0.6366 ] Network output: [ 0.01202 0.9154 0.9687 -7.645e-05 3.432e-05 0.09156 -5.762e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004372 0.001386 0.00324 0.001882 0.9902 0.9932 0.004449 0.9302 0.9521 0.01103 ] Network output: [ -0.0612 0.4849 0.8927 -0.0004854 0.0002179 0.7428 -0.0003658 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2482 0.167 0.3433 0.046 0.9853 0.9942 0.249 0.7352 0.9441 0.6381 ] Network output: [ -0.02234 0.2533 1.064 0.000116 -5.206e-05 0.7277 8.74e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06563 0.06142 0.1422 0.1115 0.9891 0.9933 0.06567 0.9034 0.945 0.1775 ] Network output: [ -0.01211 0.106 1.05 0.0001723 -7.736e-05 0.8693 0.0001299 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08024 0.0793 0.1558 0.1385 0.9849 0.9913 0.08025 0.85 0.9261 0.1723 ] Network output: [ 0.03922 0.8731 -0.01975 0.0001183 -5.313e-05 1.069 8.919e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1147 Epoch 5164 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04235 0.8317 0.9532 -4.152e-05 1.864e-05 0.1302 -3.129e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003064 -0.0028 -0.01038 0.007751 0.9678 0.9725 0.005937 0.8819 0.8839 0.01982 ] Network output: [ 1.061 -0.4298 0.02624 0.000144 -6.464e-05 0.2825 0.0001085 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2197 -0.02098 -0.1927 0.2742 0.9837 0.9933 0.2457 0.716 0.9393 0.6452 ] Network output: [ 0.008602 0.898 0.9753 -6.309e-05 2.832e-05 0.1092 -4.755e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004111 0.001295 0.002738 0.004832 0.9901 0.9932 0.004185 0.9282 0.9519 0.01006 ] Network output: [ 0.1273 -0.6992 0.9238 0.0001228 -5.514e-05 1.521 9.256e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2325 0.1553 0.327 0.2871 0.9853 0.9942 0.2333 0.7241 0.9428 0.6161 ] Network output: [ -0.0362 0.2375 1.076 0.0001113 -4.995e-05 0.7596 8.386e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06916 0.06495 0.1518 0.1391 0.9888 0.9932 0.0692 0.9051 0.9438 0.1835 ] Network output: [ -0.03531 0.2348 1.046 0.0001134 -5.09e-05 0.7903 8.545e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08308 0.08218 0.156 0.1393 0.985 0.9913 0.08309 0.8539 0.9237 0.1694 ] Network output: [ 0.02053 1.03 -0.03854 5.628e-05 -2.527e-05 0.9678 4.242e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1931 Epoch 5165 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02092 0.9315 0.9578 -9.13e-05 4.099e-05 0.06846 -6.88e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003103 -0.002661 -0.009755 0.005629 0.9677 0.9725 0.005976 0.8813 0.8803 0.01912 ] Network output: [ 0.8339 0.3956 0.09884 -0.0002646 0.0001188 -0.1634 -0.0001994 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2242 -3.403e-05 -0.1454 0.1206 0.9836 0.9933 0.2505 0.7209 0.9368 0.6286 ] Network output: [ 0.01254 0.9085 0.971 -6.903e-05 3.099e-05 0.0951 -5.202e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004415 0.001433 0.003044 0.002064 0.9901 0.9931 0.004493 0.9277 0.9506 0.01075 ] Network output: [ -0.03811 0.4049 0.8813 -0.0004257 0.0001911 0.7883 -0.0003208 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2509 0.1703 0.3345 0.06475 0.9853 0.9942 0.2516 0.7267 0.9426 0.6301 ] Network output: [ -0.0248 0.2452 1.07 0.0001188 -5.335e-05 0.7351 8.956e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.066 0.0619 0.1429 0.1141 0.989 0.9933 0.06604 0.9009 0.9437 0.1781 ] Network output: [ -0.01613 0.1044 1.056 0.0001703 -7.646e-05 0.8724 0.0001284 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08027 0.07935 0.1569 0.1398 0.9849 0.9912 0.08027 0.8467 0.9246 0.1731 ] Network output: [ 0.03048 0.8683 -0.006443 0.0001088 -4.884e-05 1.078 8.199e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09509 Epoch 5166 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04141 0.8368 0.9542 -4.133e-05 1.855e-05 0.1261 -3.115e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003087 -0.002793 -0.01026 0.007505 0.9678 0.9725 0.005968 0.8798 0.8819 0.01971 ] Network output: [ 1.04 -0.3348 0.02809 8.736e-05 -3.922e-05 0.2269 6.584e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2235 -0.01819 -0.185 0.2556 0.9836 0.9933 0.2499 0.7094 0.9376 0.6394 ] Network output: [ 0.009867 0.8931 0.976 -5.724e-05 2.57e-05 0.111 -4.314e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004187 0.001353 0.002803 0.0046 0.9901 0.9932 0.004262 0.9264 0.9506 0.01017 ] Network output: [ 0.1104 -0.5804 0.9154 6.914e-05 -3.104e-05 1.444 5.211e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2378 0.1605 0.3272 0.2643 0.9853 0.9941 0.2385 0.7181 0.9415 0.6151 ] Network output: [ -0.03651 0.2209 1.081 0.0001179 -5.293e-05 0.7718 8.885e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06921 0.06508 0.1532 0.1388 0.9888 0.9932 0.06924 0.903 0.9428 0.1862 ] Network output: [ -0.03499 0.2068 1.052 0.0001233 -5.538e-05 0.8112 9.296e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08311 0.08223 0.1586 0.1414 0.985 0.9913 0.08312 0.851 0.9229 0.1727 ] Network output: [ 0.01022 1.028 -0.02453 4.31e-05 -1.935e-05 0.9765 3.248e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1405 Epoch 5167 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02538 0.9183 0.9564 -8.001e-05 3.592e-05 0.07421 -6.029e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003129 -0.002686 -0.009884 0.005725 0.9678 0.9725 0.006018 0.8796 0.879 0.0192 ] Network output: [ 0.866 0.3687 0.06933 -0.0002443 0.0001097 -0.1711 -0.0001841 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2281 -0.000817 -0.1558 0.1259 0.9836 0.9933 0.2548 0.7146 0.9357 0.6278 ] Network output: [ 0.01325 0.8992 0.9728 -6.063e-05 2.722e-05 0.1013 -4.569e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004425 0.001446 0.002881 0.00222 0.9901 0.9931 0.004503 0.9262 0.9496 0.01067 ] Network output: [ -0.02047 0.3356 0.8754 -0.0003793 0.0001703 0.8284 -0.0002858 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2518 0.1714 0.3283 0.07962 0.9853 0.9941 0.2526 0.721 0.9415 0.6293 ] Network output: [ -0.02728 0.2242 1.077 0.0001253 -5.624e-05 0.7536 9.442e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06597 0.06191 0.1452 0.1179 0.989 0.9932 0.066 0.8995 0.9428 0.1815 ] Network output: [ -0.01999 0.08709 1.065 0.0001732 -7.773e-05 0.8889 0.0001305 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08004 0.07915 0.1601 0.1432 0.9849 0.9912 0.08005 0.8451 0.9237 0.1767 ] Network output: [ 0.02023 0.8577 0.008715 9.913e-05 -4.45e-05 1.094 7.471e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0791 Epoch 5168 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04167 0.8417 0.9533 -3.932e-05 1.765e-05 0.1215 -2.963e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003111 -0.002794 -0.01032 0.007313 0.9678 0.9725 0.006004 0.8785 0.8804 0.01978 ] Network output: [ 1.032 -0.2363 0.01589 4.519e-05 -2.029e-05 0.1566 3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2267 -0.01615 -0.1864 0.2376 0.9836 0.9933 0.2533 0.7054 0.9364 0.6391 ] Network output: [ 0.01079 0.8881 0.9764 -5.15e-05 2.312e-05 0.1137 -3.881e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004232 0.001374 0.002754 0.004334 0.9901 0.9931 0.004308 0.9254 0.9498 0.01032 ] Network output: [ 0.09674 -0.4653 0.9068 1.801e-05 -8.087e-06 1.365 1.358e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2407 0.1629 0.3248 0.2418 0.9853 0.9941 0.2414 0.7145 0.9407 0.62 ] Network output: [ -0.03772 0.1994 1.088 0.0001249 -5.606e-05 0.7889 9.412e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06864 0.06456 0.1553 0.1391 0.9888 0.9932 0.06868 0.9018 0.9422 0.1902 ] Network output: [ -0.03616 0.1696 1.062 0.0001343 -6.03e-05 0.8411 0.0001012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0825 0.08163 0.1628 0.1448 0.9851 0.9913 0.08251 0.8494 0.9224 0.1779 ] Network output: [ -0.003079 1.026 -0.007042 2.626e-05 -1.179e-05 0.9874 1.979e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09611 Epoch 5169 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02965 0.9064 0.9541 -6.865e-05 3.082e-05 0.07997 -5.174e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00314 -0.002704 -0.01004 0.005868 0.9678 0.9725 0.006037 0.8787 0.8783 0.0194 ] Network output: [ 0.891 0.3332 0.04951 -0.0002181 9.791e-05 -0.1656 -0.0001644 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2298 -0.001958 -0.1634 0.1325 0.9836 0.9933 0.2566 0.7106 0.935 0.6312 ] Network output: [ 0.01437 0.8918 0.973 -5.307e-05 2.382e-05 0.1062 -3.999e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004412 0.001438 0.002815 0.002385 0.9901 0.9931 0.00449 0.9255 0.949 0.01073 ] Network output: [ -0.009329 0.2721 0.8764 -0.0003444 0.0001546 0.8687 -0.0002595 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2513 0.171 0.3265 0.09218 0.9853 0.9941 0.252 0.7175 0.9408 0.6322 ] Network output: [ -0.02907 0.2027 1.083 0.0001318 -5.916e-05 0.7726 9.931e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06582 0.06179 0.1484 0.1219 0.989 0.9932 0.06586 0.899 0.9422 0.1858 ] Network output: [ -0.0227 0.07113 1.071 0.0001763 -7.916e-05 0.9041 0.0001329 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07983 0.07894 0.1637 0.1463 0.985 0.9913 0.07984 0.8445 0.9231 0.1807 ] Network output: [ 0.01044 0.8686 0.01791 8.337e-05 -3.743e-05 1.093 6.283e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06403 Epoch 5170 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04287 0.8455 0.951 -3.601e-05 1.617e-05 0.1176 -2.714e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003123 -0.002792 -0.01041 0.007185 0.9678 0.9725 0.006023 0.878 0.8795 0.01989 ] Network output: [ 1.027 -0.1619 0.006021 1.996e-05 -8.962e-06 0.1029 1.504e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2281 -0.01471 -0.1887 0.2243 0.9836 0.9933 0.2549 0.7032 0.9356 0.6414 ] Network output: [ 0.01227 0.8845 0.9755 -4.638e-05 2.082e-05 0.1154 -3.495e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004251 0.001374 0.002719 0.004122 0.9901 0.9931 0.004326 0.9249 0.9492 0.01048 ] Network output: [ 0.08517 -0.3774 0.9027 -2.355e-05 1.057e-05 1.304 -1.775e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2417 0.1636 0.3241 0.2239 0.9853 0.9941 0.2424 0.7126 0.9402 0.6261 ] Network output: [ -0.03801 0.182 1.092 0.0001307 -5.868e-05 0.8024 9.851e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06797 0.0639 0.1572 0.1393 0.9888 0.9932 0.068 0.9011 0.9417 0.1937 ] Network output: [ -0.03644 0.1397 1.069 0.0001434 -6.437e-05 0.8648 0.0001081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0818 0.08093 0.1664 0.1476 0.9851 0.9914 0.08181 0.8484 0.922 0.1823 ] Network output: [ -0.01244 1.031 0.004339 1.17e-05 -5.253e-06 0.9899 8.817e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0694 Epoch 5171 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03351 0.8968 0.9513 -5.881e-05 2.64e-05 0.08466 -4.432e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003139 -0.002712 -0.01015 0.006031 0.9678 0.9725 0.006035 0.8784 0.878 0.01958 ] Network output: [ 0.9085 0.2862 0.04007 -0.0001876 8.421e-05 -0.144 -0.0001414 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2298 -0.003118 -0.1674 0.141 0.9836 0.9933 0.2566 0.7082 0.9346 0.6356 ] Network output: [ 0.01589 0.8877 0.9717 -4.738e-05 2.127e-05 0.1086 -3.57e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004387 0.001427 0.002831 0.00257 0.9901 0.9931 0.004464 0.9252 0.9487 0.01082 ] Network output: [ -0.002394 0.2087 0.8822 -0.0003146 0.0001412 0.9127 -0.0002371 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2499 0.17 0.3282 0.1042 0.9853 0.9941 0.2506 0.7156 0.9404 0.6356 ] Network output: [ -0.02986 0.1869 1.087 0.0001363 -6.121e-05 0.7866 0.0001028 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06572 0.0617 0.1515 0.1252 0.989 0.9932 0.06575 0.8989 0.9419 0.1897 ] Network output: [ -0.024 0.06399 1.073 0.0001774 -7.964e-05 0.9115 0.0001337 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07973 0.07885 0.1664 0.1484 0.985 0.9913 0.07974 0.8445 0.9226 0.1838 ] Network output: [ 0.00309 0.8991 0.02 6.396e-05 -2.871e-05 1.075 4.82e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04964 Epoch 5172 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04439 0.8488 0.9483 -3.302e-05 1.482e-05 0.114 -2.488e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003124 -0.002782 -0.01046 0.007094 0.9678 0.9726 0.006021 0.8779 0.879 0.01998 ] Network output: [ 1.019 -0.1113 0.003119 5.653e-06 -2.538e-06 0.06997 4.261e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2282 -0.01342 -0.1888 0.215 0.9836 0.9933 0.255 0.7024 0.9351 0.6441 ] Network output: [ 0.01426 0.8826 0.9734 -4.258e-05 1.911e-05 0.1153 -3.209e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004253 0.001374 0.002735 0.003967 0.9901 0.9931 0.004328 0.9249 0.9489 0.01062 ] Network output: [ 0.07417 -0.3135 0.9028 -5.643e-05 2.534e-05 1.262 -4.253e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2418 0.1638 0.3259 0.2102 0.9853 0.9941 0.2426 0.7119 0.9399 0.6312 ] Network output: [ -0.0371 0.1709 1.094 0.0001347 -6.048e-05 0.8101 0.0001015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06736 0.06333 0.1587 0.1392 0.9889 0.9932 0.0674 0.9008 0.9415 0.1961 ] Network output: [ -0.03528 0.1204 1.072 0.0001498 -6.724e-05 0.8789 0.0001129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08122 0.08036 0.1687 0.1491 0.9851 0.9914 0.08123 0.8479 0.9218 0.1852 ] Network output: [ -0.01667 1.04 0.008918 1.227e-06 -5.51e-07 0.9845 9.25e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05448 Epoch 5173 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03695 0.8894 0.9485 -5.119e-05 2.298e-05 0.08805 -3.858e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003131 -0.002712 -0.01021 0.006181 0.9678 0.9725 0.00602 0.8784 0.8779 0.01971 ] Network output: [ 0.9213 0.2375 0.03596 -0.0001574 7.064e-05 -0.1167 -0.0001186 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2289 -0.004024 -0.1694 0.1498 0.9836 0.9933 0.2557 0.707 0.9344 0.6396 ] Network output: [ 0.01753 0.8857 0.9698 -4.36e-05 1.957e-05 0.1093 -3.286e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004355 0.001422 0.002877 0.002748 0.9901 0.9931 0.004432 0.9253 0.9487 0.0109 ] Network output: [ 0.002421 0.1482 0.8894 -0.0002877 0.0001292 0.9564 -0.0002168 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2483 0.1691 0.3312 0.1155 0.9853 0.9941 0.249 0.7147 0.9401 0.6383 ] Network output: [ -0.02985 0.1763 1.088 0.000139 -6.242e-05 0.7957 0.0001048 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06561 0.06163 0.154 0.1278 0.989 0.9932 0.06565 0.8992 0.9417 0.1926 ] Network output: [ -0.02425 0.06306 1.073 0.0001767 -7.931e-05 0.9129 0.0001331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07966 0.07879 0.1682 0.1494 0.985 0.9913 0.07967 0.845 0.9224 0.1857 ] Network output: [ -0.001055 0.9321 0.01826 4.702e-05 -2.111e-05 1.052 3.543e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03865 Epoch 5174 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04573 0.8522 0.9457 -3.132e-05 1.406e-05 0.1105 -2.36e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00312 -0.002768 -0.01047 0.007012 0.9678 0.9726 0.006009 0.8781 0.8789 0.02002 ] Network output: [ 1.011 -0.07223 0.003621 -4.337e-06 1.947e-06 0.04733 -3.268e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2278 -0.0121 -0.1879 0.2078 0.9836 0.9933 0.2545 0.7027 0.935 0.6465 ] Network output: [ 0.01622 0.8819 0.9711 -4.029e-05 1.809e-05 0.1143 -3.036e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004246 0.001378 0.002771 0.003839 0.9901 0.9931 0.004321 0.9252 0.9489 0.01072 ] Network output: [ 0.0637 -0.2629 0.9046 -8.367e-05 3.756e-05 1.231 -6.306e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2417 0.1641 0.3286 0.1989 0.9853 0.9941 0.2425 0.7122 0.9399 0.6353 ] Network output: [ -0.0356 0.1636 1.094 0.0001373 -6.162e-05 0.8142 0.0001034 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0668 0.06281 0.1596 0.1388 0.9889 0.9932 0.06683 0.9009 0.9415 0.1977 ] Network output: [ -0.03338 0.1072 1.073 0.0001543 -6.929e-05 0.8877 0.0001163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08071 0.07985 0.1701 0.15 0.9851 0.9914 0.08072 0.8479 0.9219 0.187 ] Network output: [ -0.0171 1.044 0.01033 -3.603e-06 1.618e-06 0.9797 -2.715e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04498 Epoch 5175 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03978 0.8843 0.9459 -4.59e-05 2.061e-05 0.09014 -3.459e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003122 -0.00271 -0.01027 0.006295 0.9678 0.9726 0.006002 0.8787 0.8782 0.0198 ] Network output: [ 0.9322 0.1999 0.0316 -0.0001319 5.922e-05 -0.09634 -9.941e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2281 -0.004663 -0.1718 0.1569 0.9836 0.9933 0.2547 0.7067 0.9345 0.6432 ] Network output: [ 0.01886 0.8846 0.9681 -4.142e-05 1.859e-05 0.1094 -3.122e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004321 0.001416 0.0029 0.002885 0.9901 0.9931 0.004397 0.9256 0.9488 0.01096 ] Network output: [ 0.006409 0.09839 0.8954 -0.0002654 0.0001192 0.9923 -0.0002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2467 0.1683 0.3335 0.1246 0.9853 0.9941 0.2475 0.7148 0.9401 0.641 ] Network output: [ -0.02959 0.1679 1.089 0.0001407 -6.314e-05 0.8027 0.000106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0654 0.06146 0.1559 0.1299 0.989 0.9933 0.06543 0.8998 0.9418 0.1947 ] Network output: [ -0.02422 0.06213 1.073 0.0001756 -7.885e-05 0.914 0.0001324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07948 0.07862 0.1695 0.1502 0.9851 0.9913 0.07949 0.8458 0.9224 0.1872 ] Network output: [ -0.002802 0.9528 0.01666 3.637e-05 -1.633e-05 1.036 2.741e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03217 Epoch 5176 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0466 0.8563 0.9435 -3.101e-05 1.392e-05 0.1069 -2.337e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003114 -0.002754 -0.0105 0.006935 0.9679 0.9726 0.005996 0.8785 0.879 0.02006 ] Network output: [ 1.004 -0.03539 0.00281 -1.303e-05 5.851e-06 0.02482 -9.823e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2274 -0.01092 -0.188 0.2013 0.9836 0.9933 0.254 0.7037 0.935 0.649 ] Network output: [ 0.0176 0.882 0.9693 -3.935e-05 1.767e-05 0.1133 -2.966e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004233 0.001378 0.002784 0.003716 0.9902 0.9932 0.004308 0.9256 0.949 0.01081 ] Network output: [ 0.05479 -0.2185 0.906 -0.0001074 4.821e-05 1.202 -8.093e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2414 0.1642 0.3304 0.189 0.9853 0.9941 0.2422 0.7131 0.94 0.6394 ] Network output: [ -0.03434 0.1573 1.094 0.0001389 -6.237e-05 0.8178 0.0001047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06619 0.06224 0.1602 0.1384 0.9889 0.9933 0.06623 0.9012 0.9417 0.1989 ] Network output: [ -0.03176 0.09446 1.073 0.0001581 -7.099e-05 0.8963 0.0001192 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08016 0.07931 0.1713 0.1509 0.9851 0.9914 0.08017 0.8482 0.9221 0.1885 ] Network output: [ -0.01608 1.038 0.01202 -3.579e-06 1.607e-06 0.9816 -2.697e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03774 Epoch 5177 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04181 0.8815 0.9436 -4.262e-05 1.913e-05 0.0911 -3.212e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003113 -0.002707 -0.01035 0.006379 0.9679 0.9726 0.005986 0.8791 0.8785 0.0199 ] Network output: [ 0.9423 0.1773 0.02503 -0.0001122 5.035e-05 -0.08741 -8.452e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2274 -0.005212 -0.1757 0.1615 0.9836 0.9933 0.254 0.707 0.9348 0.647 ] Network output: [ 0.01955 0.8843 0.967 -4.05e-05 1.818e-05 0.1095 -3.052e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004287 0.001403 0.002885 0.002972 0.9902 0.9932 0.004362 0.9261 0.949 0.011 ] Network output: [ 0.009965 0.06248 0.8994 -0.0002494 0.000112 1.017 -0.000188 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2451 0.1672 0.3344 0.1311 0.9853 0.9942 0.2458 0.7153 0.9403 0.6443 ] Network output: [ -0.02959 0.1598 1.09 0.0001418 -6.364e-05 0.8096 0.0001068 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06506 0.06115 0.1574 0.1317 0.989 0.9933 0.06509 0.9005 0.942 0.1967 ] Network output: [ -0.02452 0.05807 1.074 0.0001751 -7.859e-05 0.9179 0.0001319 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07917 0.07831 0.1709 0.1514 0.9851 0.9914 0.07918 0.8467 0.9226 0.1888 ] Network output: [ -0.003845 0.9609 0.01689 3.071e-05 -1.379e-05 1.03 2.315e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02876 Epoch 5178 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04696 0.8607 0.9417 -3.156e-05 1.417e-05 0.1035 -2.379e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003108 -0.002743 -0.01056 0.006875 0.9679 0.9726 0.005983 0.8791 0.8792 0.02012 ] Network output: [ 0.9999 -0.0009278 -0.0002862 -1.968e-05 8.833e-06 0.001429 -1.483e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2269 -0.01012 -0.1899 0.1956 0.9837 0.9933 0.2535 0.7048 0.9352 0.6521 ] Network output: [ 0.01825 0.8827 0.9681 -3.941e-05 1.769e-05 0.1125 -2.97e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004216 0.001369 0.002767 0.003603 0.9902 0.9932 0.00429 0.9261 0.9492 0.01089 ] Network output: [ 0.04804 -0.1805 0.9067 -0.0001278 5.735e-05 1.177 -9.628e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2408 0.1637 0.3312 0.1805 0.9853 0.9942 0.2415 0.7143 0.9402 0.644 ] Network output: [ -0.03366 0.151 1.095 0.0001401 -6.289e-05 0.8221 0.0001056 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06556 0.06164 0.1607 0.1384 0.989 0.9933 0.0656 0.9016 0.942 0.2002 ] Network output: [ -0.03092 0.08138 1.075 0.0001613 -7.242e-05 0.9061 0.0001216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07957 0.07872 0.1726 0.1521 0.9852 0.9914 0.07958 0.8487 0.9225 0.1902 ] Network output: [ -0.01533 1.028 0.01463 -1.766e-06 7.93e-07 0.988 -1.331e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03235 Epoch 5179 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04308 0.8805 0.9418 -4.069e-05 1.827e-05 0.09131 -3.066e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003105 -0.002706 -0.01045 0.00645 0.9679 0.9726 0.005972 0.8796 0.879 0.02002 ] Network output: [ 0.9513 0.1634 0.01807 -9.588e-05 4.304e-05 -0.08455 -7.226e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2266 -0.005802 -0.1802 0.1649 0.9837 0.9933 0.2531 0.7077 0.935 0.6512 ] Network output: [ 0.01969 0.8848 0.9662 -4.051e-05 1.819e-05 0.1095 -3.053e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004253 0.001383 0.002852 0.003029 0.9902 0.9932 0.004328 0.9266 0.9493 0.01106 ] Network output: [ 0.01291 0.03689 0.9024 -0.0002387 0.0001072 1.034 -0.0001799 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2434 0.1657 0.3346 0.1356 0.9853 0.9942 0.2441 0.7162 0.9405 0.6483 ] Network output: [ -0.0299 0.1526 1.092 0.0001424 -6.392e-05 0.8161 0.0001073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06467 0.06076 0.1586 0.1332 0.9891 0.9933 0.0647 0.9011 0.9422 0.1986 ] Network output: [ -0.02518 0.05265 1.075 0.0001747 -7.841e-05 0.9233 0.0001316 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0788 0.07794 0.1724 0.1526 0.9851 0.9914 0.07881 0.8476 0.9229 0.1906 ] Network output: [ -0.005159 0.9654 0.01788 2.642e-05 -1.186e-05 1.027 1.991e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02672 Epoch 5180 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04699 0.8651 0.9402 -3.238e-05 1.454e-05 0.1006 -2.44e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003101 -0.002734 -0.01063 0.006842 0.9679 0.9726 0.00597 0.8797 0.8796 0.0202 ] Network output: [ 0.9975 0.02529 -0.003245 -2.254e-05 1.012e-05 -0.01711 -1.698e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2261 -0.009704 -0.1924 0.1916 0.9837 0.9933 0.2526 0.7061 0.9354 0.6557 ] Network output: [ 0.01844 0.8842 0.9671 -4.009e-05 1.8e-05 0.1116 -3.021e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004196 0.001353 0.002744 0.003518 0.9902 0.9932 0.00427 0.9267 0.9494 0.01097 ] Network output: [ 0.043 -0.152 0.9077 -0.000144 6.463e-05 1.158 -0.0001085 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2397 0.1626 0.3317 0.1742 0.9853 0.9942 0.2404 0.7155 0.9405 0.6487 ] Network output: [ -0.03335 0.1459 1.095 0.0001406 -6.313e-05 0.826 0.000106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06499 0.06108 0.1612 0.1385 0.989 0.9933 0.06503 0.9021 0.9422 0.2014 ] Network output: [ -0.03054 0.07062 1.077 0.0001635 -7.341e-05 0.9146 0.0001232 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07904 0.07819 0.1738 0.1533 0.9852 0.9914 0.07904 0.8493 0.9228 0.1919 ] Network output: [ -0.01505 1.021 0.01657 -8.463e-07 3.799e-07 0.9925 -6.378e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02894 Epoch 5181 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04385 0.8806 0.9404 -3.957e-05 1.776e-05 0.09116 -2.982e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003096 -0.002703 -0.01054 0.006518 0.9679 0.9726 0.005955 0.8802 0.8794 0.02012 ] Network output: [ 0.9583 0.1502 0.01332 -8.077e-05 3.626e-05 -0.08038 -6.087e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2255 -0.006394 -0.1841 0.1681 0.9837 0.9933 0.2519 0.7085 0.9353 0.6555 ] Network output: [ 0.01958 0.8862 0.9655 -4.116e-05 1.848e-05 0.109 -3.102e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004222 0.001361 0.002829 0.003079 0.9902 0.9932 0.004296 0.9271 0.9495 0.01112 ] Network output: [ 0.01491 0.01601 0.9055 -0.0002309 0.0001037 1.048 -0.0001741 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2416 0.164 0.3351 0.1393 0.9853 0.9942 0.2423 0.7171 0.9407 0.6523 ] Network output: [ -0.03022 0.1476 1.093 0.0001423 -6.39e-05 0.8209 0.0001073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06431 0.0604 0.1596 0.1345 0.9891 0.9933 0.06434 0.9018 0.9425 0.2002 ] Network output: [ -0.02579 0.04922 1.076 0.0001738 -7.803e-05 0.9271 0.000131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07847 0.07761 0.1735 0.1536 0.9852 0.9914 0.07848 0.8485 0.9232 0.1921 ] Network output: [ -0.006438 0.9734 0.01788 2.159e-05 -9.692e-06 1.022 1.627e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02516 Epoch 5182 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0469 0.8688 0.9391 -3.324e-05 1.492e-05 0.09817 -2.505e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003092 -0.002725 -0.01068 0.006829 0.9679 0.9726 0.005953 0.8803 0.88 0.02027 ] Network output: [ 0.9952 0.04053 -0.004002 -2.16e-05 9.696e-06 -0.02711 -1.628e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.225 -0.009479 -0.1941 0.1894 0.9837 0.9933 0.2514 0.7074 0.9356 0.6593 ] Network output: [ 0.01852 0.8862 0.9662 -4.115e-05 1.848e-05 0.1104 -3.101e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004174 0.001337 0.002737 0.003464 0.9902 0.9932 0.004248 0.9273 0.9497 0.01104 ] Network output: [ 0.03883 -0.1335 0.9096 -0.0001559 6.998e-05 1.146 -0.0001175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2386 0.1615 0.3326 0.1698 0.9853 0.9942 0.2393 0.7167 0.9408 0.6529 ] Network output: [ -0.03298 0.1431 1.095 0.0001405 -6.307e-05 0.8282 0.0001059 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06453 0.06061 0.1616 0.1385 0.9891 0.9933 0.06456 0.9026 0.9425 0.2024 ] Network output: [ -0.03012 0.06416 1.077 0.0001645 -7.386e-05 0.9197 0.000124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07861 0.07776 0.1747 0.154 0.9852 0.9914 0.07862 0.85 0.9231 0.1931 ] Network output: [ -0.01452 1.02 0.0167 -1.038e-06 4.658e-07 0.9922 -7.82e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.027 Epoch 5183 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04434 0.8811 0.9392 -3.904e-05 1.753e-05 0.09085 -2.942e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003084 -0.002699 -0.0106 0.006579 0.9679 0.9726 0.005936 0.8808 0.8799 0.0202 ] Network output: [ 0.963 0.1357 0.01119 -6.653e-05 2.987e-05 -0.07321 -5.014e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2243 -0.006867 -0.1868 0.1714 0.9837 0.9933 0.2505 0.7094 0.9356 0.6592 ] Network output: [ 0.01948 0.8881 0.9648 -4.221e-05 1.895e-05 0.108 -3.181e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004192 0.001344 0.002821 0.003126 0.9903 0.9932 0.004266 0.9277 0.9498 0.01117 ] Network output: [ 0.01596 -0.002235 0.9089 -0.0002247 0.0001009 1.061 -0.0001693 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2399 0.1626 0.336 0.1425 0.9854 0.9942 0.2406 0.7181 0.941 0.6558 ] Network output: [ -0.03028 0.1449 1.093 0.0001417 -6.362e-05 0.8236 0.0001068 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.064 0.06009 0.1603 0.1354 0.9891 0.9933 0.06404 0.9025 0.9427 0.2014 ] Network output: [ -0.02603 0.0485 1.076 0.0001724 -7.74e-05 0.9284 0.0001299 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07819 0.07734 0.1743 0.1541 0.9852 0.9914 0.0782 0.8494 0.9234 0.193 ] Network output: [ -0.006961 0.9834 0.01658 1.721e-05 -7.728e-06 1.014 1.297e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02396 Epoch 5184 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04676 0.872 0.9381 -3.424e-05 1.537e-05 0.09621 -2.581e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003081 -0.002715 -0.01071 0.006825 0.9679 0.9726 0.005933 0.881 0.8804 0.02032 ] Network output: [ 0.9928 0.04891 -0.003172 -1.878e-05 8.432e-06 -0.0315 -1.416e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2238 -0.009281 -0.1952 0.1884 0.9837 0.9934 0.25 0.7087 0.9359 0.6624 ] Network output: [ 0.01859 0.8883 0.9654 -4.249e-05 1.907e-05 0.109 -3.202e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004152 0.001324 0.00274 0.003428 0.9903 0.9932 0.004225 0.9279 0.95 0.0111 ] Network output: [ 0.03516 -0.1211 0.9119 -0.0001647 7.396e-05 1.138 -0.0001242 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2374 0.1605 0.3338 0.1667 0.9854 0.9942 0.2381 0.718 0.9411 0.6564 ] Network output: [ -0.03245 0.1419 1.095 0.0001399 -6.282e-05 0.8289 0.0001054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06413 0.06022 0.1618 0.1385 0.9891 0.9933 0.06416 0.9032 0.9428 0.203 ] Network output: [ -0.02949 0.06063 1.077 0.0001647 -7.395e-05 0.9223 0.0001241 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07826 0.0774 0.1751 0.1544 0.9852 0.9914 0.07826 0.8506 0.9234 0.1938 ] Network output: [ -0.01328 1.02 0.0157 -6.542e-07 2.937e-07 0.9904 -4.93e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02579 Epoch 5185 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04464 0.8818 0.9384 -3.908e-05 1.755e-05 0.0904 -2.945e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003073 -0.002693 -0.01064 0.006625 0.9679 0.9726 0.005917 0.8815 0.8804 0.02026 ] Network output: [ 0.9665 0.1242 0.009844 -5.438e-05 2.441e-05 -0.06715 -4.098e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2231 -0.007188 -0.1892 0.1741 0.9837 0.9934 0.2491 0.7105 0.936 0.6625 ] Network output: [ 0.01935 0.8899 0.9642 -4.355e-05 1.955e-05 0.107 -3.282e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004164 0.001329 0.002812 0.003158 0.9903 0.9932 0.004237 0.9283 0.9502 0.0112 ] Network output: [ 0.01651 -0.01606 0.9118 -0.00022 9.878e-05 1.07 -0.0001658 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2384 0.1613 0.3367 0.1448 0.9854 0.9942 0.2391 0.7192 0.9413 0.6588 ] Network output: [ -0.03014 0.1434 1.092 0.0001408 -6.323e-05 0.8251 0.0001061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06369 0.05979 0.1607 0.1359 0.9891 0.9934 0.06373 0.9031 0.943 0.2022 ] Network output: [ -0.02604 0.04818 1.076 0.000171 -7.678e-05 0.9291 0.0001289 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07792 0.07706 0.1747 0.1545 0.9852 0.9914 0.07793 0.8503 0.9237 0.1937 ] Network output: [ -0.006636 0.9895 0.01521 1.506e-05 -6.76e-06 1.009 1.135e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02319 Epoch 5186 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04653 0.8748 0.9375 -3.552e-05 1.595e-05 0.09449 -2.677e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00307 -0.002706 -0.01075 0.006819 0.9679 0.9726 0.005914 0.8817 0.8809 0.02036 ] Network output: [ 0.9911 0.05673 -0.002849 -1.602e-05 7.191e-06 -0.03605 -1.207e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2227 -0.009106 -0.1967 0.1875 0.9837 0.9934 0.2488 0.71 0.9363 0.6653 ] Network output: [ 0.01853 0.8902 0.9648 -4.404e-05 1.977e-05 0.1078 -3.319e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004129 0.001312 0.002732 0.003391 0.9903 0.9932 0.004202 0.9285 0.9503 0.01115 ] Network output: [ 0.0322 -0.11 0.9138 -0.0001722 7.731e-05 1.131 -0.0001298 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2363 0.1596 0.3344 0.164 0.9854 0.9942 0.237 0.7192 0.9414 0.6597 ] Network output: [ -0.03194 0.1408 1.094 0.0001392 -6.25e-05 0.8295 0.0001049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06374 0.05984 0.1619 0.1384 0.9891 0.9934 0.06377 0.9038 0.9431 0.2035 ] Network output: [ -0.02894 0.05703 1.077 0.0001648 -7.4e-05 0.925 0.0001242 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0779 0.07705 0.1754 0.1548 0.9852 0.9914 0.07791 0.8513 0.9238 0.1944 ] Network output: [ -0.01174 1.017 0.01509 1.34e-06 -6.016e-07 0.9914 1.01e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02477 Epoch 5187 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04471 0.8829 0.9377 -3.965e-05 1.78e-05 0.08978 -2.988e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003063 -0.002687 -0.0107 0.006658 0.9679 0.9726 0.005899 0.8821 0.8809 0.02032 ] Network output: [ 0.9697 0.1185 0.007593 -4.479e-05 2.011e-05 -0.06566 -3.375e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.222 -0.007452 -0.192 0.1758 0.9837 0.9934 0.2479 0.7116 0.9363 0.6658 ] Network output: [ 0.01906 0.8915 0.9639 -4.51e-05 2.025e-05 0.1063 -3.399e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004137 0.001313 0.002786 0.00317 0.9903 0.9933 0.00421 0.9288 0.9505 0.01124 ] Network output: [ 0.01706 -0.02409 0.9137 -0.0002175 9.764e-05 1.075 -0.0001639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.237 0.1601 0.3367 0.146 0.9854 0.9942 0.2377 0.7204 0.9416 0.662 ] Network output: [ -0.03005 0.1416 1.092 0.00014 -6.285e-05 0.8268 0.0001055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06335 0.05946 0.1609 0.1364 0.9891 0.9934 0.06339 0.9037 0.9433 0.2028 ] Network output: [ -0.02615 0.04609 1.076 0.00017 -7.634e-05 0.9312 0.0001282 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0776 0.07674 0.1752 0.155 0.9852 0.9914 0.07761 0.8511 0.9241 0.1944 ] Network output: [ -0.006152 0.9895 0.01488 1.503e-05 -6.749e-06 1.008 1.133e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0227 Epoch 5188 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04616 0.8776 0.937 -3.7e-05 1.661e-05 0.09293 -2.788e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00306 -0.002698 -0.01079 0.006815 0.9679 0.9726 0.005896 0.8823 0.8814 0.02041 ] Network output: [ 0.9904 0.06563 -0.003879 -1.342e-05 6.023e-06 -0.04263 -1.011e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2216 -0.009042 -0.1989 0.1865 0.9837 0.9934 0.2476 0.7113 0.9366 0.6684 ] Network output: [ 0.01823 0.8919 0.9645 -4.575e-05 2.054e-05 0.107 -3.448e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004107 0.001296 0.002707 0.003353 0.9903 0.9933 0.00418 0.9291 0.9507 0.0112 ] Network output: [ 0.03012 -0.09904 0.9148 -0.000179 8.037e-05 1.123 -0.0001349 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2351 0.1584 0.3344 0.1614 0.9854 0.9942 0.2358 0.7205 0.9417 0.6631 ] Network output: [ -0.03165 0.1392 1.094 0.0001385 -6.22e-05 0.8307 0.0001044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06333 0.05943 0.1618 0.1385 0.9891 0.9934 0.06337 0.9043 0.9434 0.204 ] Network output: [ -0.02871 0.05225 1.077 0.000165 -7.408e-05 0.9289 0.0001244 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07752 0.07667 0.1759 0.1553 0.9852 0.9915 0.07753 0.852 0.9241 0.1951 ] Network output: [ -0.01065 1.011 0.01541 3.889e-06 -1.746e-06 0.9952 2.931e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02382 Epoch 5189 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04458 0.8844 0.9372 -4.05e-05 1.818e-05 0.08908 -3.052e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003053 -0.002683 -0.01076 0.006688 0.9679 0.9726 0.005883 0.8827 0.8815 0.02039 ] Network output: [ 0.9729 0.116 0.004831 -3.652e-05 1.64e-05 -0.06678 -2.752e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2209 -0.007752 -0.1953 0.177 0.9837 0.9934 0.2467 0.7127 0.9367 0.6691 ] Network output: [ 0.01857 0.8932 0.9638 -4.68e-05 2.101e-05 0.1057 -3.527e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004111 0.001295 0.00275 0.003172 0.9903 0.9933 0.004184 0.9294 0.9508 0.01128 ] Network output: [ 0.01765 -0.02836 0.9149 -0.0002166 9.722e-05 1.077 -0.0001632 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2355 0.1586 0.3362 0.1466 0.9854 0.9942 0.2362 0.7215 0.9419 0.6654 ] Network output: [ -0.03012 0.1397 1.092 0.0001392 -6.248e-05 0.8288 0.0001049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.063 0.0591 0.1611 0.1369 0.9892 0.9934 0.06304 0.9043 0.9436 0.2036 ] Network output: [ -0.02646 0.04287 1.076 0.0001693 -7.601e-05 0.9345 0.0001276 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07726 0.0764 0.1757 0.1556 0.9852 0.9915 0.07727 0.8519 0.9244 0.1953 ] Network output: [ -0.006116 0.9879 0.0152 1.522e-05 -6.835e-06 1.009 1.147e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02227 Epoch 5190 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04571 0.8802 0.9366 -3.844e-05 1.726e-05 0.09159 -2.897e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00305 -0.002692 -0.01084 0.006822 0.9679 0.9726 0.00588 0.883 0.8818 0.02047 ] Network output: [ 0.9904 0.07197 -0.005004 -9.739e-06 4.372e-06 -0.04784 -7.34e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2205 -0.009107 -0.2014 0.186 0.9838 0.9934 0.2463 0.7125 0.9369 0.6716 ] Network output: [ 0.01779 0.8937 0.9643 -4.755e-05 2.135e-05 0.1062 -3.584e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004085 0.001279 0.00268 0.003323 0.9903 0.9933 0.004157 0.9296 0.951 0.01124 ] Network output: [ 0.02861 -0.09036 0.9157 -0.0001847 8.293e-05 1.117 -0.0001392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2338 0.1571 0.3341 0.1593 0.9854 0.9942 0.2345 0.7217 0.942 0.6667 ] Network output: [ -0.03152 0.1379 1.094 0.0001377 -6.184e-05 0.832 0.0001038 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06296 0.05906 0.1618 0.1386 0.9892 0.9934 0.063 0.9048 0.9437 0.2046 ] Network output: [ -0.02871 0.04796 1.077 0.0001649 -7.405e-05 0.9328 0.0001243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07717 0.07632 0.1764 0.1559 0.9852 0.9915 0.07718 0.8527 0.9245 0.1959 ] Network output: [ -0.01021 1.007 0.01574 5.258e-06 -2.361e-06 0.9978 3.963e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02305 Epoch 5191 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04434 0.8858 0.9369 -4.141e-05 1.859e-05 0.08845 -3.121e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003043 -0.002678 -0.01081 0.006722 0.9679 0.9726 0.005866 0.8834 0.882 0.02045 ] Network output: [ 0.9754 0.1117 0.0032 -2.802e-05 1.258e-05 -0.0658 -2.111e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2197 -0.00807 -0.1982 0.1785 0.9838 0.9934 0.2454 0.7137 0.937 0.6725 ] Network output: [ 0.01807 0.895 0.9637 -4.857e-05 2.181e-05 0.105 -3.661e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004087 0.001276 0.002723 0.003178 0.9903 0.9933 0.004159 0.9299 0.9511 0.01132 ] Network output: [ 0.01795 -0.03233 0.9163 -0.000216 9.696e-05 1.079 -0.0001628 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.234 0.1572 0.3359 0.1472 0.9854 0.9942 0.2347 0.7226 0.9422 0.6688 ] Network output: [ -0.03017 0.1386 1.092 0.0001381 -6.202e-05 0.8301 0.0001041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06269 0.05878 0.1612 0.1372 0.9892 0.9934 0.06272 0.9049 0.9439 0.2042 ] Network output: [ -0.02674 0.04085 1.077 0.0001683 -7.558e-05 0.9368 0.0001269 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07697 0.07611 0.1762 0.1561 0.9852 0.9915 0.07698 0.8526 0.9247 0.196 ] Network output: [ -0.006354 0.9897 0.01499 1.428e-05 -6.413e-06 1.008 1.077e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02182 Epoch 5192 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04528 0.8824 0.9364 -3.975e-05 1.785e-05 0.09053 -2.996e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003039 -0.002685 -0.01089 0.006838 0.9679 0.9726 0.005862 0.8836 0.8823 0.02052 ] Network output: [ 0.9902 0.07284 -0.004772 -4.414e-06 1.981e-06 -0.04847 -3.326e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2193 -0.009206 -0.2034 0.1864 0.9838 0.9934 0.2449 0.7137 0.9372 0.6747 ] Network output: [ 0.01739 0.8957 0.9641 -4.938e-05 2.217e-05 0.1052 -3.721e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004064 0.001263 0.002664 0.003308 0.9904 0.9933 0.004135 0.9301 0.9513 0.01129 ] Network output: [ 0.02718 -0.08545 0.9172 -0.0001887 8.473e-05 1.113 -0.0001422 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2325 0.1559 0.3342 0.1581 0.9854 0.9942 0.2331 0.7228 0.9423 0.6699 ] Network output: [ -0.03131 0.1377 1.093 0.0001367 -6.136e-05 0.8323 0.000103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06265 0.05874 0.1618 0.1386 0.9892 0.9934 0.06268 0.9053 0.944 0.205 ] Network output: [ -0.0286 0.04593 1.077 0.0001643 -7.378e-05 0.9347 0.0001239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07688 0.07602 0.1767 0.1562 0.9853 0.9915 0.07689 0.8533 0.9248 0.1965 ] Network output: [ -0.009845 1.008 0.01512 5.296e-06 -2.378e-06 0.9969 3.991e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02249 Epoch 5193 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0441 0.8871 0.9366 -4.234e-05 1.901e-05 0.08794 -3.191e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003032 -0.002672 -0.01085 0.006756 0.9679 0.9726 0.005847 0.884 0.8824 0.0205 ] Network output: [ 0.9769 0.1044 0.003235 -1.918e-05 8.61e-06 -0.06146 -1.445e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2184 -0.00831 -0.2002 0.1803 0.9838 0.9934 0.244 0.7148 0.9373 0.6755 ] Network output: [ 0.01767 0.8969 0.9635 -5.037e-05 2.261e-05 0.104 -3.796e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004064 0.001262 0.002708 0.003189 0.9904 0.9933 0.004135 0.9305 0.9514 0.01136 ] Network output: [ 0.0178 -0.03692 0.9182 -0.0002151 9.657e-05 1.082 -0.0001621 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2326 0.156 0.3361 0.1478 0.9854 0.9942 0.2333 0.7237 0.9425 0.6716 ] Network output: [ -0.03001 0.1388 1.092 0.0001369 -6.146e-05 0.8302 0.0001032 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06242 0.05851 0.1612 0.1374 0.9892 0.9934 0.06245 0.9054 0.9441 0.2046 ] Network output: [ -0.02673 0.04074 1.076 0.000167 -7.499e-05 0.9373 0.0001259 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07672 0.07585 0.1764 0.1562 0.9853 0.9915 0.07673 0.8533 0.925 0.1964 ] Network output: [ -0.006245 0.9941 0.01389 1.301e-05 -5.839e-06 1.005 9.802e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02138 Epoch 5194 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04491 0.8841 0.9362 -4.104e-05 1.843e-05 0.08969 -3.093e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003028 -0.002677 -0.01091 0.006855 0.9679 0.9727 0.005842 0.8842 0.8828 0.02056 ] Network output: [ 0.9896 0.07075 -0.003551 1.481e-06 -6.647e-07 -0.04632 1.116e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.218 -0.009242 -0.2048 0.1872 0.9838 0.9934 0.2435 0.7148 0.9375 0.6774 ] Network output: [ 0.0171 0.8976 0.9638 -5.121e-05 2.299e-05 0.1042 -3.859e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004042 0.001251 0.002655 0.003299 0.9904 0.9933 0.004113 0.9307 0.9516 0.01133 ] Network output: [ 0.02576 -0.08251 0.9189 -0.0001915 8.596e-05 1.111 -0.0001443 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2312 0.1549 0.3345 0.1572 0.9854 0.9942 0.2319 0.7239 0.9426 0.6726 ] Network output: [ -0.03091 0.1383 1.092 0.0001355 -6.082e-05 0.8317 0.0001021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06236 0.05845 0.1616 0.1385 0.9892 0.9934 0.0624 0.9058 0.9442 0.2052 ] Network output: [ -0.02827 0.04535 1.077 0.0001634 -7.337e-05 0.9352 0.0001232 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07662 0.07576 0.1767 0.1563 0.9853 0.9915 0.07663 0.8539 0.9251 0.1968 ] Network output: [ -0.009038 1.009 0.01394 5.618e-06 -2.522e-06 0.9952 4.234e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02206 Epoch 5195 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04387 0.8882 0.9365 -4.343e-05 1.95e-05 0.08746 -3.273e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003021 -0.002665 -0.01089 0.006782 0.9679 0.9727 0.005828 0.8846 0.8829 0.02054 ] Network output: [ 0.9778 0.09816 0.003593 -1.128e-05 5.063e-06 -0.05748 -8.5e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2172 -0.008443 -0.2021 0.182 0.9838 0.9934 0.2426 0.7158 0.9377 0.6782 ] Network output: [ 0.01734 0.8986 0.9633 -5.221e-05 2.344e-05 0.1031 -3.934e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004041 0.00125 0.002692 0.003194 0.9904 0.9933 0.004112 0.931 0.9517 0.01139 ] Network output: [ 0.01754 -0.03994 0.9198 -0.0002144 9.625e-05 1.084 -0.0001616 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2313 0.1549 0.336 0.1482 0.9854 0.9942 0.232 0.7248 0.9428 0.6742 ] Network output: [ -0.02969 0.1392 1.091 0.0001356 -6.088e-05 0.8298 0.0001022 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06215 0.05824 0.161 0.1375 0.9892 0.9934 0.06218 0.906 0.9444 0.2049 ] Network output: [ -0.02656 0.04065 1.076 0.0001658 -7.442e-05 0.9376 0.0001249 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07646 0.0756 0.1764 0.1563 0.9853 0.9915 0.07647 0.854 0.9253 0.1967 ] Network output: [ -0.005606 0.9956 0.01285 1.307e-05 -5.868e-06 1.003 9.85e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02103 Epoch 5196 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04454 0.8857 0.9361 -4.25e-05 1.908e-05 0.08889 -3.203e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003017 -0.002669 -0.01094 0.006866 0.9679 0.9727 0.005823 0.8848 0.8832 0.02059 ] Network output: [ 0.9892 0.07064 -0.002996 6.506e-06 -2.921e-06 -0.04599 4.903e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2168 -0.009234 -0.2065 0.1877 0.9838 0.9934 0.2422 0.7159 0.9379 0.68 ] Network output: [ 0.01678 0.8992 0.9637 -5.309e-05 2.383e-05 0.1033 -4.001e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004021 0.00124 0.002637 0.003284 0.9904 0.9933 0.004092 0.9312 0.9518 0.01136 ] Network output: [ 0.0247 -0.0784 0.92 -0.000194 8.709e-05 1.108 -0.0001462 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.23 0.1539 0.3343 0.1561 0.9854 0.9942 0.2307 0.7251 0.9429 0.6752 ] Network output: [ -0.03049 0.1386 1.092 0.0001343 -6.03e-05 0.8314 0.0001012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06206 0.05816 0.1613 0.1384 0.9892 0.9934 0.0621 0.9063 0.9445 0.2054 ] Network output: [ -0.02796 0.0438 1.076 0.0001627 -7.304e-05 0.9365 0.0001226 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07634 0.07548 0.1767 0.1565 0.9853 0.9915 0.07635 0.8545 0.9254 0.1971 ] Network output: [ -0.008007 1.006 0.01338 7.338e-06 -3.294e-06 0.9964 5.53e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02163 Epoch 5197 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04355 0.8894 0.9364 -4.477e-05 2.01e-05 0.08689 -3.374e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003011 -0.002658 -0.01093 0.006802 0.968 0.9727 0.005811 0.8851 0.8834 0.02058 ] Network output: [ 0.979 0.09621 0.002863 -4.977e-06 2.234e-06 -0.05716 -3.751e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2161 -0.00855 -0.2045 0.1829 0.9838 0.9934 0.2414 0.7169 0.938 0.6808 ] Network output: [ 0.01692 0.9001 0.9633 -5.41e-05 2.429e-05 0.1025 -4.077e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004019 0.001237 0.002662 0.003188 0.9904 0.9933 0.00409 0.9314 0.952 0.01141 ] Network output: [ 0.01757 -0.03971 0.9205 -0.0002145 9.631e-05 1.083 -0.0001617 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.23 0.1538 0.3354 0.148 0.9854 0.9942 0.2307 0.7258 0.9431 0.6769 ] Network output: [ -0.02944 0.139 1.09 0.0001345 -6.038e-05 0.8301 0.0001014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06185 0.05795 0.1608 0.1376 0.9893 0.9935 0.06189 0.9064 0.9446 0.2051 ] Network output: [ -0.02651 0.03871 1.076 0.0001649 -7.403e-05 0.9394 0.0001243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07617 0.07531 0.1765 0.1566 0.9853 0.9915 0.07618 0.8546 0.9255 0.1971 ] Network output: [ -0.004998 0.9924 0.01278 1.449e-05 -6.506e-06 1.005 1.092e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02071 Epoch 5198 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04408 0.8875 0.9361 -4.41e-05 1.98e-05 0.08806 -3.323e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003007 -0.002662 -0.01099 0.006877 0.968 0.9727 0.005807 0.8854 0.8837 0.02063 ] Network output: [ 0.9895 0.07313 -0.003695 1.079e-05 -4.845e-06 -0.04846 8.133e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2157 -0.009274 -0.2089 0.1879 0.9838 0.9934 0.241 0.717 0.9382 0.6827 ] Network output: [ 0.01632 0.9007 0.9638 -5.503e-05 2.47e-05 0.1027 -4.147e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004001 0.001226 0.002604 0.003263 0.9904 0.9933 0.004071 0.9316 0.9521 0.01139 ] Network output: [ 0.02418 -0.07285 0.9203 -0.0001966 8.828e-05 1.103 -0.0001482 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2287 0.1527 0.3335 0.1549 0.9854 0.9942 0.2294 0.7261 0.9432 0.6781 ] Network output: [ -0.03025 0.1381 1.091 0.0001333 -5.984e-05 0.8318 0.0001005 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06175 0.05785 0.1611 0.1385 0.9893 0.9935 0.06179 0.9068 0.9447 0.2056 ] Network output: [ -0.02791 0.04068 1.077 0.0001622 -7.283e-05 0.9393 0.0001223 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07603 0.07517 0.177 0.1569 0.9853 0.9915 0.07604 0.8551 0.9256 0.1976 ] Network output: [ -0.007434 1.001 0.01372 9.358e-06 -4.201e-06 1 7.052e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02114 Epoch 5199 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04314 0.8909 0.9364 -4.62e-05 2.074e-05 0.08626 -3.482e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003001 -0.002653 -0.01098 0.006824 0.968 0.9727 0.005795 0.8857 0.8838 0.02064 ] Network output: [ 0.9805 0.0957 0.001578 8.295e-07 -3.724e-07 -0.05832 6.251e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.215 -0.008703 -0.2073 0.1837 0.9838 0.9934 0.2402 0.7179 0.9383 0.6837 ] Network output: [ 0.01637 0.9016 0.9635 -5.604e-05 2.516e-05 0.1019 -4.223e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003998 0.001223 0.002626 0.003179 0.9904 0.9934 0.004069 0.9319 0.9523 0.01145 ] Network output: [ 0.01778 -0.03809 0.9208 -0.0002152 9.66e-05 1.081 -0.0001622 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2286 0.1526 0.3344 0.1476 0.9854 0.9942 0.2293 0.7268 0.9433 0.6798 ] Network output: [ -0.02934 0.1383 1.09 0.0001334 -5.99e-05 0.8308 0.0001006 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06156 0.05765 0.1606 0.1377 0.9893 0.9935 0.06159 0.9069 0.9449 0.2055 ] Network output: [ -0.02667 0.03594 1.076 0.0001642 -7.371e-05 0.9421 0.0001237 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07588 0.07502 0.1768 0.1571 0.9853 0.9915 0.07589 0.8552 0.9258 0.1977 ] Network output: [ -0.004958 0.9893 0.01318 1.537e-05 -6.901e-06 1.007 1.159e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02038 Epoch 5200 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04358 0.8892 0.9361 -4.562e-05 2.048e-05 0.0873 -3.438e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002998 -0.002656 -0.01103 0.006897 0.968 0.9727 0.005791 0.8859 0.8841 0.02069 ] Network output: [ 0.9902 0.07349 -0.004224 1.584e-05 -7.111e-06 -0.0495 1.194e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2146 -0.009378 -0.2114 0.1884 0.9838 0.9934 0.2397 0.718 0.9384 0.6855 ] Network output: [ 0.01578 0.9023 0.9639 -5.699e-05 2.558e-05 0.102 -4.295e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003981 0.001212 0.002574 0.003251 0.9904 0.9934 0.004052 0.9321 0.9524 0.01144 ] Network output: [ 0.02381 -0.06879 0.9207 -0.0001987 8.92e-05 1.1 -0.0001497 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2275 0.1516 0.3327 0.154 0.9854 0.9942 0.2281 0.7271 0.9434 0.681 ] Network output: [ -0.03012 0.1379 1.091 0.0001322 -5.934e-05 0.8322 9.962e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06147 0.05756 0.1609 0.1385 0.9893 0.9935 0.0615 0.9072 0.9449 0.206 ] Network output: [ -0.02799 0.03817 1.077 0.0001615 -7.252e-05 0.9417 0.0001217 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07576 0.07489 0.1772 0.1573 0.9853 0.9915 0.07576 0.8557 0.9259 0.1982 ] Network output: [ -0.007461 0.9993 0.01387 9.809e-06 -4.404e-06 1.002 7.392e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0207 Epoch 5201 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04272 0.8922 0.9364 -4.752e-05 2.134e-05 0.08572 -3.582e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002991 -0.002647 -0.01102 0.006853 0.968 0.9727 0.005779 0.8863 0.8843 0.02069 ] Network output: [ 0.9816 0.09147 0.001509 7.593e-06 -3.409e-06 -0.05606 5.722e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2138 -0.008859 -0.2095 0.185 0.9838 0.9934 0.2389 0.7188 0.9385 0.6865 ] Network output: [ 0.01587 0.9034 0.9635 -5.797e-05 2.603e-05 0.1011 -4.369e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003978 0.00121 0.002603 0.003181 0.9904 0.9934 0.004048 0.9324 0.9525 0.01149 ] Network output: [ 0.0177 -0.03828 0.9218 -0.0002152 9.663e-05 1.08 -0.0001622 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2273 0.1514 0.3338 0.1476 0.9855 0.9942 0.228 0.7278 0.9436 0.6826 ] Network output: [ -0.02921 0.1385 1.09 0.0001322 -5.933e-05 0.8309 9.961e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06131 0.0574 0.1605 0.1378 0.9893 0.9935 0.06135 0.9073 0.9451 0.2058 ] Network output: [ -0.02675 0.03492 1.076 0.0001631 -7.32e-05 0.9434 0.0001229 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07564 0.07477 0.177 0.1573 0.9853 0.9915 0.07565 0.8558 0.926 0.1982 ] Network output: [ -0.005196 0.9914 0.01277 1.453e-05 -6.521e-06 1.006 1.095e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02001 Epoch 5202 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04316 0.8905 0.9362 -4.699e-05 2.109e-05 0.08672 -3.541e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002988 -0.002649 -0.01106 0.006923 0.968 0.9727 0.005773 0.8865 0.8845 0.02073 ] Network output: [ 0.9902 0.06871 -0.003177 2.227e-05 -9.997e-06 -0.04584 1.678e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2133 -0.009446 -0.213 0.1898 0.9838 0.9934 0.2383 0.719 0.9387 0.6881 ] Network output: [ 0.01538 0.9041 0.9639 -5.891e-05 2.645e-05 0.101 -4.44e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003962 0.001202 0.00256 0.003252 0.9904 0.9934 0.004032 0.9326 0.9526 0.01147 ] Network output: [ 0.02315 -0.06795 0.922 -0.0001993 8.95e-05 1.099 -0.0001502 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2262 0.1505 0.3325 0.1538 0.9855 0.9942 0.2269 0.7281 0.9437 0.6835 ] Network output: [ -0.02983 0.1388 1.09 0.0001308 -5.873e-05 0.8315 9.859e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06124 0.05733 0.1606 0.1385 0.9893 0.9935 0.06127 0.9077 0.9451 0.2062 ] Network output: [ -0.02786 0.03811 1.076 0.0001603 -7.197e-05 0.942 0.0001208 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07554 0.07467 0.1772 0.1574 0.9853 0.9915 0.07554 0.8563 0.9261 0.1985 ] Network output: [ -0.007411 1.003 0.01294 8.87e-06 -3.982e-06 0.9992 6.685e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02037 Epoch 5203 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04239 0.8932 0.9365 -4.879e-05 2.19e-05 0.08532 -3.677e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002981 -0.00264 -0.01104 0.006882 0.968 0.9727 0.00576 0.8868 0.8847 0.02072 ] Network output: [ 0.9817 0.08383 0.002991 1.493e-05 -6.705e-06 -0.05016 1.126e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.008911 -0.2109 0.1869 0.9838 0.9934 0.2375 0.7198 0.9388 0.6888 ] Network output: [ 0.01553 0.9051 0.9635 -5.987e-05 2.688e-05 0.1001 -4.512e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003959 0.001202 0.002594 0.003189 0.9905 0.9934 0.004028 0.9328 0.9527 0.01152 ] Network output: [ 0.01719 -0.04016 0.9232 -0.0002144 9.626e-05 1.082 -0.0001616 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2261 0.1505 0.3338 0.1479 0.9855 0.9942 0.2268 0.7287 0.9438 0.6847 ] Network output: [ -0.02884 0.1398 1.089 0.0001307 -5.868e-05 0.8298 9.851e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0611 0.05719 0.1602 0.1378 0.9893 0.9935 0.06113 0.9078 0.9453 0.2059 ] Network output: [ -0.02651 0.03589 1.075 0.0001616 -7.253e-05 0.9427 0.0001218 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07544 0.07457 0.1769 0.1573 0.9853 0.9915 0.07545 0.8563 0.9262 0.1983 ] Network output: [ -0.004934 0.9958 0.01145 1.345e-05 -6.038e-06 1.003 1.014e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01968 Epoch 5204 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04283 0.8916 0.9363 -4.838e-05 2.172e-05 0.08625 -3.646e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002977 -0.002641 -0.01108 0.006944 0.968 0.9727 0.005754 0.887 0.8849 0.02075 ] Network output: [ 0.9897 0.06293 -0.00143 2.857e-05 -1.283e-05 -0.04082 2.153e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.009399 -0.2142 0.1913 0.9839 0.9934 0.237 0.7199 0.939 0.6902 ] Network output: [ 0.01512 0.9057 0.9638 -6.081e-05 2.73e-05 0.1 -4.583e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003943 0.001196 0.00255 0.003253 0.9905 0.9934 0.004012 0.933 0.9529 0.0115 ] Network output: [ 0.02239 -0.06774 0.9232 -0.0001992 8.943e-05 1.099 -0.0001501 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.225 0.1498 0.3323 0.1536 0.9855 0.9942 0.2257 0.729 0.9439 0.6856 ] Network output: [ -0.02933 0.1401 1.089 0.0001294 -5.809e-05 0.8302 9.752e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06102 0.05712 0.1603 0.1384 0.9893 0.9935 0.06105 0.9081 0.9453 0.2062 ] Network output: [ -0.02746 0.03882 1.075 0.000159 -7.137e-05 0.9414 0.0001198 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07532 0.07446 0.177 0.1573 0.9853 0.9915 0.07533 0.8568 0.9263 0.1985 ] Network output: [ -0.006695 1.005 0.01169 8.91e-06 -4e-06 0.9971 6.715e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02012 Epoch 5205 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04208 0.8942 0.9366 -5.023e-05 2.255e-05 0.08488 -3.785e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00297 -0.002631 -0.01106 0.006901 0.968 0.9727 0.005742 0.8873 0.8851 0.02074 ] Network output: [ 0.9816 0.07917 0.004083 2.092e-05 -9.39e-06 -0.0464 1.576e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2113 -0.008856 -0.2123 0.1882 0.9839 0.9934 0.2362 0.7207 0.9391 0.6909 ] Network output: [ 0.01526 0.9065 0.9635 -6.177e-05 2.773e-05 0.09926 -4.655e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003939 0.001196 0.002576 0.003188 0.9905 0.9934 0.004009 0.9332 0.953 0.01154 ] Network output: [ 0.01683 -0.03998 0.9241 -0.0002137 9.594e-05 1.081 -0.0001611 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2249 0.1497 0.3333 0.1478 0.9855 0.9942 0.2256 0.7297 0.944 0.6867 ] Network output: [ -0.02834 0.1408 1.088 0.0001294 -5.808e-05 0.8288 9.751e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06086 0.05697 0.1598 0.1377 0.9893 0.9935 0.0609 0.9082 0.9455 0.2059 ] Network output: [ -0.02616 0.03582 1.074 0.0001603 -7.199e-05 0.9427 0.0001208 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07521 0.07434 0.1767 0.1573 0.9853 0.9915 0.07521 0.8569 0.9265 0.1984 ] Network output: [ -0.004067 0.9948 0.01065 1.442e-05 -6.472e-06 1.003 1.087e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01943 Epoch 5206 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04246 0.8928 0.9364 -5e-05 2.245e-05 0.08566 -3.768e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002967 -0.002633 -0.01111 0.006956 0.968 0.9727 0.005737 0.8875 0.8853 0.02078 ] Network output: [ 0.9897 0.06234 -0.001181 3.308e-05 -1.485e-05 -0.04038 2.493e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.00931 -0.2161 0.1919 0.9839 0.9934 0.2358 0.7209 0.9392 0.6923 ] Network output: [ 0.01478 0.907 0.9639 -6.274e-05 2.816e-05 0.09934 -4.728e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003924 0.001189 0.002522 0.003242 0.9905 0.9934 0.003994 0.9334 0.9531 0.01152 ] Network output: [ 0.02217 -0.06443 0.9234 -0.0001996 8.959e-05 1.096 -0.0001504 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2239 0.149 0.3313 0.153 0.9855 0.9942 0.2246 0.73 0.9441 0.6877 ] Network output: [ -0.02889 0.1404 1.088 0.0001282 -5.756e-05 0.8297 9.662e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06075 0.05687 0.1598 0.1383 0.9893 0.9935 0.06079 0.9085 0.9455 0.2062 ] Network output: [ -0.02721 0.03708 1.075 0.0001581 -7.099e-05 0.9428 0.0001192 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07506 0.07419 0.177 0.1575 0.9853 0.9915 0.07506 0.8573 0.9265 0.1987 ] Network output: [ -0.0058 0.9997 0.01164 1.104e-05 -4.956e-06 1 8.32e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0198 Epoch 5207 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04167 0.8955 0.9367 -5.191e-05 2.331e-05 0.08424 -3.912e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002961 -0.002625 -0.0111 0.006914 0.968 0.9727 0.005726 0.8878 0.8854 0.02078 ] Network output: [ 0.9823 0.08054 0.003253 2.496e-05 -1.121e-05 -0.04823 1.881e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2103 -0.008821 -0.2148 0.1886 0.9839 0.9934 0.235 0.7216 0.9393 0.6932 ] Network output: [ 0.01481 0.9077 0.9637 -6.372e-05 2.861e-05 0.09875 -4.802e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003921 0.001186 0.002537 0.003174 0.9905 0.9934 0.00399 0.9336 0.9532 0.01157 ] Network output: [ 0.01706 -0.03603 0.9237 -0.0002141 9.61e-05 1.077 -0.0001613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2237 0.1488 0.3319 0.1471 0.9855 0.9942 0.2244 0.7306 0.9442 0.6891 ] Network output: [ -0.02804 0.1403 1.087 0.0001283 -5.761e-05 0.8291 9.672e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06059 0.05671 0.1594 0.1378 0.9893 0.9935 0.06062 0.9086 0.9457 0.2061 ] Network output: [ -0.02612 0.03275 1.075 0.0001597 -7.171e-05 0.9454 0.0001204 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07492 0.07406 0.1768 0.1577 0.9853 0.9915 0.07493 0.8574 0.9267 0.1988 ] Network output: [ -0.003515 0.988 0.01127 1.675e-05 -7.519e-06 1.008 1.262e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01915 Epoch 5208 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04197 0.8944 0.9366 -5.176e-05 2.324e-05 0.08489 -3.9e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002959 -0.002627 -0.01116 0.006971 0.968 0.9727 0.005723 0.888 0.8857 0.02083 ] Network output: [ 0.9906 0.06514 -0.002555 3.67e-05 -1.648e-05 -0.04364 2.766e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.21 -0.009311 -0.2189 0.1921 0.9839 0.9934 0.2347 0.7218 0.9394 0.6948 ] Network output: [ 0.01423 0.9083 0.9641 -6.472e-05 2.906e-05 0.09886 -4.878e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003907 0.001177 0.002479 0.003225 0.9905 0.9934 0.003976 0.9338 0.9533 0.01156 ] Network output: [ 0.02252 -0.05932 0.9228 -0.0002004 8.998e-05 1.091 -0.0001511 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2227 0.1479 0.3297 0.1521 0.9855 0.9942 0.2234 0.7309 0.9443 0.6903 ] Network output: [ -0.02875 0.1396 1.088 0.0001272 -5.711e-05 0.8305 9.587e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06048 0.0566 0.1595 0.1384 0.9893 0.9935 0.06052 0.9088 0.9457 0.2065 ] Network output: [ -0.02735 0.03327 1.076 0.0001576 -7.077e-05 0.9463 0.0001188 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07477 0.07391 0.1772 0.1581 0.9853 0.9915 0.07478 0.8578 0.9268 0.1993 ] Network output: [ -0.00574 0.9933 0.01264 1.281e-05 -5.753e-06 1.006 9.657e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0194 Epoch 5209 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04116 0.8971 0.9369 -5.358e-05 2.405e-05 0.08351 -4.038e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002954 -0.00262 -0.01115 0.006937 0.968 0.9727 0.005713 0.8883 0.8858 0.02084 ] Network output: [ 0.9834 0.08134 0.00212 2.928e-05 -1.314e-05 -0.05006 2.206e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2093 -0.008879 -0.2175 0.1891 0.9839 0.9934 0.2339 0.7225 0.9395 0.6958 ] Network output: [ 0.01422 0.9092 0.9639 -6.57e-05 2.95e-05 0.09816 -4.952e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003904 0.001175 0.0025 0.003165 0.9905 0.9934 0.003973 0.934 0.9534 0.01161 ] Network output: [ 0.01737 -0.03238 0.9235 -0.0002146 9.633e-05 1.073 -0.0001617 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2225 0.1478 0.3305 0.1465 0.9855 0.9942 0.2232 0.7315 0.9444 0.6918 ] Network output: [ -0.02799 0.1397 1.087 0.0001273 -5.714e-05 0.8299 9.592e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06035 0.05646 0.1592 0.1379 0.9894 0.9935 0.06038 0.9089 0.9458 0.2065 ] Network output: [ -0.02634 0.02959 1.075 0.0001591 -7.141e-05 0.9485 0.0001199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07467 0.07381 0.1771 0.1582 0.9853 0.9915 0.07468 0.8579 0.9269 0.1995 ] Network output: [ -0.003923 0.9851 0.01196 1.708e-05 -7.669e-06 1.011 1.287e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01883 Epoch 5210 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04149 0.8958 0.9368 -5.329e-05 2.392e-05 0.08423 -4.016e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002951 -0.002622 -0.0112 0.007001 0.968 0.9727 0.005709 0.8885 0.886 0.02088 ] Network output: [ 0.9915 0.06233 -0.002791 4.213e-05 -1.891e-05 -0.04234 3.175e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2089 -0.009373 -0.2211 0.1933 0.9839 0.9934 0.2335 0.7226 0.9396 0.6974 ] Network output: [ 0.01368 0.91 0.9644 -6.669e-05 2.994e-05 0.09804 -5.026e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00389 0.001168 0.002452 0.003226 0.9905 0.9934 0.003959 0.9342 0.9535 0.0116 ] Network output: [ 0.02258 -0.05792 0.9231 -0.0002002 8.989e-05 1.089 -0.0001509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2215 0.147 0.3288 0.152 0.9855 0.9942 0.2221 0.7317 0.9445 0.6929 ] Network output: [ -0.02868 0.1397 1.087 0.0001259 -5.654e-05 0.8307 9.491e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06028 0.05639 0.1594 0.1385 0.9894 0.9935 0.06031 0.9092 0.9458 0.2069 ] Network output: [ -0.02753 0.03182 1.076 0.0001565 -7.028e-05 0.948 0.000118 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07456 0.0737 0.1774 0.1584 0.9853 0.9915 0.07457 0.8583 0.9269 0.1999 ] Network output: [ -0.006445 0.9956 0.01261 1.14e-05 -5.116e-06 1.005 8.589e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01906 Epoch 5211 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04075 0.8982 0.9371 -5.495e-05 2.467e-05 0.08301 -4.141e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002944 -0.002613 -0.01118 0.006971 0.968 0.9727 0.005697 0.8887 0.8861 0.02088 ] Network output: [ 0.9835 0.07389 0.003539 3.585e-05 -1.61e-05 -0.04429 2.702e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2081 -0.0089 -0.2189 0.191 0.9839 0.9935 0.2326 0.7233 0.9397 0.6981 ] Network output: [ 0.0138 0.911 0.964 -6.762e-05 3.036e-05 0.09707 -5.096e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003888 0.001169 0.002491 0.003177 0.9905 0.9934 0.003956 0.9344 0.9536 0.01165 ] Network output: [ 0.01686 -0.03355 0.9248 -0.0002136 9.587e-05 1.074 -0.0001609 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2213 0.1469 0.3302 0.1469 0.9855 0.9942 0.222 0.7323 0.9446 0.694 ] Network output: [ -0.02775 0.1408 1.086 0.0001258 -5.649e-05 0.829 9.482e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06018 0.0563 0.159 0.138 0.9894 0.9935 0.06021 0.9093 0.9459 0.2068 ] Network output: [ -0.02625 0.03047 1.075 0.0001575 -7.071e-05 0.9481 0.0001187 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07451 0.07365 0.1771 0.1582 0.9853 0.9915 0.07452 0.8583 0.927 0.1998 ] Network output: [ -0.004445 0.9923 0.01081 1.444e-05 -6.484e-06 1.006 1.088e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01848 Epoch 5212 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04119 0.8966 0.937 -5.454e-05 2.449e-05 0.08388 -4.111e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002941 -0.002614 -0.01122 0.007035 0.968 0.9727 0.005691 0.8889 0.8863 0.02091 ] Network output: [ 0.9911 0.05159 -0.0002629 4.958e-05 -2.226e-05 -0.03337 3.736e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2077 -0.009308 -0.2219 0.1957 0.9839 0.9935 0.2321 0.7234 0.9398 0.6993 ] Network output: [ 0.01342 0.9118 0.9643 -6.855e-05 3.077e-05 0.09677 -5.166e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003873 0.001166 0.002454 0.003245 0.9905 0.9934 0.003942 0.9345 0.9537 0.01163 ] Network output: [ 0.02186 -0.06147 0.9249 -0.000198 8.891e-05 1.092 -0.0001492 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2203 0.1463 0.3289 0.1527 0.9855 0.9942 0.221 0.7325 0.9447 0.6946 ] Network output: [ -0.02825 0.1418 1.086 0.0001243 -5.58e-05 0.8289 9.367e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06013 0.05626 0.1591 0.1384 0.9894 0.9935 0.06016 0.9095 0.946 0.207 ] Network output: [ -0.02718 0.03455 1.075 0.0001546 -6.941e-05 0.9457 0.0001165 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07442 0.07356 0.1771 0.1581 0.9853 0.9915 0.07443 0.8587 0.927 0.1998 ] Network output: [ -0.006504 1.005 0.0107 8.581e-06 -3.852e-06 0.9975 6.467e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01893 Epoch 5213 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04052 0.8987 0.9373 -5.625e-05 2.525e-05 0.08271 -4.239e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002933 -0.002603 -0.01118 0.006997 0.968 0.9727 0.005677 0.8892 0.8864 0.02088 ] Network output: [ 0.9823 0.0633 0.006847 4.272e-05 -1.918e-05 -0.03463 3.22e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2069 -0.008723 -0.2191 0.1933 0.9839 0.9935 0.2312 0.7241 0.9399 0.6997 ] Network output: [ 0.0137 0.9127 0.9638 -6.943e-05 3.117e-05 0.0958 -5.232e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00387 0.00117 0.002497 0.003194 0.9905 0.9934 0.003938 0.9347 0.9538 0.01167 ] Network output: [ 0.01581 -0.03646 0.9266 -0.0002113 9.488e-05 1.077 -0.0001593 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2203 0.1465 0.3305 0.1474 0.9855 0.9943 0.2209 0.7331 0.9448 0.6953 ] Network output: [ -0.02707 0.1432 1.085 0.0001242 -5.576e-05 0.8267 9.36e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06002 0.05617 0.1586 0.1377 0.9894 0.9935 0.06006 0.9096 0.9461 0.2066 ] Network output: [ -0.0256 0.03358 1.073 0.0001556 -6.987e-05 0.9452 0.0001173 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07436 0.07351 0.1766 0.1578 0.9853 0.9915 0.07437 0.8588 0.9271 0.1995 ] Network output: [ -0.003674 0.999 0.008619 1.306e-05 -5.865e-06 0.9998 9.845e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01831 Epoch 5214 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.041 0.8971 0.9371 -5.597e-05 2.513e-05 0.08355 -4.218e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00293 -0.002603 -0.01122 0.007051 0.968 0.9727 0.005671 0.8893 0.8866 0.0209 ] Network output: [ 0.9902 0.04459 0.002069 5.527e-05 -2.481e-05 -0.02679 4.165e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2065 -0.009058 -0.2225 0.1973 0.9839 0.9935 0.2308 0.7243 0.9401 0.7007 ] Network output: [ 0.01335 0.9131 0.9642 -7.034e-05 3.158e-05 0.09576 -5.301e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003856 0.001167 0.002446 0.003251 0.9905 0.9934 0.003924 0.9349 0.9538 0.01164 ] Network output: [ 0.02134 -0.06278 0.9259 -0.0001957 8.785e-05 1.093 -0.0001475 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2193 0.146 0.3286 0.153 0.9855 0.9943 0.22 0.7334 0.9448 0.6957 ] Network output: [ -0.0275 0.1436 1.085 0.0001228 -5.514e-05 0.8269 9.257e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05993 0.0561 0.1585 0.1382 0.9894 0.9935 0.05997 0.9099 0.9461 0.2067 ] Network output: [ -0.02652 0.0361 1.074 0.0001531 -6.872e-05 0.944 0.0001154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07422 0.07337 0.1766 0.1578 0.9853 0.9915 0.07423 0.8592 0.9272 0.1996 ] Network output: [ -0.005166 1.005 0.009316 9.535e-06 -4.281e-06 0.9961 7.186e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01887 Epoch 5215 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04023 0.8997 0.9375 -5.798e-05 2.603e-05 0.08215 -4.369e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002924 -0.002593 -0.0112 0.007001 0.968 0.9727 0.00566 0.8896 0.8867 0.02089 ] Network output: [ 0.9816 0.06363 0.00758 4.583e-05 -2.058e-05 -0.0342 3.454e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2059 -0.008447 -0.2206 0.1937 0.9839 0.9935 0.2301 0.725 0.9401 0.7011 ] Network output: [ 0.01353 0.9136 0.9639 -7.125e-05 3.199e-05 0.09517 -5.369e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003853 0.001169 0.002468 0.003181 0.9905 0.9934 0.003921 0.9351 0.9539 0.01168 ] Network output: [ 0.01567 -0.03269 0.9264 -0.0002106 9.453e-05 1.074 -0.0001587 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2193 0.1461 0.3293 0.1467 0.9855 0.9943 0.2199 0.734 0.945 0.6968 ] Network output: [ -0.02638 0.1435 1.084 0.0001231 -5.527e-05 0.8259 9.279e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05978 0.05596 0.158 0.1376 0.9894 0.9935 0.05982 0.9099 0.9462 0.2064 ] Network output: [ -0.02508 0.0318 1.073 0.0001549 -6.953e-05 0.9463 0.0001167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07411 0.07326 0.1763 0.158 0.9853 0.9915 0.07412 0.8592 0.9273 0.1995 ] Network output: [ -0.002037 0.9904 0.008608 1.675e-05 -7.522e-06 1.005 1.263e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01812 Epoch 5216 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04061 0.8985 0.9373 -5.791e-05 2.6e-05 0.08275 -4.364e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002923 -0.002596 -0.01126 0.007052 0.968 0.9727 0.005658 0.8897 0.8869 0.02094 ] Network output: [ 0.9908 0.0512 0.0001416 5.682e-05 -2.551e-05 -0.0328 4.283e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2057 -0.008864 -0.2254 0.1968 0.9839 0.9935 0.2299 0.7251 0.9403 0.7025 ] Network output: [ 0.01293 0.9138 0.9645 -7.223e-05 3.242e-05 0.09552 -5.443e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00384 0.001161 0.00239 0.003226 0.9905 0.9934 0.003907 0.9352 0.954 0.01166 ] Network output: [ 0.02227 -0.05603 0.9241 -0.0001955 8.778e-05 1.087 -0.0001474 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2183 0.1453 0.3262 0.1519 0.9855 0.9943 0.2189 0.7342 0.945 0.6979 ] Network output: [ -0.02715 0.1422 1.085 0.000122 -5.479e-05 0.8278 9.198e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05965 0.05583 0.158 0.1383 0.9894 0.9936 0.05969 0.9102 0.9463 0.2068 ] Network output: [ -0.02649 0.03077 1.074 0.0001529 -6.866e-05 0.9485 0.0001153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07391 0.07306 0.1768 0.1585 0.9853 0.9915 0.07392 0.8596 0.9274 0.2001 ] Network output: [ -0.004146 0.9903 0.01105 1.436e-05 -6.445e-06 1.007 1.082e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01847 Epoch 5217 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03966 0.9017 0.9377 -6.007e-05 2.697e-05 0.08108 -4.527e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002919 -0.002589 -0.01126 0.007005 0.968 0.9727 0.00565 0.89 0.887 0.02096 ] Network output: [ 0.9828 0.07473 0.004276 4.596e-05 -2.063e-05 -0.04448 3.464e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2052 -0.008364 -0.2242 0.1926 0.9839 0.9935 0.2293 0.7258 0.9403 0.7035 ] Network output: [ 0.01287 0.9144 0.9645 -7.318e-05 3.285e-05 0.09508 -5.515e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003839 0.001157 0.002402 0.003148 0.9905 0.9934 0.003907 0.9354 0.9541 0.01172 ] Network output: [ 0.01678 -0.02258 0.924 -0.0002121 9.524e-05 1.064 -0.0001599 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2182 0.1451 0.3265 0.1451 0.9855 0.9943 0.2189 0.7348 0.9451 0.6996 ] Network output: [ -0.02634 0.1408 1.084 0.0001226 -5.504e-05 0.8283 9.24e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0595 0.05567 0.1576 0.1379 0.9894 0.9936 0.05953 0.9102 0.9463 0.2069 ] Network output: [ -0.02543 0.02413 1.074 0.0001553 -6.97e-05 0.9531 0.000117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07379 0.07294 0.1769 0.1591 0.9853 0.9915 0.0738 0.8596 0.9275 0.2005 ] Network output: [ -0.001889 0.9746 0.01125 2.114e-05 -9.49e-06 1.018 1.593e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01788 Epoch 5218 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03998 0.9006 0.9376 -5.984e-05 2.686e-05 0.08166 -4.509e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002918 -0.002594 -0.01134 0.007074 0.968 0.9727 0.005651 0.8901 0.8872 0.02103 ] Network output: [ 0.9933 0.05872 -0.003731 5.87e-05 -2.635e-05 -0.04138 4.424e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.205 -0.008942 -0.2294 0.1965 0.9839 0.9935 0.2291 0.7258 0.9404 0.7053 ] Network output: [ 0.01207 0.9151 0.9652 -7.421e-05 3.332e-05 0.09525 -5.593e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003827 0.001147 0.002326 0.003208 0.9906 0.9935 0.003894 0.9355 0.9541 0.01171 ] Network output: [ 0.02377 -0.04945 0.9221 -0.000196 8.798e-05 1.079 -0.0001477 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2171 0.1441 0.3235 0.151 0.9855 0.9943 0.2178 0.7349 0.9452 0.701 ] Network output: [ -0.02749 0.1395 1.085 0.0001213 -5.447e-05 0.8306 9.145e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05941 0.05558 0.1579 0.1388 0.9894 0.9936 0.05945 0.9104 0.9463 0.2076 ] Network output: [ -0.02727 0.02394 1.076 0.0001529 -6.864e-05 0.9551 0.0001152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07364 0.07279 0.1776 0.1596 0.9853 0.9915 0.07365 0.86 0.9275 0.2013 ] Network output: [ -0.005481 0.9824 0.01356 1.518e-05 -6.815e-06 1.015 1.144e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01805 Epoch 5219 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03902 0.9036 0.938 -6.174e-05 2.772e-05 0.08009 -4.653e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002913 -0.002586 -0.01133 0.007042 0.968 0.9727 0.005642 0.8904 0.8872 0.02105 ] Network output: [ 0.9842 0.07469 0.003059 4.971e-05 -2.232e-05 -0.0459 3.746e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2042 -0.008471 -0.2269 0.1934 0.9839 0.9935 0.2282 0.7265 0.9404 0.7064 ] Network output: [ 0.0121 0.9164 0.9649 -7.512e-05 3.372e-05 0.09424 -5.661e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003827 0.001147 0.002372 0.00315 0.9906 0.9935 0.003894 0.9357 0.9542 0.01178 ] Network output: [ 0.01691 -0.01919 0.924 -0.0002126 9.547e-05 1.061 -0.0001603 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2171 0.1441 0.3252 0.1448 0.9855 0.9943 0.2177 0.7354 0.9452 0.7025 ] Network output: [ -0.02661 0.1398 1.084 0.0001216 -5.458e-05 0.8297 9.163e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05935 0.05551 0.1577 0.1383 0.9894 0.9936 0.05938 0.9105 0.9464 0.2077 ] Network output: [ -0.02599 0.02116 1.075 0.0001544 -6.934e-05 0.9564 0.0001164 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07363 0.07278 0.1774 0.1597 0.9854 0.9915 0.07364 0.86 0.9276 0.2015 ] Network output: [ -0.003842 0.9794 0.0118 1.789e-05 -8.032e-06 1.017 1.348e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01746 Epoch 5220 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03958 0.9016 0.9378 -6.094e-05 2.736e-05 0.08116 -4.592e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002911 -0.002589 -0.01137 0.007131 0.968 0.9727 0.005639 0.8905 0.8874 0.02109 ] Network output: [ 0.9944 0.04477 -0.002112 6.716e-05 -3.015e-05 -0.03121 5.061e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2038 -0.009034 -0.2306 0.1996 0.9839 0.9935 0.2278 0.7264 0.9405 0.7078 ] Network output: [ 0.01154 0.9176 0.9653 -7.606e-05 3.415e-05 0.09376 -5.732e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003813 0.001144 0.002331 0.003245 0.9906 0.9935 0.00388 0.9358 0.9542 0.01176 ] Network output: [ 0.02334 -0.05638 0.9243 -0.0001926 8.648e-05 1.085 -0.0001452 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.216 0.1434 0.3238 0.1527 0.9855 0.9943 0.2166 0.7355 0.9452 0.7031 ] Network output: [ -0.02758 0.1413 1.085 0.0001196 -5.369e-05 0.8296 9.012e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05935 0.05552 0.158 0.1389 0.9894 0.9936 0.05938 0.9107 0.9464 0.2081 ] Network output: [ -0.0275 0.02736 1.075 0.0001505 -6.758e-05 0.9529 0.0001134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07359 0.07274 0.1777 0.1594 0.9854 0.9915 0.0736 0.8604 0.9275 0.2017 ] Network output: [ -0.007737 1.002 0.01165 7.343e-06 -3.296e-06 1.002 5.534e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01783 Epoch 5221 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03882 0.9039 0.9383 -6.258e-05 2.809e-05 0.07997 -4.716e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002902 -0.002576 -0.01131 0.007095 0.968 0.9727 0.005622 0.8907 0.8875 0.02105 ] Network output: [ 0.9823 0.0518 0.009452 5.967e-05 -2.679e-05 -0.02552 4.497e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2028 -0.008319 -0.2254 0.1977 0.9839 0.9935 0.2267 0.7271 0.9406 0.7078 ] Network output: [ 0.01207 0.9189 0.9645 -7.681e-05 3.448e-05 0.09213 -5.789e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003812 0.001155 0.002423 0.003202 0.9906 0.9935 0.003879 0.936 0.9543 0.01182 ] Network output: [ 0.01437 -0.02921 0.9285 -0.0002088 9.375e-05 1.071 -0.0001574 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2161 0.1439 0.3274 0.1467 0.9855 0.9943 0.2167 0.7361 0.9453 0.7033 ] Network output: [ -0.02601 0.1443 1.082 0.0001194 -5.361e-05 0.8257 8.999e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05934 0.05553 0.1575 0.138 0.9894 0.9936 0.05937 0.9108 0.9464 0.2077 ] Network output: [ -0.02522 0.03017 1.072 0.0001512 -6.789e-05 0.9487 0.000114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07366 0.07281 0.1767 0.1586 0.9854 0.9915 0.07367 0.8604 0.9275 0.2009 ] Network output: [ -0.004593 1.007 0.007086 9.291e-06 -4.171e-06 0.9953 7.002e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01723 Epoch 5222 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03972 0.9008 0.938 -6.156e-05 2.763e-05 0.08147 -4.639e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002897 -0.002575 -0.01132 0.007172 0.968 0.9727 0.005613 0.8909 0.8877 0.02104 ] Network output: [ 0.992 0.0177 0.00546 7.847e-05 -3.523e-05 -0.006777 5.914e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2024 -0.008665 -0.2284 0.2045 0.9839 0.9935 0.2262 0.7272 0.9407 0.7082 ] Network output: [ 0.01192 0.9195 0.9647 -7.759e-05 3.483e-05 0.09167 -5.848e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003795 0.00116 0.002387 0.003303 0.9906 0.9935 0.003862 0.9361 0.9544 0.01175 ] Network output: [ 0.02123 -0.07155 0.9289 -0.0001854 8.322e-05 1.099 -0.0001397 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.215 0.1437 0.3261 0.1554 0.9855 0.9943 0.2157 0.7363 0.9454 0.7027 ] Network output: [ -0.02645 0.1473 1.082 0.0001172 -5.26e-05 0.8238 8.831e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05932 0.05555 0.1575 0.1384 0.9894 0.9936 0.05935 0.9111 0.9464 0.2074 ] Network output: [ -0.02614 0.03882 1.072 0.0001468 -6.591e-05 0.9424 0.0001106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07359 0.07275 0.1763 0.1578 0.9854 0.9915 0.0736 0.8608 0.9275 0.2004 ] Network output: [ -0.006631 1.026 0.006163 1.405e-06 -6.308e-07 0.9812 1.059e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01872 Epoch 5223 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03895 0.9033 0.9385 -6.373e-05 2.861e-05 0.08008 -4.803e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002888 -0.002559 -0.01124 0.007097 0.968 0.9727 0.005594 0.8911 0.8878 0.02096 ] Network output: [ 0.9779 0.03645 0.01674 6.586e-05 -2.957e-05 -0.008743 4.964e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2015 -0.007667 -0.2232 0.2004 0.984 0.9935 0.2252 0.728 0.9408 0.7076 ] Network output: [ 0.01266 0.9198 0.9639 -7.827e-05 3.514e-05 0.09059 -5.899e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003794 0.001172 0.00246 0.003223 0.9906 0.9935 0.003861 0.9363 0.9545 0.01179 ] Network output: [ 0.01206 -0.03451 0.9314 -0.0002045 9.18e-05 1.078 -0.0001541 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2153 0.1444 0.3287 0.1474 0.9855 0.9943 0.216 0.737 0.9455 0.7025 ] Network output: [ -0.02435 0.149 1.08 0.0001176 -5.279e-05 0.8203 8.863e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05921 0.05547 0.1566 0.1373 0.9894 0.9936 0.05924 0.9111 0.9466 0.2066 ] Network output: [ -0.02333 0.03754 1.069 0.0001488 -6.682e-05 0.9409 0.0001122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07355 0.07271 0.1751 0.1573 0.9853 0.9915 0.07355 0.8608 0.9276 0.1995 ] Network output: [ -0.0008819 1.011 0.002909 1.121e-05 -5.032e-06 0.9882 8.447e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01762 Epoch 5224 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03977 0.9009 0.9381 -6.33e-05 2.842e-05 0.08116 -4.771e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002887 -0.00256 -0.0113 0.007146 0.968 0.9727 0.005591 0.8912 0.888 0.02098 ] Network output: [ 0.9901 0.02084 0.006678 7.954e-05 -3.571e-05 -0.007304 5.994e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2015 -0.008013 -0.2292 0.2043 0.984 0.9935 0.2252 0.7281 0.9409 0.7082 ] Network output: [ 0.01221 0.9192 0.9646 -7.91e-05 3.551e-05 0.09138 -5.961e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003778 0.001168 0.002349 0.003279 0.9906 0.9935 0.003844 0.9364 0.9546 0.01172 ] Network output: [ 0.02179 -0.06779 0.9275 -0.0001821 8.177e-05 1.096 -0.0001373 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2143 0.1438 0.3241 0.1546 0.9855 0.9943 0.2149 0.7371 0.9456 0.7027 ] Network output: [ -0.02507 0.1477 1.081 0.0001163 -5.222e-05 0.822 8.766e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05903 0.05532 0.1564 0.138 0.9894 0.9936 0.05906 0.9114 0.9466 0.2066 ] Network output: [ -0.02488 0.03659 1.071 0.0001464 -6.572e-05 0.9429 0.0001103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07327 0.07245 0.1755 0.1578 0.9854 0.9915 0.07328 0.8613 0.9277 0.1998 ] Network output: [ -0.002153 1.003 0.006445 1.146e-05 -5.144e-06 0.9951 8.635e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01846 Epoch 5225 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03848 0.9054 0.9387 -6.646e-05 2.984e-05 0.0787 -5.009e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002883 -0.002551 -0.0113 0.007045 0.968 0.9727 0.005583 0.8914 0.888 0.02099 ] Network output: [ 0.9778 0.06649 0.0112 5.838e-05 -2.621e-05 -0.033 4.4e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2012 -0.007164 -0.2277 0.1959 0.984 0.9935 0.2249 0.7289 0.9409 0.7087 ] Network output: [ 0.0123 0.9189 0.9646 -7.993e-05 3.588e-05 0.09157 -6.024e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00378 0.001163 0.002345 0.003136 0.9906 0.9935 0.003847 0.9365 0.9546 0.01179 ] Network output: [ 0.01431 -0.01214 0.9255 -0.000208 9.337e-05 1.057 -0.0001567 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2146 0.1438 0.3234 0.1434 0.9855 0.9943 0.2152 0.7378 0.9457 0.7047 ] Network output: [ -0.02366 0.1435 1.08 0.0001182 -5.304e-05 0.8241 8.905e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05877 0.05506 0.1556 0.1375 0.9894 0.9936 0.05881 0.9113 0.9467 0.2066 ] Network output: [ -0.02313 0.02237 1.071 0.0001513 -6.794e-05 0.953 0.0001141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07304 0.07221 0.1756 0.1591 0.9854 0.9915 0.07305 0.8612 0.928 0.2005 ] Network output: [ 0.002978 0.9616 0.008449 2.803e-05 -1.258e-05 1.024 2.112e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0173 Epoch 5226 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03887 0.9045 0.9384 -6.638e-05 2.98e-05 0.07906 -5.002e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002889 -0.002563 -0.01146 0.007117 0.968 0.9727 0.005596 0.8915 0.8881 0.02112 ] Network output: [ 0.9951 0.06258 -0.006354 7.031e-05 -3.157e-05 -0.0462 5.299e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2016 -0.008009 -0.2378 0.1984 0.984 0.9935 0.2253 0.7288 0.941 0.7114 ] Network output: [ 0.01088 0.9187 0.9661 -8.104e-05 3.638e-05 0.09307 -6.107e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003768 0.001142 0.002171 0.003181 0.9906 0.9935 0.003834 0.9366 0.9546 0.01177 ] Network output: [ 0.02721 -0.04182 0.9182 -0.0001864 8.368e-05 1.068 -0.0001405 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2133 0.1422 0.3162 0.1506 0.9855 0.9943 0.2139 0.7378 0.9457 0.7074 ] Network output: [ -0.02582 0.1376 1.083 0.0001174 -5.273e-05 0.8311 8.851e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05857 0.05483 0.1561 0.1391 0.9894 0.9936 0.05861 0.9116 0.9467 0.2079 ] Network output: [ -0.02661 0.01303 1.077 0.00015 -6.735e-05 0.964 0.0001131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07271 0.07187 0.1776 0.1611 0.9854 0.9916 0.07272 0.8617 0.928 0.2026 ] Network output: [ -0.002089 0.9496 0.01618 2.606e-05 -1.17e-05 1.039 1.964e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01781 Epoch 5227 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0371 0.9105 0.939 -6.952e-05 3.121e-05 0.07601 -5.239e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00289 -0.00256 -0.0115 0.007051 0.968 0.9727 0.005597 0.8917 0.888 0.02122 ] Network output: [ 0.9833 0.1069 -0.001767 4.919e-05 -2.208e-05 -0.07147 3.707e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2012 -0.007549 -0.2364 0.1904 0.984 0.9935 0.2249 0.7294 0.9409 0.7135 ] Network output: [ 0.01041 0.9198 0.9663 -8.201e-05 3.682e-05 0.09269 -6.18e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003777 0.001127 0.002189 0.003053 0.9906 0.9935 0.003844 0.9368 0.9547 0.01191 ] Network output: [ 0.01848 0.01508 0.9178 -0.000216 9.695e-05 1.029 -0.0001628 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2135 0.1417 0.3168 0.1393 0.9856 0.9943 0.2141 0.7383 0.9458 0.7109 ] Network output: [ -0.02523 0.133 1.083 0.0001193 -5.356e-05 0.8345 8.991e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05846 0.05466 0.156 0.1389 0.9895 0.9936 0.05849 0.9114 0.9467 0.2089 ] Network output: [ -0.02569 -0.0005997 1.078 0.0001548 -6.95e-05 0.9747 0.0001167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07266 0.07181 0.1784 0.1627 0.9854 0.9916 0.07267 0.8615 0.9281 0.2041 ] Network output: [ -0.0004992 0.9278 0.01731 3.44e-05 -1.544e-05 1.056 2.592e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0184 Epoch 5228 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03766 0.9085 0.9388 -6.814e-05 3.059e-05 0.07711 -5.135e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002895 -0.002575 -0.01163 0.007205 0.968 0.9727 0.00561 0.8919 0.8882 0.02137 ] Network output: [ 1.003 0.06812 -0.01512 7.372e-05 -3.31e-05 -0.05851 5.556e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2012 -0.008812 -0.2444 0.1991 0.984 0.9935 0.2248 0.7289 0.9409 0.7167 ] Network output: [ 0.008843 0.922 0.9675 -8.321e-05 3.736e-05 0.09239 -6.271e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003766 0.001113 0.002093 0.003197 0.9906 0.9935 0.003832 0.9369 0.9546 0.01191 ] Network output: [ 0.03031 -0.04018 0.9161 -0.0001866 8.376e-05 1.063 -0.0001406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2119 0.1402 0.3133 0.1515 0.9856 0.9943 0.2126 0.738 0.9457 0.7131 ] Network output: [ -0.0281 0.1327 1.086 0.0001167 -5.239e-05 0.838 8.795e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05854 0.05472 0.1573 0.1405 0.9895 0.9936 0.05857 0.9117 0.9466 0.2104 ] Network output: [ -0.02965 0.004414 1.081 0.0001495 -6.713e-05 0.9746 0.0001127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07266 0.0718 0.1799 0.1632 0.9854 0.9916 0.07266 0.862 0.9279 0.2055 ] Network output: [ -0.01021 0.9671 0.01982 1.401e-05 -6.29e-06 1.034 1.056e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01712 Epoch 5229 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03629 0.9122 0.9396 -6.988e-05 3.137e-05 0.07532 -5.266e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002885 -0.002563 -0.01153 0.007184 0.968 0.9727 0.005593 0.8921 0.8882 0.02135 ] Network output: [ 0.9839 0.06992 0.004806 6.334e-05 -2.843e-05 -0.04236 4.773e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1997 -0.008066 -0.2349 0.1975 0.984 0.9935 0.2232 0.7296 0.9409 0.7173 ] Network output: [ 0.009429 0.9252 0.9664 -8.388e-05 3.766e-05 0.08926 -6.321e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003773 0.001127 0.002293 0.003162 0.9906 0.9935 0.00384 0.9372 0.9547 0.01205 ] Network output: [ 0.01381 -0.001378 0.9261 -0.0002144 9.627e-05 1.047 -0.0001616 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2124 0.1409 0.3217 0.1428 0.9856 0.9943 0.213 0.7386 0.9456 0.7137 ] Network output: [ -0.02626 0.1382 1.083 0.0001166 -5.233e-05 0.8321 8.785e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05874 0.05493 0.1573 0.1392 0.9895 0.9936 0.05877 0.9117 0.9466 0.2104 ] Network output: [ -0.02646 0.0134 1.075 0.0001501 -6.738e-05 0.9649 0.0001131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07304 0.07219 0.1786 0.1615 0.9854 0.9916 0.07305 0.8617 0.9278 0.2046 ] Network output: [ -0.00866 1.001 0.0105 6.815e-06 -3.06e-06 1.006 5.136e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01582 Epoch 5230 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03819 0.9053 0.9391 -6.637e-05 2.98e-05 0.07889 -5.002e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002877 -0.002562 -0.01151 0.007371 0.9681 0.9727 0.005578 0.8922 0.8885 0.0213 ] Network output: [ 1.001 -0.01885 0.003039 0.0001049 -4.708e-05 0.01422 7.903e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1988 -0.008863 -0.2367 0.2141 0.984 0.9935 0.2222 0.7293 0.9411 0.7173 ] Network output: [ 0.009405 0.9276 0.9662 -8.455e-05 3.796e-05 0.0871 -6.372e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003749 0.001149 0.002323 0.003418 0.9906 0.9935 0.003815 0.9372 0.9548 0.01193 ] Network output: [ 0.02353 -0.09605 0.9324 -0.0001689 7.583e-05 1.116 -0.0001273 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2108 0.141 0.3237 0.1618 0.9856 0.9943 0.2114 0.7384 0.9456 0.711 ] Network output: [ -0.02753 0.1493 1.082 0.0001112 -4.993e-05 0.8245 8.382e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05901 0.05528 0.1579 0.1396 0.9895 0.9936 0.05904 0.9121 0.9465 0.2099 ] Network output: [ -0.02816 0.04343 1.072 0.000139 -6.241e-05 0.9419 0.0001048 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07327 0.07244 0.1773 0.1583 0.9854 0.9916 0.07328 0.8623 0.9275 0.2026 ] Network output: [ -0.0159 1.082 0.003943 -2.323e-05 1.043e-05 0.946 -1.75e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02062 Epoch 5231 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03756 0.9064 0.9399 -6.802e-05 3.054e-05 0.07836 -5.126e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002853 -0.002526 -0.01121 0.00727 0.968 0.9727 0.00553 0.8924 0.8885 0.02102 ] Network output: [ 0.9702 -0.02989 0.03735 9.294e-05 -4.172e-05 0.0526 7.004e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1968 -0.006917 -0.219 0.2133 0.984 0.9935 0.22 0.7304 0.9412 0.7135 ] Network output: [ 0.01201 0.9292 0.9634 -8.463e-05 3.799e-05 0.08307 -6.378e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003748 0.0012 0.002632 0.003383 0.9906 0.9935 0.003813 0.9374 0.9549 0.01195 ] Network output: [ 0.001742 -0.05827 0.9466 -0.000196 8.801e-05 1.107 -0.0001477 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2119 0.1438 0.3359 0.1521 0.9856 0.9943 0.2125 0.7393 0.9456 0.7064 ] Network output: [ -0.02266 0.1626 1.074 0.0001103 -4.952e-05 0.8088 8.313e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05918 0.05556 0.1564 0.1366 0.9895 0.9936 0.05921 0.9121 0.9466 0.2071 ] Network output: [ -0.02106 0.06502 1.06 0.0001383 -6.21e-05 0.9176 0.0001042 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07367 0.07286 0.1729 0.154 0.9854 0.9916 0.07367 0.862 0.9275 0.1982 ] Network output: [ -0.004588 1.105 -0.01223 -1.876e-05 8.422e-06 0.9163 -1.414e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02277 Epoch 5232 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04016 0.8981 0.9391 -6.539e-05 2.935e-05 0.08228 -4.928e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002841 -0.002514 -0.01114 0.00733 0.9681 0.9727 0.005504 0.8924 0.8888 0.02082 ] Network output: [ 0.9857 -0.08908 0.0312 0.000124 -5.567e-05 0.08694 9.345e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1965 -0.006874 -0.2224 0.2244 0.984 0.9935 0.2196 0.7303 0.9415 0.7101 ] Network output: [ 0.01299 0.9273 0.9631 -8.477e-05 3.805e-05 0.08331 -6.388e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003716 0.001227 0.002545 0.003542 0.9906 0.9935 0.003781 0.9373 0.955 0.01168 ] Network output: [ 0.01718 -0.1436 0.9446 -0.0001465 6.578e-05 1.164 -0.0001104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2107 0.1446 0.3324 0.169 0.9856 0.9943 0.2113 0.7393 0.9458 0.7009 ] Network output: [ -0.02197 0.171 1.072 0.0001065 -4.781e-05 0.8009 8.026e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05908 0.05558 0.1549 0.1365 0.9894 0.9936 0.05911 0.9124 0.9467 0.2044 ] Network output: [ -0.02112 0.0837 1.058 0.0001306 -5.864e-05 0.9015 9.844e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07342 0.07265 0.1706 0.1518 0.9853 0.9915 0.07343 0.8625 0.9275 0.1951 ] Network output: [ -0.0008135 1.107 -0.01438 -1.735e-05 7.789e-06 0.9085 -1.308e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03128 Epoch 5233 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03845 0.904 0.94 -7.083e-05 3.18e-05 0.07886 -5.338e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00283 -0.002484 -0.01099 0.007054 0.9681 0.9727 0.005477 0.8925 0.8887 0.02061 ] Network output: [ 0.956 -0.002511 0.04633 7.899e-05 -3.546e-05 0.04452 5.953e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1963 -0.004541 -0.2145 0.2073 0.984 0.9935 0.2194 0.7317 0.9415 0.7065 ] Network output: [ 0.01462 0.924 0.9622 -8.51e-05 3.82e-05 0.08423 -6.413e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003721 0.001241 0.002573 0.003244 0.9906 0.9935 0.003786 0.9373 0.9551 0.01169 ] Network output: [ 0.001235 -0.03345 0.9409 -0.0001948 8.746e-05 1.089 -0.0001468 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2123 0.1463 0.3318 0.1461 0.9856 0.9943 0.2129 0.7404 0.946 0.6998 ] Network output: [ -0.01719 0.1662 1.069 0.0001106 -4.964e-05 0.7995 8.333e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05854 0.0551 0.1521 0.1344 0.9894 0.9936 0.05857 0.9122 0.947 0.2022 ] Network output: [ -0.01517 0.06047 1.056 0.0001408 -6.321e-05 0.9145 0.0001061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07293 0.07216 0.1689 0.153 0.9853 0.9915 0.07294 0.8621 0.928 0.1941 ] Network output: [ 0.01722 1.001 -0.01279 2.874e-05 -1.29e-05 0.9773 2.166e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02027 Epoch 5234 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03941 0.9023 0.9393 -7.149e-05 3.209e-05 0.07931 -5.387e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002846 -0.002503 -0.0113 0.007054 0.9681 0.9727 0.005505 0.8925 0.8889 0.02081 ] Network output: [ 0.9878 0.04218 0.004297 8.295e-05 -3.724e-05 -0.02174 6.252e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1984 -0.005482 -0.2374 0.2029 0.984 0.9935 0.2216 0.7314 0.9417 0.7092 ] Network output: [ 0.01238 0.9185 0.9656 -8.602e-05 3.862e-05 0.09078 -6.482e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003702 0.001194 0.002105 0.00318 0.9906 0.9935 0.003767 0.9373 0.9551 0.01158 ] Network output: [ 0.02948 -0.05549 0.9168 -0.0001623 7.288e-05 1.079 -0.0001223 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2107 0.1437 0.3115 0.153 0.9856 0.9943 0.2113 0.7404 0.9463 0.7044 ] Network output: [ -0.02038 0.146 1.077 0.0001126 -5.053e-05 0.8186 8.483e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05777 0.05428 0.1517 0.1374 0.9894 0.9936 0.0578 0.9125 0.9472 0.2039 ] Network output: [ -0.02126 0.02051 1.07 0.0001446 -6.493e-05 0.9523 0.000109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07181 0.07103 0.1731 0.1592 0.9853 0.9915 0.07181 0.8629 0.9285 0.1987 ] Network output: [ 0.01477 0.893 0.01154 5.461e-05 -2.452e-05 1.066 4.115e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02023 Epoch 5235 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0354 0.9171 0.9404 -7.937e-05 3.563e-05 0.07146 -5.981e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002866 -0.002515 -0.01157 0.006809 0.9681 0.9728 0.005543 0.8927 0.8886 0.02114 ] Network output: [ 0.973 0.2114 -0.01053 1.67e-05 -7.497e-06 -0.1468 1.259e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1998 -0.004917 -0.2464 0.174 0.984 0.9935 0.2232 0.7324 0.9414 0.7147 ] Network output: [ 0.01018 0.9157 0.968 -8.713e-05 3.912e-05 0.09554 -6.567e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00373 0.001121 0.001858 0.002756 0.9906 0.9935 0.003795 0.9374 0.955 0.01185 ] Network output: [ 0.02409 0.09189 0.8986 -0.000226 0.0001014 0.9604 -0.0001703 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2115 0.141 0.3001 0.1253 0.9856 0.9943 0.2121 0.7409 0.9464 0.715 ] Network output: [ -0.02091 0.119 1.082 0.0001207 -5.419e-05 0.8414 9.098e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0571 0.05342 0.1515 0.1387 0.9895 0.9936 0.05713 0.9119 0.9472 0.2071 ] Network output: [ -0.02265 -0.04462 1.083 0.0001623 -7.284e-05 1.008 0.0001223 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07109 0.07026 0.1781 0.1674 0.9854 0.9916 0.0711 0.8623 0.929 0.2054 ] Network output: [ 0.0201 0.7456 0.03102 0.0001016 -4.561e-05 1.184 7.656e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03969 Epoch 5236 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03451 0.9205 0.9402 -7.948e-05 3.568e-05 0.06999 -5.99e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002905 -0.002577 -0.01205 0.007051 0.9681 0.9728 0.005626 0.8926 0.8885 0.0217 ] Network output: [ 1.018 0.2104 -0.05753 3.651e-05 -1.639e-05 -0.1893 2.752e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.202 -0.008208 -0.2716 0.1789 0.984 0.9935 0.2257 0.7307 0.9411 0.7243 ] Network output: [ 0.004573 0.9195 0.9728 -8.958e-05 4.021e-05 0.09815 -6.751e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00374 0.001043 0.001471 0.002894 0.9907 0.9935 0.003806 0.9374 0.9547 0.01195 ] Network output: [ 0.05565 0.02474 0.8801 -0.0001842 8.269e-05 0.9831 -0.0001388 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2094 0.1361 0.284 0.1431 0.9856 0.9943 0.21 0.7395 0.9462 0.7249 ] Network output: [ -0.02997 0.1028 1.093 0.0001193 -5.357e-05 0.8644 8.993e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05719 0.0533 0.1551 0.1439 0.9895 0.9936 0.05723 0.912 0.9468 0.2128 ] Network output: [ -0.03506 -0.06973 1.1 0.0001597 -7.17e-05 1.041 0.0001204 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07094 0.07007 0.1857 0.1738 0.9854 0.9916 0.07095 0.8628 0.9286 0.213 ] Network output: [ -0.002206 0.7574 0.0494 7.864e-05 -3.53e-05 1.198 5.926e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04167 Epoch 5237 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03003 0.9336 0.9424 -8.361e-05 3.754e-05 0.0636 -6.301e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002918 -0.002594 -0.01208 0.007058 0.9681 0.9728 0.005658 0.8929 0.8881 0.02198 ] Network output: [ 0.9911 0.2479 -0.0344 2.854e-06 -1.281e-06 -0.1957 2.151e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2011 -0.008414 -0.26 0.1708 0.984 0.9935 0.2248 0.7306 0.9405 0.7302 ] Network output: [ 0.002897 0.9281 0.9728 -9.072e-05 4.073e-05 0.09298 -6.837e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003793 0.001018 0.001762 0.002783 0.9907 0.9935 0.003859 0.9377 0.9546 0.01238 ] Network output: [ 0.02692 0.1166 0.8951 -0.0002516 0.0001129 0.9334 -0.0001896 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2108 0.1352 0.2981 0.1245 0.9856 0.9943 0.2114 0.739 0.9457 0.7317 ] Network output: [ -0.03013 0.1047 1.092 0.0001216 -5.46e-05 0.8645 9.166e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05794 0.05384 0.1572 0.1435 0.9896 0.9937 0.05797 0.9113 0.9463 0.2162 ] Network output: [ -0.03328 -0.06791 1.095 0.0001648 -7.399e-05 1.04 0.0001242 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07208 0.07116 0.1867 0.1741 0.9854 0.9916 0.07209 0.8614 0.9279 0.2153 ] Network output: [ -0.009027 0.8329 0.03669 5.579e-05 -2.505e-05 1.149 4.205e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03779 Epoch 5238 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0328 0.9209 0.9423 -7.424e-05 3.333e-05 0.07092 -5.595e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002918 -0.002626 -0.01214 0.007651 0.9681 0.9728 0.005668 0.8928 0.8884 0.02213 ] Network output: [ 1.038 0.01737 -0.04086 0.0001048 -4.704e-05 -0.05153 7.896e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1996 -0.01187 -0.2645 0.2139 0.984 0.9935 0.2232 0.7281 0.9405 0.734 ] Network output: [ 0.0003299 0.9394 0.9738 -9.228e-05 4.143e-05 0.08581 -6.955e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003769 0.001053 0.001942 0.003455 0.9907 0.9936 0.003836 0.9377 0.9544 0.01232 ] Network output: [ 0.04112 -0.09632 0.9179 -0.0001623 7.286e-05 1.096 -0.0001223 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2075 0.1346 0.3091 0.1678 0.9856 0.9943 0.2082 0.7375 0.9451 0.7296 ] Network output: [ -0.03763 0.1303 1.094 0.0001084 -4.867e-05 0.8518 8.17e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05916 0.05513 0.1621 0.1453 0.9895 0.9937 0.05919 0.912 0.9457 0.2186 ] Network output: [ -0.04126 0.009202 1.089 0.0001373 -6.163e-05 0.9847 0.0001035 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07334 0.07245 0.1863 0.1666 0.9855 0.9916 0.07335 0.8624 0.9266 0.2133 ] Network output: [ -0.04424 1.119 0.02075 -5.591e-05 2.51e-05 0.9487 -4.213e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01979 Epoch 5239 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03256 0.9161 0.9442 -7.135e-05 3.203e-05 0.0743 -5.377e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002855 -0.002546 -0.0113 0.007666 0.9681 0.9728 0.005545 0.8932 0.8885 0.02146 ] Network output: [ 0.9667 -0.1319 0.06104 0.0001184 -5.315e-05 0.1379 8.922e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1934 -0.008435 -0.2108 0.2324 0.984 0.9935 0.2163 0.7297 0.9407 0.7258 ] Network output: [ 0.007166 0.9484 0.9655 -9.059e-05 4.067e-05 0.07137 -6.827e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00377 0.001208 0.003075 0.003726 0.9907 0.9936 0.003837 0.9382 0.9548 0.01244 ] Network output: [ -0.02066 -0.09494 0.9772 -0.0002059 9.242e-05 1.158 -0.0001551 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.21 0.1424 0.3563 0.1608 0.9856 0.9943 0.2107 0.7385 0.9448 0.7158 ] Network output: [ -0.02776 0.1816 1.073 0.000101 -4.535e-05 0.8009 7.614e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06065 0.05694 0.1615 0.1376 0.9895 0.9937 0.06068 0.9121 0.9456 0.2128 ] Network output: [ -0.0247 0.1182 1.051 0.0001219 -5.474e-05 0.8805 9.19e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07562 0.07479 0.1735 0.1493 0.9854 0.9916 0.07563 0.8615 0.9258 0.1998 ] Network output: [ -0.03346 1.366 -0.03673 -0.0001124 5.046e-05 0.7367 -8.47e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05976 Epoch 5240 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04153 0.8841 0.942 -5.757e-05 2.585e-05 0.09065 -4.339e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002793 -0.002483 -0.01052 0.007885 0.9681 0.9728 0.005418 0.8928 0.8892 0.02034 ] Network output: [ 0.9839 -0.4369 0.09682 0.0002343 -0.0001052 0.3734 0.0001766 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1899 -0.007628 -0.19 0.2832 0.984 0.9935 0.2123 0.7284 0.9414 0.708 ] Network output: [ 0.01439 0.9475 0.9594 -8.739e-05 3.923e-05 0.06401 -6.586e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003682 0.001359 0.003397 0.004538 0.9905 0.9935 0.003747 0.9372 0.9548 0.0114 ] Network output: [ 0.01419 -0.4737 1.001 -3.063e-05 1.375e-05 1.444 -2.308e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2081 0.1481 0.372 0.2321 0.9856 0.9943 0.2087 0.7361 0.9446 0.6839 ] Network output: [ -0.02104 0.2506 1.054 8.333e-05 -3.741e-05 0.7376 6.28e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06201 0.05875 0.1564 0.1336 0.9893 0.9936 0.06204 0.912 0.9451 0.1974 ] Network output: [ -0.01731 0.2621 1.019 8.467e-05 -3.801e-05 0.7541 6.381e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07698 0.07627 0.158 0.1312 0.9853 0.9915 0.07699 0.8606 0.9242 0.1785 ] Network output: [ -0.003268 1.492 -0.08806 -0.0001189 5.338e-05 0.6016 -8.961e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2101 Epoch 5241 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03771 0.8955 0.9455 -7.139e-05 3.205e-05 0.08326 -5.38e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002743 -0.002366 -0.009331 0.006934 0.9681 0.9728 0.005301 0.8919 0.8883 0.01878 ] Network output: [ 0.8681 -0.2831 0.1898 0.0001081 -4.851e-05 0.3576 8.144e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1891 0.000476 -0.1357 0.2453 0.984 0.9935 0.2112 0.7296 0.9412 0.6781 ] Network output: [ 0.02387 0.9415 0.9519 -8.577e-05 3.85e-05 0.05844 -6.464e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003726 0.001509 0.003801 0.003832 0.9904 0.9934 0.003792 0.936 0.9548 0.01085 ] Network output: [ -0.03213 -0.2062 0.9949 -0.0001427 6.406e-05 1.275 -0.0001075 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2162 0.1592 0.3797 0.1762 0.9856 0.9943 0.2169 0.7361 0.9448 0.6558 ] Network output: [ -0.001375 0.2871 1.03 8.646e-05 -3.882e-05 0.686 6.516e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06152 0.05858 0.1431 0.121 0.9892 0.9935 0.06156 0.91 0.9456 0.1796 ] Network output: [ 0.007311 0.2649 0.9949 0.0001016 -4.562e-05 0.726 7.658e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07659 0.07594 0.1436 0.1246 0.9851 0.9914 0.0766 0.8574 0.9249 0.1628 ] Network output: [ 0.07046 1.254 -0.1176 1.516e-05 -6.808e-06 0.7226 1.143e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1286 Epoch 5242 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04069 0.8821 0.9458 -7.29e-05 3.273e-05 0.09041 -5.494e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002767 -0.002379 -0.009711 0.006663 0.9681 0.9728 0.005333 0.891 0.8883 0.01865 ] Network output: [ 0.9233 -0.1548 0.1069 9.322e-05 -4.185e-05 0.2015 7.025e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1952 0.0005397 -0.1788 0.2268 0.984 0.9935 0.218 0.7278 0.9416 0.6714 ] Network output: [ 0.02152 0.9177 0.9598 -8.523e-05 3.826e-05 0.07908 -6.423e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003698 0.001454 0.002804 0.003432 0.9904 0.9934 0.003763 0.9351 0.9546 0.01033 ] Network output: [ 0.01423 -0.1805 0.9432 -0.0001064 4.775e-05 1.208 -8.016e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.216 0.1577 0.3371 0.1738 0.9855 0.9943 0.2167 0.7356 0.9456 0.656 ] Network output: [ -0.002882 0.2467 1.042 9.327e-05 -4.187e-05 0.7172 7.029e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0593 0.05638 0.1375 0.1235 0.9891 0.9934 0.05933 0.9097 0.9464 0.1782 ] Network output: [ 0.001552 0.1788 1.02 0.0001152 -5.17e-05 0.7984 8.679e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07363 0.07298 0.1464 0.1346 0.9851 0.9914 0.07363 0.8579 0.9266 0.1674 ] Network output: [ 0.08066 0.9477 -0.06378 0.0001025 -4.603e-05 0.9552 7.727e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06202 Epoch 5243 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03354 0.9016 0.949 -8.735e-05 3.921e-05 0.08195 -6.583e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002813 -0.002404 -0.01048 0.006182 0.9681 0.9728 0.005413 0.8909 0.8878 0.01937 ] Network output: [ 0.9274 0.2069 0.03618 -2.455e-05 1.102e-05 -0.09799 -1.85e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2013 0.001778 -0.2184 0.1677 0.984 0.9935 0.2247 0.729 0.9413 0.6821 ] Network output: [ 0.01444 0.9011 0.9698 -8.772e-05 3.938e-05 0.09984 -6.611e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003723 0.001315 0.00197 0.002517 0.9905 0.9934 0.003788 0.9352 0.9545 0.01073 ] Network output: [ 0.01261 0.1329 0.894 -0.0002195 9.853e-05 0.947 -0.0001654 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2176 0.1534 0.2986 0.1155 0.9856 0.9943 0.2182 0.7371 0.9464 0.6812 ] Network output: [ -0.008338 0.1698 1.064 0.0001103 -4.951e-05 0.7836 8.31e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05667 0.05352 0.137 0.1274 0.9893 0.9935 0.0567 0.9092 0.9471 0.1869 ] Network output: [ -0.006968 0.01658 1.06 0.0001509 -6.775e-05 0.9383 0.0001137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0706 0.0699 0.159 0.1537 0.9851 0.9914 0.07061 0.8578 0.9286 0.1843 ] Network output: [ 0.07485 0.592 0.01096 0.0001905 -8.55e-05 1.248 0.0001435 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06473 Epoch 5244 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03076 0.909 0.9487 -8.941e-05 4.014e-05 0.08044 -6.738e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002892 -0.002513 -0.01153 0.006451 0.9681 0.9728 0.005571 0.8906 0.8876 0.02056 ] Network output: [ 1.01 0.2982 -0.06871 -1.983e-05 8.901e-06 -0.2504 -1.494e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2078 -0.00327 -0.2729 0.1612 0.984 0.9935 0.232 0.7265 0.9409 0.7025 ] Network output: [ 0.003048 0.8997 0.9805 -9.085e-05 4.079e-05 0.1133 -6.847e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003733 0.001154 0.001114 0.002571 0.9906 0.9934 0.003798 0.9352 0.9541 0.01101 ] Network output: [ 0.07198 0.06277 0.848 -0.0001601 7.189e-05 0.9446 -0.0001207 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2148 0.1448 0.2608 0.1377 0.9856 0.9943 0.2154 0.7349 0.9461 0.705 ] Network output: [ -0.02556 0.1101 1.091 0.000116 -5.207e-05 0.8506 8.741e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05601 0.05252 0.1439 0.1394 0.9894 0.9935 0.05604 0.9095 0.9468 0.2 ] Network output: [ -0.03083 -0.07956 1.101 0.0001588 -7.129e-05 1.041 0.0001197 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06943 0.06865 0.1761 0.1718 0.9853 0.9915 0.06944 0.8591 0.9288 0.2028 ] Network output: [ 0.0384 0.4682 0.06766 0.0001915 -8.599e-05 1.388 0.0001443 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.105 Epoch 5245 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02162 0.9431 0.9511 -0.0001003 4.505e-05 0.0622 -7.562e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00296 -0.002585 -0.01195 0.006255 0.9681 0.9728 0.005711 0.8906 0.8863 0.02132 ] Network output: [ 0.9867 0.5095 -0.08021 -0.0001189 5.336e-05 -0.4031 -8.958e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.004573 -0.2776 0.1249 0.984 0.9935 0.2356 0.7254 0.9395 0.7166 ] Network output: [ -0.003244 0.9104 0.9841 -9.219e-05 4.139e-05 0.1116 -6.948e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003846 0.001018 0.001051 0.002142 0.9907 0.9935 0.003913 0.935 0.9534 0.0116 ] Network output: [ 0.05272 0.2438 0.8401 -0.0002676 0.0001201 0.8095 -0.0002017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2176 0.1394 0.2614 0.1047 0.9855 0.9943 0.2182 0.732 0.9452 0.7235 ] Network output: [ -0.03129 0.08941 1.098 0.0001246 -5.596e-05 0.8755 9.394e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05662 0.05259 0.1478 0.142 0.9895 0.9936 0.05665 0.9073 0.9456 0.2078 ] Network output: [ -0.03657 -0.1323 1.113 0.000177 -7.945e-05 1.093 0.0001334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07039 0.06949 0.1843 0.1809 0.9853 0.9915 0.0704 0.8556 0.9275 0.2131 ] Network output: [ 0.02677 0.4436 0.07251 0.0001967 -8.828e-05 1.431 0.0001482 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1656 Epoch 5246 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0215 0.937 0.9525 -9.016e-05 4.048e-05 0.06709 -6.795e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00301 -0.002695 -0.01224 0.007229 0.9681 0.9728 0.005829 0.8898 0.8859 0.02193 ] Network output: [ 1.065 0.1909 -0.1031 7.085e-06 -3.181e-06 -0.2171 5.34e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.01214 -0.2864 0.1856 0.984 0.9935 0.2368 0.7186 0.9385 0.7285 ] Network output: [ -0.01218 0.937 0.9887 -9.597e-05 4.309e-05 0.09826 -7.233e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00388 0.001032 0.001346 0.003128 0.9907 0.9935 0.003948 0.9349 0.9528 0.01183 ] Network output: [ 0.06885 -0.04681 0.8741 -0.0001617 7.258e-05 1.034 -0.0001218 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2149 0.1373 0.2813 0.167 0.9855 0.9943 0.2155 0.7276 0.9437 0.7286 ] Network output: [ -0.04728 0.1065 1.108 0.0001121 -5.033e-05 0.8801 8.45e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05923 0.05507 0.1594 0.1505 0.9895 0.9936 0.05926 0.9078 0.9441 0.2176 ] Network output: [ -0.0523 -0.04931 1.111 0.0001475 -6.623e-05 1.043 0.0001112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07332 0.0724 0.1893 0.1781 0.9854 0.9916 0.07333 0.8562 0.9251 0.2164 ] Network output: [ -0.02955 0.837 0.05045 4.38e-05 -1.966e-05 1.172 3.301e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03802 Epoch 5247 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01912 0.9459 0.9553 -8.707e-05 3.909e-05 0.06015 -6.562e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002972 -0.002644 -0.01172 0.007072 0.9681 0.9728 0.005761 0.8902 0.8853 0.02171 ] Network output: [ 0.9791 0.204 -0.01187 -3.331e-05 1.495e-05 -0.1505 -2.51e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2059 -0.009218 -0.2418 0.1762 0.984 0.9935 0.2302 0.7196 0.9378 0.7278 ] Network output: [ -0.005911 0.9438 0.9822 -8.994e-05 4.038e-05 0.08545 -6.778e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003939 0.00108 0.002208 0.002917 0.9907 0.9935 0.004008 0.9353 0.9526 0.01236 ] Network output: [ -0.001895 0.1402 0.9168 -0.0002878 0.0001292 0.9456 -0.0002169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2178 0.1403 0.318 0.1253 0.9855 0.9943 0.2184 0.728 0.9431 0.728 ] Network output: [ -0.03548 0.134 1.091 0.0001182 -5.307e-05 0.8461 8.909e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06021 0.056 0.1578 0.1435 0.9896 0.9936 0.06024 0.9063 0.9435 0.2157 ] Network output: [ -0.03475 -0.008721 1.085 0.000157 -7.049e-05 0.9941 0.0001183 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07506 0.07411 0.1814 0.1697 0.9854 0.9916 0.07507 0.8531 0.9238 0.2107 ] Network output: [ -0.01987 0.9829 0.01119 1.739e-05 -7.805e-06 1.046 1.31e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02525 Epoch 5248 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02964 0.9072 0.9525 -6.461e-05 2.901e-05 0.0807 -4.869e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002932 -0.002653 -0.01165 0.007936 0.9681 0.9728 0.005694 0.8898 0.8861 0.02155 ] Network output: [ 1.064 -0.201 -0.02684 0.0001581 -7.097e-05 0.1011 0.0001191 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2017 -0.01413 -0.2525 0.2507 0.984 0.9935 0.2256 0.7156 0.9383 0.7268 ] Network output: [ -0.005018 0.9482 0.9815 -8.776e-05 3.94e-05 0.07999 -6.614e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003824 0.001152 0.00232 0.004079 0.9906 0.9935 0.003892 0.9347 0.9524 0.01181 ] Network output: [ 0.04573 -0.3084 0.9461 -8.226e-05 3.693e-05 1.271 -6.199e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2101 0.1394 0.3279 0.214 0.9855 0.9943 0.2108 0.7251 0.9424 0.7149 ] Network output: [ -0.04294 0.1735 1.091 9.828e-05 -4.412e-05 0.8215 7.406e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06159 0.05768 0.1629 0.1465 0.9894 0.9936 0.06163 0.9075 0.9425 0.214 ] Network output: [ -0.04407 0.1183 1.073 0.0001117 -5.013e-05 0.8977 8.416e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07616 0.0753 0.1775 0.1564 0.9854 0.9916 0.07617 0.855 0.9219 0.202 ] Network output: [ -0.05264 1.318 -0.01082 -0.000108 4.847e-05 0.7973 -8.137e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07221 Epoch 5249 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02729 0.9138 0.9545 -6.951e-05 3.121e-05 0.07677 -5.239e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002838 -0.002497 -0.01019 0.007296 0.9681 0.9728 0.005501 0.8901 0.8856 0.02022 ] Network output: [ 0.9025 -0.1845 0.1392 6.918e-05 -3.106e-05 0.2406 5.214e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1941 -0.004942 -0.1627 0.2332 0.9839 0.9935 0.217 0.7195 0.9383 0.7067 ] Network output: [ 0.009621 0.9505 0.9663 -8.249e-05 3.703e-05 0.0637 -6.217e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003867 0.00137 0.003763 0.003785 0.9905 0.9935 0.003935 0.9349 0.9528 0.01195 ] Network output: [ -0.05296 -0.0696 0.9988 -0.0002304 0.0001034 1.176 -0.0001736 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2181 0.1529 0.3801 0.1579 0.9855 0.9943 0.2187 0.7272 0.9423 0.6924 ] Network output: [ -0.01888 0.2322 1.056 9.942e-05 -4.463e-05 0.7496 7.493e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06254 0.05905 0.155 0.1315 0.9894 0.9935 0.06258 0.9065 0.9429 0.1999 ] Network output: [ -0.01045 0.198 1.023 0.0001176 -5.28e-05 0.8006 8.864e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07808 0.0773 0.1589 0.1385 0.9853 0.9915 0.07809 0.852 0.9215 0.1828 ] Network output: [ 0.003167 1.386 -0.08079 -7.436e-05 3.338e-05 0.6883 -5.604e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09078 Epoch 5250 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04101 0.8703 0.9492 -5.226e-05 2.346e-05 0.09824 -3.939e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002804 -0.002476 -0.009942 0.007479 0.9681 0.9728 0.005427 0.8892 0.8864 0.01934 ] Network output: [ 0.9815 -0.4146 0.09595 0.0001925 -8.644e-05 0.3564 0.0001451 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1946 -0.006606 -0.1834 0.275 0.9839 0.9935 0.2174 0.7161 0.9391 0.6897 ] Network output: [ 0.01515 0.9329 0.9649 -7.704e-05 3.459e-05 0.07158 -5.806e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003749 0.001417 0.003181 0.00437 0.9904 0.9934 0.003816 0.9332 0.9527 0.01064 ] Network output: [ 0.03329 -0.4805 0.9786 -8.07e-06 3.623e-06 1.435 -6.082e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2134 0.1532 0.36 0.2383 0.9855 0.9943 0.214 0.7237 0.9424 0.6645 ] Network output: [ -0.01491 0.2613 1.048 8.999e-05 -4.04e-05 0.7205 6.782e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06249 0.05929 0.148 0.1324 0.9891 0.9934 0.06252 0.9061 0.9426 0.1865 ] Network output: [ -0.01059 0.2612 1.014 9.426e-05 -4.232e-05 0.7459 7.104e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0773 0.0766 0.1497 0.1321 0.9851 0.9914 0.07731 0.8519 0.9209 0.1689 ] Network output: [ 0.03746 1.281 -0.09125 -1.788e-05 8.027e-06 0.7356 -1.347e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1733 Epoch 5251 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03107 0.9047 0.954 -7.715e-05 3.464e-05 0.07891 -5.814e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002799 -0.002392 -0.009493 0.006367 0.9681 0.9728 0.005395 0.8887 0.8851 0.01871 ] Network output: [ 0.8624 -0.001612 0.1456 -1.384e-05 6.211e-06 0.1311 -1.043e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1973 0.002212 -0.1555 0.1957 0.9839 0.9935 0.2203 0.7192 0.9386 0.6748 ] Network output: [ 0.01866 0.9239 0.9634 -7.924e-05 3.557e-05 0.07504 -5.972e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003836 0.001465 0.003211 0.002992 0.9903 0.9933 0.003903 0.9329 0.9526 0.01083 ] Network output: [ -0.04428 0.09486 0.956 -0.0002541 0.0001141 1.037 -0.0001915 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2226 0.1609 0.3523 0.1236 0.9855 0.9943 0.2233 0.7263 0.9432 0.6648 ] Network output: [ -0.002385 0.2466 1.042 0.0001017 -4.566e-05 0.7165 7.664e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06002 0.05692 0.1381 0.121 0.9892 0.9934 0.06005 0.9041 0.9436 0.1805 ] Network output: [ 0.00572 0.1636 1.018 0.0001311 -5.884e-05 0.8078 9.877e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0749 0.07421 0.1464 0.1357 0.985 0.9913 0.07491 0.8488 0.923 0.169 ] Network output: [ 0.07273 0.9952 -0.06918 8.662e-05 -3.889e-05 0.9289 6.528e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04646 Epoch 5252 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03852 0.87 0.9514 -6.479e-05 2.909e-05 0.1013 -4.883e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002829 -0.002464 -0.01038 0.006935 0.9681 0.9728 0.005452 0.888 0.8859 0.01938 ] Network output: [ 1 -0.1391 0.023 0.0001046 -4.695e-05 0.1162 7.882e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2023 -0.003738 -0.2204 0.2292 0.9839 0.9935 0.2259 0.715 0.9392 0.6845 ] Network output: [ 0.01134 0.9067 0.9736 -7.959e-05 3.573e-05 0.0967 -5.998e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003733 0.001367 0.002203 0.003546 0.9904 0.9933 0.003799 0.9323 0.9526 0.01047 ] Network output: [ 0.05229 -0.238 0.9157 -6.261e-05 2.811e-05 1.217 -4.719e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.216 0.1538 0.3108 0.1937 0.9855 0.9943 0.2166 0.7238 0.9435 0.6717 ] Network output: [ -0.0166 0.1976 1.066 0.0001009 -4.53e-05 0.7703 7.605e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0589 0.05578 0.142 0.1335 0.9892 0.9934 0.05893 0.9056 0.9439 0.1882 ] Network output: [ -0.01611 0.1168 1.05 0.0001208 -5.425e-05 0.8655 9.107e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0729 0.07222 0.1563 0.1471 0.9851 0.9914 0.07291 0.8521 0.9238 0.1793 ] Network output: [ 0.04136 0.8876 -0.01245 8.084e-05 -3.629e-05 1.042 6.093e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04918 Epoch 5253 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02711 0.9149 0.9541 -8.656e-05 3.886e-05 0.07641 -6.523e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002865 -0.002449 -0.01071 0.006013 0.9681 0.9728 0.00551 0.8882 0.8846 0.0198 ] Network output: [ 0.9172 0.3585 0.02065 -0.0001007 4.522e-05 -0.214 -7.59e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2058 0.001352 -0.224 0.1405 0.9839 0.9935 0.2297 0.7185 0.9383 0.6907 ] Network output: [ 0.009344 0.8994 0.9761 -8.028e-05 3.604e-05 0.1055 -6.05e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003821 0.001248 0.001859 0.002159 0.9905 0.9934 0.003888 0.9325 0.9522 0.0111 ] Network output: [ -0.003822 0.3074 0.8829 -0.0003021 0.0001356 0.8161 -0.0002276 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2212 0.1519 0.2924 0.08675 0.9855 0.9943 0.2219 0.7255 0.9438 0.6939 ] Network output: [ -0.01209 0.1514 1.071 0.0001202 -5.397e-05 0.8025 9.06e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05657 0.05314 0.1374 0.129 0.9893 0.9935 0.0566 0.9029 0.9441 0.1918 ] Network output: [ -0.01098 -0.0267 1.071 0.0001662 -7.46e-05 0.9786 0.0001252 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0706 0.06983 0.1628 0.1604 0.9851 0.9914 0.07061 0.8483 0.9249 0.191 ] Network output: [ 0.05626 0.5733 0.02918 0.0001795 -8.06e-05 1.286 0.0001353 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1004 Epoch 5254 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03132 0.8917 0.952 -7.322e-05 3.287e-05 0.09339 -5.518e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002921 -0.00258 -0.01158 0.007173 0.9682 0.9728 0.005634 0.8872 0.8849 0.02081 ] Network output: [ 1.074 0.02225 -0.08551 7.855e-05 -3.526e-05 -0.08429 5.92e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2095 -0.008623 -0.2769 0.2115 0.9839 0.9935 0.234 0.7108 0.9381 0.7081 ] Network output: [ -0.003406 0.912 0.9857 -8.597e-05 3.86e-05 0.1088 -6.479e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003763 0.001189 0.001324 0.003395 0.9905 0.9934 0.003829 0.932 0.9517 0.011 ] Network output: [ 0.09225 -0.1861 0.8687 -5.975e-05 2.683e-05 1.133 -4.503e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2137 0.1448 0.2715 0.1935 0.9855 0.9942 0.2143 0.7203 0.9428 0.7027 ] Network output: [ -0.03649 0.1283 1.097 0.0001078 -4.838e-05 0.8484 8.122e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05778 0.05428 0.1499 0.1458 0.9893 0.9935 0.05782 0.9047 0.943 0.2047 ] Network output: [ -0.04202 -0.003724 1.097 0.0001342 -6.023e-05 0.9913 0.0001011 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07127 0.0705 0.1761 0.1685 0.9853 0.9915 0.07127 0.8519 0.9236 0.202 ] Network output: [ -0.004825 0.7562 0.05428 7.482e-05 -3.359e-05 1.199 5.638e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04364 Epoch 5255 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01893 0.9447 0.9556 -9.336e-05 4.191e-05 0.0615 -7.036e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002933 -0.002535 -0.01132 0.006111 0.9681 0.9728 0.005653 0.8877 0.8829 0.02084 ] Network output: [ 0.9205 0.4884 -0.004845 -0.0001532 6.877e-05 -0.3251 -0.0001155 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2083 -0.001668 -0.2372 0.1216 0.9839 0.9935 0.2326 0.7145 0.9364 0.7102 ] Network output: [ 0.0009419 0.9144 0.9806 -8.268e-05 3.712e-05 0.1028 -6.231e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003913 0.001107 0.001732 0.001987 0.9905 0.9934 0.003981 0.9319 0.9511 0.01184 ] Network output: [ -0.006929 0.4113 0.8723 -0.0003646 0.0001637 0.7288 -0.0002748 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2217 0.1449 0.2876 0.07169 0.9855 0.9943 0.2224 0.7203 0.9423 0.7163 ] Network output: [ -0.0211 0.1273 1.081 0.0001272 -5.709e-05 0.834 9.585e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05708 0.05315 0.1427 0.1348 0.9894 0.9935 0.05711 0.8999 0.9421 0.2031 ] Network output: [ -0.02142 -0.07696 1.087 0.0001791 -8.038e-05 1.034 0.0001349 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07128 0.07038 0.1729 0.1713 0.9852 0.9914 0.07129 0.844 0.9227 0.2045 ] Network output: [ 0.02857 0.5993 0.04156 0.0001548 -6.95e-05 1.303 0.0001167 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1368 Epoch 5256 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02967 0.8947 0.953 -6.583e-05 2.955e-05 0.09267 -4.961e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002955 -0.002672 -0.01193 0.007942 0.9682 0.9728 0.005722 0.886 0.8836 0.02152 ] Network output: [ 1.128 -0.1817 -0.1028 0.0001631 -7.321e-05 0.02951 0.0001229 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2084 -0.01549 -0.2866 0.2521 0.9839 0.9935 0.2329 0.7021 0.9363 0.722 ] Network output: [ -0.01107 0.9354 0.9891 -8.845e-05 3.971e-05 0.0973 -6.666e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003793 0.001133 0.001497 0.004134 0.9905 0.9934 0.00386 0.9306 0.9502 0.01131 ] Network output: [ 0.1097 -0.4053 0.8918 1.27e-05 -5.703e-06 1.294 9.574e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2097 0.1391 0.2843 0.2417 0.9855 0.9942 0.2103 0.7117 0.9404 0.7102 ] Network output: [ -0.05031 0.1443 1.105 9.773e-05 -4.387e-05 0.8522 7.365e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06001 0.05621 0.1594 0.1528 0.9892 0.9935 0.06004 0.9023 0.94 0.2129 ] Network output: [ -0.05632 0.06195 1.097 0.0001115 -5.005e-05 0.9542 8.402e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07362 0.07279 0.1808 0.167 0.9853 0.9915 0.07362 0.8485 0.9194 0.2051 ] Network output: [ -0.04784 1.054 0.0348 -3.725e-05 1.672e-05 1.006 -2.807e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06472 Epoch 5257 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01591 0.9509 0.9589 -8.941e-05 4.014e-05 0.05803 -6.738e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002896 -0.002504 -0.01052 0.006426 0.9681 0.9728 0.005593 0.8864 0.881 0.0205 ] Network output: [ 0.8512 0.2826 0.1077 -0.0001306 5.864e-05 -0.0932 -9.844e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2017 -0.001091 -0.1766 0.1516 0.9839 0.9934 0.2254 0.7087 0.9344 0.7073 ] Network output: [ 0.004728 0.9398 0.9727 -8.166e-05 3.666e-05 0.07764 -6.154e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003989 0.001251 0.003073 0.002436 0.9905 0.9934 0.004059 0.931 0.95 0.01238 ] Network output: [ -0.0748 0.4136 0.939 -0.0004183 0.0001878 0.7953 -0.0003152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2249 0.1516 0.3419 0.06923 0.9855 0.9942 0.2255 0.7145 0.94 0.7074 ] Network output: [ -0.01551 0.1866 1.063 0.0001189 -5.339e-05 0.7818 8.963e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05971 0.05588 0.1455 0.1294 0.9894 0.9935 0.05974 0.897 0.9398 0.2012 ] Network output: [ -0.008108 0.04613 1.049 0.0001629 -7.315e-05 0.9222 0.0001228 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07481 0.07393 0.1625 0.1556 0.9851 0.9914 0.07481 0.8384 0.9187 0.1935 ] Network output: [ 0.02177 0.9814 -0.02764 5.218e-05 -2.343e-05 1.003 3.933e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06594 Epoch 5258 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03902 0.8621 0.9514 -4.637e-05 2.082e-05 0.1083 -3.495e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002877 -0.002613 -0.01122 0.008268 0.9682 0.9728 0.005574 0.8843 0.8828 0.02073 ] Network output: [ 1.124 -0.4693 -0.04419 0.0002651 -0.000119 0.2671 0.0001998 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2011 -0.01635 -0.258 0.2983 0.9839 0.9934 0.2248 0.6943 0.9354 0.71 ] Network output: [ -0.001665 0.9328 0.9815 -7.998e-05 3.591e-05 0.08868 -6.027e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003732 0.001215 0.002072 0.004857 0.9904 0.9933 0.003798 0.9279 0.949 0.01082 ] Network output: [ 0.1227 -0.6955 0.9286 0.0001216 -5.46e-05 1.522 9.166e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2066 0.1407 0.3098 0.2943 0.9854 0.9942 0.2072 0.7023 0.9385 0.6851 ] Network output: [ -0.04071 0.2029 1.084 8.881e-05 -3.987e-05 0.795 6.693e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0617 0.0581 0.156 0.149 0.989 0.9933 0.06174 0.8988 0.9374 0.2011 ] Network output: [ -0.04414 0.1919 1.062 8.739e-05 -3.923e-05 0.8351 6.586e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07537 0.07459 0.1659 0.1504 0.9852 0.9914 0.07538 0.8429 0.9153 0.1862 ] Network output: [ -0.01874 1.196 -0.02103 -4.632e-05 2.079e-05 0.8624 -3.49e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1932 Epoch 5259 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01648 0.9493 0.9604 -9.13e-05 4.099e-05 0.05698 -6.881e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002839 -0.002415 -0.009445 0.006159 0.9681 0.9728 0.00547 0.884 0.8789 0.01934 ] Network output: [ 0.7765 0.2097 0.1996 -0.0001554 6.976e-05 0.03697 -0.0001171 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1984 0.003304 -0.1243 0.1569 0.9838 0.9934 0.2216 0.7018 0.9328 0.6847 ] Network output: [ 0.01346 0.9442 0.9642 -7.962e-05 3.575e-05 0.06431 -6.001e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004013 0.001423 0.003826 0.002493 0.9903 0.9933 0.004084 0.9281 0.9486 0.012 ] Network output: [ -0.109 0.4394 0.9636 -0.000439 0.0001971 0.8131 -0.0003308 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2296 0.1613 0.3664 0.0612 0.9855 0.9942 0.2302 0.706 0.9382 0.6801 ] Network output: [ -0.005671 0.2488 1.043 0.0001067 -4.789e-05 0.7198 8.04e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06063 0.05715 0.1403 0.1187 0.9892 0.9934 0.06066 0.8927 0.9378 0.1874 ] Network output: [ 0.006213 0.1419 1.018 0.0001465 -6.575e-05 0.828 0.0001104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07584 0.07505 0.1489 0.1387 0.9849 0.9913 0.07585 0.8314 0.9157 0.1754 ] Network output: [ 0.04807 1.095 -0.07146 4.363e-05 -1.959e-05 0.88 3.288e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.088 Epoch 5260 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04463 0.8382 0.9511 -4.008e-05 1.799e-05 0.1213 -3.021e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002823 -0.002538 -0.01025 0.007961 0.9682 0.9728 0.005454 0.8811 0.8808 0.0195 ] Network output: [ 1.077 -0.558 0.02055 0.0002494 -0.000112 0.3844 0.000188 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1999 -0.01362 -0.217 0.305 0.9838 0.9934 0.2234 0.6842 0.934 0.6847 ] Network output: [ 0.009193 0.918 0.9739 -7.105e-05 3.19e-05 0.08946 -5.355e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003741 0.001325 0.002522 0.005008 0.9901 0.9932 0.003807 0.924 0.9476 0.01022 ] Network output: [ 0.1253 -0.7907 0.9357 0.0001561 -7.006e-05 1.605 0.0001176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2112 0.1481 0.3236 0.308 0.9854 0.9942 0.2119 0.691 0.9368 0.6519 ] Network output: [ -0.02545 0.2403 1.062 8.975e-05 -4.029e-05 0.7489 6.764e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06268 0.0593 0.1474 0.1428 0.9887 0.9932 0.06271 0.8941 0.9358 0.1862 ] Network output: [ -0.02607 0.2545 1.033 8.5e-05 -3.816e-05 0.7654 6.406e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07633 0.07561 0.1507 0.1396 0.985 0.9913 0.07634 0.8358 0.9129 0.1686 ] Network output: [ 0.0402 1.078 -0.06072 4.094e-05 -1.838e-05 0.9026 3.086e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2579 Epoch 5261 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01685 0.951 0.9612 -9.681e-05 4.346e-05 0.05375 -7.296e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00286 -0.002403 -0.009285 0.005682 0.9681 0.9728 0.005491 0.8803 0.8761 0.01866 ] Network output: [ 0.7854 0.345 0.1641 -0.0002088 9.375e-05 -0.08067 -0.0001574 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2051 0.00594 -0.1373 0.1338 0.9837 0.9934 0.229 0.691 0.9308 0.6646 ] Network output: [ 0.01389 0.9323 0.9675 -7.815e-05 3.509e-05 0.07218 -5.89e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004059 0.00145 0.003207 0.002098 0.9901 0.9931 0.00413 0.9238 0.9464 0.01126 ] Network output: [ -0.08324 0.4988 0.9232 -0.0004294 0.0001928 0.7427 -0.0003236 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2341 0.1652 0.3395 0.05205 0.9854 0.9942 0.2348 0.6946 0.9366 0.6639 ] Network output: [ -0.005512 0.2554 1.045 0.0001041 -4.672e-05 0.711 7.843e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05973 0.05634 0.1331 0.1146 0.989 0.9932 0.05976 0.8872 0.9361 0.1795 ] Network output: [ 0.004027 0.1238 1.027 0.0001469 -6.597e-05 0.8413 0.0001107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07423 0.07346 0.1456 0.1382 0.9848 0.9911 0.07424 0.8241 0.9142 0.1711 ] Network output: [ 0.05916 0.9276 -0.04504 9.526e-05 -4.277e-05 0.9995 7.179e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1068 Epoch 5262 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04087 0.8379 0.9553 -4.512e-05 2.026e-05 0.1249 -3.4e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002843 -0.00254 -0.01012 0.007771 0.9682 0.9729 0.005478 0.8774 0.8781 0.0193 ] Network output: [ 1.068 -0.4923 0.01656 0.0001943 -8.724e-05 0.3405 0.0001465 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2053 -0.01265 -0.2119 0.292 0.9837 0.9934 0.2294 0.6722 0.9319 0.6744 ] Network output: [ 0.006985 0.9107 0.9778 -6.965e-05 3.127e-05 0.09724 -5.249e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003803 0.001359 0.002447 0.004912 0.99 0.9931 0.00387 0.9204 0.9457 0.01017 ] Network output: [ 0.1288 -0.7325 0.9165 0.000141 -6.328e-05 1.559 0.0001062 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2171 0.1527 0.3137 0.2991 0.9853 0.9942 0.2177 0.6793 0.935 0.644 ] Network output: [ -0.02776 0.2252 1.068 9.335e-05 -4.191e-05 0.7626 7.035e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0627 0.05934 0.1461 0.1436 0.9886 0.9931 0.06273 0.89 0.9343 0.1864 ] Network output: [ -0.02869 0.2288 1.041 9.048e-05 -4.062e-05 0.788 6.819e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07611 0.0754 0.151 0.1424 0.9849 0.9912 0.07612 0.8302 0.9115 0.1699 ] Network output: [ 0.03836 0.9807 -0.04005 6.517e-05 -2.926e-05 0.9829 4.911e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.2165 Epoch 5263 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01549 0.953 0.9627 -9.753e-05 4.379e-05 0.05284 -7.35e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002909 -0.002436 -0.009511 0.005492 0.9682 0.9728 0.005571 0.8768 0.8731 0.01867 ] Network output: [ 0.8152 0.4516 0.1115 -0.0002431 0.0001091 -0.1945 -0.0001832 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 0.006276 -0.159 0.1178 0.9837 0.9933 0.2374 0.6792 0.9282 0.6587 ] Network output: [ 0.009945 0.9238 0.9738 -7.512e-05 3.372e-05 0.08223 -5.661e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00412 0.001428 0.002678 0.001889 0.99 0.993 0.004192 0.92 0.9441 0.01103 ] Network output: [ -0.05789 0.5338 0.889 -0.0004194 0.0001883 0.6913 -0.000316 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2377 0.1661 0.3156 0.04899 0.9853 0.9942 0.2384 0.6826 0.9346 0.6618 ] Network output: [ -0.01178 0.238 1.056 0.000107 -4.803e-05 0.7298 8.062e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05919 0.05573 0.1318 0.116 0.9889 0.9931 0.05922 0.882 0.934 0.1807 ] Network output: [ -0.004962 0.08359 1.046 0.000152 -6.826e-05 0.881 0.0001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07316 0.07238 0.149 0.1435 0.9847 0.9911 0.07317 0.8173 0.9122 0.1754 ] Network output: [ 0.04541 0.8233 -0.008854 0.0001108 -4.973e-05 1.095 8.349e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.129 Epoch 5264 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03732 0.8415 0.9584 -4.572e-05 2.053e-05 0.1252 -3.446e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002877 -0.002569 -0.01018 0.007763 0.9682 0.9729 0.005534 0.8737 0.875 0.01942 ] Network output: [ 1.074 -0.4482 0.001337 0.0001602 -7.193e-05 0.2989 0.0001207 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2102 -0.01342 -0.2137 0.2849 0.9837 0.9933 0.2348 0.6591 0.9292 0.6716 ] Network output: [ 0.003131 0.9095 0.9822 -6.678e-05 2.998e-05 0.1018 -5.033e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003871 0.00136 0.002377 0.004932 0.99 0.993 0.00394 0.9167 0.9435 0.01029 ] Network output: [ 0.1335 -0.7013 0.9031 0.0001325 -5.949e-05 1.532 9.987e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2211 0.1546 0.3064 0.2968 0.9853 0.9941 0.2217 0.6666 0.9325 0.6436 ] Network output: [ -0.03341 0.2067 1.077 9.814e-05 -4.406e-05 0.7833 7.396e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06309 0.05966 0.1481 0.1469 0.9885 0.993 0.06313 0.8855 0.9319 0.1904 ] Network output: [ -0.03497 0.2058 1.052 9.531e-05 -4.279e-05 0.8127 7.183e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07631 0.07559 0.1543 0.1467 0.9849 0.9912 0.07632 0.8241 0.909 0.1741 ] Network output: [ 0.02426 0.9519 -0.02181 6.278e-05 -2.819e-05 1.022 4.731e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1918 Epoch 5265 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01421 0.9558 0.9642 -9.479e-05 4.256e-05 0.05123 -7.144e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002948 -0.002467 -0.009567 0.005497 0.9682 0.9728 0.005639 0.8732 0.8698 0.01874 ] Network output: [ 0.8314 0.4715 0.08979 -0.0002558 0.0001148 -0.2252 -0.0001927 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2177 0.006087 -0.1643 0.1159 0.9836 0.9933 0.2429 0.6668 0.9252 0.6559 ] Network output: [ 0.007186 0.9242 0.9772 -7.126e-05 3.199e-05 0.08392 -5.371e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004193 0.001442 0.002569 0.001937 0.99 0.9929 0.004267 0.9164 0.9415 0.01102 ] Network output: [ -0.04627 0.5248 0.876 -0.0004061 0.0001823 0.69 -0.0003061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.241 0.168 0.3087 0.0544 0.9853 0.9941 0.2418 0.67 0.9318 0.6598 ] Network output: [ -0.01734 0.2315 1.064 0.0001087 -4.88e-05 0.74 8.191e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05961 0.05611 0.1334 0.1181 0.9888 0.993 0.05964 0.877 0.9312 0.1834 ] Network output: [ -0.01155 0.0756 1.055 0.0001524 -6.843e-05 0.8932 0.0001149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07332 0.07254 0.1516 0.1459 0.9847 0.9911 0.07333 0.8103 0.909 0.1785 ] Network output: [ 0.02845 0.8394 0.005413 9.333e-05 -4.19e-05 1.099 7.034e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1286 Epoch 5266 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03622 0.8426 0.9602 -4.231e-05 1.899e-05 0.1246 -3.189e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002895 -0.002582 -0.01005 0.007748 0.9682 0.9729 0.005563 0.8701 0.8718 0.01938 ] Network output: [ 1.067 -0.4339 0.007386 0.0001355 -6.085e-05 0.2938 0.0001022 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.01371 -0.2054 0.2812 0.9836 0.9933 0.2377 0.6462 0.9262 0.6675 ] Network output: [ 0.003003 0.9087 0.9831 -6.116e-05 2.745e-05 0.102 -4.609e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00394 0.001375 0.002496 0.005013 0.9898 0.9929 0.00401 0.9129 0.9409 0.01039 ] Network output: [ 0.1322 -0.6868 0.8999 0.0001236 -5.547e-05 1.523 9.312e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2246 0.1567 0.3079 0.2961 0.9852 0.9941 0.2253 0.6536 0.9297 0.6399 ] Network output: [ -0.0349 0.1997 1.08 0.0001031 -4.629e-05 0.7906 7.771e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06391 0.06041 0.1498 0.1488 0.9884 0.9929 0.06394 0.8806 0.929 0.1925 ] Network output: [ -0.03622 0.2036 1.054 9.914e-05 -4.451e-05 0.8157 7.471e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07703 0.07629 0.1552 0.148 0.9848 0.9912 0.07704 0.8174 0.9056 0.1754 ] Network output: [ 0.01855 0.9645 -0.02023 5.733e-05 -2.574e-05 1.019 4.321e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1839 Epoch 5267 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01513 0.9531 0.9647 -8.899e-05 3.995e-05 0.05162 -6.706e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002972 -0.002486 -0.009459 0.005597 0.9682 0.9728 0.005679 0.8697 0.8666 0.01867 ] Network output: [ 0.8435 0.427 0.08446 -0.0002473 0.000111 -0.1995 -0.0001864 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2209 0.005939 -0.1611 0.1241 0.9836 0.9933 0.2465 0.6544 0.9221 0.6515 ] Network output: [ 0.00684 0.9258 0.9782 -6.655e-05 2.987e-05 0.08209 -5.015e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004256 0.001493 0.002647 0.002162 0.9899 0.9929 0.004331 0.9129 0.939 0.011 ] Network output: [ -0.0367 0.4649 0.8733 -0.0003746 0.0001682 0.7337 -0.0002823 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.244 0.1712 0.3094 0.06925 0.9852 0.9941 0.2447 0.6579 0.9289 0.6544 ] Network output: [ -0.02069 0.2308 1.067 0.0001093 -4.907e-05 0.7436 8.238e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06047 0.057 0.1359 0.1205 0.9887 0.9929 0.06051 0.8729 0.9284 0.1855 ] Network output: [ -0.01522 0.08803 1.057 0.0001487 -6.677e-05 0.8858 0.0001121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07399 0.07322 0.1528 0.146 0.9846 0.991 0.074 0.8047 0.9058 0.1793 ] Network output: [ 0.0156 0.9001 0.007427 6.669e-05 -2.994e-05 1.062 5.026e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1057 Epoch 5268 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03645 0.8443 0.9608 -3.911e-05 1.756e-05 0.1218 -2.947e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002905 -0.002576 -0.009806 0.007619 0.9682 0.9729 0.005575 0.867 0.8688 0.0192 ] Network output: [ 1.045 -0.3981 0.02428 0.0001025 -4.6e-05 0.2847 7.722e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2148 -0.01223 -0.1916 0.2725 0.9835 0.9933 0.2399 0.6358 0.9234 0.6615 ] Network output: [ 0.005572 0.9062 0.9818 -5.48e-05 2.46e-05 0.1006 -4.13e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004009 0.00142 0.002684 0.00499 0.9897 0.9928 0.004081 0.9097 0.9387 0.01048 ] Network output: [ 0.1219 -0.634 0.899 9.866e-05 -4.429e-05 1.492 7.435e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2288 0.1605 0.3123 0.2867 0.9852 0.9941 0.2295 0.6432 0.9271 0.6352 ] Network output: [ -0.03313 0.1951 1.08 0.0001084 -4.867e-05 0.792 8.17e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06453 0.06105 0.1505 0.1487 0.9883 0.9928 0.06457 0.8765 0.9266 0.1935 ] Network output: [ -0.03371 0.2007 1.052 0.0001045 -4.692e-05 0.815 7.876e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07762 0.07688 0.1552 0.1479 0.9848 0.9911 0.07763 0.8117 0.9028 0.1759 ] Network output: [ 0.0159 0.9877 -0.0215 4.931e-05 -2.213e-05 1.002 3.716e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1629 Epoch 5269 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0186 0.9433 0.9638 -8.054e-05 3.616e-05 0.05539 -6.07e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002987 -0.002498 -0.009382 0.00572 0.9682 0.9729 0.005701 0.8669 0.8643 0.0186 ] Network output: [ 0.8622 0.3678 0.07489 -0.000227 0.0001019 -0.1679 -0.0001711 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2237 0.005781 -0.1605 0.1346 0.9835 0.9932 0.2496 0.6442 0.9198 0.6476 ] Network output: [ 0.00813 0.9224 0.9783 -6.057e-05 2.719e-05 0.08278 -4.565e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004289 0.001545 0.002688 0.002421 0.9898 0.9928 0.004365 0.9101 0.9371 0.01094 ] Network output: [ -0.02329 0.3804 0.8713 -0.0003305 0.0001484 0.7936 -0.0002491 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2459 0.1742 0.3095 0.08775 0.9852 0.9941 0.2467 0.6484 0.9266 0.6491 ] Network output: [ -0.02243 0.2228 1.071 0.0001116 -5.01e-05 0.7513 8.41e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06108 0.0577 0.1384 0.1236 0.9886 0.9929 0.06112 0.8703 0.9264 0.1876 ] Network output: [ -0.01757 0.09426 1.06 0.0001461 -6.56e-05 0.8819 0.0001101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07442 0.07367 0.1542 0.1467 0.9846 0.991 0.07443 0.8013 0.9034 0.1803 ] Network output: [ 0.007229 0.9362 0.01061 4.869e-05 -2.186e-05 1.039 3.669e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08046 Epoch 5270 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03708 0.8483 0.9604 -3.74e-05 1.679e-05 0.117 -2.818e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00292 -0.002564 -0.009649 0.007426 0.9682 0.9729 0.005592 0.8649 0.8664 0.01908 ] Network output: [ 1.024 -0.329 0.03321 6.425e-05 -2.885e-05 0.2484 4.842e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2175 -0.009629 -0.1821 0.2585 0.9835 0.9933 0.2428 0.6291 0.9212 0.657 ] Network output: [ 0.008292 0.9028 0.9804 -4.939e-05 2.217e-05 0.09998 -3.722e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00407 0.001476 0.002801 0.004832 0.9897 0.9928 0.004142 0.9077 0.9371 0.01059 ] Network output: [ 0.1065 -0.5416 0.8964 6.097e-05 -2.737e-05 1.432 4.595e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2331 0.165 0.3148 0.269 0.9851 0.9941 0.2338 0.6368 0.9253 0.6339 ] Network output: [ -0.03121 0.1851 1.081 0.0001138 -5.107e-05 0.797 8.574e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06457 0.06116 0.1513 0.1482 0.9883 0.9928 0.06461 0.8739 0.9251 0.1953 ] Network output: [ -0.03117 0.1842 1.054 0.0001117 -5.013e-05 0.825 8.415e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07766 0.07694 0.1563 0.1488 0.9848 0.9911 0.07767 0.808 0.9012 0.1779 ] Network output: [ 0.009883 1.006 -0.01615 3.685e-05 -1.654e-05 0.99 2.777e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1265 Epoch 5271 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02321 0.9304 0.9618 -7.111e-05 3.193e-05 0.06107 -5.359e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002997 -0.002505 -0.009408 0.005841 0.9682 0.9729 0.005713 0.8651 0.8628 0.01863 ] Network output: [ 0.8846 0.3188 0.05933 -0.0002036 9.138e-05 -0.1482 -0.0001534 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2259 0.005504 -0.1642 0.1431 0.9835 0.9932 0.252 0.6373 0.9184 0.6466 ] Network output: [ 0.009948 0.9153 0.9783 -5.418e-05 2.432e-05 0.08634 -4.083e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004291 0.001574 0.002672 0.002633 0.9897 0.9928 0.004367 0.9084 0.9358 0.0109 ] Network output: [ -0.01017 0.3014 0.87 -0.0002897 0.0001301 0.8478 -0.0002183 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2466 0.1759 0.3085 0.1038 0.9852 0.9941 0.2474 0.6423 0.9252 0.6471 ] Network output: [ -0.02332 0.206 1.076 0.0001159 -5.203e-05 0.7654 8.734e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06124 0.05793 0.1411 0.1272 0.9885 0.9928 0.06128 0.8691 0.9253 0.1905 ] Network output: [ -0.01925 0.08765 1.063 0.0001464 -6.574e-05 0.8881 0.0001104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07445 0.07372 0.1564 0.1487 0.9846 0.991 0.07446 0.7999 0.9021 0.1825 ] Network output: [ 0.001533 0.9438 0.01682 3.959e-05 -1.777e-05 1.036 2.983e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06147 Epoch 5272 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03799 0.8533 0.9589 -3.619e-05 1.625e-05 0.1116 -2.727e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002937 -0.002556 -0.009632 0.007248 0.9683 0.9729 0.005615 0.8637 0.8647 0.01908 ] Network output: [ 1.012 -0.2486 0.03015 3.196e-05 -1.435e-05 0.1951 2.409e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2202 -0.007116 -0.1797 0.2437 0.9835 0.9932 0.2458 0.6256 0.9197 0.6558 ] Network output: [ 0.01023 0.8994 0.9795 -4.52e-05 2.029e-05 0.1004 -3.406e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004108 0.001515 0.002819 0.004613 0.9897 0.9928 0.004181 0.9068 0.936 0.01071 ] Network output: [ 0.09171 -0.4419 0.893 2.21e-05 -9.921e-06 1.366 1.665e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2361 0.1683 0.3149 0.2495 0.9851 0.9941 0.2368 0.6336 0.9243 0.6366 ] Network output: [ -0.0302 0.1704 1.084 0.0001188 -5.333e-05 0.8067 8.952e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06408 0.06075 0.1523 0.1479 0.9883 0.9928 0.06412 0.8726 0.9244 0.1981 ] Network output: [ -0.02988 0.158 1.058 0.0001193 -5.357e-05 0.8441 8.992e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07717 0.07646 0.1586 0.1508 0.9848 0.9911 0.07718 0.8061 0.9006 0.1815 ] Network output: [ 0.001746 1.015 -0.005801 2.418e-05 -1.085e-05 0.9876 1.822e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09082 Epoch 5273 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02748 0.9191 0.9593 -6.257e-05 2.809e-05 0.06639 -4.716e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003 -0.00251 -0.009502 0.005959 0.9683 0.9729 0.005715 0.8642 0.862 0.01875 ] Network output: [ 0.9051 0.2819 0.04377 -0.0001807 8.11e-05 -0.1366 -0.0001362 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2271 0.005051 -0.1695 0.1497 0.9835 0.9932 0.2533 0.6333 0.9177 0.6486 ] Network output: [ 0.01159 0.9085 0.9778 -4.873e-05 2.188e-05 0.09034 -3.673e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004273 0.001581 0.002645 0.002795 0.9897 0.9927 0.004349 0.9076 0.9352 0.01093 ] Network output: [ -0.0004679 0.2382 0.8707 -0.0002589 0.0001162 0.891 -0.0001951 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2462 0.1762 0.308 0.1159 0.9851 0.9941 0.2469 0.6389 0.9244 0.6484 ] Network output: [ -0.02387 0.1862 1.08 0.0001207 -5.418e-05 0.782 9.095e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06109 0.05784 0.1438 0.1308 0.9885 0.9928 0.06113 0.8688 0.9246 0.1941 ] Network output: [ -0.02051 0.07478 1.067 0.0001483 -6.656e-05 0.8995 0.0001117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07423 0.07352 0.159 0.1513 0.9847 0.991 0.07424 0.7997 0.9015 0.1854 ] Network output: [ -0.002938 0.943 0.023 3.355e-05 -1.506e-05 1.04 2.528e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04859 Epoch 5274 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03904 0.8583 0.9568 -3.503e-05 1.573e-05 0.1066 -2.64e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002949 -0.00255 -0.009698 0.007117 0.9683 0.9729 0.005632 0.8632 0.8637 0.01915 ] Network output: [ 1.006 -0.1774 0.02184 1.007e-05 -4.519e-06 0.143 7.587e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2221 -0.005245 -0.1815 0.2313 0.9835 0.9932 0.2478 0.6242 0.9188 0.6572 ] Network output: [ 0.01157 0.8972 0.9785 -4.22e-05 1.895e-05 0.101 -3.18e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004123 0.001531 0.002788 0.004409 0.9897 0.9927 0.004196 0.9065 0.9354 0.01083 ] Network output: [ 0.07971 -0.3577 0.8906 -1.104e-05 4.956e-06 1.308 -8.319e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2375 0.1699 0.3141 0.2327 0.9851 0.9941 0.2382 0.6325 0.9237 0.6414 ] Network output: [ -0.02971 0.1555 1.087 0.0001229 -5.519e-05 0.8175 9.266e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06335 0.06008 0.1534 0.148 0.9883 0.9928 0.06339 0.872 0.924 0.2011 ] Network output: [ -0.02934 0.1312 1.063 0.0001261 -5.659e-05 0.8647 9.5e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07643 0.07574 0.1613 0.1533 0.9848 0.9911 0.07644 0.8053 0.9003 0.1853 ] Network output: [ -0.005428 1.017 0.004325 1.403e-05 -6.298e-06 0.9897 1.057e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06518 Epoch 5275 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03089 0.911 0.9567 -5.58e-05 2.505e-05 0.07025 -4.205e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002998 -0.002511 -0.009612 0.006077 0.9683 0.9729 0.005707 0.8638 0.8617 0.01891 ] Network output: [ 0.9209 0.2511 0.03216 -0.0001588 7.129e-05 -0.1258 -0.0001197 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2273 0.00445 -0.1742 0.1554 0.9835 0.9932 0.2535 0.6311 0.9172 0.6522 ] Network output: [ 0.01288 0.9042 0.9768 -4.49e-05 2.016e-05 0.09305 -3.384e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004245 0.001574 0.002635 0.002932 0.9897 0.9927 0.00432 0.9074 0.9348 0.01101 ] Network output: [ 0.005989 0.1872 0.8733 -0.0002366 0.0001062 0.9265 -0.0001783 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.245 0.1756 0.3084 0.1253 0.9852 0.9941 0.2458 0.6372 0.9239 0.6514 ] Network output: [ -0.02421 0.1685 1.083 0.0001246 -5.594e-05 0.7971 9.391e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06082 0.0576 0.1463 0.1341 0.9885 0.9928 0.06086 0.869 0.9243 0.1976 ] Network output: [ -0.02139 0.06281 1.07 0.0001499 -6.729e-05 0.9102 0.000113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07393 0.07323 0.1615 0.1538 0.9847 0.9911 0.07394 0.8001 0.9011 0.1884 ] Network output: [ -0.006384 0.9464 0.02678 2.749e-05 -1.234e-05 1.04 2.071e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03952 Epoch 5276 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04001 0.8633 0.9545 -3.414e-05 1.533e-05 0.102 -2.573e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002955 -0.002543 -0.009782 0.007031 0.9683 0.9729 0.005638 0.8631 0.8631 0.01925 ] Network output: [ 1.003 -0.1226 0.0144 -2.791e-06 1.253e-06 0.102 -2.104e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2229 -0.003972 -0.1842 0.2221 0.9835 0.9932 0.2487 0.6239 0.9182 0.6598 ] Network output: [ 0.01261 0.8965 0.9773 -4.038e-05 1.813e-05 0.1008 -3.043e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004121 0.001532 0.002756 0.004251 0.9897 0.9928 0.004194 0.9067 0.9351 0.01095 ] Network output: [ 0.07025 -0.2944 0.89 -3.726e-05 1.673e-05 1.264 -2.808e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2377 0.1703 0.3137 0.2198 0.9851 0.9941 0.2384 0.6323 0.9234 0.6466 ] Network output: [ -0.02924 0.1435 1.089 0.0001259 -5.651e-05 0.8263 9.486e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06262 0.05938 0.1544 0.1482 0.9884 0.9928 0.06265 0.8719 0.9238 0.2038 ] Network output: [ -0.02889 0.1094 1.067 0.0001311 -5.886e-05 0.8817 9.88e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07569 0.075 0.1636 0.1554 0.9848 0.9912 0.0757 0.805 0.9001 0.1887 ] Network output: [ -0.01028 1.019 0.01132 6.448e-06 -2.895e-06 0.9904 4.86e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04925 Epoch 5277 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03348 0.906 0.9542 -5.086e-05 2.283e-05 0.07263 -3.833e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00299 -0.002509 -0.009706 0.006188 0.9683 0.9729 0.005691 0.8639 0.8616 0.01905 ] Network output: [ 0.9323 0.2225 0.02506 -0.0001382 6.202e-05 -0.1128 -0.0001041 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2266 0.003806 -0.1776 0.1609 0.9835 0.9932 0.2527 0.63 0.9171 0.6562 ] Network output: [ 0.0139 0.9023 0.9756 -4.264e-05 1.914e-05 0.09417 -3.213e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004213 0.001561 0.002644 0.003055 0.9898 0.9928 0.004288 0.9075 0.9347 0.0111 ] Network output: [ 0.0102 0.144 0.8773 -0.0002198 9.87e-05 0.9574 -0.0001657 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2434 0.1744 0.3098 0.1331 0.9852 0.9941 0.2442 0.6365 0.9237 0.6548 ] Network output: [ -0.0243 0.1549 1.085 0.0001272 -5.712e-05 0.8087 9.588e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06054 0.05734 0.1485 0.1369 0.9885 0.9928 0.06057 0.8694 0.9241 0.2007 ] Network output: [ -0.02186 0.05467 1.072 0.0001506 -6.76e-05 0.9177 0.0001135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07364 0.07295 0.1636 0.1558 0.9847 0.9911 0.07365 0.8008 0.9009 0.1909 ] Network output: [ -0.008549 0.9554 0.02778 2.139e-05 -9.601e-06 1.034 1.612e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0328 Epoch 5278 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04079 0.8683 0.9522 -3.376e-05 1.516e-05 0.0978 -2.545e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002955 -0.002535 -0.009855 0.006974 0.9683 0.9729 0.005634 0.8634 0.8629 0.01934 ] Network output: [ 1 -0.08247 0.009714 -9.689e-06 4.35e-06 0.07264 -7.302e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2229 -0.003075 -0.1864 0.2156 0.9835 0.9932 0.2486 0.6244 0.9179 0.6628 ] Network output: [ 0.01348 0.8971 0.9759 -3.961e-05 1.778e-05 0.09987 -2.985e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004111 0.001527 0.002738 0.004134 0.9897 0.9928 0.004183 0.9071 0.935 0.01106 ] Network output: [ 0.06249 -0.2481 0.8909 -5.786e-05 2.597e-05 1.232 -4.36e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2373 0.17 0.314 0.2102 0.9852 0.9941 0.238 0.6329 0.9233 0.6514 ] Network output: [ -0.02863 0.1349 1.09 0.0001276 -5.73e-05 0.8325 9.618e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06195 0.05874 0.1552 0.1484 0.9884 0.9928 0.06198 0.8719 0.9237 0.2059 ] Network output: [ -0.02826 0.09357 1.07 0.0001345 -6.039e-05 0.894 0.0001014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07502 0.07434 0.1654 0.1571 0.9848 0.9912 0.07503 0.805 0.9001 0.1913 ] Network output: [ -0.01282 1.022 0.01512 1.163e-06 -5.219e-07 0.9887 8.762e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03963 Epoch 5279 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03541 0.9031 0.952 -4.749e-05 2.132e-05 0.07388 -3.579e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002979 -0.002505 -0.009783 0.006287 0.9683 0.9729 0.005671 0.8642 0.8618 0.01918 ] Network output: [ 0.9405 0.1968 0.02091 -0.0001193 5.357e-05 -0.09926 -8.993e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2255 0.003218 -0.1802 0.1659 0.9835 0.9932 0.2514 0.6298 0.9171 0.6601 ] Network output: [ 0.01468 0.9021 0.9742 -4.161e-05 1.868e-05 0.09419 -3.136e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004181 0.001547 0.002662 0.003162 0.9898 0.9928 0.004254 0.9079 0.9348 0.01118 ] Network output: [ 0.01298 0.1073 0.8818 -0.0002068 9.286e-05 0.9841 -0.0001559 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2417 0.1732 0.3115 0.1397 0.9852 0.9941 0.2424 0.6366 0.9236 0.6581 ] Network output: [ -0.0242 0.1449 1.087 0.0001287 -5.778e-05 0.8174 9.699e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06025 0.05707 0.1503 0.1392 0.9885 0.9929 0.06028 0.87 0.924 0.2033 ] Network output: [ -0.02201 0.04971 1.073 0.0001505 -6.754e-05 0.9223 0.0001134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07337 0.07268 0.1651 0.1573 0.9848 0.9911 0.07338 0.8018 0.9008 0.1929 ] Network output: [ -0.009465 0.9658 0.02702 1.629e-05 -7.312e-06 1.026 1.227e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02795 Epoch 5280 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04132 0.873 0.9503 -3.396e-05 1.525e-05 0.09393 -2.559e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00295 -0.002527 -0.009917 0.006933 0.9683 0.9729 0.005624 0.8639 0.8629 0.01942 ] Network output: [ 0.9971 -0.05227 0.006921 -1.319e-05 5.922e-06 0.05113 -9.941e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2224 -0.002402 -0.1882 0.2109 0.9835 0.9932 0.2481 0.6254 0.9178 0.6657 ] Network output: [ 0.01415 0.8986 0.9745 -3.965e-05 1.78e-05 0.09847 -2.988e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004095 0.001518 0.002727 0.004043 0.9898 0.9928 0.004167 0.9076 0.935 0.01115 ] Network output: [ 0.05594 -0.2133 0.8927 -7.441e-05 3.341e-05 1.208 -5.608e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2365 0.1694 0.3147 0.2028 0.9852 0.9941 0.2372 0.6339 0.9234 0.6557 ] Network output: [ -0.02795 0.1289 1.091 0.0001285 -5.768e-05 0.8368 9.683e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06134 0.05816 0.1558 0.1486 0.9884 0.9928 0.06138 0.8722 0.9238 0.2076 ] Network output: [ -0.02752 0.08188 1.071 0.0001367 -6.139e-05 0.9029 0.0001031 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07443 0.07375 0.1666 0.1583 0.9849 0.9912 0.07444 0.8054 0.9002 0.1933 ] Network output: [ -0.01369 1.024 0.01694 -1.984e-06 8.905e-07 0.9866 -1.495e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03348 Epoch 5281 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03682 0.9017 0.9501 -4.538e-05 2.037e-05 0.07436 -3.42e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002968 -0.0025 -0.00985 0.006371 0.9683 0.9729 0.00565 0.8646 0.8621 0.01928 ] Network output: [ 0.9469 0.1757 0.01789 -0.0001028 4.614e-05 -0.08788 -7.746e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2242 0.002711 -0.1826 0.1702 0.9835 0.9932 0.2501 0.6301 0.9172 0.6637 ] Network output: [ 0.01519 0.9028 0.973 -4.148e-05 1.862e-05 0.09367 -3.126e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004148 0.001532 0.002675 0.003248 0.9898 0.9928 0.004221 0.9084 0.9349 0.01126 ] Network output: [ 0.01489 0.07737 0.8859 -0.0001969 8.839e-05 1.006 -0.0001484 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.24 0.1719 0.3131 0.145 0.9852 0.9941 0.2407 0.6371 0.9237 0.6612 ] Network output: [ -0.02403 0.1374 1.087 0.0001294 -5.808e-05 0.8239 9.75e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05994 0.05678 0.1517 0.141 0.9885 0.9929 0.05998 0.8708 0.9241 0.2053 ] Network output: [ -0.02204 0.04617 1.073 0.0001499 -6.729e-05 0.9257 0.000113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07307 0.07239 0.1663 0.1585 0.9848 0.9911 0.07308 0.8028 0.9008 0.1945 ] Network output: [ -0.009536 0.9741 0.02573 1.278e-05 -5.737e-06 1.019 9.63e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02465 Epoch 5282 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0416 0.8776 0.9486 -3.464e-05 1.555e-05 0.09041 -2.611e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002944 -0.002518 -0.009974 0.006904 0.9683 0.973 0.005612 0.8645 0.863 0.01949 ] Network output: [ 0.9947 -0.02809 0.004767 -1.47e-05 6.599e-06 0.03391 -1.108e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2217 -0.001903 -0.19 0.2073 0.9835 0.9932 0.2473 0.6267 0.9179 0.6686 ] Network output: [ 0.01455 0.9004 0.9733 -4.031e-05 1.81e-05 0.09697 -3.038e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004075 0.001507 0.002714 0.003969 0.9898 0.9928 0.004147 0.9082 0.9352 0.01122 ] Network output: [ 0.05046 -0.1858 0.8946 -8.806e-05 3.953e-05 1.19 -6.636e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2356 0.1687 0.3153 0.1968 0.9852 0.9941 0.2363 0.6352 0.9236 0.6597 ] Network output: [ -0.02734 0.1243 1.091 0.0001288 -5.781e-05 0.84 9.705e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06078 0.05761 0.1562 0.1488 0.9885 0.9929 0.06081 0.8727 0.9239 0.209 ] Network output: [ -0.02687 0.0725 1.072 0.0001382 -6.204e-05 0.91 0.0001041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07388 0.0732 0.1676 0.1594 0.9849 0.9912 0.07389 0.8059 0.9004 0.1949 ] Network output: [ -0.01368 1.024 0.01795 -3.407e-06 1.529e-06 0.9856 -2.567e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02922 Epoch 5283 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03778 0.9014 0.9485 -4.422e-05 1.985e-05 0.0743 -3.333e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002957 -0.002494 -0.009917 0.006439 0.9683 0.973 0.005629 0.8652 0.8624 0.01937 ] Network output: [ 0.9523 0.1598 0.01506 -8.849e-05 3.973e-05 -0.07984 -6.669e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.223 0.002261 -0.1851 0.1737 0.9835 0.9932 0.2486 0.6309 0.9175 0.6672 ] Network output: [ 0.01542 0.9041 0.972 -4.198e-05 1.885e-05 0.09294 -3.164e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004116 0.001516 0.002677 0.003312 0.9898 0.9928 0.004188 0.9089 0.9352 0.01132 ] Network output: [ 0.01627 0.05404 0.8896 -0.0001896 8.511e-05 1.023 -0.0001429 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2383 0.1705 0.3142 0.149 0.9852 0.9941 0.239 0.6381 0.9239 0.6644 ] Network output: [ -0.0239 0.1313 1.088 0.0001296 -5.816e-05 0.8292 9.763e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05961 0.05646 0.1527 0.1425 0.9886 0.9929 0.05965 0.8715 0.9243 0.2071 ] Network output: [ -0.0221 0.04286 1.073 0.0001492 -6.698e-05 0.9288 0.0001124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07276 0.07207 0.1672 0.1596 0.9848 0.9912 0.07277 0.8039 0.901 0.1959 ] Network output: [ -0.009249 0.9793 0.0246 1.067e-05 -4.789e-06 1.015 8.04e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02247 Epoch 5284 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04165 0.882 0.9473 -3.565e-05 1.6e-05 0.08726 -2.686e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002937 -0.002509 -0.01003 0.006884 0.9683 0.973 0.005598 0.8652 0.8633 0.01955 ] Network output: [ 0.9931 -0.008232 0.002691 -1.473e-05 6.612e-06 0.01938 -1.11e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2209 -0.00157 -0.1922 0.2045 0.9835 0.9933 0.2464 0.6282 0.9181 0.6716 ] Network output: [ 0.01467 0.9025 0.9724 -4.139e-05 1.858e-05 0.09553 -3.12e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004054 0.001494 0.002696 0.003906 0.9898 0.9928 0.004126 0.9089 0.9354 0.01129 ] Network output: [ 0.04599 -0.1636 0.8964 -9.945e-05 4.465e-05 1.175 -7.495e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2345 0.1677 0.3157 0.192 0.9852 0.9941 0.2352 0.6366 0.9238 0.6635 ] Network output: [ -0.02687 0.1205 1.091 0.0001287 -5.777e-05 0.8427 9.698e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06025 0.0571 0.1565 0.149 0.9885 0.9929 0.06028 0.8732 0.9242 0.2102 ] Network output: [ -0.0264 0.06443 1.073 0.0001391 -6.247e-05 0.9164 0.0001049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07336 0.07268 0.1685 0.1603 0.9849 0.9912 0.07337 0.8066 0.9007 0.1964 ] Network output: [ -0.01335 1.022 0.01869 -3.759e-06 1.688e-06 0.9859 -2.833e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02613 Epoch 5285 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03837 0.9019 0.9473 -4.375e-05 1.964e-05 0.07388 -3.297e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002945 -0.002489 -0.009985 0.006498 0.9683 0.973 0.005608 0.8658 0.8629 0.01946 ] Network output: [ 0.9569 0.148 0.01233 -7.601e-05 3.412e-05 -0.07452 -5.728e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2217 0.001836 -0.1878 0.1766 0.9835 0.9933 0.2472 0.6318 0.9178 0.6707 ] Network output: [ 0.01539 0.9057 0.9713 -4.293e-05 1.927e-05 0.09213 -3.235e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004085 0.001499 0.002669 0.003359 0.9898 0.9928 0.004157 0.9096 0.9355 0.01138 ] Network output: [ 0.01726 0.03624 0.8927 -0.0001845 8.281e-05 1.036 -0.000139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2366 0.1691 0.3149 0.1521 0.9852 0.9941 0.2373 0.6392 0.9242 0.6676 ] Network output: [ -0.02384 0.1264 1.088 0.0001294 -5.808e-05 0.8336 9.75e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05927 0.05613 0.1535 0.1437 0.9886 0.9929 0.0593 0.8723 0.9245 0.2086 ] Network output: [ -0.02223 0.03958 1.073 0.0001484 -6.663e-05 0.9321 0.0001119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07242 0.07174 0.1681 0.1606 0.9849 0.9912 0.07243 0.805 0.9012 0.1972 ] Network output: [ -0.008904 0.9825 0.02373 9.409e-06 -4.224e-06 1.012 7.091e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02097 Epoch 5286 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04152 0.8861 0.9462 -3.683e-05 1.653e-05 0.08448 -2.775e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002929 -0.002502 -0.01009 0.006874 0.9683 0.973 0.005583 0.8659 0.8637 0.01962 ] Network output: [ 0.9921 0.007625 0.0007831 -1.342e-05 6.027e-06 0.007414 -1.012e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2199 -0.001389 -0.1945 0.2025 0.9835 0.9933 0.2453 0.6297 0.9184 0.6746 ] Network output: [ 0.01458 0.9048 0.9717 -4.276e-05 1.92e-05 0.09416 -3.223e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004032 0.001479 0.002675 0.003854 0.9898 0.9928 0.004103 0.9096 0.9357 0.01136 ] Network output: [ 0.04238 -0.1459 0.8982 -0.0001089 4.891e-05 1.163 -8.21e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2333 0.1666 0.3158 0.1881 0.9852 0.9941 0.234 0.6381 0.9242 0.6672 ] Network output: [ -0.02651 0.1175 1.091 0.0001283 -5.761e-05 0.845 9.67e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05976 0.05662 0.1567 0.1492 0.9885 0.9929 0.05979 0.8738 0.9244 0.2113 ] Network output: [ -0.02609 0.05752 1.073 0.0001397 -6.271e-05 0.9219 0.0001053 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07288 0.0722 0.1692 0.1612 0.9849 0.9912 0.07289 0.8074 0.9011 0.1977 ] Network output: [ -0.01294 1.02 0.0192 -3.617e-06 1.624e-06 0.9868 -2.726e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02387 Epoch 5287 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03867 0.9029 0.9463 -4.375e-05 1.964e-05 0.07326 -3.297e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002934 -0.002484 -0.01005 0.00655 0.9683 0.973 0.005589 0.8665 0.8634 0.01954 ] Network output: [ 0.9608 0.1387 0.009963 -6.483e-05 2.91e-05 -0.07066 -4.886e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2204 0.001424 -0.1906 0.179 0.9836 0.9933 0.2458 0.6329 0.9182 0.674 ] Network output: [ 0.01519 0.9075 0.9707 -4.418e-05 1.983e-05 0.09124 -3.329e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004056 0.00148 0.002657 0.003395 0.9899 0.9928 0.004127 0.9102 0.9358 0.01144 ] Network output: [ 0.01794 0.02257 0.8954 -0.000181 8.124e-05 1.045 -0.0001364 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2349 0.1676 0.3153 0.1544 0.9852 0.9941 0.2356 0.6404 0.9245 0.6708 ] Network output: [ -0.02379 0.1225 1.088 0.0001289 -5.789e-05 0.8372 9.718e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05893 0.05579 0.1541 0.1448 0.9886 0.9929 0.05896 0.8731 0.9247 0.2099 ] Network output: [ -0.02238 0.03657 1.074 0.0001476 -6.626e-05 0.9351 0.0001112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07209 0.07141 0.1688 0.1614 0.9849 0.9912 0.0721 0.8061 0.9015 0.1983 ] Network output: [ -0.008572 0.9848 0.02294 8.574e-06 -3.849e-06 1.009 6.461e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01988 Epoch 5288 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04128 0.8898 0.9454 -3.809e-05 1.71e-05 0.08206 -2.87e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002921 -0.002495 -0.01015 0.006871 0.9684 0.973 0.005567 0.8666 0.8641 0.01969 ] Network output: [ 0.9914 0.01963 -0.000719 -1.096e-05 4.92e-06 -0.001839 -8.259e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2189 -0.001326 -0.1968 0.2011 0.9836 0.9933 0.2441 0.6312 0.9187 0.6776 ] Network output: [ 0.01436 0.9071 0.9712 -4.431e-05 1.989e-05 0.09284 -3.339e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004009 0.001463 0.002653 0.003813 0.9899 0.9929 0.00408 0.9103 0.936 0.01141 ] Network output: [ 0.03945 -0.1321 0.8999 -0.0001168 5.243e-05 1.153 -8.802e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.232 0.1654 0.3159 0.185 0.9852 0.9941 0.2327 0.6396 0.9245 0.6708 ] Network output: [ -0.02621 0.1151 1.091 0.0001277 -5.735e-05 0.8469 9.627e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05931 0.05617 0.1568 0.1494 0.9886 0.9929 0.05934 0.8744 0.9247 0.2122 ] Network output: [ -0.02586 0.05183 1.074 0.0001399 -6.279e-05 0.9267 0.0001054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07243 0.07176 0.1698 0.162 0.9849 0.9912 0.07244 0.8082 0.9014 0.1989 ] Network output: [ -0.01248 1.018 0.01939 -3.284e-06 1.474e-06 0.9875 -2.475e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02222 Epoch 5289 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03878 0.9042 0.9455 -4.408e-05 1.979e-05 0.07255 -3.322e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002923 -0.002478 -0.01011 0.006596 0.9684 0.973 0.005569 0.8672 0.8639 0.01962 ] Network output: [ 0.964 0.1309 0.008143 -5.463e-05 2.453e-05 -0.06737 -4.117e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2191 0.001033 -0.1932 0.1811 0.9836 0.9933 0.2443 0.6341 0.9186 0.6773 ] Network output: [ 0.01489 0.9095 0.9703 -4.562e-05 2.048e-05 0.0903 -3.438e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004027 0.001462 0.002642 0.003422 0.9899 0.9929 0.004098 0.9109 0.9361 0.01149 ] Network output: [ 0.01834 0.01193 0.8979 -0.0001786 8.02e-05 1.053 -0.0001346 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2333 0.1661 0.3156 0.1561 0.9852 0.9941 0.234 0.6417 0.9248 0.6739 ] Network output: [ -0.02372 0.1196 1.088 0.0001283 -5.761e-05 0.84 9.67e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0586 0.05546 0.1545 0.1456 0.9886 0.993 0.05863 0.8738 0.925 0.211 ] Network output: [ -0.02251 0.03404 1.074 0.0001467 -6.585e-05 0.9377 0.0001105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07176 0.07108 0.1694 0.1622 0.9849 0.9912 0.07177 0.8071 0.9018 0.1993 ] Network output: [ -0.008215 0.987 0.02209 8.006e-06 -3.594e-06 1.007 6.033e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01902 Epoch 5290 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04097 0.8932 0.9447 -3.939e-05 1.768e-05 0.07998 -2.968e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002912 -0.002488 -0.01021 0.006875 0.9684 0.973 0.005551 0.8673 0.8646 0.01975 ] Network output: [ 0.991 0.02831 -0.001756 -7.593e-06 3.409e-06 -0.008632 -5.723e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2177 -0.001338 -0.1991 0.2003 0.9836 0.9933 0.2429 0.6327 0.919 0.6806 ] Network output: [ 0.01406 0.9093 0.9708 -4.596e-05 2.063e-05 0.09158 -3.464e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003986 0.001446 0.002632 0.003781 0.9899 0.9929 0.004056 0.911 0.9363 0.01147 ] Network output: [ 0.03702 -0.1213 0.9016 -0.0001233 5.535e-05 1.145 -9.291e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2306 0.1641 0.3159 0.1825 0.9852 0.9941 0.2313 0.6411 0.9249 0.6741 ] Network output: [ -0.02592 0.1134 1.091 0.000127 -5.702e-05 0.8483 9.572e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05889 0.05576 0.1568 0.1496 0.9886 0.993 0.05893 0.875 0.925 0.213 ] Network output: [ -0.02566 0.04723 1.074 0.0001398 -6.275e-05 0.9306 0.0001053 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07202 0.07135 0.1703 0.1627 0.985 0.9912 0.07203 0.809 0.9017 0.1999 ] Network output: [ -0.01196 1.017 0.01928 -2.829e-06 1.27e-06 0.9879 -2.132e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02098 Epoch 5291 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03874 0.9057 0.9449 -4.465e-05 2.005e-05 0.07181 -3.365e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002912 -0.002473 -0.01017 0.006638 0.9684 0.973 0.00555 0.8679 0.8644 0.01969 ] Network output: [ 0.9666 0.1241 0.006814 -4.526e-05 2.032e-05 -0.0644 -3.411e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2177 0.0006736 -0.1958 0.183 0.9836 0.9933 0.2428 0.6353 0.919 0.6804 ] Network output: [ 0.01454 0.9115 0.9699 -4.719e-05 2.118e-05 0.08933 -3.556e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004 0.001444 0.002626 0.003443 0.9899 0.9929 0.00407 0.9116 0.9365 0.01154 ] Network output: [ 0.01852 0.003709 0.9002 -0.0001772 7.954e-05 1.058 -0.0001335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2317 0.1647 0.3157 0.1574 0.9853 0.9941 0.2324 0.643 0.9251 0.677 ] Network output: [ -0.0236 0.1174 1.088 0.0001275 -5.725e-05 0.8421 9.611e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05828 0.05514 0.1547 0.1463 0.9887 0.993 0.05831 0.8746 0.9253 0.212 ] Network output: [ -0.02258 0.03191 1.074 0.0001457 -6.542e-05 0.94 0.0001098 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07145 0.07077 0.1698 0.1629 0.9849 0.9912 0.07146 0.8081 0.9022 0.2002 ] Network output: [ -0.007801 0.9888 0.0212 7.705e-06 -3.459e-06 1.006 5.807e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01832 Epoch 5292 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04062 0.8962 0.9442 -4.07e-05 1.827e-05 0.07819 -3.068e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002902 -0.002481 -0.01026 0.006884 0.9684 0.973 0.005533 0.8681 0.865 0.01981 ] Network output: [ 0.9908 0.03448 -0.00245 -3.609e-06 1.62e-06 -0.01356 -2.72e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2165 -0.001395 -0.2013 0.1998 0.9836 0.9933 0.2415 0.6342 0.9194 0.6834 ] Network output: [ 0.01372 0.9115 0.9705 -4.769e-05 2.141e-05 0.09039 -3.594e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003963 0.001429 0.00261 0.003755 0.9899 0.9929 0.004033 0.9117 0.9367 0.01152 ] Network output: [ 0.035 -0.1127 0.9033 -0.0001287 5.777e-05 1.139 -9.698e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2293 0.1629 0.3158 0.1805 0.9853 0.9941 0.2299 0.6426 0.9252 0.6773 ] Network output: [ -0.02561 0.1122 1.09 0.0001262 -5.664e-05 0.8493 9.508e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05851 0.05538 0.1567 0.1497 0.9886 0.993 0.05854 0.8756 0.9253 0.2138 ] Network output: [ -0.02547 0.04345 1.074 0.0001395 -6.262e-05 0.9338 0.0001051 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07164 0.07097 0.1706 0.1633 0.985 0.9913 0.07165 0.8098 0.9021 0.2007 ] Network output: [ -0.01139 1.016 0.01898 -2.248e-06 1.009e-06 0.9882 -1.694e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02002 Epoch 5293 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03861 0.9071 0.9444 -4.541e-05 2.039e-05 0.07107 -3.422e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002901 -0.002467 -0.01023 0.006676 0.9684 0.973 0.005531 0.8686 0.865 0.01976 ] Network output: [ 0.9688 0.1183 0.005819 -3.663e-05 1.644e-05 -0.06182 -2.76e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2164 0.0003491 -0.1982 0.1847 0.9836 0.9933 0.2414 0.6366 0.9194 0.6834 ] Network output: [ 0.01415 0.9134 0.9697 -4.884e-05 2.193e-05 0.08839 -3.681e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003974 0.001427 0.002608 0.003458 0.9899 0.9929 0.004044 0.9122 0.9368 0.01159 ] Network output: [ 0.01855 -0.002439 0.9022 -0.0001763 7.916e-05 1.062 -0.0001329 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2301 0.1632 0.3157 0.1584 0.9853 0.9941 0.2308 0.6443 0.9255 0.6799 ] Network output: [ -0.02344 0.1157 1.088 0.0001267 -5.686e-05 0.8437 9.545e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05797 0.05483 0.1549 0.1469 0.9887 0.993 0.058 0.8753 0.9256 0.2128 ] Network output: [ -0.02261 0.03 1.074 0.0001448 -6.499e-05 0.942 0.0001091 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07114 0.07046 0.1701 0.1634 0.985 0.9912 0.07115 0.809 0.9025 0.201 ] Network output: [ -0.00735 0.99 0.02034 7.671e-06 -3.444e-06 1.004 5.781e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01775 Epoch 5294 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.04024 0.8989 0.9438 -4.204e-05 1.887e-05 0.07664 -3.168e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002892 -0.002475 -0.01031 0.006895 0.9684 0.973 0.005516 0.8688 0.8655 0.01986 ] Network output: [ 0.9907 0.03882 -0.002954 7.885e-07 -3.54e-07 -0.01717 5.942e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2153 -0.001481 -0.2035 0.1997 0.9836 0.9933 0.2402 0.6357 0.9198 0.6862 ] Network output: [ 0.01333 0.9136 0.9702 -4.945e-05 2.22e-05 0.08929 -3.727e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003941 0.001413 0.002588 0.003733 0.9899 0.9929 0.00401 0.9124 0.937 0.01157 ] Network output: [ 0.03332 -0.1057 0.9048 -0.0001332 5.98e-05 1.134 -0.0001004 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2279 0.1616 0.3156 0.1789 0.9853 0.9941 0.2286 0.644 0.9256 0.6803 ] Network output: [ -0.02531 0.1112 1.09 0.0001252 -5.622e-05 0.8501 9.438e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05815 0.05502 0.1566 0.1499 0.9887 0.993 0.05818 0.8763 0.9256 0.2144 ] Network output: [ -0.0253 0.0402 1.074 0.000139 -6.242e-05 0.9366 0.0001048 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07129 0.07061 0.1709 0.1638 0.985 0.9913 0.07129 0.8106 0.9025 0.2015 ] Network output: [ -0.01083 1.014 0.01862 -1.572e-06 7.055e-07 0.9886 -1.184e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01924 Epoch 5295 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0384 0.9086 0.9441 -4.63e-05 2.079e-05 0.07034 -3.49e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002891 -0.002461 -0.01028 0.00671 0.9684 0.973 0.005512 0.8693 0.8655 0.01982 ] Network output: [ 0.9706 0.1134 0.005047 -2.864e-05 1.286e-05 -0.05965 -2.158e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2151 5.71e-05 -0.2005 0.1862 0.9836 0.9933 0.2399 0.6378 0.9198 0.6863 ] Network output: [ 0.01373 0.9153 0.9695 -5.055e-05 2.269e-05 0.08749 -3.81e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00395 0.00141 0.002589 0.003468 0.99 0.9929 0.004019 0.9129 0.9372 0.01163 ] Network output: [ 0.01848 -0.006834 0.904 -0.000176 7.9e-05 1.065 -0.0001326 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2286 0.1619 0.3156 0.159 0.9853 0.9941 0.2292 0.6456 0.9258 0.6827 ] Network output: [ -0.02324 0.1145 1.088 0.0001257 -5.644e-05 0.845 9.474e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05766 0.05452 0.1549 0.1473 0.9887 0.993 0.05769 0.8759 0.9259 0.2135 ] Network output: [ -0.02261 0.02818 1.074 0.0001438 -6.457e-05 0.9438 0.0001084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07084 0.07015 0.1704 0.164 0.985 0.9913 0.07084 0.81 0.9028 0.2017 ] Network output: [ -0.006901 0.9907 0.01957 7.844e-06 -3.521e-06 1.004 5.911e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01725 Epoch 5296 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03983 0.9013 0.9436 -4.339e-05 1.948e-05 0.0753 -3.27e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002882 -0.002468 -0.01036 0.00691 0.9684 0.973 0.005499 0.8695 0.866 0.01992 ] Network output: [ 0.9907 0.04174 -0.003344 5.485e-06 -2.462e-06 -0.01977 4.133e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2141 -0.001585 -0.2057 0.1998 0.9836 0.9933 0.2388 0.6371 0.9201 0.6889 ] Network output: [ 0.01292 0.9156 0.9701 -5.125e-05 2.301e-05 0.08827 -3.863e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003919 0.001398 0.002565 0.003715 0.99 0.9929 0.003987 0.9131 0.9374 0.01161 ] Network output: [ 0.03195 -0.09997 0.9063 -0.000137 6.148e-05 1.129 -0.0001032 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2265 0.1603 0.3152 0.1775 0.9853 0.9941 0.2272 0.6454 0.9259 0.6832 ] Network output: [ -0.02501 0.1105 1.089 0.0001243 -5.578e-05 0.8507 9.364e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05781 0.05468 0.1565 0.15 0.9887 0.993 0.05784 0.8769 0.9259 0.2149 ] Network output: [ -0.02515 0.03734 1.074 0.0001385 -6.217e-05 0.9392 0.0001044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07095 0.07027 0.1711 0.1643 0.985 0.9913 0.07095 0.8114 0.9028 0.2022 ] Network output: [ -0.01032 1.013 0.01825 -8.734e-07 3.921e-07 0.9891 -6.583e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01857 Epoch 5297 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03813 0.9101 0.9438 -4.731e-05 2.124e-05 0.06962 -3.566e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00288 -0.002456 -0.01033 0.006743 0.9684 0.973 0.005494 0.87 0.866 0.01988 ] Network output: [ 0.9721 0.1091 0.00447 -2.119e-05 9.512e-06 -0.05775 -1.597e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2138 -0.0002049 -0.2028 0.1876 0.9836 0.9933 0.2384 0.6391 0.9201 0.689 ] Network output: [ 0.01328 0.9172 0.9694 -5.23e-05 2.348e-05 0.08661 -3.942e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003926 0.001394 0.002567 0.003475 0.99 0.9929 0.003994 0.9135 0.9375 0.01167 ] Network output: [ 0.01834 -0.009826 0.9057 -0.000176 7.9e-05 1.067 -0.0001326 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.227 0.1605 0.3153 0.1594 0.9853 0.9941 0.2277 0.6469 0.9262 0.6854 ] Network output: [ -0.02302 0.1136 1.087 0.0001247 -5.599e-05 0.8459 9.4e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05737 0.05423 0.1548 0.1477 0.9887 0.993 0.0574 0.8766 0.9262 0.2141 ] Network output: [ -0.02259 0.02643 1.074 0.0001429 -6.414e-05 0.9456 0.0001077 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07054 0.06986 0.1706 0.1644 0.985 0.9913 0.07055 0.8108 0.9031 0.2024 ] Network output: [ -0.006477 0.9909 0.01886 8.136e-06 -3.653e-06 1.003 6.132e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01681 Epoch 5298 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03941 0.9035 0.9434 -4.474e-05 2.009e-05 0.07411 -3.372e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002872 -0.002462 -0.01041 0.006927 0.9684 0.973 0.005482 0.8702 0.8665 0.01997 ] Network output: [ 0.9909 0.04343 -0.003622 1.041e-05 -4.676e-06 -0.02149 7.849e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.001699 -0.2078 0.2002 0.9837 0.9933 0.2374 0.6384 0.9205 0.6915 ] Network output: [ 0.01248 0.9176 0.97 -5.307e-05 2.383e-05 0.08729 -4e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003897 0.001383 0.002542 0.003701 0.99 0.9929 0.003965 0.9137 0.9377 0.01166 ] Network output: [ 0.03082 -0.09523 0.9076 -0.00014 6.287e-05 1.125 -0.0001055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2251 0.1591 0.3148 0.1764 0.9853 0.9941 0.2258 0.6468 0.9263 0.686 ] Network output: [ -0.0247 0.11 1.089 0.0001232 -5.532e-05 0.8511 9.287e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05749 0.05436 0.1562 0.1502 0.9887 0.993 0.05752 0.8774 0.9262 0.2154 ] Network output: [ -0.02501 0.03482 1.074 0.0001378 -6.188e-05 0.9414 0.0001039 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07062 0.06994 0.1713 0.1648 0.985 0.9913 0.07063 0.8122 0.9031 0.2028 ] Network output: [ -0.009864 1.012 0.01786 -2.203e-07 9.89e-08 0.9897 -1.66e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.018 Epoch 5299 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03782 0.9116 0.9436 -4.84e-05 2.173e-05 0.06892 -3.648e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00287 -0.00245 -0.01038 0.006773 0.9684 0.973 0.005476 0.8707 0.8665 0.01993 ] Network output: [ 0.9733 0.1052 0.004102 -1.419e-05 6.37e-06 -0.05597 -1.069e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.0004369 -0.2051 0.1889 0.9837 0.9933 0.237 0.6403 0.9205 0.6917 ] Network output: [ 0.01283 0.919 0.9693 -5.408e-05 2.428e-05 0.08575 -4.075e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003903 0.001379 0.002546 0.00348 0.99 0.993 0.003971 0.9141 0.9378 0.01172 ] Network output: [ 0.01815 -0.01173 0.9071 -0.0001762 7.909e-05 1.068 -0.0001328 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2256 0.1592 0.3149 0.1596 0.9853 0.9941 0.2263 0.6482 0.9265 0.688 ] Network output: [ -0.02276 0.1129 1.087 0.0001237 -5.554e-05 0.8466 9.323e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05709 0.05395 0.1547 0.148 0.9887 0.993 0.05712 0.8772 0.9264 0.2147 ] Network output: [ -0.02253 0.02479 1.074 0.0001419 -6.372e-05 0.9473 0.000107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07025 0.06957 0.1707 0.1649 0.985 0.9913 0.07026 0.8117 0.9034 0.203 ] Network output: [ -0.006075 0.991 0.0182 8.487e-06 -3.81e-06 1.003 6.396e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01641 Epoch 5300 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03899 0.9055 0.9433 -4.609e-05 2.069e-05 0.07306 -3.474e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002863 -0.002455 -0.01046 0.006946 0.9684 0.973 0.005465 0.8709 0.867 0.02002 ] Network output: [ 0.9911 0.04402 -0.003782 1.552e-05 -6.968e-06 -0.0224 1.17e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.001815 -0.21 0.2007 0.9837 0.9933 0.236 0.6398 0.9209 0.6941 ] Network output: [ 0.01203 0.9194 0.9699 -5.49e-05 2.464e-05 0.08636 -4.137e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003876 0.001368 0.002518 0.00369 0.99 0.993 0.003944 0.9143 0.938 0.0117 ] Network output: [ 0.0299 -0.09139 0.9087 -0.0001425 6.399e-05 1.122 -0.0001074 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2237 0.1578 0.3143 0.1755 0.9853 0.9941 0.2244 0.6481 0.9266 0.6887 ] Network output: [ -0.0244 0.1097 1.088 0.0001222 -5.485e-05 0.8514 9.208e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05719 0.05406 0.156 0.1503 0.9887 0.993 0.05722 0.878 0.9265 0.2159 ] Network output: [ -0.02488 0.03261 1.074 0.0001371 -6.155e-05 0.9435 0.0001033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07032 0.06963 0.1714 0.1652 0.985 0.9913 0.07033 0.813 0.9034 0.2034 ] Network output: [ -0.009464 1.011 0.01745 3.589e-07 -1.611e-07 0.9902 2.705e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01751 Epoch 5301 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03747 0.9131 0.9435 -4.956e-05 2.225e-05 0.06823 -3.735e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00286 -0.002444 -0.01043 0.006802 0.9684 0.973 0.005458 0.8713 0.867 0.01999 ] Network output: [ 0.9743 0.1017 0.003936 -7.599e-06 3.411e-06 -0.05421 -5.727e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2112 -0.0006371 -0.2072 0.1901 0.9837 0.9933 0.2355 0.6415 0.9209 0.6943 ] Network output: [ 0.01238 0.9208 0.9693 -5.586e-05 2.508e-05 0.0849 -4.21e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003881 0.001364 0.002524 0.003483 0.99 0.993 0.003949 0.9147 0.9382 0.01176 ] Network output: [ 0.01791 -0.01276 0.9084 -0.0001765 7.924e-05 1.068 -0.000133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2242 0.158 0.3144 0.1597 0.9853 0.9941 0.2248 0.6495 0.9268 0.6906 ] Network output: [ -0.02247 0.1124 1.086 0.0001227 -5.507e-05 0.8471 9.245e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05681 0.05367 0.1545 0.1483 0.9888 0.9931 0.05685 0.8778 0.9267 0.2152 ] Network output: [ -0.02244 0.02324 1.073 0.000141 -6.331e-05 0.9489 0.0001063 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06998 0.06929 0.1708 0.1653 0.985 0.9913 0.06998 0.8125 0.9037 0.2036 ] Network output: [ -0.005683 0.991 0.01756 8.879e-06 -3.986e-06 1.003 6.691e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01603 Epoch 5302 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03857 0.9074 0.9432 -4.745e-05 2.13e-05 0.07211 -3.576e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002853 -0.002449 -0.0105 0.006967 0.9684 0.973 0.005448 0.8715 0.8675 0.02007 ] Network output: [ 0.9914 0.04371 -0.003843 2.074e-05 -9.311e-06 -0.02261 1.563e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2103 -0.001925 -0.212 0.2013 0.9837 0.9933 0.2346 0.641 0.9212 0.6965 ] Network output: [ 0.01158 0.9212 0.9699 -5.672e-05 2.547e-05 0.08545 -4.275e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003855 0.001355 0.002494 0.003681 0.99 0.993 0.003923 0.9149 0.9383 0.01174 ] Network output: [ 0.02915 -0.08831 0.9098 -0.0001445 6.486e-05 1.12 -0.0001089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2224 0.1567 0.3137 0.1748 0.9853 0.9942 0.223 0.6494 0.9269 0.6912 ] Network output: [ -0.02408 0.1095 1.088 0.0001211 -5.437e-05 0.8516 9.127e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05691 0.05377 0.1557 0.1505 0.9888 0.9931 0.05694 0.8786 0.9267 0.2164 ] Network output: [ -0.02475 0.03067 1.074 0.0001363 -6.12e-05 0.9453 0.0001027 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07003 0.06934 0.1714 0.1656 0.985 0.9913 0.07003 0.8137 0.9038 0.204 ] Network output: [ -0.009109 1.011 0.01703 8.644e-07 -3.88e-07 0.9905 6.514e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01707 Epoch 5303 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0371 0.9145 0.9435 -5.078e-05 2.28e-05 0.06755 -3.827e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00285 -0.002438 -0.01047 0.00683 0.9684 0.973 0.005441 0.872 0.8675 0.02004 ] Network output: [ 0.975 0.0985 0.003944 -1.398e-06 6.275e-07 -0.05249 -1.053e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2099 -0.0008042 -0.2093 0.1913 0.9837 0.9933 0.2341 0.6427 0.9212 0.6967 ] Network output: [ 0.01194 0.9225 0.9693 -5.765e-05 2.588e-05 0.08405 -4.345e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00386 0.001351 0.002502 0.003485 0.99 0.993 0.003927 0.9153 0.9385 0.0118 ] Network output: [ 0.01762 -0.01304 0.9096 -0.0001769 7.943e-05 1.067 -0.0001333 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2228 0.1568 0.3139 0.1597 0.9853 0.9942 0.2234 0.6507 0.9272 0.693 ] Network output: [ -0.02215 0.1121 1.085 0.0001216 -5.46e-05 0.8473 9.166e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05655 0.05341 0.1542 0.1485 0.9888 0.9931 0.05658 0.8784 0.9269 0.2156 ] Network output: [ -0.02232 0.02177 1.073 0.0001401 -6.289e-05 0.9503 0.0001056 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06971 0.06902 0.1708 0.1657 0.985 0.9913 0.06972 0.8133 0.904 0.2041 ] Network output: [ -0.005293 0.9907 0.01694 9.312e-06 -4.18e-06 1.003 7.018e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01568 Epoch 5304 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03814 0.9091 0.9432 -4.88e-05 2.191e-05 0.07124 -3.678e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002843 -0.002443 -0.01055 0.006989 0.9684 0.973 0.005431 0.8722 0.8679 0.02012 ] Network output: [ 0.9918 0.04268 -0.003842 2.601e-05 -1.168e-05 -0.02228 1.961e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2091 -0.002024 -0.2141 0.2021 0.9837 0.9933 0.2332 0.6423 0.9216 0.6989 ] Network output: [ 0.01113 0.923 0.9699 -5.855e-05 2.628e-05 0.08457 -4.412e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003835 0.001343 0.00247 0.003675 0.99 0.993 0.003902 0.9155 0.9386 0.01178 ] Network output: [ 0.02857 -0.08587 0.9108 -0.0001459 6.55e-05 1.117 -0.00011 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.221 0.1555 0.3131 0.1743 0.9853 0.9942 0.2217 0.6506 0.9273 0.6937 ] Network output: [ -0.02376 0.1094 1.087 0.00012 -5.388e-05 0.8517 9.045e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05664 0.0535 0.1554 0.1506 0.9888 0.9931 0.05667 0.8791 0.927 0.2168 ] Network output: [ -0.02462 0.02893 1.074 0.0001355 -6.081e-05 0.9469 0.0001021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06975 0.06906 0.1715 0.1659 0.9851 0.9913 0.06976 0.8144 0.904 0.2045 ] Network output: [ -0.008799 1.01 0.01661 1.299e-06 -5.832e-07 0.9909 9.79e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01668 Epoch 5305 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03672 0.916 0.9435 -5.204e-05 2.336e-05 0.06686 -3.922e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00284 -0.002432 -0.01051 0.006856 0.9684 0.973 0.005424 0.8726 0.8679 0.02009 ] Network output: [ 0.9756 0.09562 0.004089 4.418e-06 -1.983e-06 -0.05086 3.329e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2086 -0.0009383 -0.2113 0.1924 0.9837 0.9933 0.2327 0.6439 0.9216 0.6991 ] Network output: [ 0.01149 0.9242 0.9694 -5.944e-05 2.669e-05 0.0832 -4.48e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003839 0.001339 0.00248 0.003485 0.9901 0.993 0.003906 0.9158 0.9388 0.01184 ] Network output: [ 0.01729 -0.01265 0.9107 -0.0001774 7.964e-05 1.067 -0.0001337 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2214 0.1556 0.3132 0.1595 0.9853 0.9942 0.2221 0.6519 0.9275 0.6954 ] Network output: [ -0.0218 0.1119 1.085 0.0001206 -5.413e-05 0.8475 9.087e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0563 0.05316 0.1539 0.1487 0.9888 0.9931 0.05633 0.8789 0.9272 0.216 ] Network output: [ -0.02217 0.02034 1.073 0.0001392 -6.249e-05 0.9517 0.0001049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06945 0.06876 0.1708 0.166 0.985 0.9913 0.06946 0.814 0.9043 0.2046 ] Network output: [ -0.004905 0.9904 0.01635 9.784e-06 -4.393e-06 1.003 7.374e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01534 Epoch 5306 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03773 0.9107 0.9432 -5.016e-05 2.252e-05 0.07044 -3.78e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002834 -0.002437 -0.01059 0.007012 0.9684 0.973 0.005414 0.8728 0.8684 0.02017 ] Network output: [ 0.9922 0.04106 -0.003814 3.131e-05 -1.405e-05 -0.02153 2.359e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2079 -0.00211 -0.2161 0.203 0.9837 0.9934 0.2319 0.6434 0.9219 0.7012 ] Network output: [ 0.01067 0.9247 0.97 -6.037e-05 2.71e-05 0.0837 -4.549e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003815 0.001331 0.002447 0.003672 0.9901 0.993 0.003882 0.916 0.9389 0.01182 ] Network output: [ 0.02812 -0.08402 0.9118 -0.0001469 6.593e-05 1.115 -0.0001107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2197 0.1544 0.3124 0.1739 0.9853 0.9942 0.2204 0.6518 0.9276 0.696 ] Network output: [ -0.02344 0.1093 1.086 0.0001189 -5.339e-05 0.8518 8.962e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05638 0.05325 0.1551 0.1507 0.9888 0.9931 0.05641 0.8796 0.9272 0.2172 ] Network output: [ -0.0245 0.02734 1.074 0.0001346 -6.041e-05 0.9484 0.0001014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06948 0.0688 0.1715 0.1663 0.9851 0.9913 0.06949 0.8151 0.9043 0.205 ] Network output: [ -0.008539 1.01 0.01621 1.655e-06 -7.429e-07 0.9912 1.247e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01633 Epoch 5307 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03632 0.9174 0.9436 -5.335e-05 2.395e-05 0.06616 -4.021e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00283 -0.002425 -0.01056 0.006881 0.9684 0.973 0.005407 0.8732 0.8684 0.02014 ] Network output: [ 0.976 0.09307 0.004351 9.852e-06 -4.423e-06 -0.04933 7.424e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2074 -0.00104 -0.2132 0.1934 0.9837 0.9934 0.2313 0.6451 0.9219 0.7014 ] Network output: [ 0.01106 0.9259 0.9694 -6.123e-05 2.749e-05 0.08235 -4.614e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00382 0.001328 0.002458 0.003485 0.9901 0.993 0.003887 0.9164 0.939 0.01188 ] Network output: [ 0.01693 -0.01166 0.9117 -0.0001779 7.987e-05 1.065 -0.0001341 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2201 0.1545 0.3126 0.1593 0.9854 0.9942 0.2207 0.6531 0.9278 0.6977 ] Network output: [ -0.02142 0.1118 1.084 0.0001195 -5.367e-05 0.8475 9.01e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05606 0.05293 0.1536 0.1489 0.9888 0.9931 0.05609 0.8794 0.9274 0.2164 ] Network output: [ -0.02199 0.01891 1.073 0.0001383 -6.209e-05 0.953 0.0001042 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0692 0.06851 0.1708 0.1664 0.9851 0.9913 0.0692 0.8147 0.9046 0.2051 ] Network output: [ -0.004519 0.9898 0.01579 1.029e-05 -4.618e-06 1.003 7.753e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01503 Epoch 5308 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03732 0.9122 0.9433 -5.152e-05 2.313e-05 0.06969 -3.882e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002825 -0.00243 -0.01063 0.007037 0.9684 0.9731 0.005398 0.8734 0.8688 0.02022 ] Network output: [ 0.9927 0.03892 -0.003779 3.659e-05 -1.643e-05 -0.02039 2.758e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2067 -0.002183 -0.2181 0.204 0.9837 0.9934 0.2306 0.6446 0.9222 0.7035 ] Network output: [ 0.01021 0.9264 0.9701 -6.217e-05 2.791e-05 0.08284 -4.686e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003796 0.001321 0.002423 0.00367 0.9901 0.993 0.003863 0.9165 0.9392 0.01185 ] Network output: [ 0.0278 -0.08273 0.9126 -0.0001474 6.616e-05 1.114 -0.0001111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2184 0.1534 0.3116 0.1736 0.9854 0.9942 0.2191 0.653 0.9278 0.6983 ] Network output: [ -0.02312 0.1092 1.086 0.0001178 -5.289e-05 0.8518 8.879e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05614 0.05301 0.1548 0.1509 0.9888 0.9931 0.05617 0.8801 0.9274 0.2175 ] Network output: [ -0.02438 0.02589 1.074 0.0001336 -5.999e-05 0.9498 0.0001007 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06923 0.06854 0.1715 0.1666 0.9851 0.9913 0.06924 0.8158 0.9046 0.2055 ] Network output: [ -0.008333 1.009 0.01583 1.917e-06 -8.608e-07 0.9914 1.445e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01602 Epoch 5309 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03591 0.9188 0.9437 -5.47e-05 2.456e-05 0.06544 -4.122e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002821 -0.002419 -0.0106 0.006906 0.9684 0.9731 0.005391 0.8738 0.8688 0.02019 ] Network output: [ 0.9762 0.09087 0.004725 1.491e-05 -6.692e-06 -0.04791 1.123e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2062 -0.001111 -0.215 0.1943 0.9837 0.9934 0.23 0.6462 0.9222 0.7036 ] Network output: [ 0.01063 0.9275 0.9695 -6.3e-05 2.828e-05 0.0815 -4.748e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003801 0.001318 0.002436 0.003484 0.9901 0.993 0.003867 0.9169 0.9393 0.01191 ] Network output: [ 0.01653 -0.01012 0.9126 -0.0001785 8.011e-05 1.064 -0.0001345 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2188 0.1535 0.3118 0.1591 0.9854 0.9942 0.2195 0.6542 0.928 0.6999 ] Network output: [ -0.02102 0.1118 1.083 0.0001185 -5.321e-05 0.8475 8.933e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05583 0.0527 0.1533 0.1491 0.9888 0.9931 0.05586 0.8799 0.9276 0.2168 ] Network output: [ -0.02178 0.01749 1.072 0.0001375 -6.171e-05 0.9543 0.0001036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06895 0.06827 0.1707 0.1667 0.9851 0.9913 0.06896 0.8154 0.9048 0.2056 ] Network output: [ -0.004131 0.9891 0.01525 1.081e-05 -4.855e-06 1.004 8.15e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01473 Epoch 5310 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03692 0.9136 0.9434 -5.287e-05 2.373e-05 0.06898 -3.984e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002816 -0.002424 -0.01067 0.007063 0.9684 0.9731 0.005383 0.874 0.8692 0.02027 ] Network output: [ 0.9933 0.0363 -0.003747 4.187e-05 -1.88e-05 -0.01892 3.155e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2055 -0.002241 -0.2201 0.205 0.9837 0.9934 0.2292 0.6457 0.9225 0.7057 ] Network output: [ 0.009757 0.928 0.9702 -6.396e-05 2.872e-05 0.08199 -4.821e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003778 0.001311 0.002399 0.003671 0.9901 0.993 0.003844 0.917 0.9394 0.01189 ] Network output: [ 0.02761 -0.08202 0.9133 -0.0001474 6.619e-05 1.113 -0.0001111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2171 0.1524 0.3108 0.1735 0.9854 0.9942 0.2178 0.6541 0.9281 0.7004 ] Network output: [ -0.0228 0.1092 1.085 0.0001167 -5.24e-05 0.8518 8.796e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05591 0.05279 0.1545 0.1511 0.9888 0.9931 0.05594 0.8806 0.9276 0.2179 ] Network output: [ -0.02428 0.02455 1.073 0.0001327 -5.956e-05 0.9511 9.998e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06899 0.0683 0.1715 0.167 0.9851 0.9913 0.06899 0.8165 0.9048 0.206 ] Network output: [ -0.008182 1.009 0.01547 2.076e-06 -9.321e-07 0.9915 1.565e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01574 Epoch 5311 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03549 0.9203 0.9438 -5.607e-05 2.517e-05 0.06469 -4.226e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002812 -0.002412 -0.01064 0.006929 0.9684 0.9731 0.005375 0.8744 0.8692 0.02023 ] Network output: [ 0.9762 0.08901 0.005211 1.957e-05 -8.787e-06 -0.0466 1.475e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.205 -0.001151 -0.2168 0.1952 0.9838 0.9934 0.2287 0.6473 0.9225 0.7057 ] Network output: [ 0.01021 0.9291 0.9696 -6.475e-05 2.907e-05 0.08063 -4.88e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003783 0.001308 0.002415 0.003483 0.9901 0.993 0.003849 0.9174 0.9396 0.01195 ] Network output: [ 0.01608 -0.008047 0.9134 -0.000179 8.037e-05 1.062 -0.0001349 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2176 0.1525 0.3111 0.1587 0.9854 0.9942 0.2182 0.6553 0.9283 0.702 ] Network output: [ -0.0206 0.1118 1.082 0.0001175 -5.277e-05 0.8474 8.858e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05561 0.05248 0.1529 0.1492 0.9889 0.9931 0.05564 0.8803 0.9278 0.2172 ] Network output: [ -0.02155 0.01608 1.072 0.0001366 -6.134e-05 0.9556 0.000103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06872 0.06804 0.1707 0.1671 0.9851 0.9913 0.06873 0.816 0.905 0.206 ] Network output: [ -0.003732 0.9884 0.01472 1.137e-05 -5.103e-06 1.004 8.567e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01444 Epoch 5312 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03652 0.9149 0.9435 -5.42e-05 2.433e-05 0.0683 -4.085e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002807 -0.002418 -0.01072 0.00709 0.9685 0.9731 0.005367 0.8745 0.8696 0.02032 ] Network output: [ 0.9939 0.03322 -0.003735 4.713e-05 -2.116e-05 -0.01714 3.552e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2043 -0.002285 -0.2221 0.2062 0.9838 0.9934 0.228 0.6467 0.9228 0.7078 ] Network output: [ 0.009301 0.9296 0.9704 -6.573e-05 2.951e-05 0.08113 -4.954e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00376 0.001302 0.002375 0.003674 0.9901 0.993 0.003826 0.9175 0.9397 0.01193 ] Network output: [ 0.02754 -0.0819 0.914 -0.0001471 6.604e-05 1.112 -0.0001109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2159 0.1514 0.31 0.1736 0.9854 0.9942 0.2165 0.6552 0.9284 0.7025 ] Network output: [ -0.02249 0.1091 1.084 0.0001156 -5.191e-05 0.8519 8.713e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05569 0.05258 0.1542 0.1513 0.9888 0.9931 0.05572 0.8811 0.9278 0.2183 ] Network output: [ -0.02418 0.02334 1.073 0.0001317 -5.911e-05 0.9523 9.922e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06876 0.06807 0.1714 0.1673 0.9851 0.9913 0.06876 0.8172 0.905 0.2064 ] Network output: [ -0.008083 1.009 0.01512 2.126e-06 -9.545e-07 0.9916 1.602e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0155 Epoch 5313 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03507 0.9218 0.9439 -5.747e-05 2.58e-05 0.06392 -4.331e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002804 -0.002406 -0.01067 0.006951 0.9685 0.9731 0.00536 0.8749 0.8696 0.02028 ] Network output: [ 0.9761 0.08755 0.005805 2.384e-05 -1.07e-05 -0.04544 1.796e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2038 -0.00116 -0.2184 0.196 0.9838 0.9934 0.2274 0.6484 0.9228 0.7077 ] Network output: [ 0.00981 0.9307 0.9697 -6.647e-05 2.984e-05 0.07975 -5.01e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003766 0.0013 0.002395 0.00348 0.9901 0.9931 0.003832 0.9178 0.9398 0.01199 ] Network output: [ 0.01556 -0.005439 0.9141 -0.0001797 8.066e-05 1.059 -0.0001354 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2164 0.1516 0.3103 0.1582 0.9854 0.9942 0.217 0.6564 0.9285 0.704 ] Network output: [ -0.02015 0.1118 1.082 0.0001166 -5.233e-05 0.8473 8.785e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05539 0.05227 0.1525 0.1493 0.9889 0.9931 0.05542 0.8808 0.928 0.2175 ] Network output: [ -0.02127 0.01465 1.072 0.0001359 -6.099e-05 0.9568 0.0001024 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0685 0.06781 0.1706 0.1674 0.9851 0.9913 0.06851 0.8166 0.9053 0.2064 ] Network output: [ -0.003312 0.9874 0.0142 1.196e-05 -5.37e-06 1.005 9.015e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01418 Epoch 5314 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03615 0.9161 0.9437 -5.552e-05 2.493e-05 0.06765 -4.184e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002799 -0.002412 -0.01076 0.007118 0.9685 0.9731 0.005353 0.875 0.87 0.02037 ] Network output: [ 0.9947 0.02971 -0.003764 5.237e-05 -2.351e-05 -0.01507 3.947e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2032 -0.002316 -0.2241 0.2074 0.9838 0.9934 0.2267 0.6478 0.9231 0.7098 ] Network output: [ 0.008847 0.9312 0.9705 -6.748e-05 3.029e-05 0.08028 -5.086e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003743 0.001294 0.002351 0.003679 0.9901 0.9931 0.003808 0.918 0.9399 0.01196 ] Network output: [ 0.02759 -0.08241 0.9146 -0.0001463 6.569e-05 1.112 -0.0001103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2147 0.1505 0.3091 0.1738 0.9854 0.9942 0.2153 0.6562 0.9286 0.7045 ] Network output: [ -0.02219 0.109 1.084 0.0001145 -5.142e-05 0.852 8.631e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05549 0.05238 0.1539 0.1515 0.9889 0.9931 0.05552 0.8815 0.928 0.2187 ] Network output: [ -0.0241 0.02223 1.073 0.0001306 -5.864e-05 0.9535 9.844e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06854 0.06786 0.1714 0.1676 0.9851 0.9914 0.06855 0.8178 0.9052 0.2069 ] Network output: [ -0.008042 1.01 0.0148 2.063e-06 -9.262e-07 0.9915 1.555e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0153 Epoch 5315 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03463 0.9233 0.9441 -5.889e-05 2.644e-05 0.0631 -4.438e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002795 -0.002399 -0.01071 0.006972 0.9685 0.9731 0.005345 0.8754 0.8699 0.02032 ] Network output: [ 0.9758 0.08656 0.006506 2.767e-05 -1.242e-05 -0.04448 2.085e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2027 -0.00114 -0.22 0.1968 0.9838 0.9934 0.2262 0.6494 0.923 0.7097 ] Network output: [ 0.009419 0.9322 0.9698 -6.817e-05 3.06e-05 0.07885 -5.137e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003749 0.001293 0.002375 0.003477 0.9901 0.9931 0.003815 0.9183 0.94 0.01203 ] Network output: [ 0.01498 -0.002252 0.9148 -0.0001804 8.099e-05 1.057 -0.000136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2152 0.1508 0.3095 0.1577 0.9854 0.9942 0.2158 0.6574 0.9288 0.706 ] Network output: [ -0.01967 0.1118 1.081 0.0001156 -5.192e-05 0.8471 8.715e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05519 0.05208 0.1521 0.1495 0.9889 0.9931 0.05522 0.8812 0.9281 0.2179 ] Network output: [ -0.02097 0.01319 1.071 0.0001351 -6.066e-05 0.958 0.0001018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06828 0.0676 0.1704 0.1677 0.9851 0.9913 0.06829 0.8172 0.9055 0.2069 ] Network output: [ -0.002863 0.9863 0.01367 1.261e-05 -5.662e-06 1.006 9.505e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01393 Epoch 5316 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03578 0.9173 0.9438 -5.682e-05 2.551e-05 0.06703 -4.282e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002791 -0.002406 -0.0108 0.007148 0.9685 0.9731 0.005338 0.8755 0.8703 0.02042 ] Network output: [ 0.9955 0.02575 -0.003859 5.76e-05 -2.586e-05 -0.01273 4.341e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2021 -0.002336 -0.226 0.2087 0.9838 0.9934 0.2255 0.6487 0.9233 0.7118 ] Network output: [ 0.008392 0.9328 0.9707 -6.92e-05 3.107e-05 0.07944 -5.215e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003726 0.001288 0.002328 0.003687 0.9901 0.9931 0.003791 0.9184 0.9401 0.012 ] Network output: [ 0.02776 -0.08362 0.9152 -0.0001451 6.514e-05 1.112 -0.0001093 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2135 0.1496 0.3082 0.1742 0.9854 0.9942 0.2141 0.6572 0.9288 0.7065 ] Network output: [ -0.02191 0.1089 1.083 0.0001135 -5.093e-05 0.8521 8.55e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0553 0.0522 0.1536 0.1517 0.9889 0.9931 0.05533 0.8819 0.9281 0.2191 ] Network output: [ -0.02404 0.02121 1.073 0.0001296 -5.816e-05 0.9545 9.763e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06833 0.06765 0.1714 0.168 0.9851 0.9914 0.06834 0.8184 0.9054 0.2074 ] Network output: [ -0.008062 1.01 0.01451 1.878e-06 -8.429e-07 0.9912 1.415e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01514 Epoch 5317 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0342 0.9248 0.9443 -6.033e-05 2.708e-05 0.06224 -4.546e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002787 -0.002393 -0.01074 0.006991 0.9685 0.9731 0.00533 0.8759 0.8702 0.02036 ] Network output: [ 0.9752 0.08609 0.007318 3.104e-05 -1.393e-05 -0.04375 2.339e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2016 -0.001091 -0.2215 0.1974 0.9838 0.9934 0.2249 0.6504 0.9233 0.7116 ] Network output: [ 0.009043 0.9337 0.97 -6.982e-05 3.135e-05 0.07795 -5.262e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003734 0.001286 0.002356 0.003473 0.9901 0.9931 0.003799 0.9187 0.9402 0.01207 ] Network output: [ 0.01431 0.001576 0.9154 -0.0001813 8.137e-05 1.054 -0.0001366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2141 0.1499 0.3087 0.1571 0.9854 0.9942 0.2147 0.6584 0.929 0.7079 ] Network output: [ -0.01916 0.1118 1.08 0.0001148 -5.152e-05 0.8469 8.649e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.055 0.05189 0.1517 0.1496 0.9889 0.9932 0.05503 0.8816 0.9283 0.2182 ] Network output: [ -0.02062 0.01169 1.071 0.0001344 -6.035e-05 0.9592 0.0001013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06808 0.0674 0.1703 0.168 0.9851 0.9913 0.06809 0.8178 0.9056 0.2073 ] Network output: [ -0.002374 0.9851 0.01315 1.333e-05 -5.986e-06 1.007 1.005e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0137 Epoch 5318 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03543 0.9184 0.944 -5.809e-05 2.608e-05 0.06643 -4.378e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002783 -0.0024 -0.01084 0.007178 0.9685 0.9731 0.005324 0.876 0.8707 0.02046 ] Network output: [ 0.9966 0.02131 -0.00404 6.284e-05 -2.821e-05 -0.01012 4.736e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2011 -0.002346 -0.2279 0.2101 0.9838 0.9934 0.2243 0.6497 0.9236 0.7138 ] Network output: [ 0.007935 0.9343 0.9709 -7.088e-05 3.182e-05 0.07859 -5.342e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00371 0.001282 0.002305 0.003697 0.9901 0.9931 0.003775 0.9188 0.9403 0.01204 ] Network output: [ 0.02808 -0.0856 0.9157 -0.0001434 6.438e-05 1.113 -0.0001081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2123 0.1488 0.3073 0.1747 0.9854 0.9942 0.2129 0.6582 0.929 0.7083 ] Network output: [ -0.02166 0.1087 1.083 0.0001124 -5.045e-05 0.8523 8.469e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05512 0.05203 0.1533 0.152 0.9889 0.9932 0.05515 0.8824 0.9282 0.2195 ] Network output: [ -0.02401 0.0203 1.073 0.0001284 -5.766e-05 0.9555 9.68e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06813 0.06746 0.1714 0.1683 0.9851 0.9914 0.06814 0.819 0.9056 0.2078 ] Network output: [ -0.008152 1.011 0.01424 1.554e-06 -6.979e-07 0.9908 1.171e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01502 Epoch 5319 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03375 0.9264 0.9445 -6.178e-05 2.773e-05 0.06134 -4.656e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002779 -0.002386 -0.01078 0.007008 0.9685 0.9731 0.005316 0.8764 0.8706 0.0204 ] Network output: [ 0.9745 0.08624 0.008256 3.39e-05 -1.522e-05 -0.04331 2.555e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2006 -0.001013 -0.2228 0.1979 0.9838 0.9934 0.2238 0.6514 0.9235 0.7134 ] Network output: [ 0.008683 0.9352 0.9701 -7.144e-05 3.207e-05 0.07702 -5.384e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003719 0.00128 0.002337 0.003467 0.9902 0.9931 0.003784 0.9192 0.9404 0.01211 ] Network output: [ 0.01353 0.006127 0.916 -0.0001823 8.184e-05 1.05 -0.0001374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.213 0.1492 0.3079 0.1563 0.9854 0.9942 0.2137 0.6594 0.9292 0.7098 ] Network output: [ -0.01863 0.1118 1.079 0.0001139 -5.114e-05 0.8466 8.586e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05481 0.05171 0.1513 0.1497 0.9889 0.9932 0.05484 0.8819 0.9284 0.2185 ] Network output: [ -0.02024 0.01014 1.07 0.0001338 -6.008e-05 0.9604 0.0001009 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06788 0.0672 0.1702 0.1684 0.9851 0.9914 0.06789 0.8183 0.9058 0.2077 ] Network output: [ -0.001831 0.9836 0.01261 1.415e-05 -6.353e-06 1.008 1.066e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0135 Epoch 5320 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0351 0.9195 0.9442 -5.932e-05 2.663e-05 0.06586 -4.471e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002775 -0.002394 -0.01088 0.007211 0.9685 0.9731 0.005311 0.8765 0.871 0.02051 ] Network output: [ 0.9977 0.01632 -0.00433 6.813e-05 -3.059e-05 -0.007206 5.135e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2 -0.002351 -0.2299 0.2117 0.9838 0.9934 0.2232 0.6505 0.9238 0.7156 ] Network output: [ 0.007476 0.9358 0.9712 -7.253e-05 3.256e-05 0.07775 -5.466e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003694 0.001276 0.002282 0.00371 0.9902 0.9931 0.003759 0.9192 0.9405 0.01207 ] Network output: [ 0.02854 -0.0885 0.9162 -0.0001412 6.341e-05 1.115 -0.0001064 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2111 0.148 0.3063 0.1755 0.9854 0.9942 0.2118 0.6591 0.9292 0.7101 ] Network output: [ -0.02143 0.1085 1.082 0.0001113 -4.997e-05 0.8526 8.389e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05495 0.05188 0.1531 0.1523 0.9889 0.9932 0.05498 0.8828 0.9284 0.22 ] Network output: [ -0.02401 0.01952 1.073 0.0001273 -5.715e-05 0.9564 9.594e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06795 0.06728 0.1713 0.1686 0.9851 0.9914 0.06796 0.8196 0.9057 0.2083 ] Network output: [ -0.008322 1.012 0.014 1.075e-06 -4.828e-07 0.9902 8.105e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01497 Epoch 5321 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03329 0.928 0.9448 -6.324e-05 2.839e-05 0.06037 -4.766e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002772 -0.00238 -0.01081 0.007024 0.9685 0.9731 0.005303 0.8769 0.8708 0.02044 ] Network output: [ 0.9735 0.08709 0.009341 3.622e-05 -1.626e-05 -0.04321 2.73e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1995 -0.0009071 -0.2241 0.1982 0.9838 0.9934 0.2226 0.6524 0.9237 0.7151 ] Network output: [ 0.008343 0.9367 0.9702 -7.301e-05 3.277e-05 0.07608 -5.502e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003705 0.001275 0.00232 0.00346 0.9902 0.9931 0.00377 0.9196 0.9406 0.01215 ] Network output: [ 0.01262 0.01152 0.9166 -0.0001836 8.242e-05 1.046 -0.0001384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.212 0.1485 0.3072 0.1554 0.9854 0.9942 0.2126 0.6604 0.9294 0.7116 ] Network output: [ -0.01805 0.1118 1.078 0.0001131 -5.08e-05 0.8463 8.527e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05463 0.05155 0.1508 0.1498 0.9889 0.9932 0.05466 0.8823 0.9285 0.2188 ] Network output: [ -0.0198 0.008536 1.07 0.0001333 -5.985e-05 0.9616 0.0001005 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0677 0.06702 0.17 0.1687 0.9851 0.9914 0.06771 0.8188 0.906 0.2081 ] Network output: [ -0.001217 0.9818 0.01205 1.509e-05 -6.774e-06 1.009 1.137e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01333 Epoch 5322 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03478 0.9204 0.9444 -6.051e-05 2.717e-05 0.06533 -4.56e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002768 -0.002389 -0.01092 0.007245 0.9685 0.9731 0.005298 0.8769 0.8713 0.02056 ] Network output: [ 0.9992 0.01069 -0.004752 7.351e-05 -3.3e-05 -0.003946 5.54e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.199 -0.002352 -0.2318 0.2133 0.9838 0.9934 0.2221 0.6514 0.924 0.7174 ] Network output: [ 0.007012 0.9373 0.9714 -7.413e-05 3.328e-05 0.07691 -5.587e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003679 0.001272 0.002259 0.003726 0.9902 0.9931 0.003743 0.9196 0.9407 0.0121 ] Network output: [ 0.02918 -0.09246 0.9166 -0.0001385 6.219e-05 1.117 -0.0001044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.21 0.1473 0.3054 0.1764 0.9854 0.9942 0.2106 0.6599 0.9294 0.7119 ] Network output: [ -0.02123 0.1082 1.082 0.0001103 -4.95e-05 0.8529 8.309e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0548 0.05173 0.1529 0.1526 0.9889 0.9932 0.05483 0.8832 0.9285 0.2204 ] Network output: [ -0.02404 0.01887 1.072 0.0001261 -5.661e-05 0.9573 9.504e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06778 0.06711 0.1713 0.169 0.9851 0.9914 0.06779 0.8202 0.9058 0.2087 ] Network output: [ -0.00858 1.014 0.0138 4.197e-07 -1.884e-07 0.9894 3.163e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01498 Epoch 5323 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03283 0.9297 0.945 -6.472e-05 2.906e-05 0.05934 -4.878e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002765 -0.002373 -0.01084 0.007037 0.9685 0.9731 0.005289 0.8773 0.8711 0.02048 ] Network output: [ 0.9721 0.08877 0.0106 3.792e-05 -1.702e-05 -0.04349 2.858e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1986 -0.0007724 -0.2252 0.1984 0.9838 0.9934 0.2215 0.6533 0.9239 0.7167 ] Network output: [ 0.008024 0.9382 0.9703 -7.452e-05 3.345e-05 0.07512 -5.616e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003692 0.001271 0.002305 0.003451 0.9902 0.9931 0.003756 0.9199 0.9408 0.01219 ] Network output: [ 0.01154 0.01791 0.9171 -0.0001852 8.315e-05 1.041 -0.0001396 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.211 0.1479 0.3064 0.1543 0.9854 0.9942 0.2117 0.6613 0.9296 0.7133 ] Network output: [ -0.01743 0.1118 1.078 0.0001124 -5.048e-05 0.846 8.474e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05446 0.05139 0.1504 0.1498 0.9889 0.9932 0.05449 0.8826 0.9287 0.2191 ] Network output: [ -0.0193 0.006862 1.07 0.0001329 -5.965e-05 0.9627 0.0001001 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06752 0.06684 0.1698 0.169 0.9851 0.9914 0.06753 0.8192 0.9061 0.2085 ] Network output: [ -0.0005091 0.9797 0.01145 1.619e-05 -7.268e-06 1.01 1.22e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01321 Epoch 5324 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03449 0.9213 0.9446 -6.164e-05 2.767e-05 0.06485 -4.645e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002761 -0.002384 -0.01096 0.007281 0.9685 0.9731 0.005285 0.8773 0.8716 0.02061 ] Network output: [ 1.001 0.004276 -0.005336 7.902e-05 -3.548e-05 -0.0002788 5.955e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1981 -0.002354 -0.2338 0.2151 0.9838 0.9934 0.221 0.6521 0.9242 0.7192 ] Network output: [ 0.006541 0.9388 0.9717 -7.569e-05 3.398e-05 0.07608 -5.705e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003664 0.001268 0.002236 0.003746 0.9902 0.9931 0.003728 0.92 0.9409 0.01214 ] Network output: [ 0.03001 -0.09768 0.917 -0.0001352 6.07e-05 1.12 -0.0001019 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2089 0.1466 0.3044 0.1777 0.9854 0.9942 0.2095 0.6607 0.9295 0.7135 ] Network output: [ -0.02109 0.1078 1.081 0.0001092 -4.902e-05 0.8534 8.23e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05466 0.05161 0.1527 0.153 0.9889 0.9932 0.05469 0.8836 0.9286 0.2209 ] Network output: [ -0.02413 0.01839 1.072 0.0001248 -5.605e-05 0.958 9.409e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06762 0.06695 0.1714 0.1693 0.9852 0.9914 0.06763 0.8208 0.906 0.2092 ] Network output: [ -0.008941 1.016 0.01363 -4.38e-07 1.966e-07 0.9883 -3.301e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01509 Epoch 5325 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03235 0.9315 0.9453 -6.621e-05 2.973e-05 0.05823 -4.99e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002758 -0.002366 -0.01086 0.007047 0.9685 0.9731 0.005277 0.8777 0.8714 0.02052 ] Network output: [ 0.9705 0.09143 0.01207 3.892e-05 -1.747e-05 -0.04425 2.933e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1976 -0.0006083 -0.2261 0.1984 0.9838 0.9934 0.2204 0.6542 0.9241 0.7182 ] Network output: [ 0.00773 0.9396 0.9705 -7.596e-05 3.41e-05 0.07414 -5.725e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00368 0.001267 0.002291 0.003439 0.9902 0.9931 0.003744 0.9203 0.9409 0.01224 ] Network output: [ 0.01027 0.02549 0.9176 -0.0001873 8.408e-05 1.036 -0.0001411 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2101 0.1473 0.3057 0.1529 0.9854 0.9942 0.2108 0.6622 0.9297 0.7149 ] Network output: [ -0.01677 0.1118 1.077 0.0001118 -5.019e-05 0.8455 8.426e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0543 0.05124 0.1499 0.1499 0.989 0.9932 0.05433 0.8829 0.9288 0.2194 ] Network output: [ -0.01872 0.005113 1.069 0.0001326 -5.951e-05 0.9639 9.989e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06735 0.06668 0.1695 0.1693 0.9851 0.9914 0.06736 0.8196 0.9062 0.2089 ] Network output: [ 0.0003194 0.9772 0.0108 1.749e-05 -7.853e-06 1.011 1.318e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01315 Epoch 5326 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03423 0.922 0.9449 -6.27e-05 2.815e-05 0.06442 -4.725e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002755 -0.002379 -0.01101 0.00732 0.9685 0.9731 0.005274 0.8777 0.8718 0.02066 ] Network output: [ 1.003 -0.003074 -0.006117 8.474e-05 -3.804e-05 0.003879 6.387e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1971 -0.002362 -0.2358 0.2171 0.9838 0.9934 0.2199 0.6528 0.9244 0.7209 ] Network output: [ 0.00606 0.9403 0.972 -7.721e-05 3.466e-05 0.07527 -5.819e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00365 0.001265 0.002213 0.00377 0.9902 0.9931 0.003714 0.9203 0.941 0.01217 ] Network output: [ 0.03108 -0.1044 0.9173 -0.0001312 5.891e-05 1.124 -9.889e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2078 0.1459 0.3035 0.1793 0.9854 0.9942 0.2084 0.6615 0.9297 0.715 ] Network output: [ -0.021 0.1074 1.081 0.0001081 -4.855e-05 0.8539 8.15e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05454 0.05149 0.1526 0.1534 0.9889 0.9932 0.05457 0.8839 0.9286 0.2214 ] Network output: [ -0.02429 0.01813 1.072 0.0001235 -5.545e-05 0.9586 9.308e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06748 0.06681 0.1714 0.1696 0.9852 0.9914 0.06748 0.8213 0.906 0.2096 ] Network output: [ -0.009423 1.018 0.01351 -1.53e-06 6.87e-07 0.9869 -1.153e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01533 Epoch 5327 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03185 0.9334 0.9456 -6.772e-05 3.04e-05 0.05704 -5.104e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002751 -0.00236 -0.01089 0.007053 0.9685 0.9731 0.005265 0.8781 0.8716 0.02055 ] Network output: [ 0.9683 0.09526 0.0138 3.912e-05 -1.756e-05 -0.04557 2.949e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1967 -0.0004135 -0.2269 0.1982 0.9838 0.9934 0.2194 0.6551 0.9242 0.7196 ] Network output: [ 0.007465 0.9411 0.9706 -7.734e-05 3.472e-05 0.07314 -5.829e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003668 0.001264 0.002279 0.003423 0.9902 0.9931 0.003732 0.9207 0.9411 0.01228 ] Network output: [ 0.008752 0.03452 0.9181 -0.0001899 8.525e-05 1.029 -0.0001431 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2093 0.1467 0.305 0.1513 0.9854 0.9942 0.2099 0.663 0.9299 0.7165 ] Network output: [ -0.01604 0.1119 1.076 0.0001113 -4.995e-05 0.845 8.385e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05415 0.05109 0.1494 0.1499 0.989 0.9932 0.05418 0.8831 0.9288 0.2197 ] Network output: [ -0.01807 0.003283 1.068 0.0001324 -5.942e-05 0.9651 9.975e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0672 0.06652 0.1693 0.1696 0.9851 0.9914 0.0672 0.82 0.9064 0.2093 ] Network output: [ 0.001302 0.9742 0.01009 1.906e-05 -8.556e-06 1.013 1.436e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01318 Epoch 5328 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.034 0.9226 0.9451 -6.368e-05 2.859e-05 0.06408 -4.799e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002749 -0.002374 -0.01105 0.007361 0.9685 0.9731 0.005262 0.8781 0.8721 0.02071 ] Network output: [ 1.005 -0.01158 -0.007128 9.075e-05 -4.074e-05 0.008648 6.84e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1962 -0.00238 -0.2379 0.2192 0.9838 0.9934 0.219 0.6535 0.9246 0.7226 ] Network output: [ 0.005565 0.9417 0.9724 -7.867e-05 3.532e-05 0.07447 -5.928e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003636 0.001262 0.00219 0.003799 0.9902 0.9931 0.0037 0.9206 0.9411 0.0122 ] Network output: [ 0.03244 -0.113 0.9176 -0.0001264 5.676e-05 1.13 -9.528e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2068 0.1452 0.3025 0.1812 0.9854 0.9942 0.2074 0.6621 0.9298 0.7165 ] Network output: [ -0.02098 0.1068 1.081 0.0001071 -4.806e-05 0.8546 8.068e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05443 0.0514 0.1525 0.1538 0.9889 0.9932 0.05446 0.8843 0.9287 0.2219 ] Network output: [ -0.02452 0.01813 1.072 0.0001221 -5.48e-05 0.9591 9.199e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06735 0.06668 0.1714 0.1699 0.9852 0.9914 0.06735 0.8219 0.9061 0.2101 ] Network output: [ -0.01005 1.022 0.01342 -2.897e-06 1.301e-06 0.985 -2.183e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01574 Epoch 5329 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03133 0.9354 0.9459 -6.924e-05 3.109e-05 0.05575 -5.218e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002745 -0.002353 -0.01091 0.007055 0.9685 0.9731 0.005253 0.8785 0.8718 0.02058 ] Network output: [ 0.9657 0.1005 0.01586 3.84e-05 -1.724e-05 -0.04755 2.894e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1958 -0.0001861 -0.2274 0.1976 0.9839 0.9934 0.2184 0.6559 0.9243 0.721 ] Network output: [ 0.007235 0.9424 0.9707 -7.864e-05 3.53e-05 0.07211 -5.927e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003658 0.001262 0.002269 0.003404 0.9902 0.9931 0.003722 0.921 0.9412 0.01232 ] Network output: [ 0.00694 0.04529 0.9186 -0.0001932 8.674e-05 1.021 -0.0001456 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2085 0.1463 0.3043 0.1493 0.9854 0.9942 0.2091 0.6638 0.93 0.718 ] Network output: [ -0.01523 0.112 1.075 0.0001108 -4.974e-05 0.8443 8.351e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05401 0.05096 0.1488 0.1498 0.989 0.9932 0.05404 0.8833 0.9289 0.2199 ] Network output: [ -0.0173 0.001375 1.068 0.0001323 -5.941e-05 0.9662 9.973e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06705 0.06638 0.1689 0.1699 0.9851 0.9914 0.06706 0.8203 0.9064 0.2096 ] Network output: [ 0.002481 0.9705 0.009276 2.095e-05 -9.407e-06 1.015 1.579e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01334 Epoch 5330 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0338 0.923 0.9453 -6.456e-05 2.898e-05 0.06383 -4.865e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002743 -0.00237 -0.01109 0.007407 0.9685 0.9731 0.005251 0.8784 0.8723 0.02076 ] Network output: [ 1.008 -0.02154 -0.00841 9.715e-05 -4.361e-05 0.01419 7.322e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1954 -0.002415 -0.24 0.2217 0.9839 0.9934 0.218 0.654 0.9247 0.7242 ] Network output: [ 0.005053 0.9431 0.9728 -8.006e-05 3.594e-05 0.0737 -6.034e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003623 0.00126 0.002168 0.003834 0.9902 0.9931 0.003686 0.9209 0.9413 0.01222 ] Network output: [ 0.03413 -0.1238 0.9179 -0.0001207 5.419e-05 1.137 -9.096e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2057 0.1446 0.3015 0.1836 0.9854 0.9942 0.2063 0.6627 0.9299 0.7179 ] Network output: [ -0.02105 0.1063 1.081 0.000106 -4.757e-05 0.8554 7.985e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05433 0.05132 0.1525 0.1543 0.9889 0.9932 0.05436 0.8846 0.9287 0.2224 ] Network output: [ -0.02485 0.01849 1.072 0.0001205 -5.409e-05 0.9593 9.081e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06723 0.06657 0.1715 0.1702 0.9852 0.9914 0.06724 0.8224 0.9062 0.2105 ] Network output: [ -0.01083 1.026 0.01337 -4.585e-06 2.058e-06 0.9827 -3.455e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01639 Epoch 5331 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03078 0.9376 0.9462 -7.079e-05 3.178e-05 0.05435 -5.335e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002739 -0.002346 -0.01093 0.007052 0.9685 0.9731 0.005242 0.8788 0.8719 0.0206 ] Network output: [ 0.9624 0.1073 0.01832 3.658e-05 -1.642e-05 -0.05029 2.757e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1949 7.689e-05 -0.2276 0.1968 0.9839 0.9934 0.2175 0.6567 0.9244 0.7221 ] Network output: [ 0.007047 0.9438 0.9707 -7.984e-05 3.584e-05 0.07106 -6.017e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003649 0.00126 0.002262 0.00338 0.9902 0.9931 0.003713 0.9213 0.9413 0.01237 ] Network output: [ 0.004766 0.05817 0.9191 -0.0001974 8.861e-05 1.012 -0.0001488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2078 0.1458 0.3038 0.1469 0.9855 0.9942 0.2084 0.6645 0.9301 0.7194 ] Network output: [ -0.01434 0.1123 1.073 0.0001105 -4.959e-05 0.8435 8.325e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05387 0.05083 0.1482 0.1497 0.989 0.9932 0.0539 0.8835 0.929 0.2201 ] Network output: [ -0.01642 -0.000601 1.067 0.0001325 -5.947e-05 0.9672 9.984e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06691 0.06624 0.1685 0.1702 0.9851 0.9914 0.06692 0.8205 0.9065 0.2099 ] Network output: [ 0.003908 0.9661 0.008336 2.326e-05 -1.044e-05 1.018 1.753e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01368 Epoch 5332 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03364 0.9232 0.9455 -6.532e-05 2.932e-05 0.06371 -4.923e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002737 -0.002366 -0.01113 0.007456 0.9685 0.9731 0.005241 0.8787 0.8725 0.02081 ] Network output: [ 1.012 -0.03328 -0.01 0.000104 -4.671e-05 0.0207 7.841e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1946 -0.002474 -0.2422 0.2245 0.9839 0.9934 0.2171 0.6544 0.9249 0.7257 ] Network output: [ 0.004519 0.9445 0.9732 -8.14e-05 3.654e-05 0.07295 -6.134e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00361 0.001259 0.002145 0.003875 0.9902 0.9931 0.003673 0.9212 0.9414 0.01225 ] Network output: [ 0.03623 -0.1374 0.9181 -0.0001139 5.112e-05 1.146 -8.581e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2047 0.1441 0.3006 0.1865 0.9855 0.9942 0.2053 0.6631 0.93 0.7191 ] Network output: [ -0.02122 0.1057 1.081 0.0001048 -4.705e-05 0.8564 7.899e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05426 0.05126 0.1525 0.1549 0.989 0.9932 0.05429 0.8849 0.9287 0.223 ] Network output: [ -0.02529 0.01931 1.072 0.0001188 -5.331e-05 0.9593 8.95e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06714 0.06648 0.1716 0.1705 0.9852 0.9914 0.06715 0.8229 0.9062 0.2109 ] Network output: [ -0.0118 1.031 0.01335 -6.643e-06 2.982e-06 0.9797 -5.006e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01737 Epoch 5333 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03019 0.9399 0.9466 -7.237e-05 3.249e-05 0.05282 -5.454e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002734 -0.002339 -0.01094 0.007041 0.9685 0.9731 0.005231 0.8792 0.872 0.02062 ] Network output: [ 0.9583 0.1161 0.02129 3.347e-05 -1.503e-05 -0.05394 2.523e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1942 0.0003794 -0.2275 0.1955 0.9839 0.9934 0.2166 0.6574 0.9245 0.7232 ] Network output: [ 0.00691 0.9451 0.9708 -8.094e-05 3.634e-05 0.06998 -6.1e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003641 0.001259 0.002258 0.003349 0.9902 0.9931 0.003705 0.9215 0.9414 0.01241 ] Network output: [ 0.002156 0.0736 0.9196 -0.0002026 9.096e-05 1.002 -0.0001527 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2072 0.1455 0.3033 0.144 0.9855 0.9942 0.2078 0.6651 0.9302 0.7206 ] Network output: [ -0.01333 0.1127 1.072 0.0001102 -4.949e-05 0.8424 8.307e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05375 0.05071 0.1475 0.1496 0.989 0.9932 0.05377 0.8836 0.929 0.2203 ] Network output: [ -0.01537 -0.002619 1.066 0.0001328 -5.963e-05 0.9681 0.0001001 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06679 0.06612 0.1681 0.1704 0.9852 0.9914 0.0668 0.8206 0.9066 0.2101 ] Network output: [ 0.005647 0.9608 0.007225 2.609e-05 -1.171e-05 1.021 1.966e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01429 Epoch 5334 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03352 0.9231 0.9458 -6.594e-05 2.96e-05 0.06375 -4.969e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002732 -0.002363 -0.01118 0.007511 0.9685 0.9731 0.005232 0.879 0.8727 0.02086 ] Network output: [ 1.016 -0.04725 -0.01194 0.0001116 -5.008e-05 0.02845 8.407e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1939 -0.002565 -0.2444 0.2276 0.9839 0.9934 0.2163 0.6546 0.925 0.7272 ] Network output: [ 0.003961 0.9458 0.9737 -8.265e-05 3.711e-05 0.07225 -6.229e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003597 0.001258 0.002124 0.003926 0.9902 0.9931 0.00366 0.9214 0.9414 0.01227 ] Network output: [ 0.03881 -0.1543 0.9183 -0.0001057 4.746e-05 1.158 -7.968e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2037 0.1435 0.2996 0.1902 0.9855 0.9942 0.2043 0.6634 0.9301 0.7202 ] Network output: [ -0.02153 0.1051 1.081 0.0001036 -4.651e-05 0.8575 7.808e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05421 0.05122 0.1527 0.1556 0.989 0.9932 0.05424 0.8852 0.9287 0.2235 ] Network output: [ -0.02588 0.02075 1.073 0.0001168 -5.244e-05 0.9589 8.803e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06707 0.06641 0.1717 0.1707 0.9852 0.9914 0.06707 0.8233 0.9062 0.2113 ] Network output: [ -0.01298 1.037 0.01334 -9.119e-06 4.094e-06 0.976 -6.872e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01885 Epoch 5335 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02956 0.9424 0.947 -7.398e-05 3.321e-05 0.05113 -5.575e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002728 -0.002332 -0.01094 0.007023 0.9685 0.9731 0.005221 0.8794 0.8721 0.02063 ] Network output: [ 0.9533 0.1273 0.0249 2.883e-05 -1.294e-05 -0.05864 2.172e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1934 0.0007264 -0.2269 0.1937 0.9839 0.9934 0.2158 0.658 0.9245 0.724 ] Network output: [ 0.006833 0.9464 0.9708 -8.191e-05 3.677e-05 0.06886 -6.173e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003635 0.001258 0.002259 0.00331 0.9902 0.9931 0.003699 0.9217 0.9414 0.01245 ] Network output: [ -0.0009752 0.09207 0.9202 -0.0002091 9.389e-05 0.9889 -0.0001576 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2067 0.1452 0.3029 0.1405 0.9855 0.9942 0.2073 0.6656 0.9303 0.7218 ] Network output: [ -0.0122 0.1133 1.071 0.0001101 -4.944e-05 0.8409 8.3e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05363 0.0506 0.1467 0.1493 0.989 0.9932 0.05366 0.8836 0.929 0.2203 ] Network output: [ -0.01415 -0.004632 1.065 0.0001334 -5.989e-05 0.9689 0.0001005 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06668 0.06601 0.1675 0.1706 0.9852 0.9914 0.06669 0.8206 0.9066 0.2103 ] Network output: [ 0.007773 0.9543 0.005892 2.954e-05 -1.326e-05 1.024 2.227e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01528 Epoch 5336 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03346 0.9228 0.9461 -6.639e-05 2.98e-05 0.064 -5.003e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002727 -0.00236 -0.01122 0.007571 0.9685 0.9731 0.005223 0.8792 0.8729 0.02091 ] Network output: [ 1.02 -0.06395 -0.01424 0.0001198 -5.378e-05 0.03778 9.028e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1932 -0.002697 -0.2467 0.2313 0.9839 0.9934 0.2155 0.6546 0.9251 0.7286 ] Network output: [ 0.003373 0.9471 0.9742 -8.382e-05 3.763e-05 0.07159 -6.317e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003585 0.001258 0.002103 0.003987 0.9902 0.9932 0.003648 0.9215 0.9415 0.01228 ] Network output: [ 0.04197 -0.1752 0.9185 -9.603e-05 4.311e-05 1.172 -7.237e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2027 0.143 0.2988 0.1946 0.9855 0.9942 0.2033 0.6635 0.9301 0.7211 ] Network output: [ -0.02199 0.1045 1.081 0.0001023 -4.594e-05 0.8587 7.712e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05419 0.05122 0.1529 0.1563 0.989 0.9932 0.05422 0.8854 0.9287 0.2241 ] Network output: [ -0.02663 0.02299 1.073 0.0001146 -5.145e-05 0.958 8.637e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06702 0.06637 0.1719 0.1709 0.9852 0.9914 0.06703 0.8237 0.9061 0.2116 ] Network output: [ -0.01436 1.044 0.01332 -1.205e-05 5.41e-06 0.9713 -9.081e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02101 Epoch 5337 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02887 0.9452 0.9475 -7.564e-05 3.396e-05 0.04927 -5.7e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002724 -0.002324 -0.01093 0.006994 0.9685 0.9731 0.005211 0.8796 0.8721 0.02063 ] Network output: [ 0.9471 0.1412 0.02927 2.234e-05 -1.003e-05 -0.06457 1.684e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1928 0.001123 -0.2259 0.1913 0.9839 0.9934 0.215 0.6585 0.9245 0.7246 ] Network output: [ 0.006828 0.9476 0.9707 -8.273e-05 3.714e-05 0.0677 -6.235e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003631 0.001258 0.002265 0.003263 0.9902 0.9932 0.003695 0.9219 0.9415 0.0125 ] Network output: [ -0.004719 0.1141 0.9207 -0.0002172 9.751e-05 0.9737 -0.0001637 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2064 0.1451 0.3027 0.1363 0.9855 0.9942 0.207 0.6659 0.9303 0.7227 ] Network output: [ -0.01092 0.1144 1.069 0.0001102 -4.946e-05 0.8389 8.304e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05352 0.05051 0.1458 0.1489 0.989 0.9932 0.05355 0.8834 0.929 0.2202 ] Network output: [ -0.01271 -0.006555 1.063 0.0001343 -6.027e-05 0.9693 0.0001012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06659 0.06592 0.1668 0.1707 0.9851 0.9914 0.0666 0.8204 0.9066 0.2103 ] Network output: [ 0.01037 0.9467 0.004276 3.375e-05 -1.515e-05 1.028 2.544e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01684 Epoch 5338 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03346 0.922 0.9463 -6.663e-05 2.991e-05 0.06451 -5.022e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002723 -0.002358 -0.01126 0.007638 0.9685 0.9731 0.005215 0.8793 0.873 0.02096 ] Network output: [ 1.026 -0.08399 -0.0169 0.0001289 -5.786e-05 0.04909 9.712e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1926 -0.002882 -0.249 0.2356 0.9839 0.9934 0.2149 0.6544 0.9251 0.7298 ] Network output: [ 0.002756 0.9484 0.9748 -8.486e-05 3.81e-05 0.071 -6.395e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003574 0.001259 0.002084 0.00406 0.9902 0.9932 0.003637 0.9216 0.9415 0.01229 ] Network output: [ 0.04581 -0.2011 0.9187 -8.452e-05 3.794e-05 1.19 -6.37e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2018 0.1425 0.298 0.2 0.9855 0.9942 0.2024 0.6633 0.93 0.7218 ] Network output: [ -0.02262 0.104 1.082 0.000101 -4.532e-05 0.86 7.608e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0542 0.05124 0.1532 0.1571 0.989 0.9932 0.05423 0.8856 0.9286 0.2246 ] Network output: [ -0.02757 0.0263 1.073 0.0001121 -5.032e-05 0.9563 8.448e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06701 0.06636 0.172 0.171 0.9852 0.9914 0.06702 0.8239 0.9059 0.2118 ] Network output: [ -0.01595 1.053 0.01322 -1.544e-05 6.931e-06 0.9656 -1.163e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02416 Epoch 5339 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02811 0.9483 0.948 -7.734e-05 3.472e-05 0.0472 -5.829e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002719 -0.002317 -0.01091 0.006953 0.9685 0.9731 0.005202 0.8798 0.872 0.02062 ] Network output: [ 0.9395 0.1583 0.03457 1.367e-05 -6.136e-06 -0.0719 1.03e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1922 0.001577 -0.2241 0.1882 0.9839 0.9934 0.2144 0.6587 0.9244 0.7249 ] Network output: [ 0.006907 0.9487 0.9706 -8.338e-05 3.743e-05 0.0665 -6.283e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00363 0.001259 0.002277 0.003204 0.9902 0.9932 0.003693 0.922 0.9414 0.01254 ] Network output: [ -0.009161 0.1403 0.9213 -0.000227 0.0001019 0.9558 -0.0001711 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2062 0.145 0.3026 0.1312 0.9855 0.9942 0.2068 0.666 0.9303 0.7234 ] Network output: [ -0.00948 0.1159 1.067 0.0001104 -4.955e-05 0.8364 8.317e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05344 0.05042 0.1448 0.1483 0.989 0.9932 0.05347 0.8832 0.9289 0.22 ] Network output: [ -0.01103 -0.008253 1.062 0.0001354 -6.078e-05 0.9693 0.000102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06652 0.06585 0.1659 0.1707 0.9851 0.9914 0.06653 0.82 0.9065 0.2102 ] Network output: [ 0.01352 0.9376 0.00231 3.884e-05 -1.744e-05 1.033 2.927e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01918 Epoch 5340 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03353 0.9207 0.9467 -6.663e-05 2.991e-05 0.06535 -5.021e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002719 -0.002357 -0.0113 0.007713 0.9685 0.9731 0.005208 0.8793 0.8731 0.021 ] Network output: [ 1.033 -0.108 -0.01987 0.0001388 -6.232e-05 0.06289 0.0001046 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1921 -0.00313 -0.2512 0.2405 0.9839 0.9934 0.2143 0.6538 0.9251 0.7309 ] Network output: [ 0.002113 0.9496 0.9754 -8.575e-05 3.849e-05 0.07048 -6.462e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003564 0.00126 0.00207 0.004148 0.9902 0.9932 0.003626 0.9215 0.9414 0.01229 ] Network output: [ 0.05042 -0.2328 0.9189 -7.093e-05 3.184e-05 1.213 -5.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.201 0.1421 0.2974 0.2065 0.9855 0.9942 0.2016 0.6627 0.9299 0.7221 ] Network output: [ -0.02345 0.1037 1.082 9.947e-05 -4.466e-05 0.8613 7.496e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05426 0.05131 0.1537 0.158 0.9889 0.9932 0.05429 0.8856 0.9284 0.2252 ] Network output: [ -0.02869 0.03101 1.073 0.0001092 -4.903e-05 0.9537 8.231e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06704 0.0664 0.1721 0.171 0.9852 0.9914 0.06705 0.8241 0.9057 0.2118 ] Network output: [ -0.01767 1.064 0.01295 -1.921e-05 8.626e-06 0.9587 -1.448e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02873 Epoch 5341 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02726 0.9517 0.9486 -7.91e-05 3.551e-05 0.04491 -5.961e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002716 -0.002309 -0.01088 0.006897 0.9685 0.9731 0.005194 0.8798 0.8719 0.02059 ] Network output: [ 0.9304 0.1791 0.04094 2.425e-06 -1.089e-06 -0.08082 1.828e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1917 0.002093 -0.2216 0.1844 0.9839 0.9934 0.2139 0.6587 0.9243 0.7247 ] Network output: [ 0.007079 0.9498 0.9705 -8.38e-05 3.762e-05 0.06524 -6.315e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003631 0.00126 0.002296 0.003133 0.9902 0.9932 0.003694 0.9219 0.9414 0.01257 ] Network output: [ -0.01437 0.1711 0.9218 -0.0002389 0.0001072 0.9349 -0.00018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2063 0.1451 0.3028 0.1251 0.9855 0.9942 0.2069 0.6657 0.9302 0.7237 ] Network output: [ -0.007878 0.1182 1.065 0.0001107 -4.969e-05 0.8331 8.341e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05337 0.05036 0.1437 0.1475 0.989 0.9932 0.0534 0.8827 0.9288 0.2196 ] Network output: [ -0.009097 -0.009521 1.06 0.0001368 -6.141e-05 0.9687 0.0001031 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06648 0.06581 0.1648 0.1705 0.9851 0.9914 0.06648 0.8192 0.9063 0.2099 ] Network output: [ 0.01729 0.927 -6.901e-05 4.489e-05 -2.015e-05 1.039 3.383e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02262 Epoch 5342 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03367 0.9188 0.947 -6.633e-05 2.978e-05 0.06658 -4.998e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002716 -0.002357 -0.01132 0.007794 0.9685 0.9731 0.005201 0.8792 0.8731 0.02104 ] Network output: [ 1.04 -0.1367 -0.023 0.0001495 -6.713e-05 0.07972 0.0001127 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1917 -0.003454 -0.2531 0.2462 0.9839 0.9934 0.2138 0.6526 0.9251 0.7317 ] Network output: [ 0.001455 0.9506 0.9761 -8.642e-05 3.88e-05 0.07006 -6.513e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003555 0.001263 0.002061 0.004253 0.9902 0.9932 0.003617 0.9213 0.9413 0.01228 ] Network output: [ 0.05587 -0.2713 0.9192 -5.506e-05 2.472e-05 1.24 -4.149e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2002 0.1418 0.297 0.2144 0.9855 0.9942 0.2008 0.6616 0.9297 0.722 ] Network output: [ -0.02448 0.1038 1.083 9.789e-05 -4.395e-05 0.8626 7.377e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05438 0.05144 0.1542 0.1589 0.9889 0.9932 0.05441 0.8855 0.9281 0.2256 ] Network output: [ -0.02999 0.03753 1.073 0.000106 -4.757e-05 0.9497 7.985e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06713 0.06649 0.1721 0.1708 0.9852 0.9914 0.06714 0.8239 0.9053 0.2117 ] Network output: [ -0.01938 1.076 0.01234 -2.318e-05 1.04e-05 0.9507 -1.747e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03529 Epoch 5343 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0263 0.9555 0.9492 -8.091e-05 3.632e-05 0.04236 -6.098e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002713 -0.002301 -0.01082 0.006823 0.9685 0.9731 0.005187 0.8797 0.8716 0.02054 ] Network output: [ 0.9196 0.2039 0.04845 -1.177e-05 5.285e-06 -0.09155 -8.872e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1915 0.002675 -0.2182 0.1796 0.9839 0.9934 0.2135 0.6582 0.924 0.7241 ] Network output: [ 0.007348 0.9507 0.9703 -8.395e-05 3.769e-05 0.06392 -6.327e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003636 0.001263 0.002323 0.003048 0.9902 0.9931 0.003699 0.9217 0.9412 0.0126 ] Network output: [ -0.02033 0.2068 0.9222 -0.0002528 0.0001135 0.9107 -0.0001905 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2066 0.1454 0.3031 0.1181 0.9855 0.9942 0.2072 0.6648 0.93 0.7236 ] Network output: [ -0.006146 0.1213 1.063 0.0001111 -4.987e-05 0.8288 8.372e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05333 0.05032 0.1424 0.1465 0.989 0.9932 0.05336 0.8819 0.9285 0.2189 ] Network output: [ -0.006934 -0.01008 1.057 0.0001384 -6.214e-05 0.9673 0.0001043 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06647 0.0658 0.1635 0.1702 0.9851 0.9914 0.06648 0.8181 0.906 0.2093 ] Network output: [ 0.02169 0.9151 -0.002886 5.192e-05 -2.331e-05 1.045 3.913e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02751 Epoch 5344 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03388 0.9162 0.9475 -6.567e-05 2.948e-05 0.0683 -4.949e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002713 -0.002357 -0.01134 0.007882 0.9685 0.9731 0.005196 0.8789 0.8729 0.02106 ] Network output: [ 1.048 -0.1704 -0.02603 0.0001607 -7.215e-05 0.1001 0.0001211 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1914 -0.003864 -0.2546 0.2526 0.9839 0.9934 0.2136 0.6507 0.9249 0.732 ] Network output: [ 0.0008049 0.9514 0.9768 -8.679e-05 3.896e-05 0.06975 -6.541e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003548 0.001267 0.002062 0.004378 0.9902 0.9931 0.003611 0.9209 0.9411 0.01226 ] Network output: [ 0.06217 -0.3172 0.9196 -3.682e-05 1.653e-05 1.273 -2.775e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1996 0.1417 0.297 0.2237 0.9855 0.9942 0.2002 0.6597 0.9294 0.7213 ] Network output: [ -0.02569 0.1044 1.084 9.626e-05 -4.321e-05 0.8636 7.254e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05456 0.05164 0.1549 0.16 0.9889 0.9932 0.05459 0.8852 0.9277 0.2259 ] Network output: [ -0.03142 0.04627 1.073 0.0001023 -4.593e-05 0.944 7.711e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06729 0.06666 0.172 0.1704 0.9852 0.9914 0.0673 0.8234 0.9048 0.2112 ] Network output: [ -0.02082 1.089 0.01117 -2.691e-05 1.208e-05 0.9416 -2.028e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04451 Epoch 5345 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02522 0.9596 0.9501 -8.275e-05 3.715e-05 0.03953 -6.236e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002711 -0.002294 -0.01075 0.006731 0.9685 0.9731 0.005183 0.8794 0.8711 0.02046 ] Network output: [ 0.9071 0.2329 0.05703 -2.923e-05 1.312e-05 -0.1043 -2.203e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1914 0.003322 -0.2139 0.1739 0.9839 0.9934 0.2135 0.6571 0.9235 0.7228 ] Network output: [ 0.007699 0.9516 0.9701 -8.38e-05 3.762e-05 0.06256 -6.315e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003646 0.001267 0.002358 0.002949 0.9902 0.9931 0.00371 0.9213 0.9409 0.01262 ] Network output: [ -0.02693 0.2471 0.9224 -0.0002687 0.0001206 0.8833 -0.0002025 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2073 0.146 0.3036 0.1102 0.9855 0.9942 0.2079 0.6632 0.9297 0.7229 ] Network output: [ -0.004368 0.1257 1.06 0.0001115 -5.006e-05 0.8234 8.404e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05334 0.05032 0.1409 0.1452 0.989 0.9932 0.05337 0.8807 0.9281 0.2179 ] Network output: [ -0.004623 -0.009584 1.055 0.0001402 -6.294e-05 0.9648 0.0001057 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06651 0.06583 0.162 0.1696 0.9851 0.9913 0.06652 0.8163 0.9056 0.2084 ] Network output: [ 0.02662 0.9023 -0.006086 5.977e-05 -2.683e-05 1.051 4.504e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03426 Epoch 5346 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03415 0.9128 0.9481 -6.461e-05 2.901e-05 0.07055 -4.869e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002711 -0.00236 -0.01133 0.007972 0.9685 0.9731 0.005192 0.8783 0.8726 0.02106 ] Network output: [ 1.057 -0.2091 -0.02853 0.0001717 -7.709e-05 0.1244 0.0001294 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1914 -0.004368 -0.2553 0.2598 0.9839 0.9934 0.2135 0.648 0.9246 0.7319 ] Network output: [ 0.0002008 0.952 0.9776 -8.674e-05 3.894e-05 0.06959 -6.537e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003545 0.001273 0.002078 0.004522 0.9902 0.9931 0.003607 0.9203 0.9407 0.01222 ] Network output: [ 0.06925 -0.3706 0.9201 -1.643e-05 7.374e-06 1.312 -1.238e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1993 0.1417 0.2974 0.2344 0.9854 0.9942 0.1999 0.6569 0.9289 0.7197 ] Network output: [ -0.02701 0.1057 1.085 9.467e-05 -4.25e-05 0.8641 7.134e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05484 0.05192 0.1556 0.1611 0.9889 0.9932 0.05487 0.8845 0.9271 0.2259 ] Network output: [ -0.03287 0.05761 1.072 9.841e-05 -4.418e-05 0.9361 7.416e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06756 0.06692 0.1717 0.1697 0.9852 0.9914 0.06756 0.8225 0.904 0.2103 ] Network output: [ -0.02157 1.102 0.009134 -2.968e-05 1.332e-05 0.9322 -2.237e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05713 Epoch 5347 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02401 0.9641 0.9511 -8.458e-05 3.797e-05 0.03642 -6.375e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002712 -0.002288 -0.01065 0.006618 0.9685 0.9731 0.005181 0.8788 0.8704 0.02035 ] Network output: [ 0.8934 0.2661 0.06629 -5.004e-05 2.246e-05 -0.1193 -3.771e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1917 0.004025 -0.2087 0.1673 0.9839 0.9934 0.2138 0.6551 0.9229 0.7208 ] Network output: [ 0.008089 0.9524 0.9699 -8.327e-05 3.738e-05 0.06115 -6.275e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003663 0.001273 0.002399 0.002838 0.9902 0.9931 0.003727 0.9206 0.9404 0.01262 ] Network output: [ -0.03384 0.2912 0.9221 -0.0002862 0.0001285 0.8532 -0.0002157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2084 0.1468 0.3042 0.1015 0.9855 0.9942 0.2091 0.6607 0.9291 0.7215 ] Network output: [ -0.002703 0.1314 1.058 0.0001119 -5.022e-05 0.817 8.431e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05341 0.05037 0.1393 0.1436 0.989 0.9932 0.05344 0.8791 0.9276 0.2165 ] Network output: [ -0.002346 -0.007674 1.052 0.0001419 -6.372e-05 0.9611 0.000107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06661 0.06593 0.1603 0.1686 0.9851 0.9913 0.06662 0.8139 0.9049 0.2071 ] Network output: [ 0.03175 0.8892 -0.009458 6.802e-05 -3.054e-05 1.057 5.126e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04313 Epoch 5348 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03445 0.9087 0.9488 -6.31e-05 2.833e-05 0.07334 -4.755e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00271 -0.002363 -0.0113 0.008061 0.9686 0.9732 0.00519 0.8774 0.8721 0.02103 ] Network output: [ 1.065 -0.252 -0.02987 0.0001814 -8.146e-05 0.1525 0.0001367 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1916 -0.004966 -0.2546 0.2674 0.9839 0.9934 0.2138 0.644 0.924 0.731 ] Network output: [ -0.000303 0.9522 0.9785 -8.611e-05 3.866e-05 0.0696 -6.49e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003547 0.00128 0.002114 0.004683 0.9902 0.9931 0.003609 0.9192 0.9401 0.01216 ] Network output: [ 0.07689 -0.4305 0.9208 5.473e-06 -2.457e-06 1.356 4.125e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1993 0.1419 0.2986 0.2463 0.9854 0.9942 0.1999 0.6527 0.9282 0.7172 ] Network output: [ -0.02832 0.1078 1.085 9.33e-05 -4.189e-05 0.864 7.032e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05523 0.05232 0.1563 0.1622 0.9888 0.9932 0.05527 0.8833 0.9263 0.2257 ] Network output: [ -0.03418 0.07168 1.071 9.447e-05 -4.241e-05 0.9258 7.12e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06794 0.06731 0.171 0.1687 0.9852 0.9914 0.06795 0.8208 0.9029 0.2089 ] Network output: [ -0.02107 1.112 0.005891 -3.044e-05 1.366e-05 0.9237 -2.294e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07361 Epoch 5349 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02266 0.969 0.9523 -8.634e-05 3.876e-05 0.03303 -6.506e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002715 -0.002283 -0.01053 0.006488 0.9685 0.9731 0.005185 0.8779 0.8693 0.02021 ] Network output: [ 0.8792 0.3026 0.07542 -7.385e-05 3.315e-05 -0.1368 -5.565e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1925 0.004763 -0.2029 0.1599 0.9838 0.9934 0.2147 0.6519 0.9219 0.7178 ] Network output: [ 0.008435 0.9531 0.9699 -8.234e-05 3.696e-05 0.05974 -6.205e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003688 0.001281 0.002441 0.002718 0.9902 0.9931 0.003752 0.9194 0.9396 0.0126 ] Network output: [ -0.04045 0.337 0.9209 -0.0003042 0.0001366 0.8217 -0.0002292 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2101 0.1479 0.3047 0.09264 0.9854 0.9942 0.2107 0.6568 0.9284 0.7192 ] Network output: [ -0.001403 0.1385 1.055 0.000112 -5.029e-05 0.8096 8.442e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05355 0.0505 0.1378 0.1418 0.9889 0.9932 0.05358 0.8769 0.9267 0.2148 ] Network output: [ -0.0004054 -0.004066 1.049 0.0001434 -6.438e-05 0.9562 0.0001081 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06679 0.0661 0.1585 0.1674 0.985 0.9913 0.06679 0.8105 0.9039 0.2055 ] Network output: [ 0.03649 0.8773 -0.01257 7.589e-05 -3.407e-05 1.063 5.719e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0541 Epoch 5350 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03474 0.9039 0.9498 -6.111e-05 2.744e-05 0.07661 -4.606e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002711 -0.002368 -0.01122 0.008139 0.9686 0.9732 0.005189 0.8761 0.8713 0.02096 ] Network output: [ 1.072 -0.2969 -0.02933 0.0001883 -8.452e-05 0.1834 0.0001419 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1922 -0.005643 -0.2521 0.2749 0.9838 0.9934 0.2144 0.6385 0.9232 0.7291 ] Network output: [ -0.0006414 0.9519 0.9793 -8.472e-05 3.803e-05 0.0698 -6.385e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003555 0.00129 0.002175 0.004857 0.9901 0.9931 0.003618 0.9177 0.9393 0.01209 ] Network output: [ 0.08468 -0.4941 0.9216 2.757e-05 -1.238e-05 1.403 2.078e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1999 0.1425 0.3005 0.2589 0.9854 0.9942 0.2005 0.647 0.9272 0.7134 ] Network output: [ -0.02947 0.1108 1.086 9.243e-05 -4.15e-05 0.863 6.966e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05576 0.05284 0.1569 0.1633 0.9888 0.9931 0.05579 0.8816 0.9252 0.225 ] Network output: [ -0.03514 0.08812 1.069 9.091e-05 -4.081e-05 0.9132 6.851e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06847 0.06784 0.1699 0.1675 0.9852 0.9914 0.06848 0.8182 0.9015 0.2069 ] Network output: [ -0.0187 1.118 0.001206 -2.795e-05 1.255e-05 0.9179 -2.107e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0937 Epoch 5351 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02121 0.9741 0.9537 -8.786e-05 3.944e-05 0.02944 -6.622e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002723 -0.002282 -0.01039 0.006347 0.9685 0.9731 0.005195 0.8765 0.8679 0.02004 ] Network output: [ 0.8662 0.3406 0.08315 -9.955e-05 4.469e-05 -0.1565 -7.503e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1939 0.005497 -0.197 0.1523 0.9838 0.9934 0.2162 0.6471 0.9207 0.7139 ] Network output: [ 0.008612 0.9538 0.9702 -8.097e-05 3.635e-05 0.05836 -6.102e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003722 0.001292 0.002477 0.0026 0.9901 0.993 0.003787 0.9178 0.9386 0.01255 ] Network output: [ -0.04584 0.3808 0.9185 -0.0003209 0.0001441 0.7911 -0.0002418 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2123 0.1494 0.3048 0.08438 0.9854 0.9942 0.2129 0.6512 0.9273 0.716 ] Network output: [ -0.0007902 0.1468 1.053 0.0001118 -5.02e-05 0.8018 8.427e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0538 0.05073 0.1363 0.1398 0.9889 0.9931 0.05383 0.874 0.9256 0.213 ] Network output: [ 0.0007951 0.00134 1.047 0.0001443 -6.479e-05 0.9504 0.0001088 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06705 0.06636 0.1568 0.166 0.985 0.9912 0.06706 0.8062 0.9025 0.2037 ] Network output: [ 0.03997 0.8682 -0.01475 8.221e-05 -3.691e-05 1.067 6.195e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06649 Epoch 5352 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03493 0.8988 0.951 -5.871e-05 2.636e-05 0.08012 -4.424e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002713 -0.002374 -0.0111 0.008195 0.9686 0.9732 0.005192 0.8743 0.87 0.02084 ] Network output: [ 1.076 -0.3398 -0.02621 0.0001902 -8.539e-05 0.2151 0.0001433 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1931 -0.006352 -0.2474 0.2816 0.9838 0.9934 0.2155 0.6313 0.9221 0.7261 ] Network output: [ -0.0007519 0.951 0.98 -8.242e-05 3.7e-05 0.07018 -6.211e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003573 0.001301 0.002265 0.005033 0.9901 0.993 0.003636 0.9157 0.9382 0.01202 ] Network output: [ 0.09197 -0.5557 0.9222 4.78e-05 -2.146e-05 1.45 3.603e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.201 0.1434 0.3031 0.2711 0.9854 0.9942 0.2016 0.6395 0.9258 0.7085 ] Network output: [ -0.03028 0.1144 1.085 9.237e-05 -4.147e-05 0.8611 6.961e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05644 0.05349 0.1574 0.1643 0.9887 0.9931 0.05647 0.8792 0.9238 0.224 ] Network output: [ -0.03553 0.1058 1.067 8.823e-05 -3.961e-05 0.899 6.65e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06915 0.06852 0.1684 0.1661 0.9851 0.9914 0.06916 0.8146 0.8997 0.2044 ] Network output: [ -0.01416 1.117 -0.004843 -2.137e-05 9.592e-06 0.9164 -1.61e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1157 Epoch 5353 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01974 0.979 0.9553 -8.893e-05 3.992e-05 0.02586 -6.702e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002735 -0.002285 -0.01024 0.006208 0.9686 0.9731 0.005215 0.8746 0.8661 0.01986 ] Network output: [ 0.8563 0.3762 0.08787 -0.0001251 5.614e-05 -0.1771 -9.424e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1959 0.006175 -0.1917 0.1452 0.9838 0.9934 0.2185 0.6405 0.919 0.7093 ] Network output: [ 0.008487 0.9546 0.971 -7.917e-05 3.554e-05 0.0571 -5.966e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003767 0.001307 0.002502 0.0025 0.99 0.993 0.003833 0.9157 0.9373 0.01247 ] Network output: [ -0.04891 0.4173 0.9144 -0.0003338 0.0001499 0.7648 -0.0002516 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.215 0.1513 0.3043 0.07794 0.9854 0.9942 0.2156 0.6439 0.9258 0.712 ] Network output: [ -0.001168 0.1556 1.053 0.0001113 -4.995e-05 0.7944 8.385e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05418 0.05107 0.1353 0.138 0.9888 0.9931 0.05421 0.8704 0.9241 0.2111 ] Network output: [ 0.0008509 0.008354 1.046 0.0001444 -6.483e-05 0.9441 0.0001088 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06741 0.06671 0.1554 0.1644 0.9849 0.9912 0.06742 0.801 0.9007 0.2018 ] Network output: [ 0.04116 0.8642 -0.01524 8.557e-05 -3.842e-05 1.069 6.449e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07857 Epoch 5354 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03494 0.8939 0.9525 -5.6e-05 2.514e-05 0.08345 -4.221e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002718 -0.002381 -0.01093 0.00822 0.9686 0.9732 0.005199 0.872 0.8684 0.02069 ] Network output: [ 1.076 -0.3749 -0.02016 0.0001853 -8.318e-05 0.2438 0.0001396 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1945 -0.006998 -0.2403 0.2863 0.9838 0.9934 0.2171 0.6224 0.9205 0.7221 ] Network output: [ -0.0005911 0.9495 0.9807 -7.914e-05 3.553e-05 0.07073 -5.965e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003603 0.001315 0.002381 0.005193 0.99 0.993 0.003666 0.9132 0.9368 0.01194 ] Network output: [ 0.09787 -0.607 0.9223 6.336e-05 -2.845e-05 1.489 4.775e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.203 0.1448 0.3061 0.2814 0.9853 0.9942 0.2036 0.6303 0.9241 0.7027 ] Network output: [ -0.03059 0.118 1.085 9.34e-05 -4.193e-05 0.8587 7.039e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05723 0.05426 0.1576 0.1651 0.9886 0.993 0.05726 0.876 0.922 0.2228 ] Network output: [ -0.03526 0.1228 1.063 8.701e-05 -3.906e-05 0.8848 6.557e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06996 0.06933 0.1665 0.1647 0.9851 0.9913 0.06997 0.81 0.8975 0.2017 ] Network output: [ -0.007837 1.107 -0.01158 -1.101e-05 4.942e-06 0.9202 -8.295e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1361 Epoch 5355 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01845 0.9831 0.957 -8.915e-05 4.002e-05 0.02271 -6.719e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002752 -0.002293 -0.0101 0.006092 0.9686 0.9732 0.005244 0.8722 0.8639 0.01968 ] Network output: [ 0.8517 0.4037 0.0882 -0.0001472 6.608e-05 -0.1958 -0.0001109 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1987 0.006735 -0.1878 0.1399 0.9837 0.9934 0.2216 0.6322 0.9169 0.7042 ] Network output: [ 0.007994 0.9554 0.9722 -7.69e-05 3.452e-05 0.05609 -5.795e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00382 0.001327 0.002511 0.002438 0.99 0.9929 0.003887 0.913 0.9356 0.01236 ] Network output: [ -0.04865 0.4396 0.9087 -0.00034 0.0001526 0.7476 -0.0002562 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2181 0.1536 0.3031 0.07474 0.9853 0.9942 0.2188 0.6349 0.9239 0.7073 ] Network output: [ -0.002669 0.1635 1.054 0.0001105 -4.96e-05 0.7887 8.326e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05467 0.05154 0.1348 0.1366 0.9887 0.993 0.0547 0.8664 0.9223 0.2096 ] Network output: [ -0.0004596 0.01636 1.047 0.0001436 -6.445e-05 0.9382 0.0001082 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06785 0.06715 0.1547 0.163 0.9848 0.9911 0.06786 0.7952 0.8986 0.2002 ] Network output: [ 0.03932 0.8668 -0.01349 8.488e-05 -3.811e-05 1.068 6.397e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08748 Epoch 5356 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0347 0.8901 0.9543 -5.324e-05 2.39e-05 0.08601 -4.012e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002726 -0.002389 -0.01073 0.0082 0.9686 0.9732 0.005211 0.8694 0.8663 0.0205 ] Network output: [ 1.072 -0.395 -0.0116 0.0001722 -7.73e-05 0.2642 0.0001298 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1963 -0.007423 -0.2314 0.2879 0.9837 0.9934 0.2191 0.6123 0.9185 0.7172 ] Network output: [ -0.0001495 0.9474 0.9812 -7.5e-05 3.367e-05 0.07135 -5.652e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003644 0.001332 0.002513 0.005313 0.9899 0.9929 0.003708 0.9102 0.9351 0.01189 ] Network output: [ 0.1013 -0.6377 0.9214 7.115e-05 -3.194e-05 1.514 5.362e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2056 0.1467 0.3092 0.2879 0.9853 0.9941 0.2063 0.62 0.922 0.6965 ] Network output: [ -0.03037 0.1209 1.084 9.564e-05 -4.294e-05 0.8563 7.208e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05809 0.05507 0.1576 0.1657 0.9885 0.9929 0.05812 0.8723 0.9201 0.2216 ] Network output: [ -0.03435 0.1362 1.06 8.766e-05 -3.935e-05 0.8729 6.606e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07083 0.07018 0.1646 0.1636 0.985 0.9913 0.07083 0.8046 0.8951 0.1992 ] Network output: [ -0.001173 1.092 -0.0176 9.388e-07 -4.214e-07 0.928 7.075e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1494 Epoch 5357 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01762 0.9851 0.9586 -8.807e-05 3.954e-05 0.02071 -6.637e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002774 -0.002306 -0.009983 0.006017 0.9686 0.9732 0.005279 0.8695 0.8615 0.01953 ] Network output: [ 0.8538 0.4167 0.08361 -0.0001626 7.3e-05 -0.2086 -0.0001225 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.202 0.007117 -0.1856 0.1373 0.9837 0.9933 0.2252 0.6226 0.9146 0.6991 ] Network output: [ 0.007224 0.9557 0.9739 -7.409e-05 3.326e-05 0.0556 -5.584e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003877 0.001352 0.002508 0.002436 0.9899 0.9928 0.003945 0.91 0.9336 0.01225 ] Network output: [ -0.04463 0.4415 0.9021 -0.0003369 0.0001513 0.7443 -0.0002539 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2213 0.156 0.3013 0.07593 0.9853 0.9941 0.222 0.6249 0.9218 0.7025 ] Network output: [ -0.005104 0.1688 1.056 0.0001099 -4.934e-05 0.7861 8.282e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05526 0.05211 0.135 0.1362 0.9886 0.9929 0.05529 0.8623 0.9202 0.2088 ] Network output: [ -0.002991 0.02412 1.049 0.000142 -6.376e-05 0.9336 0.000107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06835 0.06765 0.1546 0.1621 0.9848 0.9911 0.06836 0.7894 0.8963 0.1992 ] Network output: [ 0.03448 0.8757 -0.009422 8.002e-05 -3.592e-05 1.065 6.031e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09023 Epoch 5358 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03424 0.8881 0.956 -5.066e-05 2.274e-05 0.08716 -3.818e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002736 -0.002396 -0.01051 0.008132 0.9686 0.9732 0.005228 0.8665 0.864 0.02032 ] Network output: [ 1.063 -0.3936 -0.002088 0.0001513 -6.791e-05 0.2708 0.000114 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1986 -0.007443 -0.2217 0.2856 0.9837 0.9933 0.2216 0.602 0.9163 0.7121 ] Network output: [ 0.0005344 0.945 0.9817 -7.03e-05 3.156e-05 0.07199 -5.298e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003695 0.001353 0.002641 0.005368 0.9898 0.9928 0.00376 0.9071 0.9333 0.01187 ] Network output: [ 0.1014 -0.6389 0.9193 6.883e-05 -3.09e-05 1.517 5.187e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2089 0.1491 0.3115 0.2888 0.9853 0.9941 0.2095 0.6094 0.9198 0.691 ] Network output: [ -0.02968 0.1219 1.083 9.9e-05 -4.445e-05 0.8548 7.461e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05889 0.05584 0.1574 0.166 0.9884 0.9928 0.05892 0.8684 0.9181 0.2209 ] Network output: [ -0.03303 0.143 1.058 9.03e-05 -4.054e-05 0.8658 6.805e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07163 0.07098 0.1632 0.163 0.985 0.9913 0.07163 0.799 0.8927 0.1977 ] Network output: [ 0.003747 1.077 -0.02113 1.098e-05 -4.932e-06 0.937 8.279e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1505 Epoch 5359 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01758 0.9842 0.9598 -8.529e-05 3.829e-05 0.02054 -6.428e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002796 -0.002321 -0.009899 0.005998 0.9686 0.9732 0.005317 0.8667 0.859 0.01942 ] Network output: [ 0.8626 0.4124 0.07475 -0.0001691 7.59e-05 -0.213 -0.0001274 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2054 0.007278 -0.1853 0.1381 0.9836 0.9933 0.229 0.6126 0.9123 0.6949 ] Network output: [ 0.00644 0.955 0.9758 -7.066e-05 3.172e-05 0.05601 -5.325e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00393 0.001381 0.002497 0.002499 0.9898 0.9928 0.003999 0.9071 0.9316 0.01215 ] Network output: [ -0.03739 0.4213 0.8955 -0.0003244 0.0001456 0.7567 -0.0002445 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2243 0.1585 0.2994 0.08158 0.9853 0.9941 0.2249 0.6148 0.9196 0.6982 ] Network output: [ -0.007954 0.1695 1.059 0.0001101 -4.941e-05 0.7878 8.295e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05588 0.05272 0.1359 0.1368 0.9885 0.9928 0.05591 0.8586 0.9182 0.209 ] Network output: [ -0.006202 0.0298 1.052 0.0001404 -6.301e-05 0.9312 0.0001058 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06886 0.06816 0.1553 0.1619 0.9847 0.9911 0.06886 0.7842 0.894 0.199 ] Network output: [ 0.02762 0.8882 -0.003581 7.225e-05 -3.244e-05 1.06 5.445e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.08569 Epoch 5360 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03371 0.8883 0.9575 -4.842e-05 2.174e-05 0.0866 -3.649e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00275 -0.002401 -0.01033 0.008022 0.9686 0.9732 0.005249 0.8638 0.8616 0.02016 ] Network output: [ 1.051 -0.369 0.005982 0.0001251 -5.615e-05 0.2605 9.425e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.201 -0.00696 -0.213 0.2793 0.9836 0.9933 0.2243 0.5926 0.914 0.7074 ] Network output: [ 0.001372 0.9424 0.982 -6.546e-05 2.939e-05 0.07263 -4.933e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003749 0.001376 0.002744 0.005343 0.9897 0.9928 0.003816 0.9043 0.9314 0.01188 ] Network output: [ 0.09772 -0.6083 0.9161 5.632e-05 -2.528e-05 1.497 4.244e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2124 0.1517 0.3128 0.2836 0.9852 0.9941 0.2131 0.6 0.9177 0.687 ] Network output: [ -0.02869 0.12 1.083 0.0001031 -4.631e-05 0.8552 7.774e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0595 0.05643 0.1571 0.166 0.9883 0.9928 0.05954 0.8648 0.9163 0.221 ] Network output: [ -0.03156 0.1415 1.057 9.466e-05 -4.25e-05 0.8654 7.134e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07223 0.07158 0.1624 0.1631 0.9849 0.9912 0.07224 0.7939 0.8907 0.1974 ] Network output: [ 0.005408 1.065 -0.02094 1.64e-05 -7.364e-06 0.9447 1.236e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1379 Epoch 5361 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01846 0.9799 0.9604 -8.088e-05 3.631e-05 0.02243 -6.095e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002815 -0.002336 -0.00986 0.006031 0.9686 0.9732 0.00535 0.8641 0.8569 0.01937 ] Network output: [ 0.8761 0.3929 0.0633 -0.0001668 7.488e-05 -0.2092 -0.0001257 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2084 0.007211 -0.1866 0.1415 0.9836 0.9933 0.2324 0.6033 0.9101 0.6921 ] Network output: [ 0.005929 0.9529 0.9775 -6.667e-05 2.993e-05 0.05756 -5.024e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003971 0.001408 0.002487 0.002613 0.9897 0.9927 0.004041 0.9045 0.9299 0.01209 ] Network output: [ -0.02843 0.3835 0.8902 -0.0003053 0.000137 0.7819 -0.0002301 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2265 0.1606 0.2978 0.0904 0.9852 0.9941 0.2272 0.6059 0.9176 0.6951 ] Network output: [ -0.0106 0.1648 1.063 0.0001113 -4.999e-05 0.7939 8.391e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05643 0.05327 0.1375 0.1385 0.9885 0.9928 0.05646 0.8558 0.9165 0.2102 ] Network output: [ -0.009386 0.03187 1.055 0.0001392 -6.25e-05 0.932 0.0001049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06929 0.0686 0.1565 0.1624 0.9847 0.991 0.0693 0.7804 0.8921 0.1997 ] Network output: [ 0.02019 0.9004 0.003034 6.346e-05 -2.849e-05 1.056 4.783e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0757 Epoch 5362 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03333 0.8902 0.9583 -4.649e-05 2.087e-05 0.0846 -3.504e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002765 -0.002404 -0.0102 0.00789 0.9686 0.9732 0.005273 0.8616 0.8595 0.02006 ] Network output: [ 1.04 -0.3261 0.01067 9.78e-05 -4.391e-05 0.2353 7.371e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2035 -0.00606 -0.2068 0.2702 0.9836 0.9933 0.2271 0.5851 0.9119 0.704 ] Network output: [ 0.002261 0.9397 0.9822 -6.092e-05 2.735e-05 0.07334 -4.591e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0038 0.001399 0.002807 0.005247 0.9897 0.9927 0.003868 0.9021 0.9298 0.01193 ] Network output: [ 0.09117 -0.5534 0.9124 3.637e-05 -1.633e-05 1.459 2.741e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2157 0.1543 0.313 0.2736 0.9852 0.9941 0.2163 0.5926 0.9159 0.6852 ] Network output: [ -0.02758 0.1153 1.083 0.0001076 -4.829e-05 0.8576 8.107e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05986 0.05677 0.1568 0.1658 0.9882 0.9927 0.05989 0.8618 0.9148 0.2218 ] Network output: [ -0.03017 0.1323 1.057 0.0001001 -4.493e-05 0.8714 7.542e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07256 0.07191 0.1624 0.1638 0.9849 0.9912 0.07257 0.7898 0.8891 0.1983 ] Network output: [ 0.003909 1.059 -0.01722 1.7e-05 -7.632e-06 0.9507 1.281e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1162 Epoch 5363 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02007 0.9732 0.9604 -7.54e-05 3.385e-05 0.0259 -5.682e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00283 -0.002349 -0.009863 0.006103 0.9686 0.9732 0.005374 0.8621 0.8553 0.01939 ] Network output: [ 0.8916 0.3643 0.05136 -0.000158 7.091e-05 -0.1995 -0.000119 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2108 0.006947 -0.1888 0.1465 0.9835 0.9933 0.235 0.5957 0.9085 0.691 ] Network output: [ 0.005811 0.9495 0.9787 -6.246e-05 2.804e-05 0.05998 -4.707e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003996 0.001429 0.002483 0.002753 0.9897 0.9927 0.004067 0.9025 0.9285 0.01206 ] Network output: [ -0.01953 0.3368 0.887 -0.0002837 0.0001274 0.8141 -0.0002138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.228 0.162 0.2969 0.1004 0.9852 0.9941 0.2287 0.5988 0.916 0.6936 ] Network output: [ -0.01264 0.1557 1.067 0.0001136 -5.1e-05 0.8033 8.561e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05685 0.05371 0.1394 0.1409 0.9884 0.9927 0.05688 0.8539 0.9152 0.2122 ] Network output: [ -0.01202 0.03032 1.059 0.0001389 -6.234e-05 0.9354 0.0001046 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06962 0.06893 0.158 0.1636 0.9847 0.991 0.06963 0.7779 0.8906 0.201 ] Network output: [ 0.01341 0.9108 0.009278 5.501e-05 -2.47e-05 1.053 4.146e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06361 Epoch 5364 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03321 0.8931 0.9585 -4.479e-05 2.011e-05 0.08177 -3.376e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00278 -0.002405 -0.01013 0.007757 0.9686 0.9732 0.005296 0.86 0.8577 0.02001 ] Network output: [ 1.031 -0.2746 0.01174 7.34e-05 -3.295e-05 0.2015 5.532e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2057 -0.004963 -0.2034 0.2602 0.9835 0.9933 0.2295 0.5798 0.9102 0.7021 ] Network output: [ 0.003127 0.9373 0.9822 -5.698e-05 2.558e-05 0.07406 -4.294e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003841 0.001418 0.002831 0.005108 0.9896 0.9927 0.003909 0.9005 0.9286 0.012 ] Network output: [ 0.08312 -0.4875 0.9092 1.313e-05 -5.893e-06 1.412 9.892e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2183 0.1564 0.3124 0.2612 0.9852 0.9941 0.2189 0.5875 0.9146 0.6853 ] Network output: [ -0.02645 0.1087 1.083 0.0001118 -5.018e-05 0.8614 8.424e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05994 0.05686 0.1566 0.1655 0.9882 0.9927 0.05998 0.8597 0.9138 0.2231 ] Network output: [ -0.02893 0.1181 1.059 0.0001057 -4.747e-05 0.8816 7.968e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07263 0.07198 0.163 0.165 0.9849 0.9912 0.07264 0.7868 0.8881 0.2001 ] Network output: [ 0.000632 1.055 -0.01145 1.455e-05 -6.534e-06 0.9556 1.097e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09235 Epoch 5365 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02205 0.9658 0.9598 -6.97e-05 3.129e-05 0.03002 -5.253e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002839 -0.002359 -0.009897 0.006196 0.9686 0.9732 0.005388 0.8607 0.8542 0.01946 ] Network output: [ 0.9065 0.332 0.04068 -0.0001452 6.52e-05 -0.1862 -0.0001095 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 0.00654 -0.1914 0.1521 0.9835 0.9932 0.2366 0.59 0.9073 0.6915 ] Network output: [ 0.006011 0.9457 0.9793 -5.853e-05 2.627e-05 0.06264 -4.411e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004007 0.001442 0.002487 0.002897 0.9897 0.9926 0.004077 0.9012 0.9274 0.01208 ] Network output: [ -0.01187 0.2887 0.8859 -0.0002633 0.0001182 0.8481 -0.0001984 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2286 0.1628 0.2967 0.1103 0.9852 0.9941 0.2292 0.5936 0.9147 0.6934 ] Network output: [ -0.01397 0.1444 1.07 0.0001163 -5.22e-05 0.8141 8.764e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05713 0.05401 0.1413 0.1435 0.9883 0.9927 0.05716 0.8529 0.9142 0.2146 ] Network output: [ -0.01392 0.02659 1.062 0.000139 -6.242e-05 0.9402 0.0001048 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06983 0.06915 0.1597 0.1651 0.9847 0.991 0.06984 0.7765 0.8895 0.2027 ] Network output: [ 0.007881 0.9202 0.01426 4.736e-05 -2.126e-05 1.05 3.569e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05203 Epoch 5366 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03332 0.8964 0.9581 -4.333e-05 1.945e-05 0.07867 -3.265e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002791 -0.002405 -0.01011 0.00764 0.9686 0.9732 0.005314 0.859 0.8564 0.02 ] Network output: [ 1.024 -0.2235 0.01044 5.393e-05 -2.421e-05 0.166 4.064e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2074 -0.003885 -0.2022 0.2506 0.9835 0.9933 0.2313 0.5765 0.9089 0.7015 ] Network output: [ 0.003925 0.9354 0.9819 -5.382e-05 2.416e-05 0.07464 -4.056e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003869 0.001431 0.00283 0.004955 0.9896 0.9926 0.003938 0.8996 0.9277 0.01207 ] Network output: [ 0.07486 -0.4222 0.9069 -9.888e-06 4.439e-06 1.366 -7.452e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.22 0.1578 0.3115 0.2487 0.9851 0.9941 0.2207 0.5843 0.9136 0.6868 ] Network output: [ -0.02535 0.1018 1.084 0.0001154 -5.179e-05 0.8657 8.695e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05984 0.05676 0.1565 0.1652 0.9882 0.9927 0.05987 0.8582 0.913 0.2247 ] Network output: [ -0.02782 0.1024 1.06 0.0001109 -4.981e-05 0.8933 8.361e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0725 0.07185 0.1638 0.1664 0.9849 0.9912 0.07251 0.7847 0.8874 0.2022 ] Network output: [ -0.002928 1.051 -0.005344 1.101e-05 -4.944e-06 0.9598 8.3e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07156 Epoch 5367 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02402 0.9588 0.9589 -6.448e-05 2.895e-05 0.03399 -4.859e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002842 -0.002365 -0.009946 0.006297 0.9686 0.9732 0.005394 0.8598 0.8535 0.01955 ] Network output: [ 0.9194 0.2995 0.03213 -0.0001308 5.872e-05 -0.1711 -9.858e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 0.006051 -0.1939 0.1577 0.9835 0.9932 0.2374 0.586 0.9065 0.693 ] Network output: [ 0.006375 0.9425 0.9795 -5.526e-05 2.481e-05 0.06498 -4.165e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004006 0.001448 0.0025 0.003035 0.9896 0.9926 0.004077 0.9004 0.9268 0.01212 ] Network output: [ -0.005818 0.2436 0.8866 -0.0002456 0.0001102 0.8805 -0.0001851 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2285 0.163 0.2972 0.1191 0.9852 0.9941 0.2292 0.5901 0.9139 0.6942 ] Network output: [ -0.01471 0.133 1.072 0.0001189 -5.336e-05 0.8246 8.958e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0573 0.05419 0.1432 0.1461 0.9883 0.9927 0.05733 0.8524 0.9135 0.2171 ] Network output: [ -0.01515 0.02238 1.064 0.0001393 -6.256e-05 0.945 0.000105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06995 0.06928 0.1611 0.1665 0.9847 0.9911 0.06996 0.776 0.8888 0.2045 ] Network output: [ 0.003737 0.9296 0.01764 4.06e-05 -1.823e-05 1.045 3.06e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04217 Epoch 5368 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03354 0.8997 0.9574 -4.218e-05 1.894e-05 0.07559 -3.179e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002799 -0.002403 -0.01013 0.007546 0.9687 0.9732 0.005326 0.8585 0.8555 0.02003 ] Network output: [ 1.018 -0.178 0.008256 3.962e-05 -1.779e-05 0.1338 2.986e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2084 -0.002942 -0.2022 0.2424 0.9835 0.9932 0.2325 0.5747 0.908 0.702 ] Network output: [ 0.004623 0.9341 0.9815 -5.151e-05 2.312e-05 0.07492 -3.882e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003886 0.001437 0.002816 0.004812 0.9896 0.9926 0.003955 0.8992 0.9271 0.01215 ] Network output: [ 0.06717 -0.364 0.9056 -3.072e-05 1.379e-05 1.324 -2.315e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.221 0.1586 0.3107 0.2374 0.9851 0.9941 0.2217 0.5825 0.913 0.6891 ] Network output: [ -0.02425 0.09545 1.084 0.0001182 -5.306e-05 0.8696 8.908e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05962 0.05655 0.1563 0.1649 0.9882 0.9926 0.05965 0.8572 0.9126 0.2262 ] Network output: [ -0.02677 0.08762 1.062 0.0001153 -5.177e-05 0.9044 8.691e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07226 0.07161 0.1647 0.1677 0.9849 0.9912 0.07227 0.7835 0.887 0.2043 ] Network output: [ -0.005877 1.049 -3.561e-05 7.584e-06 -3.405e-06 0.963 5.716e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05553 Epoch 5369 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02576 0.953 0.9579 -6.012e-05 2.699e-05 0.03736 -4.531e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002842 -0.002368 -0.01 0.006398 0.9686 0.9732 0.005392 0.8594 0.8531 0.01965 ] Network output: [ 0.9301 0.2691 0.02575 -0.0001161 5.212e-05 -0.1555 -8.75e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 0.005537 -0.1961 0.1631 0.9835 0.9932 0.2375 0.5833 0.9061 0.6951 ] Network output: [ 0.006757 0.9402 0.9794 -5.285e-05 2.373e-05 0.0667 -3.983e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003997 0.001448 0.002517 0.00316 0.9896 0.9926 0.004068 0.9 0.9264 0.01218 ] Network output: [ -0.001211 0.2035 0.8883 -0.0002308 0.0001036 0.9096 -0.000174 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2279 0.1628 0.2981 0.1268 0.9851 0.9941 0.2286 0.588 0.9134 0.6956 ] Network output: [ -0.01503 0.1228 1.074 0.000121 -5.431e-05 0.8338 9.118e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05737 0.05428 0.1448 0.1485 0.9883 0.9927 0.0574 0.8524 0.9131 0.2195 ] Network output: [ -0.01586 0.01866 1.065 0.0001395 -6.263e-05 0.949 0.0001051 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07 0.06933 0.1624 0.1679 0.9848 0.9911 0.07001 0.7761 0.8883 0.2062 ] Network output: [ 0.0008452 0.9388 0.01955 3.479e-05 -1.562e-05 1.04 2.622e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03433 Epoch 5370 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03376 0.9031 0.9565 -4.144e-05 1.861e-05 0.07265 -3.123e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002804 -0.0024 -0.01016 0.007474 0.9687 0.9732 0.005332 0.8584 0.8549 0.02007 ] Network output: [ 1.014 -0.1399 0.006093 2.973e-05 -1.335e-05 0.1065 2.24e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2089 -0.00217 -0.203 0.2357 0.9835 0.9932 0.233 0.574 0.9074 0.7031 ] Network output: [ 0.005181 0.9336 0.981 -5.002e-05 2.245e-05 0.07484 -3.769e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003893 0.001439 0.002797 0.004687 0.9896 0.9926 0.003962 0.8992 0.9267 0.01222 ] Network output: [ 0.06036 -0.3148 0.9052 -4.871e-05 2.187e-05 1.289 -3.671e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2214 0.1589 0.31 0.2277 0.9851 0.9941 0.2221 0.5818 0.9127 0.6917 ] Network output: [ -0.02319 0.09023 1.084 0.0001202 -5.398e-05 0.8727 9.062e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05934 0.05628 0.1562 0.1647 0.9882 0.9927 0.05937 0.8567 0.9124 0.2275 ] Network output: [ -0.02578 0.07479 1.063 0.0001188 -5.332e-05 0.9141 8.95e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07197 0.07132 0.1655 0.1689 0.9849 0.9912 0.07198 0.7827 0.8869 0.2062 ] Network output: [ -0.007943 1.046 0.004111 4.792e-06 -2.151e-06 0.9655 3.611e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04389 Epoch 5371 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02718 0.9486 0.9568 -5.673e-05 2.547e-05 0.03998 -4.276e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002838 -0.002369 -0.01006 0.006492 0.9687 0.9732 0.005386 0.8593 0.853 0.01975 ] Network output: [ 0.9387 0.242 0.02107 -0.000102 4.578e-05 -0.1408 -7.686e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 0.005035 -0.1982 0.168 0.9835 0.9932 0.237 0.5817 0.9059 0.6975 ] Network output: [ 0.00706 0.9388 0.9791 -5.127e-05 2.302e-05 0.06778 -3.864e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003984 0.001444 0.002533 0.003269 0.9896 0.9926 0.004054 0.9 0.9263 0.01225 ] Network output: [ 0.002254 0.1691 0.8906 -0.0002189 9.829e-05 0.9349 -0.000165 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.227 0.1622 0.299 0.1334 0.9852 0.9941 0.2277 0.5868 0.9131 0.6974 ] Network output: [ -0.01507 0.1142 1.075 0.0001225 -5.502e-05 0.8414 9.236e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05738 0.05429 0.1461 0.1506 0.9883 0.9927 0.05741 0.8526 0.9128 0.2216 ] Network output: [ -0.01625 0.01564 1.065 0.0001395 -6.261e-05 0.9522 0.0001051 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06999 0.06933 0.1634 0.1691 0.9848 0.9911 0.07 0.7765 0.888 0.2076 ] Network output: [ -0.001043 0.9473 0.02043 3e-05 -1.347e-05 1.035 2.261e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02836 Epoch 5372 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03389 0.9065 0.9556 -4.112e-05 1.846e-05 0.0699 -3.099e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002805 -0.002397 -0.0102 0.00742 0.9687 0.9732 0.005333 0.8585 0.8547 0.02012 ] Network output: [ 1.01 -0.1088 0.004236 2.327e-05 -1.045e-05 0.08415 1.754e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.209 -0.001563 -0.2042 0.2304 0.9835 0.9932 0.2331 0.5739 0.9071 0.7045 ] Network output: [ 0.005571 0.9338 0.9804 -4.925e-05 2.211e-05 0.07444 -3.711e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003893 0.001436 0.002777 0.004582 0.9896 0.9926 0.003962 0.8993 0.9266 0.01229 ] Network output: [ 0.05451 -0.2743 0.9054 -6.389e-05 2.868e-05 1.26 -4.815e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2214 0.1588 0.3095 0.2197 0.9851 0.9941 0.222 0.5817 0.9126 0.6944 ] Network output: [ -0.02218 0.0861 1.084 0.0001216 -5.46e-05 0.8752 9.166e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05904 0.05598 0.156 0.1645 0.9882 0.9927 0.05907 0.8565 0.9123 0.2287 ] Network output: [ -0.02486 0.06403 1.064 0.0001213 -5.448e-05 0.9222 9.145e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07166 0.07101 0.1661 0.17 0.9849 0.9912 0.07167 0.7824 0.8869 0.2078 ] Network output: [ -0.009216 1.044 0.007193 2.756e-06 -1.237e-06 0.9676 2.077e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03559 Epoch 5373 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02826 0.9455 0.9558 -5.426e-05 2.436e-05 0.04188 -4.089e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002833 -0.002369 -0.01011 0.006578 0.9687 0.9732 0.005376 0.8594 0.8531 0.01984 ] Network output: [ 0.9455 0.2186 0.01758 -8.89e-05 3.991e-05 -0.1277 -6.699e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 0.004566 -0.2001 0.1724 0.9835 0.9932 0.2363 0.5809 0.9058 0.7 ] Network output: [ 0.00723 0.9383 0.9787 -5.044e-05 2.264e-05 0.06834 -3.801e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003967 0.001438 0.002546 0.003362 0.9896 0.9926 0.004037 0.9001 0.9262 0.01231 ] Network output: [ 0.004859 0.1404 0.893 -0.0002095 9.404e-05 0.956 -0.0001579 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2259 0.1615 0.2999 0.1388 0.9852 0.9941 0.2266 0.5863 0.913 0.6994 ] Network output: [ -0.01497 0.1072 1.076 0.0001236 -5.548e-05 0.8477 9.314e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05733 0.05425 0.1471 0.1523 0.9883 0.9927 0.05736 0.853 0.9127 0.2235 ] Network output: [ -0.01644 0.01316 1.065 0.0001392 -6.248e-05 0.9548 0.0001049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06994 0.06927 0.1641 0.1702 0.9848 0.9911 0.06994 0.7771 0.8879 0.209 ] Network output: [ -0.002209 0.9544 0.02068 2.62e-05 -1.176e-05 1.029 1.975e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02396 Epoch 5374 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03389 0.9098 0.9548 -4.118e-05 1.849e-05 0.06735 -3.104e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002804 -0.002392 -0.01024 0.007383 0.9687 0.9733 0.005331 0.8588 0.8546 0.02017 ] Network output: [ 1.007 -0.08367 0.00269 1.94e-05 -8.712e-06 0.06613 1.462e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2087 -0.001104 -0.2056 0.2263 0.9835 0.9933 0.2328 0.5744 0.907 0.7063 ] Network output: [ 0.005781 0.9345 0.9799 -4.907e-05 2.203e-05 0.0738 -3.698e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003888 0.001431 0.002755 0.004496 0.9896 0.9926 0.003957 0.8997 0.9266 0.01236 ] Network output: [ 0.04957 -0.2412 0.906 -7.656e-05 3.437e-05 1.236 -5.77e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.221 0.1585 0.3089 0.2131 0.9852 0.9941 0.2216 0.5822 0.9126 0.6971 ] Network output: [ -0.02127 0.08287 1.083 0.0001224 -5.497e-05 0.877 9.228e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05874 0.05569 0.1557 0.1644 0.9882 0.9927 0.05877 0.8565 0.9123 0.2298 ] Network output: [ -0.02405 0.05509 1.065 0.0001232 -5.532e-05 0.9289 9.287e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07134 0.07069 0.1666 0.171 0.9849 0.9912 0.07135 0.7824 0.887 0.2092 ] Network output: [ -0.00991 1.041 0.009446 1.392e-06 -6.251e-07 0.9694 1.049e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02965 Epoch 5375 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02902 0.9436 0.955 -5.258e-05 2.361e-05 0.04315 -3.963e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002826 -0.002367 -0.01017 0.006654 0.9687 0.9732 0.005365 0.8597 0.8534 0.01993 ] Network output: [ 0.9511 0.199 0.01487 -7.698e-05 3.456e-05 -0.1164 -5.802e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2111 0.004134 -0.2021 0.1762 0.9835 0.9932 0.2353 0.5806 0.906 0.7025 ] Network output: [ 0.007253 0.9385 0.9783 -5.02e-05 2.254e-05 0.06849 -3.783e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003949 0.00143 0.002553 0.003439 0.9897 0.9926 0.004019 0.9004 0.9263 0.01238 ] Network output: [ 0.00682 0.1169 0.8954 -0.0002021 9.072e-05 0.9732 -0.0001523 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2247 0.1606 0.3005 0.1432 0.9852 0.9941 0.2254 0.5862 0.913 0.7015 ] Network output: [ -0.01478 0.1015 1.076 0.0001242 -5.575e-05 0.8528 9.359e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05725 0.05418 0.1479 0.1539 0.9883 0.9927 0.05728 0.8535 0.9127 0.2252 ] Network output: [ -0.01653 0.01101 1.066 0.0001387 -6.228e-05 0.9571 0.0001045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06984 0.06918 0.1648 0.1712 0.9848 0.9911 0.06985 0.7779 0.8879 0.2102 ] Network output: [ -0.002891 0.9601 0.02059 2.327e-05 -1.045e-05 1.025 1.754e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02074 Epoch 5376 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03378 0.9131 0.9542 -4.155e-05 1.865e-05 0.065 -3.131e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002801 -0.002388 -0.01029 0.007358 0.9687 0.9733 0.005325 0.8593 0.8547 0.02022 ] Network output: [ 1.005 -0.06367 0.001411 1.749e-05 -7.853e-06 0.0517 1.318e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2082 -0.0007733 -0.2072 0.2232 0.9835 0.9933 0.2322 0.5751 0.907 0.7081 ] Network output: [ 0.005825 0.9356 0.9795 -4.937e-05 2.216e-05 0.073 -3.72e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00388 0.001424 0.002733 0.004425 0.9896 0.9926 0.003949 0.9001 0.9266 0.01242 ] Network output: [ 0.04545 -0.2145 0.9068 -8.708e-05 3.909e-05 1.216 -6.562e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2203 0.1579 0.3084 0.2078 0.9852 0.9941 0.221 0.5829 0.9127 0.6997 ] Network output: [ -0.02046 0.08032 1.083 0.0001229 -5.516e-05 0.8785 9.259e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05845 0.0554 0.1555 0.1644 0.9883 0.9927 0.05848 0.8566 0.9124 0.2307 ] Network output: [ -0.02336 0.04764 1.065 0.0001245 -5.591e-05 0.9346 9.385e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07104 0.07039 0.167 0.1718 0.9849 0.9912 0.07105 0.7826 0.8871 0.2105 ] Network output: [ -0.01022 1.038 0.01107 5.435e-07 -2.44e-07 0.9711 4.096e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02537 Epoch 5377 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02951 0.9425 0.9543 -5.154e-05 2.314e-05 0.04391 -3.884e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002819 -0.002365 -0.01023 0.006722 0.9687 0.9733 0.005352 0.8601 0.8537 0.02002 ] Network output: [ 0.9557 0.1826 0.01274 -6.622e-05 2.973e-05 -0.107 -4.991e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2101 0.003739 -0.204 0.1795 0.9835 0.9933 0.2342 0.5807 0.9062 0.7049 ] Network output: [ 0.007144 0.9391 0.9781 -5.043e-05 2.264e-05 0.06832 -3.8e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003931 0.001421 0.002555 0.003502 0.9897 0.9926 0.004 0.9008 0.9265 0.01244 ] Network output: [ 0.008288 0.09801 0.8976 -0.0001964 8.816e-05 0.987 -0.000148 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2235 0.1596 0.301 0.1468 0.9852 0.9941 0.2242 0.5866 0.9131 0.7036 ] Network output: [ -0.01455 0.09687 1.076 0.0001244 -5.587e-05 0.8569 9.379e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05714 0.05407 0.1485 0.1552 0.9883 0.9927 0.05717 0.854 0.9128 0.2266 ] Network output: [ -0.01656 0.009091 1.065 0.0001382 -6.203e-05 0.9591 0.0001041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06972 0.06906 0.1652 0.172 0.9849 0.9911 0.06973 0.7787 0.888 0.2113 ] Network output: [ -0.003259 0.9646 0.02031 2.104e-05 -9.444e-06 1.022 1.585e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01838 Epoch 5378 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03357 0.9162 0.9536 -4.215e-05 1.892e-05 0.06285 -3.177e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002797 -0.002383 -0.01034 0.007344 0.9687 0.9733 0.005318 0.8598 0.8549 0.02028 ] Network output: [ 1.004 -0.04794 0.0003772 1.707e-05 -7.661e-06 0.0403 1.286e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2076 -0.0005469 -0.2089 0.2209 0.9835 0.9933 0.2315 0.576 0.9071 0.7101 ] Network output: [ 0.00573 0.9371 0.9792 -5e-05 2.245e-05 0.07209 -3.768e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00387 0.001415 0.00271 0.004369 0.9897 0.9926 0.003938 0.9006 0.9268 0.01248 ] Network output: [ 0.04203 -0.1928 0.9077 -9.578e-05 4.3e-05 1.201 -7.218e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2195 0.1572 0.3079 0.2035 0.9852 0.9941 0.2202 0.5837 0.9129 0.7023 ] Network output: [ -0.01974 0.07831 1.082 0.000123 -5.52e-05 0.8797 9.267e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05817 0.05513 0.1552 0.1645 0.9883 0.9927 0.05821 0.8568 0.9125 0.2316 ] Network output: [ -0.02277 0.04144 1.065 0.0001254 -5.629e-05 0.9395 9.449e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07075 0.0701 0.1673 0.1726 0.9849 0.9912 0.07076 0.7829 0.8874 0.2116 ] Network output: [ -0.0103 1.036 0.01222 5.548e-08 -2.491e-08 0.9727 4.181e-08 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02223 Epoch 5379 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02978 0.9422 0.9538 -5.1e-05 2.289e-05 0.04428 -3.843e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002812 -0.002363 -0.01028 0.006784 0.9687 0.9733 0.00534 0.8605 0.854 0.0201 ] Network output: [ 0.9594 0.169 0.01107 -5.651e-05 2.537e-05 -0.09915 -4.259e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.209 0.00338 -0.206 0.1824 0.9835 0.9933 0.2331 0.5811 0.9064 0.7074 ] Network output: [ 0.006927 0.9402 0.9779 -5.1e-05 2.29e-05 0.06793 -3.844e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003913 0.00141 0.002553 0.003553 0.9897 0.9927 0.003982 0.9012 0.9267 0.0125 ] Network output: [ 0.00937 0.0829 0.8996 -0.000192 8.62e-05 0.9979 -0.0001447 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2222 0.1585 0.3012 0.1496 0.9852 0.9941 0.2229 0.5871 0.9133 0.7057 ] Network output: [ -0.01429 0.09317 1.076 0.0001244 -5.587e-05 0.8603 9.378e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05702 0.05395 0.1488 0.1563 0.9884 0.9927 0.05705 0.8546 0.9129 0.2279 ] Network output: [ -0.01655 0.00736 1.065 0.0001375 -6.174e-05 0.961 0.0001036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06959 0.06892 0.1656 0.1728 0.9849 0.9912 0.06959 0.7795 0.8882 0.2123 ] Network output: [ -0.003418 0.9681 0.01991 1.936e-05 -8.69e-06 1.019 1.459e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01662 Epoch 5380 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03328 0.9192 0.9532 -4.292e-05 1.927e-05 0.06088 -3.234e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002793 -0.002378 -0.01039 0.00734 0.9687 0.9733 0.005309 0.8603 0.8551 0.02034 ] Network output: [ 1.002 -0.03576 -0.0004262 1.776e-05 -7.975e-06 0.03143 1.339e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2068 -0.0004025 -0.2106 0.2192 0.9836 0.9933 0.2306 0.577 0.9073 0.7121 ] Network output: [ 0.005527 0.9387 0.9789 -5.089e-05 2.285e-05 0.07111 -3.836e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003858 0.001406 0.002688 0.004325 0.9897 0.9927 0.003926 0.9011 0.927 0.01254 ] Network output: [ 0.0392 -0.1753 0.9087 -0.000103 4.622e-05 1.188 -7.76e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2186 0.1564 0.3074 0.2 0.9852 0.9941 0.2193 0.5847 0.9131 0.7048 ] Network output: [ -0.01908 0.07673 1.081 0.0001228 -5.514e-05 0.8806 9.257e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05792 0.05487 0.1549 0.1646 0.9883 0.9927 0.05795 0.8572 0.9127 0.2324 ] Network output: [ -0.02227 0.03626 1.065 0.0001259 -5.651e-05 0.9435 9.486e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07048 0.06983 0.1675 0.1734 0.985 0.9912 0.07048 0.7834 0.8876 0.2127 ] Network output: [ -0.01022 1.033 0.01298 -1.875e-07 8.416e-08 0.974 -1.413e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01991 Epoch 5381 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02988 0.9423 0.9534 -5.084e-05 2.282e-05 0.04436 -3.832e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002805 -0.00236 -0.01033 0.006839 0.9687 0.9733 0.005327 0.861 0.8544 0.02017 ] Network output: [ 0.9625 0.1576 0.009774 -4.772e-05 2.142e-05 -0.0925 -3.596e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.208 0.003055 -0.2079 0.1849 0.9836 0.9933 0.2319 0.5817 0.9067 0.7097 ] Network output: [ 0.006632 0.9414 0.9777 -5.183e-05 2.327e-05 0.06737 -3.906e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003895 0.0014 0.002548 0.003596 0.9897 0.9927 0.003963 0.9017 0.9269 0.01255 ] Network output: [ 0.01015 0.07095 0.9015 -0.0001887 8.472e-05 1.006 -0.0001422 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2209 0.1575 0.3013 0.1519 0.9852 0.9941 0.2216 0.5878 0.9135 0.7079 ] Network output: [ -0.01401 0.0902 1.075 0.0001242 -5.577e-05 0.8629 9.363e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05688 0.05382 0.1491 0.1573 0.9884 0.9927 0.05692 0.8551 0.9131 0.229 ] Network output: [ -0.01651 0.005774 1.065 0.0001368 -6.143e-05 0.9627 0.0001031 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06944 0.06878 0.1659 0.1736 0.9849 0.9912 0.06945 0.7804 0.8884 0.2132 ] Network output: [ -0.003433 0.9708 0.01944 1.811e-05 -8.132e-06 1.017 1.365e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01527 Epoch 5382 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03293 0.922 0.9529 -4.38e-05 1.966e-05 0.0591 -3.301e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002787 -0.002374 -0.01044 0.007342 0.9687 0.9733 0.0053 0.8609 0.8554 0.0204 ] Network output: [ 1.001 -0.02652 -0.001028 1.93e-05 -8.666e-06 0.02467 1.455e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2059 -0.0003195 -0.2124 0.2181 0.9836 0.9933 0.2297 0.5781 0.9075 0.7141 ] Network output: [ 0.005246 0.9404 0.9788 -5.196e-05 2.333e-05 0.07009 -3.916e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003845 0.001396 0.002667 0.00429 0.9897 0.9927 0.003913 0.9017 0.9272 0.01259 ] Network output: [ 0.03685 -0.1611 0.9098 -0.0001089 4.888e-05 1.177 -8.206e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2177 0.1555 0.3068 0.1973 0.9852 0.9941 0.2183 0.5857 0.9134 0.7071 ] Network output: [ -0.01849 0.07545 1.081 0.0001225 -5.501e-05 0.8814 9.234e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05768 0.05463 0.1546 0.1648 0.9883 0.9927 0.05771 0.8575 0.9129 0.2332 ] Network output: [ -0.02183 0.03191 1.065 0.0001261 -5.661e-05 0.947 9.503e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07022 0.06957 0.1677 0.1741 0.985 0.9912 0.07023 0.7839 0.8879 0.2136 ] Network output: [ -0.01006 1.032 0.01346 -2.653e-07 1.191e-07 0.9751 -1.999e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01816 Epoch 5383 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02984 0.9428 0.9531 -5.098e-05 2.289e-05 0.04421 -3.842e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002797 -0.002357 -0.01039 0.006889 0.9687 0.9733 0.005314 0.8616 0.8548 0.02024 ] Network output: [ 0.965 0.148 0.008792 -3.975e-05 1.785e-05 -0.08685 -2.996e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2069 0.002764 -0.2098 0.1871 0.9836 0.9933 0.2307 0.5823 0.907 0.712 ] Network output: [ 0.00628 0.9429 0.9776 -5.284e-05 2.372e-05 0.06669 -3.982e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003877 0.001389 0.00254 0.003631 0.9897 0.9927 0.003945 0.9022 0.9272 0.01261 ] Network output: [ 0.01069 0.06161 0.9033 -0.0001862 8.36e-05 1.013 -0.0001403 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2196 0.1564 0.3012 0.1537 0.9852 0.9941 0.2203 0.5886 0.9138 0.7099 ] Network output: [ -0.0137 0.08782 1.075 0.0001239 -5.562e-05 0.8651 9.337e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05675 0.05368 0.1492 0.1581 0.9884 0.9927 0.05678 0.8557 0.9133 0.2301 ] Network output: [ -0.01643 0.004291 1.065 0.0001361 -6.11e-05 0.9642 0.0001026 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06929 0.06863 0.1661 0.1743 0.9849 0.9912 0.0693 0.7812 0.8886 0.214 ] Network output: [ -0.003346 0.9729 0.01892 1.723e-05 -7.734e-06 1.015 1.298e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01423 Epoch 5384 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03255 0.9245 0.9527 -4.476e-05 2.009e-05 0.05749 -3.373e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002782 -0.00237 -0.01049 0.00735 0.9687 0.9733 0.00529 0.8614 0.8558 0.02045 ] Network output: [ 1.001 -0.01966 -0.001469 2.147e-05 -9.638e-06 0.01962 1.618e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.205 -0.0002812 -0.2141 0.2175 0.9836 0.9933 0.2287 0.5792 0.9077 0.716 ] Network output: [ 0.004907 0.9422 0.9787 -5.316e-05 2.386e-05 0.06905 -4.006e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003832 0.001386 0.002646 0.004264 0.9897 0.9927 0.003899 0.9022 0.9275 0.01264 ] Network output: [ 0.03489 -0.1497 0.9108 -0.0001138 5.107e-05 1.169 -8.573e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2166 0.1546 0.3063 0.1951 0.9852 0.9941 0.2173 0.5868 0.9137 0.7094 ] Network output: [ -0.01793 0.07441 1.08 0.0001221 -5.482e-05 0.882 9.202e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05746 0.05441 0.1543 0.165 0.9884 0.9927 0.0575 0.8579 0.9131 0.2339 ] Network output: [ -0.02144 0.02822 1.065 0.0001261 -5.661e-05 0.95 9.504e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06999 0.06934 0.1678 0.1747 0.985 0.9912 0.07 0.7845 0.8882 0.2145 ] Network output: [ -0.009842 1.03 0.01371 -2.352e-07 1.056e-07 0.976 -1.773e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0168 Epoch 5385 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02971 0.9436 0.9529 -5.134e-05 2.305e-05 0.04389 -3.869e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00279 -0.002353 -0.01044 0.006935 0.9687 0.9733 0.005301 0.8621 0.8552 0.02031 ] Network output: [ 0.967 0.1399 0.008059 -3.251e-05 1.46e-05 -0.08205 -2.45e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2058 0.002505 -0.2116 0.1891 0.9836 0.9933 0.2295 0.5831 0.9073 0.7142 ] Network output: [ 0.005891 0.9445 0.9776 -5.397e-05 2.423e-05 0.06593 -4.068e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00386 0.001379 0.002529 0.003659 0.9897 0.9927 0.003928 0.9027 0.9275 0.01266 ] Network output: [ 0.01104 0.05443 0.9048 -0.0001844 8.278e-05 1.018 -0.000139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2184 0.1554 0.301 0.1552 0.9852 0.9941 0.219 0.5894 0.914 0.712 ] Network output: [ -0.01336 0.08588 1.075 0.0001234 -5.542e-05 0.8668 9.303e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05661 0.05354 0.1493 0.1589 0.9884 0.9927 0.05664 0.8563 0.9134 0.231 ] Network output: [ -0.01632 0.002869 1.065 0.0001354 -6.078e-05 0.9657 0.000102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06914 0.06847 0.1662 0.1749 0.9849 0.9912 0.06915 0.782 0.8888 0.2148 ] Network output: [ -0.003187 0.9744 0.01839 1.663e-05 -7.464e-06 1.014 1.253e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0134 Epoch 5386 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03214 0.927 0.9526 -4.577e-05 2.055e-05 0.05601 -3.45e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002776 -0.002365 -0.01053 0.007362 0.9687 0.9733 0.00528 0.862 0.8561 0.02051 ] Network output: [ 1 -0.01472 -0.001784 2.409e-05 -1.082e-05 0.01595 1.816e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2041 -0.0002748 -0.2158 0.2172 0.9836 0.9933 0.2276 0.5802 0.908 0.718 ] Network output: [ 0.004526 0.944 0.9787 -5.443e-05 2.444e-05 0.06802 -4.102e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003818 0.001377 0.002626 0.004244 0.9897 0.9927 0.003886 0.9027 0.9277 0.0127 ] Network output: [ 0.03325 -0.1405 0.9119 -0.0001177 5.286e-05 1.162 -8.873e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2156 0.1537 0.3057 0.1933 0.9852 0.9941 0.2162 0.5879 0.9139 0.7116 ] Network output: [ -0.01741 0.07353 1.079 0.0001216 -5.459e-05 0.8825 9.163e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05726 0.05421 0.154 0.1652 0.9884 0.9928 0.05729 0.8583 0.9133 0.2346 ] Network output: [ -0.0211 0.02505 1.065 0.000126 -5.655e-05 0.9526 9.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06977 0.06912 0.1679 0.1753 0.985 0.9912 0.06978 0.7851 0.8884 0.2153 ] Network output: [ -0.00961 1.029 0.01381 -1.418e-07 6.364e-08 0.9768 -1.068e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01574 Epoch 5387 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02951 0.9445 0.9528 -5.188e-05 2.329e-05 0.04345 -3.91e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002783 -0.00235 -0.01049 0.006978 0.9687 0.9733 0.005289 0.8626 0.8556 0.02038 ] Network output: [ 0.9686 0.133 0.007529 -2.592e-05 1.164e-05 -0.07796 -1.954e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2047 0.002278 -0.2134 0.1908 0.9836 0.9933 0.2282 0.5839 0.9076 0.7163 ] Network output: [ 0.005477 0.9461 0.9776 -5.519e-05 2.478e-05 0.06511 -4.159e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003844 0.001369 0.002517 0.003683 0.9897 0.9927 0.003912 0.9032 0.9277 0.01271 ] Network output: [ 0.01124 0.04906 0.9063 -0.0001831 8.22e-05 1.021 -0.000138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2172 0.1543 0.3007 0.1563 0.9852 0.9941 0.2178 0.5903 0.9143 0.714 ] Network output: [ -0.01299 0.08429 1.074 0.0001229 -5.519e-05 0.8682 9.265e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05648 0.05341 0.1492 0.1595 0.9884 0.9928 0.05651 0.8568 0.9136 0.2319 ] Network output: [ -0.01617 0.001477 1.064 0.0001347 -6.046e-05 0.9671 0.0001015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06899 0.06832 0.1663 0.1755 0.985 0.9912 0.069 0.7828 0.889 0.2156 ] Network output: [ -0.002978 0.9754 0.01785 1.626e-05 -7.298e-06 1.013 1.225e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01272 Epoch 5388 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03172 0.9292 0.9525 -4.682e-05 2.102e-05 0.05466 -3.528e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00277 -0.002361 -0.01058 0.007379 0.9687 0.9733 0.005269 0.8626 0.8565 0.02057 ] Network output: [ 1 -0.01138 -0.002006 2.704e-05 -1.214e-05 0.01342 2.038e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2031 -0.0002906 -0.2175 0.2171 0.9836 0.9933 0.2265 0.5813 0.9083 0.7199 ] Network output: [ 0.004117 0.9458 0.9787 -5.576e-05 2.503e-05 0.06698 -4.202e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003805 0.001367 0.002606 0.004229 0.9897 0.9927 0.003872 0.9033 0.928 0.01275 ] Network output: [ 0.03189 -0.133 0.9129 -0.000121 5.431e-05 1.156 -9.117e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2145 0.1528 0.3051 0.1919 0.9852 0.9941 0.2152 0.5889 0.9142 0.7137 ] Network output: [ -0.01692 0.07276 1.079 0.000121 -5.433e-05 0.883 9.12e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05708 0.05403 0.1538 0.1654 0.9884 0.9928 0.05711 0.8587 0.9135 0.2353 ] Network output: [ -0.02079 0.02231 1.065 0.0001257 -5.642e-05 0.9549 9.471e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06957 0.06892 0.1679 0.1759 0.985 0.9913 0.06958 0.7857 0.8887 0.216 ] Network output: [ -0.009381 1.028 0.01379 -2.017e-08 9.053e-09 0.9774 -1.52e-08 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01489 Epoch 5389 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02925 0.9456 0.9527 -5.254e-05 2.359e-05 0.04291 -3.96e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002776 -0.002346 -0.01053 0.007017 0.9687 0.9733 0.005277 0.8632 0.856 0.02044 ] Network output: [ 0.97 0.1272 0.007168 -1.992e-05 8.943e-06 -0.07446 -1.501e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2036 0.002079 -0.2151 0.1924 0.9836 0.9933 0.227 0.5847 0.908 0.7183 ] Network output: [ 0.005049 0.9477 0.9777 -5.646e-05 2.535e-05 0.06425 -4.255e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003828 0.00136 0.002504 0.003702 0.9898 0.9927 0.003896 0.9037 0.928 0.01277 ] Network output: [ 0.01132 0.04519 0.9076 -0.0001822 8.179e-05 1.024 -0.0001373 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.216 0.1533 0.3004 0.1571 0.9852 0.9941 0.2166 0.5913 0.9146 0.7159 ] Network output: [ -0.0126 0.08297 1.073 0.0001224 -5.494e-05 0.8693 9.223e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05635 0.05327 0.1491 0.1601 0.9884 0.9928 0.05638 0.8573 0.9138 0.2327 ] Network output: [ -0.016 9.916e-05 1.064 0.000134 -6.015e-05 0.9684 0.000101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06884 0.06818 0.1664 0.1761 0.985 0.9912 0.06885 0.7835 0.8893 0.2163 ] Network output: [ -0.002732 0.9761 0.01732 1.607e-05 -7.216e-06 1.012 1.211e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01216 Epoch 5390 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03129 0.9313 0.9525 -4.788e-05 2.149e-05 0.05341 -3.608e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002764 -0.002357 -0.01063 0.007398 0.9687 0.9733 0.005259 0.8631 0.8568 0.02063 ] Network output: [ 0.9999 -0.009334 -0.002156 3.022e-05 -1.357e-05 0.01182 2.278e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2021 -0.0003212 -0.2192 0.2173 0.9836 0.9933 0.2254 0.5823 0.9086 0.7218 ] Network output: [ 0.003687 0.9477 0.9788 -5.712e-05 2.564e-05 0.06596 -4.304e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003792 0.001359 0.002588 0.00422 0.9898 0.9927 0.003859 0.9038 0.9282 0.0128 ] Network output: [ 0.03075 -0.1271 0.9139 -0.0001236 5.548e-05 1.151 -9.313e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2135 0.1519 0.3045 0.1909 0.9852 0.9941 0.2141 0.5899 0.9145 0.7157 ] Network output: [ -0.01645 0.07205 1.078 0.0001204 -5.406e-05 0.8834 9.074e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05691 0.05386 0.1535 0.1657 0.9884 0.9928 0.05694 0.8591 0.9137 0.236 ] Network output: [ -0.0205 0.01991 1.065 0.0001253 -5.625e-05 0.9569 9.443e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06939 0.06873 0.1679 0.1764 0.985 0.9913 0.0694 0.7863 0.889 0.2168 ] Network output: [ -0.009169 1.027 0.01368 1.027e-07 -4.61e-08 0.9778 7.739e-08 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0142 Epoch 5391 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02896 0.9469 0.9527 -5.33e-05 2.393e-05 0.04228 -4.017e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002769 -0.002343 -0.01058 0.007053 0.9687 0.9733 0.005265 0.8637 0.8564 0.0205 ] Network output: [ 0.9711 0.1224 0.006949 -1.445e-05 6.487e-06 -0.07148 -1.089e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2026 0.001908 -0.2167 0.1938 0.9836 0.9933 0.2259 0.5856 0.9083 0.7203 ] Network output: [ 0.004614 0.9494 0.9778 -5.776e-05 2.593e-05 0.06337 -4.353e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003814 0.001351 0.00249 0.003718 0.9898 0.9927 0.003881 0.9042 0.9283 0.01281 ] Network output: [ 0.01129 0.0426 0.9088 -0.0001816 8.152e-05 1.025 -0.0001369 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2148 0.1524 0.2999 0.1577 0.9852 0.9941 0.2154 0.5922 0.9148 0.7178 ] Network output: [ -0.01219 0.08186 1.073 0.0001218 -5.468e-05 0.8702 9.18e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05622 0.05315 0.149 0.1606 0.9884 0.9928 0.05625 0.8578 0.914 0.2334 ] Network output: [ -0.01581 -0.001277 1.064 0.0001333 -5.985e-05 0.9697 0.0001005 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0687 0.06803 0.1664 0.1767 0.985 0.9912 0.0687 0.7843 0.8895 0.217 ] Network output: [ -0.002459 0.9764 0.0168 1.604e-05 -7.203e-06 1.012 1.209e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01169 Epoch 5392 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03086 0.9333 0.9526 -4.894e-05 2.197e-05 0.05226 -3.689e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002758 -0.002353 -0.01067 0.00742 0.9687 0.9733 0.005248 0.8637 0.8572 0.02068 ] Network output: [ 0.9999 -0.008385 -0.002252 3.356e-05 -1.507e-05 0.01101 2.529e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2012 -0.0003613 -0.2208 0.2177 0.9836 0.9933 0.2243 0.5833 0.9089 0.7236 ] Network output: [ 0.003244 0.9494 0.9789 -5.848e-05 2.626e-05 0.06494 -4.408e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003779 0.00135 0.002569 0.004214 0.9898 0.9927 0.003846 0.9043 0.9285 0.01284 ] Network output: [ 0.0298 -0.1224 0.9148 -0.0001256 5.64e-05 1.147 -9.467e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2124 0.151 0.3039 0.1901 0.9853 0.9941 0.213 0.591 0.9148 0.7176 ] Network output: [ -0.016 0.07138 1.077 0.0001198 -5.377e-05 0.8839 9.027e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05676 0.05371 0.1532 0.166 0.9884 0.9928 0.05679 0.8596 0.9139 0.2366 ] Network output: [ -0.02024 0.0178 1.064 0.0001249 -5.605e-05 0.9587 9.409e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06922 0.06856 0.1679 0.177 0.985 0.9913 0.06923 0.7869 0.8892 0.2175 ] Network output: [ -0.008982 1.026 0.0135 2.072e-07 -9.301e-08 0.9782 1.561e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01364 Epoch 5393 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02863 0.9481 0.9528 -5.413e-05 2.43e-05 0.04158 -4.08e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002762 -0.002339 -0.01062 0.007087 0.9687 0.9733 0.005254 0.8642 0.8568 0.02056 ] Network output: [ 0.9719 0.1183 0.006853 -9.477e-06 4.254e-06 -0.06896 -7.142e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2015 0.001763 -0.2184 0.195 0.9836 0.9933 0.2247 0.5865 0.9086 0.7221 ] Network output: [ 0.004179 0.951 0.9779 -5.907e-05 2.652e-05 0.06246 -4.451e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003799 0.001342 0.002475 0.003731 0.9898 0.9927 0.003866 0.9047 0.9285 0.01286 ] Network output: [ 0.01118 0.04109 0.9099 -0.0001813 8.137e-05 1.026 -0.0001366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2137 0.1514 0.2994 0.1581 0.9853 0.9941 0.2143 0.5931 0.9151 0.7196 ] Network output: [ -0.01176 0.0809 1.072 0.0001212 -5.442e-05 0.871 9.136e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0561 0.05303 0.1488 0.1611 0.9885 0.9928 0.05613 0.8582 0.9142 0.2341 ] Network output: [ -0.01558 -0.002658 1.063 0.0001327 -5.956e-05 0.971 9.998e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06856 0.06789 0.1664 0.1772 0.985 0.9912 0.06857 0.785 0.8898 0.2176 ] Network output: [ -0.002161 0.9765 0.01629 1.614e-05 -7.247e-06 1.012 1.217e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01129 Epoch 5394 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03043 0.9351 0.9527 -5e-05 2.245e-05 0.05118 -3.768e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002752 -0.002349 -0.01072 0.007444 0.9688 0.9733 0.005238 0.8642 0.8576 0.02074 ] Network output: [ 1 -0.008359 -0.00231 3.7e-05 -1.661e-05 0.01086 2.788e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2002 -0.0004069 -0.2224 0.2182 0.9837 0.9933 0.2233 0.5843 0.9091 0.7254 ] Network output: [ 0.002794 0.9512 0.979 -5.985e-05 2.687e-05 0.06393 -4.51e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003766 0.001342 0.002552 0.004213 0.9898 0.9927 0.003833 0.9048 0.9287 0.01289 ] Network output: [ 0.02901 -0.1189 0.9158 -0.0001272 5.709e-05 1.145 -9.584e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2113 0.1502 0.3033 0.1895 0.9853 0.9941 0.212 0.5919 0.915 0.7195 ] Network output: [ -0.01557 0.07073 1.077 0.0001191 -5.348e-05 0.8843 8.978e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05662 0.05357 0.1529 0.1663 0.9884 0.9928 0.05665 0.86 0.9141 0.2373 ] Network output: [ -0.01999 0.01593 1.064 0.0001243 -5.582e-05 0.9604 9.371e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06906 0.06841 0.1679 0.1775 0.9851 0.9913 0.06907 0.7876 0.8895 0.2181 ] Network output: [ -0.008825 1.026 0.01328 2.79e-07 -1.253e-07 0.9783 2.103e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01318 Epoch 5395 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02828 0.9495 0.9529 -5.502e-05 2.47e-05 0.04083 -4.146e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002756 -0.002336 -0.01067 0.007119 0.9688 0.9733 0.005243 0.8647 0.8572 0.02062 ] Network output: [ 0.9725 0.1149 0.006863 -4.97e-06 2.231e-06 -0.06684 -3.746e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2005 0.001641 -0.2199 0.1962 0.9837 0.9933 0.2236 0.5873 0.9089 0.7239 ] Network output: [ 0.003748 0.9527 0.9781 -6.037e-05 2.71e-05 0.06153 -4.55e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003786 0.001334 0.00246 0.003742 0.9898 0.9927 0.003853 0.9052 0.9287 0.01291 ] Network output: [ 0.011 0.04052 0.9109 -0.0001811 8.132e-05 1.026 -0.0001365 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2126 0.1505 0.2988 0.1584 0.9853 0.9941 0.2132 0.594 0.9154 0.7213 ] Network output: [ -0.0113 0.08007 1.071 0.0001206 -5.416e-05 0.8716 9.092e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05599 0.05291 0.1485 0.1616 0.9885 0.9928 0.05602 0.8587 0.9144 0.2348 ] Network output: [ -0.01533 -0.004052 1.063 0.0001321 -5.929e-05 0.9723 9.953e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06843 0.06776 0.1663 0.1777 0.985 0.9912 0.06844 0.7856 0.89 0.2183 ] Network output: [ -0.00184 0.9762 0.01579 1.635e-05 -7.341e-06 1.012 1.232e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01096 Epoch 5396 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.03001 0.9368 0.9528 -5.105e-05 2.292e-05 0.05017 -3.847e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002746 -0.002345 -0.01076 0.007469 0.9688 0.9733 0.005228 0.8647 0.8579 0.0208 ] Network output: [ 1 -0.009123 -0.002344 4.049e-05 -1.818e-05 0.01126 3.051e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1992 -0.0004553 -0.2239 0.2189 0.9837 0.9933 0.2222 0.5852 0.9094 0.7271 ] Network output: [ 0.002339 0.9529 0.9792 -6.12e-05 2.747e-05 0.06293 -4.612e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003754 0.001335 0.002536 0.004214 0.9898 0.9928 0.00382 0.9052 0.929 0.01294 ] Network output: [ 0.02836 -0.1163 0.9167 -0.0001283 5.76e-05 1.142 -9.669e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2103 0.1493 0.3027 0.1891 0.9853 0.9941 0.2109 0.5929 0.9153 0.7212 ] Network output: [ -0.01516 0.07007 1.076 0.0001185 -5.319e-05 0.8848 8.929e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0565 0.05344 0.1527 0.1667 0.9885 0.9928 0.05653 0.8604 0.9143 0.2379 ] Network output: [ -0.01977 0.01427 1.064 0.0001238 -5.557e-05 0.9619 9.329e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06892 0.06827 0.1679 0.178 0.9851 0.9913 0.06893 0.7882 0.8897 0.2188 ] Network output: [ -0.008701 1.026 0.01302 3.073e-07 -1.38e-07 0.9783 2.316e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01281 Epoch 5397 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02791 0.9509 0.953 -5.594e-05 2.511e-05 0.04002 -4.216e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00275 -0.002332 -0.01071 0.007149 0.9688 0.9733 0.005233 0.8652 0.8576 0.02068 ] Network output: [ 0.973 0.1122 0.006962 -9.102e-07 4.086e-07 -0.06511 -6.859e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1995 0.001543 -0.2214 0.1971 0.9837 0.9933 0.2225 0.5882 0.9091 0.7256 ] Network output: [ 0.003323 0.9543 0.9782 -6.166e-05 2.768e-05 0.06059 -4.647e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003773 0.001327 0.002445 0.00375 0.9898 0.9928 0.00384 0.9056 0.929 0.01296 ] Network output: [ 0.01075 0.0408 0.9118 -0.0001812 8.135e-05 1.025 -0.0001366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2115 0.1497 0.2982 0.1585 0.9853 0.9941 0.2121 0.5949 0.9156 0.723 ] Network output: [ -0.01082 0.07934 1.071 0.0001201 -5.391e-05 0.8721 9.05e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05588 0.0528 0.1483 0.162 0.9885 0.9928 0.05591 0.8591 0.9146 0.2354 ] Network output: [ -0.01505 -0.005464 1.063 0.0001315 -5.904e-05 0.9735 9.91e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0683 0.06764 0.1662 0.1782 0.985 0.9913 0.06831 0.7863 0.8902 0.2189 ] Network output: [ -0.001498 0.9758 0.0153 1.666e-05 -7.479e-06 1.012 1.255e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01067 Epoch 5398 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0296 0.9384 0.953 -5.207e-05 2.338e-05 0.04922 -3.924e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002741 -0.002342 -0.01081 0.007497 0.9688 0.9733 0.005219 0.8652 0.8583 0.02085 ] Network output: [ 1 -0.01058 -0.002366 4.4e-05 -1.976e-05 0.01216 3.316e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1983 -0.0005049 -0.2254 0.2197 0.9837 0.9933 0.2212 0.5861 0.9097 0.7288 ] Network output: [ 0.001882 0.9546 0.9794 -6.253e-05 2.807e-05 0.06194 -4.712e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003742 0.001328 0.00252 0.004219 0.9898 0.9928 0.003808 0.9057 0.9292 0.01299 ] Network output: [ 0.02784 -0.1146 0.9176 -0.000129 5.792e-05 1.141 -9.723e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2093 0.1485 0.3022 0.189 0.9853 0.9941 0.2099 0.5938 0.9155 0.7229 ] Network output: [ -0.01476 0.06939 1.075 0.0001178 -5.29e-05 0.8853 8.88e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05639 0.05333 0.1525 0.1671 0.9885 0.9928 0.05642 0.8608 0.9144 0.2386 ] Network output: [ -0.01956 0.0128 1.064 0.0001232 -5.531e-05 0.9632 9.284e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06879 0.06814 0.1679 0.1785 0.9851 0.9913 0.0688 0.7888 0.8899 0.2194 ] Network output: [ -0.008614 1.026 0.01273 2.831e-07 -1.271e-07 0.9781 2.133e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01252 Epoch 5399 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02754 0.9524 0.9532 -5.688e-05 2.554e-05 0.03916 -4.287e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002745 -0.002328 -0.01075 0.007177 0.9688 0.9733 0.005223 0.8657 0.8579 0.02073 ] Network output: [ 0.9732 0.1102 0.007141 2.718e-06 -1.22e-06 -0.06375 2.049e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1986 0.001466 -0.2228 0.198 0.9837 0.9933 0.2214 0.589 0.9094 0.7272 ] Network output: [ 0.002908 0.9559 0.9784 -6.292e-05 2.825e-05 0.05964 -4.742e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003762 0.00132 0.002429 0.003756 0.9898 0.9928 0.003828 0.906 0.9292 0.01301 ] Network output: [ 0.01043 0.04183 0.9126 -0.0001814 8.145e-05 1.024 -0.0001367 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2105 0.1489 0.2976 0.1585 0.9853 0.9941 0.2111 0.5958 0.9158 0.7246 ] Network output: [ -0.01033 0.07868 1.07 0.0001195 -5.366e-05 0.8725 9.008e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05578 0.0527 0.148 0.1624 0.9885 0.9928 0.05581 0.8595 0.9147 0.236 ] Network output: [ -0.01475 -0.006898 1.062 0.000131 -5.88e-05 0.9748 9.871e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06818 0.06752 0.1662 0.1787 0.985 0.9913 0.06819 0.7869 0.8904 0.2195 ] Network output: [ -0.001131 0.975 0.01483 1.706e-05 -7.658e-06 1.012 1.286e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01043 Epoch 5400 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02919 0.94 0.9531 -5.307e-05 2.383e-05 0.04831 -4e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002736 -0.002338 -0.01085 0.007526 0.9688 0.9733 0.005209 0.8657 0.8586 0.02091 ] Network output: [ 1.001 -0.01264 -0.002387 4.752e-05 -2.133e-05 0.01349 3.581e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1974 -0.0005548 -0.2269 0.2206 0.9837 0.9933 0.2201 0.5869 0.9099 0.7304 ] Network output: [ 0.001424 0.9563 0.9796 -6.382e-05 2.865e-05 0.06097 -4.81e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003731 0.001322 0.002505 0.004227 0.9898 0.9928 0.003797 0.9061 0.9294 0.01304 ] Network output: [ 0.02743 -0.1138 0.9185 -0.0001294 5.808e-05 1.14 -9.75e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2083 0.1477 0.3016 0.189 0.9853 0.9941 0.2089 0.5946 0.9158 0.7246 ] Network output: [ -0.01439 0.06868 1.075 0.0001172 -5.261e-05 0.8859 8.832e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05629 0.05324 0.1522 0.1675 0.9885 0.9928 0.05633 0.8612 0.9146 0.2392 ] Network output: [ -0.01938 0.0115 1.063 0.0001226 -5.503e-05 0.9644 9.237e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06868 0.06802 0.1679 0.179 0.9851 0.9913 0.06869 0.7894 0.8901 0.22 ] Network output: [ -0.008564 1.027 0.01243 1.989e-07 -8.928e-08 0.9778 1.499e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01229 Epoch 5401 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02716 0.9538 0.9534 -5.784e-05 2.597e-05 0.03825 -4.359e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002739 -0.002325 -0.01079 0.007202 0.9688 0.9733 0.005213 0.8662 0.8582 0.02078 ] Network output: [ 0.9733 0.1088 0.007391 5.925e-06 -2.66e-06 -0.06275 4.465e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1976 0.001409 -0.2242 0.1987 0.9837 0.9933 0.2204 0.5899 0.9097 0.7288 ] Network output: [ 0.002503 0.9575 0.9786 -6.414e-05 2.879e-05 0.05868 -4.834e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00375 0.001313 0.002414 0.003761 0.9898 0.9928 0.003816 0.9065 0.9294 0.01305 ] Network output: [ 0.01006 0.04357 0.9133 -0.0001818 8.162e-05 1.022 -0.000137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2095 0.1481 0.2969 0.1583 0.9853 0.9941 0.2101 0.5966 0.9161 0.7262 ] Network output: [ -0.009814 0.07808 1.069 0.000119 -5.343e-05 0.8728 8.969e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05569 0.05261 0.1477 0.1627 0.9885 0.9928 0.05572 0.8598 0.9149 0.2366 ] Network output: [ -0.01443 -0.008357 1.062 0.0001305 -5.859e-05 0.976 9.835e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06808 0.06741 0.166 0.1792 0.985 0.9913 0.06808 0.7874 0.8907 0.22 ] Network output: [ -0.0007389 0.9741 0.01437 1.754e-05 -7.876e-06 1.013 1.322e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01023 Epoch 5402 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0288 0.9414 0.9533 -5.404e-05 2.426e-05 0.04746 -4.072e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002731 -0.002335 -0.01089 0.007556 0.9688 0.9733 0.005201 0.8661 0.8589 0.02096 ] Network output: [ 1.001 -0.01528 -0.002414 5.101e-05 -2.29e-05 0.0152 3.844e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1965 -0.0006046 -0.2284 0.2216 0.9837 0.9933 0.2191 0.5877 0.9102 0.7319 ] Network output: [ 0.0009678 0.9579 0.9799 -6.508e-05 2.922e-05 0.06 -4.905e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00372 0.001317 0.002492 0.004237 0.9898 0.9928 0.003786 0.9065 0.9296 0.01308 ] Network output: [ 0.02711 -0.1138 0.9194 -0.0001294 5.809e-05 1.14 -9.752e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2073 0.147 0.3011 0.1892 0.9853 0.9941 0.208 0.5955 0.916 0.7261 ] Network output: [ -0.01403 0.06793 1.074 0.0001166 -5.233e-05 0.8865 8.785e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05621 0.05316 0.1521 0.1679 0.9885 0.9928 0.05624 0.8616 0.9147 0.2399 ] Network output: [ -0.01921 0.01035 1.063 0.0001219 -5.473e-05 0.9655 9.188e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06858 0.06792 0.1678 0.1794 0.9851 0.9913 0.06859 0.79 0.8903 0.2206 ] Network output: [ -0.008555 1.028 0.0121 4.823e-08 -2.165e-08 0.9773 3.635e-08 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01214 Epoch 5403 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02677 0.9554 0.9536 -5.88e-05 2.64e-05 0.03729 -4.432e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002734 -0.002321 -0.01083 0.007226 0.9688 0.9733 0.005204 0.8666 0.8585 0.02083 ] Network output: [ 0.9733 0.1079 0.007706 8.715e-06 -3.912e-06 -0.06211 6.568e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1967 0.001371 -0.2255 0.1994 0.9837 0.9933 0.2194 0.5907 0.9099 0.7302 ] Network output: [ 0.002112 0.959 0.9788 -6.532e-05 2.932e-05 0.05771 -4.923e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00374 0.001307 0.002398 0.003764 0.9898 0.9928 0.003806 0.9069 0.9296 0.0131 ] Network output: [ 0.009635 0.04598 0.914 -0.0001823 8.186e-05 1.02 -0.0001374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2086 0.1474 0.2962 0.158 0.9853 0.9941 0.2092 0.5974 0.9163 0.7277 ] Network output: [ -0.009282 0.07753 1.068 0.0001185 -5.321e-05 0.8731 8.933e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05561 0.05252 0.1473 0.1631 0.9885 0.9928 0.05564 0.8602 0.915 0.2372 ] Network output: [ -0.01408 -0.009843 1.061 0.0001301 -5.84e-05 0.9772 9.803e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06797 0.06731 0.1659 0.1797 0.9851 0.9913 0.06798 0.788 0.8909 0.2206 ] Network output: [ -0.0003175 0.9729 0.01393 1.812e-05 -8.133e-06 1.014 1.365e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01008 Epoch 5404 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02842 0.9427 0.9536 -5.497e-05 2.468e-05 0.04665 -4.142e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002726 -0.002332 -0.01093 0.007587 0.9688 0.9733 0.005192 0.8665 0.8592 0.02102 ] Network output: [ 1.002 -0.01844 -0.002456 5.448e-05 -2.446e-05 0.01729 4.106e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1956 -0.0006545 -0.2297 0.2226 0.9837 0.9933 0.2182 0.5885 0.9104 0.7334 ] Network output: [ 0.0005128 0.9595 0.9801 -6.629e-05 2.976e-05 0.05904 -4.996e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00371 0.001311 0.002479 0.00425 0.9898 0.9928 0.003775 0.9069 0.9298 0.01313 ] Network output: [ 0.02689 -0.1145 0.9202 -0.0001291 5.797e-05 1.14 -9.731e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2064 0.1463 0.3005 0.1895 0.9853 0.9941 0.207 0.5962 0.9162 0.7276 ] Network output: [ -0.0137 0.06713 1.074 0.000116 -5.205e-05 0.8872 8.738e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05614 0.05309 0.1519 0.1683 0.9885 0.9929 0.05617 0.8619 0.9149 0.2406 ] Network output: [ -0.01906 0.009363 1.063 0.0001212 -5.443e-05 0.9665 9.137e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06849 0.06784 0.1678 0.1799 0.9851 0.9913 0.0685 0.7906 0.8905 0.2212 ] Network output: [ -0.008586 1.029 0.01177 -1.746e-07 7.839e-08 0.9767 -1.316e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01205 Epoch 5405 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02638 0.9569 0.9538 -5.976e-05 2.683e-05 0.03629 -4.504e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002729 -0.002318 -0.01087 0.007248 0.9688 0.9733 0.005196 0.867 0.8588 0.02088 ] Network output: [ 0.9731 0.1077 0.008081 1.109e-05 -4.978e-06 -0.06185 8.357e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1959 0.00135 -0.2267 0.1999 0.9837 0.9933 0.2184 0.5914 0.9101 0.7316 ] Network output: [ 0.001734 0.9605 0.979 -6.645e-05 2.983e-05 0.05674 -5.008e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00373 0.001302 0.002383 0.003765 0.9899 0.9928 0.003796 0.9072 0.9298 0.01314 ] Network output: [ 0.009152 0.04904 0.9146 -0.000183 8.217e-05 1.017 -0.0001379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2077 0.1467 0.2955 0.1576 0.9853 0.9941 0.2083 0.5982 0.9165 0.7291 ] Network output: [ -0.008732 0.07702 1.068 0.0001181 -5.301e-05 0.8733 8.899e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05553 0.05245 0.147 0.1634 0.9885 0.9929 0.05556 0.8605 0.9151 0.2378 ] Network output: [ -0.0137 -0.01136 1.061 0.0001297 -5.823e-05 0.9784 9.775e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06788 0.06721 0.1658 0.1802 0.9851 0.9913 0.06789 0.7885 0.891 0.2212 ] Network output: [ 0.0001372 0.9715 0.01349 1.878e-05 -8.43e-06 1.015 1.415e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009979 Epoch 5406 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02805 0.944 0.9538 -5.585e-05 2.507e-05 0.04588 -4.209e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002721 -0.002329 -0.01097 0.007619 0.9688 0.9733 0.005184 0.8669 0.8595 0.02107 ] Network output: [ 1.003 -0.02213 -0.002518 5.791e-05 -2.6e-05 0.01972 4.364e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1948 -0.000705 -0.2311 0.2238 0.9837 0.9934 0.2172 0.5892 0.9107 0.7349 ] Network output: [ 5.97e-05 0.9611 0.9804 -6.746e-05 3.029e-05 0.05809 -5.084e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0037 0.001307 0.002468 0.004266 0.9899 0.9928 0.003765 0.9073 0.93 0.01317 ] Network output: [ 0.02676 -0.1161 0.9211 -0.0001285 5.77e-05 1.141 -9.686e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2055 0.1456 0.3001 0.19 0.9853 0.9941 0.2061 0.597 0.9164 0.729 ] Network output: [ -0.0134 0.06627 1.073 0.0001154 -5.179e-05 0.888 8.694e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05609 0.05303 0.1518 0.1688 0.9885 0.9929 0.05612 0.8623 0.915 0.2412 ] Network output: [ -0.01893 0.008527 1.062 0.0001206 -5.412e-05 0.9674 9.085e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06842 0.06777 0.1678 0.1803 0.9851 0.9913 0.06843 0.7911 0.8907 0.2218 ] Network output: [ -0.00866 1.03 0.01142 -4.751e-07 2.133e-07 0.9758 -3.58e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01203 Epoch 5407 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02598 0.9585 0.954 -6.071e-05 2.726e-05 0.03524 -4.576e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002725 -0.002315 -0.0109 0.007268 0.9688 0.9733 0.005188 0.8674 0.8591 0.02093 ] Network output: [ 0.9727 0.1081 0.008515 1.305e-05 -5.857e-06 -0.06196 9.832e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1951 0.001345 -0.2278 0.2003 0.9837 0.9934 0.2175 0.5922 0.9103 0.733 ] Network output: [ 0.001371 0.962 0.9792 -6.753e-05 3.032e-05 0.05576 -5.089e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003721 0.001297 0.002368 0.003764 0.9899 0.9928 0.003787 0.9076 0.93 0.01319 ] Network output: [ 0.008613 0.05276 0.9152 -0.0001839 8.254e-05 1.014 -0.0001386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2068 0.146 0.2948 0.1571 0.9853 0.9941 0.2074 0.599 0.9167 0.7305 ] Network output: [ -0.008165 0.07656 1.067 0.0001177 -5.282e-05 0.8734 8.868e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05546 0.05238 0.1466 0.1637 0.9885 0.9929 0.05549 0.8608 0.9153 0.2383 ] Network output: [ -0.0133 -0.0129 1.06 0.0001294 -5.809e-05 0.9796 9.752e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06779 0.06713 0.1656 0.1806 0.9851 0.9913 0.0678 0.7889 0.8912 0.2217 ] Network output: [ 0.0006297 0.9698 0.01306 1.953e-05 -8.769e-06 1.016 1.472e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009926 Epoch 5408 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0277 0.9452 0.954 -5.669e-05 2.545e-05 0.04516 -4.272e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002717 -0.002326 -0.01101 0.007653 0.9688 0.9733 0.005176 0.8673 0.8598 0.02113 ] Network output: [ 1.003 -0.02633 -0.002604 6.13e-05 -2.752e-05 0.0225 4.62e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.194 -0.0007568 -0.2324 0.225 0.9837 0.9934 0.2163 0.5898 0.9109 0.7363 ] Network output: [ -0.0003913 0.9627 0.9807 -6.858e-05 3.079e-05 0.05716 -5.168e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003691 0.001303 0.002458 0.004284 0.9899 0.9928 0.003756 0.9076 0.9302 0.01322 ] Network output: [ 0.02671 -0.1184 0.9219 -0.0001276 5.73e-05 1.143 -9.62e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2046 0.145 0.2996 0.1907 0.9853 0.9942 0.2052 0.5976 0.9165 0.7304 ] Network output: [ -0.01312 0.06536 1.073 0.0001148 -5.153e-05 0.8888 8.65e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05604 0.05299 0.1517 0.1693 0.9885 0.9929 0.05608 0.8626 0.9151 0.2419 ] Network output: [ -0.01882 0.007851 1.062 0.0001198 -5.38e-05 0.9681 9.031e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06836 0.06771 0.1678 0.1808 0.9851 0.9913 0.06837 0.7917 0.8908 0.2223 ] Network output: [ -0.008779 1.032 0.01106 -8.584e-07 3.854e-07 0.9747 -6.469e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01208 Epoch 5409 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02559 0.9602 0.9543 -6.165e-05 2.768e-05 0.03415 -4.646e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002721 -0.002311 -0.01094 0.007286 0.9688 0.9733 0.00518 0.8678 0.8593 0.02097 ] Network output: [ 0.9722 0.1091 0.009007 1.458e-05 -6.546e-06 -0.06247 1.099e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1943 0.001356 -0.2289 0.2005 0.9837 0.9934 0.2166 0.5929 0.9105 0.7342 ] Network output: [ 0.001025 0.9635 0.9794 -6.855e-05 3.077e-05 0.05478 -5.166e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003713 0.001292 0.002353 0.003761 0.9899 0.9928 0.003778 0.9079 0.9301 0.01323 ] Network output: [ 0.008017 0.05714 0.9157 -0.0001849 8.299e-05 1.01 -0.0001393 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.206 0.1454 0.2941 0.1565 0.9853 0.9942 0.2066 0.5997 0.9168 0.7318 ] Network output: [ -0.007579 0.07613 1.066 0.0001173 -5.266e-05 0.8735 8.84e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0554 0.05231 0.1463 0.1639 0.9886 0.9929 0.05543 0.861 0.9154 0.2388 ] Network output: [ -0.01288 -0.01447 1.06 0.0001291 -5.797e-05 0.9808 9.732e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06772 0.06705 0.1655 0.1811 0.9851 0.9913 0.06773 0.7894 0.8914 0.2222 ] Network output: [ 0.001165 0.9678 0.01264 2.038e-05 -9.151e-06 1.017 1.536e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009929 Epoch 5410 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02736 0.9463 0.9543 -5.748e-05 2.58e-05 0.04448 -4.332e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002713 -0.002324 -0.01105 0.007687 0.9688 0.9733 0.005169 0.8677 0.86 0.02118 ] Network output: [ 1.004 -0.03105 -0.00272 6.466e-05 -2.903e-05 0.02563 4.873e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1932 -0.0008109 -0.2336 0.2264 0.9837 0.9934 0.2154 0.5904 0.9111 0.7377 ] Network output: [ -0.0008404 0.9642 0.981 -6.963e-05 3.126e-05 0.05624 -5.248e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003682 0.001299 0.00245 0.004305 0.9899 0.9928 0.003747 0.9079 0.9303 0.01326 ] Network output: [ 0.02674 -0.1216 0.9228 -0.0001265 5.678e-05 1.145 -9.532e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2037 0.1443 0.2993 0.1915 0.9853 0.9942 0.2043 0.5983 0.9167 0.7316 ] Network output: [ -0.01288 0.0644 1.072 0.0001142 -5.128e-05 0.8897 8.608e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05602 0.05296 0.1516 0.1698 0.9885 0.9929 0.05605 0.8629 0.9152 0.2426 ] Network output: [ -0.01875 0.007342 1.062 0.0001191 -5.347e-05 0.9688 8.975e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06832 0.06766 0.1678 0.1812 0.9852 0.9913 0.06833 0.7922 0.891 0.2229 ] Network output: [ -0.008942 1.034 0.01069 -1.33e-06 5.969e-07 0.9734 -1.002e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01222 Epoch 5411 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0252 0.9618 0.9545 -6.257e-05 2.809e-05 0.03302 -4.715e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002717 -0.002308 -0.01097 0.007301 0.9688 0.9733 0.005173 0.8682 0.8595 0.02102 ] Network output: [ 0.9715 0.1108 0.009557 1.569e-05 -7.042e-06 -0.06339 1.182e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1935 0.001381 -0.2299 0.2006 0.9837 0.9934 0.2158 0.5936 0.9107 0.7354 ] Network output: [ 0.0006955 0.9649 0.9796 -6.951e-05 3.12e-05 0.0538 -5.238e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003706 0.001288 0.002339 0.003757 0.9899 0.9928 0.003771 0.9082 0.9303 0.01327 ] Network output: [ 0.007361 0.0622 0.9161 -0.000186 8.352e-05 1.006 -0.0001402 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2052 0.1448 0.2933 0.1557 0.9853 0.9942 0.2058 0.6003 0.917 0.733 ] Network output: [ -0.006975 0.07576 1.065 0.000117 -5.251e-05 0.8735 8.815e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05535 0.05226 0.1459 0.1642 0.9886 0.9929 0.05538 0.8613 0.9155 0.2393 ] Network output: [ -0.01243 -0.01606 1.059 0.0001289 -5.789e-05 0.982 9.717e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06765 0.06698 0.1653 0.1815 0.9851 0.9913 0.06766 0.7898 0.8915 0.2227 ] Network output: [ 0.00175 0.9656 0.01222 2.134e-05 -9.581e-06 1.019 1.608e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009996 Epoch 5412 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02703 0.9473 0.9546 -5.821e-05 2.613e-05 0.04386 -4.387e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002709 -0.002321 -0.01109 0.007723 0.9688 0.9734 0.005162 0.868 0.8603 0.02124 ] Network output: [ 1.005 -0.03633 -0.002869 6.799e-05 -3.052e-05 0.02913 5.124e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1924 -0.0008683 -0.2349 0.2278 0.9837 0.9934 0.2146 0.591 0.9112 0.739 ] Network output: [ -0.001288 0.9656 0.9813 -7.063e-05 3.171e-05 0.05534 -5.323e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003674 0.001296 0.002442 0.004329 0.9899 0.9928 0.003738 0.9082 0.9305 0.01331 ] Network output: [ 0.02685 -0.1256 0.9237 -0.000125 5.612e-05 1.148 -9.422e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2029 0.1438 0.2989 0.1925 0.9853 0.9942 0.2035 0.5988 0.9168 0.7328 ] Network output: [ -0.01267 0.06337 1.072 0.0001137 -5.104e-05 0.8907 8.567e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.056 0.05295 0.1516 0.1704 0.9885 0.9929 0.05603 0.8633 0.9153 0.2434 ] Network output: [ -0.01869 0.007013 1.062 0.0001183 -5.312e-05 0.9693 8.918e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06829 0.06763 0.1678 0.1816 0.9852 0.9914 0.0683 0.7927 0.8911 0.2234 ] Network output: [ -0.009152 1.036 0.01031 -1.894e-06 8.501e-07 0.9719 -1.427e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01245 Epoch 5413 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0248 0.9635 0.9548 -6.347e-05 2.849e-05 0.03184 -4.783e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002713 -0.002305 -0.011 0.007314 0.9688 0.9733 0.005167 0.8685 0.8597 0.02106 ] Network output: [ 0.9707 0.1132 0.01017 1.635e-05 -7.34e-06 -0.06474 1.232e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1928 0.00142 -0.2308 0.2006 0.9837 0.9934 0.215 0.5942 0.9108 0.7365 ] Network output: [ 0.0003845 0.9663 0.9798 -7.039e-05 3.16e-05 0.05283 -5.305e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003699 0.001284 0.002324 0.00375 0.9899 0.9928 0.003764 0.9085 0.9304 0.01331 ] Network output: [ 0.006643 0.06797 0.9165 -0.0001874 8.413e-05 1.001 -0.0001412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2045 0.1442 0.2926 0.1548 0.9853 0.9942 0.2051 0.6009 0.9172 0.7342 ] Network output: [ -0.006352 0.07544 1.064 0.0001167 -5.238e-05 0.8735 8.793e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0553 0.05221 0.1455 0.1644 0.9886 0.9929 0.05533 0.8615 0.9156 0.2398 ] Network output: [ -0.01195 -0.01768 1.059 0.0001288 -5.783e-05 0.9832 9.707e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06759 0.06692 0.1651 0.1819 0.9851 0.9913 0.0676 0.7901 0.8917 0.2232 ] Network output: [ 0.00239 0.963 0.01181 2.241e-05 -1.006e-05 1.021 1.689e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01014 Epoch 5414 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02672 0.9482 0.9548 -5.888e-05 2.643e-05 0.04328 -4.437e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002705 -0.002319 -0.01113 0.00776 0.9688 0.9734 0.005156 0.8683 0.8605 0.02129 ] Network output: [ 1.006 -0.04218 -0.00305 7.129e-05 -3.2e-05 0.03302 5.372e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1917 -0.0009302 -0.236 0.2293 0.9838 0.9934 0.2138 0.5914 0.9114 0.7402 ] Network output: [ -0.001733 0.9671 0.9816 -7.157e-05 3.213e-05 0.05445 -5.394e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003666 0.001294 0.002437 0.004356 0.9899 0.9928 0.003731 0.9085 0.9306 0.01335 ] Network output: [ 0.02705 -0.1305 0.9246 -0.0001233 5.534e-05 1.151 -9.29e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2021 0.1432 0.2986 0.1937 0.9853 0.9942 0.2027 0.5993 0.917 0.734 ] Network output: [ -0.01249 0.06229 1.071 0.0001132 -5.08e-05 0.8918 8.528e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.056 0.05295 0.1516 0.171 0.9886 0.9929 0.05603 0.8636 0.9153 0.2441 ] Network output: [ -0.01868 0.006881 1.061 0.0001175 -5.277e-05 0.9697 8.859e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06827 0.06762 0.1678 0.1821 0.9852 0.9914 0.06828 0.7932 0.8912 0.224 ] Network output: [ -0.00941 1.039 0.00992 -2.555e-06 1.147e-06 0.9701 -1.926e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01279 Epoch 5415 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02441 0.9653 0.955 -6.434e-05 2.888e-05 0.03061 -4.849e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00271 -0.002302 -0.01103 0.007324 0.9688 0.9734 0.005161 0.8688 0.8599 0.0211 ] Network output: [ 0.9697 0.1163 0.01085 1.656e-05 -7.433e-06 -0.06655 1.248e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1921 0.001472 -0.2317 0.2005 0.9838 0.9934 0.2142 0.5948 0.9109 0.7375 ] Network output: [ 9.288e-05 0.9676 0.98 -7.121e-05 3.197e-05 0.05185 -5.366e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003693 0.001281 0.00231 0.003741 0.9899 0.9928 0.003758 0.9088 0.9305 0.01335 ] Network output: [ 0.005861 0.07449 0.9168 -0.000189 8.483e-05 0.9962 -0.0001424 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2038 0.1437 0.2918 0.1538 0.9853 0.9942 0.2044 0.6015 0.9173 0.7353 ] Network output: [ -0.005709 0.07519 1.063 0.0001164 -5.228e-05 0.8733 8.775e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05527 0.05217 0.1451 0.1646 0.9886 0.9929 0.0553 0.8616 0.9156 0.2402 ] Network output: [ -0.01145 -0.01931 1.058 0.0001287 -5.78e-05 0.9844 9.702e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06754 0.06687 0.1649 0.1824 0.9851 0.9913 0.06755 0.7904 0.8918 0.2237 ] Network output: [ 0.003093 0.9601 0.01139 2.361e-05 -1.06e-05 1.022 1.779e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01036 Epoch 5416 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02643 0.949 0.9551 -5.948e-05 2.67e-05 0.04277 -4.483e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002702 -0.002317 -0.01116 0.007798 0.9688 0.9734 0.00515 0.8686 0.8607 0.02134 ] Network output: [ 1.007 -0.04867 -0.003267 7.457e-05 -3.348e-05 0.03733 5.62e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.191 -0.0009978 -0.2371 0.2309 0.9838 0.9934 0.213 0.5918 0.9116 0.7414 ] Network output: [ -0.002177 0.9685 0.982 -7.244e-05 3.252e-05 0.05358 -5.459e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003659 0.001291 0.002433 0.004386 0.9899 0.9928 0.003723 0.9088 0.9307 0.01339 ] Network output: [ 0.02734 -0.1365 0.9255 -0.0001212 5.442e-05 1.156 -9.136e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2013 0.1427 0.2984 0.195 0.9853 0.9942 0.202 0.5997 0.9171 0.7351 ] Network output: [ -0.01236 0.06115 1.071 0.0001127 -5.057e-05 0.8929 8.49e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05601 0.05296 0.1517 0.1716 0.9886 0.9929 0.05605 0.8638 0.9154 0.2448 ] Network output: [ -0.01869 0.006966 1.061 0.0001167 -5.24e-05 0.9699 8.797e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06828 0.06762 0.1678 0.1825 0.9852 0.9914 0.06828 0.7937 0.8913 0.2245 ] Network output: [ -0.009716 1.042 0.009508 -3.318e-06 1.489e-06 0.9681 -2.5e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01325 Epoch 5417 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02401 0.967 0.9553 -6.518e-05 2.926e-05 0.02934 -4.912e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002707 -0.0023 -0.01105 0.007332 0.9688 0.9734 0.005156 0.8691 0.8601 0.02113 ] Network output: [ 0.9686 0.1202 0.0116 1.629e-05 -7.313e-06 -0.06885 1.228e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1915 0.001538 -0.2324 0.2001 0.9838 0.9934 0.2135 0.5953 0.911 0.7384 ] Network output: [ -0.0001782 0.9689 0.9802 -7.194e-05 3.23e-05 0.05088 -5.422e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003688 0.001277 0.002297 0.003729 0.9899 0.9928 0.003753 0.909 0.9306 0.01339 ] Network output: [ 0.005011 0.08181 0.917 -0.0001908 8.564e-05 0.9904 -0.0001438 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2032 0.1432 0.291 0.1526 0.9854 0.9942 0.2038 0.602 0.9174 0.7364 ] Network output: [ -0.005045 0.07501 1.062 0.0001163 -5.219e-05 0.8731 8.761e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05524 0.05214 0.1446 0.1648 0.9886 0.9929 0.05527 0.8617 0.9157 0.2406 ] Network output: [ -0.01091 -0.02096 1.058 0.0001287 -5.78e-05 0.9855 9.702e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0675 0.06683 0.1647 0.1828 0.9851 0.9913 0.0675 0.7906 0.8919 0.2241 ] Network output: [ 0.003864 0.9568 0.01097 2.494e-05 -1.12e-05 1.025 1.879e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01069 Epoch 5418 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02615 0.9497 0.9554 -6.002e-05 2.694e-05 0.04232 -4.523e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002699 -0.002316 -0.0112 0.007837 0.9688 0.9734 0.005145 0.8689 0.8609 0.0214 ] Network output: [ 1.009 -0.05583 -0.003516 7.784e-05 -3.494e-05 0.04209 5.866e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1904 -0.001072 -0.2381 0.2326 0.9838 0.9934 0.2123 0.5921 0.9117 0.7426 ] Network output: [ -0.002619 0.9698 0.9824 -7.324e-05 3.288e-05 0.05274 -5.52e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003653 0.00129 0.002431 0.00442 0.9899 0.9929 0.003717 0.909 0.9308 0.01344 ] Network output: [ 0.02773 -0.1435 0.9264 -0.0001189 5.336e-05 1.161 -8.958e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2006 0.1422 0.2983 0.1966 0.9854 0.9942 0.2012 0.6001 0.9172 0.736 ] Network output: [ -0.01227 0.05995 1.071 0.0001122 -5.035e-05 0.8942 8.453e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05604 0.05299 0.1518 0.1722 0.9886 0.9929 0.05608 0.8641 0.9154 0.2456 ] Network output: [ -0.01874 0.007292 1.061 0.0001159 -5.202e-05 0.97 8.733e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0683 0.06764 0.1678 0.1828 0.9852 0.9914 0.0683 0.7941 0.8914 0.2249 ] Network output: [ -0.01007 1.045 0.009072 -4.185e-06 1.879e-06 0.9657 -3.154e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01387 Epoch 5419 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02362 0.9689 0.9556 -6.6e-05 2.963e-05 0.02801 -4.974e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002704 -0.002297 -0.01108 0.007336 0.9688 0.9734 0.005151 0.8694 0.8602 0.02116 ] Network output: [ 0.9672 0.1249 0.01242 1.552e-05 -6.969e-06 -0.07168 1.17e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1909 0.001617 -0.2331 0.1996 0.9838 0.9934 0.2129 0.5958 0.9111 0.7393 ] Network output: [ -0.0004274 0.9702 0.9804 -7.26e-05 3.259e-05 0.04991 -5.471e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003684 0.001274 0.002283 0.003715 0.9899 0.9928 0.003748 0.9093 0.9307 0.01343 ] Network output: [ 0.00409 0.08999 0.9172 -0.0001928 8.655e-05 0.9839 -0.0001453 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2026 0.1428 0.2903 0.1512 0.9854 0.9942 0.2032 0.6024 0.9175 0.7374 ] Network output: [ -0.00436 0.07493 1.061 0.0001161 -5.212e-05 0.8728 8.75e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05522 0.05212 0.1442 0.1649 0.9886 0.9929 0.05525 0.8618 0.9157 0.241 ] Network output: [ -0.01035 -0.02261 1.057 0.0001288 -5.783e-05 0.9866 9.708e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06746 0.0668 0.1645 0.1832 0.9851 0.9913 0.06747 0.7908 0.892 0.2245 ] Network output: [ 0.004712 0.9531 0.01055 2.641e-05 -1.186e-05 1.027 1.991e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01114 Epoch 5420 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02588 0.9503 0.9558 -6.047e-05 2.715e-05 0.04194 -4.558e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002696 -0.002314 -0.01123 0.007878 0.9688 0.9734 0.00514 0.8691 0.8611 0.02145 ] Network output: [ 1.01 -0.06374 -0.003797 8.11e-05 -3.641e-05 0.04737 6.112e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1897 -0.001155 -0.2391 0.2344 0.9838 0.9934 0.2116 0.5923 0.9118 0.7436 ] Network output: [ -0.003061 0.9712 0.9827 -7.396e-05 3.321e-05 0.05192 -5.574e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003647 0.001288 0.002432 0.004457 0.9899 0.9929 0.003711 0.9092 0.9309 0.01348 ] Network output: [ 0.02821 -0.1516 0.9274 -0.0001162 5.216e-05 1.167 -8.755e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1999 0.1417 0.2983 0.1984 0.9854 0.9942 0.2005 0.6003 0.9172 0.7369 ] Network output: [ -0.01223 0.05871 1.071 0.0001117 -5.014e-05 0.8956 8.417e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05609 0.05304 0.1519 0.1729 0.9886 0.9929 0.05612 0.8643 0.9154 0.2463 ] Network output: [ -0.01883 0.007891 1.06 0.000115 -5.162e-05 0.9699 8.666e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06833 0.06768 0.1679 0.1832 0.9852 0.9914 0.06834 0.7945 0.8914 0.2254 ] Network output: [ -0.01047 1.049 0.008606 -5.157e-06 2.315e-06 0.963 -3.887e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01466 Epoch 5421 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02322 0.9707 0.9559 -6.678e-05 2.998e-05 0.02663 -5.032e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002702 -0.002294 -0.0111 0.007337 0.9688 0.9734 0.005147 0.8696 0.8603 0.02119 ] Network output: [ 0.9656 0.1305 0.01333 1.423e-05 -6.391e-06 -0.07506 1.073e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1904 0.00171 -0.2336 0.199 0.9838 0.9934 0.2123 0.5962 0.9112 0.74 ] Network output: [ -0.0006535 0.9714 0.9806 -7.317e-05 3.285e-05 0.04895 -5.514e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00368 0.001272 0.00227 0.003698 0.9899 0.9928 0.003745 0.9094 0.9308 0.01347 ] Network output: [ 0.003094 0.09908 0.9173 -0.0001951 8.759e-05 0.9767 -0.000147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2021 0.1424 0.2895 0.1497 0.9854 0.9942 0.2027 0.6027 0.9176 0.7383 ] Network output: [ -0.003654 0.07496 1.06 0.000116 -5.208e-05 0.8723 8.743e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05521 0.05211 0.1437 0.165 0.9886 0.9929 0.05524 0.8618 0.9158 0.2414 ] Network output: [ -0.009751 -0.02426 1.057 0.000129 -5.789e-05 0.9877 9.719e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06744 0.06677 0.1642 0.1835 0.9851 0.9913 0.06745 0.7909 0.8921 0.2249 ] Network output: [ 0.005644 0.9489 0.01012 2.805e-05 -1.259e-05 1.03 2.114e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01173 Epoch 5422 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02563 0.9508 0.9561 -6.085e-05 2.732e-05 0.04165 -4.586e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002694 -0.002313 -0.01126 0.00792 0.9688 0.9734 0.005135 0.8693 0.8612 0.0215 ] Network output: [ 1.012 -0.07247 -0.004103 8.437e-05 -3.787e-05 0.05321 6.358e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1892 -0.001248 -0.24 0.2363 0.9838 0.9934 0.211 0.5924 0.9119 0.7447 ] Network output: [ -0.003501 0.9725 0.9831 -7.461e-05 3.35e-05 0.05113 -5.623e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003642 0.001288 0.002434 0.004498 0.9899 0.9929 0.003706 0.9093 0.931 0.01352 ] Network output: [ 0.02879 -0.161 0.9284 -0.0001131 5.079e-05 1.175 -8.527e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1993 0.1413 0.2983 0.2005 0.9854 0.9942 0.1999 0.6004 0.9173 0.7378 ] Network output: [ -0.01225 0.05742 1.071 0.0001112 -4.993e-05 0.897 8.382e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05616 0.05311 0.1522 0.1736 0.9886 0.9929 0.05619 0.8645 0.9154 0.2471 ] Network output: [ -0.01896 0.008796 1.06 0.0001141 -5.121e-05 0.9695 8.596e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06839 0.06773 0.1679 0.1835 0.9852 0.9914 0.06839 0.7948 0.8914 0.2258 ] Network output: [ -0.01091 1.054 0.008102 -6.234e-06 2.799e-06 0.96 -4.698e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01567 Epoch 5423 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02282 0.9726 0.9562 -6.752e-05 3.031e-05 0.02519 -5.089e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0027 -0.002292 -0.01112 0.007334 0.9688 0.9734 0.005143 0.8698 0.8603 0.02121 ] Network output: [ 0.9639 0.137 0.01433 1.239e-05 -5.562e-06 -0.07904 9.337e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1899 0.001816 -0.234 0.1981 0.9838 0.9934 0.2117 0.5965 0.9112 0.7406 ] Network output: [ -0.0008552 0.9726 0.9808 -7.365e-05 3.306e-05 0.04799 -5.551e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003678 0.001269 0.002258 0.003678 0.9899 0.9929 0.003743 0.9096 0.9308 0.0135 ] Network output: [ 0.002018 0.1092 0.9173 -0.0001977 8.876e-05 0.9687 -0.000149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2016 0.142 0.2887 0.148 0.9854 0.9942 0.2022 0.603 0.9176 0.7391 ] Network output: [ -0.002926 0.07513 1.059 0.000116 -5.206e-05 0.8717 8.739e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05521 0.0521 0.1432 0.165 0.9886 0.9929 0.05524 0.8618 0.9158 0.2416 ] Network output: [ -0.009121 -0.02588 1.056 0.0001292 -5.799e-05 0.9887 9.735e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06743 0.06676 0.1639 0.1839 0.9851 0.9913 0.06744 0.7909 0.8922 0.2252 ] Network output: [ 0.006667 0.9443 0.009685 2.985e-05 -1.34e-05 1.033 2.25e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0125 Epoch 5424 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0254 0.9511 0.9564 -6.114e-05 2.745e-05 0.04144 -4.608e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002692 -0.002313 -0.01129 0.007964 0.9688 0.9734 0.005132 0.8694 0.8613 0.02154 ] Network output: [ 1.014 -0.0821 -0.004429 8.764e-05 -3.934e-05 0.05969 6.605e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1886 -0.001351 -0.2407 0.2384 0.9838 0.9934 0.2104 0.5924 0.912 0.7456 ] Network output: [ -0.003939 0.9737 0.9835 -7.517e-05 3.375e-05 0.05037 -5.665e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003637 0.001287 0.00244 0.004544 0.9899 0.9929 0.003701 0.9095 0.931 0.01355 ] Network output: [ 0.0295 -0.1718 0.9294 -0.0001097 4.926e-05 1.183 -8.27e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1987 0.1409 0.2985 0.2028 0.9854 0.9942 0.1993 0.6004 0.9173 0.7385 ] Network output: [ -0.01232 0.0561 1.07 0.0001108 -4.973e-05 0.8986 8.347e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05624 0.05319 0.1524 0.1743 0.9886 0.9929 0.05627 0.8647 0.9154 0.2479 ] Network output: [ -0.01914 0.01005 1.06 0.0001131 -5.077e-05 0.9689 8.522e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06846 0.06781 0.168 0.1838 0.9852 0.9914 0.06847 0.7951 0.8914 0.2262 ] Network output: [ -0.0114 1.059 0.007549 -7.412e-06 3.327e-06 0.9566 -5.586e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01694 Epoch 5425 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02242 0.9746 0.9566 -6.823e-05 3.063e-05 0.0237 -5.142e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002699 -0.00229 -0.01113 0.007327 0.9688 0.9734 0.00514 0.8699 0.8604 0.02123 ] Network output: [ 0.9618 0.1446 0.01543 9.948e-06 -4.466e-06 -0.08367 7.497e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1894 0.001937 -0.2343 0.197 0.9838 0.9934 0.2112 0.5967 0.9112 0.7412 ] Network output: [ -0.001031 0.9737 0.981 -7.403e-05 3.324e-05 0.04704 -5.579e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003677 0.001267 0.002246 0.003654 0.9899 0.9929 0.003741 0.9097 0.9308 0.01353 ] Network output: [ 0.0008595 0.1203 0.9172 -0.0002006 9.007e-05 0.96 -0.0001512 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2013 0.1417 0.2879 0.146 0.9854 0.9942 0.2019 0.6031 0.9177 0.7398 ] Network output: [ -0.002176 0.07545 1.058 0.000116 -5.206e-05 0.8709 8.739e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05521 0.0521 0.1426 0.165 0.9886 0.9929 0.05525 0.8617 0.9158 0.2419 ] Network output: [ -0.008457 -0.02746 1.055 0.0001295 -5.812e-05 0.9896 9.757e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06743 0.06675 0.1636 0.1842 0.9851 0.9913 0.06743 0.7909 0.8922 0.2255 ] Network output: [ 0.007788 0.9393 0.009234 3.183e-05 -1.429e-05 1.036 2.399e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01347 Epoch 5426 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02519 0.9512 0.9568 -6.134e-05 2.754e-05 0.04134 -4.623e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00269 -0.002312 -0.01132 0.008009 0.9688 0.9734 0.005128 0.8695 0.8614 0.02159 ] Network output: [ 1.015 -0.09272 -0.004764 9.093e-05 -4.082e-05 0.06688 6.852e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1881 -0.001466 -0.2414 0.2406 0.9838 0.9934 0.2098 0.5922 0.912 0.7465 ] Network output: [ -0.004375 0.9749 0.9839 -7.565e-05 3.396e-05 0.04964 -5.701e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003634 0.001287 0.002449 0.004594 0.9899 0.9929 0.003697 0.9095 0.931 0.01359 ] Network output: [ 0.03033 -0.1841 0.9305 -0.0001059 4.755e-05 1.193 -7.983e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1981 0.1406 0.2987 0.2054 0.9854 0.9942 0.1987 0.6003 0.9173 0.7391 ] Network output: [ -0.01246 0.05474 1.07 0.0001103 -4.953e-05 0.9002 8.314e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05634 0.05329 0.1528 0.1751 0.9886 0.9929 0.05638 0.8648 0.9154 0.2487 ] Network output: [ -0.01937 0.0117 1.059 0.000112 -5.03e-05 0.9681 8.444e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06856 0.06791 0.168 0.1841 0.9853 0.9914 0.06857 0.7954 0.8913 0.2266 ] Network output: [ -0.01191 1.064 0.006933 -8.68e-06 3.897e-06 0.9529 -6.542e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01852 Epoch 5427 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02201 0.9767 0.9569 -6.891e-05 3.094e-05 0.02213 -5.193e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002698 -0.002288 -0.01114 0.007316 0.9688 0.9734 0.005138 0.87 0.8604 0.02125 ] Network output: [ 0.9596 0.1533 0.01664 6.864e-06 -3.082e-06 -0.089 5.173e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1891 0.002075 -0.2345 0.1957 0.9838 0.9934 0.2108 0.5968 0.9112 0.7416 ] Network output: [ -0.00118 0.9748 0.9812 -7.432e-05 3.336e-05 0.04609 -5.601e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003677 0.001266 0.002234 0.003627 0.9899 0.9929 0.003741 0.9097 0.9308 0.01356 ] Network output: [ -0.0003877 0.1326 0.917 -0.0002039 9.153e-05 0.9504 -0.0001537 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2009 0.1414 0.287 0.1439 0.9854 0.9942 0.2016 0.6031 0.9177 0.7404 ] Network output: [ -0.001407 0.07597 1.057 0.000116 -5.207e-05 0.87 8.742e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05523 0.05211 0.1421 0.165 0.9886 0.9929 0.05526 0.8615 0.9157 0.2421 ] Network output: [ -0.00776 -0.02898 1.055 0.0001298 -5.829e-05 0.9905 9.785e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06744 0.06676 0.1632 0.1845 0.9851 0.9913 0.06744 0.7907 0.8922 0.2257 ] Network output: [ 0.00901 0.9337 0.008767 3.399e-05 -1.526e-05 1.04 2.562e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01469 Epoch 5428 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.025 0.9513 0.9572 -6.143e-05 2.758e-05 0.04135 -4.63e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002688 -0.002312 -0.01134 0.008056 0.9688 0.9734 0.005126 0.8696 0.8615 0.02163 ] Network output: [ 1.018 -0.1045 -0.005093 9.422e-05 -4.23e-05 0.07487 7.101e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1877 -0.001595 -0.242 0.243 0.9838 0.9934 0.2093 0.5918 0.912 0.7473 ] Network output: [ -0.004807 0.976 0.9844 -7.602e-05 3.413e-05 0.04895 -5.729e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003631 0.001288 0.002461 0.00465 0.9899 0.9929 0.003695 0.9096 0.931 0.01363 ] Network output: [ 0.03131 -0.1981 0.9316 -0.0001017 4.564e-05 1.203 -7.662e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1976 0.1403 0.2991 0.2084 0.9854 0.9942 0.1982 0.5999 0.9172 0.7396 ] Network output: [ -0.01266 0.05336 1.07 0.0001099 -4.933e-05 0.9019 8.281e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05647 0.05342 0.1532 0.1759 0.9886 0.9929 0.0565 0.8649 0.9153 0.2494 ] Network output: [ -0.01965 0.0138 1.059 0.0001109 -4.981e-05 0.9669 8.361e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06868 0.06803 0.1681 0.1843 0.9853 0.9914 0.06869 0.7955 0.8912 0.2269 ] Network output: [ -0.01243 1.07 0.006236 -1.002e-05 4.5e-06 0.9487 -7.555e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02049 Epoch 5429 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0216 0.9788 0.9573 -6.955e-05 3.122e-05 0.02049 -5.241e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002697 -0.002286 -0.01115 0.007299 0.9688 0.9734 0.005137 0.8701 0.8603 0.02126 ] Network output: [ 0.957 0.1632 0.01796 3.084e-06 -1.384e-06 -0.09509 2.324e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1888 0.00223 -0.2345 0.1941 0.9838 0.9934 0.2105 0.5967 0.9111 0.7418 ] Network output: [ -0.0013 0.9758 0.9814 -7.449e-05 3.344e-05 0.04515 -5.614e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003678 0.001264 0.002223 0.003595 0.9899 0.9929 0.003743 0.9097 0.9308 0.01359 ] Network output: [ -0.001728 0.1461 0.9167 -0.0002075 9.316e-05 0.9398 -0.0001564 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2007 0.1412 0.2862 0.1415 0.9854 0.9942 0.2013 0.6029 0.9176 0.7409 ] Network output: [ -0.0006196 0.07672 1.056 0.0001161 -5.211e-05 0.8688 8.748e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05527 0.05214 0.1414 0.1649 0.9886 0.9929 0.0553 0.8613 0.9157 0.2422 ] Network output: [ -0.00703 -0.0304 1.054 0.0001303 -5.849e-05 0.9912 9.818e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06746 0.06678 0.1628 0.1847 0.9851 0.9913 0.06747 0.7904 0.8922 0.2259 ] Network output: [ 0.01034 0.9276 0.008283 3.635e-05 -1.632e-05 1.044 2.739e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0162 Epoch 5430 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02482 0.9511 0.9576 -6.142e-05 2.757e-05 0.04149 -4.628e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002687 -0.002313 -0.01136 0.008104 0.9688 0.9734 0.005123 0.8696 0.8615 0.02168 ] Network output: [ 1.02 -0.1174 -0.005398 9.753e-05 -4.378e-05 0.08377 7.35e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1873 -0.001738 -0.2423 0.2456 0.9838 0.9934 0.2089 0.5913 0.912 0.748 ] Network output: [ -0.005234 0.977 0.9848 -7.629e-05 3.425e-05 0.0483 -5.749e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003629 0.00129 0.002478 0.004712 0.9899 0.9929 0.003693 0.9095 0.931 0.01366 ] Network output: [ 0.03244 -0.214 0.9328 -9.693e-05 4.351e-05 1.216 -7.305e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1972 0.1401 0.2997 0.2117 0.9854 0.9942 0.1978 0.5994 0.9171 0.7399 ] Network output: [ -0.01294 0.05197 1.071 0.0001095 -4.914e-05 0.9037 8.249e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05663 0.05357 0.1536 0.1768 0.9886 0.9929 0.05666 0.8649 0.9151 0.2502 ] Network output: [ -0.01998 0.01642 1.059 0.0001098 -4.928e-05 0.9653 8.273e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06883 0.06818 0.1682 0.1845 0.9853 0.9914 0.06884 0.7956 0.8911 0.2271 ] Network output: [ -0.01295 1.076 0.005437 -1.142e-05 5.127e-06 0.9442 -8.606e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02292 Epoch 5431 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02117 0.981 0.9576 -7.015e-05 3.149e-05 0.01877 -5.286e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002697 -0.002285 -0.01115 0.007277 0.9688 0.9734 0.005137 0.8701 0.8602 0.02126 ] Network output: [ 0.9541 0.1744 0.01942 -1.459e-06 6.552e-07 -0.102 -1.1e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1885 0.002405 -0.2344 0.1923 0.9838 0.9934 0.2102 0.5965 0.911 0.7419 ] Network output: [ -0.001392 0.9767 0.9816 -7.455e-05 3.347e-05 0.04421 -5.618e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003681 0.001263 0.002213 0.003559 0.9899 0.9929 0.003746 0.9097 0.9307 0.01361 ] Network output: [ -0.003168 0.161 0.9162 -0.0002116 9.498e-05 0.9283 -0.0001594 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2006 0.1411 0.2854 0.1389 0.9854 0.9942 0.2012 0.6025 0.9176 0.7413 ] Network output: [ 0.0001809 0.07774 1.055 0.0001162 -5.216e-05 0.8673 8.756e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05531 0.05217 0.1408 0.1647 0.9886 0.9929 0.05534 0.8609 0.9156 0.2422 ] Network output: [ -0.006269 -0.03169 1.053 0.0001308 -5.872e-05 0.9918 9.857e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0675 0.06682 0.1624 0.1849 0.9851 0.9913 0.0675 0.7899 0.8921 0.2261 ] Network output: [ 0.01177 0.9211 0.007781 3.889e-05 -1.746e-05 1.048 2.931e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01807 Epoch 5432 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02467 0.9507 0.958 -6.128e-05 2.751e-05 0.04177 -4.618e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002686 -0.002313 -0.01138 0.008154 0.9688 0.9734 0.005122 0.8695 0.8615 0.02171 ] Network output: [ 1.022 -0.1318 -0.005651 0.0001008 -4.527e-05 0.0937 7.599e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.187 -0.001898 -0.2425 0.2483 0.9838 0.9934 0.2086 0.5905 0.912 0.7486 ] Network output: [ -0.005653 0.978 0.9853 -7.644e-05 3.431e-05 0.0477 -5.76e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003628 0.001292 0.002499 0.004781 0.9899 0.9929 0.003692 0.9094 0.9309 0.01369 ] Network output: [ 0.03376 -0.232 0.934 -9.164e-05 4.114e-05 1.23 -6.906e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1968 0.1399 0.3004 0.2154 0.9854 0.9942 0.1974 0.5986 0.917 0.7401 ] Network output: [ -0.01329 0.05059 1.071 0.000109 -4.895e-05 0.9056 8.217e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05681 0.05375 0.1542 0.1777 0.9886 0.9929 0.05684 0.8648 0.915 0.251 ] Network output: [ -0.02037 0.01964 1.058 0.0001085 -4.872e-05 0.9632 8.179e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06901 0.06836 0.1682 0.1846 0.9853 0.9914 0.06902 0.7955 0.8909 0.2273 ] Network output: [ -0.01345 1.083 0.004507 -1.283e-05 5.759e-06 0.9393 -9.668e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02593 Epoch 5433 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02073 0.9833 0.958 -7.071e-05 3.174e-05 0.01695 -5.329e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002698 -0.002283 -0.01114 0.007249 0.9688 0.9734 0.005137 0.87 0.86 0.02125 ] Network output: [ 0.9509 0.187 0.02101 -6.841e-06 3.071e-06 -0.1098 -5.156e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1884 0.002602 -0.234 0.1901 0.9838 0.9934 0.2101 0.596 0.9108 0.7419 ] Network output: [ -0.001453 0.9776 0.9817 -7.449e-05 3.344e-05 0.04327 -5.614e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003686 0.001263 0.002203 0.003519 0.9899 0.9928 0.003751 0.9095 0.9306 0.01362 ] Network output: [ -0.004712 0.1774 0.9156 -0.000216 9.699e-05 0.9156 -0.0001628 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2006 0.141 0.2845 0.136 0.9854 0.9942 0.2012 0.6019 0.9174 0.7415 ] Network output: [ 0.0009883 0.07909 1.054 0.0001163 -5.223e-05 0.8655 8.767e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05537 0.05222 0.1401 0.1644 0.9886 0.9929 0.0554 0.8604 0.9154 0.2422 ] Network output: [ -0.005483 -0.03279 1.052 0.0001314 -5.897e-05 0.9921 9.9e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06755 0.06687 0.1619 0.1851 0.9851 0.9913 0.06756 0.7893 0.8919 0.2261 ] Network output: [ 0.0133 0.9141 0.00726 4.161e-05 -1.868e-05 1.052 3.136e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02036 Epoch 5434 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02454 0.95 0.9584 -6.1e-05 2.739e-05 0.04223 -4.597e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002686 -0.002314 -0.01138 0.008206 0.9688 0.9734 0.005121 0.8694 0.8614 0.02174 ] Network output: [ 1.025 -0.1477 -0.005817 0.0001041 -4.675e-05 0.1048 7.847e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1868 -0.002075 -0.2425 0.2513 0.9838 0.9934 0.2083 0.5894 0.9118 0.749 ] Network output: [ -0.006062 0.9789 0.9858 -7.645e-05 3.432e-05 0.04716 -5.761e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003629 0.001295 0.002526 0.004858 0.9899 0.9929 0.003692 0.9092 0.9307 0.01372 ] Network output: [ 0.0353 -0.2524 0.9353 -8.575e-05 3.85e-05 1.246 -6.462e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1965 0.1398 0.3013 0.2196 0.9854 0.9942 0.1971 0.5975 0.9168 0.7402 ] Network output: [ -0.01373 0.04924 1.071 0.0001086 -4.877e-05 0.9076 8.186e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05702 0.05397 0.1548 0.1786 0.9886 0.9929 0.05705 0.8646 0.9148 0.2517 ] Network output: [ -0.0208 0.02354 1.058 0.0001072 -4.813e-05 0.9606 8.08e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06923 0.06858 0.1683 0.1847 0.9853 0.9914 0.06924 0.7953 0.8906 0.2274 ] Network output: [ -0.01387 1.09 0.003413 -1.42e-05 6.374e-06 0.934 -1.07e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02964 Epoch 5435 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02028 0.9857 0.9585 -7.123e-05 3.198e-05 0.01501 -5.368e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002699 -0.002282 -0.01113 0.007214 0.9688 0.9734 0.005139 0.8699 0.8598 0.02124 ] Network output: [ 0.9473 0.2012 0.02274 -1.315e-05 5.903e-06 -0.1187 -9.91e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1884 0.002826 -0.2334 0.1877 0.9838 0.9934 0.2101 0.5954 0.9105 0.7416 ] Network output: [ -0.001485 0.9784 0.9819 -7.429e-05 3.335e-05 0.04232 -5.599e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003693 0.001263 0.002195 0.003473 0.9899 0.9928 0.003757 0.9093 0.9304 0.01364 ] Network output: [ -0.006366 0.1953 0.9148 -0.000221 9.922e-05 0.9017 -0.0001666 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2007 0.141 0.2836 0.1328 0.9854 0.9942 0.2013 0.601 0.9172 0.7415 ] Network output: [ 0.001793 0.08083 1.053 0.0001165 -5.23e-05 0.8634 8.78e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05545 0.05229 0.1394 0.164 0.9886 0.9929 0.05548 0.8598 0.9152 0.242 ] Network output: [ -0.004676 -0.03363 1.051 0.000132 -5.926e-05 0.9922 9.948e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06762 0.06694 0.1614 0.1851 0.9851 0.9913 0.06763 0.7884 0.8917 0.2261 ] Network output: [ 0.01492 0.9068 0.006724 4.448e-05 -1.997e-05 1.057 3.352e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02316 Epoch 5436 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02443 0.9491 0.9589 -6.057e-05 2.719e-05 0.04287 -4.565e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002686 -0.002316 -0.01139 0.008259 0.9688 0.9734 0.005121 0.8691 0.8613 0.02177 ] Network output: [ 1.027 -0.1653 -0.005848 0.0001074 -4.82e-05 0.1172 8.092e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1866 -0.002271 -0.2422 0.2546 0.9838 0.9934 0.2081 0.5879 0.9117 0.7493 ] Network output: [ -0.006454 0.9796 0.9863 -7.631e-05 3.426e-05 0.04668 -5.751e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00363 0.001299 0.002559 0.004944 0.9899 0.9929 0.003694 0.9089 0.9306 0.01374 ] Network output: [ 0.03708 -0.2755 0.9366 -7.918e-05 3.554e-05 1.264 -5.967e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1964 0.1397 0.3024 0.2243 0.9854 0.9942 0.1969 0.5961 0.9165 0.7399 ] Network output: [ -0.01426 0.04793 1.071 0.0001082 -4.859e-05 0.9095 8.157e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05728 0.05422 0.1555 0.1797 0.9885 0.9929 0.05731 0.8643 0.9145 0.2524 ] Network output: [ -0.0213 0.02822 1.057 0.0001058 -4.75e-05 0.9575 7.973e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06948 0.06883 0.1683 0.1847 0.9853 0.9914 0.06949 0.795 0.8902 0.2273 ] Network output: [ -0.01417 1.098 0.002113 -1.545e-05 6.934e-06 0.9285 -1.164e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03422 Epoch 5437 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0198 0.9882 0.9589 -7.172e-05 3.22e-05 0.01295 -5.405e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002702 -0.002282 -0.01111 0.007171 0.9688 0.9734 0.005142 0.8696 0.8594 0.02121 ] Network output: [ 0.9434 0.217 0.02461 -2.049e-05 9.197e-06 -0.1286 -1.544e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1885 0.00308 -0.2326 0.1849 0.9838 0.9934 0.2102 0.5943 0.9102 0.7411 ] Network output: [ -0.001489 0.9792 0.9821 -7.395e-05 3.32e-05 0.04136 -5.573e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003702 0.001265 0.002188 0.003422 0.9899 0.9928 0.003767 0.909 0.9301 0.01364 ] Network output: [ -0.008133 0.2149 0.9138 -0.0002265 0.0001017 0.8866 -0.0001707 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.201 0.1412 0.2827 0.1293 0.9854 0.9942 0.2016 0.5998 0.917 0.7414 ] Network output: [ 0.00258 0.08303 1.051 0.0001167 -5.238e-05 0.8609 8.793e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05555 0.05238 0.1386 0.1635 0.9886 0.9929 0.05558 0.8589 0.9149 0.2417 ] Network output: [ -0.003862 -0.03413 1.05 0.0001327 -5.956e-05 0.992 9.999e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06772 0.06703 0.1608 0.1851 0.9851 0.9913 0.06772 0.7873 0.8915 0.2259 ] Network output: [ 0.0166 0.8993 0.006179 4.747e-05 -2.131e-05 1.062 3.577e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02654 Epoch 5438 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02435 0.9479 0.9595 -5.998e-05 2.693e-05 0.04373 -4.52e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002686 -0.002318 -0.01138 0.008314 0.9689 0.9734 0.005121 0.8687 0.8611 0.02179 ] Network output: [ 1.03 -0.1847 -0.005683 0.0001105 -4.962e-05 0.1312 8.33e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1865 -0.002486 -0.2415 0.258 0.9838 0.9934 0.208 0.5861 0.9114 0.7494 ] Network output: [ -0.006822 0.9803 0.9868 -7.599e-05 3.412e-05 0.04627 -5.727e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003634 0.001304 0.0026 0.005039 0.9899 0.9928 0.003698 0.9085 0.9303 0.01376 ] Network output: [ 0.03914 -0.3015 0.938 -7.184e-05 3.225e-05 1.285 -5.414e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1963 0.1398 0.3037 0.2297 0.9854 0.9942 0.1969 0.5942 0.9161 0.7395 ] Network output: [ -0.01487 0.04669 1.072 0.0001079 -4.842e-05 0.9116 8.129e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05757 0.05451 0.1563 0.1807 0.9885 0.9929 0.0576 0.8639 0.9141 0.2531 ] Network output: [ -0.02183 0.03378 1.057 0.0001043 -4.682e-05 0.9536 7.86e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06978 0.06913 0.1683 0.1847 0.9853 0.9914 0.06979 0.7944 0.8898 0.2272 ] Network output: [ -0.01428 1.105 0.0005556 -1.647e-05 7.392e-06 0.9228 -1.241e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03985 Epoch 5439 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01929 0.9909 0.9594 -7.218e-05 3.24e-05 0.01074 -5.439e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002705 -0.002281 -0.01108 0.00712 0.9688 0.9734 0.005148 0.8693 0.859 0.02118 ] Network output: [ 0.9391 0.2347 0.02661 -2.896e-05 1.3e-05 -0.1396 -2.182e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1888 0.003369 -0.2315 0.1817 0.9838 0.9934 0.2105 0.593 0.9097 0.7404 ] Network output: [ -0.001468 0.98 0.9823 -7.345e-05 3.298e-05 0.04037 -5.536e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003714 0.001267 0.002183 0.003365 0.9899 0.9928 0.003779 0.9085 0.9298 0.01365 ] Network output: [ -0.01001 0.2363 0.9126 -0.0002325 0.0001044 0.8701 -0.0001752 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2014 0.1414 0.2819 0.1255 0.9854 0.9942 0.202 0.5982 0.9166 0.741 ] Network output: [ 0.003328 0.08579 1.05 0.0001168 -5.245e-05 0.8579 8.806e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05568 0.05249 0.1378 0.163 0.9886 0.9929 0.05571 0.8579 0.9146 0.2414 ] Network output: [ -0.003057 -0.03418 1.049 0.0001334 -5.988e-05 0.9914 0.0001005 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06784 0.06715 0.1602 0.185 0.9851 0.9913 0.06784 0.7858 0.8911 0.2257 ] Network output: [ 0.0183 0.8918 0.005636 5.049e-05 -2.267e-05 1.066 3.805e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03061 Epoch 5440 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0243 0.9463 0.9601 -5.919e-05 2.657e-05 0.04484 -4.461e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002687 -0.00232 -0.01136 0.00837 0.9689 0.9734 0.005123 0.8682 0.8607 0.0218 ] Network output: [ 1.033 -0.2062 -0.005236 0.0001135 -5.097e-05 0.1469 8.556e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1865 -0.002722 -0.2405 0.2618 0.9838 0.9934 0.2081 0.5837 0.9111 0.7493 ] Network output: [ -0.007157 0.9807 0.9873 -7.547e-05 3.388e-05 0.04594 -5.688e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003639 0.001311 0.00265 0.005146 0.9899 0.9928 0.003704 0.9079 0.93 0.01377 ] Network output: [ 0.04155 -0.3307 0.9393 -6.366e-05 2.858e-05 1.308 -4.798e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1963 0.14 0.3053 0.2356 0.9854 0.9942 0.1969 0.5918 0.9157 0.7387 ] Network output: [ -0.01557 0.04557 1.072 0.0001075 -4.827e-05 0.9136 8.103e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05792 0.05485 0.1572 0.1819 0.9885 0.9929 0.05795 0.8633 0.9136 0.2536 ] Network output: [ -0.02241 0.04035 1.056 0.0001027 -4.611e-05 0.9489 7.741e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07013 0.06949 0.1682 0.1845 0.9853 0.9914 0.07014 0.7935 0.8892 0.2268 ] Network output: [ -0.0141 1.112 -0.001318 -1.71e-05 7.678e-06 0.9172 -1.289e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04674 Epoch 5441 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01875 0.9938 0.96 -7.26e-05 3.259e-05 0.008351 -5.471e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002709 -0.002282 -0.01104 0.00706 0.9688 0.9734 0.005155 0.8687 0.8584 0.02113 ] Network output: [ 0.9344 0.2542 0.02873 -3.866e-05 1.736e-05 -0.1519 -2.914e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1893 0.003699 -0.2301 0.1781 0.9838 0.9934 0.211 0.5911 0.9091 0.7394 ] Network output: [ -0.00143 0.9807 0.9825 -7.28e-05 3.268e-05 0.03934 -5.486e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003729 0.001271 0.00218 0.003303 0.9899 0.9928 0.003795 0.9079 0.9293 0.01364 ] Network output: [ -0.012 0.2596 0.9111 -0.000239 0.0001073 0.8523 -0.0001801 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2021 0.1418 0.281 0.1214 0.9854 0.9942 0.2027 0.596 0.9161 0.7404 ] Network output: [ 0.004007 0.0892 1.049 0.000117 -5.251e-05 0.8544 8.815e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05584 0.05263 0.1369 0.1623 0.9886 0.9929 0.05587 0.8566 0.9141 0.2409 ] Network output: [ -0.002287 -0.03362 1.048 0.0001341 -6.019e-05 0.9903 0.000101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06799 0.0673 0.1595 0.1848 0.9851 0.9913 0.068 0.784 0.8906 0.2253 ] Network output: [ 0.01994 0.8847 0.005116 5.345e-05 -2.4e-05 1.071 4.028e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03545 Epoch 5442 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02428 0.9443 0.9607 -5.818e-05 2.612e-05 0.04623 -4.385e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002688 -0.002323 -0.01132 0.008427 0.9689 0.9734 0.005125 0.8676 0.8603 0.02179 ] Network output: [ 1.035 -0.2299 -0.004402 0.0001162 -5.219e-05 0.1643 8.76e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1867 -0.002978 -0.2389 0.2658 0.9838 0.9934 0.2082 0.5808 0.9106 0.7489 ] Network output: [ -0.007446 0.981 0.9879 -7.471e-05 3.354e-05 0.04571 -5.63e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003647 0.001319 0.002709 0.005265 0.9899 0.9928 0.003712 0.9071 0.9295 0.01378 ] Network output: [ 0.04435 -0.3634 0.9406 -5.458e-05 2.45e-05 1.334 -4.113e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1966 0.1403 0.3071 0.2423 0.9853 0.9942 0.1971 0.5888 0.9151 0.7376 ] Network output: [ -0.01635 0.04461 1.073 0.0001072 -4.814e-05 0.9155 8.081e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05832 0.05525 0.1582 0.1831 0.9885 0.9929 0.05835 0.8625 0.913 0.2541 ] Network output: [ -0.02301 0.04799 1.055 0.0001011 -4.538e-05 0.9432 7.617e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07054 0.0699 0.1681 0.1843 0.9853 0.9914 0.07055 0.7924 0.8884 0.2263 ] Network output: [ -0.01351 1.118 -0.00357 -1.715e-05 7.697e-06 0.9121 -1.292e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05513 Epoch 5443 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01816 0.997 0.9606 -7.298e-05 3.276e-05 0.005763 -5.5e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002715 -0.002283 -0.01099 0.006992 0.9689 0.9734 0.005164 0.868 0.8576 0.02107 ] Network output: [ 0.9294 0.2755 0.0309 -4.968e-05 2.23e-05 -0.1654 -3.744e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1899 0.004076 -0.2283 0.1741 0.9837 0.9934 0.2118 0.5887 0.9084 0.738 ] Network output: [ -0.001385 0.9814 0.9828 -7.197e-05 3.231e-05 0.03825 -5.424e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003749 0.001276 0.002181 0.003236 0.9899 0.9928 0.003814 0.907 0.9288 0.01362 ] Network output: [ -0.01407 0.2844 0.9093 -0.000246 0.0001105 0.8334 -0.0001854 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.203 0.1425 0.2801 0.1171 0.9853 0.9942 0.2036 0.5933 0.9155 0.7394 ] Network output: [ 0.004574 0.09337 1.048 0.000117 -5.254e-05 0.8503 8.821e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05604 0.05282 0.1361 0.1614 0.9885 0.9928 0.05607 0.855 0.9135 0.2402 ] Network output: [ -0.001591 -0.03228 1.048 0.0001347 -6.048e-05 0.9885 0.0001015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06818 0.06749 0.1588 0.1844 0.9851 0.9913 0.06819 0.7818 0.8899 0.2248 ] Network output: [ 0.02143 0.8785 0.004652 5.616e-05 -2.521e-05 1.074 4.232e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04113 Epoch 5444 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02429 0.9418 0.9615 -5.694e-05 2.556e-05 0.0479 -4.291e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00269 -0.002326 -0.01127 0.008483 0.9689 0.9734 0.005128 0.8667 0.8597 0.02178 ] Network output: [ 1.038 -0.2555 -0.003044 0.0001185 -5.319e-05 0.1837 8.929e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.187 -0.003251 -0.2366 0.27 0.9838 0.9934 0.2086 0.5772 0.91 0.7482 ] Network output: [ -0.007668 0.981 0.9884 -7.366e-05 3.307e-05 0.0456 -5.551e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003658 0.001328 0.00278 0.005396 0.9898 0.9928 0.003722 0.9061 0.9289 0.01379 ] Network output: [ 0.04759 -0.3994 0.9417 -4.459e-05 2.002e-05 1.362 -3.36e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.197 0.1408 0.3092 0.2497 0.9853 0.9942 0.1976 0.5851 0.9143 0.736 ] Network output: [ -0.01718 0.04386 1.074 0.000107 -4.804e-05 0.9174 8.065e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05879 0.05571 0.1593 0.1843 0.9884 0.9928 0.05882 0.8613 0.9123 0.2545 ] Network output: [ -0.0236 0.05675 1.054 9.94e-05 -4.462e-05 0.9367 7.491e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07102 0.07037 0.168 0.1839 0.9853 0.9914 0.07103 0.7908 0.8875 0.2256 ] Network output: [ -0.01235 1.123 -0.006255 -1.633e-05 7.33e-06 0.9078 -1.23e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06519 Epoch 5445 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01753 1 0.9613 -7.333e-05 3.292e-05 0.002963 -5.526e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002723 -0.002285 -0.01092 0.006915 0.9689 0.9734 0.005177 0.8671 0.8567 0.02099 ] Network output: [ 0.9242 0.2982 0.03306 -6.2e-05 2.783e-05 -0.1799 -4.672e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1909 0.004504 -0.2262 0.1698 0.9837 0.9934 0.2128 0.5857 0.9075 0.7363 ] Network output: [ -0.00135 0.9822 0.9831 -7.095e-05 3.185e-05 0.03707 -5.347e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003773 0.001284 0.002186 0.003166 0.9898 0.9928 0.003839 0.906 0.928 0.0136 ] Network output: [ -0.01616 0.3105 0.9072 -0.0002534 0.0001138 0.8135 -0.000191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2042 0.1433 0.2793 0.1126 0.9853 0.9941 0.2048 0.5898 0.9147 0.7381 ] Network output: [ 0.00497 0.09836 1.047 0.000117 -5.253e-05 0.8456 8.818e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05629 0.05305 0.1352 0.1604 0.9885 0.9928 0.05632 0.8531 0.9127 0.2394 ] Network output: [ -0.001025 -0.02993 1.047 0.0001352 -6.072e-05 0.9859 0.0001019 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06842 0.06772 0.158 0.1839 0.985 0.9912 0.06843 0.7791 0.889 0.2242 ] Network output: [ 0.02261 0.874 0.004296 5.837e-05 -2.621e-05 1.077 4.399e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04761 Epoch 5446 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02433 0.939 0.9623 -5.544e-05 2.489e-05 0.04987 -4.178e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002692 -0.00233 -0.0112 0.008537 0.9689 0.9734 0.005132 0.8655 0.8589 0.02174 ] Network output: [ 1.04 -0.2826 -0.001006 0.00012 -5.385e-05 0.2046 9.04e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1874 -0.003533 -0.2337 0.2744 0.9837 0.9934 0.2091 0.5727 0.9092 0.7471 ] Network output: [ -0.0078 0.9807 0.989 -7.228e-05 3.245e-05 0.04561 -5.447e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003672 0.00134 0.002862 0.005539 0.9898 0.9928 0.003737 0.9049 0.9282 0.01378 ] Network output: [ 0.05129 -0.4383 0.9426 -3.381e-05 1.518e-05 1.393 -2.548e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1976 0.1415 0.3114 0.2577 0.9853 0.9941 0.1982 0.5805 0.9133 0.7339 ] Network output: [ -0.01804 0.04338 1.074 0.0001069 -4.8e-05 0.919 8.058e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05932 0.05624 0.1604 0.1856 0.9884 0.9928 0.05936 0.8599 0.9114 0.2547 ] Network output: [ -0.02416 0.06655 1.053 9.777e-05 -4.389e-05 0.9292 7.368e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07156 0.07092 0.1677 0.1835 0.9853 0.9914 0.07157 0.7887 0.8863 0.2246 ] Network output: [ -0.01047 1.125 -0.009394 -1.433e-05 6.435e-06 0.905 -1.08e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07694 Epoch 5447 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01684 1.004 0.9621 -7.361e-05 3.305e-05 -2.998e-05 -5.548e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002733 -0.002288 -0.01084 0.006832 0.9689 0.9734 0.005194 0.866 0.8555 0.02091 ] Network output: [ 0.919 0.3216 0.03504 -7.545e-05 3.387e-05 -0.1949 -5.686e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1921 0.004985 -0.2237 0.1654 0.9837 0.9933 0.2142 0.5818 0.9063 0.7341 ] Network output: [ -0.001351 0.9831 0.9835 -6.976e-05 3.132e-05 0.03578 -5.257e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003803 0.001295 0.002196 0.003098 0.9898 0.9927 0.003869 0.9047 0.9271 0.01357 ] Network output: [ -0.01816 0.3369 0.9047 -0.0002607 0.000117 0.7936 -0.0001965 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2057 0.1444 0.2786 0.1082 0.9853 0.9941 0.2063 0.5854 0.9137 0.7364 ] Network output: [ 0.005117 0.1042 1.046 0.0001168 -5.245e-05 0.8404 8.805e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0566 0.05334 0.1345 0.1594 0.9885 0.9928 0.05663 0.8508 0.9118 0.2386 ] Network output: [ -0.0006667 -0.02634 1.046 0.0001356 -6.086e-05 0.9825 0.0001022 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06872 0.06802 0.1573 0.1832 0.985 0.9912 0.06873 0.7758 0.8879 0.2234 ] Network output: [ 0.02327 0.872 0.004127 5.975e-05 -2.682e-05 1.078 4.503e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05468 Epoch 5448 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02439 0.9357 0.9632 -5.368e-05 2.41e-05 0.05208 -4.045e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002696 -0.002334 -0.01111 0.008584 0.9689 0.9734 0.005137 0.8641 0.8579 0.02169 ] Network output: [ 1.041 -0.3101 0.001867 0.0001202 -5.395e-05 0.2265 9.056e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1881 -0.003805 -0.2299 0.2786 0.9837 0.9934 0.2098 0.5673 0.9082 0.7456 ] Network output: [ -0.00781 0.9801 0.9895 -7.051e-05 3.165e-05 0.04577 -5.314e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00369 0.001354 0.002956 0.00569 0.9898 0.9927 0.003755 0.9033 0.9273 0.01377 ] Network output: [ 0.05541 -0.4784 0.943 -2.264e-05 1.016e-05 1.424 -1.706e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1985 0.1423 0.3138 0.2661 0.9853 0.9941 0.1991 0.5749 0.9122 0.7314 ] Network output: [ -0.01886 0.04323 1.075 0.000107 -4.805e-05 0.9202 8.065e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05993 0.05683 0.1615 0.1869 0.9883 0.9928 0.05997 0.8581 0.9102 0.2547 ] Network output: [ -0.02461 0.07709 1.052 9.63e-05 -4.323e-05 0.9209 7.258e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07218 0.07153 0.1673 0.183 0.9853 0.9914 0.07218 0.7861 0.885 0.2234 ] Network output: [ -0.00774 1.124 -0.01293 -1.087e-05 4.882e-06 0.9046 -8.195e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09004 Epoch 5449 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01611 1.008 0.963 -7.378e-05 3.312e-05 -0.003129 -5.56e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002745 -0.002292 -0.01075 0.006747 0.9689 0.9734 0.005214 0.8645 0.8541 0.0208 ] Network output: [ 0.9142 0.3441 0.03665 -8.956e-05 4.021e-05 -0.2095 -6.75e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1937 0.005512 -0.2208 0.1611 0.9837 0.9933 0.216 0.577 0.9049 0.7316 ] Network output: [ -0.001417 0.9842 0.984 -6.838e-05 3.07e-05 0.03437 -5.153e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003838 0.00131 0.002214 0.003036 0.9897 0.9927 0.003906 0.903 0.926 0.01352 ] Network output: [ -0.01988 0.3617 0.9019 -0.0002674 0.0001201 0.775 -0.0002015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2075 0.1458 0.278 0.1043 0.9853 0.9941 0.2082 0.5801 0.9125 0.7343 ] Network output: [ 0.004928 0.1107 1.045 0.0001165 -5.23e-05 0.8349 8.78e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05698 0.0537 0.1338 0.1583 0.9884 0.9927 0.05701 0.8481 0.9106 0.2377 ] Network output: [ -0.0006132 -0.02132 1.045 0.0001356 -6.088e-05 0.978 0.0001022 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06909 0.06838 0.1565 0.1824 0.9849 0.9912 0.06909 0.7719 0.8866 0.2225 ] Network output: [ 0.02314 0.8739 0.004258 5.987e-05 -2.688e-05 1.076 4.512e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06181 Epoch 5450 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02447 0.9322 0.9642 -5.168e-05 2.32e-05 0.05439 -3.895e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0027 -0.002338 -0.01099 0.00862 0.9689 0.9734 0.005143 0.8625 0.8567 0.02161 ] Network output: [ 1.041 -0.3358 0.005667 0.0001185 -5.319e-05 0.2479 8.928e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1889 -0.004035 -0.2252 0.2823 0.9837 0.9933 0.2107 0.561 0.9069 0.7435 ] Network output: [ -0.007663 0.979 0.99 -6.832e-05 3.067e-05 0.04608 -5.148e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003712 0.001369 0.003058 0.00584 0.9897 0.9927 0.003778 0.9014 0.9262 0.01376 ] Network output: [ 0.05976 -0.5167 0.9428 -1.185e-05 5.32e-06 1.454 -8.93e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1998 0.1435 0.316 0.2742 0.9853 0.9941 0.2004 0.5683 0.9108 0.7283 ] Network output: [ -0.01954 0.04344 1.075 0.0001074 -4.821e-05 0.921 8.093e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0606 0.05749 0.1624 0.1882 0.9883 0.9927 0.06063 0.8558 0.9089 0.2546 ] Network output: [ -0.02489 0.08773 1.05 9.519e-05 -4.273e-05 0.9123 7.174e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07284 0.07219 0.1667 0.1824 0.9852 0.9914 0.07285 0.7828 0.8833 0.222 ] Network output: [ -0.004178 1.118 -0.01663 -5.83e-06 2.617e-06 0.9071 -4.394e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1034 Epoch 5451 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0154 1.011 0.964 -7.373e-05 3.31e-05 -0.006122 -5.557e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002759 -0.002298 -0.01065 0.006668 0.9689 0.9734 0.005239 0.8628 0.8524 0.0207 ] Network output: [ 0.9105 0.3632 0.03759 -0.0001034 4.643e-05 -0.2222 -7.795e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1957 0.006065 -0.2177 0.1574 0.9836 0.9933 0.2182 0.5712 0.9032 0.7288 ] Network output: [ -0.001572 0.9853 0.9847 -6.68e-05 2.999e-05 0.03291 -5.034e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003879 0.00133 0.002241 0.002993 0.9897 0.9926 0.003948 0.9011 0.9246 0.01348 ] Network output: [ -0.02101 0.382 0.8988 -0.0002725 0.0001223 0.7601 -0.0002053 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2097 0.1476 0.2776 0.1015 0.9852 0.9941 0.2104 0.5739 0.911 0.7318 ] Network output: [ 0.004322 0.1173 1.045 0.000116 -5.209e-05 0.8296 8.745e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05744 0.05414 0.1334 0.1574 0.9884 0.9927 0.05747 0.8451 0.9092 0.2369 ] Network output: [ -0.0009648 -0.01487 1.045 0.0001352 -6.071e-05 0.9727 0.0001019 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.06952 0.06881 0.1559 0.1815 0.9849 0.9911 0.06953 0.7676 0.885 0.2216 ] Network output: [ 0.02195 0.8803 0.00483 5.834e-05 -2.619e-05 1.071 4.397e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06797 Epoch 5452 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02455 0.9288 0.9653 -4.953e-05 2.223e-05 0.05654 -3.732e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002705 -0.002342 -0.01085 0.008635 0.9689 0.9734 0.005151 0.8605 0.8552 0.02151 ] Network output: [ 1.04 -0.356 0.01031 0.000114 -5.12e-05 0.266 8.594e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.19 -0.004167 -0.2199 0.2849 0.9837 0.9933 0.212 0.5538 0.9054 0.7409 ] Network output: [ -0.00733 0.9774 0.9904 -6.569e-05 2.949e-05 0.04653 -4.951e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00374 0.001387 0.003162 0.005976 0.9896 0.9926 0.003807 0.8992 0.9249 0.01374 ] Network output: [ 0.06395 -0.5482 0.9415 -2.752e-06 1.235e-06 1.479 -2.074e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2013 0.1448 0.3179 0.2812 0.9852 0.9941 0.202 0.5609 0.9091 0.7248 ] Network output: [ -0.01995 0.04399 1.075 0.0001081 -4.854e-05 0.9212 8.149e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0613 0.05817 0.1632 0.1892 0.9882 0.9927 0.06133 0.8531 0.9074 0.2542 ] Network output: [ -0.02491 0.09735 1.049 9.467e-05 -4.25e-05 0.9043 7.135e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07352 0.07287 0.1661 0.1819 0.9852 0.9914 0.07353 0.779 0.8815 0.2205 ] Network output: [ -8.355e-05 1.107 -0.02006 4.916e-07 -2.207e-07 0.9129 3.705e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1149 Epoch 5453 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01479 1.014 0.965 -7.329e-05 3.29e-05 -0.008618 -5.523e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002775 -0.002306 -0.01054 0.006606 0.9689 0.9734 0.005266 0.8608 0.8504 0.02059 ] Network output: [ 0.9085 0.3756 0.03754 -0.0001157 5.192e-05 -0.2307 -8.716e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1979 0.00661 -0.2145 0.1548 0.9836 0.9933 0.2206 0.5645 0.9013 0.7258 ] Network output: [ -0.001821 0.9864 0.9855 -6.501e-05 2.919e-05 0.03154 -4.9e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003924 0.001355 0.00228 0.00298 0.9896 0.9926 0.003993 0.8989 0.9231 0.01343 ] Network output: [ -0.02117 0.3937 0.8956 -0.0002746 0.0001233 0.7519 -0.0002069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2121 0.1496 0.2775 0.1005 0.9852 0.9941 0.2128 0.5668 0.9092 0.729 ] Network output: [ 0.003273 0.1232 1.045 0.0001155 -5.186e-05 0.8254 8.706e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05797 0.05467 0.1335 0.1568 0.9883 0.9926 0.05801 0.8419 0.9076 0.2363 ] Network output: [ -0.001787 -0.007355 1.045 0.0001344 -6.035e-05 0.9668 0.0001013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07002 0.06931 0.1555 0.1806 0.9848 0.9911 0.07003 0.763 0.8831 0.2208 ] Network output: [ 0.01956 0.8916 0.005994 5.495e-05 -2.467e-05 1.064 4.141e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07174 Epoch 5454 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02461 0.926 0.9664 -4.734e-05 2.125e-05 0.05815 -3.568e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002712 -0.002346 -0.0107 0.008622 0.9689 0.9734 0.005162 0.8583 0.8535 0.02139 ] Network output: [ 1.037 -0.3664 0.01537 0.0001061 -4.764e-05 0.2775 7.997e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1914 -0.004123 -0.2141 0.2856 0.9836 0.9933 0.2135 0.5461 0.9036 0.738 ] Network output: [ -0.006804 0.9755 0.9908 -6.27e-05 2.815e-05 0.04709 -4.726e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003773 0.001407 0.003257 0.006075 0.9896 0.9926 0.003841 0.8967 0.9233 0.01372 ] Network output: [ 0.06732 -0.5662 0.9391 2.914e-06 -1.308e-06 1.492 2.196e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2033 0.1465 0.319 0.2857 0.9852 0.9941 0.2039 0.5529 0.9073 0.7212 ] Network output: [ -0.01996 0.0447 1.075 0.0001093 -4.909e-05 0.9207 8.241e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06198 0.05884 0.1635 0.1899 0.9881 0.9926 0.06201 0.8502 0.9058 0.2537 ] Network output: [ -0.02461 0.1044 1.047 9.501e-05 -4.265e-05 0.898 7.16e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07417 0.07352 0.1654 0.1816 0.9852 0.9913 0.07418 0.7748 0.8796 0.2193 ] Network output: [ 0.003856 1.094 -0.02249 7.163e-06 -3.216e-06 0.9211 5.398e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1217 Epoch 5455 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01444 1.015 0.966 -7.222e-05 3.242e-05 -0.01008 -5.443e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002792 -0.002314 -0.01044 0.006573 0.9689 0.9734 0.005294 0.8586 0.8484 0.0205 ] Network output: [ 0.9091 0.3781 0.03625 -0.0001246 5.592e-05 -0.2331 -9.388e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2003 0.007094 -0.2115 0.1541 0.9836 0.9933 0.2232 0.5572 0.8992 0.7229 ] Network output: [ -0.002119 0.9871 0.9864 -6.299e-05 2.828e-05 0.03057 -4.747e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003969 0.001385 0.002329 0.003009 0.9896 0.9925 0.00404 0.8966 0.9214 0.01339 ] Network output: [ -0.02002 0.393 0.8926 -0.0002725 0.0001224 0.7533 -0.0002054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2146 0.1518 0.2779 0.1019 0.9852 0.9941 0.2152 0.5593 0.9073 0.7261 ] Network output: [ 0.001858 0.1269 1.047 0.0001152 -5.171e-05 0.8233 8.681e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05856 0.05526 0.134 0.1568 0.9882 0.9926 0.0586 0.8388 0.9059 0.2363 ] Network output: [ -0.00305 0.0002468 1.045 0.0001333 -5.984e-05 0.9612 0.0001005 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07056 0.06985 0.1554 0.18 0.9848 0.9911 0.07057 0.7587 0.8812 0.2203 ] Network output: [ 0.01604 0.9063 0.007857 4.992e-05 -2.241e-05 1.054 3.762e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.07173 Epoch 5456 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02466 0.9243 0.9674 -4.53e-05 2.033e-05 0.0588 -3.414e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00272 -0.002349 -0.01055 0.008574 0.9689 0.9734 0.005175 0.856 0.8515 0.02127 ] Network output: [ 1.032 -0.3626 0.02004 9.451e-05 -4.243e-05 0.2787 7.122e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.193 -0.003828 -0.2086 0.2838 0.9836 0.9933 0.2153 0.5386 0.9017 0.7348 ] Network output: [ -0.006118 0.9733 0.991 -5.95e-05 2.671e-05 0.04769 -4.484e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003811 0.001429 0.003331 0.006117 0.9895 0.9925 0.003879 0.8943 0.9217 0.01371 ] Network output: [ 0.06911 -0.5649 0.9354 3.523e-06 -1.581e-06 1.491 2.655e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2055 0.1483 0.3191 0.2865 0.9851 0.9941 0.2061 0.5451 0.9054 0.7179 ] Network output: [ -0.01949 0.04521 1.075 0.0001111 -4.986e-05 0.9197 8.371e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06257 0.05942 0.1634 0.1902 0.988 0.9925 0.06261 0.8471 0.9042 0.2533 ] Network output: [ -0.02396 0.1075 1.046 9.638e-05 -4.327e-05 0.8946 7.264e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07471 0.07406 0.1648 0.1814 0.9851 0.9913 0.07472 0.7705 0.8777 0.2185 ] Network output: [ 0.00672 1.079 -0.02316 1.281e-05 -5.749e-06 0.9303 9.651e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1208 Epoch 5457 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01448 1.014 0.9668 -7.034e-05 3.158e-05 -0.009996 -5.301e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002807 -0.002322 -0.01036 0.006575 0.9689 0.9734 0.00532 0.8563 0.8464 0.02044 ] Network output: [ 0.9126 0.3692 0.0336 -0.0001289 5.785e-05 -0.2285 -9.712e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2025 0.007464 -0.2091 0.1553 0.9835 0.9932 0.2258 0.5497 0.8971 0.7205 ] Network output: [ -0.002382 0.9868 0.9873 -6.069e-05 2.724e-05 0.03038 -4.574e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00401 0.001416 0.002386 0.003084 0.9895 0.9925 0.004081 0.8943 0.9197 0.01337 ] Network output: [ -0.01747 0.3783 0.8902 -0.0002659 0.0001194 0.7654 -0.0002004 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2168 0.154 0.2785 0.1061 0.9851 0.9941 0.2175 0.5519 0.9053 0.7234 ] Network output: [ 0.0002847 0.1271 1.048 0.0001153 -5.176e-05 0.8245 8.689e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.05916 0.05587 0.135 0.1576 0.9881 0.9925 0.05919 0.8361 0.9043 0.2369 ] Network output: [ -0.004592 0.006516 1.046 0.0001321 -5.929e-05 0.9571 9.954e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0711 0.07039 0.1558 0.1797 0.9848 0.9911 0.07111 0.7549 0.8793 0.2202 ] Network output: [ 0.01182 0.9218 0.01039 4.396e-05 -1.974e-05 1.044 3.313e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06747 Epoch 5458 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0247 0.9241 0.9681 -4.352e-05 1.954e-05 0.05829 -3.28e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002731 -0.002351 -0.01042 0.008494 0.9689 0.9734 0.005192 0.8539 0.8496 0.02116 ] Network output: [ 1.026 -0.3435 0.02333 8.002e-05 -3.592e-05 0.2679 6.031e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1948 -0.003252 -0.2041 0.2794 0.9835 0.9933 0.2173 0.5317 0.8997 0.7318 ] Network output: [ -0.005351 0.971 0.9912 -5.632e-05 2.528e-05 0.0483 -4.244e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00385 0.001451 0.003374 0.006091 0.9894 0.9925 0.003918 0.892 0.9202 0.01371 ] Network output: [ 0.06878 -0.5422 0.931 -1.577e-06 7.08e-07 1.474 -1.188e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2078 0.1503 0.3181 0.2832 0.9851 0.994 0.2084 0.5382 0.9036 0.7154 ] Network output: [ -0.01856 0.04504 1.074 0.0001132 -5.083e-05 0.9187 8.534e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06301 0.05986 0.1629 0.1901 0.9879 0.9925 0.06305 0.8443 0.9027 0.2532 ] Network output: [ -0.02303 0.1055 1.046 9.876e-05 -4.434e-05 0.8951 7.443e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07509 0.07444 0.1643 0.1816 0.9851 0.9913 0.0751 0.7666 0.8761 0.2184 ] Network output: [ 0.007808 1.067 -0.02164 1.626e-05 -7.302e-06 0.939 1.226e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.1115 Epoch 5459 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01498 1.011 0.9674 -6.763e-05 3.036e-05 -0.008226 -5.097e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00282 -0.002329 -0.0103 0.006611 0.9689 0.9734 0.00534 0.8543 0.8447 0.02042 ] Network output: [ 0.9185 0.3506 0.0298 -0.0001283 5.758e-05 -0.2178 -9.666e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2045 0.007683 -0.2075 0.1582 0.9835 0.9932 0.2279 0.5429 0.8953 0.7188 ] Network output: [ -0.002521 0.9854 0.9882 -5.817e-05 2.611e-05 0.03118 -4.384e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004042 0.001446 0.002445 0.003194 0.9895 0.9924 0.004114 0.8923 0.9182 0.01337 ] Network output: [ -0.01387 0.3515 0.8886 -0.0002556 0.0001147 0.7866 -0.0001926 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2186 0.156 0.2795 0.1123 0.9851 0.994 0.2192 0.5452 0.9035 0.7213 ] Network output: [ -0.00118 0.1233 1.05 0.000116 -5.208e-05 0.829 8.743e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0597 0.05643 0.1365 0.1591 0.9881 0.9925 0.05974 0.8341 0.9028 0.2382 ] Network output: [ -0.006162 0.01021 1.047 0.0001311 -5.885e-05 0.9552 9.879e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07157 0.07088 0.1565 0.18 0.9848 0.991 0.07158 0.7521 0.8777 0.2207 ] Network output: [ 0.007491 0.9354 0.01337 3.798e-05 -1.705e-05 1.036 2.862e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05993 Epoch 5460 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02477 0.9251 0.9685 -4.205e-05 1.888e-05 0.05674 -3.169e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002743 -0.002352 -0.01033 0.008394 0.9689 0.9735 0.005211 0.8521 0.8478 0.02108 ] Network output: [ 1.02 -0.3116 0.02454 6.44e-05 -2.891e-05 0.2467 4.854e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1967 -0.002452 -0.2011 0.2729 0.9835 0.9932 0.2194 0.5262 0.8979 0.7295 ] Network output: [ -0.0046 0.9688 0.9913 -5.337e-05 2.396e-05 0.04891 -4.022e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003887 0.001472 0.003385 0.006005 0.9894 0.9924 0.003956 0.8902 0.9187 0.01372 ] Network output: [ 0.06636 -0.5019 0.9264 -1.155e-05 5.184e-06 1.443 -8.702e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.21 0.1522 0.3162 0.2763 0.9851 0.994 0.2106 0.5326 0.902 0.7139 ] Network output: [ -0.01732 0.04385 1.073 0.0001156 -5.191e-05 0.9181 8.714e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06328 0.06012 0.162 0.1897 0.9879 0.9924 0.06332 0.8419 0.9015 0.2535 ] Network output: [ -0.02195 0.09874 1.046 0.0001019 -4.572e-05 0.8994 7.676e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07528 0.07463 0.164 0.1822 0.9851 0.9912 0.07529 0.7633 0.8749 0.2191 ] Network output: [ 0.007086 1.058 -0.01813 1.728e-05 -7.757e-06 0.9463 1.302e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.09629 Epoch 5461 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01587 1.006 0.9676 -6.435e-05 2.889e-05 -0.005126 -4.85e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002829 -0.002335 -0.01027 0.006675 0.9689 0.9734 0.005354 0.8527 0.8433 0.02044 ] Network output: [ 0.9259 0.3259 0.02532 -0.0001235 5.546e-05 -0.2034 -9.311e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2059 0.007745 -0.2068 0.1622 0.9834 0.9932 0.2296 0.537 0.8938 0.7181 ] Network output: [ -0.002505 0.983 0.9889 -5.559e-05 2.495e-05 0.03285 -4.189e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004064 0.00147 0.002501 0.003325 0.9894 0.9924 0.004136 0.8907 0.917 0.0134 ] Network output: [ -0.00983 0.3175 0.888 -0.0002434 0.0001093 0.8131 -0.0001834 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2198 0.1575 0.2806 0.1196 0.9851 0.994 0.2205 0.5397 0.902 0.7201 ] Network output: [ -0.002331 0.116 1.053 0.0001173 -5.266e-05 0.8363 8.84e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06015 0.0569 0.1381 0.1612 0.988 0.9924 0.06018 0.8327 0.9017 0.24 ] Network output: [ -0.007528 0.01098 1.049 0.0001305 -5.858e-05 0.9557 9.834e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07196 0.07127 0.1574 0.1808 0.9848 0.991 0.07197 0.7503 0.8764 0.2215 ] Network output: [ 0.003589 0.9458 0.01638 3.263e-05 -1.465e-05 1.031 2.459e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05096 Epoch 5462 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02488 0.927 0.9685 -4.088e-05 1.835e-05 0.0545 -3.081e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002755 -0.002353 -0.01028 0.008289 0.9689 0.9735 0.005229 0.8508 0.8462 0.02104 ] Network output: [ 1.015 -0.2729 0.02371 4.961e-05 -2.227e-05 0.219 3.739e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1984 -0.001552 -0.1997 0.2656 0.9835 0.9932 0.2213 0.5221 0.8963 0.7279 ] Network output: [ -0.003938 0.9669 0.9913 -5.085e-05 2.283e-05 0.04946 -3.832e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003918 0.001489 0.003369 0.00588 0.9894 0.9924 0.003988 0.8888 0.9176 0.01375 ] Network output: [ 0.06242 -0.4519 0.9223 -2.448e-05 1.099e-05 1.405 -1.845e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2118 0.1538 0.314 0.2673 0.9851 0.994 0.2125 0.5286 0.9007 0.7136 ] Network output: [ -0.01594 0.04168 1.072 0.000118 -5.298e-05 0.9182 8.894e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06338 0.06023 0.161 0.1891 0.9879 0.9924 0.06342 0.84 0.9006 0.2541 ] Network output: [ -0.02083 0.08875 1.047 0.0001052 -4.724e-05 0.9064 7.93e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07532 0.07467 0.164 0.183 0.985 0.9912 0.07533 0.7608 0.874 0.2203 ] Network output: [ 0.005138 1.051 -0.01344 1.645e-05 -7.385e-06 0.9524 1.24e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.079 Epoch 5463 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01699 0.9996 0.9675 -6.09e-05 2.734e-05 -0.00138 -4.589e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002834 -0.002339 -0.01027 0.006754 0.9689 0.9735 0.005363 0.8514 0.8423 0.02049 ] Network output: [ 0.9337 0.2988 0.02079 -0.0001161 5.213e-05 -0.1875 -8.751e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2069 0.007667 -0.2068 0.1666 0.9834 0.9932 0.2306 0.5325 0.8927 0.7183 ] Network output: [ -0.002371 0.9801 0.9894 -5.317e-05 2.387e-05 0.03494 -4.007e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004075 0.001488 0.002552 0.00346 0.9894 0.9924 0.004147 0.8895 0.916 0.01344 ] Network output: [ -0.005972 0.2814 0.8884 -0.0002312 0.0001038 0.8413 -0.0001742 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2204 0.1584 0.2818 0.1269 0.9851 0.994 0.2211 0.5356 0.9009 0.7197 ] Network output: [ -0.003095 0.1067 1.055 0.0001189 -5.339e-05 0.8452 8.962e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06049 0.05726 0.1398 0.1635 0.988 0.9924 0.06052 0.8319 0.9008 0.2422 ] Network output: [ -0.008567 0.009514 1.05 0.0001302 -5.847e-05 0.9579 9.816e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07225 0.07157 0.1585 0.1818 0.9848 0.9911 0.07226 0.7493 0.8755 0.2228 ] Network output: [ 0.0004193 0.9533 0.01898 2.813e-05 -1.263e-05 1.027 2.12e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04219 Epoch 5464 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02503 0.9295 0.9683 -3.998e-05 1.795e-05 0.05194 -3.013e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002766 -0.002354 -0.01027 0.008192 0.9689 0.9735 0.005247 0.8499 0.845 0.02103 ] Network output: [ 1.011 -0.233 0.02151 3.698e-05 -1.66e-05 0.1894 2.787e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1998 -0.0006805 -0.1997 0.2583 0.9834 0.9932 0.2228 0.5194 0.895 0.7271 ] Network output: [ -0.003405 0.9654 0.9913 -4.884e-05 2.193e-05 0.04987 -3.681e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003943 0.001502 0.003339 0.005742 0.9893 0.9924 0.004013 0.888 0.9167 0.01378 ] Network output: [ 0.05772 -0.4002 0.9191 -3.838e-05 1.723e-05 1.365 -2.893e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2132 0.155 0.3117 0.2577 0.9851 0.994 0.2139 0.5259 0.8998 0.7142 ] Network output: [ -0.01458 0.03892 1.072 0.0001202 -5.394e-05 0.9189 9.056e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06336 0.06021 0.1601 0.1886 0.9879 0.9924 0.06339 0.8386 0.8999 0.255 ] Network output: [ -0.01978 0.07736 1.048 0.0001085 -4.869e-05 0.9146 8.174e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07524 0.07459 0.1642 0.184 0.985 0.9912 0.07524 0.759 0.8735 0.2218 ] Network output: [ 0.002736 1.046 -0.0085 1.467e-05 -6.587e-06 0.9573 1.106e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.06298 Epoch 5465 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01813 0.9939 0.9673 -5.764e-05 2.588e-05 0.002352 -4.344e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002837 -0.002342 -0.01029 0.006839 0.9689 0.9735 0.005366 0.8506 0.8416 0.02057 ] Network output: [ 0.9411 0.2722 0.01673 -0.0001073 4.817e-05 -0.1716 -8.087e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2073 0.00748 -0.2072 0.171 0.9834 0.9932 0.2311 0.5291 0.892 0.7191 ] Network output: [ -0.00219 0.9774 0.9897 -5.111e-05 2.294e-05 0.03701 -3.852e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004078 0.001499 0.002597 0.003589 0.9894 0.9923 0.00415 0.8887 0.9154 0.01351 ] Network output: [ -0.002651 0.2465 0.8895 -0.0002201 9.882e-05 0.8683 -0.0001659 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2206 0.1589 0.2831 0.1338 0.985 0.994 0.2212 0.5326 0.9 0.7199 ] Network output: [ -0.003507 0.09672 1.057 0.0001206 -5.413e-05 0.8543 9.087e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06074 0.05753 0.1414 0.1659 0.988 0.9924 0.06077 0.8316 0.9002 0.2445 ] Network output: [ -0.009269 0.006876 1.051 0.0001302 -5.845e-05 0.9609 9.812e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07246 0.07178 0.1595 0.1831 0.9848 0.9911 0.07246 0.749 0.8749 0.2241 ] Network output: [ -0.001938 0.9591 0.02087 2.447e-05 -1.099e-05 1.024 1.844e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03455 Epoch 5466 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02517 0.9323 0.9679 -3.933e-05 1.765e-05 0.04931 -2.964e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002775 -0.002354 -0.01029 0.008111 0.9689 0.9735 0.00526 0.8493 0.8441 0.02104 ] Network output: [ 1.008 -0.1961 0.01874 2.701e-05 -1.213e-05 0.1615 2.036e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2008 7.67e-05 -0.2004 0.2517 0.9834 0.9932 0.224 0.5178 0.8941 0.727 ] Network output: [ -0.00301 0.9646 0.9912 -4.736e-05 2.126e-05 0.05007 -3.569e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00396 0.00151 0.003303 0.005608 0.9893 0.9924 0.004031 0.8875 0.916 0.01383 ] Network output: [ 0.05288 -0.352 0.9169 -5.182e-05 2.326e-05 1.329 -3.905e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2141 0.1557 0.3097 0.2486 0.985 0.994 0.2147 0.5244 0.8992 0.7154 ] Network output: [ -0.01329 0.03603 1.071 0.0001219 -5.474e-05 0.9198 9.19e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06326 0.06012 0.1593 0.1882 0.9878 0.9924 0.0633 0.8377 0.8995 0.256 ] Network output: [ -0.0188 0.06613 1.049 0.0001113 -4.998e-05 0.923 8.39e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07509 0.07444 0.1645 0.1851 0.985 0.9912 0.0751 0.7578 0.8732 0.2234 ] Network output: [ 0.0004626 1.042 -0.004022 1.265e-05 -5.68e-06 0.9613 9.535e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04981 Epoch 5467 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01916 0.9889 0.9669 -5.481e-05 2.46e-05 0.00564 -4.13e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002837 -0.002343 -0.01032 0.006924 0.9689 0.9735 0.005365 0.8501 0.8412 0.02065 ] Network output: [ 0.9477 0.2474 0.01339 -9.809e-05 4.404e-05 -0.1566 -7.393e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2074 0.007217 -0.2078 0.1751 0.9834 0.9932 0.2311 0.5267 0.8915 0.7203 ] Network output: [ -0.002026 0.9752 0.9899 -4.951e-05 2.223e-05 0.03873 -3.731e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004076 0.001505 0.002638 0.003707 0.9894 0.9923 0.004148 0.8883 0.915 0.01358 ] Network output: [ 2.49e-05 0.2149 0.8913 -0.0002107 9.46e-05 0.8929 -0.0001588 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2203 0.159 0.2844 0.1399 0.985 0.994 0.221 0.5305 0.8995 0.7206 ] Network output: [ -0.003651 0.08728 1.058 0.0001221 -5.481e-05 0.8628 9.201e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06091 0.05771 0.1428 0.1681 0.988 0.9924 0.06095 0.8315 0.8998 0.2467 ] Network output: [ -0.009691 0.00394 1.052 0.0001302 -5.845e-05 0.964 9.813e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0726 0.07193 0.1604 0.1843 0.9848 0.9911 0.07261 0.7491 0.8745 0.2255 ] Network output: [ -0.003551 0.9638 0.022 2.154e-05 -9.671e-06 1.021 1.624e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02831 Epoch 5468 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02526 0.9352 0.9674 -3.892e-05 1.747e-05 0.04672 -2.933e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002781 -0.002354 -0.01032 0.008046 0.969 0.9735 0.005271 0.8491 0.8435 0.02107 ] Network output: [ 1.006 -0.164 0.01599 1.959e-05 -8.796e-06 0.1369 1.477e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2015 0.0006848 -0.2016 0.2461 0.9834 0.9932 0.2247 0.517 0.8934 0.7274 ] Network output: [ -0.002752 0.9643 0.9911 -4.638e-05 2.082e-05 0.04999 -3.495e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003971 0.001513 0.003267 0.005488 0.9893 0.9923 0.004042 0.8873 0.9156 0.01387 ] Network output: [ 0.0483 -0.3098 0.9157 -6.407e-05 2.876e-05 1.297 -4.828e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2146 0.1561 0.308 0.2406 0.985 0.994 0.2152 0.5236 0.8988 0.7168 ] Network output: [ -0.01211 0.03333 1.071 0.0001233 -5.537e-05 0.9209 9.294e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06313 0.05999 0.1586 0.1878 0.9879 0.9924 0.06317 0.8371 0.8993 0.257 ] Network output: [ -0.0179 0.05596 1.05 0.0001137 -5.104e-05 0.9306 8.568e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07492 0.07427 0.1648 0.1861 0.985 0.9912 0.07492 0.7571 0.8731 0.225 ] Network output: [ -0.001398 1.039 -0.000329 1.08e-05 -4.848e-06 0.9643 8.138e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03967 Epoch 5469 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02001 0.985 0.9664 -5.25e-05 2.357e-05 0.008301 -3.956e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002835 -0.002344 -0.01036 0.007005 0.969 0.9735 0.005361 0.8499 0.8411 0.02074 ] Network output: [ 0.9532 0.2254 0.01078 -8.91e-05 4e-05 -0.1429 -6.715e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2071 0.006907 -0.2086 0.1789 0.9834 0.9932 0.2309 0.5252 0.8912 0.7218 ] Network output: [ -0.001925 0.9737 0.9899 -4.838e-05 2.172e-05 0.03999 -3.646e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00407 0.001506 0.002673 0.003812 0.9894 0.9923 0.004142 0.8882 0.9148 0.01365 ] Network output: [ 0.002093 0.1873 0.8933 -0.0002031 9.116e-05 0.9144 -0.000153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2198 0.1587 0.2856 0.1452 0.985 0.994 0.2205 0.5293 0.8991 0.7216 ] Network output: [ -0.003612 0.07885 1.059 0.0001233 -5.537e-05 0.8703 9.294e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06104 0.05785 0.144 0.1701 0.988 0.9924 0.06107 0.8317 0.8996 0.2487 ] Network output: [ -0.009911 0.001164 1.052 0.0001302 -5.844e-05 0.9668 9.81e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0727 0.07203 0.1612 0.1854 0.9849 0.9911 0.07271 0.7495 0.8742 0.2268 ] Network output: [ -0.004556 0.9679 0.0225 1.925e-05 -8.644e-06 1.019 1.451e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02342 Epoch 5470 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02527 0.9381 0.9669 -3.874e-05 1.739e-05 0.04425 -2.919e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002786 -0.002353 -0.01036 0.007998 0.969 0.9735 0.005278 0.849 0.8431 0.02111 ] Network output: [ 1.004 -0.137 0.01351 1.434e-05 -6.438e-06 0.1161 1.081e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2018 0.001142 -0.203 0.2415 0.9834 0.9932 0.225 0.5168 0.893 0.7281 ] Network output: [ -0.002625 0.9645 0.9909 -4.582e-05 2.057e-05 0.04966 -3.453e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003978 0.001513 0.003234 0.005387 0.9893 0.9923 0.004049 0.8874 0.9154 0.01392 ] Network output: [ 0.04414 -0.2741 0.9152 -7.487e-05 3.361e-05 1.27 -5.643e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2147 0.1562 0.3067 0.2338 0.985 0.994 0.2153 0.5234 0.8986 0.7185 ] Network output: [ -0.01104 0.03098 1.07 0.0001243 -5.582e-05 0.9218 9.371e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06299 0.05985 0.1579 0.1876 0.9879 0.9924 0.06302 0.8367 0.8991 0.258 ] Network output: [ -0.0171 0.04716 1.05 0.0001156 -5.188e-05 0.9373 8.71e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07474 0.07409 0.165 0.1871 0.985 0.9912 0.07475 0.7567 0.873 0.2265 ] Network output: [ -0.002786 1.037 0.002544 9.282e-06 -4.167e-06 0.9666 6.995e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0321 Epoch 5471 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02066 0.9821 0.966 -5.07e-05 2.276e-05 0.01032 -3.821e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002833 -0.002345 -0.0104 0.007079 0.969 0.9735 0.005356 0.8499 0.841 0.02082 ] Network output: [ 0.9578 0.2062 0.008774 -8.065e-05 3.621e-05 -0.1309 -6.078e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2066 0.00657 -0.2095 0.1823 0.9834 0.9932 0.2303 0.5242 0.8912 0.7233 ] Network output: [ -0.001909 0.9729 0.9899 -4.767e-05 2.14e-05 0.0408 -3.592e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004062 0.001504 0.002702 0.003902 0.9894 0.9923 0.004134 0.8882 0.9147 0.01372 ] Network output: [ 0.003642 0.1638 0.8955 -0.000197 8.844e-05 0.9326 -0.0001485 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2192 0.1583 0.2867 0.1496 0.985 0.994 0.2198 0.5285 0.8989 0.7228 ] Network output: [ -0.003459 0.07161 1.059 0.0001243 -5.58e-05 0.8767 9.367e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06112 0.05793 0.1449 0.1719 0.988 0.9924 0.06116 0.832 0.8995 0.2506 ] Network output: [ -0.009998 -0.001309 1.053 0.0001301 -5.839e-05 0.9693 9.802e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07276 0.0721 0.1618 0.1865 0.9849 0.9911 0.07277 0.75 0.8741 0.228 ] Network output: [ -0.00511 0.9713 0.02254 1.752e-05 -7.865e-06 1.016 1.32e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01966 Epoch 5472 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0252 0.9411 0.9665 -3.875e-05 1.739e-05 0.04191 -2.92e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002789 -0.002353 -0.0104 0.007963 0.969 0.9735 0.005282 0.8492 0.8429 0.02116 ] Network output: [ 1.002 -0.1149 0.0114 1.083e-05 -4.86e-06 0.09894 8.158e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2019 0.001461 -0.2044 0.2378 0.9834 0.9932 0.2251 0.5169 0.8928 0.729 ] Network output: [ -0.002615 0.9651 0.9908 -4.561e-05 2.048e-05 0.04912 -3.437e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00398 0.00151 0.003205 0.005303 0.9893 0.9923 0.004051 0.8876 0.9153 0.01397 ] Network output: [ 0.04046 -0.2445 0.9152 -8.426e-05 3.783e-05 1.248 -6.35e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2145 0.156 0.3056 0.2281 0.985 0.994 0.2152 0.5235 0.8985 0.7202 ] Network output: [ -0.01009 0.02896 1.069 0.0001251 -5.614e-05 0.9226 9.425e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06285 0.05971 0.1574 0.1875 0.9879 0.9924 0.06289 0.8365 0.8991 0.259 ] Network output: [ -0.0164 0.0397 1.051 0.000117 -5.253e-05 0.943 8.819e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07457 0.07392 0.1653 0.188 0.985 0.9912 0.07458 0.7566 0.8731 0.2278 ] Network output: [ -0.003757 1.034 0.004701 8.138e-06 -3.653e-06 0.9684 6.133e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02651 Epoch 5473 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02111 0.9801 0.9657 -4.935e-05 2.216e-05 0.01175 -3.72e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002829 -0.002345 -0.01044 0.007147 0.969 0.9735 0.00535 0.85 0.8411 0.0209 ] Network output: [ 0.9616 0.1899 0.007216 -7.289e-05 3.272e-05 -0.1206 -5.493e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.206 0.00622 -0.2104 0.1853 0.9834 0.9932 0.2296 0.5237 0.8912 0.7249 ] Network output: [ -0.00198 0.9727 0.9899 -4.731e-05 2.124e-05 0.04122 -3.565e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004053 0.001499 0.002726 0.003979 0.9894 0.9923 0.004125 0.8883 0.9147 0.01379 ] Network output: [ 0.004769 0.1442 0.8977 -0.0001923 8.634e-05 0.9478 -0.0001449 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2184 0.1577 0.2876 0.1534 0.9851 0.994 0.219 0.5282 0.8989 0.7241 ] Network output: [ -0.003239 0.06549 1.059 0.000125 -5.611e-05 0.8822 9.419e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06118 0.05799 0.1457 0.1735 0.988 0.9924 0.06122 0.8324 0.8994 0.2523 ] Network output: [ -0.01 -0.003483 1.052 0.0001299 -5.831e-05 0.9715 9.789e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0728 0.07214 0.1623 0.1875 0.9849 0.9911 0.07281 0.7507 0.8741 0.2291 ] Network output: [ -0.005352 0.9742 0.02229 1.625e-05 -7.294e-06 1.014 1.225e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01681 Epoch 5474 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02506 0.9439 0.9661 -3.89e-05 1.746e-05 0.03974 -2.932e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00279 -0.002352 -0.01044 0.007939 0.969 0.9735 0.005284 0.8494 0.8429 0.02121 ] Network output: [ 1.001 -0.09694 0.009624 8.669e-06 -3.892e-06 0.08491 6.533e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2017 0.001662 -0.2059 0.235 0.9834 0.9932 0.2249 0.5173 0.8927 0.7301 ] Network output: [ -0.002708 0.9661 0.9907 -4.566e-05 2.05e-05 0.04843 -3.441e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00398 0.001505 0.00318 0.005234 0.9893 0.9924 0.004051 0.8878 0.9152 0.01402 ] Network output: [ 0.03727 -0.2202 0.9156 -9.236e-05 4.146e-05 1.23 -6.96e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2142 0.1556 0.3047 0.2234 0.9851 0.994 0.2149 0.5238 0.8985 0.7219 ] Network output: [ -0.009253 0.02725 1.068 0.0001255 -5.635e-05 0.9234 9.459e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06272 0.05957 0.157 0.1876 0.9879 0.9924 0.06276 0.8365 0.8991 0.2599 ] Network output: [ -0.01578 0.03342 1.051 0.0001181 -5.302e-05 0.9479 8.9e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07441 0.07376 0.1655 0.1888 0.985 0.9912 0.07442 0.7566 0.8733 0.229 ] Network output: [ -0.004402 1.033 0.006278 7.332e-06 -3.292e-06 0.9698 5.526e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02237 Epoch 5475 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0214 0.9789 0.9654 -4.839e-05 2.172e-05 0.0127 -3.647e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002826 -0.002345 -0.01049 0.007207 0.969 0.9735 0.005344 0.8502 0.8413 0.02098 ] Network output: [ 0.9648 0.1761 0.005993 -6.585e-05 2.956e-05 -0.1119 -4.962e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2053 0.005866 -0.2114 0.1879 0.9834 0.9932 0.2289 0.5235 0.8913 0.7265 ] Network output: [ -0.002131 0.9729 0.9898 -4.722e-05 2.12e-05 0.04133 -3.559e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004044 0.001493 0.002744 0.004044 0.9894 0.9923 0.004115 0.8885 0.9148 0.01386 ] Network output: [ 0.005561 0.1282 0.8998 -0.0001889 8.479e-05 0.9601 -0.0001423 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2175 0.157 0.2884 0.1564 0.9851 0.994 0.2182 0.5282 0.8989 0.7254 ] Network output: [ -0.002979 0.06037 1.059 0.0001255 -5.633e-05 0.8867 9.455e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06123 0.05803 0.1464 0.1749 0.988 0.9924 0.06126 0.8328 0.8995 0.2539 ] Network output: [ -0.009961 -0.005403 1.052 0.0001297 -5.821e-05 0.9735 9.772e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07282 0.07216 0.1628 0.1884 0.9849 0.9911 0.07283 0.7514 0.8742 0.2301 ] Network output: [ -0.005389 0.9764 0.02187 1.536e-05 -6.895e-06 1.013 1.157e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01465 Epoch 5476 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02485 0.9466 0.9658 -3.916e-05 1.758e-05 0.03773 -2.951e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002791 -0.002352 -0.01049 0.007925 0.969 0.9735 0.005284 0.8497 0.8429 0.02126 ] Network output: [ 1 -0.08258 0.008155 7.567e-06 -3.397e-06 0.07358 5.703e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2014 0.001763 -0.2074 0.2327 0.9834 0.9932 0.2246 0.5178 0.8927 0.7313 ] Network output: [ -0.002884 0.9673 0.9906 -4.591e-05 2.061e-05 0.04764 -3.46e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003978 0.001498 0.003157 0.005179 0.9894 0.9924 0.004049 0.8881 0.9153 0.01407 ] Network output: [ 0.03452 -0.2003 0.9163 -9.932e-05 4.459e-05 1.215 -7.485e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2137 0.155 0.304 0.2195 0.9851 0.994 0.2144 0.5244 0.8986 0.7235 ] Network output: [ -0.008508 0.02578 1.068 0.0001258 -5.647e-05 0.9241 9.48e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06261 0.05945 0.1566 0.1877 0.9879 0.9924 0.06265 0.8366 0.8992 0.2608 ] Network output: [ -0.01525 0.02817 1.051 0.0001189 -5.337e-05 0.952 8.96e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07428 0.07363 0.1656 0.1896 0.9851 0.9912 0.07429 0.7568 0.8735 0.2301 ] Network output: [ -0.004806 1.031 0.0074 6.806e-06 -3.055e-06 0.971 5.129e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01929 Epoch 5477 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02155 0.9783 0.9652 -4.773e-05 2.143e-05 0.01326 -3.597e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002822 -0.002345 -0.01053 0.007262 0.969 0.9735 0.005337 0.8505 0.8416 0.02105 ] Network output: [ 0.9674 0.1646 0.005028 -5.95e-05 2.671e-05 -0.1047 -4.484e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2046 0.005513 -0.2125 0.1901 0.9834 0.9932 0.228 0.5235 0.8915 0.7281 ] Network output: [ -0.002346 0.9734 0.9898 -4.734e-05 2.125e-05 0.04121 -3.568e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004034 0.001485 0.002758 0.004098 0.9894 0.9924 0.004106 0.8888 0.9149 0.01392 ] Network output: [ 0.006085 0.1152 0.9019 -0.0001864 8.368e-05 0.97 -0.0001405 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2166 0.1562 0.289 0.1589 0.9851 0.994 0.2173 0.5284 0.899 0.7267 ] Network output: [ -0.002694 0.05611 1.059 0.0001258 -5.646e-05 0.8905 9.478e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06126 0.05805 0.1468 0.1762 0.988 0.9924 0.06129 0.8332 0.8995 0.2552 ] Network output: [ -0.009888 -0.007116 1.052 0.0001294 -5.809e-05 0.9753 9.752e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07283 0.07216 0.1631 0.1892 0.985 0.9912 0.07284 0.7521 0.8744 0.2311 ] Network output: [ -0.005294 0.9781 0.02135 1.477e-05 -6.633e-06 1.011 1.113e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.013 Epoch 5478 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0246 0.9491 0.9656 -3.949e-05 1.773e-05 0.0359 -2.976e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00279 -0.002352 -0.01053 0.007919 0.969 0.9735 0.005284 0.8501 0.843 0.02131 ] Network output: [ 0.9999 -0.07126 0.006951 7.279e-06 -3.268e-06 0.06455 5.486e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.201 0.001784 -0.2088 0.2311 0.9835 0.9932 0.2241 0.5184 0.8928 0.7325 ] Network output: [ -0.003123 0.9686 0.9906 -4.629e-05 2.078e-05 0.0468 -3.489e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003974 0.00149 0.003138 0.005136 0.9894 0.9924 0.004045 0.8885 0.9154 0.01412 ] Network output: [ 0.03216 -0.1841 0.9171 -0.0001053 4.727e-05 1.202 -7.936e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2132 0.1544 0.3034 0.2164 0.9851 0.994 0.2138 0.525 0.8988 0.7251 ] Network output: [ -0.007845 0.0245 1.067 0.0001259 -5.653e-05 0.9248 9.489e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06252 0.05935 0.1562 0.1878 0.9879 0.9924 0.06256 0.8367 0.8993 0.2617 ] Network output: [ -0.0148 0.02377 1.051 0.0001194 -5.362e-05 0.9555 9.002e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07416 0.0735 0.1657 0.1903 0.9851 0.9912 0.07417 0.7571 0.8737 0.2311 ] Network output: [ -0.005037 1.03 0.008166 6.499e-06 -2.918e-06 0.9719 4.898e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01697 Epoch 5479 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02159 0.9781 0.965 -4.73e-05 2.123e-05 0.01352 -3.565e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002818 -0.002346 -0.01057 0.007311 0.969 0.9735 0.005331 0.8508 0.8418 0.02112 ] Network output: [ 0.9696 0.155 0.004267 -5.38e-05 2.415e-05 -0.09866 -4.054e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2038 0.005164 -0.2135 0.1921 0.9834 0.9932 0.2272 0.5237 0.8917 0.7296 ] Network output: [ -0.00261 0.9742 0.9899 -4.761e-05 2.137e-05 0.04092 -3.588e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004025 0.001476 0.002767 0.004142 0.9894 0.9924 0.004096 0.8891 0.9151 0.01398 ] Network output: [ 0.0064 0.1048 0.9038 -0.0001847 8.293e-05 0.9778 -0.0001392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2157 0.1553 0.2895 0.1608 0.9851 0.994 0.2164 0.5287 0.8992 0.7281 ] Network output: [ -0.00239 0.05258 1.059 0.0001259 -5.654e-05 0.8937 9.491e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06128 0.05806 0.1472 0.1773 0.988 0.9924 0.06131 0.8336 0.8997 0.2565 ] Network output: [ -0.009793 -0.008663 1.052 0.0001291 -5.797e-05 0.9769 9.731e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07283 0.07216 0.1634 0.19 0.985 0.9912 0.07284 0.7528 0.8745 0.2319 ] Network output: [ -0.005116 0.9795 0.02079 1.443e-05 -6.48e-06 1.01 1.088e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01175 Epoch 5480 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02432 0.9515 0.9654 -3.986e-05 1.789e-05 0.03424 -3.004e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002789 -0.002351 -0.01058 0.007919 0.969 0.9735 0.005282 0.8505 0.8432 0.02136 ] Network output: [ 0.9995 -0.0625 0.005979 7.619e-06 -3.42e-06 0.05746 5.742e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2005 0.001739 -0.2101 0.2299 0.9835 0.9932 0.2236 0.5191 0.8929 0.7337 ] Network output: [ -0.003408 0.9701 0.9906 -4.677e-05 2.1e-05 0.04593 -3.525e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00397 0.001481 0.00312 0.005102 0.9894 0.9924 0.004041 0.8889 0.9156 0.01417 ] Network output: [ 0.03014 -0.171 0.9181 -0.0001104 4.958e-05 1.192 -8.323e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2125 0.1537 0.3029 0.2139 0.9851 0.994 0.2132 0.5256 0.899 0.7267 ] Network output: [ -0.007251 0.02337 1.066 0.0001259 -5.654e-05 0.9255 9.491e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06245 0.05926 0.156 0.188 0.9879 0.9924 0.06248 0.8369 0.8995 0.2625 ] Network output: [ -0.0144 0.02008 1.051 0.0001198 -5.379e-05 0.9585 9.03e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07406 0.0734 0.1658 0.191 0.9851 0.9912 0.07406 0.7575 0.8739 0.232 ] Network output: [ -0.005147 1.029 0.008654 6.357e-06 -2.854e-06 0.9727 4.791e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01522 Epoch 5481 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02154 0.9783 0.9649 -4.705e-05 2.112e-05 0.01355 -3.546e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002815 -0.002346 -0.01061 0.007355 0.969 0.9735 0.005325 0.8512 0.8421 0.02118 ] Network output: [ 0.9714 0.147 0.003674 -4.869e-05 2.186e-05 -0.09372 -3.669e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.203 0.004823 -0.2146 0.1938 0.9835 0.9932 0.2263 0.524 0.892 0.7311 ] Network output: [ -0.002909 0.9752 0.9899 -4.798e-05 2.154e-05 0.04049 -3.616e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004016 0.001467 0.002773 0.004179 0.9894 0.9924 0.004087 0.8895 0.9153 0.01403 ] Network output: [ 0.006547 0.09678 0.9056 -0.0001837 8.249e-05 0.9838 -0.0001385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2148 0.1544 0.2898 0.1624 0.9851 0.994 0.2155 0.5291 0.8994 0.7294 ] Network output: [ -0.002071 0.04965 1.059 0.000126 -5.657e-05 0.8963 9.496e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0613 0.05806 0.1475 0.1782 0.988 0.9924 0.06133 0.834 0.8998 0.2576 ] Network output: [ -0.00968 -0.01008 1.052 0.0001288 -5.784e-05 0.9784 9.709e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07283 0.07216 0.1636 0.1907 0.985 0.9912 0.07284 0.7535 0.8747 0.2328 ] Network output: [ -0.004889 0.9804 0.02021 1.429e-05 -6.414e-06 1.009 1.077e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01077 Epoch 5482 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02402 0.9537 0.9653 -4.024e-05 1.806e-05 0.03274 -3.033e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002788 -0.002351 -0.01062 0.007924 0.969 0.9735 0.00528 0.8509 0.8434 0.02141 ] Network output: [ 0.9994 -0.05591 0.005206 8.44e-06 -3.789e-06 0.05202 6.361e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1999 0.001643 -0.2115 0.2291 0.9835 0.9932 0.2229 0.5197 0.8931 0.7349 ] Network output: [ -0.003725 0.9715 0.9907 -4.73e-05 2.123e-05 0.04506 -3.565e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003965 0.001472 0.003106 0.005077 0.9894 0.9924 0.004036 0.8893 0.9157 0.01421 ] Network output: [ 0.02842 -0.1605 0.9192 -0.0001149 5.156e-05 1.184 -8.656e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2118 0.1529 0.3025 0.2119 0.9851 0.994 0.2125 0.5263 0.8992 0.7281 ] Network output: [ -0.006715 0.02235 1.065 0.0001259 -5.651e-05 0.9262 9.486e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06239 0.05918 0.1557 0.1883 0.988 0.9924 0.06242 0.8371 0.8996 0.2633 ] Network output: [ -0.01407 0.01699 1.051 0.00012 -5.389e-05 0.9611 9.046e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07397 0.07331 0.1659 0.1916 0.9851 0.9913 0.07398 0.7579 0.8742 0.2328 ] Network output: [ -0.005173 1.028 0.008924 6.337e-06 -2.845e-06 0.9733 4.776e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01389 Epoch 5483 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02143 0.9787 0.9648 -4.694e-05 2.107e-05 0.01339 -3.537e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002811 -0.002346 -0.01065 0.007395 0.969 0.9735 0.005319 0.8516 0.8425 0.02125 ] Network output: [ 0.9729 0.1405 0.003219 -4.412e-05 1.981e-05 -0.08968 -3.325e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2022 0.004492 -0.2157 0.1952 0.9835 0.9932 0.2254 0.5244 0.8922 0.7325 ] Network output: [ -0.003229 0.9763 0.99 -4.841e-05 2.173e-05 0.03998 -3.649e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004007 0.001457 0.002775 0.004209 0.9894 0.9924 0.004078 0.8898 0.9155 0.01409 ] Network output: [ 0.00656 0.09068 0.9073 -0.0001833 8.229e-05 0.9882 -0.0001381 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2139 0.1535 0.29 0.1635 0.9851 0.994 0.2146 0.5295 0.8996 0.7307 ] Network output: [ -0.001738 0.04724 1.058 0.000126 -5.656e-05 0.8985 9.496e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06131 0.05806 0.1476 0.179 0.988 0.9924 0.06135 0.8344 0.8999 0.2586 ] Network output: [ -0.009552 -0.01139 1.051 0.0001285 -5.771e-05 0.9797 9.688e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07282 0.07215 0.1637 0.1914 0.985 0.9912 0.07283 0.7542 0.875 0.2335 ] Network output: [ -0.004631 0.981 0.01964 1.429e-05 -6.417e-06 1.009 1.077e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01002 Epoch 5484 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0237 0.9558 0.9653 -4.062e-05 1.824e-05 0.0314 -3.061e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002786 -0.002351 -0.01066 0.007934 0.969 0.9735 0.005277 0.8513 0.8437 0.02146 ] Network output: [ 0.9993 -0.05114 0.004601 9.629e-06 -4.323e-06 0.04798 7.257e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1993 0.001507 -0.2127 0.2286 0.9835 0.9932 0.2222 0.5204 0.8933 0.7361 ] Network output: [ -0.004064 0.973 0.9908 -4.786e-05 2.149e-05 0.04419 -3.607e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00396 0.001463 0.003093 0.005058 0.9894 0.9924 0.00403 0.8897 0.9159 0.01426 ] Network output: [ 0.02695 -0.1522 0.9203 -0.0001186 5.327e-05 1.178 -8.942e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2111 0.1521 0.3022 0.2103 0.9851 0.994 0.2117 0.527 0.8994 0.7295 ] Network output: [ -0.006227 0.0214 1.065 0.0001258 -5.645e-05 0.9268 9.477e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06235 0.05912 0.1555 0.1886 0.988 0.9924 0.06238 0.8374 0.8998 0.2641 ] Network output: [ -0.01377 0.0144 1.05 0.0001201 -5.393e-05 0.9633 9.054e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07391 0.07324 0.166 0.1922 0.9851 0.9913 0.07391 0.7584 0.8745 0.2336 ] Network output: [ -0.005143 1.028 0.009024 6.403e-06 -2.874e-06 0.9737 4.825e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01287 Epoch 5485 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02127 0.9794 0.9648 -4.693e-05 2.107e-05 0.01308 -3.537e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002808 -0.002346 -0.01069 0.007431 0.969 0.9735 0.005313 0.8519 0.8428 0.0213 ] Network output: [ 0.9741 0.1351 0.002881 -4.004e-05 1.798e-05 -0.08644 -3.018e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2014 0.004172 -0.2167 0.1964 0.9835 0.9932 0.2245 0.5248 0.8925 0.7338 ] Network output: [ -0.003562 0.9774 0.9901 -4.889e-05 2.195e-05 0.0394 -3.685e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003999 0.001447 0.002776 0.004232 0.9894 0.9924 0.00407 0.8902 0.9157 0.01414 ] Network output: [ 0.006467 0.08628 0.9088 -0.0001833 8.229e-05 0.9912 -0.0001381 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.213 0.1527 0.2902 0.1644 0.9851 0.994 0.2137 0.53 0.8998 0.7319 ] Network output: [ -0.001392 0.04524 1.058 0.0001259 -5.654e-05 0.9003 9.491e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06133 0.05806 0.1477 0.1797 0.988 0.9924 0.06136 0.8348 0.9001 0.2596 ] Network output: [ -0.009409 -0.01262 1.051 0.0001283 -5.758e-05 0.981 9.667e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07282 0.07214 0.1639 0.192 0.985 0.9912 0.07283 0.7548 0.8752 0.2342 ] Network output: [ -0.004356 0.9814 0.01908 1.443e-05 -6.476e-06 1.008 1.087e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009437 Epoch 5486 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02339 0.9576 0.9653 -4.099e-05 1.84e-05 0.0302 -3.09e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002784 -0.002352 -0.0107 0.007947 0.969 0.9735 0.005274 0.8517 0.844 0.02151 ] Network output: [ 0.9994 -0.04794 0.004138 1.11e-05 -4.983e-06 0.04514 8.365e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1987 0.00134 -0.2139 0.2283 0.9835 0.9932 0.2215 0.5211 0.8935 0.7373 ] Network output: [ -0.004414 0.9744 0.9909 -4.843e-05 2.174e-05 0.04335 -3.65e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003955 0.001453 0.003082 0.005046 0.9894 0.9924 0.004025 0.89 0.9161 0.0143 ] Network output: [ 0.0257 -0.1458 0.9215 -0.0001219 5.472e-05 1.172 -9.186e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2103 0.1513 0.3019 0.209 0.9851 0.994 0.211 0.5276 0.8997 0.7309 ] Network output: [ -0.005782 0.02051 1.064 0.0001256 -5.638e-05 0.9275 9.465e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06232 0.05908 0.1553 0.1889 0.988 0.9924 0.06236 0.8377 0.9 0.2648 ] Network output: [ -0.01352 0.01224 1.05 0.0001201 -5.394e-05 0.9652 9.055e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07385 0.07319 0.1661 0.1928 0.9851 0.9913 0.07386 0.7589 0.8747 0.2343 ] Network output: [ -0.005075 1.027 0.00899 6.526e-06 -2.93e-06 0.974 4.918e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01209 Epoch 5487 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02107 0.9802 0.9648 -4.699e-05 2.11e-05 0.01267 -3.542e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002804 -0.002347 -0.01073 0.007463 0.969 0.9735 0.005308 0.8523 0.8431 0.02136 ] Network output: [ 0.9751 0.1308 0.002639 -3.643e-05 1.635e-05 -0.0839 -2.745e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2006 0.003866 -0.2178 0.1974 0.9835 0.9932 0.2236 0.5253 0.8928 0.7351 ] Network output: [ -0.003899 0.9786 0.9902 -4.939e-05 2.217e-05 0.03877 -3.722e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003992 0.001437 0.002774 0.004251 0.9894 0.9924 0.004063 0.8905 0.9159 0.01419 ] Network output: [ 0.006286 0.08336 0.9102 -0.0001837 8.245e-05 0.9931 -0.0001384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2122 0.1518 0.2902 0.1649 0.9851 0.994 0.2128 0.5306 0.9 0.7331 ] Network output: [ -0.001033 0.04361 1.057 0.0001258 -5.649e-05 0.9018 9.483e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06135 0.05806 0.1478 0.1804 0.988 0.9924 0.06138 0.8351 0.9003 0.2604 ] Network output: [ -0.009254 -0.01379 1.051 0.000128 -5.747e-05 0.9823 9.647e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07281 0.07213 0.164 0.1925 0.985 0.9912 0.07282 0.7554 0.8755 0.2349 ] Network output: [ -0.004072 0.9815 0.01855 1.466e-05 -6.581e-06 1.008 1.105e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.008987 Epoch 5488 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02307 0.9593 0.9653 -4.135e-05 1.856e-05 0.02914 -3.116e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002782 -0.002352 -0.01074 0.007963 0.969 0.9735 0.00527 0.8521 0.8442 0.02156 ] Network output: [ 0.9995 -0.04609 0.003798 1.278e-05 -5.739e-06 0.04335 9.633e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.198 0.001149 -0.215 0.2283 0.9835 0.9932 0.2208 0.5217 0.8938 0.7384 ] Network output: [ -0.004771 0.9758 0.991 -4.9e-05 2.2e-05 0.04252 -3.693e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003949 0.001443 0.003073 0.005039 0.9894 0.9924 0.004019 0.8904 0.9163 0.01435 ] Network output: [ 0.02464 -0.141 0.9227 -0.0001246 5.595e-05 1.169 -9.393e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2096 0.1505 0.3017 0.2081 0.9851 0.994 0.2102 0.5282 0.8999 0.7321 ] Network output: [ -0.005376 0.01965 1.063 0.0001254 -5.63e-05 0.9282 9.451e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06231 0.05905 0.1552 0.1893 0.988 0.9924 0.06235 0.8379 0.9002 0.2656 ] Network output: [ -0.0133 0.01045 1.05 0.0001201 -5.391e-05 0.9669 9.049e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07382 0.07315 0.1661 0.1933 0.9851 0.9913 0.07383 0.7594 0.875 0.235 ] Network output: [ -0.004986 1.027 0.008848 6.684e-06 -3e-06 0.9742 5.037e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01151 Epoch 5489 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02084 0.9811 0.9648 -4.712e-05 2.115e-05 0.01216 -3.551e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002801 -0.002347 -0.01077 0.007492 0.969 0.9735 0.005303 0.8527 0.8434 0.02141 ] Network output: [ 0.9759 0.1275 0.002481 -3.325e-05 1.493e-05 -0.08199 -2.506e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1999 0.003574 -0.2188 0.1983 0.9835 0.9932 0.2228 0.5258 0.893 0.7362 ] Network output: [ -0.004234 0.9798 0.9903 -4.989e-05 2.24e-05 0.03812 -3.76e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003985 0.001427 0.002771 0.004264 0.9894 0.9924 0.004056 0.8909 0.9161 0.01423 ] Network output: [ 0.006033 0.08175 0.9116 -0.0001844 8.276e-05 0.9939 -0.0001389 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2113 0.1509 0.2901 0.1653 0.9851 0.994 0.212 0.5311 0.9003 0.7342 ] Network output: [ -0.0006617 0.04229 1.057 0.0001257 -5.643e-05 0.903 9.472e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06137 0.05806 0.1477 0.1809 0.988 0.9924 0.06141 0.8355 0.9004 0.2612 ] Network output: [ -0.009085 -0.01492 1.05 0.0001278 -5.736e-05 0.9834 9.629e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07281 0.07213 0.164 0.1931 0.9851 0.9912 0.07282 0.756 0.8757 0.2355 ] Network output: [ -0.003783 0.9813 0.01805 1.498e-05 -6.723e-06 1.008 1.129e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.008649 Epoch 5490 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02276 0.9608 0.9653 -4.167e-05 1.871e-05 0.02819 -3.141e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00278 -0.002352 -0.01078 0.007981 0.969 0.9735 0.005267 0.8525 0.8445 0.02161 ] Network output: [ 0.9997 -0.04544 0.00356 1.463e-05 -6.567e-06 0.04248 1.102e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1973 0.0009405 -0.2161 0.2285 0.9835 0.9932 0.22 0.5223 0.894 0.7395 ] Network output: [ -0.005129 0.9772 0.9911 -4.956e-05 2.225e-05 0.04172 -3.735e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003944 0.001434 0.003066 0.005037 0.9894 0.9924 0.004014 0.8908 0.9165 0.01439 ] Network output: [ 0.02376 -0.1376 0.9239 -0.0001269 5.699e-05 1.166 -9.566e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2088 0.1497 0.3015 0.2075 0.9851 0.994 0.2095 0.5288 0.9001 0.7333 ] Network output: [ -0.005005 0.0188 1.063 0.0001252 -5.62e-05 0.929 9.435e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06232 0.05904 0.1551 0.1896 0.988 0.9924 0.06235 0.8382 0.9003 0.2663 ] Network output: [ -0.01311 0.00899 1.049 0.0001199 -5.384e-05 0.9683 9.039e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0738 0.07312 0.1662 0.1938 0.9852 0.9913 0.07381 0.7598 0.8753 0.2356 ] Network output: [ -0.004883 1.027 0.008618 6.859e-06 -3.079e-06 0.9742 5.169e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01109 Epoch 5491 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0206 0.9821 0.9649 -4.728e-05 2.123e-05 0.01156 -3.563e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002799 -0.002347 -0.0108 0.007517 0.969 0.9735 0.005299 0.8531 0.8437 0.02146 ] Network output: [ 0.9765 0.1251 0.002392 -3.048e-05 1.368e-05 -0.08066 -2.297e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1991 0.003297 -0.2198 0.199 0.9835 0.9932 0.222 0.5262 0.8933 0.7374 ] Network output: [ -0.004564 0.9811 0.9904 -5.038e-05 2.262e-05 0.03743 -3.797e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003979 0.001417 0.002766 0.004274 0.9895 0.9924 0.00405 0.8912 0.9163 0.01428 ] Network output: [ 0.005717 0.08134 0.9128 -0.0001853 8.319e-05 0.9937 -0.0001397 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2105 0.15 0.29 0.1654 0.9851 0.994 0.2112 0.5316 0.9005 0.7353 ] Network output: [ -0.0002791 0.04122 1.056 0.0001255 -5.636e-05 0.9039 9.461e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0614 0.05807 0.1477 0.1814 0.988 0.9925 0.06143 0.8358 0.9006 0.2619 ] Network output: [ -0.008905 -0.01601 1.05 0.0001275 -5.726e-05 0.9846 9.613e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07281 0.07212 0.1641 0.1936 0.9851 0.9912 0.07282 0.7565 0.876 0.2361 ] Network output: [ -0.003491 0.981 0.01759 1.536e-05 -6.897e-06 1.008 1.158e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.008407 Epoch 5492 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02246 0.9621 0.9654 -4.197e-05 1.884e-05 0.02736 -3.163e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002778 -0.002353 -0.01081 0.008001 0.969 0.9735 0.005264 0.8529 0.8448 0.02166 ] Network output: [ 1 -0.04585 0.00341 1.66e-05 -7.45e-06 0.04245 1.251e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1967 0.0007181 -0.217 0.2289 0.9835 0.9932 0.2193 0.5228 0.8942 0.7406 ] Network output: [ -0.005485 0.9785 0.9913 -5.009e-05 2.249e-05 0.04095 -3.775e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003939 0.001425 0.003061 0.00504 0.9894 0.9924 0.004009 0.8911 0.9167 0.01443 ] Network output: [ 0.02304 -0.1356 0.9251 -0.0001288 5.784e-05 1.164 -9.709e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2081 0.1489 0.3014 0.2072 0.9851 0.9941 0.2087 0.5294 0.9004 0.7344 ] Network output: [ -0.004668 0.01794 1.062 0.000125 -5.61e-05 0.9298 9.418e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06233 0.05904 0.155 0.1901 0.988 0.9925 0.06237 0.8385 0.9005 0.267 ] Network output: [ -0.01295 0.007825 1.049 0.0001197 -5.376e-05 0.9694 9.024e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0738 0.07312 0.1662 0.1943 0.9852 0.9913 0.07381 0.7603 0.8755 0.2362 ] Network output: [ -0.004774 1.027 0.008314 7.038e-06 -3.16e-06 0.9741 5.304e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01081 Epoch 5493 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02034 0.9832 0.965 -4.747e-05 2.131e-05 0.0109 -3.578e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002796 -0.002348 -0.01084 0.00754 0.969 0.9735 0.005295 0.8534 0.844 0.0215 ] Network output: [ 0.977 0.1235 0.002363 -2.811e-05 1.262e-05 -0.07989 -2.119e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1984 0.003037 -0.2207 0.1995 0.9835 0.9932 0.2212 0.5267 0.8935 0.7384 ] Network output: [ -0.004886 0.9823 0.9906 -5.085e-05 2.283e-05 0.03673 -3.833e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003974 0.001408 0.00276 0.004279 0.9895 0.9924 0.004044 0.8915 0.9165 0.01432 ] Network output: [ 0.005346 0.08202 0.9138 -0.0001865 8.373e-05 0.9927 -0.0001406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2098 0.1492 0.2898 0.1653 0.9851 0.9941 0.2104 0.5321 0.9007 0.7364 ] Network output: [ 0.0001145 0.0404 1.055 0.0001254 -5.629e-05 0.9047 9.449e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06143 0.05808 0.1476 0.1818 0.988 0.9925 0.06146 0.836 0.9008 0.2626 ] Network output: [ -0.008712 -0.01707 1.049 0.0001274 -5.718e-05 0.9856 9.598e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07282 0.07213 0.1641 0.194 0.9851 0.9912 0.07283 0.757 0.8762 0.2366 ] Network output: [ -0.003197 0.9804 0.01716 1.581e-05 -7.099e-06 1.009 1.192e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.008252 Epoch 5494 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02216 0.9633 0.9655 -4.222e-05 1.896e-05 0.02663 -3.182e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002776 -0.002354 -0.01085 0.008024 0.969 0.9735 0.005261 0.8532 0.8451 0.0217 ] Network output: [ 1 -0.04725 0.003336 1.866e-05 -8.376e-06 0.04317 1.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.196 0.0004851 -0.2179 0.2294 0.9835 0.9933 0.2185 0.5233 0.8945 0.7416 ] Network output: [ -0.005836 0.9798 0.9914 -5.059e-05 2.271e-05 0.04021 -3.813e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003934 0.001416 0.003058 0.005047 0.9894 0.9924 0.004004 0.8914 0.9169 0.01447 ] Network output: [ 0.02245 -0.1349 0.9263 -0.0001303 5.851e-05 1.163 -9.822e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2073 0.1481 0.3013 0.207 0.9851 0.9941 0.208 0.5299 0.9006 0.7354 ] Network output: [ -0.004366 0.01706 1.062 0.0001247 -5.6e-05 0.9306 9.401e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06237 0.05905 0.155 0.1905 0.988 0.9925 0.06241 0.8387 0.9007 0.2677 ] Network output: [ -0.01282 0.006935 1.049 0.0001195 -5.365e-05 0.9704 9.006e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07381 0.07312 0.1663 0.1947 0.9852 0.9913 0.07382 0.7608 0.8757 0.2367 ] Network output: [ -0.004663 1.028 0.007945 7.211e-06 -3.237e-06 0.9738 5.434e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01066 Epoch 5495 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02008 0.9844 0.9651 -4.768e-05 2.141e-05 0.01016 -3.594e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002794 -0.002349 -0.01087 0.007559 0.969 0.9735 0.005291 0.8538 0.8443 0.02155 ] Network output: [ 0.9772 0.1227 0.002387 -2.613e-05 1.173e-05 -0.07967 -1.97e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1977 0.002794 -0.2216 0.1999 0.9835 0.9933 0.2204 0.5272 0.8938 0.7394 ] Network output: [ -0.005195 0.9835 0.9907 -5.13e-05 2.303e-05 0.03601 -3.866e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003969 0.001398 0.002753 0.004281 0.9895 0.9924 0.00404 0.8918 0.9167 0.01436 ] Network output: [ 0.004926 0.08375 0.9148 -0.0001879 8.436e-05 0.9908 -0.0001416 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.209 0.1484 0.2895 0.165 0.9851 0.9941 0.2097 0.5326 0.9009 0.7374 ] Network output: [ 0.000519 0.03979 1.054 0.0001252 -5.621e-05 0.9052 9.436e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06146 0.05809 0.1474 0.1822 0.9881 0.9925 0.0615 0.8362 0.9009 0.2632 ] Network output: [ -0.008507 -0.01812 1.049 0.0001272 -5.71e-05 0.9867 9.586e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07283 0.07213 0.1641 0.1945 0.9851 0.9913 0.07284 0.7574 0.8764 0.2371 ] Network output: [ -0.0029 0.9796 0.01676 1.632e-05 -7.325e-06 1.009 1.23e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.008183 Epoch 5496 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02189 0.9644 0.9657 -4.244e-05 1.905e-05 0.02601 -3.198e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002774 -0.002354 -0.01088 0.008048 0.969 0.9735 0.005257 0.8536 0.8453 0.02175 ] Network output: [ 1.001 -0.04958 0.00333 2.08e-05 -9.336e-06 0.04462 1.567e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1953 0.000244 -0.2188 0.23 0.9835 0.9933 0.2178 0.5237 0.8947 0.7426 ] Network output: [ -0.006181 0.9811 0.9916 -5.106e-05 2.292e-05 0.0395 -3.848e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00393 0.001407 0.003057 0.005058 0.9895 0.9924 0.004 0.8917 0.9171 0.01452 ] Network output: [ 0.022 -0.1354 0.9275 -0.0001315 5.901e-05 1.163 -9.907e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2066 0.1473 0.3013 0.2072 0.9851 0.9941 0.2072 0.5303 0.9008 0.7364 ] Network output: [ -0.004098 0.01616 1.061 0.0001245 -5.589e-05 0.9316 9.383e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06242 0.05908 0.155 0.1909 0.988 0.9925 0.06246 0.839 0.9008 0.2683 ] Network output: [ -0.01272 0.006306 1.048 0.0001192 -5.352e-05 0.9712 8.984e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07383 0.07314 0.1663 0.1951 0.9852 0.9913 0.07384 0.7613 0.876 0.2372 ] Network output: [ -0.004553 1.028 0.007518 7.368e-06 -3.308e-06 0.9733 5.553e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01064 Epoch 5497 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01981 0.9856 0.9652 -4.791e-05 2.151e-05 0.009358 -3.611e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002792 -0.002349 -0.0109 0.007576 0.969 0.9735 0.005288 0.8541 0.8445 0.02159 ] Network output: [ 0.9773 0.1228 0.002456 -2.455e-05 1.102e-05 -0.08 -1.85e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1971 0.002569 -0.2225 0.2001 0.9835 0.9933 0.2197 0.5276 0.894 0.7403 ] Network output: [ -0.005491 0.9846 0.9908 -5.172e-05 2.322e-05 0.03528 -3.898e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003965 0.001389 0.002745 0.00428 0.9895 0.9924 0.004036 0.8921 0.9169 0.01439 ] Network output: [ 0.004458 0.08649 0.9157 -0.0001895 8.509e-05 0.9881 -0.0001428 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2083 0.1476 0.2892 0.1645 0.9851 0.9941 0.2089 0.533 0.9011 0.7383 ] Network output: [ 0.0009341 0.03938 1.054 0.000125 -5.614e-05 0.9055 9.424e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0615 0.05811 0.1473 0.1825 0.9881 0.9925 0.06154 0.8364 0.9011 0.2637 ] Network output: [ -0.008289 -0.01915 1.049 0.0001271 -5.704e-05 0.9877 9.576e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07285 0.07214 0.164 0.1949 0.9851 0.9913 0.07286 0.7578 0.8767 0.2376 ] Network output: [ -0.002597 0.9786 0.01641 1.687e-05 -7.576e-06 1.01 1.272e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.008198 Epoch 5498 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02162 0.9653 0.9658 -4.261e-05 1.913e-05 0.02548 -3.211e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002772 -0.002355 -0.01091 0.008074 0.969 0.9736 0.005254 0.8539 0.8456 0.02179 ] Network output: [ 1.001 -0.05281 0.003383 2.3e-05 -1.032e-05 0.04677 1.733e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1947 -3.437e-06 -0.2195 0.2309 0.9835 0.9933 0.217 0.5241 0.8949 0.7435 ] Network output: [ -0.006519 0.9822 0.9918 -5.148e-05 2.311e-05 0.03881 -3.88e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003926 0.001399 0.003058 0.005074 0.9895 0.9924 0.003995 0.892 0.9173 0.01456 ] Network output: [ 0.02167 -0.137 0.9287 -0.0001322 5.935e-05 1.164 -9.964e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2059 0.1466 0.3014 0.2075 0.9851 0.9941 0.2065 0.5307 0.901 0.7373 ] Network output: [ -0.003867 0.01521 1.06 0.0001243 -5.579e-05 0.9326 9.365e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06248 0.05913 0.1551 0.1914 0.988 0.9925 0.06252 0.8392 0.9009 0.269 ] Network output: [ -0.01264 0.005936 1.048 0.0001189 -5.337e-05 0.9718 8.959e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07387 0.07318 0.1664 0.1955 0.9852 0.9913 0.07388 0.7617 0.8762 0.2377 ] Network output: [ -0.004445 1.029 0.007035 7.504e-06 -3.369e-06 0.9727 5.655e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01074 Epoch 5499 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01953 0.9869 0.9653 -4.815e-05 2.161e-05 0.008489 -3.628e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00279 -0.00235 -0.01093 0.007589 0.969 0.9735 0.005286 0.8544 0.8448 0.02162 ] Network output: [ 0.9773 0.1237 0.002567 -2.336e-05 1.049e-05 -0.08088 -1.76e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1964 0.002362 -0.2233 0.2002 0.9835 0.9933 0.219 0.528 0.8942 0.7411 ] Network output: [ -0.005772 0.9858 0.991 -5.211e-05 2.339e-05 0.03453 -3.927e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003962 0.00138 0.002736 0.004275 0.9895 0.9924 0.004032 0.8923 0.9171 0.01443 ] Network output: [ 0.003944 0.09023 0.9164 -0.0001913 8.59e-05 0.9847 -0.0001442 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2077 0.1469 0.2888 0.1638 0.9851 0.9941 0.2083 0.5334 0.9013 0.7392 ] Network output: [ 0.00136 0.03917 1.053 0.0001249 -5.606e-05 0.9057 9.411e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06155 0.05814 0.1471 0.1827 0.9881 0.9925 0.06159 0.8366 0.9012 0.2642 ] Network output: [ -0.008058 -0.02016 1.048 0.000127 -5.7e-05 0.9887 9.569e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07287 0.07216 0.164 0.1952 0.9851 0.9913 0.07288 0.7581 0.8769 0.238 ] Network output: [ -0.002288 0.9774 0.01609 1.748e-05 -7.849e-06 1.011 1.318e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.008304 Epoch 5500 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02137 0.966 0.966 -4.274e-05 1.919e-05 0.02506 -3.221e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00277 -0.002356 -0.01093 0.008102 0.969 0.9736 0.005252 0.8541 0.8459 0.02183 ] Network output: [ 1.002 -0.05693 0.003493 2.525e-05 -1.134e-05 0.04961 1.903e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.194 -0.000256 -0.2202 0.2318 0.9836 0.9933 0.2163 0.5243 0.8951 0.7444 ] Network output: [ -0.006849 0.9833 0.992 -5.187e-05 2.328e-05 0.03816 -3.909e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003922 0.001391 0.003061 0.005094 0.9895 0.9924 0.003992 0.8922 0.9174 0.0146 ] Network output: [ 0.02147 -0.1399 0.93 -0.0001326 5.953e-05 1.166 -9.993e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2052 0.1458 0.3015 0.2082 0.9852 0.9941 0.2059 0.531 0.9011 0.7381 ] Network output: [ -0.003673 0.01421 1.06 0.000124 -5.568e-05 0.9337 9.347e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06256 0.05918 0.1552 0.192 0.988 0.9925 0.0626 0.8394 0.9011 0.2697 ] Network output: [ -0.0126 0.005825 1.048 0.0001185 -5.32e-05 0.9722 8.931e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07393 0.07323 0.1664 0.1959 0.9852 0.9913 0.07394 0.7622 0.8764 0.2381 ] Network output: [ -0.004339 1.03 0.006498 7.614e-06 -3.418e-06 0.9719 5.738e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01098 Epoch 5501 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01925 0.9883 0.9655 -4.839e-05 2.172e-05 0.00755 -3.647e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002789 -0.002351 -0.01095 0.007599 0.969 0.9736 0.005284 0.8547 0.845 0.02166 ] Network output: [ 0.9771 0.1254 0.002716 -2.257e-05 1.013e-05 -0.08234 -1.701e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1959 0.002175 -0.224 0.2001 0.9836 0.9933 0.2183 0.5284 0.8943 0.7419 ] Network output: [ -0.006036 0.9869 0.9911 -5.245e-05 2.355e-05 0.03378 -3.953e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00396 0.001372 0.002726 0.004266 0.9895 0.9924 0.00403 0.8926 0.9172 0.01446 ] Network output: [ 0.003383 0.09499 0.9171 -0.0001934 8.68e-05 0.9804 -0.0001457 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.207 0.1462 0.2883 0.163 0.9852 0.9941 0.2077 0.5338 0.9015 0.74 ] Network output: [ 0.001796 0.03915 1.052 0.0001247 -5.599e-05 0.9056 9.399e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0616 0.05816 0.1468 0.1829 0.9881 0.9925 0.06164 0.8367 0.9013 0.2647 ] Network output: [ -0.007814 -0.02116 1.048 0.0001269 -5.697e-05 0.9896 9.564e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0729 0.07218 0.164 0.1956 0.9851 0.9913 0.07291 0.7584 0.8771 0.2384 ] Network output: [ -0.001969 0.976 0.01581 1.814e-05 -8.144e-06 1.012 1.367e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.008509 Epoch 5502 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02114 0.9667 0.9662 -4.282e-05 1.922e-05 0.02473 -3.227e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002768 -0.002357 -0.01096 0.008132 0.969 0.9736 0.005249 0.8544 0.8461 0.02188 ] Network output: [ 1.003 -0.06196 0.003656 2.756e-05 -1.237e-05 0.05317 2.077e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1934 -0.000513 -0.2207 0.2329 0.9836 0.9933 0.2157 0.5246 0.8953 0.7452 ] Network output: [ -0.00717 0.9844 0.9922 -5.22e-05 2.344e-05 0.03754 -3.934e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003919 0.001384 0.003067 0.005118 0.9895 0.9925 0.003988 0.8924 0.9176 0.01464 ] Network output: [ 0.0214 -0.1441 0.9312 -0.0001326 5.953e-05 1.17 -9.994e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2046 0.1452 0.3017 0.209 0.9852 0.9941 0.2052 0.5313 0.9013 0.7389 ] Network output: [ -0.003521 0.01314 1.06 0.0001238 -5.557e-05 0.9348 9.329e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06266 0.05926 0.1553 0.1925 0.988 0.9925 0.06269 0.8396 0.9012 0.2704 ] Network output: [ -0.01258 0.005981 1.047 0.0001181 -5.301e-05 0.9724 8.899e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.074 0.0733 0.1665 0.1962 0.9852 0.9913 0.07401 0.7626 0.8765 0.2385 ] Network output: [ -0.004233 1.032 0.005904 7.696e-06 -3.455e-06 0.971 5.8e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01136 Epoch 5503 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01896 0.9897 0.9656 -4.863e-05 2.183e-05 0.006538 -3.665e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002788 -0.002352 -0.01098 0.007606 0.969 0.9736 0.005282 0.8549 0.8451 0.02169 ] Network output: [ 0.9767 0.1281 0.0029 -2.219e-05 9.964e-06 -0.08442 -1.673e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1953 0.002009 -0.2248 0.1998 0.9836 0.9933 0.2177 0.5287 0.8945 0.7425 ] Network output: [ -0.006283 0.9881 0.9913 -5.275e-05 2.368e-05 0.03301 -3.976e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003958 0.001364 0.002715 0.004254 0.9895 0.9924 0.004028 0.8928 0.9173 0.01449 ] Network output: [ 0.002775 0.1008 0.9176 -0.0001956 8.78e-05 0.9753 -0.0001474 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2065 0.1455 0.2878 0.1619 0.9852 0.9941 0.2071 0.5341 0.9017 0.7407 ] Network output: [ 0.002244 0.03934 1.051 0.0001246 -5.592e-05 0.9054 9.388e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06166 0.0582 0.1465 0.183 0.9881 0.9925 0.0617 0.8367 0.9014 0.2651 ] Network output: [ -0.007555 -0.02215 1.047 0.0001269 -5.696e-05 0.9906 9.562e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07293 0.07221 0.1639 0.1959 0.9851 0.9913 0.07294 0.7586 0.8772 0.2388 ] Network output: [ -0.001638 0.9743 0.01557 1.885e-05 -8.463e-06 1.013 1.421e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.008827 Epoch 5504 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02092 0.9671 0.9664 -4.284e-05 1.923e-05 0.02452 -3.229e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002766 -0.002358 -0.01098 0.008163 0.9691 0.9736 0.005247 0.8546 0.8463 0.02192 ] Network output: [ 1.003 -0.06794 0.003874 2.992e-05 -1.343e-05 0.05746 2.255e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1928 -0.0007738 -0.2212 0.2341 0.9836 0.9933 0.215 0.5247 0.8955 0.746 ] Network output: [ -0.007482 0.9854 0.9924 -5.249e-05 2.357e-05 0.03695 -3.956e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003916 0.001378 0.003076 0.005148 0.9895 0.9925 0.003985 0.8926 0.9177 0.01467 ] Network output: [ 0.02145 -0.1497 0.9325 -0.0001322 5.937e-05 1.174 -9.966e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2039 0.1445 0.302 0.2102 0.9852 0.9941 0.2046 0.5314 0.9014 0.7395 ] Network output: [ -0.003414 0.01201 1.059 0.0001235 -5.546e-05 0.9361 9.311e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06277 0.05935 0.1555 0.1931 0.988 0.9925 0.06281 0.8398 0.9012 0.2711 ] Network output: [ -0.0126 0.00642 1.047 0.0001176 -5.28e-05 0.9724 8.864e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07409 0.07339 0.1666 0.1966 0.9852 0.9913 0.0741 0.7629 0.8767 0.2389 ] Network output: [ -0.004126 1.033 0.00525 7.747e-06 -3.478e-06 0.9698 5.839e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01192 Epoch 5505 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01868 0.9912 0.9658 -4.888e-05 2.194e-05 0.005447 -3.684e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002787 -0.002352 -0.011 0.007609 0.9691 0.9736 0.005282 0.8552 0.8453 0.02171 ] Network output: [ 0.9761 0.1317 0.00312 -2.226e-05 9.991e-06 -0.08714 -1.677e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1948 0.001865 -0.2254 0.1994 0.9836 0.9933 0.2172 0.529 0.8946 0.7431 ] Network output: [ -0.006512 0.9891 0.9914 -5.301e-05 2.38e-05 0.03223 -3.995e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003957 0.001356 0.002703 0.004238 0.9895 0.9924 0.004028 0.8929 0.9174 0.01452 ] Network output: [ 0.002117 0.1077 0.9179 -0.000198 8.889e-05 0.9693 -0.0001492 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.206 0.1449 0.2873 0.1607 0.9852 0.9941 0.2066 0.5343 0.9018 0.7414 ] Network output: [ 0.002703 0.03974 1.05 0.0001244 -5.586e-05 0.9049 9.377e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06173 0.05824 0.1462 0.1831 0.9881 0.9925 0.06176 0.8367 0.9015 0.2654 ] Network output: [ -0.007281 -0.02312 1.047 0.0001269 -5.697e-05 0.9914 9.563e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07297 0.07225 0.1638 0.1962 0.9851 0.9913 0.07298 0.7587 0.8774 0.2391 ] Network output: [ -0.001293 0.9724 0.01536 1.961e-05 -8.805e-06 1.015 1.478e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009275 Epoch 5506 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02073 0.9674 0.9666 -4.281e-05 1.922e-05 0.02441 -3.226e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002765 -0.00236 -0.011 0.008197 0.9691 0.9736 0.005244 0.8548 0.8465 0.02195 ] Network output: [ 1.004 -0.07491 0.004149 3.234e-05 -1.452e-05 0.06254 2.437e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1922 -0.001038 -0.2216 0.2355 0.9836 0.9933 0.2143 0.5247 0.8957 0.7468 ] Network output: [ -0.007786 0.9863 0.9926 -5.273e-05 2.367e-05 0.03639 -3.974e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003914 0.001372 0.003088 0.005183 0.9895 0.9925 0.003983 0.8928 0.9178 0.01471 ] Network output: [ 0.02163 -0.1566 0.9338 -0.0001314 5.901e-05 1.179 -9.906e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2033 0.1439 0.3023 0.2116 0.9852 0.9941 0.204 0.5315 0.9015 0.7401 ] Network output: [ -0.003354 0.01081 1.059 0.0001233 -5.535e-05 0.9375 9.292e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0629 0.05946 0.1558 0.1937 0.988 0.9925 0.06293 0.84 0.9013 0.2718 ] Network output: [ -0.01266 0.007162 1.046 0.0001171 -5.257e-05 0.9722 8.825e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0742 0.0735 0.1667 0.1969 0.9853 0.9914 0.07421 0.7633 0.8768 0.2392 ] Network output: [ -0.004012 1.035 0.00453 7.771e-06 -3.489e-06 0.9684 5.856e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01267 Epoch 5507 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01839 0.9928 0.966 -4.913e-05 2.205e-05 0.004271 -3.702e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002787 -0.002353 -0.01102 0.007609 0.9691 0.9736 0.005281 0.8554 0.8454 0.02174 ] Network output: [ 0.9754 0.1363 0.003377 -2.277e-05 1.022e-05 -0.09055 -1.716e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1944 0.001745 -0.226 0.1987 0.9836 0.9933 0.2167 0.5292 0.8947 0.7436 ] Network output: [ -0.006721 0.9902 0.9916 -5.322e-05 2.389e-05 0.03143 -4.011e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003958 0.001349 0.002691 0.004218 0.9895 0.9925 0.004028 0.8931 0.9175 0.01454 ] Network output: [ 0.001408 0.1158 0.9182 -0.0002006 9.008e-05 0.9624 -0.0001512 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2055 0.1443 0.2867 0.1592 0.9852 0.9941 0.2061 0.5345 0.9019 0.742 ] Network output: [ 0.003173 0.04037 1.05 0.0001243 -5.579e-05 0.9043 9.366e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0618 0.05829 0.1459 0.1831 0.9881 0.9925 0.06183 0.8367 0.9016 0.2657 ] Network output: [ -0.006991 -0.02405 1.046 0.0001269 -5.699e-05 0.9922 9.567e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07302 0.07229 0.1637 0.1965 0.9851 0.9913 0.07303 0.7587 0.8775 0.2395 ] Network output: [ -0.0009301 0.9703 0.0152 2.043e-05 -9.172e-06 1.016 1.54e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009877 Epoch 5508 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02055 0.9675 0.9668 -4.271e-05 1.917e-05 0.02442 -3.219e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002763 -0.002361 -0.01102 0.008232 0.9691 0.9736 0.005242 0.855 0.8467 0.02199 ] Network output: [ 1.005 -0.08295 0.004488 3.482e-05 -1.563e-05 0.06844 2.624e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1917 -0.001306 -0.2218 0.2371 0.9836 0.9933 0.2137 0.5246 0.8958 0.7474 ] Network output: [ -0.008079 0.9872 0.9929 -5.29e-05 2.375e-05 0.03587 -3.987e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003912 0.001367 0.003103 0.005224 0.9895 0.9925 0.003981 0.8929 0.9179 0.01475 ] Network output: [ 0.02196 -0.1651 0.9351 -0.0001302 5.846e-05 1.186 -9.814e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2028 0.1433 0.3028 0.2133 0.9852 0.9941 0.2034 0.5314 0.9016 0.7406 ] Network output: [ -0.003348 0.009527 1.059 0.0001231 -5.524e-05 0.939 9.274e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06305 0.05959 0.1561 0.1944 0.988 0.9925 0.06308 0.8401 0.9013 0.2724 ] Network output: [ -0.01275 0.008234 1.046 0.0001165 -5.232e-05 0.9718 8.782e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07433 0.07362 0.1668 0.1971 0.9853 0.9914 0.07434 0.7636 0.8769 0.2395 ] Network output: [ -0.003885 1.037 0.003736 7.77e-06 -3.488e-06 0.9668 5.856e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01367 Epoch 5509 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01809 0.9945 0.9661 -4.938e-05 2.217e-05 0.003 -3.721e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002787 -0.002355 -0.01103 0.007604 0.9691 0.9736 0.005282 0.8555 0.8455 0.02176 ] Network output: [ 0.9745 0.1419 0.003673 -2.377e-05 1.067e-05 -0.09469 -1.792e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.194 0.001651 -0.2265 0.1979 0.9836 0.9933 0.2163 0.5293 0.8948 0.744 ] Network output: [ -0.006911 0.9913 0.9917 -5.337e-05 2.396e-05 0.03062 -4.022e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003959 0.001342 0.002677 0.004194 0.9895 0.9925 0.004029 0.8932 0.9176 0.01457 ] Network output: [ 0.0006438 0.1251 0.9183 -0.0002035 9.137e-05 0.9545 -0.0001534 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2051 0.1437 0.286 0.1575 0.9852 0.9941 0.2057 0.5346 0.902 0.7426 ] Network output: [ 0.003654 0.04126 1.049 0.0001241 -5.573e-05 0.9033 9.356e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06188 0.05834 0.1455 0.183 0.9881 0.9925 0.06191 0.8366 0.9016 0.2659 ] Network output: [ -0.006684 -0.02494 1.046 0.000127 -5.703e-05 0.993 9.573e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07307 0.07234 0.1635 0.1967 0.9851 0.9913 0.07308 0.7587 0.8776 0.2397 ] Network output: [ -0.0005487 0.9679 0.01508 2.13e-05 -9.562e-06 1.018 1.605e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01066 Epoch 5510 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02038 0.9675 0.9671 -4.255e-05 1.91e-05 0.02455 -3.207e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002762 -0.002363 -0.01104 0.008269 0.9691 0.9736 0.005241 0.8551 0.8468 0.02202 ] Network output: [ 1.006 -0.09212 0.004901 3.735e-05 -1.677e-05 0.07525 2.815e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1912 -0.001576 -0.2219 0.2388 0.9836 0.9933 0.2132 0.5244 0.8959 0.748 ] Network output: [ -0.008363 0.988 0.9931 -5.302e-05 2.38e-05 0.03539 -3.996e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00391 0.001362 0.003122 0.005271 0.9895 0.9925 0.00398 0.8929 0.918 0.01479 ] Network output: [ 0.02245 -0.1753 0.9365 -0.0001285 5.771e-05 1.193 -9.687e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2023 0.1428 0.3034 0.2154 0.9852 0.9941 0.2029 0.5312 0.9016 0.741 ] Network output: [ -0.003398 0.00816 1.058 0.0001228 -5.513e-05 0.9407 9.255e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06321 0.05974 0.1565 0.1952 0.988 0.9925 0.06325 0.8402 0.9014 0.2731 ] Network output: [ -0.01288 0.009665 1.046 0.0001159 -5.203e-05 0.971 8.735e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07448 0.07376 0.1669 0.1974 0.9853 0.9914 0.07449 0.7638 0.8769 0.2398 ] Network output: [ -0.003737 1.04 0.00286 7.755e-06 -3.481e-06 0.9649 5.844e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01495 Epoch 5511 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01779 0.9963 0.9663 -4.963e-05 2.228e-05 0.001627 -3.74e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002788 -0.002356 -0.01105 0.007595 0.9691 0.9736 0.005283 0.8557 0.8455 0.02177 ] Network output: [ 0.9734 0.1487 0.00401 -2.529e-05 1.135e-05 -0.09961 -1.906e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1937 0.001584 -0.227 0.1968 0.9836 0.9933 0.2159 0.5294 0.8948 0.7443 ] Network output: [ -0.007081 0.9923 0.9919 -5.348e-05 2.401e-05 0.02978 -4.03e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003962 0.001336 0.002663 0.004165 0.9895 0.9925 0.004032 0.8932 0.9176 0.01459 ] Network output: [ -0.0001762 0.1356 0.9182 -0.0002067 9.278e-05 0.9457 -0.0001557 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2048 0.1433 0.2852 0.1556 0.9852 0.9941 0.2054 0.5345 0.902 0.743 ] Network output: [ 0.004145 0.04244 1.048 0.000124 -5.567e-05 0.9021 9.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06196 0.05841 0.1451 0.1829 0.9881 0.9925 0.062 0.8364 0.9016 0.266 ] Network output: [ -0.00636 -0.02576 1.045 0.0001272 -5.708e-05 0.9937 9.583e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07313 0.07239 0.1634 0.1969 0.9851 0.9913 0.07314 0.7585 0.8777 0.24 ] Network output: [ -0.000148 0.9653 0.01499 2.222e-05 -9.975e-06 1.02 1.675e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01166 Epoch 5512 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02024 0.9672 0.9673 -4.232e-05 1.9e-05 0.02482 -3.19e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002761 -0.002365 -0.01105 0.008309 0.9691 0.9736 0.005239 0.8551 0.8469 0.02206 ] Network output: [ 1.007 -0.1025 0.005399 3.994e-05 -1.793e-05 0.08303 3.01e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1907 -0.00185 -0.2219 0.2408 0.9836 0.9933 0.2126 0.524 0.896 0.7486 ] Network output: [ -0.008635 0.9887 0.9934 -5.307e-05 2.383e-05 0.03496 -4e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00391 0.001359 0.003145 0.005325 0.9895 0.9925 0.003979 0.8929 0.918 0.01482 ] Network output: [ 0.0231 -0.1874 0.9378 -0.0001264 5.673e-05 1.203 -9.523e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2018 0.1423 0.3041 0.2179 0.9852 0.9941 0.2024 0.5308 0.9016 0.7413 ] Network output: [ -0.00351 0.006708 1.058 0.0001226 -5.502e-05 0.9425 9.236e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0634 0.05991 0.157 0.1959 0.988 0.9925 0.06344 0.8403 0.9013 0.2738 ] Network output: [ -0.01305 0.01149 1.045 0.0001152 -5.173e-05 0.97 8.683e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07465 0.07393 0.167 0.1976 0.9853 0.9914 0.07466 0.764 0.8769 0.24 ] Network output: [ -0.003556 1.042 0.001889 7.738e-06 -3.474e-06 0.9628 5.832e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01656 Epoch 5513 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01748 0.9981 0.9665 -4.989e-05 2.24e-05 0.000142 -3.76e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002789 -0.002357 -0.01106 0.007582 0.9691 0.9736 0.005285 0.8557 0.8455 0.02178 ] Network output: [ 0.972 0.1567 0.004394 -2.735e-05 1.228e-05 -0.1053 -2.061e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1934 0.001549 -0.2273 0.1956 0.9836 0.9933 0.2156 0.5293 0.8947 0.7445 ] Network output: [ -0.00723 0.9933 0.992 -5.352e-05 2.403e-05 0.02892 -4.034e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003965 0.001331 0.002649 0.004132 0.9895 0.9925 0.004036 0.8932 0.9176 0.0146 ] Network output: [ -0.001053 0.1475 0.918 -0.00021 9.429e-05 0.9357 -0.0001583 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2046 0.1429 0.2844 0.1535 0.9852 0.9941 0.2052 0.5343 0.902 0.7434 ] Network output: [ 0.004644 0.04393 1.047 0.0001239 -5.561e-05 0.9007 9.335e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06206 0.05848 0.1446 0.1827 0.9881 0.9925 0.0621 0.8361 0.9016 0.2661 ] Network output: [ -0.006019 -0.02648 1.045 0.0001273 -5.716e-05 0.9942 9.595e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0732 0.07246 0.1632 0.197 0.9851 0.9913 0.07321 0.7583 0.8777 0.2401 ] Network output: [ 0.0002703 0.9625 0.01494 2.319e-05 -1.041e-05 1.022 1.747e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01291 Epoch 5514 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02011 0.9667 0.9676 -4.202e-05 1.887e-05 0.02523 -3.167e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00276 -0.002366 -0.01105 0.008351 0.9691 0.9736 0.005238 0.8551 0.847 0.02209 ] Network output: [ 1.008 -0.1142 0.006001 4.257e-05 -1.911e-05 0.09183 3.209e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1902 -0.002124 -0.2217 0.2429 0.9836 0.9933 0.2121 0.5234 0.896 0.749 ] Network output: [ -0.008895 0.9894 0.9936 -5.306e-05 2.382e-05 0.03457 -3.998e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00391 0.001356 0.003173 0.005386 0.9895 0.9925 0.00398 0.8929 0.918 0.01486 ] Network output: [ 0.02393 -0.2013 0.9392 -0.0001236 5.551e-05 1.214 -9.319e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2013 0.1419 0.3049 0.2207 0.9852 0.9941 0.202 0.5303 0.9016 0.7414 ] Network output: [ -0.003689 0.005175 1.058 0.0001223 -5.491e-05 0.9444 9.217e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06362 0.06011 0.1575 0.1968 0.988 0.9925 0.06365 0.8403 0.9013 0.2745 ] Network output: [ -0.01327 0.01375 1.045 0.0001145 -5.139e-05 0.9686 8.626e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07484 0.07412 0.1672 0.1978 0.9853 0.9914 0.07485 0.7641 0.8768 0.2401 ] Network output: [ -0.003325 1.045 0.0008135 7.741e-06 -3.475e-06 0.9606 5.834e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01859 Epoch 5515 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01716 1 0.9668 -5.015e-05 2.251e-05 -0.001464 -3.779e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00279 -0.002358 -0.01106 0.007563 0.9691 0.9736 0.005288 0.8557 0.8455 0.02178 ] Network output: [ 0.9705 0.1659 0.004829 -2.999e-05 1.346e-05 -0.1119 -2.26e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1932 0.001546 -0.2276 0.194 0.9836 0.9933 0.2154 0.5291 0.8946 0.7446 ] Network output: [ -0.007358 0.9943 0.9922 -5.351e-05 2.402e-05 0.02802 -4.033e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003971 0.001327 0.002634 0.004094 0.9895 0.9925 0.004041 0.8932 0.9175 0.01461 ] Network output: [ -0.001985 0.1607 0.9176 -0.0002137 9.592e-05 0.9248 -0.000161 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2044 0.1425 0.2835 0.1511 0.9852 0.9941 0.205 0.534 0.902 0.7437 ] Network output: [ 0.005146 0.04577 1.046 0.0001237 -5.555e-05 0.8988 9.325e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06217 0.05856 0.1441 0.1824 0.9881 0.9925 0.06221 0.8357 0.9015 0.2661 ] Network output: [ -0.005663 -0.02706 1.044 0.0001275 -5.724e-05 0.9946 9.609e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07329 0.07254 0.163 0.1971 0.9851 0.9913 0.0733 0.7579 0.8777 0.2403 ] Network output: [ 0.0007018 0.9596 0.01492 2.418e-05 -1.086e-05 1.024 1.823e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01446 Epoch 5516 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.02001 0.9661 0.968 -4.164e-05 1.87e-05 0.02579 -3.138e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00276 -0.002368 -0.01105 0.008394 0.9691 0.9736 0.005237 0.8551 0.847 0.02211 ] Network output: [ 1.009 -0.1271 0.006728 4.523e-05 -2.031e-05 0.1017 3.409e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1898 -0.002399 -0.2213 0.2452 0.9836 0.9933 0.2117 0.5227 0.896 0.7494 ] Network output: [ -0.009139 0.9899 0.9939 -5.296e-05 2.378e-05 0.03422 -3.991e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003911 0.001355 0.003206 0.005456 0.9895 0.9925 0.003981 0.8928 0.918 0.01489 ] Network output: [ 0.02496 -0.2172 0.9406 -0.0001204 5.405e-05 1.226 -9.073e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.201 0.1415 0.3059 0.2239 0.9852 0.9941 0.2016 0.5296 0.9015 0.7415 ] Network output: [ -0.003937 0.00357 1.058 0.000122 -5.479e-05 0.9465 9.197e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06386 0.06033 0.1582 0.1977 0.988 0.9925 0.06389 0.8402 0.9011 0.2752 ] Network output: [ -0.01353 0.01648 1.044 0.0001136 -5.102e-05 0.9669 8.564e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07506 0.07434 0.1673 0.198 0.9853 0.9914 0.07507 0.7642 0.8767 0.2402 ] Network output: [ -0.003024 1.048 -0.0003792 7.794e-06 -3.499e-06 0.9582 5.873e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0211 Epoch 5517 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01684 1.002 0.967 -5.041e-05 2.263e-05 -0.003199 -3.799e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002793 -0.00236 -0.01106 0.00754 0.9691 0.9736 0.005292 0.8557 0.8453 0.02178 ] Network output: [ 0.9687 0.1764 0.00532 -3.323e-05 1.492e-05 -0.1193 -2.504e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1931 0.00158 -0.2277 0.1923 0.9836 0.9933 0.2153 0.5287 0.8945 0.7445 ] Network output: [ -0.007467 0.9953 0.9923 -5.343e-05 2.399e-05 0.02708 -4.027e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003978 0.001323 0.002619 0.004053 0.9895 0.9924 0.004048 0.893 0.9174 0.01462 ] Network output: [ -0.002967 0.1753 0.917 -0.0002175 9.765e-05 0.9128 -0.0001639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2043 0.1423 0.2826 0.1485 0.9852 0.9941 0.2049 0.5335 0.9019 0.7438 ] Network output: [ 0.005645 0.04799 1.045 0.0001236 -5.548e-05 0.8967 9.313e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06229 0.05866 0.1435 0.182 0.9881 0.9925 0.06233 0.8353 0.9014 0.266 ] Network output: [ -0.005297 -0.02743 1.044 0.0001277 -5.733e-05 0.9948 9.625e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07338 0.07263 0.1627 0.1972 0.9851 0.9913 0.07339 0.7573 0.8777 0.2403 ] Network output: [ 0.001137 0.9567 0.01493 2.519e-05 -1.131e-05 1.026 1.899e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01633 Epoch 5518 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01992 0.9652 0.9683 -4.118e-05 1.849e-05 0.0265 -3.104e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002759 -0.00237 -0.01105 0.00844 0.9691 0.9736 0.005237 0.8549 0.847 0.02214 ] Network output: [ 1.011 -0.1414 0.007605 4.788e-05 -2.149e-05 0.1127 3.608e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1895 -0.002671 -0.2207 0.2478 0.9836 0.9933 0.2113 0.5217 0.8959 0.7497 ] Network output: [ -0.009364 0.9904 0.9942 -5.278e-05 2.369e-05 0.03393 -3.977e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003913 0.001355 0.003245 0.005533 0.9895 0.9925 0.003983 0.8926 0.9179 0.01492 ] Network output: [ 0.0262 -0.2352 0.9419 -0.0001166 5.233e-05 1.24 -8.784e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2007 0.1412 0.307 0.2276 0.9852 0.9941 0.2013 0.5286 0.9013 0.7414 ] Network output: [ -0.004255 0.001907 1.058 0.0001218 -5.467e-05 0.9486 9.177e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06412 0.06058 0.1589 0.1986 0.988 0.9925 0.06416 0.8401 0.901 0.2759 ] Network output: [ -0.01384 0.0197 1.044 0.0001127 -5.061e-05 0.9648 8.497e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07531 0.07459 0.1674 0.1981 0.9853 0.9914 0.07532 0.7641 0.8765 0.2402 ] Network output: [ -0.002629 1.051 -0.001697 7.935e-06 -3.562e-06 0.9557 5.98e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02416 Epoch 5519 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0165 1.005 0.9673 -5.068e-05 2.275e-05 -0.005063 -3.819e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002795 -0.002362 -0.01106 0.007511 0.9691 0.9736 0.005297 0.8556 0.8451 0.02177 ] Network output: [ 0.9667 0.1879 0.005868 -3.709e-05 1.665e-05 -0.1274 -2.795e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1932 0.001652 -0.2277 0.1903 0.9836 0.9933 0.2153 0.5281 0.8942 0.7443 ] Network output: [ -0.007556 0.9964 0.9924 -5.329e-05 2.392e-05 0.02609 -4.016e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003986 0.001321 0.002604 0.004007 0.9895 0.9924 0.004057 0.8928 0.9172 0.01463 ] Network output: [ -0.003988 0.1911 0.9162 -0.0002216 9.947e-05 0.8998 -0.000167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2044 0.1422 0.2816 0.1457 0.9852 0.9941 0.205 0.5327 0.9017 0.7438 ] Network output: [ 0.00613 0.05062 1.043 0.0001234 -5.54e-05 0.8942 9.301e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06243 0.05877 0.1429 0.1816 0.9881 0.9925 0.06247 0.8346 0.9012 0.2659 ] Network output: [ -0.004927 -0.02752 1.043 0.0001279 -5.743e-05 0.9947 9.641e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07349 0.07273 0.1624 0.1972 0.9851 0.9913 0.0735 0.7565 0.8775 0.2403 ] Network output: [ 0.00156 0.9538 0.01497 2.618e-05 -1.175e-05 1.028 1.973e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01857 Epoch 5520 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01985 0.964 0.9687 -4.064e-05 1.825e-05 0.02737 -3.063e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002759 -0.002372 -0.01104 0.008487 0.9691 0.9736 0.005236 0.8547 0.8469 0.02215 ] Network output: [ 1.012 -0.1568 0.00866 5.044e-05 -2.264e-05 0.1247 3.801e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1892 -0.002937 -0.2198 0.2505 0.9836 0.9933 0.211 0.5204 0.8958 0.7498 ] Network output: [ -0.009566 0.9907 0.9945 -5.25e-05 2.357e-05 0.0337 -3.956e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003916 0.001356 0.003289 0.005619 0.9895 0.9925 0.003986 0.8923 0.9177 0.01495 ] Network output: [ 0.02767 -0.2552 0.9432 -0.0001122 5.036e-05 1.256 -8.453e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2004 0.1411 0.3082 0.2317 0.9852 0.9941 0.201 0.5273 0.9011 0.7411 ] Network output: [ -0.004641 0.0002136 1.059 0.0001215 -5.455e-05 0.9509 9.157e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06442 0.06086 0.1597 0.1997 0.988 0.9925 0.06446 0.8399 0.9007 0.2765 ] Network output: [ -0.01418 0.02342 1.043 0.0001118 -5.019e-05 0.9623 8.425e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07559 0.07486 0.1676 0.1982 0.9853 0.9914 0.07559 0.7638 0.8763 0.2402 ] Network output: [ -0.002111 1.054 -0.003143 8.216e-06 -3.689e-06 0.9533 6.192e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02784 Epoch 5521 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01614 1.007 0.9675 -5.095e-05 2.287e-05 -0.007053 -3.839e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002799 -0.002363 -0.01105 0.007478 0.9691 0.9736 0.005304 0.8553 0.8448 0.02176 ] Network output: [ 0.9645 0.2004 0.006473 -4.156e-05 1.866e-05 -0.1361 -3.132e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1933 0.001764 -0.2275 0.1882 0.9836 0.9933 0.2154 0.5273 0.8939 0.7439 ] Network output: [ -0.007629 0.9974 0.9926 -5.307e-05 2.383e-05 0.02505 -4e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003997 0.00132 0.002591 0.003959 0.9895 0.9924 0.004068 0.8925 0.9169 0.01463 ] Network output: [ -0.005031 0.2078 0.9152 -0.0002257 0.0001013 0.8862 -0.0001701 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2045 0.1421 0.2806 0.1428 0.9852 0.9941 0.2052 0.5317 0.9014 0.7437 ] Network output: [ 0.006589 0.05366 1.042 0.0001232 -5.532e-05 0.8913 9.286e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06259 0.05891 0.1423 0.181 0.9881 0.9925 0.06263 0.8339 0.9009 0.2656 ] Network output: [ -0.004564 -0.02725 1.043 0.0001281 -5.752e-05 0.9943 9.656e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07362 0.07286 0.162 0.1971 0.9851 0.9913 0.07363 0.7556 0.8773 0.2403 ] Network output: [ 0.001947 0.9513 0.01504 2.71e-05 -1.217e-05 1.03 2.043e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02116 Epoch 5522 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0198 0.9627 0.9692 -4.001e-05 1.796e-05 0.02838 -3.015e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002759 -0.002375 -0.01102 0.008535 0.9691 0.9736 0.005237 0.8544 0.8467 0.02217 ] Network output: [ 1.013 -0.1732 0.009917 5.282e-05 -2.371e-05 0.1376 3.98e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.189 -0.003191 -0.2187 0.2533 0.9836 0.9933 0.2107 0.5188 0.8956 0.7498 ] Network output: [ -0.009737 0.9909 0.9948 -5.211e-05 2.34e-05 0.03352 -3.927e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003921 0.001358 0.003338 0.005713 0.9894 0.9924 0.00399 0.8919 0.9175 0.01498 ] Network output: [ 0.02937 -0.2768 0.9444 -0.0001073 4.816e-05 1.273 -8.084e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2003 0.141 0.3095 0.2362 0.9852 0.9941 0.2009 0.5257 0.9007 0.7407 ] Network output: [ -0.005086 -0.001475 1.059 0.0001212 -5.443e-05 0.9532 9.136e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06475 0.06118 0.1605 0.2007 0.988 0.9925 0.06479 0.8395 0.9004 0.2772 ] Network output: [ -0.01456 0.02762 1.043 0.0001108 -4.973e-05 0.9594 8.349e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07589 0.07517 0.1677 0.1982 0.9853 0.9914 0.0759 0.7634 0.8759 0.2401 ] Network output: [ -0.001442 1.057 -0.004709 8.696e-06 -3.904e-06 0.9511 6.553e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03216 Epoch 5523 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01578 1.01 0.9679 -5.12e-05 2.299e-05 -0.009148 -3.859e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002803 -0.002365 -0.01103 0.007441 0.9691 0.9736 0.005311 0.855 0.8444 0.02173 ] Network output: [ 0.9622 0.2136 0.007127 -4.659e-05 2.091e-05 -0.1453 -3.511e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1935 0.001918 -0.2271 0.1859 0.9836 0.9933 0.2157 0.5261 0.8935 0.7433 ] Network output: [ -0.007689 0.9984 0.9928 -5.278e-05 2.37e-05 0.02396 -3.978e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00401 0.001321 0.00258 0.003911 0.9895 0.9924 0.004081 0.8921 0.9166 0.01463 ] Network output: [ -0.006066 0.225 0.914 -0.0002299 0.0001032 0.8722 -0.0001733 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2048 0.1422 0.2795 0.1399 0.9852 0.9941 0.2055 0.5304 0.9011 0.7434 ] Network output: [ 0.007002 0.0571 1.041 0.000123 -5.522e-05 0.8881 9.269e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06277 0.05907 0.1417 0.1804 0.988 0.9924 0.06281 0.833 0.9006 0.2653 ] Network output: [ -0.004222 -0.02653 1.042 0.0001283 -5.76e-05 0.9934 9.669e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07378 0.07301 0.1617 0.1969 0.9851 0.9913 0.07379 0.7544 0.877 0.2402 ] Network output: [ 0.002264 0.9492 0.01512 2.789e-05 -1.252e-05 1.031 2.102e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02409 Epoch 5524 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01976 0.9612 0.9696 -3.93e-05 1.764e-05 0.02951 -2.962e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002759 -0.002377 -0.01099 0.008581 0.9691 0.9736 0.005237 0.8539 0.8465 0.02217 ] Network output: [ 1.014 -0.1902 0.01139 5.488e-05 -2.464e-05 0.1511 4.136e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1888 -0.003425 -0.2173 0.2561 0.9836 0.9933 0.2106 0.5169 0.8953 0.7497 ] Network output: [ -0.009871 0.991 0.9951 -5.161e-05 2.317e-05 0.0334 -3.889e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003927 0.001362 0.003393 0.005812 0.9894 0.9924 0.003997 0.8913 0.9172 0.01501 ] Network output: [ 0.03127 -0.2997 0.9454 -0.000102 4.578e-05 1.291 -7.685e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2003 0.141 0.3108 0.2409 0.9852 0.9941 0.2009 0.5237 0.9003 0.74 ] Network output: [ -0.005575 -0.003108 1.059 0.000121 -5.431e-05 0.9555 9.117e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06511 0.06152 0.1614 0.2018 0.988 0.9925 0.06515 0.8391 0.9001 0.2777 ] Network output: [ -0.01495 0.03223 1.042 0.0001098 -4.927e-05 0.9561 8.272e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07623 0.0755 0.1678 0.1982 0.9853 0.9914 0.07624 0.7628 0.8755 0.2398 ] Network output: [ -0.0005984 1.058 -0.006366 9.432e-06 -4.234e-06 0.9493 7.108e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03707 Epoch 5525 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01542 1.012 0.9682 -5.143e-05 2.309e-05 -0.01131 -3.876e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002809 -0.002368 -0.01101 0.007401 0.9691 0.9736 0.00532 0.8546 0.8439 0.0217 ] Network output: [ 0.9598 0.2269 0.007812 -5.207e-05 2.338e-05 -0.1545 -3.924e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1939 0.002111 -0.2265 0.1835 0.9836 0.9933 0.2161 0.5247 0.8929 0.7426 ] Network output: [ -0.00774 0.9995 0.993 -5.241e-05 2.353e-05 0.02282 -3.95e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004025 0.001324 0.002572 0.003863 0.9895 0.9924 0.004097 0.8916 0.9162 0.01462 ] Network output: [ -0.007051 0.2422 0.9126 -0.0002339 0.000105 0.8583 -0.0001763 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2053 0.1425 0.2786 0.137 0.9852 0.9941 0.2059 0.5287 0.9006 0.743 ] Network output: [ 0.007347 0.06087 1.04 0.0001227 -5.51e-05 0.8846 9.25e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06299 0.05926 0.141 0.1798 0.988 0.9924 0.06302 0.832 0.9002 0.265 ] Network output: [ -0.003923 -0.02526 1.041 0.0001284 -5.765e-05 0.9922 9.678e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07396 0.07319 0.1613 0.1967 0.9851 0.9912 0.07397 0.753 0.8765 0.24 ] Network output: [ 0.002467 0.9481 0.01522 2.847e-05 -1.278e-05 1.032 2.146e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02724 Epoch 5526 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01974 0.9595 0.9702 -3.851e-05 1.729e-05 0.0307 -2.902e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00276 -0.002379 -0.01095 0.008625 0.9691 0.9736 0.005239 0.8534 0.8461 0.02217 ] Network output: [ 1.015 -0.207 0.01309 5.645e-05 -2.534e-05 0.1647 4.254e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1888 -0.003629 -0.2156 0.2589 0.9836 0.9933 0.2105 0.5146 0.8949 0.7493 ] Network output: [ -0.009957 0.9909 0.9954 -5.098e-05 2.288e-05 0.03335 -3.842e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003934 0.001367 0.003451 0.005915 0.9894 0.9924 0.004005 0.8907 0.9168 0.01503 ] Network output: [ 0.03335 -0.3228 0.9462 -9.647e-05 4.331e-05 1.31 -7.27e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2004 0.1411 0.3122 0.2458 0.9852 0.9941 0.201 0.5214 0.8998 0.7392 ] Network output: [ -0.006081 -0.004625 1.06 0.0001208 -5.421e-05 0.9577 9.101e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06549 0.0619 0.1624 0.2029 0.9879 0.9924 0.06553 0.8384 0.8996 0.2782 ] Network output: [ -0.01535 0.03709 1.041 0.0001088 -4.882e-05 0.9526 8.196e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07659 0.07586 0.1679 0.1982 0.9853 0.9914 0.0766 0.762 0.8749 0.2395 ] Network output: [ 0.0004255 1.059 -0.00806 1.047e-05 -4.701e-06 0.9481 7.891e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0424 Epoch 5527 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01505 1.015 0.9686 -5.16e-05 2.317e-05 -0.01347 -3.889e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002815 -0.00237 -0.01097 0.007361 0.9691 0.9736 0.005331 0.854 0.8433 0.02167 ] Network output: [ 0.9574 0.2397 0.008499 -5.783e-05 2.596e-05 -0.1632 -4.359e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1944 0.00234 -0.2257 0.1812 0.9835 0.9933 0.2167 0.5229 0.8923 0.7418 ] Network output: [ -0.007787 1.001 0.9932 -5.195e-05 2.332e-05 0.02166 -3.915e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004043 0.001329 0.00257 0.003821 0.9894 0.9924 0.004115 0.8909 0.9157 0.01461 ] Network output: [ -0.00793 0.2584 0.9111 -0.0002376 0.0001067 0.8453 -0.000179 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2059 0.1429 0.2777 0.1344 0.9851 0.9941 0.2065 0.5267 0.9001 0.7423 ] Network output: [ 0.007599 0.06482 1.039 0.0001224 -5.497e-05 0.8811 9.228e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06323 0.05949 0.1405 0.1792 0.988 0.9924 0.06327 0.8308 0.8997 0.2646 ] Network output: [ -0.003691 -0.02339 1.041 0.0001284 -5.766e-05 0.9904 9.68e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07417 0.07339 0.1609 0.1965 0.9851 0.9912 0.07418 0.7514 0.876 0.2397 ] Network output: [ 0.002507 0.948 0.01535 2.876e-05 -1.291e-05 1.032 2.167e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03044 Epoch 5528 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01973 0.9578 0.9707 -3.766e-05 1.691e-05 0.03189 -2.839e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002762 -0.002381 -0.01091 0.008663 0.9691 0.9736 0.005241 0.8527 0.8456 0.02216 ] Network output: [ 1.015 -0.2228 0.01498 5.731e-05 -2.573e-05 0.1777 4.319e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1889 -0.003787 -0.2137 0.2615 0.9836 0.9933 0.2106 0.512 0.8943 0.7488 ] Network output: [ -0.009987 0.9907 0.9957 -5.021e-05 2.254e-05 0.03335 -3.784e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003944 0.001375 0.003512 0.006016 0.9894 0.9924 0.004015 0.8899 0.9163 0.01505 ] Network output: [ 0.03551 -0.345 0.9466 -9.104e-05 4.087e-05 1.327 -6.861e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2006 0.1414 0.3134 0.2506 0.9851 0.9941 0.2012 0.5187 0.8991 0.7382 ] Network output: [ -0.006565 -0.005956 1.06 0.0001206 -5.414e-05 0.9596 9.088e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0659 0.0623 0.1632 0.2039 0.9879 0.9924 0.06594 0.8376 0.899 0.2786 ] Network output: [ -0.01571 0.04197 1.041 0.0001078 -4.841e-05 0.949 8.127e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07696 0.07623 0.168 0.1982 0.9853 0.9914 0.07697 0.7609 0.8742 0.2392 ] Network output: [ 0.001605 1.059 -0.009699 1.182e-05 -5.307e-06 0.9477 8.909e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04784 Epoch 5529 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01471 1.017 0.969 -5.169e-05 2.321e-05 -0.01554 -3.896e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002822 -0.002373 -0.01094 0.007322 0.9691 0.9736 0.005342 0.8533 0.8425 0.02163 ] Network output: [ 0.9552 0.2511 0.009143 -6.361e-05 2.856e-05 -0.1709 -4.794e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.195 0.002595 -0.2246 0.1792 0.9835 0.9933 0.2174 0.5206 0.8915 0.7407 ] Network output: [ -0.007835 1.002 0.9934 -5.14e-05 2.308e-05 0.0205 -3.874e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004062 0.001337 0.002574 0.003788 0.9894 0.9924 0.004135 0.8901 0.915 0.0146 ] Network output: [ -0.008629 0.2726 0.9096 -0.0002405 0.000108 0.8341 -0.0001813 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2066 0.1434 0.2769 0.1322 0.9851 0.9941 0.2072 0.5243 0.8994 0.7415 ] Network output: [ 0.007734 0.06872 1.039 0.0001221 -5.484e-05 0.8777 9.205e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06352 0.05976 0.14 0.1787 0.988 0.9924 0.06355 0.8295 0.8991 0.2643 ] Network output: [ -0.003551 -0.02093 1.04 0.0001283 -5.762e-05 0.9882 9.673e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07441 0.07364 0.1605 0.1961 0.9851 0.9912 0.07442 0.7496 0.8753 0.2395 ] Network output: [ 0.002333 0.9493 0.01551 2.866e-05 -1.287e-05 1.031 2.16e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0334 Epoch 5530 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01973 0.9561 0.9713 -3.678e-05 1.651e-05 0.03298 -2.772e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002764 -0.002383 -0.01086 0.008693 0.9691 0.9736 0.005244 0.8519 0.845 0.02214 ] Network output: [ 1.015 -0.2364 0.01699 5.723e-05 -2.569e-05 0.1892 4.313e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1891 -0.003884 -0.2116 0.2636 0.9835 0.9933 0.2108 0.5091 0.8937 0.7481 ] Network output: [ -0.009954 0.9903 0.9961 -4.93e-05 2.213e-05 0.03339 -3.715e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003957 0.001383 0.003571 0.00611 0.9894 0.9924 0.004027 0.889 0.9158 0.01507 ] Network output: [ 0.03764 -0.3645 0.9466 -8.608e-05 3.864e-05 1.342 -6.487e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.201 0.1418 0.3145 0.2549 0.9851 0.9941 0.2016 0.5156 0.8984 0.737 ] Network output: [ -0.006976 -0.007036 1.06 0.0001205 -5.41e-05 0.9613 9.082e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06632 0.06271 0.164 0.2049 0.9879 0.9924 0.06636 0.8366 0.8984 0.279 ] Network output: [ -0.01601 0.04655 1.04 0.0001071 -4.807e-05 0.9456 8.069e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07735 0.07662 0.168 0.1981 0.9853 0.9914 0.07736 0.7596 0.8735 0.2388 ] Network output: [ 0.002871 1.057 -0.01115 1.343e-05 -6.031e-06 0.9484 1.012e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05289 Epoch 5531 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01441 1.019 0.9694 -5.164e-05 2.318e-05 -0.01737 -3.892e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002829 -0.002376 -0.01089 0.00729 0.9691 0.9736 0.005355 0.8525 0.8417 0.02159 ] Network output: [ 0.9535 0.26 0.009683 -6.905e-05 3.1e-05 -0.1769 -5.204e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1957 0.002863 -0.2234 0.1775 0.9835 0.9932 0.2182 0.5181 0.8906 0.7396 ] Network output: [ -0.007884 1.002 0.9937 -5.075e-05 2.278e-05 0.01942 -3.825e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004084 0.001348 0.002585 0.003768 0.9894 0.9923 0.004156 0.8892 0.9143 0.01459 ] Network output: [ -0.009073 0.2834 0.9081 -0.0002425 0.0001089 0.8256 -0.0001828 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2074 0.1441 0.2764 0.1308 0.9851 0.994 0.2081 0.5215 0.8986 0.7406 ] Network output: [ 0.007738 0.07224 1.038 0.0001219 -5.471e-05 0.8748 9.183e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06383 0.06006 0.1398 0.1783 0.988 0.9924 0.06387 0.8281 0.8984 0.2641 ] Network output: [ -0.003526 -0.01797 1.04 0.0001281 -5.752e-05 0.9857 9.657e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07469 0.07391 0.1602 0.1958 0.985 0.9912 0.0747 0.7478 0.8746 0.2392 ] Network output: [ 0.001912 0.9519 0.01573 2.815e-05 -1.264e-05 1.029 2.122e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0358 Epoch 5532 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01974 0.9547 0.9718 -3.588e-05 1.611e-05 0.03385 -2.704e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002767 -0.002385 -0.0108 0.008712 0.9691 0.9736 0.005248 0.851 0.8443 0.02211 ] Network output: [ 1.015 -0.2463 0.01897 5.6e-05 -2.514e-05 0.1981 4.22e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1894 -0.003903 -0.2094 0.2651 0.9835 0.9933 0.2112 0.5059 0.8929 0.7472 ] Network output: [ -0.009856 0.9897 0.9963 -4.827e-05 2.167e-05 0.03347 -3.638e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003971 0.001393 0.003625 0.00619 0.9893 0.9924 0.004042 0.8879 0.9151 0.01508 ] Network output: [ 0.03956 -0.3795 0.9462 -8.203e-05 3.683e-05 1.354 -6.182e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2015 0.1423 0.3153 0.2584 0.9851 0.994 0.2021 0.5124 0.8975 0.7358 ] Network output: [ -0.007259 -0.007819 1.06 0.0001206 -5.412e-05 0.9625 9.086e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06674 0.06312 0.1646 0.2057 0.9878 0.9924 0.06678 0.8355 0.8977 0.2792 ] Network output: [ -0.01621 0.05045 1.04 0.0001065 -4.783e-05 0.9426 8.029e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07772 0.07699 0.168 0.1981 0.9853 0.9914 0.07773 0.758 0.8727 0.2384 ] Network output: [ 0.004104 1.054 -0.01227 1.518e-05 -6.815e-06 0.95 1.144e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0569 Epoch 5533 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01419 1.02 0.9699 -5.141e-05 2.308e-05 -0.0188 -3.874e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002837 -0.002379 -0.01085 0.007266 0.9691 0.9736 0.005367 0.8516 0.8408 0.02155 ] Network output: [ 0.9523 0.2656 0.01005 -7.376e-05 3.311e-05 -0.1805 -5.559e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1965 0.003128 -0.222 0.1764 0.9835 0.9932 0.2191 0.5152 0.8896 0.7385 ] Network output: [ -0.007935 1.003 0.994 -5e-05 2.245e-05 0.01849 -3.768e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004106 0.00136 0.002605 0.003765 0.9894 0.9923 0.004179 0.8882 0.9136 0.01458 ] Network output: [ -0.009189 0.2897 0.9068 -0.0002433 0.0001092 0.8208 -0.0001833 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2083 0.1449 0.2762 0.1302 0.9851 0.994 0.209 0.5185 0.8977 0.7395 ] Network output: [ 0.007611 0.07499 1.038 0.0001216 -5.46e-05 0.8726 9.166e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06418 0.06041 0.1397 0.1782 0.9879 0.9923 0.06422 0.8267 0.8976 0.2641 ] Network output: [ -0.003627 -0.01472 1.039 0.0001278 -5.737e-05 0.983 9.631e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.075 0.07422 0.1599 0.1956 0.985 0.9912 0.07501 0.7459 0.8738 0.239 ] Network output: [ 0.001237 0.9559 0.01603 2.722e-05 -1.222e-05 1.026 2.051e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03727 Epoch 5534 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01975 0.9536 0.9724 -3.501e-05 1.572e-05 0.03439 -2.638e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00277 -0.002387 -0.01074 0.008717 0.9691 0.9736 0.005254 0.85 0.8435 0.02208 ] Network output: [ 1.014 -0.2515 0.02076 5.352e-05 -2.403e-05 0.2034 4.034e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1898 -0.003831 -0.2073 0.2656 0.9835 0.9932 0.2117 0.5026 0.892 0.7462 ] Network output: [ -0.009697 0.989 0.9966 -4.714e-05 2.116e-05 0.03357 -3.553e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.003987 0.001404 0.003671 0.006248 0.9893 0.9923 0.004059 0.8869 0.9144 0.0151 ] Network output: [ 0.04108 -0.3881 0.9452 -7.936e-05 3.563e-05 1.36 -5.981e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2022 0.143 0.3156 0.2606 0.9851 0.994 0.2028 0.509 0.8966 0.7346 ] Network output: [ -0.007364 -0.008299 1.06 0.0001208 -5.421e-05 0.9632 9.1e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06714 0.06351 0.165 0.2063 0.9878 0.9923 0.06718 0.8342 0.8969 0.2794 ] Network output: [ -0.01628 0.05324 1.039 0.0001063 -4.772e-05 0.9404 8.011e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07807 0.07734 0.1679 0.1981 0.9853 0.9914 0.07808 0.7564 0.8719 0.2382 ] Network output: [ 0.00515 1.05 -0.01288 1.687e-05 -7.575e-06 0.9525 1.272e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05923 Epoch 5535 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01406 1.021 0.9703 -5.093e-05 2.287e-05 -0.0197 -3.839e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002844 -0.002382 -0.0108 0.007254 0.9691 0.9736 0.00538 0.8506 0.8398 0.02152 ] Network output: [ 0.9519 0.2671 0.01019 -7.736e-05 3.473e-05 -0.1814 -5.83e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1974 0.00337 -0.2205 0.176 0.9835 0.9932 0.22 0.5121 0.8886 0.7375 ] Network output: [ -0.007981 1.004 0.9944 -4.914e-05 2.206e-05 0.01782 -3.703e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004128 0.001375 0.002634 0.003782 0.9893 0.9923 0.004201 0.8872 0.9128 0.01458 ] Network output: [ -0.008938 0.2908 0.9058 -0.0002426 0.0001089 0.8204 -0.0001828 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2092 0.1458 0.2763 0.1307 0.9851 0.994 0.2099 0.5153 0.8967 0.7385 ] Network output: [ 0.007371 0.07656 1.038 0.0001215 -5.454e-05 0.8716 9.156e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06456 0.06078 0.1398 0.1783 0.9879 0.9923 0.06459 0.8254 0.8968 0.2642 ] Network output: [ -0.003849 -0.0115 1.039 0.0001274 -5.718e-05 0.9805 9.599e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07533 0.07455 0.1598 0.1954 0.985 0.9912 0.07533 0.7441 0.8729 0.2389 ] Network output: [ 0.0003416 0.9607 0.01641 2.594e-05 -1.164e-05 1.022 1.955e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03757 Epoch 5536 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01978 0.953 0.9728 -3.42e-05 1.535e-05 0.03451 -2.577e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002775 -0.002388 -0.01068 0.008708 0.9691 0.9736 0.005262 0.849 0.8426 0.02204 ] Network output: [ 1.012 -0.2511 0.02214 4.982e-05 -2.237e-05 0.2044 3.755e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1904 -0.003663 -0.2054 0.2653 0.9835 0.9932 0.2124 0.4994 0.8911 0.7451 ] Network output: [ -0.009492 0.9883 0.9969 -4.596e-05 2.063e-05 0.03366 -3.464e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004005 0.001416 0.003705 0.00628 0.9893 0.9923 0.004077 0.8858 0.9136 0.01511 ] Network output: [ 0.04201 -0.3892 0.9438 -7.84e-05 3.52e-05 1.361 -5.909e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.203 0.1437 0.3156 0.2615 0.9851 0.994 0.2036 0.5057 0.8957 0.7335 ] Network output: [ -0.007257 -0.008522 1.06 0.0001211 -5.437e-05 0.9634 9.127e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0675 0.06386 0.1652 0.2067 0.9878 0.9923 0.06754 0.833 0.8962 0.2796 ] Network output: [ -0.0162 0.05461 1.039 0.0001064 -4.777e-05 0.9391 8.02e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07838 0.07765 0.1679 0.1982 0.9853 0.9914 0.07839 0.7547 0.8711 0.238 ] Network output: [ 0.005859 1.046 -0.01287 1.829e-05 -8.213e-06 0.9555 1.379e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05944 Epoch 5537 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01405 1.021 0.9708 -5.021e-05 2.254e-05 -0.01996 -3.784e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002852 -0.002386 -0.01076 0.007256 0.9691 0.9736 0.005392 0.8497 0.8389 0.0215 ] Network output: [ 0.9523 0.2644 0.01004 -7.958e-05 3.573e-05 -0.1793 -5.997e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1981 0.00357 -0.2191 0.1764 0.9835 0.9932 0.2209 0.5089 0.8876 0.7366 ] Network output: [ -0.008015 1.004 0.9948 -4.819e-05 2.163e-05 0.01747 -3.632e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004148 0.00139 0.002669 0.003818 0.9893 0.9923 0.004222 0.8862 0.912 0.01459 ] Network output: [ -0.008327 0.2863 0.9051 -0.0002405 0.000108 0.8243 -0.0001812 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2101 0.1467 0.2767 0.1322 0.9851 0.994 0.2107 0.5121 0.8958 0.7375 ] Network output: [ 0.007058 0.07668 1.038 0.0001215 -5.455e-05 0.8719 9.158e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06493 0.06116 0.1402 0.1788 0.9879 0.9923 0.06497 0.8243 0.8961 0.2647 ] Network output: [ -0.004165 -0.008661 1.039 0.0001269 -5.697e-05 0.9784 9.564e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07566 0.07488 0.1599 0.1954 0.985 0.9912 0.07567 0.7426 0.8721 0.2389 ] Network output: [ -0.0007021 0.9659 0.01689 2.443e-05 -1.097e-05 1.019 1.841e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03661 Epoch 5538 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0198 0.9529 0.9732 -3.347e-05 1.502e-05 0.03417 -2.522e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002781 -0.00239 -0.01063 0.008686 0.9691 0.9736 0.005271 0.8481 0.8417 0.02201 ] Network output: [ 1.011 -0.245 0.02296 4.512e-05 -2.026e-05 0.2008 3.4e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1911 -0.003404 -0.2039 0.2639 0.9835 0.9932 0.2131 0.4965 0.8901 0.7441 ] Network output: [ -0.00926 0.9875 0.9971 -4.477e-05 2.01e-05 0.03372 -3.374e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004024 0.001428 0.003726 0.006283 0.9893 0.9923 0.004097 0.8848 0.9128 0.01511 ] Network output: [ 0.04225 -0.3826 0.942 -7.929e-05 3.56e-05 1.356 -5.975e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2038 0.1445 0.315 0.2608 0.9851 0.994 0.2044 0.5026 0.8948 0.7325 ] Network output: [ -0.006935 -0.008581 1.06 0.0001216 -5.46e-05 0.9631 9.165e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06781 0.06417 0.1651 0.2069 0.9877 0.9923 0.06785 0.8317 0.8955 0.2798 ] Network output: [ -0.01598 0.05441 1.039 0.0001069 -4.798e-05 0.9391 8.054e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07864 0.07791 0.1678 0.1984 0.9853 0.9914 0.07865 0.753 0.8703 0.2381 ] Network output: [ 0.006132 1.041 -0.0122 1.928e-05 -8.656e-06 0.9588 1.453e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05743 Epoch 5539 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01417 1.02 0.9711 -4.923e-05 2.21e-05 -0.01957 -3.71e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002858 -0.002389 -0.01073 0.007271 0.9691 0.9736 0.005402 0.8488 0.838 0.02149 ] Network output: [ 0.9534 0.2578 0.009615 -8.032e-05 3.606e-05 -0.1746 -6.053e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1989 0.003716 -0.2179 0.1774 0.9834 0.9932 0.2217 0.5059 0.8867 0.7359 ] Network output: [ -0.008032 1.003 0.9952 -4.717e-05 2.118e-05 0.0175 -3.555e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004166 0.001405 0.00271 0.003872 0.9893 0.9923 0.00424 0.8852 0.9112 0.01461 ] Network output: [ -0.00742 0.2769 0.9048 -0.0002372 0.0001065 0.8323 -0.0001788 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2108 0.1476 0.2773 0.1346 0.9851 0.994 0.2115 0.5091 0.8949 0.7366 ] Network output: [ 0.00672 0.07522 1.038 0.0001217 -5.464e-05 0.8736 9.173e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06531 0.06153 0.1408 0.1796 0.9878 0.9923 0.06535 0.8233 0.8954 0.2654 ] Network output: [ -0.004538 -0.006488 1.039 0.0001264 -5.676e-05 0.9769 9.529e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07598 0.07521 0.1601 0.1956 0.985 0.9912 0.07599 0.7413 0.8713 0.2391 ] Network output: [ -0.001797 0.9708 0.01744 2.284e-05 -1.026e-05 1.015 1.722e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03457 Epoch 5540 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01983 0.9533 0.9735 -3.284e-05 1.474e-05 0.03339 -2.475e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002788 -0.002391 -0.0106 0.008652 0.9691 0.9736 0.005281 0.8472 0.8408 0.02198 ] Network output: [ 1.009 -0.234 0.02312 3.976e-05 -1.785e-05 0.1931 2.996e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1919 -0.003077 -0.2028 0.2617 0.9835 0.9932 0.2139 0.4938 0.8892 0.7432 ] Network output: [ -0.009026 0.9868 0.9973 -4.363e-05 1.959e-05 0.03376 -3.288e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004044 0.001439 0.003733 0.006259 0.9892 0.9923 0.004116 0.8839 0.9121 0.01512 ] Network output: [ 0.04177 -0.3689 0.9402 -8.191e-05 3.677e-05 1.345 -6.173e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2046 0.1452 0.3142 0.2587 0.9851 0.994 0.2052 0.4999 0.894 0.7319 ] Network output: [ -0.006426 -0.008584 1.059 0.0001222 -5.488e-05 0.9627 9.212e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06807 0.06443 0.1648 0.2068 0.9877 0.9923 0.06811 0.8306 0.8948 0.2801 ] Network output: [ -0.01564 0.05268 1.039 0.0001076 -4.831e-05 0.9401 8.11e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07885 0.07812 0.1677 0.1987 0.9853 0.9913 0.07886 0.7515 0.8697 0.2384 ] Network output: [ 0.005959 1.037 -0.01095 1.977e-05 -8.874e-06 0.962 1.49e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.05357 Epoch 5541 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01441 1.018 0.9714 -4.805e-05 2.157e-05 -0.01861 -3.621e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002863 -0.002392 -0.0107 0.007298 0.9691 0.9736 0.00541 0.8479 0.8373 0.0215 ] Network output: [ 0.9553 0.248 0.008936 -7.967e-05 3.577e-05 -0.1678 -6.004e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1994 0.003798 -0.2168 0.1789 0.9834 0.9932 0.2223 0.503 0.8859 0.7355 ] Network output: [ -0.00803 1.002 0.9955 -4.612e-05 2.071e-05 0.01786 -3.476e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00418 0.001419 0.002753 0.003938 0.9893 0.9922 0.004255 0.8844 0.9105 0.01464 ] Network output: [ -0.00632 0.2635 0.9049 -0.0002331 0.0001047 0.8433 -0.0001757 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2114 0.1483 0.2781 0.1376 0.985 0.994 0.212 0.5064 0.8941 0.736 ] Network output: [ 0.006402 0.07233 1.039 0.0001221 -5.48e-05 0.8765 9.2e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06566 0.06189 0.1416 0.1806 0.9878 0.9923 0.0657 0.8226 0.8948 0.2664 ] Network output: [ -0.004923 -0.005124 1.039 0.000126 -5.658e-05 0.9762 9.498e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07629 0.07552 0.1604 0.1959 0.985 0.9912 0.0763 0.7404 0.8707 0.2395 ] Network output: [ -0.002844 0.9752 0.01798 2.132e-05 -9.569e-06 1.013 1.606e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03175 Epoch 5542 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01986 0.9542 0.9737 -3.23e-05 1.45e-05 0.03227 -2.435e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002795 -0.002393 -0.01057 0.008612 0.9691 0.9736 0.005292 0.8465 0.84 0.02197 ] Network output: [ 1.007 -0.2192 0.02265 3.416e-05 -1.533e-05 0.1822 2.574e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1926 -0.002712 -0.2023 0.2589 0.9834 0.9932 0.2148 0.4917 0.8883 0.7424 ] Network output: [ -0.008811 0.9863 0.9974 -4.258e-05 1.912e-05 0.03375 -3.209e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004062 0.001448 0.003729 0.006214 0.9892 0.9922 0.004135 0.8831 0.9114 0.01514 ] Network output: [ 0.04066 -0.35 0.9383 -8.593e-05 3.858e-05 1.33 -6.476e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2054 0.1459 0.313 0.2555 0.985 0.994 0.206 0.4977 0.8933 0.7316 ] Network output: [ -0.005778 -0.008621 1.059 0.0001229 -5.519e-05 0.9621 9.265e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06828 0.06463 0.1643 0.2067 0.9877 0.9923 0.06832 0.8295 0.8943 0.2804 ] Network output: [ -0.01521 0.04969 1.039 0.0001086 -4.874e-05 0.9422 8.182e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.079 0.07827 0.1676 0.199 0.9853 0.9913 0.07901 0.7502 0.8692 0.2388 ] Network output: [ 0.005416 1.033 -0.00928 1.979e-05 -8.884e-06 0.965 1.491e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04848 Epoch 5543 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01472 1.016 0.9717 -4.675e-05 2.099e-05 -0.01724 -3.523e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002867 -0.002394 -0.01069 0.007335 0.9691 0.9736 0.005417 0.8472 0.8367 0.02152 ] Network output: [ 0.9576 0.2362 0.008081 -7.787e-05 3.496e-05 -0.1597 -5.868e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1999 0.003814 -0.2161 0.1807 0.9834 0.9932 0.2228 0.5005 0.8852 0.7353 ] Network output: [ -0.008017 1.001 0.9959 -4.509e-05 2.024e-05 0.01847 -3.398e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004192 0.001431 0.002796 0.004012 0.9892 0.9922 0.004266 0.8837 0.91 0.01467 ] Network output: [ -0.005142 0.2477 0.9054 -0.0002286 0.0001026 0.8563 -0.0001723 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2118 0.1488 0.2791 0.141 0.985 0.994 0.2124 0.504 0.8933 0.7356 ] Network output: [ 0.006137 0.06833 1.039 0.0001226 -5.502e-05 0.8804 9.237e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06599 0.06222 0.1425 0.1818 0.9878 0.9922 0.06603 0.822 0.8943 0.2675 ] Network output: [ -0.005285 -0.004548 1.04 0.0001257 -5.643e-05 0.9761 9.472e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07658 0.07581 0.1608 0.1964 0.985 0.9912 0.07659 0.7398 0.8701 0.24 ] Network output: [ -0.003765 0.9788 0.01847 1.994e-05 -8.952e-06 1.01 1.503e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02854 Epoch 5544 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01989 0.9554 0.9738 -3.187e-05 1.431e-05 0.03089 -2.402e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002802 -0.002395 -0.01056 0.00857 0.9691 0.9736 0.005304 0.8459 0.8393 0.02196 ] Network output: [ 1.006 -0.2022 0.02166 2.869e-05 -1.288e-05 0.1693 2.162e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1933 -0.002341 -0.2021 0.2558 0.9834 0.9932 0.2155 0.4899 0.8876 0.7419 ] Network output: [ -0.008632 0.9858 0.9976 -4.166e-05 1.87e-05 0.03368 -3.139e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004079 0.001456 0.003716 0.006155 0.9892 0.9922 0.004152 0.8825 0.9109 0.01515 ] Network output: [ 0.03905 -0.3279 0.9367 -9.095e-05 4.083e-05 1.313 -6.854e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2061 0.1465 0.3118 0.2515 0.985 0.994 0.2067 0.496 0.8927 0.7315 ] Network output: [ -0.005048 -0.008741 1.058 0.0001236 -5.551e-05 0.9614 9.318e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06844 0.06479 0.1638 0.2065 0.9877 0.9922 0.06848 0.8287 0.8939 0.2807 ] Network output: [ -0.01472 0.04583 1.039 0.0001096 -4.921e-05 0.945 8.261e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07911 0.07838 0.1676 0.1994 0.9853 0.9913 0.07912 0.7491 0.8688 0.2395 ] Network output: [ 0.00463 1.03 -0.00738 1.947e-05 -8.74e-06 0.9677 1.467e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.04289 Epoch 5545 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01508 1.013 0.9718 -4.54e-05 2.038e-05 -0.01565 -3.421e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002871 -0.002397 -0.01068 0.007377 0.9691 0.9736 0.005421 0.8467 0.8362 0.02155 ] Network output: [ 0.9601 0.2233 0.007139 -7.521e-05 3.376e-05 -0.1509 -5.668e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2001 0.003767 -0.2155 0.1828 0.9834 0.9932 0.2231 0.4984 0.8846 0.7354 ] Network output: [ -0.008003 1 0.9962 -4.412e-05 1.981e-05 0.01922 -3.325e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0042 0.00144 0.002838 0.004089 0.9892 0.9922 0.004275 0.8832 0.9095 0.01471 ] Network output: [ -0.003988 0.2306 0.9063 -0.000224 0.0001006 0.8702 -0.0001688 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.212 0.1492 0.2801 0.1445 0.985 0.994 0.2126 0.502 0.8928 0.7354 ] Network output: [ 0.00594 0.0636 1.04 0.0001231 -5.528e-05 0.8849 9.28e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06628 0.06252 0.1434 0.1832 0.9878 0.9922 0.06632 0.8217 0.8938 0.2688 ] Network output: [ -0.005601 -0.004613 1.04 0.0001254 -5.63e-05 0.9766 9.452e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07683 0.07607 0.1613 0.197 0.985 0.9912 0.07684 0.7394 0.8697 0.2406 ] Network output: [ -0.004513 0.9817 0.01883 1.877e-05 -8.426e-06 1.009 1.414e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02527 Epoch 5546 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01991 0.9569 0.9738 -3.151e-05 1.415e-05 0.02935 -2.375e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002808 -0.002397 -0.01057 0.008528 0.9691 0.9736 0.005314 0.8455 0.8387 0.02196 ] Network output: [ 1.004 -0.1845 0.02034 2.365e-05 -1.062e-05 0.1555 1.782e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1939 -0.001996 -0.2024 0.2525 0.9834 0.9932 0.2162 0.4886 0.8869 0.7416 ] Network output: [ -0.008499 0.9855 0.9977 -4.087e-05 1.835e-05 0.03355 -3.08e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004094 0.001463 0.003698 0.006088 0.9892 0.9922 0.004167 0.8821 0.9104 0.01517 ] Network output: [ 0.03711 -0.3045 0.9354 -9.652e-05 4.333e-05 1.294 -7.274e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2066 0.147 0.3105 0.2473 0.985 0.994 0.2072 0.4946 0.8922 0.7317 ] Network output: [ -0.004285 -0.008948 1.057 0.0001243 -5.582e-05 0.9609 9.37e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06856 0.0649 0.1632 0.2062 0.9877 0.9922 0.0686 0.8279 0.8935 0.2812 ] Network output: [ -0.01422 0.04151 1.039 0.0001107 -4.97e-05 0.9481 8.343e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07918 0.07845 0.1676 0.1999 0.9853 0.9913 0.07919 0.7482 0.8686 0.2402 ] Network output: [ 0.003735 1.028 -0.005441 1.894e-05 -8.503e-06 0.97 1.427e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03737 Epoch 5547 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01544 1.011 0.9719 -4.407e-05 1.979e-05 -0.014 -3.322e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002873 -0.002399 -0.01069 0.007422 0.9691 0.9736 0.005425 0.8462 0.8359 0.02159 ] Network output: [ 0.9627 0.2101 0.006192 -7.201e-05 3.233e-05 -0.142 -5.427e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2002 0.003664 -0.2152 0.185 0.9834 0.9932 0.2232 0.4967 0.8842 0.7357 ] Network output: [ -0.007998 0.9993 0.9965 -4.324e-05 1.941e-05 0.02 -3.259e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004206 0.001447 0.002878 0.004164 0.9892 0.9922 0.004281 0.8828 0.9092 0.01476 ] Network output: [ -0.002928 0.2134 0.9074 -0.0002197 9.863e-05 0.8841 -0.0001656 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.212 0.1494 0.2812 0.148 0.985 0.994 0.2127 0.5004 0.8923 0.7354 ] Network output: [ 0.005811 0.05855 1.041 0.0001237 -5.555e-05 0.8897 9.324e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06655 0.06279 0.1443 0.1845 0.9878 0.9922 0.06659 0.8215 0.8935 0.2701 ] Network output: [ -0.005864 -0.005121 1.04 0.0001252 -5.62e-05 0.9775 9.435e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07707 0.0763 0.1618 0.1976 0.985 0.9912 0.07708 0.7393 0.8694 0.2412 ] Network output: [ -0.00507 0.9839 0.01904 1.782e-05 -7.999e-06 1.007 1.343e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02218 Epoch 5548 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0199 0.9586 0.9738 -3.123e-05 1.402e-05 0.02774 -2.354e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002815 -0.002399 -0.01057 0.008489 0.9692 0.9736 0.005324 0.8452 0.8382 0.02197 ] Network output: [ 1.003 -0.167 0.01883 1.919e-05 -8.614e-06 0.1418 1.446e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1944 -0.001696 -0.2029 0.2494 0.9834 0.9932 0.2167 0.4877 0.8864 0.7414 ] Network output: [ -0.008418 0.9855 0.9979 -4.022e-05 1.805e-05 0.03333 -3.031e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004106 0.001466 0.003677 0.006019 0.9892 0.9922 0.00418 0.8818 0.91 0.01519 ] Network output: [ 0.03502 -0.2813 0.9344 -0.0001023 4.592e-05 1.276 -7.709e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.207 0.1473 0.3093 0.243 0.985 0.994 0.2076 0.4937 0.8918 0.7321 ] Network output: [ -0.003531 -0.00922 1.056 0.0001249 -5.609e-05 0.9604 9.416e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06865 0.06499 0.1626 0.206 0.9877 0.9922 0.0687 0.8274 0.8933 0.2817 ] Network output: [ -0.01373 0.03707 1.039 0.0001117 -5.016e-05 0.9515 8.421e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07924 0.07851 0.1676 0.2004 0.9853 0.9913 0.07925 0.7475 0.8684 0.2409 ] Network output: [ 0.002843 1.026 -0.003609 1.834e-05 -8.232e-06 0.972 1.382e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.03231 Epoch 5549 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01577 1.009 0.972 -4.283e-05 1.923e-05 -0.01242 -3.228e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002874 -0.002401 -0.01069 0.007469 0.9692 0.9736 0.005427 0.8459 0.8356 0.02163 ] Network output: [ 0.9653 0.1973 0.005301 -6.852e-05 3.076e-05 -0.1334 -5.164e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2002 0.003513 -0.215 0.1871 0.9834 0.9932 0.2232 0.4953 0.8839 0.7361 ] Network output: [ -0.008012 0.9984 0.9967 -4.248e-05 1.907e-05 0.02072 -3.201e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004209 0.001451 0.002914 0.004237 0.9892 0.9922 0.004284 0.8825 0.9089 0.0148 ] Network output: [ -0.002002 0.1969 0.9088 -0.0002158 9.688e-05 0.8974 -0.0001626 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.212 0.1494 0.2822 0.1513 0.985 0.994 0.2126 0.4992 0.8919 0.7356 ] Network output: [ 0.005745 0.05351 1.041 0.0001243 -5.58e-05 0.8944 9.368e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06679 0.06303 0.1451 0.1859 0.9878 0.9922 0.06683 0.8215 0.8933 0.2714 ] Network output: [ -0.006074 -0.005885 1.04 0.000125 -5.612e-05 0.9785 9.421e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07728 0.07651 0.1623 0.1982 0.9851 0.9912 0.07729 0.7394 0.8692 0.2419 ] Network output: [ -0.005445 0.9858 0.01909 1.708e-05 -7.668e-06 1.006 1.287e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01941 Epoch 5550 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01987 0.9603 0.9737 -3.102e-05 1.393e-05 0.02612 -2.338e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00282 -0.002401 -0.01059 0.008455 0.9692 0.9736 0.005334 0.845 0.8378 0.02198 ] Network output: [ 1.002 -0.1506 0.01728 1.538e-05 -6.906e-06 0.1288 1.159e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1947 -0.001455 -0.2035 0.2466 0.9834 0.9932 0.2171 0.4871 0.886 0.7415 ] Network output: [ -0.008387 0.9856 0.998 -3.97e-05 1.782e-05 0.03304 -2.992e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004117 0.001468 0.003656 0.005953 0.9892 0.9922 0.004191 0.8817 0.9098 0.01521 ] Network output: [ 0.0329 -0.2593 0.9337 -0.000108 4.848e-05 1.259 -8.139e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2073 0.1474 0.3083 0.2389 0.985 0.994 0.2079 0.4931 0.8915 0.7326 ] Network output: [ -0.00281 -0.009527 1.056 0.0001255 -5.633e-05 0.96 9.457e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06873 0.06506 0.1621 0.2058 0.9877 0.9922 0.06878 0.8269 0.8931 0.2822 ] Network output: [ -0.01327 0.03276 1.039 0.0001127 -5.059e-05 0.9548 8.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07928 0.07855 0.1676 0.2009 0.9853 0.9913 0.07929 0.7471 0.8683 0.2417 ] Network output: [ 0.002027 1.024 -0.001972 1.775e-05 -7.967e-06 0.9737 1.337e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02788 Epoch 5551 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01607 1.007 0.9721 -4.17e-05 1.872e-05 -0.01101 -3.142e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002875 -0.002403 -0.01071 0.007514 0.9692 0.9736 0.005428 0.8457 0.8355 0.02167 ] Network output: [ 0.9677 0.1852 0.0045 -6.494e-05 2.915e-05 -0.1252 -4.894e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2001 0.003322 -0.215 0.1891 0.9834 0.9932 0.223 0.4942 0.8838 0.7367 ] Network output: [ -0.008051 0.9976 0.997 -4.183e-05 1.878e-05 0.02134 -3.153e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004212 0.001453 0.002947 0.004304 0.9892 0.9922 0.004287 0.8823 0.9088 0.01485 ] Network output: [ -0.001226 0.1816 0.9103 -0.0002124 9.538e-05 0.9097 -0.0001601 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2118 0.1493 0.2832 0.1543 0.985 0.994 0.2124 0.4982 0.8917 0.7359 ] Network output: [ 0.005728 0.04869 1.042 0.0001248 -5.604e-05 0.8988 9.407e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06701 0.06324 0.1459 0.1871 0.9877 0.9922 0.06705 0.8215 0.8931 0.2727 ] Network output: [ -0.006239 -0.00676 1.04 0.0001248 -5.604e-05 0.9797 9.408e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07747 0.07671 0.1627 0.1989 0.9851 0.9912 0.07748 0.7395 0.869 0.2426 ] Network output: [ -0.00566 0.9872 0.01899 1.654e-05 -7.424e-06 1.005 1.246e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.017 Epoch 5552 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01981 0.9621 0.9736 -3.085e-05 1.385e-05 0.02454 -2.325e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002825 -0.002403 -0.01061 0.008426 0.9692 0.9737 0.005342 0.8449 0.8375 0.022 ] Network output: [ 1.002 -0.1358 0.01578 1.223e-05 -5.49e-06 0.1169 9.215e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.195 -0.001278 -0.2043 0.244 0.9834 0.9932 0.2174 0.4867 0.8857 0.7416 ] Network output: [ -0.008403 0.9859 0.9981 -3.93e-05 1.764e-05 0.03268 -2.961e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004126 0.001468 0.003635 0.005891 0.9892 0.9922 0.0042 0.8816 0.9095 0.01523 ] Network output: [ 0.03083 -0.2391 0.9334 -0.0001135 5.094e-05 1.244 -8.551e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2074 0.1474 0.3073 0.2351 0.985 0.994 0.208 0.4927 0.8913 0.7332 ] Network output: [ -0.002138 -0.009844 1.055 0.0001259 -5.653e-05 0.9598 9.49e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06881 0.06512 0.1617 0.2056 0.9877 0.9922 0.06885 0.8266 0.893 0.2827 ] Network output: [ -0.01284 0.02876 1.04 0.0001135 -5.096e-05 0.9578 8.555e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07932 0.07858 0.1677 0.2014 0.9853 0.9913 0.07933 0.7467 0.8683 0.2424 ] Network output: [ 0.001323 1.023 -0.0005707 1.723e-05 -7.733e-06 0.9751 1.298e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02414 Epoch 5553 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01631 1.005 0.9721 -4.068e-05 1.826e-05 -0.009793 -3.066e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002876 -0.002405 -0.01072 0.007558 0.9692 0.9736 0.005429 0.8456 0.8354 0.02172 ] Network output: [ 0.9698 0.174 0.003803 -6.142e-05 2.757e-05 -0.1177 -4.629e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1999 0.0031 -0.215 0.191 0.9834 0.9932 0.2228 0.4934 0.8836 0.7373 ] Network output: [ -0.008118 0.9971 0.9971 -4.13e-05 1.854e-05 0.02183 -3.112e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004213 0.001453 0.002977 0.004365 0.9892 0.9922 0.004288 0.8822 0.9087 0.0149 ] Network output: [ -0.0005974 0.1677 0.9118 -0.0002096 9.412e-05 0.9209 -0.000158 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2115 0.1491 0.2841 0.157 0.985 0.994 0.2121 0.4975 0.8915 0.7363 ] Network output: [ 0.005751 0.04424 1.042 0.0001253 -5.624e-05 0.903 9.441e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06721 0.06344 0.1467 0.1884 0.9877 0.9922 0.06725 0.8216 0.893 0.2739 ] Network output: [ -0.006366 -0.007648 1.04 0.0001247 -5.597e-05 0.9808 9.396e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07764 0.07688 0.1631 0.1995 0.9851 0.9912 0.07765 0.7398 0.869 0.2432 ] Network output: [ -0.005746 0.9884 0.01877 1.617e-05 -7.257e-06 1.004 1.218e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01496 Epoch 5554 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01973 0.9639 0.9735 -3.073e-05 1.379e-05 0.02302 -2.316e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002829 -0.002405 -0.01063 0.008403 0.9692 0.9737 0.005349 0.8449 0.8374 0.02202 ] Network output: [ 1.001 -0.1226 0.0144 9.681e-06 -4.346e-06 0.1062 7.296e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1951 -0.001164 -0.2051 0.2417 0.9834 0.9932 0.2175 0.4865 0.8854 0.7419 ] Network output: [ -0.00846 0.9863 0.9982 -3.899e-05 1.751e-05 0.03227 -2.939e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004134 0.001467 0.003616 0.005836 0.9892 0.9922 0.004208 0.8816 0.9094 0.01525 ] Network output: [ 0.02887 -0.2211 0.9333 -0.0001186 5.325e-05 1.23 -8.939e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2074 0.1473 0.3065 0.2316 0.985 0.994 0.208 0.4925 0.8912 0.7338 ] Network output: [ -0.001522 -0.01015 1.054 0.0001263 -5.67e-05 0.9596 9.518e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06888 0.06517 0.1612 0.2055 0.9877 0.9922 0.06892 0.8264 0.8929 0.2832 ] Network output: [ -0.01246 0.02515 1.04 0.0001142 -5.128e-05 0.9607 8.609e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07936 0.07862 0.1678 0.2018 0.9853 0.9913 0.07937 0.7466 0.8683 0.2431 ] Network output: [ 0.0007439 1.022 0.000589 1.68e-05 -7.544e-06 0.9763 1.266e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.02103 Epoch 5555 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01649 1.004 0.9721 -3.979e-05 1.786e-05 -0.008787 -2.999e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002876 -0.002408 -0.01074 0.007599 0.9692 0.9737 0.005429 0.8456 0.8354 0.02176 ] Network output: [ 0.9718 0.1639 0.003209 -5.805e-05 2.606e-05 -0.111 -4.375e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1996 0.002854 -0.2152 0.1927 0.9834 0.9932 0.2225 0.4928 0.8836 0.7379 ] Network output: [ -0.00821 0.9968 0.9973 -4.087e-05 1.835e-05 0.02218 -3.08e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004213 0.001451 0.003003 0.00442 0.9892 0.9922 0.004288 0.8822 0.9086 0.01494 ] Network output: [ -0.0001044 0.1553 0.9134 -0.0002074 9.31e-05 0.9307 -0.0001563 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2111 0.1488 0.285 0.1593 0.985 0.994 0.2118 0.497 0.8914 0.7367 ] Network output: [ 0.005803 0.04023 1.042 0.0001257 -5.641e-05 0.9068 9.47e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0674 0.06361 0.1473 0.1895 0.9877 0.9922 0.06744 0.8218 0.893 0.275 ] Network output: [ -0.006466 -0.008491 1.04 0.0001245 -5.59e-05 0.982 9.384e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07781 0.07704 0.1635 0.2001 0.9851 0.9912 0.07782 0.7402 0.869 0.2439 ] Network output: [ -0.005732 0.9893 0.01845 1.594e-05 -7.155e-06 1.004 1.201e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01326 Epoch 5556 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01963 0.9656 0.9735 -3.063e-05 1.375e-05 0.02159 -2.308e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002833 -0.002408 -0.01066 0.008384 0.9692 0.9737 0.005355 0.845 0.8372 0.02204 ] Network output: [ 1.001 -0.1111 0.01315 7.676e-06 -3.446e-06 0.09691 5.785e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1951 -0.001109 -0.2059 0.2398 0.9834 0.9932 0.2175 0.4864 0.8853 0.7422 ] Network output: [ -0.008552 0.9869 0.9983 -3.877e-05 1.741e-05 0.03181 -2.922e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00414 0.001464 0.003599 0.005787 0.9892 0.9922 0.004214 0.8816 0.9093 0.01528 ] Network output: [ 0.02707 -0.2051 0.9334 -0.0001234 5.539e-05 1.217 -9.298e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2073 0.1471 0.3058 0.2286 0.985 0.994 0.208 0.4924 0.8912 0.7345 ] Network output: [ -0.0009637 -0.01045 1.053 0.0001266 -5.682e-05 0.9595 9.538e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06895 0.06523 0.1609 0.2054 0.9877 0.9922 0.06899 0.8262 0.8929 0.2838 ] Network output: [ -0.01212 0.02194 1.04 0.0001148 -5.155e-05 0.9633 8.654e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0794 0.07866 0.1678 0.2023 0.9853 0.9913 0.07941 0.7465 0.8684 0.2438 ] Network output: [ 0.0002844 1.021 0.001521 1.649e-05 -7.403e-06 0.9773 1.243e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01848 Epoch 5557 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01662 1.002 0.9721 -3.902e-05 1.752e-05 -0.007982 -2.94e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002876 -0.00241 -0.01076 0.007637 0.9692 0.9737 0.005429 0.8456 0.8355 0.02181 ] Network output: [ 0.9736 0.1549 0.002713 -5.487e-05 2.464e-05 -0.105 -4.136e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1993 0.002591 -0.2154 0.1943 0.9834 0.9932 0.2221 0.4923 0.8836 0.7386 ] Network output: [ -0.008326 0.9966 0.9975 -4.052e-05 1.819e-05 0.02241 -3.054e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004213 0.001447 0.003025 0.004468 0.9892 0.9922 0.004288 0.8822 0.9086 0.01499 ] Network output: [ 0.0002683 0.1445 0.9149 -0.0002056 9.231e-05 0.9392 -0.000155 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2107 0.1484 0.2857 0.1614 0.985 0.994 0.2114 0.4967 0.8914 0.7372 ] Network output: [ 0.005878 0.03667 1.042 0.000126 -5.655e-05 0.9101 9.494e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06758 0.06378 0.1479 0.1905 0.9877 0.9922 0.06762 0.8219 0.893 0.2761 ] Network output: [ -0.006543 -0.009261 1.04 0.0001244 -5.583e-05 0.983 9.373e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07796 0.07719 0.1639 0.2007 0.9851 0.9912 0.07797 0.7405 0.869 0.2445 ] Network output: [ -0.005647 0.9901 0.01807 1.583e-05 -7.107e-06 1.003 1.193e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01185 Epoch 5558 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0195 0.9672 0.9734 -3.054e-05 1.371e-05 0.02026 -2.302e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002836 -0.00241 -0.01068 0.00837 0.9692 0.9737 0.00536 0.8451 0.8372 0.02207 ] Network output: [ 1 -0.1013 0.01206 6.144e-06 -2.758e-06 0.08884 4.63e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1951 -0.001107 -0.2068 0.2382 0.9834 0.9932 0.2175 0.4865 0.8852 0.7426 ] Network output: [ -0.008671 0.9875 0.9984 -3.861e-05 1.733e-05 0.03132 -2.91e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004145 0.001459 0.003584 0.005745 0.9892 0.9922 0.004219 0.8817 0.9093 0.0153 ] Network output: [ 0.02543 -0.1913 0.9338 -0.0001277 5.735e-05 1.206 -9.627e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2072 0.1468 0.3053 0.2259 0.985 0.994 0.2078 0.4924 0.8912 0.7352 ] Network output: [ -0.0004608 -0.01072 1.053 0.0001268 -5.691e-05 0.9595 9.554e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06903 0.06528 0.1606 0.2054 0.9877 0.9922 0.06907 0.8261 0.8929 0.2843 ] Network output: [ -0.01183 0.01915 1.039 0.0001153 -5.178e-05 0.9656 8.692e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07945 0.0787 0.1679 0.2027 0.9853 0.9913 0.07946 0.7465 0.8685 0.2444 ] Network output: [ -6.793e-05 1.02 0.00225 1.628e-05 -7.307e-06 0.9781 1.227e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01641 Epoch 5559 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01671 1.002 0.9722 -3.834e-05 1.721e-05 -0.007361 -2.889e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002876 -0.002412 -0.01078 0.007673 0.9692 0.9737 0.005429 0.8457 0.8356 0.02185 ] Network output: [ 0.9751 0.147 0.002305 -5.193e-05 2.331e-05 -0.09969 -3.914e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1989 0.002314 -0.2157 0.1957 0.9834 0.9932 0.2217 0.492 0.8837 0.7392 ] Network output: [ -0.00846 0.9966 0.9976 -4.024e-05 1.807e-05 0.02253 -3.033e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004213 0.001443 0.003045 0.00451 0.9892 0.9922 0.004288 0.8823 0.9086 0.01503 ] Network output: [ 0.000537 0.1352 0.9164 -0.0002043 9.173e-05 0.9464 -0.000154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2103 0.1479 0.2864 0.1631 0.985 0.994 0.2109 0.4965 0.8914 0.7377 ] Network output: [ 0.005971 0.03356 1.042 0.0001262 -5.666e-05 0.9131 9.512e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06775 0.06393 0.1484 0.1914 0.9877 0.9922 0.06779 0.8221 0.893 0.2771 ] Network output: [ -0.006604 -0.009947 1.04 0.0001242 -5.577e-05 0.984 9.361e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0781 0.07733 0.1642 0.2012 0.9851 0.9912 0.07811 0.7409 0.8691 0.245 ] Network output: [ -0.005513 0.9907 0.01765 1.582e-05 -7.102e-06 1.003 1.192e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0107 Epoch 5560 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01937 0.9687 0.9734 -3.046e-05 1.367e-05 0.01904 -2.295e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002838 -0.002413 -0.01071 0.00836 0.9692 0.9737 0.005365 0.8452 0.8372 0.02209 ] Network output: [ 1 -0.09304 0.01111 5.016e-06 -2.252e-06 0.08198 3.78e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.195 -0.001152 -0.2075 0.2368 0.9834 0.9932 0.2174 0.4866 0.8852 0.743 ] Network output: [ -0.008812 0.9882 0.9985 -3.849e-05 1.728e-05 0.03082 -2.901e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004149 0.001454 0.003571 0.005709 0.9892 0.9922 0.004223 0.8818 0.9093 0.01532 ] Network output: [ 0.02395 -0.1793 0.9342 -0.0001317 5.913e-05 1.197 -9.927e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.207 0.1464 0.3048 0.2236 0.985 0.994 0.2076 0.4925 0.8912 0.7358 ] Network output: [ -9.328e-06 -0.01098 1.052 0.0001269 -5.698e-05 0.9595 9.565e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06911 0.06534 0.1604 0.2054 0.9877 0.9922 0.06916 0.8261 0.893 0.2848 ] Network output: [ -0.01158 0.01674 1.039 0.0001157 -5.196e-05 0.9676 8.722e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0795 0.07875 0.168 0.2031 0.9853 0.9913 0.07951 0.7465 0.8686 0.245 ] Network output: [ -0.0003285 1.019 0.002802 1.616e-05 -7.254e-06 0.9789 1.218e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01473 Epoch 5561 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01675 1.001 0.9722 -3.774e-05 1.694e-05 -0.006901 -2.844e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002876 -0.002415 -0.0108 0.007705 0.9692 0.9737 0.005429 0.8458 0.8357 0.02189 ] Network output: [ 0.9765 0.14 0.001974 -4.924e-05 2.21e-05 -0.09507 -3.711e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1985 0.00203 -0.216 0.1969 0.9834 0.9932 0.2212 0.4918 0.8838 0.7399 ] Network output: [ -0.008609 0.9968 0.9977 -4.002e-05 1.797e-05 0.02255 -3.016e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004212 0.001438 0.003061 0.004547 0.9892 0.9922 0.004287 0.8824 0.9087 0.01507 ] Network output: [ 0.0007173 0.1273 0.9179 -0.0002034 9.134e-05 0.9525 -0.0001533 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2098 0.1474 0.2869 0.1646 0.985 0.994 0.2105 0.4963 0.8914 0.7382 ] Network output: [ 0.006079 0.03087 1.042 0.0001264 -5.675e-05 0.9157 9.526e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06792 0.06407 0.1488 0.1922 0.9877 0.9922 0.06796 0.8223 0.893 0.278 ] Network output: [ -0.006651 -0.01055 1.04 0.0001241 -5.57e-05 0.9849 9.35e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07824 0.07747 0.1644 0.2017 0.9852 0.9912 0.07825 0.7413 0.8692 0.2455 ] Network output: [ -0.005347 0.9912 0.01721 1.588e-05 -7.131e-06 1.002 1.197e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009758 Epoch 5562 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01922 0.9702 0.9734 -3.038e-05 1.364e-05 0.01792 -2.289e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00284 -0.002415 -0.01073 0.008353 0.9692 0.9737 0.005369 0.8453 0.8372 0.02212 ] Network output: [ 0.9998 -0.08617 0.01031 4.229e-06 -1.898e-06 0.07623 3.187e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1948 -0.001237 -0.2083 0.2357 0.9834 0.9932 0.2172 0.4867 0.8852 0.7434 ] Network output: [ -0.008968 0.9889 0.9986 -3.84e-05 1.724e-05 0.03032 -2.894e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004152 0.001448 0.00356 0.005678 0.9892 0.9922 0.004227 0.882 0.9093 0.01535 ] Network output: [ 0.02264 -0.1691 0.9348 -0.0001353 6.076e-05 1.189 -0.000102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2067 0.146 0.3044 0.2216 0.985 0.994 0.2073 0.4927 0.8913 0.7365 ] Network output: [ 0.0003952 -0.01124 1.051 0.000127 -5.703e-05 0.9596 9.573e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06921 0.06541 0.1602 0.2054 0.9877 0.9922 0.06925 0.826 0.893 0.2853 ] Network output: [ -0.01137 0.01469 1.039 0.0001161 -5.21e-05 0.9694 8.746e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07956 0.07881 0.1681 0.2035 0.9853 0.9913 0.07957 0.7466 0.8688 0.2455 ] Network output: [ -0.0005128 1.018 0.003203 1.612e-05 -7.236e-06 0.9795 1.215e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01338 Epoch 5563 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01675 1.001 0.9723 -3.722e-05 1.671e-05 -0.006581 -2.805e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002876 -0.002417 -0.01082 0.007734 0.9692 0.9737 0.005429 0.846 0.8358 0.02192 ] Network output: [ 0.9776 0.1339 0.001712 -4.678e-05 2.1e-05 -0.09107 -3.526e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1981 0.001741 -0.2163 0.198 0.9834 0.9932 0.2208 0.4917 0.8839 0.7405 ] Network output: [ -0.008768 0.997 0.9979 -3.984e-05 1.788e-05 0.0225 -3.002e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004212 0.001431 0.003075 0.004578 0.9892 0.9922 0.004287 0.8825 0.9088 0.0151 ] Network output: [ 0.0008232 0.1207 0.9193 -0.0002029 9.11e-05 0.9575 -0.0001529 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2094 0.1469 0.2874 0.1658 0.985 0.994 0.21 0.4963 0.8915 0.7387 ] Network output: [ 0.006198 0.02857 1.042 0.0001265 -5.681e-05 0.918 9.537e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06808 0.0642 0.1492 0.1929 0.9877 0.9922 0.06812 0.8225 0.8931 0.2788 ] Network output: [ -0.006687 -0.01107 1.039 0.0001239 -5.563e-05 0.9857 9.339e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07838 0.0776 0.1647 0.2021 0.9852 0.9913 0.07839 0.7417 0.8693 0.246 ] Network output: [ -0.005164 0.9916 0.01677 1.601e-05 -7.187e-06 1.002 1.206e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00899 Epoch 5564 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01907 0.9715 0.9734 -3.029e-05 1.36e-05 0.01692 -2.283e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002842 -0.002418 -0.01075 0.00835 0.9692 0.9737 0.005372 0.8455 0.8373 0.02215 ] Network output: [ 0.9997 -0.08058 0.009642 3.726e-06 -1.673e-06 0.07147 2.808e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1946 -0.001355 -0.209 0.2348 0.9834 0.9932 0.2169 0.4869 0.8853 0.7439 ] Network output: [ -0.009133 0.9896 0.9987 -3.833e-05 1.721e-05 0.02982 -2.889e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004155 0.001441 0.003551 0.005653 0.9892 0.9922 0.00423 0.8821 0.9094 0.01537 ] Network output: [ 0.02148 -0.1605 0.9354 -0.0001386 6.222e-05 1.182 -0.0001045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2064 0.1455 0.3041 0.2199 0.985 0.994 0.207 0.4928 0.8914 0.7371 ] Network output: [ 0.0007577 -0.01149 1.051 0.0001271 -5.705e-05 0.9598 9.577e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0693 0.06548 0.16 0.2055 0.9877 0.9922 0.06935 0.826 0.8931 0.2858 ] Network output: [ -0.0112 0.01296 1.039 0.0001163 -5.221e-05 0.971 8.764e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07963 0.07887 0.1682 0.2038 0.9853 0.9913 0.07964 0.7468 0.8689 0.246 ] Network output: [ -0.0006347 1.018 0.003477 1.614e-05 -7.247e-06 0.98 1.217e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01229 Epoch 5565 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01673 1 0.9723 -3.676e-05 1.65e-05 -0.00638 -2.77e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002876 -0.00242 -0.01084 0.00776 0.9692 0.9737 0.00543 0.8461 0.836 0.02196 ] Network output: [ 0.9787 0.1286 0.001508 -4.457e-05 2.001e-05 -0.08765 -3.359e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1976 0.001452 -0.2167 0.199 0.9834 0.9932 0.2203 0.4916 0.884 0.7411 ] Network output: [ -0.008932 0.9973 0.998 -3.969e-05 1.782e-05 0.02238 -2.991e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004212 0.001425 0.003087 0.004604 0.9892 0.9922 0.004287 0.8826 0.9089 0.01514 ] Network output: [ 0.0008666 0.1153 0.9206 -0.0002027 9.1e-05 0.9615 -0.0001528 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2089 0.1463 0.2878 0.1668 0.985 0.994 0.2095 0.4963 0.8916 0.7392 ] Network output: [ 0.006328 0.02661 1.041 0.0001266 -5.685e-05 0.9199 9.544e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06824 0.06434 0.1495 0.1936 0.9877 0.9922 0.06828 0.8226 0.8932 0.2795 ] Network output: [ -0.006713 -0.01152 1.039 0.0001238 -5.557e-05 0.9864 9.329e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07851 0.07772 0.1649 0.2025 0.9852 0.9913 0.07852 0.742 0.8695 0.2464 ] Network output: [ -0.004972 0.9918 0.01634 1.618e-05 -7.264e-06 1.002 1.219e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.008367 Epoch 5566 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01891 0.9727 0.9734 -3.019e-05 1.355e-05 0.01601 -2.275e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002844 -0.002421 -0.01078 0.008349 0.9692 0.9737 0.005375 0.8457 0.8374 0.02217 ] Network output: [ 0.9997 -0.07611 0.009095 3.46e-06 -1.553e-06 0.06763 2.608e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1943 -0.001501 -0.2096 0.2341 0.9834 0.9932 0.2166 0.4871 0.8853 0.7443 ] Network output: [ -0.009305 0.9903 0.9988 -3.827e-05 1.718e-05 0.02933 -2.884e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004158 0.001434 0.003544 0.005633 0.9892 0.9922 0.004232 0.8823 0.9095 0.0154 ] Network output: [ 0.02046 -0.1533 0.9361 -0.0001415 6.354e-05 1.176 -0.0001067 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.206 0.145 0.3039 0.2185 0.985 0.994 0.2067 0.493 0.8915 0.7377 ] Network output: [ 0.001082 -0.01174 1.05 0.0001271 -5.706e-05 0.96 9.579e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06941 0.06556 0.1599 0.2056 0.9877 0.9922 0.06945 0.8261 0.8932 0.2862 ] Network output: [ -0.01105 0.01151 1.039 0.0001165 -5.229e-05 0.9723 8.778e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07971 0.07894 0.1682 0.2041 0.9853 0.9914 0.07972 0.7469 0.8691 0.2464 ] Network output: [ -0.0007062 1.017 0.003643 1.622e-05 -7.282e-06 0.9805 1.222e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01141 Epoch 5567 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01669 1 0.9724 -3.634e-05 1.631e-05 -0.006282 -2.739e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002876 -0.002423 -0.01086 0.007784 0.9692 0.9737 0.00543 0.8463 0.8361 0.02199 ] Network output: [ 0.9795 0.1242 0.001355 -4.259e-05 1.912e-05 -0.08475 -3.209e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1972 0.001164 -0.2171 0.1998 0.9834 0.9932 0.2198 0.4916 0.8841 0.7416 ] Network output: [ -0.009099 0.9977 0.9981 -3.955e-05 1.776e-05 0.0222 -2.981e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004212 0.001418 0.003096 0.004626 0.9892 0.9922 0.004287 0.8827 0.909 0.01517 ] Network output: [ 0.0008576 0.111 0.9218 -0.0002027 9.102e-05 0.9647 -0.0001528 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2084 0.1457 0.2882 0.1676 0.985 0.994 0.2091 0.4963 0.8917 0.7397 ] Network output: [ 0.006467 0.02497 1.041 0.0001267 -5.688e-05 0.9216 9.549e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06839 0.06446 0.1498 0.1941 0.9877 0.9922 0.06843 0.8228 0.8933 0.2802 ] Network output: [ -0.006731 -0.0119 1.039 0.0001237 -5.551e-05 0.9871 9.319e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07864 0.07785 0.1651 0.2029 0.9852 0.9913 0.07865 0.7424 0.8696 0.2468 ] Network output: [ -0.004781 0.992 0.01591 1.638e-05 -7.356e-06 1.002 1.235e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.007864 Epoch 5568 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01876 0.9737 0.9734 -3.007e-05 1.35e-05 0.01521 -2.266e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002845 -0.002423 -0.0108 0.00835 0.9692 0.9737 0.005378 0.8459 0.8375 0.0222 ] Network output: [ 0.9997 -0.07266 0.008656 3.388e-06 -1.521e-06 0.06459 2.554e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.194 -0.001669 -0.2103 0.2336 0.9834 0.9932 0.2163 0.4872 0.8854 0.7447 ] Network output: [ -0.009478 0.9911 0.9989 -3.821e-05 1.716e-05 0.02886 -2.88e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00416 0.001427 0.003538 0.005617 0.9892 0.9922 0.004235 0.8824 0.9095 0.01542 ] Network output: [ 0.01957 -0.1473 0.9368 -0.0001442 6.473e-05 1.171 -0.0001087 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2057 0.1444 0.3037 0.2173 0.985 0.994 0.2063 0.4932 0.8916 0.7382 ] Network output: [ 0.001373 -0.012 1.049 0.0001271 -5.706e-05 0.9603 9.579e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06953 0.06564 0.1598 0.2057 0.9877 0.9922 0.06957 0.8261 0.8933 0.2867 ] Network output: [ -0.01093 0.01032 1.038 0.0001166 -5.235e-05 0.9735 8.788e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07979 0.07902 0.1683 0.2044 0.9853 0.9914 0.0798 0.7471 0.8692 0.2468 ] Network output: [ -0.0007369 1.017 0.003721 1.634e-05 -7.336e-06 0.9809 1.232e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01071 Epoch 5569 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01663 1 0.9724 -3.596e-05 1.615e-05 -0.006272 -2.71e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002876 -0.002425 -0.01088 0.007805 0.9692 0.9737 0.00543 0.8464 0.8363 0.02202 ] Network output: [ 0.9803 0.1204 0.001246 -4.083e-05 1.833e-05 -0.08233 -3.077e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1968 0.0008794 -0.2174 0.2005 0.9834 0.9932 0.2193 0.4917 0.8843 0.7422 ] Network output: [ -0.009265 0.9981 0.9982 -3.943e-05 1.77e-05 0.02199 -2.972e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004212 0.00141 0.003104 0.004644 0.9892 0.9922 0.004287 0.8829 0.9091 0.0152 ] Network output: [ 0.0008043 0.1076 0.923 -0.000203 9.114e-05 0.967 -0.000153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.208 0.1451 0.2885 0.1682 0.985 0.994 0.2086 0.4964 0.8918 0.7402 ] Network output: [ 0.006615 0.02361 1.041 0.0001267 -5.69e-05 0.923 9.551e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06855 0.06459 0.15 0.1946 0.9877 0.9922 0.06859 0.8229 0.8933 0.2808 ] Network output: [ -0.006742 -0.01223 1.038 0.0001235 -5.546e-05 0.9877 9.31e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07876 0.07797 0.1653 0.2032 0.9852 0.9913 0.07877 0.7427 0.8698 0.2472 ] Network output: [ -0.004593 0.9921 0.01551 1.661e-05 -7.459e-06 1.002 1.252e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.007461 Epoch 5570 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0186 0.9747 0.9734 -2.994e-05 1.344e-05 0.0145 -2.256e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002846 -0.002426 -0.01082 0.008354 0.9692 0.9737 0.00538 0.846 0.8376 0.02222 ] Network output: [ 0.9998 -0.07011 0.008315 3.476e-06 -1.56e-06 0.06228 2.62e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1937 -0.001855 -0.2108 0.2332 0.9835 0.9932 0.2159 0.4874 0.8855 0.7452 ] Network output: [ -0.009651 0.9917 0.999 -3.815e-05 1.713e-05 0.02841 -2.875e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004162 0.00142 0.003533 0.005605 0.9892 0.9922 0.004237 0.8826 0.9096 0.01544 ] Network output: [ 0.01881 -0.1425 0.9376 -0.0001465 6.579e-05 1.167 -0.0001104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2053 0.1439 0.3035 0.2164 0.985 0.994 0.2059 0.4933 0.8917 0.7387 ] Network output: [ 0.001632 -0.01228 1.049 0.0001271 -5.706e-05 0.9606 9.578e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06965 0.06574 0.1598 0.2059 0.9877 0.9922 0.06969 0.8262 0.8934 0.2871 ] Network output: [ -0.01084 0.009357 1.038 0.0001167 -5.239e-05 0.9746 8.794e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07989 0.07911 0.1684 0.2047 0.9853 0.9914 0.0799 0.7473 0.8694 0.2472 ] Network output: [ -0.0007348 1.017 0.003723 1.649e-05 -7.405e-06 0.9812 1.243e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01016 Epoch 5571 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01655 1.001 0.9725 -3.562e-05 1.599e-05 -0.006337 -2.685e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002876 -0.002428 -0.01089 0.007824 0.9692 0.9737 0.005431 0.8466 0.8365 0.02205 ] Network output: [ 0.9809 0.1172 0.001173 -3.929e-05 1.764e-05 -0.08035 -2.961e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1964 0.0006008 -0.2178 0.2011 0.9834 0.9932 0.2189 0.4917 0.8844 0.7427 ] Network output: [ -0.009429 0.9986 0.9984 -3.932e-05 1.765e-05 0.02174 -2.963e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004212 0.001403 0.00311 0.004659 0.9892 0.9922 0.004287 0.883 0.9092 0.01523 ] Network output: [ 0.0007133 0.1051 0.9241 -0.0002035 9.135e-05 0.9686 -0.0001533 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2075 0.1445 0.2887 0.1687 0.985 0.994 0.2081 0.4964 0.8919 0.7406 ] Network output: [ 0.00677 0.02249 1.04 0.0001268 -5.69e-05 0.9242 9.552e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0687 0.06471 0.1502 0.1951 0.9878 0.9922 0.06874 0.823 0.8934 0.2814 ] Network output: [ -0.006745 -0.01251 1.038 0.0001234 -5.541e-05 0.9883 9.302e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07889 0.07809 0.1654 0.2035 0.9852 0.9913 0.0789 0.7431 0.8699 0.2475 ] Network output: [ -0.004412 0.9921 0.01513 1.686e-05 -7.57e-06 1.002 1.271e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.007144 Epoch 5572 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01846 0.9756 0.9735 -2.978e-05 1.337e-05 0.01388 -2.245e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002847 -0.002429 -0.01084 0.008359 0.9692 0.9737 0.005382 0.8462 0.8377 0.02224 ] Network output: [ 0.9999 -0.06838 0.008061 3.693e-06 -1.658e-06 0.06062 2.783e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1934 -0.002056 -0.2113 0.233 0.9835 0.9932 0.2156 0.4876 0.8856 0.7456 ] Network output: [ -0.009822 0.9924 0.9991 -3.809e-05 1.71e-05 0.02798 -2.87e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004164 0.001412 0.003531 0.005597 0.9892 0.9922 0.004239 0.8827 0.9097 0.01547 ] Network output: [ 0.01815 -0.1388 0.9384 -0.0001486 6.673e-05 1.163 -0.000112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2049 0.1433 0.3034 0.2156 0.9851 0.994 0.2055 0.4935 0.8918 0.7392 ] Network output: [ 0.001864 -0.01257 1.048 0.0001271 -5.704e-05 0.961 9.575e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06978 0.06584 0.1598 0.2061 0.9877 0.9922 0.06982 0.8262 0.8934 0.2875 ] Network output: [ -0.01077 0.008599 1.038 0.0001167 -5.241e-05 0.9754 8.798e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07999 0.0792 0.1685 0.205 0.9853 0.9914 0.08 0.7475 0.8696 0.2475 ] Network output: [ -0.000706 1.016 0.003661 1.667e-05 -7.485e-06 0.9815 1.256e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009737 Epoch 5573 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01647 1.001 0.9726 -3.53e-05 1.585e-05 -0.006469 -2.661e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002876 -0.002431 -0.01091 0.007841 0.9692 0.9737 0.005432 0.8468 0.8366 0.02208 ] Network output: [ 0.9814 0.1147 0.001131 -3.796e-05 1.704e-05 -0.07879 -2.861e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1959 0.0003293 -0.2182 0.2016 0.9835 0.9932 0.2184 0.4918 0.8846 0.7431 ] Network output: [ -0.009589 0.9991 0.9985 -3.921e-05 1.76e-05 0.02147 -2.955e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004213 0.001395 0.003115 0.00467 0.9892 0.9922 0.004288 0.8831 0.9093 0.01526 ] Network output: [ 0.0005899 0.1034 0.9251 -0.0002041 9.163e-05 0.9695 -0.0001538 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2071 0.1439 0.2888 0.1689 0.9851 0.994 0.2077 0.4965 0.892 0.741 ] Network output: [ 0.006932 0.0216 1.04 0.0001267 -5.69e-05 0.9252 9.552e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06885 0.06484 0.1503 0.1954 0.9878 0.9922 0.0689 0.8232 0.8935 0.2819 ] Network output: [ -0.006742 -0.01275 1.038 0.0001233 -5.536e-05 0.9889 9.294e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07902 0.07821 0.1656 0.2038 0.9852 0.9913 0.07903 0.7433 0.8701 0.2478 ] Network output: [ -0.004241 0.9921 0.01478 1.712e-05 -7.686e-06 1.002 1.29e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.006901 Epoch 5574 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01832 0.9763 0.9736 -2.961e-05 1.329e-05 0.01335 -2.232e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002848 -0.002432 -0.01085 0.008366 0.9692 0.9737 0.005384 0.8464 0.8379 0.02227 ] Network output: [ 1 -0.06736 0.007884 4.015e-06 -1.803e-06 0.05955 3.026e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.193 -0.002267 -0.2118 0.2329 0.9835 0.9932 0.2152 0.4877 0.8857 0.746 ] Network output: [ -0.009988 0.993 0.9992 -3.801e-05 1.706e-05 0.02758 -2.864e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004166 0.001405 0.00353 0.005592 0.9892 0.9922 0.004241 0.8829 0.9098 0.01549 ] Network output: [ 0.0176 -0.136 0.9392 -0.0001505 6.756e-05 1.161 -0.0001134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2045 0.1428 0.3034 0.215 0.9851 0.994 0.2052 0.4936 0.8919 0.7397 ] Network output: [ 0.002069 -0.0129 1.048 0.000127 -5.702e-05 0.9614 9.572e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06992 0.06594 0.1598 0.2063 0.9877 0.9922 0.06996 0.8263 0.8935 0.288 ] Network output: [ -0.01072 0.008024 1.038 0.0001167 -5.241e-05 0.9762 8.798e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0801 0.07931 0.1686 0.2052 0.9853 0.9914 0.08011 0.7477 0.8698 0.2478 ] Network output: [ -0.0006551 1.016 0.003545 1.687e-05 -7.573e-06 0.9817 1.271e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009418 Epoch 5575 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01637 1.001 0.9726 -3.501e-05 1.572e-05 -0.006661 -2.638e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002876 -0.002433 -0.01093 0.007856 0.9692 0.9737 0.005433 0.8469 0.8368 0.0221 ] Network output: [ 0.9818 0.1127 0.001115 -3.683e-05 1.653e-05 -0.07761 -2.775e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1955 6.604e-05 -0.2185 0.202 0.9835 0.9932 0.2179 0.4918 0.8847 0.7436 ] Network output: [ -0.009742 0.9996 0.9986 -3.909e-05 1.755e-05 0.02118 -2.946e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004214 0.001387 0.003118 0.004678 0.9892 0.9922 0.004289 0.8833 0.9094 0.01528 ] Network output: [ 0.0004382 0.1024 0.9261 -0.0002049 9.198e-05 0.9698 -0.0001544 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2066 0.1433 0.2889 0.1691 0.9851 0.994 0.2073 0.4966 0.8921 0.7414 ] Network output: [ 0.0071 0.0209 1.039 0.0001267 -5.689e-05 0.9261 9.55e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06901 0.06496 0.1505 0.1958 0.9878 0.9922 0.06905 0.8232 0.8936 0.2824 ] Network output: [ -0.006733 -0.01295 1.038 0.0001232 -5.532e-05 0.9894 9.287e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07914 0.07833 0.1657 0.204 0.9852 0.9913 0.07915 0.7436 0.8702 0.2481 ] Network output: [ -0.004081 0.992 0.01445 1.739e-05 -7.806e-06 1.002 1.31e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.006722 Epoch 5576 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01818 0.977 0.9736 -2.942e-05 1.321e-05 0.01289 -2.217e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002849 -0.002434 -0.01087 0.008374 0.9692 0.9737 0.005386 0.8465 0.838 0.02229 ] Network output: [ 1 -0.06701 0.007777 4.422e-06 -1.985e-06 0.05902 3.333e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1927 -0.002487 -0.2122 0.2329 0.9835 0.9932 0.2148 0.4878 0.8858 0.7463 ] Network output: [ -0.01015 0.9936 0.9993 -3.792e-05 1.702e-05 0.02719 -2.858e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004168 0.001397 0.00353 0.00559 0.9892 0.9922 0.004243 0.883 0.9099 0.01551 ] Network output: [ 0.01714 -0.134 0.9401 -0.0001521 6.829e-05 1.159 -0.0001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2041 0.1422 0.3034 0.2146 0.9851 0.994 0.2048 0.4938 0.892 0.7401 ] Network output: [ 0.00225 -0.01326 1.047 0.0001269 -5.699e-05 0.9619 9.567e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07006 0.06606 0.1598 0.2065 0.9877 0.9922 0.0701 0.8263 0.8936 0.2884 ] Network output: [ -0.01069 0.007617 1.037 0.0001167 -5.24e-05 0.9768 8.797e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08021 0.07942 0.1687 0.2055 0.9853 0.9914 0.08022 0.748 0.8699 0.2481 ] Network output: [ -0.0005856 1.016 0.003381 1.708e-05 -7.667e-06 0.9819 1.287e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009194 Epoch 5577 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01627 1.001 0.9727 -3.473e-05 1.559e-05 -0.006907 -2.617e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002877 -0.002436 -0.01094 0.007868 0.9692 0.9737 0.005434 0.8471 0.8369 0.02212 ] Network output: [ 0.9822 0.1112 0.001119 -3.589e-05 1.611e-05 -0.0768 -2.705e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1952 -0.0001878 -0.2189 0.2023 0.9835 0.9932 0.2175 0.4919 0.8848 0.7439 ] Network output: [ -0.00989 1 0.9987 -3.897e-05 1.749e-05 0.02086 -2.937e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004216 0.00138 0.003121 0.004684 0.9892 0.9922 0.004291 0.8834 0.9095 0.0153 ] Network output: [ 0.0002614 0.1022 0.927 -0.0002058 9.239e-05 0.9695 -0.0001551 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2062 0.1427 0.289 0.1691 0.9851 0.994 0.2068 0.4967 0.8923 0.7418 ] Network output: [ 0.007274 0.02039 1.039 0.0001267 -5.688e-05 0.9267 9.548e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06917 0.06509 0.1506 0.196 0.9878 0.9922 0.06921 0.8233 0.8937 0.2828 ] Network output: [ -0.006717 -0.01312 1.037 0.0001232 -5.529e-05 0.9898 9.281e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07927 0.07845 0.1658 0.2043 0.9852 0.9913 0.07928 0.7438 0.8704 0.2483 ] Network output: [ -0.003932 0.9919 0.01415 1.766e-05 -7.927e-06 1.002 1.331e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.006602 Epoch 5578 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01806 0.9775 0.9737 -2.921e-05 1.311e-05 0.01251 -2.201e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00285 -0.002437 -0.01089 0.008384 0.9692 0.9737 0.005388 0.8467 0.8381 0.02231 ] Network output: [ 1 -0.06726 0.007731 4.897e-06 -2.199e-06 0.05898 3.691e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1923 -0.002712 -0.2125 0.233 0.9835 0.9932 0.2144 0.4879 0.886 0.7467 ] Network output: [ -0.0103 0.9942 0.9994 -3.781e-05 1.697e-05 0.02683 -2.849e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00417 0.00139 0.003531 0.005592 0.9892 0.9922 0.004245 0.8831 0.91 0.01553 ] Network output: [ 0.01677 -0.1329 0.9409 -0.0001535 6.892e-05 1.158 -0.0001157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2038 0.1416 0.3034 0.2144 0.9851 0.994 0.2044 0.4939 0.8921 0.7404 ] Network output: [ 0.002407 -0.01365 1.047 0.0001269 -5.696e-05 0.9625 9.563e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07021 0.06618 0.1599 0.2067 0.9877 0.9922 0.07026 0.8264 0.8937 0.2888 ] Network output: [ -0.01068 0.007363 1.037 0.0001167 -5.238e-05 0.9773 8.793e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08034 0.07953 0.1688 0.2057 0.9854 0.9914 0.08035 0.7482 0.8701 0.2483 ] Network output: [ -0.0005002 1.016 0.003177 1.73e-05 -7.766e-06 0.982 1.304e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009055 Epoch 5579 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01617 1.002 0.9728 -3.446e-05 1.547e-05 -0.007203 -2.597e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002877 -0.002439 -0.01096 0.007879 0.9692 0.9737 0.005435 0.8472 0.8371 0.02214 ] Network output: [ 0.9824 0.1102 0.001141 -3.514e-05 1.578e-05 -0.07634 -2.648e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1948 -0.0004315 -0.2192 0.2025 0.9835 0.9932 0.2171 0.4919 0.8849 0.7443 ] Network output: [ -0.01003 1.001 0.9988 -3.884e-05 1.744e-05 0.02053 -2.927e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004218 0.001372 0.003122 0.004687 0.9892 0.9922 0.004293 0.8835 0.9096 0.01532 ] Network output: [ 6.221e-05 0.1025 0.9278 -0.0002068 9.284e-05 0.9688 -0.0001559 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2058 0.1422 0.289 0.169 0.9851 0.994 0.2064 0.4968 0.8924 0.7421 ] Network output: [ 0.007453 0.02004 1.038 0.0001267 -5.686e-05 0.9272 9.545e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06933 0.06521 0.1506 0.1963 0.9878 0.9922 0.06937 0.8234 0.8937 0.2832 ] Network output: [ -0.006696 -0.01326 1.037 0.0001231 -5.526e-05 0.9903 9.276e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0794 0.07858 0.1659 0.2045 0.9853 0.9913 0.07941 0.7441 0.8705 0.2486 ] Network output: [ -0.003794 0.9917 0.01387 1.793e-05 -8.048e-06 1.002 1.351e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.006537 Epoch 5580 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01794 0.978 0.9738 -2.897e-05 1.301e-05 0.01219 -2.183e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002851 -0.00244 -0.0109 0.008394 0.9692 0.9737 0.00539 0.8468 0.8383 0.02233 ] Network output: [ 1 -0.06806 0.007742 5.426e-06 -2.436e-06 0.05939 4.089e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.192 -0.002942 -0.2128 0.2331 0.9835 0.9932 0.214 0.488 0.8861 0.747 ] Network output: [ -0.01045 0.9947 0.9996 -3.769e-05 1.692e-05 0.0265 -2.84e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004172 0.001383 0.003534 0.005596 0.9892 0.9922 0.004247 0.8832 0.9101 0.01555 ] Network output: [ 0.01648 -0.1325 0.9418 -0.0001547 6.946e-05 1.157 -0.0001166 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2034 0.1411 0.3035 0.2143 0.9851 0.994 0.204 0.4939 0.8922 0.7408 ] Network output: [ 0.002542 -0.01409 1.046 0.0001268 -5.693e-05 0.9631 9.557e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07037 0.0663 0.16 0.207 0.9877 0.9922 0.07042 0.8264 0.8938 0.2892 ] Network output: [ -0.01068 0.007252 1.037 0.0001166 -5.235e-05 0.9778 8.788e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08047 0.07966 0.1689 0.2059 0.9854 0.9914 0.08048 0.7484 0.8702 0.2486 ] Network output: [ -0.0004005 1.016 0.002936 1.752e-05 -7.867e-06 0.9821 1.321e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.008994 Epoch 5581 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01607 1.002 0.9729 -3.421e-05 1.536e-05 -0.007545 -2.578e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002878 -0.002442 -0.01097 0.007888 0.9692 0.9737 0.005437 0.8474 0.8372 0.02216 ] Network output: [ 0.9826 0.1097 0.001177 -3.458e-05 1.552e-05 -0.07621 -2.606e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1944 -0.0006643 -0.2196 0.2026 0.9835 0.9932 0.2167 0.492 0.8851 0.7446 ] Network output: [ -0.01016 1.001 0.9989 -3.87e-05 1.737e-05 0.02019 -2.916e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00422 0.001365 0.003122 0.004688 0.9892 0.9922 0.004295 0.8836 0.9096 0.01534 ] Network output: [ -0.0001575 0.1035 0.9285 -0.0002079 9.334e-05 0.9675 -0.0001567 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2054 0.1416 0.289 0.1687 0.9851 0.994 0.2061 0.4968 0.8925 0.7424 ] Network output: [ 0.007636 0.01984 1.038 0.0001266 -5.684e-05 0.9276 9.541e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06949 0.06534 0.1507 0.1965 0.9878 0.9922 0.06953 0.8234 0.8938 0.2835 ] Network output: [ -0.006668 -0.01338 1.037 0.000123 -5.523e-05 0.9906 9.272e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07953 0.0787 0.166 0.2047 0.9853 0.9913 0.07954 0.7442 0.8707 0.2488 ] Network output: [ -0.003669 0.9914 0.01363 1.819e-05 -8.168e-06 1.002 1.371e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.006522 Epoch 5582 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01783 0.9783 0.9739 -2.872e-05 1.289e-05 0.01195 -2.164e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002851 -0.002443 -0.01091 0.008406 0.9692 0.9737 0.005392 0.847 0.8384 0.02235 ] Network output: [ 1.001 -0.06938 0.007803 5.998e-06 -2.693e-06 0.06023 4.52e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1916 -0.003174 -0.2131 0.2334 0.9835 0.9932 0.2136 0.488 0.8862 0.7473 ] Network output: [ -0.01059 0.9952 0.9997 -3.755e-05 1.686e-05 0.02619 -2.83e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004175 0.001376 0.003539 0.005603 0.9892 0.9922 0.004249 0.8833 0.9102 0.01558 ] Network output: [ 0.01626 -0.1329 0.9426 -0.0001557 6.99e-05 1.157 -0.0001173 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.203 0.1406 0.3036 0.2143 0.9851 0.994 0.2036 0.494 0.8923 0.7411 ] Network output: [ 0.002655 -0.01457 1.046 0.0001267 -5.69e-05 0.9638 9.552e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07054 0.06644 0.1602 0.2073 0.9877 0.9922 0.07058 0.8265 0.8938 0.2896 ] Network output: [ -0.0107 0.007275 1.037 0.0001165 -5.23e-05 0.9781 8.78e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08061 0.07979 0.1691 0.2061 0.9854 0.9914 0.08061 0.7486 0.8703 0.2488 ] Network output: [ -0.0002878 1.016 0.002663 1.776e-05 -7.971e-06 0.9821 1.338e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009007 Epoch 5583 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01596 1.003 0.973 -3.396e-05 1.525e-05 -0.007931 -2.559e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002879 -0.002445 -0.01098 0.007895 0.9692 0.9737 0.005439 0.8475 0.8373 0.02218 ] Network output: [ 0.9827 0.1097 0.001223 -3.419e-05 1.535e-05 -0.0764 -2.577e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1941 -0.0008855 -0.2199 0.2026 0.9835 0.9932 0.2163 0.492 0.8852 0.7449 ] Network output: [ -0.01029 1.002 0.999 -3.855e-05 1.731e-05 0.01983 -2.905e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004222 0.001358 0.003122 0.004686 0.9892 0.9922 0.004298 0.8837 0.9097 0.01536 ] Network output: [ -0.0003961 0.105 0.9292 -0.0002091 9.388e-05 0.9658 -0.0001576 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2051 0.1411 0.2889 0.1684 0.9851 0.994 0.2057 0.4968 0.8925 0.7427 ] Network output: [ 0.007824 0.01978 1.037 0.0001266 -5.681e-05 0.9278 9.537e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06965 0.06547 0.1507 0.1966 0.9878 0.9922 0.06969 0.8234 0.8939 0.2838 ] Network output: [ -0.006636 -0.01346 1.036 0.000123 -5.521e-05 0.991 9.269e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07967 0.07883 0.166 0.2048 0.9853 0.9913 0.07968 0.7444 0.8708 0.249 ] Network output: [ -0.003554 0.9912 0.01341 1.846e-05 -8.286e-06 1.003 1.391e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.006557 Epoch 5584 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01773 0.9786 0.974 -2.844e-05 1.277e-05 0.01176 -2.144e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002852 -0.002446 -0.01092 0.008418 0.9692 0.9737 0.005394 0.8471 0.8385 0.02237 ] Network output: [ 1.001 -0.07117 0.00791 6.602e-06 -2.964e-06 0.06146 4.976e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1913 -0.003407 -0.2133 0.2338 0.9835 0.9932 0.2132 0.488 0.8863 0.7476 ] Network output: [ -0.01073 0.9956 0.9998 -3.738e-05 1.678e-05 0.0259 -2.817e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004177 0.00137 0.003545 0.005612 0.9892 0.9922 0.004251 0.8834 0.9102 0.0156 ] Network output: [ 0.01612 -0.134 0.9435 -0.0001565 7.026e-05 1.158 -0.0001179 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2026 0.14 0.3038 0.2145 0.9851 0.994 0.2033 0.494 0.8924 0.7413 ] Network output: [ 0.002746 -0.01509 1.046 0.0001267 -5.687e-05 0.9645 9.547e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07071 0.06658 0.1603 0.2076 0.9877 0.9922 0.07076 0.8265 0.8939 0.29 ] Network output: [ -0.01073 0.007424 1.036 0.0001164 -5.225e-05 0.9783 8.772e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08075 0.07993 0.1692 0.2063 0.9854 0.9914 0.08076 0.7488 0.8705 0.249 ] Network output: [ -0.0001626 1.016 0.00236 1.799e-05 -8.076e-06 0.9821 1.356e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009092 Epoch 5585 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01585 1.003 0.9731 -3.373e-05 1.514e-05 -0.008359 -2.542e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00288 -0.002448 -0.01099 0.007901 0.9692 0.9737 0.005442 0.8476 0.8374 0.0222 ] Network output: [ 0.9827 0.1101 0.001278 -3.398e-05 1.526e-05 -0.0769 -2.561e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1938 -0.001095 -0.2201 0.2026 0.9835 0.9932 0.216 0.4921 0.8852 0.7451 ] Network output: [ -0.0104 1.002 0.9991 -3.838e-05 1.723e-05 0.01946 -2.893e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004225 0.001351 0.003121 0.004683 0.9892 0.9922 0.004301 0.8837 0.9098 0.01538 ] Network output: [ -0.000652 0.1071 0.9298 -0.0002104 9.446e-05 0.9636 -0.0001586 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2047 0.1406 0.2888 0.168 0.9851 0.994 0.2054 0.4969 0.8926 0.743 ] Network output: [ 0.008016 0.01986 1.037 0.0001265 -5.679e-05 0.9279 9.533e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06981 0.0656 0.1507 0.1968 0.9878 0.9922 0.06985 0.8234 0.8939 0.2841 ] Network output: [ -0.006597 -0.01352 1.036 0.000123 -5.52e-05 0.9913 9.266e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0798 0.07896 0.1661 0.205 0.9853 0.9913 0.07981 0.7445 0.8709 0.2491 ] Network output: [ -0.003452 0.9909 0.01322 1.871e-05 -8.401e-06 1.003 1.41e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.006641 Epoch 5586 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01764 0.9788 0.9741 -2.815e-05 1.264e-05 0.01164 -2.121e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002853 -0.002449 -0.01093 0.008432 0.9692 0.9737 0.005395 0.8472 0.8386 0.02239 ] Network output: [ 1.001 -0.07342 0.008061 7.232e-06 -3.247e-06 0.06306 5.45e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.191 -0.00364 -0.2134 0.2342 0.9835 0.9932 0.2129 0.4879 0.8864 0.7479 ] Network output: [ -0.01086 0.996 0.9999 -3.721e-05 1.67e-05 0.02564 -2.804e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004179 0.001363 0.003552 0.005624 0.9892 0.9922 0.004254 0.8834 0.9103 0.01562 ] Network output: [ 0.01605 -0.1357 0.9443 -0.0001571 7.053e-05 1.159 -0.0001184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2023 0.1395 0.304 0.2148 0.9851 0.994 0.2029 0.4939 0.8925 0.7415 ] Network output: [ 0.002817 -0.01566 1.045 0.0001266 -5.684e-05 0.9653 9.541e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0709 0.06673 0.1605 0.2079 0.9877 0.9922 0.07094 0.8266 0.8939 0.2904 ] Network output: [ -0.01078 0.007694 1.036 0.0001163 -5.219e-05 0.9784 8.762e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0809 0.08008 0.1693 0.2064 0.9854 0.9914 0.08091 0.749 0.8706 0.2491 ] Network output: [ -2.533e-05 1.016 0.00203 1.823e-05 -8.182e-06 0.982 1.374e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009248 Epoch 5587 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01574 1.004 0.9732 -3.35e-05 1.504e-05 -0.008828 -2.524e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002881 -0.002451 -0.01101 0.007905 0.9692 0.9737 0.005444 0.8477 0.8375 0.02221 ] Network output: [ 0.9826 0.111 0.00134 -3.395e-05 1.524e-05 -0.0777 -2.558e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1935 -0.001291 -0.2204 0.2025 0.9835 0.9932 0.2157 0.4921 0.8853 0.7453 ] Network output: [ -0.01051 1.003 0.9992 -3.821e-05 1.715e-05 0.01908 -2.879e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004229 0.001344 0.003119 0.004677 0.9892 0.9922 0.004304 0.8838 0.9098 0.0154 ] Network output: [ -0.0009239 0.1096 0.9303 -0.0002118 9.506e-05 0.9611 -0.0001596 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2044 0.1401 0.2887 0.1675 0.9851 0.994 0.2051 0.4969 0.8927 0.7432 ] Network output: [ 0.00821 0.02006 1.036 0.0001264 -5.676e-05 0.9279 9.529e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.06998 0.06574 0.1507 0.1969 0.9878 0.9922 0.07002 0.8234 0.894 0.2844 ] Network output: [ -0.006554 -0.01356 1.036 0.0001229 -5.519e-05 0.9916 9.265e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.07994 0.07909 0.1661 0.2051 0.9853 0.9913 0.07995 0.7446 0.871 0.2493 ] Network output: [ -0.003361 0.9906 0.01305 1.896e-05 -8.512e-06 1.003 1.429e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.006773 Epoch 5588 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01756 0.9789 0.9743 -2.783e-05 1.25e-05 0.01157 -2.098e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002854 -0.002452 -0.01094 0.008446 0.9692 0.9737 0.005397 0.8473 0.8387 0.0224 ] Network output: [ 1.001 -0.07609 0.008252 7.879e-06 -3.537e-06 0.06502 5.938e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1906 -0.003872 -0.2135 0.2346 0.9835 0.9932 0.2125 0.4878 0.8865 0.7481 ] Network output: [ -0.01098 0.9964 1 -3.701e-05 1.661e-05 0.0254 -2.789e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004182 0.001357 0.00356 0.005639 0.9892 0.9922 0.004257 0.8835 0.9104 0.01564 ] Network output: [ 0.01604 -0.1381 0.9452 -0.0001575 7.072e-05 1.16 -0.0001187 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.202 0.1391 0.3042 0.2152 0.9851 0.994 0.2026 0.4938 0.8925 0.7417 ] Network output: [ 0.002868 -0.01627 1.045 0.0001265 -5.68e-05 0.9662 9.535e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07108 0.06689 0.1607 0.2082 0.9877 0.9922 0.07113 0.8266 0.894 0.2908 ] Network output: [ -0.01084 0.00808 1.036 0.0001161 -5.213e-05 0.9785 8.75e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08106 0.08023 0.1694 0.2066 0.9854 0.9914 0.08107 0.7491 0.8707 0.2493 ] Network output: [ 0.0001242 1.016 0.001675 1.847e-05 -8.29e-06 0.9819 1.392e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009475 Epoch 5589 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01563 1.005 0.9733 -3.327e-05 1.494e-05 -0.009336 -2.508e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002883 -0.002453 -0.01101 0.007907 0.9692 0.9737 0.005447 0.8478 0.8376 0.02222 ] Network output: [ 0.9825 0.1122 0.001407 -3.408e-05 1.53e-05 -0.0788 -2.568e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1932 -0.001475 -0.2207 0.2022 0.9835 0.9932 0.2154 0.4921 0.8854 0.7455 ] Network output: [ -0.01061 1.003 0.9993 -3.802e-05 1.707e-05 0.0187 -2.865e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004233 0.001338 0.003117 0.00467 0.9892 0.9922 0.004308 0.8838 0.9099 0.01541 ] Network output: [ -0.00121 0.1127 0.9308 -0.0002132 9.57e-05 0.9581 -0.0001606 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2042 0.1396 0.2885 0.1669 0.9851 0.994 0.2048 0.4968 0.8927 0.7434 ] Network output: [ 0.008408 0.02039 1.036 0.0001264 -5.674e-05 0.9278 9.524e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07015 0.06587 0.1506 0.1969 0.9878 0.9922 0.07019 0.8233 0.894 0.2846 ] Network output: [ -0.006506 -0.01356 1.035 0.0001229 -5.519e-05 0.9919 9.264e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08008 0.07923 0.1662 0.2052 0.9853 0.9913 0.08009 0.7446 0.8711 0.2494 ] Network output: [ -0.003281 0.9902 0.01292 1.92e-05 -8.62e-06 1.004 1.447e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.006955 Epoch 5590 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01748 0.979 0.9744 -2.75e-05 1.235e-05 0.01156 -2.073e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002854 -0.002455 -0.01095 0.00846 0.9692 0.9737 0.005399 0.8474 0.8388 0.02242 ] Network output: [ 1.002 -0.07917 0.008482 8.536e-06 -3.832e-06 0.06732 6.433e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1903 -0.004102 -0.2136 0.2352 0.9835 0.9932 0.2122 0.4877 0.8865 0.7483 ] Network output: [ -0.01109 0.9967 1 -3.679e-05 1.651e-05 0.02518 -2.772e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004185 0.001352 0.00357 0.005656 0.9892 0.9922 0.00426 0.8835 0.9104 0.01566 ] Network output: [ 0.0161 -0.141 0.946 -0.0001578 7.083e-05 1.162 -0.0001189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2016 0.1386 0.3045 0.2158 0.9851 0.994 0.2022 0.4937 0.8926 0.7419 ] Network output: [ 0.002899 -0.01694 1.045 0.0001265 -5.677e-05 0.9671 9.53e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07128 0.06706 0.161 0.2086 0.9877 0.9922 0.07132 0.8266 0.894 0.2912 ] Network output: [ -0.01091 0.008578 1.035 0.0001159 -5.205e-05 0.9784 8.738e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08123 0.08039 0.1696 0.2067 0.9854 0.9914 0.08124 0.7493 0.8708 0.2494 ] Network output: [ 0.0002861 1.016 0.001296 1.871e-05 -8.398e-06 0.9818 1.41e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009773 Epoch 5591 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01552 1.005 0.9734 -3.305e-05 1.484e-05 -0.009881 -2.491e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002884 -0.002456 -0.01102 0.007907 0.9692 0.9737 0.00545 0.8479 0.8376 0.02223 ] Network output: [ 0.9823 0.1139 0.001479 -3.438e-05 1.543e-05 -0.08017 -2.591e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.193 -0.001645 -0.2209 0.202 0.9835 0.9932 0.2151 0.492 0.8854 0.7456 ] Network output: [ -0.0107 1.004 0.9994 -3.781e-05 1.698e-05 0.0183 -2.85e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004237 0.001332 0.003114 0.004661 0.9892 0.9922 0.004312 0.8839 0.9099 0.01542 ] Network output: [ -0.00151 0.1162 0.9312 -0.0002146 9.635e-05 0.9548 -0.0001617 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2039 0.1392 0.2883 0.1662 0.9851 0.994 0.2045 0.4968 0.8928 0.7436 ] Network output: [ 0.008608 0.02083 1.035 0.0001263 -5.671e-05 0.9275 9.519e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07032 0.06601 0.1506 0.1969 0.9878 0.9922 0.07037 0.8232 0.894 0.2848 ] Network output: [ -0.006453 -0.01354 1.035 0.0001229 -5.519e-05 0.9921 9.264e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08023 0.07936 0.1662 0.2053 0.9853 0.9913 0.08024 0.7447 0.8712 0.2496 ] Network output: [ -0.003213 0.9899 0.01281 1.943e-05 -8.722e-06 1.004 1.464e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.007187 Epoch 5592 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01742 0.9789 0.9746 -2.715e-05 1.219e-05 0.01161 -2.046e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002855 -0.002457 -0.01096 0.008476 0.9692 0.9737 0.005401 0.8474 0.8389 0.02243 ] Network output: [ 1.002 -0.08262 0.008749 9.197e-06 -4.129e-06 0.06993 6.931e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.19 -0.004329 -0.2136 0.2358 0.9835 0.9932 0.2118 0.4875 0.8866 0.7485 ] Network output: [ -0.0112 0.997 1 -3.655e-05 1.641e-05 0.02499 -2.754e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004188 0.001347 0.003582 0.005676 0.9892 0.9922 0.004263 0.8835 0.9104 0.01568 ] Network output: [ 0.01622 -0.1446 0.9468 -0.0001578 7.086e-05 1.165 -0.000119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2013 0.1382 0.3047 0.2165 0.9851 0.994 0.2019 0.4936 0.8926 0.742 ] Network output: [ 0.00291 -0.01765 1.044 0.0001264 -5.674e-05 0.9681 9.524e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07148 0.06723 0.1612 0.2089 0.9877 0.9922 0.07153 0.8266 0.894 0.2916 ] Network output: [ -0.011 0.009185 1.035 0.0001158 -5.197e-05 0.9783 8.724e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0814 0.08056 0.1697 0.2069 0.9854 0.9914 0.08141 0.7494 0.8708 0.2496 ] Network output: [ 0.000461 1.017 0.0008952 1.895e-05 -8.508e-06 0.9816 1.428e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01014 Epoch 5593 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01541 1.006 0.9735 -3.284e-05 1.474e-05 -0.01046 -2.475e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002886 -0.002459 -0.01103 0.007906 0.9692 0.9737 0.005454 0.8479 0.8376 0.02224 ] Network output: [ 0.9821 0.1159 0.001554 -3.483e-05 1.564e-05 -0.08181 -2.625e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1928 -0.001802 -0.2211 0.2016 0.9835 0.9932 0.2148 0.4919 0.8854 0.7457 ] Network output: [ -0.01078 1.004 0.9995 -3.759e-05 1.688e-05 0.01789 -2.833e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004242 0.001326 0.003111 0.004651 0.9892 0.9922 0.004317 0.8839 0.9099 0.01543 ] Network output: [ -0.001821 0.1201 0.9315 -0.0002161 9.703e-05 0.9512 -0.0001629 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2037 0.1388 0.2881 0.1654 0.9851 0.994 0.2043 0.4967 0.8928 0.7437 ] Network output: [ 0.008809 0.02138 1.034 0.0001262 -5.668e-05 0.9272 9.515e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0705 0.06615 0.1505 0.1969 0.9878 0.9922 0.07054 0.8231 0.894 0.285 ] Network output: [ -0.006396 -0.01347 1.035 0.0001229 -5.519e-05 0.9923 9.265e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08037 0.0795 0.1662 0.2054 0.9853 0.9913 0.08038 0.7446 0.8712 0.2497 ] Network output: [ -0.003156 0.9895 0.01272 1.964e-05 -8.818e-06 1.004 1.48e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.007469 Epoch 5594 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01736 0.9788 0.9747 -2.678e-05 1.202e-05 0.0117 -2.018e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002856 -0.00246 -0.01096 0.008492 0.9692 0.9737 0.005403 0.8474 0.839 0.02245 ] Network output: [ 1.002 -0.08641 0.009051 9.854e-06 -4.424e-06 0.07283 7.427e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1897 -0.004551 -0.2135 0.2364 0.9835 0.9932 0.2115 0.4873 0.8866 0.7487 ] Network output: [ -0.01129 0.9972 1 -3.629e-05 1.629e-05 0.02481 -2.735e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004191 0.001342 0.003594 0.005698 0.9892 0.9922 0.004266 0.8835 0.9105 0.0157 ] Network output: [ 0.0164 -0.1487 0.9476 -0.0001577 7.082e-05 1.168 -0.0001189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.201 0.1378 0.3051 0.2173 0.9851 0.994 0.2017 0.4933 0.8926 0.7421 ] Network output: [ 0.002902 -0.0184 1.044 0.0001263 -5.67e-05 0.9691 9.519e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07169 0.06741 0.1615 0.2093 0.9877 0.9922 0.07174 0.8266 0.894 0.292 ] Network output: [ -0.0111 0.009896 1.035 0.0001156 -5.188e-05 0.9781 8.709e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08158 0.08074 0.1698 0.207 0.9854 0.9914 0.08159 0.7495 0.8709 0.2497 ] Network output: [ 0.0006491 1.017 0.0004748 1.92e-05 -8.619e-06 0.9815 1.447e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01059 Epoch 5595 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0153 1.007 0.9736 -3.263e-05 1.465e-05 -0.01108 -2.459e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002888 -0.002462 -0.01104 0.007904 0.9692 0.9737 0.005457 0.848 0.8377 0.02225 ] Network output: [ 0.9818 0.1183 0.001632 -3.544e-05 1.591e-05 -0.08369 -2.671e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1926 -0.001945 -0.2213 0.2012 0.9835 0.9932 0.2146 0.4918 0.8854 0.7457 ] Network output: [ -0.01086 1.004 0.9996 -3.736e-05 1.677e-05 0.01748 -2.816e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004247 0.001321 0.003108 0.004639 0.9892 0.9922 0.004322 0.8839 0.9099 0.01544 ] Network output: [ -0.00214 0.1244 0.9318 -0.0002177 9.772e-05 0.9472 -0.000164 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2035 0.1384 0.2878 0.1646 0.9851 0.994 0.2041 0.4965 0.8928 0.7438 ] Network output: [ 0.009011 0.02203 1.034 0.0001262 -5.665e-05 0.9267 9.51e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07068 0.0663 0.1504 0.1969 0.9878 0.9922 0.07072 0.823 0.894 0.2851 ] Network output: [ -0.006335 -0.01337 1.034 0.000123 -5.52e-05 0.9924 9.266e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08052 0.07965 0.1662 0.2054 0.9853 0.9913 0.08053 0.7446 0.8713 0.2498 ] Network output: [ -0.003112 0.9892 0.01266 1.984e-05 -8.908e-06 1.004 1.495e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.007803 Epoch 5596 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01731 0.9786 0.9749 -2.639e-05 1.185e-05 0.01184 -1.989e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002857 -0.002463 -0.01096 0.008508 0.9692 0.9737 0.005405 0.8475 0.839 0.02246 ] Network output: [ 1.003 -0.09052 0.009388 1.05e-05 -4.714e-06 0.07601 7.913e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1895 -0.004769 -0.2134 0.2371 0.9835 0.9932 0.2112 0.487 0.8867 0.7488 ] Network output: [ -0.01138 0.9974 1.001 -3.601e-05 1.616e-05 0.02466 -2.714e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004195 0.001338 0.003608 0.005722 0.9892 0.9922 0.00427 0.8835 0.9105 0.01572 ] Network output: [ 0.01664 -0.1533 0.9484 -0.0001575 7.07e-05 1.171 -0.0001187 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2008 0.1374 0.3054 0.2182 0.9851 0.994 0.2014 0.4931 0.8926 0.7421 ] Network output: [ 0.002876 -0.0192 1.044 0.0001262 -5.667e-05 0.9702 9.513e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07191 0.0676 0.1618 0.2097 0.9877 0.9922 0.07195 0.8266 0.894 0.2923 ] Network output: [ -0.01121 0.01071 1.034 0.0001154 -5.179e-05 0.9778 8.693e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08177 0.08092 0.17 0.2071 0.9854 0.9914 0.08178 0.7496 0.8709 0.2498 ] Network output: [ 0.0008509 1.017 3.687e-05 1.945e-05 -8.733e-06 0.9812 1.466e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01111 Epoch 5597 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01519 1.008 0.9737 -3.242e-05 1.456e-05 -0.01172 -2.444e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00289 -0.002465 -0.01104 0.0079 0.9692 0.9737 0.005461 0.848 0.8377 0.02225 ] Network output: [ 0.9814 0.121 0.001713 -3.62e-05 1.625e-05 -0.0858 -2.728e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1924 -0.002074 -0.2214 0.2007 0.9835 0.9932 0.2144 0.4917 0.8854 0.7457 ] Network output: [ -0.01093 1.005 0.9997 -3.711e-05 1.666e-05 0.01706 -2.797e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004252 0.001316 0.003104 0.004626 0.9892 0.9922 0.004328 0.8838 0.9099 0.01545 ] Network output: [ -0.002466 0.129 0.932 -0.0002192 9.841e-05 0.943 -0.0001652 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2033 0.138 0.2875 0.1637 0.9851 0.994 0.2039 0.4963 0.8928 0.7439 ] Network output: [ 0.009212 0.02279 1.033 0.0001261 -5.662e-05 0.9262 9.505e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07086 0.06645 0.1503 0.1969 0.9878 0.9922 0.0709 0.8228 0.894 0.2853 ] Network output: [ -0.006272 -0.01322 1.034 0.000123 -5.521e-05 0.9925 9.268e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08067 0.07979 0.1662 0.2055 0.9853 0.9913 0.08068 0.7445 0.8713 0.2498 ] Network output: [ -0.00308 0.9888 0.01261 2.002e-05 -8.989e-06 1.005 1.509e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.008187 Epoch 5598 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01726 0.9783 0.975 -2.598e-05 1.166e-05 0.01203 -1.958e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002858 -0.002466 -0.01096 0.008525 0.9692 0.9737 0.005408 0.8474 0.839 0.02247 ] Network output: [ 1.003 -0.0949 0.009758 1.112e-05 -4.993e-06 0.07942 8.383e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1892 -0.004981 -0.2132 0.2379 0.9835 0.9932 0.2109 0.4867 0.8867 0.7489 ] Network output: [ -0.01147 0.9975 1.001 -3.571e-05 1.603e-05 0.02453 -2.691e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004199 0.001334 0.003624 0.005748 0.9892 0.9922 0.004274 0.8834 0.9105 0.01574 ] Network output: [ 0.01693 -0.1584 0.9492 -0.0001571 7.052e-05 1.175 -0.0001184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2005 0.137 0.3058 0.2192 0.9851 0.994 0.2011 0.4927 0.8926 0.7421 ] Network output: [ 0.002833 -0.02003 1.044 0.0001262 -5.664e-05 0.9713 9.508e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07213 0.06779 0.1622 0.2101 0.9877 0.9922 0.07218 0.8266 0.894 0.2927 ] Network output: [ -0.01132 0.01161 1.034 0.0001151 -5.169e-05 0.9774 8.677e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08196 0.08111 0.1701 0.2072 0.9854 0.9914 0.08197 0.7497 0.8709 0.2498 ] Network output: [ 0.001066 1.017 -0.0004157 1.971e-05 -8.849e-06 0.981 1.485e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0117 Epoch 5599 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01508 1.008 0.9738 -3.222e-05 1.446e-05 -0.0124 -2.428e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002892 -0.002468 -0.01105 0.007895 0.9692 0.9737 0.005466 0.848 0.8376 0.02226 ] Network output: [ 0.9811 0.124 0.001798 -3.709e-05 1.665e-05 -0.08809 -2.795e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1923 -0.002188 -0.2215 0.2002 0.9835 0.9932 0.2143 0.4915 0.8853 0.7457 ] Network output: [ -0.01099 1.005 0.9998 -3.685e-05 1.654e-05 0.01663 -2.777e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004258 0.001312 0.003101 0.004612 0.9892 0.9922 0.004334 0.8838 0.9098 0.01546 ] Network output: [ -0.002794 0.1339 0.9321 -0.0002208 9.911e-05 0.9386 -0.0001664 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2032 0.1377 0.2872 0.1628 0.9851 0.994 0.2038 0.4961 0.8928 0.7439 ] Network output: [ 0.009411 0.02363 1.032 0.000126 -5.659e-05 0.9256 9.499e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07105 0.06661 0.1502 0.1968 0.9878 0.9922 0.07109 0.8226 0.8939 0.2854 ] Network output: [ -0.006206 -0.01303 1.033 0.000123 -5.522e-05 0.9925 9.27e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08083 0.07994 0.1662 0.2055 0.9853 0.9913 0.08084 0.7443 0.8713 0.2499 ] Network output: [ -0.003061 0.9885 0.0126 2.019e-05 -9.062e-06 1.005 1.521e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.008619 Epoch 5600 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01723 0.978 0.9752 -2.556e-05 1.148e-05 0.01225 -1.927e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00286 -0.002469 -0.01096 0.008542 0.9692 0.9737 0.00541 0.8474 0.8391 0.02248 ] Network output: [ 1.003 -0.0995 0.01016 1.171e-05 -5.258e-06 0.08304 8.827e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.189 -0.005186 -0.213 0.2386 0.9835 0.9932 0.2107 0.4863 0.8867 0.749 ] Network output: [ -0.01154 0.9977 1.001 -3.538e-05 1.589e-05 0.02442 -2.667e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004203 0.00133 0.003641 0.005776 0.9892 0.9922 0.004278 0.8834 0.9104 0.01575 ] Network output: [ 0.01727 -0.1639 0.95 -0.0001565 7.027e-05 1.179 -0.000118 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2003 0.1367 0.3062 0.2203 0.9851 0.994 0.2009 0.4923 0.8926 0.7421 ] Network output: [ 0.002776 -0.02089 1.043 0.0001261 -5.661e-05 0.9725 9.503e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07236 0.068 0.1625 0.2106 0.9877 0.9922 0.07241 0.8265 0.894 0.2931 ] Network output: [ -0.01145 0.0126 1.034 0.0001149 -5.158e-05 0.977 8.659e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08216 0.0813 0.1703 0.2074 0.9854 0.9914 0.08217 0.7497 0.8709 0.2499 ] Network output: [ 0.001296 1.018 -0.0008793 1.998e-05 -8.968e-06 0.9808 1.506e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01237 Epoch 5601 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01497 1.009 0.9739 -3.201e-05 1.437e-05 -0.01309 -2.413e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002895 -0.002471 -0.01105 0.007889 0.9692 0.9737 0.00547 0.848 0.8376 0.02226 ] Network output: [ 0.9806 0.1272 0.001886 -3.81e-05 1.711e-05 -0.09053 -2.872e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1922 -0.002289 -0.2216 0.1996 0.9835 0.9932 0.2142 0.4912 0.8853 0.7457 ] Network output: [ -0.01105 1.006 0.9999 -3.657e-05 1.642e-05 0.0162 -2.756e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004265 0.001308 0.003097 0.004598 0.9892 0.9922 0.004341 0.8837 0.9098 0.01547 ] Network output: [ -0.003121 0.1391 0.9322 -0.0002223 9.981e-05 0.9341 -0.0001676 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2031 0.1374 0.2869 0.1619 0.9851 0.994 0.2037 0.4958 0.8927 0.7439 ] Network output: [ 0.009607 0.02455 1.032 0.000126 -5.656e-05 0.9249 9.494e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07124 0.06677 0.1501 0.1967 0.9877 0.9922 0.07128 0.8224 0.8939 0.2855 ] Network output: [ -0.00614 -0.01277 1.033 0.000123 -5.523e-05 0.9924 9.272e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08099 0.0801 0.1662 0.2055 0.9853 0.9913 0.081 0.7442 0.8713 0.2499 ] Network output: [ -0.003056 0.9883 0.01259 2.032e-05 -9.124e-06 1.005 1.532e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009096 Epoch 5602 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0172 0.9776 0.9754 -2.513e-05 1.128e-05 0.01251 -1.894e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002861 -0.002472 -0.01096 0.008559 0.9692 0.9737 0.005412 0.8474 0.839 0.02249 ] Network output: [ 1.003 -0.1043 0.01059 1.226e-05 -5.503e-06 0.08681 9.238e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1888 -0.005383 -0.2127 0.2394 0.9835 0.9932 0.2104 0.4858 0.8866 0.749 ] Network output: [ -0.01161 0.9977 1.001 -3.504e-05 1.573e-05 0.02433 -2.641e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004207 0.001327 0.003658 0.005806 0.9892 0.9922 0.004283 0.8833 0.9104 0.01577 ] Network output: [ 0.01765 -0.1697 0.9507 -0.0001559 6.998e-05 1.183 -0.0001175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2001 0.1364 0.3066 0.2215 0.9851 0.994 0.2007 0.4919 0.8925 0.742 ] Network output: [ 0.002706 -0.02177 1.043 0.000126 -5.658e-05 0.9736 9.498e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0726 0.06821 0.1629 0.211 0.9877 0.9922 0.07265 0.8264 0.8939 0.2935 ] Network output: [ -0.01158 0.01366 1.033 0.0001147 -5.147e-05 0.9765 8.641e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08237 0.0815 0.1704 0.2075 0.9854 0.9914 0.08238 0.7497 0.8709 0.2499 ] Network output: [ 0.001538 1.018 -0.001349 2.025e-05 -9.091e-06 0.9806 1.526e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01309 Epoch 5603 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01487 1.01 0.9741 -3.181e-05 1.428e-05 -0.0138 -2.397e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002897 -0.002474 -0.01105 0.007882 0.9692 0.9737 0.005475 0.8479 0.8375 0.02226 ] Network output: [ 0.9802 0.1306 0.001978 -3.923e-05 1.761e-05 -0.09308 -2.956e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1921 -0.002376 -0.2217 0.199 0.9835 0.9932 0.2141 0.4909 0.8852 0.7456 ] Network output: [ -0.01109 1.006 1 -3.628e-05 1.629e-05 0.01576 -2.734e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004272 0.001304 0.003094 0.004584 0.9892 0.9922 0.004348 0.8836 0.9097 0.01548 ] Network output: [ -0.003442 0.1443 0.9323 -0.0002238 0.0001005 0.9294 -0.0001687 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.203 0.1372 0.2866 0.1609 0.9851 0.994 0.2036 0.4955 0.8927 0.7439 ] Network output: [ 0.009797 0.02553 1.031 0.0001259 -5.652e-05 0.9241 9.489e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07144 0.06694 0.15 0.1966 0.9877 0.9922 0.07148 0.8222 0.8938 0.2855 ] Network output: [ -0.006075 -0.01246 1.033 0.000123 -5.524e-05 0.9924 9.273e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08116 0.08026 0.1661 0.2056 0.9853 0.9913 0.08117 0.744 0.8713 0.25 ] Network output: [ -0.003065 0.988 0.01261 2.044e-05 -9.175e-06 1.006 1.54e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00961 Epoch 5604 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01717 0.9772 0.9756 -2.469e-05 1.108e-05 0.01279 -1.861e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002862 -0.002474 -0.01096 0.008576 0.9693 0.9737 0.005415 0.8473 0.839 0.0225 ] Network output: [ 1.004 -0.1091 0.01104 1.274e-05 -5.72e-06 0.09067 9.602e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1886 -0.005569 -0.2124 0.2402 0.9835 0.9932 0.2102 0.4853 0.8866 0.749 ] Network output: [ -0.01166 0.9978 1.001 -3.468e-05 1.557e-05 0.02426 -2.614e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004212 0.001325 0.003677 0.005837 0.9892 0.9922 0.004288 0.8832 0.9104 0.01579 ] Network output: [ 0.01807 -0.1758 0.9513 -0.0001551 6.964e-05 1.188 -0.0001169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1999 0.1361 0.307 0.2227 0.9851 0.994 0.2006 0.4914 0.8924 0.7419 ] Network output: [ 0.002626 -0.02266 1.043 0.000126 -5.655e-05 0.9748 9.493e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07284 0.06842 0.1633 0.2115 0.9877 0.9922 0.07289 0.8263 0.8938 0.2938 ] Network output: [ -0.01172 0.01479 1.033 0.0001144 -5.136e-05 0.9759 8.623e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08258 0.08171 0.1706 0.2076 0.9855 0.9914 0.08259 0.7497 0.8708 0.25 ] Network output: [ 0.001791 1.018 -0.00182 2.053e-05 -9.218e-06 0.9804 1.547e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01388 Epoch 5605 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01476 1.011 0.9742 -3.16e-05 1.419e-05 -0.01452 -2.381e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0029 -0.002477 -0.01105 0.007875 0.9692 0.9737 0.00548 0.8479 0.8374 0.02226 ] Network output: [ 0.9797 0.1341 0.002072 -4.044e-05 1.816e-05 -0.09568 -3.048e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.192 -0.002451 -0.2217 0.1984 0.9835 0.9932 0.214 0.4906 0.8851 0.7454 ] Network output: [ -0.01114 1.007 1 -3.598e-05 1.615e-05 0.01533 -2.711e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00428 0.001302 0.003091 0.004569 0.9892 0.9922 0.004356 0.8835 0.9096 0.01548 ] Network output: [ -0.003752 0.1496 0.9322 -0.0002253 0.0001012 0.9247 -0.0001698 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2029 0.137 0.2863 0.16 0.9851 0.994 0.2036 0.4951 0.8926 0.7438 ] Network output: [ 0.009981 0.02656 1.031 0.0001258 -5.649e-05 0.9233 9.483e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07164 0.06711 0.1498 0.1965 0.9877 0.9922 0.07168 0.8219 0.8937 0.2856 ] Network output: [ -0.006013 -0.01208 1.032 0.0001231 -5.525e-05 0.9922 9.275e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08133 0.08042 0.1661 0.2056 0.9853 0.9913 0.08134 0.7437 0.8712 0.25 ] Network output: [ -0.003089 0.9879 0.01264 2.052e-05 -9.213e-06 1.006 1.547e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01015 Epoch 5606 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01715 0.9768 0.9758 -2.423e-05 1.088e-05 0.01308 -1.826e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002864 -0.002477 -0.01095 0.008592 0.9693 0.9737 0.005418 0.8472 0.839 0.02251 ] Network output: [ 1.004 -0.1139 0.01152 1.315e-05 -5.902e-06 0.09456 9.908e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1884 -0.005745 -0.212 0.241 0.9835 0.9932 0.21 0.4848 0.8865 0.749 ] Network output: [ -0.01171 0.9978 1.001 -3.431e-05 1.54e-05 0.0242 -2.585e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004217 0.001323 0.003696 0.005868 0.9892 0.9922 0.004293 0.883 0.9103 0.01581 ] Network output: [ 0.01852 -0.1819 0.952 -0.0001543 6.926e-05 1.192 -0.0001163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1998 0.1359 0.3074 0.224 0.9851 0.994 0.2004 0.4908 0.8923 0.7417 ] Network output: [ 0.00254 -0.02355 1.043 0.0001259 -5.653e-05 0.976 9.489e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07309 0.06865 0.1637 0.2119 0.9877 0.9922 0.07314 0.8262 0.8937 0.2942 ] Network output: [ -0.01186 0.01595 1.033 0.0001142 -5.125e-05 0.9753 8.604e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08279 0.08192 0.1707 0.2076 0.9855 0.9914 0.0828 0.7497 0.8708 0.25 ] Network output: [ 0.002053 1.018 -0.002283 2.082e-05 -9.348e-06 0.9803 1.569e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0147 Epoch 5607 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01467 1.011 0.9743 -3.138e-05 1.409e-05 -0.01524 -2.365e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002903 -0.00248 -0.01105 0.007867 0.9693 0.9737 0.005485 0.8478 0.8373 0.02226 ] Network output: [ 0.9792 0.1375 0.002169 -4.173e-05 1.873e-05 -0.09827 -3.145e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.192 -0.002512 -0.2216 0.1978 0.9835 0.9932 0.214 0.4901 0.8849 0.7453 ] Network output: [ -0.01117 1.007 1 -3.566e-05 1.601e-05 0.0149 -2.687e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004288 0.001299 0.003089 0.004555 0.9892 0.9922 0.004364 0.8834 0.9095 0.01549 ] Network output: [ -0.004043 0.1548 0.9322 -0.0002267 0.0001018 0.9202 -0.0001708 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2029 0.1368 0.286 0.1591 0.9851 0.994 0.2035 0.4946 0.8925 0.7437 ] Network output: [ 0.01015 0.02761 1.03 0.0001258 -5.646e-05 0.9225 9.478e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07185 0.06729 0.1497 0.1964 0.9877 0.9922 0.07189 0.8216 0.8936 0.2857 ] Network output: [ -0.005955 -0.01163 1.032 0.0001231 -5.526e-05 0.992 9.276e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0815 0.08059 0.1661 0.2055 0.9853 0.9913 0.08151 0.7434 0.8712 0.25 ] Network output: [ -0.00313 0.9879 0.01269 2.058e-05 -9.237e-06 1.006 1.551e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01071 Epoch 5608 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01714 0.9763 0.976 -2.378e-05 1.067e-05 0.01339 -1.792e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002865 -0.00248 -0.01095 0.008608 0.9693 0.9737 0.005421 0.8471 0.8389 0.02252 ] Network output: [ 1.004 -0.1187 0.01201 1.346e-05 -6.042e-06 0.09839 1.014e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1882 -0.005908 -0.2116 0.2418 0.9835 0.9932 0.2099 0.4841 0.8864 0.7489 ] Network output: [ -0.01175 0.9978 1.001 -3.391e-05 1.522e-05 0.02415 -2.556e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004223 0.001322 0.003716 0.0059 0.9892 0.9922 0.004299 0.8828 0.9102 0.01583 ] Network output: [ 0.01899 -0.188 0.9525 -0.0001534 6.887e-05 1.197 -0.0001156 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1997 0.1357 0.3078 0.2252 0.9851 0.994 0.2003 0.4902 0.8922 0.7415 ] Network output: [ 0.002452 -0.02443 1.043 0.0001259 -5.65e-05 0.9772 9.485e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07334 0.06888 0.164 0.2124 0.9877 0.9922 0.07339 0.826 0.8936 0.2946 ] Network output: [ -0.01199 0.01714 1.033 0.0001139 -5.115e-05 0.9747 8.586e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08301 0.08213 0.1709 0.2077 0.9855 0.9914 0.08302 0.7496 0.8707 0.25 ] Network output: [ 0.00232 1.018 -0.002732 2.112e-05 -9.481e-06 0.9802 1.592e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01554 Epoch 5609 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01457 1.012 0.9745 -3.116e-05 1.399e-05 -0.01594 -2.348e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002906 -0.002483 -0.01104 0.007858 0.9693 0.9737 0.005491 0.8477 0.8372 0.02226 ] Network output: [ 0.9787 0.1409 0.002268 -4.305e-05 1.933e-05 -0.1008 -3.245e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.192 -0.002563 -0.2216 0.1972 0.9835 0.9932 0.214 0.4897 0.8848 0.7451 ] Network output: [ -0.01121 1.007 1 -3.532e-05 1.586e-05 0.01448 -2.662e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004296 0.001298 0.003088 0.004543 0.9892 0.9922 0.004372 0.8832 0.9094 0.01549 ] Network output: [ -0.004311 0.1598 0.9321 -0.000228 0.0001024 0.9158 -0.0001718 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2029 0.1367 0.2857 0.1582 0.9851 0.994 0.2035 0.4941 0.8923 0.7436 ] Network output: [ 0.01032 0.02865 1.029 0.0001257 -5.643e-05 0.9217 9.473e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07206 0.06747 0.1496 0.1963 0.9877 0.9922 0.0721 0.8213 0.8935 0.2857 ] Network output: [ -0.005904 -0.01111 1.032 0.0001231 -5.526e-05 0.9917 9.276e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08168 0.08077 0.166 0.2055 0.9853 0.9913 0.08169 0.7431 0.8711 0.25 ] Network output: [ -0.003187 0.9879 0.01274 2.06e-05 -9.246e-06 1.006 1.552e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01127 Epoch 5610 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01713 0.9758 0.9762 -2.331e-05 1.047e-05 0.01368 -1.757e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002867 -0.002483 -0.01094 0.008623 0.9693 0.9737 0.005424 0.8469 0.8388 0.02252 ] Network output: [ 1.004 -0.1232 0.0125 1.366e-05 -6.13e-06 0.1021 1.029e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1881 -0.006056 -0.2111 0.2425 0.9835 0.9932 0.2097 0.4834 0.8863 0.7488 ] Network output: [ -0.01178 0.9977 1.002 -3.35e-05 1.504e-05 0.02412 -2.524e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004229 0.001321 0.003736 0.005932 0.9892 0.9922 0.004305 0.8827 0.9101 0.01584 ] Network output: [ 0.01947 -0.1939 0.953 -0.0001525 6.847e-05 1.201 -0.0001149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1996 0.1355 0.3082 0.2265 0.9851 0.994 0.2002 0.4895 0.892 0.7413 ] Network output: [ 0.002366 -0.02528 1.043 0.0001258 -5.648e-05 0.9784 9.482e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0736 0.06911 0.1644 0.2128 0.9877 0.9922 0.07364 0.8258 0.8935 0.2949 ] Network output: [ -0.01212 0.01833 1.032 0.0001137 -5.104e-05 0.974 8.569e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08323 0.08235 0.171 0.2078 0.9855 0.9914 0.08324 0.7494 0.8706 0.25 ] Network output: [ 0.002588 1.018 -0.003155 2.142e-05 -9.616e-06 0.9802 1.614e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01638 Epoch 5611 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01449 1.013 0.9746 -3.093e-05 1.388e-05 -0.01661 -2.331e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002909 -0.002485 -0.01104 0.007851 0.9693 0.9737 0.005497 0.8475 0.837 0.02225 ] Network output: [ 0.9782 0.1441 0.002367 -4.439e-05 1.993e-05 -0.1031 -3.345e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.192 -0.002604 -0.2214 0.1966 0.9835 0.9932 0.214 0.4891 0.8846 0.7449 ] Network output: [ -0.01123 1.008 1 -3.497e-05 1.57e-05 0.01407 -2.636e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004305 0.001297 0.003089 0.004532 0.9892 0.9922 0.004381 0.883 0.9092 0.0155 ] Network output: [ -0.004548 0.1644 0.932 -0.0002291 0.0001029 0.9117 -0.0001727 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.203 0.1366 0.2854 0.1574 0.9851 0.994 0.2036 0.4935 0.8921 0.7435 ] Network output: [ 0.01046 0.02965 1.029 0.0001256 -5.64e-05 0.921 9.468e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07228 0.06766 0.1495 0.1962 0.9877 0.9922 0.07232 0.821 0.8933 0.2858 ] Network output: [ -0.005861 -0.01052 1.031 0.0001231 -5.525e-05 0.9913 9.275e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08186 0.08095 0.166 0.2055 0.9853 0.9913 0.08187 0.7427 0.871 0.25 ] Network output: [ -0.00326 0.9881 0.0128 2.058e-05 -9.239e-06 1.006 1.551e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01181 Epoch 5612 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01712 0.9753 0.9764 -2.285e-05 1.026e-05 0.01396 -1.722e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002869 -0.002485 -0.01093 0.008636 0.9693 0.9737 0.005427 0.8468 0.8387 0.02252 ] Network output: [ 1.004 -0.1273 0.01298 1.372e-05 -6.16e-06 0.1055 1.034e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.188 -0.006189 -0.2107 0.2432 0.9835 0.9932 0.2096 0.4827 0.8862 0.7487 ] Network output: [ -0.0118 0.9976 1.002 -3.307e-05 1.485e-05 0.02409 -2.492e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004235 0.001321 0.003756 0.005962 0.9892 0.9922 0.004311 0.8825 0.9099 0.01586 ] Network output: [ 0.01993 -0.1994 0.9535 -0.0001517 6.809e-05 1.205 -0.0001143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1995 0.1354 0.3086 0.2277 0.9851 0.994 0.2001 0.4887 0.8918 0.7411 ] Network output: [ 0.002289 -0.02609 1.043 0.0001258 -5.647e-05 0.9794 9.479e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07386 0.06934 0.1648 0.2132 0.9877 0.9922 0.0739 0.8256 0.8933 0.2952 ] Network output: [ -0.01225 0.01948 1.032 0.0001135 -5.095e-05 0.9734 8.552e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08346 0.08257 0.1711 0.2079 0.9855 0.9915 0.08347 0.7492 0.8705 0.25 ] Network output: [ 0.00285 1.018 -0.003543 2.172e-05 -9.75e-06 0.9803 1.637e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01719 Epoch 5613 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01441 1.014 0.9748 -3.068e-05 1.377e-05 -0.01724 -2.312e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002913 -0.002488 -0.01103 0.007844 0.9693 0.9737 0.005503 0.8474 0.8368 0.02225 ] Network output: [ 0.9778 0.147 0.002463 -4.571e-05 2.052e-05 -0.1052 -3.445e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.192 -0.002636 -0.2213 0.196 0.9835 0.9932 0.214 0.4885 0.8844 0.7446 ] Network output: [ -0.01126 1.008 1.001 -3.461e-05 1.554e-05 0.01368 -2.608e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004313 0.001296 0.00309 0.004523 0.9892 0.9922 0.00439 0.8828 0.909 0.0155 ] Network output: [ -0.004749 0.1686 0.9319 -0.0002301 0.0001033 0.908 -0.0001734 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.203 0.1365 0.2851 0.1567 0.9851 0.994 0.2036 0.4928 0.892 0.7433 ] Network output: [ 0.0106 0.03058 1.028 0.0001256 -5.637e-05 0.9203 9.463e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0725 0.06786 0.1495 0.1962 0.9877 0.9922 0.07254 0.8206 0.8932 0.2859 ] Network output: [ -0.00583 -0.009872 1.031 0.0001231 -5.524e-05 0.991 9.274e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08205 0.08113 0.166 0.2055 0.9853 0.9913 0.08206 0.7424 0.8708 0.2501 ] Network output: [ -0.00335 0.9884 0.01286 2.053e-05 -9.216e-06 1.005 1.547e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0123 Epoch 5614 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01711 0.9749 0.9766 -2.24e-05 1.005e-05 0.01421 -1.688e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002871 -0.002488 -0.01092 0.008648 0.9693 0.9737 0.005431 0.8466 0.8385 0.02253 ] Network output: [ 1.005 -0.131 0.01345 1.364e-05 -6.123e-06 0.1086 1.028e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.188 -0.006304 -0.2102 0.2437 0.9835 0.9932 0.2095 0.4819 0.886 0.7486 ] Network output: [ -0.01181 0.9975 1.002 -3.263e-05 1.465e-05 0.02406 -2.459e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004242 0.001321 0.003775 0.005991 0.9892 0.9922 0.004318 0.8822 0.9098 0.01587 ] Network output: [ 0.02037 -0.2043 0.9538 -0.0001509 6.774e-05 1.209 -0.0001137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1995 0.1353 0.3089 0.2287 0.9851 0.994 0.2001 0.4879 0.8917 0.7409 ] Network output: [ 0.002225 -0.02684 1.042 0.0001258 -5.645e-05 0.9805 9.477e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07412 0.06958 0.1651 0.2136 0.9877 0.9922 0.07416 0.8254 0.8932 0.2956 ] Network output: [ -0.01236 0.02058 1.032 0.0001133 -5.086e-05 0.9728 8.538e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08368 0.08279 0.1712 0.208 0.9855 0.9915 0.08369 0.749 0.8703 0.25 ] Network output: [ 0.0031 1.017 -0.003885 2.201e-05 -9.881e-06 0.9804 1.659e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01793 Epoch 5615 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01434 1.014 0.9749 -3.041e-05 1.365e-05 -0.01782 -2.292e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002916 -0.002491 -0.01103 0.007837 0.9693 0.9737 0.005509 0.8472 0.8366 0.02225 ] Network output: [ 0.9774 0.1494 0.002555 -4.697e-05 2.109e-05 -0.107 -3.54e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1921 -0.002662 -0.2211 0.1956 0.9835 0.9932 0.2141 0.4879 0.8841 0.7444 ] Network output: [ -0.01128 1.008 1.001 -3.423e-05 1.537e-05 0.01332 -2.58e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004323 0.001297 0.003093 0.004517 0.9892 0.9922 0.0044 0.8826 0.9089 0.01551 ] Network output: [ -0.004908 0.1721 0.9318 -0.000231 0.0001037 0.9049 -0.0001741 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2031 0.1364 0.2849 0.1562 0.9851 0.994 0.2037 0.4921 0.8918 0.7431 ] Network output: [ 0.01071 0.0314 1.028 0.0001255 -5.635e-05 0.9197 9.459e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07273 0.06807 0.1495 0.1962 0.9877 0.9922 0.07277 0.8203 0.893 0.286 ] Network output: [ -0.005811 -0.009172 1.031 0.000123 -5.522e-05 0.9905 9.271e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08225 0.08132 0.166 0.2055 0.9853 0.9913 0.08226 0.742 0.8707 0.2501 ] Network output: [ -0.003454 0.9888 0.01293 2.044e-05 -9.177e-06 1.005 1.541e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01274 Epoch 5616 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01711 0.9745 0.9768 -2.195e-05 9.853e-06 0.01441 -1.654e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002873 -0.00249 -0.01091 0.008658 0.9693 0.9737 0.005434 0.8464 0.8384 0.02253 ] Network output: [ 1.005 -0.1341 0.01389 1.34e-05 -6.014e-06 0.1112 1.01e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1879 -0.006402 -0.2097 0.2442 0.9835 0.9932 0.2095 0.4811 0.8858 0.7484 ] Network output: [ -0.01181 0.9974 1.002 -3.219e-05 1.445e-05 0.02404 -2.426e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004249 0.001322 0.003794 0.006017 0.9892 0.9922 0.004326 0.882 0.9096 0.01589 ] Network output: [ 0.02077 -0.2084 0.9541 -0.0001502 6.744e-05 1.212 -0.0001132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1995 0.1352 0.3092 0.2297 0.9851 0.994 0.2001 0.4871 0.8914 0.7406 ] Network output: [ 0.002179 -0.02752 1.042 0.0001257 -5.645e-05 0.9814 9.476e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07437 0.06982 0.1654 0.214 0.9876 0.9922 0.07442 0.8251 0.893 0.2959 ] Network output: [ -0.01246 0.02157 1.032 0.0001131 -5.078e-05 0.9722 8.525e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0839 0.08301 0.1714 0.2081 0.9855 0.9915 0.08391 0.7488 0.8702 0.25 ] Network output: [ 0.003329 1.017 -0.004169 2.229e-05 -1.001e-05 0.9806 1.68e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01856 Epoch 5617 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01428 1.015 0.9751 -3.012e-05 1.352e-05 -0.01832 -2.27e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00292 -0.002494 -0.01102 0.007833 0.9693 0.9737 0.005514 0.847 0.8364 0.02224 ] Network output: [ 0.9771 0.1514 0.002637 -4.814e-05 2.161e-05 -0.1084 -3.628e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1922 -0.002685 -0.2208 0.1952 0.9835 0.9932 0.2142 0.4872 0.8839 0.7441 ] Network output: [ -0.01129 1.009 1.001 -3.384e-05 1.519e-05 0.01299 -2.551e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004332 0.001297 0.003098 0.004514 0.9892 0.9922 0.004409 0.8824 0.9087 0.01551 ] Network output: [ -0.00502 0.1749 0.9318 -0.0002316 0.000104 0.9024 -0.0001745 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2032 0.1364 0.2848 0.1558 0.9851 0.994 0.2038 0.4914 0.8915 0.7429 ] Network output: [ 0.01081 0.03206 1.028 0.0001255 -5.633e-05 0.9193 9.456e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07296 0.06828 0.1495 0.1962 0.9877 0.9922 0.07301 0.8199 0.8928 0.2861 ] Network output: [ -0.005806 -0.008435 1.03 0.000123 -5.52e-05 0.9901 9.266e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08244 0.08152 0.166 0.2055 0.9853 0.9913 0.08246 0.7416 0.8705 0.2501 ] Network output: [ -0.003571 0.9894 0.01299 2.032e-05 -9.123e-06 1.005 1.532e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01309 Epoch 5618 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01711 0.9742 0.977 -2.151e-05 9.656e-06 0.01456 -1.621e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002875 -0.002493 -0.0109 0.008666 0.9693 0.9737 0.005439 0.8462 0.8382 0.02253 ] Network output: [ 1.004 -0.1364 0.01428 1.299e-05 -5.83e-06 0.1133 9.787e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1879 -0.006481 -0.2092 0.2445 0.9835 0.9932 0.2095 0.4803 0.8856 0.7482 ] Network output: [ -0.01181 0.9973 1.002 -3.173e-05 1.425e-05 0.02401 -2.391e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004257 0.001322 0.003811 0.006039 0.9892 0.9922 0.004333 0.8817 0.9094 0.0159 ] Network output: [ 0.02111 -0.2117 0.9543 -0.0001497 6.721e-05 1.215 -0.0001128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1995 0.1351 0.3094 0.2305 0.9851 0.994 0.2001 0.4863 0.8912 0.7403 ] Network output: [ 0.002158 -0.02812 1.042 0.0001257 -5.645e-05 0.9822 9.476e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07463 0.07005 0.1657 0.2144 0.9876 0.9922 0.07467 0.8249 0.8928 0.2962 ] Network output: [ -0.01254 0.02245 1.031 0.000113 -5.072e-05 0.9718 8.514e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08412 0.08322 0.1715 0.2081 0.9855 0.9915 0.08413 0.7485 0.87 0.2501 ] Network output: [ 0.003529 1.016 -0.004386 2.254e-05 -1.012e-05 0.9809 1.699e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01907 Epoch 5619 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01424 1.015 0.9753 -2.98e-05 1.338e-05 -0.01875 -2.246e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002923 -0.002497 -0.01101 0.007829 0.9693 0.9737 0.00552 0.8468 0.8362 0.02224 ] Network output: [ 0.9769 0.1528 0.002708 -4.919e-05 2.208e-05 -0.1094 -3.707e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1923 -0.002705 -0.2206 0.1949 0.9835 0.9932 0.2143 0.4864 0.8836 0.7439 ] Network output: [ -0.01131 1.009 1.001 -3.344e-05 1.501e-05 0.0127 -2.52e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004341 0.001298 0.003105 0.004514 0.9892 0.9922 0.004419 0.8821 0.9085 0.01552 ] Network output: [ -0.005083 0.1769 0.9317 -0.000232 0.0001041 0.9006 -0.0001748 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2033 0.1364 0.2847 0.1556 0.9851 0.994 0.2039 0.4906 0.8913 0.7426 ] Network output: [ 0.01089 0.03254 1.027 0.0001254 -5.631e-05 0.9191 9.453e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0732 0.0685 0.1495 0.1963 0.9877 0.9922 0.07325 0.8196 0.8926 0.2863 ] Network output: [ -0.005816 -0.007679 1.03 0.0001229 -5.517e-05 0.9896 9.261e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08265 0.08171 0.166 0.2055 0.9853 0.9913 0.08266 0.7412 0.8704 0.2501 ] Network output: [ -0.003699 0.99 0.01304 2.017e-05 -9.057e-06 1.004 1.52e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01333 Epoch 5620 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01711 0.9739 0.9772 -2.108e-05 9.465e-06 0.01463 -1.589e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002878 -0.002495 -0.01089 0.008671 0.9693 0.9737 0.005443 0.846 0.838 0.02253 ] Network output: [ 1.004 -0.138 0.01461 1.241e-05 -5.57e-06 0.1147 9.35e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1879 -0.006543 -0.2088 0.2447 0.9835 0.9932 0.2095 0.4794 0.8854 0.748 ] Network output: [ -0.0118 0.9971 1.002 -3.127e-05 1.404e-05 0.02398 -2.357e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004265 0.001324 0.003826 0.006058 0.9891 0.9922 0.004342 0.8815 0.9092 0.01591 ] Network output: [ 0.02138 -0.214 0.9544 -0.0001494 6.707e-05 1.216 -0.0001126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1996 0.1351 0.3095 0.231 0.9851 0.994 0.2002 0.4854 0.891 0.7401 ] Network output: [ 0.002164 -0.02863 1.042 0.0001258 -5.646e-05 0.9829 9.477e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07488 0.07029 0.166 0.2147 0.9876 0.9922 0.07493 0.8246 0.8926 0.2965 ] Network output: [ -0.0126 0.02318 1.031 0.0001129 -5.068e-05 0.9714 8.507e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08433 0.08344 0.1716 0.2082 0.9855 0.9915 0.08434 0.7482 0.8698 0.2501 ] Network output: [ 0.003694 1.016 -0.004529 2.277e-05 -1.022e-05 0.9813 1.716e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01941 Epoch 5621 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01421 1.015 0.9754 -2.946e-05 1.323e-05 -0.01907 -2.22e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002926 -0.002499 -0.01101 0.007828 0.9693 0.9737 0.005526 0.8466 0.8359 0.02224 ] Network output: [ 0.9767 0.1535 0.002763 -5.01e-05 2.249e-05 -0.1099 -3.776e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1923 -0.002727 -0.2203 0.1947 0.9835 0.9932 0.2144 0.4856 0.8834 0.7436 ] Network output: [ -0.01132 1.009 1.001 -3.303e-05 1.483e-05 0.01246 -2.489e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004351 0.0013 0.003113 0.004518 0.9892 0.9922 0.004428 0.8819 0.9083 0.01553 ] Network output: [ -0.005097 0.1779 0.9318 -0.0002322 0.0001042 0.8995 -0.000175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2034 0.1365 0.2846 0.1555 0.9851 0.994 0.204 0.4898 0.8911 0.7424 ] Network output: [ 0.01095 0.0328 1.027 0.0001254 -5.63e-05 0.919 9.452e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07345 0.06872 0.1496 0.1964 0.9877 0.9922 0.07349 0.8192 0.8924 0.2865 ] Network output: [ -0.005842 -0.006921 1.03 0.0001228 -5.513e-05 0.9892 9.254e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08285 0.08192 0.166 0.2055 0.9853 0.9913 0.08286 0.7409 0.8702 0.2501 ] Network output: [ -0.003832 0.9907 0.01309 2e-05 -8.98e-06 1.004 1.507e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01346 Epoch 5622 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01711 0.9737 0.9773 -2.068e-05 9.282e-06 0.01464 -1.558e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002881 -0.002498 -0.01088 0.008675 0.9693 0.9737 0.005448 0.8457 0.8378 0.02253 ] Network output: [ 1.004 -0.1387 0.01487 1.166e-05 -5.234e-06 0.1155 8.787e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.188 -0.006587 -0.2084 0.2448 0.9835 0.9932 0.2096 0.4786 0.8851 0.7477 ] Network output: [ -0.01178 0.997 1.003 -3.081e-05 1.383e-05 0.02393 -2.322e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004273 0.001325 0.003839 0.006073 0.9891 0.9922 0.00435 0.8812 0.909 0.01592 ] Network output: [ 0.02156 -0.2151 0.9545 -0.0001493 6.702e-05 1.217 -0.0001125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1996 0.1351 0.3096 0.2314 0.9851 0.994 0.2002 0.4846 0.8907 0.7398 ] Network output: [ 0.002201 -0.02905 1.042 0.0001258 -5.647e-05 0.9835 9.479e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07513 0.07052 0.1662 0.2149 0.9876 0.9922 0.07517 0.8242 0.8925 0.2967 ] Network output: [ -0.01264 0.02373 1.031 0.0001128 -5.065e-05 0.9711 8.502e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08454 0.08364 0.1716 0.2083 0.9855 0.9915 0.08455 0.7479 0.8697 0.2501 ] Network output: [ 0.003817 1.015 -0.004593 2.296e-05 -1.031e-05 0.9818 1.731e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01957 Epoch 5623 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0142 1.015 0.9756 -2.909e-05 1.306e-05 -0.0193 -2.192e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00293 -0.002502 -0.011 0.007829 0.9693 0.9737 0.005532 0.8464 0.8357 0.02224 ] Network output: [ 0.9767 0.1535 0.0028 -5.083e-05 2.282e-05 -0.1099 -3.831e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1924 -0.002752 -0.22 0.1946 0.9835 0.9932 0.2145 0.4848 0.8831 0.7434 ] Network output: [ -0.01133 1.009 1.001 -3.261e-05 1.464e-05 0.01227 -2.458e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00436 0.001302 0.003123 0.004526 0.9892 0.9922 0.004437 0.8816 0.908 0.01554 ] Network output: [ -0.005063 0.1781 0.9318 -0.0002321 0.0001042 0.8993 -0.000175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2035 0.1365 0.2847 0.1556 0.9851 0.994 0.2041 0.489 0.8908 0.7422 ] Network output: [ 0.01099 0.03283 1.027 0.0001254 -5.63e-05 0.9192 9.451e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07369 0.06895 0.1497 0.1966 0.9877 0.9922 0.07373 0.8189 0.8922 0.2868 ] Network output: [ -0.005882 -0.006181 1.03 0.0001227 -5.508e-05 0.9887 9.246e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08306 0.08212 0.1661 0.2056 0.9853 0.9913 0.08307 0.7406 0.87 0.2502 ] Network output: [ -0.003967 0.9914 0.01313 1.981e-05 -8.896e-06 1.003 1.493e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01347 Epoch 5624 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01711 0.9736 0.9775 -2.029e-05 9.108e-06 0.01456 -1.529e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002884 -0.0025 -0.01087 0.008676 0.9693 0.9738 0.005453 0.8455 0.8376 0.02253 ] Network output: [ 1.004 -0.1385 0.01505 1.076e-05 -4.83e-06 0.1155 8.109e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.188 -0.006615 -0.208 0.2447 0.9835 0.9932 0.2096 0.4778 0.8849 0.7475 ] Network output: [ -0.01176 0.9969 1.003 -3.036e-05 1.363e-05 0.02387 -2.288e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004282 0.001327 0.003851 0.006083 0.9891 0.9922 0.004359 0.8809 0.9088 0.01593 ] Network output: [ 0.02165 -0.215 0.9545 -0.0001494 6.708e-05 1.217 -0.0001126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1997 0.1351 0.3096 0.2315 0.9851 0.994 0.2003 0.4837 0.8905 0.7396 ] Network output: [ 0.002269 -0.02938 1.041 0.0001258 -5.649e-05 0.9839 9.483e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07537 0.07074 0.1663 0.2152 0.9876 0.9922 0.07542 0.8239 0.8923 0.297 ] Network output: [ -0.01265 0.0241 1.031 0.0001128 -5.064e-05 0.971 8.5e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08475 0.08384 0.1717 0.2084 0.9855 0.9915 0.08476 0.7476 0.8695 0.2502 ] Network output: [ 0.003895 1.015 -0.004577 2.312e-05 -1.038e-05 0.9823 1.742e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01954 Epoch 5625 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0142 1.015 0.9758 -2.87e-05 1.288e-05 -0.01943 -2.163e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002933 -0.002505 -0.01099 0.007832 0.9693 0.9737 0.005538 0.8461 0.8355 0.02224 ] Network output: [ 0.9768 0.1529 0.002817 -5.138e-05 2.307e-05 -0.1094 -3.872e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1925 -0.002783 -0.2197 0.1947 0.9835 0.9932 0.2145 0.484 0.8829 0.7432 ] Network output: [ -0.01134 1.009 1.001 -3.219e-05 1.445e-05 0.01214 -2.426e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004368 0.001304 0.003135 0.004537 0.9892 0.9921 0.004446 0.8814 0.9078 0.01555 ] Network output: [ -0.004983 0.1773 0.932 -0.0002319 0.0001041 0.8998 -0.0001748 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2036 0.1365 0.2848 0.1559 0.9851 0.994 0.2042 0.4882 0.8906 0.7419 ] Network output: [ 0.01101 0.03262 1.026 0.0001254 -5.631e-05 0.9196 9.452e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07394 0.06918 0.1499 0.1968 0.9877 0.9922 0.07398 0.8186 0.892 0.287 ] Network output: [ -0.005937 -0.005477 1.029 0.0001226 -5.503e-05 0.9884 9.238e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08327 0.08233 0.1661 0.2056 0.9853 0.9913 0.08328 0.7402 0.8698 0.2503 ] Network output: [ -0.004099 0.9922 0.01315 1.962e-05 -8.807e-06 1.003 1.479e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01335 Epoch 5626 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01711 0.9736 0.9777 -1.992e-05 8.942e-06 0.01439 -1.501e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002887 -0.002503 -0.01086 0.008674 0.9693 0.9738 0.005458 0.8453 0.8373 0.02252 ] Network output: [ 1.004 -0.1375 0.01514 9.723e-06 -4.365e-06 0.1149 7.327e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1881 -0.006629 -0.2077 0.2444 0.9835 0.9932 0.2097 0.477 0.8846 0.7473 ] Network output: [ -0.01174 0.9968 1.003 -2.991e-05 1.343e-05 0.0238 -2.254e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004291 0.001328 0.003859 0.006088 0.9891 0.9922 0.004368 0.8807 0.9086 0.01595 ] Network output: [ 0.02163 -0.2138 0.9545 -0.0001498 6.725e-05 1.215 -0.0001129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1998 0.1352 0.3095 0.2314 0.9851 0.994 0.2004 0.483 0.8903 0.7394 ] Network output: [ 0.002369 -0.02963 1.041 0.0001259 -5.651e-05 0.9843 9.487e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0756 0.07095 0.1664 0.2154 0.9876 0.9922 0.07565 0.8236 0.8921 0.2972 ] Network output: [ -0.01264 0.02429 1.03 0.0001128 -5.065e-05 0.971 8.502e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08494 0.08404 0.1718 0.2085 0.9855 0.9915 0.08495 0.7472 0.8693 0.2503 ] Network output: [ 0.003926 1.014 -0.004485 2.323e-05 -1.043e-05 0.9828 1.75e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01932 Epoch 5627 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01422 1.015 0.9759 -2.827e-05 1.269e-05 -0.01945 -2.131e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002936 -0.002507 -0.01099 0.007838 0.9693 0.9737 0.005543 0.8459 0.8353 0.02224 ] Network output: [ 0.9769 0.1516 0.002812 -5.174e-05 2.323e-05 -0.1085 -3.899e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1926 -0.002821 -0.2193 0.1949 0.9835 0.9932 0.2146 0.4832 0.8826 0.743 ] Network output: [ -0.01135 1.009 1.002 -3.176e-05 1.426e-05 0.01205 -2.393e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004377 0.001306 0.003148 0.004552 0.9892 0.9921 0.004455 0.8811 0.9076 0.01556 ] Network output: [ -0.004865 0.1756 0.9322 -0.0002315 0.0001039 0.901 -0.0001745 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2036 0.1366 0.2849 0.1564 0.985 0.994 0.2043 0.4874 0.8903 0.7417 ] Network output: [ 0.01103 0.03216 1.026 0.0001254 -5.632e-05 0.9202 9.454e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07418 0.06941 0.1501 0.1971 0.9877 0.9922 0.07423 0.8183 0.8918 0.2874 ] Network output: [ -0.006003 -0.004824 1.029 0.0001225 -5.497e-05 0.988 9.229e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08348 0.08254 0.1662 0.2057 0.9853 0.9913 0.08349 0.74 0.8697 0.2504 ] Network output: [ -0.004223 0.9929 0.01316 1.942e-05 -8.719e-06 1.002 1.464e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01311 Epoch 5628 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01711 0.9737 0.9778 -1.957e-05 8.786e-06 0.01415 -1.475e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00289 -0.002505 -0.01086 0.008671 0.9693 0.9738 0.005464 0.8451 0.8371 0.02252 ] Network output: [ 1.003 -0.1357 0.01514 8.573e-06 -3.849e-06 0.1136 6.461e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1882 -0.006632 -0.2075 0.244 0.9835 0.9932 0.2098 0.4763 0.8844 0.7471 ] Network output: [ -0.01172 0.9967 1.003 -2.948e-05 1.323e-05 0.02371 -2.221e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0043 0.00133 0.003866 0.006089 0.9891 0.9921 0.004377 0.8805 0.9084 0.01596 ] Network output: [ 0.02152 -0.2114 0.9544 -0.0001504 6.753e-05 1.213 -0.0001134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1999 0.1352 0.3094 0.2311 0.9851 0.994 0.2005 0.4822 0.89 0.7392 ] Network output: [ 0.0025 -0.02979 1.041 0.000126 -5.655e-05 0.9845 9.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07583 0.07116 0.1665 0.2155 0.9876 0.9922 0.07588 0.8233 0.8919 0.2975 ] Network output: [ -0.01261 0.02429 1.03 0.0001129 -5.067e-05 0.9711 8.506e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08513 0.08422 0.1718 0.2086 0.9855 0.9915 0.08514 0.7469 0.8692 0.2504 ] Network output: [ 0.00391 1.013 -0.004323 2.329e-05 -1.046e-05 0.9834 1.755e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01893 Epoch 5629 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01425 1.015 0.9761 -2.783e-05 1.249e-05 -0.01939 -2.097e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002939 -0.00251 -0.01098 0.007845 0.9693 0.9738 0.005548 0.8457 0.8351 0.02224 ] Network output: [ 0.9772 0.1497 0.002785 -5.191e-05 2.331e-05 -0.1071 -3.912e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1926 -0.002869 -0.219 0.1951 0.9835 0.9932 0.2147 0.4825 0.8824 0.7429 ] Network output: [ -0.01136 1.009 1.002 -3.132e-05 1.406e-05 0.01201 -2.361e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004385 0.001308 0.003162 0.00457 0.9891 0.9921 0.004463 0.8809 0.9074 0.01557 ] Network output: [ -0.004713 0.1731 0.9325 -0.000231 0.0001037 0.9029 -0.0001741 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2037 0.1366 0.2851 0.157 0.985 0.994 0.2043 0.4866 0.8901 0.7415 ] Network output: [ 0.01103 0.03146 1.026 0.0001255 -5.634e-05 0.9211 9.457e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07443 0.06964 0.1504 0.1974 0.9876 0.9921 0.07448 0.8181 0.8916 0.2877 ] Network output: [ -0.006081 -0.004231 1.029 0.0001223 -5.492e-05 0.9878 9.219e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08369 0.08275 0.1663 0.2059 0.9853 0.9913 0.0837 0.7398 0.8695 0.2505 ] Network output: [ -0.004334 0.9937 0.01314 1.923e-05 -8.633e-06 1.002 1.449e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01277 Epoch 5630 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0171 0.9739 0.978 -1.924e-05 8.638e-06 0.01383 -1.45e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002894 -0.002508 -0.01085 0.008666 0.9693 0.9738 0.00547 0.8449 0.8369 0.02252 ] Network output: [ 1.003 -0.1331 0.01505 7.337e-06 -3.294e-06 0.1117 5.529e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1883 -0.006628 -0.2073 0.2435 0.9835 0.9932 0.2099 0.4756 0.8841 0.7469 ] Network output: [ -0.0117 0.9966 1.003 -2.905e-05 1.304e-05 0.0236 -2.189e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004308 0.001331 0.003871 0.006085 0.9891 0.9921 0.004386 0.8802 0.9082 0.01596 ] Network output: [ 0.0213 -0.2081 0.9543 -0.0001513 6.791e-05 1.211 -0.000114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2 0.1352 0.3092 0.2306 0.985 0.994 0.2006 0.4815 0.8898 0.739 ] Network output: [ 0.002658 -0.02989 1.04 0.000126 -5.658e-05 0.9847 9.498e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07605 0.07136 0.1665 0.2156 0.9876 0.9922 0.0761 0.823 0.8917 0.2977 ] Network output: [ -0.01256 0.02411 1.03 0.000113 -5.071e-05 0.9714 8.513e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08531 0.0844 0.1719 0.2087 0.9855 0.9915 0.08532 0.7466 0.8691 0.2505 ] Network output: [ 0.003853 1.013 -0.0041 2.332e-05 -1.047e-05 0.984 1.757e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01838 Epoch 5631 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01429 1.014 0.9762 -2.736e-05 1.228e-05 -0.01923 -2.062e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002942 -0.002513 -0.01098 0.007854 0.9693 0.9738 0.005553 0.8455 0.8349 0.02225 ] Network output: [ 0.9776 0.1472 0.002739 -5.19e-05 2.33e-05 -0.1053 -3.912e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1927 -0.002928 -0.2187 0.1955 0.9835 0.9932 0.2147 0.4817 0.8822 0.7427 ] Network output: [ -0.01137 1.009 1.002 -3.089e-05 1.387e-05 0.01201 -2.328e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004393 0.00131 0.003177 0.00459 0.9891 0.9921 0.004471 0.8807 0.9073 0.01559 ] Network output: [ -0.004536 0.1698 0.9329 -0.0002303 0.0001034 0.9054 -0.0001736 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2037 0.1366 0.2854 0.1577 0.985 0.994 0.2044 0.4859 0.8899 0.7413 ] Network output: [ 0.01103 0.03055 1.026 0.0001255 -5.636e-05 0.9221 9.462e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07468 0.06987 0.1507 0.1978 0.9876 0.9921 0.07472 0.8179 0.8915 0.2882 ] Network output: [ -0.006166 -0.003705 1.029 0.0001222 -5.486e-05 0.9876 9.209e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0839 0.08295 0.1665 0.206 0.9853 0.9913 0.08391 0.7396 0.8693 0.2506 ] Network output: [ -0.004429 0.9944 0.0131 1.905e-05 -8.554e-06 1.001 1.436e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01234 Epoch 5632 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01709 0.9742 0.9781 -1.893e-05 8.499e-06 0.01345 -1.427e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002897 -0.00251 -0.01085 0.00866 0.9693 0.9738 0.005475 0.8447 0.8367 0.02252 ] Network output: [ 1.003 -0.13 0.01489 6.042e-06 -2.713e-06 0.1093 4.554e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1884 -0.006618 -0.2072 0.2429 0.9835 0.9932 0.21 0.475 0.8839 0.7467 ] Network output: [ -0.01168 0.9966 1.003 -2.864e-05 1.286e-05 0.02348 -2.159e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004317 0.001332 0.003873 0.006078 0.9891 0.9921 0.004395 0.88 0.9081 0.01597 ] Network output: [ 0.02099 -0.2038 0.9543 -0.0001523 6.838e-05 1.207 -0.0001148 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2001 0.1352 0.309 0.2299 0.985 0.994 0.2007 0.4809 0.8896 0.7389 ] Network output: [ 0.002838 -0.02992 1.04 0.0001261 -5.662e-05 0.9847 9.505e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07626 0.07156 0.1665 0.2157 0.9876 0.9921 0.07631 0.8227 0.8916 0.2979 ] Network output: [ -0.0125 0.02379 1.03 0.0001131 -5.076e-05 0.9717 8.521e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08549 0.08457 0.1719 0.2088 0.9855 0.9915 0.0855 0.7463 0.8689 0.2507 ] Network output: [ 0.003759 1.012 -0.003828 2.331e-05 -1.046e-05 0.9845 1.757e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0177 Epoch 5633 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01434 1.014 0.9764 -2.688e-05 1.207e-05 -0.01901 -2.026e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002945 -0.002515 -0.01097 0.007865 0.9693 0.9738 0.005558 0.8453 0.8347 0.02226 ] Network output: [ 0.978 0.1443 0.002676 -5.173e-05 2.322e-05 -0.1032 -3.899e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1927 -0.002997 -0.2185 0.1959 0.9835 0.9932 0.2147 0.481 0.882 0.7427 ] Network output: [ -0.01139 1.008 1.002 -3.047e-05 1.368e-05 0.01206 -2.296e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0044 0.001312 0.003193 0.004612 0.9891 0.9921 0.004479 0.8805 0.9071 0.0156 ] Network output: [ -0.004341 0.166 0.9333 -0.0002295 0.000103 0.9084 -0.000173 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2038 0.1366 0.2857 0.1586 0.985 0.994 0.2044 0.4852 0.8897 0.7412 ] Network output: [ 0.01101 0.02946 1.026 0.0001256 -5.639e-05 0.9233 9.467e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07492 0.07009 0.151 0.1982 0.9876 0.9921 0.07496 0.8177 0.8913 0.2886 ] Network output: [ -0.006259 -0.003246 1.029 0.0001221 -5.48e-05 0.9875 9.199e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08411 0.08316 0.1666 0.2061 0.9853 0.9913 0.08412 0.7394 0.8692 0.2508 ] Network output: [ -0.004504 0.9951 0.01304 1.89e-05 -8.483e-06 1.001 1.424e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01184 Epoch 5634 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01708 0.9746 0.9782 -1.864e-05 8.368e-06 0.013 -1.405e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002901 -0.002513 -0.01085 0.008652 0.9693 0.9738 0.005481 0.8446 0.8365 0.02252 ] Network output: [ 1.003 -0.1263 0.01465 4.717e-06 -2.118e-06 0.1064 3.555e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1885 -0.006608 -0.2071 0.2422 0.9835 0.9932 0.2101 0.4745 0.8837 0.7465 ] Network output: [ -0.01167 0.9966 1.003 -2.825e-05 1.268e-05 0.02334 -2.129e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004326 0.001333 0.003874 0.006067 0.9891 0.9921 0.004403 0.8799 0.9079 0.01598 ] Network output: [ 0.0206 -0.1989 0.9542 -0.0001536 6.894e-05 1.203 -0.0001157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2002 0.1352 0.3088 0.229 0.985 0.994 0.2008 0.4803 0.8894 0.7388 ] Network output: [ 0.003038 -0.02989 1.04 0.0001262 -5.666e-05 0.9847 9.511e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07647 0.07174 0.1665 0.2157 0.9876 0.9921 0.07651 0.8224 0.8914 0.2982 ] Network output: [ -0.01242 0.02333 1.03 0.0001132 -5.082e-05 0.9722 8.532e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08566 0.08474 0.172 0.209 0.9855 0.9915 0.08567 0.746 0.8688 0.2508 ] Network output: [ 0.003635 1.011 -0.003519 2.327e-05 -1.045e-05 0.9851 1.754e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01692 Epoch 5635 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01439 1.013 0.9765 -2.639e-05 1.185e-05 -0.01873 -1.989e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002947 -0.002518 -0.01097 0.007878 0.9693 0.9738 0.005562 0.8452 0.8346 0.02227 ] Network output: [ 0.9785 0.1411 0.002599 -5.141e-05 2.308e-05 -0.1008 -3.875e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1927 -0.003079 -0.2182 0.1964 0.9835 0.9932 0.2147 0.4803 0.8818 0.7426 ] Network output: [ -0.0114 1.008 1.002 -3.005e-05 1.349e-05 0.01213 -2.265e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004407 0.001313 0.00321 0.004636 0.9891 0.9921 0.004486 0.8803 0.9069 0.01562 ] Network output: [ -0.004135 0.1618 0.9339 -0.0002287 0.0001027 0.9117 -0.0001724 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2038 0.1365 0.286 0.1595 0.985 0.994 0.2044 0.4845 0.8895 0.7411 ] Network output: [ 0.011 0.02821 1.026 0.0001257 -5.643e-05 0.9247 9.473e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07516 0.07032 0.1513 0.1987 0.9876 0.9921 0.0752 0.8176 0.8912 0.289 ] Network output: [ -0.006356 -0.00285 1.029 0.0001219 -5.474e-05 0.9874 9.189e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08431 0.08336 0.1668 0.2063 0.9853 0.9913 0.08433 0.7394 0.8691 0.2509 ] Network output: [ -0.004558 0.9957 0.01296 1.876e-05 -8.422e-06 1.001 1.414e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01129 Epoch 5636 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01707 0.975 0.9783 -1.836e-05 8.243e-06 0.01251 -1.384e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002904 -0.002515 -0.01085 0.008644 0.9693 0.9738 0.005488 0.8444 0.8363 0.02253 ] Network output: [ 1.002 -0.1222 0.01435 3.386e-06 -1.52e-06 0.1032 2.552e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1886 -0.006599 -0.2071 0.2414 0.9835 0.9932 0.2102 0.474 0.8835 0.7463 ] Network output: [ -0.01166 0.9966 1.003 -2.787e-05 1.251e-05 0.02319 -2.1e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004334 0.001333 0.003874 0.006054 0.9891 0.9921 0.004412 0.8797 0.9077 0.01599 ] Network output: [ 0.02015 -0.1934 0.9541 -0.000155 6.956e-05 1.198 -0.0001168 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2003 0.1352 0.3085 0.228 0.985 0.994 0.2009 0.4798 0.8893 0.7388 ] Network output: [ 0.003251 -0.02982 1.039 0.0001263 -5.67e-05 0.9846 9.518e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07667 0.07192 0.1664 0.2157 0.9876 0.9921 0.07671 0.8222 0.8913 0.2984 ] Network output: [ -0.01234 0.02278 1.03 0.0001134 -5.09e-05 0.9727 8.544e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08582 0.0849 0.172 0.2091 0.9855 0.9915 0.08583 0.7458 0.8687 0.251 ] Network output: [ 0.003489 1.011 -0.003187 2.321e-05 -1.042e-05 0.9857 1.749e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01609 Epoch 5637 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01445 1.013 0.9767 -2.589e-05 1.162e-05 -0.0184 -1.951e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00295 -0.002521 -0.01097 0.007891 0.9693 0.9738 0.005566 0.845 0.8345 0.02228 ] Network output: [ 0.979 0.1375 0.002513 -5.097e-05 2.288e-05 -0.09828 -3.841e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1927 -0.003172 -0.218 0.197 0.9835 0.9932 0.2147 0.4797 0.8817 0.7426 ] Network output: [ -0.01142 1.008 1.002 -2.964e-05 1.331e-05 0.01222 -2.234e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004413 0.001314 0.003227 0.004661 0.9891 0.9921 0.004492 0.8801 0.9068 0.01564 ] Network output: [ -0.003925 0.1572 0.9345 -0.0002279 0.0001023 0.9153 -0.0001717 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2037 0.1365 0.2864 0.1604 0.985 0.994 0.2044 0.4839 0.8893 0.741 ] Network output: [ 0.01098 0.02685 1.026 0.0001258 -5.647e-05 0.9261 9.479e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07539 0.07053 0.1517 0.1991 0.9876 0.9921 0.07544 0.8174 0.8911 0.2895 ] Network output: [ -0.006455 -0.002512 1.029 0.0001218 -5.468e-05 0.9874 9.179e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08452 0.08356 0.167 0.2065 0.9853 0.9913 0.08453 0.7393 0.869 0.2511 ] Network output: [ -0.004589 0.9962 0.01285 1.865e-05 -8.373e-06 1 1.406e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01071 Epoch 5638 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01705 0.9755 0.9784 -1.81e-05 8.125e-06 0.01198 -1.364e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002908 -0.002518 -0.01085 0.008635 0.9693 0.9738 0.005494 0.8443 0.8361 0.02253 ] Network output: [ 1.002 -0.1179 0.014 2.072e-06 -9.303e-07 0.09979 1.562e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1887 -0.006593 -0.2072 0.2406 0.9835 0.9932 0.2103 0.4735 0.8833 0.7462 ] Network output: [ -0.01166 0.9966 1.004 -2.751e-05 1.235e-05 0.02302 -2.073e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004343 0.001333 0.003872 0.006038 0.9891 0.9921 0.00442 0.8796 0.9076 0.016 ] Network output: [ 0.01964 -0.1875 0.9541 -0.0001565 7.024e-05 1.193 -0.0001179 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2003 0.1352 0.3082 0.227 0.985 0.994 0.201 0.4793 0.8891 0.7388 ] Network output: [ 0.003474 -0.02972 1.039 0.0001264 -5.674e-05 0.9845 9.525e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07686 0.07209 0.1663 0.2157 0.9876 0.9921 0.0769 0.8219 0.8912 0.2986 ] Network output: [ -0.01225 0.02215 1.029 0.0001135 -5.097e-05 0.9733 8.557e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08598 0.08505 0.1721 0.2092 0.9855 0.9915 0.08599 0.7456 0.8687 0.2512 ] Network output: [ 0.003329 1.01 -0.002844 2.313e-05 -1.038e-05 0.9862 1.743e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01522 Epoch 5639 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01451 1.012 0.9768 -2.539e-05 1.14e-05 -0.01805 -1.914e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002952 -0.002523 -0.01097 0.007906 0.9693 0.9738 0.00557 0.8449 0.8343 0.02229 ] Network output: [ 0.9796 0.1338 0.00242 -5.043e-05 2.264e-05 -0.0956 -3.801e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1926 -0.003276 -0.2178 0.1976 0.9835 0.9932 0.2147 0.4791 0.8815 0.7426 ] Network output: [ -0.01144 1.008 1.003 -2.924e-05 1.313e-05 0.01233 -2.204e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00442 0.001314 0.003243 0.004686 0.9891 0.9921 0.004498 0.88 0.9067 0.01566 ] Network output: [ -0.003716 0.1523 0.9351 -0.000227 0.0001019 0.9191 -0.0001711 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2037 0.1364 0.2868 0.1614 0.985 0.994 0.2043 0.4834 0.8892 0.7409 ] Network output: [ 0.01096 0.02541 1.026 0.0001259 -5.65e-05 0.9277 9.485e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07563 0.07075 0.152 0.1996 0.9876 0.9921 0.07567 0.8174 0.891 0.29 ] Network output: [ -0.006556 -0.002223 1.028 0.0001217 -5.462e-05 0.9875 9.169e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08472 0.08376 0.1672 0.2067 0.9853 0.9913 0.08473 0.7393 0.8689 0.2513 ] Network output: [ -0.004599 0.9967 0.01272 1.857e-05 -8.336e-06 0.9999 1.399e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01012 Epoch 5640 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01702 0.976 0.9785 -1.785e-05 8.013e-06 0.01143 -1.345e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002911 -0.002521 -0.01085 0.008626 0.9693 0.9738 0.0055 0.8442 0.836 0.02253 ] Network output: [ 1.002 -0.1135 0.01362 7.948e-07 -3.568e-07 0.09622 5.99e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1887 -0.006595 -0.2073 0.2398 0.9835 0.9932 0.2104 0.4732 0.8831 0.7461 ] Network output: [ -0.01167 0.9967 1.004 -2.717e-05 1.22e-05 0.02284 -2.047e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004351 0.001333 0.003869 0.006021 0.9891 0.9921 0.004429 0.8794 0.9075 0.01601 ] Network output: [ 0.01909 -0.1814 0.9541 -0.0001581 7.096e-05 1.188 -0.0001191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2004 0.1351 0.3079 0.2258 0.985 0.994 0.201 0.4789 0.889 0.7388 ] Network output: [ 0.003702 -0.02959 1.038 0.0001265 -5.678e-05 0.9844 9.531e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07705 0.07226 0.1663 0.2157 0.9876 0.9921 0.07709 0.8217 0.8911 0.2988 ] Network output: [ -0.01215 0.02148 1.029 0.0001137 -5.105e-05 0.974 8.57e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08613 0.0852 0.1721 0.2093 0.9855 0.9915 0.08614 0.7454 0.8686 0.2514 ] Network output: [ 0.003161 1.01 -0.0025 2.304e-05 -1.034e-05 0.9867 1.736e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01434 Epoch 5641 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01457 1.012 0.9769 -2.489e-05 1.118e-05 -0.01768 -1.876e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002954 -0.002526 -0.01097 0.00792 0.9693 0.9738 0.005574 0.8448 0.8343 0.0223 ] Network output: [ 0.9802 0.1299 0.002324 -4.981e-05 2.236e-05 -0.09284 -3.754e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1926 -0.003392 -0.2176 0.1982 0.9835 0.9932 0.2146 0.4786 0.8814 0.7426 ] Network output: [ -0.01147 1.008 1.003 -2.885e-05 1.295e-05 0.01245 -2.174e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004425 0.001314 0.00326 0.004711 0.9891 0.9921 0.004504 0.8799 0.9066 0.01567 ] Network output: [ -0.003513 0.1474 0.9358 -0.0002262 0.0001015 0.923 -0.0001705 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2036 0.1363 0.2871 0.1624 0.985 0.994 0.2042 0.4829 0.889 0.7408 ] Network output: [ 0.01094 0.02393 1.025 0.0001259 -5.654e-05 0.9292 9.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07586 0.07096 0.1524 0.2001 0.9876 0.9921 0.0759 0.8173 0.8909 0.2905 ] Network output: [ -0.006658 -0.001973 1.028 0.0001215 -5.456e-05 0.9875 9.16e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08492 0.08395 0.1674 0.2069 0.9853 0.9913 0.08493 0.7393 0.8689 0.2514 ] Network output: [ -0.004588 0.9971 0.01256 1.851e-05 -8.31e-06 0.9996 1.395e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009537 Epoch 5642 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.017 0.9765 0.9785 -1.761e-05 7.904e-06 0.01086 -1.327e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002915 -0.002523 -0.01086 0.008617 0.9693 0.9738 0.005505 0.8441 0.8359 0.02254 ] Network output: [ 1.002 -0.109 0.01321 -4.317e-07 1.938e-07 0.0926 -3.254e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1888 -0.006604 -0.2075 0.239 0.9835 0.9932 0.2104 0.4729 0.883 0.746 ] Network output: [ -0.01168 0.9968 1.004 -2.684e-05 1.205e-05 0.02264 -2.023e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004359 0.001332 0.003866 0.006004 0.9891 0.9921 0.004437 0.8794 0.9073 0.01602 ] Network output: [ 0.01852 -0.1751 0.9541 -0.0001597 7.171e-05 1.183 -0.0001204 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2004 0.135 0.3076 0.2247 0.985 0.994 0.201 0.4786 0.8889 0.7388 ] Network output: [ 0.003931 -0.02944 1.038 0.0001265 -5.681e-05 0.9843 9.537e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07723 0.07242 0.1662 0.2157 0.9876 0.9921 0.07728 0.8215 0.891 0.2991 ] Network output: [ -0.01206 0.02078 1.029 0.0001139 -5.113e-05 0.9746 8.583e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08628 0.08535 0.1722 0.2095 0.9855 0.9915 0.08629 0.7452 0.8686 0.2516 ] Network output: [ 0.002992 1.009 -0.002164 2.295e-05 -1.03e-05 0.9871 1.729e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01347 Epoch 5643 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01463 1.011 0.977 -2.44e-05 1.095e-05 -0.0173 -1.839e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002956 -0.002529 -0.01097 0.007936 0.9693 0.9738 0.005578 0.8447 0.8342 0.02231 ] Network output: [ 0.9808 0.126 0.00223 -4.914e-05 2.206e-05 -0.09005 -3.703e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1925 -0.003518 -0.2174 0.1988 0.9835 0.9932 0.2145 0.4781 0.8813 0.7426 ] Network output: [ -0.0115 1.007 1.003 -2.848e-05 1.278e-05 0.01256 -2.146e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004431 0.001314 0.003276 0.004736 0.9891 0.9921 0.00451 0.8798 0.9065 0.01569 ] Network output: [ -0.003319 0.1424 0.9365 -0.0002254 0.0001012 0.9268 -0.0001699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2035 0.1362 0.2875 0.1634 0.985 0.994 0.2042 0.4824 0.8889 0.7408 ] Network output: [ 0.01093 0.02243 1.025 0.000126 -5.658e-05 0.9308 9.498e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07608 0.07117 0.1528 0.2006 0.9876 0.9921 0.07613 0.8172 0.8908 0.291 ] Network output: [ -0.006758 -0.001753 1.028 0.0001214 -5.451e-05 0.9877 9.151e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08511 0.08415 0.1676 0.2071 0.9854 0.9914 0.08512 0.7394 0.8688 0.2516 ] Network output: [ -0.004558 0.9975 0.01239 1.848e-05 -8.296e-06 0.9993 1.393e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.008968 Epoch 5644 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01696 0.9771 0.9786 -1.737e-05 7.799e-06 0.01029 -1.309e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002918 -0.002526 -0.01086 0.008608 0.9693 0.9738 0.005511 0.8441 0.8358 0.02254 ] Network output: [ 1.001 -0.1045 0.0128 -1.596e-06 7.167e-07 0.08898 -1.203e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1888 -0.006623 -0.2076 0.2382 0.9835 0.9932 0.2105 0.4726 0.8828 0.7459 ] Network output: [ -0.0117 0.9969 1.004 -2.653e-05 1.191e-05 0.02244 -1.999e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004366 0.001331 0.003862 0.005985 0.9891 0.9921 0.004444 0.8793 0.9073 0.01602 ] Network output: [ 0.01793 -0.1689 0.9542 -0.0001614 7.248e-05 1.178 -0.0001217 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2004 0.1349 0.3073 0.2235 0.985 0.994 0.201 0.4783 0.8888 0.7388 ] Network output: [ 0.004157 -0.02928 1.037 0.0001266 -5.684e-05 0.9841 9.543e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07741 0.07258 0.1661 0.2157 0.9876 0.9921 0.07746 0.8213 0.8909 0.2993 ] Network output: [ -0.01196 0.02009 1.029 0.0001141 -5.121e-05 0.9753 8.597e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08643 0.08549 0.1722 0.2096 0.9855 0.9915 0.08644 0.7451 0.8686 0.2517 ] Network output: [ 0.002826 1.009 -0.001843 2.286e-05 -1.026e-05 0.9876 1.723e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01264 Epoch 5645 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01468 1.01 0.9771 -2.392e-05 1.074e-05 -0.01693 -1.802e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002958 -0.002532 -0.01097 0.007951 0.9693 0.9738 0.005581 0.8447 0.8342 0.02233 ] Network output: [ 0.9814 0.1221 0.002139 -4.842e-05 2.174e-05 -0.08728 -3.649e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1924 -0.003652 -0.2173 0.1994 0.9835 0.9932 0.2144 0.4777 0.8813 0.7427 ] Network output: [ -0.01154 1.007 1.003 -2.811e-05 1.262e-05 0.01268 -2.119e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004436 0.001313 0.003291 0.004761 0.9891 0.9921 0.004515 0.8797 0.9065 0.01571 ] Network output: [ -0.003136 0.1375 0.9372 -0.0002247 0.0001009 0.9306 -0.0001693 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2034 0.136 0.288 0.1644 0.985 0.994 0.2041 0.482 0.8888 0.7408 ] Network output: [ 0.01091 0.02094 1.025 0.0001261 -5.661e-05 0.9324 9.504e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0763 0.07137 0.1532 0.2011 0.9876 0.9921 0.07635 0.8172 0.8907 0.2915 ] Network output: [ -0.006857 -0.001555 1.028 0.0001213 -5.446e-05 0.9878 9.142e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0853 0.08433 0.1678 0.2073 0.9854 0.9914 0.08531 0.7395 0.8688 0.2518 ] Network output: [ -0.004511 0.9978 0.0122 1.847e-05 -8.293e-06 0.9991 1.392e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.008423 Epoch 5646 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01693 0.9777 0.9786 -1.714e-05 7.696e-06 0.009717 -1.292e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002921 -0.002529 -0.01087 0.0086 0.9693 0.9738 0.005517 0.8441 0.8357 0.02255 ] Network output: [ 1.001 -0.1002 0.01238 -2.692e-06 1.209e-06 0.08543 -2.029e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1889 -0.006652 -0.2078 0.2374 0.9835 0.9932 0.2105 0.4724 0.8827 0.7459 ] Network output: [ -0.01172 0.9971 1.004 -2.623e-05 1.177e-05 0.02224 -1.976e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004373 0.001329 0.003858 0.005967 0.9891 0.9921 0.004452 0.8792 0.9072 0.01603 ] Network output: [ 0.01734 -0.1627 0.9544 -0.0001632 7.325e-05 1.173 -0.000123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2004 0.1348 0.307 0.2223 0.985 0.994 0.201 0.4781 0.8887 0.7389 ] Network output: [ 0.00438 -0.0291 1.037 0.0001267 -5.687e-05 0.984 9.547e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07759 0.07273 0.166 0.2157 0.9876 0.9921 0.07763 0.8211 0.8909 0.2995 ] Network output: [ -0.01188 0.0194 1.029 0.0001142 -5.129e-05 0.976 8.609e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08657 0.08563 0.1723 0.2097 0.9855 0.9915 0.08658 0.745 0.8686 0.2519 ] Network output: [ 0.002667 1.008 -0.001543 2.278e-05 -1.023e-05 0.988 1.717e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01185 Epoch 5647 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01473 1.01 0.9772 -2.344e-05 1.052e-05 -0.01656 -1.767e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00296 -0.002534 -0.01097 0.007966 0.9693 0.9738 0.005584 0.8446 0.8341 0.02234 ] Network output: [ 0.982 0.1183 0.002055 -4.769e-05 2.141e-05 -0.08456 -3.594e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1923 -0.003795 -0.2172 0.2 0.9835 0.9932 0.2143 0.4773 0.8812 0.7428 ] Network output: [ -0.01157 1.007 1.003 -2.776e-05 1.246e-05 0.01278 -2.092e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00444 0.001312 0.003306 0.004784 0.9891 0.9921 0.00452 0.8796 0.9064 0.01573 ] Network output: [ -0.002967 0.1327 0.938 -0.0002241 0.0001006 0.9343 -0.0001689 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2033 0.1358 0.2883 0.1653 0.985 0.994 0.2039 0.4817 0.8888 0.7407 ] Network output: [ 0.0109 0.01948 1.025 0.0001262 -5.665e-05 0.934 9.509e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07652 0.07156 0.1535 0.2015 0.9876 0.9921 0.07657 0.8172 0.8907 0.292 ] Network output: [ -0.006954 -0.001371 1.028 0.0001212 -5.441e-05 0.988 9.133e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08549 0.08452 0.168 0.2075 0.9854 0.9914 0.0855 0.7395 0.8688 0.252 ] Network output: [ -0.004449 0.998 0.012 1.849e-05 -8.299e-06 0.999 1.393e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.007907 Epoch 5648 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01689 0.9783 0.9787 -1.692e-05 7.595e-06 0.009153 -1.275e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002924 -0.002532 -0.01087 0.008592 0.9693 0.9738 0.005523 0.844 0.8356 0.02255 ] Network output: [ 1.001 -0.09603 0.01197 -3.715e-06 1.668e-06 0.082 -2.8e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1889 -0.006691 -0.208 0.2366 0.9835 0.9932 0.2105 0.4722 0.8826 0.7458 ] Network output: [ -0.01175 0.9973 1.004 -2.594e-05 1.164e-05 0.02202 -1.955e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00438 0.001327 0.003853 0.005948 0.9891 0.9921 0.004459 0.8792 0.9071 0.01604 ] Network output: [ 0.01675 -0.1568 0.9545 -0.0001649 7.402e-05 1.168 -0.0001243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2004 0.1346 0.3067 0.2212 0.985 0.994 0.201 0.4779 0.8886 0.7389 ] Network output: [ 0.004596 -0.02893 1.036 0.0001267 -5.69e-05 0.9838 9.551e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07776 0.07288 0.166 0.2156 0.9876 0.9921 0.07781 0.821 0.8908 0.2997 ] Network output: [ -0.01179 0.01875 1.029 0.0001144 -5.136e-05 0.9766 8.622e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08672 0.08577 0.1723 0.2098 0.9855 0.9915 0.08673 0.7449 0.8686 0.2521 ] Network output: [ 0.002518 1.008 -0.001266 2.27e-05 -1.019e-05 0.9884 1.711e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0111 Epoch 5649 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01477 1.009 0.9773 -2.297e-05 1.031e-05 -0.01622 -1.731e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002962 -0.002537 -0.01098 0.007981 0.9693 0.9738 0.005588 0.8446 0.8341 0.02235 ] Network output: [ 0.9826 0.1145 0.001978 -4.695e-05 2.108e-05 -0.08192 -3.538e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1922 -0.003945 -0.2171 0.2006 0.9835 0.9932 0.2142 0.477 0.8812 0.7428 ] Network output: [ -0.01162 1.007 1.003 -2.742e-05 1.231e-05 0.01288 -2.066e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004445 0.00131 0.00332 0.004807 0.9891 0.9921 0.004524 0.8796 0.9064 0.01575 ] Network output: [ -0.002812 0.1281 0.9388 -0.0002235 0.0001003 0.9378 -0.0001684 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2032 0.1356 0.2887 0.1662 0.985 0.994 0.2038 0.4814 0.8887 0.7408 ] Network output: [ 0.01089 0.01807 1.025 0.0001262 -5.668e-05 0.9355 9.514e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07674 0.07176 0.1539 0.202 0.9876 0.9921 0.07679 0.8172 0.8906 0.2924 ] Network output: [ -0.007048 -0.001195 1.028 0.0001211 -5.436e-05 0.9881 9.125e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08567 0.0847 0.1682 0.2076 0.9854 0.9914 0.08568 0.7397 0.8688 0.2521 ] Network output: [ -0.004376 0.9982 0.01179 1.852e-05 -8.314e-06 0.9988 1.396e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.007426 Epoch 5650 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01685 0.9789 0.9788 -1.669e-05 7.494e-06 0.008602 -1.258e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002927 -0.002535 -0.01088 0.008584 0.9693 0.9738 0.005528 0.844 0.8355 0.02256 ] Network output: [ 1.001 -0.09205 0.01157 -4.663e-06 2.094e-06 0.07872 -3.515e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1889 -0.006741 -0.2083 0.2359 0.9835 0.9932 0.2105 0.4721 0.8825 0.7458 ] Network output: [ -0.01179 0.9974 1.004 -2.566e-05 1.152e-05 0.02181 -1.934e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004387 0.001324 0.003849 0.005931 0.9891 0.9921 0.004465 0.8792 0.907 0.01605 ] Network output: [ 0.01618 -0.151 0.9547 -0.0001666 7.478e-05 1.163 -0.0001255 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2004 0.1344 0.3065 0.2201 0.985 0.994 0.201 0.4778 0.8886 0.739 ] Network output: [ 0.004804 -0.02875 1.036 0.0001268 -5.692e-05 0.9837 9.555e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07793 0.07303 0.1659 0.2156 0.9876 0.9921 0.07798 0.8209 0.8908 0.2999 ] Network output: [ -0.01172 0.01813 1.028 0.0001146 -5.143e-05 0.9773 8.633e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08686 0.08591 0.1724 0.21 0.9855 0.9915 0.08687 0.7448 0.8686 0.2523 ] Network output: [ 0.00238 1.008 -0.001015 2.264e-05 -1.016e-05 0.9888 1.706e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.01041 Epoch 5651 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01481 1.009 0.9774 -2.252e-05 1.011e-05 -0.01589 -1.697e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002964 -0.00254 -0.01098 0.007995 0.9693 0.9738 0.005591 0.8446 0.8341 0.02237 ] Network output: [ 0.9832 0.1109 0.001909 -4.622e-05 2.075e-05 -0.07937 -3.483e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.192 -0.004101 -0.217 0.2012 0.9835 0.9932 0.214 0.4767 0.8812 0.7429 ] Network output: [ -0.01166 1.007 1.003 -2.709e-05 1.216e-05 0.01296 -2.042e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004449 0.001308 0.003334 0.004829 0.9891 0.9921 0.004529 0.8795 0.9064 0.01576 ] Network output: [ -0.002671 0.1237 0.9396 -0.000223 0.0001001 0.9411 -0.0001681 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.203 0.1354 0.2891 0.167 0.985 0.994 0.2036 0.4811 0.8887 0.7408 ] Network output: [ 0.01088 0.01672 1.025 0.0001263 -5.67e-05 0.9369 9.519e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07695 0.07195 0.1542 0.2024 0.9876 0.9921 0.077 0.8172 0.8906 0.2929 ] Network output: [ -0.00714 -0.001021 1.027 0.000121 -5.431e-05 0.9883 9.118e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08586 0.08488 0.1684 0.2078 0.9854 0.9914 0.08587 0.7398 0.8688 0.2523 ] Network output: [ -0.004294 0.9984 0.01156 1.857e-05 -8.336e-06 0.9987 1.399e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.006979 Epoch 5652 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01681 0.9794 0.9788 -1.647e-05 7.395e-06 0.008071 -1.241e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00293 -0.002538 -0.01089 0.008578 0.9693 0.9738 0.005533 0.844 0.8355 0.02256 ] Network output: [ 1.001 -0.0883 0.01119 -5.538e-06 2.486e-06 0.07561 -4.174e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1888 -0.006801 -0.2085 0.2352 0.9835 0.9932 0.2105 0.4719 0.8825 0.7458 ] Network output: [ -0.01183 0.9976 1.004 -2.539e-05 1.14e-05 0.0216 -1.913e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004393 0.001322 0.003845 0.005914 0.9891 0.9921 0.004472 0.8791 0.907 0.01606 ] Network output: [ 0.01562 -0.1456 0.9549 -0.0001682 7.552e-05 1.159 -0.0001268 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2003 0.1343 0.3063 0.219 0.985 0.994 0.2009 0.4776 0.8886 0.7391 ] Network output: [ 0.005004 -0.02857 1.035 0.0001268 -5.693e-05 0.9836 9.558e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07811 0.07317 0.1658 0.2156 0.9875 0.9921 0.07815 0.8208 0.8908 0.3001 ] Network output: [ -0.01165 0.01756 1.028 0.0001147 -5.149e-05 0.9779 8.644e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.087 0.08605 0.1724 0.2101 0.9855 0.9915 0.08701 0.7448 0.8686 0.2524 ] Network output: [ 0.002255 1.007 -0.000791 2.259e-05 -1.014e-05 0.9891 1.702e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009775 Epoch 5653 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01484 1.008 0.9775 -2.208e-05 9.912e-06 -0.01558 -1.664e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002965 -0.002543 -0.01098 0.008009 0.9693 0.9738 0.005594 0.8445 0.8341 0.02238 ] Network output: [ 0.9837 0.1075 0.00185 -4.55e-05 2.043e-05 -0.07693 -3.429e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1919 -0.004261 -0.217 0.2017 0.9835 0.9932 0.2138 0.4764 0.8812 0.743 ] Network output: [ -0.0117 1.007 1.004 -2.677e-05 1.202e-05 0.01304 -2.018e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004454 0.001305 0.003347 0.004849 0.9891 0.9921 0.004533 0.8795 0.9063 0.01578 ] Network output: [ -0.002545 0.1196 0.9403 -0.0002226 9.992e-05 0.9443 -0.0001677 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2029 0.1351 0.2895 0.1678 0.985 0.994 0.2035 0.4809 0.8886 0.7408 ] Network output: [ 0.01088 0.01544 1.025 0.0001264 -5.673e-05 0.9384 9.523e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07716 0.07213 0.1545 0.2028 0.9876 0.9921 0.07721 0.8172 0.8906 0.2933 ] Network output: [ -0.007229 -0.0008465 1.027 0.0001209 -5.427e-05 0.9885 9.11e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08604 0.08505 0.1685 0.208 0.9854 0.9914 0.08605 0.7399 0.8688 0.2525 ] Network output: [ -0.004205 0.9986 0.01134 1.863e-05 -8.365e-06 0.9986 1.404e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.006568 Epoch 5654 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01676 0.98 0.9789 -1.625e-05 7.294e-06 0.00756 -1.225e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002933 -0.002541 -0.01089 0.008571 0.9693 0.9738 0.005538 0.844 0.8354 0.02257 ] Network output: [ 1.001 -0.0848 0.01083 -6.342e-06 2.847e-06 0.0727 -4.779e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1888 -0.00687 -0.2087 0.2346 0.9835 0.9932 0.2104 0.4718 0.8824 0.7458 ] Network output: [ -0.01187 0.9978 1.004 -2.513e-05 1.128e-05 0.02138 -1.894e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0044 0.001319 0.003841 0.005897 0.9891 0.9921 0.004478 0.8791 0.907 0.01606 ] Network output: [ 0.01509 -0.1404 0.9552 -0.0001698 7.624e-05 1.154 -0.000128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2002 0.134 0.306 0.218 0.985 0.994 0.2008 0.4775 0.8885 0.7392 ] Network output: [ 0.005194 -0.0284 1.035 0.0001269 -5.695e-05 0.9835 9.56e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07828 0.07332 0.1658 0.2156 0.9875 0.9921 0.07833 0.8207 0.8907 0.3003 ] Network output: [ -0.01158 0.01704 1.028 0.0001148 -5.156e-05 0.9785 8.655e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08714 0.08618 0.1725 0.2102 0.9855 0.9915 0.08715 0.7448 0.8687 0.2526 ] Network output: [ 0.002143 1.007 -0.0005935 2.255e-05 -1.012e-05 0.9894 1.699e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.009193 Epoch 5655 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01486 1.008 0.9776 -2.165e-05 9.718e-06 -0.0153 -1.631e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002967 -0.002546 -0.01099 0.008022 0.9693 0.9738 0.005597 0.8445 0.8341 0.02239 ] Network output: [ 0.9842 0.1042 0.001801 -4.481e-05 2.012e-05 -0.07462 -3.377e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1917 -0.004426 -0.2169 0.2022 0.9835 0.9932 0.2137 0.4762 0.8812 0.7431 ] Network output: [ -0.01175 1.007 1.004 -2.646e-05 1.188e-05 0.0131 -1.994e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004458 0.001302 0.003359 0.004868 0.9891 0.9921 0.004537 0.8795 0.9063 0.0158 ] Network output: [ -0.002433 0.1157 0.9411 -0.0002222 9.976e-05 0.9472 -0.0001675 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2027 0.1349 0.2898 0.1685 0.985 0.994 0.2033 0.4807 0.8886 0.7408 ] Network output: [ 0.01088 0.01424 1.025 0.0001264 -5.675e-05 0.9397 9.526e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07737 0.07231 0.1549 0.2032 0.9876 0.9921 0.07742 0.8172 0.8906 0.2937 ] Network output: [ -0.007315 -0.0006678 1.027 0.0001208 -5.423e-05 0.9886 9.104e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08621 0.08523 0.1687 0.2082 0.9854 0.9914 0.08622 0.7401 0.8689 0.2526 ] Network output: [ -0.004111 0.9987 0.01111 1.871e-05 -8.398e-06 0.9985 1.41e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.006193 Epoch 5656 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01672 0.9805 0.9789 -1.602e-05 7.194e-06 0.007074 -1.208e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002936 -0.002544 -0.0109 0.008566 0.9693 0.9738 0.005543 0.8441 0.8354 0.02258 ] Network output: [ 1.001 -0.08156 0.0105 -7.077e-06 3.177e-06 0.06999 -5.334e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1888 -0.006948 -0.2089 0.234 0.9835 0.9932 0.2104 0.4718 0.8824 0.7458 ] Network output: [ -0.01191 0.998 1.005 -2.487e-05 1.117e-05 0.02117 -1.874e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004406 0.001315 0.003838 0.005882 0.9891 0.9921 0.004484 0.8791 0.907 0.01607 ] Network output: [ 0.01458 -0.1356 0.9555 -0.0001714 7.695e-05 1.15 -0.0001292 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2001 0.1338 0.3059 0.217 0.985 0.994 0.2008 0.4775 0.8885 0.7393 ] Network output: [ 0.005375 -0.02824 1.035 0.0001269 -5.696e-05 0.9834 9.562e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07845 0.07346 0.1657 0.2156 0.9875 0.9921 0.0785 0.8206 0.8907 0.3005 ] Network output: [ -0.01153 0.01657 1.028 0.000115 -5.161e-05 0.979 8.664e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08728 0.08632 0.1725 0.2103 0.9855 0.9915 0.08729 0.7448 0.8687 0.2527 ] Network output: [ 0.002044 1.007 -0.000422 2.252e-05 -1.011e-05 0.9897 1.697e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.008663 Epoch 5657 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01488 1.007 0.9777 -2.123e-05 9.53e-06 -0.01504 -1.6e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002969 -0.002548 -0.01099 0.008035 0.9693 0.9738 0.0056 0.8446 0.8342 0.0224 ] Network output: [ 0.9847 0.1011 0.001761 -4.415e-05 1.982e-05 -0.07243 -3.327e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1916 -0.004594 -0.2169 0.2027 0.9835 0.9932 0.2135 0.476 0.8812 0.7432 ] Network output: [ -0.0118 1.007 1.004 -2.616e-05 1.174e-05 0.01314 -1.971e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004462 0.001299 0.003371 0.004886 0.9891 0.9921 0.004542 0.8795 0.9063 0.01581 ] Network output: [ -0.002335 0.112 0.9419 -0.000222 9.964e-05 0.9498 -0.0001673 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2025 0.1346 0.2901 0.1692 0.985 0.994 0.2031 0.4805 0.8886 0.7408 ] Network output: [ 0.01089 0.01311 1.025 0.0001264 -5.676e-05 0.941 9.529e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07757 0.07249 0.1552 0.2036 0.9876 0.9921 0.07762 0.8173 0.8906 0.2941 ] Network output: [ -0.007397 -0.0004833 1.027 0.0001207 -5.419e-05 0.9888 9.097e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08639 0.0854 0.1689 0.2083 0.9854 0.9914 0.0864 0.7402 0.8689 0.2527 ] Network output: [ -0.004014 0.9988 0.01088 1.879e-05 -8.435e-06 0.9984 1.416e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.005851 Epoch 5658 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01667 0.981 0.979 -1.58e-05 7.093e-06 0.006613 -1.191e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002939 -0.002547 -0.01091 0.008561 0.9693 0.9738 0.005548 0.8441 0.8354 0.02258 ] Network output: [ 1 -0.07857 0.01018 -7.749e-06 3.479e-06 0.06748 -5.84e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1887 -0.007035 -0.2091 0.2334 0.9835 0.9932 0.2103 0.4717 0.8824 0.7458 ] Network output: [ -0.01196 0.9982 1.005 -2.462e-05 1.105e-05 0.02097 -1.855e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004411 0.001312 0.003834 0.005868 0.9891 0.9921 0.00449 0.8791 0.9069 0.01608 ] Network output: [ 0.01409 -0.131 0.9558 -0.0001729 7.762e-05 1.146 -0.0001303 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2001 0.1336 0.3057 0.2161 0.985 0.994 0.2007 0.4774 0.8885 0.7394 ] Network output: [ 0.005546 -0.02809 1.034 0.0001269 -5.696e-05 0.9834 9.563e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07862 0.0736 0.1657 0.2156 0.9875 0.9921 0.07867 0.8205 0.8907 0.3007 ] Network output: [ -0.01148 0.01615 1.028 0.0001151 -5.166e-05 0.9795 8.673e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08743 0.08646 0.1726 0.2104 0.9855 0.9915 0.08744 0.7448 0.8688 0.2529 ] Network output: [ 0.001956 1.006 -0.0002753 2.25e-05 -1.01e-05 0.99 1.696e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.008184 Epoch 5659 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0149 1.007 0.9778 -2.082e-05 9.347e-06 -0.0148 -1.569e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00297 -0.002551 -0.011 0.008047 0.9693 0.9738 0.005603 0.8446 0.8342 0.02242 ] Network output: [ 0.9851 0.09817 0.00173 -4.352e-05 1.954e-05 -0.07036 -3.28e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1914 -0.004764 -0.2169 0.2032 0.9835 0.9932 0.2133 0.4758 0.8812 0.7433 ] Network output: [ -0.01185 1.007 1.004 -2.586e-05 1.161e-05 0.01318 -1.949e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004466 0.001296 0.003381 0.004902 0.9891 0.9921 0.004546 0.8795 0.9063 0.01583 ] Network output: [ -0.002249 0.1087 0.9426 -0.0002218 9.955e-05 0.9523 -0.0001671 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2023 0.1343 0.2904 0.1698 0.985 0.994 0.203 0.4804 0.8886 0.7409 ] Network output: [ 0.0109 0.01205 1.025 0.0001265 -5.677e-05 0.9422 9.531e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07778 0.07266 0.1554 0.204 0.9876 0.9921 0.07782 0.8173 0.8906 0.2945 ] Network output: [ -0.007476 -0.0002921 1.027 0.0001206 -5.415e-05 0.989 9.091e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08656 0.08557 0.1691 0.2085 0.9854 0.9914 0.08657 0.7404 0.8689 0.2529 ] Network output: [ -0.003917 0.9989 0.01066 1.888e-05 -8.475e-06 0.9983 1.423e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.005541 Epoch 5660 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01662 0.9815 0.979 -1.557e-05 6.99e-06 0.006178 -1.173e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002941 -0.00255 -0.01091 0.008557 0.9693 0.9738 0.005553 0.8441 0.8354 0.02259 ] Network output: [ 1 -0.07583 0.0099 -8.362e-06 3.754e-06 0.06517 -6.302e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1886 -0.007129 -0.2094 0.2329 0.9835 0.9932 0.2102 0.4717 0.8824 0.7458 ] Network output: [ -0.012 0.9984 1.005 -2.437e-05 1.094e-05 0.02077 -1.837e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004417 0.001308 0.003831 0.005854 0.9891 0.9921 0.004496 0.8792 0.9069 0.01609 ] Network output: [ 0.01364 -0.1268 0.9562 -0.0001744 7.827e-05 1.143 -0.0001314 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1999 0.1333 0.3055 0.2153 0.985 0.994 0.2006 0.4774 0.8885 0.7394 ] Network output: [ 0.005708 -0.02794 1.034 0.0001269 -5.697e-05 0.9834 9.563e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0788 0.07374 0.1657 0.2156 0.9875 0.9921 0.07884 0.8204 0.8907 0.3009 ] Network output: [ -0.01143 0.01579 1.028 0.0001152 -5.171e-05 0.98 8.681e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08757 0.08659 0.1727 0.2105 0.9855 0.9915 0.08758 0.7448 0.8688 0.253 ] Network output: [ 0.001881 1.006 -0.000152 2.25e-05 -1.01e-05 0.9903 1.696e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.007751 Epoch 5661 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01491 1.007 0.9779 -2.042e-05 9.168e-06 -0.01459 -1.539e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002972 -0.002554 -0.011 0.008059 0.9693 0.9738 0.005606 0.8446 0.8342 0.02243 ] Network output: [ 0.9856 0.09542 0.001707 -4.293e-05 1.927e-05 -0.06843 -3.235e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1913 -0.004935 -0.2169 0.2036 0.9835 0.9932 0.2131 0.4756 0.8812 0.7434 ] Network output: [ -0.0119 1.006 1.004 -2.557e-05 1.148e-05 0.0132 -1.927e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00447 0.001292 0.003391 0.004918 0.9891 0.9921 0.00455 0.8795 0.9064 0.01584 ] Network output: [ -0.002175 0.1055 0.9434 -0.0002216 9.949e-05 0.9545 -0.000167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2022 0.134 0.2907 0.1703 0.985 0.994 0.2028 0.4802 0.8886 0.7409 ] Network output: [ 0.01091 0.01108 1.024 0.0001265 -5.679e-05 0.9433 9.533e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07798 0.07284 0.1557 0.2043 0.9876 0.9921 0.07802 0.8173 0.8906 0.2949 ] Network output: [ -0.007552 -9.34e-05 1.027 0.0001206 -5.412e-05 0.9891 9.085e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08673 0.08573 0.1692 0.2086 0.9854 0.9914 0.08674 0.7405 0.869 0.253 ] Network output: [ -0.00382 0.999 0.01043 1.897e-05 -8.517e-06 0.9983 1.43e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.005261 Epoch 5662 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01658 0.982 0.9791 -1.534e-05 6.886e-06 0.00577 -1.156e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002943 -0.002553 -0.01092 0.008553 0.9693 0.9738 0.005557 0.8442 0.8354 0.02259 ] Network output: [ 1 -0.07334 0.00964 -8.921e-06 4.005e-06 0.06306 -6.723e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1885 -0.00723 -0.2096 0.2325 0.9835 0.9932 0.2101 0.4717 0.8824 0.7458 ] Network output: [ -0.01205 0.9987 1.005 -2.412e-05 1.083e-05 0.02057 -1.818e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004422 0.001304 0.003829 0.005842 0.9891 0.9921 0.004502 0.8792 0.9069 0.01609 ] Network output: [ 0.01321 -0.123 0.9565 -0.0001757 7.89e-05 1.139 -0.0001324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1998 0.133 0.3054 0.2145 0.985 0.994 0.2005 0.4773 0.8885 0.7395 ] Network output: [ 0.00586 -0.02782 1.033 0.0001269 -5.697e-05 0.9833 9.564e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07897 0.07389 0.1657 0.2156 0.9875 0.9921 0.07902 0.8204 0.8907 0.3011 ] Network output: [ -0.0114 0.01548 1.027 0.0001153 -5.175e-05 0.9805 8.688e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08771 0.08673 0.1727 0.2105 0.9855 0.9915 0.08772 0.7448 0.8689 0.2531 ] Network output: [ 0.001817 1.006 -5.055e-05 2.25e-05 -1.01e-05 0.9905 1.696e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.007361 Epoch 5663 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01492 1.007 0.978 -2.003e-05 8.994e-06 -0.0144 -1.51e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002973 -0.002557 -0.01101 0.00807 0.9693 0.9738 0.005609 0.8446 0.8343 0.02244 ] Network output: [ 0.986 0.09285 0.001693 -4.238e-05 1.902e-05 -0.06663 -3.194e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1911 -0.005106 -0.2169 0.204 0.9835 0.9932 0.2129 0.4755 0.8813 0.7435 ] Network output: [ -0.01194 1.006 1.004 -2.529e-05 1.135e-05 0.01321 -1.906e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004474 0.001289 0.003401 0.004932 0.9891 0.9921 0.004554 0.8795 0.9064 0.01586 ] Network output: [ -0.002112 0.1027 0.9441 -0.0002215 9.946e-05 0.9565 -0.000167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.202 0.1337 0.291 0.1708 0.985 0.994 0.2026 0.4801 0.8886 0.7409 ] Network output: [ 0.01093 0.01017 1.024 0.0001265 -5.679e-05 0.9443 9.534e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07818 0.07301 0.156 0.2046 0.9876 0.9921 0.07822 0.8173 0.8906 0.2952 ] Network output: [ -0.007625 0.0001128 1.026 0.0001205 -5.409e-05 0.9892 9.08e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0869 0.0859 0.1694 0.2087 0.9854 0.9914 0.08691 0.7407 0.8691 0.2531 ] Network output: [ -0.003724 0.999 0.01021 1.907e-05 -8.561e-06 0.9983 1.437e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.005008 Epoch 5664 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01653 0.9824 0.9791 -1.511e-05 6.781e-06 0.005388 -1.138e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002946 -0.002556 -0.01093 0.00855 0.9693 0.9738 0.005562 0.8442 0.8354 0.0226 ] Network output: [ 1 -0.07109 0.009405 -9.431e-06 4.234e-06 0.06115 -7.107e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1884 -0.007337 -0.2098 0.2321 0.9835 0.9932 0.21 0.4717 0.8824 0.7458 ] Network output: [ -0.01209 0.9988 1.005 -2.388e-05 1.072e-05 0.02038 -1.8e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004428 0.0013 0.003827 0.005831 0.9891 0.9921 0.004507 0.8792 0.9069 0.0161 ] Network output: [ 0.01282 -0.1194 0.9569 -0.0001771 7.949e-05 1.136 -0.0001334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1997 0.1328 0.3053 0.2137 0.985 0.994 0.2003 0.4773 0.8886 0.7396 ] Network output: [ 0.006004 -0.0277 1.033 0.0001269 -5.697e-05 0.9834 9.564e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07914 0.07403 0.1657 0.2156 0.9875 0.9921 0.07919 0.8203 0.8907 0.3013 ] Network output: [ -0.01137 0.01522 1.027 0.0001154 -5.179e-05 0.9809 8.694e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08785 0.08687 0.1728 0.2106 0.9855 0.9915 0.08787 0.7449 0.8689 0.2533 ] Network output: [ 0.001763 1.006 3.085e-05 2.251e-05 -1.011e-05 0.9908 1.697e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00701 Epoch 5665 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01492 1.006 0.978 -1.966e-05 8.824e-06 -0.01424 -1.481e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002975 -0.00256 -0.01101 0.00808 0.9693 0.9738 0.005612 0.8447 0.8343 0.02245 ] Network output: [ 0.9863 0.09046 0.001685 -4.187e-05 1.88e-05 -0.06495 -3.155e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1909 -0.005278 -0.2169 0.2044 0.9835 0.9932 0.2127 0.4754 0.8813 0.7435 ] Network output: [ -0.01199 1.006 1.004 -2.501e-05 1.123e-05 0.01322 -1.884e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004478 0.001285 0.00341 0.004945 0.9891 0.9921 0.004558 0.8795 0.9064 0.01587 ] Network output: [ -0.00206 0.1001 0.9448 -0.0002215 9.945e-05 0.9583 -0.0001669 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2018 0.1334 0.2913 0.1713 0.985 0.994 0.2024 0.48 0.8886 0.741 ] Network output: [ 0.01094 0.009339 1.024 0.0001265 -5.68e-05 0.9453 9.535e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07837 0.07317 0.1562 0.2049 0.9876 0.9921 0.07842 0.8174 0.8906 0.2956 ] Network output: [ -0.007695 0.0003265 1.026 0.0001204 -5.406e-05 0.9894 9.075e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08707 0.08606 0.1695 0.2088 0.9854 0.9914 0.08708 0.7408 0.8691 0.2532 ] Network output: [ -0.00363 0.9991 0.01 1.917e-05 -8.605e-06 0.9982 1.444e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00478 Epoch 5666 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01649 0.9828 0.9792 -1.487e-05 6.675e-06 0.005032 -1.121e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002948 -0.002558 -0.01093 0.008548 0.9693 0.9738 0.005566 0.8443 0.8354 0.02261 ] Network output: [ 1 -0.06906 0.009194 -9.896e-06 4.443e-06 0.05941 -7.458e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1883 -0.007449 -0.2099 0.2317 0.9835 0.9932 0.2099 0.4716 0.8824 0.7458 ] Network output: [ -0.01214 0.999 1.005 -2.364e-05 1.061e-05 0.0202 -1.781e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004433 0.001296 0.003825 0.00582 0.9891 0.9921 0.004512 0.8792 0.9069 0.01611 ] Network output: [ 0.01245 -0.1161 0.9573 -0.0001783 8.006e-05 1.133 -0.0001344 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1996 0.1325 0.3052 0.2131 0.985 0.994 0.2002 0.4773 0.8886 0.7397 ] Network output: [ 0.006139 -0.0276 1.032 0.0001269 -5.697e-05 0.9834 9.563e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07931 0.07417 0.1657 0.2156 0.9875 0.9921 0.07936 0.8203 0.8908 0.3014 ] Network output: [ -0.01135 0.01501 1.027 0.0001154 -5.183e-05 0.9812 8.7e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.088 0.08701 0.1729 0.2107 0.9855 0.9915 0.08801 0.7449 0.869 0.2534 ] Network output: [ 0.001718 1.006 9.386e-05 2.253e-05 -1.012e-05 0.991 1.698e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.006696 Epoch 5667 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01492 1.006 0.9781 -1.929e-05 8.658e-06 -0.01409 -1.453e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002976 -0.002563 -0.01102 0.00809 0.9693 0.9738 0.005615 0.8447 0.8344 0.02246 ] Network output: [ 0.9867 0.08824 0.001684 -4.14e-05 1.859e-05 -0.0634 -3.12e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1907 -0.005449 -0.2169 0.2048 0.9835 0.9932 0.2125 0.4753 0.8813 0.7436 ] Network output: [ -0.01204 1.006 1.004 -2.473e-05 1.11e-05 0.01321 -1.864e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004482 0.001281 0.003418 0.004956 0.9891 0.9921 0.004562 0.8795 0.9064 0.01588 ] Network output: [ -0.002017 0.09769 0.9455 -0.0002215 9.946e-05 0.9599 -0.000167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2016 0.1331 0.2915 0.1717 0.985 0.994 0.2022 0.4799 0.8887 0.741 ] Network output: [ 0.01097 0.008573 1.024 0.0001265 -5.68e-05 0.9463 9.535e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07857 0.07334 0.1565 0.2052 0.9876 0.9921 0.07862 0.8174 0.8906 0.2959 ] Network output: [ -0.007762 0.0005473 1.026 0.0001204 -5.403e-05 0.9895 9.071e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08724 0.08622 0.1697 0.2089 0.9854 0.9914 0.08725 0.741 0.8692 0.2533 ] Network output: [ -0.00354 0.9991 0.009793 1.926e-05 -8.649e-06 0.9982 1.452e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004575 Epoch 5668 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01644 0.9831 0.9792 -1.463e-05 6.567e-06 0.004701 -1.102e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00295 -0.002561 -0.01094 0.008546 0.9693 0.9738 0.00557 0.8443 0.8355 0.02261 ] Network output: [ 1 -0.06724 0.009005 -1.032e-05 4.634e-06 0.05786 -7.779e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1882 -0.007565 -0.2101 0.2313 0.9835 0.9932 0.2098 0.4716 0.8824 0.7458 ] Network output: [ -0.01219 0.9992 1.005 -2.339e-05 1.05e-05 0.02003 -1.763e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004438 0.001291 0.003823 0.005811 0.9891 0.9921 0.004518 0.8792 0.9069 0.01611 ] Network output: [ 0.0121 -0.1131 0.9577 -0.0001795 8.06e-05 1.13 -0.0001353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1995 0.1322 0.3051 0.2124 0.985 0.994 0.2001 0.4773 0.8886 0.7397 ] Network output: [ 0.006266 -0.02752 1.032 0.0001269 -5.697e-05 0.9835 9.563e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07949 0.07431 0.1657 0.2156 0.9875 0.9921 0.07954 0.8202 0.8908 0.3016 ] Network output: [ -0.01133 0.01484 1.027 0.0001155 -5.186e-05 0.9816 8.706e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08814 0.08715 0.1729 0.2108 0.9855 0.9915 0.08815 0.745 0.8691 0.2535 ] Network output: [ 0.001681 1.005 0.0001401 2.256e-05 -1.013e-05 0.9912 1.7e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.006414 Epoch 5669 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01492 1.006 0.9782 -1.892e-05 8.496e-06 -0.01396 -1.426e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002978 -0.002566 -0.01102 0.008099 0.9693 0.9738 0.005618 0.8447 0.8344 0.02247 ] Network output: [ 0.987 0.08619 0.001689 -4.098e-05 1.84e-05 -0.06197 -3.088e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1906 -0.00562 -0.2169 0.2051 0.9835 0.9932 0.2123 0.4752 0.8814 0.7437 ] Network output: [ -0.01208 1.006 1.004 -2.445e-05 1.098e-05 0.0132 -1.843e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004486 0.001277 0.003426 0.004967 0.9891 0.9921 0.004566 0.8795 0.9064 0.0159 ] Network output: [ -0.001983 0.09554 0.9462 -0.0002216 9.949e-05 0.9613 -0.000167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2014 0.1328 0.2917 0.1721 0.985 0.994 0.202 0.4799 0.8887 0.741 ] Network output: [ 0.01099 0.007871 1.024 0.0001265 -5.68e-05 0.9472 9.536e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07876 0.0735 0.1567 0.2055 0.9876 0.9921 0.07881 0.8174 0.8906 0.2962 ] Network output: [ -0.007825 0.0007747 1.026 0.0001203 -5.401e-05 0.9896 9.066e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0874 0.08638 0.1698 0.209 0.9854 0.9914 0.08741 0.7411 0.8692 0.2534 ] Network output: [ -0.003453 0.9992 0.009593 1.936e-05 -8.693e-06 0.9982 1.459e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004392 Epoch 5670 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0164 0.9835 0.9793 -1.438e-05 6.458e-06 0.004395 -1.084e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002953 -0.002564 -0.01095 0.008544 0.9693 0.9738 0.005574 0.8444 0.8355 0.02262 ] Network output: [ 1 -0.06563 0.008837 -1.071e-05 4.81e-06 0.05646 -8.074e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1881 -0.007685 -0.2103 0.231 0.9835 0.9932 0.2097 0.4716 0.8824 0.7458 ] Network output: [ -0.01223 0.9994 1.005 -2.315e-05 1.039e-05 0.01986 -1.745e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004443 0.001287 0.003822 0.005802 0.9891 0.9921 0.004523 0.8793 0.907 0.01612 ] Network output: [ 0.01178 -0.1104 0.9581 -0.0001807 8.112e-05 1.128 -0.0001362 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1993 0.1319 0.305 0.2118 0.9851 0.994 0.1999 0.4773 0.8886 0.7398 ] Network output: [ 0.006385 -0.02745 1.032 0.0001269 -5.696e-05 0.9836 9.562e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07966 0.07446 0.1657 0.2157 0.9875 0.9921 0.07971 0.8202 0.8908 0.3018 ] Network output: [ -0.01132 0.01472 1.026 0.0001156 -5.189e-05 0.9819 8.711e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08829 0.08729 0.173 0.2108 0.9856 0.9915 0.0883 0.745 0.8691 0.2536 ] Network output: [ 0.001653 1.005 0.0001712 2.259e-05 -1.014e-05 0.9914 1.702e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.006162 Epoch 5671 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01491 1.006 0.9783 -1.857e-05 8.337e-06 -0.01385 -1.4e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00298 -0.002569 -0.01103 0.008107 0.9693 0.9738 0.005621 0.8448 0.8345 0.02248 ] Network output: [ 0.9872 0.0843 0.001698 -4.06e-05 1.823e-05 -0.06065 -3.059e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1904 -0.005788 -0.2169 0.2054 0.9835 0.9932 0.2122 0.4751 0.8814 0.7437 ] Network output: [ -0.01213 1.006 1.004 -2.418e-05 1.086e-05 0.01317 -1.823e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00449 0.001272 0.003433 0.004977 0.9891 0.9921 0.00457 0.8796 0.9065 0.01591 ] Network output: [ -0.001958 0.0936 0.9469 -0.0002217 9.955e-05 0.9626 -0.0001671 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2012 0.1325 0.2919 0.1724 0.985 0.994 0.2019 0.4798 0.8887 0.741 ] Network output: [ 0.01102 0.007228 1.023 0.0001265 -5.68e-05 0.948 9.536e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07895 0.07367 0.1569 0.2057 0.9876 0.9921 0.079 0.8174 0.8907 0.2965 ] Network output: [ -0.007886 0.001008 1.026 0.0001203 -5.399e-05 0.9897 9.063e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08757 0.08654 0.1699 0.2091 0.9855 0.9914 0.08758 0.7413 0.8693 0.2535 ] Network output: [ -0.00337 0.9992 0.009401 1.946e-05 -8.735e-06 0.9982 1.466e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004227 Epoch 5672 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01636 0.9838 0.9793 -1.414e-05 6.347e-06 0.004112 -1.065e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002955 -0.002567 -0.01095 0.008543 0.9693 0.9738 0.005578 0.8444 0.8355 0.02262 ] Network output: [ 1 -0.0642 0.008689 -1.107e-05 4.972e-06 0.05522 -8.346e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.188 -0.007809 -0.2104 0.2307 0.9835 0.9932 0.2095 0.4716 0.8824 0.7458 ] Network output: [ -0.01227 0.9996 1.005 -2.291e-05 1.028e-05 0.0197 -1.726e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004448 0.001283 0.003822 0.005795 0.9891 0.9921 0.004528 0.8793 0.907 0.01613 ] Network output: [ 0.01149 -0.1079 0.9585 -0.0001818 8.16e-05 1.126 -0.000137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1992 0.1316 0.3049 0.2113 0.9851 0.994 0.1998 0.4773 0.8887 0.7398 ] Network output: [ 0.006497 -0.0274 1.031 0.0001269 -5.696e-05 0.9837 9.561e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07984 0.0746 0.1658 0.2157 0.9875 0.9921 0.07988 0.8201 0.8908 0.302 ] Network output: [ -0.01131 0.01463 1.026 0.0001156 -5.192e-05 0.9822 8.715e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08843 0.08743 0.1731 0.2109 0.9856 0.9915 0.08845 0.7451 0.8692 0.2536 ] Network output: [ 0.001631 1.005 0.0001887 2.262e-05 -1.016e-05 0.9916 1.705e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.005937 Epoch 5673 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01491 1.006 0.9783 -1.822e-05 8.181e-06 -0.01376 -1.373e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002981 -0.002572 -0.01103 0.008115 0.9693 0.9738 0.005624 0.8448 0.8345 0.02249 ] Network output: [ 0.9875 0.08255 0.001711 -4.026e-05 1.807e-05 -0.05944 -3.034e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1902 -0.005955 -0.217 0.2056 0.9835 0.9932 0.212 0.4751 0.8815 0.7438 ] Network output: [ -0.01217 1.007 1.005 -2.391e-05 1.074e-05 0.01314 -1.802e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004494 0.001268 0.00344 0.004986 0.9891 0.9921 0.004574 0.8796 0.9065 0.01592 ] Network output: [ -0.001941 0.09187 0.9475 -0.0002219 9.962e-05 0.9636 -0.0001672 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2011 0.1322 0.2921 0.1726 0.985 0.994 0.2017 0.4798 0.8887 0.7411 ] Network output: [ 0.01105 0.00664 1.023 0.0001265 -5.68e-05 0.9487 9.536e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07915 0.07383 0.1571 0.2059 0.9876 0.9921 0.07919 0.8174 0.8907 0.2967 ] Network output: [ -0.007944 0.001247 1.025 0.0001202 -5.397e-05 0.9898 9.059e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08773 0.0867 0.1701 0.2092 0.9855 0.9914 0.08774 0.7414 0.8694 0.2536 ] Network output: [ -0.003291 0.9992 0.009216 1.955e-05 -8.777e-06 0.9983 1.473e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00408 Epoch 5674 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01632 0.9841 0.9794 -1.389e-05 6.235e-06 0.003851 -1.047e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002957 -0.00257 -0.01096 0.008542 0.9693 0.9738 0.005582 0.8445 0.8356 0.02263 ] Network output: [ 1 -0.06295 0.008559 -1.141e-05 5.122e-06 0.05412 -8.598e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1879 -0.007934 -0.2106 0.2305 0.9835 0.9932 0.2094 0.4717 0.8824 0.7458 ] Network output: [ -0.01232 0.9997 1.005 -2.266e-05 1.017e-05 0.01955 -1.708e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004453 0.001278 0.003821 0.005788 0.9891 0.9921 0.004533 0.8793 0.907 0.01614 ] Network output: [ 0.01122 -0.1056 0.9589 -0.0001828 8.206e-05 1.124 -0.0001378 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1991 0.1313 0.3049 0.2108 0.9851 0.994 0.1997 0.4773 0.8887 0.7399 ] Network output: [ 0.006602 -0.02737 1.031 0.0001269 -5.695e-05 0.9838 9.56e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08001 0.07474 0.1658 0.2157 0.9875 0.9921 0.08006 0.8201 0.8908 0.3021 ] Network output: [ -0.0113 0.01458 1.026 0.0001157 -5.194e-05 0.9824 8.719e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08858 0.08757 0.1731 0.2109 0.9856 0.9915 0.08859 0.7452 0.8693 0.2537 ] Network output: [ 0.001616 1.005 0.0001938 2.266e-05 -1.017e-05 0.9918 1.708e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.005737 Epoch 5675 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0149 1.005 0.9784 -1.788e-05 8.029e-06 -0.01368 -1.348e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002983 -0.002574 -0.01103 0.008122 0.9693 0.9738 0.005627 0.8449 0.8346 0.02249 ] Network output: [ 0.9877 0.08096 0.001727 -3.996e-05 1.794e-05 -0.05833 -3.011e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1901 -0.00612 -0.217 0.2059 0.9835 0.9932 0.2118 0.475 0.8815 0.7438 ] Network output: [ -0.01221 1.007 1.005 -2.365e-05 1.062e-05 0.01311 -1.782e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004498 0.001264 0.003446 0.004994 0.9891 0.9921 0.004578 0.8796 0.9065 0.01593 ] Network output: [ -0.00193 0.09033 0.9481 -0.0002221 9.97e-05 0.9645 -0.0001674 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2009 0.1319 0.2923 0.1729 0.9851 0.994 0.2015 0.4797 0.8888 0.7411 ] Network output: [ 0.01108 0.006103 1.023 0.0001265 -5.68e-05 0.9495 9.535e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07934 0.07399 0.1573 0.2061 0.9876 0.9921 0.07938 0.8175 0.8907 0.297 ] Network output: [ -0.007999 0.001492 1.025 0.0001202 -5.395e-05 0.9899 9.056e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08789 0.08685 0.1702 0.2093 0.9855 0.9914 0.0879 0.7416 0.8694 0.2536 ] Network output: [ -0.003216 0.9992 0.009039 1.964e-05 -8.818e-06 0.9983 1.48e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003949 Epoch 5676 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01628 0.9843 0.9794 -1.364e-05 6.122e-06 0.003611 -1.028e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002959 -0.002573 -0.01096 0.008542 0.9693 0.9738 0.005586 0.8445 0.8356 0.02264 ] Network output: [ 1 -0.06185 0.008446 -1.172e-05 5.261e-06 0.05315 -8.832e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1878 -0.008062 -0.2107 0.2303 0.9835 0.9932 0.2093 0.4717 0.8825 0.7458 ] Network output: [ -0.01236 0.9999 1.005 -2.242e-05 1.006e-05 0.01941 -1.689e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004458 0.001274 0.003821 0.005782 0.9891 0.9921 0.004538 0.8793 0.907 0.01614 ] Network output: [ 0.01097 -0.1036 0.9593 -0.0001838 8.25e-05 1.122 -0.0001385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1989 0.131 0.3048 0.2104 0.9851 0.994 0.1995 0.4773 0.8887 0.7399 ] Network output: [ 0.006701 -0.02736 1.031 0.0001268 -5.694e-05 0.984 9.559e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08019 0.07489 0.1659 0.2158 0.9875 0.9921 0.08024 0.8201 0.8908 0.3023 ] Network output: [ -0.0113 0.01456 1.026 0.0001157 -5.196e-05 0.9827 8.723e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08873 0.08771 0.1732 0.211 0.9856 0.9915 0.08874 0.7452 0.8694 0.2538 ] Network output: [ 0.001606 1.005 0.000188 2.27e-05 -1.019e-05 0.992 1.711e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.005558 Epoch 5677 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01489 1.005 0.9785 -1.755e-05 7.879e-06 -0.01362 -1.323e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002984 -0.002577 -0.01104 0.008129 0.9693 0.9738 0.00563 0.8449 0.8346 0.0225 ] Network output: [ 0.988 0.0795 0.001746 -3.97e-05 1.782e-05 -0.05733 -2.992e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1899 -0.006283 -0.217 0.2061 0.9835 0.9932 0.2116 0.475 0.8815 0.7439 ] Network output: [ -0.01225 1.007 1.005 -2.338e-05 1.05e-05 0.01307 -1.762e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004502 0.00126 0.003452 0.005001 0.9891 0.9921 0.004582 0.8796 0.9065 0.01594 ] Network output: [ -0.001926 0.08896 0.9487 -0.0002223 9.979e-05 0.9653 -0.0001675 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2007 0.1316 0.2924 0.1731 0.9851 0.994 0.2013 0.4797 0.8888 0.7411 ] Network output: [ 0.01111 0.005613 1.023 0.0001265 -5.68e-05 0.9501 9.535e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07953 0.07414 0.1575 0.2063 0.9876 0.9921 0.07957 0.8175 0.8907 0.2972 ] Network output: [ -0.008052 0.001741 1.025 0.0001201 -5.393e-05 0.9899 9.053e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08805 0.08701 0.1703 0.2094 0.9855 0.9914 0.08806 0.7417 0.8695 0.2537 ] Network output: [ -0.003145 0.9992 0.00887 1.973e-05 -8.857e-06 0.9983 1.487e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003832 Epoch 5678 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01625 0.9846 0.9795 -1.338e-05 6.008e-06 0.003391 -1.009e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002961 -0.002576 -0.01097 0.008542 0.9693 0.9738 0.00559 0.8446 0.8356 0.02264 ] Network output: [ 1 -0.06091 0.008348 -1.201e-05 5.392e-06 0.05231 -9.051e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1876 -0.00819 -0.2108 0.2301 0.9835 0.9932 0.2091 0.4717 0.8825 0.7458 ] Network output: [ -0.0124 1 1.005 -2.217e-05 9.952e-06 0.01927 -1.671e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004463 0.001269 0.003822 0.005777 0.9891 0.9921 0.004542 0.8794 0.907 0.01615 ] Network output: [ 0.01075 -0.1018 0.9597 -0.0001847 8.291e-05 1.12 -0.0001392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1988 0.1307 0.3048 0.21 0.9851 0.994 0.1994 0.4773 0.8888 0.7399 ] Network output: [ 0.006793 -0.02736 1.03 0.0001268 -5.694e-05 0.9841 9.558e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08036 0.07503 0.166 0.2158 0.9875 0.9921 0.08041 0.8201 0.8909 0.3025 ] Network output: [ -0.01131 0.01457 1.026 0.0001158 -5.198e-05 0.9829 8.726e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08888 0.08785 0.1733 0.2111 0.9856 0.9915 0.08889 0.7453 0.8694 0.2538 ] Network output: [ 0.001602 1.005 0.0001724 2.275e-05 -1.021e-05 0.9921 1.714e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.005399 Epoch 5679 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01488 1.005 0.9786 -1.722e-05 7.732e-06 -0.01357 -1.298e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002986 -0.00258 -0.01104 0.008136 0.9693 0.9738 0.005633 0.845 0.8347 0.02251 ] Network output: [ 0.9882 0.07818 0.001767 -3.948e-05 1.772e-05 -0.05641 -2.975e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1897 -0.006443 -0.217 0.2063 0.9835 0.9932 0.2114 0.4749 0.8816 0.7439 ] Network output: [ -0.01229 1.007 1.005 -2.311e-05 1.038e-05 0.01303 -1.742e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004506 0.001255 0.003458 0.005008 0.9891 0.9921 0.004586 0.8796 0.9066 0.01595 ] Network output: [ -0.001928 0.08776 0.9493 -0.0002225 9.99e-05 0.9659 -0.0001677 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2006 0.1313 0.2926 0.1732 0.9851 0.994 0.2012 0.4796 0.8888 0.7411 ] Network output: [ 0.01115 0.005168 1.022 0.0001265 -5.679e-05 0.9508 9.534e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.07971 0.0743 0.1577 0.2065 0.9876 0.9921 0.07976 0.8175 0.8907 0.2975 ] Network output: [ -0.008102 0.001993 1.025 0.0001201 -5.392e-05 0.99 9.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08821 0.08716 0.1704 0.2095 0.9855 0.9914 0.08822 0.7418 0.8696 0.2538 ] Network output: [ -0.003079 0.9992 0.008709 1.981e-05 -8.894e-06 0.9983 1.493e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003728 Epoch 5680 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01621 0.9848 0.9796 -1.312e-05 5.892e-06 0.00319 -9.891e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002963 -0.002579 -0.01097 0.008542 0.9694 0.9738 0.005594 0.8446 0.8357 0.02265 ] Network output: [ 1 -0.0601 0.008264 -1.228e-05 5.514e-06 0.05158 -9.257e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1875 -0.00832 -0.2109 0.2299 0.9835 0.9932 0.209 0.4717 0.8825 0.7458 ] Network output: [ -0.01243 1 1.005 -2.192e-05 9.841e-06 0.01914 -1.652e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004467 0.001265 0.003822 0.005772 0.9891 0.9921 0.004547 0.8794 0.9071 0.01616 ] Network output: [ 0.01054 -0.1002 0.9602 -0.0001856 8.33e-05 1.118 -0.0001398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1986 0.1304 0.3048 0.2096 0.9851 0.994 0.1993 0.4773 0.8888 0.7399 ] Network output: [ 0.006879 -0.02737 1.03 0.0001268 -5.693e-05 0.9843 9.557e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08054 0.07518 0.166 0.2159 0.9875 0.9921 0.08059 0.82 0.8909 0.3027 ] Network output: [ -0.01132 0.01462 1.025 0.0001158 -5.2e-05 0.9831 8.729e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08902 0.08799 0.1734 0.2111 0.9856 0.9915 0.08903 0.7454 0.8695 0.2539 ] Network output: [ 0.001602 1.004 0.0001481 2.279e-05 -1.023e-05 0.9923 1.718e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.005259 Epoch 5681 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01487 1.005 0.9786 -1.69e-05 7.587e-06 -0.01354 -1.274e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002988 -0.002583 -0.01105 0.008141 0.9694 0.9738 0.005636 0.845 0.8347 0.02252 ] Network output: [ 0.9883 0.07698 0.001789 -3.93e-05 1.764e-05 -0.05559 -2.962e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1896 -0.006601 -0.2171 0.2064 0.9835 0.9932 0.2112 0.4749 0.8816 0.7439 ] Network output: [ -0.01233 1.007 1.005 -2.285e-05 1.026e-05 0.01298 -1.722e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00451 0.001251 0.003463 0.005014 0.9891 0.9921 0.004591 0.8797 0.9066 0.01596 ] Network output: [ -0.001936 0.08671 0.9499 -0.0002228 0.0001 0.9664 -0.0001679 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2004 0.131 0.2927 0.1734 0.9851 0.994 0.201 0.4796 0.8889 0.7411 ] Network output: [ 0.01118 0.004762 1.022 0.0001265 -5.679e-05 0.9514 9.533e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0799 0.07446 0.1579 0.2067 0.9876 0.9921 0.07995 0.8175 0.8908 0.2977 ] Network output: [ -0.00815 0.00225 1.024 0.0001201 -5.39e-05 0.99 9.049e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08837 0.08732 0.1705 0.2095 0.9855 0.9914 0.08838 0.742 0.8696 0.2538 ] Network output: [ -0.003018 0.9992 0.008556 1.989e-05 -8.929e-06 0.9983 1.499e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003636 Epoch 5682 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01618 0.985 0.9796 -1.287e-05 5.776e-06 0.003007 -9.696e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002965 -0.002582 -0.01098 0.008542 0.9694 0.9738 0.005597 0.8447 0.8357 0.02265 ] Network output: [ 1 -0.05942 0.008193 -1.254e-05 5.63e-06 0.05096 -9.451e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1874 -0.008451 -0.211 0.2297 0.9835 0.9932 0.2088 0.4717 0.8825 0.7458 ] Network output: [ -0.01247 1 1.006 -2.167e-05 9.729e-06 0.01902 -1.633e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004472 0.00126 0.003823 0.005768 0.9891 0.9921 0.004552 0.8794 0.9071 0.01616 ] Network output: [ 0.01035 -0.09878 0.9606 -0.0001864 8.367e-05 1.117 -0.0001405 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1985 0.1302 0.3048 0.2093 0.9851 0.994 0.1991 0.4773 0.8888 0.7399 ] Network output: [ 0.00696 -0.02741 1.029 0.0001268 -5.692e-05 0.9845 9.555e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08072 0.07532 0.1661 0.216 0.9875 0.9921 0.08077 0.82 0.8909 0.3028 ] Network output: [ -0.01133 0.01469 1.025 0.0001159 -5.201e-05 0.9832 8.731e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08917 0.08814 0.1734 0.2111 0.9856 0.9915 0.08918 0.7455 0.8696 0.254 ] Network output: [ 0.001607 1.004 0.0001161 2.284e-05 -1.025e-05 0.9924 1.721e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.005134 Epoch 5683 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01486 1.005 0.9787 -1.658e-05 7.445e-06 -0.01352 -1.25e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002989 -0.002586 -0.01105 0.008147 0.9694 0.9738 0.00564 0.845 0.8348 0.02252 ] Network output: [ 0.9885 0.07589 0.001812 -3.915e-05 1.758e-05 -0.05486 -2.951e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1894 -0.006756 -0.2171 0.2066 0.9835 0.9932 0.2111 0.4748 0.8817 0.7439 ] Network output: [ -0.01236 1.007 1.005 -2.259e-05 1.014e-05 0.01292 -1.702e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004514 0.001247 0.003468 0.005019 0.9891 0.9921 0.004595 0.8797 0.9066 0.01597 ] Network output: [ -0.001949 0.08581 0.9504 -0.0002231 0.0001001 0.9667 -0.0001681 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2002 0.1307 0.2929 0.1735 0.9851 0.994 0.2008 0.4796 0.8889 0.7411 ] Network output: [ 0.01122 0.004394 1.022 0.0001265 -5.679e-05 0.9519 9.533e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08009 0.07461 0.158 0.2069 0.9876 0.9921 0.08014 0.8175 0.8908 0.2979 ] Network output: [ -0.008195 0.00251 1.024 0.00012 -5.389e-05 0.9901 9.047e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08853 0.08747 0.1706 0.2096 0.9855 0.9914 0.08854 0.7421 0.8697 0.2539 ] Network output: [ -0.00296 0.9992 0.00841 1.996e-05 -8.963e-06 0.9984 1.505e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003554 Epoch 5684 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01615 0.9851 0.9797 -1.26e-05 5.659e-06 0.00284 -9.499e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002967 -0.002585 -0.01098 0.008543 0.9694 0.9738 0.005601 0.8447 0.8357 0.02266 ] Network output: [ 1 -0.05886 0.008134 -1.279e-05 5.741e-06 0.05043 -9.637e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1873 -0.008582 -0.2111 0.2296 0.9835 0.9932 0.2087 0.4717 0.8826 0.7458 ] Network output: [ -0.0125 1 1.006 -2.142e-05 9.617e-06 0.0189 -1.614e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004477 0.001256 0.003825 0.005765 0.9891 0.9921 0.004557 0.8794 0.9071 0.01617 ] Network output: [ 0.01018 -0.09753 0.961 -0.0001872 8.402e-05 1.115 -0.000141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1984 0.1299 0.3048 0.2089 0.9851 0.994 0.199 0.4773 0.8888 0.7399 ] Network output: [ 0.007035 -0.02746 1.029 0.0001268 -5.691e-05 0.9848 9.554e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0809 0.07547 0.1662 0.216 0.9875 0.9921 0.08095 0.82 0.8909 0.303 ] Network output: [ -0.01134 0.01478 1.025 0.0001159 -5.203e-05 0.9834 8.734e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08932 0.08828 0.1735 0.2112 0.9856 0.9915 0.08933 0.7456 0.8696 0.254 ] Network output: [ 0.001615 1.004 7.732e-05 2.289e-05 -1.027e-05 0.9926 1.725e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.005025 Epoch 5685 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01484 1.005 0.9788 -1.627e-05 7.305e-06 -0.0135 -1.226e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002991 -0.002589 -0.01105 0.008152 0.9694 0.9738 0.005643 0.8451 0.8348 0.02253 ] Network output: [ 0.9886 0.07492 0.001835 -3.904e-05 1.753e-05 -0.0542 -2.942e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1893 -0.006908 -0.2171 0.2067 0.9835 0.9932 0.2109 0.4748 0.8817 0.7439 ] Network output: [ -0.01239 1.007 1.005 -2.232e-05 1.002e-05 0.01287 -1.682e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004519 0.001242 0.003473 0.005023 0.9891 0.9921 0.004599 0.8797 0.9066 0.01598 ] Network output: [ -0.001966 0.08504 0.951 -0.0002234 0.0001003 0.967 -0.0001683 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2001 0.1304 0.293 0.1736 0.9851 0.994 0.2007 0.4796 0.8889 0.7411 ] Network output: [ 0.01126 0.004059 1.021 0.0001265 -5.678e-05 0.9524 9.532e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08027 0.07477 0.1582 0.207 0.9876 0.9921 0.08032 0.8175 0.8908 0.2981 ] Network output: [ -0.008239 0.002773 1.024 0.00012 -5.388e-05 0.9901 9.045e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08869 0.08762 0.1707 0.2096 0.9855 0.9914 0.0887 0.7422 0.8697 0.2539 ] Network output: [ -0.002907 0.9992 0.008272 2.003e-05 -8.994e-06 0.9984 1.51e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003483 Epoch 5686 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01613 0.9853 0.9797 -1.234e-05 5.541e-06 0.002689 -9.301e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002969 -0.002588 -0.01099 0.008544 0.9694 0.9738 0.005605 0.8447 0.8358 0.02266 ] Network output: [ 1 -0.0584 0.008084 -1.302e-05 5.847e-06 0.05 -9.815e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1872 -0.008713 -0.2112 0.2295 0.9835 0.9932 0.2086 0.4717 0.8826 0.7458 ] Network output: [ -0.01253 1 1.006 -2.117e-05 9.504e-06 0.01879 -1.595e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004481 0.001252 0.003826 0.005763 0.9891 0.9921 0.004561 0.8795 0.9071 0.01618 ] Network output: [ 0.01002 -0.09644 0.9615 -0.0001879 8.435e-05 1.114 -0.0001416 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1982 0.1296 0.3048 0.2087 0.9851 0.994 0.1988 0.4773 0.8889 0.7399 ] Network output: [ 0.007106 -0.02752 1.029 0.0001268 -5.691e-05 0.985 9.553e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08107 0.07562 0.1663 0.2161 0.9875 0.9921 0.08112 0.82 0.8909 0.3031 ] Network output: [ -0.01136 0.0149 1.025 0.0001159 -5.204e-05 0.9835 8.736e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08947 0.08842 0.1736 0.2112 0.9856 0.9915 0.08948 0.7457 0.8697 0.2541 ] Network output: [ 0.001627 1.004 3.256e-05 2.293e-05 -1.03e-05 0.9927 1.728e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004929 Epoch 5687 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01483 1.005 0.9788 -1.596e-05 7.167e-06 -0.0135 -1.203e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002993 -0.002592 -0.01106 0.008156 0.9694 0.9738 0.005646 0.8451 0.8349 0.02253 ] Network output: [ 0.9888 0.07405 0.001859 -3.896e-05 1.749e-05 -0.05362 -2.936e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1891 -0.007057 -0.2171 0.2068 0.9835 0.9932 0.2108 0.4748 0.8817 0.7439 ] Network output: [ -0.01242 1.007 1.005 -2.206e-05 9.904e-06 0.01281 -1.663e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004523 0.001238 0.003477 0.005027 0.9891 0.9921 0.004604 0.8797 0.9067 0.01598 ] Network output: [ -0.001988 0.0844 0.9515 -0.0002237 0.0001004 0.9672 -0.0001686 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1999 0.1301 0.2931 0.1736 0.9851 0.994 0.2005 0.4795 0.889 0.741 ] Network output: [ 0.0113 0.003755 1.021 0.0001265 -5.678e-05 0.9529 9.531e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08046 0.07492 0.1583 0.2072 0.9876 0.9921 0.08051 0.8175 0.8908 0.2983 ] Network output: [ -0.00828 0.003038 1.024 0.00012 -5.388e-05 0.9902 9.044e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08884 0.08777 0.1708 0.2097 0.9855 0.9914 0.08886 0.7423 0.8698 0.2539 ] Network output: [ -0.002858 0.9992 0.008141 2.01e-05 -9.023e-06 0.9985 1.515e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00342 Epoch 5688 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0161 0.9854 0.9798 -1.208e-05 5.422e-06 0.002553 -9.102e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002971 -0.002591 -0.01099 0.008545 0.9694 0.9738 0.005608 0.8448 0.8358 0.02266 ] Network output: [ 1 -0.05804 0.008044 -1.325e-05 5.949e-06 0.04964 -9.986e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.187 -0.008844 -0.2112 0.2294 0.9835 0.9932 0.2084 0.4717 0.8826 0.7458 ] Network output: [ -0.01257 1.001 1.006 -2.092e-05 9.39e-06 0.01869 -1.576e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004486 0.001248 0.003828 0.00576 0.9891 0.9921 0.004566 0.8795 0.9071 0.01618 ] Network output: [ 0.009877 -0.09549 0.9619 -0.0001886 8.465e-05 1.113 -0.0001421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1981 0.1293 0.3048 0.2084 0.9851 0.994 0.1987 0.4773 0.8889 0.7399 ] Network output: [ 0.007172 -0.0276 1.028 0.0001267 -5.69e-05 0.9853 9.552e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08125 0.07576 0.1664 0.2162 0.9875 0.9921 0.0813 0.82 0.891 0.3033 ] Network output: [ -0.01137 0.01503 1.025 0.0001159 -5.205e-05 0.9836 8.738e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08962 0.08857 0.1737 0.2113 0.9856 0.9915 0.08963 0.7457 0.8697 0.2541 ] Network output: [ 0.001642 1.004 -1.741e-05 2.298e-05 -1.032e-05 0.9929 1.732e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004845 Epoch 5689 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01482 1.005 0.9789 -1.566e-05 7.031e-06 -0.01351 -1.18e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002994 -0.002594 -0.01106 0.00816 0.9694 0.9738 0.005649 0.8452 0.8349 0.02254 ] Network output: [ 0.9889 0.07328 0.001882 -3.892e-05 1.747e-05 -0.0531 -2.933e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.189 -0.007203 -0.2171 0.2069 0.9835 0.9932 0.2106 0.4747 0.8818 0.7439 ] Network output: [ -0.01245 1.007 1.005 -2.18e-05 9.786e-06 0.01275 -1.643e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004527 0.001234 0.003482 0.005031 0.9891 0.9921 0.004608 0.8797 0.9067 0.01599 ] Network output: [ -0.002014 0.08387 0.952 -0.000224 0.0001006 0.9672 -0.0001688 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1998 0.1298 0.2932 0.1737 0.9851 0.994 0.2004 0.4795 0.889 0.741 ] Network output: [ 0.01134 0.00348 1.021 0.0001265 -5.677e-05 0.9534 9.53e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08064 0.07507 0.1585 0.2073 0.9876 0.9921 0.08069 0.8175 0.8908 0.2985 ] Network output: [ -0.008319 0.003306 1.024 0.00012 -5.387e-05 0.9902 9.043e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.089 0.08792 0.1709 0.2097 0.9855 0.9914 0.08901 0.7424 0.8699 0.254 ] Network output: [ -0.002813 0.9992 0.008018 2.016e-05 -9.05e-06 0.9985 1.519e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003365 Epoch 5690 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01608 0.9855 0.9799 -1.181e-05 5.303e-06 0.00243 -8.902e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002973 -0.002593 -0.01099 0.008546 0.9694 0.9738 0.005612 0.8448 0.8358 0.02267 ] Network output: [ 1 -0.05777 0.008012 -1.347e-05 6.048e-06 0.04936 -1.015e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1869 -0.008974 -0.2113 0.2293 0.9835 0.9932 0.2083 0.4717 0.8827 0.7458 ] Network output: [ -0.0126 1.001 1.006 -2.066e-05 9.276e-06 0.01859 -1.557e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004491 0.001244 0.00383 0.005759 0.9891 0.9921 0.004571 0.8795 0.9071 0.01619 ] Network output: [ 0.009749 -0.09468 0.9623 -0.0001892 8.494e-05 1.112 -0.0001426 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.198 0.129 0.3049 0.2082 0.9851 0.994 0.1986 0.4773 0.8889 0.7399 ] Network output: [ 0.007233 -0.0277 1.028 0.0001267 -5.689e-05 0.9856 9.55e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08143 0.07591 0.1665 0.2162 0.9875 0.9921 0.08148 0.8199 0.891 0.3035 ] Network output: [ -0.01139 0.01519 1.024 0.000116 -5.206e-05 0.9837 8.74e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08977 0.08871 0.1738 0.2113 0.9856 0.9915 0.08978 0.7458 0.8698 0.2541 ] Network output: [ 0.001659 1.004 -7.188e-05 2.303e-05 -1.034e-05 0.993 1.736e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004772 Epoch 5691 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01481 1.005 0.979 -1.536e-05 6.897e-06 -0.01352 -1.158e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002996 -0.002597 -0.01106 0.008164 0.9694 0.9738 0.005653 0.8452 0.835 0.02254 ] Network output: [ 0.989 0.0726 0.001904 -3.89e-05 1.746e-05 -0.05266 -2.932e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1889 -0.007346 -0.2172 0.207 0.9835 0.9932 0.2104 0.4747 0.8818 0.7439 ] Network output: [ -0.01248 1.007 1.005 -2.154e-05 9.668e-06 0.01269 -1.623e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004532 0.00123 0.003486 0.005034 0.9891 0.9921 0.004612 0.8797 0.9067 0.016 ] Network output: [ -0.002043 0.08345 0.9525 -0.0002243 0.0001007 0.9672 -0.0001691 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1996 0.1295 0.2933 0.1737 0.9851 0.994 0.2002 0.4795 0.889 0.741 ] Network output: [ 0.01139 0.003231 1.021 0.0001264 -5.676e-05 0.9538 9.529e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08083 0.07522 0.1586 0.2074 0.9876 0.9921 0.08087 0.8175 0.8908 0.2987 ] Network output: [ -0.008357 0.003577 1.023 0.00012 -5.386e-05 0.9902 9.042e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08916 0.08807 0.171 0.2098 0.9855 0.9914 0.08917 0.7425 0.8699 0.254 ] Network output: [ -0.002771 0.9992 0.007901 2.021e-05 -9.075e-06 0.9985 1.523e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003318 Epoch 5692 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01605 0.9856 0.9799 -1.155e-05 5.184e-06 0.00232 -8.702e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002974 -0.002596 -0.011 0.008547 0.9694 0.9738 0.005616 0.8449 0.8359 0.02267 ] Network output: [ 1 -0.05758 0.007987 -1.369e-05 6.144e-06 0.04914 -1.031e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1868 -0.009103 -0.2113 0.2292 0.9835 0.9932 0.2082 0.4716 0.8827 0.7457 ] Network output: [ -0.01262 1.001 1.006 -2.041e-05 9.162e-06 0.0185 -1.538e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004495 0.001239 0.003833 0.005758 0.9891 0.9921 0.004576 0.8795 0.9072 0.01619 ] Network output: [ 0.009633 -0.094 0.9628 -0.0001898 8.522e-05 1.111 -0.0001431 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1979 0.1288 0.3049 0.208 0.9851 0.994 0.1985 0.4772 0.889 0.7399 ] Network output: [ 0.00729 -0.0278 1.028 0.0001267 -5.688e-05 0.9859 9.549e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08161 0.07606 0.1666 0.2163 0.9875 0.9921 0.08166 0.8199 0.891 0.3036 ] Network output: [ -0.01142 0.01536 1.024 0.000116 -5.207e-05 0.9838 8.741e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08992 0.08886 0.1738 0.2113 0.9856 0.9915 0.08993 0.7459 0.8699 0.2542 ] Network output: [ 0.001679 1.004 -0.0001302 2.308e-05 -1.036e-05 0.9931 1.739e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004709 Epoch 5693 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01479 1.005 0.979 -1.507e-05 6.765e-06 -0.01354 -1.136e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002998 -0.0026 -0.01107 0.008168 0.9694 0.9738 0.005656 0.8453 0.835 0.02255 ] Network output: [ 0.9891 0.07201 0.001926 -3.891e-05 1.747e-05 -0.05227 -2.933e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1887 -0.007486 -0.2172 0.207 0.9835 0.9932 0.2103 0.4747 0.8818 0.7439 ] Network output: [ -0.01251 1.007 1.005 -2.127e-05 9.551e-06 0.01263 -1.603e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004536 0.001226 0.00349 0.005037 0.9891 0.9921 0.004617 0.8798 0.9067 0.01601 ] Network output: [ -0.002075 0.08312 0.953 -0.0002247 0.0001009 0.9671 -0.0001693 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1995 0.1292 0.2934 0.1737 0.9851 0.994 0.2001 0.4794 0.889 0.741 ] Network output: [ 0.01143 0.003005 1.02 0.0001264 -5.676e-05 0.9543 9.528e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08101 0.07538 0.1588 0.2075 0.9876 0.9921 0.08106 0.8175 0.8908 0.2989 ] Network output: [ -0.008393 0.003849 1.023 0.00012 -5.386e-05 0.9903 9.042e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08931 0.08822 0.1711 0.2098 0.9855 0.9914 0.08932 0.7426 0.87 0.254 ] Network output: [ -0.002733 0.9992 0.007791 2.026e-05 -9.098e-06 0.9986 1.527e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003277 Epoch 5694 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01603 0.9857 0.98 -1.128e-05 5.064e-06 0.002222 -8.501e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002976 -0.002599 -0.011 0.008548 0.9694 0.9738 0.005619 0.8449 0.8359 0.02268 ] Network output: [ 1 -0.05746 0.007969 -1.39e-05 6.239e-06 0.04899 -1.047e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1867 -0.009232 -0.2114 0.2292 0.9835 0.9932 0.2081 0.4716 0.8827 0.7457 ] Network output: [ -0.01265 1.001 1.006 -2.015e-05 9.047e-06 0.01842 -1.519e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0045 0.001235 0.003835 0.005757 0.9891 0.9921 0.00458 0.8795 0.9072 0.0162 ] Network output: [ 0.009529 -0.09343 0.9632 -0.0001904 8.547e-05 1.11 -0.0001435 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1977 0.1285 0.3049 0.2078 0.9851 0.994 0.1983 0.4772 0.889 0.7399 ] Network output: [ 0.007343 -0.02793 1.028 0.0001267 -5.688e-05 0.9862 9.548e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08179 0.0762 0.1667 0.2164 0.9875 0.9921 0.08184 0.8199 0.891 0.3038 ] Network output: [ -0.01144 0.01555 1.024 0.000116 -5.208e-05 0.9839 8.743e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09007 0.089 0.1739 0.2114 0.9856 0.9915 0.09008 0.746 0.8699 0.2542 ] Network output: [ 0.001701 1.004 -0.0001919 2.312e-05 -1.038e-05 0.9932 1.743e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004656 Epoch 5695 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01478 1.005 0.9791 -1.478e-05 6.634e-06 -0.01357 -1.114e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003 -0.002603 -0.01107 0.008171 0.9694 0.9738 0.005659 0.8453 0.835 0.02255 ] Network output: [ 0.9892 0.07149 0.001946 -3.895e-05 1.749e-05 -0.05194 -2.935e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1886 -0.007623 -0.2172 0.2071 0.9835 0.9932 0.2102 0.4746 0.8819 0.7439 ] Network output: [ -0.01253 1.007 1.005 -2.101e-05 9.433e-06 0.01256 -1.584e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00454 0.001222 0.003493 0.005039 0.9891 0.9921 0.004622 0.8798 0.9067 0.01601 ] Network output: [ -0.002111 0.08288 0.9535 -0.000225 0.000101 0.967 -0.0001696 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1994 0.129 0.2935 0.1737 0.9851 0.994 0.2 0.4794 0.889 0.7409 ] Network output: [ 0.01147 0.002801 1.02 0.0001264 -5.675e-05 0.9547 9.527e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08119 0.07553 0.1589 0.2076 0.9876 0.9921 0.08124 0.8175 0.8909 0.2991 ] Network output: [ -0.008427 0.004123 1.023 0.00012 -5.386e-05 0.9903 9.041e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08946 0.08837 0.1712 0.2098 0.9855 0.9915 0.08948 0.7427 0.87 0.2541 ] Network output: [ -0.002699 0.9992 0.007687 2.031e-05 -9.118e-06 0.9986 1.531e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003242 Epoch 5696 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01601 0.9857 0.98 -1.101e-05 4.944e-06 0.002135 -8.299e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002978 -0.002602 -0.011 0.00855 0.9694 0.9738 0.005623 0.8449 0.8359 0.02268 ] Network output: [ 1 -0.05741 0.007957 -1.411e-05 6.333e-06 0.04889 -1.063e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1866 -0.009359 -0.2114 0.2291 0.9835 0.9932 0.2079 0.4716 0.8827 0.7457 ] Network output: [ -0.01267 1.001 1.006 -1.989e-05 8.931e-06 0.01834 -1.499e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004504 0.001231 0.003838 0.005757 0.9891 0.9921 0.004585 0.8795 0.9072 0.01621 ] Network output: [ 0.009437 -0.09297 0.9636 -0.0001909 8.571e-05 1.11 -0.0001439 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1976 0.1282 0.305 0.2077 0.9851 0.994 0.1982 0.4772 0.889 0.7398 ] Network output: [ 0.007393 -0.02806 1.027 0.0001267 -5.687e-05 0.9865 9.547e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08197 0.07635 0.1668 0.2165 0.9875 0.9921 0.08202 0.8199 0.891 0.3039 ] Network output: [ -0.01147 0.01575 1.024 0.000116 -5.209e-05 0.984 8.744e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09022 0.08915 0.174 0.2114 0.9856 0.9915 0.09023 0.7461 0.87 0.2542 ] Network output: [ 0.001725 1.004 -0.0002562 2.317e-05 -1.04e-05 0.9933 1.746e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00461 Epoch 5697 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01477 1.005 0.9792 -1.449e-05 6.506e-06 -0.01361 -1.092e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003001 -0.002606 -0.01107 0.008174 0.9694 0.9738 0.005663 0.8453 0.8351 0.02256 ] Network output: [ 0.9892 0.07105 0.001965 -3.901e-05 1.751e-05 -0.05167 -2.94e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1885 -0.007757 -0.2172 0.2071 0.9835 0.9932 0.21 0.4746 0.8819 0.7438 ] Network output: [ -0.01256 1.007 1.006 -2.075e-05 9.316e-06 0.0125 -1.564e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004545 0.001218 0.003497 0.005041 0.9891 0.9921 0.004626 0.8798 0.9068 0.01602 ] Network output: [ -0.002148 0.08273 0.9539 -0.0002253 0.0001012 0.9667 -0.0001698 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1992 0.1287 0.2936 0.1737 0.9851 0.994 0.1999 0.4794 0.8891 0.7409 ] Network output: [ 0.01152 0.002616 1.02 0.0001264 -5.675e-05 0.955 9.526e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08137 0.07568 0.159 0.2078 0.9876 0.9921 0.08142 0.8175 0.8909 0.2992 ] Network output: [ -0.00846 0.004399 1.023 0.00012 -5.386e-05 0.9903 9.041e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08962 0.08852 0.1712 0.2099 0.9855 0.9915 0.08963 0.7428 0.8701 0.2541 ] Network output: [ -0.002668 0.9992 0.00759 2.035e-05 -9.137e-06 0.9986 1.534e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003214 Epoch 5698 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.016 0.9858 0.9801 -1.074e-05 4.824e-06 0.002058 -8.097e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00298 -0.002605 -0.01101 0.008551 0.9694 0.9738 0.005626 0.845 0.836 0.02268 ] Network output: [ 1 -0.05741 0.00795 -1.431e-05 6.425e-06 0.04884 -1.079e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1865 -0.009485 -0.2114 0.2291 0.9835 0.9932 0.2078 0.4716 0.8828 0.7456 ] Network output: [ -0.0127 1.001 1.006 -1.964e-05 8.815e-06 0.01827 -1.48e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004509 0.001228 0.003841 0.005757 0.9891 0.9921 0.00459 0.8795 0.9072 0.01621 ] Network output: [ 0.009354 -0.09261 0.964 -0.0001914 8.594e-05 1.109 -0.0001443 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1975 0.128 0.305 0.2076 0.9851 0.994 0.1981 0.4772 0.889 0.7398 ] Network output: [ 0.007439 -0.0282 1.027 0.0001267 -5.686e-05 0.9868 9.546e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08214 0.0765 0.1669 0.2166 0.9875 0.9921 0.08219 0.8199 0.891 0.3041 ] Network output: [ -0.01149 0.01597 1.023 0.000116 -5.21e-05 0.984 8.746e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09037 0.08929 0.1741 0.2114 0.9856 0.9915 0.09038 0.7461 0.87 0.2542 ] Network output: [ 0.001751 1.003 -0.0003229 2.321e-05 -1.042e-05 0.9935 1.749e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004573 Epoch 5699 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01476 1.005 0.9792 -1.421e-05 6.379e-06 -0.01365 -1.071e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003003 -0.002608 -0.01107 0.008176 0.9694 0.9738 0.005666 0.8454 0.8351 0.02256 ] Network output: [ 0.9893 0.07067 0.001984 -3.91e-05 1.755e-05 -0.05144 -2.947e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1884 -0.007888 -0.2172 0.2071 0.9835 0.9932 0.2099 0.4746 0.8819 0.7438 ] Network output: [ -0.01258 1.007 1.006 -2.049e-05 9.199e-06 0.01243 -1.544e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004549 0.001214 0.003501 0.005043 0.9891 0.9921 0.004631 0.8798 0.9068 0.01602 ] Network output: [ -0.002188 0.08264 0.9543 -0.0002257 0.0001013 0.9665 -0.0001701 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1991 0.1284 0.2936 0.1736 0.9851 0.994 0.1997 0.4794 0.8891 0.7408 ] Network output: [ 0.01156 0.002448 1.02 0.0001264 -5.674e-05 0.9554 9.525e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08155 0.07583 0.1592 0.2079 0.9876 0.9921 0.0816 0.8175 0.8909 0.2994 ] Network output: [ -0.008491 0.004676 1.023 0.00012 -5.386e-05 0.9903 9.041e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08977 0.08867 0.1713 0.2099 0.9855 0.9915 0.08978 0.7429 0.8701 0.2541 ] Network output: [ -0.00264 0.9992 0.007498 2.039e-05 -9.153e-06 0.9987 1.537e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00319 Epoch 5700 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01598 0.9858 0.9802 -1.048e-05 4.703e-06 0.00199 -7.896e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002982 -0.002607 -0.01101 0.008553 0.9694 0.9738 0.00563 0.845 0.836 0.02269 ] Network output: [ 1 -0.05747 0.007948 -1.452e-05 6.517e-06 0.04884 -1.094e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1864 -0.00961 -0.2115 0.229 0.9835 0.9932 0.2077 0.4716 0.8828 0.7456 ] Network output: [ -0.01272 1.001 1.006 -1.938e-05 8.699e-06 0.0182 -1.46e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004514 0.001224 0.003844 0.005757 0.9891 0.9921 0.004594 0.8796 0.9072 0.01622 ] Network output: [ 0.00928 -0.09234 0.9644 -0.0001919 8.615e-05 1.109 -0.0001446 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1974 0.1277 0.3051 0.2075 0.9851 0.994 0.198 0.4772 0.889 0.7397 ] Network output: [ 0.007482 -0.02836 1.027 0.0001267 -5.686e-05 0.9872 9.545e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08232 0.07665 0.1671 0.2167 0.9875 0.9921 0.08237 0.8198 0.891 0.3042 ] Network output: [ -0.01152 0.01619 1.023 0.0001161 -5.21e-05 0.984 8.747e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09052 0.08944 0.1741 0.2114 0.9856 0.9915 0.09053 0.7462 0.8701 0.2543 ] Network output: [ 0.001778 1.003 -0.0003913 2.325e-05 -1.044e-05 0.9936 1.753e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004542 Epoch 5701 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01474 1.005 0.9793 -1.393e-05 6.253e-06 -0.0137 -1.05e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003005 -0.002611 -0.01108 0.008178 0.9694 0.9738 0.00567 0.8454 0.8351 0.02256 ] Network output: [ 0.9894 0.07036 0.002 -3.921e-05 1.76e-05 -0.05126 -2.955e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1883 -0.008016 -0.2172 0.2071 0.9835 0.9932 0.2098 0.4745 0.8819 0.7438 ] Network output: [ -0.0126 1.007 1.006 -2.023e-05 9.082e-06 0.01236 -1.525e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004554 0.00121 0.003504 0.005044 0.9891 0.9921 0.004635 0.8798 0.9068 0.01603 ] Network output: [ -0.00223 0.08263 0.9548 -0.000226 0.0001015 0.9661 -0.0001703 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.199 0.1282 0.2937 0.1735 0.9851 0.994 0.1996 0.4793 0.8891 0.7408 ] Network output: [ 0.0116 0.002296 1.019 0.0001264 -5.674e-05 0.9557 9.524e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08173 0.07597 0.1593 0.208 0.9876 0.9921 0.08178 0.8174 0.8909 0.2995 ] Network output: [ -0.008521 0.004955 1.022 0.00012 -5.386e-05 0.9903 9.041e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.08992 0.08881 0.1714 0.2099 0.9855 0.9915 0.08993 0.7429 0.8702 0.2541 ] Network output: [ -0.002615 0.9992 0.007412 2.042e-05 -9.167e-06 0.9987 1.539e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003171 Epoch 5702 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01597 0.9859 0.9802 -1.021e-05 4.583e-06 0.001931 -7.694e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002984 -0.00261 -0.01101 0.008555 0.9694 0.9738 0.005633 0.845 0.836 0.02269 ] Network output: [ 1 -0.05757 0.007949 -1.472e-05 6.609e-06 0.04888 -1.109e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1863 -0.009733 -0.2115 0.229 0.9835 0.9932 0.2076 0.4715 0.8828 0.7455 ] Network output: [ -0.01274 1.001 1.006 -1.912e-05 8.583e-06 0.01813 -1.441e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004518 0.00122 0.003847 0.005758 0.9891 0.9921 0.004599 0.8796 0.9072 0.01622 ] Network output: [ 0.009215 -0.09215 0.9648 -0.0001923 8.635e-05 1.108 -0.0001449 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1973 0.1275 0.3051 0.2074 0.9851 0.994 0.1979 0.4771 0.8891 0.7397 ] Network output: [ 0.007522 -0.02852 1.026 0.0001266 -5.685e-05 0.9875 9.544e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0825 0.0768 0.1672 0.2168 0.9875 0.9921 0.08255 0.8198 0.891 0.3044 ] Network output: [ -0.01155 0.01643 1.023 0.0001161 -5.211e-05 0.9841 8.748e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09067 0.08958 0.1742 0.2115 0.9856 0.9915 0.09068 0.7463 0.8701 0.2543 ] Network output: [ 0.001807 1.003 -0.0004611 2.33e-05 -1.046e-05 0.9937 1.756e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004517 Epoch 5703 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01473 1.005 0.9794 -1.365e-05 6.129e-06 -0.01375 -1.029e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003007 -0.002614 -0.01108 0.008181 0.9694 0.9738 0.005673 0.8454 0.8352 0.02257 ] Network output: [ 0.9894 0.07011 0.002015 -3.934e-05 1.766e-05 -0.05112 -2.964e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1881 -0.008141 -0.2172 0.2071 0.9835 0.9932 0.2097 0.4745 0.882 0.7437 ] Network output: [ -0.01262 1.007 1.006 -1.997e-05 8.965e-06 0.0123 -1.505e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004559 0.001207 0.003507 0.005045 0.9891 0.9921 0.00464 0.8798 0.9068 0.01604 ] Network output: [ -0.002274 0.08267 0.9552 -0.0002264 0.0001016 0.9658 -0.0001706 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1989 0.1279 0.2938 0.1735 0.9851 0.994 0.1995 0.4793 0.8891 0.7407 ] Network output: [ 0.01165 0.002158 1.019 0.0001264 -5.673e-05 0.956 9.524e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08191 0.07612 0.1594 0.208 0.9876 0.9921 0.08196 0.8174 0.8909 0.2997 ] Network output: [ -0.00855 0.005236 1.022 0.00012 -5.386e-05 0.9903 9.041e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09007 0.08896 0.1715 0.2099 0.9855 0.9915 0.09008 0.743 0.8702 0.2541 ] Network output: [ -0.002593 0.9992 0.007331 2.045e-05 -9.18e-06 0.9987 1.541e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003156 Epoch 5704 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01595 0.9859 0.9803 -9.942e-06 4.463e-06 0.00188 -7.493e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002986 -0.002613 -0.01101 0.008557 0.9694 0.9738 0.005637 0.8451 0.836 0.02269 ] Network output: [ 1 -0.05772 0.007953 -1.493e-05 6.701e-06 0.04895 -1.125e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1862 -0.009855 -0.2115 0.229 0.9835 0.9932 0.2075 0.4715 0.8828 0.7455 ] Network output: [ -0.01276 1.001 1.006 -1.886e-05 8.467e-06 0.01807 -1.421e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004523 0.001217 0.003851 0.005759 0.9891 0.9921 0.004604 0.8796 0.9072 0.01623 ] Network output: [ 0.009158 -0.09203 0.9652 -0.0001927 8.653e-05 1.108 -0.0001453 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1972 0.1272 0.3052 0.2073 0.9851 0.994 0.1978 0.4771 0.8891 0.7396 ] Network output: [ 0.007559 -0.0287 1.026 0.0001266 -5.685e-05 0.9879 9.543e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08268 0.07694 0.1673 0.2169 0.9875 0.9921 0.08273 0.8198 0.891 0.3046 ] Network output: [ -0.01158 0.01668 1.023 0.0001161 -5.212e-05 0.9841 8.749e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09082 0.08973 0.1743 0.2115 0.9856 0.9915 0.09083 0.7464 0.8702 0.2543 ] Network output: [ 0.001837 1.003 -0.0005318 2.333e-05 -1.048e-05 0.9938 1.759e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004498 Epoch 5705 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01472 1.005 0.9794 -1.338e-05 6.006e-06 -0.0138 -1.008e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003009 -0.002617 -0.01108 0.008182 0.9694 0.9738 0.005677 0.8454 0.8352 0.02257 ] Network output: [ 0.9895 0.0699 0.002029 -3.948e-05 1.773e-05 -0.05102 -2.976e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.188 -0.008263 -0.2172 0.2071 0.9835 0.9932 0.2095 0.4745 0.882 0.7437 ] Network output: [ -0.01264 1.007 1.006 -1.971e-05 8.849e-06 0.01223 -1.486e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004563 0.001203 0.00351 0.005046 0.9891 0.9921 0.004645 0.8798 0.9068 0.01604 ] Network output: [ -0.002319 0.08276 0.9556 -0.0002267 0.0001018 0.9654 -0.0001709 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1988 0.1277 0.2938 0.1734 0.9851 0.994 0.1994 0.4793 0.8891 0.7407 ] Network output: [ 0.01169 0.002032 1.019 0.0001264 -5.673e-05 0.9564 9.523e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08209 0.07627 0.1595 0.2081 0.9876 0.9921 0.08214 0.8174 0.8909 0.2998 ] Network output: [ -0.008578 0.005518 1.022 0.00012 -5.386e-05 0.9902 9.042e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09022 0.0891 0.1715 0.2099 0.9855 0.9915 0.09024 0.7431 0.8702 0.2541 ] Network output: [ -0.002573 0.9992 0.007255 2.047e-05 -9.19e-06 0.9988 1.543e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003145 Epoch 5706 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01594 0.9859 0.9804 -9.676e-06 4.344e-06 0.001836 -7.292e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002987 -0.002616 -0.01101 0.008558 0.9694 0.9738 0.00564 0.8451 0.8361 0.0227 ] Network output: [ 1 -0.0579 0.007961 -1.513e-05 6.794e-06 0.04905 -1.14e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1861 -0.009975 -0.2115 0.229 0.9835 0.9932 0.2073 0.4715 0.8828 0.7454 ] Network output: [ -0.01278 1.001 1.006 -1.86e-05 8.351e-06 0.01802 -1.402e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004527 0.001213 0.003854 0.00576 0.9891 0.9921 0.004608 0.8796 0.9072 0.01623 ] Network output: [ 0.009108 -0.09198 0.9656 -0.0001931 8.67e-05 1.107 -0.0001456 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1971 0.127 0.3053 0.2072 0.9851 0.994 0.1977 0.4771 0.8891 0.7396 ] Network output: [ 0.007594 -0.02888 1.026 0.0001266 -5.684e-05 0.9882 9.542e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08286 0.07709 0.1674 0.217 0.9875 0.9921 0.08291 0.8198 0.891 0.3047 ] Network output: [ -0.01161 0.01694 1.023 0.0001161 -5.212e-05 0.9841 8.75e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09097 0.08987 0.1744 0.2115 0.9856 0.9915 0.09098 0.7464 0.8702 0.2543 ] Network output: [ 0.001867 1.003 -0.0006031 2.337e-05 -1.049e-05 0.9939 1.761e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004484 Epoch 5707 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01471 1.005 0.9795 -1.311e-05 5.885e-06 -0.01386 -9.879e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00301 -0.002619 -0.01108 0.008184 0.9694 0.9738 0.00568 0.8455 0.8352 0.02257 ] Network output: [ 0.9895 0.06975 0.002042 -3.965e-05 1.78e-05 -0.05095 -2.988e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1879 -0.008383 -0.2172 0.2071 0.9835 0.9932 0.2094 0.4744 0.882 0.7436 ] Network output: [ -0.01265 1.007 1.006 -1.945e-05 8.733e-06 0.01217 -1.466e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004568 0.001199 0.003514 0.005046 0.9891 0.9921 0.004649 0.8798 0.9068 0.01605 ] Network output: [ -0.002365 0.0829 0.956 -0.0002271 0.0001019 0.9649 -0.0001711 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1987 0.1275 0.2939 0.1733 0.9851 0.994 0.1993 0.4792 0.8891 0.7406 ] Network output: [ 0.01173 0.001917 1.018 0.0001263 -5.672e-05 0.9567 9.522e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08226 0.07642 0.1596 0.2082 0.9876 0.9921 0.08231 0.8174 0.8909 0.3 ] Network output: [ -0.008605 0.005801 1.022 0.00012 -5.386e-05 0.9902 9.042e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09037 0.08925 0.1716 0.21 0.9855 0.9915 0.09038 0.7432 0.8703 0.2541 ] Network output: [ -0.002556 0.9992 0.007184 2.049e-05 -9.199e-06 0.9988 1.544e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003137 Epoch 5708 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01593 0.9859 0.9804 -9.411e-06 4.225e-06 0.001798 -7.092e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002989 -0.002618 -0.01102 0.00856 0.9694 0.9738 0.005644 0.8451 0.8361 0.0227 ] Network output: [ 1 -0.0581 0.00797 -1.534e-05 6.887e-06 0.04918 -1.156e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.186 -0.01009 -0.2114 0.229 0.9835 0.9932 0.2072 0.4714 0.8828 0.7454 ] Network output: [ -0.01279 1.001 1.006 -1.834e-05 8.234e-06 0.01797 -1.382e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004532 0.00121 0.003858 0.005762 0.9891 0.9921 0.004613 0.8796 0.9073 0.01624 ] Network output: [ 0.009064 -0.09198 0.966 -0.0001935 8.687e-05 1.107 -0.0001458 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.197 0.1268 0.3053 0.2072 0.9851 0.994 0.1976 0.477 0.8891 0.7395 ] Network output: [ 0.007627 -0.02907 1.026 0.0001266 -5.684e-05 0.9886 9.541e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08303 0.07724 0.1676 0.2171 0.9875 0.9921 0.08308 0.8197 0.891 0.3049 ] Network output: [ -0.01164 0.0172 1.022 0.0001161 -5.213e-05 0.9841 8.75e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09112 0.09001 0.1744 0.2115 0.9856 0.9915 0.09113 0.7465 0.8702 0.2543 ] Network output: [ 0.001898 1.003 -0.0006746 2.341e-05 -1.051e-05 0.9939 1.764e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004474 Epoch 5709 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0147 1.005 0.9796 -1.284e-05 5.765e-06 -0.01392 -9.678e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003012 -0.002622 -0.01108 0.008185 0.9694 0.9738 0.005684 0.8455 0.8352 0.02258 ] Network output: [ 0.9895 0.06963 0.002052 -3.983e-05 1.788e-05 -0.05091 -3.001e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1879 -0.008499 -0.2172 0.2071 0.9835 0.9932 0.2093 0.4744 0.882 0.7435 ] Network output: [ -0.01267 1.007 1.006 -1.92e-05 8.618e-06 0.0121 -1.447e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004573 0.001196 0.003517 0.005047 0.9891 0.9921 0.004654 0.8798 0.9068 0.01605 ] Network output: [ -0.002411 0.08307 0.9564 -0.0002274 0.0001021 0.9645 -0.0001714 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1986 0.1272 0.2939 0.1732 0.9851 0.994 0.1992 0.4792 0.8892 0.7405 ] Network output: [ 0.01178 0.001811 1.018 0.0001263 -5.672e-05 0.9569 9.521e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08244 0.07656 0.1598 0.2083 0.9876 0.9921 0.08249 0.8173 0.8909 0.3001 ] Network output: [ -0.00863 0.006085 1.021 0.00012 -5.387e-05 0.9902 9.043e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09052 0.08939 0.1717 0.21 0.9855 0.9915 0.09053 0.7432 0.8703 0.2541 ] Network output: [ -0.002542 0.9992 0.007117 2.05e-05 -9.205e-06 0.9988 1.545e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003132 Epoch 5710 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01592 0.9859 0.9805 -9.146e-06 4.106e-06 0.001766 -6.893e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002991 -0.002621 -0.01102 0.008562 0.9694 0.9738 0.005647 0.8451 0.8361 0.0227 ] Network output: [ 1 -0.05834 0.007981 -1.555e-05 6.981e-06 0.04933 -1.172e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1859 -0.01021 -0.2114 0.2289 0.9835 0.9932 0.2071 0.4714 0.8828 0.7453 ] Network output: [ -0.01281 1.001 1.007 -1.808e-05 8.118e-06 0.01792 -1.363e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004537 0.001206 0.003862 0.005764 0.9891 0.9921 0.004618 0.8796 0.9073 0.01624 ] Network output: [ 0.009026 -0.09203 0.9664 -0.0001938 8.702e-05 1.107 -0.0001461 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1969 0.1265 0.3054 0.2071 0.9851 0.994 0.1975 0.477 0.8891 0.7394 ] Network output: [ 0.007657 -0.02926 1.025 0.0001266 -5.683e-05 0.989 9.541e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08321 0.07739 0.1677 0.2172 0.9875 0.9921 0.08326 0.8197 0.891 0.305 ] Network output: [ -0.01167 0.01747 1.022 0.0001161 -5.213e-05 0.9841 8.751e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09127 0.09016 0.1745 0.2116 0.9856 0.9915 0.09128 0.7466 0.8703 0.2543 ] Network output: [ 0.00193 1.003 -0.000746 2.344e-05 -1.052e-05 0.994 1.767e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004468 Epoch 5711 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01469 1.005 0.9796 -1.258e-05 5.647e-06 -0.01398 -9.48e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003014 -0.002625 -0.01109 0.008187 0.9694 0.9738 0.005687 0.8455 0.8352 0.02258 ] Network output: [ 0.9896 0.06955 0.002062 -4.002e-05 1.797e-05 -0.05089 -3.016e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1878 -0.008614 -0.2172 0.207 0.9835 0.9932 0.2092 0.4744 0.882 0.7435 ] Network output: [ -0.01269 1.007 1.006 -1.894e-05 8.502e-06 0.01204 -1.427e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004577 0.001193 0.00352 0.005047 0.9891 0.9921 0.004659 0.8798 0.9068 0.01606 ] Network output: [ -0.002459 0.08328 0.9568 -0.0002277 0.0001022 0.964 -0.0001716 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1985 0.127 0.294 0.1731 0.9851 0.994 0.1991 0.4791 0.8892 0.7405 ] Network output: [ 0.01182 0.001712 1.018 0.0001263 -5.671e-05 0.9572 9.52e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08262 0.07671 0.1599 0.2084 0.9876 0.9921 0.08267 0.8173 0.8909 0.3003 ] Network output: [ -0.008655 0.006371 1.021 0.00012 -5.387e-05 0.9902 9.043e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09067 0.08953 0.1717 0.21 0.9856 0.9915 0.09068 0.7433 0.8703 0.2542 ] Network output: [ -0.002529 0.9992 0.007054 2.052e-05 -9.21e-06 0.9989 1.546e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00313 Epoch 5712 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01591 0.9858 0.9806 -8.883e-06 3.988e-06 0.001739 -6.695e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002993 -0.002623 -0.01102 0.008564 0.9694 0.9738 0.005651 0.8452 0.8361 0.0227 ] Network output: [ 1.001 -0.05859 0.007994 -1.576e-05 7.076e-06 0.0495 -1.188e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1858 -0.01033 -0.2114 0.2289 0.9835 0.9932 0.207 0.4713 0.8829 0.7452 ] Network output: [ -0.01282 1.001 1.007 -1.782e-05 8.002e-06 0.01788 -1.343e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004541 0.001203 0.003866 0.005766 0.9891 0.9921 0.004622 0.8796 0.9073 0.01625 ] Network output: [ 0.008993 -0.09213 0.9668 -0.0001942 8.716e-05 1.107 -0.0001463 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1968 0.1263 0.3055 0.2071 0.9851 0.994 0.1974 0.4769 0.8891 0.7394 ] Network output: [ 0.007686 -0.02947 1.025 0.0001266 -5.683e-05 0.9894 9.54e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08339 0.07753 0.1678 0.2173 0.9875 0.9921 0.08344 0.8197 0.891 0.3051 ] Network output: [ -0.0117 0.01774 1.022 0.0001161 -5.214e-05 0.9841 8.752e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09142 0.0903 0.1746 0.2116 0.9856 0.9915 0.09143 0.7466 0.8703 0.2543 ] Network output: [ 0.001962 1.003 -0.000817 2.347e-05 -1.054e-05 0.9941 1.769e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004465 Epoch 5713 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01468 1.005 0.9797 -1.232e-05 5.53e-06 -0.01404 -9.283e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003016 -0.002627 -0.01109 0.008188 0.9694 0.9738 0.005691 0.8455 0.8352 0.02258 ] Network output: [ 0.9896 0.0695 0.00207 -4.022e-05 1.806e-05 -0.0509 -3.031e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1877 -0.008725 -0.2172 0.207 0.9835 0.9932 0.2091 0.4743 0.882 0.7434 ] Network output: [ -0.0127 1.007 1.006 -1.868e-05 8.388e-06 0.01197 -1.408e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004582 0.001189 0.003523 0.005048 0.9891 0.9921 0.004664 0.8798 0.9068 0.01606 ] Network output: [ -0.002506 0.08351 0.9571 -0.000228 0.0001024 0.9635 -0.0001719 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1984 0.1268 0.294 0.173 0.9851 0.994 0.199 0.4791 0.8892 0.7404 ] Network output: [ 0.01186 0.001619 1.018 0.0001263 -5.671e-05 0.9575 9.519e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08279 0.07686 0.16 0.2084 0.9876 0.9921 0.08284 0.8173 0.8909 0.3004 ] Network output: [ -0.00868 0.006657 1.021 0.00012 -5.387e-05 0.9901 9.044e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09082 0.08968 0.1718 0.21 0.9856 0.9915 0.09083 0.7433 0.8704 0.2542 ] Network output: [ -0.002519 0.9992 0.006995 2.052e-05 -9.213e-06 0.9989 1.547e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00313 Epoch 5714 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0159 0.9858 0.9806 -8.622e-06 3.871e-06 0.001717 -6.498e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002995 -0.002626 -0.01102 0.008566 0.9694 0.9738 0.005654 0.8452 0.8361 0.02271 ] Network output: [ 1.001 -0.05885 0.008008 -1.598e-05 7.173e-06 0.04968 -1.204e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1857 -0.01044 -0.2114 0.2289 0.9835 0.9932 0.2069 0.4713 0.8829 0.7452 ] Network output: [ -0.01284 1.001 1.007 -1.757e-05 7.886e-06 0.01783 -1.324e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004546 0.0012 0.00387 0.005768 0.9891 0.9921 0.004627 0.8796 0.9073 0.01626 ] Network output: [ 0.008964 -0.09226 0.9672 -0.0001945 8.73e-05 1.106 -0.0001466 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1967 0.1261 0.3056 0.2071 0.9851 0.994 0.1973 0.4769 0.8891 0.7393 ] Network output: [ 0.007713 -0.02967 1.025 0.0001266 -5.682e-05 0.9898 9.539e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08356 0.07768 0.168 0.2174 0.9875 0.9921 0.08361 0.8197 0.891 0.3053 ] Network output: [ -0.01174 0.01802 1.022 0.0001161 -5.214e-05 0.9841 8.753e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09157 0.09045 0.1747 0.2116 0.9856 0.9915 0.09158 0.7467 0.8703 0.2543 ] Network output: [ 0.001994 1.003 -0.0008872 2.35e-05 -1.055e-05 0.9942 1.771e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004465 Epoch 5715 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01467 1.005 0.9798 -1.206e-05 5.414e-06 -0.01411 -9.088e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003018 -0.00263 -0.01109 0.008189 0.9694 0.9738 0.005694 0.8455 0.8353 0.02258 ] Network output: [ 0.9896 0.06948 0.002076 -4.044e-05 1.816e-05 -0.05092 -3.048e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1876 -0.008835 -0.2172 0.2069 0.9835 0.9932 0.209 0.4743 0.882 0.7433 ] Network output: [ -0.01271 1.007 1.006 -1.843e-05 8.273e-06 0.01191 -1.389e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004587 0.001186 0.003526 0.005048 0.9891 0.9921 0.004669 0.8798 0.9068 0.01607 ] Network output: [ -0.002553 0.08377 0.9575 -0.0002284 0.0001025 0.9629 -0.0001721 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1983 0.1266 0.2941 0.1729 0.9851 0.994 0.1989 0.4791 0.8892 0.7403 ] Network output: [ 0.0119 0.001531 1.017 0.0001263 -5.67e-05 0.9578 9.518e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08297 0.077 0.1601 0.2085 0.9876 0.9921 0.08302 0.8172 0.8909 0.3005 ] Network output: [ -0.008703 0.006945 1.021 0.00012 -5.388e-05 0.9901 9.044e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09097 0.08982 0.1719 0.21 0.9856 0.9915 0.09098 0.7434 0.8704 0.2542 ] Network output: [ -0.00251 0.9993 0.00694 2.053e-05 -9.215e-06 0.9989 1.547e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003132 Epoch 5716 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0159 0.9858 0.9807 -8.362e-06 3.754e-06 0.001698 -6.302e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002997 -0.002629 -0.01102 0.008567 0.9694 0.9738 0.005658 0.8452 0.8361 0.02271 ] Network output: [ 1.001 -0.05913 0.008022 -1.62e-05 7.271e-06 0.04988 -1.221e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1856 -0.01055 -0.2113 0.2289 0.9835 0.9932 0.2068 0.4712 0.8829 0.7451 ] Network output: [ -0.01285 1.001 1.007 -1.731e-05 7.771e-06 0.0178 -1.305e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00455 0.001197 0.003874 0.00577 0.9891 0.9921 0.004632 0.8796 0.9073 0.01626 ] Network output: [ 0.008939 -0.09242 0.9675 -0.0001947 8.743e-05 1.106 -0.0001468 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1966 0.1259 0.3056 0.207 0.9851 0.994 0.1972 0.4768 0.8891 0.7392 ] Network output: [ 0.007738 -0.02988 1.025 0.0001266 -5.682e-05 0.9902 9.539e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08374 0.07783 0.1681 0.2175 0.9875 0.9921 0.08379 0.8196 0.891 0.3054 ] Network output: [ -0.01177 0.0183 1.022 0.0001161 -5.214e-05 0.984 8.753e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09172 0.09059 0.1747 0.2116 0.9856 0.9915 0.09173 0.7468 0.8704 0.2543 ] Network output: [ 0.002026 1.003 -0.0009564 2.353e-05 -1.056e-05 0.9943 1.773e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004467 Epoch 5717 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01466 1.005 0.9798 -1.18e-05 5.299e-06 -0.01417 -8.896e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00302 -0.002632 -0.01109 0.00819 0.9694 0.9738 0.005698 0.8456 0.8353 0.02258 ] Network output: [ 0.9896 0.06948 0.002081 -4.067e-05 1.826e-05 -0.05096 -3.065e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1875 -0.008941 -0.2172 0.2069 0.9835 0.9932 0.2089 0.4742 0.882 0.7432 ] Network output: [ -0.01273 1.007 1.006 -1.818e-05 8.16e-06 0.01185 -1.37e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004591 0.001183 0.003529 0.005048 0.9891 0.9921 0.004673 0.8798 0.9068 0.01607 ] Network output: [ -0.0026 0.08403 0.9578 -0.0002287 0.0001027 0.9624 -0.0001723 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1982 0.1264 0.2941 0.1728 0.9851 0.994 0.1989 0.479 0.8892 0.7402 ] Network output: [ 0.01195 0.001447 1.017 0.0001263 -5.67e-05 0.958 9.518e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08314 0.07715 0.1602 0.2086 0.9876 0.9921 0.08319 0.8172 0.8909 0.3006 ] Network output: [ -0.008726 0.007234 1.021 0.00012 -5.388e-05 0.9901 9.045e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09111 0.08996 0.1719 0.21 0.9856 0.9915 0.09112 0.7434 0.8704 0.2542 ] Network output: [ -0.002503 0.9993 0.006888 2.053e-05 -9.215e-06 0.9989 1.547e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003136 Epoch 5718 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01589 0.9857 0.9808 -8.104e-06 3.638e-06 0.001682 -6.107e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.002998 -0.002631 -0.01102 0.008569 0.9694 0.9738 0.005661 0.8452 0.8361 0.02271 ] Network output: [ 1.001 -0.05941 0.008037 -1.642e-05 7.371e-06 0.05007 -1.237e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1855 -0.01066 -0.2113 0.2289 0.9835 0.9932 0.2068 0.4711 0.8829 0.745 ] Network output: [ -0.01286 1.001 1.007 -1.705e-05 7.656e-06 0.01776 -1.285e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004555 0.001193 0.003878 0.005772 0.9891 0.9921 0.004637 0.8795 0.9073 0.01626 ] Network output: [ 0.008917 -0.0926 0.9679 -0.000195 8.755e-05 1.106 -0.000147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1965 0.1257 0.3057 0.207 0.9851 0.994 0.1971 0.4768 0.8891 0.7391 ] Network output: [ 0.007762 -0.0301 1.025 0.0001266 -5.682e-05 0.9905 9.538e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08391 0.07797 0.1682 0.2176 0.9875 0.9921 0.08396 0.8196 0.891 0.3056 ] Network output: [ -0.0118 0.01859 1.021 0.0001162 -5.215e-05 0.984 8.754e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09186 0.09073 0.1748 0.2116 0.9857 0.9915 0.09187 0.7468 0.8704 0.2543 ] Network output: [ 0.002058 1.003 -0.001024 2.356e-05 -1.058e-05 0.9944 1.775e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004471 Epoch 5719 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01465 1.005 0.9799 -1.155e-05 5.186e-06 -0.01423 -8.706e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003022 -0.002635 -0.01109 0.00819 0.9694 0.9738 0.005701 0.8456 0.8353 0.02259 ] Network output: [ 0.9896 0.0695 0.002085 -4.09e-05 1.836e-05 -0.05101 -3.082e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1874 -0.009046 -0.2172 0.2068 0.9835 0.9932 0.2089 0.4742 0.882 0.7432 ] Network output: [ -0.01274 1.007 1.006 -1.792e-05 8.046e-06 0.01179 -1.351e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004596 0.00118 0.003532 0.005048 0.9891 0.9921 0.004678 0.8798 0.9068 0.01607 ] Network output: [ -0.002646 0.08431 0.9582 -0.000229 0.0001028 0.9619 -0.0001726 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1982 0.1262 0.2941 0.1727 0.9851 0.994 0.1988 0.4789 0.8892 0.7402 ] Network output: [ 0.01199 0.001364 1.017 0.0001263 -5.669e-05 0.9583 9.517e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08331 0.07729 0.1603 0.2087 0.9876 0.9921 0.08336 0.8172 0.8908 0.3008 ] Network output: [ -0.008749 0.007524 1.02 0.00012 -5.389e-05 0.99 9.046e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09126 0.0901 0.172 0.21 0.9856 0.9915 0.09127 0.7435 0.8705 0.2542 ] Network output: [ -0.002497 0.9993 0.006839 2.052e-05 -9.213e-06 0.9989 1.547e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00314 Epoch 5720 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01588 0.9857 0.9808 -7.848e-06 3.523e-06 0.001669 -5.915e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003 -0.002634 -0.01102 0.008571 0.9694 0.9738 0.005664 0.8452 0.8362 0.02271 ] Network output: [ 1.001 -0.05969 0.008051 -1.664e-05 7.472e-06 0.05027 -1.254e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1855 -0.01077 -0.2113 0.2289 0.9835 0.9932 0.2067 0.4711 0.8829 0.7449 ] Network output: [ -0.01287 1.001 1.007 -1.68e-05 7.541e-06 0.01773 -1.266e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00456 0.00119 0.003882 0.005774 0.9891 0.9921 0.004641 0.8795 0.9073 0.01627 ] Network output: [ 0.008898 -0.09279 0.9683 -0.0001953 8.766e-05 1.106 -0.0001472 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1964 0.1255 0.3058 0.207 0.9851 0.994 0.197 0.4767 0.8891 0.739 ] Network output: [ 0.007786 -0.03031 1.024 0.0001266 -5.681e-05 0.9909 9.537e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08408 0.07812 0.1684 0.2177 0.9875 0.9921 0.08413 0.8196 0.891 0.3057 ] Network output: [ -0.01183 0.01887 1.021 0.0001162 -5.215e-05 0.984 8.755e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09201 0.09087 0.1749 0.2116 0.9857 0.9915 0.09202 0.7469 0.8704 0.2543 ] Network output: [ 0.00209 1.003 -0.001091 2.358e-05 -1.059e-05 0.9945 1.777e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004476 Epoch 5721 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01465 1.005 0.9799 -1.13e-05 5.074e-06 -0.0143 -8.517e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003024 -0.002637 -0.01109 0.008191 0.9694 0.9738 0.005705 0.8456 0.8353 0.02259 ] Network output: [ 0.9896 0.06953 0.002088 -4.114e-05 1.847e-05 -0.05107 -3.101e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1874 -0.009148 -0.2171 0.2068 0.9835 0.9932 0.2088 0.4741 0.882 0.7431 ] Network output: [ -0.01275 1.007 1.006 -1.767e-05 7.934e-06 0.01173 -1.332e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004601 0.001177 0.003535 0.005048 0.9891 0.9921 0.004683 0.8798 0.9068 0.01608 ] Network output: [ -0.002692 0.08459 0.9585 -0.0002292 0.0001029 0.9613 -0.0001728 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1981 0.126 0.2942 0.1726 0.9851 0.994 0.1987 0.4789 0.8892 0.7401 ] Network output: [ 0.01203 0.001282 1.017 0.0001263 -5.669e-05 0.9585 9.516e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08348 0.07743 0.1604 0.2087 0.9876 0.9921 0.08353 0.8171 0.8908 0.3009 ] Network output: [ -0.008771 0.007814 1.02 0.00012 -5.389e-05 0.99 9.047e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0914 0.09024 0.1721 0.21 0.9856 0.9915 0.09141 0.7435 0.8705 0.2542 ] Network output: [ -0.002493 0.9993 0.006792 2.052e-05 -9.21e-06 0.999 1.546e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003145 Epoch 5722 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01588 0.9856 0.9809 -7.595e-06 3.409e-06 0.001658 -5.724e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003002 -0.002636 -0.01102 0.008572 0.9694 0.9738 0.005668 0.8452 0.8362 0.02271 ] Network output: [ 1.001 -0.05997 0.008065 -1.687e-05 7.576e-06 0.05047 -1.272e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1854 -0.01087 -0.2112 0.2289 0.9835 0.9932 0.2066 0.471 0.8829 0.7449 ] Network output: [ -0.01288 1.001 1.007 -1.654e-05 7.427e-06 0.0177 -1.247e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004564 0.001188 0.003886 0.005777 0.9891 0.9921 0.004646 0.8795 0.9072 0.01627 ] Network output: [ 0.008881 -0.09299 0.9686 -0.0001955 8.777e-05 1.106 -0.0001473 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1963 0.1253 0.3059 0.207 0.9851 0.994 0.197 0.4766 0.8891 0.7389 ] Network output: [ 0.007808 -0.03053 1.024 0.0001265 -5.681e-05 0.9913 9.537e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08426 0.07826 0.1685 0.2178 0.9875 0.9921 0.08431 0.8196 0.891 0.3059 ] Network output: [ -0.01186 0.01916 1.021 0.0001162 -5.215e-05 0.9839 8.755e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09215 0.09101 0.175 0.2117 0.9857 0.9915 0.09217 0.7469 0.8705 0.2543 ] Network output: [ 0.002121 1.002 -0.001155 2.36e-05 -1.059e-05 0.9946 1.779e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004482 Epoch 5723 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01464 1.005 0.98 -1.105e-05 4.963e-06 -0.01436 -8.331e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003026 -0.00264 -0.01109 0.008192 0.9694 0.9738 0.005708 0.8456 0.8353 0.02259 ] Network output: [ 0.9897 0.06957 0.00209 -4.139e-05 1.858e-05 -0.05114 -3.119e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1873 -0.009249 -0.2171 0.2067 0.9835 0.9932 0.2087 0.474 0.882 0.743 ] Network output: [ -0.01276 1.007 1.006 -1.742e-05 7.822e-06 0.01167 -1.313e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004606 0.001174 0.003538 0.005048 0.9891 0.9921 0.004688 0.8798 0.9068 0.01608 ] Network output: [ -0.002736 0.08487 0.9588 -0.0002295 0.000103 0.9608 -0.000173 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.198 0.1258 0.2942 0.1725 0.9851 0.994 0.1986 0.4788 0.8892 0.74 ] Network output: [ 0.01206 0.001199 1.016 0.0001263 -5.668e-05 0.9588 9.515e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08365 0.07758 0.1605 0.2088 0.9876 0.9921 0.0837 0.8171 0.8908 0.301 ] Network output: [ -0.008793 0.008106 1.02 0.00012 -5.389e-05 0.9899 9.047e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09154 0.09038 0.1721 0.21 0.9856 0.9915 0.09156 0.7436 0.8705 0.2541 ] Network output: [ -0.00249 0.9993 0.006748 2.051e-05 -9.206e-06 0.999 1.545e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003151 Epoch 5724 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01587 0.9856 0.981 -7.343e-06 3.297e-06 0.001648 -5.534e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003004 -0.002639 -0.01102 0.008574 0.9694 0.9738 0.005671 0.8452 0.8362 0.02271 ] Network output: [ 1.001 -0.06023 0.008078 -1.711e-05 7.681e-06 0.05067 -1.289e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1853 -0.01097 -0.2112 0.2289 0.9835 0.9932 0.2065 0.471 0.8829 0.7448 ] Network output: [ -0.01289 1.001 1.007 -1.629e-05 7.314e-06 0.01767 -1.228e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004569 0.001185 0.00389 0.005779 0.9891 0.9921 0.004651 0.8795 0.9072 0.01628 ] Network output: [ 0.008865 -0.09319 0.969 -0.0001957 8.787e-05 1.106 -0.0001475 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1963 0.1251 0.306 0.207 0.9851 0.994 0.1969 0.4766 0.8891 0.7389 ] Network output: [ 0.00783 -0.03075 1.024 0.0001265 -5.681e-05 0.9917 9.536e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08443 0.07841 0.1687 0.2179 0.9875 0.9921 0.08448 0.8195 0.891 0.306 ] Network output: [ -0.01189 0.01945 1.021 0.0001162 -5.216e-05 0.9839 8.756e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0923 0.09115 0.175 0.2117 0.9857 0.9915 0.09231 0.747 0.8705 0.2543 ] Network output: [ 0.002152 1.002 -0.001217 2.362e-05 -1.06e-05 0.9946 1.78e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004488 Epoch 5725 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01463 1.005 0.9801 -1.081e-05 4.853e-06 -0.01442 -8.146e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003028 -0.002642 -0.01109 0.008192 0.9694 0.9738 0.005712 0.8456 0.8353 0.02259 ] Network output: [ 0.9897 0.06961 0.00209 -4.164e-05 1.869e-05 -0.0512 -3.138e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1872 -0.009347 -0.2171 0.2067 0.9835 0.9932 0.2086 0.474 0.882 0.7429 ] Network output: [ -0.01277 1.007 1.006 -1.718e-05 7.711e-06 0.01162 -1.294e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004611 0.001171 0.003541 0.005048 0.9891 0.9921 0.004693 0.8797 0.9068 0.01609 ] Network output: [ -0.002779 0.08513 0.9592 -0.0002298 0.0001032 0.9603 -0.0001732 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.198 0.1256 0.2943 0.1724 0.9851 0.994 0.1986 0.4788 0.8892 0.7399 ] Network output: [ 0.0121 0.001115 1.016 0.0001262 -5.668e-05 0.959 9.514e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08382 0.07772 0.1606 0.2089 0.9876 0.9921 0.08387 0.817 0.8908 0.3011 ] Network output: [ -0.008814 0.008398 1.02 0.0001201 -5.39e-05 0.9899 9.048e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09169 0.09052 0.1722 0.21 0.9856 0.9915 0.0917 0.7436 0.8705 0.2541 ] Network output: [ -0.002488 0.9994 0.006706 2.049e-05 -9.201e-06 0.999 1.545e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003157 Epoch 5726 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01587 0.9856 0.981 -7.095e-06 3.185e-06 0.001639 -5.347e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003006 -0.002641 -0.01102 0.008575 0.9694 0.9738 0.005675 0.8452 0.8362 0.02272 ] Network output: [ 1.001 -0.06049 0.00809 -1.735e-05 7.789e-06 0.05085 -1.307e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1852 -0.01108 -0.2111 0.2289 0.9835 0.9932 0.2064 0.4709 0.8829 0.7447 ] Network output: [ -0.0129 1.001 1.007 -1.604e-05 7.201e-06 0.01764 -1.209e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004574 0.001182 0.003894 0.005781 0.9891 0.9921 0.004655 0.8795 0.9072 0.01628 ] Network output: [ 0.00885 -0.09339 0.9693 -0.000196 8.797e-05 1.106 -0.0001477 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1962 0.1249 0.306 0.207 0.9851 0.994 0.1968 0.4765 0.8891 0.7388 ] Network output: [ 0.007851 -0.03097 1.024 0.0001265 -5.68e-05 0.9921 9.536e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0846 0.07855 0.1688 0.218 0.9875 0.9921 0.08465 0.8195 0.891 0.3061 ] Network output: [ -0.01192 0.01974 1.021 0.0001162 -5.216e-05 0.9838 8.756e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09244 0.09129 0.1751 0.2117 0.9857 0.9915 0.09245 0.747 0.8705 0.2543 ] Network output: [ 0.002181 1.002 -0.001278 2.363e-05 -1.061e-05 0.9947 1.781e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004494 Epoch 5727 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01462 1.005 0.9801 -1.057e-05 4.744e-06 -0.01448 -7.963e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003029 -0.002645 -0.01109 0.008193 0.9694 0.9738 0.005716 0.8456 0.8353 0.02259 ] Network output: [ 0.9897 0.06965 0.002089 -4.189e-05 1.88e-05 -0.05126 -3.157e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1872 -0.009443 -0.2171 0.2066 0.9835 0.9932 0.2086 0.4739 0.882 0.7428 ] Network output: [ -0.01277 1.007 1.007 -1.693e-05 7.6e-06 0.01156 -1.276e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004615 0.001168 0.003544 0.005048 0.9891 0.9921 0.004698 0.8797 0.9068 0.01609 ] Network output: [ -0.002821 0.08539 0.9595 -0.00023 0.0001033 0.9598 -0.0001734 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1979 0.1254 0.2943 0.1723 0.9851 0.994 0.1985 0.4787 0.8891 0.7398 ] Network output: [ 0.01214 0.001028 1.016 0.0001262 -5.667e-05 0.9593 9.514e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08399 0.07786 0.1607 0.2089 0.9876 0.9921 0.08404 0.817 0.8908 0.3013 ] Network output: [ -0.008836 0.00869 1.02 0.0001201 -5.39e-05 0.9898 9.049e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09183 0.09065 0.1722 0.21 0.9856 0.9915 0.09184 0.7436 0.8705 0.2541 ] Network output: [ -0.002486 0.9994 0.006665 2.048e-05 -9.194e-06 0.999 1.543e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003162 Epoch 5728 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01587 0.9855 0.9811 -6.849e-06 3.075e-06 0.00163 -5.161e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003008 -0.002644 -0.01102 0.008577 0.9694 0.9738 0.005678 0.8452 0.8362 0.02272 ] Network output: [ 1.001 -0.06073 0.008101 -1.759e-05 7.899e-06 0.05103 -1.326e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1852 -0.01117 -0.2111 0.2289 0.9835 0.9932 0.2064 0.4708 0.8829 0.7446 ] Network output: [ -0.01291 1.001 1.007 -1.579e-05 7.089e-06 0.01761 -1.19e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004578 0.001179 0.003899 0.005783 0.9891 0.9921 0.00466 0.8795 0.9072 0.01629 ] Network output: [ 0.008836 -0.09357 0.9696 -0.0001962 8.807e-05 1.105 -0.0001478 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1961 0.1247 0.3061 0.207 0.9851 0.994 0.1967 0.4764 0.8891 0.7387 ] Network output: [ 0.007872 -0.03118 1.023 0.0001265 -5.68e-05 0.9925 9.535e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08477 0.0787 0.1689 0.2181 0.9875 0.9921 0.08482 0.8195 0.891 0.3063 ] Network output: [ -0.01195 0.02002 1.021 0.0001162 -5.217e-05 0.9838 8.757e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09258 0.09143 0.1752 0.2117 0.9857 0.9915 0.0926 0.7471 0.8705 0.2543 ] Network output: [ 0.00221 1.002 -0.001335 2.365e-05 -1.062e-05 0.9948 1.782e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0045 Epoch 5729 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01462 1.005 0.9802 -1.033e-05 4.636e-06 -0.01454 -7.782e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003031 -0.002647 -0.01109 0.008193 0.9694 0.9738 0.005719 0.8456 0.8353 0.02259 ] Network output: [ 0.9897 0.06969 0.002088 -4.214e-05 1.892e-05 -0.05132 -3.176e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1871 -0.009538 -0.217 0.2066 0.9835 0.9932 0.2085 0.4739 0.882 0.7427 ] Network output: [ -0.01278 1.007 1.007 -1.668e-05 7.49e-06 0.01151 -1.257e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00462 0.001166 0.003547 0.005049 0.9891 0.9921 0.004703 0.8797 0.9068 0.01609 ] Network output: [ -0.002861 0.08563 0.9598 -0.0002303 0.0001034 0.9593 -0.0001735 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1978 0.1252 0.2944 0.1722 0.9851 0.994 0.1984 0.4786 0.8891 0.7397 ] Network output: [ 0.01217 0.0009368 1.016 0.0001262 -5.667e-05 0.9596 9.513e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08416 0.078 0.1608 0.209 0.9875 0.9921 0.08421 0.817 0.8908 0.3014 ] Network output: [ -0.008857 0.008984 1.019 0.0001201 -5.391e-05 0.9898 9.049e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09197 0.09079 0.1723 0.21 0.9856 0.9915 0.09198 0.7437 0.8705 0.2541 ] Network output: [ -0.002486 0.9994 0.006626 2.046e-05 -9.187e-06 0.999 1.542e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003167 Epoch 5730 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01587 0.9855 0.9812 -6.606e-06 2.966e-06 0.001621 -4.978e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003009 -0.002646 -0.01102 0.008578 0.9694 0.9738 0.005681 0.8452 0.8362 0.02272 ] Network output: [ 1.001 -0.06095 0.008109 -1.784e-05 8.011e-06 0.05119 -1.345e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1851 -0.01127 -0.211 0.2289 0.9835 0.9932 0.2063 0.4707 0.8829 0.7445 ] Network output: [ -0.01292 1.001 1.007 -1.554e-05 6.978e-06 0.01759 -1.171e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004583 0.001177 0.003903 0.005786 0.9891 0.9921 0.004665 0.8795 0.9072 0.01629 ] Network output: [ 0.008821 -0.09374 0.97 -0.0001964 8.816e-05 1.105 -0.000148 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1961 0.1245 0.3062 0.207 0.9851 0.994 0.1967 0.4764 0.8891 0.7386 ] Network output: [ 0.007893 -0.0314 1.023 0.0001265 -5.68e-05 0.9929 9.535e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08494 0.07884 0.1691 0.2182 0.9875 0.9921 0.08499 0.8194 0.8909 0.3064 ] Network output: [ -0.01198 0.02031 1.02 0.0001162 -5.217e-05 0.9838 8.758e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09273 0.09156 0.1752 0.2117 0.9857 0.9915 0.09274 0.7471 0.8705 0.2543 ] Network output: [ 0.002237 1.002 -0.00139 2.366e-05 -1.062e-05 0.9949 1.783e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004504 Epoch 5731 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01461 1.005 0.9803 -1.009e-05 4.529e-06 -0.01459 -7.603e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003033 -0.00265 -0.01109 0.008194 0.9694 0.9738 0.005723 0.8456 0.8353 0.02259 ] Network output: [ 0.9897 0.06972 0.002085 -4.239e-05 1.903e-05 -0.05138 -3.195e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1871 -0.009631 -0.217 0.2065 0.9835 0.9932 0.2084 0.4738 0.882 0.7426 ] Network output: [ -0.01279 1.007 1.007 -1.644e-05 7.381e-06 0.01146 -1.239e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004625 0.001163 0.00355 0.005049 0.9891 0.9921 0.004707 0.8797 0.9067 0.0161 ] Network output: [ -0.002899 0.08584 0.9601 -0.0002305 0.0001035 0.9589 -0.0001737 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1978 0.1251 0.2944 0.1721 0.9851 0.994 0.1984 0.4786 0.8891 0.7396 ] Network output: [ 0.01221 0.0008406 1.015 0.0001262 -5.666e-05 0.9598 9.512e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08433 0.07814 0.161 0.2091 0.9875 0.9921 0.08438 0.8169 0.8908 0.3015 ] Network output: [ -0.008879 0.009277 1.019 0.0001201 -5.391e-05 0.9897 9.05e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09211 0.09092 0.1723 0.21 0.9856 0.9915 0.09212 0.7437 0.8706 0.2541 ] Network output: [ -0.002485 0.9995 0.006588 2.044e-05 -9.178e-06 0.999 1.541e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003172 Epoch 5732 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01586 0.9854 0.9812 -6.365e-06 2.858e-06 0.001611 -4.797e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003011 -0.002649 -0.01102 0.008579 0.9694 0.9738 0.005685 0.8452 0.8362 0.02272 ] Network output: [ 1.001 -0.06114 0.008116 -1.81e-05 8.125e-06 0.05133 -1.364e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1851 -0.01137 -0.211 0.2289 0.9835 0.9932 0.2062 0.4707 0.8828 0.7444 ] Network output: [ -0.01292 1.001 1.007 -1.53e-05 6.868e-06 0.01757 -1.153e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004588 0.001174 0.003907 0.005788 0.9891 0.9921 0.00467 0.8795 0.9072 0.0163 ] Network output: [ 0.008806 -0.09389 0.9703 -0.0001966 8.825e-05 1.105 -0.0001481 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.196 0.1244 0.3062 0.2069 0.9851 0.994 0.1966 0.4763 0.8891 0.7385 ] Network output: [ 0.007914 -0.03161 1.023 0.0001265 -5.68e-05 0.9932 9.534e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0851 0.07898 0.1692 0.2183 0.9875 0.9921 0.08516 0.8194 0.8909 0.3065 ] Network output: [ -0.01201 0.02059 1.02 0.0001162 -5.217e-05 0.9837 8.758e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09287 0.0917 0.1753 0.2117 0.9857 0.9916 0.09288 0.7472 0.8705 0.2543 ] Network output: [ 0.002264 1.002 -0.001442 2.367e-05 -1.063e-05 0.995 1.784e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004507 Epoch 5733 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01461 1.005 0.9803 -9.852e-06 4.423e-06 -0.01464 -7.425e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003035 -0.002652 -0.01109 0.008194 0.9694 0.9738 0.005726 0.8456 0.8353 0.02259 ] Network output: [ 0.9897 0.06974 0.002082 -4.265e-05 1.914e-05 -0.05142 -3.214e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.187 -0.009722 -0.217 0.2065 0.9835 0.9932 0.2084 0.4737 0.882 0.7425 ] Network output: [ -0.0128 1.007 1.007 -1.62e-05 7.273e-06 0.01142 -1.221e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00463 0.001161 0.003553 0.005049 0.9891 0.9921 0.004712 0.8797 0.9067 0.0161 ] Network output: [ -0.002935 0.08603 0.9604 -0.0002307 0.0001036 0.9585 -0.0001739 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1977 0.1249 0.2945 0.1721 0.9851 0.994 0.1983 0.4785 0.8891 0.7395 ] Network output: [ 0.01224 0.0007384 1.015 0.0001262 -5.666e-05 0.9601 9.511e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08449 0.07828 0.1611 0.2091 0.9875 0.9921 0.08454 0.8169 0.8907 0.3016 ] Network output: [ -0.008901 0.009571 1.019 0.0001201 -5.391e-05 0.9896 9.05e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09225 0.09106 0.1724 0.21 0.9856 0.9915 0.09226 0.7437 0.8706 0.2541 ] Network output: [ -0.002486 0.9995 0.006551 2.042e-05 -9.169e-06 0.999 1.539e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003175 Epoch 5734 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01586 0.9854 0.9813 -6.128e-06 2.751e-06 0.0016 -4.618e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003013 -0.002651 -0.01102 0.00858 0.9694 0.9738 0.005688 0.8452 0.8362 0.02272 ] Network output: [ 1.001 -0.06132 0.00812 -1.836e-05 8.242e-06 0.05145 -1.384e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.185 -0.01146 -0.2109 0.2289 0.9835 0.9932 0.2062 0.4706 0.8828 0.7443 ] Network output: [ -0.01293 1.001 1.007 -1.505e-05 6.759e-06 0.01754 -1.135e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004592 0.001172 0.003911 0.00579 0.9891 0.9921 0.004674 0.8794 0.9072 0.0163 ] Network output: [ 0.00879 -0.09401 0.9706 -0.0001968 8.834e-05 1.105 -0.0001483 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.196 0.1242 0.3063 0.2069 0.9851 0.994 0.1966 0.4762 0.8891 0.7384 ] Network output: [ 0.007935 -0.03182 1.023 0.0001265 -5.679e-05 0.9936 9.534e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08527 0.07912 0.1693 0.2183 0.9875 0.9921 0.08532 0.8193 0.8909 0.3067 ] Network output: [ -0.01204 0.02086 1.02 0.0001162 -5.218e-05 0.9837 8.759e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09301 0.09184 0.1754 0.2117 0.9857 0.9916 0.09302 0.7472 0.8706 0.2543 ] Network output: [ 0.002289 1.002 -0.001492 2.368e-05 -1.063e-05 0.995 1.784e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004508 Epoch 5735 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01461 1.005 0.9804 -9.618e-06 4.318e-06 -0.01469 -7.248e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003037 -0.002655 -0.01109 0.008195 0.9694 0.9738 0.005729 0.8456 0.8353 0.02259 ] Network output: [ 0.9897 0.06975 0.002078 -4.289e-05 1.926e-05 -0.05145 -3.233e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.187 -0.009812 -0.2169 0.2064 0.9835 0.9932 0.2083 0.4736 0.882 0.7424 ] Network output: [ -0.0128 1.007 1.007 -1.596e-05 7.165e-06 0.01137 -1.203e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004634 0.001158 0.003557 0.00505 0.9891 0.9921 0.004717 0.8797 0.9067 0.0161 ] Network output: [ -0.002969 0.0862 0.9607 -0.0002309 0.0001037 0.9581 -0.000174 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1977 0.1247 0.2945 0.172 0.9851 0.994 0.1983 0.4784 0.8891 0.7394 ] Network output: [ 0.01227 0.0006292 1.015 0.0001262 -5.665e-05 0.9604 9.511e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08466 0.07842 0.1612 0.2092 0.9875 0.9921 0.08471 0.8168 0.8907 0.3017 ] Network output: [ -0.008923 0.009864 1.019 0.0001201 -5.391e-05 0.9896 9.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09239 0.09119 0.1725 0.21 0.9856 0.9915 0.0924 0.7438 0.8706 0.2541 ] Network output: [ -0.002486 0.9996 0.006515 2.04e-05 -9.159e-06 0.999 1.538e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003176 Epoch 5736 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01586 0.9853 0.9814 -5.894e-06 2.646e-06 0.001587 -4.442e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003015 -0.002653 -0.01102 0.008581 0.9694 0.9738 0.005692 0.8452 0.8362 0.02272 ] Network output: [ 1.001 -0.06146 0.008122 -1.862e-05 8.361e-06 0.05156 -1.404e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1849 -0.01155 -0.2109 0.2288 0.9835 0.9932 0.2061 0.4705 0.8828 0.7442 ] Network output: [ -0.01293 1.001 1.007 -1.481e-05 6.65e-06 0.01752 -1.116e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004597 0.001169 0.003915 0.005792 0.9891 0.9921 0.004679 0.8794 0.9072 0.0163 ] Network output: [ 0.008772 -0.09411 0.9709 -0.000197 8.843e-05 1.105 -0.0001484 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1959 0.124 0.3064 0.2069 0.9851 0.994 0.1965 0.4761 0.889 0.7383 ] Network output: [ 0.007956 -0.03203 1.023 0.0001265 -5.679e-05 0.994 9.533e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08544 0.07926 0.1695 0.2184 0.9875 0.9921 0.08549 0.8193 0.8909 0.3068 ] Network output: [ -0.01206 0.02114 1.02 0.0001162 -5.218e-05 0.9836 8.76e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09314 0.09197 0.1754 0.2117 0.9857 0.9916 0.09315 0.7472 0.8706 0.2543 ] Network output: [ 0.002312 1.002 -0.001538 2.368e-05 -1.063e-05 0.9951 1.785e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004507 Epoch 5737 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0146 1.005 0.9804 -9.386e-06 4.214e-06 -0.01473 -7.074e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003039 -0.002657 -0.01109 0.008195 0.9694 0.9738 0.005733 0.8456 0.8353 0.02259 ] Network output: [ 0.9897 0.06973 0.002073 -4.314e-05 1.937e-05 -0.05146 -3.251e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1869 -0.009901 -0.2169 0.2064 0.9835 0.9932 0.2083 0.4736 0.8819 0.7423 ] Network output: [ -0.01281 1.007 1.007 -1.572e-05 7.059e-06 0.01133 -1.185e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004639 0.001156 0.00356 0.005051 0.9891 0.9921 0.004722 0.8796 0.9067 0.01611 ] Network output: [ -0.003001 0.08632 0.961 -0.0002311 0.0001037 0.9577 -0.0001742 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1976 0.1246 0.2945 0.1719 0.9851 0.994 0.1982 0.4783 0.8891 0.7393 ] Network output: [ 0.0123 0.0005123 1.015 0.0001262 -5.665e-05 0.9606 9.51e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08482 0.07856 0.1613 0.2093 0.9875 0.9921 0.08487 0.8168 0.8907 0.3019 ] Network output: [ -0.008945 0.01016 1.019 0.0001201 -5.392e-05 0.9895 9.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09252 0.09132 0.1725 0.21 0.9856 0.9915 0.09254 0.7438 0.8706 0.2541 ] Network output: [ -0.002487 0.9996 0.006479 2.038e-05 -9.148e-06 0.999 1.536e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003176 Epoch 5738 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01586 0.9853 0.9814 -5.664e-06 2.543e-06 0.001572 -4.268e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003017 -0.002656 -0.01102 0.008582 0.9694 0.9738 0.005695 0.8452 0.8361 0.02272 ] Network output: [ 1.001 -0.06157 0.008122 -1.89e-05 8.483e-06 0.05164 -1.424e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1849 -0.01164 -0.2108 0.2288 0.9835 0.9932 0.206 0.4704 0.8828 0.7441 ] Network output: [ -0.01294 1.001 1.008 -1.457e-05 6.543e-06 0.0175 -1.098e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004601 0.001167 0.003919 0.005793 0.9891 0.9921 0.004684 0.8794 0.9072 0.01631 ] Network output: [ 0.008753 -0.09416 0.9712 -0.0001972 8.852e-05 1.105 -0.0001486 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1958 0.1239 0.3064 0.2069 0.9851 0.994 0.1964 0.4761 0.889 0.7382 ] Network output: [ 0.007978 -0.03223 1.022 0.0001265 -5.679e-05 0.9943 9.533e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0856 0.0794 0.1696 0.2185 0.9875 0.9921 0.08565 0.8193 0.8909 0.3069 ] Network output: [ -0.01209 0.0214 1.02 0.0001162 -5.219e-05 0.9836 8.761e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09328 0.0921 0.1755 0.2117 0.9857 0.9916 0.09329 0.7473 0.8706 0.2543 ] Network output: [ 0.002334 1.002 -0.001581 2.368e-05 -1.063e-05 0.9952 1.785e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004504 Epoch 5739 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0146 1.005 0.9805 -9.156e-06 4.11e-06 -0.01477 -6.9e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003041 -0.002659 -0.01109 0.008196 0.9694 0.9738 0.005736 0.8456 0.8352 0.02259 ] Network output: [ 0.9898 0.0697 0.002067 -4.338e-05 1.948e-05 -0.05146 -3.27e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1868 -0.009989 -0.2168 0.2063 0.9835 0.9932 0.2082 0.4735 0.8819 0.7422 ] Network output: [ -0.01282 1.007 1.007 -1.549e-05 6.953e-06 0.01129 -1.167e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004644 0.001153 0.003563 0.005051 0.9891 0.9921 0.004727 0.8796 0.9067 0.01611 ] Network output: [ -0.00303 0.08641 0.9614 -0.0002313 0.0001038 0.9574 -0.0001743 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1976 0.1244 0.2946 0.1719 0.9851 0.994 0.1982 0.4783 0.8891 0.7392 ] Network output: [ 0.01233 0.0003868 1.015 0.0001262 -5.665e-05 0.9609 9.509e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08499 0.0787 0.1614 0.2093 0.9875 0.9921 0.08504 0.8167 0.8907 0.302 ] Network output: [ -0.008967 0.01045 1.019 0.0001201 -5.392e-05 0.9894 9.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09266 0.09146 0.1726 0.21 0.9856 0.9915 0.09267 0.7438 0.8706 0.2541 ] Network output: [ -0.002487 0.9996 0.006444 2.035e-05 -9.137e-06 0.999 1.534e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003174 Epoch 5740 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01586 0.9852 0.9815 -5.436e-06 2.441e-06 0.001554 -4.097e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003019 -0.002658 -0.01102 0.008583 0.9694 0.9739 0.005698 0.8452 0.8361 0.02272 ] Network output: [ 1.001 -0.06165 0.008118 -1.917e-05 8.607e-06 0.05169 -1.445e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1848 -0.01173 -0.2108 0.2288 0.9835 0.9932 0.206 0.4703 0.8828 0.744 ] Network output: [ -0.01294 1.001 1.008 -1.434e-05 6.437e-06 0.01747 -1.081e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004606 0.001164 0.003923 0.005795 0.9891 0.9921 0.004688 0.8794 0.9071 0.01631 ] Network output: [ 0.008731 -0.09418 0.9715 -0.0001974 8.86e-05 1.104 -0.0001487 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1958 0.1237 0.3065 0.2068 0.9851 0.994 0.1964 0.476 0.889 0.738 ] Network output: [ 0.008 -0.03242 1.022 0.0001265 -5.679e-05 0.9947 9.533e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08576 0.07954 0.1697 0.2186 0.9875 0.9921 0.08582 0.8192 0.8909 0.3071 ] Network output: [ -0.01211 0.02167 1.019 0.0001163 -5.219e-05 0.9835 8.761e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09342 0.09223 0.1755 0.2117 0.9857 0.9916 0.09343 0.7473 0.8706 0.2543 ] Network output: [ 0.002354 1.002 -0.00162 2.368e-05 -1.063e-05 0.9953 1.785e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004498 Epoch 5741 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0146 1.005 0.9806 -8.928e-06 4.008e-06 -0.01481 -6.728e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003043 -0.002662 -0.01109 0.008196 0.9694 0.9738 0.00574 0.8456 0.8352 0.0226 ] Network output: [ 0.9898 0.06964 0.002061 -4.362e-05 1.958e-05 -0.05144 -3.288e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1868 -0.01008 -0.2168 0.2063 0.9835 0.9932 0.2081 0.4734 0.8819 0.7421 ] Network output: [ -0.01282 1.007 1.007 -1.525e-05 6.848e-06 0.01126 -1.15e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004648 0.001151 0.003567 0.005052 0.9891 0.9921 0.004731 0.8796 0.9067 0.01611 ] Network output: [ -0.003057 0.08647 0.9617 -0.0002314 0.0001039 0.9571 -0.0001744 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1975 0.1243 0.2947 0.1718 0.9851 0.994 0.1981 0.4782 0.8891 0.7391 ] Network output: [ 0.01236 0.0002521 1.014 0.0001262 -5.664e-05 0.9612 9.508e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08515 0.07883 0.1615 0.2094 0.9875 0.9921 0.0852 0.8167 0.8907 0.3021 ] Network output: [ -0.00899 0.01074 1.018 0.0001201 -5.392e-05 0.9894 9.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0928 0.09159 0.1726 0.21 0.9856 0.9915 0.09281 0.7439 0.8706 0.2541 ] Network output: [ -0.002487 0.9997 0.006408 2.033e-05 -9.125e-06 0.999 1.532e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003171 Epoch 5742 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01585 0.9852 0.9815 -5.212e-06 2.34e-06 0.001534 -3.928e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00302 -0.00266 -0.01102 0.008583 0.9694 0.9739 0.005702 0.8452 0.8361 0.02272 ] Network output: [ 1.001 -0.06169 0.008111 -1.945e-05 8.733e-06 0.05171 -1.466e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1848 -0.01181 -0.2107 0.2287 0.9835 0.9932 0.2059 0.4703 0.8828 0.7439 ] Network output: [ -0.01295 1.001 1.008 -1.41e-05 6.331e-06 0.01745 -1.063e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004611 0.001162 0.003927 0.005796 0.9891 0.9921 0.004693 0.8794 0.9071 0.01631 ] Network output: [ 0.008707 -0.09416 0.9718 -0.0001976 8.869e-05 1.104 -0.0001489 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1957 0.1236 0.3066 0.2068 0.9851 0.994 0.1963 0.4759 0.889 0.7379 ] Network output: [ 0.008023 -0.03261 1.022 0.0001265 -5.678e-05 0.995 9.532e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08592 0.07967 0.1698 0.2187 0.9875 0.9921 0.08598 0.8192 0.8908 0.3072 ] Network output: [ -0.01213 0.02192 1.019 0.0001163 -5.22e-05 0.9835 8.762e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09355 0.09236 0.1756 0.2117 0.9857 0.9916 0.09356 0.7473 0.8706 0.2543 ] Network output: [ 0.002372 1.002 -0.001657 2.368e-05 -1.063e-05 0.9954 1.784e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004489 Epoch 5743 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0146 1.005 0.9806 -8.701e-06 3.906e-06 -0.01484 -6.558e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003044 -0.002664 -0.01109 0.008197 0.9694 0.9738 0.005743 0.8456 0.8352 0.0226 ] Network output: [ 0.9898 0.06956 0.002054 -4.385e-05 1.969e-05 -0.0514 -3.305e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1868 -0.01016 -0.2167 0.2062 0.9835 0.9932 0.2081 0.4733 0.8819 0.742 ] Network output: [ -0.01283 1.007 1.007 -1.502e-05 6.744e-06 0.01122 -1.132e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004653 0.001149 0.003571 0.005054 0.9891 0.9921 0.004736 0.8796 0.9066 0.01612 ] Network output: [ -0.003081 0.08648 0.962 -0.0002316 0.000104 0.9568 -0.0001745 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1975 0.1241 0.2947 0.1718 0.9851 0.994 0.1981 0.4781 0.889 0.739 ] Network output: [ 0.01238 0.0001076 1.014 0.0001262 -5.664e-05 0.9615 9.508e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08531 0.07897 0.1616 0.2095 0.9875 0.9921 0.08536 0.8167 0.8906 0.3022 ] Network output: [ -0.009013 0.01104 1.018 0.0001201 -5.392e-05 0.9893 9.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09293 0.09172 0.1727 0.21 0.9856 0.9915 0.09294 0.7439 0.8706 0.2541 ] Network output: [ -0.002487 0.9997 0.006373 2.03e-05 -9.113e-06 0.999 1.53e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003164 Epoch 5744 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01585 0.9852 0.9816 -4.992e-06 2.241e-06 0.001511 -3.762e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003022 -0.002663 -0.01102 0.008584 0.9694 0.9739 0.005705 0.8452 0.8361 0.02272 ] Network output: [ 1.001 -0.0617 0.008102 -1.974e-05 8.862e-06 0.05171 -1.488e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1848 -0.0119 -0.2107 0.2287 0.9835 0.9932 0.2059 0.4702 0.8828 0.7438 ] Network output: [ -0.01295 1.001 1.008 -1.387e-05 6.227e-06 0.01743 -1.045e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004615 0.00116 0.00393 0.005797 0.9891 0.9921 0.004698 0.8793 0.9071 0.01632 ] Network output: [ 0.008679 -0.09409 0.9721 -0.0001977 8.878e-05 1.104 -0.000149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1957 0.1234 0.3066 0.2067 0.9851 0.994 0.1963 0.4758 0.889 0.7378 ] Network output: [ 0.008047 -0.03279 1.022 0.0001265 -5.678e-05 0.9953 9.532e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08608 0.07981 0.1699 0.2188 0.9875 0.9921 0.08614 0.8191 0.8908 0.3073 ] Network output: [ -0.01215 0.02218 1.019 0.0001163 -5.22e-05 0.9834 8.763e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09368 0.09249 0.1757 0.2118 0.9857 0.9916 0.09369 0.7473 0.8706 0.2543 ] Network output: [ 0.002389 1.002 -0.00169 2.367e-05 -1.063e-05 0.9954 1.784e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004477 Epoch 5745 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0146 1.005 0.9807 -8.477e-06 3.805e-06 -0.01486 -6.388e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003046 -0.002666 -0.01109 0.008198 0.9694 0.9739 0.005746 0.8456 0.8352 0.0226 ] Network output: [ 0.9898 0.06945 0.002047 -4.408e-05 1.979e-05 -0.05134 -3.322e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1867 -0.01024 -0.2167 0.2062 0.9835 0.9932 0.208 0.4733 0.8819 0.7419 ] Network output: [ -0.01283 1.007 1.007 -1.479e-05 6.641e-06 0.01119 -1.115e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004658 0.001146 0.003574 0.005055 0.9891 0.9921 0.004741 0.8796 0.9066 0.01612 ] Network output: [ -0.003103 0.08644 0.9623 -0.0002317 0.000104 0.9566 -0.0001746 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1974 0.124 0.2948 0.1717 0.9851 0.994 0.198 0.478 0.889 0.7389 ] Network output: [ 0.01241 -4.706e-05 1.014 0.0001261 -5.663e-05 0.9618 9.507e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08547 0.0791 0.1617 0.2096 0.9875 0.9921 0.08552 0.8166 0.8906 0.3024 ] Network output: [ -0.009036 0.01133 1.018 0.0001201 -5.392e-05 0.9892 9.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09306 0.09185 0.1727 0.21 0.9856 0.9915 0.09307 0.7439 0.8706 0.2541 ] Network output: [ -0.002486 0.9998 0.006337 2.027e-05 -9.101e-06 0.999 1.528e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003156 Epoch 5746 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01585 0.9851 0.9817 -4.774e-06 2.143e-06 0.001484 -3.598e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003024 -0.002665 -0.01102 0.008584 0.9694 0.9739 0.005708 0.8452 0.8361 0.02272 ] Network output: [ 1.001 -0.06167 0.008089 -2.003e-05 8.993e-06 0.05168 -1.51e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1847 -0.01198 -0.2106 0.2286 0.9835 0.9932 0.2058 0.4701 0.8827 0.7437 ] Network output: [ -0.01295 1.001 1.008 -1.364e-05 6.124e-06 0.01741 -1.028e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00462 0.001157 0.003934 0.005798 0.9891 0.9921 0.004702 0.8793 0.9071 0.01632 ] Network output: [ 0.008649 -0.09397 0.9724 -0.0001979 8.887e-05 1.104 -0.0001492 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1957 0.1233 0.3067 0.2067 0.9851 0.994 0.1963 0.4757 0.889 0.7377 ] Network output: [ 0.008072 -0.03297 1.022 0.0001265 -5.678e-05 0.9957 9.531e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08624 0.07994 0.1701 0.2189 0.9875 0.9921 0.0863 0.8191 0.8908 0.3074 ] Network output: [ -0.01218 0.02242 1.019 0.0001163 -5.221e-05 0.9834 8.764e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09381 0.09262 0.1757 0.2118 0.9857 0.9916 0.09383 0.7474 0.8706 0.2543 ] Network output: [ 0.002403 1.001 -0.001719 2.366e-05 -1.062e-05 0.9955 1.783e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004461 Epoch 5747 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0146 1.005 0.9807 -8.254e-06 3.705e-06 -0.01488 -6.22e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003048 -0.002669 -0.01109 0.008199 0.9694 0.9739 0.00575 0.8456 0.8352 0.0226 ] Network output: [ 0.9899 0.06932 0.002039 -4.43e-05 1.989e-05 -0.05126 -3.338e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1867 -0.01033 -0.2166 0.2062 0.9835 0.9932 0.208 0.4732 0.8819 0.7418 ] Network output: [ -0.01284 1.007 1.007 -1.456e-05 6.539e-06 0.01116 -1.098e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004662 0.001144 0.003578 0.005056 0.9891 0.9921 0.004746 0.8795 0.9066 0.01612 ] Network output: [ -0.003122 0.08636 0.9626 -0.0002318 0.0001041 0.9564 -0.0001747 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1974 0.1238 0.2948 0.1717 0.9851 0.994 0.198 0.4779 0.889 0.7388 ] Network output: [ 0.01243 -0.0002124 1.014 0.0001261 -5.663e-05 0.9621 9.506e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08563 0.07924 0.1619 0.2097 0.9875 0.9921 0.08568 0.8166 0.8906 0.3025 ] Network output: [ -0.00906 0.01162 1.018 0.0001201 -5.392e-05 0.9891 9.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0932 0.09197 0.1728 0.21 0.9856 0.9915 0.09321 0.7439 0.8706 0.2541 ] Network output: [ -0.002485 0.9998 0.0063 2.024e-05 -9.088e-06 0.9989 1.526e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003145 Epoch 5748 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01585 0.9851 0.9817 -4.561e-06 2.047e-06 0.001455 -3.437e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003026 -0.002667 -0.01102 0.008584 0.9694 0.9739 0.005712 0.8452 0.8361 0.02272 ] Network output: [ 1.001 -0.06159 0.008072 -2.033e-05 9.126e-06 0.05161 -1.532e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1847 -0.01206 -0.2106 0.2285 0.9835 0.9933 0.2058 0.47 0.8827 0.7436 ] Network output: [ -0.01296 1.001 1.008 -1.341e-05 6.022e-06 0.01738 -1.011e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004624 0.001155 0.003937 0.005799 0.989 0.9921 0.004707 0.8793 0.9071 0.01632 ] Network output: [ 0.008615 -0.0938 0.9726 -0.0001981 8.896e-05 1.103 -0.0001493 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1956 0.1231 0.3067 0.2066 0.9851 0.994 0.1962 0.4756 0.8889 0.7376 ] Network output: [ 0.008097 -0.03314 1.021 0.0001265 -5.677e-05 0.996 9.531e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0864 0.08007 0.1702 0.2189 0.9875 0.9921 0.08645 0.819 0.8908 0.3076 ] Network output: [ -0.01219 0.02266 1.019 0.0001163 -5.221e-05 0.9834 8.765e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09394 0.09274 0.1758 0.2118 0.9857 0.9916 0.09396 0.7474 0.8706 0.2543 ] Network output: [ 0.002416 1.001 -0.001745 2.365e-05 -1.062e-05 0.9956 1.783e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004442 Epoch 5749 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0146 1.005 0.9808 -8.032e-06 3.606e-06 -0.01489 -6.053e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00305 -0.002671 -0.01109 0.008199 0.9694 0.9739 0.005753 0.8456 0.8352 0.0226 ] Network output: [ 0.9899 0.06915 0.002031 -4.451e-05 1.998e-05 -0.05116 -3.354e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1866 -0.01041 -0.2166 0.2062 0.9835 0.9932 0.2079 0.4731 0.8818 0.7417 ] Network output: [ -0.01284 1.007 1.007 -1.434e-05 6.437e-06 0.01114 -1.081e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004667 0.001142 0.003582 0.005058 0.9891 0.9921 0.00475 0.8795 0.9066 0.01613 ] Network output: [ -0.003138 0.08624 0.9629 -0.0002319 0.0001041 0.9562 -0.0001748 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1973 0.1237 0.2949 0.1717 0.9851 0.994 0.1979 0.4779 0.889 0.7387 ] Network output: [ 0.01245 -0.0003886 1.014 0.0001261 -5.662e-05 0.9624 9.505e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08579 0.07937 0.162 0.2097 0.9875 0.9921 0.08584 0.8165 0.8906 0.3026 ] Network output: [ -0.009085 0.01191 1.018 0.0001201 -5.392e-05 0.9891 9.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09333 0.0921 0.1728 0.21 0.9856 0.9915 0.09334 0.744 0.8706 0.2541 ] Network output: [ -0.002483 0.9999 0.006263 2.022e-05 -9.076e-06 0.9989 1.524e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003131 Epoch 5750 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01585 0.9851 0.9818 -4.35e-06 1.953e-06 0.001421 -3.279e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003028 -0.002669 -0.01102 0.008584 0.9694 0.9739 0.005715 0.8452 0.8361 0.02272 ] Network output: [ 1.001 -0.06148 0.008052 -2.063e-05 9.261e-06 0.05152 -1.555e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1846 -0.01214 -0.2105 0.2285 0.9835 0.9933 0.2058 0.47 0.8827 0.7435 ] Network output: [ -0.01296 1.001 1.008 -1.319e-05 5.922e-06 0.01736 -9.941e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004629 0.001153 0.003941 0.005799 0.989 0.9921 0.004712 0.8793 0.907 0.01633 ] Network output: [ 0.008578 -0.09359 0.9729 -0.0001984 8.905e-05 1.103 -0.0001495 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1956 0.123 0.3067 0.2065 0.9851 0.994 0.1962 0.4756 0.8889 0.7375 ] Network output: [ 0.008124 -0.03331 1.021 0.0001265 -5.677e-05 0.9963 9.53e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08656 0.0802 0.1703 0.219 0.9875 0.9921 0.08661 0.819 0.8907 0.3077 ] Network output: [ -0.01221 0.0229 1.019 0.0001163 -5.222e-05 0.9833 8.766e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09407 0.09287 0.1758 0.2118 0.9857 0.9916 0.09409 0.7474 0.8706 0.2543 ] Network output: [ 0.002426 1.001 -0.001768 2.364e-05 -1.061e-05 0.9957 1.782e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004419 Epoch 5751 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0146 1.005 0.9809 -7.813e-06 3.507e-06 -0.0149 -5.888e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003052 -0.002673 -0.01109 0.0082 0.9694 0.9739 0.005756 0.8456 0.8352 0.0226 ] Network output: [ 0.9899 0.06895 0.002023 -4.471e-05 2.007e-05 -0.05103 -3.37e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1866 -0.01049 -0.2165 0.2061 0.9835 0.9932 0.2079 0.473 0.8818 0.7416 ] Network output: [ -0.01284 1.007 1.007 -1.412e-05 6.337e-06 0.01112 -1.064e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004671 0.00114 0.003586 0.00506 0.9891 0.9921 0.004755 0.8795 0.9066 0.01613 ] Network output: [ -0.003151 0.08607 0.9632 -0.000232 0.0001041 0.9561 -0.0001748 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1973 0.1235 0.295 0.1717 0.9851 0.994 0.1979 0.4778 0.889 0.7386 ] Network output: [ 0.01247 -0.0005759 1.013 0.0001261 -5.662e-05 0.9628 9.505e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08594 0.0795 0.1621 0.2098 0.9875 0.9921 0.086 0.8165 0.8905 0.3027 ] Network output: [ -0.00911 0.0122 1.018 0.0001201 -5.392e-05 0.989 9.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09346 0.09223 0.1729 0.21 0.9856 0.9915 0.09347 0.744 0.8706 0.254 ] Network output: [ -0.00248 0.9999 0.006224 2.019e-05 -9.063e-06 0.9989 1.521e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003115 Epoch 5752 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01585 0.9851 0.9818 -4.144e-06 1.86e-06 0.001384 -3.123e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00303 -0.002672 -0.01102 0.008584 0.9694 0.9739 0.005718 0.8452 0.8361 0.02272 ] Network output: [ 1.001 -0.06133 0.008029 -2.093e-05 9.398e-06 0.05139 -1.578e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1846 -0.01222 -0.2105 0.2284 0.9835 0.9933 0.2057 0.4699 0.8827 0.7434 ] Network output: [ -0.01296 1.001 1.008 -1.297e-05 5.823e-06 0.01733 -9.774e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004634 0.001151 0.003944 0.0058 0.989 0.9921 0.004716 0.8793 0.907 0.01633 ] Network output: [ 0.008537 -0.09332 0.9732 -0.0001986 8.914e-05 1.102 -0.0001496 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1955 0.1228 0.3068 0.2064 0.9851 0.994 0.1961 0.4755 0.8889 0.7374 ] Network output: [ 0.008151 -0.03346 1.021 0.0001265 -5.677e-05 0.9966 9.53e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08671 0.08034 0.1704 0.2191 0.9875 0.9921 0.08676 0.8189 0.8907 0.3078 ] Network output: [ -0.01223 0.02312 1.019 0.0001163 -5.223e-05 0.9833 8.767e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0942 0.09299 0.1759 0.2118 0.9857 0.9916 0.09421 0.7474 0.8706 0.2543 ] Network output: [ 0.002435 1.001 -0.001787 2.363e-05 -1.061e-05 0.9958 1.78e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004393 Epoch 5753 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0146 1.005 0.9809 -7.594e-06 3.409e-06 -0.01491 -5.723e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003053 -0.002675 -0.01109 0.008201 0.9694 0.9739 0.00576 0.8456 0.8352 0.0226 ] Network output: [ 0.99 0.06873 0.002014 -4.491e-05 2.016e-05 -0.05087 -3.384e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1865 -0.01058 -0.2165 0.2061 0.9835 0.9932 0.2078 0.4729 0.8818 0.7415 ] Network output: [ -0.01285 1.007 1.007 -1.389e-05 6.238e-06 0.0111 -1.047e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004676 0.001138 0.00359 0.005062 0.9891 0.9921 0.004759 0.8795 0.9066 0.01613 ] Network output: [ -0.003162 0.08585 0.9635 -0.000232 0.0001042 0.9561 -0.0001749 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1972 0.1234 0.295 0.1717 0.9851 0.994 0.1978 0.4777 0.8889 0.7385 ] Network output: [ 0.01249 -0.0007744 1.013 0.0001261 -5.661e-05 0.9631 9.504e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0861 0.07964 0.1622 0.2099 0.9875 0.9921 0.08615 0.8164 0.8905 0.3029 ] Network output: [ -0.009135 0.01249 1.017 0.0001201 -5.391e-05 0.9889 9.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09359 0.09235 0.1729 0.21 0.9856 0.9915 0.0936 0.744 0.8706 0.254 ] Network output: [ -0.002476 0.9999 0.006185 2.016e-05 -9.05e-06 0.9989 1.519e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003097 Epoch 5754 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01585 0.9851 0.9819 -3.94e-06 1.769e-06 0.001343 -2.97e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003031 -0.002674 -0.01102 0.008584 0.9694 0.9739 0.005722 0.8452 0.8361 0.02272 ] Network output: [ 1.001 -0.06114 0.008002 -2.124e-05 9.537e-06 0.05123 -1.601e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1846 -0.01229 -0.2104 0.2283 0.9835 0.9933 0.2057 0.4698 0.8827 0.7433 ] Network output: [ -0.01296 1.001 1.008 -1.275e-05 5.724e-06 0.01731 -9.61e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004638 0.001149 0.003947 0.0058 0.989 0.9921 0.004721 0.8792 0.907 0.01633 ] Network output: [ 0.008493 -0.093 0.9734 -0.0001988 8.924e-05 1.102 -0.0001498 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1955 0.1227 0.3068 0.2063 0.9851 0.994 0.1961 0.4754 0.8889 0.7373 ] Network output: [ 0.008179 -0.03361 1.021 0.0001264 -5.676e-05 0.9969 9.529e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08686 0.08046 0.1705 0.2191 0.9875 0.9921 0.08692 0.8189 0.8907 0.3079 ] Network output: [ -0.01224 0.02334 1.018 0.0001164 -5.223e-05 0.9833 8.769e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09433 0.09311 0.1759 0.2118 0.9857 0.9916 0.09434 0.7475 0.8706 0.2543 ] Network output: [ 0.002442 1.001 -0.001803 2.361e-05 -1.06e-05 0.9959 1.779e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004363 Epoch 5755 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0146 1.005 0.981 -7.378e-06 3.312e-06 -0.01491 -5.56e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003055 -0.002678 -0.01109 0.008202 0.9694 0.9739 0.005763 0.8456 0.8352 0.0226 ] Network output: [ 0.99 0.06847 0.002005 -4.509e-05 2.024e-05 -0.05069 -3.398e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1865 -0.01066 -0.2164 0.2061 0.9835 0.9932 0.2078 0.4728 0.8818 0.7414 ] Network output: [ -0.01285 1.007 1.007 -1.368e-05 6.139e-06 0.01108 -1.031e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00468 0.001136 0.003594 0.005064 0.9891 0.9921 0.004764 0.8795 0.9065 0.01614 ] Network output: [ -0.003171 0.08558 0.9638 -0.0002321 0.0001042 0.9561 -0.0001749 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1972 0.1232 0.2951 0.1717 0.9851 0.994 0.1978 0.4776 0.8889 0.7384 ] Network output: [ 0.0125 -0.000984 1.013 0.0001261 -5.661e-05 0.9635 9.503e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08625 0.07977 0.1623 0.21 0.9875 0.9921 0.08631 0.8164 0.8905 0.303 ] Network output: [ -0.009161 0.01277 1.017 0.0001201 -5.391e-05 0.9888 9.05e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09371 0.09248 0.173 0.21 0.9856 0.9915 0.09373 0.7441 0.8706 0.254 ] Network output: [ -0.002471 1 0.006144 2.013e-05 -9.038e-06 0.9989 1.517e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003075 Epoch 5756 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01585 0.985 0.9819 -3.741e-06 1.679e-06 0.001299 -2.819e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003033 -0.002676 -0.01102 0.008584 0.9694 0.9739 0.005725 0.8452 0.836 0.02272 ] Network output: [ 1.001 -0.06091 0.007971 -2.156e-05 9.677e-06 0.05104 -1.625e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1845 -0.01237 -0.2104 0.2282 0.9835 0.9933 0.2056 0.4697 0.8826 0.7432 ] Network output: [ -0.01296 1.001 1.008 -1.254e-05 5.627e-06 0.01728 -9.447e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004643 0.001147 0.00395 0.0058 0.989 0.9921 0.004726 0.8792 0.907 0.01633 ] Network output: [ 0.008444 -0.09263 0.9737 -0.000199 8.934e-05 1.101 -0.00015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1955 0.1226 0.3068 0.2062 0.9851 0.994 0.1961 0.4753 0.8889 0.7372 ] Network output: [ 0.008209 -0.03375 1.021 0.0001264 -5.676e-05 0.9971 9.529e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08701 0.08059 0.1706 0.2192 0.9875 0.9921 0.08707 0.8188 0.8907 0.308 ] Network output: [ -0.01226 0.02356 1.018 0.0001164 -5.224e-05 0.9832 8.77e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09445 0.09323 0.176 0.2118 0.9857 0.9916 0.09446 0.7475 0.8706 0.2543 ] Network output: [ 0.002447 1.001 -0.001816 2.359e-05 -1.059e-05 0.9959 1.778e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004329 Epoch 5757 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01461 1.005 0.981 -7.163e-06 3.216e-06 -0.0149 -5.398e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003057 -0.00268 -0.01109 0.008204 0.9694 0.9739 0.005766 0.8456 0.8352 0.0226 ] Network output: [ 0.9901 0.06817 0.001996 -4.527e-05 2.032e-05 -0.05049 -3.412e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1865 -0.01074 -0.2163 0.2061 0.9835 0.9932 0.2077 0.4728 0.8818 0.7413 ] Network output: [ -0.01286 1.007 1.007 -1.346e-05 6.042e-06 0.01107 -1.014e-05 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004685 0.001134 0.003598 0.005066 0.9891 0.9921 0.004768 0.8794 0.9065 0.01614 ] Network output: [ -0.003176 0.08527 0.9641 -0.0002322 0.0001042 0.9561 -0.000175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1972 0.1231 0.2952 0.1717 0.9851 0.994 0.1978 0.4775 0.8889 0.7382 ] Network output: [ 0.01252 -0.001205 1.013 0.0001261 -5.66e-05 0.9638 9.502e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08641 0.0799 0.1625 0.2101 0.9875 0.9921 0.08646 0.8164 0.8905 0.3031 ] Network output: [ -0.009188 0.01306 1.017 0.0001201 -5.391e-05 0.9888 9.05e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09384 0.0926 0.1731 0.2101 0.9856 0.9915 0.09385 0.7441 0.8706 0.254 ] Network output: [ -0.002465 1 0.006103 2.01e-05 -9.026e-06 0.9989 1.515e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003051 Epoch 5758 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01584 0.985 0.982 -3.544e-06 1.591e-06 0.00125 -2.671e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003035 -0.002678 -0.01102 0.008583 0.9694 0.9739 0.005728 0.8452 0.836 0.02272 ] Network output: [ 1.001 -0.06065 0.007937 -2.187e-05 9.819e-06 0.05082 -1.648e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1845 -0.01244 -0.2103 0.2281 0.9835 0.9933 0.2056 0.4697 0.8826 0.7431 ] Network output: [ -0.01297 1 1.008 -1.232e-05 5.532e-06 0.01726 -9.286e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004647 0.001145 0.003953 0.005799 0.989 0.9921 0.00473 0.8792 0.907 0.01634 ] Network output: [ 0.008392 -0.09221 0.9739 -0.0001992 8.944e-05 1.101 -0.0001501 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1954 0.1224 0.3069 0.2061 0.9851 0.994 0.196 0.4753 0.8889 0.7371 ] Network output: [ 0.008239 -0.03388 1.021 0.0001264 -5.676e-05 0.9974 9.528e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08716 0.08072 0.1707 0.2193 0.9875 0.9921 0.08722 0.8188 0.8906 0.3081 ] Network output: [ -0.01227 0.02376 1.018 0.0001164 -5.225e-05 0.9832 8.771e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09457 0.09335 0.176 0.2118 0.9857 0.9916 0.09459 0.7475 0.8706 0.2542 ] Network output: [ 0.002449 1.001 -0.001826 2.357e-05 -1.058e-05 0.996 1.776e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004292 Epoch 5759 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01461 1.005 0.9811 -6.949e-06 3.12e-06 -0.01489 -5.237e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003059 -0.002682 -0.01109 0.008205 0.9694 0.9739 0.005769 0.8456 0.8351 0.0226 ] Network output: [ 0.9901 0.06785 0.001987 -4.544e-05 2.04e-05 -0.05026 -3.424e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1864 -0.01082 -0.2163 0.2061 0.9835 0.9932 0.2077 0.4727 0.8818 0.7412 ] Network output: [ -0.01286 1.007 1.008 -1.324e-05 5.946e-06 0.01106 -9.981e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004689 0.001132 0.003602 0.005069 0.9891 0.9921 0.004773 0.8794 0.9065 0.01614 ] Network output: [ -0.00318 0.08491 0.9644 -0.0002322 0.0001042 0.9561 -0.000175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1971 0.123 0.2953 0.1717 0.9851 0.994 0.1977 0.4774 0.8889 0.7381 ] Network output: [ 0.01253 -0.001436 1.013 0.0001261 -5.66e-05 0.9642 9.501e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08656 0.08003 0.1626 0.2102 0.9875 0.9921 0.08661 0.8163 0.8904 0.3032 ] Network output: [ -0.009215 0.01334 1.017 0.0001201 -5.39e-05 0.9887 9.049e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09397 0.09272 0.1731 0.2101 0.9856 0.9915 0.09398 0.7441 0.8706 0.254 ] Network output: [ -0.002458 1 0.00606 2.008e-05 -9.013e-06 0.9989 1.513e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003025 Epoch 5760 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01584 0.9851 0.9821 -3.351e-06 1.504e-06 0.001198 -2.525e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003037 -0.00268 -0.01102 0.008583 0.9694 0.9739 0.005731 0.8452 0.836 0.02272 ] Network output: [ 1.001 -0.06034 0.0079 -2.219e-05 9.962e-06 0.05057 -1.672e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1845 -0.01252 -0.2103 0.228 0.9835 0.9933 0.2056 0.4696 0.8826 0.743 ] Network output: [ -0.01297 1 1.008 -1.211e-05 5.437e-06 0.01723 -9.128e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004652 0.001142 0.003956 0.005798 0.989 0.9921 0.004735 0.8792 0.9069 0.01634 ] Network output: [ 0.008336 -0.09174 0.9742 -0.0001995 8.954e-05 1.1 -0.0001503 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1954 0.1223 0.3069 0.206 0.9851 0.994 0.196 0.4752 0.8888 0.737 ] Network output: [ 0.00827 -0.03401 1.02 0.0001264 -5.675e-05 0.9977 9.527e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08731 0.08084 0.1708 0.2193 0.9875 0.9921 0.08737 0.8187 0.8906 0.3082 ] Network output: [ -0.01228 0.02396 1.018 0.0001164 -5.226e-05 0.9832 8.773e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0947 0.09347 0.1761 0.2118 0.9857 0.9916 0.09471 0.7475 0.8706 0.2542 ] Network output: [ 0.00245 1.001 -0.001834 2.355e-05 -1.057e-05 0.9961 1.774e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004251 Epoch 5761 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01461 1.004 0.9811 -6.737e-06 3.024e-06 -0.01487 -5.077e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00306 -0.002684 -0.01109 0.008206 0.9694 0.9739 0.005772 0.8456 0.8351 0.0226 ] Network output: [ 0.9902 0.0675 0.001977 -4.56e-05 2.047e-05 -0.05001 -3.436e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1864 -0.0109 -0.2162 0.2062 0.9835 0.9932 0.2077 0.4726 0.8817 0.7411 ] Network output: [ -0.01286 1.007 1.008 -1.303e-05 5.85e-06 0.01105 -9.821e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004693 0.00113 0.003607 0.005072 0.9891 0.9921 0.004777 0.8794 0.9065 0.01615 ] Network output: [ -0.003181 0.08451 0.9647 -0.0002322 0.0001043 0.9562 -0.000175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1971 0.1228 0.2953 0.1718 0.9851 0.994 0.1977 0.4774 0.8889 0.738 ] Network output: [ 0.01254 -0.001678 1.013 0.0001261 -5.659e-05 0.9646 9.501e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08671 0.08015 0.1627 0.2103 0.9875 0.9921 0.08676 0.8163 0.8904 0.3034 ] Network output: [ -0.009242 0.01363 1.017 0.0001201 -5.39e-05 0.9886 9.048e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09409 0.09284 0.1732 0.2101 0.9856 0.9915 0.0941 0.7442 0.8706 0.254 ] Network output: [ -0.00245 1 0.006015 2.005e-05 -9.002e-06 0.9988 1.511e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002996 Epoch 5762 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01584 0.9851 0.9821 -3.161e-06 1.419e-06 0.001142 -2.382e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003038 -0.002683 -0.01102 0.008582 0.9694 0.9739 0.005735 0.8452 0.836 0.02272 ] Network output: [ 1.001 -0.06 0.00786 -2.251e-05 1.011e-05 0.05028 -1.697e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1844 -0.01259 -0.2103 0.2279 0.9835 0.9933 0.2055 0.4695 0.8826 0.7429 ] Network output: [ -0.01297 1 1.008 -1.19e-05 5.344e-06 0.0172 -8.971e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004656 0.00114 0.003958 0.005797 0.989 0.9921 0.004739 0.8792 0.9069 0.01634 ] Network output: [ 0.008276 -0.09122 0.9744 -0.0001997 8.965e-05 1.099 -0.0001505 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1954 0.1221 0.3069 0.2058 0.9851 0.994 0.196 0.4751 0.8888 0.7369 ] Network output: [ 0.008302 -0.03412 1.02 0.0001264 -5.675e-05 0.9979 9.527e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08746 0.08096 0.1709 0.2194 0.9875 0.9921 0.08751 0.8187 0.8906 0.3083 ] Network output: [ -0.01229 0.02416 1.018 0.0001164 -5.227e-05 0.9832 8.774e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09482 0.09359 0.1761 0.2118 0.9857 0.9916 0.09483 0.7475 0.8706 0.2542 ] Network output: [ 0.002449 1.001 -0.001838 2.352e-05 -1.056e-05 0.9962 1.773e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004208 Epoch 5763 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01462 1.004 0.9812 -6.526e-06 2.93e-06 -0.01485 -4.918e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003062 -0.002686 -0.01109 0.008207 0.9694 0.9739 0.005775 0.8456 0.8351 0.0226 ] Network output: [ 0.9902 0.06711 0.001968 -4.575e-05 2.054e-05 -0.04974 -3.448e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1863 -0.01098 -0.2162 0.2062 0.9835 0.9932 0.2076 0.4725 0.8817 0.741 ] Network output: [ -0.01287 1.007 1.008 -1.282e-05 5.756e-06 0.01105 -9.663e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004697 0.001128 0.003611 0.005074 0.9891 0.9921 0.004781 0.8794 0.9065 0.01615 ] Network output: [ -0.003179 0.08406 0.965 -0.0002322 0.0001043 0.9564 -0.000175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.197 0.1227 0.2954 0.1718 0.9851 0.994 0.1976 0.4773 0.8888 0.7379 ] Network output: [ 0.01255 -0.00193 1.012 0.0001261 -5.659e-05 0.965 9.5e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08686 0.08028 0.1629 0.2104 0.9875 0.9921 0.08691 0.8163 0.8904 0.3035 ] Network output: [ -0.00927 0.01391 1.017 0.0001201 -5.39e-05 0.9886 9.048e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09421 0.09296 0.1732 0.2101 0.9856 0.9915 0.09423 0.7442 0.8706 0.254 ] Network output: [ -0.002441 1 0.00597 2.003e-05 -8.99e-06 0.9988 1.509e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002965 Epoch 5764 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01584 0.9851 0.9822 -2.975e-06 1.335e-06 0.001083 -2.242e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00304 -0.002685 -0.01102 0.008581 0.9694 0.9739 0.005738 0.8452 0.836 0.02272 ] Network output: [ 1.001 -0.05962 0.007817 -2.284e-05 1.025e-05 0.04997 -1.721e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1844 -0.01266 -0.2102 0.2278 0.9835 0.9933 0.2055 0.4695 0.8826 0.7428 ] Network output: [ -0.01297 1 1.008 -1.17e-05 5.252e-06 0.01717 -8.817e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00466 0.001138 0.003961 0.005796 0.989 0.9921 0.004744 0.8791 0.9069 0.01634 ] Network output: [ 0.008212 -0.09065 0.9746 -0.0001999 8.976e-05 1.099 -0.0001507 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1953 0.122 0.3069 0.2057 0.9851 0.994 0.1959 0.4751 0.8888 0.7368 ] Network output: [ 0.008335 -0.03423 1.02 0.0001264 -5.675e-05 0.9982 9.526e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0876 0.08109 0.171 0.2194 0.9875 0.9921 0.08766 0.8186 0.8906 0.3084 ] Network output: [ -0.0123 0.02434 1.018 0.0001164 -5.228e-05 0.9832 8.776e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09493 0.0937 0.1761 0.2118 0.9857 0.9916 0.09495 0.7475 0.8706 0.2542 ] Network output: [ 0.002447 1.001 -0.00184 2.349e-05 -1.055e-05 0.9963 1.771e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004161 Epoch 5765 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01462 1.004 0.9813 -6.317e-06 2.836e-06 -0.01482 -4.761e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003064 -0.002688 -0.01109 0.008209 0.9694 0.9739 0.005778 0.8456 0.8351 0.0226 ] Network output: [ 0.9903 0.0667 0.001958 -4.589e-05 2.06e-05 -0.04944 -3.458e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1863 -0.01106 -0.2161 0.2062 0.9835 0.9932 0.2076 0.4724 0.8817 0.7409 ] Network output: [ -0.01287 1.007 1.008 -1.261e-05 5.663e-06 0.01105 -9.506e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004702 0.001126 0.003615 0.005077 0.9891 0.9921 0.004786 0.8794 0.9064 0.01615 ] Network output: [ -0.003176 0.08358 0.9653 -0.0002322 0.0001043 0.9566 -0.000175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.197 0.1226 0.2955 0.1719 0.9851 0.994 0.1976 0.4772 0.8888 0.7378 ] Network output: [ 0.01256 -0.002192 1.012 0.000126 -5.658e-05 0.9654 9.499e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08701 0.08041 0.163 0.2105 0.9875 0.9921 0.08706 0.8162 0.8904 0.3036 ] Network output: [ -0.009299 0.01418 1.016 0.00012 -5.389e-05 0.9885 9.047e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09434 0.09308 0.1733 0.2101 0.9856 0.9915 0.09435 0.7442 0.8706 0.254 ] Network output: [ -0.00243 1 0.005923 2e-05 -8.979e-06 0.9988 1.507e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002932 Epoch 5766 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01584 0.9851 0.9822 -2.791e-06 1.253e-06 0.001021 -2.104e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003042 -0.002687 -0.01102 0.00858 0.9694 0.9739 0.005741 0.8452 0.836 0.02272 ] Network output: [ 1.001 -0.05922 0.00777 -2.316e-05 1.04e-05 0.04964 -1.745e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1844 -0.01273 -0.2102 0.2277 0.9835 0.9933 0.2055 0.4694 0.8825 0.7426 ] Network output: [ -0.01297 1 1.008 -1.15e-05 5.162e-06 0.01714 -8.665e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004665 0.001136 0.003963 0.005795 0.989 0.9921 0.004748 0.8791 0.9069 0.01635 ] Network output: [ 0.008145 -0.09003 0.9749 -0.0002002 8.987e-05 1.098 -0.0001509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1953 0.1219 0.307 0.2055 0.9851 0.994 0.1959 0.475 0.8888 0.7367 ] Network output: [ 0.008369 -0.03434 1.02 0.0001264 -5.674e-05 0.9984 9.525e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08775 0.08121 0.171 0.2195 0.9875 0.9921 0.0878 0.8186 0.8906 0.3085 ] Network output: [ -0.01231 0.02453 1.017 0.0001165 -5.229e-05 0.9831 8.777e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09505 0.09381 0.1762 0.2118 0.9857 0.9916 0.09506 0.7475 0.8706 0.2542 ] Network output: [ 0.002442 1.001 -0.001839 2.347e-05 -1.054e-05 0.9963 1.769e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004111 Epoch 5767 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01463 1.004 0.9813 -6.109e-06 2.743e-06 -0.01479 -4.604e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003065 -0.00269 -0.01109 0.00821 0.9694 0.9739 0.005781 0.8456 0.8351 0.0226 ] Network output: [ 0.9904 0.06626 0.001949 -4.602e-05 2.066e-05 -0.04912 -3.468e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1863 -0.01114 -0.216 0.2062 0.9835 0.9932 0.2075 0.4723 0.8817 0.7408 ] Network output: [ -0.01287 1.007 1.008 -1.241e-05 5.57e-06 0.01105 -9.351e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004706 0.001124 0.00362 0.00508 0.9891 0.9921 0.00479 0.8793 0.9064 0.01616 ] Network output: [ -0.00317 0.08305 0.9656 -0.0002322 0.0001043 0.9568 -0.000175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1969 0.1224 0.2956 0.1719 0.9851 0.994 0.1975 0.4771 0.8888 0.7377 ] Network output: [ 0.01257 -0.002463 1.012 0.000126 -5.658e-05 0.9658 9.498e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08716 0.08053 0.1631 0.2106 0.9875 0.9921 0.08721 0.8162 0.8903 0.3037 ] Network output: [ -0.009328 0.01446 1.016 0.00012 -5.389e-05 0.9884 9.046e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09446 0.0932 0.1733 0.2101 0.9856 0.9915 0.09447 0.7443 0.8706 0.254 ] Network output: [ -0.002418 1 0.005874 1.998e-05 -8.968e-06 0.9988 1.506e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002896 Epoch 5768 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01583 0.9851 0.9822 -2.611e-06 1.172e-06 0.000955 -1.968e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003044 -0.002689 -0.01102 0.008579 0.9694 0.9739 0.005744 0.8452 0.836 0.02272 ] Network output: [ 1.001 -0.05877 0.007721 -2.349e-05 1.054e-05 0.04928 -1.77e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1844 -0.0128 -0.2102 0.2276 0.9835 0.9933 0.2054 0.4693 0.8825 0.7425 ] Network output: [ -0.01297 1 1.008 -1.13e-05 5.072e-06 0.01711 -8.515e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004669 0.001134 0.003965 0.005793 0.989 0.9921 0.004753 0.8791 0.9069 0.01635 ] Network output: [ 0.008075 -0.08938 0.9751 -0.0002004 8.999e-05 1.097 -0.0001511 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1953 0.1218 0.307 0.2053 0.9851 0.994 0.1959 0.4749 0.8888 0.7366 ] Network output: [ 0.008403 -0.03443 1.02 0.0001264 -5.674e-05 0.9986 9.525e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08789 0.08133 0.1711 0.2195 0.9875 0.9921 0.08794 0.8186 0.8905 0.3086 ] Network output: [ -0.01232 0.0247 1.017 0.0001165 -5.23e-05 0.9831 8.779e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09517 0.09393 0.1762 0.2118 0.9857 0.9916 0.09518 0.7476 0.8706 0.2542 ] Network output: [ 0.002437 1.001 -0.001836 2.344e-05 -1.052e-05 0.9964 1.766e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004059 Epoch 5769 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01464 1.004 0.9814 -5.903e-06 2.65e-06 -0.01475 -4.449e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003067 -0.002692 -0.01109 0.008212 0.9694 0.9739 0.005784 0.8456 0.8351 0.0226 ] Network output: [ 0.9904 0.06579 0.001939 -4.614e-05 2.071e-05 -0.04879 -3.477e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1862 -0.01122 -0.216 0.2063 0.9835 0.9932 0.2075 0.4723 0.8817 0.7407 ] Network output: [ -0.01288 1.007 1.008 -1.22e-05 5.479e-06 0.01105 -9.197e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00471 0.001121 0.003624 0.005084 0.9891 0.9921 0.004794 0.8793 0.9064 0.01616 ] Network output: [ -0.003163 0.08248 0.9659 -0.0002322 0.0001043 0.957 -0.000175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1969 0.1223 0.2957 0.172 0.9851 0.994 0.1975 0.4771 0.8888 0.7376 ] Network output: [ 0.01258 -0.002743 1.012 0.000126 -5.657e-05 0.9662 9.497e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0873 0.08065 0.1633 0.2107 0.9875 0.9921 0.08736 0.8162 0.8903 0.3039 ] Network output: [ -0.009357 0.01474 1.016 0.00012 -5.388e-05 0.9883 9.045e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09458 0.09331 0.1734 0.2101 0.9856 0.9915 0.09459 0.7443 0.8706 0.254 ] Network output: [ -0.002406 1 0.005824 1.995e-05 -8.958e-06 0.9988 1.504e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002859 Epoch 5770 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01583 0.9851 0.9823 -2.433e-06 1.092e-06 0.0008863 -1.834e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003045 -0.002691 -0.01102 0.008578 0.9694 0.9739 0.005747 0.8452 0.8359 0.02272 ] Network output: [ 1.001 -0.0583 0.007669 -2.381e-05 1.069e-05 0.04889 -1.795e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1843 -0.01287 -0.2102 0.2274 0.9835 0.9933 0.2054 0.4693 0.8825 0.7424 ] Network output: [ -0.01298 1 1.008 -1.11e-05 4.984e-06 0.01708 -8.367e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004673 0.001132 0.003967 0.005791 0.989 0.9921 0.004757 0.8791 0.9068 0.01635 ] Network output: [ 0.008001 -0.08868 0.9753 -0.0002007 9.01e-05 1.097 -0.0001513 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1952 0.1216 0.307 0.2052 0.9851 0.994 0.1958 0.4749 0.8887 0.7365 ] Network output: [ 0.008438 -0.03452 1.019 0.0001264 -5.673e-05 0.9988 9.524e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08803 0.08144 0.1712 0.2196 0.9875 0.9921 0.08808 0.8185 0.8905 0.3087 ] Network output: [ -0.01232 0.02487 1.017 0.0001165 -5.231e-05 0.9831 8.78e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09528 0.09404 0.1763 0.2118 0.9857 0.9916 0.09529 0.7476 0.8707 0.2542 ] Network output: [ 0.002429 1.001 -0.001831 2.341e-05 -1.051e-05 0.9965 1.764e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.004004 Epoch 5771 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01464 1.004 0.9814 -5.698e-06 2.558e-06 -0.01471 -4.294e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003068 -0.002694 -0.01109 0.008214 0.9694 0.9739 0.005787 0.8455 0.8351 0.0226 ] Network output: [ 0.9905 0.0653 0.00193 -4.626e-05 2.077e-05 -0.04843 -3.486e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1862 -0.0113 -0.2159 0.2063 0.9835 0.9932 0.2074 0.4722 0.8817 0.7407 ] Network output: [ -0.01288 1.007 1.008 -1.2e-05 5.388e-06 0.01105 -9.045e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004714 0.001119 0.003629 0.005087 0.989 0.9921 0.004798 0.8793 0.9064 0.01616 ] Network output: [ -0.003153 0.08188 0.9662 -0.0002322 0.0001042 0.9573 -0.000175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1969 0.1221 0.2958 0.1721 0.9851 0.994 0.1975 0.477 0.8888 0.7375 ] Network output: [ 0.01258 -0.003031 1.012 0.000126 -5.657e-05 0.9666 9.496e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08745 0.08078 0.1634 0.2108 0.9875 0.9921 0.0875 0.8161 0.8903 0.304 ] Network output: [ -0.009387 0.01501 1.016 0.00012 -5.387e-05 0.9883 9.044e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0947 0.09343 0.1735 0.2101 0.9856 0.9915 0.09471 0.7443 0.8706 0.254 ] Network output: [ -0.002391 1 0.005773 1.993e-05 -8.948e-06 0.9988 1.502e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00282 Epoch 5772 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01583 0.9852 0.9823 -2.259e-06 1.014e-06 0.0008148 -1.702e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003047 -0.002693 -0.01102 0.008576 0.9694 0.9739 0.005751 0.8452 0.8359 0.02272 ] Network output: [ 1.001 -0.05781 0.007615 -2.414e-05 1.084e-05 0.04848 -1.819e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1843 -0.01293 -0.2101 0.2273 0.9835 0.9933 0.2054 0.4692 0.8825 0.7423 ] Network output: [ -0.01298 1 1.009 -1.091e-05 4.897e-06 0.01705 -8.22e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004678 0.00113 0.003969 0.005789 0.989 0.9921 0.004761 0.8791 0.9068 0.01635 ] Network output: [ 0.007925 -0.08794 0.9756 -0.000201 9.022e-05 1.096 -0.0001515 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1952 0.1215 0.307 0.205 0.9851 0.994 0.1958 0.4748 0.8887 0.7364 ] Network output: [ 0.008474 -0.0346 1.019 0.0001264 -5.673e-05 0.999 9.523e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08817 0.08156 0.1713 0.2196 0.9875 0.9921 0.08822 0.8185 0.8905 0.3088 ] Network output: [ -0.01233 0.02504 1.017 0.0001165 -5.232e-05 0.9831 8.782e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09539 0.09414 0.1763 0.2118 0.9857 0.9916 0.0954 0.7476 0.8707 0.2542 ] Network output: [ 0.00242 1.001 -0.001824 2.338e-05 -1.049e-05 0.9966 1.762e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003947 Epoch 5773 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01465 1.004 0.9815 -5.495e-06 2.467e-06 -0.01466 -4.141e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00307 -0.002697 -0.01109 0.008215 0.9694 0.9739 0.00579 0.8455 0.8351 0.0226 ] Network output: [ 0.9906 0.06478 0.001921 -4.636e-05 2.081e-05 -0.04805 -3.494e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1861 -0.01138 -0.2158 0.2063 0.9835 0.9932 0.2074 0.4721 0.8817 0.7406 ] Network output: [ -0.01288 1.007 1.008 -1.18e-05 5.299e-06 0.01106 -8.895e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004718 0.001117 0.003634 0.00509 0.989 0.9921 0.004802 0.8793 0.9064 0.01617 ] Network output: [ -0.003142 0.08125 0.9665 -0.0002322 0.0001042 0.9576 -0.000175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1968 0.122 0.2959 0.1722 0.9851 0.994 0.1974 0.4769 0.8887 0.7374 ] Network output: [ 0.01259 -0.003326 1.012 0.000126 -5.656e-05 0.967 9.495e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08759 0.0809 0.1635 0.2109 0.9875 0.9921 0.08765 0.8161 0.8903 0.3041 ] Network output: [ -0.009417 0.01528 1.016 0.00012 -5.387e-05 0.9882 9.043e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09481 0.09354 0.1735 0.2101 0.9856 0.9915 0.09482 0.7444 0.8706 0.254 ] Network output: [ -0.002376 1 0.005721 1.991e-05 -8.939e-06 0.9988 1.501e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00278 Epoch 5774 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01582 0.9852 0.9824 -2.087e-06 9.371e-07 0.0007409 -1.573e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003049 -0.002695 -0.01102 0.008575 0.9694 0.9739 0.005754 0.8452 0.8359 0.02272 ] Network output: [ 1.001 -0.05729 0.007559 -2.447e-05 1.098e-05 0.04805 -1.844e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1843 -0.013 -0.2101 0.2272 0.9835 0.9933 0.2053 0.4692 0.8825 0.7422 ] Network output: [ -0.01298 1 1.009 -1.072e-05 4.811e-06 0.01702 -8.076e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004682 0.001128 0.003971 0.005787 0.989 0.9921 0.004766 0.8791 0.9068 0.01635 ] Network output: [ 0.007845 -0.08717 0.9758 -0.0002012 9.035e-05 1.095 -0.0001517 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1952 0.1214 0.307 0.2048 0.9851 0.994 0.1958 0.4748 0.8887 0.7363 ] Network output: [ 0.008511 -0.03468 1.019 0.0001263 -5.672e-05 0.9992 9.522e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08831 0.08167 0.1714 0.2196 0.9875 0.9921 0.08836 0.8184 0.8905 0.3089 ] Network output: [ -0.01233 0.0252 1.017 0.0001166 -5.233e-05 0.9831 8.784e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0955 0.09425 0.1763 0.2118 0.9857 0.9916 0.09552 0.7476 0.8707 0.2542 ] Network output: [ 0.00241 1 -0.001815 2.334e-05 -1.048e-05 0.9966 1.759e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003888 Epoch 5775 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01465 1.004 0.9815 -5.294e-06 2.376e-06 -0.01462 -3.989e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003071 -0.002699 -0.01109 0.008217 0.9694 0.9739 0.005793 0.8455 0.8351 0.0226 ] Network output: [ 0.9907 0.06424 0.001912 -4.646e-05 2.086e-05 -0.04766 -3.502e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1861 -0.01146 -0.2158 0.2064 0.9835 0.9932 0.2073 0.472 0.8816 0.7405 ] Network output: [ -0.01289 1.007 1.008 -1.161e-05 5.21e-06 0.01107 -8.746e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004722 0.001115 0.003638 0.005094 0.989 0.9921 0.004806 0.8793 0.9064 0.01617 ] Network output: [ -0.003129 0.08059 0.9668 -0.0002321 0.0001042 0.9579 -0.000175 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1968 0.1219 0.296 0.1723 0.9851 0.994 0.1974 0.4768 0.8887 0.7373 ] Network output: [ 0.01259 -0.003628 1.011 0.000126 -5.656e-05 0.9675 9.494e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08774 0.08102 0.1637 0.211 0.9875 0.9921 0.08779 0.8161 0.8903 0.3043 ] Network output: [ -0.009447 0.01555 1.016 0.00012 -5.386e-05 0.9881 9.042e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09493 0.09365 0.1736 0.2101 0.9857 0.9915 0.09494 0.7444 0.8706 0.254 ] Network output: [ -0.00236 1 0.005667 1.989e-05 -8.93e-06 0.9988 1.499e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002738 Epoch 5776 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01582 0.9853 0.9824 -1.918e-06 8.613e-07 0.0006646 -1.446e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003051 -0.002697 -0.01102 0.008574 0.9694 0.9739 0.005757 0.8452 0.8359 0.02272 ] Network output: [ 1.001 -0.05674 0.007501 -2.479e-05 1.113e-05 0.0476 -1.869e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1842 -0.01307 -0.2101 0.227 0.9835 0.9933 0.2053 0.4691 0.8824 0.7421 ] Network output: [ -0.01298 1 1.009 -1.053e-05 4.726e-06 0.01698 -7.934e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004686 0.001125 0.003973 0.005785 0.989 0.9921 0.00477 0.879 0.9068 0.01636 ] Network output: [ 0.007763 -0.08636 0.976 -0.0002015 9.047e-05 1.094 -0.0001519 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1951 0.1212 0.307 0.2046 0.9851 0.994 0.1957 0.4747 0.8887 0.7362 ] Network output: [ 0.008548 -0.03475 1.019 0.0001263 -5.672e-05 0.9994 9.521e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08844 0.08179 0.1714 0.2197 0.9875 0.9921 0.0885 0.8184 0.8904 0.309 ] Network output: [ -0.01234 0.02536 1.017 0.0001166 -5.234e-05 0.9831 8.786e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09561 0.09436 0.1764 0.2118 0.9857 0.9916 0.09563 0.7476 0.8707 0.2542 ] Network output: [ 0.002399 1 -0.001805 2.331e-05 -1.046e-05 0.9967 1.757e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003828 Epoch 5777 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01466 1.004 0.9816 -5.094e-06 2.287e-06 -0.01456 -3.839e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003073 -0.002701 -0.01109 0.008219 0.9694 0.9739 0.005795 0.8455 0.8351 0.0226 ] Network output: [ 0.9907 0.06368 0.001903 -4.656e-05 2.09e-05 -0.04725 -3.509e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.186 -0.01155 -0.2157 0.2064 0.9835 0.9932 0.2073 0.472 0.8816 0.7404 ] Network output: [ -0.01289 1.007 1.008 -1.141e-05 5.123e-06 0.01108 -8.599e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004726 0.001113 0.003643 0.005097 0.989 0.9921 0.00481 0.8793 0.9063 0.01617 ] Network output: [ -0.003115 0.0799 0.9671 -0.0002321 0.0001042 0.9582 -0.0001749 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1967 0.1217 0.2961 0.1723 0.9851 0.994 0.1973 0.4768 0.8887 0.7372 ] Network output: [ 0.01259 -0.003936 1.011 0.000126 -5.655e-05 0.9679 9.493e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08788 0.08114 0.1638 0.2111 0.9875 0.9921 0.08793 0.816 0.8902 0.3044 ] Network output: [ -0.009477 0.01582 1.016 0.00012 -5.385e-05 0.9881 9.041e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09504 0.09376 0.1736 0.2101 0.9857 0.9915 0.09506 0.7444 0.8706 0.254 ] Network output: [ -0.002342 1 0.005612 1.987e-05 -8.922e-06 0.9988 1.498e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002696 Epoch 5778 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01582 0.9853 0.9825 -1.752e-06 7.866e-07 0.0005863 -1.32e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003052 -0.002699 -0.01102 0.008572 0.9694 0.9739 0.00576 0.8452 0.8359 0.02272 ] Network output: [ 1.001 -0.05617 0.00744 -2.512e-05 1.128e-05 0.04714 -1.893e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1842 -0.01314 -0.2101 0.2269 0.9835 0.9933 0.2053 0.4691 0.8824 0.742 ] Network output: [ -0.01298 1 1.009 -1.034e-05 4.643e-06 0.01695 -7.794e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00469 0.001123 0.003975 0.005782 0.989 0.9921 0.004774 0.879 0.9068 0.01636 ] Network output: [ 0.007678 -0.08553 0.9762 -0.0002018 9.059e-05 1.093 -0.0001521 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1951 0.1211 0.307 0.2044 0.9851 0.994 0.1957 0.4747 0.8887 0.7361 ] Network output: [ 0.008585 -0.03481 1.019 0.0001263 -5.671e-05 0.9996 9.52e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08858 0.0819 0.1715 0.2197 0.9875 0.9921 0.08863 0.8183 0.8904 0.3091 ] Network output: [ -0.01234 0.02551 1.017 0.0001166 -5.235e-05 0.9831 8.788e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09572 0.09446 0.1764 0.2118 0.9857 0.9916 0.09573 0.7476 0.8707 0.2542 ] Network output: [ 0.002387 1 -0.001794 2.327e-05 -1.045e-05 0.9968 1.754e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003766 Epoch 5779 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01467 1.004 0.9816 -4.895e-06 2.198e-06 -0.01451 -3.689e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003074 -0.002703 -0.01109 0.008221 0.9694 0.9739 0.005798 0.8455 0.8351 0.0226 ] Network output: [ 0.9908 0.0631 0.001895 -4.664e-05 2.094e-05 -0.04683 -3.515e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.186 -0.01163 -0.2157 0.2065 0.9835 0.9932 0.2072 0.4719 0.8816 0.7403 ] Network output: [ -0.0129 1.007 1.008 -1.122e-05 5.036e-06 0.01109 -8.454e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00473 0.001111 0.003648 0.005101 0.989 0.9921 0.004814 0.8792 0.9063 0.01618 ] Network output: [ -0.0031 0.07919 0.9675 -0.0002321 0.0001042 0.9586 -0.0001749 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1967 0.1216 0.2962 0.1724 0.9851 0.994 0.1973 0.4767 0.8887 0.7371 ] Network output: [ 0.0126 -0.00425 1.011 0.000126 -5.654e-05 0.9683 9.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08802 0.08125 0.1639 0.2112 0.9875 0.9921 0.08807 0.816 0.8902 0.3045 ] Network output: [ -0.009508 0.01609 1.015 0.0001199 -5.385e-05 0.988 9.039e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09516 0.09387 0.1737 0.2101 0.9857 0.9915 0.09517 0.7445 0.8707 0.2539 ] Network output: [ -0.002324 1 0.005555 1.985e-05 -8.914e-06 0.9988 1.496e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002652 Epoch 5780 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01581 0.9854 0.9825 -1.588e-06 7.131e-07 0.0005062 -1.197e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003054 -0.002701 -0.01102 0.00857 0.9694 0.9739 0.005763 0.8452 0.8359 0.02272 ] Network output: [ 1.001 -0.05559 0.007379 -2.545e-05 1.142e-05 0.04666 -1.918e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1842 -0.0132 -0.2101 0.2267 0.9835 0.9933 0.2052 0.469 0.8824 0.7419 ] Network output: [ -0.01298 1 1.009 -1.016e-05 4.56e-06 0.01691 -7.655e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004695 0.001121 0.003977 0.005779 0.989 0.9921 0.004779 0.879 0.9068 0.01636 ] Network output: [ 0.007592 -0.08467 0.9765 -0.0002021 9.072e-05 1.092 -0.0001523 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1951 0.121 0.307 0.2042 0.9851 0.994 0.1957 0.4746 0.8887 0.736 ] Network output: [ 0.008623 -0.03487 1.018 0.0001263 -5.671e-05 0.9998 9.519e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08871 0.08201 0.1716 0.2197 0.9875 0.9921 0.08876 0.8183 0.8904 0.3092 ] Network output: [ -0.01234 0.02566 1.016 0.0001166 -5.236e-05 0.9831 8.789e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09583 0.09456 0.1764 0.2118 0.9857 0.9916 0.09584 0.7476 0.8707 0.2542 ] Network output: [ 0.002373 1 -0.001781 2.324e-05 -1.043e-05 0.9969 1.751e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003703 Epoch 5781 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01468 1.003 0.9817 -4.698e-06 2.109e-06 -0.01445 -3.541e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003076 -0.002705 -0.01109 0.008223 0.9694 0.9739 0.005801 0.8455 0.8351 0.0226 ] Network output: [ 0.9909 0.06251 0.001887 -4.672e-05 2.097e-05 -0.04639 -3.521e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.186 -0.01171 -0.2156 0.2065 0.9835 0.9933 0.2072 0.4718 0.8816 0.7402 ] Network output: [ -0.0129 1.006 1.008 -1.103e-05 4.95e-06 0.01111 -8.31e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004734 0.001109 0.003652 0.005105 0.989 0.9921 0.004818 0.8792 0.9063 0.01618 ] Network output: [ -0.003083 0.07845 0.9678 -0.000232 0.0001042 0.959 -0.0001749 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1966 0.1215 0.2963 0.1725 0.9851 0.994 0.1972 0.4766 0.8887 0.737 ] Network output: [ 0.0126 -0.004568 1.011 0.0001259 -5.654e-05 0.9688 9.491e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08815 0.08137 0.1641 0.2113 0.9875 0.9921 0.08821 0.816 0.8902 0.3047 ] Network output: [ -0.009539 0.01635 1.015 0.0001199 -5.384e-05 0.9879 9.038e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09527 0.09398 0.1737 0.2101 0.9857 0.9915 0.09528 0.7445 0.8707 0.2539 ] Network output: [ -0.002304 1 0.005498 1.984e-05 -8.906e-06 0.9988 1.495e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002607 Epoch 5782 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01581 0.9854 0.9825 -1.427e-06 6.407e-07 0.0004247 -1.076e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003056 -0.002703 -0.01102 0.008569 0.9694 0.9739 0.005766 0.8452 0.8359 0.02272 ] Network output: [ 1.001 -0.05499 0.007316 -2.577e-05 1.157e-05 0.04616 -1.942e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1842 -0.01327 -0.2101 0.2266 0.9835 0.9933 0.2052 0.469 0.8824 0.7418 ] Network output: [ -0.01299 1 1.009 -9.977e-06 4.479e-06 0.01688 -7.519e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004699 0.001119 0.003978 0.005777 0.989 0.9921 0.004783 0.879 0.9067 0.01636 ] Network output: [ 0.007503 -0.08378 0.9767 -0.0002024 9.085e-05 1.091 -0.0001525 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.195 0.1208 0.307 0.2039 0.9851 0.994 0.1956 0.4746 0.8887 0.7359 ] Network output: [ 0.008661 -0.03492 1.018 0.0001263 -5.67e-05 0.9999 9.518e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08884 0.08212 0.1716 0.2198 0.9875 0.9921 0.0889 0.8182 0.8904 0.3093 ] Network output: [ -0.01235 0.0258 1.016 0.0001167 -5.237e-05 0.9831 8.791e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09593 0.09467 0.1765 0.2118 0.9857 0.9916 0.09595 0.7477 0.8707 0.2542 ] Network output: [ 0.002359 1 -0.001768 2.32e-05 -1.042e-05 0.9969 1.749e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003639 Epoch 5783 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01468 1.003 0.9817 -4.503e-06 2.022e-06 -0.01439 -3.394e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003077 -0.002707 -0.01109 0.008224 0.9694 0.9739 0.005803 0.8455 0.8351 0.02261 ] Network output: [ 0.991 0.0619 0.001879 -4.679e-05 2.101e-05 -0.04594 -3.526e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1859 -0.01179 -0.2155 0.2066 0.9835 0.9933 0.2071 0.4718 0.8816 0.7401 ] Network output: [ -0.0129 1.006 1.008 -1.084e-05 4.866e-06 0.01112 -8.168e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004737 0.001107 0.003657 0.005108 0.989 0.9921 0.004822 0.8792 0.9063 0.01618 ] Network output: [ -0.003066 0.07769 0.9681 -0.000232 0.0001041 0.9594 -0.0001748 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1966 0.1213 0.2964 0.1726 0.9851 0.994 0.1972 0.4766 0.8887 0.7369 ] Network output: [ 0.0126 -0.004891 1.011 0.0001259 -5.653e-05 0.9692 9.49e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08829 0.08148 0.1642 0.2114 0.9875 0.9921 0.08835 0.816 0.8902 0.3048 ] Network output: [ -0.00957 0.01662 1.015 0.0001199 -5.383e-05 0.9879 9.037e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09538 0.09409 0.1738 0.2101 0.9857 0.9915 0.09539 0.7446 0.8707 0.2539 ] Network output: [ -0.002284 1 0.00544 1.982e-05 -8.899e-06 0.9987 1.494e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002562 Epoch 5784 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01581 0.9855 0.9826 -1.268e-06 5.694e-07 0.0003418 -9.558e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003057 -0.002705 -0.01102 0.008567 0.9694 0.9739 0.005769 0.8452 0.8359 0.02272 ] Network output: [ 1.001 -0.05438 0.007252 -2.609e-05 1.171e-05 0.04566 -1.966e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1841 -0.01333 -0.2101 0.2264 0.9835 0.9933 0.2052 0.469 0.8824 0.7417 ] Network output: [ -0.01299 1 1.009 -9.798e-06 4.399e-06 0.01684 -7.384e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004703 0.001117 0.00398 0.005774 0.989 0.9921 0.004787 0.879 0.9067 0.01636 ] Network output: [ 0.007413 -0.08288 0.9769 -0.0002027 9.098e-05 1.09 -0.0001527 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.195 0.1207 0.307 0.2037 0.9851 0.994 0.1956 0.4745 0.8886 0.7358 ] Network output: [ 0.008699 -0.03497 1.018 0.0001263 -5.669e-05 1 9.517e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08897 0.08222 0.1717 0.2198 0.9875 0.9921 0.08903 0.8182 0.8904 0.3094 ] Network output: [ -0.01235 0.02595 1.016 0.0001167 -5.238e-05 0.9831 8.793e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09604 0.09477 0.1765 0.2117 0.9857 0.9916 0.09605 0.7477 0.8707 0.2542 ] Network output: [ 0.002344 1 -0.001753 2.317e-05 -1.04e-05 0.997 1.746e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003574 Epoch 5785 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01469 1.003 0.9818 -4.309e-06 1.935e-06 -0.01432 -3.248e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003079 -0.002708 -0.01109 0.008226 0.9694 0.9739 0.005806 0.8455 0.8351 0.02261 ] Network output: [ 0.9911 0.06128 0.001872 -4.686e-05 2.104e-05 -0.04549 -3.531e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1859 -0.01187 -0.2155 0.2066 0.9835 0.9933 0.2071 0.4717 0.8816 0.74 ] Network output: [ -0.01291 1.006 1.008 -1.065e-05 4.782e-06 0.01114 -8.027e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004741 0.001105 0.003662 0.005112 0.989 0.9921 0.004826 0.8792 0.9063 0.01619 ] Network output: [ -0.003047 0.07692 0.9684 -0.0002319 0.0001041 0.9598 -0.0001748 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1965 0.1212 0.2965 0.1727 0.9851 0.994 0.1971 0.4765 0.8887 0.7368 ] Network output: [ 0.01259 -0.005217 1.011 0.0001259 -5.653e-05 0.9697 9.489e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08843 0.0816 0.1644 0.2115 0.9875 0.9921 0.08848 0.8159 0.8902 0.3049 ] Network output: [ -0.009601 0.01688 1.015 0.0001199 -5.383e-05 0.9878 9.036e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09549 0.0942 0.1739 0.2101 0.9857 0.9915 0.0955 0.7446 0.8707 0.2539 ] Network output: [ -0.002262 1 0.00538 1.981e-05 -8.893e-06 0.9987 1.493e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002517 Epoch 5786 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0158 0.9855 0.9826 -1.112e-06 4.99e-07 0.0002579 -8.377e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003059 -0.002707 -0.01102 0.008565 0.9694 0.9739 0.005772 0.8452 0.8358 0.02272 ] Network output: [ 1.001 -0.05375 0.007187 -2.641e-05 1.186e-05 0.04514 -1.99e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1841 -0.0134 -0.2101 0.2263 0.9835 0.9933 0.2052 0.4689 0.8824 0.7416 ] Network output: [ -0.01299 1 1.009 -9.621e-06 4.319e-06 0.01681 -7.251e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004707 0.001115 0.003981 0.00577 0.989 0.9921 0.004791 0.879 0.9067 0.01636 ] Network output: [ 0.007321 -0.08195 0.9771 -0.0002029 9.111e-05 1.089 -0.0001529 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.195 0.1206 0.307 0.2035 0.9851 0.994 0.1956 0.4745 0.8886 0.7358 ] Network output: [ 0.008737 -0.03501 1.018 0.0001263 -5.669e-05 1 9.516e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0891 0.08233 0.1718 0.2198 0.9875 0.9921 0.08915 0.8181 0.8903 0.3094 ] Network output: [ -0.01235 0.02609 1.016 0.0001167 -5.239e-05 0.9831 8.795e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09614 0.09486 0.1765 0.2117 0.9857 0.9916 0.09615 0.7477 0.8707 0.2541 ] Network output: [ 0.002328 1 -0.001738 2.313e-05 -1.038e-05 0.9971 1.743e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003509 Epoch 5787 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0147 1.003 0.9818 -4.117e-06 1.848e-06 -0.01426 -3.103e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00308 -0.00271 -0.01109 0.008228 0.9694 0.9739 0.005809 0.8456 0.8351 0.02261 ] Network output: [ 0.9912 0.06065 0.001865 -4.692e-05 2.106e-05 -0.04502 -3.536e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1858 -0.01195 -0.2154 0.2067 0.9835 0.9933 0.207 0.4717 0.8816 0.74 ] Network output: [ -0.01291 1.006 1.008 -1.047e-05 4.699e-06 0.01116 -7.888e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004745 0.001103 0.003666 0.005116 0.989 0.9921 0.004829 0.8792 0.9063 0.01619 ] Network output: [ -0.003028 0.07613 0.9687 -0.0002319 0.0001041 0.9603 -0.0001747 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1965 0.1211 0.2966 0.1728 0.9851 0.994 0.1971 0.4765 0.8887 0.7367 ] Network output: [ 0.01259 -0.005545 1.011 0.0001259 -5.652e-05 0.9701 9.488e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08856 0.08171 0.1645 0.2116 0.9875 0.9921 0.08862 0.8159 0.8901 0.305 ] Network output: [ -0.009632 0.01714 1.015 0.0001199 -5.382e-05 0.9877 9.035e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0956 0.0943 0.1739 0.2101 0.9857 0.9915 0.09561 0.7447 0.8707 0.2539 ] Network output: [ -0.00224 1 0.00532 1.979e-05 -8.886e-06 0.9987 1.492e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002471 Epoch 5788 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0158 0.9856 0.9827 -9.572e-07 4.297e-07 0.0001733 -7.213e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00306 -0.002709 -0.01102 0.008564 0.9694 0.9739 0.005775 0.8452 0.8358 0.02272 ] Network output: [ 1.001 -0.05312 0.007121 -2.673e-05 1.2e-05 0.04462 -2.014e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1841 -0.01346 -0.21 0.2261 0.9835 0.9933 0.2051 0.4689 0.8823 0.7415 ] Network output: [ -0.01299 1 1.009 -9.447e-06 4.241e-06 0.01677 -7.12e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004711 0.001112 0.003982 0.005767 0.989 0.9921 0.004795 0.879 0.9067 0.01636 ] Network output: [ 0.007228 -0.08101 0.9773 -0.0002032 9.124e-05 1.088 -0.0001532 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.195 0.1205 0.307 0.2033 0.9851 0.994 0.1955 0.4745 0.8886 0.7357 ] Network output: [ 0.008775 -0.03505 1.018 0.0001263 -5.668e-05 1 9.515e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08923 0.08243 0.1718 0.2198 0.9875 0.9921 0.08928 0.8181 0.8903 0.3095 ] Network output: [ -0.01235 0.02622 1.016 0.0001167 -5.24e-05 0.9831 8.797e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09624 0.09496 0.1766 0.2117 0.9857 0.9916 0.09625 0.7477 0.8707 0.2541 ] Network output: [ 0.002312 1 -0.001722 2.309e-05 -1.037e-05 0.9972 1.74e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003444 Epoch 5789 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01471 1.003 0.9818 -3.927e-06 1.763e-06 -0.01419 -2.96e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003082 -0.002712 -0.01108 0.00823 0.9694 0.9739 0.005811 0.8456 0.8351 0.02261 ] Network output: [ 0.9912 0.06001 0.001858 -4.698e-05 2.109e-05 -0.04455 -3.54e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1858 -0.01203 -0.2153 0.2068 0.9835 0.9933 0.207 0.4716 0.8816 0.7399 ] Network output: [ -0.01291 1.006 1.008 -1.028e-05 4.617e-06 0.01118 -7.751e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004748 0.001101 0.003671 0.00512 0.989 0.9921 0.004833 0.8792 0.9063 0.01619 ] Network output: [ -0.003008 0.07533 0.969 -0.0002318 0.0001041 0.9607 -0.0001747 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1964 0.1209 0.2968 0.1729 0.9851 0.994 0.197 0.4764 0.8886 0.7366 ] Network output: [ 0.01259 -0.005876 1.011 0.0001259 -5.651e-05 0.9706 9.487e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0887 0.08182 0.1646 0.2117 0.9875 0.9921 0.08875 0.8159 0.8901 0.3052 ] Network output: [ -0.009663 0.01739 1.015 0.0001199 -5.381e-05 0.9877 9.033e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09571 0.0944 0.174 0.2102 0.9857 0.9915 0.09572 0.7447 0.8707 0.2539 ] Network output: [ -0.002217 1.001 0.005259 1.978e-05 -8.881e-06 0.9987 1.491e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002426 Epoch 5790 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01579 0.9856 0.9827 -8.048e-07 3.613e-07 8.816e-05 -6.066e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003062 -0.002711 -0.01102 0.008562 0.9694 0.9739 0.005778 0.8452 0.8358 0.02271 ] Network output: [ 1.001 -0.05248 0.007054 -2.704e-05 1.214e-05 0.04409 -2.038e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1841 -0.01353 -0.21 0.2259 0.9835 0.9933 0.2051 0.4689 0.8823 0.7414 ] Network output: [ -0.01299 1 1.009 -9.275e-06 4.164e-06 0.01674 -6.99e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004715 0.00111 0.003984 0.005764 0.989 0.9921 0.004799 0.879 0.9067 0.01637 ] Network output: [ 0.007134 -0.08006 0.9775 -0.0002035 9.137e-05 1.087 -0.0001534 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1949 0.1203 0.307 0.203 0.9851 0.994 0.1955 0.4744 0.8886 0.7356 ] Network output: [ 0.008813 -0.03509 1.017 0.0001262 -5.667e-05 1.001 9.514e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08935 0.08253 0.1719 0.2199 0.9875 0.9921 0.08941 0.818 0.8903 0.3096 ] Network output: [ -0.01235 0.02636 1.016 0.0001168 -5.241e-05 0.9831 8.799e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09634 0.09506 0.1766 0.2117 0.9858 0.9916 0.09635 0.7477 0.8707 0.2541 ] Network output: [ 0.002295 1 -0.001706 2.305e-05 -1.035e-05 0.9972 1.737e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003379 Epoch 5791 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01471 1.003 0.9819 -3.739e-06 1.678e-06 -0.01412 -2.818e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003083 -0.002714 -0.01108 0.008232 0.9694 0.9739 0.005814 0.8456 0.8351 0.02261 ] Network output: [ 0.9913 0.05936 0.001852 -4.703e-05 2.111e-05 -0.04407 -3.544e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1857 -0.01212 -0.2153 0.2068 0.9835 0.9933 0.2069 0.4715 0.8816 0.7398 ] Network output: [ -0.01292 1.006 1.008 -1.01e-05 4.536e-06 0.0112 -7.615e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004752 0.001099 0.003676 0.005123 0.989 0.9921 0.004837 0.8792 0.9063 0.01619 ] Network output: [ -0.002987 0.07452 0.9694 -0.0002317 0.000104 0.9612 -0.0001746 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1964 0.1208 0.2969 0.173 0.9851 0.994 0.197 0.4764 0.8886 0.7365 ] Network output: [ 0.01259 -0.006208 1.01 0.0001259 -5.65e-05 0.971 9.485e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08883 0.08193 0.1648 0.2118 0.9875 0.9921 0.08888 0.8159 0.8901 0.3053 ] Network output: [ -0.009694 0.01765 1.015 0.0001198 -5.38e-05 0.9876 9.032e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09581 0.09451 0.174 0.2102 0.9857 0.9915 0.09583 0.7448 0.8707 0.2539 ] Network output: [ -0.002194 1.001 0.005198 1.977e-05 -8.875e-06 0.9987 1.49e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00238 Epoch 5792 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01579 0.9857 0.9827 -6.546e-07 2.939e-07 2.724e-06 -4.933e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003064 -0.002713 -0.01102 0.00856 0.9694 0.9739 0.005781 0.8452 0.8358 0.02271 ] Network output: [ 1.001 -0.05183 0.006988 -2.735e-05 1.228e-05 0.04356 -2.061e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.184 -0.0136 -0.21 0.2258 0.9835 0.9933 0.2051 0.4689 0.8823 0.7413 ] Network output: [ -0.013 1 1.009 -9.105e-06 4.088e-06 0.0167 -6.862e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004719 0.001108 0.003985 0.00576 0.989 0.9921 0.004803 0.879 0.9067 0.01637 ] Network output: [ 0.00704 -0.07911 0.9777 -0.0002038 9.15e-05 1.086 -0.0001536 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1949 0.1202 0.307 0.2028 0.9851 0.994 0.1955 0.4744 0.8886 0.7355 ] Network output: [ 0.008851 -0.03512 1.017 0.0001262 -5.666e-05 1.001 9.512e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08948 0.08264 0.172 0.2199 0.9875 0.9921 0.08953 0.818 0.8903 0.3097 ] Network output: [ -0.01235 0.02649 1.016 0.0001168 -5.243e-05 0.9831 8.801e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09644 0.09515 0.1766 0.2117 0.9858 0.9916 0.09645 0.7477 0.8707 0.2541 ] Network output: [ 0.002278 0.9999 -0.00169 2.301e-05 -1.033e-05 0.9973 1.734e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003314 Epoch 5793 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01472 1.003 0.9819 -3.552e-06 1.595e-06 -0.01405 -2.677e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003084 -0.002716 -0.01108 0.008234 0.9694 0.9739 0.005816 0.8456 0.8351 0.02261 ] Network output: [ 0.9914 0.0587 0.001846 -4.708e-05 2.114e-05 -0.04359 -3.548e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1857 -0.0122 -0.2152 0.2069 0.9835 0.9933 0.2069 0.4715 0.8816 0.7397 ] Network output: [ -0.01292 1.006 1.008 -9.925e-06 4.456e-06 0.01122 -7.48e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004755 0.001097 0.00368 0.005127 0.989 0.9921 0.00484 0.8792 0.9063 0.0162 ] Network output: [ -0.002966 0.07371 0.9697 -0.0002317 0.000104 0.9616 -0.0001746 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1963 0.1207 0.297 0.1732 0.9851 0.994 0.1969 0.4763 0.8886 0.7364 ] Network output: [ 0.01258 -0.006541 1.01 0.0001258 -5.65e-05 0.9715 9.484e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08896 0.08204 0.1649 0.2119 0.9875 0.9921 0.08901 0.8159 0.8901 0.3054 ] Network output: [ -0.009725 0.01791 1.014 0.0001198 -5.38e-05 0.9875 9.031e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09592 0.09461 0.1741 0.2102 0.9857 0.9915 0.09593 0.7448 0.8707 0.2539 ] Network output: [ -0.002169 1.001 0.005136 1.976e-05 -8.87e-06 0.9988 1.489e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002335 Epoch 5794 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01578 0.9858 0.9828 -5.063e-07 2.273e-07 -8.277e-05 -3.815e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003065 -0.002715 -0.01102 0.008558 0.9694 0.9739 0.005784 0.8452 0.8358 0.02271 ] Network output: [ 1.001 -0.05118 0.006921 -2.766e-05 1.242e-05 0.04302 -2.085e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.184 -0.01366 -0.21 0.2256 0.9835 0.9933 0.205 0.4688 0.8823 0.7412 ] Network output: [ -0.013 1 1.009 -8.937e-06 4.012e-06 0.01667 -6.736e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004723 0.001105 0.003986 0.005757 0.989 0.9921 0.004807 0.879 0.9067 0.01637 ] Network output: [ 0.006945 -0.07814 0.978 -0.0002041 9.163e-05 1.085 -0.0001538 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1949 0.1201 0.307 0.2026 0.9851 0.994 0.1955 0.4744 0.8886 0.7354 ] Network output: [ 0.008889 -0.03515 1.017 0.0001262 -5.666e-05 1.001 9.511e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0896 0.08274 0.172 0.2199 0.9875 0.9921 0.08965 0.818 0.8903 0.3098 ] Network output: [ -0.01235 0.02663 1.015 0.0001168 -5.244e-05 0.9831 8.803e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09654 0.09525 0.1766 0.2117 0.9858 0.9916 0.09655 0.7477 0.8707 0.2541 ] Network output: [ 0.002261 0.9999 -0.001674 2.298e-05 -1.032e-05 0.9974 1.732e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003249 Epoch 5795 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01473 1.003 0.982 -3.367e-06 1.512e-06 -0.01397 -2.537e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003086 -0.002718 -0.01108 0.008236 0.9694 0.9739 0.005819 0.8456 0.8351 0.02261 ] Network output: [ 0.9915 0.05804 0.00184 -4.712e-05 2.116e-05 -0.0431 -3.551e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1856 -0.01228 -0.2152 0.207 0.9835 0.9933 0.2068 0.4714 0.8816 0.7396 ] Network output: [ -0.01293 1.006 1.009 -9.749e-06 4.377e-06 0.01124 -7.347e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004759 0.001094 0.003685 0.005131 0.989 0.9921 0.004844 0.8792 0.9063 0.0162 ] Network output: [ -0.002944 0.07289 0.97 -0.0002316 0.000104 0.9621 -0.0001745 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1963 0.1205 0.2971 0.1733 0.9851 0.994 0.1969 0.4763 0.8886 0.7363 ] Network output: [ 0.01258 -0.006874 1.01 0.0001258 -5.649e-05 0.972 9.483e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08909 0.08215 0.165 0.212 0.9875 0.9921 0.08914 0.8159 0.8901 0.3056 ] Network output: [ -0.009756 0.01816 1.014 0.0001198 -5.379e-05 0.9875 9.03e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09602 0.09471 0.1741 0.2102 0.9857 0.9915 0.09603 0.7449 0.8707 0.2539 ] Network output: [ -0.002145 1.001 0.005073 1.975e-05 -8.866e-06 0.9988 1.488e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00229 Epoch 5796 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01578 0.9858 0.9828 -3.598e-07 1.615e-07 -0.0001681 -2.712e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003067 -0.002717 -0.01102 0.008556 0.9694 0.9739 0.005787 0.8453 0.8358 0.02271 ] Network output: [ 1.001 -0.05053 0.006854 -2.797e-05 1.255e-05 0.04248 -2.108e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.184 -0.01373 -0.21 0.2255 0.9835 0.9933 0.205 0.4688 0.8823 0.7411 ] Network output: [ -0.013 1 1.009 -8.772e-06 3.938e-06 0.01663 -6.611e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004727 0.001103 0.003987 0.005753 0.989 0.9921 0.004811 0.879 0.9067 0.01637 ] Network output: [ 0.00685 -0.07717 0.9782 -0.0002044 9.177e-05 1.084 -0.000154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1948 0.1199 0.307 0.2023 0.9851 0.994 0.1954 0.4744 0.8886 0.7353 ] Network output: [ 0.008927 -0.03518 1.017 0.0001262 -5.665e-05 1.001 9.51e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08972 0.08284 0.1721 0.2199 0.9875 0.9921 0.08977 0.8179 0.8903 0.3098 ] Network output: [ -0.01235 0.02676 1.015 0.0001168 -5.245e-05 0.9831 8.805e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09663 0.09534 0.1767 0.2117 0.9858 0.9916 0.09664 0.7478 0.8707 0.2541 ] Network output: [ 0.002243 0.9998 -0.001657 2.294e-05 -1.03e-05 0.9974 1.729e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003185 Epoch 5797 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01474 1.002 0.982 -3.184e-06 1.429e-06 -0.0139 -2.399e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003087 -0.00272 -0.01108 0.008238 0.9694 0.9739 0.005821 0.8456 0.8351 0.02261 ] Network output: [ 0.9916 0.05738 0.001835 -4.716e-05 2.117e-05 -0.04262 -3.554e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1856 -0.01236 -0.2151 0.207 0.9835 0.9933 0.2068 0.4714 0.8816 0.7396 ] Network output: [ -0.01293 1.006 1.009 -9.574e-06 4.298e-06 0.01127 -7.215e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004762 0.001092 0.003689 0.005134 0.989 0.9921 0.004847 0.8791 0.9063 0.0162 ] Network output: [ -0.002922 0.07207 0.9703 -0.0002315 0.0001039 0.9625 -0.0001745 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1962 0.1204 0.2972 0.1734 0.9851 0.994 0.1968 0.4762 0.8886 0.7362 ] Network output: [ 0.01258 -0.007207 1.01 0.0001258 -5.648e-05 0.9724 9.482e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08922 0.08225 0.1652 0.2122 0.9875 0.9921 0.08927 0.8158 0.8901 0.3057 ] Network output: [ -0.009787 0.01841 1.014 0.0001198 -5.378e-05 0.9874 9.028e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09612 0.09481 0.1742 0.2102 0.9857 0.9915 0.09614 0.7449 0.8707 0.2539 ] Network output: [ -0.002119 1.001 0.00501 1.974e-05 -8.861e-06 0.9988 1.488e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002245 Epoch 5798 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01577 0.9859 0.9828 -2.153e-07 9.664e-08 -0.0002531 -1.622e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003068 -0.002719 -0.01102 0.008554 0.9694 0.9739 0.005789 0.8453 0.8358 0.02271 ] Network output: [ 1.001 -0.04988 0.006787 -2.827e-05 1.269e-05 0.04194 -2.13e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.184 -0.01379 -0.21 0.2253 0.9835 0.9933 0.205 0.4688 0.8823 0.741 ] Network output: [ -0.013 1 1.009 -8.608e-06 3.864e-06 0.0166 -6.487e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00473 0.001101 0.003988 0.005749 0.989 0.9921 0.004815 0.879 0.9066 0.01637 ] Network output: [ 0.006754 -0.0762 0.9784 -0.0002047 9.19e-05 1.083 -0.0001543 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1948 0.1198 0.307 0.2021 0.9851 0.994 0.1954 0.4744 0.8886 0.7352 ] Network output: [ 0.008964 -0.0352 1.017 0.0001262 -5.664e-05 1.001 9.508e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08984 0.08293 0.1721 0.2199 0.9875 0.9921 0.08989 0.8179 0.8902 0.3099 ] Network output: [ -0.01235 0.02689 1.015 0.0001169 -5.246e-05 0.9831 8.807e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09673 0.09543 0.1767 0.2117 0.9858 0.9916 0.09674 0.7478 0.8707 0.2541 ] Network output: [ 0.002225 0.9998 -0.001641 2.29e-05 -1.028e-05 0.9975 1.726e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003122 Epoch 5799 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01474 1.002 0.9821 -3.002e-06 1.348e-06 -0.01382 -2.263e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003088 -0.002722 -0.01108 0.00824 0.9694 0.9739 0.005824 0.8456 0.8351 0.02261 ] Network output: [ 0.9917 0.05672 0.001831 -4.72e-05 2.119e-05 -0.04213 -3.557e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1855 -0.01244 -0.2151 0.2071 0.9835 0.9933 0.2067 0.4714 0.8816 0.7395 ] Network output: [ -0.01293 1.006 1.009 -9.401e-06 4.221e-06 0.01129 -7.085e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004766 0.00109 0.003694 0.005138 0.989 0.9921 0.004851 0.8791 0.9062 0.01621 ] Network output: [ -0.0029 0.07124 0.9706 -0.0002315 0.0001039 0.963 -0.0001744 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1962 0.1202 0.2973 0.1735 0.9851 0.994 0.1968 0.4762 0.8886 0.7361 ] Network output: [ 0.01257 -0.007539 1.01 0.0001258 -5.647e-05 0.9729 9.48e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08934 0.08236 0.1653 0.2123 0.9875 0.9921 0.0894 0.8158 0.8901 0.3058 ] Network output: [ -0.009817 0.01866 1.014 0.0001198 -5.377e-05 0.9873 9.027e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09623 0.0949 0.1742 0.2102 0.9857 0.9915 0.09624 0.745 0.8707 0.2539 ] Network output: [ -0.002094 1.001 0.004947 1.973e-05 -8.857e-06 0.9988 1.487e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002201 Epoch 5800 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01577 0.9859 0.9829 -7.244e-08 3.252e-08 -0.0003375 -5.46e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00307 -0.002721 -0.01102 0.008552 0.9694 0.9739 0.005792 0.8453 0.8358 0.02271 ] Network output: [ 1 -0.04922 0.006721 -2.857e-05 1.282e-05 0.04141 -2.153e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1839 -0.01386 -0.21 0.2252 0.9835 0.9933 0.205 0.4688 0.8823 0.7409 ] Network output: [ -0.013 1 1.009 -8.446e-06 3.792e-06 0.01656 -6.365e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004734 0.001098 0.003989 0.005746 0.989 0.9921 0.004819 0.8789 0.9066 0.01637 ] Network output: [ 0.006659 -0.07523 0.9786 -0.000205 9.203e-05 1.082 -0.0001545 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1948 0.1197 0.307 0.2018 0.9851 0.994 0.1954 0.4743 0.8886 0.7352 ] Network output: [ 0.009001 -0.03522 1.017 0.0001261 -5.663e-05 1.001 9.507e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08996 0.08303 0.1722 0.2199 0.9875 0.9921 0.09001 0.8178 0.8902 0.31 ] Network output: [ -0.01235 0.02702 1.015 0.0001169 -5.247e-05 0.9831 8.808e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09682 0.09552 0.1767 0.2117 0.9858 0.9916 0.09683 0.7478 0.8707 0.2541 ] Network output: [ 0.002207 0.9998 -0.001625 2.286e-05 -1.026e-05 0.9975 1.723e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.003059 Epoch 5801 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01475 1.002 0.9821 -2.823e-06 1.267e-06 -0.01375 -2.127e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003089 -0.002724 -0.01108 0.008242 0.9694 0.9739 0.005826 0.8456 0.8351 0.02261 ] Network output: [ 0.9918 0.05605 0.001827 -4.724e-05 2.121e-05 -0.04164 -3.56e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1855 -0.01253 -0.215 0.2072 0.9835 0.9933 0.2067 0.4713 0.8816 0.7394 ] Network output: [ -0.01294 1.006 1.009 -9.231e-06 4.144e-06 0.01131 -6.956e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004769 0.001088 0.003698 0.005142 0.989 0.9921 0.004854 0.8791 0.9062 0.01621 ] Network output: [ -0.002878 0.07042 0.9709 -0.0002314 0.0001039 0.9635 -0.0001744 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1961 0.1201 0.2974 0.1736 0.9851 0.994 0.1968 0.4762 0.8886 0.736 ] Network output: [ 0.01257 -0.007871 1.01 0.0001258 -5.647e-05 0.9733 9.479e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08947 0.08246 0.1655 0.2124 0.9875 0.9921 0.08952 0.8158 0.89 0.3059 ] Network output: [ -0.009848 0.01891 1.014 0.0001198 -5.377e-05 0.9873 9.026e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09633 0.095 0.1743 0.2102 0.9857 0.9915 0.09634 0.745 0.8707 0.2538 ] Network output: [ -0.002068 1.001 0.004883 1.972e-05 -8.853e-06 0.9988 1.486e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002158 Epoch 5802 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01576 0.986 0.9829 6.866e-08 -3.082e-08 -0.0004213 5.175e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003071 -0.002722 -0.01102 0.00855 0.9694 0.9739 0.005795 0.8453 0.8358 0.02271 ] Network output: [ 1 -0.04858 0.006655 -2.886e-05 1.296e-05 0.04087 -2.175e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1839 -0.01392 -0.21 0.225 0.9835 0.9933 0.2049 0.4688 0.8823 0.7408 ] Network output: [ -0.01301 1 1.009 -8.287e-06 3.72e-06 0.01653 -6.245e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004738 0.001096 0.00399 0.005742 0.989 0.9921 0.004823 0.8789 0.9066 0.01637 ] Network output: [ 0.006564 -0.07426 0.9788 -0.0002053 9.216e-05 1.081 -0.0001547 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1947 0.1195 0.307 0.2016 0.9851 0.994 0.1953 0.4743 0.8886 0.7351 ] Network output: [ 0.009038 -0.03524 1.016 0.0001261 -5.662e-05 1.001 9.505e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09008 0.08312 0.1723 0.2199 0.9875 0.9921 0.09013 0.8178 0.8902 0.31 ] Network output: [ -0.01235 0.02715 1.015 0.0001169 -5.248e-05 0.9831 8.81e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09691 0.09561 0.1767 0.2117 0.9858 0.9916 0.09692 0.7478 0.8708 0.2541 ] Network output: [ 0.00219 0.9997 -0.001609 2.282e-05 -1.025e-05 0.9976 1.72e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002998 Epoch 5803 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01476 1.002 0.9821 -2.645e-06 1.187e-06 -0.01367 -1.993e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003091 -0.002725 -0.01108 0.008243 0.9694 0.9739 0.005828 0.8456 0.8351 0.02261 ] Network output: [ 0.9919 0.05539 0.001823 -4.727e-05 2.122e-05 -0.04115 -3.563e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1855 -0.01261 -0.2149 0.2072 0.9835 0.9933 0.2066 0.4713 0.8816 0.7393 ] Network output: [ -0.01294 1.006 1.009 -9.062e-06 4.068e-06 0.01134 -6.829e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004772 0.001085 0.003703 0.005145 0.989 0.9921 0.004858 0.8791 0.9062 0.01621 ] Network output: [ -0.002856 0.0696 0.9712 -0.0002313 0.0001039 0.9639 -0.0001743 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1961 0.12 0.2975 0.1737 0.9851 0.994 0.1967 0.4761 0.8886 0.7359 ] Network output: [ 0.01256 -0.008201 1.01 0.0001258 -5.646e-05 0.9738 9.478e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08959 0.08256 0.1656 0.2125 0.9875 0.9921 0.08964 0.8158 0.89 0.306 ] Network output: [ -0.009878 0.01916 1.014 0.0001197 -5.376e-05 0.9872 9.025e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09642 0.09509 0.1743 0.2102 0.9857 0.9915 0.09644 0.7451 0.8707 0.2538 ] Network output: [ -0.002041 1.001 0.00482 1.971e-05 -8.85e-06 0.9988 1.486e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002115 Epoch 5804 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01576 0.9861 0.9829 2.081e-07 -9.343e-08 -0.0005041 1.568e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003073 -0.002724 -0.01102 0.008548 0.9694 0.9739 0.005798 0.8453 0.8358 0.02271 ] Network output: [ 1 -0.04793 0.00659 -2.915e-05 1.309e-05 0.04033 -2.197e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1839 -0.01399 -0.21 0.2248 0.9835 0.9933 0.2049 0.4688 0.8823 0.7407 ] Network output: [ -0.01301 1 1.009 -8.129e-06 3.649e-06 0.01649 -6.126e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004741 0.001094 0.003991 0.005738 0.989 0.9921 0.004826 0.8789 0.9066 0.01637 ] Network output: [ 0.00647 -0.07329 0.979 -0.0002056 9.229e-05 1.08 -0.0001549 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1947 0.1194 0.307 0.2013 0.9851 0.994 0.1953 0.4743 0.8886 0.735 ] Network output: [ 0.009074 -0.03526 1.016 0.0001261 -5.661e-05 1.001 9.504e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09019 0.08322 0.1723 0.22 0.9875 0.9921 0.09025 0.8178 0.8902 0.3101 ] Network output: [ -0.01235 0.02728 1.015 0.0001169 -5.249e-05 0.9831 8.812e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.097 0.09569 0.1768 0.2116 0.9858 0.9916 0.09702 0.7478 0.8708 0.254 ] Network output: [ 0.002172 0.9997 -0.001594 2.278e-05 -1.023e-05 0.9977 1.717e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002938 Epoch 5805 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01477 1.002 0.9822 -2.469e-06 1.109e-06 -0.01359 -1.861e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003092 -0.002727 -0.01108 0.008245 0.9694 0.9739 0.005831 0.8456 0.8351 0.02261 ] Network output: [ 0.992 0.05473 0.001819 -4.731e-05 2.124e-05 -0.04066 -3.565e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1854 -0.01269 -0.2149 0.2073 0.9835 0.9933 0.2066 0.4712 0.8816 0.7393 ] Network output: [ -0.01294 1.006 1.009 -8.894e-06 3.993e-06 0.01137 -6.703e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004776 0.001083 0.003707 0.005149 0.989 0.9921 0.004861 0.8791 0.9062 0.01621 ] Network output: [ -0.002834 0.06878 0.9716 -0.0002313 0.0001038 0.9644 -0.0001743 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.196 0.1198 0.2977 0.1738 0.9851 0.994 0.1967 0.4761 0.8886 0.7358 ] Network output: [ 0.01255 -0.008529 1.01 0.0001257 -5.645e-05 0.9742 9.476e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08971 0.08266 0.1657 0.2126 0.9875 0.9921 0.08977 0.8158 0.89 0.3062 ] Network output: [ -0.009908 0.0194 1.014 0.0001197 -5.375e-05 0.9872 9.023e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09652 0.09519 0.1744 0.2102 0.9857 0.9915 0.09653 0.7451 0.8707 0.2538 ] Network output: [ -0.002015 1.001 0.004756 1.97e-05 -8.846e-06 0.9988 1.485e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002073 Epoch 5806 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01575 0.9861 0.9829 3.46e-07 -1.553e-07 -0.000586 2.607e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003074 -0.002726 -0.01102 0.008546 0.9694 0.9739 0.0058 0.8453 0.8358 0.02271 ] Network output: [ 1 -0.04729 0.006525 -2.944e-05 1.322e-05 0.03981 -2.218e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1839 -0.01405 -0.21 0.2247 0.9835 0.9933 0.2049 0.4688 0.8823 0.7406 ] Network output: [ -0.01301 1 1.009 -7.972e-06 3.579e-06 0.01646 -6.008e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004745 0.001091 0.003992 0.005734 0.989 0.9921 0.00483 0.8789 0.9066 0.01637 ] Network output: [ 0.006376 -0.07233 0.9792 -0.0002059 9.241e-05 1.08 -0.0001551 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1947 0.1193 0.307 0.2011 0.9851 0.994 0.1953 0.4743 0.8886 0.7349 ] Network output: [ 0.00911 -0.03528 1.016 0.0001261 -5.661e-05 1.002 9.502e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09031 0.08331 0.1724 0.22 0.9875 0.9921 0.09036 0.8177 0.8902 0.3102 ] Network output: [ -0.01234 0.02741 1.015 0.000117 -5.25e-05 0.9831 8.814e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09709 0.09578 0.1768 0.2116 0.9858 0.9916 0.09711 0.7479 0.8708 0.254 ] Network output: [ 0.002154 0.9996 -0.001578 2.275e-05 -1.021e-05 0.9977 1.714e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002878 Epoch 5807 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01477 1.002 0.9822 -2.295e-06 1.03e-06 -0.01352 -1.73e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003093 -0.002729 -0.01108 0.008247 0.9694 0.9739 0.005833 0.8456 0.8351 0.02261 ] Network output: [ 0.992 0.05408 0.001816 -4.734e-05 2.125e-05 -0.04018 -3.568e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1854 -0.01277 -0.2148 0.2074 0.9835 0.9933 0.2065 0.4712 0.8816 0.7392 ] Network output: [ -0.01295 1.006 1.009 -8.729e-06 3.919e-06 0.01139 -6.579e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004779 0.001081 0.003712 0.005152 0.989 0.9921 0.004864 0.8791 0.9062 0.01622 ] Network output: [ -0.002812 0.06797 0.9719 -0.0002312 0.0001038 0.9648 -0.0001743 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.196 0.1197 0.2978 0.1739 0.9851 0.994 0.1966 0.4761 0.8886 0.7357 ] Network output: [ 0.01255 -0.008854 1.01 0.0001257 -5.644e-05 0.9746 9.475e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08983 0.08276 0.1659 0.2126 0.9875 0.9921 0.08989 0.8158 0.89 0.3063 ] Network output: [ -0.009937 0.01965 1.014 0.0001197 -5.374e-05 0.9871 9.022e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09662 0.09528 0.1744 0.2102 0.9857 0.9915 0.09663 0.7452 0.8708 0.2538 ] Network output: [ -0.001988 1.001 0.004693 1.97e-05 -8.843e-06 0.9988 1.484e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002032 Epoch 5808 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01575 0.9862 0.983 4.822e-07 -2.165e-07 -0.0006666 3.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003076 -0.002728 -0.01102 0.008544 0.9694 0.9739 0.005803 0.8453 0.8358 0.0227 ] Network output: [ 1 -0.04666 0.006461 -2.972e-05 1.334e-05 0.03928 -2.24e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1838 -0.01412 -0.2101 0.2245 0.9835 0.9933 0.2048 0.4688 0.8822 0.7405 ] Network output: [ -0.01301 1 1.009 -7.818e-06 3.51e-06 0.01643 -5.892e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004749 0.001089 0.003993 0.005731 0.989 0.9921 0.004834 0.8789 0.9066 0.01637 ] Network output: [ 0.006282 -0.07138 0.9794 -0.0002061 9.254e-05 1.079 -0.0001553 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1946 0.1191 0.307 0.2009 0.9851 0.994 0.1952 0.4743 0.8886 0.7348 ] Network output: [ 0.009145 -0.0353 1.016 0.0001261 -5.66e-05 1.002 9.501e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09042 0.0834 0.1724 0.22 0.9875 0.9921 0.09048 0.8177 0.8902 0.3102 ] Network output: [ -0.01234 0.02754 1.014 0.000117 -5.252e-05 0.9831 8.816e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09718 0.09586 0.1768 0.2116 0.9858 0.9916 0.09719 0.7479 0.8708 0.254 ] Network output: [ 0.002136 0.9996 -0.001564 2.271e-05 -1.019e-05 0.9978 1.711e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00282 Epoch 5809 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01478 1.002 0.9822 -2.123e-06 9.532e-07 -0.01344 -1.6e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003094 -0.002731 -0.01108 0.008249 0.9694 0.9739 0.005835 0.8456 0.8351 0.02261 ] Network output: [ 0.9921 0.05343 0.001814 -4.737e-05 2.127e-05 -0.0397 -3.57e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1853 -0.01285 -0.2148 0.2074 0.9835 0.9933 0.2065 0.4712 0.8816 0.7391 ] Network output: [ -0.01295 1.006 1.009 -8.566e-06 3.846e-06 0.01142 -6.455e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004782 0.001079 0.003716 0.005155 0.989 0.9921 0.004867 0.8791 0.9062 0.01622 ] Network output: [ -0.00279 0.06717 0.9722 -0.0002312 0.0001038 0.9653 -0.0001742 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1959 0.1196 0.2979 0.174 0.9851 0.994 0.1966 0.476 0.8886 0.7357 ] Network output: [ 0.01254 -0.009177 1.01 0.0001257 -5.643e-05 0.9751 9.473e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.08995 0.08286 0.166 0.2127 0.9875 0.9921 0.09001 0.8158 0.89 0.3064 ] Network output: [ -0.009967 0.01989 1.013 0.0001197 -5.374e-05 0.987 9.021e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09671 0.09537 0.1745 0.2102 0.9857 0.9916 0.09672 0.7452 0.8708 0.2538 ] Network output: [ -0.001962 1.001 0.00463 1.969e-05 -8.84e-06 0.9988 1.484e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001992 Epoch 5810 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01574 0.9863 0.983 6.17e-07 -2.77e-07 -0.000746 4.65e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003077 -0.00273 -0.01102 0.008542 0.9694 0.9739 0.005806 0.8453 0.8358 0.0227 ] Network output: [ 1 -0.04603 0.006397 -3e-05 1.347e-05 0.03876 -2.261e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1838 -0.01418 -0.2101 0.2244 0.9835 0.9933 0.2048 0.4688 0.8822 0.7404 ] Network output: [ -0.01301 1 1.009 -7.666e-06 3.441e-06 0.01639 -5.777e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004752 0.001087 0.003994 0.005727 0.989 0.9921 0.004837 0.8789 0.9066 0.01637 ] Network output: [ 0.00619 -0.07043 0.9796 -0.0002064 9.267e-05 1.078 -0.0001556 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1946 0.119 0.307 0.2006 0.9851 0.994 0.1952 0.4743 0.8886 0.7347 ] Network output: [ 0.009179 -0.03532 1.016 0.000126 -5.659e-05 1.002 9.499e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09053 0.08349 0.1725 0.22 0.9875 0.9921 0.09059 0.8177 0.8902 0.3103 ] Network output: [ -0.01234 0.02767 1.014 0.000117 -5.253e-05 0.9831 8.818e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09727 0.09595 0.1768 0.2116 0.9858 0.9916 0.09728 0.7479 0.8708 0.254 ] Network output: [ 0.002118 0.9996 -0.00155 2.267e-05 -1.018e-05 0.9978 1.708e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002763 Epoch 5811 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01479 1.001 0.9823 -1.953e-06 8.768e-07 -0.01336 -1.472e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003096 -0.002733 -0.01108 0.00825 0.9694 0.9739 0.005837 0.8456 0.8351 0.02261 ] Network output: [ 0.9922 0.05279 0.001812 -4.74e-05 2.128e-05 -0.03922 -3.572e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1853 -0.01294 -0.2147 0.2075 0.9835 0.9933 0.2064 0.4711 0.8816 0.739 ] Network output: [ -0.01295 1.006 1.009 -8.404e-06 3.773e-06 0.01144 -6.334e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004785 0.001076 0.00372 0.005159 0.989 0.9921 0.004871 0.8791 0.9062 0.01622 ] Network output: [ -0.002768 0.06637 0.9725 -0.0002311 0.0001037 0.9657 -0.0001742 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1959 0.1194 0.298 0.1741 0.9851 0.994 0.1965 0.476 0.8886 0.7356 ] Network output: [ 0.01254 -0.009498 1.009 0.0001257 -5.642e-05 0.9755 9.472e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09007 0.08296 0.1661 0.2128 0.9875 0.9921 0.09013 0.8158 0.89 0.3065 ] Network output: [ -0.009995 0.02013 1.013 0.0001197 -5.373e-05 0.987 9.02e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09681 0.09546 0.1745 0.2102 0.9857 0.9916 0.09682 0.7453 0.8708 0.2538 ] Network output: [ -0.001935 1.001 0.004566 1.968e-05 -8.837e-06 0.9988 1.483e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001953 Epoch 5812 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01574 0.9863 0.983 7.502e-07 -3.368e-07 -0.0008241 5.654e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003078 -0.002732 -0.01102 0.00854 0.9694 0.9739 0.005808 0.8454 0.8358 0.0227 ] Network output: [ 1 -0.04541 0.006335 -3.027e-05 1.359e-05 0.03825 -2.282e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1838 -0.01425 -0.2101 0.2242 0.9835 0.9933 0.2048 0.4688 0.8822 0.7404 ] Network output: [ -0.01302 1 1.009 -7.515e-06 3.374e-06 0.01636 -5.663e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004756 0.001084 0.003995 0.005723 0.989 0.9921 0.004841 0.8789 0.9066 0.01637 ] Network output: [ 0.006099 -0.0695 0.9799 -0.0002067 9.279e-05 1.077 -0.0001558 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1946 0.1189 0.307 0.2004 0.9851 0.994 0.1952 0.4743 0.8886 0.7347 ] Network output: [ 0.009213 -0.03533 1.015 0.000126 -5.658e-05 1.002 9.498e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09064 0.08358 0.1725 0.22 0.9875 0.9921 0.0907 0.8176 0.8902 0.3104 ] Network output: [ -0.01234 0.0278 1.014 0.000117 -5.254e-05 0.9831 8.819e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09736 0.09603 0.1769 0.2116 0.9858 0.9916 0.09737 0.7479 0.8708 0.254 ] Network output: [ 0.002101 0.9995 -0.001536 2.263e-05 -1.016e-05 0.9979 1.706e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002708 Epoch 5813 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01479 1.001 0.9823 -1.785e-06 8.013e-07 -0.01328 -1.345e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003097 -0.002734 -0.01108 0.008252 0.9694 0.9739 0.00584 0.8457 0.8351 0.02261 ] Network output: [ 0.9923 0.05215 0.00181 -4.743e-05 2.129e-05 -0.03875 -3.574e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1852 -0.01302 -0.2147 0.2075 0.9835 0.9933 0.2063 0.4711 0.8816 0.739 ] Network output: [ -0.01296 1.005 1.009 -8.244e-06 3.701e-06 0.01147 -6.213e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004788 0.001074 0.003724 0.005162 0.989 0.9921 0.004874 0.8791 0.9062 0.01622 ] Network output: [ -0.002747 0.06559 0.9728 -0.000231 0.0001037 0.9662 -0.0001741 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1958 0.1193 0.2981 0.1742 0.9851 0.994 0.1964 0.476 0.8886 0.7355 ] Network output: [ 0.01253 -0.009815 1.009 0.0001257 -5.641e-05 0.9759 9.47e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09019 0.08305 0.1663 0.2129 0.9875 0.9921 0.09024 0.8157 0.89 0.3066 ] Network output: [ -0.01002 0.02037 1.013 0.0001197 -5.372e-05 0.9869 9.018e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0969 0.09555 0.1746 0.2102 0.9857 0.9916 0.09691 0.7453 0.8708 0.2538 ] Network output: [ -0.001908 1.001 0.004504 1.968e-05 -8.834e-06 0.9988 1.483e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001915 Epoch 5814 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01573 0.9864 0.983 8.821e-07 -3.96e-07 -0.0009006 6.647e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00308 -0.002733 -0.01102 0.008538 0.9694 0.9739 0.005811 0.8454 0.8358 0.0227 ] Network output: [ 1 -0.0448 0.006274 -3.054e-05 1.371e-05 0.03774 -2.302e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1837 -0.01431 -0.2101 0.2241 0.9835 0.9933 0.2047 0.4688 0.8822 0.7403 ] Network output: [ -0.01302 1 1.009 -7.365e-06 3.307e-06 0.01633 -5.551e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004759 0.001082 0.003996 0.005719 0.989 0.9921 0.004844 0.879 0.9066 0.01637 ] Network output: [ 0.006008 -0.06858 0.9801 -0.000207 9.291e-05 1.076 -0.000156 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1945 0.1187 0.307 0.2001 0.9851 0.994 0.1951 0.4743 0.8886 0.7346 ] Network output: [ 0.009247 -0.03535 1.015 0.000126 -5.657e-05 1.002 9.496e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09075 0.08367 0.1726 0.22 0.9875 0.9921 0.09081 0.8176 0.8901 0.3104 ] Network output: [ -0.01234 0.02793 1.014 0.000117 -5.255e-05 0.9831 8.821e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09744 0.09611 0.1769 0.2116 0.9858 0.9916 0.09745 0.7479 0.8708 0.254 ] Network output: [ 0.002084 0.9995 -0.001523 2.259e-05 -1.014e-05 0.9979 1.703e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002654 Epoch 5815 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0148 1.001 0.9823 -1.618e-06 7.266e-07 -0.01321 -1.22e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003098 -0.002736 -0.01108 0.008254 0.9694 0.9739 0.005842 0.8457 0.8351 0.02261 ] Network output: [ 0.9924 0.05152 0.001808 -4.746e-05 2.13e-05 -0.03828 -3.576e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1852 -0.0131 -0.2146 0.2076 0.9835 0.9933 0.2063 0.4711 0.8816 0.7389 ] Network output: [ -0.01296 1.005 1.009 -8.086e-06 3.63e-06 0.0115 -6.094e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004791 0.001072 0.003729 0.005165 0.989 0.9921 0.004877 0.8791 0.9062 0.01622 ] Network output: [ -0.002726 0.06481 0.9731 -0.000231 0.0001037 0.9666 -0.0001741 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1958 0.1191 0.2982 0.1743 0.9851 0.994 0.1964 0.476 0.8886 0.7354 ] Network output: [ 0.01252 -0.01013 1.009 0.0001256 -5.64e-05 0.9764 9.469e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09031 0.08315 0.1664 0.213 0.9875 0.9921 0.09036 0.8157 0.89 0.3067 ] Network output: [ -0.01005 0.02061 1.013 0.0001197 -5.372e-05 0.9868 9.017e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09699 0.09564 0.1746 0.2102 0.9857 0.9916 0.097 0.7454 0.8708 0.2537 ] Network output: [ -0.001881 1.001 0.004441 1.967e-05 -8.831e-06 0.9989 1.482e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001878 Epoch 5816 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01573 0.9865 0.9831 1.012e-06 -4.545e-07 -0.0009755 7.63e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003081 -0.002735 -0.01102 0.008535 0.9694 0.9739 0.005813 0.8454 0.8358 0.0227 ] Network output: [ 1 -0.0442 0.006213 -3.081e-05 1.383e-05 0.03724 -2.322e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1837 -0.01438 -0.2101 0.2239 0.9835 0.9933 0.2047 0.4688 0.8822 0.7402 ] Network output: [ -0.01302 1 1.009 -7.218e-06 3.24e-06 0.0163 -5.44e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004763 0.001079 0.003997 0.005715 0.989 0.9921 0.004848 0.879 0.9066 0.01637 ] Network output: [ 0.005919 -0.06767 0.9803 -0.0002072 9.303e-05 1.075 -0.0001562 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1945 0.1186 0.307 0.1999 0.9851 0.994 0.1951 0.4743 0.8886 0.7345 ] Network output: [ 0.009279 -0.03537 1.015 0.000126 -5.656e-05 1.002 9.494e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09086 0.08375 0.1726 0.22 0.9875 0.9921 0.09092 0.8176 0.8901 0.3105 ] Network output: [ -0.01234 0.02806 1.014 0.0001171 -5.256e-05 0.9831 8.823e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09752 0.09619 0.1769 0.2115 0.9858 0.9916 0.09754 0.748 0.8708 0.2539 ] Network output: [ 0.002067 0.9995 -0.001511 2.256e-05 -1.013e-05 0.998 1.7e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002601 Epoch 5817 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01481 1.001 0.9824 -1.454e-06 6.528e-07 -0.01313 -1.096e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003099 -0.002738 -0.01108 0.008255 0.9694 0.9739 0.005844 0.8457 0.8351 0.02261 ] Network output: [ 0.9925 0.05089 0.001807 -4.748e-05 2.132e-05 -0.03782 -3.579e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1851 -0.01318 -0.2146 0.2077 0.9835 0.9933 0.2062 0.4711 0.8816 0.7388 ] Network output: [ -0.01296 1.005 1.009 -7.93e-06 3.56e-06 0.01152 -5.976e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004794 0.001069 0.003733 0.005168 0.989 0.9921 0.00488 0.8791 0.9062 0.01623 ] Network output: [ -0.002705 0.06405 0.9734 -0.0002309 0.0001037 0.967 -0.000174 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1957 0.119 0.2983 0.1743 0.9851 0.994 0.1963 0.476 0.8886 0.7353 ] Network output: [ 0.01252 -0.01044 1.009 0.0001256 -5.639e-05 0.9768 9.467e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09042 0.08324 0.1665 0.2131 0.9875 0.9921 0.09047 0.8157 0.89 0.3068 ] Network output: [ -0.01008 0.02085 1.013 0.0001196 -5.371e-05 0.9868 9.016e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09708 0.09572 0.1747 0.2102 0.9857 0.9916 0.09709 0.7454 0.8708 0.2537 ] Network output: [ -0.001855 1.001 0.004379 1.966e-05 -8.828e-06 0.9989 1.482e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001842 Epoch 5818 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01572 0.9865 0.9831 1.141e-06 -5.124e-07 -0.001049 8.602e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003083 -0.002737 -0.01102 0.008533 0.9695 0.9739 0.005816 0.8454 0.8358 0.0227 ] Network output: [ 1 -0.04361 0.006154 -3.107e-05 1.395e-05 0.03675 -2.342e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1837 -0.01444 -0.2101 0.2238 0.9836 0.9933 0.2047 0.4688 0.8822 0.7401 ] Network output: [ -0.01302 1 1.009 -7.072e-06 3.175e-06 0.01627 -5.33e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004766 0.001077 0.003997 0.005711 0.989 0.9921 0.004851 0.879 0.9066 0.01637 ] Network output: [ 0.005831 -0.06677 0.9805 -0.0002075 9.315e-05 1.074 -0.0001564 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1945 0.1185 0.307 0.1997 0.9851 0.994 0.1951 0.4743 0.8886 0.7344 ] Network output: [ 0.009311 -0.03538 1.015 0.000126 -5.655e-05 1.002 9.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09097 0.08384 0.1727 0.22 0.9875 0.9921 0.09102 0.8175 0.8901 0.3106 ] Network output: [ -0.01234 0.0282 1.014 0.0001171 -5.257e-05 0.9831 8.825e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09761 0.09627 0.1769 0.2115 0.9858 0.9916 0.09762 0.748 0.8708 0.2539 ] Network output: [ 0.002051 0.9994 -0.001499 2.252e-05 -1.011e-05 0.9981 1.697e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00255 Epoch 5819 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01481 1.001 0.9824 -1.292e-06 5.798e-07 -0.01305 -9.733e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0031 -0.00274 -0.01108 0.008257 0.9695 0.9739 0.005846 0.8457 0.8352 0.02261 ] Network output: [ 0.9925 0.05028 0.001806 -4.751e-05 2.133e-05 -0.03737 -3.581e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1851 -0.01326 -0.2145 0.2077 0.9835 0.9933 0.2062 0.471 0.8816 0.7387 ] Network output: [ -0.01296 1.005 1.009 -7.775e-06 3.491e-06 0.01155 -5.86e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004797 0.001067 0.003737 0.005171 0.989 0.9921 0.004883 0.8791 0.9062 0.01623 ] Network output: [ -0.002684 0.06329 0.9737 -0.0002308 0.0001036 0.9674 -0.000174 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1957 0.1189 0.2984 0.1744 0.9851 0.994 0.1963 0.4759 0.8886 0.7352 ] Network output: [ 0.01251 -0.01075 1.009 0.0001256 -5.638e-05 0.9772 9.465e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09053 0.08333 0.1666 0.2132 0.9875 0.9921 0.09059 0.8157 0.89 0.3069 ] Network output: [ -0.01011 0.02108 1.013 0.0001196 -5.37e-05 0.9867 9.015e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09717 0.09581 0.1747 0.2102 0.9857 0.9916 0.09718 0.7455 0.8708 0.2537 ] Network output: [ -0.001828 1.001 0.004317 1.966e-05 -8.825e-06 0.9989 1.481e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001807 Epoch 5820 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01572 0.9866 0.9831 1.269e-06 -5.697e-07 -0.00112 9.564e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003084 -0.002739 -0.01102 0.008531 0.9695 0.9739 0.005818 0.8454 0.8358 0.0227 ] Network output: [ 1 -0.04303 0.006096 -3.133e-05 1.407e-05 0.03627 -2.361e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1837 -0.01451 -0.2101 0.2236 0.9836 0.9933 0.2046 0.4688 0.8822 0.74 ] Network output: [ -0.01302 1 1.01 -6.927e-06 3.11e-06 0.01624 -5.221e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004769 0.001074 0.003998 0.005707 0.989 0.9921 0.004855 0.879 0.9066 0.01637 ] Network output: [ 0.005745 -0.06589 0.9807 -0.0002077 9.327e-05 1.073 -0.0001566 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1944 0.1183 0.307 0.1994 0.9851 0.994 0.195 0.4743 0.8886 0.7343 ] Network output: [ 0.009343 -0.0354 1.015 0.0001259 -5.654e-05 1.002 9.491e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09108 0.08392 0.1727 0.22 0.9875 0.9921 0.09113 0.8175 0.8901 0.3106 ] Network output: [ -0.01233 0.02833 1.014 0.0001171 -5.258e-05 0.9831 8.826e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09769 0.09635 0.1769 0.2115 0.9858 0.9916 0.0977 0.748 0.8709 0.2539 ] Network output: [ 0.002034 0.9994 -0.001488 2.248e-05 -1.009e-05 0.9981 1.694e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0025 Epoch 5821 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01482 1.001 0.9824 -1.131e-06 5.077e-07 -0.01298 -8.523e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003101 -0.002741 -0.01108 0.008258 0.9695 0.9739 0.005848 0.8457 0.8352 0.02261 ] Network output: [ 0.9926 0.04968 0.001805 -4.754e-05 2.134e-05 -0.03692 -3.583e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.185 -0.01334 -0.2145 0.2078 0.9835 0.9933 0.2061 0.471 0.8816 0.7387 ] Network output: [ -0.01297 1.005 1.009 -7.622e-06 3.422e-06 0.01158 -5.744e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0048 0.001065 0.003741 0.005174 0.989 0.9921 0.004886 0.8791 0.9062 0.01623 ] Network output: [ -0.002664 0.06255 0.974 -0.0002308 0.0001036 0.9678 -0.0001739 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1956 0.1187 0.2985 0.1745 0.9851 0.994 0.1962 0.4759 0.8886 0.7351 ] Network output: [ 0.0125 -0.01105 1.009 0.0001256 -5.637e-05 0.9776 9.464e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09064 0.08342 0.1668 0.2133 0.9875 0.9921 0.0907 0.8157 0.8899 0.307 ] Network output: [ -0.01013 0.02132 1.013 0.0001196 -5.37e-05 0.9867 9.014e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09725 0.09589 0.1748 0.2102 0.9857 0.9916 0.09727 0.7456 0.8708 0.2537 ] Network output: [ -0.001802 1.001 0.004256 1.965e-05 -8.822e-06 0.9989 1.481e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001773 Epoch 5822 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01571 0.9866 0.9831 1.395e-06 -6.264e-07 -0.00119 1.052e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003085 -0.00274 -0.01102 0.008529 0.9695 0.9739 0.005821 0.8454 0.8358 0.0227 ] Network output: [ 1 -0.04246 0.006039 -3.159e-05 1.418e-05 0.0358 -2.381e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1836 -0.01458 -0.2101 0.2235 0.9836 0.9933 0.2046 0.4688 0.8822 0.7399 ] Network output: [ -0.01302 1 1.01 -6.785e-06 3.046e-06 0.01621 -5.113e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004773 0.001072 0.003999 0.005704 0.989 0.9921 0.004858 0.879 0.9066 0.01637 ] Network output: [ 0.005659 -0.06502 0.9809 -0.000208 9.338e-05 1.072 -0.0001568 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1944 0.1182 0.307 0.1992 0.9851 0.994 0.195 0.4743 0.8886 0.7343 ] Network output: [ 0.009374 -0.03542 1.015 0.0001259 -5.652e-05 1.003 9.489e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09118 0.084 0.1728 0.22 0.9875 0.9921 0.09124 0.8175 0.8901 0.3107 ] Network output: [ -0.01233 0.02846 1.014 0.0001171 -5.259e-05 0.9831 8.828e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09777 0.09642 0.1769 0.2115 0.9858 0.9916 0.09778 0.748 0.8709 0.2539 ] Network output: [ 0.002018 0.9994 -0.001477 2.245e-05 -1.008e-05 0.9982 1.692e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002452 Epoch 5823 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01483 1.001 0.9825 -9.723e-07 4.365e-07 -0.0129 -7.328e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003102 -0.002743 -0.01108 0.008259 0.9695 0.9739 0.00585 0.8457 0.8352 0.02261 ] Network output: [ 0.9927 0.04908 0.001804 -4.757e-05 2.136e-05 -0.03648 -3.585e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.185 -0.01342 -0.2144 0.2078 0.9835 0.9933 0.2061 0.471 0.8816 0.7386 ] Network output: [ -0.01297 1.005 1.009 -7.471e-06 3.354e-06 0.0116 -5.63e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004803 0.001062 0.003745 0.005176 0.989 0.9921 0.004889 0.8791 0.9062 0.01623 ] Network output: [ -0.002645 0.06182 0.9743 -0.0002307 0.0001036 0.9682 -0.0001739 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1956 0.1186 0.2986 0.1746 0.9851 0.994 0.1962 0.4759 0.8886 0.735 ] Network output: [ 0.01249 -0.01135 1.009 0.0001256 -5.636e-05 0.978 9.462e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09076 0.08351 0.1669 0.2134 0.9875 0.9921 0.09081 0.8157 0.8899 0.3071 ] Network output: [ -0.01016 0.02155 1.013 0.0001196 -5.369e-05 0.9866 9.013e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09734 0.09597 0.1748 0.2102 0.9857 0.9916 0.09735 0.7456 0.8708 0.2537 ] Network output: [ -0.001776 1.001 0.004196 1.964e-05 -8.819e-06 0.9989 1.481e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00174 Epoch 5824 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01571 0.9867 0.9832 1.52e-06 -6.825e-07 -0.001258 1.146e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003087 -0.002742 -0.01102 0.008527 0.9695 0.9739 0.005823 0.8455 0.8358 0.02269 ] Network output: [ 1 -0.0419 0.005983 -3.184e-05 1.429e-05 0.03533 -2.4e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1836 -0.01464 -0.2101 0.2233 0.9836 0.9933 0.2046 0.4688 0.8822 0.7398 ] Network output: [ -0.01303 1 1.01 -6.643e-06 2.982e-06 0.01619 -5.007e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004776 0.001069 0.004 0.0057 0.989 0.9921 0.004861 0.879 0.9066 0.01637 ] Network output: [ 0.005575 -0.06417 0.9811 -0.0002083 9.349e-05 1.071 -0.0001569 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1944 0.1181 0.307 0.199 0.9851 0.994 0.195 0.4743 0.8886 0.7342 ] Network output: [ 0.009404 -0.03543 1.014 0.0001259 -5.651e-05 1.003 9.487e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09128 0.08409 0.1728 0.22 0.9875 0.9921 0.09134 0.8174 0.8901 0.3107 ] Network output: [ -0.01233 0.0286 1.013 0.0001172 -5.26e-05 0.9831 8.829e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09785 0.0965 0.177 0.2115 0.9858 0.9916 0.09786 0.748 0.8709 0.2539 ] Network output: [ 0.002003 0.9994 -0.001467 2.241e-05 -1.006e-05 0.9982 1.689e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002405 Epoch 5825 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01483 1.001 0.9825 -8.157e-07 3.662e-07 -0.01283 -6.147e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003103 -0.002745 -0.01108 0.008261 0.9695 0.9739 0.005852 0.8457 0.8352 0.02261 ] Network output: [ 0.9928 0.0485 0.001804 -4.76e-05 2.137e-05 -0.03605 -3.587e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1849 -0.0135 -0.2144 0.2079 0.9835 0.9933 0.206 0.471 0.8816 0.7385 ] Network output: [ -0.01297 1.005 1.009 -7.321e-06 3.287e-06 0.01163 -5.517e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004806 0.00106 0.003749 0.005179 0.989 0.9921 0.004892 0.8791 0.9062 0.01623 ] Network output: [ -0.002625 0.06111 0.9746 -0.0002307 0.0001036 0.9686 -0.0001738 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1956 0.1184 0.2987 0.1747 0.9851 0.994 0.1961 0.4759 0.8886 0.7349 ] Network output: [ 0.01249 -0.01164 1.009 0.0001255 -5.635e-05 0.9784 9.46e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09086 0.0836 0.167 0.2135 0.9875 0.9921 0.09092 0.8157 0.8899 0.3072 ] Network output: [ -0.01019 0.02178 1.013 0.0001196 -5.368e-05 0.9865 9.012e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09742 0.09605 0.1749 0.2101 0.9857 0.9916 0.09744 0.7457 0.8708 0.2536 ] Network output: [ -0.00175 1.001 0.004136 1.964e-05 -8.816e-06 0.9989 1.48e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001708 Epoch 5826 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0157 0.9867 0.9832 1.644e-06 -7.38e-07 -0.001325 1.239e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003088 -0.002744 -0.01102 0.008525 0.9695 0.9739 0.005826 0.8455 0.8358 0.02269 ] Network output: [ 1 -0.04136 0.005928 -3.209e-05 1.441e-05 0.03487 -2.418e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1836 -0.01471 -0.2101 0.2232 0.9836 0.9933 0.2045 0.4688 0.8822 0.7397 ] Network output: [ -0.01303 1 1.01 -6.503e-06 2.92e-06 0.01616 -4.901e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004779 0.001067 0.004001 0.005696 0.989 0.9921 0.004865 0.879 0.9066 0.01637 ] Network output: [ 0.005493 -0.06333 0.9813 -0.0002085 9.36e-05 1.07 -0.0001571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1943 0.1179 0.307 0.1988 0.9851 0.994 0.1949 0.4743 0.8886 0.7341 ] Network output: [ 0.009433 -0.03545 1.014 0.0001259 -5.65e-05 1.003 9.485e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09139 0.08417 0.1729 0.22 0.9875 0.9921 0.09144 0.8174 0.8901 0.3108 ] Network output: [ -0.01233 0.02873 1.013 0.0001172 -5.261e-05 0.9831 8.831e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09793 0.09657 0.177 0.2114 0.9858 0.9916 0.09794 0.7481 0.8709 0.2538 ] Network output: [ 0.001987 0.9993 -0.001458 2.237e-05 -1.004e-05 0.9983 1.686e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00236 Epoch 5827 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01484 1.001 0.9825 -6.609e-07 2.967e-07 -0.01276 -4.981e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003105 -0.002746 -0.01107 0.008262 0.9695 0.9739 0.005854 0.8458 0.8352 0.02261 ] Network output: [ 0.9929 0.04792 0.001804 -4.763e-05 2.138e-05 -0.03562 -3.589e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1849 -0.01358 -0.2143 0.2079 0.9835 0.9933 0.206 0.471 0.8816 0.7384 ] Network output: [ -0.01297 1.005 1.009 -7.173e-06 3.22e-06 0.01165 -5.406e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004809 0.001057 0.003753 0.005181 0.989 0.9921 0.004895 0.8791 0.9062 0.01624 ] Network output: [ -0.002607 0.06041 0.9749 -0.0002306 0.0001035 0.969 -0.0001738 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1955 0.1183 0.2988 0.1747 0.9851 0.994 0.1961 0.4759 0.8886 0.7348 ] Network output: [ 0.01248 -0.01194 1.009 0.0001255 -5.634e-05 0.9788 9.458e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09097 0.08368 0.1671 0.2135 0.9875 0.9921 0.09103 0.8157 0.8899 0.3073 ] Network output: [ -0.01021 0.02201 1.012 0.0001196 -5.368e-05 0.9865 9.011e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09751 0.09613 0.1749 0.2101 0.9857 0.9916 0.09752 0.7457 0.8709 0.2536 ] Network output: [ -0.001724 1 0.004077 1.963e-05 -8.813e-06 0.999 1.48e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001677 Epoch 5828 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0157 0.9868 0.9832 1.766e-06 -7.929e-07 -0.001389 1.331e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003089 -0.002745 -0.01102 0.008523 0.9695 0.9739 0.005828 0.8455 0.8358 0.02269 ] Network output: [ 1 -0.04082 0.005874 -3.233e-05 1.451e-05 0.03443 -2.437e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1835 -0.01477 -0.2101 0.2231 0.9836 0.9933 0.2045 0.4688 0.8822 0.7396 ] Network output: [ -0.01303 1 1.01 -6.365e-06 2.858e-06 0.01613 -4.797e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004782 0.001064 0.004002 0.005692 0.989 0.9921 0.004868 0.879 0.9066 0.01637 ] Network output: [ 0.005412 -0.06251 0.9815 -0.0002087 9.371e-05 1.069 -0.0001573 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1943 0.1178 0.307 0.1985 0.9851 0.994 0.1949 0.4743 0.8886 0.734 ] Network output: [ 0.009462 -0.03547 1.014 0.0001258 -5.649e-05 1.003 9.483e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09149 0.08425 0.1729 0.22 0.9875 0.9921 0.09154 0.8174 0.8901 0.3109 ] Network output: [ -0.01233 0.02887 1.013 0.0001172 -5.261e-05 0.9831 8.832e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.098 0.09665 0.177 0.2114 0.9858 0.9916 0.09801 0.7481 0.8709 0.2538 ] Network output: [ 0.001973 0.9993 -0.00145 2.234e-05 -1.003e-05 0.9983 1.683e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002316 Epoch 5829 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01485 1 0.9826 -5.081e-07 2.281e-07 -0.01268 -3.829e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003106 -0.002748 -0.01107 0.008263 0.9695 0.9739 0.005856 0.8458 0.8352 0.02261 ] Network output: [ 0.9929 0.04736 0.001804 -4.766e-05 2.14e-05 -0.03521 -3.592e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1848 -0.01366 -0.2143 0.208 0.9836 0.9933 0.2059 0.471 0.8817 0.7384 ] Network output: [ -0.01298 1.005 1.009 -7.027e-06 3.155e-06 0.01168 -5.296e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004812 0.001055 0.003756 0.005184 0.989 0.9921 0.004898 0.8791 0.9062 0.01624 ] Network output: [ -0.002588 0.05972 0.9752 -0.0002306 0.0001035 0.9694 -0.0001738 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1955 0.1182 0.2989 0.1748 0.9851 0.994 0.1961 0.4759 0.8886 0.7348 ] Network output: [ 0.01247 -0.01222 1.009 0.0001255 -5.633e-05 0.9792 9.457e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09108 0.08377 0.1673 0.2136 0.9875 0.9921 0.09113 0.8157 0.8899 0.3074 ] Network output: [ -0.01024 0.02224 1.012 0.0001195 -5.367e-05 0.9864 9.01e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09759 0.09621 0.1749 0.2101 0.9857 0.9916 0.0976 0.7458 0.8709 0.2536 ] Network output: [ -0.001699 1 0.004018 1.962e-05 -8.81e-06 0.999 1.479e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001647 Epoch 5830 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01569 0.9869 0.9832 1.887e-06 -8.472e-07 -0.001451 1.422e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00309 -0.002747 -0.01102 0.008521 0.9695 0.9739 0.00583 0.8455 0.8358 0.02269 ] Network output: [ 1 -0.0403 0.005822 -3.257e-05 1.462e-05 0.03399 -2.455e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1835 -0.01484 -0.2101 0.2229 0.9836 0.9933 0.2045 0.4689 0.8822 0.7395 ] Network output: [ -0.01303 1 1.01 -6.228e-06 2.796e-06 0.01611 -4.694e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004786 0.001062 0.004003 0.005689 0.989 0.9921 0.004871 0.879 0.9066 0.01637 ] Network output: [ 0.005333 -0.0617 0.9817 -0.000209 9.381e-05 1.068 -0.0001575 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1943 0.1177 0.307 0.1983 0.9851 0.994 0.1949 0.4744 0.8886 0.7339 ] Network output: [ 0.009489 -0.03549 1.014 0.0001258 -5.648e-05 1.003 9.481e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09159 0.08432 0.173 0.22 0.9875 0.9921 0.09164 0.8174 0.8901 0.3109 ] Network output: [ -0.01233 0.02901 1.013 0.0001172 -5.262e-05 0.983 8.834e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09808 0.09672 0.177 0.2114 0.9858 0.9916 0.09809 0.7481 0.8709 0.2538 ] Network output: [ 0.001958 0.9993 -0.001442 2.23e-05 -1.001e-05 0.9983 1.681e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002274 Epoch 5831 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01485 1 0.9826 -3.572e-07 1.604e-07 -0.01261 -2.692e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003107 -0.00275 -0.01107 0.008264 0.9695 0.9739 0.005858 0.8458 0.8352 0.02261 ] Network output: [ 0.993 0.0468 0.001804 -4.769e-05 2.141e-05 -0.0348 -3.594e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1848 -0.01374 -0.2142 0.208 0.9836 0.9933 0.2059 0.471 0.8817 0.7383 ] Network output: [ -0.01298 1.005 1.009 -6.882e-06 3.09e-06 0.01171 -5.186e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004815 0.001053 0.00376 0.005186 0.989 0.9921 0.004901 0.8791 0.9062 0.01624 ] Network output: [ -0.002571 0.05905 0.9754 -0.0002305 0.0001035 0.9697 -0.0001737 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1954 0.118 0.299 0.1749 0.9851 0.994 0.196 0.4759 0.8886 0.7347 ] Network output: [ 0.01247 -0.01251 1.008 0.0001255 -5.632e-05 0.9796 9.455e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09118 0.08385 0.1674 0.2137 0.9875 0.9921 0.09124 0.8157 0.8899 0.3075 ] Network output: [ -0.01026 0.02247 1.012 0.0001195 -5.366e-05 0.9863 9.009e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09767 0.09629 0.175 0.2101 0.9857 0.9916 0.09768 0.7458 0.8709 0.2536 ] Network output: [ -0.001674 1 0.00396 1.962e-05 -8.807e-06 0.999 1.478e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001619 Epoch 5832 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01569 0.9869 0.9832 2.007e-06 -9.01e-07 -0.001512 1.513e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003092 -0.002749 -0.01102 0.008519 0.9695 0.9739 0.005833 0.8455 0.8358 0.02269 ] Network output: [ 1 -0.03978 0.005771 -3.281e-05 1.473e-05 0.03356 -2.473e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1835 -0.0149 -0.2101 0.2228 0.9836 0.9933 0.2044 0.4689 0.8822 0.7394 ] Network output: [ -0.01303 1 1.01 -6.093e-06 2.735e-06 0.01609 -4.592e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004789 0.001059 0.004003 0.005685 0.989 0.9921 0.004874 0.879 0.9066 0.01637 ] Network output: [ 0.005255 -0.06091 0.9819 -0.0002092 9.392e-05 1.068 -0.0001577 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1942 0.1175 0.307 0.1981 0.9851 0.994 0.1948 0.4744 0.8886 0.7339 ] Network output: [ 0.009517 -0.03551 1.014 0.0001258 -5.647e-05 1.003 9.479e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09169 0.0844 0.173 0.22 0.9875 0.9921 0.09174 0.8173 0.8901 0.311 ] Network output: [ -0.01233 0.02914 1.013 0.0001172 -5.263e-05 0.983 8.835e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09815 0.09679 0.177 0.2114 0.9858 0.9916 0.09817 0.7481 0.8709 0.2538 ] Network output: [ 0.001944 0.9992 -0.001435 2.226e-05 -9.995e-06 0.9984 1.678e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002233 Epoch 5833 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01486 1 0.9826 -2.083e-07 9.349e-08 -0.01254 -1.569e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003108 -0.002751 -0.01107 0.008266 0.9695 0.9739 0.00586 0.8458 0.8352 0.0226 ] Network output: [ 0.9931 0.04626 0.001804 -4.772e-05 2.143e-05 -0.0344 -3.597e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1847 -0.01382 -0.2142 0.2081 0.9836 0.9933 0.2058 0.4709 0.8817 0.7382 ] Network output: [ -0.01298 1.005 1.009 -6.739e-06 3.025e-06 0.01173 -5.079e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004817 0.00105 0.003764 0.005189 0.989 0.9921 0.004904 0.8791 0.9062 0.01624 ] Network output: [ -0.002553 0.05839 0.9757 -0.0002305 0.0001035 0.9701 -0.0001737 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1954 0.1179 0.2991 0.1749 0.9851 0.994 0.196 0.4759 0.8886 0.7346 ] Network output: [ 0.01246 -0.01279 1.008 0.0001254 -5.631e-05 0.98 9.453e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09129 0.08394 0.1675 0.2138 0.9875 0.9921 0.09134 0.8157 0.8899 0.3076 ] Network output: [ -0.01029 0.0227 1.012 0.0001195 -5.366e-05 0.9863 9.008e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09775 0.09637 0.175 0.2101 0.9857 0.9916 0.09776 0.7459 0.8709 0.2535 ] Network output: [ -0.00165 1 0.003903 1.961e-05 -8.804e-06 0.999 1.478e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001591 Epoch 5834 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01568 0.987 0.9833 2.125e-06 -9.542e-07 -0.00157 1.602e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003093 -0.00275 -0.01102 0.008517 0.9695 0.9739 0.005835 0.8456 0.8358 0.02269 ] Network output: [ 1 -0.03928 0.005721 -3.304e-05 1.483e-05 0.03315 -2.49e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1835 -0.01497 -0.2101 0.2227 0.9836 0.9933 0.2044 0.4689 0.8822 0.7394 ] Network output: [ -0.01303 1 1.01 -5.959e-06 2.675e-06 0.01606 -4.491e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004792 0.001057 0.004004 0.005681 0.989 0.9921 0.004877 0.879 0.9066 0.01637 ] Network output: [ 0.005178 -0.06014 0.9821 -0.0002094 9.402e-05 1.067 -0.0001578 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1942 0.1174 0.307 0.1979 0.9851 0.994 0.1948 0.4744 0.8886 0.7338 ] Network output: [ 0.009543 -0.03554 1.014 0.0001258 -5.646e-05 1.003 9.477e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09178 0.08448 0.1731 0.22 0.9875 0.9921 0.09184 0.8173 0.8901 0.311 ] Network output: [ -0.01233 0.02928 1.013 0.0001173 -5.264e-05 0.983 8.837e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09823 0.09686 0.177 0.2113 0.9858 0.9916 0.09824 0.7482 0.8709 0.2537 ] Network output: [ 0.00193 0.9992 -0.001428 2.223e-05 -9.979e-06 0.9984 1.675e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002193 Epoch 5835 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01486 1 0.9826 -6.123e-08 2.749e-08 -0.01247 -4.614e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003109 -0.002753 -0.01107 0.008267 0.9695 0.9739 0.005862 0.8458 0.8353 0.0226 ] Network output: [ 0.9931 0.04573 0.001804 -4.776e-05 2.144e-05 -0.034 -3.599e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1847 -0.0139 -0.2141 0.2081 0.9836 0.9933 0.2058 0.4709 0.8817 0.7381 ] Network output: [ -0.01298 1.005 1.009 -6.597e-06 2.962e-06 0.01176 -4.972e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00482 0.001048 0.003768 0.005191 0.989 0.9921 0.004906 0.8792 0.9062 0.01624 ] Network output: [ -0.002536 0.05775 0.976 -0.0002304 0.0001034 0.9704 -0.0001736 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1953 0.1177 0.2992 0.175 0.9851 0.994 0.1959 0.4759 0.8886 0.7345 ] Network output: [ 0.01245 -0.01306 1.008 0.0001254 -5.63e-05 0.9804 9.451e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09139 0.08402 0.1676 0.2138 0.9875 0.9921 0.09145 0.8156 0.8899 0.3077 ] Network output: [ -0.01031 0.02292 1.012 0.0001195 -5.365e-05 0.9862 9.007e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09783 0.09644 0.175 0.2101 0.9857 0.9916 0.09784 0.7459 0.8709 0.2535 ] Network output: [ -0.001625 1 0.003847 1.96e-05 -8.8e-06 0.999 1.477e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001564 Epoch 5836 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01568 0.987 0.9833 2.243e-06 -1.007e-06 -0.001627 1.69e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003094 -0.002752 -0.01102 0.008515 0.9695 0.9739 0.005837 0.8456 0.8358 0.02268 ] Network output: [ 1 -0.03879 0.005672 -3.327e-05 1.494e-05 0.03274 -2.507e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1834 -0.01503 -0.2101 0.2225 0.9836 0.9933 0.2044 0.4689 0.8822 0.7393 ] Network output: [ -0.01303 1 1.01 -5.826e-06 2.616e-06 0.01604 -4.391e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004795 0.001054 0.004005 0.005678 0.989 0.9921 0.004881 0.879 0.9066 0.01637 ] Network output: [ 0.005104 -0.05939 0.9823 -0.0002096 9.411e-05 1.066 -0.000158 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1942 0.1173 0.307 0.1977 0.9851 0.994 0.1948 0.4744 0.8886 0.7337 ] Network output: [ 0.009569 -0.03556 1.014 0.0001257 -5.644e-05 1.003 9.475e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09188 0.08455 0.1731 0.22 0.9875 0.9921 0.09194 0.8173 0.89 0.3111 ] Network output: [ -0.01233 0.02942 1.013 0.0001173 -5.265e-05 0.983 8.838e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0983 0.09693 0.1771 0.2113 0.9858 0.9916 0.09831 0.7482 0.8709 0.2537 ] Network output: [ 0.001917 0.9992 -0.001422 2.219e-05 -9.963e-06 0.9985 1.673e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002154 Epoch 5837 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01487 1 0.9827 8.388e-08 -3.766e-08 -0.0124 6.321e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00311 -0.002755 -0.01107 0.008268 0.9695 0.9739 0.005864 0.8458 0.8353 0.0226 ] Network output: [ 0.9932 0.04521 0.001805 -4.779e-05 2.146e-05 -0.03362 -3.602e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1847 -0.01398 -0.2141 0.2082 0.9836 0.9933 0.2057 0.4709 0.8817 0.7381 ] Network output: [ -0.01298 1.005 1.009 -6.457e-06 2.899e-06 0.01179 -4.866e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004823 0.001045 0.003771 0.005193 0.989 0.9921 0.004909 0.8792 0.9062 0.01624 ] Network output: [ -0.00252 0.05712 0.9763 -0.0002304 0.0001034 0.9707 -0.0001736 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1953 0.1176 0.2993 0.1751 0.9851 0.994 0.1959 0.4759 0.8886 0.7344 ] Network output: [ 0.01244 -0.01333 1.008 0.0001254 -5.629e-05 0.9808 9.449e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09149 0.0841 0.1677 0.2139 0.9875 0.9921 0.09155 0.8156 0.8899 0.3078 ] Network output: [ -0.01033 0.02315 1.012 0.0001195 -5.365e-05 0.9861 9.006e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09791 0.09652 0.1751 0.2101 0.9857 0.9916 0.09792 0.746 0.8709 0.2535 ] Network output: [ -0.001601 1 0.003791 1.959e-05 -8.797e-06 0.9991 1.477e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001538 Epoch 5838 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01568 0.987 0.9833 2.359e-06 -1.059e-06 -0.001682 1.778e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003095 -0.002754 -0.01102 0.008513 0.9695 0.9739 0.005839 0.8456 0.8358 0.02268 ] Network output: [ 1 -0.03832 0.005624 -3.35e-05 1.504e-05 0.03234 -2.524e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1834 -0.0151 -0.2101 0.2224 0.9836 0.9933 0.2043 0.4689 0.8822 0.7392 ] Network output: [ -0.01303 1 1.01 -5.695e-06 2.557e-06 0.01602 -4.292e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004798 0.001052 0.004006 0.005674 0.989 0.9921 0.004884 0.879 0.9066 0.01637 ] Network output: [ 0.00503 -0.05865 0.9825 -0.0002098 9.421e-05 1.065 -0.0001581 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1941 0.1171 0.307 0.1975 0.9851 0.994 0.1947 0.4744 0.8886 0.7336 ] Network output: [ 0.009594 -0.03559 1.013 0.0001257 -5.643e-05 1.004 9.473e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09198 0.08463 0.1732 0.22 0.9875 0.9921 0.09203 0.8172 0.89 0.3111 ] Network output: [ -0.01233 0.02956 1.013 0.0001173 -5.265e-05 0.983 8.839e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09837 0.09699 0.1771 0.2113 0.9858 0.9916 0.09838 0.7482 0.871 0.2537 ] Network output: [ 0.001904 0.9992 -0.001417 2.216e-05 -9.947e-06 0.9985 1.67e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002117 Epoch 5839 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01487 0.9999 0.9827 2.271e-07 -1.019e-07 -0.01233 1.711e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003111 -0.002756 -0.01107 0.008269 0.9695 0.9739 0.005866 0.8459 0.8353 0.0226 ] Network output: [ 0.9933 0.0447 0.001805 -4.783e-05 2.147e-05 -0.03324 -3.604e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1846 -0.01405 -0.214 0.2082 0.9836 0.9933 0.2057 0.4709 0.8817 0.738 ] Network output: [ -0.01298 1.005 1.009 -6.319e-06 2.837e-06 0.01181 -4.762e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004826 0.001043 0.003775 0.005195 0.989 0.9921 0.004912 0.8792 0.9062 0.01624 ] Network output: [ -0.002504 0.0565 0.9766 -0.0002303 0.0001034 0.971 -0.0001736 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1952 0.1175 0.2994 0.1751 0.9851 0.994 0.1958 0.4759 0.8886 0.7343 ] Network output: [ 0.01244 -0.0136 1.008 0.0001254 -5.628e-05 0.9811 9.447e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09159 0.08418 0.1678 0.214 0.9875 0.9921 0.09165 0.8156 0.8899 0.3079 ] Network output: [ -0.01036 0.02337 1.012 0.0001195 -5.364e-05 0.9861 9.005e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09798 0.09659 0.1751 0.2101 0.9857 0.9916 0.09799 0.746 0.8709 0.2535 ] Network output: [ -0.001578 1 0.003736 1.959e-05 -8.793e-06 0.9991 1.476e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001514 Epoch 5840 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01567 0.9871 0.9833 2.473e-06 -1.11e-06 -0.001735 1.864e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003096 -0.002755 -0.01102 0.008511 0.9695 0.9739 0.005842 0.8456 0.8358 0.02268 ] Network output: [ 1 -0.03785 0.005577 -3.372e-05 1.514e-05 0.03195 -2.541e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1834 -0.01516 -0.2101 0.2223 0.9836 0.9933 0.2043 0.4689 0.8822 0.7391 ] Network output: [ -0.01303 1 1.01 -5.565e-06 2.498e-06 0.016 -4.194e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004801 0.001049 0.004007 0.005671 0.989 0.9921 0.004887 0.879 0.9066 0.01637 ] Network output: [ 0.004959 -0.05793 0.9827 -0.0002101 9.43e-05 1.064 -0.0001583 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1941 0.117 0.307 0.1973 0.9851 0.994 0.1947 0.4744 0.8886 0.7336 ] Network output: [ 0.009618 -0.03561 1.013 0.0001257 -5.642e-05 1.004 9.471e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09207 0.0847 0.1733 0.22 0.9875 0.9921 0.09213 0.8172 0.89 0.3112 ] Network output: [ -0.01233 0.0297 1.012 0.0001173 -5.266e-05 0.9829 8.84e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09844 0.09706 0.1771 0.2113 0.9858 0.9916 0.09845 0.7482 0.871 0.2537 ] Network output: [ 0.001891 0.9992 -0.001412 2.212e-05 -9.931e-06 0.9986 1.667e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002082 Epoch 5841 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01488 0.9998 0.9827 3.683e-07 -1.654e-07 -0.01226 2.776e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003112 -0.002758 -0.01107 0.008269 0.9695 0.9739 0.005868 0.8459 0.8353 0.0226 ] Network output: [ 0.9933 0.04421 0.001806 -4.787e-05 2.149e-05 -0.03288 -3.607e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1846 -0.01413 -0.214 0.2083 0.9836 0.9933 0.2056 0.4709 0.8817 0.7379 ] Network output: [ -0.01298 1.005 1.009 -6.182e-06 2.775e-06 0.01184 -4.659e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004828 0.00104 0.003778 0.005197 0.989 0.9921 0.004915 0.8792 0.9063 0.01625 ] Network output: [ -0.002489 0.0559 0.9768 -0.0002303 0.0001034 0.9713 -0.0001735 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1952 0.1173 0.2995 0.1752 0.9851 0.994 0.1958 0.4759 0.8886 0.7342 ] Network output: [ 0.01243 -0.01386 1.008 0.0001253 -5.626e-05 0.9815 9.445e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09169 0.08426 0.1679 0.214 0.9875 0.9921 0.09175 0.8156 0.8899 0.308 ] Network output: [ -0.01038 0.02359 1.012 0.0001195 -5.363e-05 0.986 9.004e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09806 0.09666 0.1752 0.2101 0.9857 0.9916 0.09807 0.7461 0.8709 0.2535 ] Network output: [ -0.001555 1 0.003682 1.958e-05 -8.789e-06 0.9991 1.475e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00149 Epoch 5842 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01567 0.9871 0.9833 2.587e-06 -1.161e-06 -0.001786 1.949e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003097 -0.002757 -0.01102 0.008509 0.9695 0.9739 0.005844 0.8456 0.8359 0.02268 ] Network output: [ 1 -0.0374 0.005532 -3.394e-05 1.524e-05 0.03157 -2.558e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1833 -0.01523 -0.2101 0.2221 0.9836 0.9933 0.2043 0.469 0.8822 0.739 ] Network output: [ -0.01304 1 1.01 -5.437e-06 2.441e-06 0.01598 -4.097e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004804 0.001047 0.004008 0.005667 0.989 0.9921 0.00489 0.879 0.9066 0.01637 ] Network output: [ 0.004888 -0.05722 0.9829 -0.0002103 9.439e-05 1.064 -0.0001585 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1941 0.1169 0.307 0.1971 0.9851 0.994 0.1947 0.4745 0.8886 0.7335 ] Network output: [ 0.009642 -0.03564 1.013 0.0001256 -5.641e-05 1.004 9.469e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09216 0.08477 0.1733 0.22 0.9875 0.9921 0.09222 0.8172 0.89 0.3112 ] Network output: [ -0.01233 0.02985 1.012 0.0001173 -5.267e-05 0.9829 8.842e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09851 0.09713 0.1771 0.2112 0.9858 0.9916 0.09852 0.7483 0.871 0.2536 ] Network output: [ 0.001879 0.9991 -0.001408 2.209e-05 -9.916e-06 0.9986 1.665e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002047 Epoch 5843 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01488 0.9997 0.9827 5.077e-07 -2.279e-07 -0.0122 3.826e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003113 -0.002759 -0.01107 0.00827 0.9695 0.9739 0.00587 0.8459 0.8353 0.0226 ] Network output: [ 0.9934 0.04372 0.001806 -4.79e-05 2.151e-05 -0.03252 -3.61e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1845 -0.01421 -0.214 0.2083 0.9836 0.9933 0.2056 0.4709 0.8817 0.7378 ] Network output: [ -0.01299 1.005 1.009 -6.047e-06 2.715e-06 0.01186 -4.557e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004831 0.001038 0.003782 0.005199 0.989 0.9921 0.004917 0.8792 0.9063 0.01625 ] Network output: [ -0.002474 0.05532 0.9771 -0.0002302 0.0001033 0.9716 -0.0001735 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1951 0.1172 0.2996 0.1752 0.9851 0.994 0.1957 0.4759 0.8886 0.7341 ] Network output: [ 0.01242 -0.01412 1.008 0.0001253 -5.625e-05 0.9819 9.443e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09179 0.08433 0.1681 0.2141 0.9875 0.9921 0.09185 0.8156 0.8899 0.3081 ] Network output: [ -0.0104 0.02381 1.012 0.0001195 -5.363e-05 0.9859 9.003e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09813 0.09673 0.1752 0.2101 0.9857 0.9916 0.09814 0.7461 0.8709 0.2534 ] Network output: [ -0.001532 1 0.003629 1.957e-05 -8.785e-06 0.9991 1.475e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001467 Epoch 5844 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01566 0.9872 0.9833 2.699e-06 -1.212e-06 -0.001835 2.034e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003099 -0.002759 -0.01102 0.008508 0.9695 0.9739 0.005846 0.8457 0.8359 0.02268 ] Network output: [ 1 -0.03695 0.005487 -3.415e-05 1.533e-05 0.0312 -2.574e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1833 -0.01529 -0.2101 0.222 0.9836 0.9933 0.2042 0.469 0.8823 0.7389 ] Network output: [ -0.01304 1 1.01 -5.31e-06 2.384e-06 0.01596 -4.002e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004807 0.001044 0.004009 0.005664 0.989 0.9921 0.004893 0.879 0.9066 0.01637 ] Network output: [ 0.00482 -0.05653 0.9831 -0.0002105 9.448e-05 1.063 -0.0001586 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.194 0.1167 0.307 0.1969 0.9851 0.994 0.1946 0.4745 0.8886 0.7334 ] Network output: [ 0.009665 -0.03567 1.013 0.0001256 -5.64e-05 1.004 9.467e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09226 0.08484 0.1734 0.22 0.9875 0.9921 0.09231 0.8172 0.89 0.3113 ] Network output: [ -0.01232 0.02999 1.012 0.0001173 -5.268e-05 0.9829 8.843e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09858 0.09719 0.1771 0.2112 0.9858 0.9916 0.09859 0.7483 0.871 0.2536 ] Network output: [ 0.001867 0.9991 -0.001405 2.205e-05 -9.9e-06 0.9987 1.662e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.002014 Epoch 5845 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01489 0.9996 0.9828 6.452e-07 -2.896e-07 -0.01213 4.862e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003114 -0.002761 -0.01107 0.008271 0.9695 0.9739 0.005872 0.8459 0.8353 0.0226 ] Network output: [ 0.9935 0.04325 0.001807 -4.794e-05 2.152e-05 -0.03217 -3.613e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1845 -0.01428 -0.2139 0.2083 0.9836 0.9933 0.2055 0.4709 0.8817 0.7378 ] Network output: [ -0.01299 1.005 1.009 -5.913e-06 2.655e-06 0.01189 -4.456e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004834 0.001036 0.003785 0.0052 0.989 0.9921 0.00492 0.8792 0.9063 0.01625 ] Network output: [ -0.00246 0.05475 0.9774 -0.0002302 0.0001033 0.9719 -0.0001735 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1951 0.1171 0.2997 0.1753 0.9851 0.994 0.1957 0.4759 0.8886 0.7341 ] Network output: [ 0.01242 -0.01438 1.008 0.0001253 -5.624e-05 0.9822 9.441e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09189 0.08441 0.1682 0.2142 0.9875 0.9921 0.09194 0.8156 0.8899 0.3081 ] Network output: [ -0.01042 0.02403 1.011 0.0001194 -5.362e-05 0.9859 9.002e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0982 0.0968 0.1752 0.21 0.9857 0.9916 0.09822 0.7462 0.871 0.2534 ] Network output: [ -0.00151 1 0.003577 1.956e-05 -8.781e-06 0.9991 1.474e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001445 Epoch 5846 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01566 0.9872 0.9834 2.81e-06 -1.261e-06 -0.001883 2.118e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0031 -0.00276 -0.01102 0.008506 0.9695 0.9739 0.005848 0.8457 0.8359 0.02267 ] Network output: [ 1 -0.03652 0.005444 -3.436e-05 1.543e-05 0.03084 -2.59e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1833 -0.01536 -0.2101 0.2219 0.9836 0.9933 0.2042 0.469 0.8823 0.7388 ] Network output: [ -0.01304 1 1.01 -5.184e-06 2.327e-06 0.01594 -3.907e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00481 0.001042 0.00401 0.00566 0.989 0.9921 0.004896 0.879 0.9066 0.01637 ] Network output: [ 0.004753 -0.05586 0.9833 -0.0002106 9.456e-05 1.062 -0.0001587 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.194 0.1166 0.3071 0.1967 0.9851 0.994 0.1946 0.4745 0.8886 0.7333 ] Network output: [ 0.009687 -0.0357 1.013 0.0001256 -5.638e-05 1.004 9.465e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09235 0.08491 0.1734 0.22 0.9875 0.9921 0.0924 0.8171 0.89 0.3113 ] Network output: [ -0.01232 0.03013 1.012 0.0001173 -5.268e-05 0.9828 8.844e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09865 0.09726 0.1771 0.2112 0.9858 0.9916 0.09866 0.7483 0.871 0.2536 ] Network output: [ 0.001855 0.9991 -0.001402 2.202e-05 -9.884e-06 0.9987 1.659e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001982 Epoch 5847 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01489 0.9995 0.9828 7.807e-07 -3.505e-07 -0.01207 5.884e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003115 -0.002762 -0.01107 0.008272 0.9695 0.9739 0.005874 0.8459 0.8353 0.0226 ] Network output: [ 0.9935 0.04279 0.001807 -4.798e-05 2.154e-05 -0.03183 -3.616e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1844 -0.01436 -0.2139 0.2084 0.9836 0.9933 0.2055 0.4709 0.8817 0.7377 ] Network output: [ -0.01299 1.005 1.009 -5.781e-06 2.595e-06 0.01191 -4.357e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004836 0.001033 0.003788 0.005202 0.989 0.9921 0.004923 0.8792 0.9063 0.01625 ] Network output: [ -0.002446 0.05419 0.9776 -0.0002301 0.0001033 0.9721 -0.0001734 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.195 0.1169 0.2998 0.1753 0.9851 0.994 0.1956 0.4759 0.8887 0.734 ] Network output: [ 0.01241 -0.01463 1.008 0.0001252 -5.623e-05 0.9826 9.439e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09198 0.08449 0.1683 0.2142 0.9875 0.9921 0.09204 0.8156 0.8899 0.3082 ] Network output: [ -0.01044 0.02424 1.011 0.0001194 -5.362e-05 0.9858 9.001e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09828 0.09687 0.1753 0.21 0.9857 0.9916 0.09829 0.7462 0.871 0.2534 ] Network output: [ -0.001488 1 0.003525 1.955e-05 -8.776e-06 0.9992 1.473e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001424 Epoch 5848 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01566 0.9872 0.9834 2.919e-06 -1.311e-06 -0.001928 2.2e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003101 -0.002762 -0.01102 0.008504 0.9695 0.9739 0.00585 0.8457 0.8359 0.02267 ] Network output: [ 1 -0.0361 0.005402 -3.457e-05 1.552e-05 0.03049 -2.606e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1832 -0.01542 -0.2101 0.2218 0.9836 0.9933 0.2042 0.469 0.8823 0.7387 ] Network output: [ -0.01304 1 1.01 -5.06e-06 2.271e-06 0.01593 -3.813e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004812 0.001039 0.004011 0.005657 0.989 0.9921 0.004899 0.879 0.9066 0.01637 ] Network output: [ 0.004687 -0.05521 0.9835 -0.0002108 9.465e-05 1.061 -0.0001589 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.194 0.1165 0.3071 0.1965 0.9851 0.994 0.1946 0.4745 0.8886 0.7332 ] Network output: [ 0.009708 -0.03573 1.013 0.0001256 -5.637e-05 1.004 9.463e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09244 0.08498 0.1735 0.22 0.9875 0.9921 0.09249 0.8171 0.89 0.3114 ] Network output: [ -0.01232 0.03028 1.012 0.0001174 -5.269e-05 0.9828 8.845e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09871 0.09732 0.1771 0.2112 0.9858 0.9916 0.09872 0.7483 0.871 0.2535 ] Network output: [ 0.001844 0.9991 -0.001399 2.198e-05 -9.869e-06 0.9987 1.657e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001951 Epoch 5849 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0149 0.9994 0.9828 9.144e-07 -4.105e-07 -0.012 6.891e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003115 -0.002764 -0.01107 0.008272 0.9695 0.9739 0.005875 0.8459 0.8353 0.0226 ] Network output: [ 0.9936 0.04234 0.001808 -4.802e-05 2.156e-05 -0.0315 -3.619e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1844 -0.01444 -0.2138 0.2084 0.9836 0.9933 0.2054 0.4709 0.8817 0.7376 ] Network output: [ -0.01299 1.005 1.009 -5.65e-06 2.537e-06 0.01194 -4.258e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004839 0.001031 0.003792 0.005204 0.989 0.9921 0.004925 0.8792 0.9063 0.01625 ] Network output: [ -0.002433 0.05365 0.9779 -0.0002301 0.0001033 0.9724 -0.0001734 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.195 0.1168 0.2999 0.1753 0.9851 0.994 0.1956 0.4759 0.8887 0.7339 ] Network output: [ 0.0124 -0.01487 1.008 0.0001252 -5.622e-05 0.9829 9.437e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09208 0.08456 0.1684 0.2143 0.9875 0.9921 0.09213 0.8156 0.8899 0.3083 ] Network output: [ -0.01046 0.02446 1.011 0.0001194 -5.361e-05 0.9857 9e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09835 0.09693 0.1753 0.21 0.9858 0.9916 0.09836 0.7462 0.871 0.2533 ] Network output: [ -0.001467 1 0.003474 1.954e-05 -8.772e-06 0.9992 1.473e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001403 Epoch 5850 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01565 0.9873 0.9834 3.028e-06 -1.359e-06 -0.001972 2.282e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003102 -0.002763 -0.01102 0.008502 0.9695 0.9739 0.005852 0.8457 0.8359 0.02267 ] Network output: [ 1 -0.0357 0.00536 -3.478e-05 1.561e-05 0.03015 -2.621e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1832 -0.01548 -0.2101 0.2217 0.9836 0.9933 0.2041 0.469 0.8823 0.7387 ] Network output: [ -0.01304 1 1.01 -4.937e-06 2.216e-06 0.01591 -3.72e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004815 0.001037 0.004012 0.005654 0.989 0.9921 0.004901 0.8791 0.9066 0.01637 ] Network output: [ 0.004623 -0.05457 0.9837 -0.000211 9.473e-05 1.061 -0.000159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1939 0.1164 0.3071 0.1963 0.9851 0.994 0.1945 0.4745 0.8886 0.7332 ] Network output: [ 0.009729 -0.03577 1.013 0.0001255 -5.636e-05 1.004 9.461e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09253 0.08505 0.1735 0.22 0.9875 0.9921 0.09258 0.8171 0.89 0.3114 ] Network output: [ -0.01232 0.03042 1.012 0.0001174 -5.269e-05 0.9828 8.846e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09878 0.09738 0.1771 0.2111 0.9858 0.9916 0.09879 0.7483 0.871 0.2535 ] Network output: [ 0.001833 0.999 -0.001398 2.195e-05 -9.853e-06 0.9988 1.654e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001922 Epoch 5851 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0149 0.9993 0.9828 1.046e-06 -4.697e-07 -0.01194 7.885e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003116 -0.002765 -0.01107 0.008273 0.9695 0.9739 0.005877 0.846 0.8354 0.0226 ] Network output: [ 0.9936 0.0419 0.001808 -4.807e-05 2.158e-05 -0.03117 -3.622e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1843 -0.01451 -0.2138 0.2084 0.9836 0.9933 0.2054 0.4709 0.8818 0.7375 ] Network output: [ -0.01299 1.004 1.01 -5.521e-06 2.479e-06 0.01196 -4.161e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004841 0.001028 0.003795 0.005205 0.989 0.9921 0.004928 0.8792 0.9063 0.01625 ] Network output: [ -0.002421 0.05312 0.9781 -0.00023 0.0001033 0.9726 -0.0001733 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1949 0.1167 0.3 0.1754 0.9851 0.994 0.1955 0.4759 0.8887 0.7338 ] Network output: [ 0.01239 -0.01512 1.008 0.0001252 -5.62e-05 0.9833 9.435e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09217 0.08463 0.1685 0.2144 0.9875 0.9921 0.09222 0.8156 0.8898 0.3084 ] Network output: [ -0.01048 0.02467 1.011 0.0001194 -5.361e-05 0.9857 8.999e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09842 0.097 0.1753 0.21 0.9858 0.9916 0.09843 0.7463 0.871 0.2533 ] Network output: [ -0.001446 1 0.003425 1.953e-05 -8.767e-06 0.9992 1.472e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001384 Epoch 5852 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01565 0.9873 0.9834 3.135e-06 -1.407e-06 -0.002014 2.363e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003103 -0.002765 -0.01102 0.0085 0.9695 0.9739 0.005854 0.8457 0.8359 0.02267 ] Network output: [ 1 -0.0353 0.00532 -3.498e-05 1.57e-05 0.02981 -2.636e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1832 -0.01555 -0.2101 0.2216 0.9836 0.9933 0.2041 0.4691 0.8823 0.7386 ] Network output: [ -0.01304 1 1.01 -4.815e-06 2.162e-06 0.01589 -3.629e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004818 0.001034 0.004013 0.00565 0.989 0.9921 0.004904 0.8791 0.9066 0.01637 ] Network output: [ 0.004561 -0.05395 0.9839 -0.0002112 9.48e-05 1.06 -0.0001591 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1939 0.1162 0.3071 0.1961 0.9851 0.994 0.1945 0.4746 0.8887 0.7331 ] Network output: [ 0.009749 -0.0358 1.012 0.0001255 -5.634e-05 1.004 9.459e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09261 0.08512 0.1736 0.22 0.9875 0.9921 0.09267 0.8171 0.89 0.3115 ] Network output: [ -0.01232 0.03057 1.012 0.0001174 -5.27e-05 0.9828 8.847e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09884 0.09744 0.1771 0.2111 0.9858 0.9916 0.09885 0.7484 0.871 0.2535 ] Network output: [ 0.001822 0.999 -0.001396 2.191e-05 -9.838e-06 0.9988 1.652e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001893 Epoch 5853 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01491 0.9992 0.9829 1.176e-06 -5.28e-07 -0.01188 8.864e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003117 -0.002767 -0.01107 0.008274 0.9695 0.9739 0.005879 0.846 0.8354 0.0226 ] Network output: [ 0.9937 0.04147 0.001809 -4.811e-05 2.16e-05 -0.03086 -3.626e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1843 -0.01458 -0.2137 0.2085 0.9836 0.9933 0.2053 0.4709 0.8818 0.7375 ] Network output: [ -0.01299 1.004 1.01 -5.393e-06 2.421e-06 0.01199 -4.065e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004844 0.001026 0.003798 0.005206 0.989 0.9921 0.00493 0.8792 0.9063 0.01625 ] Network output: [ -0.002408 0.05261 0.9784 -0.00023 0.0001032 0.9729 -0.0001733 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1949 0.1165 0.3001 0.1754 0.9851 0.994 0.1955 0.4759 0.8887 0.7337 ] Network output: [ 0.01239 -0.01536 1.007 0.0001252 -5.619e-05 0.9836 9.433e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09226 0.0847 0.1686 0.2144 0.9875 0.9921 0.09232 0.8156 0.8898 0.3085 ] Network output: [ -0.0105 0.02489 1.011 0.0001194 -5.36e-05 0.9856 8.998e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09848 0.09706 0.1753 0.21 0.9858 0.9916 0.0985 0.7463 0.871 0.2533 ] Network output: [ -0.001425 1 0.003376 1.952e-05 -8.762e-06 0.9992 1.471e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001365 Epoch 5854 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01565 0.9873 0.9834 3.241e-06 -1.455e-06 -0.002054 2.443e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003104 -0.002766 -0.01102 0.008498 0.9695 0.9739 0.005856 0.8458 0.8359 0.02267 ] Network output: [ 1 -0.03491 0.005281 -3.518e-05 1.579e-05 0.02949 -2.651e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1832 -0.01561 -0.2101 0.2214 0.9836 0.9933 0.2041 0.4691 0.8823 0.7385 ] Network output: [ -0.01304 1 1.01 -4.695e-06 2.108e-06 0.01588 -3.538e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004821 0.001032 0.004014 0.005647 0.989 0.9921 0.004907 0.8791 0.9066 0.01637 ] Network output: [ 0.0045 -0.05334 0.9841 -0.0002113 9.488e-05 1.059 -0.0001593 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1939 0.1161 0.3071 0.1959 0.9851 0.994 0.1945 0.4746 0.8887 0.733 ] Network output: [ 0.009769 -0.03584 1.012 0.0001255 -5.633e-05 1.005 9.456e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0927 0.08518 0.1736 0.22 0.9875 0.9921 0.09276 0.817 0.89 0.3115 ] Network output: [ -0.01232 0.03071 1.012 0.0001174 -5.271e-05 0.9827 8.848e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09891 0.0975 0.1772 0.2111 0.9858 0.9916 0.09892 0.7484 0.871 0.2535 ] Network output: [ 0.001812 0.999 -0.001395 2.188e-05 -9.823e-06 0.9988 1.649e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001866 Epoch 5855 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01491 0.9991 0.9829 1.304e-06 -5.855e-07 -0.01182 9.83e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003118 -0.002768 -0.01106 0.008274 0.9695 0.9739 0.005881 0.846 0.8354 0.02259 ] Network output: [ 0.9937 0.04105 0.001809 -4.815e-05 2.162e-05 -0.03055 -3.629e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1843 -0.01466 -0.2137 0.2085 0.9836 0.9933 0.2053 0.4709 0.8818 0.7374 ] Network output: [ -0.01299 1.004 1.01 -5.267e-06 2.365e-06 0.01201 -3.969e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004846 0.001024 0.003801 0.005208 0.989 0.9921 0.004933 0.8792 0.9063 0.01625 ] Network output: [ -0.002397 0.05211 0.9787 -0.0002299 0.0001032 0.9731 -0.0001733 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1948 0.1164 0.3002 0.1754 0.9851 0.994 0.1954 0.4759 0.8887 0.7336 ] Network output: [ 0.01238 -0.01559 1.007 0.0001251 -5.618e-05 0.984 9.431e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09235 0.08477 0.1687 0.2145 0.9875 0.9921 0.09241 0.8156 0.8898 0.3085 ] Network output: [ -0.01052 0.0251 1.011 0.0001194 -5.359e-05 0.9855 8.997e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09855 0.09713 0.1754 0.21 0.9858 0.9916 0.09856 0.7464 0.871 0.2533 ] Network output: [ -0.001405 1 0.003328 1.951e-05 -8.757e-06 0.9992 1.47e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001347 Epoch 5856 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01564 0.9874 0.9834 3.346e-06 -1.502e-06 -0.002093 2.521e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003105 -0.002768 -0.01102 0.008496 0.9695 0.9739 0.005858 0.8458 0.8359 0.02266 ] Network output: [ 1 -0.03454 0.005242 -3.538e-05 1.588e-05 0.02917 -2.666e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1831 -0.01567 -0.2101 0.2213 0.9836 0.9933 0.204 0.4691 0.8823 0.7384 ] Network output: [ -0.01304 1 1.01 -4.576e-06 2.054e-06 0.01587 -3.448e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004824 0.001029 0.004015 0.005644 0.989 0.9921 0.00491 0.8791 0.9066 0.01637 ] Network output: [ 0.00444 -0.05275 0.9843 -0.0002115 9.495e-05 1.059 -0.0001594 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1938 0.116 0.3071 0.1957 0.9851 0.994 0.1944 0.4746 0.8887 0.7329 ] Network output: [ 0.009788 -0.03588 1.012 0.0001254 -5.632e-05 1.005 9.454e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09279 0.08525 0.1737 0.22 0.9875 0.9921 0.09284 0.817 0.89 0.3115 ] Network output: [ -0.01232 0.03086 1.012 0.0001174 -5.271e-05 0.9827 8.849e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09897 0.09756 0.1772 0.2111 0.9858 0.9916 0.09898 0.7484 0.8711 0.2534 ] Network output: [ 0.001802 0.999 -0.001395 2.185e-05 -9.807e-06 0.9989 1.646e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001839 Epoch 5857 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01492 0.999 0.9829 1.431e-06 -6.422e-07 -0.01176 1.078e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003119 -0.00277 -0.01106 0.008275 0.9695 0.9739 0.005882 0.846 0.8354 0.02259 ] Network output: [ 0.9938 0.04064 0.00181 -4.82e-05 2.164e-05 -0.03025 -3.633e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1842 -0.01473 -0.2137 0.2085 0.9836 0.9933 0.2052 0.4709 0.8818 0.7373 ] Network output: [ -0.01299 1.004 1.01 -5.142e-06 2.309e-06 0.01204 -3.876e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004849 0.001021 0.003804 0.005209 0.989 0.9921 0.004935 0.8792 0.9063 0.01625 ] Network output: [ -0.002386 0.05162 0.9789 -0.0002299 0.0001032 0.9733 -0.0001732 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1948 0.1163 0.3003 0.1755 0.9851 0.994 0.1954 0.4759 0.8887 0.7335 ] Network output: [ 0.01237 -0.01582 1.007 0.0001251 -5.617e-05 0.9843 9.429e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09244 0.08484 0.1688 0.2145 0.9875 0.9921 0.0925 0.8156 0.8898 0.3086 ] Network output: [ -0.01054 0.02531 1.011 0.0001194 -5.359e-05 0.9855 8.996e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09862 0.09719 0.1754 0.2099 0.9858 0.9916 0.09863 0.7464 0.871 0.2532 ] Network output: [ -0.001386 1 0.00328 1.949e-05 -8.752e-06 0.9993 1.469e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00133 Epoch 5858 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01564 0.9874 0.9835 3.449e-06 -1.548e-06 -0.002129 2.599e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003106 -0.002769 -0.01102 0.008494 0.9695 0.9739 0.00586 0.8458 0.8359 0.02266 ] Network output: [ 1 -0.03417 0.005205 -3.557e-05 1.597e-05 0.02886 -2.681e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1831 -0.01574 -0.2101 0.2212 0.9836 0.9933 0.204 0.4691 0.8823 0.7383 ] Network output: [ -0.01304 1 1.01 -4.458e-06 2.001e-06 0.01585 -3.36e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004826 0.001027 0.004016 0.005641 0.989 0.9921 0.004913 0.8791 0.9066 0.01637 ] Network output: [ 0.004382 -0.05218 0.9844 -0.0002117 9.502e-05 1.058 -0.0001595 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1938 0.1158 0.3071 0.1955 0.9851 0.994 0.1944 0.4746 0.8887 0.7328 ] Network output: [ 0.009806 -0.03591 1.012 0.0001254 -5.631e-05 1.005 9.452e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09287 0.08531 0.1737 0.22 0.9875 0.9921 0.09293 0.817 0.89 0.3116 ] Network output: [ -0.01232 0.031 1.011 0.0001174 -5.272e-05 0.9826 8.849e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09903 0.09762 0.1772 0.211 0.9858 0.9916 0.09904 0.7484 0.8711 0.2534 ] Network output: [ 0.001793 0.999 -0.001395 2.181e-05 -9.792e-06 0.9989 1.644e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001814 Epoch 5859 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01492 0.9989 0.9829 1.555e-06 -6.981e-07 -0.0117 1.172e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00312 -0.002771 -0.01106 0.008275 0.9695 0.9739 0.005884 0.846 0.8354 0.02259 ] Network output: [ 0.9939 0.04025 0.00181 -4.825e-05 2.166e-05 -0.02996 -3.636e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1842 -0.0148 -0.2136 0.2085 0.9836 0.9933 0.2052 0.4709 0.8818 0.7372 ] Network output: [ -0.01299 1.004 1.01 -5.019e-06 2.253e-06 0.01206 -3.783e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004851 0.001019 0.003807 0.00521 0.989 0.9921 0.004938 0.8792 0.9063 0.01625 ] Network output: [ -0.002375 0.05115 0.9792 -0.0002298 0.0001032 0.9735 -0.0001732 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1948 0.1161 0.3003 0.1755 0.9851 0.994 0.1954 0.4759 0.8887 0.7335 ] Network output: [ 0.01237 -0.01605 1.007 0.0001251 -5.615e-05 0.9846 9.426e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09253 0.08491 0.1689 0.2146 0.9875 0.9921 0.09258 0.8155 0.8898 0.3087 ] Network output: [ -0.01056 0.02552 1.011 0.0001194 -5.358e-05 0.9854 8.995e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09868 0.09725 0.1754 0.2099 0.9858 0.9916 0.09869 0.7465 0.871 0.2532 ] Network output: [ -0.001366 1 0.003234 1.948e-05 -8.746e-06 0.9993 1.468e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001314 Epoch 5860 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01564 0.9874 0.9835 3.551e-06 -1.594e-06 -0.002165 2.676e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003107 -0.002771 -0.01102 0.008492 0.9695 0.9739 0.005862 0.8458 0.8359 0.02266 ] Network output: [ 1 -0.03381 0.005168 -3.576e-05 1.605e-05 0.02857 -2.695e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1831 -0.0158 -0.2101 0.2211 0.9836 0.9933 0.204 0.4692 0.8823 0.7382 ] Network output: [ -0.01304 1 1.01 -4.342e-06 1.949e-06 0.01584 -3.272e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004829 0.001024 0.004017 0.005637 0.989 0.9921 0.004915 0.8791 0.9066 0.01637 ] Network output: [ 0.004325 -0.05162 0.9846 -0.0002118 9.509e-05 1.057 -0.0001596 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1938 0.1157 0.3072 0.1954 0.9851 0.994 0.1944 0.4746 0.8887 0.7328 ] Network output: [ 0.009823 -0.03596 1.012 0.0001254 -5.629e-05 1.005 9.45e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09296 0.08538 0.1738 0.22 0.9875 0.9921 0.09301 0.817 0.89 0.3116 ] Network output: [ -0.01232 0.03115 1.011 0.0001174 -5.272e-05 0.9826 8.85e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09909 0.09767 0.1772 0.211 0.9858 0.9916 0.0991 0.7485 0.8711 0.2534 ] Network output: [ 0.001784 0.999 -0.001395 2.178e-05 -9.777e-06 0.999 1.641e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001789 Epoch 5861 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01492 0.9989 0.9829 1.678e-06 -7.531e-07 -0.01164 1.264e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003121 -0.002773 -0.01106 0.008275 0.9695 0.9739 0.005886 0.8461 0.8354 0.02259 ] Network output: [ 0.9939 0.03986 0.00181 -4.83e-05 2.168e-05 -0.02967 -3.64e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1841 -0.01488 -0.2136 0.2085 0.9836 0.9933 0.2051 0.471 0.8818 0.7372 ] Network output: [ -0.01299 1.004 1.01 -4.898e-06 2.199e-06 0.01209 -3.691e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004854 0.001016 0.00381 0.005211 0.989 0.9921 0.00494 0.8792 0.9063 0.01625 ] Network output: [ -0.002365 0.05069 0.9794 -0.0002298 0.0001032 0.9737 -0.0001732 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1947 0.116 0.3004 0.1755 0.9851 0.994 0.1953 0.4759 0.8887 0.7334 ] Network output: [ 0.01236 -0.01628 1.007 0.000125 -5.614e-05 0.9849 9.424e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09262 0.08498 0.169 0.2146 0.9875 0.9921 0.09267 0.8155 0.8898 0.3087 ] Network output: [ -0.01058 0.02572 1.011 0.0001193 -5.358e-05 0.9853 8.994e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09874 0.09731 0.1754 0.2099 0.9858 0.9916 0.09876 0.7465 0.871 0.2532 ] Network output: [ -0.001348 1 0.003188 1.947e-05 -8.741e-06 0.9993 1.467e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001298 Epoch 5862 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01564 0.9875 0.9835 3.652e-06 -1.64e-06 -0.002198 2.753e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003108 -0.002772 -0.01102 0.008491 0.9695 0.9739 0.005864 0.8458 0.8359 0.02266 ] Network output: [ 1 -0.03347 0.005133 -3.595e-05 1.614e-05 0.02827 -2.709e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.183 -0.01586 -0.21 0.221 0.9836 0.9933 0.2039 0.4692 0.8823 0.7381 ] Network output: [ -0.01303 1 1.01 -4.226e-06 1.897e-06 0.01583 -3.185e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004832 0.001022 0.004018 0.005634 0.989 0.9921 0.004918 0.8791 0.9066 0.01637 ] Network output: [ 0.004269 -0.05108 0.9848 -0.0002119 9.515e-05 1.057 -0.0001597 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1937 0.1156 0.3072 0.1952 0.9851 0.994 0.1943 0.4747 0.8887 0.7327 ] Network output: [ 0.00984 -0.036 1.012 0.0001254 -5.628e-05 1.005 9.447e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09304 0.08544 0.1738 0.2201 0.9875 0.9921 0.09309 0.817 0.8899 0.3117 ] Network output: [ -0.01232 0.0313 1.011 0.0001174 -5.272e-05 0.9826 8.851e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09915 0.09773 0.1772 0.211 0.9858 0.9916 0.09916 0.7485 0.8711 0.2533 ] Network output: [ 0.001775 0.9989 -0.001396 2.174e-05 -9.762e-06 0.999 1.639e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001766 Epoch 5863 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01493 0.9988 0.983 1.798e-06 -8.073e-07 -0.01158 1.355e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003122 -0.002774 -0.01106 0.008276 0.9695 0.9739 0.005887 0.8461 0.8354 0.02259 ] Network output: [ 0.994 0.03949 0.00181 -4.834e-05 2.17e-05 -0.0294 -3.643e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1841 -0.01495 -0.2135 0.2086 0.9836 0.9933 0.2051 0.471 0.8818 0.7371 ] Network output: [ -0.01299 1.004 1.01 -4.777e-06 2.145e-06 0.01211 -3.6e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004856 0.001014 0.003813 0.005212 0.989 0.9921 0.004943 0.8792 0.9063 0.01625 ] Network output: [ -0.002355 0.05024 0.9796 -0.0002297 0.0001031 0.9739 -0.0001731 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1947 0.1159 0.3005 0.1755 0.9851 0.994 0.1953 0.4759 0.8887 0.7333 ] Network output: [ 0.01235 -0.0165 1.007 0.000125 -5.613e-05 0.9853 9.422e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0927 0.08504 0.1691 0.2147 0.9875 0.9921 0.09276 0.8155 0.8898 0.3088 ] Network output: [ -0.0106 0.02593 1.01 0.0001193 -5.357e-05 0.9853 8.993e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09881 0.09737 0.1755 0.2099 0.9858 0.9916 0.09882 0.7465 0.871 0.2531 ] Network output: [ -0.001329 1 0.003144 1.946e-05 -8.735e-06 0.9993 1.466e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001283 Epoch 5864 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01563 0.9875 0.9835 3.752e-06 -1.684e-06 -0.00223 2.828e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003109 -0.002774 -0.01102 0.008489 0.9695 0.9739 0.005866 0.8459 0.8359 0.02266 ] Network output: [ 0.9999 -0.03313 0.005098 -3.613e-05 1.622e-05 0.02799 -2.723e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.183 -0.01593 -0.21 0.2209 0.9836 0.9933 0.2039 0.4692 0.8823 0.7381 ] Network output: [ -0.01303 1 1.01 -4.113e-06 1.846e-06 0.01582 -3.099e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004834 0.00102 0.004019 0.005631 0.989 0.9921 0.004921 0.8791 0.9066 0.01637 ] Network output: [ 0.004215 -0.05055 0.985 -0.0002121 9.521e-05 1.056 -0.0001598 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1937 0.1155 0.3072 0.195 0.9851 0.994 0.1943 0.4747 0.8887 0.7326 ] Network output: [ 0.009857 -0.03604 1.012 0.0001253 -5.626e-05 1.005 9.445e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09312 0.0855 0.1739 0.2201 0.9875 0.9921 0.09318 0.8169 0.8899 0.3117 ] Network output: [ -0.01232 0.03144 1.011 0.0001175 -5.273e-05 0.9825 8.852e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09921 0.09778 0.1772 0.2109 0.9858 0.9916 0.09922 0.7485 0.8711 0.2533 ] Network output: [ 0.001766 0.9989 -0.001398 2.171e-05 -9.746e-06 0.999 1.636e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001743 Epoch 5865 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01493 0.9987 0.983 1.917e-06 -8.608e-07 -0.01152 1.445e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003123 -0.002776 -0.01106 0.008276 0.9695 0.9739 0.005889 0.8461 0.8355 0.02259 ] Network output: [ 0.994 0.03912 0.00181 -4.839e-05 2.173e-05 -0.02913 -3.647e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1841 -0.01502 -0.2135 0.2086 0.9836 0.9933 0.2051 0.471 0.8818 0.737 ] Network output: [ -0.01299 1.004 1.01 -4.658e-06 2.091e-06 0.01213 -3.511e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004858 0.001012 0.003816 0.005213 0.989 0.9921 0.004945 0.8792 0.9063 0.01625 ] Network output: [ -0.002346 0.04981 0.9799 -0.0002297 0.0001031 0.9741 -0.0001731 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1946 0.1158 0.3006 0.1755 0.9851 0.994 0.1952 0.476 0.8887 0.7332 ] Network output: [ 0.01234 -0.01672 1.007 0.000125 -5.611e-05 0.9856 9.42e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09279 0.08511 0.1692 0.2147 0.9875 0.9921 0.09284 0.8155 0.8898 0.3089 ] Network output: [ -0.01061 0.02613 1.01 0.0001193 -5.357e-05 0.9852 8.992e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09887 0.09743 0.1755 0.2099 0.9858 0.9916 0.09888 0.7466 0.8711 0.2531 ] Network output: [ -0.001312 1 0.0031 1.944e-05 -8.729e-06 0.9993 1.465e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001269 Epoch 5866 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01563 0.9875 0.9835 3.851e-06 -1.729e-06 -0.002261 2.902e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00311 -0.002775 -0.01102 0.008487 0.9695 0.9739 0.005868 0.8459 0.8359 0.02265 ] Network output: [ 0.9999 -0.03281 0.005064 -3.632e-05 1.63e-05 0.02772 -2.737e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.183 -0.01599 -0.21 0.2208 0.9836 0.9933 0.2039 0.4692 0.8823 0.738 ] Network output: [ -0.01303 1 1.01 -4e-06 1.796e-06 0.01581 -3.015e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004837 0.001017 0.00402 0.005628 0.989 0.9921 0.004923 0.8791 0.9066 0.01636 ] Network output: [ 0.004162 -0.05003 0.9852 -0.0002122 9.527e-05 1.056 -0.0001599 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1937 0.1153 0.3072 0.1949 0.9851 0.994 0.1943 0.4747 0.8887 0.7325 ] Network output: [ 0.009872 -0.03609 1.011 0.0001253 -5.625e-05 1.005 9.443e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0932 0.08556 0.1739 0.2201 0.9875 0.9921 0.09326 0.8169 0.8899 0.3118 ] Network output: [ -0.01232 0.03159 1.011 0.0001175 -5.273e-05 0.9825 8.852e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09926 0.09784 0.1772 0.2109 0.9858 0.9916 0.09927 0.7485 0.8711 0.2533 ] Network output: [ 0.001758 0.9989 -0.001399 2.168e-05 -9.731e-06 0.9991 1.634e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001722 Epoch 5867 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01494 0.9986 0.983 2.034e-06 -9.134e-07 -0.01147 1.533e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003123 -0.002777 -0.01106 0.008276 0.9695 0.9739 0.005891 0.8461 0.8355 0.02258 ] Network output: [ 0.994 0.03877 0.00181 -4.845e-05 2.175e-05 -0.02887 -3.651e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.184 -0.01509 -0.2135 0.2086 0.9836 0.9933 0.205 0.471 0.8818 0.7369 ] Network output: [ -0.01299 1.004 1.01 -4.541e-06 2.039e-06 0.01216 -3.422e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004861 0.001009 0.003819 0.005214 0.989 0.9921 0.004948 0.8793 0.9063 0.01625 ] Network output: [ -0.002337 0.04939 0.9801 -0.0002296 0.0001031 0.9742 -0.0001731 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1946 0.1156 0.3007 0.1756 0.9851 0.994 0.1952 0.476 0.8887 0.7331 ] Network output: [ 0.01234 -0.01693 1.007 0.000125 -5.61e-05 0.9859 9.417e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09287 0.08517 0.1693 0.2148 0.9875 0.9921 0.09293 0.8155 0.8898 0.3089 ] Network output: [ -0.01063 0.02634 1.01 0.0001193 -5.356e-05 0.9851 8.991e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09893 0.09749 0.1755 0.2098 0.9858 0.9916 0.09894 0.7466 0.8711 0.2531 ] Network output: [ -0.001294 1 0.003057 1.943e-05 -8.722e-06 0.9994 1.464e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001255 Epoch 5868 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01563 0.9875 0.9835 3.948e-06 -1.772e-06 -0.00229 2.975e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003111 -0.002776 -0.01102 0.008485 0.9695 0.9739 0.005869 0.8459 0.836 0.02265 ] Network output: [ 0.9999 -0.03249 0.005031 -3.65e-05 1.638e-05 0.02745 -2.751e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.183 -0.01605 -0.21 0.2207 0.9836 0.9933 0.2038 0.4693 0.8823 0.7379 ] Network output: [ -0.01303 1 1.01 -3.889e-06 1.746e-06 0.0158 -2.931e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004839 0.001015 0.004021 0.005625 0.989 0.9921 0.004926 0.8791 0.9066 0.01636 ] Network output: [ 0.004111 -0.04953 0.9854 -0.0002123 9.533e-05 1.055 -0.00016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1936 0.1152 0.3072 0.1947 0.9851 0.994 0.1942 0.4747 0.8887 0.7324 ] Network output: [ 0.009888 -0.03613 1.011 0.0001253 -5.624e-05 1.006 9.44e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09328 0.08562 0.174 0.2201 0.9875 0.9921 0.09334 0.8169 0.8899 0.3118 ] Network output: [ -0.01232 0.03174 1.011 0.0001175 -5.274e-05 0.9824 8.853e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09932 0.09789 0.1772 0.2109 0.9858 0.9916 0.09933 0.7485 0.8711 0.2532 ] Network output: [ 0.00175 0.9989 -0.001402 2.164e-05 -9.716e-06 0.9991 1.631e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001701 Epoch 5869 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01494 0.9985 0.983 2.15e-06 -9.652e-07 -0.01141 1.62e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003124 -0.002778 -0.01106 0.008276 0.9695 0.9739 0.005892 0.8461 0.8355 0.02258 ] Network output: [ 0.9941 0.03842 0.00181 -4.85e-05 2.177e-05 -0.02862 -3.655e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.184 -0.01516 -0.2134 0.2086 0.9836 0.9933 0.205 0.471 0.8819 0.7369 ] Network output: [ -0.01299 1.004 1.01 -4.425e-06 1.987e-06 0.01218 -3.335e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004863 0.001007 0.003822 0.005215 0.989 0.9921 0.00495 0.8793 0.9063 0.01625 ] Network output: [ -0.002329 0.04898 0.9803 -0.0002296 0.0001031 0.9744 -0.000173 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1946 0.1155 0.3008 0.1756 0.9851 0.994 0.1951 0.476 0.8887 0.733 ] Network output: [ 0.01233 -0.01714 1.007 0.0001249 -5.608e-05 0.9862 9.415e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09295 0.08524 0.1694 0.2148 0.9875 0.9921 0.09301 0.8155 0.8898 0.309 ] Network output: [ -0.01064 0.02654 1.01 0.0001193 -5.355e-05 0.985 8.99e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09899 0.09754 0.1755 0.2098 0.9858 0.9916 0.099 0.7467 0.8711 0.253 ] Network output: [ -0.001277 1 0.003014 1.941e-05 -8.716e-06 0.9994 1.463e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001242 Epoch 5870 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01563 0.9875 0.9835 4.044e-06 -1.815e-06 -0.002317 3.048e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003112 -0.002778 -0.01102 0.008483 0.9695 0.9739 0.005871 0.8459 0.836 0.02265 ] Network output: [ 0.9999 -0.03218 0.004998 -3.667e-05 1.646e-05 0.02719 -2.764e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1829 -0.01611 -0.21 0.2206 0.9836 0.9933 0.2038 0.4693 0.8823 0.7378 ] Network output: [ -0.01303 1 1.01 -3.779e-06 1.697e-06 0.01579 -2.848e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004842 0.001012 0.004022 0.005622 0.989 0.9921 0.004928 0.8791 0.9066 0.01636 ] Network output: [ 0.004061 -0.04904 0.9856 -0.0002125 9.539e-05 1.055 -0.0001601 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1936 0.1151 0.3073 0.1945 0.9851 0.994 0.1942 0.4748 0.8887 0.7324 ] Network output: [ 0.009902 -0.03618 1.011 0.0001252 -5.622e-05 1.006 9.438e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09336 0.08568 0.174 0.2201 0.9875 0.9921 0.09341 0.8169 0.8899 0.3118 ] Network output: [ -0.01232 0.03189 1.011 0.0001175 -5.274e-05 0.9824 8.853e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09937 0.09794 0.1772 0.2109 0.9858 0.9916 0.09939 0.7486 0.8711 0.2532 ] Network output: [ 0.001742 0.9989 -0.001404 2.161e-05 -9.701e-06 0.9991 1.629e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001681 Epoch 5871 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01494 0.9985 0.983 2.264e-06 -1.016e-06 -0.01136 1.706e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003125 -0.00278 -0.01106 0.008276 0.9695 0.9739 0.005894 0.8461 0.8355 0.02258 ] Network output: [ 0.9941 0.03809 0.001809 -4.855e-05 2.18e-05 -0.02837 -3.659e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1839 -0.01523 -0.2134 0.2086 0.9836 0.9933 0.2049 0.471 0.8819 0.7368 ] Network output: [ -0.01299 1.004 1.01 -4.311e-06 1.935e-06 0.0122 -3.249e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004865 0.001005 0.003825 0.005215 0.989 0.9921 0.004952 0.8793 0.9063 0.01625 ] Network output: [ -0.002321 0.04858 0.9806 -0.0002295 0.000103 0.9746 -0.000173 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1945 0.1154 0.3008 0.1756 0.9851 0.994 0.1951 0.476 0.8887 0.7329 ] Network output: [ 0.01232 -0.01735 1.007 0.0001249 -5.607e-05 0.9865 9.413e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09303 0.0853 0.1694 0.2149 0.9875 0.9921 0.09309 0.8155 0.8898 0.3091 ] Network output: [ -0.01066 0.02674 1.01 0.0001193 -5.355e-05 0.985 8.989e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09905 0.0976 0.1756 0.2098 0.9858 0.9916 0.09906 0.7467 0.8711 0.253 ] Network output: [ -0.001261 1 0.002973 1.94e-05 -8.709e-06 0.9994 1.462e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001229 Epoch 5872 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01562 0.9876 0.9835 4.139e-06 -1.858e-06 -0.002343 3.119e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003113 -0.002779 -0.01102 0.008482 0.9695 0.9739 0.005873 0.8459 0.836 0.02265 ] Network output: [ 0.9999 -0.03188 0.004967 -3.685e-05 1.654e-05 0.02694 -2.777e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1829 -0.01617 -0.21 0.2205 0.9836 0.9933 0.2038 0.4693 0.8823 0.7377 ] Network output: [ -0.01303 1 1.01 -3.671e-06 1.648e-06 0.01578 -2.766e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004844 0.00101 0.004023 0.005619 0.989 0.9921 0.004931 0.8791 0.9066 0.01636 ] Network output: [ 0.004012 -0.04857 0.9857 -0.0002126 9.544e-05 1.054 -0.0001602 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1936 0.115 0.3073 0.1944 0.9851 0.994 0.1942 0.4748 0.8887 0.7323 ] Network output: [ 0.009916 -0.03623 1.011 0.0001252 -5.621e-05 1.006 9.436e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09343 0.08574 0.1741 0.2201 0.9875 0.9921 0.09349 0.8168 0.8899 0.3119 ] Network output: [ -0.01232 0.03204 1.011 0.0001175 -5.274e-05 0.9824 8.854e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09943 0.09799 0.1772 0.2108 0.9858 0.9916 0.09944 0.7486 0.8711 0.2532 ] Network output: [ 0.001735 0.9989 -0.001407 2.158e-05 -9.686e-06 0.9992 1.626e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001662 Epoch 5873 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01495 0.9984 0.983 2.375e-06 -1.066e-06 -0.01131 1.79e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003126 -0.002781 -0.01106 0.008276 0.9695 0.9739 0.005895 0.8462 0.8355 0.02258 ] Network output: [ 0.9942 0.03776 0.001809 -4.86e-05 2.182e-05 -0.02813 -3.663e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1839 -0.0153 -0.2133 0.2086 0.9836 0.9933 0.2049 0.471 0.8819 0.7367 ] Network output: [ -0.01299 1.004 1.01 -4.198e-06 1.885e-06 0.01222 -3.164e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004868 0.001002 0.003828 0.005216 0.989 0.9921 0.004955 0.8793 0.9063 0.01625 ] Network output: [ -0.002313 0.04819 0.9808 -0.0002295 0.000103 0.9747 -0.0001729 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1945 0.1153 0.3009 0.1756 0.9851 0.994 0.1951 0.476 0.8887 0.7329 ] Network output: [ 0.01232 -0.01755 1.007 0.0001249 -5.606e-05 0.9868 9.41e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09311 0.08536 0.1695 0.2149 0.9875 0.9921 0.09317 0.8155 0.8898 0.3091 ] Network output: [ -0.01068 0.02693 1.01 0.0001193 -5.354e-05 0.9849 8.988e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0991 0.09765 0.1756 0.2098 0.9858 0.9916 0.09911 0.7467 0.8711 0.253 ] Network output: [ -0.001244 1 0.002932 1.938e-05 -8.703e-06 0.9994 1.461e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001217 Epoch 5874 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01562 0.9876 0.9836 4.232e-06 -1.9e-06 -0.002368 3.19e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003114 -0.002781 -0.01101 0.00848 0.9695 0.9739 0.005875 0.846 0.836 0.02264 ] Network output: [ 0.9999 -0.03159 0.004936 -3.702e-05 1.662e-05 0.02669 -2.79e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1829 -0.01623 -0.21 0.2204 0.9836 0.9933 0.2038 0.4693 0.8823 0.7376 ] Network output: [ -0.01303 1 1.01 -3.563e-06 1.6e-06 0.01578 -2.685e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004847 0.001008 0.004024 0.005617 0.989 0.9921 0.004933 0.8792 0.9066 0.01636 ] Network output: [ 0.003964 -0.04811 0.9859 -0.0002127 9.549e-05 1.053 -0.0001603 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1935 0.1149 0.3073 0.1942 0.9851 0.994 0.1941 0.4748 0.8887 0.7322 ] Network output: [ 0.00993 -0.03628 1.011 0.0001252 -5.619e-05 1.006 9.433e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09351 0.0858 0.1741 0.2201 0.9875 0.9921 0.09357 0.8168 0.8899 0.3119 ] Network output: [ -0.01232 0.03218 1.011 0.0001175 -5.274e-05 0.9823 8.854e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09948 0.09804 0.1772 0.2108 0.9858 0.9916 0.09949 0.7486 0.8711 0.2531 ] Network output: [ 0.001728 0.9989 -0.00141 2.154e-05 -9.671e-06 0.9992 1.624e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001643 Epoch 5875 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01495 0.9983 0.9831 2.486e-06 -1.116e-06 -0.01126 1.873e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003127 -0.002783 -0.01106 0.008276 0.9695 0.9739 0.005897 0.8462 0.8355 0.02258 ] Network output: [ 0.9942 0.03745 0.001808 -4.866e-05 2.184e-05 -0.0279 -3.667e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1839 -0.01537 -0.2133 0.2086 0.9836 0.9933 0.2048 0.471 0.8819 0.7366 ] Network output: [ -0.01299 1.004 1.01 -4.086e-06 1.834e-06 0.01225 -3.08e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00487 0.0009999 0.00383 0.005217 0.989 0.9921 0.004957 0.8793 0.9063 0.01625 ] Network output: [ -0.002306 0.04782 0.981 -0.0002294 0.000103 0.9748 -0.0001729 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1944 0.1151 0.301 0.1756 0.9851 0.994 0.195 0.476 0.8887 0.7328 ] Network output: [ 0.01231 -0.01776 1.007 0.0001248 -5.604e-05 0.9871 9.408e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09319 0.08542 0.1696 0.215 0.9875 0.9921 0.09325 0.8155 0.8898 0.3092 ] Network output: [ -0.01069 0.02713 1.01 0.0001193 -5.354e-05 0.9848 8.987e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09916 0.0977 0.1756 0.2098 0.9858 0.9916 0.09917 0.7468 0.8711 0.2529 ] Network output: [ -0.001229 1 0.002893 1.937e-05 -8.696e-06 0.9995 1.46e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001206 Epoch 5876 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01562 0.9876 0.9836 4.324e-06 -1.941e-06 -0.002391 3.259e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003115 -0.002782 -0.01101 0.008478 0.9695 0.9739 0.005876 0.846 0.836 0.02264 ] Network output: [ 0.9999 -0.03131 0.004906 -3.719e-05 1.67e-05 0.02646 -2.803e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1829 -0.01629 -0.21 0.2203 0.9836 0.9933 0.2037 0.4694 0.8823 0.7376 ] Network output: [ -0.01303 1 1.01 -3.457e-06 1.552e-06 0.01577 -2.606e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004849 0.001005 0.004025 0.005614 0.989 0.9921 0.004936 0.8792 0.9066 0.01636 ] Network output: [ 0.003917 -0.04766 0.9861 -0.0002128 9.554e-05 1.053 -0.0001604 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1935 0.1147 0.3073 0.1941 0.9851 0.994 0.1941 0.4748 0.8887 0.7321 ] Network output: [ 0.009943 -0.03634 1.011 0.0001251 -5.618e-05 1.006 9.431e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09359 0.08585 0.1742 0.2201 0.9875 0.9921 0.09364 0.8168 0.8899 0.312 ] Network output: [ -0.01232 0.03233 1.011 0.0001175 -5.275e-05 0.9823 8.854e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09953 0.09809 0.1772 0.2108 0.9858 0.9916 0.09955 0.7486 0.8711 0.2531 ] Network output: [ 0.001721 0.9988 -0.001413 2.151e-05 -9.656e-06 0.9992 1.621e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001626 Epoch 5877 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01495 0.9982 0.9831 2.594e-06 -1.165e-06 -0.01121 1.955e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003127 -0.002784 -0.01105 0.008276 0.9695 0.9739 0.005898 0.8462 0.8355 0.02258 ] Network output: [ 0.9943 0.03714 0.001807 -4.871e-05 2.187e-05 -0.02768 -3.671e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1838 -0.01544 -0.2133 0.2087 0.9836 0.9933 0.2048 0.471 0.8819 0.7366 ] Network output: [ -0.01299 1.004 1.01 -3.976e-06 1.785e-06 0.01227 -2.996e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004872 0.0009975 0.003833 0.005217 0.989 0.9921 0.004959 0.8793 0.9063 0.01625 ] Network output: [ -0.0023 0.04746 0.9812 -0.0002294 0.000103 0.975 -0.0001729 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1944 0.115 0.3011 0.1756 0.9851 0.994 0.195 0.476 0.8888 0.7327 ] Network output: [ 0.0123 -0.01795 1.007 0.0001248 -5.603e-05 0.9874 9.405e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09327 0.08548 0.1697 0.215 0.9875 0.9921 0.09333 0.8155 0.8898 0.3093 ] Network output: [ -0.0107 0.02733 1.01 0.0001192 -5.353e-05 0.9848 8.986e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09921 0.09775 0.1756 0.2097 0.9858 0.9916 0.09922 0.7468 0.8711 0.2529 ] Network output: [ -0.001213 1 0.002854 1.935e-05 -8.688e-06 0.9995 1.459e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001195 Epoch 5878 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01562 0.9876 0.9836 4.416e-06 -1.982e-06 -0.002413 3.328e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003116 -0.002783 -0.01101 0.008476 0.9695 0.9739 0.005878 0.846 0.836 0.02264 ] Network output: [ 0.9999 -0.03104 0.004876 -3.736e-05 1.677e-05 0.02622 -2.815e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1828 -0.01635 -0.21 0.2202 0.9836 0.9933 0.2037 0.4694 0.8823 0.7375 ] Network output: [ -0.01303 1 1.01 -3.353e-06 1.505e-06 0.01577 -2.527e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004852 0.001003 0.004026 0.005611 0.989 0.9921 0.004938 0.8792 0.9066 0.01636 ] Network output: [ 0.003872 -0.04723 0.9863 -0.0002129 9.558e-05 1.052 -0.0001605 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1935 0.1146 0.3074 0.1939 0.9851 0.994 0.1941 0.4749 0.8887 0.732 ] Network output: [ 0.009955 -0.03639 1.011 0.0001251 -5.616e-05 1.006 9.428e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09366 0.08591 0.1742 0.2201 0.9875 0.9921 0.09372 0.8168 0.8899 0.312 ] Network output: [ -0.01232 0.03248 1.01 0.0001175 -5.275e-05 0.9822 8.855e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09958 0.09814 0.1772 0.2107 0.9858 0.9916 0.0996 0.7486 0.8711 0.2531 ] Network output: [ 0.001714 0.9988 -0.001417 2.148e-05 -9.641e-06 0.9993 1.619e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001609 Epoch 5879 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01496 0.9982 0.9831 2.701e-06 -1.212e-06 -0.01115 2.035e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003128 -0.002785 -0.01105 0.008276 0.9695 0.9739 0.0059 0.8462 0.8355 0.02257 ] Network output: [ 0.9943 0.03684 0.001806 -4.877e-05 2.189e-05 -0.02746 -3.675e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1838 -0.0155 -0.2132 0.2087 0.9836 0.9933 0.2048 0.471 0.8819 0.7365 ] Network output: [ -0.01299 1.004 1.01 -3.867e-06 1.736e-06 0.01229 -2.915e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004874 0.0009952 0.003836 0.005217 0.989 0.9921 0.004961 0.8793 0.9063 0.01625 ] Network output: [ -0.002293 0.04711 0.9815 -0.0002293 0.000103 0.9751 -0.0001728 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1944 0.1149 0.3011 0.1756 0.9851 0.994 0.195 0.476 0.8888 0.7326 ] Network output: [ 0.0123 -0.01815 1.006 0.0001248 -5.601e-05 0.9876 9.403e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09335 0.08554 0.1698 0.2151 0.9875 0.9921 0.0934 0.8154 0.8898 0.3093 ] Network output: [ -0.01072 0.02752 1.01 0.0001192 -5.352e-05 0.9847 8.985e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09927 0.0978 0.1756 0.2097 0.9858 0.9916 0.09928 0.7468 0.8711 0.2529 ] Network output: [ -0.001198 1 0.002815 1.934e-05 -8.681e-06 0.9995 1.457e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001184 Epoch 5880 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01561 0.9876 0.9836 4.505e-06 -2.023e-06 -0.002434 3.395e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003117 -0.002785 -0.01101 0.008474 0.9695 0.9739 0.00588 0.846 0.836 0.02264 ] Network output: [ 0.9999 -0.03078 0.004848 -3.752e-05 1.684e-05 0.026 -2.828e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1828 -0.01641 -0.21 0.2202 0.9836 0.9933 0.2037 0.4694 0.8823 0.7374 ] Network output: [ -0.01303 1 1.01 -3.249e-06 1.459e-06 0.01576 -2.449e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004854 0.001 0.004027 0.005608 0.989 0.9921 0.004941 0.8792 0.9066 0.01636 ] Network output: [ 0.003827 -0.04681 0.9864 -0.000213 9.563e-05 1.052 -0.0001605 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1934 0.1145 0.3074 0.1938 0.9851 0.994 0.194 0.4749 0.8887 0.732 ] Network output: [ 0.009967 -0.03644 1.011 0.0001251 -5.615e-05 1.006 9.426e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09373 0.08596 0.1743 0.2201 0.9875 0.9921 0.09379 0.8168 0.8899 0.312 ] Network output: [ -0.01232 0.03263 1.01 0.0001175 -5.275e-05 0.9822 8.855e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09963 0.09819 0.1772 0.2107 0.9858 0.9916 0.09965 0.7487 0.8711 0.253 ] Network output: [ 0.001708 0.9988 -0.001421 2.144e-05 -9.627e-06 0.9993 1.616e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001593 Epoch 5881 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01496 0.9981 0.9831 2.806e-06 -1.26e-06 -0.01111 2.114e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003129 -0.002786 -0.01105 0.008276 0.9695 0.9739 0.005901 0.8462 0.8356 0.02257 ] Network output: [ 0.9943 0.03656 0.001805 -4.883e-05 2.192e-05 -0.02725 -3.68e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1838 -0.01557 -0.2132 0.2087 0.9836 0.9933 0.2047 0.4711 0.8819 0.7364 ] Network output: [ -0.01299 1.004 1.01 -3.76e-06 1.688e-06 0.01231 -2.834e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004876 0.0009929 0.003838 0.005218 0.989 0.9921 0.004964 0.8793 0.9063 0.01625 ] Network output: [ -0.002288 0.04677 0.9817 -0.0002293 0.0001029 0.9752 -0.0001728 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1943 0.1148 0.3012 0.1756 0.9851 0.994 0.1949 0.4761 0.8888 0.7325 ] Network output: [ 0.01229 -0.01834 1.006 0.0001247 -5.6e-05 0.9879 9.4e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09342 0.0856 0.1699 0.2151 0.9875 0.9921 0.09348 0.8154 0.8897 0.3094 ] Network output: [ -0.01073 0.02771 1.01 0.0001192 -5.352e-05 0.9846 8.984e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09932 0.09785 0.1756 0.2097 0.9858 0.9916 0.09933 0.7469 0.8711 0.2528 ] Network output: [ -0.001184 1 0.002778 1.932e-05 -8.673e-06 0.9995 1.456e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001174 Epoch 5882 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01561 0.9876 0.9836 4.594e-06 -2.062e-06 -0.002453 3.462e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003117 -0.002786 -0.01101 0.008473 0.9695 0.9739 0.005882 0.846 0.836 0.02263 ] Network output: [ 0.9999 -0.03052 0.00482 -3.768e-05 1.692e-05 0.02578 -2.84e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1828 -0.01647 -0.2099 0.2201 0.9836 0.9933 0.2036 0.4694 0.8823 0.7373 ] Network output: [ -0.01302 1 1.01 -3.147e-06 1.413e-06 0.01576 -2.372e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004856 0.000998 0.004028 0.005605 0.989 0.9921 0.004943 0.8792 0.9066 0.01636 ] Network output: [ 0.003784 -0.0464 0.9866 -0.0002131 9.567e-05 1.051 -0.0001606 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1934 0.1144 0.3074 0.1936 0.9851 0.994 0.194 0.4749 0.8888 0.7319 ] Network output: [ 0.009978 -0.0365 1.011 0.000125 -5.613e-05 1.006 9.423e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09381 0.08601 0.1743 0.2201 0.9875 0.9921 0.09386 0.8167 0.8899 0.3121 ] Network output: [ -0.01232 0.03278 1.01 0.0001175 -5.275e-05 0.9821 8.855e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09968 0.09823 0.1772 0.2107 0.9858 0.9916 0.0997 0.7487 0.8711 0.253 ] Network output: [ 0.001702 0.9988 -0.001425 2.141e-05 -9.612e-06 0.9993 1.614e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001577 Epoch 5883 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01496 0.998 0.9831 2.909e-06 -1.306e-06 -0.01106 2.192e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00313 -0.002788 -0.01105 0.008276 0.9695 0.9739 0.005903 0.8463 0.8356 0.02257 ] Network output: [ 0.9944 0.03628 0.001804 -4.888e-05 2.195e-05 -0.02705 -3.684e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1837 -0.01564 -0.2132 0.2087 0.9836 0.9933 0.2047 0.4711 0.8819 0.7363 ] Network output: [ -0.01298 1.004 1.01 -3.654e-06 1.64e-06 0.01234 -2.754e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004879 0.0009906 0.003841 0.005218 0.989 0.9921 0.004966 0.8793 0.9063 0.01625 ] Network output: [ -0.002282 0.04644 0.9819 -0.0002292 0.0001029 0.9753 -0.0001727 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1943 0.1146 0.3013 0.1756 0.9851 0.994 0.1949 0.4761 0.8888 0.7324 ] Network output: [ 0.01228 -0.01853 1.006 0.0001247 -5.598e-05 0.9882 9.398e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0935 0.08565 0.17 0.2151 0.9875 0.9921 0.09356 0.8154 0.8897 0.3094 ] Network output: [ -0.01075 0.0279 1.01 0.0001192 -5.351e-05 0.9845 8.983e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09937 0.0979 0.1757 0.2097 0.9858 0.9916 0.09938 0.7469 0.8711 0.2528 ] Network output: [ -0.00117 1 0.002741 1.93e-05 -8.666e-06 0.9995 1.455e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001165 Epoch 5884 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01561 0.9877 0.9836 4.681e-06 -2.102e-06 -0.002471 3.528e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003118 -0.002787 -0.01101 0.008471 0.9695 0.9739 0.005883 0.8461 0.836 0.02263 ] Network output: [ 0.9999 -0.03027 0.004792 -3.784e-05 1.699e-05 0.02557 -2.852e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1827 -0.01653 -0.2099 0.22 0.9836 0.9933 0.2036 0.4695 0.8824 0.7372 ] Network output: [ -0.01302 1 1.01 -3.046e-06 1.367e-06 0.01575 -2.295e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004859 0.0009957 0.004029 0.005603 0.989 0.9921 0.004945 0.8792 0.9066 0.01636 ] Network output: [ 0.003742 -0.046 0.9868 -0.0002132 9.571e-05 1.051 -0.0001607 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1934 0.1143 0.3074 0.1935 0.9851 0.994 0.194 0.4749 0.8888 0.7318 ] Network output: [ 0.009989 -0.03656 1.01 0.000125 -5.612e-05 1.007 9.421e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09388 0.08607 0.1744 0.2201 0.9875 0.9921 0.09393 0.8167 0.8899 0.3121 ] Network output: [ -0.01232 0.03293 1.01 0.0001175 -5.275e-05 0.9821 8.855e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09973 0.09828 0.1772 0.2106 0.9858 0.9916 0.09974 0.7487 0.8712 0.2529 ] Network output: [ 0.001696 0.9988 -0.00143 2.138e-05 -9.597e-06 0.9993 1.611e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001562 Epoch 5885 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01497 0.9979 0.9831 3.011e-06 -1.352e-06 -0.01101 2.269e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003131 -0.002789 -0.01105 0.008276 0.9695 0.9739 0.005904 0.8463 0.8356 0.02257 ] Network output: [ 0.9944 0.03601 0.001802 -4.894e-05 2.197e-05 -0.02685 -3.688e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1837 -0.0157 -0.2131 0.2087 0.9836 0.9933 0.2046 0.4711 0.8819 0.7363 ] Network output: [ -0.01298 1.004 1.01 -3.549e-06 1.593e-06 0.01236 -2.675e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004881 0.0009883 0.003843 0.005218 0.989 0.9921 0.004968 0.8793 0.9063 0.01625 ] Network output: [ -0.002277 0.04612 0.9821 -0.0002292 0.0001029 0.9754 -0.0001727 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1942 0.1145 0.3014 0.1756 0.9851 0.994 0.1948 0.4761 0.8888 0.7324 ] Network output: [ 0.01228 -0.01872 1.006 0.0001247 -5.597e-05 0.9885 9.395e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09357 0.08571 0.17 0.2152 0.9875 0.9921 0.09363 0.8154 0.8897 0.3095 ] Network output: [ -0.01076 0.02809 1.009 0.0001192 -5.35e-05 0.9845 8.982e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09942 0.09795 0.1757 0.2096 0.9858 0.9916 0.09943 0.7469 0.8711 0.2528 ] Network output: [ -0.001156 1 0.002705 1.929e-05 -8.658e-06 0.9996 1.453e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001156 Epoch 5886 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01561 0.9877 0.9836 4.767e-06 -2.14e-06 -0.002488 3.593e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003119 -0.002788 -0.01101 0.008469 0.9695 0.9739 0.005885 0.8461 0.836 0.02263 ] Network output: [ 0.9999 -0.03003 0.004765 -3.8e-05 1.706e-05 0.02537 -2.864e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1827 -0.01659 -0.2099 0.2199 0.9836 0.9933 0.2036 0.4695 0.8824 0.7371 ] Network output: [ -0.01302 1 1.01 -2.946e-06 1.323e-06 0.01575 -2.22e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004861 0.0009934 0.00403 0.0056 0.989 0.9921 0.004948 0.8792 0.9066 0.01635 ] Network output: [ 0.003701 -0.04562 0.987 -0.0002133 9.574e-05 1.05 -0.0001607 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1934 0.1141 0.3075 0.1933 0.9851 0.994 0.1939 0.475 0.8888 0.7317 ] Network output: [ 0.01 -0.03662 1.01 0.000125 -5.61e-05 1.007 9.418e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09395 0.08612 0.1744 0.2201 0.9875 0.9921 0.09401 0.8167 0.8899 0.3121 ] Network output: [ -0.01232 0.03307 1.01 0.0001175 -5.275e-05 0.982 8.855e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09978 0.09832 0.1772 0.2106 0.9858 0.9916 0.09979 0.7487 0.8712 0.2529 ] Network output: [ 0.00169 0.9988 -0.001435 2.134e-05 -9.582e-06 0.9994 1.609e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001548 Epoch 5887 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01497 0.9979 0.9832 3.111e-06 -1.396e-06 -0.01096 2.344e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003131 -0.00279 -0.01105 0.008275 0.9695 0.9739 0.005906 0.8463 0.8356 0.02257 ] Network output: [ 0.9945 0.03574 0.001801 -4.9e-05 2.2e-05 -0.02666 -3.693e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1837 -0.01577 -0.2131 0.2087 0.9836 0.9933 0.2046 0.4711 0.8819 0.7362 ] Network output: [ -0.01298 1.004 1.01 -3.446e-06 1.547e-06 0.01238 -2.597e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004883 0.0009861 0.003846 0.005219 0.989 0.9921 0.00497 0.8793 0.9063 0.01625 ] Network output: [ -0.002273 0.04581 0.9823 -0.0002291 0.0001029 0.9755 -0.0001727 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1942 0.1144 0.3014 0.1756 0.9851 0.994 0.1948 0.4761 0.8888 0.7323 ] Network output: [ 0.01227 -0.0189 1.006 0.0001246 -5.595e-05 0.9887 9.393e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09364 0.08576 0.1701 0.2152 0.9875 0.9921 0.0937 0.8154 0.8897 0.3095 ] Network output: [ -0.01077 0.02828 1.009 0.0001192 -5.35e-05 0.9844 8.981e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09947 0.098 0.1757 0.2096 0.9858 0.9916 0.09948 0.747 0.8711 0.2527 ] Network output: [ -0.001143 1 0.00267 1.927e-05 -8.65e-06 0.9996 1.452e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001147 Epoch 5888 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01561 0.9877 0.9836 4.852e-06 -2.178e-06 -0.002504 3.657e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00312 -0.00279 -0.01101 0.008467 0.9695 0.9739 0.005886 0.8461 0.836 0.02263 ] Network output: [ 0.9999 -0.0298 0.004739 -3.815e-05 1.713e-05 0.02517 -2.875e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1827 -0.01665 -0.2099 0.2198 0.9836 0.9933 0.2035 0.4695 0.8824 0.7371 ] Network output: [ -0.01302 1 1.01 -2.848e-06 1.278e-06 0.01575 -2.146e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004863 0.0009911 0.004032 0.005597 0.989 0.9921 0.00495 0.8792 0.9066 0.01635 ] Network output: [ 0.003661 -0.04524 0.9871 -0.0002133 9.578e-05 1.05 -0.0001608 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1933 0.114 0.3075 0.1932 0.9851 0.994 0.1939 0.475 0.8888 0.7316 ] Network output: [ 0.01001 -0.03668 1.01 0.0001249 -5.609e-05 1.007 9.416e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09402 0.08617 0.1745 0.2201 0.9875 0.9921 0.09407 0.8167 0.8898 0.3122 ] Network output: [ -0.01232 0.03322 1.01 0.0001175 -5.275e-05 0.982 8.856e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09983 0.09836 0.1772 0.2106 0.9858 0.9916 0.09984 0.7487 0.8712 0.2529 ] Network output: [ 0.001685 0.9988 -0.00144 2.131e-05 -9.567e-06 0.9994 1.606e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001534 Epoch 5889 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01497 0.9978 0.9832 3.209e-06 -1.441e-06 -0.01092 2.418e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003132 -0.002792 -0.01105 0.008275 0.9695 0.9739 0.005907 0.8463 0.8356 0.02256 ] Network output: [ 0.9945 0.03549 0.001799 -4.906e-05 2.202e-05 -0.02647 -3.697e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1836 -0.01583 -0.2131 0.2087 0.9836 0.9933 0.2046 0.4711 0.8819 0.7361 ] Network output: [ -0.01298 1.004 1.01 -3.344e-06 1.501e-06 0.0124 -2.52e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004885 0.0009838 0.003848 0.005219 0.989 0.9921 0.004972 0.8793 0.9063 0.01625 ] Network output: [ -0.002268 0.04551 0.9825 -0.0002291 0.0001028 0.9756 -0.0001726 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1942 0.1143 0.3015 0.1756 0.9851 0.994 0.1948 0.4761 0.8888 0.7322 ] Network output: [ 0.01226 -0.01909 1.006 0.0001246 -5.594e-05 0.989 9.39e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09372 0.08581 0.1702 0.2152 0.9875 0.9921 0.09377 0.8154 0.8897 0.3096 ] Network output: [ -0.01078 0.02847 1.009 0.0001192 -5.349e-05 0.9843 8.98e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09952 0.09804 0.1757 0.2096 0.9858 0.9916 0.09953 0.747 0.8711 0.2527 ] Network output: [ -0.00113 1 0.002636 1.925e-05 -8.642e-06 0.9996 1.451e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001139 Epoch 5890 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01561 0.9877 0.9837 4.936e-06 -2.216e-06 -0.002518 3.72e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003121 -0.002791 -0.01101 0.008466 0.9695 0.9739 0.005888 0.8461 0.836 0.02262 ] Network output: [ 0.9999 -0.02957 0.004713 -3.83e-05 1.72e-05 0.02497 -2.887e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1827 -0.01671 -0.2099 0.2197 0.9836 0.9933 0.2035 0.4695 0.8824 0.737 ] Network output: [ -0.01302 1 1.01 -2.75e-06 1.235e-06 0.01574 -2.073e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004865 0.0009888 0.004033 0.005595 0.989 0.9921 0.004952 0.8792 0.9066 0.01635 ] Network output: [ 0.003622 -0.04488 0.9873 -0.0002134 9.581e-05 1.049 -0.0001608 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1933 0.1139 0.3075 0.193 0.9851 0.994 0.1939 0.475 0.8888 0.7316 ] Network output: [ 0.01002 -0.03674 1.01 0.0001249 -5.607e-05 1.007 9.413e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09409 0.08622 0.1745 0.2201 0.9875 0.9921 0.09414 0.8166 0.8898 0.3122 ] Network output: [ -0.01232 0.03337 1.01 0.0001175 -5.275e-05 0.9819 8.856e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09987 0.09841 0.1772 0.2105 0.9858 0.9916 0.09988 0.7488 0.8712 0.2528 ] Network output: [ 0.00168 0.9987 -0.001445 2.128e-05 -9.552e-06 0.9994 1.604e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001521 Epoch 5891 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01498 0.9977 0.9832 3.306e-06 -1.484e-06 -0.01087 2.491e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003133 -0.002793 -0.01105 0.008275 0.9695 0.9739 0.005909 0.8463 0.8356 0.02256 ] Network output: [ 0.9945 0.03524 0.001797 -4.912e-05 2.205e-05 -0.02629 -3.702e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1836 -0.0159 -0.213 0.2087 0.9836 0.9933 0.2045 0.4711 0.882 0.736 ] Network output: [ -0.01298 1.004 1.01 -3.244e-06 1.456e-06 0.01242 -2.445e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004887 0.0009815 0.00385 0.005219 0.989 0.9921 0.004974 0.8793 0.9063 0.01625 ] Network output: [ -0.002265 0.04522 0.9827 -0.000229 0.0001028 0.9756 -0.0001726 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1941 0.1142 0.3016 0.1756 0.9851 0.994 0.1947 0.4761 0.8888 0.7321 ] Network output: [ 0.01226 -0.01926 1.006 0.0001246 -5.592e-05 0.9893 9.388e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09379 0.08587 0.1703 0.2153 0.9875 0.9921 0.09384 0.8154 0.8897 0.3096 ] Network output: [ -0.01079 0.02865 1.009 0.0001191 -5.348e-05 0.9843 8.978e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09957 0.09809 0.1757 0.2095 0.9858 0.9916 0.09958 0.747 0.8711 0.2527 ] Network output: [ -0.001117 1 0.002602 1.923e-05 -8.633e-06 0.9996 1.449e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001132 Epoch 5892 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0156 0.9877 0.9837 5.018e-06 -2.253e-06 -0.002532 3.782e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003122 -0.002792 -0.01101 0.008464 0.9695 0.9739 0.005889 0.8461 0.836 0.02262 ] Network output: [ 0.9999 -0.02935 0.004688 -3.845e-05 1.726e-05 0.02479 -2.898e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1826 -0.01677 -0.2099 0.2196 0.9836 0.9933 0.2035 0.4696 0.8824 0.7369 ] Network output: [ -0.01302 1 1.01 -2.654e-06 1.192e-06 0.01574 -2e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004868 0.0009865 0.004034 0.005592 0.989 0.9921 0.004955 0.8792 0.9066 0.01635 ] Network output: [ 0.003584 -0.04453 0.9875 -0.0002135 9.584e-05 1.049 -0.0001609 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1933 0.1138 0.3075 0.1929 0.9851 0.994 0.1939 0.475 0.8888 0.7315 ] Network output: [ 0.01003 -0.0368 1.01 0.0001249 -5.606e-05 1.007 9.41e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09415 0.08627 0.1746 0.2201 0.9875 0.9921 0.09421 0.8166 0.8898 0.3122 ] Network output: [ -0.01232 0.03352 1.01 0.0001175 -5.275e-05 0.9819 8.855e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09992 0.09845 0.1772 0.2105 0.9858 0.9916 0.09993 0.7488 0.8712 0.2528 ] Network output: [ 0.001675 0.9987 -0.001451 2.125e-05 -9.538e-06 0.9994 1.601e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001509 Epoch 5893 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01498 0.9977 0.9832 3.401e-06 -1.527e-06 -0.01083 2.563e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003133 -0.002794 -0.01105 0.008274 0.9695 0.9739 0.00591 0.8463 0.8356 0.02256 ] Network output: [ 0.9946 0.035 0.001795 -4.918e-05 2.208e-05 -0.02612 -3.706e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1836 -0.01596 -0.213 0.2087 0.9836 0.9933 0.2045 0.4711 0.882 0.736 ] Network output: [ -0.01298 1.004 1.01 -3.145e-06 1.412e-06 0.01244 -2.37e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004889 0.0009793 0.003853 0.005219 0.989 0.9921 0.004976 0.8793 0.9063 0.01625 ] Network output: [ -0.002261 0.04494 0.9829 -0.0002289 0.0001028 0.9757 -0.0001725 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1941 0.1141 0.3016 0.1755 0.9851 0.994 0.1947 0.4762 0.8888 0.732 ] Network output: [ 0.01225 -0.01944 1.006 0.0001245 -5.591e-05 0.9895 9.385e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09386 0.08592 0.1704 0.2153 0.9875 0.9921 0.09391 0.8154 0.8897 0.3097 ] Network output: [ -0.01081 0.02884 1.009 0.0001191 -5.348e-05 0.9842 8.977e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09961 0.09813 0.1757 0.2095 0.9858 0.9916 0.09963 0.7471 0.8711 0.2526 ] Network output: [ -0.001105 1 0.002569 1.921e-05 -8.625e-06 0.9996 1.448e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001124 Epoch 5894 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0156 0.9877 0.9837 5.1e-06 -2.289e-06 -0.002544 3.843e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003122 -0.002794 -0.01101 0.008462 0.9695 0.9739 0.005891 0.8462 0.8361 0.02262 ] Network output: [ 0.9999 -0.02914 0.004664 -3.86e-05 1.733e-05 0.02461 -2.909e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1826 -0.01683 -0.2099 0.2196 0.9836 0.9933 0.2035 0.4696 0.8824 0.7368 ] Network output: [ -0.01301 1 1.01 -2.56e-06 1.149e-06 0.01574 -1.929e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00487 0.0009842 0.004035 0.005589 0.989 0.9921 0.004957 0.8792 0.9066 0.01635 ] Network output: [ 0.003547 -0.04419 0.9876 -0.0002136 9.587e-05 1.049 -0.0001609 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1932 0.1137 0.3076 0.1928 0.9851 0.994 0.1938 0.4751 0.8888 0.7314 ] Network output: [ 0.01004 -0.03686 1.01 0.0001248 -5.604e-05 1.007 9.408e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09422 0.08632 0.1746 0.2201 0.9875 0.9921 0.09428 0.8166 0.8898 0.3123 ] Network output: [ -0.01232 0.03366 1.01 0.0001175 -5.275e-05 0.9818 8.855e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09996 0.09849 0.1772 0.2105 0.9858 0.9916 0.09997 0.7488 0.8712 0.2528 ] Network output: [ 0.00167 0.9987 -0.001457 2.121e-05 -9.523e-06 0.9995 1.599e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001497 Epoch 5895 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01498 0.9976 0.9832 3.494e-06 -1.569e-06 -0.01078 2.633e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003134 -0.002795 -0.01104 0.008274 0.9695 0.9739 0.005911 0.8464 0.8356 0.02256 ] Network output: [ 0.9946 0.03477 0.001793 -4.924e-05 2.21e-05 -0.02595 -3.711e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1835 -0.01602 -0.2129 0.2087 0.9836 0.9933 0.2045 0.4712 0.882 0.7359 ] Network output: [ -0.01298 1.004 1.01 -3.047e-06 1.368e-06 0.01246 -2.296e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004891 0.0009771 0.003855 0.005219 0.989 0.9921 0.004979 0.8794 0.9063 0.01625 ] Network output: [ -0.002258 0.04467 0.9831 -0.0002289 0.0001028 0.9758 -0.0001725 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1941 0.1139 0.3017 0.1755 0.9851 0.994 0.1947 0.4762 0.8888 0.7319 ] Network output: [ 0.01224 -0.01962 1.006 0.0001245 -5.589e-05 0.9898 9.382e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09392 0.08597 0.1704 0.2153 0.9875 0.9921 0.09398 0.8153 0.8897 0.3097 ] Network output: [ -0.01082 0.02902 1.009 0.0001191 -5.347e-05 0.9841 8.976e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09966 0.09817 0.1757 0.2095 0.9858 0.9916 0.09967 0.7471 0.8711 0.2526 ] Network output: [ -0.001093 1 0.002536 1.919e-05 -8.616e-06 0.9997 1.446e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001118 Epoch 5896 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0156 0.9877 0.9837 5.18e-06 -2.325e-06 -0.002556 3.904e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003123 -0.002795 -0.011 0.00846 0.9695 0.9739 0.005892 0.8462 0.8361 0.02262 ] Network output: [ 0.9999 -0.02893 0.00464 -3.875e-05 1.739e-05 0.02443 -2.92e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1826 -0.01688 -0.2098 0.2195 0.9836 0.9933 0.2034 0.4696 0.8824 0.7367 ] Network output: [ -0.01301 1 1.01 -2.466e-06 1.107e-06 0.01574 -1.859e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004872 0.0009819 0.004036 0.005587 0.989 0.9921 0.004959 0.8792 0.9066 0.01635 ] Network output: [ 0.003511 -0.04386 0.9878 -0.0002136 9.59e-05 1.048 -0.000161 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1932 0.1136 0.3076 0.1926 0.9851 0.994 0.1938 0.4751 0.8888 0.7313 ] Network output: [ 0.01004 -0.03693 1.01 0.0001248 -5.603e-05 1.007 9.405e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09429 0.08637 0.1747 0.2201 0.9875 0.9921 0.09434 0.8166 0.8898 0.3123 ] Network output: [ -0.01232 0.03381 1.01 0.0001175 -5.275e-05 0.9817 8.855e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1 0.09853 0.1772 0.2104 0.9858 0.9916 0.1 0.7488 0.8712 0.2527 ] Network output: [ 0.001665 0.9987 -0.001463 2.118e-05 -9.508e-06 0.9995 1.596e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001485 Epoch 5897 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01499 0.9976 0.9832 3.586e-06 -1.61e-06 -0.01074 2.703e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003135 -0.002796 -0.01104 0.008273 0.9695 0.9739 0.005913 0.8464 0.8356 0.02255 ] Network output: [ 0.9946 0.03455 0.001791 -4.93e-05 2.213e-05 -0.02579 -3.715e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1835 -0.01609 -0.2129 0.2087 0.9836 0.9933 0.2044 0.4712 0.882 0.7358 ] Network output: [ -0.01297 1.003 1.01 -2.951e-06 1.325e-06 0.01248 -2.224e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004893 0.0009748 0.003858 0.005219 0.989 0.9921 0.004981 0.8794 0.9064 0.01625 ] Network output: [ -0.002255 0.04441 0.9833 -0.0002288 0.0001027 0.9758 -0.0001725 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.194 0.1138 0.3018 0.1755 0.9851 0.994 0.1946 0.4762 0.8888 0.7319 ] Network output: [ 0.01224 -0.01979 1.006 0.0001245 -5.587e-05 0.99 9.38e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09399 0.08602 0.1705 0.2154 0.9875 0.9921 0.09405 0.8153 0.8897 0.3098 ] Network output: [ -0.01083 0.0292 1.009 0.0001191 -5.346e-05 0.984 8.975e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0997 0.09821 0.1757 0.2095 0.9858 0.9916 0.09972 0.7471 0.8711 0.2525 ] Network output: [ -0.001081 1 0.002505 1.917e-05 -8.607e-06 0.9997 1.445e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001111 Epoch 5898 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0156 0.9877 0.9837 5.259e-06 -2.361e-06 -0.002566 3.963e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003124 -0.002796 -0.011 0.008459 0.9695 0.9739 0.005894 0.8462 0.8361 0.02261 ] Network output: [ 0.9998 -0.02873 0.004616 -3.889e-05 1.746e-05 0.02426 -2.931e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1826 -0.01694 -0.2098 0.2194 0.9836 0.9933 0.2034 0.4696 0.8824 0.7367 ] Network output: [ -0.01301 1 1.01 -2.374e-06 1.066e-06 0.01574 -1.789e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004874 0.0009797 0.004037 0.005584 0.989 0.9921 0.004961 0.8793 0.9066 0.01635 ] Network output: [ 0.003476 -0.04354 0.988 -0.0002137 9.592e-05 1.048 -0.000161 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1932 0.1135 0.3076 0.1925 0.9851 0.994 0.1938 0.4751 0.8888 0.7313 ] Network output: [ 0.01005 -0.03699 1.01 0.0001248 -5.601e-05 1.008 9.402e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09435 0.08641 0.1747 0.2201 0.9875 0.9921 0.09441 0.8166 0.8898 0.3123 ] Network output: [ -0.01232 0.03396 1.009 0.0001175 -5.275e-05 0.9817 8.855e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1 0.09857 0.1772 0.2104 0.9858 0.9916 0.1001 0.7488 0.8712 0.2527 ] Network output: [ 0.001661 0.9987 -0.001469 2.115e-05 -9.494e-06 0.9995 1.594e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001474 Epoch 5899 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01499 0.9975 0.9832 3.676e-06 -1.65e-06 -0.0107 2.771e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003135 -0.002798 -0.01104 0.008273 0.9695 0.9739 0.005914 0.8464 0.8357 0.02255 ] Network output: [ 0.9947 0.03433 0.001788 -4.936e-05 2.216e-05 -0.02563 -3.72e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1835 -0.01615 -0.2129 0.2087 0.9836 0.9933 0.2044 0.4712 0.882 0.7358 ] Network output: [ -0.01297 1.003 1.01 -2.856e-06 1.282e-06 0.0125 -2.152e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004895 0.0009726 0.00386 0.005219 0.989 0.9921 0.004983 0.8794 0.9064 0.01625 ] Network output: [ -0.002252 0.04415 0.9835 -0.0002288 0.0001027 0.9759 -0.0001724 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.194 0.1137 0.3018 0.1755 0.9851 0.994 0.1946 0.4762 0.8888 0.7318 ] Network output: [ 0.01223 -0.01996 1.006 0.0001244 -5.586e-05 0.9903 9.377e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09406 0.08607 0.1706 0.2154 0.9875 0.9921 0.09412 0.8153 0.8897 0.3098 ] Network output: [ -0.01084 0.02938 1.009 0.0001191 -5.346e-05 0.984 8.974e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09975 0.09826 0.1757 0.2094 0.9858 0.9916 0.09976 0.7471 0.8711 0.2525 ] Network output: [ -0.00107 1 0.002474 1.915e-05 -8.598e-06 0.9997 1.443e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001105 Epoch 5900 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0156 0.9877 0.9837 5.336e-06 -2.396e-06 -0.002576 4.022e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003125 -0.002797 -0.011 0.008457 0.9695 0.9739 0.005895 0.8462 0.8361 0.02261 ] Network output: [ 0.9998 -0.02854 0.004594 -3.903e-05 1.752e-05 0.0241 -2.941e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1825 -0.017 -0.2098 0.2193 0.9836 0.9933 0.2034 0.4697 0.8824 0.7366 ] Network output: [ -0.01301 1 1.01 -2.283e-06 1.025e-06 0.01574 -1.72e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004876 0.0009774 0.004038 0.005582 0.989 0.9921 0.004963 0.8793 0.9066 0.01634 ] Network output: [ 0.003442 -0.04323 0.9881 -0.0002137 9.594e-05 1.047 -0.0001611 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1932 0.1133 0.3076 0.1924 0.9851 0.994 0.1938 0.4751 0.8888 0.7312 ] Network output: [ 0.01006 -0.03706 1.01 0.0001247 -5.599e-05 1.008 9.4e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09441 0.08646 0.1748 0.2201 0.9875 0.9921 0.09447 0.8165 0.8898 0.3124 ] Network output: [ -0.01232 0.0341 1.009 0.0001175 -5.275e-05 0.9816 8.855e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1001 0.0986 0.1772 0.2104 0.9858 0.9916 0.1001 0.7488 0.8712 0.2527 ] Network output: [ 0.001657 0.9987 -0.001475 2.111e-05 -9.479e-06 0.9996 1.591e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001464 Epoch 5901 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01499 0.9974 0.9833 3.765e-06 -1.69e-06 -0.01066 2.837e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003136 -0.002799 -0.01104 0.008272 0.9695 0.9739 0.005915 0.8464 0.8357 0.02255 ] Network output: [ 0.9947 0.03412 0.001786 -4.942e-05 2.219e-05 -0.02548 -3.724e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1834 -0.01621 -0.2128 0.2087 0.9836 0.9933 0.2043 0.4712 0.882 0.7357 ] Network output: [ -0.01297 1.003 1.01 -2.762e-06 1.24e-06 0.01252 -2.081e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004897 0.0009704 0.003862 0.005218 0.989 0.9921 0.004985 0.8794 0.9064 0.01625 ] Network output: [ -0.00225 0.04391 0.9837 -0.0002287 0.0001027 0.9759 -0.0001724 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.194 0.1136 0.3019 0.1755 0.9851 0.994 0.1946 0.4762 0.8888 0.7317 ] Network output: [ 0.01222 -0.02012 1.006 0.0001244 -5.584e-05 0.9905 9.374e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09412 0.08612 0.1707 0.2154 0.9875 0.9921 0.09418 0.8153 0.8897 0.3099 ] Network output: [ -0.01085 0.02956 1.009 0.0001191 -5.345e-05 0.9839 8.972e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09979 0.0983 0.1758 0.2094 0.9858 0.9916 0.0998 0.7472 0.8711 0.2525 ] Network output: [ -0.001059 1 0.002443 1.913e-05 -8.589e-06 0.9997 1.442e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001099 Epoch 5902 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0156 0.9877 0.9837 5.413e-06 -2.43e-06 -0.002584 4.079e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003125 -0.002798 -0.011 0.008455 0.9695 0.9739 0.005897 0.8462 0.8361 0.02261 ] Network output: [ 0.9998 -0.02835 0.004571 -3.917e-05 1.758e-05 0.02394 -2.952e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1825 -0.01705 -0.2098 0.2193 0.9836 0.9933 0.2033 0.4697 0.8824 0.7365 ] Network output: [ -0.01301 1 1.01 -2.193e-06 9.845e-07 0.01574 -1.653e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004878 0.0009752 0.00404 0.005579 0.989 0.9921 0.004965 0.8793 0.9066 0.01634 ] Network output: [ 0.003408 -0.04293 0.9883 -0.0002138 9.597e-05 1.047 -0.0001611 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1931 0.1132 0.3077 0.1922 0.9851 0.994 0.1937 0.4752 0.8888 0.7311 ] Network output: [ 0.01007 -0.03713 1.01 0.0001247 -5.598e-05 1.008 9.397e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09448 0.0865 0.1748 0.2201 0.9875 0.9921 0.09453 0.8165 0.8898 0.3124 ] Network output: [ -0.01232 0.03425 1.009 0.0001175 -5.275e-05 0.9816 8.854e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1001 0.09864 0.1772 0.2103 0.9858 0.9916 0.1001 0.7488 0.8712 0.2526 ] Network output: [ 0.001653 0.9987 -0.001482 2.108e-05 -9.464e-06 0.9996 1.589e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001454 Epoch 5903 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01499 0.9974 0.9833 3.852e-06 -1.729e-06 -0.01061 2.903e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003137 -0.0028 -0.01104 0.008272 0.9695 0.9739 0.005917 0.8464 0.8357 0.02255 ] Network output: [ 0.9947 0.03392 0.001783 -4.948e-05 2.221e-05 -0.02534 -3.729e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1834 -0.01627 -0.2128 0.2086 0.9836 0.9933 0.2043 0.4712 0.882 0.7356 ] Network output: [ -0.01297 1.003 1.01 -2.669e-06 1.198e-06 0.01254 -2.012e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004899 0.0009682 0.003864 0.005218 0.989 0.9921 0.004987 0.8794 0.9064 0.01625 ] Network output: [ -0.002248 0.04367 0.9839 -0.0002287 0.0001026 0.976 -0.0001723 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1939 0.1135 0.302 0.1754 0.9851 0.994 0.1945 0.4762 0.8888 0.7316 ] Network output: [ 0.01222 -0.02029 1.006 0.0001243 -5.582e-05 0.9908 9.371e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09419 0.08616 0.1707 0.2155 0.9875 0.9921 0.09425 0.8153 0.8897 0.3099 ] Network output: [ -0.01086 0.02973 1.009 0.000119 -5.344e-05 0.9838 8.971e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09983 0.09833 0.1758 0.2094 0.9858 0.9916 0.09984 0.7472 0.8711 0.2524 ] Network output: [ -0.001049 1 0.002413 1.911e-05 -8.58e-06 0.9997 1.44e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001094 Epoch 5904 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0156 0.9877 0.9837 5.488e-06 -2.464e-06 -0.002592 4.136e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003126 -0.002799 -0.011 0.008453 0.9695 0.9739 0.005898 0.8463 0.8361 0.0226 ] Network output: [ 0.9998 -0.02817 0.004549 -3.931e-05 1.765e-05 0.02378 -2.962e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1825 -0.01711 -0.2098 0.2192 0.9836 0.9933 0.2033 0.4697 0.8824 0.7364 ] Network output: [ -0.013 0.9999 1.01 -2.104e-06 9.447e-07 0.01574 -1.586e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00488 0.000973 0.004041 0.005577 0.989 0.9921 0.004968 0.8793 0.9066 0.01634 ] Network output: [ 0.003376 -0.04264 0.9884 -0.0002138 9.598e-05 1.047 -0.0001611 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1931 0.1131 0.3077 0.1921 0.9851 0.994 0.1937 0.4752 0.8888 0.731 ] Network output: [ 0.01007 -0.03719 1.009 0.0001247 -5.596e-05 1.008 9.394e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09454 0.08655 0.1749 0.2201 0.9875 0.9921 0.0946 0.8165 0.8898 0.3124 ] Network output: [ -0.01232 0.0344 1.009 0.0001175 -5.274e-05 0.9815 8.854e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1002 0.09868 0.1772 0.2103 0.9858 0.9916 0.1002 0.7489 0.8712 0.2526 ] Network output: [ 0.001649 0.9987 -0.001488 2.105e-05 -9.45e-06 0.9996 1.586e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001444 Epoch 5905 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.015 0.9973 0.9833 3.938e-06 -1.768e-06 -0.01057 2.968e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003137 -0.002801 -0.01104 0.008271 0.9695 0.9739 0.005918 0.8465 0.8357 0.02254 ] Network output: [ 0.9947 0.03372 0.00178 -4.954e-05 2.224e-05 -0.0252 -3.734e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1834 -0.01633 -0.2128 0.2086 0.9836 0.9933 0.2043 0.4712 0.882 0.7355 ] Network output: [ -0.01297 1.003 1.01 -2.578e-06 1.158e-06 0.01256 -1.943e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004901 0.0009661 0.003866 0.005218 0.989 0.9921 0.004989 0.8794 0.9064 0.01625 ] Network output: [ -0.002247 0.04345 0.9841 -0.0002286 0.0001026 0.976 -0.0001723 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1939 0.1134 0.302 0.1754 0.9851 0.994 0.1945 0.4763 0.8888 0.7315 ] Network output: [ 0.01221 -0.02045 1.005 0.0001243 -5.581e-05 0.991 9.368e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09425 0.08621 0.1708 0.2155 0.9875 0.9921 0.09431 0.8153 0.8896 0.31 ] Network output: [ -0.01087 0.02991 1.009 0.000119 -5.343e-05 0.9838 8.97e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09987 0.09837 0.1758 0.2093 0.9858 0.9916 0.09988 0.7472 0.8711 0.2524 ] Network output: [ -0.001039 1 0.002384 1.909e-05 -8.57e-06 0.9998 1.439e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001088 Epoch 5906 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0156 0.9877 0.9837 5.562e-06 -2.497e-06 -0.002598 4.192e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003127 -0.002801 -0.011 0.008452 0.9695 0.9739 0.0059 0.8463 0.8361 0.0226 ] Network output: [ 0.9998 -0.028 0.004528 -3.944e-05 1.771e-05 0.02363 -2.972e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1825 -0.01717 -0.2098 0.2191 0.9836 0.9933 0.2033 0.4697 0.8824 0.7363 ] Network output: [ -0.013 0.9999 1.01 -2.017e-06 9.054e-07 0.01574 -1.52e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004882 0.0009708 0.004042 0.005575 0.989 0.9921 0.00497 0.8793 0.9066 0.01634 ] Network output: [ 0.003344 -0.04236 0.9886 -0.0002138 9.6e-05 1.046 -0.0001612 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1931 0.113 0.3077 0.192 0.9851 0.994 0.1937 0.4752 0.8888 0.7309 ] Network output: [ 0.01008 -0.03726 1.009 0.0001246 -5.594e-05 1.008 9.391e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0946 0.08659 0.1749 0.2201 0.9875 0.9921 0.09466 0.8165 0.8898 0.3125 ] Network output: [ -0.01232 0.03454 1.009 0.0001175 -5.274e-05 0.9815 8.854e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1002 0.09871 0.1772 0.2103 0.9858 0.9916 0.1002 0.7489 0.8712 0.2525 ] Network output: [ 0.001645 0.9987 -0.001495 2.102e-05 -9.435e-06 0.9996 1.584e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001435 Epoch 5907 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.015 0.9973 0.9833 4.022e-06 -1.806e-06 -0.01054 3.031e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003138 -0.002802 -0.01104 0.008271 0.9695 0.9739 0.005919 0.8465 0.8357 0.02254 ] Network output: [ 0.9948 0.03354 0.001777 -4.96e-05 2.227e-05 -0.02506 -3.738e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1833 -0.01639 -0.2127 0.2086 0.9836 0.9933 0.2042 0.4712 0.882 0.7355 ] Network output: [ -0.01297 1.003 1.01 -2.489e-06 1.117e-06 0.01258 -1.875e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004903 0.0009639 0.003869 0.005218 0.989 0.9921 0.004991 0.8794 0.9064 0.01624 ] Network output: [ -0.002246 0.04323 0.9843 -0.0002285 0.0001026 0.976 -0.0001722 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1939 0.1133 0.3021 0.1754 0.9851 0.994 0.1945 0.4763 0.8888 0.7315 ] Network output: [ 0.01221 -0.02061 1.005 0.0001243 -5.579e-05 0.9913 9.366e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09431 0.08626 0.1709 0.2155 0.9875 0.9921 0.09437 0.8153 0.8896 0.31 ] Network output: [ -0.01088 0.03008 1.008 0.000119 -5.343e-05 0.9837 8.969e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09991 0.09841 0.1758 0.2093 0.9858 0.9916 0.09992 0.7472 0.8711 0.2524 ] Network output: [ -0.001029 1 0.002356 1.907e-05 -8.561e-06 0.9998 1.437e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001084 Epoch 5908 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01559 0.9877 0.9837 5.635e-06 -2.53e-06 -0.002604 4.247e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003128 -0.002802 -0.011 0.00845 0.9695 0.9739 0.005901 0.8463 0.8361 0.0226 ] Network output: [ 0.9998 -0.02783 0.004507 -3.957e-05 1.777e-05 0.02349 -2.982e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.01722 -0.2098 0.219 0.9836 0.9933 0.2033 0.4698 0.8824 0.7363 ] Network output: [ -0.013 0.9999 1.01 -1.931e-06 8.667e-07 0.01575 -1.455e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004884 0.0009686 0.004043 0.005572 0.989 0.9921 0.004972 0.8793 0.9066 0.01634 ] Network output: [ 0.003313 -0.04208 0.9887 -0.0002139 9.602e-05 1.046 -0.0001612 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1931 0.1129 0.3078 0.1919 0.9851 0.994 0.1936 0.4752 0.8888 0.7309 ] Network output: [ 0.01009 -0.03733 1.009 0.0001246 -5.593e-05 1.008 9.388e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09466 0.08664 0.1749 0.2201 0.9875 0.9921 0.09472 0.8165 0.8898 0.3125 ] Network output: [ -0.01232 0.03469 1.009 0.0001175 -5.274e-05 0.9814 8.853e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1002 0.09875 0.1772 0.2102 0.9858 0.9916 0.1003 0.7489 0.8712 0.2525 ] Network output: [ 0.001642 0.9987 -0.001502 2.098e-05 -9.421e-06 0.9996 1.581e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001427 Epoch 5909 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.015 0.9972 0.9833 4.105e-06 -1.843e-06 -0.0105 3.093e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003139 -0.002803 -0.01103 0.00827 0.9695 0.9739 0.00592 0.8465 0.8357 0.02254 ] Network output: [ 0.9948 0.03335 0.001774 -4.966e-05 2.23e-05 -0.02493 -3.743e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1833 -0.01645 -0.2127 0.2086 0.9836 0.9933 0.2042 0.4713 0.882 0.7354 ] Network output: [ -0.01296 1.003 1.01 -2.4e-06 1.077e-06 0.0126 -1.809e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004905 0.0009617 0.003871 0.005217 0.989 0.9921 0.004992 0.8794 0.9064 0.01624 ] Network output: [ -0.002245 0.04302 0.9845 -0.0002285 0.0001026 0.9761 -0.0001722 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1938 0.1132 0.3022 0.1754 0.9851 0.994 0.1944 0.4763 0.8888 0.7314 ] Network output: [ 0.0122 -0.02076 1.005 0.0001242 -5.577e-05 0.9915 9.363e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09438 0.0863 0.1709 0.2155 0.9875 0.9921 0.09443 0.8152 0.8896 0.3101 ] Network output: [ -0.01088 0.03025 1.008 0.000119 -5.342e-05 0.9836 8.967e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09995 0.09844 0.1758 0.2093 0.9858 0.9916 0.09996 0.7473 0.8711 0.2523 ] Network output: [ -0.001019 1 0.002328 1.905e-05 -8.551e-06 0.9998 1.435e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001079 Epoch 5910 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01559 0.9877 0.9837 5.707e-06 -2.562e-06 -0.002609 4.301e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003128 -0.002803 -0.011 0.008448 0.9695 0.9739 0.005902 0.8463 0.8361 0.0226 ] Network output: [ 0.9998 -0.02767 0.004486 -3.97e-05 1.782e-05 0.02335 -2.992e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.01728 -0.2097 0.219 0.9836 0.9933 0.2032 0.4698 0.8824 0.7362 ] Network output: [ -0.013 0.9999 1.01 -1.845e-06 8.285e-07 0.01575 -1.391e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004886 0.0009664 0.004044 0.00557 0.989 0.9921 0.004974 0.8793 0.9066 0.01634 ] Network output: [ 0.003283 -0.04182 0.9889 -0.0002139 9.603e-05 1.045 -0.0001612 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.193 0.1128 0.3078 0.1918 0.9851 0.994 0.1936 0.4752 0.8888 0.7308 ] Network output: [ 0.01009 -0.0374 1.009 0.0001245 -5.591e-05 1.009 9.386e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09472 0.08668 0.175 0.2201 0.9875 0.9921 0.09478 0.8164 0.8897 0.3125 ] Network output: [ -0.01232 0.03483 1.009 0.0001175 -5.273e-05 0.9813 8.853e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1003 0.09878 0.1772 0.2102 0.9858 0.9916 0.1003 0.7489 0.8712 0.2525 ] Network output: [ 0.001639 0.9986 -0.00151 2.095e-05 -9.406e-06 0.9997 1.579e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001419 Epoch 5911 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.015 0.9972 0.9833 4.186e-06 -1.879e-06 -0.01046 3.155e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003139 -0.002805 -0.01103 0.008269 0.9695 0.9739 0.005922 0.8465 0.8357 0.02254 ] Network output: [ 0.9948 0.03318 0.00177 -4.973e-05 2.232e-05 -0.02481 -3.747e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1833 -0.01651 -0.2127 0.2086 0.9836 0.9933 0.2042 0.4713 0.882 0.7353 ] Network output: [ -0.01296 1.003 1.01 -2.313e-06 1.038e-06 0.01262 -1.743e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004907 0.0009596 0.003873 0.005217 0.989 0.9921 0.004994 0.8794 0.9064 0.01624 ] Network output: [ -0.002244 0.04281 0.9846 -0.0002284 0.0001025 0.9761 -0.0001721 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1938 0.113 0.3022 0.1753 0.9851 0.994 0.1944 0.4763 0.8888 0.7313 ] Network output: [ 0.01219 -0.02092 1.005 0.0001242 -5.576e-05 0.9917 9.36e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09444 0.08634 0.171 0.2156 0.9875 0.9921 0.09449 0.8152 0.8896 0.3101 ] Network output: [ -0.01089 0.03042 1.008 0.000119 -5.341e-05 0.9835 8.966e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09999 0.09848 0.1758 0.2093 0.9858 0.9916 0.1 0.7473 0.8711 0.2523 ] Network output: [ -0.00101 1 0.002301 1.903e-05 -8.541e-06 0.9998 1.434e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001075 Epoch 5912 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01559 0.9877 0.9838 5.777e-06 -2.594e-06 -0.002613 4.354e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003129 -0.002804 -0.011 0.008447 0.9695 0.9739 0.005904 0.8463 0.8361 0.02259 ] Network output: [ 0.9998 -0.02751 0.004466 -3.983e-05 1.788e-05 0.02321 -3.002e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.01733 -0.2097 0.2189 0.9836 0.9933 0.2032 0.4698 0.8824 0.7361 ] Network output: [ -0.013 0.9999 1.01 -1.762e-06 7.908e-07 0.01575 -1.328e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004888 0.0009643 0.004045 0.005567 0.989 0.9921 0.004976 0.8793 0.9066 0.01634 ] Network output: [ 0.003254 -0.04157 0.9891 -0.0002139 9.604e-05 1.045 -0.0001612 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.193 0.1127 0.3078 0.1916 0.9851 0.994 0.1936 0.4753 0.8888 0.7307 ] Network output: [ 0.0101 -0.03748 1.009 0.0001245 -5.589e-05 1.009 9.383e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09478 0.08672 0.175 0.2201 0.9875 0.9921 0.09483 0.8164 0.8897 0.3125 ] Network output: [ -0.01232 0.03497 1.009 0.0001175 -5.273e-05 0.9813 8.852e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1003 0.09881 0.1772 0.2102 0.9858 0.9916 0.1003 0.7489 0.8712 0.2524 ] Network output: [ 0.001636 0.9986 -0.001517 2.092e-05 -9.392e-06 0.9997 1.577e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001411 Epoch 5913 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.015 0.9971 0.9833 4.266e-06 -1.915e-06 -0.01042 3.215e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00314 -0.002806 -0.01103 0.008268 0.9695 0.9739 0.005923 0.8465 0.8357 0.02253 ] Network output: [ 0.9949 0.03301 0.001767 -4.979e-05 2.235e-05 -0.02469 -3.752e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1832 -0.01657 -0.2126 0.2086 0.9836 0.9933 0.2041 0.4713 0.882 0.7352 ] Network output: [ -0.01296 1.003 1.01 -2.227e-06 9.997e-07 0.01264 -1.678e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004909 0.0009575 0.003875 0.005216 0.989 0.9921 0.004996 0.8794 0.9064 0.01624 ] Network output: [ -0.002244 0.04262 0.9848 -0.0002283 0.0001025 0.9761 -0.0001721 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1938 0.1129 0.3023 0.1753 0.9851 0.994 0.1944 0.4763 0.8889 0.7312 ] Network output: [ 0.01219 -0.02107 1.005 0.0001242 -5.574e-05 0.992 9.357e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0945 0.08639 0.1711 0.2156 0.9875 0.9921 0.09455 0.8152 0.8896 0.3102 ] Network output: [ -0.0109 0.03059 1.008 0.0001189 -5.34e-05 0.9835 8.964e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1 0.09851 0.1758 0.2092 0.9858 0.9916 0.1 0.7473 0.8711 0.2522 ] Network output: [ -0.001001 1 0.002274 1.9e-05 -8.531e-06 0.9998 1.432e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001071 Epoch 5914 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01559 0.9877 0.9838 5.847e-06 -2.625e-06 -0.002616 4.406e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00313 -0.002805 -0.01099 0.008445 0.9695 0.9739 0.005905 0.8463 0.8361 0.02259 ] Network output: [ 0.9998 -0.02736 0.004446 -3.996e-05 1.794e-05 0.02308 -3.011e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.01739 -0.2097 0.2188 0.9836 0.9933 0.2032 0.4698 0.8824 0.736 ] Network output: [ -0.01299 0.9999 1.01 -1.679e-06 7.537e-07 0.01575 -1.265e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00489 0.0009621 0.004047 0.005565 0.989 0.9921 0.004978 0.8793 0.9066 0.01633 ] Network output: [ 0.003226 -0.04132 0.9892 -0.000214 9.605e-05 1.045 -0.0001612 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.193 0.1126 0.3079 0.1915 0.9851 0.994 0.1936 0.4753 0.8888 0.7306 ] Network output: [ 0.0101 -0.03755 1.009 0.0001245 -5.587e-05 1.009 9.38e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09483 0.08676 0.1751 0.2201 0.9875 0.9921 0.09489 0.8164 0.8897 0.3126 ] Network output: [ -0.01232 0.03512 1.009 0.0001174 -5.273e-05 0.9812 8.851e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1003 0.09885 0.1772 0.2101 0.9858 0.9916 0.1004 0.7489 0.8712 0.2524 ] Network output: [ 0.001633 0.9986 -0.001524 2.089e-05 -9.377e-06 0.9997 1.574e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001404 Epoch 5915 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01501 0.9971 0.9833 4.344e-06 -1.95e-06 -0.01038 3.274e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003141 -0.002807 -0.01103 0.008268 0.9695 0.9739 0.005924 0.8465 0.8357 0.02253 ] Network output: [ 0.9949 0.03285 0.001763 -4.985e-05 2.238e-05 -0.02457 -3.757e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1832 -0.01663 -0.2126 0.2086 0.9836 0.9933 0.2041 0.4713 0.882 0.7352 ] Network output: [ -0.01296 1.003 1.01 -2.142e-06 9.616e-07 0.01266 -1.614e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00491 0.0009553 0.003877 0.005216 0.989 0.9921 0.004998 0.8794 0.9064 0.01624 ] Network output: [ -0.002243 0.04243 0.985 -0.0002283 0.0001025 0.9761 -0.000172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1937 0.1128 0.3023 0.1753 0.9851 0.994 0.1943 0.4763 0.8889 0.7311 ] Network output: [ 0.01218 -0.02122 1.005 0.0001241 -5.572e-05 0.9922 9.354e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09455 0.08643 0.1711 0.2156 0.9875 0.9921 0.09461 0.8152 0.8896 0.3102 ] Network output: [ -0.01091 0.03076 1.008 0.0001189 -5.339e-05 0.9834 8.963e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1001 0.09855 0.1758 0.2092 0.9858 0.9916 0.1001 0.7473 0.8711 0.2522 ] Network output: [ -0.0009923 1 0.002248 1.898e-05 -8.521e-06 0.9998 1.43e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001067 Epoch 5916 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01559 0.9877 0.9838 5.915e-06 -2.656e-06 -0.002619 4.458e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00313 -0.002806 -0.01099 0.008443 0.9695 0.9739 0.005906 0.8464 0.8361 0.02259 ] Network output: [ 0.9998 -0.02721 0.004427 -4.008e-05 1.799e-05 0.02295 -3.021e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.01744 -0.2097 0.2188 0.9836 0.9933 0.2032 0.4699 0.8824 0.736 ] Network output: [ -0.01299 0.9998 1.01 -1.597e-06 7.171e-07 0.01576 -1.204e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004892 0.00096 0.004048 0.005563 0.989 0.9921 0.00498 0.8793 0.9066 0.01633 ] Network output: [ 0.003198 -0.04108 0.9894 -0.000214 9.606e-05 1.044 -0.0001613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1929 0.1125 0.3079 0.1914 0.9851 0.994 0.1935 0.4753 0.8888 0.7306 ] Network output: [ 0.0101 -0.03762 1.009 0.0001244 -5.586e-05 1.009 9.377e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09489 0.0868 0.1751 0.2201 0.9875 0.9921 0.09495 0.8164 0.8897 0.3126 ] Network output: [ -0.01232 0.03526 1.009 0.0001174 -5.272e-05 0.9812 8.851e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1004 0.09888 0.1772 0.2101 0.9858 0.9916 0.1004 0.7489 0.8712 0.2523 ] Network output: [ 0.00163 0.9986 -0.001532 2.086e-05 -9.363e-06 0.9997 1.572e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001397 Epoch 5917 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01501 0.997 0.9833 4.421e-06 -1.985e-06 -0.01035 3.331e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003141 -0.002808 -0.01103 0.008267 0.9695 0.9739 0.005925 0.8466 0.8357 0.02253 ] Network output: [ 0.9949 0.03269 0.00176 -4.991e-05 2.241e-05 -0.02446 -3.761e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1832 -0.01669 -0.2126 0.2086 0.9836 0.9933 0.2041 0.4713 0.8821 0.7351 ] Network output: [ -0.01296 1.003 1.01 -2.058e-06 9.241e-07 0.01268 -1.551e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004912 0.0009532 0.003879 0.005215 0.989 0.9921 0.005 0.8794 0.9064 0.01624 ] Network output: [ -0.002244 0.04225 0.9852 -0.0002282 0.0001024 0.9761 -0.000172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1937 0.1127 0.3024 0.1752 0.9851 0.994 0.1943 0.4764 0.8889 0.7311 ] Network output: [ 0.01217 -0.02137 1.005 0.0001241 -5.57e-05 0.9924 9.351e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09461 0.08647 0.1712 0.2156 0.9875 0.9921 0.09467 0.8152 0.8896 0.3102 ] Network output: [ -0.01092 0.03093 1.008 0.0001189 -5.338e-05 0.9833 8.962e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1001 0.09858 0.1758 0.2092 0.9858 0.9916 0.1001 0.7473 0.8711 0.2522 ] Network output: [ -0.000984 1 0.002223 1.896e-05 -8.511e-06 0.9999 1.429e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001064 Epoch 5918 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01559 0.9877 0.9838 5.983e-06 -2.686e-06 -0.002621 4.509e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003131 -0.002807 -0.01099 0.008441 0.9695 0.9739 0.005908 0.8464 0.8361 0.02258 ] Network output: [ 0.9998 -0.02707 0.004408 -4.02e-05 1.805e-05 0.02283 -3.03e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.01749 -0.2097 0.2187 0.9836 0.9933 0.2031 0.4699 0.8824 0.7359 ] Network output: [ -0.01299 0.9998 1.01 -1.517e-06 6.809e-07 0.01576 -1.143e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004894 0.0009578 0.004049 0.005561 0.989 0.9921 0.004982 0.8793 0.9066 0.01633 ] Network output: [ 0.003171 -0.04085 0.9895 -0.000214 9.606e-05 1.044 -0.0001613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1929 0.1124 0.3079 0.1913 0.9851 0.994 0.1935 0.4753 0.8889 0.7305 ] Network output: [ 0.01011 -0.0377 1.009 0.0001244 -5.584e-05 1.009 9.374e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09495 0.08684 0.1752 0.2201 0.9875 0.9921 0.095 0.8163 0.8897 0.3126 ] Network output: [ -0.01232 0.0354 1.009 0.0001174 -5.272e-05 0.9811 8.85e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1004 0.09891 0.1772 0.2101 0.9859 0.9916 0.1004 0.7489 0.8712 0.2523 ] Network output: [ 0.001627 0.9986 -0.00154 2.082e-05 -9.349e-06 0.9998 1.569e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00139 Epoch 5919 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01501 0.997 0.9834 4.496e-06 -2.018e-06 -0.01031 3.388e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003142 -0.002809 -0.01103 0.008266 0.9695 0.9739 0.005926 0.8466 0.8357 0.02253 ] Network output: [ 0.9949 0.03254 0.001756 -4.997e-05 2.243e-05 -0.02435 -3.766e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1832 -0.01674 -0.2126 0.2085 0.9836 0.9933 0.2041 0.4713 0.8821 0.735 ] Network output: [ -0.01295 1.003 1.01 -1.976e-06 8.872e-07 0.0127 -1.489e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004914 0.0009511 0.003881 0.005215 0.989 0.9921 0.005002 0.8794 0.9064 0.01624 ] Network output: [ -0.002244 0.04208 0.9853 -0.0002281 0.0001024 0.9761 -0.0001719 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1937 0.1126 0.3024 0.1752 0.9851 0.994 0.1943 0.4764 0.8889 0.731 ] Network output: [ 0.01217 -0.02152 1.005 0.000124 -5.568e-05 0.9926 9.348e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09467 0.08651 0.1713 0.2156 0.9875 0.9921 0.09473 0.8152 0.8896 0.3103 ] Network output: [ -0.01092 0.03109 1.008 0.0001189 -5.337e-05 0.9832 8.96e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1001 0.09861 0.1758 0.2091 0.9858 0.9916 0.1001 0.7474 0.8711 0.2521 ] Network output: [ -0.000976 0.9999 0.002198 1.894e-05 -8.501e-06 0.9999 1.427e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001061 Epoch 5920 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01559 0.9877 0.9838 6.049e-06 -2.715e-06 -0.002622 4.558e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003132 -0.002808 -0.01099 0.00844 0.9695 0.9739 0.005909 0.8464 0.8361 0.02258 ] Network output: [ 0.9998 -0.02694 0.00439 -4.032e-05 1.81e-05 0.02272 -3.039e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.01755 -0.2096 0.2186 0.9836 0.9933 0.2031 0.4699 0.8824 0.7358 ] Network output: [ -0.01299 0.9998 1.01 -1.437e-06 6.453e-07 0.01577 -1.083e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004896 0.0009557 0.00405 0.005558 0.989 0.9921 0.004984 0.8793 0.9066 0.01633 ] Network output: [ 0.003145 -0.04063 0.9897 -0.000214 9.607e-05 1.044 -0.0001613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1929 0.1123 0.3079 0.1912 0.9851 0.994 0.1935 0.4754 0.8889 0.7304 ] Network output: [ 0.01011 -0.03777 1.009 0.0001243 -5.582e-05 1.009 9.371e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.095 0.08688 0.1752 0.2201 0.9875 0.9921 0.09506 0.8163 0.8897 0.3127 ] Network output: [ -0.01232 0.03554 1.009 0.0001174 -5.271e-05 0.981 8.849e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1004 0.09894 0.1772 0.21 0.9859 0.9916 0.1005 0.749 0.8712 0.2523 ] Network output: [ 0.001625 0.9986 -0.001548 2.079e-05 -9.334e-06 0.9998 1.567e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001384 Epoch 5921 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01501 0.9969 0.9834 4.57e-06 -2.052e-06 -0.01028 3.444e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003142 -0.00281 -0.01103 0.008265 0.9695 0.9739 0.005928 0.8466 0.8358 0.02252 ] Network output: [ 0.995 0.03239 0.001752 -5.003e-05 2.246e-05 -0.02425 -3.771e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1831 -0.0168 -0.2125 0.2085 0.9836 0.9933 0.204 0.4714 0.8821 0.7349 ] Network output: [ -0.01295 1.003 1.01 -1.895e-06 8.508e-07 0.01271 -1.428e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004916 0.000949 0.003883 0.005214 0.989 0.9921 0.005004 0.8794 0.9064 0.01624 ] Network output: [ -0.002245 0.04191 0.9855 -0.0002281 0.0001024 0.9761 -0.0001719 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1937 0.1125 0.3025 0.1752 0.9851 0.994 0.1943 0.4764 0.8889 0.7309 ] Network output: [ 0.01216 -0.02166 1.005 0.000124 -5.567e-05 0.9929 9.345e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09473 0.08655 0.1713 0.2157 0.9875 0.9921 0.09478 0.8152 0.8896 0.3103 ] Network output: [ -0.01093 0.03125 1.008 0.0001189 -5.337e-05 0.9832 8.958e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1002 0.09864 0.1758 0.2091 0.9858 0.9916 0.1002 0.7474 0.8711 0.2521 ] Network output: [ -0.0009682 0.9999 0.002173 1.891e-05 -8.49e-06 0.9999 1.425e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001058 Epoch 5922 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01559 0.9877 0.9838 6.114e-06 -2.745e-06 -0.002622 4.607e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003132 -0.002809 -0.01099 0.008438 0.9695 0.9739 0.00591 0.8464 0.8362 0.02258 ] Network output: [ 0.9998 -0.02681 0.004372 -4.044e-05 1.815e-05 0.0226 -3.048e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.0176 -0.2096 0.2186 0.9836 0.9933 0.2031 0.4699 0.8824 0.7357 ] Network output: [ -0.01298 0.9998 1.01 -1.359e-06 6.102e-07 0.01577 -1.024e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004898 0.0009536 0.004051 0.005556 0.989 0.9921 0.004985 0.8793 0.9066 0.01633 ] Network output: [ 0.003119 -0.04042 0.9898 -0.000214 9.607e-05 1.044 -0.0001613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1929 0.1122 0.308 0.1911 0.9851 0.994 0.1935 0.4754 0.8889 0.7303 ] Network output: [ 0.01012 -0.03785 1.009 0.0001243 -5.58e-05 1.009 9.368e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09505 0.08692 0.1753 0.2201 0.9875 0.9921 0.09511 0.8163 0.8897 0.3127 ] Network output: [ -0.01232 0.03568 1.008 0.0001174 -5.271e-05 0.981 8.848e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1005 0.09897 0.1772 0.21 0.9859 0.9916 0.1005 0.749 0.8712 0.2522 ] Network output: [ 0.001623 0.9986 -0.001556 2.076e-05 -9.32e-06 0.9998 1.565e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001378 Epoch 5923 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01501 0.9969 0.9834 4.642e-06 -2.084e-06 -0.01024 3.499e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003143 -0.002811 -0.01102 0.008264 0.9695 0.9739 0.005929 0.8466 0.8358 0.02252 ] Network output: [ 0.995 0.03225 0.001748 -5.009e-05 2.249e-05 -0.02415 -3.775e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1831 -0.01686 -0.2125 0.2085 0.9836 0.9933 0.204 0.4714 0.8821 0.7349 ] Network output: [ -0.01295 1.003 1.01 -1.815e-06 8.15e-07 0.01273 -1.368e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004918 0.000947 0.003885 0.005213 0.989 0.9921 0.005005 0.8795 0.9064 0.01624 ] Network output: [ -0.002246 0.04175 0.9857 -0.000228 0.0001024 0.9761 -0.0001718 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1936 0.1124 0.3026 0.1751 0.9851 0.994 0.1942 0.4764 0.8889 0.7308 ] Network output: [ 0.01216 -0.02181 1.005 0.000124 -5.565e-05 0.9931 9.342e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09478 0.08659 0.1714 0.2157 0.9875 0.9921 0.09484 0.8151 0.8896 0.3103 ] Network output: [ -0.01094 0.03142 1.008 0.0001189 -5.336e-05 0.9831 8.957e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1002 0.09867 0.1758 0.2091 0.9858 0.9916 0.1002 0.7474 0.8711 0.252 ] Network output: [ -0.0009607 0.9999 0.00215 1.889e-05 -8.48e-06 0.9999 1.424e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001055 Epoch 5924 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01559 0.9877 0.9838 6.177e-06 -2.773e-06 -0.002621 4.656e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003133 -0.00281 -0.01099 0.008436 0.9695 0.9739 0.005911 0.8464 0.8362 0.02257 ] Network output: [ 0.9998 -0.02668 0.004354 -4.056e-05 1.821e-05 0.02249 -3.056e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.01765 -0.2096 0.2185 0.9836 0.9933 0.2031 0.47 0.8825 0.7356 ] Network output: [ -0.01298 0.9998 1.01 -1.282e-06 5.757e-07 0.01577 -9.664e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0049 0.0009515 0.004053 0.005554 0.989 0.9921 0.004987 0.8794 0.9066 0.01633 ] Network output: [ 0.003094 -0.04021 0.9899 -0.000214 9.607e-05 1.043 -0.0001613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.1121 0.308 0.191 0.9851 0.994 0.1934 0.4754 0.8889 0.7302 ] Network output: [ 0.01012 -0.03793 1.009 0.0001243 -5.579e-05 1.01 9.365e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09511 0.08695 0.1753 0.2201 0.9875 0.9921 0.09517 0.8163 0.8897 0.3127 ] Network output: [ -0.01232 0.03583 1.008 0.0001174 -5.27e-05 0.9809 8.847e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1005 0.09899 0.1772 0.2099 0.9859 0.9916 0.1005 0.749 0.8712 0.2522 ] Network output: [ 0.00162 0.9986 -0.001564 2.073e-05 -9.306e-06 0.9998 1.562e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001372 Epoch 5925 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01501 0.9968 0.9834 4.714e-06 -2.116e-06 -0.01021 3.552e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003144 -0.002812 -0.01102 0.008263 0.9695 0.9739 0.00593 0.8466 0.8358 0.02252 ] Network output: [ 0.995 0.03212 0.001743 -5.016e-05 2.252e-05 -0.02406 -3.78e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1831 -0.01691 -0.2125 0.2085 0.9836 0.9933 0.204 0.4714 0.8821 0.7348 ] Network output: [ -0.01295 1.003 1.01 -1.737e-06 7.797e-07 0.01275 -1.309e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004919 0.0009449 0.003887 0.005213 0.989 0.9921 0.005007 0.8795 0.9064 0.01623 ] Network output: [ -0.002247 0.0416 0.9859 -0.0002279 0.0001023 0.9761 -0.0001718 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1936 0.1123 0.3026 0.1751 0.9851 0.994 0.1942 0.4764 0.8889 0.7307 ] Network output: [ 0.01215 -0.02195 1.005 0.0001239 -5.563e-05 0.9933 9.338e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09483 0.08663 0.1715 0.2157 0.9875 0.9921 0.09489 0.8151 0.8895 0.3104 ] Network output: [ -0.01094 0.03158 1.008 0.0001188 -5.335e-05 0.983 8.955e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1002 0.0987 0.1758 0.209 0.9858 0.9916 0.1002 0.7474 0.8711 0.252 ] Network output: [ -0.0009535 0.9999 0.002126 1.887e-05 -8.469e-06 0.9999 1.422e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001053 Epoch 5926 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01559 0.9877 0.9838 6.24e-06 -2.801e-06 -0.00262 4.703e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003133 -0.002812 -0.01099 0.008435 0.9695 0.9739 0.005913 0.8464 0.8362 0.02257 ] Network output: [ 0.9998 -0.02656 0.004337 -4.067e-05 1.826e-05 0.02239 -3.065e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.0177 -0.2096 0.2184 0.9836 0.9933 0.203 0.47 0.8825 0.7356 ] Network output: [ -0.01298 0.9997 1.01 -1.206e-06 5.416e-07 0.01578 -9.091e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004902 0.0009494 0.004054 0.005552 0.989 0.9921 0.004989 0.8794 0.9066 0.01632 ] Network output: [ 0.00307 -0.04002 0.9901 -0.000214 9.607e-05 1.043 -0.0001613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.112 0.308 0.1909 0.9851 0.994 0.1934 0.4754 0.8889 0.7302 ] Network output: [ 0.01012 -0.038 1.009 0.0001242 -5.577e-05 1.01 9.362e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09516 0.08699 0.1754 0.2201 0.9875 0.9921 0.09522 0.8163 0.8897 0.3127 ] Network output: [ -0.01232 0.03597 1.008 0.0001174 -5.27e-05 0.9808 8.846e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1005 0.09902 0.1772 0.2099 0.9859 0.9916 0.1005 0.749 0.8712 0.2521 ] Network output: [ 0.001618 0.9986 -0.001572 2.07e-05 -9.292e-06 0.9998 1.56e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001367 Epoch 5927 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01502 0.9968 0.9834 4.783e-06 -2.147e-06 -0.01018 3.605e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003144 -0.002813 -0.01102 0.008262 0.9695 0.9739 0.005931 0.8466 0.8358 0.02251 ] Network output: [ 0.995 0.03199 0.001739 -5.022e-05 2.254e-05 -0.02397 -3.785e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1831 -0.01697 -0.2124 0.2085 0.9836 0.9933 0.2039 0.4714 0.8821 0.7347 ] Network output: [ -0.01294 1.003 1.01 -1.659e-06 7.449e-07 0.01277 -1.251e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004921 0.0009428 0.003888 0.005212 0.989 0.9921 0.005009 0.8795 0.9064 0.01623 ] Network output: [ -0.002248 0.04145 0.986 -0.0002278 0.0001023 0.9761 -0.0001717 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1936 0.1122 0.3027 0.1751 0.9851 0.994 0.1942 0.4765 0.8889 0.7307 ] Network output: [ 0.01214 -0.02209 1.005 0.0001239 -5.561e-05 0.9935 9.335e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09489 0.08667 0.1715 0.2157 0.9875 0.9921 0.09495 0.8151 0.8895 0.3104 ] Network output: [ -0.01095 0.03174 1.008 0.0001188 -5.334e-05 0.983 8.954e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1003 0.09873 0.1758 0.209 0.9858 0.9916 0.1003 0.7474 0.8711 0.252 ] Network output: [ -0.0009466 0.9999 0.002104 1.884e-05 -8.459e-06 1 1.42e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001051 Epoch 5928 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01559 0.9877 0.9838 6.302e-06 -2.829e-06 -0.002619 4.749e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003134 -0.002813 -0.01099 0.008433 0.9695 0.9739 0.005914 0.8465 0.8362 0.02257 ] Network output: [ 0.9998 -0.02644 0.00432 -4.078e-05 1.831e-05 0.02229 -3.073e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.01776 -0.2096 0.2184 0.9836 0.9933 0.203 0.47 0.8825 0.7355 ] Network output: [ -0.01298 0.9997 1.01 -1.132e-06 5.08e-07 0.01578 -8.528e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004903 0.0009474 0.004055 0.00555 0.989 0.9921 0.004991 0.8794 0.9066 0.01632 ] Network output: [ 0.003047 -0.03982 0.9902 -0.000214 9.607e-05 1.043 -0.0001613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.1119 0.3081 0.1908 0.9851 0.994 0.1934 0.4754 0.8889 0.7301 ] Network output: [ 0.01012 -0.03808 1.009 0.0001242 -5.575e-05 1.01 9.359e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09521 0.08702 0.1754 0.2201 0.9875 0.9921 0.09527 0.8162 0.8896 0.3128 ] Network output: [ -0.01232 0.03611 1.008 0.0001174 -5.269e-05 0.9808 8.845e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.09905 0.1772 0.2099 0.9859 0.9916 0.1006 0.749 0.8712 0.2521 ] Network output: [ 0.001616 0.9986 -0.00158 2.067e-05 -9.277e-06 0.9999 1.557e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001362 Epoch 5929 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01502 0.9967 0.9834 4.852e-06 -2.178e-06 -0.01015 3.656e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003145 -0.002814 -0.01102 0.008261 0.9695 0.9739 0.005932 0.8467 0.8358 0.02251 ] Network output: [ 0.995 0.03186 0.001735 -5.028e-05 2.257e-05 -0.02388 -3.789e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.183 -0.01702 -0.2124 0.2085 0.9836 0.9933 0.2039 0.4714 0.8821 0.7347 ] Network output: [ -0.01294 1.003 1.01 -1.583e-06 7.107e-07 0.01278 -1.193e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004923 0.0009408 0.00389 0.005211 0.989 0.9921 0.005011 0.8795 0.9064 0.01623 ] Network output: [ -0.00225 0.04131 0.9862 -0.0002278 0.0001023 0.9761 -0.0001717 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1935 0.1121 0.3027 0.175 0.9851 0.994 0.1941 0.4765 0.8889 0.7306 ] Network output: [ 0.01214 -0.02222 1.005 0.0001238 -5.559e-05 0.9937 9.332e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09494 0.0867 0.1716 0.2157 0.9875 0.9921 0.095 0.8151 0.8895 0.3105 ] Network output: [ -0.01095 0.03189 1.008 0.0001188 -5.333e-05 0.9829 8.952e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1003 0.09876 0.1758 0.209 0.9858 0.9916 0.1003 0.7474 0.8711 0.2519 ] Network output: [ -0.0009399 0.9999 0.002082 1.882e-05 -8.448e-06 1 1.418e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001049 Epoch 5930 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01558 0.9876 0.9838 6.362e-06 -2.856e-06 -0.002616 4.795e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003135 -0.002814 -0.01098 0.008431 0.9695 0.9739 0.005915 0.8465 0.8362 0.02256 ] Network output: [ 0.9998 -0.02633 0.004303 -4.089e-05 1.836e-05 0.02219 -3.082e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.01781 -0.2095 0.2183 0.9836 0.9933 0.203 0.47 0.8825 0.7354 ] Network output: [ -0.01297 0.9997 1.01 -1.058e-06 4.749e-07 0.01579 -7.972e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004905 0.0009453 0.004056 0.005547 0.989 0.9921 0.004993 0.8794 0.9066 0.01632 ] Network output: [ 0.003024 -0.03964 0.9904 -0.000214 9.607e-05 1.042 -0.0001613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.1118 0.3081 0.1907 0.9851 0.994 0.1934 0.4755 0.8889 0.73 ] Network output: [ 0.01013 -0.03816 1.008 0.0001241 -5.573e-05 1.01 9.355e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09526 0.08706 0.1754 0.2201 0.9874 0.9921 0.09532 0.8162 0.8896 0.3128 ] Network output: [ -0.01232 0.03624 1.008 0.0001174 -5.269e-05 0.9807 8.844e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.09907 0.1772 0.2098 0.9859 0.9916 0.1006 0.749 0.8712 0.2521 ] Network output: [ 0.001615 0.9986 -0.001589 2.063e-05 -9.263e-06 0.9999 1.555e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001357 Epoch 5931 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01502 0.9967 0.9834 4.919e-06 -2.208e-06 -0.01011 3.707e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003145 -0.002815 -0.01102 0.00826 0.9695 0.9739 0.005933 0.8467 0.8358 0.02251 ] Network output: [ 0.9951 0.03175 0.00173 -5.034e-05 2.26e-05 -0.0238 -3.794e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.183 -0.01708 -0.2124 0.2084 0.9836 0.9933 0.2039 0.4714 0.8821 0.7346 ] Network output: [ -0.01294 1.003 1.01 -1.508e-06 6.77e-07 0.0128 -1.137e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004924 0.0009388 0.003892 0.00521 0.989 0.9921 0.005012 0.8795 0.9064 0.01623 ] Network output: [ -0.002252 0.04118 0.9863 -0.0002277 0.0001022 0.9761 -0.0001716 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1935 0.112 0.3028 0.175 0.9851 0.994 0.1941 0.4765 0.8889 0.7305 ] Network output: [ 0.01213 -0.02236 1.005 0.0001238 -5.557e-05 0.9939 9.329e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09499 0.08674 0.1716 0.2157 0.9875 0.9921 0.09505 0.8151 0.8895 0.3105 ] Network output: [ -0.01096 0.03205 1.008 0.0001188 -5.332e-05 0.9828 8.95e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1003 0.09878 0.1758 0.2089 0.9858 0.9916 0.1003 0.7474 0.8711 0.2519 ] Network output: [ -0.0009335 0.9999 0.00206 1.879e-05 -8.437e-06 1 1.416e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001047 Epoch 5932 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01558 0.9876 0.9838 6.422e-06 -2.883e-06 -0.002613 4.84e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003135 -0.002815 -0.01098 0.00843 0.9695 0.9739 0.005916 0.8465 0.8362 0.02256 ] Network output: [ 0.9998 -0.02623 0.004287 -4.1e-05 1.841e-05 0.02209 -3.09e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.01786 -0.2095 0.2183 0.9836 0.9933 0.203 0.47 0.8825 0.7353 ] Network output: [ -0.01297 0.9997 1.01 -9.852e-07 4.423e-07 0.0158 -7.425e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004907 0.0009433 0.004057 0.005545 0.989 0.9921 0.004995 0.8794 0.9066 0.01632 ] Network output: [ 0.003002 -0.03946 0.9905 -0.000214 9.606e-05 1.042 -0.0001613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.1117 0.3081 0.1906 0.9851 0.994 0.1933 0.4755 0.8889 0.7299 ] Network output: [ 0.01013 -0.03824 1.008 0.0001241 -5.571e-05 1.01 9.352e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09531 0.08709 0.1755 0.2201 0.9874 0.9921 0.09537 0.8162 0.8896 0.3128 ] Network output: [ -0.01232 0.03638 1.008 0.0001173 -5.268e-05 0.9807 8.843e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.0991 0.1772 0.2098 0.9859 0.9916 0.1006 0.749 0.8712 0.252 ] Network output: [ 0.001613 0.9986 -0.001597 2.06e-05 -9.249e-06 0.9999 1.553e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001353 Epoch 5933 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01502 0.9966 0.9834 4.985e-06 -2.238e-06 -0.01008 3.757e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003146 -0.002816 -0.01102 0.008259 0.9695 0.9739 0.005934 0.8467 0.8358 0.02251 ] Network output: [ 0.9951 0.03163 0.001725 -5.04e-05 2.263e-05 -0.02372 -3.798e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.183 -0.01713 -0.2123 0.2084 0.9836 0.9933 0.2038 0.4715 0.8821 0.7345 ] Network output: [ -0.01294 1.003 1.01 -1.434e-06 6.439e-07 0.01282 -1.081e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004926 0.0009368 0.003894 0.005209 0.989 0.9921 0.005014 0.8795 0.9064 0.01623 ] Network output: [ -0.002254 0.04105 0.9865 -0.0002276 0.0001022 0.976 -0.0001715 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1935 0.1119 0.3028 0.1749 0.9851 0.994 0.1941 0.4765 0.8889 0.7304 ] Network output: [ 0.01213 -0.02249 1.005 0.0001237 -5.555e-05 0.9941 9.326e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09504 0.08678 0.1717 0.2158 0.9875 0.9921 0.0951 0.815 0.8895 0.3105 ] Network output: [ -0.01096 0.03221 1.007 0.0001187 -5.331e-05 0.9827 8.949e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1003 0.09881 0.1758 0.2089 0.9858 0.9916 0.1004 0.7475 0.8711 0.2519 ] Network output: [ -0.0009274 0.9999 0.002039 1.877e-05 -8.426e-06 1 1.415e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001046 Epoch 5934 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01558 0.9876 0.9838 6.48e-06 -2.909e-06 -0.00261 4.884e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003136 -0.002816 -0.01098 0.008428 0.9695 0.9739 0.005917 0.8465 0.8362 0.02256 ] Network output: [ 0.9998 -0.02612 0.004271 -4.111e-05 1.845e-05 0.022 -3.098e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.01791 -0.2095 0.2182 0.9836 0.9933 0.2029 0.4701 0.8825 0.7353 ] Network output: [ -0.01297 0.9997 1.01 -9.136e-07 4.102e-07 0.0158 -6.885e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004909 0.0009412 0.004059 0.005543 0.989 0.9921 0.004996 0.8794 0.9066 0.01632 ] Network output: [ 0.00298 -0.0393 0.9907 -0.000214 9.606e-05 1.042 -0.0001612 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.1116 0.3082 0.1905 0.9851 0.994 0.1933 0.4755 0.8889 0.7299 ] Network output: [ 0.01013 -0.03832 1.008 0.0001241 -5.569e-05 1.01 9.349e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09536 0.08713 0.1755 0.2201 0.9874 0.9921 0.09542 0.8162 0.8896 0.3128 ] Network output: [ -0.01232 0.03652 1.008 0.0001173 -5.267e-05 0.9806 8.842e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.09912 0.1772 0.2098 0.9859 0.9916 0.1007 0.749 0.8712 0.252 ] Network output: [ 0.001612 0.9985 -0.001606 2.057e-05 -9.235e-06 0.9999 1.55e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001349 Epoch 5935 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01502 0.9966 0.9834 5.049e-06 -2.267e-06 -0.01005 3.805e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003146 -0.002817 -0.01102 0.008258 0.9695 0.9739 0.005935 0.8467 0.8358 0.0225 ] Network output: [ 0.9951 0.03152 0.001721 -5.046e-05 2.265e-05 -0.02365 -3.803e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.183 -0.01718 -0.2123 0.2084 0.9836 0.9933 0.2038 0.4715 0.8821 0.7344 ] Network output: [ -0.01293 1.003 1.01 -1.361e-06 6.112e-07 0.01284 -1.026e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004928 0.0009347 0.003896 0.005208 0.989 0.9921 0.005016 0.8795 0.9064 0.01623 ] Network output: [ -0.002256 0.04093 0.9867 -0.0002275 0.0001022 0.976 -0.0001715 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1935 0.1118 0.3029 0.1749 0.9851 0.994 0.1941 0.4765 0.8889 0.7304 ] Network output: [ 0.01212 -0.02262 1.005 0.0001237 -5.553e-05 0.9943 9.322e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09509 0.08681 0.1718 0.2158 0.9875 0.9921 0.09515 0.815 0.8895 0.3106 ] Network output: [ -0.01097 0.03236 1.007 0.0001187 -5.33e-05 0.9827 8.947e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1004 0.09883 0.1758 0.2089 0.9858 0.9916 0.1004 0.7475 0.8711 0.2518 ] Network output: [ -0.0009215 0.9999 0.002018 1.874e-05 -8.415e-06 1 1.413e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001044 Epoch 5936 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01558 0.9876 0.9838 6.538e-06 -2.935e-06 -0.002605 4.927e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003136 -0.002816 -0.01098 0.008426 0.9695 0.9739 0.005918 0.8465 0.8362 0.02255 ] Network output: [ 0.9998 -0.02603 0.004256 -4.121e-05 1.85e-05 0.02192 -3.106e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.01796 -0.2095 0.2182 0.9836 0.9933 0.2029 0.4701 0.8825 0.7352 ] Network output: [ -0.01296 0.9996 1.01 -8.432e-07 3.785e-07 0.01581 -6.354e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00491 0.0009392 0.00406 0.005541 0.989 0.9921 0.004998 0.8794 0.9066 0.01632 ] Network output: [ 0.002959 -0.03913 0.9908 -0.0002139 9.605e-05 1.042 -0.0001612 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.1115 0.3082 0.1904 0.9851 0.994 0.1933 0.4755 0.8889 0.7298 ] Network output: [ 0.01013 -0.0384 1.008 0.000124 -5.567e-05 1.01 9.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09541 0.08716 0.1756 0.2201 0.9874 0.9921 0.09547 0.8161 0.8896 0.3129 ] Network output: [ -0.01231 0.03666 1.008 0.0001173 -5.267e-05 0.9805 8.841e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09914 0.1771 0.2097 0.9859 0.9916 0.1007 0.749 0.8712 0.252 ] Network output: [ 0.00161 0.9985 -0.001615 2.054e-05 -9.221e-06 0.9999 1.548e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001345 Epoch 5937 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01502 0.9966 0.9834 5.112e-06 -2.295e-06 -0.01002 3.853e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003147 -0.002818 -0.01101 0.008257 0.9695 0.9739 0.005936 0.8467 0.8358 0.0225 ] Network output: [ 0.9951 0.03142 0.001716 -5.052e-05 2.268e-05 -0.02358 -3.807e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1829 -0.01724 -0.2123 0.2084 0.9836 0.9933 0.2038 0.4715 0.8821 0.7344 ] Network output: [ -0.01293 1.003 1.01 -1.29e-06 5.791e-07 0.01285 -9.721e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004929 0.0009327 0.003897 0.005208 0.989 0.9921 0.005017 0.8795 0.9064 0.01623 ] Network output: [ -0.002259 0.04082 0.9868 -0.0002275 0.0001021 0.976 -0.0001714 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1934 0.1117 0.3029 0.1749 0.9851 0.994 0.194 0.4765 0.8889 0.7303 ] Network output: [ 0.01211 -0.02275 1.005 0.0001237 -5.551e-05 0.9945 9.319e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09514 0.08684 0.1718 0.2158 0.9875 0.9921 0.0952 0.815 0.8895 0.3106 ] Network output: [ -0.01097 0.03251 1.007 0.0001187 -5.329e-05 0.9826 8.945e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1004 0.09886 0.1758 0.2088 0.9858 0.9916 0.1004 0.7475 0.8711 0.2518 ] Network output: [ -0.0009159 0.9999 0.001998 1.872e-05 -8.404e-06 1 1.411e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001043 Epoch 5938 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01558 0.9876 0.9839 6.594e-06 -2.96e-06 -0.002601 4.97e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003137 -0.002817 -0.01098 0.008424 0.9695 0.9739 0.005919 0.8465 0.8362 0.02255 ] Network output: [ 0.9998 -0.02593 0.00424 -4.131e-05 1.855e-05 0.02183 -3.113e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.01801 -0.2095 0.2181 0.9836 0.9933 0.2029 0.4701 0.8825 0.7351 ] Network output: [ -0.01296 0.9996 1.01 -7.737e-07 3.474e-07 0.01582 -5.831e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004912 0.0009372 0.004061 0.005539 0.989 0.9921 0.005 0.8794 0.9066 0.01631 ] Network output: [ 0.002939 -0.03898 0.9909 -0.0002139 9.604e-05 1.041 -0.0001612 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.1114 0.3082 0.1903 0.9851 0.994 0.1933 0.4755 0.8889 0.7297 ] Network output: [ 0.01013 -0.03848 1.008 0.000124 -5.565e-05 1.011 9.343e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09546 0.08719 0.1756 0.2201 0.9874 0.9921 0.09551 0.8161 0.8896 0.3129 ] Network output: [ -0.01231 0.03679 1.008 0.0001173 -5.266e-05 0.9805 8.84e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09917 0.1771 0.2097 0.9859 0.9916 0.1007 0.749 0.8712 0.2519 ] Network output: [ 0.001609 0.9985 -0.001624 2.051e-05 -9.207e-06 1 1.546e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001342 Epoch 5939 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01502 0.9965 0.9835 5.174e-06 -2.323e-06 -0.009994 3.899e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003147 -0.002819 -0.01101 0.008256 0.9695 0.9739 0.005937 0.8467 0.8358 0.0225 ] Network output: [ 0.9951 0.03132 0.001711 -5.058e-05 2.271e-05 -0.02351 -3.812e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1829 -0.01729 -0.2123 0.2083 0.9836 0.9933 0.2038 0.4715 0.8821 0.7343 ] Network output: [ -0.01293 1.003 1.01 -1.219e-06 5.474e-07 0.01287 -9.19e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004931 0.0009308 0.003899 0.005207 0.989 0.9921 0.005019 0.8795 0.9064 0.01622 ] Network output: [ -0.002262 0.04071 0.987 -0.0002274 0.0001021 0.9759 -0.0001714 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1934 0.1116 0.303 0.1748 0.9851 0.994 0.194 0.4766 0.8889 0.7302 ] Network output: [ 0.01211 -0.02288 1.004 0.0001236 -5.549e-05 0.9947 9.316e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09519 0.08688 0.1719 0.2158 0.9875 0.9921 0.09525 0.815 0.8895 0.3106 ] Network output: [ -0.01098 0.03266 1.007 0.0001187 -5.328e-05 0.9825 8.943e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1004 0.09888 0.1758 0.2088 0.9858 0.9916 0.1004 0.7475 0.8711 0.2517 ] Network output: [ -0.0009105 0.9999 0.001978 1.87e-05 -8.393e-06 1 1.409e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001042 Epoch 5940 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01558 0.9876 0.9839 6.65e-06 -2.985e-06 -0.002595 5.011e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003138 -0.002818 -0.01098 0.008423 0.9695 0.9739 0.00592 0.8466 0.8362 0.02255 ] Network output: [ 0.9998 -0.02584 0.004226 -4.141e-05 1.859e-05 0.02176 -3.121e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.01806 -0.2094 0.218 0.9836 0.9933 0.2029 0.4701 0.8825 0.735 ] Network output: [ -0.01296 0.9996 1.01 -7.054e-07 3.167e-07 0.01582 -5.316e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004914 0.0009352 0.004062 0.005537 0.989 0.9921 0.005002 0.8794 0.9066 0.01631 ] Network output: [ 0.002919 -0.03883 0.9911 -0.0002139 9.603e-05 1.041 -0.0001612 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.1113 0.3083 0.1902 0.9851 0.994 0.1932 0.4756 0.8889 0.7296 ] Network output: [ 0.01013 -0.03856 1.008 0.0001239 -5.563e-05 1.011 9.339e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0955 0.08723 0.1757 0.2201 0.9874 0.9921 0.09556 0.8161 0.8896 0.3129 ] Network output: [ -0.01231 0.03693 1.008 0.0001173 -5.265e-05 0.9804 8.838e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09919 0.1771 0.2097 0.9859 0.9916 0.1007 0.749 0.8712 0.2519 ] Network output: [ 0.001608 0.9985 -0.001633 2.048e-05 -9.193e-06 1 1.543e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001339 Epoch 5941 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01502 0.9965 0.9835 5.235e-06 -2.35e-06 -0.009966 3.945e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003148 -0.00282 -0.01101 0.008255 0.9695 0.9739 0.005938 0.8467 0.8358 0.02249 ] Network output: [ 0.9952 0.03123 0.001706 -5.064e-05 2.273e-05 -0.02344 -3.816e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1829 -0.01734 -0.2122 0.2083 0.9836 0.9933 0.2037 0.4715 0.8821 0.7342 ] Network output: [ -0.01293 1.003 1.01 -1.15e-06 5.163e-07 0.01288 -8.667e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004933 0.0009288 0.003901 0.005206 0.989 0.9921 0.005021 0.8795 0.9064 0.01622 ] Network output: [ -0.002265 0.0406 0.9871 -0.0002273 0.000102 0.9759 -0.0001713 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1934 0.1115 0.303 0.1748 0.9851 0.994 0.194 0.4766 0.8889 0.7301 ] Network output: [ 0.0121 -0.02301 1.004 0.0001236 -5.547e-05 0.9949 9.312e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09524 0.08691 0.1719 0.2158 0.9875 0.9921 0.09529 0.815 0.8895 0.3106 ] Network output: [ -0.01098 0.03281 1.007 0.0001186 -5.326e-05 0.9825 8.941e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1005 0.0989 0.1758 0.2088 0.9858 0.9916 0.1005 0.7475 0.8711 0.2517 ] Network output: [ -0.0009053 0.9999 0.001959 1.867e-05 -8.382e-06 1 1.407e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001042 Epoch 5942 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01558 0.9876 0.9839 6.704e-06 -3.01e-06 -0.00259 5.052e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003138 -0.002819 -0.01097 0.008421 0.9695 0.9739 0.005921 0.8466 0.8362 0.02255 ] Network output: [ 0.9998 -0.02576 0.004211 -4.151e-05 1.864e-05 0.02168 -3.129e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.01811 -0.2094 0.218 0.9836 0.9933 0.2028 0.4701 0.8825 0.735 ] Network output: [ -0.01296 0.9996 1.011 -6.381e-07 2.864e-07 0.01583 -4.809e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004915 0.0009332 0.004063 0.005535 0.989 0.9921 0.005003 0.8794 0.9066 0.01631 ] Network output: [ 0.0029 -0.03868 0.9912 -0.0002139 9.602e-05 1.041 -0.0001612 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.1112 0.3083 0.1901 0.9851 0.994 0.1932 0.4756 0.8889 0.7296 ] Network output: [ 0.01013 -0.03864 1.008 0.0001239 -5.561e-05 1.011 9.336e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09555 0.08726 0.1757 0.2201 0.9874 0.9921 0.09561 0.8161 0.8896 0.3129 ] Network output: [ -0.01231 0.03706 1.008 0.0001173 -5.264e-05 0.9803 8.837e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09921 0.1771 0.2096 0.9859 0.9916 0.1008 0.749 0.8711 0.2518 ] Network output: [ 0.001607 0.9985 -0.001642 2.045e-05 -9.179e-06 1 1.541e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001336 Epoch 5943 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9964 0.9835 5.294e-06 -2.377e-06 -0.009938 3.99e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003148 -0.002821 -0.01101 0.008254 0.9695 0.9739 0.005939 0.8468 0.8358 0.02249 ] Network output: [ 0.9952 0.03114 0.001701 -5.07e-05 2.276e-05 -0.02338 -3.821e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1829 -0.01739 -0.2122 0.2083 0.9836 0.9933 0.2037 0.4715 0.8821 0.7342 ] Network output: [ -0.01292 1.003 1.01 -1.082e-06 4.857e-07 0.0129 -8.153e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004934 0.0009268 0.003902 0.005205 0.989 0.9921 0.005022 0.8795 0.9064 0.01622 ] Network output: [ -0.002268 0.04051 0.9873 -0.0002272 0.000102 0.9759 -0.0001712 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1934 0.1114 0.3031 0.1747 0.9851 0.994 0.194 0.4766 0.8889 0.7301 ] Network output: [ 0.0121 -0.02313 1.004 0.0001235 -5.545e-05 0.9951 9.309e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09528 0.08694 0.172 0.2158 0.9875 0.9921 0.09534 0.8149 0.8894 0.3107 ] Network output: [ -0.01099 0.03296 1.007 0.0001186 -5.325e-05 0.9824 8.94e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1005 0.09892 0.1758 0.2087 0.9858 0.9916 0.1005 0.7475 0.8711 0.2517 ] Network output: [ -0.0009004 0.9998 0.00194 1.864e-05 -8.37e-06 1 1.405e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001041 Epoch 5944 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01558 0.9876 0.9839 6.757e-06 -3.034e-06 -0.002583 5.092e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003139 -0.00282 -0.01097 0.008419 0.9695 0.9739 0.005922 0.8466 0.8362 0.02254 ] Network output: [ 0.9999 -0.02568 0.004197 -4.161e-05 1.868e-05 0.02161 -3.136e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.01816 -0.2094 0.2179 0.9836 0.9933 0.2028 0.4702 0.8825 0.7349 ] Network output: [ -0.01295 0.9996 1.011 -5.718e-07 2.567e-07 0.01584 -4.309e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004917 0.0009313 0.004065 0.005533 0.989 0.9921 0.005005 0.8794 0.9066 0.01631 ] Network output: [ 0.002882 -0.03855 0.9913 -0.0002138 9.6e-05 1.041 -0.0001612 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.1111 0.3083 0.19 0.9851 0.994 0.1932 0.4756 0.8889 0.7295 ] Network output: [ 0.01013 -0.03873 1.008 0.0001238 -5.559e-05 1.011 9.333e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09559 0.08729 0.1757 0.2201 0.9874 0.9921 0.09565 0.816 0.8895 0.313 ] Network output: [ -0.01231 0.0372 1.008 0.0001172 -5.263e-05 0.9803 8.836e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09923 0.1771 0.2096 0.9859 0.9916 0.1008 0.749 0.8711 0.2518 ] Network output: [ 0.001606 0.9985 -0.001651 2.042e-05 -9.165e-06 1 1.539e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001333 Epoch 5945 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9964 0.9835 5.353e-06 -2.403e-06 -0.00991 4.034e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003149 -0.002822 -0.01101 0.008253 0.9695 0.9739 0.00594 0.8468 0.8358 0.02249 ] Network output: [ 0.9952 0.03105 0.001695 -5.076e-05 2.279e-05 -0.02333 -3.825e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1828 -0.01745 -0.2122 0.2083 0.9836 0.9933 0.2037 0.4715 0.8821 0.7341 ] Network output: [ -0.01292 1.003 1.01 -1.015e-06 4.555e-07 0.01292 -7.647e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004936 0.0009249 0.003904 0.005203 0.989 0.9921 0.005024 0.8795 0.9064 0.01622 ] Network output: [ -0.002271 0.04041 0.9874 -0.0002271 0.000102 0.9758 -0.0001712 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1933 0.1113 0.3031 0.1747 0.9851 0.994 0.1939 0.4766 0.8889 0.73 ] Network output: [ 0.01209 -0.02325 1.004 0.0001235 -5.543e-05 0.9953 9.305e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09533 0.08697 0.172 0.2158 0.9875 0.9921 0.09539 0.8149 0.8894 0.3107 ] Network output: [ -0.01099 0.03311 1.007 0.0001186 -5.324e-05 0.9823 8.938e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1005 0.09894 0.1758 0.2087 0.9858 0.9916 0.1005 0.7475 0.8711 0.2516 ] Network output: [ -0.0008957 0.9998 0.001922 1.862e-05 -8.359e-06 1 1.403e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001041 Epoch 5946 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01558 0.9876 0.9839 6.81e-06 -3.057e-06 -0.002576 5.132e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003139 -0.002821 -0.01097 0.008418 0.9695 0.9739 0.005924 0.8466 0.8362 0.02254 ] Network output: [ 0.9999 -0.0256 0.004183 -4.171e-05 1.872e-05 0.02154 -3.143e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.01821 -0.2094 0.2179 0.9836 0.9933 0.2028 0.4702 0.8825 0.7348 ] Network output: [ -0.01295 0.9995 1.011 -5.065e-07 2.274e-07 0.01584 -3.817e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004919 0.0009293 0.004066 0.005531 0.989 0.9921 0.005007 0.8794 0.9066 0.01631 ] Network output: [ 0.002864 -0.03842 0.9915 -0.0002138 9.599e-05 1.04 -0.0001611 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.111 0.3084 0.1899 0.9851 0.994 0.1932 0.4756 0.8889 0.7294 ] Network output: [ 0.01013 -0.03881 1.008 0.0001238 -5.557e-05 1.011 9.329e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09564 0.08732 0.1758 0.2201 0.9874 0.9921 0.0957 0.816 0.8895 0.313 ] Network output: [ -0.01231 0.03733 1.008 0.0001172 -5.262e-05 0.9802 8.834e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09925 0.1771 0.2095 0.9859 0.9916 0.1008 0.749 0.8711 0.2518 ] Network output: [ 0.001606 0.9985 -0.00166 2.038e-05 -9.151e-06 1 1.536e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001331 Epoch 5947 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9964 0.9835 5.409e-06 -2.429e-06 -0.009883 4.077e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003149 -0.002823 -0.01101 0.008252 0.9695 0.9739 0.005941 0.8468 0.8359 0.02248 ] Network output: [ 0.9952 0.03097 0.00169 -5.082e-05 2.281e-05 -0.02327 -3.83e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1828 -0.0175 -0.2121 0.2082 0.9836 0.9933 0.2037 0.4716 0.8821 0.734 ] Network output: [ -0.01292 1.003 1.01 -9.486e-07 4.259e-07 0.01293 -7.149e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004937 0.0009229 0.003906 0.005202 0.989 0.9921 0.005026 0.8795 0.9064 0.01622 ] Network output: [ -0.002275 0.04033 0.9875 -0.0002271 0.0001019 0.9758 -0.0001711 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1933 0.1112 0.3032 0.1746 0.9851 0.994 0.1939 0.4766 0.8889 0.7299 ] Network output: [ 0.01209 -0.02337 1.004 0.0001234 -5.541e-05 0.9955 9.302e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09537 0.087 0.1721 0.2158 0.9875 0.9921 0.09543 0.8149 0.8894 0.3107 ] Network output: [ -0.01099 0.03325 1.007 0.0001186 -5.323e-05 0.9822 8.936e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1005 0.09896 0.1758 0.2087 0.9858 0.9916 0.1005 0.7475 0.8711 0.2516 ] Network output: [ -0.0008912 0.9998 0.001904 1.859e-05 -8.348e-06 1 1.401e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00104 Epoch 5948 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01558 0.9875 0.9839 6.861e-06 -3.08e-06 -0.002569 5.171e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00314 -0.002822 -0.01097 0.008416 0.9695 0.9739 0.005925 0.8466 0.8362 0.02253 ] Network output: [ 0.9999 -0.02553 0.004169 -4.18e-05 1.877e-05 0.02147 -3.15e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.01826 -0.2093 0.2178 0.9836 0.9933 0.2028 0.4702 0.8825 0.7348 ] Network output: [ -0.01295 0.9995 1.011 -4.423e-07 1.985e-07 0.01585 -3.333e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00492 0.0009274 0.004067 0.005529 0.989 0.9921 0.005008 0.8794 0.9066 0.0163 ] Network output: [ 0.002846 -0.03829 0.9916 -0.0002138 9.597e-05 1.04 -0.0001611 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.1109 0.3084 0.1898 0.9851 0.994 0.1932 0.4756 0.8889 0.7294 ] Network output: [ 0.01013 -0.03889 1.008 0.0001237 -5.555e-05 1.011 9.326e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09568 0.08735 0.1758 0.2201 0.9874 0.9921 0.09574 0.816 0.8895 0.313 ] Network output: [ -0.01231 0.03747 1.007 0.0001172 -5.262e-05 0.9801 8.833e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09927 0.1771 0.2095 0.9859 0.9916 0.1008 0.749 0.8711 0.2517 ] Network output: [ 0.001605 0.9985 -0.001669 2.035e-05 -9.138e-06 1 1.534e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001328 Epoch 5949 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9963 0.9835 5.465e-06 -2.454e-06 -0.009857 4.119e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00315 -0.002823 -0.011 0.00825 0.9695 0.9739 0.005942 0.8468 0.8359 0.02248 ] Network output: [ 0.9952 0.0309 0.001684 -5.087e-05 2.284e-05 -0.02322 -3.834e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1828 -0.01755 -0.2121 0.2082 0.9836 0.9933 0.2036 0.4716 0.8821 0.7339 ] Network output: [ -0.01292 1.003 1.01 -8.837e-07 3.967e-07 0.01295 -6.659e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004939 0.000921 0.003907 0.005201 0.989 0.9921 0.005027 0.8795 0.9064 0.01622 ] Network output: [ -0.002279 0.04025 0.9877 -0.000227 0.0001019 0.9757 -0.0001711 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1933 0.1111 0.3032 0.1746 0.9851 0.994 0.1939 0.4766 0.8889 0.7298 ] Network output: [ 0.01208 -0.02349 1.004 0.0001234 -5.539e-05 0.9957 9.298e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09542 0.08703 0.1721 0.2159 0.9875 0.9921 0.09548 0.8149 0.8894 0.3108 ] Network output: [ -0.011 0.0334 1.007 0.0001185 -5.322e-05 0.9822 8.934e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1005 0.09898 0.1758 0.2086 0.9858 0.9916 0.1006 0.7475 0.8711 0.2515 ] Network output: [ -0.000887 0.9998 0.001887 1.857e-05 -8.336e-06 1 1.399e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00104 Epoch 5950 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01558 0.9875 0.9839 6.911e-06 -3.103e-06 -0.002561 5.208e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00314 -0.002823 -0.01097 0.008414 0.9695 0.9739 0.005926 0.8466 0.8362 0.02253 ] Network output: [ 0.9999 -0.02546 0.004156 -4.189e-05 1.881e-05 0.02141 -3.157e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.01831 -0.2093 0.2178 0.9836 0.9933 0.2028 0.4702 0.8825 0.7347 ] Network output: [ -0.01294 0.9995 1.011 -3.79e-07 1.702e-07 0.01586 -2.856e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004922 0.0009254 0.004068 0.005527 0.989 0.9921 0.00501 0.8794 0.9066 0.0163 ] Network output: [ 0.002829 -0.03817 0.9917 -0.0002137 9.595e-05 1.04 -0.0001611 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.1108 0.3084 0.1897 0.9851 0.994 0.1931 0.4757 0.8889 0.7293 ] Network output: [ 0.01013 -0.03898 1.008 0.0001237 -5.553e-05 1.011 9.323e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09572 0.08737 0.1759 0.2201 0.9874 0.9921 0.09578 0.816 0.8895 0.313 ] Network output: [ -0.01231 0.0376 1.007 0.0001172 -5.261e-05 0.9801 8.831e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09929 0.1771 0.2095 0.9859 0.9916 0.1008 0.749 0.8711 0.2517 ] Network output: [ 0.001605 0.9985 -0.001678 2.032e-05 -9.124e-06 1 1.532e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001326 Epoch 5951 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9963 0.9835 5.52e-06 -2.478e-06 -0.009831 4.16e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00315 -0.002824 -0.011 0.008249 0.9695 0.9739 0.005943 0.8468 0.8359 0.02248 ] Network output: [ 0.9952 0.03082 0.001679 -5.093e-05 2.287e-05 -0.02318 -3.838e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1828 -0.0176 -0.2121 0.2082 0.9836 0.9933 0.2036 0.4716 0.8821 0.7339 ] Network output: [ -0.01291 1.003 1.01 -8.197e-07 3.68e-07 0.01296 -6.178e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00494 0.0009191 0.003909 0.0052 0.989 0.9921 0.005029 0.8795 0.9064 0.01621 ] Network output: [ -0.002283 0.04017 0.9878 -0.0002269 0.0001019 0.9756 -0.000171 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1933 0.111 0.3033 0.1745 0.9851 0.994 0.1939 0.4766 0.8889 0.7298 ] Network output: [ 0.01207 -0.02361 1.004 0.0001233 -5.537e-05 0.9958 9.295e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09546 0.08706 0.1722 0.2159 0.9875 0.9921 0.09552 0.8149 0.8894 0.3108 ] Network output: [ -0.011 0.03354 1.007 0.0001185 -5.321e-05 0.9821 8.932e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.099 0.1758 0.2086 0.9858 0.9916 0.1006 0.7475 0.8711 0.2515 ] Network output: [ -0.0008829 0.9998 0.00187 1.854e-05 -8.324e-06 1 1.397e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00104 Epoch 5952 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01558 0.9875 0.9839 6.961e-06 -3.125e-06 -0.002553 5.246e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003141 -0.002824 -0.01097 0.008413 0.9695 0.9739 0.005926 0.8467 0.8362 0.02253 ] Network output: [ 0.9999 -0.02539 0.004143 -4.198e-05 1.885e-05 0.02135 -3.164e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.01836 -0.2093 0.2177 0.9836 0.9933 0.2027 0.4702 0.8825 0.7346 ] Network output: [ -0.01294 0.9995 1.011 -3.168e-07 1.422e-07 0.01587 -2.387e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004923 0.0009235 0.004069 0.005525 0.989 0.9921 0.005011 0.8794 0.9066 0.0163 ] Network output: [ 0.002813 -0.03806 0.9918 -0.0002137 9.593e-05 1.04 -0.000161 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.1107 0.3085 0.1896 0.9851 0.994 0.1931 0.4757 0.8889 0.7292 ] Network output: [ 0.01013 -0.03906 1.008 0.0001237 -5.551e-05 1.012 9.319e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09577 0.0874 0.1759 0.2201 0.9874 0.9921 0.09583 0.816 0.8895 0.3131 ] Network output: [ -0.01231 0.03773 1.007 0.0001172 -5.26e-05 0.98 8.829e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.0993 0.1771 0.2094 0.9859 0.9916 0.1009 0.749 0.8711 0.2516 ] Network output: [ 0.001604 0.9985 -0.001688 2.029e-05 -9.11e-06 1 1.529e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001325 Epoch 5953 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9963 0.9835 5.573e-06 -2.502e-06 -0.009806 4.2e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003151 -0.002825 -0.011 0.008248 0.9695 0.9739 0.005944 0.8468 0.8359 0.02247 ] Network output: [ 0.9952 0.03076 0.001673 -5.099e-05 2.289e-05 -0.02313 -3.843e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1827 -0.01765 -0.2121 0.2082 0.9836 0.9933 0.2036 0.4716 0.8821 0.7338 ] Network output: [ -0.01291 1.003 1.01 -7.569e-07 3.398e-07 0.01297 -5.704e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004942 0.0009172 0.00391 0.005199 0.989 0.9921 0.00503 0.8795 0.9064 0.01621 ] Network output: [ -0.002287 0.0401 0.988 -0.0002268 0.0001018 0.9756 -0.0001709 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1932 0.1109 0.3033 0.1745 0.9851 0.994 0.1938 0.4767 0.8889 0.7297 ] Network output: [ 0.01207 -0.02373 1.004 0.0001233 -5.535e-05 0.996 9.291e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0955 0.08709 0.1722 0.2159 0.9875 0.9921 0.09556 0.8148 0.8894 0.3108 ] Network output: [ -0.011 0.03368 1.007 0.0001185 -5.319e-05 0.982 8.93e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.09902 0.1757 0.2086 0.9858 0.9916 0.1006 0.7475 0.8711 0.2515 ] Network output: [ -0.0008791 0.9998 0.001853 1.852e-05 -8.313e-06 1 1.395e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001041 Epoch 5954 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01558 0.9875 0.9839 7.009e-06 -3.147e-06 -0.002544 5.282e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003141 -0.002825 -0.01096 0.008411 0.9695 0.9739 0.005927 0.8467 0.8362 0.02252 ] Network output: [ 0.9999 -0.02533 0.00413 -4.207e-05 1.889e-05 0.02129 -3.171e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.0184 -0.2093 0.2177 0.9836 0.9933 0.2027 0.4703 0.8825 0.7345 ] Network output: [ -0.01294 0.9995 1.011 -2.555e-07 1.147e-07 0.01588 -1.926e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004925 0.0009216 0.004071 0.005523 0.989 0.9921 0.005013 0.8794 0.9066 0.0163 ] Network output: [ 0.002797 -0.03795 0.992 -0.0002136 9.591e-05 1.04 -0.000161 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.1106 0.3085 0.1895 0.9851 0.994 0.1931 0.4757 0.8889 0.7291 ] Network output: [ 0.01013 -0.03915 1.008 0.0001236 -5.549e-05 1.012 9.316e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09581 0.08743 0.176 0.2201 0.9874 0.9921 0.09587 0.8159 0.8895 0.3131 ] Network output: [ -0.0123 0.03786 1.007 0.0001171 -5.259e-05 0.9799 8.828e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09932 0.1771 0.2094 0.9859 0.9916 0.1009 0.749 0.8711 0.2516 ] Network output: [ 0.001604 0.9985 -0.001697 2.026e-05 -9.096e-06 1 1.527e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001323 Epoch 5955 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9962 0.9835 5.625e-06 -2.525e-06 -0.009782 4.24e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003151 -0.002826 -0.011 0.008246 0.9695 0.9739 0.005945 0.8468 0.8359 0.02247 ] Network output: [ 0.9953 0.03069 0.001668 -5.105e-05 2.292e-05 -0.02309 -3.847e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1827 -0.0177 -0.212 0.2081 0.9836 0.9933 0.2036 0.4716 0.8821 0.7337 ] Network output: [ -0.01291 1.003 1.01 -6.951e-07 3.12e-07 0.01299 -5.238e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004943 0.0009153 0.003912 0.005198 0.989 0.9921 0.005032 0.8795 0.9064 0.01621 ] Network output: [ -0.002292 0.04003 0.9881 -0.0002267 0.0001018 0.9755 -0.0001709 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1932 0.1109 0.3034 0.1744 0.9851 0.994 0.1938 0.4767 0.8889 0.7296 ] Network output: [ 0.01206 -0.02384 1.004 0.0001232 -5.533e-05 0.9962 9.288e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09555 0.08712 0.1723 0.2159 0.9875 0.9921 0.0956 0.8148 0.8894 0.3108 ] Network output: [ -0.011 0.03382 1.007 0.0001185 -5.318e-05 0.982 8.928e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.09904 0.1757 0.2085 0.9858 0.9916 0.1006 0.7475 0.8711 0.2514 ] Network output: [ -0.0008755 0.9998 0.001837 1.849e-05 -8.301e-06 1 1.394e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001041 Epoch 5956 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01558 0.9875 0.9839 7.056e-06 -3.168e-06 -0.002535 5.318e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003142 -0.002826 -0.01096 0.008409 0.9695 0.9739 0.005928 0.8467 0.8362 0.02252 ] Network output: [ 0.9999 -0.02527 0.004118 -4.216e-05 1.893e-05 0.02124 -3.177e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.01845 -0.2093 0.2176 0.9836 0.9933 0.2027 0.4703 0.8825 0.7345 ] Network output: [ -0.01294 0.9994 1.011 -1.952e-07 8.763e-08 0.01588 -1.471e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004926 0.0009197 0.004072 0.005521 0.989 0.9921 0.005015 0.8794 0.9066 0.0163 ] Network output: [ 0.002781 -0.03785 0.9921 -0.0002136 9.589e-05 1.039 -0.000161 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.1105 0.3085 0.1895 0.9851 0.994 0.1931 0.4757 0.8889 0.7291 ] Network output: [ 0.01013 -0.03923 1.008 0.0001236 -5.547e-05 1.012 9.312e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09585 0.08746 0.176 0.2201 0.9874 0.9921 0.09591 0.8159 0.8895 0.3131 ] Network output: [ -0.0123 0.03799 1.007 0.0001171 -5.258e-05 0.9799 8.826e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09934 0.177 0.2094 0.9859 0.9916 0.1009 0.749 0.8711 0.2516 ] Network output: [ 0.001604 0.9985 -0.001707 2.023e-05 -9.083e-06 1 1.525e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001322 Epoch 5957 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9962 0.9835 5.677e-06 -2.548e-06 -0.009758 4.278e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003152 -0.002827 -0.011 0.008245 0.9695 0.9739 0.005946 0.8469 0.8359 0.02247 ] Network output: [ 0.9953 0.03063 0.001662 -5.11e-05 2.294e-05 -0.02306 -3.851e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1827 -0.01775 -0.212 0.2081 0.9836 0.9933 0.2035 0.4716 0.8821 0.7337 ] Network output: [ -0.01291 1.003 1.01 -6.343e-07 2.848e-07 0.013 -4.78e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004945 0.0009134 0.003914 0.005196 0.989 0.9921 0.005033 0.8795 0.9064 0.01621 ] Network output: [ -0.002296 0.03997 0.9882 -0.0002266 0.0001017 0.9755 -0.0001708 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1932 0.1108 0.3034 0.1744 0.9851 0.994 0.1938 0.4767 0.8889 0.7295 ] Network output: [ 0.01206 -0.02395 1.004 0.0001232 -5.531e-05 0.9964 9.284e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09559 0.08715 0.1723 0.2159 0.9875 0.9921 0.09564 0.8148 0.8894 0.3109 ] Network output: [ -0.01101 0.03396 1.007 0.0001184 -5.317e-05 0.9819 8.925e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.09905 0.1757 0.2085 0.9858 0.9916 0.1006 0.7475 0.8711 0.2514 ] Network output: [ -0.0008722 0.9998 0.001821 1.846e-05 -8.289e-06 1 1.392e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001042 Epoch 5958 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01558 0.9875 0.9839 7.103e-06 -3.189e-06 -0.002525 5.353e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003142 -0.002827 -0.01096 0.008408 0.9695 0.9739 0.005929 0.8467 0.8362 0.02252 ] Network output: [ 0.9999 -0.02522 0.004106 -4.224e-05 1.896e-05 0.02119 -3.183e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.0185 -0.2092 0.2176 0.9836 0.9933 0.2027 0.4703 0.8825 0.7344 ] Network output: [ -0.01293 0.9994 1.011 -1.359e-07 6.1e-08 0.01589 -1.024e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004928 0.0009178 0.004073 0.005519 0.989 0.9921 0.005016 0.8795 0.9066 0.01629 ] Network output: [ 0.002766 -0.03775 0.9922 -0.0002135 9.587e-05 1.039 -0.0001609 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.1104 0.3086 0.1894 0.9851 0.994 0.193 0.4757 0.8889 0.729 ] Network output: [ 0.01013 -0.03932 1.008 0.0001235 -5.545e-05 1.012 9.309e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09589 0.08748 0.176 0.2201 0.9874 0.9921 0.09595 0.8159 0.8895 0.3131 ] Network output: [ -0.0123 0.03812 1.007 0.0001171 -5.257e-05 0.9798 8.824e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09935 0.177 0.2093 0.9859 0.9917 0.1009 0.749 0.8711 0.2515 ] Network output: [ 0.001604 0.9985 -0.001717 2.02e-05 -9.069e-06 1 1.522e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001321 Epoch 5959 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9962 0.9835 5.727e-06 -2.571e-06 -0.009734 4.316e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003152 -0.002828 -0.011 0.008244 0.9695 0.9739 0.005947 0.8469 0.8359 0.02246 ] Network output: [ 0.9953 0.03058 0.001656 -5.116e-05 2.297e-05 -0.02302 -3.856e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1827 -0.01779 -0.212 0.2081 0.9836 0.9933 0.2035 0.4717 0.8821 0.7336 ] Network output: [ -0.0129 1.003 1.01 -5.745e-07 2.579e-07 0.01302 -4.33e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004946 0.0009115 0.003915 0.005195 0.989 0.9921 0.005035 0.8795 0.9064 0.01621 ] Network output: [ -0.002301 0.03992 0.9884 -0.0002265 0.0001017 0.9754 -0.0001707 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1932 0.1107 0.3034 0.1743 0.9851 0.994 0.1938 0.4767 0.8889 0.7295 ] Network output: [ 0.01205 -0.02406 1.004 0.0001231 -5.528e-05 0.9965 9.281e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09563 0.08717 0.1724 0.2159 0.9875 0.9921 0.09569 0.8148 0.8893 0.3109 ] Network output: [ -0.01101 0.0341 1.007 0.0001184 -5.316e-05 0.9818 8.923e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.09907 0.1757 0.2085 0.9858 0.9916 0.1007 0.7475 0.8711 0.2513 ] Network output: [ -0.000869 0.9998 0.001806 1.844e-05 -8.278e-06 1 1.39e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001042 Epoch 5960 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01557 0.9875 0.9839 7.148e-06 -3.209e-06 -0.002515 5.387e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003143 -0.002827 -0.01096 0.008406 0.9695 0.9739 0.00593 0.8467 0.8363 0.02251 ] Network output: [ 0.9999 -0.02517 0.004094 -4.232e-05 1.9e-05 0.02114 -3.19e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.01855 -0.2092 0.2175 0.9836 0.9933 0.2026 0.4703 0.8825 0.7343 ] Network output: [ -0.01293 0.9994 1.011 -7.749e-08 3.479e-08 0.0159 -5.84e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004929 0.0009159 0.004074 0.005517 0.989 0.9921 0.005018 0.8795 0.9066 0.01629 ] Network output: [ 0.002752 -0.03766 0.9923 -0.0002135 9.584e-05 1.039 -0.0001609 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.1104 0.3086 0.1893 0.9851 0.994 0.193 0.4757 0.8889 0.7289 ] Network output: [ 0.01013 -0.03941 1.007 0.0001235 -5.543e-05 1.012 9.305e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09593 0.08751 0.1761 0.2201 0.9874 0.9921 0.09599 0.8159 0.8894 0.3131 ] Network output: [ -0.0123 0.03825 1.007 0.0001171 -5.255e-05 0.9797 8.822e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09937 0.177 0.2093 0.9859 0.9917 0.1009 0.749 0.8711 0.2515 ] Network output: [ 0.001604 0.9985 -0.001726 2.017e-05 -9.056e-06 1 1.52e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00132 Epoch 5961 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9961 0.9835 5.775e-06 -2.593e-06 -0.009711 4.352e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003153 -0.002829 -0.01099 0.008242 0.9695 0.9739 0.005947 0.8469 0.8359 0.02246 ] Network output: [ 0.9953 0.03053 0.00165 -5.121e-05 2.299e-05 -0.02299 -3.86e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1827 -0.01784 -0.2119 0.2081 0.9836 0.9933 0.2035 0.4717 0.8822 0.7335 ] Network output: [ -0.0129 1.002 1.01 -5.158e-07 2.315e-07 0.01303 -3.887e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004948 0.0009097 0.003917 0.005194 0.989 0.9921 0.005036 0.8795 0.9064 0.01621 ] Network output: [ -0.002306 0.03987 0.9885 -0.0002265 0.0001017 0.9753 -0.0001707 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1932 0.1106 0.3035 0.1743 0.9851 0.994 0.1937 0.4767 0.8889 0.7294 ] Network output: [ 0.01205 -0.02417 1.004 0.0001231 -5.526e-05 0.9967 9.277e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09567 0.0872 0.1724 0.2159 0.9875 0.9921 0.09572 0.8147 0.8893 0.3109 ] Network output: [ -0.01101 0.03423 1.007 0.0001184 -5.314e-05 0.9818 8.921e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09908 0.1757 0.2084 0.9858 0.9916 0.1007 0.7475 0.871 0.2513 ] Network output: [ -0.000866 0.9998 0.001791 1.841e-05 -8.266e-06 1 1.388e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001043 Epoch 5962 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01557 0.9874 0.9839 7.193e-06 -3.229e-06 -0.002505 5.421e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003143 -0.002828 -0.01096 0.008404 0.9695 0.974 0.005931 0.8467 0.8363 0.02251 ] Network output: [ 0.9999 -0.02512 0.004082 -4.241e-05 1.904e-05 0.0211 -3.196e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.01859 -0.2092 0.2175 0.9836 0.9933 0.2026 0.4703 0.8825 0.7343 ] Network output: [ -0.01293 0.9994 1.011 -2.005e-08 9.003e-09 0.01591 -1.511e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004931 0.0009141 0.004076 0.005515 0.989 0.9921 0.005019 0.8795 0.9066 0.01629 ] Network output: [ 0.002738 -0.03758 0.9925 -0.0002134 9.582e-05 1.039 -0.0001608 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.1103 0.3086 0.1892 0.9851 0.994 0.193 0.4758 0.8889 0.7288 ] Network output: [ 0.01013 -0.03949 1.007 0.0001234 -5.541e-05 1.012 9.301e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09597 0.08753 0.1761 0.2201 0.9874 0.9921 0.09603 0.8158 0.8894 0.3132 ] Network output: [ -0.0123 0.03838 1.007 0.000117 -5.254e-05 0.9797 8.821e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09938 0.177 0.2093 0.9859 0.9917 0.101 0.749 0.8711 0.2514 ] Network output: [ 0.001604 0.9985 -0.001736 2.014e-05 -9.042e-06 1 1.518e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001319 Epoch 5963 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9961 0.9835 5.823e-06 -2.614e-06 -0.009689 4.388e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003153 -0.002829 -0.01099 0.008241 0.9695 0.9739 0.005948 0.8469 0.8359 0.02246 ] Network output: [ 0.9953 0.03048 0.001644 -5.127e-05 2.302e-05 -0.02296 -3.864e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1826 -0.01789 -0.2119 0.208 0.9836 0.9933 0.2035 0.4717 0.8822 0.7335 ] Network output: [ -0.0129 1.002 1.01 -4.58e-07 2.056e-07 0.01304 -3.452e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004949 0.0009078 0.003918 0.005193 0.989 0.9921 0.005038 0.8796 0.9064 0.0162 ] Network output: [ -0.002311 0.03982 0.9886 -0.0002264 0.0001016 0.9753 -0.0001706 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1931 0.1105 0.3035 0.1742 0.9851 0.994 0.1937 0.4767 0.8889 0.7293 ] Network output: [ 0.01204 -0.02428 1.004 0.000123 -5.524e-05 0.9969 9.273e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09571 0.08722 0.1725 0.2159 0.9875 0.9921 0.09576 0.8147 0.8893 0.3109 ] Network output: [ -0.01101 0.03437 1.006 0.0001183 -5.313e-05 0.9817 8.919e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.0991 0.1757 0.2084 0.9858 0.9916 0.1007 0.7475 0.871 0.2513 ] Network output: [ -0.0008632 0.9998 0.001777 1.839e-05 -8.254e-06 1 1.386e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001044 Epoch 5964 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01557 0.9874 0.9839 7.237e-06 -3.249e-06 -0.002494 5.454e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003143 -0.002829 -0.01096 0.008403 0.9695 0.974 0.005932 0.8467 0.8363 0.02251 ] Network output: [ 0.9999 -0.02508 0.004071 -4.249e-05 1.907e-05 0.02106 -3.202e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.01864 -0.2092 0.2174 0.9836 0.9933 0.2026 0.4703 0.8825 0.7342 ] Network output: [ -0.01292 0.9994 1.011 3.645e-08 -1.636e-08 0.01592 2.747e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004932 0.0009122 0.004077 0.005513 0.989 0.9921 0.005021 0.8795 0.9066 0.01629 ] Network output: [ 0.002725 -0.0375 0.9926 -0.0002134 9.579e-05 1.039 -0.0001608 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.1102 0.3087 0.1891 0.9851 0.994 0.193 0.4758 0.8889 0.7288 ] Network output: [ 0.01013 -0.03958 1.007 0.0001234 -5.539e-05 1.012 9.298e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09601 0.08756 0.1761 0.2201 0.9874 0.9921 0.09606 0.8158 0.8894 0.3132 ] Network output: [ -0.0123 0.03851 1.007 0.000117 -5.253e-05 0.9796 8.819e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.101 0.09939 0.177 0.2092 0.9859 0.9917 0.101 0.749 0.8711 0.2514 ] Network output: [ 0.001605 0.9985 -0.001746 2.011e-05 -9.029e-06 1 1.516e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001319 Epoch 5965 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9961 0.9836 5.87e-06 -2.635e-06 -0.009667 4.424e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003153 -0.00283 -0.01099 0.00824 0.9695 0.974 0.005949 0.8469 0.8359 0.02245 ] Network output: [ 0.9953 0.03043 0.001638 -5.133e-05 2.304e-05 -0.02294 -3.868e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1826 -0.01794 -0.2119 0.208 0.9836 0.9933 0.2034 0.4717 0.8822 0.7334 ] Network output: [ -0.01289 1.002 1.01 -4.012e-07 1.801e-07 0.01306 -3.024e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004951 0.000906 0.003919 0.005191 0.989 0.9921 0.005039 0.8796 0.9064 0.0162 ] Network output: [ -0.002316 0.03978 0.9888 -0.0002263 0.0001016 0.9752 -0.0001705 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1931 0.1104 0.3036 0.1742 0.9851 0.994 0.1937 0.4767 0.8889 0.7293 ] Network output: [ 0.01204 -0.02439 1.004 0.000123 -5.522e-05 0.997 9.269e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09574 0.08725 0.1725 0.2159 0.9875 0.9921 0.0958 0.8147 0.8893 0.311 ] Network output: [ -0.01101 0.0345 1.006 0.0001183 -5.312e-05 0.9816 8.916e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09911 0.1757 0.2084 0.9858 0.9916 0.1007 0.7475 0.871 0.2512 ] Network output: [ -0.0008606 0.9998 0.001763 1.836e-05 -8.242e-06 1 1.384e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001045 Epoch 5966 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01557 0.9874 0.9839 7.28e-06 -3.268e-06 -0.002483 5.486e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003144 -0.00283 -0.01095 0.008401 0.9695 0.974 0.005933 0.8467 0.8363 0.0225 ] Network output: [ 0.9999 -0.02504 0.00406 -4.257e-05 1.911e-05 0.02102 -3.208e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.01869 -0.2091 0.2174 0.9836 0.9933 0.2026 0.4704 0.8825 0.7341 ] Network output: [ -0.01292 0.9993 1.011 9.203e-08 -4.132e-08 0.01593 6.936e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004934 0.0009104 0.004078 0.005512 0.989 0.9921 0.005022 0.8795 0.9066 0.01629 ] Network output: [ 0.002712 -0.03742 0.9927 -0.0002133 9.576e-05 1.038 -0.0001608 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.1101 0.3087 0.189 0.9851 0.9941 0.193 0.4758 0.8889 0.7287 ] Network output: [ 0.01012 -0.03967 1.007 0.0001233 -5.537e-05 1.013 9.294e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09604 0.08758 0.1762 0.2201 0.9874 0.9921 0.0961 0.8158 0.8894 0.3132 ] Network output: [ -0.0123 0.03863 1.007 0.000117 -5.252e-05 0.9795 8.817e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.101 0.09941 0.177 0.2092 0.9859 0.9917 0.101 0.749 0.8711 0.2514 ] Network output: [ 0.001605 0.9984 -0.001756 2.008e-05 -9.015e-06 1 1.513e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001318 Epoch 5967 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.996 0.9836 5.915e-06 -2.656e-06 -0.009645 4.458e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003154 -0.002831 -0.01099 0.008238 0.9695 0.974 0.00595 0.8469 0.8359 0.02245 ] Network output: [ 0.9953 0.03039 0.001632 -5.138e-05 2.307e-05 -0.02291 -3.872e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1826 -0.01798 -0.2119 0.208 0.9836 0.9933 0.2034 0.4717 0.8822 0.7333 ] Network output: [ -0.01289 1.002 1.01 -3.454e-07 1.551e-07 0.01307 -2.603e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004952 0.0009041 0.003921 0.00519 0.989 0.9921 0.005041 0.8796 0.9064 0.0162 ] Network output: [ -0.002322 0.03974 0.9889 -0.0002262 0.0001015 0.9751 -0.0001705 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1931 0.1103 0.3036 0.1741 0.9851 0.994 0.1937 0.4768 0.8889 0.7292 ] Network output: [ 0.01203 -0.02449 1.004 0.0001229 -5.519e-05 0.9972 9.266e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09578 0.08727 0.1725 0.2159 0.9875 0.9921 0.09584 0.8147 0.8893 0.311 ] Network output: [ -0.01101 0.03463 1.006 0.0001183 -5.31e-05 0.9815 8.914e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09913 0.1757 0.2083 0.9858 0.9916 0.1007 0.7475 0.871 0.2512 ] Network output: [ -0.0008582 0.9998 0.001749 1.833e-05 -8.23e-06 1 1.382e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001047 Epoch 5968 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01557 0.9874 0.984 7.322e-06 -3.287e-06 -0.002471 5.518e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003144 -0.002831 -0.01095 0.008399 0.9695 0.974 0.005934 0.8468 0.8363 0.0225 ] Network output: [ 0.9999 -0.025 0.004049 -4.264e-05 1.914e-05 0.02098 -3.214e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.01873 -0.2091 0.2173 0.9836 0.9933 0.2026 0.4704 0.8825 0.7341 ] Network output: [ -0.01292 0.9993 1.011 1.467e-07 -6.586e-08 0.01594 1.106e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004935 0.0009085 0.004079 0.00551 0.989 0.9921 0.005024 0.8795 0.9066 0.01628 ] Network output: [ 0.002699 -0.03735 0.9928 -0.0002132 9.573e-05 1.038 -0.0001607 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.11 0.3088 0.189 0.9851 0.9941 0.1929 0.4758 0.8889 0.7286 ] Network output: [ 0.01012 -0.03976 1.007 0.0001233 -5.534e-05 1.013 9.291e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09608 0.0876 0.1762 0.2201 0.9874 0.9921 0.09614 0.8158 0.8894 0.3132 ] Network output: [ -0.01229 0.03876 1.007 0.000117 -5.251e-05 0.9795 8.815e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.101 0.09942 0.177 0.2091 0.9859 0.9917 0.101 0.749 0.8711 0.2513 ] Network output: [ 0.001606 0.9984 -0.001766 2.005e-05 -9.002e-06 1 1.511e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001318 Epoch 5969 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.996 0.9836 5.96e-06 -2.676e-06 -0.009624 4.492e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003154 -0.002832 -0.01099 0.008237 0.9695 0.974 0.005951 0.8469 0.8359 0.02245 ] Network output: [ 0.9954 0.03035 0.001626 -5.143e-05 2.309e-05 -0.02289 -3.876e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1826 -0.01803 -0.2118 0.2079 0.9836 0.9933 0.2034 0.4717 0.8822 0.7333 ] Network output: [ -0.01289 1.002 1.01 -2.906e-07 1.305e-07 0.01308 -2.19e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004954 0.0009023 0.003922 0.005188 0.989 0.9921 0.005042 0.8796 0.9064 0.0162 ] Network output: [ -0.002327 0.03971 0.989 -0.0002261 0.0001015 0.975 -0.0001704 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1931 0.1102 0.3036 0.174 0.9851 0.994 0.1937 0.4768 0.8889 0.7291 ] Network output: [ 0.01203 -0.0246 1.004 0.0001229 -5.517e-05 0.9974 9.262e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09582 0.0873 0.1726 0.2159 0.9875 0.9921 0.09588 0.8147 0.8893 0.311 ] Network output: [ -0.01102 0.03477 1.006 0.0001183 -5.309e-05 0.9815 8.912e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09914 0.1757 0.2083 0.9858 0.9916 0.1007 0.7475 0.871 0.2511 ] Network output: [ -0.000856 0.9998 0.001736 1.831e-05 -8.218e-06 1 1.38e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001048 Epoch 5970 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01557 0.9874 0.984 7.363e-06 -3.305e-06 -0.00246 5.549e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003145 -0.002831 -0.01095 0.008398 0.9695 0.974 0.005935 0.8468 0.8363 0.0225 ] Network output: [ 0.9999 -0.02497 0.004039 -4.272e-05 1.918e-05 0.02095 -3.219e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.01878 -0.2091 0.2173 0.9836 0.9933 0.2025 0.4704 0.8825 0.734 ] Network output: [ -0.01292 0.9993 1.011 2.005e-07 -8.999e-08 0.01595 1.511e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004937 0.0009067 0.00408 0.005508 0.989 0.9921 0.005025 0.8795 0.9066 0.01628 ] Network output: [ 0.002687 -0.03729 0.9929 -0.0002132 9.57e-05 1.038 -0.0001607 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.1099 0.3088 0.1889 0.9851 0.9941 0.1929 0.4758 0.8889 0.7286 ] Network output: [ 0.01012 -0.03984 1.007 0.0001232 -5.532e-05 1.013 9.287e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09612 0.08763 0.1763 0.2201 0.9874 0.9921 0.09617 0.8157 0.8894 0.3132 ] Network output: [ -0.01229 0.03888 1.007 0.0001169 -5.25e-05 0.9794 8.813e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.101 0.09943 0.177 0.2091 0.9859 0.9917 0.101 0.749 0.871 0.2513 ] Network output: [ 0.001606 0.9984 -0.001776 2.002e-05 -8.989e-06 1 1.509e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001318 Epoch 5971 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.996 0.9836 6.003e-06 -2.695e-06 -0.009604 4.524e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003155 -0.002833 -0.01098 0.008235 0.9695 0.974 0.005952 0.847 0.8359 0.02244 ] Network output: [ 0.9954 0.03032 0.001619 -5.149e-05 2.311e-05 -0.02287 -3.88e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1826 -0.01808 -0.2118 0.2079 0.9836 0.9933 0.2034 0.4717 0.8822 0.7332 ] Network output: [ -0.01289 1.002 1.01 -2.367e-07 1.063e-07 0.0131 -1.784e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004955 0.0009005 0.003924 0.005187 0.989 0.9921 0.005043 0.8796 0.9064 0.0162 ] Network output: [ -0.002333 0.03968 0.9891 -0.000226 0.0001015 0.9749 -0.0001703 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.193 0.1101 0.3037 0.174 0.9851 0.994 0.1936 0.4768 0.8889 0.729 ] Network output: [ 0.01202 -0.0247 1.004 0.0001228 -5.515e-05 0.9975 9.258e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09585 0.08732 0.1726 0.2159 0.9875 0.9921 0.09591 0.8146 0.8893 0.311 ] Network output: [ -0.01102 0.0349 1.006 0.0001182 -5.307e-05 0.9814 8.909e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09915 0.1757 0.2083 0.9858 0.9916 0.1007 0.7475 0.871 0.2511 ] Network output: [ -0.000854 0.9998 0.001723 1.828e-05 -8.206e-06 1 1.378e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00105 Epoch 5972 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01557 0.9874 0.984 7.403e-06 -3.323e-06 -0.002447 5.579e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003145 -0.002832 -0.01095 0.008396 0.9695 0.974 0.005935 0.8468 0.8363 0.02249 ] Network output: [ 0.9999 -0.02494 0.004029 -4.279e-05 1.921e-05 0.02091 -3.225e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.01882 -0.2091 0.2173 0.9836 0.9933 0.2025 0.4704 0.8825 0.7339 ] Network output: [ -0.01291 0.9993 1.011 2.533e-07 -1.137e-07 0.01595 1.909e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004938 0.0009049 0.004082 0.005506 0.989 0.9921 0.005026 0.8795 0.9066 0.01628 ] Network output: [ 0.002675 -0.03723 0.993 -0.0002131 9.567e-05 1.038 -0.0001606 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.1098 0.3088 0.1888 0.9851 0.9941 0.1929 0.4758 0.8889 0.7285 ] Network output: [ 0.01012 -0.03993 1.007 0.0001232 -5.53e-05 1.013 9.283e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09615 0.08765 0.1763 0.2201 0.9874 0.9921 0.09621 0.8157 0.8894 0.3133 ] Network output: [ -0.01229 0.03901 1.007 0.0001169 -5.248e-05 0.9793 8.81e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.101 0.09944 0.1769 0.2091 0.9859 0.9917 0.101 0.749 0.871 0.2512 ] Network output: [ 0.001607 0.9984 -0.001786 1.999e-05 -8.975e-06 1 1.507e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001318 Epoch 5973 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.996 0.9836 6.046e-06 -2.714e-06 -0.009584 4.556e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003155 -0.002833 -0.01098 0.008234 0.9695 0.974 0.005952 0.847 0.8359 0.02244 ] Network output: [ 0.9954 0.03029 0.001613 -5.154e-05 2.314e-05 -0.02286 -3.884e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1825 -0.01812 -0.2118 0.2079 0.9836 0.9933 0.2034 0.4717 0.8822 0.7331 ] Network output: [ -0.01288 1.002 1.01 -1.838e-07 8.251e-08 0.01311 -1.385e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004956 0.0008987 0.003925 0.005186 0.989 0.9921 0.005045 0.8796 0.9064 0.01619 ] Network output: [ -0.002339 0.03965 0.9893 -0.0002259 0.0001014 0.9748 -0.0001702 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.193 0.1101 0.3037 0.1739 0.9851 0.994 0.1936 0.4768 0.8889 0.729 ] Network output: [ 0.01202 -0.0248 1.004 0.0001228 -5.513e-05 0.9977 9.254e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09589 0.08734 0.1727 0.2159 0.9875 0.9921 0.09595 0.8146 0.8892 0.3111 ] Network output: [ -0.01102 0.03502 1.006 0.0001182 -5.306e-05 0.9813 8.907e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09916 0.1756 0.2082 0.9858 0.9916 0.1008 0.7475 0.871 0.2511 ] Network output: [ -0.0008521 0.9998 0.00171 1.825e-05 -8.194e-06 1 1.375e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001051 Epoch 5974 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01557 0.9874 0.984 7.442e-06 -3.341e-06 -0.002435 5.609e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003146 -0.002833 -0.01095 0.008394 0.9695 0.974 0.005936 0.8468 0.8363 0.02249 ] Network output: [ 0.9999 -0.02491 0.004019 -4.286e-05 1.924e-05 0.02089 -3.23e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.01887 -0.209 0.2172 0.9836 0.9933 0.2025 0.4704 0.8825 0.7339 ] Network output: [ -0.01291 0.9993 1.011 3.053e-07 -1.371e-07 0.01596 2.301e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004939 0.0009031 0.004083 0.005504 0.989 0.9921 0.005028 0.8795 0.9066 0.01628 ] Network output: [ 0.002664 -0.03717 0.9932 -0.000213 9.564e-05 1.038 -0.0001606 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.1097 0.3089 0.1887 0.9851 0.9941 0.1929 0.4758 0.8889 0.7284 ] Network output: [ 0.01012 -0.04002 1.007 0.0001231 -5.528e-05 1.013 9.279e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09619 0.08767 0.1763 0.2201 0.9874 0.9921 0.09624 0.8157 0.8893 0.3133 ] Network output: [ -0.01229 0.03913 1.007 0.0001169 -5.247e-05 0.9793 8.808e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.101 0.09945 0.1769 0.209 0.9859 0.9917 0.101 0.749 0.871 0.2512 ] Network output: [ 0.001608 0.9984 -0.001796 1.996e-05 -8.962e-06 1 1.504e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001319 Epoch 5975 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9959 0.9836 6.087e-06 -2.733e-06 -0.009565 4.588e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003155 -0.002834 -0.01098 0.008232 0.9695 0.974 0.005953 0.847 0.8359 0.02244 ] Network output: [ 0.9954 0.03026 0.001607 -5.159e-05 2.316e-05 -0.02285 -3.888e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1825 -0.01817 -0.2118 0.2078 0.9836 0.9933 0.2033 0.4718 0.8822 0.7331 ] Network output: [ -0.01288 1.002 1.01 -1.318e-07 5.916e-08 0.01312 -9.931e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004958 0.0008969 0.003926 0.005184 0.989 0.9921 0.005046 0.8796 0.9064 0.01619 ] Network output: [ -0.002345 0.03963 0.9894 -0.0002258 0.0001014 0.9748 -0.0001702 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.193 0.11 0.3038 0.1739 0.9851 0.994 0.1936 0.4768 0.8889 0.7289 ] Network output: [ 0.01201 -0.0249 1.004 0.0001227 -5.51e-05 0.9979 9.25e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09592 0.08736 0.1727 0.2159 0.9875 0.9921 0.09598 0.8146 0.8892 0.3111 ] Network output: [ -0.01102 0.03515 1.006 0.0001182 -5.304e-05 0.9813 8.905e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09917 0.1756 0.2082 0.9858 0.9916 0.1008 0.7475 0.871 0.251 ] Network output: [ -0.0008505 0.9998 0.001698 1.822e-05 -8.182e-06 1 1.373e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001053 Epoch 5976 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01557 0.9873 0.984 7.481e-06 -3.358e-06 -0.002422 5.638e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003146 -0.002834 -0.01094 0.008392 0.9695 0.974 0.005937 0.8468 0.8363 0.02249 ] Network output: [ 0.9999 -0.02488 0.004009 -4.293e-05 1.927e-05 0.02086 -3.236e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.01891 -0.209 0.2172 0.9836 0.9933 0.2025 0.4704 0.8825 0.7338 ] Network output: [ -0.01291 0.9992 1.011 3.564e-07 -1.6e-07 0.01597 2.686e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004941 0.0009013 0.004084 0.005502 0.989 0.9921 0.005029 0.8795 0.9066 0.01628 ] Network output: [ 0.002653 -0.03712 0.9933 -0.000213 9.561e-05 1.038 -0.0001605 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.1097 0.3089 0.1887 0.9851 0.9941 0.1929 0.4759 0.8889 0.7284 ] Network output: [ 0.01011 -0.04011 1.007 0.0001231 -5.525e-05 1.013 9.276e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09622 0.08769 0.1764 0.22 0.9874 0.9921 0.09628 0.8157 0.8893 0.3133 ] Network output: [ -0.01229 0.03925 1.007 0.0001168 -5.246e-05 0.9792 8.806e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.101 0.09946 0.1769 0.209 0.9859 0.9917 0.101 0.749 0.871 0.2512 ] Network output: [ 0.001609 0.9984 -0.001806 1.993e-05 -8.949e-06 1 1.502e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001319 Epoch 5977 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9959 0.9836 6.128e-06 -2.751e-06 -0.009546 4.618e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003156 -0.002835 -0.01098 0.008231 0.9695 0.974 0.005954 0.847 0.8359 0.02243 ] Network output: [ 0.9954 0.03024 0.0016 -5.164e-05 2.319e-05 -0.02284 -3.892e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1825 -0.01821 -0.2117 0.2078 0.9836 0.9933 0.2033 0.4718 0.8822 0.733 ] Network output: [ -0.01288 1.002 1.01 -8.068e-08 3.622e-08 0.01313 -6.08e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004959 0.0008951 0.003928 0.005183 0.989 0.9921 0.005048 0.8796 0.9063 0.01619 ] Network output: [ -0.002351 0.03962 0.9895 -0.0002257 0.0001013 0.9747 -0.0001701 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.193 0.1099 0.3038 0.1738 0.9851 0.994 0.1936 0.4768 0.8889 0.7288 ] Network output: [ 0.01201 -0.025 1.003 0.0001227 -5.508e-05 0.998 9.246e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09596 0.08739 0.1728 0.2159 0.9875 0.9921 0.09602 0.8146 0.8892 0.3111 ] Network output: [ -0.01102 0.03528 1.006 0.0001181 -5.303e-05 0.9812 8.902e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09918 0.1756 0.2082 0.9858 0.9916 0.1008 0.7475 0.871 0.251 ] Network output: [ -0.000849 0.9997 0.001686 1.82e-05 -8.169e-06 1 1.371e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001055 Epoch 5978 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01557 0.9873 0.984 7.518e-06 -3.375e-06 -0.002409 5.666e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003146 -0.002835 -0.01094 0.008391 0.9695 0.974 0.005938 0.8468 0.8363 0.02248 ] Network output: [ 0.9999 -0.02486 0.004 -4.3e-05 1.931e-05 0.02084 -3.241e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.01896 -0.209 0.2171 0.9836 0.9933 0.2025 0.4704 0.8825 0.7337 ] Network output: [ -0.0129 0.9992 1.011 4.067e-07 -1.826e-07 0.01598 3.065e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004942 0.0008996 0.004085 0.005501 0.989 0.9921 0.005031 0.8795 0.9066 0.01627 ] Network output: [ 0.002643 -0.03708 0.9934 -0.0002129 9.557e-05 1.038 -0.0001604 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.1096 0.3089 0.1886 0.9851 0.9941 0.1928 0.4759 0.8889 0.7283 ] Network output: [ 0.01011 -0.0402 1.007 0.000123 -5.523e-05 1.013 9.272e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09625 0.08771 0.1764 0.22 0.9874 0.9921 0.09631 0.8156 0.8893 0.3133 ] Network output: [ -0.01229 0.03938 1.007 0.0001168 -5.244e-05 0.9791 8.804e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.101 0.09947 0.1769 0.209 0.9859 0.9917 0.1011 0.749 0.871 0.2511 ] Network output: [ 0.00161 0.9984 -0.001816 1.99e-05 -8.936e-06 1 1.5e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00132 Epoch 5979 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9959 0.9836 6.167e-06 -2.769e-06 -0.009527 4.648e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003156 -0.002836 -0.01098 0.008229 0.9695 0.974 0.005955 0.847 0.8359 0.02243 ] Network output: [ 0.9954 0.03022 0.001594 -5.17e-05 2.321e-05 -0.02283 -3.896e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1825 -0.01826 -0.2117 0.2078 0.9836 0.9933 0.2033 0.4718 0.8822 0.7329 ] Network output: [ -0.01287 1.002 1.01 -3.049e-08 1.369e-08 0.01314 -2.298e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00496 0.0008934 0.003929 0.005181 0.989 0.9921 0.005049 0.8796 0.9063 0.01619 ] Network output: [ -0.002358 0.03961 0.9896 -0.0002256 0.0001013 0.9746 -0.00017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.193 0.1098 0.3038 0.1737 0.9851 0.9941 0.1936 0.4768 0.8889 0.7288 ] Network output: [ 0.012 -0.0251 1.003 0.0001226 -5.506e-05 0.9982 9.242e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09599 0.08741 0.1728 0.2159 0.9875 0.9921 0.09605 0.8145 0.8892 0.3111 ] Network output: [ -0.01102 0.0354 1.006 0.0001181 -5.301e-05 0.9811 8.9e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09919 0.1756 0.2081 0.9858 0.9916 0.1008 0.7475 0.871 0.2509 ] Network output: [ -0.0008477 0.9997 0.001675 1.817e-05 -8.157e-06 1 1.369e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001057 Epoch 5980 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01557 0.9873 0.984 7.555e-06 -3.392e-06 -0.002395 5.694e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003147 -0.002835 -0.01094 0.008389 0.9695 0.974 0.005939 0.8468 0.8363 0.02248 ] Network output: [ 0.9999 -0.02484 0.00399 -4.307e-05 1.934e-05 0.02082 -3.246e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.019 -0.209 0.2171 0.9836 0.9933 0.2024 0.4704 0.8825 0.7337 ] Network output: [ -0.0129 0.9992 1.011 4.561e-07 -2.048e-07 0.01599 3.437e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004944 0.0008978 0.004087 0.005499 0.989 0.9921 0.005032 0.8795 0.9066 0.01627 ] Network output: [ 0.002633 -0.03703 0.9935 -0.0002128 9.554e-05 1.037 -0.0001604 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.1095 0.309 0.1885 0.9851 0.9941 0.1928 0.4759 0.8889 0.7282 ] Network output: [ 0.01011 -0.04029 1.007 0.000123 -5.521e-05 1.014 9.268e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09629 0.08773 0.1765 0.22 0.9874 0.9921 0.09634 0.8156 0.8893 0.3133 ] Network output: [ -0.01228 0.0395 1.006 0.0001168 -5.243e-05 0.9791 8.801e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09948 0.1769 0.2089 0.9859 0.9917 0.1011 0.749 0.871 0.2511 ] Network output: [ 0.001611 0.9984 -0.001826 1.988e-05 -8.923e-06 1 1.498e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001321 Epoch 5981 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9959 0.9836 6.206e-06 -2.786e-06 -0.009509 4.677e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003157 -0.002836 -0.01097 0.008228 0.9695 0.974 0.005955 0.847 0.8359 0.02243 ] Network output: [ 0.9954 0.0302 0.001587 -5.175e-05 2.323e-05 -0.02282 -3.9e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1825 -0.0183 -0.2117 0.2077 0.9836 0.9933 0.2033 0.4718 0.8822 0.7329 ] Network output: [ -0.01287 1.002 1.01 1.88e-08 -8.438e-09 0.01316 1.416e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004962 0.0008916 0.00393 0.00518 0.989 0.9921 0.00505 0.8796 0.9063 0.01619 ] Network output: [ -0.002364 0.0396 0.9897 -0.0002255 0.0001012 0.9745 -0.0001699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1929 0.1097 0.3039 0.1737 0.9851 0.9941 0.1935 0.4768 0.8889 0.7287 ] Network output: [ 0.012 -0.02519 1.003 0.0001226 -5.503e-05 0.9983 9.238e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09603 0.08743 0.1728 0.2159 0.9875 0.9921 0.09608 0.8145 0.8892 0.3111 ] Network output: [ -0.01102 0.03553 1.006 0.0001181 -5.3e-05 0.9811 8.897e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.0992 0.1756 0.2081 0.9858 0.9916 0.1008 0.7475 0.8709 0.2509 ] Network output: [ -0.0008465 0.9997 0.001664 1.814e-05 -8.145e-06 1 1.367e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001059 Epoch 5982 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01557 0.9873 0.984 7.591e-06 -3.408e-06 -0.002382 5.721e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003147 -0.002836 -0.01094 0.008387 0.9695 0.974 0.005939 0.8469 0.8363 0.02247 ] Network output: [ 0.9999 -0.02483 0.003981 -4.314e-05 1.937e-05 0.0208 -3.251e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.01905 -0.2089 0.217 0.9836 0.9933 0.2024 0.4705 0.8825 0.7336 ] Network output: [ -0.0129 0.9992 1.011 5.047e-07 -2.266e-07 0.016 3.803e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004945 0.000896 0.004088 0.005497 0.989 0.9921 0.005033 0.8795 0.9065 0.01627 ] Network output: [ 0.002623 -0.037 0.9936 -0.0002127 9.55e-05 1.037 -0.0001603 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.1094 0.309 0.1885 0.9851 0.9941 0.1928 0.4759 0.8889 0.7282 ] Network output: [ 0.0101 -0.04038 1.007 0.0001229 -5.519e-05 1.014 9.264e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09632 0.08775 0.1765 0.22 0.9874 0.9921 0.09638 0.8156 0.8893 0.3133 ] Network output: [ -0.01228 0.03962 1.006 0.0001168 -5.242e-05 0.979 8.799e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09949 0.1769 0.2089 0.9859 0.9917 0.1011 0.749 0.871 0.251 ] Network output: [ 0.001612 0.9984 -0.001837 1.985e-05 -8.91e-06 1 1.496e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001322 Epoch 5983 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9958 0.9836 6.243e-06 -2.803e-06 -0.009492 4.705e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003157 -0.002837 -0.01097 0.008226 0.9695 0.974 0.005956 0.847 0.8359 0.02242 ] Network output: [ 0.9954 0.03019 0.001581 -5.18e-05 2.325e-05 -0.02282 -3.904e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.01835 -0.2117 0.2077 0.9836 0.9933 0.2033 0.4718 0.8822 0.7328 ] Network output: [ -0.01287 1.002 1.01 6.72e-08 -3.017e-08 0.01317 5.064e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004963 0.0008899 0.003932 0.005178 0.989 0.9921 0.005052 0.8796 0.9063 0.01618 ] Network output: [ -0.002371 0.0396 0.9898 -0.0002254 0.0001012 0.9744 -0.0001699 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1929 0.1096 0.3039 0.1736 0.9851 0.9941 0.1935 0.4769 0.8889 0.7286 ] Network output: [ 0.01199 -0.02529 1.003 0.0001225 -5.501e-05 0.9985 9.234e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09606 0.08745 0.1729 0.2159 0.9875 0.9921 0.09612 0.8145 0.8892 0.3112 ] Network output: [ -0.01102 0.03565 1.006 0.000118 -5.298e-05 0.981 8.894e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09921 0.1756 0.208 0.9858 0.9916 0.1008 0.7475 0.8709 0.2509 ] Network output: [ -0.0008456 0.9997 0.001653 1.812e-05 -8.133e-06 1 1.365e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001061 Epoch 5984 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01557 0.9873 0.984 7.626e-06 -3.424e-06 -0.002368 5.748e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003147 -0.002837 -0.01094 0.008386 0.9695 0.974 0.00594 0.8469 0.8363 0.02247 ] Network output: [ 0.9999 -0.02481 0.003973 -4.32e-05 1.94e-05 0.02078 -3.256e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.01909 -0.2089 0.217 0.9836 0.9933 0.2024 0.4705 0.8825 0.7335 ] Network output: [ -0.01289 0.9991 1.011 5.524e-07 -2.48e-07 0.01601 4.163e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004946 0.0008943 0.004089 0.005495 0.989 0.9921 0.005035 0.8795 0.9065 0.01627 ] Network output: [ 0.002614 -0.03696 0.9937 -0.0002126 9.546e-05 1.037 -0.0001603 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.1093 0.309 0.1884 0.9851 0.9941 0.1928 0.4759 0.8889 0.7281 ] Network output: [ 0.0101 -0.04047 1.007 0.0001229 -5.516e-05 1.014 9.26e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09635 0.08777 0.1765 0.22 0.9874 0.9921 0.09641 0.8155 0.8893 0.3134 ] Network output: [ -0.01228 0.03974 1.006 0.0001167 -5.24e-05 0.9789 8.797e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09949 0.1769 0.2089 0.9859 0.9917 0.1011 0.749 0.871 0.251 ] Network output: [ 0.001613 0.9984 -0.001847 1.982e-05 -8.897e-06 1 1.494e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001323 Epoch 5985 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9958 0.9836 6.28e-06 -2.819e-06 -0.009475 4.733e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003157 -0.002838 -0.01097 0.008225 0.9695 0.974 0.005957 0.847 0.8359 0.02242 ] Network output: [ 0.9954 0.03017 0.001574 -5.185e-05 2.328e-05 -0.02282 -3.907e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.01839 -0.2116 0.2077 0.9836 0.9933 0.2032 0.4718 0.8822 0.7327 ] Network output: [ -0.01287 1.002 1.01 1.147e-07 -5.151e-08 0.01318 8.646e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004964 0.0008881 0.003933 0.005176 0.989 0.9921 0.005053 0.8796 0.9063 0.01618 ] Network output: [ -0.002377 0.0396 0.99 -0.0002253 0.0001011 0.9743 -0.0001698 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1929 0.1095 0.304 0.1736 0.9851 0.9941 0.1935 0.4769 0.8889 0.7286 ] Network output: [ 0.01199 -0.02538 1.003 0.0001225 -5.498e-05 0.9986 9.23e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09609 0.08747 0.1729 0.2159 0.9875 0.9921 0.09615 0.8144 0.8891 0.3112 ] Network output: [ -0.01102 0.03577 1.006 0.000118 -5.297e-05 0.9809 8.892e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09921 0.1756 0.208 0.9858 0.9916 0.1008 0.7475 0.8709 0.2508 ] Network output: [ -0.0008448 0.9997 0.001642 1.809e-05 -8.121e-06 1 1.363e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001064 Epoch 5986 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01557 0.9872 0.984 7.661e-06 -3.439e-06 -0.002353 5.773e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003148 -0.002838 -0.01094 0.008384 0.9695 0.974 0.005941 0.8469 0.8363 0.02247 ] Network output: [ 0.9999 -0.0248 0.003964 -4.327e-05 1.942e-05 0.02077 -3.261e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.01913 -0.2089 0.217 0.9836 0.9933 0.2024 0.4705 0.8825 0.7335 ] Network output: [ -0.01289 0.9991 1.011 5.993e-07 -2.69e-07 0.01602 4.517e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004948 0.0008926 0.00409 0.005494 0.989 0.9921 0.005036 0.8795 0.9065 0.01626 ] Network output: [ 0.002605 -0.03693 0.9938 -0.0002126 9.542e-05 1.037 -0.0001602 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.1092 0.3091 0.1883 0.9851 0.9941 0.1928 0.4759 0.8889 0.728 ] Network output: [ 0.0101 -0.04056 1.007 0.0001228 -5.514e-05 1.014 9.256e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09638 0.08779 0.1766 0.22 0.9874 0.9921 0.09644 0.8155 0.8892 0.3134 ] Network output: [ -0.01228 0.03986 1.006 0.0001167 -5.239e-05 0.9789 8.794e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.0995 0.1768 0.2088 0.9859 0.9917 0.1011 0.749 0.871 0.251 ] Network output: [ 0.001615 0.9984 -0.001857 1.979e-05 -8.884e-06 1 1.491e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001325 Epoch 5987 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9958 0.9836 6.315e-06 -2.835e-06 -0.009458 4.759e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003158 -0.002839 -0.01097 0.008223 0.9695 0.974 0.005958 0.847 0.8359 0.02241 ] Network output: [ 0.9954 0.03017 0.001568 -5.19e-05 2.33e-05 -0.02282 -3.911e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.01843 -0.2116 0.2076 0.9836 0.9933 0.2032 0.4718 0.8822 0.7327 ] Network output: [ -0.01286 1.002 1.01 1.614e-07 -7.245e-08 0.01319 1.216e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004966 0.0008864 0.003934 0.005175 0.989 0.9921 0.005054 0.8796 0.9063 0.01618 ] Network output: [ -0.002384 0.0396 0.9901 -0.0002252 0.0001011 0.9742 -0.0001697 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1929 0.1095 0.304 0.1735 0.9851 0.9941 0.1935 0.4769 0.8889 0.7285 ] Network output: [ 0.01198 -0.02547 1.003 0.0001224 -5.496e-05 0.9988 9.226e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09612 0.08748 0.1729 0.2159 0.9875 0.9921 0.09618 0.8144 0.8891 0.3112 ] Network output: [ -0.01102 0.03589 1.006 0.0001179 -5.295e-05 0.9809 8.889e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09922 0.1756 0.208 0.9858 0.9916 0.1008 0.7475 0.8709 0.2508 ] Network output: [ -0.0008441 0.9997 0.001632 1.806e-05 -8.108e-06 1 1.361e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001066 Epoch 5988 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01557 0.9872 0.984 7.694e-06 -3.454e-06 -0.002339 5.799e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003148 -0.002838 -0.01093 0.008382 0.9695 0.974 0.005942 0.8469 0.8363 0.02246 ] Network output: [ 1 -0.0248 0.003956 -4.333e-05 1.945e-05 0.02076 -3.265e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.01917 -0.2089 0.2169 0.9836 0.9933 0.2024 0.4705 0.8825 0.7334 ] Network output: [ -0.01289 0.9991 1.011 6.454e-07 -2.897e-07 0.01603 4.864e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004949 0.0008909 0.004091 0.005492 0.989 0.9921 0.005037 0.8795 0.9065 0.01626 ] Network output: [ 0.002596 -0.03691 0.9939 -0.0002125 9.538e-05 1.037 -0.0001601 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.1092 0.3091 0.1883 0.9851 0.9941 0.1928 0.4759 0.8889 0.728 ] Network output: [ 0.01009 -0.04065 1.007 0.0001228 -5.511e-05 1.014 9.252e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09641 0.08781 0.1766 0.22 0.9874 0.9921 0.09647 0.8155 0.8892 0.3134 ] Network output: [ -0.01228 0.03998 1.006 0.0001167 -5.237e-05 0.9788 8.792e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09951 0.1768 0.2088 0.9859 0.9917 0.1011 0.749 0.8709 0.2509 ] Network output: [ 0.001616 0.9984 -0.001868 1.976e-05 -8.871e-06 1 1.489e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001326 Epoch 5989 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9958 0.9836 6.35e-06 -2.851e-06 -0.009442 4.786e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003158 -0.002839 -0.01097 0.008221 0.9695 0.974 0.005958 0.8471 0.8359 0.02241 ] Network output: [ 0.9954 0.03016 0.001561 -5.195e-05 2.332e-05 -0.02282 -3.915e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.01848 -0.2116 0.2076 0.9836 0.9933 0.2032 0.4718 0.8822 0.7326 ] Network output: [ -0.01286 1.002 1.01 2.072e-07 -9.302e-08 0.0132 1.562e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004967 0.0008847 0.003935 0.005173 0.989 0.9921 0.005056 0.8796 0.9063 0.01618 ] Network output: [ -0.002391 0.03961 0.9902 -0.0002251 0.0001011 0.9741 -0.0001696 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1929 0.1094 0.304 0.1734 0.9851 0.9941 0.1935 0.4769 0.8889 0.7284 ] Network output: [ 0.01198 -0.02556 1.003 0.0001224 -5.493e-05 0.9989 9.222e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09615 0.0875 0.173 0.2159 0.9875 0.9921 0.09621 0.8144 0.8891 0.3112 ] Network output: [ -0.01102 0.03601 1.006 0.0001179 -5.294e-05 0.9808 8.886e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09923 0.1755 0.2079 0.9858 0.9916 0.1008 0.7475 0.8709 0.2508 ] Network output: [ -0.0008436 0.9997 0.001622 1.803e-05 -8.096e-06 1 1.359e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001069 Epoch 5990 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01557 0.9872 0.984 7.727e-06 -3.469e-06 -0.002324 5.824e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003149 -0.002839 -0.01093 0.008381 0.9695 0.974 0.005942 0.8469 0.8363 0.02246 ] Network output: [ 1 -0.02479 0.003948 -4.339e-05 1.948e-05 0.02075 -3.27e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.01922 -0.2088 0.2169 0.9836 0.9933 0.2024 0.4705 0.8825 0.7333 ] Network output: [ -0.01289 0.9991 1.011 6.907e-07 -3.101e-07 0.01604 5.205e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00495 0.0008891 0.004093 0.00549 0.989 0.9921 0.005039 0.8795 0.9065 0.01626 ] Network output: [ 0.002588 -0.03689 0.994 -0.0002124 9.534e-05 1.037 -0.0001601 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.1091 0.3091 0.1882 0.9851 0.9941 0.1927 0.4759 0.8889 0.7279 ] Network output: [ 0.01009 -0.04074 1.007 0.0001227 -5.509e-05 1.014 9.248e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09644 0.08783 0.1766 0.22 0.9874 0.9921 0.0965 0.8155 0.8892 0.3134 ] Network output: [ -0.01227 0.04009 1.006 0.0001166 -5.236e-05 0.9787 8.789e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09951 0.1768 0.2088 0.9859 0.9917 0.1011 0.749 0.8709 0.2509 ] Network output: [ 0.001618 0.9984 -0.001878 1.973e-05 -8.858e-06 1 1.487e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001328 Epoch 5991 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9958 0.9836 6.384e-06 -2.866e-06 -0.009427 4.811e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003158 -0.00284 -0.01097 0.00822 0.9695 0.974 0.005959 0.8471 0.8359 0.02241 ] Network output: [ 0.9955 0.03016 0.001554 -5.2e-05 2.334e-05 -0.02283 -3.919e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.01852 -0.2116 0.2076 0.9836 0.9933 0.2032 0.4718 0.8822 0.7325 ] Network output: [ -0.01286 1.002 1.01 2.522e-07 -1.132e-07 0.01321 1.9e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004968 0.000883 0.003936 0.005172 0.989 0.9921 0.005057 0.8796 0.9063 0.01617 ] Network output: [ -0.002398 0.03962 0.9903 -0.000225 0.000101 0.974 -0.0001696 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.1093 0.3041 0.1734 0.9851 0.9941 0.1934 0.4769 0.8889 0.7284 ] Network output: [ 0.01197 -0.02565 1.003 0.0001223 -5.491e-05 0.999 9.218e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09618 0.08752 0.173 0.2159 0.9875 0.9921 0.09624 0.8144 0.8891 0.3112 ] Network output: [ -0.01102 0.03613 1.006 0.0001179 -5.292e-05 0.9807 8.884e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09923 0.1755 0.2079 0.9858 0.9916 0.1008 0.7475 0.8709 0.2507 ] Network output: [ -0.0008433 0.9997 0.001613 1.801e-05 -8.084e-06 1 1.357e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001071 Epoch 5992 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01557 0.9872 0.984 7.759e-06 -3.483e-06 -0.002309 5.848e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003149 -0.00284 -0.01093 0.008379 0.9695 0.974 0.005943 0.8469 0.8363 0.02246 ] Network output: [ 1 -0.02479 0.00394 -4.345e-05 1.951e-05 0.02074 -3.274e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.01926 -0.2088 0.2169 0.9836 0.9933 0.2023 0.4705 0.8825 0.7333 ] Network output: [ -0.01288 0.9991 1.011 7.352e-07 -3.301e-07 0.01605 5.541e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004951 0.0008874 0.004094 0.005488 0.989 0.9921 0.00504 0.8795 0.9065 0.01626 ] Network output: [ 0.002581 -0.03687 0.9941 -0.0002123 9.53e-05 1.037 -0.00016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.109 0.3092 0.1881 0.9851 0.9941 0.1927 0.476 0.8889 0.7278 ] Network output: [ 0.01008 -0.04083 1.007 0.0001227 -5.507e-05 1.015 9.244e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09647 0.08785 0.1767 0.22 0.9874 0.9921 0.09653 0.8154 0.8892 0.3134 ] Network output: [ -0.01227 0.04021 1.006 0.0001166 -5.234e-05 0.9787 8.787e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09952 0.1768 0.2087 0.9859 0.9917 0.1011 0.7489 0.8709 0.2508 ] Network output: [ 0.001619 0.9984 -0.001889 1.97e-05 -8.846e-06 1 1.485e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001329 Epoch 5993 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9957 0.9836 6.417e-06 -2.881e-06 -0.009412 4.836e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003159 -0.002841 -0.01096 0.008218 0.9695 0.974 0.00596 0.8471 0.8359 0.0224 ] Network output: [ 0.9955 0.03016 0.001547 -5.204e-05 2.336e-05 -0.02284 -3.922e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.01856 -0.2115 0.2075 0.9836 0.9933 0.2032 0.4719 0.8822 0.7325 ] Network output: [ -0.01285 1.002 1.01 2.963e-07 -1.33e-07 0.01322 2.233e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004969 0.0008813 0.003938 0.00517 0.989 0.9921 0.005058 0.8796 0.9063 0.01617 ] Network output: [ -0.002406 0.03963 0.9904 -0.0002249 0.000101 0.9739 -0.0001695 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.1092 0.3041 0.1733 0.9851 0.9941 0.1934 0.4769 0.8889 0.7283 ] Network output: [ 0.01197 -0.02574 1.003 0.0001223 -5.488e-05 0.9992 9.213e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09621 0.08754 0.173 0.2159 0.9875 0.9921 0.09627 0.8143 0.8891 0.3112 ] Network output: [ -0.01101 0.03624 1.006 0.0001178 -5.29e-05 0.9807 8.881e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09924 0.1755 0.2079 0.9858 0.9916 0.1009 0.7475 0.8709 0.2507 ] Network output: [ -0.0008431 0.9997 0.001604 1.798e-05 -8.071e-06 1 1.355e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001074 Epoch 5994 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01556 0.9872 0.984 7.791e-06 -3.498e-06 -0.002294 5.871e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003149 -0.00284 -0.01093 0.008377 0.9695 0.974 0.005944 0.8469 0.8363 0.02245 ] Network output: [ 1 -0.02479 0.003932 -4.351e-05 1.953e-05 0.02073 -3.279e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.0193 -0.2088 0.2168 0.9836 0.9933 0.2023 0.4705 0.8825 0.7332 ] Network output: [ -0.01288 0.999 1.011 7.789e-07 -3.497e-07 0.01606 5.87e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004953 0.0008858 0.004095 0.005487 0.989 0.9921 0.005041 0.8795 0.9065 0.01626 ] Network output: [ 0.002573 -0.03686 0.9942 -0.0002122 9.526e-05 1.037 -0.0001599 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.1089 0.3092 0.1881 0.9851 0.9941 0.1927 0.476 0.8889 0.7278 ] Network output: [ 0.01008 -0.04092 1.007 0.0001226 -5.504e-05 1.015 9.24e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0965 0.08786 0.1767 0.22 0.9874 0.9921 0.09656 0.8154 0.8892 0.3134 ] Network output: [ -0.01227 0.04033 1.006 0.0001166 -5.233e-05 0.9786 8.784e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09952 0.1768 0.2087 0.9859 0.9917 0.1011 0.7489 0.8709 0.2508 ] Network output: [ 0.001621 0.9984 -0.001899 1.968e-05 -8.833e-06 1 1.483e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001331 Epoch 5995 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9957 0.9836 6.449e-06 -2.895e-06 -0.009397 4.86e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003159 -0.002841 -0.01096 0.008217 0.9695 0.974 0.00596 0.8471 0.8359 0.0224 ] Network output: [ 0.9955 0.03016 0.001541 -5.209e-05 2.339e-05 -0.02285 -3.926e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.0186 -0.2115 0.2075 0.9836 0.9933 0.2031 0.4719 0.8822 0.7324 ] Network output: [ -0.01285 1.002 1.01 3.396e-07 -1.525e-07 0.01323 2.56e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004971 0.0008796 0.003939 0.005168 0.989 0.9921 0.005059 0.8796 0.9063 0.01617 ] Network output: [ -0.002413 0.03965 0.9905 -0.0002248 0.0001009 0.9738 -0.0001694 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.1091 0.3041 0.1732 0.9851 0.9941 0.1934 0.4769 0.8889 0.7282 ] Network output: [ 0.01196 -0.02582 1.003 0.0001222 -5.486e-05 0.9993 9.209e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09624 0.08755 0.1731 0.2159 0.9875 0.9921 0.0963 0.8143 0.8891 0.3113 ] Network output: [ -0.01101 0.03636 1.006 0.0001178 -5.289e-05 0.9806 8.878e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09924 0.1755 0.2078 0.9858 0.9916 0.1009 0.7474 0.8709 0.2506 ] Network output: [ -0.0008431 0.9997 0.001595 1.795e-05 -8.059e-06 1 1.353e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001077 Epoch 5996 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01556 0.9871 0.984 7.821e-06 -3.511e-06 -0.002278 5.894e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00315 -0.002841 -0.01093 0.008376 0.9695 0.974 0.005944 0.8469 0.8363 0.02245 ] Network output: [ 1 -0.02479 0.003925 -4.356e-05 1.956e-05 0.02073 -3.283e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.01934 -0.2088 0.2168 0.9836 0.9933 0.2023 0.4705 0.8825 0.7331 ] Network output: [ -0.01288 0.999 1.011 8.219e-07 -3.69e-07 0.01607 6.194e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004954 0.0008841 0.004096 0.005485 0.989 0.9921 0.005042 0.8795 0.9065 0.01625 ] Network output: [ 0.002566 -0.03685 0.9943 -0.0002121 9.521e-05 1.037 -0.0001598 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.1088 0.3092 0.188 0.9851 0.9941 0.1927 0.476 0.8889 0.7277 ] Network output: [ 0.01008 -0.04101 1.007 0.0001226 -5.502e-05 1.015 9.236e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09653 0.08788 0.1767 0.22 0.9874 0.9921 0.09659 0.8154 0.8892 0.3135 ] Network output: [ -0.01227 0.04044 1.006 0.0001165 -5.231e-05 0.9785 8.781e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09952 0.1768 0.2086 0.9859 0.9917 0.1011 0.7489 0.8709 0.2508 ] Network output: [ 0.001623 0.9984 -0.00191 1.965e-05 -8.82e-06 1 1.481e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001333 Epoch 5997 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9957 0.9837 6.48e-06 -2.909e-06 -0.009383 4.883e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003159 -0.002842 -0.01096 0.008215 0.9695 0.974 0.005961 0.8471 0.8359 0.0224 ] Network output: [ 0.9955 0.03017 0.001534 -5.214e-05 2.341e-05 -0.02286 -3.929e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.01865 -0.2115 0.2074 0.9836 0.9933 0.2031 0.4719 0.8822 0.7324 ] Network output: [ -0.01285 1.002 1.01 3.821e-07 -1.716e-07 0.01324 2.88e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004972 0.0008779 0.00394 0.005166 0.989 0.9921 0.005061 0.8796 0.9063 0.01617 ] Network output: [ -0.002421 0.03967 0.9906 -0.0002247 0.0001009 0.9737 -0.0001693 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.1091 0.3042 0.1732 0.9851 0.9941 0.1934 0.4769 0.8889 0.7282 ] Network output: [ 0.01196 -0.02591 1.003 0.0001221 -5.483e-05 0.9995 9.205e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09627 0.08757 0.1731 0.2159 0.9875 0.9921 0.09632 0.8143 0.889 0.3113 ] Network output: [ -0.01101 0.03647 1.005 0.0001178 -5.287e-05 0.9805 8.875e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09924 0.1755 0.2078 0.9858 0.9916 0.1009 0.7474 0.8708 0.2506 ] Network output: [ -0.0008432 0.9997 0.001586 1.792e-05 -8.046e-06 1 1.351e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00108 Epoch 5998 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01556 0.9871 0.984 7.851e-06 -3.525e-06 -0.002262 5.917e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00315 -0.002842 -0.01092 0.008374 0.9695 0.974 0.005945 0.8469 0.8363 0.02244 ] Network output: [ 1 -0.0248 0.003918 -4.362e-05 1.958e-05 0.02073 -3.287e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.01939 -0.2087 0.2168 0.9836 0.9933 0.2023 0.4705 0.8825 0.7331 ] Network output: [ -0.01287 0.999 1.011 8.64e-07 -3.879e-07 0.01608 6.512e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004955 0.0008824 0.004097 0.005483 0.989 0.9921 0.005044 0.8795 0.9065 0.01625 ] Network output: [ 0.00256 -0.03684 0.9944 -0.000212 9.517e-05 1.036 -0.0001598 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.1088 0.3093 0.1879 0.9851 0.9941 0.1927 0.476 0.8889 0.7276 ] Network output: [ 0.01007 -0.0411 1.007 0.0001225 -5.499e-05 1.015 9.232e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09655 0.08789 0.1768 0.22 0.9874 0.9921 0.09661 0.8154 0.8891 0.3135 ] Network output: [ -0.01227 0.04056 1.006 0.0001165 -5.229e-05 0.9785 8.779e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09953 0.1767 0.2086 0.9859 0.9917 0.1011 0.7489 0.8709 0.2507 ] Network output: [ 0.001625 0.9984 -0.00192 1.962e-05 -8.808e-06 1 1.479e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001336 Epoch 5999 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9957 0.9837 6.51e-06 -2.923e-06 -0.009369 4.906e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00316 -0.002843 -0.01096 0.008213 0.9695 0.974 0.005962 0.8471 0.8359 0.02239 ] Network output: [ 0.9955 0.03018 0.001527 -5.218e-05 2.343e-05 -0.02288 -3.933e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.01869 -0.2115 0.2074 0.9836 0.9933 0.2031 0.4719 0.8822 0.7323 ] Network output: [ -0.01285 1.002 1.01 4.238e-07 -1.903e-07 0.01325 3.194e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004973 0.0008762 0.003941 0.005165 0.989 0.9921 0.005062 0.8796 0.9063 0.01617 ] Network output: [ -0.002428 0.03969 0.9907 -0.0002246 0.0001008 0.9736 -0.0001692 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.109 0.3042 0.1731 0.9851 0.9941 0.1934 0.4769 0.8889 0.7281 ] Network output: [ 0.01195 -0.02599 1.003 0.0001221 -5.481e-05 0.9996 9.201e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09629 0.08759 0.1731 0.2159 0.9875 0.9921 0.09635 0.8143 0.889 0.3113 ] Network output: [ -0.01101 0.03658 1.005 0.0001177 -5.285e-05 0.9805 8.872e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09925 0.1755 0.2078 0.9858 0.9916 0.1009 0.7474 0.8708 0.2506 ] Network output: [ -0.0008435 0.9997 0.001578 1.79e-05 -8.034e-06 1 1.349e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001083 Epoch 6000 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01556 0.9871 0.984 7.88e-06 -3.538e-06 -0.002247 5.939e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00315 -0.002842 -0.01092 0.008372 0.9695 0.974 0.005946 0.847 0.8363 0.02244 ] Network output: [ 1 -0.02481 0.003911 -4.367e-05 1.961e-05 0.02073 -3.291e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.01943 -0.2087 0.2167 0.9836 0.9933 0.2023 0.4705 0.8825 0.733 ] Network output: [ -0.01287 0.999 1.011 9.055e-07 -4.065e-07 0.01609 6.824e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004956 0.0008808 0.004099 0.005482 0.989 0.9921 0.005045 0.8795 0.9065 0.01625 ] Network output: [ 0.002553 -0.03684 0.9945 -0.0002119 9.512e-05 1.036 -0.0001597 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.1087 0.3093 0.1879 0.9851 0.9941 0.1926 0.476 0.8889 0.7276 ] Network output: [ 0.01007 -0.04119 1.006 0.0001224 -5.497e-05 1.015 9.228e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09658 0.08791 0.1768 0.22 0.9874 0.9921 0.09664 0.8153 0.8891 0.3135 ] Network output: [ -0.01226 0.04067 1.006 0.0001164 -5.228e-05 0.9784 8.776e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09953 0.1767 0.2086 0.9859 0.9917 0.1011 0.7489 0.8709 0.2507 ] Network output: [ 0.001627 0.9984 -0.001931 1.959e-05 -8.795e-06 1 1.476e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001338 Epoch 6001 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01502 0.9957 0.9837 6.54e-06 -2.936e-06 -0.009355 4.928e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00316 -0.002843 -0.01095 0.008211 0.9695 0.974 0.005962 0.8471 0.8359 0.02239 ] Network output: [ 0.9955 0.03019 0.00152 -5.223e-05 2.345e-05 -0.02289 -3.936e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.01873 -0.2114 0.2074 0.9836 0.9933 0.2031 0.4719 0.8822 0.7322 ] Network output: [ -0.01284 1.002 1.01 4.648e-07 -2.086e-07 0.01326 3.503e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004974 0.0008746 0.003942 0.005163 0.989 0.9921 0.005063 0.8796 0.9063 0.01616 ] Network output: [ -0.002436 0.03972 0.9908 -0.0002245 0.0001008 0.9734 -0.0001692 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.1089 0.3042 0.173 0.9851 0.9941 0.1933 0.4769 0.8889 0.728 ] Network output: [ 0.01195 -0.02607 1.003 0.000122 -5.478e-05 0.9997 9.196e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09632 0.0876 0.1732 0.2159 0.9875 0.9921 0.09638 0.8142 0.889 0.3113 ] Network output: [ -0.01101 0.0367 1.005 0.0001177 -5.283e-05 0.9804 8.869e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09925 0.1754 0.2077 0.9858 0.9916 0.1009 0.7474 0.8708 0.2505 ] Network output: [ -0.0008439 0.9997 0.00157 1.787e-05 -8.022e-06 1 1.347e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001086 Epoch 6002 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01556 0.9871 0.984 7.909e-06 -3.55e-06 -0.00223 5.96e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00315 -0.002843 -0.01092 0.008371 0.9695 0.974 0.005946 0.847 0.8363 0.02244 ] Network output: [ 1 -0.02482 0.003904 -4.372e-05 1.963e-05 0.02073 -3.295e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.01947 -0.2087 0.2167 0.9836 0.9933 0.2022 0.4706 0.8825 0.7329 ] Network output: [ -0.01287 0.999 1.011 9.461e-07 -4.248e-07 0.0161 7.13e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004957 0.0008791 0.0041 0.00548 0.989 0.9921 0.005046 0.8795 0.9065 0.01625 ] Network output: [ 0.002547 -0.03684 0.9946 -0.0002118 9.508e-05 1.036 -0.0001596 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.1086 0.3094 0.1878 0.9851 0.9941 0.1926 0.476 0.8889 0.7275 ] Network output: [ 0.01006 -0.04128 1.006 0.0001224 -5.494e-05 1.015 9.224e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09661 0.08793 0.1768 0.22 0.9874 0.9921 0.09667 0.8153 0.8891 0.3135 ] Network output: [ -0.01226 0.04079 1.006 0.0001164 -5.226e-05 0.9783 8.773e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09953 0.1767 0.2085 0.9859 0.9917 0.1011 0.7489 0.8708 0.2507 ] Network output: [ 0.001629 0.9984 -0.001941 1.956e-05 -8.783e-06 1 1.474e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00134 Epoch 6003 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01502 0.9957 0.9837 6.568e-06 -2.949e-06 -0.009343 4.95e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00316 -0.002844 -0.01095 0.00821 0.9695 0.974 0.005963 0.8471 0.8359 0.02238 ] Network output: [ 0.9955 0.0302 0.001513 -5.227e-05 2.347e-05 -0.02291 -3.94e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.01877 -0.2114 0.2073 0.9836 0.9933 0.2031 0.4719 0.8822 0.7322 ] Network output: [ -0.01284 1.002 1.01 5.049e-07 -2.267e-07 0.01327 3.805e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004975 0.0008729 0.003943 0.005161 0.989 0.9921 0.005064 0.8796 0.9063 0.01616 ] Network output: [ -0.002444 0.03975 0.9909 -0.0002244 0.0001007 0.9733 -0.0001691 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.1088 0.3043 0.173 0.9851 0.9941 0.1933 0.4769 0.8889 0.728 ] Network output: [ 0.01194 -0.02616 1.003 0.000122 -5.476e-05 0.9999 9.192e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09635 0.08762 0.1732 0.2159 0.9875 0.9921 0.09641 0.8142 0.889 0.3113 ] Network output: [ -0.01101 0.03681 1.005 0.0001176 -5.282e-05 0.9803 8.866e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09925 0.1754 0.2077 0.9858 0.9916 0.1009 0.7474 0.8708 0.2505 ] Network output: [ -0.0008444 0.9997 0.001563 1.784e-05 -8.009e-06 1.001 1.345e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001089 Epoch 6004 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01556 0.9871 0.9841 7.936e-06 -3.563e-06 -0.002214 5.981e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003151 -0.002844 -0.01092 0.008369 0.9695 0.974 0.005947 0.847 0.8363 0.02243 ] Network output: [ 1 -0.02483 0.003898 -4.377e-05 1.965e-05 0.02074 -3.299e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.01951 -0.2087 0.2166 0.9836 0.9933 0.2022 0.4706 0.8825 0.7329 ] Network output: [ -0.01286 0.9989 1.011 9.861e-07 -4.427e-07 0.01611 7.432e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004959 0.0008775 0.004101 0.005479 0.989 0.9921 0.005047 0.8795 0.9065 0.01624 ] Network output: [ 0.002542 -0.03685 0.9947 -0.0002117 9.503e-05 1.036 -0.0001595 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.1085 0.3094 0.1877 0.9851 0.9941 0.1926 0.476 0.8889 0.7274 ] Network output: [ 0.01006 -0.04138 1.006 0.0001223 -5.492e-05 1.015 9.219e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09663 0.08794 0.1769 0.22 0.9874 0.9921 0.09669 0.8153 0.8891 0.3135 ] Network output: [ -0.01226 0.0409 1.006 0.0001164 -5.224e-05 0.9783 8.77e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09953 0.1767 0.2085 0.9859 0.9917 0.1012 0.7489 0.8708 0.2506 ] Network output: [ 0.001631 0.9984 -0.001952 1.954e-05 -8.77e-06 1 1.472e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001343 Epoch 6005 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01502 0.9956 0.9837 6.596e-06 -2.961e-06 -0.00933 4.971e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00316 -0.002845 -0.01095 0.008208 0.9695 0.974 0.005963 0.8471 0.8359 0.02238 ] Network output: [ 0.9955 0.03021 0.001506 -5.232e-05 2.349e-05 -0.02293 -3.943e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.01881 -0.2114 0.2073 0.9836 0.9933 0.203 0.4719 0.8822 0.7321 ] Network output: [ -0.01284 1.002 1.01 5.443e-07 -2.443e-07 0.01328 4.102e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004977 0.0008713 0.003945 0.005159 0.989 0.9921 0.005066 0.8796 0.9063 0.01616 ] Network output: [ -0.002452 0.03979 0.991 -0.0002242 0.0001007 0.9732 -0.000169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.1088 0.3043 0.1729 0.9851 0.9941 0.1933 0.477 0.8889 0.7279 ] Network output: [ 0.01194 -0.02624 1.003 0.0001219 -5.473e-05 1 9.188e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09637 0.08763 0.1732 0.2159 0.9875 0.9921 0.09643 0.8142 0.889 0.3113 ] Network output: [ -0.01101 0.03692 1.005 0.0001176 -5.28e-05 0.9803 8.863e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09925 0.1754 0.2076 0.9858 0.9916 0.1009 0.7474 0.8708 0.2504 ] Network output: [ -0.0008451 0.9997 0.001555 1.781e-05 -7.997e-06 1.001 1.342e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001092 Epoch 6006 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01556 0.987 0.9841 7.963e-06 -3.575e-06 -0.002198 6.001e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003151 -0.002844 -0.01091 0.008367 0.9695 0.974 0.005947 0.847 0.8363 0.02243 ] Network output: [ 1 -0.02485 0.003891 -4.382e-05 1.967e-05 0.02075 -3.303e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.01955 -0.2086 0.2166 0.9836 0.9933 0.2022 0.4706 0.8825 0.7328 ] Network output: [ -0.01286 0.9989 1.011 1.025e-06 -4.603e-07 0.01612 7.727e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00496 0.0008759 0.004102 0.005477 0.989 0.9921 0.005049 0.8795 0.9065 0.01624 ] Network output: [ 0.002536 -0.03686 0.9948 -0.0002116 9.498e-05 1.036 -0.0001594 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.1085 0.3094 0.1877 0.9851 0.9941 0.1926 0.476 0.8889 0.7274 ] Network output: [ 0.01005 -0.04147 1.006 0.0001223 -5.489e-05 1.015 9.215e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09666 0.08795 0.1769 0.22 0.9874 0.9921 0.09672 0.8152 0.8891 0.3135 ] Network output: [ -0.01226 0.04101 1.006 0.0001163 -5.223e-05 0.9782 8.767e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09954 0.1767 0.2085 0.9859 0.9917 0.1012 0.7489 0.8708 0.2506 ] Network output: [ 0.001633 0.9984 -0.001963 1.951e-05 -8.758e-06 1 1.47e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001345 Epoch 6007 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01502 0.9956 0.9837 6.623e-06 -2.973e-06 -0.009318 4.991e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003161 -0.002845 -0.01095 0.008206 0.9695 0.974 0.005964 0.8472 0.8359 0.02238 ] Network output: [ 0.9955 0.03023 0.001499 -5.236e-05 2.351e-05 -0.02295 -3.946e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.01885 -0.2114 0.2073 0.9836 0.9933 0.203 0.4719 0.8822 0.732 ] Network output: [ -0.01283 1.002 1.01 5.829e-07 -2.617e-07 0.01329 4.393e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004978 0.0008697 0.003946 0.005158 0.989 0.9921 0.005067 0.8796 0.9063 0.01616 ] Network output: [ -0.00246 0.03982 0.9911 -0.0002241 0.0001006 0.9731 -0.0001689 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.1087 0.3043 0.1728 0.9851 0.9941 0.1933 0.477 0.8889 0.7279 ] Network output: [ 0.01193 -0.02631 1.003 0.0001219 -5.47e-05 1 9.183e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0964 0.08765 0.1733 0.2159 0.9875 0.9921 0.09646 0.8141 0.889 0.3113 ] Network output: [ -0.011 0.03702 1.005 0.0001176 -5.278e-05 0.9802 8.86e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09925 0.1754 0.2076 0.9858 0.9916 0.1009 0.7474 0.8708 0.2504 ] Network output: [ -0.0008459 0.9997 0.001548 1.778e-05 -7.984e-06 1.001 1.34e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001095 Epoch 6008 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01556 0.987 0.9841 7.989e-06 -3.587e-06 -0.002181 6.021e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003151 -0.002845 -0.01091 0.008366 0.9695 0.974 0.005948 0.847 0.8363 0.02242 ] Network output: [ 1 -0.02486 0.003885 -4.387e-05 1.97e-05 0.02076 -3.306e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.01959 -0.2086 0.2166 0.9836 0.9933 0.2022 0.4706 0.8825 0.7328 ] Network output: [ -0.01286 0.9989 1.011 1.064e-06 -4.776e-07 0.01613 8.017e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004961 0.0008742 0.004104 0.005475 0.989 0.9921 0.00505 0.8795 0.9065 0.01624 ] Network output: [ 0.002531 -0.03687 0.9949 -0.0002115 9.493e-05 1.036 -0.0001594 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.1084 0.3095 0.1876 0.9851 0.9941 0.1926 0.476 0.8889 0.7273 ] Network output: [ 0.01005 -0.04156 1.006 0.0001222 -5.487e-05 1.016 9.211e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09669 0.08797 0.1769 0.22 0.9874 0.9921 0.09674 0.8152 0.8891 0.3136 ] Network output: [ -0.01225 0.04112 1.006 0.0001163 -5.221e-05 0.9781 8.764e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09954 0.1767 0.2084 0.9859 0.9917 0.1012 0.7488 0.8708 0.2505 ] Network output: [ 0.001635 0.9984 -0.001973 1.948e-05 -8.745e-06 1 1.468e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001348 Epoch 6009 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01502 0.9956 0.9837 6.649e-06 -2.985e-06 -0.009306 5.011e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003161 -0.002846 -0.01095 0.008204 0.9695 0.974 0.005965 0.8472 0.8359 0.02237 ] Network output: [ 0.9955 0.03025 0.001492 -5.241e-05 2.353e-05 -0.02298 -3.95e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.01889 -0.2114 0.2072 0.9836 0.9933 0.203 0.4719 0.8822 0.732 ] Network output: [ -0.01283 1.002 1.01 6.207e-07 -2.787e-07 0.01329 4.678e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004979 0.0008681 0.003947 0.005156 0.989 0.9921 0.005068 0.8796 0.9063 0.01615 ] Network output: [ -0.002468 0.03986 0.9912 -0.000224 0.0001006 0.973 -0.0001688 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.1086 0.3044 0.1728 0.9851 0.9941 0.1933 0.477 0.8889 0.7278 ] Network output: [ 0.01193 -0.02639 1.003 0.0001218 -5.468e-05 1 9.179e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09642 0.08766 0.1733 0.2159 0.9874 0.9921 0.09648 0.8141 0.8889 0.3114 ] Network output: [ -0.011 0.03713 1.005 0.0001175 -5.276e-05 0.9802 8.857e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09925 0.1754 0.2076 0.9858 0.9916 0.1009 0.7474 0.8707 0.2504 ] Network output: [ -0.0008469 0.9997 0.001541 1.776e-05 -7.972e-06 1.001 1.338e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001099 Epoch 6010 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01556 0.987 0.9841 8.015e-06 -3.598e-06 -0.002164 6.04e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003152 -0.002846 -0.01091 0.008364 0.9695 0.974 0.005949 0.847 0.8363 0.02242 ] Network output: [ 1 -0.02488 0.003879 -4.392e-05 1.972e-05 0.02077 -3.31e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.01963 -0.2086 0.2165 0.9836 0.9933 0.2022 0.4706 0.8825 0.7327 ] Network output: [ -0.01286 0.9989 1.011 1.102e-06 -4.946e-07 0.01614 8.302e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004962 0.0008726 0.004105 0.005474 0.989 0.9921 0.005051 0.8795 0.9065 0.01624 ] Network output: [ 0.002526 -0.03689 0.995 -0.0002113 9.488e-05 1.036 -0.0001593 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.1083 0.3095 0.1876 0.9851 0.9941 0.1926 0.476 0.8889 0.7272 ] Network output: [ 0.01004 -0.04165 1.006 0.0001222 -5.484e-05 1.016 9.207e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09671 0.08798 0.177 0.22 0.9874 0.9921 0.09677 0.8152 0.889 0.3136 ] Network output: [ -0.01225 0.04123 1.006 0.0001163 -5.219e-05 0.9781 8.761e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09954 0.1766 0.2084 0.9859 0.9917 0.1012 0.7488 0.8708 0.2505 ] Network output: [ 0.001638 0.9984 -0.001984 1.945e-05 -8.733e-06 1 1.466e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001351 Epoch 6011 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01502 0.9956 0.9837 6.674e-06 -2.996e-06 -0.009295 5.03e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003161 -0.002847 -0.01094 0.008203 0.9695 0.974 0.005965 0.8472 0.8359 0.02237 ] Network output: [ 0.9955 0.03028 0.001485 -5.245e-05 2.355e-05 -0.023 -3.953e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.01893 -0.2113 0.2072 0.9836 0.9933 0.203 0.4719 0.8822 0.7319 ] Network output: [ -0.01283 1.002 1.01 6.578e-07 -2.953e-07 0.0133 4.958e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00498 0.0008665 0.003948 0.005154 0.989 0.9921 0.005069 0.8796 0.9063 0.01615 ] Network output: [ -0.002476 0.03991 0.9913 -0.0002239 0.0001005 0.9729 -0.0001687 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.1085 0.3044 0.1727 0.9851 0.9941 0.1933 0.477 0.8889 0.7277 ] Network output: [ 0.01192 -0.02647 1.003 0.0001217 -5.465e-05 1 9.174e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09645 0.08767 0.1733 0.2159 0.9874 0.9921 0.09651 0.8141 0.8889 0.3114 ] Network output: [ -0.011 0.03724 1.005 0.0001175 -5.274e-05 0.9801 8.854e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09925 0.1754 0.2075 0.9858 0.9916 0.1009 0.7473 0.8707 0.2503 ] Network output: [ -0.0008479 0.9997 0.001535 1.773e-05 -7.959e-06 1.001 1.336e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001102 Epoch 6012 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01556 0.987 0.9841 8.04e-06 -3.609e-06 -0.002147 6.059e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003152 -0.002846 -0.01091 0.008362 0.9695 0.974 0.005949 0.847 0.8363 0.02242 ] Network output: [ 1 -0.0249 0.003874 -4.397e-05 1.974e-05 0.02078 -3.313e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.01967 -0.2085 0.2165 0.9836 0.9933 0.2022 0.4706 0.8825 0.7326 ] Network output: [ -0.01285 0.9989 1.011 1.139e-06 -5.112e-07 0.01615 8.582e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004963 0.000871 0.004106 0.005472 0.989 0.9921 0.005052 0.8795 0.9065 0.01623 ] Network output: [ 0.002522 -0.0369 0.9951 -0.0002112 9.483e-05 1.036 -0.0001592 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.1082 0.3095 0.1875 0.9851 0.9941 0.1925 0.476 0.8889 0.7272 ] Network output: [ 0.01004 -0.04174 1.006 0.0001221 -5.482e-05 1.016 9.202e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09673 0.08799 0.177 0.22 0.9874 0.9921 0.09679 0.8151 0.889 0.3136 ] Network output: [ -0.01225 0.04134 1.006 0.0001162 -5.217e-05 0.978 8.758e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09954 0.1766 0.2084 0.9859 0.9917 0.1012 0.7488 0.8708 0.2505 ] Network output: [ 0.00164 0.9984 -0.001995 1.943e-05 -8.721e-06 1 1.464e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001354 Epoch 6013 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01502 0.9956 0.9837 6.699e-06 -3.007e-06 -0.009284 5.048e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003162 -0.002847 -0.01094 0.008201 0.9695 0.974 0.005966 0.8472 0.8359 0.02236 ] Network output: [ 0.9955 0.0303 0.001478 -5.249e-05 2.357e-05 -0.02303 -3.956e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.01897 -0.2113 0.2071 0.9836 0.9933 0.203 0.4719 0.8822 0.7319 ] Network output: [ -0.01283 1.002 1.01 6.942e-07 -3.117e-07 0.01331 5.232e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004981 0.0008649 0.003949 0.005152 0.989 0.9921 0.00507 0.8796 0.9063 0.01615 ] Network output: [ -0.002484 0.03995 0.9914 -0.0002238 0.0001005 0.9727 -0.0001687 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.1085 0.3044 0.1726 0.9851 0.9941 0.1932 0.477 0.8889 0.7277 ] Network output: [ 0.01192 -0.02655 1.003 0.0001217 -5.462e-05 1 9.17e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09647 0.08768 0.1734 0.2159 0.9874 0.9921 0.09653 0.814 0.8889 0.3114 ] Network output: [ -0.011 0.03734 1.005 0.0001174 -5.272e-05 0.98 8.851e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09925 0.1753 0.2075 0.9858 0.9916 0.1009 0.7473 0.8707 0.2503 ] Network output: [ -0.0008491 0.9997 0.001529 1.77e-05 -7.947e-06 1.001 1.334e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001106 Epoch 6014 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01556 0.987 0.9841 8.064e-06 -3.62e-06 -0.00213 6.077e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003152 -0.002847 -0.01091 0.00836 0.9695 0.974 0.00595 0.847 0.8363 0.02241 ] Network output: [ 1 -0.02493 0.003868 -4.401e-05 1.976e-05 0.02079 -3.317e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.01971 -0.2085 0.2165 0.9836 0.9933 0.2021 0.4706 0.8825 0.7326 ] Network output: [ -0.01285 0.9988 1.011 1.175e-06 -5.276e-07 0.01616 8.856e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004964 0.0008695 0.004107 0.00547 0.989 0.9921 0.005053 0.8795 0.9065 0.01623 ] Network output: [ 0.002518 -0.03693 0.9952 -0.0002111 9.478e-05 1.036 -0.0001591 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.1082 0.3096 0.1875 0.9851 0.9941 0.1925 0.476 0.8889 0.7271 ] Network output: [ 0.01003 -0.04183 1.006 0.000122 -5.479e-05 1.016 9.198e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09676 0.08801 0.177 0.22 0.9874 0.9921 0.09682 0.8151 0.889 0.3136 ] Network output: [ -0.01225 0.04145 1.006 0.0001162 -5.215e-05 0.9779 8.755e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09954 0.1766 0.2083 0.9859 0.9917 0.1012 0.7488 0.8708 0.2504 ] Network output: [ 0.001643 0.9984 -0.002006 1.94e-05 -8.709e-06 1 1.462e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001357 Epoch 6015 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01502 0.9956 0.9837 6.723e-06 -3.018e-06 -0.009274 5.066e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003162 -0.002848 -0.01094 0.008199 0.9695 0.974 0.005966 0.8472 0.8359 0.02236 ] Network output: [ 0.9955 0.03033 0.001471 -5.253e-05 2.358e-05 -0.02306 -3.959e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.01901 -0.2113 0.2071 0.9836 0.9933 0.203 0.4719 0.8821 0.7318 ] Network output: [ -0.01282 1.002 1.01 7.299e-07 -3.277e-07 0.01332 5.501e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004982 0.0008633 0.00395 0.00515 0.989 0.9921 0.005071 0.8796 0.9063 0.01615 ] Network output: [ -0.002493 0.04 0.9915 -0.0002237 0.0001004 0.9726 -0.0001686 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.1084 0.3045 0.1726 0.9851 0.9941 0.1932 0.477 0.8889 0.7276 ] Network output: [ 0.01192 -0.02662 1.003 0.0001216 -5.46e-05 1.001 9.165e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09649 0.0877 0.1734 0.2159 0.9874 0.9921 0.09655 0.814 0.8889 0.3114 ] Network output: [ -0.01099 0.03745 1.005 0.0001174 -5.271e-05 0.98 8.848e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09925 0.1753 0.2075 0.9858 0.9916 0.1009 0.7473 0.8707 0.2503 ] Network output: [ -0.0008505 0.9997 0.001523 1.767e-05 -7.934e-06 1.001 1.332e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001109 Epoch 6016 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01556 0.9869 0.9841 8.088e-06 -3.631e-06 -0.002113 6.095e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003153 -0.002847 -0.0109 0.008359 0.9695 0.974 0.00595 0.847 0.8363 0.02241 ] Network output: [ 1 -0.02495 0.003863 -4.405e-05 1.978e-05 0.02081 -3.32e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.01975 -0.2085 0.2165 0.9836 0.9933 0.2021 0.4706 0.8825 0.7325 ] Network output: [ -0.01285 0.9988 1.011 1.211e-06 -5.436e-07 0.01617 9.125e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004965 0.0008679 0.004108 0.005469 0.989 0.9921 0.005054 0.8795 0.9065 0.01623 ] Network output: [ 0.002514 -0.03695 0.9953 -0.000211 9.473e-05 1.036 -0.000159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.1081 0.3096 0.1874 0.9851 0.9941 0.1925 0.476 0.8889 0.727 ] Network output: [ 0.01003 -0.04192 1.006 0.000122 -5.477e-05 1.016 9.194e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09678 0.08802 0.1771 0.22 0.9874 0.9921 0.09684 0.8151 0.889 0.3136 ] Network output: [ -0.01224 0.04156 1.006 0.0001161 -5.214e-05 0.9779 8.752e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09953 0.1766 0.2083 0.9859 0.9917 0.1012 0.7488 0.8707 0.2504 ] Network output: [ 0.001645 0.9984 -0.002016 1.937e-05 -8.697e-06 1 1.46e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00136 Epoch 6017 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01501 0.9956 0.9837 6.746e-06 -3.028e-06 -0.009264 5.084e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003162 -0.002848 -0.01094 0.008197 0.9695 0.974 0.005967 0.8472 0.8359 0.02236 ] Network output: [ 0.9955 0.03036 0.001464 -5.258e-05 2.36e-05 -0.02309 -3.962e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.01904 -0.2113 0.2071 0.9836 0.9933 0.2029 0.4719 0.8821 0.7317 ] Network output: [ -0.01282 1.002 1.01 7.649e-07 -3.434e-07 0.01333 5.764e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004983 0.0008617 0.003951 0.005148 0.989 0.9921 0.005073 0.8796 0.9063 0.01614 ] Network output: [ -0.002501 0.04005 0.9916 -0.0002236 0.0001004 0.9725 -0.0001685 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.1083 0.3045 0.1725 0.9851 0.9941 0.1932 0.477 0.8889 0.7275 ] Network output: [ 0.01191 -0.02669 1.003 0.0001216 -5.457e-05 1.001 9.161e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09652 0.08771 0.1734 0.2159 0.9874 0.9921 0.09658 0.814 0.8889 0.3114 ] Network output: [ -0.01099 0.03755 1.005 0.0001174 -5.269e-05 0.9799 8.844e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09925 0.1753 0.2074 0.9858 0.9916 0.1009 0.7473 0.8707 0.2502 ] Network output: [ -0.0008519 0.9997 0.001517 1.765e-05 -7.922e-06 1.001 1.33e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001113 Epoch 6018 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01556 0.9869 0.9841 8.111e-06 -3.641e-06 -0.002096 6.112e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003153 -0.002848 -0.0109 0.008357 0.9695 0.974 0.005951 0.847 0.8363 0.0224 ] Network output: [ 1 -0.02498 0.003858 -4.41e-05 1.98e-05 0.02083 -3.323e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.01979 -0.2085 0.2164 0.9836 0.9933 0.2021 0.4706 0.8825 0.7325 ] Network output: [ -0.01284 0.9988 1.011 1.246e-06 -5.593e-07 0.01618 9.39e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004967 0.0008663 0.00411 0.005467 0.989 0.9921 0.005055 0.8795 0.9065 0.01623 ] Network output: [ 0.00251 -0.03698 0.9954 -0.0002109 9.467e-05 1.036 -0.0001589 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.108 0.3096 0.1873 0.9851 0.9941 0.1925 0.476 0.8889 0.727 ] Network output: [ 0.01002 -0.04202 1.006 0.0001219 -5.474e-05 1.016 9.189e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0968 0.08803 0.1771 0.22 0.9874 0.9921 0.09686 0.8151 0.889 0.3136 ] Network output: [ -0.01224 0.04167 1.005 0.0001161 -5.212e-05 0.9778 8.749e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09953 0.1766 0.2082 0.9859 0.9917 0.1012 0.7488 0.8707 0.2504 ] Network output: [ 0.001648 0.9984 -0.002027 1.934e-05 -8.684e-06 1 1.458e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001364 Epoch 6019 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01501 0.9955 0.9837 6.768e-06 -3.038e-06 -0.009254 5.101e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003162 -0.002849 -0.01094 0.008195 0.9695 0.974 0.005968 0.8472 0.8359 0.02235 ] Network output: [ 0.9955 0.03039 0.001457 -5.262e-05 2.362e-05 -0.02312 -3.965e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.01908 -0.2112 0.207 0.9836 0.9933 0.2029 0.4719 0.8821 0.7317 ] Network output: [ -0.01282 1.002 1.01 7.991e-07 -3.588e-07 0.01333 6.023e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004985 0.0008601 0.003952 0.005147 0.989 0.9921 0.005074 0.8796 0.9063 0.01614 ] Network output: [ -0.00251 0.04011 0.9916 -0.0002234 0.0001003 0.9724 -0.0001684 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.1082 0.3045 0.1724 0.9851 0.9941 0.1932 0.477 0.8889 0.7275 ] Network output: [ 0.01191 -0.02677 1.003 0.0001215 -5.454e-05 1.001 9.156e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09654 0.08772 0.1734 0.2159 0.9874 0.9921 0.0966 0.8139 0.8888 0.3114 ] Network output: [ -0.01099 0.03765 1.005 0.0001173 -5.267e-05 0.9798 8.841e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09925 0.1753 0.2074 0.9858 0.9916 0.1009 0.7473 0.8707 0.2502 ] Network output: [ -0.0008535 0.9997 0.001511 1.762e-05 -7.909e-06 1.001 1.328e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001117 Epoch 6020 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01555 0.9869 0.9841 8.133e-06 -3.651e-06 -0.002078 6.129e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003153 -0.002849 -0.0109 0.008355 0.9695 0.974 0.005951 0.847 0.8363 0.0224 ] Network output: [ 1 -0.02501 0.003853 -4.414e-05 1.981e-05 0.02085 -3.326e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.01983 -0.2084 0.2164 0.9836 0.9933 0.2021 0.4706 0.8825 0.7324 ] Network output: [ -0.01284 0.9988 1.011 1.28e-06 -5.748e-07 0.01619 9.649e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004968 0.0008648 0.004111 0.005466 0.989 0.9921 0.005057 0.8795 0.9065 0.01623 ] Network output: [ 0.002507 -0.03701 0.9955 -0.0002108 9.462e-05 1.036 -0.0001588 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.1079 0.3097 0.1873 0.9851 0.9941 0.1925 0.476 0.8889 0.7269 ] Network output: [ 0.01002 -0.04211 1.006 0.0001219 -5.471e-05 1.016 9.185e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09683 0.08804 0.1771 0.22 0.9874 0.9921 0.09688 0.815 0.8889 0.3136 ] Network output: [ -0.01224 0.04177 1.005 0.000116 -5.21e-05 0.9777 8.746e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09953 0.1765 0.2082 0.9859 0.9917 0.1012 0.7487 0.8707 0.2503 ] Network output: [ 0.00165 0.9983 -0.002038 1.932e-05 -8.672e-06 1 1.456e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001367 Epoch 6021 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01501 0.9955 0.9837 6.79e-06 -3.048e-06 -0.009245 5.117e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003163 -0.00285 -0.01093 0.008194 0.9695 0.974 0.005968 0.8472 0.8359 0.02235 ] Network output: [ 0.9955 0.03042 0.00145 -5.266e-05 2.364e-05 -0.02315 -3.968e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.01912 -0.2112 0.207 0.9836 0.9933 0.2029 0.4719 0.8821 0.7316 ] Network output: [ -0.01281 1.002 1.01 8.327e-07 -3.738e-07 0.01334 6.276e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004986 0.0008586 0.003953 0.005145 0.989 0.9921 0.005075 0.8796 0.9063 0.01614 ] Network output: [ -0.002518 0.04016 0.9917 -0.0002233 0.0001003 0.9722 -0.0001683 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.1082 0.3045 0.1724 0.9851 0.9941 0.1932 0.477 0.8889 0.7274 ] Network output: [ 0.0119 -0.02684 1.003 0.0001214 -5.451e-05 1.001 9.151e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09656 0.08773 0.1735 0.2159 0.9874 0.9921 0.09662 0.8139 0.8888 0.3114 ] Network output: [ -0.01099 0.03775 1.005 0.0001173 -5.265e-05 0.9798 8.838e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09925 0.1753 0.2074 0.9858 0.9916 0.1009 0.7472 0.8706 0.2501 ] Network output: [ -0.0008551 0.9997 0.001506 1.759e-05 -7.897e-06 1.001 1.326e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001121 Epoch 6022 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01555 0.9869 0.9841 8.154e-06 -3.661e-06 -0.002061 6.145e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003153 -0.002849 -0.0109 0.008354 0.9695 0.974 0.005952 0.8471 0.8363 0.0224 ] Network output: [ 1 -0.02504 0.003848 -4.418e-05 1.983e-05 0.02087 -3.329e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.01987 -0.2084 0.2164 0.9836 0.9933 0.2021 0.4706 0.8825 0.7323 ] Network output: [ -0.01284 0.9988 1.011 1.314e-06 -5.899e-07 0.01621 9.903e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004969 0.0008632 0.004112 0.005464 0.989 0.9921 0.005058 0.8795 0.9065 0.01622 ] Network output: [ 0.002504 -0.03704 0.9956 -0.0002106 9.456e-05 1.036 -0.0001587 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.1079 0.3097 0.1872 0.9851 0.9941 0.1925 0.476 0.8889 0.7269 ] Network output: [ 0.01001 -0.0422 1.006 0.0001218 -5.469e-05 1.017 9.18e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.08805 0.1772 0.22 0.9874 0.9921 0.09691 0.815 0.8889 0.3137 ] Network output: [ -0.01224 0.04188 1.005 0.000116 -5.208e-05 0.9777 8.742e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09953 0.1765 0.2082 0.9859 0.9917 0.1012 0.7487 0.8707 0.2503 ] Network output: [ 0.001653 0.9983 -0.002049 1.929e-05 -8.66e-06 1 1.454e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00137 Epoch 6023 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01501 0.9955 0.9837 6.811e-06 -3.058e-06 -0.009236 5.133e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003163 -0.00285 -0.01093 0.008192 0.9695 0.974 0.005969 0.8472 0.8359 0.02234 ] Network output: [ 0.9955 0.03046 0.001443 -5.27e-05 2.366e-05 -0.02319 -3.971e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.01916 -0.2112 0.2069 0.9836 0.9933 0.2029 0.472 0.8821 0.7316 ] Network output: [ -0.01281 1.002 1.01 8.656e-07 -3.886e-07 0.01335 6.524e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004987 0.000857 0.003954 0.005143 0.989 0.9921 0.005076 0.8796 0.9062 0.01614 ] Network output: [ -0.002527 0.04022 0.9918 -0.0002232 0.0001002 0.9721 -0.0001682 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.1081 0.3046 0.1723 0.9851 0.9941 0.1932 0.477 0.8889 0.7274 ] Network output: [ 0.0119 -0.02691 1.003 0.0001214 -5.449e-05 1.001 9.147e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09658 0.08774 0.1735 0.2158 0.9874 0.9921 0.09664 0.8139 0.8888 0.3114 ] Network output: [ -0.01098 0.03785 1.005 0.0001172 -5.263e-05 0.9797 8.835e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09924 0.1752 0.2073 0.9858 0.9916 0.1009 0.7472 0.8706 0.2501 ] Network output: [ -0.0008569 0.9997 0.001501 1.756e-05 -7.884e-06 1.001 1.324e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001124 Epoch 6024 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01555 0.9869 0.9841 8.175e-06 -3.67e-06 -0.002043 6.161e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003154 -0.00285 -0.01089 0.008352 0.9695 0.974 0.005953 0.8471 0.8363 0.02239 ] Network output: [ 1 -0.02507 0.003843 -4.422e-05 1.985e-05 0.02089 -3.332e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.0199 -0.2084 0.2163 0.9836 0.9933 0.2021 0.4706 0.8825 0.7323 ] Network output: [ -0.01283 0.9987 1.011 1.347e-06 -6.048e-07 0.01622 1.015e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00497 0.0008617 0.004113 0.005463 0.989 0.9921 0.005059 0.8795 0.9065 0.01622 ] Network output: [ 0.002501 -0.03708 0.9957 -0.0002105 9.451e-05 1.036 -0.0001587 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.1078 0.3097 0.1872 0.9851 0.9941 0.1924 0.476 0.8889 0.7268 ] Network output: [ 0.01001 -0.04229 1.006 0.0001218 -5.466e-05 1.017 9.176e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09687 0.08807 0.1772 0.22 0.9874 0.9921 0.09693 0.815 0.8889 0.3137 ] Network output: [ -0.01223 0.04198 1.005 0.000116 -5.206e-05 0.9776 8.739e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09953 0.1765 0.2081 0.9859 0.9917 0.1012 0.7487 0.8707 0.2502 ] Network output: [ 0.001656 0.9983 -0.00206 1.926e-05 -8.648e-06 1 1.452e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001374 Epoch 6025 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01501 0.9955 0.9837 6.831e-06 -3.067e-06 -0.009227 5.148e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003163 -0.002851 -0.01093 0.00819 0.9695 0.974 0.005969 0.8472 0.8359 0.02234 ] Network output: [ 0.9955 0.03049 0.001436 -5.273e-05 2.367e-05 -0.02322 -3.974e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.0192 -0.2112 0.2069 0.9836 0.9933 0.2029 0.472 0.8821 0.7315 ] Network output: [ -0.01281 1.002 1.01 8.978e-07 -4.031e-07 0.01335 6.766e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004988 0.0008555 0.003955 0.005141 0.989 0.9921 0.005077 0.8796 0.9062 0.01613 ] Network output: [ -0.002536 0.04028 0.9919 -0.0002231 0.0001002 0.972 -0.0001681 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.108 0.3046 0.1722 0.9851 0.9941 0.1931 0.477 0.8889 0.7273 ] Network output: [ 0.01189 -0.02698 1.002 0.0001213 -5.446e-05 1.001 9.142e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0966 0.08775 0.1735 0.2158 0.9874 0.9921 0.09666 0.8138 0.8888 0.3114 ] Network output: [ -0.01098 0.03795 1.005 0.0001172 -5.261e-05 0.9797 8.831e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09924 0.1752 0.2073 0.9858 0.9916 0.1009 0.7472 0.8706 0.2501 ] Network output: [ -0.0008588 0.9997 0.001497 1.753e-05 -7.872e-06 1.001 1.321e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001128 Epoch 6026 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01555 0.9868 0.9841 8.196e-06 -3.679e-06 -0.002025 6.177e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003154 -0.00285 -0.01089 0.00835 0.9695 0.974 0.005953 0.8471 0.8363 0.02239 ] Network output: [ 1 -0.02511 0.003839 -4.425e-05 1.987e-05 0.02091 -3.335e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.01994 -0.2084 0.2163 0.9836 0.9933 0.2021 0.4706 0.8825 0.7322 ] Network output: [ -0.01283 0.9987 1.011 1.38e-06 -6.194e-07 0.01623 1.04e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004971 0.0008602 0.004114 0.005461 0.989 0.9921 0.00506 0.8795 0.9064 0.01622 ] Network output: [ 0.002498 -0.03712 0.9957 -0.0002104 9.445e-05 1.036 -0.0001586 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.1077 0.3098 0.1871 0.9851 0.9941 0.1924 0.476 0.8889 0.7267 ] Network output: [ 0.01 -0.04238 1.006 0.0001217 -5.463e-05 1.017 9.171e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09689 0.08808 0.1772 0.22 0.9874 0.9921 0.09695 0.8149 0.8889 0.3137 ] Network output: [ -0.01223 0.04209 1.005 0.0001159 -5.204e-05 0.9775 8.736e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09952 0.1765 0.2081 0.9859 0.9917 0.1012 0.7487 0.8706 0.2502 ] Network output: [ 0.001659 0.9983 -0.00207 1.924e-05 -8.636e-06 1 1.45e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001378 Epoch 6027 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01501 0.9955 0.9837 6.85e-06 -3.075e-06 -0.009219 5.163e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003163 -0.002851 -0.01093 0.008188 0.9695 0.974 0.00597 0.8472 0.8359 0.02234 ] Network output: [ 0.9955 0.03053 0.001428 -5.277e-05 2.369e-05 -0.02326 -3.977e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.01923 -0.2112 0.2069 0.9836 0.9933 0.2029 0.472 0.8821 0.7314 ] Network output: [ -0.01281 1.002 1.01 9.294e-07 -4.172e-07 0.01336 7.004e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004989 0.000854 0.003956 0.005139 0.989 0.9921 0.005078 0.8796 0.9062 0.01613 ] Network output: [ -0.002545 0.04034 0.992 -0.000223 0.0001001 0.9718 -0.000168 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.1079 0.3046 0.1721 0.9851 0.9941 0.1931 0.477 0.8889 0.7272 ] Network output: [ 0.01189 -0.02705 1.002 0.0001212 -5.443e-05 1.001 9.137e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09662 0.08776 0.1736 0.2158 0.9874 0.9921 0.09668 0.8138 0.8888 0.3115 ] Network output: [ -0.01098 0.03805 1.005 0.0001171 -5.259e-05 0.9796 8.828e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09924 0.1752 0.2072 0.9858 0.9916 0.1009 0.7472 0.8706 0.25 ] Network output: [ -0.0008608 0.9997 0.001492 1.751e-05 -7.859e-06 1.001 1.319e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001132 Epoch 6028 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01555 0.9868 0.9841 8.216e-06 -3.688e-06 -0.002007 6.192e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003154 -0.002851 -0.01089 0.008349 0.9695 0.974 0.005954 0.8471 0.8363 0.02238 ] Network output: [ 1 -0.02515 0.003834 -4.429e-05 1.988e-05 0.02094 -3.338e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.01998 -0.2083 0.2163 0.9836 0.9933 0.202 0.4706 0.8825 0.7322 ] Network output: [ -0.01283 0.9987 1.011 1.411e-06 -6.337e-07 0.01624 1.064e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004972 0.0008587 0.004116 0.00546 0.989 0.9921 0.005061 0.8795 0.9064 0.01622 ] Network output: [ 0.002496 -0.03716 0.9958 -0.0002103 9.44e-05 1.035 -0.0001585 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.1077 0.3098 0.1871 0.9851 0.9941 0.1924 0.476 0.8889 0.7267 ] Network output: [ 0.009995 -0.04247 1.006 0.0001216 -5.461e-05 1.017 9.167e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08809 0.1772 0.22 0.9874 0.9921 0.09697 0.8149 0.8889 0.3137 ] Network output: [ -0.01223 0.04219 1.005 0.0001159 -5.202e-05 0.9775 8.732e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09952 0.1765 0.2081 0.9859 0.9917 0.1012 0.7487 0.8706 0.2502 ] Network output: [ 0.001662 0.9983 -0.002081 1.921e-05 -8.625e-06 1 1.448e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001381 Epoch 6029 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.015 0.9955 0.9837 6.869e-06 -3.084e-06 -0.009211 5.177e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003164 -0.002852 -0.01092 0.008186 0.9695 0.974 0.00597 0.8473 0.8359 0.02233 ] Network output: [ 0.9955 0.03057 0.001421 -5.281e-05 2.371e-05 -0.0233 -3.98e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.01927 -0.2111 0.2068 0.9836 0.9933 0.2029 0.472 0.8821 0.7314 ] Network output: [ -0.0128 1.002 1.01 9.603e-07 -4.311e-07 0.01337 7.237e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00499 0.0008525 0.003957 0.005137 0.989 0.9921 0.005079 0.8796 0.9062 0.01613 ] Network output: [ -0.002554 0.04041 0.9921 -0.0002228 0.0001 0.9717 -0.0001679 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.1079 0.3047 0.1721 0.9851 0.9941 0.1931 0.477 0.8889 0.7272 ] Network output: [ 0.01189 -0.02711 1.002 0.0001212 -5.44e-05 1.001 9.133e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09664 0.08777 0.1736 0.2158 0.9874 0.9921 0.0967 0.8138 0.8887 0.3115 ] Network output: [ -0.01097 0.03815 1.005 0.0001171 -5.257e-05 0.9795 8.824e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09923 0.1752 0.2072 0.9858 0.9916 0.1009 0.7472 0.8706 0.25 ] Network output: [ -0.000863 0.9997 0.001488 1.748e-05 -7.847e-06 1.001 1.317e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001136 Epoch 6030 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01555 0.9868 0.9841 8.235e-06 -3.697e-06 -0.001989 6.206e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003154 -0.002851 -0.01089 0.008347 0.9695 0.974 0.005954 0.8471 0.8363 0.02238 ] Network output: [ 1 -0.02518 0.00383 -4.433e-05 1.99e-05 0.02096 -3.341e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02002 -0.2083 0.2163 0.9836 0.9933 0.202 0.4706 0.8825 0.7321 ] Network output: [ -0.01283 0.9987 1.011 1.443e-06 -6.477e-07 0.01625 1.087e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004973 0.0008572 0.004117 0.005458 0.989 0.9921 0.005062 0.8795 0.9064 0.01621 ] Network output: [ 0.002494 -0.0372 0.9959 -0.0002101 9.434e-05 1.035 -0.0001584 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.1076 0.3099 0.187 0.9851 0.9941 0.1924 0.476 0.8889 0.7266 ] Network output: [ 0.009989 -0.04257 1.006 0.0001216 -5.458e-05 1.017 9.162e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.0881 0.1773 0.22 0.9874 0.9921 0.09699 0.8149 0.8888 0.3137 ] Network output: [ -0.01222 0.04229 1.005 0.0001158 -5.2e-05 0.9774 8.729e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09951 0.1765 0.208 0.9859 0.9917 0.1012 0.7486 0.8706 0.2501 ] Network output: [ 0.001665 0.9983 -0.002092 1.918e-05 -8.613e-06 1.001 1.446e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001385 Epoch 6031 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.015 0.9955 0.9837 6.887e-06 -3.092e-06 -0.009203 5.19e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003164 -0.002852 -0.01092 0.008184 0.9695 0.974 0.005971 0.8473 0.8359 0.02233 ] Network output: [ 0.9955 0.03061 0.001414 -5.285e-05 2.373e-05 -0.02334 -3.983e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.01931 -0.2111 0.2068 0.9836 0.9933 0.2028 0.472 0.8821 0.7313 ] Network output: [ -0.0128 1.002 1.01 9.906e-07 -4.447e-07 0.01337 7.466e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004991 0.0008509 0.003958 0.005135 0.989 0.9921 0.00508 0.8796 0.9062 0.01613 ] Network output: [ -0.002562 0.04048 0.9922 -0.0002227 9.999e-05 0.9716 -0.0001679 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.1078 0.3047 0.172 0.9851 0.9941 0.1931 0.477 0.8889 0.7271 ] Network output: [ 0.01188 -0.02718 1.002 0.0001211 -5.437e-05 1.002 9.128e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09666 0.08778 0.1736 0.2158 0.9874 0.9921 0.09672 0.8137 0.8887 0.3115 ] Network output: [ -0.01097 0.03824 1.005 0.000117 -5.255e-05 0.9795 8.821e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09923 0.1752 0.2072 0.9858 0.9916 0.1009 0.7471 0.8706 0.25 ] Network output: [ -0.0008652 0.9997 0.001484 1.745e-05 -7.834e-06 1.001 1.315e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001141 Epoch 6032 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01555 0.9868 0.9841 8.253e-06 -3.705e-06 -0.001971 6.22e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003154 -0.002852 -0.01089 0.008345 0.9695 0.974 0.005955 0.8471 0.8363 0.02238 ] Network output: [ 1 -0.02522 0.003826 -4.436e-05 1.992e-05 0.02099 -3.343e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02006 -0.2083 0.2162 0.9836 0.9933 0.202 0.4706 0.8825 0.7321 ] Network output: [ -0.01282 0.9987 1.011 1.473e-06 -6.614e-07 0.01626 1.11e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004974 0.0008557 0.004118 0.005457 0.989 0.9921 0.005063 0.8795 0.9064 0.01621 ] Network output: [ 0.002492 -0.03725 0.996 -0.00021 9.428e-05 1.035 -0.0001583 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.1075 0.3099 0.187 0.9851 0.9941 0.1924 0.476 0.8889 0.7266 ] Network output: [ 0.009983 -0.04266 1.006 0.0001215 -5.455e-05 1.017 9.158e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09695 0.08811 0.1773 0.22 0.9874 0.9921 0.09701 0.8148 0.8888 0.3137 ] Network output: [ -0.01222 0.0424 1.005 0.0001158 -5.198e-05 0.9773 8.725e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09951 0.1764 0.208 0.9859 0.9917 0.1011 0.7486 0.8706 0.2501 ] Network output: [ 0.001668 0.9983 -0.002103 1.916e-05 -8.601e-06 1.001 1.444e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001389 Epoch 6033 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.015 0.9955 0.9837 6.905e-06 -3.1e-06 -0.009196 5.204e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003164 -0.002853 -0.01092 0.008182 0.9695 0.974 0.005971 0.8473 0.8359 0.02232 ] Network output: [ 0.9956 0.03066 0.001407 -5.288e-05 2.374e-05 -0.02338 -3.986e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.01934 -0.2111 0.2067 0.9836 0.9933 0.2028 0.472 0.8821 0.7313 ] Network output: [ -0.0128 1.002 1.01 1.02e-06 -4.58e-07 0.01338 7.689e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004992 0.0008494 0.003959 0.005133 0.989 0.9921 0.005081 0.8796 0.9062 0.01612 ] Network output: [ -0.002571 0.04054 0.9922 -0.0002226 9.993e-05 0.9715 -0.0001678 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.1077 0.3047 0.1719 0.9851 0.9941 0.1931 0.477 0.8889 0.7271 ] Network output: [ 0.01188 -0.02725 1.002 0.0001211 -5.435e-05 1.002 9.123e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09668 0.08779 0.1736 0.2158 0.9874 0.9921 0.09674 0.8137 0.8887 0.3115 ] Network output: [ -0.01097 0.03834 1.005 0.000117 -5.252e-05 0.9794 8.817e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09922 0.1751 0.2071 0.9858 0.9916 0.1009 0.7471 0.8705 0.2499 ] Network output: [ -0.0008675 0.9997 0.00148 1.742e-05 -7.822e-06 1.001 1.313e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001145 Epoch 6034 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01555 0.9868 0.9841 8.271e-06 -3.713e-06 -0.001953 6.234e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003155 -0.002853 -0.01088 0.008343 0.9695 0.974 0.005955 0.8471 0.8363 0.02237 ] Network output: [ 1 -0.02527 0.003822 -4.44e-05 1.993e-05 0.02102 -3.346e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02009 -0.2082 0.2162 0.9836 0.9933 0.202 0.4706 0.8825 0.732 ] Network output: [ -0.01282 0.9987 1.011 1.503e-06 -6.749e-07 0.01627 1.133e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004975 0.0008542 0.004119 0.005455 0.989 0.9921 0.005064 0.8795 0.9064 0.01621 ] Network output: [ 0.00249 -0.0373 0.9961 -0.0002099 9.422e-05 1.035 -0.0001582 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.1074 0.3099 0.1869 0.9851 0.9941 0.1924 0.476 0.8888 0.7265 ] Network output: [ 0.009977 -0.04275 1.006 0.0001215 -5.452e-05 1.017 9.153e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09697 0.08811 0.1773 0.22 0.9874 0.9921 0.09703 0.8148 0.8888 0.3137 ] Network output: [ -0.01222 0.0425 1.005 0.0001157 -5.196e-05 0.9773 8.722e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.0995 0.1764 0.208 0.9859 0.9917 0.1011 0.7486 0.8706 0.2501 ] Network output: [ 0.001671 0.9983 -0.002113 1.913e-05 -8.589e-06 1.001 1.442e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001393 Epoch 6035 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.015 0.9955 0.9838 6.921e-06 -3.107e-06 -0.009189 5.216e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003164 -0.002853 -0.01092 0.00818 0.9695 0.974 0.005972 0.8473 0.8359 0.02232 ] Network output: [ 0.9956 0.0307 0.0014 -5.292e-05 2.376e-05 -0.02342 -3.988e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.01938 -0.2111 0.2067 0.9836 0.9933 0.2028 0.472 0.8821 0.7312 ] Network output: [ -0.01279 1.002 1.01 1.049e-06 -4.711e-07 0.01338 7.908e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004993 0.000848 0.00396 0.005131 0.989 0.9921 0.005082 0.8796 0.9062 0.01612 ] Network output: [ -0.00258 0.04062 0.9923 -0.0002225 9.988e-05 0.9713 -0.0001677 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.1077 0.3047 0.1719 0.9851 0.9941 0.1931 0.477 0.8889 0.727 ] Network output: [ 0.01187 -0.02731 1.002 0.000121 -5.432e-05 1.002 9.118e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0967 0.0878 0.1737 0.2158 0.9874 0.9921 0.09676 0.8137 0.8887 0.3115 ] Network output: [ -0.01096 0.03843 1.005 0.000117 -5.25e-05 0.9794 8.814e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09922 0.1751 0.2071 0.9858 0.9916 0.1009 0.7471 0.8705 0.2499 ] Network output: [ -0.0008699 0.9997 0.001476 1.74e-05 -7.81e-06 1.001 1.311e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001149 Epoch 6036 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01555 0.9867 0.9841 8.289e-06 -3.721e-06 -0.001935 6.247e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003155 -0.002853 -0.01088 0.008342 0.9695 0.974 0.005955 0.8471 0.8363 0.02237 ] Network output: [ 1 -0.02531 0.003819 -4.443e-05 1.995e-05 0.02105 -3.348e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02013 -0.2082 0.2162 0.9836 0.9933 0.202 0.4706 0.8825 0.7319 ] Network output: [ -0.01282 0.9986 1.011 1.533e-06 -6.881e-07 0.01628 1.155e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004976 0.0008527 0.00412 0.005453 0.989 0.9921 0.005065 0.8795 0.9064 0.01621 ] Network output: [ 0.002489 -0.03735 0.9962 -0.0002097 9.416e-05 1.035 -0.0001581 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.1074 0.31 0.1869 0.9851 0.9941 0.1924 0.476 0.8888 0.7264 ] Network output: [ 0.009971 -0.04284 1.006 0.0001214 -5.45e-05 1.018 9.148e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09699 0.08812 0.1774 0.22 0.9874 0.9921 0.09705 0.8148 0.8888 0.3137 ] Network output: [ -0.01222 0.0426 1.005 0.0001157 -5.194e-05 0.9772 8.718e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.0995 0.1764 0.2079 0.9859 0.9917 0.1011 0.7486 0.8706 0.25 ] Network output: [ 0.001674 0.9983 -0.002124 1.911e-05 -8.578e-06 1.001 1.44e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001397 Epoch 6037 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.015 0.9955 0.9838 6.938e-06 -3.115e-06 -0.009183 5.228e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003165 -0.002854 -0.01092 0.008178 0.9695 0.974 0.005972 0.8473 0.8359 0.02232 ] Network output: [ 0.9956 0.03075 0.001393 -5.296e-05 2.377e-05 -0.02347 -3.991e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.01941 -0.211 0.2067 0.9836 0.9933 0.2028 0.472 0.8821 0.7312 ] Network output: [ -0.01279 1.002 1.01 1.078e-06 -4.838e-07 0.01339 8.122e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004994 0.0008465 0.003961 0.005129 0.989 0.9921 0.005084 0.8796 0.9062 0.01612 ] Network output: [ -0.002589 0.04069 0.9924 -0.0002224 9.982e-05 0.9712 -0.0001676 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.1076 0.3048 0.1718 0.9851 0.9941 0.1931 0.477 0.8889 0.7269 ] Network output: [ 0.01187 -0.02737 1.002 0.0001209 -5.429e-05 1.002 9.113e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09672 0.08781 0.1737 0.2158 0.9874 0.9921 0.09678 0.8136 0.8887 0.3115 ] Network output: [ -0.01096 0.03852 1.005 0.0001169 -5.248e-05 0.9793 8.81e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09921 0.1751 0.2071 0.9858 0.9916 0.1009 0.7471 0.8705 0.2499 ] Network output: [ -0.0008724 0.9997 0.001473 1.737e-05 -7.797e-06 1.001 1.309e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001153 Epoch 6038 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01555 0.9867 0.9841 8.306e-06 -3.729e-06 -0.001917 6.259e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003155 -0.002854 -0.01088 0.00834 0.9695 0.974 0.005956 0.8471 0.8363 0.02236 ] Network output: [ 1 -0.02535 0.003815 -4.446e-05 1.996e-05 0.02108 -3.351e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02017 -0.2082 0.2161 0.9836 0.9933 0.202 0.4706 0.8825 0.7319 ] Network output: [ -0.01281 0.9986 1.011 1.561e-06 -7.01e-07 0.01629 1.177e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004977 0.0008513 0.004122 0.005452 0.989 0.9921 0.005066 0.8795 0.9064 0.0162 ] Network output: [ 0.002488 -0.0374 0.9963 -0.0002096 9.41e-05 1.035 -0.000158 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.1073 0.31 0.1868 0.9851 0.9941 0.1923 0.476 0.8888 0.7264 ] Network output: [ 0.009965 -0.04293 1.006 0.0001213 -5.447e-05 1.018 9.144e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.097 0.08813 0.1774 0.22 0.9874 0.9921 0.09706 0.8147 0.8888 0.3138 ] Network output: [ -0.01221 0.0427 1.005 0.0001156 -5.191e-05 0.9771 8.715e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09949 0.1764 0.2079 0.9859 0.9917 0.1011 0.7486 0.8705 0.25 ] Network output: [ 0.001677 0.9983 -0.002135 1.908e-05 -8.566e-06 1.001 1.438e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001401 Epoch 6039 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01499 0.9955 0.9838 6.953e-06 -3.122e-06 -0.009177 5.24e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003165 -0.002855 -0.01091 0.008176 0.9695 0.974 0.005973 0.8473 0.8359 0.02231 ] Network output: [ 0.9956 0.0308 0.001386 -5.299e-05 2.379e-05 -0.02351 -3.994e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.01945 -0.211 0.2066 0.9836 0.9933 0.2028 0.472 0.8821 0.7311 ] Network output: [ -0.01279 1.002 1.01 1.105e-06 -4.963e-07 0.0134 8.331e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004995 0.000845 0.003961 0.005127 0.989 0.9921 0.005085 0.8796 0.9062 0.01612 ] Network output: [ -0.002599 0.04076 0.9925 -0.0002222 9.977e-05 0.9711 -0.0001675 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.1075 0.3048 0.1717 0.9851 0.9941 0.193 0.477 0.8889 0.7269 ] Network output: [ 0.01187 -0.02744 1.002 0.0001209 -5.426e-05 1.002 9.108e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09674 0.08782 0.1737 0.2158 0.9874 0.9921 0.0968 0.8136 0.8886 0.3115 ] Network output: [ -0.01095 0.03861 1.005 0.0001169 -5.246e-05 0.9792 8.807e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09921 0.1751 0.207 0.9858 0.9916 0.1009 0.747 0.8705 0.2498 ] Network output: [ -0.0008749 0.9997 0.00147 1.734e-05 -7.785e-06 1.001 1.307e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001158 Epoch 6040 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01555 0.9867 0.9841 8.322e-06 -3.736e-06 -0.001899 6.272e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003155 -0.002854 -0.01088 0.008338 0.9695 0.974 0.005956 0.8471 0.8363 0.02236 ] Network output: [ 1 -0.0254 0.003812 -4.449e-05 1.997e-05 0.02111 -3.353e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.0202 -0.2082 0.2161 0.9836 0.9933 0.2019 0.4706 0.8825 0.7318 ] Network output: [ -0.01281 0.9986 1.011 1.59e-06 -7.137e-07 0.0163 1.198e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004978 0.0008498 0.004123 0.00545 0.989 0.9921 0.005067 0.8795 0.9064 0.0162 ] Network output: [ 0.002487 -0.03745 0.9963 -0.0002095 9.404e-05 1.035 -0.0001579 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.1072 0.31 0.1868 0.9851 0.9941 0.1923 0.476 0.8888 0.7263 ] Network output: [ 0.009959 -0.04302 1.006 0.0001213 -5.444e-05 1.018 9.139e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09702 0.08814 0.1774 0.22 0.9874 0.9921 0.09708 0.8147 0.8887 0.3138 ] Network output: [ -0.01221 0.0428 1.005 0.0001156 -5.189e-05 0.9771 8.711e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09949 0.1763 0.2079 0.9859 0.9917 0.1011 0.7485 0.8705 0.25 ] Network output: [ 0.00168 0.9983 -0.002146 1.905e-05 -8.554e-06 1.001 1.436e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001405 Epoch 6041 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01499 0.9954 0.9838 6.968e-06 -3.128e-06 -0.009171 5.251e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003165 -0.002855 -0.01091 0.008174 0.9695 0.974 0.005973 0.8473 0.8359 0.02231 ] Network output: [ 0.9956 0.03084 0.001379 -5.303e-05 2.38e-05 -0.02356 -3.996e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.01948 -0.211 0.2066 0.9836 0.9933 0.2028 0.472 0.8821 0.731 ] Network output: [ -0.01279 1.002 1.01 1.133e-06 -5.085e-07 0.0134 8.536e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004996 0.0008435 0.003962 0.005125 0.989 0.9921 0.005086 0.8796 0.9062 0.01611 ] Network output: [ -0.002608 0.04084 0.9925 -0.0002221 9.971e-05 0.9709 -0.0001674 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.1075 0.3048 0.1716 0.9851 0.9941 0.193 0.477 0.8888 0.7268 ] Network output: [ 0.01186 -0.0275 1.002 0.0001208 -5.423e-05 1.002 9.103e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09675 0.08782 0.1737 0.2158 0.9874 0.9921 0.09681 0.8136 0.8886 0.3115 ] Network output: [ -0.01095 0.0387 1.004 0.0001168 -5.244e-05 0.9792 8.803e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.0992 0.1751 0.207 0.9858 0.9916 0.1009 0.747 0.8705 0.2498 ] Network output: [ -0.0008776 0.9997 0.001467 1.731e-05 -7.772e-06 1.001 1.305e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001162 Epoch 6042 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01554 0.9867 0.9842 8.338e-06 -3.743e-06 -0.001881 6.284e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003156 -0.002855 -0.01087 0.008337 0.9695 0.974 0.005957 0.8471 0.8363 0.02236 ] Network output: [ 1 -0.02544 0.003809 -4.452e-05 1.999e-05 0.02115 -3.355e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02024 -0.2081 0.2161 0.9836 0.9933 0.2019 0.4706 0.8825 0.7318 ] Network output: [ -0.01281 0.9986 1.011 1.617e-06 -7.261e-07 0.01631 1.219e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004979 0.0008484 0.004124 0.005449 0.989 0.9921 0.005068 0.8795 0.9064 0.0162 ] Network output: [ 0.002486 -0.03751 0.9964 -0.0002093 9.398e-05 1.035 -0.0001578 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.1072 0.3101 0.1867 0.9851 0.9941 0.1923 0.476 0.8888 0.7263 ] Network output: [ 0.009953 -0.04311 1.006 0.0001212 -5.441e-05 1.018 9.134e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09704 0.08815 0.1774 0.22 0.9874 0.9921 0.0971 0.8147 0.8887 0.3138 ] Network output: [ -0.01221 0.04289 1.005 0.0001155 -5.187e-05 0.977 8.708e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09948 0.1763 0.2078 0.9859 0.9917 0.1011 0.7485 0.8705 0.2499 ] Network output: [ 0.001683 0.9983 -0.002156 1.903e-05 -8.543e-06 1.001 1.434e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001409 Epoch 6043 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01499 0.9954 0.9838 6.982e-06 -3.135e-06 -0.009165 5.262e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003165 -0.002856 -0.01091 0.008173 0.9695 0.974 0.005974 0.8473 0.8359 0.0223 ] Network output: [ 0.9956 0.0309 0.001371 -5.306e-05 2.382e-05 -0.0236 -3.999e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.01952 -0.211 0.2065 0.9836 0.9933 0.2028 0.472 0.8821 0.731 ] Network output: [ -0.01278 1.002 1.01 1.159e-06 -5.205e-07 0.01341 8.737e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004997 0.0008421 0.003963 0.005123 0.989 0.9921 0.005087 0.8796 0.9062 0.01611 ] Network output: [ -0.002617 0.04092 0.9926 -0.000222 9.965e-05 0.9708 -0.0001673 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.1074 0.3048 0.1716 0.9851 0.9941 0.193 0.477 0.8888 0.7268 ] Network output: [ 0.01186 -0.02756 1.002 0.0001207 -5.42e-05 1.002 9.099e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09677 0.08783 0.1737 0.2157 0.9874 0.9921 0.09683 0.8135 0.8886 0.3115 ] Network output: [ -0.01095 0.03879 1.004 0.0001168 -5.242e-05 0.9791 8.799e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09919 0.175 0.207 0.9858 0.9916 0.1009 0.747 0.8704 0.2497 ] Network output: [ -0.0008804 0.9997 0.001464 1.728e-05 -7.76e-06 1.001 1.303e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001166 Epoch 6044 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01554 0.9867 0.9842 8.353e-06 -3.75e-06 -0.001862 6.295e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003156 -0.002855 -0.01087 0.008335 0.9695 0.974 0.005957 0.8471 0.8363 0.02235 ] Network output: [ 1 -0.02549 0.003806 -4.455e-05 2e-05 0.02118 -3.357e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02028 -0.2081 0.2161 0.9836 0.9933 0.2019 0.4706 0.8824 0.7317 ] Network output: [ -0.0128 0.9986 1.011 1.644e-06 -7.383e-07 0.01632 1.239e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00498 0.0008469 0.004125 0.005448 0.989 0.9921 0.005069 0.8795 0.9064 0.0162 ] Network output: [ 0.002485 -0.03757 0.9965 -0.0002092 9.392e-05 1.035 -0.0001577 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.1071 0.3101 0.1867 0.9851 0.9941 0.1923 0.476 0.8888 0.7262 ] Network output: [ 0.009947 -0.0432 1.006 0.0001211 -5.439e-05 1.018 9.13e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09706 0.08816 0.1775 0.22 0.9874 0.9921 0.09712 0.8146 0.8887 0.3138 ] Network output: [ -0.0122 0.04299 1.005 0.0001155 -5.185e-05 0.9769 8.704e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09947 0.1763 0.2078 0.9859 0.9917 0.1011 0.7485 0.8705 0.2499 ] Network output: [ 0.001687 0.9983 -0.002167 1.9e-05 -8.531e-06 1.001 1.432e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001414 Epoch 6045 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01499 0.9954 0.9838 6.996e-06 -3.141e-06 -0.00916 5.272e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003166 -0.002856 -0.01091 0.008171 0.9695 0.974 0.005974 0.8473 0.8359 0.0223 ] Network output: [ 0.9956 0.03095 0.001364 -5.309e-05 2.383e-05 -0.02365 -4.001e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.01955 -0.211 0.2065 0.9836 0.9933 0.2027 0.472 0.8821 0.7309 ] Network output: [ -0.01278 1.002 1.01 1.185e-06 -5.321e-07 0.01341 8.933e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004998 0.0008406 0.003964 0.005121 0.989 0.9921 0.005088 0.8796 0.9062 0.01611 ] Network output: [ -0.002626 0.041 0.9927 -0.0002219 9.96e-05 0.9707 -0.0001672 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.1073 0.3049 0.1715 0.9851 0.9941 0.193 0.477 0.8888 0.7267 ] Network output: [ 0.01185 -0.02762 1.002 0.0001207 -5.417e-05 1.002 9.094e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09679 0.08784 0.1738 0.2157 0.9874 0.9921 0.09685 0.8135 0.8886 0.3115 ] Network output: [ -0.01094 0.03888 1.004 0.0001167 -5.239e-05 0.9791 8.796e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09918 0.175 0.2069 0.9858 0.9916 0.1008 0.747 0.8704 0.2497 ] Network output: [ -0.0008832 0.9997 0.001461 1.726e-05 -7.747e-06 1.001 1.301e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001171 Epoch 6046 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01554 0.9866 0.9842 8.368e-06 -3.757e-06 -0.001844 6.306e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003156 -0.002856 -0.01087 0.008333 0.9695 0.974 0.005958 0.8471 0.8363 0.02235 ] Network output: [ 1 -0.02554 0.003803 -4.458e-05 2.001e-05 0.02122 -3.359e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02031 -0.2081 0.216 0.9836 0.9933 0.2019 0.4706 0.8824 0.7317 ] Network output: [ -0.0128 0.9985 1.011 1.671e-06 -7.502e-07 0.01632 1.259e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004981 0.0008455 0.004126 0.005446 0.989 0.9921 0.00507 0.8795 0.9064 0.01619 ] Network output: [ 0.002485 -0.03763 0.9966 -0.0002091 9.385e-05 1.035 -0.0001576 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.107 0.3101 0.1867 0.9851 0.9941 0.1923 0.476 0.8888 0.7261 ] Network output: [ 0.009941 -0.04329 1.006 0.0001211 -5.436e-05 1.018 9.125e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09707 0.08816 0.1775 0.22 0.9874 0.9921 0.09713 0.8146 0.8887 0.3138 ] Network output: [ -0.0122 0.04309 1.005 0.0001154 -5.183e-05 0.9769 8.7e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09947 0.1763 0.2078 0.9859 0.9917 0.1011 0.7485 0.8705 0.2498 ] Network output: [ 0.00169 0.9983 -0.002178 1.898e-05 -8.52e-06 1.001 1.43e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001418 Epoch 6047 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01499 0.9954 0.9838 7.009e-06 -3.147e-06 -0.009155 5.282e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003166 -0.002857 -0.0109 0.008169 0.9695 0.974 0.005974 0.8473 0.8359 0.02229 ] Network output: [ 0.9956 0.031 0.001357 -5.312e-05 2.385e-05 -0.0237 -4.004e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.01959 -0.2109 0.2065 0.9836 0.9933 0.2027 0.472 0.8821 0.7309 ] Network output: [ -0.01278 1.002 1.01 1.211e-06 -5.436e-07 0.01342 9.125e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004999 0.0008392 0.003965 0.005119 0.989 0.9921 0.005089 0.8796 0.9062 0.0161 ] Network output: [ -0.002635 0.04108 0.9928 -0.0002217 9.954e-05 0.9705 -0.0001671 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.1073 0.3049 0.1714 0.9851 0.9941 0.193 0.477 0.8888 0.7267 ] Network output: [ 0.01185 -0.02768 1.002 0.0001206 -5.414e-05 1.002 9.089e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0968 0.08784 0.1738 0.2157 0.9874 0.9921 0.09686 0.8135 0.8886 0.3115 ] Network output: [ -0.01094 0.03897 1.004 0.0001167 -5.237e-05 0.979 8.792e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09918 0.175 0.2069 0.9858 0.9916 0.1008 0.7469 0.8704 0.2497 ] Network output: [ -0.0008861 0.9997 0.001459 1.723e-05 -7.735e-06 1.001 1.298e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001175 Epoch 6048 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01554 0.9866 0.9842 8.382e-06 -3.763e-06 -0.001826 6.317e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003156 -0.002856 -0.01087 0.008331 0.9695 0.974 0.005958 0.8471 0.8363 0.02234 ] Network output: [ 1 -0.02559 0.0038 -4.46e-05 2.002e-05 0.02125 -3.361e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02035 -0.208 0.216 0.9836 0.9933 0.2019 0.4706 0.8824 0.7316 ] Network output: [ -0.0128 0.9985 1.011 1.697e-06 -7.619e-07 0.01633 1.279e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004982 0.0008441 0.004128 0.005445 0.989 0.9921 0.005071 0.8795 0.9064 0.01619 ] Network output: [ 0.002484 -0.03769 0.9966 -0.0002089 9.379e-05 1.035 -0.0001574 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.107 0.3102 0.1866 0.9851 0.9941 0.1923 0.476 0.8888 0.7261 ] Network output: [ 0.009934 -0.04338 1.006 0.000121 -5.433e-05 1.018 9.12e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09709 0.08817 0.1775 0.22 0.9874 0.9921 0.09715 0.8146 0.8887 0.3138 ] Network output: [ -0.0122 0.04318 1.005 0.0001154 -5.18e-05 0.9768 8.696e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09946 0.1763 0.2077 0.9859 0.9917 0.1011 0.7484 0.8704 0.2498 ] Network output: [ 0.001693 0.9983 -0.002188 1.895e-05 -8.508e-06 1.001 1.428e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001422 Epoch 6049 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01498 0.9954 0.9838 7.022e-06 -3.152e-06 -0.00915 5.292e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003166 -0.002857 -0.0109 0.008167 0.9695 0.974 0.005975 0.8473 0.8359 0.02229 ] Network output: [ 0.9956 0.03105 0.00135 -5.316e-05 2.386e-05 -0.02375 -4.006e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.01962 -0.2109 0.2064 0.9836 0.9933 0.2027 0.472 0.8821 0.7308 ] Network output: [ -0.01277 1.002 1.01 1.236e-06 -5.548e-07 0.01342 9.313e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005 0.0008378 0.003966 0.005117 0.989 0.9921 0.00509 0.8796 0.9062 0.0161 ] Network output: [ -0.002645 0.04116 0.9928 -0.0002216 9.948e-05 0.9704 -0.000167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.1072 0.3049 0.1714 0.9851 0.9941 0.193 0.477 0.8888 0.7266 ] Network output: [ 0.01185 -0.02774 1.002 0.0001205 -5.411e-05 1.003 9.083e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09682 0.08785 0.1738 0.2157 0.9874 0.9921 0.09688 0.8134 0.8885 0.3115 ] Network output: [ -0.01093 0.03906 1.004 0.0001166 -5.235e-05 0.9789 8.788e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09917 0.175 0.2068 0.9858 0.9916 0.1008 0.7469 0.8704 0.2496 ] Network output: [ -0.0008891 0.9997 0.001457 1.72e-05 -7.723e-06 1.001 1.296e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00118 Epoch 6050 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01554 0.9866 0.9842 8.395e-06 -3.769e-06 -0.001808 6.327e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003156 -0.002857 -0.01086 0.00833 0.9695 0.974 0.005959 0.8471 0.8363 0.02234 ] Network output: [ 1 -0.02564 0.003797 -4.463e-05 2.004e-05 0.02129 -3.363e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02038 -0.208 0.216 0.9836 0.9933 0.2019 0.4706 0.8824 0.7316 ] Network output: [ -0.0128 0.9985 1.011 1.722e-06 -7.733e-07 0.01634 1.298e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004983 0.0008427 0.004129 0.005443 0.989 0.9921 0.005072 0.8795 0.9064 0.01619 ] Network output: [ 0.002484 -0.03775 0.9967 -0.0002088 9.373e-05 1.035 -0.0001573 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.1069 0.3102 0.1866 0.9851 0.9941 0.1923 0.476 0.8888 0.726 ] Network output: [ 0.009928 -0.04347 1.006 0.000121 -5.43e-05 1.018 9.115e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09711 0.08818 0.1776 0.22 0.9874 0.9921 0.09716 0.8145 0.8886 0.3138 ] Network output: [ -0.01219 0.04328 1.005 0.0001153 -5.178e-05 0.9768 8.693e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09945 0.1762 0.2077 0.9859 0.9917 0.1011 0.7484 0.8704 0.2498 ] Network output: [ 0.001697 0.9983 -0.002199 1.893e-05 -8.497e-06 1.001 1.426e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001427 Epoch 6051 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01498 0.9954 0.9838 7.034e-06 -3.158e-06 -0.009146 5.301e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003166 -0.002858 -0.0109 0.008165 0.9695 0.974 0.005975 0.8473 0.8359 0.02229 ] Network output: [ 0.9956 0.03111 0.001343 -5.319e-05 2.388e-05 -0.0238 -4.008e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.01966 -0.2109 0.2064 0.9836 0.9933 0.2027 0.472 0.8821 0.7308 ] Network output: [ -0.01277 1.002 1.01 1.26e-06 -5.657e-07 0.01342 9.496e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005001 0.0008364 0.003967 0.005115 0.989 0.9921 0.005091 0.8796 0.9062 0.0161 ] Network output: [ -0.002654 0.04124 0.9929 -0.0002215 9.942e-05 0.9702 -0.0001669 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.1071 0.3049 0.1713 0.9851 0.9941 0.193 0.477 0.8888 0.7266 ] Network output: [ 0.01184 -0.0278 1.002 0.0001205 -5.408e-05 1.003 9.078e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09683 0.08786 0.1738 0.2157 0.9874 0.9921 0.09689 0.8134 0.8885 0.3116 ] Network output: [ -0.01093 0.03914 1.004 0.0001166 -5.233e-05 0.9789 8.784e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09916 0.1749 0.2068 0.9858 0.9916 0.1008 0.7469 0.8704 0.2496 ] Network output: [ -0.0008921 0.9997 0.001455 1.717e-05 -7.71e-06 1.001 1.294e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001184 Epoch 6052 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01554 0.9866 0.9842 8.408e-06 -3.775e-06 -0.00179 6.337e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003157 -0.002857 -0.01086 0.008328 0.9696 0.974 0.005959 0.8472 0.8363 0.02234 ] Network output: [ 1 -0.02569 0.003794 -4.465e-05 2.005e-05 0.02133 -3.365e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02042 -0.208 0.216 0.9836 0.9933 0.2019 0.4706 0.8824 0.7315 ] Network output: [ -0.01279 0.9985 1.011 1.747e-06 -7.845e-07 0.01635 1.317e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004984 0.0008413 0.00413 0.005442 0.989 0.9921 0.005073 0.8795 0.9064 0.01619 ] Network output: [ 0.002484 -0.03781 0.9968 -0.0002086 9.366e-05 1.035 -0.0001572 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.1068 0.3102 0.1865 0.9851 0.9941 0.1922 0.476 0.8888 0.726 ] Network output: [ 0.009922 -0.04356 1.006 0.0001209 -5.427e-05 1.019 9.11e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09712 0.08818 0.1776 0.22 0.9874 0.9921 0.09718 0.8145 0.8886 0.3138 ] Network output: [ -0.01219 0.04337 1.005 0.0001153 -5.176e-05 0.9767 8.689e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09944 0.1762 0.2077 0.9859 0.9917 0.1011 0.7484 0.8704 0.2497 ] Network output: [ 0.0017 0.9983 -0.002209 1.89e-05 -8.486e-06 1.001 1.424e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001431 Epoch 6053 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01498 0.9954 0.9838 7.045e-06 -3.163e-06 -0.009141 5.31e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003166 -0.002858 -0.0109 0.008163 0.9696 0.974 0.005976 0.8473 0.8359 0.02228 ] Network output: [ 0.9956 0.03116 0.001336 -5.322e-05 2.389e-05 -0.02385 -4.011e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.01969 -0.2109 0.2063 0.9836 0.9933 0.2027 0.472 0.8821 0.7307 ] Network output: [ -0.01277 1.002 1.01 1.284e-06 -5.764e-07 0.01343 9.676e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005002 0.000835 0.003968 0.005113 0.989 0.9921 0.005092 0.8796 0.9061 0.0161 ] Network output: [ -0.002663 0.04133 0.993 -0.0002213 9.936e-05 0.9701 -0.0001668 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.1071 0.305 0.1712 0.9851 0.9941 0.193 0.477 0.8888 0.7265 ] Network output: [ 0.01184 -0.02785 1.002 0.0001204 -5.405e-05 1.003 9.073e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.08786 0.1738 0.2157 0.9874 0.9921 0.09691 0.8134 0.8885 0.3116 ] Network output: [ -0.01093 0.03923 1.004 0.0001165 -5.23e-05 0.9788 8.78e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09915 0.1749 0.2068 0.9858 0.9916 0.1008 0.7468 0.8703 0.2496 ] Network output: [ -0.0008953 0.9997 0.001453 1.715e-05 -7.698e-06 1.001 1.292e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001189 Epoch 6054 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01554 0.9865 0.9842 8.421e-06 -3.78e-06 -0.001771 6.346e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003157 -0.002858 -0.01086 0.008326 0.9696 0.974 0.005959 0.8472 0.8363 0.02233 ] Network output: [ 1 -0.02575 0.003792 -4.468e-05 2.006e-05 0.02137 -3.367e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02045 -0.208 0.2159 0.9836 0.9933 0.2018 0.4706 0.8824 0.7315 ] Network output: [ -0.01279 0.9985 1.011 1.772e-06 -7.954e-07 0.01636 1.335e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004985 0.0008399 0.004131 0.00544 0.989 0.9921 0.005074 0.8795 0.9064 0.01618 ] Network output: [ 0.002484 -0.03788 0.9969 -0.0002085 9.36e-05 1.035 -0.0001571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1068 0.3103 0.1865 0.9851 0.9941 0.1922 0.476 0.8888 0.7259 ] Network output: [ 0.009915 -0.04365 1.006 0.0001208 -5.424e-05 1.019 9.106e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09714 0.08819 0.1776 0.22 0.9874 0.9921 0.0972 0.8145 0.8886 0.3138 ] Network output: [ -0.01219 0.04347 1.005 0.0001152 -5.174e-05 0.9766 8.685e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09943 0.1762 0.2076 0.9859 0.9917 0.1011 0.7484 0.8704 0.2497 ] Network output: [ 0.001703 0.9983 -0.00222 1.888e-05 -8.474e-06 1.001 1.423e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001436 Epoch 6055 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01498 0.9954 0.9838 7.056e-06 -3.168e-06 -0.009137 5.318e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.002859 -0.01089 0.008161 0.9696 0.974 0.005976 0.8473 0.8359 0.02228 ] Network output: [ 0.9956 0.03122 0.001329 -5.325e-05 2.39e-05 -0.0239 -4.013e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.01972 -0.2109 0.2063 0.9836 0.9933 0.2027 0.472 0.8821 0.7307 ] Network output: [ -0.01277 1.002 1.01 1.307e-06 -5.868e-07 0.01343 9.851e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005003 0.0008336 0.003968 0.005111 0.989 0.9921 0.005093 0.8796 0.9061 0.01609 ] Network output: [ -0.002672 0.04141 0.9931 -0.0002212 9.93e-05 0.97 -0.0001667 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.107 0.305 0.1711 0.9851 0.9941 0.1929 0.477 0.8888 0.7264 ] Network output: [ 0.01183 -0.02791 1.002 0.0001203 -5.402e-05 1.003 9.068e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09686 0.08787 0.1739 0.2157 0.9874 0.9921 0.09692 0.8133 0.8885 0.3116 ] Network output: [ -0.01092 0.03931 1.004 0.0001165 -5.228e-05 0.9788 8.776e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09914 0.1749 0.2067 0.9858 0.9916 0.1008 0.7468 0.8703 0.2495 ] Network output: [ -0.0008984 0.9997 0.001451 1.712e-05 -7.686e-06 1.001 1.29e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001193 Epoch 6056 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01554 0.9865 0.9842 8.433e-06 -3.786e-06 -0.001753 6.355e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003157 -0.002858 -0.01086 0.008324 0.9696 0.974 0.00596 0.8472 0.8363 0.02233 ] Network output: [ 1 -0.0258 0.00379 -4.47e-05 2.007e-05 0.02141 -3.369e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02049 -0.2079 0.2159 0.9836 0.9933 0.2018 0.4706 0.8824 0.7314 ] Network output: [ -0.01279 0.9985 1.011 1.796e-06 -8.062e-07 0.01637 1.353e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004986 0.0008385 0.004132 0.005439 0.989 0.9921 0.005075 0.8795 0.9063 0.01618 ] Network output: [ 0.002485 -0.03795 0.9969 -0.0002083 9.353e-05 1.035 -0.000157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1067 0.3103 0.1864 0.9851 0.9941 0.1922 0.476 0.8888 0.7259 ] Network output: [ 0.009909 -0.04374 1.006 0.0001208 -5.421e-05 1.019 9.101e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09715 0.08819 0.1776 0.22 0.9874 0.9921 0.09721 0.8144 0.8886 0.3139 ] Network output: [ -0.01218 0.04356 1.005 0.0001152 -5.171e-05 0.9766 8.681e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09942 0.1762 0.2076 0.9859 0.9917 0.1011 0.7483 0.8703 0.2497 ] Network output: [ 0.001707 0.9983 -0.00223 1.885e-05 -8.463e-06 1.001 1.421e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001441 Epoch 6057 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01497 0.9954 0.9838 7.066e-06 -3.172e-06 -0.009134 5.326e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.002859 -0.01089 0.008159 0.9696 0.974 0.005977 0.8473 0.8359 0.02227 ] Network output: [ 0.9956 0.03128 0.001322 -5.328e-05 2.392e-05 -0.02395 -4.015e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.01976 -0.2108 0.2063 0.9836 0.9933 0.2027 0.472 0.8821 0.7306 ] Network output: [ -0.01276 1.002 1.01 1.33e-06 -5.971e-07 0.01344 1.002e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005004 0.0008322 0.003969 0.005109 0.989 0.9921 0.005094 0.8796 0.9061 0.01609 ] Network output: [ -0.002681 0.0415 0.9931 -0.0002211 9.924e-05 0.9698 -0.0001666 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.1069 0.305 0.1711 0.9851 0.9941 0.1929 0.477 0.8888 0.7264 ] Network output: [ 0.01183 -0.02797 1.002 0.0001203 -5.399e-05 1.003 9.063e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09688 0.08787 0.1739 0.2157 0.9874 0.9921 0.09694 0.8133 0.8885 0.3116 ] Network output: [ -0.01092 0.0394 1.004 0.0001164 -5.226e-05 0.9787 8.772e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09913 0.1749 0.2067 0.9858 0.9916 0.1008 0.7468 0.8703 0.2495 ] Network output: [ -0.0009017 0.9997 0.00145 1.709e-05 -7.673e-06 1.001 1.288e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001198 Epoch 6058 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01553 0.9865 0.9842 8.445e-06 -3.791e-06 -0.001735 6.364e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003157 -0.002859 -0.01085 0.008323 0.9696 0.974 0.00596 0.8472 0.8363 0.02232 ] Network output: [ 1 -0.02585 0.003787 -4.472e-05 2.008e-05 0.02145 -3.371e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02052 -0.2079 0.2159 0.9836 0.9933 0.2018 0.4706 0.8824 0.7313 ] Network output: [ -0.01278 0.9984 1.011 1.819e-06 -8.167e-07 0.01638 1.371e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004987 0.0008371 0.004133 0.005437 0.989 0.9921 0.005076 0.8795 0.9063 0.01618 ] Network output: [ 0.002485 -0.03801 0.997 -0.0002082 9.347e-05 1.035 -0.0001569 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1067 0.3103 0.1864 0.9851 0.9941 0.1922 0.476 0.8888 0.7258 ] Network output: [ 0.009903 -0.04383 1.005 0.0001207 -5.418e-05 1.019 9.096e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09717 0.0882 0.1777 0.22 0.9874 0.9921 0.09723 0.8144 0.8886 0.3139 ] Network output: [ -0.01218 0.04365 1.005 0.0001151 -5.169e-05 0.9765 8.677e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.09941 0.1762 0.2076 0.9859 0.9917 0.1011 0.7483 0.8703 0.2496 ] Network output: [ 0.00171 0.9983 -0.002241 1.883e-05 -8.452e-06 1.001 1.419e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001445 Epoch 6059 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01497 0.9954 0.9838 7.076e-06 -3.177e-06 -0.00913 5.333e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.002859 -0.01089 0.008157 0.9696 0.974 0.005977 0.8474 0.8359 0.02227 ] Network output: [ 0.9956 0.03134 0.001315 -5.33e-05 2.393e-05 -0.02401 -4.017e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.01979 -0.2108 0.2062 0.9836 0.9933 0.2027 0.472 0.8821 0.7305 ] Network output: [ -0.01276 1.002 1.01 1.352e-06 -6.07e-07 0.01344 1.019e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005005 0.0008308 0.00397 0.005107 0.989 0.9921 0.005095 0.8796 0.9061 0.01609 ] Network output: [ -0.002691 0.04159 0.9932 -0.0002209 9.918e-05 0.9697 -0.0001665 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.1069 0.305 0.171 0.9851 0.9941 0.1929 0.477 0.8888 0.7263 ] Network output: [ 0.01183 -0.02802 1.002 0.0001202 -5.396e-05 1.003 9.058e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09689 0.08788 0.1739 0.2157 0.9874 0.9921 0.09695 0.8132 0.8884 0.3116 ] Network output: [ -0.01091 0.03948 1.004 0.0001164 -5.223e-05 0.9787 8.769e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09912 0.1748 0.2067 0.9858 0.9916 0.1008 0.7468 0.8703 0.2495 ] Network output: [ -0.000905 0.9997 0.001448 1.706e-05 -7.661e-06 1.001 1.286e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001203 Epoch 6060 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01553 0.9865 0.9842 8.456e-06 -3.796e-06 -0.001717 6.372e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003157 -0.002859 -0.01085 0.008321 0.9696 0.974 0.005961 0.8472 0.8363 0.02232 ] Network output: [ 1 -0.02591 0.003785 -4.475e-05 2.009e-05 0.02149 -3.372e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02056 -0.2079 0.2159 0.9836 0.9933 0.2018 0.4706 0.8824 0.7313 ] Network output: [ -0.01278 0.9984 1.011 1.842e-06 -8.269e-07 0.01639 1.388e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004988 0.0008358 0.004135 0.005436 0.989 0.9921 0.005077 0.8795 0.9063 0.01618 ] Network output: [ 0.002486 -0.03808 0.9971 -0.000208 9.34e-05 1.035 -0.0001568 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1066 0.3104 0.1863 0.9851 0.9941 0.1922 0.476 0.8888 0.7258 ] Network output: [ 0.009896 -0.04391 1.005 0.0001206 -5.415e-05 1.019 9.091e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09718 0.08821 0.1777 0.2199 0.9874 0.9921 0.09724 0.8144 0.8885 0.3139 ] Network output: [ -0.01218 0.04374 1.005 0.0001151 -5.166e-05 0.9764 8.673e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1011 0.0994 0.1761 0.2075 0.9859 0.9917 0.1011 0.7483 0.8703 0.2496 ] Network output: [ 0.001714 0.9983 -0.002251 1.88e-05 -8.441e-06 1.001 1.417e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00145 Epoch 6061 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01497 0.9954 0.9838 7.086e-06 -3.181e-06 -0.009127 5.34e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.00286 -0.01089 0.008154 0.9696 0.974 0.005977 0.8474 0.8359 0.02226 ] Network output: [ 0.9956 0.0314 0.001308 -5.333e-05 2.394e-05 -0.02406 -4.019e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.01982 -0.2108 0.2062 0.9836 0.9933 0.2026 0.472 0.882 0.7305 ] Network output: [ -0.01276 1.002 1.01 1.374e-06 -6.168e-07 0.01344 1.035e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005006 0.0008294 0.003971 0.005105 0.989 0.9921 0.005096 0.8796 0.9061 0.01609 ] Network output: [ -0.0027 0.04167 0.9933 -0.0002208 9.912e-05 0.9696 -0.0001664 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.1068 0.305 0.1709 0.9851 0.9941 0.1929 0.477 0.8888 0.7263 ] Network output: [ 0.01182 -0.02808 1.002 0.0001201 -5.393e-05 1.003 9.053e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08788 0.1739 0.2156 0.9874 0.9921 0.09697 0.8132 0.8884 0.3116 ] Network output: [ -0.01091 0.03956 1.004 0.0001163 -5.221e-05 0.9786 8.765e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09911 0.1748 0.2066 0.9858 0.9916 0.1008 0.7467 0.8702 0.2494 ] Network output: [ -0.0009083 0.9997 0.001447 1.704e-05 -7.649e-06 1.001 1.284e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001207 Epoch 6062 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01553 0.9865 0.9842 8.466e-06 -3.801e-06 -0.001699 6.38e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003157 -0.00286 -0.01085 0.008319 0.9696 0.974 0.005961 0.8472 0.8363 0.02231 ] Network output: [ 1 -0.02596 0.003783 -4.477e-05 2.01e-05 0.02153 -3.374e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02059 -0.2078 0.2159 0.9836 0.9933 0.2018 0.4706 0.8824 0.7312 ] Network output: [ -0.01278 0.9984 1.011 1.864e-06 -8.37e-07 0.0164 1.405e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004989 0.0008344 0.004136 0.005434 0.989 0.9921 0.005078 0.8795 0.9063 0.01617 ] Network output: [ 0.002486 -0.03815 0.9972 -0.0002079 9.333e-05 1.035 -0.0001567 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1065 0.3104 0.1863 0.9851 0.9941 0.1922 0.476 0.8888 0.7257 ] Network output: [ 0.00989 -0.044 1.005 0.0001206 -5.412e-05 1.019 9.086e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09719 0.08821 0.1777 0.2199 0.9874 0.9921 0.09725 0.8143 0.8885 0.3139 ] Network output: [ -0.01217 0.04383 1.005 0.000115 -5.164e-05 0.9764 8.669e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.101 0.09939 0.1761 0.2075 0.9859 0.9917 0.1011 0.7482 0.8703 0.2496 ] Network output: [ 0.001717 0.9983 -0.002262 1.878e-05 -8.429e-06 1.001 1.415e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001455 Epoch 6063 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01497 0.9954 0.9838 7.094e-06 -3.185e-06 -0.009124 5.347e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.00286 -0.01089 0.008152 0.9696 0.974 0.005978 0.8474 0.8359 0.02226 ] Network output: [ 0.9956 0.03145 0.001301 -5.336e-05 2.396e-05 -0.02411 -4.021e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.01985 -0.2108 0.2061 0.9836 0.9933 0.2026 0.472 0.882 0.7304 ] Network output: [ -0.01276 1.002 1.01 1.395e-06 -6.263e-07 0.01345 1.051e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005007 0.0008281 0.003972 0.005103 0.989 0.9921 0.005097 0.8796 0.9061 0.01608 ] Network output: [ -0.002709 0.04176 0.9933 -0.0002207 9.906e-05 0.9694 -0.0001663 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.1068 0.3051 0.1709 0.9851 0.9941 0.1929 0.477 0.8888 0.7262 ] Network output: [ 0.01182 -0.02813 1.002 0.0001201 -5.39e-05 1.003 9.047e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08789 0.1739 0.2156 0.9874 0.9921 0.09698 0.8132 0.8884 0.3116 ] Network output: [ -0.0109 0.03964 1.004 0.0001162 -5.219e-05 0.9786 8.76e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.0991 0.1748 0.2066 0.9858 0.9916 0.1008 0.7467 0.8702 0.2494 ] Network output: [ -0.0009117 0.9997 0.001446 1.701e-05 -7.636e-06 1.001 1.282e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001212 Epoch 6064 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01553 0.9864 0.9842 8.476e-06 -3.805e-06 -0.001681 6.388e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003158 -0.00286 -0.01085 0.008318 0.9696 0.974 0.005961 0.8472 0.8363 0.02231 ] Network output: [ 1 -0.02602 0.003781 -4.479e-05 2.011e-05 0.02157 -3.375e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02062 -0.2078 0.2158 0.9836 0.9933 0.2018 0.4706 0.8824 0.7312 ] Network output: [ -0.01277 0.9984 1.011 1.886e-06 -8.468e-07 0.01641 1.422e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00499 0.0008331 0.004137 0.005433 0.989 0.9921 0.005079 0.8795 0.9063 0.01617 ] Network output: [ 0.002487 -0.03822 0.9972 -0.0002077 9.326e-05 1.035 -0.0001566 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1065 0.3104 0.1863 0.9851 0.9941 0.1922 0.476 0.8888 0.7256 ] Network output: [ 0.009883 -0.04409 1.005 0.0001205 -5.409e-05 1.019 9.081e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09721 0.08821 0.1777 0.2199 0.9874 0.9921 0.09727 0.8143 0.8885 0.3139 ] Network output: [ -0.01217 0.04392 1.005 0.000115 -5.162e-05 0.9763 8.665e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.101 0.09938 0.1761 0.2075 0.9859 0.9917 0.101 0.7482 0.8703 0.2495 ] Network output: [ 0.001721 0.9983 -0.002272 1.875e-05 -8.418e-06 1.001 1.413e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001459 Epoch 6065 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01496 0.9954 0.9838 7.103e-06 -3.189e-06 -0.009121 5.353e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.002861 -0.01088 0.00815 0.9696 0.974 0.005978 0.8474 0.8359 0.02226 ] Network output: [ 0.9956 0.03151 0.001294 -5.339e-05 2.397e-05 -0.02417 -4.023e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.01989 -0.2108 0.2061 0.9836 0.9933 0.2026 0.4719 0.882 0.7304 ] Network output: [ -0.01275 1.002 1.01 1.416e-06 -6.356e-07 0.01345 1.067e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005008 0.0008267 0.003972 0.0051 0.989 0.9921 0.005098 0.8796 0.9061 0.01608 ] Network output: [ -0.002718 0.04185 0.9934 -0.0002205 9.9e-05 0.9693 -0.0001662 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.1067 0.3051 0.1708 0.9851 0.9941 0.1929 0.477 0.8888 0.7262 ] Network output: [ 0.01182 -0.02819 1.002 0.00012 -5.386e-05 1.003 9.042e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08789 0.174 0.2156 0.9874 0.9921 0.09699 0.8131 0.8884 0.3116 ] Network output: [ -0.0109 0.03972 1.004 0.0001162 -5.216e-05 0.9785 8.756e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09909 0.1748 0.2066 0.9858 0.9916 0.1008 0.7467 0.8702 0.2494 ] Network output: [ -0.0009152 0.9997 0.001445 1.698e-05 -7.624e-06 1.001 1.28e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001217 Epoch 6066 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01553 0.9864 0.9842 8.486e-06 -3.81e-06 -0.001664 6.395e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003158 -0.002861 -0.01084 0.008316 0.9696 0.974 0.005962 0.8472 0.8363 0.02231 ] Network output: [ 1 -0.02608 0.003779 -4.481e-05 2.012e-05 0.02161 -3.377e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02066 -0.2078 0.2158 0.9836 0.9933 0.2018 0.4705 0.8824 0.7311 ] Network output: [ -0.01277 0.9984 1.011 1.908e-06 -8.564e-07 0.01642 1.438e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00499 0.0008317 0.004138 0.005431 0.989 0.9921 0.00508 0.8795 0.9063 0.01617 ] Network output: [ 0.002488 -0.0383 0.9973 -0.0002076 9.32e-05 1.035 -0.0001564 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1064 0.3105 0.1862 0.9851 0.9941 0.1921 0.476 0.8888 0.7256 ] Network output: [ 0.009877 -0.04418 1.005 0.0001204 -5.406e-05 1.02 9.076e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09722 0.08822 0.1778 0.2199 0.9874 0.9921 0.09728 0.8142 0.8885 0.3139 ] Network output: [ -0.01217 0.04401 1.005 0.0001149 -5.159e-05 0.9762 8.661e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.101 0.09937 0.1761 0.2074 0.9859 0.9917 0.101 0.7482 0.8702 0.2495 ] Network output: [ 0.001724 0.9983 -0.002282 1.873e-05 -8.407e-06 1.001 1.411e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001464 Epoch 6067 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01496 0.9954 0.9838 7.111e-06 -3.192e-06 -0.009118 5.359e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.002861 -0.01088 0.008148 0.9696 0.974 0.005978 0.8474 0.8359 0.02225 ] Network output: [ 0.9956 0.03158 0.001287 -5.341e-05 2.398e-05 -0.02422 -4.025e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.01992 -0.2107 0.206 0.9836 0.9933 0.2026 0.4719 0.882 0.7303 ] Network output: [ -0.01275 1.002 1.01 1.436e-06 -6.447e-07 0.01345 1.082e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005009 0.0008254 0.003973 0.005098 0.989 0.9921 0.005099 0.8796 0.9061 0.01608 ] Network output: [ -0.002727 0.04194 0.9935 -0.0002204 9.894e-05 0.9692 -0.0001661 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.1066 0.3051 0.1707 0.9851 0.9941 0.1929 0.477 0.8888 0.7261 ] Network output: [ 0.01181 -0.02824 1.002 0.0001199 -5.383e-05 1.003 9.037e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09695 0.08789 0.174 0.2156 0.9874 0.9921 0.097 0.8131 0.8883 0.3116 ] Network output: [ -0.01089 0.0398 1.004 0.0001161 -5.214e-05 0.9784 8.752e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09908 0.1748 0.2065 0.9858 0.9916 0.1008 0.7466 0.8702 0.2493 ] Network output: [ -0.0009187 0.9997 0.001444 1.696e-05 -7.612e-06 1.001 1.278e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001221 Epoch 6068 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01553 0.9864 0.9842 8.495e-06 -3.814e-06 -0.001646 6.402e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003158 -0.002861 -0.01084 0.008314 0.9696 0.974 0.005962 0.8472 0.8363 0.0223 ] Network output: [ 1 -0.02613 0.003778 -4.483e-05 2.012e-05 0.02165 -3.378e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02069 -0.2078 0.2158 0.9836 0.9933 0.2018 0.4705 0.8824 0.7311 ] Network output: [ -0.01277 0.9984 1.011 1.929e-06 -8.658e-07 0.01643 1.453e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004991 0.0008304 0.004139 0.00543 0.989 0.9921 0.005081 0.8795 0.9063 0.01616 ] Network output: [ 0.002489 -0.03837 0.9974 -0.0002074 9.313e-05 1.035 -0.0001563 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1063 0.3105 0.1862 0.9851 0.9941 0.1921 0.476 0.8887 0.7255 ] Network output: [ 0.00987 -0.04426 1.005 0.0001204 -5.403e-05 1.02 9.071e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09723 0.08822 0.1778 0.2199 0.9874 0.9921 0.09729 0.8142 0.8884 0.3139 ] Network output: [ -0.01216 0.0441 1.005 0.0001149 -5.157e-05 0.9762 8.657e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.101 0.09936 0.176 0.2074 0.9859 0.9917 0.101 0.7482 0.8702 0.2495 ] Network output: [ 0.001728 0.9983 -0.002292 1.87e-05 -8.396e-06 1.001 1.409e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001469 Epoch 6069 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01496 0.9954 0.9838 7.118e-06 -3.196e-06 -0.009116 5.364e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.002862 -0.01088 0.008146 0.9696 0.974 0.005979 0.8474 0.8359 0.02225 ] Network output: [ 0.9956 0.03164 0.00128 -5.344e-05 2.399e-05 -0.02427 -4.027e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.01995 -0.2107 0.206 0.9836 0.9933 0.2026 0.4719 0.882 0.7303 ] Network output: [ -0.01275 1.002 1.01 1.456e-06 -6.536e-07 0.01346 1.097e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00501 0.000824 0.003974 0.005096 0.989 0.9921 0.005099 0.8796 0.9061 0.01607 ] Network output: [ -0.002737 0.04203 0.9935 -0.0002203 9.888e-05 0.969 -0.000166 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.1066 0.3051 0.1706 0.9851 0.9941 0.1929 0.477 0.8888 0.7261 ] Network output: [ 0.01181 -0.02829 1.002 0.0001198 -5.38e-05 1.003 9.032e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09696 0.0879 0.174 0.2156 0.9874 0.9921 0.09702 0.813 0.8883 0.3116 ] Network output: [ -0.01089 0.03988 1.004 0.0001161 -5.211e-05 0.9784 8.748e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09906 0.1747 0.2065 0.9858 0.9916 0.1007 0.7466 0.8702 0.2493 ] Network output: [ -0.0009222 0.9997 0.001443 1.693e-05 -7.6e-06 1.001 1.276e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001226 Epoch 6070 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01553 0.9864 0.9842 8.504e-06 -3.818e-06 -0.001628 6.409e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003158 -0.002861 -0.01084 0.008312 0.9696 0.974 0.005962 0.8472 0.8363 0.0223 ] Network output: [ 1 -0.02619 0.003776 -4.484e-05 2.013e-05 0.0217 -3.38e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02072 -0.2077 0.2158 0.9836 0.9933 0.2017 0.4705 0.8824 0.731 ] Network output: [ -0.01277 0.9983 1.011 1.949e-06 -8.75e-07 0.01643 1.469e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004992 0.0008291 0.00414 0.005428 0.989 0.9921 0.005082 0.8795 0.9063 0.01616 ] Network output: [ 0.00249 -0.03844 0.9974 -0.0002073 9.306e-05 1.035 -0.0001562 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1063 0.3105 0.1861 0.9851 0.9941 0.1921 0.476 0.8887 0.7255 ] Network output: [ 0.009864 -0.04435 1.005 0.0001203 -5.4e-05 1.02 9.065e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09725 0.08823 0.1778 0.2199 0.9874 0.9921 0.09731 0.8142 0.8884 0.3139 ] Network output: [ -0.01216 0.04419 1.004 0.0001148 -5.154e-05 0.9761 8.653e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.101 0.09935 0.176 0.2074 0.9859 0.9917 0.101 0.7481 0.8702 0.2494 ] Network output: [ 0.001731 0.9983 -0.002302 1.868e-05 -8.385e-06 1.001 1.408e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001473 Epoch 6071 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01496 0.9954 0.9838 7.125e-06 -3.199e-06 -0.009114 5.369e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.002862 -0.01088 0.008144 0.9696 0.974 0.005979 0.8474 0.8359 0.02224 ] Network output: [ 0.9956 0.0317 0.001273 -5.346e-05 2.4e-05 -0.02433 -4.029e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.01998 -0.2107 0.206 0.9836 0.9933 0.2026 0.4719 0.882 0.7302 ] Network output: [ -0.01274 1.002 1.01 1.475e-06 -6.623e-07 0.01346 1.112e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005011 0.0008227 0.003975 0.005094 0.989 0.9921 0.0051 0.8796 0.9061 0.01607 ] Network output: [ -0.002746 0.04212 0.9936 -0.0002201 9.882e-05 0.9689 -0.0001659 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.1065 0.3052 0.1706 0.9851 0.9941 0.1928 0.477 0.8887 0.726 ] Network output: [ 0.0118 -0.02834 1.002 0.0001198 -5.377e-05 1.004 9.026e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09697 0.0879 0.174 0.2156 0.9874 0.9921 0.09703 0.813 0.8883 0.3116 ] Network output: [ -0.01088 0.03995 1.004 0.000116 -5.209e-05 0.9783 8.744e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09905 0.1747 0.2065 0.9858 0.9916 0.1007 0.7466 0.8701 0.2493 ] Network output: [ -0.0009258 0.9997 0.001443 1.69e-05 -7.588e-06 1.001 1.274e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001231 Epoch 6072 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01552 0.9864 0.9842 8.512e-06 -3.821e-06 -0.001611 6.415e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003158 -0.002862 -0.01084 0.00831 0.9696 0.974 0.005963 0.8472 0.8362 0.02229 ] Network output: [ 1 -0.02625 0.003774 -4.486e-05 2.014e-05 0.02174 -3.381e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02076 -0.2077 0.2157 0.9836 0.9933 0.2017 0.4705 0.8824 0.731 ] Network output: [ -0.01276 0.9983 1.011 1.969e-06 -8.84e-07 0.01644 1.484e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004993 0.0008278 0.004142 0.005427 0.989 0.9921 0.005083 0.8795 0.9063 0.01616 ] Network output: [ 0.002491 -0.03851 0.9975 -0.0002071 9.299e-05 1.035 -0.0001561 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1062 0.3106 0.1861 0.9851 0.9941 0.1921 0.476 0.8887 0.7254 ] Network output: [ 0.009857 -0.04444 1.005 0.0001202 -5.397e-05 1.02 9.06e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09726 0.08823 0.1778 0.2199 0.9874 0.9921 0.09732 0.8141 0.8884 0.3139 ] Network output: [ -0.01215 0.04427 1.004 0.0001148 -5.152e-05 0.9761 8.648e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.101 0.09934 0.176 0.2073 0.9859 0.9917 0.101 0.7481 0.8702 0.2494 ] Network output: [ 0.001735 0.9983 -0.002312 1.865e-05 -8.374e-06 1.001 1.406e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001478 Epoch 6073 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01495 0.9954 0.9838 7.131e-06 -3.201e-06 -0.009112 5.374e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.002863 -0.01087 0.008142 0.9696 0.974 0.00598 0.8474 0.8359 0.02224 ] Network output: [ 0.9956 0.03176 0.001266 -5.349e-05 2.401e-05 -0.02438 -4.031e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02001 -0.2107 0.2059 0.9836 0.9933 0.2026 0.4719 0.882 0.7302 ] Network output: [ -0.01274 1.002 1.01 1.494e-06 -6.707e-07 0.01346 1.126e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005012 0.0008214 0.003976 0.005092 0.989 0.9921 0.005101 0.8796 0.9061 0.01607 ] Network output: [ -0.002755 0.04221 0.9936 -0.00022 9.875e-05 0.9688 -0.0001658 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.1065 0.3052 0.1705 0.9851 0.9941 0.1928 0.4769 0.8887 0.726 ] Network output: [ 0.0118 -0.02839 1.002 0.0001197 -5.374e-05 1.004 9.021e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09698 0.0879 0.174 0.2156 0.9874 0.9921 0.09704 0.813 0.8883 0.3116 ] Network output: [ -0.01088 0.04003 1.004 0.000116 -5.206e-05 0.9783 8.74e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09904 0.1747 0.2064 0.9858 0.9916 0.1007 0.7465 0.8701 0.2492 ] Network output: [ -0.0009294 0.9997 0.001442 1.687e-05 -7.576e-06 1.001 1.272e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001235 Epoch 6074 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01552 0.9863 0.9842 8.52e-06 -3.825e-06 -0.001594 6.421e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003158 -0.002862 -0.01083 0.008309 0.9696 0.974 0.005963 0.8472 0.8362 0.02229 ] Network output: [ 1 -0.02631 0.003773 -4.488e-05 2.015e-05 0.02178 -3.382e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.181 -0.02079 -0.2077 0.2157 0.9836 0.9933 0.2017 0.4705 0.8824 0.7309 ] Network output: [ -0.01276 0.9983 1.011 1.989e-06 -8.927e-07 0.01645 1.499e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004994 0.0008265 0.004143 0.005426 0.989 0.9921 0.005083 0.8795 0.9063 0.01616 ] Network output: [ 0.002492 -0.03859 0.9976 -0.000207 9.292e-05 1.035 -0.000156 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1062 0.3106 0.186 0.9851 0.9941 0.1921 0.4759 0.8887 0.7254 ] Network output: [ 0.009851 -0.04452 1.005 0.0001202 -5.394e-05 1.02 9.055e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09727 0.08823 0.1779 0.2199 0.9874 0.9921 0.09733 0.8141 0.8884 0.3139 ] Network output: [ -0.01215 0.04436 1.004 0.0001147 -5.149e-05 0.976 8.644e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.101 0.09933 0.176 0.2073 0.9859 0.9917 0.101 0.7481 0.8701 0.2494 ] Network output: [ 0.001738 0.9983 -0.002322 1.863e-05 -8.363e-06 1.001 1.404e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001483 Epoch 6075 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01495 0.9954 0.9839 7.137e-06 -3.204e-06 -0.00911 5.379e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.002863 -0.01087 0.00814 0.9696 0.974 0.00598 0.8474 0.8359 0.02223 ] Network output: [ 0.9956 0.03182 0.001259 -5.351e-05 2.402e-05 -0.02444 -4.033e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02004 -0.2107 0.2059 0.9836 0.9933 0.2026 0.4719 0.882 0.7301 ] Network output: [ -0.01274 1.002 1.01 1.512e-06 -6.79e-07 0.01346 1.14e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005013 0.0008201 0.003976 0.00509 0.989 0.9921 0.005102 0.8795 0.906 0.01607 ] Network output: [ -0.002764 0.0423 0.9937 -0.0002198 9.869e-05 0.9686 -0.0001657 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.1064 0.3052 0.1704 0.9851 0.9941 0.1928 0.4769 0.8887 0.7259 ] Network output: [ 0.0118 -0.02844 1.002 0.0001196 -5.371e-05 1.004 9.015e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09699 0.08791 0.174 0.2156 0.9874 0.9921 0.09705 0.8129 0.8882 0.3116 ] Network output: [ -0.01087 0.04011 1.004 0.0001159 -5.204e-05 0.9782 8.736e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09903 0.1747 0.2064 0.9858 0.9916 0.1007 0.7465 0.8701 0.2492 ] Network output: [ -0.000933 0.9997 0.001442 1.685e-05 -7.564e-06 1.001 1.27e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00124 Epoch 6076 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01552 0.9863 0.9842 8.527e-06 -3.828e-06 -0.001576 6.426e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003159 -0.002863 -0.01083 0.008307 0.9696 0.974 0.005963 0.8472 0.8362 0.02228 ] Network output: [ 1 -0.02636 0.003771 -4.489e-05 2.015e-05 0.02183 -3.383e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.181 -0.02082 -0.2076 0.2157 0.9836 0.9933 0.2017 0.4705 0.8824 0.7309 ] Network output: [ -0.01276 0.9983 1.011 2.008e-06 -9.013e-07 0.01646 1.513e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004995 0.0008252 0.004144 0.005424 0.989 0.9921 0.005084 0.8795 0.9063 0.01615 ] Network output: [ 0.002493 -0.03866 0.9976 -0.0002068 9.285e-05 1.035 -0.0001559 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1061 0.3106 0.186 0.9851 0.9941 0.1921 0.4759 0.8887 0.7253 ] Network output: [ 0.009844 -0.04461 1.005 0.0001201 -5.391e-05 1.02 9.05e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09728 0.08824 0.1779 0.2199 0.9874 0.9921 0.09734 0.8141 0.8884 0.314 ] Network output: [ -0.01215 0.04445 1.004 0.0001146 -5.147e-05 0.9759 8.64e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.101 0.09931 0.176 0.2073 0.9859 0.9917 0.101 0.748 0.8701 0.2493 ] Network output: [ 0.001742 0.9983 -0.002332 1.86e-05 -8.352e-06 1.001 1.402e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001488 Epoch 6077 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01495 0.9954 0.9839 7.143e-06 -3.207e-06 -0.009108 5.383e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.002863 -0.01087 0.008138 0.9696 0.974 0.00598 0.8474 0.8359 0.02223 ] Network output: [ 0.9956 0.03188 0.001253 -5.353e-05 2.403e-05 -0.02449 -4.034e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02007 -0.2106 0.2058 0.9836 0.9933 0.2026 0.4719 0.882 0.7301 ] Network output: [ -0.01274 1.002 1.011 1.53e-06 -6.87e-07 0.01347 1.153e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005014 0.0008187 0.003977 0.005088 0.989 0.9921 0.005103 0.8795 0.906 0.01606 ] Network output: [ -0.002773 0.04239 0.9938 -0.0002197 9.863e-05 0.9685 -0.0001656 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.1063 0.3052 0.1703 0.9851 0.9941 0.1928 0.4769 0.8887 0.7259 ] Network output: [ 0.01179 -0.02849 1.002 0.0001196 -5.367e-05 1.004 9.01e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.097 0.08791 0.1741 0.2155 0.9874 0.9921 0.09706 0.8129 0.8882 0.3116 ] Network output: [ -0.01087 0.04018 1.004 0.0001159 -5.201e-05 0.9782 8.731e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09901 0.1746 0.2064 0.9858 0.9916 0.1007 0.7464 0.8701 0.2492 ] Network output: [ -0.0009367 0.9997 0.001442 1.682e-05 -7.551e-06 1.001 1.268e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001245 Epoch 6078 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01552 0.9863 0.9843 8.534e-06 -3.831e-06 -0.001559 6.432e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003159 -0.002863 -0.01083 0.008305 0.9696 0.974 0.005964 0.8472 0.8362 0.02228 ] Network output: [ 1 -0.02642 0.00377 -4.491e-05 2.016e-05 0.02187 -3.384e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.181 -0.02085 -0.2076 0.2157 0.9836 0.9933 0.2017 0.4705 0.8823 0.7308 ] Network output: [ -0.01275 0.9983 1.011 2.026e-06 -9.097e-07 0.01647 1.527e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004996 0.0008239 0.004145 0.005423 0.989 0.9921 0.005085 0.8794 0.9063 0.01615 ] Network output: [ 0.002495 -0.03873 0.9977 -0.0002067 9.278e-05 1.035 -0.0001557 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.106 0.3107 0.186 0.9851 0.9941 0.1921 0.4759 0.8887 0.7253 ] Network output: [ 0.009838 -0.04469 1.005 0.00012 -5.388e-05 1.02 9.045e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.08824 0.1779 0.2199 0.9874 0.9921 0.09735 0.814 0.8883 0.314 ] Network output: [ -0.01214 0.04453 1.004 0.0001146 -5.144e-05 0.9759 8.636e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.101 0.0993 0.1759 0.2072 0.9859 0.9917 0.101 0.748 0.8701 0.2493 ] Network output: [ 0.001745 0.9983 -0.002342 1.858e-05 -8.341e-06 1.001 1.4e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001493 Epoch 6079 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01495 0.9954 0.9839 7.148e-06 -3.209e-06 -0.009106 5.387e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002864 -0.01087 0.008136 0.9696 0.974 0.005981 0.8474 0.8359 0.02223 ] Network output: [ 0.9956 0.03194 0.001246 -5.355e-05 2.404e-05 -0.02455 -4.036e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.0201 -0.2106 0.2058 0.9836 0.9933 0.2025 0.4719 0.882 0.73 ] Network output: [ -0.01273 1.002 1.011 1.548e-06 -6.949e-07 0.01347 1.167e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005014 0.0008175 0.003978 0.005086 0.989 0.9921 0.005104 0.8795 0.906 0.01606 ] Network output: [ -0.002782 0.04248 0.9938 -0.0002195 9.856e-05 0.9684 -0.0001655 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.1063 0.3052 0.1703 0.9851 0.9941 0.1928 0.4769 0.8887 0.7258 ] Network output: [ 0.01179 -0.02854 1.002 0.0001195 -5.364e-05 1.004 9.005e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09701 0.08791 0.1741 0.2155 0.9874 0.9921 0.09707 0.8128 0.8882 0.3116 ] Network output: [ -0.01086 0.04025 1.004 0.0001158 -5.199e-05 0.9781 8.727e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.099 0.1746 0.2063 0.9858 0.9916 0.1007 0.7464 0.87 0.2491 ] Network output: [ -0.0009404 0.9997 0.001442 1.679e-05 -7.539e-06 1.001 1.266e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001249 Epoch 6080 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01552 0.9863 0.9843 8.541e-06 -3.834e-06 -0.001542 6.437e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003159 -0.002864 -0.01083 0.008303 0.9696 0.974 0.005964 0.8472 0.8362 0.02228 ] Network output: [ 1 -0.02648 0.003768 -4.492e-05 2.017e-05 0.02191 -3.386e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.181 -0.02089 -0.2076 0.2157 0.9836 0.9933 0.2017 0.4705 0.8823 0.7308 ] Network output: [ -0.01275 0.9983 1.011 2.045e-06 -9.179e-07 0.01648 1.541e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004997 0.0008226 0.004146 0.005421 0.989 0.9921 0.005086 0.8794 0.9062 0.01615 ] Network output: [ 0.002496 -0.03881 0.9978 -0.0002065 9.271e-05 1.035 -0.0001556 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.106 0.3107 0.1859 0.9851 0.9941 0.1921 0.4759 0.8887 0.7252 ] Network output: [ 0.009831 -0.04478 1.005 0.0001199 -5.385e-05 1.02 9.04e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08824 0.1779 0.2199 0.9874 0.9921 0.09736 0.814 0.8883 0.314 ] Network output: [ -0.01214 0.04461 1.004 0.0001145 -5.142e-05 0.9758 8.631e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09929 0.1759 0.2072 0.9859 0.9917 0.101 0.748 0.8701 0.2493 ] Network output: [ 0.001748 0.9983 -0.002352 1.856e-05 -8.331e-06 1.001 1.398e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001497 Epoch 6081 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01494 0.9954 0.9839 7.153e-06 -3.211e-06 -0.009105 5.39e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002864 -0.01086 0.008134 0.9696 0.974 0.005981 0.8474 0.8358 0.02222 ] Network output: [ 0.9956 0.032 0.001239 -5.357e-05 2.405e-05 -0.0246 -4.038e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02013 -0.2106 0.2058 0.9836 0.9933 0.2025 0.4719 0.882 0.73 ] Network output: [ -0.01273 1.001 1.011 1.565e-06 -7.026e-07 0.01347 1.179e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005015 0.0008162 0.003979 0.005084 0.989 0.9921 0.005105 0.8795 0.906 0.01606 ] Network output: [ -0.00279 0.04257 0.9939 -0.0002194 9.85e-05 0.9682 -0.0001653 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.1062 0.3053 0.1702 0.9851 0.9941 0.1928 0.4769 0.8887 0.7258 ] Network output: [ 0.01178 -0.02859 1.002 0.0001194 -5.361e-05 1.004 8.999e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09702 0.08791 0.1741 0.2155 0.9874 0.9921 0.09708 0.8128 0.8882 0.3116 ] Network output: [ -0.01086 0.04033 1.004 0.0001157 -5.196e-05 0.9781 8.723e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09899 0.1746 0.2063 0.9858 0.9916 0.1007 0.7464 0.87 0.2491 ] Network output: [ -0.0009441 0.9997 0.001441 1.677e-05 -7.528e-06 1.001 1.264e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001254 Epoch 6082 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01552 0.9863 0.9843 8.547e-06 -3.837e-06 -0.001525 6.441e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003159 -0.002864 -0.01082 0.008302 0.9696 0.974 0.005964 0.8472 0.8362 0.02227 ] Network output: [ 1 -0.02654 0.003767 -4.494e-05 2.017e-05 0.02196 -3.387e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.181 -0.02092 -0.2076 0.2156 0.9836 0.9933 0.2017 0.4705 0.8823 0.7307 ] Network output: [ -0.01275 0.9982 1.011 2.062e-06 -9.259e-07 0.01648 1.554e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004998 0.0008214 0.004147 0.00542 0.989 0.9921 0.005087 0.8794 0.9062 0.01615 ] Network output: [ 0.002497 -0.03888 0.9978 -0.0002063 9.264e-05 1.035 -0.0001555 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1059 0.3107 0.1859 0.9851 0.9941 0.1921 0.4759 0.8887 0.7252 ] Network output: [ 0.009825 -0.04486 1.005 0.0001199 -5.382e-05 1.021 9.034e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09731 0.08825 0.178 0.2199 0.9874 0.9921 0.09737 0.8139 0.8883 0.314 ] Network output: [ -0.01213 0.0447 1.004 0.0001145 -5.139e-05 0.9758 8.627e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09927 0.1759 0.2072 0.9859 0.9917 0.1009 0.7479 0.87 0.2492 ] Network output: [ 0.001752 0.9983 -0.002361 1.853e-05 -8.32e-06 1.001 1.397e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001502 Epoch 6083 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01494 0.9954 0.9839 7.157e-06 -3.213e-06 -0.009103 5.394e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002865 -0.01086 0.008132 0.9696 0.974 0.005981 0.8474 0.8358 0.02222 ] Network output: [ 0.9956 0.03207 0.001233 -5.359e-05 2.406e-05 -0.02466 -4.039e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02016 -0.2106 0.2057 0.9836 0.9933 0.2025 0.4719 0.882 0.7299 ] Network output: [ -0.01273 1.001 1.011 1.582e-06 -7.101e-07 0.01347 1.192e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005016 0.0008149 0.003979 0.005082 0.989 0.9921 0.005106 0.8795 0.906 0.01605 ] Network output: [ -0.002799 0.04266 0.9939 -0.0002193 9.843e-05 0.9681 -0.0001652 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.1062 0.3053 0.1701 0.9851 0.9941 0.1928 0.4769 0.8887 0.7257 ] Network output: [ 0.01178 -0.02864 1.001 0.0001193 -5.358e-05 1.004 8.994e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09704 0.08792 0.1741 0.2155 0.9874 0.9921 0.09709 0.8128 0.8882 0.3116 ] Network output: [ -0.01085 0.0404 1.004 0.0001157 -5.194e-05 0.978 8.718e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.09897 0.1746 0.2062 0.9858 0.9916 0.1007 0.7463 0.87 0.2491 ] Network output: [ -0.0009478 0.9997 0.001441 1.674e-05 -7.516e-06 1.001 1.262e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001259 Epoch 6084 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01552 0.9862 0.9843 8.552e-06 -3.839e-06 -0.001509 6.445e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003159 -0.002864 -0.01082 0.0083 0.9696 0.974 0.005965 0.8472 0.8362 0.02227 ] Network output: [ 1 -0.02659 0.003766 -4.495e-05 2.018e-05 0.022 -3.388e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.181 -0.02095 -0.2075 0.2156 0.9836 0.9933 0.2017 0.4705 0.8823 0.7307 ] Network output: [ -0.01275 0.9982 1.011 2.08e-06 -9.337e-07 0.01649 1.567e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004998 0.0008201 0.004148 0.005418 0.989 0.9921 0.005088 0.8794 0.9062 0.01614 ] Network output: [ 0.002499 -0.03895 0.9979 -0.0002062 9.257e-05 1.035 -0.0001554 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1059 0.3108 0.1858 0.9851 0.9941 0.192 0.4759 0.8887 0.7251 ] Network output: [ 0.009818 -0.04494 1.005 0.0001198 -5.379e-05 1.021 9.029e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09733 0.08825 0.178 0.2199 0.9874 0.9921 0.09738 0.8139 0.8883 0.314 ] Network output: [ -0.01213 0.04478 1.004 0.0001144 -5.136e-05 0.9757 8.623e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09926 0.1759 0.2071 0.9859 0.9917 0.1009 0.7479 0.87 0.2492 ] Network output: [ 0.001755 0.9983 -0.002371 1.851e-05 -8.309e-06 1.001 1.395e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001507 Epoch 6085 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01494 0.9954 0.9839 7.161e-06 -3.215e-06 -0.009102 5.397e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002865 -0.01086 0.00813 0.9696 0.974 0.005982 0.8474 0.8358 0.02221 ] Network output: [ 0.9956 0.03213 0.001226 -5.361e-05 2.407e-05 -0.02471 -4.041e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02019 -0.2105 0.2057 0.9836 0.9933 0.2025 0.4719 0.882 0.7299 ] Network output: [ -0.01273 1.001 1.011 1.598e-06 -7.174e-07 0.01348 1.204e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005017 0.0008136 0.00398 0.005079 0.989 0.9921 0.005107 0.8795 0.906 0.01605 ] Network output: [ -0.002808 0.04275 0.994 -0.0002191 9.837e-05 0.968 -0.0001651 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.1061 0.3053 0.1701 0.9851 0.9941 0.1928 0.4769 0.8887 0.7257 ] Network output: [ 0.01178 -0.02869 1.001 0.0001193 -5.354e-05 1.004 8.988e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09705 0.08792 0.1741 0.2155 0.9874 0.9921 0.0971 0.8127 0.8881 0.3116 ] Network output: [ -0.01085 0.04047 1.004 0.0001156 -5.191e-05 0.978 8.714e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.09896 0.1745 0.2062 0.9858 0.9916 0.1006 0.7463 0.87 0.249 ] Network output: [ -0.0009515 0.9997 0.001442 1.671e-05 -7.504e-06 1.001 1.26e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001264 Epoch 6086 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01551 0.9862 0.9843 8.558e-06 -3.842e-06 -0.001492 6.449e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003159 -0.002865 -0.01082 0.008298 0.9696 0.974 0.005965 0.8472 0.8362 0.02226 ] Network output: [ 1 -0.02665 0.003765 -4.496e-05 2.019e-05 0.02204 -3.389e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.181 -0.02098 -0.2075 0.2156 0.9836 0.9933 0.2017 0.4704 0.8823 0.7306 ] Network output: [ -0.01274 0.9982 1.011 2.097e-06 -9.413e-07 0.0165 1.58e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.004999 0.0008189 0.00415 0.005417 0.989 0.9921 0.005089 0.8794 0.9062 0.01614 ] Network output: [ 0.0025 -0.03903 0.998 -0.000206 9.249e-05 1.035 -0.0001553 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1058 0.3108 0.1858 0.9851 0.9941 0.192 0.4759 0.8887 0.7251 ] Network output: [ 0.009812 -0.04502 1.005 0.0001197 -5.375e-05 1.021 9.024e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09734 0.08825 0.178 0.2199 0.9874 0.9921 0.09739 0.8139 0.8882 0.314 ] Network output: [ -0.01213 0.04486 1.004 0.0001144 -5.134e-05 0.9756 8.618e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09925 0.1758 0.2071 0.9859 0.9917 0.1009 0.7479 0.87 0.2492 ] Network output: [ 0.001759 0.9983 -0.00238 1.848e-05 -8.298e-06 1.001 1.393e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001512 Epoch 6087 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01493 0.9954 0.9839 7.164e-06 -3.216e-06 -0.009101 5.399e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002866 -0.01086 0.008127 0.9696 0.974 0.005982 0.8474 0.8358 0.02221 ] Network output: [ 0.9956 0.03219 0.001219 -5.363e-05 2.408e-05 -0.02477 -4.042e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02022 -0.2105 0.2056 0.9836 0.9933 0.2025 0.4719 0.8819 0.7298 ] Network output: [ -0.01272 1.001 1.011 1.614e-06 -7.245e-07 0.01348 1.216e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005018 0.0008124 0.003981 0.005077 0.989 0.9921 0.005108 0.8795 0.906 0.01605 ] Network output: [ -0.002817 0.04284 0.994 -0.000219 9.83e-05 0.9679 -0.000165 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.106 0.3053 0.17 0.9851 0.9941 0.1928 0.4769 0.8887 0.7256 ] Network output: [ 0.01177 -0.02874 1.001 0.0001192 -5.351e-05 1.004 8.983e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09705 0.08792 0.1741 0.2155 0.9874 0.9921 0.09711 0.8127 0.8881 0.3116 ] Network output: [ -0.01084 0.04054 1.004 0.0001156 -5.188e-05 0.9779 8.71e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.09894 0.1745 0.2062 0.9858 0.9916 0.1006 0.7463 0.8699 0.249 ] Network output: [ -0.0009552 0.9997 0.001442 1.669e-05 -7.492e-06 1.001 1.258e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001268 Epoch 6088 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01551 0.9862 0.9843 8.563e-06 -3.844e-06 -0.001476 6.453e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003159 -0.002865 -0.01081 0.008296 0.9696 0.974 0.005965 0.8472 0.8362 0.02226 ] Network output: [ 1 -0.02671 0.003763 -4.498e-05 2.019e-05 0.02208 -3.389e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.181 -0.02101 -0.2075 0.2156 0.9836 0.9933 0.2016 0.4704 0.8823 0.7306 ] Network output: [ -0.01274 0.9982 1.011 2.113e-06 -9.487e-07 0.01651 1.593e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005 0.0008176 0.004151 0.005415 0.989 0.9921 0.00509 0.8794 0.9062 0.01614 ] Network output: [ 0.002502 -0.0391 0.998 -0.0002059 9.242e-05 1.035 -0.0001551 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1058 0.3108 0.1858 0.9851 0.9941 0.192 0.4759 0.8887 0.725 ] Network output: [ 0.009806 -0.04511 1.005 0.0001197 -5.372e-05 1.021 9.019e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09735 0.08825 0.178 0.2199 0.9874 0.9921 0.0974 0.8138 0.8882 0.314 ] Network output: [ -0.01212 0.04494 1.004 0.0001143 -5.131e-05 0.9756 8.614e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09923 0.1758 0.2071 0.9859 0.9917 0.1009 0.7478 0.87 0.2491 ] Network output: [ 0.001762 0.9983 -0.00239 1.846e-05 -8.287e-06 1.001 1.391e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001517 Epoch 6089 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01493 0.9954 0.9839 7.168e-06 -3.218e-06 -0.0091 5.402e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002866 -0.01085 0.008125 0.9696 0.974 0.005982 0.8474 0.8358 0.0222 ] Network output: [ 0.9956 0.03225 0.001213 -5.365e-05 2.409e-05 -0.02482 -4.043e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02025 -0.2105 0.2056 0.9836 0.9933 0.2025 0.4719 0.8819 0.7298 ] Network output: [ -0.01272 1.001 1.011 1.629e-06 -7.314e-07 0.01348 1.228e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005019 0.0008111 0.003982 0.005075 0.989 0.9921 0.005109 0.8795 0.906 0.01605 ] Network output: [ -0.002825 0.04293 0.9941 -0.0002188 9.824e-05 0.9677 -0.0001649 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.106 0.3053 0.1699 0.9851 0.9941 0.1928 0.4769 0.8887 0.7256 ] Network output: [ 0.01177 -0.02879 1.001 0.0001191 -5.348e-05 1.004 8.977e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09706 0.08792 0.1742 0.2155 0.9874 0.9921 0.09712 0.8126 0.8881 0.3117 ] Network output: [ -0.01084 0.04061 1.004 0.0001155 -5.186e-05 0.9779 8.705e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.09893 0.1745 0.2061 0.9858 0.9916 0.1006 0.7462 0.8699 0.249 ] Network output: [ -0.000959 0.9997 0.001442 1.666e-05 -7.48e-06 1.001 1.256e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001273 Epoch 6090 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01551 0.9862 0.9843 8.567e-06 -3.846e-06 -0.00146 6.456e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003159 -0.002866 -0.01081 0.008294 0.9696 0.974 0.005965 0.8472 0.8362 0.02225 ] Network output: [ 1 -0.02676 0.003762 -4.499e-05 2.02e-05 0.02213 -3.39e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.181 -0.02104 -0.2074 0.2155 0.9836 0.9933 0.2016 0.4704 0.8823 0.7305 ] Network output: [ -0.01274 0.9982 1.011 2.129e-06 -9.56e-07 0.01651 1.605e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005001 0.0008164 0.004152 0.005414 0.989 0.9921 0.00509 0.8794 0.9062 0.01614 ] Network output: [ 0.002503 -0.03917 0.9981 -0.0002057 9.235e-05 1.035 -0.000155 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1057 0.3109 0.1857 0.9851 0.9941 0.192 0.4759 0.8887 0.725 ] Network output: [ 0.009799 -0.04519 1.005 0.0001196 -5.369e-05 1.021 9.013e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09736 0.08826 0.178 0.2199 0.9874 0.9921 0.09741 0.8138 0.8882 0.314 ] Network output: [ -0.01212 0.04502 1.004 0.0001142 -5.128e-05 0.9755 8.609e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09922 0.1758 0.207 0.9859 0.9917 0.1009 0.7478 0.8699 0.2491 ] Network output: [ 0.001765 0.9983 -0.002399 1.844e-05 -8.277e-06 1.001 1.389e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001521 Epoch 6091 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01493 0.9954 0.9839 7.17e-06 -3.219e-06 -0.009099 5.404e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.002866 -0.01085 0.008123 0.9696 0.974 0.005983 0.8474 0.8358 0.0222 ] Network output: [ 0.9956 0.03231 0.001206 -5.367e-05 2.409e-05 -0.02487 -4.045e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02028 -0.2105 0.2056 0.9836 0.9933 0.2025 0.4718 0.8819 0.7297 ] Network output: [ -0.01272 1.001 1.011 1.644e-06 -7.382e-07 0.01348 1.239e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00502 0.0008098 0.003982 0.005073 0.989 0.9921 0.00511 0.8795 0.906 0.01604 ] Network output: [ -0.002834 0.04302 0.9942 -0.0002187 9.817e-05 0.9676 -0.0001648 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.1059 0.3054 0.1699 0.9851 0.9941 0.1927 0.4769 0.8887 0.7255 ] Network output: [ 0.01176 -0.02883 1.001 0.000119 -5.344e-05 1.004 8.971e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09707 0.08792 0.1742 0.2154 0.9874 0.9921 0.09713 0.8126 0.8881 0.3117 ] Network output: [ -0.01083 0.04068 1.004 0.0001154 -5.183e-05 0.9778 8.701e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.09891 0.1745 0.2061 0.9858 0.9916 0.1006 0.7462 0.8699 0.2489 ] Network output: [ -0.0009627 0.9997 0.001442 1.664e-05 -7.468e-06 1.001 1.254e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001277 Epoch 6092 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01551 0.9862 0.9843 8.571e-06 -3.848e-06 -0.001444 6.459e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00316 -0.002866 -0.01081 0.008293 0.9696 0.974 0.005966 0.8472 0.8362 0.02225 ] Network output: [ 1 -0.02682 0.003761 -4.5e-05 2.02e-05 0.02217 -3.391e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.181 -0.02108 -0.2074 0.2155 0.9836 0.9933 0.2016 0.4704 0.8823 0.7305 ] Network output: [ -0.01273 0.9982 1.011 2.145e-06 -9.63e-07 0.01652 1.617e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005002 0.0008151 0.004153 0.005412 0.989 0.9921 0.005091 0.8794 0.9062 0.01613 ] Network output: [ 0.002505 -0.03924 0.9981 -0.0002055 9.228e-05 1.035 -0.0001549 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1056 0.3109 0.1857 0.9851 0.9941 0.192 0.4758 0.8886 0.7249 ] Network output: [ 0.009793 -0.04527 1.005 0.0001195 -5.366e-05 1.021 9.008e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09736 0.08826 0.1781 0.2199 0.9874 0.9921 0.09742 0.8137 0.8882 0.314 ] Network output: [ -0.01211 0.0451 1.004 0.0001142 -5.126e-05 0.9755 8.605e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.0992 0.1758 0.207 0.9859 0.9917 0.1009 0.7478 0.8699 0.2491 ] Network output: [ 0.001769 0.9983 -0.002408 1.841e-05 -8.266e-06 1.001 1.388e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001526 Epoch 6093 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01492 0.9954 0.9839 7.173e-06 -3.22e-06 -0.009098 5.406e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.002867 -0.01085 0.008121 0.9696 0.974 0.005983 0.8474 0.8358 0.02219 ] Network output: [ 0.9956 0.03237 0.0012 -5.369e-05 2.41e-05 -0.02493 -4.046e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02031 -0.2105 0.2055 0.9836 0.9933 0.2025 0.4718 0.8819 0.7297 ] Network output: [ -0.01272 1.001 1.011 1.659e-06 -7.448e-07 0.01348 1.25e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005021 0.0008086 0.003983 0.005071 0.989 0.9921 0.00511 0.8795 0.906 0.01604 ] Network output: [ -0.002842 0.0431 0.9942 -0.0002185 9.81e-05 0.9675 -0.0001647 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.1059 0.3054 0.1698 0.9851 0.9941 0.1927 0.4768 0.8887 0.7255 ] Network output: [ 0.01176 -0.02888 1.001 0.000119 -5.341e-05 1.005 8.966e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09708 0.08792 0.1742 0.2154 0.9874 0.9921 0.09714 0.8126 0.888 0.3117 ] Network output: [ -0.01083 0.04075 1.004 0.0001154 -5.18e-05 0.9778 8.696e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.0989 0.1744 0.2061 0.9858 0.9916 0.1006 0.7461 0.8699 0.2489 ] Network output: [ -0.0009664 0.9997 0.001443 1.661e-05 -7.456e-06 1.001 1.252e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001282 Epoch 6094 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01551 0.9862 0.9843 8.575e-06 -3.849e-06 -0.001429 6.462e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00316 -0.002866 -0.01081 0.008291 0.9696 0.974 0.005966 0.8472 0.8362 0.02224 ] Network output: [ 1 -0.02688 0.00376 -4.501e-05 2.021e-05 0.02221 -3.392e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.02111 -0.2074 0.2155 0.9836 0.9933 0.2016 0.4704 0.8823 0.7305 ] Network output: [ -0.01273 0.9982 1.011 2.161e-06 -9.699e-07 0.01653 1.628e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005003 0.0008139 0.004154 0.005411 0.989 0.9921 0.005092 0.8794 0.9062 0.01613 ] Network output: [ 0.002506 -0.03931 0.9982 -0.0002054 9.22e-05 1.035 -0.0001548 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1056 0.3109 0.1857 0.9851 0.9941 0.192 0.4758 0.8886 0.7249 ] Network output: [ 0.009787 -0.04535 1.005 0.0001195 -5.363e-05 1.021 9.002e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09737 0.08826 0.1781 0.2199 0.9874 0.9921 0.09743 0.8137 0.8881 0.314 ] Network output: [ -0.01211 0.04517 1.004 0.0001141 -5.123e-05 0.9754 8.6e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1009 0.09919 0.1757 0.207 0.9859 0.9917 0.1009 0.7477 0.8699 0.249 ] Network output: [ 0.001772 0.9983 -0.002417 1.839e-05 -8.255e-06 1.001 1.386e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001531 Epoch 6095 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01492 0.9954 0.9839 7.175e-06 -3.221e-06 -0.009097 5.407e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.002867 -0.01085 0.008119 0.9696 0.974 0.005983 0.8474 0.8358 0.02219 ] Network output: [ 0.9956 0.03243 0.001193 -5.37e-05 2.411e-05 -0.02498 -4.047e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02034 -0.2104 0.2055 0.9836 0.9933 0.2025 0.4718 0.8819 0.7296 ] Network output: [ -0.01271 1.001 1.011 1.673e-06 -7.512e-07 0.01348 1.261e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005021 0.0008074 0.003984 0.005069 0.989 0.9921 0.005111 0.8795 0.9059 0.01604 ] Network output: [ -0.00285 0.04319 0.9943 -0.0002184 9.804e-05 0.9674 -0.0001646 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.1058 0.3054 0.1697 0.9851 0.9941 0.1927 0.4768 0.8886 0.7254 ] Network output: [ 0.01176 -0.02893 1.001 0.0001189 -5.338e-05 1.005 8.96e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09709 0.08792 0.1742 0.2154 0.9874 0.9921 0.09715 0.8125 0.888 0.3117 ] Network output: [ -0.01082 0.04082 1.004 0.0001153 -5.178e-05 0.9777 8.692e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.09888 0.1744 0.206 0.9858 0.9916 0.1006 0.7461 0.8698 0.2489 ] Network output: [ -0.0009701 0.9997 0.001443 1.658e-05 -7.445e-06 1.001 1.25e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001286 Epoch 6096 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0155 0.9861 0.9843 8.578e-06 -3.851e-06 -0.001413 6.465e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00316 -0.002867 -0.0108 0.008289 0.9696 0.974 0.005966 0.8472 0.8362 0.02224 ] Network output: [ 1 -0.02693 0.003759 -4.502e-05 2.021e-05 0.02225 -3.393e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.02114 -0.2074 0.2155 0.9836 0.9933 0.2016 0.4704 0.8823 0.7304 ] Network output: [ -0.01273 0.9981 1.011 2.176e-06 -9.767e-07 0.01654 1.64e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005003 0.0008127 0.004155 0.005409 0.989 0.9921 0.005093 0.8794 0.9062 0.01613 ] Network output: [ 0.002507 -0.03938 0.9982 -0.0002052 9.213e-05 1.035 -0.0001547 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1055 0.311 0.1856 0.9851 0.9941 0.192 0.4758 0.8886 0.7248 ] Network output: [ 0.00978 -0.04543 1.005 0.0001194 -5.359e-05 1.021 8.997e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09738 0.08826 0.1781 0.2199 0.9874 0.9921 0.09744 0.8137 0.8881 0.314 ] Network output: [ -0.0121 0.04525 1.004 0.0001141 -5.12e-05 0.9754 8.596e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09917 0.1757 0.2069 0.9859 0.9917 0.1009 0.7477 0.8699 0.249 ] Network output: [ 0.001775 0.9983 -0.002426 1.836e-05 -8.245e-06 1.001 1.384e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001535 Epoch 6097 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01492 0.9954 0.9839 7.177e-06 -3.222e-06 -0.009096 5.408e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.002867 -0.01084 0.008117 0.9696 0.974 0.005983 0.8474 0.8358 0.02219 ] Network output: [ 0.9956 0.03248 0.001187 -5.372e-05 2.412e-05 -0.02503 -4.048e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02037 -0.2104 0.2054 0.9836 0.9933 0.2025 0.4718 0.8819 0.7296 ] Network output: [ -0.01271 1.001 1.011 1.687e-06 -7.574e-07 0.01349 1.272e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005022 0.0008062 0.003985 0.005067 0.989 0.9921 0.005112 0.8795 0.9059 0.01603 ] Network output: [ -0.002858 0.04328 0.9943 -0.0002182 9.797e-05 0.9672 -0.0001645 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.1058 0.3054 0.1696 0.9851 0.9941 0.1927 0.4768 0.8886 0.7254 ] Network output: [ 0.01175 -0.02898 1.001 0.0001188 -5.334e-05 1.005 8.955e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0971 0.08793 0.1742 0.2154 0.9874 0.9921 0.09716 0.8125 0.888 0.3117 ] Network output: [ -0.01082 0.04088 1.004 0.0001153 -5.175e-05 0.9777 8.687e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.09887 0.1744 0.206 0.9858 0.9916 0.1006 0.7461 0.8698 0.2488 ] Network output: [ -0.0009739 0.9997 0.001443 1.656e-05 -7.433e-06 1.001 1.248e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001291 Epoch 6098 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0155 0.9861 0.9843 8.581e-06 -3.852e-06 -0.001398 6.467e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00316 -0.002867 -0.0108 0.008287 0.9696 0.974 0.005967 0.8472 0.8362 0.02224 ] Network output: [ 1 -0.02698 0.003758 -4.503e-05 2.021e-05 0.02229 -3.393e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.02117 -0.2073 0.2155 0.9836 0.9933 0.2016 0.4704 0.8823 0.7304 ] Network output: [ -0.01273 0.9981 1.011 2.19e-06 -9.832e-07 0.01654 1.651e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005004 0.0008115 0.004156 0.005408 0.989 0.9921 0.005094 0.8794 0.9062 0.01612 ] Network output: [ 0.002509 -0.03945 0.9983 -0.0002051 9.206e-05 1.035 -0.0001545 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1055 0.311 0.1856 0.9851 0.9941 0.192 0.4758 0.8886 0.7248 ] Network output: [ 0.009774 -0.04551 1.005 0.0001193 -5.356e-05 1.021 8.992e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09739 0.08826 0.1781 0.2199 0.9874 0.9921 0.09745 0.8136 0.8881 0.314 ] Network output: [ -0.0121 0.04533 1.004 0.000114 -5.118e-05 0.9753 8.591e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09916 0.1757 0.2069 0.9859 0.9917 0.1008 0.7476 0.8698 0.249 ] Network output: [ 0.001778 0.9983 -0.002435 1.834e-05 -8.234e-06 1.001 1.382e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00154 Epoch 6099 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01492 0.9954 0.9839 7.178e-06 -3.222e-06 -0.009095 5.409e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.002868 -0.01084 0.008115 0.9696 0.974 0.005984 0.8474 0.8358 0.02218 ] Network output: [ 0.9956 0.03254 0.001181 -5.373e-05 2.412e-05 -0.02508 -4.049e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.0204 -0.2104 0.2054 0.9836 0.9933 0.2024 0.4718 0.8819 0.7295 ] Network output: [ -0.01271 1.001 1.011 1.701e-06 -7.635e-07 0.01349 1.282e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005023 0.0008049 0.003985 0.005065 0.989 0.9921 0.005113 0.8795 0.9059 0.01603 ] Network output: [ -0.002866 0.04336 0.9944 -0.0002181 9.79e-05 0.9671 -0.0001643 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.1057 0.3055 0.1696 0.9851 0.9941 0.1927 0.4768 0.8886 0.7253 ] Network output: [ 0.01175 -0.02902 1.001 0.0001187 -5.331e-05 1.005 8.949e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09711 0.08793 0.1742 0.2154 0.9874 0.9921 0.09717 0.8124 0.888 0.3117 ] Network output: [ -0.01081 0.04095 1.004 0.0001152 -5.172e-05 0.9776 8.682e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1005 0.09885 0.1744 0.206 0.9858 0.9916 0.1006 0.746 0.8698 0.2488 ] Network output: [ -0.0009776 0.9997 0.001444 1.653e-05 -7.421e-06 1.001 1.246e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001295 Epoch 6100 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0155 0.9861 0.9843 8.583e-06 -3.853e-06 -0.001383 6.469e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00316 -0.002868 -0.0108 0.008285 0.9696 0.974 0.005967 0.8472 0.8362 0.02223 ] Network output: [ 1 -0.02704 0.003757 -4.504e-05 2.022e-05 0.02233 -3.394e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.0212 -0.2073 0.2154 0.9836 0.9933 0.2016 0.4703 0.8823 0.7303 ] Network output: [ -0.01272 0.9981 1.011 2.204e-06 -9.896e-07 0.01655 1.661e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005005 0.0008103 0.004157 0.005406 0.989 0.9921 0.005095 0.8794 0.9061 0.01612 ] Network output: [ 0.00251 -0.03952 0.9984 -0.0002049 9.198e-05 1.035 -0.0001544 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1054 0.311 0.1855 0.9851 0.9941 0.192 0.4758 0.8886 0.7247 ] Network output: [ 0.009768 -0.04559 1.005 0.0001192 -5.353e-05 1.022 8.986e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0974 0.08826 0.1781 0.2199 0.9874 0.9921 0.09746 0.8136 0.8881 0.3141 ] Network output: [ -0.01209 0.0454 1.004 0.0001139 -5.115e-05 0.9752 8.586e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09914 0.1757 0.2069 0.9859 0.9917 0.1008 0.7476 0.8698 0.249 ] Network output: [ 0.001782 0.9983 -0.002443 1.832e-05 -8.223e-06 1.001 1.38e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001544 Epoch 6101 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01491 0.9954 0.9839 7.179e-06 -3.223e-06 -0.009094 5.41e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.002868 -0.01084 0.008113 0.9696 0.974 0.005984 0.8474 0.8358 0.02218 ] Network output: [ 0.9956 0.0326 0.001174 -5.375e-05 2.413e-05 -0.02513 -4.05e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02043 -0.2104 0.2054 0.9836 0.9933 0.2024 0.4718 0.8819 0.7295 ] Network output: [ -0.01271 1.001 1.011 1.714e-06 -7.695e-07 0.01349 1.292e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005024 0.0008037 0.003986 0.005063 0.989 0.9921 0.005114 0.8795 0.9059 0.01603 ] Network output: [ -0.002874 0.04344 0.9944 -0.0002179 9.783e-05 0.967 -0.0001642 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.1057 0.3055 0.1695 0.9851 0.9941 0.1927 0.4768 0.8886 0.7253 ] Network output: [ 0.01174 -0.02907 1.001 0.0001187 -5.327e-05 1.005 8.943e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09712 0.08793 0.1742 0.2154 0.9874 0.9921 0.09717 0.8124 0.8879 0.3117 ] Network output: [ -0.01081 0.04102 1.004 0.0001151 -5.169e-05 0.9776 8.678e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1005 0.09884 0.1743 0.2059 0.9858 0.9916 0.1005 0.746 0.8698 0.2488 ] Network output: [ -0.0009812 0.9997 0.001444 1.651e-05 -7.41e-06 1.001 1.244e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0013 Epoch 6102 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0155 0.9861 0.9843 8.586e-06 -3.854e-06 -0.001368 6.47e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00316 -0.002868 -0.0108 0.008283 0.9696 0.974 0.005967 0.8472 0.8362 0.02223 ] Network output: [ 1 -0.02709 0.003756 -4.505e-05 2.022e-05 0.02237 -3.395e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.02123 -0.2073 0.2154 0.9836 0.9933 0.2016 0.4703 0.8823 0.7303 ] Network output: [ -0.01272 0.9981 1.011 2.218e-06 -9.958e-07 0.01656 1.672e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005006 0.0008091 0.004158 0.005405 0.989 0.9921 0.005096 0.8794 0.9061 0.01612 ] Network output: [ 0.002511 -0.03959 0.9984 -0.0002047 9.191e-05 1.035 -0.0001543 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1054 0.3111 0.1855 0.9851 0.9941 0.1919 0.4758 0.8886 0.7247 ] Network output: [ 0.009762 -0.04566 1.005 0.0001192 -5.35e-05 1.022 8.981e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09741 0.08826 0.1782 0.2198 0.9874 0.9921 0.09747 0.8135 0.888 0.3141 ] Network output: [ -0.01209 0.04548 1.004 0.0001139 -5.112e-05 0.9752 8.582e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09913 0.1757 0.2068 0.9859 0.9917 0.1008 0.7476 0.8698 0.2489 ] Network output: [ 0.001785 0.9983 -0.002452 1.829e-05 -8.213e-06 1.001 1.379e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001549 Epoch 6103 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01491 0.9954 0.9839 7.18e-06 -3.223e-06 -0.009094 5.411e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.002869 -0.01083 0.008111 0.9696 0.974 0.005984 0.8474 0.8358 0.02217 ] Network output: [ 0.9956 0.03265 0.001168 -5.376e-05 2.413e-05 -0.02518 -4.051e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02046 -0.2104 0.2053 0.9836 0.9933 0.2024 0.4718 0.8819 0.7294 ] Network output: [ -0.0127 1.001 1.011 1.727e-06 -7.752e-07 0.01349 1.301e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005025 0.0008025 0.003987 0.005061 0.989 0.9921 0.005115 0.8795 0.9059 0.01603 ] Network output: [ -0.002882 0.04352 0.9945 -0.0002178 9.776e-05 0.9669 -0.0001641 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.1056 0.3055 0.1694 0.9851 0.9941 0.1927 0.4768 0.8886 0.7252 ] Network output: [ 0.01174 -0.02912 1.001 0.0001186 -5.324e-05 1.005 8.937e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09712 0.08793 0.1743 0.2154 0.9874 0.9921 0.09718 0.8123 0.8879 0.3117 ] Network output: [ -0.0108 0.04108 1.003 0.0001151 -5.167e-05 0.9775 8.673e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1005 0.09882 0.1743 0.2059 0.9858 0.9916 0.1005 0.7459 0.8697 0.2487 ] Network output: [ -0.0009849 0.9997 0.001445 1.648e-05 -7.398e-06 1.001 1.242e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001304 Epoch 6104 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0155 0.9861 0.9843 8.587e-06 -3.855e-06 -0.001354 6.472e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00316 -0.002868 -0.01079 0.008281 0.9696 0.974 0.005967 0.8472 0.8362 0.02222 ] Network output: [ 1 -0.02714 0.003755 -4.505e-05 2.023e-05 0.02241 -3.395e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.02126 -0.2072 0.2154 0.9836 0.9933 0.2016 0.4703 0.8822 0.7302 ] Network output: [ -0.01272 0.9981 1.011 2.232e-06 -1.002e-06 0.01656 1.682e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005007 0.0008079 0.004159 0.005403 0.989 0.9921 0.005096 0.8794 0.9061 0.01612 ] Network output: [ 0.002513 -0.03965 0.9985 -0.0002046 9.183e-05 1.035 -0.0001542 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1913 0.1053 0.3111 0.1855 0.9851 0.9941 0.1919 0.4757 0.8886 0.7246 ] Network output: [ 0.009756 -0.04574 1.005 0.0001191 -5.346e-05 1.022 8.975e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09742 0.08826 0.1782 0.2198 0.9874 0.9921 0.09747 0.8135 0.888 0.3141 ] Network output: [ -0.01209 0.04555 1.004 0.0001138 -5.109e-05 0.9751 8.577e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09911 0.1756 0.2068 0.9859 0.9917 0.1008 0.7475 0.8698 0.2489 ] Network output: [ 0.001788 0.9983 -0.00246 1.827e-05 -8.202e-06 1.001 1.377e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001553 Epoch 6105 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01491 0.9954 0.9839 7.18e-06 -3.223e-06 -0.009093 5.411e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.002869 -0.01083 0.008108 0.9696 0.974 0.005985 0.8474 0.8358 0.02217 ] Network output: [ 0.9956 0.03271 0.001162 -5.377e-05 2.414e-05 -0.02523 -4.052e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02049 -0.2103 0.2053 0.9836 0.9933 0.2024 0.4718 0.8819 0.7294 ] Network output: [ -0.0127 1.001 1.011 1.739e-06 -7.809e-07 0.01349 1.311e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005026 0.0008013 0.003988 0.005059 0.989 0.9921 0.005116 0.8795 0.9059 0.01602 ] Network output: [ -0.00289 0.0436 0.9945 -0.0002176 9.769e-05 0.9668 -0.000164 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.1055 0.3055 0.1694 0.9851 0.9941 0.1927 0.4768 0.8886 0.7252 ] Network output: [ 0.01174 -0.02916 1.001 0.0001185 -5.321e-05 1.005 8.932e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09713 0.08793 0.1743 0.2153 0.9874 0.9921 0.09719 0.8123 0.8879 0.3117 ] Network output: [ -0.01079 0.04115 1.003 0.000115 -5.164e-05 0.9775 8.668e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1005 0.0988 0.1743 0.2059 0.9858 0.9916 0.1005 0.7459 0.8697 0.2487 ] Network output: [ -0.0009885 0.9997 0.001446 1.645e-05 -7.387e-06 1.001 1.24e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001309 Epoch 6106 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01549 0.9861 0.9843 8.589e-06 -3.856e-06 -0.00134 6.473e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00316 -0.002869 -0.01079 0.00828 0.9696 0.974 0.005968 0.8472 0.8362 0.02222 ] Network output: [ 1 -0.02719 0.003754 -4.506e-05 2.023e-05 0.02245 -3.396e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.02129 -0.2072 0.2154 0.9836 0.9933 0.2016 0.4703 0.8822 0.7302 ] Network output: [ -0.01271 0.9981 1.011 2.245e-06 -1.008e-06 0.01657 1.692e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005008 0.0008068 0.00416 0.005402 0.989 0.9921 0.005097 0.8794 0.9061 0.01611 ] Network output: [ 0.002514 -0.03971 0.9985 -0.0002044 9.176e-05 1.035 -0.000154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1913 0.1053 0.3111 0.1854 0.9851 0.9941 0.1919 0.4757 0.8886 0.7246 ] Network output: [ 0.00975 -0.04582 1.005 0.000119 -5.343e-05 1.022 8.97e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09742 0.08826 0.1782 0.2198 0.9874 0.9921 0.09748 0.8135 0.888 0.3141 ] Network output: [ -0.01208 0.04562 1.004 0.0001137 -5.107e-05 0.9751 8.572e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09909 0.1756 0.2068 0.9859 0.9917 0.1008 0.7475 0.8697 0.2489 ] Network output: [ 0.001791 0.9983 -0.002469 1.825e-05 -8.192e-06 1.001 1.375e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001558 Epoch 6107 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0149 0.9954 0.9839 7.18e-06 -3.223e-06 -0.009092 5.411e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.002869 -0.01083 0.008106 0.9696 0.974 0.005985 0.8474 0.8358 0.02216 ] Network output: [ 0.9956 0.03276 0.001156 -5.378e-05 2.415e-05 -0.02528 -4.053e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02051 -0.2103 0.2052 0.9836 0.9933 0.2024 0.4717 0.8819 0.7293 ] Network output: [ -0.0127 1.001 1.011 1.752e-06 -7.864e-07 0.01349 1.32e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005027 0.0008001 0.003988 0.005057 0.989 0.9921 0.005117 0.8795 0.9059 0.01602 ] Network output: [ -0.002897 0.04368 0.9946 -0.0002175 9.763e-05 0.9667 -0.0001639 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.1055 0.3055 0.1693 0.9851 0.9941 0.1927 0.4768 0.8886 0.7251 ] Network output: [ 0.01173 -0.02921 1.001 0.0001184 -5.317e-05 1.005 8.926e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09714 0.08793 0.1743 0.2153 0.9874 0.9921 0.0972 0.8122 0.8879 0.3117 ] Network output: [ -0.01079 0.04121 1.003 0.000115 -5.161e-05 0.9774 8.664e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1005 0.09879 0.1743 0.2058 0.9858 0.9916 0.1005 0.7459 0.8697 0.2487 ] Network output: [ -0.0009921 0.9997 0.001446 1.643e-05 -7.375e-06 1.001 1.238e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001313 Epoch 6108 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01549 0.986 0.9843 8.59e-06 -3.856e-06 -0.001326 6.474e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003161 -0.002869 -0.01079 0.008278 0.9696 0.974 0.005968 0.8472 0.8361 0.02221 ] Network output: [ 1 -0.02724 0.003753 -4.507e-05 2.023e-05 0.02249 -3.397e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.02132 -0.2072 0.2154 0.9836 0.9933 0.2015 0.4703 0.8822 0.7301 ] Network output: [ -0.01271 0.9981 1.011 2.258e-06 -1.014e-06 0.01657 1.702e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005008 0.0008056 0.004161 0.0054 0.989 0.9921 0.005098 0.8794 0.9061 0.01611 ] Network output: [ 0.002515 -0.03978 0.9986 -0.0002042 9.169e-05 1.035 -0.0001539 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1913 0.1052 0.3112 0.1854 0.9851 0.9941 0.1919 0.4757 0.8886 0.7245 ] Network output: [ 0.009744 -0.04589 1.005 0.0001189 -5.34e-05 1.022 8.964e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09743 0.08826 0.1782 0.2198 0.9874 0.9921 0.09749 0.8134 0.888 0.3141 ] Network output: [ -0.01208 0.0457 1.004 0.0001137 -5.104e-05 0.975 8.568e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1008 0.09908 0.1756 0.2067 0.9859 0.9917 0.1008 0.7474 0.8697 0.2488 ] Network output: [ 0.001794 0.9983 -0.002477 1.822e-05 -8.181e-06 1.001 1.373e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001562 Epoch 6109 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0149 0.9954 0.9839 7.18e-06 -3.223e-06 -0.009091 5.411e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.00287 -0.01083 0.008104 0.9696 0.974 0.005985 0.8474 0.8358 0.02216 ] Network output: [ 0.9956 0.03281 0.00115 -5.379e-05 2.415e-05 -0.02533 -4.054e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02054 -0.2103 0.2052 0.9836 0.9933 0.2024 0.4717 0.8818 0.7293 ] Network output: [ -0.01269 1.001 1.011 1.763e-06 -7.917e-07 0.01349 1.329e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005027 0.000799 0.003989 0.005055 0.989 0.9921 0.005117 0.8794 0.9059 0.01602 ] Network output: [ -0.002905 0.04376 0.9946 -0.0002173 9.756e-05 0.9666 -0.0001638 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.1054 0.3056 0.1692 0.9851 0.9941 0.1927 0.4767 0.8886 0.7251 ] Network output: [ 0.01173 -0.02925 1.001 0.0001184 -5.314e-05 1.005 8.92e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09714 0.08793 0.1743 0.2153 0.9874 0.9921 0.0972 0.8122 0.8878 0.3117 ] Network output: [ -0.01078 0.04127 1.003 0.0001149 -5.158e-05 0.9774 8.659e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1005 0.09877 0.1742 0.2058 0.9858 0.9916 0.1005 0.7458 0.8696 0.2486 ] Network output: [ -0.0009957 0.9997 0.001447 1.64e-05 -7.364e-06 1.001 1.236e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001317 Epoch 6110 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01549 0.986 0.9843 8.591e-06 -3.857e-06 -0.001312 6.474e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003161 -0.002869 -0.01078 0.008276 0.9696 0.974 0.005968 0.8472 0.8361 0.02221 ] Network output: [ 1 -0.02729 0.003752 -4.508e-05 2.024e-05 0.02252 -3.397e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.02135 -0.2072 0.2153 0.9836 0.9933 0.2015 0.4703 0.8822 0.7301 ] Network output: [ -0.01271 0.998 1.011 2.27e-06 -1.019e-06 0.01658 1.711e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005009 0.0008044 0.004163 0.005399 0.989 0.9921 0.005099 0.8793 0.9061 0.01611 ] Network output: [ 0.002516 -0.03984 0.9986 -0.0002041 9.161e-05 1.035 -0.0001538 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1913 0.1051 0.3112 0.1853 0.9851 0.9941 0.1919 0.4757 0.8886 0.7245 ] Network output: [ 0.009738 -0.04597 1.005 0.0001189 -5.336e-05 1.022 8.958e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09744 0.08826 0.1782 0.2198 0.9874 0.9921 0.0975 0.8134 0.8879 0.3141 ] Network output: [ -0.01207 0.04577 1.004 0.0001136 -5.101e-05 0.975 8.563e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09906 0.1756 0.2067 0.9859 0.9917 0.1007 0.7474 0.8697 0.2488 ] Network output: [ 0.001797 0.9983 -0.002485 1.82e-05 -8.171e-06 1.001 1.372e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001567 Epoch 6111 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0149 0.9954 0.9839 7.18e-06 -3.223e-06 -0.009091 5.411e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.00287 -0.01082 0.008102 0.9696 0.974 0.005985 0.8474 0.8358 0.02215 ] Network output: [ 0.9956 0.03286 0.001144 -5.38e-05 2.415e-05 -0.02538 -4.055e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02057 -0.2103 0.2052 0.9836 0.9933 0.2024 0.4717 0.8818 0.7292 ] Network output: [ -0.01269 1.001 1.011 1.775e-06 -7.969e-07 0.01349 1.338e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005028 0.0007978 0.00399 0.005052 0.989 0.9921 0.005118 0.8794 0.9059 0.01601 ] Network output: [ -0.002912 0.04383 0.9947 -0.0002171 9.748e-05 0.9664 -0.0001636 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.1054 0.3056 0.1692 0.9851 0.9941 0.1926 0.4767 0.8886 0.725 ] Network output: [ 0.01172 -0.0293 1.001 0.0001183 -5.31e-05 1.005 8.914e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09715 0.08792 0.1743 0.2153 0.9874 0.9921 0.09721 0.8122 0.8878 0.3117 ] Network output: [ -0.01078 0.04133 1.003 0.0001148 -5.155e-05 0.9773 8.654e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1004 0.09875 0.1742 0.2058 0.9858 0.9916 0.1005 0.7458 0.8696 0.2486 ] Network output: [ -0.0009992 0.9997 0.001447 1.638e-05 -7.353e-06 1.001 1.234e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001321 Epoch 6112 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01549 0.986 0.9843 8.591e-06 -3.857e-06 -0.001299 6.475e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003161 -0.00287 -0.01078 0.008274 0.9696 0.974 0.005968 0.8472 0.8361 0.0222 ] Network output: [ 1 -0.02734 0.003751 -4.508e-05 2.024e-05 0.02256 -3.398e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.02137 -0.2071 0.2153 0.9836 0.9933 0.2015 0.4703 0.8822 0.73 ] Network output: [ -0.01271 0.998 1.011 2.282e-06 -1.025e-06 0.01659 1.72e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00501 0.0008033 0.004164 0.005397 0.989 0.9921 0.0051 0.8793 0.9061 0.01611 ] Network output: [ 0.002517 -0.0399 0.9987 -0.0002039 9.154e-05 1.035 -0.0001537 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1913 0.1051 0.3112 0.1853 0.9851 0.9941 0.1919 0.4757 0.8885 0.7244 ] Network output: [ 0.009732 -0.04604 1.005 0.0001188 -5.333e-05 1.022 8.953e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09744 0.08826 0.1783 0.2198 0.9874 0.9921 0.0975 0.8133 0.8879 0.3141 ] Network output: [ -0.01207 0.04584 1.004 0.0001136 -5.098e-05 0.9749 8.558e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09904 0.1755 0.2067 0.9859 0.9917 0.1007 0.7474 0.8696 0.2488 ] Network output: [ 0.001799 0.9983 -0.002493 1.818e-05 -8.16e-06 1.001 1.37e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001571 Epoch 6113 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01489 0.9954 0.984 7.179e-06 -3.223e-06 -0.00909 5.41e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.00287 -0.01082 0.0081 0.9696 0.974 0.005986 0.8474 0.8357 0.02215 ] Network output: [ 0.9956 0.03291 0.001138 -5.381e-05 2.416e-05 -0.02542 -4.056e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.0206 -0.2103 0.2051 0.9836 0.9933 0.2024 0.4717 0.8818 0.7292 ] Network output: [ -0.01269 1.001 1.011 1.786e-06 -8.019e-07 0.0135 1.346e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005029 0.0007966 0.003991 0.00505 0.989 0.9921 0.005119 0.8794 0.9058 0.01601 ] Network output: [ -0.00292 0.04391 0.9947 -0.000217 9.741e-05 0.9663 -0.0001635 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.1053 0.3056 0.1691 0.9851 0.9941 0.1926 0.4767 0.8886 0.725 ] Network output: [ 0.01172 -0.02934 1.001 0.0001182 -5.307e-05 1.005 8.908e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09716 0.08792 0.1743 0.2153 0.9874 0.9921 0.09722 0.8121 0.8878 0.3117 ] Network output: [ -0.01077 0.0414 1.003 0.0001148 -5.152e-05 0.9773 8.649e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1004 0.09873 0.1742 0.2057 0.9858 0.9916 0.1004 0.7457 0.8696 0.2486 ] Network output: [ -0.001003 0.9997 0.001448 1.635e-05 -7.341e-06 1.001 1.232e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001325 Epoch 6114 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01549 0.986 0.9844 8.591e-06 -3.857e-06 -0.001285 6.475e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003161 -0.00287 -0.01078 0.008272 0.9696 0.974 0.005969 0.8472 0.8361 0.0222 ] Network output: [ 1 -0.02738 0.00375 -4.509e-05 2.024e-05 0.02259 -3.398e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.0214 -0.2071 0.2153 0.9836 0.9933 0.2015 0.4702 0.8822 0.73 ] Network output: [ -0.0127 0.998 1.011 2.294e-06 -1.03e-06 0.01659 1.729e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005011 0.0008021 0.004165 0.005396 0.989 0.9921 0.005101 0.8793 0.9061 0.0161 ] Network output: [ 0.002518 -0.03995 0.9987 -0.0002037 9.146e-05 1.035 -0.0001535 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1913 0.105 0.3112 0.1853 0.9851 0.9941 0.1919 0.4757 0.8885 0.7244 ] Network output: [ 0.009726 -0.04612 1.005 0.0001187 -5.33e-05 1.022 8.947e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09745 0.08826 0.1783 0.2198 0.9874 0.9921 0.09751 0.8133 0.8879 0.3141 ] Network output: [ -0.01206 0.04591 1.004 0.0001135 -5.095e-05 0.9748 8.553e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09903 0.1755 0.2066 0.9859 0.9917 0.1007 0.7473 0.8696 0.2487 ] Network output: [ 0.001802 0.9983 -0.0025 1.815e-05 -8.15e-06 1.001 1.368e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001575 Epoch 6115 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01489 0.9954 0.984 7.178e-06 -3.222e-06 -0.009089 5.41e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.002871 -0.01082 0.008098 0.9696 0.974 0.005986 0.8474 0.8357 0.02214 ] Network output: [ 0.9956 0.03296 0.001132 -5.382e-05 2.416e-05 -0.02547 -4.056e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02062 -0.2102 0.2051 0.9836 0.9933 0.2024 0.4717 0.8818 0.7292 ] Network output: [ -0.01269 1.001 1.011 1.797e-06 -8.069e-07 0.0135 1.354e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00503 0.0007955 0.003991 0.005048 0.989 0.9921 0.00512 0.8794 0.9058 0.01601 ] Network output: [ -0.002927 0.04398 0.9948 -0.0002168 9.734e-05 0.9662 -0.0001634 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.1053 0.3056 0.1691 0.9851 0.9941 0.1926 0.4767 0.8885 0.7249 ] Network output: [ 0.01171 -0.02939 1.001 0.0001181 -5.303e-05 1.005 8.903e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09716 0.08792 0.1743 0.2153 0.9874 0.9921 0.09722 0.8121 0.8877 0.3117 ] Network output: [ -0.01077 0.04146 1.003 0.0001147 -5.149e-05 0.9772 8.644e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1004 0.09872 0.1742 0.2057 0.9858 0.9916 0.1004 0.7457 0.8696 0.2486 ] Network output: [ -0.001006 0.9997 0.001448 1.633e-05 -7.33e-06 1.001 1.231e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001329 Epoch 6116 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01548 0.986 0.9844 8.591e-06 -3.857e-06 -0.001273 6.474e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003161 -0.00287 -0.01078 0.00827 0.9696 0.974 0.005969 0.8472 0.8361 0.0222 ] Network output: [ 1 -0.02743 0.003749 -4.51e-05 2.025e-05 0.02262 -3.399e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.02143 -0.2071 0.2153 0.9836 0.9933 0.2015 0.4702 0.8822 0.7299 ] Network output: [ -0.0127 0.998 1.011 2.306e-06 -1.035e-06 0.0166 1.738e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005012 0.000801 0.004166 0.005394 0.989 0.9921 0.005101 0.8793 0.9061 0.0161 ] Network output: [ 0.002518 -0.04001 0.9988 -0.0002036 9.139e-05 1.035 -0.0001534 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1913 0.105 0.3113 0.1852 0.9851 0.9941 0.1919 0.4756 0.8885 0.7243 ] Network output: [ 0.00972 -0.04619 1.005 0.0001186 -5.326e-05 1.022 8.941e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09746 0.08826 0.1783 0.2198 0.9874 0.9921 0.09752 0.8132 0.8879 0.3141 ] Network output: [ -0.01206 0.04598 1.004 0.0001134 -5.092e-05 0.9748 8.549e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09901 0.1755 0.2066 0.9859 0.9917 0.1007 0.7473 0.8696 0.2487 ] Network output: [ 0.001805 0.9983 -0.002508 1.813e-05 -8.139e-06 1.001 1.366e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001579 Epoch 6117 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01489 0.9954 0.984 7.177e-06 -3.222e-06 -0.009088 5.409e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.002871 -0.01082 0.008096 0.9696 0.974 0.005986 0.8474 0.8357 0.02214 ] Network output: [ 0.9956 0.03301 0.001127 -5.383e-05 2.417e-05 -0.02551 -4.057e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02065 -0.2102 0.2051 0.9836 0.9933 0.2024 0.4717 0.8818 0.7291 ] Network output: [ -0.01269 1.001 1.011 1.808e-06 -8.116e-07 0.0135 1.363e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005031 0.0007943 0.003992 0.005046 0.989 0.9921 0.005121 0.8794 0.9058 0.01601 ] Network output: [ -0.002933 0.04405 0.9948 -0.0002167 9.727e-05 0.9661 -0.0001633 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.1052 0.3056 0.169 0.9851 0.9941 0.1926 0.4767 0.8885 0.7249 ] Network output: [ 0.01171 -0.02944 1.001 0.0001181 -5.3e-05 1.005 8.897e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09717 0.08792 0.1743 0.2153 0.9874 0.9921 0.09723 0.812 0.8877 0.3117 ] Network output: [ -0.01076 0.04152 1.003 0.0001146 -5.147e-05 0.9772 8.64e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1004 0.0987 0.1741 0.2057 0.9858 0.9916 0.1004 0.7456 0.8695 0.2485 ] Network output: [ -0.00101 0.9997 0.001449 1.63e-05 -7.319e-06 1.001 1.229e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001333 Epoch 6118 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01548 0.986 0.9844 8.59e-06 -3.857e-06 -0.00126 6.474e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003161 -0.002871 -0.01077 0.008268 0.9696 0.974 0.005969 0.8472 0.8361 0.02219 ] Network output: [ 1 -0.02747 0.003748 -4.51e-05 2.025e-05 0.02266 -3.399e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02146 -0.207 0.2152 0.9836 0.9933 0.2015 0.4702 0.8822 0.7299 ] Network output: [ -0.0127 0.998 1.011 2.317e-06 -1.04e-06 0.0166 1.746e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005012 0.0007998 0.004167 0.005393 0.989 0.9921 0.005102 0.8793 0.906 0.0161 ] Network output: [ 0.002519 -0.04006 0.9988 -0.0002034 9.131e-05 1.035 -0.0001533 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1913 0.1049 0.3113 0.1852 0.9851 0.9941 0.1919 0.4756 0.8885 0.7243 ] Network output: [ 0.009714 -0.04626 1.005 0.0001186 -5.323e-05 1.023 8.936e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09746 0.08826 0.1783 0.2198 0.9874 0.9921 0.09752 0.8132 0.8878 0.3141 ] Network output: [ -0.01205 0.04604 1.004 0.0001134 -5.089e-05 0.9747 8.544e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09899 0.1755 0.2066 0.9859 0.9917 0.1007 0.7472 0.8696 0.2487 ] Network output: [ 0.001807 0.9983 -0.002515 1.811e-05 -8.129e-06 1.001 1.365e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001583 Epoch 6119 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01488 0.9954 0.984 7.175e-06 -3.221e-06 -0.009087 5.408e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.002871 -0.01081 0.008094 0.9696 0.974 0.005986 0.8474 0.8357 0.02214 ] Network output: [ 0.9956 0.03306 0.001121 -5.384e-05 2.417e-05 -0.02555 -4.057e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02068 -0.2102 0.205 0.9836 0.9933 0.2024 0.4717 0.8818 0.7291 ] Network output: [ -0.01268 1.001 1.011 1.818e-06 -8.163e-07 0.0135 1.37e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005032 0.0007932 0.003993 0.005044 0.989 0.9921 0.005122 0.8794 0.9058 0.016 ] Network output: [ -0.00294 0.04412 0.9949 -0.0002165 9.72e-05 0.966 -0.0001632 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.1052 0.3057 0.1689 0.9851 0.9941 0.1926 0.4767 0.8885 0.7248 ] Network output: [ 0.01171 -0.02948 1.001 0.000118 -5.296e-05 1.006 8.891e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09718 0.08792 0.1744 0.2152 0.9874 0.9921 0.09724 0.812 0.8877 0.3117 ] Network output: [ -0.01076 0.04158 1.003 0.0001146 -5.144e-05 0.9771 8.635e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1004 0.09868 0.1741 0.2056 0.9858 0.9916 0.1004 0.7456 0.8695 0.2485 ] Network output: [ -0.001013 0.9997 0.001449 1.628e-05 -7.308e-06 1.001 1.227e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001337 Epoch 6120 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01548 0.986 0.9844 8.59e-06 -3.856e-06 -0.001248 6.473e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003161 -0.002871 -0.01077 0.008266 0.9696 0.974 0.005969 0.8472 0.8361 0.02219 ] Network output: [ 1 -0.02751 0.003746 -4.511e-05 2.025e-05 0.02269 -3.399e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02149 -0.207 0.2152 0.9836 0.9933 0.2015 0.4702 0.8822 0.7299 ] Network output: [ -0.0127 0.998 1.011 2.328e-06 -1.045e-06 0.01661 1.754e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005013 0.0007987 0.004168 0.005391 0.989 0.9921 0.005103 0.8793 0.906 0.01609 ] Network output: [ 0.002519 -0.04011 0.9989 -0.0002032 9.124e-05 1.035 -0.0001532 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1913 0.1049 0.3113 0.1851 0.9851 0.9941 0.1919 0.4756 0.8885 0.7242 ] Network output: [ 0.009709 -0.04633 1.005 0.0001185 -5.32e-05 1.023 8.93e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09747 0.08826 0.1783 0.2198 0.9874 0.9921 0.09753 0.8132 0.8878 0.3141 ] Network output: [ -0.01205 0.04611 1.004 0.0001133 -5.087e-05 0.9747 8.539e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1007 0.09897 0.1754 0.2065 0.9859 0.9917 0.1007 0.7472 0.8695 0.2487 ] Network output: [ 0.00181 0.9983 -0.002523 1.808e-05 -8.118e-06 1.001 1.363e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001587 Epoch 6121 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01488 0.9954 0.984 7.174e-06 -3.22e-06 -0.009086 5.406e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.002872 -0.01081 0.008091 0.9696 0.974 0.005987 0.8474 0.8357 0.02213 ] Network output: [ 0.9956 0.0331 0.001115 -5.384e-05 2.417e-05 -0.02559 -4.058e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02071 -0.2102 0.205 0.9836 0.9933 0.2024 0.4716 0.8818 0.729 ] Network output: [ -0.01268 1.001 1.011 1.828e-06 -8.208e-07 0.0135 1.378e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005032 0.000792 0.003994 0.005042 0.989 0.9921 0.005122 0.8794 0.9058 0.016 ] Network output: [ -0.002947 0.04418 0.9949 -0.0002163 9.713e-05 0.9659 -0.000163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.1051 0.3057 0.1689 0.9851 0.9941 0.1926 0.4766 0.8885 0.7248 ] Network output: [ 0.0117 -0.02953 1.001 0.0001179 -5.293e-05 1.006 8.885e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09718 0.08792 0.1744 0.2152 0.9874 0.9921 0.09724 0.8119 0.8877 0.3117 ] Network output: [ -0.01075 0.04164 1.003 0.0001145 -5.141e-05 0.9771 8.63e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1004 0.09866 0.1741 0.2056 0.9858 0.9916 0.1004 0.7455 0.8695 0.2485 ] Network output: [ -0.001016 0.9997 0.00145 1.625e-05 -7.297e-06 1.001 1.225e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001341 Epoch 6122 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01548 0.9859 0.9844 8.588e-06 -3.856e-06 -0.001236 6.472e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003161 -0.002872 -0.01077 0.008264 0.9696 0.974 0.00597 0.8472 0.8361 0.02218 ] Network output: [ 1 -0.02755 0.003745 -4.511e-05 2.025e-05 0.02272 -3.4e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02152 -0.207 0.2152 0.9836 0.9933 0.2015 0.4702 0.8822 0.7298 ] Network output: [ -0.01269 0.998 1.011 2.338e-06 -1.05e-06 0.01661 1.762e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005014 0.0007976 0.004169 0.00539 0.989 0.9921 0.005104 0.8793 0.906 0.01609 ] Network output: [ 0.00252 -0.04016 0.9989 -0.0002031 9.116e-05 1.035 -0.000153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1913 0.1048 0.3114 0.1851 0.9851 0.9941 0.1918 0.4756 0.8885 0.7242 ] Network output: [ 0.009703 -0.0464 1.005 0.0001184 -5.316e-05 1.023 8.924e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09748 0.08826 0.1783 0.2198 0.9874 0.9921 0.09754 0.8131 0.8878 0.3141 ] Network output: [ -0.01204 0.04618 1.004 0.0001132 -5.084e-05 0.9746 8.534e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.09895 0.1754 0.2065 0.9859 0.9917 0.1006 0.7471 0.8695 0.2486 ] Network output: [ 0.001812 0.9983 -0.00253 1.806e-05 -8.108e-06 1.001 1.361e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001591 Epoch 6123 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01488 0.9954 0.984 7.172e-06 -3.22e-06 -0.009085 5.405e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.002872 -0.01081 0.008089 0.9696 0.974 0.005987 0.8474 0.8357 0.02213 ] Network output: [ 0.9956 0.03314 0.00111 -5.385e-05 2.418e-05 -0.02564 -4.058e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02073 -0.2102 0.2049 0.9836 0.9933 0.2023 0.4716 0.8818 0.729 ] Network output: [ -0.01268 1.001 1.011 1.838e-06 -8.252e-07 0.0135 1.385e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005033 0.0007909 0.003994 0.00504 0.989 0.9921 0.005123 0.8794 0.9058 0.016 ] Network output: [ -0.002953 0.04425 0.9949 -0.0002162 9.705e-05 0.9658 -0.0001629 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.1051 0.3057 0.1688 0.9851 0.9941 0.1926 0.4766 0.8885 0.7248 ] Network output: [ 0.0117 -0.02957 1.001 0.0001178 -5.289e-05 1.006 8.879e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09719 0.08792 0.1744 0.2152 0.9874 0.9921 0.09725 0.8119 0.8876 0.3117 ] Network output: [ -0.01075 0.04169 1.003 0.0001144 -5.138e-05 0.977 8.625e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1003 0.09864 0.1741 0.2056 0.9858 0.9916 0.1004 0.7455 0.8694 0.2484 ] Network output: [ -0.001019 0.9997 0.00145 1.623e-05 -7.286e-06 1.001 1.223e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001345 Epoch 6124 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01547 0.9859 0.9844 8.587e-06 -3.855e-06 -0.001225 6.471e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003161 -0.002872 -0.01077 0.008262 0.9696 0.974 0.00597 0.8472 0.8361 0.02218 ] Network output: [ 1 -0.02759 0.003744 -4.512e-05 2.025e-05 0.02275 -3.4e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02154 -0.207 0.2152 0.9836 0.9933 0.2015 0.4701 0.8821 0.7298 ] Network output: [ -0.01269 0.998 1.011 2.349e-06 -1.054e-06 0.01662 1.77e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005015 0.0007965 0.00417 0.005388 0.989 0.9921 0.005105 0.8793 0.906 0.01609 ] Network output: [ 0.00252 -0.04021 0.999 -0.0002029 9.109e-05 1.035 -0.0001529 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1913 0.1048 0.3114 0.185 0.9851 0.9941 0.1918 0.4756 0.8885 0.7241 ] Network output: [ 0.009698 -0.04647 1.005 0.0001183 -5.313e-05 1.023 8.919e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09748 0.08826 0.1784 0.2198 0.9874 0.9921 0.09754 0.8131 0.8878 0.3141 ] Network output: [ -0.01204 0.04624 1.004 0.0001132 -5.081e-05 0.9746 8.529e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.09894 0.1754 0.2065 0.9859 0.9917 0.1006 0.7471 0.8695 0.2486 ] Network output: [ 0.001815 0.9983 -0.002537 1.804e-05 -8.098e-06 1.001 1.359e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001595 Epoch 6125 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01487 0.9954 0.984 7.17e-06 -3.219e-06 -0.009084 5.403e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002872 -0.01081 0.008087 0.9696 0.974 0.005987 0.8474 0.8357 0.02212 ] Network output: [ 0.9956 0.03319 0.001104 -5.385e-05 2.418e-05 -0.02567 -4.059e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02076 -0.2101 0.2049 0.9836 0.9933 0.2023 0.4716 0.8818 0.7289 ] Network output: [ -0.01268 1.001 1.011 1.848e-06 -8.295e-07 0.0135 1.393e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005034 0.0007898 0.003995 0.005038 0.989 0.9921 0.005124 0.8794 0.9058 0.01599 ] Network output: [ -0.00296 0.04431 0.995 -0.000216 9.698e-05 0.9657 -0.0001628 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.105 0.3057 0.1687 0.9851 0.9941 0.1926 0.4766 0.8885 0.7247 ] Network output: [ 0.01169 -0.02962 1.001 0.0001177 -5.286e-05 1.006 8.873e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09719 0.08792 0.1744 0.2152 0.9874 0.9921 0.09725 0.8118 0.8876 0.3117 ] Network output: [ -0.01074 0.04175 1.003 0.0001144 -5.135e-05 0.977 8.62e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1003 0.09863 0.174 0.2055 0.9858 0.9916 0.1003 0.7455 0.8694 0.2484 ] Network output: [ -0.001023 0.9997 0.001451 1.621e-05 -7.275e-06 1.001 1.221e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001349 Epoch 6126 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01547 0.9859 0.9844 8.585e-06 -3.854e-06 -0.001213 6.47e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003161 -0.002872 -0.01076 0.008261 0.9696 0.974 0.00597 0.8472 0.8361 0.02217 ] Network output: [ 1 -0.02763 0.003743 -4.512e-05 2.026e-05 0.02277 -3.4e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02157 -0.2069 0.2152 0.9836 0.9933 0.2015 0.4701 0.8821 0.7297 ] Network output: [ -0.01269 0.998 1.011 2.359e-06 -1.059e-06 0.01662 1.777e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005016 0.0007953 0.004171 0.005386 0.989 0.9921 0.005105 0.8793 0.906 0.01609 ] Network output: [ 0.00252 -0.04026 0.999 -0.0002027 9.101e-05 1.035 -0.0001528 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.1047 0.3114 0.185 0.9851 0.9941 0.1918 0.4755 0.8885 0.7241 ] Network output: [ 0.009692 -0.04654 1.005 0.0001183 -5.309e-05 1.023 8.913e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09749 0.08826 0.1784 0.2198 0.9874 0.9921 0.09755 0.813 0.8877 0.3141 ] Network output: [ -0.01203 0.04631 1.004 0.0001131 -5.078e-05 0.9745 8.524e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.09892 0.1754 0.2064 0.9859 0.9917 0.1006 0.7471 0.8694 0.2486 ] Network output: [ 0.001817 0.9983 -0.002543 1.801e-05 -8.087e-06 1.001 1.358e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001598 Epoch 6127 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01487 0.9954 0.984 7.167e-06 -3.218e-06 -0.009082 5.401e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002873 -0.0108 0.008085 0.9696 0.974 0.005987 0.8474 0.8357 0.02212 ] Network output: [ 0.9956 0.03323 0.001099 -5.386e-05 2.418e-05 -0.02571 -4.059e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02079 -0.2101 0.2049 0.9836 0.9933 0.2023 0.4716 0.8817 0.7289 ] Network output: [ -0.01267 1.001 1.011 1.857e-06 -8.337e-07 0.0135 1.4e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005035 0.0007886 0.003996 0.005036 0.989 0.992 0.005125 0.8794 0.9058 0.01599 ] Network output: [ -0.002966 0.04437 0.995 -0.0002159 9.691e-05 0.9657 -0.0001627 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.105 0.3057 0.1687 0.9851 0.9941 0.1926 0.4766 0.8885 0.7247 ] Network output: [ 0.01169 -0.02966 1.001 0.0001177 -5.282e-05 1.006 8.867e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0972 0.08792 0.1744 0.2152 0.9874 0.9921 0.09726 0.8118 0.8876 0.3117 ] Network output: [ -0.01074 0.04181 1.003 0.0001143 -5.132e-05 0.9769 8.615e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1003 0.09861 0.174 0.2055 0.9858 0.9916 0.1003 0.7454 0.8694 0.2484 ] Network output: [ -0.001026 0.9997 0.001451 1.618e-05 -7.264e-06 1.001 1.219e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001352 Epoch 6128 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01547 0.9859 0.9844 8.583e-06 -3.853e-06 -0.001202 6.468e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003162 -0.002873 -0.01076 0.008259 0.9696 0.974 0.00597 0.8472 0.8361 0.02217 ] Network output: [ 1 -0.02766 0.003741 -4.513e-05 2.026e-05 0.0228 -3.401e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.0216 -0.2069 0.2151 0.9836 0.9933 0.2015 0.4701 0.8821 0.7297 ] Network output: [ -0.01268 0.9979 1.011 2.368e-06 -1.063e-06 0.01662 1.785e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005016 0.0007942 0.004172 0.005385 0.989 0.9921 0.005106 0.8793 0.906 0.01608 ] Network output: [ 0.00252 -0.0403 0.9991 -0.0002026 9.094e-05 1.035 -0.0001527 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.1047 0.3115 0.185 0.9851 0.9941 0.1918 0.4755 0.8885 0.724 ] Network output: [ 0.009687 -0.0466 1.005 0.0001182 -5.306e-05 1.023 8.907e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09749 0.08825 0.1784 0.2197 0.9874 0.9921 0.09755 0.813 0.8877 0.3141 ] Network output: [ -0.01202 0.04637 1.004 0.000113 -5.075e-05 0.9745 8.519e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.0989 0.1753 0.2064 0.9859 0.9917 0.1006 0.747 0.8694 0.2485 ] Network output: [ 0.001819 0.9983 -0.00255 1.799e-05 -8.077e-06 1.001 1.356e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001602 Epoch 6129 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01487 0.9954 0.984 7.165e-06 -3.216e-06 -0.009081 5.399e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002873 -0.0108 0.008083 0.9696 0.974 0.005988 0.8474 0.8357 0.02211 ] Network output: [ 0.9956 0.03326 0.001093 -5.386e-05 2.418e-05 -0.02575 -4.059e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02081 -0.2101 0.2048 0.9836 0.9933 0.2023 0.4716 0.8817 0.7288 ] Network output: [ -0.01267 1.001 1.011 1.866e-06 -8.378e-07 0.0135 1.406e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005036 0.0007875 0.003997 0.005034 0.989 0.992 0.005126 0.8794 0.9057 0.01599 ] Network output: [ -0.002972 0.04442 0.9951 -0.0002157 9.683e-05 0.9656 -0.0001626 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.1049 0.3058 0.1686 0.9851 0.9941 0.1926 0.4766 0.8885 0.7246 ] Network output: [ 0.01168 -0.02971 1.001 0.0001176 -5.278e-05 1.006 8.861e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0972 0.08791 0.1744 0.2152 0.9874 0.992 0.09726 0.8117 0.8876 0.3117 ] Network output: [ -0.01073 0.04187 1.003 0.0001142 -5.129e-05 0.9769 8.61e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1003 0.09859 0.174 0.2055 0.9858 0.9916 0.1003 0.7454 0.8694 0.2484 ] Network output: [ -0.001029 0.9997 0.001452 1.616e-05 -7.253e-06 1.001 1.218e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001356 Epoch 6130 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01547 0.9859 0.9844 8.581e-06 -3.852e-06 -0.001192 6.467e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003162 -0.002873 -0.01076 0.008257 0.9696 0.974 0.005971 0.8472 0.8361 0.02216 ] Network output: [ 1 -0.02769 0.00374 -4.513e-05 2.026e-05 0.02282 -3.401e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02163 -0.2069 0.2151 0.9836 0.9933 0.2015 0.4701 0.8821 0.7296 ] Network output: [ -0.01268 0.9979 1.011 2.378e-06 -1.067e-06 0.01663 1.792e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005017 0.0007931 0.004173 0.005383 0.989 0.9921 0.005107 0.8793 0.906 0.01608 ] Network output: [ 0.00252 -0.04034 0.9991 -0.0002024 9.086e-05 1.035 -0.0001525 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.1046 0.3115 0.1849 0.9851 0.9941 0.1918 0.4755 0.8884 0.724 ] Network output: [ 0.009681 -0.04667 1.005 0.0001181 -5.302e-05 1.023 8.901e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0975 0.08825 0.1784 0.2197 0.9874 0.9921 0.09756 0.8129 0.8877 0.3142 ] Network output: [ -0.01202 0.04643 1.004 0.000113 -5.072e-05 0.9744 8.514e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1006 0.09888 0.1753 0.2064 0.9859 0.9917 0.1006 0.747 0.8694 0.2485 ] Network output: [ 0.001821 0.9983 -0.002557 1.797e-05 -8.067e-06 1.001 1.354e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001605 Epoch 6131 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01486 0.9954 0.984 7.162e-06 -3.215e-06 -0.009079 5.397e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002873 -0.0108 0.008081 0.9696 0.974 0.005988 0.8474 0.8357 0.02211 ] Network output: [ 0.9956 0.0333 0.001088 -5.386e-05 2.418e-05 -0.02578 -4.059e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02084 -0.2101 0.2048 0.9836 0.9933 0.2023 0.4715 0.8817 0.7288 ] Network output: [ -0.01267 1.001 1.011 1.875e-06 -8.417e-07 0.0135 1.413e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005036 0.0007864 0.003997 0.005032 0.989 0.992 0.005126 0.8794 0.9057 0.01598 ] Network output: [ -0.002978 0.04448 0.9951 -0.0002155 9.676e-05 0.9655 -0.0001624 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.1049 0.3058 0.1686 0.9851 0.9941 0.1926 0.4765 0.8885 0.7246 ] Network output: [ 0.01168 -0.02976 1.001 0.0001175 -5.275e-05 1.006 8.855e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09721 0.08791 0.1744 0.2152 0.9874 0.992 0.09727 0.8117 0.8875 0.3117 ] Network output: [ -0.01073 0.04192 1.003 0.0001142 -5.126e-05 0.9769 8.605e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1003 0.09857 0.174 0.2055 0.9858 0.9916 0.1003 0.7453 0.8693 0.2483 ] Network output: [ -0.001032 0.9997 0.001452 1.613e-05 -7.243e-06 1.001 1.216e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001359 Epoch 6132 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01546 0.9859 0.9844 8.578e-06 -3.851e-06 -0.001182 6.465e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003162 -0.002873 -0.01075 0.008255 0.9696 0.974 0.005971 0.8472 0.8361 0.02216 ] Network output: [ 1 -0.02773 0.003739 -4.513e-05 2.026e-05 0.02285 -3.401e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02166 -0.2069 0.2151 0.9836 0.9933 0.2014 0.4701 0.8821 0.7296 ] Network output: [ -0.01268 0.9979 1.011 2.387e-06 -1.071e-06 0.01663 1.799e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005018 0.000792 0.004174 0.005382 0.989 0.9921 0.005108 0.8793 0.906 0.01608 ] Network output: [ 0.002519 -0.04038 0.9992 -0.0002022 9.078e-05 1.035 -0.0001524 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.1046 0.3115 0.1849 0.9851 0.9941 0.1918 0.4755 0.8884 0.7239 ] Network output: [ 0.009676 -0.04674 1.005 0.000118 -5.299e-05 1.023 8.895e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0975 0.08825 0.1784 0.2197 0.9874 0.9921 0.09756 0.8129 0.8877 0.3142 ] Network output: [ -0.01201 0.0465 1.004 0.0001129 -5.069e-05 0.9744 8.509e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1005 0.09886 0.1753 0.2064 0.9859 0.9917 0.1006 0.7469 0.8694 0.2485 ] Network output: [ 0.001823 0.9983 -0.002563 1.795e-05 -8.056e-06 1.001 1.352e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001609 Epoch 6133 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01486 0.9954 0.984 7.159e-06 -3.214e-06 -0.009078 5.395e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002874 -0.01079 0.008079 0.9696 0.974 0.005988 0.8474 0.8357 0.0221 ] Network output: [ 0.9956 0.03333 0.001083 -5.387e-05 2.418e-05 -0.02582 -4.059e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02087 -0.2101 0.2048 0.9836 0.9933 0.2023 0.4715 0.8817 0.7288 ] Network output: [ -0.01267 1.001 1.011 1.883e-06 -8.455e-07 0.0135 1.419e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005037 0.0007853 0.003998 0.00503 0.989 0.992 0.005127 0.8793 0.9057 0.01598 ] Network output: [ -0.002983 0.04453 0.9952 -0.0002154 9.668e-05 0.9654 -0.0001623 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.1048 0.3058 0.1685 0.9851 0.9941 0.1926 0.4765 0.8885 0.7245 ] Network output: [ 0.01167 -0.0298 1.001 0.0001174 -5.271e-05 1.006 8.849e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09721 0.08791 0.1744 0.2151 0.9874 0.992 0.09727 0.8117 0.8875 0.3117 ] Network output: [ -0.01072 0.04198 1.003 0.0001141 -5.123e-05 0.9768 8.6e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1003 0.09855 0.1739 0.2054 0.9858 0.9916 0.1003 0.7453 0.8693 0.2483 ] Network output: [ -0.001035 0.9997 0.001452 1.611e-05 -7.232e-06 1.001 1.214e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001363 Epoch 6134 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01546 0.9859 0.9844 8.575e-06 -3.85e-06 -0.001172 6.462e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003162 -0.002874 -0.01075 0.008253 0.9696 0.974 0.005971 0.8472 0.836 0.02215 ] Network output: [ 1 -0.02776 0.003737 -4.514e-05 2.026e-05 0.02287 -3.402e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02168 -0.2068 0.2151 0.9836 0.9933 0.2014 0.47 0.8821 0.7296 ] Network output: [ -0.01268 0.9979 1.011 2.396e-06 -1.075e-06 0.01663 1.805e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005019 0.0007909 0.004175 0.00538 0.989 0.9921 0.005109 0.8792 0.9059 0.01607 ] Network output: [ 0.002519 -0.04041 0.9992 -0.0002021 9.071e-05 1.035 -0.0001523 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.1045 0.3115 0.1848 0.9851 0.9941 0.1918 0.4755 0.8884 0.7239 ] Network output: [ 0.009671 -0.0468 1.005 0.000118 -5.295e-05 1.023 8.89e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09751 0.08825 0.1784 0.2197 0.9874 0.9921 0.09757 0.8128 0.8876 0.3142 ] Network output: [ -0.01201 0.04656 1.004 0.0001128 -5.066e-05 0.9743 8.504e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1005 0.09884 0.1753 0.2063 0.9859 0.9917 0.1005 0.7469 0.8693 0.2485 ] Network output: [ 0.001825 0.9983 -0.002569 1.792e-05 -8.046e-06 1.001 1.351e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001612 Epoch 6135 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01486 0.9954 0.984 7.156e-06 -3.212e-06 -0.009076 5.393e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002874 -0.01079 0.008077 0.9696 0.974 0.005988 0.8474 0.8357 0.0221 ] Network output: [ 0.9956 0.03337 0.001078 -5.387e-05 2.418e-05 -0.02585 -4.06e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02089 -0.21 0.2047 0.9836 0.9933 0.2023 0.4715 0.8817 0.7287 ] Network output: [ -0.01266 1.001 1.011 1.892e-06 -8.493e-07 0.0135 1.426e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005038 0.0007842 0.003999 0.005028 0.989 0.992 0.005128 0.8793 0.9057 0.01598 ] Network output: [ -0.002989 0.04458 0.9952 -0.0002152 9.661e-05 0.9653 -0.0001622 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.1048 0.3058 0.1684 0.9851 0.9941 0.1926 0.4765 0.8884 0.7245 ] Network output: [ 0.01167 -0.02985 1.001 0.0001173 -5.268e-05 1.006 8.843e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09722 0.08791 0.1745 0.2151 0.9874 0.992 0.09728 0.8116 0.8875 0.3117 ] Network output: [ -0.01072 0.04203 1.003 0.000114 -5.12e-05 0.9768 8.595e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1002 0.09853 0.1739 0.2054 0.9858 0.9916 0.1002 0.7452 0.8693 0.2483 ] Network output: [ -0.001037 0.9997 0.001452 1.609e-05 -7.221e-06 1.001 1.212e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001366 Epoch 6136 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01546 0.9859 0.9844 8.572e-06 -3.848e-06 -0.001162 6.46e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003162 -0.002874 -0.01075 0.008251 0.9696 0.974 0.005971 0.8472 0.836 0.02215 ] Network output: [ 1 -0.02778 0.003736 -4.514e-05 2.026e-05 0.02289 -3.402e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02171 -0.2068 0.215 0.9836 0.9933 0.2014 0.47 0.8821 0.7295 ] Network output: [ -0.01267 0.9979 1.011 2.404e-06 -1.079e-06 0.01664 1.812e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005019 0.0007899 0.004176 0.005378 0.989 0.9921 0.005109 0.8792 0.9059 0.01607 ] Network output: [ 0.002518 -0.04044 0.9993 -0.0002019 9.063e-05 1.035 -0.0001521 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.1045 0.3116 0.1848 0.9851 0.9941 0.1918 0.4754 0.8884 0.7239 ] Network output: [ 0.009666 -0.04686 1.005 0.0001179 -5.292e-05 1.023 8.884e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09751 0.08825 0.1785 0.2197 0.9874 0.9921 0.09757 0.8128 0.8876 0.3142 ] Network output: [ -0.012 0.04662 1.004 0.0001128 -5.063e-05 0.9743 8.499e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1005 0.09882 0.1752 0.2063 0.9859 0.9917 0.1005 0.7468 0.8693 0.2484 ] Network output: [ 0.001827 0.9983 -0.002575 1.79e-05 -8.036e-06 1.001 1.349e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001615 Epoch 6137 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01485 0.9954 0.984 7.152e-06 -3.211e-06 -0.009074 5.39e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002874 -0.01079 0.008074 0.9696 0.974 0.005989 0.8474 0.8356 0.02209 ] Network output: [ 0.9956 0.0334 0.001073 -5.387e-05 2.418e-05 -0.02588 -4.06e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02092 -0.21 0.2047 0.9836 0.9933 0.2023 0.4715 0.8817 0.7287 ] Network output: [ -0.01266 1.001 1.011 1.9e-06 -8.529e-07 0.01351 1.432e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005039 0.0007831 0.004 0.005027 0.989 0.992 0.005129 0.8793 0.9057 0.01598 ] Network output: [ -0.002994 0.04462 0.9953 -0.000215 9.653e-05 0.9652 -0.0001621 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.1047 0.3058 0.1684 0.9851 0.9941 0.1925 0.4765 0.8884 0.7244 ] Network output: [ 0.01166 -0.0299 1.001 0.0001173 -5.264e-05 1.006 8.837e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09722 0.0879 0.1745 0.2151 0.9874 0.992 0.09728 0.8116 0.8875 0.3117 ] Network output: [ -0.01071 0.04209 1.003 0.000114 -5.117e-05 0.9767 8.589e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1002 0.09851 0.1739 0.2054 0.9858 0.9916 0.1002 0.7452 0.8692 0.2482 ] Network output: [ -0.00104 0.9997 0.001452 1.606e-05 -7.211e-06 1.001 1.21e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001369 Epoch 6138 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01546 0.9859 0.9844 8.568e-06 -3.847e-06 -0.001153 6.457e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003162 -0.002874 -0.01075 0.008249 0.9696 0.974 0.005971 0.8472 0.836 0.02215 ] Network output: [ 1 -0.02781 0.003734 -4.514e-05 2.027e-05 0.02291 -3.402e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02174 -0.2068 0.215 0.9836 0.9933 0.2014 0.47 0.8821 0.7295 ] Network output: [ -0.01267 0.9979 1.011 2.413e-06 -1.083e-06 0.01664 1.818e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00502 0.0007888 0.004176 0.005377 0.989 0.9921 0.00511 0.8792 0.9059 0.01607 ] Network output: [ 0.002517 -0.04047 0.9993 -0.0002017 9.056e-05 1.035 -0.000152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.1044 0.3116 0.1847 0.9851 0.9941 0.1918 0.4754 0.8884 0.7238 ] Network output: [ 0.009661 -0.04693 1.005 0.0001178 -5.288e-05 1.024 8.878e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09752 0.08825 0.1785 0.2197 0.9874 0.9921 0.09758 0.8127 0.8876 0.3142 ] Network output: [ -0.012 0.04668 1.004 0.0001127 -5.06e-05 0.9742 8.494e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1005 0.0988 0.1752 0.2063 0.9859 0.9917 0.1005 0.7468 0.8693 0.2484 ] Network output: [ 0.001829 0.9983 -0.002581 1.788e-05 -8.025e-06 1.001 1.347e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001618 Epoch 6139 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01485 0.9954 0.984 7.149e-06 -3.209e-06 -0.009072 5.388e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002875 -0.01079 0.008072 0.9696 0.974 0.005989 0.8474 0.8356 0.02209 ] Network output: [ 0.9956 0.03343 0.001068 -5.387e-05 2.418e-05 -0.02591 -4.06e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02094 -0.21 0.2047 0.9836 0.9933 0.2023 0.4715 0.8817 0.7286 ] Network output: [ -0.01266 1.001 1.011 1.908e-06 -8.564e-07 0.01351 1.438e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00504 0.000782 0.004 0.005025 0.989 0.992 0.00513 0.8793 0.9057 0.01597 ] Network output: [ -0.002999 0.04467 0.9953 -0.0002149 9.646e-05 0.9652 -0.0001619 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.1047 0.3059 0.1683 0.9851 0.9941 0.1925 0.4765 0.8884 0.7244 ] Network output: [ 0.01166 -0.02994 1.001 0.0001172 -5.26e-05 1.006 8.831e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09723 0.0879 0.1745 0.2151 0.9874 0.992 0.09729 0.8115 0.8874 0.3117 ] Network output: [ -0.0107 0.04214 1.003 0.0001139 -5.114e-05 0.9767 8.584e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1002 0.09849 0.1738 0.2053 0.9858 0.9916 0.1002 0.7451 0.8692 0.2482 ] Network output: [ -0.001043 0.9997 0.001452 1.604e-05 -7.2e-06 1.001 1.209e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001372 Epoch 6140 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01545 0.9858 0.9844 8.565e-06 -3.845e-06 -0.001145 6.455e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003162 -0.002874 -0.01074 0.008247 0.9696 0.974 0.005972 0.8472 0.836 0.02214 ] Network output: [ 1 -0.02783 0.003732 -4.515e-05 2.027e-05 0.02292 -3.402e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02176 -0.2067 0.215 0.9836 0.9933 0.2014 0.47 0.8821 0.7294 ] Network output: [ -0.01267 0.9979 1.011 2.421e-06 -1.087e-06 0.01664 1.824e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005021 0.0007877 0.004177 0.005375 0.989 0.9921 0.005111 0.8792 0.9059 0.01607 ] Network output: [ 0.002516 -0.0405 0.9993 -0.0002015 9.048e-05 1.035 -0.0001519 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.1044 0.3116 0.1847 0.9851 0.9941 0.1918 0.4754 0.8884 0.7238 ] Network output: [ 0.009656 -0.04699 1.005 0.0001177 -5.285e-05 1.024 8.872e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09752 0.08824 0.1785 0.2197 0.9874 0.9921 0.09758 0.8127 0.8875 0.3142 ] Network output: [ -0.01199 0.04674 1.004 0.0001126 -5.057e-05 0.9742 8.489e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1005 0.09878 0.1752 0.2062 0.9859 0.9917 0.1005 0.7467 0.8692 0.2484 ] Network output: [ 0.001831 0.9983 -0.002586 1.785e-05 -8.015e-06 1.001 1.345e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001621 Epoch 6141 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01485 0.9954 0.984 7.145e-06 -3.208e-06 -0.009069 5.385e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002875 -0.01078 0.00807 0.9696 0.974 0.005989 0.8474 0.8356 0.02208 ] Network output: [ 0.9956 0.03345 0.001063 -5.386e-05 2.418e-05 -0.02593 -4.059e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02097 -0.21 0.2046 0.9836 0.9933 0.2023 0.4714 0.8817 0.7286 ] Network output: [ -0.01266 1.001 1.011 1.915e-06 -8.599e-07 0.01351 1.443e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00504 0.000781 0.004001 0.005023 0.989 0.992 0.005131 0.8793 0.9057 0.01597 ] Network output: [ -0.003004 0.04471 0.9953 -0.0002147 9.638e-05 0.9651 -0.0001618 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.1046 0.3059 0.1683 0.9851 0.9941 0.1925 0.4764 0.8884 0.7244 ] Network output: [ 0.01165 -0.02999 1.001 0.0001171 -5.257e-05 1.006 8.824e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09723 0.0879 0.1745 0.2151 0.9874 0.992 0.09729 0.8115 0.8874 0.3117 ] Network output: [ -0.0107 0.04219 1.003 0.0001138 -5.11e-05 0.9766 8.579e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1002 0.09847 0.1738 0.2053 0.9858 0.9916 0.1002 0.7451 0.8692 0.2482 ] Network output: [ -0.001045 0.9997 0.001452 1.602e-05 -7.19e-06 1.001 1.207e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001375 Epoch 6142 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01545 0.9858 0.9844 8.561e-06 -3.843e-06 -0.001136 6.452e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003162 -0.002875 -0.01074 0.008245 0.9696 0.974 0.005972 0.8472 0.836 0.02214 ] Network output: [ 1.001 -0.02785 0.00373 -4.515e-05 2.027e-05 0.02294 -3.402e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02179 -0.2067 0.215 0.9836 0.9933 0.2014 0.47 0.882 0.7294 ] Network output: [ -0.01267 0.9979 1.011 2.428e-06 -1.09e-06 0.01665 1.83e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005022 0.0007866 0.004178 0.005373 0.989 0.9921 0.005112 0.8792 0.9059 0.01606 ] Network output: [ 0.002515 -0.04052 0.9994 -0.0002014 9.041e-05 1.035 -0.0001518 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.1043 0.3116 0.1847 0.9852 0.9941 0.1918 0.4754 0.8884 0.7237 ] Network output: [ 0.009651 -0.04705 1.005 0.0001176 -5.281e-05 1.024 8.866e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09753 0.08824 0.1785 0.2197 0.9874 0.9921 0.09758 0.8127 0.8875 0.3142 ] Network output: [ -0.01198 0.04679 1.004 0.0001126 -5.054e-05 0.9741 8.484e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1005 0.09876 0.1752 0.2062 0.9859 0.9917 0.1005 0.7467 0.8692 0.2483 ] Network output: [ 0.001832 0.9983 -0.002592 1.783e-05 -8.005e-06 1.001 1.344e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001624 Epoch 6143 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01484 0.9954 0.984 7.141e-06 -3.206e-06 -0.009067 5.382e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002875 -0.01078 0.008068 0.9696 0.974 0.005989 0.8474 0.8356 0.02208 ] Network output: [ 0.9956 0.03348 0.001059 -5.386e-05 2.418e-05 -0.02596 -4.059e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.021 -0.2099 0.2046 0.9836 0.9933 0.2023 0.4714 0.8817 0.7285 ] Network output: [ -0.01265 1.001 1.011 1.923e-06 -8.632e-07 0.01351 1.449e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005041 0.0007799 0.004002 0.005021 0.989 0.992 0.005131 0.8793 0.9057 0.01597 ] Network output: [ -0.003009 0.04474 0.9954 -0.0002145 9.63e-05 0.965 -0.0001617 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.1046 0.3059 0.1682 0.9851 0.9941 0.1925 0.4764 0.8884 0.7243 ] Network output: [ 0.01165 -0.03004 1.001 0.000117 -5.253e-05 1.007 8.818e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09724 0.0879 0.1745 0.2151 0.9874 0.992 0.09729 0.8114 0.8874 0.3117 ] Network output: [ -0.01069 0.04225 1.003 0.0001138 -5.107e-05 0.9766 8.574e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1002 0.09845 0.1738 0.2053 0.9858 0.9916 0.1002 0.745 0.8692 0.2482 ] Network output: [ -0.001048 0.9997 0.001452 1.599e-05 -7.18e-06 1.001 1.205e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001378 Epoch 6144 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01545 0.9858 0.9844 8.557e-06 -3.841e-06 -0.001128 6.448e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003162 -0.002875 -0.01074 0.008243 0.9696 0.974 0.005972 0.8472 0.836 0.02213 ] Network output: [ 1.001 -0.02787 0.003729 -4.515e-05 2.027e-05 0.02295 -3.403e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02182 -0.2067 0.2149 0.9836 0.9933 0.2014 0.4699 0.882 0.7294 ] Network output: [ -0.01266 0.9979 1.011 2.436e-06 -1.094e-06 0.01665 1.836e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005023 0.0007856 0.004179 0.005371 0.989 0.9921 0.005113 0.8792 0.9059 0.01606 ] Network output: [ 0.002513 -0.04054 0.9994 -0.0002012 9.033e-05 1.035 -0.0001516 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.1043 0.3117 0.1846 0.9852 0.9941 0.1918 0.4753 0.8884 0.7237 ] Network output: [ 0.009646 -0.04711 1.004 0.0001176 -5.278e-05 1.024 8.86e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09753 0.08824 0.1785 0.2197 0.9874 0.9921 0.09759 0.8126 0.8875 0.3142 ] Network output: [ -0.01198 0.04685 1.003 0.0001125 -5.051e-05 0.9741 8.479e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1004 0.09874 0.1751 0.2062 0.9859 0.9917 0.1004 0.7466 0.8692 0.2483 ] Network output: [ 0.001834 0.9983 -0.002597 1.781e-05 -7.994e-06 1.001 1.342e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001627 Epoch 6145 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01484 0.9954 0.984 7.137e-06 -3.204e-06 -0.009064 5.379e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002876 -0.01078 0.008066 0.9696 0.974 0.005989 0.8474 0.8356 0.02208 ] Network output: [ 0.9956 0.0335 0.001054 -5.386e-05 2.418e-05 -0.02598 -4.059e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02102 -0.2099 0.2046 0.9836 0.9933 0.2023 0.4714 0.8816 0.7285 ] Network output: [ -0.01265 1.001 1.011 1.93e-06 -8.664e-07 0.01351 1.455e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005042 0.0007788 0.004003 0.005019 0.989 0.992 0.005132 0.8793 0.9056 0.01596 ] Network output: [ -0.003013 0.04478 0.9954 -0.0002143 9.623e-05 0.965 -0.0001615 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.1045 0.3059 0.1682 0.9851 0.9941 0.1925 0.4764 0.8884 0.7243 ] Network output: [ 0.01164 -0.03008 1.001 0.0001169 -5.249e-05 1.007 8.812e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09724 0.08789 0.1745 0.2151 0.9874 0.992 0.0973 0.8114 0.8873 0.3117 ] Network output: [ -0.01069 0.0423 1.003 0.0001137 -5.104e-05 0.9765 8.569e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1001 0.09843 0.1738 0.2052 0.9858 0.9916 0.1002 0.745 0.8691 0.2481 ] Network output: [ -0.00105 0.9997 0.001452 1.597e-05 -7.169e-06 1.001 1.204e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001381 Epoch 6146 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01544 0.9858 0.9844 8.552e-06 -3.839e-06 -0.00112 6.445e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003162 -0.002875 -0.01073 0.008241 0.9696 0.974 0.005972 0.8472 0.836 0.02213 ] Network output: [ 1.001 -0.02789 0.003727 -4.515e-05 2.027e-05 0.02296 -3.403e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02184 -0.2067 0.2149 0.9836 0.9933 0.2014 0.4699 0.882 0.7293 ] Network output: [ -0.01266 0.9979 1.011 2.443e-06 -1.097e-06 0.01665 1.841e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005023 0.0007845 0.00418 0.00537 0.989 0.9921 0.005113 0.8792 0.9059 0.01606 ] Network output: [ 0.002512 -0.04056 0.9995 -0.000201 9.026e-05 1.035 -0.0001515 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.1042 0.3117 0.1846 0.9852 0.9941 0.1918 0.4753 0.8884 0.7236 ] Network output: [ 0.009642 -0.04717 1.004 0.0001175 -5.274e-05 1.024 8.854e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09753 0.08824 0.1785 0.2197 0.9874 0.9921 0.09759 0.8126 0.8875 0.3142 ] Network output: [ -0.01197 0.04691 1.003 0.0001124 -5.048e-05 0.974 8.474e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1004 0.09872 0.1751 0.2061 0.9859 0.9917 0.1004 0.7466 0.8691 0.2483 ] Network output: [ 0.001835 0.9983 -0.002602 1.778e-05 -7.984e-06 1.001 1.34e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00163 Epoch 6147 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01484 0.9954 0.984 7.133e-06 -3.202e-06 -0.009062 5.376e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002876 -0.01077 0.008064 0.9696 0.974 0.00599 0.8474 0.8356 0.02207 ] Network output: [ 0.9956 0.03352 0.001049 -5.386e-05 2.418e-05 -0.02601 -4.059e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02105 -0.2099 0.2046 0.9836 0.9933 0.2023 0.4714 0.8816 0.7285 ] Network output: [ -0.01265 1.001 1.011 1.937e-06 -8.696e-07 0.01351 1.46e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005043 0.0007777 0.004003 0.005017 0.989 0.992 0.005133 0.8793 0.9056 0.01596 ] Network output: [ -0.003018 0.04481 0.9955 -0.0002142 9.615e-05 0.9649 -0.0001614 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.1045 0.3059 0.1681 0.9851 0.9941 0.1925 0.4764 0.8884 0.7242 ] Network output: [ 0.01164 -0.03013 1.001 0.0001168 -5.246e-05 1.007 8.806e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09724 0.08789 0.1745 0.215 0.9874 0.992 0.0973 0.8113 0.8873 0.3117 ] Network output: [ -0.01068 0.04235 1.003 0.0001136 -5.101e-05 0.9765 8.563e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1001 0.09841 0.1737 0.2052 0.9858 0.9916 0.1001 0.7449 0.8691 0.2481 ] Network output: [ -0.001053 0.9997 0.001452 1.595e-05 -7.159e-06 1.001 1.202e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001384 Epoch 6148 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01544 0.9858 0.9844 8.547e-06 -3.837e-06 -0.001113 6.442e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003162 -0.002876 -0.01073 0.008239 0.9696 0.974 0.005972 0.8472 0.836 0.02212 ] Network output: [ 1.001 -0.02791 0.003725 -4.515e-05 2.027e-05 0.02297 -3.403e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02187 -0.2066 0.2149 0.9836 0.9933 0.2014 0.4699 0.882 0.7293 ] Network output: [ -0.01266 0.9979 1.011 2.451e-06 -1.1e-06 0.01665 1.847e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005024 0.0007835 0.004181 0.005368 0.989 0.9921 0.005114 0.8792 0.9059 0.01605 ] Network output: [ 0.00251 -0.04057 0.9995 -0.0002009 9.018e-05 1.035 -0.0001514 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.1042 0.3117 0.1845 0.9852 0.9941 0.1917 0.4753 0.8883 0.7236 ] Network output: [ 0.009637 -0.04723 1.004 0.0001174 -5.271e-05 1.024 8.848e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09754 0.08823 0.1785 0.2196 0.9874 0.992 0.0976 0.8125 0.8874 0.3142 ] Network output: [ -0.01197 0.04696 1.003 0.0001124 -5.045e-05 0.974 8.469e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1004 0.0987 0.1751 0.2061 0.9859 0.9917 0.1004 0.7465 0.8691 0.2483 ] Network output: [ 0.001836 0.9983 -0.002607 1.776e-05 -7.974e-06 1.001 1.339e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001632 Epoch 6149 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01483 0.9954 0.9841 7.129e-06 -3.2e-06 -0.009059 5.373e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.002876 -0.01077 0.008062 0.9696 0.974 0.00599 0.8474 0.8356 0.02207 ] Network output: [ 0.9956 0.03354 0.001045 -5.385e-05 2.418e-05 -0.02603 -4.058e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02107 -0.2099 0.2045 0.9836 0.9933 0.2023 0.4714 0.8816 0.7284 ] Network output: [ -0.01265 1.001 1.011 1.944e-06 -8.727e-07 0.01351 1.465e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005043 0.0007767 0.004004 0.005015 0.989 0.992 0.005134 0.8793 0.9056 0.01596 ] Network output: [ -0.003022 0.04484 0.9955 -0.000214 9.607e-05 0.9648 -0.0001613 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.1044 0.306 0.168 0.9851 0.9941 0.1925 0.4763 0.8884 0.7242 ] Network output: [ 0.01163 -0.03018 1.001 0.0001168 -5.242e-05 1.007 8.8e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09725 0.08789 0.1745 0.215 0.9874 0.992 0.09731 0.8113 0.8873 0.3117 ] Network output: [ -0.01068 0.0424 1.003 0.0001136 -5.098e-05 0.9765 8.558e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1001 0.09839 0.1737 0.2052 0.9858 0.9916 0.1001 0.7449 0.8691 0.2481 ] Network output: [ -0.001055 0.9997 0.001451 1.592e-05 -7.149e-06 1.001 1.2e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001386 Epoch 6150 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01544 0.9858 0.9845 8.542e-06 -3.835e-06 -0.001106 6.438e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003162 -0.002876 -0.01073 0.008237 0.9696 0.974 0.005973 0.8472 0.836 0.02212 ] Network output: [ 1.001 -0.02792 0.003723 -4.516e-05 2.027e-05 0.02298 -3.403e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02189 -0.2066 0.2149 0.9836 0.9933 0.2014 0.4699 0.882 0.7292 ] Network output: [ -0.01266 0.9978 1.011 2.457e-06 -1.103e-06 0.01666 1.852e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005025 0.0007824 0.004182 0.005366 0.989 0.9921 0.005115 0.8792 0.9058 0.01605 ] Network output: [ 0.002508 -0.04059 0.9995 -0.0002007 9.011e-05 1.035 -0.0001513 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.1041 0.3118 0.1845 0.9852 0.9941 0.1917 0.4753 0.8883 0.7236 ] Network output: [ 0.009632 -0.04728 1.004 0.0001173 -5.267e-05 1.024 8.842e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09754 0.08823 0.1786 0.2196 0.9874 0.992 0.0976 0.8125 0.8874 0.3142 ] Network output: [ -0.01196 0.04702 1.003 0.0001123 -5.042e-05 0.9739 8.463e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1004 0.09868 0.1751 0.2061 0.9859 0.9917 0.1004 0.7465 0.8691 0.2482 ] Network output: [ 0.001837 0.9983 -0.002611 1.774e-05 -7.964e-06 1.001 1.337e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001635 Epoch 6151 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01483 0.9954 0.9841 7.125e-06 -3.199e-06 -0.009056 5.369e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.002877 -0.01077 0.00806 0.9696 0.974 0.00599 0.8474 0.8356 0.02206 ] Network output: [ 0.9956 0.03355 0.00104 -5.385e-05 2.417e-05 -0.02605 -4.058e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.0211 -0.2099 0.2045 0.9836 0.9933 0.2022 0.4713 0.8816 0.7284 ] Network output: [ -0.01265 1.001 1.011 1.951e-06 -8.757e-07 0.01351 1.47e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005044 0.0007756 0.004005 0.005013 0.989 0.992 0.005134 0.8793 0.9056 0.01595 ] Network output: [ -0.003026 0.04487 0.9955 -0.0002138 9.599e-05 0.9648 -0.0001611 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.1044 0.306 0.168 0.9851 0.9941 0.1925 0.4763 0.8883 0.7242 ] Network output: [ 0.01163 -0.03023 1.001 0.0001167 -5.238e-05 1.007 8.794e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09725 0.08789 0.1745 0.215 0.9874 0.992 0.09731 0.8112 0.8873 0.3117 ] Network output: [ -0.01067 0.04245 1.003 0.0001135 -5.095e-05 0.9764 8.553e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1001 0.09837 0.1737 0.2051 0.9858 0.9916 0.1001 0.7448 0.869 0.2481 ] Network output: [ -0.001057 0.9997 0.001451 1.59e-05 -7.139e-06 1.001 1.198e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001389 Epoch 6152 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01544 0.9858 0.9845 8.537e-06 -3.833e-06 -0.0011 6.434e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003163 -0.002876 -0.01073 0.008234 0.9696 0.974 0.005973 0.8472 0.836 0.02211 ] Network output: [ 1.001 -0.02793 0.003721 -4.516e-05 2.027e-05 0.02299 -3.403e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02192 -0.2066 0.2148 0.9836 0.9933 0.2014 0.4698 0.882 0.7292 ] Network output: [ -0.01265 0.9978 1.011 2.464e-06 -1.106e-06 0.01666 1.857e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005026 0.0007814 0.004183 0.005364 0.989 0.9921 0.005116 0.8792 0.9058 0.01605 ] Network output: [ 0.002506 -0.04059 0.9996 -0.0002005 9.003e-05 1.035 -0.0001511 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.1041 0.3118 0.1844 0.9852 0.9941 0.1917 0.4752 0.8883 0.7235 ] Network output: [ 0.009628 -0.04734 1.004 0.0001172 -5.263e-05 1.024 8.836e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09754 0.08823 0.1786 0.2196 0.9874 0.992 0.0976 0.8124 0.8874 0.3142 ] Network output: [ -0.01195 0.04707 1.003 0.0001122 -5.039e-05 0.9739 8.458e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1004 0.09866 0.175 0.206 0.9859 0.9917 0.1004 0.7464 0.8691 0.2482 ] Network output: [ 0.001838 0.9983 -0.002616 1.772e-05 -7.953e-06 1.001 1.335e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001637 Epoch 6153 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01483 0.9954 0.9841 7.12e-06 -3.196e-06 -0.009052 5.366e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.002877 -0.01077 0.008058 0.9696 0.974 0.00599 0.8474 0.8356 0.02206 ] Network output: [ 0.9956 0.03357 0.001036 -5.384e-05 2.417e-05 -0.02606 -4.057e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02112 -0.2098 0.2045 0.9836 0.9933 0.2022 0.4713 0.8816 0.7283 ] Network output: [ -0.01264 1.001 1.011 1.957e-06 -8.786e-07 0.01351 1.475e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005045 0.0007746 0.004006 0.005011 0.989 0.992 0.005135 0.8792 0.9056 0.01595 ] Network output: [ -0.003029 0.04489 0.9956 -0.0002137 9.592e-05 0.9647 -0.000161 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.1043 0.306 0.1679 0.9851 0.9941 0.1925 0.4763 0.8883 0.7241 ] Network output: [ 0.01162 -0.03027 1.001 0.0001166 -5.235e-05 1.007 8.787e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09725 0.08788 0.1746 0.215 0.9874 0.992 0.09731 0.8112 0.8872 0.3117 ] Network output: [ -0.01067 0.0425 1.003 0.0001134 -5.092e-05 0.9764 8.547e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1001 0.09835 0.1737 0.2051 0.9858 0.9916 0.1001 0.7448 0.869 0.248 ] Network output: [ -0.001059 0.9997 0.00145 1.588e-05 -7.129e-06 1.001 1.197e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001391 Epoch 6154 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01543 0.9858 0.9845 8.532e-06 -3.83e-06 -0.001094 6.43e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003163 -0.002877 -0.01072 0.008232 0.9696 0.974 0.005973 0.8472 0.836 0.02211 ] Network output: [ 1.001 -0.02794 0.003718 -4.516e-05 2.027e-05 0.02299 -3.403e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02195 -0.2066 0.2148 0.9836 0.9933 0.2014 0.4698 0.882 0.7292 ] Network output: [ -0.01265 0.9978 1.011 2.471e-06 -1.109e-06 0.01666 1.862e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005026 0.0007803 0.004184 0.005363 0.989 0.992 0.005116 0.8792 0.9058 0.01604 ] Network output: [ 0.002503 -0.0406 0.9996 -0.0002004 8.996e-05 1.035 -0.000151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.1041 0.3118 0.1844 0.9852 0.9941 0.1917 0.4752 0.8883 0.7235 ] Network output: [ 0.009624 -0.04739 1.004 0.0001172 -5.26e-05 1.024 8.83e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09755 0.08822 0.1786 0.2196 0.9874 0.992 0.09761 0.8124 0.8874 0.3142 ] Network output: [ -0.01195 0.04712 1.003 0.0001122 -5.035e-05 0.9738 8.453e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1003 0.09864 0.175 0.206 0.9859 0.9917 0.1003 0.7464 0.869 0.2482 ] Network output: [ 0.001839 0.9983 -0.00262 1.769e-05 -7.943e-06 1.001 1.333e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001639 Epoch 6155 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01482 0.9954 0.9841 7.116e-06 -3.194e-06 -0.009049 5.362e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.002877 -0.01076 0.008056 0.9696 0.974 0.00599 0.8474 0.8356 0.02205 ] Network output: [ 0.9956 0.03358 0.001032 -5.383e-05 2.417e-05 -0.02608 -4.057e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02115 -0.2098 0.2044 0.9836 0.9933 0.2022 0.4713 0.8816 0.7283 ] Network output: [ -0.01264 1.001 1.011 1.963e-06 -8.814e-07 0.01351 1.48e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005046 0.0007735 0.004006 0.00501 0.989 0.992 0.005136 0.8792 0.9056 0.01595 ] Network output: [ -0.003033 0.04491 0.9956 -0.0002135 9.584e-05 0.9647 -0.0001609 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.1043 0.306 0.1679 0.9851 0.9941 0.1925 0.4763 0.8883 0.7241 ] Network output: [ 0.01162 -0.03032 1.001 0.0001165 -5.231e-05 1.007 8.781e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09726 0.08788 0.1746 0.215 0.9874 0.992 0.09732 0.8111 0.8872 0.3117 ] Network output: [ -0.01066 0.04255 1.003 0.0001133 -5.088e-05 0.9763 8.542e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1 0.09833 0.1736 0.2051 0.9858 0.9916 0.1001 0.7447 0.869 0.248 ] Network output: [ -0.001061 0.9997 0.001449 1.586e-05 -7.119e-06 1.001 1.195e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001394 Epoch 6156 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01543 0.9858 0.9845 8.526e-06 -3.828e-06 -0.001088 6.426e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003163 -0.002877 -0.01072 0.00823 0.9696 0.974 0.005973 0.8472 0.836 0.0221 ] Network output: [ 1.001 -0.02795 0.003716 -4.516e-05 2.027e-05 0.023 -3.404e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02197 -0.2065 0.2148 0.9836 0.9933 0.2014 0.4698 0.882 0.7291 ] Network output: [ -0.01265 0.9978 1.011 2.477e-06 -1.112e-06 0.01666 1.867e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005027 0.0007793 0.004184 0.005361 0.989 0.992 0.005117 0.8791 0.9058 0.01604 ] Network output: [ 0.0025 -0.0406 0.9997 -0.0002002 8.988e-05 1.035 -0.0001509 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.104 0.3118 0.1843 0.9852 0.9941 0.1917 0.4752 0.8883 0.7234 ] Network output: [ 0.009619 -0.04745 1.004 0.0001171 -5.256e-05 1.024 8.824e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09755 0.08822 0.1786 0.2196 0.9874 0.992 0.09761 0.8123 0.8873 0.3142 ] Network output: [ -0.01194 0.04718 1.003 0.0001121 -5.032e-05 0.9738 8.448e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1003 0.09862 0.175 0.206 0.9859 0.9916 0.1003 0.7463 0.869 0.2481 ] Network output: [ 0.00184 0.9983 -0.002624 1.767e-05 -7.933e-06 1.001 1.332e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001641 Epoch 6157 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01482 0.9954 0.9841 7.111e-06 -3.192e-06 -0.009045 5.359e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.002877 -0.01076 0.008054 0.9696 0.974 0.005991 0.8474 0.8356 0.02205 ] Network output: [ 0.9956 0.03358 0.001028 -5.382e-05 2.416e-05 -0.02609 -4.056e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02117 -0.2098 0.2044 0.9836 0.9933 0.2022 0.4713 0.8816 0.7283 ] Network output: [ -0.01264 1.001 1.011 1.969e-06 -8.842e-07 0.01351 1.484e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005046 0.0007725 0.004007 0.005008 0.989 0.992 0.005137 0.8792 0.9056 0.01595 ] Network output: [ -0.003036 0.04493 0.9957 -0.0002133 9.576e-05 0.9646 -0.0001607 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.1042 0.3061 0.1678 0.9851 0.9941 0.1925 0.4762 0.8883 0.724 ] Network output: [ 0.01161 -0.03037 1.001 0.0001164 -5.227e-05 1.007 8.775e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09726 0.08788 0.1746 0.215 0.9874 0.992 0.09732 0.8111 0.8872 0.3117 ] Network output: [ -0.01066 0.0426 1.003 0.0001133 -5.085e-05 0.9763 8.537e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1 0.09831 0.1736 0.2051 0.9858 0.9916 0.1 0.7447 0.8689 0.248 ] Network output: [ -0.001063 0.9997 0.001449 1.584e-05 -7.109e-06 1.001 1.193e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001396 Epoch 6158 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01543 0.9858 0.9845 8.52e-06 -3.825e-06 -0.001083 6.421e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003163 -0.002877 -0.01072 0.008228 0.9696 0.974 0.005973 0.8472 0.8359 0.0221 ] Network output: [ 1.001 -0.02795 0.003714 -4.516e-05 2.028e-05 0.023 -3.404e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.022 -0.2065 0.2147 0.9836 0.9933 0.2014 0.4698 0.8819 0.7291 ] Network output: [ -0.01265 0.9978 1.011 2.483e-06 -1.115e-06 0.01666 1.871e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005028 0.0007782 0.004185 0.005359 0.989 0.992 0.005118 0.8791 0.9058 0.01604 ] Network output: [ 0.002498 -0.0406 0.9997 -0.0002 8.981e-05 1.035 -0.0001508 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.104 0.3119 0.1843 0.9852 0.9941 0.1917 0.4752 0.8883 0.7234 ] Network output: [ 0.009615 -0.0475 1.004 0.000117 -5.253e-05 1.024 8.818e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09755 0.08822 0.1786 0.2196 0.9874 0.992 0.09761 0.8123 0.8873 0.3142 ] Network output: [ -0.01193 0.04723 1.003 0.000112 -5.029e-05 0.9738 8.443e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1003 0.0986 0.1749 0.206 0.9859 0.9916 0.1003 0.7463 0.869 0.2481 ] Network output: [ 0.001841 0.9983 -0.002628 1.765e-05 -7.923e-06 1.001 1.33e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001643 Epoch 6159 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01482 0.9954 0.9841 7.106e-06 -3.19e-06 -0.009041 5.355e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.002878 -0.01076 0.008051 0.9696 0.974 0.005991 0.8474 0.8355 0.02204 ] Network output: [ 0.9956 0.03359 0.001024 -5.382e-05 2.416e-05 -0.0261 -4.056e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.0212 -0.2098 0.2044 0.9836 0.9933 0.2022 0.4712 0.8816 0.7282 ] Network output: [ -0.01264 1.001 1.011 1.975e-06 -8.869e-07 0.01352 1.489e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005047 0.0007714 0.004008 0.005006 0.989 0.992 0.005138 0.8792 0.9056 0.01594 ] Network output: [ -0.00304 0.04494 0.9957 -0.0002131 9.568e-05 0.9646 -0.0001606 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.1042 0.3061 0.1678 0.9851 0.9941 0.1925 0.4762 0.8883 0.724 ] Network output: [ 0.01161 -0.03042 1.001 0.0001164 -5.223e-05 1.007 8.769e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09726 0.08787 0.1746 0.215 0.9874 0.992 0.09732 0.811 0.8872 0.3117 ] Network output: [ -0.01065 0.04265 1.003 0.0001132 -5.082e-05 0.9763 8.531e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1 0.09829 0.1736 0.205 0.9858 0.9916 0.1 0.7446 0.8689 0.2479 ] Network output: [ -0.001064 0.9997 0.001448 1.581e-05 -7.099e-06 1.001 1.192e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001398 Epoch 6160 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01542 0.9858 0.9845 8.514e-06 -3.822e-06 -0.001078 6.417e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003163 -0.002878 -0.01071 0.008226 0.9696 0.974 0.005974 0.8472 0.8359 0.02209 ] Network output: [ 1.001 -0.02796 0.003711 -4.517e-05 2.028e-05 0.023 -3.404e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02202 -0.2065 0.2147 0.9836 0.9933 0.2014 0.4697 0.8819 0.729 ] Network output: [ -0.01264 0.9978 1.011 2.489e-06 -1.117e-06 0.01666 1.876e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005029 0.0007772 0.004186 0.005357 0.989 0.992 0.005119 0.8791 0.9058 0.01604 ] Network output: [ 0.002494 -0.0406 0.9997 -0.0001999 8.973e-05 1.035 -0.0001506 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.1039 0.3119 0.1842 0.9852 0.9941 0.1917 0.4751 0.8883 0.7234 ] Network output: [ 0.009611 -0.04755 1.004 0.0001169 -5.249e-05 1.024 8.811e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09755 0.08821 0.1786 0.2196 0.9874 0.992 0.09761 0.8122 0.8873 0.3142 ] Network output: [ -0.01193 0.04728 1.003 0.000112 -5.026e-05 0.9737 8.437e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1003 0.09858 0.1749 0.2059 0.9859 0.9916 0.1003 0.7462 0.8689 0.2481 ] Network output: [ 0.001841 0.9984 -0.002632 1.763e-05 -7.913e-06 1.001 1.328e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001645 Epoch 6161 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01481 0.9953 0.9841 7.101e-06 -3.188e-06 -0.009037 5.352e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.002878 -0.01076 0.008049 0.9696 0.974 0.005991 0.8474 0.8355 0.02204 ] Network output: [ 0.9956 0.0336 0.00102 -5.381e-05 2.416e-05 -0.02611 -4.055e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02122 -0.2097 0.2043 0.9836 0.9933 0.2022 0.4712 0.8815 0.7282 ] Network output: [ -0.01263 1.001 1.011 1.981e-06 -8.895e-07 0.01352 1.493e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005048 0.0007704 0.004009 0.005004 0.989 0.992 0.005138 0.8792 0.9055 0.01594 ] Network output: [ -0.003043 0.04495 0.9957 -0.0002129 9.56e-05 0.9645 -0.0001605 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.1042 0.3061 0.1677 0.9851 0.9941 0.1925 0.4762 0.8883 0.7239 ] Network output: [ 0.0116 -0.03047 1.001 0.0001163 -5.22e-05 1.007 8.762e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09726 0.08787 0.1746 0.2149 0.9874 0.992 0.09732 0.811 0.8871 0.3118 ] Network output: [ -0.01065 0.0427 1.003 0.0001131 -5.079e-05 0.9762 8.526e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09998 0.09826 0.1736 0.205 0.9858 0.9916 0.09999 0.7446 0.8689 0.2479 ] Network output: [ -0.001066 0.9997 0.001447 1.579e-05 -7.09e-06 1.001 1.19e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0014 Epoch 6162 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01542 0.9858 0.9845 8.508e-06 -3.82e-06 -0.001073 6.412e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003163 -0.002878 -0.01071 0.008224 0.9696 0.974 0.005974 0.8472 0.8359 0.02209 ] Network output: [ 1.001 -0.02796 0.003709 -4.517e-05 2.028e-05 0.02299 -3.404e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02205 -0.2065 0.2147 0.9836 0.9933 0.2013 0.4697 0.8819 0.729 ] Network output: [ -0.01264 0.9978 1.011 2.494e-06 -1.12e-06 0.01666 1.88e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005029 0.0007762 0.004187 0.005355 0.989 0.992 0.00512 0.8791 0.9058 0.01603 ] Network output: [ 0.002491 -0.04059 0.9998 -0.0001997 8.966e-05 1.035 -0.0001505 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.1039 0.3119 0.1842 0.9852 0.9941 0.1917 0.4751 0.8883 0.7233 ] Network output: [ 0.009607 -0.0476 1.004 0.0001168 -5.245e-05 1.025 8.805e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09756 0.08821 0.1786 0.2196 0.9874 0.992 0.09762 0.8122 0.8872 0.3142 ] Network output: [ -0.01192 0.04733 1.003 0.0001119 -5.023e-05 0.9737 8.432e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1003 0.09855 0.1749 0.2059 0.9859 0.9916 0.1003 0.7462 0.8689 0.2481 ] Network output: [ 0.001842 0.9984 -0.002635 1.76e-05 -7.902e-06 1.001 1.327e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001647 Epoch 6163 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01481 0.9953 0.9841 7.096e-06 -3.186e-06 -0.009032 5.348e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.002878 -0.01075 0.008047 0.9696 0.974 0.005991 0.8474 0.8355 0.02203 ] Network output: [ 0.9956 0.0336 0.001016 -5.38e-05 2.415e-05 -0.02612 -4.054e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02125 -0.2097 0.2043 0.9836 0.9933 0.2022 0.4712 0.8815 0.7281 ] Network output: [ -0.01263 1.001 1.011 1.987e-06 -8.92e-07 0.01352 1.497e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005049 0.0007694 0.00401 0.005002 0.989 0.992 0.005139 0.8792 0.9055 0.01594 ] Network output: [ -0.003045 0.04496 0.9958 -0.0002128 9.552e-05 0.9645 -0.0001603 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.1041 0.3061 0.1677 0.9851 0.9941 0.1924 0.4762 0.8883 0.7239 ] Network output: [ 0.01159 -0.03052 1 0.0001162 -5.216e-05 1.007 8.756e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09727 0.08787 0.1746 0.2149 0.9874 0.992 0.09733 0.8109 0.8871 0.3118 ] Network output: [ -0.01065 0.04274 1.003 0.0001131 -5.076e-05 0.9762 8.52e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09996 0.09824 0.1735 0.205 0.9858 0.9916 0.09997 0.7445 0.8688 0.2479 ] Network output: [ -0.001068 0.9997 0.001445 1.577e-05 -7.08e-06 1.001 1.189e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001402 Epoch 6164 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01542 0.9858 0.9845 8.502e-06 -3.817e-06 -0.001069 6.407e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003163 -0.002878 -0.01071 0.008222 0.9696 0.974 0.005974 0.8472 0.8359 0.02208 ] Network output: [ 1.001 -0.02795 0.003706 -4.517e-05 2.028e-05 0.02299 -3.404e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02207 -0.2064 0.2147 0.9836 0.9933 0.2013 0.4697 0.8819 0.729 ] Network output: [ -0.01264 0.9978 1.011 2.5e-06 -1.122e-06 0.01666 1.884e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00503 0.0007752 0.004188 0.005353 0.989 0.992 0.00512 0.8791 0.9058 0.01603 ] Network output: [ 0.002488 -0.04058 0.9998 -0.0001995 8.958e-05 1.035 -0.0001504 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.1038 0.3119 0.1841 0.9852 0.9941 0.1917 0.4751 0.8882 0.7233 ] Network output: [ 0.009603 -0.04765 1.004 0.0001168 -5.242e-05 1.025 8.799e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09756 0.08821 0.1786 0.2196 0.9874 0.992 0.09762 0.8121 0.8872 0.3142 ] Network output: [ -0.01191 0.04738 1.003 0.0001118 -5.02e-05 0.9736 8.427e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1002 0.09853 0.1749 0.2059 0.9859 0.9916 0.1002 0.7461 0.8689 0.248 ] Network output: [ 0.001842 0.9984 -0.002639 1.758e-05 -7.892e-06 1.001 1.325e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001648 Epoch 6165 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01481 0.9953 0.9841 7.091e-06 -3.183e-06 -0.009028 5.344e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.002879 -0.01075 0.008045 0.9696 0.974 0.005991 0.8474 0.8355 0.02203 ] Network output: [ 0.9957 0.0336 0.001013 -5.379e-05 2.415e-05 -0.02613 -4.054e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02127 -0.2097 0.2043 0.9836 0.9933 0.2022 0.4712 0.8815 0.7281 ] Network output: [ -0.01263 1.001 1.011 1.992e-06 -8.945e-07 0.01352 1.502e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005049 0.0007684 0.00401 0.005 0.989 0.992 0.00514 0.8792 0.9055 0.01593 ] Network output: [ -0.003048 0.04496 0.9958 -0.0002126 9.544e-05 0.9644 -0.0001602 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.1041 0.3062 0.1676 0.9851 0.9941 0.1924 0.4761 0.8883 0.7239 ] Network output: [ 0.01159 -0.03057 1 0.0001161 -5.212e-05 1.007 8.75e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09727 0.08786 0.1746 0.2149 0.9874 0.992 0.09733 0.8109 0.8871 0.3118 ] Network output: [ -0.01064 0.04279 1.003 0.000113 -5.072e-05 0.9761 8.515e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09994 0.09822 0.1735 0.2049 0.9858 0.9916 0.09995 0.7445 0.8688 0.2479 ] Network output: [ -0.001069 0.9997 0.001444 1.575e-05 -7.07e-06 1.001 1.187e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001404 Epoch 6166 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01541 0.9858 0.9845 8.495e-06 -3.814e-06 -0.001065 6.402e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003163 -0.002878 -0.01071 0.00822 0.9696 0.974 0.005974 0.8472 0.8359 0.02208 ] Network output: [ 1.001 -0.02795 0.003703 -4.517e-05 2.028e-05 0.02298 -3.404e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.0221 -0.2064 0.2146 0.9836 0.9933 0.2013 0.4697 0.8819 0.7289 ] Network output: [ -0.01264 0.9978 1.011 2.505e-06 -1.125e-06 0.01666 1.888e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005031 0.0007741 0.004189 0.005352 0.989 0.992 0.005121 0.8791 0.9057 0.01603 ] Network output: [ 0.002484 -0.04056 0.9998 -0.0001994 8.951e-05 1.035 -0.0001503 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.1038 0.3119 0.1841 0.9852 0.9941 0.1917 0.4751 0.8882 0.7232 ] Network output: [ 0.009599 -0.0477 1.004 0.0001167 -5.238e-05 1.025 8.793e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09756 0.0882 0.1786 0.2195 0.9874 0.992 0.09762 0.8121 0.8872 0.3142 ] Network output: [ -0.01191 0.04742 1.003 0.0001117 -5.017e-05 0.9736 8.422e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1002 0.09851 0.1748 0.2058 0.9859 0.9916 0.1002 0.7461 0.8688 0.248 ] Network output: [ 0.001842 0.9984 -0.002642 1.756e-05 -7.882e-06 1.001 1.323e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00165 Epoch 6167 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0148 0.9953 0.9841 7.086e-06 -3.181e-06 -0.009023 5.34e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.002879 -0.01075 0.008043 0.9696 0.974 0.005991 0.8474 0.8355 0.02202 ] Network output: [ 0.9957 0.03359 0.001009 -5.378e-05 2.414e-05 -0.02613 -4.053e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.0213 -0.2097 0.2043 0.9836 0.9933 0.2022 0.4711 0.8815 0.7281 ] Network output: [ -0.01263 1.001 1.011 1.998e-06 -8.969e-07 0.01352 1.506e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00505 0.0007673 0.004011 0.004999 0.989 0.992 0.005141 0.8792 0.9055 0.01593 ] Network output: [ -0.00305 0.04496 0.9959 -0.0002124 9.536e-05 0.9644 -0.0001601 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.104 0.3062 0.1676 0.9851 0.9941 0.1924 0.4761 0.8882 0.7238 ] Network output: [ 0.01158 -0.03062 1 0.000116 -5.208e-05 1.007 8.743e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09727 0.08786 0.1746 0.2149 0.9874 0.992 0.09733 0.8108 0.887 0.3118 ] Network output: [ -0.01064 0.04284 1.003 0.0001129 -5.069e-05 0.9761 8.509e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09992 0.0982 0.1735 0.2049 0.9858 0.9916 0.09993 0.7444 0.8688 0.2478 ] Network output: [ -0.00107 0.9997 0.001443 1.573e-05 -7.061e-06 1.001 1.185e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001405 Epoch 6168 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01541 0.9858 0.9845 8.488e-06 -3.811e-06 -0.001062 6.397e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003163 -0.002879 -0.0107 0.008218 0.9696 0.974 0.005974 0.8472 0.8359 0.02208 ] Network output: [ 1.001 -0.02794 0.0037 -4.517e-05 2.028e-05 0.02298 -3.404e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02212 -0.2064 0.2146 0.9836 0.9933 0.2013 0.4696 0.8819 0.7289 ] Network output: [ -0.01263 0.9978 1.011 2.51e-06 -1.127e-06 0.01667 1.892e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005032 0.0007731 0.004189 0.00535 0.989 0.992 0.005122 0.8791 0.9057 0.01602 ] Network output: [ 0.00248 -0.04054 0.9999 -0.0001992 8.943e-05 1.035 -0.0001501 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.1037 0.312 0.184 0.9852 0.9941 0.1917 0.475 0.8882 0.7232 ] Network output: [ 0.009596 -0.04775 1.004 0.0001166 -5.234e-05 1.025 8.787e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09756 0.0882 0.1787 0.2195 0.9874 0.992 0.09762 0.812 0.8872 0.3142 ] Network output: [ -0.0119 0.04747 1.003 0.0001117 -5.014e-05 0.9735 8.416e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1002 0.09849 0.1748 0.2058 0.9859 0.9916 0.1002 0.746 0.8688 0.248 ] Network output: [ 0.001843 0.9984 -0.002645 1.753e-05 -7.872e-06 1.001 1.321e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001651 Epoch 6169 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0148 0.9953 0.9841 7.081e-06 -3.179e-06 -0.009018 5.336e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.002879 -0.01074 0.008041 0.9696 0.974 0.005992 0.8474 0.8355 0.02202 ] Network output: [ 0.9957 0.03359 0.001005 -5.376e-05 2.414e-05 -0.02613 -4.052e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02132 -0.2097 0.2042 0.9836 0.9933 0.2022 0.4711 0.8815 0.728 ] Network output: [ -0.01263 1.001 1.011 2.003e-06 -8.993e-07 0.01352 1.51e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005051 0.0007663 0.004012 0.004997 0.989 0.992 0.005141 0.8792 0.9055 0.01593 ] Network output: [ -0.003052 0.04496 0.9959 -0.0002122 9.528e-05 0.9644 -0.0001599 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.104 0.3062 0.1675 0.9851 0.9941 0.1924 0.4761 0.8882 0.7238 ] Network output: [ 0.01158 -0.03067 1 0.0001159 -5.205e-05 1.008 8.737e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09727 0.08785 0.1747 0.2149 0.9874 0.992 0.09733 0.8108 0.887 0.3118 ] Network output: [ -0.01063 0.04288 1.003 0.0001128 -5.066e-05 0.976 8.504e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0999 0.09818 0.1735 0.2049 0.9858 0.9916 0.09991 0.7444 0.8688 0.2478 ] Network output: [ -0.001071 0.9997 0.001441 1.571e-05 -7.051e-06 1.001 1.184e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001407 Epoch 6170 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01541 0.9858 0.9845 8.481e-06 -3.808e-06 -0.001059 6.392e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003163 -0.002879 -0.0107 0.008215 0.9696 0.974 0.005975 0.8472 0.8359 0.02207 ] Network output: [ 1.001 -0.02793 0.003697 -4.517e-05 2.028e-05 0.02296 -3.404e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02215 -0.2064 0.2146 0.9836 0.9933 0.2013 0.4696 0.8819 0.7288 ] Network output: [ -0.01263 0.9978 1.011 2.515e-06 -1.129e-06 0.01667 1.895e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005032 0.0007721 0.00419 0.005348 0.989 0.992 0.005123 0.8791 0.9057 0.01602 ] Network output: [ 0.002475 -0.04052 0.9999 -0.000199 8.936e-05 1.035 -0.00015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.1037 0.312 0.184 0.9852 0.9941 0.1917 0.475 0.8882 0.7232 ] Network output: [ 0.009592 -0.0478 1.004 0.0001165 -5.23e-05 1.025 8.78e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09757 0.08819 0.1787 0.2195 0.9874 0.992 0.09763 0.812 0.8871 0.3142 ] Network output: [ -0.01189 0.04752 1.003 0.0001116 -5.01e-05 0.9735 8.411e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1002 0.09847 0.1748 0.2058 0.9859 0.9916 0.1002 0.746 0.8688 0.248 ] Network output: [ 0.001843 0.9984 -0.002648 1.751e-05 -7.862e-06 1.001 1.32e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001652 Epoch 6171 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0148 0.9953 0.9841 7.075e-06 -3.176e-06 -0.009013 5.332e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.002879 -0.01074 0.008039 0.9696 0.974 0.005992 0.8474 0.8355 0.02201 ] Network output: [ 0.9957 0.03358 0.001002 -5.375e-05 2.413e-05 -0.02614 -4.051e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02135 -0.2096 0.2042 0.9836 0.9933 0.2022 0.4711 0.8815 0.728 ] Network output: [ -0.01262 1.001 1.011 2.008e-06 -9.016e-07 0.01352 1.513e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005052 0.0007653 0.004013 0.004995 0.989 0.992 0.005142 0.8792 0.9055 0.01592 ] Network output: [ -0.003054 0.04495 0.9959 -0.000212 9.519e-05 0.9644 -0.0001598 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.1039 0.3062 0.1675 0.9851 0.9941 0.1924 0.4761 0.8882 0.7238 ] Network output: [ 0.01157 -0.03072 1 0.0001158 -5.201e-05 1.008 8.731e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09728 0.08785 0.1747 0.2149 0.9874 0.992 0.09734 0.8107 0.887 0.3118 ] Network output: [ -0.01063 0.04293 1.003 0.0001128 -5.062e-05 0.976 8.498e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09988 0.09816 0.1734 0.2048 0.9858 0.9916 0.09989 0.7443 0.8687 0.2478 ] Network output: [ -0.001072 0.9997 0.001439 1.569e-05 -7.042e-06 1.001 1.182e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001408 Epoch 6172 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0154 0.9858 0.9845 8.474e-06 -3.804e-06 -0.001056 6.386e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003163 -0.002879 -0.0107 0.008213 0.9696 0.974 0.005975 0.8472 0.8359 0.02207 ] Network output: [ 1.001 -0.02792 0.003694 -4.517e-05 2.028e-05 0.02295 -3.404e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02217 -0.2063 0.2145 0.9836 0.9933 0.2013 0.4696 0.8819 0.7288 ] Network output: [ -0.01263 0.9978 1.011 2.52e-06 -1.131e-06 0.01666 1.899e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005033 0.0007711 0.004191 0.005346 0.989 0.992 0.005123 0.8791 0.9057 0.01602 ] Network output: [ 0.002471 -0.0405 1 -0.0001989 8.929e-05 1.035 -0.0001499 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.1036 0.312 0.1839 0.9852 0.9941 0.1917 0.475 0.8882 0.7231 ] Network output: [ 0.009588 -0.04784 1.004 0.0001164 -5.227e-05 1.025 8.774e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09757 0.08819 0.1787 0.2195 0.9874 0.992 0.09763 0.8119 0.8871 0.3142 ] Network output: [ -0.01189 0.04756 1.003 0.0001115 -5.007e-05 0.9735 8.406e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1001 0.09844 0.1748 0.2057 0.9859 0.9916 0.1002 0.7459 0.8687 0.2479 ] Network output: [ 0.001842 0.9984 -0.00265 1.749e-05 -7.852e-06 1.001 1.318e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001653 Epoch 6173 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01479 0.9953 0.9841 7.07e-06 -3.174e-06 -0.009007 5.328e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.00288 -0.01074 0.008037 0.9696 0.974 0.005992 0.8474 0.8355 0.02201 ] Network output: [ 0.9957 0.03357 0.0009987 -5.374e-05 2.412e-05 -0.02613 -4.05e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02137 -0.2096 0.2042 0.9836 0.9933 0.2022 0.4711 0.8815 0.728 ] Network output: [ -0.01262 1.001 1.011 2.013e-06 -9.038e-07 0.01352 1.517e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005052 0.0007643 0.004014 0.004993 0.989 0.992 0.005143 0.8791 0.9055 0.01592 ] Network output: [ -0.003056 0.04494 0.996 -0.0002119 9.511e-05 0.9643 -0.0001597 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.1039 0.3062 0.1674 0.9851 0.9941 0.1924 0.476 0.8882 0.7237 ] Network output: [ 0.01156 -0.03077 1 0.0001158 -5.197e-05 1.008 8.724e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09728 0.08785 0.1747 0.2149 0.9874 0.992 0.09734 0.8107 0.887 0.3118 ] Network output: [ -0.01062 0.04297 1.003 0.0001127 -5.059e-05 0.976 8.493e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09985 0.09813 0.1734 0.2048 0.9858 0.9916 0.09987 0.7443 0.8687 0.2478 ] Network output: [ -0.001073 0.9997 0.001438 1.567e-05 -7.033e-06 1.001 1.181e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001409 Epoch 6174 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0154 0.9858 0.9845 8.467e-06 -3.801e-06 -0.001054 6.381e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003163 -0.00288 -0.01069 0.008211 0.9696 0.974 0.005975 0.8472 0.8359 0.02206 ] Network output: [ 1.001 -0.02791 0.003691 -4.517e-05 2.028e-05 0.02294 -3.404e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.0222 -0.2063 0.2145 0.9836 0.9933 0.2013 0.4696 0.8819 0.7288 ] Network output: [ -0.01263 0.9978 1.011 2.525e-06 -1.133e-06 0.01666 1.903e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005034 0.0007701 0.004192 0.005344 0.989 0.992 0.005124 0.879 0.9057 0.01601 ] Network output: [ 0.002466 -0.04047 1 -0.0001987 8.921e-05 1.035 -0.0001498 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.1036 0.312 0.1839 0.9852 0.9941 0.1917 0.475 0.8882 0.7231 ] Network output: [ 0.009585 -0.04789 1.004 0.0001163 -5.223e-05 1.025 8.768e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09757 0.08818 0.1787 0.2195 0.9874 0.992 0.09763 0.8119 0.8871 0.3142 ] Network output: [ -0.01188 0.04761 1.003 0.0001115 -5.004e-05 0.9734 8.4e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1001 0.09842 0.1747 0.2057 0.9859 0.9916 0.1001 0.7459 0.8687 0.2479 ] Network output: [ 0.001842 0.9984 -0.002653 1.747e-05 -7.842e-06 1.001 1.316e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001654 Epoch 6175 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01479 0.9953 0.9841 7.064e-06 -3.171e-06 -0.009001 5.324e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.00288 -0.01073 0.008035 0.9696 0.974 0.005992 0.8474 0.8355 0.02201 ] Network output: [ 0.9957 0.03356 0.0009955 -5.372e-05 2.412e-05 -0.02613 -4.049e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.0214 -0.2096 0.2042 0.9836 0.9933 0.2022 0.471 0.8815 0.7279 ] Network output: [ -0.01262 1.001 1.011 2.018e-06 -9.06e-07 0.01353 1.521e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005053 0.0007633 0.004015 0.004992 0.989 0.992 0.005144 0.8791 0.9055 0.01592 ] Network output: [ -0.003058 0.04493 0.996 -0.0002117 9.503e-05 0.9643 -0.0001595 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.1038 0.3063 0.1674 0.9851 0.9941 0.1924 0.476 0.8882 0.7237 ] Network output: [ 0.01156 -0.03082 1 0.0001157 -5.193e-05 1.008 8.718e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09728 0.08784 0.1747 0.2149 0.9874 0.992 0.09734 0.8106 0.8869 0.3118 ] Network output: [ -0.01062 0.04302 1.003 0.0001126 -5.056e-05 0.9759 8.487e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09983 0.09811 0.1734 0.2048 0.9858 0.9916 0.09985 0.7442 0.8687 0.2477 ] Network output: [ -0.001074 0.9997 0.001436 1.565e-05 -7.024e-06 1.001 1.179e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001411 Epoch 6176 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01539 0.9858 0.9845 8.459e-06 -3.798e-06 -0.001052 6.375e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003163 -0.00288 -0.01069 0.008209 0.9696 0.974 0.005975 0.8472 0.8359 0.02206 ] Network output: [ 1.001 -0.02789 0.003688 -4.517e-05 2.028e-05 0.02292 -3.404e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02222 -0.2063 0.2145 0.9836 0.9933 0.2013 0.4695 0.8818 0.7287 ] Network output: [ -0.01262 0.9978 1.011 2.529e-06 -1.135e-06 0.01666 1.906e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005035 0.0007691 0.004193 0.005342 0.989 0.992 0.005125 0.879 0.9057 0.01601 ] Network output: [ 0.002461 -0.04044 1 -0.0001986 8.914e-05 1.035 -0.0001496 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.1035 0.3121 0.1838 0.9852 0.9941 0.1917 0.4749 0.8882 0.723 ] Network output: [ 0.009582 -0.04793 1.004 0.0001163 -5.219e-05 1.025 8.762e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09757 0.08818 0.1787 0.2195 0.9874 0.992 0.09763 0.8118 0.887 0.3142 ] Network output: [ -0.01187 0.04765 1.003 0.0001114 -5.001e-05 0.9734 8.395e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1001 0.0984 0.1747 0.2057 0.9859 0.9916 0.1001 0.7458 0.8687 0.2479 ] Network output: [ 0.001842 0.9984 -0.002655 1.744e-05 -7.831e-06 1.001 1.315e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001655 Epoch 6177 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01479 0.9953 0.9841 7.059e-06 -3.169e-06 -0.008995 5.32e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.00288 -0.01073 0.008033 0.9696 0.974 0.005992 0.8474 0.8355 0.022 ] Network output: [ 0.9957 0.03354 0.0009924 -5.371e-05 2.411e-05 -0.02613 -4.048e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02142 -0.2096 0.2041 0.9836 0.9933 0.2022 0.471 0.8815 0.7279 ] Network output: [ -0.01262 1.001 1.011 2.023e-06 -9.081e-07 0.01353 1.524e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005054 0.0007623 0.004015 0.00499 0.989 0.992 0.005144 0.8791 0.9054 0.01592 ] Network output: [ -0.003059 0.04492 0.9961 -0.0002115 9.495e-05 0.9643 -0.0001594 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.1038 0.3063 0.1674 0.9851 0.9941 0.1924 0.476 0.8882 0.7236 ] Network output: [ 0.01155 -0.03087 1 0.0001156 -5.189e-05 1.008 8.712e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09728 0.08784 0.1747 0.2148 0.9874 0.992 0.09734 0.8106 0.8869 0.3118 ] Network output: [ -0.01061 0.04306 1.003 0.0001125 -5.053e-05 0.9759 8.482e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09981 0.09809 0.1734 0.2048 0.9858 0.9916 0.09982 0.7442 0.8686 0.2477 ] Network output: [ -0.001075 0.9997 0.001434 1.562e-05 -7.015e-06 1.001 1.178e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001412 Epoch 6178 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01539 0.9858 0.9845 8.451e-06 -3.794e-06 -0.001051 6.369e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003163 -0.00288 -0.01069 0.008207 0.9696 0.974 0.005975 0.8472 0.8358 0.02205 ] Network output: [ 1.001 -0.02787 0.003684 -4.517e-05 2.028e-05 0.0229 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02224 -0.2063 0.2145 0.9836 0.9933 0.2013 0.4695 0.8818 0.7287 ] Network output: [ -0.01262 0.9978 1.011 2.533e-06 -1.137e-06 0.01666 1.909e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005035 0.0007681 0.004193 0.00534 0.989 0.992 0.005126 0.879 0.9057 0.01601 ] Network output: [ 0.002456 -0.0404 1 -0.0001984 8.906e-05 1.035 -0.0001495 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.1035 0.3121 0.1837 0.9852 0.9941 0.1917 0.4749 0.8882 0.723 ] Network output: [ 0.009578 -0.04797 1.004 0.0001162 -5.216e-05 1.025 8.755e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09757 0.08818 0.1787 0.2195 0.9874 0.992 0.09763 0.8118 0.887 0.3142 ] Network output: [ -0.01187 0.04769 1.003 0.0001113 -4.998e-05 0.9733 8.39e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1001 0.09838 0.1747 0.2057 0.9859 0.9916 0.1001 0.7458 0.8687 0.2479 ] Network output: [ 0.001841 0.9984 -0.002657 1.742e-05 -7.821e-06 1.001 1.313e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001655 Epoch 6179 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01478 0.9953 0.9841 7.053e-06 -3.167e-06 -0.008989 5.316e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.00288 -0.01073 0.008031 0.9696 0.974 0.005992 0.8474 0.8354 0.022 ] Network output: [ 0.9957 0.03352 0.0009895 -5.369e-05 2.41e-05 -0.02612 -4.046e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02145 -0.2095 0.2041 0.9836 0.9933 0.2022 0.471 0.8814 0.7278 ] Network output: [ -0.01261 1.001 1.011 2.027e-06 -9.102e-07 0.01353 1.528e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005055 0.0007613 0.004016 0.004988 0.989 0.992 0.005145 0.8791 0.9054 0.01591 ] Network output: [ -0.00306 0.0449 0.9961 -0.0002113 9.487e-05 0.9643 -0.0001593 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.1037 0.3063 0.1673 0.9851 0.9941 0.1924 0.476 0.8882 0.7236 ] Network output: [ 0.01154 -0.03093 1 0.0001155 -5.186e-05 1.008 8.705e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09728 0.08783 0.1747 0.2148 0.9874 0.992 0.09734 0.8105 0.8869 0.3118 ] Network output: [ -0.01061 0.04311 1.003 0.0001125 -5.049e-05 0.9759 8.476e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09979 0.09807 0.1734 0.2047 0.9858 0.9916 0.0998 0.7441 0.8686 0.2477 ] Network output: [ -0.001075 0.9996 0.001431 1.56e-05 -7.005e-06 1.001 1.176e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001413 Epoch 6180 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01539 0.9858 0.9845 8.444e-06 -3.791e-06 -0.00105 6.363e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003164 -0.00288 -0.01068 0.008205 0.9696 0.974 0.005976 0.8472 0.8358 0.02205 ] Network output: [ 1.001 -0.02785 0.003681 -4.518e-05 2.028e-05 0.02288 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02227 -0.2062 0.2144 0.9836 0.9933 0.2013 0.4695 0.8818 0.7287 ] Network output: [ -0.01262 0.9978 1.011 2.537e-06 -1.139e-06 0.01666 1.912e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005036 0.0007671 0.004194 0.005338 0.989 0.992 0.005126 0.879 0.9057 0.01601 ] Network output: [ 0.00245 -0.04036 1 -0.0001982 8.899e-05 1.035 -0.0001494 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.1035 0.3121 0.1837 0.9852 0.9941 0.1916 0.4749 0.8881 0.723 ] Network output: [ 0.009575 -0.04801 1.004 0.0001161 -5.212e-05 1.025 8.749e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09757 0.08817 0.1787 0.2194 0.9874 0.992 0.09763 0.8117 0.887 0.3142 ] Network output: [ -0.01186 0.04774 1.003 0.0001113 -4.994e-05 0.9733 8.384e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1001 0.09836 0.1747 0.2056 0.9859 0.9916 0.1001 0.7457 0.8686 0.2478 ] Network output: [ 0.001841 0.9984 -0.002659 1.74e-05 -7.811e-06 1.001 1.311e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001656 Epoch 6181 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01478 0.9953 0.9842 7.048e-06 -3.164e-06 -0.008982 5.311e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002881 -0.01073 0.008029 0.9696 0.974 0.005993 0.8474 0.8354 0.02199 ] Network output: [ 0.9957 0.0335 0.0009866 -5.368e-05 2.41e-05 -0.02611 -4.045e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02147 -0.2095 0.2041 0.9836 0.9933 0.2022 0.471 0.8814 0.7278 ] Network output: [ -0.01261 1.001 1.011 2.032e-06 -9.123e-07 0.01353 1.531e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005055 0.0007603 0.004017 0.004987 0.989 0.992 0.005146 0.8791 0.9054 0.01591 ] Network output: [ -0.003061 0.04488 0.9961 -0.0002111 9.478e-05 0.9643 -0.0001591 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.1037 0.3063 0.1673 0.9851 0.9941 0.1924 0.4759 0.8882 0.7236 ] Network output: [ 0.01154 -0.03098 1 0.0001154 -5.182e-05 1.008 8.699e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.08783 0.1747 0.2148 0.9874 0.992 0.09735 0.8105 0.8868 0.3118 ] Network output: [ -0.0106 0.04315 1.003 0.0001124 -5.046e-05 0.9758 8.47e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09977 0.09805 0.1733 0.2047 0.9858 0.9916 0.09978 0.744 0.8686 0.2477 ] Network output: [ -0.001076 0.9996 0.001429 1.558e-05 -6.997e-06 1.001 1.175e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001414 Epoch 6182 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01538 0.9858 0.9845 8.436e-06 -3.787e-06 -0.001049 6.357e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003164 -0.002881 -0.01068 0.008202 0.9696 0.974 0.005976 0.8472 0.8358 0.02204 ] Network output: [ 1.001 -0.02783 0.003677 -4.518e-05 2.028e-05 0.02286 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02229 -0.2062 0.2144 0.9836 0.9933 0.2013 0.4695 0.8818 0.7286 ] Network output: [ -0.01262 0.9978 1.011 2.541e-06 -1.141e-06 0.01666 1.915e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005037 0.0007661 0.004195 0.005336 0.989 0.992 0.005127 0.879 0.9056 0.016 ] Network output: [ 0.002445 -0.04032 1 -0.0001981 8.892e-05 1.035 -0.0001493 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.1034 0.3121 0.1836 0.9852 0.9941 0.1916 0.4749 0.8881 0.7229 ] Network output: [ 0.009572 -0.04805 1.004 0.000116 -5.208e-05 1.025 8.743e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08817 0.1787 0.2194 0.9874 0.992 0.09764 0.8117 0.887 0.3142 ] Network output: [ -0.01185 0.04778 1.003 0.0001112 -4.991e-05 0.9733 8.379e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1 0.09833 0.1746 0.2056 0.9859 0.9916 0.1001 0.7457 0.8686 0.2478 ] Network output: [ 0.00184 0.9984 -0.00266 1.738e-05 -7.801e-06 1.001 1.31e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001656 Epoch 6183 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01478 0.9953 0.9842 7.042e-06 -3.162e-06 -0.008976 5.307e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002881 -0.01072 0.008027 0.9696 0.974 0.005993 0.8474 0.8354 0.02199 ] Network output: [ 0.9957 0.03348 0.0009838 -5.366e-05 2.409e-05 -0.0261 -4.044e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.0215 -0.2095 0.2041 0.9836 0.9933 0.2022 0.4709 0.8814 0.7278 ] Network output: [ -0.01261 1.001 1.011 2.036e-06 -9.143e-07 0.01353 1.535e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005056 0.0007593 0.004018 0.004985 0.989 0.992 0.005147 0.8791 0.9054 0.01591 ] Network output: [ -0.003062 0.04485 0.9962 -0.0002109 9.47e-05 0.9642 -0.000159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.1037 0.3064 0.1672 0.9851 0.9941 0.1924 0.4759 0.8881 0.7235 ] Network output: [ 0.01153 -0.03103 1 0.0001153 -5.178e-05 1.008 8.692e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.08783 0.1747 0.2148 0.9874 0.992 0.09735 0.8104 0.8868 0.3118 ] Network output: [ -0.0106 0.04319 1.003 0.0001123 -5.042e-05 0.9758 8.465e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09975 0.09802 0.1733 0.2047 0.9858 0.9916 0.09976 0.744 0.8685 0.2476 ] Network output: [ -0.001076 0.9996 0.001427 1.556e-05 -6.988e-06 1.001 1.173e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001414 Epoch 6184 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01538 0.9858 0.9845 8.428e-06 -3.783e-06 -0.001049 6.351e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003164 -0.002881 -0.01068 0.0082 0.9696 0.974 0.005976 0.8472 0.8358 0.02204 ] Network output: [ 1.001 -0.0278 0.003674 -4.518e-05 2.028e-05 0.02284 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02232 -0.2062 0.2144 0.9836 0.9933 0.2013 0.4694 0.8818 0.7286 ] Network output: [ -0.01261 0.9977 1.011 2.545e-06 -1.143e-06 0.01666 1.918e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005038 0.0007651 0.004195 0.005334 0.989 0.992 0.005128 0.879 0.9056 0.016 ] Network output: [ 0.002439 -0.04028 1 -0.0001979 8.885e-05 1.034 -0.0001491 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.1034 0.3122 0.1836 0.9852 0.9941 0.1916 0.4748 0.8881 0.7229 ] Network output: [ 0.009569 -0.04809 1.004 0.0001159 -5.204e-05 1.025 8.736e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08816 0.1787 0.2194 0.9874 0.992 0.09764 0.8116 0.8869 0.3142 ] Network output: [ -0.01184 0.04782 1.003 0.0001111 -4.988e-05 0.9732 8.373e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1 0.09831 0.1746 0.2056 0.9859 0.9916 0.1 0.7456 0.8686 0.2478 ] Network output: [ 0.001839 0.9984 -0.002662 1.735e-05 -7.791e-06 1.001 1.308e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001656 Epoch 6185 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01477 0.9953 0.9842 7.037e-06 -3.159e-06 -0.008968 5.303e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002881 -0.01072 0.008025 0.9696 0.974 0.005993 0.8474 0.8354 0.02198 ] Network output: [ 0.9957 0.03346 0.0009811 -5.364e-05 2.408e-05 -0.02609 -4.043e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02152 -0.2095 0.204 0.9836 0.9933 0.2021 0.4709 0.8814 0.7277 ] Network output: [ -0.01261 1.001 1.011 2.041e-06 -9.162e-07 0.01353 1.538e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005057 0.0007583 0.004019 0.004983 0.989 0.992 0.005147 0.8791 0.9054 0.0159 ] Network output: [ -0.003063 0.04482 0.9962 -0.0002108 9.462e-05 0.9642 -0.0001588 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.1036 0.3064 0.1672 0.9851 0.9941 0.1924 0.4759 0.8881 0.7235 ] Network output: [ 0.01153 -0.03108 1 0.0001153 -5.174e-05 1.008 8.686e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.08782 0.1748 0.2148 0.9874 0.992 0.09735 0.8104 0.8868 0.3118 ] Network output: [ -0.01059 0.04324 1.003 0.0001122 -5.039e-05 0.9757 8.459e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09973 0.098 0.1733 0.2046 0.9858 0.9916 0.09974 0.7439 0.8685 0.2476 ] Network output: [ -0.001076 0.9996 0.001424 1.555e-05 -6.979e-06 1.001 1.172e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001415 Epoch 6186 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01538 0.9858 0.9846 8.419e-06 -3.78e-06 -0.001049 6.345e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003164 -0.002881 -0.01068 0.008198 0.9696 0.974 0.005976 0.8472 0.8358 0.02203 ] Network output: [ 1.001 -0.02777 0.00367 -4.518e-05 2.028e-05 0.02281 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02234 -0.2062 0.2143 0.9836 0.9933 0.2013 0.4694 0.8818 0.7285 ] Network output: [ -0.01261 0.9977 1.011 2.549e-06 -1.144e-06 0.01666 1.921e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005038 0.0007641 0.004196 0.005332 0.989 0.992 0.005129 0.879 0.9056 0.016 ] Network output: [ 0.002433 -0.04023 1 -0.0001977 8.877e-05 1.034 -0.000149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.1033 0.3122 0.1835 0.9852 0.9941 0.1916 0.4748 0.8881 0.7228 ] Network output: [ 0.009566 -0.04813 1.004 0.0001158 -5.2e-05 1.025 8.73e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08816 0.1787 0.2194 0.9874 0.992 0.09764 0.8116 0.8869 0.3142 ] Network output: [ -0.01184 0.04786 1.003 0.000111 -4.985e-05 0.9732 8.368e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1 0.09829 0.1746 0.2055 0.9859 0.9916 0.1 0.7455 0.8685 0.2478 ] Network output: [ 0.001838 0.9984 -0.002663 1.733e-05 -7.781e-06 1.001 1.306e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001656 Epoch 6187 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01477 0.9953 0.9842 7.031e-06 -3.156e-06 -0.008961 5.299e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002882 -0.01072 0.008023 0.9696 0.974 0.005993 0.8474 0.8354 0.02198 ] Network output: [ 0.9957 0.03343 0.0009786 -5.362e-05 2.407e-05 -0.02607 -4.041e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02155 -0.2094 0.204 0.9836 0.9933 0.2021 0.4709 0.8814 0.7277 ] Network output: [ -0.01261 1.001 1.011 2.045e-06 -9.181e-07 0.01354 1.541e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005057 0.0007573 0.00402 0.004981 0.989 0.992 0.005148 0.8791 0.9054 0.0159 ] Network output: [ -0.003063 0.04479 0.9962 -0.0002106 9.453e-05 0.9642 -0.0001587 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.1036 0.3064 0.1671 0.9851 0.9941 0.1924 0.4758 0.8881 0.7234 ] Network output: [ 0.01152 -0.03114 1 0.0001152 -5.17e-05 1.008 8.679e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.08782 0.1748 0.2148 0.9874 0.992 0.09735 0.8103 0.8868 0.3118 ] Network output: [ -0.01059 0.04328 1.003 0.0001122 -5.036e-05 0.9757 8.453e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0997 0.09798 0.1733 0.2046 0.9858 0.9916 0.09972 0.7439 0.8685 0.2476 ] Network output: [ -0.001077 0.9996 0.001421 1.553e-05 -6.97e-06 1.001 1.17e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001416 Epoch 6188 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01537 0.9858 0.9846 8.411e-06 -3.776e-06 -0.001049 6.339e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003164 -0.002882 -0.01067 0.008196 0.9696 0.974 0.005976 0.8472 0.8358 0.02203 ] Network output: [ 1.001 -0.02774 0.003666 -4.518e-05 2.028e-05 0.02279 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02236 -0.2061 0.2143 0.9836 0.9933 0.2013 0.4694 0.8818 0.7285 ] Network output: [ -0.01261 0.9977 1.011 2.553e-06 -1.146e-06 0.01666 1.924e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005039 0.0007631 0.004197 0.00533 0.989 0.992 0.005129 0.879 0.9056 0.01599 ] Network output: [ 0.002426 -0.04018 1 -0.0001976 8.87e-05 1.034 -0.0001489 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.1033 0.3122 0.1835 0.9852 0.9941 0.1916 0.4748 0.8881 0.7228 ] Network output: [ 0.009563 -0.04817 1.004 0.0001158 -5.197e-05 1.026 8.724e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08815 0.1787 0.2194 0.9874 0.992 0.09764 0.8115 0.8869 0.3142 ] Network output: [ -0.01183 0.0479 1.003 0.000111 -4.982e-05 0.9731 8.363e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09997 0.09826 0.1746 0.2055 0.9859 0.9916 0.09999 0.7455 0.8685 0.2477 ] Network output: [ 0.001837 0.9984 -0.002664 1.731e-05 -7.771e-06 1.001 1.305e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001656 Epoch 6189 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01477 0.9953 0.9842 7.025e-06 -3.154e-06 -0.008954 5.294e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002882 -0.01071 0.008021 0.9696 0.974 0.005993 0.8474 0.8354 0.02197 ] Network output: [ 0.9957 0.0334 0.0009761 -5.36e-05 2.406e-05 -0.02606 -4.04e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02157 -0.2094 0.204 0.9836 0.9933 0.2021 0.4708 0.8814 0.7277 ] Network output: [ -0.0126 1.001 1.011 2.049e-06 -9.2e-07 0.01354 1.544e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005058 0.0007563 0.00402 0.00498 0.989 0.992 0.005149 0.8791 0.9054 0.0159 ] Network output: [ -0.003063 0.04475 0.9963 -0.0002104 9.445e-05 0.9642 -0.0001585 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.1035 0.3065 0.1671 0.9851 0.9941 0.1924 0.4758 0.8881 0.7234 ] Network output: [ 0.01151 -0.03119 1 0.0001151 -5.166e-05 1.008 8.673e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.08781 0.1748 0.2148 0.9874 0.992 0.09735 0.8103 0.8867 0.3118 ] Network output: [ -0.01059 0.04332 1.003 0.0001121 -5.032e-05 0.9757 8.448e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09968 0.09796 0.1732 0.2046 0.9858 0.9916 0.09969 0.7438 0.8684 0.2476 ] Network output: [ -0.001077 0.9996 0.001418 1.551e-05 -6.961e-06 1.001 1.169e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001416 Epoch 6190 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01537 0.9858 0.9846 8.402e-06 -3.772e-06 -0.00105 6.332e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003164 -0.002882 -0.01067 0.008193 0.9696 0.974 0.005976 0.8472 0.8358 0.02202 ] Network output: [ 1.001 -0.02771 0.003662 -4.518e-05 2.028e-05 0.02276 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02239 -0.2061 0.2143 0.9836 0.9933 0.2013 0.4694 0.8817 0.7285 ] Network output: [ -0.01261 0.9977 1.011 2.556e-06 -1.147e-06 0.01665 1.926e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00504 0.0007621 0.004198 0.005328 0.989 0.992 0.00513 0.879 0.9056 0.01599 ] Network output: [ 0.00242 -0.04012 1 -0.0001974 8.863e-05 1.034 -0.0001488 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.1032 0.3122 0.1834 0.9852 0.9941 0.1916 0.4747 0.8881 0.7228 ] Network output: [ 0.00956 -0.0482 1.004 0.0001157 -5.193e-05 1.026 8.717e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08815 0.1787 0.2194 0.9874 0.992 0.09764 0.8115 0.8868 0.3142 ] Network output: [ -0.01182 0.04794 1.003 0.0001109 -4.978e-05 0.9731 8.357e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09995 0.09824 0.1745 0.2055 0.9859 0.9916 0.09996 0.7454 0.8685 0.2477 ] Network output: [ 0.001836 0.9984 -0.002665 1.729e-05 -7.761e-06 1.001 1.303e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001656 Epoch 6191 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01476 0.9953 0.9842 7.019e-06 -3.151e-06 -0.008946 5.29e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002882 -0.01071 0.008019 0.9696 0.974 0.005993 0.8474 0.8354 0.02197 ] Network output: [ 0.9957 0.03337 0.0009738 -5.358e-05 2.406e-05 -0.02604 -4.038e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.0216 -0.2094 0.204 0.9836 0.9933 0.2021 0.4708 0.8814 0.7276 ] Network output: [ -0.0126 1.001 1.011 2.053e-06 -9.218e-07 0.01354 1.547e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005059 0.0007553 0.004021 0.004978 0.989 0.992 0.005149 0.879 0.9053 0.01589 ] Network output: [ -0.003063 0.04472 0.9963 -0.0002102 9.436e-05 0.9642 -0.0001584 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.1035 0.3065 0.1671 0.9851 0.9941 0.1924 0.4758 0.8881 0.7234 ] Network output: [ 0.0115 -0.03124 1 0.000115 -5.163e-05 1.008 8.666e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.08781 0.1748 0.2148 0.9874 0.992 0.09735 0.8102 0.8867 0.3118 ] Network output: [ -0.01058 0.04336 1.003 0.000112 -5.029e-05 0.9756 8.442e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09966 0.09793 0.1732 0.2046 0.9858 0.9916 0.09967 0.7438 0.8684 0.2475 ] Network output: [ -0.001076 0.9996 0.001415 1.549e-05 -6.953e-06 1.001 1.167e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001417 Epoch 6192 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01536 0.9858 0.9846 8.394e-06 -3.768e-06 -0.001051 6.326e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003164 -0.002882 -0.01067 0.008191 0.9696 0.974 0.005977 0.8472 0.8358 0.02202 ] Network output: [ 1.001 -0.02767 0.003658 -4.518e-05 2.028e-05 0.02272 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02241 -0.2061 0.2142 0.9836 0.9933 0.2013 0.4693 0.8817 0.7284 ] Network output: [ -0.0126 0.9977 1.011 2.559e-06 -1.149e-06 0.01665 1.929e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005041 0.0007611 0.004198 0.005326 0.989 0.992 0.005131 0.8789 0.9056 0.01599 ] Network output: [ 0.002413 -0.04006 1 -0.0001973 8.856e-05 1.034 -0.0001487 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.1032 0.3122 0.1833 0.9852 0.9941 0.1916 0.4747 0.8881 0.7227 ] Network output: [ 0.009557 -0.04824 1.004 0.0001156 -5.189e-05 1.026 8.711e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08814 0.1788 0.2193 0.9874 0.992 0.09764 0.8114 0.8868 0.3142 ] Network output: [ -0.01182 0.04797 1.003 0.0001108 -4.975e-05 0.9731 8.352e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09993 0.09822 0.1745 0.2055 0.9859 0.9916 0.09994 0.7454 0.8684 0.2477 ] Network output: [ 0.001835 0.9984 -0.002666 1.727e-05 -7.751e-06 1.001 1.301e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001656 Epoch 6193 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01476 0.9953 0.9842 7.014e-06 -3.149e-06 -0.008938 5.286e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002882 -0.01071 0.008017 0.9696 0.974 0.005994 0.8474 0.8354 0.02196 ] Network output: [ 0.9957 0.03333 0.0009715 -5.356e-05 2.405e-05 -0.02602 -4.037e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02162 -0.2094 0.204 0.9836 0.9933 0.2021 0.4708 0.8814 0.7276 ] Network output: [ -0.0126 1.001 1.011 2.057e-06 -9.236e-07 0.01354 1.55e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00506 0.0007543 0.004022 0.004977 0.989 0.992 0.00515 0.879 0.9053 0.01589 ] Network output: [ -0.003063 0.04467 0.9964 -0.00021 9.428e-05 0.9642 -0.0001583 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.1034 0.3065 0.167 0.9851 0.9941 0.1924 0.4758 0.8881 0.7233 ] Network output: [ 0.0115 -0.0313 1 0.0001149 -5.159e-05 1.009 8.66e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.0878 0.1748 0.2148 0.9874 0.992 0.09735 0.8102 0.8867 0.3118 ] Network output: [ -0.01058 0.0434 1.003 0.0001119 -5.026e-05 0.9756 8.436e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09964 0.09791 0.1732 0.2045 0.9858 0.9916 0.09965 0.7437 0.8684 0.2475 ] Network output: [ -0.001076 0.9996 0.001412 1.547e-05 -6.944e-06 1.001 1.166e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001417 Epoch 6194 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01536 0.9858 0.9846 8.385e-06 -3.764e-06 -0.001052 6.319e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003164 -0.002882 -0.01066 0.008189 0.9696 0.974 0.005977 0.8472 0.8358 0.02201 ] Network output: [ 1.001 -0.02764 0.003654 -4.518e-05 2.028e-05 0.02269 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02244 -0.2061 0.2142 0.9836 0.9933 0.2013 0.4693 0.8817 0.7284 ] Network output: [ -0.0126 0.9977 1.011 2.563e-06 -1.15e-06 0.01665 1.931e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005041 0.0007601 0.004199 0.005324 0.989 0.992 0.005132 0.8789 0.9056 0.01598 ] Network output: [ 0.002406 -0.04 1 -0.0001971 8.848e-05 1.034 -0.0001485 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.1032 0.3123 0.1833 0.9852 0.9941 0.1916 0.4747 0.888 0.7227 ] Network output: [ 0.009554 -0.04827 1.004 0.0001155 -5.185e-05 1.026 8.704e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08814 0.1788 0.2193 0.9874 0.992 0.09764 0.8114 0.8868 0.3142 ] Network output: [ -0.01181 0.04801 1.003 0.0001107 -4.972e-05 0.973 8.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09991 0.09819 0.1745 0.2054 0.9859 0.9916 0.09992 0.7453 0.8684 0.2477 ] Network output: [ 0.001833 0.9984 -0.002666 1.724e-05 -7.741e-06 1.001 1.299e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001655 Epoch 6195 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01476 0.9953 0.9842 7.008e-06 -3.146e-06 -0.00893 5.281e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002883 -0.01071 0.008015 0.9696 0.974 0.005994 0.8474 0.8354 0.02196 ] Network output: [ 0.9958 0.03329 0.0009694 -5.354e-05 2.404e-05 -0.02599 -4.035e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02164 -0.2093 0.2039 0.9836 0.9933 0.2021 0.4708 0.8813 0.7276 ] Network output: [ -0.0126 1.001 1.011 2.061e-06 -9.254e-07 0.01354 1.553e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00506 0.0007533 0.004023 0.004975 0.989 0.992 0.005151 0.879 0.9053 0.01589 ] Network output: [ -0.003063 0.04463 0.9964 -0.0002098 9.419e-05 0.9642 -0.0001581 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.1034 0.3065 0.167 0.9851 0.9941 0.1923 0.4757 0.8881 0.7233 ] Network output: [ 0.01149 -0.03135 1 0.0001148 -5.155e-05 1.009 8.653e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.0878 0.1748 0.2147 0.9874 0.992 0.09735 0.8101 0.8867 0.3118 ] Network output: [ -0.01057 0.04344 1.003 0.0001119 -5.022e-05 0.9756 8.431e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09962 0.09789 0.1732 0.2045 0.9858 0.9916 0.09963 0.7437 0.8683 0.2475 ] Network output: [ -0.001076 0.9996 0.001409 1.545e-05 -6.936e-06 1.001 1.164e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001417 Epoch 6196 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01536 0.9858 0.9846 8.376e-06 -3.76e-06 -0.001054 6.312e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003164 -0.002883 -0.01066 0.008187 0.9696 0.974 0.005977 0.8472 0.8358 0.02201 ] Network output: [ 1.001 -0.0276 0.00365 -4.518e-05 2.028e-05 0.02266 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02246 -0.206 0.2142 0.9836 0.9933 0.2013 0.4693 0.8817 0.7284 ] Network output: [ -0.0126 0.9977 1.011 2.566e-06 -1.152e-06 0.01665 1.934e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005042 0.0007591 0.0042 0.005321 0.989 0.992 0.005132 0.8789 0.9055 0.01598 ] Network output: [ 0.002398 -0.03994 1 -0.0001969 8.841e-05 1.034 -0.0001484 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.1031 0.3123 0.1832 0.9852 0.9941 0.1916 0.4747 0.888 0.7227 ] Network output: [ 0.009552 -0.04831 1.004 0.0001154 -5.181e-05 1.026 8.698e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08813 0.1788 0.2193 0.9874 0.992 0.09764 0.8113 0.8868 0.3142 ] Network output: [ -0.0118 0.04805 1.003 0.0001107 -4.969e-05 0.973 8.341e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09988 0.09817 0.1744 0.2054 0.9859 0.9916 0.0999 0.7453 0.8684 0.2476 ] Network output: [ 0.001832 0.9984 -0.002667 1.722e-05 -7.731e-06 1.001 1.298e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001655 Epoch 6197 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01475 0.9952 0.9842 7.002e-06 -3.143e-06 -0.008921 5.277e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002883 -0.0107 0.008013 0.9696 0.974 0.005994 0.8473 0.8354 0.02195 ] Network output: [ 0.9958 0.03325 0.0009673 -5.352e-05 2.403e-05 -0.02597 -4.033e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02167 -0.2093 0.2039 0.9836 0.9933 0.2021 0.4707 0.8813 0.7275 ] Network output: [ -0.0126 1.001 1.011 2.065e-06 -9.271e-07 0.01355 1.556e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005061 0.0007524 0.004024 0.004973 0.989 0.992 0.005152 0.879 0.9053 0.01589 ] Network output: [ -0.003062 0.04458 0.9964 -0.0002096 9.411e-05 0.9643 -0.000158 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.1033 0.3066 0.1669 0.9851 0.9941 0.1923 0.4757 0.888 0.7233 ] Network output: [ 0.01148 -0.03141 1 0.0001147 -5.151e-05 1.009 8.647e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08779 0.1748 0.2147 0.9874 0.992 0.09736 0.8101 0.8866 0.3118 ] Network output: [ -0.01057 0.04348 1.003 0.0001118 -5.019e-05 0.9755 8.425e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09959 0.09787 0.1731 0.2045 0.9858 0.9916 0.09961 0.7436 0.8683 0.2475 ] Network output: [ -0.001075 0.9996 0.001406 1.543e-05 -6.927e-06 1.001 1.163e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001417 Epoch 6198 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01535 0.9858 0.9846 8.367e-06 -3.756e-06 -0.001056 6.306e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003164 -0.002883 -0.01066 0.008184 0.9696 0.974 0.005977 0.8472 0.8357 0.022 ] Network output: [ 1.001 -0.02756 0.003645 -4.518e-05 2.028e-05 0.02262 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02248 -0.206 0.2141 0.9836 0.9933 0.2013 0.4693 0.8817 0.7283 ] Network output: [ -0.0126 0.9977 1.011 2.569e-06 -1.153e-06 0.01665 1.936e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005043 0.0007582 0.0042 0.005319 0.989 0.992 0.005133 0.8789 0.9055 0.01598 ] Network output: [ 0.002391 -0.03987 1 -0.0001968 8.834e-05 1.034 -0.0001483 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.1031 0.3123 0.1832 0.9852 0.9941 0.1916 0.4746 0.888 0.7226 ] Network output: [ 0.009549 -0.04834 1.004 0.0001153 -5.177e-05 1.026 8.691e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08813 0.1788 0.2193 0.9874 0.992 0.09764 0.8113 0.8867 0.3142 ] Network output: [ -0.01179 0.04808 1.003 0.0001106 -4.965e-05 0.973 8.335e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09986 0.09815 0.1744 0.2054 0.9859 0.9916 0.09987 0.7452 0.8683 0.2476 ] Network output: [ 0.00183 0.9984 -0.002667 1.72e-05 -7.721e-06 1.001 1.296e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001654 Epoch 6199 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01475 0.9952 0.9842 6.996e-06 -3.141e-06 -0.008912 5.273e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002883 -0.0107 0.008011 0.9696 0.974 0.005994 0.8473 0.8353 0.02195 ] Network output: [ 0.9958 0.03321 0.0009654 -5.35e-05 2.402e-05 -0.02595 -4.032e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02169 -0.2093 0.2039 0.9836 0.9933 0.2021 0.4707 0.8813 0.7275 ] Network output: [ -0.01259 1.001 1.011 2.069e-06 -9.288e-07 0.01355 1.559e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005062 0.0007514 0.004025 0.004972 0.989 0.992 0.005152 0.879 0.9053 0.01588 ] Network output: [ -0.003062 0.04453 0.9965 -0.0002094 9.402e-05 0.9643 -0.0001578 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.1033 0.3066 0.1669 0.9851 0.9941 0.1923 0.4757 0.888 0.7232 ] Network output: [ 0.01148 -0.03146 1 0.0001146 -5.147e-05 1.009 8.64e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08779 0.1748 0.2147 0.9874 0.992 0.09736 0.81 0.8866 0.3118 ] Network output: [ -0.01056 0.04352 1.003 0.0001117 -5.015e-05 0.9755 8.419e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09957 0.09784 0.1731 0.2044 0.9858 0.9916 0.09958 0.7436 0.8683 0.2475 ] Network output: [ -0.001075 0.9996 0.001402 1.541e-05 -6.919e-06 1.001 1.162e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001417 Epoch 6200 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01535 0.9858 0.9846 8.358e-06 -3.752e-06 -0.001059 6.299e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003164 -0.002883 -0.01065 0.008182 0.9696 0.974 0.005977 0.8472 0.8357 0.022 ] Network output: [ 1.001 -0.02751 0.003641 -4.518e-05 2.028e-05 0.02258 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02251 -0.206 0.2141 0.9836 0.9933 0.2013 0.4692 0.8817 0.7283 ] Network output: [ -0.01259 0.9977 1.011 2.572e-06 -1.154e-06 0.01664 1.938e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005044 0.0007572 0.004201 0.005317 0.989 0.992 0.005134 0.8789 0.9055 0.01597 ] Network output: [ 0.002383 -0.0398 1 -0.0001966 8.827e-05 1.034 -0.0001482 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.103 0.3123 0.1831 0.9852 0.9941 0.1916 0.4746 0.888 0.7226 ] Network output: [ 0.009547 -0.04837 1.004 0.0001152 -5.174e-05 1.026 8.685e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08812 0.1788 0.2193 0.9874 0.992 0.09764 0.8112 0.8867 0.3142 ] Network output: [ -0.01178 0.04812 1.003 0.0001105 -4.962e-05 0.9729 8.33e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09984 0.09812 0.1744 0.2053 0.9859 0.9916 0.09985 0.7451 0.8683 0.2476 ] Network output: [ 0.001829 0.9984 -0.002667 1.718e-05 -7.711e-06 1.001 1.294e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001653 Epoch 6201 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01475 0.9952 0.9842 6.99e-06 -3.138e-06 -0.008903 5.268e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002883 -0.0107 0.008009 0.9696 0.974 0.005994 0.8473 0.8353 0.02194 ] Network output: [ 0.9958 0.03317 0.0009636 -5.348e-05 2.401e-05 -0.02592 -4.03e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02172 -0.2093 0.2039 0.9836 0.9933 0.2021 0.4707 0.8813 0.7275 ] Network output: [ -0.01259 1.001 1.011 2.073e-06 -9.305e-07 0.01355 1.562e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005062 0.0007504 0.004026 0.00497 0.989 0.992 0.005153 0.879 0.9053 0.01588 ] Network output: [ -0.003061 0.04448 0.9965 -0.0002092 9.394e-05 0.9643 -0.0001577 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.1033 0.3066 0.1669 0.9851 0.9941 0.1923 0.4756 0.888 0.7232 ] Network output: [ 0.01147 -0.03152 1 0.0001146 -5.143e-05 1.009 8.634e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08778 0.1749 0.2147 0.9874 0.992 0.09736 0.81 0.8866 0.3118 ] Network output: [ -0.01056 0.04356 1.003 0.0001116 -5.012e-05 0.9755 8.413e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09955 0.09782 0.1731 0.2044 0.9858 0.9916 0.09956 0.7435 0.8682 0.2474 ] Network output: [ -0.001074 0.9996 0.001398 1.539e-05 -6.911e-06 1.001 1.16e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001417 Epoch 6202 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01534 0.9858 0.9846 8.349e-06 -3.748e-06 -0.001062 6.292e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003164 -0.002883 -0.01065 0.00818 0.9696 0.974 0.005978 0.8471 0.8357 0.02199 ] Network output: [ 1.001 -0.02747 0.003637 -4.518e-05 2.028e-05 0.02254 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02253 -0.206 0.2141 0.9836 0.9933 0.2013 0.4692 0.8817 0.7283 ] Network output: [ -0.01259 0.9977 1.011 2.574e-06 -1.156e-06 0.01664 1.94e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005044 0.0007562 0.004201 0.005315 0.989 0.992 0.005135 0.8789 0.9055 0.01597 ] Network output: [ 0.002375 -0.03972 1 -0.0001965 8.82e-05 1.034 -0.0001481 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.103 0.3123 0.183 0.9852 0.9941 0.1916 0.4746 0.888 0.7226 ] Network output: [ 0.009544 -0.0484 1.004 0.0001152 -5.17e-05 1.026 8.678e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08811 0.1788 0.2193 0.9874 0.992 0.09764 0.8112 0.8867 0.3142 ] Network output: [ -0.01178 0.04815 1.003 0.0001105 -4.959e-05 0.9729 8.324e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09982 0.0981 0.1744 0.2053 0.9859 0.9916 0.09983 0.7451 0.8683 0.2476 ] Network output: [ 0.001827 0.9984 -0.002667 1.715e-05 -7.701e-06 1.001 1.293e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001653 Epoch 6203 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01474 0.9952 0.9842 6.985e-06 -3.136e-06 -0.008894 5.264e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002884 -0.01069 0.008007 0.9696 0.974 0.005994 0.8473 0.8353 0.02194 ] Network output: [ 0.9958 0.03312 0.0009619 -5.345e-05 2.4e-05 -0.02589 -4.028e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02174 -0.2092 0.2039 0.9836 0.9933 0.2021 0.4706 0.8813 0.7274 ] Network output: [ -0.01259 1.001 1.011 2.076e-06 -9.321e-07 0.01355 1.565e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005063 0.0007494 0.004026 0.004968 0.989 0.992 0.005154 0.879 0.9053 0.01588 ] Network output: [ -0.00306 0.04442 0.9965 -0.0002091 9.385e-05 0.9643 -0.0001575 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.1032 0.3066 0.1668 0.9851 0.9941 0.1923 0.4756 0.888 0.7232 ] Network output: [ 0.01146 -0.03157 1 0.0001145 -5.139e-05 1.009 8.627e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08778 0.1749 0.2147 0.9874 0.992 0.09736 0.8099 0.8865 0.3118 ] Network output: [ -0.01056 0.0436 1.003 0.0001116 -5.008e-05 0.9754 8.408e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09953 0.0978 0.1731 0.2044 0.9858 0.9916 0.09954 0.7434 0.8682 0.2474 ] Network output: [ -0.001073 0.9996 0.001394 1.538e-05 -6.903e-06 1.001 1.159e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001416 Epoch 6204 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01534 0.9858 0.9846 8.339e-06 -3.744e-06 -0.001065 6.285e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003164 -0.002884 -0.01065 0.008177 0.9696 0.974 0.005978 0.8471 0.8357 0.02199 ] Network output: [ 1.001 -0.02742 0.003632 -4.518e-05 2.028e-05 0.0225 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02255 -0.206 0.214 0.9836 0.9933 0.2013 0.4692 0.8817 0.7282 ] Network output: [ -0.01259 0.9977 1.011 2.577e-06 -1.157e-06 0.01664 1.942e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005045 0.0007552 0.004202 0.005313 0.989 0.992 0.005135 0.8789 0.9055 0.01597 ] Network output: [ 0.002367 -0.03965 1 -0.0001963 8.812e-05 1.034 -0.0001479 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.1029 0.3124 0.183 0.9852 0.9941 0.1916 0.4746 0.888 0.7225 ] Network output: [ 0.009542 -0.04843 1.004 0.0001151 -5.166e-05 1.026 8.672e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08811 0.1788 0.2192 0.9874 0.992 0.09764 0.8111 0.8866 0.3142 ] Network output: [ -0.01177 0.04818 1.003 0.0001104 -4.956e-05 0.9729 8.319e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09979 0.09808 0.1743 0.2053 0.9859 0.9916 0.0998 0.745 0.8682 0.2476 ] Network output: [ 0.001825 0.9984 -0.002667 1.713e-05 -7.691e-06 1.001 1.291e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001652 Epoch 6205 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01474 0.9952 0.9842 6.979e-06 -3.133e-06 -0.008885 5.259e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002884 -0.01069 0.008005 0.9696 0.974 0.005994 0.8473 0.8353 0.02193 ] Network output: [ 0.9958 0.03307 0.0009602 -5.343e-05 2.399e-05 -0.02586 -4.027e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02177 -0.2092 0.2038 0.9836 0.9933 0.2021 0.4706 0.8813 0.7274 ] Network output: [ -0.01259 1.001 1.011 2.08e-06 -9.337e-07 0.01356 1.567e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005064 0.0007485 0.004027 0.004967 0.989 0.992 0.005155 0.879 0.9053 0.01587 ] Network output: [ -0.003059 0.04436 0.9966 -0.0002089 9.377e-05 0.9643 -0.0001574 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.1032 0.3067 0.1668 0.9851 0.9941 0.1923 0.4756 0.888 0.7231 ] Network output: [ 0.01146 -0.03163 1 0.0001144 -5.135e-05 1.009 8.621e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08777 0.1749 0.2147 0.9874 0.992 0.09736 0.8099 0.8865 0.3118 ] Network output: [ -0.01055 0.04364 1.003 0.0001115 -5.005e-05 0.9754 8.402e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0995 0.09777 0.173 0.2044 0.9858 0.9916 0.09952 0.7434 0.8682 0.2474 ] Network output: [ -0.001072 0.9996 0.00139 1.536e-05 -6.895e-06 1.001 1.157e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001416 Epoch 6206 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01534 0.9858 0.9846 8.33e-06 -3.74e-06 -0.001068 6.278e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003164 -0.002884 -0.01065 0.008175 0.9696 0.974 0.005978 0.8471 0.8357 0.02198 ] Network output: [ 1.001 -0.02737 0.003627 -4.518e-05 2.028e-05 0.02245 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02257 -0.2059 0.214 0.9836 0.9933 0.2013 0.4692 0.8816 0.7282 ] Network output: [ -0.01259 0.9977 1.011 2.58e-06 -1.158e-06 0.01663 1.944e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005046 0.0007542 0.004203 0.005311 0.989 0.992 0.005136 0.8789 0.9055 0.01596 ] Network output: [ 0.002359 -0.03956 1 -0.0001961 8.805e-05 1.034 -0.0001478 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.1029 0.3124 0.1829 0.9852 0.9941 0.1916 0.4745 0.888 0.7225 ] Network output: [ 0.00954 -0.04846 1.004 0.000115 -5.162e-05 1.026 8.665e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.0881 0.1788 0.2192 0.9874 0.992 0.09764 0.8111 0.8866 0.3142 ] Network output: [ -0.01176 0.04822 1.003 0.0001103 -4.952e-05 0.9728 8.313e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09977 0.09805 0.1743 0.2052 0.9859 0.9916 0.09978 0.745 0.8682 0.2475 ] Network output: [ 0.001823 0.9984 -0.002666 1.711e-05 -7.681e-06 1.001 1.289e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00165 Epoch 6207 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01474 0.9952 0.9842 6.973e-06 -3.13e-06 -0.008875 5.255e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002884 -0.01069 0.008003 0.9696 0.974 0.005995 0.8473 0.8353 0.02193 ] Network output: [ 0.9958 0.03302 0.0009587 -5.34e-05 2.398e-05 -0.02583 -4.025e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02179 -0.2092 0.2038 0.9836 0.9933 0.2021 0.4706 0.8813 0.7274 ] Network output: [ -0.01259 1.001 1.011 2.083e-06 -9.353e-07 0.01356 1.57e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005064 0.0007475 0.004028 0.004965 0.989 0.992 0.005155 0.8789 0.9052 0.01587 ] Network output: [ -0.003057 0.0443 0.9966 -0.0002087 9.368e-05 0.9643 -0.0001573 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.1031 0.3067 0.1668 0.9851 0.9941 0.1923 0.4755 0.888 0.7231 ] Network output: [ 0.01145 -0.03168 1 0.0001143 -5.131e-05 1.009 8.614e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08777 0.1749 0.2147 0.9874 0.992 0.09736 0.8098 0.8865 0.3118 ] Network output: [ -0.01055 0.04368 1.003 0.0001114 -5.001e-05 0.9754 8.396e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09948 0.09775 0.173 0.2043 0.9858 0.9916 0.09949 0.7433 0.8681 0.2474 ] Network output: [ -0.001071 0.9996 0.001386 1.534e-05 -6.887e-06 1.001 1.156e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001416 Epoch 6208 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01533 0.9858 0.9846 8.32e-06 -3.735e-06 -0.001072 6.27e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003164 -0.002884 -0.01064 0.008173 0.9696 0.974 0.005978 0.8471 0.8357 0.02198 ] Network output: [ 1.001 -0.02732 0.003623 -4.518e-05 2.028e-05 0.02241 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.0226 -0.2059 0.2139 0.9836 0.9933 0.2012 0.4691 0.8816 0.7282 ] Network output: [ -0.01258 0.9977 1.011 2.582e-06 -1.159e-06 0.01663 1.946e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005046 0.0007533 0.004203 0.005308 0.989 0.992 0.005137 0.8789 0.9055 0.01596 ] Network output: [ 0.00235 -0.03948 1.001 -0.000196 8.798e-05 1.033 -0.0001477 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.1029 0.3124 0.1828 0.9852 0.9941 0.1916 0.4745 0.888 0.7225 ] Network output: [ 0.009538 -0.04848 1.004 0.0001149 -5.158e-05 1.026 8.659e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.0881 0.1788 0.2192 0.9874 0.992 0.09764 0.811 0.8866 0.3142 ] Network output: [ -0.01175 0.04825 1.003 0.0001102 -4.949e-05 0.9728 8.308e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09975 0.09803 0.1743 0.2052 0.9859 0.9916 0.09976 0.7449 0.8682 0.2475 ] Network output: [ 0.001821 0.9984 -0.002666 1.709e-05 -7.671e-06 1.001 1.288e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001649 Epoch 6209 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01473 0.9952 0.9842 6.967e-06 -3.128e-06 -0.008865 5.251e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002884 -0.01068 0.008001 0.9696 0.974 0.005995 0.8473 0.8353 0.02193 ] Network output: [ 0.9958 0.03297 0.0009573 -5.338e-05 2.396e-05 -0.02579 -4.023e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02181 -0.2092 0.2038 0.9836 0.9933 0.2021 0.4706 0.8813 0.7273 ] Network output: [ -0.01258 1.001 1.011 2.087e-06 -9.369e-07 0.01356 1.573e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005065 0.0007465 0.004029 0.004964 0.989 0.992 0.005156 0.8789 0.9052 0.01587 ] Network output: [ -0.003056 0.04423 0.9967 -0.0002085 9.359e-05 0.9644 -0.0001571 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.1031 0.3067 0.1667 0.9851 0.9941 0.1923 0.4755 0.888 0.7231 ] Network output: [ 0.01144 -0.03174 1 0.0001142 -5.127e-05 1.009 8.607e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08776 0.1749 0.2147 0.9874 0.992 0.09736 0.8098 0.8865 0.3118 ] Network output: [ -0.01054 0.04372 1.003 0.0001113 -4.998e-05 0.9753 8.39e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09946 0.09773 0.173 0.2043 0.9858 0.9916 0.09947 0.7433 0.8681 0.2473 ] Network output: [ -0.00107 0.9996 0.001382 1.532e-05 -6.879e-06 1.001 1.155e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001415 Epoch 6210 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01533 0.9858 0.9846 8.311e-06 -3.731e-06 -0.001076 6.263e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003165 -0.002884 -0.01064 0.00817 0.9696 0.974 0.005978 0.8471 0.8357 0.02197 ] Network output: [ 1.001 -0.02726 0.003618 -4.518e-05 2.029e-05 0.02236 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02262 -0.2059 0.2139 0.9836 0.9933 0.2012 0.4691 0.8816 0.7281 ] Network output: [ -0.01258 0.9977 1.011 2.585e-06 -1.16e-06 0.01663 1.948e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005047 0.0007523 0.004204 0.005306 0.989 0.992 0.005138 0.8788 0.9054 0.01596 ] Network output: [ 0.002341 -0.03939 1.001 -0.0001958 8.791e-05 1.033 -0.0001476 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.1028 0.3124 0.1828 0.9852 0.9941 0.1916 0.4745 0.8879 0.7224 ] Network output: [ 0.009535 -0.04851 1.004 0.0001148 -5.154e-05 1.026 8.652e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08809 0.1788 0.2192 0.9874 0.992 0.09764 0.811 0.8866 0.3142 ] Network output: [ -0.01175 0.04828 1.003 0.0001102 -4.946e-05 0.9728 8.302e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09972 0.098 0.1743 0.2052 0.9859 0.9916 0.09974 0.7449 0.8681 0.2475 ] Network output: [ 0.001818 0.9984 -0.002665 1.706e-05 -7.661e-06 1.001 1.286e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001648 Epoch 6211 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01473 0.9952 0.9842 6.961e-06 -3.125e-06 -0.008855 5.246e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002885 -0.01068 0.007999 0.9696 0.974 0.005995 0.8473 0.8353 0.02192 ] Network output: [ 0.9958 0.03292 0.000956 -5.335e-05 2.395e-05 -0.02576 -4.021e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02184 -0.2091 0.2038 0.9836 0.9933 0.2021 0.4705 0.8812 0.7273 ] Network output: [ -0.01258 1.001 1.011 2.09e-06 -9.384e-07 0.01356 1.575e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005066 0.0007456 0.00403 0.004962 0.989 0.992 0.005157 0.8789 0.9052 0.01586 ] Network output: [ -0.003054 0.04417 0.9967 -0.0002083 9.351e-05 0.9644 -0.000157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.103 0.3068 0.1667 0.9852 0.9941 0.1923 0.4755 0.888 0.723 ] Network output: [ 0.01143 -0.03179 1 0.0001141 -5.123e-05 1.009 8.601e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08776 0.1749 0.2147 0.9874 0.992 0.09736 0.8097 0.8864 0.3118 ] Network output: [ -0.01054 0.04376 1.002 0.0001113 -4.994e-05 0.9753 8.384e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09944 0.0977 0.173 0.2043 0.9858 0.9916 0.09945 0.7432 0.8681 0.2473 ] Network output: [ -0.001069 0.9996 0.001378 1.53e-05 -6.871e-06 1.001 1.153e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001414 Epoch 6212 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01532 0.9858 0.9846 8.301e-06 -3.727e-06 -0.001081 6.256e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003165 -0.002885 -0.01064 0.008168 0.9696 0.974 0.005978 0.8471 0.8357 0.02197 ] Network output: [ 1.001 -0.02721 0.003613 -4.519e-05 2.029e-05 0.02231 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02264 -0.2059 0.2139 0.9836 0.9933 0.2012 0.4691 0.8816 0.7281 ] Network output: [ -0.01258 0.9977 1.011 2.587e-06 -1.161e-06 0.01662 1.95e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005048 0.0007513 0.004205 0.005304 0.989 0.992 0.005138 0.8788 0.9054 0.01596 ] Network output: [ 0.002332 -0.0393 1.001 -0.0001957 8.784e-05 1.033 -0.0001475 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.1028 0.3124 0.1827 0.9852 0.9941 0.1916 0.4744 0.8879 0.7224 ] Network output: [ 0.009533 -0.04853 1.004 0.0001147 -5.15e-05 1.026 8.646e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08809 0.1788 0.2192 0.9874 0.992 0.09764 0.8109 0.8865 0.3142 ] Network output: [ -0.01174 0.04831 1.003 0.0001101 -4.942e-05 0.9727 8.297e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0997 0.09798 0.1742 0.2052 0.9859 0.9916 0.09971 0.7448 0.8681 0.2475 ] Network output: [ 0.001816 0.9984 -0.002664 1.704e-05 -7.651e-06 1.001 1.284e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001646 Epoch 6213 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01473 0.9952 0.9842 6.955e-06 -3.123e-06 -0.008844 5.242e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002885 -0.01068 0.007997 0.9696 0.974 0.005995 0.8473 0.8353 0.02192 ] Network output: [ 0.9958 0.03286 0.0009548 -5.333e-05 2.394e-05 -0.02572 -4.019e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02186 -0.2091 0.2038 0.9836 0.9933 0.2021 0.4705 0.8812 0.7273 ] Network output: [ -0.01258 1.001 1.011 2.094e-06 -9.4e-07 0.01357 1.578e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005067 0.0007446 0.004031 0.004961 0.989 0.992 0.005157 0.8789 0.9052 0.01586 ] Network output: [ -0.003052 0.0441 0.9967 -0.0002081 9.342e-05 0.9644 -0.0001568 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.103 0.3068 0.1667 0.9852 0.9941 0.1923 0.4755 0.8879 0.723 ] Network output: [ 0.01143 -0.03185 1 0.000114 -5.12e-05 1.009 8.594e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08775 0.1749 0.2146 0.9874 0.992 0.09736 0.8097 0.8864 0.3118 ] Network output: [ -0.01054 0.04379 1.002 0.0001112 -4.991e-05 0.9753 8.378e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09941 0.09768 0.1729 0.2043 0.9858 0.9916 0.09943 0.7432 0.868 0.2473 ] Network output: [ -0.001068 0.9996 0.001373 1.529e-05 -6.863e-06 1.001 1.152e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001414 Epoch 6214 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01532 0.9859 0.9846 8.291e-06 -3.722e-06 -0.001085 6.249e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003165 -0.002885 -0.01063 0.008166 0.9696 0.974 0.005979 0.8471 0.8357 0.02196 ] Network output: [ 1.001 -0.02715 0.003608 -4.519e-05 2.029e-05 0.02226 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02267 -0.2058 0.2138 0.9836 0.9933 0.2012 0.469 0.8816 0.7281 ] Network output: [ -0.01258 0.9977 1.011 2.589e-06 -1.162e-06 0.01662 1.951e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005049 0.0007503 0.004205 0.005302 0.989 0.992 0.005139 0.8788 0.9054 0.01595 ] Network output: [ 0.002323 -0.03921 1.001 -0.0001955 8.777e-05 1.033 -0.0001473 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.1027 0.3125 0.1826 0.9852 0.9941 0.1916 0.4744 0.8879 0.7224 ] Network output: [ 0.009531 -0.04856 1.004 0.0001146 -5.146e-05 1.026 8.639e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08808 0.1788 0.2191 0.9874 0.992 0.09764 0.8109 0.8865 0.3142 ] Network output: [ -0.01173 0.04834 1.003 0.00011 -4.939e-05 0.9727 8.291e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09968 0.09796 0.1742 0.2051 0.9859 0.9916 0.09969 0.7447 0.8681 0.2474 ] Network output: [ 0.001814 0.9985 -0.002663 1.702e-05 -7.641e-06 1.001 1.283e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001645 Epoch 6215 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01472 0.9952 0.9843 6.95e-06 -3.12e-06 -0.008834 5.237e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002885 -0.01068 0.007995 0.9696 0.974 0.005995 0.8473 0.8353 0.02191 ] Network output: [ 0.9959 0.0328 0.0009537 -5.33e-05 2.393e-05 -0.02568 -4.017e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02189 -0.2091 0.2038 0.9836 0.9933 0.2021 0.4705 0.8812 0.7272 ] Network output: [ -0.01258 1.001 1.011 2.097e-06 -9.415e-07 0.01357 1.58e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005067 0.0007436 0.004032 0.004959 0.989 0.992 0.005158 0.8789 0.9052 0.01586 ] Network output: [ -0.00305 0.04402 0.9968 -0.0002079 9.333e-05 0.9645 -0.0001567 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.103 0.3068 0.1666 0.9852 0.9941 0.1923 0.4754 0.8879 0.723 ] Network output: [ 0.01142 -0.03191 1 0.0001139 -5.116e-05 1.009 8.588e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08775 0.1749 0.2146 0.9874 0.992 0.09736 0.8096 0.8864 0.3118 ] Network output: [ -0.01053 0.04383 1.002 0.0001111 -4.988e-05 0.9752 8.373e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09939 0.09766 0.1729 0.2042 0.9858 0.9916 0.0994 0.7431 0.868 0.2473 ] Network output: [ -0.001066 0.9996 0.001368 1.527e-05 -6.855e-06 1.001 1.151e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001413 Epoch 6216 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01531 0.9859 0.9846 8.281e-06 -3.718e-06 -0.00109 6.241e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003165 -0.002885 -0.01063 0.008163 0.9696 0.974 0.005979 0.8471 0.8356 0.02196 ] Network output: [ 1.001 -0.02709 0.003603 -4.519e-05 2.029e-05 0.02221 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02269 -0.2058 0.2138 0.9836 0.9933 0.2012 0.469 0.8816 0.728 ] Network output: [ -0.01258 0.9977 1.011 2.591e-06 -1.163e-06 0.01662 1.953e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005049 0.0007494 0.004206 0.005299 0.989 0.992 0.00514 0.8788 0.9054 0.01595 ] Network output: [ 0.002314 -0.03912 1.001 -0.0001954 8.77e-05 1.033 -0.0001472 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.1027 0.3125 0.1826 0.9852 0.9941 0.1916 0.4744 0.8879 0.7223 ] Network output: [ 0.009529 -0.04858 1.004 0.0001145 -5.142e-05 1.026 8.633e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08807 0.1788 0.2191 0.9874 0.992 0.09764 0.8108 0.8865 0.3142 ] Network output: [ -0.01172 0.04837 1.003 0.0001099 -4.936e-05 0.9727 8.286e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09965 0.09793 0.1742 0.2051 0.9859 0.9916 0.09966 0.7447 0.868 0.2474 ] Network output: [ 0.001811 0.9985 -0.002662 1.7e-05 -7.631e-06 1.001 1.281e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001643 Epoch 6217 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01472 0.9951 0.9843 6.944e-06 -3.117e-06 -0.008823 5.233e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002885 -0.01067 0.007993 0.9696 0.974 0.005995 0.8473 0.8353 0.02191 ] Network output: [ 0.9959 0.03274 0.0009527 -5.327e-05 2.392e-05 -0.02564 -4.015e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02191 -0.2091 0.2037 0.9836 0.9933 0.2021 0.4704 0.8812 0.7272 ] Network output: [ -0.01258 1.001 1.011 2.1e-06 -9.43e-07 0.01357 1.583e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005068 0.0007427 0.004033 0.004958 0.989 0.992 0.005159 0.8789 0.9052 0.01586 ] Network output: [ -0.003048 0.04395 0.9968 -0.0002077 9.325e-05 0.9645 -0.0001565 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.1029 0.3068 0.1666 0.9852 0.9941 0.1923 0.4754 0.8879 0.7229 ] Network output: [ 0.01141 -0.03196 1 0.0001139 -5.112e-05 1.01 8.581e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08774 0.175 0.2146 0.9874 0.992 0.09736 0.8096 0.8863 0.3118 ] Network output: [ -0.01053 0.04387 1.002 0.000111 -4.984e-05 0.9752 8.367e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09937 0.09763 0.1729 0.2042 0.9858 0.9916 0.09938 0.7431 0.868 0.2473 ] Network output: [ -0.001064 0.9996 0.001364 1.525e-05 -6.848e-06 1.001 1.15e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001412 Epoch 6218 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01531 0.9859 0.9846 8.272e-06 -3.713e-06 -0.001096 6.234e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003165 -0.002885 -0.01063 0.008161 0.9696 0.974 0.005979 0.8471 0.8356 0.02196 ] Network output: [ 1.001 -0.02703 0.003598 -4.519e-05 2.029e-05 0.02215 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02271 -0.2058 0.2138 0.9836 0.9933 0.2012 0.469 0.8816 0.728 ] Network output: [ -0.01257 0.9977 1.011 2.593e-06 -1.164e-06 0.01661 1.955e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00505 0.0007484 0.004206 0.005297 0.989 0.992 0.005141 0.8788 0.9054 0.01595 ] Network output: [ 0.002304 -0.03902 1.001 -0.0001952 8.763e-05 1.033 -0.0001471 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.1026 0.3125 0.1825 0.9852 0.9941 0.1916 0.4744 0.8879 0.7223 ] Network output: [ 0.009527 -0.0486 1.004 0.0001145 -5.138e-05 1.026 8.626e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08807 0.1788 0.2191 0.9874 0.992 0.09764 0.8107 0.8864 0.3142 ] Network output: [ -0.01171 0.0484 1.003 0.0001099 -4.933e-05 0.9726 8.28e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09963 0.09791 0.1742 0.2051 0.9859 0.9916 0.09964 0.7446 0.868 0.2474 ] Network output: [ 0.001809 0.9985 -0.002661 1.698e-05 -7.621e-06 1.001 1.279e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001641 Epoch 6219 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01472 0.9951 0.9843 6.938e-06 -3.115e-06 -0.008812 5.229e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002886 -0.01067 0.007991 0.9696 0.974 0.005995 0.8473 0.8352 0.0219 ] Network output: [ 0.9959 0.03268 0.0009518 -5.325e-05 2.39e-05 -0.0256 -4.013e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02194 -0.209 0.2037 0.9836 0.9933 0.2021 0.4704 0.8812 0.7272 ] Network output: [ -0.01257 1.001 1.011 2.104e-06 -9.444e-07 0.01358 1.585e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005069 0.0007417 0.004034 0.004956 0.989 0.992 0.005159 0.8789 0.9052 0.01585 ] Network output: [ -0.003045 0.04387 0.9968 -0.0002075 9.316e-05 0.9645 -0.0001564 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.1029 0.3069 0.1666 0.9852 0.9941 0.1923 0.4754 0.8879 0.7229 ] Network output: [ 0.0114 -0.03202 1 0.0001138 -5.108e-05 1.01 8.574e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08774 0.175 0.2146 0.9874 0.992 0.09736 0.8095 0.8863 0.3118 ] Network output: [ -0.01053 0.0439 1.002 0.0001109 -4.981e-05 0.9752 8.361e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09935 0.09761 0.1729 0.2042 0.9858 0.9916 0.09936 0.743 0.8679 0.2472 ] Network output: [ -0.001063 0.9996 0.001359 1.524e-05 -6.84e-06 1.001 1.148e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001411 Epoch 6220 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01531 0.9859 0.9846 8.262e-06 -3.709e-06 -0.001101 6.226e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003165 -0.002886 -0.01063 0.008159 0.9696 0.974 0.005979 0.8471 0.8356 0.02195 ] Network output: [ 1.001 -0.02696 0.003593 -4.519e-05 2.029e-05 0.0221 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02273 -0.2058 0.2137 0.9836 0.9933 0.2012 0.469 0.8816 0.728 ] Network output: [ -0.01257 0.9977 1.011 2.596e-06 -1.165e-06 0.01661 1.956e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005051 0.0007474 0.004207 0.005295 0.989 0.992 0.005141 0.8788 0.9054 0.01594 ] Network output: [ 0.002294 -0.03892 1.001 -0.000195 8.756e-05 1.033 -0.000147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.1026 0.3125 0.1824 0.9852 0.9941 0.1916 0.4743 0.8879 0.7223 ] Network output: [ 0.009526 -0.04863 1.004 0.0001144 -5.135e-05 1.026 8.619e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08806 0.1788 0.2191 0.9874 0.992 0.09764 0.8107 0.8864 0.3142 ] Network output: [ -0.0117 0.04843 1.003 0.0001098 -4.929e-05 0.9726 8.275e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09961 0.09788 0.1741 0.205 0.9859 0.9916 0.09962 0.7446 0.868 0.2474 ] Network output: [ 0.001806 0.9985 -0.002659 1.695e-05 -7.612e-06 1.001 1.278e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00164 Epoch 6221 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01471 0.9951 0.9843 6.932e-06 -3.112e-06 -0.008801 5.224e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002886 -0.01067 0.007989 0.9696 0.974 0.005995 0.8473 0.8352 0.0219 ] Network output: [ 0.9959 0.03261 0.000951 -5.322e-05 2.389e-05 -0.02555 -4.011e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02196 -0.209 0.2037 0.9836 0.9933 0.202 0.4704 0.8812 0.7272 ] Network output: [ -0.01257 1.001 1.011 2.107e-06 -9.459e-07 0.01358 1.588e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005069 0.0007408 0.004034 0.004954 0.989 0.992 0.00516 0.8789 0.9051 0.01585 ] Network output: [ -0.003043 0.04379 0.9969 -0.0002073 9.307e-05 0.9646 -0.0001562 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.1028 0.3069 0.1665 0.9852 0.9941 0.1923 0.4753 0.8879 0.7229 ] Network output: [ 0.01139 -0.03208 1 0.0001137 -5.104e-05 1.01 8.568e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08773 0.175 0.2146 0.9874 0.992 0.09736 0.8095 0.8863 0.3118 ] Network output: [ -0.01052 0.04394 1.002 0.0001109 -4.977e-05 0.9751 8.355e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09932 0.09759 0.1729 0.2042 0.9858 0.9916 0.09933 0.743 0.8679 0.2472 ] Network output: [ -0.001061 0.9996 0.001354 1.522e-05 -6.833e-06 1.001 1.147e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00141 Epoch 6222 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0153 0.9859 0.9846 8.251e-06 -3.704e-06 -0.001107 6.219e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003165 -0.002886 -0.01062 0.008156 0.9696 0.974 0.005979 0.8471 0.8356 0.02195 ] Network output: [ 1.001 -0.0269 0.003587 -4.519e-05 2.029e-05 0.02204 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02276 -0.2058 0.2137 0.9836 0.9933 0.2012 0.4689 0.8815 0.7279 ] Network output: [ -0.01257 0.9977 1.011 2.598e-06 -1.166e-06 0.0166 1.958e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005052 0.0007465 0.004207 0.005292 0.989 0.992 0.005142 0.8788 0.9054 0.01594 ] Network output: [ 0.002284 -0.03882 1.001 -0.0001949 8.749e-05 1.033 -0.0001469 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.1026 0.3125 0.1824 0.9852 0.9941 0.1916 0.4743 0.8879 0.7222 ] Network output: [ 0.009524 -0.04865 1.004 0.0001143 -5.131e-05 1.026 8.613e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08805 0.1788 0.2191 0.9874 0.992 0.09764 0.8106 0.8864 0.3142 ] Network output: [ -0.0117 0.04846 1.003 0.0001097 -4.926e-05 0.9726 8.269e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09958 0.09786 0.1741 0.205 0.9859 0.9916 0.09959 0.7445 0.8679 0.2473 ] Network output: [ 0.001803 0.9985 -0.002658 1.693e-05 -7.602e-06 1.001 1.276e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001638 Epoch 6223 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01471 0.9951 0.9843 6.926e-06 -3.11e-06 -0.008789 5.22e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002886 -0.01066 0.007987 0.9696 0.974 0.005996 0.8473 0.8352 0.02189 ] Network output: [ 0.9959 0.03254 0.0009503 -5.319e-05 2.388e-05 -0.02551 -4.009e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02198 -0.209 0.2037 0.9836 0.9933 0.202 0.4704 0.8812 0.7271 ] Network output: [ -0.01257 1.001 1.011 2.11e-06 -9.473e-07 0.01358 1.59e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00507 0.0007398 0.004035 0.004953 0.989 0.992 0.005161 0.8788 0.9051 0.01585 ] Network output: [ -0.00304 0.0437 0.9969 -0.0002071 9.298e-05 0.9646 -0.0001561 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.1028 0.3069 0.1665 0.9852 0.9941 0.1923 0.4753 0.8879 0.7228 ] Network output: [ 0.01139 -0.03214 1 0.0001136 -5.1e-05 1.01 8.561e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08773 0.175 0.2146 0.9874 0.992 0.09736 0.8094 0.8863 0.3118 ] Network output: [ -0.01052 0.04397 1.002 0.0001108 -4.973e-05 0.9751 8.349e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0993 0.09756 0.1728 0.2041 0.9858 0.9916 0.09931 0.7429 0.8679 0.2472 ] Network output: [ -0.001059 0.9996 0.001349 1.52e-05 -6.825e-06 1.001 1.146e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001409 Epoch 6224 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0153 0.9859 0.9847 8.241e-06 -3.7e-06 -0.001113 6.211e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003165 -0.002886 -0.01062 0.008154 0.9696 0.974 0.005979 0.8471 0.8356 0.02194 ] Network output: [ 1.001 -0.02683 0.003582 -4.519e-05 2.029e-05 0.02198 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02278 -0.2057 0.2137 0.9836 0.9933 0.2012 0.4689 0.8815 0.7279 ] Network output: [ -0.01257 0.9977 1.011 2.599e-06 -1.167e-06 0.0166 1.959e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005052 0.0007455 0.004208 0.00529 0.989 0.992 0.005143 0.8788 0.9054 0.01594 ] Network output: [ 0.002274 -0.03871 1.001 -0.0001947 8.742e-05 1.033 -0.0001468 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.1025 0.3126 0.1823 0.9852 0.9941 0.1915 0.4743 0.8879 0.7222 ] Network output: [ 0.009522 -0.04867 1.004 0.0001142 -5.127e-05 1.027 8.606e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08805 0.1788 0.219 0.9874 0.992 0.09764 0.8106 0.8864 0.3142 ] Network output: [ -0.01169 0.04848 1.003 0.0001097 -4.923e-05 0.9725 8.264e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09956 0.09783 0.1741 0.205 0.9859 0.9916 0.09957 0.7444 0.8679 0.2473 ] Network output: [ 0.0018 0.9985 -0.002656 1.691e-05 -7.592e-06 1.001 1.274e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001636 Epoch 6225 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01471 0.9951 0.9843 6.921e-06 -3.107e-06 -0.008778 5.216e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002886 -0.01066 0.007985 0.9696 0.974 0.005996 0.8473 0.8352 0.02189 ] Network output: [ 0.9959 0.03248 0.0009497 -5.316e-05 2.387e-05 -0.02546 -4.006e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02201 -0.209 0.2037 0.9836 0.9933 0.202 0.4703 0.8812 0.7271 ] Network output: [ -0.01257 1.001 1.011 2.113e-06 -9.487e-07 0.01358 1.593e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005071 0.0007388 0.004036 0.004951 0.989 0.992 0.005162 0.8788 0.9051 0.01584 ] Network output: [ -0.003038 0.04362 0.997 -0.0002069 9.29e-05 0.9647 -0.0001559 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.1027 0.307 0.1665 0.9852 0.9941 0.1923 0.4753 0.8879 0.7228 ] Network output: [ 0.01138 -0.03219 1 0.0001135 -5.096e-05 1.01 8.554e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08772 0.175 0.2146 0.9874 0.992 0.09736 0.8094 0.8862 0.3118 ] Network output: [ -0.01051 0.04401 1.002 0.0001107 -4.97e-05 0.9751 8.343e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09928 0.09754 0.1728 0.2041 0.9858 0.9916 0.09929 0.7428 0.8679 0.2472 ] Network output: [ -0.001057 0.9996 0.001343 1.519e-05 -6.818e-06 1.001 1.144e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001408 Epoch 6226 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01529 0.9859 0.9847 8.231e-06 -3.695e-06 -0.00112 6.203e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003165 -0.002887 -0.01062 0.008151 0.9696 0.974 0.00598 0.8471 0.8356 0.02194 ] Network output: [ 1.001 -0.02676 0.003577 -4.519e-05 2.029e-05 0.02192 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.0228 -0.2057 0.2136 0.9836 0.9933 0.2012 0.4689 0.8815 0.7279 ] Network output: [ -0.01256 0.9977 1.011 2.601e-06 -1.168e-06 0.01659 1.96e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005053 0.0007445 0.004208 0.005288 0.989 0.992 0.005144 0.8787 0.9053 0.01593 ] Network output: [ 0.002264 -0.0386 1.001 -0.0001946 8.735e-05 1.033 -0.0001466 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.1025 0.3126 0.1822 0.9852 0.9941 0.1915 0.4742 0.8878 0.7222 ] Network output: [ 0.00952 -0.04868 1.004 0.0001141 -5.123e-05 1.027 8.599e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08804 0.1788 0.219 0.9874 0.992 0.09764 0.8105 0.8863 0.3142 ] Network output: [ -0.01168 0.04851 1.003 0.0001096 -4.919e-05 0.9725 8.258e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09953 0.09781 0.174 0.205 0.9859 0.9916 0.09955 0.7444 0.8679 0.2473 ] Network output: [ 0.001797 0.9985 -0.002654 1.689e-05 -7.582e-06 1.001 1.273e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001633 Epoch 6227 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0147 0.9951 0.9843 6.915e-06 -3.104e-06 -0.008766 5.211e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002887 -0.01066 0.007983 0.9696 0.974 0.005996 0.8473 0.8352 0.02188 ] Network output: [ 0.9959 0.03241 0.0009492 -5.313e-05 2.385e-05 -0.02542 -4.004e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02203 -0.2089 0.2037 0.9836 0.9933 0.202 0.4703 0.8811 0.7271 ] Network output: [ -0.01257 1.001 1.011 2.116e-06 -9.502e-07 0.01359 1.595e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005071 0.0007379 0.004037 0.00495 0.989 0.992 0.005162 0.8788 0.9051 0.01584 ] Network output: [ -0.003035 0.04353 0.997 -0.0002067 9.281e-05 0.9647 -0.0001558 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.1027 0.307 0.1664 0.9852 0.9941 0.1923 0.4752 0.8879 0.7228 ] Network output: [ 0.01137 -0.03225 1 0.0001134 -5.092e-05 1.01 8.548e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08771 0.175 0.2146 0.9874 0.992 0.09736 0.8093 0.8862 0.3118 ] Network output: [ -0.01051 0.04404 1.002 0.0001106 -4.966e-05 0.975 8.337e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09925 0.09751 0.1728 0.2041 0.9858 0.9916 0.09927 0.7428 0.8678 0.2471 ] Network output: [ -0.001055 0.9996 0.001338 1.517e-05 -6.81e-06 1.001 1.143e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001406 Epoch 6228 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01529 0.9859 0.9847 8.221e-06 -3.691e-06 -0.001126 6.196e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003165 -0.002887 -0.01061 0.008149 0.9696 0.974 0.00598 0.8471 0.8356 0.02193 ] Network output: [ 1.001 -0.02669 0.003571 -4.519e-05 2.029e-05 0.02186 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02282 -0.2057 0.2136 0.9836 0.9933 0.2012 0.4689 0.8815 0.7278 ] Network output: [ -0.01256 0.9977 1.011 2.603e-06 -1.169e-06 0.01659 1.962e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005054 0.0007436 0.004209 0.005285 0.989 0.992 0.005144 0.8787 0.9053 0.01593 ] Network output: [ 0.002253 -0.03849 1.001 -0.0001944 8.728e-05 1.032 -0.0001465 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.1024 0.3126 0.1822 0.9852 0.9941 0.1915 0.4742 0.8878 0.7221 ] Network output: [ 0.009519 -0.0487 1.004 0.000114 -5.119e-05 1.027 8.593e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08803 0.1788 0.219 0.9874 0.992 0.09764 0.8105 0.8863 0.3142 ] Network output: [ -0.01167 0.04853 1.003 0.0001095 -4.916e-05 0.9725 8.253e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09951 0.09778 0.174 0.2049 0.9859 0.9916 0.09952 0.7443 0.8678 0.2473 ] Network output: [ 0.001794 0.9985 -0.002652 1.687e-05 -7.572e-06 1.001 1.271e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001631 Epoch 6229 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0147 0.9951 0.9843 6.909e-06 -3.102e-06 -0.008754 5.207e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002887 -0.01065 0.007981 0.9696 0.974 0.005996 0.8473 0.8352 0.02188 ] Network output: [ 0.9959 0.03233 0.0009488 -5.31e-05 2.384e-05 -0.02537 -4.002e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02206 -0.2089 0.2037 0.9836 0.9933 0.202 0.4703 0.8811 0.727 ] Network output: [ -0.01256 1.001 1.011 2.12e-06 -9.516e-07 0.01359 1.597e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005072 0.0007369 0.004038 0.004948 0.989 0.992 0.005163 0.8788 0.9051 0.01584 ] Network output: [ -0.003032 0.04344 0.997 -0.0002065 9.272e-05 0.9648 -0.0001556 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.1027 0.307 0.1664 0.9852 0.9941 0.1922 0.4752 0.8878 0.7227 ] Network output: [ 0.01136 -0.03231 0.9999 0.0001133 -5.088e-05 1.01 8.541e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08771 0.175 0.2146 0.9874 0.992 0.09736 0.8093 0.8862 0.3118 ] Network output: [ -0.01051 0.04408 1.002 0.0001105 -4.963e-05 0.975 8.331e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09923 0.09749 0.1728 0.204 0.9858 0.9916 0.09924 0.7427 0.8678 0.2471 ] Network output: [ -0.001053 0.9996 0.001332 1.515e-05 -6.803e-06 1.001 1.142e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001405 Epoch 6230 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01528 0.9859 0.9847 8.211e-06 -3.686e-06 -0.001133 6.188e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003165 -0.002887 -0.01061 0.008147 0.9696 0.974 0.00598 0.8471 0.8356 0.02193 ] Network output: [ 1.001 -0.02662 0.003566 -4.519e-05 2.029e-05 0.0218 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02285 -0.2057 0.2135 0.9836 0.9933 0.2012 0.4688 0.8815 0.7278 ] Network output: [ -0.01256 0.9977 1.011 2.605e-06 -1.169e-06 0.01658 1.963e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005055 0.0007426 0.004209 0.005283 0.989 0.992 0.005145 0.8787 0.9053 0.01593 ] Network output: [ 0.002243 -0.03838 1.001 -0.0001943 8.721e-05 1.032 -0.0001464 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.1024 0.3126 0.1821 0.9852 0.9941 0.1915 0.4742 0.8878 0.7221 ] Network output: [ 0.009517 -0.04872 1.003 0.0001139 -5.115e-05 1.027 8.586e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09757 0.08803 0.1788 0.219 0.9874 0.992 0.09763 0.8104 0.8863 0.3142 ] Network output: [ -0.01166 0.04856 1.003 0.0001094 -4.913e-05 0.9725 8.247e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09949 0.09776 0.174 0.2049 0.9859 0.9916 0.0995 0.7443 0.8678 0.2473 ] Network output: [ 0.001791 0.9985 -0.00265 1.685e-05 -7.563e-06 1.001 1.27e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001629 Epoch 6231 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0147 0.9951 0.9843 6.904e-06 -3.099e-06 -0.008741 5.203e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002887 -0.01065 0.007979 0.9696 0.974 0.005996 0.8473 0.8352 0.02187 ] Network output: [ 0.9959 0.03226 0.0009485 -5.307e-05 2.383e-05 -0.02532 -4e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02208 -0.2089 0.2036 0.9836 0.9933 0.202 0.4702 0.8811 0.727 ] Network output: [ -0.01256 1.001 1.011 2.123e-06 -9.53e-07 0.01359 1.6e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005073 0.000736 0.004039 0.004947 0.989 0.992 0.005164 0.8788 0.9051 0.01583 ] Network output: [ -0.003028 0.04334 0.9971 -0.0002063 9.263e-05 0.9648 -0.0001555 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1026 0.3071 0.1664 0.9852 0.9941 0.1922 0.4752 0.8878 0.7227 ] Network output: [ 0.01135 -0.03237 0.9999 0.0001132 -5.084e-05 1.01 8.534e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.0877 0.175 0.2146 0.9874 0.992 0.09736 0.8092 0.8861 0.3118 ] Network output: [ -0.0105 0.04411 1.002 0.0001105 -4.959e-05 0.975 8.325e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09921 0.09747 0.1727 0.204 0.9858 0.9916 0.09922 0.7427 0.8678 0.2471 ] Network output: [ -0.001051 0.9996 0.001327 1.514e-05 -6.796e-06 1.001 1.141e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001404 Epoch 6232 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01528 0.9859 0.9847 8.2e-06 -3.681e-06 -0.00114 6.18e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003165 -0.002887 -0.01061 0.008144 0.9696 0.974 0.00598 0.8471 0.8356 0.02192 ] Network output: [ 1.001 -0.02654 0.00356 -4.519e-05 2.029e-05 0.02174 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02287 -0.2057 0.2135 0.9836 0.9933 0.2012 0.4688 0.8815 0.7278 ] Network output: [ -0.01256 0.9977 1.011 2.607e-06 -1.17e-06 0.01658 1.964e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005055 0.0007416 0.00421 0.00528 0.989 0.992 0.005146 0.8787 0.9053 0.01592 ] Network output: [ 0.002232 -0.03826 1.001 -0.0001941 8.715e-05 1.032 -0.0001463 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1023 0.3126 0.182 0.9852 0.9941 0.1915 0.4742 0.8878 0.7221 ] Network output: [ 0.009515 -0.04874 1.003 0.0001138 -5.111e-05 1.027 8.579e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09757 0.08802 0.1788 0.219 0.9874 0.992 0.09763 0.8104 0.8862 0.3142 ] Network output: [ -0.01165 0.04858 1.003 0.0001094 -4.909e-05 0.9724 8.241e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09946 0.09773 0.174 0.2049 0.9859 0.9916 0.09947 0.7442 0.8678 0.2472 ] Network output: [ 0.001788 0.9985 -0.002648 1.682e-05 -7.553e-06 1.001 1.268e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001627 Epoch 6233 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01469 0.9951 0.9843 6.898e-06 -3.097e-06 -0.008729 5.198e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002887 -0.01065 0.007977 0.9696 0.974 0.005996 0.8473 0.8352 0.02187 ] Network output: [ 0.996 0.03218 0.0009483 -5.304e-05 2.381e-05 -0.02526 -3.997e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02211 -0.2089 0.2036 0.9836 0.9933 0.202 0.4702 0.8811 0.727 ] Network output: [ -0.01256 1.001 1.011 2.126e-06 -9.543e-07 0.0136 1.602e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005073 0.000735 0.00404 0.004945 0.989 0.992 0.005164 0.8788 0.9051 0.01583 ] Network output: [ -0.003025 0.04325 0.9971 -0.0002061 9.254e-05 0.9649 -0.0001554 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1026 0.3071 0.1663 0.9852 0.9941 0.1922 0.4751 0.8878 0.7227 ] Network output: [ 0.01135 -0.03242 0.9999 0.0001132 -5.08e-05 1.01 8.528e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.0877 0.1751 0.2145 0.9874 0.992 0.09736 0.8092 0.8861 0.3118 ] Network output: [ -0.0105 0.04415 1.002 0.0001104 -4.956e-05 0.9749 8.319e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09918 0.09744 0.1727 0.204 0.9858 0.9916 0.0992 0.7426 0.8677 0.2471 ] Network output: [ -0.001048 0.9996 0.001321 1.512e-05 -6.789e-06 1.001 1.14e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001402 Epoch 6234 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01527 0.986 0.9847 8.19e-06 -3.677e-06 -0.001148 6.172e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003165 -0.002888 -0.0106 0.008142 0.9696 0.974 0.00598 0.8471 0.8355 0.02192 ] Network output: [ 1.001 -0.02647 0.003554 -4.519e-05 2.029e-05 0.02167 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02289 -0.2056 0.2135 0.9836 0.9933 0.2012 0.4688 0.8815 0.7277 ] Network output: [ -0.01256 0.9977 1.011 2.608e-06 -1.171e-06 0.01657 1.966e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005056 0.0007407 0.00421 0.005278 0.989 0.992 0.005147 0.8787 0.9053 0.01592 ] Network output: [ 0.002221 -0.03815 1.001 -0.000194 8.708e-05 1.032 -0.0001462 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1023 0.3126 0.182 0.9852 0.9941 0.1915 0.4741 0.8878 0.722 ] Network output: [ 0.009514 -0.04875 1.003 0.0001138 -5.107e-05 1.027 8.573e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09757 0.08801 0.1788 0.2189 0.9874 0.992 0.09763 0.8103 0.8862 0.3142 ] Network output: [ -0.01165 0.04861 1.003 0.0001093 -4.906e-05 0.9724 8.236e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09944 0.09771 0.1739 0.2048 0.9859 0.9916 0.09945 0.7441 0.8677 0.2472 ] Network output: [ 0.001784 0.9985 -0.002645 1.68e-05 -7.543e-06 1.001 1.266e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001624 Epoch 6235 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01469 0.995 0.9843 6.892e-06 -3.094e-06 -0.008716 5.194e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002888 -0.01065 0.007975 0.9696 0.974 0.005996 0.8473 0.8352 0.02186 ] Network output: [ 0.996 0.03211 0.0009482 -5.301e-05 2.38e-05 -0.02521 -3.995e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02213 -0.2088 0.2036 0.9836 0.9933 0.202 0.4702 0.8811 0.7269 ] Network output: [ -0.01256 1.001 1.011 2.129e-06 -9.557e-07 0.0136 1.604e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005074 0.0007341 0.004041 0.004944 0.989 0.992 0.005165 0.8788 0.9051 0.01583 ] Network output: [ -0.003022 0.04315 0.9971 -0.0002059 9.245e-05 0.9649 -0.0001552 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1025 0.3071 0.1663 0.9852 0.9941 0.1922 0.4751 0.8878 0.7226 ] Network output: [ 0.01134 -0.03248 0.9999 0.0001131 -5.076e-05 1.01 8.521e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08769 0.1751 0.2145 0.9874 0.992 0.09736 0.8091 0.8861 0.3118 ] Network output: [ -0.0105 0.04418 1.002 0.0001103 -4.952e-05 0.9749 8.313e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09916 0.09742 0.1727 0.204 0.9858 0.9916 0.09917 0.7426 0.8677 0.2471 ] Network output: [ -0.001046 0.9996 0.001315 1.511e-05 -6.781e-06 1.001 1.138e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001401 Epoch 6236 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01527 0.986 0.9847 8.18e-06 -3.672e-06 -0.001155 6.164e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003165 -0.002888 -0.0106 0.008139 0.9696 0.974 0.005981 0.8471 0.8355 0.02191 ] Network output: [ 1.001 -0.02639 0.003549 -4.519e-05 2.029e-05 0.02161 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02291 -0.2056 0.2134 0.9836 0.9933 0.2012 0.4687 0.8814 0.7277 ] Network output: [ -0.01255 0.9977 1.011 2.61e-06 -1.172e-06 0.01657 1.967e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005057 0.0007397 0.004211 0.005276 0.989 0.992 0.005147 0.8787 0.9053 0.01592 ] Network output: [ 0.00221 -0.03803 1.001 -0.0001938 8.701e-05 1.032 -0.0001461 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1023 0.3127 0.1819 0.9852 0.9941 0.1915 0.4741 0.8878 0.722 ] Network output: [ 0.009512 -0.04877 1.003 0.0001137 -5.103e-05 1.027 8.566e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09757 0.08801 0.1788 0.2189 0.9874 0.992 0.09763 0.8103 0.8862 0.3142 ] Network output: [ -0.01164 0.04863 1.003 0.0001092 -4.903e-05 0.9724 8.23e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09941 0.09768 0.1739 0.2048 0.9859 0.9916 0.09943 0.7441 0.8677 0.2472 ] Network output: [ 0.001781 0.9985 -0.002643 1.678e-05 -7.533e-06 1.001 1.265e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001622 Epoch 6237 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01469 0.995 0.9843 6.886e-06 -3.092e-06 -0.008704 5.19e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002888 -0.01064 0.007973 0.9696 0.974 0.005996 0.8473 0.8352 0.02186 ] Network output: [ 0.996 0.03203 0.0009481 -5.298e-05 2.378e-05 -0.02516 -3.993e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02215 -0.2088 0.2036 0.9836 0.9933 0.202 0.4701 0.8811 0.7269 ] Network output: [ -0.01256 1.001 1.011 2.132e-06 -9.571e-07 0.0136 1.607e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005075 0.0007331 0.004042 0.004942 0.989 0.992 0.005166 0.8788 0.905 0.01582 ] Network output: [ -0.003018 0.04305 0.9972 -0.0002057 9.237e-05 0.965 -0.0001551 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1025 0.3072 0.1663 0.9852 0.9941 0.1922 0.4751 0.8878 0.7226 ] Network output: [ 0.01133 -0.03254 0.9999 0.000113 -5.072e-05 1.01 8.514e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08769 0.1751 0.2145 0.9874 0.992 0.09736 0.8091 0.8861 0.3118 ] Network output: [ -0.01049 0.04421 1.002 0.0001102 -4.949e-05 0.9749 8.307e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09914 0.09739 0.1727 0.2039 0.9858 0.9916 0.09915 0.7425 0.8677 0.247 ] Network output: [ -0.001043 0.9996 0.001309 1.509e-05 -6.774e-06 1.001 1.137e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001399 Epoch 6238 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01526 0.986 0.9847 8.169e-06 -3.667e-06 -0.001163 6.157e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003165 -0.002888 -0.0106 0.008137 0.9696 0.974 0.005981 0.8471 0.8355 0.02191 ] Network output: [ 1.001 -0.02631 0.003543 -4.519e-05 2.029e-05 0.02154 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02294 -0.2056 0.2134 0.9836 0.9933 0.2012 0.4687 0.8814 0.7277 ] Network output: [ -0.01255 0.9977 1.011 2.611e-06 -1.172e-06 0.01656 1.968e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005057 0.0007387 0.004211 0.005273 0.989 0.992 0.005148 0.8787 0.9053 0.01591 ] Network output: [ 0.002199 -0.03791 1.001 -0.0001937 8.694e-05 1.032 -0.0001459 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1022 0.3127 0.1818 0.9852 0.9941 0.1915 0.4741 0.8878 0.722 ] Network output: [ 0.009511 -0.04878 1.003 0.0001136 -5.099e-05 1.027 8.559e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09757 0.088 0.1789 0.2189 0.9874 0.992 0.09763 0.8102 0.8862 0.3142 ] Network output: [ -0.01163 0.04865 1.003 0.0001091 -4.899e-05 0.9723 8.225e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09939 0.09766 0.1739 0.2048 0.9859 0.9916 0.0994 0.744 0.8677 0.2472 ] Network output: [ 0.001778 0.9985 -0.00264 1.676e-05 -7.524e-06 1.001 1.263e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001619 Epoch 6239 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01468 0.995 0.9843 6.881e-06 -3.089e-06 -0.008691 5.186e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002888 -0.01064 0.007971 0.9696 0.974 0.005997 0.8473 0.8352 0.02185 ] Network output: [ 0.996 0.03195 0.0009482 -5.295e-05 2.377e-05 -0.0251 -3.99e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02218 -0.2088 0.2036 0.9836 0.9933 0.202 0.4701 0.8811 0.7269 ] Network output: [ -0.01255 1.001 1.011 2.135e-06 -9.584e-07 0.01361 1.609e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005075 0.0007322 0.004043 0.004941 0.989 0.992 0.005166 0.8788 0.905 0.01582 ] Network output: [ -0.003014 0.04295 0.9972 -0.0002055 9.228e-05 0.965 -0.0001549 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1024 0.3072 0.1663 0.9852 0.9941 0.1922 0.4751 0.8878 0.7226 ] Network output: [ 0.01132 -0.0326 0.9999 0.0001129 -5.068e-05 1.011 8.507e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08768 0.1751 0.2145 0.9874 0.992 0.09736 0.809 0.886 0.3119 ] Network output: [ -0.01049 0.04424 1.002 0.0001102 -4.945e-05 0.9748 8.301e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09911 0.09737 0.1727 0.2039 0.9858 0.9916 0.09913 0.7424 0.8676 0.247 ] Network output: [ -0.001041 0.9996 0.001303 1.507e-05 -6.767e-06 1.001 1.136e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001397 Epoch 6240 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01526 0.986 0.9847 8.159e-06 -3.663e-06 -0.001171 6.149e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003165 -0.002888 -0.0106 0.008134 0.9696 0.974 0.005981 0.8471 0.8355 0.0219 ] Network output: [ 1.001 -0.02624 0.003537 -4.519e-05 2.029e-05 0.02147 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02296 -0.2056 0.2133 0.9836 0.9933 0.2012 0.4687 0.8814 0.7276 ] Network output: [ -0.01255 0.9977 1.011 2.613e-06 -1.173e-06 0.01656 1.969e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005058 0.0007378 0.004212 0.005271 0.989 0.992 0.005149 0.8787 0.9052 0.01591 ] Network output: [ 0.002187 -0.03778 1.001 -0.0001935 8.687e-05 1.032 -0.0001458 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1022 0.3127 0.1817 0.9852 0.9941 0.1915 0.474 0.8877 0.722 ] Network output: [ 0.009509 -0.04879 1.003 0.0001135 -5.095e-05 1.027 8.552e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09757 0.08799 0.1789 0.2189 0.9874 0.992 0.09763 0.8102 0.8861 0.3142 ] Network output: [ -0.01162 0.04868 1.003 0.0001091 -4.896e-05 0.9723 8.219e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09936 0.09763 0.1739 0.2048 0.9859 0.9916 0.09938 0.744 0.8676 0.2472 ] Network output: [ 0.001774 0.9985 -0.002638 1.674e-05 -7.514e-06 1.001 1.261e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001616 Epoch 6241 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01468 0.995 0.9843 6.875e-06 -3.087e-06 -0.008678 5.181e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002888 -0.01064 0.007969 0.9696 0.974 0.005997 0.8472 0.8351 0.02185 ] Network output: [ 0.996 0.03187 0.0009483 -5.292e-05 2.376e-05 -0.02504 -3.988e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.0222 -0.2088 0.2036 0.9836 0.9933 0.202 0.4701 0.8811 0.7269 ] Network output: [ -0.01255 1.001 1.011 2.138e-06 -9.598e-07 0.01361 1.611e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005076 0.0007312 0.004044 0.00494 0.989 0.992 0.005167 0.8787 0.905 0.01582 ] Network output: [ -0.003011 0.04284 0.9972 -0.0002053 9.219e-05 0.9651 -0.0001548 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1024 0.3072 0.1662 0.9852 0.9941 0.1922 0.475 0.8878 0.7225 ] Network output: [ 0.01131 -0.03266 0.9999 0.0001128 -5.064e-05 1.011 8.501e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08767 0.1751 0.2145 0.9874 0.992 0.09736 0.809 0.886 0.3119 ] Network output: [ -0.01048 0.04428 1.002 0.0001101 -4.942e-05 0.9748 8.295e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09909 0.09735 0.1726 0.2039 0.9858 0.9916 0.0991 0.7424 0.8676 0.247 ] Network output: [ -0.001038 0.9996 0.001297 1.506e-05 -6.76e-06 1.001 1.135e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001396 Epoch 6242 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01525 0.986 0.9847 8.148e-06 -3.658e-06 -0.00118 6.141e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003165 -0.002889 -0.01059 0.008132 0.9696 0.974 0.005981 0.8471 0.8355 0.0219 ] Network output: [ 1.001 -0.02615 0.003531 -4.519e-05 2.029e-05 0.0214 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02298 -0.2056 0.2133 0.9836 0.9933 0.2012 0.4687 0.8814 0.7276 ] Network output: [ -0.01255 0.9977 1.011 2.614e-06 -1.174e-06 0.01655 1.97e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005059 0.0007368 0.004212 0.005268 0.989 0.992 0.00515 0.8786 0.9052 0.01591 ] Network output: [ 0.002176 -0.03766 1.001 -0.0001934 8.68e-05 1.032 -0.0001457 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1021 0.3127 0.1817 0.9852 0.9941 0.1915 0.474 0.8877 0.7219 ] Network output: [ 0.009508 -0.04881 1.003 0.0001134 -5.091e-05 1.027 8.546e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09757 0.08799 0.1789 0.2188 0.9874 0.992 0.09763 0.8101 0.8861 0.3142 ] Network output: [ -0.01161 0.0487 1.003 0.000109 -4.893e-05 0.9723 8.213e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09934 0.09761 0.1738 0.2047 0.9859 0.9916 0.09935 0.7439 0.8676 0.2471 ] Network output: [ 0.001771 0.9985 -0.002635 1.672e-05 -7.504e-06 1.001 1.26e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001614 Epoch 6243 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01468 0.995 0.9843 6.87e-06 -3.084e-06 -0.008664 5.177e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002889 -0.01063 0.007967 0.9696 0.974 0.005997 0.8472 0.8351 0.02184 ] Network output: [ 0.996 0.03178 0.0009486 -5.288e-05 2.374e-05 -0.02499 -3.986e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02223 -0.2087 0.2036 0.9836 0.9933 0.202 0.47 0.881 0.7268 ] Network output: [ -0.01255 1.001 1.011 2.141e-06 -9.611e-07 0.01361 1.613e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005077 0.0007303 0.004045 0.004938 0.989 0.992 0.005168 0.8787 0.905 0.01582 ] Network output: [ -0.003007 0.04274 0.9973 -0.0002051 9.21e-05 0.9652 -0.0001546 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1024 0.3073 0.1662 0.9852 0.9941 0.1922 0.475 0.8878 0.7225 ] Network output: [ 0.0113 -0.03271 0.9999 0.0001127 -5.06e-05 1.011 8.494e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08767 0.1751 0.2145 0.9874 0.992 0.09736 0.8089 0.886 0.3119 ] Network output: [ -0.01048 0.04431 1.002 0.00011 -4.938e-05 0.9748 8.289e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09907 0.09732 0.1726 0.2039 0.9858 0.9916 0.09908 0.7423 0.8676 0.247 ] Network output: [ -0.001035 0.9996 0.001291 1.504e-05 -6.754e-06 1.001 1.134e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001394 Epoch 6244 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01525 0.986 0.9847 8.138e-06 -3.653e-06 -0.001188 6.133e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003166 -0.002889 -0.01059 0.008129 0.9696 0.974 0.005981 0.8471 0.8355 0.02189 ] Network output: [ 1.001 -0.02607 0.003526 -4.519e-05 2.029e-05 0.02133 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.023 -0.2055 0.2133 0.9836 0.9933 0.2012 0.4686 0.8814 0.7276 ] Network output: [ -0.01254 0.9977 1.011 2.616e-06 -1.174e-06 0.01655 1.971e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00506 0.0007358 0.004213 0.005266 0.989 0.992 0.00515 0.8786 0.9052 0.0159 ] Network output: [ 0.002164 -0.03753 1.001 -0.0001932 8.673e-05 1.031 -0.0001456 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1021 0.3127 0.1816 0.9852 0.9941 0.1915 0.474 0.8877 0.7219 ] Network output: [ 0.009507 -0.04882 1.003 0.0001133 -5.087e-05 1.027 8.539e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09756 0.08798 0.1789 0.2188 0.9874 0.992 0.09762 0.81 0.8861 0.3142 ] Network output: [ -0.0116 0.04872 1.003 0.0001089 -4.889e-05 0.9723 8.208e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09932 0.09758 0.1738 0.2047 0.9859 0.9916 0.09933 0.7438 0.8676 0.2471 ] Network output: [ 0.001767 0.9985 -0.002632 1.669e-05 -7.495e-06 1.001 1.258e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001611 Epoch 6245 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01467 0.995 0.9843 6.864e-06 -3.082e-06 -0.008651 5.173e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002889 -0.01063 0.007965 0.9696 0.974 0.005997 0.8472 0.8351 0.02184 ] Network output: [ 0.996 0.0317 0.0009489 -5.285e-05 2.373e-05 -0.02493 -3.983e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02225 -0.2087 0.2036 0.9836 0.9933 0.202 0.47 0.881 0.7268 ] Network output: [ -0.01255 1.001 1.011 2.144e-06 -9.625e-07 0.01362 1.616e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005077 0.0007293 0.004046 0.004937 0.989 0.992 0.005168 0.8787 0.905 0.01581 ] Network output: [ -0.003003 0.04263 0.9973 -0.000205 9.201e-05 0.9652 -0.0001545 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1023 0.3073 0.1662 0.9852 0.9941 0.1922 0.475 0.8877 0.7225 ] Network output: [ 0.0113 -0.03277 0.9998 0.0001126 -5.056e-05 1.011 8.487e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08766 0.1751 0.2145 0.9874 0.992 0.09736 0.8089 0.8859 0.3119 ] Network output: [ -0.01048 0.04434 1.002 0.0001099 -4.934e-05 0.9748 8.283e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09904 0.0973 0.1726 0.2038 0.9858 0.9916 0.09906 0.7423 0.8675 0.247 ] Network output: [ -0.001032 0.9996 0.001285 1.503e-05 -6.747e-06 1.001 1.133e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001392 Epoch 6246 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01524 0.986 0.9847 8.127e-06 -3.648e-06 -0.001197 6.125e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003166 -0.002889 -0.01059 0.008127 0.9696 0.974 0.005981 0.8471 0.8355 0.02189 ] Network output: [ 1.001 -0.02599 0.00352 -4.519e-05 2.029e-05 0.02126 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02302 -0.2055 0.2132 0.9836 0.9933 0.2012 0.4686 0.8814 0.7276 ] Network output: [ -0.01254 0.9977 1.011 2.617e-06 -1.175e-06 0.01654 1.972e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00506 0.0007349 0.004213 0.005263 0.989 0.992 0.005151 0.8786 0.9052 0.0159 ] Network output: [ 0.002152 -0.0374 1.001 -0.000193 8.667e-05 1.031 -0.0001455 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1021 0.3128 0.1815 0.9852 0.9941 0.1915 0.474 0.8877 0.7219 ] Network output: [ 0.009505 -0.04883 1.003 0.0001132 -5.083e-05 1.027 8.532e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09756 0.08797 0.1789 0.2188 0.9874 0.992 0.09762 0.81 0.886 0.3142 ] Network output: [ -0.01159 0.04874 1.003 0.0001088 -4.886e-05 0.9722 8.202e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09929 0.09756 0.1738 0.2047 0.9859 0.9916 0.0993 0.7438 0.8675 0.2471 ] Network output: [ 0.001763 0.9985 -0.002629 1.667e-05 -7.485e-06 1.001 1.256e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001608 Epoch 6247 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01467 0.995 0.9844 6.859e-06 -3.079e-06 -0.008637 5.169e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002889 -0.01063 0.007963 0.9696 0.974 0.005997 0.8472 0.8351 0.02184 ] Network output: [ 0.996 0.03161 0.0009493 -5.282e-05 2.371e-05 -0.02487 -3.981e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02228 -0.2087 0.2035 0.9836 0.9933 0.202 0.47 0.881 0.7268 ] Network output: [ -0.01255 1.001 1.011 2.147e-06 -9.638e-07 0.01362 1.618e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005078 0.0007284 0.004046 0.004935 0.989 0.992 0.005169 0.8787 0.905 0.01581 ] Network output: [ -0.002999 0.04252 0.9974 -0.0002048 9.192e-05 0.9653 -0.0001543 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1023 0.3073 0.1662 0.9852 0.9941 0.1922 0.4749 0.8877 0.7224 ] Network output: [ 0.01129 -0.03283 0.9998 0.0001125 -5.052e-05 1.011 8.48e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08766 0.1752 0.2145 0.9874 0.992 0.09736 0.8088 0.8859 0.3119 ] Network output: [ -0.01047 0.04437 1.002 0.0001098 -4.931e-05 0.9747 8.277e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09902 0.09727 0.1726 0.2038 0.9858 0.9916 0.09903 0.7422 0.8675 0.2469 ] Network output: [ -0.001029 0.9996 0.001278 1.501e-05 -6.74e-06 1.001 1.131e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00139 Epoch 6248 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01524 0.9861 0.9847 8.116e-06 -3.644e-06 -0.001205 6.117e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003166 -0.002889 -0.01058 0.008124 0.9696 0.974 0.005982 0.847 0.8355 0.02188 ] Network output: [ 1.001 -0.0259 0.003514 -4.519e-05 2.029e-05 0.02118 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02305 -0.2055 0.2132 0.9836 0.9933 0.2012 0.4686 0.8814 0.7275 ] Network output: [ -0.01254 0.9977 1.011 2.619e-06 -1.176e-06 0.01654 1.973e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005061 0.0007339 0.004214 0.005261 0.989 0.992 0.005152 0.8786 0.9052 0.0159 ] Network output: [ 0.00214 -0.03727 1.001 -0.0001929 8.66e-05 1.031 -0.0001454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.102 0.3128 0.1815 0.9852 0.9941 0.1915 0.4739 0.8877 0.7218 ] Network output: [ 0.009504 -0.04884 1.003 0.0001131 -5.078e-05 1.027 8.525e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09756 0.08796 0.1789 0.2188 0.9874 0.992 0.09762 0.8099 0.886 0.3142 ] Network output: [ -0.01159 0.04876 1.003 0.0001088 -4.883e-05 0.9722 8.196e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09927 0.09753 0.1737 0.2046 0.9859 0.9916 0.09928 0.7437 0.8675 0.2471 ] Network output: [ 0.001759 0.9985 -0.002626 1.665e-05 -7.475e-06 1.001 1.255e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001605 Epoch 6249 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01467 0.995 0.9844 6.853e-06 -3.077e-06 -0.008624 5.165e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002889 -0.01062 0.007961 0.9696 0.974 0.005997 0.8472 0.8351 0.02183 ] Network output: [ 0.9961 0.03152 0.0009498 -5.279e-05 2.37e-05 -0.02481 -3.978e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.0223 -0.2087 0.2035 0.9836 0.9933 0.202 0.47 0.881 0.7267 ] Network output: [ -0.01255 1.001 1.011 2.15e-06 -9.651e-07 0.01363 1.62e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005079 0.0007274 0.004047 0.004934 0.989 0.992 0.00517 0.8787 0.905 0.01581 ] Network output: [ -0.002994 0.04241 0.9974 -0.0002046 9.183e-05 0.9654 -0.0001542 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1022 0.3073 0.1661 0.9852 0.9941 0.1922 0.4749 0.8877 0.7224 ] Network output: [ 0.01128 -0.03289 0.9998 0.0001124 -5.048e-05 1.011 8.474e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08765 0.1752 0.2145 0.9874 0.992 0.09736 0.8088 0.8859 0.3119 ] Network output: [ -0.01047 0.0444 1.002 0.0001098 -4.927e-05 0.9747 8.271e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.099 0.09725 0.1725 0.2038 0.9858 0.9916 0.09901 0.7422 0.8675 0.2469 ] Network output: [ -0.001026 0.9996 0.001272 1.5e-05 -6.733e-06 1.001 1.13e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001388 Epoch 6250 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01523 0.9861 0.9847 8.106e-06 -3.639e-06 -0.001214 6.109e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003166 -0.00289 -0.01058 0.008122 0.9696 0.974 0.005982 0.847 0.8355 0.02188 ] Network output: [ 1.001 -0.02582 0.003508 -4.519e-05 2.029e-05 0.02111 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02307 -0.2055 0.2131 0.9836 0.9933 0.2012 0.4686 0.8814 0.7275 ] Network output: [ -0.01254 0.9977 1.011 2.62e-06 -1.176e-06 0.01653 1.974e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005062 0.000733 0.004214 0.005258 0.989 0.992 0.005153 0.8786 0.9052 0.01589 ] Network output: [ 0.002128 -0.03714 1.001 -0.0001927 8.653e-05 1.031 -0.0001453 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.102 0.3128 0.1814 0.9852 0.9941 0.1915 0.4739 0.8877 0.7218 ] Network output: [ 0.009503 -0.04885 1.003 0.000113 -5.074e-05 1.027 8.518e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09756 0.08796 0.1789 0.2187 0.9874 0.992 0.09762 0.8099 0.886 0.3141 ] Network output: [ -0.01158 0.04878 1.003 0.0001087 -4.879e-05 0.9722 8.191e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09924 0.09751 0.1737 0.2046 0.9859 0.9916 0.09925 0.7437 0.8675 0.247 ] Network output: [ 0.001756 0.9985 -0.002623 1.663e-05 -7.466e-06 1.001 1.253e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001602 Epoch 6251 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01466 0.9949 0.9844 6.848e-06 -3.074e-06 -0.00861 5.161e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.00289 -0.01062 0.007959 0.9696 0.974 0.005997 0.8472 0.8351 0.02183 ] Network output: [ 0.9961 0.03144 0.0009504 -5.275e-05 2.368e-05 -0.02475 -3.976e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02232 -0.2086 0.2035 0.9836 0.9933 0.202 0.4699 0.881 0.7267 ] Network output: [ -0.01254 1.001 1.011 2.153e-06 -9.664e-07 0.01363 1.622e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005079 0.0007265 0.004048 0.004932 0.989 0.992 0.00517 0.8787 0.905 0.0158 ] Network output: [ -0.00299 0.0423 0.9974 -0.0002044 9.174e-05 0.9654 -0.000154 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1022 0.3074 0.1661 0.9852 0.9941 0.1922 0.4749 0.8877 0.7224 ] Network output: [ 0.01127 -0.03295 0.9998 0.0001123 -5.044e-05 1.011 8.467e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08764 0.1752 0.2144 0.9874 0.992 0.09736 0.8087 0.8859 0.3119 ] Network output: [ -0.01047 0.04443 1.002 0.0001097 -4.924e-05 0.9747 8.265e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09897 0.09722 0.1725 0.2038 0.9858 0.9916 0.09898 0.7421 0.8674 0.2469 ] Network output: [ -0.001023 0.9996 0.001265 1.498e-05 -6.726e-06 1.001 1.129e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001386 Epoch 6252 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01523 0.9861 0.9847 8.095e-06 -3.634e-06 -0.001224 6.101e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003166 -0.00289 -0.01058 0.008119 0.9696 0.974 0.005982 0.847 0.8355 0.02187 ] Network output: [ 1.001 -0.02573 0.003502 -4.519e-05 2.029e-05 0.02104 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02309 -0.2055 0.2131 0.9836 0.9933 0.2012 0.4685 0.8813 0.7275 ] Network output: [ -0.01254 0.9977 1.011 2.621e-06 -1.177e-06 0.01652 1.975e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005063 0.000732 0.004215 0.005256 0.989 0.992 0.005153 0.8786 0.9052 0.01589 ] Network output: [ 0.002116 -0.037 1.001 -0.0001926 8.646e-05 1.031 -0.0001451 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1019 0.3128 0.1813 0.9852 0.9941 0.1915 0.4739 0.8877 0.7218 ] Network output: [ 0.009501 -0.04886 1.003 0.0001129 -5.07e-05 1.027 8.512e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09756 0.08795 0.1789 0.2187 0.9874 0.992 0.09762 0.8098 0.886 0.3141 ] Network output: [ -0.01157 0.0488 1.003 0.0001086 -4.876e-05 0.9722 8.185e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09922 0.09748 0.1737 0.2046 0.9859 0.9916 0.09923 0.7436 0.8674 0.247 ] Network output: [ 0.001752 0.9985 -0.00262 1.661e-05 -7.456e-06 1.001 1.252e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001599 Epoch 6253 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01466 0.9949 0.9844 6.842e-06 -3.072e-06 -0.008596 5.156e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.00289 -0.01062 0.007957 0.9696 0.974 0.005997 0.8472 0.8351 0.02182 ] Network output: [ 0.9961 0.03135 0.000951 -5.272e-05 2.367e-05 -0.02468 -3.973e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02235 -0.2086 0.2035 0.9836 0.9933 0.202 0.4699 0.881 0.7267 ] Network output: [ -0.01254 1.001 1.011 2.156e-06 -9.678e-07 0.01363 1.625e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00508 0.0007255 0.004049 0.004931 0.989 0.992 0.005171 0.8787 0.9049 0.0158 ] Network output: [ -0.002986 0.04219 0.9975 -0.0002042 9.165e-05 0.9655 -0.0001539 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1021 0.3074 0.1661 0.9852 0.9941 0.1922 0.4748 0.8877 0.7224 ] Network output: [ 0.01126 -0.03301 0.9998 0.0001123 -5.04e-05 1.011 8.46e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.08764 0.1752 0.2144 0.9874 0.992 0.09735 0.8087 0.8858 0.3119 ] Network output: [ -0.01046 0.04446 1.002 0.0001096 -4.92e-05 0.9746 8.259e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09895 0.0972 0.1725 0.2037 0.9858 0.9916 0.09896 0.742 0.8674 0.2469 ] Network output: [ -0.00102 0.9996 0.001258 1.497e-05 -6.72e-06 1.001 1.128e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001384 Epoch 6254 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01522 0.9861 0.9847 8.084e-06 -3.629e-06 -0.001233 6.093e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003166 -0.00289 -0.01057 0.008117 0.9696 0.974 0.005982 0.847 0.8354 0.02187 ] Network output: [ 1.001 -0.02565 0.003496 -4.519e-05 2.029e-05 0.02096 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02311 -0.2054 0.2131 0.9836 0.9933 0.2012 0.4685 0.8813 0.7274 ] Network output: [ -0.01253 0.9977 1.011 2.623e-06 -1.177e-06 0.01652 1.976e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005063 0.000731 0.004215 0.005253 0.989 0.992 0.005154 0.8786 0.9051 0.01589 ] Network output: [ 0.002104 -0.03687 1.001 -0.0001924 8.639e-05 1.031 -0.000145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1019 0.3128 0.1812 0.9852 0.9941 0.1915 0.4738 0.8877 0.7217 ] Network output: [ 0.0095 -0.04886 1.003 0.0001129 -5.066e-05 1.027 8.505e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09756 0.08794 0.1789 0.2187 0.9874 0.992 0.09762 0.8098 0.8859 0.3141 ] Network output: [ -0.01156 0.04882 1.003 0.0001085 -4.873e-05 0.9721 8.18e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09919 0.09746 0.1737 0.2046 0.9859 0.9916 0.09921 0.7435 0.8674 0.247 ] Network output: [ 0.001748 0.9986 -0.002616 1.659e-05 -7.446e-06 1.001 1.25e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001596 Epoch 6255 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01466 0.9949 0.9844 6.837e-06 -3.069e-06 -0.008582 5.152e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.00289 -0.01061 0.007955 0.9696 0.974 0.005997 0.8472 0.8351 0.02182 ] Network output: [ 0.9961 0.03125 0.0009517 -5.269e-05 2.365e-05 -0.02462 -3.971e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02237 -0.2086 0.2035 0.9836 0.9933 0.202 0.4699 0.881 0.7267 ] Network output: [ -0.01254 1.001 1.011 2.159e-06 -9.691e-07 0.01364 1.627e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005081 0.0007246 0.00405 0.004929 0.989 0.992 0.005172 0.8787 0.9049 0.0158 ] Network output: [ -0.002981 0.04207 0.9975 -0.000204 9.156e-05 0.9656 -0.0001537 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1021 0.3075 0.166 0.9852 0.9941 0.1922 0.4748 0.8877 0.7223 ] Network output: [ 0.01125 -0.03306 0.9998 0.0001122 -5.036e-05 1.011 8.453e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.08763 0.1752 0.2144 0.9874 0.992 0.09735 0.8086 0.8858 0.3119 ] Network output: [ -0.01046 0.04449 1.002 0.0001095 -4.916e-05 0.9746 8.253e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09892 0.09718 0.1725 0.2037 0.9858 0.9916 0.09894 0.742 0.8674 0.2468 ] Network output: [ -0.001017 0.9996 0.001251 1.495e-05 -6.713e-06 1.001 1.127e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001382 Epoch 6256 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01522 0.9861 0.9847 8.074e-06 -3.625e-06 -0.001242 6.085e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003166 -0.00289 -0.01057 0.008114 0.9696 0.974 0.005982 0.847 0.8354 0.02186 ] Network output: [ 1.001 -0.02556 0.00349 -4.519e-05 2.029e-05 0.02088 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02313 -0.2054 0.213 0.9836 0.9933 0.2012 0.4685 0.8813 0.7274 ] Network output: [ -0.01253 0.9978 1.011 2.624e-06 -1.178e-06 0.01651 1.977e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005064 0.0007301 0.004216 0.005251 0.989 0.992 0.005155 0.8786 0.9051 0.01588 ] Network output: [ 0.002092 -0.03673 1.001 -0.0001923 8.633e-05 1.031 -0.0001449 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1019 0.3128 0.1812 0.9852 0.9941 0.1915 0.4738 0.8876 0.7217 ] Network output: [ 0.009499 -0.04887 1.003 0.0001128 -5.062e-05 1.027 8.498e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09755 0.08794 0.1789 0.2187 0.9874 0.992 0.09761 0.8097 0.8859 0.3141 ] Network output: [ -0.01155 0.04883 1.003 0.0001085 -4.869e-05 0.9721 8.174e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09917 0.09743 0.1736 0.2045 0.9859 0.9916 0.09918 0.7435 0.8674 0.247 ] Network output: [ 0.001744 0.9986 -0.002613 1.657e-05 -7.437e-06 1.001 1.248e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001593 Epoch 6257 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01465 0.9949 0.9844 6.831e-06 -3.067e-06 -0.008567 5.148e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.00289 -0.01061 0.007953 0.9696 0.974 0.005998 0.8472 0.8351 0.02181 ] Network output: [ 0.9961 0.03116 0.0009526 -5.265e-05 2.364e-05 -0.02455 -3.968e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.0224 -0.2085 0.2035 0.9836 0.9933 0.202 0.4698 0.881 0.7266 ] Network output: [ -0.01254 1.001 1.011 2.162e-06 -9.704e-07 0.01364 1.629e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005081 0.0007236 0.004051 0.004928 0.989 0.992 0.005172 0.8786 0.9049 0.01579 ] Network output: [ -0.002977 0.04195 0.9975 -0.0002038 9.147e-05 0.9656 -0.0001536 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1021 0.3075 0.166 0.9852 0.9941 0.1922 0.4748 0.8877 0.7223 ] Network output: [ 0.01124 -0.03312 0.9998 0.0001121 -5.032e-05 1.011 8.447e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.08762 0.1752 0.2144 0.9874 0.992 0.09735 0.8086 0.8858 0.3119 ] Network output: [ -0.01046 0.04452 1.002 0.0001094 -4.913e-05 0.9746 8.247e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0989 0.09715 0.1725 0.2037 0.9858 0.9916 0.09891 0.7419 0.8673 0.2468 ] Network output: [ -0.001013 0.9996 0.001245 1.494e-05 -6.707e-06 1.001 1.126e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00138 Epoch 6258 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01521 0.9861 0.9847 8.063e-06 -3.62e-06 -0.001252 6.076e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003166 -0.002891 -0.01057 0.008112 0.9696 0.974 0.005982 0.847 0.8354 0.02186 ] Network output: [ 1 -0.02547 0.003484 -4.519e-05 2.029e-05 0.02081 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02316 -0.2054 0.213 0.9836 0.9933 0.2012 0.4685 0.8813 0.7274 ] Network output: [ -0.01253 0.9978 1.011 2.625e-06 -1.179e-06 0.01651 1.978e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005065 0.0007291 0.004216 0.005248 0.989 0.992 0.005156 0.8786 0.9051 0.01588 ] Network output: [ 0.002079 -0.03659 1.001 -0.0001921 8.626e-05 1.031 -0.0001448 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1018 0.3129 0.1811 0.9852 0.9941 0.1915 0.4738 0.8876 0.7217 ] Network output: [ 0.009498 -0.04888 1.003 0.0001127 -5.058e-05 1.027 8.491e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09755 0.08793 0.1789 0.2187 0.9874 0.992 0.09761 0.8097 0.8859 0.3141 ] Network output: [ -0.01154 0.04885 1.003 0.0001084 -4.866e-05 0.9721 8.168e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09914 0.09741 0.1736 0.2045 0.9859 0.9916 0.09916 0.7434 0.8673 0.247 ] Network output: [ 0.00174 0.9986 -0.002609 1.654e-05 -7.427e-06 1.001 1.247e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00159 Epoch 6259 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01465 0.9949 0.9844 6.826e-06 -3.064e-06 -0.008553 5.144e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002891 -0.01061 0.007951 0.9696 0.974 0.005998 0.8472 0.8351 0.02181 ] Network output: [ 0.9961 0.03107 0.0009534 -5.262e-05 2.362e-05 -0.02449 -3.965e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02242 -0.2085 0.2035 0.9836 0.9933 0.202 0.4698 0.8809 0.7266 ] Network output: [ -0.01254 1.001 1.011 2.164e-06 -9.717e-07 0.01364 1.631e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005082 0.0007227 0.004052 0.004926 0.989 0.992 0.005173 0.8786 0.9049 0.01579 ] Network output: [ -0.002972 0.04184 0.9976 -0.0002036 9.138e-05 0.9657 -0.0001534 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.102 0.3075 0.166 0.9852 0.9941 0.1922 0.4747 0.8876 0.7223 ] Network output: [ 0.01124 -0.03318 0.9998 0.000112 -5.028e-05 1.011 8.44e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.08762 0.1752 0.2144 0.9874 0.992 0.09735 0.8085 0.8858 0.3119 ] Network output: [ -0.01045 0.04455 1.002 0.0001094 -4.909e-05 0.9746 8.241e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09888 0.09713 0.1724 0.2037 0.9858 0.9916 0.09889 0.7419 0.8673 0.2468 ] Network output: [ -0.00101 0.9996 0.001238 1.492e-05 -6.7e-06 1.001 1.125e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001378 Epoch 6260 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01521 0.9861 0.9847 8.052e-06 -3.615e-06 -0.001262 6.068e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003166 -0.002891 -0.01056 0.008109 0.9696 0.974 0.005983 0.847 0.8354 0.02185 ] Network output: [ 1 -0.02538 0.003477 -4.519e-05 2.029e-05 0.02073 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02318 -0.2054 0.2129 0.9836 0.9933 0.2012 0.4684 0.8813 0.7273 ] Network output: [ -0.01253 0.9978 1.011 2.626e-06 -1.179e-06 0.0165 1.979e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005065 0.0007281 0.004216 0.005246 0.989 0.992 0.005156 0.8785 0.9051 0.01588 ] Network output: [ 0.002067 -0.03645 1.001 -0.000192 8.619e-05 1.03 -0.0001447 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1018 0.3129 0.181 0.9852 0.9941 0.1915 0.4738 0.8876 0.7217 ] Network output: [ 0.009497 -0.04888 1.003 0.0001126 -5.054e-05 1.027 8.484e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09755 0.08792 0.1789 0.2186 0.9874 0.992 0.09761 0.8096 0.8859 0.3141 ] Network output: [ -0.01153 0.04887 1.003 0.0001083 -4.862e-05 0.9721 8.163e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09912 0.09738 0.1736 0.2045 0.9859 0.9916 0.09913 0.7433 0.8673 0.2469 ] Network output: [ 0.001736 0.9986 -0.002606 1.652e-05 -7.418e-06 1.001 1.245e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001586 Epoch 6261 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01465 0.9949 0.9844 6.82e-06 -3.062e-06 -0.008539 5.14e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002891 -0.0106 0.00795 0.9696 0.974 0.005998 0.8472 0.835 0.0218 ] Network output: [ 0.9961 0.03097 0.0009544 -5.258e-05 2.361e-05 -0.02442 -3.963e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02245 -0.2085 0.2035 0.9836 0.9933 0.202 0.4698 0.8809 0.7266 ] Network output: [ -0.01253 1.001 1.011 2.167e-06 -9.73e-07 0.01365 1.633e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005083 0.0007217 0.004053 0.004925 0.989 0.992 0.005174 0.8786 0.9049 0.01579 ] Network output: [ -0.002967 0.04172 0.9976 -0.0002034 9.129e-05 0.9658 -0.0001533 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.102 0.3076 0.166 0.9852 0.9941 0.1922 0.4747 0.8876 0.7222 ] Network output: [ 0.01123 -0.03324 0.9998 0.0001119 -5.024e-05 1.011 8.433e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.08761 0.1752 0.2144 0.9874 0.992 0.09735 0.8085 0.8857 0.3119 ] Network output: [ -0.01045 0.04458 1.002 0.0001093 -4.906e-05 0.9745 8.235e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09885 0.0971 0.1724 0.2036 0.9858 0.9916 0.09887 0.7418 0.8673 0.2468 ] Network output: [ -0.001006 0.9996 0.001231 1.491e-05 -6.694e-06 1.001 1.124e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001375 Epoch 6262 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0152 0.9862 0.9847 8.041e-06 -3.61e-06 -0.001272 6.06e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003166 -0.002891 -0.01056 0.008107 0.9696 0.974 0.005983 0.847 0.8354 0.02185 ] Network output: [ 1 -0.02528 0.003471 -4.519e-05 2.029e-05 0.02065 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.0232 -0.2054 0.2129 0.9836 0.9933 0.2012 0.4684 0.8813 0.7273 ] Network output: [ -0.01252 0.9978 1.011 2.628e-06 -1.18e-06 0.01649 1.98e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005066 0.0007272 0.004217 0.005243 0.989 0.992 0.005157 0.8785 0.9051 0.01587 ] Network output: [ 0.002054 -0.03631 1.001 -0.0001918 8.612e-05 1.03 -0.0001446 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1017 0.3129 0.1809 0.9852 0.9941 0.1915 0.4737 0.8876 0.7216 ] Network output: [ 0.009495 -0.04889 1.003 0.0001125 -5.05e-05 1.027 8.477e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09755 0.08791 0.1789 0.2186 0.9874 0.992 0.09761 0.8096 0.8858 0.3141 ] Network output: [ -0.01152 0.04889 1.003 0.0001082 -4.859e-05 0.972 8.157e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09909 0.09736 0.1736 0.2044 0.9859 0.9916 0.09911 0.7433 0.8673 0.2469 ] Network output: [ 0.001731 0.9986 -0.002602 1.65e-05 -7.408e-06 1.001 1.244e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001583 Epoch 6263 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01464 0.9949 0.9844 6.815e-06 -3.059e-06 -0.008524 5.136e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002891 -0.0106 0.007948 0.9696 0.974 0.005998 0.8472 0.835 0.0218 ] Network output: [ 0.9962 0.03088 0.0009554 -5.255e-05 2.359e-05 -0.02436 -3.96e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02247 -0.2085 0.2035 0.9836 0.9933 0.2019 0.4697 0.8809 0.7266 ] Network output: [ -0.01253 1.001 1.011 2.17e-06 -9.743e-07 0.01365 1.636e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005083 0.0007208 0.004054 0.004924 0.989 0.992 0.005175 0.8786 0.9049 0.01578 ] Network output: [ -0.002962 0.04159 0.9976 -0.0002032 9.121e-05 0.9659 -0.0001531 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1019 0.3076 0.1659 0.9852 0.9941 0.1922 0.4747 0.8876 0.7222 ] Network output: [ 0.01122 -0.0333 0.9997 0.0001118 -5.019e-05 1.012 8.426e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.0876 0.1753 0.2144 0.9874 0.992 0.09735 0.8084 0.8857 0.3119 ] Network output: [ -0.01045 0.04461 1.002 0.0001092 -4.902e-05 0.9745 8.229e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09883 0.09708 0.1724 0.2036 0.9858 0.9916 0.09884 0.7418 0.8672 0.2468 ] Network output: [ -0.001003 0.9995 0.001224 1.49e-05 -6.687e-06 1.001 1.123e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001373 Epoch 6264 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0152 0.9862 0.9847 8.031e-06 -3.605e-06 -0.001282 6.052e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003166 -0.002891 -0.01056 0.008104 0.9696 0.974 0.005983 0.847 0.8354 0.02184 ] Network output: [ 1 -0.02519 0.003465 -4.519e-05 2.029e-05 0.02057 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02322 -0.2053 0.2128 0.9836 0.9933 0.2012 0.4684 0.8813 0.7273 ] Network output: [ -0.01252 0.9978 1.011 2.629e-06 -1.18e-06 0.01649 1.981e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005067 0.0007262 0.004217 0.005241 0.989 0.992 0.005158 0.8785 0.9051 0.01587 ] Network output: [ 0.002041 -0.03616 1.001 -0.0001917 8.606e-05 1.03 -0.0001445 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1017 0.3129 0.1809 0.9852 0.9941 0.1915 0.4737 0.8876 0.7216 ] Network output: [ 0.009494 -0.04889 1.003 0.0001124 -5.046e-05 1.027 8.471e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09755 0.08791 0.1789 0.2186 0.9874 0.992 0.09761 0.8095 0.8858 0.3141 ] Network output: [ -0.01152 0.0489 1.003 0.0001082 -4.856e-05 0.972 8.151e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09907 0.09733 0.1735 0.2044 0.9859 0.9916 0.09908 0.7432 0.8672 0.2469 ] Network output: [ 0.001727 0.9986 -0.002598 1.648e-05 -7.399e-06 1.001 1.242e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00158 Epoch 6265 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01464 0.9948 0.9844 6.81e-06 -3.057e-06 -0.008509 5.132e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002891 -0.0106 0.007946 0.9696 0.974 0.005998 0.8472 0.835 0.02179 ] Network output: [ 0.9962 0.03078 0.0009566 -5.251e-05 2.357e-05 -0.02429 -3.957e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02249 -0.2084 0.2035 0.9836 0.9933 0.2019 0.4697 0.8809 0.7265 ] Network output: [ -0.01253 1.001 1.011 2.173e-06 -9.756e-07 0.01366 1.638e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005084 0.0007198 0.004055 0.004922 0.989 0.992 0.005175 0.8786 0.9049 0.01578 ] Network output: [ -0.002957 0.04147 0.9977 -0.000203 9.112e-05 0.9659 -0.000153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1019 0.3076 0.1659 0.9852 0.9941 0.1921 0.4746 0.8876 0.7222 ] Network output: [ 0.01121 -0.03335 0.9997 0.0001117 -5.015e-05 1.012 8.419e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.0876 0.1753 0.2144 0.9874 0.992 0.09735 0.8084 0.8857 0.3119 ] Network output: [ -0.01044 0.04464 1.002 0.0001091 -4.898e-05 0.9745 8.223e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09881 0.09705 0.1724 0.2036 0.9858 0.9916 0.09882 0.7417 0.8672 0.2467 ] Network output: [ -0.0009994 0.9995 0.001216 1.488e-05 -6.681e-06 1.001 1.122e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001371 Epoch 6266 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01519 0.9862 0.9848 8.02e-06 -3.6e-06 -0.001292 6.044e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003166 -0.002892 -0.01056 0.008102 0.9696 0.974 0.005983 0.847 0.8354 0.02184 ] Network output: [ 1 -0.0251 0.003459 -4.519e-05 2.029e-05 0.02049 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02324 -0.2053 0.2128 0.9836 0.9933 0.2012 0.4684 0.8812 0.7273 ] Network output: [ -0.01252 0.9978 1.011 2.63e-06 -1.181e-06 0.01648 1.982e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005068 0.0007253 0.004218 0.005238 0.989 0.992 0.005159 0.8785 0.9051 0.01587 ] Network output: [ 0.002029 -0.03602 1.001 -0.0001915 8.599e-05 1.03 -0.0001444 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1016 0.3129 0.1808 0.9852 0.9941 0.1915 0.4737 0.8876 0.7216 ] Network output: [ 0.009493 -0.0489 1.003 0.0001123 -5.042e-05 1.027 8.464e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09754 0.0879 0.1789 0.2186 0.9874 0.992 0.0976 0.8094 0.8858 0.3141 ] Network output: [ -0.01151 0.04892 1.003 0.0001081 -4.852e-05 0.972 8.146e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09904 0.09731 0.1735 0.2044 0.9859 0.9916 0.09906 0.7432 0.8672 0.2469 ] Network output: [ 0.001723 0.9986 -0.002595 1.646e-05 -7.389e-06 1.001 1.24e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001576 Epoch 6267 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01464 0.9948 0.9844 6.804e-06 -3.055e-06 -0.008495 5.128e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002892 -0.0106 0.007944 0.9696 0.974 0.005998 0.8472 0.835 0.02179 ] Network output: [ 0.9962 0.03069 0.0009577 -5.248e-05 2.356e-05 -0.02422 -3.955e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02252 -0.2084 0.2034 0.9836 0.9933 0.2019 0.4697 0.8809 0.7265 ] Network output: [ -0.01253 1.001 1.011 2.176e-06 -9.769e-07 0.01366 1.64e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005085 0.0007189 0.004056 0.004921 0.989 0.992 0.005176 0.8786 0.9049 0.01578 ] Network output: [ -0.002953 0.04135 0.9977 -0.0002028 9.103e-05 0.966 -0.0001528 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.1019 0.3077 0.1659 0.9852 0.9941 0.1921 0.4746 0.8876 0.7221 ] Network output: [ 0.0112 -0.03341 0.9997 0.0001116 -5.011e-05 1.012 8.413e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.08759 0.1753 0.2144 0.9874 0.992 0.09735 0.8083 0.8856 0.3119 ] Network output: [ -0.01044 0.04467 1.002 0.000109 -4.895e-05 0.9744 8.217e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09878 0.09703 0.1723 0.2036 0.9858 0.9916 0.09879 0.7416 0.8672 0.2467 ] Network output: [ -0.0009958 0.9995 0.001209 1.487e-05 -6.675e-06 1.001 1.12e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001369 Epoch 6268 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01519 0.9862 0.9848 8.009e-06 -3.596e-06 -0.001302 6.036e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003166 -0.002892 -0.01055 0.008099 0.9696 0.974 0.005983 0.847 0.8354 0.02183 ] Network output: [ 1 -0.02501 0.003453 -4.519e-05 2.029e-05 0.02041 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02327 -0.2053 0.2128 0.9836 0.9933 0.2012 0.4683 0.8812 0.7272 ] Network output: [ -0.01252 0.9978 1.011 2.631e-06 -1.181e-06 0.01647 1.983e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005068 0.0007243 0.004218 0.005235 0.989 0.992 0.005159 0.8785 0.9051 0.01586 ] Network output: [ 0.002016 -0.03588 1.001 -0.0001914 8.592e-05 1.03 -0.0001442 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1016 0.3129 0.1807 0.9852 0.9941 0.1915 0.4737 0.8876 0.7216 ] Network output: [ 0.009492 -0.0489 1.003 0.0001122 -5.038e-05 1.027 8.457e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09754 0.08789 0.1789 0.2185 0.9874 0.992 0.0976 0.8094 0.8857 0.3141 ] Network output: [ -0.0115 0.04893 1.003 0.000108 -4.849e-05 0.972 8.14e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09902 0.09728 0.1735 0.2044 0.9859 0.9916 0.09903 0.7431 0.8672 0.2469 ] Network output: [ 0.001719 0.9986 -0.002591 1.644e-05 -7.38e-06 1.001 1.239e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001573 Epoch 6269 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01463 0.9948 0.9844 6.799e-06 -3.052e-06 -0.00848 5.124e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002892 -0.01059 0.007942 0.9696 0.974 0.005998 0.8472 0.835 0.02178 ] Network output: [ 0.9962 0.03059 0.000959 -5.244e-05 2.354e-05 -0.02415 -3.952e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02254 -0.2084 0.2034 0.9836 0.9933 0.2019 0.4696 0.8809 0.7265 ] Network output: [ -0.01253 1.001 1.011 2.179e-06 -9.782e-07 0.01366 1.642e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005085 0.0007179 0.004057 0.004919 0.989 0.992 0.005177 0.8786 0.9048 0.01577 ] Network output: [ -0.002948 0.04122 0.9977 -0.0002026 9.094e-05 0.9661 -0.0001527 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1018 0.3077 0.1659 0.9852 0.9941 0.1921 0.4746 0.8876 0.7221 ] Network output: [ 0.01119 -0.03347 0.9997 0.0001115 -5.007e-05 1.012 8.406e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.08759 0.1753 0.2144 0.9874 0.992 0.09735 0.8083 0.8856 0.3119 ] Network output: [ -0.01043 0.0447 1.002 0.0001089 -4.891e-05 0.9744 8.211e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09876 0.097 0.1723 0.2035 0.9858 0.9916 0.09877 0.7416 0.8671 0.2467 ] Network output: [ -0.0009921 0.9995 0.001202 1.485e-05 -6.668e-06 1.001 1.119e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001366 Epoch 6270 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01518 0.9862 0.9848 7.998e-06 -3.591e-06 -0.001313 6.028e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003166 -0.002892 -0.01055 0.008097 0.9696 0.974 0.005983 0.847 0.8354 0.02183 ] Network output: [ 1 -0.02491 0.003447 -4.519e-05 2.029e-05 0.02032 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02329 -0.2053 0.2127 0.9836 0.9933 0.2012 0.4683 0.8812 0.7272 ] Network output: [ -0.01252 0.9978 1.011 2.632e-06 -1.182e-06 0.01647 1.984e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005069 0.0007233 0.004218 0.005233 0.989 0.992 0.00516 0.8785 0.905 0.01586 ] Network output: [ 0.002003 -0.03573 1.001 -0.0001912 8.586e-05 1.03 -0.0001441 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1016 0.313 0.1806 0.9852 0.9941 0.1915 0.4736 0.8876 0.7215 ] Network output: [ 0.009491 -0.04891 1.003 0.0001121 -5.034e-05 1.027 8.45e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09754 0.08788 0.1789 0.2185 0.9874 0.992 0.0976 0.8093 0.8857 0.3141 ] Network output: [ -0.01149 0.04895 1.003 0.0001079 -4.846e-05 0.972 8.134e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09899 0.09725 0.1734 0.2043 0.9859 0.9916 0.09901 0.743 0.8672 0.2468 ] Network output: [ 0.001714 0.9986 -0.002587 1.642e-05 -7.371e-06 1.001 1.237e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00157 Epoch 6271 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01463 0.9948 0.9844 6.794e-06 -3.05e-06 -0.008465 5.12e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002892 -0.01059 0.00794 0.9696 0.974 0.005998 0.8472 0.835 0.02178 ] Network output: [ 0.9962 0.03049 0.0009603 -5.241e-05 2.353e-05 -0.02408 -3.949e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02257 -0.2084 0.2034 0.9836 0.9933 0.2019 0.4696 0.8809 0.7265 ] Network output: [ -0.01252 1.001 1.011 2.182e-06 -9.795e-07 0.01367 1.644e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005086 0.000717 0.004058 0.004918 0.989 0.992 0.005177 0.8785 0.9048 0.01577 ] Network output: [ -0.002942 0.0411 0.9978 -0.0002024 9.085e-05 0.9662 -0.0001525 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1018 0.3077 0.1659 0.9852 0.9941 0.1921 0.4746 0.8876 0.7221 ] Network output: [ 0.01118 -0.03353 0.9997 0.0001114 -5.003e-05 1.012 8.399e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09728 0.08758 0.1753 0.2143 0.9874 0.992 0.09734 0.8082 0.8856 0.3119 ] Network output: [ -0.01043 0.04472 1.002 0.0001089 -4.887e-05 0.9744 8.205e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09873 0.09698 0.1723 0.2035 0.9858 0.9916 0.09875 0.7415 0.8671 0.2467 ] Network output: [ -0.0009884 0.9995 0.001195 1.484e-05 -6.662e-06 1.001 1.118e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001364 Epoch 6272 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01518 0.9862 0.9848 7.988e-06 -3.586e-06 -0.001323 6.02e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003166 -0.002892 -0.01055 0.008094 0.9696 0.974 0.005984 0.847 0.8353 0.02182 ] Network output: [ 1 -0.02482 0.00344 -4.519e-05 2.029e-05 0.02024 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02331 -0.2053 0.2127 0.9836 0.9933 0.2012 0.4683 0.8812 0.7272 ] Network output: [ -0.01251 0.9978 1.011 2.633e-06 -1.182e-06 0.01646 1.985e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00507 0.0007224 0.004219 0.00523 0.989 0.992 0.005161 0.8785 0.905 0.01586 ] Network output: [ 0.00199 -0.03558 1.001 -0.0001911 8.579e-05 1.03 -0.000144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1015 0.313 0.1805 0.9852 0.9941 0.1915 0.4736 0.8875 0.7215 ] Network output: [ 0.009489 -0.04891 1.003 0.000112 -5.029e-05 1.027 8.443e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09754 0.08787 0.1789 0.2185 0.9874 0.992 0.0976 0.8093 0.8857 0.3141 ] Network output: [ -0.01148 0.04896 1.002 0.0001079 -4.842e-05 0.9719 8.129e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09897 0.09723 0.1734 0.2043 0.9859 0.9916 0.09898 0.743 0.8671 0.2468 ] Network output: [ 0.00171 0.9986 -0.002583 1.64e-05 -7.361e-06 1.001 1.236e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001566 Epoch 6273 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01463 0.9948 0.9844 6.788e-06 -3.048e-06 -0.00845 5.116e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002892 -0.01059 0.007938 0.9696 0.974 0.005998 0.8472 0.835 0.02177 ] Network output: [ 0.9962 0.03039 0.0009617 -5.237e-05 2.351e-05 -0.02401 -3.947e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02259 -0.2083 0.2034 0.9836 0.9933 0.2019 0.4696 0.8809 0.7264 ] Network output: [ -0.01252 1.001 1.011 2.185e-06 -9.808e-07 0.01367 1.647e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005087 0.0007161 0.004059 0.004916 0.989 0.992 0.005178 0.8785 0.9048 0.01577 ] Network output: [ -0.002937 0.04097 0.9978 -0.0002022 9.076e-05 0.9663 -0.0001524 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1017 0.3078 0.1658 0.9852 0.9941 0.1921 0.4745 0.8876 0.7221 ] Network output: [ 0.01117 -0.03358 0.9997 0.0001114 -4.999e-05 1.012 8.392e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09728 0.08757 0.1753 0.2143 0.9874 0.992 0.09734 0.8082 0.8856 0.3119 ] Network output: [ -0.01043 0.04475 1.002 0.0001088 -4.884e-05 0.9744 8.198e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09871 0.09696 0.1723 0.2035 0.9858 0.9916 0.09872 0.7415 0.8671 0.2467 ] Network output: [ -0.0009846 0.9995 0.001187 1.483e-05 -6.656e-06 1.001 1.117e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001362 Epoch 6274 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01517 0.9863 0.9848 7.977e-06 -3.581e-06 -0.001334 6.012e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003166 -0.002893 -0.01054 0.008092 0.9696 0.974 0.005984 0.847 0.8353 0.02182 ] Network output: [ 1 -0.02472 0.003434 -4.519e-05 2.029e-05 0.02016 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02333 -0.2052 0.2126 0.9836 0.9933 0.2012 0.4682 0.8812 0.7272 ] Network output: [ -0.01251 0.9978 1.011 2.634e-06 -1.183e-06 0.01645 1.985e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00507 0.0007214 0.004219 0.005228 0.989 0.992 0.005161 0.8785 0.905 0.01585 ] Network output: [ 0.001977 -0.03543 1.001 -0.0001909 8.572e-05 1.029 -0.0001439 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1015 0.313 0.1805 0.9852 0.9941 0.1915 0.4736 0.8875 0.7215 ] Network output: [ 0.009488 -0.04891 1.003 0.0001119 -5.025e-05 1.027 8.436e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09753 0.08787 0.1789 0.2185 0.9874 0.992 0.09759 0.8092 0.8857 0.3141 ] Network output: [ -0.01147 0.04898 1.002 0.0001078 -4.839e-05 0.9719 8.123e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09894 0.0972 0.1734 0.2043 0.9859 0.9916 0.09896 0.7429 0.8671 0.2468 ] Network output: [ 0.001706 0.9986 -0.002579 1.638e-05 -7.352e-06 1.001 1.234e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001563 Epoch 6275 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01462 0.9948 0.9844 6.783e-06 -3.045e-06 -0.008435 5.112e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002893 -0.01058 0.007936 0.9696 0.974 0.005999 0.8472 0.835 0.02177 ] Network output: [ 0.9962 0.03029 0.0009631 -5.233e-05 2.349e-05 -0.02394 -3.944e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02262 -0.2083 0.2034 0.9836 0.9933 0.2019 0.4696 0.8809 0.7264 ] Network output: [ -0.01252 1.001 1.011 2.188e-06 -9.821e-07 0.01367 1.649e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005087 0.0007151 0.00406 0.004915 0.989 0.992 0.005179 0.8785 0.9048 0.01577 ] Network output: [ -0.002932 0.04085 0.9979 -0.000202 9.067e-05 0.9663 -0.0001522 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1017 0.3078 0.1658 0.9852 0.9941 0.1921 0.4745 0.8875 0.722 ] Network output: [ 0.01116 -0.03364 0.9997 0.0001113 -4.995e-05 1.012 8.385e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09728 0.08757 0.1753 0.2143 0.9874 0.992 0.09734 0.8081 0.8855 0.3119 ] Network output: [ -0.01042 0.04478 1.002 0.0001087 -4.88e-05 0.9743 8.192e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09869 0.09693 0.1723 0.2035 0.9858 0.9916 0.0987 0.7414 0.867 0.2466 ] Network output: [ -0.0009808 0.9995 0.00118 1.481e-05 -6.65e-06 1.001 1.116e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001359 Epoch 6276 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01517 0.9863 0.9848 7.966e-06 -3.576e-06 -0.001345 6.003e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003166 -0.002893 -0.01054 0.008089 0.9696 0.974 0.005984 0.847 0.8353 0.02181 ] Network output: [ 1 -0.02462 0.003428 -4.519e-05 2.029e-05 0.02008 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02335 -0.2052 0.2126 0.9836 0.9933 0.2012 0.4682 0.8812 0.7271 ] Network output: [ -0.01251 0.9978 1.011 2.636e-06 -1.183e-06 0.01645 1.986e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005071 0.0007205 0.00422 0.005225 0.989 0.992 0.005162 0.8784 0.905 0.01585 ] Network output: [ 0.001964 -0.03529 1.001 -0.0001908 8.566e-05 1.029 -0.0001438 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1014 0.313 0.1804 0.9852 0.9941 0.1915 0.4735 0.8875 0.7214 ] Network output: [ 0.009487 -0.04891 1.003 0.0001118 -5.021e-05 1.027 8.429e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09753 0.08786 0.1789 0.2184 0.9874 0.992 0.09759 0.8092 0.8856 0.3141 ] Network output: [ -0.01146 0.04899 1.002 0.0001077 -4.835e-05 0.9719 8.117e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09892 0.09718 0.1734 0.2043 0.9858 0.9916 0.09893 0.7428 0.8671 0.2468 ] Network output: [ 0.001701 0.9986 -0.002575 1.636e-05 -7.342e-06 1.001 1.233e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001559 Epoch 6277 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01462 0.9948 0.9844 6.778e-06 -3.043e-06 -0.00842 5.108e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002893 -0.01058 0.007934 0.9696 0.974 0.005999 0.8472 0.835 0.02176 ] Network output: [ 0.9963 0.03019 0.0009646 -5.23e-05 2.348e-05 -0.02387 -3.941e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02264 -0.2083 0.2034 0.9836 0.9933 0.2019 0.4695 0.8808 0.7264 ] Network output: [ -0.01252 1.001 1.011 2.191e-06 -9.835e-07 0.01368 1.651e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005088 0.0007142 0.004061 0.004913 0.989 0.992 0.005179 0.8785 0.9048 0.01576 ] Network output: [ -0.002927 0.04072 0.9979 -0.0002018 9.058e-05 0.9664 -0.0001521 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1016 0.3078 0.1658 0.9852 0.9941 0.1921 0.4745 0.8875 0.722 ] Network output: [ 0.01116 -0.0337 0.9997 0.0001112 -4.991e-05 1.012 8.378e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09728 0.08756 0.1754 0.2143 0.9874 0.992 0.09734 0.8081 0.8855 0.3119 ] Network output: [ -0.01042 0.0448 1.002 0.0001086 -4.877e-05 0.9743 8.186e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09866 0.09691 0.1722 0.2034 0.9858 0.9916 0.09867 0.7413 0.867 0.2466 ] Network output: [ -0.000977 0.9995 0.001172 1.48e-05 -6.643e-06 1.001 1.115e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001357 Epoch 6278 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01516 0.9863 0.9848 7.955e-06 -3.571e-06 -0.001356 5.995e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.002893 -0.01054 0.008087 0.9696 0.974 0.005984 0.847 0.8353 0.02181 ] Network output: [ 1 -0.02453 0.003422 -4.519e-05 2.029e-05 0.01999 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02338 -0.2052 0.2125 0.9836 0.9933 0.2012 0.4682 0.8812 0.7271 ] Network output: [ -0.01251 0.9978 1.011 2.637e-06 -1.184e-06 0.01644 1.987e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005072 0.0007195 0.00422 0.005222 0.989 0.992 0.005163 0.8784 0.905 0.01584 ] Network output: [ 0.001951 -0.03514 1.001 -0.0001906 8.559e-05 1.029 -0.0001437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1014 0.313 0.1803 0.9852 0.9941 0.1915 0.4735 0.8875 0.7214 ] Network output: [ 0.009486 -0.04891 1.003 0.0001118 -5.017e-05 1.028 8.422e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09753 0.08785 0.1789 0.2184 0.9874 0.992 0.09759 0.8091 0.8856 0.3141 ] Network output: [ -0.01145 0.049 1.002 0.0001076 -4.832e-05 0.9719 8.111e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09889 0.09715 0.1733 0.2042 0.9858 0.9916 0.09891 0.7428 0.867 0.2468 ] Network output: [ 0.001697 0.9986 -0.002571 1.633e-05 -7.333e-06 1.001 1.231e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001556 Epoch 6279 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01462 0.9947 0.9845 6.773e-06 -3.04e-06 -0.008405 5.104e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002893 -0.01058 0.007932 0.9696 0.974 0.005999 0.8471 0.835 0.02176 ] Network output: [ 0.9963 0.03009 0.0009662 -5.226e-05 2.346e-05 -0.0238 -3.939e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02266 -0.2083 0.2034 0.9836 0.9933 0.2019 0.4695 0.8808 0.7264 ] Network output: [ -0.01252 1.001 1.011 2.194e-06 -9.848e-07 0.01368 1.653e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005089 0.0007132 0.004062 0.004912 0.989 0.992 0.00518 0.8785 0.9048 0.01576 ] Network output: [ -0.002922 0.04059 0.9979 -0.0002016 9.049e-05 0.9665 -0.0001519 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1016 0.3079 0.1658 0.9852 0.9941 0.1921 0.4744 0.8875 0.722 ] Network output: [ 0.01115 -0.03375 0.9997 0.0001111 -4.987e-05 1.012 8.372e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09728 0.08755 0.1754 0.2143 0.9874 0.992 0.09734 0.808 0.8855 0.3119 ] Network output: [ -0.01042 0.04483 1.002 0.0001085 -4.873e-05 0.9743 8.18e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09864 0.09688 0.1722 0.2034 0.9858 0.9916 0.09865 0.7413 0.867 0.2466 ] Network output: [ -0.000973 0.9995 0.001165 1.478e-05 -6.637e-06 1.001 1.114e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001354 Epoch 6280 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01516 0.9863 0.9848 7.944e-06 -3.566e-06 -0.001367 5.987e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.002893 -0.01053 0.008084 0.9696 0.974 0.005984 0.847 0.8353 0.0218 ] Network output: [ 1 -0.02443 0.003416 -4.519e-05 2.029e-05 0.01991 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.0234 -0.2052 0.2125 0.9836 0.9933 0.2012 0.4682 0.8812 0.7271 ] Network output: [ -0.01251 0.9978 1.011 2.638e-06 -1.184e-06 0.01643 1.988e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005073 0.0007186 0.00422 0.00522 0.989 0.992 0.005164 0.8784 0.905 0.01584 ] Network output: [ 0.001938 -0.03499 1.001 -0.0001905 8.552e-05 1.029 -0.0001436 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1014 0.3131 0.1802 0.9852 0.9941 0.1915 0.4735 0.8875 0.7214 ] Network output: [ 0.009485 -0.04891 1.003 0.0001117 -5.013e-05 1.028 8.415e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09753 0.08784 0.1789 0.2184 0.9874 0.992 0.09759 0.8091 0.8856 0.314 ] Network output: [ -0.01144 0.04902 1.002 0.0001076 -4.829e-05 0.9719 8.106e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09887 0.09713 0.1733 0.2042 0.9858 0.9916 0.09888 0.7427 0.867 0.2467 ] Network output: [ 0.001692 0.9986 -0.002566 1.631e-05 -7.324e-06 1.001 1.229e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001552 Epoch 6281 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01461 0.9947 0.9845 6.767e-06 -3.038e-06 -0.008389 5.1e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002893 -0.01057 0.00793 0.9696 0.974 0.005999 0.8471 0.835 0.02175 ] Network output: [ 0.9963 0.02999 0.0009679 -5.223e-05 2.345e-05 -0.02373 -3.936e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02269 -0.2082 0.2034 0.9836 0.9933 0.2019 0.4695 0.8808 0.7263 ] Network output: [ -0.01252 1.001 1.011 2.196e-06 -9.861e-07 0.01369 1.655e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005089 0.0007123 0.004062 0.004911 0.989 0.992 0.005181 0.8785 0.9048 0.01576 ] Network output: [ -0.002916 0.04046 0.998 -0.0002014 9.04e-05 0.9666 -0.0001517 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1016 0.3079 0.1657 0.9852 0.9941 0.1921 0.4744 0.8875 0.7219 ] Network output: [ 0.01114 -0.03381 0.9996 0.000111 -4.983e-05 1.012 8.365e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09728 0.08754 0.1754 0.2143 0.9874 0.992 0.09734 0.8079 0.8854 0.3119 ] Network output: [ -0.01041 0.04486 1.002 0.0001085 -4.869e-05 0.9743 8.174e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09861 0.09686 0.1722 0.2034 0.9858 0.9916 0.09863 0.7412 0.8669 0.2466 ] Network output: [ -0.0009691 0.9995 0.001157 1.477e-05 -6.631e-06 1.001 1.113e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001352 Epoch 6282 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01515 0.9863 0.9848 7.933e-06 -3.562e-06 -0.001378 5.979e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.002894 -0.01053 0.008081 0.9696 0.974 0.005985 0.847 0.8353 0.0218 ] Network output: [ 1 -0.02433 0.003409 -4.519e-05 2.029e-05 0.01982 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02342 -0.2052 0.2125 0.9836 0.9933 0.2012 0.4681 0.8811 0.727 ] Network output: [ -0.0125 0.9978 1.011 2.639e-06 -1.185e-06 0.01642 1.989e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005073 0.0007176 0.004221 0.005217 0.989 0.992 0.005164 0.8784 0.905 0.01584 ] Network output: [ 0.001924 -0.03483 1.001 -0.0001903 8.545e-05 1.029 -0.0001435 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1013 0.3131 0.1802 0.9852 0.9941 0.1915 0.4735 0.8875 0.7214 ] Network output: [ 0.009484 -0.04891 1.003 0.0001116 -5.009e-05 1.028 8.408e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09752 0.08783 0.1789 0.2184 0.9874 0.992 0.09758 0.809 0.8855 0.314 ] Network output: [ -0.01143 0.04903 1.002 0.0001075 -4.825e-05 0.9718 8.1e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09884 0.0971 0.1733 0.2042 0.9858 0.9916 0.09886 0.7426 0.867 0.2467 ] Network output: [ 0.001688 0.9986 -0.002562 1.629e-05 -7.315e-06 1.001 1.228e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001549 Epoch 6283 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01461 0.9947 0.9845 6.762e-06 -3.036e-06 -0.008374 5.096e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002894 -0.01057 0.007928 0.9696 0.974 0.005999 0.8471 0.8349 0.02175 ] Network output: [ 0.9963 0.02988 0.0009695 -5.219e-05 2.343e-05 -0.02365 -3.933e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02271 -0.2082 0.2034 0.9836 0.9933 0.2019 0.4694 0.8808 0.7263 ] Network output: [ -0.01251 1.001 1.011 2.199e-06 -9.874e-07 0.01369 1.657e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00509 0.0007114 0.004063 0.004909 0.989 0.992 0.005181 0.8785 0.9048 0.01575 ] Network output: [ -0.002911 0.04033 0.998 -0.0002012 9.031e-05 0.9667 -0.0001516 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1015 0.3079 0.1657 0.9852 0.9941 0.1921 0.4744 0.8875 0.7219 ] Network output: [ 0.01113 -0.03387 0.9996 0.0001109 -4.979e-05 1.012 8.358e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09728 0.08754 0.1754 0.2143 0.9874 0.992 0.09734 0.8079 0.8854 0.3119 ] Network output: [ -0.01041 0.04488 1.002 0.0001084 -4.866e-05 0.9742 8.168e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09859 0.09683 0.1722 0.2034 0.9858 0.9916 0.0986 0.7412 0.8669 0.2466 ] Network output: [ -0.0009651 0.9995 0.00115 1.476e-05 -6.625e-06 1.001 1.112e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001349 Epoch 6284 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01515 0.9863 0.9848 7.923e-06 -3.557e-06 -0.001389 5.971e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.002894 -0.01053 0.008079 0.9696 0.974 0.005985 0.847 0.8353 0.02179 ] Network output: [ 1 -0.02423 0.003403 -4.519e-05 2.029e-05 0.01974 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02344 -0.2051 0.2124 0.9836 0.9933 0.2012 0.4681 0.8811 0.727 ] Network output: [ -0.0125 0.9978 1.011 2.64e-06 -1.185e-06 0.01642 1.989e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005074 0.0007166 0.004221 0.005214 0.989 0.992 0.005165 0.8784 0.9049 0.01583 ] Network output: [ 0.001911 -0.03468 1.001 -0.0001902 8.539e-05 1.029 -0.0001433 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1013 0.3131 0.1801 0.9852 0.9941 0.1915 0.4734 0.8875 0.7213 ] Network output: [ 0.009483 -0.04891 1.003 0.0001115 -5.005e-05 1.028 8.401e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09752 0.08783 0.1789 0.2183 0.9874 0.992 0.09758 0.8089 0.8855 0.314 ] Network output: [ -0.01143 0.04904 1.002 0.0001074 -4.822e-05 0.9718 8.094e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09882 0.09707 0.1733 0.2041 0.9858 0.9916 0.09883 0.7426 0.8669 0.2467 ] Network output: [ 0.001683 0.9986 -0.002558 1.627e-05 -7.305e-06 1.001 1.226e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001545 Epoch 6285 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01461 0.9947 0.9845 6.757e-06 -3.033e-06 -0.008359 5.092e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002894 -0.01057 0.007926 0.9696 0.974 0.005999 0.8471 0.8349 0.02174 ] Network output: [ 0.9963 0.02978 0.0009713 -5.215e-05 2.341e-05 -0.02358 -3.93e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02274 -0.2082 0.2034 0.9836 0.9933 0.2019 0.4694 0.8808 0.7263 ] Network output: [ -0.01251 1.001 1.011 2.202e-06 -9.887e-07 0.01369 1.66e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00509 0.0007104 0.004064 0.004908 0.989 0.992 0.005182 0.8785 0.9047 0.01575 ] Network output: [ -0.002905 0.0402 0.998 -0.000201 9.022e-05 0.9668 -0.0001514 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1015 0.308 0.1657 0.9852 0.9941 0.1921 0.4743 0.8875 0.7219 ] Network output: [ 0.01112 -0.03392 0.9996 0.0001108 -4.975e-05 1.013 8.351e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09727 0.08753 0.1754 0.2143 0.9874 0.992 0.09733 0.8078 0.8854 0.3119 ] Network output: [ -0.01041 0.04491 1.002 0.0001083 -4.862e-05 0.9742 8.162e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09857 0.09681 0.1722 0.2033 0.9858 0.9916 0.09858 0.7411 0.8669 0.2466 ] Network output: [ -0.0009611 0.9995 0.001142 1.474e-05 -6.619e-06 1.001 1.111e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001347 Epoch 6286 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01514 0.9864 0.9848 7.912e-06 -3.552e-06 -0.0014 5.963e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.002894 -0.01053 0.008076 0.9696 0.974 0.005985 0.847 0.8353 0.02179 ] Network output: [ 1 -0.02414 0.003397 -4.519e-05 2.029e-05 0.01965 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02346 -0.2051 0.2124 0.9836 0.9933 0.2012 0.4681 0.8811 0.727 ] Network output: [ -0.0125 0.9978 1.011 2.641e-06 -1.186e-06 0.01641 1.99e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005075 0.0007157 0.004221 0.005212 0.989 0.992 0.005166 0.8784 0.9049 0.01583 ] Network output: [ 0.001898 -0.03453 1.001 -0.0001901 8.532e-05 1.029 -0.0001432 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1012 0.3131 0.18 0.9852 0.9941 0.1915 0.4734 0.8875 0.7213 ] Network output: [ 0.009481 -0.04891 1.003 0.0001114 -5.001e-05 1.028 8.394e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09752 0.08782 0.1789 0.2183 0.9874 0.992 0.09758 0.8089 0.8855 0.314 ] Network output: [ -0.01142 0.04905 1.002 0.0001073 -4.818e-05 0.9718 8.089e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09879 0.09705 0.1732 0.2041 0.9858 0.9916 0.09881 0.7425 0.8669 0.2467 ] Network output: [ 0.001679 0.9986 -0.002554 1.625e-05 -7.296e-06 1.001 1.225e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001542 Epoch 6287 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0146 0.9947 0.9845 6.752e-06 -3.031e-06 -0.008343 5.088e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002894 -0.01056 0.007924 0.9696 0.974 0.005999 0.8471 0.8349 0.02174 ] Network output: [ 0.9963 0.02968 0.0009731 -5.212e-05 2.34e-05 -0.02351 -3.928e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02276 -0.2081 0.2034 0.9836 0.9933 0.2019 0.4694 0.8808 0.7263 ] Network output: [ -0.01251 1.001 1.011 2.205e-06 -9.899e-07 0.0137 1.662e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005091 0.0007095 0.004065 0.004906 0.989 0.992 0.005183 0.8784 0.9047 0.01575 ] Network output: [ -0.0029 0.04007 0.9981 -0.0002008 9.013e-05 0.9669 -0.0001513 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1014 0.308 0.1657 0.9852 0.9941 0.1921 0.4743 0.8875 0.7219 ] Network output: [ 0.01111 -0.03398 0.9996 0.0001107 -4.971e-05 1.013 8.344e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09727 0.08752 0.1754 0.2143 0.9874 0.992 0.09733 0.8078 0.8854 0.3119 ] Network output: [ -0.0104 0.04493 1.002 0.0001082 -4.858e-05 0.9742 8.156e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09854 0.09678 0.1721 0.2033 0.9858 0.9916 0.09855 0.7411 0.8668 0.2465 ] Network output: [ -0.000957 0.9995 0.001134 1.473e-05 -6.613e-06 1.001 1.11e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001344 Epoch 6288 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01513 0.9864 0.9848 7.901e-06 -3.547e-06 -0.001411 5.954e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.002894 -0.01052 0.008074 0.9696 0.974 0.005985 0.847 0.8353 0.02178 ] Network output: [ 1 -0.02404 0.003391 -4.519e-05 2.029e-05 0.01957 -3.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02348 -0.2051 0.2123 0.9836 0.9933 0.2012 0.4681 0.8811 0.727 ] Network output: [ -0.0125 0.9978 1.011 2.642e-06 -1.186e-06 0.0164 1.991e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005075 0.0007147 0.004222 0.005209 0.989 0.992 0.005167 0.8784 0.9049 0.01583 ] Network output: [ 0.001885 -0.03438 1.001 -0.0001899 8.525e-05 1.028 -0.0001431 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1012 0.3131 0.1799 0.9852 0.9941 0.1915 0.4734 0.8874 0.7213 ] Network output: [ 0.00948 -0.04891 1.003 0.0001113 -4.996e-05 1.028 8.387e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09752 0.08781 0.1789 0.2183 0.9874 0.992 0.09758 0.8088 0.8855 0.314 ] Network output: [ -0.01141 0.04907 1.002 0.0001073 -4.815e-05 0.9718 8.083e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09877 0.09702 0.1732 0.2041 0.9858 0.9916 0.09878 0.7424 0.8669 0.2467 ] Network output: [ 0.001674 0.9986 -0.002549 1.623e-05 -7.287e-06 1.001 1.223e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001538 Epoch 6289 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0146 0.9947 0.9845 6.747e-06 -3.029e-06 -0.008328 5.084e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002894 -0.01056 0.007922 0.9696 0.974 0.005999 0.8471 0.8349 0.02173 ] Network output: [ 0.9963 0.02957 0.0009749 -5.208e-05 2.338e-05 -0.02343 -3.925e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02279 -0.2081 0.2033 0.9836 0.9933 0.2019 0.4693 0.8808 0.7262 ] Network output: [ -0.01251 1.001 1.011 2.208e-06 -9.912e-07 0.0137 1.664e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005092 0.0007085 0.004066 0.004905 0.989 0.992 0.005183 0.8784 0.9047 0.01574 ] Network output: [ -0.002894 0.03993 0.9981 -0.0002006 9.004e-05 0.9669 -0.0001511 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1014 0.3081 0.1656 0.9852 0.9941 0.1921 0.4743 0.8875 0.7218 ] Network output: [ 0.0111 -0.03403 0.9996 0.0001106 -4.966e-05 1.013 8.337e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09727 0.08752 0.1754 0.2142 0.9874 0.992 0.09733 0.8077 0.8853 0.3119 ] Network output: [ -0.0104 0.04496 1.002 0.0001081 -4.855e-05 0.9742 8.15e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09852 0.09676 0.1721 0.2033 0.9858 0.9916 0.09853 0.741 0.8668 0.2465 ] Network output: [ -0.0009529 0.9995 0.001127 1.472e-05 -6.607e-06 1.001 1.109e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001342 Epoch 6290 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01513 0.9864 0.9848 7.89e-06 -3.542e-06 -0.001423 5.946e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.002895 -0.01052 0.008071 0.9696 0.974 0.005985 0.8469 0.8353 0.02178 ] Network output: [ 1 -0.02394 0.003385 -4.519e-05 2.029e-05 0.01948 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02351 -0.2051 0.2123 0.9836 0.9933 0.2012 0.468 0.8811 0.7269 ] Network output: [ -0.0125 0.9978 1.011 2.643e-06 -1.187e-06 0.0164 1.992e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005076 0.0007138 0.004222 0.005206 0.989 0.992 0.005167 0.8784 0.9049 0.01582 ] Network output: [ 0.001871 -0.03422 1.001 -0.0001898 8.519e-05 1.028 -0.000143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1012 0.3131 0.1798 0.9852 0.9941 0.1915 0.4733 0.8874 0.7213 ] Network output: [ 0.009479 -0.04891 1.003 0.0001112 -4.992e-05 1.028 8.381e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09751 0.0878 0.1789 0.2182 0.9874 0.992 0.09757 0.8088 0.8854 0.314 ] Network output: [ -0.0114 0.04908 1.002 0.0001072 -4.812e-05 0.9718 8.077e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09874 0.097 0.1732 0.2041 0.9858 0.9916 0.09875 0.7424 0.8668 0.2466 ] Network output: [ 0.001669 0.9986 -0.002545 1.621e-05 -7.278e-06 1.001 1.222e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001534 Epoch 6291 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0146 0.9947 0.9845 6.741e-06 -3.026e-06 -0.008312 5.08e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002895 -0.01056 0.00792 0.9696 0.974 0.005999 0.8471 0.8349 0.02173 ] Network output: [ 0.9964 0.02947 0.0009768 -5.204e-05 2.336e-05 -0.02336 -3.922e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02281 -0.2081 0.2033 0.9836 0.9933 0.2019 0.4693 0.8808 0.7262 ] Network output: [ -0.01251 1.001 1.011 2.211e-06 -9.925e-07 0.01371 1.666e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005092 0.0007076 0.004067 0.004903 0.989 0.992 0.005184 0.8784 0.9047 0.01574 ] Network output: [ -0.002889 0.0398 0.9981 -0.0002004 8.994e-05 0.967 -0.000151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1014 0.3081 0.1656 0.9852 0.9941 0.1921 0.4742 0.8875 0.7218 ] Network output: [ 0.01109 -0.03409 0.9996 0.0001105 -4.962e-05 1.013 8.33e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09727 0.08751 0.1754 0.2142 0.9874 0.992 0.09733 0.8077 0.8853 0.3119 ] Network output: [ -0.0104 0.04498 1.002 0.0001081 -4.851e-05 0.9741 8.143e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09849 0.09673 0.1721 0.2033 0.9858 0.9916 0.09851 0.7409 0.8668 0.2465 ] Network output: [ -0.0009488 0.9995 0.001119 1.47e-05 -6.601e-06 1.001 1.108e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001339 Epoch 6292 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01512 0.9864 0.9848 7.879e-06 -3.537e-06 -0.001434 5.938e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.002895 -0.01052 0.008068 0.9696 0.974 0.005985 0.8469 0.8352 0.02177 ] Network output: [ 1 -0.02384 0.003378 -4.519e-05 2.029e-05 0.01939 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02353 -0.2051 0.2122 0.9836 0.9933 0.2012 0.468 0.8811 0.7269 ] Network output: [ -0.01249 0.9978 1.011 2.644e-06 -1.187e-06 0.01639 1.993e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005077 0.0007128 0.004222 0.005204 0.989 0.992 0.005168 0.8783 0.9049 0.01582 ] Network output: [ 0.001858 -0.03407 1.001 -0.0001896 8.512e-05 1.028 -0.0001429 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1011 0.3132 0.1798 0.9852 0.9941 0.1915 0.4733 0.8874 0.7212 ] Network output: [ 0.009478 -0.04891 1.003 0.0001111 -4.988e-05 1.028 8.374e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09751 0.08779 0.1789 0.2182 0.9874 0.992 0.09757 0.8087 0.8854 0.314 ] Network output: [ -0.01139 0.04909 1.002 0.0001071 -4.808e-05 0.9717 8.071e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09872 0.09697 0.1732 0.204 0.9858 0.9916 0.09873 0.7423 0.8668 0.2466 ] Network output: [ 0.001665 0.9987 -0.00254 1.619e-05 -7.268e-06 1.001 1.22e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001531 Epoch 6293 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01459 0.9946 0.9845 6.736e-06 -3.024e-06 -0.008297 5.077e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002895 -0.01055 0.007918 0.9696 0.974 0.006 0.8471 0.8349 0.02172 ] Network output: [ 0.9964 0.02936 0.0009788 -5.201e-05 2.335e-05 -0.02328 -3.919e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02283 -0.2081 0.2033 0.9836 0.9933 0.2019 0.4693 0.8808 0.7262 ] Network output: [ -0.0125 1.001 1.011 2.214e-06 -9.938e-07 0.01371 1.668e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005093 0.0007067 0.004068 0.004902 0.989 0.992 0.005184 0.8784 0.9047 0.01574 ] Network output: [ -0.002883 0.03967 0.9982 -0.0002002 8.985e-05 0.9671 -0.0001508 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1013 0.3081 0.1656 0.9852 0.9941 0.1921 0.4742 0.8874 0.7218 ] Network output: [ 0.01108 -0.03414 0.9996 0.0001104 -4.958e-05 1.013 8.323e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09727 0.0875 0.1755 0.2142 0.9874 0.992 0.09733 0.8076 0.8853 0.3119 ] Network output: [ -0.01039 0.04501 1.002 0.000108 -4.847e-05 0.9741 8.137e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09847 0.09671 0.1721 0.2032 0.9858 0.9916 0.09848 0.7409 0.8667 0.2465 ] Network output: [ -0.0009446 0.9995 0.001111 1.469e-05 -6.595e-06 1.001 1.107e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001337 Epoch 6294 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01512 0.9864 0.9848 7.869e-06 -3.533e-06 -0.001446 5.93e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.002895 -0.01051 0.008066 0.9696 0.974 0.005986 0.8469 0.8352 0.02177 ] Network output: [ 1 -0.02374 0.003372 -4.519e-05 2.029e-05 0.01931 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02355 -0.2051 0.2122 0.9836 0.9933 0.2012 0.468 0.8811 0.7269 ] Network output: [ -0.01249 0.9978 1.011 2.645e-06 -1.187e-06 0.01638 1.993e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005078 0.0007119 0.004223 0.005201 0.989 0.992 0.005169 0.8783 0.9049 0.01582 ] Network output: [ 0.001845 -0.03392 1.001 -0.0001895 8.505e-05 1.028 -0.0001428 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1011 0.3132 0.1797 0.9852 0.9941 0.1915 0.4733 0.8874 0.7212 ] Network output: [ 0.009477 -0.0489 1.003 0.000111 -4.984e-05 1.028 8.367e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09751 0.08779 0.1789 0.2182 0.9874 0.992 0.09757 0.8087 0.8854 0.314 ] Network output: [ -0.01138 0.0491 1.002 0.000107 -4.805e-05 0.9717 8.066e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09869 0.09694 0.1731 0.204 0.9858 0.9916 0.0987 0.7423 0.8668 0.2466 ] Network output: [ 0.00166 0.9987 -0.002536 1.617e-05 -7.259e-06 1.001 1.219e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001527 Epoch 6295 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01459 0.9946 0.9845 6.731e-06 -3.022e-06 -0.008281 5.073e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002895 -0.01055 0.007916 0.9696 0.974 0.006 0.8471 0.8349 0.02172 ] Network output: [ 0.9964 0.02926 0.0009808 -5.197e-05 2.333e-05 -0.02321 -3.917e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02286 -0.208 0.2033 0.9836 0.9933 0.2019 0.4692 0.8807 0.7262 ] Network output: [ -0.0125 1.001 1.011 2.217e-06 -9.951e-07 0.01371 1.671e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005094 0.0007057 0.004069 0.0049 0.989 0.992 0.005185 0.8784 0.9047 0.01573 ] Network output: [ -0.002878 0.03953 0.9982 -0.0001999 8.976e-05 0.9672 -0.0001507 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1013 0.3082 0.1656 0.9852 0.9941 0.1921 0.4742 0.8874 0.7218 ] Network output: [ 0.01107 -0.0342 0.9996 0.0001104 -4.954e-05 1.013 8.316e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09727 0.0875 0.1755 0.2142 0.9874 0.992 0.09733 0.8076 0.8853 0.3119 ] Network output: [ -0.01039 0.04503 1.002 0.0001079 -4.844e-05 0.9741 8.131e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09844 0.09668 0.172 0.2032 0.9858 0.9916 0.09846 0.7408 0.8667 0.2465 ] Network output: [ -0.0009405 0.9995 0.001103 1.468e-05 -6.59e-06 1.001 1.106e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001334 Epoch 6296 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01511 0.9864 0.9848 7.858e-06 -3.528e-06 -0.001457 5.922e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.002895 -0.01051 0.008063 0.9696 0.974 0.005986 0.8469 0.8352 0.02176 ] Network output: [ 1 -0.02364 0.003366 -4.518e-05 2.028e-05 0.01922 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02357 -0.205 0.2122 0.9836 0.9933 0.2012 0.468 0.8811 0.7269 ] Network output: [ -0.01249 0.9978 1.011 2.646e-06 -1.188e-06 0.01637 1.994e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005078 0.0007109 0.004223 0.005198 0.989 0.992 0.00517 0.8783 0.9049 0.01581 ] Network output: [ 0.001831 -0.03376 1.002 -0.0001893 8.499e-05 1.028 -0.0001427 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.101 0.3132 0.1796 0.9852 0.9941 0.1915 0.4733 0.8874 0.7212 ] Network output: [ 0.009476 -0.0489 1.003 0.0001109 -4.98e-05 1.028 8.36e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0975 0.08778 0.1789 0.2182 0.9874 0.992 0.09756 0.8086 0.8853 0.314 ] Network output: [ -0.01137 0.04911 1.002 0.0001069 -4.801e-05 0.9717 8.06e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09867 0.09692 0.1731 0.204 0.9858 0.9916 0.09868 0.7422 0.8667 0.2466 ] Network output: [ 0.001655 0.9987 -0.002531 1.615e-05 -7.25e-06 1.001 1.217e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001523 Epoch 6297 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01459 0.9946 0.9845 6.726e-06 -3.019e-06 -0.008266 5.069e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002895 -0.01055 0.007914 0.9696 0.974 0.006 0.8471 0.8349 0.02172 ] Network output: [ 0.9964 0.02915 0.0009829 -5.193e-05 2.331e-05 -0.02314 -3.914e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02288 -0.208 0.2033 0.9836 0.9933 0.2019 0.4692 0.8807 0.7261 ] Network output: [ -0.0125 1.001 1.011 2.22e-06 -9.964e-07 0.01372 1.673e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005094 0.0007048 0.00407 0.004899 0.989 0.992 0.005186 0.8784 0.9047 0.01573 ] Network output: [ -0.002872 0.0394 0.9982 -0.0001997 8.967e-05 0.9673 -0.0001505 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1012 0.3082 0.1656 0.9852 0.9941 0.1921 0.4742 0.8874 0.7217 ] Network output: [ 0.01106 -0.03425 0.9996 0.0001103 -4.95e-05 1.013 8.31e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09726 0.08749 0.1755 0.2142 0.9874 0.992 0.09732 0.8075 0.8852 0.3119 ] Network output: [ -0.01038 0.04505 1.002 0.0001078 -4.84e-05 0.9741 8.125e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09842 0.09666 0.172 0.2032 0.9858 0.9916 0.09843 0.7408 0.8667 0.2464 ] Network output: [ -0.0009363 0.9995 0.001096 1.467e-05 -6.584e-06 1.001 1.105e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001332 Epoch 6298 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01511 0.9865 0.9848 7.847e-06 -3.523e-06 -0.001469 5.914e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.002896 -0.01051 0.008061 0.9696 0.974 0.005986 0.8469 0.8352 0.02176 ] Network output: [ 1 -0.02354 0.00336 -4.518e-05 2.028e-05 0.01913 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02359 -0.205 0.2121 0.9836 0.9933 0.2012 0.4679 0.881 0.7268 ] Network output: [ -0.01249 0.9978 1.011 2.647e-06 -1.188e-06 0.01637 1.995e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005079 0.00071 0.004224 0.005196 0.989 0.992 0.00517 0.8783 0.9049 0.01581 ] Network output: [ 0.001818 -0.03361 1.002 -0.0001892 8.492e-05 1.028 -0.0001426 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.101 0.3132 0.1795 0.9852 0.9941 0.1915 0.4732 0.8874 0.7212 ] Network output: [ 0.009474 -0.0489 1.003 0.0001108 -4.976e-05 1.028 8.353e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0975 0.08777 0.1789 0.2181 0.9874 0.992 0.09756 0.8086 0.8853 0.314 ] Network output: [ -0.01136 0.04912 1.002 0.0001069 -4.798e-05 0.9717 8.054e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09864 0.09689 0.1731 0.2039 0.9858 0.9916 0.09865 0.7421 0.8667 0.2466 ] Network output: [ 0.001651 0.9987 -0.002527 1.613e-05 -7.241e-06 1.001 1.216e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00152 Epoch 6299 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01458 0.9946 0.9845 6.721e-06 -3.017e-06 -0.00825 5.065e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002896 -0.01054 0.007912 0.9696 0.974 0.006 0.8471 0.8349 0.02171 ] Network output: [ 0.9964 0.02905 0.000985 -5.189e-05 2.33e-05 -0.02306 -3.911e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02291 -0.208 0.2033 0.9836 0.9933 0.2019 0.4692 0.8807 0.7261 ] Network output: [ -0.0125 1.001 1.011 2.222e-06 -9.977e-07 0.01372 1.675e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005095 0.0007039 0.004071 0.004898 0.989 0.992 0.005186 0.8784 0.9047 0.01573 ] Network output: [ -0.002866 0.03927 0.9983 -0.0001995 8.958e-05 0.9674 -0.0001504 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1012 0.3082 0.1655 0.9852 0.9941 0.1921 0.4741 0.8874 0.7217 ] Network output: [ 0.01106 -0.03431 0.9995 0.0001102 -4.946e-05 1.013 8.303e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09726 0.08748 0.1755 0.2142 0.9874 0.992 0.09732 0.8075 0.8852 0.3119 ] Network output: [ -0.01038 0.04508 1.002 0.0001077 -4.836e-05 0.9741 8.119e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0984 0.09663 0.172 0.2032 0.9858 0.9916 0.09841 0.7407 0.8666 0.2464 ] Network output: [ -0.000932 0.9995 0.001088 1.465e-05 -6.578e-06 1.001 1.104e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001329 Epoch 6300 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0151 0.9865 0.9848 7.836e-06 -3.518e-06 -0.00148 5.906e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.002896 -0.0105 0.008058 0.9696 0.974 0.005986 0.8469 0.8352 0.02175 ] Network output: [ 1 -0.02344 0.003354 -4.518e-05 2.028e-05 0.01905 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02361 -0.205 0.2121 0.9836 0.9933 0.2012 0.4679 0.881 0.7268 ] Network output: [ -0.01248 0.9978 1.011 2.648e-06 -1.189e-06 0.01636 1.996e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00508 0.000709 0.004224 0.005193 0.989 0.992 0.005171 0.8783 0.9048 0.01581 ] Network output: [ 0.001805 -0.03345 1.002 -0.000189 8.485e-05 1.028 -0.0001424 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.101 0.3132 0.1794 0.9852 0.9941 0.1915 0.4732 0.8874 0.7211 ] Network output: [ 0.009473 -0.04889 1.003 0.0001107 -4.971e-05 1.028 8.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0975 0.08776 0.1789 0.2181 0.9874 0.992 0.09756 0.8085 0.8853 0.314 ] Network output: [ -0.01135 0.04913 1.002 0.0001068 -4.794e-05 0.9717 8.048e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09862 0.09687 0.1731 0.2039 0.9858 0.9916 0.09863 0.7421 0.8667 0.2465 ] Network output: [ 0.001646 0.9987 -0.002522 1.611e-05 -7.232e-06 1.001 1.214e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001516 Epoch 6301 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01458 0.9946 0.9845 6.716e-06 -3.015e-06 -0.008235 5.061e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002896 -0.01054 0.00791 0.9696 0.974 0.006 0.8471 0.8349 0.02171 ] Network output: [ 0.9964 0.02894 0.0009871 -5.186e-05 2.328e-05 -0.02298 -3.908e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02293 -0.2079 0.2033 0.9836 0.9933 0.2019 0.4692 0.8807 0.7261 ] Network output: [ -0.0125 1.001 1.011 2.225e-06 -9.99e-07 0.01373 1.677e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005096 0.0007029 0.004072 0.004896 0.989 0.992 0.005187 0.8784 0.9046 0.01572 ] Network output: [ -0.002861 0.03913 0.9983 -0.0001993 8.949e-05 0.9675 -0.0001502 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1012 0.3083 0.1655 0.9852 0.9941 0.1921 0.4741 0.8874 0.7217 ] Network output: [ 0.01105 -0.03436 0.9995 0.0001101 -4.942e-05 1.013 8.296e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09726 0.08747 0.1755 0.2142 0.9874 0.992 0.09732 0.8074 0.8852 0.3119 ] Network output: [ -0.01038 0.0451 1.002 0.0001076 -4.833e-05 0.974 8.113e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09837 0.09661 0.172 0.2031 0.9858 0.9916 0.09838 0.7406 0.8666 0.2464 ] Network output: [ -0.0009278 0.9995 0.00108 1.464e-05 -6.572e-06 1.001 1.103e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001327 Epoch 6302 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0151 0.9865 0.9848 7.825e-06 -3.513e-06 -0.001492 5.898e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.002896 -0.0105 0.008055 0.9696 0.974 0.005986 0.8469 0.8352 0.02175 ] Network output: [ 1 -0.02334 0.003348 -4.518e-05 2.028e-05 0.01896 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02363 -0.205 0.212 0.9836 0.9933 0.2012 0.4679 0.881 0.7268 ] Network output: [ -0.01248 0.9978 1.011 2.649e-06 -1.189e-06 0.01635 1.996e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00508 0.0007081 0.004224 0.00519 0.989 0.992 0.005172 0.8783 0.9048 0.0158 ] Network output: [ 0.001791 -0.0333 1.002 -0.0001889 8.479e-05 1.027 -0.0001423 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1009 0.3133 0.1794 0.9852 0.9941 0.1915 0.4732 0.8874 0.7211 ] Network output: [ 0.009472 -0.04889 1.003 0.0001106 -4.967e-05 1.028 8.339e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0975 0.08775 0.1789 0.2181 0.9874 0.992 0.09756 0.8084 0.8853 0.314 ] Network output: [ -0.01134 0.04913 1.002 0.0001067 -4.791e-05 0.9717 8.043e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09859 0.09684 0.173 0.2039 0.9858 0.9916 0.0986 0.742 0.8666 0.2465 ] Network output: [ 0.001641 0.9987 -0.002518 1.609e-05 -7.223e-06 1.001 1.212e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001513 Epoch 6303 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01458 0.9946 0.9845 6.71e-06 -3.013e-06 -0.008219 5.057e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002896 -0.01054 0.007908 0.9696 0.974 0.006 0.8471 0.8349 0.0217 ] Network output: [ 0.9964 0.02883 0.0009893 -5.182e-05 2.326e-05 -0.02291 -3.905e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02296 -0.2079 0.2033 0.9836 0.9933 0.2019 0.4691 0.8807 0.7261 ] Network output: [ -0.0125 1.001 1.011 2.228e-06 -1e-06 0.01373 1.679e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005096 0.000702 0.004073 0.004895 0.989 0.992 0.005188 0.8783 0.9046 0.01572 ] Network output: [ -0.002855 0.039 0.9983 -0.0001991 8.94e-05 0.9676 -0.0001501 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1011 0.3083 0.1655 0.9852 0.9941 0.1921 0.4741 0.8874 0.7217 ] Network output: [ 0.01104 -0.03442 0.9995 0.00011 -4.938e-05 1.013 8.289e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09726 0.08747 0.1755 0.2142 0.9874 0.992 0.09732 0.8074 0.8851 0.3119 ] Network output: [ -0.01037 0.04512 1.002 0.0001076 -4.829e-05 0.974 8.106e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09835 0.09658 0.172 0.2031 0.9858 0.9916 0.09836 0.7406 0.8666 0.2464 ] Network output: [ -0.0009235 0.9995 0.001072 1.463e-05 -6.566e-06 1.001 1.102e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001324 Epoch 6304 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01509 0.9865 0.9848 7.815e-06 -3.508e-06 -0.001504 5.889e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.002896 -0.0105 0.008053 0.9696 0.974 0.005986 0.8469 0.8352 0.02174 ] Network output: [ 1 -0.02324 0.003342 -4.518e-05 2.028e-05 0.01887 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02366 -0.205 0.212 0.9836 0.9933 0.2012 0.4679 0.881 0.7268 ] Network output: [ -0.01248 0.9979 1.011 2.65e-06 -1.19e-06 0.01634 1.997e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005081 0.0007071 0.004225 0.005188 0.989 0.992 0.005172 0.8783 0.9048 0.0158 ] Network output: [ 0.001778 -0.03314 1.002 -0.0001887 8.472e-05 1.027 -0.0001422 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1009 0.3133 0.1793 0.9852 0.9941 0.1915 0.4732 0.8873 0.7211 ] Network output: [ 0.009471 -0.04888 1.003 0.0001106 -4.963e-05 1.028 8.332e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09749 0.08774 0.1788 0.2181 0.9874 0.992 0.09755 0.8084 0.8852 0.314 ] Network output: [ -0.01133 0.04914 1.002 0.0001066 -4.788e-05 0.9716 8.037e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09857 0.09681 0.173 0.2039 0.9858 0.9916 0.09858 0.7419 0.8666 0.2465 ] Network output: [ 0.001637 0.9987 -0.002513 1.607e-05 -7.214e-06 1.001 1.211e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001509 Epoch 6305 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01457 0.9946 0.9845 6.705e-06 -3.01e-06 -0.008204 5.053e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002896 -0.01054 0.007906 0.9696 0.974 0.006 0.8471 0.8348 0.0217 ] Network output: [ 0.9965 0.02873 0.0009915 -5.178e-05 2.325e-05 -0.02283 -3.903e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02298 -0.2079 0.2033 0.9836 0.9933 0.2019 0.4691 0.8807 0.7261 ] Network output: [ -0.01249 1.001 1.011 2.231e-06 -1.002e-06 0.01373 1.681e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005097 0.0007011 0.004074 0.004893 0.989 0.992 0.005188 0.8783 0.9046 0.01572 ] Network output: [ -0.002849 0.03886 0.9984 -0.0001989 8.931e-05 0.9677 -0.0001499 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1011 0.3083 0.1655 0.9852 0.9941 0.1921 0.474 0.8874 0.7216 ] Network output: [ 0.01103 -0.03447 0.9995 0.0001099 -4.933e-05 1.013 8.282e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09726 0.08746 0.1755 0.2142 0.9874 0.992 0.09732 0.8073 0.8851 0.3119 ] Network output: [ -0.01037 0.04515 1.002 0.0001075 -4.825e-05 0.974 8.1e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09832 0.09656 0.1719 0.2031 0.9858 0.9916 0.09834 0.7405 0.8666 0.2464 ] Network output: [ -0.0009192 0.9995 0.001064 1.461e-05 -6.561e-06 1.001 1.101e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001322 Epoch 6306 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01509 0.9865 0.9848 7.804e-06 -3.503e-06 -0.001515 5.881e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.002897 -0.01049 0.00805 0.9696 0.9741 0.005987 0.8469 0.8352 0.02174 ] Network output: [ 1 -0.02313 0.003336 -4.518e-05 2.028e-05 0.01879 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02368 -0.2049 0.2119 0.9836 0.9933 0.2012 0.4678 0.881 0.7267 ] Network output: [ -0.01248 0.9979 1.011 2.651e-06 -1.19e-06 0.01634 1.998e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005082 0.0007061 0.004225 0.005185 0.989 0.992 0.005173 0.8783 0.9048 0.0158 ] Network output: [ 0.001764 -0.03299 1.002 -0.0001886 8.465e-05 1.027 -0.0001421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1008 0.3133 0.1792 0.9852 0.9941 0.1915 0.4731 0.8873 0.7211 ] Network output: [ 0.009469 -0.04888 1.003 0.0001105 -4.959e-05 1.028 8.324e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09749 0.08773 0.1788 0.218 0.9874 0.992 0.09755 0.8083 0.8852 0.3139 ] Network output: [ -0.01133 0.04915 1.002 0.0001066 -4.784e-05 0.9716 8.031e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09854 0.09679 0.173 0.2038 0.9858 0.9916 0.09855 0.7419 0.8666 0.2465 ] Network output: [ 0.001632 0.9987 -0.002509 1.605e-05 -7.205e-06 1.001 1.209e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001505 Epoch 6307 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01457 0.9945 0.9845 6.7e-06 -3.008e-06 -0.008188 5.049e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002897 -0.01053 0.007904 0.9696 0.974 0.006 0.8471 0.8348 0.02169 ] Network output: [ 0.9965 0.02862 0.0009938 -5.175e-05 2.323e-05 -0.02276 -3.9e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.023 -0.2079 0.2033 0.9836 0.9933 0.2019 0.4691 0.8807 0.726 ] Network output: [ -0.01249 1.001 1.011 2.234e-06 -1.003e-06 0.01374 1.684e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005098 0.0007001 0.004075 0.004892 0.989 0.992 0.005189 0.8783 0.9046 0.01571 ] Network output: [ -0.002843 0.03872 0.9984 -0.0001987 8.922e-05 0.9678 -0.0001498 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.101 0.3084 0.1654 0.9852 0.9941 0.1921 0.474 0.8874 0.7216 ] Network output: [ 0.01102 -0.03452 0.9995 0.0001098 -4.929e-05 1.013 8.275e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09725 0.08745 0.1755 0.2141 0.9874 0.992 0.09731 0.8073 0.8851 0.3119 ] Network output: [ -0.01037 0.04517 1.002 0.0001074 -4.822e-05 0.974 8.094e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0983 0.09653 0.1719 0.2031 0.9858 0.9916 0.09831 0.7405 0.8665 0.2463 ] Network output: [ -0.0009149 0.9995 0.001056 1.46e-05 -6.555e-06 1.001 1.1e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001319 Epoch 6308 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01508 0.9866 0.9848 7.793e-06 -3.499e-06 -0.001527 5.873e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.002897 -0.01049 0.008048 0.9696 0.9741 0.005987 0.8469 0.8352 0.02173 ] Network output: [ 1 -0.02303 0.003329 -4.518e-05 2.028e-05 0.0187 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.0237 -0.2049 0.2119 0.9836 0.9933 0.2012 0.4678 0.881 0.7267 ] Network output: [ -0.01248 0.9979 1.011 2.652e-06 -1.191e-06 0.01633 1.999e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005083 0.0007052 0.004225 0.005182 0.989 0.992 0.005174 0.8782 0.9048 0.01579 ] Network output: [ 0.001751 -0.03283 1.002 -0.0001884 8.459e-05 1.027 -0.000142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1008 0.3133 0.1791 0.9852 0.9941 0.1915 0.4731 0.8873 0.721 ] Network output: [ 0.009468 -0.04887 1.003 0.0001104 -4.955e-05 1.028 8.317e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09749 0.08773 0.1788 0.218 0.9874 0.992 0.09755 0.8083 0.8852 0.3139 ] Network output: [ -0.01132 0.04916 1.002 0.0001065 -4.781e-05 0.9716 8.025e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09851 0.09676 0.1729 0.2038 0.9858 0.9916 0.09853 0.7418 0.8665 0.2465 ] Network output: [ 0.001627 0.9987 -0.002504 1.603e-05 -7.196e-06 1.001 1.208e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001502 Epoch 6309 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01457 0.9945 0.9845 6.695e-06 -3.006e-06 -0.008173 5.046e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002897 -0.01053 0.007902 0.9696 0.974 0.006 0.8471 0.8348 0.02169 ] Network output: [ 0.9965 0.02851 0.0009961 -5.171e-05 2.321e-05 -0.02268 -3.897e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02303 -0.2078 0.2033 0.9836 0.9933 0.2019 0.469 0.8807 0.726 ] Network output: [ -0.01249 1.001 1.011 2.237e-06 -1.004e-06 0.01374 1.686e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005098 0.0006992 0.004075 0.00489 0.989 0.992 0.00519 0.8783 0.9046 0.01571 ] Network output: [ -0.002838 0.03859 0.9984 -0.0001985 8.913e-05 0.9678 -0.0001496 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.101 0.3084 0.1654 0.9852 0.9941 0.1921 0.474 0.8873 0.7216 ] Network output: [ 0.01101 -0.03458 0.9995 0.0001097 -4.925e-05 1.014 8.268e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09725 0.08745 0.1756 0.2141 0.9874 0.992 0.09731 0.8072 0.8851 0.3119 ] Network output: [ -0.01036 0.04519 1.002 0.0001073 -4.818e-05 0.9739 8.088e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09827 0.09651 0.1719 0.203 0.9858 0.9916 0.09829 0.7404 0.8665 0.2463 ] Network output: [ -0.0009105 0.9995 0.001048 1.459e-05 -6.549e-06 1.001 1.099e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001317 Epoch 6310 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01507 0.9866 0.9849 7.782e-06 -3.494e-06 -0.001539 5.865e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.002897 -0.01049 0.008045 0.9696 0.9741 0.005987 0.8469 0.8351 0.02173 ] Network output: [ 1 -0.02293 0.003323 -4.517e-05 2.028e-05 0.01861 -3.404e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02372 -0.2049 0.2119 0.9836 0.9933 0.2012 0.4678 0.881 0.7267 ] Network output: [ -0.01247 0.9979 1.011 2.653e-06 -1.191e-06 0.01632 1.999e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005083 0.0007042 0.004226 0.00518 0.989 0.992 0.005175 0.8782 0.9048 0.01579 ] Network output: [ 0.001738 -0.03267 1.002 -0.0001883 8.452e-05 1.027 -0.0001419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1008 0.3133 0.1791 0.9852 0.9941 0.1915 0.4731 0.8873 0.721 ] Network output: [ 0.009467 -0.04887 1.002 0.0001103 -4.95e-05 1.028 8.31e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09748 0.08772 0.1788 0.218 0.9874 0.992 0.09754 0.8082 0.8852 0.3139 ] Network output: [ -0.01131 0.04917 1.002 0.0001064 -4.777e-05 0.9716 8.02e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09849 0.09674 0.1729 0.2038 0.9858 0.9916 0.0985 0.7417 0.8665 0.2464 ] Network output: [ 0.001622 0.9987 -0.002499 1.601e-05 -7.187e-06 1.001 1.206e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001498 Epoch 6311 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01456 0.9945 0.9846 6.69e-06 -3.003e-06 -0.008157 5.042e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002897 -0.01053 0.0079 0.9696 0.974 0.006001 0.8471 0.8348 0.02168 ] Network output: [ 0.9965 0.02841 0.0009984 -5.167e-05 2.32e-05 -0.02261 -3.894e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02305 -0.2078 0.2033 0.9836 0.9933 0.2019 0.469 0.8807 0.726 ] Network output: [ -0.01249 1.001 1.011 2.24e-06 -1.005e-06 0.01374 1.688e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005099 0.0006983 0.004076 0.004889 0.989 0.992 0.00519 0.8783 0.9046 0.01571 ] Network output: [ -0.002832 0.03845 0.9985 -0.0001983 8.904e-05 0.9679 -0.0001495 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1009 0.3085 0.1654 0.9852 0.9941 0.1921 0.4739 0.8873 0.7216 ] Network output: [ 0.011 -0.03463 0.9995 0.0001096 -4.921e-05 1.014 8.261e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09725 0.08744 0.1756 0.2141 0.9874 0.992 0.09731 0.8072 0.885 0.3119 ] Network output: [ -0.01036 0.04521 1.002 0.0001072 -4.814e-05 0.9739 8.082e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09825 0.09648 0.1719 0.203 0.9858 0.9916 0.09826 0.7403 0.8665 0.2463 ] Network output: [ -0.0009062 0.9995 0.001041 1.458e-05 -6.543e-06 1.001 1.098e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001314 Epoch 6312 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01507 0.9866 0.9849 7.772e-06 -3.489e-06 -0.001551 5.857e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003167 -0.002897 -0.01049 0.008042 0.9696 0.9741 0.005987 0.8469 0.8351 0.02172 ] Network output: [ 1 -0.02283 0.003317 -4.517e-05 2.028e-05 0.01852 -3.404e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02374 -0.2049 0.2118 0.9836 0.9933 0.2012 0.4678 0.881 0.7267 ] Network output: [ -0.01247 0.9979 1.011 2.654e-06 -1.191e-06 0.01631 2e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005084 0.0007033 0.004226 0.005177 0.989 0.992 0.005175 0.8782 0.9048 0.01579 ] Network output: [ 0.001724 -0.03252 1.002 -0.0001881 8.445e-05 1.027 -0.0001418 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1007 0.3133 0.179 0.9852 0.9941 0.1915 0.473 0.8873 0.721 ] Network output: [ 0.009465 -0.04886 1.002 0.0001102 -4.946e-05 1.028 8.303e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09748 0.08771 0.1788 0.2179 0.9874 0.992 0.09754 0.8082 0.8851 0.3139 ] Network output: [ -0.0113 0.04917 1.002 0.0001063 -4.774e-05 0.9716 8.014e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09846 0.09671 0.1729 0.2038 0.9858 0.9916 0.09848 0.7417 0.8665 0.2464 ] Network output: [ 0.001618 0.9987 -0.002495 1.599e-05 -7.178e-06 1.001 1.205e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001495 Epoch 6313 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01456 0.9945 0.9846 6.685e-06 -3.001e-06 -0.008142 5.038e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002897 -0.01052 0.007898 0.9696 0.9741 0.006001 0.8471 0.8348 0.02168 ] Network output: [ 0.9965 0.0283 0.001001 -5.163e-05 2.318e-05 -0.02253 -3.891e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02308 -0.2078 0.2033 0.9836 0.9933 0.2019 0.469 0.8806 0.726 ] Network output: [ -0.01249 1.001 1.011 2.243e-06 -1.007e-06 0.01375 1.69e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0051 0.0006973 0.004077 0.004887 0.989 0.992 0.005191 0.8783 0.9046 0.0157 ] Network output: [ -0.002826 0.03832 0.9985 -0.0001981 8.895e-05 0.968 -0.0001493 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1009 0.3085 0.1654 0.9852 0.9941 0.192 0.4739 0.8873 0.7215 ] Network output: [ 0.01099 -0.03468 0.9995 0.0001095 -4.917e-05 1.014 8.254e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09725 0.08743 0.1756 0.2141 0.9874 0.992 0.09731 0.8071 0.885 0.3119 ] Network output: [ -0.01036 0.04523 1.002 0.0001072 -4.811e-05 0.9739 8.076e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09823 0.09646 0.1719 0.203 0.9858 0.9916 0.09824 0.7403 0.8664 0.2463 ] Network output: [ -0.0009018 0.9995 0.001033 1.456e-05 -6.538e-06 1.001 1.097e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001311 Epoch 6314 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01506 0.9866 0.9849 7.761e-06 -3.484e-06 -0.001563 5.849e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.002898 -0.01048 0.00804 0.9696 0.9741 0.005987 0.8469 0.8351 0.02172 ] Network output: [ 1 -0.02273 0.003311 -4.517e-05 2.028e-05 0.01843 -3.404e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02376 -0.2049 0.2118 0.9837 0.9933 0.2012 0.4677 0.8809 0.7266 ] Network output: [ -0.01247 0.9979 1.011 2.655e-06 -1.192e-06 0.01631 2.001e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005085 0.0007024 0.004226 0.005174 0.989 0.992 0.005176 0.8782 0.9048 0.01578 ] Network output: [ 0.001711 -0.03236 1.002 -0.000188 8.439e-05 1.027 -0.0001417 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1007 0.3134 0.1789 0.9852 0.9941 0.1915 0.473 0.8873 0.721 ] Network output: [ 0.009464 -0.04886 1.002 0.0001101 -4.942e-05 1.028 8.296e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09748 0.0877 0.1788 0.2179 0.9874 0.992 0.09754 0.8081 0.8851 0.3139 ] Network output: [ -0.01129 0.04918 1.002 0.0001063 -4.77e-05 0.9716 8.008e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09844 0.09668 0.1729 0.2037 0.9858 0.9916 0.09845 0.7416 0.8664 0.2464 ] Network output: [ 0.001613 0.9987 -0.00249 1.597e-05 -7.169e-06 1.001 1.203e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001491 Epoch 6315 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01455 0.9945 0.9846 6.68e-06 -2.999e-06 -0.008126 5.034e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002897 -0.01052 0.007896 0.9696 0.9741 0.006001 0.847 0.8348 0.02167 ] Network output: [ 0.9965 0.02819 0.001003 -5.16e-05 2.316e-05 -0.02245 -3.888e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.0231 -0.2077 0.2032 0.9836 0.9933 0.2019 0.4689 0.8806 0.7259 ] Network output: [ -0.01248 1.001 1.011 2.245e-06 -1.008e-06 0.01375 1.692e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0051 0.0006964 0.004078 0.004886 0.989 0.992 0.005192 0.8783 0.9046 0.0157 ] Network output: [ -0.00282 0.03818 0.9985 -0.0001979 8.886e-05 0.9681 -0.0001492 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1009 0.3085 0.1653 0.9852 0.9941 0.192 0.4739 0.8873 0.7215 ] Network output: [ 0.01098 -0.03474 0.9995 0.0001094 -4.913e-05 1.014 8.247e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09725 0.08742 0.1756 0.2141 0.9874 0.992 0.09731 0.8071 0.885 0.3119 ] Network output: [ -0.01035 0.04525 1.002 0.0001071 -4.807e-05 0.9739 8.069e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0982 0.09643 0.1718 0.203 0.9858 0.9916 0.09821 0.7402 0.8664 0.2463 ] Network output: [ -0.0008974 0.9995 0.001025 1.455e-05 -6.532e-06 1.001 1.097e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001309 Epoch 6316 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01506 0.9866 0.9849 7.75e-06 -3.479e-06 -0.001575 5.841e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.002898 -0.01048 0.008037 0.9696 0.9741 0.005988 0.8469 0.8351 0.02171 ] Network output: [ 1 -0.02263 0.003305 -4.517e-05 2.028e-05 0.01835 -3.404e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02379 -0.2048 0.2117 0.9837 0.9933 0.2012 0.4677 0.8809 0.7266 ] Network output: [ -0.01247 0.9979 1.011 2.656e-06 -1.192e-06 0.0163 2.002e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005085 0.0007014 0.004226 0.005171 0.989 0.992 0.005177 0.8782 0.9047 0.01578 ] Network output: [ 0.001698 -0.03221 1.002 -0.0001878 8.432e-05 1.026 -0.0001415 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1006 0.3134 0.1788 0.9852 0.9941 0.1915 0.473 0.8873 0.721 ] Network output: [ 0.009463 -0.04885 1.002 0.00011 -4.938e-05 1.028 8.289e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09747 0.08769 0.1788 0.2179 0.9874 0.992 0.09753 0.8081 0.8851 0.3139 ] Network output: [ -0.01128 0.04919 1.002 0.0001062 -4.767e-05 0.9716 8.002e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09841 0.09666 0.1728 0.2037 0.9858 0.9916 0.09843 0.7415 0.8664 0.2464 ] Network output: [ 0.001608 0.9987 -0.002485 1.595e-05 -7.16e-06 1.001 1.202e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001487 Epoch 6317 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01455 0.9945 0.9846 6.675e-06 -2.996e-06 -0.008111 5.03e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002898 -0.01052 0.007894 0.9696 0.9741 0.006001 0.847 0.8348 0.02167 ] Network output: [ 0.9965 0.02808 0.001006 -5.156e-05 2.315e-05 -0.02238 -3.886e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02313 -0.2077 0.2032 0.9836 0.9933 0.2019 0.4689 0.8806 0.7259 ] Network output: [ -0.01248 1.001 1.011 2.248e-06 -1.009e-06 0.01375 1.694e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005101 0.0006955 0.004079 0.004884 0.989 0.992 0.005192 0.8783 0.9045 0.0157 ] Network output: [ -0.002814 0.03805 0.9986 -0.0001977 8.877e-05 0.9682 -0.000149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.1008 0.3086 0.1653 0.9852 0.9941 0.192 0.4738 0.8873 0.7215 ] Network output: [ 0.01097 -0.03479 0.9995 0.0001093 -4.909e-05 1.014 8.24e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09724 0.08742 0.1756 0.2141 0.9874 0.992 0.0973 0.807 0.885 0.3119 ] Network output: [ -0.01035 0.04527 1.002 0.000107 -4.803e-05 0.9739 8.063e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09818 0.09641 0.1718 0.2029 0.9858 0.9916 0.09819 0.7402 0.8664 0.2462 ] Network output: [ -0.0008931 0.9994 0.001017 1.454e-05 -6.526e-06 1.001 1.096e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001306 Epoch 6318 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01505 0.9866 0.9849 7.739e-06 -3.474e-06 -0.001587 5.833e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.002898 -0.01048 0.008034 0.9696 0.9741 0.005988 0.8469 0.8351 0.02171 ] Network output: [ 1 -0.02253 0.003299 -4.517e-05 2.028e-05 0.01826 -3.404e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02381 -0.2048 0.2117 0.9837 0.9933 0.2012 0.4677 0.8809 0.7266 ] Network output: [ -0.01247 0.9979 1.011 2.657e-06 -1.193e-06 0.01629 2.002e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005086 0.0007005 0.004227 0.005169 0.989 0.992 0.005178 0.8782 0.9047 0.01577 ] Network output: [ 0.001684 -0.03205 1.002 -0.0001877 8.425e-05 1.026 -0.0001414 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1006 0.3134 0.1787 0.9852 0.9941 0.1915 0.473 0.8873 0.7209 ] Network output: [ 0.009461 -0.04884 1.002 0.0001099 -4.934e-05 1.028 8.282e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09747 0.08768 0.1788 0.2179 0.9874 0.992 0.09753 0.808 0.885 0.3139 ] Network output: [ -0.01127 0.04919 1.002 0.0001061 -4.763e-05 0.9715 7.997e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09839 0.09663 0.1728 0.2037 0.9858 0.9916 0.0984 0.7415 0.8664 0.2464 ] Network output: [ 0.001603 0.9987 -0.00248 1.593e-05 -7.151e-06 1.001 1.2e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001484 Epoch 6319 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01455 0.9945 0.9846 6.669e-06 -2.994e-06 -0.008096 5.026e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002898 -0.01051 0.007892 0.9696 0.9741 0.006001 0.847 0.8348 0.02166 ] Network output: [ 0.9966 0.02798 0.001008 -5.152e-05 2.313e-05 -0.0223 -3.883e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02315 -0.2077 0.2032 0.9836 0.9933 0.2019 0.4689 0.8806 0.7259 ] Network output: [ -0.01248 1.001 1.011 2.251e-06 -1.011e-06 0.01376 1.697e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005101 0.0006945 0.00408 0.004883 0.989 0.992 0.005193 0.8782 0.9045 0.0157 ] Network output: [ -0.002809 0.03791 0.9986 -0.0001975 8.868e-05 0.9683 -0.0001489 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1008 0.3086 0.1653 0.9852 0.9941 0.192 0.4738 0.8873 0.7215 ] Network output: [ 0.01096 -0.03484 0.9994 0.0001092 -4.905e-05 1.014 8.233e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09724 0.08741 0.1756 0.2141 0.9874 0.992 0.0973 0.807 0.8849 0.3119 ] Network output: [ -0.01034 0.04529 1.002 0.0001069 -4.8e-05 0.9738 8.057e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09815 0.09638 0.1718 0.2029 0.9858 0.9916 0.09817 0.7401 0.8663 0.2462 ] Network output: [ -0.0008887 0.9994 0.001009 1.452e-05 -6.521e-06 1.001 1.095e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001304 Epoch 6320 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01505 0.9867 0.9849 7.729e-06 -3.47e-06 -0.001598 5.825e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.002898 -0.01047 0.008032 0.9696 0.9741 0.005988 0.8469 0.8351 0.0217 ] Network output: [ 1 -0.02243 0.003293 -4.517e-05 2.028e-05 0.01817 -3.404e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02383 -0.2048 0.2116 0.9837 0.9933 0.2012 0.4677 0.8809 0.7266 ] Network output: [ -0.01246 0.9979 1.011 2.658e-06 -1.193e-06 0.01628 2.003e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005087 0.0006995 0.004227 0.005166 0.989 0.992 0.005178 0.8782 0.9047 0.01577 ] Network output: [ 0.001671 -0.0319 1.002 -0.0001875 8.418e-05 1.026 -0.0001413 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1006 0.3134 0.1787 0.9852 0.9941 0.1915 0.4729 0.8873 0.7209 ] Network output: [ 0.00946 -0.04883 1.002 0.0001098 -4.929e-05 1.028 8.275e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09747 0.08767 0.1788 0.2178 0.9874 0.992 0.09753 0.8079 0.885 0.3139 ] Network output: [ -0.01126 0.0492 1.002 0.000106 -4.76e-05 0.9715 7.991e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09836 0.09661 0.1728 0.2036 0.9858 0.9916 0.09837 0.7414 0.8663 0.2463 ] Network output: [ 0.001599 0.9987 -0.002476 1.591e-05 -7.142e-06 1.001 1.199e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00148 Epoch 6321 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01454 0.9944 0.9846 6.664e-06 -2.992e-06 -0.00808 5.022e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002898 -0.01051 0.00789 0.9696 0.9741 0.006001 0.847 0.8348 0.02166 ] Network output: [ 0.9966 0.02787 0.001011 -5.148e-05 2.311e-05 -0.02223 -3.88e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02317 -0.2077 0.2032 0.9836 0.9933 0.2019 0.4689 0.8806 0.7259 ] Network output: [ -0.01248 1.001 1.011 2.254e-06 -1.012e-06 0.01376 1.699e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005102 0.0006936 0.004081 0.004881 0.989 0.992 0.005194 0.8782 0.9045 0.01569 ] Network output: [ -0.002803 0.03777 0.9986 -0.0001973 8.859e-05 0.9684 -0.0001487 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1007 0.3086 0.1653 0.9852 0.9941 0.192 0.4738 0.8873 0.7214 ] Network output: [ 0.01095 -0.03489 0.9994 0.0001092 -4.9e-05 1.014 8.226e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09724 0.0874 0.1756 0.2141 0.9874 0.992 0.0973 0.8069 0.8849 0.3119 ] Network output: [ -0.01034 0.04531 1.002 0.0001068 -4.796e-05 0.9738 8.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09813 0.09636 0.1718 0.2029 0.9858 0.9916 0.09814 0.74 0.8663 0.2462 ] Network output: [ -0.0008843 0.9994 0.001001 1.451e-05 -6.515e-06 1.001 1.094e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001301 Epoch 6322 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01504 0.9867 0.9849 7.718e-06 -3.465e-06 -0.00161 5.816e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.002899 -0.01047 0.008029 0.9696 0.9741 0.005988 0.8469 0.8351 0.0217 ] Network output: [ 1 -0.02233 0.003287 -4.516e-05 2.028e-05 0.01808 -3.404e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02385 -0.2048 0.2116 0.9837 0.9933 0.2012 0.4676 0.8809 0.7265 ] Network output: [ -0.01246 0.9979 1.011 2.659e-06 -1.194e-06 0.01628 2.004e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005088 0.0006986 0.004227 0.005163 0.989 0.992 0.005179 0.8782 0.9047 0.01577 ] Network output: [ 0.001658 -0.03174 1.002 -0.0001874 8.412e-05 1.026 -0.0001412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1005 0.3134 0.1786 0.9852 0.9941 0.1915 0.4729 0.8872 0.7209 ] Network output: [ 0.009459 -0.04883 1.002 0.0001097 -4.925e-05 1.028 8.268e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09746 0.08767 0.1788 0.2178 0.9874 0.992 0.09752 0.8079 0.885 0.3139 ] Network output: [ -0.01125 0.04921 1.002 0.000106 -4.757e-05 0.9715 7.985e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09834 0.09658 0.1728 0.2036 0.9858 0.9916 0.09835 0.7413 0.8663 0.2463 ] Network output: [ 0.001594 0.9987 -0.002471 1.589e-05 -7.133e-06 1.001 1.197e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001477 Epoch 6323 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01454 0.9944 0.9846 6.659e-06 -2.989e-06 -0.008065 5.018e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002898 -0.01051 0.007888 0.9696 0.9741 0.006001 0.847 0.8348 0.02165 ] Network output: [ 0.9966 0.02776 0.001013 -5.145e-05 2.31e-05 -0.02215 -3.877e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.0232 -0.2076 0.2032 0.9836 0.9933 0.2018 0.4688 0.8806 0.7259 ] Network output: [ -0.01248 1.001 1.011 2.257e-06 -1.013e-06 0.01377 1.701e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005103 0.0006927 0.004082 0.00488 0.989 0.992 0.005194 0.8782 0.9045 0.01569 ] Network output: [ -0.002797 0.03764 0.9987 -0.0001971 8.85e-05 0.9685 -0.0001486 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1007 0.3087 0.1653 0.9852 0.9941 0.192 0.4738 0.8873 0.7214 ] Network output: [ 0.01094 -0.03494 0.9994 0.0001091 -4.896e-05 1.014 8.219e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09724 0.08739 0.1757 0.214 0.9874 0.992 0.0973 0.8069 0.8849 0.3119 ] Network output: [ -0.01034 0.04533 1.002 0.0001067 -4.792e-05 0.9738 8.045e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0981 0.09633 0.1718 0.2029 0.9858 0.9916 0.09812 0.74 0.8663 0.2462 ] Network output: [ -0.0008799 0.9994 0.0009931 1.45e-05 -6.51e-06 1.001 1.093e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001299 Epoch 6324 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9867 0.9849 7.707e-06 -3.46e-06 -0.001622 5.808e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.002899 -0.01047 0.008027 0.9696 0.9741 0.005988 0.8469 0.8351 0.02169 ] Network output: [ 1 -0.02223 0.003282 -4.516e-05 2.027e-05 0.018 -3.403e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02387 -0.2048 0.2116 0.9837 0.9933 0.2012 0.4676 0.8809 0.7265 ] Network output: [ -0.01246 0.9979 1.011 2.66e-06 -1.194e-06 0.01627 2.005e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005088 0.0006976 0.004228 0.005161 0.989 0.992 0.00518 0.8781 0.9047 0.01576 ] Network output: [ 0.001645 -0.03159 1.002 -0.0001872 8.405e-05 1.026 -0.0001411 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1005 0.3135 0.1785 0.9852 0.9941 0.1915 0.4729 0.8872 0.7209 ] Network output: [ 0.009457 -0.04882 1.002 0.0001096 -4.921e-05 1.028 8.261e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09746 0.08766 0.1788 0.2178 0.9874 0.992 0.09752 0.8078 0.885 0.3139 ] Network output: [ -0.01124 0.04921 1.002 0.0001059 -4.753e-05 0.9715 7.979e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09831 0.09655 0.1727 0.2036 0.9858 0.9916 0.09832 0.7413 0.8663 0.2463 ] Network output: [ 0.001589 0.9987 -0.002466 1.587e-05 -7.124e-06 1.001 1.196e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001473 Epoch 6325 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01454 0.9944 0.9846 6.654e-06 -2.987e-06 -0.008049 5.015e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002899 -0.0105 0.007886 0.9696 0.9741 0.006001 0.847 0.8348 0.02165 ] Network output: [ 0.9966 0.02766 0.001016 -5.141e-05 2.308e-05 -0.02207 -3.874e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02322 -0.2076 0.2032 0.9836 0.9933 0.2018 0.4688 0.8806 0.7258 ] Network output: [ -0.01247 1.001 1.011 2.26e-06 -1.014e-06 0.01377 1.703e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005103 0.0006918 0.004083 0.004878 0.989 0.992 0.005195 0.8782 0.9045 0.01569 ] Network output: [ -0.002791 0.0375 0.9987 -0.0001969 8.841e-05 0.9686 -0.0001484 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1007 0.3087 0.1652 0.9852 0.9941 0.192 0.4737 0.8872 0.7214 ] Network output: [ 0.01093 -0.03499 0.9994 0.000109 -4.892e-05 1.014 8.212e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09723 0.08739 0.1757 0.214 0.9874 0.992 0.09729 0.8068 0.8849 0.3119 ] Network output: [ -0.01033 0.04535 1.002 0.0001067 -4.788e-05 0.9738 8.038e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09808 0.09631 0.1717 0.2029 0.9858 0.9916 0.09809 0.7399 0.8662 0.2462 ] Network output: [ -0.0008754 0.9994 0.0009852 1.449e-05 -6.504e-06 1.001 1.092e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001296 Epoch 6326 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01503 0.9867 0.9849 7.697e-06 -3.455e-06 -0.001634 5.8e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.002899 -0.01046 0.008024 0.9696 0.9741 0.005988 0.8469 0.8351 0.02169 ] Network output: [ 1 -0.02213 0.003276 -4.516e-05 2.027e-05 0.01791 -3.403e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02389 -0.2048 0.2115 0.9837 0.9933 0.2012 0.4676 0.8809 0.7265 ] Network output: [ -0.01246 0.9979 1.011 2.661e-06 -1.195e-06 0.01626 2.005e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005089 0.0006967 0.004228 0.005158 0.989 0.992 0.00518 0.8781 0.9047 0.01576 ] Network output: [ 0.001631 -0.03143 1.002 -0.0001871 8.398e-05 1.026 -0.000141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1005 0.3135 0.1784 0.9852 0.9941 0.1915 0.4729 0.8872 0.7208 ] Network output: [ 0.009456 -0.04881 1.002 0.0001095 -4.917e-05 1.028 8.254e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09746 0.08765 0.1788 0.2177 0.9874 0.992 0.09752 0.8078 0.8849 0.3138 ] Network output: [ -0.01123 0.04922 1.002 0.0001058 -4.75e-05 0.9715 7.973e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09829 0.09653 0.1727 0.2036 0.9858 0.9916 0.0983 0.7412 0.8662 0.2463 ] Network output: [ 0.001584 0.9987 -0.002461 1.585e-05 -7.115e-06 1.001 1.194e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001469 Epoch 6327 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01453 0.9944 0.9846 6.649e-06 -2.985e-06 -0.008034 5.011e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002899 -0.0105 0.007884 0.9696 0.9741 0.006001 0.847 0.8347 0.02164 ] Network output: [ 0.9966 0.02755 0.001018 -5.137e-05 2.306e-05 -0.022 -3.872e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02325 -0.2076 0.2032 0.9836 0.9933 0.2018 0.4688 0.8806 0.7258 ] Network output: [ -0.01247 1.001 1.011 2.263e-06 -1.016e-06 0.01377 1.705e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005104 0.0006908 0.004084 0.004877 0.989 0.992 0.005196 0.8782 0.9045 0.01568 ] Network output: [ -0.002785 0.03737 0.9987 -0.0001967 8.832e-05 0.9687 -0.0001483 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1006 0.3088 0.1652 0.9852 0.9941 0.192 0.4737 0.8872 0.7214 ] Network output: [ 0.01093 -0.03504 0.9994 0.0001089 -4.888e-05 1.014 8.205e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09723 0.08738 0.1757 0.214 0.9874 0.992 0.09729 0.8068 0.8848 0.3119 ] Network output: [ -0.01033 0.04537 1.002 0.0001066 -4.785e-05 0.9738 8.032e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09806 0.09628 0.1717 0.2028 0.9858 0.9916 0.09807 0.7399 0.8662 0.2462 ] Network output: [ -0.000871 0.9994 0.0009774 1.448e-05 -6.498e-06 1.001 1.091e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001294 Epoch 6328 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01502 0.9867 0.9849 7.686e-06 -3.45e-06 -0.001646 5.792e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.002899 -0.01046 0.008021 0.9696 0.9741 0.005989 0.8469 0.8351 0.02168 ] Network output: [ 1 -0.02203 0.00327 -4.516e-05 2.027e-05 0.01782 -3.403e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02391 -0.2047 0.2115 0.9837 0.9933 0.2012 0.4676 0.8809 0.7265 ] Network output: [ -0.01246 0.9979 1.011 2.662e-06 -1.195e-06 0.01625 2.006e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00509 0.0006957 0.004228 0.005155 0.989 0.992 0.005181 0.8781 0.9047 0.01576 ] Network output: [ 0.001618 -0.03128 1.002 -0.0001869 8.392e-05 1.025 -0.0001409 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1004 0.3135 0.1783 0.9852 0.9941 0.1915 0.4728 0.8872 0.7208 ] Network output: [ 0.009454 -0.0488 1.002 0.0001094 -4.913e-05 1.028 8.247e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09745 0.08764 0.1788 0.2177 0.9874 0.992 0.09751 0.8077 0.8849 0.3138 ] Network output: [ -0.01122 0.04922 1.002 0.0001057 -4.746e-05 0.9715 7.967e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09826 0.0965 0.1727 0.2035 0.9858 0.9916 0.09827 0.7411 0.8662 0.2463 ] Network output: [ 0.00158 0.9987 -0.002457 1.583e-05 -7.107e-06 1.001 1.193e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001466 Epoch 6329 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01453 0.9944 0.9846 6.644e-06 -2.983e-06 -0.008019 5.007e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002899 -0.0105 0.007882 0.9696 0.9741 0.006002 0.847 0.8347 0.02164 ] Network output: [ 0.9966 0.02744 0.001021 -5.134e-05 2.305e-05 -0.02192 -3.869e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02327 -0.2075 0.2032 0.9836 0.9933 0.2018 0.4687 0.8806 0.7258 ] Network output: [ -0.01247 1.001 1.011 2.265e-06 -1.017e-06 0.01378 1.707e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005105 0.0006899 0.004085 0.004875 0.989 0.992 0.005196 0.8782 0.9045 0.01568 ] Network output: [ -0.002779 0.03723 0.9988 -0.0001965 8.823e-05 0.9688 -0.0001481 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1006 0.3088 0.1652 0.9852 0.9941 0.192 0.4737 0.8872 0.7213 ] Network output: [ 0.01092 -0.03509 0.9994 0.0001088 -4.884e-05 1.014 8.198e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09723 0.08737 0.1757 0.214 0.9874 0.992 0.09729 0.8067 0.8848 0.3119 ] Network output: [ -0.01032 0.04539 1.002 0.0001065 -4.781e-05 0.9737 8.026e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09803 0.09626 0.1717 0.2028 0.9858 0.9916 0.09804 0.7398 0.8662 0.2461 ] Network output: [ -0.0008666 0.9994 0.0009695 1.446e-05 -6.493e-06 1.001 1.09e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001291 Epoch 6330 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01502 0.9868 0.9849 7.675e-06 -3.446e-06 -0.001658 5.784e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.0029 -0.01046 0.008019 0.9696 0.9741 0.005989 0.8468 0.835 0.02168 ] Network output: [ 1 -0.02193 0.003264 -4.515e-05 2.027e-05 0.01773 -3.403e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02393 -0.2047 0.2114 0.9837 0.9933 0.2012 0.4675 0.8809 0.7264 ] Network output: [ -0.01245 0.9979 1.011 2.663e-06 -1.195e-06 0.01624 2.007e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00509 0.0006948 0.004229 0.005152 0.989 0.992 0.005182 0.8781 0.9047 0.01575 ] Network output: [ 0.001605 -0.03112 1.002 -0.0001868 8.385e-05 1.025 -0.0001408 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1004 0.3135 0.1783 0.9852 0.9941 0.1915 0.4728 0.8872 0.7208 ] Network output: [ 0.009453 -0.04879 1.002 0.0001093 -4.908e-05 1.028 8.24e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09745 0.08763 0.1788 0.2177 0.9874 0.992 0.09751 0.8077 0.8849 0.3138 ] Network output: [ -0.01121 0.04923 1.002 0.0001056 -4.743e-05 0.9715 7.962e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09823 0.09647 0.1727 0.2035 0.9858 0.9916 0.09825 0.7411 0.8662 0.2462 ] Network output: [ 0.001575 0.9988 -0.002452 1.581e-05 -7.098e-06 1.001 1.192e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001462 Epoch 6331 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01453 0.9944 0.9846 6.638e-06 -2.98e-06 -0.008004 5.003e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002899 -0.01049 0.00788 0.9696 0.9741 0.006002 0.847 0.8347 0.02163 ] Network output: [ 0.9966 0.02734 0.001023 -5.13e-05 2.303e-05 -0.02185 -3.866e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02329 -0.2075 0.2032 0.9836 0.9933 0.2018 0.4687 0.8805 0.7258 ] Network output: [ -0.01247 1.001 1.011 2.268e-06 -1.018e-06 0.01378 1.709e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005105 0.000689 0.004086 0.004874 0.989 0.992 0.005197 0.8782 0.9045 0.01568 ] Network output: [ -0.002774 0.0371 0.9988 -0.0001963 8.814e-05 0.9689 -0.000148 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1005 0.3088 0.1652 0.9852 0.9941 0.192 0.4736 0.8872 0.7213 ] Network output: [ 0.01091 -0.03514 0.9994 0.0001087 -4.88e-05 1.014 8.191e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09723 0.08736 0.1757 0.214 0.9874 0.992 0.09729 0.8067 0.8848 0.3119 ] Network output: [ -0.01032 0.04541 1.002 0.0001064 -4.777e-05 0.9737 8.02e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09801 0.09623 0.1717 0.2028 0.9858 0.9916 0.09802 0.7397 0.8661 0.2461 ] Network output: [ -0.0008622 0.9994 0.0009616 1.445e-05 -6.487e-06 1.001 1.089e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001289 Epoch 6332 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01501 0.9868 0.9849 7.664e-06 -3.441e-06 -0.00167 5.776e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.0029 -0.01045 0.008016 0.9696 0.9741 0.005989 0.8468 0.835 0.02167 ] Network output: [ 1 -0.02183 0.003258 -4.515e-05 2.027e-05 0.01765 -3.403e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02396 -0.2047 0.2114 0.9837 0.9933 0.2012 0.4675 0.8808 0.7264 ] Network output: [ -0.01245 0.9979 1.011 2.664e-06 -1.196e-06 0.01624 2.007e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005091 0.0006939 0.004229 0.00515 0.989 0.992 0.005183 0.8781 0.9046 0.01575 ] Network output: [ 0.001592 -0.03097 1.002 -0.0001866 8.378e-05 1.025 -0.0001406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1003 0.3135 0.1782 0.9852 0.9941 0.1915 0.4728 0.8872 0.7208 ] Network output: [ 0.009451 -0.04879 1.002 0.0001092 -4.904e-05 1.028 8.233e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09745 0.08762 0.1788 0.2177 0.9874 0.992 0.09751 0.8076 0.8849 0.3138 ] Network output: [ -0.01121 0.04923 1.002 0.0001056 -4.739e-05 0.9714 7.956e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09821 0.09645 0.1726 0.2035 0.9858 0.9916 0.09822 0.741 0.8661 0.2462 ] Network output: [ 0.00157 0.9988 -0.002447 1.579e-05 -7.089e-06 1.001 1.19e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001459 Epoch 6333 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01452 0.9944 0.9846 6.633e-06 -2.978e-06 -0.007989 4.999e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.0029 -0.01049 0.007878 0.9696 0.9741 0.006002 0.847 0.8347 0.02163 ] Network output: [ 0.9967 0.02723 0.001026 -5.126e-05 2.301e-05 -0.02177 -3.863e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02332 -0.2075 0.2032 0.9836 0.9933 0.2018 0.4687 0.8805 0.7258 ] Network output: [ -0.01247 1.001 1.011 2.271e-06 -1.02e-06 0.01378 1.712e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005106 0.0006881 0.004086 0.004872 0.989 0.992 0.005198 0.8782 0.9045 0.01567 ] Network output: [ -0.002768 0.03696 0.9988 -0.0001961 8.805e-05 0.969 -0.0001478 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1005 0.3089 0.1651 0.9852 0.9941 0.192 0.4736 0.8872 0.7213 ] Network output: [ 0.0109 -0.03519 0.9994 0.0001086 -4.875e-05 1.014 8.184e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09723 0.08736 0.1757 0.214 0.9874 0.992 0.09729 0.8066 0.8847 0.3119 ] Network output: [ -0.01032 0.04543 1.002 0.0001063 -4.774e-05 0.9737 8.014e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09798 0.09621 0.1717 0.2028 0.9858 0.9916 0.09799 0.7397 0.8661 0.2461 ] Network output: [ -0.0008577 0.9994 0.0009538 1.444e-05 -6.482e-06 1.001 1.088e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001286 Epoch 6334 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01501 0.9868 0.9849 7.654e-06 -3.436e-06 -0.001682 5.768e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.0029 -0.01045 0.008013 0.9696 0.9741 0.005989 0.8468 0.835 0.02167 ] Network output: [ 1 -0.02173 0.003252 -4.515e-05 2.027e-05 0.01756 -3.402e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02398 -0.2047 0.2113 0.9837 0.9933 0.2012 0.4675 0.8808 0.7264 ] Network output: [ -0.01245 0.9979 1.011 2.665e-06 -1.196e-06 0.01623 2.008e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005092 0.0006929 0.004229 0.005147 0.989 0.992 0.005183 0.8781 0.9046 0.01575 ] Network output: [ 0.001579 -0.03082 1.002 -0.0001865 8.371e-05 1.025 -0.0001405 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1003 0.3136 0.1781 0.9852 0.9941 0.1915 0.4728 0.8872 0.7208 ] Network output: [ 0.00945 -0.04878 1.002 0.0001091 -4.9e-05 1.028 8.225e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09744 0.08761 0.1788 0.2176 0.9874 0.992 0.0975 0.8075 0.8848 0.3138 ] Network output: [ -0.0112 0.04924 1.002 0.0001055 -4.736e-05 0.9714 7.95e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09818 0.09642 0.1726 0.2035 0.9858 0.9916 0.0982 0.741 0.8661 0.2462 ] Network output: [ 0.001565 0.9988 -0.002442 1.577e-05 -7.08e-06 1.001 1.189e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001455 Epoch 6335 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01452 0.9943 0.9846 6.628e-06 -2.976e-06 -0.007973 4.995e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.0029 -0.01049 0.007876 0.9696 0.9741 0.006002 0.847 0.8347 0.02162 ] Network output: [ 0.9967 0.02712 0.001029 -5.122e-05 2.3e-05 -0.02169 -3.86e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02334 -0.2075 0.2032 0.9836 0.9933 0.2018 0.4686 0.8805 0.7257 ] Network output: [ -0.01247 1.001 1.011 2.274e-06 -1.021e-06 0.01379 1.714e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005107 0.0006872 0.004087 0.004871 0.989 0.992 0.005198 0.8781 0.9044 0.01567 ] Network output: [ -0.002762 0.03683 0.9989 -0.0001959 8.796e-05 0.969 -0.0001477 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1005 0.3089 0.1651 0.9852 0.9941 0.192 0.4736 0.8872 0.7213 ] Network output: [ 0.01089 -0.03524 0.9994 0.0001085 -4.871e-05 1.015 8.177e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09722 0.08735 0.1757 0.214 0.9874 0.992 0.09728 0.8065 0.8847 0.3119 ] Network output: [ -0.01031 0.04545 1.002 0.0001063 -4.77e-05 0.9737 8.007e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09796 0.09618 0.1716 0.2027 0.9858 0.9916 0.09797 0.7396 0.8661 0.2461 ] Network output: [ -0.0008533 0.9994 0.0009459 1.443e-05 -6.476e-06 1.001 1.087e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001284 Epoch 6336 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.015 0.9868 0.9849 7.643e-06 -3.431e-06 -0.001694 5.76e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.0029 -0.01045 0.008011 0.9696 0.9741 0.005989 0.8468 0.835 0.02166 ] Network output: [ 1 -0.02163 0.003247 -4.514e-05 2.027e-05 0.01747 -3.402e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.024 -0.2047 0.2113 0.9837 0.9933 0.2012 0.4675 0.8808 0.7264 ] Network output: [ -0.01245 0.9979 1.011 2.666e-06 -1.197e-06 0.01622 2.009e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005093 0.000692 0.00423 0.005144 0.989 0.992 0.005184 0.8781 0.9046 0.01574 ] Network output: [ 0.001566 -0.03066 1.002 -0.0001863 8.365e-05 1.025 -0.0001404 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1003 0.3136 0.178 0.9852 0.9941 0.1915 0.4727 0.8872 0.7207 ] Network output: [ 0.009448 -0.04877 1.002 0.000109 -4.896e-05 1.028 8.218e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09744 0.08761 0.1788 0.2176 0.9874 0.992 0.0975 0.8075 0.8848 0.3138 ] Network output: [ -0.01119 0.04924 1.002 0.0001054 -4.732e-05 0.9714 7.944e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09816 0.0964 0.1726 0.2034 0.9858 0.9916 0.09817 0.7409 0.8661 0.2462 ] Network output: [ 0.001561 0.9988 -0.002437 1.575e-05 -7.071e-06 1.001 1.187e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001452 Epoch 6337 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01451 0.9943 0.9846 6.623e-06 -2.973e-06 -0.007958 4.991e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.0029 -0.01048 0.007874 0.9696 0.9741 0.006002 0.847 0.8347 0.02162 ] Network output: [ 0.9967 0.02702 0.001031 -5.119e-05 2.298e-05 -0.02162 -3.858e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02337 -0.2074 0.2032 0.9836 0.9933 0.2018 0.4686 0.8805 0.7257 ] Network output: [ -0.01246 1.001 1.011 2.277e-06 -1.022e-06 0.01379 1.716e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005107 0.0006862 0.004088 0.004869 0.989 0.992 0.005199 0.8781 0.9044 0.01567 ] Network output: [ -0.002756 0.03669 0.9989 -0.0001957 8.787e-05 0.9691 -0.0001475 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1004 0.309 0.1651 0.9852 0.9941 0.192 0.4735 0.8872 0.7212 ] Network output: [ 0.01088 -0.03529 0.9994 0.0001084 -4.867e-05 1.015 8.17e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09722 0.08734 0.1757 0.214 0.9874 0.992 0.09728 0.8065 0.8847 0.3119 ] Network output: [ -0.01031 0.04546 1.002 0.0001062 -4.766e-05 0.9737 8.001e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09793 0.09616 0.1716 0.2027 0.9858 0.9916 0.09795 0.7396 0.866 0.2461 ] Network output: [ -0.0008489 0.9994 0.0009381 1.441e-05 -6.471e-06 1.001 1.086e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001281 Epoch 6338 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01499 0.9868 0.9849 7.632e-06 -3.426e-06 -0.001706 5.752e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.0029 -0.01045 0.008008 0.9696 0.9741 0.00599 0.8468 0.835 0.02166 ] Network output: [ 1 -0.02153 0.003241 -4.514e-05 2.027e-05 0.01739 -3.402e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02402 -0.2046 0.2112 0.9837 0.9933 0.2012 0.4674 0.8808 0.7263 ] Network output: [ -0.01245 0.9979 1.011 2.666e-06 -1.197e-06 0.01621 2.01e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005093 0.000691 0.00423 0.005141 0.989 0.992 0.005185 0.8781 0.9046 0.01574 ] Network output: [ 0.001553 -0.03051 1.002 -0.0001862 8.358e-05 1.025 -0.0001403 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1002 0.3136 0.1779 0.9852 0.9941 0.1915 0.4727 0.8871 0.7207 ] Network output: [ 0.009446 -0.04876 1.002 0.000109 -4.891e-05 1.028 8.211e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09744 0.0876 0.1788 0.2176 0.9874 0.992 0.0975 0.8074 0.8848 0.3138 ] Network output: [ -0.01118 0.04924 1.002 0.0001053 -4.729e-05 0.9714 7.938e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09813 0.09637 0.1726 0.2034 0.9858 0.9916 0.09815 0.7408 0.866 0.2462 ] Network output: [ 0.001556 0.9988 -0.002432 1.573e-05 -7.063e-06 1.001 1.186e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001448 Epoch 6339 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01451 0.9943 0.9846 6.618e-06 -2.971e-06 -0.007943 4.987e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.0029 -0.01048 0.007872 0.9696 0.9741 0.006002 0.847 0.8347 0.02161 ] Network output: [ 0.9967 0.02691 0.001034 -5.115e-05 2.296e-05 -0.02154 -3.855e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02339 -0.2074 0.2032 0.9836 0.9933 0.2018 0.4686 0.8805 0.7257 ] Network output: [ -0.01246 1.001 1.011 2.279e-06 -1.023e-06 0.01379 1.718e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005108 0.0006853 0.004089 0.004868 0.989 0.992 0.0052 0.8781 0.9044 0.01566 ] Network output: [ -0.00275 0.03656 0.9989 -0.0001955 8.778e-05 0.9692 -0.0001474 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1004 0.309 0.1651 0.9852 0.9941 0.192 0.4735 0.8872 0.7212 ] Network output: [ 0.01087 -0.03534 0.9993 0.0001083 -4.863e-05 1.015 8.163e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09722 0.08733 0.1757 0.2139 0.9874 0.992 0.09728 0.8064 0.8847 0.3119 ] Network output: [ -0.0103 0.04548 1.002 0.0001061 -4.763e-05 0.9736 7.995e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09791 0.09613 0.1716 0.2027 0.9858 0.9916 0.09792 0.7395 0.866 0.2461 ] Network output: [ -0.0008445 0.9994 0.0009303 1.44e-05 -6.465e-06 1.001 1.085e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001279 Epoch 6340 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01499 0.9869 0.9849 7.622e-06 -3.422e-06 -0.001718 5.744e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.002901 -0.01044 0.008005 0.9696 0.9741 0.00599 0.8468 0.835 0.02165 ] Network output: [ 1 -0.02143 0.003235 -4.514e-05 2.026e-05 0.0173 -3.402e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02404 -0.2046 0.2112 0.9837 0.9933 0.2012 0.4674 0.8808 0.7263 ] Network output: [ -0.01244 0.998 1.011 2.667e-06 -1.197e-06 0.01621 2.01e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005094 0.0006901 0.00423 0.005139 0.989 0.992 0.005186 0.878 0.9046 0.01573 ] Network output: [ 0.00154 -0.03036 1.002 -0.000186 8.351e-05 1.025 -0.0001402 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1002 0.3136 0.1779 0.9852 0.9941 0.1915 0.4727 0.8871 0.7207 ] Network output: [ 0.009445 -0.04875 1.002 0.0001089 -4.887e-05 1.028 8.204e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09743 0.08759 0.1788 0.2175 0.9874 0.992 0.09749 0.8074 0.8847 0.3138 ] Network output: [ -0.01117 0.04925 1.002 0.0001053 -4.725e-05 0.9714 7.932e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09811 0.09634 0.1725 0.2034 0.9858 0.9916 0.09812 0.7408 0.866 0.2462 ] Network output: [ 0.001551 0.9988 -0.002428 1.571e-05 -7.054e-06 1.001 1.184e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001445 Epoch 6341 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01451 0.9943 0.9846 6.612e-06 -2.968e-06 -0.007928 4.983e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002901 -0.01048 0.00787 0.9696 0.9741 0.006002 0.847 0.8347 0.02161 ] Network output: [ 0.9967 0.0268 0.001037 -5.111e-05 2.295e-05 -0.02147 -3.852e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02341 -0.2074 0.2031 0.9836 0.9933 0.2018 0.4685 0.8805 0.7257 ] Network output: [ -0.01246 1.001 1.011 2.282e-06 -1.025e-06 0.0138 1.72e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005109 0.0006844 0.00409 0.004866 0.989 0.992 0.0052 0.8781 0.9044 0.01566 ] Network output: [ -0.002744 0.03642 0.999 -0.0001953 8.769e-05 0.9693 -0.0001472 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1003 0.309 0.165 0.9852 0.9941 0.192 0.4735 0.8872 0.7212 ] Network output: [ 0.01086 -0.03539 0.9993 0.0001082 -4.859e-05 1.015 8.156e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09722 0.08733 0.1758 0.2139 0.9874 0.992 0.09728 0.8064 0.8846 0.3119 ] Network output: [ -0.0103 0.0455 1.002 0.000106 -4.759e-05 0.9736 7.989e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09788 0.09611 0.1716 0.2027 0.9858 0.9916 0.0979 0.7394 0.866 0.246 ] Network output: [ -0.00084 0.9994 0.0009225 1.439e-05 -6.46e-06 1.001 1.084e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001276 Epoch 6342 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01498 0.9869 0.9849 7.611e-06 -3.417e-06 -0.00173 5.736e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.002901 -0.01044 0.008003 0.9696 0.9741 0.00599 0.8468 0.835 0.02164 ] Network output: [ 1 -0.02133 0.00323 -4.514e-05 2.026e-05 0.01721 -3.402e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02406 -0.2046 0.2112 0.9837 0.9933 0.2012 0.4674 0.8808 0.7263 ] Network output: [ -0.01244 0.998 1.011 2.668e-06 -1.198e-06 0.0162 2.011e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005095 0.0006892 0.004231 0.005136 0.989 0.992 0.005186 0.878 0.9046 0.01573 ] Network output: [ 0.001527 -0.0302 1.002 -0.0001859 8.344e-05 1.024 -0.0001401 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1001 0.3136 0.1778 0.9852 0.9941 0.1915 0.4726 0.8871 0.7207 ] Network output: [ 0.009443 -0.04874 1.002 0.0001088 -4.883e-05 1.028 8.197e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09743 0.08758 0.1788 0.2175 0.9874 0.992 0.09749 0.8073 0.8847 0.3138 ] Network output: [ -0.01116 0.04925 1.002 0.0001052 -4.722e-05 0.9714 7.927e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09808 0.09632 0.1725 0.2033 0.9858 0.9916 0.09809 0.7407 0.866 0.2461 ] Network output: [ 0.001546 0.9988 -0.002423 1.569e-05 -7.045e-06 1.001 1.183e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001442 Epoch 6343 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0145 0.9943 0.9846 6.607e-06 -2.966e-06 -0.007913 4.979e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002901 -0.01047 0.007868 0.9696 0.9741 0.006002 0.847 0.8347 0.0216 ] Network output: [ 0.9967 0.0267 0.00104 -5.108e-05 2.293e-05 -0.02139 -3.849e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02344 -0.2073 0.2031 0.9836 0.9933 0.2018 0.4685 0.8805 0.7257 ] Network output: [ -0.01246 1.001 1.011 2.285e-06 -1.026e-06 0.0138 1.722e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005109 0.0006835 0.004091 0.004865 0.989 0.992 0.005201 0.8781 0.9044 0.01566 ] Network output: [ -0.002739 0.03629 0.999 -0.0001951 8.76e-05 0.9694 -0.0001471 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1003 0.3091 0.165 0.9852 0.9941 0.192 0.4734 0.8871 0.7212 ] Network output: [ 0.01085 -0.03543 0.9993 0.0001081 -4.855e-05 1.015 8.149e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09721 0.08732 0.1758 0.2139 0.9874 0.992 0.09727 0.8063 0.8846 0.3119 ] Network output: [ -0.0103 0.04551 1.002 0.0001059 -4.755e-05 0.9736 7.983e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09786 0.09608 0.1716 0.2026 0.9858 0.9916 0.09787 0.7394 0.8659 0.246 ] Network output: [ -0.0008356 0.9994 0.0009147 1.438e-05 -6.454e-06 1.001 1.083e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001274 Epoch 6344 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01498 0.9869 0.9849 7.6e-06 -3.412e-06 -0.001742 5.728e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.002901 -0.01044 0.008 0.9696 0.9741 0.00599 0.8468 0.835 0.02164 ] Network output: [ 1 -0.02123 0.003224 -4.513e-05 2.026e-05 0.01713 -3.401e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02408 -0.2046 0.2111 0.9837 0.9933 0.2012 0.4674 0.8808 0.7263 ] Network output: [ -0.01244 0.998 1.011 2.669e-06 -1.198e-06 0.01619 2.012e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005095 0.0006882 0.004231 0.005133 0.989 0.992 0.005187 0.878 0.9046 0.01573 ] Network output: [ 0.001514 -0.03005 1.002 -0.0001857 8.338e-05 1.024 -0.00014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1001 0.3137 0.1777 0.9852 0.9941 0.1915 0.4726 0.8871 0.7206 ] Network output: [ 0.009441 -0.04873 1.002 0.0001087 -4.879e-05 1.028 8.19e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09743 0.08757 0.1788 0.2175 0.9874 0.992 0.09749 0.8073 0.8847 0.3138 ] Network output: [ -0.01115 0.04925 1.002 0.0001051 -4.718e-05 0.9714 7.921e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09806 0.09629 0.1725 0.2033 0.9858 0.9916 0.09807 0.7406 0.8659 0.2461 ] Network output: [ 0.001542 0.9988 -0.002418 1.567e-05 -7.037e-06 1.001 1.181e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001438 Epoch 6345 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0145 0.9943 0.9847 6.602e-06 -2.964e-06 -0.007899 4.975e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002901 -0.01047 0.007866 0.9696 0.9741 0.006003 0.847 0.8347 0.0216 ] Network output: [ 0.9967 0.02659 0.001042 -5.104e-05 2.291e-05 -0.02132 -3.846e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02346 -0.2073 0.2031 0.9836 0.9933 0.2018 0.4685 0.8805 0.7256 ] Network output: [ -0.01246 1.001 1.011 2.288e-06 -1.027e-06 0.0138 1.724e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00511 0.0006826 0.004092 0.004863 0.989 0.992 0.005202 0.8781 0.9044 0.01565 ] Network output: [ -0.002733 0.03615 0.999 -0.0001949 8.751e-05 0.9695 -0.0001469 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1003 0.3091 0.165 0.9852 0.9941 0.192 0.4734 0.8871 0.7211 ] Network output: [ 0.01084 -0.03548 0.9993 0.000108 -4.85e-05 1.015 8.142e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09721 0.08731 0.1758 0.2139 0.9874 0.992 0.09727 0.8063 0.8846 0.3119 ] Network output: [ -0.01029 0.04553 1.002 0.0001058 -4.751e-05 0.9736 7.976e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09784 0.09606 0.1715 0.2026 0.9858 0.9916 0.09785 0.7393 0.8659 0.246 ] Network output: [ -0.0008312 0.9994 0.000907 1.436e-05 -6.449e-06 1.001 1.083e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001272 Epoch 6346 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01497 0.9869 0.9849 7.59e-06 -3.407e-06 -0.001754 5.72e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.002901 -0.01043 0.007998 0.9696 0.9741 0.00599 0.8468 0.835 0.02163 ] Network output: [ 1 -0.02114 0.003218 -4.513e-05 2.026e-05 0.01704 -3.401e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.0241 -0.2046 0.2111 0.9837 0.9933 0.2012 0.4673 0.8808 0.7262 ] Network output: [ -0.01244 0.998 1.011 2.67e-06 -1.199e-06 0.01618 2.012e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005096 0.0006873 0.004231 0.005131 0.989 0.992 0.005188 0.878 0.9046 0.01572 ] Network output: [ 0.001501 -0.0299 1.002 -0.0001856 8.331e-05 1.024 -0.0001398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1001 0.3137 0.1776 0.9852 0.9941 0.1915 0.4726 0.8871 0.7206 ] Network output: [ 0.00944 -0.04872 1.002 0.0001086 -4.874e-05 1.028 8.183e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09742 0.08756 0.1788 0.2175 0.9874 0.992 0.09748 0.8072 0.8847 0.3137 ] Network output: [ -0.01114 0.04925 1.002 0.000105 -4.715e-05 0.9713 7.915e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09803 0.09627 0.1725 0.2033 0.9858 0.9916 0.09804 0.7406 0.8659 0.2461 ] Network output: [ 0.001537 0.9988 -0.002413 1.565e-05 -7.028e-06 1.001 1.18e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001435 Epoch 6347 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0145 0.9943 0.9847 6.596e-06 -2.961e-06 -0.007884 4.971e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002901 -0.01047 0.007864 0.9696 0.9741 0.006003 0.8469 0.8347 0.02159 ] Network output: [ 0.9968 0.02649 0.001045 -5.1e-05 2.29e-05 -0.02124 -3.844e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02348 -0.2073 0.2031 0.9836 0.9933 0.2018 0.4685 0.8805 0.7256 ] Network output: [ -0.01245 1.001 1.011 2.29e-06 -1.028e-06 0.0138 1.726e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00511 0.0006817 0.004093 0.004862 0.989 0.992 0.005202 0.8781 0.9044 0.01565 ] Network output: [ -0.002727 0.03602 0.999 -0.0001947 8.742e-05 0.9696 -0.0001468 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1002 0.3092 0.165 0.9852 0.9941 0.192 0.4734 0.8871 0.7211 ] Network output: [ 0.01083 -0.03553 0.9993 0.0001079 -4.846e-05 1.015 8.135e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09721 0.0873 0.1758 0.2139 0.9874 0.992 0.09727 0.8062 0.8846 0.3118 ] Network output: [ -0.01029 0.04555 1.002 0.0001058 -4.748e-05 0.9736 7.97e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09781 0.09603 0.1715 0.2026 0.9858 0.9916 0.09782 0.7393 0.8659 0.246 ] Network output: [ -0.0008268 0.9994 0.0008992 1.435e-05 -6.443e-06 1.001 1.082e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001269 Epoch 6348 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01497 0.9869 0.9849 7.579e-06 -3.403e-06 -0.001766 5.712e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003168 -0.002902 -0.01043 0.007995 0.9696 0.9741 0.005991 0.8468 0.835 0.02163 ] Network output: [ 1 -0.02104 0.003213 -4.513e-05 2.026e-05 0.01696 -3.401e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02413 -0.2045 0.211 0.9837 0.9933 0.2012 0.4673 0.8807 0.7262 ] Network output: [ -0.01244 0.998 1.011 2.671e-06 -1.199e-06 0.01617 2.013e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005097 0.0006864 0.004231 0.005128 0.989 0.992 0.005189 0.878 0.9045 0.01572 ] Network output: [ 0.001488 -0.02975 1.002 -0.0001854 8.324e-05 1.024 -0.0001397 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.1 0.3137 0.1776 0.9852 0.9941 0.1915 0.4726 0.8871 0.7206 ] Network output: [ 0.009438 -0.04871 1.002 0.0001085 -4.87e-05 1.028 8.176e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09742 0.08755 0.1788 0.2174 0.9874 0.992 0.09748 0.8072 0.8846 0.3137 ] Network output: [ -0.01113 0.04926 1.002 0.0001049 -4.711e-05 0.9713 7.909e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09801 0.09624 0.1724 0.2033 0.9858 0.9916 0.09802 0.7405 0.8659 0.2461 ] Network output: [ 0.001532 0.9988 -0.002408 1.564e-05 -7.019e-06 1.001 1.178e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001431 Epoch 6349 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01449 0.9942 0.9847 6.591e-06 -2.959e-06 -0.007869 4.967e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002902 -0.01046 0.007862 0.9696 0.9741 0.006003 0.8469 0.8347 0.02159 ] Network output: [ 0.9968 0.02638 0.001048 -5.096e-05 2.288e-05 -0.02117 -3.841e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02351 -0.2073 0.2031 0.9836 0.9933 0.2018 0.4684 0.8805 0.7256 ] Network output: [ -0.01245 1.001 1.011 2.293e-06 -1.03e-06 0.01381 1.728e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005111 0.0006808 0.004094 0.00486 0.989 0.992 0.005203 0.8781 0.9044 0.01565 ] Network output: [ -0.002721 0.03589 0.9991 -0.0001945 8.733e-05 0.9697 -0.0001466 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1002 0.3092 0.165 0.9852 0.9941 0.192 0.4734 0.8871 0.7211 ] Network output: [ 0.01082 -0.03557 0.9993 0.0001079 -4.842e-05 1.015 8.128e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09721 0.0873 0.1758 0.2139 0.9874 0.992 0.09726 0.8062 0.8845 0.3118 ] Network output: [ -0.01028 0.04556 1.002 0.0001057 -4.744e-05 0.9735 7.964e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09779 0.09601 0.1715 0.2026 0.9858 0.9916 0.0978 0.7392 0.8658 0.246 ] Network output: [ -0.0008224 0.9994 0.0008915 1.434e-05 -6.438e-06 1.001 1.081e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001267 Epoch 6350 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01496 0.9869 0.9849 7.569e-06 -3.398e-06 -0.001778 5.704e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002902 -0.01043 0.007992 0.9696 0.9741 0.005991 0.8468 0.8349 0.02162 ] Network output: [ 1 -0.02094 0.003207 -4.512e-05 2.026e-05 0.01687 -3.401e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02415 -0.2045 0.211 0.9837 0.9933 0.2012 0.4673 0.8807 0.7262 ] Network output: [ -0.01243 0.998 1.011 2.672e-06 -1.2e-06 0.01617 2.014e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005098 0.0006855 0.004232 0.005125 0.989 0.992 0.005189 0.878 0.9045 0.01572 ] Network output: [ 0.001476 -0.0296 1.002 -0.0001853 8.317e-05 1.024 -0.0001396 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09999 0.3137 0.1775 0.9852 0.9941 0.1915 0.4725 0.8871 0.7206 ] Network output: [ 0.009436 -0.04869 1.002 0.0001084 -4.866e-05 1.028 8.168e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09741 0.08754 0.1788 0.2174 0.9874 0.992 0.09747 0.8071 0.8846 0.3137 ] Network output: [ -0.01112 0.04926 1.002 0.0001049 -4.708e-05 0.9713 7.903e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09798 0.09621 0.1724 0.2032 0.9858 0.9916 0.09799 0.7404 0.8658 0.2461 ] Network output: [ 0.001528 0.9988 -0.002404 1.562e-05 -7.011e-06 1.001 1.177e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001428 Epoch 6351 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01449 0.9942 0.9847 6.586e-06 -2.957e-06 -0.007855 4.963e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002902 -0.01046 0.00786 0.9696 0.9741 0.006003 0.8469 0.8346 0.02158 ] Network output: [ 0.9968 0.02628 0.001051 -5.093e-05 2.286e-05 -0.02109 -3.838e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02353 -0.2072 0.2031 0.9837 0.9933 0.2018 0.4684 0.8804 0.7256 ] Network output: [ -0.01245 1.001 1.011 2.296e-06 -1.031e-06 0.01381 1.73e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005112 0.0006798 0.004094 0.004859 0.989 0.992 0.005204 0.878 0.9043 0.01564 ] Network output: [ -0.002716 0.03576 0.9991 -0.0001943 8.724e-05 0.9698 -0.0001464 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1002 0.3092 0.1649 0.9852 0.9941 0.192 0.4733 0.8871 0.7211 ] Network output: [ 0.01081 -0.03562 0.9993 0.0001078 -4.838e-05 1.015 8.121e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0972 0.08729 0.1758 0.2139 0.9874 0.992 0.09726 0.8061 0.8845 0.3118 ] Network output: [ -0.01028 0.04558 1.002 0.0001056 -4.74e-05 0.9735 7.958e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09776 0.09598 0.1715 0.2025 0.9858 0.9916 0.09777 0.7391 0.8658 0.2459 ] Network output: [ -0.000818 0.9994 0.0008839 1.433e-05 -6.432e-06 1.001 1.08e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001264 Epoch 6352 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01495 0.987 0.9849 7.558e-06 -3.393e-06 -0.00179 5.696e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002902 -0.01042 0.00799 0.9696 0.9741 0.005991 0.8468 0.8349 0.02162 ] Network output: [ 1 -0.02084 0.003202 -4.512e-05 2.026e-05 0.01679 -3.4e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02417 -0.2045 0.2109 0.9837 0.9933 0.2012 0.4673 0.8807 0.7262 ] Network output: [ -0.01243 0.998 1.011 2.673e-06 -1.2e-06 0.01616 2.014e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005098 0.0006845 0.004232 0.005122 0.989 0.992 0.00519 0.878 0.9045 0.01571 ] Network output: [ 0.001463 -0.02945 1.002 -0.0001851 8.31e-05 1.024 -0.0001395 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09995 0.3137 0.1774 0.9852 0.9941 0.1915 0.4725 0.8871 0.7206 ] Network output: [ 0.009434 -0.04868 1.002 0.0001083 -4.862e-05 1.028 8.161e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09741 0.08753 0.1788 0.2174 0.9874 0.992 0.09747 0.807 0.8846 0.3137 ] Network output: [ -0.01111 0.04926 1.002 0.0001048 -4.704e-05 0.9713 7.897e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09796 0.09619 0.1724 0.2032 0.9858 0.9916 0.09797 0.7404 0.8658 0.246 ] Network output: [ 0.001523 0.9988 -0.002399 1.56e-05 -7.002e-06 1.001 1.175e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001425 Epoch 6353 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01448 0.9942 0.9847 6.581e-06 -2.954e-06 -0.00784 4.959e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002902 -0.01046 0.007858 0.9696 0.9741 0.006003 0.8469 0.8346 0.02158 ] Network output: [ 0.9968 0.02617 0.001054 -5.089e-05 2.285e-05 -0.02102 -3.835e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02356 -0.2072 0.2031 0.9837 0.9933 0.2018 0.4684 0.8804 0.7256 ] Network output: [ -0.01245 1.001 1.011 2.299e-06 -1.032e-06 0.01381 1.732e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005112 0.0006789 0.004095 0.004857 0.989 0.992 0.005204 0.878 0.9043 0.01564 ] Network output: [ -0.00271 0.03562 0.9991 -0.0001941 8.715e-05 0.9699 -0.0001463 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1001 0.3093 0.1649 0.9852 0.9941 0.192 0.4733 0.8871 0.7211 ] Network output: [ 0.0108 -0.03567 0.9993 0.0001077 -4.834e-05 1.015 8.114e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0972 0.08728 0.1758 0.2139 0.9874 0.992 0.09726 0.8061 0.8845 0.3118 ] Network output: [ -0.01028 0.04559 1.002 0.0001055 -4.737e-05 0.9735 7.951e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09774 0.09596 0.1715 0.2025 0.9858 0.9916 0.09775 0.7391 0.8658 0.2459 ] Network output: [ -0.0008136 0.9994 0.0008762 1.432e-05 -6.427e-06 1.001 1.079e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001262 Epoch 6354 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01495 0.987 0.985 7.547e-06 -3.388e-06 -0.001802 5.688e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002902 -0.01042 0.007987 0.9696 0.9741 0.005991 0.8468 0.8349 0.02161 ] Network output: [ 1 -0.02075 0.003196 -4.511e-05 2.025e-05 0.0167 -3.4e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02419 -0.2045 0.2109 0.9837 0.9933 0.2012 0.4672 0.8807 0.7262 ] Network output: [ -0.01243 0.998 1.011 2.674e-06 -1.2e-06 0.01615 2.015e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005099 0.0006836 0.004232 0.00512 0.989 0.992 0.005191 0.878 0.9045 0.01571 ] Network output: [ 0.00145 -0.0293 1.002 -0.000185 8.304e-05 1.024 -0.0001394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09992 0.3138 0.1773 0.9852 0.9941 0.1915 0.4725 0.8871 0.7205 ] Network output: [ 0.009433 -0.04867 1.002 0.0001082 -4.857e-05 1.028 8.154e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09741 0.08753 0.1788 0.2173 0.9874 0.992 0.09747 0.807 0.8846 0.3137 ] Network output: [ -0.0111 0.04926 1.002 0.0001047 -4.701e-05 0.9713 7.891e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09793 0.09616 0.1724 0.2032 0.9858 0.9916 0.09794 0.7403 0.8658 0.246 ] Network output: [ 0.001518 0.9988 -0.002394 1.558e-05 -6.994e-06 1.001 1.174e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001421 Epoch 6355 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01448 0.9942 0.9847 6.575e-06 -2.952e-06 -0.007825 4.955e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002902 -0.01045 0.007855 0.9696 0.9741 0.006003 0.8469 0.8346 0.02157 ] Network output: [ 0.9968 0.02607 0.001056 -5.085e-05 2.283e-05 -0.02095 -3.832e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02358 -0.2072 0.2031 0.9837 0.9933 0.2018 0.4683 0.8804 0.7255 ] Network output: [ -0.01245 1.001 1.011 2.301e-06 -1.033e-06 0.01382 1.734e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005113 0.000678 0.004096 0.004856 0.989 0.992 0.005205 0.878 0.9043 0.01564 ] Network output: [ -0.002704 0.03549 0.9992 -0.0001939 8.706e-05 0.97 -0.0001461 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1001 0.3093 0.1649 0.9852 0.9941 0.192 0.4733 0.8871 0.721 ] Network output: [ 0.01079 -0.03571 0.9993 0.0001076 -4.829e-05 1.015 8.107e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0972 0.08727 0.1758 0.2138 0.9874 0.992 0.09726 0.806 0.8845 0.3118 ] Network output: [ -0.01027 0.04561 1.002 0.0001054 -4.733e-05 0.9735 7.945e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09771 0.09593 0.1714 0.2025 0.9858 0.9916 0.09773 0.739 0.8657 0.2459 ] Network output: [ -0.0008092 0.9994 0.0008686 1.43e-05 -6.422e-06 1.001 1.078e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00126 Epoch 6356 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01494 0.987 0.985 7.537e-06 -3.383e-06 -0.001813 5.68e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002903 -0.01042 0.007984 0.9696 0.9741 0.005991 0.8468 0.8349 0.02161 ] Network output: [ 1 -0.02065 0.003191 -4.511e-05 2.025e-05 0.01662 -3.4e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02421 -0.2045 0.2109 0.9837 0.9933 0.2012 0.4672 0.8807 0.7261 ] Network output: [ -0.01243 0.998 1.011 2.675e-06 -1.201e-06 0.01614 2.016e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0051 0.0006827 0.004233 0.005117 0.989 0.992 0.005191 0.8779 0.9045 0.01571 ] Network output: [ 0.001438 -0.02915 1.002 -0.0001848 8.297e-05 1.023 -0.0001393 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09988 0.3138 0.1772 0.9852 0.9941 0.1915 0.4725 0.887 0.7205 ] Network output: [ 0.009431 -0.04866 1.002 0.0001081 -4.853e-05 1.028 8.147e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0974 0.08752 0.1788 0.2173 0.9874 0.992 0.09746 0.8069 0.8845 0.3137 ] Network output: [ -0.01109 0.04926 1.002 0.0001046 -4.697e-05 0.9713 7.885e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0979 0.09614 0.1723 0.2032 0.9858 0.9916 0.09792 0.7402 0.8657 0.246 ] Network output: [ 0.001514 0.9988 -0.002389 1.556e-05 -6.985e-06 1.001 1.173e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001418 Epoch 6357 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01448 0.9942 0.9847 6.57e-06 -2.949e-06 -0.007811 4.951e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002903 -0.01045 0.007853 0.9696 0.9741 0.006003 0.8469 0.8346 0.02157 ] Network output: [ 0.9968 0.02597 0.001059 -5.082e-05 2.281e-05 -0.02087 -3.83e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.0236 -0.2071 0.2031 0.9837 0.9933 0.2018 0.4683 0.8804 0.7255 ] Network output: [ -0.01244 1.001 1.011 2.304e-06 -1.034e-06 0.01382 1.736e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005114 0.0006771 0.004097 0.004854 0.989 0.992 0.005206 0.878 0.9043 0.01563 ] Network output: [ -0.002698 0.03536 0.9992 -0.0001937 8.697e-05 0.9701 -0.000146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1 0.3093 0.1649 0.9852 0.9941 0.192 0.4732 0.8871 0.721 ] Network output: [ 0.01079 -0.03576 0.9993 0.0001075 -4.825e-05 1.015 8.1e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09719 0.08727 0.1759 0.2138 0.9874 0.992 0.09725 0.806 0.8844 0.3118 ] Network output: [ -0.01027 0.04562 1.002 0.0001053 -4.729e-05 0.9735 7.939e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09769 0.09591 0.1714 0.2025 0.9858 0.9916 0.0977 0.739 0.8657 0.2459 ] Network output: [ -0.0008048 0.9994 0.0008609 1.429e-05 -6.416e-06 1.001 1.077e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001257 Epoch 6358 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01494 0.987 0.985 7.526e-06 -3.379e-06 -0.001825 5.672e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002903 -0.01041 0.007982 0.9696 0.9741 0.005991 0.8468 0.8349 0.0216 ] Network output: [ 1 -0.02056 0.003185 -4.511e-05 2.025e-05 0.01653 -3.399e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02423 -0.2044 0.2108 0.9837 0.9933 0.2012 0.4672 0.8807 0.7261 ] Network output: [ -0.01243 0.998 1.011 2.675e-06 -1.201e-06 0.01613 2.016e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0051 0.0006817 0.004233 0.005114 0.989 0.992 0.005192 0.8779 0.9045 0.0157 ] Network output: [ 0.001425 -0.029 1.002 -0.0001847 8.29e-05 1.023 -0.0001392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09984 0.3138 0.1772 0.9852 0.9941 0.1915 0.4724 0.887 0.7205 ] Network output: [ 0.009429 -0.04865 1.002 0.000108 -4.849e-05 1.028 8.14e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0974 0.08751 0.1788 0.2173 0.9874 0.992 0.09746 0.8069 0.8845 0.3137 ] Network output: [ -0.01109 0.04926 1.002 0.0001046 -4.694e-05 0.9713 7.88e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09788 0.09611 0.1723 0.2031 0.9858 0.9916 0.09789 0.7402 0.8657 0.246 ] Network output: [ 0.001509 0.9988 -0.002384 1.554e-05 -6.977e-06 1.001 1.171e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001415 Epoch 6359 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01447 0.9942 0.9847 6.564e-06 -2.947e-06 -0.007797 4.947e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002903 -0.01045 0.007851 0.9696 0.9741 0.006003 0.8469 0.8346 0.02156 ] Network output: [ 0.9968 0.02586 0.001062 -5.078e-05 2.28e-05 -0.0208 -3.827e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02363 -0.2071 0.2031 0.9837 0.9933 0.2018 0.4683 0.8804 0.7255 ] Network output: [ -0.01244 1.001 1.011 2.307e-06 -1.036e-06 0.01382 1.738e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005114 0.0006762 0.004098 0.004853 0.989 0.992 0.005206 0.878 0.9043 0.01563 ] Network output: [ -0.002693 0.03523 0.9992 -0.0001935 8.688e-05 0.9701 -0.0001458 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.1 0.3094 0.1648 0.9852 0.9941 0.192 0.4732 0.8871 0.721 ] Network output: [ 0.01078 -0.0358 0.9993 0.0001074 -4.821e-05 1.015 8.093e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09719 0.08726 0.1759 0.2138 0.9874 0.992 0.09725 0.8059 0.8844 0.3118 ] Network output: [ -0.01026 0.04564 1.002 0.0001053 -4.725e-05 0.9735 7.933e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09767 0.09588 0.1714 0.2024 0.9858 0.9916 0.09768 0.7389 0.8657 0.2459 ] Network output: [ -0.0008004 0.9994 0.0008534 1.428e-05 -6.411e-06 1.001 1.076e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001255 Epoch 6360 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01493 0.987 0.985 7.515e-06 -3.374e-06 -0.001837 5.664e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002903 -0.01041 0.007979 0.9696 0.9741 0.005992 0.8468 0.8349 0.0216 ] Network output: [ 1 -0.02046 0.00318 -4.51e-05 2.025e-05 0.01645 -3.399e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02425 -0.2044 0.2108 0.9837 0.9933 0.2012 0.4672 0.8807 0.7261 ] Network output: [ -0.01242 0.998 1.011 2.676e-06 -1.201e-06 0.01613 2.017e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005101 0.0006808 0.004233 0.005111 0.989 0.992 0.005193 0.8779 0.9045 0.0157 ] Network output: [ 0.001413 -0.02885 1.002 -0.0001845 8.283e-05 1.023 -0.000139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.0998 0.3138 0.1771 0.9852 0.9941 0.1915 0.4724 0.887 0.7205 ] Network output: [ 0.009427 -0.04864 1.002 0.0001079 -4.845e-05 1.028 8.133e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0974 0.0875 0.1788 0.2173 0.9874 0.992 0.09746 0.8068 0.8845 0.3137 ] Network output: [ -0.01108 0.04926 1.002 0.0001045 -4.69e-05 0.9713 7.874e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09785 0.09608 0.1723 0.2031 0.9858 0.9916 0.09787 0.7401 0.8657 0.246 ] Network output: [ 0.001504 0.9988 -0.002379 1.552e-05 -6.968e-06 1.001 1.17e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001411 Epoch 6361 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01447 0.9942 0.9847 6.559e-06 -2.945e-06 -0.007782 4.943e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002903 -0.01044 0.007849 0.9696 0.9741 0.006004 0.8469 0.8346 0.02156 ] Network output: [ 0.9968 0.02576 0.001065 -5.074e-05 2.278e-05 -0.02073 -3.824e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02365 -0.2071 0.2031 0.9837 0.9933 0.2018 0.4682 0.8804 0.7255 ] Network output: [ -0.01244 1.001 1.011 2.309e-06 -1.037e-06 0.01382 1.74e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005115 0.0006753 0.004099 0.004851 0.989 0.992 0.005207 0.878 0.9043 0.01563 ] Network output: [ -0.002687 0.0351 0.9993 -0.0001933 8.679e-05 0.9702 -0.0001457 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09996 0.3094 0.1648 0.9852 0.9941 0.192 0.4732 0.887 0.721 ] Network output: [ 0.01077 -0.03585 0.9992 0.0001073 -4.817e-05 1.016 8.086e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09719 0.08725 0.1759 0.2138 0.9874 0.992 0.09725 0.8059 0.8844 0.3118 ] Network output: [ -0.01026 0.04565 1.002 0.0001052 -4.722e-05 0.9734 7.926e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09764 0.09586 0.1714 0.2024 0.9858 0.9916 0.09765 0.7388 0.8656 0.2459 ] Network output: [ -0.0007961 0.9994 0.0008458 1.427e-05 -6.405e-06 1.001 1.075e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001253 Epoch 6362 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01493 0.9871 0.985 7.505e-06 -3.369e-06 -0.001849 5.656e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002903 -0.01041 0.007976 0.9696 0.9741 0.005992 0.8468 0.8349 0.02159 ] Network output: [ 1 -0.02036 0.003174 -4.51e-05 2.025e-05 0.01636 -3.399e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1805 -0.02427 -0.2044 0.2107 0.9837 0.9933 0.2012 0.4671 0.8807 0.7261 ] Network output: [ -0.01242 0.998 1.011 2.677e-06 -1.202e-06 0.01612 2.018e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005102 0.0006799 0.004234 0.005109 0.989 0.992 0.005194 0.8779 0.9045 0.01569 ] Network output: [ 0.0014 -0.02871 1.002 -0.0001844 8.276e-05 1.023 -0.0001389 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09977 0.3138 0.177 0.9852 0.9941 0.1915 0.4724 0.887 0.7205 ] Network output: [ 0.009425 -0.04862 1.002 0.0001078 -4.84e-05 1.028 8.125e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09739 0.08749 0.1788 0.2172 0.9874 0.992 0.09745 0.8068 0.8844 0.3137 ] Network output: [ -0.01107 0.04927 1.002 0.0001044 -4.687e-05 0.9713 7.868e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09783 0.09606 0.1723 0.2031 0.9858 0.9916 0.09784 0.74 0.8656 0.246 ] Network output: [ 0.0015 0.9988 -0.002375 1.55e-05 -6.96e-06 1.001 1.168e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001408 Epoch 6363 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01446 0.9942 0.9847 6.554e-06 -2.942e-06 -0.007768 4.939e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002903 -0.01044 0.007847 0.9696 0.9741 0.006004 0.8469 0.8346 0.02155 ] Network output: [ 0.9969 0.02566 0.001068 -5.071e-05 2.276e-05 -0.02065 -3.821e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02367 -0.2071 0.2031 0.9837 0.9933 0.2018 0.4682 0.8804 0.7255 ] Network output: [ -0.01244 1.001 1.011 2.312e-06 -1.038e-06 0.01383 1.742e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005116 0.0006744 0.0041 0.004849 0.989 0.992 0.005208 0.878 0.9043 0.01562 ] Network output: [ -0.002681 0.03497 0.9993 -0.0001931 8.67e-05 0.9703 -0.0001455 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09992 0.3095 0.1648 0.9852 0.9941 0.192 0.4731 0.887 0.7209 ] Network output: [ 0.01076 -0.03589 0.9992 0.0001072 -4.813e-05 1.016 8.079e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09719 0.08724 0.1759 0.2138 0.9874 0.992 0.09725 0.8058 0.8843 0.3118 ] Network output: [ -0.01025 0.04567 1.002 0.0001051 -4.718e-05 0.9734 7.92e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09762 0.09583 0.1714 0.2024 0.9858 0.9916 0.09763 0.7388 0.8656 0.2458 ] Network output: [ -0.0007917 0.9994 0.0008383 1.426e-05 -6.4e-06 1.001 1.074e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00125 Epoch 6364 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01492 0.9871 0.985 7.494e-06 -3.364e-06 -0.001861 5.648e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002904 -0.0104 0.007974 0.9697 0.9741 0.005992 0.8467 0.8349 0.02159 ] Network output: [ 1 -0.02027 0.003169 -4.509e-05 2.024e-05 0.01628 -3.398e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02429 -0.2044 0.2107 0.9837 0.9933 0.2012 0.4671 0.8807 0.726 ] Network output: [ -0.01242 0.998 1.011 2.678e-06 -1.202e-06 0.01611 2.018e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005103 0.000679 0.004234 0.005106 0.989 0.992 0.005194 0.8779 0.9044 0.01569 ] Network output: [ 0.001388 -0.02856 1.002 -0.0001842 8.269e-05 1.023 -0.0001388 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09973 0.3139 0.1769 0.9852 0.9941 0.1915 0.4723 0.887 0.7204 ] Network output: [ 0.009423 -0.04861 1.002 0.0001077 -4.836e-05 1.028 8.118e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09739 0.08748 0.1788 0.2172 0.9874 0.992 0.09745 0.8067 0.8844 0.3136 ] Network output: [ -0.01106 0.04927 1.002 0.0001043 -4.683e-05 0.9713 7.862e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0978 0.09603 0.1722 0.203 0.9858 0.9916 0.09781 0.74 0.8656 0.2459 ] Network output: [ 0.001495 0.9988 -0.00237 1.548e-05 -6.951e-06 1.001 1.167e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001405 Epoch 6365 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01446 0.9941 0.9847 6.548e-06 -2.94e-06 -0.007754 4.935e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002904 -0.01044 0.007845 0.9697 0.9741 0.006004 0.8469 0.8346 0.02155 ] Network output: [ 0.9969 0.02555 0.001071 -5.067e-05 2.275e-05 -0.02058 -3.819e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.0237 -0.207 0.203 0.9837 0.9933 0.2018 0.4682 0.8804 0.7254 ] Network output: [ -0.01244 1 1.011 2.315e-06 -1.039e-06 0.01383 1.744e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005116 0.0006735 0.004101 0.004848 0.989 0.992 0.005208 0.8779 0.9043 0.01562 ] Network output: [ -0.002676 0.03484 0.9993 -0.0001929 8.661e-05 0.9704 -0.0001454 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09988 0.3095 0.1648 0.9852 0.9941 0.192 0.4731 0.887 0.7209 ] Network output: [ 0.01075 -0.03593 0.9992 0.0001071 -4.808e-05 1.016 8.072e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09718 0.08723 0.1759 0.2138 0.9874 0.992 0.09724 0.8058 0.8843 0.3118 ] Network output: [ -0.01025 0.04568 1.002 0.000105 -4.714e-05 0.9734 7.914e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09759 0.09581 0.1713 0.2024 0.9858 0.9916 0.0976 0.7387 0.8656 0.2458 ] Network output: [ -0.0007874 0.9994 0.0008308 1.424e-05 -6.394e-06 1.001 1.073e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001248 Epoch 6366 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01491 0.9871 0.985 7.484e-06 -3.36e-06 -0.001873 5.64e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002904 -0.0104 0.007971 0.9697 0.9741 0.005992 0.8467 0.8349 0.02158 ] Network output: [ 1 -0.02017 0.003164 -4.509e-05 2.024e-05 0.0162 -3.398e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02432 -0.2044 0.2107 0.9837 0.9933 0.2012 0.4671 0.8806 0.726 ] Network output: [ -0.01242 0.998 1.011 2.679e-06 -1.203e-06 0.0161 2.019e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005103 0.0006781 0.004234 0.005103 0.989 0.992 0.005195 0.8779 0.9044 0.01569 ] Network output: [ 0.001376 -0.02841 1.002 -0.000184 8.263e-05 1.023 -0.0001387 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09969 0.3139 0.1768 0.9852 0.9941 0.1915 0.4723 0.887 0.7204 ] Network output: [ 0.009421 -0.0486 1.002 0.0001076 -4.832e-05 1.028 8.111e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09738 0.08747 0.1788 0.2172 0.9874 0.992 0.09744 0.8066 0.8844 0.3136 ] Network output: [ -0.01105 0.04927 1.002 0.0001042 -4.68e-05 0.9712 7.856e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09778 0.09601 0.1722 0.203 0.9858 0.9916 0.09779 0.7399 0.8656 0.2459 ] Network output: [ 0.001491 0.9988 -0.002365 1.546e-05 -6.943e-06 1.001 1.165e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001402 Epoch 6367 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01446 0.9941 0.9847 6.543e-06 -2.937e-06 -0.00774 4.931e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002904 -0.01043 0.007843 0.9697 0.9741 0.006004 0.8469 0.8346 0.02154 ] Network output: [ 0.9969 0.02545 0.001074 -5.063e-05 2.273e-05 -0.02051 -3.816e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02372 -0.207 0.203 0.9837 0.9933 0.2018 0.4682 0.8804 0.7254 ] Network output: [ -0.01244 1 1.011 2.317e-06 -1.04e-06 0.01383 1.746e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005117 0.0006726 0.004101 0.004846 0.989 0.992 0.005209 0.8779 0.9043 0.01562 ] Network output: [ -0.00267 0.03471 0.9993 -0.0001927 8.652e-05 0.9705 -0.0001452 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09985 0.3095 0.1647 0.9852 0.9941 0.192 0.4731 0.887 0.7209 ] Network output: [ 0.01074 -0.03598 0.9992 0.000107 -4.804e-05 1.016 8.065e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09718 0.08723 0.1759 0.2138 0.9874 0.992 0.09724 0.8057 0.8843 0.3118 ] Network output: [ -0.01025 0.04569 1.002 0.0001049 -4.71e-05 0.9734 7.908e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09757 0.09578 0.1713 0.2024 0.9858 0.9916 0.09758 0.7387 0.8655 0.2458 ] Network output: [ -0.000783 0.9994 0.0008233 1.423e-05 -6.389e-06 1.001 1.073e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001246 Epoch 6368 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01491 0.9871 0.985 7.473e-06 -3.355e-06 -0.001885 5.632e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002904 -0.0104 0.007968 0.9697 0.9741 0.005992 0.8467 0.8349 0.02158 ] Network output: [ 1 -0.02008 0.003158 -4.508e-05 2.024e-05 0.01611 -3.398e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02434 -0.2043 0.2106 0.9837 0.9933 0.2012 0.4671 0.8806 0.726 ] Network output: [ -0.01241 0.998 1.011 2.68e-06 -1.203e-06 0.01609 2.019e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005104 0.0006772 0.004234 0.005101 0.989 0.992 0.005196 0.8779 0.9044 0.01568 ] Network output: [ 0.001363 -0.02827 1.002 -0.0001839 8.256e-05 1.023 -0.0001386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09965 0.3139 0.1768 0.9852 0.9941 0.1915 0.4723 0.887 0.7204 ] Network output: [ 0.009419 -0.04859 1.002 0.0001075 -4.827e-05 1.028 8.104e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09738 0.08746 0.1788 0.2171 0.9874 0.992 0.09744 0.8066 0.8844 0.3136 ] Network output: [ -0.01104 0.04927 1.002 0.0001042 -4.676e-05 0.9712 7.85e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09775 0.09598 0.1722 0.203 0.9858 0.9916 0.09776 0.7398 0.8656 0.2459 ] Network output: [ 0.001486 0.9989 -0.00236 1.545e-05 -6.934e-06 1.001 1.164e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001399 Epoch 6369 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01445 0.9941 0.9847 6.537e-06 -2.935e-06 -0.007726 4.927e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002904 -0.01043 0.007841 0.9697 0.9741 0.006004 0.8469 0.8346 0.02154 ] Network output: [ 0.9969 0.02535 0.001076 -5.06e-05 2.271e-05 -0.02044 -3.813e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02374 -0.207 0.203 0.9837 0.9933 0.2018 0.4681 0.8803 0.7254 ] Network output: [ -0.01243 1 1.011 2.32e-06 -1.041e-06 0.01384 1.748e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005118 0.0006717 0.004102 0.004845 0.989 0.992 0.00521 0.8779 0.9042 0.01561 ] Network output: [ -0.002664 0.03458 0.9994 -0.0001925 8.643e-05 0.9706 -0.0001451 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09981 0.3096 0.1647 0.9852 0.9941 0.192 0.4731 0.887 0.7209 ] Network output: [ 0.01073 -0.03602 0.9992 0.0001069 -4.8e-05 1.016 8.058e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09718 0.08722 0.1759 0.2137 0.9874 0.992 0.09724 0.8057 0.8843 0.3118 ] Network output: [ -0.01024 0.04571 1.002 0.0001048 -4.707e-05 0.9734 7.901e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09754 0.09576 0.1713 0.2023 0.9858 0.9916 0.09756 0.7386 0.8655 0.2458 ] Network output: [ -0.0007787 0.9994 0.0008159 1.422e-05 -6.384e-06 1.001 1.072e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001244 Epoch 6370 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0149 0.9871 0.985 7.462e-06 -3.35e-06 -0.001896 5.624e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002904 -0.01039 0.007966 0.9697 0.9741 0.005993 0.8467 0.8348 0.02157 ] Network output: [ 1 -0.01999 0.003153 -4.508e-05 2.024e-05 0.01603 -3.397e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02436 -0.2043 0.2106 0.9837 0.9933 0.2012 0.467 0.8806 0.726 ] Network output: [ -0.01241 0.998 1.011 2.68e-06 -1.203e-06 0.01608 2.02e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005105 0.0006762 0.004235 0.005098 0.989 0.992 0.005197 0.8779 0.9044 0.01568 ] Network output: [ 0.001351 -0.02812 1.002 -0.0001837 8.249e-05 1.023 -0.0001385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09962 0.3139 0.1767 0.9852 0.9941 0.1915 0.4723 0.887 0.7204 ] Network output: [ 0.009417 -0.04857 1.002 0.0001074 -4.823e-05 1.028 8.097e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09738 0.08745 0.1788 0.2171 0.9874 0.992 0.09744 0.8065 0.8843 0.3136 ] Network output: [ -0.01103 0.04926 1.002 0.0001041 -4.673e-05 0.9712 7.844e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09773 0.09595 0.1722 0.203 0.9858 0.9916 0.09774 0.7398 0.8655 0.2459 ] Network output: [ 0.001481 0.9989 -0.002355 1.543e-05 -6.926e-06 1.001 1.163e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001395 Epoch 6371 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01445 0.9941 0.9847 6.532e-06 -2.932e-06 -0.007712 4.922e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002904 -0.01043 0.007839 0.9697 0.9741 0.006004 0.8469 0.8346 0.02153 ] Network output: [ 0.9969 0.02525 0.001079 -5.056e-05 2.27e-05 -0.02036 -3.81e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02377 -0.2069 0.203 0.9837 0.9933 0.2018 0.4681 0.8803 0.7254 ] Network output: [ -0.01243 1 1.011 2.322e-06 -1.043e-06 0.01384 1.75e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005118 0.0006708 0.004103 0.004843 0.989 0.992 0.00521 0.8779 0.9042 0.01561 ] Network output: [ -0.002659 0.03445 0.9994 -0.0001923 8.634e-05 0.9707 -0.0001449 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09977 0.3096 0.1647 0.9852 0.9941 0.192 0.473 0.887 0.7209 ] Network output: [ 0.01072 -0.03606 0.9992 0.0001068 -4.796e-05 1.016 8.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09718 0.08721 0.1759 0.2137 0.9874 0.992 0.09724 0.8056 0.8842 0.3118 ] Network output: [ -0.01024 0.04572 1.002 0.0001048 -4.703e-05 0.9734 7.895e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09752 0.09573 0.1713 0.2023 0.9858 0.9916 0.09753 0.7385 0.8655 0.2458 ] Network output: [ -0.0007744 0.9993 0.0008085 1.421e-05 -6.378e-06 1.001 1.071e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001241 Epoch 6372 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0149 0.9871 0.985 7.452e-06 -3.345e-06 -0.001908 5.616e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002905 -0.01039 0.007963 0.9697 0.9741 0.005993 0.8467 0.8348 0.02157 ] Network output: [ 1 -0.01989 0.003148 -4.507e-05 2.024e-05 0.01595 -3.397e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02438 -0.2043 0.2105 0.9837 0.9933 0.2012 0.467 0.8806 0.726 ] Network output: [ -0.01241 0.998 1.011 2.681e-06 -1.204e-06 0.01608 2.021e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005105 0.0006753 0.004235 0.005095 0.989 0.992 0.005197 0.8778 0.9044 0.01568 ] Network output: [ 0.001339 -0.02798 1.002 -0.0001836 8.242e-05 1.022 -0.0001384 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09958 0.3139 0.1766 0.9852 0.9941 0.1915 0.4722 0.887 0.7204 ] Network output: [ 0.009415 -0.04856 1.002 0.0001073 -4.819e-05 1.028 8.089e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09737 0.08745 0.1788 0.2171 0.9874 0.992 0.09743 0.8065 0.8843 0.3136 ] Network output: [ -0.01102 0.04926 1.002 0.000104 -4.669e-05 0.9712 7.838e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0977 0.09593 0.1721 0.2029 0.9858 0.9916 0.09771 0.7397 0.8655 0.2459 ] Network output: [ 0.001477 0.9989 -0.002351 1.541e-05 -6.917e-06 1.001 1.161e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001392 Epoch 6373 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01444 0.9941 0.9847 6.526e-06 -2.93e-06 -0.007698 4.918e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002905 -0.01043 0.007837 0.9697 0.9741 0.006004 0.8469 0.8345 0.02153 ] Network output: [ 0.9969 0.02515 0.001082 -5.052e-05 2.268e-05 -0.02029 -3.808e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02379 -0.2069 0.203 0.9837 0.9933 0.2018 0.4681 0.8803 0.7254 ] Network output: [ -0.01243 1 1.011 2.325e-06 -1.044e-06 0.01384 1.752e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005119 0.0006699 0.004104 0.004842 0.989 0.992 0.005211 0.8779 0.9042 0.01561 ] Network output: [ -0.002653 0.03432 0.9994 -0.0001921 8.625e-05 0.9708 -0.0001448 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09973 0.3097 0.1647 0.9852 0.9941 0.192 0.473 0.887 0.7208 ] Network output: [ 0.01071 -0.03611 0.9992 0.0001067 -4.792e-05 1.016 8.044e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09717 0.0872 0.1759 0.2137 0.9874 0.992 0.09723 0.8056 0.8842 0.3118 ] Network output: [ -0.01023 0.04573 1.002 0.0001047 -4.699e-05 0.9734 7.889e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09749 0.09571 0.1713 0.2023 0.9858 0.9916 0.09751 0.7385 0.8654 0.2458 ] Network output: [ -0.0007701 0.9993 0.0008011 1.42e-05 -6.373e-06 1.001 1.07e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001239 Epoch 6374 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01489 0.9872 0.985 7.441e-06 -3.341e-06 -0.00192 5.608e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002905 -0.01039 0.00796 0.9697 0.9741 0.005993 0.8467 0.8348 0.02156 ] Network output: [ 1 -0.0198 0.003143 -4.507e-05 2.023e-05 0.01587 -3.397e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.0244 -0.2043 0.2105 0.9837 0.9933 0.2012 0.467 0.8806 0.7259 ] Network output: [ -0.01241 0.9981 1.011 2.682e-06 -1.204e-06 0.01607 2.021e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005106 0.0006744 0.004235 0.005092 0.989 0.992 0.005198 0.8778 0.9044 0.01567 ] Network output: [ 0.001327 -0.02783 1.002 -0.0001834 8.235e-05 1.022 -0.0001382 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09954 0.314 0.1765 0.9852 0.9941 0.1915 0.4722 0.8869 0.7203 ] Network output: [ 0.009413 -0.04855 1.002 0.0001072 -4.815e-05 1.028 8.082e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09737 0.08744 0.1788 0.2171 0.9874 0.992 0.09743 0.8064 0.8843 0.3136 ] Network output: [ -0.01101 0.04926 1.002 0.0001039 -4.666e-05 0.9712 7.832e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09768 0.0959 0.1721 0.2029 0.9858 0.9916 0.09769 0.7396 0.8655 0.2458 ] Network output: [ 0.001472 0.9989 -0.002346 1.539e-05 -6.909e-06 1.001 1.16e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001389 Epoch 6375 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01444 0.9941 0.9847 6.521e-06 -2.927e-06 -0.007684 4.914e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002905 -0.01042 0.007835 0.9697 0.9741 0.006004 0.8469 0.8345 0.02152 ] Network output: [ 0.9969 0.02505 0.001085 -5.049e-05 2.267e-05 -0.02022 -3.805e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02382 -0.2069 0.203 0.9837 0.9933 0.2018 0.468 0.8803 0.7253 ] Network output: [ -0.01243 1 1.011 2.327e-06 -1.045e-06 0.01384 1.754e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00512 0.000669 0.004105 0.00484 0.989 0.992 0.005212 0.8779 0.9042 0.0156 ] Network output: [ -0.002648 0.03419 0.9995 -0.0001919 8.616e-05 0.9709 -0.0001446 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.0997 0.3097 0.1646 0.9852 0.9941 0.192 0.473 0.887 0.7208 ] Network output: [ 0.0107 -0.03615 0.9992 0.0001066 -4.787e-05 1.016 8.036e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09717 0.0872 0.176 0.2137 0.9874 0.992 0.09723 0.8055 0.8842 0.3118 ] Network output: [ -0.01023 0.04574 1.002 0.0001046 -4.696e-05 0.9733 7.882e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09747 0.09568 0.1712 0.2023 0.9858 0.9916 0.09748 0.7384 0.8654 0.2457 ] Network output: [ -0.0007658 0.9993 0.0007937 1.418e-05 -6.368e-06 1.001 1.069e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001237 Epoch 6376 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01488 0.9872 0.985 7.431e-06 -3.336e-06 -0.001932 5.6e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002905 -0.01039 0.007958 0.9697 0.9741 0.005993 0.8467 0.8348 0.02156 ] Network output: [ 1 -0.01971 0.003137 -4.506e-05 2.023e-05 0.01579 -3.396e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02442 -0.2043 0.2104 0.9837 0.9933 0.2013 0.467 0.8806 0.7259 ] Network output: [ -0.01241 0.9981 1.011 2.683e-06 -1.204e-06 0.01606 2.022e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005107 0.0006735 0.004236 0.00509 0.989 0.992 0.005199 0.8778 0.9044 0.01567 ] Network output: [ 0.001315 -0.02769 1.002 -0.0001833 8.228e-05 1.022 -0.0001381 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09951 0.314 0.1765 0.9852 0.9941 0.1915 0.4722 0.8869 0.7203 ] Network output: [ 0.009411 -0.04854 1.002 0.0001071 -4.81e-05 1.028 8.075e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09737 0.08743 0.1788 0.217 0.9874 0.992 0.09743 0.8064 0.8843 0.3136 ] Network output: [ -0.011 0.04926 1.002 0.0001038 -4.662e-05 0.9712 7.826e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09765 0.09588 0.1721 0.2029 0.9858 0.9916 0.09766 0.7396 0.8654 0.2458 ] Network output: [ 0.001468 0.9989 -0.002341 1.537e-05 -6.901e-06 1.001 1.158e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001386 Epoch 6377 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01444 0.9941 0.9848 6.515e-06 -2.925e-06 -0.007671 4.91e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002905 -0.01042 0.007833 0.9697 0.9741 0.006005 0.8469 0.8345 0.02152 ] Network output: [ 0.997 0.02495 0.001088 -5.045e-05 2.265e-05 -0.02015 -3.802e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02384 -0.2068 0.203 0.9837 0.9933 0.2018 0.468 0.8803 0.7253 ] Network output: [ -0.01243 1 1.011 2.33e-06 -1.046e-06 0.01384 1.756e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00512 0.0006682 0.004106 0.004838 0.989 0.992 0.005212 0.8779 0.9042 0.0156 ] Network output: [ -0.002642 0.03407 0.9995 -0.0001917 8.607e-05 0.971 -0.0001445 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09966 0.3097 0.1646 0.9852 0.9941 0.192 0.4729 0.8869 0.7208 ] Network output: [ 0.01069 -0.03619 0.9992 0.0001065 -4.783e-05 1.016 8.029e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09717 0.08719 0.176 0.2137 0.9874 0.992 0.09723 0.8054 0.8842 0.3118 ] Network output: [ -0.01022 0.04576 1.002 0.0001045 -4.692e-05 0.9733 7.876e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09745 0.09566 0.1712 0.2022 0.9858 0.9916 0.09746 0.7383 0.8654 0.2457 ] Network output: [ -0.0007615 0.9993 0.0007864 1.417e-05 -6.362e-06 1.001 1.068e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001235 Epoch 6378 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01488 0.9872 0.985 7.42e-06 -3.331e-06 -0.001944 5.592e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002905 -0.01038 0.007955 0.9697 0.9741 0.005993 0.8467 0.8348 0.02155 ] Network output: [ 1 -0.01961 0.003132 -4.506e-05 2.023e-05 0.0157 -3.396e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02444 -0.2042 0.2104 0.9837 0.9933 0.2013 0.4669 0.8806 0.7259 ] Network output: [ -0.0124 0.9981 1.011 2.684e-06 -1.205e-06 0.01605 2.022e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005108 0.0006726 0.004236 0.005087 0.989 0.992 0.0052 0.8778 0.9044 0.01567 ] Network output: [ 0.001303 -0.02755 1.002 -0.0001831 8.221e-05 1.022 -0.000138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09947 0.314 0.1764 0.9852 0.9941 0.1915 0.4722 0.8869 0.7203 ] Network output: [ 0.009409 -0.04852 1.002 0.0001071 -4.806e-05 1.028 8.068e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09736 0.08742 0.1788 0.217 0.9874 0.992 0.09742 0.8063 0.8842 0.3136 ] Network output: [ -0.01099 0.04926 1.002 0.0001038 -4.659e-05 0.9712 7.82e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09763 0.09585 0.1721 0.2029 0.9858 0.9916 0.09764 0.7395 0.8654 0.2458 ] Network output: [ 0.001463 0.9989 -0.002336 1.535e-05 -6.892e-06 1.001 1.157e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001383 Epoch 6379 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01443 0.9941 0.9848 6.509e-06 -2.922e-06 -0.007657 4.906e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002905 -0.01042 0.007831 0.9697 0.9741 0.006005 0.8468 0.8345 0.02151 ] Network output: [ 0.997 0.02485 0.001091 -5.041e-05 2.263e-05 -0.02008 -3.799e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02386 -0.2068 0.203 0.9837 0.9933 0.2018 0.468 0.8803 0.7253 ] Network output: [ -0.01242 1 1.011 2.333e-06 -1.047e-06 0.01385 1.758e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005121 0.0006673 0.004107 0.004837 0.989 0.992 0.005213 0.8779 0.9042 0.0156 ] Network output: [ -0.002636 0.03394 0.9995 -0.0001915 8.598e-05 0.971 -0.0001443 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09962 0.3098 0.1646 0.9852 0.9941 0.192 0.4729 0.8869 0.7208 ] Network output: [ 0.01068 -0.03623 0.9992 0.0001064 -4.779e-05 1.016 8.022e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09716 0.08718 0.176 0.2137 0.9874 0.992 0.09722 0.8054 0.8841 0.3118 ] Network output: [ -0.01022 0.04577 1.002 0.0001044 -4.688e-05 0.9733 7.87e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09742 0.09563 0.1712 0.2022 0.9858 0.9916 0.09743 0.7383 0.8654 0.2457 ] Network output: [ -0.0007573 0.9993 0.0007791 1.416e-05 -6.357e-06 1.001 1.067e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001232 Epoch 6380 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01487 0.9872 0.985 7.409e-06 -3.326e-06 -0.001955 5.584e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002906 -0.01038 0.007952 0.9697 0.9741 0.005994 0.8467 0.8348 0.02155 ] Network output: [ 1 -0.01952 0.003127 -4.505e-05 2.023e-05 0.01562 -3.395e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02446 -0.2042 0.2104 0.9837 0.9933 0.2013 0.4669 0.8806 0.7259 ] Network output: [ -0.0124 0.9981 1.011 2.684e-06 -1.205e-06 0.01604 2.023e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005108 0.0006717 0.004236 0.005084 0.989 0.992 0.0052 0.8778 0.9043 0.01566 ] Network output: [ 0.001291 -0.0274 1.002 -0.000183 8.214e-05 1.022 -0.0001379 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09944 0.314 0.1763 0.9852 0.9941 0.1915 0.4721 0.8869 0.7203 ] Network output: [ 0.009406 -0.04851 1.002 0.000107 -4.802e-05 1.028 8.061e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09736 0.08741 0.1788 0.217 0.9874 0.992 0.09742 0.8062 0.8842 0.3135 ] Network output: [ -0.01098 0.04926 1.002 0.0001037 -4.655e-05 0.9712 7.814e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0976 0.09582 0.172 0.2028 0.9858 0.9916 0.09761 0.7394 0.8654 0.2458 ] Network output: [ 0.001459 0.9989 -0.002331 1.533e-05 -6.884e-06 1.001 1.156e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00138 Epoch 6381 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01443 0.994 0.9848 6.504e-06 -2.92e-06 -0.007644 4.901e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002905 -0.01041 0.007828 0.9697 0.9741 0.006005 0.8468 0.8345 0.02151 ] Network output: [ 0.997 0.02475 0.001094 -5.038e-05 2.262e-05 -0.02001 -3.797e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02389 -0.2068 0.203 0.9837 0.9933 0.2018 0.468 0.8803 0.7253 ] Network output: [ -0.01242 1 1.011 2.335e-06 -1.048e-06 0.01385 1.76e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005122 0.0006664 0.004107 0.004835 0.989 0.992 0.005214 0.8778 0.9042 0.01559 ] Network output: [ -0.002631 0.03381 0.9995 -0.0001913 8.589e-05 0.9711 -0.0001442 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09958 0.3098 0.1646 0.9852 0.9941 0.192 0.4729 0.8869 0.7207 ] Network output: [ 0.01067 -0.03627 0.9992 0.0001064 -4.775e-05 1.016 8.015e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09716 0.08717 0.176 0.2136 0.9874 0.992 0.09722 0.8053 0.8841 0.3118 ] Network output: [ -0.01021 0.04578 1.002 0.0001043 -4.684e-05 0.9733 7.864e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0974 0.09561 0.1712 0.2022 0.9858 0.9916 0.09741 0.7382 0.8653 0.2457 ] Network output: [ -0.000753 0.9993 0.0007719 1.415e-05 -6.351e-06 1.001 1.066e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00123 Epoch 6382 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01487 0.9872 0.985 7.399e-06 -3.322e-06 -0.001967 5.576e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002906 -0.01038 0.00795 0.9697 0.9741 0.005994 0.8467 0.8348 0.02154 ] Network output: [ 1 -0.01943 0.003122 -4.505e-05 2.022e-05 0.01554 -3.395e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02448 -0.2042 0.2103 0.9837 0.9933 0.2013 0.4669 0.8805 0.7259 ] Network output: [ -0.0124 0.9981 1.011 2.685e-06 -1.205e-06 0.01604 2.024e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005109 0.0006708 0.004237 0.005081 0.989 0.992 0.005201 0.8778 0.9043 0.01566 ] Network output: [ 0.001279 -0.02726 1.002 -0.0001828 8.207e-05 1.022 -0.0001378 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.0994 0.3141 0.1762 0.9852 0.9941 0.1915 0.4721 0.8869 0.7203 ] Network output: [ 0.009404 -0.0485 1.002 0.0001069 -4.797e-05 1.028 8.053e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09735 0.0874 0.1788 0.2169 0.9874 0.992 0.09741 0.8062 0.8842 0.3135 ] Network output: [ -0.01097 0.04926 1.002 0.0001036 -4.651e-05 0.9712 7.808e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09757 0.0958 0.172 0.2028 0.9858 0.9916 0.09759 0.7393 0.8653 0.2458 ] Network output: [ 0.001454 0.9989 -0.002327 1.532e-05 -6.876e-06 1.001 1.154e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001377 Epoch 6383 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01442 0.994 0.9848 6.498e-06 -2.917e-06 -0.00763 4.897e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002906 -0.01041 0.007826 0.9697 0.9741 0.006005 0.8468 0.8345 0.0215 ] Network output: [ 0.997 0.02465 0.001097 -5.034e-05 2.26e-05 -0.01994 -3.794e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02391 -0.2068 0.203 0.9837 0.9933 0.2018 0.4679 0.8803 0.7253 ] Network output: [ -0.01242 1 1.011 2.337e-06 -1.049e-06 0.01385 1.762e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005122 0.0006655 0.004108 0.004834 0.989 0.992 0.005214 0.8778 0.9042 0.01559 ] Network output: [ -0.002625 0.03369 0.9996 -0.0001911 8.58e-05 0.9712 -0.000144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09955 0.3099 0.1645 0.9852 0.9941 0.192 0.4728 0.8869 0.7207 ] Network output: [ 0.01066 -0.03631 0.9991 0.0001063 -4.77e-05 1.016 8.008e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09716 0.08716 0.176 0.2136 0.9874 0.992 0.09722 0.8053 0.8841 0.3118 ] Network output: [ -0.01021 0.04579 1.002 0.0001043 -4.681e-05 0.9733 7.857e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09737 0.09558 0.1712 0.2022 0.9858 0.9916 0.09739 0.7382 0.8653 0.2457 ] Network output: [ -0.0007488 0.9993 0.0007646 1.414e-05 -6.346e-06 1.001 1.065e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001228 Epoch 6384 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01486 0.9873 0.985 7.388e-06 -3.317e-06 -0.001979 5.568e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003169 -0.002906 -0.01037 0.007947 0.9697 0.9741 0.005994 0.8467 0.8348 0.02154 ] Network output: [ 1 -0.01934 0.003117 -4.504e-05 2.022e-05 0.01546 -3.395e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.0245 -0.2042 0.2103 0.9837 0.9933 0.2013 0.4669 0.8805 0.7258 ] Network output: [ -0.0124 0.9981 1.011 2.686e-06 -1.206e-06 0.01603 2.024e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00511 0.0006699 0.004237 0.005079 0.989 0.992 0.005202 0.8778 0.9043 0.01565 ] Network output: [ 0.001267 -0.02712 1.002 -0.0001827 8.2e-05 1.022 -0.0001377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09936 0.3141 0.1762 0.9852 0.9941 0.1915 0.4721 0.8869 0.7202 ] Network output: [ 0.009402 -0.04848 1.002 0.0001068 -4.793e-05 1.028 8.046e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09735 0.08739 0.1788 0.2169 0.9874 0.992 0.09741 0.8061 0.8842 0.3135 ] Network output: [ -0.01096 0.04926 1.002 0.0001035 -4.648e-05 0.9712 7.802e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09755 0.09577 0.172 0.2028 0.9858 0.9916 0.09756 0.7393 0.8653 0.2458 ] Network output: [ 0.00145 0.9989 -0.002322 1.53e-05 -6.867e-06 1.001 1.153e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001374 Epoch 6385 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01442 0.994 0.9848 6.492e-06 -2.915e-06 -0.007617 4.893e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002906 -0.01041 0.007824 0.9697 0.9741 0.006005 0.8468 0.8345 0.0215 ] Network output: [ 0.997 0.02455 0.001099 -5.031e-05 2.258e-05 -0.01987 -3.791e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02393 -0.2067 0.203 0.9837 0.9933 0.2018 0.4679 0.8803 0.7253 ] Network output: [ -0.01242 1 1.011 2.34e-06 -1.05e-06 0.01385 1.763e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005123 0.0006646 0.004109 0.004832 0.989 0.992 0.005215 0.8778 0.9042 0.01559 ] Network output: [ -0.00262 0.03356 0.9996 -0.0001909 8.571e-05 0.9713 -0.0001439 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09951 0.3099 0.1645 0.9852 0.9941 0.192 0.4728 0.8869 0.7207 ] Network output: [ 0.01066 -0.03635 0.9991 0.0001062 -4.766e-05 1.016 8.001e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09716 0.08716 0.176 0.2136 0.9874 0.992 0.09722 0.8052 0.8841 0.3118 ] Network output: [ -0.01021 0.0458 1.002 0.0001042 -4.677e-05 0.9733 7.851e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09735 0.09556 0.1711 0.2021 0.9858 0.9916 0.09736 0.7381 0.8653 0.2457 ] Network output: [ -0.0007446 0.9993 0.0007575 1.412e-05 -6.341e-06 1.001 1.064e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001226 Epoch 6386 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01486 0.9873 0.985 7.378e-06 -3.312e-06 -0.00199 5.56e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.002906 -0.01037 0.007944 0.9697 0.9741 0.005994 0.8467 0.8348 0.02153 ] Network output: [ 1 -0.01925 0.003112 -4.504e-05 2.022e-05 0.01538 -3.394e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02453 -0.2042 0.2102 0.9837 0.9933 0.2013 0.4668 0.8805 0.7258 ] Network output: [ -0.0124 0.9981 1.011 2.687e-06 -1.206e-06 0.01602 2.025e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00511 0.000669 0.004237 0.005076 0.989 0.992 0.005202 0.8778 0.9043 0.01565 ] Network output: [ 0.001255 -0.02698 1.002 -0.0001825 8.193e-05 1.021 -0.0001375 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09933 0.3141 0.1761 0.9852 0.9941 0.1915 0.4721 0.8869 0.7202 ] Network output: [ 0.0094 -0.04847 1.002 0.0001067 -4.789e-05 1.028 8.039e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09735 0.08738 0.1788 0.2169 0.9874 0.992 0.09741 0.8061 0.8841 0.3135 ] Network output: [ -0.01096 0.04925 1.002 0.0001035 -4.644e-05 0.9711 7.797e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09752 0.09575 0.172 0.2028 0.9858 0.9916 0.09754 0.7392 0.8653 0.2457 ] Network output: [ 0.001445 0.9989 -0.002317 1.528e-05 -6.859e-06 1.001 1.151e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001371 Epoch 6387 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01442 0.994 0.9848 6.487e-06 -2.912e-06 -0.007604 4.889e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002906 -0.0104 0.007822 0.9697 0.9741 0.006005 0.8468 0.8345 0.02149 ] Network output: [ 0.997 0.02445 0.001102 -5.027e-05 2.257e-05 -0.0198 -3.788e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02396 -0.2067 0.2029 0.9837 0.9933 0.2018 0.4679 0.8803 0.7252 ] Network output: [ -0.01242 1 1.011 2.342e-06 -1.052e-06 0.01385 1.765e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005124 0.0006637 0.00411 0.00483 0.989 0.992 0.005216 0.8778 0.9041 0.01558 ] Network output: [ -0.002614 0.03344 0.9996 -0.0001907 8.562e-05 0.9714 -0.0001437 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09947 0.3099 0.1645 0.9852 0.9941 0.192 0.4728 0.8869 0.7207 ] Network output: [ 0.01065 -0.03639 0.9991 0.0001061 -4.762e-05 1.016 7.994e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09715 0.08715 0.176 0.2136 0.9874 0.992 0.09721 0.8052 0.884 0.3118 ] Network output: [ -0.0102 0.04581 1.002 0.0001041 -4.673e-05 0.9732 7.845e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09732 0.09553 0.1711 0.2021 0.9858 0.9916 0.09734 0.738 0.8652 0.2456 ] Network output: [ -0.0007404 0.9993 0.0007503 1.411e-05 -6.335e-06 1.001 1.064e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001224 Epoch 6388 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01485 0.9873 0.985 7.367e-06 -3.307e-06 -0.002002 5.552e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.002907 -0.01037 0.007942 0.9697 0.9741 0.005994 0.8467 0.8347 0.02153 ] Network output: [ 1 -0.01916 0.003107 -4.503e-05 2.022e-05 0.0153 -3.394e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02455 -0.2041 0.2102 0.9837 0.9933 0.2013 0.4668 0.8805 0.7258 ] Network output: [ -0.01239 0.9981 1.011 2.687e-06 -1.206e-06 0.01601 2.025e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005111 0.0006681 0.004237 0.005073 0.989 0.992 0.005203 0.8777 0.9043 0.01565 ] Network output: [ 0.001243 -0.02684 1.002 -0.0001824 8.186e-05 1.021 -0.0001374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09929 0.3141 0.176 0.9852 0.9941 0.1915 0.472 0.8869 0.7202 ] Network output: [ 0.009397 -0.04845 1.002 0.0001066 -4.784e-05 1.029 8.032e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09734 0.08737 0.1788 0.2168 0.9874 0.992 0.0974 0.806 0.8841 0.3135 ] Network output: [ -0.01095 0.04925 1.002 0.0001034 -4.641e-05 0.9711 7.791e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0975 0.09572 0.1719 0.2027 0.9858 0.9916 0.09751 0.7391 0.8652 0.2457 ] Network output: [ 0.001441 0.9989 -0.002312 1.526e-05 -6.851e-06 1.001 1.15e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001368 Epoch 6389 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01441 0.994 0.9848 6.481e-06 -2.91e-06 -0.00759 4.884e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002906 -0.0104 0.00782 0.9697 0.9741 0.006005 0.8468 0.8345 0.02149 ] Network output: [ 0.997 0.02435 0.001105 -5.023e-05 2.255e-05 -0.01973 -3.786e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02398 -0.2067 0.2029 0.9837 0.9933 0.2018 0.4678 0.8802 0.7252 ] Network output: [ -0.01241 1 1.011 2.345e-06 -1.053e-06 0.01386 1.767e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005124 0.0006628 0.004111 0.004829 0.989 0.992 0.005216 0.8778 0.9041 0.01558 ] Network output: [ -0.002609 0.03331 0.9997 -0.0001905 8.553e-05 0.9715 -0.0001436 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09944 0.31 0.1645 0.9852 0.9941 0.192 0.4728 0.8869 0.7207 ] Network output: [ 0.01064 -0.03643 0.9991 0.000106 -4.758e-05 1.016 7.987e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09715 0.08714 0.176 0.2136 0.9874 0.992 0.09721 0.8051 0.884 0.3118 ] Network output: [ -0.0102 0.04582 1.002 0.000104 -4.669e-05 0.9732 7.838e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0973 0.09551 0.1711 0.2021 0.9858 0.9916 0.09731 0.738 0.8652 0.2456 ] Network output: [ -0.0007362 0.9993 0.0007432 1.41e-05 -6.33e-06 1.001 1.063e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001222 Epoch 6390 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01484 0.9873 0.985 7.356e-06 -3.303e-06 -0.002014 5.544e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.002907 -0.01036 0.007939 0.9697 0.9741 0.005995 0.8467 0.8347 0.02152 ] Network output: [ 1 -0.01907 0.003102 -4.503e-05 2.021e-05 0.01522 -3.393e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02457 -0.2041 0.2102 0.9837 0.9933 0.2013 0.4668 0.8805 0.7258 ] Network output: [ -0.01239 0.9981 1.011 2.688e-06 -1.207e-06 0.016 2.026e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005112 0.0006672 0.004238 0.005071 0.989 0.992 0.005204 0.8777 0.9043 0.01564 ] Network output: [ 0.001232 -0.0267 1.002 -0.0001822 8.179e-05 1.021 -0.0001373 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09925 0.3141 0.1759 0.9852 0.9941 0.1915 0.472 0.8869 0.7202 ] Network output: [ 0.009395 -0.04844 1.002 0.0001065 -4.78e-05 1.029 8.024e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09734 0.08737 0.1788 0.2168 0.9874 0.992 0.0974 0.806 0.8841 0.3135 ] Network output: [ -0.01094 0.04925 1.002 0.0001033 -4.637e-05 0.9711 7.785e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09747 0.09569 0.1719 0.2027 0.9858 0.9916 0.09749 0.7391 0.8652 0.2457 ] Network output: [ 0.001436 0.9989 -0.002308 1.524e-05 -6.842e-06 1.001 1.149e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001365 Epoch 6391 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01441 0.994 0.9848 6.475e-06 -2.907e-06 -0.007577 4.88e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002907 -0.0104 0.007818 0.9697 0.9741 0.006006 0.8468 0.8345 0.02148 ] Network output: [ 0.997 0.02425 0.001108 -5.02e-05 2.254e-05 -0.01966 -3.783e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.024 -0.2066 0.2029 0.9837 0.9933 0.2018 0.4678 0.8802 0.7252 ] Network output: [ -0.01241 1 1.011 2.347e-06 -1.054e-06 0.01386 1.769e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005125 0.000662 0.004112 0.004827 0.989 0.992 0.005217 0.8778 0.9041 0.01558 ] Network output: [ -0.002604 0.03319 0.9997 -0.0001903 8.544e-05 0.9716 -0.0001434 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.0994 0.31 0.1644 0.9852 0.9941 0.192 0.4727 0.8869 0.7206 ] Network output: [ 0.01063 -0.03647 0.9991 0.0001059 -4.753e-05 1.017 7.98e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09715 0.08713 0.176 0.2136 0.9874 0.992 0.09721 0.8051 0.884 0.3118 ] Network output: [ -0.01019 0.04583 1.002 0.0001039 -4.666e-05 0.9732 7.832e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09728 0.09549 0.1711 0.2021 0.9858 0.9916 0.09729 0.7379 0.8652 0.2456 ] Network output: [ -0.000732 0.9993 0.0007361 1.409e-05 -6.325e-06 1.001 1.062e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00122 Epoch 6392 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01484 0.9873 0.985 7.346e-06 -3.298e-06 -0.002025 5.536e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.002907 -0.01036 0.007936 0.9697 0.9741 0.005995 0.8467 0.8347 0.02152 ] Network output: [ 1 -0.01898 0.003097 -4.502e-05 2.021e-05 0.01514 -3.393e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02459 -0.2041 0.2101 0.9837 0.9933 0.2013 0.4668 0.8805 0.7258 ] Network output: [ -0.01239 0.9981 1.011 2.689e-06 -1.207e-06 0.01599 2.026e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005113 0.0006663 0.004238 0.005068 0.989 0.992 0.005205 0.8777 0.9043 0.01564 ] Network output: [ 0.00122 -0.02656 1.002 -0.000182 8.173e-05 1.021 -0.0001372 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09922 0.3142 0.1758 0.9852 0.9941 0.1915 0.472 0.8868 0.7202 ] Network output: [ 0.009393 -0.04842 1.002 0.0001064 -4.776e-05 1.029 8.017e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09733 0.08736 0.1788 0.2168 0.9874 0.992 0.09739 0.8059 0.884 0.3135 ] Network output: [ -0.01093 0.04924 1.002 0.0001032 -4.634e-05 0.9711 7.779e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09745 0.09567 0.1719 0.2027 0.9858 0.9916 0.09746 0.739 0.8652 0.2457 ] Network output: [ 0.001432 0.9989 -0.002303 1.522e-05 -6.834e-06 1.001 1.147e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001362 Epoch 6393 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0144 0.994 0.9848 6.469e-06 -2.904e-06 -0.007564 4.876e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002907 -0.01039 0.007816 0.9697 0.9741 0.006006 0.8468 0.8345 0.02148 ] Network output: [ 0.9971 0.02415 0.001111 -5.016e-05 2.252e-05 -0.01959 -3.78e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02402 -0.2066 0.2029 0.9837 0.9933 0.2018 0.4678 0.8802 0.7252 ] Network output: [ -0.01241 1 1.011 2.35e-06 -1.055e-06 0.01386 1.771e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005125 0.0006611 0.004113 0.004826 0.989 0.992 0.005218 0.8778 0.9041 0.01557 ] Network output: [ -0.002598 0.03306 0.9997 -0.0001901 8.535e-05 0.9717 -0.0001433 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09936 0.3101 0.1644 0.9852 0.9941 0.192 0.4727 0.8869 0.7206 ] Network output: [ 0.01062 -0.03651 0.9991 0.0001058 -4.749e-05 1.017 7.973e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09714 0.08713 0.1761 0.2136 0.9874 0.992 0.0972 0.805 0.8839 0.3118 ] Network output: [ -0.01019 0.04584 1.002 0.0001038 -4.662e-05 0.9732 7.826e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09725 0.09546 0.1711 0.2021 0.9858 0.9916 0.09726 0.7378 0.8651 0.2456 ] Network output: [ -0.0007278 0.9993 0.000729 1.408e-05 -6.319e-06 1.001 1.061e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001217 Epoch 6394 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01483 0.9874 0.9851 7.335e-06 -3.293e-06 -0.002037 5.528e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.002907 -0.01036 0.007934 0.9697 0.9741 0.005995 0.8467 0.8347 0.02151 ] Network output: [ 1 -0.01889 0.003092 -4.501e-05 2.021e-05 0.01506 -3.392e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02461 -0.2041 0.2101 0.9837 0.9933 0.2013 0.4667 0.8805 0.7257 ] Network output: [ -0.01239 0.9981 1.011 2.689e-06 -1.207e-06 0.01599 2.027e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005113 0.0006654 0.004238 0.005065 0.989 0.992 0.005205 0.8777 0.9043 0.01564 ] Network output: [ 0.001209 -0.02642 1.002 -0.0001819 8.166e-05 1.021 -0.0001371 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09918 0.3142 0.1758 0.9852 0.9941 0.1915 0.4719 0.8868 0.7201 ] Network output: [ 0.00939 -0.04841 1.002 0.0001063 -4.771e-05 1.029 8.01e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09733 0.08735 0.1788 0.2168 0.9874 0.992 0.09739 0.8059 0.884 0.3135 ] Network output: [ -0.01092 0.04924 1.002 0.0001031 -4.63e-05 0.9711 7.773e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09742 0.09564 0.1719 0.2027 0.9858 0.9916 0.09744 0.7389 0.8651 0.2457 ] Network output: [ 0.001428 0.9989 -0.002298 1.52e-05 -6.826e-06 1.001 1.146e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001359 Epoch 6395 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0144 0.994 0.9848 6.464e-06 -2.902e-06 -0.007551 4.871e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002907 -0.01039 0.007814 0.9697 0.9741 0.006006 0.8468 0.8344 0.02147 ] Network output: [ 0.9971 0.02406 0.001114 -5.013e-05 2.25e-05 -0.01952 -3.778e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02405 -0.2066 0.2029 0.9837 0.9933 0.2018 0.4677 0.8802 0.7252 ] Network output: [ -0.01241 1 1.011 2.352e-06 -1.056e-06 0.01386 1.772e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005126 0.0006602 0.004113 0.004824 0.989 0.992 0.005218 0.8777 0.9041 0.01557 ] Network output: [ -0.002593 0.03294 0.9997 -0.0001899 8.526e-05 0.9717 -0.0001431 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09933 0.3101 0.1644 0.9852 0.9941 0.192 0.4727 0.8868 0.7206 ] Network output: [ 0.01061 -0.03655 0.9991 0.0001057 -4.745e-05 1.017 7.965e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09714 0.08712 0.1761 0.2135 0.9874 0.992 0.0972 0.805 0.8839 0.3118 ] Network output: [ -0.01018 0.04585 1.002 0.0001038 -4.658e-05 0.9732 7.819e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09723 0.09544 0.171 0.202 0.9858 0.9916 0.09724 0.7378 0.8651 0.2456 ] Network output: [ -0.0007237 0.9993 0.000722 1.406e-05 -6.314e-06 1.001 1.06e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001215 Epoch 6396 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01483 0.9874 0.9851 7.324e-06 -3.288e-06 -0.002049 5.52e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.002908 -0.01035 0.007931 0.9697 0.9741 0.005995 0.8466 0.8347 0.02151 ] Network output: [ 1 -0.0188 0.003087 -4.501e-05 2.021e-05 0.01498 -3.392e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02463 -0.2041 0.21 0.9837 0.9933 0.2013 0.4667 0.8805 0.7257 ] Network output: [ -0.01239 0.9981 1.011 2.69e-06 -1.208e-06 0.01598 2.027e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005114 0.0006645 0.004239 0.005062 0.989 0.992 0.005206 0.8777 0.9043 0.01563 ] Network output: [ 0.001197 -0.02629 1.002 -0.0001817 8.159e-05 1.021 -0.000137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09915 0.3142 0.1757 0.9852 0.9941 0.1915 0.4719 0.8868 0.7201 ] Network output: [ 0.009388 -0.0484 1.001 0.0001062 -4.767e-05 1.029 8.003e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09733 0.08734 0.1788 0.2167 0.9874 0.992 0.09739 0.8058 0.884 0.3134 ] Network output: [ -0.01091 0.04924 1.002 0.0001031 -4.627e-05 0.9711 7.767e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0974 0.09562 0.1718 0.2026 0.9858 0.9916 0.09741 0.7389 0.8651 0.2457 ] Network output: [ 0.001423 0.9989 -0.002293 1.519e-05 -6.818e-06 1.001 1.145e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001356 Epoch 6397 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0144 0.994 0.9848 6.458e-06 -2.899e-06 -0.007538 4.867e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002907 -0.01039 0.007812 0.9697 0.9741 0.006006 0.8468 0.8344 0.02147 ] Network output: [ 0.9971 0.02396 0.001117 -5.009e-05 2.249e-05 -0.01945 -3.775e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02407 -0.2065 0.2029 0.9837 0.9933 0.2018 0.4677 0.8802 0.7252 ] Network output: [ -0.01241 1 1.011 2.354e-06 -1.057e-06 0.01386 1.774e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005127 0.0006593 0.004114 0.004822 0.989 0.992 0.005219 0.8777 0.9041 0.01556 ] Network output: [ -0.002587 0.03282 0.9998 -0.0001897 8.517e-05 0.9718 -0.000143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09929 0.3101 0.1644 0.9852 0.9941 0.192 0.4726 0.8868 0.7206 ] Network output: [ 0.0106 -0.03659 0.9991 0.0001056 -4.741e-05 1.017 7.958e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09714 0.08711 0.1761 0.2135 0.9874 0.992 0.0972 0.8049 0.8839 0.3118 ] Network output: [ -0.01018 0.04586 1.002 0.0001037 -4.654e-05 0.9732 7.813e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0972 0.09541 0.171 0.202 0.9858 0.9916 0.09722 0.7377 0.8651 0.2456 ] Network output: [ -0.0007195 0.9993 0.000715 1.405e-05 -6.309e-06 1.001 1.059e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001213 Epoch 6398 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01482 0.9874 0.9851 7.314e-06 -3.283e-06 -0.00206 5.512e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.002908 -0.01035 0.007928 0.9697 0.9741 0.005995 0.8466 0.8347 0.0215 ] Network output: [ 1 -0.01871 0.003082 -4.5e-05 2.02e-05 0.01491 -3.391e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02465 -0.204 0.21 0.9837 0.9933 0.2013 0.4667 0.8805 0.7257 ] Network output: [ -0.01238 0.9981 1.011 2.691e-06 -1.208e-06 0.01597 2.028e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005115 0.0006636 0.004239 0.00506 0.989 0.992 0.005207 0.8777 0.9042 0.01563 ] Network output: [ 0.001186 -0.02615 1.002 -0.0001816 8.152e-05 1.021 -0.0001368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09911 0.3142 0.1756 0.9852 0.9941 0.1915 0.4719 0.8868 0.7201 ] Network output: [ 0.009386 -0.04838 1.001 0.0001061 -4.763e-05 1.029 7.995e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09732 0.08733 0.1788 0.2167 0.9874 0.992 0.09738 0.8057 0.884 0.3134 ] Network output: [ -0.0109 0.04923 1.002 0.000103 -4.623e-05 0.9711 7.761e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09737 0.09559 0.1718 0.2026 0.9858 0.9916 0.09739 0.7388 0.8651 0.2456 ] Network output: [ 0.001419 0.9989 -0.002289 1.517e-05 -6.81e-06 1.001 1.143e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001353 Epoch 6399 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01439 0.9939 0.9848 6.452e-06 -2.896e-06 -0.007526 4.862e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002908 -0.01038 0.007809 0.9697 0.9741 0.006006 0.8468 0.8344 0.02146 ] Network output: [ 0.9971 0.02386 0.00112 -5.005e-05 2.247e-05 -0.01938 -3.772e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02409 -0.2065 0.2029 0.9837 0.9933 0.2018 0.4677 0.8802 0.7251 ] Network output: [ -0.0124 1 1.011 2.356e-06 -1.058e-06 0.01387 1.776e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005127 0.0006585 0.004115 0.004821 0.989 0.992 0.00522 0.8777 0.9041 0.01556 ] Network output: [ -0.002582 0.03269 0.9998 -0.0001895 8.508e-05 0.9719 -0.0001428 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09925 0.3102 0.1643 0.9852 0.9941 0.192 0.4726 0.8868 0.7206 ] Network output: [ 0.01059 -0.03662 0.9991 0.0001055 -4.736e-05 1.017 7.951e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09713 0.0871 0.1761 0.2135 0.9874 0.992 0.09719 0.8049 0.8839 0.3117 ] Network output: [ -0.01017 0.04587 1.002 0.0001036 -4.65e-05 0.9732 7.807e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09718 0.09539 0.171 0.202 0.9858 0.9916 0.09719 0.7377 0.865 0.2455 ] Network output: [ -0.0007154 0.9993 0.0007081 1.404e-05 -6.303e-06 1.001 1.058e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001211 Epoch 6400 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01481 0.9874 0.9851 7.303e-06 -3.279e-06 -0.002072 5.504e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.002908 -0.01035 0.007926 0.9697 0.9741 0.005996 0.8466 0.8347 0.0215 ] Network output: [ 1 -0.01862 0.003077 -4.499e-05 2.02e-05 0.01483 -3.391e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02467 -0.204 0.2099 0.9837 0.9933 0.2013 0.4667 0.8804 0.7257 ] Network output: [ -0.01238 0.9981 1.011 2.691e-06 -1.208e-06 0.01596 2.028e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005116 0.0006627 0.004239 0.005057 0.989 0.992 0.005208 0.8777 0.9042 0.01562 ] Network output: [ 0.001174 -0.02601 1.002 -0.0001814 8.144e-05 1.021 -0.0001367 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09908 0.3143 0.1755 0.9852 0.9941 0.1915 0.4719 0.8868 0.7201 ] Network output: [ 0.009383 -0.04837 1.001 0.000106 -4.758e-05 1.029 7.988e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09732 0.08732 0.1788 0.2167 0.9874 0.992 0.09738 0.8057 0.8839 0.3134 ] Network output: [ -0.01089 0.04923 1.002 0.0001029 -4.619e-05 0.9711 7.755e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09735 0.09557 0.1718 0.2026 0.9858 0.9916 0.09736 0.7387 0.865 0.2456 ] Network output: [ 0.001414 0.9989 -0.002284 1.515e-05 -6.801e-06 1.001 1.142e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00135 Epoch 6401 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01439 0.9939 0.9848 6.446e-06 -2.894e-06 -0.007513 4.858e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002908 -0.01038 0.007807 0.9697 0.9741 0.006006 0.8468 0.8344 0.02146 ] Network output: [ 0.9971 0.02377 0.001123 -5.002e-05 2.245e-05 -0.01931 -3.769e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02412 -0.2065 0.2029 0.9837 0.9933 0.2018 0.4677 0.8802 0.7251 ] Network output: [ -0.0124 1 1.011 2.359e-06 -1.059e-06 0.01387 1.778e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005128 0.0006576 0.004116 0.004819 0.989 0.992 0.00522 0.8777 0.9041 0.01556 ] Network output: [ -0.002577 0.03257 0.9998 -0.0001893 8.499e-05 0.972 -0.0001427 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09922 0.3102 0.1643 0.9852 0.9941 0.192 0.4726 0.8868 0.7205 ] Network output: [ 0.01058 -0.03666 0.9991 0.0001054 -4.732e-05 1.017 7.944e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09713 0.08709 0.1761 0.2135 0.9874 0.992 0.09719 0.8048 0.8838 0.3117 ] Network output: [ -0.01017 0.04588 1.002 0.0001035 -4.647e-05 0.9732 7.8e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09715 0.09536 0.171 0.202 0.9858 0.9916 0.09717 0.7376 0.865 0.2455 ] Network output: [ -0.0007113 0.9993 0.0007012 1.403e-05 -6.298e-06 1.001 1.057e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001209 Epoch 6402 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01481 0.9874 0.9851 7.293e-06 -3.274e-06 -0.002084 5.496e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.002908 -0.01034 0.007923 0.9697 0.9741 0.005996 0.8466 0.8347 0.02149 ] Network output: [ 1 -0.01853 0.003072 -4.499e-05 2.02e-05 0.01475 -3.39e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02469 -0.204 0.2099 0.9837 0.9933 0.2013 0.4666 0.8804 0.7257 ] Network output: [ -0.01238 0.9981 1.011 2.692e-06 -1.208e-06 0.01595 2.029e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005116 0.0006618 0.00424 0.005054 0.989 0.992 0.005208 0.8776 0.9042 0.01562 ] Network output: [ 0.001163 -0.02588 1.002 -0.0001813 8.137e-05 1.02 -0.0001366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09904 0.3143 0.1755 0.9852 0.9941 0.1915 0.4718 0.8868 0.7201 ] Network output: [ 0.009381 -0.04835 1.001 0.0001059 -4.754e-05 1.029 7.981e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09732 0.08731 0.1788 0.2166 0.9874 0.992 0.09738 0.8056 0.8839 0.3134 ] Network output: [ -0.01088 0.04923 1.002 0.0001028 -4.616e-05 0.9711 7.749e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09732 0.09554 0.1718 0.2026 0.9858 0.9916 0.09734 0.7387 0.865 0.2456 ] Network output: [ 0.00141 0.9989 -0.002279 1.513e-05 -6.793e-06 1.001 1.14e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001347 Epoch 6403 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01438 0.9939 0.9848 6.44e-06 -2.891e-06 -0.0075 4.853e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002908 -0.01038 0.007805 0.9697 0.9741 0.006006 0.8468 0.8344 0.02146 ] Network output: [ 0.9971 0.02367 0.001125 -4.998e-05 2.244e-05 -0.01924 -3.767e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02414 -0.2065 0.2029 0.9837 0.9933 0.2018 0.4676 0.8802 0.7251 ] Network output: [ -0.0124 1 1.011 2.361e-06 -1.06e-06 0.01387 1.779e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005129 0.0006567 0.004117 0.004817 0.989 0.992 0.005221 0.8777 0.9041 0.01555 ] Network output: [ -0.002571 0.03245 0.9998 -0.0001891 8.49e-05 0.9721 -0.0001425 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09918 0.3103 0.1643 0.9852 0.9941 0.192 0.4725 0.8868 0.7205 ] Network output: [ 0.01057 -0.0367 0.999 0.0001053 -4.728e-05 1.017 7.937e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09713 0.08709 0.1761 0.2135 0.9874 0.992 0.09719 0.8048 0.8838 0.3117 ] Network output: [ -0.01016 0.04589 1.002 0.0001034 -4.643e-05 0.9731 7.794e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09713 0.09534 0.171 0.2019 0.9858 0.9916 0.09714 0.7375 0.865 0.2455 ] Network output: [ -0.0007072 0.9993 0.0006943 1.402e-05 -6.292e-06 1.001 1.056e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001207 Epoch 6404 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0148 0.9874 0.9851 7.282e-06 -3.269e-06 -0.002095 5.488e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.002909 -0.01034 0.00792 0.9697 0.9741 0.005996 0.8466 0.8347 0.02149 ] Network output: [ 1 -0.01845 0.003067 -4.498e-05 2.019e-05 0.01467 -3.39e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02471 -0.204 0.2099 0.9837 0.9933 0.2013 0.4666 0.8804 0.7256 ] Network output: [ -0.01238 0.9981 1.011 2.692e-06 -1.209e-06 0.01594 2.029e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005117 0.000661 0.00424 0.005052 0.989 0.992 0.005209 0.8776 0.9042 0.01562 ] Network output: [ 0.001152 -0.02574 1.002 -0.0001811 8.13e-05 1.02 -0.0001365 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09901 0.3143 0.1754 0.9852 0.9941 0.1915 0.4718 0.8868 0.7201 ] Network output: [ 0.009378 -0.04834 1.001 0.0001058 -4.75e-05 1.029 7.974e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09731 0.0873 0.1787 0.2166 0.9874 0.992 0.09737 0.8056 0.8839 0.3134 ] Network output: [ -0.01087 0.04922 1.002 0.0001027 -4.612e-05 0.9711 7.743e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0973 0.09551 0.1717 0.2025 0.9858 0.9916 0.09731 0.7386 0.865 0.2456 ] Network output: [ 0.001406 0.9989 -0.002274 1.511e-05 -6.785e-06 1.001 1.139e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001344 Epoch 6405 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01438 0.9939 0.9848 6.434e-06 -2.889e-06 -0.007488 4.849e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002908 -0.01037 0.007803 0.9697 0.9741 0.006007 0.8468 0.8344 0.02145 ] Network output: [ 0.9971 0.02358 0.001128 -4.995e-05 2.242e-05 -0.01918 -3.764e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02416 -0.2064 0.2029 0.9837 0.9933 0.2018 0.4676 0.8802 0.7251 ] Network output: [ -0.0124 1 1.011 2.363e-06 -1.061e-06 0.01387 1.781e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005129 0.0006559 0.004118 0.004816 0.989 0.992 0.005222 0.8777 0.904 0.01555 ] Network output: [ -0.002566 0.03233 0.9999 -0.0001889 8.481e-05 0.9722 -0.0001424 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09915 0.3103 0.1643 0.9852 0.9941 0.192 0.4725 0.8868 0.7205 ] Network output: [ 0.01056 -0.03674 0.999 0.0001052 -4.724e-05 1.017 7.93e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09713 0.08708 0.1761 0.2135 0.9874 0.992 0.09719 0.8047 0.8838 0.3117 ] Network output: [ -0.01016 0.04589 1.002 0.0001033 -4.639e-05 0.9731 7.788e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09711 0.09531 0.1709 0.2019 0.9858 0.9916 0.09712 0.7375 0.8649 0.2455 ] Network output: [ -0.0007031 0.9993 0.0006874 1.4e-05 -6.287e-06 1.001 1.055e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001205 Epoch 6406 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0148 0.9875 0.9851 7.271e-06 -3.264e-06 -0.002107 5.48e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.002909 -0.01034 0.007918 0.9697 0.9741 0.005996 0.8466 0.8347 0.02148 ] Network output: [ 1 -0.01836 0.003062 -4.497e-05 2.019e-05 0.0146 -3.389e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02473 -0.2039 0.2098 0.9837 0.9933 0.2013 0.4666 0.8804 0.7256 ] Network output: [ -0.01238 0.9982 1.011 2.693e-06 -1.209e-06 0.01594 2.03e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005118 0.0006601 0.00424 0.005049 0.989 0.992 0.00521 0.8776 0.9042 0.01561 ] Network output: [ 0.00114 -0.02561 1.002 -0.0001809 8.123e-05 1.02 -0.0001364 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09897 0.3143 0.1753 0.9852 0.9941 0.1915 0.4718 0.8868 0.72 ] Network output: [ 0.009376 -0.04832 1.001 0.0001057 -4.745e-05 1.029 7.966e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09731 0.08729 0.1787 0.2166 0.9874 0.992 0.09737 0.8055 0.8839 0.3134 ] Network output: [ -0.01086 0.04922 1.002 0.0001027 -4.609e-05 0.9711 7.736e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09727 0.09549 0.1717 0.2025 0.9858 0.9916 0.09729 0.7385 0.8649 0.2456 ] Network output: [ 0.001401 0.9989 -0.00227 1.51e-05 -6.777e-06 1.001 1.138e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001342 Epoch 6407 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01438 0.9939 0.9849 6.428e-06 -2.886e-06 -0.007476 4.844e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002909 -0.01037 0.007801 0.9697 0.9741 0.006007 0.8467 0.8344 0.02145 ] Network output: [ 0.9971 0.02348 0.001131 -4.991e-05 2.241e-05 -0.01911 -3.761e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02419 -0.2064 0.2029 0.9837 0.9933 0.2018 0.4676 0.8801 0.7251 ] Network output: [ -0.0124 1 1.011 2.365e-06 -1.062e-06 0.01387 1.783e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00513 0.000655 0.004118 0.004814 0.989 0.992 0.005222 0.8777 0.904 0.01555 ] Network output: [ -0.002561 0.03221 0.9999 -0.0001887 8.472e-05 0.9723 -0.0001422 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09911 0.3103 0.1642 0.9852 0.9941 0.192 0.4725 0.8868 0.7205 ] Network output: [ 0.01055 -0.03677 0.999 0.0001051 -4.719e-05 1.017 7.923e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09712 0.08707 0.1761 0.2134 0.9874 0.992 0.09718 0.8046 0.8838 0.3117 ] Network output: [ -0.01015 0.0459 1.002 0.0001033 -4.635e-05 0.9731 7.781e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09708 0.09529 0.1709 0.2019 0.9858 0.9916 0.09709 0.7374 0.8649 0.2455 ] Network output: [ -0.0006991 0.9993 0.0006806 1.399e-05 -6.282e-06 1.001 1.055e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001203 Epoch 6408 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01479 0.9875 0.9851 7.261e-06 -3.26e-06 -0.002118 5.472e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.002909 -0.01033 0.007915 0.9697 0.9741 0.005996 0.8466 0.8346 0.02148 ] Network output: [ 1 -0.01827 0.003058 -4.497e-05 2.019e-05 0.01452 -3.389e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02475 -0.2039 0.2098 0.9837 0.9933 0.2013 0.4666 0.8804 0.7256 ] Network output: [ -0.01237 0.9982 1.011 2.694e-06 -1.209e-06 0.01593 2.03e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005118 0.0006592 0.00424 0.005046 0.989 0.992 0.005211 0.8776 0.9042 0.01561 ] Network output: [ 0.001129 -0.02547 1.002 -0.0001808 8.116e-05 1.02 -0.0001362 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09894 0.3144 0.1752 0.9852 0.9941 0.1915 0.4718 0.8868 0.72 ] Network output: [ 0.009373 -0.04831 1.001 0.0001056 -4.741e-05 1.029 7.959e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08729 0.1787 0.2165 0.9874 0.992 0.09736 0.8055 0.8838 0.3134 ] Network output: [ -0.01085 0.04921 1.002 0.0001026 -4.605e-05 0.9711 7.73e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09725 0.09546 0.1717 0.2025 0.9858 0.9916 0.09726 0.7385 0.8649 0.2456 ] Network output: [ 0.001397 0.9989 -0.002265 1.508e-05 -6.769e-06 1.001 1.136e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001339 Epoch 6409 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01437 0.9939 0.9849 6.422e-06 -2.883e-06 -0.007463 4.84e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002909 -0.01037 0.007799 0.9697 0.9741 0.006007 0.8467 0.8344 0.02144 ] Network output: [ 0.9972 0.02339 0.001134 -4.987e-05 2.239e-05 -0.01904 -3.759e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02421 -0.2064 0.2028 0.9837 0.9933 0.2018 0.4675 0.8801 0.7251 ] Network output: [ -0.0124 1 1.011 2.368e-06 -1.063e-06 0.01387 1.784e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005131 0.0006541 0.004119 0.004813 0.989 0.992 0.005223 0.8777 0.904 0.01554 ] Network output: [ -0.002555 0.03209 0.9999 -0.0001885 8.463e-05 0.9723 -0.0001421 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09907 0.3104 0.1642 0.9852 0.9941 0.192 0.4725 0.8868 0.7205 ] Network output: [ 0.01055 -0.03681 0.999 0.000105 -4.715e-05 1.017 7.915e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09712 0.08706 0.1761 0.2134 0.9874 0.992 0.09718 0.8046 0.8837 0.3117 ] Network output: [ -0.01015 0.04591 1.002 0.0001032 -4.632e-05 0.9731 7.775e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09706 0.09526 0.1709 0.2019 0.9858 0.9916 0.09707 0.7373 0.8649 0.2455 ] Network output: [ -0.000695 0.9993 0.0006738 1.398e-05 -6.276e-06 1.001 1.054e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001201 Epoch 6410 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01478 0.9875 0.9851 7.25e-06 -3.255e-06 -0.00213 5.464e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.002909 -0.01033 0.007912 0.9697 0.9741 0.005997 0.8466 0.8346 0.02147 ] Network output: [ 1 -0.01818 0.003053 -4.496e-05 2.018e-05 0.01444 -3.388e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02477 -0.2039 0.2097 0.9837 0.9933 0.2013 0.4665 0.8804 0.7256 ] Network output: [ -0.01237 0.9982 1.011 2.694e-06 -1.209e-06 0.01592 2.03e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005119 0.0006583 0.004241 0.005043 0.989 0.992 0.005211 0.8776 0.9042 0.01561 ] Network output: [ 0.001118 -0.02534 1.002 -0.0001806 8.109e-05 1.02 -0.0001361 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.0989 0.3144 0.1752 0.9852 0.9941 0.1915 0.4717 0.8867 0.72 ] Network output: [ 0.009371 -0.04829 1.001 0.0001055 -4.737e-05 1.029 7.952e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08728 0.1787 0.2165 0.9874 0.992 0.09736 0.8054 0.8838 0.3134 ] Network output: [ -0.01084 0.04921 1.002 0.0001025 -4.601e-05 0.9711 7.724e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09722 0.09544 0.1717 0.2025 0.9858 0.9916 0.09724 0.7384 0.8649 0.2455 ] Network output: [ 0.001393 0.999 -0.00226 1.506e-05 -6.761e-06 1.001 1.135e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001336 Epoch 6411 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01437 0.9939 0.9849 6.416e-06 -2.88e-06 -0.007451 4.835e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002909 -0.01036 0.007797 0.9697 0.9741 0.006007 0.8467 0.8344 0.02144 ] Network output: [ 0.9972 0.02329 0.001137 -4.984e-05 2.237e-05 -0.01898 -3.756e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02423 -0.2063 0.2028 0.9837 0.9933 0.2018 0.4675 0.8801 0.725 ] Network output: [ -0.01239 1 1.011 2.37e-06 -1.064e-06 0.01387 1.786e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005131 0.0006533 0.00412 0.004811 0.989 0.992 0.005224 0.8776 0.904 0.01554 ] Network output: [ -0.00255 0.03197 0.9999 -0.0001883 8.454e-05 0.9724 -0.0001419 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09904 0.3104 0.1642 0.9852 0.9941 0.192 0.4724 0.8868 0.7204 ] Network output: [ 0.01054 -0.03684 0.999 0.0001049 -4.711e-05 1.017 7.908e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09712 0.08705 0.1761 0.2134 0.9874 0.992 0.09718 0.8045 0.8837 0.3117 ] Network output: [ -0.01014 0.04592 1.002 0.0001031 -4.628e-05 0.9731 7.769e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09703 0.09524 0.1709 0.2018 0.9858 0.9916 0.09705 0.7373 0.8648 0.2454 ] Network output: [ -0.000691 0.9993 0.0006671 1.397e-05 -6.271e-06 1.001 1.053e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001199 Epoch 6412 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01478 0.9875 0.9851 7.24e-06 -3.25e-06 -0.002141 5.456e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.00291 -0.01033 0.00791 0.9697 0.9741 0.005997 0.8466 0.8346 0.02147 ] Network output: [ 1 -0.0181 0.003048 -4.495e-05 2.018e-05 0.01437 -3.388e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02479 -0.2039 0.2097 0.9837 0.9933 0.2013 0.4665 0.8804 0.7256 ] Network output: [ -0.01237 0.9982 1.011 2.695e-06 -1.21e-06 0.01591 2.031e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00512 0.0006574 0.004241 0.005041 0.989 0.992 0.005212 0.8776 0.9042 0.0156 ] Network output: [ 0.001107 -0.0252 1.002 -0.0001805 8.102e-05 1.02 -0.000136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09887 0.3144 0.1751 0.9852 0.9941 0.1915 0.4717 0.8867 0.72 ] Network output: [ 0.009368 -0.04827 1.001 0.0001054 -4.732e-05 1.029 7.944e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08727 0.1787 0.2165 0.9874 0.992 0.09736 0.8053 0.8838 0.3133 ] Network output: [ -0.01083 0.0492 1.002 0.0001024 -4.598e-05 0.9711 7.718e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0972 0.09541 0.1716 0.2024 0.9858 0.9916 0.09721 0.7383 0.8648 0.2455 ] Network output: [ 0.001388 0.999 -0.002256 1.504e-05 -6.753e-06 1.001 1.134e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001333 Epoch 6413 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01436 0.9939 0.9849 6.41e-06 -2.878e-06 -0.007439 4.831e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002909 -0.01036 0.007794 0.9697 0.9741 0.006007 0.8467 0.8344 0.02143 ] Network output: [ 0.9972 0.0232 0.00114 -4.98e-05 2.236e-05 -0.01891 -3.753e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02425 -0.2063 0.2028 0.9837 0.9933 0.2018 0.4675 0.8801 0.725 ] Network output: [ -0.01239 1 1.011 2.372e-06 -1.065e-06 0.01387 1.788e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005132 0.0006524 0.004121 0.004809 0.989 0.992 0.005224 0.8776 0.904 0.01554 ] Network output: [ -0.002545 0.03185 1 -0.0001881 8.445e-05 0.9725 -0.0001418 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.099 0.3105 0.1642 0.9852 0.9941 0.192 0.4724 0.8867 0.7204 ] Network output: [ 0.01053 -0.03688 0.999 0.0001048 -4.707e-05 1.017 7.901e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09711 0.08705 0.1762 0.2134 0.9874 0.992 0.09717 0.8045 0.8837 0.3117 ] Network output: [ -0.01014 0.04593 1.002 0.000103 -4.624e-05 0.9731 7.762e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09701 0.09521 0.1709 0.2018 0.9858 0.9916 0.09702 0.7372 0.8648 0.2454 ] Network output: [ -0.0006869 0.9993 0.0006603 1.396e-05 -6.266e-06 1.001 1.052e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001197 Epoch 6414 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01477 0.9875 0.9851 7.229e-06 -3.245e-06 -0.002153 5.448e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.00291 -0.01032 0.007907 0.9697 0.9741 0.005997 0.8466 0.8346 0.02146 ] Network output: [ 1 -0.01801 0.003043 -4.495e-05 2.018e-05 0.01429 -3.387e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02481 -0.2039 0.2097 0.9837 0.9933 0.2013 0.4665 0.8804 0.7255 ] Network output: [ -0.01237 0.9982 1.011 2.695e-06 -1.21e-06 0.0159 2.031e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005121 0.0006566 0.004241 0.005038 0.989 0.992 0.005213 0.8776 0.9041 0.0156 ] Network output: [ 0.001096 -0.02507 1.002 -0.0001803 8.095e-05 1.02 -0.0001359 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1909 0.09883 0.3144 0.175 0.9852 0.9941 0.1915 0.4717 0.8867 0.72 ] Network output: [ 0.009365 -0.04826 1.001 0.0001053 -4.728e-05 1.029 7.937e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.08726 0.1787 0.2165 0.9874 0.992 0.09735 0.8053 0.8838 0.3133 ] Network output: [ -0.01083 0.0492 1.002 0.0001023 -4.594e-05 0.971 7.712e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09717 0.09539 0.1716 0.2024 0.9858 0.9916 0.09719 0.7383 0.8648 0.2455 ] Network output: [ 0.001384 0.999 -0.002251 1.502e-05 -6.745e-06 1.001 1.132e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00133 Epoch 6415 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01436 0.9939 0.9849 6.404e-06 -2.875e-06 -0.007427 4.826e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.00291 -0.01036 0.007792 0.9697 0.9741 0.006007 0.8467 0.8344 0.02143 ] Network output: [ 0.9972 0.02311 0.001143 -4.977e-05 2.234e-05 -0.01884 -3.751e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02428 -0.2063 0.2028 0.9837 0.9933 0.2019 0.4674 0.8801 0.725 ] Network output: [ -0.01239 1 1.011 2.374e-06 -1.066e-06 0.01388 1.789e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005133 0.0006516 0.004122 0.004808 0.989 0.992 0.005225 0.8776 0.904 0.01553 ] Network output: [ -0.002539 0.03173 1 -0.0001879 8.436e-05 0.9726 -0.0001416 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09897 0.3105 0.1641 0.9852 0.9941 0.192 0.4724 0.8867 0.7204 ] Network output: [ 0.01052 -0.03692 0.999 0.0001047 -4.702e-05 1.017 7.894e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09711 0.08704 0.1762 0.2134 0.9874 0.992 0.09717 0.8044 0.8837 0.3117 ] Network output: [ -0.01013 0.04593 1.002 0.0001029 -4.62e-05 0.9731 7.756e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09699 0.09519 0.1708 0.2018 0.9858 0.9916 0.097 0.7371 0.8648 0.2454 ] Network output: [ -0.0006829 0.9993 0.0006537 1.394e-05 -6.26e-06 1.001 1.051e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001195 Epoch 6416 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01477 0.9876 0.9851 7.218e-06 -3.241e-06 -0.002164 5.44e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00317 -0.00291 -0.01032 0.007904 0.9697 0.9741 0.005997 0.8466 0.8346 0.02146 ] Network output: [ 1 -0.01793 0.003039 -4.494e-05 2.017e-05 0.01422 -3.387e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02483 -0.2038 0.2096 0.9837 0.9933 0.2013 0.4665 0.8803 0.7255 ] Network output: [ -0.01237 0.9982 1.011 2.696e-06 -1.21e-06 0.01589 2.032e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005121 0.0006557 0.004242 0.005035 0.989 0.992 0.005214 0.8776 0.9041 0.01559 ] Network output: [ 0.001085 -0.02494 1.002 -0.0001802 8.088e-05 1.02 -0.0001358 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.0988 0.3144 0.1749 0.9852 0.9941 0.1915 0.4717 0.8867 0.7199 ] Network output: [ 0.009363 -0.04824 1.001 0.0001052 -4.724e-05 1.029 7.93e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.08725 0.1787 0.2164 0.9874 0.992 0.09735 0.8052 0.8837 0.3133 ] Network output: [ -0.01082 0.04919 1.002 0.0001023 -4.591e-05 0.971 7.706e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09715 0.09536 0.1716 0.2024 0.9858 0.9916 0.09716 0.7382 0.8648 0.2455 ] Network output: [ 0.00138 0.999 -0.002246 1.501e-05 -6.737e-06 1.001 1.131e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001328 Epoch 6417 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01435 0.9938 0.9849 6.398e-06 -2.872e-06 -0.007415 4.822e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.00291 -0.01035 0.00779 0.9697 0.9741 0.006008 0.8467 0.8343 0.02142 ] Network output: [ 0.9972 0.02301 0.001145 -4.973e-05 2.233e-05 -0.01878 -3.748e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.0243 -0.2062 0.2028 0.9837 0.9933 0.2019 0.4674 0.8801 0.725 ] Network output: [ -0.01239 1 1.011 2.376e-06 -1.067e-06 0.01388 1.791e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005133 0.0006507 0.004122 0.004806 0.989 0.992 0.005226 0.8776 0.904 0.01553 ] Network output: [ -0.002534 0.03161 1 -0.0001877 8.427e-05 0.9727 -0.0001415 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09893 0.3105 0.1641 0.9852 0.9941 0.192 0.4723 0.8867 0.7204 ] Network output: [ 0.01051 -0.03695 0.999 0.0001046 -4.698e-05 1.017 7.887e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09711 0.08703 0.1762 0.2134 0.9874 0.992 0.09717 0.8044 0.8836 0.3117 ] Network output: [ -0.01013 0.04594 1.002 0.0001028 -4.616e-05 0.9731 7.75e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09696 0.09516 0.1708 0.2018 0.9858 0.9916 0.09697 0.7371 0.8647 0.2454 ] Network output: [ -0.0006789 0.9993 0.000647 1.393e-05 -6.255e-06 1.001 1.05e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001193 Epoch 6418 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01476 0.9876 0.9851 7.208e-06 -3.236e-06 -0.002176 5.432e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.00291 -0.01032 0.007902 0.9697 0.9741 0.005997 0.8466 0.8346 0.02145 ] Network output: [ 1 -0.01784 0.003034 -4.493e-05 2.017e-05 0.01414 -3.386e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02485 -0.2038 0.2096 0.9837 0.9933 0.2013 0.4664 0.8803 0.7255 ] Network output: [ -0.01236 0.9982 1.011 2.696e-06 -1.21e-06 0.01588 2.032e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005122 0.0006548 0.004242 0.005033 0.989 0.992 0.005214 0.8775 0.9041 0.01559 ] Network output: [ 0.001074 -0.02481 1.002 -0.00018 8.081e-05 1.019 -0.0001357 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.09876 0.3145 0.1749 0.9852 0.9941 0.1915 0.4716 0.8867 0.7199 ] Network output: [ 0.00936 -0.04823 1.001 0.0001051 -4.719e-05 1.029 7.923e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09728 0.08724 0.1787 0.2164 0.9874 0.992 0.09734 0.8052 0.8837 0.3133 ] Network output: [ -0.01081 0.04919 1.002 0.0001022 -4.587e-05 0.971 7.7e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09712 0.09534 0.1716 0.2024 0.9858 0.9916 0.09714 0.7381 0.8647 0.2455 ] Network output: [ 0.001376 0.999 -0.002241 1.499e-05 -6.728e-06 1.001 1.13e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001325 Epoch 6419 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01435 0.9938 0.9849 6.392e-06 -2.869e-06 -0.007403 4.817e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.00291 -0.01035 0.007788 0.9697 0.9741 0.006008 0.8467 0.8343 0.02142 ] Network output: [ 0.9972 0.02292 0.001148 -4.97e-05 2.231e-05 -0.01871 -3.745e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02432 -0.2062 0.2028 0.9837 0.9933 0.2019 0.4674 0.8801 0.725 ] Network output: [ -0.01239 1 1.011 2.378e-06 -1.068e-06 0.01388 1.792e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005134 0.0006499 0.004123 0.004804 0.989 0.992 0.005227 0.8776 0.904 0.01553 ] Network output: [ -0.002529 0.0315 1 -0.0001875 8.418e-05 0.9728 -0.0001413 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.0989 0.3106 0.1641 0.9852 0.9941 0.192 0.4723 0.8867 0.7204 ] Network output: [ 0.0105 -0.03699 0.999 0.0001046 -4.694e-05 1.017 7.88e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0971 0.08702 0.1762 0.2134 0.9874 0.992 0.09716 0.8043 0.8836 0.3117 ] Network output: [ -0.01012 0.04595 1.002 0.0001027 -4.613e-05 0.973 7.743e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09694 0.09514 0.1708 0.2018 0.9858 0.9916 0.09695 0.737 0.8647 0.2454 ] Network output: [ -0.000675 0.9993 0.0006404 1.392e-05 -6.25e-06 1.001 1.049e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001191 Epoch 6420 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01475 0.9876 0.9851 7.197e-06 -3.231e-06 -0.002187 5.424e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.002911 -0.01031 0.007899 0.9697 0.9741 0.005998 0.8466 0.8346 0.02145 ] Network output: [ 1 -0.01776 0.003029 -4.492e-05 2.017e-05 0.01406 -3.386e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02488 -0.2038 0.2095 0.9837 0.9933 0.2013 0.4664 0.8803 0.7255 ] Network output: [ -0.01236 0.9982 1.011 2.697e-06 -1.211e-06 0.01588 2.032e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005123 0.000654 0.004242 0.00503 0.989 0.992 0.005215 0.8775 0.9041 0.01559 ] Network output: [ 0.001063 -0.02468 1.002 -0.0001798 8.074e-05 1.019 -0.0001355 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.09873 0.3145 0.1748 0.9852 0.9941 0.1916 0.4716 0.8867 0.7199 ] Network output: [ 0.009357 -0.04821 1.001 0.000105 -4.715e-05 1.029 7.915e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09728 0.08723 0.1787 0.2164 0.9874 0.992 0.09734 0.8051 0.8837 0.3133 ] Network output: [ -0.0108 0.04918 1.002 0.0001021 -4.583e-05 0.971 7.694e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0971 0.09531 0.1715 0.2023 0.9858 0.9916 0.09711 0.7381 0.8647 0.2455 ] Network output: [ 0.001371 0.999 -0.002237 1.497e-05 -6.72e-06 1.001 1.128e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001322 Epoch 6421 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01435 0.9938 0.9849 6.386e-06 -2.867e-06 -0.007391 4.812e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.00291 -0.01035 0.007786 0.9697 0.9741 0.006008 0.8467 0.8343 0.02141 ] Network output: [ 0.9972 0.02283 0.001151 -4.966e-05 2.229e-05 -0.01865 -3.743e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02435 -0.2062 0.2028 0.9837 0.9933 0.2019 0.4674 0.8801 0.725 ] Network output: [ -0.01238 1 1.011 2.38e-06 -1.069e-06 0.01388 1.794e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005135 0.000649 0.004124 0.004803 0.989 0.992 0.005227 0.8776 0.9039 0.01552 ] Network output: [ -0.002524 0.03138 1 -0.0001873 8.409e-05 0.9728 -0.0001412 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09886 0.3106 0.1641 0.9852 0.9941 0.192 0.4723 0.8867 0.7203 ] Network output: [ 0.01049 -0.03702 0.999 0.0001045 -4.69e-05 1.017 7.872e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0971 0.08702 0.1762 0.2133 0.9874 0.992 0.09716 0.8043 0.8836 0.3117 ] Network output: [ -0.01012 0.04595 1.002 0.0001027 -4.609e-05 0.973 7.737e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09691 0.09512 0.1708 0.2017 0.9858 0.9916 0.09693 0.737 0.8647 0.2454 ] Network output: [ -0.000671 0.9993 0.0006338 1.391e-05 -6.244e-06 1.001 1.048e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001189 Epoch 6422 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01475 0.9876 0.9851 7.186e-06 -3.226e-06 -0.002199 5.416e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.002911 -0.01031 0.007896 0.9697 0.9741 0.005998 0.8466 0.8346 0.02144 ] Network output: [ 1 -0.01767 0.003024 -4.492e-05 2.016e-05 0.01399 -3.385e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.0249 -0.2038 0.2095 0.9837 0.9933 0.2014 0.4664 0.8803 0.7255 ] Network output: [ -0.01236 0.9982 1.011 2.697e-06 -1.211e-06 0.01587 2.033e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005124 0.0006531 0.004242 0.005027 0.989 0.992 0.005216 0.8775 0.9041 0.01558 ] Network output: [ 0.001053 -0.02455 1.002 -0.0001797 8.067e-05 1.019 -0.0001354 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.0987 0.3145 0.1747 0.9852 0.9941 0.1916 0.4716 0.8867 0.7199 ] Network output: [ 0.009355 -0.04819 1.001 0.0001049 -4.711e-05 1.029 7.908e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09728 0.08722 0.1787 0.2163 0.9874 0.992 0.09734 0.8051 0.8836 0.3133 ] Network output: [ -0.01079 0.04917 1.002 0.000102 -4.58e-05 0.971 7.688e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09707 0.09528 0.1715 0.2023 0.9858 0.9916 0.09709 0.738 0.8647 0.2454 ] Network output: [ 0.001367 0.999 -0.002232 1.495e-05 -6.712e-06 1.001 1.127e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001319 Epoch 6423 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01434 0.9938 0.9849 6.379e-06 -2.864e-06 -0.007379 4.808e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002911 -0.01034 0.007784 0.9697 0.9741 0.006008 0.8467 0.8343 0.02141 ] Network output: [ 0.9972 0.02273 0.001154 -4.963e-05 2.228e-05 -0.01858 -3.74e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02437 -0.2062 0.2028 0.9837 0.9933 0.2019 0.4673 0.8801 0.7249 ] Network output: [ -0.01238 1 1.011 2.382e-06 -1.069e-06 0.01388 1.795e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005135 0.0006482 0.004125 0.004801 0.989 0.992 0.005228 0.8776 0.9039 0.01552 ] Network output: [ -0.002519 0.03126 1 -0.0001871 8.4e-05 0.9729 -0.000141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09883 0.3107 0.164 0.9852 0.9941 0.192 0.4722 0.8867 0.7203 ] Network output: [ 0.01048 -0.03705 0.999 0.0001044 -4.685e-05 1.018 7.865e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0971 0.08701 0.1762 0.2133 0.9874 0.992 0.09716 0.8042 0.8836 0.3117 ] Network output: [ -0.01011 0.04596 1.002 0.0001026 -4.605e-05 0.973 7.731e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09689 0.09509 0.1708 0.2017 0.9858 0.9916 0.0969 0.7369 0.8646 0.2454 ] Network output: [ -0.000667 0.9993 0.0006273 1.39e-05 -6.239e-06 1.001 1.047e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001187 Epoch 6424 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01474 0.9876 0.9851 7.176e-06 -3.221e-06 -0.00221 5.408e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.002911 -0.01031 0.007893 0.9697 0.9741 0.005998 0.8466 0.8346 0.02143 ] Network output: [ 1 -0.01759 0.00302 -4.491e-05 2.016e-05 0.01392 -3.384e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1806 -0.02492 -0.2038 0.2095 0.9837 0.9933 0.2014 0.4664 0.8803 0.7254 ] Network output: [ -0.01236 0.9982 1.011 2.697e-06 -1.211e-06 0.01586 2.033e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005124 0.0006522 0.004243 0.005025 0.989 0.992 0.005217 0.8775 0.9041 0.01558 ] Network output: [ 0.001042 -0.02442 1.002 -0.0001795 8.06e-05 1.019 -0.0001353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.09866 0.3145 0.1746 0.9852 0.9941 0.1916 0.4715 0.8867 0.7199 ] Network output: [ 0.009352 -0.04818 1.001 0.0001048 -4.706e-05 1.029 7.901e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09727 0.08721 0.1787 0.2163 0.9874 0.992 0.09733 0.805 0.8836 0.3133 ] Network output: [ -0.01078 0.04917 1.002 0.0001019 -4.576e-05 0.971 7.682e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09705 0.09526 0.1715 0.2023 0.9858 0.9916 0.09706 0.7379 0.8646 0.2454 ] Network output: [ 0.001363 0.999 -0.002227 1.493e-05 -6.704e-06 1.001 1.125e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001317 Epoch 6425 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01434 0.9938 0.9849 6.373e-06 -2.861e-06 -0.007368 4.803e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002911 -0.01034 0.007782 0.9697 0.9741 0.006008 0.8467 0.8343 0.0214 ] Network output: [ 0.9973 0.02264 0.001157 -4.959e-05 2.226e-05 -0.01851 -3.737e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02439 -0.2061 0.2028 0.9837 0.9933 0.2019 0.4673 0.8801 0.7249 ] Network output: [ -0.01238 1 1.011 2.384e-06 -1.07e-06 0.01388 1.797e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005136 0.0006473 0.004126 0.004799 0.989 0.992 0.005229 0.8775 0.9039 0.01552 ] Network output: [ -0.002513 0.03114 1 -0.0001869 8.391e-05 0.973 -0.0001409 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09879 0.3107 0.164 0.9852 0.9941 0.192 0.4722 0.8867 0.7203 ] Network output: [ 0.01047 -0.03709 0.9989 0.0001043 -4.681e-05 1.018 7.858e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0971 0.087 0.1762 0.2133 0.9874 0.992 0.09716 0.8042 0.8835 0.3117 ] Network output: [ -0.01011 0.04596 1.002 0.0001025 -4.601e-05 0.973 7.724e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09687 0.09507 0.1707 0.2017 0.9858 0.9916 0.09688 0.7368 0.8646 0.2453 ] Network output: [ -0.0006631 0.9993 0.0006207 1.389e-05 -6.234e-06 1.001 1.046e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001185 Epoch 6426 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01474 0.9876 0.9851 7.165e-06 -3.217e-06 -0.002222 5.4e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.002911 -0.0103 0.007891 0.9697 0.9741 0.005998 0.8465 0.8346 0.02143 ] Network output: [ 1 -0.01751 0.003015 -4.49e-05 2.016e-05 0.01384 -3.384e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02494 -0.2037 0.2094 0.9837 0.9933 0.2014 0.4663 0.8803 0.7254 ] Network output: [ -0.01236 0.9982 1.011 2.698e-06 -1.211e-06 0.01585 2.033e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005125 0.0006514 0.004243 0.005022 0.989 0.992 0.005217 0.8775 0.9041 0.01558 ] Network output: [ 0.001031 -0.02429 1.003 -0.0001794 8.052e-05 1.019 -0.0001352 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.09863 0.3146 0.1746 0.9852 0.9941 0.1916 0.4715 0.8867 0.7199 ] Network output: [ 0.009349 -0.04816 1.001 0.0001047 -4.702e-05 1.029 7.893e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09727 0.08721 0.1787 0.2163 0.9874 0.992 0.09733 0.8049 0.8836 0.3133 ] Network output: [ -0.01077 0.04916 1.002 0.0001019 -4.573e-05 0.971 7.676e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09702 0.09523 0.1715 0.2023 0.9858 0.9916 0.09704 0.7378 0.8646 0.2454 ] Network output: [ 0.001359 0.999 -0.002223 1.492e-05 -6.696e-06 1.001 1.124e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001314 Epoch 6427 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01433 0.9938 0.9849 6.367e-06 -2.858e-06 -0.007356 4.798e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002911 -0.01034 0.007779 0.9697 0.9741 0.006008 0.8467 0.8343 0.0214 ] Network output: [ 0.9973 0.02255 0.001159 -4.955e-05 2.225e-05 -0.01845 -3.735e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02441 -0.2061 0.2028 0.9837 0.9933 0.2019 0.4673 0.88 0.7249 ] Network output: [ -0.01238 1 1.011 2.386e-06 -1.071e-06 0.01388 1.798e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005137 0.0006465 0.004127 0.004798 0.989 0.992 0.005229 0.8775 0.9039 0.01551 ] Network output: [ -0.002508 0.03103 1 -0.0001867 8.382e-05 0.9731 -0.0001407 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09876 0.3107 0.164 0.9852 0.9941 0.192 0.4722 0.8867 0.7203 ] Network output: [ 0.01046 -0.03712 0.9989 0.0001042 -4.677e-05 1.018 7.851e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09709 0.08699 0.1762 0.2133 0.9874 0.992 0.09715 0.8041 0.8835 0.3117 ] Network output: [ -0.0101 0.04597 1.002 0.0001024 -4.597e-05 0.973 7.718e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09684 0.09504 0.1707 0.2017 0.9858 0.9916 0.09685 0.7368 0.8646 0.2453 ] Network output: [ -0.0006592 0.9993 0.0006143 1.387e-05 -6.228e-06 1.001 1.046e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001184 Epoch 6428 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01473 0.9877 0.9851 7.154e-06 -3.212e-06 -0.002233 5.392e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.002912 -0.0103 0.007888 0.9697 0.9741 0.005998 0.8465 0.8345 0.02142 ] Network output: [ 1 -0.01742 0.003011 -4.489e-05 2.015e-05 0.01377 -3.383e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02496 -0.2037 0.2094 0.9837 0.9933 0.2014 0.4663 0.8803 0.7254 ] Network output: [ -0.01235 0.9982 1.011 2.698e-06 -1.211e-06 0.01584 2.033e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005126 0.0006505 0.004243 0.005019 0.989 0.992 0.005218 0.8775 0.9041 0.01557 ] Network output: [ 0.00102 -0.02416 1.003 -0.0001792 8.045e-05 1.019 -0.0001351 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.09859 0.3146 0.1745 0.9852 0.9941 0.1916 0.4715 0.8866 0.7198 ] Network output: [ 0.009346 -0.04815 1.001 0.0001046 -4.698e-05 1.029 7.886e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09726 0.0872 0.1787 0.2162 0.9874 0.992 0.09732 0.8049 0.8836 0.3132 ] Network output: [ -0.01076 0.04915 1.002 0.0001018 -4.569e-05 0.971 7.67e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.097 0.09521 0.1715 0.2022 0.9858 0.9916 0.09701 0.7378 0.8646 0.2454 ] Network output: [ 0.001355 0.999 -0.002218 1.49e-05 -6.688e-06 1.001 1.123e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001311 Epoch 6429 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01433 0.9938 0.9849 6.361e-06 -2.856e-06 -0.007345 4.794e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002911 -0.01033 0.007777 0.9697 0.9741 0.006008 0.8467 0.8343 0.02139 ] Network output: [ 0.9973 0.02246 0.001162 -4.952e-05 2.223e-05 -0.01838 -3.732e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02444 -0.2061 0.2027 0.9837 0.9933 0.2019 0.4672 0.88 0.7249 ] Network output: [ -0.01238 1 1.011 2.388e-06 -1.072e-06 0.01388 1.8e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005138 0.0006456 0.004127 0.004796 0.989 0.992 0.00523 0.8775 0.9039 0.01551 ] Network output: [ -0.002503 0.03091 1 -0.0001865 8.373e-05 0.9732 -0.0001406 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09872 0.3108 0.1639 0.9852 0.9941 0.192 0.4722 0.8867 0.7203 ] Network output: [ 0.01045 -0.03715 0.9989 0.0001041 -4.672e-05 1.018 7.844e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09709 0.08698 0.1762 0.2133 0.9874 0.992 0.09715 0.8041 0.8835 0.3117 ] Network output: [ -0.0101 0.04597 1.002 0.0001023 -4.594e-05 0.973 7.711e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09682 0.09502 0.1707 0.2016 0.9858 0.9916 0.09683 0.7367 0.8645 0.2453 ] Network output: [ -0.0006553 0.9993 0.0006078 1.386e-05 -6.223e-06 1.001 1.045e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001182 Epoch 6430 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01472 0.9877 0.9851 7.144e-06 -3.207e-06 -0.002245 5.384e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.002912 -0.0103 0.007885 0.9697 0.9741 0.005999 0.8465 0.8345 0.02142 ] Network output: [ 1 -0.01734 0.003006 -4.488e-05 2.015e-05 0.01369 -3.383e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02498 -0.2037 0.2093 0.9837 0.9933 0.2014 0.4663 0.8803 0.7254 ] Network output: [ -0.01235 0.9982 1.011 2.699e-06 -1.211e-06 0.01583 2.034e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005126 0.0006496 0.004244 0.005016 0.989 0.992 0.005219 0.8775 0.904 0.01557 ] Network output: [ 0.00101 -0.02403 1.003 -0.000179 8.038e-05 1.019 -0.0001349 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.09856 0.3146 0.1744 0.9852 0.9941 0.1916 0.4715 0.8866 0.7198 ] Network output: [ 0.009343 -0.04813 1.001 0.0001045 -4.693e-05 1.029 7.879e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09726 0.08719 0.1787 0.2162 0.9874 0.992 0.09732 0.8048 0.8835 0.3132 ] Network output: [ -0.01075 0.04915 1.002 0.0001017 -4.565e-05 0.971 7.664e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09697 0.09518 0.1714 0.2022 0.9858 0.9916 0.09699 0.7377 0.8645 0.2454 ] Network output: [ 0.00135 0.999 -0.002213 1.488e-05 -6.68e-06 1.001 1.121e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001309 Epoch 6431 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01432 0.9938 0.9849 6.354e-06 -2.853e-06 -0.007333 4.789e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002912 -0.01033 0.007775 0.9697 0.9741 0.006009 0.8467 0.8343 0.02139 ] Network output: [ 0.9973 0.02237 0.001165 -4.948e-05 2.222e-05 -0.01832 -3.729e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02446 -0.206 0.2027 0.9837 0.9933 0.2019 0.4672 0.88 0.7249 ] Network output: [ -0.01237 1 1.011 2.39e-06 -1.073e-06 0.01388 1.801e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005138 0.0006448 0.004128 0.004794 0.989 0.992 0.005231 0.8775 0.9039 0.01551 ] Network output: [ -0.002498 0.0308 1 -0.0001863 8.364e-05 0.9733 -0.0001404 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09869 0.3108 0.1639 0.9852 0.9941 0.192 0.4721 0.8866 0.7203 ] Network output: [ 0.01044 -0.03719 0.9989 0.000104 -4.668e-05 1.018 7.836e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09709 0.08698 0.1762 0.2133 0.9874 0.992 0.09715 0.804 0.8835 0.3117 ] Network output: [ -0.01009 0.04598 1.002 0.0001022 -4.59e-05 0.973 7.705e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09679 0.09499 0.1707 0.2016 0.9858 0.9916 0.09681 0.7366 0.8645 0.2453 ] Network output: [ -0.0006514 0.9993 0.0006014 1.385e-05 -6.218e-06 1.001 1.044e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00118 Epoch 6432 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01472 0.9877 0.9852 7.133e-06 -3.202e-06 -0.002256 5.376e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.002912 -0.0103 0.007883 0.9697 0.9741 0.005999 0.8465 0.8345 0.02141 ] Network output: [ 1 -0.01726 0.003001 -4.488e-05 2.015e-05 0.01362 -3.382e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.025 -0.2037 0.2093 0.9837 0.9933 0.2014 0.4663 0.8803 0.7254 ] Network output: [ -0.01235 0.9982 1.011 2.699e-06 -1.212e-06 0.01582 2.034e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005127 0.0006488 0.004244 0.005014 0.989 0.992 0.00522 0.8774 0.904 0.01556 ] Network output: [ 0.0009993 -0.0239 1.003 -0.0001789 8.031e-05 1.019 -0.0001348 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.09853 0.3146 0.1744 0.9852 0.9941 0.1916 0.4714 0.8866 0.7198 ] Network output: [ 0.009341 -0.04811 1.001 0.0001044 -4.689e-05 1.029 7.871e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09726 0.08718 0.1787 0.2162 0.9874 0.992 0.09732 0.8048 0.8835 0.3132 ] Network output: [ -0.01074 0.04914 1.002 0.0001016 -4.562e-05 0.971 7.658e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09695 0.09516 0.1714 0.2022 0.9858 0.9916 0.09696 0.7376 0.8645 0.2454 ] Network output: [ 0.001346 0.999 -0.002209 1.486e-05 -6.673e-06 1.001 1.12e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001306 Epoch 6433 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01432 0.9938 0.9849 6.348e-06 -2.85e-06 -0.007322 4.784e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002912 -0.01033 0.007773 0.9697 0.9741 0.006009 0.8466 0.8343 0.02138 ] Network output: [ 0.9973 0.02228 0.001168 -4.945e-05 2.22e-05 -0.01826 -3.727e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02448 -0.206 0.2027 0.9837 0.9933 0.2019 0.4672 0.88 0.7249 ] Network output: [ -0.01237 1 1.011 2.392e-06 -1.074e-06 0.01388 1.803e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005139 0.0006439 0.004129 0.004793 0.989 0.992 0.005231 0.8775 0.9039 0.0155 ] Network output: [ -0.002493 0.03068 1 -0.0001861 8.355e-05 0.9733 -0.0001403 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09865 0.3109 0.1639 0.9852 0.9941 0.192 0.4721 0.8866 0.7202 ] Network output: [ 0.01044 -0.03722 0.9989 0.0001039 -4.664e-05 1.018 7.829e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09708 0.08697 0.1763 0.2132 0.9874 0.992 0.09714 0.804 0.8834 0.3117 ] Network output: [ -0.01009 0.04598 1.002 0.0001022 -4.586e-05 0.973 7.699e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09677 0.09497 0.1707 0.2016 0.9858 0.9916 0.09678 0.7366 0.8645 0.2453 ] Network output: [ -0.0006475 0.9993 0.000595 1.384e-05 -6.212e-06 1.001 1.043e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001178 Epoch 6434 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01471 0.9877 0.9852 7.122e-06 -3.197e-06 -0.002267 5.367e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.002912 -0.01029 0.00788 0.9697 0.9741 0.005999 0.8465 0.8345 0.02141 ] Network output: [ 1 -0.01717 0.002997 -4.487e-05 2.014e-05 0.01355 -3.381e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02502 -0.2036 0.2093 0.9837 0.9933 0.2014 0.4662 0.8802 0.7254 ] Network output: [ -0.01235 0.9982 1.011 2.699e-06 -1.212e-06 0.01581 2.034e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005128 0.0006479 0.004244 0.005011 0.989 0.992 0.00522 0.8774 0.904 0.01556 ] Network output: [ 0.0009888 -0.02378 1.003 -0.0001787 8.024e-05 1.019 -0.0001347 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.09849 0.3147 0.1743 0.9852 0.9941 0.1916 0.4714 0.8866 0.7198 ] Network output: [ 0.009338 -0.04809 1.001 0.0001043 -4.685e-05 1.029 7.864e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09725 0.08717 0.1787 0.2162 0.9874 0.992 0.09731 0.8047 0.8835 0.3132 ] Network output: [ -0.01073 0.04913 1.002 0.0001015 -4.558e-05 0.971 7.652e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09693 0.09513 0.1714 0.2022 0.9858 0.9916 0.09694 0.7376 0.8645 0.2454 ] Network output: [ 0.001342 0.999 -0.002204 1.485e-05 -6.665e-06 1.001 1.119e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001304 Epoch 6435 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01432 0.9938 0.9849 6.342e-06 -2.847e-06 -0.007311 4.779e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002912 -0.01032 0.007771 0.9697 0.9741 0.006009 0.8466 0.8343 0.02138 ] Network output: [ 0.9973 0.02219 0.001171 -4.941e-05 2.218e-05 -0.01819 -3.724e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.0245 -0.206 0.2027 0.9837 0.9933 0.2019 0.4672 0.88 0.7248 ] Network output: [ -0.01237 1 1.011 2.394e-06 -1.075e-06 0.01388 1.804e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00514 0.0006431 0.00413 0.004791 0.989 0.992 0.005232 0.8775 0.9039 0.0155 ] Network output: [ -0.002488 0.03057 1 -0.0001859 8.346e-05 0.9734 -0.0001401 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09862 0.3109 0.1639 0.9852 0.9941 0.192 0.4721 0.8866 0.7202 ] Network output: [ 0.01043 -0.03725 0.9989 0.0001038 -4.659e-05 1.018 7.822e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09708 0.08696 0.1763 0.2132 0.9874 0.992 0.09714 0.8039 0.8834 0.3116 ] Network output: [ -0.01008 0.04599 1.002 0.0001021 -4.582e-05 0.973 7.692e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09675 0.09494 0.1706 0.2016 0.9858 0.9916 0.09676 0.7365 0.8644 0.2453 ] Network output: [ -0.0006436 0.9993 0.0005886 1.383e-05 -6.207e-06 1.001 1.042e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001176 Epoch 6436 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01471 0.9877 0.9852 7.111e-06 -3.193e-06 -0.002279 5.359e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.002913 -0.01029 0.007877 0.9697 0.9741 0.005999 0.8465 0.8345 0.0214 ] Network output: [ 1 -0.01709 0.002992 -4.486e-05 2.014e-05 0.01348 -3.381e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02504 -0.2036 0.2092 0.9837 0.9933 0.2014 0.4662 0.8802 0.7253 ] Network output: [ -0.01235 0.9983 1.011 2.7e-06 -1.212e-06 0.01581 2.034e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005129 0.0006471 0.004245 0.005008 0.989 0.992 0.005221 0.8774 0.904 0.01556 ] Network output: [ 0.0009784 -0.02365 1.003 -0.0001786 8.017e-05 1.018 -0.0001346 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.09846 0.3147 0.1742 0.9852 0.9941 0.1916 0.4714 0.8866 0.7198 ] Network output: [ 0.009335 -0.04808 1.001 0.0001042 -4.68e-05 1.029 7.857e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09725 0.08716 0.1787 0.2161 0.9874 0.992 0.09731 0.8047 0.8835 0.3132 ] Network output: [ -0.01072 0.04913 1.002 0.0001014 -4.554e-05 0.971 7.645e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0969 0.09511 0.1714 0.2021 0.9858 0.9916 0.09691 0.7375 0.8644 0.2453 ] Network output: [ 0.001338 0.999 -0.002199 1.483e-05 -6.657e-06 1.001 1.117e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001301 Epoch 6437 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01431 0.9938 0.985 6.335e-06 -2.844e-06 -0.007299 4.774e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002912 -0.01032 0.007769 0.9697 0.9741 0.006009 0.8466 0.8343 0.02137 ] Network output: [ 0.9973 0.0221 0.001173 -4.938e-05 2.217e-05 -0.01813 -3.721e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02453 -0.2059 0.2027 0.9837 0.9933 0.2019 0.4671 0.88 0.7248 ] Network output: [ -0.01237 1 1.011 2.396e-06 -1.076e-06 0.01388 1.805e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00514 0.0006423 0.004131 0.004789 0.989 0.992 0.005233 0.8775 0.9039 0.0155 ] Network output: [ -0.002483 0.03045 1 -0.0001857 8.337e-05 0.9735 -0.00014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09859 0.3109 0.1638 0.9852 0.9941 0.192 0.472 0.8866 0.7202 ] Network output: [ 0.01042 -0.03728 0.9989 0.0001037 -4.655e-05 1.018 7.815e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09708 0.08695 0.1763 0.2132 0.9874 0.992 0.09714 0.8038 0.8834 0.3116 ] Network output: [ -0.01008 0.04599 1.002 0.000102 -4.578e-05 0.9729 7.686e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09672 0.09492 0.1706 0.2016 0.9858 0.9916 0.09674 0.7364 0.8644 0.2452 ] Network output: [ -0.0006397 0.9993 0.0005823 1.381e-05 -6.202e-06 1.001 1.041e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001174 Epoch 6438 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0147 0.9878 0.9852 7.101e-06 -3.188e-06 -0.00229 5.351e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.002913 -0.01029 0.007875 0.9697 0.9741 0.006 0.8465 0.8345 0.0214 ] Network output: [ 1 -0.01701 0.002988 -4.485e-05 2.013e-05 0.0134 -3.38e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02506 -0.2036 0.2092 0.9837 0.9933 0.2014 0.4662 0.8802 0.7253 ] Network output: [ -0.01234 0.9983 1.011 2.7e-06 -1.212e-06 0.0158 2.035e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005129 0.0006462 0.004245 0.005006 0.989 0.992 0.005222 0.8774 0.904 0.01555 ] Network output: [ 0.000968 -0.02352 1.003 -0.0001784 8.009e-05 1.018 -0.0001345 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.09843 0.3147 0.1741 0.9852 0.9941 0.1916 0.4714 0.8866 0.7198 ] Network output: [ 0.009332 -0.04806 1.001 0.0001042 -4.676e-05 1.029 7.849e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09725 0.08715 0.1787 0.2161 0.9874 0.992 0.09731 0.8046 0.8834 0.3132 ] Network output: [ -0.01071 0.04912 1.002 0.0001014 -4.551e-05 0.971 7.639e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09688 0.09508 0.1713 0.2021 0.9858 0.9916 0.09689 0.7374 0.8644 0.2453 ] Network output: [ 0.001334 0.999 -0.002195 1.481e-05 -6.649e-06 1.001 1.116e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001298 Epoch 6439 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01431 0.9937 0.985 6.329e-06 -2.841e-06 -0.007288 4.77e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002913 -0.01032 0.007766 0.9697 0.9741 0.006009 0.8466 0.8342 0.02137 ] Network output: [ 0.9973 0.02201 0.001176 -4.934e-05 2.215e-05 -0.01806 -3.719e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02455 -0.2059 0.2027 0.9837 0.9933 0.2019 0.4671 0.88 0.7248 ] Network output: [ -0.01237 1 1.011 2.398e-06 -1.076e-06 0.01389 1.807e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005141 0.0006414 0.004131 0.004788 0.989 0.992 0.005233 0.8774 0.9038 0.01549 ] Network output: [ -0.002478 0.03034 1 -0.0001855 8.328e-05 0.9736 -0.0001398 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09855 0.311 0.1638 0.9852 0.9941 0.192 0.472 0.8866 0.7202 ] Network output: [ 0.01041 -0.03731 0.9989 0.0001036 -4.651e-05 1.018 7.807e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09707 0.08695 0.1763 0.2132 0.9874 0.992 0.09713 0.8038 0.8833 0.3116 ] Network output: [ -0.01007 0.046 1.002 0.0001019 -4.575e-05 0.9729 7.679e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0967 0.0949 0.1706 0.2015 0.9858 0.9916 0.09671 0.7364 0.8644 0.2452 ] Network output: [ -0.0006359 0.9993 0.000576 1.38e-05 -6.196e-06 1.001 1.04e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001172 Epoch 6440 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01469 0.9878 0.9852 7.09e-06 -3.183e-06 -0.002302 5.343e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.002913 -0.01028 0.007872 0.9697 0.9741 0.006 0.8465 0.8345 0.02139 ] Network output: [ 1 -0.01693 0.002983 -4.484e-05 2.013e-05 0.01333 -3.379e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02508 -0.2036 0.2091 0.9837 0.9933 0.2014 0.4662 0.8802 0.7253 ] Network output: [ -0.01234 0.9983 1.011 2.7e-06 -1.212e-06 0.01579 2.035e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00513 0.0006454 0.004245 0.005003 0.989 0.992 0.005223 0.8774 0.904 0.01555 ] Network output: [ 0.0009577 -0.0234 1.003 -0.0001782 8.002e-05 1.018 -0.0001343 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.09839 0.3147 0.1741 0.9852 0.9941 0.1916 0.4713 0.8866 0.7197 ] Network output: [ 0.009329 -0.04804 1.001 0.0001041 -4.671e-05 1.029 7.842e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09724 0.08715 0.1787 0.2161 0.9874 0.992 0.0973 0.8045 0.8834 0.3132 ] Network output: [ -0.01071 0.04911 1.002 0.0001013 -4.547e-05 0.971 7.633e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09685 0.09506 0.1713 0.2021 0.9858 0.9916 0.09686 0.7374 0.8644 0.2453 ] Network output: [ 0.00133 0.999 -0.00219 1.479e-05 -6.641e-06 1.001 1.115e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001296 Epoch 6441 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0143 0.9937 0.985 6.322e-06 -2.838e-06 -0.007277 4.765e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002913 -0.01031 0.007764 0.9697 0.9741 0.006009 0.8466 0.8342 0.02136 ] Network output: [ 0.9974 0.02192 0.001179 -4.931e-05 2.214e-05 -0.018 -3.716e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02457 -0.2059 0.2027 0.9837 0.9933 0.2019 0.4671 0.88 0.7248 ] Network output: [ -0.01236 1 1.011 2.399e-06 -1.077e-06 0.01389 1.808e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005142 0.0006406 0.004132 0.004786 0.989 0.992 0.005234 0.8774 0.9038 0.01549 ] Network output: [ -0.002473 0.03023 1 -0.0001853 8.319e-05 0.9737 -0.0001397 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09852 0.311 0.1638 0.9852 0.9941 0.192 0.472 0.8866 0.7202 ] Network output: [ 0.0104 -0.03734 0.9989 0.0001035 -4.647e-05 1.018 7.8e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09707 0.08694 0.1763 0.2132 0.9874 0.992 0.09713 0.8037 0.8833 0.3116 ] Network output: [ -0.01007 0.046 1.002 0.0001018 -4.571e-05 0.9729 7.673e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09668 0.09487 0.1706 0.2015 0.9858 0.9916 0.09669 0.7363 0.8643 0.2452 ] Network output: [ -0.0006321 0.9993 0.0005697 1.379e-05 -6.191e-06 1.001 1.039e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001171 Epoch 6442 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01469 0.9878 0.9852 7.079e-06 -3.178e-06 -0.002313 5.335e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.002913 -0.01028 0.007869 0.9697 0.9741 0.006 0.8465 0.8345 0.02139 ] Network output: [ 1 -0.01685 0.002979 -4.483e-05 2.013e-05 0.01326 -3.379e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.0251 -0.2035 0.2091 0.9837 0.9933 0.2014 0.4661 0.8802 0.7253 ] Network output: [ -0.01234 0.9983 1.011 2.7e-06 -1.212e-06 0.01578 2.035e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005131 0.0006445 0.004245 0.005 0.989 0.992 0.005223 0.8774 0.904 0.01555 ] Network output: [ 0.0009474 -0.02327 1.003 -0.0001781 7.995e-05 1.018 -0.0001342 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.09836 0.3148 0.174 0.9852 0.9941 0.1916 0.4713 0.8866 0.7197 ] Network output: [ 0.009326 -0.04803 1.001 0.000104 -4.667e-05 1.029 7.835e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09724 0.08714 0.1787 0.216 0.9874 0.992 0.0973 0.8045 0.8834 0.3132 ] Network output: [ -0.0107 0.0491 1.002 0.0001012 -4.543e-05 0.971 7.627e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09683 0.09503 0.1713 0.2021 0.9858 0.9916 0.09684 0.7373 0.8643 0.2453 ] Network output: [ 0.001326 0.999 -0.002185 1.477e-05 -6.633e-06 1.001 1.113e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001293 Epoch 6443 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0143 0.9937 0.985 6.316e-06 -2.835e-06 -0.007266 4.76e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002913 -0.01031 0.007762 0.9697 0.9741 0.00601 0.8466 0.8342 0.02136 ] Network output: [ 0.9974 0.02183 0.001182 -4.927e-05 2.212e-05 -0.01794 -3.713e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02459 -0.2058 0.2027 0.9837 0.9933 0.2019 0.467 0.88 0.7248 ] Network output: [ -0.01236 1 1.011 2.401e-06 -1.078e-06 0.01389 1.81e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005142 0.0006398 0.004133 0.004784 0.989 0.992 0.005235 0.8774 0.9038 0.01549 ] Network output: [ -0.002468 0.03011 1 -0.0001851 8.31e-05 0.9737 -0.0001395 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09849 0.3111 0.1638 0.9852 0.9941 0.192 0.4719 0.8866 0.7201 ] Network output: [ 0.01039 -0.03738 0.9989 0.0001034 -4.642e-05 1.018 7.793e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09707 0.08693 0.1763 0.2131 0.9874 0.992 0.09713 0.8037 0.8833 0.3116 ] Network output: [ -0.01006 0.046 1.002 0.0001017 -4.567e-05 0.9729 7.667e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09665 0.09485 0.1706 0.2015 0.9858 0.9916 0.09666 0.7362 0.8643 0.2452 ] Network output: [ -0.0006282 0.9993 0.0005635 1.378e-05 -6.186e-06 1.001 1.038e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001169 Epoch 6444 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01468 0.9878 0.9852 7.069e-06 -3.173e-06 -0.002325 5.327e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.002914 -0.01028 0.007867 0.9697 0.9741 0.006 0.8465 0.8345 0.02138 ] Network output: [ 1 -0.01677 0.002974 -4.482e-05 2.012e-05 0.01319 -3.378e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02512 -0.2035 0.2091 0.9837 0.9933 0.2014 0.4661 0.8802 0.7253 ] Network output: [ -0.01234 0.9983 1.011 2.701e-06 -1.212e-06 0.01577 2.035e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005132 0.0006437 0.004246 0.004998 0.989 0.992 0.005224 0.8774 0.904 0.01554 ] Network output: [ 0.0009372 -0.02315 1.003 -0.0001779 7.988e-05 1.018 -0.0001341 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.09833 0.3148 0.1739 0.9852 0.9941 0.1916 0.4713 0.8865 0.7197 ] Network output: [ 0.009323 -0.04801 1.001 0.0001039 -4.663e-05 1.029 7.827e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09723 0.08713 0.1787 0.216 0.9874 0.992 0.09729 0.8044 0.8834 0.3131 ] Network output: [ -0.01069 0.04909 1.002 0.0001011 -4.54e-05 0.971 7.621e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0968 0.09501 0.1713 0.202 0.9858 0.9916 0.09681 0.7372 0.8643 0.2453 ] Network output: [ 0.001321 0.999 -0.002181 1.476e-05 -6.625e-06 1.001 1.112e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001291 Epoch 6445 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01429 0.9937 0.985 6.309e-06 -2.833e-06 -0.007256 4.755e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002913 -0.01031 0.00776 0.9697 0.9741 0.00601 0.8466 0.8342 0.02135 ] Network output: [ 0.9974 0.02174 0.001184 -4.924e-05 2.21e-05 -0.01787 -3.711e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02461 -0.2058 0.2027 0.9837 0.9933 0.2019 0.467 0.88 0.7248 ] Network output: [ -0.01236 1 1.011 2.403e-06 -1.079e-06 0.01389 1.811e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005143 0.000639 0.004134 0.004783 0.989 0.992 0.005236 0.8774 0.9038 0.01548 ] Network output: [ -0.002463 0.03 1 -0.0001849 8.301e-05 0.9738 -0.0001394 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09845 0.3111 0.1637 0.9852 0.9941 0.192 0.4719 0.8866 0.7201 ] Network output: [ 0.01038 -0.03741 0.9989 0.0001033 -4.638e-05 1.018 7.786e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09707 0.08692 0.1763 0.2131 0.9874 0.992 0.09713 0.8036 0.8833 0.3116 ] Network output: [ -0.01006 0.04601 1.002 0.0001016 -4.563e-05 0.9729 7.66e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09663 0.09482 0.1706 0.2015 0.9858 0.9916 0.09664 0.7362 0.8643 0.2452 ] Network output: [ -0.0006244 0.9992 0.0005573 1.377e-05 -6.18e-06 1.001 1.037e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001167 Epoch 6446 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01468 0.9878 0.9852 7.058e-06 -3.169e-06 -0.002336 5.319e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003171 -0.002914 -0.01027 0.007864 0.9697 0.9741 0.006 0.8465 0.8345 0.02138 ] Network output: [ 1 -0.01669 0.00297 -4.481e-05 2.012e-05 0.01312 -3.377e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02514 -0.2035 0.209 0.9837 0.9933 0.2014 0.4661 0.8802 0.7253 ] Network output: [ -0.01233 0.9983 1.011 2.701e-06 -1.212e-06 0.01576 2.035e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005132 0.0006428 0.004246 0.004995 0.989 0.992 0.005225 0.8774 0.904 0.01554 ] Network output: [ 0.000927 -0.02302 1.003 -0.0001778 7.981e-05 1.018 -0.000134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.0983 0.3148 0.1738 0.9852 0.9941 0.1916 0.4712 0.8865 0.7197 ] Network output: [ 0.00932 -0.04799 1.001 0.0001038 -4.658e-05 1.029 7.82e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09723 0.08712 0.1787 0.216 0.9874 0.992 0.09729 0.8044 0.8833 0.3131 ] Network output: [ -0.01068 0.04908 1.002 0.000101 -4.536e-05 0.971 7.615e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09678 0.09498 0.1712 0.202 0.9858 0.9916 0.09679 0.7372 0.8643 0.2453 ] Network output: [ 0.001317 0.999 -0.002176 1.474e-05 -6.617e-06 1.001 1.111e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001288 Epoch 6447 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01429 0.9937 0.985 6.303e-06 -2.83e-06 -0.007245 4.75e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002914 -0.0103 0.007758 0.9697 0.9741 0.00601 0.8466 0.8342 0.02135 ] Network output: [ 0.9974 0.02165 0.001187 -4.92e-05 2.209e-05 -0.01781 -3.708e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02464 -0.2058 0.2027 0.9837 0.9933 0.2019 0.467 0.8799 0.7248 ] Network output: [ -0.01236 1 1.011 2.404e-06 -1.079e-06 0.01389 1.812e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005144 0.0006381 0.004134 0.004781 0.989 0.992 0.005236 0.8774 0.9038 0.01548 ] Network output: [ -0.002457 0.02989 1 -0.0001847 8.292e-05 0.9739 -0.0001392 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09842 0.3112 0.1637 0.9852 0.9941 0.192 0.4719 0.8866 0.7201 ] Network output: [ 0.01037 -0.03744 0.9989 0.0001032 -4.634e-05 1.018 7.779e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09706 0.08692 0.1763 0.2131 0.9874 0.992 0.09712 0.8036 0.8832 0.3116 ] Network output: [ -0.01005 0.04601 1.002 0.0001016 -4.559e-05 0.9729 7.654e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0966 0.0948 0.1705 0.2015 0.9858 0.9916 0.09662 0.7361 0.8642 0.2452 ] Network output: [ -0.0006206 0.9992 0.0005511 1.375e-05 -6.175e-06 1.001 1.037e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001165 Epoch 6448 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01467 0.9878 0.9852 7.047e-06 -3.164e-06 -0.002347 5.311e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002914 -0.01027 0.007861 0.9697 0.9741 0.006001 0.8465 0.8344 0.02137 ] Network output: [ 1 -0.01661 0.002965 -4.48e-05 2.011e-05 0.01304 -3.377e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02516 -0.2035 0.209 0.9837 0.9933 0.2014 0.4661 0.8802 0.7252 ] Network output: [ -0.01233 0.9983 1.011 2.701e-06 -1.213e-06 0.01575 2.036e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005133 0.000642 0.004246 0.004992 0.989 0.992 0.005226 0.8773 0.9039 0.01553 ] Network output: [ 0.0009168 -0.0229 1.003 -0.0001776 7.973e-05 1.018 -0.0001338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.09826 0.3148 0.1738 0.9852 0.9941 0.1916 0.4712 0.8865 0.7197 ] Network output: [ 0.009317 -0.04797 1.001 0.0001037 -4.654e-05 1.029 7.812e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09723 0.08711 0.1787 0.2159 0.9874 0.992 0.09729 0.8043 0.8833 0.3131 ] Network output: [ -0.01067 0.04908 1.002 0.000101 -4.532e-05 0.971 7.609e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09675 0.09496 0.1712 0.202 0.9858 0.9916 0.09677 0.7371 0.8642 0.2452 ] Network output: [ 0.001313 0.999 -0.002171 1.472e-05 -6.609e-06 1.001 1.11e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001286 Epoch 6449 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01428 0.9937 0.985 6.296e-06 -2.827e-06 -0.007234 4.745e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002914 -0.0103 0.007755 0.9697 0.9741 0.00601 0.8466 0.8342 0.02134 ] Network output: [ 0.9974 0.02156 0.00119 -4.917e-05 2.207e-05 -0.01775 -3.705e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02466 -0.2057 0.2026 0.9837 0.9933 0.2019 0.4669 0.8799 0.7247 ] Network output: [ -0.01236 1 1.011 2.406e-06 -1.08e-06 0.01389 1.813e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005144 0.0006373 0.004135 0.004779 0.989 0.992 0.005237 0.8774 0.9038 0.01547 ] Network output: [ -0.002452 0.02978 1 -0.0001845 8.283e-05 0.974 -0.0001391 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09839 0.3112 0.1637 0.9852 0.9941 0.192 0.4719 0.8865 0.7201 ] Network output: [ 0.01036 -0.03747 0.9988 0.0001031 -4.629e-05 1.018 7.771e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09706 0.08691 0.1763 0.2131 0.9874 0.992 0.09712 0.8035 0.8832 0.3116 ] Network output: [ -0.01005 0.04601 1.002 0.0001015 -4.555e-05 0.9729 7.647e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09658 0.09477 0.1705 0.2014 0.9858 0.9916 0.09659 0.7361 0.8642 0.2452 ] Network output: [ -0.0006169 0.9992 0.000545 1.374e-05 -6.17e-06 1.001 1.036e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001163 Epoch 6450 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01466 0.9879 0.9852 7.036e-06 -3.159e-06 -0.002359 5.303e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002914 -0.01027 0.007859 0.9697 0.9741 0.006001 0.8465 0.8344 0.02137 ] Network output: [ 1 -0.01653 0.002961 -4.479e-05 2.011e-05 0.01297 -3.376e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02518 -0.2035 0.209 0.9837 0.9933 0.2014 0.466 0.8802 0.7252 ] Network output: [ -0.01233 0.9983 1.011 2.701e-06 -1.213e-06 0.01574 2.036e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005134 0.0006412 0.004247 0.00499 0.989 0.992 0.005226 0.8773 0.9039 0.01553 ] Network output: [ 0.0009067 -0.02278 1.003 -0.0001774 7.966e-05 1.018 -0.0001337 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.09823 0.3149 0.1737 0.9852 0.9941 0.1916 0.4712 0.8865 0.7197 ] Network output: [ 0.009314 -0.04796 1.001 0.0001036 -4.649e-05 1.029 7.805e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09722 0.0871 0.1787 0.2159 0.9874 0.992 0.09728 0.8043 0.8833 0.3131 ] Network output: [ -0.01066 0.04907 1.002 0.0001009 -4.529e-05 0.971 7.602e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09673 0.09493 0.1712 0.202 0.9858 0.9916 0.09674 0.737 0.8642 0.2452 ] Network output: [ 0.001309 0.999 -0.002167 1.47e-05 -6.601e-06 1.001 1.108e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001283 Epoch 6451 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01428 0.9937 0.985 6.29e-06 -2.824e-06 -0.007223 4.74e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002914 -0.0103 0.007753 0.9697 0.9741 0.00601 0.8466 0.8342 0.02134 ] Network output: [ 0.9974 0.02148 0.001192 -4.913e-05 2.206e-05 -0.01769 -3.703e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02468 -0.2057 0.2026 0.9837 0.9933 0.2019 0.4669 0.8799 0.7247 ] Network output: [ -0.01235 1 1.011 2.408e-06 -1.081e-06 0.01389 1.815e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005145 0.0006365 0.004136 0.004778 0.989 0.992 0.005238 0.8774 0.9038 0.01547 ] Network output: [ -0.002447 0.02967 1 -0.0001843 8.274e-05 0.9741 -0.0001389 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09835 0.3112 0.1637 0.9852 0.9941 0.192 0.4718 0.8865 0.7201 ] Network output: [ 0.01035 -0.0375 0.9988 0.000103 -4.625e-05 1.018 7.764e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09706 0.0869 0.1763 0.2131 0.9874 0.992 0.09712 0.8035 0.8832 0.3116 ] Network output: [ -0.01004 0.04602 1.002 0.0001014 -4.552e-05 0.9729 7.641e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09656 0.09475 0.1705 0.2014 0.9858 0.9916 0.09657 0.736 0.8642 0.2451 ] Network output: [ -0.0006131 0.9992 0.0005388 1.373e-05 -6.164e-06 1.001 1.035e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001161 Epoch 6452 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01466 0.9879 0.9852 7.026e-06 -3.154e-06 -0.00237 5.295e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002915 -0.01026 0.007856 0.9697 0.9741 0.006001 0.8465 0.8344 0.02136 ] Network output: [ 1 -0.01645 0.002956 -4.478e-05 2.011e-05 0.0129 -3.375e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.0252 -0.2034 0.2089 0.9837 0.9933 0.2014 0.466 0.8801 0.7252 ] Network output: [ -0.01233 0.9983 1.011 2.701e-06 -1.213e-06 0.01574 2.036e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005135 0.0006403 0.004247 0.004987 0.989 0.992 0.005227 0.8773 0.9039 0.01553 ] Network output: [ 0.0008967 -0.02266 1.003 -0.0001773 7.959e-05 1.017 -0.0001336 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.0982 0.3149 0.1736 0.9852 0.9941 0.1916 0.4712 0.8865 0.7196 ] Network output: [ 0.009311 -0.04794 1.001 0.0001035 -4.645e-05 1.029 7.798e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09722 0.08709 0.1787 0.2159 0.9874 0.992 0.09728 0.8042 0.8833 0.3131 ] Network output: [ -0.01065 0.04906 1.002 0.0001008 -4.525e-05 0.971 7.596e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0967 0.09491 0.1712 0.2019 0.9858 0.9916 0.09672 0.737 0.8642 0.2452 ] Network output: [ 0.001305 0.999 -0.002162 1.469e-05 -6.594e-06 1.001 1.107e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001281 Epoch 6453 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01428 0.9937 0.985 6.283e-06 -2.821e-06 -0.007213 4.735e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002914 -0.01029 0.007751 0.9697 0.9741 0.00601 0.8466 0.8342 0.02133 ] Network output: [ 0.9974 0.02139 0.001195 -4.91e-05 2.204e-05 -0.01762 -3.7e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.0247 -0.2057 0.2026 0.9837 0.9933 0.2019 0.4669 0.8799 0.7247 ] Network output: [ -0.01235 1 1.011 2.409e-06 -1.082e-06 0.01389 1.816e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005146 0.0006357 0.004137 0.004776 0.989 0.992 0.005238 0.8774 0.9038 0.01547 ] Network output: [ -0.002442 0.02955 1 -0.0001841 8.265e-05 0.9741 -0.0001388 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09832 0.3113 0.1636 0.9852 0.9941 0.192 0.4718 0.8865 0.7201 ] Network output: [ 0.01035 -0.03753 0.9988 0.0001029 -4.621e-05 1.018 7.757e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09705 0.08689 0.1764 0.2131 0.9874 0.992 0.09711 0.8034 0.8832 0.3116 ] Network output: [ -0.01003 0.04602 1.002 0.0001013 -4.548e-05 0.9729 7.634e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09653 0.09473 0.1705 0.2014 0.9858 0.9916 0.09655 0.7359 0.8642 0.2451 ] Network output: [ -0.0006093 0.9992 0.0005328 1.372e-05 -6.159e-06 1.002 1.034e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00116 Epoch 6454 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01465 0.9879 0.9852 7.015e-06 -3.149e-06 -0.002382 5.287e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002915 -0.01026 0.007853 0.9697 0.9741 0.006001 0.8464 0.8344 0.02136 ] Network output: [ 1 -0.01637 0.002952 -4.477e-05 2.01e-05 0.01283 -3.374e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02522 -0.2034 0.2089 0.9837 0.9933 0.2015 0.466 0.8801 0.7252 ] Network output: [ -0.01233 0.9983 1.011 2.701e-06 -1.213e-06 0.01573 2.036e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005135 0.0006395 0.004247 0.004984 0.989 0.992 0.005228 0.8773 0.9039 0.01552 ] Network output: [ 0.0008867 -0.02253 1.003 -0.0001771 7.952e-05 1.017 -0.0001335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.09817 0.3149 0.1735 0.9852 0.9941 0.1916 0.4711 0.8865 0.7196 ] Network output: [ 0.009308 -0.04792 1.001 0.0001034 -4.641e-05 1.029 7.79e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09721 0.08709 0.1787 0.2159 0.9874 0.992 0.09727 0.8041 0.8832 0.3131 ] Network output: [ -0.01064 0.04905 1.002 0.0001007 -4.521e-05 0.971 7.59e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09668 0.09488 0.1712 0.2019 0.9858 0.9916 0.09669 0.7369 0.8641 0.2452 ] Network output: [ 0.001301 0.9991 -0.002157 1.467e-05 -6.586e-06 1.001 1.106e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001278 Epoch 6455 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01427 0.9937 0.985 6.277e-06 -2.818e-06 -0.007202 4.73e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002914 -0.01029 0.007749 0.9697 0.9741 0.006011 0.8466 0.8342 0.02133 ] Network output: [ 0.9974 0.0213 0.001198 -4.906e-05 2.203e-05 -0.01756 -3.697e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02472 -0.2057 0.2026 0.9837 0.9933 0.2019 0.4669 0.8799 0.7247 ] Network output: [ -0.01235 1 1.011 2.411e-06 -1.082e-06 0.01389 1.817e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005146 0.0006349 0.004138 0.004774 0.989 0.992 0.005239 0.8773 0.9038 0.01546 ] Network output: [ -0.002437 0.02944 1 -0.0001839 8.256e-05 0.9742 -0.0001386 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09829 0.3113 0.1636 0.9852 0.9941 0.192 0.4718 0.8865 0.72 ] Network output: [ 0.01034 -0.03756 0.9988 0.0001028 -4.616e-05 1.018 7.75e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09705 0.08689 0.1764 0.213 0.9874 0.992 0.09711 0.8034 0.8831 0.3116 ] Network output: [ -0.01003 0.04602 1.002 0.0001012 -4.544e-05 0.9729 7.628e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09651 0.0947 0.1705 0.2014 0.9858 0.9916 0.09652 0.7359 0.8641 0.2451 ] Network output: [ -0.0006056 0.9992 0.0005267 1.371e-05 -6.154e-06 1.002 1.033e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001158 Epoch 6456 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01465 0.9879 0.9852 7.004e-06 -3.144e-06 -0.002393 5.278e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002915 -0.01026 0.007851 0.9697 0.9741 0.006002 0.8464 0.8344 0.02135 ] Network output: [ 1 -0.01629 0.002947 -4.477e-05 2.01e-05 0.01276 -3.374e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02524 -0.2034 0.2088 0.9837 0.9933 0.2015 0.4659 0.8801 0.7252 ] Network output: [ -0.01232 0.9983 1.011 2.701e-06 -1.213e-06 0.01572 2.036e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005136 0.0006387 0.004248 0.004982 0.989 0.992 0.005229 0.8773 0.9039 0.01552 ] Network output: [ 0.0008767 -0.02241 1.003 -0.000177 7.944e-05 1.017 -0.0001334 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.191 0.09814 0.3149 0.1735 0.9852 0.9941 0.1916 0.4711 0.8865 0.7196 ] Network output: [ 0.009305 -0.0479 1.001 0.0001033 -4.636e-05 1.029 7.783e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09721 0.08708 0.1787 0.2158 0.9874 0.992 0.09727 0.8041 0.8832 0.3131 ] Network output: [ -0.01063 0.04904 1.002 0.0001006 -4.518e-05 0.971 7.584e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09666 0.09486 0.1711 0.2019 0.9858 0.9916 0.09667 0.7368 0.8641 0.2452 ] Network output: [ 0.001297 0.9991 -0.002153 1.465e-05 -6.578e-06 1.001 1.104e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001276 Epoch 6457 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01427 0.9937 0.985 6.27e-06 -2.815e-06 -0.007192 4.725e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002915 -0.01028 0.007747 0.9697 0.9741 0.006011 0.8466 0.8342 0.02132 ] Network output: [ 0.9974 0.02122 0.001201 -4.903e-05 2.201e-05 -0.0175 -3.695e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02475 -0.2056 0.2026 0.9837 0.9933 0.2019 0.4668 0.8799 0.7247 ] Network output: [ -0.01235 1 1.011 2.413e-06 -1.083e-06 0.01389 1.818e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005147 0.000634 0.004138 0.004773 0.989 0.992 0.00524 0.8773 0.9037 0.01546 ] Network output: [ -0.002432 0.02933 1 -0.0001837 8.247e-05 0.9743 -0.0001385 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09825 0.3114 0.1636 0.9852 0.9941 0.1921 0.4717 0.8865 0.72 ] Network output: [ 0.01033 -0.03758 0.9988 0.0001027 -4.612e-05 1.019 7.742e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09705 0.08688 0.1764 0.213 0.9874 0.992 0.09711 0.8033 0.8831 0.3116 ] Network output: [ -0.01002 0.04602 1.002 0.0001011 -4.54e-05 0.9728 7.622e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09649 0.09468 0.1704 0.2013 0.9858 0.9916 0.0965 0.7358 0.8641 0.2451 ] Network output: [ -0.0006018 0.9992 0.0005207 1.37e-05 -6.148e-06 1.002 1.032e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001156 Epoch 6458 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01464 0.9879 0.9852 6.993e-06 -3.14e-06 -0.002404 5.27e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002915 -0.01025 0.007848 0.9697 0.9741 0.006002 0.8464 0.8344 0.02135 ] Network output: [ 1 -0.01621 0.002943 -4.476e-05 2.009e-05 0.01269 -3.373e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02526 -0.2034 0.2088 0.9837 0.9933 0.2015 0.4659 0.8801 0.7251 ] Network output: [ -0.01232 0.9983 1.011 2.702e-06 -1.213e-06 0.01571 2.036e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005137 0.0006378 0.004248 0.004979 0.989 0.992 0.005229 0.8773 0.9039 0.01552 ] Network output: [ 0.0008668 -0.02229 1.003 -0.0001768 7.937e-05 1.017 -0.0001332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.0981 0.315 0.1734 0.9852 0.9941 0.1916 0.4711 0.8865 0.7196 ] Network output: [ 0.009302 -0.04788 1.001 0.0001032 -4.632e-05 1.029 7.776e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09721 0.08707 0.1787 0.2158 0.9874 0.992 0.09727 0.804 0.8832 0.313 ] Network output: [ -0.01062 0.04903 1.002 0.0001006 -4.514e-05 0.971 7.578e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09663 0.09483 0.1711 0.2019 0.9858 0.9916 0.09664 0.7367 0.8641 0.2452 ] Network output: [ 0.001293 0.9991 -0.002148 1.464e-05 -6.57e-06 1.001 1.103e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001273 Epoch 6459 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01426 0.9937 0.985 6.263e-06 -2.812e-06 -0.007182 4.72e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002915 -0.01028 0.007745 0.9697 0.9741 0.006011 0.8465 0.8342 0.02132 ] Network output: [ 0.9975 0.02113 0.001203 -4.899e-05 2.199e-05 -0.01744 -3.692e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02477 -0.2056 0.2026 0.9837 0.9933 0.2019 0.4668 0.8799 0.7247 ] Network output: [ -0.01235 1 1.011 2.414e-06 -1.084e-06 0.01389 1.819e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005148 0.0006332 0.004139 0.004771 0.989 0.992 0.005241 0.8773 0.9037 0.01546 ] Network output: [ -0.002427 0.02922 1.001 -0.0001835 8.238e-05 0.9744 -0.0001383 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09822 0.3114 0.1635 0.9852 0.9941 0.1921 0.4717 0.8865 0.72 ] Network output: [ 0.01032 -0.03761 0.9988 0.0001026 -4.608e-05 1.019 7.735e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09705 0.08687 0.1764 0.213 0.9874 0.992 0.0971 0.8032 0.8831 0.3116 ] Network output: [ -0.01002 0.04602 1.002 0.000101 -4.536e-05 0.9728 7.615e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09646 0.09465 0.1704 0.2013 0.9858 0.9916 0.09648 0.7357 0.8641 0.2451 ] Network output: [ -0.0005981 0.9992 0.0005146 1.368e-05 -6.143e-06 1.002 1.031e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001154 Epoch 6460 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01463 0.9879 0.9852 6.982e-06 -3.135e-06 -0.002416 5.262e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002916 -0.01025 0.007845 0.9697 0.9741 0.006002 0.8464 0.8344 0.02134 ] Network output: [ 1 -0.01613 0.002939 -4.475e-05 2.009e-05 0.01262 -3.372e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.02528 -0.2033 0.2088 0.9837 0.9933 0.2015 0.4659 0.8801 0.7251 ] Network output: [ -0.01232 0.9983 1.011 2.702e-06 -1.213e-06 0.0157 2.036e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005138 0.000637 0.004248 0.004976 0.989 0.992 0.00523 0.8773 0.9039 0.01551 ] Network output: [ 0.0008569 -0.02217 1.003 -0.0001766 7.93e-05 1.017 -0.0001331 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.09807 0.315 0.1733 0.9852 0.9941 0.1917 0.4711 0.8865 0.7196 ] Network output: [ 0.009298 -0.04786 1.001 0.0001031 -4.627e-05 1.029 7.768e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0972 0.08706 0.1787 0.2158 0.9874 0.992 0.09726 0.804 0.8831 0.313 ] Network output: [ -0.01061 0.04902 1.002 0.0001005 -4.51e-05 0.9709 7.572e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09661 0.09481 0.1711 0.2018 0.9858 0.9916 0.09662 0.7367 0.864 0.2452 ] Network output: [ 0.001289 0.9991 -0.002143 1.462e-05 -6.562e-06 1.001 1.102e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001271 Epoch 6461 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01426 0.9936 0.985 6.256e-06 -2.809e-06 -0.007171 4.715e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002915 -0.01028 0.007742 0.9697 0.9741 0.006011 0.8465 0.8341 0.02131 ] Network output: [ 0.9975 0.02104 0.001206 -4.896e-05 2.198e-05 -0.01738 -3.689e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02479 -0.2056 0.2026 0.9837 0.9933 0.2019 0.4668 0.8799 0.7246 ] Network output: [ -0.01234 1 1.011 2.416e-06 -1.084e-06 0.01389 1.821e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005148 0.0006324 0.00414 0.004769 0.989 0.992 0.005241 0.8773 0.9037 0.01545 ] Network output: [ -0.002423 0.02911 1.001 -0.0001833 8.23e-05 0.9745 -0.0001381 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09819 0.3114 0.1635 0.9852 0.9941 0.1921 0.4717 0.8865 0.72 ] Network output: [ 0.01031 -0.03764 0.9988 0.0001025 -4.603e-05 1.019 7.728e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09704 0.08686 0.1764 0.213 0.9874 0.992 0.0971 0.8032 0.8831 0.3116 ] Network output: [ -0.01001 0.04603 1.002 0.000101 -4.532e-05 0.9728 7.609e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09644 0.09463 0.1704 0.2013 0.9858 0.9916 0.09645 0.7357 0.864 0.2451 ] Network output: [ -0.0005944 0.9992 0.0005087 1.367e-05 -6.138e-06 1.002 1.03e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001153 Epoch 6462 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01463 0.988 0.9852 6.972e-06 -3.13e-06 -0.002427 5.254e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002916 -0.01025 0.007843 0.9697 0.9741 0.006002 0.8464 0.8344 0.02134 ] Network output: [ 1 -0.01605 0.002934 -4.473e-05 2.008e-05 0.01255 -3.371e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1807 -0.0253 -0.2033 0.2087 0.9837 0.9933 0.2015 0.4659 0.8801 0.7251 ] Network output: [ -0.01232 0.9983 1.011 2.702e-06 -1.213e-06 0.01569 2.036e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005138 0.0006362 0.004248 0.004974 0.989 0.992 0.005231 0.8772 0.9039 0.01551 ] Network output: [ 0.0008471 -0.02205 1.003 -0.0001765 7.923e-05 1.017 -0.000133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.09804 0.315 0.1733 0.9852 0.9941 0.1917 0.471 0.8864 0.7196 ] Network output: [ 0.009295 -0.04785 1.001 0.000103 -4.623e-05 1.029 7.761e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0972 0.08705 0.1787 0.2157 0.9874 0.992 0.09726 0.8039 0.8831 0.313 ] Network output: [ -0.0106 0.04901 1.002 0.0001004 -4.507e-05 0.9709 7.565e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09658 0.09478 0.1711 0.2018 0.9858 0.9916 0.0966 0.7366 0.864 0.2451 ] Network output: [ 0.001285 0.9991 -0.002139 1.46e-05 -6.555e-06 1.001 1.1e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001268 Epoch 6463 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01425 0.9936 0.985 6.25e-06 -2.806e-06 -0.007161 4.71e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002915 -0.01027 0.00774 0.9697 0.9741 0.006011 0.8465 0.8341 0.02131 ] Network output: [ 0.9975 0.02096 0.001209 -4.892e-05 2.196e-05 -0.01732 -3.687e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02481 -0.2055 0.2026 0.9837 0.9933 0.2019 0.4667 0.8799 0.7246 ] Network output: [ -0.01234 1 1.011 2.417e-06 -1.085e-06 0.01389 1.822e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005149 0.0006316 0.004141 0.004767 0.989 0.992 0.005242 0.8773 0.9037 0.01545 ] Network output: [ -0.002418 0.029 1.001 -0.0001831 8.221e-05 0.9745 -0.000138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09816 0.3115 0.1635 0.9852 0.9941 0.1921 0.4716 0.8865 0.72 ] Network output: [ 0.0103 -0.03767 0.9988 0.0001024 -4.599e-05 1.019 7.721e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09704 0.08686 0.1764 0.213 0.9874 0.992 0.0971 0.8031 0.883 0.3116 ] Network output: [ -0.01001 0.04603 1.002 0.0001009 -4.529e-05 0.9728 7.602e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09642 0.09461 0.1704 0.2013 0.9858 0.9916 0.09643 0.7356 0.864 0.2451 ] Network output: [ -0.0005907 0.9992 0.0005027 1.366e-05 -6.132e-06 1.002 1.029e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001151 Epoch 6464 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01462 0.988 0.9852 6.961e-06 -3.125e-06 -0.002439 5.246e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002916 -0.01024 0.00784 0.9697 0.9741 0.006003 0.8464 0.8344 0.02133 ] Network output: [ 1 -0.01598 0.00293 -4.472e-05 2.008e-05 0.01248 -3.371e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02532 -0.2033 0.2087 0.9837 0.9933 0.2015 0.4658 0.8801 0.7251 ] Network output: [ -0.01232 0.9983 1.011 2.702e-06 -1.213e-06 0.01568 2.036e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005139 0.0006354 0.004249 0.004971 0.989 0.992 0.005232 0.8772 0.9038 0.0155 ] Network output: [ 0.0008373 -0.02193 1.003 -0.0001763 7.915e-05 1.017 -0.0001329 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.09801 0.315 0.1732 0.9852 0.9941 0.1917 0.471 0.8864 0.7196 ] Network output: [ 0.009292 -0.04783 1.001 0.0001029 -4.619e-05 1.029 7.753e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0972 0.08704 0.1787 0.2157 0.9874 0.992 0.09726 0.8038 0.8831 0.313 ] Network output: [ -0.01059 0.049 1.002 0.0001003 -4.503e-05 0.9709 7.559e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09656 0.09476 0.171 0.2018 0.9858 0.9916 0.09657 0.7365 0.864 0.2451 ] Network output: [ 0.001281 0.9991 -0.002134 1.458e-05 -6.547e-06 1.001 1.099e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001266 Epoch 6465 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01425 0.9936 0.985 6.243e-06 -2.803e-06 -0.007151 4.705e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002916 -0.01027 0.007738 0.9697 0.9741 0.006012 0.8465 0.8341 0.0213 ] Network output: [ 0.9975 0.02087 0.001211 -4.889e-05 2.195e-05 -0.01726 -3.684e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02483 -0.2055 0.2026 0.9837 0.9933 0.2019 0.4667 0.8798 0.7246 ] Network output: [ -0.01234 1 1.011 2.419e-06 -1.086e-06 0.01389 1.823e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00515 0.0006308 0.004142 0.004766 0.989 0.992 0.005243 0.8773 0.9037 0.01545 ] Network output: [ -0.002413 0.02889 1.001 -0.0001829 8.212e-05 0.9746 -0.0001378 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09812 0.3115 0.1635 0.9852 0.9941 0.1921 0.4716 0.8865 0.72 ] Network output: [ 0.01029 -0.0377 0.9988 0.0001023 -4.595e-05 1.019 7.713e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09704 0.08685 0.1764 0.213 0.9874 0.992 0.0971 0.8031 0.883 0.3115 ] Network output: [ -0.01 0.04603 1.002 0.0001008 -4.525e-05 0.9728 7.596e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09639 0.09458 0.1704 0.2013 0.9858 0.9916 0.0964 0.7355 0.864 0.245 ] Network output: [ -0.000587 0.9992 0.0004968 1.365e-05 -6.127e-06 1.002 1.029e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001149 Epoch 6466 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01462 0.988 0.9852 6.95e-06 -3.12e-06 -0.00245 5.238e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002916 -0.01024 0.007837 0.9697 0.9741 0.006003 0.8464 0.8343 0.02133 ] Network output: [ 1 -0.0159 0.002925 -4.471e-05 2.007e-05 0.01241 -3.37e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02534 -0.2033 0.2086 0.9837 0.9933 0.2015 0.4658 0.8801 0.7251 ] Network output: [ -0.01231 0.9984 1.011 2.702e-06 -1.213e-06 0.01567 2.036e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00514 0.0006346 0.004249 0.004968 0.989 0.992 0.005232 0.8772 0.9038 0.0155 ] Network output: [ 0.0008276 -0.02181 1.003 -0.0001761 7.908e-05 1.017 -0.0001328 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.09798 0.3151 0.1731 0.9852 0.9941 0.1917 0.471 0.8864 0.7195 ] Network output: [ 0.009289 -0.04781 1.001 0.0001028 -4.614e-05 1.029 7.746e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09719 0.08703 0.1787 0.2157 0.9874 0.992 0.09725 0.8038 0.8831 0.313 ] Network output: [ -0.01058 0.04899 1.002 0.0001002 -4.499e-05 0.9709 7.553e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09653 0.09473 0.171 0.2018 0.9858 0.9916 0.09655 0.7365 0.864 0.2451 ] Network output: [ 0.001277 0.9991 -0.002129 1.457e-05 -6.539e-06 1.001 1.098e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001264 Epoch 6467 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01424 0.9936 0.9851 6.236e-06 -2.8e-06 -0.007141 4.7e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002916 -0.01027 0.007736 0.9697 0.9741 0.006012 0.8465 0.8341 0.0213 ] Network output: [ 0.9975 0.02078 0.001214 -4.885e-05 2.193e-05 -0.01719 -3.681e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02486 -0.2055 0.2025 0.9837 0.9933 0.2019 0.4667 0.8798 0.7246 ] Network output: [ -0.01234 1 1.011 2.42e-06 -1.086e-06 0.01389 1.824e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005151 0.00063 0.004142 0.004764 0.989 0.992 0.005243 0.8773 0.9037 0.01544 ] Network output: [ -0.002408 0.02879 1.001 -0.0001827 8.203e-05 0.9747 -0.0001377 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09809 0.3116 0.1634 0.9852 0.9941 0.1921 0.4716 0.8864 0.7199 ] Network output: [ 0.01028 -0.03773 0.9988 0.0001023 -4.59e-05 1.019 7.706e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09703 0.08684 0.1764 0.2129 0.9874 0.992 0.09709 0.803 0.883 0.3115 ] Network output: [ -0.009996 0.04603 1.002 0.0001007 -4.521e-05 0.9728 7.589e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09637 0.09456 0.1703 0.2012 0.9858 0.9916 0.09638 0.7355 0.8639 0.245 ] Network output: [ -0.0005833 0.9992 0.0004909 1.364e-05 -6.122e-06 1.002 1.028e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001147 Epoch 6468 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01461 0.988 0.9853 6.939e-06 -3.115e-06 -0.002461 5.23e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002917 -0.01024 0.007835 0.9697 0.9741 0.006003 0.8464 0.8343 0.02132 ] Network output: [ 1 -0.01582 0.002921 -4.47e-05 2.007e-05 0.01235 -3.369e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02536 -0.2032 0.2086 0.9837 0.9933 0.2015 0.4658 0.88 0.7251 ] Network output: [ -0.01231 0.9984 1.011 2.702e-06 -1.213e-06 0.01566 2.036e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005141 0.0006337 0.004249 0.004966 0.989 0.992 0.005233 0.8772 0.9038 0.0155 ] Network output: [ 0.0008179 -0.02169 1.003 -0.000176 7.901e-05 1.017 -0.0001326 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.09795 0.3151 0.173 0.9852 0.9941 0.1917 0.4709 0.8864 0.7195 ] Network output: [ 0.009286 -0.04779 1.001 0.0001027 -4.61e-05 1.029 7.739e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09719 0.08703 0.1787 0.2156 0.9874 0.992 0.09725 0.8037 0.883 0.313 ] Network output: [ -0.01058 0.04898 1.002 0.0001001 -4.496e-05 0.9709 7.547e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09651 0.09471 0.171 0.2017 0.9858 0.9916 0.09652 0.7364 0.8639 0.2451 ] Network output: [ 0.001273 0.9991 -0.002125 1.455e-05 -6.531e-06 1.001 1.096e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001261 Epoch 6469 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01424 0.9936 0.9851 6.229e-06 -2.797e-06 -0.007131 4.695e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002916 -0.01026 0.007734 0.9697 0.9741 0.006012 0.8465 0.8341 0.02129 ] Network output: [ 0.9975 0.0207 0.001217 -4.881e-05 2.191e-05 -0.01713 -3.679e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02488 -0.2054 0.2025 0.9837 0.9933 0.202 0.4666 0.8798 0.7246 ] Network output: [ -0.01234 1 1.011 2.421e-06 -1.087e-06 0.01389 1.825e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005151 0.0006292 0.004143 0.004762 0.989 0.992 0.005244 0.8772 0.9037 0.01544 ] Network output: [ -0.002403 0.02868 1.001 -0.0001825 8.194e-05 0.9748 -0.0001375 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09806 0.3116 0.1634 0.9852 0.9941 0.1921 0.4715 0.8864 0.7199 ] Network output: [ 0.01027 -0.03775 0.9988 0.0001022 -4.586e-05 1.019 7.699e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09703 0.08683 0.1764 0.2129 0.9874 0.992 0.09709 0.803 0.883 0.3115 ] Network output: [ -0.009991 0.04603 1.002 0.0001006 -4.517e-05 0.9728 7.583e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09635 0.09453 0.1703 0.2012 0.9858 0.9916 0.09636 0.7354 0.8639 0.245 ] Network output: [ -0.0005796 0.9992 0.000485 1.362e-05 -6.116e-06 1.002 1.027e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001146 Epoch 6470 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0146 0.988 0.9853 6.928e-06 -3.11e-06 -0.002473 5.221e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002917 -0.01023 0.007832 0.9697 0.9741 0.006003 0.8464 0.8343 0.02132 ] Network output: [ 1 -0.01574 0.002917 -4.469e-05 2.006e-05 0.01228 -3.368e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02538 -0.2032 0.2086 0.9837 0.9933 0.2015 0.4658 0.88 0.725 ] Network output: [ -0.01231 0.9984 1.011 2.701e-06 -1.213e-06 0.01565 2.036e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005141 0.0006329 0.00425 0.004963 0.989 0.992 0.005234 0.8772 0.9038 0.01549 ] Network output: [ 0.0008082 -0.02157 1.003 -0.0001758 7.893e-05 1.016 -0.0001325 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.09791 0.3151 0.173 0.9852 0.9941 0.1917 0.4709 0.8864 0.7195 ] Network output: [ 0.009282 -0.04777 1.001 0.0001026 -4.605e-05 1.029 7.731e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09718 0.08702 0.1787 0.2156 0.9874 0.992 0.09725 0.8037 0.883 0.313 ] Network output: [ -0.01057 0.04896 1.002 0.0001001 -4.492e-05 0.9709 7.541e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09649 0.09468 0.171 0.2017 0.9858 0.9916 0.0965 0.7363 0.8639 0.2451 ] Network output: [ 0.001269 0.9991 -0.00212 1.453e-05 -6.524e-06 1.001 1.095e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001259 Epoch 6471 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01424 0.9936 0.9851 6.223e-06 -2.794e-06 -0.007121 4.689e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002916 -0.01026 0.007731 0.9697 0.9741 0.006012 0.8465 0.8341 0.02129 ] Network output: [ 0.9975 0.02061 0.001219 -4.878e-05 2.19e-05 -0.01707 -3.676e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.0249 -0.2054 0.2025 0.9837 0.9933 0.202 0.4666 0.8798 0.7246 ] Network output: [ -0.01233 1 1.011 2.423e-06 -1.088e-06 0.01388 1.826e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005152 0.0006284 0.004144 0.004761 0.989 0.992 0.005245 0.8772 0.9037 0.01544 ] Network output: [ -0.002398 0.02857 1.001 -0.0001823 8.185e-05 0.9749 -0.0001374 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09803 0.3116 0.1634 0.9852 0.9941 0.1921 0.4715 0.8864 0.7199 ] Network output: [ 0.01026 -0.03778 0.9987 0.0001021 -4.582e-05 1.019 7.691e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09703 0.08683 0.1764 0.2129 0.9874 0.992 0.09709 0.8029 0.8829 0.3115 ] Network output: [ -0.009985 0.04603 1.002 0.0001005 -4.513e-05 0.9728 7.576e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09632 0.09451 0.1703 0.2012 0.9858 0.9916 0.09633 0.7353 0.8639 0.245 ] Network output: [ -0.000576 0.9992 0.0004791 1.361e-05 -6.111e-06 1.002 1.026e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001144 Epoch 6472 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0146 0.9881 0.9853 6.917e-06 -3.106e-06 -0.002484 5.213e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002917 -0.01023 0.007829 0.9697 0.9741 0.006003 0.8464 0.8343 0.02131 ] Network output: [ 1 -0.01567 0.002912 -4.468e-05 2.006e-05 0.01221 -3.367e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.0254 -0.2032 0.2085 0.9837 0.9933 0.2015 0.4657 0.88 0.725 ] Network output: [ -0.01231 0.9984 1.011 2.701e-06 -1.213e-06 0.01565 2.036e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005142 0.0006321 0.00425 0.00496 0.989 0.992 0.005235 0.8772 0.9038 0.01549 ] Network output: [ 0.0007986 -0.02145 1.003 -0.0001757 7.886e-05 1.016 -0.0001324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.09788 0.3151 0.1729 0.9852 0.9941 0.1917 0.4709 0.8864 0.7195 ] Network output: [ 0.009279 -0.04775 1.001 0.0001025 -4.601e-05 1.029 7.724e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09718 0.08701 0.1787 0.2156 0.9874 0.992 0.09724 0.8036 0.883 0.3129 ] Network output: [ -0.01056 0.04895 1.002 9.997e-05 -4.488e-05 0.9709 7.534e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09646 0.09466 0.171 0.2017 0.9858 0.9916 0.09647 0.7363 0.8639 0.2451 ] Network output: [ 0.001265 0.9991 -0.002115 1.451e-05 -6.516e-06 1.001 1.094e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001256 Epoch 6473 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01423 0.9936 0.9851 6.216e-06 -2.79e-06 -0.007111 4.684e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002917 -0.01026 0.007729 0.9697 0.9741 0.006012 0.8465 0.8341 0.02128 ] Network output: [ 0.9975 0.02053 0.001222 -4.874e-05 2.188e-05 -0.01701 -3.674e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02492 -0.2054 0.2025 0.9837 0.9933 0.202 0.4666 0.8798 0.7246 ] Network output: [ -0.01233 1 1.011 2.424e-06 -1.088e-06 0.01388 1.827e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005153 0.0006276 0.004145 0.004759 0.989 0.992 0.005246 0.8772 0.9037 0.01543 ] Network output: [ -0.002393 0.02846 1.001 -0.0001821 8.176e-05 0.9749 -0.0001372 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.098 0.3117 0.1634 0.9852 0.9941 0.1921 0.4715 0.8864 0.7199 ] Network output: [ 0.01026 -0.03781 0.9987 0.000102 -4.577e-05 1.019 7.684e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09702 0.08682 0.1764 0.2129 0.9874 0.992 0.09708 0.8029 0.8829 0.3115 ] Network output: [ -0.00998 0.04603 1.002 0.0001004 -4.509e-05 0.9728 7.57e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0963 0.09449 0.1703 0.2012 0.9858 0.9916 0.09631 0.7353 0.8638 0.245 ] Network output: [ -0.0005723 0.9992 0.0004733 1.36e-05 -6.106e-06 1.002 1.025e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001142 Epoch 6474 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01459 0.9881 0.9853 6.907e-06 -3.101e-06 -0.002496 5.205e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003172 -0.002917 -0.01023 0.007827 0.9697 0.9741 0.006004 0.8464 0.8343 0.02131 ] Network output: [ 1 -0.01559 0.002908 -4.467e-05 2.005e-05 0.01214 -3.367e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02541 -0.2032 0.2085 0.9837 0.9933 0.2015 0.4657 0.88 0.725 ] Network output: [ -0.01231 0.9984 1.011 2.701e-06 -1.213e-06 0.01564 2.036e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005143 0.0006313 0.00425 0.004958 0.989 0.992 0.005236 0.8772 0.9038 0.01549 ] Network output: [ 0.000789 -0.02133 1.003 -0.0001755 7.879e-05 1.016 -0.0001323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.09785 0.3152 0.1728 0.9852 0.9941 0.1917 0.4709 0.8864 0.7195 ] Network output: [ 0.009276 -0.04773 1.001 0.0001024 -4.597e-05 1.029 7.716e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09718 0.087 0.1787 0.2156 0.9874 0.992 0.09724 0.8036 0.883 0.3129 ] Network output: [ -0.01055 0.04894 1.002 9.989e-05 -4.485e-05 0.9709 7.528e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09644 0.09463 0.1709 0.2017 0.9858 0.9916 0.09645 0.7362 0.8638 0.2451 ] Network output: [ 0.001261 0.9991 -0.00211 1.45e-05 -6.508e-06 1.001 1.093e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001254 Epoch 6475 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01423 0.9936 0.9851 6.209e-06 -2.787e-06 -0.007102 4.679e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002917 -0.01025 0.007727 0.9697 0.9741 0.006013 0.8465 0.8341 0.02128 ] Network output: [ 0.9975 0.02044 0.001224 -4.871e-05 2.187e-05 -0.01695 -3.671e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02494 -0.2053 0.2025 0.9837 0.9933 0.202 0.4666 0.8798 0.7245 ] Network output: [ -0.01233 1 1.011 2.425e-06 -1.089e-06 0.01388 1.828e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005153 0.0006268 0.004145 0.004757 0.989 0.992 0.005246 0.8772 0.9036 0.01543 ] Network output: [ -0.002388 0.02835 1.001 -0.0001819 8.167e-05 0.975 -0.0001371 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09796 0.3117 0.1633 0.9852 0.9941 0.1921 0.4715 0.8864 0.7199 ] Network output: [ 0.01025 -0.03783 0.9987 0.0001019 -4.573e-05 1.019 7.677e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09702 0.08681 0.1765 0.2129 0.9874 0.992 0.09708 0.8028 0.8829 0.3115 ] Network output: [ -0.009974 0.04603 1.002 0.0001004 -4.506e-05 0.9728 7.563e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09628 0.09446 0.1703 0.2011 0.9858 0.9916 0.09629 0.7352 0.8638 0.245 ] Network output: [ -0.0005687 0.9992 0.0004675 1.359e-05 -6.1e-06 1.002 1.024e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001141 Epoch 6476 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01458 0.9881 0.9853 6.896e-06 -3.096e-06 -0.002507 5.197e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.002918 -0.01022 0.007824 0.9697 0.9741 0.006004 0.8464 0.8343 0.0213 ] Network output: [ 1 -0.01551 0.002904 -4.466e-05 2.005e-05 0.01207 -3.366e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02543 -0.2032 0.2084 0.9837 0.9933 0.2015 0.4657 0.88 0.725 ] Network output: [ -0.0123 0.9984 1.011 2.701e-06 -1.213e-06 0.01563 2.036e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005144 0.0006305 0.00425 0.004955 0.989 0.992 0.005236 0.8771 0.9038 0.01548 ] Network output: [ 0.0007794 -0.02121 1.003 -0.0001753 7.871e-05 1.016 -0.0001321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.09782 0.3152 0.1728 0.9852 0.9941 0.1917 0.4708 0.8864 0.7195 ] Network output: [ 0.009273 -0.04771 1.001 0.0001023 -4.592e-05 1.029 7.709e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09717 0.08699 0.1787 0.2155 0.9874 0.992 0.09723 0.8035 0.8829 0.3129 ] Network output: [ -0.01054 0.04893 1.002 9.981e-05 -4.481e-05 0.9709 7.522e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09641 0.09461 0.1709 0.2016 0.9858 0.9916 0.09643 0.7361 0.8638 0.245 ] Network output: [ 0.001257 0.9991 -0.002106 1.448e-05 -6.501e-06 1.001 1.091e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001252 Epoch 6477 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01422 0.9936 0.9851 6.202e-06 -2.784e-06 -0.007092 4.674e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002917 -0.01025 0.007725 0.9697 0.9741 0.006013 0.8465 0.8341 0.02127 ] Network output: [ 0.9976 0.02036 0.001227 -4.867e-05 2.185e-05 -0.01689 -3.668e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02496 -0.2053 0.2025 0.9837 0.9933 0.202 0.4665 0.8798 0.7245 ] Network output: [ -0.01233 1 1.011 2.427e-06 -1.089e-06 0.01388 1.829e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005154 0.000626 0.004146 0.004756 0.989 0.992 0.005247 0.8772 0.9036 0.01543 ] Network output: [ -0.002383 0.02825 1.001 -0.0001817 8.158e-05 0.9751 -0.0001369 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09793 0.3118 0.1633 0.9852 0.9941 0.1921 0.4714 0.8864 0.7198 ] Network output: [ 0.01024 -0.03786 0.9987 0.0001018 -4.569e-05 1.019 7.67e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09702 0.0868 0.1765 0.2129 0.9874 0.992 0.09708 0.8028 0.8829 0.3115 ] Network output: [ -0.009969 0.04603 1.002 0.0001003 -4.502e-05 0.9728 7.557e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09625 0.09444 0.1703 0.2011 0.9858 0.9916 0.09627 0.7351 0.8638 0.245 ] Network output: [ -0.000565 0.9992 0.0004617 1.358e-05 -6.095e-06 1.002 1.023e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001139 Epoch 6478 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01458 0.9881 0.9853 6.885e-06 -3.091e-06 -0.002519 5.189e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.002918 -0.01022 0.007821 0.9697 0.9741 0.006004 0.8463 0.8343 0.0213 ] Network output: [ 1 -0.01544 0.002899 -4.465e-05 2.004e-05 0.012 -3.365e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02545 -0.2031 0.2084 0.9837 0.9933 0.2015 0.4657 0.88 0.725 ] Network output: [ -0.0123 0.9984 1.011 2.701e-06 -1.213e-06 0.01562 2.036e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005144 0.0006297 0.004251 0.004952 0.989 0.992 0.005237 0.8771 0.9038 0.01548 ] Network output: [ 0.0007699 -0.0211 1.003 -0.0001752 7.864e-05 1.016 -0.000132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.09779 0.3152 0.1727 0.9852 0.9941 0.1917 0.4708 0.8864 0.7194 ] Network output: [ 0.009269 -0.04769 1.001 0.0001022 -4.588e-05 1.029 7.702e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09717 0.08698 0.1787 0.2155 0.9874 0.992 0.09723 0.8034 0.8829 0.3129 ] Network output: [ -0.01053 0.04892 1.002 9.973e-05 -4.477e-05 0.9709 7.516e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09639 0.09459 0.1709 0.2016 0.9858 0.9916 0.0964 0.736 0.8638 0.245 ] Network output: [ 0.001253 0.9991 -0.002101 1.446e-05 -6.493e-06 1.001 1.09e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001249 Epoch 6479 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01422 0.9936 0.9851 6.195e-06 -2.781e-06 -0.007082 4.669e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002917 -0.01025 0.007722 0.9697 0.9741 0.006013 0.8465 0.8341 0.02127 ] Network output: [ 0.9976 0.02027 0.00123 -4.864e-05 2.184e-05 -0.01683 -3.666e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02499 -0.2053 0.2025 0.9837 0.9933 0.202 0.4665 0.8798 0.7245 ] Network output: [ -0.01233 1 1.011 2.428e-06 -1.09e-06 0.01388 1.83e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005155 0.0006252 0.004147 0.004754 0.989 0.992 0.005248 0.8772 0.9036 0.01542 ] Network output: [ -0.002378 0.02814 1.001 -0.0001815 8.149e-05 0.9752 -0.0001368 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.0979 0.3118 0.1633 0.9852 0.9941 0.1921 0.4714 0.8864 0.7198 ] Network output: [ 0.01023 -0.03789 0.9987 0.0001017 -4.564e-05 1.019 7.662e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09702 0.0868 0.1765 0.2128 0.9874 0.992 0.09708 0.8027 0.8828 0.3115 ] Network output: [ -0.009963 0.04603 1.002 0.0001002 -4.498e-05 0.9728 7.55e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09623 0.09442 0.1702 0.2011 0.9858 0.9916 0.09624 0.7351 0.8637 0.2449 ] Network output: [ -0.0005614 0.9992 0.000456 1.356e-05 -6.09e-06 1.002 1.022e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001137 Epoch 6480 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01457 0.9881 0.9853 6.874e-06 -3.086e-06 -0.00253 5.18e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.002918 -0.01022 0.007819 0.9697 0.9741 0.006004 0.8463 0.8343 0.02129 ] Network output: [ 1 -0.01536 0.002895 -4.464e-05 2.004e-05 0.01194 -3.364e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02547 -0.2031 0.2084 0.9837 0.9933 0.2015 0.4656 0.88 0.725 ] Network output: [ -0.0123 0.9984 1.011 2.701e-06 -1.213e-06 0.01561 2.035e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005145 0.0006289 0.004251 0.00495 0.989 0.992 0.005238 0.8771 0.9038 0.01547 ] Network output: [ 0.0007605 -0.02098 1.003 -0.000175 7.857e-05 1.016 -0.0001319 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.09776 0.3153 0.1726 0.9852 0.9941 0.1917 0.4708 0.8863 0.7194 ] Network output: [ 0.009266 -0.04767 1.001 0.0001021 -4.583e-05 1.029 7.694e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09717 0.08698 0.1787 0.2155 0.9874 0.992 0.09723 0.8034 0.8829 0.3129 ] Network output: [ -0.01052 0.04891 1.002 9.964e-05 -4.473e-05 0.9709 7.509e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09637 0.09456 0.1709 0.2016 0.9858 0.9916 0.09638 0.736 0.8637 0.245 ] Network output: [ 0.001249 0.9991 -0.002096 1.445e-05 -6.485e-06 1.001 1.089e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001247 Epoch 6481 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01421 0.9936 0.9851 6.188e-06 -2.778e-06 -0.007073 4.663e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002918 -0.01024 0.00772 0.9697 0.9741 0.006013 0.8464 0.8341 0.02126 ] Network output: [ 0.9976 0.02019 0.001232 -4.86e-05 2.182e-05 -0.01677 -3.663e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02501 -0.2052 0.2025 0.9837 0.9933 0.202 0.4665 0.8798 0.7245 ] Network output: [ -0.01232 1 1.011 2.429e-06 -1.091e-06 0.01388 1.831e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005155 0.0006245 0.004148 0.004752 0.989 0.992 0.005248 0.8772 0.9036 0.01542 ] Network output: [ -0.002373 0.02803 1.001 -0.0001813 8.14e-05 0.9752 -0.0001366 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09787 0.3119 0.1632 0.9852 0.9941 0.1921 0.4714 0.8864 0.7198 ] Network output: [ 0.01022 -0.03791 0.9987 0.0001016 -4.56e-05 1.019 7.655e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09701 0.08679 0.1765 0.2128 0.9874 0.992 0.09707 0.8026 0.8828 0.3115 ] Network output: [ -0.009957 0.04603 1.002 0.0001001 -4.494e-05 0.9728 7.544e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09621 0.09439 0.1702 0.2011 0.9858 0.9916 0.09622 0.735 0.8637 0.2449 ] Network output: [ -0.0005578 0.9992 0.0004502 1.355e-05 -6.084e-06 1.002 1.021e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001135 Epoch 6482 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01457 0.9881 0.9853 6.863e-06 -3.081e-06 -0.002542 5.172e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.002918 -0.01021 0.007816 0.9697 0.9741 0.006005 0.8463 0.8343 0.02129 ] Network output: [ 1 -0.01528 0.002891 -4.463e-05 2.003e-05 0.01187 -3.363e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02549 -0.2031 0.2083 0.9837 0.9933 0.2016 0.4656 0.88 0.7249 ] Network output: [ -0.0123 0.9984 1.011 2.701e-06 -1.212e-06 0.0156 2.035e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005146 0.0006281 0.004251 0.004947 0.989 0.992 0.005239 0.8771 0.9037 0.01547 ] Network output: [ 0.000751 -0.02086 1.003 -0.0001748 7.849e-05 1.016 -0.0001318 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.09773 0.3153 0.1726 0.9852 0.9941 0.1917 0.4708 0.8863 0.7194 ] Network output: [ 0.009263 -0.04765 1.001 0.000102 -4.579e-05 1.029 7.687e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09716 0.08697 0.1787 0.2154 0.9874 0.992 0.09722 0.8033 0.8829 0.3129 ] Network output: [ -0.01051 0.0489 1.002 9.956e-05 -4.47e-05 0.9709 7.503e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09634 0.09454 0.1708 0.2016 0.9858 0.9916 0.09635 0.7359 0.8637 0.245 ] Network output: [ 0.001245 0.9991 -0.002092 1.443e-05 -6.478e-06 1.001 1.087e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001245 Epoch 6483 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01421 0.9936 0.9851 6.181e-06 -2.775e-06 -0.007063 4.658e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002918 -0.01024 0.007718 0.9697 0.9741 0.006013 0.8464 0.834 0.02126 ] Network output: [ 0.9976 0.02011 0.001235 -4.857e-05 2.18e-05 -0.01671 -3.66e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02503 -0.2052 0.2025 0.9837 0.9933 0.202 0.4664 0.8798 0.7245 ] Network output: [ -0.01232 1 1.011 2.431e-06 -1.091e-06 0.01388 1.832e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005156 0.0006237 0.004148 0.00475 0.989 0.992 0.005249 0.8771 0.9036 0.01542 ] Network output: [ -0.002368 0.02792 1.001 -0.0001811 8.131e-05 0.9753 -0.0001365 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09784 0.3119 0.1632 0.9852 0.9941 0.1921 0.4713 0.8864 0.7198 ] Network output: [ 0.01021 -0.03794 0.9987 0.0001015 -4.556e-05 1.019 7.648e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09701 0.08678 0.1765 0.2128 0.9874 0.992 0.09707 0.8026 0.8828 0.3115 ] Network output: [ -0.009952 0.04603 1.002 0.0001 -4.49e-05 0.9728 7.537e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09618 0.09437 0.1702 0.2011 0.9858 0.9916 0.0962 0.7349 0.8637 0.2449 ] Network output: [ -0.0005542 0.9992 0.0004445 1.354e-05 -6.079e-06 1.002 1.021e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001134 Epoch 6484 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01456 0.9882 0.9853 6.852e-06 -3.076e-06 -0.002553 5.164e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.002919 -0.01021 0.007813 0.9697 0.9741 0.006005 0.8463 0.8343 0.02128 ] Network output: [ 1 -0.01521 0.002886 -4.462e-05 2.003e-05 0.0118 -3.362e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02551 -0.2031 0.2083 0.9837 0.9933 0.2016 0.4656 0.88 0.7249 ] Network output: [ -0.0123 0.9984 1.011 2.7e-06 -1.212e-06 0.01559 2.035e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005147 0.0006273 0.004252 0.004944 0.989 0.992 0.005239 0.8771 0.9037 0.01547 ] Network output: [ 0.0007417 -0.02075 1.003 -0.0001747 7.842e-05 1.016 -0.0001316 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.0977 0.3153 0.1725 0.9852 0.9941 0.1917 0.4707 0.8863 0.7194 ] Network output: [ 0.009259 -0.04763 1.001 0.0001019 -4.575e-05 1.029 7.679e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09716 0.08696 0.1787 0.2154 0.9874 0.992 0.09722 0.8033 0.8828 0.3129 ] Network output: [ -0.0105 0.04888 1.002 9.948e-05 -4.466e-05 0.9709 7.497e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09632 0.09451 0.1708 0.2015 0.9858 0.9916 0.09633 0.7358 0.8637 0.245 ] Network output: [ 0.001241 0.9991 -0.002087 1.441e-05 -6.47e-06 1.001 1.086e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001242 Epoch 6485 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0142 0.9936 0.9851 6.174e-06 -2.772e-06 -0.007054 4.653e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002918 -0.01024 0.007716 0.9697 0.9741 0.006013 0.8464 0.834 0.02125 ] Network output: [ 0.9976 0.02002 0.001237 -4.853e-05 2.179e-05 -0.01665 -3.658e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02505 -0.2052 0.2024 0.9837 0.9933 0.202 0.4664 0.8797 0.7245 ] Network output: [ -0.01232 1 1.011 2.432e-06 -1.092e-06 0.01388 1.833e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005157 0.0006229 0.004149 0.004749 0.989 0.992 0.00525 0.8771 0.9036 0.01541 ] Network output: [ -0.002363 0.02782 1.001 -0.0001809 8.122e-05 0.9754 -0.0001363 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09781 0.3119 0.1632 0.9852 0.9941 0.1921 0.4713 0.8863 0.7198 ] Network output: [ 0.0102 -0.03796 0.9987 0.0001014 -4.551e-05 1.019 7.64e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09701 0.08677 0.1765 0.2128 0.9874 0.992 0.09707 0.8025 0.8828 0.3115 ] Network output: [ -0.009946 0.04603 1.002 9.993e-05 -4.486e-05 0.9727 7.531e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09616 0.09435 0.1702 0.201 0.9858 0.9916 0.09617 0.7349 0.8636 0.2449 ] Network output: [ -0.0005506 0.9992 0.0004388 1.353e-05 -6.074e-06 1.002 1.02e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001132 Epoch 6486 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01455 0.9882 0.9853 6.841e-06 -3.071e-06 -0.002565 5.156e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.002919 -0.01021 0.007811 0.9697 0.9741 0.006005 0.8463 0.8342 0.02128 ] Network output: [ 1 -0.01513 0.002882 -4.46e-05 2.002e-05 0.01173 -3.361e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02553 -0.203 0.2083 0.9837 0.9933 0.2016 0.4656 0.8799 0.7249 ] Network output: [ -0.01229 0.9984 1.011 2.7e-06 -1.212e-06 0.01558 2.035e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005147 0.0006265 0.004252 0.004942 0.989 0.992 0.00524 0.8771 0.9037 0.01546 ] Network output: [ 0.0007323 -0.02063 1.003 -0.0001745 7.835e-05 1.016 -0.0001315 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.09767 0.3153 0.1724 0.9852 0.9941 0.1917 0.4707 0.8863 0.7194 ] Network output: [ 0.009256 -0.04761 1.001 0.0001018 -4.57e-05 1.029 7.672e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09716 0.08695 0.1787 0.2154 0.9874 0.992 0.09722 0.8032 0.8828 0.3128 ] Network output: [ -0.01049 0.04887 1.002 9.94e-05 -4.462e-05 0.9709 7.491e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09629 0.09449 0.1708 0.2015 0.9858 0.9916 0.09631 0.7358 0.8636 0.245 ] Network output: [ 0.001237 0.9991 -0.002082 1.439e-05 -6.462e-06 1.001 1.085e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00124 Epoch 6487 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0142 0.9935 0.9851 6.167e-06 -2.769e-06 -0.007045 4.648e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002918 -0.01023 0.007714 0.9697 0.9741 0.006014 0.8464 0.834 0.02125 ] Network output: [ 0.9976 0.01994 0.00124 -4.85e-05 2.177e-05 -0.01659 -3.655e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02507 -0.2051 0.2024 0.9837 0.9933 0.202 0.4664 0.8797 0.7245 ] Network output: [ -0.01232 1 1.011 2.433e-06 -1.092e-06 0.01388 1.833e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005158 0.0006221 0.00415 0.004747 0.989 0.992 0.005251 0.8771 0.9036 0.01541 ] Network output: [ -0.002359 0.02771 1.001 -0.0001807 8.113e-05 0.9755 -0.0001362 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09778 0.312 0.1632 0.9852 0.9941 0.1921 0.4713 0.8863 0.7198 ] Network output: [ 0.01019 -0.03799 0.9987 0.0001013 -4.547e-05 1.019 7.633e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.097 0.08677 0.1765 0.2128 0.9874 0.992 0.09706 0.8025 0.8827 0.3115 ] Network output: [ -0.00994 0.04603 1.002 9.984e-05 -4.482e-05 0.9727 7.524e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09614 0.09432 0.1702 0.201 0.9858 0.9916 0.09615 0.7348 0.8636 0.2449 ] Network output: [ -0.000547 0.9992 0.0004332 1.352e-05 -6.068e-06 1.002 1.019e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00113 Epoch 6488 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01455 0.9882 0.9853 6.83e-06 -3.066e-06 -0.002576 5.148e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.002919 -0.0102 0.007808 0.9697 0.9741 0.006005 0.8463 0.8342 0.02127 ] Network output: [ 1 -0.01506 0.002878 -4.459e-05 2.002e-05 0.01167 -3.361e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02555 -0.203 0.2082 0.9837 0.9933 0.2016 0.4655 0.8799 0.7249 ] Network output: [ -0.01229 0.9984 1.011 2.7e-06 -1.212e-06 0.01557 2.035e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005148 0.0006257 0.004252 0.004939 0.989 0.992 0.005241 0.8771 0.9037 0.01546 ] Network output: [ 0.000723 -0.02051 1.003 -0.0001743 7.827e-05 1.016 -0.0001314 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1911 0.09764 0.3154 0.1723 0.9852 0.9941 0.1917 0.4707 0.8863 0.7194 ] Network output: [ 0.009253 -0.04759 1.001 0.0001017 -4.566e-05 1.029 7.664e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09715 0.08694 0.1787 0.2153 0.9874 0.992 0.09721 0.8032 0.8828 0.3128 ] Network output: [ -0.01048 0.04886 1.002 9.931e-05 -4.458e-05 0.9709 7.484e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09627 0.09446 0.1708 0.2015 0.9858 0.9916 0.09628 0.7357 0.8636 0.245 ] Network output: [ 0.001234 0.9991 -0.002078 1.438e-05 -6.455e-06 1.001 1.084e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001238 Epoch 6489 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01419 0.9935 0.9851 6.16e-06 -2.765e-06 -0.007035 4.642e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002919 -0.01023 0.007711 0.9697 0.9741 0.006014 0.8464 0.834 0.02124 ] Network output: [ 0.9976 0.01986 0.001242 -4.846e-05 2.176e-05 -0.01653 -3.652e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02509 -0.2051 0.2024 0.9837 0.9933 0.202 0.4663 0.8797 0.7245 ] Network output: [ -0.01232 1 1.011 2.434e-06 -1.093e-06 0.01388 1.834e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005158 0.0006213 0.004151 0.004745 0.989 0.992 0.005251 0.8771 0.9036 0.0154 ] Network output: [ -0.002354 0.02761 1.001 -0.0001805 8.104e-05 0.9756 -0.000136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09775 0.312 0.1631 0.9851 0.9941 0.1921 0.4712 0.8863 0.7197 ] Network output: [ 0.01018 -0.03801 0.9987 0.0001012 -4.543e-05 1.019 7.626e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.097 0.08676 0.1765 0.2127 0.9874 0.992 0.09706 0.8024 0.8827 0.3115 ] Network output: [ -0.009934 0.04602 1.002 9.976e-05 -4.478e-05 0.9727 7.518e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09611 0.0943 0.1701 0.201 0.9858 0.9916 0.09613 0.7347 0.8636 0.2449 ] Network output: [ -0.0005434 0.9992 0.0004275 1.351e-05 -6.063e-06 1.002 1.018e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001129 Epoch 6490 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01454 0.9882 0.9853 6.819e-06 -3.062e-06 -0.002588 5.139e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.002919 -0.0102 0.007805 0.9697 0.9741 0.006006 0.8463 0.8342 0.02127 ] Network output: [ 1 -0.01498 0.002873 -4.458e-05 2.001e-05 0.0116 -3.36e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02557 -0.203 0.2082 0.9837 0.9933 0.2016 0.4655 0.8799 0.7249 ] Network output: [ -0.01229 0.9984 1.011 2.7e-06 -1.212e-06 0.01556 2.035e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005149 0.0006249 0.004253 0.004937 0.989 0.992 0.005242 0.877 0.9037 0.01546 ] Network output: [ 0.0007137 -0.0204 1.003 -0.0001742 7.82e-05 1.015 -0.0001313 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.09761 0.3154 0.1723 0.9852 0.9941 0.1917 0.4706 0.8863 0.7194 ] Network output: [ 0.009249 -0.04757 1.001 0.0001016 -4.561e-05 1.029 7.657e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09715 0.08694 0.1787 0.2153 0.9874 0.992 0.09721 0.8031 0.8828 0.3128 ] Network output: [ -0.01047 0.04884 1.002 9.923e-05 -4.455e-05 0.9709 7.478e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09625 0.09444 0.1708 0.2015 0.9858 0.9916 0.09626 0.7356 0.8636 0.2449 ] Network output: [ 0.00123 0.9991 -0.002073 1.436e-05 -6.447e-06 1.001 1.082e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001235 Epoch 6491 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01419 0.9935 0.9851 6.153e-06 -2.762e-06 -0.007026 4.637e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002919 -0.01023 0.007709 0.9697 0.9741 0.006014 0.8464 0.834 0.02124 ] Network output: [ 0.9976 0.01977 0.001245 -4.843e-05 2.174e-05 -0.01647 -3.65e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02512 -0.2051 0.2024 0.9837 0.9933 0.202 0.4663 0.8797 0.7244 ] Network output: [ -0.01231 1 1.01 2.435e-06 -1.093e-06 0.01388 1.835e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005159 0.0006205 0.004151 0.004744 0.989 0.992 0.005252 0.8771 0.9036 0.0154 ] Network output: [ -0.002349 0.0275 1.001 -0.0001803 8.095e-05 0.9756 -0.0001359 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09772 0.3121 0.1631 0.9851 0.9941 0.1921 0.4712 0.8863 0.7197 ] Network output: [ 0.01018 -0.03804 0.9987 0.0001011 -4.538e-05 1.019 7.618e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.097 0.08675 0.1765 0.2127 0.9874 0.992 0.09706 0.8024 0.8827 0.3114 ] Network output: [ -0.009929 0.04602 1.002 9.967e-05 -4.475e-05 0.9727 7.511e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09609 0.09427 0.1701 0.201 0.9858 0.9916 0.0961 0.7347 0.8635 0.2449 ] Network output: [ -0.0005398 0.9992 0.0004219 1.349e-05 -6.058e-06 1.002 1.017e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001127 Epoch 6492 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01454 0.9882 0.9853 6.809e-06 -3.057e-06 -0.002599 5.131e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.00292 -0.0102 0.007803 0.9697 0.9741 0.006006 0.8463 0.8342 0.02126 ] Network output: [ 1 -0.01491 0.002869 -4.457e-05 2.001e-05 0.01153 -3.359e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02559 -0.203 0.2081 0.9837 0.9933 0.2016 0.4655 0.8799 0.7249 ] Network output: [ -0.01229 0.9984 1.011 2.699e-06 -1.212e-06 0.01555 2.034e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00515 0.0006241 0.004253 0.004934 0.989 0.992 0.005243 0.877 0.9037 0.01545 ] Network output: [ 0.0007044 -0.02028 1.003 -0.000174 7.812e-05 1.015 -0.0001311 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.09758 0.3154 0.1722 0.9852 0.9941 0.1918 0.4706 0.8863 0.7193 ] Network output: [ 0.009246 -0.04755 1.001 0.0001015 -4.557e-05 1.029 7.649e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09715 0.08693 0.1787 0.2153 0.9874 0.992 0.09721 0.803 0.8827 0.3128 ] Network output: [ -0.01046 0.04883 1.002 9.915e-05 -4.451e-05 0.9709 7.472e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09622 0.09442 0.1707 0.2015 0.9858 0.9916 0.09624 0.7356 0.8635 0.2449 ] Network output: [ 0.001226 0.9991 -0.002068 1.434e-05 -6.439e-06 1.001 1.081e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001233 Epoch 6493 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01419 0.9935 0.9851 6.146e-06 -2.759e-06 -0.007017 4.632e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002919 -0.01022 0.007707 0.9697 0.9741 0.006014 0.8464 0.834 0.02123 ] Network output: [ 0.9976 0.01969 0.001248 -4.839e-05 2.173e-05 -0.01641 -3.647e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02514 -0.205 0.2024 0.9837 0.9933 0.202 0.4663 0.8797 0.7244 ] Network output: [ -0.01231 1 1.01 2.436e-06 -1.094e-06 0.01388 1.836e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00516 0.0006198 0.004152 0.004742 0.989 0.992 0.005253 0.8771 0.9035 0.0154 ] Network output: [ -0.002344 0.02739 1.001 -0.0001801 8.086e-05 0.9757 -0.0001357 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09769 0.3121 0.1631 0.9851 0.9941 0.1921 0.4712 0.8863 0.7197 ] Network output: [ 0.01017 -0.03806 0.9987 0.000101 -4.534e-05 1.019 7.611e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.097 0.08675 0.1765 0.2127 0.9874 0.992 0.09706 0.8023 0.8826 0.3114 ] Network output: [ -0.009923 0.04602 1.002 9.958e-05 -4.471e-05 0.9727 7.505e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09607 0.09425 0.1701 0.201 0.9858 0.9916 0.09608 0.7346 0.8635 0.2449 ] Network output: [ -0.0005362 0.9992 0.0004163 1.348e-05 -6.052e-06 1.002 1.016e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001125 Epoch 6494 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01453 0.9882 0.9853 6.798e-06 -3.052e-06 -0.002611 5.123e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.00292 -0.01019 0.0078 0.9697 0.9741 0.006006 0.8463 0.8342 0.02125 ] Network output: [ 1 -0.01483 0.002865 -4.456e-05 2e-05 0.01147 -3.358e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1808 -0.02561 -0.2029 0.2081 0.9837 0.9933 0.2016 0.4655 0.8799 0.7249 ] Network output: [ -0.01229 0.9984 1.011 2.699e-06 -1.212e-06 0.01554 2.034e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00515 0.0006233 0.004253 0.004931 0.989 0.992 0.005243 0.877 0.9037 0.01545 ] Network output: [ 0.0006952 -0.02017 1.003 -0.0001739 7.805e-05 1.015 -0.000131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.09755 0.3154 0.1721 0.9852 0.9941 0.1918 0.4706 0.8863 0.7193 ] Network output: [ 0.009242 -0.04753 1.001 0.0001014 -4.552e-05 1.029 7.642e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09714 0.08692 0.1787 0.2153 0.9874 0.992 0.0972 0.803 0.8827 0.3128 ] Network output: [ -0.01045 0.04882 1.002 9.906e-05 -4.447e-05 0.9709 7.466e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0962 0.09439 0.1707 0.2014 0.9858 0.9916 0.09621 0.7355 0.8635 0.2449 ] Network output: [ 0.001222 0.9991 -0.002063 1.433e-05 -6.432e-06 1.001 1.08e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001231 Epoch 6495 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01418 0.9935 0.9852 6.138e-06 -2.756e-06 -0.007008 4.626e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002919 -0.01022 0.007705 0.9697 0.9741 0.006014 0.8464 0.834 0.02123 ] Network output: [ 0.9977 0.01961 0.00125 -4.836e-05 2.171e-05 -0.01635 -3.644e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02516 -0.205 0.2024 0.9837 0.9933 0.202 0.4663 0.8797 0.7244 ] Network output: [ -0.01231 1 1.01 2.437e-06 -1.094e-06 0.01388 1.837e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00516 0.000619 0.004153 0.00474 0.989 0.992 0.005254 0.8771 0.9035 0.01539 ] Network output: [ -0.002339 0.02729 1.001 -0.0001799 8.077e-05 0.9758 -0.0001356 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09766 0.3121 0.163 0.9851 0.9941 0.1921 0.4712 0.8863 0.7197 ] Network output: [ 0.01016 -0.03809 0.9986 0.0001009 -4.53e-05 1.02 7.604e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09699 0.08674 0.1765 0.2127 0.9874 0.992 0.09705 0.8023 0.8826 0.3114 ] Network output: [ -0.009917 0.04602 1.002 9.95e-05 -4.467e-05 0.9727 7.498e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09605 0.09423 0.1701 0.2009 0.9858 0.9916 0.09606 0.7345 0.8635 0.2448 ] Network output: [ -0.0005327 0.9992 0.0004107 1.347e-05 -6.047e-06 1.002 1.015e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001124 Epoch 6496 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01452 0.9883 0.9853 6.787e-06 -3.047e-06 -0.002622 5.115e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.00292 -0.01019 0.007797 0.9697 0.9741 0.006006 0.8463 0.8342 0.02125 ] Network output: [ 1 -0.01476 0.00286 -4.454e-05 2e-05 0.0114 -3.357e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.02563 -0.2029 0.2081 0.9837 0.9933 0.2016 0.4654 0.8799 0.7248 ] Network output: [ -0.01228 0.9985 1.011 2.699e-06 -1.212e-06 0.01553 2.034e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005151 0.0006225 0.004253 0.004929 0.989 0.992 0.005244 0.877 0.9037 0.01544 ] Network output: [ 0.000686 -0.02005 1.003 -0.0001737 7.798e-05 1.015 -0.0001309 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.09752 0.3155 0.1721 0.9852 0.9941 0.1918 0.4706 0.8863 0.7193 ] Network output: [ 0.009239 -0.04751 1 0.0001013 -4.548e-05 1.029 7.635e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09714 0.08691 0.1787 0.2152 0.9874 0.992 0.0972 0.8029 0.8827 0.3128 ] Network output: [ -0.01044 0.0488 1.002 9.898e-05 -4.444e-05 0.9709 7.459e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09618 0.09437 0.1707 0.2014 0.9858 0.9916 0.09619 0.7354 0.8635 0.2449 ] Network output: [ 0.001218 0.9991 -0.002059 1.431e-05 -6.424e-06 1.001 1.078e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001229 Epoch 6497 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01418 0.9935 0.9852 6.131e-06 -2.753e-06 -0.006999 4.621e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.00292 -0.01022 0.007703 0.9697 0.9741 0.006015 0.8464 0.834 0.02122 ] Network output: [ 0.9977 0.01952 0.001253 -4.832e-05 2.169e-05 -0.01629 -3.642e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02518 -0.205 0.2024 0.9837 0.9933 0.202 0.4662 0.8797 0.7244 ] Network output: [ -0.01231 1 1.01 2.438e-06 -1.095e-06 0.01387 1.837e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005161 0.0006182 0.004154 0.004738 0.989 0.992 0.005254 0.877 0.9035 0.01539 ] Network output: [ -0.002334 0.02718 1.001 -0.0001797 8.068e-05 0.9759 -0.0001354 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09762 0.3122 0.163 0.9851 0.9941 0.1921 0.4711 0.8863 0.7197 ] Network output: [ 0.01015 -0.03811 0.9986 0.0001008 -4.525e-05 1.02 7.596e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09699 0.08673 0.1765 0.2127 0.9874 0.992 0.09705 0.8022 0.8826 0.3114 ] Network output: [ -0.009911 0.04602 1.002 9.941e-05 -4.463e-05 0.9727 7.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09602 0.0942 0.1701 0.2009 0.9858 0.9916 0.09604 0.7345 0.8634 0.2448 ] Network output: [ -0.0005291 0.9992 0.0004051 1.346e-05 -6.042e-06 1.002 1.014e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001122 Epoch 6498 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01452 0.9883 0.9853 6.776e-06 -3.042e-06 -0.002634 5.106e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.00292 -0.01019 0.007794 0.9697 0.9741 0.006007 0.8463 0.8342 0.02124 ] Network output: [ 1 -0.01468 0.002856 -4.453e-05 1.999e-05 0.01134 -3.356e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.02565 -0.2029 0.208 0.9837 0.9933 0.2016 0.4654 0.8799 0.7248 ] Network output: [ -0.01228 0.9985 1.011 2.698e-06 -1.211e-06 0.01552 2.033e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005152 0.0006217 0.004254 0.004926 0.989 0.992 0.005245 0.877 0.9036 0.01544 ] Network output: [ 0.0006769 -0.01994 1.003 -0.0001735 7.79e-05 1.015 -0.0001308 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.09749 0.3155 0.172 0.9852 0.9941 0.1918 0.4705 0.8862 0.7193 ] Network output: [ 0.009235 -0.04749 1 0.0001012 -4.543e-05 1.029 7.627e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09713 0.0869 0.1787 0.2152 0.9874 0.992 0.09719 0.8029 0.8826 0.3128 ] Network output: [ -0.01044 0.04879 1.002 9.89e-05 -4.44e-05 0.9709 7.453e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09615 0.09434 0.1707 0.2014 0.9858 0.9916 0.09616 0.7353 0.8634 0.2449 ] Network output: [ 0.001214 0.9991 -0.002054 1.429e-05 -6.417e-06 1.001 1.077e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001226 Epoch 6499 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01417 0.9935 0.9852 6.124e-06 -2.749e-06 -0.00699 4.615e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.00292 -0.01021 0.0077 0.9697 0.9741 0.006015 0.8464 0.834 0.02122 ] Network output: [ 0.9977 0.01944 0.001255 -4.829e-05 2.168e-05 -0.01624 -3.639e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.0252 -0.2049 0.2024 0.9837 0.9933 0.202 0.4662 0.8797 0.7244 ] Network output: [ -0.01231 1 1.01 2.439e-06 -1.095e-06 0.01387 1.838e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005162 0.0006175 0.004155 0.004737 0.989 0.992 0.005255 0.877 0.9035 0.01539 ] Network output: [ -0.002329 0.02708 1.001 -0.0001795 8.059e-05 0.9759 -0.0001353 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09759 0.3122 0.163 0.9851 0.9941 0.1921 0.4711 0.8863 0.7197 ] Network output: [ 0.01014 -0.03813 0.9986 0.0001007 -4.521e-05 1.02 7.589e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09699 0.08672 0.1766 0.2127 0.9874 0.992 0.09705 0.8022 0.8826 0.3114 ] Network output: [ -0.009906 0.04601 1.002 9.932e-05 -4.459e-05 0.9727 7.485e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.096 0.09418 0.1701 0.2009 0.9858 0.9916 0.09601 0.7344 0.8634 0.2448 ] Network output: [ -0.0005256 0.9992 0.0003996 1.345e-05 -6.037e-06 1.002 1.013e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00112 Epoch 6500 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01451 0.9883 0.9854 6.765e-06 -3.037e-06 -0.002645 5.098e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003173 -0.002921 -0.01018 0.007792 0.9697 0.9741 0.006007 0.8463 0.8342 0.02124 ] Network output: [ 1 -0.01461 0.002852 -4.452e-05 1.999e-05 0.01127 -3.355e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.02567 -0.2029 0.208 0.9837 0.9933 0.2016 0.4654 0.8799 0.7248 ] Network output: [ -0.01228 0.9985 1.011 2.698e-06 -1.211e-06 0.01552 2.033e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005153 0.0006209 0.004254 0.004923 0.989 0.992 0.005246 0.877 0.9036 0.01544 ] Network output: [ 0.0006678 -0.01983 1.003 -0.0001734 7.783e-05 1.015 -0.0001307 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.09746 0.3155 0.1719 0.9852 0.9941 0.1918 0.4705 0.8862 0.7193 ] Network output: [ 0.009232 -0.04747 1 0.0001011 -4.539e-05 1.029 7.62e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09713 0.0869 0.1787 0.2152 0.9874 0.992 0.09719 0.8028 0.8826 0.3127 ] Network output: [ -0.01043 0.04878 1.002 9.881e-05 -4.436e-05 0.9709 7.447e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09613 0.09432 0.1706 0.2014 0.9858 0.9916 0.09614 0.7353 0.8634 0.2449 ] Network output: [ 0.00121 0.9991 -0.002049 1.428e-05 -6.409e-06 1.001 1.076e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001224 Epoch 6501 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01417 0.9935 0.9852 6.117e-06 -2.746e-06 -0.006981 4.61e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.00292 -0.01021 0.007698 0.9697 0.9741 0.006015 0.8464 0.834 0.02121 ] Network output: [ 0.9977 0.01936 0.001258 -4.825e-05 2.166e-05 -0.01618 -3.636e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02522 -0.2049 0.2024 0.9837 0.9933 0.202 0.4662 0.8797 0.7244 ] Network output: [ -0.0123 1 1.01 2.44e-06 -1.095e-06 0.01387 1.839e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005163 0.0006167 0.004155 0.004735 0.989 0.992 0.005256 0.877 0.9035 0.01538 ] Network output: [ -0.002324 0.02697 1.001 -0.0001793 8.05e-05 0.976 -0.0001351 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09756 0.3123 0.163 0.9851 0.9941 0.1922 0.4711 0.8863 0.7197 ] Network output: [ 0.01013 -0.03816 0.9986 0.0001006 -4.516e-05 1.02 7.582e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09699 0.08672 0.1766 0.2126 0.9874 0.992 0.09705 0.8021 0.8825 0.3114 ] Network output: [ -0.0099 0.04601 1.001 9.924e-05 -4.455e-05 0.9727 7.479e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09598 0.09416 0.17 0.2009 0.9858 0.9916 0.09599 0.7343 0.8634 0.2448 ] Network output: [ -0.000522 0.9992 0.0003941 1.343e-05 -6.031e-06 1.002 1.012e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001119 Epoch 6502 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01451 0.9883 0.9854 6.754e-06 -3.032e-06 -0.002657 5.09e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002921 -0.01018 0.007789 0.9697 0.9741 0.006007 0.8462 0.8342 0.02123 ] Network output: [ 1 -0.01453 0.002848 -4.451e-05 1.998e-05 0.0112 -3.354e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.02569 -0.2028 0.208 0.9837 0.9933 0.2016 0.4653 0.8799 0.7248 ] Network output: [ -0.01228 0.9985 1.011 2.697e-06 -1.211e-06 0.01551 2.033e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005153 0.0006202 0.004254 0.004921 0.989 0.992 0.005246 0.877 0.9036 0.01543 ] Network output: [ 0.0006587 -0.01971 1.003 -0.0001732 7.775e-05 1.015 -0.0001305 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.09743 0.3155 0.1718 0.9852 0.9941 0.1918 0.4705 0.8862 0.7193 ] Network output: [ 0.009228 -0.04745 1 0.000101 -4.535e-05 1.029 7.612e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09713 0.08689 0.1787 0.2151 0.9874 0.992 0.09719 0.8027 0.8826 0.3127 ] Network output: [ -0.01042 0.04876 1.002 9.873e-05 -4.432e-05 0.9709 7.441e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09611 0.09429 0.1706 0.2013 0.9858 0.9916 0.09612 0.7352 0.8634 0.2449 ] Network output: [ 0.001206 0.9991 -0.002044 1.426e-05 -6.402e-06 1.001 1.075e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001222 Epoch 6503 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01416 0.9935 0.9852 6.11e-06 -2.743e-06 -0.006972 4.604e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.00292 -0.01021 0.007696 0.9697 0.9741 0.006015 0.8464 0.8339 0.02121 ] Network output: [ 0.9977 0.01928 0.00126 -4.822e-05 2.165e-05 -0.01612 -3.634e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02524 -0.2049 0.2023 0.9837 0.9933 0.2021 0.4661 0.8796 0.7244 ] Network output: [ -0.0123 1 1.01 2.441e-06 -1.096e-06 0.01387 1.84e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005163 0.0006159 0.004156 0.004733 0.989 0.992 0.005256 0.877 0.9035 0.01538 ] Network output: [ -0.002319 0.02687 1.001 -0.0001791 8.041e-05 0.9761 -0.000135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09753 0.3123 0.1629 0.9851 0.9941 0.1922 0.471 0.8862 0.7196 ] Network output: [ 0.01012 -0.03818 0.9986 0.0001005 -4.512e-05 1.02 7.574e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09698 0.08671 0.1766 0.2126 0.9874 0.992 0.09704 0.802 0.8825 0.3114 ] Network output: [ -0.009894 0.04601 1.001 9.915e-05 -4.451e-05 0.9727 7.472e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09595 0.09413 0.17 0.2009 0.9858 0.9916 0.09597 0.7343 0.8633 0.2448 ] Network output: [ -0.0005185 0.9992 0.0003885 1.342e-05 -6.026e-06 1.002 1.012e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001117 Epoch 6504 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0145 0.9883 0.9854 6.743e-06 -3.027e-06 -0.002668 5.082e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002921 -0.01018 0.007786 0.9697 0.9741 0.006007 0.8462 0.8341 0.02123 ] Network output: [ 1 -0.01446 0.002843 -4.449e-05 1.998e-05 0.01114 -3.353e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.02571 -0.2028 0.2079 0.9837 0.9933 0.2017 0.4653 0.8798 0.7248 ] Network output: [ -0.01228 0.9985 1.011 2.697e-06 -1.211e-06 0.0155 2.032e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005154 0.0006194 0.004255 0.004918 0.989 0.992 0.005247 0.8769 0.9036 0.01543 ] Network output: [ 0.0006496 -0.0196 1.003 -0.000173 7.768e-05 1.015 -0.0001304 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.0974 0.3156 0.1718 0.9852 0.9941 0.1918 0.4704 0.8862 0.7193 ] Network output: [ 0.009225 -0.04743 1 0.0001009 -4.53e-05 1.029 7.605e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09712 0.08688 0.1786 0.2151 0.9874 0.992 0.09718 0.8027 0.8826 0.3127 ] Network output: [ -0.01041 0.04875 1.002 9.865e-05 -4.429e-05 0.9709 7.434e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09608 0.09427 0.1706 0.2013 0.9858 0.9916 0.09609 0.7351 0.8633 0.2448 ] Network output: [ 0.001202 0.9992 -0.00204 1.424e-05 -6.394e-06 1.001 1.073e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00122 Epoch 6505 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01416 0.9935 0.9852 6.102e-06 -2.74e-06 -0.006963 4.599e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002921 -0.0102 0.007694 0.9697 0.9741 0.006015 0.8463 0.8339 0.0212 ] Network output: [ 0.9977 0.0192 0.001263 -4.818e-05 2.163e-05 -0.01606 -3.631e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02526 -0.2048 0.2023 0.9837 0.9933 0.2021 0.4661 0.8796 0.7243 ] Network output: [ -0.0123 1 1.01 2.442e-06 -1.096e-06 0.01387 1.84e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005164 0.0006152 0.004157 0.004732 0.989 0.992 0.005257 0.877 0.9035 0.01538 ] Network output: [ -0.002315 0.02676 1.001 -0.0001789 8.032e-05 0.9762 -0.0001348 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09751 0.3124 0.1629 0.9851 0.9941 0.1922 0.471 0.8862 0.7196 ] Network output: [ 0.01011 -0.0382 0.9986 0.0001004 -4.508e-05 1.02 7.567e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09698 0.0867 0.1766 0.2126 0.9874 0.992 0.09704 0.802 0.8825 0.3114 ] Network output: [ -0.009888 0.04601 1.001 9.906e-05 -4.447e-05 0.9727 7.466e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09593 0.09411 0.17 0.2008 0.9857 0.9916 0.09594 0.7342 0.8633 0.2448 ] Network output: [ -0.0005149 0.9992 0.0003831 1.341e-05 -6.021e-06 1.002 1.011e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001116 Epoch 6506 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01449 0.9884 0.9854 6.732e-06 -3.022e-06 -0.00268 5.073e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002921 -0.01017 0.007784 0.9697 0.9741 0.006008 0.8462 0.8341 0.02122 ] Network output: [ 1 -0.01439 0.002839 -4.448e-05 1.997e-05 0.01107 -3.352e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.02572 -0.2028 0.2079 0.9837 0.9933 0.2017 0.4653 0.8798 0.7248 ] Network output: [ -0.01227 0.9985 1.011 2.696e-06 -1.211e-06 0.01549 2.032e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005155 0.0006186 0.004255 0.004915 0.989 0.992 0.005248 0.8769 0.9036 0.01543 ] Network output: [ 0.0006406 -0.01949 1.003 -0.0001729 7.761e-05 1.015 -0.0001303 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.09737 0.3156 0.1717 0.9852 0.9941 0.1918 0.4704 0.8862 0.7192 ] Network output: [ 0.009221 -0.04741 1 0.0001008 -4.526e-05 1.029 7.597e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09712 0.08687 0.1786 0.2151 0.9874 0.992 0.09718 0.8026 0.8825 0.3127 ] Network output: [ -0.0104 0.04873 1.002 9.856e-05 -4.425e-05 0.9709 7.428e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09606 0.09425 0.1706 0.2013 0.9858 0.9916 0.09607 0.7351 0.8633 0.2448 ] Network output: [ 0.001198 0.9992 -0.002035 1.423e-05 -6.386e-06 1.001 1.072e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001217 Epoch 6507 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01415 0.9935 0.9852 6.095e-06 -2.736e-06 -0.006954 4.593e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002921 -0.0102 0.007691 0.9697 0.9741 0.006016 0.8463 0.8339 0.0212 ] Network output: [ 0.9977 0.01911 0.001265 -4.814e-05 2.161e-05 -0.016 -3.628e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02529 -0.2048 0.2023 0.9837 0.9933 0.2021 0.4661 0.8796 0.7243 ] Network output: [ -0.0123 1 1.01 2.443e-06 -1.097e-06 0.01387 1.841e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005165 0.0006144 0.004158 0.00473 0.989 0.992 0.005258 0.877 0.9035 0.01537 ] Network output: [ -0.00231 0.02666 1.001 -0.0001787 8.023e-05 0.9763 -0.0001347 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09748 0.3124 0.1629 0.9851 0.9941 0.1922 0.471 0.8862 0.7196 ] Network output: [ 0.0101 -0.03823 0.9986 0.0001003 -4.503e-05 1.02 7.56e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09698 0.0867 0.1766 0.2126 0.9874 0.992 0.09704 0.8019 0.8825 0.3114 ] Network output: [ -0.009882 0.046 1.001 9.898e-05 -4.443e-05 0.9727 7.459e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09591 0.09409 0.17 0.2008 0.9857 0.9916 0.09592 0.7341 0.8633 0.2448 ] Network output: [ -0.0005114 0.9992 0.0003776 1.34e-05 -6.015e-06 1.002 1.01e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001114 Epoch 6508 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01449 0.9884 0.9854 6.721e-06 -3.017e-06 -0.002692 5.065e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002922 -0.01017 0.007781 0.9697 0.9741 0.006008 0.8462 0.8341 0.02122 ] Network output: [ 1 -0.01431 0.002835 -4.447e-05 1.996e-05 0.01101 -3.351e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.02574 -0.2028 0.2078 0.9837 0.9933 0.2017 0.4653 0.8798 0.7247 ] Network output: [ -0.01227 0.9985 1.011 2.696e-06 -1.21e-06 0.01548 2.032e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005156 0.0006178 0.004255 0.004913 0.989 0.992 0.005249 0.8769 0.9036 0.01542 ] Network output: [ 0.0006316 -0.01937 1.003 -0.0001727 7.753e-05 1.014 -0.0001302 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.09734 0.3156 0.1716 0.9852 0.9941 0.1918 0.4704 0.8862 0.7192 ] Network output: [ 0.009218 -0.04739 1 0.0001007 -4.521e-05 1.029 7.59e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09712 0.08686 0.1786 0.215 0.9874 0.992 0.09718 0.8026 0.8825 0.3127 ] Network output: [ -0.01039 0.04872 1.002 9.848e-05 -4.421e-05 0.9709 7.422e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09604 0.09422 0.1706 0.2013 0.9858 0.9916 0.09605 0.735 0.8633 0.2448 ] Network output: [ 0.001195 0.9992 -0.00203 1.421e-05 -6.379e-06 1.001 1.071e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001215 Epoch 6509 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01415 0.9935 0.9852 6.088e-06 -2.733e-06 -0.006945 4.588e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002921 -0.0102 0.007689 0.9697 0.9741 0.006016 0.8463 0.8339 0.02119 ] Network output: [ 0.9977 0.01903 0.001268 -4.811e-05 2.16e-05 -0.01594 -3.626e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02531 -0.2048 0.2023 0.9837 0.9933 0.2021 0.466 0.8796 0.7243 ] Network output: [ -0.0123 1 1.01 2.444e-06 -1.097e-06 0.01387 1.842e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005166 0.0006137 0.004158 0.004728 0.989 0.992 0.005259 0.8769 0.9035 0.01537 ] Network output: [ -0.002305 0.02656 1.001 -0.0001785 8.014e-05 0.9763 -0.0001345 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09745 0.3124 0.1629 0.9851 0.9941 0.1922 0.4709 0.8862 0.7196 ] Network output: [ 0.0101 -0.03825 0.9986 0.0001002 -4.499e-05 1.02 7.552e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09697 0.08669 0.1766 0.2126 0.9874 0.992 0.09703 0.8019 0.8824 0.3114 ] Network output: [ -0.009876 0.046 1.001 9.889e-05 -4.439e-05 0.9727 7.453e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09589 0.09407 0.17 0.2008 0.9857 0.9916 0.0959 0.7341 0.8632 0.2447 ] Network output: [ -0.0005079 0.9992 0.0003721 1.339e-05 -6.01e-06 1.002 1.009e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001112 Epoch 6510 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01448 0.9884 0.9854 6.71e-06 -3.012e-06 -0.002703 5.057e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002922 -0.01017 0.007778 0.9697 0.9741 0.006008 0.8462 0.8341 0.02121 ] Network output: [ 1 -0.01424 0.00283 -4.446e-05 1.996e-05 0.01094 -3.35e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.02576 -0.2027 0.2078 0.9837 0.9933 0.2017 0.4652 0.8798 0.7247 ] Network output: [ -0.01227 0.9985 1.011 2.695e-06 -1.21e-06 0.01547 2.031e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005157 0.0006171 0.004256 0.00491 0.989 0.992 0.00525 0.8769 0.9036 0.01542 ] Network output: [ 0.0006227 -0.01926 1.003 -0.0001725 7.746e-05 1.014 -0.00013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.09732 0.3157 0.1716 0.9852 0.9941 0.1918 0.4704 0.8862 0.7192 ] Network output: [ 0.009214 -0.04736 1 0.0001006 -4.517e-05 1.029 7.582e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09711 0.08686 0.1786 0.215 0.9874 0.992 0.09717 0.8025 0.8825 0.3127 ] Network output: [ -0.01038 0.04871 1.002 9.839e-05 -4.417e-05 0.9709 7.415e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09601 0.0942 0.1705 0.2012 0.9858 0.9916 0.09602 0.7349 0.8632 0.2448 ] Network output: [ 0.001191 0.9992 -0.002025 1.419e-05 -6.371e-06 1.001 1.07e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001213 Epoch 6511 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01414 0.9935 0.9852 6.08e-06 -2.73e-06 -0.006937 4.582e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002921 -0.01019 0.007687 0.9697 0.9741 0.006016 0.8463 0.8339 0.02119 ] Network output: [ 0.9977 0.01895 0.00127 -4.807e-05 2.158e-05 -0.01588 -3.623e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02533 -0.2047 0.2023 0.9837 0.9933 0.2021 0.466 0.8796 0.7243 ] Network output: [ -0.01229 1 1.01 2.445e-06 -1.097e-06 0.01387 1.842e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005166 0.0006129 0.004159 0.004726 0.989 0.992 0.005259 0.8769 0.9034 0.01537 ] Network output: [ -0.0023 0.02645 1.001 -0.0001783 8.005e-05 0.9764 -0.0001344 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09742 0.3125 0.1628 0.9851 0.9941 0.1922 0.4709 0.8862 0.7196 ] Network output: [ 0.01009 -0.03827 0.9986 0.0001001 -4.495e-05 1.02 7.545e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09697 0.08668 0.1766 0.2125 0.9874 0.992 0.09703 0.8018 0.8824 0.3114 ] Network output: [ -0.00987 0.04599 1.001 9.88e-05 -4.436e-05 0.9727 7.446e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09586 0.09404 0.1699 0.2008 0.9857 0.9916 0.09588 0.734 0.8632 0.2447 ] Network output: [ -0.0005044 0.9992 0.0003667 1.338e-05 -6.005e-06 1.001 1.008e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001111 Epoch 6512 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01447 0.9884 0.9854 6.699e-06 -3.007e-06 -0.002715 5.048e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002922 -0.01016 0.007776 0.9697 0.9741 0.006008 0.8462 0.8341 0.02121 ] Network output: [ 1 -0.01417 0.002826 -4.444e-05 1.995e-05 0.01088 -3.349e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.02578 -0.2027 0.2078 0.9837 0.9933 0.2017 0.4652 0.8798 0.7247 ] Network output: [ -0.01227 0.9985 1.011 2.695e-06 -1.21e-06 0.01546 2.031e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005157 0.0006163 0.004256 0.004908 0.989 0.992 0.00525 0.8769 0.9036 0.01541 ] Network output: [ 0.0006137 -0.01915 1.003 -0.0001724 7.738e-05 1.014 -0.0001299 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.09729 0.3157 0.1715 0.9852 0.9941 0.1918 0.4703 0.8862 0.7192 ] Network output: [ 0.009211 -0.04734 1 0.0001005 -4.512e-05 1.029 7.575e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09711 0.08685 0.1786 0.215 0.9874 0.992 0.09717 0.8025 0.8825 0.3127 ] Network output: [ -0.01037 0.04869 1.002 9.831e-05 -4.413e-05 0.9709 7.409e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09599 0.09418 0.1705 0.2012 0.9858 0.9916 0.096 0.7348 0.8632 0.2448 ] Network output: [ 0.001187 0.9992 -0.002021 1.418e-05 -6.364e-06 1.001 1.068e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001211 Epoch 6513 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01414 0.9935 0.9852 6.073e-06 -2.726e-06 -0.006928 4.577e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002922 -0.01019 0.007685 0.9697 0.9741 0.006016 0.8463 0.8339 0.02118 ] Network output: [ 0.9977 0.01887 0.001273 -4.804e-05 2.157e-05 -0.01582 -3.62e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02535 -0.2047 0.2023 0.9837 0.9933 0.2021 0.466 0.8796 0.7243 ] Network output: [ -0.01229 1 1.01 2.445e-06 -1.098e-06 0.01386 1.843e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005167 0.0006121 0.00416 0.004725 0.989 0.992 0.00526 0.8769 0.9034 0.01536 ] Network output: [ -0.002295 0.02635 1.001 -0.0001781 7.996e-05 0.9765 -0.0001342 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09739 0.3125 0.1628 0.9851 0.9941 0.1922 0.4709 0.8862 0.7196 ] Network output: [ 0.01008 -0.03829 0.9986 0.0001 -4.49e-05 1.02 7.538e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09697 0.08667 0.1766 0.2125 0.9874 0.992 0.09703 0.8018 0.8824 0.3114 ] Network output: [ -0.009864 0.04599 1.001 9.872e-05 -4.432e-05 0.9727 7.439e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09584 0.09402 0.1699 0.2008 0.9857 0.9916 0.09585 0.7339 0.8632 0.2447 ] Network output: [ -0.0005008 0.9992 0.0003613 1.336e-05 -5.999e-06 1.001 1.007e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001109 Epoch 6514 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01447 0.9884 0.9854 6.688e-06 -3.002e-06 -0.002726 5.04e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002922 -0.01016 0.007773 0.9697 0.9741 0.006009 0.8462 0.8341 0.0212 ] Network output: [ 1 -0.01409 0.002822 -4.443e-05 1.995e-05 0.01081 -3.348e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.0258 -0.2027 0.2077 0.9837 0.9933 0.2017 0.4652 0.8798 0.7247 ] Network output: [ -0.01226 0.9985 1.011 2.694e-06 -1.21e-06 0.01545 2.03e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005158 0.0006155 0.004256 0.004905 0.989 0.992 0.005251 0.8769 0.9035 0.01541 ] Network output: [ 0.0006048 -0.01903 1.003 -0.0001722 7.731e-05 1.014 -0.0001298 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1912 0.09726 0.3157 0.1714 0.9852 0.9941 0.1918 0.4703 0.8862 0.7192 ] Network output: [ 0.009207 -0.04732 1 0.0001004 -4.508e-05 1.029 7.567e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09711 0.08684 0.1786 0.215 0.9874 0.992 0.09717 0.8024 0.8824 0.3126 ] Network output: [ -0.01036 0.04868 1.002 9.823e-05 -4.41e-05 0.9709 7.403e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09597 0.09415 0.1705 0.2012 0.9858 0.9916 0.09598 0.7348 0.8632 0.2448 ] Network output: [ 0.001183 0.9992 -0.002016 1.416e-05 -6.356e-06 1.001 1.067e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001208 Epoch 6515 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01413 0.9934 0.9852 6.066e-06 -2.723e-06 -0.00692 4.571e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002922 -0.01019 0.007683 0.9697 0.9741 0.006017 0.8463 0.8339 0.02118 ] Network output: [ 0.9978 0.01879 0.001275 -4.8e-05 2.155e-05 -0.01577 -3.618e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02537 -0.2047 0.2023 0.9837 0.9933 0.2021 0.466 0.8796 0.7243 ] Network output: [ -0.01229 1 1.01 2.446e-06 -1.098e-06 0.01386 1.843e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005168 0.0006114 0.004161 0.004723 0.989 0.992 0.005261 0.8769 0.9034 0.01536 ] Network output: [ -0.00229 0.02624 1.001 -0.0001779 7.987e-05 0.9766 -0.0001341 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09736 0.3126 0.1628 0.9851 0.9941 0.1922 0.4708 0.8862 0.7195 ] Network output: [ 0.01007 -0.03832 0.9986 9.992e-05 -4.486e-05 1.02 7.53e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09697 0.08667 0.1766 0.2125 0.9874 0.992 0.09703 0.8017 0.8824 0.3114 ] Network output: [ -0.009858 0.04599 1.001 9.863e-05 -4.428e-05 0.9727 7.433e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09582 0.094 0.1699 0.2007 0.9857 0.9916 0.09583 0.7339 0.8631 0.2447 ] Network output: [ -0.0004973 0.9992 0.0003558 1.335e-05 -5.994e-06 1.001 1.006e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001107 Epoch 6516 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01446 0.9884 0.9854 6.677e-06 -2.997e-06 -0.002738 5.032e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002923 -0.01016 0.00777 0.9697 0.9741 0.006009 0.8462 0.8341 0.0212 ] Network output: [ 1 -0.01402 0.002818 -4.442e-05 1.994e-05 0.01075 -3.347e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.02582 -0.2027 0.2077 0.9837 0.9933 0.2017 0.4652 0.8798 0.7247 ] Network output: [ -0.01226 0.9985 1.011 2.694e-06 -1.209e-06 0.01544 2.03e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005159 0.0006148 0.004256 0.004902 0.989 0.992 0.005252 0.8769 0.9035 0.01541 ] Network output: [ 0.000596 -0.01892 1.003 -0.000172 7.723e-05 1.014 -0.0001297 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1913 0.09723 0.3157 0.1714 0.9852 0.9941 0.1919 0.4703 0.8861 0.7192 ] Network output: [ 0.009204 -0.0473 1 0.0001003 -4.503e-05 1.029 7.56e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0971 0.08683 0.1786 0.2149 0.9874 0.992 0.09716 0.8023 0.8824 0.3126 ] Network output: [ -0.01035 0.04866 1.002 9.814e-05 -4.406e-05 0.9709 7.396e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09594 0.09413 0.1705 0.2012 0.9858 0.9916 0.09595 0.7347 0.8631 0.2448 ] Network output: [ 0.001179 0.9992 -0.002011 1.414e-05 -6.349e-06 1.001 1.066e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001206 Epoch 6517 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01413 0.9934 0.9852 6.058e-06 -2.72e-06 -0.006911 4.566e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002922 -0.01018 0.00768 0.9697 0.9741 0.006017 0.8463 0.8339 0.02117 ] Network output: [ 0.9978 0.01871 0.001278 -4.797e-05 2.153e-05 -0.01571 -3.615e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02539 -0.2046 0.2023 0.9837 0.9933 0.2021 0.4659 0.8796 0.7243 ] Network output: [ -0.01229 1 1.01 2.447e-06 -1.098e-06 0.01386 1.844e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005168 0.0006106 0.004161 0.004721 0.989 0.992 0.005262 0.8769 0.9034 0.01536 ] Network output: [ -0.002285 0.02614 1.001 -0.0001777 7.978e-05 0.9766 -0.0001339 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09733 0.3126 0.1627 0.9851 0.9941 0.1922 0.4708 0.8862 0.7195 ] Network output: [ 0.01006 -0.03834 0.9986 9.982e-05 -4.481e-05 1.02 7.523e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09696 0.08666 0.1766 0.2125 0.9874 0.992 0.09702 0.8017 0.8823 0.3113 ] Network output: [ -0.009853 0.04598 1.001 9.854e-05 -4.424e-05 0.9727 7.426e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0958 0.09397 0.1699 0.2007 0.9857 0.9916 0.09581 0.7338 0.8631 0.2447 ] Network output: [ -0.0004938 0.9992 0.0003505 1.334e-05 -5.989e-06 1.001 1.005e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001106 Epoch 6518 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01446 0.9885 0.9854 6.665e-06 -2.992e-06 -0.00275 5.023e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002923 -0.01015 0.007768 0.9697 0.9741 0.006009 0.8462 0.8341 0.02119 ] Network output: [ 1 -0.01395 0.002813 -4.44e-05 1.993e-05 0.01068 -3.346e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.02584 -0.2026 0.2077 0.9837 0.9933 0.2017 0.4651 0.8798 0.7247 ] Network output: [ -0.01226 0.9985 1.011 2.693e-06 -1.209e-06 0.01543 2.03e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00516 0.000614 0.004257 0.0049 0.989 0.992 0.005253 0.8768 0.9035 0.0154 ] Network output: [ 0.0005871 -0.01881 1.003 -0.0001719 7.716e-05 1.014 -0.0001295 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1913 0.0972 0.3158 0.1713 0.9852 0.9941 0.1919 0.4702 0.8861 0.7192 ] Network output: [ 0.0092 -0.04728 1 0.0001002 -4.499e-05 1.029 7.552e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0971 0.08682 0.1786 0.2149 0.9874 0.992 0.09716 0.8023 0.8824 0.3126 ] Network output: [ -0.01034 0.04864 1.002 9.806e-05 -4.402e-05 0.9709 7.39e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09592 0.0941 0.1705 0.2012 0.9858 0.9916 0.09593 0.7346 0.8631 0.2448 ] Network output: [ 0.001175 0.9992 -0.002006 1.413e-05 -6.341e-06 1.001 1.065e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001204 Epoch 6519 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01413 0.9934 0.9852 6.051e-06 -2.716e-06 -0.006903 4.56e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002922 -0.01018 0.007678 0.9697 0.9741 0.006017 0.8463 0.8339 0.02117 ] Network output: [ 0.9978 0.01862 0.00128 -4.793e-05 2.152e-05 -0.01565 -3.612e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02541 -0.2046 0.2022 0.9837 0.9933 0.2021 0.4659 0.8796 0.7243 ] Network output: [ -0.01229 1 1.01 2.448e-06 -1.099e-06 0.01386 1.845e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005169 0.0006099 0.004162 0.00472 0.989 0.992 0.005262 0.8769 0.9034 0.01535 ] Network output: [ -0.00228 0.02604 1.001 -0.0001775 7.969e-05 0.9767 -0.0001338 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.0973 0.3126 0.1627 0.9851 0.9941 0.1922 0.4708 0.8861 0.7195 ] Network output: [ 0.01005 -0.03836 0.9985 9.972e-05 -4.477e-05 1.02 7.516e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09696 0.08665 0.1766 0.2125 0.9874 0.992 0.09702 0.8016 0.8823 0.3113 ] Network output: [ -0.009847 0.04598 1.001 9.845e-05 -4.42e-05 0.9727 7.42e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09577 0.09395 0.1699 0.2007 0.9857 0.9915 0.09579 0.7337 0.8631 0.2447 ] Network output: [ -0.0004903 0.9992 0.0003451 1.333e-05 -5.983e-06 1.001 1.004e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001104 Epoch 6520 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01445 0.9885 0.9854 6.654e-06 -2.987e-06 -0.002762 5.015e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002923 -0.01015 0.007765 0.9697 0.9741 0.006009 0.8462 0.8341 0.02119 ] Network output: [ 1 -0.01387 0.002809 -4.439e-05 1.993e-05 0.01062 -3.345e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1809 -0.02586 -0.2026 0.2076 0.9837 0.9933 0.2017 0.4651 0.8797 0.7247 ] Network output: [ -0.01226 0.9985 1.011 2.692e-06 -1.209e-06 0.01542 2.029e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00516 0.0006132 0.004257 0.004897 0.989 0.992 0.005254 0.8768 0.9035 0.0154 ] Network output: [ 0.0005783 -0.0187 1.003 -0.0001717 7.708e-05 1.014 -0.0001294 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1913 0.09717 0.3158 0.1712 0.9852 0.9941 0.1919 0.4702 0.8861 0.7191 ] Network output: [ 0.009196 -0.04726 1 0.0001001 -4.494e-05 1.029 7.545e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0971 0.08682 0.1786 0.2149 0.9874 0.992 0.09716 0.8022 0.8824 0.3126 ] Network output: [ -0.01033 0.04863 1.002 9.797e-05 -4.398e-05 0.9709 7.384e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0959 0.09408 0.1704 0.2011 0.9858 0.9916 0.09591 0.7346 0.8631 0.2447 ] Network output: [ 0.001171 0.9992 -0.002001 1.411e-05 -6.334e-06 1.001 1.063e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001202 Epoch 6521 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01412 0.9934 0.9852 6.043e-06 -2.713e-06 -0.006894 4.555e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002923 -0.01018 0.007676 0.9697 0.9741 0.006017 0.8463 0.8339 0.02116 ] Network output: [ 0.9978 0.01854 0.001282 -4.79e-05 2.15e-05 -0.01559 -3.61e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02543 -0.2046 0.2022 0.9837 0.9933 0.2021 0.4659 0.8795 0.7242 ] Network output: [ -0.01228 1 1.01 2.448e-06 -1.099e-06 0.01386 1.845e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00517 0.0006092 0.004163 0.004718 0.989 0.992 0.005263 0.8769 0.9034 0.01535 ] Network output: [ -0.002276 0.02593 1.001 -0.0001773 7.96e-05 0.9768 -0.0001336 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09727 0.3127 0.1627 0.9851 0.9941 0.1922 0.4708 0.8861 0.7195 ] Network output: [ 0.01004 -0.03838 0.9985 9.963e-05 -4.473e-05 1.02 7.508e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09696 0.08665 0.1767 0.2125 0.9874 0.992 0.09702 0.8015 0.8823 0.3113 ] Network output: [ -0.009841 0.04597 1.001 9.837e-05 -4.416e-05 0.9726 7.413e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09575 0.09393 0.1699 0.2007 0.9857 0.9915 0.09576 0.7337 0.863 0.2447 ] Network output: [ -0.0004868 0.9992 0.0003397 1.332e-05 -5.978e-06 1.001 1.004e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001103 Epoch 6522 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01444 0.9885 0.9854 6.643e-06 -2.982e-06 -0.002773 5.007e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003174 -0.002923 -0.01015 0.007762 0.9697 0.9741 0.00601 0.8462 0.8341 0.02118 ] Network output: [ 1 -0.0138 0.002805 -4.438e-05 1.992e-05 0.01055 -3.344e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.181 -0.02588 -0.2026 0.2076 0.9837 0.9933 0.2017 0.4651 0.8797 0.7246 ] Network output: [ -0.01226 0.9985 1.011 2.692e-06 -1.208e-06 0.01541 2.029e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005161 0.0006125 0.004257 0.004895 0.989 0.992 0.005254 0.8768 0.9035 0.0154 ] Network output: [ 0.0005695 -0.01859 1.003 -0.0001715 7.701e-05 1.014 -0.0001293 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1913 0.09715 0.3158 0.1712 0.9852 0.9941 0.1919 0.4702 0.8861 0.7191 ] Network output: [ 0.009193 -0.04723 1 0.0001 -4.49e-05 1.029 7.537e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09709 0.08681 0.1786 0.2148 0.9874 0.992 0.09715 0.8022 0.8823 0.3126 ] Network output: [ -0.01032 0.04861 1.001 9.789e-05 -4.395e-05 0.9709 7.377e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09587 0.09406 0.1704 0.2011 0.9858 0.9916 0.09589 0.7345 0.863 0.2447 ] Network output: [ 0.001168 0.9992 -0.001996 1.409e-05 -6.326e-06 1.001 1.062e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0012 Epoch 6523 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01412 0.9934 0.9853 6.036e-06 -2.71e-06 -0.006886 4.549e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002923 -0.01017 0.007674 0.9697 0.9741 0.006017 0.8463 0.8338 0.02116 ] Network output: [ 0.9978 0.01846 0.001285 -4.786e-05 2.149e-05 -0.01553 -3.607e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02545 -0.2045 0.2022 0.9837 0.9933 0.2021 0.4658 0.8795 0.7242 ] Network output: [ -0.01228 1 1.01 2.449e-06 -1.099e-06 0.01386 1.846e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005171 0.0006084 0.004164 0.004716 0.989 0.992 0.005264 0.8768 0.9034 0.01534 ] Network output: [ -0.002271 0.02583 1.001 -0.0001771 7.951e-05 0.9769 -0.0001335 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09724 0.3127 0.1627 0.9851 0.9941 0.1922 0.4707 0.8861 0.7195 ] Network output: [ 0.01003 -0.0384 0.9985 9.953e-05 -4.468e-05 1.02 7.501e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09696 0.08664 0.1767 0.2124 0.9874 0.992 0.09702 0.8015 0.8823 0.3113 ] Network output: [ -0.009835 0.04597 1.001 9.828e-05 -4.412e-05 0.9726 7.407e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09573 0.09391 0.1698 0.2007 0.9857 0.9915 0.09574 0.7336 0.863 0.2447 ] Network output: [ -0.0004833 0.9992 0.0003344 1.33e-05 -5.973e-06 1.001 1.003e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001101 Epoch 6524 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01444 0.9885 0.9854 6.632e-06 -2.977e-06 -0.002785 4.998e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002924 -0.01014 0.00776 0.9697 0.9741 0.00601 0.8462 0.834 0.02118 ] Network output: [ 1 -0.01373 0.002801 -4.436e-05 1.992e-05 0.01049 -3.343e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.181 -0.0259 -0.2026 0.2075 0.9837 0.9933 0.2018 0.4651 0.8797 0.7246 ] Network output: [ -0.01225 0.9985 1.011 2.691e-06 -1.208e-06 0.0154 2.028e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005162 0.0006117 0.004258 0.004892 0.9889 0.992 0.005255 0.8768 0.9035 0.01539 ] Network output: [ 0.0005608 -0.01848 1.003 -0.0001714 7.693e-05 1.014 -0.0001291 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1913 0.09712 0.3158 0.1711 0.9852 0.9941 0.1919 0.4702 0.8861 0.7191 ] Network output: [ 0.009189 -0.04721 1 9.991e-05 -4.485e-05 1.029 7.53e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09709 0.0868 0.1786 0.2148 0.9874 0.992 0.09715 0.8021 0.8823 0.3126 ] Network output: [ -0.01031 0.0486 1.001 9.78e-05 -4.391e-05 0.9709 7.371e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09585 0.09403 0.1704 0.2011 0.9858 0.9916 0.09586 0.7344 0.863 0.2447 ] Network output: [ 0.001164 0.9992 -0.001992 1.408e-05 -6.319e-06 1.001 1.061e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001197 Epoch 6525 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01411 0.9934 0.9853 6.029e-06 -2.706e-06 -0.006877 4.543e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002923 -0.01017 0.007671 0.9697 0.9741 0.006018 0.8462 0.8338 0.02115 ] Network output: [ 0.9978 0.01838 0.001287 -4.782e-05 2.147e-05 -0.01548 -3.604e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02547 -0.2045 0.2022 0.9837 0.9933 0.2021 0.4658 0.8795 0.7242 ] Network output: [ -0.01228 1 1.01 2.449e-06 -1.1e-06 0.01385 1.846e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005171 0.0006077 0.004164 0.004714 0.989 0.992 0.005265 0.8768 0.9034 0.01534 ] Network output: [ -0.002266 0.02573 1.001 -0.0001769 7.942e-05 0.9769 -0.0001333 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09721 0.3128 0.1626 0.9851 0.9941 0.1922 0.4707 0.8861 0.7195 ] Network output: [ 0.01002 -0.03842 0.9985 9.943e-05 -4.464e-05 1.02 7.493e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09695 0.08663 0.1767 0.2124 0.9874 0.992 0.09701 0.8014 0.8822 0.3113 ] Network output: [ -0.009829 0.04596 1.001 9.819e-05 -4.408e-05 0.9726 7.4e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09571 0.09388 0.1698 0.2006 0.9857 0.9915 0.09572 0.7335 0.863 0.2446 ] Network output: [ -0.0004799 0.9992 0.000329 1.329e-05 -5.967e-06 1.001 1.002e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001099 Epoch 6526 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01443 0.9885 0.9854 6.621e-06 -2.972e-06 -0.002797 4.99e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002924 -0.01014 0.007757 0.9697 0.9741 0.00601 0.8461 0.834 0.02117 ] Network output: [ 1 -0.01365 0.002796 -4.435e-05 1.991e-05 0.01042 -3.342e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.181 -0.02591 -0.2025 0.2075 0.9837 0.9933 0.2018 0.465 0.8797 0.7246 ] Network output: [ -0.01225 0.9986 1.011 2.69e-06 -1.208e-06 0.01539 2.027e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005163 0.000611 0.004258 0.004889 0.9889 0.992 0.005256 0.8768 0.9035 0.01539 ] Network output: [ 0.000552 -0.01837 1.003 -0.0001712 7.686e-05 1.014 -0.000129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1913 0.09709 0.3159 0.171 0.9852 0.9941 0.1919 0.4701 0.8861 0.7191 ] Network output: [ 0.009185 -0.04719 1 9.981e-05 -4.481e-05 1.029 7.522e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09709 0.08679 0.1786 0.2148 0.9874 0.992 0.09715 0.8021 0.8823 0.3126 ] Network output: [ -0.01031 0.04858 1.001 9.772e-05 -4.387e-05 0.9709 7.365e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09583 0.09401 0.1704 0.2011 0.9858 0.9916 0.09584 0.7343 0.863 0.2447 ] Network output: [ 0.00116 0.9992 -0.001987 1.406e-05 -6.311e-06 1.001 1.06e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001195 Epoch 6527 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01411 0.9934 0.9853 6.021e-06 -2.703e-06 -0.006869 4.538e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002923 -0.01017 0.007669 0.9697 0.9741 0.006018 0.8462 0.8338 0.02115 ] Network output: [ 0.9978 0.0183 0.00129 -4.779e-05 2.145e-05 -0.01542 -3.602e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02549 -0.2045 0.2022 0.9837 0.9933 0.2021 0.4658 0.8795 0.7242 ] Network output: [ -0.01228 1 1.01 2.45e-06 -1.1e-06 0.01385 1.846e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005172 0.0006069 0.004165 0.004713 0.989 0.992 0.005265 0.8768 0.9034 0.01534 ] Network output: [ -0.002261 0.02562 1.001 -0.0001767 7.933e-05 0.977 -0.0001332 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09719 0.3128 0.1626 0.9851 0.9941 0.1922 0.4707 0.8861 0.7195 ] Network output: [ 0.01002 -0.03844 0.9985 9.933e-05 -4.459e-05 1.02 7.486e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09695 0.08663 0.1767 0.2124 0.9874 0.992 0.09701 0.8014 0.8822 0.3113 ] Network output: [ -0.009823 0.04596 1.001 9.81e-05 -4.404e-05 0.9726 7.393e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09569 0.09386 0.1698 0.2006 0.9857 0.9915 0.0957 0.7335 0.8629 0.2446 ] Network output: [ -0.0004764 0.9992 0.0003237 1.328e-05 -5.962e-06 1.001 1.001e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001098 Epoch 6528 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01442 0.9885 0.9854 6.61e-06 -2.967e-06 -0.002809 4.982e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002924 -0.01014 0.007754 0.9697 0.9741 0.00601 0.8461 0.834 0.02117 ] Network output: [ 1 -0.01358 0.002792 -4.433e-05 1.99e-05 0.01036 -3.341e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.181 -0.02593 -0.2025 0.2075 0.9837 0.9933 0.2018 0.465 0.8797 0.7246 ] Network output: [ -0.01225 0.9986 1.011 2.689e-06 -1.207e-06 0.01538 2.027e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005164 0.0006102 0.004258 0.004887 0.9889 0.992 0.005257 0.8768 0.9035 0.01538 ] Network output: [ 0.0005433 -0.01826 1.003 -0.000171 7.678e-05 1.013 -0.0001289 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1913 0.09706 0.3159 0.171 0.9851 0.9941 0.1919 0.4701 0.8861 0.7191 ] Network output: [ 0.009182 -0.04717 1 9.971e-05 -4.477e-05 1.029 7.515e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09708 0.08679 0.1786 0.2147 0.9874 0.992 0.09714 0.802 0.8823 0.3125 ] Network output: [ -0.0103 0.04856 1.001 9.764e-05 -4.383e-05 0.9709 7.358e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0958 0.09399 0.1703 0.201 0.9858 0.9916 0.09582 0.7343 0.8629 0.2447 ] Network output: [ 0.001156 0.9992 -0.001982 1.404e-05 -6.304e-06 1.001 1.058e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001193 Epoch 6529 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0141 0.9934 0.9853 6.014e-06 -2.7e-06 -0.006861 4.532e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002924 -0.01016 0.007667 0.9697 0.9741 0.006018 0.8462 0.8338 0.02114 ] Network output: [ 0.9978 0.01822 0.001292 -4.775e-05 2.144e-05 -0.01536 -3.599e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02552 -0.2044 0.2022 0.9837 0.9933 0.2022 0.4657 0.8795 0.7242 ] Network output: [ -0.01228 1 1.01 2.451e-06 -1.1e-06 0.01385 1.847e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005173 0.0006062 0.004166 0.004711 0.989 0.992 0.005266 0.8768 0.9033 0.01533 ] Network output: [ -0.002256 0.02552 1.001 -0.0001765 7.924e-05 0.9771 -0.000133 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09716 0.3129 0.1626 0.9851 0.9941 0.1922 0.4706 0.8861 0.7194 ] Network output: [ 0.01001 -0.03846 0.9985 9.923e-05 -4.455e-05 1.02 7.479e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09695 0.08662 0.1767 0.2124 0.9874 0.992 0.09701 0.8013 0.8822 0.3113 ] Network output: [ -0.009817 0.04595 1.001 9.802e-05 -4.4e-05 0.9726 7.387e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09566 0.09384 0.1698 0.2006 0.9857 0.9915 0.09567 0.7334 0.8629 0.2446 ] Network output: [ -0.0004729 0.9992 0.0003184 1.327e-05 -5.957e-06 1.001 1e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001096 Epoch 6530 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01442 0.9886 0.9854 6.599e-06 -2.962e-06 -0.00282 4.973e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002924 -0.01013 0.007752 0.9697 0.9741 0.006011 0.8461 0.834 0.02116 ] Network output: [ 1 -0.01351 0.002788 -4.432e-05 1.99e-05 0.0103 -3.34e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.181 -0.02595 -0.2025 0.2074 0.9837 0.9933 0.2018 0.465 0.8797 0.7246 ] Network output: [ -0.01225 0.9986 1.011 2.689e-06 -1.207e-06 0.01537 2.026e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005164 0.0006095 0.004258 0.004884 0.9889 0.992 0.005258 0.8768 0.9035 0.01538 ] Network output: [ 0.0005346 -0.01814 1.003 -0.0001709 7.671e-05 1.013 -0.0001288 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1913 0.09703 0.3159 0.1709 0.9851 0.9941 0.1919 0.4701 0.8861 0.7191 ] Network output: [ 0.009178 -0.04714 1 9.961e-05 -4.472e-05 1.029 7.507e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09708 0.08678 0.1786 0.2147 0.9874 0.992 0.09714 0.8019 0.8822 0.3125 ] Network output: [ -0.01029 0.04855 1.001 9.755e-05 -4.379e-05 0.9709 7.352e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09578 0.09396 0.1703 0.201 0.9858 0.9916 0.09579 0.7342 0.8629 0.2447 ] Network output: [ 0.001152 0.9992 -0.001977 1.403e-05 -6.297e-06 1.001 1.057e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001191 Epoch 6531 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0141 0.9934 0.9853 6.006e-06 -2.696e-06 -0.006853 4.526e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002924 -0.01016 0.007665 0.9697 0.9741 0.006018 0.8462 0.8338 0.02114 ] Network output: [ 0.9978 0.01814 0.001295 -4.772e-05 2.142e-05 -0.0153 -3.596e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02554 -0.2044 0.2022 0.9837 0.9933 0.2022 0.4657 0.8795 0.7242 ] Network output: [ -0.01227 1 1.01 2.451e-06 -1.1e-06 0.01385 1.847e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005174 0.0006055 0.004167 0.004709 0.989 0.992 0.005267 0.8768 0.9033 0.01533 ] Network output: [ -0.002251 0.02542 1.001 -0.0001763 7.915e-05 0.9772 -0.0001329 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.09713 0.3129 0.1626 0.9851 0.9941 0.1923 0.4706 0.8861 0.7194 ] Network output: [ 0.009999 -0.03848 0.9985 9.914e-05 -4.451e-05 1.02 7.471e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09695 0.08661 0.1767 0.2124 0.9874 0.992 0.09701 0.8013 0.8822 0.3113 ] Network output: [ -0.009811 0.04595 1.001 9.793e-05 -4.396e-05 0.9726 7.38e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09564 0.09381 0.1698 0.2006 0.9857 0.9915 0.09565 0.7333 0.8629 0.2446 ] Network output: [ -0.0004694 0.9992 0.0003131 1.326e-05 -5.952e-06 1.001 9.991e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001095 Epoch 6532 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01441 0.9886 0.9855 6.588e-06 -2.957e-06 -0.002832 4.965e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002925 -0.01013 0.007749 0.9697 0.9741 0.006011 0.8461 0.834 0.02116 ] Network output: [ 1 -0.01344 0.002783 -4.431e-05 1.989e-05 0.01023 -3.339e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.181 -0.02597 -0.2025 0.2074 0.9837 0.9933 0.2018 0.4649 0.8797 0.7246 ] Network output: [ -0.01225 0.9986 1.011 2.688e-06 -1.207e-06 0.01536 2.026e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005165 0.0006087 0.004259 0.004881 0.9889 0.992 0.005258 0.8767 0.9034 0.01538 ] Network output: [ 0.000526 -0.01803 1.003 -0.0001707 7.663e-05 1.013 -0.0001286 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1913 0.09701 0.316 0.1708 0.9851 0.9941 0.1919 0.47 0.8861 0.7191 ] Network output: [ 0.009174 -0.04712 1 9.951e-05 -4.468e-05 1.029 7.5e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09708 0.08677 0.1786 0.2147 0.9874 0.992 0.09714 0.8019 0.8822 0.3125 ] Network output: [ -0.01028 0.04853 1.001 9.747e-05 -4.376e-05 0.9709 7.345e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09576 0.09394 0.1703 0.201 0.9858 0.9916 0.09577 0.7341 0.8629 0.2447 ] Network output: [ 0.001148 0.9992 -0.001972 1.401e-05 -6.289e-06 1.001 1.056e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001189 Epoch 6533 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01409 0.9934 0.9853 5.998e-06 -2.693e-06 -0.006845 4.521e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002924 -0.01016 0.007662 0.9697 0.9741 0.006019 0.8462 0.8338 0.02113 ] Network output: [ 0.9978 0.01806 0.001297 -4.768e-05 2.141e-05 -0.01524 -3.593e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02556 -0.2044 0.2022 0.9837 0.9933 0.2022 0.4657 0.8795 0.7242 ] Network output: [ -0.01227 1 1.01 2.452e-06 -1.101e-06 0.01385 1.848e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005174 0.0006047 0.004167 0.004707 0.989 0.992 0.005268 0.8768 0.9033 0.01533 ] Network output: [ -0.002246 0.02532 1.001 -0.0001761 7.906e-05 0.9773 -0.0001327 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.0971 0.3129 0.1625 0.9851 0.9941 0.1923 0.4706 0.8861 0.7194 ] Network output: [ 0.00999 -0.0385 0.9985 9.904e-05 -4.446e-05 1.02 7.464e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09694 0.08661 0.1767 0.2123 0.9874 0.992 0.097 0.8012 0.8821 0.3113 ] Network output: [ -0.009804 0.04594 1.001 9.784e-05 -4.392e-05 0.9726 7.374e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09562 0.09379 0.1698 0.2006 0.9857 0.9915 0.09563 0.7333 0.8629 0.2446 ] Network output: [ -0.0004659 0.9992 0.0003078 1.325e-05 -5.946e-06 1.001 9.982e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001093 Epoch 6534 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01441 0.9886 0.9855 6.577e-06 -2.952e-06 -0.002844 4.956e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002925 -0.01013 0.007746 0.9697 0.9741 0.006011 0.8461 0.834 0.02115 ] Network output: [ 1 -0.01337 0.002779 -4.429e-05 1.988e-05 0.01017 -3.338e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.181 -0.02599 -0.2024 0.2074 0.9837 0.9933 0.2018 0.4649 0.8797 0.7246 ] Network output: [ -0.01224 0.9986 1.011 2.687e-06 -1.206e-06 0.01535 2.025e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005166 0.000608 0.004259 0.004879 0.9889 0.992 0.005259 0.8767 0.9034 0.01537 ] Network output: [ 0.0005173 -0.01792 1.003 -0.0001705 7.656e-05 1.013 -0.0001285 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1913 0.09698 0.316 0.1707 0.9851 0.9941 0.1919 0.47 0.886 0.719 ] Network output: [ 0.009171 -0.0471 1 9.941e-05 -4.463e-05 1.029 7.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09708 0.08676 0.1786 0.2147 0.9874 0.992 0.09714 0.8018 0.8822 0.3125 ] Network output: [ -0.01027 0.04851 1.001 9.738e-05 -4.372e-05 0.9709 7.339e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09574 0.09392 0.1703 0.201 0.9858 0.9916 0.09575 0.7341 0.8628 0.2447 ] Network output: [ 0.001144 0.9992 -0.001967 1.399e-05 -6.282e-06 1.001 1.055e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001187 Epoch 6535 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01409 0.9934 0.9853 5.991e-06 -2.69e-06 -0.006837 4.515e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002924 -0.01015 0.00766 0.9697 0.9741 0.006019 0.8462 0.8338 0.02113 ] Network output: [ 0.9979 0.01798 0.0013 -4.765e-05 2.139e-05 -0.01519 -3.591e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02558 -0.2043 0.2022 0.9837 0.9933 0.2022 0.4656 0.8795 0.7242 ] Network output: [ -0.01227 1 1.01 2.452e-06 -1.101e-06 0.01385 1.848e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005175 0.000604 0.004168 0.004706 0.989 0.992 0.005268 0.8768 0.9033 0.01532 ] Network output: [ -0.002241 0.02521 1.001 -0.0001759 7.897e-05 0.9773 -0.0001326 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.09707 0.313 0.1625 0.9851 0.9941 0.1923 0.4705 0.8861 0.7194 ] Network output: [ 0.009981 -0.03853 0.9985 9.894e-05 -4.442e-05 1.02 7.456e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09694 0.0866 0.1767 0.2123 0.9874 0.992 0.097 0.8012 0.8821 0.3113 ] Network output: [ -0.009798 0.04594 1.001 9.775e-05 -4.389e-05 0.9726 7.367e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0956 0.09377 0.1697 0.2005 0.9857 0.9915 0.09561 0.7332 0.8628 0.2446 ] Network output: [ -0.0004625 0.9992 0.0003026 1.323e-05 -5.941e-06 1.001 9.973e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001091 Epoch 6536 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0144 0.9886 0.9855 6.565e-06 -2.947e-06 -0.002856 4.948e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002925 -0.01012 0.007744 0.9697 0.9741 0.006012 0.8461 0.834 0.02115 ] Network output: [ 1 -0.01329 0.002775 -4.428e-05 1.988e-05 0.0101 -3.337e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.181 -0.02601 -0.2024 0.2073 0.9837 0.9933 0.2018 0.4649 0.8797 0.7245 ] Network output: [ -0.01224 0.9986 1.011 2.686e-06 -1.206e-06 0.01534 2.024e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005167 0.0006072 0.004259 0.004876 0.9889 0.992 0.00526 0.8767 0.9034 0.01537 ] Network output: [ 0.0005087 -0.01781 1.003 -0.0001704 7.648e-05 1.013 -0.0001284 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1913 0.09695 0.316 0.1707 0.9851 0.9941 0.1919 0.47 0.886 0.719 ] Network output: [ 0.009167 -0.04707 1 9.931e-05 -4.459e-05 1.029 7.485e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09707 0.08676 0.1786 0.2146 0.9874 0.992 0.09713 0.8018 0.8822 0.3125 ] Network output: [ -0.01026 0.0485 1.001 9.73e-05 -4.368e-05 0.9709 7.333e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09571 0.09389 0.1703 0.201 0.9858 0.9916 0.09573 0.734 0.8628 0.2446 ] Network output: [ 0.001141 0.9992 -0.001962 1.398e-05 -6.274e-06 1.001 1.053e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001184 Epoch 6537 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01408 0.9934 0.9853 5.983e-06 -2.686e-06 -0.006829 4.509e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002925 -0.01015 0.007658 0.9697 0.9741 0.006019 0.8462 0.8338 0.02112 ] Network output: [ 0.9979 0.0179 0.001302 -4.761e-05 2.137e-05 -0.01513 -3.588e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.0256 -0.2043 0.2021 0.9837 0.9933 0.2022 0.4656 0.8795 0.7241 ] Network output: [ -0.01227 1 1.01 2.453e-06 -1.101e-06 0.01384 1.848e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005176 0.0006033 0.004169 0.004704 0.989 0.992 0.005269 0.8767 0.9033 0.01532 ] Network output: [ -0.002236 0.02511 1.001 -0.0001757 7.888e-05 0.9774 -0.0001324 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.09705 0.313 0.1625 0.9851 0.9941 0.1923 0.4705 0.886 0.7194 ] Network output: [ 0.009972 -0.03854 0.9985 9.884e-05 -4.437e-05 1.021 7.449e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09694 0.08659 0.1767 0.2123 0.9874 0.992 0.097 0.8011 0.8821 0.3113 ] Network output: [ -0.009792 0.04593 1.001 9.767e-05 -4.385e-05 0.9726 7.36e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09557 0.09375 0.1697 0.2005 0.9857 0.9915 0.09559 0.7331 0.8628 0.2446 ] Network output: [ -0.000459 0.9992 0.0002973 1.322e-05 -5.936e-06 1.001 9.964e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00109 Epoch 6538 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01439 0.9886 0.9855 6.554e-06 -2.942e-06 -0.002868 4.94e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002925 -0.01012 0.007741 0.9697 0.9741 0.006012 0.8461 0.834 0.02114 ] Network output: [ 1 -0.01322 0.002771 -4.426e-05 1.987e-05 0.01004 -3.336e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.181 -0.02603 -0.2024 0.2073 0.9837 0.9933 0.2018 0.4649 0.8796 0.7245 ] Network output: [ -0.01224 0.9986 1.011 2.685e-06 -1.206e-06 0.01533 2.024e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005167 0.0006065 0.00426 0.004874 0.9889 0.992 0.005261 0.8767 0.9034 0.01536 ] Network output: [ 0.0005001 -0.0177 1.003 -0.0001702 7.641e-05 1.013 -0.0001283 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09692 0.316 0.1706 0.9851 0.9941 0.1919 0.47 0.886 0.719 ] Network output: [ 0.009163 -0.04705 1 9.921e-05 -4.454e-05 1.029 7.477e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09707 0.08675 0.1786 0.2146 0.9874 0.992 0.09713 0.8017 0.8821 0.3125 ] Network output: [ -0.01025 0.04848 1.001 9.721e-05 -4.364e-05 0.971 7.326e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09569 0.09387 0.1702 0.2009 0.9858 0.9916 0.0957 0.7339 0.8628 0.2446 ] Network output: [ 0.001137 0.9992 -0.001957 1.396e-05 -6.267e-06 1.001 1.052e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001182 Epoch 6539 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01408 0.9934 0.9853 5.976e-06 -2.683e-06 -0.006821 4.503e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002925 -0.01015 0.007656 0.9697 0.9741 0.006019 0.8462 0.8338 0.02112 ] Network output: [ 0.9979 0.01782 0.001304 -4.757e-05 2.136e-05 -0.01507 -3.585e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02562 -0.2043 0.2021 0.9837 0.9933 0.2022 0.4656 0.8795 0.7241 ] Network output: [ -0.01227 1 1.01 2.453e-06 -1.101e-06 0.01384 1.849e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005177 0.0006026 0.004169 0.004702 0.989 0.992 0.00527 0.8767 0.9033 0.01532 ] Network output: [ -0.002231 0.02501 1.001 -0.0001755 7.879e-05 0.9775 -0.0001323 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.09702 0.3131 0.1624 0.9851 0.9941 0.1923 0.4705 0.886 0.7194 ] Network output: [ 0.009963 -0.03856 0.9985 9.874e-05 -4.433e-05 1.021 7.442e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09694 0.08659 0.1767 0.2123 0.9874 0.992 0.097 0.8011 0.8821 0.3112 ] Network output: [ -0.009786 0.04593 1.001 9.758e-05 -4.381e-05 0.9726 7.354e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09555 0.09372 0.1697 0.2005 0.9857 0.9915 0.09556 0.7331 0.8628 0.2446 ] Network output: [ -0.0004555 0.9992 0.0002921 1.321e-05 -5.93e-06 1.001 9.955e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001088 Epoch 6540 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01439 0.9887 0.9855 6.543e-06 -2.937e-06 -0.002879 4.931e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002926 -0.01012 0.007738 0.9697 0.9741 0.006012 0.8461 0.834 0.02114 ] Network output: [ 1 -0.01315 0.002766 -4.425e-05 1.986e-05 0.009976 -3.335e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.181 -0.02604 -0.2024 0.2072 0.9837 0.9933 0.2018 0.4648 0.8796 0.7245 ] Network output: [ -0.01224 0.9986 1.011 2.684e-06 -1.205e-06 0.01532 2.023e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005168 0.0006057 0.00426 0.004871 0.9889 0.992 0.005262 0.8767 0.9034 0.01536 ] Network output: [ 0.0004916 -0.01759 1.003 -0.00017 7.633e-05 1.013 -0.0001281 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.0969 0.3161 0.1705 0.9851 0.9941 0.192 0.4699 0.886 0.719 ] Network output: [ 0.009159 -0.04703 1 9.911e-05 -4.45e-05 1.029 7.47e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09707 0.08674 0.1786 0.2146 0.9874 0.992 0.09713 0.8016 0.8821 0.3125 ] Network output: [ -0.01024 0.04846 1.001 9.713e-05 -4.36e-05 0.971 7.32e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09567 0.09385 0.1702 0.2009 0.9858 0.9916 0.09568 0.7339 0.8627 0.2446 ] Network output: [ 0.001133 0.9992 -0.001953 1.394e-05 -6.26e-06 1.001 1.051e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00118 Epoch 6541 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01407 0.9934 0.9853 5.968e-06 -2.679e-06 -0.006813 4.498e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002925 -0.01014 0.007654 0.9697 0.9741 0.006019 0.8462 0.8338 0.02111 ] Network output: [ 0.9979 0.01774 0.001307 -4.754e-05 2.134e-05 -0.01501 -3.583e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02564 -0.2042 0.2021 0.9837 0.9933 0.2022 0.4656 0.8794 0.7241 ] Network output: [ -0.01226 1 1.01 2.454e-06 -1.101e-06 0.01384 1.849e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005177 0.0006018 0.00417 0.004701 0.989 0.992 0.005271 0.8767 0.9033 0.01531 ] Network output: [ -0.002226 0.02491 1.001 -0.0001753 7.87e-05 0.9776 -0.0001321 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.09699 0.3131 0.1624 0.9851 0.9941 0.1923 0.4704 0.886 0.7193 ] Network output: [ 0.009955 -0.03858 0.9985 9.865e-05 -4.429e-05 1.021 7.434e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09694 0.08658 0.1767 0.2123 0.9874 0.992 0.097 0.801 0.882 0.3112 ] Network output: [ -0.00978 0.04592 1.001 9.749e-05 -4.377e-05 0.9726 7.347e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09553 0.0937 0.1697 0.2005 0.9857 0.9915 0.09554 0.733 0.8627 0.2445 ] Network output: [ -0.0004521 0.9992 0.0002868 1.32e-05 -5.925e-06 1.001 9.946e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001087 Epoch 6542 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01438 0.9887 0.9855 6.532e-06 -2.932e-06 -0.002891 4.923e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002926 -0.01011 0.007736 0.9697 0.9741 0.006012 0.8461 0.8339 0.02113 ] Network output: [ 1 -0.01308 0.002762 -4.423e-05 1.986e-05 0.009913 -3.334e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.181 -0.02606 -0.2023 0.2072 0.9837 0.9933 0.2018 0.4648 0.8796 0.7245 ] Network output: [ -0.01224 0.9986 1.011 2.684e-06 -1.205e-06 0.01531 2.022e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005169 0.000605 0.00426 0.004868 0.9889 0.992 0.005262 0.8767 0.9034 0.01536 ] Network output: [ 0.000483 -0.01748 1.003 -0.0001699 7.626e-05 1.013 -0.000128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09687 0.3161 0.1705 0.9851 0.9941 0.192 0.4699 0.886 0.719 ] Network output: [ 0.009156 -0.047 1 9.901e-05 -4.445e-05 1.029 7.462e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09706 0.08673 0.1786 0.2145 0.9874 0.992 0.09712 0.8016 0.8821 0.3124 ] Network output: [ -0.01023 0.04845 1.001 9.704e-05 -4.357e-05 0.971 7.313e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09564 0.09382 0.1702 0.2009 0.9858 0.9916 0.09566 0.7338 0.8627 0.2446 ] Network output: [ 0.001129 0.9992 -0.001948 1.393e-05 -6.252e-06 1.001 1.05e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001178 Epoch 6543 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01407 0.9934 0.9853 5.96e-06 -2.676e-06 -0.006805 4.492e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002925 -0.01014 0.007651 0.9697 0.9741 0.00602 0.8462 0.8337 0.02111 ] Network output: [ 0.9979 0.01766 0.001309 -4.75e-05 2.133e-05 -0.01495 -3.58e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02566 -0.2042 0.2021 0.9837 0.9933 0.2022 0.4655 0.8794 0.7241 ] Network output: [ -0.01226 1 1.01 2.454e-06 -1.102e-06 0.01384 1.849e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005178 0.0006011 0.004171 0.004699 0.989 0.992 0.005272 0.8767 0.9033 0.01531 ] Network output: [ -0.002221 0.0248 1.001 -0.0001751 7.861e-05 0.9776 -0.000132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.09696 0.3132 0.1624 0.9851 0.9941 0.1923 0.4704 0.886 0.7193 ] Network output: [ 0.009946 -0.0386 0.9985 9.855e-05 -4.424e-05 1.021 7.427e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08657 0.1767 0.2122 0.9874 0.992 0.09699 0.8009 0.882 0.3112 ] Network output: [ -0.009774 0.04591 1.001 9.74e-05 -4.373e-05 0.9726 7.341e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09551 0.09368 0.1697 0.2005 0.9857 0.9915 0.09552 0.7329 0.8627 0.2445 ] Network output: [ -0.0004486 0.9992 0.0002816 1.319e-05 -5.92e-06 1.001 9.938e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001085 Epoch 6544 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01438 0.9887 0.9855 6.521e-06 -2.927e-06 -0.002903 4.914e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003175 -0.002926 -0.01011 0.007733 0.9697 0.9741 0.006013 0.8461 0.8339 0.02113 ] Network output: [ 1 -0.01301 0.002758 -4.422e-05 1.985e-05 0.009849 -3.332e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02608 -0.2023 0.2072 0.9837 0.9933 0.2019 0.4648 0.8796 0.7245 ] Network output: [ -0.01223 0.9986 1.011 2.683e-06 -1.204e-06 0.0153 2.022e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00517 0.0006043 0.00426 0.004866 0.9889 0.992 0.005263 0.8766 0.9034 0.01535 ] Network output: [ 0.0004745 -0.01737 1.003 -0.0001697 7.618e-05 1.013 -0.0001279 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09684 0.3161 0.1704 0.9851 0.9941 0.192 0.4699 0.886 0.719 ] Network output: [ 0.009152 -0.04698 1 9.891e-05 -4.441e-05 1.029 7.454e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09706 0.08673 0.1786 0.2145 0.9874 0.992 0.09712 0.8015 0.8821 0.3124 ] Network output: [ -0.01022 0.04843 1.001 9.696e-05 -4.353e-05 0.971 7.307e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09562 0.0938 0.1702 0.2009 0.9858 0.9916 0.09563 0.7337 0.8627 0.2446 ] Network output: [ 0.001125 0.9992 -0.001943 1.391e-05 -6.245e-06 1.001 1.048e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001176 Epoch 6545 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01406 0.9934 0.9853 5.953e-06 -2.672e-06 -0.006797 4.486e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002926 -0.01014 0.007649 0.9697 0.9741 0.00602 0.8461 0.8337 0.0211 ] Network output: [ 0.9979 0.01758 0.001312 -4.747e-05 2.131e-05 -0.0149 -3.577e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02568 -0.2042 0.2021 0.9837 0.9933 0.2022 0.4655 0.8794 0.7241 ] Network output: [ -0.01226 1 1.01 2.454e-06 -1.102e-06 0.01384 1.85e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005179 0.0006004 0.004172 0.004697 0.989 0.992 0.005272 0.8767 0.9033 0.01531 ] Network output: [ -0.002216 0.0247 1.001 -0.0001749 7.852e-05 0.9777 -0.0001318 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.09693 0.3132 0.1624 0.9851 0.9941 0.1923 0.4704 0.886 0.7193 ] Network output: [ 0.009937 -0.03862 0.9984 9.845e-05 -4.42e-05 1.021 7.419e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08657 0.1768 0.2122 0.9874 0.992 0.09699 0.8009 0.882 0.3112 ] Network output: [ -0.009768 0.04591 1.001 9.731e-05 -4.369e-05 0.9726 7.334e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09549 0.09366 0.1697 0.2004 0.9857 0.9915 0.0955 0.7329 0.8627 0.2445 ] Network output: [ -0.0004451 0.9992 0.0002764 1.317e-05 -5.914e-06 1.001 9.929e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001084 Epoch 6546 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01437 0.9887 0.9855 6.51e-06 -2.922e-06 -0.002915 4.906e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002926 -0.01011 0.00773 0.9697 0.9741 0.006013 0.846 0.8339 0.02112 ] Network output: [ 1 -0.01293 0.002753 -4.42e-05 1.984e-05 0.009786 -3.331e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.0261 -0.2023 0.2071 0.9837 0.9933 0.2019 0.4648 0.8796 0.7245 ] Network output: [ -0.01223 0.9986 1.011 2.682e-06 -1.204e-06 0.01529 2.021e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005171 0.0006035 0.004261 0.004863 0.9889 0.992 0.005264 0.8766 0.9034 0.01535 ] Network output: [ 0.000466 -0.01727 1.003 -0.0001695 7.611e-05 1.013 -0.0001278 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09682 0.3162 0.1703 0.9851 0.9941 0.192 0.4698 0.886 0.719 ] Network output: [ 0.009148 -0.04696 1 9.881e-05 -4.436e-05 1.029 7.447e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09706 0.08672 0.1786 0.2145 0.9874 0.992 0.09712 0.8015 0.882 0.3124 ] Network output: [ -0.01021 0.04841 1.001 9.687e-05 -4.349e-05 0.971 7.301e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0956 0.09378 0.1702 0.2008 0.9858 0.9916 0.09561 0.7336 0.8626 0.2446 ] Network output: [ 0.001121 0.9992 -0.001938 1.389e-05 -6.237e-06 1.001 1.047e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001174 Epoch 6547 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01406 0.9933 0.9853 5.945e-06 -2.669e-06 -0.006789 4.48e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002926 -0.01013 0.007647 0.9697 0.9741 0.00602 0.8461 0.8337 0.0211 ] Network output: [ 0.9979 0.0175 0.001314 -4.743e-05 2.129e-05 -0.01484 -3.574e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.0257 -0.2041 0.2021 0.9837 0.9933 0.2022 0.4655 0.8794 0.7241 ] Network output: [ -0.01226 1 1.01 2.455e-06 -1.102e-06 0.01383 1.85e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00518 0.0005997 0.004172 0.004695 0.989 0.992 0.005273 0.8767 0.9032 0.0153 ] Network output: [ -0.002212 0.0246 1.001 -0.0001747 7.843e-05 0.9778 -0.0001317 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.09691 0.3132 0.1623 0.9851 0.9941 0.1923 0.4703 0.886 0.7193 ] Network output: [ 0.009928 -0.03864 0.9984 9.835e-05 -4.415e-05 1.021 7.412e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08656 0.1768 0.2122 0.9874 0.992 0.09699 0.8008 0.882 0.3112 ] Network output: [ -0.009762 0.0459 1.001 9.723e-05 -4.365e-05 0.9726 7.327e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09546 0.09364 0.1696 0.2004 0.9857 0.9915 0.09548 0.7328 0.8626 0.2445 ] Network output: [ -0.0004417 0.9992 0.0002712 1.316e-05 -5.909e-06 1.001 9.92e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001082 Epoch 6548 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01436 0.9887 0.9855 6.498e-06 -2.917e-06 -0.002927 4.897e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002927 -0.0101 0.007728 0.9697 0.9741 0.006013 0.846 0.8339 0.02112 ] Network output: [ 1 -0.01286 0.002749 -4.419e-05 1.984e-05 0.009722 -3.33e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02612 -0.2022 0.2071 0.9837 0.9933 0.2019 0.4647 0.8796 0.7245 ] Network output: [ -0.01223 0.9986 1.011 2.681e-06 -1.203e-06 0.01528 2.02e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005171 0.0006028 0.004261 0.004861 0.9889 0.992 0.005265 0.8766 0.9033 0.01535 ] Network output: [ 0.0004576 -0.01716 1.003 -0.0001694 7.603e-05 1.013 -0.0001276 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09679 0.3162 0.1703 0.9851 0.9941 0.192 0.4698 0.886 0.719 ] Network output: [ 0.009144 -0.04693 1 9.871e-05 -4.432e-05 1.029 7.439e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09705 0.08671 0.1786 0.2144 0.9874 0.992 0.09711 0.8014 0.882 0.3124 ] Network output: [ -0.0102 0.04839 1.001 9.679e-05 -4.345e-05 0.971 7.294e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09558 0.09375 0.1701 0.2008 0.9858 0.9916 0.09559 0.7336 0.8626 0.2446 ] Network output: [ 0.001117 0.9992 -0.001933 1.388e-05 -6.23e-06 1.001 1.046e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001172 Epoch 6549 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01405 0.9933 0.9854 5.937e-06 -2.665e-06 -0.006781 4.475e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002926 -0.01013 0.007645 0.9697 0.9741 0.00602 0.8461 0.8337 0.02109 ] Network output: [ 0.9979 0.01742 0.001316 -4.739e-05 2.128e-05 -0.01478 -3.572e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02572 -0.2041 0.2021 0.9837 0.9933 0.2022 0.4654 0.8794 0.7241 ] Network output: [ -0.01225 1 1.01 2.455e-06 -1.102e-06 0.01383 1.85e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00518 0.000599 0.004173 0.004694 0.989 0.992 0.005274 0.8766 0.9032 0.0153 ] Network output: [ -0.002207 0.0245 1.001 -0.0001745 7.834e-05 0.9779 -0.0001315 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.09688 0.3133 0.1623 0.9851 0.9941 0.1923 0.4703 0.886 0.7193 ] Network output: [ 0.009919 -0.03866 0.9984 9.825e-05 -4.411e-05 1.021 7.405e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08655 0.1768 0.2122 0.9874 0.992 0.09699 0.8008 0.8819 0.3112 ] Network output: [ -0.009756 0.04589 1.001 9.714e-05 -4.361e-05 0.9726 7.321e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09544 0.09361 0.1696 0.2004 0.9857 0.9915 0.09546 0.7327 0.8626 0.2445 ] Network output: [ -0.0004382 0.9992 0.000266 1.315e-05 -5.904e-06 1.001 9.911e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00108 Epoch 6550 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01436 0.9887 0.9855 6.487e-06 -2.912e-06 -0.002939 4.889e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002927 -0.0101 0.007725 0.9697 0.9741 0.006013 0.846 0.8339 0.02111 ] Network output: [ 1 -0.01279 0.002745 -4.417e-05 1.983e-05 0.009659 -3.329e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02614 -0.2022 0.2071 0.9837 0.9933 0.2019 0.4647 0.8796 0.7244 ] Network output: [ -0.01223 0.9986 1.011 2.68e-06 -1.203e-06 0.01527 2.019e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005172 0.0006021 0.004261 0.004858 0.9889 0.992 0.005266 0.8766 0.9033 0.01534 ] Network output: [ 0.0004491 -0.01705 1.003 -0.0001692 7.596e-05 1.012 -0.0001275 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09676 0.3162 0.1702 0.9851 0.9941 0.192 0.4698 0.886 0.7189 ] Network output: [ 0.00914 -0.04691 1 9.861e-05 -4.427e-05 1.029 7.432e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09705 0.08671 0.1786 0.2144 0.9874 0.992 0.09711 0.8013 0.882 0.3124 ] Network output: [ -0.01019 0.04837 1.001 9.67e-05 -4.341e-05 0.971 7.288e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09555 0.09373 0.1701 0.2008 0.9858 0.9916 0.09557 0.7335 0.8626 0.2446 ] Network output: [ 0.001114 0.9992 -0.001928 1.386e-05 -6.223e-06 1.001 1.045e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00117 Epoch 6551 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01405 0.9933 0.9854 5.93e-06 -2.662e-06 -0.006773 4.469e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002927 -0.01012 0.007642 0.9697 0.9741 0.006021 0.8461 0.8337 0.02109 ] Network output: [ 0.9979 0.01734 0.001319 -4.736e-05 2.126e-05 -0.01472 -3.569e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02574 -0.2041 0.2021 0.9837 0.9933 0.2022 0.4654 0.8794 0.7241 ] Network output: [ -0.01225 1 1.01 2.455e-06 -1.102e-06 0.01383 1.85e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005181 0.0005983 0.004174 0.004692 0.989 0.992 0.005275 0.8766 0.9032 0.01529 ] Network output: [ -0.002202 0.0244 1.001 -0.0001743 7.825e-05 0.9779 -0.0001314 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.09685 0.3133 0.1623 0.9851 0.9941 0.1923 0.4703 0.886 0.7193 ] Network output: [ 0.009911 -0.03868 0.9984 9.815e-05 -4.407e-05 1.021 7.397e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08655 0.1768 0.2122 0.9874 0.992 0.09698 0.8007 0.8819 0.3112 ] Network output: [ -0.009749 0.04589 1.001 9.705e-05 -4.357e-05 0.9726 7.314e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09542 0.09359 0.1696 0.2004 0.9857 0.9915 0.09543 0.7327 0.8626 0.2445 ] Network output: [ -0.0004348 0.9992 0.0002608 1.314e-05 -5.899e-06 1.001 9.902e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001079 Epoch 6552 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01435 0.9888 0.9855 6.476e-06 -2.907e-06 -0.002951 4.881e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002927 -0.0101 0.007722 0.9697 0.9741 0.006014 0.846 0.8339 0.02111 ] Network output: [ 1 -0.01272 0.002741 -4.416e-05 1.982e-05 0.009596 -3.328e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02616 -0.2022 0.207 0.9837 0.9933 0.2019 0.4647 0.8796 0.7244 ] Network output: [ -0.01223 0.9986 1.011 2.679e-06 -1.203e-06 0.01526 2.019e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005173 0.0006013 0.004262 0.004855 0.9889 0.992 0.005267 0.8766 0.9033 0.01534 ] Network output: [ 0.0004407 -0.01694 1.003 -0.000169 7.588e-05 1.012 -0.0001274 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09674 0.3162 0.1701 0.9851 0.9941 0.192 0.4698 0.8859 0.7189 ] Network output: [ 0.009137 -0.04688 1 9.851e-05 -4.423e-05 1.029 7.424e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09705 0.0867 0.1786 0.2144 0.9874 0.992 0.09711 0.8013 0.8819 0.3124 ] Network output: [ -0.01018 0.04836 1.001 9.662e-05 -4.337e-05 0.971 7.281e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09553 0.09371 0.1701 0.2008 0.9858 0.9916 0.09554 0.7334 0.8625 0.2445 ] Network output: [ 0.00111 0.9992 -0.001923 1.384e-05 -6.215e-06 1.001 1.043e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001167 Epoch 6553 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01405 0.9933 0.9854 5.922e-06 -2.659e-06 -0.006766 4.463e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002927 -0.01012 0.00764 0.9697 0.9741 0.006021 0.8461 0.8337 0.02108 ] Network output: [ 0.9979 0.01726 0.001321 -4.732e-05 2.124e-05 -0.01466 -3.566e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02576 -0.204 0.2021 0.9837 0.9933 0.2023 0.4654 0.8794 0.724 ] Network output: [ -0.01225 1 1.01 2.455e-06 -1.102e-06 0.01383 1.85e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005182 0.0005975 0.004175 0.00469 0.989 0.992 0.005275 0.8766 0.9032 0.01529 ] Network output: [ -0.002197 0.02429 1.001 -0.0001741 7.816e-05 0.978 -0.0001312 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.09683 0.3134 0.1623 0.9851 0.9941 0.1923 0.4703 0.886 0.7193 ] Network output: [ 0.009902 -0.0387 0.9984 9.806e-05 -4.402e-05 1.021 7.39e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08654 0.1768 0.2122 0.9874 0.992 0.09698 0.8007 0.8819 0.3112 ] Network output: [ -0.009743 0.04588 1.001 9.696e-05 -4.353e-05 0.9726 7.307e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0954 0.09357 0.1696 0.2004 0.9857 0.9915 0.09541 0.7326 0.8625 0.2445 ] Network output: [ -0.0004313 0.9992 0.0002557 1.313e-05 -5.893e-06 1.001 9.893e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001077 Epoch 6554 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01434 0.9888 0.9855 6.465e-06 -2.902e-06 -0.002963 4.872e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002927 -0.01009 0.00772 0.9697 0.9741 0.006014 0.846 0.8339 0.0211 ] Network output: [ 1 -0.01265 0.002736 -4.414e-05 1.982e-05 0.009533 -3.327e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02617 -0.2022 0.207 0.9837 0.9933 0.2019 0.4646 0.8795 0.7244 ] Network output: [ -0.01222 0.9987 1.011 2.678e-06 -1.202e-06 0.01525 2.018e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005174 0.0006006 0.004262 0.004853 0.9889 0.992 0.005267 0.8766 0.9033 0.01533 ] Network output: [ 0.0004323 -0.01683 1.003 -0.0001689 7.58e-05 1.012 -0.0001273 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09671 0.3163 0.1701 0.9851 0.9941 0.192 0.4697 0.8859 0.7189 ] Network output: [ 0.009133 -0.04686 1 9.841e-05 -4.418e-05 1.029 7.417e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09705 0.08669 0.1786 0.2143 0.9874 0.992 0.09711 0.8012 0.8819 0.3124 ] Network output: [ -0.01018 0.04834 1.001 9.653e-05 -4.334e-05 0.971 7.275e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09551 0.09369 0.1701 0.2008 0.9858 0.9916 0.09552 0.7333 0.8625 0.2445 ] Network output: [ 0.001106 0.9992 -0.001918 1.383e-05 -6.208e-06 1.001 1.042e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001165 Epoch 6555 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01404 0.9933 0.9854 5.914e-06 -2.655e-06 -0.006758 4.457e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002927 -0.01012 0.007638 0.9697 0.9741 0.006021 0.8461 0.8337 0.02108 ] Network output: [ 0.998 0.01718 0.001323 -4.728e-05 2.123e-05 -0.01461 -3.564e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02578 -0.204 0.202 0.9837 0.9933 0.2023 0.4653 0.8794 0.724 ] Network output: [ -0.01225 1 1.01 2.456e-06 -1.102e-06 0.01382 1.851e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005183 0.0005968 0.004175 0.004689 0.989 0.992 0.005276 0.8766 0.9032 0.01529 ] Network output: [ -0.002192 0.02419 1.001 -0.0001739 7.807e-05 0.9781 -0.0001311 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.0968 0.3134 0.1622 0.9851 0.9941 0.1923 0.4702 0.8859 0.7192 ] Network output: [ 0.009893 -0.03872 0.9984 9.796e-05 -4.398e-05 1.021 7.382e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08654 0.1768 0.2121 0.9874 0.992 0.09698 0.8006 0.8819 0.3112 ] Network output: [ -0.009737 0.04587 1.001 9.687e-05 -4.349e-05 0.9726 7.301e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09538 0.09355 0.1696 0.2003 0.9857 0.9915 0.09539 0.7325 0.8625 0.2445 ] Network output: [ -0.0004278 0.9992 0.0002505 1.312e-05 -5.888e-06 1.001 9.884e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001076 Epoch 6556 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01434 0.9888 0.9855 6.454e-06 -2.897e-06 -0.002975 4.864e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002928 -0.01009 0.007717 0.9697 0.9741 0.006014 0.846 0.8339 0.0211 ] Network output: [ 1 -0.01258 0.002732 -4.413e-05 1.981e-05 0.00947 -3.326e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02619 -0.2021 0.2069 0.9837 0.9933 0.2019 0.4646 0.8795 0.7244 ] Network output: [ -0.01222 0.9987 1.011 2.676e-06 -1.202e-06 0.01524 2.017e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005175 0.0005999 0.004262 0.00485 0.9889 0.992 0.005268 0.8766 0.9033 0.01533 ] Network output: [ 0.0004239 -0.01672 1.003 -0.0001687 7.573e-05 1.012 -0.0001271 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1914 0.09669 0.3163 0.17 0.9851 0.9941 0.192 0.4697 0.8859 0.7189 ] Network output: [ 0.009129 -0.04683 1 9.831e-05 -4.414e-05 1.029 7.409e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09704 0.08668 0.1786 0.2143 0.9874 0.992 0.0971 0.8012 0.8819 0.3123 ] Network output: [ -0.01017 0.04832 1.001 9.645e-05 -4.33e-05 0.971 7.268e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09549 0.09366 0.1701 0.2007 0.9858 0.9916 0.0955 0.7333 0.8625 0.2445 ] Network output: [ 0.001102 0.9992 -0.001913 1.381e-05 -6.201e-06 1.001 1.041e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001163 Epoch 6557 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01404 0.9933 0.9854 5.906e-06 -2.652e-06 -0.00675 4.451e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002927 -0.01011 0.007636 0.9697 0.9741 0.006021 0.8461 0.8337 0.02107 ] Network output: [ 0.998 0.0171 0.001326 -4.725e-05 2.121e-05 -0.01455 -3.561e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.0258 -0.204 0.202 0.9837 0.9933 0.2023 0.4653 0.8794 0.724 ] Network output: [ -0.01225 1 1.01 2.456e-06 -1.102e-06 0.01382 1.851e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005183 0.0005961 0.004176 0.004687 0.989 0.992 0.005277 0.8766 0.9032 0.01528 ] Network output: [ -0.002187 0.02409 1.001 -0.0001737 7.798e-05 0.9782 -0.0001309 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.09677 0.3135 0.1622 0.9851 0.9941 0.1924 0.4702 0.8859 0.7192 ] Network output: [ 0.009884 -0.03873 0.9984 9.786e-05 -4.393e-05 1.021 7.375e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08653 0.1768 0.2121 0.9874 0.992 0.09698 0.8006 0.8818 0.3112 ] Network output: [ -0.009731 0.04586 1.001 9.679e-05 -4.345e-05 0.9726 7.294e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09536 0.09352 0.1696 0.2003 0.9857 0.9915 0.09537 0.7325 0.8625 0.2445 ] Network output: [ -0.0004244 0.9992 0.0002454 1.31e-05 -5.883e-06 1.001 9.875e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001074 Epoch 6558 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01433 0.9888 0.9855 6.442e-06 -2.892e-06 -0.002987 4.855e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002928 -0.01009 0.007714 0.9697 0.9741 0.006015 0.846 0.8339 0.02109 ] Network output: [ 1 -0.0125 0.002728 -4.411e-05 1.98e-05 0.009407 -3.324e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02621 -0.2021 0.2069 0.9837 0.9933 0.2019 0.4646 0.8795 0.7244 ] Network output: [ -0.01222 0.9987 1.011 2.675e-06 -1.201e-06 0.01523 2.016e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005175 0.0005992 0.004263 0.004848 0.9889 0.992 0.005269 0.8765 0.9033 0.01533 ] Network output: [ 0.0004155 -0.01661 1.003 -0.0001685 7.565e-05 1.012 -0.000127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09666 0.3163 0.1699 0.9851 0.9941 0.192 0.4697 0.8859 0.7189 ] Network output: [ 0.009125 -0.04681 0.9999 9.821e-05 -4.409e-05 1.029 7.402e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09704 0.08668 0.1786 0.2143 0.9874 0.992 0.0971 0.8011 0.8819 0.3123 ] Network output: [ -0.01016 0.0483 1.001 9.636e-05 -4.326e-05 0.971 7.262e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09547 0.09364 0.17 0.2007 0.9858 0.9916 0.09548 0.7332 0.8624 0.2445 ] Network output: [ 0.001098 0.9992 -0.001908 1.38e-05 -6.193e-06 1.001 1.04e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001161 Epoch 6559 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01403 0.9933 0.9854 5.898e-06 -2.648e-06 -0.006743 4.445e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002928 -0.01011 0.007633 0.9697 0.9741 0.006022 0.8461 0.8337 0.02107 ] Network output: [ 0.998 0.01702 0.001328 -4.721e-05 2.12e-05 -0.01449 -3.558e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02582 -0.2039 0.202 0.9837 0.9933 0.2023 0.4653 0.8793 0.724 ] Network output: [ -0.01224 1 1.01 2.456e-06 -1.103e-06 0.01382 1.851e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005184 0.0005954 0.004177 0.004685 0.989 0.992 0.005278 0.8766 0.9032 0.01528 ] Network output: [ -0.002182 0.02399 1.001 -0.0001735 7.789e-05 0.9783 -0.0001308 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.09675 0.3135 0.1622 0.9851 0.9941 0.1924 0.4702 0.8859 0.7192 ] Network output: [ 0.009876 -0.03875 0.9984 9.776e-05 -4.389e-05 1.021 7.368e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08652 0.1768 0.2121 0.9874 0.992 0.09698 0.8005 0.8818 0.3112 ] Network output: [ -0.009725 0.04586 1.001 9.67e-05 -4.341e-05 0.9726 7.287e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09533 0.0935 0.1695 0.2003 0.9857 0.9915 0.09535 0.7324 0.8624 0.2444 ] Network output: [ -0.0004209 0.9992 0.0002402 1.309e-05 -5.877e-06 1.001 9.866e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001073 Epoch 6560 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01433 0.9888 0.9855 6.431e-06 -2.887e-06 -0.002999 4.847e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002928 -0.01008 0.007712 0.9697 0.9741 0.006015 0.846 0.8338 0.02109 ] Network output: [ 1 -0.01243 0.002723 -4.41e-05 1.98e-05 0.009344 -3.323e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02623 -0.2021 0.2069 0.9837 0.9933 0.2019 0.4646 0.8795 0.7244 ] Network output: [ -0.01222 0.9987 1.011 2.674e-06 -1.201e-06 0.01522 2.015e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005176 0.0005985 0.004263 0.004845 0.9889 0.992 0.00527 0.8765 0.9033 0.01532 ] Network output: [ 0.0004072 -0.0165 1.003 -0.0001683 7.558e-05 1.012 -0.0001269 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09663 0.3163 0.1699 0.9851 0.9941 0.1921 0.4696 0.8859 0.7189 ] Network output: [ 0.009121 -0.04679 0.9999 9.811e-05 -4.405e-05 1.029 7.394e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09704 0.08667 0.1786 0.2143 0.9874 0.992 0.0971 0.8011 0.8818 0.3123 ] Network output: [ -0.01015 0.04828 1.001 9.627e-05 -4.322e-05 0.971 7.256e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09544 0.09362 0.17 0.2007 0.9858 0.9916 0.09546 0.7331 0.8624 0.2445 ] Network output: [ 0.001094 0.9992 -0.001903 1.378e-05 -6.186e-06 1.001 1.038e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001159 Epoch 6561 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01403 0.9933 0.9854 5.891e-06 -2.645e-06 -0.006735 4.439e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002928 -0.01011 0.007631 0.9697 0.9741 0.006022 0.8461 0.8337 0.02106 ] Network output: [ 0.998 0.01694 0.00133 -4.718e-05 2.118e-05 -0.01443 -3.555e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02584 -0.2039 0.202 0.9837 0.9933 0.2023 0.4652 0.8793 0.724 ] Network output: [ -0.01224 1 1.01 2.456e-06 -1.103e-06 0.01382 1.851e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005185 0.0005947 0.004178 0.004683 0.989 0.992 0.005278 0.8766 0.9032 0.01528 ] Network output: [ -0.002177 0.02389 1.001 -0.0001733 7.78e-05 0.9783 -0.0001306 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.09672 0.3135 0.1621 0.9851 0.9941 0.1924 0.4701 0.8859 0.7192 ] Network output: [ 0.009867 -0.03877 0.9984 9.766e-05 -4.384e-05 1.021 7.36e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08652 0.1768 0.2121 0.9874 0.992 0.09697 0.8004 0.8818 0.3112 ] Network output: [ -0.009719 0.04585 1.001 9.661e-05 -4.337e-05 0.9726 7.281e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09531 0.09348 0.1695 0.2003 0.9857 0.9915 0.09533 0.7323 0.8624 0.2444 ] Network output: [ -0.0004175 0.9992 0.0002351 1.308e-05 -5.872e-06 1.001 9.858e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001071 Epoch 6562 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01432 0.9888 0.9856 6.42e-06 -2.882e-06 -0.003011 4.838e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002928 -0.01008 0.007709 0.9697 0.9741 0.006015 0.846 0.8338 0.02108 ] Network output: [ 1 -0.01236 0.002719 -4.408e-05 1.979e-05 0.009281 -3.322e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02625 -0.2021 0.2068 0.9837 0.9933 0.202 0.4645 0.8795 0.7244 ] Network output: [ -0.01221 0.9987 1.011 2.673e-06 -1.2e-06 0.01521 2.014e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005177 0.0005977 0.004263 0.004842 0.9889 0.992 0.005271 0.8765 0.9033 0.01532 ] Network output: [ 0.0003988 -0.0164 1.003 -0.0001682 7.55e-05 1.012 -0.0001267 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09661 0.3164 0.1698 0.9851 0.9941 0.1921 0.4696 0.8859 0.7189 ] Network output: [ 0.009117 -0.04676 0.9999 9.801e-05 -4.4e-05 1.029 7.386e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09703 0.08666 0.1786 0.2142 0.9874 0.992 0.09709 0.801 0.8818 0.3123 ] Network output: [ -0.01014 0.04826 1.001 9.619e-05 -4.318e-05 0.971 7.249e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09542 0.0936 0.17 0.2007 0.9858 0.9916 0.09543 0.7331 0.8624 0.2445 ] Network output: [ 0.001091 0.9993 -0.001898 1.376e-05 -6.179e-06 1.001 1.037e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001157 Epoch 6563 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01402 0.9933 0.9854 5.883e-06 -2.641e-06 -0.006728 4.433e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002928 -0.0101 0.007629 0.9697 0.9741 0.006022 0.8461 0.8336 0.02106 ] Network output: [ 0.998 0.01686 0.001333 -4.714e-05 2.116e-05 -0.01438 -3.553e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02586 -0.2039 0.202 0.9837 0.9933 0.2023 0.4652 0.8793 0.724 ] Network output: [ -0.01224 1 1.01 2.456e-06 -1.103e-06 0.01382 1.851e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005186 0.000594 0.004178 0.004682 0.989 0.992 0.005279 0.8765 0.9032 0.01527 ] Network output: [ -0.002172 0.02379 1.001 -0.0001731 7.771e-05 0.9784 -0.0001304 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.09669 0.3136 0.1621 0.9851 0.9941 0.1924 0.4701 0.8859 0.7192 ] Network output: [ 0.009858 -0.03879 0.9984 9.756e-05 -4.38e-05 1.021 7.353e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08651 0.1768 0.2121 0.9874 0.992 0.09697 0.8004 0.8818 0.3111 ] Network output: [ -0.009712 0.04584 1.001 9.652e-05 -4.333e-05 0.9726 7.274e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09529 0.09346 0.1695 0.2003 0.9857 0.9915 0.0953 0.7323 0.8624 0.2444 ] Network output: [ -0.000414 0.9992 0.00023 1.307e-05 -5.867e-06 1.001 9.849e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00107 Epoch 6564 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01431 0.9889 0.9856 6.409e-06 -2.877e-06 -0.003023 4.83e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002929 -0.01008 0.007706 0.9697 0.9741 0.006015 0.846 0.8338 0.02108 ] Network output: [ 1 -0.01229 0.002715 -4.406e-05 1.978e-05 0.009218 -3.321e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1811 -0.02627 -0.202 0.2068 0.9837 0.9933 0.202 0.4645 0.8795 0.7243 ] Network output: [ -0.01221 0.9987 1.011 2.672e-06 -1.199e-06 0.0152 2.014e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005178 0.000597 0.004263 0.00484 0.9889 0.992 0.005271 0.8765 0.9033 0.01532 ] Network output: [ 0.0003905 -0.01629 1.003 -0.000168 7.543e-05 1.012 -0.0001266 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09658 0.3164 0.1697 0.9851 0.9941 0.1921 0.4696 0.8859 0.7189 ] Network output: [ 0.009114 -0.04674 0.9999 9.791e-05 -4.396e-05 1.029 7.379e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09703 0.08666 0.1786 0.2142 0.9874 0.992 0.09709 0.8009 0.8818 0.3123 ] Network output: [ -0.01013 0.04824 1.001 9.61e-05 -4.314e-05 0.971 7.243e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0954 0.09357 0.17 0.2007 0.9858 0.9916 0.09541 0.733 0.8624 0.2445 ] Network output: [ 0.001087 0.9993 -0.001893 1.375e-05 -6.171e-06 1.001 1.036e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001155 Epoch 6565 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01402 0.9933 0.9854 5.875e-06 -2.637e-06 -0.00672 4.428e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002928 -0.0101 0.007627 0.9697 0.9741 0.006022 0.846 0.8336 0.02105 ] Network output: [ 0.998 0.01679 0.001335 -4.71e-05 2.115e-05 -0.01432 -3.55e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02588 -0.2038 0.202 0.9837 0.9933 0.2023 0.4652 0.8793 0.724 ] Network output: [ -0.01224 1 1.01 2.456e-06 -1.103e-06 0.01381 1.851e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005186 0.0005933 0.004179 0.00468 0.989 0.992 0.00528 0.8765 0.9031 0.01527 ] Network output: [ -0.002167 0.02368 1.001 -0.0001729 7.762e-05 0.9785 -0.0001303 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.09667 0.3136 0.1621 0.9851 0.9941 0.1924 0.4701 0.8859 0.7192 ] Network output: [ 0.009849 -0.0388 0.9984 9.746e-05 -4.376e-05 1.021 7.345e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.0865 0.1768 0.212 0.9874 0.992 0.09697 0.8003 0.8817 0.3111 ] Network output: [ -0.009706 0.04583 1.001 9.643e-05 -4.329e-05 0.9726 7.267e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09527 0.09344 0.1695 0.2002 0.9857 0.9915 0.09528 0.7322 0.8623 0.2444 ] Network output: [ -0.0004106 0.9992 0.0002248 1.306e-05 -5.862e-06 1.001 9.84e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001068 Epoch 6566 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01431 0.9889 0.9856 6.397e-06 -2.872e-06 -0.003036 4.821e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003176 -0.002929 -0.01007 0.007704 0.9697 0.9741 0.006016 0.846 0.8338 0.02107 ] Network output: [ 1 -0.01222 0.00271 -4.405e-05 1.977e-05 0.009155 -3.32e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02628 -0.202 0.2068 0.9837 0.9933 0.202 0.4645 0.8795 0.7243 ] Network output: [ -0.01221 0.9987 1.011 2.671e-06 -1.199e-06 0.01519 2.013e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005179 0.0005963 0.004264 0.004837 0.9889 0.992 0.005272 0.8765 0.9032 0.01531 ] Network output: [ 0.0003822 -0.01618 1.003 -0.0001678 7.535e-05 1.012 -0.0001265 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09656 0.3164 0.1697 0.9851 0.9941 0.1921 0.4696 0.8859 0.7188 ] Network output: [ 0.00911 -0.04671 0.9999 9.781e-05 -4.391e-05 1.029 7.371e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09703 0.08665 0.1786 0.2142 0.9874 0.992 0.09709 0.8009 0.8818 0.3123 ] Network output: [ -0.01012 0.04822 1.001 9.602e-05 -4.311e-05 0.971 7.236e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09538 0.09355 0.17 0.2006 0.9858 0.9916 0.09539 0.7329 0.8623 0.2445 ] Network output: [ 0.001083 0.9993 -0.001888 1.373e-05 -6.164e-06 1.001 1.035e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001153 Epoch 6567 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01401 0.9933 0.9854 5.867e-06 -2.634e-06 -0.006713 4.422e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003181 -0.002929 -0.0101 0.007624 0.9697 0.9741 0.006023 0.846 0.8336 0.02105 ] Network output: [ 0.998 0.01671 0.001337 -4.707e-05 2.113e-05 -0.01426 -3.547e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.0259 -0.2038 0.202 0.9837 0.9933 0.2023 0.4652 0.8793 0.724 ] Network output: [ -0.01224 1 1.01 2.456e-06 -1.103e-06 0.01381 1.851e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005187 0.0005926 0.00418 0.004678 0.9889 0.992 0.005281 0.8765 0.9031 0.01527 ] Network output: [ -0.002162 0.02358 1.001 -0.0001727 7.753e-05 0.9786 -0.0001301 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.09664 0.3137 0.1621 0.9851 0.9941 0.1924 0.47 0.8859 0.7192 ] Network output: [ 0.009841 -0.03882 0.9984 9.737e-05 -4.371e-05 1.021 7.338e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.0865 0.1768 0.212 0.9874 0.992 0.09697 0.8003 0.8817 0.3111 ] Network output: [ -0.0097 0.04582 1.001 9.634e-05 -4.325e-05 0.9726 7.261e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09525 0.09341 0.1695 0.2002 0.9857 0.9915 0.09526 0.7321 0.8623 0.2444 ] Network output: [ -0.0004071 0.9992 0.0002197 1.304e-05 -5.856e-06 1.001 9.831e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001067 Epoch 6568 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0143 0.9889 0.9856 6.386e-06 -2.867e-06 -0.003048 4.813e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002929 -0.01007 0.007701 0.9697 0.9741 0.006016 0.8459 0.8338 0.02107 ] Network output: [ 1 -0.01215 0.002706 -4.403e-05 1.977e-05 0.009092 -3.318e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.0263 -0.202 0.2067 0.9837 0.9933 0.202 0.4645 0.8795 0.7243 ] Network output: [ -0.01221 0.9987 1.011 2.669e-06 -1.198e-06 0.01518 2.012e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00518 0.0005956 0.004264 0.004835 0.9889 0.992 0.005273 0.8765 0.9032 0.01531 ] Network output: [ 0.000374 -0.01607 1.003 -0.0001677 7.527e-05 1.012 -0.0001264 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09653 0.3165 0.1696 0.9851 0.9941 0.1921 0.4695 0.8858 0.7188 ] Network output: [ 0.009106 -0.04669 0.9999 9.771e-05 -4.387e-05 1.029 7.364e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09703 0.08664 0.1786 0.2141 0.9874 0.992 0.09709 0.8008 0.8817 0.3122 ] Network output: [ -0.01011 0.0482 1.001 9.593e-05 -4.307e-05 0.971 7.23e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09535 0.09353 0.1699 0.2006 0.9857 0.9916 0.09537 0.7328 0.8623 0.2444 ] Network output: [ 0.001079 0.9993 -0.001883 1.371e-05 -6.157e-06 1.001 1.034e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001151 Epoch 6569 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01401 0.9933 0.9854 5.859e-06 -2.63e-06 -0.006705 4.416e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003181 -0.002929 -0.01009 0.007622 0.9697 0.9741 0.006023 0.846 0.8336 0.02104 ] Network output: [ 0.998 0.01663 0.00134 -4.703e-05 2.111e-05 -0.0142 -3.544e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02592 -0.2038 0.202 0.9837 0.9933 0.2023 0.4651 0.8793 0.724 ] Network output: [ -0.01223 1 1.01 2.456e-06 -1.103e-06 0.01381 1.851e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005188 0.0005919 0.00418 0.004676 0.9889 0.992 0.005282 0.8765 0.9031 0.01526 ] Network output: [ -0.002157 0.02348 1.001 -0.0001725 7.744e-05 0.9786 -0.00013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.09662 0.3137 0.162 0.9851 0.9941 0.1924 0.47 0.8859 0.7191 ] Network output: [ 0.009832 -0.03884 0.9984 9.727e-05 -4.367e-05 1.021 7.33e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08649 0.1769 0.212 0.9874 0.992 0.09697 0.8002 0.8817 0.3111 ] Network output: [ -0.009694 0.04581 1.001 9.625e-05 -4.321e-05 0.9726 7.254e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09523 0.09339 0.1695 0.2002 0.9857 0.9915 0.09524 0.7321 0.8623 0.2444 ] Network output: [ -0.0004037 0.9992 0.0002146 1.303e-05 -5.851e-06 1.001 9.822e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001065 Epoch 6570 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01429 0.9889 0.9856 6.375e-06 -2.862e-06 -0.00306 4.804e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002929 -0.01007 0.007698 0.9697 0.9741 0.006016 0.8459 0.8338 0.02106 ] Network output: [ 1 -0.01208 0.002702 -4.402e-05 1.976e-05 0.00903 -3.317e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02632 -0.202 0.2067 0.9837 0.9933 0.202 0.4644 0.8795 0.7243 ] Network output: [ -0.01221 0.9987 1.011 2.668e-06 -1.198e-06 0.01517 2.011e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00518 0.0005949 0.004264 0.004832 0.9889 0.992 0.005274 0.8765 0.9032 0.0153 ] Network output: [ 0.0003657 -0.01596 1.003 -0.0001675 7.52e-05 1.011 -0.0001262 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09651 0.3165 0.1695 0.9851 0.9941 0.1921 0.4695 0.8858 0.7188 ] Network output: [ 0.009102 -0.04666 0.9998 9.761e-05 -4.382e-05 1.029 7.356e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09702 0.08663 0.1786 0.2141 0.9874 0.992 0.09708 0.8008 0.8817 0.3122 ] Network output: [ -0.0101 0.04818 1.001 9.585e-05 -4.303e-05 0.971 7.223e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09533 0.09351 0.1699 0.2006 0.9857 0.9916 0.09534 0.7328 0.8623 0.2444 ] Network output: [ 0.001075 0.9993 -0.001878 1.37e-05 -6.149e-06 1.001 1.032e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001149 Epoch 6571 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.014 0.9933 0.9854 5.851e-06 -2.627e-06 -0.006698 4.41e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003181 -0.002929 -0.01009 0.00762 0.9697 0.9741 0.006023 0.846 0.8336 0.02104 ] Network output: [ 0.998 0.01655 0.001342 -4.699e-05 2.11e-05 -0.01415 -3.542e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02594 -0.2037 0.2019 0.9837 0.9933 0.2023 0.4651 0.8793 0.7239 ] Network output: [ -0.01223 1 1.01 2.456e-06 -1.103e-06 0.01381 1.851e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005189 0.0005913 0.004181 0.004675 0.9889 0.992 0.005282 0.8765 0.9031 0.01526 ] Network output: [ -0.002152 0.02338 1.002 -0.0001723 7.735e-05 0.9787 -0.0001298 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.09659 0.3138 0.162 0.9851 0.9941 0.1924 0.47 0.8859 0.7191 ] Network output: [ 0.009823 -0.03886 0.9983 9.717e-05 -4.362e-05 1.021 7.323e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0969 0.08649 0.1769 0.212 0.9874 0.992 0.09696 0.8002 0.8817 0.3111 ] Network output: [ -0.009687 0.04581 1.001 9.617e-05 -4.317e-05 0.9726 7.247e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09521 0.09337 0.1694 0.2002 0.9857 0.9915 0.09522 0.732 0.8622 0.2444 ] Network output: [ -0.0004002 0.9992 0.0002095 1.302e-05 -5.846e-06 1.001 9.813e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001063 Epoch 6572 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01429 0.9889 0.9856 6.363e-06 -2.857e-06 -0.003072 4.796e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.00293 -0.01006 0.007696 0.9697 0.9741 0.006017 0.8459 0.8338 0.02105 ] Network output: [ 1 -0.012 0.002697 -4.4e-05 1.975e-05 0.008967 -3.316e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02634 -0.2019 0.2066 0.9837 0.9933 0.202 0.4644 0.8794 0.7243 ] Network output: [ -0.0122 0.9987 1.011 2.667e-06 -1.197e-06 0.01516 2.01e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005181 0.0005942 0.004265 0.00483 0.9889 0.992 0.005275 0.8764 0.9032 0.0153 ] Network output: [ 0.0003575 -0.01586 1.003 -0.0001673 7.512e-05 1.011 -0.0001261 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09648 0.3165 0.1695 0.9851 0.9941 0.1921 0.4695 0.8858 0.7188 ] Network output: [ 0.009098 -0.04663 0.9998 9.751e-05 -4.378e-05 1.029 7.349e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09702 0.08663 0.1786 0.2141 0.9874 0.992 0.09708 0.8007 0.8817 0.3122 ] Network output: [ -0.01009 0.04816 1.001 9.576e-05 -4.299e-05 0.971 7.217e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09531 0.09348 0.1699 0.2006 0.9857 0.9916 0.09532 0.7327 0.8622 0.2444 ] Network output: [ 0.001071 0.9993 -0.001872 1.368e-05 -6.142e-06 1.001 1.031e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001146 Epoch 6573 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.014 0.9933 0.9854 5.843e-06 -2.623e-06 -0.006691 4.404e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003181 -0.002929 -0.01009 0.007618 0.9697 0.9741 0.006023 0.846 0.8336 0.02103 ] Network output: [ 0.998 0.01647 0.001345 -4.696e-05 2.108e-05 -0.01409 -3.539e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02596 -0.2037 0.2019 0.9837 0.9933 0.2024 0.4651 0.8793 0.7239 ] Network output: [ -0.01223 1 1.01 2.456e-06 -1.103e-06 0.0138 1.851e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005189 0.0005906 0.004182 0.004673 0.9889 0.992 0.005283 0.8765 0.9031 0.01526 ] Network output: [ -0.002146 0.02328 1.002 -0.0001721 7.726e-05 0.9788 -0.0001297 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.09656 0.3138 0.162 0.9851 0.9941 0.1924 0.4699 0.8858 0.7191 ] Network output: [ 0.009814 -0.03887 0.9983 9.707e-05 -4.358e-05 1.021 7.315e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0969 0.08648 0.1769 0.212 0.9874 0.992 0.09696 0.8001 0.8816 0.3111 ] Network output: [ -0.009681 0.0458 1.001 9.608e-05 -4.313e-05 0.9726 7.241e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09519 0.09335 0.1694 0.2002 0.9857 0.9915 0.0952 0.7319 0.8622 0.2444 ] Network output: [ -0.0003968 0.9992 0.0002044 1.301e-05 -5.84e-06 1.001 9.804e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001062 Epoch 6574 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01428 0.989 0.9856 6.352e-06 -2.852e-06 -0.003084 4.787e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.00293 -0.01006 0.007693 0.9697 0.9741 0.006017 0.8459 0.8338 0.02105 ] Network output: [ 1 -0.01193 0.002693 -4.398e-05 1.975e-05 0.008904 -3.315e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02636 -0.2019 0.2066 0.9837 0.9933 0.202 0.4644 0.8794 0.7243 ] Network output: [ -0.0122 0.9987 1.011 2.665e-06 -1.197e-06 0.01515 2.009e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005182 0.0005935 0.004265 0.004827 0.9889 0.992 0.005276 0.8764 0.9032 0.0153 ] Network output: [ 0.0003493 -0.01575 1.003 -0.0001672 7.505e-05 1.011 -0.000126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09646 0.3165 0.1694 0.9851 0.9941 0.1921 0.4694 0.8858 0.7188 ] Network output: [ 0.009094 -0.04661 0.9998 9.741e-05 -4.373e-05 1.029 7.341e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09702 0.08662 0.1786 0.214 0.9874 0.992 0.09708 0.8006 0.8817 0.3122 ] Network output: [ -0.01008 0.04814 1.001 9.567e-05 -4.295e-05 0.971 7.21e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09529 0.09346 0.1699 0.2005 0.9857 0.9916 0.0953 0.7326 0.8622 0.2444 ] Network output: [ 0.001067 0.9993 -0.001867 1.367e-05 -6.135e-06 1.001 1.03e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001144 Epoch 6575 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01399 0.9933 0.9855 5.835e-06 -2.62e-06 -0.006683 4.398e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003181 -0.00293 -0.01008 0.007615 0.9697 0.9741 0.006024 0.846 0.8336 0.02103 ] Network output: [ 0.9981 0.01639 0.001347 -4.692e-05 2.106e-05 -0.01403 -3.536e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02598 -0.2037 0.2019 0.9837 0.9933 0.2024 0.465 0.8793 0.7239 ] Network output: [ -0.01223 1 1.01 2.456e-06 -1.103e-06 0.0138 1.851e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00519 0.0005899 0.004183 0.004671 0.9889 0.992 0.005284 0.8764 0.9031 0.01525 ] Network output: [ -0.002141 0.02318 1.002 -0.0001719 7.717e-05 0.9789 -0.0001295 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.09654 0.3138 0.162 0.9851 0.9941 0.1924 0.4699 0.8858 0.7191 ] Network output: [ 0.009806 -0.03889 0.9983 9.697e-05 -4.353e-05 1.021 7.308e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0969 0.08647 0.1769 0.2119 0.9874 0.992 0.09696 0.8001 0.8816 0.3111 ] Network output: [ -0.009675 0.04579 1.001 9.599e-05 -4.309e-05 0.9726 7.234e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09516 0.09333 0.1694 0.2002 0.9857 0.9915 0.09518 0.7319 0.8622 0.2444 ] Network output: [ -0.0003933 0.9992 0.0001994 1.3e-05 -5.835e-06 1.001 9.795e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00106 Epoch 6576 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01428 0.989 0.9856 6.341e-06 -2.847e-06 -0.003097 4.779e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.00293 -0.01006 0.00769 0.9697 0.9741 0.006017 0.8459 0.8338 0.02104 ] Network output: [ 1 -0.01186 0.002689 -4.397e-05 1.974e-05 0.008842 -3.313e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02637 -0.2019 0.2066 0.9837 0.9933 0.202 0.4643 0.8794 0.7243 ] Network output: [ -0.0122 0.9987 1.011 2.664e-06 -1.196e-06 0.01514 2.008e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005183 0.0005928 0.004265 0.004824 0.9889 0.992 0.005276 0.8764 0.9032 0.01529 ] Network output: [ 0.0003411 -0.01564 1.003 -0.000167 7.497e-05 1.011 -0.0001259 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1915 0.09643 0.3166 0.1693 0.9851 0.9941 0.1921 0.4694 0.8858 0.7188 ] Network output: [ 0.00909 -0.04658 0.9998 9.731e-05 -4.368e-05 1.029 7.333e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09702 0.08661 0.1786 0.214 0.9874 0.992 0.09708 0.8006 0.8816 0.3122 ] Network output: [ -0.01007 0.04812 1.001 9.559e-05 -4.291e-05 0.971 7.204e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09527 0.09344 0.1699 0.2005 0.9857 0.9916 0.09528 0.7326 0.8622 0.2444 ] Network output: [ 0.001064 0.9993 -0.001862 1.365e-05 -6.128e-06 1.001 1.029e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001142 Epoch 6577 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01399 0.9933 0.9855 5.827e-06 -2.616e-06 -0.006676 4.392e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003181 -0.00293 -0.01008 0.007613 0.9697 0.9741 0.006024 0.846 0.8336 0.02102 ] Network output: [ 0.9981 0.01631 0.001349 -4.688e-05 2.105e-05 -0.01397 -3.533e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.026 -0.2036 0.2019 0.9837 0.9933 0.2024 0.465 0.8792 0.7239 ] Network output: [ -0.01223 1 1.01 2.456e-06 -1.103e-06 0.0138 1.851e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005191 0.0005892 0.004183 0.00467 0.9889 0.992 0.005285 0.8764 0.9031 0.01525 ] Network output: [ -0.002136 0.02308 1.002 -0.0001717 7.708e-05 0.9789 -0.0001294 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.09651 0.3139 0.1619 0.9851 0.9941 0.1924 0.4699 0.8858 0.7191 ] Network output: [ 0.009797 -0.0389 0.9983 9.687e-05 -4.349e-05 1.021 7.301e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0969 0.08647 0.1769 0.2119 0.9874 0.992 0.09696 0.8 0.8816 0.3111 ] Network output: [ -0.009668 0.04578 1.001 9.59e-05 -4.305e-05 0.9726 7.227e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09514 0.09331 0.1694 0.2001 0.9857 0.9915 0.09516 0.7318 0.8621 0.2443 ] Network output: [ -0.0003899 0.9992 0.0001943 1.299e-05 -5.83e-06 1.001 9.787e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001059 Epoch 6578 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01427 0.989 0.9856 6.329e-06 -2.841e-06 -0.003109 4.77e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002931 -0.01005 0.007688 0.9697 0.9741 0.006018 0.8459 0.8337 0.02104 ] Network output: [ 1 -0.01179 0.002684 -4.395e-05 1.973e-05 0.008779 -3.312e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02639 -0.2019 0.2065 0.9837 0.9933 0.2021 0.4643 0.8794 0.7242 ] Network output: [ -0.0122 0.9987 1.011 2.663e-06 -1.195e-06 0.01513 2.007e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005184 0.0005921 0.004265 0.004822 0.9889 0.992 0.005277 0.8764 0.9032 0.01529 ] Network output: [ 0.0003329 -0.01553 1.003 -0.0001668 7.489e-05 1.011 -0.0001257 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09641 0.3166 0.1693 0.9851 0.9941 0.1922 0.4694 0.8858 0.7188 ] Network output: [ 0.009086 -0.04656 0.9998 9.721e-05 -4.364e-05 1.029 7.326e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09701 0.08661 0.1786 0.214 0.9874 0.992 0.09707 0.8005 0.8816 0.3122 ] Network output: [ -0.01006 0.0481 1.001 9.55e-05 -4.287e-05 0.971 7.197e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09525 0.09342 0.1698 0.2005 0.9857 0.9915 0.09526 0.7325 0.8621 0.2444 ] Network output: [ 0.00106 0.9993 -0.001857 1.363e-05 -6.12e-06 1.001 1.027e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00114 Epoch 6579 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01398 0.9933 0.9855 5.819e-06 -2.613e-06 -0.006669 4.386e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003181 -0.00293 -0.01008 0.007611 0.9697 0.9741 0.006024 0.846 0.8336 0.02102 ] Network output: [ 0.9981 0.01623 0.001351 -4.685e-05 2.103e-05 -0.01391 -3.53e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02602 -0.2036 0.2019 0.9837 0.9933 0.2024 0.465 0.8792 0.7239 ] Network output: [ -0.01222 1 1.01 2.456e-06 -1.103e-06 0.01379 1.851e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005192 0.0005885 0.004184 0.004668 0.9889 0.992 0.005286 0.8764 0.9031 0.01524 ] Network output: [ -0.002131 0.02297 1.002 -0.0001715 7.699e-05 0.979 -0.0001292 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.09649 0.3139 0.1619 0.9851 0.9941 0.1924 0.4698 0.8858 0.7191 ] Network output: [ 0.009788 -0.03892 0.9983 9.677e-05 -4.344e-05 1.021 7.293e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0969 0.08646 0.1769 0.2119 0.9874 0.992 0.09696 0.7999 0.8815 0.3111 ] Network output: [ -0.009662 0.04577 1.001 9.581e-05 -4.301e-05 0.9726 7.221e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09512 0.09329 0.1694 0.2001 0.9857 0.9915 0.09513 0.7317 0.8621 0.2443 ] Network output: [ -0.0003864 0.9992 0.0001892 1.297e-05 -5.825e-06 1.001 9.778e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001057 Epoch 6580 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01426 0.989 0.9856 6.318e-06 -2.836e-06 -0.003121 4.761e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002931 -0.01005 0.007685 0.9697 0.9741 0.006018 0.8459 0.8337 0.02103 ] Network output: [ 1 -0.01172 0.00268 -4.393e-05 1.972e-05 0.008717 -3.311e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02641 -0.2018 0.2065 0.9837 0.9933 0.2021 0.4643 0.8794 0.7242 ] Network output: [ -0.0122 0.9988 1.011 2.661e-06 -1.195e-06 0.01512 2.006e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005184 0.0005914 0.004266 0.004819 0.9889 0.992 0.005278 0.8764 0.9032 0.01529 ] Network output: [ 0.0003247 -0.01543 1.003 -0.0001667 7.482e-05 1.011 -0.0001256 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09638 0.3166 0.1692 0.9851 0.9941 0.1922 0.4694 0.8858 0.7188 ] Network output: [ 0.009082 -0.04653 0.9998 9.711e-05 -4.359e-05 1.029 7.318e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09701 0.0866 0.1786 0.214 0.9874 0.992 0.09707 0.8005 0.8816 0.3122 ] Network output: [ -0.01005 0.04808 1.001 9.541e-05 -4.284e-05 0.971 7.191e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09522 0.09339 0.1698 0.2005 0.9857 0.9915 0.09524 0.7324 0.8621 0.2444 ] Network output: [ 0.001056 0.9993 -0.001852 1.362e-05 -6.113e-06 1.001 1.026e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001138 Epoch 6581 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01398 0.9932 0.9855 5.811e-06 -2.609e-06 -0.006662 4.38e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003181 -0.00293 -0.01007 0.007609 0.9697 0.9741 0.006024 0.846 0.8336 0.02101 ] Network output: [ 0.9981 0.01615 0.001354 -4.681e-05 2.101e-05 -0.01386 -3.528e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02604 -0.2036 0.2019 0.9837 0.9933 0.2024 0.4649 0.8792 0.7239 ] Network output: [ -0.01222 1 1.01 2.456e-06 -1.103e-06 0.01379 1.851e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005193 0.0005878 0.004185 0.004666 0.9889 0.992 0.005286 0.8764 0.903 0.01524 ] Network output: [ -0.002126 0.02287 1.002 -0.0001713 7.69e-05 0.9791 -0.0001291 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.09646 0.314 0.1619 0.9851 0.9941 0.1925 0.4698 0.8858 0.7191 ] Network output: [ 0.00978 -0.03894 0.9983 9.667e-05 -4.34e-05 1.021 7.286e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09689 0.08646 0.1769 0.2119 0.9874 0.992 0.09695 0.7999 0.8815 0.3111 ] Network output: [ -0.009656 0.04576 1.001 9.572e-05 -4.297e-05 0.9726 7.214e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0951 0.09326 0.1694 0.2001 0.9857 0.9915 0.09511 0.7317 0.8621 0.2443 ] Network output: [ -0.000383 0.9992 0.0001842 1.296e-05 -5.819e-06 1.001 9.769e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001056 Epoch 6582 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01426 0.989 0.9856 6.307e-06 -2.831e-06 -0.003133 4.753e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002931 -0.01005 0.007682 0.9697 0.9741 0.006018 0.8459 0.8337 0.02103 ] Network output: [ 1 -0.01165 0.002676 -4.392e-05 1.972e-05 0.008654 -3.31e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1812 -0.02643 -0.2018 0.2065 0.9837 0.9933 0.2021 0.4643 0.8794 0.7242 ] Network output: [ -0.01219 0.9988 1.011 2.66e-06 -1.194e-06 0.01511 2.005e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005185 0.0005907 0.004266 0.004817 0.9889 0.992 0.005279 0.8764 0.9031 0.01528 ] Network output: [ 0.0003166 -0.01532 1.003 -0.0001665 7.474e-05 1.011 -0.0001255 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09636 0.3167 0.1691 0.9851 0.9941 0.1922 0.4693 0.8858 0.7187 ] Network output: [ 0.009078 -0.0465 0.9997 9.7e-05 -4.355e-05 1.029 7.311e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09701 0.08659 0.1786 0.2139 0.9874 0.992 0.09707 0.8004 0.8816 0.3121 ] Network output: [ -0.01005 0.04806 1.001 9.533e-05 -4.28e-05 0.971 7.184e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0952 0.09337 0.1698 0.2005 0.9857 0.9915 0.09521 0.7323 0.8621 0.2444 ] Network output: [ 0.001052 0.9993 -0.001847 1.36e-05 -6.106e-06 1.001 1.025e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001136 Epoch 6583 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01397 0.9932 0.9855 5.803e-06 -2.605e-06 -0.006655 4.374e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003181 -0.002931 -0.01007 0.007606 0.9697 0.9741 0.006025 0.846 0.8335 0.02101 ] Network output: [ 0.9981 0.01607 0.001356 -4.677e-05 2.1e-05 -0.0138 -3.525e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02606 -0.2035 0.2019 0.9837 0.9933 0.2024 0.4649 0.8792 0.7239 ] Network output: [ -0.01222 1 1.01 2.456e-06 -1.103e-06 0.01379 1.851e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005193 0.0005872 0.004186 0.004664 0.9889 0.992 0.005287 0.8764 0.903 0.01524 ] Network output: [ -0.002121 0.02277 1.002 -0.0001711 7.681e-05 0.9792 -0.0001289 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.09644 0.314 0.1619 0.9851 0.9941 0.1925 0.4698 0.8858 0.7191 ] Network output: [ 0.009771 -0.03895 0.9983 9.657e-05 -4.336e-05 1.022 7.278e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09689 0.08645 0.1769 0.2119 0.9874 0.992 0.09695 0.7998 0.8815 0.3111 ] Network output: [ -0.00965 0.04575 1.001 9.563e-05 -4.293e-05 0.9726 7.207e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09508 0.09324 0.1693 0.2001 0.9857 0.9915 0.09509 0.7316 0.862 0.2443 ] Network output: [ -0.0003795 0.9992 0.0001791 1.295e-05 -5.814e-06 1.001 9.76e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001054 Epoch 6584 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01425 0.989 0.9856 6.295e-06 -2.826e-06 -0.003146 4.744e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003177 -0.002931 -0.01004 0.00768 0.9697 0.9741 0.006018 0.8459 0.8337 0.02102 ] Network output: [ 1 -0.01158 0.002671 -4.39e-05 1.971e-05 0.008592 -3.308e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02645 -0.2018 0.2064 0.9837 0.9933 0.2021 0.4642 0.8794 0.7242 ] Network output: [ -0.01219 0.9988 1.011 2.659e-06 -1.194e-06 0.0151 2.004e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005186 0.00059 0.004266 0.004814 0.9889 0.992 0.00528 0.8763 0.9031 0.01528 ] Network output: [ 0.0003085 -0.01521 1.003 -0.0001663 7.467e-05 1.011 -0.0001253 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09633 0.3167 0.1691 0.9851 0.9941 0.1922 0.4693 0.8858 0.7187 ] Network output: [ 0.009074 -0.04648 0.9997 9.69e-05 -4.35e-05 1.029 7.303e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09701 0.08659 0.1786 0.2139 0.9874 0.992 0.09707 0.8004 0.8815 0.3121 ] Network output: [ -0.01004 0.04804 1.001 9.524e-05 -4.276e-05 0.971 7.178e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09518 0.09335 0.1698 0.2004 0.9857 0.9915 0.09519 0.7323 0.862 0.2444 ] Network output: [ 0.001048 0.9993 -0.001842 1.358e-05 -6.099e-06 1.001 1.024e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001134 Epoch 6585 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01397 0.9932 0.9855 5.795e-06 -2.602e-06 -0.006647 4.368e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003181 -0.002931 -0.01007 0.007604 0.9697 0.9741 0.006025 0.8459 0.8335 0.021 ] Network output: [ 0.9981 0.016 0.001358 -4.673e-05 2.098e-05 -0.01374 -3.522e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02608 -0.2035 0.2019 0.9837 0.9933 0.2024 0.4649 0.8792 0.7239 ] Network output: [ -0.01222 1 1.01 2.456e-06 -1.102e-06 0.01379 1.851e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005194 0.0005865 0.004186 0.004663 0.9889 0.992 0.005288 0.8764 0.903 0.01523 ] Network output: [ -0.002116 0.02267 1.002 -0.0001709 7.672e-05 0.9793 -0.0001288 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.09641 0.3141 0.1618 0.9851 0.9941 0.1925 0.4697 0.8858 0.719 ] Network output: [ 0.009762 -0.03897 0.9983 9.647e-05 -4.331e-05 1.022 7.271e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09689 0.08645 0.1769 0.2119 0.9874 0.992 0.09695 0.7998 0.8815 0.311 ] Network output: [ -0.009643 0.04574 1.001 9.554e-05 -4.289e-05 0.9726 7.2e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09506 0.09322 0.1693 0.2001 0.9857 0.9915 0.09507 0.7315 0.862 0.2443 ] Network output: [ -0.0003761 0.9992 0.0001741 1.294e-05 -5.809e-06 1.001 9.751e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001053 Epoch 6586 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01424 0.9891 0.9856 6.284e-06 -2.821e-06 -0.003158 4.736e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002932 -0.01004 0.007677 0.9697 0.9741 0.006019 0.8459 0.8337 0.02102 ] Network output: [ 1 -0.01151 0.002667 -4.388e-05 1.97e-05 0.00853 -3.307e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02646 -0.2017 0.2064 0.9837 0.9933 0.2021 0.4642 0.8794 0.7242 ] Network output: [ -0.01219 0.9988 1.011 2.657e-06 -1.193e-06 0.01509 2.002e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005187 0.0005893 0.004267 0.004812 0.9889 0.992 0.005281 0.8763 0.9031 0.01527 ] Network output: [ 0.0003003 -0.0151 1.003 -0.0001661 7.459e-05 1.011 -0.0001252 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09631 0.3167 0.169 0.9851 0.9941 0.1922 0.4693 0.8857 0.7187 ] Network output: [ 0.00907 -0.04645 0.9997 9.68e-05 -4.346e-05 1.029 7.295e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.097 0.08658 0.1786 0.2139 0.9874 0.992 0.09706 0.8003 0.8815 0.3121 ] Network output: [ -0.01003 0.04802 1.001 9.516e-05 -4.272e-05 0.9711 7.171e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09516 0.09333 0.1698 0.2004 0.9857 0.9915 0.09517 0.7322 0.862 0.2443 ] Network output: [ 0.001044 0.9993 -0.001837 1.357e-05 -6.091e-06 1.001 1.023e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001132 Epoch 6587 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01396 0.9932 0.9855 5.787e-06 -2.598e-06 -0.00664 4.362e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003181 -0.002931 -0.01006 0.007602 0.9697 0.9741 0.006025 0.8459 0.8335 0.021 ] Network output: [ 0.9981 0.01592 0.001361 -4.67e-05 2.096e-05 -0.01368 -3.519e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.0261 -0.2034 0.2019 0.9837 0.9933 0.2024 0.4648 0.8792 0.7239 ] Network output: [ -0.01222 1 1.01 2.456e-06 -1.102e-06 0.01378 1.851e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005195 0.0005858 0.004187 0.004661 0.9889 0.992 0.005289 0.8764 0.903 0.01523 ] Network output: [ -0.002111 0.02257 1.002 -0.0001707 7.663e-05 0.9793 -0.0001286 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.09639 0.3141 0.1618 0.9851 0.9941 0.1925 0.4697 0.8858 0.719 ] Network output: [ 0.009753 -0.03898 0.9983 9.638e-05 -4.327e-05 1.022 7.263e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09689 0.08644 0.1769 0.2118 0.9874 0.992 0.09695 0.7997 0.8814 0.311 ] Network output: [ -0.009637 0.04573 1.001 9.545e-05 -4.285e-05 0.9726 7.194e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09504 0.0932 0.1693 0.2 0.9857 0.9915 0.09505 0.7315 0.862 0.2443 ] Network output: [ -0.0003726 0.9992 0.000169 1.293e-05 -5.803e-06 1.001 9.742e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001051 Epoch 6588 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01424 0.9891 0.9856 6.273e-06 -2.816e-06 -0.00317 4.727e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002932 -0.01004 0.007674 0.9697 0.9741 0.006019 0.8458 0.8337 0.02101 ] Network output: [ 1 -0.01144 0.002662 -4.386e-05 1.969e-05 0.008467 -3.306e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02648 -0.2017 0.2064 0.9837 0.9933 0.2021 0.4642 0.8793 0.7242 ] Network output: [ -0.01219 0.9988 1.011 2.656e-06 -1.192e-06 0.01507 2.001e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005188 0.0005886 0.004267 0.004809 0.9889 0.992 0.005281 0.8763 0.9031 0.01527 ] Network output: [ 0.0002923 -0.015 1.003 -0.000166 7.451e-05 1.011 -0.0001251 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09628 0.3168 0.1689 0.9851 0.9941 0.1922 0.4692 0.8857 0.7187 ] Network output: [ 0.009066 -0.04643 0.9997 9.67e-05 -4.341e-05 1.029 7.288e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.097 0.08657 0.1786 0.2138 0.9874 0.992 0.09706 0.8002 0.8815 0.3121 ] Network output: [ -0.01002 0.048 1.001 9.507e-05 -4.268e-05 0.9711 7.165e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09514 0.09331 0.1698 0.2004 0.9857 0.9915 0.09515 0.7321 0.862 0.2443 ] Network output: [ 0.00104 0.9993 -0.001831 1.355e-05 -6.084e-06 1.001 1.021e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00113 Epoch 6589 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01396 0.9932 0.9855 5.779e-06 -2.595e-06 -0.006633 4.355e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003181 -0.002931 -0.01006 0.0076 0.9697 0.9741 0.006025 0.8459 0.8335 0.02099 ] Network output: [ 0.9981 0.01584 0.001363 -4.666e-05 2.095e-05 -0.01362 -3.517e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02612 -0.2034 0.2018 0.9837 0.9933 0.2024 0.4648 0.8792 0.7238 ] Network output: [ -0.01221 1 1.01 2.455e-06 -1.102e-06 0.01378 1.85e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005196 0.0005851 0.004188 0.004659 0.9889 0.992 0.00529 0.8763 0.903 0.01523 ] Network output: [ -0.002106 0.02247 1.002 -0.0001705 7.654e-05 0.9794 -0.0001285 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.09636 0.3141 0.1618 0.9851 0.9941 0.1925 0.4697 0.8858 0.719 ] Network output: [ 0.009745 -0.039 0.9983 9.628e-05 -4.322e-05 1.022 7.256e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09689 0.08643 0.1769 0.2118 0.9874 0.992 0.09695 0.7997 0.8814 0.311 ] Network output: [ -0.009631 0.04572 1.001 9.537e-05 -4.281e-05 0.9726 7.187e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09502 0.09318 0.1693 0.2 0.9857 0.9915 0.09503 0.7314 0.8619 0.2443 ] Network output: [ -0.0003692 0.9992 0.000164 1.292e-05 -5.798e-06 1.001 9.733e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00105 Epoch 6590 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01423 0.9891 0.9856 6.261e-06 -2.811e-06 -0.003183 4.719e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002932 -0.01003 0.007672 0.9697 0.9741 0.006019 0.8458 0.8337 0.02101 ] Network output: [ 1 -0.01137 0.002658 -4.385e-05 1.968e-05 0.008405 -3.304e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.0265 -0.2017 0.2063 0.9837 0.9933 0.2021 0.4642 0.8793 0.7242 ] Network output: [ -0.01219 0.9988 1.011 2.654e-06 -1.192e-06 0.01506 2e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005189 0.0005879 0.004267 0.004806 0.9889 0.992 0.005282 0.8763 0.9031 0.01527 ] Network output: [ 0.0002842 -0.01489 1.003 -0.0001658 7.444e-05 1.01 -0.000125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09626 0.3168 0.1689 0.9851 0.9941 0.1922 0.4692 0.8857 0.7187 ] Network output: [ 0.009062 -0.0464 0.9997 9.66e-05 -4.337e-05 1.029 7.28e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.097 0.08657 0.1786 0.2138 0.9874 0.992 0.09706 0.8002 0.8815 0.3121 ] Network output: [ -0.01001 0.04798 1.001 9.498e-05 -4.264e-05 0.9711 7.158e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09512 0.09328 0.1697 0.2004 0.9857 0.9915 0.09513 0.7321 0.8619 0.2443 ] Network output: [ 0.001037 0.9993 -0.001826 1.354e-05 -6.077e-06 1.001 1.02e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001128 Epoch 6591 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01395 0.9932 0.9855 5.771e-06 -2.591e-06 -0.006626 4.349e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003182 -0.002932 -0.01006 0.007598 0.9697 0.9741 0.006026 0.8459 0.8335 0.02099 ] Network output: [ 0.9981 0.01576 0.001365 -4.662e-05 2.093e-05 -0.01357 -3.514e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02614 -0.2034 0.2018 0.9837 0.9933 0.2024 0.4648 0.8792 0.7238 ] Network output: [ -0.01221 1 1.01 2.455e-06 -1.102e-06 0.01378 1.85e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005196 0.0005845 0.004188 0.004658 0.9889 0.992 0.00529 0.8763 0.903 0.01522 ] Network output: [ -0.002101 0.02237 1.002 -0.0001703 7.645e-05 0.9795 -0.0001283 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.09634 0.3142 0.1617 0.9851 0.9941 0.1925 0.4697 0.8857 0.719 ] Network output: [ 0.009736 -0.03902 0.9983 9.618e-05 -4.318e-05 1.022 7.248e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09688 0.08643 0.1769 0.2118 0.9874 0.992 0.09694 0.7996 0.8814 0.311 ] Network output: [ -0.009624 0.04571 1.001 9.528e-05 -4.277e-05 0.9726 7.18e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.095 0.09316 0.1693 0.2 0.9857 0.9915 0.09501 0.7313 0.8619 0.2443 ] Network output: [ -0.0003657 0.9992 0.000159 1.29e-05 -5.793e-06 1.001 9.725e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001048 Epoch 6592 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01423 0.9891 0.9857 6.25e-06 -2.806e-06 -0.003195 4.71e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002932 -0.01003 0.007669 0.9697 0.9741 0.00602 0.8458 0.8337 0.021 ] Network output: [ 1 -0.01129 0.002654 -4.383e-05 1.968e-05 0.008343 -3.303e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02652 -0.2017 0.2063 0.9837 0.9933 0.2021 0.4641 0.8793 0.7242 ] Network output: [ -0.01218 0.9988 1.011 2.653e-06 -1.191e-06 0.01505 1.999e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005189 0.0005872 0.004267 0.004804 0.9889 0.992 0.005283 0.8763 0.9031 0.01526 ] Network output: [ 0.0002761 -0.01478 1.003 -0.0001656 7.436e-05 1.01 -0.0001248 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09624 0.3168 0.1688 0.9851 0.9941 0.1922 0.4692 0.8857 0.7187 ] Network output: [ 0.009058 -0.04637 0.9997 9.65e-05 -4.332e-05 1.029 7.272e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.097 0.08656 0.1786 0.2138 0.9874 0.992 0.09706 0.8001 0.8814 0.3121 ] Network output: [ -0.009999 0.04795 1.001 9.49e-05 -4.26e-05 0.9711 7.152e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09509 0.09326 0.1697 0.2004 0.9857 0.9915 0.09511 0.732 0.8619 0.2443 ] Network output: [ 0.001033 0.9993 -0.001821 1.352e-05 -6.07e-06 1.001 1.019e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001126 Epoch 6593 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01395 0.9932 0.9855 5.763e-06 -2.587e-06 -0.006619 4.343e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003182 -0.002932 -0.01005 0.007595 0.9697 0.9741 0.006026 0.8459 0.8335 0.02098 ] Network output: [ 0.9981 0.01568 0.001368 -4.659e-05 2.091e-05 -0.01351 -3.511e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02616 -0.2033 0.2018 0.9837 0.9933 0.2025 0.4647 0.8792 0.7238 ] Network output: [ -0.01221 1 1.01 2.455e-06 -1.102e-06 0.01377 1.85e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005197 0.0005838 0.004189 0.004656 0.9889 0.992 0.005291 0.8763 0.903 0.01522 ] Network output: [ -0.002095 0.02226 1.002 -0.0001701 7.636e-05 0.9796 -0.0001282 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.09631 0.3142 0.1617 0.9851 0.9941 0.1925 0.4696 0.8857 0.719 ] Network output: [ 0.009727 -0.03903 0.9983 9.608e-05 -4.313e-05 1.022 7.241e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09688 0.08642 0.1769 0.2118 0.9874 0.992 0.09694 0.7995 0.8814 0.311 ] Network output: [ -0.009618 0.0457 1.001 9.519e-05 -4.273e-05 0.9726 7.174e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09498 0.09314 0.1693 0.2 0.9857 0.9915 0.09499 0.7312 0.8619 0.2443 ] Network output: [ -0.0003623 0.9992 0.0001539 1.289e-05 -5.788e-06 1.001 9.716e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001047 Epoch 6594 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01422 0.9891 0.9857 6.238e-06 -2.801e-06 -0.003208 4.702e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002933 -0.01003 0.007666 0.9697 0.9741 0.00602 0.8458 0.8337 0.021 ] Network output: [ 1 -0.01122 0.002649 -4.381e-05 1.967e-05 0.008281 -3.302e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02654 -0.2016 0.2062 0.9837 0.9933 0.2022 0.4641 0.8793 0.7241 ] Network output: [ -0.01218 0.9988 1.011 2.651e-06 -1.19e-06 0.01504 1.998e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00519 0.0005866 0.004268 0.004801 0.9889 0.992 0.005284 0.8763 0.9031 0.01526 ] Network output: [ 0.0002681 -0.01468 1.003 -0.0001655 7.428e-05 1.01 -0.0001247 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1916 0.09621 0.3168 0.1687 0.9851 0.9941 0.1922 0.4692 0.8857 0.7187 ] Network output: [ 0.009054 -0.04635 0.9996 9.64e-05 -4.328e-05 1.029 7.265e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09699 0.08655 0.1786 0.2137 0.9874 0.992 0.09705 0.8001 0.8814 0.312 ] Network output: [ -0.00999 0.04793 1.001 9.481e-05 -4.256e-05 0.9711 7.145e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09507 0.09324 0.1697 0.2003 0.9857 0.9915 0.09508 0.7319 0.8619 0.2443 ] Network output: [ 0.001029 0.9993 -0.001816 1.35e-05 -6.062e-06 1.001 1.018e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001124 Epoch 6595 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01394 0.9932 0.9855 5.755e-06 -2.584e-06 -0.006612 4.337e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003182 -0.002932 -0.01005 0.007593 0.9697 0.9741 0.006026 0.8459 0.8335 0.02098 ] Network output: [ 0.9981 0.0156 0.00137 -4.655e-05 2.09e-05 -0.01345 -3.508e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02618 -0.2033 0.2018 0.9837 0.9933 0.2025 0.4647 0.8791 0.7238 ] Network output: [ -0.01221 1 1.01 2.454e-06 -1.102e-06 0.01377 1.85e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005198 0.0005831 0.00419 0.004654 0.9889 0.992 0.005292 0.8763 0.903 0.01522 ] Network output: [ -0.00209 0.02216 1.002 -0.0001699 7.627e-05 0.9797 -0.000128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.09629 0.3143 0.1617 0.9851 0.9941 0.1925 0.4696 0.8857 0.719 ] Network output: [ 0.009719 -0.03905 0.9983 9.598e-05 -4.309e-05 1.022 7.233e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09688 0.08642 0.177 0.2118 0.9874 0.992 0.09694 0.7995 0.8813 0.311 ] Network output: [ -0.009611 0.04569 1.001 9.51e-05 -4.269e-05 0.9726 7.167e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09495 0.09311 0.1692 0.2 0.9857 0.9915 0.09497 0.7312 0.8618 0.2443 ] Network output: [ -0.0003588 0.9992 0.0001489 1.288e-05 -5.782e-06 1.001 9.707e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001045 Epoch 6596 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01421 0.9892 0.9857 6.227e-06 -2.796e-06 -0.00322 4.693e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002933 -0.01002 0.007664 0.9697 0.9741 0.00602 0.8458 0.8336 0.02099 ] Network output: [ 1 -0.01115 0.002645 -4.379e-05 1.966e-05 0.008218 -3.3e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02655 -0.2016 0.2062 0.9837 0.9933 0.2022 0.4641 0.8793 0.7241 ] Network output: [ -0.01218 0.9988 1.011 2.649e-06 -1.189e-06 0.01503 1.997e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005191 0.0005859 0.004268 0.004799 0.9889 0.992 0.005285 0.8763 0.9031 0.01525 ] Network output: [ 0.00026 -0.01457 1.003 -0.0001653 7.421e-05 1.01 -0.0001246 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.09619 0.3169 0.1687 0.9851 0.9941 0.1923 0.4691 0.8857 0.7187 ] Network output: [ 0.00905 -0.04632 0.9996 9.63e-05 -4.323e-05 1.029 7.257e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09699 0.08655 0.1786 0.2137 0.9874 0.992 0.09705 0.8 0.8814 0.312 ] Network output: [ -0.00998 0.04791 1.001 9.472e-05 -4.252e-05 0.9711 7.139e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09505 0.09322 0.1697 0.2003 0.9857 0.9915 0.09506 0.7318 0.8618 0.2443 ] Network output: [ 0.001025 0.9993 -0.00181 1.349e-05 -6.055e-06 1.001 1.016e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001122 Epoch 6597 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01394 0.9932 0.9855 5.747e-06 -2.58e-06 -0.006605 4.331e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003182 -0.002932 -0.01005 0.007591 0.9697 0.9741 0.006026 0.8459 0.8335 0.02097 ] Network output: [ 0.9982 0.01552 0.001372 -4.651e-05 2.088e-05 -0.01339 -3.505e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.0262 -0.2033 0.2018 0.9837 0.9933 0.2025 0.4647 0.8791 0.7238 ] Network output: [ -0.0122 1 1.01 2.454e-06 -1.102e-06 0.01377 1.85e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005199 0.0005825 0.004191 0.004652 0.9889 0.992 0.005293 0.8763 0.903 0.01521 ] Network output: [ -0.002085 0.02206 1.002 -0.0001697 7.618e-05 0.9797 -0.0001279 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.09626 0.3143 0.1617 0.9851 0.9941 0.1925 0.4696 0.8857 0.719 ] Network output: [ 0.00971 -0.03906 0.9983 9.588e-05 -4.304e-05 1.022 7.226e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09688 0.08641 0.177 0.2117 0.9874 0.992 0.09694 0.7994 0.8813 0.311 ] Network output: [ -0.009605 0.04568 1.001 9.501e-05 -4.265e-05 0.9726 7.16e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09493 0.09309 0.1692 0.1999 0.9857 0.9915 0.09495 0.7311 0.8618 0.2442 ] Network output: [ -0.0003553 0.9992 0.0001439 1.287e-05 -5.777e-06 1.001 9.698e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001044 Epoch 6598 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01421 0.9892 0.9857 6.216e-06 -2.79e-06 -0.003232 4.684e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002933 -0.01002 0.007661 0.9697 0.9741 0.006021 0.8458 0.8336 0.02099 ] Network output: [ 1 -0.01108 0.00264 -4.378e-05 1.965e-05 0.008156 -3.299e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02657 -0.2016 0.2062 0.9837 0.9933 0.2022 0.464 0.8793 0.7241 ] Network output: [ -0.01218 0.9988 1.011 2.648e-06 -1.189e-06 0.01502 1.996e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005192 0.0005852 0.004268 0.004796 0.9889 0.992 0.005286 0.8762 0.9031 0.01525 ] Network output: [ 0.000252 -0.01446 1.003 -0.0001651 7.413e-05 1.01 -0.0001244 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.09616 0.3169 0.1686 0.9851 0.9941 0.1923 0.4691 0.8857 0.7186 ] Network output: [ 0.009046 -0.04629 0.9996 9.62e-05 -4.319e-05 1.029 7.25e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09699 0.08654 0.1786 0.2137 0.9874 0.992 0.09705 0.7999 0.8814 0.312 ] Network output: [ -0.009971 0.04789 1.001 9.464e-05 -4.249e-05 0.9711 7.132e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09503 0.0932 0.1697 0.2003 0.9857 0.9915 0.09504 0.7318 0.8618 0.2443 ] Network output: [ 0.001021 0.9993 -0.001805 1.347e-05 -6.048e-06 1.001 1.015e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00112 Epoch 6599 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01394 0.9932 0.9855 5.739e-06 -2.576e-06 -0.006598 4.325e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003182 -0.002933 -0.01004 0.007589 0.9697 0.9741 0.006027 0.8459 0.8335 0.02097 ] Network output: [ 0.9982 0.01544 0.001375 -4.647e-05 2.086e-05 -0.01334 -3.502e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02622 -0.2032 0.2018 0.9837 0.9933 0.2025 0.4647 0.8791 0.7238 ] Network output: [ -0.0122 1 1.01 2.454e-06 -1.102e-06 0.01377 1.849e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0052 0.0005818 0.004191 0.004651 0.9889 0.992 0.005294 0.8763 0.9029 0.01521 ] Network output: [ -0.00208 0.02196 1.002 -0.0001695 7.609e-05 0.9798 -0.0001277 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.09624 0.3144 0.1616 0.9851 0.9941 0.1925 0.4695 0.8857 0.7189 ] Network output: [ 0.009701 -0.03908 0.9982 9.578e-05 -4.3e-05 1.022 7.218e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09688 0.08641 0.177 0.2117 0.9874 0.992 0.09694 0.7994 0.8813 0.311 ] Network output: [ -0.009599 0.04567 1.001 9.492e-05 -4.261e-05 0.9726 7.153e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09491 0.09307 0.1692 0.1999 0.9857 0.9915 0.09493 0.731 0.8618 0.2442 ] Network output: [ -0.0003519 0.9992 0.0001389 1.286e-05 -5.772e-06 1.001 9.689e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001042 Epoch 6600 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0142 0.9892 0.9857 6.204e-06 -2.785e-06 -0.003245 4.676e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002933 -0.01002 0.007658 0.9697 0.9741 0.006021 0.8458 0.8336 0.02098 ] Network output: [ 1 -0.01101 0.002636 -4.376e-05 1.964e-05 0.008094 -3.298e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1813 -0.02659 -0.2016 0.2061 0.9837 0.9933 0.2022 0.464 0.8793 0.7241 ] Network output: [ -0.01217 0.9988 1.011 2.646e-06 -1.188e-06 0.01501 1.994e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005193 0.0005845 0.004268 0.004794 0.9889 0.992 0.005287 0.8762 0.903 0.01525 ] Network output: [ 0.000244 -0.01436 1.003 -0.000165 7.406e-05 1.01 -0.0001243 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.09614 0.3169 0.1685 0.9851 0.9941 0.1923 0.4691 0.8857 0.7186 ] Network output: [ 0.009042 -0.04626 0.9996 9.609e-05 -4.314e-05 1.029 7.242e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09699 0.08654 0.1786 0.2137 0.9874 0.992 0.09705 0.7999 0.8813 0.312 ] Network output: [ -0.009962 0.04787 1.001 9.455e-05 -4.245e-05 0.9711 7.125e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09501 0.09317 0.1696 0.2003 0.9857 0.9915 0.09502 0.7317 0.8618 0.2443 ] Network output: [ 0.001017 0.9993 -0.0018 1.346e-05 -6.041e-06 1.001 1.014e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001118 Epoch 6601 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01393 0.9932 0.9856 5.731e-06 -2.573e-06 -0.006591 4.319e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003182 -0.002933 -0.01004 0.007586 0.9697 0.9741 0.006027 0.8459 0.8335 0.02096 ] Network output: [ 0.9982 0.01536 0.001377 -4.644e-05 2.085e-05 -0.01328 -3.5e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02624 -0.2032 0.2018 0.9837 0.9933 0.2025 0.4646 0.8791 0.7238 ] Network output: [ -0.0122 1 1.01 2.453e-06 -1.101e-06 0.01376 1.849e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0052 0.0005812 0.004192 0.004649 0.9889 0.992 0.005294 0.8762 0.9029 0.01521 ] Network output: [ -0.002075 0.02186 1.002 -0.0001693 7.599e-05 0.9799 -0.0001276 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.09622 0.3144 0.1616 0.9851 0.9941 0.1926 0.4695 0.8857 0.7189 ] Network output: [ 0.009693 -0.03909 0.9982 9.568e-05 -4.295e-05 1.022 7.211e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09688 0.0864 0.177 0.2117 0.9874 0.992 0.09694 0.7993 0.8813 0.311 ] Network output: [ -0.009592 0.04566 1.001 9.483e-05 -4.257e-05 0.9726 7.147e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09489 0.09305 0.1692 0.1999 0.9857 0.9915 0.09491 0.731 0.8617 0.2442 ] Network output: [ -0.0003484 0.9992 0.0001339 1.285e-05 -5.767e-06 1.001 9.68e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001041 Epoch 6602 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01419 0.9892 0.9857 6.193e-06 -2.78e-06 -0.003257 4.667e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002934 -0.01001 0.007656 0.9697 0.9741 0.006021 0.8458 0.8336 0.02098 ] Network output: [ 1 -0.01094 0.002632 -4.374e-05 1.964e-05 0.008032 -3.296e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02661 -0.2015 0.2061 0.9837 0.9933 0.2022 0.464 0.8793 0.7241 ] Network output: [ -0.01217 0.9988 1.011 2.645e-06 -1.187e-06 0.015 1.993e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005194 0.0005839 0.004269 0.004791 0.9889 0.992 0.005287 0.8762 0.903 0.01524 ] Network output: [ 0.000236 -0.01425 1.003 -0.0001648 7.398e-05 1.01 -0.0001242 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.09612 0.317 0.1685 0.9851 0.9941 0.1923 0.469 0.8857 0.7186 ] Network output: [ 0.009038 -0.04624 0.9996 9.599e-05 -4.309e-05 1.029 7.234e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09698 0.08653 0.1786 0.2136 0.9874 0.992 0.09704 0.7998 0.8813 0.312 ] Network output: [ -0.009952 0.04784 1.001 9.446e-05 -4.241e-05 0.9711 7.119e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09499 0.09315 0.1696 0.2003 0.9857 0.9915 0.095 0.7316 0.8617 0.2443 ] Network output: [ 0.001013 0.9993 -0.001795 1.344e-05 -6.034e-06 1.001 1.013e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001116 Epoch 6603 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01393 0.9932 0.9856 5.722e-06 -2.569e-06 -0.006585 4.313e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003182 -0.002933 -0.01004 0.007584 0.9697 0.9741 0.006027 0.8458 0.8334 0.02096 ] Network output: [ 0.9982 0.01529 0.001379 -4.64e-05 2.083e-05 -0.01322 -3.497e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02626 -0.2032 0.2018 0.9837 0.9933 0.2025 0.4646 0.8791 0.7238 ] Network output: [ -0.0122 1 1.01 2.453e-06 -1.101e-06 0.01376 1.849e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005201 0.0005805 0.004193 0.004647 0.9889 0.992 0.005295 0.8762 0.9029 0.0152 ] Network output: [ -0.00207 0.02176 1.002 -0.0001691 7.59e-05 0.98 -0.0001274 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.09619 0.3144 0.1616 0.9851 0.9941 0.1926 0.4695 0.8857 0.7189 ] Network output: [ 0.009684 -0.0391 0.9982 9.558e-05 -4.291e-05 1.022 7.203e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09688 0.08639 0.177 0.2117 0.9874 0.992 0.09694 0.7993 0.8812 0.311 ] Network output: [ -0.009586 0.04565 1.001 9.474e-05 -4.253e-05 0.9726 7.14e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09487 0.09303 0.1692 0.1999 0.9857 0.9915 0.09488 0.7309 0.8617 0.2442 ] Network output: [ -0.000345 0.9992 0.0001289 1.283e-05 -5.761e-06 1.001 9.672e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001039 Epoch 6604 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01419 0.9892 0.9857 6.181e-06 -2.775e-06 -0.00327 4.658e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003178 -0.002934 -0.01001 0.007653 0.9697 0.9741 0.006022 0.8458 0.8336 0.02097 ] Network output: [ 1 -0.01087 0.002627 -4.372e-05 1.963e-05 0.00797 -3.295e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02662 -0.2015 0.2061 0.9837 0.9933 0.2022 0.464 0.8793 0.7241 ] Network output: [ -0.01217 0.9988 1.011 2.643e-06 -1.187e-06 0.01499 1.992e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005194 0.0005832 0.004269 0.004788 0.9889 0.992 0.005288 0.8762 0.903 0.01524 ] Network output: [ 0.0002281 -0.01414 1.003 -0.0001646 7.39e-05 1.01 -0.0001241 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.09609 0.317 0.1684 0.9851 0.9941 0.1923 0.469 0.8856 0.7186 ] Network output: [ 0.009034 -0.04621 0.9996 9.589e-05 -4.305e-05 1.029 7.227e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09698 0.08652 0.1786 0.2136 0.9874 0.992 0.09704 0.7998 0.8813 0.312 ] Network output: [ -0.009943 0.04782 1.001 9.437e-05 -4.237e-05 0.9711 7.112e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09497 0.09313 0.1696 0.2002 0.9857 0.9915 0.09498 0.7316 0.8617 0.2443 ] Network output: [ 0.001009 0.9993 -0.001789 1.342e-05 -6.026e-06 1.001 1.012e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001114 Epoch 6605 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01392 0.9932 0.9856 5.714e-06 -2.565e-06 -0.006578 4.306e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003182 -0.002934 -0.01003 0.007582 0.9697 0.9741 0.006028 0.8458 0.8334 0.02095 ] Network output: [ 0.9982 0.01521 0.001381 -4.636e-05 2.081e-05 -0.01316 -3.494e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02628 -0.2031 0.2017 0.9837 0.9933 0.2025 0.4646 0.8791 0.7238 ] Network output: [ -0.0122 1 1.01 2.453e-06 -1.101e-06 0.01376 1.848e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005202 0.0005798 0.004194 0.004646 0.9889 0.992 0.005296 0.8762 0.9029 0.0152 ] Network output: [ -0.002064 0.02166 1.002 -0.0001689 7.581e-05 0.98 -0.0001273 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.09617 0.3145 0.1616 0.9851 0.9941 0.1926 0.4694 0.8857 0.7189 ] Network output: [ 0.009675 -0.03912 0.9982 9.548e-05 -4.287e-05 1.022 7.196e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09687 0.08639 0.177 0.2117 0.9874 0.992 0.09693 0.7992 0.8812 0.311 ] Network output: [ -0.009579 0.04564 1.001 9.465e-05 -4.249e-05 0.9726 7.133e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09485 0.09301 0.1692 0.1999 0.9857 0.9915 0.09486 0.7308 0.8617 0.2442 ] Network output: [ -0.0003415 0.9992 0.0001239 1.282e-05 -5.756e-06 1.001 9.663e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001038 Epoch 6606 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01418 0.9892 0.9857 6.17e-06 -2.77e-06 -0.003282 4.65e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002934 -0.01001 0.00765 0.9697 0.9741 0.006022 0.8457 0.8336 0.02097 ] Network output: [ 1 -0.0108 0.002623 -4.37e-05 1.962e-05 0.007908 -3.294e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02664 -0.2015 0.206 0.9837 0.9933 0.2022 0.4639 0.8792 0.7241 ] Network output: [ -0.01217 0.9989 1.011 2.641e-06 -1.186e-06 0.01498 1.991e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005195 0.0005825 0.004269 0.004786 0.9889 0.992 0.005289 0.8762 0.903 0.01524 ] Network output: [ 0.0002201 -0.01404 1.003 -0.0001644 7.383e-05 1.01 -0.0001239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.09607 0.317 0.1683 0.9851 0.9941 0.1923 0.469 0.8856 0.7186 ] Network output: [ 0.00903 -0.04618 0.9995 9.579e-05 -4.3e-05 1.029 7.219e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09698 0.08652 0.1786 0.2136 0.9874 0.992 0.09704 0.7997 0.8813 0.312 ] Network output: [ -0.009934 0.0478 1.001 9.429e-05 -4.233e-05 0.9711 7.106e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09495 0.09311 0.1696 0.2002 0.9857 0.9915 0.09496 0.7315 0.8617 0.2442 ] Network output: [ 0.001006 0.9993 -0.001784 1.341e-05 -6.019e-06 1.001 1.01e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001112 Epoch 6607 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01392 0.9932 0.9856 5.706e-06 -2.562e-06 -0.006571 4.3e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003182 -0.002934 -0.01003 0.00758 0.9697 0.9741 0.006028 0.8458 0.8334 0.02095 ] Network output: [ 0.9982 0.01513 0.001384 -4.632e-05 2.08e-05 -0.0131 -3.491e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.0263 -0.2031 0.2017 0.9837 0.9933 0.2025 0.4645 0.8791 0.7237 ] Network output: [ -0.01219 1 1.01 2.452e-06 -1.101e-06 0.01375 1.848e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005203 0.0005792 0.004194 0.004644 0.9889 0.992 0.005297 0.8762 0.9029 0.01519 ] Network output: [ -0.002059 0.02156 1.002 -0.0001687 7.572e-05 0.9801 -0.0001271 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.09614 0.3145 0.1615 0.9851 0.9941 0.1926 0.4694 0.8856 0.7189 ] Network output: [ 0.009667 -0.03913 0.9982 9.538e-05 -4.282e-05 1.022 7.188e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09687 0.08638 0.177 0.2116 0.9874 0.992 0.09693 0.7992 0.8812 0.3109 ] Network output: [ -0.009573 0.04563 1.001 9.456e-05 -4.245e-05 0.9726 7.126e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09483 0.09299 0.1692 0.1999 0.9857 0.9915 0.09484 0.7308 0.8617 0.2442 ] Network output: [ -0.000338 0.9992 0.0001189 1.281e-05 -5.751e-06 1.001 9.654e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001036 Epoch 6608 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01418 0.9893 0.9857 6.159e-06 -2.765e-06 -0.003295 4.641e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002934 -0.01 0.007648 0.9697 0.9741 0.006022 0.8457 0.8336 0.02096 ] Network output: [ 1 -0.01073 0.002618 -4.369e-05 1.961e-05 0.007846 -3.292e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02666 -0.2014 0.206 0.9837 0.9933 0.2022 0.4639 0.8792 0.7241 ] Network output: [ -0.01217 0.9989 1.011 2.64e-06 -1.185e-06 0.01497 1.989e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005196 0.0005818 0.00427 0.004783 0.9889 0.992 0.00529 0.8762 0.903 0.01523 ] Network output: [ 0.0002122 -0.01393 1.003 -0.0001643 7.375e-05 1.01 -0.0001238 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.09605 0.317 0.1683 0.9851 0.9941 0.1923 0.4689 0.8856 0.7186 ] Network output: [ 0.009026 -0.04615 0.9995 9.569e-05 -4.296e-05 1.029 7.211e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09698 0.08651 0.1786 0.2135 0.9874 0.992 0.09704 0.7996 0.8812 0.3119 ] Network output: [ -0.009925 0.04778 1.001 9.42e-05 -4.229e-05 0.9711 7.099e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09492 0.09309 0.1696 0.2002 0.9857 0.9915 0.09494 0.7314 0.8616 0.2442 ] Network output: [ 0.001002 0.9993 -0.001779 1.339e-05 -6.012e-06 1.001 1.009e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00111 Epoch 6609 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01391 0.9932 0.9856 5.698e-06 -2.558e-06 -0.006564 4.294e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003182 -0.002934 -0.01002 0.007577 0.9697 0.9741 0.006028 0.8458 0.8334 0.02094 ] Network output: [ 0.9982 0.01505 0.001386 -4.629e-05 2.078e-05 -0.01305 -3.488e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02632 -0.2031 0.2017 0.9837 0.9933 0.2026 0.4645 0.8791 0.7237 ] Network output: [ -0.01219 1 1.01 2.452e-06 -1.101e-06 0.01375 1.848e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005204 0.0005785 0.004195 0.004642 0.9889 0.992 0.005298 0.8762 0.9029 0.01519 ] Network output: [ -0.002054 0.02145 1.002 -0.0001685 7.563e-05 0.9802 -0.000127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.09612 0.3146 0.1615 0.9851 0.9941 0.1926 0.4694 0.8856 0.7189 ] Network output: [ 0.009658 -0.03915 0.9982 9.528e-05 -4.278e-05 1.022 7.181e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09687 0.08638 0.177 0.2116 0.9874 0.992 0.09693 0.7991 0.8812 0.3109 ] Network output: [ -0.009567 0.04561 1.001 9.447e-05 -4.241e-05 0.9726 7.12e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09481 0.09297 0.1691 0.1998 0.9857 0.9915 0.09482 0.7307 0.8616 0.2442 ] Network output: [ -0.0003346 0.9992 0.0001139 1.28e-05 -5.746e-06 1.001 9.645e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001035 Epoch 6610 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01417 0.9893 0.9857 6.147e-06 -2.76e-06 -0.003308 4.633e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002935 -0.01 0.007645 0.9697 0.9741 0.006022 0.8457 0.8336 0.02096 ] Network output: [ 1 -0.01066 0.002614 -4.367e-05 1.96e-05 0.007784 -3.291e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02668 -0.2014 0.2059 0.9837 0.9933 0.2023 0.4639 0.8792 0.724 ] Network output: [ -0.01216 0.9989 1.011 2.638e-06 -1.184e-06 0.01496 1.988e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005197 0.0005812 0.00427 0.004781 0.9889 0.992 0.005291 0.8761 0.903 0.01523 ] Network output: [ 0.0002042 -0.01382 1.003 -0.0001641 7.367e-05 1.01 -0.0001237 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1917 0.09602 0.3171 0.1682 0.9851 0.9941 0.1923 0.4689 0.8856 0.7186 ] Network output: [ 0.009022 -0.04613 0.9995 9.559e-05 -4.291e-05 1.029 7.204e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09698 0.0865 0.1786 0.2135 0.9874 0.992 0.09704 0.7996 0.8812 0.3119 ] Network output: [ -0.009915 0.04775 1.001 9.411e-05 -4.225e-05 0.9711 7.093e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0949 0.09307 0.1695 0.2002 0.9857 0.9915 0.09492 0.7313 0.8616 0.2442 ] Network output: [ 0.0009978 0.9993 -0.001774 1.338e-05 -6.005e-06 1.001 1.008e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001108 Epoch 6611 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01391 0.9932 0.9856 5.69e-06 -2.554e-06 -0.006557 4.288e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003182 -0.002934 -0.01002 0.007575 0.9697 0.9741 0.006028 0.8458 0.8334 0.02094 ] Network output: [ 0.9982 0.01497 0.001388 -4.625e-05 2.076e-05 -0.01299 -3.486e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02634 -0.203 0.2017 0.9837 0.9933 0.2026 0.4645 0.8791 0.7237 ] Network output: [ -0.01219 1 1.01 2.451e-06 -1.1e-06 0.01375 1.847e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005204 0.0005779 0.004196 0.004641 0.9889 0.992 0.005298 0.8762 0.9029 0.01519 ] Network output: [ -0.002049 0.02135 1.002 -0.0001683 7.554e-05 0.9803 -0.0001268 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.0961 0.3146 0.1615 0.9851 0.9941 0.1926 0.4693 0.8856 0.7189 ] Network output: [ 0.009649 -0.03916 0.9982 9.518e-05 -4.273e-05 1.022 7.173e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09687 0.08637 0.177 0.2116 0.9874 0.992 0.09693 0.799 0.8811 0.3109 ] Network output: [ -0.00956 0.0456 1.001 9.438e-05 -4.237e-05 0.9726 7.113e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09479 0.09295 0.1691 0.1998 0.9857 0.9915 0.0948 0.7306 0.8616 0.2442 ] Network output: [ -0.0003311 0.9992 0.0001089 1.279e-05 -5.74e-06 1.001 9.636e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001033 Epoch 6612 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01416 0.9893 0.9857 6.136e-06 -2.754e-06 -0.00332 4.624e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002935 -0.009997 0.007642 0.9697 0.9741 0.006023 0.8457 0.8336 0.02095 ] Network output: [ 1 -0.01059 0.002609 -4.365e-05 1.96e-05 0.007722 -3.29e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02669 -0.2014 0.2059 0.9837 0.9933 0.2023 0.4638 0.8792 0.724 ] Network output: [ -0.01216 0.9989 1.011 2.636e-06 -1.183e-06 0.01495 1.987e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005198 0.0005805 0.00427 0.004778 0.9889 0.992 0.005292 0.8761 0.903 0.01522 ] Network output: [ 0.0001963 -0.01372 1.003 -0.0001639 7.36e-05 1.009 -0.0001235 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.096 0.3171 0.1681 0.9851 0.9941 0.1924 0.4689 0.8856 0.7186 ] Network output: [ 0.009018 -0.0461 0.9995 9.549e-05 -4.287e-05 1.029 7.196e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09697 0.0865 0.1786 0.2135 0.9874 0.992 0.09703 0.7995 0.8812 0.3119 ] Network output: [ -0.009906 0.04773 1.001 9.403e-05 -4.221e-05 0.9711 7.086e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09488 0.09305 0.1695 0.2002 0.9857 0.9915 0.09489 0.7313 0.8616 0.2442 ] Network output: [ 0.0009939 0.9993 -0.001768 1.336e-05 -5.998e-06 1.001 1.007e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001106 Epoch 6613 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0139 0.9932 0.9856 5.681e-06 -2.551e-06 -0.006551 4.282e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003182 -0.002935 -0.01002 0.007573 0.9697 0.9741 0.006029 0.8458 0.8334 0.02093 ] Network output: [ 0.9982 0.01489 0.00139 -4.621e-05 2.075e-05 -0.01293 -3.483e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02636 -0.203 0.2017 0.9837 0.9933 0.2026 0.4644 0.8791 0.7237 ] Network output: [ -0.01219 1 1.01 2.451e-06 -1.1e-06 0.01374 1.847e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005205 0.0005772 0.004196 0.004639 0.9889 0.992 0.005299 0.8761 0.9029 0.01518 ] Network output: [ -0.002043 0.02125 1.002 -0.0001681 7.545e-05 0.9804 -0.0001267 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.09607 0.3147 0.1615 0.9851 0.9941 0.1926 0.4693 0.8856 0.7189 ] Network output: [ 0.009641 -0.03917 0.9982 9.508e-05 -4.269e-05 1.022 7.166e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09687 0.08637 0.177 0.2116 0.9874 0.992 0.09693 0.799 0.8811 0.3109 ] Network output: [ -0.009554 0.04559 1.001 9.429e-05 -4.233e-05 0.9726 7.106e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09477 0.09293 0.1691 0.1998 0.9857 0.9915 0.09478 0.7306 0.8616 0.2442 ] Network output: [ -0.0003277 0.9992 0.000104 1.277e-05 -5.735e-06 1.001 9.627e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001032 Epoch 6614 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01416 0.9893 0.9857 6.124e-06 -2.749e-06 -0.003333 4.615e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002935 -0.009994 0.00764 0.9697 0.9741 0.006023 0.8457 0.8335 0.02095 ] Network output: [ 1 -0.01052 0.002605 -4.363e-05 1.959e-05 0.00766 -3.288e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02671 -0.2014 0.2059 0.9837 0.9933 0.2023 0.4638 0.8792 0.724 ] Network output: [ -0.01216 0.9989 1.011 2.634e-06 -1.183e-06 0.01494 1.985e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005199 0.0005798 0.00427 0.004776 0.9889 0.992 0.005293 0.8761 0.903 0.01522 ] Network output: [ 0.0001884 -0.01361 1.003 -0.0001638 7.352e-05 1.009 -0.0001234 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.09598 0.3171 0.1681 0.9851 0.9941 0.1924 0.4689 0.8856 0.7186 ] Network output: [ 0.009014 -0.04607 0.9995 9.538e-05 -4.282e-05 1.029 7.189e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09697 0.08649 0.1786 0.2134 0.9874 0.992 0.09703 0.7995 0.8812 0.3119 ] Network output: [ -0.009897 0.04771 1.001 9.394e-05 -4.217e-05 0.9711 7.08e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09486 0.09302 0.1695 0.2001 0.9857 0.9915 0.09487 0.7312 0.8615 0.2442 ] Network output: [ 0.00099 0.9993 -0.001763 1.334e-05 -5.991e-06 1.001 1.006e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001104 Epoch 6615 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0139 0.9932 0.9856 5.673e-06 -2.547e-06 -0.006544 4.275e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003183 -0.002935 -0.01001 0.007571 0.9697 0.9741 0.006029 0.8458 0.8334 0.02093 ] Network output: [ 0.9982 0.01481 0.001393 -4.617e-05 2.073e-05 -0.01287 -3.48e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02638 -0.2029 0.2017 0.9837 0.9933 0.2026 0.4644 0.879 0.7237 ] Network output: [ -0.01219 1 1.01 2.45e-06 -1.1e-06 0.01374 1.846e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005206 0.0005766 0.004197 0.004637 0.9889 0.992 0.0053 0.8761 0.9029 0.01518 ] Network output: [ -0.002038 0.02115 1.002 -0.0001679 7.536e-05 0.9804 -0.0001265 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.09605 0.3147 0.1614 0.9851 0.9941 0.1926 0.4693 0.8856 0.7188 ] Network output: [ 0.009632 -0.03919 0.9982 9.498e-05 -4.264e-05 1.022 7.158e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09687 0.08636 0.177 0.2116 0.9874 0.992 0.09693 0.7989 0.8811 0.3109 ] Network output: [ -0.009547 0.04558 1.001 9.42e-05 -4.229e-05 0.9726 7.099e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09475 0.09291 0.1691 0.1998 0.9857 0.9915 0.09476 0.7305 0.8615 0.2442 ] Network output: [ -0.0003242 0.9992 9.899e-05 1.276e-05 -5.73e-06 1.001 9.619e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00103 Epoch 6616 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01415 0.9893 0.9857 6.113e-06 -2.744e-06 -0.003345 4.607e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002935 -0.009991 0.007637 0.9697 0.9741 0.006023 0.8457 0.8335 0.02094 ] Network output: [ 1 -0.01044 0.0026 -4.361e-05 1.958e-05 0.007599 -3.287e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02673 -0.2013 0.2058 0.9837 0.9933 0.2023 0.4638 0.8792 0.724 ] Network output: [ -0.01216 0.9989 1.011 2.632e-06 -1.182e-06 0.01493 1.984e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005199 0.0005792 0.004271 0.004773 0.9889 0.992 0.005293 0.8761 0.9029 0.01522 ] Network output: [ 0.0001806 -0.01351 1.003 -0.0001636 7.344e-05 1.009 -0.0001233 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.09595 0.3172 0.168 0.9851 0.9941 0.1924 0.4688 0.8856 0.7185 ] Network output: [ 0.00901 -0.04604 0.9995 9.528e-05 -4.278e-05 1.029 7.181e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09697 0.08649 0.1786 0.2134 0.9874 0.992 0.09703 0.7994 0.8811 0.3119 ] Network output: [ -0.009888 0.04768 1.001 9.385e-05 -4.213e-05 0.9712 7.073e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09484 0.093 0.1695 0.2001 0.9857 0.9915 0.09485 0.7311 0.8615 0.2442 ] Network output: [ 0.0009861 0.9993 -0.001758 1.333e-05 -5.983e-06 1.001 1.004e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001102 Epoch 6617 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01389 0.9932 0.9856 5.665e-06 -2.543e-06 -0.006537 4.269e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003183 -0.002935 -0.01001 0.007568 0.9697 0.9741 0.006029 0.8458 0.8334 0.02092 ] Network output: [ 0.9982 0.01474 0.001395 -4.614e-05 2.071e-05 -0.01281 -3.477e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.0264 -0.2029 0.2017 0.9837 0.9933 0.2026 0.4644 0.879 0.7237 ] Network output: [ -0.01218 1 1.01 2.449e-06 -1.1e-06 0.01374 1.846e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005207 0.000576 0.004198 0.004635 0.9889 0.992 0.005301 0.8761 0.9028 0.01518 ] Network output: [ -0.002033 0.02105 1.002 -0.0001677 7.527e-05 0.9805 -0.0001264 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.09603 0.3148 0.1614 0.9851 0.9941 0.1926 0.4692 0.8856 0.7188 ] Network output: [ 0.009623 -0.0392 0.9982 9.488e-05 -4.26e-05 1.022 7.151e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09687 0.08636 0.177 0.2115 0.9874 0.992 0.09693 0.7989 0.8811 0.3109 ] Network output: [ -0.009541 0.04557 1.001 9.411e-05 -4.225e-05 0.9726 7.093e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09473 0.09289 0.1691 0.1998 0.9857 0.9915 0.09474 0.7304 0.8615 0.2441 ] Network output: [ -0.0003207 0.9992 9.402e-05 1.275e-05 -5.724e-06 1.001 9.61e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001029 Epoch 6618 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01414 0.9893 0.9857 6.101e-06 -2.739e-06 -0.003358 4.598e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002936 -0.009987 0.007634 0.9697 0.9741 0.006024 0.8457 0.8335 0.02094 ] Network output: [ 1 -0.01037 0.002596 -4.359e-05 1.957e-05 0.007537 -3.285e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1814 -0.02675 -0.2013 0.2058 0.9837 0.9933 0.2023 0.4638 0.8792 0.724 ] Network output: [ -0.01216 0.9989 1.011 2.631e-06 -1.181e-06 0.01491 1.983e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0052 0.0005785 0.004271 0.004771 0.9889 0.992 0.005294 0.8761 0.9029 0.01521 ] Network output: [ 0.0001727 -0.0134 1.003 -0.0001634 7.337e-05 1.009 -0.0001232 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.09593 0.3172 0.1679 0.9851 0.9941 0.1924 0.4688 0.8856 0.7185 ] Network output: [ 0.009006 -0.04601 0.9994 9.518e-05 -4.273e-05 1.029 7.173e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09697 0.08648 0.1786 0.2134 0.9874 0.992 0.09703 0.7994 0.8811 0.3119 ] Network output: [ -0.009878 0.04766 1.001 9.376e-05 -4.209e-05 0.9712 7.066e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09482 0.09298 0.1695 0.2001 0.9857 0.9915 0.09483 0.731 0.8615 0.2442 ] Network output: [ 0.0009822 0.9993 -0.001752 1.331e-05 -5.976e-06 1.001 1.003e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0011 Epoch 6619 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01389 0.9932 0.9856 5.657e-06 -2.539e-06 -0.006531 4.263e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003183 -0.002935 -0.01001 0.007566 0.9697 0.9741 0.00603 0.8458 0.8334 0.02092 ] Network output: [ 0.9983 0.01466 0.001397 -4.61e-05 2.07e-05 -0.01275 -3.474e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02642 -0.2029 0.2017 0.9837 0.9933 0.2026 0.4643 0.879 0.7237 ] Network output: [ -0.01218 1 1.01 2.449e-06 -1.099e-06 0.01373 1.846e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005207 0.0005753 0.004199 0.004634 0.9889 0.992 0.005302 0.8761 0.9028 0.01517 ] Network output: [ -0.002028 0.02095 1.002 -0.0001675 7.518e-05 0.9806 -0.0001262 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.096 0.3148 0.1614 0.9851 0.9941 0.1927 0.4692 0.8856 0.7188 ] Network output: [ 0.009615 -0.03921 0.9982 9.479e-05 -4.255e-05 1.022 7.143e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09686 0.08635 0.1771 0.2115 0.9874 0.992 0.09692 0.7988 0.881 0.3109 ] Network output: [ -0.009534 0.04556 1.001 9.402e-05 -4.221e-05 0.9726 7.086e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09471 0.09287 0.1691 0.1998 0.9857 0.9915 0.09472 0.7304 0.8615 0.2441 ] Network output: [ -0.0003173 0.9992 8.906e-05 1.274e-05 -5.719e-06 1.001 9.601e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001027 Epoch 6620 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01414 0.9894 0.9858 6.09e-06 -2.734e-06 -0.003371 4.589e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003179 -0.002936 -0.009984 0.007632 0.9697 0.9741 0.006024 0.8457 0.8335 0.02093 ] Network output: [ 1 -0.0103 0.002591 -4.357e-05 1.956e-05 0.007475 -3.284e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02676 -0.2013 0.2058 0.9837 0.9933 0.2023 0.4637 0.8792 0.724 ] Network output: [ -0.01215 0.9989 1.011 2.629e-06 -1.18e-06 0.0149 1.981e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005201 0.0005779 0.004271 0.004768 0.9889 0.992 0.005295 0.8761 0.9029 0.01521 ] Network output: [ 0.0001648 -0.01329 1.003 -0.0001632 7.329e-05 1.009 -0.000123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.09591 0.3172 0.1679 0.9851 0.9941 0.1924 0.4688 0.8856 0.7185 ] Network output: [ 0.009002 -0.04598 0.9994 9.508e-05 -4.268e-05 1.029 7.166e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09697 0.08647 0.1785 0.2134 0.9874 0.992 0.09703 0.7993 0.8811 0.3118 ] Network output: [ -0.009869 0.04764 1.001 9.368e-05 -4.205e-05 0.9712 7.06e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0948 0.09296 0.1695 0.2001 0.9857 0.9915 0.09481 0.731 0.8614 0.2442 ] Network output: [ 0.0009783 0.9993 -0.001747 1.33e-05 -5.969e-06 1.001 1.002e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001098 Epoch 6621 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01388 0.9931 0.9856 5.648e-06 -2.536e-06 -0.006524 4.257e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003183 -0.002936 -0.01 0.007564 0.9697 0.9741 0.00603 0.8457 0.8334 0.02091 ] Network output: [ 0.9983 0.01458 0.0014 -4.606e-05 2.068e-05 -0.0127 -3.471e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02643 -0.2028 0.2017 0.9837 0.9933 0.2026 0.4643 0.879 0.7237 ] Network output: [ -0.01218 1 1.01 2.448e-06 -1.099e-06 0.01373 1.845e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005208 0.0005747 0.004199 0.004632 0.9889 0.992 0.005303 0.8761 0.9028 0.01517 ] Network output: [ -0.002022 0.02085 1.002 -0.0001673 7.509e-05 0.9807 -0.0001261 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.09598 0.3148 0.1614 0.9851 0.9941 0.1927 0.4692 0.8856 0.7188 ] Network output: [ 0.009606 -0.03923 0.9982 9.469e-05 -4.251e-05 1.022 7.136e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09686 0.08635 0.1771 0.2115 0.9874 0.992 0.09692 0.7988 0.881 0.3109 ] Network output: [ -0.009528 0.04554 1.001 9.393e-05 -4.217e-05 0.9726 7.079e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09469 0.09284 0.169 0.1997 0.9857 0.9915 0.0947 0.7303 0.8614 0.2441 ] Network output: [ -0.0003138 0.9992 8.41e-05 1.273e-05 -5.714e-06 1.001 9.592e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001026 Epoch 6622 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01413 0.9894 0.9858 6.078e-06 -2.729e-06 -0.003383 4.581e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002936 -0.009981 0.007629 0.9697 0.9741 0.006024 0.8457 0.8335 0.02093 ] Network output: [ 1 -0.01023 0.002587 -4.355e-05 1.955e-05 0.007413 -3.282e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02678 -0.2012 0.2057 0.9837 0.9933 0.2023 0.4637 0.8791 0.724 ] Network output: [ -0.01215 0.9989 1.011 2.627e-06 -1.179e-06 0.01489 1.98e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005202 0.0005772 0.004272 0.004766 0.9889 0.992 0.005296 0.876 0.9029 0.01521 ] Network output: [ 0.000157 -0.01319 1.003 -0.0001631 7.321e-05 1.009 -0.0001229 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.09589 0.3172 0.1678 0.9851 0.9941 0.1924 0.4687 0.8855 0.7185 ] Network output: [ 0.008997 -0.04596 0.9994 9.498e-05 -4.264e-05 1.029 7.158e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09696 0.08647 0.1785 0.2133 0.9874 0.992 0.09702 0.7992 0.8811 0.3118 ] Network output: [ -0.00986 0.04761 1.001 9.359e-05 -4.202e-05 0.9712 7.053e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09478 0.09294 0.1694 0.2001 0.9857 0.9915 0.09479 0.7309 0.8614 0.2442 ] Network output: [ 0.0009744 0.9993 -0.001741 1.328e-05 -5.962e-06 1.001 1.001e-05 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001096 Epoch 6623 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01388 0.9931 0.9856 5.64e-06 -2.532e-06 -0.006517 4.25e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003183 -0.002936 -0.01 0.007562 0.9697 0.9741 0.00603 0.8457 0.8333 0.02091 ] Network output: [ 0.9983 0.0145 0.001402 -4.602e-05 2.066e-05 -0.01264 -3.468e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02645 -0.2028 0.2016 0.9837 0.9933 0.2026 0.4643 0.879 0.7237 ] Network output: [ -0.01218 1 1.01 2.448e-06 -1.099e-06 0.01373 1.845e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005209 0.000574 0.0042 0.00463 0.9889 0.992 0.005303 0.8761 0.9028 0.01517 ] Network output: [ -0.002017 0.02075 1.002 -0.0001671 7.5e-05 0.9808 -0.0001259 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.09596 0.3149 0.1613 0.9851 0.9941 0.1927 0.4691 0.8856 0.7188 ] Network output: [ 0.009597 -0.03924 0.9982 9.459e-05 -4.246e-05 1.022 7.128e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09686 0.08634 0.1771 0.2115 0.9874 0.992 0.09692 0.7987 0.881 0.3109 ] Network output: [ -0.009521 0.04553 1.001 9.384e-05 -4.213e-05 0.9726 7.072e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09467 0.09282 0.169 0.1997 0.9857 0.9915 0.09468 0.7302 0.8614 0.2441 ] Network output: [ -0.0003103 0.9992 7.914e-05 1.272e-05 -5.709e-06 1.001 9.583e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001024 Epoch 6624 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01412 0.9894 0.9858 6.067e-06 -2.724e-06 -0.003396 4.572e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002937 -0.009977 0.007626 0.9697 0.9741 0.006025 0.8456 0.8335 0.02092 ] Network output: [ 1 -0.01016 0.002582 -4.354e-05 1.954e-05 0.007352 -3.281e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.0268 -0.2012 0.2057 0.9837 0.9933 0.2024 0.4637 0.8791 0.724 ] Network output: [ -0.01215 0.9989 1.011 2.625e-06 -1.178e-06 0.01488 1.978e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005203 0.0005766 0.004272 0.004763 0.9889 0.992 0.005297 0.876 0.9029 0.0152 ] Network output: [ 0.0001492 -0.01308 1.003 -0.0001629 7.313e-05 1.009 -0.0001228 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.09586 0.3173 0.1677 0.9851 0.9941 0.1924 0.4687 0.8855 0.7185 ] Network output: [ 0.008993 -0.04593 0.9994 9.488e-05 -4.259e-05 1.029 7.15e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09696 0.08646 0.1785 0.2133 0.9874 0.992 0.09702 0.7992 0.881 0.3118 ] Network output: [ -0.009851 0.04759 1.001 9.35e-05 -4.198e-05 0.9712 7.047e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09476 0.09292 0.1694 0.2 0.9857 0.9915 0.09477 0.7308 0.8614 0.2442 ] Network output: [ 0.0009705 0.9993 -0.001736 1.326e-05 -5.955e-06 1.001 9.996e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001094 Epoch 6625 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01387 0.9931 0.9857 5.632e-06 -2.528e-06 -0.006511 4.244e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003183 -0.002936 -0.009997 0.007559 0.9697 0.9741 0.00603 0.8457 0.8333 0.0209 ] Network output: [ 0.9983 0.01442 0.001404 -4.598e-05 2.064e-05 -0.01258 -3.466e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02647 -0.2028 0.2016 0.9837 0.9933 0.2026 0.4642 0.879 0.7237 ] Network output: [ -0.01217 1 1.01 2.447e-06 -1.098e-06 0.01372 1.844e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00521 0.0005734 0.004201 0.004629 0.9889 0.992 0.005304 0.8761 0.9028 0.01516 ] Network output: [ -0.002012 0.02064 1.002 -0.0001669 7.491e-05 0.9808 -0.0001258 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.09593 0.3149 0.1613 0.9851 0.9941 0.1927 0.4691 0.8855 0.7188 ] Network output: [ 0.009589 -0.03925 0.9982 9.449e-05 -4.242e-05 1.022 7.121e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09686 0.08634 0.1771 0.2115 0.9874 0.992 0.09692 0.7987 0.881 0.3109 ] Network output: [ -0.009515 0.04552 1.001 9.375e-05 -4.209e-05 0.9726 7.065e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09465 0.0928 0.169 0.1997 0.9857 0.9915 0.09466 0.7302 0.8614 0.2441 ] Network output: [ -0.0003068 0.9992 7.419e-05 1.27e-05 -5.703e-06 1.001 9.574e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001023 Epoch 6626 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01412 0.9894 0.9858 6.055e-06 -2.718e-06 -0.003409 4.563e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002937 -0.009974 0.007624 0.9697 0.9741 0.006025 0.8456 0.8335 0.02092 ] Network output: [ 1 -0.01009 0.002578 -4.352e-05 1.954e-05 0.00729 -3.28e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02682 -0.2012 0.2057 0.9837 0.9933 0.2024 0.4637 0.8791 0.7239 ] Network output: [ -0.01215 0.9989 1.011 2.623e-06 -1.178e-06 0.01487 1.977e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005204 0.0005759 0.004272 0.00476 0.9889 0.992 0.005298 0.876 0.9029 0.0152 ] Network output: [ 0.0001414 -0.01298 1.003 -0.0001627 7.306e-05 1.009 -0.0001226 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1918 0.09584 0.3173 0.1677 0.9851 0.9941 0.1924 0.4687 0.8855 0.7185 ] Network output: [ 0.008989 -0.0459 0.9994 9.478e-05 -4.255e-05 1.029 7.143e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09696 0.08646 0.1785 0.2133 0.9874 0.992 0.09702 0.7991 0.881 0.3118 ] Network output: [ -0.009842 0.04757 1.001 9.341e-05 -4.194e-05 0.9712 7.04e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09474 0.0929 0.1694 0.2 0.9857 0.9915 0.09475 0.7308 0.8613 0.2441 ] Network output: [ 0.0009666 0.9993 -0.001731 1.325e-05 -5.948e-06 1.001 9.985e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001092 Epoch 6627 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01387 0.9931 0.9857 5.623e-06 -2.524e-06 -0.006504 4.238e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003183 -0.002936 -0.009994 0.007557 0.9697 0.9741 0.006031 0.8457 0.8333 0.0209 ] Network output: [ 0.9983 0.01434 0.001406 -4.595e-05 2.063e-05 -0.01252 -3.463e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02649 -0.2027 0.2016 0.9837 0.9933 0.2027 0.4642 0.879 0.7236 ] Network output: [ -0.01217 1 1.01 2.446e-06 -1.098e-06 0.01372 1.844e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005211 0.0005728 0.004201 0.004627 0.9889 0.992 0.005305 0.876 0.9028 0.01516 ] Network output: [ -0.002006 0.02054 1.002 -0.0001667 7.482e-05 0.9809 -0.0001256 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.09591 0.315 0.1613 0.9851 0.9941 0.1927 0.4691 0.8855 0.7188 ] Network output: [ 0.00958 -0.03927 0.9981 9.439e-05 -4.237e-05 1.022 7.113e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09686 0.08633 0.1771 0.2115 0.9874 0.992 0.09692 0.7986 0.8809 0.3108 ] Network output: [ -0.009508 0.04551 1.001 9.366e-05 -4.205e-05 0.9726 7.059e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09463 0.09278 0.169 0.1997 0.9857 0.9915 0.09464 0.7301 0.8613 0.2441 ] Network output: [ -0.0003034 0.9992 6.925e-05 1.269e-05 -5.698e-06 1.001 9.566e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001021 Epoch 6628 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01411 0.9894 0.9858 6.044e-06 -2.713e-06 -0.003422 4.555e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002937 -0.009971 0.007621 0.9697 0.9741 0.006025 0.8456 0.8335 0.02091 ] Network output: [ 1 -0.01002 0.002573 -4.35e-05 1.953e-05 0.007228 -3.278e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02683 -0.2012 0.2056 0.9837 0.9933 0.2024 0.4636 0.8791 0.7239 ] Network output: [ -0.01215 0.9989 1.011 2.621e-06 -1.177e-06 0.01486 1.975e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005204 0.0005753 0.004272 0.004758 0.9889 0.992 0.005299 0.876 0.9029 0.01519 ] Network output: [ 0.0001336 -0.01287 1.003 -0.0001626 7.298e-05 1.009 -0.0001225 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.09582 0.3173 0.1676 0.9851 0.9941 0.1925 0.4686 0.8855 0.7185 ] Network output: [ 0.008985 -0.04587 0.9994 9.467e-05 -4.25e-05 1.029 7.135e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09696 0.08645 0.1785 0.2132 0.9874 0.992 0.09702 0.7991 0.881 0.3118 ] Network output: [ -0.009832 0.04754 1.001 9.333e-05 -4.19e-05 0.9712 7.033e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09472 0.09288 0.1694 0.2 0.9857 0.9915 0.09473 0.7307 0.8613 0.2441 ] Network output: [ 0.0009627 0.9993 -0.001725 1.323e-05 -5.941e-06 1.001 9.973e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00109 Epoch 6629 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01386 0.9931 0.9857 5.615e-06 -2.521e-06 -0.006498 4.232e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003183 -0.002937 -0.00999 0.007555 0.9697 0.9741 0.006031 0.8457 0.8333 0.02089 ] Network output: [ 0.9983 0.01426 0.001409 -4.591e-05 2.061e-05 -0.01246 -3.46e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02651 -0.2027 0.2016 0.9837 0.9933 0.2027 0.4642 0.879 0.7236 ] Network output: [ -0.01217 1 1.01 2.445e-06 -1.098e-06 0.01372 1.843e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005212 0.0005721 0.004202 0.004625 0.9889 0.992 0.005306 0.876 0.9028 0.01516 ] Network output: [ -0.002001 0.02044 1.002 -0.0001665 7.473e-05 0.981 -0.0001255 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.09589 0.315 0.1613 0.9851 0.9941 0.1927 0.469 0.8855 0.7188 ] Network output: [ 0.009571 -0.03928 0.9981 9.429e-05 -4.233e-05 1.022 7.106e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09686 0.08633 0.1771 0.2114 0.9874 0.992 0.09692 0.7985 0.8809 0.3108 ] Network output: [ -0.009502 0.04549 1.001 9.357e-05 -4.201e-05 0.9726 7.052e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09461 0.09276 0.169 0.1997 0.9857 0.9915 0.09462 0.73 0.8613 0.2441 ] Network output: [ -0.0002999 0.9992 6.43e-05 1.268e-05 -5.693e-06 1.001 9.557e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00102 Epoch 6630 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01411 0.9895 0.9858 6.032e-06 -2.708e-06 -0.003434 4.546e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002937 -0.009967 0.007618 0.9697 0.9741 0.006026 0.8456 0.8334 0.02091 ] Network output: [ 1 -0.00995 0.002569 -4.348e-05 1.952e-05 0.007167 -3.277e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02685 -0.2011 0.2056 0.9837 0.9933 0.2024 0.4636 0.8791 0.7239 ] Network output: [ -0.01214 0.9989 1.011 2.619e-06 -1.176e-06 0.01485 1.974e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005205 0.0005746 0.004273 0.004755 0.9889 0.992 0.0053 0.876 0.9029 0.01519 ] Network output: [ 0.0001258 -0.01276 1.003 -0.0001624 7.29e-05 1.009 -0.0001224 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.0958 0.3174 0.1675 0.9851 0.9941 0.1925 0.4686 0.8855 0.7185 ] Network output: [ 0.008981 -0.04584 0.9994 9.457e-05 -4.246e-05 1.029 7.127e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09696 0.08644 0.1785 0.2132 0.9874 0.992 0.09702 0.799 0.881 0.3118 ] Network output: [ -0.009823 0.04752 1.001 9.324e-05 -4.186e-05 0.9712 7.027e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0947 0.09286 0.1694 0.2 0.9857 0.9915 0.09471 0.7306 0.8613 0.2441 ] Network output: [ 0.0009588 0.9993 -0.00172 1.322e-05 -5.934e-06 1.001 9.961e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001088 Epoch 6631 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01386 0.9931 0.9857 5.607e-06 -2.517e-06 -0.006491 4.225e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003183 -0.002937 -0.009987 0.007553 0.9697 0.9741 0.006031 0.8457 0.8333 0.02089 ] Network output: [ 0.9983 0.01419 0.001411 -4.587e-05 2.059e-05 -0.0124 -3.457e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02653 -0.2027 0.2016 0.9837 0.9933 0.2027 0.4642 0.879 0.7236 ] Network output: [ -0.01217 1 1.01 2.445e-06 -1.098e-06 0.01371 1.842e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005212 0.0005715 0.004203 0.004624 0.9889 0.992 0.005307 0.876 0.9028 0.01515 ] Network output: [ -0.001996 0.02034 1.002 -0.0001663 7.464e-05 0.9811 -0.0001253 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.09587 0.3151 0.1612 0.9851 0.9941 0.1927 0.469 0.8855 0.7187 ] Network output: [ 0.009563 -0.03929 0.9981 9.419e-05 -4.228e-05 1.022 7.098e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09686 0.08632 0.1771 0.2114 0.9874 0.992 0.09692 0.7985 0.8809 0.3108 ] Network output: [ -0.009495 0.04548 1.001 9.348e-05 -4.197e-05 0.9726 7.045e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09459 0.09274 0.169 0.1996 0.9857 0.9915 0.0946 0.7299 0.8613 0.2441 ] Network output: [ -0.0002964 0.9992 5.936e-05 1.267e-05 -5.688e-06 1.001 9.548e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001018 Epoch 6632 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0141 0.9895 0.9858 6.021e-06 -2.703e-06 -0.003447 4.537e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002938 -0.009964 0.007616 0.9697 0.9741 0.006026 0.8456 0.8334 0.0209 ] Network output: [ 1 -0.009879 0.002564 -4.346e-05 1.951e-05 0.007105 -3.275e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02687 -0.2011 0.2055 0.9837 0.9933 0.2024 0.4636 0.8791 0.7239 ] Network output: [ -0.01214 0.999 1.011 2.617e-06 -1.175e-06 0.01484 1.972e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005206 0.000574 0.004273 0.004753 0.9889 0.992 0.0053 0.876 0.9029 0.01519 ] Network output: [ 0.000118 -0.01266 1.003 -0.0001622 7.283e-05 1.009 -0.0001223 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.09578 0.3174 0.1675 0.9851 0.9941 0.1925 0.4686 0.8855 0.7185 ] Network output: [ 0.008977 -0.04581 0.9993 9.447e-05 -4.241e-05 1.029 7.12e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09695 0.08644 0.1785 0.2132 0.9874 0.992 0.09701 0.7989 0.8809 0.3118 ] Network output: [ -0.009814 0.04749 1.001 9.315e-05 -4.182e-05 0.9712 7.02e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09468 0.09283 0.1693 0.2 0.9857 0.9915 0.09469 0.7305 0.8612 0.2441 ] Network output: [ 0.0009549 0.9993 -0.001714 1.32e-05 -5.926e-06 1.001 9.949e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001086 Epoch 6633 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01385 0.9931 0.9857 5.598e-06 -2.513e-06 -0.006485 4.219e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003183 -0.002937 -0.009983 0.00755 0.9697 0.9741 0.006032 0.8457 0.8333 0.02088 ] Network output: [ 0.9983 0.01411 0.001413 -4.583e-05 2.058e-05 -0.01235 -3.454e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02655 -0.2026 0.2016 0.9837 0.9933 0.2027 0.4641 0.8789 0.7236 ] Network output: [ -0.01217 1 1.01 2.444e-06 -1.097e-06 0.01371 1.842e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005213 0.0005709 0.004204 0.004622 0.9889 0.992 0.005307 0.876 0.9028 0.01515 ] Network output: [ -0.00199 0.02024 1.002 -0.0001661 7.455e-05 0.9811 -0.0001252 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.09584 0.3151 0.1612 0.9851 0.9941 0.1927 0.469 0.8855 0.7187 ] Network output: [ 0.009554 -0.0393 0.9981 9.409e-05 -4.224e-05 1.022 7.091e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09686 0.08632 0.1771 0.2114 0.9874 0.992 0.09692 0.7984 0.8809 0.3108 ] Network output: [ -0.009489 0.04547 1.001 9.339e-05 -4.193e-05 0.9726 7.038e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09457 0.09272 0.169 0.1996 0.9857 0.9915 0.09458 0.7299 0.8612 0.2441 ] Network output: [ -0.0002929 0.9992 5.443e-05 1.266e-05 -5.682e-06 1.001 9.539e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001017 Epoch 6634 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01409 0.9895 0.9858 6.009e-06 -2.698e-06 -0.00346 4.529e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002938 -0.009961 0.007613 0.9697 0.9741 0.006026 0.8456 0.8334 0.0209 ] Network output: [ 1 -0.009808 0.00256 -4.344e-05 1.95e-05 0.007043 -3.274e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1815 -0.02688 -0.2011 0.2055 0.9837 0.9933 0.2024 0.4635 0.8791 0.7239 ] Network output: [ -0.01214 0.999 1.011 2.615e-06 -1.174e-06 0.01483 1.971e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005207 0.0005733 0.004273 0.00475 0.9889 0.992 0.005301 0.876 0.9028 0.01518 ] Network output: [ 0.0001103 -0.01255 1.003 -0.000162 7.275e-05 1.008 -0.0001221 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.09575 0.3174 0.1674 0.9851 0.9941 0.1925 0.4686 0.8855 0.7184 ] Network output: [ 0.008973 -0.04578 0.9993 9.437e-05 -4.237e-05 1.029 7.112e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09695 0.08643 0.1785 0.2131 0.9874 0.992 0.09701 0.7989 0.8809 0.3117 ] Network output: [ -0.009805 0.04747 1.001 9.306e-05 -4.178e-05 0.9712 7.013e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09466 0.09281 0.1693 0.1999 0.9857 0.9915 0.09467 0.7305 0.8612 0.2441 ] Network output: [ 0.000951 0.9994 -0.001709 1.319e-05 -5.919e-06 1.001 9.937e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001084 Epoch 6635 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01385 0.9931 0.9857 5.59e-06 -2.509e-06 -0.006478 4.213e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003184 -0.002938 -0.00998 0.007548 0.9697 0.9741 0.006032 0.8457 0.8333 0.02088 ] Network output: [ 0.9983 0.01403 0.001415 -4.579e-05 2.056e-05 -0.01229 -3.451e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02657 -0.2026 0.2016 0.9837 0.9933 0.2027 0.4641 0.8789 0.7236 ] Network output: [ -0.01216 1 1.01 2.443e-06 -1.097e-06 0.01371 1.841e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005214 0.0005703 0.004204 0.00462 0.9889 0.992 0.005308 0.876 0.9027 0.01515 ] Network output: [ -0.001985 0.02014 1.002 -0.0001659 7.446e-05 0.9812 -0.000125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.09582 0.3151 0.1612 0.9851 0.9941 0.1927 0.4689 0.8855 0.7187 ] Network output: [ 0.009546 -0.03932 0.9981 9.399e-05 -4.219e-05 1.022 7.083e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.08631 0.1771 0.2114 0.9874 0.992 0.09691 0.7984 0.8808 0.3108 ] Network output: [ -0.009482 0.04546 1.001 9.33e-05 -4.189e-05 0.9726 7.031e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09455 0.0927 0.1689 0.1996 0.9857 0.9915 0.09456 0.7298 0.8612 0.2441 ] Network output: [ -0.0002895 0.9992 4.95e-05 1.265e-05 -5.677e-06 1.001 9.53e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001015 Epoch 6636 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01409 0.9895 0.9858 5.998e-06 -2.693e-06 -0.003473 4.52e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002938 -0.009957 0.00761 0.9697 0.9741 0.006027 0.8456 0.8334 0.02089 ] Network output: [ 1 -0.009738 0.002555 -4.342e-05 1.949e-05 0.006982 -3.272e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.0269 -0.2011 0.2055 0.9837 0.9933 0.2024 0.4635 0.8791 0.7239 ] Network output: [ -0.01214 0.999 1.011 2.613e-06 -1.173e-06 0.01482 1.969e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005208 0.0005727 0.004273 0.004748 0.9889 0.992 0.005302 0.8759 0.9028 0.01518 ] Network output: [ 0.0001026 -0.01245 1.003 -0.0001619 7.267e-05 1.008 -0.000122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.09573 0.3175 0.1674 0.9851 0.9941 0.1925 0.4685 0.8855 0.7184 ] Network output: [ 0.008968 -0.04575 0.9993 9.427e-05 -4.232e-05 1.029 7.104e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09695 0.08643 0.1785 0.2131 0.9874 0.992 0.09701 0.7988 0.8809 0.3117 ] Network output: [ -0.009795 0.04744 1.001 9.297e-05 -4.174e-05 0.9712 7.007e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09464 0.09279 0.1693 0.1999 0.9857 0.9915 0.09465 0.7304 0.8612 0.2441 ] Network output: [ 0.000947 0.9994 -0.001704 1.317e-05 -5.912e-06 1.001 9.925e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001082 Epoch 6637 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01384 0.9931 0.9857 5.581e-06 -2.506e-06 -0.006472 4.206e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003184 -0.002938 -0.009976 0.007546 0.9697 0.9741 0.006032 0.8457 0.8333 0.02087 ] Network output: [ 0.9983 0.01395 0.001418 -4.576e-05 2.054e-05 -0.01223 -3.448e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02659 -0.2025 0.2016 0.9837 0.9933 0.2027 0.4641 0.8789 0.7236 ] Network output: [ -0.01216 1 1.01 2.442e-06 -1.096e-06 0.0137 1.841e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005215 0.0005696 0.004205 0.004619 0.9889 0.992 0.005309 0.876 0.9027 0.01514 ] Network output: [ -0.00198 0.02004 1.002 -0.0001657 7.437e-05 0.9813 -0.0001248 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.0958 0.3152 0.1612 0.9851 0.9941 0.1928 0.4689 0.8855 0.7187 ] Network output: [ 0.009537 -0.03933 0.9981 9.389e-05 -4.215e-05 1.023 7.076e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.08631 0.1771 0.2114 0.9874 0.992 0.09691 0.7983 0.8808 0.3108 ] Network output: [ -0.009476 0.04544 1.001 9.321e-05 -4.185e-05 0.9726 7.025e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09453 0.09268 0.1689 0.1996 0.9857 0.9915 0.09454 0.7297 0.8612 0.2441 ] Network output: [ -0.000286 0.9992 4.457e-05 1.263e-05 -5.672e-06 1.001 9.521e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001014 Epoch 6638 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01408 0.9895 0.9858 5.986e-06 -2.687e-06 -0.003485 4.511e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00318 -0.002938 -0.009954 0.007608 0.9697 0.9741 0.006027 0.8456 0.8334 0.02089 ] Network output: [ 1 -0.009667 0.002551 -4.34e-05 1.948e-05 0.00692 -3.271e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02692 -0.201 0.2054 0.9837 0.9933 0.2025 0.4635 0.8791 0.7239 ] Network output: [ -0.01213 0.999 1.011 2.611e-06 -1.172e-06 0.01481 1.968e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005209 0.0005721 0.004274 0.004745 0.9889 0.992 0.005303 0.8759 0.9028 0.01518 ] Network output: [ 9.484e-05 -0.01234 1.003 -0.0001617 7.26e-05 1.008 -0.0001219 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.09571 0.3175 0.1673 0.9851 0.9941 0.1925 0.4685 0.8854 0.7184 ] Network output: [ 0.008964 -0.04572 0.9993 9.416e-05 -4.227e-05 1.029 7.097e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09695 0.08642 0.1785 0.2131 0.9874 0.992 0.09701 0.7988 0.8809 0.3117 ] Network output: [ -0.009786 0.04742 1.001 9.289e-05 -4.17e-05 0.9712 7e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09461 0.09277 0.1693 0.1999 0.9857 0.9915 0.09463 0.7303 0.8612 0.2441 ] Network output: [ 0.0009431 0.9994 -0.001698 1.315e-05 -5.905e-06 1.001 9.913e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00108 Epoch 6639 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01384 0.9931 0.9857 5.573e-06 -2.502e-06 -0.006465 4.2e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003184 -0.002938 -0.009973 0.007544 0.9697 0.9741 0.006032 0.8456 0.8333 0.02087 ] Network output: [ 0.9983 0.01387 0.00142 -4.572e-05 2.052e-05 -0.01217 -3.445e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02661 -0.2025 0.2016 0.9837 0.9933 0.2027 0.464 0.8789 0.7236 ] Network output: [ -0.01216 1 1.01 2.441e-06 -1.096e-06 0.0137 1.84e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005216 0.000569 0.004206 0.004617 0.9889 0.992 0.00531 0.8759 0.9027 0.01514 ] Network output: [ -0.001974 0.01994 1.002 -0.0001655 7.428e-05 0.9814 -0.0001247 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.09578 0.3152 0.1611 0.9851 0.9941 0.1928 0.4689 0.8855 0.7187 ] Network output: [ 0.009528 -0.03934 0.9981 9.379e-05 -4.21e-05 1.023 7.068e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.0863 0.1771 0.2113 0.9874 0.992 0.09691 0.7983 0.8808 0.3108 ] Network output: [ -0.009469 0.04543 1.001 9.312e-05 -4.18e-05 0.9726 7.018e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09451 0.09266 0.1689 0.1996 0.9857 0.9915 0.09452 0.7297 0.8611 0.244 ] Network output: [ -0.0002825 0.9992 3.965e-05 1.262e-05 -5.667e-06 1.001 9.513e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001013 Epoch 6640 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01407 0.9895 0.9858 5.975e-06 -2.682e-06 -0.003498 4.503e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003181 -0.002939 -0.009951 0.007605 0.9697 0.9741 0.006027 0.8456 0.8334 0.02088 ] Network output: [ 1 -0.009597 0.002546 -4.338e-05 1.947e-05 0.006859 -3.269e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02694 -0.201 0.2054 0.9837 0.9933 0.2025 0.4635 0.879 0.7239 ] Network output: [ -0.01213 0.999 1.01 2.609e-06 -1.171e-06 0.01479 1.966e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00521 0.0005714 0.004274 0.004743 0.9889 0.992 0.005304 0.8759 0.9028 0.01517 ] Network output: [ 8.713e-05 -0.01224 1.003 -0.0001615 7.252e-05 1.008 -0.0001217 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.09569 0.3175 0.1672 0.9851 0.9941 0.1925 0.4685 0.8854 0.7184 ] Network output: [ 0.00896 -0.04569 0.9993 9.406e-05 -4.223e-05 1.029 7.089e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09695 0.08642 0.1785 0.213 0.9874 0.992 0.09701 0.7987 0.8808 0.3117 ] Network output: [ -0.009777 0.04739 1.001 9.28e-05 -4.166e-05 0.9712 6.994e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09459 0.09275 0.1693 0.1999 0.9857 0.9915 0.09461 0.7302 0.8611 0.2441 ] Network output: [ 0.0009392 0.9994 -0.001693 1.314e-05 -5.898e-06 1.001 9.901e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001078 Epoch 6641 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01383 0.9931 0.9857 5.564e-06 -2.498e-06 -0.006459 4.194e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003184 -0.002938 -0.009969 0.007541 0.9697 0.9741 0.006033 0.8456 0.8332 0.02086 ] Network output: [ 0.9984 0.01379 0.001422 -4.568e-05 2.051e-05 -0.01211 -3.443e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02663 -0.2025 0.2015 0.9837 0.9933 0.2027 0.464 0.8789 0.7236 ] Network output: [ -0.01216 1 1.01 2.441e-06 -1.096e-06 0.0137 1.839e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005216 0.0005684 0.004206 0.004615 0.9889 0.992 0.005311 0.8759 0.9027 0.01513 ] Network output: [ -0.001969 0.01983 1.002 -0.0001653 7.419e-05 0.9815 -0.0001245 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.09576 0.3153 0.1611 0.9851 0.9941 0.1928 0.4688 0.8855 0.7187 ] Network output: [ 0.00952 -0.03935 0.9981 9.369e-05 -4.206e-05 1.023 7.061e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.0863 0.1771 0.2113 0.9874 0.992 0.09691 0.7982 0.8808 0.3108 ] Network output: [ -0.009463 0.04542 1.001 9.303e-05 -4.176e-05 0.9726 7.011e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09449 0.09264 0.1689 0.1996 0.9857 0.9915 0.0945 0.7296 0.8611 0.244 ] Network output: [ -0.000279 0.9992 3.473e-05 1.261e-05 -5.661e-06 1.001 9.504e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001011 Epoch 6642 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01407 0.9896 0.9858 5.963e-06 -2.677e-06 -0.003511 4.494e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003181 -0.002939 -0.009947 0.007602 0.9697 0.9741 0.006028 0.8456 0.8334 0.02088 ] Network output: [ 1 -0.009526 0.002542 -4.336e-05 1.947e-05 0.006797 -3.268e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02695 -0.201 0.2054 0.9837 0.9933 0.2025 0.4634 0.879 0.7239 ] Network output: [ -0.01213 0.999 1.01 2.607e-06 -1.17e-06 0.01478 1.965e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00521 0.0005708 0.004274 0.00474 0.9889 0.992 0.005305 0.8759 0.9028 0.01517 ] Network output: [ 7.944e-05 -0.01213 1.003 -0.0001614 7.244e-05 1.008 -0.0001216 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1919 0.09567 0.3175 0.1672 0.9851 0.9941 0.1925 0.4684 0.8854 0.7184 ] Network output: [ 0.008956 -0.04566 0.9993 9.396e-05 -4.218e-05 1.029 7.081e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09695 0.08641 0.1785 0.213 0.9874 0.992 0.09701 0.7986 0.8808 0.3117 ] Network output: [ -0.009768 0.04737 1.001 9.271e-05 -4.162e-05 0.9713 6.987e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09457 0.09273 0.1693 0.1999 0.9857 0.9915 0.09459 0.7302 0.8611 0.2441 ] Network output: [ 0.0009353 0.9994 -0.001687 1.312e-05 -5.891e-06 1.001 9.889e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001076 Epoch 6643 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01383 0.9931 0.9857 5.556e-06 -2.494e-06 -0.006453 4.187e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003184 -0.002939 -0.009966 0.007539 0.9697 0.9741 0.006033 0.8456 0.8332 0.02086 ] Network output: [ 0.9984 0.01372 0.001424 -4.564e-05 2.049e-05 -0.01205 -3.44e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02664 -0.2024 0.2015 0.9837 0.9933 0.2028 0.464 0.8789 0.7236 ] Network output: [ -0.01216 1 1.01 2.44e-06 -1.095e-06 0.01369 1.839e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005217 0.0005678 0.004207 0.004613 0.9889 0.992 0.005312 0.8759 0.9027 0.01513 ] Network output: [ -0.001964 0.01973 1.002 -0.0001651 7.41e-05 0.9815 -0.0001244 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.09573 0.3153 0.1611 0.9851 0.9941 0.1928 0.4688 0.8854 0.7187 ] Network output: [ 0.009511 -0.03936 0.9981 9.359e-05 -4.201e-05 1.023 7.053e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.08629 0.1771 0.2113 0.9874 0.992 0.09691 0.7981 0.8807 0.3108 ] Network output: [ -0.009456 0.0454 1.001 9.294e-05 -4.172e-05 0.9726 7.004e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09447 0.09262 0.1689 0.1995 0.9857 0.9915 0.09448 0.7295 0.8611 0.244 ] Network output: [ -0.0002755 0.9992 2.982e-05 1.26e-05 -5.656e-06 1.001 9.495e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00101 Epoch 6644 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01406 0.9896 0.9858 5.951e-06 -2.672e-06 -0.003524 4.485e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003181 -0.002939 -0.009944 0.0076 0.9697 0.9741 0.006028 0.8455 0.8334 0.02087 ] Network output: [ 1 -0.009455 0.002537 -4.334e-05 1.946e-05 0.006736 -3.266e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02697 -0.2009 0.2053 0.9837 0.9933 0.2025 0.4634 0.879 0.7238 ] Network output: [ -0.01213 0.999 1.01 2.605e-06 -1.169e-06 0.01477 1.963e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005211 0.0005702 0.004275 0.004738 0.9889 0.992 0.005306 0.8759 0.9028 0.01516 ] Network output: [ 7.176e-05 -0.01203 1.003 -0.0001612 7.236e-05 1.008 -0.0001215 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.09565 0.3176 0.1671 0.9851 0.9941 0.1926 0.4684 0.8854 0.7184 ] Network output: [ 0.008952 -0.04563 0.9992 9.386e-05 -4.214e-05 1.029 7.073e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09694 0.0864 0.1785 0.213 0.9874 0.992 0.097 0.7986 0.8808 0.3117 ] Network output: [ -0.009759 0.04734 1.001 9.262e-05 -4.158e-05 0.9713 6.98e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09455 0.09271 0.1692 0.1998 0.9857 0.9915 0.09457 0.7301 0.8611 0.2441 ] Network output: [ 0.0009314 0.9994 -0.001682 1.311e-05 -5.884e-06 1.001 9.877e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001074 Epoch 6645 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01382 0.9931 0.9857 5.548e-06 -2.491e-06 -0.006446 4.181e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003184 -0.002939 -0.009963 0.007537 0.9697 0.9741 0.006033 0.8456 0.8332 0.02085 ] Network output: [ 0.9984 0.01364 0.001426 -4.56e-05 2.047e-05 -0.012 -3.437e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02666 -0.2024 0.2015 0.9837 0.9933 0.2028 0.4639 0.8789 0.7236 ] Network output: [ -0.01215 1 1.01 2.439e-06 -1.095e-06 0.01369 1.838e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005218 0.0005672 0.004208 0.004612 0.9889 0.992 0.005313 0.8759 0.9027 0.01513 ] Network output: [ -0.001958 0.01963 1.002 -0.0001649 7.401e-05 0.9816 -0.0001242 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.09571 0.3154 0.1611 0.9851 0.9941 0.1928 0.4688 0.8854 0.7187 ] Network output: [ 0.009503 -0.03937 0.9981 9.349e-05 -4.197e-05 1.023 7.045e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.08629 0.1772 0.2113 0.9874 0.992 0.09691 0.7981 0.8807 0.3108 ] Network output: [ -0.00945 0.04539 1.001 9.285e-05 -4.168e-05 0.9726 6.997e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09445 0.0926 0.1689 0.1995 0.9857 0.9915 0.09447 0.7295 0.861 0.244 ] Network output: [ -0.0002721 0.9992 2.491e-05 1.259e-05 -5.651e-06 1.001 9.486e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001008 Epoch 6646 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01406 0.9896 0.9858 5.94e-06 -2.667e-06 -0.003537 4.476e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003181 -0.00294 -0.009941 0.007597 0.9697 0.9741 0.006028 0.8455 0.8334 0.02087 ] Network output: [ 1 -0.009385 0.002533 -4.332e-05 1.945e-05 0.006675 -3.265e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02699 -0.2009 0.2053 0.9837 0.9933 0.2025 0.4634 0.879 0.7238 ] Network output: [ -0.01213 0.999 1.01 2.603e-06 -1.168e-06 0.01476 1.961e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005212 0.0005695 0.004275 0.004735 0.9889 0.992 0.005307 0.8759 0.9028 0.01516 ] Network output: [ 6.41e-05 -0.01192 1.003 -0.000161 7.229e-05 1.008 -0.0001213 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.09563 0.3176 0.167 0.9851 0.9941 0.1926 0.4684 0.8854 0.7184 ] Network output: [ 0.008948 -0.0456 0.9992 9.376e-05 -4.209e-05 1.029 7.066e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09694 0.0864 0.1785 0.213 0.9874 0.992 0.097 0.7985 0.8807 0.3116 ] Network output: [ -0.009749 0.04732 1.001 9.253e-05 -4.154e-05 0.9713 6.974e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09453 0.09269 0.1692 0.1998 0.9857 0.9915 0.09455 0.73 0.861 0.244 ] Network output: [ 0.0009274 0.9994 -0.001676 1.309e-05 -5.877e-06 1.001 9.865e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001072 Epoch 6647 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01382 0.9931 0.9857 5.539e-06 -2.487e-06 -0.00644 4.174e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003184 -0.002939 -0.009959 0.007535 0.9697 0.9741 0.006034 0.8456 0.8332 0.02085 ] Network output: [ 0.9984 0.01356 0.001429 -4.556e-05 2.046e-05 -0.01194 -3.434e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.02668 -0.2024 0.2015 0.9837 0.9933 0.2028 0.4639 0.8789 0.7235 ] Network output: [ -0.01215 1 1.01 2.438e-06 -1.094e-06 0.01369 1.837e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005219 0.0005666 0.004209 0.00461 0.9889 0.992 0.005313 0.8759 0.9027 0.01512 ] Network output: [ -0.001953 0.01953 1.002 -0.0001647 7.392e-05 0.9817 -0.0001241 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.09569 0.3154 0.161 0.9851 0.9941 0.1928 0.4688 0.8854 0.7186 ] Network output: [ 0.009494 -0.03939 0.9981 9.339e-05 -4.192e-05 1.023 7.038e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.08628 0.1772 0.2113 0.9874 0.992 0.09691 0.798 0.8807 0.3107 ] Network output: [ -0.009443 0.04537 1.001 9.276e-05 -4.164e-05 0.9726 6.991e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09443 0.09258 0.1688 0.1995 0.9857 0.9915 0.09445 0.7294 0.861 0.244 ] Network output: [ -0.0002686 0.9992 2e-05 1.258e-05 -5.646e-06 1.001 9.477e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001007 Epoch 6648 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01405 0.9896 0.9859 5.928e-06 -2.661e-06 -0.00355 4.468e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003181 -0.00294 -0.009937 0.007594 0.9697 0.9741 0.006029 0.8455 0.8333 0.02086 ] Network output: [ 1 -0.009314 0.002528 -4.33e-05 1.944e-05 0.006613 -3.263e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.027 -0.2009 0.2053 0.9837 0.9933 0.2025 0.4633 0.879 0.7238 ] Network output: [ -0.01212 0.999 1.01 2.6e-06 -1.167e-06 0.01475 1.96e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005213 0.0005689 0.004275 0.004733 0.9889 0.992 0.005307 0.8758 0.9028 0.01516 ] Network output: [ 5.645e-05 -0.01182 1.003 -0.0001608 7.221e-05 1.008 -0.0001212 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.09561 0.3176 0.167 0.9851 0.9941 0.1926 0.4684 0.8854 0.7184 ] Network output: [ 0.008943 -0.04557 0.9992 9.365e-05 -4.204e-05 1.029 7.058e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09694 0.08639 0.1785 0.2129 0.9874 0.992 0.097 0.7985 0.8807 0.3116 ] Network output: [ -0.00974 0.04729 1.001 9.245e-05 -4.15e-05 0.9713 6.967e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09451 0.09267 0.1692 0.1998 0.9857 0.9915 0.09453 0.73 0.861 0.244 ] Network output: [ 0.0009235 0.9994 -0.001671 1.307e-05 -5.87e-06 1.001 9.854e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00107 Epoch 6649 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01381 0.9931 0.9857 5.531e-06 -2.483e-06 -0.006434 4.168e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003184 -0.002939 -0.009956 0.007533 0.9697 0.9741 0.006034 0.8456 0.8332 0.02084 ] Network output: [ 0.9984 0.01348 0.001431 -4.552e-05 2.044e-05 -0.01188 -3.431e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.0267 -0.2023 0.2015 0.9837 0.9933 0.2028 0.4639 0.8789 0.7235 ] Network output: [ -0.01215 1 1.01 2.437e-06 -1.094e-06 0.01368 1.836e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00522 0.000566 0.004209 0.004608 0.9889 0.992 0.005314 0.8759 0.9027 0.01512 ] Network output: [ -0.001947 0.01943 1.002 -0.0001645 7.383e-05 0.9818 -0.0001239 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.09567 0.3155 0.161 0.9851 0.9941 0.1928 0.4687 0.8854 0.7186 ] Network output: [ 0.009485 -0.0394 0.9981 9.329e-05 -4.188e-05 1.023 7.03e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.08628 0.1772 0.2112 0.9874 0.992 0.09691 0.798 0.8807 0.3107 ] Network output: [ -0.009437 0.04536 1.001 9.267e-05 -4.16e-05 0.9726 6.984e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09441 0.09256 0.1688 0.1995 0.9857 0.9915 0.09443 0.7293 0.861 0.244 ] Network output: [ -0.0002651 0.9992 1.51e-05 1.256e-05 -5.64e-06 1.001 9.468e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001005 Epoch 6650 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01404 0.9896 0.9859 5.917e-06 -2.656e-06 -0.003563 4.459e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003181 -0.00294 -0.009934 0.007592 0.9697 0.9741 0.006029 0.8455 0.8333 0.02086 ] Network output: [ 1 -0.009244 0.002524 -4.328e-05 1.943e-05 0.006552 -3.262e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1816 -0.02702 -0.2009 0.2052 0.9837 0.9933 0.2026 0.4633 0.879 0.7238 ] Network output: [ -0.01212 0.999 1.01 2.598e-06 -1.166e-06 0.01474 1.958e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005214 0.0005683 0.004275 0.00473 0.9889 0.992 0.005308 0.8758 0.9027 0.01515 ] Network output: [ 4.882e-05 -0.01171 1.003 -0.0001607 7.213e-05 1.008 -0.0001211 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.09558 0.3177 0.1669 0.9851 0.9941 0.1926 0.4683 0.8854 0.7184 ] Network output: [ 0.008939 -0.04554 0.9992 9.355e-05 -4.2e-05 1.029 7.05e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09694 0.08639 0.1785 0.2129 0.9874 0.992 0.097 0.7984 0.8807 0.3116 ] Network output: [ -0.009731 0.04727 1.001 9.236e-05 -4.146e-05 0.9713 6.96e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09449 0.09265 0.1692 0.1998 0.9857 0.9915 0.09451 0.7299 0.861 0.244 ] Network output: [ 0.0009196 0.9994 -0.001665 1.306e-05 -5.863e-06 1.001 9.842e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001069 Epoch 6651 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01381 0.9931 0.9858 5.522e-06 -2.479e-06 -0.006428 4.162e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003184 -0.00294 -0.009952 0.00753 0.9697 0.9741 0.006034 0.8456 0.8332 0.02084 ] Network output: [ 0.9984 0.0134 0.001433 -4.549e-05 2.042e-05 -0.01182 -3.428e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.02672 -0.2023 0.2015 0.9837 0.9933 0.2028 0.4638 0.8788 0.7235 ] Network output: [ -0.01215 1 1.01 2.436e-06 -1.093e-06 0.01368 1.836e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005221 0.0005654 0.00421 0.004607 0.9889 0.992 0.005315 0.8758 0.9027 0.01512 ] Network output: [ -0.001942 0.01933 1.002 -0.0001643 7.374e-05 0.9819 -0.0001238 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.09565 0.3155 0.161 0.9851 0.9941 0.1928 0.4687 0.8854 0.7186 ] Network output: [ 0.009477 -0.03941 0.9981 9.319e-05 -4.184e-05 1.023 7.023e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.08627 0.1772 0.2112 0.9874 0.992 0.09691 0.7979 0.8806 0.3107 ] Network output: [ -0.00943 0.04535 1.001 9.258e-05 -4.156e-05 0.9726 6.977e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0944 0.09254 0.1688 0.1995 0.9857 0.9915 0.09441 0.7293 0.8609 0.244 ] Network output: [ -0.0002616 0.9992 1.02e-05 1.255e-05 -5.635e-06 1.001 9.46e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001004 Epoch 6652 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01404 0.9897 0.9859 5.905e-06 -2.651e-06 -0.003576 4.45e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003181 -0.00294 -0.00993 0.007589 0.9697 0.9741 0.006029 0.8455 0.8333 0.02085 ] Network output: [ 1 -0.009173 0.002519 -4.326e-05 1.942e-05 0.006491 -3.26e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02704 -0.2008 0.2052 0.9837 0.9933 0.2026 0.4633 0.879 0.7238 ] Network output: [ -0.01212 0.999 1.01 2.596e-06 -1.165e-06 0.01473 1.956e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005215 0.0005677 0.004276 0.004728 0.9889 0.992 0.005309 0.8758 0.9027 0.01515 ] Network output: [ 4.12e-05 -0.01161 1.003 -0.0001605 7.206e-05 1.008 -0.000121 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.09556 0.3177 0.1668 0.9851 0.9941 0.1926 0.4683 0.8854 0.7183 ] Network output: [ 0.008935 -0.04551 0.9992 9.345e-05 -4.195e-05 1.029 7.043e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09694 0.08638 0.1785 0.2129 0.9874 0.992 0.097 0.7983 0.8807 0.3116 ] Network output: [ -0.009722 0.04724 1.001 9.227e-05 -4.142e-05 0.9713 6.954e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09447 0.09263 0.1692 0.1998 0.9857 0.9915 0.09449 0.7298 0.8609 0.244 ] Network output: [ 0.0009157 0.9994 -0.00166 1.304e-05 -5.856e-06 1.001 9.83e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001067 Epoch 6653 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0138 0.9931 0.9858 5.514e-06 -2.475e-06 -0.006421 4.155e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003184 -0.00294 -0.009949 0.007528 0.9697 0.9741 0.006035 0.8456 0.8332 0.02083 ] Network output: [ 0.9984 0.01332 0.001435 -4.545e-05 2.04e-05 -0.01176 -3.425e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.02674 -0.2022 0.2015 0.9837 0.9933 0.2028 0.4638 0.8788 0.7235 ] Network output: [ -0.01214 1 1.01 2.435e-06 -1.093e-06 0.01368 1.835e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005221 0.0005647 0.004211 0.004605 0.9889 0.992 0.005316 0.8758 0.9026 0.01511 ] Network output: [ -0.001936 0.01923 1.002 -0.0001641 7.365e-05 0.9819 -0.0001236 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.09563 0.3155 0.161 0.9851 0.9941 0.1928 0.4687 0.8854 0.7186 ] Network output: [ 0.009468 -0.03942 0.9981 9.309e-05 -4.179e-05 1.023 7.015e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.08627 0.1772 0.2112 0.9874 0.992 0.09691 0.7979 0.8806 0.3107 ] Network output: [ -0.009423 0.04533 1.001 9.249e-05 -4.152e-05 0.9726 6.97e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09438 0.09252 0.1688 0.1995 0.9857 0.9915 0.09439 0.7292 0.8609 0.244 ] Network output: [ -0.0002581 0.9992 5.308e-06 1.254e-05 -5.63e-06 1.001 9.451e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001002 Epoch 6654 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01403 0.9897 0.9859 5.894e-06 -2.646e-06 -0.003589 4.442e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003181 -0.002941 -0.009927 0.007586 0.9697 0.9741 0.00603 0.8455 0.8333 0.02085 ] Network output: [ 1 -0.009103 0.002514 -4.324e-05 1.941e-05 0.00643 -3.258e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02705 -0.2008 0.2051 0.9837 0.9933 0.2026 0.4633 0.879 0.7238 ] Network output: [ -0.01212 0.999 1.01 2.594e-06 -1.164e-06 0.01472 1.955e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005216 0.000567 0.004276 0.004725 0.9889 0.992 0.00531 0.8758 0.9027 0.01514 ] Network output: [ 3.359e-05 -0.0115 1.003 -0.0001603 7.198e-05 1.008 -0.0001208 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.09554 0.3177 0.1668 0.9851 0.9941 0.1926 0.4683 0.8854 0.7183 ] Network output: [ 0.008931 -0.04548 0.9992 9.335e-05 -4.191e-05 1.029 7.035e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09694 0.08638 0.1785 0.2128 0.9874 0.992 0.097 0.7983 0.8806 0.3116 ] Network output: [ -0.009713 0.04722 1.001 9.218e-05 -4.138e-05 0.9713 6.947e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09445 0.09261 0.1692 0.1998 0.9857 0.9915 0.09447 0.7297 0.8609 0.244 ] Network output: [ 0.0009117 0.9994 -0.001654 1.303e-05 -5.849e-06 1.001 9.818e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001065 Epoch 6655 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0138 0.9931 0.9858 5.505e-06 -2.471e-06 -0.006415 4.149e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003185 -0.00294 -0.009945 0.007526 0.9697 0.9741 0.006035 0.8455 0.8332 0.02083 ] Network output: [ 0.9984 0.01325 0.001438 -4.541e-05 2.039e-05 -0.0117 -3.422e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.02676 -0.2022 0.2015 0.9837 0.9933 0.2028 0.4638 0.8788 0.7235 ] Network output: [ -0.01214 1 1.01 2.434e-06 -1.093e-06 0.01367 1.834e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005222 0.0005641 0.004211 0.004603 0.9889 0.992 0.005317 0.8758 0.9026 0.01511 ] Network output: [ -0.001931 0.01913 1.002 -0.0001639 7.356e-05 0.982 -0.0001235 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.09561 0.3156 0.1609 0.9851 0.9941 0.1929 0.4686 0.8854 0.7186 ] Network output: [ 0.00946 -0.03943 0.9981 9.299e-05 -4.175e-05 1.023 7.008e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.08626 0.1772 0.2112 0.9874 0.992 0.09691 0.7978 0.8806 0.3107 ] Network output: [ -0.009417 0.04532 1.001 9.239e-05 -4.148e-05 0.9726 6.963e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09436 0.09251 0.1688 0.1994 0.9857 0.9915 0.09437 0.7291 0.8609 0.244 ] Network output: [ -0.0002546 0.9992 4.188e-07 1.253e-05 -5.625e-06 1.001 9.442e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001001 Epoch 6656 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01402 0.9897 0.9859 5.882e-06 -2.641e-06 -0.003602 4.433e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003182 -0.002941 -0.009924 0.007584 0.9697 0.9741 0.00603 0.8455 0.8333 0.02084 ] Network output: [ 1 -0.009032 0.00251 -4.322e-05 1.94e-05 0.006368 -3.257e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02707 -0.2008 0.2051 0.9837 0.9933 0.2026 0.4632 0.8789 0.7238 ] Network output: [ -0.01212 0.999 1.01 2.591e-06 -1.163e-06 0.01471 1.953e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005217 0.0005664 0.004276 0.004722 0.9889 0.992 0.005311 0.8758 0.9027 0.01514 ] Network output: [ 2.6e-05 -0.0114 1.003 -0.0001602 7.19e-05 1.007 -0.0001207 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.192 0.09552 0.3178 0.1667 0.9851 0.9941 0.1926 0.4682 0.8853 0.7183 ] Network output: [ 0.008926 -0.04545 0.9992 9.325e-05 -4.186e-05 1.029 7.027e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09694 0.08637 0.1785 0.2128 0.9874 0.992 0.097 0.7982 0.8806 0.3116 ] Network output: [ -0.009703 0.04719 1.001 9.209e-05 -4.134e-05 0.9713 6.94e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09443 0.09259 0.1691 0.1997 0.9857 0.9915 0.09445 0.7297 0.8609 0.244 ] Network output: [ 0.0009078 0.9994 -0.001648 1.301e-05 -5.842e-06 1.001 9.806e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001063 Epoch 6657 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01379 0.9931 0.9858 5.496e-06 -2.468e-06 -0.006409 4.142e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003185 -0.00294 -0.009942 0.007524 0.9697 0.9741 0.006035 0.8455 0.8332 0.02082 ] Network output: [ 0.9984 0.01317 0.00144 -4.537e-05 2.037e-05 -0.01164 -3.419e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.02678 -0.2022 0.2014 0.9837 0.9933 0.2029 0.4637 0.8788 0.7235 ] Network output: [ -0.01214 1 1.01 2.433e-06 -1.092e-06 0.01367 1.833e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005223 0.0005635 0.004212 0.004602 0.9889 0.992 0.005318 0.8758 0.9026 0.01511 ] Network output: [ -0.001926 0.01902 1.002 -0.0001637 7.347e-05 0.9821 -0.0001233 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.09558 0.3156 0.1609 0.9851 0.9941 0.1929 0.4686 0.8854 0.7186 ] Network output: [ 0.009451 -0.03944 0.998 9.289e-05 -4.17e-05 1.023 7e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08626 0.1772 0.2112 0.9874 0.992 0.0969 0.7978 0.8806 0.3107 ] Network output: [ -0.00941 0.0453 1.001 9.23e-05 -4.144e-05 0.9726 6.956e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09434 0.09249 0.1688 0.1994 0.9857 0.9915 0.09435 0.7291 0.8608 0.244 ] Network output: [ -0.0002511 0.9992 -4.466e-06 1.252e-05 -5.619e-06 1.001 9.433e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009995 Epoch 6658 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01402 0.9897 0.9859 5.87e-06 -2.635e-06 -0.003615 4.424e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003182 -0.002941 -0.00992 0.007581 0.9697 0.9741 0.00603 0.8455 0.8333 0.02084 ] Network output: [ 1 -0.008962 0.002505 -4.319e-05 1.939e-05 0.006307 -3.255e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02709 -0.2007 0.2051 0.9837 0.9933 0.2026 0.4632 0.8789 0.7238 ] Network output: [ -0.01211 0.9991 1.01 2.589e-06 -1.162e-06 0.01469 1.951e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005217 0.0005658 0.004276 0.00472 0.9889 0.992 0.005312 0.8758 0.9027 0.01514 ] Network output: [ 1.843e-05 -0.01129 1.003 -0.00016 7.182e-05 1.007 -0.0001206 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.0955 0.3178 0.1666 0.9851 0.9941 0.1927 0.4682 0.8853 0.7183 ] Network output: [ 0.008922 -0.04542 0.9991 9.314e-05 -4.182e-05 1.029 7.02e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08637 0.1785 0.2128 0.9874 0.992 0.09699 0.7982 0.8806 0.3116 ] Network output: [ -0.009694 0.04716 1.001 9.2e-05 -4.13e-05 0.9713 6.934e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09441 0.09257 0.1691 0.1997 0.9857 0.9915 0.09443 0.7296 0.8608 0.244 ] Network output: [ 0.0009039 0.9994 -0.001643 1.3e-05 -5.834e-06 1.001 9.794e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001061 Epoch 6659 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01379 0.9931 0.9858 5.488e-06 -2.464e-06 -0.006403 4.136e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003185 -0.002941 -0.009938 0.007521 0.9697 0.9741 0.006036 0.8455 0.8332 0.02082 ] Network output: [ 0.9984 0.01309 0.001442 -4.533e-05 2.035e-05 -0.01159 -3.416e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.0268 -0.2021 0.2014 0.9837 0.9933 0.2029 0.4637 0.8788 0.7235 ] Network output: [ -0.01214 1 1.01 2.431e-06 -1.092e-06 0.01366 1.832e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005224 0.0005629 0.004213 0.0046 0.9889 0.992 0.005318 0.8758 0.9026 0.0151 ] Network output: [ -0.00192 0.01892 1.002 -0.0001635 7.338e-05 0.9822 -0.0001232 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.09556 0.3157 0.1609 0.9851 0.9941 0.1929 0.4686 0.8853 0.7186 ] Network output: [ 0.009443 -0.03945 0.998 9.279e-05 -4.166e-05 1.023 6.993e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08626 0.1772 0.2112 0.9874 0.992 0.0969 0.7977 0.8805 0.3107 ] Network output: [ -0.009404 0.04529 1.001 9.221e-05 -4.14e-05 0.9726 6.949e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09432 0.09247 0.1688 0.1994 0.9857 0.9915 0.09433 0.729 0.8608 0.244 ] Network output: [ -0.0002477 0.9992 -9.346e-06 1.251e-05 -5.614e-06 1.001 9.424e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009981 Epoch 6660 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01401 0.9897 0.9859 5.859e-06 -2.63e-06 -0.003627 4.415e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003182 -0.002941 -0.009917 0.007579 0.9697 0.9741 0.006031 0.8454 0.8333 0.02083 ] Network output: [ 1 -0.008892 0.002501 -4.317e-05 1.938e-05 0.006246 -3.254e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.0271 -0.2007 0.205 0.9837 0.9933 0.2026 0.4632 0.8789 0.7237 ] Network output: [ -0.01211 0.9991 1.01 2.587e-06 -1.161e-06 0.01468 1.95e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005218 0.0005652 0.004277 0.004717 0.9889 0.992 0.005313 0.8757 0.9027 0.01513 ] Network output: [ 1.087e-05 -0.01119 1.003 -0.0001598 7.175e-05 1.007 -0.0001204 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.09548 0.3178 0.1666 0.9851 0.9941 0.1927 0.4682 0.8853 0.7183 ] Network output: [ 0.008918 -0.04539 0.9991 9.304e-05 -4.177e-05 1.029 7.012e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08636 0.1785 0.2127 0.9874 0.992 0.09699 0.7981 0.8806 0.3115 ] Network output: [ -0.009685 0.04714 1.001 9.191e-05 -4.126e-05 0.9713 6.927e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0944 0.09255 0.1691 0.1997 0.9857 0.9915 0.09441 0.7295 0.8608 0.244 ] Network output: [ 0.0008999 0.9994 -0.001637 1.298e-05 -5.827e-06 1.001 9.783e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001059 Epoch 6661 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01379 0.9931 0.9858 5.479e-06 -2.46e-06 -0.006397 4.129e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003185 -0.002941 -0.009935 0.007519 0.9697 0.9741 0.006036 0.8455 0.8331 0.02081 ] Network output: [ 0.9984 0.01301 0.001444 -4.529e-05 2.033e-05 -0.01153 -3.413e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.02681 -0.2021 0.2014 0.9837 0.9933 0.2029 0.4637 0.8788 0.7235 ] Network output: [ -0.01214 1 1.01 2.43e-06 -1.091e-06 0.01366 1.832e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005225 0.0005623 0.004213 0.004598 0.9889 0.992 0.005319 0.8758 0.9026 0.0151 ] Network output: [ -0.001915 0.01882 1.002 -0.0001633 7.329e-05 0.9823 -0.000123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.09554 0.3157 0.1609 0.9851 0.9941 0.1929 0.4685 0.8853 0.7186 ] Network output: [ 0.009434 -0.03946 0.998 9.269e-05 -4.161e-05 1.023 6.985e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08625 0.1772 0.2111 0.9874 0.992 0.0969 0.7976 0.8805 0.3107 ] Network output: [ -0.009397 0.04528 1.001 9.212e-05 -4.136e-05 0.9726 6.943e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0943 0.09245 0.1687 0.1994 0.9857 0.9915 0.09431 0.7289 0.8608 0.2439 ] Network output: [ -0.0002442 0.9992 -1.422e-05 1.249e-05 -5.609e-06 1.001 9.415e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009967 Epoch 6662 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.014 0.9898 0.9859 5.847e-06 -2.625e-06 -0.00364 4.407e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003182 -0.002942 -0.009914 0.007576 0.9697 0.9741 0.006031 0.8454 0.8333 0.02083 ] Network output: [ 1 -0.008821 0.002496 -4.315e-05 1.937e-05 0.006185 -3.252e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02712 -0.2007 0.205 0.9837 0.9933 0.2026 0.4631 0.8789 0.7237 ] Network output: [ -0.01211 0.9991 1.01 2.584e-06 -1.16e-06 0.01467 1.948e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005219 0.0005646 0.004277 0.004715 0.9889 0.992 0.005314 0.8757 0.9027 0.01513 ] Network output: [ 3.326e-06 -0.01108 1.003 -0.0001596 7.167e-05 1.007 -0.0001203 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.09546 0.3178 0.1665 0.9851 0.9941 0.1927 0.4681 0.8853 0.7183 ] Network output: [ 0.008914 -0.04536 0.9991 9.294e-05 -4.172e-05 1.029 7.004e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08636 0.1785 0.2127 0.9874 0.992 0.09699 0.7981 0.8805 0.3115 ] Network output: [ -0.009676 0.04711 1.001 9.183e-05 -4.122e-05 0.9713 6.92e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09438 0.09253 0.1691 0.1997 0.9857 0.9915 0.09439 0.7294 0.8608 0.244 ] Network output: [ 0.000896 0.9994 -0.001632 1.296e-05 -5.82e-06 1.001 9.771e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001057 Epoch 6663 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01378 0.993 0.9858 5.471e-06 -2.456e-06 -0.006391 4.123e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003185 -0.002941 -0.009931 0.007517 0.9697 0.9741 0.006036 0.8455 0.8331 0.02081 ] Network output: [ 0.9985 0.01293 0.001446 -4.525e-05 2.032e-05 -0.01147 -3.41e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.02683 -0.2021 0.2014 0.9837 0.9933 0.2029 0.4636 0.8788 0.7235 ] Network output: [ -0.01213 1 1.01 2.429e-06 -1.091e-06 0.01366 1.831e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005225 0.0005618 0.004214 0.004597 0.9889 0.992 0.00532 0.8757 0.9026 0.0151 ] Network output: [ -0.001909 0.01872 1.002 -0.000163 7.32e-05 0.9823 -0.0001229 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.09552 0.3158 0.1608 0.9851 0.9941 0.1929 0.4685 0.8853 0.7185 ] Network output: [ 0.009426 -0.03947 0.998 9.259e-05 -4.157e-05 1.023 6.978e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08625 0.1772 0.2111 0.9874 0.992 0.0969 0.7976 0.8805 0.3107 ] Network output: [ -0.00939 0.04526 1.001 9.203e-05 -4.132e-05 0.9726 6.936e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09428 0.09243 0.1687 0.1994 0.9857 0.9915 0.09429 0.7288 0.8608 0.2439 ] Network output: [ -0.0002407 0.9992 -1.909e-05 1.248e-05 -5.603e-06 1.001 9.407e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009953 Epoch 6664 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.014 0.9898 0.9859 5.836e-06 -2.62e-06 -0.003653 4.398e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003182 -0.002942 -0.00991 0.007573 0.9697 0.9741 0.006032 0.8454 0.8333 0.02082 ] Network output: [ 1 -0.008751 0.002491 -4.313e-05 1.936e-05 0.006124 -3.25e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02714 -0.2007 0.205 0.9837 0.9933 0.2027 0.4631 0.8789 0.7237 ] Network output: [ -0.01211 0.9991 1.01 2.582e-06 -1.159e-06 0.01466 1.946e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00522 0.000564 0.004277 0.004712 0.9889 0.992 0.005315 0.8757 0.9027 0.01513 ] Network output: [ -4.203e-06 -0.01098 1.003 -0.0001595 7.159e-05 1.007 -0.0001202 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.09544 0.3179 0.1664 0.9851 0.9941 0.1927 0.4681 0.8853 0.7183 ] Network output: [ 0.008909 -0.04533 0.9991 9.284e-05 -4.168e-05 1.029 6.997e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08635 0.1785 0.2127 0.9874 0.992 0.09699 0.798 0.8805 0.3115 ] Network output: [ -0.009667 0.04708 1.001 9.174e-05 -4.118e-05 0.9714 6.914e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09436 0.09251 0.1691 0.1997 0.9857 0.9915 0.09437 0.7294 0.8607 0.244 ] Network output: [ 0.0008921 0.9994 -0.001626 1.295e-05 -5.813e-06 1.001 9.759e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001055 Epoch 6665 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01378 0.993 0.9858 5.462e-06 -2.452e-06 -0.006385 4.116e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003185 -0.002942 -0.009928 0.007515 0.9697 0.9741 0.006036 0.8455 0.8331 0.0208 ] Network output: [ 0.9985 0.01285 0.001449 -4.521e-05 2.03e-05 -0.01141 -3.407e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.02685 -0.202 0.2014 0.9837 0.9933 0.2029 0.4636 0.8788 0.7235 ] Network output: [ -0.01213 1 1.01 2.428e-06 -1.09e-06 0.01365 1.83e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005226 0.0005612 0.004215 0.004595 0.9889 0.992 0.005321 0.8757 0.9026 0.01509 ] Network output: [ -0.001904 0.01862 1.002 -0.0001628 7.311e-05 0.9824 -0.0001227 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.0955 0.3158 0.1608 0.9851 0.9941 0.1929 0.4685 0.8853 0.7185 ] Network output: [ 0.009417 -0.03948 0.998 9.249e-05 -4.152e-05 1.023 6.97e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08624 0.1772 0.2111 0.9874 0.992 0.0969 0.7975 0.8805 0.3107 ] Network output: [ -0.009384 0.04525 1.001 9.194e-05 -4.128e-05 0.9726 6.929e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09426 0.09241 0.1687 0.1994 0.9857 0.9915 0.09427 0.7288 0.8607 0.2439 ] Network output: [ -0.0002372 0.9992 -2.396e-05 1.247e-05 -5.598e-06 1.001 9.398e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009939 Epoch 6666 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01399 0.9898 0.9859 5.824e-06 -2.615e-06 -0.003667 4.389e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003182 -0.002942 -0.009907 0.007571 0.9697 0.9741 0.006032 0.8454 0.8332 0.02082 ] Network output: [ 1 -0.008681 0.002487 -4.311e-05 1.935e-05 0.006063 -3.249e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1817 -0.02715 -0.2006 0.2049 0.9837 0.9933 0.2027 0.4631 0.8789 0.7237 ] Network output: [ -0.0121 0.9991 1.01 2.58e-06 -1.158e-06 0.01465 1.944e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005221 0.0005633 0.004278 0.00471 0.9889 0.992 0.005316 0.8757 0.9027 0.01512 ] Network output: [ -1.172e-05 -0.01087 1.003 -0.0001593 7.151e-05 1.007 -0.0001201 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.09542 0.3179 0.1664 0.9851 0.9941 0.1927 0.4681 0.8853 0.7183 ] Network output: [ 0.008905 -0.0453 0.9991 9.273e-05 -4.163e-05 1.029 6.989e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08635 0.1785 0.2127 0.9874 0.992 0.09699 0.7979 0.8805 0.3115 ] Network output: [ -0.009658 0.04706 1.001 9.165e-05 -4.114e-05 0.9714 6.907e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09434 0.09249 0.169 0.1996 0.9857 0.9915 0.09435 0.7293 0.8607 0.244 ] Network output: [ 0.0008881 0.9994 -0.001621 1.293e-05 -5.806e-06 1.001 9.747e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001053 Epoch 6667 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01377 0.993 0.9858 5.454e-06 -2.448e-06 -0.006379 4.11e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003185 -0.002942 -0.009924 0.007512 0.9697 0.9741 0.006037 0.8455 0.8331 0.0208 ] Network output: [ 0.9985 0.01278 0.001451 -4.517e-05 2.028e-05 -0.01135 -3.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.02687 -0.202 0.2014 0.9837 0.9933 0.2029 0.4636 0.8788 0.7234 ] Network output: [ -0.01213 1 1.01 2.427e-06 -1.089e-06 0.01365 1.829e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005227 0.0005606 0.004216 0.004593 0.9889 0.992 0.005322 0.8757 0.9026 0.01509 ] Network output: [ -0.001898 0.01852 1.002 -0.0001626 7.302e-05 0.9825 -0.0001226 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.09548 0.3159 0.1608 0.9851 0.9941 0.1929 0.4684 0.8853 0.7185 ] Network output: [ 0.009408 -0.03949 0.998 9.239e-05 -4.148e-05 1.023 6.962e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08624 0.1772 0.2111 0.9874 0.992 0.0969 0.7975 0.8805 0.3107 ] Network output: [ -0.009377 0.04523 1.001 9.185e-05 -4.123e-05 0.9726 6.922e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09424 0.09239 0.1687 0.1993 0.9857 0.9915 0.09425 0.7287 0.8607 0.2439 ] Network output: [ -0.0002337 0.9992 -2.882e-05 1.246e-05 -5.593e-06 1.001 9.389e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009924 Epoch 6668 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01399 0.9898 0.9859 5.812e-06 -2.609e-06 -0.00368 4.38e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003182 -0.002942 -0.009904 0.007568 0.9697 0.9741 0.006032 0.8454 0.8332 0.02081 ] Network output: [ 1 -0.00861 0.002482 -4.309e-05 1.934e-05 0.006002 -3.247e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02717 -0.2006 0.2049 0.9837 0.9933 0.2027 0.4631 0.8789 0.7237 ] Network output: [ -0.0121 0.9991 1.01 2.577e-06 -1.157e-06 0.01464 1.942e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005222 0.0005627 0.004278 0.004707 0.9889 0.992 0.005316 0.8757 0.9026 0.01512 ] Network output: [ -1.921e-05 -0.01077 1.003 -0.0001591 7.144e-05 1.007 -0.0001199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.0954 0.3179 0.1663 0.9851 0.9941 0.1927 0.4681 0.8853 0.7183 ] Network output: [ 0.008901 -0.04527 0.9991 9.263e-05 -4.159e-05 1.029 6.981e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08634 0.1785 0.2126 0.9874 0.992 0.09699 0.7979 0.8805 0.3115 ] Network output: [ -0.009648 0.04703 1.001 9.156e-05 -4.11e-05 0.9714 6.9e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09432 0.09247 0.169 0.1996 0.9857 0.9915 0.09433 0.7292 0.8607 0.244 ] Network output: [ 0.0008842 0.9994 -0.001615 1.292e-05 -5.799e-06 1.001 9.735e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001051 Epoch 6669 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01377 0.993 0.9858 5.445e-06 -2.444e-06 -0.006373 4.103e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003185 -0.002942 -0.009921 0.00751 0.9697 0.9741 0.006037 0.8455 0.8331 0.02079 ] Network output: [ 0.9985 0.0127 0.001453 -4.514e-05 2.026e-05 -0.01129 -3.402e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.02689 -0.2019 0.2014 0.9837 0.9933 0.2029 0.4635 0.8787 0.7234 ] Network output: [ -0.01213 1 1.01 2.426e-06 -1.089e-06 0.01364 1.828e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005228 0.00056 0.004216 0.004592 0.9889 0.992 0.005323 0.8757 0.9026 0.01508 ] Network output: [ -0.001893 0.01842 1.002 -0.0001624 7.293e-05 0.9826 -0.0001224 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.09546 0.3159 0.1608 0.9851 0.9941 0.1929 0.4684 0.8853 0.7185 ] Network output: [ 0.0094 -0.0395 0.998 9.228e-05 -4.143e-05 1.023 6.955e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08623 0.1773 0.2111 0.9874 0.992 0.0969 0.7974 0.8804 0.3106 ] Network output: [ -0.009371 0.04522 1.001 9.176e-05 -4.119e-05 0.9726 6.915e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09422 0.09237 0.1687 0.1993 0.9857 0.9915 0.09424 0.7286 0.8607 0.2439 ] Network output: [ -0.0002302 0.9992 -3.367e-05 1.245e-05 -5.588e-06 1.001 9.38e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000991 Epoch 6670 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01398 0.9898 0.9859 5.801e-06 -2.604e-06 -0.003693 4.372e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003183 -0.002943 -0.0099 0.007565 0.9697 0.9741 0.006033 0.8454 0.8332 0.02081 ] Network output: [ 1 -0.00854 0.002478 -4.307e-05 1.933e-05 0.005941 -3.246e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02719 -0.2006 0.2049 0.9837 0.9933 0.2027 0.463 0.8789 0.7237 ] Network output: [ -0.0121 0.9991 1.01 2.575e-06 -1.156e-06 0.01463 1.941e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005223 0.0005621 0.004278 0.004705 0.9889 0.992 0.005317 0.8757 0.9026 0.01511 ] Network output: [ -2.67e-05 -0.01067 1.003 -0.000159 7.136e-05 1.007 -0.0001198 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1921 0.09538 0.318 0.1663 0.9851 0.9941 0.1927 0.468 0.8853 0.7183 ] Network output: [ 0.008896 -0.04523 0.9991 9.253e-05 -4.154e-05 1.029 6.973e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08634 0.1785 0.2126 0.9874 0.992 0.09699 0.7978 0.8804 0.3115 ] Network output: [ -0.009639 0.047 1.001 9.147e-05 -4.106e-05 0.9714 6.894e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0943 0.09245 0.169 0.1996 0.9857 0.9915 0.09431 0.7292 0.8606 0.2439 ] Network output: [ 0.0008802 0.9994 -0.001609 1.29e-05 -5.792e-06 1.001 9.724e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00105 Epoch 6671 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01376 0.993 0.9858 5.436e-06 -2.441e-06 -0.006367 4.097e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003185 -0.002942 -0.009917 0.007508 0.9697 0.9741 0.006037 0.8455 0.8331 0.02079 ] Network output: [ 0.9985 0.01262 0.001455 -4.51e-05 2.025e-05 -0.01123 -3.399e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.02691 -0.2019 0.2014 0.9837 0.9933 0.203 0.4635 0.8787 0.7234 ] Network output: [ -0.01212 1 1.01 2.424e-06 -1.088e-06 0.01364 1.827e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005229 0.0005594 0.004217 0.00459 0.9889 0.992 0.005324 0.8757 0.9025 0.01508 ] Network output: [ -0.001887 0.01832 1.002 -0.0001622 7.284e-05 0.9827 -0.0001223 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.09544 0.3159 0.1607 0.9851 0.9941 0.193 0.4684 0.8853 0.7185 ] Network output: [ 0.009391 -0.03951 0.998 9.218e-05 -4.139e-05 1.023 6.947e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08623 0.1773 0.211 0.9874 0.992 0.0969 0.7974 0.8804 0.3106 ] Network output: [ -0.009364 0.0452 1.001 9.167e-05 -4.115e-05 0.9726 6.908e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09421 0.09235 0.1687 0.1993 0.9857 0.9915 0.09422 0.7286 0.8606 0.2439 ] Network output: [ -0.0002267 0.9992 -3.852e-05 1.243e-05 -5.582e-06 1.001 9.371e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009896 Epoch 6672 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01397 0.9898 0.9859 5.789e-06 -2.599e-06 -0.003706 4.363e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003183 -0.002943 -0.009897 0.007563 0.9697 0.9741 0.006033 0.8454 0.8332 0.0208 ] Network output: [ 1 -0.00847 0.002473 -4.304e-05 1.932e-05 0.005881 -3.244e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.0272 -0.2005 0.2048 0.9837 0.9933 0.2027 0.463 0.8788 0.7237 ] Network output: [ -0.0121 0.9991 1.01 2.573e-06 -1.155e-06 0.01462 1.939e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005224 0.0005615 0.004278 0.004702 0.9889 0.992 0.005318 0.8756 0.9026 0.01511 ] Network output: [ -3.416e-05 -0.01056 1.003 -0.0001588 7.128e-05 1.007 -0.0001197 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.09536 0.318 0.1662 0.9851 0.9941 0.1928 0.468 0.8853 0.7182 ] Network output: [ 0.008892 -0.0452 0.999 9.243e-05 -4.149e-05 1.029 6.966e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08633 0.1785 0.2126 0.9874 0.992 0.09699 0.7978 0.8804 0.3114 ] Network output: [ -0.00963 0.04698 1.001 9.138e-05 -4.102e-05 0.9714 6.887e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09428 0.09243 0.169 0.1996 0.9857 0.9915 0.09429 0.7291 0.8606 0.2439 ] Network output: [ 0.0008763 0.9994 -0.001604 1.289e-05 -5.785e-06 1.001 9.712e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001048 Epoch 6673 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01376 0.993 0.9858 5.428e-06 -2.437e-06 -0.006361 4.09e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003185 -0.002943 -0.009914 0.007506 0.9697 0.9741 0.006038 0.8454 0.8331 0.02078 ] Network output: [ 0.9985 0.01254 0.001457 -4.506e-05 2.023e-05 -0.01118 -3.396e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.02693 -0.2019 0.2014 0.9837 0.9933 0.203 0.4635 0.8787 0.7234 ] Network output: [ -0.01212 1 1.01 2.423e-06 -1.088e-06 0.01364 1.826e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00523 0.0005588 0.004218 0.004588 0.9889 0.992 0.005324 0.8757 0.9025 0.01508 ] Network output: [ -0.001882 0.01822 1.002 -0.000162 7.275e-05 0.9827 -0.0001221 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.09542 0.316 0.1607 0.9851 0.9941 0.193 0.4683 0.8853 0.7185 ] Network output: [ 0.009383 -0.03952 0.998 9.208e-05 -4.134e-05 1.023 6.94e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08622 0.1773 0.211 0.9874 0.992 0.0969 0.7973 0.8804 0.3106 ] Network output: [ -0.009357 0.04519 1.001 9.158e-05 -4.111e-05 0.9726 6.901e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09419 0.09233 0.1687 0.1993 0.9857 0.9915 0.0942 0.7285 0.8606 0.2439 ] Network output: [ -0.0002232 0.9992 -4.337e-05 1.242e-05 -5.577e-06 1.001 9.362e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009882 Epoch 6674 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01397 0.9899 0.9859 5.777e-06 -2.594e-06 -0.003719 4.354e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003183 -0.002943 -0.009894 0.00756 0.9697 0.9741 0.006033 0.8454 0.8332 0.0208 ] Network output: [ 1 -0.0084 0.002468 -4.302e-05 1.931e-05 0.00582 -3.242e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02722 -0.2005 0.2048 0.9837 0.9933 0.2027 0.463 0.8788 0.7237 ] Network output: [ -0.0121 0.9991 1.01 2.57e-06 -1.154e-06 0.01461 1.937e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005224 0.0005609 0.004279 0.0047 0.9889 0.992 0.005319 0.8756 0.9026 0.01511 ] Network output: [ -4.162e-05 -0.01046 1.003 -0.0001586 7.12e-05 1.007 -0.0001195 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.09534 0.318 0.1661 0.9851 0.9941 0.1928 0.468 0.8852 0.7182 ] Network output: [ 0.008888 -0.04517 0.999 9.233e-05 -4.145e-05 1.029 6.958e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08633 0.1785 0.2125 0.9874 0.992 0.09698 0.7977 0.8804 0.3114 ] Network output: [ -0.009621 0.04695 1.001 9.129e-05 -4.098e-05 0.9714 6.88e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09426 0.09241 0.169 0.1996 0.9857 0.9915 0.09427 0.729 0.8606 0.2439 ] Network output: [ 0.0008724 0.9994 -0.001598 1.287e-05 -5.778e-06 1.001 9.7e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001046 Epoch 6675 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01375 0.993 0.9859 5.419e-06 -2.433e-06 -0.006355 4.084e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003186 -0.002943 -0.009911 0.007503 0.9697 0.9741 0.006038 0.8454 0.8331 0.02078 ] Network output: [ 0.9985 0.01247 0.00146 -4.502e-05 2.021e-05 -0.01112 -3.393e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.02694 -0.2018 0.2013 0.9837 0.9933 0.203 0.4634 0.8787 0.7234 ] Network output: [ -0.01212 1 1.01 2.422e-06 -1.087e-06 0.01363 1.825e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005231 0.0005582 0.004218 0.004587 0.9889 0.992 0.005325 0.8756 0.9025 0.01507 ] Network output: [ -0.001876 0.01812 1.002 -0.0001618 7.266e-05 0.9828 -0.000122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.0954 0.316 0.1607 0.9851 0.9941 0.193 0.4683 0.8853 0.7185 ] Network output: [ 0.009374 -0.03953 0.998 9.198e-05 -4.129e-05 1.023 6.932e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08622 0.1773 0.211 0.9874 0.992 0.0969 0.7973 0.8804 0.3106 ] Network output: [ -0.009351 0.04517 1.001 9.148e-05 -4.107e-05 0.9726 6.895e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09417 0.09231 0.1686 0.1993 0.9857 0.9915 0.09418 0.7284 0.8606 0.2439 ] Network output: [ -0.0002198 0.9992 -4.821e-05 1.241e-05 -5.572e-06 1.001 9.354e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009868 Epoch 6676 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01396 0.9899 0.986 5.766e-06 -2.588e-06 -0.003732 4.345e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003183 -0.002944 -0.00989 0.007557 0.9697 0.9741 0.006034 0.8454 0.8332 0.02079 ] Network output: [ 1 -0.00833 0.002464 -4.3e-05 1.93e-05 0.005759 -3.241e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02724 -0.2005 0.2047 0.9837 0.9933 0.2028 0.4629 0.8788 0.7237 ] Network output: [ -0.01209 0.9991 1.01 2.568e-06 -1.153e-06 0.01459 1.935e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005225 0.0005603 0.004279 0.004697 0.9889 0.992 0.00532 0.8756 0.9026 0.0151 ] Network output: [ -4.905e-05 -0.01035 1.003 -0.0001584 7.113e-05 1.007 -0.0001194 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.09532 0.3181 0.1661 0.9851 0.9941 0.1928 0.4679 0.8852 0.7182 ] Network output: [ 0.008884 -0.04514 0.999 9.222e-05 -4.14e-05 1.029 6.95e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08632 0.1785 0.2125 0.9874 0.992 0.09698 0.7976 0.8804 0.3114 ] Network output: [ -0.009612 0.04692 1.001 9.12e-05 -4.094e-05 0.9714 6.873e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09424 0.09239 0.169 0.1995 0.9857 0.9915 0.09425 0.7289 0.8605 0.2439 ] Network output: [ 0.0008684 0.9994 -0.001593 1.286e-05 -5.771e-06 1.001 9.688e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001044 Epoch 6677 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01375 0.993 0.9859 5.41e-06 -2.429e-06 -0.006349 4.077e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003186 -0.002943 -0.009907 0.007501 0.9697 0.9741 0.006038 0.8454 0.8331 0.02077 ] Network output: [ 0.9985 0.01239 0.001462 -4.498e-05 2.019e-05 -0.01106 -3.39e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.02696 -0.2018 0.2013 0.9837 0.9933 0.203 0.4634 0.8787 0.7234 ] Network output: [ -0.01212 1 1.01 2.42e-06 -1.087e-06 0.01363 1.824e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005231 0.0005576 0.004219 0.004585 0.9889 0.992 0.005326 0.8756 0.9025 0.01507 ] Network output: [ -0.001871 0.01802 1.002 -0.0001616 7.257e-05 0.9829 -0.0001218 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.09538 0.3161 0.1607 0.9851 0.9941 0.193 0.4683 0.8852 0.7185 ] Network output: [ 0.009366 -0.03954 0.998 9.188e-05 -4.125e-05 1.023 6.925e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08622 0.1773 0.211 0.9874 0.992 0.0969 0.7972 0.8803 0.3106 ] Network output: [ -0.009344 0.04516 1.001 9.139e-05 -4.103e-05 0.9726 6.888e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09415 0.09229 0.1686 0.1993 0.9857 0.9915 0.09416 0.7284 0.8605 0.2439 ] Network output: [ -0.0002163 0.9992 -5.304e-05 1.24e-05 -5.567e-06 1.001 9.345e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009854 Epoch 6678 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01395 0.9899 0.986 5.754e-06 -2.583e-06 -0.003745 4.336e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003183 -0.002944 -0.009887 0.007555 0.9697 0.9741 0.006034 0.8453 0.8332 0.02079 ] Network output: [ 1 -0.00826 0.002459 -4.298e-05 1.929e-05 0.005699 -3.239e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02725 -0.2004 0.2047 0.9837 0.9933 0.2028 0.4629 0.8788 0.7236 ] Network output: [ -0.01209 0.9991 1.01 2.565e-06 -1.152e-06 0.01458 1.933e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005226 0.0005597 0.004279 0.004695 0.9889 0.992 0.005321 0.8756 0.9026 0.0151 ] Network output: [ -5.647e-05 -0.01025 1.003 -0.0001583 7.105e-05 1.006 -0.0001193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.0953 0.3181 0.166 0.9851 0.9941 0.1928 0.4679 0.8852 0.7182 ] Network output: [ 0.008879 -0.04511 0.999 9.212e-05 -4.136e-05 1.029 6.943e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08632 0.1785 0.2125 0.9874 0.992 0.09698 0.7976 0.8803 0.3114 ] Network output: [ -0.009603 0.0469 1.001 9.111e-05 -4.09e-05 0.9714 6.867e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09422 0.09237 0.1689 0.1995 0.9857 0.9915 0.09423 0.7289 0.8605 0.2439 ] Network output: [ 0.0008645 0.9994 -0.001587 1.284e-05 -5.764e-06 1.001 9.677e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001042 Epoch 6679 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01374 0.993 0.9859 5.402e-06 -2.425e-06 -0.006343 4.071e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003186 -0.002943 -0.009904 0.007499 0.9697 0.9741 0.006039 0.8454 0.833 0.02077 ] Network output: [ 0.9985 0.01231 0.001464 -4.494e-05 2.017e-05 -0.011 -3.387e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.02698 -0.2018 0.2013 0.9837 0.9933 0.203 0.4634 0.8787 0.7234 ] Network output: [ -0.01212 1 1.01 2.419e-06 -1.086e-06 0.01362 1.823e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005232 0.0005571 0.00422 0.004583 0.9889 0.992 0.005327 0.8756 0.9025 0.01507 ] Network output: [ -0.001865 0.01791 1.002 -0.0001614 7.248e-05 0.983 -0.0001217 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.09536 0.3161 0.1606 0.9851 0.9941 0.193 0.4682 0.8852 0.7185 ] Network output: [ 0.009357 -0.03955 0.998 9.178e-05 -4.12e-05 1.023 6.917e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08621 0.1773 0.211 0.9874 0.992 0.0969 0.7971 0.8803 0.3106 ] Network output: [ -0.009337 0.04514 1.001 9.13e-05 -4.099e-05 0.9726 6.881e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09413 0.09227 0.1686 0.1992 0.9857 0.9915 0.09414 0.7283 0.8605 0.2439 ] Network output: [ -0.0002128 0.9992 -5.787e-05 1.239e-05 -5.561e-06 1.001 9.336e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000984 Epoch 6680 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01395 0.9899 0.986 5.742e-06 -2.578e-06 -0.003758 4.328e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003183 -0.002944 -0.009884 0.007552 0.9697 0.9741 0.006034 0.8453 0.8332 0.02078 ] Network output: [ 1 -0.008189 0.002455 -4.296e-05 1.928e-05 0.005638 -3.237e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1818 -0.02727 -0.2004 0.2047 0.9837 0.9933 0.2028 0.4629 0.8788 0.7236 ] Network output: [ -0.01209 0.9991 1.01 2.563e-06 -1.15e-06 0.01457 1.931e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005227 0.0005591 0.004279 0.004692 0.9889 0.992 0.005322 0.8756 0.9026 0.0151 ] Network output: [ -6.387e-05 -0.01015 1.003 -0.0001581 7.097e-05 1.006 -0.0001191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.09528 0.3181 0.1659 0.9851 0.9941 0.1928 0.4679 0.8852 0.7182 ] Network output: [ 0.008875 -0.04508 0.999 9.202e-05 -4.131e-05 1.029 6.935e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08631 0.1785 0.2124 0.9874 0.992 0.09698 0.7975 0.8803 0.3114 ] Network output: [ -0.009594 0.04687 1.001 9.103e-05 -4.086e-05 0.9714 6.86e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0942 0.09235 0.1689 0.1995 0.9857 0.9915 0.09421 0.7288 0.8605 0.2439 ] Network output: [ 0.0008605 0.9994 -0.001581 1.282e-05 -5.757e-06 1.001 9.665e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00104 Epoch 6681 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01374 0.993 0.9859 5.393e-06 -2.421e-06 -0.006337 4.064e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003186 -0.002944 -0.0099 0.007497 0.9697 0.9741 0.006039 0.8454 0.833 0.02076 ] Network output: [ 0.9985 0.01223 0.001466 -4.49e-05 2.016e-05 -0.01094 -3.384e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.027 -0.2017 0.2013 0.9837 0.9933 0.203 0.4634 0.8787 0.7234 ] Network output: [ -0.01211 1 1.01 2.418e-06 -1.085e-06 0.01362 1.822e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005233 0.0005565 0.004221 0.004582 0.9889 0.992 0.005328 0.8756 0.9025 0.01506 ] Network output: [ -0.00186 0.01781 1.002 -0.0001612 7.239e-05 0.9831 -0.0001215 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.09534 0.3162 0.1606 0.9851 0.9941 0.193 0.4682 0.8852 0.7184 ] Network output: [ 0.009349 -0.03956 0.998 9.168e-05 -4.116e-05 1.023 6.909e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08621 0.1773 0.2109 0.9874 0.992 0.0969 0.7971 0.8803 0.3106 ] Network output: [ -0.009331 0.04513 1.001 9.121e-05 -4.095e-05 0.9726 6.874e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09411 0.09226 0.1686 0.1992 0.9857 0.9915 0.09412 0.7282 0.8605 0.2439 ] Network output: [ -0.0002093 0.9992 -6.27e-05 1.238e-05 -5.556e-06 1.001 9.327e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009827 Epoch 6682 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01394 0.9899 0.986 5.731e-06 -2.573e-06 -0.003771 4.319e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003183 -0.002944 -0.00988 0.007549 0.9697 0.9741 0.006035 0.8453 0.8331 0.02078 ] Network output: [ 1 -0.00812 0.00245 -4.293e-05 1.927e-05 0.005577 -3.236e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.02729 -0.2004 0.2046 0.9837 0.9933 0.2028 0.4629 0.8788 0.7236 ] Network output: [ -0.01209 0.9991 1.01 2.56e-06 -1.149e-06 0.01456 1.929e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005228 0.0005585 0.00428 0.00469 0.9889 0.992 0.005323 0.8756 0.9026 0.01509 ] Network output: [ -7.126e-05 -0.01004 1.003 -0.0001579 7.089e-05 1.006 -0.000119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.09526 0.3181 0.1659 0.9851 0.9941 0.1928 0.4678 0.8852 0.7182 ] Network output: [ 0.008871 -0.04505 0.999 9.192e-05 -4.126e-05 1.029 6.927e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08631 0.1785 0.2124 0.9874 0.992 0.09698 0.7975 0.8803 0.3114 ] Network output: [ -0.009585 0.04684 1.001 9.094e-05 -4.082e-05 0.9715 6.853e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09418 0.09233 0.1689 0.1995 0.9857 0.9915 0.09419 0.7287 0.8604 0.2439 ] Network output: [ 0.0008566 0.9994 -0.001576 1.281e-05 -5.75e-06 1.001 9.653e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001038 Epoch 6683 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01373 0.993 0.9859 5.384e-06 -2.417e-06 -0.006331 4.058e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003186 -0.002944 -0.009897 0.007495 0.9697 0.9741 0.006039 0.8454 0.833 0.02076 ] Network output: [ 0.9985 0.01215 0.001468 -4.486e-05 2.014e-05 -0.01088 -3.381e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.02702 -0.2017 0.2013 0.9837 0.9933 0.203 0.4633 0.8787 0.7234 ] Network output: [ -0.01211 1 1.01 2.416e-06 -1.085e-06 0.01362 1.821e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005234 0.0005559 0.004221 0.00458 0.9889 0.992 0.005329 0.8756 0.9025 0.01506 ] Network output: [ -0.001854 0.01771 1.002 -0.000161 7.23e-05 0.9831 -0.0001214 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.09532 0.3162 0.1606 0.9851 0.9941 0.193 0.4682 0.8852 0.7184 ] Network output: [ 0.00934 -0.03957 0.998 9.158e-05 -4.111e-05 1.023 6.902e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.0862 0.1773 0.2109 0.9874 0.992 0.0969 0.797 0.8803 0.3106 ] Network output: [ -0.009324 0.04511 1.001 9.112e-05 -4.091e-05 0.9726 6.867e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09409 0.09224 0.1686 0.1992 0.9857 0.9915 0.09411 0.7282 0.8604 0.2439 ] Network output: [ -0.0002058 0.9992 -6.751e-05 1.236e-05 -5.551e-06 1.001 9.318e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009813 Epoch 6684 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01394 0.99 0.986 5.719e-06 -2.568e-06 -0.003784 4.31e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003183 -0.002945 -0.009877 0.007547 0.9697 0.9741 0.006035 0.8453 0.8331 0.02077 ] Network output: [ 1 -0.00805 0.002445 -4.291e-05 1.926e-05 0.005517 -3.234e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.0273 -0.2004 0.2046 0.9837 0.9933 0.2028 0.4628 0.8788 0.7236 ] Network output: [ -0.01209 0.9992 1.01 2.557e-06 -1.148e-06 0.01455 1.927e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005229 0.000558 0.00428 0.004687 0.9889 0.992 0.005324 0.8755 0.9025 0.01509 ] Network output: [ -7.863e-05 -0.00994 1.003 -0.0001577 7.082e-05 1.006 -0.0001189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1922 0.09524 0.3182 0.1658 0.9851 0.9941 0.1928 0.4678 0.8852 0.7182 ] Network output: [ 0.008866 -0.04501 0.999 9.181e-05 -4.122e-05 1.029 6.919e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.0863 0.1785 0.2124 0.9874 0.992 0.09698 0.7974 0.8803 0.3113 ] Network output: [ -0.009575 0.04681 1.001 9.085e-05 -4.078e-05 0.9715 6.847e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09416 0.09231 0.1689 0.1995 0.9857 0.9915 0.09417 0.7286 0.8604 0.2439 ] Network output: [ 0.0008527 0.9994 -0.00157 1.279e-05 -5.743e-06 1.001 9.641e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001037 Epoch 6685 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01373 0.993 0.9859 5.375e-06 -2.413e-06 -0.006325 4.051e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003186 -0.002944 -0.009893 0.007492 0.9697 0.9741 0.00604 0.8454 0.833 0.02075 ] Network output: [ 0.9985 0.01208 0.00147 -4.482e-05 2.012e-05 -0.01082 -3.378e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.02704 -0.2016 0.2013 0.9837 0.9933 0.2031 0.4633 0.8787 0.7234 ] Network output: [ -0.01211 1 1.01 2.415e-06 -1.084e-06 0.01361 1.82e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005235 0.0005553 0.004222 0.004578 0.9889 0.992 0.00533 0.8756 0.9025 0.01506 ] Network output: [ -0.001848 0.01761 1.002 -0.0001608 7.221e-05 0.9832 -0.0001212 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.0953 0.3163 0.1606 0.9851 0.9941 0.1931 0.4681 0.8852 0.7184 ] Network output: [ 0.009332 -0.03958 0.998 9.148e-05 -4.107e-05 1.023 6.894e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.0862 0.1773 0.2109 0.9874 0.992 0.0969 0.797 0.8802 0.3106 ] Network output: [ -0.009317 0.04509 1.001 9.103e-05 -4.087e-05 0.9726 6.86e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09407 0.09222 0.1686 0.1992 0.9857 0.9915 0.09409 0.7281 0.8604 0.2439 ] Network output: [ -0.0002023 0.9992 -7.232e-05 1.235e-05 -5.546e-06 1.001 9.309e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009799 Epoch 6686 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01393 0.99 0.986 5.707e-06 -2.562e-06 -0.003797 4.301e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003184 -0.002945 -0.009874 0.007544 0.9697 0.9741 0.006035 0.8453 0.8331 0.02077 ] Network output: [ 1 -0.00798 0.002441 -4.289e-05 1.925e-05 0.005457 -3.232e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.02732 -0.2003 0.2046 0.9837 0.9933 0.2028 0.4628 0.8788 0.7236 ] Network output: [ -0.01208 0.9992 1.01 2.555e-06 -1.147e-06 0.01454 1.925e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00523 0.0005574 0.00428 0.004685 0.9889 0.992 0.005325 0.8755 0.9025 0.01508 ] Network output: [ -8.598e-05 -0.009836 1.003 -0.0001576 7.074e-05 1.006 -0.0001187 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.09523 0.3182 0.1657 0.9851 0.9941 0.1929 0.4678 0.8852 0.7182 ] Network output: [ 0.008862 -0.04498 0.9989 9.171e-05 -4.117e-05 1.029 6.912e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.0863 0.1785 0.2124 0.9874 0.992 0.09698 0.7973 0.8803 0.3113 ] Network output: [ -0.009566 0.04679 1.001 9.076e-05 -4.074e-05 0.9715 6.84e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09414 0.09229 0.1689 0.1995 0.9857 0.9915 0.09416 0.7286 0.8604 0.2439 ] Network output: [ 0.0008487 0.9994 -0.001564 1.278e-05 -5.736e-06 1.001 9.63e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001035 Epoch 6687 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01372 0.993 0.9859 5.367e-06 -2.409e-06 -0.00632 4.045e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003186 -0.002945 -0.00989 0.00749 0.9697 0.9741 0.00604 0.8454 0.833 0.02075 ] Network output: [ 0.9986 0.012 0.001473 -4.478e-05 2.01e-05 -0.01077 -3.375e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.02705 -0.2016 0.2013 0.9837 0.9933 0.2031 0.4633 0.8786 0.7234 ] Network output: [ -0.01211 1 1.01 2.413e-06 -1.084e-06 0.01361 1.819e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005236 0.0005548 0.004223 0.004577 0.9889 0.992 0.00533 0.8755 0.9025 0.01505 ] Network output: [ -0.001843 0.01751 1.002 -0.0001606 7.212e-05 0.9833 -0.0001211 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.09528 0.3163 0.1605 0.9851 0.9941 0.1931 0.4681 0.8852 0.7184 ] Network output: [ 0.009323 -0.03959 0.998 9.138e-05 -4.102e-05 1.023 6.887e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.0862 0.1773 0.2109 0.9874 0.992 0.0969 0.7969 0.8802 0.3106 ] Network output: [ -0.009311 0.04508 1.001 9.094e-05 -4.082e-05 0.9726 6.853e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09406 0.0922 0.1686 0.1992 0.9857 0.9915 0.09407 0.728 0.8604 0.2438 ] Network output: [ -0.0001989 0.9992 -7.713e-05 1.234e-05 -5.54e-06 1.001 9.301e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009785 Epoch 6688 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01392 0.99 0.986 5.696e-06 -2.557e-06 -0.00381 4.293e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003184 -0.002945 -0.00987 0.007542 0.9697 0.9741 0.006036 0.8453 0.8331 0.02076 ] Network output: [ 1 -0.00791 0.002436 -4.287e-05 1.924e-05 0.005396 -3.231e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.02734 -0.2003 0.2045 0.9837 0.9933 0.2029 0.4628 0.8788 0.7236 ] Network output: [ -0.01208 0.9992 1.01 2.552e-06 -1.146e-06 0.01453 1.923e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005231 0.0005568 0.00428 0.004682 0.9889 0.992 0.005325 0.8755 0.9025 0.01508 ] Network output: [ -9.332e-05 -0.009733 1.003 -0.0001574 7.066e-05 1.006 -0.0001186 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.09521 0.3182 0.1657 0.9851 0.9941 0.1929 0.4677 0.8852 0.7182 ] Network output: [ 0.008858 -0.04495 0.9989 9.161e-05 -4.113e-05 1.029 6.904e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08629 0.1785 0.2123 0.9874 0.992 0.09698 0.7973 0.8802 0.3113 ] Network output: [ -0.009557 0.04676 1.001 9.067e-05 -4.07e-05 0.9715 6.833e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09412 0.09227 0.1689 0.1994 0.9857 0.9915 0.09414 0.7285 0.8604 0.2439 ] Network output: [ 0.0008448 0.9994 -0.001559 1.276e-05 -5.729e-06 1.001 9.618e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001033 Epoch 6689 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01372 0.993 0.9859 5.358e-06 -2.405e-06 -0.006314 4.038e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003186 -0.002945 -0.009886 0.007488 0.9697 0.9741 0.00604 0.8453 0.833 0.02074 ] Network output: [ 0.9986 0.01192 0.001475 -4.474e-05 2.009e-05 -0.01071 -3.372e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.02707 -0.2016 0.2013 0.9837 0.9933 0.2031 0.4632 0.8786 0.7233 ] Network output: [ -0.0121 1 1.01 2.412e-06 -1.083e-06 0.0136 1.818e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005236 0.0005542 0.004223 0.004575 0.9889 0.992 0.005331 0.8755 0.9024 0.01505 ] Network output: [ -0.001837 0.01741 1.002 -0.0001604 7.203e-05 0.9834 -0.0001209 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.09526 0.3163 0.1605 0.9851 0.9941 0.1931 0.4681 0.8852 0.7184 ] Network output: [ 0.009315 -0.03959 0.9979 9.128e-05 -4.098e-05 1.023 6.879e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08619 0.1773 0.2109 0.9874 0.992 0.0969 0.7969 0.8802 0.3105 ] Network output: [ -0.009304 0.04506 1.001 9.084e-05 -4.078e-05 0.9726 6.846e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09404 0.09218 0.1686 0.1992 0.9857 0.9915 0.09405 0.728 0.8603 0.2438 ] Network output: [ -0.0001954 0.9992 -8.193e-05 1.233e-05 -5.535e-06 1.001 9.292e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009771 Epoch 6690 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01392 0.99 0.986 5.684e-06 -2.552e-06 -0.003824 4.284e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003184 -0.002945 -0.009867 0.007539 0.9697 0.9741 0.006036 0.8453 0.8331 0.02076 ] Network output: [ 0.9999 -0.00784 0.002431 -4.284e-05 1.923e-05 0.005336 -3.229e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.02735 -0.2003 0.2045 0.9837 0.9933 0.2029 0.4627 0.8787 0.7236 ] Network output: [ -0.01208 0.9992 1.01 2.55e-06 -1.145e-06 0.01451 1.921e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005232 0.0005562 0.004281 0.00468 0.9889 0.992 0.005326 0.8755 0.9025 0.01508 ] Network output: [ -0.0001006 -0.00963 1.003 -0.0001572 7.058e-05 1.006 -0.0001185 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.09519 0.3183 0.1656 0.9851 0.9941 0.1929 0.4677 0.8852 0.7182 ] Network output: [ 0.008853 -0.04492 0.9989 9.151e-05 -4.108e-05 1.029 6.896e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08629 0.1785 0.2123 0.9874 0.992 0.09698 0.7972 0.8802 0.3113 ] Network output: [ -0.009548 0.04673 1.001 9.058e-05 -4.066e-05 0.9715 6.826e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09411 0.09225 0.1688 0.1994 0.9857 0.9915 0.09412 0.7284 0.8603 0.2439 ] Network output: [ 0.0008409 0.9994 -0.001553 1.275e-05 -5.722e-06 1.001 9.606e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001031 Epoch 6691 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01371 0.993 0.9859 5.349e-06 -2.401e-06 -0.006308 4.031e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003187 -0.002945 -0.009883 0.007486 0.9697 0.9741 0.006041 0.8453 0.833 0.02074 ] Network output: [ 0.9986 0.01184 0.001477 -4.47e-05 2.007e-05 -0.01065 -3.369e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.02709 -0.2015 0.2013 0.9837 0.9933 0.2031 0.4632 0.8786 0.7233 ] Network output: [ -0.0121 1 1.01 2.411e-06 -1.082e-06 0.0136 1.817e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005237 0.0005536 0.004224 0.004573 0.9889 0.992 0.005332 0.8755 0.9024 0.01505 ] Network output: [ -0.001832 0.01731 1.002 -0.0001602 7.194e-05 0.9835 -0.0001208 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.09524 0.3164 0.1605 0.9851 0.9941 0.1931 0.468 0.8852 0.7184 ] Network output: [ 0.009307 -0.0396 0.9979 9.118e-05 -4.093e-05 1.023 6.872e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08619 0.1773 0.2108 0.9874 0.992 0.0969 0.7968 0.8802 0.3105 ] Network output: [ -0.009297 0.04505 1.001 9.075e-05 -4.074e-05 0.9726 6.839e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09402 0.09216 0.1685 0.1992 0.9857 0.9915 0.09403 0.7279 0.8603 0.2438 ] Network output: [ -0.0001919 0.9992 -8.672e-05 1.232e-05 -5.53e-06 1.001 9.283e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009758 Epoch 6692 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01391 0.99 0.986 5.672e-06 -2.547e-06 -0.003837 4.275e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003184 -0.002946 -0.009864 0.007536 0.9697 0.9741 0.006037 0.8453 0.8331 0.02075 ] Network output: [ 0.9999 -0.00777 0.002427 -4.282e-05 1.922e-05 0.005276 -3.227e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.02737 -0.2002 0.2045 0.9837 0.9933 0.2029 0.4627 0.8787 0.7236 ] Network output: [ -0.01208 0.9992 1.01 2.547e-06 -1.143e-06 0.0145 1.919e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005232 0.0005556 0.004281 0.004677 0.9889 0.992 0.005327 0.8755 0.9025 0.01507 ] Network output: [ -0.0001079 -0.009527 1.003 -0.000157 7.05e-05 1.006 -0.0001184 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.09517 0.3183 0.1656 0.9851 0.9941 0.1929 0.4677 0.8851 0.7181 ] Network output: [ 0.008849 -0.04488 0.9989 9.14e-05 -4.103e-05 1.029 6.888e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08628 0.1785 0.2123 0.9874 0.992 0.09698 0.7972 0.8802 0.3113 ] Network output: [ -0.009539 0.0467 1.001 9.049e-05 -4.062e-05 0.9715 6.82e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09409 0.09223 0.1688 0.1994 0.9857 0.9915 0.0941 0.7284 0.8603 0.2439 ] Network output: [ 0.0008369 0.9994 -0.001547 1.273e-05 -5.716e-06 1.001 9.595e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001029 Epoch 6693 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01371 0.993 0.9859 5.34e-06 -2.398e-06 -0.006303 4.025e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003187 -0.002945 -0.009879 0.007483 0.9697 0.9741 0.006041 0.8453 0.833 0.02074 ] Network output: [ 0.9986 0.01177 0.001479 -4.466e-05 2.005e-05 -0.01059 -3.366e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.02711 -0.2015 0.2012 0.9837 0.9933 0.2031 0.4632 0.8786 0.7233 ] Network output: [ -0.0121 1 1.01 2.409e-06 -1.081e-06 0.0136 1.816e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005238 0.0005531 0.004225 0.004572 0.9889 0.992 0.005333 0.8755 0.9024 0.01504 ] Network output: [ -0.001826 0.01721 1.002 -0.00016 7.185e-05 0.9835 -0.0001206 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.09522 0.3164 0.1605 0.9851 0.9941 0.1931 0.468 0.8852 0.7184 ] Network output: [ 0.009298 -0.03961 0.9979 9.108e-05 -4.089e-05 1.023 6.864e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08619 0.1773 0.2108 0.9874 0.992 0.0969 0.7967 0.8801 0.3105 ] Network output: [ -0.009291 0.04503 1.001 9.066e-05 -4.07e-05 0.9727 6.832e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.094 0.09214 0.1685 0.1991 0.9857 0.9915 0.09401 0.7278 0.8603 0.2438 ] Network output: [ -0.0001884 0.9992 -9.15e-05 1.231e-05 -5.524e-06 1.001 9.274e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009744 Epoch 6694 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0139 0.99 0.986 5.661e-06 -2.541e-06 -0.00385 4.266e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003184 -0.002946 -0.00986 0.007534 0.9697 0.9741 0.006037 0.8452 0.8331 0.02075 ] Network output: [ 0.9999 -0.007701 0.002422 -4.28e-05 1.921e-05 0.005216 -3.225e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1819 -0.02738 -0.2002 0.2044 0.9837 0.9933 0.2029 0.4627 0.8787 0.7236 ] Network output: [ -0.01207 0.9992 1.01 2.544e-06 -1.142e-06 0.01449 1.917e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005233 0.000555 0.004281 0.004675 0.9889 0.992 0.005328 0.8755 0.9025 0.01507 ] Network output: [ -0.0001152 -0.009424 1.003 -0.0001569 7.043e-05 1.006 -0.0001182 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.09515 0.3183 0.1655 0.9851 0.9941 0.1929 0.4677 0.8851 0.7181 ] Network output: [ 0.008844 -0.04485 0.9989 9.13e-05 -4.099e-05 1.029 6.881e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08628 0.1785 0.2122 0.9874 0.992 0.09698 0.7971 0.8802 0.3113 ] Network output: [ -0.00953 0.04667 1.001 9.04e-05 -4.058e-05 0.9715 6.813e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09407 0.09221 0.1688 0.1994 0.9857 0.9915 0.09408 0.7283 0.8603 0.2438 ] Network output: [ 0.000833 0.9994 -0.001542 1.272e-05 -5.709e-06 1.001 9.583e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001027 Epoch 6695 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0137 0.993 0.9859 5.332e-06 -2.394e-06 -0.006297 4.018e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003187 -0.002946 -0.009876 0.007481 0.9697 0.9741 0.006041 0.8453 0.833 0.02073 ] Network output: [ 0.9986 0.01169 0.001481 -4.462e-05 2.003e-05 -0.01053 -3.363e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.02713 -0.2014 0.2012 0.9837 0.9933 0.2031 0.4631 0.8786 0.7233 ] Network output: [ -0.0121 1 1.01 2.407e-06 -1.081e-06 0.01359 1.814e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005239 0.0005525 0.004225 0.00457 0.9889 0.992 0.005334 0.8755 0.9024 0.01504 ] Network output: [ -0.001821 0.01711 1.002 -0.0001598 7.176e-05 0.9836 -0.0001205 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.0952 0.3165 0.1604 0.9851 0.9941 0.1931 0.468 0.8851 0.7184 ] Network output: [ 0.00929 -0.03962 0.9979 9.098e-05 -4.084e-05 1.023 6.856e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08618 0.1774 0.2108 0.9874 0.992 0.0969 0.7967 0.8801 0.3105 ] Network output: [ -0.009284 0.04501 1.001 9.057e-05 -4.066e-05 0.9727 6.826e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09398 0.09212 0.1685 0.1991 0.9857 0.9915 0.094 0.7277 0.8602 0.2438 ] Network output: [ -0.0001849 0.9992 -9.628e-05 1.229e-05 -5.519e-06 1.001 9.265e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009731 Epoch 6696 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0139 0.9901 0.986 5.649e-06 -2.536e-06 -0.003863 4.257e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003184 -0.002946 -0.009857 0.007531 0.9697 0.9741 0.006037 0.8452 0.8331 0.02074 ] Network output: [ 0.9999 -0.007631 0.002417 -4.278e-05 1.92e-05 0.005156 -3.224e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.0274 -0.2002 0.2044 0.9837 0.9933 0.2029 0.4627 0.8787 0.7235 ] Network output: [ -0.01207 0.9992 1.01 2.542e-06 -1.141e-06 0.01448 1.915e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005234 0.0005544 0.004282 0.004673 0.9889 0.992 0.005329 0.8754 0.9025 0.01507 ] Network output: [ -0.0001225 -0.009322 1.003 -0.0001567 7.035e-05 1.006 -0.0001181 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1923 0.09513 0.3184 0.1654 0.9851 0.9941 0.1929 0.4676 0.8851 0.7181 ] Network output: [ 0.00884 -0.04482 0.9989 9.12e-05 -4.094e-05 1.029 6.873e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08627 0.1785 0.2122 0.9874 0.992 0.09697 0.797 0.8801 0.3113 ] Network output: [ -0.009521 0.04665 1.001 9.031e-05 -4.054e-05 0.9715 6.806e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09405 0.0922 0.1688 0.1994 0.9857 0.9915 0.09406 0.7282 0.8602 0.2438 ] Network output: [ 0.000829 0.9994 -0.001536 1.27e-05 -5.702e-06 1.001 9.571e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001026 Epoch 6697 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0137 0.993 0.9859 5.323e-06 -2.39e-06 -0.006291 4.011e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003187 -0.002946 -0.009872 0.007479 0.9697 0.9741 0.006042 0.8453 0.8329 0.02073 ] Network output: [ 0.9986 0.01161 0.001483 -4.458e-05 2.001e-05 -0.01047 -3.36e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.02715 -0.2014 0.2012 0.9837 0.9933 0.2031 0.4631 0.8786 0.7233 ] Network output: [ -0.0121 1 1.01 2.406e-06 -1.08e-06 0.01359 1.813e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00524 0.0005519 0.004226 0.004568 0.9889 0.992 0.005335 0.8755 0.9024 0.01504 ] Network output: [ -0.001815 0.01701 1.002 -0.0001596 7.167e-05 0.9837 -0.0001203 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.09518 0.3165 0.1604 0.9851 0.9941 0.1931 0.4679 0.8851 0.7183 ] Network output: [ 0.009281 -0.03963 0.9979 9.088e-05 -4.08e-05 1.024 6.849e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08618 0.1774 0.2108 0.9874 0.992 0.0969 0.7966 0.8801 0.3105 ] Network output: [ -0.009277 0.045 1.001 9.048e-05 -4.062e-05 0.9727 6.819e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09397 0.09211 0.1685 0.1991 0.9857 0.9915 0.09398 0.7277 0.8602 0.2438 ] Network output: [ -0.0001815 0.9992 -0.0001011 1.228e-05 -5.514e-06 1.001 9.256e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009717 Epoch 6698 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01389 0.9901 0.986 5.637e-06 -2.531e-06 -0.003876 4.249e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003184 -0.002947 -0.009854 0.007528 0.9697 0.9741 0.006038 0.8452 0.8331 0.02074 ] Network output: [ 0.9999 -0.007562 0.002413 -4.275e-05 1.919e-05 0.005096 -3.222e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.02742 -0.2002 0.2044 0.9837 0.9933 0.2029 0.4626 0.8787 0.7235 ] Network output: [ -0.01207 0.9992 1.01 2.539e-06 -1.14e-06 0.01447 1.913e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005235 0.0005539 0.004282 0.00467 0.9889 0.992 0.00533 0.8754 0.9025 0.01506 ] Network output: [ -0.0001297 -0.009219 1.003 -0.0001565 7.027e-05 1.006 -0.000118 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.09511 0.3184 0.1654 0.9851 0.9941 0.193 0.4676 0.8851 0.7181 ] Network output: [ 0.008836 -0.04479 0.9989 9.11e-05 -4.09e-05 1.029 6.865e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08627 0.1785 0.2122 0.9874 0.992 0.09697 0.797 0.8801 0.3112 ] Network output: [ -0.009512 0.04662 1.001 9.022e-05 -4.05e-05 0.9715 6.799e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09403 0.09218 0.1688 0.1993 0.9857 0.9915 0.09404 0.7281 0.8602 0.2438 ] Network output: [ 0.0008251 0.9994 -0.00153 1.268e-05 -5.695e-06 1.001 9.56e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001024 Epoch 6699 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01369 0.993 0.986 5.314e-06 -2.386e-06 -0.006286 4.005e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003187 -0.002946 -0.009869 0.007477 0.9697 0.9741 0.006042 0.8453 0.8329 0.02072 ] Network output: [ 0.9986 0.01154 0.001485 -4.454e-05 2e-05 -0.01041 -3.357e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.02716 -0.2014 0.2012 0.9837 0.9933 0.2032 0.4631 0.8786 0.7233 ] Network output: [ -0.01209 1 1.01 2.404e-06 -1.079e-06 0.01358 1.812e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005241 0.0005514 0.004227 0.004567 0.9889 0.992 0.005336 0.8754 0.9024 0.01503 ] Network output: [ -0.001809 0.01691 1.002 -0.0001594 7.158e-05 0.9838 -0.0001202 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.09517 0.3166 0.1604 0.9851 0.9941 0.1931 0.4679 0.8851 0.7183 ] Network output: [ 0.009273 -0.03963 0.9979 9.078e-05 -4.075e-05 1.024 6.841e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08617 0.1774 0.2108 0.9874 0.992 0.0969 0.7966 0.8801 0.3105 ] Network output: [ -0.009271 0.04498 1.001 9.039e-05 -4.058e-05 0.9727 6.812e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09395 0.09209 0.1685 0.1991 0.9857 0.9915 0.09396 0.7276 0.8602 0.2438 ] Network output: [ -0.000178 0.9992 -0.0001058 1.227e-05 -5.509e-06 1.001 9.247e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009704 Epoch 6700 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01388 0.9901 0.986 5.626e-06 -2.526e-06 -0.003889 4.24e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003185 -0.002947 -0.00985 0.007526 0.9697 0.9741 0.006038 0.8452 0.833 0.02073 ] Network output: [ 0.9999 -0.007492 0.002408 -4.273e-05 1.918e-05 0.005036 -3.22e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.02743 -0.2001 0.2043 0.9837 0.9933 0.203 0.4626 0.8787 0.7235 ] Network output: [ -0.01207 0.9992 1.01 2.536e-06 -1.139e-06 0.01446 1.911e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005236 0.0005533 0.004282 0.004668 0.9889 0.992 0.005331 0.8754 0.9025 0.01506 ] Network output: [ -0.000137 -0.009117 1.003 -0.0001564 7.019e-05 1.005 -0.0001178 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.0951 0.3184 0.1653 0.9851 0.9941 0.193 0.4676 0.8851 0.7181 ] Network output: [ 0.008831 -0.04476 0.9988 9.099e-05 -4.085e-05 1.029 6.857e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08627 0.1785 0.2122 0.9874 0.992 0.09697 0.7969 0.8801 0.3112 ] Network output: [ -0.009503 0.04659 1.001 9.013e-05 -4.046e-05 0.9716 6.793e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09401 0.09216 0.1688 0.1993 0.9857 0.9915 0.09402 0.7281 0.8602 0.2438 ] Network output: [ 0.0008212 0.9994 -0.001524 1.267e-05 -5.688e-06 1.001 9.548e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001022 Epoch 6701 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01369 0.993 0.986 5.305e-06 -2.382e-06 -0.00628 3.998e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003187 -0.002947 -0.009866 0.007474 0.9697 0.9741 0.006042 0.8453 0.8329 0.02072 ] Network output: [ 0.9986 0.01146 0.001488 -4.45e-05 1.998e-05 -0.01036 -3.354e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.02718 -0.2013 0.2012 0.9837 0.9933 0.2032 0.463 0.8786 0.7233 ] Network output: [ -0.01209 1 1.01 2.403e-06 -1.079e-06 0.01358 1.811e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005242 0.0005508 0.004227 0.004565 0.9889 0.992 0.005337 0.8754 0.9024 0.01503 ] Network output: [ -0.001804 0.01681 1.002 -0.0001592 7.149e-05 0.9839 -0.00012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.09515 0.3166 0.1604 0.9851 0.9941 0.1932 0.4679 0.8851 0.7183 ] Network output: [ 0.009264 -0.03964 0.9979 9.068e-05 -4.071e-05 1.024 6.834e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08617 0.1774 0.2108 0.9874 0.992 0.0969 0.7965 0.88 0.3105 ] Network output: [ -0.009264 0.04496 1.001 9.029e-05 -4.054e-05 0.9727 6.805e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09393 0.09207 0.1685 0.1991 0.9857 0.9915 0.09394 0.7275 0.8601 0.2438 ] Network output: [ -0.0001745 0.9992 -0.0001106 1.226e-05 -5.503e-06 1.001 9.239e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000969 Epoch 6702 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01388 0.9901 0.9861 5.614e-06 -2.52e-06 -0.003902 4.231e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003185 -0.002947 -0.009847 0.007523 0.9697 0.9741 0.006038 0.8452 0.833 0.02073 ] Network output: [ 0.9999 -0.007423 0.002403 -4.271e-05 1.917e-05 0.004976 -3.218e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.02745 -0.2001 0.2043 0.9837 0.9933 0.203 0.4626 0.8787 0.7235 ] Network output: [ -0.01207 0.9992 1.01 2.533e-06 -1.137e-06 0.01445 1.909e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005237 0.0005527 0.004282 0.004665 0.9889 0.992 0.005332 0.8754 0.9024 0.01505 ] Network output: [ -0.0001442 -0.009015 1.003 -0.0001562 7.012e-05 1.005 -0.0001177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.09508 0.3185 0.1652 0.9851 0.9941 0.193 0.4675 0.8851 0.7181 ] Network output: [ 0.008827 -0.04472 0.9988 9.089e-05 -4.08e-05 1.029 6.85e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08626 0.1785 0.2121 0.9874 0.992 0.09697 0.7969 0.8801 0.3112 ] Network output: [ -0.009494 0.04656 1.001 9.004e-05 -4.042e-05 0.9716 6.786e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09399 0.09214 0.1687 0.1993 0.9857 0.9915 0.09401 0.728 0.8601 0.2438 ] Network output: [ 0.0008172 0.9994 -0.001519 1.265e-05 -5.681e-06 1.001 9.536e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00102 Epoch 6703 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01368 0.993 0.986 5.296e-06 -2.378e-06 -0.006274 3.992e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003187 -0.002947 -0.009862 0.007472 0.9697 0.9741 0.006043 0.8453 0.8329 0.02071 ] Network output: [ 0.9986 0.01138 0.00149 -4.446e-05 1.996e-05 -0.0103 -3.351e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.0272 -0.2013 0.2012 0.9836 0.9933 0.2032 0.463 0.8786 0.7233 ] Network output: [ -0.01209 1 1.01 2.401e-06 -1.078e-06 0.01357 1.81e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005242 0.0005503 0.004228 0.004563 0.9889 0.992 0.005338 0.8754 0.9024 0.01502 ] Network output: [ -0.001798 0.01671 1.002 -0.000159 7.14e-05 0.9839 -0.0001199 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.09513 0.3167 0.1603 0.9851 0.9941 0.1932 0.4678 0.8851 0.7183 ] Network output: [ 0.009256 -0.03965 0.9979 9.058e-05 -4.066e-05 1.024 6.826e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08617 0.1774 0.2107 0.9874 0.992 0.0969 0.7965 0.88 0.3105 ] Network output: [ -0.009257 0.04495 1.001 9.02e-05 -4.049e-05 0.9727 6.798e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09391 0.09205 0.1685 0.1991 0.9857 0.9915 0.09392 0.7275 0.8601 0.2438 ] Network output: [ -0.0001711 0.9992 -0.0001153 1.225e-05 -5.498e-06 1.001 9.23e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009677 Epoch 6704 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01387 0.9901 0.9861 5.602e-06 -2.515e-06 -0.003916 4.222e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003185 -0.002947 -0.009844 0.00752 0.9697 0.9741 0.006039 0.8452 0.833 0.02072 ] Network output: [ 0.9999 -0.007354 0.002399 -4.268e-05 1.916e-05 0.004916 -3.217e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.02747 -0.2001 0.2042 0.9837 0.9933 0.203 0.4625 0.8787 0.7235 ] Network output: [ -0.01206 0.9992 1.01 2.531e-06 -1.136e-06 0.01443 1.907e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005238 0.0005522 0.004283 0.004663 0.9889 0.992 0.005333 0.8754 0.9024 0.01505 ] Network output: [ -0.0001514 -0.008912 1.003 -0.000156 7.004e-05 1.005 -0.0001176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.09506 0.3185 0.1652 0.9851 0.9941 0.193 0.4675 0.8851 0.7181 ] Network output: [ 0.008822 -0.04469 0.9988 9.079e-05 -4.076e-05 1.029 6.842e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08626 0.1785 0.2121 0.9874 0.992 0.09697 0.7968 0.88 0.3112 ] Network output: [ -0.009485 0.04653 1.001 8.995e-05 -4.038e-05 0.9716 6.779e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09397 0.09212 0.1687 0.1993 0.9857 0.9915 0.09399 0.7279 0.8601 0.2438 ] Network output: [ 0.0008133 0.9994 -0.001513 1.264e-05 -5.674e-06 1.001 9.525e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001018 Epoch 6705 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01368 0.993 0.986 5.288e-06 -2.374e-06 -0.006269 3.985e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003187 -0.002947 -0.009859 0.00747 0.9697 0.9741 0.006043 0.8452 0.8329 0.02071 ] Network output: [ 0.9986 0.0113 0.001492 -4.442e-05 1.994e-05 -0.01024 -3.348e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.02722 -0.2013 0.2012 0.9836 0.9933 0.2032 0.463 0.8785 0.7233 ] Network output: [ -0.01209 1 1.01 2.399e-06 -1.077e-06 0.01357 1.808e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005243 0.0005497 0.004229 0.004562 0.9889 0.992 0.005338 0.8754 0.9024 0.01502 ] Network output: [ -0.001793 0.01661 1.002 -0.0001588 7.131e-05 0.984 -0.0001197 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.09511 0.3167 0.1603 0.9851 0.9941 0.1932 0.4678 0.8851 0.7183 ] Network output: [ 0.009248 -0.03966 0.9979 9.048e-05 -4.062e-05 1.024 6.818e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08616 0.1774 0.2107 0.9874 0.992 0.0969 0.7964 0.88 0.3105 ] Network output: [ -0.00925 0.04493 1.001 9.011e-05 -4.045e-05 0.9727 6.791e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09389 0.09203 0.1684 0.199 0.9857 0.9915 0.09391 0.7274 0.8601 0.2438 ] Network output: [ -0.0001676 0.9992 -0.0001201 1.224e-05 -5.493e-06 1.001 9.221e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009663 Epoch 6706 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01387 0.9902 0.9861 5.591e-06 -2.51e-06 -0.003929 4.213e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003185 -0.002948 -0.00984 0.007518 0.9697 0.9741 0.006039 0.8452 0.833 0.02072 ] Network output: [ 0.9999 -0.007284 0.002394 -4.266e-05 1.915e-05 0.004857 -3.215e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.02748 -0.2 0.2042 0.9837 0.9933 0.203 0.4625 0.8786 0.7235 ] Network output: [ -0.01206 0.9992 1.01 2.528e-06 -1.135e-06 0.01442 1.905e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005239 0.0005516 0.004283 0.00466 0.9889 0.992 0.005334 0.8754 0.9024 0.01505 ] Network output: [ -0.0001586 -0.008811 1.003 -0.0001558 6.996e-05 1.005 -0.0001174 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.09504 0.3185 0.1651 0.9851 0.9941 0.193 0.4675 0.8851 0.7181 ] Network output: [ 0.008818 -0.04466 0.9988 9.068e-05 -4.071e-05 1.029 6.834e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08625 0.1785 0.2121 0.9874 0.992 0.09697 0.7967 0.88 0.3112 ] Network output: [ -0.009476 0.0465 1.001 8.986e-05 -4.034e-05 0.9716 6.772e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09396 0.0921 0.1687 0.1993 0.9857 0.9915 0.09397 0.7278 0.8601 0.2438 ] Network output: [ 0.0008094 0.9994 -0.001507 1.262e-05 -5.667e-06 1.001 9.513e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001017 Epoch 6707 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01367 0.993 0.986 5.279e-06 -2.37e-06 -0.006263 3.978e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003187 -0.002947 -0.009855 0.007468 0.9697 0.9741 0.006043 0.8452 0.8329 0.0207 ] Network output: [ 0.9986 0.01123 0.001494 -4.438e-05 1.993e-05 -0.01018 -3.345e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.02724 -0.2012 0.2012 0.9836 0.9933 0.2032 0.4629 0.8785 0.7233 ] Network output: [ -0.01208 1 1.01 2.398e-06 -1.076e-06 0.01357 1.807e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005244 0.0005492 0.00423 0.00456 0.9889 0.992 0.005339 0.8754 0.9023 0.01502 ] Network output: [ -0.001787 0.01651 1.002 -0.0001586 7.122e-05 0.9841 -0.0001196 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.09509 0.3167 0.1603 0.9851 0.9941 0.1932 0.4678 0.8851 0.7183 ] Network output: [ 0.009239 -0.03967 0.9979 9.037e-05 -4.057e-05 1.024 6.811e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08616 0.1774 0.2107 0.9874 0.992 0.0969 0.7963 0.88 0.3105 ] Network output: [ -0.009244 0.04491 1.001 9.002e-05 -4.041e-05 0.9727 6.784e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09388 0.09201 0.1684 0.199 0.9857 0.9915 0.09389 0.7273 0.8601 0.2438 ] Network output: [ -0.0001641 0.9992 -0.0001248 1.222e-05 -5.488e-06 1.001 9.212e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000965 Epoch 6708 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01386 0.9902 0.9861 5.579e-06 -2.505e-06 -0.003942 4.205e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003185 -0.002948 -0.009837 0.007515 0.9697 0.9741 0.00604 0.8452 0.833 0.02071 ] Network output: [ 0.9999 -0.007215 0.002389 -4.264e-05 1.914e-05 0.004797 -3.213e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.182 -0.0275 -0.2 0.2042 0.9837 0.9933 0.203 0.4625 0.8786 0.7235 ] Network output: [ -0.01206 0.9993 1.01 2.525e-06 -1.134e-06 0.01441 1.903e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00524 0.000551 0.004283 0.004658 0.9889 0.992 0.005335 0.8753 0.9024 0.01504 ] Network output: [ -0.0001657 -0.008709 1.003 -0.0001557 6.988e-05 1.005 -0.0001173 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.09502 0.3185 0.1651 0.9851 0.9941 0.193 0.4674 0.885 0.7181 ] Network output: [ 0.008814 -0.04462 0.9988 9.058e-05 -4.067e-05 1.029 6.827e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08625 0.1785 0.212 0.9874 0.992 0.09697 0.7967 0.88 0.3112 ] Network output: [ -0.009466 0.04647 1.001 8.977e-05 -4.03e-05 0.9716 6.765e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09394 0.09208 0.1687 0.1993 0.9857 0.9915 0.09395 0.7278 0.86 0.2438 ] Network output: [ 0.0008055 0.9994 -0.001502 1.261e-05 -5.66e-06 1.001 9.502e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001015 Epoch 6709 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01367 0.9929 0.986 5.27e-06 -2.366e-06 -0.006258 3.971e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003188 -0.002948 -0.009852 0.007466 0.9697 0.9741 0.006044 0.8452 0.8329 0.0207 ] Network output: [ 0.9986 0.01115 0.001496 -4.434e-05 1.991e-05 -0.01012 -3.342e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.02725 -0.2012 0.2012 0.9836 0.9933 0.2032 0.4629 0.8785 0.7233 ] Network output: [ -0.01208 1 1.01 2.396e-06 -1.076e-06 0.01356 1.806e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005245 0.0005486 0.00423 0.004558 0.9889 0.992 0.00534 0.8754 0.9023 0.01501 ] Network output: [ -0.001781 0.01641 1.002 -0.0001584 7.113e-05 0.9842 -0.0001194 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.09507 0.3168 0.1603 0.9851 0.9941 0.1932 0.4677 0.8851 0.7183 ] Network output: [ 0.009231 -0.03967 0.9979 9.027e-05 -4.053e-05 1.024 6.803e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08616 0.1774 0.2107 0.9874 0.992 0.0969 0.7963 0.8799 0.3104 ] Network output: [ -0.009237 0.0449 1.001 8.993e-05 -4.037e-05 0.9727 6.777e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09386 0.092 0.1684 0.199 0.9857 0.9915 0.09387 0.7273 0.86 0.2438 ] Network output: [ -0.0001607 0.9992 -0.0001295 1.221e-05 -5.482e-06 1.001 9.203e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009637 Epoch 6710 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01385 0.9902 0.9861 5.567e-06 -2.499e-06 -0.003955 4.196e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003185 -0.002948 -0.009834 0.007513 0.9697 0.9741 0.00604 0.8452 0.833 0.02071 ] Network output: [ 0.9999 -0.007146 0.002385 -4.261e-05 1.913e-05 0.004738 -3.211e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.02751 -0.2 0.2041 0.9837 0.9933 0.203 0.4625 0.8786 0.7235 ] Network output: [ -0.01206 0.9993 1.01 2.522e-06 -1.132e-06 0.0144 1.901e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005241 0.0005505 0.004283 0.004655 0.9889 0.992 0.005336 0.8753 0.9024 0.01504 ] Network output: [ -0.0001728 -0.008607 1.003 -0.0001555 6.98e-05 1.005 -0.0001172 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1924 0.09501 0.3186 0.165 0.9851 0.9941 0.193 0.4674 0.885 0.7181 ] Network output: [ 0.008809 -0.04459 0.9988 9.048e-05 -4.062e-05 1.029 6.819e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08624 0.1785 0.212 0.9874 0.992 0.09697 0.7966 0.88 0.3111 ] Network output: [ -0.009457 0.04645 1.001 8.968e-05 -4.026e-05 0.9716 6.759e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09392 0.09206 0.1687 0.1992 0.9857 0.9915 0.09393 0.7277 0.86 0.2438 ] Network output: [ 0.0008015 0.9994 -0.001496 1.259e-05 -5.653e-06 1.001 9.49e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001013 Epoch 6711 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01366 0.9929 0.986 5.261e-06 -2.362e-06 -0.006252 3.965e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003188 -0.002948 -0.009848 0.007463 0.9697 0.9741 0.006044 0.8452 0.8329 0.02069 ] Network output: [ 0.9987 0.01107 0.001498 -4.43e-05 1.989e-05 -0.01006 -3.339e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.02727 -0.2011 0.2011 0.9836 0.9933 0.2032 0.4629 0.8785 0.7232 ] Network output: [ -0.01208 1 1.01 2.394e-06 -1.075e-06 0.01356 1.804e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005246 0.0005481 0.004231 0.004557 0.9889 0.992 0.005341 0.8753 0.9023 0.01501 ] Network output: [ -0.001776 0.01631 1.002 -0.0001582 7.104e-05 0.9843 -0.0001193 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.09505 0.3168 0.1602 0.9851 0.9941 0.1932 0.4677 0.885 0.7183 ] Network output: [ 0.009222 -0.03968 0.9979 9.017e-05 -4.048e-05 1.024 6.796e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08615 0.1774 0.2107 0.9874 0.992 0.0969 0.7962 0.8799 0.3104 ] Network output: [ -0.00923 0.04488 1.001 8.983e-05 -4.033e-05 0.9727 6.77e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09384 0.09198 0.1684 0.199 0.9857 0.9915 0.09385 0.7272 0.86 0.2438 ] Network output: [ -0.0001572 0.9992 -0.0001342 1.22e-05 -5.477e-06 1.001 9.194e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009623 Epoch 6712 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01385 0.9902 0.9861 5.556e-06 -2.494e-06 -0.003968 4.187e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003185 -0.002949 -0.00983 0.00751 0.9697 0.9741 0.00604 0.8451 0.833 0.0207 ] Network output: [ 0.9999 -0.007077 0.00238 -4.259e-05 1.912e-05 0.004678 -3.21e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.02753 -0.1999 0.2041 0.9837 0.9933 0.2031 0.4624 0.8786 0.7235 ] Network output: [ -0.01206 0.9993 1.01 2.519e-06 -1.131e-06 0.01439 1.899e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005242 0.0005499 0.004284 0.004653 0.9889 0.992 0.005337 0.8753 0.9024 0.01504 ] Network output: [ -0.00018 -0.008506 1.003 -0.0001553 6.972e-05 1.005 -0.000117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.09499 0.3186 0.1649 0.9851 0.9941 0.1931 0.4674 0.885 0.7181 ] Network output: [ 0.008805 -0.04456 0.9988 9.038e-05 -4.057e-05 1.029 6.811e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08624 0.1785 0.212 0.9874 0.992 0.09697 0.7966 0.8799 0.3111 ] Network output: [ -0.009448 0.04642 1.001 8.959e-05 -4.022e-05 0.9716 6.752e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0939 0.09204 0.1687 0.1992 0.9857 0.9915 0.09391 0.7276 0.86 0.2438 ] Network output: [ 0.0007976 0.9994 -0.00149 1.258e-05 -5.646e-06 1.001 9.478e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001011 Epoch 6713 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01366 0.9929 0.986 5.252e-06 -2.358e-06 -0.006247 3.958e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003188 -0.002948 -0.009845 0.007461 0.9697 0.9741 0.006044 0.8452 0.8329 0.02069 ] Network output: [ 0.9987 0.011 0.0015 -4.426e-05 1.987e-05 -0.01001 -3.336e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.02729 -0.2011 0.2011 0.9836 0.9933 0.2033 0.4628 0.8785 0.7232 ] Network output: [ -0.01208 1 1.01 2.393e-06 -1.074e-06 0.01355 1.803e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005247 0.0005475 0.004232 0.004555 0.9889 0.992 0.005342 0.8753 0.9023 0.01501 ] Network output: [ -0.00177 0.01621 1.002 -0.000158 7.095e-05 0.9843 -0.0001191 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.09504 0.3169 0.1602 0.9851 0.9941 0.1932 0.4677 0.885 0.7183 ] Network output: [ 0.009214 -0.03969 0.9979 9.007e-05 -4.044e-05 1.024 6.788e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08615 0.1774 0.2106 0.9874 0.992 0.0969 0.7962 0.8799 0.3104 ] Network output: [ -0.009224 0.04486 1.001 8.974e-05 -4.029e-05 0.9727 6.763e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09382 0.09196 0.1684 0.199 0.9857 0.9915 0.09383 0.7271 0.86 0.2437 ] Network output: [ -0.0001538 0.9992 -0.0001389 1.219e-05 -5.472e-06 1.001 9.185e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000961 Epoch 6714 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01384 0.9902 0.9861 5.544e-06 -2.489e-06 -0.003981 4.178e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003186 -0.002949 -0.009827 0.007507 0.9697 0.9741 0.006041 0.8451 0.833 0.0207 ] Network output: [ 0.9999 -0.007008 0.002375 -4.256e-05 1.911e-05 0.004619 -3.208e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.02755 -0.1999 0.2041 0.9837 0.9933 0.2031 0.4624 0.8786 0.7235 ] Network output: [ -0.01205 0.9993 1.01 2.516e-06 -1.13e-06 0.01438 1.896e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005242 0.0005493 0.004284 0.00465 0.9889 0.992 0.005338 0.8753 0.9024 0.01503 ] Network output: [ -0.0001871 -0.008404 1.003 -0.0001551 6.965e-05 1.005 -0.0001169 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.09497 0.3186 0.1649 0.9851 0.9941 0.1931 0.4674 0.885 0.718 ] Network output: [ 0.0088 -0.04453 0.9987 9.027e-05 -4.053e-05 1.029 6.803e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08624 0.1785 0.2119 0.9874 0.992 0.09697 0.7965 0.8799 0.3111 ] Network output: [ -0.009439 0.04639 1.001 8.95e-05 -4.018e-05 0.9716 6.745e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09388 0.09203 0.1687 0.1992 0.9857 0.9915 0.09389 0.7275 0.8599 0.2438 ] Network output: [ 0.0007937 0.9994 -0.001485 1.256e-05 -5.639e-06 1.001 9.467e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00101 Epoch 6715 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01365 0.9929 0.986 5.243e-06 -2.354e-06 -0.006242 3.951e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003188 -0.002948 -0.009841 0.007459 0.9697 0.9741 0.006045 0.8452 0.8328 0.02068 ] Network output: [ 0.9987 0.01092 0.001502 -4.422e-05 1.985e-05 -0.009948 -3.333e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.02731 -0.2011 0.2011 0.9836 0.9933 0.2033 0.4628 0.8785 0.7232 ] Network output: [ -0.01208 1 1.01 2.391e-06 -1.073e-06 0.01355 1.802e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005248 0.000547 0.004232 0.004553 0.9889 0.992 0.005343 0.8753 0.9023 0.015 ] Network output: [ -0.001764 0.01611 1.002 -0.0001578 7.086e-05 0.9844 -0.0001189 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.09502 0.3169 0.1602 0.9851 0.9941 0.1933 0.4676 0.885 0.7182 ] Network output: [ 0.009206 -0.03969 0.9979 8.997e-05 -4.039e-05 1.024 6.781e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08615 0.1774 0.2106 0.9874 0.992 0.0969 0.7961 0.8799 0.3104 ] Network output: [ -0.009217 0.04484 1.001 8.965e-05 -4.025e-05 0.9727 6.756e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0938 0.09194 0.1684 0.199 0.9857 0.9915 0.09382 0.7271 0.8599 0.2437 ] Network output: [ -0.0001503 0.9992 -0.0001436 1.218e-05 -5.466e-06 1.001 9.176e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009597 Epoch 6716 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01383 0.9902 0.9861 5.532e-06 -2.484e-06 -0.003995 4.169e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003186 -0.002949 -0.009824 0.007505 0.9697 0.9741 0.006041 0.8451 0.8329 0.02069 ] Network output: [ 0.9999 -0.006939 0.002371 -4.254e-05 1.91e-05 0.00456 -3.206e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.02756 -0.1999 0.204 0.9836 0.9933 0.2031 0.4624 0.8786 0.7234 ] Network output: [ -0.01205 0.9993 1.01 2.513e-06 -1.128e-06 0.01437 1.894e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005243 0.0005488 0.004284 0.004648 0.9889 0.992 0.005338 0.8753 0.9024 0.01503 ] Network output: [ -0.0001941 -0.008303 1.003 -0.000155 6.957e-05 1.005 -0.0001168 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.09495 0.3187 0.1648 0.9851 0.9941 0.1931 0.4673 0.885 0.718 ] Network output: [ 0.008796 -0.04449 0.9987 9.017e-05 -4.048e-05 1.029 6.796e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08623 0.1785 0.2119 0.9874 0.992 0.09697 0.7965 0.8799 0.3111 ] Network output: [ -0.00943 0.04636 1.001 8.941e-05 -4.014e-05 0.9716 6.738e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09386 0.09201 0.1686 0.1992 0.9857 0.9915 0.09388 0.7275 0.8599 0.2438 ] Network output: [ 0.0007898 0.9994 -0.001479 1.255e-05 -5.632e-06 1.001 9.455e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001008 Epoch 6717 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01365 0.9929 0.986 5.234e-06 -2.35e-06 -0.006236 3.945e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003188 -0.002949 -0.009838 0.007457 0.9697 0.9741 0.006045 0.8452 0.8328 0.02068 ] Network output: [ 0.9987 0.01085 0.001504 -4.418e-05 1.983e-05 -0.00989 -3.33e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.02733 -0.201 0.2011 0.9836 0.9933 0.2033 0.4628 0.8785 0.7232 ] Network output: [ -0.01207 1 1.01 2.389e-06 -1.073e-06 0.01354 1.8e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005249 0.0005464 0.004233 0.004552 0.9889 0.992 0.005344 0.8753 0.9023 0.015 ] Network output: [ -0.001759 0.01602 1.002 -0.0001576 7.077e-05 0.9845 -0.0001188 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.095 0.317 0.1602 0.9851 0.9941 0.1933 0.4676 0.885 0.7182 ] Network output: [ 0.009197 -0.0397 0.9979 8.987e-05 -4.035e-05 1.024 6.773e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08614 0.1774 0.2106 0.9874 0.992 0.0969 0.7961 0.8798 0.3104 ] Network output: [ -0.00921 0.04483 1.001 8.956e-05 -4.021e-05 0.9727 6.749e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09379 0.09192 0.1684 0.1989 0.9857 0.9915 0.0938 0.727 0.8599 0.2437 ] Network output: [ -0.0001469 0.9992 -0.0001483 1.216e-05 -5.461e-06 1.001 9.168e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009584 Epoch 6718 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01383 0.9903 0.9861 5.52e-06 -2.478e-06 -0.004008 4.16e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003186 -0.002949 -0.00982 0.007502 0.9697 0.9741 0.006041 0.8451 0.8329 0.02069 ] Network output: [ 0.9999 -0.006871 0.002366 -4.252e-05 1.909e-05 0.004501 -3.204e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.02758 -0.1999 0.204 0.9836 0.9933 0.2031 0.4623 0.8786 0.7234 ] Network output: [ -0.01205 0.9993 1.01 2.51e-06 -1.127e-06 0.01435 1.892e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005244 0.0005482 0.004284 0.004645 0.9889 0.992 0.005339 0.8753 0.9023 0.01502 ] Network output: [ -0.0002012 -0.008202 1.003 -0.0001548 6.949e-05 1.005 -0.0001167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.09494 0.3187 0.1647 0.9851 0.9941 0.1931 0.4673 0.885 0.718 ] Network output: [ 0.008791 -0.04446 0.9987 9.007e-05 -4.043e-05 1.029 6.788e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08623 0.1785 0.2119 0.9874 0.992 0.09697 0.7964 0.8799 0.3111 ] Network output: [ -0.009421 0.04633 1.001 8.932e-05 -4.01e-05 0.9717 6.732e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09385 0.09199 0.1686 0.1992 0.9857 0.9915 0.09386 0.7274 0.8599 0.2438 ] Network output: [ 0.0007859 0.9994 -0.001473 1.253e-05 -5.625e-06 1.001 9.444e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001006 Epoch 6719 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01364 0.9929 0.986 5.225e-06 -2.346e-06 -0.006231 3.938e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003188 -0.002949 -0.009834 0.007454 0.9697 0.9741 0.006045 0.8452 0.8328 0.02067 ] Network output: [ 0.9987 0.01077 0.001506 -4.414e-05 1.982e-05 -0.009832 -3.327e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.02734 -0.201 0.2011 0.9836 0.9933 0.2033 0.4627 0.8785 0.7232 ] Network output: [ -0.01207 1 1.01 2.387e-06 -1.072e-06 0.01354 1.799e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005249 0.0005459 0.004234 0.00455 0.9889 0.992 0.005345 0.8753 0.9023 0.015 ] Network output: [ -0.001753 0.01592 1.002 -0.0001574 7.068e-05 0.9846 -0.0001186 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.09498 0.317 0.1602 0.9851 0.9941 0.1933 0.4676 0.885 0.7182 ] Network output: [ 0.009189 -0.03971 0.9979 8.977e-05 -4.03e-05 1.024 6.765e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08614 0.1775 0.2106 0.9874 0.992 0.0969 0.796 0.8798 0.3104 ] Network output: [ -0.009203 0.04481 1.001 8.947e-05 -4.016e-05 0.9727 6.742e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09377 0.09191 0.1683 0.1989 0.9857 0.9915 0.09378 0.7269 0.8599 0.2437 ] Network output: [ -0.0001434 0.9992 -0.000153 1.215e-05 -5.456e-06 1.001 9.159e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009571 Epoch 6720 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01382 0.9903 0.9861 5.509e-06 -2.473e-06 -0.004021 4.152e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003186 -0.00295 -0.009817 0.007499 0.9697 0.9741 0.006042 0.8451 0.8329 0.02068 ] Network output: [ 0.9999 -0.006802 0.002361 -4.249e-05 1.908e-05 0.004442 -3.202e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.02759 -0.1998 0.204 0.9836 0.9933 0.2031 0.4623 0.8786 0.7234 ] Network output: [ -0.01205 0.9993 1.01 2.507e-06 -1.126e-06 0.01434 1.89e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005245 0.0005477 0.004285 0.004643 0.9889 0.992 0.00534 0.8752 0.9023 0.01502 ] Network output: [ -0.0002082 -0.008102 1.003 -0.0001546 6.941e-05 1.005 -0.0001165 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.09492 0.3187 0.1647 0.9851 0.9941 0.1931 0.4673 0.885 0.718 ] Network output: [ 0.008787 -0.04443 0.9987 8.996e-05 -4.039e-05 1.029 6.78e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08622 0.1785 0.2119 0.9874 0.992 0.09697 0.7963 0.8798 0.3111 ] Network output: [ -0.009412 0.0463 1.001 8.923e-05 -4.006e-05 0.9717 6.725e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09383 0.09197 0.1686 0.1992 0.9857 0.9915 0.09384 0.7273 0.8598 0.2437 ] Network output: [ 0.0007819 0.9994 -0.001467 1.252e-05 -5.619e-06 1.001 9.432e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001004 Epoch 6721 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01364 0.9929 0.986 5.216e-06 -2.342e-06 -0.006226 3.931e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003188 -0.002949 -0.009831 0.007452 0.9697 0.9741 0.006046 0.8451 0.8328 0.02067 ] Network output: [ 0.9987 0.01069 0.001509 -4.41e-05 1.98e-05 -0.009774 -3.324e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.02736 -0.2009 0.2011 0.9836 0.9933 0.2033 0.4627 0.8785 0.7232 ] Network output: [ -0.01207 1 1.01 2.385e-06 -1.071e-06 0.01354 1.798e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00525 0.0005454 0.004234 0.004548 0.9889 0.992 0.005346 0.8753 0.9023 0.01499 ] Network output: [ -0.001748 0.01582 1.002 -0.0001572 7.059e-05 0.9847 -0.0001185 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.09497 0.3171 0.1601 0.9851 0.9941 0.1933 0.4675 0.885 0.7182 ] Network output: [ 0.00918 -0.03971 0.9978 8.967e-05 -4.026e-05 1.024 6.758e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08614 0.1775 0.2106 0.9874 0.992 0.0969 0.796 0.8798 0.3104 ] Network output: [ -0.009197 0.04479 1.001 8.937e-05 -4.012e-05 0.9727 6.735e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09375 0.09189 0.1683 0.1989 0.9857 0.9915 0.09376 0.7268 0.8598 0.2437 ] Network output: [ -0.00014 0.9992 -0.0001577 1.214e-05 -5.451e-06 1.001 9.15e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009558 Epoch 6722 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01381 0.9903 0.9861 5.497e-06 -2.468e-06 -0.004034 4.143e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003186 -0.00295 -0.009814 0.007497 0.9697 0.9741 0.006042 0.8451 0.8329 0.02068 ] Network output: [ 0.9999 -0.006734 0.002357 -4.247e-05 1.907e-05 0.004383 -3.2e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1821 -0.02761 -0.1998 0.2039 0.9836 0.9933 0.2031 0.4623 0.8786 0.7234 ] Network output: [ -0.01204 0.9993 1.01 2.505e-06 -1.124e-06 0.01433 1.887e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005246 0.0005471 0.004285 0.004641 0.9889 0.992 0.005341 0.8752 0.9023 0.01502 ] Network output: [ -0.0002153 -0.008001 1.003 -0.0001544 6.933e-05 1.005 -0.0001164 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1925 0.0949 0.3188 0.1646 0.9851 0.9941 0.1931 0.4672 0.885 0.718 ] Network output: [ 0.008782 -0.04439 0.9987 8.986e-05 -4.034e-05 1.029 6.772e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08622 0.1785 0.2118 0.9874 0.992 0.09697 0.7963 0.8798 0.3111 ] Network output: [ -0.009403 0.04627 1.001 8.914e-05 -4.002e-05 0.9717 6.718e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09381 0.09195 0.1686 0.1991 0.9857 0.9915 0.09382 0.7273 0.8598 0.2437 ] Network output: [ 0.000778 0.9994 -0.001462 1.25e-05 -5.612e-06 1.001 9.42e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001003 Epoch 6723 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01363 0.9929 0.9861 5.207e-06 -2.338e-06 -0.00622 3.924e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003188 -0.00295 -0.009828 0.00745 0.9697 0.9741 0.006046 0.8451 0.8328 0.02066 ] Network output: [ 0.9987 0.01062 0.001511 -4.406e-05 1.978e-05 -0.009716 -3.321e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.02738 -0.2009 0.2011 0.9836 0.9933 0.2033 0.4627 0.8784 0.7232 ] Network output: [ -0.01207 1 1.01 2.383e-06 -1.07e-06 0.01353 1.796e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005251 0.0005448 0.004235 0.004547 0.9889 0.992 0.005346 0.8752 0.9023 0.01499 ] Network output: [ -0.001742 0.01572 1.002 -0.000157 7.05e-05 0.9847 -0.0001183 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.09495 0.3171 0.1601 0.9851 0.9941 0.1933 0.4675 0.885 0.7182 ] Network output: [ 0.009172 -0.03972 0.9978 8.957e-05 -4.021e-05 1.024 6.75e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08613 0.1775 0.2105 0.9874 0.992 0.0969 0.7959 0.8798 0.3104 ] Network output: [ -0.00919 0.04477 1.001 8.928e-05 -4.008e-05 0.9727 6.729e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09373 0.09187 0.1683 0.1989 0.9857 0.9915 0.09375 0.7268 0.8598 0.2437 ] Network output: [ -0.0001365 0.9992 -0.0001623 1.213e-05 -5.445e-06 1.001 9.141e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009545 Epoch 6724 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01381 0.9903 0.9861 5.485e-06 -2.463e-06 -0.004047 4.134e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003186 -0.00295 -0.00981 0.007494 0.9697 0.9741 0.006043 0.8451 0.8329 0.02067 ] Network output: [ 0.9999 -0.006665 0.002352 -4.244e-05 1.905e-05 0.004324 -3.199e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.02763 -0.1998 0.2039 0.9836 0.9933 0.2032 0.4623 0.8785 0.7234 ] Network output: [ -0.01204 0.9993 1.01 2.502e-06 -1.123e-06 0.01432 1.885e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005247 0.0005466 0.004285 0.004638 0.9889 0.992 0.005342 0.8752 0.9023 0.01501 ] Network output: [ -0.0002223 -0.007901 1.003 -0.0001543 6.926e-05 1.004 -0.0001163 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.09488 0.3188 0.1646 0.9851 0.9941 0.1932 0.4672 0.885 0.718 ] Network output: [ 0.008778 -0.04436 0.9987 8.976e-05 -4.03e-05 1.028 6.765e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08622 0.1785 0.2118 0.9874 0.992 0.09697 0.7962 0.8798 0.311 ] Network output: [ -0.009394 0.04624 1.001 8.905e-05 -3.998e-05 0.9717 6.711e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09379 0.09193 0.1686 0.1991 0.9857 0.9915 0.0938 0.7272 0.8598 0.2437 ] Network output: [ 0.0007741 0.9994 -0.001456 1.248e-05 -5.605e-06 1.001 9.409e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.001001 Epoch 6725 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01363 0.9929 0.9861 5.198e-06 -2.334e-06 -0.006215 3.918e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003189 -0.00295 -0.009824 0.007448 0.9697 0.9741 0.006047 0.8451 0.8328 0.02066 ] Network output: [ 0.9987 0.01054 0.001513 -4.402e-05 1.976e-05 -0.009658 -3.318e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.0274 -0.2009 0.2011 0.9836 0.9933 0.2034 0.4626 0.8784 0.7232 ] Network output: [ -0.01206 1 1.01 2.382e-06 -1.069e-06 0.01353 1.795e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005252 0.0005443 0.004236 0.004545 0.9889 0.992 0.005347 0.8752 0.9022 0.01499 ] Network output: [ -0.001736 0.01562 1.002 -0.0001568 7.041e-05 0.9848 -0.0001182 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.09493 0.3172 0.1601 0.9851 0.9941 0.1933 0.4675 0.885 0.7182 ] Network output: [ 0.009164 -0.03973 0.9978 8.947e-05 -4.016e-05 1.024 6.743e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08613 0.1775 0.2105 0.9874 0.992 0.0969 0.7958 0.8797 0.3104 ] Network output: [ -0.009183 0.04476 1.001 8.919e-05 -4.004e-05 0.9727 6.722e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09372 0.09185 0.1683 0.1989 0.9857 0.9915 0.09373 0.7267 0.8598 0.2437 ] Network output: [ -0.0001331 0.9992 -0.000167 1.212e-05 -5.44e-06 1.001 9.132e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009532 Epoch 6726 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0138 0.9903 0.9861 5.474e-06 -2.457e-06 -0.00406 4.125e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003186 -0.00295 -0.009807 0.007492 0.9697 0.9741 0.006043 0.8451 0.8329 0.02067 ] Network output: [ 0.9999 -0.006597 0.002347 -4.242e-05 1.904e-05 0.004265 -3.197e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.02764 -0.1997 0.2039 0.9836 0.9933 0.2032 0.4622 0.8785 0.7234 ] Network output: [ -0.01204 0.9993 1.01 2.498e-06 -1.122e-06 0.01431 1.883e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005248 0.000546 0.004286 0.004636 0.9889 0.992 0.005343 0.8752 0.9023 0.01501 ] Network output: [ -0.0002292 -0.007801 1.003 -0.0001541 6.918e-05 1.004 -0.0001161 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.09487 0.3188 0.1645 0.9851 0.9941 0.1932 0.4672 0.8849 0.718 ] Network output: [ 0.008774 -0.04433 0.9987 8.966e-05 -4.025e-05 1.028 6.757e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08621 0.1785 0.2118 0.9874 0.992 0.09697 0.7962 0.8798 0.311 ] Network output: [ -0.009385 0.04621 1.001 8.896e-05 -3.994e-05 0.9717 6.704e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09377 0.09191 0.1686 0.1991 0.9857 0.9915 0.09379 0.7271 0.8598 0.2437 ] Network output: [ 0.0007702 0.9994 -0.00145 1.247e-05 -5.598e-06 1.001 9.397e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009991 Epoch 6727 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01362 0.9929 0.9861 5.189e-06 -2.33e-06 -0.00621 3.911e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003189 -0.00295 -0.009821 0.007446 0.9697 0.9741 0.006047 0.8451 0.8328 0.02065 ] Network output: [ 0.9987 0.01047 0.001515 -4.398e-05 1.974e-05 -0.009601 -3.314e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.02741 -0.2008 0.201 0.9836 0.9933 0.2034 0.4626 0.8784 0.7232 ] Network output: [ -0.01206 1 1.01 2.38e-06 -1.068e-06 0.01352 1.793e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005253 0.0005438 0.004236 0.004544 0.9889 0.992 0.005348 0.8752 0.9022 0.01498 ] Network output: [ -0.001731 0.01552 1.002 -0.0001566 7.032e-05 0.9849 -0.000118 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.09491 0.3172 0.1601 0.9851 0.9941 0.1934 0.4674 0.885 0.7182 ] Network output: [ 0.009156 -0.03973 0.9978 8.937e-05 -4.012e-05 1.024 6.735e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08613 0.1775 0.2105 0.9874 0.992 0.0969 0.7958 0.8797 0.3104 ] Network output: [ -0.009176 0.04474 1.001 8.91e-05 -4e-05 0.9727 6.715e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0937 0.09184 0.1683 0.1989 0.9857 0.9915 0.09371 0.7266 0.8597 0.2437 ] Network output: [ -0.0001297 0.9992 -0.0001716 1.211e-05 -5.435e-06 1.001 9.123e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009519 Epoch 6728 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0138 0.9904 0.9861 5.462e-06 -2.452e-06 -0.004074 4.116e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003187 -0.002951 -0.009803 0.007489 0.9697 0.9741 0.006043 0.845 0.8329 0.02066 ] Network output: [ 0.9999 -0.006529 0.002343 -4.239e-05 1.903e-05 0.004207 -3.195e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.02766 -0.1997 0.2038 0.9836 0.9933 0.2032 0.4622 0.8785 0.7234 ] Network output: [ -0.01204 0.9993 1.01 2.495e-06 -1.12e-06 0.0143 1.881e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005249 0.0005455 0.004286 0.004633 0.9889 0.992 0.005344 0.8752 0.9023 0.01501 ] Network output: [ -0.0002362 -0.007701 1.003 -0.0001539 6.91e-05 1.004 -0.000116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.09485 0.3189 0.1644 0.9851 0.9941 0.1932 0.4671 0.8849 0.718 ] Network output: [ 0.008769 -0.04429 0.9986 8.955e-05 -4.02e-05 1.028 6.749e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08621 0.1785 0.2117 0.9874 0.992 0.09697 0.7961 0.8797 0.311 ] Network output: [ -0.009376 0.04618 1.001 8.887e-05 -3.99e-05 0.9717 6.697e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09376 0.0919 0.1685 0.1991 0.9857 0.9915 0.09377 0.727 0.8597 0.2437 ] Network output: [ 0.0007663 0.9994 -0.001444 1.245e-05 -5.591e-06 1.001 9.386e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009974 Epoch 6729 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01362 0.9929 0.9861 5.18e-06 -2.326e-06 -0.006205 3.904e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003189 -0.00295 -0.009817 0.007443 0.9697 0.9741 0.006047 0.8451 0.8328 0.02065 ] Network output: [ 0.9987 0.01039 0.001517 -4.394e-05 1.973e-05 -0.009543 -3.311e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.02743 -0.2008 0.201 0.9836 0.9933 0.2034 0.4626 0.8784 0.7232 ] Network output: [ -0.01206 1 1.01 2.378e-06 -1.067e-06 0.01352 1.792e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005254 0.0005432 0.004237 0.004542 0.9889 0.992 0.005349 0.8752 0.9022 0.01498 ] Network output: [ -0.001725 0.01542 1.002 -0.0001564 7.023e-05 0.985 -0.0001179 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.0949 0.3172 0.16 0.9851 0.9941 0.1934 0.4674 0.8849 0.7182 ] Network output: [ 0.009147 -0.03974 0.9978 8.926e-05 -4.007e-05 1.024 6.727e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08612 0.1775 0.2105 0.9874 0.992 0.0969 0.7957 0.8797 0.3103 ] Network output: [ -0.009169 0.04472 1.001 8.9e-05 -3.996e-05 0.9727 6.708e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09368 0.09182 0.1683 0.1989 0.9857 0.9915 0.09369 0.7266 0.8597 0.2437 ] Network output: [ -0.0001263 0.9992 -0.0001762 1.209e-05 -5.429e-06 1.001 9.114e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009506 Epoch 6730 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01379 0.9904 0.9862 5.45e-06 -2.447e-06 -0.004087 4.107e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003187 -0.002951 -0.0098 0.007486 0.9697 0.9741 0.006044 0.845 0.8329 0.02066 ] Network output: [ 0.9999 -0.006461 0.002338 -4.237e-05 1.902e-05 0.004148 -3.193e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.02767 -0.1997 0.2038 0.9836 0.9933 0.2032 0.4622 0.8785 0.7234 ] Network output: [ -0.01204 0.9993 1.01 2.492e-06 -1.119e-06 0.01429 1.878e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00525 0.0005449 0.004286 0.004631 0.9889 0.992 0.005345 0.8752 0.9023 0.015 ] Network output: [ -0.0002431 -0.007601 1.003 -0.0001537 6.902e-05 1.004 -0.0001159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.09483 0.3189 0.1644 0.9851 0.9941 0.1932 0.4671 0.8849 0.718 ] Network output: [ 0.008765 -0.04426 0.9986 8.945e-05 -4.016e-05 1.028 6.741e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.0862 0.1785 0.2117 0.9874 0.992 0.09697 0.796 0.8797 0.311 ] Network output: [ -0.009367 0.04615 1.001 8.878e-05 -3.986e-05 0.9717 6.691e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09374 0.09188 0.1685 0.1991 0.9857 0.9915 0.09375 0.727 0.8597 0.2437 ] Network output: [ 0.0007624 0.9994 -0.001439 1.244e-05 -5.584e-06 1.001 9.374e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009957 Epoch 6731 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01361 0.9929 0.9861 5.171e-06 -2.322e-06 -0.0062 3.897e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003189 -0.002951 -0.009814 0.007441 0.9697 0.9741 0.006048 0.8451 0.8328 0.02064 ] Network output: [ 0.9987 0.01031 0.001519 -4.39e-05 1.971e-05 -0.009485 -3.308e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.02745 -0.2007 0.201 0.9836 0.9933 0.2034 0.4625 0.8784 0.7232 ] Network output: [ -0.01206 1 1.01 2.376e-06 -1.067e-06 0.01351 1.79e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005255 0.0005427 0.004238 0.00454 0.9889 0.992 0.00535 0.8752 0.9022 0.01498 ] Network output: [ -0.001719 0.01533 1.002 -0.0001562 7.014e-05 0.9851 -0.0001177 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.09488 0.3173 0.16 0.9851 0.9941 0.1934 0.4674 0.8849 0.7182 ] Network output: [ 0.009139 -0.03974 0.9978 8.916e-05 -4.003e-05 1.024 6.72e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08612 0.1775 0.2105 0.9874 0.992 0.0969 0.7957 0.8797 0.3103 ] Network output: [ -0.009163 0.0447 1.001 8.891e-05 -3.992e-05 0.9727 6.701e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09367 0.0918 0.1683 0.1988 0.9857 0.9915 0.09368 0.7265 0.8597 0.2437 ] Network output: [ -0.0001228 0.9992 -0.0001809 1.208e-05 -5.424e-06 1.001 9.105e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009493 Epoch 6732 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01378 0.9904 0.9862 5.438e-06 -2.442e-06 -0.0041 4.099e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003187 -0.002951 -0.009797 0.007484 0.9697 0.9741 0.006044 0.845 0.8329 0.02065 ] Network output: [ 0.9999 -0.006393 0.002333 -4.234e-05 1.901e-05 0.00409 -3.191e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.02769 -0.1996 0.2037 0.9836 0.9933 0.2032 0.4621 0.8785 0.7234 ] Network output: [ -0.01203 0.9993 1.01 2.489e-06 -1.118e-06 0.01427 1.876e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005251 0.0005444 0.004286 0.004628 0.9889 0.992 0.005346 0.8751 0.9023 0.015 ] Network output: [ -0.00025 -0.007501 1.003 -0.0001536 6.894e-05 1.004 -0.0001157 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.09482 0.3189 0.1643 0.9851 0.9941 0.1932 0.4671 0.8849 0.718 ] Network output: [ 0.00876 -0.04422 0.9986 8.935e-05 -4.011e-05 1.028 6.733e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.0862 0.1785 0.2117 0.9874 0.992 0.09697 0.796 0.8797 0.311 ] Network output: [ -0.009358 0.04612 1.001 8.869e-05 -3.982e-05 0.9717 6.684e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09372 0.09186 0.1685 0.1991 0.9857 0.9915 0.09373 0.7269 0.8597 0.2437 ] Network output: [ 0.0007586 0.9994 -0.001433 1.242e-05 -5.577e-06 1.001 9.363e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009939 Epoch 6733 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01361 0.9929 0.9861 5.162e-06 -2.318e-06 -0.006195 3.89e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003189 -0.002951 -0.00981 0.007439 0.9697 0.9741 0.006048 0.8451 0.8327 0.02064 ] Network output: [ 0.9987 0.01024 0.001521 -4.386e-05 1.969e-05 -0.009428 -3.305e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.02747 -0.2007 0.201 0.9836 0.9933 0.2034 0.4625 0.8784 0.7232 ] Network output: [ -0.01206 1 1.01 2.374e-06 -1.066e-06 0.01351 1.789e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005256 0.0005422 0.004238 0.004539 0.9889 0.992 0.005351 0.8752 0.9022 0.01497 ] Network output: [ -0.001714 0.01523 1.002 -0.000156 7.005e-05 0.9851 -0.0001176 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.09486 0.3173 0.16 0.9851 0.9941 0.1934 0.4673 0.8849 0.7182 ] Network output: [ 0.009131 -0.03975 0.9978 8.906e-05 -3.998e-05 1.024 6.712e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08612 0.1775 0.2105 0.9874 0.992 0.0969 0.7956 0.8796 0.3103 ] Network output: [ -0.009156 0.04468 1.001 8.882e-05 -3.987e-05 0.9727 6.694e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09365 0.09178 0.1683 0.1988 0.9857 0.9915 0.09366 0.7264 0.8596 0.2437 ] Network output: [ -0.0001194 0.9992 -0.0001855 1.207e-05 -5.419e-06 1.001 9.096e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000948 Epoch 6734 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01378 0.9904 0.9862 5.427e-06 -2.436e-06 -0.004113 4.09e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003187 -0.002952 -0.009793 0.007481 0.9697 0.9741 0.006045 0.845 0.8328 0.02065 ] Network output: [ 0.9999 -0.006325 0.002329 -4.232e-05 1.9e-05 0.004032 -3.189e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1822 -0.0277 -0.1996 0.2037 0.9836 0.9933 0.2033 0.4621 0.8785 0.7233 ] Network output: [ -0.01203 0.9994 1.01 2.486e-06 -1.116e-06 0.01426 1.874e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005252 0.0005438 0.004287 0.004626 0.9889 0.992 0.005347 0.8751 0.9023 0.01499 ] Network output: [ -0.0002569 -0.007402 1.003 -0.0001534 6.886e-05 1.004 -0.0001156 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1926 0.0948 0.3189 0.1643 0.9851 0.9941 0.1932 0.467 0.8849 0.7179 ] Network output: [ 0.008755 -0.04419 0.9986 8.924e-05 -4.006e-05 1.028 6.726e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.0862 0.1785 0.2116 0.9874 0.992 0.09697 0.7959 0.8797 0.311 ] Network output: [ -0.009349 0.04609 1.001 8.86e-05 -3.977e-05 0.9718 6.677e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0937 0.09184 0.1685 0.199 0.9857 0.9915 0.09372 0.7268 0.8596 0.2437 ] Network output: [ 0.0007547 0.9994 -0.001427 1.241e-05 -5.57e-06 1.001 9.351e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009922 Epoch 6735 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01361 0.9929 0.9861 5.153e-06 -2.313e-06 -0.00619 3.884e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003189 -0.002951 -0.009807 0.007437 0.9697 0.9741 0.006048 0.845 0.8327 0.02063 ] Network output: [ 0.9988 0.01016 0.001523 -4.382e-05 1.967e-05 -0.00937 -3.302e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.02748 -0.2007 0.201 0.9836 0.9933 0.2034 0.4625 0.8784 0.7231 ] Network output: [ -0.01205 1 1.01 2.372e-06 -1.065e-06 0.0135 1.787e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005256 0.0005417 0.004239 0.004537 0.9889 0.992 0.005352 0.8751 0.9022 0.01497 ] Network output: [ -0.001708 0.01513 1.002 -0.0001558 6.996e-05 0.9852 -0.0001174 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.09484 0.3174 0.16 0.9851 0.9941 0.1934 0.4673 0.8849 0.7181 ] Network output: [ 0.009122 -0.03975 0.9978 8.896e-05 -3.994e-05 1.024 6.704e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08611 0.1775 0.2104 0.9874 0.992 0.0969 0.7956 0.8796 0.3103 ] Network output: [ -0.009149 0.04467 1.001 8.873e-05 -3.983e-05 0.9727 6.687e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09363 0.09177 0.1682 0.1988 0.9857 0.9915 0.09364 0.7264 0.8596 0.2437 ] Network output: [ -0.000116 0.9992 -0.0001901 1.206e-05 -5.413e-06 1.001 9.088e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009468 Epoch 6736 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01377 0.9904 0.9862 5.415e-06 -2.431e-06 -0.004126 4.081e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003187 -0.002952 -0.00979 0.007479 0.9697 0.9741 0.006045 0.845 0.8328 0.02064 ] Network output: [ 0.9999 -0.006258 0.002324 -4.229e-05 1.899e-05 0.003974 -3.187e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.02772 -0.1996 0.2037 0.9836 0.9933 0.2033 0.4621 0.8785 0.7233 ] Network output: [ -0.01203 0.9994 1.01 2.483e-06 -1.115e-06 0.01425 1.871e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005253 0.0005433 0.004287 0.004624 0.9889 0.992 0.005348 0.8751 0.9022 0.01499 ] Network output: [ -0.0002638 -0.007303 1.003 -0.0001532 6.879e-05 1.004 -0.0001155 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.09478 0.319 0.1642 0.9851 0.9941 0.1933 0.467 0.8849 0.7179 ] Network output: [ 0.008751 -0.04416 0.9986 8.914e-05 -4.002e-05 1.028 6.718e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08619 0.1785 0.2116 0.9874 0.992 0.09697 0.7959 0.8796 0.3109 ] Network output: [ -0.009341 0.04606 1.001 8.851e-05 -3.973e-05 0.9718 6.67e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09369 0.09182 0.1685 0.199 0.9857 0.9915 0.0937 0.7267 0.8596 0.2437 ] Network output: [ 0.0007508 0.9994 -0.001422 1.239e-05 -5.564e-06 1.001 9.34e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009906 Epoch 6737 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0136 0.9929 0.9861 5.144e-06 -2.309e-06 -0.006185 3.877e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003189 -0.002952 -0.009803 0.007434 0.9697 0.9741 0.006049 0.845 0.8327 0.02063 ] Network output: [ 0.9988 0.01009 0.001525 -4.378e-05 1.965e-05 -0.009313 -3.299e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.0275 -0.2006 0.201 0.9836 0.9933 0.2035 0.4624 0.8784 0.7231 ] Network output: [ -0.01205 1 1.01 2.37e-06 -1.064e-06 0.0135 1.786e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005257 0.0005411 0.00424 0.004535 0.9889 0.992 0.005353 0.8751 0.9022 0.01496 ] Network output: [ -0.001702 0.01503 1.002 -0.0001556 6.987e-05 0.9853 -0.0001173 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.09483 0.3174 0.1599 0.9851 0.9941 0.1934 0.4673 0.8849 0.7181 ] Network output: [ 0.009114 -0.03976 0.9978 8.886e-05 -3.989e-05 1.024 6.697e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08611 0.1775 0.2104 0.9874 0.992 0.0969 0.7955 0.8796 0.3103 ] Network output: [ -0.009142 0.04465 1.001 8.863e-05 -3.979e-05 0.9728 6.68e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09361 0.09175 0.1682 0.1988 0.9857 0.9915 0.09363 0.7263 0.8596 0.2437 ] Network output: [ -0.0001126 0.9992 -0.0001946 1.205e-05 -5.408e-06 1.001 9.079e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009455 Epoch 6738 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01376 0.9904 0.9862 5.403e-06 -2.426e-06 -0.004139 4.072e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003187 -0.002952 -0.009787 0.007476 0.9697 0.9741 0.006045 0.845 0.8328 0.02064 ] Network output: [ 0.9999 -0.00619 0.002319 -4.227e-05 1.898e-05 0.003916 -3.185e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.02774 -0.1995 0.2036 0.9836 0.9933 0.2033 0.462 0.8785 0.7233 ] Network output: [ -0.01203 0.9994 1.01 2.48e-06 -1.113e-06 0.01424 1.869e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005253 0.0005428 0.004287 0.004621 0.9889 0.992 0.005349 0.8751 0.9022 0.01499 ] Network output: [ -0.0002706 -0.007204 1.003 -0.000153 6.871e-05 1.004 -0.0001153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.09477 0.319 0.1641 0.9851 0.9941 0.1933 0.467 0.8849 0.7179 ] Network output: [ 0.008746 -0.04412 0.9986 8.904e-05 -3.997e-05 1.028 6.71e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08619 0.1785 0.2116 0.9874 0.992 0.09697 0.7958 0.8796 0.3109 ] Network output: [ -0.009332 0.04603 1.001 8.842e-05 -3.969e-05 0.9718 6.663e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09367 0.09181 0.1685 0.199 0.9857 0.9915 0.09368 0.7267 0.8596 0.2437 ] Network output: [ 0.0007469 0.9995 -0.001416 1.238e-05 -5.557e-06 1.001 9.328e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009889 Epoch 6739 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0136 0.9929 0.9861 5.135e-06 -2.305e-06 -0.00618 3.87e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00319 -0.002952 -0.0098 0.007432 0.9697 0.9741 0.006049 0.845 0.8327 0.02062 ] Network output: [ 0.9988 0.01001 0.001527 -4.374e-05 1.963e-05 -0.009256 -3.296e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.02752 -0.2006 0.201 0.9836 0.9933 0.2035 0.4624 0.8784 0.7231 ] Network output: [ -0.01205 1 1.01 2.368e-06 -1.063e-06 0.01349 1.784e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005258 0.0005406 0.004241 0.004534 0.9889 0.992 0.005354 0.8751 0.9022 0.01496 ] Network output: [ -0.001697 0.01494 1.002 -0.0001554 6.978e-05 0.9854 -0.0001171 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.09481 0.3175 0.1599 0.9851 0.9941 0.1934 0.4672 0.8849 0.7181 ] Network output: [ 0.009106 -0.03976 0.9978 8.876e-05 -3.985e-05 1.024 6.689e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08611 0.1775 0.2104 0.9874 0.992 0.0969 0.7954 0.8796 0.3103 ] Network output: [ -0.009136 0.04463 1.001 8.854e-05 -3.975e-05 0.9728 6.673e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0936 0.09173 0.1682 0.1988 0.9857 0.9915 0.09361 0.7262 0.8595 0.2437 ] Network output: [ -0.0001092 0.9992 -0.0001992 1.203e-05 -5.403e-06 1.001 9.07e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009442 Epoch 6740 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01376 0.9905 0.9862 5.392e-06 -2.42e-06 -0.004153 4.063e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003187 -0.002952 -0.009783 0.007473 0.9697 0.9741 0.006046 0.845 0.8328 0.02063 ] Network output: [ 0.9999 -0.006123 0.002315 -4.224e-05 1.896e-05 0.003858 -3.184e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.02775 -0.1995 0.2036 0.9836 0.9933 0.2033 0.462 0.8784 0.7233 ] Network output: [ -0.01203 0.9994 1.01 2.477e-06 -1.112e-06 0.01423 1.867e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005254 0.0005422 0.004287 0.004619 0.9889 0.992 0.00535 0.8751 0.9022 0.01498 ] Network output: [ -0.0002774 -0.007105 1.003 -0.0001529 6.863e-05 1.004 -0.0001152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.09475 0.319 0.1641 0.9851 0.9941 0.1933 0.467 0.8849 0.7179 ] Network output: [ 0.008742 -0.04409 0.9986 8.893e-05 -3.993e-05 1.028 6.702e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08619 0.1785 0.2116 0.9874 0.992 0.09697 0.7957 0.8796 0.3109 ] Network output: [ -0.009323 0.046 1.001 8.833e-05 -3.965e-05 0.9718 6.657e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09365 0.09179 0.1685 0.199 0.9857 0.9915 0.09366 0.7266 0.8595 0.2437 ] Network output: [ 0.000743 0.9995 -0.00141 1.236e-05 -5.55e-06 1.001 9.317e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009872 Epoch 6741 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01359 0.9929 0.9861 5.126e-06 -2.301e-06 -0.006175 3.863e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00319 -0.002952 -0.009796 0.00743 0.9697 0.9741 0.006049 0.845 0.8327 0.02062 ] Network output: [ 0.9988 0.009939 0.001529 -4.37e-05 1.962e-05 -0.009198 -3.293e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.02754 -0.2006 0.201 0.9836 0.9933 0.2035 0.4624 0.8783 0.7231 ] Network output: [ -0.01205 1 1.01 2.366e-06 -1.062e-06 0.01349 1.783e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005259 0.0005401 0.004241 0.004532 0.9889 0.992 0.005355 0.8751 0.9022 0.01496 ] Network output: [ -0.001691 0.01484 1.002 -0.0001552 6.969e-05 0.9855 -0.000117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1929 0.09479 0.3175 0.1599 0.9851 0.9941 0.1935 0.4672 0.8849 0.7181 ] Network output: [ 0.009097 -0.03977 0.9978 8.866e-05 -3.98e-05 1.024 6.682e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.08611 0.1775 0.2104 0.9874 0.992 0.0969 0.7954 0.8795 0.3103 ] Network output: [ -0.009129 0.04461 1.001 8.845e-05 -3.971e-05 0.9728 6.666e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09358 0.09171 0.1682 0.1988 0.9857 0.9915 0.09359 0.7262 0.8595 0.2437 ] Network output: [ -0.0001058 0.9992 -0.0002038 1.202e-05 -5.398e-06 1.001 9.061e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000943 Epoch 6742 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01375 0.9905 0.9862 5.38e-06 -2.415e-06 -0.004166 4.054e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003188 -0.002953 -0.00978 0.007471 0.9697 0.9741 0.006046 0.845 0.8328 0.02063 ] Network output: [ 0.9999 -0.006055 0.00231 -4.222e-05 1.895e-05 0.003801 -3.182e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.02777 -0.1995 0.2036 0.9836 0.9933 0.2033 0.462 0.8784 0.7233 ] Network output: [ -0.01202 0.9994 1.01 2.474e-06 -1.111e-06 0.01422 1.864e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005255 0.0005417 0.004288 0.004616 0.9889 0.992 0.005351 0.8751 0.9022 0.01498 ] Network output: [ -0.0002842 -0.007007 1.003 -0.0001527 6.855e-05 1.004 -0.0001151 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.09473 0.3191 0.164 0.9851 0.9941 0.1933 0.4669 0.8849 0.7179 ] Network output: [ 0.008737 -0.04406 0.9985 8.883e-05 -3.988e-05 1.028 6.695e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08618 0.1785 0.2115 0.9874 0.992 0.09697 0.7957 0.8796 0.3109 ] Network output: [ -0.009314 0.04597 1.001 8.824e-05 -3.961e-05 0.9718 6.65e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09363 0.09177 0.1684 0.199 0.9857 0.9915 0.09364 0.7265 0.8595 0.2437 ] Network output: [ 0.0007392 0.9995 -0.001404 1.235e-05 -5.543e-06 1.001 9.305e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009855 Epoch 6743 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01359 0.9929 0.9861 5.117e-06 -2.297e-06 -0.00617 3.856e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00319 -0.002952 -0.009793 0.007428 0.9697 0.9741 0.00605 0.845 0.8327 0.02061 ] Network output: [ 0.9988 0.009864 0.001531 -4.365e-05 1.96e-05 -0.009141 -3.29e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1825 -0.02755 -0.2005 0.201 0.9836 0.9933 0.2035 0.4623 0.8783 0.7231 ] Network output: [ -0.01204 1 1.01 2.363e-06 -1.061e-06 0.01348 1.781e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00526 0.0005396 0.004242 0.00453 0.9889 0.992 0.005356 0.8751 0.9021 0.01495 ] Network output: [ -0.001686 0.01474 1.002 -0.000155 6.96e-05 0.9855 -0.0001168 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1929 0.09478 0.3176 0.1599 0.9851 0.9941 0.1935 0.4672 0.8849 0.7181 ] Network output: [ 0.009089 -0.03978 0.9978 8.856e-05 -3.976e-05 1.024 6.674e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.0861 0.1775 0.2104 0.9874 0.992 0.0969 0.7953 0.8795 0.3103 ] Network output: [ -0.009122 0.04459 1.001 8.836e-05 -3.967e-05 0.9728 6.659e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09356 0.0917 0.1682 0.1988 0.9857 0.9915 0.09358 0.7261 0.8595 0.2436 ] Network output: [ -0.0001024 0.9992 -0.0002083 1.201e-05 -5.392e-06 1.001 9.052e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009417 Epoch 6744 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01375 0.9905 0.9862 5.368e-06 -2.41e-06 -0.004179 4.046e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003188 -0.002953 -0.009777 0.007468 0.9697 0.9741 0.006047 0.8449 0.8328 0.02062 ] Network output: [ 0.9999 -0.005988 0.002305 -4.219e-05 1.894e-05 0.003743 -3.18e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.02778 -0.1995 0.2035 0.9836 0.9933 0.2033 0.462 0.8784 0.7233 ] Network output: [ -0.01202 0.9994 1.01 2.47e-06 -1.109e-06 0.0142 1.862e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005256 0.0005412 0.004288 0.004614 0.9889 0.992 0.005352 0.875 0.9022 0.01498 ] Network output: [ -0.000291 -0.006909 1.003 -0.0001525 6.847e-05 1.004 -0.0001149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.09472 0.3191 0.164 0.9851 0.9941 0.1933 0.4669 0.8848 0.7179 ] Network output: [ 0.008733 -0.04402 0.9985 8.873e-05 -3.983e-05 1.028 6.687e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08618 0.1785 0.2115 0.9874 0.992 0.09697 0.7956 0.8795 0.3109 ] Network output: [ -0.009305 0.04594 1.001 8.814e-05 -3.957e-05 0.9718 6.643e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09362 0.09175 0.1684 0.199 0.9857 0.9915 0.09363 0.7265 0.8595 0.2437 ] Network output: [ 0.0007353 0.9995 -0.001399 1.233e-05 -5.536e-06 1.001 9.294e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009838 Epoch 6745 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01358 0.9929 0.9861 5.108e-06 -2.293e-06 -0.006165 3.849e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00319 -0.002953 -0.00979 0.007426 0.9697 0.9741 0.00605 0.845 0.8327 0.02061 ] Network output: [ 0.9988 0.009789 0.001533 -4.361e-05 1.958e-05 -0.009084 -3.287e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1825 -0.02757 -0.2005 0.2009 0.9836 0.9933 0.2035 0.4623 0.8783 0.7231 ] Network output: [ -0.01204 1 1.01 2.361e-06 -1.06e-06 0.01348 1.78e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005261 0.0005391 0.004243 0.004529 0.9889 0.992 0.005356 0.875 0.9021 0.01495 ] Network output: [ -0.00168 0.01464 1.002 -0.0001548 6.951e-05 0.9856 -0.0001167 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1929 0.09476 0.3176 0.1598 0.9851 0.9941 0.1935 0.4671 0.8849 0.7181 ] Network output: [ 0.009081 -0.03978 0.9978 8.846e-05 -3.971e-05 1.024 6.666e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09684 0.0861 0.1776 0.2103 0.9874 0.992 0.0969 0.7953 0.8795 0.3103 ] Network output: [ -0.009115 0.04457 1.001 8.826e-05 -3.962e-05 0.9728 6.652e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09355 0.09168 0.1682 0.1987 0.9857 0.9915 0.09356 0.726 0.8595 0.2436 ] Network output: [ -9.907e-05 0.9992 -0.0002128 1.2e-05 -5.387e-06 1.001 9.043e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009405 Epoch 6746 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01374 0.9905 0.9862 5.356e-06 -2.405e-06 -0.004192 4.037e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003188 -0.002953 -0.009773 0.007466 0.9697 0.9741 0.006047 0.8449 0.8328 0.02062 ] Network output: [ 0.9999 -0.005921 0.002301 -4.217e-05 1.893e-05 0.003686 -3.178e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.0278 -0.1994 0.2035 0.9836 0.9933 0.2034 0.4619 0.8784 0.7233 ] Network output: [ -0.01202 0.9994 1.01 2.467e-06 -1.108e-06 0.01419 1.859e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005257 0.0005406 0.004288 0.004611 0.9889 0.992 0.005353 0.875 0.9022 0.01497 ] Network output: [ -0.0002978 -0.006811 1.003 -0.0001523 6.839e-05 1.003 -0.0001148 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1927 0.0947 0.3191 0.1639 0.9851 0.9941 0.1933 0.4669 0.8848 0.7179 ] Network output: [ 0.008728 -0.04399 0.9985 8.863e-05 -3.979e-05 1.028 6.679e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08618 0.1785 0.2115 0.9874 0.992 0.09697 0.7956 0.8795 0.3109 ] Network output: [ -0.009296 0.04591 1.001 8.805e-05 -3.953e-05 0.9718 6.636e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0936 0.09173 0.1684 0.1989 0.9857 0.9915 0.09361 0.7264 0.8594 0.2437 ] Network output: [ 0.0007315 0.9995 -0.001393 1.232e-05 -5.529e-06 1.001 9.282e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009822 Epoch 6747 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01358 0.9929 0.9862 5.099e-06 -2.289e-06 -0.00616 3.843e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00319 -0.002953 -0.009786 0.007423 0.9697 0.9741 0.00605 0.845 0.8327 0.0206 ] Network output: [ 0.9988 0.009715 0.001535 -4.357e-05 1.956e-05 -0.009027 -3.284e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1825 -0.02759 -0.2004 0.2009 0.9836 0.9933 0.2035 0.4623 0.8783 0.7231 ] Network output: [ -0.01204 1 1.01 2.359e-06 -1.059e-06 0.01347 1.778e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005262 0.0005386 0.004243 0.004527 0.9889 0.992 0.005357 0.875 0.9021 0.01495 ] Network output: [ -0.001674 0.01455 1.002 -0.0001546 6.942e-05 0.9857 -0.0001165 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1929 0.09474 0.3176 0.1598 0.9851 0.9941 0.1935 0.4671 0.8848 0.7181 ] Network output: [ 0.009073 -0.03978 0.9978 8.836e-05 -3.967e-05 1.024 6.659e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.0861 0.1776 0.2103 0.9874 0.992 0.09691 0.7952 0.8795 0.3103 ] Network output: [ -0.009108 0.04455 1.001 8.817e-05 -3.958e-05 0.9728 6.645e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09353 0.09166 0.1682 0.1987 0.9857 0.9915 0.09354 0.7259 0.8594 0.2436 ] Network output: [ -9.57e-05 0.9992 -0.0002174 1.199e-05 -5.382e-06 1.001 9.034e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009392 Epoch 6748 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01373 0.9905 0.9862 5.345e-06 -2.399e-06 -0.004205 4.028e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003188 -0.002954 -0.00977 0.007463 0.9697 0.9741 0.006047 0.8449 0.8328 0.02061 ] Network output: [ 0.9999 -0.005854 0.002296 -4.214e-05 1.892e-05 0.003628 -3.176e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1823 -0.02781 -0.1994 0.2035 0.9836 0.9933 0.2034 0.4619 0.8784 0.7233 ] Network output: [ -0.01202 0.9994 1.01 2.464e-06 -1.106e-06 0.01418 1.857e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005258 0.0005401 0.004288 0.004609 0.9889 0.992 0.005354 0.875 0.9022 0.01497 ] Network output: [ -0.0003045 -0.006713 1.003 -0.0001522 6.832e-05 1.003 -0.0001147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.09469 0.3192 0.1638 0.9851 0.9941 0.1934 0.4668 0.8848 0.7179 ] Network output: [ 0.008724 -0.04395 0.9985 8.852e-05 -3.974e-05 1.028 6.671e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08617 0.1785 0.2114 0.9874 0.992 0.09697 0.7955 0.8795 0.3108 ] Network output: [ -0.009287 0.04588 1.001 8.796e-05 -3.949e-05 0.9719 6.629e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09358 0.09172 0.1684 0.1989 0.9857 0.9915 0.09359 0.7263 0.8594 0.2437 ] Network output: [ 0.0007276 0.9995 -0.001387 1.23e-05 -5.523e-06 1.001 9.271e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009805 Epoch 6749 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01357 0.9929 0.9862 5.09e-06 -2.285e-06 -0.006155 3.836e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00319 -0.002953 -0.009783 0.007421 0.9697 0.9741 0.006051 0.845 0.8327 0.0206 ] Network output: [ 0.9988 0.009641 0.001537 -4.353e-05 1.954e-05 -0.00897 -3.281e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1825 -0.02761 -0.2004 0.2009 0.9836 0.9933 0.2036 0.4623 0.8783 0.7231 ] Network output: [ -0.01204 1 1.01 2.357e-06 -1.058e-06 0.01347 1.776e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005263 0.000538 0.004244 0.004525 0.9889 0.992 0.005358 0.875 0.9021 0.01494 ] Network output: [ -0.001669 0.01445 1.002 -0.0001544 6.933e-05 0.9858 -0.0001164 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1929 0.09473 0.3177 0.1598 0.9851 0.9941 0.1935 0.4671 0.8848 0.7181 ] Network output: [ 0.009065 -0.03979 0.9978 8.825e-05 -3.962e-05 1.024 6.651e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.08609 0.1776 0.2103 0.9874 0.992 0.09691 0.7952 0.8795 0.3102 ] Network output: [ -0.009101 0.04453 1.001 8.808e-05 -3.954e-05 0.9728 6.638e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09351 0.09165 0.1682 0.1987 0.9857 0.9915 0.09352 0.7259 0.8594 0.2436 ] Network output: [ -9.233e-05 0.9992 -0.0002219 1.198e-05 -5.376e-06 1.001 9.025e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000938 Epoch 6750 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01373 0.9906 0.9862 5.333e-06 -2.394e-06 -0.004218 4.019e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003188 -0.002954 -0.009767 0.00746 0.9697 0.9741 0.006048 0.8449 0.8327 0.02061 ] Network output: [ 0.9999 -0.005788 0.002291 -4.211e-05 1.891e-05 0.003571 -3.174e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.02783 -0.1994 0.2034 0.9836 0.9933 0.2034 0.4619 0.8784 0.7233 ] Network output: [ -0.01201 0.9994 1.01 2.461e-06 -1.105e-06 0.01417 1.855e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005259 0.0005396 0.004289 0.004607 0.9889 0.992 0.005355 0.875 0.9022 0.01496 ] Network output: [ -0.0003112 -0.006616 1.003 -0.000152 6.824e-05 1.003 -0.0001145 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.09467 0.3192 0.1638 0.9851 0.9941 0.1934 0.4668 0.8848 0.7179 ] Network output: [ 0.008719 -0.04392 0.9985 8.842e-05 -3.969e-05 1.028 6.664e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08617 0.1785 0.2114 0.9874 0.992 0.09697 0.7954 0.8795 0.3108 ] Network output: [ -0.009278 0.04585 1.001 8.787e-05 -3.945e-05 0.9719 6.622e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09356 0.0917 0.1684 0.1989 0.9857 0.9915 0.09358 0.7262 0.8594 0.2436 ] Network output: [ 0.0007238 0.9995 -0.001381 1.229e-05 -5.516e-06 1.001 9.259e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009789 Epoch 6751 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01357 0.9929 0.9862 5.081e-06 -2.281e-06 -0.00615 3.829e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00319 -0.002954 -0.009779 0.007419 0.9697 0.9741 0.006051 0.8449 0.8326 0.02059 ] Network output: [ 0.9988 0.009567 0.001539 -4.349e-05 1.952e-05 -0.008913 -3.278e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1825 -0.02762 -0.2004 0.2009 0.9836 0.9933 0.2036 0.4622 0.8783 0.7231 ] Network output: [ -0.01203 1 1.01 2.355e-06 -1.057e-06 0.01347 1.775e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005264 0.0005375 0.004245 0.004524 0.9889 0.992 0.005359 0.875 0.9021 0.01494 ] Network output: [ -0.001663 0.01436 1.002 -0.0001542 6.924e-05 0.9859 -0.0001162 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1929 0.09471 0.3177 0.1598 0.9851 0.9941 0.1935 0.467 0.8848 0.7181 ] Network output: [ 0.009056 -0.03979 0.9978 8.815e-05 -3.958e-05 1.024 6.644e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.08609 0.1776 0.2103 0.9874 0.992 0.09691 0.7951 0.8794 0.3102 ] Network output: [ -0.009095 0.04452 1.001 8.799e-05 -3.95e-05 0.9728 6.631e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0935 0.09163 0.1681 0.1987 0.9857 0.9915 0.09351 0.7258 0.8594 0.2436 ] Network output: [ -8.897e-05 0.9992 -0.0002264 1.196e-05 -5.371e-06 1.001 9.016e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009368 Epoch 6752 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01372 0.9906 0.9862 5.321e-06 -2.389e-06 -0.004231 4.01e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003188 -0.002954 -0.009764 0.007458 0.9697 0.9741 0.006048 0.8449 0.8327 0.0206 ] Network output: [ 0.9999 -0.005721 0.002287 -4.209e-05 1.89e-05 0.003514 -3.172e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.02784 -0.1993 0.2034 0.9836 0.9933 0.2034 0.4618 0.8784 0.7233 ] Network output: [ -0.01201 0.9994 1.01 2.458e-06 -1.103e-06 0.01416 1.852e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00526 0.000539 0.004289 0.004604 0.9889 0.992 0.005356 0.875 0.9021 0.01496 ] Network output: [ -0.0003179 -0.006518 1.003 -0.0001518 6.816e-05 1.003 -0.0001144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.09465 0.3192 0.1637 0.9851 0.9941 0.1934 0.4668 0.8848 0.7179 ] Network output: [ 0.008715 -0.04389 0.9985 8.832e-05 -3.965e-05 1.028 6.656e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08616 0.1785 0.2114 0.9874 0.992 0.09697 0.7954 0.8794 0.3108 ] Network output: [ -0.009269 0.04582 1.001 8.778e-05 -3.941e-05 0.9719 6.615e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09355 0.09168 0.1684 0.1989 0.9857 0.9915 0.09356 0.7262 0.8593 0.2436 ] Network output: [ 0.0007199 0.9995 -0.001376 1.227e-05 -5.509e-06 1.001 9.248e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009772 Epoch 6753 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01356 0.9929 0.9862 5.071e-06 -2.277e-06 -0.006145 3.822e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00319 -0.002954 -0.009776 0.007417 0.9697 0.9741 0.006052 0.8449 0.8326 0.02059 ] Network output: [ 0.9988 0.009493 0.001541 -4.345e-05 1.951e-05 -0.008857 -3.275e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1825 -0.02764 -0.2003 0.2009 0.9836 0.9933 0.2036 0.4622 0.8783 0.7231 ] Network output: [ -0.01203 1 1.01 2.353e-06 -1.056e-06 0.01346 1.773e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005265 0.000537 0.004245 0.004522 0.9889 0.992 0.00536 0.875 0.9021 0.01494 ] Network output: [ -0.001657 0.01426 1.003 -0.000154 6.915e-05 0.9859 -0.0001161 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1929 0.0947 0.3178 0.1598 0.9851 0.9941 0.1936 0.467 0.8848 0.718 ] Network output: [ 0.009048 -0.0398 0.9978 8.805e-05 -3.953e-05 1.024 6.636e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.08609 0.1776 0.2103 0.9874 0.992 0.09691 0.7951 0.8794 0.3102 ] Network output: [ -0.009088 0.0445 1.001 8.789e-05 -3.946e-05 0.9728 6.624e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09348 0.09161 0.1681 0.1987 0.9857 0.9915 0.09349 0.7257 0.8593 0.2436 ] Network output: [ -8.562e-05 0.9992 -0.0002308 1.195e-05 -5.366e-06 1.001 9.007e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009355 Epoch 6754 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01371 0.9906 0.9862 5.309e-06 -2.384e-06 -0.004244 4.001e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003189 -0.002954 -0.00976 0.007455 0.9697 0.9741 0.006049 0.8449 0.8327 0.0206 ] Network output: [ 0.9999 -0.005655 0.002282 -4.206e-05 1.888e-05 0.003458 -3.17e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.02786 -0.1993 0.2034 0.9836 0.9933 0.2034 0.4618 0.8784 0.7232 ] Network output: [ -0.01201 0.9994 1.01 2.454e-06 -1.102e-06 0.01415 1.85e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005261 0.0005385 0.004289 0.004602 0.9889 0.992 0.005356 0.875 0.9021 0.01496 ] Network output: [ -0.0003245 -0.006422 1.003 -0.0001516 6.808e-05 1.003 -0.0001143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.09464 0.3193 0.1637 0.9851 0.9941 0.1934 0.4667 0.8848 0.7179 ] Network output: [ 0.00871 -0.04385 0.9985 8.821e-05 -3.96e-05 1.028 6.648e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08616 0.1785 0.2114 0.9874 0.992 0.09697 0.7953 0.8794 0.3108 ] Network output: [ -0.00926 0.04579 1.001 8.769e-05 -3.937e-05 0.9719 6.609e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09353 0.09166 0.1683 0.1989 0.9857 0.9915 0.09354 0.7261 0.8593 0.2436 ] Network output: [ 0.0007161 0.9995 -0.00137 1.226e-05 -5.502e-06 1.001 9.236e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009756 Epoch 6755 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01356 0.9929 0.9862 5.062e-06 -2.273e-06 -0.006141 3.815e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003191 -0.002954 -0.009772 0.007414 0.9697 0.9741 0.006052 0.8449 0.8326 0.02058 ] Network output: [ 0.9988 0.009419 0.001543 -4.341e-05 1.949e-05 -0.0088 -3.271e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1825 -0.02766 -0.2003 0.2009 0.9836 0.9933 0.2036 0.4622 0.8783 0.7231 ] Network output: [ -0.01203 1 1.01 2.35e-06 -1.055e-06 0.01346 1.771e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005265 0.0005365 0.004246 0.004521 0.9889 0.992 0.005361 0.875 0.9021 0.01493 ] Network output: [ -0.001652 0.01416 1.003 -0.0001538 6.906e-05 0.986 -0.0001159 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.193 0.09468 0.3178 0.1597 0.9851 0.9941 0.1936 0.467 0.8848 0.718 ] Network output: [ 0.00904 -0.0398 0.9977 8.795e-05 -3.948e-05 1.024 6.628e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.08609 0.1776 0.2102 0.9874 0.992 0.09691 0.795 0.8794 0.3102 ] Network output: [ -0.009081 0.04448 1.001 8.78e-05 -3.942e-05 0.9728 6.617e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09346 0.09159 0.1681 0.1987 0.9857 0.9915 0.09347 0.7257 0.8593 0.2436 ] Network output: [ -8.227e-05 0.9992 -0.0002353 1.194e-05 -5.36e-06 1.001 8.998e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009343 Epoch 6756 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01371 0.9906 0.9863 5.298e-06 -2.378e-06 -0.004257 3.992e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003189 -0.002955 -0.009757 0.007453 0.9697 0.9741 0.006049 0.8449 0.8327 0.02059 ] Network output: [ 0.9999 -0.005589 0.002277 -4.204e-05 1.887e-05 0.003401 -3.168e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.02787 -0.1993 0.2033 0.9836 0.9933 0.2035 0.4618 0.8783 0.7232 ] Network output: [ -0.01201 0.9994 1.01 2.451e-06 -1.1e-06 0.01414 1.847e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005262 0.000538 0.004289 0.004599 0.9889 0.992 0.005357 0.8749 0.9021 0.01495 ] Network output: [ -0.0003311 -0.006325 1.003 -0.0001515 6.8e-05 1.003 -0.0001142 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.09462 0.3193 0.1636 0.9851 0.9941 0.1934 0.4667 0.8848 0.7178 ] Network output: [ 0.008705 -0.04382 0.9985 8.811e-05 -3.956e-05 1.028 6.64e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08616 0.1785 0.2113 0.9874 0.992 0.09697 0.7953 0.8794 0.3108 ] Network output: [ -0.009251 0.04576 1.001 8.76e-05 -3.933e-05 0.9719 6.602e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09351 0.09165 0.1683 0.1989 0.9857 0.9915 0.09352 0.726 0.8593 0.2436 ] Network output: [ 0.0007123 0.9995 -0.001364 1.224e-05 -5.495e-06 1.001 9.225e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000974 Epoch 6757 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01355 0.9929 0.9862 5.053e-06 -2.268e-06 -0.006136 3.808e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003191 -0.002954 -0.009769 0.007412 0.9697 0.9741 0.006052 0.8449 0.8326 0.02058 ] Network output: [ 0.9988 0.009346 0.001544 -4.337e-05 1.947e-05 -0.008744 -3.268e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1826 -0.02768 -0.2002 0.2009 0.9836 0.9933 0.2036 0.4621 0.8783 0.7231 ] Network output: [ -0.01203 1 1.01 2.348e-06 -1.054e-06 0.01345 1.77e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005266 0.000536 0.004247 0.004519 0.9889 0.992 0.005362 0.8749 0.9021 0.01493 ] Network output: [ -0.001646 0.01407 1.003 -0.0001536 6.897e-05 0.9861 -0.0001158 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.193 0.09466 0.3179 0.1597 0.9851 0.9941 0.1936 0.4669 0.8848 0.718 ] Network output: [ 0.009032 -0.03981 0.9977 8.785e-05 -3.944e-05 1.024 6.621e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.08608 0.1776 0.2102 0.9874 0.992 0.09691 0.7949 0.8794 0.3102 ] Network output: [ -0.009074 0.04446 1.001 8.771e-05 -3.937e-05 0.9728 6.61e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09345 0.09158 0.1681 0.1987 0.9857 0.9915 0.09346 0.7256 0.8593 0.2436 ] Network output: [ -7.894e-05 0.9993 -0.0002398 1.193e-05 -5.355e-06 1.001 8.989e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009331 Epoch 6758 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0137 0.9906 0.9863 5.286e-06 -2.373e-06 -0.00427 3.984e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003189 -0.002955 -0.009754 0.00745 0.9697 0.9741 0.006049 0.8448 0.8327 0.02059 ] Network output: [ 0.9999 -0.005523 0.002273 -4.201e-05 1.886e-05 0.003344 -3.166e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.02789 -0.1992 0.2033 0.9836 0.9933 0.2035 0.4618 0.8783 0.7232 ] Network output: [ -0.01201 0.9994 1.01 2.448e-06 -1.099e-06 0.01412 1.845e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005263 0.0005375 0.00429 0.004597 0.9889 0.992 0.005358 0.8749 0.9021 0.01495 ] Network output: [ -0.0003377 -0.006228 1.003 -0.0001513 6.792e-05 1.003 -0.000114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1928 0.09461 0.3193 0.1635 0.9851 0.9941 0.1934 0.4667 0.8848 0.7178 ] Network output: [ 0.008701 -0.04378 0.9984 8.801e-05 -3.951e-05 1.028 6.632e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08616 0.1785 0.2113 0.9874 0.992 0.09697 0.7952 0.8794 0.3108 ] Network output: [ -0.009243 0.04573 1.001 8.751e-05 -3.929e-05 0.9719 6.595e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09349 0.09163 0.1683 0.1988 0.9857 0.9915 0.09351 0.7259 0.8592 0.2436 ] Network output: [ 0.0007084 0.9995 -0.001359 1.223e-05 -5.488e-06 1.001 9.213e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009723 Epoch 6759 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01355 0.9929 0.9862 5.044e-06 -2.264e-06 -0.006131 3.801e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003191 -0.002955 -0.009765 0.00741 0.9697 0.9741 0.006053 0.8449 0.8326 0.02058 ] Network output: [ 0.9988 0.009273 0.001546 -4.333e-05 1.945e-05 -0.008687 -3.265e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1826 -0.02769 -0.2002 0.2009 0.9836 0.9933 0.2036 0.4621 0.8782 0.723 ] Network output: [ -0.01203 1 1.01 2.346e-06 -1.053e-06 0.01345 1.768e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005267 0.0005355 0.004247 0.004517 0.9889 0.992 0.005363 0.8749 0.9021 0.01493 ] Network output: [ -0.001641 0.01397 1.003 -0.0001534 6.888e-05 0.9862 -0.0001156 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.193 0.09465 0.3179 0.1597 0.9851 0.9941 0.1936 0.4669 0.8848 0.718 ] Network output: [ 0.009024 -0.03981 0.9977 8.775e-05 -3.939e-05 1.024 6.613e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.08608 0.1776 0.2102 0.9874 0.992 0.09691 0.7949 0.8793 0.3102 ] Network output: [ -0.009067 0.04444 1.001 8.761e-05 -3.933e-05 0.9728 6.603e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09343 0.09156 0.1681 0.1986 0.9857 0.9915 0.09344 0.7255 0.8592 0.2436 ] Network output: [ -7.56e-05 0.9993 -0.0002442 1.192e-05 -5.35e-06 1.001 8.98e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009319 Epoch 6760 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0137 0.9906 0.9863 5.274e-06 -2.368e-06 -0.004283 3.975e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003189 -0.002955 -0.00975 0.007447 0.9697 0.9741 0.00605 0.8448 0.8327 0.02058 ] Network output: [ 0.9999 -0.005457 0.002268 -4.198e-05 1.885e-05 0.003288 -3.164e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1824 -0.02791 -0.1992 0.2033 0.9836 0.9933 0.2035 0.4617 0.8783 0.7232 ] Network output: [ -0.012 0.9995 1.01 2.444e-06 -1.097e-06 0.01411 1.842e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005264 0.000537 0.00429 0.004595 0.9889 0.992 0.005359 0.8749 0.9021 0.01495 ] Network output: [ -0.0003443 -0.006132 1.003 -0.0001511 6.784e-05 1.003 -0.0001139 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1929 0.09459 0.3194 0.1635 0.9851 0.9941 0.1935 0.4666 0.8847 0.7178 ] Network output: [ 0.008696 -0.04375 0.9984 8.79e-05 -3.946e-05 1.028 6.625e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08615 0.1785 0.2113 0.9874 0.992 0.09697 0.7951 0.8793 0.3108 ] Network output: [ -0.009234 0.04569 1.001 8.742e-05 -3.924e-05 0.9719 6.588e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09348 0.09161 0.1683 0.1988 0.9857 0.9915 0.09349 0.7259 0.8592 0.2436 ] Network output: [ 0.0007046 0.9995 -0.001353 1.221e-05 -5.482e-06 1.001 9.202e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009707 Epoch 6761 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01354 0.9929 0.9862 5.035e-06 -2.26e-06 -0.006127 3.794e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003191 -0.002955 -0.009762 0.007408 0.9697 0.9741 0.006053 0.8449 0.8326 0.02057 ] Network output: [ 0.9989 0.009199 0.001548 -4.329e-05 1.943e-05 -0.008631 -3.262e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1826 -0.02771 -0.2002 0.2009 0.9836 0.9933 0.2037 0.4621 0.8782 0.723 ] Network output: [ -0.01202 1 1.01 2.343e-06 -1.052e-06 0.01344 1.766e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005268 0.000535 0.004248 0.004516 0.9889 0.992 0.005364 0.8749 0.902 0.01492 ] Network output: [ -0.001635 0.01388 1.003 -0.0001532 6.879e-05 0.9862 -0.0001155 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.193 0.09463 0.318 0.1597 0.9851 0.9941 0.1936 0.4669 0.8848 0.718 ] Network output: [ 0.009015 -0.03981 0.9977 8.765e-05 -3.935e-05 1.024 6.605e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.08608 0.1776 0.2102 0.9874 0.992 0.09691 0.7948 0.8793 0.3102 ] Network output: [ -0.00906 0.04442 1.001 8.752e-05 -3.929e-05 0.9728 6.596e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09341 0.09154 0.1681 0.1986 0.9857 0.9915 0.09343 0.7255 0.8592 0.2436 ] Network output: [ -7.228e-05 0.9993 -0.0002486 1.19e-05 -5.344e-06 1.001 8.971e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009307 Epoch 6762 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01369 0.9907 0.9863 5.262e-06 -2.362e-06 -0.004297 3.966e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003189 -0.002956 -0.009747 0.007445 0.9697 0.9741 0.00605 0.8448 0.8327 0.02058 ] Network output: [ 0.9999 -0.005391 0.002263 -4.196e-05 1.884e-05 0.003232 -3.162e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1825 -0.02792 -0.1992 0.2032 0.9836 0.9933 0.2035 0.4617 0.8783 0.7232 ] Network output: [ -0.012 0.9995 1.01 2.441e-06 -1.096e-06 0.0141 1.84e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005265 0.0005365 0.00429 0.004592 0.9889 0.992 0.00536 0.8749 0.9021 0.01494 ] Network output: [ -0.0003509 -0.006037 1.003 -0.0001509 6.776e-05 1.003 -0.0001138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1929 0.09458 0.3194 0.1634 0.9851 0.9941 0.1935 0.4666 0.8847 0.7178 ] Network output: [ 0.008692 -0.04371 0.9984 8.78e-05 -3.942e-05 1.028 6.617e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08615 0.1785 0.2112 0.9874 0.992 0.09697 0.7951 0.8793 0.3107 ] Network output: [ -0.009225 0.04566 1.001 8.733e-05 -3.92e-05 0.9719 6.581e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09346 0.09159 0.1683 0.1988 0.9857 0.9915 0.09347 0.7258 0.8592 0.2436 ] Network output: [ 0.0007008 0.9995 -0.001347 1.22e-05 -5.475e-06 1.001 9.191e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009691 Epoch 6763 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01354 0.9929 0.9862 5.025e-06 -2.256e-06 -0.006122 3.787e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003191 -0.002955 -0.009759 0.007406 0.9697 0.9741 0.006053 0.8449 0.8326 0.02057 ] Network output: [ 0.9989 0.009126 0.00155 -4.324e-05 1.941e-05 -0.008575 -3.259e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1826 -0.02773 -0.2001 0.2008 0.9836 0.9933 0.2037 0.462 0.8782 0.723 ] Network output: [ -0.01202 1 1.01 2.341e-06 -1.051e-06 0.01344 1.764e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005269 0.0005345 0.004249 0.004514 0.9889 0.992 0.005365 0.8749 0.902 0.01492 ] Network output: [ -0.00163 0.01378 1.003 -0.000153 6.871e-05 0.9863 -0.0001153 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.193 0.09462 0.318 0.1596 0.9851 0.9941 0.1936 0.4668 0.8847 0.718 ] Network output: [ 0.009007 -0.03982 0.9977 8.755e-05 -3.93e-05 1.024 6.598e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.08608 0.1776 0.2102 0.9874 0.992 0.09691 0.7948 0.8793 0.3102 ] Network output: [ -0.009054 0.0444 1.001 8.743e-05 -3.925e-05 0.9728 6.589e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0934 0.09153 0.1681 0.1986 0.9857 0.9915 0.09341 0.7254 0.8592 0.2436 ] Network output: [ -6.897e-05 0.9993 -0.000253 1.189e-05 -5.339e-06 1.001 8.962e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009295 Epoch 6764 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01368 0.9907 0.9863 5.251e-06 -2.357e-06 -0.00431 3.957e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003189 -0.002956 -0.009744 0.007442 0.9697 0.9741 0.006051 0.8448 0.8327 0.02057 ] Network output: [ 0.9999 -0.005326 0.002259 -4.193e-05 1.882e-05 0.003176 -3.16e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1825 -0.02794 -0.1991 0.2032 0.9836 0.9933 0.2035 0.4617 0.8783 0.7232 ] Network output: [ -0.012 0.9995 1.01 2.437e-06 -1.094e-06 0.01409 1.837e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005266 0.000536 0.004291 0.00459 0.9889 0.992 0.005361 0.8749 0.9021 0.01494 ] Network output: [ -0.0003574 -0.005941 1.003 -0.0001508 6.769e-05 1.003 -0.0001136 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1929 0.09456 0.3194 0.1634 0.9851 0.9941 0.1935 0.4666 0.8847 0.7178 ] Network output: [ 0.008687 -0.04368 0.9984 8.77e-05 -3.937e-05 1.028 6.609e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08615 0.1785 0.2112 0.9874 0.992 0.09697 0.795 0.8793 0.3107 ] Network output: [ -0.009216 0.04563 1.001 8.723e-05 -3.916e-05 0.972 6.574e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09344 0.09158 0.1683 0.1988 0.9857 0.9915 0.09346 0.7257 0.8592 0.2436 ] Network output: [ 0.000697 0.9995 -0.001342 1.218e-05 -5.468e-06 1.001 9.179e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009675 Epoch 6765 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01353 0.9928 0.9862 5.016e-06 -2.252e-06 -0.006118 3.78e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003191 -0.002956 -0.009755 0.007403 0.9697 0.9741 0.006054 0.8449 0.8326 0.02056 ] Network output: [ 0.9989 0.009054 0.001552 -4.32e-05 1.94e-05 -0.008519 -3.256e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1826 -0.02774 -0.2001 0.2008 0.9836 0.9933 0.2037 0.462 0.8782 0.723 ] Network output: [ -0.01202 1 1.01 2.339e-06 -1.05e-06 0.01343 1.763e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00527 0.000534 0.004249 0.004512 0.9889 0.992 0.005366 0.8749 0.902 0.01492 ] Network output: [ -0.001624 0.01369 1.003 -0.0001528 6.862e-05 0.9864 -0.0001152 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.193 0.0946 0.3181 0.1596 0.9851 0.9941 0.1936 0.4668 0.8847 0.718 ] Network output: [ 0.008999 -0.03982 0.9977 8.745e-05 -3.926e-05 1.024 6.59e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.08607 0.1776 0.2102 0.9874 0.992 0.09691 0.7947 0.8793 0.3102 ] Network output: [ -0.009047 0.04438 1.001 8.734e-05 -3.921e-05 0.9728 6.582e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09338 0.09151 0.1681 0.1986 0.9857 0.9915 0.09339 0.7253 0.8591 0.2436 ] Network output: [ -6.566e-05 0.9993 -0.0002574 1.188e-05 -5.334e-06 1.001 8.953e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009283 Epoch 6766 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01368 0.9907 0.9863 5.239e-06 -2.352e-06 -0.004323 3.948e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00319 -0.002956 -0.00974 0.00744 0.9697 0.9741 0.006051 0.8448 0.8326 0.02057 ] Network output: [ 0.9999 -0.00526 0.002254 -4.19e-05 1.881e-05 0.00312 -3.158e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1825 -0.02795 -0.1991 0.2032 0.9836 0.9933 0.2035 0.4616 0.8783 0.7232 ] Network output: [ -0.012 0.9995 1.01 2.434e-06 -1.093e-06 0.01408 1.834e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005267 0.0005354 0.004291 0.004587 0.9889 0.992 0.005362 0.8748 0.9021 0.01493 ] Network output: [ -0.0003639 -0.005846 1.003 -0.0001506 6.761e-05 1.003 -0.0001135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1929 0.09455 0.3194 0.1633 0.9851 0.9941 0.1935 0.4666 0.8847 0.7178 ] Network output: [ 0.008682 -0.04365 0.9984 8.759e-05 -3.932e-05 1.028 6.601e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08614 0.1785 0.2112 0.9874 0.992 0.09697 0.795 0.8793 0.3107 ] Network output: [ -0.009207 0.0456 1.001 8.714e-05 -3.912e-05 0.972 6.567e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09343 0.09156 0.1683 0.1988 0.9857 0.9915 0.09344 0.7256 0.8591 0.2436 ] Network output: [ 0.0006932 0.9995 -0.001336 1.216e-05 -5.461e-06 1.001 9.168e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009659 Epoch 6767 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01353 0.9928 0.9862 5.007e-06 -2.248e-06 -0.006113 3.773e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003191 -0.002956 -0.009752 0.007401 0.9697 0.9741 0.006054 0.8448 0.8326 0.02056 ] Network output: [ 0.9989 0.008981 0.001554 -4.316e-05 1.938e-05 -0.008463 -3.253e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1826 -0.02776 -0.2 0.2008 0.9836 0.9933 0.2037 0.462 0.8782 0.723 ] Network output: [ -0.01202 1 1.01 2.336e-06 -1.049e-06 0.01343 1.761e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005271 0.0005336 0.00425 0.004511 0.9889 0.992 0.005367 0.8749 0.902 0.01491 ] Network output: [ -0.001618 0.01359 1.003 -0.0001526 6.853e-05 0.9865 -0.000115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1931 0.09458 0.3181 0.1596 0.9851 0.994 0.1937 0.4668 0.8847 0.718 ] Network output: [ 0.008991 -0.03982 0.9977 8.734e-05 -3.921e-05 1.024 6.583e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09685 0.08607 0.1776 0.2101 0.9874 0.992 0.09692 0.7947 0.8792 0.3102 ] Network output: [ -0.00904 0.04436 1.001 8.724e-05 -3.917e-05 0.9728 6.575e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09336 0.09149 0.168 0.1986 0.9857 0.9915 0.09338 0.7252 0.8591 0.2436 ] Network output: [ -6.236e-05 0.9993 -0.0002618 1.187e-05 -5.328e-06 1.001 8.944e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009271 Epoch 6768 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01367 0.9907 0.9863 5.227e-06 -2.347e-06 -0.004336 3.939e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00319 -0.002956 -0.009737 0.007437 0.9697 0.9741 0.006051 0.8448 0.8326 0.02056 ] Network output: [ 0.9999 -0.005195 0.002249 -4.188e-05 1.88e-05 0.003065 -3.156e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1825 -0.02797 -0.1991 0.2031 0.9836 0.9933 0.2036 0.4616 0.8783 0.7232 ] Network output: [ -0.01199 0.9995 1.01 2.431e-06 -1.091e-06 0.01407 1.832e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005268 0.0005349 0.004291 0.004585 0.9889 0.992 0.005363 0.8748 0.9021 0.01493 ] Network output: [ -0.0003703 -0.005751 1.003 -0.0001504 6.753e-05 1.003 -0.0001134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1929 0.09453 0.3195 0.1632 0.9851 0.9941 0.1935 0.4665 0.8847 0.7178 ] Network output: [ 0.008678 -0.04361 0.9984 8.749e-05 -3.928e-05 1.028 6.594e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08614 0.1785 0.2112 0.9874 0.992 0.09697 0.7949 0.8792 0.3107 ] Network output: [ -0.009198 0.04557 1.001 8.705e-05 -3.908e-05 0.972 6.56e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09341 0.09154 0.1682 0.1988 0.9857 0.9915 0.09342 0.7256 0.8591 0.2436 ] Network output: [ 0.0006894 0.9995 -0.00133 1.215e-05 -5.454e-06 1.001 9.156e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009643 Epoch 6769 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01352 0.9928 0.9862 4.998e-06 -2.244e-06 -0.006109 3.766e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003192 -0.002956 -0.009748 0.007399 0.9697 0.9741 0.006055 0.8448 0.8325 0.02055 ] Network output: [ 0.9989 0.008909 0.001556 -4.312e-05 1.936e-05 -0.008407 -3.25e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1827 -0.02778 -0.2 0.2008 0.9836 0.9933 0.2037 0.4619 0.8782 0.723 ] Network output: [ -0.01201 1 1.01 2.334e-06 -1.048e-06 0.01342 1.759e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005272 0.0005331 0.004251 0.004509 0.9889 0.992 0.005368 0.8748 0.902 0.01491 ] Network output: [ -0.001613 0.0135 1.003 -0.0001524 6.844e-05 0.9865 -0.0001149 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1931 0.09457 0.3181 0.1596 0.9851 0.994 0.1937 0.4667 0.8847 0.718 ] Network output: [ 0.008983 -0.03983 0.9977 8.724e-05 -3.917e-05 1.025 6.575e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09686 0.08607 0.1776 0.2101 0.9874 0.992 0.09692 0.7946 0.8792 0.3101 ] Network output: [ -0.009033 0.04434 1.001 8.715e-05 -3.912e-05 0.9728 6.568e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09335 0.09148 0.168 0.1986 0.9857 0.9915 0.09336 0.7252 0.8591 0.2436 ] Network output: [ -5.908e-05 0.9993 -0.0002661 1.186e-05 -5.323e-06 1.001 8.936e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009259 Epoch 6770 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01366 0.9907 0.9863 5.215e-06 -2.341e-06 -0.004349 3.931e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00319 -0.002957 -0.009734 0.007434 0.9697 0.9741 0.006052 0.8448 0.8326 0.02056 ] Network output: [ 0.9999 -0.00513 0.002245 -4.185e-05 1.879e-05 0.003009 -3.154e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1825 -0.02798 -0.1991 0.2031 0.9836 0.9933 0.2036 0.4616 0.8783 0.7232 ] Network output: [ -0.01199 0.9995 1.01 2.427e-06 -1.09e-06 0.01405 1.829e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005269 0.0005344 0.004291 0.004583 0.9889 0.992 0.005364 0.8748 0.902 0.01493 ] Network output: [ -0.0003768 -0.005656 1.003 -0.0001502 6.745e-05 1.003 -0.0001132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1929 0.09452 0.3195 0.1632 0.9851 0.9941 0.1936 0.4665 0.8847 0.7178 ] Network output: [ 0.008673 -0.04358 0.9984 8.739e-05 -3.923e-05 1.028 6.586e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08614 0.1785 0.2111 0.9874 0.992 0.09697 0.7949 0.8792 0.3107 ] Network output: [ -0.00919 0.04554 1.001 8.696e-05 -3.904e-05 0.972 6.554e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09339 0.09153 0.1682 0.1987 0.9857 0.9915 0.0934 0.7255 0.8591 0.2436 ] Network output: [ 0.0006857 0.9995 -0.001325 1.213e-05 -5.448e-06 1.001 9.145e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009627 Epoch 6771 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01352 0.9928 0.9863 4.988e-06 -2.239e-06 -0.006104 3.759e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003192 -0.002956 -0.009745 0.007397 0.9697 0.9741 0.006055 0.8448 0.8325 0.02055 ] Network output: [ 0.9989 0.008837 0.001558 -4.308e-05 1.934e-05 -0.008352 -3.247e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1827 -0.0278 -0.2 0.2008 0.9836 0.9933 0.2037 0.4619 0.8782 0.723 ] Network output: [ -0.01201 1 1.01 2.331e-06 -1.047e-06 0.01342 1.757e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005273 0.0005326 0.004251 0.004508 0.9889 0.992 0.005368 0.8748 0.902 0.0149 ] Network output: [ -0.001607 0.01341 1.003 -0.0001522 6.835e-05 0.9866 -0.0001147 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1931 0.09455 0.3182 0.1595 0.9851 0.994 0.1937 0.4667 0.8847 0.7179 ] Network output: [ 0.008975 -0.03983 0.9977 8.714e-05 -3.912e-05 1.025 6.567e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09686 0.08607 0.1777 0.2101 0.9874 0.992 0.09692 0.7945 0.8792 0.3101 ] Network output: [ -0.009026 0.04432 1.001 8.706e-05 -3.908e-05 0.9729 6.561e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09333 0.09146 0.168 0.1986 0.9857 0.9915 0.09334 0.7251 0.859 0.2436 ] Network output: [ -5.58e-05 0.9993 -0.0002705 1.184e-05 -5.318e-06 1.001 8.927e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009247 Epoch 6772 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01366 0.9908 0.9863 5.204e-06 -2.336e-06 -0.004362 3.922e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00319 -0.002957 -0.00973 0.007432 0.9697 0.9741 0.006052 0.8448 0.8326 0.02055 ] Network output: [ 0.9999 -0.005066 0.00224 -4.182e-05 1.878e-05 0.002954 -3.152e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1825 -0.028 -0.199 0.2031 0.9836 0.9933 0.2036 0.4615 0.8783 0.7232 ] Network output: [ -0.01199 0.9995 1.01 2.424e-06 -1.088e-06 0.01404 1.827e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005269 0.0005339 0.004292 0.00458 0.9889 0.992 0.005365 0.8748 0.902 0.01492 ] Network output: [ -0.0003832 -0.005562 1.003 -0.0001501 6.737e-05 1.002 -0.0001131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.193 0.0945 0.3195 0.1631 0.9851 0.9941 0.1936 0.4665 0.8847 0.7178 ] Network output: [ 0.008668 -0.04354 0.9984 8.728e-05 -3.919e-05 1.028 6.578e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08613 0.1785 0.2111 0.9874 0.992 0.09697 0.7948 0.8792 0.3107 ] Network output: [ -0.009181 0.04551 1.001 8.687e-05 -3.9e-05 0.972 6.547e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09338 0.09151 0.1682 0.1987 0.9857 0.9915 0.09339 0.7254 0.859 0.2436 ] Network output: [ 0.0006819 0.9995 -0.001319 1.212e-05 -5.441e-06 1.001 9.134e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009611 Epoch 6773 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01351 0.9928 0.9863 4.979e-06 -2.235e-06 -0.0061 3.752e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003192 -0.002957 -0.009741 0.007395 0.9697 0.9741 0.006055 0.8448 0.8325 0.02054 ] Network output: [ 0.9989 0.008765 0.001559 -4.304e-05 1.932e-05 -0.008296 -3.243e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1827 -0.02781 -0.1999 0.2008 0.9836 0.9933 0.2038 0.4619 0.8782 0.723 ] Network output: [ -0.01201 1 1.01 2.329e-06 -1.046e-06 0.01341 1.755e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005274 0.0005321 0.004252 0.004506 0.9889 0.992 0.005369 0.8748 0.902 0.0149 ] Network output: [ -0.001602 0.01331 1.003 -0.000152 6.826e-05 0.9867 -0.0001146 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1931 0.09454 0.3182 0.1595 0.9851 0.994 0.1937 0.4667 0.8847 0.7179 ] Network output: [ 0.008967 -0.03983 0.9977 8.704e-05 -3.908e-05 1.025 6.56e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09686 0.08607 0.1777 0.2101 0.9874 0.992 0.09692 0.7945 0.8792 0.3101 ] Network output: [ -0.009019 0.0443 1.001 8.696e-05 -3.904e-05 0.9729 6.554e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09332 0.09144 0.168 0.1985 0.9857 0.9915 0.09333 0.725 0.859 0.2436 ] Network output: [ -5.252e-05 0.9993 -0.0002748 1.183e-05 -5.312e-06 1.001 8.918e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009235 Epoch 6774 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01365 0.9908 0.9863 5.192e-06 -2.331e-06 -0.004374 3.913e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00319 -0.002957 -0.009727 0.007429 0.9697 0.9741 0.006053 0.8447 0.8326 0.02055 ] Network output: [ 0.9998 -0.005001 0.002236 -4.179e-05 1.876e-05 0.002899 -3.15e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1826 -0.02801 -0.199 0.203 0.9836 0.9933 0.2036 0.4615 0.8782 0.7231 ] Network output: [ -0.01199 0.9995 1.01 2.42e-06 -1.087e-06 0.01403 1.824e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00527 0.0005334 0.004292 0.004578 0.9889 0.992 0.005366 0.8748 0.902 0.01492 ] Network output: [ -0.0003896 -0.005468 1.003 -0.0001499 6.729e-05 1.002 -0.000113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.193 0.09449 0.3196 0.1631 0.9851 0.994 0.1936 0.4664 0.8847 0.7178 ] Network output: [ 0.008664 -0.04351 0.9983 8.718e-05 -3.914e-05 1.028 6.57e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08613 0.1785 0.2111 0.9874 0.992 0.09697 0.7947 0.8792 0.3106 ] Network output: [ -0.009172 0.04548 1.001 8.678e-05 -3.896e-05 0.972 6.54e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09336 0.09149 0.1682 0.1987 0.9857 0.9915 0.09337 0.7254 0.859 0.2436 ] Network output: [ 0.0006781 0.9995 -0.001313 1.21e-05 -5.434e-06 1.001 9.122e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009596 Epoch 6775 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01351 0.9928 0.9863 4.97e-06 -2.231e-06 -0.006096 3.745e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003192 -0.002957 -0.009738 0.007392 0.9697 0.9741 0.006056 0.8448 0.8325 0.02054 ] Network output: [ 0.9989 0.008693 0.001561 -4.3e-05 1.93e-05 -0.008241 -3.24e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1827 -0.02783 -0.1999 0.2008 0.9836 0.9933 0.2038 0.4618 0.8782 0.723 ] Network output: [ -0.01201 1 1.01 2.327e-06 -1.044e-06 0.01341 1.753e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005275 0.0005316 0.004253 0.004504 0.9889 0.992 0.00537 0.8748 0.902 0.0149 ] Network output: [ -0.001596 0.01322 1.003 -0.0001518 6.817e-05 0.9868 -0.0001144 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1931 0.09452 0.3183 0.1595 0.9851 0.994 0.1937 0.4666 0.8847 0.7179 ] Network output: [ 0.008959 -0.03983 0.9977 8.694e-05 -3.903e-05 1.025 6.552e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09686 0.08606 0.1777 0.2101 0.9874 0.992 0.09692 0.7944 0.8791 0.3101 ] Network output: [ -0.009012 0.04428 1.001 8.687e-05 -3.9e-05 0.9729 6.547e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0933 0.09143 0.168 0.1985 0.9857 0.9915 0.09331 0.725 0.859 0.2436 ] Network output: [ -4.926e-05 0.9993 -0.0002791 1.182e-05 -5.307e-06 1.001 8.909e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009223 Epoch 6776 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01365 0.9908 0.9863 5.18e-06 -2.326e-06 -0.004387 3.904e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00319 -0.002958 -0.009724 0.007427 0.9697 0.9741 0.006053 0.8447 0.8326 0.02054 ] Network output: [ 0.9998 -0.004937 0.002231 -4.177e-05 1.875e-05 0.002844 -3.148e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1826 -0.02803 -0.199 0.203 0.9836 0.9933 0.2036 0.4615 0.8782 0.7231 ] Network output: [ -0.01199 0.9995 1.01 2.417e-06 -1.085e-06 0.01402 1.821e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005271 0.0005329 0.004292 0.004576 0.9889 0.992 0.005367 0.8748 0.902 0.01492 ] Network output: [ -0.0003959 -0.005374 1.003 -0.0001497 6.721e-05 1.002 -0.0001128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.193 0.09447 0.3196 0.163 0.9851 0.994 0.1936 0.4664 0.8847 0.7178 ] Network output: [ 0.008659 -0.04347 0.9983 8.708e-05 -3.909e-05 1.028 6.563e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08613 0.1785 0.211 0.9874 0.992 0.09697 0.7947 0.8791 0.3106 ] Network output: [ -0.009163 0.04544 1.001 8.669e-05 -3.892e-05 0.972 6.533e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09334 0.09147 0.1682 0.1987 0.9857 0.9915 0.09335 0.7253 0.859 0.2436 ] Network output: [ 0.0006744 0.9995 -0.001308 1.209e-05 -5.427e-06 1.001 9.111e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000958 Epoch 6777 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0135 0.9928 0.9863 4.961e-06 -2.227e-06 -0.006091 3.738e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003192 -0.002957 -0.009734 0.00739 0.9697 0.9741 0.006056 0.8448 0.8325 0.02053 ] Network output: [ 0.9989 0.008621 0.001563 -4.295e-05 1.928e-05 -0.008186 -3.237e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1827 -0.02785 -0.1998 0.2008 0.9836 0.9933 0.2038 0.4618 0.8781 0.723 ] Network output: [ -0.012 1 1.01 2.324e-06 -1.043e-06 0.0134 1.751e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005275 0.0005311 0.004253 0.004503 0.9889 0.992 0.005371 0.8748 0.902 0.01489 ] Network output: [ -0.001591 0.01313 1.003 -0.0001516 6.808e-05 0.9869 -0.0001143 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1931 0.09451 0.3183 0.1595 0.9851 0.994 0.1937 0.4666 0.8847 0.7179 ] Network output: [ 0.00895 -0.03984 0.9977 8.684e-05 -3.898e-05 1.025 6.544e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09686 0.08606 0.1777 0.21 0.9874 0.992 0.09692 0.7944 0.8791 0.3101 ] Network output: [ -0.009006 0.04426 1.001 8.678e-05 -3.896e-05 0.9729 6.54e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09328 0.09141 0.168 0.1985 0.9857 0.9915 0.0933 0.7249 0.859 0.2435 ] Network output: [ -4.601e-05 0.9993 -0.0002834 1.181e-05 -5.301e-06 1.001 8.9e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009212 Epoch 6778 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01364 0.9908 0.9863 5.168e-06 -2.32e-06 -0.0044 3.895e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00319 -0.002958 -0.00972 0.007424 0.9697 0.9741 0.006054 0.8447 0.8326 0.02054 ] Network output: [ 0.9998 -0.004873 0.002226 -4.174e-05 1.874e-05 0.002789 -3.146e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1826 -0.02804 -0.1989 0.2029 0.9836 0.9933 0.2037 0.4615 0.8782 0.7231 ] Network output: [ -0.01198 0.9995 1.01 2.413e-06 -1.083e-06 0.01401 1.819e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005272 0.0005324 0.004292 0.004573 0.9889 0.992 0.005368 0.8747 0.902 0.01491 ] Network output: [ -0.0004023 -0.005281 1.003 -0.0001495 6.713e-05 1.002 -0.0001127 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.193 0.09446 0.3196 0.1629 0.9851 0.994 0.1936 0.4664 0.8846 0.7177 ] Network output: [ 0.008654 -0.04344 0.9983 8.698e-05 -3.905e-05 1.028 6.555e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08613 0.1785 0.211 0.9874 0.992 0.09697 0.7946 0.8791 0.3106 ] Network output: [ -0.009154 0.04541 1.001 8.659e-05 -3.888e-05 0.9721 6.526e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09333 0.09146 0.1682 0.1987 0.9857 0.9915 0.09334 0.7252 0.8589 0.2436 ] Network output: [ 0.0006707 0.9995 -0.001302 1.207e-05 -5.421e-06 1.001 9.1e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009565 Epoch 6779 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0135 0.9928 0.9863 4.951e-06 -2.223e-06 -0.006087 3.731e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003192 -0.002958 -0.009731 0.007388 0.9697 0.9741 0.006057 0.8448 0.8325 0.02053 ] Network output: [ 0.9989 0.00855 0.001565 -4.291e-05 1.927e-05 -0.008131 -3.234e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1827 -0.02786 -0.1998 0.2007 0.9836 0.9933 0.2038 0.4618 0.8781 0.723 ] Network output: [ -0.012 1 1.01 2.321e-06 -1.042e-06 0.01339 1.75e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005276 0.0005306 0.004254 0.004501 0.9889 0.992 0.005372 0.8748 0.9019 0.01489 ] Network output: [ -0.001585 0.01303 1.003 -0.0001514 6.799e-05 0.9869 -0.0001141 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1932 0.09449 0.3184 0.1595 0.9851 0.994 0.1938 0.4666 0.8847 0.7179 ] Network output: [ 0.008942 -0.03984 0.9977 8.674e-05 -3.894e-05 1.025 6.537e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09686 0.08606 0.1777 0.21 0.9874 0.992 0.09692 0.7943 0.8791 0.3101 ] Network output: [ -0.008999 0.04424 1.001 8.668e-05 -3.892e-05 0.9729 6.533e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09327 0.0914 0.168 0.1985 0.9857 0.9915 0.09328 0.7248 0.8589 0.2435 ] Network output: [ -4.277e-05 0.9993 -0.0002877 1.18e-05 -5.296e-06 1.001 8.891e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00092 Epoch 6780 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01363 0.9908 0.9863 5.157e-06 -2.315e-06 -0.004413 3.886e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003191 -0.002958 -0.009717 0.007422 0.9697 0.9741 0.006054 0.8447 0.8326 0.02053 ] Network output: [ 0.9998 -0.004809 0.002222 -4.171e-05 1.873e-05 0.002735 -3.144e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1826 -0.02806 -0.1989 0.2029 0.9836 0.9933 0.2037 0.4614 0.8782 0.7231 ] Network output: [ -0.01198 0.9995 1.01 2.41e-06 -1.082e-06 0.014 1.816e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005273 0.0005319 0.004293 0.004571 0.9889 0.992 0.005369 0.8747 0.902 0.01491 ] Network output: [ -0.0004086 -0.005188 1.003 -0.0001494 6.705e-05 1.002 -0.0001126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.193 0.09444 0.3197 0.1629 0.9851 0.994 0.1936 0.4663 0.8846 0.7177 ] Network output: [ 0.00865 -0.0434 0.9983 8.687e-05 -3.9e-05 1.028 6.547e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08612 0.1785 0.211 0.9874 0.992 0.09698 0.7946 0.8791 0.3106 ] Network output: [ -0.009146 0.04538 1.001 8.65e-05 -3.883e-05 0.9721 6.519e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09331 0.09144 0.1682 0.1987 0.9857 0.9915 0.09332 0.7251 0.8589 0.2436 ] Network output: [ 0.0006669 0.9995 -0.001296 1.206e-05 -5.414e-06 1.001 9.088e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009549 Epoch 6781 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01349 0.9928 0.9863 4.942e-06 -2.219e-06 -0.006083 3.724e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003192 -0.002958 -0.009728 0.007386 0.9697 0.9741 0.006057 0.8447 0.8325 0.02052 ] Network output: [ 0.9989 0.008479 0.001567 -4.287e-05 1.925e-05 -0.008076 -3.231e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1827 -0.02788 -0.1998 0.2007 0.9836 0.9933 0.2038 0.4617 0.8781 0.723 ] Network output: [ -0.012 1 1.01 2.319e-06 -1.041e-06 0.01339 1.748e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005277 0.0005302 0.004255 0.004499 0.9889 0.992 0.005373 0.8747 0.9019 0.01489 ] Network output: [ -0.00158 0.01294 1.003 -0.0001513 6.79e-05 0.987 -0.000114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1932 0.09448 0.3184 0.1594 0.9851 0.994 0.1938 0.4665 0.8846 0.7179 ] Network output: [ 0.008934 -0.03984 0.9977 8.663e-05 -3.889e-05 1.025 6.529e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09686 0.08606 0.1777 0.21 0.9874 0.992 0.09692 0.7943 0.8791 0.3101 ] Network output: [ -0.008992 0.04422 1.001 8.659e-05 -3.887e-05 0.9729 6.526e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09325 0.09138 0.168 0.1985 0.9857 0.9915 0.09326 0.7248 0.8589 0.2435 ] Network output: [ -3.954e-05 0.9993 -0.0002919 1.178e-05 -5.291e-06 1.001 8.882e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009188 Epoch 6782 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01363 0.9908 0.9864 5.145e-06 -2.31e-06 -0.004426 3.877e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003191 -0.002958 -0.009714 0.007419 0.9697 0.9741 0.006054 0.8447 0.8325 0.02053 ] Network output: [ 0.9998 -0.004745 0.002217 -4.168e-05 1.871e-05 0.00268 -3.141e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1826 -0.02807 -0.1989 0.2029 0.9836 0.9933 0.2037 0.4614 0.8782 0.7231 ] Network output: [ -0.01198 0.9995 1.01 2.406e-06 -1.08e-06 0.01398 1.813e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005274 0.0005314 0.004293 0.004568 0.9889 0.992 0.00537 0.8747 0.902 0.01491 ] Network output: [ -0.0004148 -0.005095 1.003 -0.0001492 6.698e-05 1.002 -0.0001124 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1931 0.09443 0.3197 0.1628 0.9851 0.994 0.1937 0.4663 0.8846 0.7177 ] Network output: [ 0.008645 -0.04337 0.9983 8.677e-05 -3.895e-05 1.028 6.539e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08612 0.1785 0.211 0.9874 0.992 0.09698 0.7945 0.8791 0.3106 ] Network output: [ -0.009137 0.04535 1.001 8.641e-05 -3.879e-05 0.9721 6.512e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09329 0.09142 0.1681 0.1986 0.9857 0.9915 0.09331 0.7251 0.8589 0.2436 ] Network output: [ 0.0006632 0.9995 -0.001291 1.204e-05 -5.407e-06 1.001 9.077e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009534 Epoch 6783 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01349 0.9928 0.9863 4.933e-06 -2.214e-06 -0.006079 3.717e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003193 -0.002958 -0.009724 0.007384 0.9697 0.9741 0.006057 0.8447 0.8325 0.02052 ] Network output: [ 0.9989 0.008408 0.001569 -4.283e-05 1.923e-05 -0.008021 -3.228e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1828 -0.0279 -0.1997 0.2007 0.9836 0.9933 0.2039 0.4617 0.8781 0.7229 ] Network output: [ -0.012 1 1.01 2.316e-06 -1.04e-06 0.01338 1.746e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005278 0.0005297 0.004255 0.004498 0.9889 0.992 0.005374 0.8747 0.9019 0.01488 ] Network output: [ -0.001574 0.01285 1.003 -0.0001511 6.781e-05 0.9871 -0.0001138 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1932 0.09447 0.3185 0.1594 0.9851 0.994 0.1938 0.4665 0.8846 0.7179 ] Network output: [ 0.008926 -0.03984 0.9977 8.653e-05 -3.885e-05 1.025 6.521e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09686 0.08606 0.1777 0.21 0.9874 0.992 0.09693 0.7942 0.879 0.3101 ] Network output: [ -0.008985 0.0442 1.001 8.65e-05 -3.883e-05 0.9729 6.519e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09324 0.09136 0.1679 0.1985 0.9857 0.9915 0.09325 0.7247 0.8589 0.2435 ] Network output: [ -3.631e-05 0.9993 -0.0002962 1.177e-05 -5.285e-06 1.001 8.873e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009177 Epoch 6784 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01362 0.9909 0.9864 5.133e-06 -2.305e-06 -0.004439 3.869e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003191 -0.002959 -0.00971 0.007416 0.9697 0.9741 0.006055 0.8447 0.8325 0.02052 ] Network output: [ 0.9998 -0.004681 0.002213 -4.166e-05 1.87e-05 0.002626 -3.139e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1826 -0.02809 -0.1988 0.2028 0.9836 0.9933 0.2037 0.4614 0.8782 0.7231 ] Network output: [ -0.01198 0.9996 1.01 2.403e-06 -1.079e-06 0.01397 1.811e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005275 0.000531 0.004293 0.004566 0.9889 0.992 0.005371 0.8747 0.902 0.0149 ] Network output: [ -0.0004211 -0.005003 1.003 -0.000149 6.69e-05 1.002 -0.0001123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1931 0.09442 0.3197 0.1628 0.9851 0.994 0.1937 0.4663 0.8846 0.7177 ] Network output: [ 0.00864 -0.04334 0.9983 8.667e-05 -3.891e-05 1.028 6.531e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08612 0.1785 0.2109 0.9874 0.992 0.09698 0.7944 0.8791 0.3106 ] Network output: [ -0.009128 0.04532 1.001 8.632e-05 -3.875e-05 0.9721 6.505e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09328 0.09141 0.1681 0.1986 0.9857 0.9915 0.09329 0.725 0.8588 0.2435 ] Network output: [ 0.0006595 0.9995 -0.001285 1.203e-05 -5.4e-06 1.001 9.066e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009518 Epoch 6785 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01348 0.9928 0.9863 4.923e-06 -2.21e-06 -0.006075 3.71e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003193 -0.002958 -0.009721 0.007381 0.9697 0.9741 0.006058 0.8447 0.8325 0.02051 ] Network output: [ 0.9989 0.008337 0.00157 -4.279e-05 1.921e-05 -0.007967 -3.225e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1828 -0.02791 -0.1997 0.2007 0.9836 0.9933 0.2039 0.4617 0.8781 0.7229 ] Network output: [ -0.012 1 1.01 2.314e-06 -1.039e-06 0.01338 1.744e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005279 0.0005292 0.004256 0.004496 0.9889 0.992 0.005375 0.8747 0.9019 0.01488 ] Network output: [ -0.001568 0.01276 1.003 -0.0001509 6.772e-05 0.9872 -0.0001137 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1932 0.09445 0.3185 0.1594 0.9851 0.994 0.1938 0.4665 0.8846 0.7179 ] Network output: [ 0.008918 -0.03985 0.9977 8.643e-05 -3.88e-05 1.025 6.514e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09687 0.08605 0.1777 0.21 0.9874 0.992 0.09693 0.7941 0.879 0.3101 ] Network output: [ -0.008978 0.04418 1.001 8.64e-05 -3.879e-05 0.9729 6.512e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09322 0.09135 0.1679 0.1985 0.9857 0.9915 0.09323 0.7246 0.8588 0.2435 ] Network output: [ -3.31e-05 0.9993 -0.0003004 1.176e-05 -5.28e-06 1.001 8.863e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009165 Epoch 6786 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01361 0.9909 0.9864 5.122e-06 -2.299e-06 -0.004452 3.86e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003191 -0.002959 -0.009707 0.007414 0.9697 0.9741 0.006055 0.8447 0.8325 0.02052 ] Network output: [ 0.9998 -0.004618 0.002208 -4.163e-05 1.869e-05 0.002572 -3.137e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1827 -0.0281 -0.1988 0.2028 0.9836 0.9933 0.2037 0.4613 0.8782 0.7231 ] Network output: [ -0.01197 0.9996 1.01 2.399e-06 -1.077e-06 0.01396 1.808e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005276 0.0005305 0.004293 0.004564 0.9889 0.992 0.005372 0.8747 0.9019 0.0149 ] Network output: [ -0.0004273 -0.004911 1.003 -0.0001488 6.682e-05 1.002 -0.0001122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1931 0.0944 0.3198 0.1627 0.9851 0.994 0.1937 0.4662 0.8846 0.7177 ] Network output: [ 0.008636 -0.0433 0.9983 8.656e-05 -3.886e-05 1.028 6.524e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08611 0.1785 0.2109 0.9874 0.992 0.09698 0.7944 0.879 0.3106 ] Network output: [ -0.009119 0.04529 1.001 8.623e-05 -3.871e-05 0.9721 6.498e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09326 0.09139 0.1681 0.1986 0.9857 0.9915 0.09327 0.7249 0.8588 0.2435 ] Network output: [ 0.0006558 0.9995 -0.001279 1.201e-05 -5.394e-06 1.001 9.054e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009503 Epoch 6787 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01348 0.9928 0.9863 4.914e-06 -2.206e-06 -0.00607 3.703e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003193 -0.002959 -0.009717 0.007379 0.9697 0.9741 0.006058 0.8447 0.8324 0.02051 ] Network output: [ 0.9989 0.008267 0.001572 -4.275e-05 1.919e-05 -0.007912 -3.222e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1828 -0.02793 -0.1996 0.2007 0.9836 0.9933 0.2039 0.4616 0.8781 0.7229 ] Network output: [ -0.01199 1 1.01 2.311e-06 -1.038e-06 0.01337 1.742e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00528 0.0005287 0.004257 0.004495 0.9889 0.992 0.005376 0.8747 0.9019 0.01488 ] Network output: [ -0.001563 0.01266 1.003 -0.0001507 6.763e-05 0.9872 -0.0001135 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1932 0.09444 0.3185 0.1594 0.9851 0.994 0.1938 0.4664 0.8846 0.7179 ] Network output: [ 0.00891 -0.03985 0.9977 8.633e-05 -3.876e-05 1.025 6.506e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09687 0.08605 0.1777 0.21 0.9874 0.992 0.09693 0.7941 0.879 0.3101 ] Network output: [ -0.008971 0.04416 1.001 8.631e-05 -3.875e-05 0.9729 6.505e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0932 0.09133 0.1679 0.1984 0.9857 0.9915 0.09322 0.7246 0.8588 0.2435 ] Network output: [ -2.99e-05 0.9993 -0.0003046 1.175e-05 -5.275e-06 1.001 8.854e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009154 Epoch 6788 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01361 0.9909 0.9864 5.11e-06 -2.294e-06 -0.004465 3.851e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003191 -0.002959 -0.009704 0.007411 0.9697 0.9741 0.006056 0.8447 0.8325 0.02051 ] Network output: [ 0.9998 -0.004555 0.002203 -4.16e-05 1.868e-05 0.002518 -3.135e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1827 -0.02812 -0.1988 0.2028 0.9836 0.9933 0.2038 0.4613 0.8782 0.7231 ] Network output: [ -0.01197 0.9996 1.01 2.395e-06 -1.075e-06 0.01395 1.805e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005277 0.00053 0.004294 0.004561 0.9889 0.992 0.005373 0.8747 0.9019 0.01489 ] Network output: [ -0.0004335 -0.004819 1.003 -0.0001487 6.674e-05 1.002 -0.000112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1931 0.09439 0.3198 0.1627 0.9851 0.994 0.1937 0.4662 0.8846 0.7177 ] Network output: [ 0.008631 -0.04327 0.9983 8.646e-05 -3.881e-05 1.028 6.516e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08611 0.1785 0.2109 0.9874 0.992 0.09698 0.7943 0.879 0.3105 ] Network output: [ -0.009111 0.04525 1.001 8.614e-05 -3.867e-05 0.9721 6.491e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09324 0.09137 0.1681 0.1986 0.9857 0.9915 0.09326 0.7248 0.8588 0.2435 ] Network output: [ 0.0006521 0.9995 -0.001274 1.2e-05 -5.387e-06 1.001 9.043e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009488 Epoch 6789 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01347 0.9928 0.9863 4.905e-06 -2.202e-06 -0.006066 3.696e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003193 -0.002959 -0.009714 0.007377 0.9697 0.9741 0.006059 0.8447 0.8324 0.0205 ] Network output: [ 0.999 0.008197 0.001574 -4.271e-05 1.917e-05 -0.007858 -3.218e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1828 -0.02795 -0.1996 0.2007 0.9836 0.9933 0.2039 0.4616 0.8781 0.7229 ] Network output: [ -0.01199 1 1.01 2.309e-06 -1.036e-06 0.01337 1.74e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005281 0.0005283 0.004257 0.004493 0.9889 0.992 0.005377 0.8747 0.9019 0.01487 ] Network output: [ -0.001557 0.01257 1.003 -0.0001505 6.755e-05 0.9873 -0.0001134 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1932 0.09442 0.3186 0.1593 0.9851 0.994 0.1938 0.4664 0.8846 0.7179 ] Network output: [ 0.008902 -0.03985 0.9977 8.623e-05 -3.871e-05 1.025 6.499e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09687 0.08605 0.1777 0.2099 0.9874 0.992 0.09693 0.794 0.879 0.31 ] Network output: [ -0.008964 0.04414 1.001 8.622e-05 -3.871e-05 0.9729 6.498e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09319 0.09131 0.1679 0.1984 0.9857 0.9915 0.0932 0.7245 0.8588 0.2435 ] Network output: [ -2.671e-05 0.9993 -0.0003088 1.174e-05 -5.269e-06 1.001 8.845e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009142 Epoch 6790 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0136 0.9909 0.9864 5.098e-06 -2.289e-06 -0.004477 3.842e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003191 -0.00296 -0.0097 0.007409 0.9697 0.9741 0.006056 0.8446 0.8325 0.02051 ] Network output: [ 0.9998 -0.004492 0.002199 -4.157e-05 1.866e-05 0.002465 -3.133e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1827 -0.02813 -0.1987 0.2027 0.9836 0.9933 0.2038 0.4613 0.8781 0.7231 ] Network output: [ -0.01197 0.9996 1.01 2.392e-06 -1.074e-06 0.01394 1.803e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005278 0.0005295 0.004294 0.004559 0.9889 0.992 0.005374 0.8746 0.9019 0.01489 ] Network output: [ -0.0004396 -0.004728 1.003 -0.0001485 6.666e-05 1.002 -0.0001119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1931 0.09437 0.3198 0.1626 0.9851 0.994 0.1937 0.4662 0.8846 0.7177 ] Network output: [ 0.008626 -0.04323 0.9982 8.636e-05 -3.877e-05 1.028 6.508e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08611 0.1785 0.2108 0.9874 0.992 0.09698 0.7943 0.879 0.3105 ] Network output: [ -0.009102 0.04522 1.001 8.604e-05 -3.863e-05 0.9721 6.485e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09323 0.09136 0.1681 0.1986 0.9857 0.9915 0.09324 0.7248 0.8587 0.2435 ] Network output: [ 0.0006484 0.9995 -0.001268 1.198e-05 -5.38e-06 1.001 9.032e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009472 Epoch 6791 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01347 0.9928 0.9863 4.895e-06 -2.198e-06 -0.006062 3.689e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003193 -0.002959 -0.00971 0.007375 0.9697 0.9741 0.006059 0.8447 0.8324 0.0205 ] Network output: [ 0.999 0.008127 0.001576 -4.266e-05 1.915e-05 -0.007804 -3.215e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1828 -0.02796 -0.1996 0.2007 0.9836 0.9933 0.2039 0.4616 0.8781 0.7229 ] Network output: [ -0.01199 1 1.01 2.306e-06 -1.035e-06 0.01336 1.738e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005282 0.0005278 0.004258 0.004491 0.9889 0.992 0.005378 0.8746 0.9019 0.01487 ] Network output: [ -0.001552 0.01248 1.003 -0.0001503 6.746e-05 0.9874 -0.0001132 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1933 0.09441 0.3186 0.1593 0.9851 0.994 0.1939 0.4664 0.8846 0.7178 ] Network output: [ 0.008894 -0.03985 0.9976 8.613e-05 -3.867e-05 1.025 6.491e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09687 0.08605 0.1777 0.2099 0.9874 0.992 0.09693 0.794 0.8789 0.31 ] Network output: [ -0.008957 0.04412 1.001 8.612e-05 -3.866e-05 0.9729 6.49e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09317 0.0913 0.1679 0.1984 0.9857 0.9915 0.09318 0.7244 0.8587 0.2435 ] Network output: [ -2.353e-05 0.9993 -0.0003129 1.173e-05 -5.264e-06 1.001 8.836e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009131 Epoch 6792 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0136 0.9909 0.9864 5.086e-06 -2.283e-06 -0.00449 3.833e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003192 -0.00296 -0.009697 0.007406 0.9697 0.9741 0.006057 0.8446 0.8325 0.0205 ] Network output: [ 0.9998 -0.00443 0.002194 -4.154e-05 1.865e-05 0.002412 -3.131e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1827 -0.02815 -0.1987 0.2027 0.9836 0.9933 0.2038 0.4613 0.8781 0.7231 ] Network output: [ -0.01197 0.9996 1.01 2.388e-06 -1.072e-06 0.01393 1.8e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005279 0.000529 0.004294 0.004557 0.9889 0.992 0.005375 0.8746 0.9019 0.01489 ] Network output: [ -0.0004457 -0.004637 1.003 -0.0001483 6.658e-05 1.002 -0.0001118 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1931 0.09436 0.3199 0.1625 0.9851 0.994 0.1937 0.4661 0.8846 0.7177 ] Network output: [ 0.008621 -0.0432 0.9982 8.625e-05 -3.872e-05 1.028 6.5e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08611 0.1785 0.2108 0.9874 0.992 0.09698 0.7942 0.879 0.3105 ] Network output: [ -0.009093 0.04519 1.001 8.595e-05 -3.859e-05 0.9722 6.478e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09321 0.09134 0.1681 0.1986 0.9857 0.9915 0.09322 0.7247 0.8587 0.2435 ] Network output: [ 0.0006447 0.9995 -0.001263 1.197e-05 -5.373e-06 1.001 9.02e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009457 Epoch 6793 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01346 0.9928 0.9863 4.886e-06 -2.193e-06 -0.006058 3.682e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003193 -0.00296 -0.009707 0.007372 0.9697 0.9741 0.006059 0.8447 0.8324 0.02049 ] Network output: [ 0.999 0.008057 0.001577 -4.262e-05 1.913e-05 -0.00775 -3.212e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1828 -0.02798 -0.1995 0.2007 0.9836 0.9933 0.2039 0.4615 0.8781 0.7229 ] Network output: [ -0.01199 1 1.01 2.303e-06 -1.034e-06 0.01336 1.736e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005283 0.0005273 0.004259 0.00449 0.9889 0.992 0.005379 0.8746 0.9019 0.01487 ] Network output: [ -0.001547 0.01239 1.003 -0.0001501 6.737e-05 0.9875 -0.0001131 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1933 0.09439 0.3187 0.1593 0.9851 0.994 0.1939 0.4663 0.8846 0.7178 ] Network output: [ 0.008886 -0.03985 0.9976 8.603e-05 -3.862e-05 1.025 6.483e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09687 0.08605 0.1777 0.2099 0.9874 0.992 0.09693 0.7939 0.8789 0.31 ] Network output: [ -0.00895 0.04409 1.001 8.603e-05 -3.862e-05 0.9729 6.483e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09316 0.09128 0.1679 0.1984 0.9857 0.9915 0.09317 0.7243 0.8587 0.2435 ] Network output: [ -2.037e-05 0.9993 -0.000317 1.171e-05 -5.258e-06 1.001 8.827e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000912 Epoch 6794 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01359 0.9909 0.9864 5.075e-06 -2.278e-06 -0.004503 3.824e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003192 -0.00296 -0.009694 0.007404 0.9697 0.9741 0.006057 0.8446 0.8325 0.0205 ] Network output: [ 0.9998 -0.004367 0.00219 -4.152e-05 1.864e-05 0.002359 -3.129e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1827 -0.02816 -0.1987 0.2027 0.9836 0.9933 0.2038 0.4612 0.8781 0.7231 ] Network output: [ -0.01197 0.9996 1.01 2.384e-06 -1.07e-06 0.01392 1.797e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00528 0.0005285 0.004295 0.004554 0.9889 0.992 0.005376 0.8746 0.9019 0.01488 ] Network output: [ -0.0004518 -0.004546 1.003 -0.0001481 6.65e-05 1.002 -0.0001116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1932 0.09435 0.3199 0.1625 0.9851 0.994 0.1938 0.4661 0.8846 0.7177 ] Network output: [ 0.008617 -0.04316 0.9982 8.615e-05 -3.868e-05 1.028 6.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.0861 0.1785 0.2108 0.9874 0.992 0.09698 0.7941 0.8789 0.3105 ] Network output: [ -0.009084 0.04516 1.001 8.586e-05 -3.855e-05 0.9722 6.471e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0932 0.09132 0.1681 0.1986 0.9857 0.9915 0.09321 0.7246 0.8587 0.2435 ] Network output: [ 0.0006411 0.9995 -0.001257 1.195e-05 -5.367e-06 1.001 9.009e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009442 Epoch 6795 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01346 0.9928 0.9864 4.876e-06 -2.189e-06 -0.006055 3.675e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003193 -0.00296 -0.009704 0.00737 0.9697 0.9741 0.00606 0.8446 0.8324 0.02049 ] Network output: [ 0.999 0.007988 0.001579 -4.258e-05 1.912e-05 -0.007696 -3.209e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1828 -0.028 -0.1995 0.2006 0.9836 0.9933 0.204 0.4615 0.878 0.7229 ] Network output: [ -0.01198 1 1.01 2.3e-06 -1.033e-06 0.01335 1.734e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005284 0.0005269 0.004259 0.004488 0.9889 0.992 0.00538 0.8746 0.9018 0.01486 ] Network output: [ -0.001541 0.0123 1.003 -0.0001499 6.728e-05 0.9875 -0.0001129 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1933 0.09438 0.3187 0.1593 0.9851 0.994 0.1939 0.4663 0.8846 0.7178 ] Network output: [ 0.008878 -0.03985 0.9976 8.593e-05 -3.857e-05 1.025 6.476e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09687 0.08604 0.1777 0.2099 0.9874 0.992 0.09693 0.7939 0.8789 0.31 ] Network output: [ -0.008944 0.04407 1.001 8.594e-05 -3.858e-05 0.9729 6.476e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09314 0.09127 0.1679 0.1984 0.9857 0.9915 0.09315 0.7243 0.8587 0.2435 ] Network output: [ -1.721e-05 0.9993 -0.0003212 1.17e-05 -5.253e-06 1.001 8.818e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009108 Epoch 6796 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01358 0.991 0.9864 5.063e-06 -2.273e-06 -0.004516 3.816e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003192 -0.00296 -0.00969 0.007401 0.9697 0.9741 0.006057 0.8446 0.8325 0.0205 ] Network output: [ 0.9998 -0.004305 0.002185 -4.149e-05 1.863e-05 0.002306 -3.127e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1827 -0.02818 -0.1986 0.2026 0.9836 0.9933 0.2038 0.4612 0.8781 0.723 ] Network output: [ -0.01196 0.9996 1.01 2.381e-06 -1.069e-06 0.0139 1.794e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005281 0.0005281 0.004295 0.004552 0.9889 0.992 0.005377 0.8746 0.9019 0.01488 ] Network output: [ -0.0004579 -0.004456 1.003 -0.000148 6.642e-05 1.001 -0.0001115 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1932 0.09433 0.3199 0.1624 0.9851 0.994 0.1938 0.4661 0.8845 0.7177 ] Network output: [ 0.008612 -0.04313 0.9982 8.605e-05 -3.863e-05 1.028 6.485e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.0861 0.1785 0.2108 0.9874 0.992 0.09698 0.7941 0.8789 0.3105 ] Network output: [ -0.009076 0.04512 1.001 8.577e-05 -3.85e-05 0.9722 6.464e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09318 0.09131 0.168 0.1985 0.9857 0.9915 0.09319 0.7246 0.8587 0.2435 ] Network output: [ 0.0006374 0.9995 -0.001252 1.194e-05 -5.36e-06 1.001 8.998e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009427 Epoch 6797 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01345 0.9928 0.9864 4.867e-06 -2.185e-06 -0.006051 3.668e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003194 -0.00296 -0.0097 0.007368 0.9697 0.9741 0.00606 0.8446 0.8324 0.02048 ] Network output: [ 0.999 0.007919 0.001581 -4.254e-05 1.91e-05 -0.007643 -3.206e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1829 -0.02801 -0.1994 0.2006 0.9836 0.9933 0.204 0.4615 0.878 0.7229 ] Network output: [ -0.01198 1 1.01 2.298e-06 -1.032e-06 0.01335 1.732e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005285 0.0005264 0.00426 0.004486 0.9889 0.992 0.005381 0.8746 0.9018 0.01486 ] Network output: [ -0.001536 0.01221 1.003 -0.0001497 6.719e-05 0.9876 -0.0001128 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1933 0.09437 0.3188 0.1592 0.9851 0.994 0.1939 0.4663 0.8845 0.7178 ] Network output: [ 0.00887 -0.03985 0.9976 8.582e-05 -3.853e-05 1.025 6.468e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09687 0.08604 0.1778 0.2099 0.9874 0.992 0.09694 0.7938 0.8789 0.31 ] Network output: [ -0.008937 0.04405 1.001 8.584e-05 -3.854e-05 0.9729 6.469e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09313 0.09125 0.1679 0.1984 0.9857 0.9915 0.09314 0.7242 0.8586 0.2435 ] Network output: [ -1.407e-05 0.9993 -0.0003253 1.169e-05 -5.248e-06 1.001 8.809e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009097 Epoch 6798 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01358 0.991 0.9864 5.051e-06 -2.268e-06 -0.004529 3.807e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003192 -0.002961 -0.009687 0.007399 0.9697 0.9741 0.006058 0.8446 0.8325 0.02049 ] Network output: [ 0.9998 -0.004244 0.002181 -4.146e-05 1.861e-05 0.002253 -3.124e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1828 -0.02819 -0.1986 0.2026 0.9836 0.9933 0.2039 0.4612 0.8781 0.723 ] Network output: [ -0.01196 0.9996 1.01 2.377e-06 -1.067e-06 0.01389 1.791e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005282 0.0005276 0.004295 0.00455 0.9889 0.992 0.005378 0.8746 0.9019 0.01488 ] Network output: [ -0.0004639 -0.004367 1.003 -0.0001478 6.634e-05 1.001 -0.0001114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1932 0.09432 0.32 0.1624 0.9851 0.994 0.1938 0.4661 0.8845 0.7177 ] Network output: [ 0.008607 -0.04309 0.9982 8.594e-05 -3.858e-05 1.028 6.477e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.0861 0.1785 0.2107 0.9874 0.992 0.09698 0.794 0.8789 0.3105 ] Network output: [ -0.009067 0.04509 1.001 8.568e-05 -3.846e-05 0.9722 6.457e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09316 0.09129 0.168 0.1985 0.9857 0.9915 0.09318 0.7245 0.8586 0.2435 ] Network output: [ 0.0006338 0.9995 -0.001246 1.192e-05 -5.353e-06 1.001 8.986e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009412 Epoch 6799 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01345 0.9928 0.9864 4.857e-06 -2.181e-06 -0.006047 3.661e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003194 -0.002961 -0.009697 0.007366 0.9697 0.9741 0.006061 0.8446 0.8324 0.02048 ] Network output: [ 0.999 0.00785 0.001582 -4.25e-05 1.908e-05 -0.007589 -3.203e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1829 -0.02803 -0.1994 0.2006 0.9836 0.9933 0.204 0.4614 0.878 0.7229 ] Network output: [ -0.01198 1 1.01 2.295e-06 -1.03e-06 0.01334 1.73e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005286 0.000526 0.00426 0.004485 0.9889 0.992 0.005382 0.8746 0.9018 0.01486 ] Network output: [ -0.00153 0.01212 1.003 -0.0001495 6.71e-05 0.9877 -0.0001126 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1933 0.09435 0.3188 0.1592 0.9851 0.994 0.1939 0.4662 0.8845 0.7178 ] Network output: [ 0.008862 -0.03985 0.9976 8.572e-05 -3.848e-05 1.025 6.46e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09688 0.08604 0.1778 0.2098 0.9874 0.992 0.09694 0.7938 0.8789 0.31 ] Network output: [ -0.00893 0.04403 1.001 8.575e-05 -3.85e-05 0.9729 6.462e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09311 0.09124 0.1679 0.1984 0.9857 0.9915 0.09312 0.7241 0.8586 0.2435 ] Network output: [ -1.093e-05 0.9993 -0.0003293 1.168e-05 -5.242e-06 1.001 8.8e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009086 Epoch 6800 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01357 0.991 0.9864 5.039e-06 -2.262e-06 -0.004541 3.798e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003192 -0.002961 -0.009684 0.007396 0.9697 0.9741 0.006058 0.8446 0.8324 0.02049 ] Network output: [ 0.9998 -0.004182 0.002176 -4.143e-05 1.86e-05 0.002201 -3.122e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1828 -0.02821 -0.1986 0.2026 0.9836 0.9933 0.2039 0.4611 0.8781 0.723 ] Network output: [ -0.01196 0.9996 1.01 2.373e-06 -1.065e-06 0.01388 1.789e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005283 0.0005271 0.004295 0.004547 0.9889 0.992 0.005379 0.8746 0.9019 0.01487 ] Network output: [ -0.0004699 -0.004277 1.003 -0.0001476 6.626e-05 1.001 -0.0001112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1932 0.09431 0.32 0.1623 0.9851 0.994 0.1938 0.466 0.8845 0.7177 ] Network output: [ 0.008602 -0.04306 0.9982 8.584e-05 -3.854e-05 1.028 6.469e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.0861 0.1785 0.2107 0.9874 0.992 0.09698 0.794 0.8789 0.3104 ] Network output: [ -0.009058 0.04506 1.001 8.558e-05 -3.842e-05 0.9722 6.45e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09315 0.09128 0.168 0.1985 0.9857 0.9915 0.09316 0.7244 0.8586 0.2435 ] Network output: [ 0.0006301 0.9995 -0.001241 1.191e-05 -5.346e-06 1.001 8.975e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009398 Epoch 6801 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01344 0.9928 0.9864 4.848e-06 -2.176e-06 -0.006043 3.654e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003194 -0.002961 -0.009693 0.007364 0.9697 0.9741 0.006061 0.8446 0.8324 0.02047 ] Network output: [ 0.999 0.007781 0.001584 -4.245e-05 1.906e-05 -0.007536 -3.2e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1829 -0.02805 -0.1994 0.2006 0.9836 0.9933 0.204 0.4614 0.878 0.7229 ] Network output: [ -0.01198 1 1.01 2.292e-06 -1.029e-06 0.01334 1.727e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005287 0.0005255 0.004261 0.004483 0.9889 0.992 0.005383 0.8746 0.9018 0.01485 ] Network output: [ -0.001525 0.01203 1.003 -0.0001493 6.701e-05 0.9878 -0.0001125 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1933 0.09434 0.3189 0.1592 0.9851 0.994 0.1939 0.4662 0.8845 0.7178 ] Network output: [ 0.008854 -0.03986 0.9976 8.562e-05 -3.844e-05 1.025 6.453e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09688 0.08604 0.1778 0.2098 0.9874 0.992 0.09694 0.7937 0.8788 0.31 ] Network output: [ -0.008923 0.04401 1.001 8.566e-05 -3.845e-05 0.973 6.455e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0931 0.09122 0.1678 0.1983 0.9857 0.9915 0.09311 0.7241 0.8586 0.2435 ] Network output: [ -7.815e-06 0.9993 -0.0003334 1.166e-05 -5.237e-06 1.001 8.791e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009075 Epoch 6802 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01356 0.991 0.9864 5.028e-06 -2.257e-06 -0.004554 3.789e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003193 -0.002961 -0.00968 0.007393 0.9697 0.9741 0.006059 0.8446 0.8324 0.02048 ] Network output: [ 0.9998 -0.004121 0.002171 -4.14e-05 1.859e-05 0.002148 -3.12e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1828 -0.02822 -0.1985 0.2025 0.9836 0.9933 0.2039 0.4611 0.8781 0.723 ] Network output: [ -0.01196 0.9996 1.01 2.37e-06 -1.064e-06 0.01387 1.786e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005284 0.0005266 0.004296 0.004545 0.9889 0.992 0.00538 0.8745 0.9019 0.01487 ] Network output: [ -0.0004758 -0.004188 1.003 -0.0001474 6.618e-05 1.001 -0.0001111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1932 0.09429 0.32 0.1622 0.9851 0.994 0.1938 0.466 0.8845 0.7176 ] Network output: [ 0.008598 -0.04302 0.9982 8.574e-05 -3.849e-05 1.028 6.461e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.0861 0.1785 0.2107 0.9874 0.992 0.09699 0.7939 0.8788 0.3104 ] Network output: [ -0.00905 0.04503 1.001 8.549e-05 -3.838e-05 0.9722 6.443e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09313 0.09126 0.168 0.1985 0.9857 0.9915 0.09314 0.7243 0.8586 0.2435 ] Network output: [ 0.0006265 0.9995 -0.001235 1.189e-05 -5.34e-06 1.001 8.964e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009383 Epoch 6803 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01344 0.9928 0.9864 4.839e-06 -2.172e-06 -0.006039 3.647e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003194 -0.002961 -0.00969 0.007361 0.9697 0.9741 0.006061 0.8446 0.8324 0.02047 ] Network output: [ 0.999 0.007713 0.001586 -4.241e-05 1.904e-05 -0.007483 -3.196e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1829 -0.02806 -0.1993 0.2006 0.9836 0.9933 0.204 0.4614 0.878 0.7229 ] Network output: [ -0.01197 1 1.01 2.289e-06 -1.028e-06 0.01333 1.725e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005287 0.000525 0.004262 0.004482 0.9889 0.992 0.005384 0.8745 0.9018 0.01485 ] Network output: [ -0.001519 0.01194 1.003 -0.0001491 6.692e-05 0.9878 -0.0001123 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1934 0.09432 0.3189 0.1592 0.9851 0.994 0.194 0.4662 0.8845 0.7178 ] Network output: [ 0.008847 -0.03986 0.9976 8.552e-05 -3.839e-05 1.025 6.445e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09688 0.08604 0.1778 0.2098 0.9874 0.992 0.09694 0.7936 0.8788 0.31 ] Network output: [ -0.008916 0.04399 1.001 8.556e-05 -3.841e-05 0.973 6.448e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09308 0.0912 0.1678 0.1983 0.9857 0.9915 0.09309 0.724 0.8586 0.2435 ] Network output: [ -4.708e-06 0.9993 -0.0003374 1.165e-05 -5.231e-06 1.001 8.782e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009064 Epoch 6804 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01356 0.991 0.9864 5.016e-06 -2.252e-06 -0.004567 3.78e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003193 -0.002962 -0.009677 0.007391 0.9697 0.9741 0.006059 0.8445 0.8324 0.02048 ] Network output: [ 0.9998 -0.00406 0.002167 -4.137e-05 1.857e-05 0.002096 -3.118e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1828 -0.02824 -0.1985 0.2025 0.9836 0.9933 0.2039 0.4611 0.8781 0.723 ] Network output: [ -0.01196 0.9996 1.01 2.366e-06 -1.062e-06 0.01386 1.783e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005285 0.0005262 0.004296 0.004543 0.9889 0.992 0.005381 0.8745 0.9018 0.01486 ] Network output: [ -0.0004818 -0.004099 1.003 -0.0001472 6.61e-05 1.001 -0.000111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1933 0.09428 0.3201 0.1622 0.9851 0.994 0.1939 0.466 0.8845 0.7176 ] Network output: [ 0.008593 -0.04299 0.9982 8.563e-05 -3.844e-05 1.028 6.454e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08609 0.1785 0.2106 0.9874 0.992 0.09699 0.7938 0.8788 0.3104 ] Network output: [ -0.009041 0.045 1.001 8.54e-05 -3.834e-05 0.9722 6.436e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09312 0.09124 0.168 0.1985 0.9857 0.9915 0.09313 0.7243 0.8585 0.2435 ] Network output: [ 0.0006229 0.9995 -0.001229 1.188e-05 -5.333e-06 1.001 8.953e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009368 Epoch 6805 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01343 0.9928 0.9864 4.829e-06 -2.168e-06 -0.006036 3.639e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003194 -0.002961 -0.009686 0.007359 0.9697 0.9741 0.006062 0.8446 0.8323 0.02047 ] Network output: [ 0.999 0.007645 0.001587 -4.237e-05 1.902e-05 -0.00743 -3.193e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1829 -0.02808 -0.1993 0.2006 0.9836 0.9933 0.2041 0.4613 0.878 0.7229 ] Network output: [ -0.01197 1 1.01 2.287e-06 -1.027e-06 0.01333 1.723e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005288 0.0005246 0.004262 0.00448 0.9889 0.992 0.005385 0.8745 0.9018 0.01485 ] Network output: [ -0.001514 0.01186 1.003 -0.0001489 6.683e-05 0.9879 -0.0001122 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1934 0.09431 0.3189 0.1592 0.9851 0.994 0.194 0.4661 0.8845 0.7178 ] Network output: [ 0.008839 -0.03986 0.9976 8.542e-05 -3.835e-05 1.025 6.437e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09688 0.08604 0.1778 0.2098 0.9874 0.992 0.09694 0.7936 0.8788 0.31 ] Network output: [ -0.008909 0.04397 1.001 8.547e-05 -3.837e-05 0.973 6.441e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09306 0.09119 0.1678 0.1983 0.9857 0.9915 0.09308 0.7239 0.8585 0.2435 ] Network output: [ -1.614e-06 0.9993 -0.0003414 1.164e-05 -5.226e-06 1.001 8.773e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009053 Epoch 6806 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01355 0.991 0.9864 5.004e-06 -2.247e-06 -0.004579 3.771e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003193 -0.002962 -0.009674 0.007388 0.9697 0.9741 0.00606 0.8445 0.8324 0.02047 ] Network output: [ 0.9998 -0.003999 0.002162 -4.134e-05 1.856e-05 0.002045 -3.116e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1828 -0.02825 -0.1985 0.2025 0.9836 0.9933 0.2039 0.461 0.878 0.723 ] Network output: [ -0.01195 0.9996 1.01 2.362e-06 -1.06e-06 0.01385 1.78e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005286 0.0005257 0.004296 0.00454 0.9889 0.992 0.005382 0.8745 0.9018 0.01486 ] Network output: [ -0.0004877 -0.004011 1.003 -0.0001471 6.603e-05 1.001 -0.0001108 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1933 0.09427 0.3201 0.1621 0.9851 0.994 0.1939 0.4659 0.8845 0.7176 ] Network output: [ 0.008588 -0.04296 0.9981 8.553e-05 -3.84e-05 1.028 6.446e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08609 0.1785 0.2106 0.9874 0.992 0.09699 0.7938 0.8788 0.3104 ] Network output: [ -0.009032 0.04496 1.001 8.531e-05 -3.83e-05 0.9723 6.429e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0931 0.09123 0.168 0.1985 0.9857 0.9915 0.09311 0.7242 0.8585 0.2435 ] Network output: [ 0.0006193 0.9995 -0.001224 1.186e-05 -5.326e-06 1.001 8.941e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009353 Epoch 6807 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01343 0.9928 0.9864 4.82e-06 -2.164e-06 -0.006032 3.632e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003194 -0.002962 -0.009683 0.007357 0.9697 0.9741 0.006062 0.8446 0.8323 0.02046 ] Network output: [ 0.999 0.007577 0.001589 -4.233e-05 1.9e-05 -0.007378 -3.19e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1829 -0.0281 -0.1992 0.2006 0.9836 0.9933 0.2041 0.4613 0.878 0.7229 ] Network output: [ -0.01197 1 1.01 2.284e-06 -1.025e-06 0.01332 1.721e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005289 0.0005241 0.004263 0.004478 0.9889 0.992 0.005386 0.8745 0.9018 0.01484 ] Network output: [ -0.001509 0.01177 1.003 -0.0001487 6.674e-05 0.988 -0.000112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1934 0.0943 0.319 0.1591 0.9851 0.994 0.194 0.4661 0.8845 0.7178 ] Network output: [ 0.008831 -0.03986 0.9976 8.532e-05 -3.83e-05 1.025 6.43e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09688 0.08603 0.1778 0.2098 0.9874 0.992 0.09694 0.7935 0.8788 0.31 ] Network output: [ -0.008902 0.04395 1.001 8.537e-05 -3.833e-05 0.973 6.434e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09305 0.09117 0.1678 0.1983 0.9857 0.9915 0.09306 0.7239 0.8585 0.2435 ] Network output: [ 1.468e-06 0.9993 -0.0003454 1.163e-05 -5.221e-06 1.001 8.764e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009042 Epoch 6808 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01355 0.9911 0.9865 4.993e-06 -2.241e-06 -0.004592 3.763e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003193 -0.002962 -0.009671 0.007386 0.9697 0.9741 0.00606 0.8445 0.8324 0.02047 ] Network output: [ 0.9998 -0.003938 0.002158 -4.131e-05 1.855e-05 0.001993 -3.114e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1828 -0.02826 -0.1984 0.2024 0.9836 0.9933 0.204 0.461 0.878 0.723 ] Network output: [ -0.01195 0.9996 1.01 2.358e-06 -1.059e-06 0.01383 1.777e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005287 0.0005252 0.004296 0.004538 0.9889 0.992 0.005383 0.8745 0.9018 0.01486 ] Network output: [ -0.0004935 -0.003923 1.003 -0.0001469 6.595e-05 1.001 -0.0001107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1933 0.09425 0.3201 0.1621 0.9851 0.994 0.1939 0.4659 0.8845 0.7176 ] Network output: [ 0.008583 -0.04292 0.9981 8.543e-05 -3.835e-05 1.028 6.438e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08609 0.1785 0.2106 0.9874 0.992 0.09699 0.7937 0.8788 0.3104 ] Network output: [ -0.009024 0.04493 1.001 8.522e-05 -3.826e-05 0.9723 6.422e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09308 0.09121 0.168 0.1985 0.9857 0.9915 0.0931 0.7241 0.8585 0.2435 ] Network output: [ 0.0006157 0.9995 -0.001219 1.185e-05 -5.32e-06 1.001 8.93e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009339 Epoch 6809 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01342 0.9928 0.9864 4.81e-06 -2.159e-06 -0.006028 3.625e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003195 -0.002962 -0.00968 0.007355 0.9697 0.9741 0.006063 0.8445 0.8323 0.02046 ] Network output: [ 0.999 0.007509 0.001591 -4.229e-05 1.898e-05 -0.007325 -3.187e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.183 -0.02811 -0.1992 0.2006 0.9836 0.9933 0.2041 0.4613 0.878 0.7228 ] Network output: [ -0.01197 1 1.01 2.281e-06 -1.024e-06 0.01331 1.719e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00529 0.0005237 0.004264 0.004477 0.9889 0.992 0.005386 0.8745 0.9018 0.01484 ] Network output: [ -0.001503 0.01168 1.003 -0.0001485 6.666e-05 0.988 -0.0001119 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1934 0.09428 0.319 0.1591 0.9851 0.994 0.194 0.4661 0.8845 0.7178 ] Network output: [ 0.008823 -0.03986 0.9976 8.521e-05 -3.826e-05 1.025 6.422e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09689 0.08603 0.1778 0.2097 0.9874 0.992 0.09695 0.7935 0.8787 0.3099 ] Network output: [ -0.008895 0.04392 1.001 8.528e-05 -3.829e-05 0.973 6.427e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09303 0.09116 0.1678 0.1983 0.9857 0.9915 0.09305 0.7238 0.8585 0.2435 ] Network output: [ 4.536e-06 0.9993 -0.0003494 1.162e-05 -5.215e-06 1.001 8.755e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009031 Epoch 6810 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01354 0.9911 0.9865 4.981e-06 -2.236e-06 -0.004604 3.754e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003193 -0.002962 -0.009667 0.007383 0.9697 0.9741 0.00606 0.8445 0.8324 0.02046 ] Network output: [ 0.9998 -0.003878 0.002153 -4.128e-05 1.853e-05 0.001942 -3.111e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1829 -0.02828 -0.1984 0.2024 0.9836 0.9933 0.204 0.461 0.878 0.723 ] Network output: [ -0.01195 0.9997 1.01 2.355e-06 -1.057e-06 0.01382 1.774e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005288 0.0005248 0.004297 0.004536 0.9889 0.992 0.005384 0.8745 0.9018 0.01485 ] Network output: [ -0.0004993 -0.003836 1.003 -0.0001467 6.587e-05 1.001 -0.0001106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1933 0.09424 0.3201 0.162 0.9851 0.994 0.1939 0.4659 0.8845 0.7176 ] Network output: [ 0.008579 -0.04289 0.9981 8.532e-05 -3.83e-05 1.028 6.43e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08609 0.1785 0.2106 0.9874 0.992 0.09699 0.7937 0.8787 0.3104 ] Network output: [ -0.009015 0.0449 1.001 8.512e-05 -3.822e-05 0.9723 6.415e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09307 0.0912 0.168 0.1984 0.9857 0.9915 0.09308 0.724 0.8584 0.2435 ] Network output: [ 0.0006122 0.9995 -0.001213 1.183e-05 -5.313e-06 1.001 8.919e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009324 Epoch 6811 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01342 0.9928 0.9864 4.801e-06 -2.155e-06 -0.006025 3.618e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003195 -0.002962 -0.009676 0.007353 0.9697 0.9741 0.006063 0.8445 0.8323 0.02045 ] Network output: [ 0.999 0.007442 0.001592 -4.225e-05 1.897e-05 -0.007273 -3.184e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.183 -0.02813 -0.1992 0.2005 0.9836 0.9933 0.2041 0.4612 0.878 0.7228 ] Network output: [ -0.01197 1 1.01 2.278e-06 -1.023e-06 0.01331 1.717e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005291 0.0005232 0.004264 0.004475 0.9889 0.992 0.005387 0.8745 0.9018 0.01484 ] Network output: [ -0.001498 0.01159 1.003 -0.0001483 6.657e-05 0.9881 -0.0001117 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1934 0.09427 0.3191 0.1591 0.9851 0.994 0.194 0.466 0.8845 0.7177 ] Network output: [ 0.008815 -0.03986 0.9976 8.511e-05 -3.821e-05 1.025 6.414e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09689 0.08603 0.1778 0.2097 0.9874 0.992 0.09695 0.7934 0.8787 0.3099 ] Network output: [ -0.008888 0.0439 1.001 8.519e-05 -3.824e-05 0.973 6.42e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09302 0.09114 0.1678 0.1983 0.9857 0.9915 0.09303 0.7237 0.8584 0.2435 ] Network output: [ 7.591e-06 0.9993 -0.0003533 1.16e-05 -5.21e-06 1.001 8.746e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000902 Epoch 6812 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01353 0.9911 0.9865 4.969e-06 -2.231e-06 -0.004617 3.745e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003193 -0.002963 -0.009664 0.007381 0.9697 0.9741 0.006061 0.8445 0.8324 0.02046 ] Network output: [ 0.9998 -0.003818 0.002149 -4.126e-05 1.852e-05 0.001891 -3.109e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1829 -0.02829 -0.1984 0.2024 0.9836 0.9933 0.204 0.461 0.878 0.723 ] Network output: [ -0.01195 0.9997 1.01 2.351e-06 -1.055e-06 0.01381 1.772e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005289 0.0005243 0.004297 0.004534 0.9889 0.992 0.005385 0.8744 0.9018 0.01485 ] Network output: [ -0.0005051 -0.003749 1.003 -0.0001465 6.579e-05 1.001 -0.0001104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1933 0.09423 0.3202 0.162 0.9851 0.994 0.1939 0.4658 0.8844 0.7176 ] Network output: [ 0.008574 -0.04285 0.9981 8.522e-05 -3.826e-05 1.028 6.422e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08608 0.1785 0.2105 0.9874 0.992 0.09699 0.7936 0.8787 0.3104 ] Network output: [ -0.009006 0.04487 1.001 8.503e-05 -3.817e-05 0.9723 6.408e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09305 0.09118 0.1679 0.1984 0.9857 0.9915 0.09307 0.724 0.8584 0.2435 ] Network output: [ 0.0006086 0.9995 -0.001208 1.182e-05 -5.306e-06 1.001 8.908e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000931 Epoch 6813 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01341 0.9928 0.9864 4.791e-06 -2.151e-06 -0.006021 3.611e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003195 -0.002963 -0.009673 0.00735 0.9697 0.9741 0.006063 0.8445 0.8323 0.02045 ] Network output: [ 0.999 0.007375 0.001594 -4.22e-05 1.895e-05 -0.007221 -3.181e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.183 -0.02815 -0.1991 0.2005 0.9836 0.9933 0.2041 0.4612 0.8779 0.7228 ] Network output: [ -0.01196 1 1.01 2.275e-06 -1.021e-06 0.0133 1.715e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005292 0.0005228 0.004265 0.004474 0.9889 0.992 0.005388 0.8745 0.9017 0.01483 ] Network output: [ -0.001493 0.0115 1.003 -0.0001481 6.648e-05 0.9882 -0.0001116 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1934 0.09426 0.3191 0.1591 0.9851 0.994 0.1941 0.466 0.8845 0.7177 ] Network output: [ 0.008807 -0.03986 0.9976 8.501e-05 -3.816e-05 1.025 6.407e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09689 0.08603 0.1778 0.2097 0.9874 0.992 0.09695 0.7934 0.8787 0.3099 ] Network output: [ -0.008881 0.04388 1.001 8.509e-05 -3.82e-05 0.973 6.413e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.093 0.09113 0.1678 0.1983 0.9857 0.9915 0.09302 0.7237 0.8584 0.2435 ] Network output: [ 1.063e-05 0.9993 -0.0003573 1.159e-05 -5.204e-06 1.001 8.737e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009009 Epoch 6814 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01353 0.9911 0.9865 4.957e-06 -2.226e-06 -0.004629 3.736e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003194 -0.002963 -0.009661 0.007378 0.9697 0.9741 0.006061 0.8445 0.8324 0.02045 ] Network output: [ 0.9998 -0.003758 0.002144 -4.123e-05 1.851e-05 0.00184 -3.107e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1829 -0.02831 -0.1983 0.2023 0.9836 0.9933 0.204 0.4609 0.878 0.723 ] Network output: [ -0.01194 0.9997 1.01 2.347e-06 -1.054e-06 0.0138 1.769e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00529 0.0005238 0.004297 0.004531 0.9889 0.992 0.005386 0.8744 0.9018 0.01485 ] Network output: [ -0.0005109 -0.003662 1.003 -0.0001464 6.571e-05 1.001 -0.0001103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1933 0.09422 0.3202 0.1619 0.9851 0.994 0.194 0.4658 0.8844 0.7176 ] Network output: [ 0.008569 -0.04282 0.9981 8.512e-05 -3.821e-05 1.028 6.415e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08608 0.1785 0.2105 0.9874 0.992 0.09699 0.7935 0.8787 0.3103 ] Network output: [ -0.008998 0.04483 1.001 8.494e-05 -3.813e-05 0.9723 6.401e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09304 0.09116 0.1679 0.1984 0.9857 0.9915 0.09305 0.7239 0.8584 0.2435 ] Network output: [ 0.0006051 0.9995 -0.001202 1.18e-05 -5.3e-06 1.001 8.896e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009295 Epoch 6815 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01341 0.9928 0.9864 4.782e-06 -2.147e-06 -0.006018 3.604e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003195 -0.002963 -0.009669 0.007348 0.9697 0.9741 0.006064 0.8445 0.8323 0.02044 ] Network output: [ 0.999 0.007308 0.001595 -4.216e-05 1.893e-05 -0.007169 -3.177e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.183 -0.02816 -0.1991 0.2005 0.9836 0.9933 0.2041 0.4612 0.8779 0.7228 ] Network output: [ -0.01196 1 1.01 2.272e-06 -1.02e-06 0.0133 1.712e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005293 0.0005223 0.004266 0.004472 0.9889 0.992 0.005389 0.8744 0.9017 0.01483 ] Network output: [ -0.001487 0.01142 1.003 -0.0001479 6.639e-05 0.9883 -0.0001114 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1935 0.09424 0.3192 0.159 0.9851 0.994 0.1941 0.466 0.8844 0.7177 ] Network output: [ 0.008799 -0.03986 0.9976 8.491e-05 -3.812e-05 1.025 6.399e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09689 0.08603 0.1778 0.2097 0.9874 0.992 0.09695 0.7933 0.8787 0.3099 ] Network output: [ -0.008874 0.04386 1.001 8.5e-05 -3.816e-05 0.973 6.406e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09299 0.09111 0.1678 0.1983 0.9857 0.9915 0.093 0.7236 0.8584 0.2435 ] Network output: [ 1.366e-05 0.9993 -0.0003612 1.158e-05 -5.199e-06 1.001 8.727e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008998 Epoch 6816 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01352 0.9911 0.9865 4.946e-06 -2.22e-06 -0.004642 3.727e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003194 -0.002963 -0.009657 0.007376 0.9697 0.9741 0.006062 0.8445 0.8323 0.02045 ] Network output: [ 0.9998 -0.003699 0.00214 -4.12e-05 1.849e-05 0.00179 -3.105e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1829 -0.02832 -0.1983 0.2023 0.9836 0.9933 0.204 0.4609 0.878 0.7229 ] Network output: [ -0.01194 0.9997 1.01 2.343e-06 -1.052e-06 0.01379 1.766e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005291 0.0005234 0.004297 0.004529 0.9889 0.992 0.005387 0.8744 0.9018 0.01484 ] Network output: [ -0.0005166 -0.003576 1.003 -0.0001462 6.563e-05 1.001 -0.0001102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1934 0.0942 0.3202 0.1619 0.9851 0.994 0.194 0.4658 0.8844 0.7176 ] Network output: [ 0.008564 -0.04278 0.9981 8.501e-05 -3.817e-05 1.028 6.407e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08608 0.1785 0.2105 0.9874 0.992 0.09699 0.7935 0.8787 0.3103 ] Network output: [ -0.008989 0.0448 1.001 8.485e-05 -3.809e-05 0.9723 6.394e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09302 0.09115 0.1679 0.1984 0.9857 0.9915 0.09303 0.7238 0.8583 0.2435 ] Network output: [ 0.0006015 0.9995 -0.001197 1.179e-05 -5.293e-06 1.001 8.885e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009281 Epoch 6817 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01341 0.9928 0.9864 4.772e-06 -2.142e-06 -0.006014 3.596e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003195 -0.002963 -0.009666 0.007346 0.9697 0.9741 0.006064 0.8445 0.8323 0.02044 ] Network output: [ 0.9991 0.007242 0.001597 -4.212e-05 1.891e-05 -0.007118 -3.174e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.183 -0.02818 -0.199 0.2005 0.9836 0.9933 0.2042 0.4611 0.8779 0.7228 ] Network output: [ -0.01196 1 1.01 2.269e-06 -1.019e-06 0.01329 1.71e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005294 0.0005219 0.004266 0.00447 0.9889 0.992 0.00539 0.8744 0.9017 0.01482 ] Network output: [ -0.001482 0.01133 1.003 -0.0001477 6.63e-05 0.9883 -0.0001113 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1935 0.09423 0.3192 0.159 0.9851 0.994 0.1941 0.4659 0.8844 0.7177 ] Network output: [ 0.008791 -0.03986 0.9976 8.481e-05 -3.807e-05 1.025 6.391e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09689 0.08603 0.1778 0.2097 0.9874 0.992 0.09695 0.7932 0.8786 0.3099 ] Network output: [ -0.008867 0.04384 1.001 8.491e-05 -3.812e-05 0.973 6.399e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09297 0.0911 0.1678 0.1982 0.9856 0.9915 0.09299 0.7235 0.8583 0.2434 ] Network output: [ 1.667e-05 0.9993 -0.000365 1.157e-05 -5.193e-06 1.001 8.718e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008987 Epoch 6818 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01352 0.9912 0.9865 4.934e-06 -2.215e-06 -0.004654 3.718e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003194 -0.002964 -0.009654 0.007373 0.9697 0.9741 0.006062 0.8444 0.8323 0.02044 ] Network output: [ 0.9998 -0.00364 0.002135 -4.117e-05 1.848e-05 0.001739 -3.102e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1829 -0.02834 -0.1983 0.2023 0.9836 0.9933 0.2041 0.4609 0.878 0.7229 ] Network output: [ -0.01194 0.9997 1.01 2.339e-06 -1.05e-06 0.01378 1.763e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005292 0.0005229 0.004298 0.004527 0.9889 0.992 0.005388 0.8744 0.9018 0.01484 ] Network output: [ -0.0005223 -0.00349 1.003 -0.000146 6.555e-05 1.001 -0.00011 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1934 0.09419 0.3203 0.1618 0.9851 0.994 0.194 0.4657 0.8844 0.7176 ] Network output: [ 0.008559 -0.04275 0.9981 8.491e-05 -3.812e-05 1.028 6.399e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09694 0.08608 0.1785 0.2104 0.9874 0.992 0.097 0.7934 0.8786 0.3103 ] Network output: [ -0.008981 0.04477 1.001 8.475e-05 -3.805e-05 0.9723 6.387e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09301 0.09113 0.1679 0.1984 0.9857 0.9915 0.09302 0.7238 0.8583 0.2435 ] Network output: [ 0.000598 0.9995 -0.001191 1.178e-05 -5.286e-06 1.001 8.874e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009267 Epoch 6819 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0134 0.9928 0.9865 4.762e-06 -2.138e-06 -0.006011 3.589e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003195 -0.002963 -0.009662 0.007344 0.9697 0.9741 0.006065 0.8445 0.8323 0.02043 ] Network output: [ 0.9991 0.007176 0.001598 -4.208e-05 1.889e-05 -0.007066 -3.171e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.183 -0.02819 -0.199 0.2005 0.9836 0.9933 0.2042 0.4611 0.8779 0.7228 ] Network output: [ -0.01196 1 1.01 2.266e-06 -1.017e-06 0.01329 1.708e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005295 0.0005215 0.004267 0.004469 0.9889 0.992 0.005391 0.8744 0.9017 0.01482 ] Network output: [ -0.001477 0.01125 1.003 -0.0001475 6.621e-05 0.9884 -0.0001112 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1935 0.09422 0.3193 0.159 0.9851 0.994 0.1941 0.4659 0.8844 0.7177 ] Network output: [ 0.008784 -0.03985 0.9976 8.471e-05 -3.803e-05 1.025 6.384e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09689 0.08603 0.1778 0.2097 0.9874 0.992 0.09696 0.7932 0.8786 0.3099 ] Network output: [ -0.00886 0.04381 1.001 8.481e-05 -3.808e-05 0.973 6.392e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09296 0.09108 0.1677 0.1982 0.9856 0.9915 0.09297 0.7234 0.8583 0.2434 ] Network output: [ 1.967e-05 0.9993 -0.0003689 1.156e-05 -5.188e-06 1.001 8.709e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008977 Epoch 6820 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01351 0.9912 0.9865 4.922e-06 -2.21e-06 -0.004667 3.71e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003194 -0.002964 -0.009651 0.007371 0.9697 0.9741 0.006063 0.8444 0.8323 0.02044 ] Network output: [ 0.9998 -0.003581 0.002131 -4.114e-05 1.847e-05 0.001689 -3.1e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1829 -0.02835 -0.1983 0.2022 0.9836 0.9933 0.2041 0.4608 0.878 0.7229 ] Network output: [ -0.01194 0.9997 1.01 2.335e-06 -1.048e-06 0.01377 1.76e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005293 0.0005225 0.004298 0.004524 0.9889 0.992 0.005389 0.8744 0.9017 0.01484 ] Network output: [ -0.0005279 -0.003405 1.003 -0.0001458 6.547e-05 1.001 -0.0001099 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1934 0.09418 0.3203 0.1617 0.9851 0.994 0.194 0.4657 0.8844 0.7176 ] Network output: [ 0.008554 -0.04271 0.9981 8.481e-05 -3.807e-05 1.028 6.391e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09694 0.08608 0.1785 0.2104 0.9874 0.992 0.097 0.7934 0.8786 0.3103 ] Network output: [ -0.008972 0.04473 1.001 8.466e-05 -3.801e-05 0.9724 6.38e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09299 0.09112 0.1679 0.1984 0.9857 0.9915 0.093 0.7237 0.8583 0.2435 ] Network output: [ 0.0005945 0.9995 -0.001186 1.176e-05 -5.28e-06 1.001 8.863e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009253 Epoch 6821 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0134 0.9928 0.9865 4.753e-06 -2.134e-06 -0.006008 3.582e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003195 -0.002964 -0.009659 0.007342 0.9697 0.9741 0.006065 0.8445 0.8322 0.02043 ] Network output: [ 0.9991 0.00711 0.0016 -4.204e-05 1.887e-05 -0.007015 -3.168e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1831 -0.02821 -0.1989 0.2005 0.9836 0.9933 0.2042 0.4611 0.8779 0.7228 ] Network output: [ -0.01195 1 1.01 2.263e-06 -1.016e-06 0.01328 1.706e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005296 0.000521 0.004268 0.004467 0.9889 0.992 0.005392 0.8744 0.9017 0.01482 ] Network output: [ -0.001471 0.01116 1.003 -0.0001473 6.612e-05 0.9885 -0.000111 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1935 0.09421 0.3193 0.159 0.9851 0.994 0.1941 0.4659 0.8844 0.7177 ] Network output: [ 0.008776 -0.03985 0.9976 8.461e-05 -3.798e-05 1.025 6.376e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0969 0.08603 0.1778 0.2096 0.9874 0.992 0.09696 0.7931 0.8786 0.3099 ] Network output: [ -0.008853 0.04379 1.001 8.472e-05 -3.803e-05 0.973 6.385e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09294 0.09107 0.1677 0.1982 0.9856 0.9915 0.09296 0.7234 0.8583 0.2434 ] Network output: [ 2.266e-05 0.9993 -0.0003727 1.154e-05 -5.183e-06 1.001 8.7e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008966 Epoch 6822 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0135 0.9912 0.9865 4.911e-06 -2.205e-06 -0.004679 3.701e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003194 -0.002964 -0.009647 0.007368 0.9697 0.9741 0.006063 0.8444 0.8323 0.02043 ] Network output: [ 0.9998 -0.003522 0.002127 -4.111e-05 1.845e-05 0.00164 -3.098e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.183 -0.02837 -0.1982 0.2022 0.9836 0.9933 0.2041 0.4608 0.8779 0.7229 ] Network output: [ -0.01194 0.9997 1.01 2.331e-06 -1.047e-06 0.01375 1.757e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005294 0.000522 0.004298 0.004522 0.9889 0.992 0.00539 0.8744 0.9017 0.01483 ] Network output: [ -0.0005336 -0.00332 1.003 -0.0001457 6.539e-05 1.001 -0.0001098 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1934 0.09417 0.3203 0.1617 0.9851 0.994 0.194 0.4657 0.8844 0.7176 ] Network output: [ 0.00855 -0.04268 0.998 8.47e-05 -3.803e-05 1.028 6.383e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09694 0.08608 0.1785 0.2104 0.9874 0.992 0.097 0.7933 0.8786 0.3103 ] Network output: [ -0.008964 0.0447 1.001 8.457e-05 -3.797e-05 0.9724 6.373e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09298 0.0911 0.1679 0.1983 0.9857 0.9915 0.09299 0.7236 0.8583 0.2434 ] Network output: [ 0.000591 0.9995 -0.001181 1.175e-05 -5.273e-06 1.001 8.852e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009239 Epoch 6823 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01339 0.9928 0.9865 4.743e-06 -2.129e-06 -0.006005 3.575e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003196 -0.002964 -0.009656 0.007339 0.9697 0.9741 0.006065 0.8444 0.8322 0.02042 ] Network output: [ 0.9991 0.007045 0.001601 -4.199e-05 1.885e-05 -0.006964 -3.165e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1831 -0.02823 -0.1989 0.2005 0.9836 0.9933 0.2042 0.461 0.8779 0.7228 ] Network output: [ -0.01195 1 1.01 2.26e-06 -1.015e-06 0.01327 1.703e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005297 0.0005206 0.004268 0.004465 0.9889 0.992 0.005393 0.8744 0.9017 0.01481 ] Network output: [ -0.001466 0.01107 1.003 -0.0001471 6.604e-05 0.9885 -0.0001109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1935 0.09419 0.3193 0.1589 0.9851 0.994 0.1941 0.4658 0.8844 0.7177 ] Network output: [ 0.008768 -0.03985 0.9976 8.45e-05 -3.794e-05 1.025 6.369e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0969 0.08602 0.1779 0.2096 0.9874 0.992 0.09696 0.7931 0.8786 0.3099 ] Network output: [ -0.008846 0.04377 1.001 8.462e-05 -3.799e-05 0.973 6.378e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09293 0.09105 0.1677 0.1982 0.9856 0.9915 0.09294 0.7233 0.8582 0.2434 ] Network output: [ 2.563e-05 0.9993 -0.0003765 1.153e-05 -5.177e-06 1.001 8.691e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008955 Epoch 6824 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0135 0.9912 0.9865 4.899e-06 -2.199e-06 -0.004692 3.692e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003194 -0.002965 -0.009644 0.007365 0.9697 0.9741 0.006064 0.8444 0.8323 0.02043 ] Network output: [ 0.9998 -0.003464 0.002122 -4.108e-05 1.844e-05 0.00159 -3.096e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.183 -0.02838 -0.1982 0.2022 0.9836 0.9933 0.2041 0.4608 0.8779 0.7229 ] Network output: [ -0.01193 0.9997 1.01 2.327e-06 -1.045e-06 0.01374 1.754e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005295 0.0005216 0.004299 0.00452 0.9889 0.992 0.005391 0.8743 0.9017 0.01483 ] Network output: [ -0.0005391 -0.003236 1.003 -0.0001455 6.531e-05 1 -0.0001096 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1934 0.09415 0.3204 0.1616 0.9851 0.994 0.194 0.4656 0.8844 0.7176 ] Network output: [ 0.008545 -0.04264 0.998 8.46e-05 -3.798e-05 1.028 6.376e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09694 0.08607 0.1785 0.2104 0.9874 0.992 0.097 0.7933 0.8786 0.3103 ] Network output: [ -0.008955 0.04467 1.001 8.448e-05 -3.792e-05 0.9724 6.366e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09296 0.09108 0.1679 0.1983 0.9857 0.9915 0.09297 0.7235 0.8582 0.2434 ] Network output: [ 0.0005875 0.9995 -0.001175 1.173e-05 -5.266e-06 1.001 8.841e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009225 Epoch 6825 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01339 0.9928 0.9865 4.734e-06 -2.125e-06 -0.006001 3.568e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003196 -0.002964 -0.009652 0.007337 0.9697 0.9741 0.006066 0.8444 0.8322 0.02042 ] Network output: [ 0.9991 0.00698 0.001603 -4.195e-05 1.883e-05 -0.006913 -3.162e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1831 -0.02824 -0.1989 0.2005 0.9836 0.9933 0.2042 0.461 0.8779 0.7228 ] Network output: [ -0.01195 1 1.01 2.257e-06 -1.013e-06 0.01327 1.701e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005298 0.0005201 0.004269 0.004464 0.9889 0.992 0.005394 0.8743 0.9017 0.01481 ] Network output: [ -0.001461 0.01099 1.003 -0.0001469 6.595e-05 0.9886 -0.0001107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1936 0.09418 0.3194 0.1589 0.9851 0.994 0.1942 0.4658 0.8844 0.7177 ] Network output: [ 0.00876 -0.03985 0.9976 8.44e-05 -3.789e-05 1.025 6.361e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0969 0.08602 0.1779 0.2096 0.9874 0.992 0.09696 0.793 0.8785 0.3099 ] Network output: [ -0.008839 0.04375 1.001 8.453e-05 -3.795e-05 0.973 6.37e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09291 0.09104 0.1677 0.1982 0.9856 0.9915 0.09293 0.7232 0.8582 0.2434 ] Network output: [ 2.858e-05 0.9993 -0.0003803 1.152e-05 -5.172e-06 1.001 8.682e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008945 Epoch 6826 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01349 0.9912 0.9865 4.887e-06 -2.194e-06 -0.004704 3.683e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003195 -0.002965 -0.009641 0.007363 0.9697 0.9741 0.006064 0.8444 0.8323 0.02042 ] Network output: [ 0.9998 -0.003406 0.002118 -4.105e-05 1.843e-05 0.001541 -3.093e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.183 -0.0284 -0.1982 0.2021 0.9836 0.9933 0.2041 0.4607 0.8779 0.7229 ] Network output: [ -0.01193 0.9997 1.01 2.323e-06 -1.043e-06 0.01373 1.751e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005296 0.0005211 0.004299 0.004518 0.9889 0.992 0.005392 0.8743 0.9017 0.01482 ] Network output: [ -0.0005447 -0.003152 1.003 -0.0001453 6.523e-05 1 -0.0001095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1935 0.09414 0.3204 0.1616 0.9851 0.994 0.1941 0.4656 0.8844 0.7175 ] Network output: [ 0.00854 -0.04261 0.998 8.45e-05 -3.793e-05 1.028 6.368e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09694 0.08607 0.1785 0.2103 0.9874 0.992 0.097 0.7932 0.8785 0.3103 ] Network output: [ -0.008946 0.04464 1.001 8.438e-05 -3.788e-05 0.9724 6.359e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09295 0.09107 0.1678 0.1983 0.9857 0.9915 0.09296 0.7235 0.8582 0.2434 ] Network output: [ 0.0005841 0.9995 -0.00117 1.172e-05 -5.26e-06 1.001 8.829e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009211 Epoch 6827 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01338 0.9928 0.9865 4.724e-06 -2.121e-06 -0.005998 3.56e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003196 -0.002965 -0.009649 0.007335 0.9697 0.9741 0.006066 0.8444 0.8322 0.02041 ] Network output: [ 0.9991 0.006915 0.001604 -4.191e-05 1.881e-05 -0.006863 -3.158e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1831 -0.02826 -0.1988 0.2004 0.9836 0.9933 0.2043 0.461 0.8779 0.7228 ] Network output: [ -0.01195 1 1.01 2.254e-06 -1.012e-06 0.01326 1.699e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005299 0.0005197 0.004269 0.004462 0.9889 0.992 0.005395 0.8743 0.9017 0.01481 ] Network output: [ -0.001456 0.01091 1.003 -0.0001467 6.586e-05 0.9887 -0.0001106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1936 0.09417 0.3194 0.1589 0.9851 0.994 0.1942 0.4658 0.8844 0.7177 ] Network output: [ 0.008753 -0.03985 0.9975 8.43e-05 -3.785e-05 1.025 6.353e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0969 0.08602 0.1779 0.2096 0.9874 0.992 0.09696 0.793 0.8785 0.3099 ] Network output: [ -0.008833 0.04372 1.001 8.444e-05 -3.791e-05 0.9731 6.363e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0929 0.09102 0.1677 0.1982 0.9856 0.9915 0.09291 0.7232 0.8582 0.2434 ] Network output: [ 3.152e-05 0.9993 -0.0003841 1.151e-05 -5.166e-06 1.001 8.673e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008934 Epoch 6828 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01348 0.9912 0.9865 4.875e-06 -2.189e-06 -0.004716 3.674e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003195 -0.002965 -0.009637 0.00736 0.9697 0.9741 0.006064 0.8444 0.8323 0.02042 ] Network output: [ 0.9998 -0.003348 0.002113 -4.102e-05 1.841e-05 0.001492 -3.091e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.183 -0.02841 -0.1981 0.2021 0.9836 0.9933 0.2042 0.4607 0.8779 0.7229 ] Network output: [ -0.01193 0.9997 1.01 2.32e-06 -1.041e-06 0.01372 1.748e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005297 0.0005207 0.004299 0.004515 0.9889 0.992 0.005393 0.8743 0.9017 0.01482 ] Network output: [ -0.0005502 -0.003069 1.003 -0.0001451 6.515e-05 1 -0.0001094 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1935 0.09413 0.3204 0.1615 0.9851 0.994 0.1941 0.4656 0.8844 0.7175 ] Network output: [ 0.008535 -0.04258 0.998 8.439e-05 -3.789e-05 1.028 6.36e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09694 0.08607 0.1785 0.2103 0.9874 0.992 0.097 0.7931 0.8785 0.3102 ] Network output: [ -0.008938 0.0446 1.001 8.429e-05 -3.784e-05 0.9724 6.353e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09293 0.09105 0.1678 0.1983 0.9857 0.9915 0.09294 0.7234 0.8582 0.2434 ] Network output: [ 0.0005806 0.9995 -0.001164 1.17e-05 -5.253e-06 1.001 8.818e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009197 Epoch 6829 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01338 0.9928 0.9865 4.715e-06 -2.117e-06 -0.005995 3.553e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003196 -0.002965 -0.009645 0.007333 0.9697 0.9741 0.006067 0.8444 0.8322 0.02041 ] Network output: [ 0.9991 0.00685 0.001606 -4.187e-05 1.88e-05 -0.006813 -3.155e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1831 -0.02827 -0.1988 0.2004 0.9836 0.9933 0.2043 0.4609 0.8779 0.7228 ] Network output: [ -0.01194 1 1.01 2.251e-06 -1.011e-06 0.01326 1.696e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0053 0.0005193 0.00427 0.004461 0.9889 0.992 0.005396 0.8743 0.9017 0.0148 ] Network output: [ -0.00145 0.01082 1.003 -0.0001465 6.577e-05 0.9888 -0.0001104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1936 0.09416 0.3195 0.1589 0.9851 0.994 0.1942 0.4657 0.8844 0.7177 ] Network output: [ 0.008745 -0.03985 0.9975 8.42e-05 -3.78e-05 1.025 6.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08602 0.1779 0.2096 0.9874 0.992 0.09697 0.7929 0.8785 0.3098 ] Network output: [ -0.008826 0.0437 1.001 8.434e-05 -3.786e-05 0.9731 6.356e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09288 0.09101 0.1677 0.1982 0.9856 0.9915 0.0929 0.7231 0.8582 0.2434 ] Network output: [ 3.445e-05 0.9993 -0.0003878 1.15e-05 -5.161e-06 1.001 8.663e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008923 Epoch 6830 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01348 0.9913 0.9865 4.864e-06 -2.184e-06 -0.004729 3.666e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003195 -0.002965 -0.009634 0.007358 0.9697 0.9741 0.006065 0.8444 0.8323 0.02041 ] Network output: [ 0.9998 -0.003291 0.002109 -4.099e-05 1.84e-05 0.001444 -3.089e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.183 -0.02842 -0.1981 0.2021 0.9836 0.9933 0.2042 0.4607 0.8779 0.7229 ] Network output: [ -0.01193 0.9997 1.01 2.316e-06 -1.04e-06 0.01371 1.745e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005298 0.0005202 0.004299 0.004513 0.9889 0.992 0.005394 0.8743 0.9017 0.01482 ] Network output: [ -0.0005557 -0.002985 1.003 -0.0001449 6.507e-05 1 -0.0001092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1935 0.09412 0.3205 0.1615 0.9851 0.994 0.1941 0.4655 0.8843 0.7175 ] Network output: [ 0.00853 -0.04254 0.998 8.429e-05 -3.784e-05 1.028 6.352e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09695 0.08607 0.1785 0.2103 0.9874 0.992 0.09701 0.7931 0.8785 0.3102 ] Network output: [ -0.008929 0.04457 1.001 8.42e-05 -3.78e-05 0.9724 6.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09292 0.09104 0.1678 0.1983 0.9857 0.9915 0.09293 0.7233 0.8581 0.2434 ] Network output: [ 0.0005772 0.9995 -0.001159 1.169e-05 -5.246e-06 1.001 8.807e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009183 Epoch 6831 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01337 0.9928 0.9865 4.705e-06 -2.112e-06 -0.005992 3.546e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003196 -0.002965 -0.009642 0.007331 0.9697 0.9741 0.006067 0.8444 0.8322 0.0204 ] Network output: [ 0.9991 0.006786 0.001607 -4.182e-05 1.878e-05 -0.006763 -3.152e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1831 -0.02829 -0.1987 0.2004 0.9836 0.9933 0.2043 0.4609 0.8778 0.7228 ] Network output: [ -0.01194 1 1.01 2.248e-06 -1.009e-06 0.01325 1.694e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005301 0.0005189 0.004271 0.004459 0.9889 0.992 0.005397 0.8743 0.9016 0.0148 ] Network output: [ -0.001445 0.01074 1.003 -0.0001463 6.568e-05 0.9888 -0.0001103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1936 0.09414 0.3195 0.1588 0.9851 0.994 0.1942 0.4657 0.8843 0.7176 ] Network output: [ 0.008737 -0.03985 0.9975 8.41e-05 -3.775e-05 1.025 6.338e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08602 0.1779 0.2095 0.9874 0.992 0.09697 0.7928 0.8785 0.3098 ] Network output: [ -0.008819 0.04368 1.001 8.425e-05 -3.782e-05 0.9731 6.349e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09287 0.09099 0.1677 0.1982 0.9856 0.9915 0.09288 0.723 0.8581 0.2434 ] Network output: [ 3.736e-05 0.9993 -0.0003915 1.148e-05 -5.155e-06 1.001 8.654e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008913 Epoch 6832 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01347 0.9913 0.9865 4.852e-06 -2.178e-06 -0.004741 3.657e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003195 -0.002966 -0.009631 0.007355 0.9697 0.9741 0.006065 0.8444 0.8322 0.02041 ] Network output: [ 0.9998 -0.003234 0.002104 -4.095e-05 1.839e-05 0.001395 -3.086e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1831 -0.02844 -0.1981 0.202 0.9836 0.9933 0.2042 0.4606 0.8779 0.7229 ] Network output: [ -0.01192 0.9997 1.01 2.312e-06 -1.038e-06 0.0137 1.742e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005299 0.0005198 0.0043 0.004511 0.9889 0.992 0.005395 0.8743 0.9017 0.01481 ] Network output: [ -0.0005611 -0.002903 1.003 -0.0001448 6.499e-05 1 -0.0001091 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1935 0.09411 0.3205 0.1614 0.9851 0.994 0.1941 0.4655 0.8843 0.7175 ] Network output: [ 0.008525 -0.04251 0.998 8.419e-05 -3.779e-05 1.028 6.345e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09695 0.08607 0.1785 0.2103 0.9874 0.992 0.09701 0.793 0.8785 0.3102 ] Network output: [ -0.008921 0.04454 1.001 8.411e-05 -3.776e-05 0.9725 6.339e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0929 0.09102 0.1678 0.1983 0.9857 0.9915 0.09291 0.7232 0.8581 0.2434 ] Network output: [ 0.0005738 0.9995 -0.001154 1.167e-05 -5.24e-06 1.001 8.796e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009169 Epoch 6833 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01337 0.9928 0.9865 4.695e-06 -2.108e-06 -0.005989 3.539e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003196 -0.002966 -0.009639 0.007328 0.9697 0.9741 0.006068 0.8444 0.8322 0.0204 ] Network output: [ 0.9991 0.006722 0.001609 -4.178e-05 1.876e-05 -0.006713 -3.149e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1832 -0.02831 -0.1987 0.2004 0.9836 0.9933 0.2043 0.4609 0.8778 0.7228 ] Network output: [ -0.01194 1 1.01 2.245e-06 -1.008e-06 0.01325 1.692e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005302 0.0005184 0.004271 0.004457 0.9889 0.992 0.005398 0.8743 0.9016 0.0148 ] Network output: [ -0.00144 0.01065 1.003 -0.0001461 6.559e-05 0.9889 -0.0001101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1936 0.09413 0.3196 0.1588 0.9851 0.994 0.1942 0.4657 0.8843 0.7176 ] Network output: [ 0.008729 -0.03985 0.9975 8.4e-05 -3.771e-05 1.025 6.33e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08602 0.1779 0.2095 0.9874 0.992 0.09697 0.7928 0.8785 0.3098 ] Network output: [ -0.008812 0.04366 1.001 8.415e-05 -3.778e-05 0.9731 6.342e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09286 0.09098 0.1677 0.1981 0.9856 0.9915 0.09287 0.723 0.8581 0.2434 ] Network output: [ 4.025e-05 0.9993 -0.0003952 1.147e-05 -5.15e-06 1.001 8.645e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008903 Epoch 6834 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01347 0.9913 0.9866 4.84e-06 -2.173e-06 -0.004753 3.648e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003195 -0.002966 -0.009628 0.007353 0.9697 0.9741 0.006066 0.8443 0.8322 0.0204 ] Network output: [ 0.9998 -0.003177 0.0021 -4.092e-05 1.837e-05 0.001347 -3.084e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1831 -0.02845 -0.198 0.202 0.9836 0.9933 0.2042 0.4606 0.8779 0.7229 ] Network output: [ -0.01192 0.9997 1.01 2.308e-06 -1.036e-06 0.01369 1.739e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0053 0.0005193 0.0043 0.004508 0.9889 0.992 0.005396 0.8743 0.9017 0.01481 ] Network output: [ -0.0005665 -0.002821 1.003 -0.0001446 6.491e-05 1 -0.000109 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1935 0.09409 0.3205 0.1614 0.9851 0.994 0.1941 0.4655 0.8843 0.7175 ] Network output: [ 0.00852 -0.04247 0.998 8.408e-05 -3.775e-05 1.028 6.337e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09695 0.08607 0.1785 0.2102 0.9874 0.992 0.09701 0.793 0.8785 0.3102 ] Network output: [ -0.008912 0.0445 1.001 8.401e-05 -3.772e-05 0.9725 6.332e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09289 0.09101 0.1678 0.1983 0.9857 0.9915 0.0929 0.7232 0.8581 0.2434 ] Network output: [ 0.0005704 0.9995 -0.001149 1.166e-05 -5.233e-06 1.001 8.785e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009155 Epoch 6835 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01336 0.9928 0.9865 4.686e-06 -2.104e-06 -0.005986 3.531e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003197 -0.002966 -0.009635 0.007326 0.9697 0.9741 0.006068 0.8444 0.8322 0.02039 ] Network output: [ 0.9991 0.006659 0.00161 -4.174e-05 1.874e-05 -0.006663 -3.146e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1832 -0.02832 -0.1987 0.2004 0.9836 0.9933 0.2043 0.4608 0.8778 0.7227 ] Network output: [ -0.01194 1 1.01 2.241e-06 -1.006e-06 0.01324 1.689e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005303 0.000518 0.004272 0.004456 0.9889 0.992 0.005399 0.8743 0.9016 0.01479 ] Network output: [ -0.001435 0.01057 1.003 -0.0001459 6.551e-05 0.989 -0.00011 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1936 0.09412 0.3196 0.1588 0.9851 0.994 0.1942 0.4656 0.8843 0.7176 ] Network output: [ 0.008722 -0.03984 0.9975 8.389e-05 -3.766e-05 1.025 6.323e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08602 0.1779 0.2095 0.9874 0.992 0.09697 0.7927 0.8784 0.3098 ] Network output: [ -0.008805 0.04363 1.001 8.406e-05 -3.774e-05 0.9731 6.335e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09284 0.09096 0.1677 0.1981 0.9856 0.9915 0.09285 0.7229 0.8581 0.2434 ] Network output: [ 4.313e-05 0.9993 -0.0003988 1.146e-05 -5.144e-06 1.001 8.636e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008892 Epoch 6836 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01346 0.9913 0.9866 4.829e-06 -2.168e-06 -0.004765 3.639e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003195 -0.002966 -0.009624 0.00735 0.9697 0.9741 0.006066 0.8443 0.8322 0.0204 ] Network output: [ 0.9998 -0.003121 0.002096 -4.089e-05 1.836e-05 0.001299 -3.082e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1831 -0.02847 -0.198 0.202 0.9836 0.9933 0.2042 0.4606 0.8779 0.7229 ] Network output: [ -0.01192 0.9998 1.01 2.304e-06 -1.034e-06 0.01367 1.736e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005301 0.0005189 0.0043 0.004506 0.9889 0.992 0.005397 0.8742 0.9017 0.01481 ] Network output: [ -0.0005719 -0.002739 1.003 -0.0001444 6.483e-05 1 -0.0001088 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1936 0.09408 0.3206 0.1613 0.9851 0.994 0.1942 0.4654 0.8843 0.7175 ] Network output: [ 0.008515 -0.04244 0.998 8.398e-05 -3.77e-05 1.028 6.329e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09695 0.08606 0.1785 0.2102 0.9874 0.992 0.09701 0.7929 0.8784 0.3102 ] Network output: [ -0.008904 0.04447 1.001 8.392e-05 -3.768e-05 0.9725 6.325e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09287 0.09099 0.1678 0.1982 0.9857 0.9915 0.09288 0.7231 0.858 0.2434 ] Network output: [ 0.000567 0.9995 -0.001143 1.164e-05 -5.226e-06 1.001 8.774e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009142 Epoch 6837 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01336 0.9928 0.9865 4.676e-06 -2.099e-06 -0.005983 3.524e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003197 -0.002966 -0.009632 0.007324 0.9697 0.9741 0.006068 0.8443 0.8322 0.02039 ] Network output: [ 0.9991 0.006595 0.001611 -4.17e-05 1.872e-05 -0.006614 -3.142e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1832 -0.02834 -0.1986 0.2004 0.9836 0.9933 0.2044 0.4608 0.8778 0.7227 ] Network output: [ -0.01193 1 1.01 2.238e-06 -1.005e-06 0.01323 1.687e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005304 0.0005176 0.004273 0.004454 0.9889 0.992 0.0054 0.8742 0.9016 0.01479 ] Network output: [ -0.00143 0.01049 1.003 -0.0001457 6.542e-05 0.989 -0.0001098 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1937 0.09411 0.3197 0.1588 0.9851 0.994 0.1943 0.4656 0.8843 0.7176 ] Network output: [ 0.008714 -0.03984 0.9975 8.379e-05 -3.762e-05 1.025 6.315e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09691 0.08602 0.1779 0.2095 0.9874 0.992 0.09697 0.7927 0.8784 0.3098 ] Network output: [ -0.008798 0.04361 1.001 8.397e-05 -3.77e-05 0.9731 6.328e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09283 0.09095 0.1676 0.1981 0.9856 0.9915 0.09284 0.7228 0.858 0.2434 ] Network output: [ 4.599e-05 0.9993 -0.0004024 1.145e-05 -5.139e-06 1.001 8.627e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008882 Epoch 6838 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01345 0.9913 0.9866 4.817e-06 -2.163e-06 -0.004777 3.63e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003196 -0.002967 -0.009621 0.007348 0.9697 0.9741 0.006067 0.8443 0.8322 0.02039 ] Network output: [ 0.9998 -0.003064 0.002091 -4.086e-05 1.834e-05 0.001252 -3.08e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1831 -0.02848 -0.198 0.2019 0.9836 0.9933 0.2043 0.4606 0.8779 0.7228 ] Network output: [ -0.01192 0.9998 1.01 2.3e-06 -1.032e-06 0.01366 1.733e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005302 0.0005185 0.0043 0.004504 0.9889 0.992 0.005398 0.8742 0.9016 0.0148 ] Network output: [ -0.0005772 -0.002658 1.003 -0.0001442 6.475e-05 1 -0.0001087 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1936 0.09407 0.3206 0.1612 0.9851 0.994 0.1942 0.4654 0.8843 0.7175 ] Network output: [ 0.008511 -0.0424 0.998 8.388e-05 -3.766e-05 1.028 6.321e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09695 0.08606 0.1785 0.2102 0.9874 0.992 0.09701 0.7928 0.8784 0.3102 ] Network output: [ -0.008895 0.04444 1.001 8.383e-05 -3.763e-05 0.9725 6.318e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09286 0.09098 0.1678 0.1982 0.9856 0.9915 0.09287 0.723 0.858 0.2434 ] Network output: [ 0.0005636 0.9995 -0.001138 1.163e-05 -5.22e-06 1.001 8.763e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009128 Epoch 6839 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01335 0.9928 0.9865 4.666e-06 -2.095e-06 -0.00598 3.517e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003197 -0.002966 -0.009628 0.007322 0.9697 0.9741 0.006069 0.8443 0.8321 0.02038 ] Network output: [ 0.9991 0.006533 0.001613 -4.166e-05 1.87e-05 -0.006565 -3.139e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1832 -0.02835 -0.1986 0.2004 0.9836 0.9933 0.2044 0.4608 0.8778 0.7227 ] Network output: [ -0.01193 1 1.01 2.235e-06 -1.003e-06 0.01323 1.684e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005305 0.0005172 0.004273 0.004453 0.9889 0.992 0.005401 0.8742 0.9016 0.01479 ] Network output: [ -0.001425 0.01041 1.003 -0.0001455 6.533e-05 0.9891 -0.0001097 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1937 0.0941 0.3197 0.1588 0.9851 0.994 0.1943 0.4656 0.8843 0.7176 ] Network output: [ 0.008706 -0.03984 0.9975 8.369e-05 -3.757e-05 1.025 6.307e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08602 0.1779 0.2095 0.9874 0.992 0.09698 0.7926 0.8784 0.3098 ] Network output: [ -0.008791 0.04359 1.001 8.387e-05 -3.765e-05 0.9731 6.321e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09281 0.09093 0.1676 0.1981 0.9856 0.9915 0.09282 0.7227 0.858 0.2434 ] Network output: [ 4.883e-05 0.9993 -0.000406 1.143e-05 -5.133e-06 1.001 8.618e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008871 Epoch 6840 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01345 0.9913 0.9866 4.805e-06 -2.157e-06 -0.004789 3.622e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003196 -0.002967 -0.009618 0.007345 0.9697 0.9741 0.006067 0.8443 0.8322 0.02039 ] Network output: [ 0.9998 -0.003009 0.002087 -4.083e-05 1.833e-05 0.001204 -3.077e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1831 -0.0285 -0.1979 0.2019 0.9836 0.9933 0.2043 0.4605 0.8778 0.7228 ] Network output: [ -0.01192 0.9998 1.01 2.295e-06 -1.031e-06 0.01365 1.73e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005303 0.000518 0.004301 0.004502 0.9889 0.992 0.005399 0.8742 0.9016 0.0148 ] Network output: [ -0.0005825 -0.002577 1.003 -0.0001441 6.467e-05 0.9999 -0.0001086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1936 0.09406 0.3206 0.1612 0.9851 0.994 0.1942 0.4654 0.8843 0.7175 ] Network output: [ 0.008506 -0.04237 0.9979 8.377e-05 -3.761e-05 1.028 6.313e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09695 0.08606 0.1785 0.2101 0.9874 0.992 0.09701 0.7928 0.8784 0.3101 ] Network output: [ -0.008887 0.04441 1.001 8.374e-05 -3.759e-05 0.9725 6.311e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09284 0.09096 0.1678 0.1982 0.9856 0.9915 0.09285 0.723 0.858 0.2434 ] Network output: [ 0.0005602 0.9995 -0.001133 1.161e-05 -5.213e-06 1.001 8.751e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009114 Epoch 6841 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01335 0.9928 0.9865 4.657e-06 -2.091e-06 -0.005978 3.509e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003197 -0.002967 -0.009625 0.00732 0.9697 0.9741 0.006069 0.8443 0.8321 0.02038 ] Network output: [ 0.9991 0.00647 0.001614 -4.161e-05 1.868e-05 -0.006516 -3.136e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1832 -0.02837 -0.1985 0.2004 0.9836 0.9933 0.2044 0.4607 0.8778 0.7227 ] Network output: [ -0.01193 1 1.01 2.232e-06 -1.002e-06 0.01322 1.682e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005306 0.0005167 0.004274 0.004451 0.9889 0.992 0.005402 0.8742 0.9016 0.01478 ] Network output: [ -0.00142 0.01033 1.003 -0.0001453 6.524e-05 0.9892 -0.0001095 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1937 0.09408 0.3197 0.1587 0.9851 0.994 0.1943 0.4655 0.8843 0.7176 ] Network output: [ 0.008699 -0.03984 0.9975 8.359e-05 -3.753e-05 1.025 6.3e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08602 0.1779 0.2094 0.9874 0.992 0.09698 0.7926 0.8784 0.3098 ] Network output: [ -0.008784 0.04356 1.001 8.378e-05 -3.761e-05 0.9731 6.314e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0928 0.09092 0.1676 0.1981 0.9856 0.9915 0.09281 0.7227 0.858 0.2434 ] Network output: [ 5.166e-05 0.9993 -0.0004096 1.142e-05 -5.128e-06 1.001 8.608e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008861 Epoch 6842 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01344 0.9914 0.9866 4.794e-06 -2.152e-06 -0.004802 3.613e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003196 -0.002967 -0.009614 0.007343 0.9697 0.9741 0.006068 0.8443 0.8322 0.02038 ] Network output: [ 0.9998 -0.002953 0.002083 -4.08e-05 1.832e-05 0.001157 -3.075e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1831 -0.02851 -0.1979 0.2019 0.9836 0.9933 0.2043 0.4605 0.8778 0.7228 ] Network output: [ -0.01191 0.9998 1.01 2.291e-06 -1.029e-06 0.01364 1.727e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005304 0.0005176 0.004301 0.004499 0.9889 0.992 0.0054 0.8742 0.9016 0.0148 ] Network output: [ -0.0005877 -0.002497 1.003 -0.0001439 6.459e-05 0.9999 -0.0001084 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1936 0.09405 0.3207 0.1611 0.9851 0.994 0.1942 0.4654 0.8843 0.7175 ] Network output: [ 0.008501 -0.04233 0.9979 8.367e-05 -3.756e-05 1.028 6.306e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09696 0.08606 0.1785 0.2101 0.9874 0.992 0.09702 0.7927 0.8784 0.3101 ] Network output: [ -0.008878 0.04437 1.001 8.364e-05 -3.755e-05 0.9725 6.304e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09283 0.09095 0.1677 0.1982 0.9856 0.9915 0.09284 0.7229 0.8579 0.2434 ] Network output: [ 0.0005569 0.9995 -0.001128 1.16e-05 -5.207e-06 1.001 8.74e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009101 Epoch 6843 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01334 0.9928 0.9866 4.647e-06 -2.086e-06 -0.005975 3.502e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003197 -0.002967 -0.009622 0.007317 0.9697 0.9741 0.00607 0.8443 0.8321 0.02038 ] Network output: [ 0.9991 0.006408 0.001616 -4.157e-05 1.866e-05 -0.006467 -3.133e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1832 -0.02839 -0.1985 0.2003 0.9836 0.9933 0.2044 0.4607 0.8778 0.7227 ] Network output: [ -0.01193 1 1.01 2.229e-06 -1.001e-06 0.01322 1.68e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005307 0.0005163 0.004274 0.004449 0.9889 0.992 0.005403 0.8742 0.9016 0.01478 ] Network output: [ -0.001415 0.01025 1.003 -0.0001451 6.515e-05 0.9892 -0.0001094 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1937 0.09407 0.3198 0.1587 0.9851 0.994 0.1943 0.4655 0.8843 0.7176 ] Network output: [ 0.008691 -0.03983 0.9975 8.349e-05 -3.748e-05 1.025 6.292e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08601 0.1779 0.2094 0.9874 0.992 0.09698 0.7925 0.8783 0.3098 ] Network output: [ -0.008777 0.04354 1.001 8.368e-05 -3.757e-05 0.9731 6.307e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09278 0.0909 0.1676 0.1981 0.9856 0.9915 0.0928 0.7226 0.8579 0.2434 ] Network output: [ 5.447e-05 0.9993 -0.0004131 1.141e-05 -5.123e-06 1.001 8.599e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008851 Epoch 6844 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01344 0.9914 0.9866 4.782e-06 -2.147e-06 -0.004814 3.604e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003196 -0.002967 -0.009611 0.00734 0.9697 0.9741 0.006068 0.8443 0.8322 0.02038 ] Network output: [ 0.9998 -0.002898 0.002078 -4.077e-05 1.83e-05 0.001111 -3.072e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1832 -0.02852 -0.1979 0.2018 0.9836 0.9933 0.2043 0.4605 0.8778 0.7228 ] Network output: [ -0.01191 0.9998 1.01 2.287e-06 -1.027e-06 0.01363 1.724e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005305 0.0005172 0.004301 0.004497 0.9889 0.992 0.005401 0.8742 0.9016 0.01479 ] Network output: [ -0.0005929 -0.002417 1.003 -0.0001437 6.451e-05 0.9998 -0.0001083 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1936 0.09404 0.3207 0.1611 0.9851 0.994 0.1942 0.4653 0.8843 0.7175 ] Network output: [ 0.008496 -0.0423 0.9979 8.357e-05 -3.752e-05 1.028 6.298e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09696 0.08606 0.1785 0.2101 0.9874 0.992 0.09702 0.7927 0.8783 0.3101 ] Network output: [ -0.00887 0.04434 1.001 8.355e-05 -3.751e-05 0.9725 6.297e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09281 0.09093 0.1677 0.1982 0.9856 0.9915 0.09282 0.7228 0.8579 0.2434 ] Network output: [ 0.0005536 0.9995 -0.001122 1.158e-05 -5.2e-06 1.001 8.729e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009087 Epoch 6845 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01334 0.9928 0.9866 4.637e-06 -2.082e-06 -0.005972 3.495e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003197 -0.002967 -0.009618 0.007315 0.9697 0.9741 0.00607 0.8443 0.8321 0.02037 ] Network output: [ 0.9991 0.006346 0.001617 -4.153e-05 1.864e-05 -0.006419 -3.13e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1833 -0.0284 -0.1985 0.2003 0.9836 0.9933 0.2044 0.4607 0.8778 0.7227 ] Network output: [ -0.01192 1 1.01 2.225e-06 -9.99e-07 0.01321 1.677e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005307 0.0005159 0.004275 0.004448 0.9889 0.992 0.005404 0.8742 0.9016 0.01478 ] Network output: [ -0.00141 0.01017 1.003 -0.0001449 6.506e-05 0.9893 -0.0001092 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1937 0.09406 0.3198 0.1587 0.9851 0.994 0.1943 0.4655 0.8843 0.7176 ] Network output: [ 0.008683 -0.03983 0.9975 8.339e-05 -3.744e-05 1.025 6.284e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09692 0.08601 0.1779 0.2094 0.9874 0.992 0.09698 0.7924 0.8783 0.3098 ] Network output: [ -0.00877 0.04352 1.001 8.359e-05 -3.753e-05 0.9731 6.3e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09277 0.09089 0.1676 0.1981 0.9856 0.9915 0.09278 0.7225 0.8579 0.2434 ] Network output: [ 5.727e-05 0.9993 -0.0004167 1.14e-05 -5.117e-06 1.001 8.59e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008841 Epoch 6846 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01343 0.9914 0.9866 4.77e-06 -2.142e-06 -0.004826 3.595e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003196 -0.002968 -0.009608 0.007338 0.9697 0.9741 0.006068 0.8443 0.8322 0.02037 ] Network output: [ 0.9998 -0.002843 0.002074 -4.074e-05 1.829e-05 0.001064 -3.07e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1832 -0.02854 -0.1978 0.2018 0.9836 0.9933 0.2043 0.4604 0.8778 0.7228 ] Network output: [ -0.01191 0.9998 1.01 2.283e-06 -1.025e-06 0.01362 1.721e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005306 0.0005167 0.004302 0.004495 0.9889 0.992 0.005402 0.8741 0.9016 0.01479 ] Network output: [ -0.0005981 -0.002338 1.003 -0.0001435 6.443e-05 0.9997 -0.0001082 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1937 0.09403 0.3207 0.161 0.9851 0.994 0.1943 0.4653 0.8843 0.7175 ] Network output: [ 0.008491 -0.04227 0.9979 8.346e-05 -3.747e-05 1.028 6.29e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09696 0.08606 0.1785 0.2101 0.9874 0.992 0.09702 0.7926 0.8783 0.3101 ] Network output: [ -0.008861 0.04431 1.001 8.346e-05 -3.747e-05 0.9726 6.29e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0928 0.09092 0.1677 0.1982 0.9856 0.9915 0.09281 0.7227 0.8579 0.2434 ] Network output: [ 0.0005502 0.9995 -0.001117 1.157e-05 -5.193e-06 1.001 8.718e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009074 Epoch 6847 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01333 0.9928 0.9866 4.628e-06 -2.077e-06 -0.005969 3.487e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003198 -0.002968 -0.009615 0.007313 0.9697 0.9741 0.006071 0.8443 0.8321 0.02037 ] Network output: [ 0.9991 0.006284 0.001618 -4.149e-05 1.862e-05 -0.006371 -3.126e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1833 -0.02842 -0.1984 0.2003 0.9836 0.9933 0.2045 0.4606 0.8777 0.7227 ] Network output: [ -0.01192 1 1.01 2.222e-06 -9.976e-07 0.0132 1.675e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005308 0.0005155 0.004276 0.004446 0.9889 0.992 0.005405 0.8741 0.9016 0.01477 ] Network output: [ -0.001404 0.01009 1.003 -0.0001447 6.498e-05 0.9894 -0.0001091 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1938 0.09405 0.3199 0.1587 0.9851 0.994 0.1944 0.4654 0.8843 0.7176 ] Network output: [ 0.008676 -0.03983 0.9975 8.329e-05 -3.739e-05 1.025 6.277e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08601 0.1779 0.2094 0.9874 0.992 0.09699 0.7924 0.8783 0.3097 ] Network output: [ -0.008763 0.04349 1.001 8.35e-05 -3.748e-05 0.9731 6.292e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09275 0.09087 0.1676 0.1981 0.9856 0.9915 0.09277 0.7225 0.8579 0.2434 ] Network output: [ 6.005e-05 0.9993 -0.0004202 1.139e-05 -5.112e-06 1.001 8.581e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008831 Epoch 6848 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01342 0.9914 0.9866 4.759e-06 -2.136e-06 -0.004838 3.586e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003197 -0.002968 -0.009604 0.007335 0.9697 0.9741 0.006069 0.8442 0.8321 0.02037 ] Network output: [ 0.9998 -0.002789 0.00207 -4.071e-05 1.827e-05 0.001018 -3.068e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1832 -0.02855 -0.1978 0.2018 0.9836 0.9933 0.2044 0.4604 0.8778 0.7228 ] Network output: [ -0.01191 0.9998 1.01 2.279e-06 -1.023e-06 0.01361 1.718e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005307 0.0005163 0.004302 0.004493 0.9889 0.992 0.005403 0.8741 0.9016 0.01478 ] Network output: [ -0.0006032 -0.00226 1.003 -0.0001433 6.435e-05 0.9997 -0.000108 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1937 0.09401 0.3208 0.161 0.9851 0.994 0.1943 0.4653 0.8842 0.7175 ] Network output: [ 0.008486 -0.04223 0.9979 8.336e-05 -3.742e-05 1.028 6.282e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09696 0.08606 0.1785 0.21 0.9874 0.992 0.09702 0.7925 0.8783 0.3101 ] Network output: [ -0.008853 0.04427 1.001 8.336e-05 -3.743e-05 0.9726 6.283e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09278 0.0909 0.1677 0.1982 0.9856 0.9915 0.09279 0.7227 0.8579 0.2434 ] Network output: [ 0.0005469 0.9995 -0.001112 1.155e-05 -5.187e-06 1.001 8.707e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009061 Epoch 6849 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01333 0.9928 0.9866 4.618e-06 -2.073e-06 -0.005967 3.48e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003198 -0.002968 -0.009611 0.007311 0.9697 0.9741 0.006071 0.8443 0.8321 0.02036 ] Network output: [ 0.9992 0.006223 0.001619 -4.144e-05 1.861e-05 -0.006323 -3.123e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1833 -0.02843 -0.1984 0.2003 0.9836 0.9933 0.2045 0.4606 0.8777 0.7227 ] Network output: [ -0.01192 1 1.01 2.219e-06 -9.961e-07 0.0132 1.672e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005309 0.0005151 0.004276 0.004445 0.9889 0.992 0.005406 0.8741 0.9015 0.01477 ] Network output: [ -0.001399 0.01001 1.003 -0.0001445 6.489e-05 0.9894 -0.0001089 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1938 0.09404 0.3199 0.1586 0.9851 0.994 0.1944 0.4654 0.8842 0.7176 ] Network output: [ 0.008668 -0.03982 0.9975 8.318e-05 -3.734e-05 1.025 6.269e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08601 0.178 0.2094 0.9874 0.992 0.09699 0.7923 0.8783 0.3097 ] Network output: [ -0.008756 0.04347 1.001 8.34e-05 -3.744e-05 0.9731 6.285e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09274 0.09086 0.1676 0.198 0.9856 0.9915 0.09275 0.7224 0.8578 0.2434 ] Network output: [ 6.281e-05 0.9993 -0.0004236 1.137e-05 -5.106e-06 1.001 8.572e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000882 Epoch 6850 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01342 0.9914 0.9866 4.747e-06 -2.131e-06 -0.00485 3.578e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003197 -0.002968 -0.009601 0.007333 0.9697 0.9741 0.006069 0.8442 0.8321 0.02036 ] Network output: [ 0.9998 -0.002735 0.002065 -4.067e-05 1.826e-05 0.0009725 -3.065e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1832 -0.02857 -0.1978 0.2018 0.9836 0.9933 0.2044 0.4604 0.8778 0.7228 ] Network output: [ -0.0119 0.9998 1.01 2.275e-06 -1.021e-06 0.01359 1.715e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005308 0.0005159 0.004302 0.004491 0.9889 0.992 0.005404 0.8741 0.9016 0.01478 ] Network output: [ -0.0006083 -0.002181 1.003 -0.0001432 6.427e-05 0.9996 -0.0001079 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1937 0.094 0.3208 0.1609 0.9851 0.994 0.1943 0.4652 0.8842 0.7174 ] Network output: [ 0.008481 -0.0422 0.9979 8.326e-05 -3.738e-05 1.028 6.275e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09696 0.08605 0.1785 0.21 0.9874 0.992 0.09702 0.7925 0.8783 0.3101 ] Network output: [ -0.008845 0.04424 1.001 8.327e-05 -3.738e-05 0.9726 6.276e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09277 0.09089 0.1677 0.1981 0.9856 0.9915 0.09278 0.7226 0.8578 0.2434 ] Network output: [ 0.0005437 0.9995 -0.001107 1.154e-05 -5.18e-06 1.001 8.696e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009048 Epoch 6851 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01332 0.9928 0.9866 4.608e-06 -2.069e-06 -0.005964 3.473e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003198 -0.002968 -0.009608 0.007309 0.9697 0.9741 0.006071 0.8442 0.8321 0.02036 ] Network output: [ 0.9992 0.006162 0.001621 -4.14e-05 1.859e-05 -0.006276 -3.12e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1833 -0.02845 -0.1983 0.2003 0.9836 0.9933 0.2045 0.4606 0.8777 0.7227 ] Network output: [ -0.01192 1 1.01 2.215e-06 -9.946e-07 0.01319 1.67e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00531 0.0005147 0.004277 0.004443 0.9889 0.992 0.005407 0.8741 0.9015 0.01477 ] Network output: [ -0.001394 0.009927 1.003 -0.0001443 6.48e-05 0.9895 -0.0001088 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1938 0.09403 0.32 0.1586 0.9851 0.994 0.1944 0.4654 0.8842 0.7175 ] Network output: [ 0.00866 -0.03982 0.9975 8.308e-05 -3.73e-05 1.025 6.261e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08601 0.178 0.2094 0.9874 0.992 0.09699 0.7923 0.8782 0.3097 ] Network output: [ -0.008749 0.04345 1.001 8.331e-05 -3.74e-05 0.9732 6.278e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09273 0.09084 0.1676 0.198 0.9856 0.9915 0.09274 0.7223 0.8578 0.2434 ] Network output: [ 6.555e-05 0.9993 -0.000427 1.136e-05 -5.101e-06 1.001 8.562e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000881 Epoch 6852 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01341 0.9914 0.9866 4.736e-06 -2.126e-06 -0.004861 3.569e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003197 -0.002969 -0.009598 0.00733 0.9697 0.9741 0.00607 0.8442 0.8321 0.02036 ] Network output: [ 0.9998 -0.002681 0.002061 -4.064e-05 1.825e-05 0.000927 -3.063e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1832 -0.02858 -0.1977 0.2017 0.9836 0.9933 0.2044 0.4603 0.8778 0.7228 ] Network output: [ -0.0119 0.9998 1.01 2.271e-06 -1.019e-06 0.01358 1.711e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005309 0.0005155 0.004302 0.004488 0.9889 0.992 0.005405 0.8741 0.9016 0.01478 ] Network output: [ -0.0006134 -0.002104 1.003 -0.000143 6.419e-05 0.9995 -0.0001078 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1937 0.09399 0.3208 0.1609 0.9851 0.994 0.1943 0.4652 0.8842 0.7174 ] Network output: [ 0.008476 -0.04216 0.9979 8.315e-05 -3.733e-05 1.028 6.267e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09697 0.08605 0.1785 0.21 0.9874 0.992 0.09703 0.7924 0.8782 0.3101 ] Network output: [ -0.008836 0.04421 1.001 8.318e-05 -3.734e-05 0.9726 6.269e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09275 0.09087 0.1677 0.1981 0.9856 0.9915 0.09276 0.7225 0.8578 0.2434 ] Network output: [ 0.0005404 0.9995 -0.001102 1.152e-05 -5.174e-06 1.001 8.685e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009034 Epoch 6853 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01332 0.9928 0.9866 4.598e-06 -2.064e-06 -0.005962 3.465e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003198 -0.002968 -0.009605 0.007306 0.9697 0.9741 0.006072 0.8442 0.8321 0.02035 ] Network output: [ 0.9992 0.006102 0.001622 -4.136e-05 1.857e-05 -0.006228 -3.117e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1833 -0.02846 -0.1983 0.2003 0.9836 0.9933 0.2045 0.4605 0.8777 0.7227 ] Network output: [ -0.01192 1 1.01 2.212e-06 -9.931e-07 0.01319 1.667e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005311 0.0005143 0.004277 0.004441 0.9889 0.992 0.005408 0.8741 0.9015 0.01476 ] Network output: [ -0.00139 0.009848 1.003 -0.0001441 6.471e-05 0.9896 -0.0001086 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1938 0.09402 0.32 0.1586 0.9851 0.994 0.1944 0.4653 0.8842 0.7175 ] Network output: [ 0.008653 -0.03982 0.9975 8.298e-05 -3.725e-05 1.025 6.254e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09693 0.08601 0.178 0.2093 0.9874 0.992 0.09699 0.7922 0.8782 0.3097 ] Network output: [ -0.008742 0.04342 1.001 8.321e-05 -3.736e-05 0.9732 6.271e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09271 0.09083 0.1676 0.198 0.9856 0.9915 0.09272 0.7223 0.8578 0.2434 ] Network output: [ 6.828e-05 0.9993 -0.0004304 1.135e-05 -5.095e-06 1.001 8.553e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00088 Epoch 6854 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0134 0.9914 0.9866 4.724e-06 -2.121e-06 -0.004873 3.56e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003197 -0.002969 -0.009595 0.007328 0.9697 0.9741 0.00607 0.8442 0.8321 0.02036 ] Network output: [ 0.9998 -0.002628 0.002057 -4.061e-05 1.823e-05 0.0008819 -3.061e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1832 -0.02859 -0.1977 0.2017 0.9836 0.9933 0.2044 0.4603 0.8778 0.7228 ] Network output: [ -0.0119 0.9998 1.01 2.267e-06 -1.018e-06 0.01357 1.708e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00531 0.000515 0.004303 0.004486 0.9889 0.992 0.005406 0.8741 0.9015 0.01477 ] Network output: [ -0.0006184 -0.002027 1.003 -0.0001428 6.411e-05 0.9995 -0.0001076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1937 0.09398 0.3209 0.1608 0.9851 0.994 0.1943 0.4652 0.8842 0.7174 ] Network output: [ 0.008471 -0.04213 0.9979 8.305e-05 -3.728e-05 1.028 6.259e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09697 0.08605 0.1785 0.21 0.9874 0.992 0.09703 0.7924 0.8782 0.31 ] Network output: [ -0.008828 0.04417 1.001 8.309e-05 -3.73e-05 0.9726 6.262e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09274 0.09086 0.1677 0.1981 0.9856 0.9915 0.09275 0.7224 0.8578 0.2434 ] Network output: [ 0.0005371 0.9995 -0.001097 1.151e-05 -5.167e-06 1.001 8.674e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009021 Epoch 6855 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01331 0.9928 0.9866 4.589e-06 -2.06e-06 -0.005959 3.458e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003198 -0.002969 -0.009601 0.007304 0.9697 0.9741 0.006072 0.8442 0.832 0.02035 ] Network output: [ 0.9992 0.006042 0.001623 -4.132e-05 1.855e-05 -0.006181 -3.114e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1833 -0.02848 -0.1983 0.2003 0.9836 0.9933 0.2045 0.4605 0.8777 0.7227 ] Network output: [ -0.01191 1 1.01 2.209e-06 -9.916e-07 0.01318 1.665e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005312 0.0005139 0.004278 0.00444 0.9889 0.992 0.005409 0.8741 0.9015 0.01476 ] Network output: [ -0.001385 0.00977 1.003 -0.0001439 6.462e-05 0.9896 -0.0001085 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1938 0.094 0.32 0.1586 0.9851 0.994 0.1944 0.4653 0.8842 0.7175 ] Network output: [ 0.008645 -0.03981 0.9975 8.288e-05 -3.721e-05 1.025 6.246e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09694 0.08601 0.178 0.2093 0.9874 0.992 0.097 0.7922 0.8782 0.3097 ] Network output: [ -0.008735 0.0434 1.001 8.312e-05 -3.731e-05 0.9732 6.264e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0927 0.09082 0.1675 0.198 0.9856 0.9915 0.09271 0.7222 0.8578 0.2434 ] Network output: [ 7.098e-05 0.9994 -0.0004338 1.134e-05 -5.09e-06 1.001 8.544e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000879 Epoch 6856 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0134 0.9915 0.9866 4.712e-06 -2.116e-06 -0.004885 3.551e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003197 -0.002969 -0.009591 0.007325 0.9697 0.9741 0.006071 0.8442 0.8321 0.02035 ] Network output: [ 0.9998 -0.002575 0.002053 -4.058e-05 1.822e-05 0.000837 -3.058e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1833 -0.02861 -0.1977 0.2017 0.9836 0.9933 0.2045 0.4603 0.8777 0.7228 ] Network output: [ -0.0119 0.9998 1.01 2.263e-06 -1.016e-06 0.01356 1.705e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005311 0.0005146 0.004303 0.004484 0.9889 0.992 0.005407 0.8741 0.9015 0.01477 ] Network output: [ -0.0006234 -0.00195 1.003 -0.0001426 6.403e-05 0.9994 -0.0001075 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1938 0.09397 0.3209 0.1608 0.9851 0.994 0.1944 0.4651 0.8842 0.7174 ] Network output: [ 0.008466 -0.04209 0.9979 8.295e-05 -3.724e-05 1.028 6.251e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09697 0.08605 0.1785 0.2099 0.9874 0.992 0.09703 0.7923 0.8782 0.31 ] Network output: [ -0.008819 0.04414 1.001 8.299e-05 -3.726e-05 0.9726 6.255e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09272 0.09084 0.1677 0.1981 0.9856 0.9915 0.09274 0.7224 0.8577 0.2434 ] Network output: [ 0.0005339 0.9995 -0.001092 1.149e-05 -5.16e-06 1.001 8.663e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0009008 Epoch 6857 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01331 0.9928 0.9866 4.579e-06 -2.056e-06 -0.005957 3.451e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003198 -0.002969 -0.009598 0.007302 0.9697 0.9741 0.006073 0.8442 0.832 0.02034 ] Network output: [ 0.9992 0.005982 0.001624 -4.127e-05 1.853e-05 -0.006134 -3.11e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1834 -0.0285 -0.1982 0.2003 0.9836 0.9933 0.2046 0.4605 0.8777 0.7227 ] Network output: [ -0.01191 1 1.01 2.205e-06 -9.9e-07 0.01317 1.662e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005313 0.0005134 0.004279 0.004438 0.9889 0.992 0.00541 0.8741 0.9015 0.01476 ] Network output: [ -0.00138 0.009693 1.003 -0.0001438 6.454e-05 0.9897 -0.0001083 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1938 0.09399 0.3201 0.1585 0.9851 0.994 0.1945 0.4653 0.8842 0.7175 ] Network output: [ 0.008638 -0.03981 0.9975 8.278e-05 -3.716e-05 1.025 6.238e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09694 0.08601 0.178 0.2093 0.9874 0.992 0.097 0.7921 0.8782 0.3097 ] Network output: [ -0.008728 0.04338 1.001 8.302e-05 -3.727e-05 0.9732 6.257e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09268 0.0908 0.1675 0.198 0.9856 0.9915 0.0927 0.7221 0.8577 0.2434 ] Network output: [ 7.367e-05 0.9994 -0.0004372 1.132e-05 -5.084e-06 1.001 8.535e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000878 Epoch 6858 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01339 0.9915 0.9866 4.701e-06 -2.11e-06 -0.004897 3.543e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003197 -0.00297 -0.009588 0.007323 0.9697 0.9741 0.006071 0.8442 0.8321 0.02035 ] Network output: [ 0.9998 -0.002522 0.002048 -4.055e-05 1.82e-05 0.0007924 -3.056e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1833 -0.02862 -0.1976 0.2016 0.9836 0.9933 0.2045 0.4602 0.8777 0.7228 ] Network output: [ -0.0119 0.9998 1.01 2.258e-06 -1.014e-06 0.01355 1.702e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005312 0.0005142 0.004303 0.004482 0.9889 0.992 0.005408 0.874 0.9015 0.01477 ] Network output: [ -0.0006283 -0.001874 1.003 -0.0001425 6.395e-05 0.9993 -0.0001074 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1938 0.09396 0.3209 0.1607 0.9851 0.994 0.1944 0.4651 0.8842 0.7174 ] Network output: [ 0.008461 -0.04206 0.9978 8.284e-05 -3.719e-05 1.028 6.243e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09697 0.08605 0.1785 0.2099 0.9874 0.992 0.09703 0.7922 0.8782 0.31 ] Network output: [ -0.008811 0.04411 1.001 8.29e-05 -3.722e-05 0.9727 6.248e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09271 0.09083 0.1676 0.1981 0.9856 0.9915 0.09272 0.7223 0.8577 0.2434 ] Network output: [ 0.0005307 0.9995 -0.001087 1.148e-05 -5.154e-06 1.001 8.652e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008995 Epoch 6859 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0133 0.9928 0.9866 4.569e-06 -2.051e-06 -0.005955 3.443e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003199 -0.002969 -0.009594 0.0073 0.9697 0.9741 0.006073 0.8442 0.832 0.02034 ] Network output: [ 0.9992 0.005923 0.001626 -4.123e-05 1.851e-05 -0.006088 -3.107e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1834 -0.02851 -0.1982 0.2002 0.9836 0.9933 0.2046 0.4604 0.8777 0.7227 ] Network output: [ -0.01191 1 1.01 2.202e-06 -9.885e-07 0.01317 1.659e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005314 0.000513 0.004279 0.004436 0.9889 0.992 0.005411 0.874 0.9015 0.01475 ] Network output: [ -0.001375 0.009616 1.003 -0.0001436 6.445e-05 0.9898 -0.0001082 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1939 0.09398 0.3201 0.1585 0.9851 0.994 0.1945 0.4652 0.8842 0.7175 ] Network output: [ 0.00863 -0.03981 0.9975 8.268e-05 -3.712e-05 1.025 6.231e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09694 0.08601 0.178 0.2093 0.9874 0.992 0.097 0.792 0.8781 0.3097 ] Network output: [ -0.008721 0.04335 1.001 8.293e-05 -3.723e-05 0.9732 6.25e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09267 0.09079 0.1675 0.198 0.9856 0.9915 0.09268 0.7221 0.8577 0.2434 ] Network output: [ 7.634e-05 0.9994 -0.0004405 1.131e-05 -5.079e-06 1.001 8.525e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000877 Epoch 6860 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01339 0.9915 0.9867 4.689e-06 -2.105e-06 -0.004909 3.534e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003198 -0.00297 -0.009585 0.00732 0.9697 0.9741 0.006072 0.8442 0.8321 0.02034 ] Network output: [ 0.9998 -0.002469 0.002044 -4.051e-05 1.819e-05 0.0007482 -3.053e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1833 -0.02864 -0.1976 0.2016 0.9836 0.9933 0.2045 0.4602 0.8777 0.7227 ] Network output: [ -0.01189 0.9998 1.01 2.254e-06 -1.012e-06 0.01354 1.699e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005313 0.0005138 0.004304 0.004479 0.9889 0.992 0.005409 0.874 0.9015 0.01476 ] Network output: [ -0.0006332 -0.001798 1.003 -0.0001423 6.387e-05 0.9993 -0.0001072 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1938 0.09395 0.321 0.1607 0.9851 0.994 0.1944 0.4651 0.8842 0.7174 ] Network output: [ 0.008456 -0.04203 0.9978 8.274e-05 -3.715e-05 1.028 6.236e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09697 0.08605 0.1785 0.2099 0.9874 0.992 0.09704 0.7922 0.8781 0.31 ] Network output: [ -0.008803 0.04407 1.001 8.281e-05 -3.717e-05 0.9727 6.241e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09269 0.09081 0.1676 0.1981 0.9856 0.9915 0.09271 0.7222 0.8577 0.2434 ] Network output: [ 0.0005275 0.9995 -0.001082 1.147e-05 -5.147e-06 1.001 8.641e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008982 Epoch 6861 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0133 0.9927 0.9866 4.559e-06 -2.047e-06 -0.005953 3.436e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003199 -0.00297 -0.009591 0.007298 0.9697 0.9741 0.006074 0.8442 0.832 0.02033 ] Network output: [ 0.9992 0.005864 0.001627 -4.119e-05 1.849e-05 -0.006042 -3.104e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1834 -0.02853 -0.1981 0.2002 0.9836 0.9933 0.2046 0.4604 0.8777 0.7226 ] Network output: [ -0.01191 1 1.01 2.198e-06 -9.87e-07 0.01316 1.657e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005315 0.0005126 0.00428 0.004435 0.9889 0.992 0.005412 0.874 0.9015 0.01475 ] Network output: [ -0.00137 0.009539 1.003 -0.0001434 6.436e-05 0.9898 -0.000108 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1939 0.09397 0.3202 0.1585 0.9851 0.994 0.1945 0.4652 0.8842 0.7175 ] Network output: [ 0.008623 -0.0398 0.9975 8.257e-05 -3.707e-05 1.025 6.223e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09694 0.08601 0.178 0.2093 0.9874 0.992 0.09701 0.792 0.8781 0.3097 ] Network output: [ -0.008714 0.04333 1.001 8.284e-05 -3.719e-05 0.9732 6.243e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09266 0.09077 0.1675 0.198 0.9856 0.9915 0.09267 0.722 0.8577 0.2434 ] Network output: [ 7.9e-05 0.9994 -0.0004438 1.13e-05 -5.073e-06 1.001 8.516e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008761 Epoch 6862 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01338 0.9915 0.9867 4.677e-06 -2.1e-06 -0.00492 3.525e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003198 -0.00297 -0.009581 0.007318 0.9698 0.9741 0.006072 0.8441 0.8321 0.02034 ] Network output: [ 0.9998 -0.002418 0.00204 -4.048e-05 1.817e-05 0.0007043 -3.051e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1833 -0.02865 -0.1976 0.2016 0.9836 0.9933 0.2045 0.4602 0.8777 0.7227 ] Network output: [ -0.01189 0.9999 1.01 2.25e-06 -1.01e-06 0.01353 1.696e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005314 0.0005134 0.004304 0.004477 0.9889 0.992 0.00541 0.874 0.9015 0.01476 ] Network output: [ -0.0006381 -0.001723 1.003 -0.0001421 6.379e-05 0.9992 -0.0001071 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1938 0.09394 0.321 0.1606 0.9851 0.994 0.1944 0.465 0.8842 0.7174 ] Network output: [ 0.008451 -0.04199 0.9978 8.264e-05 -3.71e-05 1.028 6.228e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09698 0.08605 0.1785 0.2098 0.9874 0.992 0.09704 0.7921 0.8781 0.31 ] Network output: [ -0.008794 0.04404 1.001 8.271e-05 -3.713e-05 0.9727 6.233e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09268 0.0908 0.1676 0.1981 0.9856 0.9915 0.09269 0.7222 0.8576 0.2434 ] Network output: [ 0.0005243 0.9995 -0.001077 1.145e-05 -5.141e-06 1.001 8.63e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008969 Epoch 6863 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01329 0.9927 0.9866 4.55e-06 -2.042e-06 -0.00595 3.429e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003199 -0.00297 -0.009588 0.007296 0.9698 0.9741 0.006074 0.8442 0.832 0.02033 ] Network output: [ 0.9992 0.005805 0.001628 -4.115e-05 1.847e-05 -0.005996 -3.101e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1834 -0.02854 -0.1981 0.2002 0.9836 0.9933 0.2046 0.4604 0.8777 0.7226 ] Network output: [ -0.0119 1 1.01 2.195e-06 -9.854e-07 0.01316 1.654e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005316 0.0005122 0.004281 0.004433 0.9889 0.992 0.005413 0.874 0.9015 0.01475 ] Network output: [ -0.001365 0.009463 1.003 -0.0001432 6.427e-05 0.9899 -0.0001079 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1939 0.09396 0.3202 0.1585 0.9851 0.994 0.1945 0.4652 0.8842 0.7175 ] Network output: [ 0.008615 -0.0398 0.9974 8.247e-05 -3.702e-05 1.025 6.215e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09695 0.08601 0.178 0.2092 0.9874 0.992 0.09701 0.7919 0.8781 0.3097 ] Network output: [ -0.008707 0.0433 1.001 8.274e-05 -3.715e-05 0.9732 6.236e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09264 0.09076 0.1675 0.198 0.9856 0.9915 0.09265 0.7219 0.8576 0.2434 ] Network output: [ 8.163e-05 0.9994 -0.000447 1.129e-05 -5.067e-06 1.001 8.507e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008751 Epoch 6864 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01337 0.9915 0.9867 4.666e-06 -2.095e-06 -0.004932 3.516e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003198 -0.00297 -0.009578 0.007315 0.9698 0.9741 0.006073 0.8441 0.832 0.02033 ] Network output: [ 0.9998 -0.002366 0.002036 -4.045e-05 1.816e-05 0.0006607 -3.048e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1833 -0.02866 -0.1975 0.2015 0.9836 0.9933 0.2045 0.4602 0.8777 0.7227 ] Network output: [ -0.01189 0.9999 1.01 2.246e-06 -1.008e-06 0.01352 1.692e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005315 0.000513 0.004304 0.004475 0.9889 0.992 0.005411 0.874 0.9015 0.01476 ] Network output: [ -0.0006429 -0.001649 1.003 -0.0001419 6.371e-05 0.9991 -0.000107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1938 0.09393 0.321 0.1606 0.9851 0.994 0.1944 0.465 0.8841 0.7174 ] Network output: [ 0.008446 -0.04196 0.9978 8.253e-05 -3.705e-05 1.028 6.22e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09698 0.08605 0.1785 0.2098 0.9874 0.992 0.09704 0.7921 0.8781 0.31 ] Network output: [ -0.008786 0.044 1.001 8.262e-05 -3.709e-05 0.9727 6.226e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09267 0.09078 0.1676 0.1981 0.9856 0.9915 0.09268 0.7221 0.8576 0.2434 ] Network output: [ 0.0005212 0.9995 -0.001072 1.144e-05 -5.134e-06 1.001 8.619e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008956 Epoch 6865 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01329 0.9927 0.9866 4.54e-06 -2.038e-06 -0.005948 3.421e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003199 -0.00297 -0.009584 0.007293 0.9698 0.9741 0.006074 0.8441 0.832 0.02032 ] Network output: [ 0.9992 0.005747 0.001629 -4.11e-05 1.845e-05 -0.00595 -3.098e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1834 -0.02856 -0.1981 0.2002 0.9836 0.9933 0.2046 0.4603 0.8776 0.7226 ] Network output: [ -0.0119 1 1.01 2.192e-06 -9.838e-07 0.01315 1.652e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005317 0.0005119 0.004281 0.004432 0.9889 0.992 0.005414 0.874 0.9015 0.01474 ] Network output: [ -0.00136 0.009387 1.003 -0.000143 6.419e-05 0.9899 -0.0001077 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1939 0.09395 0.3203 0.1585 0.9851 0.994 0.1945 0.4651 0.8841 0.7175 ] Network output: [ 0.008608 -0.03979 0.9974 8.237e-05 -3.698e-05 1.025 6.208e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09695 0.08601 0.178 0.2092 0.9874 0.992 0.09701 0.7919 0.8781 0.3097 ] Network output: [ -0.0087 0.04328 1.001 8.265e-05 -3.71e-05 0.9732 6.229e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09263 0.09075 0.1675 0.1979 0.9856 0.9915 0.09264 0.7218 0.8576 0.2433 ] Network output: [ 8.425e-05 0.9994 -0.0004503 1.128e-05 -5.062e-06 1.001 8.498e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008741 Epoch 6866 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01337 0.9915 0.9867 4.654e-06 -2.089e-06 -0.004944 3.508e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003198 -0.002971 -0.009575 0.007313 0.9698 0.9741 0.006073 0.8441 0.832 0.02033 ] Network output: [ 0.9998 -0.002315 0.002032 -4.042e-05 1.815e-05 0.0006174 -3.046e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1834 -0.02868 -0.1975 0.2015 0.9836 0.9933 0.2046 0.4601 0.8777 0.7227 ] Network output: [ -0.01189 0.9999 1.01 2.241e-06 -1.006e-06 0.0135 1.689e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005316 0.0005126 0.004304 0.004473 0.9889 0.992 0.005412 0.874 0.9015 0.01475 ] Network output: [ -0.0006477 -0.001575 1.003 -0.0001417 6.363e-05 0.9991 -0.0001068 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1939 0.09392 0.3211 0.1605 0.9851 0.994 0.1945 0.465 0.8841 0.7174 ] Network output: [ 0.008441 -0.04192 0.9978 8.243e-05 -3.701e-05 1.028 6.212e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09698 0.08605 0.1785 0.2098 0.9874 0.992 0.09704 0.792 0.8781 0.31 ] Network output: [ -0.008778 0.04397 1.001 8.253e-05 -3.705e-05 0.9727 6.219e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09265 0.09077 0.1676 0.198 0.9856 0.9915 0.09266 0.722 0.8576 0.2434 ] Network output: [ 0.000518 0.9995 -0.001067 1.142e-05 -5.128e-06 1.001 8.608e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008944 Epoch 6867 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01328 0.9927 0.9867 4.53e-06 -2.034e-06 -0.005946 3.414e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003199 -0.002971 -0.009581 0.007291 0.9698 0.9741 0.006075 0.8441 0.832 0.02032 ] Network output: [ 0.9992 0.005689 0.00163 -4.106e-05 1.843e-05 -0.005905 -3.094e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1834 -0.02857 -0.198 0.2002 0.9836 0.9933 0.2047 0.4603 0.8776 0.7226 ] Network output: [ -0.0119 1 1.01 2.188e-06 -9.823e-07 0.01314 1.649e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005318 0.0005115 0.004282 0.00443 0.9889 0.992 0.005415 0.874 0.9014 0.01474 ] Network output: [ -0.001355 0.009312 1.003 -0.0001428 6.41e-05 0.99 -0.0001076 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1939 0.09394 0.3203 0.1584 0.9851 0.994 0.1945 0.4651 0.8841 0.7175 ] Network output: [ 0.0086 -0.03979 0.9974 8.227e-05 -3.693e-05 1.025 6.2e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09695 0.08601 0.178 0.2092 0.9874 0.992 0.09701 0.7918 0.8781 0.3096 ] Network output: [ -0.008693 0.04326 1.001 8.255e-05 -3.706e-05 0.9732 6.221e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09262 0.09073 0.1675 0.1979 0.9856 0.9915 0.09263 0.7218 0.8576 0.2433 ] Network output: [ 8.684e-05 0.9994 -0.0004535 1.126e-05 -5.056e-06 1.001 8.488e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008731 Epoch 6868 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01336 0.9916 0.9867 4.643e-06 -2.084e-06 -0.004955 3.499e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003198 -0.002971 -0.009572 0.00731 0.9698 0.9741 0.006074 0.8441 0.832 0.02032 ] Network output: [ 0.9997 -0.002264 0.002027 -4.039e-05 1.813e-05 0.0005744 -3.044e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1834 -0.02869 -0.1975 0.2015 0.9836 0.9933 0.2046 0.4601 0.8777 0.7227 ] Network output: [ -0.01188 0.9999 1.01 2.237e-06 -1.004e-06 0.01349 1.686e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005317 0.0005122 0.004305 0.004471 0.9889 0.992 0.005413 0.8739 0.9015 0.01475 ] Network output: [ -0.0006524 -0.001501 1.003 -0.0001416 6.355e-05 0.999 -0.0001067 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1939 0.09391 0.3211 0.1605 0.9851 0.994 0.1945 0.4649 0.8841 0.7174 ] Network output: [ 0.008436 -0.04189 0.9978 8.233e-05 -3.696e-05 1.028 6.205e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09698 0.08605 0.1785 0.2098 0.9874 0.992 0.09704 0.792 0.8781 0.3099 ] Network output: [ -0.008769 0.04394 1.001 8.243e-05 -3.701e-05 0.9727 6.212e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09264 0.09076 0.1676 0.198 0.9856 0.9915 0.09265 0.7219 0.8576 0.2434 ] Network output: [ 0.0005149 0.9995 -0.001062 1.141e-05 -5.121e-06 1.001 8.597e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008931 Epoch 6869 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01328 0.9927 0.9867 4.52e-06 -2.029e-06 -0.005944 3.406e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003199 -0.002971 -0.009577 0.007289 0.9698 0.9741 0.006075 0.8441 0.832 0.02031 ] Network output: [ 0.9992 0.005632 0.001631 -4.102e-05 1.841e-05 -0.00586 -3.091e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1835 -0.02859 -0.198 0.2002 0.9836 0.9933 0.2047 0.4603 0.8776 0.7226 ] Network output: [ -0.0119 1 1.01 2.184e-06 -9.807e-07 0.01314 1.646e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005319 0.0005111 0.004282 0.004428 0.9889 0.992 0.005416 0.8739 0.9014 0.01474 ] Network output: [ -0.001351 0.009237 1.003 -0.0001426 6.401e-05 0.9901 -0.0001075 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.194 0.09393 0.3203 0.1584 0.9851 0.994 0.1946 0.4651 0.8841 0.7175 ] Network output: [ 0.008593 -0.03978 0.9974 8.217e-05 -3.689e-05 1.026 6.192e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09696 0.08601 0.178 0.2092 0.9874 0.992 0.09702 0.7918 0.878 0.3096 ] Network output: [ -0.008686 0.04323 1.001 8.246e-05 -3.702e-05 0.9732 6.214e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0926 0.09072 0.1675 0.1979 0.9856 0.9915 0.09261 0.7217 0.8575 0.2433 ] Network output: [ 8.942e-05 0.9994 -0.0004566 1.125e-05 -5.051e-06 1.001 8.479e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008721 Epoch 6870 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01336 0.9916 0.9867 4.631e-06 -2.079e-06 -0.004967 3.49e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003199 -0.002971 -0.009568 0.007308 0.9698 0.9741 0.006074 0.8441 0.832 0.02032 ] Network output: [ 0.9997 -0.002213 0.002023 -4.035e-05 1.812e-05 0.0005318 -3.041e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1834 -0.02871 -0.1974 0.2014 0.9836 0.9933 0.2046 0.4601 0.8777 0.7227 ] Network output: [ -0.01188 0.9999 1.01 2.233e-06 -1.002e-06 0.01348 1.683e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005318 0.0005118 0.004305 0.004468 0.9889 0.992 0.005414 0.8739 0.9015 0.01474 ] Network output: [ -0.0006571 -0.001428 1.003 -0.0001414 6.347e-05 0.999 -0.0001066 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1939 0.0939 0.3211 0.1604 0.9851 0.994 0.1945 0.4649 0.8841 0.7174 ] Network output: [ 0.008431 -0.04186 0.9978 8.222e-05 -3.691e-05 1.028 6.197e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09699 0.08605 0.1785 0.2097 0.9874 0.992 0.09705 0.7919 0.878 0.3099 ] Network output: [ -0.008761 0.0439 1.001 8.234e-05 -3.697e-05 0.9728 6.205e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09262 0.09074 0.1676 0.198 0.9856 0.9915 0.09264 0.7219 0.8575 0.2433 ] Network output: [ 0.0005118 0.9995 -0.001057 1.139e-05 -5.114e-06 1.001 8.586e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008918 Epoch 6871 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01327 0.9927 0.9867 4.51e-06 -2.025e-06 -0.005942 3.399e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0032 -0.002971 -0.009574 0.007287 0.9698 0.9741 0.006076 0.8441 0.832 0.02031 ] Network output: [ 0.9992 0.005575 0.001632 -4.097e-05 1.84e-05 -0.005815 -3.088e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1835 -0.0286 -0.1979 0.2002 0.9836 0.9933 0.2047 0.4602 0.8776 0.7226 ] Network output: [ -0.01189 1 1.01 2.181e-06 -9.791e-07 0.01313 1.644e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00532 0.0005107 0.004283 0.004427 0.9889 0.992 0.005417 0.8739 0.9014 0.01473 ] Network output: [ -0.001346 0.009163 1.003 -0.0001424 6.392e-05 0.9901 -0.0001073 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.194 0.09392 0.3204 0.1584 0.9851 0.994 0.1946 0.465 0.8841 0.7174 ] Network output: [ 0.008585 -0.03978 0.9974 8.207e-05 -3.684e-05 1.026 6.185e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09696 0.08601 0.178 0.2092 0.9874 0.992 0.09702 0.7917 0.878 0.3096 ] Network output: [ -0.008679 0.04321 1.001 8.236e-05 -3.698e-05 0.9732 6.207e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09259 0.0907 0.1675 0.1979 0.9856 0.9915 0.0926 0.7216 0.8575 0.2433 ] Network output: [ 9.198e-05 0.9994 -0.0004598 1.124e-05 -5.045e-06 1.001 8.47e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008711 Epoch 6872 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01335 0.9916 0.9867 4.619e-06 -2.074e-06 -0.004978 3.481e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003199 -0.002972 -0.009565 0.007305 0.9698 0.9742 0.006074 0.8441 0.832 0.02031 ] Network output: [ 0.9997 -0.002163 0.002019 -4.032e-05 1.81e-05 0.0004894 -3.039e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1834 -0.02872 -0.1974 0.2014 0.9836 0.9933 0.2046 0.46 0.8776 0.7227 ] Network output: [ -0.01188 0.9999 1.01 2.229e-06 -1.001e-06 0.01347 1.68e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005319 0.0005114 0.004305 0.004466 0.9889 0.992 0.005416 0.8739 0.9014 0.01474 ] Network output: [ -0.0006617 -0.001356 1.003 -0.0001412 6.339e-05 0.9989 -0.0001064 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1939 0.09389 0.3212 0.1603 0.9851 0.994 0.1945 0.4649 0.8841 0.7173 ] Network output: [ 0.008426 -0.04182 0.9978 8.212e-05 -3.687e-05 1.028 6.189e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09699 0.08604 0.1785 0.2097 0.9874 0.992 0.09705 0.7918 0.878 0.3099 ] Network output: [ -0.008753 0.04387 1.001 8.225e-05 -3.692e-05 0.9728 6.198e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09261 0.09073 0.1676 0.198 0.9856 0.9915 0.09262 0.7218 0.8575 0.2433 ] Network output: [ 0.0005087 0.9995 -0.001052 1.138e-05 -5.108e-06 1.001 8.575e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008906 Epoch 6873 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01327 0.9927 0.9867 4.5e-06 -2.02e-06 -0.00594 3.392e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0032 -0.002971 -0.009571 0.007285 0.9698 0.9742 0.006076 0.8441 0.8319 0.02031 ] Network output: [ 0.9992 0.005518 0.001633 -4.093e-05 1.838e-05 -0.00577 -3.085e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1835 -0.02862 -0.1979 0.2001 0.9836 0.9933 0.2047 0.4602 0.8776 0.7226 ] Network output: [ -0.01189 1 1.01 2.177e-06 -9.775e-07 0.01312 1.641e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005321 0.0005103 0.004284 0.004425 0.9889 0.992 0.005418 0.8739 0.9014 0.01473 ] Network output: [ -0.001341 0.00909 1.003 -0.0001422 6.384e-05 0.9902 -0.0001072 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.194 0.09391 0.3204 0.1584 0.9851 0.994 0.1946 0.465 0.8841 0.7174 ] Network output: [ 0.008578 -0.03977 0.9974 8.196e-05 -3.68e-05 1.026 6.177e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09696 0.08601 0.178 0.2091 0.9874 0.992 0.09702 0.7917 0.878 0.3096 ] Network output: [ -0.008672 0.04318 1.001 8.227e-05 -3.693e-05 0.9733 6.2e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09257 0.09069 0.1674 0.1979 0.9856 0.9915 0.09259 0.7216 0.8575 0.2433 ] Network output: [ 9.452e-05 0.9994 -0.0004629 1.123e-05 -5.04e-06 1.001 8.46e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008702 Epoch 6874 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01334 0.9916 0.9867 4.608e-06 -2.069e-06 -0.00499 3.473e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003199 -0.002972 -0.009562 0.007303 0.9698 0.9742 0.006075 0.8441 0.832 0.02031 ] Network output: [ 0.9997 -0.002113 0.002015 -4.029e-05 1.809e-05 0.0004474 -3.036e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1834 -0.02873 -0.1974 0.2014 0.9836 0.9933 0.2046 0.46 0.8776 0.7227 ] Network output: [ -0.01188 0.9999 1.01 2.224e-06 -9.986e-07 0.01346 1.676e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00532 0.000511 0.004306 0.004464 0.9889 0.992 0.005417 0.8739 0.9014 0.01474 ] Network output: [ -0.0006663 -0.001284 1.003 -0.000141 6.331e-05 0.9988 -0.0001063 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1939 0.09388 0.3212 0.1603 0.9851 0.994 0.1945 0.4648 0.8841 0.7173 ] Network output: [ 0.008421 -0.04179 0.9978 8.202e-05 -3.682e-05 1.028 6.181e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09699 0.08604 0.1785 0.2097 0.9874 0.992 0.09705 0.7918 0.878 0.3099 ] Network output: [ -0.008744 0.04384 1.001 8.215e-05 -3.688e-05 0.9728 6.191e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0926 0.09071 0.1675 0.198 0.9856 0.9915 0.09261 0.7217 0.8575 0.2433 ] Network output: [ 0.0005056 0.9995 -0.001047 1.136e-05 -5.101e-06 1.001 8.564e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008893 Epoch 6875 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01326 0.9927 0.9867 4.491e-06 -2.016e-06 -0.005938 3.384e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0032 -0.002972 -0.009567 0.007282 0.9698 0.9742 0.006077 0.8441 0.8319 0.0203 ] Network output: [ 0.9992 0.005462 0.001634 -4.089e-05 1.836e-05 -0.005726 -3.082e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1835 -0.02863 -0.1978 0.2001 0.9836 0.9933 0.2047 0.4602 0.8776 0.7226 ] Network output: [ -0.01189 1 1.01 2.174e-06 -9.759e-07 0.01312 1.638e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005322 0.0005099 0.004284 0.004424 0.9889 0.992 0.005419 0.8739 0.9014 0.01472 ] Network output: [ -0.001336 0.009017 1.003 -0.000142 6.375e-05 0.9903 -0.000107 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.194 0.0939 0.3205 0.1583 0.9851 0.994 0.1946 0.4649 0.8841 0.7174 ] Network output: [ 0.008571 -0.03977 0.9974 8.186e-05 -3.675e-05 1.026 6.169e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09696 0.08601 0.178 0.2091 0.9874 0.992 0.09702 0.7916 0.878 0.3096 ] Network output: [ -0.008665 0.04316 1.001 8.217e-05 -3.689e-05 0.9733 6.193e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09256 0.09068 0.1674 0.1979 0.9856 0.9915 0.09257 0.7215 0.8574 0.2433 ] Network output: [ 9.704e-05 0.9994 -0.0004659 1.121e-05 -5.034e-06 1.001 8.451e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008692 Epoch 6876 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01334 0.9916 0.9867 4.596e-06 -2.063e-06 -0.005001 3.464e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003199 -0.002972 -0.009558 0.007301 0.9698 0.9742 0.006075 0.844 0.832 0.0203 ] Network output: [ 0.9997 -0.002064 0.002011 -4.025e-05 1.807e-05 0.0004058 -3.034e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1835 -0.02875 -0.1973 0.2013 0.9836 0.9933 0.2047 0.46 0.8776 0.7227 ] Network output: [ -0.01187 0.9999 1.01 2.22e-06 -9.966e-07 0.01345 1.673e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005321 0.0005106 0.004306 0.004462 0.9889 0.992 0.005418 0.8739 0.9014 0.01473 ] Network output: [ -0.0006709 -0.001213 1.003 -0.0001409 6.323e-05 0.9988 -0.0001062 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.194 0.09387 0.3212 0.1602 0.9851 0.994 0.1946 0.4648 0.8841 0.7173 ] Network output: [ 0.008416 -0.04176 0.9977 8.192e-05 -3.677e-05 1.028 6.173e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09699 0.08604 0.1785 0.2097 0.9874 0.992 0.09705 0.7917 0.878 0.3099 ] Network output: [ -0.008736 0.0438 1.001 8.206e-05 -3.684e-05 0.9728 6.184e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09258 0.0907 0.1675 0.198 0.9856 0.9915 0.09259 0.7217 0.8574 0.2433 ] Network output: [ 0.0005026 0.9995 -0.001042 1.135e-05 -5.095e-06 1.001 8.553e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008881 Epoch 6877 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01326 0.9927 0.9867 4.481e-06 -2.012e-06 -0.005936 3.377e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0032 -0.002972 -0.009564 0.00728 0.9698 0.9742 0.006077 0.8441 0.8319 0.0203 ] Network output: [ 0.9992 0.005406 0.001635 -4.085e-05 1.834e-05 -0.005682 -3.078e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1835 -0.02865 -0.1978 0.2001 0.9836 0.9933 0.2048 0.4601 0.8776 0.7226 ] Network output: [ -0.01189 1 1.01 2.17e-06 -9.743e-07 0.01311 1.636e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005323 0.0005095 0.004285 0.004422 0.9889 0.992 0.00542 0.8739 0.9014 0.01472 ] Network output: [ -0.001332 0.008944 1.003 -0.0001418 6.366e-05 0.9903 -0.0001069 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.194 0.09389 0.3205 0.1583 0.9851 0.994 0.1946 0.4649 0.8841 0.7174 ] Network output: [ 0.008563 -0.03976 0.9974 8.176e-05 -3.671e-05 1.026 6.162e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09697 0.08601 0.1781 0.2091 0.9874 0.992 0.09703 0.7915 0.8779 0.3096 ] Network output: [ -0.008658 0.04313 1.001 8.208e-05 -3.685e-05 0.9733 6.186e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09255 0.09066 0.1674 0.1979 0.9856 0.9915 0.09256 0.7214 0.8574 0.2433 ] Network output: [ 9.954e-05 0.9994 -0.000469 1.12e-05 -5.029e-06 1.001 8.442e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008682 Epoch 6878 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01333 0.9916 0.9867 4.585e-06 -2.058e-06 -0.005013 3.455e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003199 -0.002972 -0.009555 0.007298 0.9698 0.9742 0.006076 0.844 0.832 0.0203 ] Network output: [ 0.9997 -0.002015 0.002007 -4.022e-05 1.806e-05 0.0003644 -3.031e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1835 -0.02876 -0.1973 0.2013 0.9836 0.9933 0.2047 0.4599 0.8776 0.7227 ] Network output: [ -0.01187 0.9999 1.01 2.216e-06 -9.947e-07 0.01344 1.67e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005322 0.0005102 0.004306 0.00446 0.9889 0.992 0.005419 0.8739 0.9014 0.01473 ] Network output: [ -0.0006754 -0.001142 1.003 -0.0001407 6.315e-05 0.9987 -0.000106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.194 0.09386 0.3213 0.1602 0.9851 0.994 0.1946 0.4648 0.8841 0.7173 ] Network output: [ 0.008411 -0.04172 0.9977 8.181e-05 -3.673e-05 1.027 6.166e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.097 0.08604 0.1785 0.2096 0.9874 0.992 0.09706 0.7917 0.8779 0.3099 ] Network output: [ -0.008728 0.04377 1.001 8.197e-05 -3.68e-05 0.9728 6.177e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09257 0.09068 0.1675 0.198 0.9856 0.9915 0.09258 0.7216 0.8574 0.2433 ] Network output: [ 0.0004996 0.9995 -0.001037 1.133e-05 -5.088e-06 1.001 8.542e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008868 Epoch 6879 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01325 0.9927 0.9867 4.471e-06 -2.007e-06 -0.005935 3.369e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0032 -0.002972 -0.009561 0.007278 0.9698 0.9742 0.006078 0.844 0.8319 0.02029 ] Network output: [ 0.9992 0.00535 0.001636 -4.08e-05 1.832e-05 -0.005639 -3.075e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1835 -0.02866 -0.1978 0.2001 0.9836 0.9933 0.2048 0.4601 0.8776 0.7226 ] Network output: [ -0.01188 1 1.01 2.167e-06 -9.727e-07 0.0131 1.633e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005324 0.0005091 0.004285 0.00442 0.9889 0.992 0.005421 0.8739 0.9014 0.01472 ] Network output: [ -0.001327 0.008872 1.003 -0.0001416 6.357e-05 0.9904 -0.0001067 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1941 0.09388 0.3206 0.1583 0.9851 0.994 0.1947 0.4649 0.8841 0.7174 ] Network output: [ 0.008556 -0.03976 0.9974 8.166e-05 -3.666e-05 1.026 6.154e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09697 0.08601 0.1781 0.2091 0.9874 0.992 0.09703 0.7915 0.8779 0.3096 ] Network output: [ -0.008651 0.04311 1.001 8.199e-05 -3.681e-05 0.9733 6.179e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09253 0.09065 0.1674 0.1979 0.9856 0.9915 0.09255 0.7214 0.8574 0.2433 ] Network output: [ 0.000102 0.9994 -0.000472 1.119e-05 -5.023e-06 1.001 8.432e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008673 Epoch 6880 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01333 0.9917 0.9867 4.573e-06 -2.053e-06 -0.005024 3.446e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0032 -0.002973 -0.009552 0.007296 0.9698 0.9742 0.006076 0.844 0.8319 0.02029 ] Network output: [ 0.9997 -0.001966 0.002003 -4.019e-05 1.804e-05 0.0003234 -3.029e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1835 -0.02877 -0.1973 0.2013 0.9836 0.9933 0.2047 0.4599 0.8776 0.7227 ] Network output: [ -0.01187 0.9999 1.01 2.211e-06 -9.927e-07 0.01343 1.667e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005323 0.0005098 0.004306 0.004458 0.9889 0.992 0.00542 0.8738 0.9014 0.01473 ] Network output: [ -0.0006799 -0.001072 1.003 -0.0001405 6.307e-05 0.9987 -0.0001059 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.194 0.09385 0.3213 0.1601 0.9851 0.994 0.1946 0.4647 0.8841 0.7173 ] Network output: [ 0.008406 -0.04169 0.9977 8.171e-05 -3.668e-05 1.027 6.158e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.097 0.08604 0.1785 0.2096 0.9874 0.992 0.09706 0.7916 0.8779 0.3099 ] Network output: [ -0.00872 0.04374 1.001 8.187e-05 -3.676e-05 0.9728 6.17e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09255 0.09067 0.1675 0.1979 0.9856 0.9915 0.09257 0.7215 0.8574 0.2433 ] Network output: [ 0.0004965 0.9995 -0.001032 1.132e-05 -5.082e-06 1.001 8.531e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008856 Epoch 6881 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01325 0.9927 0.9867 4.461e-06 -2.003e-06 -0.005933 3.362e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003201 -0.002973 -0.009557 0.007276 0.9698 0.9742 0.006078 0.844 0.8319 0.02029 ] Network output: [ 0.9992 0.005295 0.001637 -4.076e-05 1.83e-05 -0.005595 -3.072e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1836 -0.02868 -0.1977 0.2001 0.9836 0.9933 0.2048 0.4601 0.8776 0.7226 ] Network output: [ -0.01188 1 1.01 2.163e-06 -9.71e-07 0.0131 1.63e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005325 0.0005087 0.004286 0.004419 0.9889 0.992 0.005422 0.8738 0.9014 0.01471 ] Network output: [ -0.001322 0.008801 1.003 -0.0001414 6.349e-05 0.9904 -0.0001066 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1941 0.09387 0.3206 0.1583 0.9851 0.994 0.1947 0.4648 0.8841 0.7174 ] Network output: [ 0.008548 -0.03975 0.9974 8.156e-05 -3.661e-05 1.026 6.146e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09697 0.08601 0.1781 0.2091 0.9874 0.992 0.09703 0.7914 0.8779 0.3096 ] Network output: [ -0.008644 0.04308 1.001 8.189e-05 -3.676e-05 0.9733 6.172e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09252 0.09063 0.1674 0.1978 0.9856 0.9915 0.09253 0.7213 0.8574 0.2433 ] Network output: [ 0.0001045 0.9994 -0.000475 1.118e-05 -5.018e-06 1.001 8.423e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008663 Epoch 6882 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01332 0.9917 0.9867 4.562e-06 -2.048e-06 -0.005035 3.438e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0032 -0.002973 -0.009549 0.007293 0.9698 0.9742 0.006077 0.844 0.8319 0.02029 ] Network output: [ 0.9997 -0.001918 0.001998 -4.015e-05 1.803e-05 0.0002828 -3.026e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1835 -0.02879 -0.1972 0.2012 0.9836 0.9933 0.2047 0.4599 0.8776 0.7226 ] Network output: [ -0.01187 0.9999 1.01 2.207e-06 -9.908e-07 0.01341 1.663e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005324 0.0005094 0.004307 0.004455 0.9889 0.992 0.005421 0.8738 0.9014 0.01472 ] Network output: [ -0.0006844 -0.001002 1.003 -0.0001403 6.299e-05 0.9986 -0.0001057 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.194 0.09384 0.3213 0.1601 0.9851 0.994 0.1946 0.4647 0.884 0.7173 ] Network output: [ 0.008401 -0.04166 0.9977 8.161e-05 -3.664e-05 1.027 6.15e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.097 0.08604 0.1785 0.2096 0.9874 0.992 0.09706 0.7915 0.8779 0.3098 ] Network output: [ -0.008711 0.0437 1.001 8.178e-05 -3.671e-05 0.9728 6.163e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09254 0.09066 0.1675 0.1979 0.9856 0.9915 0.09255 0.7214 0.8573 0.2433 ] Network output: [ 0.0004936 0.9995 -0.001028 1.131e-05 -5.075e-06 1.001 8.52e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008843 Epoch 6883 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01324 0.9927 0.9867 4.451e-06 -1.998e-06 -0.005931 3.354e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003201 -0.002973 -0.009554 0.007274 0.9698 0.9742 0.006079 0.844 0.8319 0.02028 ] Network output: [ 0.9993 0.005241 0.001638 -4.072e-05 1.828e-05 -0.005552 -3.069e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1836 -0.02869 -0.1977 0.2001 0.9836 0.9933 0.2048 0.46 0.8775 0.7226 ] Network output: [ -0.01188 1 1.01 2.159e-06 -9.694e-07 0.01309 1.627e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005326 0.0005084 0.004286 0.004417 0.9889 0.992 0.005423 0.8738 0.9014 0.01471 ] Network output: [ -0.001318 0.00873 1.003 -0.0001412 6.34e-05 0.9905 -0.0001064 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1941 0.09386 0.3207 0.1582 0.9851 0.994 0.1947 0.4648 0.884 0.7174 ] Network output: [ 0.008541 -0.03975 0.9974 8.146e-05 -3.657e-05 1.026 6.139e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09698 0.08601 0.1781 0.2091 0.9874 0.992 0.09704 0.7914 0.8779 0.3096 ] Network output: [ -0.008637 0.04306 1.001 8.18e-05 -3.672e-05 0.9733 6.164e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09251 0.09062 0.1674 0.1978 0.9856 0.9915 0.09252 0.7212 0.8573 0.2433 ] Network output: [ 0.0001069 0.9994 -0.0004779 1.116e-05 -5.012e-06 1.001 8.414e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008654 Epoch 6884 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01331 0.9917 0.9868 4.55e-06 -2.043e-06 -0.005047 3.429e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0032 -0.002973 -0.009545 0.007291 0.9698 0.9742 0.006077 0.844 0.8319 0.02028 ] Network output: [ 0.9997 -0.00187 0.001994 -4.012e-05 1.801e-05 0.0002425 -3.024e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1835 -0.0288 -0.1972 0.2012 0.9836 0.9933 0.2048 0.4598 0.8776 0.7226 ] Network output: [ -0.01187 0.9999 1.01 2.203e-06 -9.888e-07 0.0134 1.66e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005325 0.000509 0.004307 0.004453 0.9889 0.992 0.005422 0.8738 0.9014 0.01472 ] Network output: [ -0.0006888 -0.0009331 1.003 -0.0001401 6.291e-05 0.9986 -0.0001056 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.194 0.09383 0.3214 0.16 0.9851 0.994 0.1946 0.4647 0.884 0.7173 ] Network output: [ 0.008396 -0.04162 0.9977 8.15e-05 -3.659e-05 1.027 6.142e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.097 0.08604 0.1785 0.2096 0.9874 0.992 0.09707 0.7915 0.8779 0.3098 ] Network output: [ -0.008703 0.04367 1.001 8.169e-05 -3.667e-05 0.9729 6.156e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09253 0.09064 0.1675 0.1979 0.9856 0.9915 0.09254 0.7214 0.8573 0.2433 ] Network output: [ 0.0004906 0.9995 -0.001023 1.129e-05 -5.069e-06 1.001 8.509e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008831 Epoch 6885 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01324 0.9927 0.9867 4.441e-06 -1.994e-06 -0.00593 3.347e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003201 -0.002973 -0.00955 0.007271 0.9698 0.9742 0.006079 0.844 0.8319 0.02028 ] Network output: [ 0.9993 0.005186 0.001639 -4.068e-05 1.826e-05 -0.00551 -3.065e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1836 -0.02871 -0.1976 0.2001 0.9836 0.9933 0.2048 0.46 0.8775 0.7226 ] Network output: [ -0.01188 1 1.01 2.156e-06 -9.677e-07 0.01309 1.625e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005327 0.000508 0.004287 0.004416 0.9889 0.992 0.005424 0.8738 0.9013 0.01471 ] Network output: [ -0.001313 0.008659 1.003 -0.000141 6.331e-05 0.9906 -0.0001063 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1941 0.09385 0.3207 0.1582 0.9851 0.994 0.1947 0.4648 0.884 0.7174 ] Network output: [ 0.008534 -0.03974 0.9974 8.135e-05 -3.652e-05 1.026 6.131e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09698 0.08601 0.1781 0.209 0.9874 0.992 0.09704 0.7913 0.8778 0.3096 ] Network output: [ -0.00863 0.04303 1.001 8.17e-05 -3.668e-05 0.9733 6.157e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09249 0.09061 0.1674 0.1978 0.9856 0.9915 0.09251 0.7211 0.8573 0.2433 ] Network output: [ 0.0001093 0.9994 -0.0004808 1.115e-05 -5.006e-06 1.001 8.404e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008644 Epoch 6886 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01331 0.9917 0.9868 4.538e-06 -2.037e-06 -0.005058 3.42e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0032 -0.002974 -0.009542 0.007288 0.9698 0.9742 0.006078 0.844 0.8319 0.02028 ] Network output: [ 0.9997 -0.001823 0.00199 -4.009e-05 1.8e-05 0.0002025 -3.021e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1835 -0.02882 -0.1971 0.2012 0.9836 0.9933 0.2048 0.4598 0.8776 0.7226 ] Network output: [ -0.01186 0.9999 1.01 2.198e-06 -9.869e-07 0.01339 1.657e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005326 0.0005086 0.004307 0.004451 0.9889 0.992 0.005423 0.8738 0.9014 0.01472 ] Network output: [ -0.0006931 -0.0008646 1.003 -0.00014 6.283e-05 0.9985 -0.0001055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1941 0.09382 0.3214 0.16 0.9851 0.994 0.1947 0.4646 0.884 0.7173 ] Network output: [ 0.008391 -0.04159 0.9977 8.14e-05 -3.654e-05 1.027 6.135e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09701 0.08604 0.1785 0.2095 0.9874 0.992 0.09707 0.7914 0.8778 0.3098 ] Network output: [ -0.008695 0.04363 1.001 8.159e-05 -3.663e-05 0.9729 6.149e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09251 0.09063 0.1675 0.1979 0.9856 0.9915 0.09252 0.7213 0.8573 0.2433 ] Network output: [ 0.0004876 0.9995 -0.001018 1.128e-05 -5.062e-06 1.001 8.498e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008819 Epoch 6887 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01323 0.9927 0.9867 4.431e-06 -1.989e-06 -0.005928 3.339e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003201 -0.002974 -0.009547 0.007269 0.9698 0.9742 0.006079 0.844 0.8319 0.02027 ] Network output: [ 0.9993 0.005133 0.00164 -4.063e-05 1.824e-05 -0.005467 -3.062e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1836 -0.02872 -0.1976 0.2 0.9836 0.9933 0.2049 0.46 0.8775 0.7225 ] Network output: [ -0.01187 1 1.01 2.152e-06 -9.661e-07 0.01308 1.622e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005328 0.0005076 0.004288 0.004414 0.9889 0.992 0.005425 0.8738 0.9013 0.0147 ] Network output: [ -0.001309 0.008589 1.003 -0.0001408 6.322e-05 0.9906 -0.0001061 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1941 0.09384 0.3207 0.1582 0.9851 0.994 0.1947 0.4647 0.884 0.7174 ] Network output: [ 0.008526 -0.03973 0.9974 8.125e-05 -3.648e-05 1.026 6.123e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09698 0.08601 0.1781 0.209 0.9874 0.992 0.09704 0.7913 0.8778 0.3095 ] Network output: [ -0.008623 0.04301 1.001 8.161e-05 -3.664e-05 0.9733 6.15e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09248 0.09059 0.1674 0.1978 0.9856 0.9915 0.09249 0.7211 0.8573 0.2433 ] Network output: [ 0.0001117 0.9994 -0.0004837 1.114e-05 -5.001e-06 1.001 8.395e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008635 Epoch 6888 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0133 0.9917 0.9868 4.527e-06 -2.032e-06 -0.005069 3.412e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0032 -0.002974 -0.009539 0.007286 0.9698 0.9742 0.006078 0.844 0.8319 0.02027 ] Network output: [ 0.9997 -0.001776 0.001986 -4.005e-05 1.798e-05 0.0001628 -3.019e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1836 -0.02883 -0.1971 0.2012 0.9836 0.9933 0.2048 0.4598 0.8775 0.7226 ] Network output: [ -0.01186 0.9999 1.01 2.194e-06 -9.849e-07 0.01338 1.653e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005327 0.0005082 0.004308 0.004449 0.9889 0.992 0.005424 0.8738 0.9014 0.01471 ] Network output: [ -0.0006974 -0.0007967 1.003 -0.0001398 6.275e-05 0.9984 -0.0001053 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1941 0.09381 0.3214 0.1599 0.9851 0.994 0.1947 0.4646 0.884 0.7173 ] Network output: [ 0.008386 -0.04155 0.9977 8.13e-05 -3.65e-05 1.027 6.127e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09701 0.08604 0.1785 0.2095 0.9874 0.992 0.09707 0.7914 0.8778 0.3098 ] Network output: [ -0.008687 0.0436 1.001 8.15e-05 -3.659e-05 0.9729 6.142e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0925 0.09061 0.1675 0.1979 0.9856 0.9915 0.09251 0.7212 0.8572 0.2433 ] Network output: [ 0.0004847 0.9995 -0.001014 1.126e-05 -5.056e-06 1.001 8.487e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008807 Epoch 6889 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01323 0.9927 0.9867 4.421e-06 -1.985e-06 -0.005927 3.332e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003201 -0.002974 -0.009544 0.007267 0.9698 0.9742 0.00608 0.844 0.8318 0.02027 ] Network output: [ 0.9993 0.005079 0.001641 -4.059e-05 1.822e-05 -0.005425 -3.059e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1836 -0.02874 -0.1976 0.2 0.9836 0.9933 0.2049 0.4599 0.8775 0.7225 ] Network output: [ -0.01187 1 1.01 2.148e-06 -9.644e-07 0.01307 1.619e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005329 0.0005072 0.004288 0.004412 0.9889 0.992 0.005426 0.8738 0.9013 0.0147 ] Network output: [ -0.001304 0.00852 1.003 -0.0001406 6.314e-05 0.9907 -0.000106 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1942 0.09383 0.3208 0.1582 0.9851 0.994 0.1948 0.4647 0.884 0.7174 ] Network output: [ 0.008519 -0.03973 0.9974 8.115e-05 -3.643e-05 1.026 6.116e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09699 0.08601 0.1781 0.209 0.9874 0.992 0.09705 0.7912 0.8778 0.3095 ] Network output: [ -0.008615 0.04298 1.001 8.151e-05 -3.659e-05 0.9733 6.143e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09247 0.09058 0.1674 0.1978 0.9856 0.9915 0.09248 0.721 0.8572 0.2433 ] Network output: [ 0.0001141 0.9994 -0.0004866 1.113e-05 -4.995e-06 1.001 8.386e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008625 Epoch 6890 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0133 0.9917 0.9868 4.515e-06 -2.027e-06 -0.00508 3.403e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0032 -0.002974 -0.009535 0.007283 0.9698 0.9742 0.006079 0.8439 0.8319 0.02027 ] Network output: [ 0.9997 -0.001729 0.001982 -4.002e-05 1.797e-05 0.0001235 -3.016e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1836 -0.02884 -0.1971 0.2011 0.9836 0.9933 0.2048 0.4598 0.8775 0.7226 ] Network output: [ -0.01186 1 1.01 2.189e-06 -9.829e-07 0.01337 1.65e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005328 0.0005078 0.004308 0.004447 0.9889 0.992 0.005425 0.8737 0.9013 0.01471 ] Network output: [ -0.0007017 -0.0007294 1.003 -0.0001396 6.267e-05 0.9984 -0.0001052 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1941 0.0938 0.3215 0.1599 0.9851 0.994 0.1947 0.4646 0.884 0.7173 ] Network output: [ 0.00838 -0.04152 0.9977 8.119e-05 -3.645e-05 1.027 6.119e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09701 0.08604 0.1785 0.2095 0.9874 0.992 0.09707 0.7913 0.8778 0.3098 ] Network output: [ -0.008679 0.04357 1.001 8.141e-05 -3.655e-05 0.9729 6.135e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09249 0.0906 0.1675 0.1979 0.9856 0.9915 0.0925 0.7211 0.8572 0.2433 ] Network output: [ 0.0004818 0.9995 -0.001009 1.125e-05 -5.049e-06 1.001 8.476e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008795 Epoch 6891 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01322 0.9927 0.9868 4.411e-06 -1.98e-06 -0.005925 3.325e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003201 -0.002974 -0.00954 0.007265 0.9698 0.9742 0.00608 0.8439 0.8318 0.02026 ] Network output: [ 0.9993 0.005026 0.001642 -4.055e-05 1.82e-05 -0.005383 -3.056e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1837 -0.02875 -0.1975 0.2 0.9836 0.9933 0.2049 0.4599 0.8775 0.7225 ] Network output: [ -0.01187 1 1.01 2.144e-06 -9.627e-07 0.01307 1.616e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00533 0.0005069 0.004289 0.004411 0.9889 0.992 0.005427 0.8737 0.9013 0.0147 ] Network output: [ -0.0013 0.008451 1.003 -0.0001404 6.305e-05 0.9907 -0.0001058 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1942 0.09382 0.3208 0.1581 0.9851 0.994 0.1948 0.4647 0.884 0.7174 ] Network output: [ 0.008512 -0.03972 0.9974 8.105e-05 -3.639e-05 1.026 6.108e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09699 0.08601 0.1781 0.209 0.9874 0.992 0.09705 0.7911 0.8778 0.3095 ] Network output: [ -0.008608 0.04296 1.001 8.142e-05 -3.655e-05 0.9733 6.136e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09245 0.09057 0.1674 0.1978 0.9856 0.9915 0.09247 0.7209 0.8572 0.2433 ] Network output: [ 0.0001165 0.9994 -0.0004894 1.111e-05 -4.99e-06 1.001 8.376e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008616 Epoch 6892 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01329 0.9917 0.9868 4.504e-06 -2.022e-06 -0.005091 3.394e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003201 -0.002975 -0.009532 0.007281 0.9698 0.9742 0.006079 0.8439 0.8319 0.02027 ] Network output: [ 0.9997 -0.001683 0.001978 -3.999e-05 1.795e-05 8.458e-05 -3.013e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1836 -0.02886 -0.197 0.2011 0.9836 0.9933 0.2048 0.4597 0.8775 0.7226 ] Network output: [ -0.01186 1 1.01 2.185e-06 -9.809e-07 0.01336 1.647e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005329 0.0005074 0.004308 0.004445 0.9889 0.992 0.005426 0.8737 0.9013 0.01471 ] Network output: [ -0.0007059 -0.0006627 1.003 -0.0001394 6.259e-05 0.9983 -0.0001051 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1941 0.09379 0.3215 0.1598 0.9851 0.994 0.1947 0.4645 0.884 0.7173 ] Network output: [ 0.008375 -0.04149 0.9977 8.109e-05 -3.64e-05 1.027 6.111e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09702 0.08604 0.1785 0.2095 0.9874 0.992 0.09708 0.7912 0.8778 0.3098 ] Network output: [ -0.00867 0.04353 1.001 8.131e-05 -3.65e-05 0.9729 6.128e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09247 0.09059 0.1674 0.1979 0.9856 0.9915 0.09248 0.7211 0.8572 0.2433 ] Network output: [ 0.0004789 0.9995 -0.001004 1.123e-05 -5.043e-06 1.001 8.465e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008782 Epoch 6893 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01322 0.9927 0.9868 4.401e-06 -1.976e-06 -0.005924 3.317e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003202 -0.002975 -0.009537 0.007263 0.9698 0.9742 0.006081 0.8439 0.8318 0.02026 ] Network output: [ 0.9993 0.004973 0.001643 -4.05e-05 1.818e-05 -0.005342 -3.053e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1837 -0.02877 -0.1975 0.2 0.9836 0.9933 0.2049 0.4599 0.8775 0.7225 ] Network output: [ -0.01187 1 1.01 2.141e-06 -9.61e-07 0.01306 1.613e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005331 0.0005065 0.004289 0.004409 0.9889 0.992 0.005428 0.8737 0.9013 0.01469 ] Network output: [ -0.001295 0.008383 1.003 -0.0001402 6.296e-05 0.9908 -0.0001057 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1942 0.09381 0.3209 0.1581 0.9851 0.994 0.1948 0.4646 0.884 0.7173 ] Network output: [ 0.008504 -0.03971 0.9974 8.095e-05 -3.634e-05 1.026 6.1e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09699 0.08601 0.1781 0.209 0.9874 0.992 0.09705 0.7911 0.8778 0.3095 ] Network output: [ -0.008601 0.04293 1.001 8.132e-05 -3.651e-05 0.9734 6.129e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09244 0.09055 0.1673 0.1978 0.9856 0.9915 0.09245 0.7209 0.8572 0.2433 ] Network output: [ 0.0001188 0.9994 -0.0004922 1.11e-05 -4.984e-06 1.001 8.367e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008606 Epoch 6894 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01328 0.9918 0.9868 4.492e-06 -2.017e-06 -0.005102 3.386e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003201 -0.002975 -0.009529 0.007278 0.9698 0.9742 0.00608 0.8439 0.8319 0.02026 ] Network output: [ 0.9997 -0.001637 0.001974 -3.995e-05 1.794e-05 4.597e-05 -3.011e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1836 -0.02887 -0.197 0.2011 0.9836 0.9933 0.2049 0.4597 0.8775 0.7226 ] Network output: [ -0.01185 1 1.01 2.181e-06 -9.789e-07 0.01335 1.643e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00533 0.0005071 0.004309 0.004443 0.9889 0.992 0.005427 0.8737 0.9013 0.0147 ] Network output: [ -0.0007101 -0.0005965 1.003 -0.0001392 6.251e-05 0.9983 -0.0001049 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1941 0.09378 0.3215 0.1598 0.9851 0.994 0.1947 0.4645 0.884 0.7173 ] Network output: [ 0.00837 -0.04146 0.9977 8.099e-05 -3.636e-05 1.027 6.103e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09702 0.08604 0.1785 0.2094 0.9873 0.992 0.09708 0.7912 0.8778 0.3098 ] Network output: [ -0.008662 0.0435 1.001 8.122e-05 -3.646e-05 0.9729 6.121e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09246 0.09057 0.1674 0.1979 0.9856 0.9915 0.09247 0.721 0.8572 0.2433 ] Network output: [ 0.000476 0.9995 -0.0009995 1.122e-05 -5.036e-06 1.001 8.454e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000877 Epoch 6895 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01321 0.9927 0.9868 4.391e-06 -1.971e-06 -0.005923 3.31e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003202 -0.002975 -0.009534 0.00726 0.9698 0.9742 0.006081 0.8439 0.8318 0.02025 ] Network output: [ 0.9993 0.004921 0.001643 -4.046e-05 1.816e-05 -0.005301 -3.049e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1837 -0.02878 -0.1974 0.2 0.9836 0.9933 0.2049 0.4598 0.8775 0.7225 ] Network output: [ -0.01187 1 1.01 2.137e-06 -9.593e-07 0.01305 1.61e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005332 0.0005061 0.00429 0.004408 0.9889 0.992 0.005429 0.8737 0.9013 0.01469 ] Network output: [ -0.001291 0.008315 1.003 -0.0001401 6.288e-05 0.9908 -0.0001055 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1942 0.0938 0.3209 0.1581 0.9851 0.994 0.1948 0.4646 0.884 0.7173 ] Network output: [ 0.008497 -0.03971 0.9974 8.085e-05 -3.629e-05 1.026 6.093e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.097 0.08601 0.1781 0.2089 0.9874 0.992 0.09706 0.791 0.8777 0.3095 ] Network output: [ -0.008594 0.04291 1.001 8.123e-05 -3.647e-05 0.9734 6.122e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09243 0.09054 0.1673 0.1978 0.9856 0.9915 0.09244 0.7208 0.8571 0.2433 ] Network output: [ 0.0001211 0.9994 -0.0004949 1.109e-05 -4.979e-06 1.001 8.357e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008597 Epoch 6896 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01328 0.9918 0.9868 4.481e-06 -2.012e-06 -0.005113 3.377e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003201 -0.002975 -0.009526 0.007276 0.9698 0.9742 0.00608 0.8439 0.8318 0.02026 ] Network output: [ 0.9997 -0.001592 0.00197 -3.992e-05 1.792e-05 7.714e-06 -3.008e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1836 -0.02888 -0.197 0.201 0.9836 0.9933 0.2049 0.4597 0.8775 0.7226 ] Network output: [ -0.01185 1 1.01 2.176e-06 -9.769e-07 0.01334 1.64e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005331 0.0005067 0.004309 0.004441 0.9889 0.992 0.005428 0.8737 0.9013 0.0147 ] Network output: [ -0.0007142 -0.0005309 1.003 -0.0001391 6.243e-05 0.9982 -0.0001048 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1942 0.09378 0.3216 0.1597 0.9851 0.994 0.1948 0.4645 0.884 0.7173 ] Network output: [ 0.008365 -0.04142 0.9976 8.088e-05 -3.631e-05 1.027 6.096e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09702 0.08604 0.1785 0.2094 0.9873 0.992 0.09708 0.7911 0.8777 0.3097 ] Network output: [ -0.008654 0.04347 1.001 8.112e-05 -3.642e-05 0.973 6.114e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09245 0.09056 0.1674 0.1978 0.9856 0.9915 0.09246 0.7209 0.8571 0.2433 ] Network output: [ 0.0004731 0.9995 -0.0009949 1.12e-05 -5.03e-06 1.001 8.444e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008758 Epoch 6897 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01321 0.9927 0.9868 4.382e-06 -1.967e-06 -0.005922 3.302e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003202 -0.002975 -0.00953 0.007258 0.9698 0.9742 0.006082 0.8439 0.8318 0.02025 ] Network output: [ 0.9993 0.004869 0.001644 -4.042e-05 1.815e-05 -0.00526 -3.046e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1837 -0.0288 -0.1974 0.2 0.9836 0.9933 0.205 0.4598 0.8775 0.7225 ] Network output: [ -0.01186 1 1.01 2.133e-06 -9.576e-07 0.01305 1.608e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005333 0.0005058 0.004291 0.004406 0.9889 0.992 0.00543 0.8737 0.9013 0.01469 ] Network output: [ -0.001286 0.008248 1.003 -0.0001399 6.279e-05 0.9909 -0.0001054 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1942 0.09379 0.3209 0.1581 0.9851 0.994 0.1948 0.4646 0.884 0.7173 ] Network output: [ 0.00849 -0.0397 0.9974 8.074e-05 -3.625e-05 1.026 6.085e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.097 0.08601 0.1781 0.2089 0.9874 0.992 0.09706 0.791 0.8777 0.3095 ] Network output: [ -0.008587 0.04288 1.001 8.113e-05 -3.642e-05 0.9734 6.114e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09242 0.09053 0.1673 0.1978 0.9856 0.9915 0.09243 0.7207 0.8571 0.2433 ] Network output: [ 0.0001234 0.9994 -0.0004977 1.108e-05 -4.973e-06 1.001 8.348e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008587 Epoch 6898 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01327 0.9918 0.9868 4.469e-06 -2.006e-06 -0.005124 3.368e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003201 -0.002975 -0.009522 0.007274 0.9698 0.9742 0.006081 0.8439 0.8318 0.02025 ] Network output: [ 0.9997 -0.001547 0.001966 -3.988e-05 1.79e-05 -3.02e-05 -3.006e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1837 -0.0289 -0.1969 0.201 0.9836 0.9933 0.2049 0.4596 0.8775 0.7226 ] Network output: [ -0.01185 1 1.01 2.172e-06 -9.749e-07 0.01333 1.637e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005332 0.0005063 0.004309 0.004438 0.9889 0.992 0.005429 0.8737 0.9013 0.01469 ] Network output: [ -0.0007183 -0.0004659 1.003 -0.0001389 6.235e-05 0.9982 -0.0001047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1942 0.09377 0.3216 0.1597 0.9851 0.994 0.1948 0.4644 0.8839 0.7172 ] Network output: [ 0.00836 -0.04139 0.9976 8.078e-05 -3.627e-05 1.027 6.088e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09702 0.08604 0.1785 0.2094 0.9873 0.992 0.09709 0.7911 0.8777 0.3097 ] Network output: [ -0.008646 0.04343 1.001 8.103e-05 -3.638e-05 0.973 6.107e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09243 0.09055 0.1674 0.1978 0.9856 0.9915 0.09244 0.7209 0.8571 0.2433 ] Network output: [ 0.0004703 0.9995 -0.0009903 1.119e-05 -5.023e-06 1.001 8.433e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008746 Epoch 6899 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0132 0.9927 0.9868 4.372e-06 -1.963e-06 -0.00592 3.295e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003202 -0.002975 -0.009527 0.007256 0.9698 0.9742 0.006082 0.8439 0.8318 0.02024 ] Network output: [ 0.9993 0.004818 0.001645 -4.038e-05 1.813e-05 -0.005219 -3.043e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1837 -0.02881 -0.1974 0.2 0.9836 0.9933 0.205 0.4598 0.8774 0.7225 ] Network output: [ -0.01186 1 1.01 2.129e-06 -9.559e-07 0.01304 1.605e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005334 0.0005054 0.004291 0.004404 0.9889 0.992 0.005431 0.8737 0.9013 0.01468 ] Network output: [ -0.001282 0.008181 1.003 -0.0001397 6.27e-05 0.991 -0.0001053 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1943 0.09378 0.321 0.158 0.9851 0.994 0.1949 0.4645 0.8839 0.7173 ] Network output: [ 0.008483 -0.03969 0.9973 8.064e-05 -3.62e-05 1.026 6.077e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.097 0.08601 0.1781 0.2089 0.9874 0.992 0.09706 0.7909 0.8777 0.3095 ] Network output: [ -0.00858 0.04286 1.001 8.104e-05 -3.638e-05 0.9734 6.107e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0924 0.09051 0.1673 0.1977 0.9856 0.9915 0.09241 0.7207 0.8571 0.2433 ] Network output: [ 0.0001257 0.9994 -0.0005004 1.106e-05 -4.967e-06 1.001 8.339e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008578 Epoch 6900 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01327 0.9918 0.9868 4.458e-06 -2.001e-06 -0.005135 3.36e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003201 -0.002976 -0.009519 0.007271 0.9698 0.9742 0.006081 0.8439 0.8318 0.02025 ] Network output: [ 0.9997 -0.001502 0.001962 -3.985e-05 1.789e-05 -6.776e-05 -3.003e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1837 -0.02891 -0.1969 0.201 0.9836 0.9933 0.2049 0.4596 0.8775 0.7226 ] Network output: [ -0.01185 1 1.01 2.167e-06 -9.729e-07 0.01331 1.633e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005333 0.0005059 0.004309 0.004436 0.9889 0.992 0.00543 0.8736 0.9013 0.01469 ] Network output: [ -0.0007224 -0.0004015 1.003 -0.0001387 6.227e-05 0.9981 -0.0001045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1942 0.09376 0.3216 0.1596 0.9851 0.994 0.1948 0.4644 0.8839 0.7172 ] Network output: [ 0.008355 -0.04136 0.9976 8.068e-05 -3.622e-05 1.027 6.08e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09703 0.08604 0.1785 0.2094 0.9873 0.992 0.09709 0.791 0.8777 0.3097 ] Network output: [ -0.008638 0.0434 1.001 8.094e-05 -3.634e-05 0.973 6.1e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09242 0.09053 0.1674 0.1978 0.9856 0.9915 0.09243 0.7208 0.8571 0.2433 ] Network output: [ 0.0004675 0.9995 -0.0009858 1.118e-05 -5.017e-06 1.001 8.422e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008735 Epoch 6901 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0132 0.9927 0.9868 4.362e-06 -1.958e-06 -0.005919 3.287e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003202 -0.002976 -0.009523 0.007254 0.9698 0.9742 0.006083 0.8439 0.8318 0.02024 ] Network output: [ 0.9993 0.004767 0.001646 -4.033e-05 1.811e-05 -0.005179 -3.04e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1837 -0.02883 -0.1973 0.1999 0.9836 0.9933 0.205 0.4597 0.8774 0.7225 ] Network output: [ -0.01186 1 1.01 2.125e-06 -9.542e-07 0.01303 1.602e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005335 0.000505 0.004292 0.004403 0.9889 0.992 0.005432 0.8736 0.9013 0.01468 ] Network output: [ -0.001278 0.008115 1.003 -0.0001395 6.262e-05 0.991 -0.0001051 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1943 0.09377 0.321 0.158 0.9851 0.994 0.1949 0.4645 0.8839 0.7173 ] Network output: [ 0.008476 -0.03969 0.9973 8.054e-05 -3.616e-05 1.026 6.07e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09701 0.08601 0.1781 0.2089 0.9874 0.992 0.09707 0.7909 0.8777 0.3095 ] Network output: [ -0.008573 0.04283 1.001 8.094e-05 -3.634e-05 0.9734 6.1e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09239 0.0905 0.1673 0.1977 0.9856 0.9915 0.0924 0.7206 0.8571 0.2433 ] Network output: [ 0.0001279 0.9994 -0.000503 1.105e-05 -4.962e-06 1.001 8.329e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008569 Epoch 6902 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01326 0.9918 0.9868 4.446e-06 -1.996e-06 -0.005146 3.351e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003202 -0.002976 -0.009516 0.007269 0.9698 0.9742 0.006082 0.8438 0.8318 0.02024 ] Network output: [ 0.9997 -0.001458 0.001959 -3.981e-05 1.787e-05 -0.000105 -3e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1837 -0.02892 -0.1969 0.2009 0.9836 0.9933 0.205 0.4596 0.8775 0.7226 ] Network output: [ -0.01185 1 1.01 2.163e-06 -9.709e-07 0.0133 1.63e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005334 0.0005056 0.00431 0.004434 0.9889 0.992 0.005431 0.8736 0.9013 0.01469 ] Network output: [ -0.0007264 -0.0003376 1.003 -0.0001385 6.219e-05 0.9981 -0.0001044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1942 0.09375 0.3217 0.1596 0.9851 0.994 0.1948 0.4644 0.8839 0.7172 ] Network output: [ 0.00835 -0.04132 0.9976 8.058e-05 -3.617e-05 1.027 6.072e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09703 0.08604 0.1785 0.2093 0.9873 0.992 0.09709 0.791 0.8777 0.3097 ] Network output: [ -0.00863 0.04336 1.001 8.084e-05 -3.629e-05 0.973 6.093e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09241 0.09052 0.1674 0.1978 0.9856 0.9915 0.09242 0.7207 0.857 0.2433 ] Network output: [ 0.0004647 0.9995 -0.0009813 1.116e-05 -5.01e-06 1.001 8.411e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008723 Epoch 6903 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01319 0.9927 0.9868 4.352e-06 -1.954e-06 -0.005918 3.28e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003203 -0.002976 -0.00952 0.007252 0.9698 0.9742 0.006083 0.8439 0.8318 0.02024 ] Network output: [ 0.9993 0.004716 0.001646 -4.029e-05 1.809e-05 -0.005139 -3.036e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1838 -0.02884 -0.1973 0.1999 0.9836 0.9933 0.205 0.4597 0.8774 0.7225 ] Network output: [ -0.01186 1 1.01 2.122e-06 -9.525e-07 0.01303 1.599e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005336 0.0005047 0.004292 0.004401 0.9889 0.992 0.005433 0.8736 0.9012 0.01468 ] Network output: [ -0.001273 0.00805 1.003 -0.0001393 6.253e-05 0.9911 -0.000105 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1943 0.09376 0.3211 0.158 0.9851 0.994 0.1949 0.4645 0.8839 0.7173 ] Network output: [ 0.008468 -0.03968 0.9973 8.044e-05 -3.611e-05 1.026 6.062e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09701 0.08601 0.1781 0.2089 0.9874 0.992 0.09707 0.7908 0.8776 0.3095 ] Network output: [ -0.008566 0.04281 1.001 8.085e-05 -3.63e-05 0.9734 6.093e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09238 0.09049 0.1673 0.1977 0.9856 0.9915 0.09239 0.7205 0.857 0.2433 ] Network output: [ 0.0001302 0.9994 -0.0005056 1.104e-05 -4.956e-06 1.001 8.32e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008559 Epoch 6904 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01326 0.9918 0.9868 4.435e-06 -1.991e-06 -0.005157 3.342e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003202 -0.002976 -0.009513 0.007266 0.9698 0.9742 0.006082 0.8438 0.8318 0.02024 ] Network output: [ 0.9997 -0.001414 0.001955 -3.978e-05 1.786e-05 -0.0001418 -2.998e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1837 -0.02894 -0.1968 0.2009 0.9836 0.9933 0.205 0.4595 0.8775 0.7226 ] Network output: [ -0.01184 1 1.01 2.158e-06 -9.689e-07 0.01329 1.626e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005335 0.0005052 0.00431 0.004432 0.9889 0.992 0.005432 0.8736 0.9013 0.01468 ] Network output: [ -0.0007304 -0.0002744 1.003 -0.0001383 6.211e-05 0.998 -0.0001043 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1942 0.09374 0.3217 0.1595 0.9851 0.994 0.1949 0.4643 0.8839 0.7172 ] Network output: [ 0.008345 -0.04129 0.9976 8.047e-05 -3.613e-05 1.027 6.065e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09703 0.08604 0.1785 0.2093 0.9873 0.992 0.09709 0.7909 0.8776 0.3097 ] Network output: [ -0.008622 0.04333 1.001 8.075e-05 -3.625e-05 0.973 6.086e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09239 0.09051 0.1674 0.1978 0.9856 0.9915 0.0924 0.7206 0.857 0.2433 ] Network output: [ 0.0004619 0.9995 -0.0009768 1.115e-05 -5.004e-06 1.001 8.4e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008711 Epoch 6905 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01319 0.9927 0.9868 4.342e-06 -1.949e-06 -0.005917 3.272e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003203 -0.002976 -0.009517 0.00725 0.9698 0.9742 0.006084 0.8438 0.8318 0.02023 ] Network output: [ 0.9993 0.004666 0.001647 -4.025e-05 1.807e-05 -0.005099 -3.033e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1838 -0.02886 -0.1972 0.1999 0.9836 0.9933 0.2051 0.4597 0.8774 0.7225 ] Network output: [ -0.01185 1 1.01 2.118e-06 -9.507e-07 0.01302 1.596e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005337 0.0005043 0.004293 0.004399 0.9889 0.992 0.005434 0.8736 0.9012 0.01467 ] Network output: [ -0.001269 0.007985 1.003 -0.0001391 6.244e-05 0.9911 -0.0001048 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1943 0.09376 0.3211 0.158 0.9851 0.994 0.1949 0.4644 0.8839 0.7173 ] Network output: [ 0.008461 -0.03967 0.9973 8.034e-05 -3.607e-05 1.026 6.054e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09701 0.08601 0.1781 0.2089 0.9874 0.992 0.09707 0.7907 0.8776 0.3095 ] Network output: [ -0.008559 0.04278 1.001 8.075e-05 -3.625e-05 0.9734 6.086e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09236 0.09048 0.1673 0.1977 0.9856 0.9915 0.09238 0.7205 0.857 0.2433 ] Network output: [ 0.0001324 0.9994 -0.0005082 1.103e-05 -4.95e-06 1.001 8.31e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000855 Epoch 6906 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01325 0.9918 0.9868 4.423e-06 -1.986e-06 -0.005167 3.334e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003202 -0.002977 -0.009509 0.007264 0.9698 0.9742 0.006083 0.8438 0.8318 0.02023 ] Network output: [ 0.9997 -0.00137 0.001951 -3.974e-05 1.784e-05 -0.0001783 -2.995e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1837 -0.02895 -0.1968 0.2009 0.9836 0.9933 0.205 0.4595 0.8774 0.7225 ] Network output: [ -0.01184 1 1.01 2.154e-06 -9.669e-07 0.01328 1.623e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005336 0.0005048 0.00431 0.00443 0.9889 0.992 0.005433 0.8736 0.9012 0.01468 ] Network output: [ -0.0007343 -0.0002117 1.003 -0.0001382 6.203e-05 0.998 -0.0001041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1943 0.09373 0.3217 0.1595 0.9851 0.994 0.1949 0.4643 0.8839 0.7172 ] Network output: [ 0.008339 -0.04126 0.9976 8.037e-05 -3.608e-05 1.027 6.057e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09704 0.08604 0.1785 0.2093 0.9873 0.992 0.0971 0.7908 0.8776 0.3097 ] Network output: [ -0.008614 0.0433 1.001 8.066e-05 -3.621e-05 0.973 6.078e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09238 0.09049 0.1674 0.1978 0.9856 0.9915 0.09239 0.7206 0.857 0.2433 ] Network output: [ 0.0004592 0.9995 -0.0009723 1.113e-05 -4.997e-06 1.001 8.389e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008699 Epoch 6907 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01318 0.9927 0.9868 4.332e-06 -1.945e-06 -0.005916 3.264e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003203 -0.002977 -0.009513 0.007247 0.9698 0.9742 0.006084 0.8438 0.8317 0.02023 ] Network output: [ 0.9993 0.004617 0.001648 -4.02e-05 1.805e-05 -0.00506 -3.03e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1838 -0.02887 -0.1972 0.1999 0.9836 0.9933 0.2051 0.4596 0.8774 0.7225 ] Network output: [ -0.01185 1 1.01 2.114e-06 -9.49e-07 0.01301 1.593e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005338 0.000504 0.004293 0.004398 0.9889 0.992 0.005435 0.8736 0.9012 0.01467 ] Network output: [ -0.001265 0.00792 1.003 -0.0001389 6.235e-05 0.9912 -0.0001047 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1943 0.09375 0.3212 0.1579 0.9851 0.994 0.1949 0.4644 0.8839 0.7173 ] Network output: [ 0.008454 -0.03966 0.9973 8.024e-05 -3.602e-05 1.026 6.047e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09702 0.08601 0.1782 0.2088 0.9874 0.992 0.09708 0.7907 0.8776 0.3094 ] Network output: [ -0.008552 0.04275 1.001 8.066e-05 -3.621e-05 0.9734 6.079e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09235 0.09046 0.1673 0.1977 0.9856 0.9915 0.09236 0.7204 0.857 0.2433 ] Network output: [ 0.0001346 0.9994 -0.0005108 1.101e-05 -4.945e-06 1.001 8.301e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008541 Epoch 6908 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01324 0.9919 0.9868 4.412e-06 -1.981e-06 -0.005178 3.325e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003202 -0.002977 -0.009506 0.007261 0.9698 0.9742 0.006083 0.8438 0.8318 0.02023 ] Network output: [ 0.9997 -0.001327 0.001947 -3.971e-05 1.783e-05 -0.0002145 -2.993e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1838 -0.02896 -0.1968 0.2009 0.9836 0.9933 0.205 0.4595 0.8774 0.7225 ] Network output: [ -0.01184 1 1.01 2.149e-06 -9.648e-07 0.01327 1.62e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005337 0.0005045 0.004311 0.004428 0.9889 0.992 0.005434 0.8736 0.9012 0.01468 ] Network output: [ -0.0007382 -0.0001496 1.003 -0.000138 6.195e-05 0.9979 -0.000104 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1943 0.09372 0.3218 0.1595 0.9851 0.994 0.1949 0.4643 0.8839 0.7172 ] Network output: [ 0.008334 -0.04122 0.9976 8.027e-05 -3.603e-05 1.027 6.049e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09704 0.08604 0.1785 0.2092 0.9873 0.992 0.0971 0.7908 0.8776 0.3097 ] Network output: [ -0.008605 0.04326 1.001 8.056e-05 -3.617e-05 0.9731 6.071e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09237 0.09048 0.1674 0.1978 0.9856 0.9915 0.09238 0.7205 0.8569 0.2433 ] Network output: [ 0.0004564 0.9995 -0.0009678 1.112e-05 -4.991e-06 1.001 8.378e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008687 Epoch 6909 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01318 0.9927 0.9868 4.322e-06 -1.94e-06 -0.005916 3.257e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003203 -0.002977 -0.00951 0.007245 0.9698 0.9742 0.006084 0.8438 0.8317 0.02022 ] Network output: [ 0.9993 0.004567 0.001648 -4.016e-05 1.803e-05 -0.005021 -3.027e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1838 -0.02889 -0.1972 0.1999 0.9836 0.9933 0.2051 0.4596 0.8774 0.7225 ] Network output: [ -0.01185 1 1.01 2.11e-06 -9.472e-07 0.01301 1.59e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005339 0.0005036 0.004294 0.004396 0.9889 0.992 0.005437 0.8736 0.9012 0.01467 ] Network output: [ -0.00126 0.007856 1.003 -0.0001387 6.227e-05 0.9912 -0.0001045 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1944 0.09374 0.3212 0.1579 0.9851 0.994 0.195 0.4644 0.8839 0.7173 ] Network output: [ 0.008447 -0.03965 0.9973 8.013e-05 -3.597e-05 1.026 6.039e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09702 0.08601 0.1782 0.2088 0.9874 0.992 0.09708 0.7906 0.8776 0.3094 ] Network output: [ -0.008545 0.04273 1.001 8.057e-05 -3.617e-05 0.9734 6.072e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09234 0.09045 0.1673 0.1977 0.9856 0.9915 0.09235 0.7203 0.8569 0.2433 ] Network output: [ 0.0001367 0.9994 -0.0005133 1.1e-05 -4.939e-06 1.001 8.292e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008532 Epoch 6910 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01324 0.9919 0.9869 4.401e-06 -1.976e-06 -0.005189 3.316e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003202 -0.002977 -0.009503 0.007259 0.9698 0.9742 0.006084 0.8438 0.8318 0.02022 ] Network output: [ 0.9997 -0.001285 0.001943 -3.967e-05 1.781e-05 -0.0002503 -2.99e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1838 -0.02898 -0.1967 0.2008 0.9836 0.9933 0.205 0.4594 0.8774 0.7225 ] Network output: [ -0.01184 1 1.01 2.145e-06 -9.628e-07 0.01326 1.616e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005338 0.0005041 0.004311 0.004426 0.9889 0.992 0.005435 0.8736 0.9012 0.01467 ] Network output: [ -0.000742 -8.817e-05 1.003 -0.0001378 6.187e-05 0.9979 -0.0001039 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1943 0.09372 0.3218 0.1594 0.9851 0.994 0.1949 0.4642 0.8839 0.7172 ] Network output: [ 0.008329 -0.04119 0.9976 8.016e-05 -3.599e-05 1.027 6.041e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09704 0.08604 0.1785 0.2092 0.9873 0.992 0.0971 0.7907 0.8776 0.3096 ] Network output: [ -0.008597 0.04323 1.001 8.047e-05 -3.612e-05 0.9731 6.064e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09235 0.09047 0.1673 0.1978 0.9856 0.9915 0.09237 0.7204 0.8569 0.2433 ] Network output: [ 0.0004537 0.9995 -0.0009634 1.11e-05 -4.985e-06 1.001 8.368e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008676 Epoch 6911 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01317 0.9927 0.9868 4.312e-06 -1.936e-06 -0.005915 3.249e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003203 -0.002977 -0.009507 0.007243 0.9698 0.9742 0.006085 0.8438 0.8317 0.02022 ] Network output: [ 0.9993 0.004518 0.001649 -4.012e-05 1.801e-05 -0.004983 -3.023e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1838 -0.0289 -0.1971 0.1999 0.9836 0.9933 0.2051 0.4596 0.8774 0.7225 ] Network output: [ -0.01185 1 1.01 2.106e-06 -9.455e-07 0.013 1.587e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00534 0.0005032 0.004295 0.004395 0.9889 0.992 0.005438 0.8736 0.9012 0.01466 ] Network output: [ -0.001256 0.007793 1.003 -0.0001385 6.218e-05 0.9913 -0.0001044 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1944 0.09373 0.3212 0.1579 0.9851 0.994 0.195 0.4643 0.8839 0.7173 ] Network output: [ 0.00844 -0.03965 0.9973 8.003e-05 -3.593e-05 1.026 6.031e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09702 0.08601 0.1782 0.2088 0.9874 0.992 0.09708 0.7906 0.8775 0.3094 ] Network output: [ -0.008538 0.0427 1.001 8.047e-05 -3.613e-05 0.9735 6.065e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09233 0.09044 0.1673 0.1977 0.9856 0.9915 0.09234 0.7202 0.8569 0.2433 ] Network output: [ 0.0001389 0.9994 -0.0005158 1.099e-05 -4.934e-06 1.001 8.282e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008522 Epoch 6912 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01323 0.9919 0.9869 4.389e-06 -1.97e-06 -0.005199 3.308e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003203 -0.002978 -0.0095 0.007257 0.9698 0.9742 0.006084 0.8438 0.8317 0.02022 ] Network output: [ 0.9997 -0.001243 0.001939 -3.964e-05 1.78e-05 -0.0002857 -2.987e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1838 -0.02899 -0.1967 0.2008 0.9836 0.9933 0.2051 0.4594 0.8774 0.7225 ] Network output: [ -0.01183 1 1.01 2.14e-06 -9.608e-07 0.01325 1.613e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005339 0.0005037 0.004311 0.004424 0.9889 0.992 0.005437 0.8735 0.9012 0.01467 ] Network output: [ -0.0007458 -2.729e-05 1.003 -0.0001376 6.179e-05 0.9978 -0.0001037 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1943 0.09371 0.3218 0.1594 0.9851 0.994 0.1949 0.4642 0.8839 0.7172 ] Network output: [ 0.008324 -0.04116 0.9976 8.006e-05 -3.594e-05 1.027 6.034e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09705 0.08604 0.1785 0.2092 0.9873 0.992 0.09711 0.7907 0.8775 0.3096 ] Network output: [ -0.008589 0.04319 1.001 8.037e-05 -3.608e-05 0.9731 6.057e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09234 0.09045 0.1673 0.1977 0.9856 0.9915 0.09235 0.7204 0.8569 0.2433 ] Network output: [ 0.000451 0.9995 -0.000959 1.109e-05 -4.978e-06 1.001 8.357e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008664 Epoch 6913 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01317 0.9927 0.9868 4.302e-06 -1.931e-06 -0.005914 3.242e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003203 -0.002978 -0.009503 0.007241 0.9698 0.9742 0.006085 0.8438 0.8317 0.02021 ] Network output: [ 0.9993 0.00447 0.00165 -4.007e-05 1.799e-05 -0.004944 -3.02e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1839 -0.02892 -0.1971 0.1999 0.9836 0.9933 0.2051 0.4595 0.8774 0.7224 ] Network output: [ -0.01184 1 1.01 2.102e-06 -9.437e-07 0.01299 1.584e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005341 0.0005029 0.004295 0.004393 0.9889 0.992 0.005439 0.8735 0.9012 0.01466 ] Network output: [ -0.001252 0.00773 1.003 -0.0001383 6.21e-05 0.9913 -0.0001042 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1944 0.09372 0.3213 0.1579 0.9851 0.994 0.195 0.4643 0.8839 0.7173 ] Network output: [ 0.008433 -0.03964 0.9973 7.993e-05 -3.588e-05 1.026 6.024e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09703 0.08601 0.1782 0.2088 0.9874 0.992 0.09709 0.7905 0.8775 0.3094 ] Network output: [ -0.008531 0.04268 1.001 8.038e-05 -3.608e-05 0.9735 6.057e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09231 0.09042 0.1672 0.1977 0.9856 0.9915 0.09233 0.7202 0.8569 0.2433 ] Network output: [ 0.000141 0.9994 -0.0005183 1.098e-05 -4.928e-06 1.001 8.273e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008513 Epoch 6914 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01323 0.9919 0.9869 4.378e-06 -1.965e-06 -0.00521 3.299e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003203 -0.002978 -0.009496 0.007254 0.9698 0.9742 0.006085 0.8438 0.8317 0.02021 ] Network output: [ 0.9997 -0.001201 0.001935 -3.96e-05 1.778e-05 -0.0003208 -2.985e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1838 -0.029 -0.1967 0.2008 0.9836 0.9933 0.2051 0.4594 0.8774 0.7225 ] Network output: [ -0.01183 1 1.01 2.136e-06 -9.587e-07 0.01324 1.609e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00534 0.0005034 0.004311 0.004422 0.9889 0.992 0.005438 0.8735 0.9012 0.01467 ] Network output: [ -0.0007496 3.3e-05 1.003 -0.0001375 6.171e-05 0.9978 -0.0001036 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1943 0.0937 0.3219 0.1593 0.9851 0.994 0.195 0.4642 0.8839 0.7172 ] Network output: [ 0.008319 -0.04113 0.9976 7.996e-05 -3.59e-05 1.027 6.026e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09705 0.08604 0.1785 0.2092 0.9873 0.992 0.09711 0.7906 0.8775 0.3096 ] Network output: [ -0.008581 0.04316 1.001 8.028e-05 -3.604e-05 0.9731 6.05e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09233 0.09044 0.1673 0.1977 0.9856 0.9915 0.09234 0.7203 0.8569 0.2433 ] Network output: [ 0.0004484 0.9995 -0.0009546 1.107e-05 -4.972e-06 1.001 8.346e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008653 Epoch 6915 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01316 0.9928 0.9869 4.292e-06 -1.927e-06 -0.005913 3.234e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003204 -0.002978 -0.0095 0.007239 0.9698 0.9742 0.006086 0.8438 0.8317 0.02021 ] Network output: [ 0.9993 0.004422 0.00165 -4.003e-05 1.797e-05 -0.004906 -3.017e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1839 -0.02893 -0.197 0.1998 0.9836 0.9933 0.2052 0.4595 0.8774 0.7224 ] Network output: [ -0.01184 1 1.01 2.098e-06 -9.419e-07 0.01298 1.581e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005342 0.0005025 0.004296 0.004391 0.9889 0.992 0.00544 0.8735 0.9012 0.01466 ] Network output: [ -0.001248 0.007668 1.003 -0.0001381 6.201e-05 0.9914 -0.0001041 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1944 0.09371 0.3213 0.1578 0.9851 0.994 0.195 0.4643 0.8839 0.7172 ] Network output: [ 0.008425 -0.03963 0.9973 7.983e-05 -3.584e-05 1.026 6.016e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09703 0.08601 0.1782 0.2088 0.9874 0.992 0.09709 0.7905 0.8775 0.3094 ] Network output: [ -0.008524 0.04265 1.001 8.028e-05 -3.604e-05 0.9735 6.05e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0923 0.09041 0.1672 0.1977 0.9856 0.9915 0.09231 0.7201 0.8568 0.2433 ] Network output: [ 0.0001431 0.9994 -0.0005207 1.096e-05 -4.922e-06 1.001 8.263e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008504 Epoch 6916 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01322 0.9919 0.9869 4.366e-06 -1.96e-06 -0.00522 3.291e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003203 -0.002978 -0.009493 0.007252 0.9698 0.9742 0.006085 0.8437 0.8317 0.02021 ] Network output: [ 0.9997 -0.001159 0.001932 -3.957e-05 1.776e-05 -0.0003555 -2.982e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1838 -0.02902 -0.1966 0.2007 0.9836 0.9933 0.2051 0.4593 0.8774 0.7225 ] Network output: [ -0.01183 1 1.01 2.131e-06 -9.567e-07 0.01323 1.606e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005341 0.000503 0.004312 0.00442 0.9889 0.992 0.005439 0.8735 0.9012 0.01466 ] Network output: [ -0.0007533 9.269e-05 1.003 -0.0001373 6.163e-05 0.9977 -0.0001035 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1944 0.09369 0.3219 0.1593 0.9851 0.994 0.195 0.4642 0.8838 0.7172 ] Network output: [ 0.008314 -0.04109 0.9975 7.985e-05 -3.585e-05 1.027 6.018e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09705 0.08604 0.1785 0.2091 0.9873 0.992 0.09711 0.7905 0.8775 0.3096 ] Network output: [ -0.008573 0.04313 1.001 8.019e-05 -3.6e-05 0.9731 6.043e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09231 0.09043 0.1673 0.1977 0.9856 0.9915 0.09233 0.7202 0.8568 0.2433 ] Network output: [ 0.0004457 0.9995 -0.0009503 1.106e-05 -4.965e-06 1.001 8.335e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008641 Epoch 6917 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01316 0.9928 0.9869 4.282e-06 -1.922e-06 -0.005913 3.227e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003204 -0.002978 -0.009497 0.007236 0.9698 0.9742 0.006086 0.8438 0.8317 0.0202 ] Network output: [ 0.9993 0.004374 0.001651 -3.999e-05 1.795e-05 -0.004869 -3.014e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1839 -0.02895 -0.197 0.1998 0.9836 0.9933 0.2052 0.4595 0.8773 0.7224 ] Network output: [ -0.01184 1 1.01 2.094e-06 -9.401e-07 0.01298 1.578e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005343 0.0005022 0.004296 0.00439 0.9889 0.992 0.005441 0.8735 0.9012 0.01465 ] Network output: [ -0.001244 0.007607 1.003 -0.0001379 6.192e-05 0.9914 -0.0001039 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1944 0.0937 0.3214 0.1578 0.9851 0.994 0.195 0.4642 0.8838 0.7172 ] Network output: [ 0.008418 -0.03962 0.9973 7.973e-05 -3.579e-05 1.026 6.008e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09703 0.08601 0.1782 0.2087 0.9874 0.992 0.09709 0.7904 0.8775 0.3094 ] Network output: [ -0.008517 0.04262 1.001 8.019e-05 -3.6e-05 0.9735 6.043e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09229 0.0904 0.1672 0.1976 0.9856 0.9915 0.0923 0.72 0.8568 0.2433 ] Network output: [ 0.0001452 0.9994 -0.0005231 1.095e-05 -4.917e-06 1.001 8.254e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008495 Epoch 6918 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01321 0.9919 0.9869 4.355e-06 -1.955e-06 -0.005231 3.282e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003203 -0.002979 -0.00949 0.007249 0.9698 0.9742 0.006086 0.8437 0.8317 0.0202 ] Network output: [ 0.9997 -0.001119 0.001928 -3.953e-05 1.775e-05 -0.0003899 -2.979e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1839 -0.02903 -0.1966 0.2007 0.9836 0.9933 0.2051 0.4593 0.8774 0.7225 ] Network output: [ -0.01183 1 1.01 2.126e-06 -9.546e-07 0.01322 1.603e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005342 0.0005026 0.004312 0.004417 0.9889 0.992 0.00544 0.8735 0.9012 0.01466 ] Network output: [ -0.0007569 0.0001518 1.003 -0.0001371 6.155e-05 0.9977 -0.0001033 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1944 0.09368 0.3219 0.1592 0.9851 0.994 0.195 0.4641 0.8838 0.7172 ] Network output: [ 0.008308 -0.04106 0.9975 7.975e-05 -3.58e-05 1.027 6.01e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09706 0.08604 0.1785 0.2091 0.9873 0.992 0.09712 0.7905 0.8775 0.3096 ] Network output: [ -0.008565 0.04309 1.001 8.009e-05 -3.596e-05 0.9731 6.036e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0923 0.09041 0.1673 0.1977 0.9856 0.9915 0.09231 0.7201 0.8568 0.2433 ] Network output: [ 0.0004431 0.9995 -0.000946 1.105e-05 -4.959e-06 1.001 8.324e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000863 Epoch 6919 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01315 0.9928 0.9869 4.271e-06 -1.918e-06 -0.005912 3.219e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003204 -0.002978 -0.009493 0.007234 0.9698 0.9742 0.006087 0.8437 0.8317 0.0202 ] Network output: [ 0.9993 0.004327 0.001651 -3.995e-05 1.793e-05 -0.004831 -3.01e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1839 -0.02896 -0.1969 0.1998 0.9836 0.9933 0.2052 0.4594 0.8773 0.7224 ] Network output: [ -0.01184 1 1.01 2.09e-06 -9.383e-07 0.01297 1.575e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005344 0.0005019 0.004297 0.004388 0.9889 0.992 0.005442 0.8735 0.9011 0.01465 ] Network output: [ -0.00124 0.007546 1.003 -0.0001377 6.184e-05 0.9915 -0.0001038 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1945 0.0937 0.3214 0.1578 0.9851 0.994 0.1951 0.4642 0.8838 0.7172 ] Network output: [ 0.008411 -0.03961 0.9973 7.963e-05 -3.575e-05 1.026 6.001e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09704 0.08602 0.1782 0.2087 0.9874 0.992 0.0971 0.7903 0.8775 0.3094 ] Network output: [ -0.00851 0.0426 1.001 8.009e-05 -3.596e-05 0.9735 6.036e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09228 0.09039 0.1672 0.1976 0.9856 0.9915 0.09229 0.72 0.8568 0.2433 ] Network output: [ 0.0001472 0.9994 -0.0005254 1.094e-05 -4.911e-06 1.001 8.244e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008486 Epoch 6920 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01321 0.9919 0.9869 4.343e-06 -1.95e-06 -0.005241 3.273e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003203 -0.002979 -0.009486 0.007247 0.9698 0.9742 0.006086 0.8437 0.8317 0.0202 ] Network output: [ 0.9997 -0.001078 0.001924 -3.95e-05 1.773e-05 -0.0004239 -2.977e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1839 -0.02904 -0.1966 0.2007 0.9836 0.9933 0.2052 0.4593 0.8774 0.7225 ] Network output: [ -0.01183 1 1.01 2.122e-06 -9.526e-07 0.0132 1.599e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005343 0.0005023 0.004312 0.004415 0.9889 0.992 0.005441 0.8735 0.9012 0.01466 ] Network output: [ -0.0007605 0.0002103 1.003 -0.0001369 6.147e-05 0.9976 -0.0001032 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1944 0.09368 0.322 0.1592 0.9851 0.994 0.195 0.4641 0.8838 0.7172 ] Network output: [ 0.008303 -0.04103 0.9975 7.965e-05 -3.576e-05 1.027 6.003e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09706 0.08604 0.1785 0.2091 0.9873 0.992 0.09712 0.7904 0.8774 0.3096 ] Network output: [ -0.008557 0.04306 1.001 8e-05 -3.591e-05 0.9732 6.029e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09229 0.0904 0.1673 0.1977 0.9856 0.9915 0.0923 0.7201 0.8568 0.2433 ] Network output: [ 0.0004405 0.9995 -0.0009417 1.103e-05 -4.952e-06 1.001 8.314e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008618 Epoch 6921 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01315 0.9928 0.9869 4.261e-06 -1.913e-06 -0.005912 3.212e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003204 -0.002979 -0.00949 0.007232 0.9698 0.9742 0.006087 0.8437 0.8317 0.02019 ] Network output: [ 0.9993 0.004281 0.001652 -3.99e-05 1.791e-05 -0.004794 -3.007e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1839 -0.02897 -0.1969 0.1998 0.9836 0.9933 0.2052 0.4594 0.8773 0.7224 ] Network output: [ -0.01183 1 1.01 2.086e-06 -9.365e-07 0.01296 1.572e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005345 0.0005015 0.004297 0.004387 0.9889 0.992 0.005443 0.8735 0.9011 0.01465 ] Network output: [ -0.001236 0.007485 1.003 -0.0001375 6.175e-05 0.9915 -0.0001037 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1945 0.09369 0.3215 0.1578 0.9851 0.994 0.1951 0.4642 0.8838 0.7172 ] Network output: [ 0.008404 -0.0396 0.9973 7.952e-05 -3.57e-05 1.026 5.993e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09704 0.08602 0.1782 0.2087 0.9874 0.992 0.0971 0.7903 0.8774 0.3094 ] Network output: [ -0.008503 0.04257 1.001 8e-05 -3.591e-05 0.9735 6.029e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09226 0.09037 0.1672 0.1976 0.9856 0.9915 0.09228 0.7199 0.8568 0.2433 ] Network output: [ 0.0001493 0.9994 -0.0005278 1.093e-05 -4.905e-06 1.001 8.235e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008476 Epoch 6922 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0132 0.992 0.9869 4.332e-06 -1.945e-06 -0.005252 3.265e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003204 -0.002979 -0.009483 0.007245 0.9698 0.9742 0.006087 0.8437 0.8317 0.0202 ] Network output: [ 0.9997 -0.001038 0.00192 -3.946e-05 1.772e-05 -0.0004575 -2.974e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1839 -0.02906 -0.1965 0.2007 0.9836 0.9933 0.2052 0.4592 0.8773 0.7225 ] Network output: [ -0.01182 1 1.01 2.117e-06 -9.505e-07 0.01319 1.596e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005344 0.0005019 0.004313 0.004413 0.9889 0.992 0.005442 0.8734 0.9012 0.01465 ] Network output: [ -0.0007641 0.0002682 1.003 -0.0001367 6.138e-05 0.9976 -0.000103 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1944 0.09367 0.322 0.1591 0.9851 0.994 0.195 0.4641 0.8838 0.7171 ] Network output: [ 0.008298 -0.041 0.9975 7.955e-05 -3.571e-05 1.027 5.995e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09706 0.08604 0.1785 0.2091 0.9873 0.992 0.09712 0.7904 0.8774 0.3096 ] Network output: [ -0.008549 0.04302 1.001 7.99e-05 -3.587e-05 0.9732 6.022e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09228 0.09039 0.1673 0.1977 0.9856 0.9915 0.09229 0.72 0.8567 0.2433 ] Network output: [ 0.0004379 0.9995 -0.0009374 1.102e-05 -4.946e-06 1.001 8.303e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008607 Epoch 6923 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01314 0.9928 0.9869 4.251e-06 -1.909e-06 -0.005911 3.204e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003204 -0.002979 -0.009487 0.00723 0.9698 0.9742 0.006088 0.8437 0.8316 0.02019 ] Network output: [ 0.9994 0.004234 0.001652 -3.986e-05 1.789e-05 -0.004758 -3.004e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1839 -0.02899 -0.1969 0.1998 0.9836 0.9933 0.2052 0.4594 0.8773 0.7224 ] Network output: [ -0.01183 1 1.01 2.082e-06 -9.347e-07 0.01296 1.569e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005346 0.0005012 0.004298 0.004385 0.9889 0.992 0.005444 0.8734 0.9011 0.01464 ] Network output: [ -0.001232 0.007425 1.003 -0.0001374 6.166e-05 0.9916 -0.0001035 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1945 0.09368 0.3215 0.1577 0.9851 0.994 0.1951 0.4641 0.8838 0.7172 ] Network output: [ 0.008397 -0.03959 0.9973 7.942e-05 -3.566e-05 1.026 5.986e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09704 0.08602 0.1782 0.2087 0.9874 0.992 0.09711 0.7902 0.8774 0.3094 ] Network output: [ -0.008496 0.04254 1.001 7.99e-05 -3.587e-05 0.9735 6.022e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09225 0.09036 0.1672 0.1976 0.9856 0.9915 0.09226 0.7198 0.8567 0.2433 ] Network output: [ 0.0001513 0.9994 -0.00053 1.091e-05 -4.9e-06 1.001 8.225e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008467 Epoch 6924 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0132 0.992 0.9869 4.321e-06 -1.94e-06 -0.005262 3.256e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003204 -0.002979 -0.00948 0.007242 0.9698 0.9742 0.006087 0.8437 0.8317 0.02019 ] Network output: [ 0.9997 -0.0009983 0.001916 -3.943e-05 1.77e-05 -0.0004908 -2.971e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1839 -0.02907 -0.1965 0.2006 0.9836 0.9933 0.2052 0.4592 0.8773 0.7225 ] Network output: [ -0.01182 1 1.01 2.113e-06 -9.484e-07 0.01318 1.592e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005345 0.0005016 0.004313 0.004411 0.9889 0.992 0.005443 0.8734 0.9011 0.01465 ] Network output: [ -0.0007676 0.0003254 1.003 -0.0001366 6.13e-05 0.9975 -0.0001029 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1945 0.09366 0.322 0.1591 0.9851 0.994 0.1951 0.464 0.8838 0.7171 ] Network output: [ 0.008293 -0.04096 0.9975 7.944e-05 -3.566e-05 1.027 5.987e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09707 0.08604 0.1785 0.209 0.9873 0.992 0.09713 0.7903 0.8774 0.3096 ] Network output: [ -0.008541 0.04299 1.001 7.981e-05 -3.583e-05 0.9732 6.015e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09226 0.09037 0.1673 0.1977 0.9856 0.9915 0.09228 0.7199 0.8567 0.2433 ] Network output: [ 0.0004353 0.9995 -0.0009332 1.1e-05 -4.94e-06 1.001 8.292e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008595 Epoch 6925 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01314 0.9928 0.9869 4.241e-06 -1.904e-06 -0.005911 3.196e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003205 -0.002979 -0.009483 0.007228 0.9698 0.9742 0.006088 0.8437 0.8316 0.02019 ] Network output: [ 0.9994 0.004188 0.001653 -3.982e-05 1.787e-05 -0.004721 -3.001e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.184 -0.029 -0.1968 0.1998 0.9836 0.9933 0.2053 0.4593 0.8773 0.7224 ] Network output: [ -0.01183 1 1.01 2.078e-06 -9.329e-07 0.01295 1.566e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005347 0.0005008 0.004298 0.004383 0.9889 0.992 0.005445 0.8734 0.9011 0.01464 ] Network output: [ -0.001228 0.007366 1.003 -0.0001372 6.158e-05 0.9916 -0.0001034 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1945 0.09367 0.3215 0.1577 0.9851 0.994 0.1951 0.4641 0.8838 0.7172 ] Network output: [ 0.00839 -0.03958 0.9973 7.932e-05 -3.561e-05 1.026 5.978e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09705 0.08602 0.1782 0.2087 0.9874 0.992 0.09711 0.7902 0.8774 0.3094 ] Network output: [ -0.008489 0.04252 1.001 7.981e-05 -3.583e-05 0.9735 6.014e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09224 0.09035 0.1672 0.1976 0.9856 0.9915 0.09225 0.7198 0.8567 0.2433 ] Network output: [ 0.0001533 0.9994 -0.0005323 1.09e-05 -4.894e-06 1.001 8.216e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008458 Epoch 6926 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01319 0.992 0.9869 4.309e-06 -1.935e-06 -0.005272 3.248e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003204 -0.00298 -0.009477 0.00724 0.9698 0.9742 0.006087 0.8437 0.8317 0.02019 ] Network output: [ 0.9997 -0.000959 0.001913 -3.939e-05 1.768e-05 -0.0005237 -2.969e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1839 -0.02908 -0.1965 0.2006 0.9836 0.9933 0.2052 0.4592 0.8773 0.7225 ] Network output: [ -0.01182 1 1.01 2.108e-06 -9.463e-07 0.01317 1.589e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005346 0.0005012 0.004313 0.004409 0.9889 0.992 0.005444 0.8734 0.9011 0.01465 ] Network output: [ -0.0007711 0.0003821 1.003 -0.0001364 6.122e-05 0.9975 -0.0001028 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1945 0.09365 0.3221 0.159 0.9851 0.994 0.1951 0.464 0.8838 0.7171 ] Network output: [ 0.008288 -0.04093 0.9975 7.934e-05 -3.562e-05 1.027 5.979e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09707 0.08604 0.1785 0.209 0.9873 0.992 0.09713 0.7902 0.8774 0.3095 ] Network output: [ -0.008533 0.04296 1.001 7.972e-05 -3.579e-05 0.9732 6.008e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09225 0.09036 0.1673 0.1977 0.9856 0.9915 0.09226 0.7199 0.8567 0.2433 ] Network output: [ 0.0004328 0.9995 -0.000929 1.099e-05 -4.933e-06 1.001 8.281e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008584 Epoch 6927 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01313 0.9928 0.9869 4.231e-06 -1.9e-06 -0.00591 3.189e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003205 -0.00298 -0.00948 0.007226 0.9698 0.9742 0.006089 0.8437 0.8316 0.02018 ] Network output: [ 0.9994 0.004143 0.001653 -3.977e-05 1.786e-05 -0.004685 -2.997e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.184 -0.02902 -0.1968 0.1998 0.9836 0.9933 0.2053 0.4593 0.8773 0.7224 ] Network output: [ -0.01183 1 1.01 2.074e-06 -9.311e-07 0.01294 1.563e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005348 0.0005005 0.004299 0.004382 0.9889 0.992 0.005446 0.8734 0.9011 0.01464 ] Network output: [ -0.001224 0.007307 1.003 -0.000137 6.149e-05 0.9917 -0.0001032 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1945 0.09366 0.3216 0.1577 0.9851 0.994 0.1951 0.4641 0.8838 0.7172 ] Network output: [ 0.008383 -0.03957 0.9973 7.922e-05 -3.556e-05 1.026 5.97e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09705 0.08602 0.1782 0.2086 0.9874 0.992 0.09711 0.7901 0.8774 0.3093 ] Network output: [ -0.008482 0.04249 1.001 7.971e-05 -3.579e-05 0.9735 6.007e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09223 0.09034 0.1672 0.1976 0.9856 0.9915 0.09224 0.7197 0.8567 0.2433 ] Network output: [ 0.0001553 0.9994 -0.0005345 1.089e-05 -4.888e-06 1.001 8.206e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008449 Epoch 6928 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01319 0.992 0.9869 4.298e-06 -1.929e-06 -0.005282 3.239e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003204 -0.00298 -0.009473 0.007237 0.9698 0.9742 0.006088 0.8437 0.8316 0.02018 ] Network output: [ 0.9997 -0.0009202 0.001909 -3.936e-05 1.767e-05 -0.0005562 -2.966e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1839 -0.0291 -0.1964 0.2006 0.9836 0.9933 0.2052 0.4592 0.8773 0.7224 ] Network output: [ -0.01182 1 1.01 2.103e-06 -9.443e-07 0.01316 1.585e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005348 0.0005009 0.004314 0.004407 0.9889 0.992 0.005445 0.8734 0.9011 0.01464 ] Network output: [ -0.0007746 0.0004382 1.003 -0.0001362 6.114e-05 0.9974 -0.0001026 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1945 0.09364 0.3221 0.159 0.9851 0.994 0.1951 0.464 0.8838 0.7171 ] Network output: [ 0.008282 -0.0409 0.9975 7.924e-05 -3.557e-05 1.027 5.972e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09707 0.08604 0.1785 0.209 0.9873 0.992 0.09713 0.7902 0.8774 0.3095 ] Network output: [ -0.008525 0.04292 1.001 7.962e-05 -3.575e-05 0.9732 6.001e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09224 0.09035 0.1672 0.1976 0.9856 0.9915 0.09225 0.7198 0.8567 0.2433 ] Network output: [ 0.0004303 0.9995 -0.0009248 1.097e-05 -4.927e-06 1.001 8.271e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008573 Epoch 6929 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01313 0.9928 0.9869 4.221e-06 -1.895e-06 -0.00591 3.181e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003205 -0.00298 -0.009477 0.007223 0.9698 0.9742 0.006089 0.8437 0.8316 0.02018 ] Network output: [ 0.9994 0.004098 0.001654 -3.973e-05 1.784e-05 -0.00465 -2.994e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.184 -0.02903 -0.1967 0.1997 0.9836 0.9933 0.2053 0.4593 0.8773 0.7224 ] Network output: [ -0.01182 1 1.01 2.07e-06 -9.293e-07 0.01294 1.56e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005349 0.0005001 0.0043 0.00438 0.9889 0.992 0.005447 0.8734 0.9011 0.01463 ] Network output: [ -0.00122 0.007249 1.003 -0.0001368 6.14e-05 0.9917 -0.0001031 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1946 0.09366 0.3216 0.1577 0.9851 0.994 0.1952 0.464 0.8838 0.7172 ] Network output: [ 0.008376 -0.03956 0.9973 7.912e-05 -3.552e-05 1.026 5.963e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09706 0.08602 0.1782 0.2086 0.9874 0.992 0.09712 0.7901 0.8773 0.3093 ] Network output: [ -0.008475 0.04246 1.001 7.962e-05 -3.574e-05 0.9735 6e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09221 0.09032 0.1672 0.1976 0.9856 0.9914 0.09223 0.7196 0.8566 0.2433 ] Network output: [ 0.0001572 0.9994 -0.0005367 1.088e-05 -4.883e-06 1.001 8.197e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000844 Epoch 6930 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01318 0.992 0.9869 4.287e-06 -1.924e-06 -0.005293 3.23e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003204 -0.00298 -0.00947 0.007235 0.9698 0.9742 0.006088 0.8436 0.8316 0.02018 ] Network output: [ 0.9997 -0.0008818 0.001905 -3.932e-05 1.765e-05 -0.0005884 -2.963e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.184 -0.02911 -0.1964 0.2005 0.9836 0.9933 0.2053 0.4591 0.8773 0.7224 ] Network output: [ -0.01181 1 1.01 2.099e-06 -9.422e-07 0.01315 1.582e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005349 0.0005005 0.004314 0.004405 0.9889 0.992 0.005446 0.8734 0.9011 0.01464 ] Network output: [ -0.000778 0.0004937 1.003 -0.000136 6.106e-05 0.9974 -0.0001025 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1945 0.09364 0.3221 0.1589 0.9851 0.994 0.1951 0.4639 0.8838 0.7171 ] Network output: [ 0.008277 -0.04087 0.9975 7.913e-05 -3.553e-05 1.027 5.964e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09708 0.08604 0.1785 0.209 0.9873 0.992 0.09714 0.7901 0.8773 0.3095 ] Network output: [ -0.008517 0.04289 1.001 7.953e-05 -3.57e-05 0.9732 5.993e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09223 0.09034 0.1672 0.1976 0.9856 0.9915 0.09224 0.7197 0.8566 0.2433 ] Network output: [ 0.0004278 0.9995 -0.0009207 1.096e-05 -4.92e-06 1.001 8.26e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008561 Epoch 6931 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01312 0.9928 0.9869 4.211e-06 -1.891e-06 -0.00591 3.174e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003205 -0.00298 -0.009473 0.007221 0.9698 0.9742 0.00609 0.8436 0.8316 0.02017 ] Network output: [ 0.9994 0.004054 0.001654 -3.969e-05 1.782e-05 -0.004614 -2.991e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.184 -0.02905 -0.1967 0.1997 0.9836 0.9933 0.2053 0.4592 0.8773 0.7224 ] Network output: [ -0.01182 1 1.01 2.066e-06 -9.274e-07 0.01293 1.557e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00535 0.0004998 0.0043 0.004379 0.9889 0.992 0.005448 0.8734 0.9011 0.01463 ] Network output: [ -0.001216 0.007192 1.003 -0.0001366 6.132e-05 0.9918 -0.0001029 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1946 0.09365 0.3217 0.1576 0.9851 0.994 0.1952 0.464 0.8838 0.7172 ] Network output: [ 0.008369 -0.03955 0.9973 7.902e-05 -3.547e-05 1.026 5.955e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09706 0.08602 0.1782 0.2086 0.9873 0.992 0.09712 0.79 0.8773 0.3093 ] Network output: [ -0.008468 0.04244 1.001 7.952e-05 -3.57e-05 0.9736 5.993e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0922 0.09031 0.1672 0.1976 0.9856 0.9914 0.09221 0.7195 0.8566 0.2433 ] Network output: [ 0.0001591 0.9994 -0.0005389 1.086e-05 -4.877e-06 1.001 8.187e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008431 Epoch 6932 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01317 0.992 0.9869 4.275e-06 -1.919e-06 -0.005303 3.222e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003205 -0.002981 -0.009467 0.007233 0.9698 0.9742 0.006089 0.8436 0.8316 0.02017 ] Network output: [ 0.9997 -0.0008438 0.001902 -3.928e-05 1.764e-05 -0.0006202 -2.961e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.184 -0.02912 -0.1963 0.2005 0.9836 0.9933 0.2053 0.4591 0.8773 0.7224 ] Network output: [ -0.01181 1 1.01 2.094e-06 -9.401e-07 0.01314 1.578e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00535 0.0005002 0.004314 0.004403 0.9889 0.992 0.005447 0.8733 0.9011 0.01463 ] Network output: [ -0.0007813 0.0005486 1.003 -0.0001358 6.098e-05 0.9973 -0.0001024 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1945 0.09363 0.3222 0.1589 0.9851 0.994 0.1952 0.4639 0.8837 0.7171 ] Network output: [ 0.008272 -0.04084 0.9975 7.903e-05 -3.548e-05 1.027 5.956e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09708 0.08604 0.1785 0.2089 0.9873 0.992 0.09714 0.7901 0.8773 0.3095 ] Network output: [ -0.008509 0.04285 1.001 7.943e-05 -3.566e-05 0.9733 5.986e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09221 0.09032 0.1672 0.1976 0.9856 0.9915 0.09223 0.7196 0.8566 0.2433 ] Network output: [ 0.0004253 0.9995 -0.0009166 1.095e-05 -4.914e-06 1.001 8.249e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000855 Epoch 6933 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01312 0.9928 0.9869 4.201e-06 -1.886e-06 -0.00591 3.166e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003205 -0.002981 -0.00947 0.007219 0.9698 0.9742 0.00609 0.8436 0.8316 0.02017 ] Network output: [ 0.9994 0.004009 0.001654 -3.964e-05 1.78e-05 -0.004579 -2.988e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.184 -0.02906 -0.1967 0.1997 0.9836 0.9933 0.2054 0.4592 0.8772 0.7224 ] Network output: [ -0.01182 1 1.01 2.062e-06 -9.256e-07 0.01292 1.554e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005351 0.0004995 0.004301 0.004377 0.9889 0.992 0.005449 0.8733 0.9011 0.01463 ] Network output: [ -0.001212 0.007135 1.003 -0.0001364 6.123e-05 0.9918 -0.0001028 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1946 0.09364 0.3217 0.1576 0.9851 0.994 0.1952 0.4639 0.8837 0.7172 ] Network output: [ 0.008362 -0.03954 0.9973 7.891e-05 -3.543e-05 1.026 5.947e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09706 0.08602 0.1782 0.2086 0.9873 0.992 0.09713 0.7899 0.8773 0.3093 ] Network output: [ -0.008461 0.04241 1.001 7.943e-05 -3.566e-05 0.9736 5.986e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09219 0.0903 0.1672 0.1976 0.9856 0.9914 0.0922 0.7195 0.8566 0.2433 ] Network output: [ 0.000161 0.9994 -0.000541 1.085e-05 -4.871e-06 1.001 8.178e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008422 Epoch 6934 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01317 0.992 0.9869 4.264e-06 -1.914e-06 -0.005313 3.213e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003205 -0.002981 -0.009464 0.00723 0.9698 0.9742 0.006089 0.8436 0.8316 0.02017 ] Network output: [ 0.9997 -0.0008062 0.001898 -3.925e-05 1.762e-05 -0.0006517 -2.958e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.184 -0.02914 -0.1963 0.2005 0.9836 0.9933 0.2053 0.4591 0.8773 0.7224 ] Network output: [ -0.01181 1 1.01 2.089e-06 -9.38e-07 0.01313 1.575e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005351 0.0004999 0.004315 0.004401 0.9889 0.992 0.005448 0.8733 0.9011 0.01463 ] Network output: [ -0.0007846 0.0006028 1.003 -0.0001357 6.09e-05 0.9973 -0.0001022 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1946 0.09362 0.3222 0.1588 0.9851 0.994 0.1952 0.4639 0.8837 0.7171 ] Network output: [ 0.008267 -0.0408 0.9975 7.893e-05 -3.543e-05 1.027 5.948e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09708 0.08604 0.1785 0.2089 0.9873 0.992 0.09715 0.79 0.8773 0.3095 ] Network output: [ -0.008502 0.04282 1.001 7.934e-05 -3.562e-05 0.9733 5.979e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0922 0.09031 0.1672 0.1976 0.9856 0.9915 0.09221 0.7196 0.8566 0.2433 ] Network output: [ 0.0004228 0.9995 -0.0009125 1.093e-05 -4.908e-06 1.001 8.238e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008539 Epoch 6935 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01311 0.9928 0.9869 4.191e-06 -1.881e-06 -0.00591 3.158e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003206 -0.002981 -0.009467 0.007217 0.9698 0.9742 0.006091 0.8436 0.8316 0.02016 ] Network output: [ 0.9994 0.003966 0.001655 -3.96e-05 1.778e-05 -0.004545 -2.984e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1841 -0.02907 -0.1966 0.1997 0.9836 0.9933 0.2054 0.4592 0.8772 0.7224 ] Network output: [ -0.01182 1 1.01 2.058e-06 -9.237e-07 0.01291 1.551e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005352 0.0004991 0.004301 0.004375 0.9889 0.992 0.00545 0.8733 0.9011 0.01462 ] Network output: [ -0.001208 0.007078 1.003 -0.0001362 6.115e-05 0.9919 -0.0001026 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1946 0.09363 0.3217 0.1576 0.9851 0.994 0.1952 0.4639 0.8837 0.7171 ] Network output: [ 0.008355 -0.03953 0.9972 7.881e-05 -3.538e-05 1.026 5.94e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09707 0.08602 0.1782 0.2086 0.9873 0.992 0.09713 0.7899 0.8773 0.3093 ] Network output: [ -0.008453 0.04238 1.001 7.933e-05 -3.562e-05 0.9736 5.979e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09218 0.09029 0.1671 0.1975 0.9856 0.9914 0.09219 0.7194 0.8565 0.2433 ] Network output: [ 0.0001629 0.9994 -0.0005431 1.084e-05 -4.866e-06 1.001 8.168e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008413 Epoch 6936 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01316 0.9921 0.987 4.253e-06 -1.909e-06 -0.005323 3.205e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003205 -0.002981 -0.00946 0.007228 0.9698 0.9742 0.00609 0.8436 0.8316 0.02016 ] Network output: [ 0.9997 -0.000769 0.001894 -3.921e-05 1.76e-05 -0.0006828 -2.955e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.184 -0.02915 -0.1963 0.2004 0.9836 0.9933 0.2053 0.459 0.8773 0.7224 ] Network output: [ -0.01181 1 1.01 2.085e-06 -9.359e-07 0.01312 1.571e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005352 0.0004995 0.004315 0.004399 0.9889 0.992 0.005449 0.8733 0.9011 0.01463 ] Network output: [ -0.0007879 0.0006565 1.003 -0.0001355 6.082e-05 0.9973 -0.0001021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1946 0.09362 0.3222 0.1588 0.9851 0.994 0.1952 0.4638 0.8837 0.7171 ] Network output: [ 0.008261 -0.04077 0.9974 7.883e-05 -3.539e-05 1.027 5.941e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09709 0.08605 0.1785 0.2089 0.9873 0.992 0.09715 0.79 0.8773 0.3095 ] Network output: [ -0.008494 0.04279 1.001 7.925e-05 -3.558e-05 0.9733 5.972e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09219 0.0903 0.1672 0.1976 0.9856 0.9915 0.0922 0.7195 0.8565 0.2433 ] Network output: [ 0.0004204 0.9995 -0.0009084 1.092e-05 -4.901e-06 1.001 8.228e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008528 Epoch 6937 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01311 0.9928 0.9869 4.181e-06 -1.877e-06 -0.00591 3.151e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003206 -0.002981 -0.009463 0.007215 0.9698 0.9742 0.006091 0.8436 0.8316 0.02016 ] Network output: [ 0.9994 0.003923 0.001655 -3.956e-05 1.776e-05 -0.00451 -2.981e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1841 -0.02909 -0.1966 0.1997 0.9836 0.9933 0.2054 0.4591 0.8772 0.7224 ] Network output: [ -0.01181 1 1.01 2.053e-06 -9.218e-07 0.01291 1.548e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005353 0.0004988 0.004302 0.004374 0.9889 0.992 0.005451 0.8733 0.901 0.01462 ] Network output: [ -0.001204 0.007023 1.003 -0.000136 6.106e-05 0.9919 -0.0001025 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1946 0.09363 0.3218 0.1576 0.9851 0.994 0.1952 0.4639 0.8837 0.7171 ] Network output: [ 0.008348 -0.03952 0.9972 7.871e-05 -3.534e-05 1.026 5.932e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09707 0.08602 0.1783 0.2086 0.9873 0.992 0.09713 0.7898 0.8772 0.3093 ] Network output: [ -0.008446 0.04236 1.001 7.924e-05 -3.557e-05 0.9736 5.972e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09217 0.09027 0.1671 0.1975 0.9856 0.9914 0.09218 0.7193 0.8565 0.2432 ] Network output: [ 0.0001648 0.9994 -0.0005451 1.083e-05 -4.86e-06 1.001 8.159e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008404 Epoch 6938 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01316 0.9921 0.987 4.241e-06 -1.904e-06 -0.005333 3.196e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003205 -0.002982 -0.009457 0.007225 0.9698 0.9742 0.00609 0.8436 0.8316 0.02016 ] Network output: [ 0.9997 -0.0007323 0.001891 -3.917e-05 1.759e-05 -0.0007135 -2.952e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.184 -0.02916 -0.1962 0.2004 0.9836 0.9933 0.2054 0.459 0.8772 0.7224 ] Network output: [ -0.0118 1 1.01 2.08e-06 -9.338e-07 0.01311 1.568e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005353 0.0004992 0.004315 0.004397 0.9889 0.992 0.00545 0.8733 0.9011 0.01462 ] Network output: [ -0.0007911 0.0007096 1.003 -0.0001353 6.074e-05 0.9972 -0.000102 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1946 0.09361 0.3223 0.1587 0.9851 0.994 0.1952 0.4638 0.8837 0.7171 ] Network output: [ 0.008256 -0.04074 0.9974 7.872e-05 -3.534e-05 1.027 5.933e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09709 0.08605 0.1785 0.2089 0.9873 0.992 0.09715 0.7899 0.8772 0.3095 ] Network output: [ -0.008486 0.04275 1.001 7.915e-05 -3.553e-05 0.9733 5.965e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09218 0.09029 0.1672 0.1976 0.9856 0.9914 0.09219 0.7194 0.8565 0.2433 ] Network output: [ 0.000418 0.9995 -0.0009044 1.09e-05 -4.895e-06 1.001 8.217e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008517 Epoch 6939 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0131 0.9928 0.987 4.171e-06 -1.872e-06 -0.00591 3.143e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003206 -0.002982 -0.00946 0.007213 0.9698 0.9742 0.006092 0.8436 0.8315 0.02015 ] Network output: [ 0.9994 0.00388 0.001655 -3.951e-05 1.774e-05 -0.004476 -2.978e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1841 -0.0291 -0.1965 0.1997 0.9836 0.9933 0.2054 0.4591 0.8772 0.7223 ] Network output: [ -0.01181 1 1.01 2.049e-06 -9.2e-07 0.0129 1.544e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005354 0.0004985 0.004302 0.004372 0.9889 0.992 0.005452 0.8733 0.901 0.01462 ] Network output: [ -0.0012 0.006967 1.003 -0.0001358 6.097e-05 0.992 -0.0001024 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1947 0.09362 0.3218 0.1575 0.9851 0.994 0.1953 0.4638 0.8837 0.7171 ] Network output: [ 0.008341 -0.03951 0.9972 7.861e-05 -3.529e-05 1.026 5.924e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09708 0.08603 0.1783 0.2085 0.9873 0.992 0.09714 0.7898 0.8772 0.3093 ] Network output: [ -0.008439 0.04233 1.001 7.914e-05 -3.553e-05 0.9736 5.964e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09215 0.09026 0.1671 0.1975 0.9856 0.9914 0.09217 0.7193 0.8565 0.2432 ] Network output: [ 0.0001666 0.9994 -0.0005471 1.081e-05 -4.854e-06 1.001 8.149e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008395 Epoch 6940 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01315 0.9921 0.987 4.23e-06 -1.899e-06 -0.005343 3.188e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003205 -0.002982 -0.009454 0.007223 0.9698 0.9742 0.006091 0.8436 0.8316 0.02015 ] Network output: [ 0.9997 -0.000696 0.001887 -3.914e-05 1.757e-05 -0.0007439 -2.95e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1841 -0.02917 -0.1962 0.2004 0.9836 0.9933 0.2054 0.459 0.8772 0.7224 ] Network output: [ -0.0118 1 1.01 2.075e-06 -9.317e-07 0.0131 1.564e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005354 0.0004988 0.004315 0.004395 0.9889 0.992 0.005452 0.8733 0.901 0.01462 ] Network output: [ -0.0007943 0.000762 1.003 -0.0001351 6.066e-05 0.9972 -0.0001018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1946 0.0936 0.3223 0.1587 0.9851 0.994 0.1952 0.4638 0.8837 0.7171 ] Network output: [ 0.008251 -0.04071 0.9974 7.862e-05 -3.53e-05 1.027 5.925e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0971 0.08605 0.1785 0.2089 0.9873 0.992 0.09716 0.7898 0.8772 0.3094 ] Network output: [ -0.008478 0.04272 1.001 7.906e-05 -3.549e-05 0.9733 5.958e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09216 0.09027 0.1672 0.1976 0.9856 0.9914 0.09218 0.7194 0.8565 0.2433 ] Network output: [ 0.0004156 0.9995 -0.0009004 1.089e-05 -4.888e-06 1.001 8.206e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008506 Epoch 6941 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0131 0.9928 0.987 4.161e-06 -1.868e-06 -0.00591 3.136e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003206 -0.002982 -0.009457 0.00721 0.9698 0.9742 0.006092 0.8436 0.8315 0.02015 ] Network output: [ 0.9994 0.003837 0.001656 -3.947e-05 1.772e-05 -0.004443 -2.975e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1841 -0.02912 -0.1965 0.1996 0.9836 0.9933 0.2054 0.4591 0.8772 0.7223 ] Network output: [ -0.01181 1 1.01 2.045e-06 -9.181e-07 0.01289 1.541e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005356 0.0004982 0.004303 0.004371 0.9889 0.992 0.005453 0.8733 0.901 0.01461 ] Network output: [ -0.001197 0.006913 1.003 -0.0001356 6.089e-05 0.992 -0.0001022 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1947 0.09361 0.3219 0.1575 0.9851 0.994 0.1953 0.4638 0.8837 0.7171 ] Network output: [ 0.008335 -0.0395 0.9972 7.851e-05 -3.525e-05 1.026 5.917e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09708 0.08603 0.1783 0.2085 0.9873 0.992 0.09714 0.7897 0.8772 0.3093 ] Network output: [ -0.008432 0.0423 1.001 7.905e-05 -3.549e-05 0.9736 5.957e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09214 0.09025 0.1671 0.1975 0.9856 0.9914 0.09215 0.7192 0.8565 0.2432 ] Network output: [ 0.0001684 0.9994 -0.0005491 1.08e-05 -4.849e-06 1.001 8.14e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008386 Epoch 6942 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01314 0.9921 0.987 4.219e-06 -1.894e-06 -0.005352 3.179e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003206 -0.002982 -0.009451 0.007221 0.9698 0.9742 0.006091 0.8435 0.8316 0.02015 ] Network output: [ 0.9997 -0.0006602 0.001883 -3.91e-05 1.755e-05 -0.0007739 -2.947e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1841 -0.02919 -0.1962 0.2004 0.9836 0.9933 0.2054 0.4589 0.8772 0.7224 ] Network output: [ -0.0118 1 1.01 2.071e-06 -9.296e-07 0.01309 1.56e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005355 0.0004985 0.004316 0.004393 0.9889 0.992 0.005453 0.8733 0.901 0.01462 ] Network output: [ -0.0007974 0.0008139 1.003 -0.0001349 6.058e-05 0.9971 -0.0001017 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1946 0.09359 0.3223 0.1587 0.9851 0.994 0.1953 0.4637 0.8837 0.7171 ] Network output: [ 0.008246 -0.04068 0.9974 7.852e-05 -3.525e-05 1.027 5.917e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0971 0.08605 0.1785 0.2088 0.9873 0.992 0.09716 0.7898 0.8772 0.3094 ] Network output: [ -0.00847 0.04268 1.001 7.896e-05 -3.545e-05 0.9733 5.951e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09215 0.09026 0.1672 0.1976 0.9856 0.9914 0.09216 0.7193 0.8564 0.2432 ] Network output: [ 0.0004132 0.9995 -0.0008964 1.087e-05 -4.882e-06 1.001 8.196e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008495 Epoch 6943 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0131 0.9928 0.987 4.15e-06 -1.863e-06 -0.00591 3.128e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003206 -0.002982 -0.009453 0.007208 0.9698 0.9742 0.006093 0.8436 0.8315 0.02014 ] Network output: [ 0.9994 0.003796 0.001656 -3.943e-05 1.77e-05 -0.004409 -2.971e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1841 -0.02913 -0.1965 0.1996 0.9836 0.9933 0.2055 0.459 0.8772 0.7223 ] Network output: [ -0.01181 1 1.01 2.041e-06 -9.162e-07 0.01289 1.538e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005357 0.0004978 0.004303 0.004369 0.9889 0.992 0.005454 0.8733 0.901 0.01461 ] Network output: [ -0.001193 0.006859 1.003 -0.0001354 6.08e-05 0.9921 -0.0001021 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1947 0.09361 0.3219 0.1575 0.9851 0.994 0.1953 0.4638 0.8837 0.7171 ] Network output: [ 0.008328 -0.03949 0.9972 7.841e-05 -3.52e-05 1.026 5.909e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09708 0.08603 0.1783 0.2085 0.9873 0.992 0.09715 0.7897 0.8772 0.3093 ] Network output: [ -0.008425 0.04228 1.001 7.895e-05 -3.544e-05 0.9736 5.95e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09213 0.09024 0.1671 0.1975 0.9856 0.9914 0.09214 0.7191 0.8564 0.2432 ] Network output: [ 0.0001702 0.9994 -0.000551 1.079e-05 -4.843e-06 1.001 8.13e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008377 Epoch 6944 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01314 0.9921 0.987 4.207e-06 -1.889e-06 -0.005362 3.171e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003206 -0.002982 -0.009447 0.007218 0.9698 0.9742 0.006092 0.8435 0.8315 0.02014 ] Network output: [ 0.9997 -0.0006247 0.00188 -3.906e-05 1.754e-05 -0.0008036 -2.944e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1841 -0.0292 -0.1961 0.2003 0.9836 0.9933 0.2054 0.4589 0.8772 0.7224 ] Network output: [ -0.0118 1 1.01 2.066e-06 -9.275e-07 0.01308 1.557e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005356 0.0004982 0.004316 0.004391 0.9889 0.992 0.005454 0.8732 0.901 0.01461 ] Network output: [ -0.0008005 0.0008652 1.003 -0.0001348 6.05e-05 0.9971 -0.0001016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1947 0.09359 0.3224 0.1586 0.9851 0.994 0.1953 0.4637 0.8837 0.7171 ] Network output: [ 0.00824 -0.04065 0.9974 7.842e-05 -3.52e-05 1.027 5.91e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0971 0.08605 0.1785 0.2088 0.9873 0.992 0.09716 0.7897 0.8772 0.3094 ] Network output: [ -0.008462 0.04265 1.001 7.887e-05 -3.541e-05 0.9734 5.944e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09214 0.09025 0.1672 0.1976 0.9856 0.9914 0.09215 0.7192 0.8564 0.2432 ] Network output: [ 0.0004109 0.9995 -0.0008925 1.086e-05 -4.876e-06 1.001 8.185e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008484 Epoch 6945 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01309 0.9928 0.987 4.14e-06 -1.859e-06 -0.00591 3.12e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003206 -0.002982 -0.00945 0.007206 0.9698 0.9742 0.006093 0.8435 0.8315 0.02014 ] Network output: [ 0.9994 0.003754 0.001656 -3.938e-05 1.768e-05 -0.004376 -2.968e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1842 -0.02914 -0.1964 0.1996 0.9836 0.9933 0.2055 0.459 0.8772 0.7223 ] Network output: [ -0.0118 1 1.01 2.037e-06 -9.143e-07 0.01288 1.535e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005358 0.0004975 0.004304 0.004367 0.9889 0.992 0.005455 0.8732 0.901 0.01461 ] Network output: [ -0.001189 0.006805 1.003 -0.0001352 6.072e-05 0.9921 -0.0001019 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1947 0.0936 0.322 0.1575 0.9851 0.994 0.1953 0.4637 0.8837 0.7171 ] Network output: [ 0.008321 -0.03948 0.9972 7.83e-05 -3.515e-05 1.026 5.901e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09709 0.08603 0.1783 0.2085 0.9873 0.992 0.09715 0.7896 0.8772 0.3092 ] Network output: [ -0.008418 0.04225 1.001 7.886e-05 -3.54e-05 0.9736 5.943e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09212 0.09023 0.1671 0.1975 0.9856 0.9914 0.09213 0.7191 0.8564 0.2432 ] Network output: [ 0.000172 0.9995 -0.000553 1.078e-05 -4.837e-06 1.001 8.121e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008368 Epoch 6946 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01313 0.9921 0.987 4.196e-06 -1.884e-06 -0.005372 3.162e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003206 -0.002983 -0.009444 0.007216 0.9698 0.9742 0.006092 0.8435 0.8315 0.02014 ] Network output: [ 0.9997 -0.0005897 0.001876 -3.903e-05 1.752e-05 -0.0008328 -2.941e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1841 -0.02921 -0.1961 0.2003 0.9836 0.9933 0.2055 0.4589 0.8772 0.7224 ] Network output: [ -0.0118 1 1.01 2.061e-06 -9.253e-07 0.01307 1.553e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005357 0.0004978 0.004316 0.004389 0.9889 0.992 0.005455 0.8732 0.901 0.01461 ] Network output: [ -0.0008035 0.0009158 1.003 -0.0001346 6.042e-05 0.997 -0.0001014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1947 0.09358 0.3224 0.1586 0.9851 0.994 0.1953 0.4637 0.8837 0.717 ] Network output: [ 0.008235 -0.04061 0.9974 7.831e-05 -3.516e-05 1.027 5.902e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09711 0.08605 0.1785 0.2088 0.9873 0.992 0.09717 0.7897 0.8772 0.3094 ] Network output: [ -0.008454 0.04262 1.001 7.877e-05 -3.536e-05 0.9734 5.937e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09213 0.09024 0.1671 0.1975 0.9856 0.9914 0.09214 0.7191 0.8564 0.2432 ] Network output: [ 0.0004086 0.9995 -0.0008886 1.085e-05 -4.869e-06 1.001 8.174e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008473 Epoch 6947 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01309 0.9928 0.987 4.13e-06 -1.854e-06 -0.00591 3.113e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003207 -0.002983 -0.009447 0.007204 0.9698 0.9742 0.006094 0.8435 0.8315 0.02013 ] Network output: [ 0.9994 0.003713 0.001656 -3.934e-05 1.766e-05 -0.004344 -2.965e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1842 -0.02916 -0.1964 0.1996 0.9836 0.9933 0.2055 0.4589 0.8772 0.7223 ] Network output: [ -0.0118 1 1.01 2.032e-06 -9.124e-07 0.01287 1.532e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005359 0.0004972 0.004304 0.004366 0.9889 0.992 0.005456 0.8732 0.901 0.0146 ] Network output: [ -0.001186 0.006752 1.003 -0.0001351 6.063e-05 0.9922 -0.0001018 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1947 0.09359 0.322 0.1574 0.9851 0.994 0.1954 0.4637 0.8837 0.7171 ] Network output: [ 0.008314 -0.03947 0.9972 7.82e-05 -3.511e-05 1.026 5.894e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09709 0.08603 0.1783 0.2085 0.9873 0.992 0.09715 0.7895 0.8771 0.3092 ] Network output: [ -0.008411 0.04222 1.001 7.876e-05 -3.536e-05 0.9737 5.936e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09211 0.09021 0.1671 0.1975 0.9856 0.9914 0.09212 0.719 0.8564 0.2432 ] Network output: [ 0.0001737 0.9995 -0.0005548 1.076e-05 -4.832e-06 1.001 8.111e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008359 Epoch 6948 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01313 0.9921 0.987 4.185e-06 -1.879e-06 -0.005382 3.154e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003206 -0.002983 -0.009441 0.007214 0.9698 0.9742 0.006093 0.8435 0.8315 0.02014 ] Network output: [ 0.9997 -0.0005551 0.001873 -3.899e-05 1.75e-05 -0.0008617 -2.938e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1841 -0.02923 -0.1961 0.2003 0.9836 0.9933 0.2055 0.4588 0.8772 0.7224 ] Network output: [ -0.01179 1 1.01 2.056e-06 -9.232e-07 0.01305 1.55e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005358 0.0004975 0.004317 0.004387 0.9889 0.992 0.005456 0.8732 0.901 0.01461 ] Network output: [ -0.0008065 0.0009659 1.003 -0.0001344 6.034e-05 0.997 -0.0001013 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1947 0.09357 0.3224 0.1585 0.9851 0.994 0.1953 0.4636 0.8837 0.717 ] Network output: [ 0.00823 -0.04058 0.9974 7.821e-05 -3.511e-05 1.027 5.894e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09711 0.08605 0.1785 0.2088 0.9873 0.992 0.09717 0.7896 0.8771 0.3094 ] Network output: [ -0.008446 0.04258 1.001 7.868e-05 -3.532e-05 0.9734 5.93e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09212 0.09022 0.1671 0.1975 0.9856 0.9914 0.09213 0.7191 0.8564 0.2432 ] Network output: [ 0.0004063 0.9995 -0.0008847 1.083e-05 -4.863e-06 1.001 8.164e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008462 Epoch 6949 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01308 0.9928 0.987 4.12e-06 -1.85e-06 -0.005911 3.105e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003207 -0.002983 -0.009443 0.007202 0.9698 0.9742 0.006094 0.8435 0.8315 0.02013 ] Network output: [ 0.9994 0.003672 0.001657 -3.93e-05 1.764e-05 -0.004311 -2.962e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1842 -0.02917 -0.1963 0.1996 0.9836 0.9933 0.2055 0.4589 0.8772 0.7223 ] Network output: [ -0.0118 1 1.01 2.028e-06 -9.105e-07 0.01286 1.528e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00536 0.0004969 0.004305 0.004364 0.9889 0.992 0.005458 0.8732 0.901 0.0146 ] Network output: [ -0.001182 0.0067 1.003 -0.0001349 6.055e-05 0.9922 -0.0001016 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1948 0.09358 0.322 0.1574 0.9851 0.994 0.1954 0.4637 0.8837 0.7171 ] Network output: [ 0.008307 -0.03946 0.9972 7.81e-05 -3.506e-05 1.026 5.886e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0971 0.08603 0.1783 0.2084 0.9873 0.992 0.09716 0.7895 0.8771 0.3092 ] Network output: [ -0.008404 0.04219 1.001 7.867e-05 -3.532e-05 0.9737 5.929e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0921 0.0902 0.1671 0.1975 0.9856 0.9914 0.09211 0.7189 0.8563 0.2432 ] Network output: [ 0.0001754 0.9995 -0.0005567 1.075e-05 -4.826e-06 1.001 8.101e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000835 Epoch 6950 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01312 0.9922 0.987 4.173e-06 -1.874e-06 -0.005391 3.145e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003206 -0.002983 -0.009438 0.007211 0.9698 0.9742 0.006093 0.8435 0.8315 0.02013 ] Network output: [ 0.9997 -0.0005209 0.001869 -3.895e-05 1.749e-05 -0.0008903 -2.936e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1842 -0.02924 -0.196 0.2003 0.9836 0.9933 0.2055 0.4588 0.8772 0.7224 ] Network output: [ -0.01179 1 1.01 2.052e-06 -9.211e-07 0.01304 1.546e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005359 0.0004972 0.004317 0.004385 0.9889 0.992 0.005457 0.8732 0.901 0.0146 ] Network output: [ -0.0008095 0.001015 1.003 -0.0001342 6.026e-05 0.997 -0.0001012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1947 0.09357 0.3225 0.1585 0.9851 0.994 0.1953 0.4636 0.8836 0.717 ] Network output: [ 0.008225 -0.04055 0.9974 7.811e-05 -3.507e-05 1.027 5.886e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09711 0.08605 0.1785 0.2087 0.9873 0.992 0.09718 0.7895 0.8771 0.3094 ] Network output: [ -0.008439 0.04255 1.001 7.859e-05 -3.528e-05 0.9734 5.922e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0921 0.09021 0.1671 0.1975 0.9856 0.9914 0.09212 0.719 0.8563 0.2432 ] Network output: [ 0.000404 0.9995 -0.0008808 1.082e-05 -4.857e-06 1.001 8.153e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008451 Epoch 6951 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01308 0.9928 0.987 4.11e-06 -1.845e-06 -0.005911 3.097e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003207 -0.002983 -0.00944 0.007199 0.9698 0.9742 0.006095 0.8435 0.8315 0.02013 ] Network output: [ 0.9994 0.003632 0.001657 -3.925e-05 1.762e-05 -0.004279 -2.958e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1842 -0.02919 -0.1963 0.1996 0.9836 0.9933 0.2056 0.4589 0.8771 0.7223 ] Network output: [ -0.0118 1 1.01 2.024e-06 -9.086e-07 0.01286 1.525e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005361 0.0004966 0.004305 0.004362 0.9889 0.992 0.005459 0.8732 0.901 0.0146 ] Network output: [ -0.001178 0.006648 1.003 -0.0001347 6.046e-05 0.9923 -0.0001015 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1948 0.09358 0.3221 0.1574 0.9851 0.994 0.1954 0.4636 0.8836 0.7171 ] Network output: [ 0.0083 -0.03944 0.9972 7.8e-05 -3.502e-05 1.026 5.878e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0971 0.08603 0.1783 0.2084 0.9873 0.992 0.09716 0.7894 0.8771 0.3092 ] Network output: [ -0.008397 0.04217 1.001 7.857e-05 -3.527e-05 0.9737 5.921e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09208 0.09019 0.1671 0.1975 0.9856 0.9914 0.0921 0.7189 0.8563 0.2432 ] Network output: [ 0.0001771 0.9995 -0.0005585 1.074e-05 -4.82e-06 1.001 8.092e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008341 Epoch 6952 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01312 0.9922 0.987 4.162e-06 -1.869e-06 -0.005401 3.137e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003207 -0.002984 -0.009434 0.007209 0.9698 0.9742 0.006094 0.8435 0.8315 0.02013 ] Network output: [ 0.9997 -0.0004871 0.001866 -3.892e-05 1.747e-05 -0.0009185 -2.933e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1842 -0.02925 -0.196 0.2002 0.9836 0.9933 0.2055 0.4588 0.8772 0.7223 ] Network output: [ -0.01179 1 1.01 2.047e-06 -9.189e-07 0.01303 1.543e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00536 0.0004969 0.004317 0.004383 0.9889 0.992 0.005458 0.8732 0.901 0.0146 ] Network output: [ -0.0008124 0.001064 1.003 -0.000134 6.017e-05 0.9969 -0.000101 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1948 0.09356 0.3225 0.1584 0.9851 0.994 0.1954 0.4636 0.8836 0.717 ] Network output: [ 0.008219 -0.04052 0.9974 7.801e-05 -3.502e-05 1.027 5.879e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09712 0.08605 0.1785 0.2087 0.9873 0.992 0.09718 0.7895 0.8771 0.3094 ] Network output: [ -0.008431 0.04251 1.001 7.849e-05 -3.524e-05 0.9734 5.915e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09209 0.0902 0.1671 0.1975 0.9856 0.9914 0.0921 0.7189 0.8563 0.2432 ] Network output: [ 0.0004017 0.9995 -0.000877 1.08e-05 -4.85e-06 1.001 8.142e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000844 Epoch 6953 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01307 0.9928 0.987 4.1e-06 -1.841e-06 -0.005911 3.09e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003207 -0.002984 -0.009437 0.007197 0.9698 0.9742 0.006095 0.8435 0.8315 0.02012 ] Network output: [ 0.9994 0.003593 0.001657 -3.921e-05 1.76e-05 -0.004247 -2.955e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1842 -0.0292 -0.1963 0.1996 0.9836 0.9933 0.2056 0.4588 0.8771 0.7223 ] Network output: [ -0.0118 1 1.01 2.02e-06 -9.067e-07 0.01285 1.522e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005362 0.0004962 0.004306 0.004361 0.9889 0.992 0.00546 0.8732 0.901 0.01459 ] Network output: [ -0.001175 0.006597 1.003 -0.0001345 6.038e-05 0.9923 -0.0001014 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1948 0.09357 0.3221 0.1574 0.9851 0.994 0.1954 0.4636 0.8836 0.7171 ] Network output: [ 0.008294 -0.03943 0.9972 7.79e-05 -3.497e-05 1.026 5.871e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09711 0.08603 0.1783 0.2084 0.9873 0.992 0.09717 0.7894 0.8771 0.3092 ] Network output: [ -0.00839 0.04214 1.001 7.848e-05 -3.523e-05 0.9737 5.914e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09207 0.09018 0.1671 0.1975 0.9856 0.9914 0.09208 0.7188 0.8563 0.2432 ] Network output: [ 0.0001788 0.9995 -0.0005603 1.072e-05 -4.815e-06 1.001 8.082e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008332 Epoch 6954 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01311 0.9922 0.987 4.151e-06 -1.864e-06 -0.005411 3.128e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003207 -0.002984 -0.009431 0.007206 0.9698 0.9742 0.006095 0.8435 0.8315 0.02012 ] Network output: [ 0.9997 -0.0004538 0.001862 -3.888e-05 1.745e-05 -0.0009463 -2.93e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1842 -0.02926 -0.196 0.2002 0.9836 0.9933 0.2055 0.4587 0.8771 0.7223 ] Network output: [ -0.01179 1 1.01 2.042e-06 -9.168e-07 0.01302 1.539e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005361 0.0004965 0.004318 0.004381 0.9889 0.992 0.005459 0.8731 0.901 0.0146 ] Network output: [ -0.0008153 0.001112 1.003 -0.0001339 6.009e-05 0.9969 -0.0001009 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1948 0.09355 0.3225 0.1584 0.9851 0.994 0.1954 0.4635 0.8836 0.717 ] Network output: [ 0.008214 -0.04049 0.9974 7.79e-05 -3.497e-05 1.027 5.871e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09712 0.08605 0.1785 0.2087 0.9873 0.992 0.09718 0.7894 0.8771 0.3094 ] Network output: [ -0.008423 0.04248 1.001 7.84e-05 -3.52e-05 0.9734 5.908e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09208 0.09019 0.1671 0.1975 0.9856 0.9914 0.09209 0.7189 0.8563 0.2432 ] Network output: [ 0.0003995 0.9995 -0.0008732 1.079e-05 -4.844e-06 1.001 8.132e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008429 Epoch 6955 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01307 0.9928 0.987 4.09e-06 -1.836e-06 -0.005912 3.082e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003207 -0.002984 -0.009434 0.007195 0.9698 0.9742 0.006096 0.8435 0.8314 0.02012 ] Network output: [ 0.9994 0.003553 0.001657 -3.917e-05 1.758e-05 -0.004216 -2.952e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1843 -0.02921 -0.1962 0.1995 0.9836 0.9933 0.2056 0.4588 0.8771 0.7223 ] Network output: [ -0.01179 1 1.01 2.015e-06 -9.047e-07 0.01284 1.519e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005363 0.0004959 0.004306 0.004359 0.9889 0.992 0.005461 0.8731 0.9009 0.01459 ] Network output: [ -0.001171 0.006546 1.003 -0.0001343 6.029e-05 0.9924 -0.0001012 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1948 0.09356 0.3222 0.1573 0.9851 0.994 0.1954 0.4636 0.8836 0.7171 ] Network output: [ 0.008287 -0.03942 0.9972 7.78e-05 -3.493e-05 1.026 5.863e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09711 0.08604 0.1783 0.2084 0.9873 0.992 0.09717 0.7893 0.877 0.3092 ] Network output: [ -0.008383 0.04211 1.001 7.838e-05 -3.519e-05 0.9737 5.907e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09206 0.09017 0.1671 0.1974 0.9856 0.9914 0.09207 0.7187 0.8563 0.2432 ] Network output: [ 0.0001805 0.9995 -0.000562 1.071e-05 -4.809e-06 1.001 8.073e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008323 Epoch 6956 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0131 0.9922 0.987 4.14e-06 -1.858e-06 -0.00542 3.12e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003207 -0.002984 -0.009428 0.007204 0.9698 0.9742 0.006095 0.8434 0.8315 0.02012 ] Network output: [ 0.9997 -0.0004209 0.001859 -3.884e-05 1.744e-05 -0.0009738 -2.927e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1842 -0.02928 -0.1959 0.2002 0.9836 0.9933 0.2056 0.4587 0.8771 0.7223 ] Network output: [ -0.01178 1 1.01 2.037e-06 -9.147e-07 0.01301 1.535e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005362 0.0004962 0.004318 0.004379 0.9889 0.992 0.00546 0.8731 0.901 0.01459 ] Network output: [ -0.0008181 0.00116 1.003 -0.0001337 6.001e-05 0.9968 -0.0001007 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1948 0.09355 0.3226 0.1583 0.9851 0.994 0.1954 0.4635 0.8836 0.717 ] Network output: [ 0.008209 -0.04046 0.9974 7.78e-05 -3.493e-05 1.027 5.863e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09713 0.08606 0.1785 0.2087 0.9873 0.992 0.09719 0.7894 0.877 0.3093 ] Network output: [ -0.008415 0.04245 1.001 7.83e-05 -3.515e-05 0.9734 5.901e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09207 0.09018 0.1671 0.1975 0.9856 0.9914 0.09208 0.7188 0.8562 0.2432 ] Network output: [ 0.0003973 0.9996 -0.0008694 1.078e-05 -4.838e-06 1.001 8.121e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008418 Epoch 6957 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01306 0.9928 0.987 4.079e-06 -1.831e-06 -0.005912 3.074e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003208 -0.002984 -0.00943 0.007193 0.9698 0.9742 0.006096 0.8434 0.8314 0.02011 ] Network output: [ 0.9994 0.003514 0.001657 -3.912e-05 1.756e-05 -0.004185 -2.949e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1843 -0.02923 -0.1962 0.1995 0.9836 0.9933 0.2056 0.4588 0.8771 0.7223 ] Network output: [ -0.01179 1 1.01 2.011e-06 -9.028e-07 0.01284 1.516e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005364 0.0004956 0.004307 0.004358 0.9889 0.992 0.005462 0.8731 0.9009 0.01459 ] Network output: [ -0.001168 0.006496 1.003 -0.0001341 6.02e-05 0.9924 -0.0001011 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1948 0.09356 0.3222 0.1573 0.9851 0.994 0.1955 0.4635 0.8836 0.717 ] Network output: [ 0.00828 -0.03941 0.9972 7.77e-05 -3.488e-05 1.026 5.855e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09711 0.08604 0.1783 0.2084 0.9873 0.992 0.09718 0.7893 0.877 0.3092 ] Network output: [ -0.008376 0.04208 1.001 7.829e-05 -3.515e-05 0.9737 5.9e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09205 0.09015 0.167 0.1974 0.9856 0.9914 0.09206 0.7186 0.8562 0.2432 ] Network output: [ 0.0001821 0.9995 -0.0005637 1.07e-05 -4.803e-06 1.001 8.063e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008314 Epoch 6958 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0131 0.9922 0.987 4.128e-06 -1.853e-06 -0.00543 3.111e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003207 -0.002985 -0.009425 0.007202 0.9698 0.9742 0.006096 0.8434 0.8314 0.02011 ] Network output: [ 0.9997 -0.0003884 0.001855 -3.881e-05 1.742e-05 -0.001001 -2.924e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1842 -0.02929 -0.1959 0.2001 0.9836 0.9933 0.2056 0.4587 0.8771 0.7223 ] Network output: [ -0.01178 1 1.01 2.033e-06 -9.125e-07 0.013 1.532e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005363 0.0004959 0.004318 0.004377 0.9889 0.992 0.005461 0.8731 0.9009 0.01459 ] Network output: [ -0.0008209 0.001207 1.003 -0.0001335 5.993e-05 0.9968 -0.0001006 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1948 0.09354 0.3226 0.1583 0.9851 0.994 0.1954 0.4635 0.8836 0.717 ] Network output: [ 0.008203 -0.04043 0.9973 7.77e-05 -3.488e-05 1.027 5.856e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09713 0.08606 0.1785 0.2086 0.9873 0.992 0.09719 0.7893 0.877 0.3093 ] Network output: [ -0.008407 0.04241 1.001 7.821e-05 -3.511e-05 0.9735 5.894e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09206 0.09016 0.1671 0.1975 0.9856 0.9914 0.09207 0.7187 0.8562 0.2432 ] Network output: [ 0.0003951 0.9996 -0.0008657 1.076e-05 -4.831e-06 1.001 8.11e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008408 Epoch 6959 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01306 0.9928 0.987 4.069e-06 -1.827e-06 -0.005913 3.067e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003208 -0.002985 -0.009427 0.007191 0.9698 0.9742 0.006097 0.8434 0.8314 0.02011 ] Network output: [ 0.9994 0.003476 0.001657 -3.908e-05 1.754e-05 -0.004154 -2.945e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1843 -0.02924 -0.1961 0.1995 0.9836 0.9933 0.2056 0.4587 0.8771 0.7223 ] Network output: [ -0.01179 1 1.01 2.007e-06 -9.009e-07 0.01283 1.512e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005365 0.0004953 0.004308 0.004356 0.9889 0.992 0.005463 0.8731 0.9009 0.01458 ] Network output: [ -0.001164 0.006447 1.003 -0.0001339 6.012e-05 0.9924 -0.0001009 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1949 0.09355 0.3222 0.1573 0.9851 0.994 0.1955 0.4635 0.8836 0.717 ] Network output: [ 0.008273 -0.0394 0.9972 7.759e-05 -3.483e-05 1.026 5.848e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09712 0.08604 0.1783 0.2083 0.9873 0.992 0.09718 0.7892 0.877 0.3092 ] Network output: [ -0.008369 0.04205 1.001 7.819e-05 -3.51e-05 0.9737 5.893e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09204 0.09014 0.167 0.1974 0.9856 0.9914 0.09205 0.7186 0.8562 0.2432 ] Network output: [ 0.0001837 0.9995 -0.0005654 1.069e-05 -4.797e-06 1.001 8.053e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008305 Epoch 6960 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01309 0.9922 0.9871 4.117e-06 -1.848e-06 -0.005439 3.103e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003208 -0.002985 -0.009422 0.007199 0.9698 0.9742 0.006096 0.8434 0.8314 0.02011 ] Network output: [ 0.9997 -0.0003563 0.001852 -3.877e-05 1.74e-05 -0.001028 -2.922e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1843 -0.0293 -0.1958 0.2001 0.9836 0.9933 0.2056 0.4586 0.8771 0.7223 ] Network output: [ -0.01178 1 1.01 2.028e-06 -9.104e-07 0.01299 1.528e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005364 0.0004956 0.004319 0.004375 0.9889 0.992 0.005462 0.8731 0.9009 0.01459 ] Network output: [ -0.0008236 0.001254 1.003 -0.0001333 5.985e-05 0.9968 -0.0001005 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1948 0.09354 0.3226 0.1583 0.9851 0.994 0.1955 0.4634 0.8836 0.717 ] Network output: [ 0.008198 -0.0404 0.9973 7.76e-05 -3.484e-05 1.027 5.848e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09713 0.08606 0.1785 0.2086 0.9873 0.992 0.0972 0.7893 0.877 0.3093 ] Network output: [ -0.0084 0.04238 1.001 7.811e-05 -3.507e-05 0.9735 5.887e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09205 0.09015 0.1671 0.1975 0.9856 0.9914 0.09206 0.7186 0.8562 0.2432 ] Network output: [ 0.0003929 0.9996 -0.000862 1.075e-05 -4.825e-06 1.001 8.1e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008397 Epoch 6961 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01305 0.9928 0.987 4.059e-06 -1.822e-06 -0.005914 3.059e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003208 -0.002985 -0.009424 0.007189 0.9698 0.9742 0.006097 0.8434 0.8314 0.0201 ] Network output: [ 0.9994 0.003438 0.001657 -3.904e-05 1.753e-05 -0.004124 -2.942e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1843 -0.02926 -0.1961 0.1995 0.9836 0.9933 0.2057 0.4587 0.8771 0.7223 ] Network output: [ -0.01179 1 1.01 2.002e-06 -8.989e-07 0.01282 1.509e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005366 0.000495 0.004308 0.004354 0.9889 0.992 0.005464 0.8731 0.9009 0.01458 ] Network output: [ -0.001161 0.006398 1.003 -0.0001337 6.003e-05 0.9925 -0.0001008 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1949 0.09354 0.3223 0.1573 0.9851 0.994 0.1955 0.4635 0.8836 0.717 ] Network output: [ 0.008266 -0.03938 0.9972 7.749e-05 -3.479e-05 1.026 5.84e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09712 0.08604 0.1783 0.2083 0.9873 0.992 0.09718 0.7891 0.877 0.3092 ] Network output: [ -0.008362 0.04203 1.001 7.81e-05 -3.506e-05 0.9737 5.886e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09203 0.09013 0.167 0.1974 0.9856 0.9914 0.09204 0.7185 0.8562 0.2432 ] Network output: [ 0.0001853 0.9995 -0.000567 1.067e-05 -4.792e-06 1.001 8.044e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008296 Epoch 6962 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01309 0.9922 0.9871 4.106e-06 -1.843e-06 -0.005448 3.094e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003208 -0.002985 -0.009418 0.007197 0.9698 0.9742 0.006097 0.8434 0.8314 0.0201 ] Network output: [ 0.9997 -0.0003247 0.001848 -3.873e-05 1.739e-05 -0.001054 -2.919e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1843 -0.02932 -0.1958 0.2001 0.9836 0.9933 0.2056 0.4586 0.8771 0.7223 ] Network output: [ -0.01178 1 1.01 2.023e-06 -9.082e-07 0.01298 1.525e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005365 0.0004953 0.004319 0.004373 0.9889 0.992 0.005463 0.8731 0.9009 0.01458 ] Network output: [ -0.0008263 0.001299 1.003 -0.0001331 5.977e-05 0.9967 -0.0001003 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1949 0.09353 0.3227 0.1582 0.9851 0.994 0.1955 0.4634 0.8836 0.717 ] Network output: [ 0.008193 -0.04036 0.9973 7.749e-05 -3.479e-05 1.027 5.84e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09714 0.08606 0.1785 0.2086 0.9873 0.992 0.0972 0.7892 0.877 0.3093 ] Network output: [ -0.008392 0.04234 1.001 7.802e-05 -3.503e-05 0.9735 5.88e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09203 0.09014 0.1671 0.1975 0.9856 0.9914 0.09205 0.7186 0.8561 0.2432 ] Network output: [ 0.0003907 0.9996 -0.0008583 1.073e-05 -4.819e-06 1.001 8.089e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008386 Epoch 6963 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01305 0.9928 0.987 4.049e-06 -1.818e-06 -0.005914 3.051e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003208 -0.002985 -0.00942 0.007186 0.9698 0.9742 0.006098 0.8434 0.8314 0.0201 ] Network output: [ 0.9994 0.0034 0.001657 -3.899e-05 1.751e-05 -0.004094 -2.939e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1843 -0.02927 -0.1961 0.1995 0.9836 0.9933 0.2057 0.4587 0.8771 0.7223 ] Network output: [ -0.01178 1 1.01 1.998e-06 -8.97e-07 0.01281 1.506e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005367 0.0004947 0.004309 0.004353 0.9889 0.992 0.005465 0.8731 0.9009 0.01458 ] Network output: [ -0.001157 0.00635 1.003 -0.0001335 5.995e-05 0.9925 -0.0001006 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1949 0.09354 0.3223 0.1572 0.9851 0.994 0.1955 0.4634 0.8836 0.717 ] Network output: [ 0.00826 -0.03937 0.9972 7.739e-05 -3.474e-05 1.026 5.832e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09713 0.08604 0.1783 0.2083 0.9873 0.992 0.09719 0.7891 0.877 0.3092 ] Network output: [ -0.008355 0.042 1.001 7.8e-05 -3.502e-05 0.9737 5.878e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09201 0.09012 0.167 0.1974 0.9856 0.9914 0.09203 0.7184 0.8561 0.2432 ] Network output: [ 0.0001869 0.9995 -0.0005686 1.066e-05 -4.786e-06 1.001 8.034e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008287 Epoch 6964 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01308 0.9922 0.9871 4.095e-06 -1.838e-06 -0.005458 3.086e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003208 -0.002986 -0.009415 0.007195 0.9698 0.9742 0.006097 0.8434 0.8314 0.0201 ] Network output: [ 0.9997 -0.0002934 0.001845 -3.869e-05 1.737e-05 -0.00108 -2.916e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1843 -0.02933 -0.1958 0.2001 0.9836 0.9933 0.2057 0.4586 0.8771 0.7223 ] Network output: [ -0.01177 1 1.01 2.018e-06 -9.061e-07 0.01297 1.521e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005367 0.0004949 0.004319 0.004371 0.9889 0.992 0.005465 0.873 0.9009 0.01458 ] Network output: [ -0.000829 0.001345 1.003 -0.000133 5.969e-05 0.9967 -0.0001002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1949 0.09352 0.3227 0.1582 0.9851 0.994 0.1955 0.4634 0.8836 0.717 ] Network output: [ 0.008187 -0.04033 0.9973 7.739e-05 -3.474e-05 1.027 5.832e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09714 0.08606 0.1785 0.2086 0.9873 0.992 0.0972 0.7891 0.8769 0.3093 ] Network output: [ -0.008384 0.04231 1.001 7.793e-05 -3.498e-05 0.9735 5.873e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09202 0.09013 0.1671 0.1974 0.9856 0.9914 0.09203 0.7185 0.8561 0.2432 ] Network output: [ 0.0003886 0.9996 -0.0008546 1.072e-05 -4.812e-06 1.001 8.079e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008376 Epoch 6965 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01304 0.9928 0.9871 4.039e-06 -1.813e-06 -0.005915 3.044e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003208 -0.002986 -0.009417 0.007184 0.9698 0.9742 0.006098 0.8434 0.8314 0.02009 ] Network output: [ 0.9994 0.003363 0.001657 -3.895e-05 1.749e-05 -0.004064 -2.935e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1843 -0.02928 -0.196 0.1995 0.9836 0.9933 0.2057 0.4586 0.8771 0.7222 ] Network output: [ -0.01178 1 1.01 1.994e-06 -8.95e-07 0.01281 1.502e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005368 0.0004944 0.004309 0.004351 0.9889 0.992 0.005466 0.873 0.9009 0.01457 ] Network output: [ -0.001154 0.006302 1.003 -0.0001333 5.986e-05 0.9926 -0.0001005 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1949 0.09353 0.3224 0.1572 0.9851 0.994 0.1955 0.4634 0.8836 0.717 ] Network output: [ 0.008253 -0.03936 0.9972 7.729e-05 -3.47e-05 1.026 5.825e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09713 0.08604 0.1783 0.2083 0.9873 0.992 0.09719 0.789 0.8769 0.3091 ] Network output: [ -0.008348 0.04197 1.001 7.791e-05 -3.498e-05 0.9738 5.871e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.092 0.09011 0.167 0.1974 0.9856 0.9914 0.09202 0.7184 0.8561 0.2432 ] Network output: [ 0.0001884 0.9995 -0.0005702 1.065e-05 -4.78e-06 1.001 8.025e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008279 Epoch 6966 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01308 0.9923 0.9871 4.084e-06 -1.833e-06 -0.005467 3.078e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003208 -0.002986 -0.009412 0.007192 0.9698 0.9742 0.006098 0.8434 0.8314 0.02009 ] Network output: [ 0.9997 -0.0002626 0.001842 -3.865e-05 1.735e-05 -0.001106 -2.913e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1843 -0.02934 -0.1957 0.2 0.9836 0.9933 0.2057 0.4585 0.8771 0.7223 ] Network output: [ -0.01177 1 1.01 2.013e-06 -9.039e-07 0.01296 1.517e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005368 0.0004946 0.00432 0.004369 0.9889 0.992 0.005466 0.873 0.9009 0.01458 ] Network output: [ -0.0008316 0.001389 1.003 -0.0001328 5.961e-05 0.9967 -0.0001001 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1949 0.09352 0.3227 0.1581 0.9851 0.994 0.1955 0.4633 0.8835 0.717 ] Network output: [ 0.008182 -0.0403 0.9973 7.729e-05 -3.47e-05 1.027 5.825e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09715 0.08606 0.1785 0.2085 0.9873 0.992 0.09721 0.7891 0.8769 0.3093 ] Network output: [ -0.008376 0.04228 1.001 7.783e-05 -3.494e-05 0.9735 5.866e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09201 0.09012 0.167 0.1974 0.9856 0.9914 0.09202 0.7184 0.8561 0.2432 ] Network output: [ 0.0003865 0.9996 -0.000851 1.071e-05 -4.806e-06 1.001 8.068e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008365 Epoch 6967 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01304 0.9928 0.9871 4.029e-06 -1.809e-06 -0.005916 3.036e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003209 -0.002986 -0.009414 0.007182 0.9698 0.9742 0.006099 0.8434 0.8314 0.02009 ] Network output: [ 0.9994 0.003327 0.001657 -3.891e-05 1.747e-05 -0.004035 -2.932e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1844 -0.0293 -0.196 0.1994 0.9836 0.9933 0.2057 0.4586 0.8771 0.7222 ] Network output: [ -0.01178 1 1.01 1.989e-06 -8.93e-07 0.0128 1.499e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005369 0.0004941 0.00431 0.00435 0.9889 0.992 0.005467 0.873 0.9009 0.01457 ] Network output: [ -0.001151 0.006255 1.003 -0.0001332 5.978e-05 0.9926 -0.0001004 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.195 0.09353 0.3224 0.1572 0.9851 0.994 0.1956 0.4634 0.8835 0.717 ] Network output: [ 0.008246 -0.03935 0.9972 7.719e-05 -3.465e-05 1.026 5.817e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09714 0.08605 0.1783 0.2083 0.9873 0.992 0.0972 0.789 0.8769 0.3091 ] Network output: [ -0.008341 0.04194 1.001 7.781e-05 -3.493e-05 0.9738 5.864e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09199 0.0901 0.167 0.1974 0.9856 0.9914 0.092 0.7183 0.8561 0.2432 ] Network output: [ 0.0001899 0.9995 -0.0005717 1.064e-05 -4.774e-06 1.001 8.015e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000827 Epoch 6968 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01307 0.9923 0.9871 4.073e-06 -1.828e-06 -0.005476 3.069e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003208 -0.002986 -0.009409 0.00719 0.9698 0.9742 0.006098 0.8433 0.8314 0.02009 ] Network output: [ 0.9997 -0.0002322 0.001838 -3.862e-05 1.734e-05 -0.001131 -2.91e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1843 -0.02935 -0.1957 0.2 0.9836 0.9933 0.2057 0.4585 0.8771 0.7223 ] Network output: [ -0.01177 1 1.01 2.009e-06 -9.017e-07 0.01295 1.514e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005369 0.0004943 0.00432 0.004367 0.9889 0.992 0.005467 0.873 0.9009 0.01457 ] Network output: [ -0.0008342 0.001433 1.003 -0.0001326 5.953e-05 0.9966 -9.993e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1949 0.09351 0.3228 0.1581 0.9851 0.994 0.1955 0.4633 0.8835 0.717 ] Network output: [ 0.008177 -0.04027 0.9973 7.719e-05 -3.465e-05 1.027 5.817e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09715 0.08606 0.1786 0.2085 0.9873 0.992 0.09721 0.789 0.8769 0.3093 ] Network output: [ -0.008369 0.04224 1.001 7.774e-05 -3.49e-05 0.9735 5.858e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.092 0.0901 0.167 0.1974 0.9856 0.9914 0.09201 0.7184 0.8561 0.2432 ] Network output: [ 0.0003844 0.9996 -0.0008474 1.069e-05 -4.8e-06 1.001 8.057e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008354 Epoch 6969 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01303 0.9928 0.9871 4.018e-06 -1.804e-06 -0.005917 3.028e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003209 -0.002986 -0.00941 0.00718 0.9698 0.9742 0.006099 0.8433 0.8314 0.02008 ] Network output: [ 0.9995 0.00329 0.001657 -3.886e-05 1.745e-05 -0.004006 -2.929e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1844 -0.02931 -0.1959 0.1994 0.9836 0.9933 0.2058 0.4586 0.877 0.7222 ] Network output: [ -0.01178 1 1.01 1.985e-06 -8.911e-07 0.01279 1.496e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00537 0.0004938 0.00431 0.004348 0.9889 0.992 0.005468 0.873 0.9009 0.01457 ] Network output: [ -0.001147 0.006208 1.003 -0.000133 5.969e-05 0.9926 -0.0001002 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.195 0.09352 0.3224 0.1572 0.9851 0.994 0.1956 0.4633 0.8835 0.717 ] Network output: [ 0.00824 -0.03933 0.9972 7.709e-05 -3.461e-05 1.026 5.81e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09714 0.08605 0.1784 0.2083 0.9873 0.992 0.0972 0.7889 0.8769 0.3091 ] Network output: [ -0.008334 0.04191 1.001 7.772e-05 -3.489e-05 0.9738 5.857e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09198 0.09009 0.167 0.1974 0.9856 0.9914 0.09199 0.7182 0.856 0.2432 ] Network output: [ 0.0001914 0.9995 -0.0005732 1.062e-05 -4.769e-06 1.001 8.005e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008261 Epoch 6970 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01306 0.9923 0.9871 4.061e-06 -1.823e-06 -0.005485 3.061e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003209 -0.002986 -0.009405 0.007188 0.9698 0.9742 0.006099 0.8433 0.8314 0.02008 ] Network output: [ 0.9997 -0.0002022 0.001835 -3.858e-05 1.732e-05 -0.001156 -2.907e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1844 -0.02937 -0.1957 0.2 0.9836 0.9933 0.2057 0.4585 0.8771 0.7223 ] Network output: [ -0.01177 1 1.01 2.004e-06 -8.996e-07 0.01294 1.51e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00537 0.000494 0.00432 0.004365 0.9889 0.992 0.005468 0.873 0.9009 0.01457 ] Network output: [ -0.0008367 0.001477 1.003 -0.0001324 5.945e-05 0.9966 -9.979e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.195 0.09351 0.3228 0.158 0.9851 0.994 0.1956 0.4632 0.8835 0.7169 ] Network output: [ 0.008171 -0.04024 0.9973 7.708e-05 -3.461e-05 1.027 5.809e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09716 0.08606 0.1786 0.2085 0.9873 0.992 0.09722 0.789 0.8769 0.3093 ] Network output: [ -0.008361 0.04221 1.001 7.764e-05 -3.486e-05 0.9736 5.851e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09199 0.09009 0.167 0.1974 0.9856 0.9914 0.092 0.7183 0.856 0.2432 ] Network output: [ 0.0003824 0.9996 -0.0008439 1.068e-05 -4.793e-06 1.001 8.047e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008344 Epoch 6971 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01303 0.9928 0.9871 4.008e-06 -1.799e-06 -0.005918 3.021e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003209 -0.002986 -0.009407 0.007178 0.9698 0.9742 0.0061 0.8433 0.8313 0.02008 ] Network output: [ 0.9995 0.003255 0.001657 -3.882e-05 1.743e-05 -0.003977 -2.926e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1844 -0.02932 -0.1959 0.1994 0.9836 0.9933 0.2058 0.4585 0.877 0.7222 ] Network output: [ -0.01177 1 1.01 1.98e-06 -8.891e-07 0.01278 1.492e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005371 0.0004935 0.004311 0.004346 0.9889 0.992 0.005469 0.873 0.9009 0.01456 ] Network output: [ -0.001144 0.006162 1.003 -0.0001328 5.961e-05 0.9927 -0.0001001 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.195 0.09351 0.3225 0.1571 0.9851 0.994 0.1956 0.4633 0.8835 0.717 ] Network output: [ 0.008233 -0.03932 0.9971 7.699e-05 -3.456e-05 1.026 5.802e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09715 0.08605 0.1784 0.2082 0.9873 0.992 0.09721 0.7889 0.8769 0.3091 ] Network output: [ -0.008327 0.04189 1.001 7.762e-05 -3.485e-05 0.9738 5.85e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09197 0.09007 0.167 0.1974 0.9856 0.9914 0.09198 0.7182 0.856 0.2432 ] Network output: [ 0.0001929 0.9995 -0.0005747 1.061e-05 -4.763e-06 1.001 7.996e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008252 Epoch 6972 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01306 0.9923 0.9871 4.05e-06 -1.818e-06 -0.005494 3.052e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003209 -0.002987 -0.009402 0.007185 0.9698 0.9742 0.006099 0.8433 0.8314 0.02008 ] Network output: [ 0.9997 -0.0001727 0.001832 -3.854e-05 1.73e-05 -0.001181 -2.905e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1844 -0.02938 -0.1956 0.2 0.9836 0.9933 0.2058 0.4584 0.877 0.7223 ] Network output: [ -0.01177 1 1.01 1.999e-06 -8.974e-07 0.01293 1.506e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005371 0.0004937 0.004321 0.004363 0.9889 0.992 0.005469 0.873 0.9009 0.01457 ] Network output: [ -0.0008392 0.00152 1.003 -0.0001322 5.937e-05 0.9966 -9.966e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.195 0.0935 0.3228 0.158 0.9851 0.994 0.1956 0.4632 0.8835 0.7169 ] Network output: [ 0.008166 -0.04021 0.9973 7.698e-05 -3.456e-05 1.027 5.802e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09716 0.08607 0.1786 0.2085 0.9873 0.992 0.09722 0.7889 0.8769 0.3092 ] Network output: [ -0.008353 0.04217 1.001 7.755e-05 -3.481e-05 0.9736 5.844e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09198 0.09008 0.167 0.1974 0.9856 0.9914 0.09199 0.7182 0.856 0.2432 ] Network output: [ 0.0003803 0.9996 -0.0008403 1.066e-05 -4.787e-06 1.001 8.036e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008333 Epoch 6973 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01302 0.9928 0.9871 3.998e-06 -1.795e-06 -0.005919 3.013e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003209 -0.002987 -0.009404 0.007176 0.9698 0.9742 0.0061 0.8433 0.8313 0.02008 ] Network output: [ 0.9995 0.003219 0.001657 -3.878e-05 1.741e-05 -0.003949 -2.922e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1844 -0.02934 -0.1958 0.1994 0.9836 0.9933 0.2058 0.4585 0.877 0.7222 ] Network output: [ -0.01177 1 1.01 1.976e-06 -8.871e-07 0.01278 1.489e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005372 0.0004932 0.004311 0.004345 0.9889 0.992 0.00547 0.873 0.9008 0.01456 ] Network output: [ -0.001141 0.006117 1.003 -0.0001326 5.952e-05 0.9927 -9.992e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.195 0.09351 0.3225 0.1571 0.9851 0.994 0.1956 0.4633 0.8835 0.717 ] Network output: [ 0.008226 -0.03931 0.9971 7.688e-05 -3.452e-05 1.026 5.794e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09715 0.08605 0.1784 0.2082 0.9873 0.992 0.09721 0.7888 0.8768 0.3091 ] Network output: [ -0.00832 0.04186 1.001 7.753e-05 -3.48e-05 0.9738 5.843e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09196 0.09006 0.167 0.1973 0.9856 0.9914 0.09197 0.7181 0.856 0.2432 ] Network output: [ 0.0001944 0.9995 -0.0005761 1.06e-05 -4.757e-06 1.001 7.986e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008243 Epoch 6974 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01305 0.9923 0.9871 4.039e-06 -1.813e-06 -0.005503 3.044e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003209 -0.002987 -0.009399 0.007183 0.9698 0.9742 0.0061 0.8433 0.8313 0.02008 ] Network output: [ 0.9997 -0.0001435 0.001828 -3.85e-05 1.729e-05 -0.001205 -2.902e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1844 -0.02939 -0.1956 0.1999 0.9836 0.9933 0.2058 0.4584 0.877 0.7223 ] Network output: [ -0.01176 1 1.01 1.994e-06 -8.952e-07 0.01292 1.503e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005372 0.0004934 0.004321 0.004361 0.9889 0.992 0.00547 0.873 0.9008 0.01456 ] Network output: [ -0.0008416 0.001562 1.003 -0.0001321 5.929e-05 0.9965 -9.952e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.195 0.09349 0.3229 0.158 0.9851 0.994 0.1956 0.4632 0.8835 0.7169 ] Network output: [ 0.008161 -0.04018 0.9973 7.688e-05 -3.451e-05 1.027 5.794e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09716 0.08607 0.1786 0.2084 0.9873 0.992 0.09723 0.7888 0.8768 0.3092 ] Network output: [ -0.008346 0.04214 1.001 7.745e-05 -3.477e-05 0.9736 5.837e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09196 0.09007 0.167 0.1974 0.9856 0.9914 0.09198 0.7181 0.856 0.2432 ] Network output: [ 0.0003783 0.9996 -0.0008368 1.065e-05 -4.781e-06 1.001 8.026e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008323 Epoch 6975 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01302 0.9928 0.9871 3.988e-06 -1.79e-06 -0.00592 3.005e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003209 -0.002987 -0.009401 0.007173 0.9698 0.9742 0.006101 0.8433 0.8313 0.02007 ] Network output: [ 0.9995 0.003184 0.001657 -3.873e-05 1.739e-05 -0.003921 -2.919e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1844 -0.02935 -0.1958 0.1994 0.9836 0.9933 0.2058 0.4585 0.877 0.7222 ] Network output: [ -0.01177 1 1.01 1.972e-06 -8.851e-07 0.01277 1.486e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005373 0.0004929 0.004312 0.004343 0.9889 0.992 0.005471 0.8729 0.9008 0.01456 ] Network output: [ -0.001137 0.006072 1.003 -0.0001324 5.944e-05 0.9928 -9.978e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.195 0.0935 0.3226 0.1571 0.9851 0.994 0.1957 0.4632 0.8835 0.717 ] Network output: [ 0.00822 -0.03929 0.9971 7.678e-05 -3.447e-05 1.026 5.787e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09715 0.08605 0.1784 0.2082 0.9873 0.992 0.09722 0.7887 0.8768 0.3091 ] Network output: [ -0.008313 0.04183 1.001 7.743e-05 -3.476e-05 0.9738 5.836e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09195 0.09005 0.167 0.1973 0.9856 0.9914 0.09196 0.718 0.856 0.2432 ] Network output: [ 0.0001958 0.9995 -0.0005775 1.058e-05 -4.752e-06 1.001 7.976e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008234 Epoch 6976 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01305 0.9923 0.9871 4.028e-06 -1.808e-06 -0.005512 3.036e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003209 -0.002987 -0.009396 0.007181 0.9698 0.9742 0.0061 0.8433 0.8313 0.02007 ] Network output: [ 0.9997 -0.0001148 0.001825 -3.846e-05 1.727e-05 -0.001229 -2.899e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1844 -0.0294 -0.1956 0.1999 0.9836 0.9933 0.2058 0.4584 0.877 0.7222 ] Network output: [ -0.01176 1 1.01 1.989e-06 -8.93e-07 0.01291 1.499e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005373 0.0004931 0.004321 0.004359 0.9889 0.992 0.005471 0.8729 0.9008 0.01456 ] Network output: [ -0.000844 0.001604 1.003 -0.0001319 5.92e-05 0.9965 -9.939e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.195 0.09349 0.3229 0.1579 0.9851 0.994 0.1956 0.4631 0.8835 0.7169 ] Network output: [ 0.008155 -0.04015 0.9973 7.678e-05 -3.447e-05 1.027 5.786e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09717 0.08607 0.1786 0.2084 0.9873 0.992 0.09723 0.7888 0.8768 0.3092 ] Network output: [ -0.008338 0.04211 1.001 7.736e-05 -3.473e-05 0.9736 5.83e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09195 0.09006 0.167 0.1974 0.9856 0.9914 0.09197 0.7181 0.8559 0.2432 ] Network output: [ 0.0003763 0.9996 -0.0008333 1.064e-05 -4.775e-06 1.001 8.015e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008312 Epoch 6977 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01301 0.9928 0.9871 3.978e-06 -1.786e-06 -0.005921 2.998e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00321 -0.002987 -0.009397 0.007171 0.9698 0.9742 0.006101 0.8433 0.8313 0.02007 ] Network output: [ 0.9995 0.00315 0.001657 -3.869e-05 1.737e-05 -0.003893 -2.916e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1845 -0.02936 -0.1958 0.1994 0.9836 0.9933 0.2058 0.4584 0.877 0.7222 ] Network output: [ -0.01177 1 1.01 1.967e-06 -8.831e-07 0.01276 1.482e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005374 0.0004926 0.004312 0.004342 0.9889 0.992 0.005473 0.8729 0.9008 0.01455 ] Network output: [ -0.001134 0.006027 1.003 -0.0001322 5.935e-05 0.9928 -9.964e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1951 0.0935 0.3226 0.1571 0.9851 0.994 0.1957 0.4632 0.8835 0.717 ] Network output: [ 0.008213 -0.03928 0.9971 7.668e-05 -3.442e-05 1.026 5.779e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09716 0.08605 0.1784 0.2082 0.9873 0.992 0.09722 0.7887 0.8768 0.3091 ] Network output: [ -0.008306 0.0418 1.001 7.734e-05 -3.472e-05 0.9738 5.828e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09194 0.09004 0.167 0.1973 0.9856 0.9914 0.09195 0.718 0.8559 0.2432 ] Network output: [ 0.0001972 0.9995 -0.0005789 1.057e-05 -4.746e-06 1.001 7.967e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008225 Epoch 6978 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01304 0.9923 0.9871 4.017e-06 -1.803e-06 -0.005521 3.027e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003209 -0.002988 -0.009393 0.007178 0.9698 0.9742 0.006101 0.8433 0.8313 0.02007 ] Network output: [ 0.9997 -8.643e-05 0.001822 -3.843e-05 1.725e-05 -0.001252 -2.896e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1844 -0.02942 -0.1955 0.1999 0.9836 0.9933 0.2058 0.4583 0.877 0.7222 ] Network output: [ -0.01176 1 1.01 1.984e-06 -8.908e-07 0.0129 1.495e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005374 0.0004928 0.004322 0.004357 0.9889 0.992 0.005472 0.8729 0.9008 0.01456 ] Network output: [ -0.0008464 0.001645 1.003 -0.0001317 5.912e-05 0.9965 -9.925e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.195 0.09348 0.3229 0.1579 0.9851 0.994 0.1957 0.4631 0.8835 0.7169 ] Network output: [ 0.00815 -0.04012 0.9973 7.667e-05 -3.442e-05 1.027 5.778e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09717 0.08607 0.1786 0.2084 0.9873 0.992 0.09723 0.7887 0.8768 0.3092 ] Network output: [ -0.00833 0.04207 1.001 7.726e-05 -3.469e-05 0.9736 5.823e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09194 0.09005 0.167 0.1974 0.9856 0.9914 0.09195 0.718 0.8559 0.2432 ] Network output: [ 0.0003743 0.9996 -0.0008299 1.062e-05 -4.768e-06 1.001 8.005e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008302 Epoch 6979 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01301 0.9928 0.9871 3.968e-06 -1.781e-06 -0.005922 2.99e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00321 -0.002988 -0.009394 0.007169 0.9698 0.9742 0.006102 0.8433 0.8313 0.02006 ] Network output: [ 0.9995 0.003115 0.001657 -3.865e-05 1.735e-05 -0.003866 -2.913e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1845 -0.02938 -0.1957 0.1993 0.9836 0.9933 0.2059 0.4584 0.877 0.7222 ] Network output: [ -0.01176 1 1.01 1.963e-06 -8.811e-07 0.01275 1.479e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005375 0.0004923 0.004313 0.00434 0.9889 0.992 0.005474 0.8729 0.9008 0.01455 ] Network output: [ -0.001131 0.005984 1.003 -0.000132 5.927e-05 0.9928 -9.95e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1951 0.09349 0.3226 0.157 0.9851 0.994 0.1957 0.4632 0.8835 0.7169 ] Network output: [ 0.008206 -0.03926 0.9971 7.658e-05 -3.438e-05 1.026 5.771e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09716 0.08606 0.1784 0.2082 0.9873 0.992 0.09723 0.7886 0.8768 0.3091 ] Network output: [ -0.008299 0.04177 1.001 7.724e-05 -3.468e-05 0.9738 5.821e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09193 0.09003 0.1669 0.1973 0.9856 0.9914 0.09194 0.7179 0.8559 0.2432 ] Network output: [ 0.0001986 0.9995 -0.0005803 1.056e-05 -4.74e-06 1.001 7.957e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008216 Epoch 6980 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01304 0.9923 0.9871 4.006e-06 -1.798e-06 -0.00553 3.019e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00321 -0.002988 -0.009389 0.007176 0.9698 0.9742 0.006101 0.8432 0.8313 0.02006 ] Network output: [ 0.9997 -5.85e-05 0.001819 -3.839e-05 1.723e-05 -0.001275 -2.893e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1845 -0.02943 -0.1955 0.1998 0.9836 0.9933 0.2059 0.4583 0.877 0.7222 ] Network output: [ -0.01176 1 1.01 1.979e-06 -8.887e-07 0.01289 1.492e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005375 0.0004925 0.004322 0.004355 0.9889 0.992 0.005473 0.8729 0.9008 0.01455 ] Network output: [ -0.0008487 0.001686 1.003 -0.0001315 5.904e-05 0.9964 -9.912e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1951 0.09348 0.323 0.1578 0.9851 0.994 0.1957 0.4631 0.8835 0.7169 ] Network output: [ 0.008145 -0.04009 0.9973 7.657e-05 -3.438e-05 1.027 5.771e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09718 0.08607 0.1786 0.2084 0.9873 0.992 0.09724 0.7887 0.8768 0.3092 ] Network output: [ -0.008323 0.04204 1.001 7.717e-05 -3.464e-05 0.9736 5.816e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09193 0.09004 0.167 0.1974 0.9856 0.9914 0.09194 0.7179 0.8559 0.2432 ] Network output: [ 0.0003724 0.9996 -0.0008265 1.061e-05 -4.762e-06 1.001 7.994e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008291 Epoch 6981 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.013 0.9928 0.9871 3.957e-06 -1.777e-06 -0.005923 2.982e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00321 -0.002988 -0.009391 0.007167 0.9698 0.9742 0.006102 0.8433 0.8313 0.02006 ] Network output: [ 0.9995 0.003082 0.001657 -3.86e-05 1.733e-05 -0.003839 -2.909e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1845 -0.02939 -0.1957 0.1993 0.9836 0.9933 0.2059 0.4584 0.877 0.7222 ] Network output: [ -0.01176 1 1.01 1.958e-06 -8.791e-07 0.01275 1.476e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005376 0.000492 0.004313 0.004338 0.9889 0.992 0.005475 0.8729 0.9008 0.01455 ] Network output: [ -0.001128 0.00594 1.003 -0.0001318 5.918e-05 0.9929 -9.935e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1951 0.09348 0.3227 0.157 0.9851 0.994 0.1957 0.4631 0.8835 0.7169 ] Network output: [ 0.0082 -0.03925 0.9971 7.648e-05 -3.433e-05 1.026 5.764e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09717 0.08606 0.1784 0.2081 0.9873 0.992 0.09723 0.7886 0.8768 0.3091 ] Network output: [ -0.008292 0.04174 1.001 7.715e-05 -3.463e-05 0.9739 5.814e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09191 0.09002 0.1669 0.1973 0.9856 0.9914 0.09193 0.7178 0.8559 0.2432 ] Network output: [ 0.0002 0.9995 -0.0005816 1.055e-05 -4.734e-06 1.001 7.947e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008208 Epoch 6982 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01303 0.9924 0.9871 3.995e-06 -1.793e-06 -0.005539 3.01e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00321 -0.002988 -0.009386 0.007174 0.9698 0.9742 0.006102 0.8432 0.8313 0.02006 ] Network output: [ 0.9997 -3.098e-05 0.001815 -3.835e-05 1.722e-05 -0.001298 -2.89e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1845 -0.02944 -0.1954 0.1998 0.9836 0.9933 0.2059 0.4583 0.877 0.7222 ] Network output: [ -0.01175 1 1.01 1.975e-06 -8.865e-07 0.01288 1.488e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005376 0.0004922 0.004322 0.004354 0.9889 0.992 0.005474 0.8729 0.9008 0.01455 ] Network output: [ -0.000851 0.001726 1.003 -0.0001313 5.896e-05 0.9964 -9.898e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1951 0.09347 0.323 0.1578 0.9851 0.994 0.1957 0.463 0.8835 0.7169 ] Network output: [ 0.008139 -0.04006 0.9972 7.647e-05 -3.433e-05 1.027 5.763e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09718 0.08607 0.1786 0.2084 0.9873 0.992 0.09724 0.7886 0.8767 0.3092 ] Network output: [ -0.008315 0.042 1.001 7.707e-05 -3.46e-05 0.9737 5.809e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09192 0.09002 0.167 0.1973 0.9856 0.9914 0.09193 0.7179 0.8559 0.2432 ] Network output: [ 0.0003704 0.9996 -0.0008231 1.059e-05 -4.756e-06 1.001 7.984e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008281 Epoch 6983 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.013 0.9928 0.9871 3.947e-06 -1.772e-06 -0.005924 2.975e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00321 -0.002988 -0.009387 0.007165 0.9698 0.9742 0.006103 0.8432 0.8313 0.02005 ] Network output: [ 0.9995 0.003048 0.001656 -3.856e-05 1.731e-05 -0.003812 -2.906e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1845 -0.0294 -0.1956 0.1993 0.9836 0.9933 0.2059 0.4583 0.877 0.7222 ] Network output: [ -0.01176 1 1.01 1.954e-06 -8.771e-07 0.01274 1.472e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005378 0.0004917 0.004314 0.004337 0.9889 0.992 0.005476 0.8729 0.9008 0.01454 ] Network output: [ -0.001125 0.005898 1.003 -0.0001316 5.91e-05 0.9929 -9.921e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1951 0.09348 0.3227 0.157 0.9851 0.994 0.1957 0.4631 0.8834 0.7169 ] Network output: [ 0.008193 -0.03924 0.9971 7.638e-05 -3.429e-05 1.026 5.756e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09717 0.08606 0.1784 0.2081 0.9873 0.992 0.09723 0.7885 0.8767 0.3091 ] Network output: [ -0.008285 0.04171 1.001 7.705e-05 -3.459e-05 0.9739 5.807e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0919 0.09001 0.1669 0.1973 0.9856 0.9914 0.09192 0.7177 0.8558 0.2432 ] Network output: [ 0.0002013 0.9995 -0.0005828 1.053e-05 -4.729e-06 1.001 7.938e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008199 Epoch 6984 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01303 0.9924 0.9871 3.984e-06 -1.788e-06 -0.005548 3.002e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00321 -0.002989 -0.009383 0.007171 0.9698 0.9742 0.006102 0.8432 0.8313 0.02005 ] Network output: [ 0.9997 -3.875e-06 0.001812 -3.831e-05 1.72e-05 -0.001321 -2.887e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1845 -0.02945 -0.1954 0.1998 0.9836 0.9933 0.2059 0.4583 0.877 0.7222 ] Network output: [ -0.01175 1 1.01 1.97e-06 -8.843e-07 0.01287 1.484e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005377 0.0004919 0.004323 0.004352 0.9889 0.992 0.005476 0.8729 0.9008 0.01454 ] Network output: [ -0.0008532 0.001765 1.003 -0.0001312 5.888e-05 0.9964 -9.884e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1951 0.09347 0.323 0.1577 0.9851 0.994 0.1957 0.463 0.8834 0.7169 ] Network output: [ 0.008134 -0.04003 0.9972 7.637e-05 -3.428e-05 1.027 5.755e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09719 0.08608 0.1786 0.2083 0.9873 0.992 0.09725 0.7886 0.8767 0.3092 ] Network output: [ -0.008307 0.04197 1.001 7.698e-05 -3.456e-05 0.9737 5.801e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09191 0.09001 0.167 0.1973 0.9856 0.9914 0.09192 0.7178 0.8558 0.2432 ] Network output: [ 0.0003685 0.9996 -0.0008197 1.058e-05 -4.75e-06 1.001 7.973e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000827 Epoch 6985 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01299 0.9928 0.9871 3.937e-06 -1.767e-06 -0.005926 2.967e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003211 -0.002989 -0.009384 0.007163 0.9698 0.9742 0.006103 0.8432 0.8313 0.02005 ] Network output: [ 0.9995 0.003016 0.001656 -3.852e-05 1.729e-05 -0.003786 -2.903e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1845 -0.02942 -0.1956 0.1993 0.9836 0.9933 0.2059 0.4583 0.8769 0.7222 ] Network output: [ -0.01176 1 1.01 1.949e-06 -8.75e-07 0.01273 1.469e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005379 0.0004914 0.004314 0.004335 0.9889 0.992 0.005477 0.8729 0.9008 0.01454 ] Network output: [ -0.001122 0.005856 1.003 -0.0001315 5.902e-05 0.993 -9.907e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1951 0.09347 0.3228 0.157 0.9851 0.994 0.1958 0.463 0.8834 0.7169 ] Network output: [ 0.008187 -0.03922 0.9971 7.628e-05 -3.424e-05 1.026 5.748e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09718 0.08606 0.1784 0.2081 0.9873 0.992 0.09724 0.7885 0.8767 0.309 ] Network output: [ -0.008278 0.04169 1.001 7.696e-05 -3.455e-05 0.9739 5.8e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09189 0.09 0.1669 0.1973 0.9856 0.9914 0.0919 0.7177 0.8558 0.2432 ] Network output: [ 0.0002026 0.9995 -0.0005841 1.052e-05 -4.723e-06 1.001 7.928e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000819 Epoch 6986 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01302 0.9924 0.9872 3.973e-06 -1.783e-06 -0.005556 2.994e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00321 -0.002989 -0.00938 0.007169 0.9698 0.9742 0.006103 0.8432 0.8313 0.02005 ] Network output: [ 0.9997 2.283e-05 0.001809 -3.827e-05 1.718e-05 -0.001343 -2.884e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1845 -0.02947 -0.1954 0.1998 0.9836 0.9933 0.2059 0.4582 0.877 0.7222 ] Network output: [ -0.01175 1 1.01 1.965e-06 -8.821e-07 0.01286 1.481e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005378 0.0004916 0.004323 0.00435 0.9889 0.992 0.005477 0.8728 0.9008 0.01454 ] Network output: [ -0.0008554 0.001804 1.003 -0.000131 5.88e-05 0.9963 -9.871e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1951 0.09346 0.3231 0.1577 0.9851 0.994 0.1957 0.463 0.8834 0.7169 ] Network output: [ 0.008129 -0.04 0.9972 7.627e-05 -3.424e-05 1.027 5.748e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09719 0.08608 0.1786 0.2083 0.9873 0.992 0.09725 0.7885 0.8767 0.3092 ] Network output: [ -0.0083 0.04194 1.001 7.689e-05 -3.452e-05 0.9737 5.794e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0919 0.09 0.167 0.1973 0.9856 0.9914 0.09191 0.7177 0.8558 0.2432 ] Network output: [ 0.0003666 0.9996 -0.0008164 1.057e-05 -4.743e-06 1.001 7.963e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000826 Epoch 6987 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01299 0.9928 0.9871 3.927e-06 -1.763e-06 -0.005927 2.959e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003211 -0.002989 -0.009381 0.00716 0.9698 0.9742 0.006104 0.8432 0.8312 0.02004 ] Network output: [ 0.9995 0.002983 0.001656 -3.847e-05 1.727e-05 -0.00376 -2.899e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1846 -0.02943 -0.1956 0.1993 0.9836 0.9933 0.206 0.4583 0.8769 0.7222 ] Network output: [ -0.01175 1 1.01 1.945e-06 -8.73e-07 0.01272 1.466e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00538 0.0004911 0.004315 0.004334 0.9889 0.992 0.005478 0.8728 0.9008 0.01454 ] Network output: [ -0.001119 0.005814 1.003 -0.0001313 5.893e-05 0.993 -9.893e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1952 0.09347 0.3228 0.1569 0.9851 0.994 0.1958 0.463 0.8834 0.7169 ] Network output: [ 0.00818 -0.03921 0.9971 7.617e-05 -3.42e-05 1.026 5.741e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09718 0.08606 0.1784 0.2081 0.9873 0.992 0.09724 0.7884 0.8767 0.309 ] Network output: [ -0.008271 0.04166 1.001 7.686e-05 -3.451e-05 0.9739 5.793e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09188 0.08998 0.1669 0.1973 0.9856 0.9914 0.09189 0.7176 0.8558 0.2432 ] Network output: [ 0.0002039 0.9995 -0.0005853 1.051e-05 -4.717e-06 1.001 7.918e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008181 Epoch 6988 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01301 0.9924 0.9872 3.961e-06 -1.778e-06 -0.005565 2.985e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00321 -0.002989 -0.009376 0.007167 0.9698 0.9742 0.006103 0.8432 0.8313 0.02004 ] Network output: [ 0.9997 4.913e-05 0.001806 -3.823e-05 1.716e-05 -0.001365 -2.881e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1845 -0.02948 -0.1953 0.1997 0.9836 0.9933 0.2059 0.4582 0.8769 0.7222 ] Network output: [ -0.01175 1 1.01 1.96e-06 -8.799e-07 0.01285 1.477e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005379 0.0004913 0.004323 0.004348 0.9889 0.992 0.005478 0.8728 0.9008 0.01454 ] Network output: [ -0.0008575 0.001842 1.003 -0.0001308 5.872e-05 0.9963 -9.857e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1952 0.09346 0.3231 0.1577 0.9851 0.994 0.1958 0.4629 0.8834 0.7169 ] Network output: [ 0.008123 -0.03997 0.9972 7.616e-05 -3.419e-05 1.027 5.74e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0972 0.08608 0.1786 0.2083 0.9873 0.992 0.09726 0.7884 0.8767 0.3091 ] Network output: [ -0.008292 0.0419 1.001 7.679e-05 -3.447e-05 0.9737 5.787e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09189 0.08999 0.1669 0.1973 0.9856 0.9914 0.0919 0.7176 0.8558 0.2432 ] Network output: [ 0.0003648 0.9996 -0.0008131 1.055e-05 -4.737e-06 1.001 7.952e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000825 Epoch 6989 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01298 0.9928 0.9872 3.917e-06 -1.758e-06 -0.005928 2.952e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003211 -0.002989 -0.009378 0.007158 0.9698 0.9742 0.006104 0.8432 0.8312 0.02004 ] Network output: [ 0.9995 0.002951 0.001656 -3.843e-05 1.725e-05 -0.003734 -2.896e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1846 -0.02944 -0.1955 0.1993 0.9836 0.9933 0.206 0.4582 0.8769 0.7222 ] Network output: [ -0.01175 1 1.01 1.94e-06 -8.71e-07 0.01271 1.462e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005381 0.0004908 0.004315 0.004332 0.9889 0.992 0.005479 0.8728 0.9008 0.01453 ] Network output: [ -0.001116 0.005773 1.003 -0.0001311 5.885e-05 0.993 -9.879e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1952 0.09346 0.3228 0.1569 0.9851 0.994 0.1958 0.463 0.8834 0.7169 ] Network output: [ 0.008174 -0.03919 0.9971 7.607e-05 -3.415e-05 1.026 5.733e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09719 0.08607 0.1784 0.2081 0.9873 0.992 0.09725 0.7883 0.8767 0.309 ] Network output: [ -0.008264 0.04163 1.001 7.677e-05 -3.446e-05 0.9739 5.785e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09187 0.08997 0.1669 0.1973 0.9856 0.9914 0.09188 0.7175 0.8558 0.2432 ] Network output: [ 0.0002052 0.9995 -0.0005865 1.049e-05 -4.711e-06 1.001 7.909e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008172 Epoch 6990 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01301 0.9924 0.9872 3.95e-06 -1.773e-06 -0.005574 2.977e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003211 -0.00299 -0.009373 0.007164 0.9698 0.9742 0.006104 0.8432 0.8312 0.02004 ] Network output: [ 0.9997 7.503e-05 0.001802 -3.819e-05 1.715e-05 -0.001386 -2.878e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1846 -0.02949 -0.1953 0.1997 0.9836 0.9933 0.206 0.4582 0.8769 0.7222 ] Network output: [ -0.01174 1 1.01 1.955e-06 -8.777e-07 0.01284 1.473e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005381 0.000491 0.004323 0.004346 0.9889 0.992 0.005479 0.8728 0.9008 0.01453 ] Network output: [ -0.0008596 0.00188 1.003 -0.0001306 5.864e-05 0.9963 -9.844e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1952 0.09345 0.3231 0.1576 0.9851 0.994 0.1958 0.4629 0.8834 0.7169 ] Network output: [ 0.008118 -0.03994 0.9972 7.606e-05 -3.415e-05 1.027 5.732e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0972 0.08608 0.1786 0.2083 0.9873 0.992 0.09726 0.7884 0.8767 0.3091 ] Network output: [ -0.008285 0.04187 1.001 7.67e-05 -3.443e-05 0.9737 5.78e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09188 0.08998 0.1669 0.1973 0.9856 0.9914 0.09189 0.7176 0.8557 0.2432 ] Network output: [ 0.0003629 0.9996 -0.0008098 1.054e-05 -4.731e-06 1.001 7.942e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008239 Epoch 6991 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01298 0.9928 0.9872 3.906e-06 -1.754e-06 -0.00593 2.944e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003211 -0.00299 -0.009374 0.007156 0.9698 0.9742 0.006105 0.8432 0.8312 0.02004 ] Network output: [ 0.9995 0.002919 0.001655 -3.839e-05 1.723e-05 -0.003708 -2.893e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1846 -0.02946 -0.1955 0.1992 0.9836 0.9933 0.206 0.4582 0.8769 0.7221 ] Network output: [ -0.01175 1 1.01 1.936e-06 -8.689e-07 0.01271 1.459e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005382 0.0004906 0.004316 0.00433 0.9889 0.992 0.00548 0.8728 0.9007 0.01453 ] Network output: [ -0.001113 0.005732 1.003 -0.0001309 5.876e-05 0.9931 -9.864e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1952 0.09346 0.3229 0.1569 0.9851 0.994 0.1958 0.4629 0.8834 0.7169 ] Network output: [ 0.008167 -0.03918 0.9971 7.597e-05 -3.411e-05 1.026 5.725e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09719 0.08607 0.1784 0.208 0.9873 0.992 0.09725 0.7883 0.8766 0.309 ] Network output: [ -0.008257 0.0416 1.001 7.667e-05 -3.442e-05 0.9739 5.778e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09186 0.08996 0.1669 0.1973 0.9856 0.9914 0.09187 0.7175 0.8557 0.2432 ] Network output: [ 0.0002065 0.9995 -0.0005876 1.048e-05 -4.705e-06 1.001 7.899e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008163 Epoch 6992 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.013 0.9924 0.9872 3.939e-06 -1.769e-06 -0.005582 2.969e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003211 -0.00299 -0.00937 0.007162 0.9698 0.9742 0.006104 0.8432 0.8312 0.02003 ] Network output: [ 0.9997 0.0001005 0.001799 -3.815e-05 1.713e-05 -0.001407 -2.875e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1846 -0.0295 -0.1953 0.1997 0.9836 0.9933 0.206 0.4581 0.8769 0.7222 ] Network output: [ -0.01174 1 1.01 1.95e-06 -8.755e-07 0.01283 1.47e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005382 0.0004907 0.004324 0.004344 0.9889 0.992 0.00548 0.8728 0.9007 0.01453 ] Network output: [ -0.0008617 0.001917 1.003 -0.0001304 5.856e-05 0.9962 -9.83e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1952 0.09345 0.3232 0.1576 0.9851 0.994 0.1958 0.4629 0.8834 0.7169 ] Network output: [ 0.008112 -0.03991 0.9972 7.596e-05 -3.41e-05 1.027 5.725e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09721 0.08608 0.1786 0.2082 0.9873 0.992 0.09727 0.7883 0.8766 0.3091 ] Network output: [ -0.008277 0.04183 1.001 7.66e-05 -3.439e-05 0.9737 5.773e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09186 0.08997 0.1669 0.1973 0.9856 0.9914 0.09188 0.7175 0.8557 0.2432 ] Network output: [ 0.0003611 0.9996 -0.0008065 1.052e-05 -4.724e-06 1.001 7.931e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008229 Epoch 6993 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01297 0.9928 0.9872 3.896e-06 -1.749e-06 -0.005931 2.936e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003211 -0.00299 -0.009371 0.007154 0.9698 0.9742 0.006105 0.8432 0.8312 0.02003 ] Network output: [ 0.9995 0.002888 0.001655 -3.834e-05 1.721e-05 -0.003683 -2.89e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1846 -0.02947 -0.1954 0.1992 0.9836 0.9933 0.206 0.4582 0.8769 0.7221 ] Network output: [ -0.01175 1 1.01 1.931e-06 -8.669e-07 0.0127 1.455e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005383 0.0004903 0.004316 0.004329 0.9889 0.992 0.005481 0.8728 0.9007 0.01453 ] Network output: [ -0.00111 0.005693 1.003 -0.0001307 5.868e-05 0.9931 -9.85e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1952 0.09345 0.3229 0.1569 0.9851 0.994 0.1958 0.4629 0.8834 0.7169 ] Network output: [ 0.008161 -0.03916 0.9971 7.587e-05 -3.406e-05 1.026 5.718e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0972 0.08607 0.1784 0.208 0.9873 0.992 0.09726 0.7882 0.8766 0.309 ] Network output: [ -0.00825 0.04157 1.001 7.658e-05 -3.438e-05 0.9739 5.771e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09185 0.08995 0.1669 0.1973 0.9856 0.9914 0.09186 0.7174 0.8557 0.2432 ] Network output: [ 0.0002077 0.9995 -0.0005887 1.047e-05 -4.7e-06 1.001 7.889e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008154 Epoch 6994 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.013 0.9924 0.9872 3.928e-06 -1.764e-06 -0.005591 2.961e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003211 -0.00299 -0.009367 0.00716 0.9698 0.9742 0.006105 0.8431 0.8312 0.02003 ] Network output: [ 0.9997 0.0001256 0.001796 -3.812e-05 1.711e-05 -0.001428 -2.873e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1846 -0.02952 -0.1952 0.1997 0.9836 0.9933 0.206 0.4581 0.8769 0.7222 ] Network output: [ -0.01174 1 1.01 1.945e-06 -8.733e-07 0.01282 1.466e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005383 0.0004904 0.004324 0.004342 0.9889 0.992 0.005481 0.8728 0.9007 0.01453 ] Network output: [ -0.0008637 0.001954 1.003 -0.0001303 5.848e-05 0.9962 -9.817e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1952 0.09344 0.3232 0.1575 0.9851 0.994 0.1958 0.4628 0.8834 0.7168 ] Network output: [ 0.008107 -0.03988 0.9972 7.586e-05 -3.406e-05 1.027 5.717e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09721 0.08608 0.1786 0.2082 0.9873 0.992 0.09727 0.7883 0.8766 0.3091 ] Network output: [ -0.008269 0.0418 1.001 7.651e-05 -3.435e-05 0.9738 5.766e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09185 0.08996 0.1669 0.1973 0.9856 0.9914 0.09187 0.7174 0.8557 0.2432 ] Network output: [ 0.0003593 0.9996 -0.0008033 1.051e-05 -4.718e-06 1.001 7.921e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008219 Epoch 6995 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01297 0.9928 0.9872 3.886e-06 -1.745e-06 -0.005933 2.929e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003212 -0.00299 -0.009368 0.007152 0.9698 0.9742 0.006106 0.8431 0.8312 0.02003 ] Network output: [ 0.9995 0.002857 0.001655 -3.83e-05 1.719e-05 -0.003658 -2.886e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1846 -0.02948 -0.1954 0.1992 0.9836 0.9933 0.2061 0.4581 0.8769 0.7221 ] Network output: [ -0.01174 1 1.01 1.926e-06 -8.648e-07 0.01269 1.452e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005384 0.00049 0.004317 0.004327 0.9889 0.992 0.005482 0.8728 0.9007 0.01452 ] Network output: [ -0.001107 0.005653 1.003 -0.0001305 5.859e-05 0.9931 -9.836e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1953 0.09345 0.323 0.1568 0.9851 0.994 0.1959 0.4629 0.8834 0.7169 ] Network output: [ 0.008154 -0.03915 0.9971 7.577e-05 -3.402e-05 1.026 5.71e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0972 0.08607 0.1784 0.208 0.9873 0.992 0.09726 0.7882 0.8766 0.309 ] Network output: [ -0.008242 0.04154 1.001 7.648e-05 -3.434e-05 0.9739 5.764e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09184 0.08994 0.1669 0.1972 0.9856 0.9914 0.09185 0.7173 0.8557 0.2432 ] Network output: [ 0.0002089 0.9995 -0.0005898 1.046e-05 -4.694e-06 1.001 7.88e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008145 Epoch 6996 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01299 0.9924 0.9872 3.917e-06 -1.759e-06 -0.005599 2.952e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003211 -0.00299 -0.009364 0.007158 0.9698 0.9742 0.006105 0.8431 0.8312 0.02003 ] Network output: [ 0.9997 0.0001504 0.001793 -3.808e-05 1.709e-05 -0.001448 -2.87e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1846 -0.02953 -0.1952 0.1996 0.9836 0.9933 0.206 0.4581 0.8769 0.7222 ] Network output: [ -0.01174 1 1.01 1.94e-06 -8.711e-07 0.0128 1.462e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005384 0.0004902 0.004324 0.00434 0.9889 0.992 0.005482 0.8727 0.9007 0.01452 ] Network output: [ -0.0008657 0.00199 1.003 -0.0001301 5.84e-05 0.9962 -9.803e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1952 0.09344 0.3232 0.1575 0.9851 0.994 0.1959 0.4628 0.8834 0.7168 ] Network output: [ 0.008102 -0.03985 0.9972 7.576e-05 -3.401e-05 1.027 5.709e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09721 0.08609 0.1786 0.2082 0.9873 0.992 0.09728 0.7882 0.8766 0.3091 ] Network output: [ -0.008262 0.04177 1.001 7.641e-05 -3.43e-05 0.9738 5.759e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09184 0.08995 0.1669 0.1973 0.9856 0.9914 0.09186 0.7174 0.8557 0.2432 ] Network output: [ 0.0003575 0.9996 -0.0008001 1.05e-05 -4.712e-06 1.001 7.91e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008208 Epoch 6997 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01296 0.9928 0.9872 3.876e-06 -1.74e-06 -0.005934 2.921e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003212 -0.002991 -0.009365 0.00715 0.9698 0.9742 0.006106 0.8431 0.8312 0.02002 ] Network output: [ 0.9995 0.002827 0.001654 -3.825e-05 1.717e-05 -0.003634 -2.883e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1847 -0.0295 -0.1954 0.1992 0.9836 0.9933 0.2061 0.4581 0.8769 0.7221 ] Network output: [ -0.01174 1 1.01 1.922e-06 -8.628e-07 0.01268 1.448e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005385 0.0004897 0.004317 0.004326 0.9889 0.992 0.005483 0.8727 0.9007 0.01452 ] Network output: [ -0.001104 0.005614 1.003 -0.0001303 5.851e-05 0.9932 -9.822e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1953 0.09344 0.323 0.1568 0.9851 0.994 0.1959 0.4628 0.8834 0.7169 ] Network output: [ 0.008148 -0.03913 0.9971 7.567e-05 -3.397e-05 1.026 5.703e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09721 0.08608 0.1784 0.208 0.9873 0.992 0.09727 0.7881 0.8766 0.309 ] Network output: [ -0.008235 0.04151 1.001 7.639e-05 -3.429e-05 0.974 5.757e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09183 0.08993 0.1669 0.1972 0.9856 0.9914 0.09184 0.7173 0.8556 0.2432 ] Network output: [ 0.0002101 0.9995 -0.0005908 1.044e-05 -4.688e-06 1.001 7.87e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008137 Epoch 6998 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01299 0.9924 0.9872 3.906e-06 -1.754e-06 -0.005608 2.944e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003212 -0.002991 -0.00936 0.007155 0.9698 0.9742 0.006106 0.8431 0.8312 0.02002 ] Network output: [ 0.9997 0.0001747 0.00179 -3.804e-05 1.708e-05 -0.001469 -2.867e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1847 -0.02954 -0.1952 0.1996 0.9836 0.9933 0.2061 0.458 0.8769 0.7222 ] Network output: [ -0.01173 1 1.01 1.935e-06 -8.688e-07 0.01279 1.459e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005385 0.0004899 0.004325 0.004338 0.9889 0.992 0.005483 0.8727 0.9007 0.01452 ] Network output: [ -0.0008677 0.002025 1.003 -0.0001299 5.832e-05 0.9962 -9.79e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1953 0.09343 0.3233 0.1575 0.9851 0.994 0.1959 0.4628 0.8834 0.7168 ] Network output: [ 0.008096 -0.03982 0.9972 7.565e-05 -3.396e-05 1.027 5.701e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09722 0.08609 0.1786 0.2082 0.9873 0.992 0.09728 0.7881 0.8766 0.3091 ] Network output: [ -0.008254 0.04173 1.001 7.632e-05 -3.426e-05 0.9738 5.752e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09183 0.08993 0.1669 0.1973 0.9856 0.9914 0.09184 0.7173 0.8556 0.2432 ] Network output: [ 0.0003557 0.9996 -0.000797 1.048e-05 -4.706e-06 1.001 7.9e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008198 Epoch 6999 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01296 0.9928 0.9872 3.866e-06 -1.735e-06 -0.005936 2.913e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003212 -0.002991 -0.009361 0.007148 0.9698 0.9742 0.006107 0.8431 0.8312 0.02002 ] Network output: [ 0.9995 0.002797 0.001654 -3.821e-05 1.715e-05 -0.00361 -2.88e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1847 -0.02951 -0.1953 0.1992 0.9836 0.9933 0.2061 0.4581 0.8769 0.7221 ] Network output: [ -0.01174 1 1.01 1.917e-06 -8.607e-07 0.01268 1.445e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005386 0.0004894 0.004318 0.004324 0.9889 0.992 0.005485 0.8727 0.9007 0.01452 ] Network output: [ -0.001101 0.005576 1.003 -0.0001301 5.843e-05 0.9932 -9.808e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1953 0.09344 0.323 0.1568 0.9851 0.994 0.1959 0.4628 0.8834 0.7169 ] Network output: [ 0.008141 -0.03912 0.9971 7.557e-05 -3.392e-05 1.026 5.695e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09721 0.08608 0.1784 0.208 0.9873 0.992 0.09727 0.7881 0.8765 0.309 ] Network output: [ -0.008228 0.04148 1.001 7.629e-05 -3.425e-05 0.974 5.75e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09182 0.08992 0.1669 0.1972 0.9856 0.9914 0.09183 0.7172 0.8556 0.2432 ] Network output: [ 0.0002113 0.9995 -0.0005918 1.043e-05 -4.682e-06 1.001 7.86e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008128 Epoch 7000 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01298 0.9925 0.9872 3.895e-06 -1.749e-06 -0.005616 2.936e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003212 -0.002991 -0.009357 0.007153 0.9698 0.9742 0.006106 0.8431 0.8312 0.02002 ] Network output: [ 0.9997 0.0001986 0.001787 -3.8e-05 1.706e-05 -0.001488 -2.864e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1847 -0.02955 -0.1951 0.1996 0.9836 0.9933 0.2061 0.458 0.8769 0.7221 ] Network output: [ -0.01173 1 1.01 1.93e-06 -8.666e-07 0.01278 1.455e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005386 0.0004896 0.004325 0.004336 0.9889 0.992 0.005484 0.8727 0.9007 0.01452 ] Network output: [ -0.0008696 0.00206 1.003 -0.0001297 5.823e-05 0.9961 -9.776e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1953 0.09343 0.3233 0.1574 0.9851 0.994 0.1959 0.4627 0.8833 0.7168 ] Network output: [ 0.008091 -0.03979 0.9972 7.555e-05 -3.392e-05 1.027 5.694e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09722 0.08609 0.1786 0.2081 0.9873 0.992 0.09729 0.7881 0.8765 0.3091 ] Network output: [ -0.008247 0.0417 1.001 7.622e-05 -3.422e-05 0.9738 5.744e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09182 0.08992 0.1669 0.1973 0.9856 0.9914 0.09183 0.7172 0.8556 0.2432 ] Network output: [ 0.000354 0.9996 -0.0007938 1.047e-05 -4.7e-06 1.001 7.889e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008188 Epoch 7001 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01295 0.9928 0.9872 3.855e-06 -1.731e-06 -0.005938 2.906e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003212 -0.002991 -0.009358 0.007145 0.9698 0.9742 0.006107 0.8431 0.8312 0.02001 ] Network output: [ 0.9995 0.002767 0.001654 -3.817e-05 1.713e-05 -0.003586 -2.876e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1847 -0.02952 -0.1953 0.1992 0.9836 0.9933 0.2061 0.458 0.8768 0.7221 ] Network output: [ -0.01174 1 1.01 1.913e-06 -8.586e-07 0.01267 1.441e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005387 0.0004892 0.004318 0.004322 0.9889 0.992 0.005486 0.8727 0.9007 0.01451 ] Network output: [ -0.001098 0.005538 1.003 -0.00013 5.834e-05 0.9932 -9.794e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1953 0.09343 0.3231 0.1567 0.9851 0.994 0.1959 0.4628 0.8833 0.7168 ] Network output: [ 0.008135 -0.0391 0.9971 7.547e-05 -3.388e-05 1.026 5.687e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09722 0.08608 0.1785 0.208 0.9873 0.992 0.09728 0.788 0.8765 0.309 ] Network output: [ -0.008221 0.04145 1.001 7.62e-05 -3.421e-05 0.974 5.742e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09181 0.08991 0.1669 0.1972 0.9856 0.9914 0.09182 0.7171 0.8556 0.2432 ] Network output: [ 0.0002125 0.9995 -0.0005928 1.042e-05 -4.677e-06 1.001 7.851e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008119 Epoch 7002 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01298 0.9925 0.9872 3.884e-06 -1.744e-06 -0.005624 2.927e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003212 -0.002991 -0.009354 0.007151 0.9698 0.9742 0.006107 0.8431 0.8312 0.02001 ] Network output: [ 0.9997 0.0002221 0.001784 -3.796e-05 1.704e-05 -0.001508 -2.861e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1847 -0.02957 -0.1951 0.1996 0.9836 0.9933 0.2061 0.458 0.8769 0.7221 ] Network output: [ -0.01173 1 1.01 1.925e-06 -8.644e-07 0.01277 1.451e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005387 0.0004893 0.004325 0.004335 0.9889 0.992 0.005486 0.8727 0.9007 0.01451 ] Network output: [ -0.0008714 0.002094 1.003 -0.0001295 5.815e-05 0.9961 -9.762e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1953 0.09342 0.3233 0.1574 0.9851 0.994 0.1959 0.4627 0.8833 0.7168 ] Network output: [ 0.008085 -0.03976 0.9972 7.545e-05 -3.387e-05 1.027 5.686e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09723 0.08609 0.1786 0.2081 0.9873 0.992 0.09729 0.788 0.8765 0.3091 ] Network output: [ -0.008239 0.04166 1.001 7.613e-05 -3.418e-05 0.9738 5.737e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09181 0.08991 0.1669 0.1972 0.9856 0.9914 0.09182 0.7171 0.8556 0.2432 ] Network output: [ 0.0003523 0.9996 -0.0007907 1.045e-05 -4.693e-06 1.001 7.879e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008178 Epoch 7003 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01295 0.9929 0.9872 3.845e-06 -1.726e-06 -0.00594 2.898e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003212 -0.002991 -0.009355 0.007143 0.9698 0.9742 0.006108 0.8431 0.8311 0.02001 ] Network output: [ 0.9995 0.002738 0.001653 -3.812e-05 1.712e-05 -0.003562 -2.873e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1847 -0.02954 -0.1952 0.1991 0.9836 0.9933 0.2061 0.458 0.8768 0.7221 ] Network output: [ -0.01173 1 1.01 1.908e-06 -8.566e-07 0.01266 1.438e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005388 0.0004889 0.004319 0.004321 0.9889 0.992 0.005487 0.8727 0.9007 0.01451 ] Network output: [ -0.001096 0.005501 1.003 -0.0001298 5.826e-05 0.9933 -9.78e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1953 0.09343 0.3231 0.1567 0.9851 0.994 0.196 0.4627 0.8833 0.7168 ] Network output: [ 0.008128 -0.03909 0.9971 7.536e-05 -3.383e-05 1.026 5.68e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09722 0.08608 0.1785 0.2079 0.9873 0.992 0.09728 0.788 0.8765 0.309 ] Network output: [ -0.008214 0.04142 1.001 7.61e-05 -3.416e-05 0.974 5.735e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0918 0.0899 0.1668 0.1972 0.9856 0.9914 0.09181 0.7171 0.8556 0.2432 ] Network output: [ 0.0002136 0.9995 -0.0005938 1.04e-05 -4.671e-06 1.001 7.841e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000811 Epoch 7004 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01297 0.9925 0.9872 3.873e-06 -1.739e-06 -0.005633 2.919e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003212 -0.002992 -0.009351 0.007148 0.9698 0.9742 0.006107 0.8431 0.8312 0.02001 ] Network output: [ 0.9997 0.0002453 0.001781 -3.792e-05 1.702e-05 -0.001527 -2.858e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1847 -0.02958 -0.195 0.1995 0.9836 0.9933 0.2061 0.4579 0.8768 0.7221 ] Network output: [ -0.01173 1 1.01 1.92e-06 -8.622e-07 0.01276 1.447e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005388 0.000489 0.004326 0.004333 0.9889 0.992 0.005487 0.8727 0.9007 0.01451 ] Network output: [ -0.0008733 0.002128 1.003 -0.0001294 5.807e-05 0.9961 -9.749e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1953 0.09342 0.3234 0.1573 0.9851 0.994 0.1959 0.4627 0.8833 0.7168 ] Network output: [ 0.00808 -0.03973 0.9972 7.535e-05 -3.383e-05 1.027 5.678e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09723 0.0861 0.1786 0.2081 0.9873 0.992 0.0973 0.788 0.8765 0.309 ] Network output: [ -0.008232 0.04163 1.001 7.603e-05 -3.413e-05 0.9738 5.73e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0918 0.0899 0.1669 0.1972 0.9856 0.9914 0.09181 0.7171 0.8555 0.2432 ] Network output: [ 0.0003506 0.9996 -0.0007877 1.044e-05 -4.687e-06 1.001 7.868e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008167 Epoch 7005 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01294 0.9929 0.9872 3.835e-06 -1.722e-06 -0.005941 2.89e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003213 -0.002992 -0.009352 0.007141 0.9698 0.9742 0.006108 0.8431 0.8311 0.02 ] Network output: [ 0.9995 0.002709 0.001653 -3.808e-05 1.71e-05 -0.003539 -2.87e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1847 -0.02955 -0.1952 0.1991 0.9836 0.9933 0.2062 0.458 0.8768 0.7221 ] Network output: [ -0.01173 1 1.01 1.903e-06 -8.545e-07 0.01265 1.434e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005389 0.0004886 0.004319 0.004319 0.9889 0.992 0.005488 0.8727 0.9007 0.01451 ] Network output: [ -0.001093 0.005464 1.003 -0.0001296 5.817e-05 0.9933 -9.766e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1954 0.09342 0.3232 0.1567 0.9851 0.994 0.196 0.4627 0.8833 0.7168 ] Network output: [ 0.008122 -0.03907 0.997 7.526e-05 -3.379e-05 1.026 5.672e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09723 0.08609 0.1785 0.2079 0.9873 0.992 0.09729 0.7879 0.8765 0.3089 ] Network output: [ -0.008207 0.04139 1.001 7.601e-05 -3.412e-05 0.974 5.728e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09179 0.08989 0.1668 0.1972 0.9856 0.9914 0.0918 0.717 0.8555 0.2432 ] Network output: [ 0.0002147 0.9995 -0.0005947 1.039e-05 -4.665e-06 1.001 7.831e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008101 Epoch 7006 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01296 0.9925 0.9872 3.862e-06 -1.734e-06 -0.005641 2.911e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003212 -0.002992 -0.009348 0.007146 0.9698 0.9742 0.006108 0.843 0.8311 0.02 ] Network output: [ 0.9997 0.0002681 0.001778 -3.788e-05 1.701e-05 -0.001546 -2.855e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1847 -0.02959 -0.195 0.1995 0.9836 0.9933 0.2062 0.4579 0.8768 0.7221 ] Network output: [ -0.01173 1 1.01 1.916e-06 -8.599e-07 0.01275 1.444e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005389 0.0004888 0.004326 0.004331 0.9889 0.992 0.005488 0.8726 0.9007 0.01451 ] Network output: [ -0.0008751 0.002162 1.003 -0.0001292 5.799e-05 0.996 -9.735e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1954 0.09341 0.3234 0.1573 0.9851 0.994 0.196 0.4626 0.8833 0.7168 ] Network output: [ 0.008075 -0.0397 0.9971 7.525e-05 -3.378e-05 1.027 5.671e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09724 0.0861 0.1786 0.2081 0.9873 0.992 0.0973 0.7879 0.8765 0.309 ] Network output: [ -0.008224 0.0416 1.001 7.594e-05 -3.409e-05 0.9739 5.723e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09179 0.08989 0.1669 0.1972 0.9856 0.9914 0.0918 0.717 0.8555 0.2432 ] Network output: [ 0.0003489 0.9996 -0.0007846 1.043e-05 -4.681e-06 1.001 7.858e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008157 Epoch 7007 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01294 0.9929 0.9872 3.825e-06 -1.717e-06 -0.005943 2.882e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003213 -0.002992 -0.009348 0.007139 0.9698 0.9742 0.006109 0.843 0.8311 0.02 ] Network output: [ 0.9995 0.002681 0.001652 -3.804e-05 1.708e-05 -0.003516 -2.867e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1848 -0.02956 -0.1952 0.1991 0.9836 0.9933 0.2062 0.4579 0.8768 0.7221 ] Network output: [ -0.01173 1 1.01 1.899e-06 -8.524e-07 0.01265 1.431e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00539 0.0004883 0.00432 0.004318 0.9889 0.992 0.005489 0.8726 0.9006 0.0145 ] Network output: [ -0.00109 0.005428 1.003 -0.0001294 5.809e-05 0.9933 -9.751e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1954 0.09342 0.3232 0.1567 0.9851 0.994 0.196 0.4627 0.8833 0.7168 ] Network output: [ 0.008115 -0.03906 0.997 7.516e-05 -3.374e-05 1.026 5.664e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09723 0.08609 0.1785 0.2079 0.9873 0.992 0.09729 0.7878 0.8765 0.3089 ] Network output: [ -0.0082 0.04137 1.001 7.591e-05 -3.408e-05 0.974 5.721e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09178 0.08988 0.1668 0.1972 0.9856 0.9914 0.09179 0.7169 0.8555 0.2432 ] Network output: [ 0.0002158 0.9995 -0.0005956 1.038e-05 -4.659e-06 1.001 7.821e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008092 Epoch 7008 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01296 0.9925 0.9872 3.852e-06 -1.729e-06 -0.005649 2.903e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003213 -0.002992 -0.009344 0.007144 0.9698 0.9742 0.006108 0.843 0.8311 0.02 ] Network output: [ 0.9997 0.0002904 0.001775 -3.784e-05 1.699e-05 -0.001564 -2.852e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1848 -0.0296 -0.195 0.1995 0.9836 0.9933 0.2062 0.4579 0.8768 0.7221 ] Network output: [ -0.01172 1 1.01 1.911e-06 -8.577e-07 0.01274 1.44e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00539 0.0004885 0.004326 0.004329 0.9889 0.992 0.005489 0.8726 0.9007 0.0145 ] Network output: [ -0.0008768 0.002194 1.003 -0.000129 5.791e-05 0.996 -9.722e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1954 0.09341 0.3235 0.1573 0.9851 0.994 0.196 0.4626 0.8833 0.7168 ] Network output: [ 0.008069 -0.03967 0.9971 7.514e-05 -3.373e-05 1.027 5.663e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09724 0.0861 0.1786 0.2081 0.9873 0.992 0.09731 0.7879 0.8765 0.309 ] Network output: [ -0.008217 0.04156 1.001 7.584e-05 -3.405e-05 0.9739 5.716e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09178 0.08988 0.1669 0.1972 0.9856 0.9914 0.09179 0.7169 0.8555 0.2432 ] Network output: [ 0.0003472 0.9996 -0.0007816 1.041e-05 -4.675e-06 1.001 7.847e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008147 Epoch 7009 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01293 0.9929 0.9872 3.815e-06 -1.712e-06 -0.005945 2.875e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003213 -0.002992 -0.009345 0.007137 0.9698 0.9742 0.006109 0.843 0.8311 0.02 ] Network output: [ 0.9995 0.002653 0.001652 -3.799e-05 1.706e-05 -0.003493 -2.863e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1848 -0.02957 -0.1951 0.1991 0.9836 0.9933 0.2062 0.4579 0.8768 0.7221 ] Network output: [ -0.01173 1 1.01 1.894e-06 -8.503e-07 0.01264 1.427e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005391 0.0004881 0.00432 0.004316 0.9889 0.992 0.00549 0.8726 0.9006 0.0145 ] Network output: [ -0.001087 0.005392 1.003 -0.0001292 5.801e-05 0.9934 -9.737e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1954 0.09341 0.3232 0.1566 0.9851 0.994 0.196 0.4626 0.8833 0.7168 ] Network output: [ 0.008109 -0.03904 0.997 7.506e-05 -3.37e-05 1.026 5.657e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09724 0.08609 0.1785 0.2079 0.9873 0.992 0.0973 0.7878 0.8764 0.3089 ] Network output: [ -0.008193 0.04134 1.001 7.582e-05 -3.404e-05 0.974 5.714e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09177 0.08987 0.1668 0.1972 0.9856 0.9914 0.09178 0.7168 0.8555 0.2432 ] Network output: [ 0.0002169 0.9995 -0.0005965 1.037e-05 -4.653e-06 1.001 7.812e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008083 Epoch 7010 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01295 0.9925 0.9872 3.841e-06 -1.724e-06 -0.005657 2.894e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003213 -0.002993 -0.009341 0.007142 0.9698 0.9742 0.006109 0.843 0.8311 0.01999 ] Network output: [ 0.9997 0.0003124 0.001772 -3.78e-05 1.697e-05 -0.001582 -2.849e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1848 -0.02961 -0.1949 0.1995 0.9836 0.9933 0.2062 0.4578 0.8768 0.7221 ] Network output: [ -0.01172 1 1.01 1.906e-06 -8.555e-07 0.01273 1.436e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005391 0.0004882 0.004327 0.004327 0.9889 0.992 0.00549 0.8726 0.9006 0.0145 ] Network output: [ -0.0008785 0.002227 1.003 -0.0001288 5.783e-05 0.996 -9.708e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1954 0.0934 0.3235 0.1572 0.9851 0.994 0.196 0.4626 0.8833 0.7168 ] Network output: [ 0.008064 -0.03964 0.9971 7.504e-05 -3.369e-05 1.027 5.655e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09725 0.0861 0.1786 0.208 0.9873 0.992 0.09731 0.7878 0.8764 0.309 ] Network output: [ -0.008209 0.04153 1.001 7.575e-05 -3.401e-05 0.9739 5.709e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09177 0.08987 0.1668 0.1972 0.9856 0.9914 0.09178 0.7169 0.8554 0.2432 ] Network output: [ 0.0003456 0.9996 -0.0007786 1.04e-05 -4.668e-06 1.001 7.837e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008137 Epoch 7011 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01293 0.9929 0.9872 3.804e-06 -1.708e-06 -0.005947 2.867e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003213 -0.002993 -0.009342 0.007135 0.9698 0.9742 0.00611 0.843 0.8311 0.01999 ] Network output: [ 0.9995 0.002625 0.001651 -3.795e-05 1.704e-05 -0.003471 -2.86e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1848 -0.02959 -0.1951 0.1991 0.9836 0.9933 0.2062 0.4579 0.8768 0.7221 ] Network output: [ -0.01172 1 1.01 1.889e-06 -8.482e-07 0.01263 1.424e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005393 0.0004878 0.00432 0.004314 0.9889 0.992 0.005491 0.8726 0.9006 0.0145 ] Network output: [ -0.001085 0.005357 1.003 -0.000129 5.792e-05 0.9934 -9.723e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1954 0.09341 0.3233 0.1566 0.9851 0.994 0.196 0.4626 0.8833 0.7168 ] Network output: [ 0.008103 -0.03902 0.997 7.496e-05 -3.365e-05 1.026 5.649e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09724 0.08609 0.1785 0.2079 0.9873 0.992 0.0973 0.7877 0.8764 0.3089 ] Network output: [ -0.008187 0.04131 1.001 7.572e-05 -3.399e-05 0.9741 5.707e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09176 0.08986 0.1668 0.1972 0.9856 0.9914 0.09177 0.7168 0.8554 0.2432 ] Network output: [ 0.0002179 0.9995 -0.0005973 1.035e-05 -4.648e-06 1.001 7.802e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008075 Epoch 7012 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01295 0.9925 0.9873 3.83e-06 -1.719e-06 -0.005665 2.886e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003213 -0.002993 -0.009338 0.007139 0.9698 0.9742 0.00611 0.843 0.8311 0.01999 ] Network output: [ 0.9997 0.0003341 0.001769 -3.776e-05 1.695e-05 -0.0016 -2.846e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1848 -0.02963 -0.1949 0.1994 0.9836 0.9933 0.2062 0.4578 0.8768 0.7221 ] Network output: [ -0.01172 1 1.01 1.901e-06 -8.533e-07 0.01272 1.432e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005392 0.0004879 0.004327 0.004325 0.9889 0.992 0.005491 0.8726 0.9006 0.0145 ] Network output: [ -0.0008802 0.002258 1.003 -0.0001286 5.775e-05 0.996 -9.695e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1954 0.0934 0.3235 0.1572 0.9851 0.994 0.196 0.4625 0.8833 0.7168 ] Network output: [ 0.008058 -0.03961 0.9971 7.494e-05 -3.364e-05 1.027 5.648e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09725 0.0861 0.1786 0.208 0.9873 0.992 0.09732 0.7877 0.8764 0.309 ] Network output: [ -0.008202 0.04149 1.001 7.566e-05 -3.396e-05 0.9739 5.702e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09176 0.08986 0.1668 0.1972 0.9856 0.9914 0.09177 0.7168 0.8554 0.2432 ] Network output: [ 0.000344 0.9996 -0.0007756 1.039e-05 -4.662e-06 1.001 7.827e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008127 Epoch 7013 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01292 0.9929 0.9873 3.794e-06 -1.703e-06 -0.005949 2.859e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003213 -0.002993 -0.009339 0.007132 0.9698 0.9742 0.00611 0.843 0.8311 0.01999 ] Network output: [ 0.9995 0.002598 0.001651 -3.791e-05 1.702e-05 -0.003449 -2.857e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1848 -0.0296 -0.195 0.1991 0.9836 0.9933 0.2063 0.4578 0.8768 0.7221 ] Network output: [ -0.01172 1 1.01 1.885e-06 -8.461e-07 0.01262 1.42e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005394 0.0004875 0.004321 0.004313 0.9889 0.992 0.005492 0.8726 0.9006 0.01449 ] Network output: [ -0.001082 0.005323 1.003 -0.0001288 5.784e-05 0.9934 -9.709e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1955 0.09341 0.3233 0.1566 0.9851 0.994 0.1961 0.4626 0.8833 0.7168 ] Network output: [ 0.008096 -0.03901 0.997 7.486e-05 -3.361e-05 1.026 5.642e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09725 0.0861 0.1785 0.2078 0.9873 0.992 0.09731 0.7877 0.8764 0.3089 ] Network output: [ -0.00818 0.04128 1.001 7.563e-05 -3.395e-05 0.9741 5.699e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09175 0.08985 0.1668 0.1972 0.9856 0.9914 0.09176 0.7167 0.8554 0.2432 ] Network output: [ 0.0002189 0.9995 -0.0005981 1.034e-05 -4.642e-06 1.001 7.792e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008066 Epoch 7014 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01294 0.9925 0.9873 3.819e-06 -1.714e-06 -0.005673 2.878e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003213 -0.002993 -0.009335 0.007137 0.9698 0.9742 0.00611 0.843 0.8311 0.01999 ] Network output: [ 0.9997 0.0003553 0.001766 -3.772e-05 1.693e-05 -0.001617 -2.843e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1848 -0.02964 -0.1949 0.1994 0.9836 0.9933 0.2063 0.4578 0.8768 0.7221 ] Network output: [ -0.01172 1 1.01 1.896e-06 -8.51e-07 0.01271 1.429e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005394 0.0004877 0.004327 0.004323 0.9889 0.992 0.005492 0.8726 0.9006 0.01449 ] Network output: [ -0.0008818 0.002289 1.003 -0.0001285 5.767e-05 0.996 -9.681e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1954 0.0934 0.3236 0.1571 0.9851 0.994 0.1961 0.4625 0.8833 0.7168 ] Network output: [ 0.008053 -0.03958 0.9971 7.484e-05 -3.36e-05 1.027 5.64e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09726 0.08611 0.1786 0.208 0.9873 0.992 0.09732 0.7877 0.8764 0.309 ] Network output: [ -0.008195 0.04146 1.001 7.556e-05 -3.392e-05 0.9739 5.695e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09175 0.08985 0.1668 0.1972 0.9856 0.9914 0.09176 0.7167 0.8554 0.2432 ] Network output: [ 0.0003423 0.9996 -0.0007727 1.037e-05 -4.656e-06 1.001 7.816e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008117 Epoch 7015 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01292 0.9929 0.9873 3.784e-06 -1.699e-06 -0.005951 2.852e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003214 -0.002993 -0.009335 0.00713 0.9698 0.9742 0.006111 0.843 0.8311 0.01998 ] Network output: [ 0.9995 0.002571 0.00165 -3.786e-05 1.7e-05 -0.003427 -2.853e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1848 -0.02961 -0.195 0.199 0.9836 0.9933 0.2063 0.4578 0.8768 0.7221 ] Network output: [ -0.01172 1 1.01 1.88e-06 -8.44e-07 0.01261 1.417e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005395 0.0004873 0.004321 0.004311 0.9889 0.992 0.005493 0.8726 0.9006 0.01449 ] Network output: [ -0.001079 0.005288 1.003 -0.0001286 5.775e-05 0.9935 -9.695e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1955 0.0934 0.3234 0.1566 0.9851 0.994 0.1961 0.4625 0.8833 0.7168 ] Network output: [ 0.00809 -0.03899 0.997 7.476e-05 -3.356e-05 1.026 5.634e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09725 0.0861 0.1785 0.2078 0.9873 0.992 0.09732 0.7876 0.8764 0.3089 ] Network output: [ -0.008173 0.04125 1.001 7.553e-05 -3.391e-05 0.9741 5.692e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09174 0.08984 0.1668 0.1971 0.9856 0.9914 0.09175 0.7166 0.8554 0.2432 ] Network output: [ 0.0002199 0.9995 -0.0005989 1.033e-05 -4.636e-06 1.001 7.782e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008057 Epoch 7016 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01294 0.9926 0.9873 3.808e-06 -1.71e-06 -0.005681 2.87e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003214 -0.002994 -0.009332 0.007135 0.9698 0.9742 0.006111 0.843 0.8311 0.01998 ] Network output: [ 0.9997 0.0003762 0.001763 -3.768e-05 1.692e-05 -0.001635 -2.84e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1848 -0.02965 -0.1948 0.1994 0.9836 0.9933 0.2063 0.4577 0.8768 0.7221 ] Network output: [ -0.01171 1 1.01 1.891e-06 -8.488e-07 0.0127 1.425e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005395 0.0004874 0.004328 0.004321 0.9889 0.992 0.005493 0.8725 0.9006 0.01449 ] Network output: [ -0.0008834 0.00232 1.003 -0.0001283 5.759e-05 0.9959 -9.667e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1955 0.09339 0.3236 0.1571 0.9851 0.994 0.1961 0.4625 0.8833 0.7168 ] Network output: [ 0.008048 -0.03956 0.9971 7.474e-05 -3.355e-05 1.027 5.632e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09726 0.08611 0.1786 0.208 0.9873 0.992 0.09733 0.7876 0.8764 0.309 ] Network output: [ -0.008187 0.04143 1.001 7.547e-05 -3.388e-05 0.9739 5.687e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09174 0.08984 0.1668 0.1972 0.9856 0.9914 0.09175 0.7167 0.8554 0.2432 ] Network output: [ 0.0003408 0.9996 -0.0007698 1.036e-05 -4.65e-06 1.001 7.806e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008107 Epoch 7017 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01291 0.9929 0.9873 3.774e-06 -1.694e-06 -0.005953 2.844e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003214 -0.002994 -0.009332 0.007128 0.9698 0.9742 0.006111 0.843 0.8311 0.01998 ] Network output: [ 0.9995 0.002545 0.00165 -3.782e-05 1.698e-05 -0.003406 -2.85e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1849 -0.02963 -0.195 0.199 0.9836 0.9933 0.2063 0.4578 0.8768 0.722 ] Network output: [ -0.01172 1 1.01 1.875e-06 -8.419e-07 0.01261 1.413e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005396 0.000487 0.004322 0.004309 0.9889 0.992 0.005494 0.8725 0.9006 0.01449 ] Network output: [ -0.001077 0.005255 1.003 -0.0001285 5.767e-05 0.9935 -9.681e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1955 0.0934 0.3234 0.1565 0.9851 0.994 0.1961 0.4625 0.8832 0.7168 ] Network output: [ 0.008084 -0.03897 0.997 7.466e-05 -3.352e-05 1.026 5.626e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09726 0.0861 0.1785 0.2078 0.9873 0.992 0.09732 0.7876 0.8763 0.3089 ] Network output: [ -0.008166 0.04122 1.001 7.544e-05 -3.387e-05 0.9741 5.685e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09173 0.08982 0.1668 0.1971 0.9856 0.9914 0.09174 0.7166 0.8553 0.2432 ] Network output: [ 0.0002209 0.9995 -0.0005996 1.031e-05 -4.63e-06 1.001 7.773e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008048 Epoch 7018 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01293 0.9926 0.9873 3.797e-06 -1.705e-06 -0.005689 2.862e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003214 -0.002994 -0.009328 0.007132 0.9698 0.9742 0.006111 0.8429 0.8311 0.01998 ] Network output: [ 0.9997 0.0003967 0.00176 -3.764e-05 1.69e-05 -0.001651 -2.837e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1849 -0.02966 -0.1948 0.1994 0.9836 0.9933 0.2063 0.4577 0.8768 0.7221 ] Network output: [ -0.01171 1 1.01 1.886e-06 -8.465e-07 0.01269 1.421e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005396 0.0004871 0.004328 0.00432 0.9889 0.992 0.005494 0.8725 0.9006 0.01449 ] Network output: [ -0.000885 0.00235 1.003 -0.0001281 5.751e-05 0.9959 -9.654e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1955 0.09339 0.3236 0.1571 0.9851 0.994 0.1961 0.4624 0.8832 0.7167 ] Network output: [ 0.008042 -0.03953 0.9971 7.464e-05 -3.351e-05 1.027 5.625e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09727 0.08611 0.1786 0.2079 0.9873 0.992 0.09733 0.7876 0.8763 0.309 ] Network output: [ -0.00818 0.04139 1.001 7.537e-05 -3.384e-05 0.974 5.68e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09173 0.08983 0.1668 0.1972 0.9856 0.9914 0.09174 0.7166 0.8553 0.2432 ] Network output: [ 0.0003392 0.9996 -0.0007669 1.034e-05 -4.644e-06 1.001 7.795e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008097 Epoch 7019 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01291 0.9929 0.9873 3.764e-06 -1.69e-06 -0.005955 2.836e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003214 -0.002994 -0.009329 0.007126 0.9698 0.9742 0.006112 0.8429 0.831 0.01997 ] Network output: [ 0.9995 0.002519 0.001649 -3.777e-05 1.696e-05 -0.003384 -2.847e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1849 -0.02964 -0.1949 0.199 0.9836 0.9933 0.2063 0.4577 0.8767 0.722 ] Network output: [ -0.01171 1 1.01 1.871e-06 -8.398e-07 0.0126 1.41e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005397 0.0004868 0.004322 0.004308 0.9889 0.992 0.005496 0.8725 0.9006 0.01448 ] Network output: [ -0.001074 0.005222 1.003 -0.0001283 5.759e-05 0.9935 -9.667e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1955 0.09339 0.3234 0.1565 0.9851 0.994 0.1961 0.4625 0.8832 0.7168 ] Network output: [ 0.008077 -0.03896 0.997 7.456e-05 -3.347e-05 1.026 5.619e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09726 0.0861 0.1785 0.2078 0.9873 0.992 0.09733 0.7875 0.8763 0.3089 ] Network output: [ -0.008159 0.04119 1.001 7.534e-05 -3.382e-05 0.9741 5.678e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09172 0.08981 0.1668 0.1971 0.9856 0.9914 0.09173 0.7165 0.8553 0.2432 ] Network output: [ 0.0002219 0.9995 -0.0006003 1.03e-05 -4.624e-06 1.001 7.763e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008039 Epoch 7020 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01293 0.9926 0.9873 3.786e-06 -1.7e-06 -0.005697 2.853e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003214 -0.002994 -0.009325 0.00713 0.9698 0.9742 0.006112 0.8429 0.8311 0.01997 ] Network output: [ 0.9997 0.0004169 0.001757 -3.76e-05 1.688e-05 -0.001668 -2.834e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1849 -0.02967 -0.1947 0.1993 0.9836 0.9933 0.2063 0.4577 0.8767 0.7221 ] Network output: [ -0.01171 1 1.01 1.881e-06 -8.443e-07 0.01268 1.417e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005397 0.0004869 0.004328 0.004318 0.9889 0.992 0.005496 0.8725 0.9006 0.01448 ] Network output: [ -0.0008865 0.00238 1.003 -0.0001279 5.743e-05 0.9959 -9.64e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1955 0.09338 0.3237 0.157 0.9851 0.994 0.1961 0.4624 0.8832 0.7167 ] Network output: [ 0.008037 -0.0395 0.9971 7.453e-05 -3.346e-05 1.027 5.617e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09727 0.08611 0.1786 0.2079 0.9873 0.992 0.09734 0.7875 0.8763 0.3089 ] Network output: [ -0.008172 0.04136 1.001 7.528e-05 -3.379e-05 0.974 5.673e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09172 0.08982 0.1668 0.1972 0.9856 0.9914 0.09173 0.7165 0.8553 0.2432 ] Network output: [ 0.0003377 0.9996 -0.000764 1.033e-05 -4.638e-06 1.001 7.785e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008087 Epoch 7021 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0129 0.9929 0.9873 3.753e-06 -1.685e-06 -0.005958 2.829e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003214 -0.002994 -0.009326 0.007124 0.9698 0.9742 0.006112 0.8429 0.831 0.01997 ] Network output: [ 0.9995 0.002493 0.001648 -3.773e-05 1.694e-05 -0.003364 -2.843e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1849 -0.02965 -0.1949 0.199 0.9836 0.9933 0.2064 0.4577 0.8767 0.722 ] Network output: [ -0.01171 1 1.01 1.866e-06 -8.377e-07 0.01259 1.406e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005398 0.0004865 0.004323 0.004306 0.9889 0.992 0.005497 0.8725 0.9006 0.01448 ] Network output: [ -0.001072 0.005189 1.003 -0.0001281 5.75e-05 0.9935 -9.653e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1955 0.09339 0.3235 0.1565 0.9851 0.994 0.1962 0.4624 0.8832 0.7168 ] Network output: [ 0.008071 -0.03894 0.997 7.445e-05 -3.343e-05 1.026 5.611e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09727 0.08611 0.1785 0.2078 0.9873 0.992 0.09733 0.7874 0.8763 0.3089 ] Network output: [ -0.008152 0.04116 1.001 7.525e-05 -3.378e-05 0.9741 5.671e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09171 0.0898 0.1668 0.1971 0.9856 0.9914 0.09172 0.7164 0.8553 0.2432 ] Network output: [ 0.0002228 0.9995 -0.000601 1.029e-05 -4.619e-06 1.001 7.753e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000803 Epoch 7022 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01292 0.9926 0.9873 3.775e-06 -1.695e-06 -0.005705 2.845e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003214 -0.002995 -0.009322 0.007128 0.9698 0.9742 0.006112 0.8429 0.831 0.01997 ] Network output: [ 0.9997 0.0004367 0.001754 -3.756e-05 1.686e-05 -0.001684 -2.831e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1849 -0.02969 -0.1947 0.1993 0.9836 0.9933 0.2064 0.4576 0.8767 0.7221 ] Network output: [ -0.01171 1 1.01 1.876e-06 -8.42e-07 0.01267 1.414e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005398 0.0004866 0.004329 0.004316 0.9889 0.992 0.005497 0.8725 0.9006 0.01448 ] Network output: [ -0.000888 0.002409 1.003 -0.0001277 5.735e-05 0.9959 -9.627e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1955 0.09338 0.3237 0.157 0.9851 0.994 0.1961 0.4624 0.8832 0.7167 ] Network output: [ 0.008031 -0.03947 0.9971 7.443e-05 -3.342e-05 1.027 5.609e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09728 0.08612 0.1786 0.2079 0.9873 0.992 0.09734 0.7875 0.8763 0.3089 ] Network output: [ -0.008165 0.04132 1.001 7.518e-05 -3.375e-05 0.974 5.666e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09171 0.08981 0.1668 0.1971 0.9856 0.9914 0.09172 0.7164 0.8553 0.2432 ] Network output: [ 0.0003361 0.9996 -0.0007612 1.032e-05 -4.631e-06 1.001 7.775e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008077 Epoch 7023 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0129 0.9929 0.9873 3.743e-06 -1.68e-06 -0.00596 2.821e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003215 -0.002995 -0.009322 0.007122 0.9698 0.9742 0.006113 0.8429 0.831 0.01996 ] Network output: [ 0.9995 0.002468 0.001648 -3.769e-05 1.692e-05 -0.003343 -2.84e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1849 -0.02966 -0.1948 0.199 0.9836 0.9933 0.2064 0.4577 0.8767 0.722 ] Network output: [ -0.01171 1 1.01 1.861e-06 -8.356e-07 0.01258 1.403e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005399 0.0004862 0.004323 0.004305 0.9889 0.992 0.005498 0.8725 0.9006 0.01448 ] Network output: [ -0.001069 0.005157 1.003 -0.0001279 5.742e-05 0.9936 -9.639e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1956 0.09338 0.3235 0.1565 0.9851 0.994 0.1962 0.4624 0.8832 0.7167 ] Network output: [ 0.008065 -0.03892 0.997 7.435e-05 -3.338e-05 1.026 5.604e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09728 0.08611 0.1785 0.2077 0.9873 0.992 0.09734 0.7874 0.8763 0.3089 ] Network output: [ -0.008145 0.04113 1.001 7.515e-05 -3.374e-05 0.9741 5.664e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0917 0.08979 0.1668 0.1971 0.9856 0.9914 0.09171 0.7164 0.8553 0.2432 ] Network output: [ 0.0002238 0.9995 -0.0006016 1.027e-05 -4.613e-06 1.001 7.743e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008021 Epoch 7024 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01292 0.9926 0.9873 3.765e-06 -1.69e-06 -0.005713 2.837e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003214 -0.002995 -0.009319 0.007126 0.9698 0.9742 0.006113 0.8429 0.831 0.01996 ] Network output: [ 0.9997 0.0004561 0.001751 -3.752e-05 1.684e-05 -0.0017 -2.828e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1849 -0.0297 -0.1947 0.1993 0.9836 0.9933 0.2064 0.4576 0.8767 0.722 ] Network output: [ -0.0117 1 1.01 1.871e-06 -8.398e-07 0.01266 1.41e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005399 0.0004863 0.004329 0.004314 0.9889 0.992 0.005498 0.8725 0.9006 0.01448 ] Network output: [ -0.0008895 0.002437 1.003 -0.0001276 5.727e-05 0.9958 -9.613e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1956 0.09338 0.3237 0.157 0.9851 0.994 0.1962 0.4623 0.8832 0.7167 ] Network output: [ 0.008026 -0.03944 0.9971 7.433e-05 -3.337e-05 1.027 5.602e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.08612 0.1786 0.2079 0.9873 0.992 0.09735 0.7874 0.8763 0.3089 ] Network output: [ -0.008158 0.04129 1.001 7.509e-05 -3.371e-05 0.974 5.659e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0917 0.0898 0.1668 0.1971 0.9856 0.9914 0.09171 0.7164 0.8552 0.2432 ] Network output: [ 0.0003346 0.9996 -0.0007584 1.03e-05 -4.625e-06 1.001 7.764e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008067 Epoch 7025 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01289 0.9929 0.9873 3.733e-06 -1.676e-06 -0.005962 2.813e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003215 -0.002995 -0.009319 0.00712 0.9698 0.9742 0.006113 0.8429 0.831 0.01996 ] Network output: [ 0.9995 0.002443 0.001647 -3.764e-05 1.69e-05 -0.003323 -2.837e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1849 -0.02968 -0.1948 0.1989 0.9836 0.9933 0.2064 0.4576 0.8767 0.722 ] Network output: [ -0.01171 1 1.01 1.856e-06 -8.334e-07 0.01257 1.399e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0054 0.000486 0.004324 0.004303 0.9889 0.992 0.005499 0.8725 0.9005 0.01447 ] Network output: [ -0.001067 0.005125 1.003 -0.0001277 5.734e-05 0.9936 -9.625e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1956 0.09338 0.3236 0.1564 0.9851 0.994 0.1962 0.4623 0.8832 0.7167 ] Network output: [ 0.008058 -0.03891 0.997 7.425e-05 -3.333e-05 1.026 5.596e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09728 0.08611 0.1785 0.2077 0.9873 0.992 0.09734 0.7873 0.8763 0.3088 ] Network output: [ -0.008138 0.0411 1.001 7.506e-05 -3.369e-05 0.9741 5.656e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09169 0.08978 0.1668 0.1971 0.9856 0.9914 0.0917 0.7163 0.8552 0.2432 ] Network output: [ 0.0002247 0.9995 -0.0006023 1.026e-05 -4.607e-06 1.001 7.734e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008012 Epoch 7026 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01291 0.9926 0.9873 3.754e-06 -1.685e-06 -0.005721 2.829e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003215 -0.002995 -0.009316 0.007123 0.9698 0.9742 0.006113 0.8429 0.831 0.01996 ] Network output: [ 0.9997 0.0004751 0.001748 -3.748e-05 1.683e-05 -0.001716 -2.825e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1849 -0.02971 -0.1946 0.1993 0.9836 0.9933 0.2064 0.4576 0.8767 0.722 ] Network output: [ -0.0117 1 1.01 1.866e-06 -8.376e-07 0.01265 1.406e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0054 0.0004861 0.00433 0.004312 0.9889 0.992 0.005499 0.8725 0.9005 0.01447 ] Network output: [ -0.0008909 0.002465 1.003 -0.0001274 5.718e-05 0.9958 -9.6e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1956 0.09337 0.3238 0.1569 0.9851 0.994 0.1962 0.4623 0.8832 0.7167 ] Network output: [ 0.00802 -0.03941 0.9971 7.423e-05 -3.332e-05 1.027 5.594e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.08612 0.1786 0.2079 0.9873 0.992 0.09735 0.7873 0.8763 0.3089 ] Network output: [ -0.00815 0.04126 1.001 7.499e-05 -3.367e-05 0.974 5.652e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09169 0.08979 0.1668 0.1971 0.9856 0.9914 0.0917 0.7163 0.8552 0.2432 ] Network output: [ 0.0003332 0.9996 -0.0007556 1.029e-05 -4.619e-06 1.001 7.754e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008057 Epoch 7027 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01289 0.9929 0.9873 3.723e-06 -1.671e-06 -0.005964 2.806e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003215 -0.002995 -0.009316 0.007117 0.9698 0.9742 0.006114 0.8429 0.831 0.01996 ] Network output: [ 0.9995 0.002418 0.001646 -3.76e-05 1.688e-05 -0.003303 -2.834e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.185 -0.02969 -0.1948 0.1989 0.9836 0.9933 0.2064 0.4576 0.8767 0.722 ] Network output: [ -0.01171 1 1.01 1.852e-06 -8.313e-07 0.01257 1.396e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005401 0.0004857 0.004324 0.004301 0.9889 0.992 0.0055 0.8724 0.9005 0.01447 ] Network output: [ -0.001064 0.005094 1.003 -0.0001275 5.725e-05 0.9936 -9.611e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1956 0.09338 0.3236 0.1564 0.9851 0.994 0.1962 0.4623 0.8832 0.7167 ] Network output: [ 0.008052 -0.03889 0.997 7.415e-05 -3.329e-05 1.026 5.588e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.08611 0.1785 0.2077 0.9873 0.992 0.09735 0.7873 0.8762 0.3088 ] Network output: [ -0.008131 0.04107 1.001 7.496e-05 -3.365e-05 0.9742 5.649e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09168 0.08977 0.1667 0.1971 0.9856 0.9914 0.09169 0.7162 0.8552 0.2432 ] Network output: [ 0.0002256 0.9995 -0.0006029 1.025e-05 -4.601e-06 1.001 7.724e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008004 Epoch 7028 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0129 0.9926 0.9873 3.743e-06 -1.68e-06 -0.005728 2.821e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003215 -0.002995 -0.009312 0.007121 0.9698 0.9742 0.006114 0.8429 0.831 0.01995 ] Network output: [ 0.9997 0.0004939 0.001745 -3.744e-05 1.681e-05 -0.001731 -2.822e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.185 -0.02972 -0.1946 0.1992 0.9836 0.9933 0.2064 0.4575 0.8767 0.722 ] Network output: [ -0.0117 1 1.01 1.861e-06 -8.353e-07 0.01264 1.402e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005401 0.0004858 0.00433 0.00431 0.9889 0.992 0.0055 0.8724 0.9005 0.01447 ] Network output: [ -0.0008923 0.002493 1.003 -0.0001272 5.71e-05 0.9958 -9.586e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1956 0.09337 0.3238 0.1569 0.9851 0.994 0.1962 0.4623 0.8832 0.7167 ] Network output: [ 0.008015 -0.03938 0.9971 7.413e-05 -3.328e-05 1.027 5.586e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08612 0.1786 0.2078 0.9873 0.992 0.09736 0.7873 0.8762 0.3089 ] Network output: [ -0.008143 0.04122 1.001 7.49e-05 -3.362e-05 0.974 5.645e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09168 0.08978 0.1668 0.1971 0.9856 0.9914 0.09169 0.7162 0.8552 0.2432 ] Network output: [ 0.0003317 0.9996 -0.0007528 1.027e-05 -4.613e-06 1.001 7.744e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008047 Epoch 7029 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01288 0.9929 0.9873 3.712e-06 -1.667e-06 -0.005967 2.798e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003215 -0.002996 -0.009313 0.007115 0.9698 0.9742 0.006114 0.8429 0.831 0.01995 ] Network output: [ 0.9995 0.002394 0.001646 -3.756e-05 1.686e-05 -0.003283 -2.83e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.185 -0.0297 -0.1947 0.1989 0.9836 0.9933 0.2065 0.4576 0.8767 0.722 ] Network output: [ -0.0117 1 1.01 1.847e-06 -8.292e-07 0.01256 1.392e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005402 0.0004855 0.004325 0.0043 0.9889 0.992 0.005501 0.8724 0.9005 0.01447 ] Network output: [ -0.001062 0.005063 1.003 -0.0001273 5.717e-05 0.9937 -9.597e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1956 0.09337 0.3236 0.1564 0.9851 0.994 0.1962 0.4623 0.8832 0.7167 ] Network output: [ 0.008046 -0.03887 0.997 7.405e-05 -3.324e-05 1.026 5.581e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09729 0.08612 0.1785 0.2077 0.9873 0.992 0.09735 0.7872 0.8762 0.3088 ] Network output: [ -0.008124 0.04104 1.001 7.487e-05 -3.361e-05 0.9742 5.642e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09167 0.08976 0.1667 0.1971 0.9856 0.9914 0.09168 0.7162 0.8552 0.2432 ] Network output: [ 0.0002264 0.9995 -0.0006034 1.024e-05 -4.595e-06 1.001 7.714e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007995 Epoch 7030 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0129 0.9926 0.9873 3.732e-06 -1.675e-06 -0.005736 2.813e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003215 -0.002996 -0.009309 0.007119 0.9698 0.9742 0.006114 0.8428 0.831 0.01995 ] Network output: [ 0.9997 0.0005122 0.001742 -3.74e-05 1.679e-05 -0.001746 -2.819e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.185 -0.02973 -0.1946 0.1992 0.9836 0.9933 0.2064 0.4575 0.8767 0.722 ] Network output: [ -0.0117 1 1.01 1.856e-06 -8.33e-07 0.01264 1.398e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005402 0.0004856 0.00433 0.004309 0.9889 0.992 0.005501 0.8724 0.9005 0.01447 ] Network output: [ -0.0008936 0.00252 1.003 -0.000127 5.702e-05 0.9958 -9.573e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1956 0.09337 0.3238 0.1568 0.9851 0.994 0.1962 0.4622 0.8832 0.7167 ] Network output: [ 0.008009 -0.03935 0.9971 7.403e-05 -3.323e-05 1.027 5.579e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08613 0.1786 0.2078 0.9873 0.992 0.09736 0.7872 0.8762 0.3089 ] Network output: [ -0.008135 0.04119 1.001 7.48e-05 -3.358e-05 0.9741 5.637e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09167 0.08977 0.1668 0.1971 0.9856 0.9914 0.09168 0.7162 0.8552 0.2432 ] Network output: [ 0.0003302 0.9996 -0.0007501 1.026e-05 -4.607e-06 1.001 7.733e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008037 Epoch 7031 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01288 0.9929 0.9873 3.702e-06 -1.662e-06 -0.005969 2.79e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003215 -0.002996 -0.009309 0.007113 0.9698 0.9742 0.006115 0.8428 0.831 0.01995 ] Network output: [ 0.9995 0.00237 0.001645 -3.751e-05 1.684e-05 -0.003263 -2.827e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.185 -0.02971 -0.1947 0.1989 0.9836 0.9933 0.2065 0.4575 0.8767 0.722 ] Network output: [ -0.0117 1 1.01 1.842e-06 -8.27e-07 0.01255 1.388e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005403 0.0004852 0.004325 0.004298 0.9889 0.992 0.005502 0.8724 0.9005 0.01446 ] Network output: [ -0.001059 0.005033 1.003 -0.0001272 5.709e-05 0.9937 -9.583e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1957 0.09337 0.3237 0.1563 0.9851 0.994 0.1963 0.4622 0.8832 0.7167 ] Network output: [ 0.00804 -0.03886 0.997 7.395e-05 -3.32e-05 1.026 5.573e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08612 0.1785 0.2077 0.9873 0.992 0.09736 0.7872 0.8762 0.3088 ] Network output: [ -0.008117 0.04101 1.001 7.477e-05 -3.357e-05 0.9742 5.635e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09166 0.08975 0.1667 0.1971 0.9856 0.9914 0.09167 0.7161 0.8551 0.2432 ] Network output: [ 0.0002273 0.9995 -0.000604 1.022e-05 -4.589e-06 1.001 7.704e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007986 Epoch 7032 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01289 0.9926 0.9873 3.721e-06 -1.671e-06 -0.005744 2.805e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003215 -0.002996 -0.009306 0.007117 0.9698 0.9742 0.006115 0.8428 0.831 0.01995 ] Network output: [ 0.9997 0.0005302 0.001739 -3.736e-05 1.677e-05 -0.001761 -2.815e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.185 -0.02975 -0.1945 0.1992 0.9836 0.9933 0.2065 0.4575 0.8767 0.722 ] Network output: [ -0.0117 1 1.01 1.851e-06 -8.308e-07 0.01263 1.395e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005403 0.0004853 0.004331 0.004307 0.9889 0.992 0.005502 0.8724 0.9005 0.01446 ] Network output: [ -0.0008949 0.002547 1.003 -0.0001268 5.694e-05 0.9958 -9.559e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1956 0.09336 0.3239 0.1568 0.9851 0.994 0.1963 0.4622 0.8832 0.7167 ] Network output: [ 0.008004 -0.03933 0.997 7.392e-05 -3.319e-05 1.027 5.571e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09731 0.08613 0.1786 0.2078 0.9873 0.992 0.09737 0.7872 0.8762 0.3089 ] Network output: [ -0.008128 0.04116 1.001 7.471e-05 -3.354e-05 0.9741 5.63e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09166 0.08976 0.1667 0.1971 0.9856 0.9914 0.09167 0.7161 0.8551 0.2432 ] Network output: [ 0.0003288 0.9996 -0.0007474 1.025e-05 -4.6e-06 1.001 7.723e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008027 Epoch 7033 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01287 0.9929 0.9873 3.692e-06 -1.658e-06 -0.005972 2.782e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003216 -0.002996 -0.009306 0.007111 0.9698 0.9742 0.006116 0.8428 0.831 0.01994 ] Network output: [ 0.9996 0.002346 0.001644 -3.747e-05 1.682e-05 -0.003244 -2.824e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.185 -0.02973 -0.1946 0.1989 0.9836 0.9933 0.2065 0.4575 0.8767 0.722 ] Network output: [ -0.0117 1 1.01 1.837e-06 -8.249e-07 0.01254 1.385e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005404 0.000485 0.004326 0.004297 0.9889 0.992 0.005503 0.8724 0.9005 0.01446 ] Network output: [ -0.001057 0.005004 1.003 -0.000127 5.7e-05 0.9937 -9.569e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1957 0.09337 0.3237 0.1563 0.9851 0.994 0.1963 0.4622 0.8832 0.7167 ] Network output: [ 0.008033 -0.03884 0.997 7.385e-05 -3.315e-05 1.026 5.566e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0973 0.08612 0.1785 0.2077 0.9873 0.992 0.09736 0.7871 0.8762 0.3088 ] Network output: [ -0.00811 0.04098 1.001 7.467e-05 -3.352e-05 0.9742 5.628e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09165 0.08974 0.1667 0.1971 0.9856 0.9914 0.09166 0.716 0.8551 0.2432 ] Network output: [ 0.0002281 0.9995 -0.0006045 1.021e-05 -4.584e-06 1.001 7.695e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007977 Epoch 7034 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01289 0.9926 0.9873 3.711e-06 -1.666e-06 -0.005751 2.796e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003216 -0.002996 -0.009303 0.007114 0.9698 0.9742 0.006115 0.8428 0.831 0.01994 ] Network output: [ 0.9997 0.0005479 0.001737 -3.732e-05 1.675e-05 -0.001775 -2.812e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.185 -0.02976 -0.1945 0.1992 0.9836 0.9933 0.2065 0.4574 0.8767 0.722 ] Network output: [ -0.01169 1 1.01 1.846e-06 -8.285e-07 0.01262 1.391e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005405 0.000485 0.004331 0.004305 0.9889 0.992 0.005503 0.8724 0.9005 0.01446 ] Network output: [ -0.0008962 0.002573 1.003 -0.0001267 5.686e-05 0.9957 -9.545e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1957 0.09336 0.3239 0.1568 0.9851 0.994 0.1963 0.4622 0.8831 0.7167 ] Network output: [ 0.007999 -0.0393 0.997 7.382e-05 -3.314e-05 1.027 5.564e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09731 0.08613 0.1786 0.2078 0.9873 0.992 0.09737 0.7871 0.8762 0.3089 ] Network output: [ -0.008121 0.04112 1.001 7.461e-05 -3.35e-05 0.9741 5.623e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09165 0.08975 0.1667 0.1971 0.9856 0.9914 0.09166 0.716 0.8551 0.2432 ] Network output: [ 0.0003274 0.9996 -0.0007447 1.023e-05 -4.594e-06 1.001 7.713e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008017 Epoch 7035 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01287 0.9929 0.9873 3.682e-06 -1.653e-06 -0.005974 2.775e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003216 -0.002996 -0.009303 0.007109 0.9698 0.9742 0.006116 0.8428 0.8309 0.01994 ] Network output: [ 0.9996 0.002323 0.001644 -3.742e-05 1.68e-05 -0.003225 -2.82e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.185 -0.02974 -0.1946 0.1989 0.9836 0.9933 0.2065 0.4575 0.8766 0.722 ] Network output: [ -0.0117 1 1.01 1.833e-06 -8.227e-07 0.01253 1.381e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005406 0.0004847 0.004326 0.004295 0.9889 0.992 0.005504 0.8724 0.9005 0.01446 ] Network output: [ -0.001055 0.004974 1.003 -0.0001268 5.692e-05 0.9937 -9.555e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1957 0.09336 0.3237 0.1563 0.9851 0.994 0.1963 0.4622 0.8831 0.7167 ] Network output: [ 0.008027 -0.03882 0.997 7.375e-05 -3.311e-05 1.026 5.558e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09731 0.08613 0.1786 0.2076 0.9873 0.992 0.09737 0.787 0.8762 0.3088 ] Network output: [ -0.008103 0.04095 1.001 7.458e-05 -3.348e-05 0.9742 5.621e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09164 0.08973 0.1667 0.1971 0.9856 0.9914 0.09165 0.7159 0.8551 0.2432 ] Network output: [ 0.000229 0.9995 -0.000605 1.02e-05 -4.578e-06 1.001 7.685e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007968 Epoch 7036 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01288 0.9927 0.9873 3.7e-06 -1.661e-06 -0.005759 2.788e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003216 -0.002997 -0.0093 0.007112 0.9698 0.9742 0.006116 0.8428 0.8309 0.01994 ] Network output: [ 0.9997 0.0005652 0.001734 -3.728e-05 1.674e-05 -0.001789 -2.809e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.185 -0.02977 -0.1944 0.1991 0.9836 0.9933 0.2065 0.4574 0.8767 0.722 ] Network output: [ -0.01169 1 1.01 1.84e-06 -8.263e-07 0.01261 1.387e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005406 0.0004848 0.004331 0.004303 0.9889 0.992 0.005505 0.8724 0.9005 0.01446 ] Network output: [ -0.0008974 0.002598 1.003 -0.0001265 5.678e-05 0.9957 -9.532e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1957 0.09335 0.3239 0.1567 0.9851 0.994 0.1963 0.4621 0.8831 0.7167 ] Network output: [ 0.007993 -0.03927 0.997 7.372e-05 -3.31e-05 1.027 5.556e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09732 0.08614 0.1787 0.2078 0.9873 0.992 0.09738 0.787 0.8761 0.3089 ] Network output: [ -0.008114 0.04109 1.001 7.452e-05 -3.345e-05 0.9741 5.616e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09164 0.08974 0.1667 0.1971 0.9856 0.9914 0.09165 0.716 0.8551 0.2432 ] Network output: [ 0.000326 0.9996 -0.0007421 1.022e-05 -4.588e-06 1.001 7.702e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0008007 Epoch 7037 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01286 0.9929 0.9873 3.672e-06 -1.648e-06 -0.005977 2.767e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003216 -0.002997 -0.0093 0.007107 0.9698 0.9742 0.006117 0.8428 0.8309 0.01993 ] Network output: [ 0.9996 0.002301 0.001643 -3.738e-05 1.678e-05 -0.003207 -2.817e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1851 -0.02975 -0.1946 0.1988 0.9836 0.9933 0.2065 0.4574 0.8766 0.722 ] Network output: [ -0.01169 1.001 1.01 1.828e-06 -8.206e-07 0.01253 1.378e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005407 0.0004845 0.004326 0.004293 0.9889 0.992 0.005506 0.8723 0.9005 0.01445 ] Network output: [ -0.001052 0.004946 1.003 -0.0001266 5.684e-05 0.9938 -9.541e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1957 0.09336 0.3238 0.1563 0.9851 0.994 0.1963 0.4621 0.8831 0.7167 ] Network output: [ 0.008021 -0.0388 0.997 7.365e-05 -3.306e-05 1.026 5.55e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09731 0.08613 0.1786 0.2076 0.9873 0.992 0.09738 0.787 0.8761 0.3088 ] Network output: [ -0.008096 0.04092 1.001 7.448e-05 -3.344e-05 0.9742 5.613e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09163 0.08972 0.1667 0.197 0.9856 0.9914 0.09164 0.7159 0.8551 0.2432 ] Network output: [ 0.0002297 0.9995 -0.0006054 1.018e-05 -4.572e-06 1.001 7.675e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007959 Epoch 7038 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01288 0.9927 0.9874 3.689e-06 -1.656e-06 -0.005766 2.78e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003216 -0.002997 -0.009296 0.00711 0.9698 0.9742 0.006117 0.8428 0.8309 0.01993 ] Network output: [ 0.9997 0.0005822 0.001731 -3.724e-05 1.672e-05 -0.001803 -2.806e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1851 -0.02978 -0.1944 0.1991 0.9836 0.9933 0.2065 0.4574 0.8766 0.722 ] Network output: [ -0.01169 1 1.01 1.835e-06 -8.24e-07 0.0126 1.383e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005407 0.0004845 0.004332 0.004301 0.9889 0.992 0.005506 0.8723 0.9005 0.01445 ] Network output: [ -0.0008986 0.002624 1.003 -0.0001263 5.67e-05 0.9957 -9.518e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1957 0.09335 0.324 0.1567 0.9851 0.994 0.1963 0.4621 0.8831 0.7167 ] Network output: [ 0.007988 -0.03924 0.997 7.362e-05 -3.305e-05 1.027 5.548e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09732 0.08614 0.1787 0.2077 0.9873 0.992 0.09738 0.787 0.8761 0.3088 ] Network output: [ -0.008106 0.04105 1.001 7.442e-05 -3.341e-05 0.9741 5.609e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09163 0.08973 0.1667 0.1971 0.9856 0.9914 0.09164 0.7159 0.855 0.2432 ] Network output: [ 0.0003246 0.9996 -0.0007395 1.021e-05 -4.582e-06 1.001 7.692e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007997 Epoch 7039 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01286 0.9929 0.9874 3.662e-06 -1.644e-06 -0.005979 2.759e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003216 -0.002997 -0.009297 0.007105 0.9698 0.9742 0.006117 0.8428 0.8309 0.01993 ] Network output: [ 0.9996 0.002278 0.001642 -3.734e-05 1.676e-05 -0.003188 -2.814e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1851 -0.02976 -0.1945 0.1988 0.9836 0.9933 0.2066 0.4574 0.8766 0.722 ] Network output: [ -0.01169 1.001 1.01 1.823e-06 -8.184e-07 0.01252 1.374e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005408 0.0004842 0.004327 0.004292 0.9889 0.992 0.005507 0.8723 0.9005 0.01445 ] Network output: [ -0.00105 0.004917 1.003 -0.0001264 5.675e-05 0.9938 -9.527e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1957 0.09336 0.3238 0.1562 0.9851 0.994 0.1964 0.4621 0.8831 0.7167 ] Network output: [ 0.008015 -0.03878 0.9969 7.355e-05 -3.302e-05 1.026 5.543e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09732 0.08613 0.1786 0.2076 0.9873 0.992 0.09738 0.7869 0.8761 0.3088 ] Network output: [ -0.008089 0.04089 1.001 7.439e-05 -3.34e-05 0.9742 5.606e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09162 0.08971 0.1667 0.197 0.9856 0.9914 0.09163 0.7158 0.855 0.2432 ] Network output: [ 0.0002305 0.9995 -0.0006058 1.017e-05 -4.566e-06 1.001 7.665e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000795 Epoch 7040 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01287 0.9927 0.9874 3.678e-06 -1.651e-06 -0.005774 2.772e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003216 -0.002997 -0.009293 0.007108 0.9698 0.9742 0.006117 0.8428 0.8309 0.01993 ] Network output: [ 0.9997 0.0005988 0.001728 -3.72e-05 1.67e-05 -0.001817 -2.803e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1851 -0.02979 -0.1944 0.1991 0.9836 0.9933 0.2066 0.4573 0.8766 0.722 ] Network output: [ -0.01169 1 1.01 1.83e-06 -8.217e-07 0.01259 1.379e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005408 0.0004843 0.004332 0.0043 0.9889 0.992 0.005507 0.8723 0.9005 0.01445 ] Network output: [ -0.0008998 0.002648 1.003 -0.0001261 5.662e-05 0.9957 -9.505e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1957 0.09335 0.324 0.1567 0.9851 0.994 0.1964 0.4621 0.8831 0.7167 ] Network output: [ 0.007982 -0.03921 0.997 7.352e-05 -3.301e-05 1.027 5.541e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09733 0.08614 0.1787 0.2077 0.9873 0.992 0.09739 0.7869 0.8761 0.3088 ] Network output: [ -0.008099 0.04102 1.001 7.433e-05 -3.337e-05 0.9741 5.602e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09162 0.08972 0.1667 0.1971 0.9856 0.9914 0.09163 0.7158 0.855 0.2432 ] Network output: [ 0.0003233 0.9996 -0.0007369 1.019e-05 -4.576e-06 1.001 7.682e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007987 Epoch 7041 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01285 0.9929 0.9874 3.651e-06 -1.639e-06 -0.005982 2.752e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003216 -0.002997 -0.009293 0.007102 0.9698 0.9742 0.006118 0.8428 0.8309 0.01992 ] Network output: [ 0.9996 0.002256 0.001641 -3.729e-05 1.674e-05 -0.00317 -2.81e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1851 -0.02978 -0.1945 0.1988 0.9836 0.9933 0.2066 0.4574 0.8766 0.722 ] Network output: [ -0.01169 1.001 1.01 1.818e-06 -8.163e-07 0.01251 1.37e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005409 0.000484 0.004327 0.00429 0.9889 0.992 0.005508 0.8723 0.9005 0.01445 ] Network output: [ -0.001048 0.004889 1.003 -0.0001262 5.667e-05 0.9938 -9.513e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1958 0.09335 0.3239 0.1562 0.9851 0.994 0.1964 0.4621 0.8831 0.7167 ] Network output: [ 0.008009 -0.03877 0.9969 7.345e-05 -3.297e-05 1.026 5.535e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09733 0.08614 0.1786 0.2076 0.9873 0.992 0.09739 0.7869 0.8761 0.3088 ] Network output: [ -0.008082 0.04086 1.001 7.429e-05 -3.335e-05 0.9743 5.599e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09161 0.08971 0.1667 0.197 0.9856 0.9914 0.09162 0.7157 0.855 0.2432 ] Network output: [ 0.0002313 0.9995 -0.0006062 1.016e-05 -4.56e-06 1.001 7.655e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007941 Epoch 7042 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01287 0.9927 0.9874 3.668e-06 -1.647e-06 -0.005781 2.764e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003216 -0.002998 -0.00929 0.007105 0.9698 0.9742 0.006118 0.8428 0.8309 0.01992 ] Network output: [ 0.9997 0.0006151 0.001725 -3.715e-05 1.668e-05 -0.00183 -2.8e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1851 -0.02981 -0.1943 0.1991 0.9836 0.9933 0.2066 0.4573 0.8766 0.722 ] Network output: [ -0.01168 1 1.01 1.825e-06 -8.195e-07 0.01258 1.376e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005409 0.0004841 0.004332 0.004298 0.9889 0.992 0.005508 0.8723 0.9005 0.01445 ] Network output: [ -0.0009009 0.002672 1.003 -0.0001259 5.654e-05 0.9957 -9.491e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1958 0.09335 0.324 0.1566 0.9851 0.994 0.1964 0.462 0.8831 0.7166 ] Network output: [ 0.007977 -0.03918 0.997 7.342e-05 -3.296e-05 1.027 5.533e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09733 0.08614 0.1787 0.2077 0.9873 0.992 0.0974 0.7869 0.8761 0.3088 ] Network output: [ -0.008092 0.04099 1.001 7.424e-05 -3.333e-05 0.9742 5.595e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09161 0.08971 0.1667 0.197 0.9856 0.9914 0.09162 0.7157 0.855 0.2432 ] Network output: [ 0.000322 0.9996 -0.0007343 1.018e-05 -4.57e-06 1.001 7.671e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007977 Epoch 7043 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01285 0.9929 0.9874 3.641e-06 -1.635e-06 -0.005984 2.744e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003217 -0.002998 -0.00929 0.0071 0.9698 0.9742 0.006118 0.8427 0.8309 0.01992 ] Network output: [ 0.9996 0.002234 0.00164 -3.725e-05 1.672e-05 -0.003152 -2.807e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1851 -0.02979 -0.1944 0.1988 0.9836 0.9933 0.2066 0.4573 0.8766 0.7219 ] Network output: [ -0.01169 1.001 1.01 1.813e-06 -8.141e-07 0.0125 1.367e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00541 0.0004838 0.004328 0.004289 0.9889 0.992 0.005509 0.8723 0.9004 0.01444 ] Network output: [ -0.001045 0.004862 1.003 -0.0001261 5.659e-05 0.9938 -9.5e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1958 0.09335 0.3239 0.1562 0.9851 0.994 0.1964 0.462 0.8831 0.7167 ] Network output: [ 0.008002 -0.03875 0.9969 7.335e-05 -3.293e-05 1.026 5.528e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09733 0.08614 0.1786 0.2076 0.9873 0.992 0.09739 0.7868 0.8761 0.3088 ] Network output: [ -0.008075 0.04083 1.001 7.42e-05 -3.331e-05 0.9743 5.592e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0916 0.0897 0.1667 0.197 0.9856 0.9914 0.09161 0.7157 0.855 0.2432 ] Network output: [ 0.000232 0.9995 -0.0006066 1.015e-05 -4.554e-06 1.001 7.646e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007932 Epoch 7044 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01286 0.9927 0.9874 3.657e-06 -1.642e-06 -0.005788 2.756e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003217 -0.002998 -0.009287 0.007103 0.9698 0.9742 0.006118 0.8427 0.8309 0.01992 ] Network output: [ 0.9997 0.0006311 0.001723 -3.711e-05 1.666e-05 -0.001843 -2.797e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1851 -0.02982 -0.1943 0.199 0.9836 0.9933 0.2066 0.4573 0.8766 0.722 ] Network output: [ -0.01168 1 1.01 1.82e-06 -8.172e-07 0.01257 1.372e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00541 0.0004838 0.004333 0.004296 0.9889 0.992 0.005509 0.8723 0.9004 0.01444 ] Network output: [ -0.000902 0.002696 1.003 -0.0001258 5.646e-05 0.9956 -9.478e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1958 0.09334 0.3241 0.1566 0.9851 0.994 0.1964 0.462 0.8831 0.7166 ] Network output: [ 0.007971 -0.03916 0.997 7.332e-05 -3.291e-05 1.027 5.525e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09734 0.08615 0.1787 0.2077 0.9873 0.992 0.0974 0.7868 0.8761 0.3088 ] Network output: [ -0.008084 0.04095 1.001 7.414e-05 -3.328e-05 0.9742 5.587e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0916 0.0897 0.1667 0.197 0.9856 0.9914 0.09161 0.7157 0.855 0.2432 ] Network output: [ 0.0003206 0.9996 -0.0007317 1.017e-05 -4.564e-06 1.001 7.661e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007967 Epoch 7045 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01284 0.9929 0.9874 3.631e-06 -1.63e-06 -0.005987 2.736e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003217 -0.002998 -0.009287 0.007098 0.9698 0.9742 0.006119 0.8427 0.8309 0.01992 ] Network output: [ 0.9996 0.002213 0.001639 -3.72e-05 1.67e-05 -0.003135 -2.804e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1852 -0.0298 -0.1944 0.1988 0.9836 0.9933 0.2066 0.4573 0.8766 0.7219 ] Network output: [ -0.01168 1.001 1.01 1.809e-06 -8.119e-07 0.01249 1.363e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005411 0.0004835 0.004328 0.004287 0.9889 0.992 0.00551 0.8723 0.9004 0.01444 ] Network output: [ -0.001043 0.004835 1.003 -0.0001259 5.651e-05 0.9939 -9.486e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1958 0.09335 0.3239 0.1562 0.9851 0.994 0.1964 0.462 0.8831 0.7167 ] Network output: [ 0.007996 -0.03873 0.9969 7.324e-05 -3.288e-05 1.026 5.52e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09734 0.08614 0.1786 0.2075 0.9873 0.992 0.0974 0.7868 0.876 0.3087 ] Network output: [ -0.008068 0.0408 1.001 7.41e-05 -3.327e-05 0.9743 5.585e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09159 0.08969 0.1667 0.197 0.9856 0.9914 0.0916 0.7156 0.8549 0.2432 ] Network output: [ 0.0002328 0.9996 -0.000607 1.013e-05 -4.549e-06 1.001 7.636e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007923 Epoch 7046 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01286 0.9927 0.9874 3.646e-06 -1.637e-06 -0.005796 2.748e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003217 -0.002998 -0.009284 0.007101 0.9698 0.9742 0.006119 0.8427 0.8309 0.01991 ] Network output: [ 0.9997 0.0006468 0.00172 -3.707e-05 1.664e-05 -0.001855 -2.794e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1851 -0.02983 -0.1943 0.199 0.9836 0.9933 0.2066 0.4572 0.8766 0.722 ] Network output: [ -0.01168 1 1.01 1.815e-06 -8.149e-07 0.01256 1.368e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005411 0.0004836 0.004333 0.004294 0.9889 0.992 0.00551 0.8723 0.9004 0.01444 ] Network output: [ -0.0009031 0.002719 1.003 -0.0001256 5.638e-05 0.9956 -9.464e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1958 0.09334 0.3241 0.1565 0.9851 0.994 0.1964 0.462 0.8831 0.7166 ] Network output: [ 0.007966 -0.03913 0.997 7.322e-05 -3.287e-05 1.027 5.518e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09735 0.08615 0.1787 0.2076 0.9873 0.992 0.09741 0.7868 0.876 0.3088 ] Network output: [ -0.008077 0.04092 1.001 7.405e-05 -3.324e-05 0.9742 5.58e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09159 0.08969 0.1667 0.197 0.9856 0.9914 0.0916 0.7156 0.8549 0.2432 ] Network output: [ 0.0003193 0.9996 -0.0007292 1.015e-05 -4.558e-06 1.001 7.651e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007957 Epoch 7047 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01284 0.9929 0.9874 3.621e-06 -1.626e-06 -0.00599 2.729e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003217 -0.002998 -0.009284 0.007096 0.9698 0.9742 0.006119 0.8427 0.8309 0.01991 ] Network output: [ 0.9996 0.002192 0.001639 -3.716e-05 1.668e-05 -0.003118 -2.801e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1852 -0.02981 -0.1944 0.1988 0.9836 0.9933 0.2067 0.4573 0.8766 0.7219 ] Network output: [ -0.01168 1.001 1.01 1.804e-06 -8.098e-07 0.01249 1.359e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005412 0.0004833 0.004329 0.004286 0.9889 0.992 0.005511 0.8722 0.9004 0.01444 ] Network output: [ -0.001041 0.004808 1.003 -0.0001257 5.642e-05 0.9939 -9.472e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1958 0.09334 0.324 0.1561 0.9851 0.994 0.1965 0.462 0.8831 0.7166 ] Network output: [ 0.00799 -0.03871 0.9969 7.314e-05 -3.284e-05 1.026 5.512e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09734 0.08614 0.1786 0.2075 0.9873 0.992 0.0974 0.7867 0.876 0.3087 ] Network output: [ -0.008061 0.04077 1.001 7.401e-05 -3.323e-05 0.9743 5.578e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09158 0.08968 0.1667 0.197 0.9856 0.9914 0.09159 0.7155 0.8549 0.2432 ] Network output: [ 0.0002335 0.9996 -0.0006073 1.012e-05 -4.543e-06 1.001 7.626e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007915 Epoch 7048 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01285 0.9927 0.9874 3.636e-06 -1.632e-06 -0.005803 2.74e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003217 -0.002999 -0.009281 0.007099 0.9698 0.9742 0.006119 0.8427 0.8309 0.01991 ] Network output: [ 0.9997 0.0006621 0.001717 -3.703e-05 1.662e-05 -0.001868 -2.791e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1852 -0.02984 -0.1942 0.199 0.9836 0.9933 0.2067 0.4572 0.8766 0.7219 ] Network output: [ -0.01168 1 1.01 1.81e-06 -8.127e-07 0.01255 1.364e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005412 0.0004833 0.004333 0.004292 0.9889 0.992 0.005511 0.8722 0.9004 0.01444 ] Network output: [ -0.0009041 0.002742 1.003 -0.0001254 5.63e-05 0.9956 -9.451e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1958 0.09334 0.3241 0.1565 0.9851 0.994 0.1964 0.4619 0.8831 0.7166 ] Network output: [ 0.00796 -0.0391 0.997 7.311e-05 -3.282e-05 1.026 5.51e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09735 0.08615 0.1787 0.2076 0.9873 0.992 0.09741 0.7867 0.876 0.3088 ] Network output: [ -0.00807 0.04089 1.001 7.395e-05 -3.32e-05 0.9742 5.573e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09158 0.08968 0.1667 0.197 0.9856 0.9914 0.09159 0.7155 0.8549 0.2432 ] Network output: [ 0.0003181 0.9996 -0.0007267 1.014e-05 -4.551e-06 1.001 7.64e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007948 Epoch 7049 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01283 0.9929 0.9874 3.611e-06 -1.621e-06 -0.005993 2.721e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003217 -0.002999 -0.009281 0.007094 0.9698 0.9742 0.00612 0.8427 0.8309 0.01991 ] Network output: [ 0.9996 0.002171 0.001638 -3.712e-05 1.666e-05 -0.003101 -2.797e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1852 -0.02983 -0.1943 0.1987 0.9836 0.9933 0.2067 0.4572 0.8766 0.7219 ] Network output: [ -0.01168 1.001 1.01 1.799e-06 -8.076e-07 0.01248 1.356e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005413 0.000483 0.004329 0.004284 0.9889 0.992 0.005512 0.8722 0.9004 0.01443 ] Network output: [ -0.001039 0.004782 1.003 -0.0001255 5.634e-05 0.9939 -9.458e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1959 0.09334 0.324 0.1561 0.9851 0.994 0.1965 0.4619 0.8831 0.7166 ] Network output: [ 0.007984 -0.03869 0.9969 7.304e-05 -3.279e-05 1.026 5.505e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09735 0.08615 0.1786 0.2075 0.9873 0.992 0.09741 0.7866 0.876 0.3087 ] Network output: [ -0.008054 0.04073 1.001 7.392e-05 -3.318e-05 0.9743 5.57e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09157 0.08967 0.1667 0.197 0.9856 0.9914 0.09158 0.7155 0.8549 0.2432 ] Network output: [ 0.0002341 0.9996 -0.0006076 1.011e-05 -4.537e-06 1.001 7.616e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007906 Epoch 7050 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01285 0.9927 0.9874 3.625e-06 -1.627e-06 -0.00581 2.732e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003217 -0.002999 -0.009277 0.007096 0.9698 0.9742 0.00612 0.8427 0.8309 0.01991 ] Network output: [ 0.9997 0.0006771 0.001715 -3.699e-05 1.661e-05 -0.00188 -2.788e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1852 -0.02985 -0.1942 0.199 0.9836 0.9933 0.2067 0.4572 0.8766 0.7219 ] Network output: [ -0.01167 1 1.01 1.805e-06 -8.104e-07 0.01254 1.36e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005413 0.0004831 0.004334 0.004291 0.9889 0.992 0.005512 0.8722 0.9004 0.01443 ] Network output: [ -0.0009051 0.002765 1.003 -0.0001252 5.622e-05 0.9956 -9.437e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1959 0.09333 0.3242 0.1565 0.9851 0.994 0.1965 0.4619 0.8831 0.7166 ] Network output: [ 0.007955 -0.03907 0.997 7.301e-05 -3.278e-05 1.026 5.502e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09736 0.08616 0.1787 0.2076 0.9873 0.992 0.09742 0.7866 0.876 0.3088 ] Network output: [ -0.008063 0.04085 1.001 7.386e-05 -3.316e-05 0.9742 5.566e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09157 0.08967 0.1667 0.197 0.9856 0.9914 0.09158 0.7155 0.8549 0.2432 ] Network output: [ 0.0003168 0.9996 -0.0007242 1.012e-05 -4.545e-06 1.001 7.63e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007938 Epoch 7051 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01283 0.993 0.9874 3.601e-06 -1.616e-06 -0.005995 2.713e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003218 -0.002999 -0.009277 0.007092 0.9698 0.9742 0.00612 0.8427 0.8308 0.0199 ] Network output: [ 0.9996 0.002151 0.001637 -3.707e-05 1.664e-05 -0.003084 -2.794e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1852 -0.02984 -0.1943 0.1987 0.9836 0.9933 0.2067 0.4572 0.8766 0.7219 ] Network output: [ -0.01168 1.001 1.01 1.794e-06 -8.054e-07 0.01247 1.352e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005414 0.0004828 0.00433 0.004282 0.9889 0.992 0.005513 0.8722 0.9004 0.01443 ] Network output: [ -0.001037 0.004757 1.003 -0.0001253 5.626e-05 0.9939 -9.444e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1959 0.09334 0.3241 0.1561 0.9851 0.994 0.1965 0.4619 0.883 0.7166 ] Network output: [ 0.007978 -0.03867 0.9969 7.294e-05 -3.275e-05 1.026 5.497e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09735 0.08615 0.1786 0.2075 0.9873 0.992 0.09742 0.7866 0.876 0.3087 ] Network output: [ -0.008047 0.0407 1.001 7.382e-05 -3.314e-05 0.9743 5.563e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09156 0.08966 0.1667 0.197 0.9856 0.9914 0.09157 0.7154 0.8549 0.2432 ] Network output: [ 0.0002348 0.9996 -0.0006079 1.009e-05 -4.531e-06 1.001 7.606e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007897 Epoch 7052 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01284 0.9927 0.9874 3.614e-06 -1.623e-06 -0.005817 2.724e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003218 -0.002999 -0.009274 0.007094 0.9698 0.9742 0.00612 0.8427 0.8308 0.0199 ] Network output: [ 0.9997 0.0006918 0.001712 -3.695e-05 1.659e-05 -0.001892 -2.785e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1852 -0.02986 -0.1941 0.1989 0.9836 0.9933 0.2067 0.4571 0.8766 0.7219 ] Network output: [ -0.01167 1 1.01 1.8e-06 -8.081e-07 0.01253 1.357e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005414 0.0004828 0.004334 0.004289 0.9889 0.992 0.005514 0.8722 0.9004 0.01443 ] Network output: [ -0.0009061 0.002786 1.003 -0.000125 5.614e-05 0.9956 -9.424e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1959 0.09333 0.3242 0.1564 0.9851 0.994 0.1965 0.4619 0.883 0.7166 ] Network output: [ 0.007949 -0.03904 0.997 7.291e-05 -3.273e-05 1.026 5.495e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09736 0.08616 0.1787 0.2076 0.9873 0.992 0.09742 0.7866 0.876 0.3088 ] Network output: [ -0.008055 0.04082 1.001 7.376e-05 -3.311e-05 0.9742 5.559e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09156 0.08966 0.1667 0.197 0.9856 0.9914 0.09157 0.7154 0.8548 0.2432 ] Network output: [ 0.0003155 0.9996 -0.0007218 1.011e-05 -4.539e-06 1.001 7.62e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007928 Epoch 7053 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01282 0.993 0.9874 3.59e-06 -1.612e-06 -0.005998 2.706e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003218 -0.002999 -0.009274 0.00709 0.9698 0.9742 0.006121 0.8427 0.8308 0.0199 ] Network output: [ 0.9996 0.002131 0.001636 -3.703e-05 1.662e-05 -0.003067 -2.791e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1852 -0.02985 -0.1942 0.1987 0.9836 0.9933 0.2067 0.4571 0.8765 0.7219 ] Network output: [ -0.01167 1.001 1.01 1.789e-06 -8.032e-07 0.01246 1.348e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005415 0.0004826 0.00433 0.004281 0.9889 0.992 0.005515 0.8722 0.9004 0.01443 ] Network output: [ -0.001035 0.004731 1.003 -0.0001251 5.617e-05 0.994 -9.43e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1959 0.09333 0.3241 0.1561 0.9851 0.994 0.1965 0.4619 0.883 0.7166 ] Network output: [ 0.007972 -0.03866 0.9969 7.284e-05 -3.27e-05 1.026 5.49e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09736 0.08615 0.1786 0.2075 0.9873 0.992 0.09742 0.7865 0.876 0.3087 ] Network output: [ -0.008041 0.04067 1.001 7.373e-05 -3.31e-05 0.9743 5.556e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09155 0.08965 0.1666 0.197 0.9856 0.9914 0.09156 0.7153 0.8548 0.2432 ] Network output: [ 0.0002355 0.9996 -0.0006081 1.008e-05 -4.525e-06 1.001 7.597e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007888 Epoch 7054 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01283 0.9927 0.9874 3.604e-06 -1.618e-06 -0.005824 2.716e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003218 -0.003 -0.009271 0.007092 0.9698 0.9742 0.006121 0.8427 0.8308 0.0199 ] Network output: [ 0.9997 0.0007062 0.001709 -3.691e-05 1.657e-05 -0.001903 -2.781e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1852 -0.02988 -0.1941 0.1989 0.9836 0.9933 0.2067 0.4571 0.8765 0.7219 ] Network output: [ -0.01167 1 1.01 1.795e-06 -8.058e-07 0.01252 1.353e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005416 0.0004826 0.004334 0.004287 0.9889 0.992 0.005515 0.8722 0.9004 0.01443 ] Network output: [ -0.0009071 0.002808 1.003 -0.0001249 5.606e-05 0.9956 -9.41e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1959 0.09333 0.3242 0.1564 0.9851 0.994 0.1965 0.4618 0.883 0.7166 ] Network output: [ 0.007944 -0.03902 0.997 7.281e-05 -3.269e-05 1.026 5.487e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09737 0.08616 0.1787 0.2076 0.9873 0.992 0.09743 0.7865 0.8759 0.3088 ] Network output: [ -0.008048 0.04078 1.001 7.367e-05 -3.307e-05 0.9743 5.552e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09155 0.08965 0.1667 0.197 0.9856 0.9914 0.09156 0.7153 0.8548 0.2432 ] Network output: [ 0.0003143 0.9996 -0.0007193 1.01e-05 -4.533e-06 1.001 7.61e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007918 Epoch 7055 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01282 0.993 0.9874 3.58e-06 -1.607e-06 -0.006001 2.698e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003218 -0.003 -0.009271 0.007087 0.9698 0.9742 0.006121 0.8427 0.8308 0.01989 ] Network output: [ 0.9996 0.002111 0.001635 -3.699e-05 1.66e-05 -0.003051 -2.787e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1853 -0.02986 -0.1942 0.1987 0.9836 0.9933 0.2068 0.4571 0.8765 0.7219 ] Network output: [ -0.01167 1.001 1.01 1.784e-06 -8.011e-07 0.01245 1.345e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005417 0.0004823 0.004331 0.004279 0.9889 0.992 0.005516 0.8722 0.9004 0.01442 ] Network output: [ -0.001032 0.004707 1.003 -0.0001249 5.609e-05 0.994 -9.416e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1959 0.09333 0.3241 0.156 0.9851 0.994 0.1965 0.4618 0.883 0.7166 ] Network output: [ 0.007966 -0.03864 0.9969 7.274e-05 -3.266e-05 1.026 5.482e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09737 0.08616 0.1786 0.2075 0.9873 0.992 0.09743 0.7865 0.8759 0.3087 ] Network output: [ -0.008034 0.04064 1.001 7.363e-05 -3.306e-05 0.9744 5.549e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09154 0.08964 0.1666 0.197 0.9856 0.9914 0.09155 0.7153 0.8548 0.2432 ] Network output: [ 0.0002361 0.9996 -0.0006083 1.007e-05 -4.519e-06 1.001 7.587e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007879 Epoch 7056 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01283 0.9928 0.9874 3.593e-06 -1.613e-06 -0.005831 2.708e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003218 -0.003 -0.009268 0.00709 0.9698 0.9742 0.006121 0.8426 0.8308 0.01989 ] Network output: [ 0.9997 0.0007202 0.001707 -3.687e-05 1.655e-05 -0.001915 -2.778e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1853 -0.02989 -0.1941 0.1989 0.9836 0.9933 0.2068 0.4571 0.8765 0.7219 ] Network output: [ -0.01167 1 1.01 1.79e-06 -8.036e-07 0.01251 1.349e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005417 0.0004824 0.004335 0.004285 0.9889 0.992 0.005516 0.8722 0.9004 0.01442 ] Network output: [ -0.000908 0.002829 1.003 -0.0001247 5.598e-05 0.9955 -9.397e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1959 0.09332 0.3243 0.1564 0.9851 0.994 0.1965 0.4618 0.883 0.7166 ] Network output: [ 0.007939 -0.03899 0.997 7.271e-05 -3.264e-05 1.026 5.48e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09737 0.08617 0.1787 0.2075 0.9873 0.992 0.09744 0.7865 0.8759 0.3087 ] Network output: [ -0.008041 0.04075 1.001 7.357e-05 -3.303e-05 0.9743 5.545e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09154 0.08964 0.1666 0.197 0.9856 0.9914 0.09155 0.7153 0.8548 0.2432 ] Network output: [ 0.0003131 0.9996 -0.0007169 1.008e-05 -4.527e-06 1.001 7.599e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007908 Epoch 7057 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01281 0.993 0.9874 3.57e-06 -1.603e-06 -0.006004 2.69e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003218 -0.003 -0.009268 0.007085 0.9698 0.9742 0.006122 0.8426 0.8308 0.01989 ] Network output: [ 0.9996 0.002091 0.001634 -3.694e-05 1.658e-05 -0.003035 -2.784e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1853 -0.02987 -0.1942 0.1987 0.9836 0.9933 0.2068 0.4571 0.8765 0.7219 ] Network output: [ -0.01167 1.001 1.01 1.779e-06 -7.989e-07 0.01245 1.341e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005418 0.0004821 0.004331 0.004278 0.9889 0.992 0.005517 0.8721 0.9004 0.01442 ] Network output: [ -0.00103 0.004682 1.003 -0.0001248 5.601e-05 0.994 -9.402e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1959 0.09333 0.3242 0.156 0.9851 0.994 0.1966 0.4618 0.883 0.7166 ] Network output: [ 0.00796 -0.03862 0.9969 7.264e-05 -3.261e-05 1.026 5.474e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09737 0.08616 0.1786 0.2074 0.9873 0.992 0.09743 0.7864 0.8759 0.3087 ] Network output: [ -0.008027 0.04061 1.001 7.354e-05 -3.301e-05 0.9744 5.542e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09153 0.08963 0.1666 0.197 0.9856 0.9914 0.09154 0.7152 0.8548 0.2432 ] Network output: [ 0.0002367 0.9996 -0.0006085 1.005e-05 -4.514e-06 1.001 7.577e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000787 Epoch 7058 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01282 0.9928 0.9874 3.582e-06 -1.608e-06 -0.005839 2.7e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003218 -0.003 -0.009265 0.007088 0.9698 0.9742 0.006122 0.8426 0.8308 0.01989 ] Network output: [ 0.9997 0.000734 0.001704 -3.682e-05 1.653e-05 -0.001926 -2.775e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1853 -0.0299 -0.194 0.1989 0.9836 0.9933 0.2068 0.457 0.8765 0.7219 ] Network output: [ -0.01166 1 1.01 1.785e-06 -8.013e-07 0.0125 1.345e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005418 0.0004821 0.004335 0.004284 0.9889 0.992 0.005517 0.8721 0.9004 0.01442 ] Network output: [ -0.0009088 0.00285 1.003 -0.0001245 5.589e-05 0.9955 -9.383e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1959 0.09332 0.3243 0.1563 0.9851 0.994 0.1966 0.4618 0.883 0.7166 ] Network output: [ 0.007933 -0.03896 0.9969 7.261e-05 -3.26e-05 1.026 5.472e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09738 0.08617 0.1787 0.2075 0.9873 0.992 0.09744 0.7864 0.8759 0.3087 ] Network output: [ -0.008034 0.04072 1.001 7.348e-05 -3.299e-05 0.9743 5.538e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09153 0.08963 0.1666 0.197 0.9856 0.9914 0.09154 0.7152 0.8548 0.2432 ] Network output: [ 0.0003119 0.9996 -0.0007146 1.007e-05 -4.521e-06 1.001 7.589e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007899 Epoch 7059 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01281 0.993 0.9874 3.56e-06 -1.598e-06 -0.006007 2.683e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003218 -0.003 -0.009265 0.007083 0.9698 0.9742 0.006122 0.8426 0.8308 0.01988 ] Network output: [ 0.9996 0.002072 0.001633 -3.69e-05 1.656e-05 -0.003019 -2.781e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1853 -0.02989 -0.1941 0.1986 0.9836 0.9933 0.2068 0.457 0.8765 0.7219 ] Network output: [ -0.01167 1.001 1.01 1.775e-06 -7.967e-07 0.01244 1.337e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005419 0.0004819 0.004331 0.004276 0.9889 0.992 0.005518 0.8721 0.9004 0.01442 ] Network output: [ -0.001028 0.004658 1.003 -0.0001246 5.593e-05 0.994 -9.389e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.196 0.09332 0.3242 0.156 0.9851 0.994 0.1966 0.4618 0.883 0.7166 ] Network output: [ 0.007953 -0.0386 0.9969 7.254e-05 -3.257e-05 1.026 5.467e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09738 0.08616 0.1786 0.2074 0.9873 0.992 0.09744 0.7864 0.8759 0.3087 ] Network output: [ -0.00802 0.04058 1.001 7.344e-05 -3.297e-05 0.9744 5.535e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09152 0.08962 0.1666 0.197 0.9856 0.9914 0.09154 0.7151 0.8548 0.2432 ] Network output: [ 0.0002373 0.9996 -0.0006087 1.004e-05 -4.508e-06 1.001 7.567e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007861 Epoch 7060 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01282 0.9928 0.9874 3.572e-06 -1.604e-06 -0.005846 2.692e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003218 -0.003 -0.009262 0.007085 0.9698 0.9742 0.006123 0.8426 0.8308 0.01988 ] Network output: [ 0.9997 0.0007475 0.001701 -3.678e-05 1.651e-05 -0.001936 -2.772e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1853 -0.02991 -0.194 0.1988 0.9836 0.9933 0.2068 0.457 0.8765 0.7219 ] Network output: [ -0.01166 1 1.01 1.78e-06 -7.99e-07 0.01249 1.341e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005419 0.0004819 0.004335 0.004282 0.9889 0.992 0.005518 0.8721 0.9004 0.01442 ] Network output: [ -0.0009097 0.00287 1.003 -0.0001243 5.581e-05 0.9955 -9.37e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.196 0.09332 0.3243 0.1563 0.9851 0.994 0.1966 0.4617 0.883 0.7166 ] Network output: [ 0.007928 -0.03893 0.9969 7.251e-05 -3.255e-05 1.026 5.464e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09739 0.08617 0.1787 0.2075 0.9873 0.992 0.09745 0.7864 0.8759 0.3087 ] Network output: [ -0.008027 0.04068 1.001 7.338e-05 -3.294e-05 0.9743 5.53e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09152 0.08962 0.1666 0.197 0.9856 0.9914 0.09154 0.7151 0.8547 0.2432 ] Network output: [ 0.0003107 0.9996 -0.0007122 1.006e-05 -4.515e-06 1.001 7.579e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007889 Epoch 7061 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0128 0.993 0.9874 3.55e-06 -1.594e-06 -0.00601 2.675e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003219 -0.003001 -0.009261 0.007081 0.9698 0.9742 0.006123 0.8426 0.8308 0.01988 ] Network output: [ 0.9996 0.002053 0.001632 -3.685e-05 1.654e-05 -0.003003 -2.777e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1853 -0.0299 -0.1941 0.1986 0.9836 0.9933 0.2068 0.457 0.8765 0.7219 ] Network output: [ -0.01166 1.001 1.01 1.77e-06 -7.945e-07 0.01243 1.334e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00542 0.0004816 0.004332 0.004274 0.9889 0.992 0.005519 0.8721 0.9003 0.01441 ] Network output: [ -0.001026 0.004635 1.003 -0.0001244 5.585e-05 0.994 -9.375e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.196 0.09332 0.3242 0.1559 0.9851 0.994 0.1966 0.4617 0.883 0.7166 ] Network output: [ 0.007947 -0.03858 0.9969 7.244e-05 -3.252e-05 1.026 5.459e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09738 0.08617 0.1786 0.2074 0.9873 0.992 0.09745 0.7863 0.8759 0.3087 ] Network output: [ -0.008013 0.04055 1.001 7.335e-05 -3.293e-05 0.9744 5.528e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09151 0.08961 0.1666 0.1969 0.9856 0.9914 0.09153 0.7151 0.8547 0.2432 ] Network output: [ 0.0002379 0.9996 -0.0006088 1.003e-05 -4.502e-06 1.001 7.557e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007852 Epoch 7062 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01281 0.9928 0.9875 3.561e-06 -1.599e-06 -0.005852 2.684e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003219 -0.003001 -0.009258 0.007083 0.9698 0.9742 0.006123 0.8426 0.8308 0.01988 ] Network output: [ 0.9997 0.0007606 0.001699 -3.674e-05 1.649e-05 -0.001947 -2.769e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1853 -0.02992 -0.194 0.1988 0.9836 0.9933 0.2068 0.457 0.8765 0.7219 ] Network output: [ -0.01166 1 1.01 1.775e-06 -7.967e-07 0.01248 1.337e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00542 0.0004817 0.004336 0.00428 0.9889 0.992 0.005519 0.8721 0.9003 0.01441 ] Network output: [ -0.0009105 0.00289 1.003 -0.0001241 5.573e-05 0.9955 -9.356e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.196 0.09332 0.3244 0.1563 0.9851 0.994 0.1966 0.4617 0.883 0.7166 ] Network output: [ 0.007922 -0.03891 0.9969 7.241e-05 -3.251e-05 1.026 5.457e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09739 0.08618 0.1787 0.2075 0.9873 0.992 0.09745 0.7863 0.8759 0.3087 ] Network output: [ -0.00802 0.04065 1.001 7.329e-05 -3.29e-05 0.9743 5.523e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09151 0.08961 0.1666 0.197 0.9856 0.9914 0.09153 0.7151 0.8547 0.2432 ] Network output: [ 0.0003096 0.9996 -0.0007099 1.004e-05 -4.509e-06 1.001 7.569e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007879 Epoch 7063 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0128 0.993 0.9875 3.54e-06 -1.589e-06 -0.006013 2.668e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003219 -0.003001 -0.009258 0.007079 0.9698 0.9742 0.006124 0.8426 0.8308 0.01988 ] Network output: [ 0.9996 0.002035 0.001631 -3.681e-05 1.653e-05 -0.002988 -2.774e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1853 -0.02991 -0.194 0.1986 0.9836 0.9933 0.2069 0.457 0.8765 0.7219 ] Network output: [ -0.01166 1.001 1.01 1.765e-06 -7.923e-07 0.01242 1.33e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005421 0.0004814 0.004332 0.004273 0.9889 0.992 0.00552 0.8721 0.9003 0.01441 ] Network output: [ -0.001024 0.004612 1.003 -0.0001242 5.576e-05 0.9941 -9.361e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.196 0.09332 0.3243 0.1559 0.9851 0.994 0.1966 0.4617 0.883 0.7166 ] Network output: [ 0.007941 -0.03856 0.9969 7.234e-05 -3.248e-05 1.026 5.452e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09739 0.08617 0.1786 0.2074 0.9873 0.992 0.09745 0.7862 0.8758 0.3087 ] Network output: [ -0.008006 0.04052 1.001 7.325e-05 -3.288e-05 0.9744 5.52e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09151 0.0896 0.1666 0.1969 0.9856 0.9914 0.09152 0.715 0.8547 0.2432 ] Network output: [ 0.0002385 0.9996 -0.000609 1.001e-05 -4.496e-06 1.001 7.548e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007843 Epoch 7064 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01281 0.9928 0.9875 3.551e-06 -1.594e-06 -0.005859 2.676e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003219 -0.003001 -0.009255 0.007081 0.9698 0.9742 0.006124 0.8426 0.8308 0.01987 ] Network output: [ 0.9997 0.0007735 0.001696 -3.67e-05 1.648e-05 -0.001957 -2.766e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1853 -0.02993 -0.1939 0.1988 0.9836 0.9933 0.2069 0.4569 0.8765 0.7219 ] Network output: [ -0.01166 1 1.01 1.77e-06 -7.944e-07 0.01247 1.334e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005421 0.0004814 0.004336 0.004278 0.9889 0.992 0.00552 0.8721 0.9003 0.01441 ] Network output: [ -0.0009113 0.002909 1.003 -0.000124 5.565e-05 0.9955 -9.343e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.196 0.09331 0.3244 0.1562 0.9851 0.994 0.1966 0.4616 0.883 0.7166 ] Network output: [ 0.007917 -0.03888 0.9969 7.231e-05 -3.246e-05 1.026 5.449e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0974 0.08618 0.1787 0.2075 0.9873 0.992 0.09746 0.7862 0.8758 0.3087 ] Network output: [ -0.008012 0.04062 1.001 7.319e-05 -3.286e-05 0.9743 5.516e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0915 0.0896 0.1666 0.1969 0.9856 0.9914 0.09152 0.715 0.8547 0.2432 ] Network output: [ 0.0003084 0.9996 -0.0007075 1.003e-05 -4.503e-06 1.001 7.558e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007869 Epoch 7065 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01279 0.993 0.9875 3.529e-06 -1.585e-06 -0.006016 2.66e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003219 -0.003001 -0.009255 0.007077 0.9698 0.9742 0.006124 0.8426 0.8308 0.01987 ] Network output: [ 0.9996 0.002017 0.00163 -3.677e-05 1.651e-05 -0.002973 -2.771e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1854 -0.02992 -0.194 0.1986 0.9836 0.9933 0.2069 0.4569 0.8765 0.7219 ] Network output: [ -0.01166 1.001 1.01 1.76e-06 -7.901e-07 0.01241 1.326e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005422 0.0004812 0.004333 0.004271 0.9889 0.992 0.005521 0.8721 0.9003 0.01441 ] Network output: [ -0.001022 0.004589 1.003 -0.000124 5.568e-05 0.9941 -9.347e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.196 0.09332 0.3243 0.1559 0.9851 0.994 0.1967 0.4617 0.883 0.7166 ] Network output: [ 0.007935 -0.03854 0.9969 7.224e-05 -3.243e-05 1.026 5.444e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0974 0.08618 0.1786 0.2074 0.9873 0.992 0.09746 0.7862 0.8758 0.3086 ] Network output: [ -0.007999 0.04049 1.001 7.316e-05 -3.284e-05 0.9744 5.513e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0915 0.08959 0.1666 0.1969 0.9856 0.9914 0.09151 0.7149 0.8547 0.2432 ] Network output: [ 0.0002391 0.9996 -0.0006091 1e-05 -4.49e-06 1.001 7.538e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007834 Epoch 7066 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0128 0.9928 0.9875 3.54e-06 -1.589e-06 -0.005866 2.668e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003219 -0.003001 -0.009252 0.007079 0.9698 0.9742 0.006124 0.8426 0.8308 0.01987 ] Network output: [ 0.9997 0.000786 0.001693 -3.666e-05 1.646e-05 -0.001967 -2.763e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1854 -0.02995 -0.1939 0.1988 0.9836 0.9933 0.2069 0.4569 0.8765 0.7219 ] Network output: [ -0.01165 1 1.01 1.764e-06 -7.921e-07 0.01246 1.33e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005422 0.0004812 0.004336 0.004277 0.9889 0.992 0.005522 0.8721 0.9003 0.01441 ] Network output: [ -0.000912 0.002928 1.003 -0.0001238 5.557e-05 0.9955 -9.329e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.196 0.09331 0.3245 0.1562 0.9851 0.994 0.1967 0.4616 0.883 0.7165 ] Network output: [ 0.007911 -0.03885 0.9969 7.221e-05 -3.242e-05 1.026 5.442e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0974 0.08618 0.1787 0.2074 0.9873 0.992 0.09747 0.7862 0.8758 0.3087 ] Network output: [ -0.008005 0.04058 1.001 7.31e-05 -3.282e-05 0.9744 5.509e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0915 0.08959 0.1666 0.1969 0.9856 0.9914 0.09151 0.7149 0.8547 0.2432 ] Network output: [ 0.0003073 0.9996 -0.0007052 1.002e-05 -4.496e-06 1.001 7.548e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000786 Epoch 7067 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01279 0.993 0.9875 3.519e-06 -1.58e-06 -0.006019 2.652e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003219 -0.003002 -0.009252 0.007075 0.9698 0.9742 0.006125 0.8426 0.8307 0.01987 ] Network output: [ 0.9996 0.001999 0.001629 -3.672e-05 1.649e-05 -0.002958 -2.767e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1854 -0.02993 -0.194 0.1986 0.9836 0.9933 0.2069 0.4569 0.8765 0.7219 ] Network output: [ -0.01166 1.001 1.01 1.755e-06 -7.879e-07 0.0124 1.323e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005423 0.000481 0.004333 0.00427 0.9889 0.992 0.005522 0.8721 0.9003 0.0144 ] Network output: [ -0.00102 0.004567 1.003 -0.0001238 5.56e-05 0.9941 -9.333e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1961 0.09331 0.3244 0.1559 0.9851 0.994 0.1967 0.4616 0.883 0.7166 ] Network output: [ 0.007929 -0.03852 0.9969 7.214e-05 -3.239e-05 1.026 5.437e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0974 0.08618 0.1786 0.2073 0.9873 0.992 0.09746 0.7861 0.8758 0.3086 ] Network output: [ -0.007992 0.04046 1.001 7.306e-05 -3.28e-05 0.9744 5.506e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09149 0.08958 0.1666 0.1969 0.9856 0.9914 0.0915 0.7148 0.8546 0.2432 ] Network output: [ 0.0002396 0.9996 -0.0006092 9.989e-06 -4.484e-06 1.001 7.528e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007825 Epoch 7068 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0128 0.9928 0.9875 3.53e-06 -1.585e-06 -0.005873 2.66e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003219 -0.003002 -0.009249 0.007077 0.9698 0.9742 0.006125 0.8425 0.8307 0.01987 ] Network output: [ 0.9997 0.0007983 0.001691 -3.662e-05 1.644e-05 -0.001977 -2.76e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1854 -0.02996 -0.1938 0.1988 0.9836 0.9933 0.2069 0.4569 0.8765 0.7219 ] Network output: [ -0.01165 1.001 1.01 1.759e-06 -7.899e-07 0.01245 1.326e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005423 0.000481 0.004337 0.004275 0.9889 0.992 0.005523 0.872 0.9003 0.0144 ] Network output: [ -0.0009128 0.002946 1.003 -0.0001236 5.549e-05 0.9955 -9.316e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1961 0.09331 0.3245 0.1561 0.9851 0.994 0.1967 0.4616 0.8829 0.7165 ] Network output: [ 0.007906 -0.03882 0.9969 7.21e-05 -3.237e-05 1.026 5.434e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09741 0.08619 0.1787 0.2074 0.9873 0.992 0.09747 0.7861 0.8758 0.3087 ] Network output: [ -0.007998 0.04055 1.001 7.3e-05 -3.277e-05 0.9744 5.502e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09149 0.08958 0.1666 0.1969 0.9856 0.9914 0.0915 0.7148 0.8546 0.2432 ] Network output: [ 0.0003062 0.9996 -0.000703 1e-05 -4.49e-06 1.001 7.538e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000785 Epoch 7069 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01278 0.993 0.9875 3.509e-06 -1.575e-06 -0.006022 2.645e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00322 -0.003002 -0.009249 0.007073 0.9698 0.9742 0.006125 0.8425 0.8307 0.01986 ] Network output: [ 0.9996 0.001981 0.001628 -3.668e-05 1.647e-05 -0.002944 -2.764e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1854 -0.02995 -0.1939 0.1985 0.9836 0.9933 0.2069 0.4569 0.8764 0.7218 ] Network output: [ -0.01165 1.001 1.01 1.75e-06 -7.857e-07 0.0124 1.319e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005424 0.0004807 0.004334 0.004268 0.9889 0.992 0.005524 0.872 0.9003 0.0144 ] Network output: [ -0.001018 0.004545 1.003 -0.0001237 5.552e-05 0.9941 -9.32e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1961 0.09331 0.3244 0.1558 0.9851 0.994 0.1967 0.4616 0.8829 0.7165 ] Network output: [ 0.007923 -0.0385 0.9969 7.204e-05 -3.234e-05 1.026 5.429e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09741 0.08618 0.1786 0.2073 0.9873 0.992 0.09747 0.7861 0.8758 0.3086 ] Network output: [ -0.007985 0.04043 1.001 7.297e-05 -3.276e-05 0.9745 5.499e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09148 0.08957 0.1666 0.1969 0.9856 0.9914 0.09149 0.7148 0.8546 0.2432 ] Network output: [ 0.0002401 0.9996 -0.0006092 9.976e-06 -4.479e-06 1.001 7.518e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007816 Epoch 7070 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01279 0.9928 0.9875 3.519e-06 -1.58e-06 -0.00588 2.652e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00322 -0.003002 -0.009246 0.007074 0.9698 0.9742 0.006125 0.8425 0.8307 0.01986 ] Network output: [ 0.9997 0.0008103 0.001688 -3.657e-05 1.642e-05 -0.001987 -2.756e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1854 -0.02997 -0.1938 0.1987 0.9836 0.9933 0.2069 0.4568 0.8764 0.7219 ] Network output: [ -0.01165 1.001 1.01 1.754e-06 -7.876e-07 0.01244 1.322e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005425 0.0004808 0.004337 0.004273 0.9889 0.992 0.005524 0.872 0.9003 0.0144 ] Network output: [ -0.0009135 0.002964 1.003 -0.0001234 5.541e-05 0.9954 -9.302e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1961 0.09331 0.3245 0.1561 0.9851 0.994 0.1967 0.4615 0.8829 0.7165 ] Network output: [ 0.0079 -0.0388 0.9969 7.2e-05 -3.233e-05 1.026 5.426e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09742 0.08619 0.1787 0.2074 0.9873 0.992 0.09748 0.7861 0.8758 0.3087 ] Network output: [ -0.007991 0.04052 1.001 7.291e-05 -3.273e-05 0.9744 5.495e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09148 0.08957 0.1666 0.1969 0.9856 0.9914 0.09149 0.7148 0.8546 0.2432 ] Network output: [ 0.0003051 0.9996 -0.0007007 9.989e-06 -4.484e-06 1.001 7.528e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000784 Epoch 7071 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01278 0.993 0.9875 3.499e-06 -1.571e-06 -0.006025 2.637e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00322 -0.003002 -0.009245 0.00707 0.9698 0.9742 0.006126 0.8425 0.8307 0.01986 ] Network output: [ 0.9996 0.001964 0.001627 -3.663e-05 1.645e-05 -0.002929 -2.761e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1854 -0.02996 -0.1939 0.1985 0.9836 0.9933 0.207 0.4568 0.8764 0.7218 ] Network output: [ -0.01165 1.001 1.01 1.745e-06 -7.835e-07 0.01239 1.315e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005425 0.0004805 0.004334 0.004266 0.9889 0.992 0.005525 0.872 0.9003 0.0144 ] Network output: [ -0.001017 0.004523 1.003 -0.0001235 5.543e-05 0.9941 -9.306e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1961 0.09331 0.3244 0.1558 0.9851 0.994 0.1967 0.4615 0.8829 0.7165 ] Network output: [ 0.007917 -0.03848 0.9969 7.194e-05 -3.23e-05 1.026 5.421e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09741 0.08619 0.1787 0.2073 0.9873 0.992 0.09748 0.786 0.8758 0.3086 ] Network output: [ -0.007979 0.0404 1.001 7.287e-05 -3.271e-05 0.9745 5.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09147 0.08956 0.1666 0.1969 0.9856 0.9914 0.09148 0.7147 0.8546 0.2432 ] Network output: [ 0.0002406 0.9996 -0.0006092 9.963e-06 -4.473e-06 1.001 7.508e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007807 Epoch 7072 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01279 0.9928 0.9875 3.509e-06 -1.575e-06 -0.005887 2.644e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00322 -0.003002 -0.009243 0.007072 0.9698 0.9742 0.006126 0.8425 0.8307 0.01986 ] Network output: [ 0.9997 0.000822 0.001686 -3.653e-05 1.64e-05 -0.001996 -2.753e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1854 -0.02998 -0.1938 0.1987 0.9836 0.9933 0.207 0.4568 0.8764 0.7218 ] Network output: [ -0.01165 1.001 1.01 1.749e-06 -7.853e-07 0.01243 1.318e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005426 0.0004805 0.004337 0.004271 0.9889 0.992 0.005525 0.872 0.9003 0.0144 ] Network output: [ -0.0009141 0.002982 1.003 -0.0001233 5.533e-05 0.9954 -9.289e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1961 0.09331 0.3246 0.1561 0.9851 0.994 0.1967 0.4615 0.8829 0.7165 ] Network output: [ 0.007895 -0.03877 0.9969 7.19e-05 -3.228e-05 1.026 5.419e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09742 0.08619 0.1787 0.2074 0.9873 0.992 0.09748 0.786 0.8757 0.3087 ] Network output: [ -0.007984 0.04048 1.001 7.282e-05 -3.269e-05 0.9744 5.488e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09147 0.08956 0.1666 0.1969 0.9856 0.9914 0.09148 0.7147 0.8546 0.2432 ] Network output: [ 0.000304 0.9996 -0.0006985 9.975e-06 -4.478e-06 1.001 7.518e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000783 Epoch 7073 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01277 0.993 0.9875 3.489e-06 -1.566e-06 -0.006029 2.629e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00322 -0.003003 -0.009242 0.007068 0.9698 0.9742 0.006126 0.8425 0.8307 0.01985 ] Network output: [ 0.9996 0.001947 0.001626 -3.659e-05 1.643e-05 -0.002915 -2.757e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1854 -0.02997 -0.1938 0.1985 0.9836 0.9933 0.207 0.4568 0.8764 0.7218 ] Network output: [ -0.01165 1.001 1.01 1.74e-06 -7.812e-07 0.01238 1.311e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005426 0.0004803 0.004334 0.004265 0.9889 0.992 0.005526 0.872 0.9003 0.01439 ] Network output: [ -0.001015 0.004502 1.003 -0.0001233 5.535e-05 0.9942 -9.292e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1961 0.09331 0.3245 0.1558 0.9851 0.994 0.1967 0.4615 0.8829 0.7165 ] Network output: [ 0.007911 -0.03846 0.9969 7.184e-05 -3.225e-05 1.026 5.414e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09742 0.08619 0.1787 0.2073 0.9873 0.992 0.09748 0.786 0.8757 0.3086 ] Network output: [ -0.007972 0.04037 1.001 7.278e-05 -3.267e-05 0.9745 5.485e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09146 0.08956 0.1666 0.1969 0.9856 0.9914 0.09147 0.7146 0.8546 0.2432 ] Network output: [ 0.0002411 0.9996 -0.0006092 9.95e-06 -4.467e-06 1.001 7.498e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007798 Epoch 7074 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01278 0.9928 0.9875 3.498e-06 -1.57e-06 -0.005893 2.636e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00322 -0.003003 -0.00924 0.00707 0.9698 0.9742 0.006126 0.8425 0.8307 0.01985 ] Network output: [ 0.9997 0.0008335 0.001683 -3.649e-05 1.638e-05 -0.002005 -2.75e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1854 -0.02999 -0.1937 0.1987 0.9836 0.9933 0.207 0.4568 0.8764 0.7218 ] Network output: [ -0.01165 1.001 1.01 1.744e-06 -7.83e-07 0.01242 1.314e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005427 0.0004803 0.004338 0.00427 0.9889 0.992 0.005526 0.872 0.9003 0.01439 ] Network output: [ -0.0009148 0.002999 1.003 -0.0001231 5.525e-05 0.9954 -9.275e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1961 0.0933 0.3246 0.156 0.9851 0.994 0.1968 0.4615 0.8829 0.7165 ] Network output: [ 0.007889 -0.03874 0.9969 7.18e-05 -3.223e-05 1.026 5.411e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09743 0.0862 0.1787 0.2074 0.9873 0.992 0.09749 0.786 0.8757 0.3086 ] Network output: [ -0.007977 0.04045 1.001 7.272e-05 -3.265e-05 0.9744 5.48e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09146 0.08955 0.1666 0.1969 0.9856 0.9914 0.09147 0.7146 0.8545 0.2432 ] Network output: [ 0.0003029 0.9996 -0.0006963 9.962e-06 -4.472e-06 1.001 7.507e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007821 Epoch 7075 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01277 0.993 0.9875 3.479e-06 -1.562e-06 -0.006032 2.622e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00322 -0.003003 -0.009239 0.007066 0.9698 0.9742 0.006127 0.8425 0.8307 0.01985 ] Network output: [ 0.9996 0.00193 0.001625 -3.655e-05 1.641e-05 -0.002901 -2.754e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1855 -0.02998 -0.1938 0.1985 0.9836 0.9933 0.207 0.4568 0.8764 0.7218 ] Network output: [ -0.01165 1.001 1.01 1.735e-06 -7.79e-07 0.01237 1.308e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005428 0.0004801 0.004335 0.004263 0.9889 0.992 0.005527 0.872 0.9003 0.01439 ] Network output: [ -0.001013 0.004481 1.003 -0.0001231 5.527e-05 0.9942 -9.278e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1962 0.09331 0.3245 0.1558 0.9851 0.994 0.1968 0.4615 0.8829 0.7165 ] Network output: [ 0.007905 -0.03844 0.9968 7.174e-05 -3.221e-05 1.026 5.406e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09743 0.08619 0.1787 0.2073 0.9873 0.992 0.09749 0.7859 0.8757 0.3086 ] Network output: [ -0.007965 0.04033 1.001 7.268e-05 -3.263e-05 0.9745 5.478e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09145 0.08955 0.1666 0.1969 0.9856 0.9914 0.09146 0.7146 0.8545 0.2432 ] Network output: [ 0.0002416 0.9996 -0.0006092 9.937e-06 -4.461e-06 1.001 7.489e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007789 Epoch 7076 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01278 0.9929 0.9875 3.488e-06 -1.566e-06 -0.0059 2.628e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00322 -0.003003 -0.009236 0.007068 0.9698 0.9742 0.006127 0.8425 0.8307 0.01985 ] Network output: [ 0.9997 0.0008446 0.001681 -3.645e-05 1.636e-05 -0.002014 -2.747e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1855 -0.03 -0.1937 0.1987 0.9836 0.9933 0.207 0.4567 0.8764 0.7218 ] Network output: [ -0.01164 1.001 1.01 1.739e-06 -7.807e-07 0.01241 1.311e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005428 0.0004801 0.004338 0.004268 0.9889 0.992 0.005527 0.872 0.9003 0.01439 ] Network output: [ -0.0009154 0.003016 1.003 -0.0001229 5.517e-05 0.9954 -9.262e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1962 0.0933 0.3246 0.156 0.9851 0.994 0.1968 0.4614 0.8829 0.7165 ] Network output: [ 0.007884 -0.03871 0.9969 7.17e-05 -3.219e-05 1.026 5.404e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09743 0.0862 0.1787 0.2073 0.9873 0.992 0.0975 0.7859 0.8757 0.3086 ] Network output: [ -0.00797 0.04041 1.001 7.263e-05 -3.26e-05 0.9744 5.473e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09145 0.08955 0.1666 0.1969 0.9856 0.9914 0.09146 0.7146 0.8545 0.2432 ] Network output: [ 0.0003019 0.9996 -0.0006941 9.948e-06 -4.466e-06 1.001 7.497e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007811 Epoch 7077 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01276 0.993 0.9875 3.469e-06 -1.557e-06 -0.006035 2.614e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003221 -0.003003 -0.009236 0.007064 0.9698 0.9742 0.006127 0.8425 0.8307 0.01985 ] Network output: [ 0.9996 0.001914 0.001624 -3.65e-05 1.639e-05 -0.002887 -2.751e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1855 -0.02999 -0.1938 0.1985 0.9836 0.9933 0.207 0.4567 0.8764 0.7218 ] Network output: [ -0.01164 1.001 1.01 1.73e-06 -7.768e-07 0.01236 1.304e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005429 0.0004799 0.004335 0.004262 0.9889 0.992 0.005528 0.872 0.9003 0.01439 ] Network output: [ -0.001011 0.004461 1.003 -0.0001229 5.519e-05 0.9942 -9.265e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1962 0.0933 0.3245 0.1557 0.9851 0.994 0.1968 0.4614 0.8829 0.7165 ] Network output: [ 0.007899 -0.03842 0.9968 7.164e-05 -3.216e-05 1.026 5.399e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09743 0.0862 0.1787 0.2072 0.9873 0.992 0.0975 0.7858 0.8757 0.3086 ] Network output: [ -0.007958 0.0403 1.001 7.259e-05 -3.259e-05 0.9745 5.47e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09144 0.08954 0.1666 0.1969 0.9856 0.9914 0.09145 0.7145 0.8545 0.2432 ] Network output: [ 0.0002421 0.9996 -0.0006092 9.924e-06 -4.455e-06 1.001 7.479e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000778 Epoch 7078 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01277 0.9929 0.9875 3.477e-06 -1.561e-06 -0.005907 2.62e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003221 -0.003003 -0.009233 0.007066 0.9698 0.9742 0.006127 0.8425 0.8307 0.01984 ] Network output: [ 0.9997 0.0008555 0.001678 -3.641e-05 1.634e-05 -0.002022 -2.744e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1855 -0.03001 -0.1937 0.1986 0.9836 0.9933 0.207 0.4567 0.8764 0.7218 ] Network output: [ -0.01164 1.001 1.01 1.734e-06 -7.784e-07 0.0124 1.307e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005429 0.0004799 0.004338 0.004266 0.9889 0.992 0.005528 0.8719 0.9003 0.01439 ] Network output: [ -0.000916 0.003033 1.003 -0.0001227 5.509e-05 0.9954 -9.248e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1962 0.0933 0.3247 0.156 0.9851 0.994 0.1968 0.4614 0.8829 0.7165 ] Network output: [ 0.007878 -0.03869 0.9969 7.16e-05 -3.214e-05 1.026 5.396e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09744 0.0862 0.1787 0.2073 0.9873 0.992 0.0975 0.7858 0.8757 0.3086 ] Network output: [ -0.007963 0.04038 1.001 7.253e-05 -3.256e-05 0.9745 5.466e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09144 0.08954 0.1666 0.1969 0.9856 0.9914 0.09145 0.7145 0.8545 0.2432 ] Network output: [ 0.0003008 0.9996 -0.0006919 9.935e-06 -4.46e-06 1.001 7.487e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007801 Epoch 7079 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01276 0.993 0.9875 3.459e-06 -1.553e-06 -0.006038 2.607e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003221 -0.003003 -0.009233 0.007062 0.9698 0.9742 0.006128 0.8425 0.8307 0.01984 ] Network output: [ 0.9996 0.001898 0.001622 -3.646e-05 1.637e-05 -0.002874 -2.748e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1855 -0.03001 -0.1937 0.1985 0.9836 0.9933 0.2071 0.4567 0.8764 0.7218 ] Network output: [ -0.01164 1.001 1.01 1.725e-06 -7.746e-07 0.01236 1.3e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00543 0.0004797 0.004336 0.00426 0.9889 0.992 0.005529 0.8719 0.9002 0.01438 ] Network output: [ -0.001009 0.004441 1.003 -0.0001227 5.511e-05 0.9942 -9.251e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1962 0.0933 0.3246 0.1557 0.9851 0.994 0.1968 0.4614 0.8829 0.7165 ] Network output: [ 0.007893 -0.0384 0.9968 7.154e-05 -3.212e-05 1.026 5.391e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09744 0.0862 0.1787 0.2072 0.9873 0.992 0.0975 0.7858 0.8757 0.3086 ] Network output: [ -0.007951 0.04027 1.001 7.249e-05 -3.254e-05 0.9745 5.463e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09143 0.08953 0.1665 0.1969 0.9856 0.9914 0.09145 0.7144 0.8545 0.2432 ] Network output: [ 0.0002425 0.9996 -0.0006091 9.911e-06 -4.449e-06 1.001 7.469e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007771 Epoch 7080 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01277 0.9929 0.9875 3.467e-06 -1.556e-06 -0.005913 2.613e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003221 -0.003004 -0.00923 0.007063 0.9698 0.9742 0.006128 0.8424 0.8307 0.01984 ] Network output: [ 0.9997 0.0008661 0.001676 -3.636e-05 1.633e-05 -0.002031 -2.741e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1855 -0.03003 -0.1936 0.1986 0.9836 0.9933 0.2071 0.4567 0.8764 0.7218 ] Network output: [ -0.01164 1.001 1.01 1.729e-06 -7.761e-07 0.01239 1.303e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00543 0.0004797 0.004339 0.004264 0.9889 0.992 0.00553 0.8719 0.9002 0.01438 ] Network output: [ -0.0009165 0.003049 1.003 -0.0001225 5.501e-05 0.9954 -9.235e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1962 0.0933 0.3247 0.1559 0.9851 0.994 0.1968 0.4614 0.8829 0.7165 ] Network output: [ 0.007873 -0.03866 0.9969 7.15e-05 -3.21e-05 1.026 5.389e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09745 0.08621 0.1787 0.2073 0.9873 0.992 0.09751 0.7858 0.8757 0.3086 ] Network output: [ -0.007956 0.04035 1.001 7.244e-05 -3.252e-05 0.9745 5.459e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09143 0.08953 0.1666 0.1969 0.9856 0.9914 0.09145 0.7144 0.8545 0.2432 ] Network output: [ 0.0002998 0.9996 -0.0006898 9.921e-06 -4.454e-06 1.001 7.477e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007792 Epoch 7081 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01275 0.993 0.9875 3.449e-06 -1.548e-06 -0.006042 2.599e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003221 -0.003004 -0.00923 0.00706 0.9698 0.9742 0.006128 0.8424 0.8307 0.01984 ] Network output: [ 0.9996 0.001882 0.001621 -3.641e-05 1.635e-05 -0.002861 -2.744e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1855 -0.03002 -0.1937 0.1984 0.9836 0.9933 0.2071 0.4567 0.8764 0.7218 ] Network output: [ -0.01164 1.001 1.01 1.72e-06 -7.724e-07 0.01235 1.297e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005431 0.0004794 0.004336 0.004258 0.9889 0.992 0.00553 0.8719 0.9002 0.01438 ] Network output: [ -0.001007 0.004421 1.003 -0.0001226 5.502e-05 0.9942 -9.237e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1962 0.0933 0.3246 0.1557 0.9851 0.994 0.1968 0.4614 0.8829 0.7165 ] Network output: [ 0.007887 -0.03838 0.9968 7.144e-05 -3.207e-05 1.026 5.384e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09745 0.0862 0.1787 0.2072 0.9873 0.992 0.09751 0.7857 0.8756 0.3086 ] Network output: [ -0.007944 0.04024 1.001 7.24e-05 -3.25e-05 0.9745 5.456e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09143 0.08952 0.1665 0.1969 0.9856 0.9914 0.09144 0.7144 0.8544 0.2432 ] Network output: [ 0.0002429 0.9996 -0.0006091 9.898e-06 -4.443e-06 1.001 7.459e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007762 Epoch 7082 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01276 0.9929 0.9875 3.456e-06 -1.552e-06 -0.00592 2.605e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003221 -0.003004 -0.009227 0.007061 0.9698 0.9742 0.006129 0.8424 0.8307 0.01984 ] Network output: [ 0.9997 0.0008765 0.001673 -3.632e-05 1.631e-05 -0.002039 -2.737e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1855 -0.03004 -0.1936 0.1986 0.9836 0.9933 0.2071 0.4566 0.8764 0.7218 ] Network output: [ -0.01164 1.001 1.01 1.724e-06 -7.738e-07 0.01239 1.299e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005431 0.0004794 0.004339 0.004263 0.9889 0.992 0.005531 0.8719 0.9002 0.01438 ] Network output: [ -0.0009171 0.003065 1.003 -0.0001224 5.493e-05 0.9954 -9.221e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1962 0.0933 0.3247 0.1559 0.9851 0.994 0.1968 0.4613 0.8829 0.7165 ] Network output: [ 0.007867 -0.03863 0.9969 7.14e-05 -3.205e-05 1.026 5.381e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09745 0.08621 0.1787 0.2073 0.9873 0.992 0.09751 0.7857 0.8756 0.3086 ] Network output: [ -0.007948 0.04031 1.001 7.234e-05 -3.248e-05 0.9745 5.452e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09142 0.08952 0.1665 0.1969 0.9856 0.9914 0.09144 0.7144 0.8544 0.2432 ] Network output: [ 0.0002988 0.9996 -0.0006877 9.908e-06 -4.448e-06 1.001 7.467e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007782 Epoch 7083 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01275 0.993 0.9875 3.438e-06 -1.544e-06 -0.006045 2.591e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003221 -0.003004 -0.009226 0.007058 0.9698 0.9742 0.006129 0.8424 0.8306 0.01983 ] Network output: [ 0.9996 0.001866 0.00162 -3.637e-05 1.633e-05 -0.002848 -2.741e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1855 -0.03003 -0.1936 0.1984 0.9836 0.9933 0.2071 0.4566 0.8764 0.7218 ] Network output: [ -0.01164 1.001 1.01 1.715e-06 -7.701e-07 0.01234 1.293e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005432 0.0004792 0.004337 0.004257 0.9889 0.992 0.005532 0.8719 0.9002 0.01438 ] Network output: [ -0.001006 0.004402 1.003 -0.0001224 5.494e-05 0.9943 -9.223e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1962 0.0933 0.3247 0.1556 0.9851 0.994 0.1969 0.4613 0.8829 0.7165 ] Network output: [ 0.007881 -0.03836 0.9968 7.134e-05 -3.203e-05 1.026 5.376e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09745 0.08621 0.1787 0.2072 0.9873 0.992 0.09751 0.7857 0.8756 0.3086 ] Network output: [ -0.007938 0.04021 1.001 7.23e-05 -3.246e-05 0.9746 5.449e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09142 0.08951 0.1665 0.1969 0.9856 0.9914 0.09143 0.7143 0.8544 0.2432 ] Network output: [ 0.0002434 0.9996 -0.000609 9.885e-06 -4.438e-06 1.001 7.449e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007753 Epoch 7084 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01276 0.9929 0.9875 3.446e-06 -1.547e-06 -0.005926 2.597e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003221 -0.003004 -0.009224 0.007059 0.9698 0.9742 0.006129 0.8424 0.8306 0.01983 ] Network output: [ 0.9997 0.0008865 0.001671 -3.628e-05 1.629e-05 -0.002047 -2.734e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1856 -0.03005 -0.1935 0.1986 0.9836 0.9933 0.2071 0.4566 0.8764 0.7218 ] Network output: [ -0.01163 1.001 1.01 1.719e-06 -7.715e-07 0.01238 1.295e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005432 0.0004792 0.004339 0.004261 0.9889 0.992 0.005532 0.8719 0.9002 0.01438 ] Network output: [ -0.0009176 0.00308 1.003 -0.0001222 5.485e-05 0.9954 -9.208e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1962 0.09329 0.3248 0.1559 0.9851 0.994 0.1969 0.4613 0.8829 0.7165 ] Network output: [ 0.007862 -0.0386 0.9969 7.13e-05 -3.201e-05 1.026 5.373e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09746 0.08621 0.1787 0.2073 0.9873 0.992 0.09752 0.7857 0.8756 0.3086 ] Network output: [ -0.007941 0.04028 1.001 7.225e-05 -3.243e-05 0.9745 5.445e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09142 0.08951 0.1665 0.1969 0.9856 0.9914 0.09143 0.7143 0.8544 0.2432 ] Network output: [ 0.0002978 0.9996 -0.0006856 9.894e-06 -4.442e-06 1.001 7.456e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007772 Epoch 7085 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01274 0.993 0.9875 3.428e-06 -1.539e-06 -0.006048 2.584e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003221 -0.003004 -0.009223 0.007056 0.9698 0.9742 0.00613 0.8424 0.8306 0.01983 ] Network output: [ 0.9996 0.001851 0.001619 -3.632e-05 1.631e-05 -0.002835 -2.738e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1856 -0.03004 -0.1936 0.1984 0.9836 0.9933 0.2071 0.4566 0.8764 0.7218 ] Network output: [ -0.01163 1.001 1.01 1.711e-06 -7.679e-07 0.01233 1.289e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005433 0.000479 0.004337 0.004255 0.9889 0.992 0.005533 0.8719 0.9002 0.01437 ] Network output: [ -0.001004 0.004383 1.003 -0.0001222 5.486e-05 0.9943 -9.21e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1963 0.0933 0.3247 0.1556 0.9851 0.994 0.1969 0.4613 0.8828 0.7165 ] Network output: [ 0.007876 -0.03834 0.9968 7.124e-05 -3.198e-05 1.026 5.369e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09746 0.08621 0.1787 0.2072 0.9873 0.992 0.09752 0.7856 0.8756 0.3085 ] Network output: [ -0.007931 0.04018 1.001 7.221e-05 -3.242e-05 0.9746 5.442e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09141 0.0895 0.1665 0.1968 0.9856 0.9914 0.09142 0.7142 0.8544 0.2432 ] Network output: [ 0.0002438 0.9996 -0.0006089 9.872e-06 -4.432e-06 1.001 7.44e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007744 Epoch 7086 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01275 0.9929 0.9875 3.435e-06 -1.542e-06 -0.005933 2.589e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003221 -0.003005 -0.009221 0.007057 0.9698 0.9742 0.00613 0.8424 0.8306 0.01983 ] Network output: [ 0.9997 0.0008964 0.001668 -3.624e-05 1.627e-05 -0.002054 -2.731e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1856 -0.03006 -0.1935 0.1985 0.9836 0.9933 0.2071 0.4566 0.8764 0.7218 ] Network output: [ -0.01163 1.001 1.01 1.713e-06 -7.692e-07 0.01237 1.291e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005434 0.000479 0.00434 0.004259 0.9889 0.992 0.005533 0.8719 0.9002 0.01437 ] Network output: [ -0.000918 0.003095 1.003 -0.000122 5.477e-05 0.9954 -9.194e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1963 0.09329 0.3248 0.1558 0.9851 0.994 0.1969 0.4613 0.8828 0.7165 ] Network output: [ 0.007856 -0.03858 0.9969 7.12e-05 -3.196e-05 1.026 5.366e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09747 0.08622 0.1787 0.2072 0.9873 0.992 0.09753 0.7856 0.8756 0.3086 ] Network output: [ -0.007934 0.04025 1.001 7.215e-05 -3.239e-05 0.9745 5.438e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09141 0.0895 0.1665 0.1969 0.9856 0.9914 0.09142 0.7142 0.8544 0.2432 ] Network output: [ 0.0002968 0.9996 -0.0006835 9.881e-06 -4.436e-06 1.001 7.446e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007763 Epoch 7087 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01274 0.993 0.9875 3.418e-06 -1.535e-06 -0.006052 2.576e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003222 -0.003005 -0.00922 0.007053 0.9698 0.9742 0.00613 0.8424 0.8306 0.01982 ] Network output: [ 0.9996 0.001836 0.001618 -3.628e-05 1.629e-05 -0.002822 -2.734e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1856 -0.03005 -0.1936 0.1984 0.9836 0.9933 0.2072 0.4566 0.8763 0.7218 ] Network output: [ -0.01163 1.001 1.01 1.706e-06 -7.657e-07 0.01232 1.285e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005434 0.0004788 0.004337 0.004254 0.9889 0.992 0.005534 0.8719 0.9002 0.01437 ] Network output: [ -0.001002 0.004364 1.003 -0.000122 5.478e-05 0.9943 -9.196e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1963 0.09329 0.3247 0.1556 0.9851 0.994 0.1969 0.4613 0.8828 0.7165 ] Network output: [ 0.00787 -0.03832 0.9968 7.114e-05 -3.194e-05 1.026 5.361e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09747 0.08622 0.1787 0.2072 0.9873 0.992 0.09753 0.7856 0.8756 0.3085 ] Network output: [ -0.007924 0.04015 1.001 7.211e-05 -3.237e-05 0.9746 5.435e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0914 0.08949 0.1665 0.1968 0.9856 0.9914 0.09141 0.7142 0.8544 0.2432 ] Network output: [ 0.0002442 0.9996 -0.0006087 9.858e-06 -4.426e-06 1.001 7.43e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007735 Epoch 7088 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01275 0.9929 0.9876 3.425e-06 -1.538e-06 -0.005939 2.581e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003222 -0.003005 -0.009218 0.007055 0.9698 0.9742 0.00613 0.8424 0.8306 0.01982 ] Network output: [ 0.9997 0.000906 0.001666 -3.62e-05 1.625e-05 -0.002062 -2.728e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1856 -0.03007 -0.1935 0.1985 0.9836 0.9933 0.2072 0.4565 0.8763 0.7218 ] Network output: [ -0.01163 1.001 1.01 1.708e-06 -7.669e-07 0.01236 1.287e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005435 0.0004788 0.00434 0.004258 0.9889 0.992 0.005534 0.8718 0.9002 0.01437 ] Network output: [ -0.0009185 0.00311 1.003 -0.0001218 5.469e-05 0.9953 -9.181e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1963 0.09329 0.3248 0.1558 0.9851 0.994 0.1969 0.4612 0.8828 0.7164 ] Network output: [ 0.007851 -0.03855 0.9968 7.11e-05 -3.192e-05 1.026 5.358e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09747 0.08622 0.1788 0.2072 0.9873 0.992 0.09753 0.7856 0.8756 0.3086 ] Network output: [ -0.007927 0.04021 1.001 7.206e-05 -3.235e-05 0.9745 5.431e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0914 0.08949 0.1665 0.1968 0.9856 0.9914 0.09141 0.7142 0.8543 0.2432 ] Network output: [ 0.0002958 0.9996 -0.0006814 9.867e-06 -4.43e-06 1.001 7.436e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007753 Epoch 7089 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01273 0.9931 0.9876 3.408e-06 -1.53e-06 -0.006055 2.569e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003222 -0.003005 -0.009217 0.007051 0.9698 0.9742 0.006131 0.8424 0.8306 0.01982 ] Network output: [ 0.9996 0.001821 0.001617 -3.624e-05 1.627e-05 -0.00281 -2.731e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1856 -0.03006 -0.1935 0.1984 0.9836 0.9933 0.2072 0.4565 0.8763 0.7218 ] Network output: [ -0.01163 1.001 1.01 1.701e-06 -7.635e-07 0.01231 1.282e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005435 0.0004786 0.004338 0.004252 0.9889 0.992 0.005535 0.8718 0.9002 0.01437 ] Network output: [ -0.001 0.004346 1.003 -0.0001218 5.47e-05 0.9943 -9.182e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1963 0.09329 0.3248 0.1556 0.9851 0.994 0.1969 0.4612 0.8828 0.7165 ] Network output: [ 0.007864 -0.0383 0.9968 7.104e-05 -3.189e-05 1.026 5.354e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09747 0.08622 0.1787 0.2071 0.9873 0.992 0.09753 0.7855 0.8756 0.3085 ] Network output: [ -0.007917 0.04012 1.001 7.202e-05 -3.233e-05 0.9746 5.428e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09139 0.08948 0.1665 0.1968 0.9856 0.9914 0.0914 0.7141 0.8543 0.2432 ] Network output: [ 0.0002445 0.9996 -0.0006086 9.845e-06 -4.42e-06 1.001 7.42e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007726 Epoch 7090 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01274 0.9929 0.9876 3.415e-06 -1.533e-06 -0.005946 2.573e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003222 -0.003005 -0.009214 0.007053 0.9698 0.9742 0.006131 0.8424 0.8306 0.01982 ] Network output: [ 0.9997 0.0009153 0.001663 -3.615e-05 1.623e-05 -0.002069 -2.725e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1856 -0.03008 -0.1934 0.1985 0.9836 0.9933 0.2072 0.4565 0.8763 0.7218 ] Network output: [ -0.01163 1.001 1.01 1.703e-06 -7.646e-07 0.01235 1.284e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005436 0.0004786 0.00434 0.004256 0.9889 0.992 0.005535 0.8718 0.9002 0.01437 ] Network output: [ -0.0009189 0.003125 1.003 -0.0001216 5.461e-05 0.9953 -9.167e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1963 0.09329 0.3249 0.1558 0.9851 0.994 0.1969 0.4612 0.8828 0.7164 ] Network output: [ 0.007846 -0.03852 0.9968 7.1e-05 -3.187e-05 1.026 5.351e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09748 0.08623 0.1788 0.2072 0.9873 0.992 0.09754 0.7855 0.8756 0.3086 ] Network output: [ -0.00792 0.04018 1.001 7.196e-05 -3.231e-05 0.9746 5.423e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09139 0.08948 0.1665 0.1968 0.9856 0.9914 0.0914 0.7141 0.8543 0.2432 ] Network output: [ 0.0002949 0.9996 -0.0006794 9.854e-06 -4.424e-06 1.001 7.426e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007744 Epoch 7091 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01273 0.9931 0.9876 3.398e-06 -1.526e-06 -0.006059 2.561e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003222 -0.003005 -0.009214 0.007049 0.9698 0.9742 0.006131 0.8424 0.8306 0.01982 ] Network output: [ 0.9996 0.001806 0.001615 -3.619e-05 1.625e-05 -0.002797 -2.728e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1856 -0.03008 -0.1935 0.1983 0.9836 0.9933 0.2072 0.4565 0.8763 0.7218 ] Network output: [ -0.01163 1.001 1.01 1.696e-06 -7.612e-07 0.01231 1.278e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005437 0.0004784 0.004338 0.004251 0.9889 0.992 0.005536 0.8718 0.9002 0.01436 ] Network output: [ -0.0009986 0.004328 1.003 -0.0001217 5.462e-05 0.9943 -9.169e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1963 0.09329 0.3248 0.1555 0.9851 0.994 0.197 0.4612 0.8828 0.7164 ] Network output: [ 0.007858 -0.03828 0.9968 7.094e-05 -3.185e-05 1.026 5.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09748 0.08622 0.1787 0.2071 0.9873 0.992 0.09754 0.7855 0.8755 0.3085 ] Network output: [ -0.00791 0.04008 1.001 7.192e-05 -3.229e-05 0.9746 5.42e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09138 0.08948 0.1665 0.1968 0.9856 0.9914 0.09139 0.714 0.8543 0.2432 ] Network output: [ 0.0002449 0.9996 -0.0006084 9.832e-06 -4.414e-06 1.001 7.41e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007717 Epoch 7092 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01274 0.9929 0.9876 3.404e-06 -1.528e-06 -0.005952 2.566e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003222 -0.003006 -0.009211 0.00705 0.9698 0.9742 0.006131 0.8423 0.8306 0.01981 ] Network output: [ 0.9997 0.0009244 0.001661 -3.611e-05 1.621e-05 -0.002076 -2.721e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1856 -0.03009 -0.1934 0.1985 0.9836 0.9933 0.2072 0.4565 0.8763 0.7218 ] Network output: [ -0.01162 1.001 1.01 1.698e-06 -7.623e-07 0.01234 1.28e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005437 0.0004784 0.004341 0.004254 0.9889 0.992 0.005537 0.8718 0.9002 0.01436 ] Network output: [ -0.0009193 0.003139 1.003 -0.0001215 5.453e-05 0.9953 -9.154e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1963 0.09329 0.3249 0.1557 0.9851 0.994 0.197 0.4612 0.8828 0.7164 ] Network output: [ 0.00784 -0.0385 0.9968 7.09e-05 -3.183e-05 1.026 5.343e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09748 0.08623 0.1788 0.2072 0.9873 0.992 0.09755 0.7854 0.8755 0.3085 ] Network output: [ -0.007913 0.04015 1.001 7.187e-05 -3.227e-05 0.9746 5.416e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09138 0.08948 0.1665 0.1968 0.9856 0.9914 0.09139 0.714 0.8543 0.2432 ] Network output: [ 0.000294 0.9996 -0.0006774 9.84e-06 -4.418e-06 1.001 7.416e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007734 Epoch 7093 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01272 0.9931 0.9876 3.388e-06 -1.521e-06 -0.006062 2.553e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003222 -0.003006 -0.009211 0.007047 0.9698 0.9742 0.006132 0.8423 0.8306 0.01981 ] Network output: [ 0.9996 0.001792 0.001614 -3.615e-05 1.623e-05 -0.002785 -2.724e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1857 -0.03009 -0.1934 0.1983 0.9836 0.9933 0.2072 0.4565 0.8763 0.7218 ] Network output: [ -0.01162 1.001 1.01 1.691e-06 -7.59e-07 0.0123 1.274e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005438 0.0004782 0.004339 0.004249 0.9889 0.992 0.005537 0.8718 0.9002 0.01436 ] Network output: [ -0.0009969 0.004311 1.003 -0.0001215 5.454e-05 0.9944 -9.155e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1964 0.09329 0.3248 0.1555 0.9851 0.994 0.197 0.4612 0.8828 0.7164 ] Network output: [ 0.007852 -0.03826 0.9968 7.084e-05 -3.18e-05 1.026 5.338e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09748 0.08623 0.1787 0.2071 0.9873 0.992 0.09755 0.7854 0.8755 0.3085 ] Network output: [ -0.007903 0.04005 1.001 7.183e-05 -3.225e-05 0.9746 5.413e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09137 0.08947 0.1665 0.1968 0.9856 0.9914 0.09139 0.714 0.8543 0.2432 ] Network output: [ 0.0002452 0.9996 -0.0006082 9.819e-06 -4.408e-06 1.001 7.4e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007708 Epoch 7094 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01273 0.9929 0.9876 3.394e-06 -1.524e-06 -0.005959 2.558e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003222 -0.003006 -0.009208 0.007048 0.9698 0.9742 0.006132 0.8423 0.8306 0.01981 ] Network output: [ 0.9997 0.0009332 0.001658 -3.607e-05 1.619e-05 -0.002083 -2.718e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1857 -0.03011 -0.1934 0.1985 0.9836 0.9933 0.2072 0.4564 0.8763 0.7218 ] Network output: [ -0.01162 1.001 1.01 1.693e-06 -7.6e-07 0.01233 1.276e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005438 0.0004782 0.004341 0.004253 0.9889 0.992 0.005538 0.8718 0.9002 0.01436 ] Network output: [ -0.0009197 0.003152 1.003 -0.0001213 5.445e-05 0.9953 -9.14e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1964 0.09329 0.3249 0.1557 0.9851 0.994 0.197 0.4611 0.8828 0.7164 ] Network output: [ 0.007835 -0.03847 0.9968 7.08e-05 -3.178e-05 1.026 5.336e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09749 0.08623 0.1788 0.2072 0.9873 0.992 0.09755 0.7854 0.8755 0.3085 ] Network output: [ -0.007906 0.04011 1.001 7.178e-05 -3.222e-05 0.9746 5.409e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09137 0.08947 0.1665 0.1968 0.9856 0.9914 0.09138 0.7139 0.8543 0.2432 ] Network output: [ 0.000293 0.9996 -0.0006754 9.827e-06 -4.412e-06 1.001 7.406e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007724 Epoch 7095 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01272 0.9931 0.9876 3.378e-06 -1.517e-06 -0.006066 2.546e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003223 -0.003006 -0.009208 0.007045 0.9698 0.9742 0.006132 0.8423 0.8306 0.01981 ] Network output: [ 0.9996 0.001778 0.001613 -3.61e-05 1.621e-05 -0.002773 -2.721e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1857 -0.0301 -0.1934 0.1983 0.9836 0.9933 0.2073 0.4564 0.8763 0.7217 ] Network output: [ -0.01162 1.001 1.01 1.686e-06 -7.567e-07 0.01229 1.27e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005439 0.000478 0.004339 0.004247 0.9889 0.992 0.005538 0.8718 0.9002 0.01436 ] Network output: [ -0.0009953 0.004293 1.003 -0.0001213 5.445e-05 0.9944 -9.141e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1964 0.09329 0.3249 0.1555 0.9851 0.994 0.197 0.4611 0.8828 0.7164 ] Network output: [ 0.007846 -0.03824 0.9968 7.074e-05 -3.176e-05 1.026 5.331e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09749 0.08623 0.1787 0.2071 0.9873 0.992 0.09755 0.7853 0.8755 0.3085 ] Network output: [ -0.007897 0.04002 1.001 7.173e-05 -3.22e-05 0.9746 5.406e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09137 0.08946 0.1665 0.1968 0.9856 0.9914 0.09138 0.7139 0.8542 0.2432 ] Network output: [ 0.0002456 0.9996 -0.000608 9.806e-06 -4.402e-06 1.001 7.39e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007699 Epoch 7096 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01272 0.9929 0.9876 3.384e-06 -1.519e-06 -0.005965 2.55e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003223 -0.003006 -0.009205 0.007046 0.9698 0.9742 0.006132 0.8423 0.8306 0.01981 ] Network output: [ 0.9997 0.0009418 0.001656 -3.603e-05 1.617e-05 -0.002089 -2.715e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1857 -0.03012 -0.1933 0.1984 0.9836 0.9933 0.2073 0.4564 0.8763 0.7217 ] Network output: [ -0.01162 1.001 1.01 1.688e-06 -7.577e-07 0.01232 1.272e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005439 0.000478 0.004342 0.004251 0.9889 0.992 0.005539 0.8718 0.9001 0.01436 ] Network output: [ -0.00092 0.003166 1.003 -0.0001211 5.437e-05 0.9953 -9.127e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1964 0.09328 0.325 0.1557 0.9851 0.994 0.197 0.4611 0.8828 0.7164 ] Network output: [ 0.007829 -0.03844 0.9968 7.07e-05 -3.174e-05 1.026 5.328e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0975 0.08624 0.1788 0.2071 0.9873 0.992 0.09756 0.7853 0.8755 0.3085 ] Network output: [ -0.007899 0.04008 1.001 7.168e-05 -3.218e-05 0.9746 5.402e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09136 0.08946 0.1665 0.1968 0.9856 0.9914 0.09138 0.7139 0.8542 0.2432 ] Network output: [ 0.0002921 0.9996 -0.0006734 9.813e-06 -4.406e-06 1.001 7.396e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007715 Epoch 7097 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01271 0.9931 0.9876 3.368e-06 -1.512e-06 -0.006069 2.538e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003223 -0.003006 -0.009204 0.007043 0.9698 0.9742 0.006133 0.8423 0.8306 0.0198 ] Network output: [ 0.9996 0.001764 0.001612 -3.606e-05 1.619e-05 -0.002762 -2.718e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1857 -0.03011 -0.1934 0.1983 0.9836 0.9933 0.2073 0.4564 0.8763 0.7217 ] Network output: [ -0.01162 1.001 1.01 1.681e-06 -7.545e-07 0.01228 1.267e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00544 0.0004778 0.004339 0.004246 0.9889 0.992 0.00554 0.8718 0.9001 0.01436 ] Network output: [ -0.0009936 0.004276 1.003 -0.0001211 5.437e-05 0.9944 -9.128e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1964 0.09329 0.3249 0.1555 0.9851 0.994 0.197 0.4611 0.8828 0.7164 ] Network output: [ 0.00784 -0.03822 0.9968 7.064e-05 -3.171e-05 1.026 5.323e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0975 0.08624 0.1787 0.2071 0.9873 0.992 0.09756 0.7853 0.8755 0.3085 ] Network output: [ -0.00789 0.03999 1.001 7.164e-05 -3.216e-05 0.9747 5.399e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09136 0.08945 0.1665 0.1968 0.9856 0.9914 0.09137 0.7138 0.8542 0.2432 ] Network output: [ 0.0002459 0.9996 -0.0006077 9.793e-06 -4.397e-06 1.001 7.38e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000769 Epoch 7098 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01272 0.993 0.9876 3.373e-06 -1.514e-06 -0.005971 2.542e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003223 -0.003007 -0.009202 0.007044 0.9698 0.9742 0.006133 0.8423 0.8306 0.0198 ] Network output: [ 0.9997 0.0009501 0.001654 -3.598e-05 1.615e-05 -0.002096 -2.712e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1857 -0.03013 -0.1933 0.1984 0.9836 0.9933 0.2073 0.4564 0.8763 0.7217 ] Network output: [ -0.01162 1.001 1.01 1.683e-06 -7.554e-07 0.01231 1.268e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00544 0.0004778 0.004342 0.004249 0.9889 0.992 0.00554 0.8717 0.9001 0.01435 ] Network output: [ -0.0009204 0.003179 1.003 -0.0001209 5.429e-05 0.9953 -9.114e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1964 0.09328 0.325 0.1556 0.9851 0.994 0.197 0.4611 0.8828 0.7164 ] Network output: [ 0.007824 -0.03842 0.9968 7.06e-05 -3.169e-05 1.026 5.32e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0975 0.08624 0.1788 0.2071 0.9873 0.992 0.09757 0.7853 0.8755 0.3085 ] Network output: [ -0.007892 0.04005 1.001 7.159e-05 -3.214e-05 0.9746 5.395e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09136 0.08945 0.1665 0.1968 0.9856 0.9914 0.09137 0.7138 0.8542 0.2432 ] Network output: [ 0.0002912 0.9996 -0.0006714 9.8e-06 -4.4e-06 1.001 7.385e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007705 Epoch 7099 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01271 0.9931 0.9876 3.358e-06 -1.508e-06 -0.006073 2.531e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003223 -0.003007 -0.009201 0.007041 0.9698 0.9742 0.006133 0.8423 0.8305 0.0198 ] Network output: [ 0.9996 0.001751 0.00161 -3.602e-05 1.617e-05 -0.00275 -2.714e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1857 -0.03012 -0.1933 0.1983 0.9836 0.9933 0.2073 0.4564 0.8763 0.7217 ] Network output: [ -0.01162 1.001 1.01 1.676e-06 -7.523e-07 0.01227 1.263e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005441 0.0004776 0.00434 0.004244 0.9889 0.992 0.005541 0.8717 0.9001 0.01435 ] Network output: [ -0.000992 0.00426 1.003 -0.0001209 5.429e-05 0.9944 -9.114e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1964 0.09328 0.3249 0.1554 0.9851 0.994 0.1971 0.4611 0.8828 0.7164 ] Network output: [ 0.007834 -0.0382 0.9968 7.054e-05 -3.167e-05 1.026 5.316e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0975 0.08624 0.1787 0.207 0.9873 0.992 0.09757 0.7852 0.8755 0.3085 ] Network output: [ -0.007883 0.03996 1.001 7.154e-05 -3.212e-05 0.9747 5.392e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09135 0.08944 0.1665 0.1968 0.9856 0.9914 0.09136 0.7138 0.8542 0.2432 ] Network output: [ 0.0002462 0.9996 -0.0006075 9.78e-06 -4.391e-06 1.001 7.371e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007681 Epoch 7100 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01271 0.993 0.9876 3.363e-06 -1.51e-06 -0.005977 2.534e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003223 -0.003007 -0.009199 0.007042 0.9698 0.9742 0.006134 0.8423 0.8305 0.0198 ] Network output: [ 0.9997 0.0009583 0.001651 -3.594e-05 1.613e-05 -0.002102 -2.709e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1857 -0.03014 -0.1932 0.1984 0.9836 0.9933 0.2073 0.4563 0.8763 0.7217 ] Network output: [ -0.01161 1.001 1.01 1.678e-06 -7.531e-07 0.0123 1.264e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005441 0.0004776 0.004342 0.004247 0.9889 0.992 0.005541 0.8717 0.9001 0.01435 ] Network output: [ -0.0009207 0.003191 1.003 -0.0001207 5.421e-05 0.9953 -9.1e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1964 0.09328 0.325 0.1556 0.9851 0.994 0.1971 0.461 0.8828 0.7164 ] Network output: [ 0.007818 -0.03839 0.9968 7.05e-05 -3.165e-05 1.026 5.313e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09751 0.08625 0.1788 0.2071 0.9873 0.992 0.09757 0.7852 0.8754 0.3085 ] Network output: [ -0.007885 0.04001 1.001 7.149e-05 -3.21e-05 0.9746 5.388e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09135 0.08944 0.1665 0.1968 0.9856 0.9914 0.09136 0.7137 0.8542 0.2432 ] Network output: [ 0.0002903 0.9996 -0.0006695 9.786e-06 -4.393e-06 1.001 7.375e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007696 Epoch 7101 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0127 0.9931 0.9876 3.348e-06 -1.503e-06 -0.006076 2.523e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003223 -0.003007 -0.009198 0.007039 0.9698 0.9742 0.006134 0.8423 0.8305 0.01979 ] Network output: [ 0.9996 0.001738 0.001609 -3.597e-05 1.615e-05 -0.002739 -2.711e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1857 -0.03013 -0.1933 0.1983 0.9836 0.9933 0.2073 0.4563 0.8763 0.7217 ] Network output: [ -0.01161 1.001 1.01 1.671e-06 -7.5e-07 0.01226 1.259e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005442 0.0004774 0.00434 0.004243 0.9889 0.992 0.005542 0.8717 0.9001 0.01435 ] Network output: [ -0.0009904 0.004243 1.003 -0.0001208 5.421e-05 0.9944 -9.1e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1965 0.09328 0.325 0.1554 0.9851 0.994 0.1971 0.461 0.8828 0.7164 ] Network output: [ 0.007828 -0.03817 0.9968 7.044e-05 -3.162e-05 1.026 5.308e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09751 0.08624 0.1787 0.207 0.9873 0.992 0.09757 0.7852 0.8754 0.3085 ] Network output: [ -0.007876 0.03993 1.001 7.145e-05 -3.208e-05 0.9747 5.385e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09134 0.08943 0.1665 0.1968 0.9856 0.9914 0.09135 0.7137 0.8542 0.2432 ] Network output: [ 0.0002465 0.9996 -0.0006072 9.767e-06 -4.385e-06 1.001 7.361e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007672 Epoch 7102 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01271 0.993 0.9876 3.353e-06 -1.505e-06 -0.005984 2.527e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003223 -0.003007 -0.009196 0.00704 0.9698 0.9742 0.006134 0.8423 0.8305 0.01979 ] Network output: [ 0.9997 0.0009662 0.001649 -3.59e-05 1.612e-05 -0.002108 -2.705e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1857 -0.03015 -0.1932 0.1984 0.9836 0.9933 0.2073 0.4563 0.8763 0.7217 ] Network output: [ -0.01161 1.001 1.01 1.672e-06 -7.508e-07 0.01229 1.26e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005443 0.0004774 0.004343 0.004246 0.9889 0.992 0.005542 0.8717 0.9001 0.01435 ] Network output: [ -0.0009209 0.003204 1.003 -0.0001206 5.413e-05 0.9953 -9.087e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1965 0.09328 0.3251 0.1556 0.9851 0.994 0.1971 0.461 0.8827 0.7164 ] Network output: [ 0.007813 -0.03836 0.9968 7.04e-05 -3.16e-05 1.026 5.305e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09752 0.08625 0.1788 0.2071 0.9873 0.992 0.09758 0.7852 0.8754 0.3085 ] Network output: [ -0.007879 0.03998 1.001 7.14e-05 -3.205e-05 0.9747 5.381e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09134 0.08943 0.1665 0.1968 0.9856 0.9914 0.09135 0.7137 0.8541 0.2432 ] Network output: [ 0.0002895 0.9996 -0.0006675 9.773e-06 -4.387e-06 1.001 7.365e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007686 Epoch 7103 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0127 0.9931 0.9876 3.338e-06 -1.499e-06 -0.00608 2.516e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003224 -0.003007 -0.009195 0.007037 0.9698 0.9742 0.006135 0.8423 0.8305 0.01979 ] Network output: [ 0.9996 0.001725 0.001608 -3.593e-05 1.613e-05 -0.002728 -2.708e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1858 -0.03014 -0.1932 0.1982 0.9836 0.9933 0.2074 0.4563 0.8762 0.7217 ] Network output: [ -0.01161 1.001 1.01 1.666e-06 -7.478e-07 0.01226 1.255e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005443 0.0004772 0.004341 0.004241 0.9889 0.992 0.005543 0.8717 0.9001 0.01435 ] Network output: [ -0.0009888 0.004227 1.003 -0.0001206 5.413e-05 0.9944 -9.087e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1965 0.09328 0.325 0.1554 0.9851 0.994 0.1971 0.461 0.8827 0.7164 ] Network output: [ 0.007823 -0.03815 0.9968 7.034e-05 -3.158e-05 1.026 5.301e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09752 0.08625 0.1787 0.207 0.9873 0.992 0.09758 0.7851 0.8754 0.3085 ] Network output: [ -0.00787 0.0399 1.001 7.136e-05 -3.203e-05 0.9747 5.378e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09133 0.08943 0.1665 0.1968 0.9856 0.9914 0.09134 0.7136 0.8541 0.2432 ] Network output: [ 0.0002468 0.9996 -0.0006069 9.754e-06 -4.379e-06 1.001 7.351e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007663 Epoch 7104 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0127 0.993 0.9876 3.342e-06 -1.501e-06 -0.00599 2.519e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003224 -0.003007 -0.009193 0.007037 0.9698 0.9742 0.006135 0.8422 0.8305 0.01979 ] Network output: [ 0.9997 0.0009738 0.001646 -3.585e-05 1.61e-05 -0.002114 -2.702e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1858 -0.03016 -0.1932 0.1983 0.9836 0.9933 0.2074 0.4563 0.8762 0.7217 ] Network output: [ -0.01161 1.001 1.01 1.667e-06 -7.485e-07 0.01228 1.257e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005444 0.0004772 0.004343 0.004244 0.9889 0.992 0.005543 0.8717 0.9001 0.01434 ] Network output: [ -0.0009212 0.003216 1.003 -0.0001204 5.405e-05 0.9953 -9.073e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1965 0.09328 0.3251 0.1555 0.9851 0.994 0.1971 0.461 0.8827 0.7164 ] Network output: [ 0.007807 -0.03834 0.9968 7.03e-05 -3.156e-05 1.026 5.298e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09752 0.08625 0.1788 0.2071 0.9873 0.992 0.09759 0.7851 0.8754 0.3085 ] Network output: [ -0.007872 0.03995 1.001 7.13e-05 -3.201e-05 0.9747 5.374e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09133 0.08942 0.1665 0.1968 0.9856 0.9914 0.09134 0.7136 0.8541 0.2432 ] Network output: [ 0.0002886 0.9996 -0.0006656 9.76e-06 -4.381e-06 1.001 7.355e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007677 Epoch 7105 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01269 0.9931 0.9876 3.328e-06 -1.494e-06 -0.006084 2.508e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003224 -0.003008 -0.009192 0.007034 0.9698 0.9742 0.006135 0.8422 0.8305 0.01979 ] Network output: [ 0.9996 0.001712 0.001606 -3.588e-05 1.611e-05 -0.002717 -2.704e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1858 -0.03016 -0.1932 0.1982 0.9836 0.9933 0.2074 0.4563 0.8762 0.7217 ] Network output: [ -0.01161 1.001 1.01 1.661e-06 -7.455e-07 0.01225 1.252e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005444 0.000477 0.004341 0.00424 0.9889 0.992 0.005544 0.8717 0.9001 0.01434 ] Network output: [ -0.0009872 0.004212 1.003 -0.0001204 5.405e-05 0.9945 -9.073e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1965 0.09328 0.3251 0.1553 0.9851 0.994 0.1971 0.461 0.8827 0.7164 ] Network output: [ 0.007817 -0.03813 0.9968 7.024e-05 -3.153e-05 1.026 5.293e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09752 0.08625 0.1787 0.207 0.9873 0.992 0.09759 0.7851 0.8754 0.3084 ] Network output: [ -0.007863 0.03986 1.001 7.126e-05 -3.199e-05 0.9747 5.37e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09132 0.08942 0.1665 0.1968 0.9856 0.9914 0.09134 0.7135 0.8541 0.2432 ] Network output: [ 0.0002471 0.9996 -0.0006066 9.741e-06 -4.373e-06 1.001 7.341e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007654 Epoch 7106 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0127 0.993 0.9876 3.332e-06 -1.496e-06 -0.005996 2.511e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003224 -0.003008 -0.009189 0.007035 0.9698 0.9742 0.006135 0.8422 0.8305 0.01978 ] Network output: [ 0.9997 0.0009813 0.001644 -3.581e-05 1.608e-05 -0.002119 -2.699e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1858 -0.03017 -0.1931 0.1983 0.9836 0.9933 0.2074 0.4562 0.8762 0.7217 ] Network output: [ -0.01161 1.001 1.01 1.662e-06 -7.462e-07 0.01227 1.253e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005445 0.000477 0.004343 0.004242 0.9889 0.992 0.005545 0.8717 0.9001 0.01434 ] Network output: [ -0.0009214 0.003227 1.003 -0.0001202 5.397e-05 0.9953 -9.06e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1965 0.09328 0.3251 0.1555 0.9851 0.994 0.1971 0.4609 0.8827 0.7164 ] Network output: [ 0.007802 -0.03831 0.9968 7.02e-05 -3.151e-05 1.026 5.29e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09753 0.08626 0.1788 0.207 0.9873 0.992 0.09759 0.785 0.8754 0.3085 ] Network output: [ -0.007865 0.03991 1.001 7.121e-05 -3.197e-05 0.9747 5.367e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09132 0.08942 0.1665 0.1968 0.9856 0.9914 0.09133 0.7135 0.8541 0.2432 ] Network output: [ 0.0002878 0.9996 -0.0006637 9.746e-06 -4.375e-06 1.001 7.345e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007667 Epoch 7107 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01269 0.9931 0.9876 3.318e-06 -1.49e-06 -0.006087 2.501e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003224 -0.003008 -0.009189 0.007032 0.9698 0.9742 0.006136 0.8422 0.8305 0.01978 ] Network output: [ 0.9996 0.001699 0.001605 -3.584e-05 1.609e-05 -0.002706 -2.701e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1858 -0.03017 -0.1932 0.1982 0.9836 0.9933 0.2074 0.4562 0.8762 0.7217 ] Network output: [ -0.01161 1.001 1.01 1.656e-06 -7.433e-07 0.01224 1.248e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005446 0.0004768 0.004341 0.004238 0.9889 0.992 0.005545 0.8717 0.9001 0.01434 ] Network output: [ -0.0009856 0.004196 1.003 -0.0001202 5.397e-05 0.9945 -9.059e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1965 0.09328 0.3251 0.1553 0.9851 0.994 0.1971 0.4609 0.8827 0.7164 ] Network output: [ 0.007811 -0.03811 0.9968 7.014e-05 -3.149e-05 1.026 5.286e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09753 0.08626 0.1788 0.207 0.9873 0.992 0.09759 0.785 0.8754 0.3084 ] Network output: [ -0.007856 0.03983 1.001 7.117e-05 -3.195e-05 0.9747 5.363e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09132 0.08941 0.1664 0.1968 0.9856 0.9914 0.09133 0.7135 0.8541 0.2432 ] Network output: [ 0.0002473 0.9996 -0.0006063 9.728e-06 -4.367e-06 1.001 7.331e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007645 Epoch 7108 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01269 0.993 0.9876 3.322e-06 -1.491e-06 -0.006002 2.503e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003224 -0.003008 -0.009186 0.007033 0.9698 0.9742 0.006136 0.8422 0.8305 0.01978 ] Network output: [ 0.9997 0.0009885 0.001642 -3.577e-05 1.606e-05 -0.002125 -2.696e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1858 -0.03018 -0.1931 0.1983 0.9836 0.9933 0.2074 0.4562 0.8762 0.7217 ] Network output: [ -0.0116 1.001 1.01 1.657e-06 -7.439e-07 0.01227 1.249e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005446 0.0004768 0.004344 0.004241 0.9889 0.992 0.005546 0.8716 0.9001 0.01434 ] Network output: [ -0.0009216 0.003239 1.003 -0.00012 5.389e-05 0.9953 -9.046e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1965 0.09328 0.3252 0.1555 0.9851 0.994 0.1972 0.4609 0.8827 0.7164 ] Network output: [ 0.007796 -0.03828 0.9968 7.01e-05 -3.147e-05 1.026 5.283e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09754 0.08626 0.1788 0.207 0.9873 0.992 0.0976 0.785 0.8754 0.3085 ] Network output: [ -0.007858 0.03988 1.001 7.111e-05 -3.193e-05 0.9747 5.359e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09131 0.08941 0.1665 0.1968 0.9856 0.9914 0.09133 0.7135 0.8541 0.2432 ] Network output: [ 0.0002869 0.9996 -0.0006618 9.733e-06 -4.369e-06 1.001 7.335e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007658 Epoch 7109 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01268 0.9931 0.9876 3.308e-06 -1.485e-06 -0.006091 2.493e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003224 -0.003008 -0.009186 0.00703 0.9698 0.9742 0.006136 0.8422 0.8305 0.01978 ] Network output: [ 0.9996 0.001687 0.001604 -3.579e-05 1.607e-05 -0.002695 -2.698e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1858 -0.03018 -0.1931 0.1982 0.9836 0.9933 0.2074 0.4562 0.8762 0.7217 ] Network output: [ -0.0116 1.001 1.01 1.651e-06 -7.41e-07 0.01223 1.244e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005447 0.0004766 0.004342 0.004236 0.9889 0.992 0.005547 0.8716 0.9001 0.01434 ] Network output: [ -0.0009841 0.004181 1.003 -0.00012 5.389e-05 0.9945 -9.046e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1965 0.09328 0.3251 0.1553 0.9851 0.994 0.1972 0.4609 0.8827 0.7164 ] Network output: [ 0.007805 -0.03809 0.9967 7.004e-05 -3.144e-05 1.026 5.278e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09754 0.08626 0.1788 0.207 0.9873 0.992 0.0976 0.7849 0.8753 0.3084 ] Network output: [ -0.007849 0.0398 1.001 7.107e-05 -3.191e-05 0.9748 5.356e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09131 0.0894 0.1664 0.1968 0.9856 0.9914 0.09132 0.7134 0.8541 0.2432 ] Network output: [ 0.0002476 0.9996 -0.0006059 9.715e-06 -4.361e-06 1.001 7.321e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007636 Epoch 7110 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01269 0.993 0.9876 3.312e-06 -1.487e-06 -0.006008 2.496e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003224 -0.003008 -0.009183 0.007031 0.9698 0.9742 0.006136 0.8422 0.8305 0.01978 ] Network output: [ 0.9997 0.0009955 0.001639 -3.573e-05 1.604e-05 -0.00213 -2.692e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1858 -0.03019 -0.193 0.1983 0.9836 0.9933 0.2074 0.4562 0.8762 0.7217 ] Network output: [ -0.0116 1.001 1.01 1.652e-06 -7.416e-07 0.01226 1.245e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005447 0.0004766 0.004344 0.004239 0.9889 0.992 0.005547 0.8716 0.9001 0.01434 ] Network output: [ -0.0009218 0.00325 1.003 -0.0001199 5.381e-05 0.9953 -9.033e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1966 0.09328 0.3252 0.1554 0.9851 0.994 0.1972 0.4609 0.8827 0.7164 ] Network output: [ 0.007791 -0.03826 0.9968 7e-05 -3.142e-05 1.026 5.275e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09754 0.08627 0.1788 0.207 0.9873 0.992 0.09761 0.7849 0.8753 0.3085 ] Network output: [ -0.007851 0.03985 1.001 7.102e-05 -3.188e-05 0.9747 5.352e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09131 0.0894 0.1664 0.1968 0.9856 0.9914 0.09132 0.7134 0.854 0.2432 ] Network output: [ 0.0002861 0.9996 -0.00066 9.719e-06 -4.363e-06 1.001 7.325e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007648 Epoch 7111 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01268 0.9931 0.9876 3.298e-06 -1.481e-06 -0.006095 2.486e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003224 -0.003009 -0.009182 0.007028 0.9698 0.9742 0.006137 0.8422 0.8305 0.01977 ] Network output: [ 0.9996 0.001675 0.001602 -3.575e-05 1.605e-05 -0.002685 -2.694e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1858 -0.03019 -0.1931 0.1982 0.9836 0.9933 0.2075 0.4562 0.8762 0.7217 ] Network output: [ -0.0116 1.001 1.01 1.646e-06 -7.388e-07 0.01222 1.24e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005448 0.0004764 0.004342 0.004235 0.9889 0.992 0.005548 0.8716 0.9001 0.01433 ] Network output: [ -0.0009826 0.004167 1.003 -0.0001199 5.381e-05 0.9945 -9.032e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1966 0.09328 0.3252 0.1553 0.9851 0.994 0.1972 0.4608 0.8827 0.7164 ] Network output: [ 0.007799 -0.03807 0.9967 6.994e-05 -3.14e-05 1.026 5.271e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09754 0.08627 0.1788 0.2069 0.9873 0.992 0.09761 0.7849 0.8753 0.3084 ] Network output: [ -0.007842 0.03977 1.001 7.098e-05 -3.186e-05 0.9748 5.349e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0913 0.08939 0.1664 0.1967 0.9856 0.9914 0.09131 0.7133 0.854 0.2432 ] Network output: [ 0.0002478 0.9996 -0.0006056 9.702e-06 -4.356e-06 1.001 7.312e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007627 Epoch 7112 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01268 0.993 0.9876 3.301e-06 -1.482e-06 -0.006014 2.488e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003225 -0.003009 -0.00918 0.007029 0.9698 0.9742 0.006137 0.8422 0.8305 0.01977 ] Network output: [ 0.9997 0.001002 0.001637 -3.568e-05 1.602e-05 -0.002135 -2.689e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1859 -0.03021 -0.193 0.1983 0.9836 0.9933 0.2075 0.4561 0.8762 0.7217 ] Network output: [ -0.0116 1.001 1.01 1.647e-06 -7.393e-07 0.01225 1.241e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005448 0.0004764 0.004344 0.004237 0.9889 0.992 0.005548 0.8716 0.9001 0.01433 ] Network output: [ -0.000922 0.003261 1.003 -0.0001197 5.373e-05 0.9953 -9.02e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1966 0.09327 0.3252 0.1554 0.9851 0.994 0.1972 0.4608 0.8827 0.7163 ] Network output: [ 0.007785 -0.03823 0.9968 6.99e-05 -3.138e-05 1.026 5.268e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09755 0.08627 0.1788 0.207 0.9873 0.992 0.09761 0.7849 0.8753 0.3084 ] Network output: [ -0.007844 0.03981 1.001 7.093e-05 -3.184e-05 0.9747 5.345e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0913 0.08939 0.1664 0.1967 0.9856 0.9914 0.09131 0.7133 0.854 0.2432 ] Network output: [ 0.0002853 0.9996 -0.0006581 9.706e-06 -4.357e-06 1.001 7.315e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007638 Epoch 7113 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01267 0.9931 0.9876 3.288e-06 -1.476e-06 -0.006098 2.478e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003225 -0.003009 -0.009179 0.007026 0.9698 0.9742 0.006137 0.8422 0.8305 0.01977 ] Network output: [ 0.9996 0.001663 0.001601 -3.571e-05 1.603e-05 -0.002675 -2.691e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1859 -0.0302 -0.193 0.1981 0.9836 0.9933 0.2075 0.4561 0.8762 0.7217 ] Network output: [ -0.0116 1.001 1.01 1.641e-06 -7.365e-07 0.01221 1.236e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005449 0.0004762 0.004343 0.004233 0.9889 0.992 0.005549 0.8716 0.9 0.01433 ] Network output: [ -0.0009811 0.004152 1.003 -0.0001197 5.372e-05 0.9945 -9.019e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1966 0.09328 0.3252 0.1552 0.9851 0.994 0.1972 0.4608 0.8827 0.7163 ] Network output: [ 0.007794 -0.03805 0.9967 6.984e-05 -3.135e-05 1.026 5.263e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09755 0.08627 0.1788 0.2069 0.9873 0.992 0.09761 0.7848 0.8753 0.3084 ] Network output: [ -0.007836 0.03974 1.001 7.088e-05 -3.182e-05 0.9748 5.342e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09129 0.08938 0.1664 0.1967 0.9856 0.9914 0.0913 0.7133 0.854 0.2432 ] Network output: [ 0.000248 0.9996 -0.0006052 9.689e-06 -4.35e-06 1.001 7.302e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007618 Epoch 7114 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01268 0.993 0.9877 3.291e-06 -1.478e-06 -0.00602 2.48e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003225 -0.003009 -0.009177 0.007027 0.9698 0.9742 0.006138 0.8422 0.8304 0.01977 ] Network output: [ 0.9997 0.001009 0.001635 -3.564e-05 1.6e-05 -0.00214 -2.686e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1859 -0.03022 -0.193 0.1982 0.9836 0.9933 0.2075 0.4561 0.8762 0.7217 ] Network output: [ -0.0116 1.001 1.01 1.642e-06 -7.37e-07 0.01224 1.237e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005449 0.0004762 0.004345 0.004236 0.9889 0.992 0.005549 0.8716 0.9 0.01433 ] Network output: [ -0.0009221 0.003271 1.003 -0.0001195 5.365e-05 0.9952 -9.006e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1966 0.09327 0.3253 0.1554 0.9851 0.994 0.1972 0.4608 0.8827 0.7163 ] Network output: [ 0.00778 -0.0382 0.9968 6.98e-05 -3.133e-05 1.026 5.26e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09756 0.08628 0.1788 0.207 0.9873 0.992 0.09762 0.7848 0.8753 0.3084 ] Network output: [ -0.007837 0.03978 1.001 7.083e-05 -3.18e-05 0.9747 5.338e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09129 0.08938 0.1664 0.1967 0.9856 0.9914 0.0913 0.7133 0.854 0.2432 ] Network output: [ 0.0002845 0.9996 -0.0006563 9.693e-06 -4.351e-06 1.001 7.305e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007629 Epoch 7115 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01267 0.9931 0.9877 3.278e-06 -1.472e-06 -0.006102 2.471e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003225 -0.003009 -0.009176 0.007024 0.9698 0.9742 0.006138 0.8422 0.8304 0.01976 ] Network output: [ 0.9996 0.001651 0.0016 -3.566e-05 1.601e-05 -0.002665 -2.688e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1859 -0.03021 -0.193 0.1981 0.9836 0.9933 0.2075 0.4561 0.8762 0.7217 ] Network output: [ -0.0116 1.001 1.01 1.636e-06 -7.342e-07 0.01221 1.233e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00545 0.000476 0.004343 0.004232 0.9889 0.992 0.00555 0.8716 0.9 0.01433 ] Network output: [ -0.0009796 0.004138 1.003 -0.0001195 5.364e-05 0.9945 -9.005e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1966 0.09328 0.3252 0.1552 0.9851 0.994 0.1972 0.4608 0.8827 0.7163 ] Network output: [ 0.007788 -0.03802 0.9967 6.974e-05 -3.131e-05 1.026 5.256e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09756 0.08627 0.1788 0.2069 0.9873 0.992 0.09762 0.7848 0.8753 0.3084 ] Network output: [ -0.007829 0.03971 1.001 7.079e-05 -3.178e-05 0.9748 5.335e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09128 0.08938 0.1664 0.1967 0.9856 0.9914 0.0913 0.7132 0.854 0.2432 ] Network output: [ 0.0002483 0.9996 -0.0006048 9.676e-06 -4.344e-06 1.001 7.292e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007609 Epoch 7116 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01267 0.993 0.9877 3.281e-06 -1.473e-06 -0.006026 2.473e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003225 -0.003009 -0.009174 0.007024 0.9698 0.9742 0.006138 0.8421 0.8304 0.01976 ] Network output: [ 0.9997 0.001015 0.001632 -3.56e-05 1.598e-05 -0.002145 -2.683e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1859 -0.03023 -0.1929 0.1982 0.9836 0.9933 0.2075 0.4561 0.8762 0.7217 ] Network output: [ -0.01159 1.001 1.01 1.637e-06 -7.347e-07 0.01223 1.233e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00545 0.000476 0.004345 0.004234 0.9889 0.992 0.00555 0.8716 0.9 0.01433 ] Network output: [ -0.0009222 0.003281 1.003 -0.0001193 5.357e-05 0.9952 -8.993e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1966 0.09327 0.3253 0.1554 0.9851 0.994 0.1972 0.4608 0.8827 0.7163 ] Network output: [ 0.007775 -0.03818 0.9968 6.97e-05 -3.129e-05 1.026 5.253e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09756 0.08628 0.1788 0.2069 0.9873 0.992 0.09763 0.7848 0.8753 0.3084 ] Network output: [ -0.00783 0.03975 1.001 7.074e-05 -3.176e-05 0.9748 5.331e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09128 0.08937 0.1664 0.1967 0.9856 0.9914 0.09129 0.7132 0.854 0.2432 ] Network output: [ 0.0002837 0.9996 -0.0006545 9.679e-06 -4.345e-06 1.001 7.295e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007619 Epoch 7117 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01266 0.9931 0.9877 3.268e-06 -1.467e-06 -0.006106 2.463e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003225 -0.00301 -0.009173 0.007022 0.9698 0.9742 0.006139 0.8421 0.8304 0.01976 ] Network output: [ 0.9996 0.00164 0.001598 -3.562e-05 1.599e-05 -0.002655 -2.684e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1859 -0.03022 -0.193 0.1981 0.9836 0.9933 0.2075 0.4561 0.8762 0.7217 ] Network output: [ -0.01159 1.001 1.01 1.63e-06 -7.32e-07 0.0122 1.229e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005451 0.0004758 0.004343 0.00423 0.9889 0.992 0.005551 0.8716 0.9 0.01432 ] Network output: [ -0.0009781 0.004124 1.003 -0.0001193 5.356e-05 0.9945 -8.992e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1966 0.09327 0.3253 0.1552 0.9851 0.994 0.1973 0.4607 0.8827 0.7163 ] Network output: [ 0.007782 -0.038 0.9967 6.964e-05 -3.126e-05 1.026 5.248e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09757 0.08628 0.1788 0.2069 0.9873 0.992 0.09763 0.7847 0.8753 0.3084 ] Network output: [ -0.007822 0.03967 1.001 7.069e-05 -3.174e-05 0.9748 5.328e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09128 0.08937 0.1664 0.1967 0.9856 0.9914 0.09129 0.7131 0.8539 0.2432 ] Network output: [ 0.0002485 0.9996 -0.0006044 9.663e-06 -4.338e-06 1.001 7.282e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00076 Epoch 7118 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01267 0.993 0.9877 3.271e-06 -1.468e-06 -0.006032 2.465e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003225 -0.00301 -0.009171 0.007022 0.9698 0.9742 0.006139 0.8421 0.8304 0.01976 ] Network output: [ 0.9997 0.001022 0.00163 -3.555e-05 1.596e-05 -0.002149 -2.679e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1859 -0.03024 -0.1929 0.1982 0.9836 0.9933 0.2075 0.456 0.8762 0.7217 ] Network output: [ -0.01159 1.001 1.01 1.631e-06 -7.324e-07 0.01222 1.229e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005452 0.0004758 0.004345 0.004232 0.9889 0.992 0.005552 0.8715 0.9 0.01432 ] Network output: [ -0.0009223 0.003291 1.003 -0.0001191 5.349e-05 0.9952 -8.979e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1966 0.09327 0.3254 0.1553 0.9851 0.994 0.1973 0.4607 0.8827 0.7163 ] Network output: [ 0.007769 -0.03815 0.9967 6.96e-05 -3.125e-05 1.026 5.245e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09757 0.08628 0.1788 0.2069 0.9873 0.992 0.09763 0.7847 0.8753 0.3084 ] Network output: [ -0.007823 0.03971 1.001 7.064e-05 -3.171e-05 0.9748 5.324e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09127 0.08937 0.1664 0.1967 0.9856 0.9914 0.09129 0.7131 0.8539 0.2432 ] Network output: [ 0.0002829 0.9996 -0.0006527 9.666e-06 -4.339e-06 1 7.285e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000761 Epoch 7119 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01266 0.9931 0.9877 3.258e-06 -1.463e-06 -0.00611 2.456e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003225 -0.00301 -0.00917 0.00702 0.9698 0.9742 0.006139 0.8421 0.8304 0.01976 ] Network output: [ 0.9996 0.001628 0.001597 -3.557e-05 1.597e-05 -0.002645 -2.681e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1859 -0.03024 -0.1929 0.1981 0.9836 0.9933 0.2076 0.456 0.8762 0.7216 ] Network output: [ -0.01159 1.001 1.01 1.625e-06 -7.297e-07 0.01219 1.225e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005452 0.0004756 0.004344 0.004229 0.9889 0.992 0.005552 0.8715 0.9 0.01432 ] Network output: [ -0.0009766 0.00411 1.003 -0.0001191 5.348e-05 0.9946 -8.978e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1967 0.09327 0.3253 0.1552 0.9851 0.994 0.1973 0.4607 0.8826 0.7163 ] Network output: [ 0.007776 -0.03798 0.9967 6.954e-05 -3.122e-05 1.026 5.241e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09757 0.08628 0.1788 0.2069 0.9873 0.992 0.09763 0.7847 0.8752 0.3084 ] Network output: [ -0.007816 0.03964 1.001 7.06e-05 -3.169e-05 0.9748 5.321e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09127 0.08936 0.1664 0.1967 0.9856 0.9914 0.09128 0.7131 0.8539 0.2432 ] Network output: [ 0.0002487 0.9996 -0.000604 9.65e-06 -4.332e-06 1.001 7.272e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007591 Epoch 7120 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01266 0.9931 0.9877 3.261e-06 -1.464e-06 -0.006038 2.457e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003225 -0.00301 -0.009168 0.00702 0.9698 0.9742 0.006139 0.8421 0.8304 0.01975 ] Network output: [ 0.9997 0.001028 0.001628 -3.551e-05 1.594e-05 -0.002153 -2.676e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1859 -0.03025 -0.1929 0.1982 0.9836 0.9933 0.2076 0.456 0.8761 0.7217 ] Network output: [ -0.01159 1.001 1.01 1.626e-06 -7.301e-07 0.01221 1.226e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005453 0.0004756 0.004346 0.004231 0.9889 0.992 0.005553 0.8715 0.9 0.01432 ] Network output: [ -0.0009224 0.003301 1.003 -0.000119 5.341e-05 0.9952 -8.966e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1967 0.09327 0.3254 0.1553 0.9851 0.994 0.1973 0.4607 0.8826 0.7163 ] Network output: [ 0.007764 -0.03812 0.9967 6.95e-05 -3.12e-05 1.026 5.238e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08629 0.1788 0.2069 0.9873 0.992 0.09764 0.7846 0.8752 0.3084 ] Network output: [ -0.007816 0.03968 1.001 7.055e-05 -3.167e-05 0.9748 5.317e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09127 0.08936 0.1664 0.1967 0.9856 0.9914 0.09128 0.7131 0.8539 0.2432 ] Network output: [ 0.0002822 0.9996 -0.0006509 9.653e-06 -4.333e-06 1 7.275e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00076 Epoch 7121 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01265 0.9931 0.9877 3.248e-06 -1.458e-06 -0.006113 2.448e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003226 -0.00301 -0.009167 0.007018 0.9698 0.9742 0.00614 0.8421 0.8304 0.01975 ] Network output: [ 0.9996 0.001617 0.001595 -3.553e-05 1.595e-05 -0.002635 -2.678e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.186 -0.03025 -0.1929 0.1981 0.9836 0.9933 0.2076 0.456 0.8761 0.7216 ] Network output: [ -0.01159 1.001 1.01 1.62e-06 -7.275e-07 0.01218 1.221e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005453 0.0004755 0.004344 0.004227 0.9889 0.992 0.005554 0.8715 0.9 0.01432 ] Network output: [ -0.0009752 0.004097 1.003 -0.000119 5.34e-05 0.9946 -8.965e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1967 0.09327 0.3253 0.1551 0.9851 0.994 0.1973 0.4607 0.8826 0.7163 ] Network output: [ 0.007771 -0.03796 0.9967 6.944e-05 -3.117e-05 1.026 5.233e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08629 0.1788 0.2068 0.9873 0.992 0.09764 0.7846 0.8752 0.3084 ] Network output: [ -0.007809 0.03961 1.001 7.05e-05 -3.165e-05 0.9748 5.313e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09126 0.08935 0.1664 0.1967 0.9856 0.9914 0.09127 0.713 0.8539 0.2432 ] Network output: [ 0.0002488 0.9996 -0.0006035 9.637e-06 -4.326e-06 1.001 7.262e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007581 Epoch 7122 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01266 0.9931 0.9877 3.25e-06 -1.459e-06 -0.006044 2.45e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003226 -0.00301 -0.009165 0.007018 0.9698 0.9742 0.00614 0.8421 0.8304 0.01975 ] Network output: [ 0.9997 0.001033 0.001625 -3.547e-05 1.592e-05 -0.002158 -2.673e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.186 -0.03026 -0.1928 0.1981 0.9836 0.9933 0.2076 0.456 0.8761 0.7216 ] Network output: [ -0.01159 1.001 1.01 1.621e-06 -7.278e-07 0.0122 1.222e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005454 0.0004754 0.004346 0.004229 0.9889 0.992 0.005554 0.8715 0.9 0.01432 ] Network output: [ -0.0009225 0.00331 1.003 -0.0001188 5.333e-05 0.9952 -8.953e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1967 0.09327 0.3254 0.1553 0.9851 0.994 0.1973 0.4606 0.8826 0.7163 ] Network output: [ 0.007758 -0.0381 0.9967 6.94e-05 -3.116e-05 1.026 5.23e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09758 0.08629 0.1788 0.2069 0.9873 0.992 0.09765 0.7846 0.8752 0.3084 ] Network output: [ -0.00781 0.03965 1.001 7.045e-05 -3.163e-05 0.9748 5.31e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09126 0.08935 0.1664 0.1967 0.9856 0.9914 0.09127 0.713 0.8539 0.2432 ] Network output: [ 0.0002814 0.9996 -0.0006492 9.639e-06 -4.327e-06 1 7.264e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007591 Epoch 7123 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01265 0.9931 0.9877 3.238e-06 -1.454e-06 -0.006117 2.441e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003226 -0.00301 -0.009164 0.007016 0.9698 0.9742 0.00614 0.8421 0.8304 0.01975 ] Network output: [ 0.9996 0.001606 0.001594 -3.548e-05 1.593e-05 -0.002626 -2.674e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.186 -0.03026 -0.1928 0.198 0.9836 0.9933 0.2076 0.456 0.8761 0.7216 ] Network output: [ -0.01159 1.001 1.01 1.615e-06 -7.252e-07 0.01217 1.217e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005455 0.0004753 0.004345 0.004225 0.9889 0.992 0.005555 0.8715 0.9 0.01431 ] Network output: [ -0.0009738 0.004084 1.003 -0.0001188 5.332e-05 0.9946 -8.951e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1967 0.09327 0.3254 0.1551 0.9851 0.994 0.1973 0.4606 0.8826 0.7163 ] Network output: [ 0.007765 -0.03794 0.9967 6.934e-05 -3.113e-05 1.026 5.226e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09759 0.08629 0.1788 0.2068 0.9873 0.992 0.09765 0.7845 0.8752 0.3084 ] Network output: [ -0.007802 0.03958 1.001 7.041e-05 -3.161e-05 0.9749 5.306e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09125 0.08934 0.1664 0.1967 0.9856 0.9914 0.09126 0.7129 0.8539 0.2432 ] Network output: [ 0.000249 0.9996 -0.0006031 9.623e-06 -4.32e-06 1.001 7.253e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007572 Epoch 7124 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01265 0.9931 0.9877 3.24e-06 -1.455e-06 -0.00605 2.442e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003226 -0.003011 -0.009162 0.007016 0.9698 0.9742 0.00614 0.8421 0.8304 0.01975 ] Network output: [ 0.9997 0.001039 0.001623 -3.542e-05 1.59e-05 -0.002162 -2.67e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.186 -0.03027 -0.1928 0.1981 0.9836 0.9933 0.2076 0.4559 0.8761 0.7216 ] Network output: [ -0.01158 1.001 1.01 1.616e-06 -7.255e-07 0.01219 1.218e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005455 0.0004752 0.004346 0.004228 0.9889 0.992 0.005555 0.8715 0.9 0.01431 ] Network output: [ -0.0009225 0.003319 1.003 -0.0001186 5.325e-05 0.9952 -8.939e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1967 0.09327 0.3255 0.1552 0.9851 0.994 0.1973 0.4606 0.8826 0.7163 ] Network output: [ 0.007753 -0.03807 0.9967 6.93e-05 -3.111e-05 1.026 5.223e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09759 0.0863 0.1788 0.2069 0.9873 0.992 0.09765 0.7845 0.8752 0.3084 ] Network output: [ -0.007803 0.03961 1.001 7.036e-05 -3.159e-05 0.9748 5.303e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09125 0.08934 0.1664 0.1967 0.9856 0.9914 0.09126 0.7129 0.8538 0.2432 ] Network output: [ 0.0002807 0.9996 -0.0006474 9.626e-06 -4.321e-06 1 7.254e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007582 Epoch 7125 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01264 0.9932 0.9877 3.228e-06 -1.449e-06 -0.006121 2.433e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003226 -0.003011 -0.009161 0.007013 0.9698 0.9742 0.006141 0.8421 0.8304 0.01974 ] Network output: [ 0.9996 0.001596 0.001593 -3.544e-05 1.591e-05 -0.002617 -2.671e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.186 -0.03027 -0.1928 0.198 0.9836 0.9933 0.2076 0.4559 0.8761 0.7216 ] Network output: [ -0.01159 1.001 1.01 1.61e-06 -7.229e-07 0.01216 1.214e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005456 0.0004751 0.004345 0.004224 0.9889 0.992 0.005556 0.8715 0.9 0.01431 ] Network output: [ -0.0009723 0.004071 1.003 -0.0001186 5.324e-05 0.9946 -8.938e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1967 0.09327 0.3254 0.1551 0.9851 0.994 0.1974 0.4606 0.8826 0.7163 ] Network output: [ 0.007759 -0.03791 0.9967 6.924e-05 -3.109e-05 1.026 5.218e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09759 0.0863 0.1788 0.2068 0.9873 0.992 0.09765 0.7845 0.8752 0.3084 ] Network output: [ -0.007795 0.03955 1.001 7.032e-05 -3.157e-05 0.9749 5.299e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09125 0.08934 0.1664 0.1967 0.9856 0.9914 0.09126 0.7129 0.8538 0.2432 ] Network output: [ 0.0002492 0.9996 -0.0006026 9.61e-06 -4.314e-06 1.001 7.243e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007563 Epoch 7126 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01265 0.9931 0.9877 3.23e-06 -1.45e-06 -0.006055 2.434e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003226 -0.003011 -0.009158 0.007014 0.9698 0.9742 0.006141 0.8421 0.8304 0.01974 ] Network output: [ 0.9997 0.001044 0.001621 -3.538e-05 1.588e-05 -0.002166 -2.666e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.186 -0.03028 -0.1927 0.1981 0.9836 0.9933 0.2076 0.4559 0.8761 0.7216 ] Network output: [ -0.01158 1.001 1.01 1.611e-06 -7.232e-07 0.01218 1.214e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005456 0.0004751 0.004347 0.004226 0.9889 0.992 0.005556 0.8715 0.9 0.01431 ] Network output: [ -0.0009225 0.003328 1.003 -0.0001184 5.317e-05 0.9952 -8.926e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1967 0.09327 0.3255 0.1552 0.9851 0.994 0.1974 0.4606 0.8826 0.7163 ] Network output: [ 0.007747 -0.03805 0.9967 6.92e-05 -3.107e-05 1.026 5.215e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0976 0.0863 0.1788 0.2068 0.9873 0.992 0.09766 0.7845 0.8752 0.3084 ] Network output: [ -0.007796 0.03958 1.001 7.026e-05 -3.154e-05 0.9748 5.295e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09124 0.08933 0.1664 0.1967 0.9856 0.9914 0.09126 0.7128 0.8538 0.2432 ] Network output: [ 0.0002799 0.9996 -0.0006457 9.613e-06 -4.315e-06 1 7.244e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007572 Epoch 7127 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01264 0.9932 0.9877 3.219e-06 -1.445e-06 -0.006125 2.426e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003226 -0.003011 -0.009157 0.007011 0.9698 0.9742 0.006141 0.842 0.8304 0.01974 ] Network output: [ 0.9996 0.001585 0.001591 -3.54e-05 1.589e-05 -0.002608 -2.668e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.186 -0.03028 -0.1928 0.198 0.9836 0.9933 0.2076 0.4559 0.8761 0.7216 ] Network output: [ -0.01158 1.001 1.01 1.605e-06 -7.207e-07 0.01216 1.21e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005457 0.0004749 0.004345 0.004222 0.9889 0.992 0.005557 0.8715 0.9 0.01431 ] Network output: [ -0.0009709 0.004058 1.003 -0.0001184 5.316e-05 0.9946 -8.924e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1968 0.09327 0.3255 0.155 0.9851 0.994 0.1974 0.4606 0.8826 0.7163 ] Network output: [ 0.007753 -0.03789 0.9967 6.914e-05 -3.104e-05 1.026 5.211e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0976 0.0863 0.1788 0.2068 0.9873 0.992 0.09766 0.7844 0.8752 0.3083 ] Network output: [ -0.007789 0.03952 1.001 7.022e-05 -3.152e-05 0.9749 5.292e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09124 0.08933 0.1664 0.1967 0.9856 0.9914 0.09125 0.7128 0.8538 0.2432 ] Network output: [ 0.0002493 0.9996 -0.0006021 9.597e-06 -4.309e-06 1.001 7.233e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007554 Epoch 7128 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01264 0.9931 0.9877 3.22e-06 -1.446e-06 -0.006061 2.427e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003226 -0.003011 -0.009155 0.007012 0.9698 0.9742 0.006142 0.842 0.8304 0.01974 ] Network output: [ 0.9997 0.00105 0.001619 -3.534e-05 1.586e-05 -0.002169 -2.663e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.186 -0.03029 -0.1927 0.1981 0.9836 0.9933 0.2077 0.4559 0.8761 0.7216 ] Network output: [ -0.01158 1.001 1.01 1.606e-06 -7.209e-07 0.01217 1.21e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005457 0.0004749 0.004347 0.004224 0.9889 0.992 0.005557 0.8714 0.9 0.01431 ] Network output: [ -0.0009225 0.003337 1.003 -0.0001183 5.309e-05 0.9952 -8.913e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1968 0.09327 0.3255 0.1552 0.9851 0.994 0.1974 0.4605 0.8826 0.7163 ] Network output: [ 0.007742 -0.03802 0.9967 6.91e-05 -3.102e-05 1.026 5.208e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09761 0.08631 0.1788 0.2068 0.9873 0.992 0.09767 0.7844 0.8751 0.3084 ] Network output: [ -0.007789 0.03955 1.001 7.017e-05 -3.15e-05 0.9749 5.288e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09124 0.08933 0.1664 0.1967 0.9856 0.9914 0.09125 0.7128 0.8538 0.2432 ] Network output: [ 0.0002792 0.9996 -0.000644 9.599e-06 -4.309e-06 1 7.234e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007563 Epoch 7129 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01263 0.9932 0.9877 3.209e-06 -1.44e-06 -0.006129 2.418e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003227 -0.003011 -0.009154 0.007009 0.9698 0.9742 0.006142 0.842 0.8303 0.01973 ] Network output: [ 0.9996 0.001575 0.00159 -3.535e-05 1.587e-05 -0.002599 -2.664e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.186 -0.03029 -0.1927 0.198 0.9836 0.9933 0.2077 0.4559 0.8761 0.7216 ] Network output: [ -0.01158 1.001 1.01 1.6e-06 -7.184e-07 0.01215 1.206e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005458 0.0004747 0.004346 0.004221 0.9889 0.992 0.005558 0.8714 0.9 0.0143 ] Network output: [ -0.0009696 0.004046 1.003 -0.0001182 5.308e-05 0.9946 -8.911e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1968 0.09327 0.3255 0.155 0.9851 0.994 0.1974 0.4605 0.8826 0.7163 ] Network output: [ 0.007748 -0.03787 0.9967 6.904e-05 -3.1e-05 1.026 5.203e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09761 0.08631 0.1788 0.2068 0.9873 0.992 0.09767 0.7844 0.8751 0.3083 ] Network output: [ -0.007782 0.03948 1.001 7.013e-05 -3.148e-05 0.9749 5.285e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09123 0.08932 0.1664 0.1967 0.9856 0.9914 0.09124 0.7127 0.8538 0.2432 ] Network output: [ 0.0002495 0.9996 -0.0006016 9.584e-06 -4.303e-06 1.001 7.223e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007545 Epoch 7130 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01264 0.9931 0.9877 3.21e-06 -1.441e-06 -0.006067 2.419e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003227 -0.003012 -0.009152 0.00701 0.9698 0.9742 0.006142 0.842 0.8303 0.01973 ] Network output: [ 0.9997 0.001055 0.001616 -3.529e-05 1.585e-05 -0.002173 -2.66e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.186 -0.0303 -0.1927 0.1981 0.9836 0.9933 0.2077 0.4558 0.8761 0.7216 ] Network output: [ -0.01158 1.001 1.01 1.601e-06 -7.186e-07 0.01217 1.206e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005458 0.0004747 0.004347 0.004223 0.9889 0.992 0.005559 0.8714 0.9 0.0143 ] Network output: [ -0.0009225 0.003345 1.003 -0.0001181 5.301e-05 0.9952 -8.899e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1968 0.09327 0.3256 0.1551 0.9851 0.994 0.1974 0.4605 0.8826 0.7163 ] Network output: [ 0.007736 -0.03799 0.9967 6.9e-05 -3.098e-05 1.026 5.2e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09761 0.08631 0.1788 0.2068 0.9873 0.992 0.09767 0.7844 0.8751 0.3083 ] Network output: [ -0.007782 0.03951 1.001 7.008e-05 -3.146e-05 0.9749 5.281e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09123 0.08932 0.1664 0.1967 0.9856 0.9914 0.09124 0.7127 0.8538 0.2432 ] Network output: [ 0.0002785 0.9996 -0.0006423 9.586e-06 -4.304e-06 1 7.224e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007553 Epoch 7131 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01263 0.9932 0.9877 3.199e-06 -1.436e-06 -0.006133 2.411e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003227 -0.003012 -0.009151 0.007007 0.9698 0.9742 0.006142 0.842 0.8303 0.01973 ] Network output: [ 0.9996 0.001565 0.001588 -3.531e-05 1.585e-05 -0.00259 -2.661e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1861 -0.0303 -0.1927 0.198 0.9836 0.9933 0.2077 0.4558 0.8761 0.7216 ] Network output: [ -0.01158 1.001 1.01 1.595e-06 -7.161e-07 0.01214 1.202e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005459 0.0004746 0.004346 0.004219 0.9889 0.992 0.005559 0.8714 0.8999 0.0143 ] Network output: [ -0.0009682 0.004034 1.003 -0.0001181 5.3e-05 0.9946 -8.897e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1968 0.09327 0.3255 0.155 0.9851 0.994 0.1974 0.4605 0.8826 0.7163 ] Network output: [ 0.007742 -0.03785 0.9967 6.894e-05 -3.095e-05 1.026 5.196e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09761 0.08631 0.1788 0.2068 0.9873 0.992 0.09768 0.7843 0.8751 0.3083 ] Network output: [ -0.007775 0.03945 1.001 7.003e-05 -3.144e-05 0.9749 5.278e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09122 0.08931 0.1664 0.1967 0.9856 0.9914 0.09123 0.7127 0.8537 0.2432 ] Network output: [ 0.0002496 0.9996 -0.0006011 9.571e-06 -4.297e-06 1.001 7.213e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007536 Epoch 7132 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01263 0.9931 0.9877 3.2e-06 -1.437e-06 -0.006073 2.412e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003227 -0.003012 -0.009149 0.007007 0.9698 0.9742 0.006143 0.842 0.8303 0.01973 ] Network output: [ 0.9997 0.00106 0.001614 -3.525e-05 1.583e-05 -0.002176 -2.657e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1861 -0.03032 -0.1926 0.198 0.9836 0.9933 0.2077 0.4558 0.8761 0.7216 ] Network output: [ -0.01158 1.001 1.01 1.595e-06 -7.163e-07 0.01216 1.202e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00546 0.0004745 0.004348 0.004221 0.9889 0.992 0.00556 0.8714 0.8999 0.0143 ] Network output: [ -0.0009225 0.003353 1.003 -0.0001179 5.293e-05 0.9952 -8.886e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1968 0.09327 0.3256 0.1551 0.9851 0.994 0.1974 0.4605 0.8826 0.7163 ] Network output: [ 0.007731 -0.03797 0.9967 6.89e-05 -3.093e-05 1.026 5.193e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09762 0.08632 0.1788 0.2068 0.9873 0.992 0.09768 0.7843 0.8751 0.3083 ] Network output: [ -0.007775 0.03948 1.001 6.998e-05 -3.142e-05 0.9749 5.274e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09122 0.08931 0.1664 0.1967 0.9856 0.9914 0.09123 0.7126 0.8537 0.2432 ] Network output: [ 0.0002778 0.9996 -0.0006406 9.573e-06 -4.298e-06 1 7.214e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007544 Epoch 7133 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01263 0.9932 0.9877 3.189e-06 -1.432e-06 -0.006137 2.403e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003227 -0.003012 -0.009148 0.007005 0.9698 0.9742 0.006143 0.842 0.8303 0.01973 ] Network output: [ 0.9996 0.001555 0.001587 -3.526e-05 1.583e-05 -0.002581 -2.657e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1861 -0.03031 -0.1926 0.1979 0.9836 0.9933 0.2077 0.4558 0.8761 0.7216 ] Network output: [ -0.01158 1.001 1.01 1.59e-06 -7.139e-07 0.01213 1.198e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00546 0.0004744 0.004347 0.004218 0.9889 0.992 0.005561 0.8714 0.8999 0.0143 ] Network output: [ -0.0009668 0.004022 1.003 -0.0001179 5.292e-05 0.9946 -8.884e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1968 0.09327 0.3256 0.155 0.9851 0.994 0.1975 0.4605 0.8826 0.7163 ] Network output: [ 0.007736 -0.03783 0.9967 6.885e-05 -3.091e-05 1.026 5.188e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09762 0.08632 0.1788 0.2067 0.9873 0.992 0.09768 0.7843 0.8751 0.3083 ] Network output: [ -0.007769 0.03942 1.001 6.994e-05 -3.14e-05 0.9749 5.271e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09121 0.08931 0.1664 0.1967 0.9856 0.9914 0.09123 0.7126 0.8537 0.2432 ] Network output: [ 0.0002497 0.9996 -0.0006006 9.558e-06 -4.291e-06 1.001 7.203e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007527 Epoch 7134 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01263 0.9931 0.9877 3.19e-06 -1.432e-06 -0.006078 2.404e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003227 -0.003012 -0.009146 0.007005 0.9698 0.9742 0.006143 0.842 0.8303 0.01972 ] Network output: [ 0.9997 0.001064 0.001612 -3.521e-05 1.581e-05 -0.002179 -2.653e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1861 -0.03033 -0.1926 0.198 0.9836 0.9933 0.2077 0.4558 0.8761 0.7216 ] Network output: [ -0.01157 1.001 1.01 1.59e-06 -7.14e-07 0.01215 1.199e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005461 0.0004743 0.004348 0.004219 0.9889 0.992 0.005561 0.8714 0.8999 0.0143 ] Network output: [ -0.0009224 0.003361 1.003 -0.0001177 5.285e-05 0.9952 -8.873e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1968 0.09327 0.3256 0.1551 0.9851 0.994 0.1975 0.4604 0.8826 0.7162 ] Network output: [ 0.007725 -0.03794 0.9967 6.88e-05 -3.089e-05 1.026 5.185e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09763 0.08632 0.1788 0.2068 0.9873 0.992 0.09769 0.7842 0.8751 0.3083 ] Network output: [ -0.007769 0.03945 1.001 6.989e-05 -3.138e-05 0.9749 5.267e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09121 0.0893 0.1664 0.1967 0.9856 0.9914 0.09122 0.7126 0.8537 0.2432 ] Network output: [ 0.0002771 0.9996 -0.0006389 9.559e-06 -4.292e-06 1 7.204e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007534 Epoch 7135 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01262 0.9932 0.9877 3.179e-06 -1.427e-06 -0.006141 2.396e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003227 -0.003012 -0.009145 0.007003 0.9698 0.9742 0.006144 0.842 0.8303 0.01972 ] Network output: [ 0.9996 0.001545 0.001585 -3.522e-05 1.581e-05 -0.002573 -2.654e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1861 -0.03032 -0.1926 0.1979 0.9836 0.9933 0.2077 0.4558 0.8761 0.7216 ] Network output: [ -0.01157 1.001 1.01 1.585e-06 -7.116e-07 0.01212 1.195e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005461 0.0004742 0.004347 0.004216 0.9889 0.992 0.005562 0.8714 0.8999 0.01429 ] Network output: [ -0.0009655 0.00401 1.003 -0.0001177 5.284e-05 0.9947 -8.87e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1969 0.09327 0.3256 0.1549 0.9851 0.994 0.1975 0.4604 0.8826 0.7162 ] Network output: [ 0.007731 -0.0378 0.9967 6.875e-05 -3.086e-05 1.026 5.181e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09763 0.08632 0.1788 0.2067 0.9873 0.992 0.09769 0.7842 0.8751 0.3083 ] Network output: [ -0.007762 0.03939 1.001 6.984e-05 -3.136e-05 0.975 5.264e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09121 0.0893 0.1664 0.1967 0.9855 0.9914 0.09122 0.7125 0.8537 0.2432 ] Network output: [ 0.0002498 0.9996 -0.0006 9.545e-06 -4.285e-06 1.001 7.194e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007518 Epoch 7136 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01262 0.9931 0.9877 3.18e-06 -1.428e-06 -0.006084 2.396e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003227 -0.003013 -0.009143 0.007003 0.9698 0.9742 0.006144 0.842 0.8303 0.01972 ] Network output: [ 0.9997 0.001069 0.00161 -3.516e-05 1.579e-05 -0.002182 -2.65e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1861 -0.03034 -0.1925 0.198 0.9836 0.9933 0.2078 0.4557 0.8761 0.7216 ] Network output: [ -0.01157 1.001 1.01 1.585e-06 -7.116e-07 0.01214 1.195e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005462 0.0004742 0.004348 0.004218 0.9889 0.992 0.005562 0.8714 0.8999 0.01429 ] Network output: [ -0.0009224 0.003369 1.003 -0.0001176 5.277e-05 0.9952 -8.859e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1969 0.09327 0.3257 0.155 0.9851 0.994 0.1975 0.4604 0.8825 0.7162 ] Network output: [ 0.00772 -0.03791 0.9967 6.87e-05 -3.084e-05 1.026 5.178e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09763 0.08632 0.1789 0.2067 0.9873 0.992 0.0977 0.7842 0.8751 0.3083 ] Network output: [ -0.007762 0.03941 1.001 6.979e-05 -3.133e-05 0.9749 5.26e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09121 0.0893 0.1664 0.1967 0.9855 0.9914 0.09122 0.7125 0.8537 0.2432 ] Network output: [ 0.0002764 0.9996 -0.0006372 9.546e-06 -4.286e-06 1 7.194e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007525 Epoch 7137 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01262 0.9932 0.9877 3.169e-06 -1.423e-06 -0.006144 2.388e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003228 -0.003013 -0.009142 0.007001 0.9698 0.9742 0.006144 0.842 0.8303 0.01972 ] Network output: [ 0.9997 0.001536 0.001584 -3.517e-05 1.579e-05 -0.002564 -2.651e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1861 -0.03034 -0.1926 0.1979 0.9836 0.9933 0.2078 0.4557 0.876 0.7216 ] Network output: [ -0.01157 1.001 1.01 1.58e-06 -7.093e-07 0.01211 1.191e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005463 0.000474 0.004347 0.004215 0.9889 0.992 0.005563 0.8714 0.8999 0.01429 ] Network output: [ -0.0009641 0.003999 1.003 -0.0001175 5.276e-05 0.9947 -8.857e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1969 0.09327 0.3256 0.1549 0.9851 0.994 0.1975 0.4604 0.8825 0.7162 ] Network output: [ 0.007725 -0.03778 0.9967 6.865e-05 -3.082e-05 1.026 5.173e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09764 0.08632 0.1788 0.2067 0.9873 0.992 0.0977 0.7842 0.8751 0.3083 ] Network output: [ -0.007755 0.03936 1.001 6.975e-05 -3.131e-05 0.975 5.257e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0912 0.08929 0.1663 0.1966 0.9855 0.9914 0.09121 0.7125 0.8537 0.2432 ] Network output: [ 0.0002499 0.9996 -0.0005995 9.532e-06 -4.279e-06 1.001 7.184e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007509 Epoch 7138 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01262 0.9931 0.9877 3.17e-06 -1.423e-06 -0.00609 2.389e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003228 -0.003013 -0.00914 0.007001 0.9698 0.9742 0.006144 0.842 0.8303 0.01972 ] Network output: [ 0.9997 0.001073 0.001607 -3.512e-05 1.577e-05 -0.002185 -2.647e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1861 -0.03035 -0.1925 0.198 0.9836 0.9933 0.2078 0.4557 0.876 0.7216 ] Network output: [ -0.01157 1.001 1.01 1.58e-06 -7.093e-07 0.01213 1.191e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005463 0.000474 0.004349 0.004216 0.9889 0.992 0.005563 0.8713 0.8999 0.01429 ] Network output: [ -0.0009223 0.003376 1.003 -0.0001174 5.27e-05 0.9952 -8.846e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1969 0.09327 0.3257 0.155 0.9851 0.994 0.1975 0.4604 0.8825 0.7162 ] Network output: [ 0.007715 -0.03789 0.9967 6.86e-05 -3.08e-05 1.026 5.17e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09764 0.08633 0.1789 0.2067 0.9873 0.992 0.0977 0.7841 0.875 0.3083 ] Network output: [ -0.007755 0.03938 1.001 6.97e-05 -3.129e-05 0.9749 5.253e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0912 0.08929 0.1663 0.1966 0.9855 0.9914 0.09121 0.7124 0.8537 0.2432 ] Network output: [ 0.0002758 0.9996 -0.0006356 9.533e-06 -4.28e-06 1 7.184e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007515 Epoch 7139 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01261 0.9932 0.9877 3.159e-06 -1.418e-06 -0.006148 2.381e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003228 -0.003013 -0.009139 0.006999 0.9698 0.9742 0.006145 0.8419 0.8303 0.01971 ] Network output: [ 0.9997 0.001526 0.001582 -3.513e-05 1.577e-05 -0.002556 -2.647e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1861 -0.03035 -0.1925 0.1979 0.9836 0.9933 0.2078 0.4557 0.876 0.7216 ] Network output: [ -0.01157 1.001 1.01 1.575e-06 -7.071e-07 0.01211 1.187e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005464 0.0004739 0.004348 0.004213 0.9889 0.992 0.005564 0.8713 0.8999 0.01429 ] Network output: [ -0.0009628 0.003988 1.003 -0.0001173 5.268e-05 0.9947 -8.843e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1969 0.09327 0.3257 0.1549 0.9851 0.994 0.1975 0.4604 0.8825 0.7162 ] Network output: [ 0.007719 -0.03776 0.9967 6.855e-05 -3.077e-05 1.026 5.166e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09764 0.08633 0.1788 0.2067 0.9873 0.992 0.0977 0.7841 0.875 0.3083 ] Network output: [ -0.007749 0.03932 1.001 6.966e-05 -3.127e-05 0.975 5.249e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09119 0.08928 0.1663 0.1966 0.9855 0.9914 0.0912 0.7124 0.8536 0.2432 ] Network output: [ 0.00025 0.9996 -0.0005989 9.519e-06 -4.273e-06 1.001 7.174e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00075 Epoch 7140 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01261 0.9931 0.9877 3.16e-06 -1.419e-06 -0.006095 2.381e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003228 -0.003013 -0.009137 0.006999 0.9698 0.9742 0.006145 0.8419 0.8303 0.01971 ] Network output: [ 0.9997 0.001077 0.001605 -3.508e-05 1.575e-05 -0.002188 -2.644e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1862 -0.03036 -0.1925 0.198 0.9836 0.9933 0.2078 0.4557 0.876 0.7216 ] Network output: [ -0.01157 1.001 1.01 1.575e-06 -7.07e-07 0.01212 1.187e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005464 0.0004738 0.004349 0.004214 0.9889 0.992 0.005564 0.8713 0.8999 0.01429 ] Network output: [ -0.0009222 0.003383 1.003 -0.0001172 5.262e-05 0.9952 -8.833e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1969 0.09327 0.3257 0.155 0.9851 0.994 0.1975 0.4603 0.8825 0.7162 ] Network output: [ 0.007709 -0.03786 0.9967 6.85e-05 -3.075e-05 1.026 5.163e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09765 0.08633 0.1789 0.2067 0.9873 0.992 0.09771 0.7841 0.875 0.3083 ] Network output: [ -0.007748 0.03935 1.001 6.961e-05 -3.125e-05 0.975 5.246e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09119 0.08928 0.1663 0.1966 0.9855 0.9914 0.0912 0.7124 0.8536 0.2432 ] Network output: [ 0.0002751 0.9996 -0.000634 9.52e-06 -4.274e-06 1 7.174e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007506 Epoch 7141 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01261 0.9932 0.9878 3.149e-06 -1.414e-06 -0.006152 2.374e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003228 -0.003013 -0.009136 0.006997 0.9698 0.9742 0.006145 0.8419 0.8303 0.01971 ] Network output: [ 0.9997 0.001517 0.001581 -3.508e-05 1.575e-05 -0.002548 -2.644e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1862 -0.03036 -0.1925 0.1979 0.9836 0.9933 0.2078 0.4557 0.876 0.7216 ] Network output: [ -0.01157 1.001 1.01 1.57e-06 -7.048e-07 0.0121 1.183e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005465 0.0004737 0.004348 0.004211 0.9889 0.992 0.005565 0.8713 0.8999 0.01428 ] Network output: [ -0.0009615 0.003977 1.003 -0.0001172 5.26e-05 0.9947 -8.83e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1969 0.09327 0.3257 0.1549 0.9851 0.994 0.1975 0.4603 0.8825 0.7162 ] Network output: [ 0.007714 -0.03774 0.9967 6.845e-05 -3.073e-05 1.026 5.159e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09765 0.08633 0.1788 0.2067 0.9873 0.992 0.09771 0.784 0.875 0.3083 ] Network output: [ -0.007742 0.03929 1.001 6.956e-05 -3.123e-05 0.975 5.242e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09118 0.08927 0.1663 0.1966 0.9855 0.9914 0.0912 0.7123 0.8536 0.2432 ] Network output: [ 0.0002501 0.9996 -0.0005983 9.506e-06 -4.268e-06 1.001 7.164e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007491 Epoch 7142 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01261 0.9931 0.9878 3.15e-06 -1.414e-06 -0.006101 2.374e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003228 -0.003014 -0.009134 0.006997 0.9698 0.9742 0.006146 0.8419 0.8303 0.01971 ] Network output: [ 0.9997 0.001081 0.001603 -3.503e-05 1.573e-05 -0.002191 -2.64e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1862 -0.03037 -0.1924 0.1979 0.9836 0.9933 0.2078 0.4556 0.876 0.7216 ] Network output: [ -0.01156 1.001 1.01 1.57e-06 -7.047e-07 0.01211 1.183e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005465 0.0004736 0.004349 0.004213 0.9889 0.992 0.005566 0.8713 0.8999 0.01428 ] Network output: [ -0.000922 0.00339 1.003 -0.000117 5.254e-05 0.9952 -8.819e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1969 0.09327 0.3258 0.1549 0.9851 0.994 0.1976 0.4603 0.8825 0.7162 ] Network output: [ 0.007704 -0.03784 0.9967 6.841e-05 -3.071e-05 1.026 5.155e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09765 0.08634 0.1789 0.2067 0.9873 0.992 0.09772 0.784 0.875 0.3083 ] Network output: [ -0.007742 0.03931 1.001 6.951e-05 -3.121e-05 0.975 5.239e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09118 0.08927 0.1663 0.1966 0.9855 0.9914 0.09119 0.7123 0.8536 0.2432 ] Network output: [ 0.0002744 0.9996 -0.0006323 9.506e-06 -4.268e-06 1 7.164e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007497 Epoch 7143 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0126 0.9932 0.9878 3.14e-06 -1.41e-06 -0.006156 2.366e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003228 -0.003014 -0.009133 0.006995 0.9698 0.9742 0.006146 0.8419 0.8303 0.0197 ] Network output: [ 0.9997 0.001508 0.001579 -3.504e-05 1.573e-05 -0.00254 -2.641e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1862 -0.03037 -0.1924 0.1979 0.9836 0.9933 0.2079 0.4556 0.876 0.7216 ] Network output: [ -0.01156 1.001 1.01 1.565e-06 -7.025e-07 0.01209 1.179e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005466 0.0004735 0.004348 0.00421 0.9889 0.992 0.005566 0.8713 0.8999 0.01428 ] Network output: [ -0.0009602 0.003966 1.003 -0.000117 5.252e-05 0.9947 -8.816e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1969 0.09327 0.3257 0.1548 0.9851 0.994 0.1976 0.4603 0.8825 0.7162 ] Network output: [ 0.007708 -0.03771 0.9966 6.835e-05 -3.068e-05 1.026 5.151e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09766 0.08634 0.1788 0.2067 0.9873 0.992 0.09772 0.784 0.875 0.3083 ] Network output: [ -0.007735 0.03926 1.001 6.947e-05 -3.119e-05 0.975 5.235e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09118 0.08927 0.1663 0.1966 0.9855 0.9914 0.09119 0.7123 0.8536 0.2432 ] Network output: [ 0.0002502 0.9996 -0.0005977 9.493e-06 -4.262e-06 1.001 7.154e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007482 Epoch 7144 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0126 0.9932 0.9878 3.14e-06 -1.41e-06 -0.006107 2.366e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003228 -0.003014 -0.009131 0.006995 0.9698 0.9742 0.006146 0.8419 0.8303 0.0197 ] Network output: [ 0.9997 0.001085 0.001601 -3.499e-05 1.571e-05 -0.002193 -2.637e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1862 -0.03038 -0.1924 0.1979 0.9836 0.9933 0.2079 0.4556 0.876 0.7216 ] Network output: [ -0.01156 1.001 1.01 1.565e-06 -7.024e-07 0.0121 1.179e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005466 0.0004735 0.00435 0.004211 0.9889 0.992 0.005567 0.8713 0.8999 0.01428 ] Network output: [ -0.0009219 0.003397 1.003 -0.0001168 5.246e-05 0.9952 -8.806e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.197 0.09327 0.3258 0.1549 0.9851 0.994 0.1976 0.4603 0.8825 0.7162 ] Network output: [ 0.007698 -0.03781 0.9967 6.831e-05 -3.067e-05 1.026 5.148e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09766 0.08634 0.1789 0.2067 0.9873 0.992 0.09772 0.784 0.875 0.3083 ] Network output: [ -0.007735 0.03928 1.001 6.942e-05 -3.116e-05 0.975 5.232e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09118 0.08927 0.1663 0.1966 0.9855 0.9914 0.09119 0.7122 0.8536 0.2432 ] Network output: [ 0.0002738 0.9996 -0.0006307 9.493e-06 -4.262e-06 1 7.154e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007487 Epoch 7145 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0126 0.9932 0.9878 3.13e-06 -1.405e-06 -0.00616 2.359e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003229 -0.003014 -0.00913 0.006993 0.9698 0.9742 0.006147 0.8419 0.8302 0.0197 ] Network output: [ 0.9997 0.001499 0.001578 -3.5e-05 1.571e-05 -0.002532 -2.637e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1862 -0.03038 -0.1924 0.1978 0.9836 0.9933 0.2079 0.4556 0.876 0.7215 ] Network output: [ -0.01156 1.001 1.01 1.56e-06 -7.003e-07 0.01208 1.176e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005467 0.0004733 0.004349 0.004208 0.9889 0.992 0.005568 0.8713 0.8999 0.01428 ] Network output: [ -0.0009589 0.003955 1.003 -0.0001168 5.244e-05 0.9947 -8.803e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.197 0.09327 0.3258 0.1548 0.9851 0.994 0.1976 0.4603 0.8825 0.7162 ] Network output: [ 0.007702 -0.03769 0.9966 6.825e-05 -3.064e-05 1.026 5.144e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09766 0.08634 0.1789 0.2066 0.9873 0.992 0.09773 0.7839 0.875 0.3083 ] Network output: [ -0.007729 0.03923 1.001 6.937e-05 -3.114e-05 0.975 5.228e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09117 0.08926 0.1663 0.1966 0.9855 0.9914 0.09118 0.7122 0.8536 0.2432 ] Network output: [ 0.0002502 0.9996 -0.0005971 9.48e-06 -4.256e-06 1.001 7.144e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007473 Epoch 7146 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0126 0.9932 0.9878 3.13e-06 -1.405e-06 -0.006112 2.359e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003229 -0.003014 -0.009128 0.006993 0.9698 0.9742 0.006147 0.8419 0.8302 0.0197 ] Network output: [ 0.9997 0.001089 0.001599 -3.495e-05 1.569e-05 -0.002196 -2.634e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1862 -0.03039 -0.1924 0.1979 0.9836 0.9933 0.2079 0.4556 0.876 0.7215 ] Network output: [ -0.01156 1.001 1.01 1.56e-06 -7.001e-07 0.01209 1.175e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005468 0.0004733 0.00435 0.00421 0.9889 0.992 0.005568 0.8713 0.8999 0.01428 ] Network output: [ -0.0009217 0.003403 1.003 -0.0001167 5.238e-05 0.9952 -8.793e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.197 0.09327 0.3258 0.1549 0.9851 0.994 0.1976 0.4602 0.8825 0.7162 ] Network output: [ 0.007693 -0.03778 0.9967 6.821e-05 -3.062e-05 1.026 5.14e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09767 0.08635 0.1789 0.2067 0.9873 0.992 0.09773 0.7839 0.875 0.3083 ] Network output: [ -0.007728 0.03925 1.001 6.932e-05 -3.112e-05 0.975 5.224e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09117 0.08926 0.1663 0.1966 0.9855 0.9914 0.09118 0.7122 0.8535 0.2432 ] Network output: [ 0.0002732 0.9996 -0.0006291 9.48e-06 -4.256e-06 1 7.144e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007478 Epoch 7147 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01259 0.9932 0.9878 3.12e-06 -1.401e-06 -0.006164 2.351e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003229 -0.003014 -0.009126 0.006991 0.9698 0.9742 0.006147 0.8419 0.8302 0.0197 ] Network output: [ 0.9997 0.001491 0.001576 -3.495e-05 1.569e-05 -0.002524 -2.634e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1862 -0.03039 -0.1924 0.1978 0.9836 0.9933 0.2079 0.4556 0.876 0.7215 ] Network output: [ -0.01156 1.001 1.01 1.555e-06 -6.98e-07 0.01207 1.172e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005468 0.0004732 0.004349 0.004207 0.9889 0.992 0.005569 0.8713 0.8999 0.01428 ] Network output: [ -0.0009577 0.003945 1.003 -0.0001166 5.236e-05 0.9947 -8.79e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.197 0.09327 0.3258 0.1548 0.9851 0.994 0.1976 0.4602 0.8825 0.7162 ] Network output: [ 0.007697 -0.03767 0.9966 6.815e-05 -3.06e-05 1.026 5.136e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09767 0.08635 0.1789 0.2066 0.9873 0.992 0.09773 0.7839 0.8749 0.3083 ] Network output: [ -0.007722 0.0392 1.001 6.928e-05 -3.11e-05 0.975 5.221e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09116 0.08925 0.1663 0.1966 0.9855 0.9914 0.09117 0.7121 0.8535 0.2432 ] Network output: [ 0.0002503 0.9996 -0.0005965 9.467e-06 -4.25e-06 1.001 7.135e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007463 Epoch 7148 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01259 0.9932 0.9878 3.12e-06 -1.401e-06 -0.006118 2.351e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003229 -0.003015 -0.009124 0.006991 0.9698 0.9742 0.006147 0.8419 0.8302 0.01969 ] Network output: [ 0.9997 0.001093 0.001596 -3.49e-05 1.567e-05 -0.002198 -2.63e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1862 -0.0304 -0.1923 0.1979 0.9836 0.9933 0.2079 0.4555 0.876 0.7215 ] Network output: [ -0.01156 1.001 1.01 1.554e-06 -6.978e-07 0.01209 1.171e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005469 0.0004731 0.004351 0.004208 0.9889 0.992 0.005569 0.8712 0.8999 0.01427 ] Network output: [ -0.0009215 0.003409 1.003 -0.0001165 5.23e-05 0.9952 -8.779e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.197 0.09327 0.3259 0.1549 0.9851 0.994 0.1976 0.4602 0.8825 0.7162 ] Network output: [ 0.007687 -0.03776 0.9967 6.811e-05 -3.058e-05 1.026 5.133e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09768 0.08635 0.1789 0.2066 0.9873 0.992 0.09774 0.7838 0.8749 0.3083 ] Network output: [ -0.007721 0.03922 1.001 6.923e-05 -3.108e-05 0.975 5.217e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09116 0.08925 0.1663 0.1966 0.9855 0.9914 0.09117 0.7121 0.8535 0.2432 ] Network output: [ 0.0002725 0.9996 -0.0006276 9.467e-06 -4.25e-06 1 7.134e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007468 Epoch 7149 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01259 0.9932 0.9878 3.11e-06 -1.396e-06 -0.006168 2.344e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003229 -0.003015 -0.009123 0.006989 0.9698 0.9742 0.006148 0.8419 0.8302 0.01969 ] Network output: [ 0.9997 0.001482 0.001575 -3.491e-05 1.567e-05 -0.002516 -2.631e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1863 -0.0304 -0.1923 0.1978 0.9836 0.9933 0.2079 0.4555 0.876 0.7215 ] Network output: [ -0.01156 1.001 1.01 1.55e-06 -6.957e-07 0.01206 1.168e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005469 0.000473 0.00435 0.004205 0.9889 0.992 0.00557 0.8712 0.8998 0.01427 ] Network output: [ -0.0009564 0.003935 1.003 -0.0001165 5.228e-05 0.9947 -8.776e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.197 0.09327 0.3258 0.1547 0.9851 0.994 0.1976 0.4602 0.8825 0.7162 ] Network output: [ 0.007691 -0.03764 0.9966 6.805e-05 -3.055e-05 1.026 5.129e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09768 0.08635 0.1789 0.2066 0.9873 0.992 0.09774 0.7838 0.8749 0.3082 ] Network output: [ -0.007715 0.03916 1.001 6.918e-05 -3.106e-05 0.9751 5.214e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09116 0.08924 0.1663 0.1966 0.9855 0.9914 0.09117 0.7121 0.8535 0.2432 ] Network output: [ 0.0002503 0.9996 -0.0005959 9.454e-06 -4.244e-06 1 7.125e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007454 Epoch 7150 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01259 0.9932 0.9878 3.11e-06 -1.396e-06 -0.006123 2.344e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003229 -0.003015 -0.009121 0.006988 0.9698 0.9742 0.006148 0.8419 0.8302 0.01969 ] Network output: [ 0.9997 0.001096 0.001594 -3.486e-05 1.565e-05 -0.0022 -2.627e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1863 -0.03041 -0.1923 0.1978 0.9836 0.9933 0.2079 0.4555 0.876 0.7215 ] Network output: [ -0.01155 1.001 1.01 1.549e-06 -6.955e-07 0.01208 1.168e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00547 0.000473 0.004351 0.004206 0.9889 0.992 0.00557 0.8712 0.8998 0.01427 ] Network output: [ -0.0009214 0.003415 1.003 -0.0001163 5.222e-05 0.9952 -8.766e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.197 0.09327 0.3259 0.1548 0.9851 0.994 0.1977 0.4602 0.8825 0.7162 ] Network output: [ 0.007682 -0.03773 0.9966 6.801e-05 -3.053e-05 1.026 5.125e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09768 0.08636 0.1789 0.2066 0.9873 0.992 0.09775 0.7838 0.8749 0.3083 ] Network output: [ -0.007715 0.03918 1.001 6.914e-05 -3.104e-05 0.975 5.21e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09115 0.08924 0.1663 0.1966 0.9855 0.9914 0.09117 0.712 0.8535 0.2432 ] Network output: [ 0.0002719 0.9996 -0.000626 9.453e-06 -4.244e-06 1 7.124e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007459 Epoch 7151 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01258 0.9932 0.9878 3.1e-06 -1.392e-06 -0.006172 2.337e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003229 -0.003015 -0.00912 0.006986 0.9698 0.9742 0.006148 0.8418 0.8302 0.01969 ] Network output: [ 0.9997 0.001474 0.001573 -3.486e-05 1.565e-05 -0.002509 -2.627e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1863 -0.03041 -0.1923 0.1978 0.9836 0.9933 0.208 0.4555 0.876 0.7215 ] Network output: [ -0.01155 1.001 1.01 1.545e-06 -6.934e-07 0.01206 1.164e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005471 0.0004728 0.00435 0.004204 0.9889 0.992 0.005571 0.8712 0.8998 0.01427 ] Network output: [ -0.0009552 0.003925 1.003 -0.0001163 5.22e-05 0.9947 -8.763e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.197 0.09327 0.3259 0.1547 0.9851 0.994 0.1977 0.4602 0.8825 0.7162 ] Network output: [ 0.007685 -0.03762 0.9966 6.795e-05 -3.051e-05 1.026 5.121e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09769 0.08636 0.1789 0.2066 0.9873 0.992 0.09775 0.7838 0.8749 0.3082 ] Network output: [ -0.007709 0.03913 1.001 6.909e-05 -3.102e-05 0.9751 5.207e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09115 0.08924 0.1663 0.1966 0.9855 0.9914 0.09116 0.712 0.8535 0.2432 ] Network output: [ 0.0002504 0.9996 -0.0005953 9.441e-06 -4.238e-06 1 7.115e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007445 Epoch 7152 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01258 0.9932 0.9878 3.1e-06 -1.392e-06 -0.006129 2.336e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003229 -0.003015 -0.009118 0.006986 0.9698 0.9742 0.006148 0.8418 0.8302 0.01969 ] Network output: [ 0.9997 0.001099 0.001592 -3.482e-05 1.563e-05 -0.002202 -2.624e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1863 -0.03042 -0.1922 0.1978 0.9836 0.9933 0.208 0.4555 0.876 0.7215 ] Network output: [ -0.01155 1.001 1.01 1.544e-06 -6.932e-07 0.01207 1.164e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005471 0.0004728 0.004351 0.004205 0.9889 0.992 0.005572 0.8712 0.8998 0.01427 ] Network output: [ -0.0009211 0.003421 1.003 -0.0001161 5.214e-05 0.9952 -8.753e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1971 0.09327 0.3259 0.1548 0.9851 0.994 0.1977 0.4601 0.8825 0.7162 ] Network output: [ 0.007677 -0.03771 0.9966 6.791e-05 -3.049e-05 1.026 5.118e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09769 0.08636 0.1789 0.2066 0.9873 0.992 0.09775 0.7837 0.8749 0.3082 ] Network output: [ -0.007708 0.03915 1.001 6.904e-05 -3.1e-05 0.9751 5.203e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09115 0.08924 0.1663 0.1966 0.9855 0.9914 0.09116 0.712 0.8535 0.2432 ] Network output: [ 0.0002713 0.9996 -0.0006244 9.44e-06 -4.238e-06 1 7.114e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000745 Epoch 7153 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01258 0.9932 0.9878 3.091e-06 -1.388e-06 -0.006176 2.329e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003229 -0.003015 -0.009117 0.006984 0.9698 0.9742 0.006149 0.8418 0.8302 0.01968 ] Network output: [ 0.9997 0.001466 0.001572 -3.482e-05 1.563e-05 -0.002501 -2.624e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1863 -0.03042 -0.1922 0.1978 0.9836 0.9933 0.208 0.4555 0.876 0.7215 ] Network output: [ -0.01155 1.001 1.01 1.54e-06 -6.912e-07 0.01205 1.16e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005472 0.0004727 0.00435 0.004202 0.9889 0.992 0.005572 0.8712 0.8998 0.01427 ] Network output: [ -0.0009539 0.003915 1.003 -0.0001161 5.212e-05 0.9948 -8.749e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1971 0.09327 0.3259 0.1547 0.9851 0.994 0.1977 0.4601 0.8825 0.7162 ] Network output: [ 0.00768 -0.0376 0.9966 6.786e-05 -3.046e-05 1.026 5.114e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09769 0.08636 0.1789 0.2066 0.9873 0.992 0.09776 0.7837 0.8749 0.3082 ] Network output: [ -0.007702 0.0391 1.001 6.9e-05 -3.098e-05 0.9751 5.2e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09114 0.08923 0.1663 0.1966 0.9855 0.9914 0.09115 0.7119 0.8535 0.2432 ] Network output: [ 0.0002504 0.9996 -0.0005946 9.428e-06 -4.232e-06 1 7.105e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007436 Epoch 7154 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01258 0.9932 0.9878 3.09e-06 -1.387e-06 -0.006134 2.329e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00323 -0.003016 -0.009115 0.006984 0.9698 0.9742 0.006149 0.8418 0.8302 0.01968 ] Network output: [ 0.9997 0.001102 0.00159 -3.477e-05 1.561e-05 -0.002204 -2.621e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1863 -0.03043 -0.1922 0.1978 0.9836 0.9933 0.208 0.4554 0.8759 0.7215 ] Network output: [ -0.01155 1.001 1.01 1.539e-06 -6.909e-07 0.01206 1.16e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005472 0.0004726 0.004352 0.004203 0.9889 0.992 0.005573 0.8712 0.8998 0.01426 ] Network output: [ -0.0009209 0.003427 1.003 -0.000116 5.206e-05 0.9952 -8.74e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1971 0.09327 0.326 0.1548 0.9851 0.994 0.1977 0.4601 0.8824 0.7162 ] Network output: [ 0.007671 -0.03768 0.9966 6.781e-05 -3.044e-05 1.026 5.11e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0977 0.08637 0.1789 0.2066 0.9873 0.992 0.09776 0.7837 0.8749 0.3082 ] Network output: [ -0.007701 0.03912 1.001 6.895e-05 -3.095e-05 0.9751 5.196e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09114 0.08923 0.1663 0.1966 0.9855 0.9914 0.09115 0.7119 0.8534 0.2432 ] Network output: [ 0.0002707 0.9996 -0.0006229 9.427e-06 -4.232e-06 1 7.104e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000744 Epoch 7155 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01257 0.9932 0.9878 3.081e-06 -1.383e-06 -0.006181 2.322e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00323 -0.003016 -0.009114 0.006982 0.9698 0.9742 0.006149 0.8418 0.8302 0.01968 ] Network output: [ 0.9997 0.001458 0.00157 -3.477e-05 1.561e-05 -0.002494 -2.621e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1863 -0.03043 -0.1922 0.1977 0.9836 0.9933 0.208 0.4554 0.8759 0.7215 ] Network output: [ -0.01155 1.001 1.01 1.534e-06 -6.889e-07 0.01204 1.156e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005473 0.0004725 0.004351 0.004201 0.9889 0.992 0.005573 0.8712 0.8998 0.01426 ] Network output: [ -0.0009527 0.003906 1.003 -0.0001159 5.204e-05 0.9948 -8.736e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1971 0.09327 0.326 0.1547 0.9851 0.994 0.1977 0.4601 0.8824 0.7162 ] Network output: [ 0.007674 -0.03758 0.9966 6.776e-05 -3.042e-05 1.026 5.106e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0977 0.08637 0.1789 0.2065 0.9873 0.992 0.09776 0.7836 0.8749 0.3082 ] Network output: [ -0.007696 0.03907 1.001 6.89e-05 -3.093e-05 0.9751 5.193e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09113 0.08922 0.1663 0.1966 0.9855 0.9914 0.09115 0.7119 0.8534 0.2432 ] Network output: [ 0.0002504 0.9996 -0.0005939 9.415e-06 -4.227e-06 1 7.095e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007427 Epoch 7156 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01257 0.9932 0.9878 3.08e-06 -1.383e-06 -0.006139 2.321e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00323 -0.003016 -0.009112 0.006982 0.9698 0.9742 0.00615 0.8418 0.8302 0.01968 ] Network output: [ 0.9997 0.001105 0.001588 -3.473e-05 1.559e-05 -0.002205 -2.617e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1863 -0.03044 -0.1922 0.1978 0.9836 0.9933 0.208 0.4554 0.8759 0.7215 ] Network output: [ -0.01155 1.001 1.01 1.534e-06 -6.886e-07 0.01205 1.156e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005473 0.0004725 0.004352 0.004202 0.9889 0.992 0.005574 0.8712 0.8998 0.01426 ] Network output: [ -0.0009207 0.003432 1.003 -0.0001158 5.198e-05 0.9952 -8.726e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1971 0.09327 0.326 0.1547 0.9851 0.994 0.1977 0.4601 0.8824 0.7162 ] Network output: [ 0.007666 -0.03766 0.9966 6.771e-05 -3.04e-05 1.026 5.103e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09771 0.08637 0.1789 0.2066 0.9873 0.992 0.09777 0.7836 0.8749 0.3082 ] Network output: [ -0.007695 0.03908 1.001 6.885e-05 -3.091e-05 0.9751 5.189e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09113 0.08922 0.1663 0.1966 0.9855 0.9914 0.09114 0.7118 0.8534 0.2432 ] Network output: [ 0.0002701 0.9996 -0.0006213 9.414e-06 -4.226e-06 1 7.094e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007431 Epoch 7157 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01257 0.9933 0.9878 3.071e-06 -1.379e-06 -0.006185 2.315e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00323 -0.003016 -0.009111 0.00698 0.9698 0.9742 0.00615 0.8418 0.8302 0.01967 ] Network output: [ 0.9997 0.00145 0.001568 -3.473e-05 1.559e-05 -0.002487 -2.617e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1863 -0.03044 -0.1922 0.1977 0.9836 0.9933 0.208 0.4554 0.8759 0.7215 ] Network output: [ -0.01155 1.001 1.01 1.529e-06 -6.866e-07 0.01203 1.153e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005474 0.0004724 0.004351 0.004199 0.9889 0.992 0.005575 0.8712 0.8998 0.01426 ] Network output: [ -0.0009515 0.003897 1.003 -0.0001157 5.196e-05 0.9948 -8.723e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1971 0.09327 0.326 0.1546 0.9851 0.994 0.1977 0.46 0.8824 0.7162 ] Network output: [ 0.007669 -0.03755 0.9966 6.766e-05 -3.037e-05 1.026 5.099e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09771 0.08637 0.1789 0.2065 0.9873 0.992 0.09777 0.7836 0.8748 0.3082 ] Network output: [ -0.007689 0.03904 1.001 6.881e-05 -3.089e-05 0.9751 5.186e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09113 0.08922 0.1663 0.1966 0.9855 0.9914 0.09114 0.7118 0.8534 0.2432 ] Network output: [ 0.0002505 0.9996 -0.0005933 9.402e-06 -4.221e-06 1 7.085e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007418 Epoch 7158 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01257 0.9932 0.9878 3.07e-06 -1.378e-06 -0.006145 2.314e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00323 -0.003016 -0.009109 0.00698 0.9698 0.9742 0.00615 0.8418 0.8302 0.01967 ] Network output: [ 0.9997 0.001108 0.001586 -3.468e-05 1.557e-05 -0.002207 -2.614e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1864 -0.03045 -0.1921 0.1978 0.9836 0.9933 0.208 0.4554 0.8759 0.7215 ] Network output: [ -0.01154 1.001 1.01 1.529e-06 -6.863e-07 0.01204 1.152e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005475 0.0004723 0.004352 0.0042 0.9889 0.992 0.005575 0.8712 0.8998 0.01426 ] Network output: [ -0.0009204 0.003437 1.003 -0.0001156 5.19e-05 0.9952 -8.713e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1971 0.09327 0.326 0.1547 0.9851 0.994 0.1978 0.46 0.8824 0.7161 ] Network output: [ 0.00766 -0.03763 0.9966 6.761e-05 -3.035e-05 1.026 5.096e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09771 0.08638 0.1789 0.2065 0.9873 0.992 0.09778 0.7836 0.8748 0.3082 ] Network output: [ -0.007688 0.03905 1.001 6.876e-05 -3.087e-05 0.9751 5.182e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09112 0.08921 0.1663 0.1966 0.9855 0.9914 0.09114 0.7118 0.8534 0.2432 ] Network output: [ 0.0002695 0.9996 -0.0006198 9.4e-06 -4.22e-06 1 7.084e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007422 Epoch 7159 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01256 0.9933 0.9878 3.061e-06 -1.374e-06 -0.006189 2.307e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00323 -0.003016 -0.009108 0.006978 0.9698 0.9742 0.006151 0.8418 0.8302 0.01967 ] Network output: [ 0.9997 0.001442 0.001567 -3.468e-05 1.557e-05 -0.00248 -2.614e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1864 -0.03045 -0.1921 0.1977 0.9836 0.9933 0.2081 0.4554 0.8759 0.7215 ] Network output: [ -0.01154 1.001 1.01 1.524e-06 -6.843e-07 0.01202 1.149e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005475 0.0004722 0.004351 0.004198 0.9889 0.992 0.005576 0.8711 0.8998 0.01426 ] Network output: [ -0.0009503 0.003888 1.003 -0.0001156 5.188e-05 0.9948 -8.709e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1971 0.09327 0.326 0.1546 0.9851 0.994 0.1978 0.46 0.8824 0.7161 ] Network output: [ 0.007663 -0.03753 0.9966 6.756e-05 -3.033e-05 1.026 5.092e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09772 0.08638 0.1789 0.2065 0.9873 0.992 0.09778 0.7835 0.8748 0.3082 ] Network output: [ -0.007682 0.039 1.002 6.871e-05 -3.085e-05 0.9751 5.179e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09112 0.08921 0.1663 0.1966 0.9855 0.9914 0.09113 0.7117 0.8534 0.2432 ] Network output: [ 0.0002505 0.9996 -0.0005926 9.389e-06 -4.215e-06 1 7.076e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007409 Epoch 7160 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01256 0.9932 0.9878 3.061e-06 -1.374e-06 -0.00615 2.306e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00323 -0.003016 -0.009106 0.006978 0.9698 0.9742 0.006151 0.8418 0.8302 0.01967 ] Network output: [ 0.9997 0.001111 0.001584 -3.464e-05 1.555e-05 -0.002208 -2.611e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1864 -0.03046 -0.1921 0.1977 0.9836 0.9933 0.2081 0.4553 0.8759 0.7215 ] Network output: [ -0.01154 1.001 1.01 1.524e-06 -6.84e-07 0.01203 1.148e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005476 0.0004721 0.004353 0.004198 0.9889 0.992 0.005576 0.8711 0.8998 0.01426 ] Network output: [ -0.0009202 0.003442 1.003 -0.0001154 5.182e-05 0.9952 -8.7e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1971 0.09327 0.3261 0.1547 0.9851 0.994 0.1978 0.46 0.8824 0.7161 ] Network output: [ 0.007655 -0.0376 0.9966 6.752e-05 -3.031e-05 1.026 5.088e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09772 0.08638 0.1789 0.2065 0.9873 0.992 0.09778 0.7835 0.8748 0.3082 ] Network output: [ -0.007681 0.03902 1.002 6.867e-05 -3.083e-05 0.9751 5.175e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09112 0.08921 0.1663 0.1966 0.9855 0.9914 0.09113 0.7117 0.8534 0.2432 ] Network output: [ 0.000269 0.9996 -0.0006183 9.387e-06 -4.214e-06 1 7.075e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007412 Epoch 7161 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01256 0.9933 0.9878 3.052e-06 -1.37e-06 -0.006193 2.3e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00323 -0.003017 -0.009105 0.006976 0.9698 0.9742 0.006151 0.8418 0.8301 0.01967 ] Network output: [ 0.9997 0.001434 0.001565 -3.464e-05 1.555e-05 -0.002473 -2.611e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1864 -0.03047 -0.1921 0.1977 0.9836 0.9933 0.2081 0.4553 0.8759 0.7215 ] Network output: [ -0.01154 1.001 1.01 1.519e-06 -6.821e-07 0.01201 1.145e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005476 0.000472 0.004352 0.004196 0.9889 0.992 0.005577 0.8711 0.8998 0.01425 ] Network output: [ -0.0009491 0.003879 1.003 -0.0001154 5.18e-05 0.9948 -8.696e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1972 0.09327 0.3261 0.1546 0.9851 0.994 0.1978 0.46 0.8824 0.7161 ] Network output: [ 0.007657 -0.03751 0.9966 6.746e-05 -3.029e-05 1.026 5.084e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09772 0.08638 0.1789 0.2065 0.9873 0.992 0.09779 0.7835 0.8748 0.3082 ] Network output: [ -0.007676 0.03897 1.002 6.862e-05 -3.081e-05 0.9752 5.171e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09111 0.0892 0.1663 0.1966 0.9855 0.9914 0.09112 0.7117 0.8533 0.2432 ] Network output: [ 0.0002505 0.9996 -0.0005919 9.376e-06 -4.209e-06 1 7.066e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00074 Epoch 7162 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01256 0.9932 0.9878 3.051e-06 -1.37e-06 -0.006156 2.299e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003231 -0.003017 -0.009103 0.006976 0.9698 0.9742 0.006151 0.8418 0.8301 0.01966 ] Network output: [ 0.9997 0.001113 0.001581 -3.46e-05 1.553e-05 -0.00221 -2.607e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1864 -0.03048 -0.192 0.1977 0.9836 0.9933 0.2081 0.4553 0.8759 0.7215 ] Network output: [ -0.01154 1.001 1.01 1.518e-06 -6.817e-07 0.01202 1.144e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005477 0.000472 0.004353 0.004197 0.9889 0.992 0.005577 0.8711 0.8998 0.01425 ] Network output: [ -0.0009199 0.003447 1.003 -0.0001153 5.175e-05 0.9952 -8.687e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1972 0.09327 0.3261 0.1546 0.9851 0.994 0.1978 0.46 0.8824 0.7161 ] Network output: [ 0.00765 -0.03758 0.9966 6.742e-05 -3.027e-05 1.026 5.081e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09773 0.08639 0.1789 0.2065 0.9873 0.992 0.09779 0.7835 0.8748 0.3082 ] Network output: [ -0.007675 0.03898 1.002 6.857e-05 -3.078e-05 0.9751 5.168e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09111 0.0892 0.1663 0.1966 0.9855 0.9914 0.09112 0.7116 0.8533 0.2432 ] Network output: [ 0.0002684 0.9997 -0.0006168 9.374e-06 -4.208e-06 1 7.065e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007403 Epoch 7163 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01255 0.9933 0.9878 3.042e-06 -1.366e-06 -0.006197 2.293e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003231 -0.003017 -0.009102 0.006974 0.9698 0.9742 0.006152 0.8417 0.8301 0.01966 ] Network output: [ 0.9997 0.001427 0.001564 -3.46e-05 1.553e-05 -0.002466 -2.607e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1864 -0.03048 -0.192 0.1977 0.9836 0.9933 0.2081 0.4553 0.8759 0.7215 ] Network output: [ -0.01154 1.001 1.01 1.514e-06 -6.798e-07 0.01201 1.141e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005478 0.0004719 0.004352 0.004194 0.9889 0.992 0.005578 0.8711 0.8998 0.01425 ] Network output: [ -0.0009479 0.00387 1.003 -0.0001152 5.172e-05 0.9948 -8.683e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1972 0.09327 0.3261 0.1546 0.9851 0.994 0.1978 0.4599 0.8824 0.7161 ] Network output: [ 0.007652 -0.03748 0.9966 6.736e-05 -3.024e-05 1.026 5.077e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09773 0.08639 0.1789 0.2065 0.9873 0.992 0.09779 0.7834 0.8748 0.3082 ] Network output: [ -0.007669 0.03894 1.002 6.853e-05 -3.076e-05 0.9752 5.164e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09111 0.08919 0.1663 0.1966 0.9855 0.9914 0.09112 0.7116 0.8533 0.2432 ] Network output: [ 0.0002505 0.9996 -0.0005912 9.363e-06 -4.203e-06 1 7.056e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007391 Epoch 7164 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01255 0.9932 0.9878 3.041e-06 -1.365e-06 -0.006161 2.292e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003231 -0.003017 -0.0091 0.006974 0.9698 0.9742 0.006152 0.8417 0.8301 0.01966 ] Network output: [ 0.9997 0.001116 0.001579 -3.455e-05 1.551e-05 -0.002211 -2.604e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1864 -0.03049 -0.192 0.1977 0.9836 0.9933 0.2081 0.4553 0.8759 0.7215 ] Network output: [ -0.01154 1.001 1.01 1.513e-06 -6.794e-07 0.01202 1.14e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005478 0.0004718 0.004353 0.004195 0.9889 0.992 0.005579 0.8711 0.8998 0.01425 ] Network output: [ -0.0009196 0.003452 1.003 -0.0001151 5.167e-05 0.9952 -8.673e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1972 0.09327 0.3261 0.1546 0.9851 0.994 0.1978 0.4599 0.8824 0.7161 ] Network output: [ 0.007644 -0.03755 0.9966 6.732e-05 -3.022e-05 1.026 5.073e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09774 0.08639 0.1789 0.2065 0.9873 0.992 0.0978 0.7834 0.8748 0.3082 ] Network output: [ -0.007668 0.03895 1.002 6.848e-05 -3.074e-05 0.9752 5.161e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0911 0.08919 0.1663 0.1966 0.9855 0.9914 0.09112 0.7116 0.8533 0.2432 ] Network output: [ 0.0002678 0.9997 -0.0006153 9.361e-06 -4.202e-06 1 7.055e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007394 Epoch 7165 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01255 0.9933 0.9878 3.032e-06 -1.361e-06 -0.006201 2.285e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003231 -0.003017 -0.009099 0.006972 0.9698 0.9742 0.006152 0.8417 0.8301 0.01966 ] Network output: [ 0.9997 0.00142 0.001562 -3.455e-05 1.551e-05 -0.002459 -2.604e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1864 -0.03049 -0.192 0.1976 0.9836 0.9933 0.2081 0.4553 0.8759 0.7215 ] Network output: [ -0.01154 1.001 1.01 1.509e-06 -6.775e-07 0.012 1.137e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005479 0.0004717 0.004353 0.004193 0.9889 0.992 0.005579 0.8711 0.8998 0.01425 ] Network output: [ -0.0009467 0.003861 1.003 -0.000115 5.164e-05 0.9948 -8.669e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1972 0.09327 0.3261 0.1545 0.9851 0.994 0.1978 0.4599 0.8824 0.7161 ] Network output: [ 0.007646 -0.03746 0.9966 6.726e-05 -3.02e-05 1.026 5.069e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09774 0.08639 0.1789 0.2065 0.9873 0.992 0.0978 0.7834 0.8748 0.3082 ] Network output: [ -0.007663 0.03891 1.002 6.843e-05 -3.072e-05 0.9752 5.157e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0911 0.08919 0.1663 0.1965 0.9855 0.9914 0.09111 0.7115 0.8533 0.2432 ] Network output: [ 0.0002504 0.9996 -0.0005905 9.35e-06 -4.197e-06 1 7.046e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007382 Epoch 7166 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01255 0.9932 0.9878 3.031e-06 -1.361e-06 -0.006166 2.284e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003231 -0.003017 -0.009097 0.006972 0.9698 0.9742 0.006153 0.8417 0.8301 0.01966 ] Network output: [ 0.9997 0.001118 0.001577 -3.451e-05 1.549e-05 -0.002212 -2.601e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1864 -0.0305 -0.192 0.1977 0.9836 0.9933 0.2081 0.4552 0.8759 0.7215 ] Network output: [ -0.01153 1.001 1.01 1.508e-06 -6.771e-07 0.01201 1.137e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005479 0.0004717 0.004354 0.004194 0.9889 0.992 0.00558 0.8711 0.8998 0.01425 ] Network output: [ -0.0009193 0.003456 1.003 -0.0001149 5.159e-05 0.9952 -8.66e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1972 0.09327 0.3262 0.1546 0.9851 0.994 0.1978 0.4599 0.8824 0.7161 ] Network output: [ 0.007639 -0.03753 0.9966 6.722e-05 -3.018e-05 1.026 5.066e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09774 0.0864 0.1789 0.2065 0.9873 0.992 0.09781 0.7833 0.8747 0.3082 ] Network output: [ -0.007661 0.03892 1.002 6.838e-05 -3.07e-05 0.9752 5.154e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0911 0.08918 0.1663 0.1965 0.9855 0.9914 0.09111 0.7115 0.8533 0.2432 ] Network output: [ 0.0002673 0.9997 -0.0006139 9.348e-06 -4.197e-06 1 7.045e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007384 Epoch 7167 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01254 0.9933 0.9878 3.023e-06 -1.357e-06 -0.006205 2.278e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003231 -0.003018 -0.009096 0.00697 0.9698 0.9742 0.006153 0.8417 0.8301 0.01965 ] Network output: [ 0.9997 0.001412 0.00156 -3.451e-05 1.549e-05 -0.002453 -2.6e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1865 -0.0305 -0.192 0.1976 0.9836 0.9933 0.2082 0.4552 0.8759 0.7215 ] Network output: [ -0.01153 1.001 1.01 1.504e-06 -6.752e-07 0.01199 1.134e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00548 0.0004716 0.004353 0.004191 0.9889 0.992 0.00558 0.8711 0.8997 0.01424 ] Network output: [ -0.0009456 0.003853 1.003 -0.0001149 5.156e-05 0.9948 -8.656e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1972 0.09327 0.3262 0.1545 0.9851 0.994 0.1979 0.4599 0.8824 0.7161 ] Network output: [ 0.007641 -0.03744 0.9966 6.717e-05 -3.015e-05 1.026 5.062e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09775 0.0864 0.1789 0.2064 0.9873 0.992 0.09781 0.7833 0.8747 0.3082 ] Network output: [ -0.007656 0.03888 1.002 6.834e-05 -3.068e-05 0.9752 5.15e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09109 0.08918 0.1663 0.1965 0.9855 0.9914 0.0911 0.7115 0.8533 0.2432 ] Network output: [ 0.0002504 0.9996 -0.0005898 9.337e-06 -4.192e-06 1 7.036e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007373 Epoch 7168 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01254 0.9932 0.9879 3.021e-06 -1.356e-06 -0.006171 2.277e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003231 -0.003018 -0.009094 0.00697 0.9698 0.9742 0.006153 0.8417 0.8301 0.01965 ] Network output: [ 0.9997 0.00112 0.001575 -3.447e-05 1.547e-05 -0.002213 -2.597e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1865 -0.03051 -0.1919 0.1977 0.9836 0.9933 0.2082 0.4552 0.8759 0.7215 ] Network output: [ -0.01153 1.001 1.01 1.503e-06 -6.748e-07 0.012 1.133e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00548 0.0004715 0.004354 0.004192 0.9889 0.992 0.005581 0.8711 0.8997 0.01424 ] Network output: [ -0.000919 0.00346 1.003 -0.0001147 5.151e-05 0.9952 -8.647e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1972 0.09327 0.3262 0.1546 0.9851 0.994 0.1979 0.4599 0.8824 0.7161 ] Network output: [ 0.007633 -0.0375 0.9966 6.712e-05 -3.013e-05 1.026 5.058e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09775 0.0864 0.1789 0.2064 0.9873 0.992 0.09781 0.7833 0.8747 0.3082 ] Network output: [ -0.007655 0.03888 1.002 6.829e-05 -3.066e-05 0.9752 5.147e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09109 0.08918 0.1663 0.1965 0.9855 0.9914 0.0911 0.7114 0.8532 0.2432 ] Network output: [ 0.0002667 0.9997 -0.0006124 9.335e-06 -4.191e-06 1 7.035e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007375 Epoch 7169 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01254 0.9933 0.9879 3.013e-06 -1.353e-06 -0.006209 2.271e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003231 -0.003018 -0.009093 0.006968 0.9698 0.9742 0.006153 0.8417 0.8301 0.01965 ] Network output: [ 0.9997 0.001405 0.001559 -3.446e-05 1.547e-05 -0.002446 -2.597e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1865 -0.03051 -0.1919 0.1976 0.9836 0.9933 0.2082 0.4552 0.8759 0.7214 ] Network output: [ -0.01153 1.001 1.01 1.499e-06 -6.73e-07 0.01198 1.13e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005481 0.0004714 0.004353 0.00419 0.9889 0.992 0.005582 0.871 0.8997 0.01424 ] Network output: [ -0.0009444 0.003845 1.003 -0.0001147 5.148e-05 0.9948 -8.643e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1973 0.09327 0.3262 0.1545 0.9851 0.994 0.1979 0.4598 0.8824 0.7161 ] Network output: [ 0.007635 -0.03741 0.9966 6.707e-05 -3.011e-05 1.026 5.054e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09775 0.0864 0.1789 0.2064 0.9873 0.992 0.09782 0.7833 0.8747 0.3081 ] Network output: [ -0.00765 0.03884 1.002 6.824e-05 -3.064e-05 0.9752 5.143e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09109 0.08917 0.1662 0.1965 0.9855 0.9914 0.0911 0.7114 0.8532 0.2432 ] Network output: [ 0.0002504 0.9996 -0.000589 9.324e-06 -4.186e-06 1 7.026e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007364 Epoch 7170 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01254 0.9933 0.9879 3.011e-06 -1.352e-06 -0.006177 2.269e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003232 -0.003018 -0.009091 0.006968 0.9698 0.9742 0.006154 0.8417 0.8301 0.01965 ] Network output: [ 0.9997 0.001123 0.001573 -3.442e-05 1.545e-05 -0.002214 -2.594e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1865 -0.03052 -0.1919 0.1976 0.9836 0.9933 0.2082 0.4552 0.8759 0.7214 ] Network output: [ -0.01153 1.001 1.01 1.498e-06 -6.725e-07 0.01199 1.129e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005481 0.0004714 0.004354 0.004191 0.9889 0.992 0.005582 0.871 0.8997 0.01424 ] Network output: [ -0.0009186 0.003465 1.003 -0.0001146 5.143e-05 0.9952 -8.634e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1973 0.09327 0.3262 0.1545 0.9851 0.994 0.1979 0.4598 0.8824 0.7161 ] Network output: [ 0.007628 -0.03747 0.9966 6.702e-05 -3.009e-05 1.026 5.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09776 0.08641 0.1789 0.2064 0.9873 0.992 0.09782 0.7832 0.8747 0.3082 ] Network output: [ -0.007648 0.03885 1.002 6.82e-05 -3.062e-05 0.9752 5.139e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09108 0.08917 0.1662 0.1965 0.9855 0.9914 0.09109 0.7114 0.8532 0.2432 ] Network output: [ 0.0002662 0.9997 -0.0006109 9.321e-06 -4.185e-06 1 7.025e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007366 Epoch 7171 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01253 0.9933 0.9879 3.003e-06 -1.348e-06 -0.006213 2.263e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003232 -0.003018 -0.00909 0.006966 0.9698 0.9742 0.006154 0.8417 0.8301 0.01965 ] Network output: [ 0.9997 0.001398 0.001557 -3.442e-05 1.545e-05 -0.00244 -2.594e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1865 -0.03052 -0.1919 0.1976 0.9836 0.9933 0.2082 0.4552 0.8758 0.7214 ] Network output: [ -0.01153 1.001 1.01 1.494e-06 -6.707e-07 0.01197 1.126e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005482 0.0004713 0.004354 0.004188 0.9889 0.992 0.005583 0.871 0.8997 0.01424 ] Network output: [ -0.0009432 0.003837 1.003 -0.0001145 5.141e-05 0.9948 -8.629e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1973 0.09327 0.3262 0.1544 0.9851 0.994 0.1979 0.4598 0.8823 0.7161 ] Network output: [ 0.00763 -0.03739 0.9966 6.697e-05 -3.007e-05 1.026 5.047e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09776 0.08641 0.1789 0.2064 0.9873 0.992 0.09782 0.7832 0.8747 0.3081 ] Network output: [ -0.007643 0.03881 1.002 6.815e-05 -3.06e-05 0.9752 5.136e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09108 0.08917 0.1662 0.1965 0.9855 0.9914 0.09109 0.7113 0.8532 0.2432 ] Network output: [ 0.0002504 0.9996 -0.0005883 9.31e-06 -4.18e-06 1 7.017e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007354 Epoch 7172 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01253 0.9933 0.9879 3.002e-06 -1.348e-06 -0.006182 2.262e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003232 -0.003018 -0.009088 0.006965 0.9698 0.9742 0.006154 0.8417 0.8301 0.01964 ] Network output: [ 0.9997 0.001125 0.001571 -3.438e-05 1.543e-05 -0.002215 -2.591e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1865 -0.03053 -0.1918 0.1976 0.9836 0.9933 0.2082 0.4551 0.8758 0.7214 ] Network output: [ -0.01153 1.001 1.01 1.493e-06 -6.702e-07 0.01198 1.125e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005483 0.0004712 0.004355 0.004189 0.9889 0.992 0.005583 0.871 0.8997 0.01424 ] Network output: [ -0.0009183 0.003468 1.003 -0.0001144 5.135e-05 0.9952 -8.621e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1973 0.09327 0.3263 0.1545 0.9851 0.994 0.1979 0.4598 0.8823 0.7161 ] Network output: [ 0.007623 -0.03745 0.9966 6.692e-05 -3.004e-05 1.026 5.044e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09777 0.08641 0.1789 0.2064 0.9873 0.992 0.09783 0.7832 0.8747 0.3081 ] Network output: [ -0.007641 0.03882 1.002 6.81e-05 -3.057e-05 0.9752 5.132e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09108 0.08916 0.1662 0.1965 0.9855 0.9914 0.09109 0.7113 0.8532 0.2432 ] Network output: [ 0.0002657 0.9997 -0.0006095 9.308e-06 -4.179e-06 1 7.015e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007356 Epoch 7173 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01253 0.9933 0.9879 2.994e-06 -1.344e-06 -0.006217 2.256e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003232 -0.003019 -0.009086 0.006964 0.9698 0.9742 0.006155 0.8417 0.8301 0.01964 ] Network output: [ 0.9997 0.001392 0.001555 -3.437e-05 1.543e-05 -0.002433 -2.59e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1865 -0.03053 -0.1918 0.1976 0.9836 0.9933 0.2082 0.4551 0.8758 0.7214 ] Network output: [ -0.01153 1.001 1.01 1.489e-06 -6.684e-07 0.01197 1.122e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005483 0.0004711 0.004354 0.004187 0.9889 0.992 0.005584 0.871 0.8997 0.01423 ] Network output: [ -0.0009421 0.003829 1.003 -0.0001143 5.133e-05 0.9949 -8.616e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1973 0.09327 0.3263 0.1544 0.9851 0.994 0.1979 0.4598 0.8823 0.7161 ] Network output: [ 0.007624 -0.03737 0.9966 6.687e-05 -3.002e-05 1.026 5.04e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09777 0.08642 0.1789 0.2064 0.9873 0.992 0.09783 0.7831 0.8747 0.3081 ] Network output: [ -0.007636 0.03878 1.002 6.806e-05 -3.055e-05 0.9752 5.129e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09107 0.08916 0.1662 0.1965 0.9855 0.9914 0.09108 0.7113 0.8532 0.2432 ] Network output: [ 0.0002503 0.9997 -0.0005875 9.297e-06 -4.174e-06 1 7.007e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007345 Epoch 7174 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01253 0.9933 0.9879 2.992e-06 -1.343e-06 -0.006187 2.255e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003232 -0.003019 -0.009085 0.006963 0.9698 0.9742 0.006155 0.8416 0.8301 0.01964 ] Network output: [ 0.9997 0.001126 0.001569 -3.433e-05 1.541e-05 -0.002216 -2.587e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1865 -0.03054 -0.1918 0.1976 0.9836 0.9933 0.2082 0.4551 0.8758 0.7214 ] Network output: [ -0.01152 1.001 1.01 1.488e-06 -6.679e-07 0.01197 1.121e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005484 0.0004711 0.004355 0.004187 0.9889 0.992 0.005585 0.871 0.8997 0.01423 ] Network output: [ -0.0009179 0.003472 1.003 -0.0001142 5.127e-05 0.9952 -8.607e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1973 0.09327 0.3263 0.1545 0.9851 0.994 0.1979 0.4597 0.8823 0.7161 ] Network output: [ 0.007617 -0.03742 0.9966 6.683e-05 -3e-05 1.026 5.036e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09777 0.08642 0.1789 0.2064 0.9873 0.992 0.09784 0.7831 0.8747 0.3081 ] Network output: [ -0.007635 0.03879 1.002 6.801e-05 -3.053e-05 0.9752 5.125e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09107 0.08916 0.1662 0.1965 0.9855 0.9914 0.09108 0.7112 0.8532 0.2432 ] Network output: [ 0.0002651 0.9997 -0.0006081 9.295e-06 -4.173e-06 1 7.005e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007347 Epoch 7175 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01252 0.9933 0.9879 2.984e-06 -1.34e-06 -0.006222 2.249e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003232 -0.003019 -0.009083 0.006962 0.9698 0.9742 0.006155 0.8416 0.8301 0.01964 ] Network output: [ 0.9997 0.001385 0.001554 -3.433e-05 1.541e-05 -0.002427 -2.587e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1865 -0.03054 -0.1918 0.1975 0.9836 0.9933 0.2083 0.4551 0.8758 0.7214 ] Network output: [ -0.01152 1.001 1.01 1.484e-06 -6.661e-07 0.01196 1.118e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005484 0.000471 0.004354 0.004185 0.9889 0.992 0.005585 0.871 0.8997 0.01423 ] Network output: [ -0.000941 0.003821 1.003 -0.0001142 5.125e-05 0.9949 -8.603e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1973 0.09327 0.3263 0.1544 0.9851 0.994 0.198 0.4597 0.8823 0.7161 ] Network output: [ 0.007619 -0.03734 0.9966 6.677e-05 -2.998e-05 1.026 5.032e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09778 0.08642 0.1789 0.2064 0.9873 0.992 0.09784 0.7831 0.8747 0.3081 ] Network output: [ -0.00763 0.03875 1.002 6.796e-05 -3.051e-05 0.9753 5.122e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09107 0.08915 0.1662 0.1965 0.9855 0.9914 0.09108 0.7112 0.8532 0.2433 ] Network output: [ 0.0002503 0.9997 -0.0005868 9.284e-06 -4.168e-06 1 6.997e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007336 Epoch 7176 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01252 0.9933 0.9879 2.982e-06 -1.339e-06 -0.006192 2.247e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003232 -0.003019 -0.009082 0.006961 0.9698 0.9742 0.006155 0.8416 0.8301 0.01964 ] Network output: [ 0.9997 0.001128 0.001567 -3.429e-05 1.539e-05 -0.002216 -2.584e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1865 -0.03055 -0.1918 0.1976 0.9836 0.9933 0.2083 0.4551 0.8758 0.7214 ] Network output: [ -0.01152 1.001 1.01 1.483e-06 -6.656e-07 0.01196 1.117e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005485 0.0004709 0.004355 0.004186 0.9889 0.992 0.005586 0.871 0.8997 0.01423 ] Network output: [ -0.0009175 0.003476 1.003 -0.000114 5.119e-05 0.9952 -8.594e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1973 0.09327 0.3263 0.1544 0.9851 0.994 0.198 0.4597 0.8823 0.7161 ] Network output: [ 0.007612 -0.0374 0.9966 6.673e-05 -2.996e-05 1.026 5.029e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09778 0.08642 0.1789 0.2064 0.9873 0.992 0.09784 0.7831 0.8746 0.3081 ] Network output: [ -0.007628 0.03875 1.002 6.791e-05 -3.049e-05 0.9753 5.118e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09106 0.08915 0.1662 0.1965 0.9855 0.9914 0.09107 0.7112 0.8531 0.2433 ] Network output: [ 0.0002646 0.9997 -0.0006066 9.282e-06 -4.167e-06 1 6.995e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007338 Epoch 7177 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01252 0.9933 0.9879 2.974e-06 -1.335e-06 -0.006226 2.242e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003232 -0.003019 -0.00908 0.00696 0.9698 0.9742 0.006156 0.8416 0.83 0.01963 ] Network output: [ 0.9997 0.001378 0.001552 -3.428e-05 1.539e-05 -0.002421 -2.584e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1866 -0.03055 -0.1918 0.1975 0.9836 0.9933 0.2083 0.455 0.8758 0.7214 ] Network output: [ -0.01152 1.001 1.01 1.479e-06 -6.638e-07 0.01195 1.114e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005486 0.0004708 0.004355 0.004184 0.9889 0.992 0.005586 0.871 0.8997 0.01423 ] Network output: [ -0.0009399 0.003813 1.003 -0.000114 5.117e-05 0.9949 -8.59e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1974 0.09328 0.3263 0.1544 0.9851 0.994 0.198 0.4597 0.8823 0.7161 ] Network output: [ 0.007613 -0.03732 0.9966 6.667e-05 -2.993e-05 1.026 5.025e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09778 0.08643 0.1789 0.2063 0.9873 0.992 0.09785 0.783 0.8746 0.3081 ] Network output: [ -0.007623 0.03871 1.002 6.787e-05 -3.047e-05 0.9753 5.115e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09106 0.08915 0.1662 0.1965 0.9855 0.9914 0.09107 0.7111 0.8531 0.2433 ] Network output: [ 0.0002502 0.9997 -0.000586 9.271e-06 -4.162e-06 1 6.987e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007327 Epoch 7178 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01252 0.9933 0.9879 2.972e-06 -1.334e-06 -0.006198 2.24e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003233 -0.003019 -0.009079 0.006959 0.9698 0.9742 0.006156 0.8416 0.83 0.01963 ] Network output: [ 0.9997 0.00113 0.001565 -3.425e-05 1.537e-05 -0.002217 -2.581e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1866 -0.03056 -0.1917 0.1976 0.9836 0.9933 0.2083 0.455 0.8758 0.7214 ] Network output: [ -0.01152 1.001 1.01 1.477e-06 -6.633e-07 0.01195 1.113e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005486 0.0004708 0.004356 0.004184 0.9889 0.992 0.005587 0.871 0.8997 0.01423 ] Network output: [ -0.0009171 0.003479 1.003 -0.0001139 5.112e-05 0.9952 -8.581e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1974 0.09328 0.3264 0.1544 0.9851 0.994 0.198 0.4597 0.8823 0.7161 ] Network output: [ 0.007606 -0.03737 0.9966 6.663e-05 -2.991e-05 1.026 5.021e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09779 0.08643 0.179 0.2064 0.9873 0.992 0.09785 0.783 0.8746 0.3081 ] Network output: [ -0.007622 0.03872 1.002 6.782e-05 -3.045e-05 0.9753 5.111e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09106 0.08914 0.1662 0.1965 0.9855 0.9914 0.09107 0.7111 0.8531 0.2433 ] Network output: [ 0.0002641 0.9997 -0.0006052 9.269e-06 -4.161e-06 1 6.985e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007329 Epoch 7179 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01251 0.9933 0.9879 2.965e-06 -1.331e-06 -0.00623 2.234e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003233 -0.00302 -0.009077 0.006958 0.9698 0.9742 0.006156 0.8416 0.83 0.01963 ] Network output: [ 0.9997 0.001372 0.001551 -3.424e-05 1.537e-05 -0.002415 -2.58e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1866 -0.03056 -0.1917 0.1975 0.9836 0.9933 0.2083 0.455 0.8758 0.7214 ] Network output: [ -0.01152 1.001 1.01 1.474e-06 -6.616e-07 0.01194 1.111e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005487 0.0004707 0.004355 0.004182 0.9889 0.992 0.005588 0.8709 0.8997 0.01422 ] Network output: [ -0.0009387 0.003806 1.003 -0.0001138 5.109e-05 0.9949 -8.576e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1974 0.09328 0.3264 0.1543 0.9851 0.994 0.198 0.4597 0.8823 0.7161 ] Network output: [ 0.007608 -0.0373 0.9965 6.658e-05 -2.989e-05 1.026 5.017e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09779 0.08643 0.1789 0.2063 0.9873 0.992 0.09785 0.783 0.8746 0.3081 ] Network output: [ -0.007617 0.03868 1.002 6.778e-05 -3.043e-05 0.9753 5.108e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09105 0.08914 0.1662 0.1965 0.9855 0.9914 0.09106 0.7111 0.8531 0.2433 ] Network output: [ 0.0002502 0.9997 -0.0005852 9.258e-06 -4.156e-06 1 6.977e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007318 Epoch 7180 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01251 0.9933 0.9879 2.963e-06 -1.33e-06 -0.006203 2.233e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003233 -0.00302 -0.009076 0.006957 0.9698 0.9742 0.006157 0.8416 0.83 0.01963 ] Network output: [ 0.9997 0.001131 0.001562 -3.42e-05 1.535e-05 -0.002217 -2.577e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1866 -0.03057 -0.1917 0.1975 0.9836 0.9933 0.2083 0.455 0.8758 0.7214 ] Network output: [ -0.01152 1.001 1.01 1.472e-06 -6.61e-07 0.01195 1.11e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005487 0.0004706 0.004356 0.004183 0.9889 0.992 0.005588 0.8709 0.8997 0.01422 ] Network output: [ -0.0009167 0.003482 1.003 -0.0001137 5.104e-05 0.9952 -8.568e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1974 0.09328 0.3264 0.1544 0.9851 0.994 0.198 0.4596 0.8823 0.716 ] Network output: [ 0.007601 -0.03735 0.9966 6.653e-05 -2.987e-05 1.026 5.014e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0978 0.08644 0.179 0.2063 0.9873 0.992 0.09786 0.7829 0.8746 0.3081 ] Network output: [ -0.007615 0.03869 1.002 6.773e-05 -3.041e-05 0.9753 5.104e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09105 0.08914 0.1662 0.1965 0.9855 0.9914 0.09106 0.711 0.8531 0.2433 ] Network output: [ 0.0002636 0.9997 -0.0006038 9.256e-06 -4.155e-06 1 6.975e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007319 Epoch 7181 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01251 0.9933 0.9879 2.955e-06 -1.327e-06 -0.006234 2.227e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003233 -0.00302 -0.009074 0.006956 0.9698 0.9742 0.006157 0.8416 0.83 0.01962 ] Network output: [ 0.9997 0.001366 0.001549 -3.419e-05 1.535e-05 -0.002409 -2.577e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1866 -0.03057 -0.1917 0.1975 0.9836 0.9933 0.2083 0.455 0.8758 0.7214 ] Network output: [ -0.01152 1.001 1.01 1.469e-06 -6.593e-07 0.01193 1.107e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005488 0.0004705 0.004355 0.004181 0.9889 0.992 0.005589 0.8709 0.8997 0.01422 ] Network output: [ -0.0009376 0.003799 1.003 -0.0001136 5.101e-05 0.9949 -8.563e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1974 0.09328 0.3264 0.1543 0.9851 0.994 0.198 0.4596 0.8823 0.716 ] Network output: [ 0.007602 -0.03727 0.9965 6.648e-05 -2.984e-05 1.026 5.01e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0978 0.08644 0.1789 0.2063 0.9873 0.992 0.09786 0.7829 0.8746 0.3081 ] Network output: [ -0.00761 0.03865 1.002 6.768e-05 -3.039e-05 0.9753 5.101e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09105 0.08913 0.1662 0.1965 0.9855 0.9914 0.09106 0.711 0.8531 0.2433 ] Network output: [ 0.0002501 0.9997 -0.0005844 9.245e-06 -4.151e-06 1 6.968e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007309 Epoch 7182 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01251 0.9933 0.9879 2.953e-06 -1.326e-06 -0.006208 2.225e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003233 -0.00302 -0.009073 0.006955 0.9698 0.9742 0.006157 0.8416 0.83 0.01962 ] Network output: [ 0.9997 0.001133 0.00156 -3.416e-05 1.533e-05 -0.002218 -2.574e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1866 -0.03058 -0.1917 0.1975 0.9836 0.9933 0.2083 0.455 0.8758 0.7214 ] Network output: [ -0.01151 1.001 1.01 1.467e-06 -6.587e-07 0.01194 1.106e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005488 0.0004705 0.004356 0.004181 0.9889 0.992 0.005589 0.8709 0.8997 0.01422 ] Network output: [ -0.0009163 0.003486 1.003 -0.0001135 5.096e-05 0.9952 -8.555e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1974 0.09328 0.3265 0.1544 0.9851 0.994 0.198 0.4596 0.8823 0.716 ] Network output: [ 0.007596 -0.03732 0.9965 6.643e-05 -2.982e-05 1.026 5.007e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0978 0.08644 0.179 0.2063 0.9873 0.992 0.09787 0.7829 0.8746 0.3081 ] Network output: [ -0.007609 0.03865 1.002 6.763e-05 -3.036e-05 0.9753 5.097e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09104 0.08913 0.1662 0.1965 0.9855 0.9914 0.09105 0.711 0.8531 0.2433 ] Network output: [ 0.0002631 0.9997 -0.0006024 9.242e-06 -4.149e-06 1 6.965e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000731 Epoch 7183 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0125 0.9933 0.9879 2.946e-06 -1.322e-06 -0.006238 2.22e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003233 -0.00302 -0.009071 0.006953 0.9698 0.9742 0.006158 0.8416 0.83 0.01962 ] Network output: [ 0.9997 0.001359 0.001547 -3.415e-05 1.533e-05 -0.002403 -2.574e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1866 -0.03058 -0.1917 0.1975 0.9836 0.9933 0.2084 0.4549 0.8758 0.7214 ] Network output: [ -0.01151 1.001 1.01 1.463e-06 -6.57e-07 0.01192 1.103e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005489 0.0004704 0.004356 0.004179 0.9889 0.992 0.00559 0.8709 0.8997 0.01422 ] Network output: [ -0.0009365 0.003791 1.003 -0.0001134 5.093e-05 0.9949 -8.55e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1974 0.09328 0.3264 0.1543 0.9851 0.994 0.1981 0.4596 0.8823 0.716 ] Network output: [ 0.007597 -0.03725 0.9965 6.638e-05 -2.98e-05 1.026 5.003e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09781 0.08644 0.179 0.2063 0.9873 0.992 0.09787 0.7829 0.8746 0.3081 ] Network output: [ -0.007604 0.03862 1.002 6.759e-05 -3.034e-05 0.9753 5.094e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09104 0.08913 0.1662 0.1965 0.9855 0.9914 0.09105 0.7109 0.853 0.2433 ] Network output: [ 0.00025 0.9997 -0.0005837 9.232e-06 -4.145e-06 1 6.958e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00073 Epoch 7184 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0125 0.9933 0.9879 2.943e-06 -1.321e-06 -0.006213 2.218e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003233 -0.00302 -0.009069 0.006953 0.9698 0.9742 0.006158 0.8416 0.83 0.01962 ] Network output: [ 0.9997 0.001134 0.001558 -3.411e-05 1.531e-05 -0.002218 -2.571e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1866 -0.03059 -0.1916 0.1975 0.9836 0.9933 0.2084 0.4549 0.8758 0.7214 ] Network output: [ -0.01151 1.001 1.01 1.462e-06 -6.564e-07 0.01193 1.102e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00549 0.0004703 0.004357 0.00418 0.9889 0.992 0.005591 0.8709 0.8997 0.01422 ] Network output: [ -0.0009159 0.003489 1.003 -0.0001133 5.088e-05 0.9952 -8.541e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1974 0.09328 0.3265 0.1543 0.9851 0.994 0.1981 0.4596 0.8823 0.716 ] Network output: [ 0.00759 -0.0373 0.9965 6.633e-05 -2.978e-05 1.026 4.999e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09781 0.08645 0.179 0.2063 0.9873 0.992 0.09787 0.7828 0.8746 0.3081 ] Network output: [ -0.007602 0.03862 1.002 6.754e-05 -3.032e-05 0.9753 5.09e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09104 0.08912 0.1662 0.1965 0.9855 0.9914 0.09105 0.7109 0.853 0.2433 ] Network output: [ 0.0002626 0.9997 -0.000601 9.229e-06 -4.143e-06 1 6.956e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007301 Epoch 7185 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0125 0.9933 0.9879 2.936e-06 -1.318e-06 -0.006242 2.213e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003233 -0.003021 -0.009068 0.006951 0.9698 0.9742 0.006158 0.8416 0.83 0.01962 ] Network output: [ 0.9997 0.001353 0.001546 -3.41e-05 1.531e-05 -0.002397 -2.57e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1866 -0.03059 -0.1916 0.1974 0.9836 0.9933 0.2084 0.4549 0.8758 0.7214 ] Network output: [ -0.01151 1.001 1.01 1.458e-06 -6.547e-07 0.01192 1.099e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00549 0.0004702 0.004356 0.004178 0.9889 0.992 0.005591 0.8709 0.8996 0.01422 ] Network output: [ -0.0009355 0.003784 1.003 -0.0001133 5.085e-05 0.9949 -8.537e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1975 0.09328 0.3265 0.1543 0.9851 0.994 0.1981 0.4596 0.8823 0.716 ] Network output: [ 0.007591 -0.03723 0.9965 6.628e-05 -2.976e-05 1.026 4.995e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09782 0.08645 0.179 0.2063 0.9873 0.992 0.09788 0.7828 0.8746 0.3081 ] Network output: [ -0.007597 0.03859 1.002 6.749e-05 -3.03e-05 0.9753 5.087e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09103 0.08912 0.1662 0.1965 0.9855 0.9914 0.09104 0.7109 0.853 0.2433 ] Network output: [ 0.00025 0.9997 -0.0005829 9.219e-06 -4.139e-06 1 6.948e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007291 Epoch 7186 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0125 0.9933 0.9879 2.934e-06 -1.317e-06 -0.006218 2.211e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003234 -0.003021 -0.009066 0.006951 0.9698 0.9742 0.006158 0.8415 0.83 0.01961 ] Network output: [ 0.9997 0.001135 0.001556 -3.407e-05 1.529e-05 -0.002218 -2.568e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1867 -0.0306 -0.1916 0.1975 0.9836 0.9933 0.2084 0.4549 0.8758 0.7214 ] Network output: [ -0.01151 1.001 1.01 1.457e-06 -6.541e-07 0.01192 1.098e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005491 0.0004702 0.004357 0.004178 0.9889 0.992 0.005592 0.8709 0.8996 0.01421 ] Network output: [ -0.0009155 0.003491 1.003 -0.0001132 5.08e-05 0.9952 -8.528e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1975 0.09328 0.3265 0.1543 0.9851 0.994 0.1981 0.4595 0.8823 0.716 ] Network output: [ 0.007585 -0.03727 0.9965 6.624e-05 -2.974e-05 1.026 4.992e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09782 0.08645 0.179 0.2063 0.9873 0.992 0.09788 0.7828 0.8745 0.3081 ] Network output: [ -0.007595 0.03859 1.002 6.745e-05 -3.028e-05 0.9753 5.083e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09103 0.08912 0.1662 0.1965 0.9855 0.9914 0.09104 0.7108 0.853 0.2433 ] Network output: [ 0.0002621 0.9997 -0.0005996 9.216e-06 -4.138e-06 1 6.946e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007292 Epoch 7187 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01249 0.9933 0.9879 2.926e-06 -1.314e-06 -0.006247 2.205e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003234 -0.003021 -0.009065 0.006949 0.9698 0.9742 0.006159 0.8415 0.83 0.01961 ] Network output: [ 0.9997 0.001347 0.001544 -3.406e-05 1.529e-05 -0.002391 -2.567e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1867 -0.0306 -0.1916 0.1974 0.9836 0.9933 0.2084 0.4549 0.8758 0.7214 ] Network output: [ -0.01151 1.001 1.01 1.453e-06 -6.525e-07 0.01191 1.095e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005491 0.0004701 0.004357 0.004176 0.9889 0.992 0.005592 0.8709 0.8996 0.01421 ] Network output: [ -0.0009344 0.003778 1.003 -0.0001131 5.077e-05 0.9949 -8.524e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1975 0.09328 0.3265 0.1542 0.9851 0.994 0.1981 0.4595 0.8823 0.716 ] Network output: [ 0.007586 -0.0372 0.9965 6.618e-05 -2.971e-05 1.026 4.988e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09782 0.08645 0.179 0.2063 0.9873 0.992 0.09789 0.7827 0.8745 0.3081 ] Network output: [ -0.007591 0.03855 1.002 6.74e-05 -3.026e-05 0.9754 5.08e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09103 0.08911 0.1662 0.1965 0.9855 0.9914 0.09104 0.7108 0.853 0.2433 ] Network output: [ 0.0002499 0.9997 -0.000582 9.206e-06 -4.133e-06 1 6.938e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007282 Epoch 7188 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01249 0.9933 0.9879 2.924e-06 -1.313e-06 -0.006223 2.204e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003234 -0.003021 -0.009063 0.006949 0.9698 0.9742 0.006159 0.8415 0.83 0.01961 ] Network output: [ 0.9997 0.001136 0.001554 -3.402e-05 1.527e-05 -0.002218 -2.564e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1867 -0.03061 -0.1915 0.1975 0.9836 0.9933 0.2084 0.4549 0.8758 0.7214 ] Network output: [ -0.01151 1.001 1.01 1.452e-06 -6.518e-07 0.01191 1.094e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005492 0.00047 0.004357 0.004176 0.9889 0.992 0.005593 0.8709 0.8996 0.01421 ] Network output: [ -0.000915 0.003494 1.003 -0.000113 5.072e-05 0.9952 -8.515e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1975 0.09328 0.3266 0.1543 0.9851 0.994 0.1981 0.4595 0.8823 0.716 ] Network output: [ 0.00758 -0.03724 0.9965 6.614e-05 -2.969e-05 1.026 4.984e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09783 0.08646 0.179 0.2063 0.9873 0.992 0.09789 0.7827 0.8745 0.3081 ] Network output: [ -0.007589 0.03856 1.002 6.735e-05 -3.024e-05 0.9754 5.076e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09102 0.08911 0.1662 0.1965 0.9855 0.9914 0.09104 0.7108 0.853 0.2433 ] Network output: [ 0.0002617 0.9997 -0.0005983 9.203e-06 -4.132e-06 1 6.936e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007282 Epoch 7189 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01249 0.9934 0.9879 2.917e-06 -1.309e-06 -0.006251 2.198e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003234 -0.003021 -0.009062 0.006947 0.9698 0.9742 0.006159 0.8415 0.83 0.01961 ] Network output: [ 0.9997 0.001342 0.001542 -3.402e-05 1.527e-05 -0.002385 -2.563e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1867 -0.03061 -0.1915 0.1974 0.9836 0.9933 0.2084 0.4548 0.8757 0.7214 ] Network output: [ -0.01151 1.001 1.01 1.448e-06 -6.502e-07 0.0119 1.091e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005493 0.0004699 0.004357 0.004175 0.9889 0.992 0.005594 0.8708 0.8996 0.01421 ] Network output: [ -0.0009333 0.003771 1.003 -0.0001129 5.07e-05 0.9949 -8.51e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1975 0.09328 0.3266 0.1542 0.9851 0.994 0.1981 0.4595 0.8822 0.716 ] Network output: [ 0.00758 -0.03718 0.9965 6.609e-05 -2.967e-05 1.026 4.981e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09783 0.08646 0.179 0.2062 0.9873 0.992 0.09789 0.7827 0.8745 0.3081 ] Network output: [ -0.007584 0.03852 1.002 6.731e-05 -3.022e-05 0.9754 5.073e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09102 0.08911 0.1662 0.1965 0.9855 0.9914 0.09103 0.7107 0.853 0.2433 ] Network output: [ 0.0002498 0.9997 -0.0005812 9.193e-06 -4.127e-06 1 6.928e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007273 Epoch 7190 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01249 0.9933 0.9879 2.914e-06 -1.308e-06 -0.006228 2.196e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003234 -0.003021 -0.00906 0.006947 0.9698 0.9742 0.00616 0.8415 0.83 0.01961 ] Network output: [ 0.9997 0.001137 0.001552 -3.398e-05 1.525e-05 -0.002218 -2.561e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1867 -0.03062 -0.1915 0.1974 0.9836 0.9933 0.2084 0.4548 0.8757 0.7214 ] Network output: [ -0.0115 1.001 1.01 1.447e-06 -6.495e-07 0.0119 1.09e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005493 0.0004699 0.004358 0.004175 0.9889 0.992 0.005594 0.8708 0.8996 0.01421 ] Network output: [ -0.0009146 0.003497 1.003 -0.0001128 5.065e-05 0.9952 -8.502e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1975 0.09328 0.3266 0.1542 0.9851 0.994 0.1981 0.4595 0.8822 0.716 ] Network output: [ 0.007574 -0.03722 0.9965 6.604e-05 -2.965e-05 1.026 4.977e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09784 0.08646 0.179 0.2063 0.9873 0.992 0.0979 0.7827 0.8745 0.3081 ] Network output: [ -0.007582 0.03852 1.002 6.726e-05 -3.02e-05 0.9754 5.069e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09102 0.0891 0.1662 0.1965 0.9855 0.9914 0.09103 0.7107 0.853 0.2433 ] Network output: [ 0.0002612 0.9997 -0.0005969 9.19e-06 -4.126e-06 1 6.926e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007273 Epoch 7191 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01249 0.9934 0.9879 2.907e-06 -1.305e-06 -0.006255 2.191e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003234 -0.003021 -0.009059 0.006945 0.9698 0.9742 0.00616 0.8415 0.83 0.0196 ] Network output: [ 0.9997 0.001336 0.001541 -3.397e-05 1.525e-05 -0.00238 -2.56e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1867 -0.03062 -0.1915 0.1974 0.9836 0.9933 0.2085 0.4548 0.8757 0.7214 ] Network output: [ -0.0115 1.001 1.01 1.443e-06 -6.479e-07 0.01189 1.088e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005494 0.0004698 0.004357 0.004173 0.9889 0.992 0.005595 0.8708 0.8996 0.01421 ] Network output: [ -0.0009322 0.003764 1.003 -0.0001127 5.062e-05 0.9949 -8.497e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1975 0.09328 0.3266 0.1542 0.9851 0.994 0.1982 0.4595 0.8822 0.716 ] Network output: [ 0.007575 -0.03715 0.9965 6.599e-05 -2.962e-05 1.026 4.973e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09784 0.08646 0.179 0.2062 0.9873 0.992 0.0979 0.7826 0.8745 0.308 ] Network output: [ -0.007578 0.03849 1.002 6.721e-05 -3.017e-05 0.9754 5.065e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09101 0.0891 0.1662 0.1965 0.9855 0.9914 0.09102 0.7107 0.8529 0.2433 ] Network output: [ 0.0002497 0.9997 -0.0005804 9.18e-06 -4.121e-06 1 6.919e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007264 Epoch 7192 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01248 0.9933 0.9879 2.905e-06 -1.304e-06 -0.006233 2.189e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003234 -0.003022 -0.009057 0.006945 0.9698 0.9742 0.00616 0.8415 0.8299 0.0196 ] Network output: [ 0.9997 0.001138 0.00155 -3.394e-05 1.524e-05 -0.002218 -2.558e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1867 -0.03063 -0.1915 0.1974 0.9836 0.9933 0.2085 0.4548 0.8757 0.7214 ] Network output: [ -0.0115 1.001 1.01 1.442e-06 -6.473e-07 0.01189 1.087e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005494 0.0004697 0.004358 0.004173 0.9889 0.992 0.005595 0.8708 0.8996 0.0142 ] Network output: [ -0.0009141 0.003499 1.003 -0.0001126 5.057e-05 0.9952 -8.489e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1975 0.09328 0.3266 0.1542 0.9851 0.994 0.1982 0.4594 0.8822 0.716 ] Network output: [ 0.007569 -0.03719 0.9965 6.594e-05 -2.96e-05 1.026 4.97e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09784 0.08647 0.179 0.2062 0.9873 0.992 0.09791 0.7826 0.8745 0.3081 ] Network output: [ -0.007576 0.03849 1.002 6.717e-05 -3.015e-05 0.9754 5.062e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09101 0.0891 0.1662 0.1965 0.9855 0.9914 0.09102 0.7106 0.8529 0.2433 ] Network output: [ 0.0002607 0.9997 -0.0005956 9.177e-06 -4.12e-06 1 6.916e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007264 Epoch 7193 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01248 0.9934 0.9879 2.898e-06 -1.301e-06 -0.006259 2.184e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003234 -0.003022 -0.009056 0.006943 0.9698 0.9742 0.00616 0.8415 0.8299 0.0196 ] Network output: [ 0.9997 0.00133 0.001539 -3.393e-05 1.523e-05 -0.002374 -2.557e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1867 -0.03063 -0.1915 0.1974 0.9836 0.9933 0.2085 0.4548 0.8757 0.7213 ] Network output: [ -0.0115 1.001 1.01 1.438e-06 -6.456e-07 0.01188 1.084e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005495 0.0004697 0.004358 0.004172 0.9889 0.992 0.005596 0.8708 0.8996 0.0142 ] Network output: [ -0.0009312 0.003758 1.003 -0.0001126 5.054e-05 0.9949 -8.484e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1975 0.09328 0.3266 0.1542 0.9851 0.994 0.1982 0.4594 0.8822 0.716 ] Network output: [ 0.007569 -0.03713 0.9965 6.589e-05 -2.958e-05 1.026 4.966e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09785 0.08647 0.179 0.2062 0.9873 0.992 0.09791 0.7826 0.8745 0.308 ] Network output: [ -0.007571 0.03846 1.002 6.712e-05 -3.013e-05 0.9754 5.058e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09101 0.08909 0.1662 0.1965 0.9855 0.9914 0.09102 0.7106 0.8529 0.2433 ] Network output: [ 0.0002496 0.9997 -0.0005796 9.167e-06 -4.116e-06 1 6.909e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007255 Epoch 7194 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01248 0.9933 0.9879 2.895e-06 -1.3e-06 -0.006238 2.182e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003235 -0.003022 -0.009054 0.006943 0.9698 0.9742 0.006161 0.8415 0.8299 0.0196 ] Network output: [ 0.9997 0.001139 0.001548 -3.389e-05 1.522e-05 -0.002218 -2.554e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1867 -0.03064 -0.1914 0.1974 0.9836 0.9933 0.2085 0.4548 0.8757 0.7213 ] Network output: [ -0.0115 1.001 1.01 1.437e-06 -6.45e-07 0.01189 1.083e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005495 0.0004696 0.004358 0.004172 0.9889 0.992 0.005596 0.8708 0.8996 0.0142 ] Network output: [ -0.0009136 0.003501 1.003 -0.0001125 5.049e-05 0.9952 -8.476e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1976 0.09328 0.3267 0.1542 0.9851 0.994 0.1982 0.4594 0.8822 0.716 ] Network output: [ 0.007563 -0.03717 0.9965 6.584e-05 -2.956e-05 1.026 4.962e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09785 0.08647 0.179 0.2062 0.9873 0.992 0.09791 0.7826 0.8745 0.308 ] Network output: [ -0.007569 0.03846 1.002 6.707e-05 -3.011e-05 0.9754 5.055e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.091 0.08909 0.1662 0.1965 0.9855 0.9914 0.09102 0.7106 0.8529 0.2433 ] Network output: [ 0.0002603 0.9997 -0.0005942 9.164e-06 -4.114e-06 1 6.906e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007255 Epoch 7195 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01248 0.9934 0.988 2.888e-06 -1.297e-06 -0.006263 2.177e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003235 -0.003022 -0.009053 0.006941 0.9698 0.9742 0.006161 0.8415 0.8299 0.0196 ] Network output: [ 0.9997 0.001325 0.001537 -3.388e-05 1.521e-05 -0.002369 -2.553e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1868 -0.03064 -0.1914 0.1974 0.9836 0.9933 0.2085 0.4547 0.8757 0.7213 ] Network output: [ -0.0115 1.001 1.01 1.433e-06 -6.434e-07 0.01187 1.08e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005496 0.0004695 0.004358 0.00417 0.9889 0.992 0.005597 0.8708 0.8996 0.0142 ] Network output: [ -0.0009301 0.003751 1.003 -0.0001124 5.046e-05 0.995 -8.471e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1976 0.09329 0.3267 0.1541 0.9851 0.994 0.1982 0.4594 0.8822 0.716 ] Network output: [ 0.007564 -0.03711 0.9965 6.579e-05 -2.954e-05 1.026 4.958e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09785 0.08648 0.179 0.2062 0.9873 0.992 0.09792 0.7825 0.8744 0.308 ] Network output: [ -0.007565 0.03842 1.002 6.703e-05 -3.009e-05 0.9754 5.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.091 0.08909 0.1662 0.1964 0.9855 0.9914 0.09101 0.7105 0.8529 0.2433 ] Network output: [ 0.0002495 0.9997 -0.0005787 9.154e-06 -4.11e-06 1 6.899e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007246 Epoch 7196 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01248 0.9933 0.988 2.886e-06 -1.295e-06 -0.006243 2.175e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003235 -0.003022 -0.009051 0.006941 0.9698 0.9742 0.006161 0.8415 0.8299 0.01959 ] Network output: [ 0.9997 0.00114 0.001546 -3.385e-05 1.52e-05 -0.002217 -2.551e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1868 -0.03065 -0.1914 0.1974 0.9836 0.9933 0.2085 0.4547 0.8757 0.7213 ] Network output: [ -0.0115 1.001 1.01 1.432e-06 -6.427e-07 0.01188 1.079e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005497 0.0004695 0.004359 0.00417 0.9889 0.992 0.005598 0.8708 0.8996 0.0142 ] Network output: [ -0.0009132 0.003504 1.003 -0.0001123 5.041e-05 0.9952 -8.463e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1976 0.09329 0.3267 0.1542 0.9851 0.994 0.1982 0.4594 0.8822 0.716 ] Network output: [ 0.007558 -0.03714 0.9965 6.575e-05 -2.952e-05 1.026 4.955e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09786 0.08648 0.179 0.2062 0.9873 0.992 0.09792 0.7825 0.8744 0.308 ] Network output: [ -0.007563 0.03842 1.002 6.698e-05 -3.007e-05 0.9754 5.048e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.091 0.08908 0.1662 0.1964 0.9855 0.9914 0.09101 0.7105 0.8529 0.2433 ] Network output: [ 0.0002598 0.9997 -0.0005929 9.151e-06 -4.108e-06 1 6.896e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007245 Epoch 7197 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01247 0.9934 0.988 2.879e-06 -1.292e-06 -0.006268 2.169e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003235 -0.003022 -0.00905 0.006939 0.9698 0.9742 0.006162 0.8415 0.8299 0.01959 ] Network output: [ 0.9997 0.001319 0.001535 -3.384e-05 1.519e-05 -0.002364 -2.55e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1868 -0.03065 -0.1914 0.1973 0.9836 0.9933 0.2085 0.4547 0.8757 0.7213 ] Network output: [ -0.0115 1.001 1.01 1.428e-06 -6.411e-07 0.01187 1.076e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005497 0.0004694 0.004358 0.004169 0.9889 0.992 0.005598 0.8708 0.8996 0.0142 ] Network output: [ -0.0009291 0.003745 1.003 -0.0001122 5.038e-05 0.995 -8.458e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1976 0.09329 0.3267 0.1541 0.9851 0.994 0.1982 0.4594 0.8822 0.716 ] Network output: [ 0.007558 -0.03708 0.9965 6.57e-05 -2.949e-05 1.026 4.951e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09786 0.08648 0.179 0.2062 0.9873 0.992 0.09792 0.7825 0.8744 0.308 ] Network output: [ -0.007559 0.03839 1.002 6.693e-05 -3.005e-05 0.9754 5.044e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09099 0.08908 0.1662 0.1964 0.9855 0.9914 0.09101 0.7105 0.8529 0.2433 ] Network output: [ 0.0002494 0.9997 -0.0005779 9.142e-06 -4.104e-06 1 6.889e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007237 Epoch 7198 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01247 0.9934 0.988 2.876e-06 -1.291e-06 -0.006248 2.167e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003235 -0.003023 -0.009048 0.006939 0.9698 0.9742 0.006162 0.8414 0.8299 0.01959 ] Network output: [ 0.9997 0.001141 0.001544 -3.38e-05 1.518e-05 -0.002217 -2.548e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1868 -0.03066 -0.1913 0.1974 0.9836 0.9933 0.2085 0.4547 0.8757 0.7213 ] Network output: [ -0.01149 1.001 1.01 1.426e-06 -6.404e-07 0.01187 1.075e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005498 0.0004693 0.004359 0.004169 0.9889 0.992 0.005599 0.8708 0.8996 0.0142 ] Network output: [ -0.0009127 0.003506 1.003 -0.0001121 5.033e-05 0.9952 -8.45e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1976 0.09329 0.3267 0.1541 0.9851 0.994 0.1982 0.4593 0.8822 0.716 ] Network output: [ 0.007553 -0.03712 0.9965 6.565e-05 -2.947e-05 1.026 4.948e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09787 0.08649 0.179 0.2062 0.9873 0.992 0.09793 0.7824 0.8744 0.308 ] Network output: [ -0.007556 0.03839 1.002 6.689e-05 -3.003e-05 0.9754 5.041e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09099 0.08908 0.1662 0.1964 0.9855 0.9914 0.091 0.7104 0.8529 0.2433 ] Network output: [ 0.0002593 0.9997 -0.0005915 9.138e-06 -4.102e-06 1 6.886e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007236 Epoch 7199 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01247 0.9934 0.988 2.869e-06 -1.288e-06 -0.006272 2.162e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003235 -0.003023 -0.009047 0.006937 0.9698 0.9742 0.006162 0.8414 0.8299 0.01959 ] Network output: [ 0.9997 0.001314 0.001534 -3.379e-05 1.517e-05 -0.002358 -2.547e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1868 -0.03066 -0.1913 0.1973 0.9836 0.9933 0.2086 0.4547 0.8757 0.7213 ] Network output: [ -0.01149 1.001 1.01 1.423e-06 -6.388e-07 0.01186 1.072e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005498 0.0004692 0.004359 0.004167 0.9889 0.992 0.0056 0.8707 0.8996 0.01419 ] Network output: [ -0.000928 0.003739 1.003 -0.0001121 5.03e-05 0.995 -8.444e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1976 0.09329 0.3267 0.1541 0.9851 0.994 0.1982 0.4593 0.8822 0.716 ] Network output: [ 0.007553 -0.03706 0.9965 6.56e-05 -2.945e-05 1.026 4.944e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09787 0.08649 0.179 0.2062 0.9873 0.992 0.09793 0.7824 0.8744 0.308 ] Network output: [ -0.007552 0.03836 1.002 6.684e-05 -3.001e-05 0.9755 5.037e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09099 0.08907 0.1662 0.1964 0.9855 0.9914 0.091 0.7104 0.8528 0.2433 ] Network output: [ 0.0002493 0.9997 -0.0005771 9.129e-06 -4.098e-06 1 6.88e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007227 Epoch 7200 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01247 0.9934 0.988 2.866e-06 -1.287e-06 -0.006253 2.16e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003235 -0.003023 -0.009045 0.006937 0.9698 0.9742 0.006162 0.8414 0.8299 0.01959 ] Network output: [ 0.9997 0.001141 0.001542 -3.376e-05 1.516e-05 -0.002217 -2.544e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1868 -0.03067 -0.1913 0.1973 0.9836 0.9933 0.2086 0.4547 0.8757 0.7213 ] Network output: [ -0.01149 1.001 1.01 1.421e-06 -6.381e-07 0.01186 1.071e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005499 0.0004692 0.004359 0.004167 0.9889 0.992 0.0056 0.8707 0.8996 0.01419 ] Network output: [ -0.0009122 0.003507 1.003 -0.0001119 5.026e-05 0.9952 -8.436e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1976 0.09329 0.3268 0.1541 0.9851 0.994 0.1983 0.4593 0.8822 0.716 ] Network output: [ 0.007547 -0.03709 0.9965 6.555e-05 -2.943e-05 1.026 4.94e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09788 0.08649 0.179 0.2062 0.9873 0.992 0.09794 0.7824 0.8744 0.308 ] Network output: [ -0.00755 0.03836 1.002 6.679e-05 -2.999e-05 0.9754 5.034e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09098 0.08907 0.1662 0.1964 0.9855 0.9914 0.091 0.7104 0.8528 0.2433 ] Network output: [ 0.0002589 0.9997 -0.0005902 9.125e-06 -4.096e-06 1 6.877e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007227 Epoch 7201 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01246 0.9934 0.988 2.86e-06 -1.284e-06 -0.006276 2.155e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003235 -0.003023 -0.009044 0.006935 0.9698 0.9742 0.006163 0.8414 0.8299 0.01958 ] Network output: [ 0.9997 0.001308 0.001532 -3.375e-05 1.515e-05 -0.002353 -2.543e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1868 -0.03067 -0.1913 0.1973 0.9836 0.9933 0.2086 0.4546 0.8757 0.7213 ] Network output: [ -0.01149 1.001 1.01 1.418e-06 -6.366e-07 0.01185 1.069e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0055 0.0004691 0.004359 0.004166 0.9889 0.992 0.005601 0.8707 0.8996 0.01419 ] Network output: [ -0.000927 0.003733 1.003 -0.0001119 5.023e-05 0.995 -8.431e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1976 0.09329 0.3268 0.1541 0.9851 0.994 0.1983 0.4593 0.8822 0.716 ] Network output: [ 0.007547 -0.03704 0.9965 6.55e-05 -2.941e-05 1.026 4.936e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09788 0.08649 0.179 0.2061 0.9873 0.992 0.09794 0.7824 0.8744 0.308 ] Network output: [ -0.007546 0.03833 1.002 6.675e-05 -2.997e-05 0.9755 5.03e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09098 0.08907 0.1662 0.1964 0.9855 0.9914 0.09099 0.7103 0.8528 0.2433 ] Network output: [ 0.0002492 0.9997 -0.0005762 9.116e-06 -4.092e-06 1 6.87e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007218 Epoch 7202 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01246 0.9934 0.988 2.857e-06 -1.283e-06 -0.006258 2.153e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003236 -0.003023 -0.009042 0.006935 0.9698 0.9742 0.006163 0.8414 0.8299 0.01958 ] Network output: [ 0.9997 0.001142 0.00154 -3.371e-05 1.514e-05 -0.002216 -2.541e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1868 -0.03068 -0.1913 0.1973 0.9836 0.9933 0.2086 0.4546 0.8757 0.7213 ] Network output: [ -0.01149 1.001 1.01 1.416e-06 -6.358e-07 0.01185 1.067e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0055 0.0004691 0.00436 0.004166 0.9889 0.992 0.005601 0.8707 0.8996 0.01419 ] Network output: [ -0.0009116 0.003509 1.003 -0.0001118 5.018e-05 0.9952 -8.423e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1977 0.09329 0.3268 0.1541 0.9851 0.994 0.1983 0.4593 0.8822 0.7159 ] Network output: [ 0.007542 -0.03707 0.9965 6.545e-05 -2.938e-05 1.026 4.933e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09788 0.0865 0.179 0.2061 0.9873 0.992 0.09795 0.7823 0.8744 0.308 ] Network output: [ -0.007543 0.03833 1.002 6.67e-05 -2.994e-05 0.9755 5.027e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09098 0.08906 0.1662 0.1964 0.9855 0.9914 0.09099 0.7103 0.8528 0.2433 ] Network output: [ 0.0002585 0.9997 -0.0005889 9.112e-06 -4.091e-06 1 6.867e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007218 Epoch 7203 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01246 0.9934 0.988 2.85e-06 -1.28e-06 -0.00628 2.148e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003236 -0.003023 -0.009041 0.006933 0.9698 0.9742 0.006163 0.8414 0.8299 0.01958 ] Network output: [ 0.9997 0.001303 0.00153 -3.37e-05 1.513e-05 -0.002348 -2.54e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1868 -0.03068 -0.1913 0.1973 0.9836 0.9933 0.2086 0.4546 0.8757 0.7213 ] Network output: [ -0.01149 1.001 1.01 1.413e-06 -6.343e-07 0.01184 1.065e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005501 0.000469 0.004359 0.004164 0.9889 0.992 0.005602 0.8707 0.8995 0.01419 ] Network output: [ -0.000926 0.003727 1.003 -0.0001117 5.015e-05 0.995 -8.418e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1977 0.09329 0.3268 0.154 0.9851 0.994 0.1983 0.4593 0.8822 0.7159 ] Network output: [ 0.007542 -0.03701 0.9965 6.54e-05 -2.936e-05 1.026 4.929e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09789 0.0865 0.179 0.2061 0.9873 0.992 0.09795 0.7823 0.8744 0.308 ] Network output: [ -0.007539 0.0383 1.002 6.665e-05 -2.992e-05 0.9755 5.023e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09098 0.08906 0.1661 0.1964 0.9855 0.9914 0.09099 0.7103 0.8528 0.2433 ] Network output: [ 0.0002491 0.9997 -0.0005753 9.103e-06 -4.086e-06 1 6.86e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007209 Epoch 7204 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01246 0.9934 0.988 2.847e-06 -1.278e-06 -0.006263 2.146e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003236 -0.003024 -0.009039 0.006932 0.9698 0.9742 0.006164 0.8414 0.8299 0.01958 ] Network output: [ 0.9997 0.001142 0.001538 -3.367e-05 1.512e-05 -0.002215 -2.537e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1869 -0.03069 -0.1912 0.1973 0.9836 0.9933 0.2086 0.4546 0.8757 0.7213 ] Network output: [ -0.01149 1.001 1.01 1.411e-06 -6.335e-07 0.01184 1.064e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005501 0.0004689 0.00436 0.004164 0.9889 0.992 0.005602 0.8707 0.8995 0.01419 ] Network output: [ -0.0009111 0.003511 1.003 -0.0001116 5.01e-05 0.9952 -8.41e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1977 0.09329 0.3268 0.1541 0.9851 0.994 0.1983 0.4592 0.8822 0.7159 ] Network output: [ 0.007537 -0.03704 0.9965 6.536e-05 -2.934e-05 1.026 4.925e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09789 0.0865 0.179 0.2061 0.9873 0.992 0.09795 0.7823 0.8744 0.308 ] Network output: [ -0.007537 0.03829 1.002 6.661e-05 -2.99e-05 0.9755 5.02e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09097 0.08906 0.1661 0.1964 0.9855 0.9914 0.09098 0.7102 0.8528 0.2433 ] Network output: [ 0.000258 0.9997 -0.0005876 9.099e-06 -4.085e-06 1 6.857e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007209 Epoch 7205 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01245 0.9934 0.988 2.841e-06 -1.275e-06 -0.006284 2.141e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003236 -0.003024 -0.009038 0.006931 0.9698 0.9742 0.006164 0.8414 0.8299 0.01958 ] Network output: [ 0.9997 0.001298 0.001529 -3.366e-05 1.511e-05 -0.002343 -2.537e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1869 -0.03069 -0.1912 0.1973 0.9836 0.9933 0.2086 0.4546 0.8757 0.7213 ] Network output: [ -0.01149 1.001 1.01 1.408e-06 -6.32e-07 0.01183 1.061e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005502 0.0004689 0.00436 0.004163 0.9889 0.992 0.005603 0.8707 0.8995 0.01418 ] Network output: [ -0.000925 0.003721 1.003 -0.0001115 5.007e-05 0.995 -8.405e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1977 0.09329 0.3268 0.154 0.9851 0.994 0.1983 0.4592 0.8822 0.7159 ] Network output: [ 0.007537 -0.03699 0.9965 6.531e-05 -2.932e-05 1.026 4.922e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09789 0.0865 0.179 0.2061 0.9873 0.992 0.09796 0.7822 0.8743 0.308 ] Network output: [ -0.007533 0.03826 1.002 6.656e-05 -2.988e-05 0.9755 5.016e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09097 0.08905 0.1661 0.1964 0.9855 0.9914 0.09098 0.7102 0.8528 0.2433 ] Network output: [ 0.0002489 0.9997 -0.0005745 9.09e-06 -4.081e-06 1 6.85e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00072 Epoch 7206 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01245 0.9934 0.988 2.838e-06 -1.274e-06 -0.006268 2.139e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003236 -0.003024 -0.009036 0.00693 0.9698 0.9742 0.006164 0.8414 0.8299 0.01957 ] Network output: [ 0.9997 0.001142 0.001536 -3.363e-05 1.51e-05 -0.002215 -2.534e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1869 -0.0307 -0.1912 0.1973 0.9836 0.9933 0.2086 0.4546 0.8756 0.7213 ] Network output: [ -0.01149 1.001 1.01 1.406e-06 -6.313e-07 0.01184 1.06e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005502 0.0004688 0.00436 0.004163 0.9889 0.992 0.005604 0.8707 0.8995 0.01418 ] Network output: [ -0.0009106 0.003512 1.003 -0.0001114 5.002e-05 0.9952 -8.397e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1977 0.09329 0.3269 0.154 0.9851 0.994 0.1983 0.4592 0.8821 0.7159 ] Network output: [ 0.007531 -0.03702 0.9965 6.526e-05 -2.93e-05 1.026 4.918e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0979 0.08651 0.179 0.2061 0.9873 0.992 0.09796 0.7822 0.8743 0.308 ] Network output: [ -0.007531 0.03826 1.002 6.651e-05 -2.986e-05 0.9755 5.013e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09097 0.08905 0.1661 0.1964 0.9855 0.9914 0.09098 0.7102 0.8527 0.2433 ] Network output: [ 0.0002576 0.9997 -0.0005863 9.086e-06 -4.079e-06 1 6.847e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007199 Epoch 7207 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01245 0.9934 0.988 2.831e-06 -1.271e-06 -0.006289 2.134e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003236 -0.003024 -0.009035 0.006929 0.9698 0.9742 0.006165 0.8414 0.8299 0.01957 ] Network output: [ 0.9997 0.001293 0.001527 -3.361e-05 1.509e-05 -0.002338 -2.533e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1869 -0.0307 -0.1912 0.1972 0.9836 0.9933 0.2087 0.4545 0.8756 0.7213 ] Network output: [ -0.01148 1.001 1.01 1.403e-06 -6.298e-07 0.01182 1.057e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005503 0.0004687 0.00436 0.004161 0.9889 0.992 0.005604 0.8707 0.8995 0.01418 ] Network output: [ -0.0009239 0.003716 1.003 -0.0001114 4.999e-05 0.995 -8.392e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1977 0.09329 0.3269 0.154 0.9851 0.994 0.1983 0.4592 0.8821 0.7159 ] Network output: [ 0.007531 -0.03696 0.9965 6.521e-05 -2.927e-05 1.026 4.914e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0979 0.08651 0.179 0.2061 0.9873 0.992 0.09797 0.7822 0.8743 0.308 ] Network output: [ -0.007526 0.03823 1.002 6.647e-05 -2.984e-05 0.9755 5.009e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09096 0.08905 0.1661 0.1964 0.9855 0.9914 0.09097 0.7101 0.8527 0.2433 ] Network output: [ 0.0002488 0.9997 -0.0005736 9.077e-06 -4.075e-06 1 6.84e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007191 Epoch 7208 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01245 0.9934 0.988 2.828e-06 -1.27e-06 -0.006273 2.132e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003236 -0.003024 -0.009033 0.006928 0.9698 0.9742 0.006165 0.8414 0.8298 0.01957 ] Network output: [ 0.9997 0.001143 0.001534 -3.358e-05 1.508e-05 -0.002214 -2.531e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1869 -0.03071 -0.1912 0.1973 0.9836 0.9933 0.2087 0.4545 0.8756 0.7213 ] Network output: [ -0.01148 1.001 1.01 1.401e-06 -6.29e-07 0.01183 1.056e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005504 0.0004687 0.004361 0.004161 0.9889 0.992 0.005605 0.8707 0.8995 0.01418 ] Network output: [ -0.00091 0.003514 1.003 -0.0001113 4.994e-05 0.9952 -8.384e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1977 0.0933 0.3269 0.154 0.9851 0.994 0.1984 0.4592 0.8821 0.7159 ] Network output: [ 0.007526 -0.03699 0.9965 6.516e-05 -2.925e-05 1.026 4.911e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09791 0.08651 0.179 0.2061 0.9873 0.992 0.09797 0.7822 0.8743 0.308 ] Network output: [ -0.007524 0.03823 1.002 6.642e-05 -2.982e-05 0.9755 5.006e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09096 0.08905 0.1661 0.1964 0.9855 0.9914 0.09097 0.7101 0.8527 0.2433 ] Network output: [ 0.0002572 0.9997 -0.000585 9.072e-06 -4.073e-06 1 6.837e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000719 Epoch 7209 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01244 0.9934 0.988 2.822e-06 -1.267e-06 -0.006293 2.127e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003236 -0.003024 -0.009032 0.006927 0.9698 0.9742 0.006165 0.8414 0.8298 0.01957 ] Network output: [ 0.9997 0.001288 0.001525 -3.357e-05 1.507e-05 -0.002333 -2.53e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1869 -0.03071 -0.1911 0.1972 0.9836 0.9933 0.2087 0.4545 0.8756 0.7213 ] Network output: [ -0.01148 1.001 1.01 1.398e-06 -6.275e-07 0.01182 1.053e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005504 0.0004686 0.00436 0.00416 0.9889 0.992 0.005605 0.8706 0.8995 0.01418 ] Network output: [ -0.0009229 0.00371 1.003 -0.0001112 4.991e-05 0.995 -8.379e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1977 0.0933 0.3269 0.1539 0.9851 0.994 0.1984 0.4591 0.8821 0.7159 ] Network output: [ 0.007526 -0.03694 0.9965 6.511e-05 -2.923e-05 1.026 4.907e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09791 0.08652 0.179 0.2061 0.9873 0.992 0.09797 0.7821 0.8743 0.308 ] Network output: [ -0.00752 0.0382 1.002 6.637e-05 -2.98e-05 0.9755 5.002e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09096 0.08904 0.1661 0.1964 0.9855 0.9914 0.09097 0.7101 0.8527 0.2433 ] Network output: [ 0.0002487 0.9997 -0.0005727 9.064e-06 -4.069e-06 1 6.831e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007182 Epoch 7210 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01244 0.9934 0.988 2.819e-06 -1.266e-06 -0.006277 2.124e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003237 -0.003025 -0.00903 0.006926 0.9698 0.9742 0.006165 0.8413 0.8298 0.01956 ] Network output: [ 0.9997 0.001143 0.001532 -3.354e-05 1.506e-05 -0.002213 -2.527e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1869 -0.03072 -0.1911 0.1972 0.9836 0.9933 0.2087 0.4545 0.8756 0.7213 ] Network output: [ -0.01148 1.001 1.01 1.396e-06 -6.267e-07 0.01182 1.052e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005505 0.0004685 0.004361 0.00416 0.9889 0.992 0.005606 0.8706 0.8995 0.01418 ] Network output: [ -0.0009095 0.003515 1.003 -0.0001111 4.987e-05 0.9952 -8.371e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1978 0.0933 0.3269 0.154 0.9851 0.994 0.1984 0.4591 0.8821 0.7159 ] Network output: [ 0.007521 -0.03696 0.9965 6.506e-05 -2.921e-05 1.026 4.903e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09792 0.08652 0.179 0.2061 0.9873 0.992 0.09798 0.7821 0.8743 0.308 ] Network output: [ -0.007518 0.0382 1.002 6.633e-05 -2.978e-05 0.9755 4.999e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09095 0.08904 0.1661 0.1964 0.9855 0.9914 0.09097 0.71 0.8527 0.2433 ] Network output: [ 0.0002567 0.9997 -0.0005837 9.059e-06 -4.067e-06 1 6.827e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007181 Epoch 7211 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01244 0.9934 0.988 2.813e-06 -1.263e-06 -0.006297 2.12e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003237 -0.003025 -0.009029 0.006925 0.9698 0.9742 0.006166 0.8413 0.8298 0.01956 ] Network output: [ 0.9997 0.001283 0.001523 -3.352e-05 1.505e-05 -0.002328 -2.526e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1869 -0.03072 -0.1911 0.1972 0.9836 0.9933 0.2087 0.4545 0.8756 0.7213 ] Network output: [ -0.01148 1.001 1.01 1.393e-06 -6.252e-07 0.01181 1.05e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005505 0.0004685 0.004361 0.004158 0.9889 0.992 0.005607 0.8706 0.8995 0.01418 ] Network output: [ -0.0009219 0.003705 1.003 -0.000111 4.983e-05 0.995 -8.366e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1978 0.0933 0.3269 0.1539 0.9851 0.994 0.1984 0.4591 0.8821 0.7159 ] Network output: [ 0.00752 -0.03692 0.9965 6.501e-05 -2.919e-05 1.026 4.9e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09792 0.08652 0.179 0.2061 0.9873 0.992 0.09798 0.7821 0.8743 0.308 ] Network output: [ -0.007514 0.03817 1.002 6.628e-05 -2.976e-05 0.9755 4.995e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09095 0.08904 0.1661 0.1964 0.9855 0.9914 0.09096 0.71 0.8527 0.2433 ] Network output: [ 0.0002485 0.9997 -0.0005718 9.051e-06 -4.063e-06 1 6.821e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007173 Epoch 7212 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01244 0.9934 0.988 2.809e-06 -1.261e-06 -0.006282 2.117e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003237 -0.003025 -0.009027 0.006924 0.9698 0.9742 0.006166 0.8413 0.8298 0.01956 ] Network output: [ 0.9997 0.001143 0.00153 -3.349e-05 1.504e-05 -0.002212 -2.524e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1869 -0.03073 -0.1911 0.1972 0.9836 0.9933 0.2087 0.4545 0.8756 0.7213 ] Network output: [ -0.01148 1.001 1.01 1.391e-06 -6.244e-07 0.01181 1.048e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005506 0.0004684 0.004361 0.004158 0.9889 0.992 0.005607 0.8706 0.8995 0.01417 ] Network output: [ -0.0009089 0.003516 1.003 -0.0001109 4.979e-05 0.9952 -8.358e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1978 0.0933 0.327 0.1539 0.9851 0.994 0.1984 0.4591 0.8821 0.7159 ] Network output: [ 0.007515 -0.03694 0.9965 6.497e-05 -2.917e-05 1.026 4.896e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09792 0.08653 0.179 0.2061 0.9873 0.992 0.09799 0.7821 0.8743 0.308 ] Network output: [ -0.007511 0.03816 1.002 6.623e-05 -2.973e-05 0.9755 4.992e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09095 0.08903 0.1661 0.1964 0.9855 0.9914 0.09096 0.71 0.8527 0.2433 ] Network output: [ 0.0002563 0.9997 -0.0005825 9.046e-06 -4.061e-06 1 6.818e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007172 Epoch 7213 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01243 0.9934 0.988 2.803e-06 -1.258e-06 -0.006301 2.113e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003237 -0.003025 -0.009026 0.006923 0.9698 0.9742 0.006166 0.8413 0.8298 0.01956 ] Network output: [ 0.9997 0.001278 0.001522 -3.348e-05 1.503e-05 -0.002323 -2.523e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.187 -0.03073 -0.1911 0.1972 0.9836 0.9933 0.2087 0.4544 0.8756 0.7213 ] Network output: [ -0.01148 1.001 1.01 1.388e-06 -6.23e-07 0.0118 1.046e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005507 0.0004683 0.004361 0.004157 0.9889 0.992 0.005608 0.8706 0.8995 0.01417 ] Network output: [ -0.0009209 0.003699 1.003 -0.0001108 4.976e-05 0.995 -8.353e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1978 0.0933 0.327 0.1539 0.9851 0.994 0.1984 0.4591 0.8821 0.7159 ] Network output: [ 0.007515 -0.03689 0.9965 6.492e-05 -2.914e-05 1.026 4.892e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09793 0.08653 0.179 0.206 0.9873 0.992 0.09799 0.782 0.8743 0.308 ] Network output: [ -0.007507 0.03813 1.002 6.619e-05 -2.971e-05 0.9756 4.988e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09094 0.08903 0.1661 0.1964 0.9855 0.9914 0.09096 0.7099 0.8527 0.2433 ] Network output: [ 0.0002484 0.9997 -0.000571 9.038e-06 -4.057e-06 1 6.811e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007164 Epoch 7214 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01243 0.9934 0.988 2.8e-06 -1.257e-06 -0.006287 2.11e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003237 -0.003025 -0.009024 0.006922 0.9698 0.9742 0.006167 0.8413 0.8298 0.01956 ] Network output: [ 0.9997 0.001143 0.001528 -3.345e-05 1.502e-05 -0.002212 -2.521e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.187 -0.03074 -0.191 0.1972 0.9836 0.9933 0.2087 0.4544 0.8756 0.7213 ] Network output: [ -0.01148 1.001 1.01 1.386e-06 -6.221e-07 0.0118 1.044e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005507 0.0004683 0.004362 0.004157 0.9889 0.992 0.005608 0.8706 0.8995 0.01417 ] Network output: [ -0.0009084 0.003518 1.003 -0.0001107 4.971e-05 0.9952 -8.345e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1978 0.0933 0.327 0.1539 0.9851 0.994 0.1984 0.4591 0.8821 0.7159 ] Network output: [ 0.00751 -0.03691 0.9965 6.487e-05 -2.912e-05 1.026 4.889e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09793 0.08653 0.179 0.206 0.9873 0.992 0.09799 0.782 0.8743 0.308 ] Network output: [ -0.007505 0.03813 1.002 6.614e-05 -2.969e-05 0.9756 4.985e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09094 0.08903 0.1661 0.1964 0.9855 0.9914 0.09095 0.7099 0.8526 0.2433 ] Network output: [ 0.0002559 0.9997 -0.0005812 9.033e-06 -4.055e-06 1 6.808e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007163 Epoch 7215 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01243 0.9934 0.988 2.794e-06 -1.254e-06 -0.006306 2.105e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003237 -0.003025 -0.009023 0.006921 0.9698 0.9742 0.006167 0.8413 0.8298 0.01955 ] Network output: [ 0.9997 0.001274 0.00152 -3.343e-05 1.501e-05 -0.002318 -2.52e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.187 -0.03074 -0.191 0.1972 0.9836 0.9933 0.2088 0.4544 0.8756 0.7213 ] Network output: [ -0.01147 1.001 1.01 1.383e-06 -6.207e-07 0.01179 1.042e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005508 0.0004682 0.004361 0.004155 0.9889 0.992 0.005609 0.8706 0.8995 0.01417 ] Network output: [ -0.0009199 0.003694 1.003 -0.0001107 4.968e-05 0.995 -8.34e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1978 0.0933 0.327 0.1539 0.9851 0.994 0.1984 0.459 0.8821 0.7159 ] Network output: [ 0.00751 -0.03687 0.9964 6.482e-05 -2.91e-05 1.026 4.885e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09794 0.08653 0.179 0.206 0.9873 0.992 0.098 0.782 0.8742 0.3079 ] Network output: [ -0.007501 0.0381 1.002 6.609e-05 -2.967e-05 0.9756 4.981e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09094 0.08902 0.1661 0.1964 0.9855 0.9914 0.09095 0.7099 0.8526 0.2433 ] Network output: [ 0.0002482 0.9997 -0.0005701 9.025e-06 -4.052e-06 1 6.801e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007155 Epoch 7216 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01243 0.9934 0.988 2.791e-06 -1.253e-06 -0.006292 2.103e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003237 -0.003026 -0.009021 0.00692 0.9698 0.9742 0.006167 0.8413 0.8298 0.01955 ] Network output: [ 0.9997 0.001143 0.001526 -3.34e-05 1.5e-05 -0.002211 -2.517e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.187 -0.03075 -0.191 0.1972 0.9836 0.9933 0.2088 0.4544 0.8756 0.7213 ] Network output: [ -0.01147 1.001 1.01 1.381e-06 -6.199e-07 0.01179 1.041e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005508 0.0004682 0.004362 0.004155 0.9889 0.992 0.00561 0.8706 0.8995 0.01417 ] Network output: [ -0.0009078 0.003519 1.003 -0.0001106 4.963e-05 0.9952 -8.332e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1978 0.0933 0.327 0.1539 0.9851 0.994 0.1985 0.459 0.8821 0.7159 ] Network output: [ 0.007505 -0.03689 0.9965 6.477e-05 -2.908e-05 1.026 4.881e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09794 0.08654 0.179 0.206 0.9873 0.992 0.098 0.7819 0.8742 0.3079 ] Network output: [ -0.007498 0.0381 1.002 6.605e-05 -2.965e-05 0.9756 4.978e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09094 0.08902 0.1661 0.1964 0.9855 0.9914 0.09095 0.7098 0.8526 0.2433 ] Network output: [ 0.0002555 0.9997 -0.0005799 9.02e-06 -4.05e-06 1 6.798e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007154 Epoch 7217 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01242 0.9934 0.988 2.784e-06 -1.25e-06 -0.00631 2.098e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003237 -0.003026 -0.00902 0.006919 0.9698 0.9742 0.006168 0.8413 0.8298 0.01955 ] Network output: [ 0.9997 0.001269 0.001518 -3.339e-05 1.499e-05 -0.002313 -2.516e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.187 -0.03075 -0.191 0.1971 0.9836 0.9933 0.2088 0.4544 0.8756 0.7212 ] Network output: [ -0.01147 1.001 1.01 1.378e-06 -6.184e-07 0.01178 1.038e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005509 0.0004681 0.004362 0.004154 0.9889 0.992 0.00561 0.8706 0.8995 0.01417 ] Network output: [ -0.0009189 0.003689 1.003 -0.0001105 4.96e-05 0.995 -8.327e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1978 0.0933 0.327 0.1538 0.9851 0.994 0.1985 0.459 0.8821 0.7159 ] Network output: [ 0.007504 -0.03684 0.9964 6.472e-05 -2.906e-05 1.026 4.878e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09794 0.08654 0.179 0.206 0.9873 0.992 0.09801 0.7819 0.8742 0.3079 ] Network output: [ -0.007494 0.03807 1.002 6.6e-05 -2.963e-05 0.9756 4.974e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09093 0.08902 0.1661 0.1964 0.9855 0.9914 0.09094 0.7098 0.8526 0.2433 ] Network output: [ 0.0002481 0.9997 -0.0005692 9.012e-06 -4.046e-06 1 6.792e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007146 Epoch 7218 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01242 0.9934 0.988 2.781e-06 -1.249e-06 -0.006297 2.096e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003238 -0.003026 -0.009018 0.006918 0.9698 0.9742 0.006168 0.8413 0.8298 0.01955 ] Network output: [ 0.9997 0.001143 0.001524 -3.336e-05 1.498e-05 -0.00221 -2.514e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.187 -0.03076 -0.191 0.1972 0.9836 0.9933 0.2088 0.4544 0.8756 0.7212 ] Network output: [ -0.01147 1.001 1.01 1.376e-06 -6.176e-07 0.01179 1.037e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005509 0.000468 0.004362 0.004154 0.9889 0.992 0.005611 0.8706 0.8995 0.01416 ] Network output: [ -0.0009072 0.00352 1.003 -0.0001104 4.956e-05 0.9952 -8.319e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1979 0.0933 0.3271 0.1539 0.9851 0.994 0.1985 0.459 0.8821 0.7159 ] Network output: [ 0.0075 -0.03686 0.9964 6.468e-05 -2.904e-05 1.026 4.874e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09795 0.08654 0.1791 0.206 0.9873 0.992 0.09801 0.7819 0.8742 0.3079 ] Network output: [ -0.007492 0.03806 1.002 6.595e-05 -2.961e-05 0.9756 4.971e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09093 0.08902 0.1661 0.1964 0.9855 0.9914 0.09094 0.7098 0.8526 0.2433 ] Network output: [ 0.0002551 0.9997 -0.0005787 9.007e-06 -4.044e-06 1 6.788e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007145 Epoch 7219 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01242 0.9935 0.988 2.775e-06 -1.246e-06 -0.006314 2.091e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003238 -0.003026 -0.009017 0.006917 0.9698 0.9742 0.006168 0.8413 0.8298 0.01955 ] Network output: [ 0.9997 0.001265 0.001516 -3.334e-05 1.497e-05 -0.002309 -2.513e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.187 -0.03076 -0.1909 0.1971 0.9836 0.9933 0.2088 0.4543 0.8756 0.7212 ] Network output: [ -0.01147 1.001 1.01 1.372e-06 -6.162e-07 0.01178 1.034e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00551 0.000468 0.004362 0.004152 0.9889 0.992 0.005611 0.8705 0.8995 0.01416 ] Network output: [ -0.0009179 0.003684 1.003 -0.0001103 4.952e-05 0.995 -8.314e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1979 0.09331 0.3271 0.1538 0.9851 0.994 0.1985 0.459 0.8821 0.7159 ] Network output: [ 0.007499 -0.03682 0.9964 6.463e-05 -2.901e-05 1.026 4.87e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09795 0.08655 0.179 0.206 0.9873 0.992 0.09801 0.7819 0.8742 0.3079 ] Network output: [ -0.007488 0.03804 1.002 6.591e-05 -2.959e-05 0.9756 4.967e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09093 0.08901 0.1661 0.1964 0.9855 0.9914 0.09094 0.7097 0.8526 0.2433 ] Network output: [ 0.0002479 0.9997 -0.0005683 8.999e-06 -4.04e-06 1 6.782e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007137 Epoch 7220 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01242 0.9934 0.988 2.772e-06 -1.244e-06 -0.006301 2.089e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003238 -0.003026 -0.009015 0.006916 0.9698 0.9742 0.006168 0.8413 0.8298 0.01954 ] Network output: [ 0.9997 0.001143 0.001522 -3.331e-05 1.496e-05 -0.002208 -2.511e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.187 -0.03077 -0.1909 0.1971 0.9836 0.9933 0.2088 0.4543 0.8756 0.7212 ] Network output: [ -0.01147 1.001 1.01 1.371e-06 -6.153e-07 0.01178 1.033e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005511 0.0004679 0.004363 0.004152 0.9889 0.992 0.005612 0.8705 0.8995 0.01416 ] Network output: [ -0.0009066 0.00352 1.003 -0.0001102 4.948e-05 0.9952 -8.306e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1979 0.09331 0.3271 0.1538 0.9851 0.994 0.1985 0.459 0.8821 0.7159 ] Network output: [ 0.007494 -0.03684 0.9964 6.458e-05 -2.899e-05 1.026 4.867e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09796 0.08655 0.1791 0.206 0.9873 0.992 0.09802 0.7818 0.8742 0.3079 ] Network output: [ -0.007486 0.03803 1.002 6.586e-05 -2.957e-05 0.9756 4.963e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09092 0.08901 0.1661 0.1964 0.9855 0.9914 0.09094 0.7097 0.8526 0.2433 ] Network output: [ 0.0002547 0.9997 -0.0005774 8.994e-06 -4.038e-06 1 6.778e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007135 Epoch 7221 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01241 0.9935 0.988 2.766e-06 -1.242e-06 -0.006318 2.084e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003238 -0.003026 -0.009014 0.006915 0.9698 0.9742 0.006169 0.8412 0.8298 0.01954 ] Network output: [ 0.9997 0.00126 0.001515 -3.33e-05 1.495e-05 -0.002304 -2.51e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.187 -0.03077 -0.1909 0.1971 0.9836 0.9933 0.2088 0.4543 0.8756 0.7212 ] Network output: [ -0.01147 1.001 1.01 1.367e-06 -6.139e-07 0.01177 1.031e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005511 0.0004678 0.004363 0.004151 0.9889 0.992 0.005613 0.8705 0.8995 0.01416 ] Network output: [ -0.000917 0.003679 1.003 -0.0001101 4.945e-05 0.995 -8.301e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1979 0.09331 0.3271 0.1538 0.9851 0.994 0.1985 0.4589 0.8821 0.7159 ] Network output: [ 0.007493 -0.03679 0.9964 6.453e-05 -2.897e-05 1.026 4.863e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09796 0.08655 0.1791 0.206 0.9873 0.992 0.09802 0.7818 0.8742 0.3079 ] Network output: [ -0.007482 0.03801 1.002 6.582e-05 -2.955e-05 0.9756 4.96e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09092 0.08901 0.1661 0.1964 0.9855 0.9914 0.09093 0.7097 0.8525 0.2433 ] Network output: [ 0.0002478 0.9997 -0.0005674 8.986e-06 -4.034e-06 1 6.772e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007128 Epoch 7222 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01241 0.9934 0.988 2.762e-06 -1.24e-06 -0.006306 2.082e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003238 -0.003027 -0.009012 0.006914 0.9698 0.9742 0.006169 0.8412 0.8298 0.01954 ] Network output: [ 0.9997 0.001142 0.00152 -3.327e-05 1.494e-05 -0.002207 -2.507e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1871 -0.03078 -0.1909 0.1971 0.9836 0.9933 0.2089 0.4543 0.8756 0.7212 ] Network output: [ -0.01147 1.001 1.01 1.366e-06 -6.13e-07 0.01177 1.029e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005512 0.0004678 0.004363 0.004151 0.9889 0.992 0.005613 0.8705 0.8994 0.01416 ] Network output: [ -0.000906 0.003521 1.003 -0.00011 4.94e-05 0.9952 -8.293e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1979 0.09331 0.3271 0.1538 0.9851 0.994 0.1985 0.4589 0.8821 0.7159 ] Network output: [ 0.007489 -0.03681 0.9964 6.448e-05 -2.895e-05 1.026 4.86e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09796 0.08656 0.1791 0.206 0.9873 0.992 0.09803 0.7818 0.8742 0.3079 ] Network output: [ -0.007479 0.038 1.002 6.577e-05 -2.953e-05 0.9756 4.956e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09092 0.089 0.1661 0.1964 0.9855 0.9914 0.09093 0.7096 0.8525 0.2433 ] Network output: [ 0.0002543 0.9997 -0.0005762 8.981e-06 -4.032e-06 1 6.769e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007126 Epoch 7223 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01241 0.9935 0.9881 2.756e-06 -1.237e-06 -0.006322 2.077e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003238 -0.003027 -0.009011 0.006913 0.9698 0.9742 0.006169 0.8412 0.8298 0.01954 ] Network output: [ 0.9997 0.001256 0.001513 -3.325e-05 1.493e-05 -0.0023 -2.506e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1871 -0.03078 -0.1909 0.1971 0.9836 0.9933 0.2089 0.4543 0.8755 0.7212 ] Network output: [ -0.01146 1.001 1.01 1.362e-06 -6.116e-07 0.01176 1.027e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005512 0.0004677 0.004363 0.004149 0.9889 0.992 0.005614 0.8705 0.8994 0.01416 ] Network output: [ -0.000916 0.003674 1.003 -0.00011 4.937e-05 0.9951 -8.287e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1979 0.09331 0.3271 0.1538 0.9851 0.994 0.1985 0.4589 0.8821 0.7159 ] Network output: [ 0.007488 -0.03677 0.9964 6.443e-05 -2.893e-05 1.026 4.856e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09797 0.08656 0.1791 0.206 0.9873 0.992 0.09803 0.7817 0.8742 0.3079 ] Network output: [ -0.007475 0.03797 1.002 6.572e-05 -2.951e-05 0.9756 4.953e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09092 0.089 0.1661 0.1964 0.9855 0.9914 0.09093 0.7096 0.8525 0.2433 ] Network output: [ 0.0002476 0.9997 -0.0005664 8.973e-06 -4.028e-06 1 6.762e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007119 Epoch 7224 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01241 0.9934 0.9881 2.753e-06 -1.236e-06 -0.006311 2.075e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003238 -0.003027 -0.009009 0.006912 0.9698 0.9742 0.00617 0.8412 0.8297 0.01954 ] Network output: [ 0.9997 0.001142 0.001518 -3.323e-05 1.492e-05 -0.002206 -2.504e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1871 -0.03079 -0.1908 0.1971 0.9836 0.9933 0.2089 0.4543 0.8755 0.7212 ] Network output: [ -0.01146 1.001 1.01 1.36e-06 -6.108e-07 0.01176 1.025e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005513 0.0004677 0.004363 0.004149 0.9889 0.992 0.005614 0.8705 0.8994 0.01416 ] Network output: [ -0.0009054 0.003522 1.003 -0.0001099 4.932e-05 0.9952 -8.28e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1979 0.09331 0.3272 0.1538 0.9851 0.994 0.1986 0.4589 0.882 0.7158 ] Network output: [ 0.007484 -0.03679 0.9964 6.438e-05 -2.89e-05 1.026 4.852e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09797 0.08656 0.1791 0.206 0.9873 0.992 0.09804 0.7817 0.8742 0.3079 ] Network output: [ -0.007473 0.03797 1.002 6.567e-05 -2.948e-05 0.9756 4.949e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09091 0.089 0.1661 0.1964 0.9855 0.9914 0.09092 0.7096 0.8525 0.2433 ] Network output: [ 0.0002539 0.9997 -0.0005749 8.968e-06 -4.026e-06 1 6.759e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007117 Epoch 7225 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0124 0.9935 0.9881 2.747e-06 -1.233e-06 -0.006327 2.07e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003238 -0.003027 -0.009008 0.006911 0.9698 0.9742 0.00617 0.8412 0.8297 0.01953 ] Network output: [ 0.9997 0.001251 0.001511 -3.321e-05 1.491e-05 -0.002295 -2.503e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1871 -0.03079 -0.1908 0.1971 0.9836 0.9933 0.2089 0.4542 0.8755 0.7212 ] Network output: [ -0.01146 1.001 1.01 1.357e-06 -6.094e-07 0.01175 1.023e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005514 0.0004676 0.004363 0.004148 0.9889 0.992 0.005615 0.8705 0.8994 0.01415 ] Network output: [ -0.000915 0.003669 1.003 -0.0001098 4.929e-05 0.9951 -8.274e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1979 0.09331 0.3272 0.1537 0.9851 0.994 0.1986 0.4589 0.882 0.7158 ] Network output: [ 0.007483 -0.03675 0.9964 6.433e-05 -2.888e-05 1.026 4.848e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09798 0.08656 0.1791 0.2059 0.9873 0.992 0.09804 0.7817 0.8741 0.3079 ] Network output: [ -0.007469 0.03794 1.002 6.563e-05 -2.946e-05 0.9757 4.946e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09091 0.08899 0.1661 0.1964 0.9855 0.9914 0.09092 0.7095 0.8525 0.2433 ] Network output: [ 0.0002475 0.9997 -0.0005655 8.96e-06 -4.023e-06 1 6.753e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000711 Epoch 7226 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0124 0.9935 0.9881 2.744e-06 -1.232e-06 -0.006316 2.068e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003239 -0.003027 -0.009006 0.00691 0.9698 0.9742 0.00617 0.8412 0.8297 0.01953 ] Network output: [ 0.9997 0.001142 0.001516 -3.318e-05 1.49e-05 -0.002205 -2.501e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1871 -0.0308 -0.1908 0.1971 0.9836 0.9933 0.2089 0.4542 0.8755 0.7212 ] Network output: [ -0.01146 1.001 1.01 1.355e-06 -6.085e-07 0.01175 1.021e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005514 0.0004676 0.004364 0.004148 0.9889 0.992 0.005616 0.8705 0.8994 0.01415 ] Network output: [ -0.0009048 0.003522 1.003 -0.0001097 4.925e-05 0.9952 -8.267e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1979 0.09331 0.3272 0.1538 0.9851 0.994 0.1986 0.4589 0.882 0.7158 ] Network output: [ 0.007478 -0.03676 0.9964 6.429e-05 -2.886e-05 1.026 4.845e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09798 0.08657 0.1791 0.2059 0.9873 0.992 0.09804 0.7817 0.8741 0.3079 ] Network output: [ -0.007466 0.03793 1.002 6.558e-05 -2.944e-05 0.9757 4.942e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09091 0.08899 0.1661 0.1964 0.9855 0.9914 0.09092 0.7095 0.8525 0.2433 ] Network output: [ 0.0002535 0.9997 -0.0005737 8.955e-06 -4.02e-06 1 6.749e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007108 Epoch 7227 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0124 0.9935 0.9881 2.738e-06 -1.229e-06 -0.006331 2.063e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003239 -0.003027 -0.009005 0.006909 0.9698 0.9742 0.006171 0.8412 0.8297 0.01953 ] Network output: [ 0.9997 0.001247 0.001509 -3.317e-05 1.489e-05 -0.002291 -2.499e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1871 -0.0308 -0.1908 0.197 0.9836 0.9933 0.2089 0.4542 0.8755 0.7212 ] Network output: [ -0.01146 1.001 1.01 1.352e-06 -6.071e-07 0.01174 1.019e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005515 0.0004675 0.004364 0.004146 0.9889 0.992 0.005616 0.8705 0.8994 0.01415 ] Network output: [ -0.000914 0.003664 1.003 -0.0001096 4.921e-05 0.9951 -8.261e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.198 0.09331 0.3272 0.1537 0.9851 0.994 0.1986 0.4588 0.882 0.7158 ] Network output: [ 0.007477 -0.03672 0.9964 6.424e-05 -2.884e-05 1.026 4.841e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09799 0.08657 0.1791 0.2059 0.9873 0.992 0.09805 0.7816 0.8741 0.3079 ] Network output: [ -0.007463 0.03791 1.002 6.554e-05 -2.942e-05 0.9757 4.939e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0909 0.08899 0.1661 0.1963 0.9855 0.9914 0.09092 0.7095 0.8525 0.2433 ] Network output: [ 0.0002473 0.9997 -0.0005646 8.947e-06 -4.017e-06 1 6.743e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007101 Epoch 7228 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0124 0.9935 0.9881 2.734e-06 -1.228e-06 -0.00632 2.061e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003239 -0.003028 -0.009003 0.006908 0.9698 0.9742 0.006171 0.8412 0.8297 0.01953 ] Network output: [ 0.9997 0.001141 0.001514 -3.314e-05 1.488e-05 -0.002203 -2.497e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1871 -0.03081 -0.1908 0.1971 0.9836 0.9933 0.2089 0.4542 0.8755 0.7212 ] Network output: [ -0.01146 1.001 1.01 1.35e-06 -6.062e-07 0.01174 1.018e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005515 0.0004674 0.004364 0.004146 0.9889 0.992 0.005617 0.8705 0.8994 0.01415 ] Network output: [ -0.0009042 0.003523 1.003 -0.0001095 4.917e-05 0.9952 -8.254e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.198 0.09331 0.3272 0.1537 0.9851 0.994 0.1986 0.4588 0.882 0.7158 ] Network output: [ 0.007473 -0.03674 0.9964 6.419e-05 -2.882e-05 1.026 4.838e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09799 0.08657 0.1791 0.2059 0.9873 0.992 0.09805 0.7816 0.8741 0.3079 ] Network output: [ -0.00746 0.0379 1.002 6.549e-05 -2.94e-05 0.9757 4.935e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0909 0.08899 0.1661 0.1963 0.9855 0.9914 0.09091 0.7094 0.8525 0.2433 ] Network output: [ 0.0002531 0.9997 -0.0005725 8.942e-06 -4.015e-06 1 6.739e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007099 Epoch 7229 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01239 0.9935 0.9881 2.729e-06 -1.225e-06 -0.006335 2.056e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003239 -0.003028 -0.009002 0.006907 0.9698 0.9742 0.006171 0.8412 0.8297 0.01953 ] Network output: [ 0.9997 0.001243 0.001508 -3.312e-05 1.487e-05 -0.002286 -2.496e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1871 -0.03081 -0.1908 0.197 0.9836 0.9933 0.2089 0.4542 0.8755 0.7212 ] Network output: [ -0.01146 1.001 1.01 1.347e-06 -6.049e-07 0.01174 1.015e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005516 0.0004674 0.004364 0.004145 0.9889 0.992 0.005617 0.8704 0.8994 0.01415 ] Network output: [ -0.0009131 0.00366 1.003 -0.0001094 4.914e-05 0.9951 -8.248e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.198 0.09332 0.3272 0.1537 0.9851 0.994 0.1986 0.4588 0.882 0.7158 ] Network output: [ 0.007472 -0.0367 0.9964 6.414e-05 -2.88e-05 1.026 4.834e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09799 0.08658 0.1791 0.2059 0.9873 0.992 0.09806 0.7816 0.8741 0.3079 ] Network output: [ -0.007456 0.03788 1.002 6.544e-05 -2.938e-05 0.9757 4.932e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0909 0.08898 0.1661 0.1963 0.9855 0.9914 0.09091 0.7094 0.8524 0.2433 ] Network output: [ 0.0002471 0.9997 -0.0005637 8.934e-06 -4.011e-06 1 6.733e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007092 Epoch 7230 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01239 0.9935 0.9881 2.725e-06 -1.223e-06 -0.006325 2.054e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003239 -0.003028 -0.009 0.006906 0.9698 0.9742 0.006171 0.8412 0.8297 0.01952 ] Network output: [ 0.9997 0.001141 0.001512 -3.309e-05 1.486e-05 -0.002202 -2.494e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1871 -0.03082 -0.1907 0.197 0.9836 0.9933 0.209 0.4542 0.8755 0.7212 ] Network output: [ -0.01146 1.001 1.01 1.345e-06 -6.04e-07 0.01174 1.014e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005517 0.0004673 0.004364 0.004145 0.9889 0.992 0.005618 0.8704 0.8994 0.01415 ] Network output: [ -0.0009035 0.003523 1.003 -0.0001094 4.909e-05 0.9952 -8.241e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.198 0.09332 0.3273 0.1537 0.9851 0.994 0.1986 0.4588 0.882 0.7158 ] Network output: [ 0.007468 -0.03671 0.9964 6.409e-05 -2.877e-05 1.026 4.83e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.098 0.08658 0.1791 0.2059 0.9873 0.992 0.09806 0.7816 0.8741 0.3079 ] Network output: [ -0.007454 0.03787 1.002 6.54e-05 -2.936e-05 0.9757 4.928e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0909 0.08898 0.1661 0.1963 0.9855 0.9914 0.09091 0.7094 0.8524 0.2433 ] Network output: [ 0.0002528 0.9997 -0.0005713 8.929e-06 -4.009e-06 1 6.73e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000709 Epoch 7231 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01239 0.9935 0.9881 2.719e-06 -1.221e-06 -0.006339 2.049e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003239 -0.003028 -0.008999 0.006905 0.9698 0.9742 0.006172 0.8412 0.8297 0.01952 ] Network output: [ 0.9997 0.001239 0.001506 -3.308e-05 1.485e-05 -0.002282 -2.493e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1872 -0.03082 -0.1907 0.197 0.9836 0.9933 0.209 0.4541 0.8755 0.7212 ] Network output: [ -0.01145 1.001 1.01 1.342e-06 -6.026e-07 0.01173 1.012e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005517 0.0004672 0.004364 0.004143 0.9889 0.992 0.005619 0.8704 0.8994 0.01414 ] Network output: [ -0.0009121 0.003655 1.003 -0.0001093 4.906e-05 0.9951 -8.235e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.198 0.09332 0.3273 0.1537 0.9851 0.994 0.1986 0.4588 0.882 0.7158 ] Network output: [ 0.007467 -0.03667 0.9964 6.404e-05 -2.875e-05 1.026 4.827e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.098 0.08658 0.1791 0.2059 0.9873 0.992 0.09806 0.7815 0.8741 0.3079 ] Network output: [ -0.00745 0.03784 1.002 6.535e-05 -2.934e-05 0.9757 4.925e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09089 0.08898 0.1661 0.1963 0.9855 0.9914 0.0909 0.7093 0.8524 0.2433 ] Network output: [ 0.000247 0.9997 -0.0005628 8.922e-06 -4.005e-06 1 6.724e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007083 Epoch 7232 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01239 0.9935 0.9881 2.716e-06 -1.219e-06 -0.00633 2.047e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003239 -0.003028 -0.008997 0.006904 0.9698 0.9742 0.006172 0.8412 0.8297 0.01952 ] Network output: [ 0.9997 0.001141 0.00151 -3.305e-05 1.484e-05 -0.002201 -2.491e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1872 -0.03083 -0.1907 0.197 0.9836 0.9933 0.209 0.4541 0.8755 0.7212 ] Network output: [ -0.01145 1.001 1.01 1.34e-06 -6.017e-07 0.01173 1.01e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005518 0.0004672 0.004365 0.004143 0.9889 0.992 0.005619 0.8704 0.8994 0.01414 ] Network output: [ -0.0009029 0.003523 1.003 -0.0001092 4.902e-05 0.9952 -8.228e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.198 0.09332 0.3273 0.1537 0.9851 0.994 0.1987 0.4588 0.882 0.7158 ] Network output: [ 0.007463 -0.03669 0.9964 6.4e-05 -2.873e-05 1.026 4.823e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09801 0.08659 0.1791 0.2059 0.9873 0.992 0.09807 0.7815 0.8741 0.3079 ] Network output: [ -0.007447 0.03784 1.002 6.53e-05 -2.932e-05 0.9757 4.921e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09089 0.08897 0.1661 0.1963 0.9855 0.9914 0.0909 0.7093 0.8524 0.2433 ] Network output: [ 0.0002524 0.9997 -0.00057 8.917e-06 -4.003e-06 1 6.72e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007081 Epoch 7233 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01239 0.9935 0.9881 2.71e-06 -1.217e-06 -0.006344 2.042e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003239 -0.003028 -0.008996 0.006903 0.9698 0.9742 0.006172 0.8411 0.8297 0.01952 ] Network output: [ 0.9997 0.001235 0.001504 -3.303e-05 1.483e-05 -0.002277 -2.489e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1872 -0.03083 -0.1907 0.197 0.9836 0.9933 0.209 0.4541 0.8755 0.7212 ] Network output: [ -0.01145 1.001 1.01 1.337e-06 -6.004e-07 0.01172 1.008e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005518 0.0004671 0.004365 0.004142 0.9889 0.992 0.00562 0.8704 0.8994 0.01414 ] Network output: [ -0.0009112 0.003651 1.003 -0.0001091 4.898e-05 0.9951 -8.223e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.198 0.09332 0.3273 0.1536 0.9851 0.994 0.1987 0.4587 0.882 0.7158 ] Network output: [ 0.007461 -0.03665 0.9964 6.395e-05 -2.871e-05 1.026 4.819e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09801 0.08659 0.1791 0.2059 0.9873 0.992 0.09807 0.7815 0.8741 0.3079 ] Network output: [ -0.007444 0.03781 1.002 6.526e-05 -2.93e-05 0.9757 4.918e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09089 0.08897 0.1661 0.1963 0.9855 0.9914 0.0909 0.7093 0.8524 0.2433 ] Network output: [ 0.0002468 0.9997 -0.0005618 8.909e-06 -3.999e-06 1 6.714e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007074 Epoch 7234 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01238 0.9935 0.9881 2.706e-06 -1.215e-06 -0.006334 2.04e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00324 -0.003028 -0.008994 0.006902 0.9698 0.9742 0.006173 0.8411 0.8297 0.01952 ] Network output: [ 0.9997 0.00114 0.001508 -3.3e-05 1.482e-05 -0.002199 -2.487e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1872 -0.03084 -0.1906 0.197 0.9836 0.9933 0.209 0.4541 0.8755 0.7212 ] Network output: [ -0.01145 1.001 1.01 1.335e-06 -5.994e-07 0.01172 1.006e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005519 0.0004671 0.004365 0.004142 0.9889 0.992 0.00562 0.8704 0.8994 0.01414 ] Network output: [ -0.0009023 0.003524 1.003 -0.000109 4.894e-05 0.9952 -8.215e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.198 0.09332 0.3273 0.1536 0.9851 0.994 0.1987 0.4587 0.882 0.7158 ] Network output: [ 0.007457 -0.03666 0.9964 6.39e-05 -2.869e-05 1.026 4.816e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09801 0.08659 0.1791 0.2059 0.9873 0.992 0.09808 0.7814 0.8741 0.3079 ] Network output: [ -0.007441 0.0378 1.002 6.521e-05 -2.928e-05 0.9757 4.914e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09088 0.08897 0.1661 0.1963 0.9855 0.9914 0.0909 0.7092 0.8524 0.2433 ] Network output: [ 0.000252 0.9997 -0.0005688 8.904e-06 -3.997e-06 1 6.71e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007072 Epoch 7235 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01238 0.9935 0.9881 2.701e-06 -1.212e-06 -0.006348 2.035e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00324 -0.003029 -0.008993 0.006901 0.9698 0.9742 0.006173 0.8411 0.8297 0.01951 ] Network output: [ 0.9997 0.001231 0.001502 -3.299e-05 1.481e-05 -0.002273 -2.486e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1872 -0.03084 -0.1906 0.197 0.9836 0.9933 0.209 0.4541 0.8755 0.7212 ] Network output: [ -0.01145 1.001 1.01 1.332e-06 -5.981e-07 0.01171 1.004e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00552 0.000467 0.004365 0.00414 0.9889 0.992 0.005621 0.8704 0.8994 0.01414 ] Network output: [ -0.0009102 0.003646 1.003 -0.0001089 4.89e-05 0.9951 -8.21e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1981 0.09332 0.3273 0.1536 0.9851 0.994 0.1987 0.4587 0.882 0.7158 ] Network output: [ 0.007456 -0.03662 0.9964 6.385e-05 -2.867e-05 1.026 4.812e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09802 0.08659 0.1791 0.2059 0.9873 0.992 0.09808 0.7814 0.874 0.3079 ] Network output: [ -0.007437 0.03778 1.002 6.516e-05 -2.925e-05 0.9757 4.911e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09088 0.08897 0.1661 0.1963 0.9855 0.9914 0.09089 0.7092 0.8524 0.2433 ] Network output: [ 0.0002466 0.9997 -0.0005609 8.896e-06 -3.994e-06 1 6.704e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007065 Epoch 7236 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01238 0.9935 0.9881 2.697e-06 -1.211e-06 -0.006339 2.033e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00324 -0.003029 -0.008991 0.0069 0.9698 0.9742 0.006173 0.8411 0.8297 0.01951 ] Network output: [ 0.9997 0.001139 0.001506 -3.296e-05 1.48e-05 -0.002198 -2.484e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1872 -0.03085 -0.1906 0.197 0.9836 0.9933 0.209 0.4541 0.8755 0.7212 ] Network output: [ -0.01145 1.001 1.01 1.33e-06 -5.972e-07 0.01171 1.002e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00552 0.000467 0.004365 0.00414 0.9889 0.992 0.005622 0.8704 0.8994 0.01414 ] Network output: [ -0.0009016 0.003524 1.003 -0.0001088 4.886e-05 0.9952 -8.202e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1981 0.09332 0.3274 0.1536 0.9851 0.994 0.1987 0.4587 0.882 0.7158 ] Network output: [ 0.007452 -0.03664 0.9964 6.38e-05 -2.864e-05 1.026 4.808e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09802 0.0866 0.1791 0.2058 0.9873 0.992 0.09809 0.7814 0.874 0.3079 ] Network output: [ -0.007435 0.03777 1.002 6.512e-05 -2.923e-05 0.9757 4.908e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09088 0.08896 0.1661 0.1963 0.9855 0.9914 0.09089 0.7092 0.8524 0.2433 ] Network output: [ 0.0002516 0.9997 -0.0005676 8.891e-06 -3.991e-06 1 6.7e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007063 Epoch 7237 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01238 0.9935 0.9881 2.692e-06 -1.208e-06 -0.006352 2.028e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00324 -0.003029 -0.00899 0.006899 0.9698 0.9742 0.006174 0.8411 0.8297 0.01951 ] Network output: [ 0.9997 0.001227 0.001501 -3.294e-05 1.479e-05 -0.002269 -2.483e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1872 -0.03085 -0.1906 0.1969 0.9836 0.9933 0.209 0.454 0.8755 0.7212 ] Network output: [ -0.01145 1.001 1.01 1.327e-06 -5.958e-07 0.0117 1e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005521 0.0004669 0.004365 0.004139 0.9889 0.992 0.005622 0.8704 0.8994 0.01414 ] Network output: [ -0.0009093 0.003642 1.003 -0.0001088 4.883e-05 0.9951 -8.197e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1981 0.09333 0.3274 0.1536 0.9851 0.994 0.1987 0.4587 0.882 0.7158 ] Network output: [ 0.007451 -0.0366 0.9964 6.375e-05 -2.862e-05 1.026 4.805e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09803 0.0866 0.1791 0.2058 0.9873 0.992 0.09809 0.7814 0.874 0.3078 ] Network output: [ -0.007431 0.03775 1.002 6.507e-05 -2.921e-05 0.9758 4.904e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09088 0.08896 0.1661 0.1963 0.9855 0.9914 0.09089 0.7091 0.8523 0.2433 ] Network output: [ 0.0002464 0.9997 -0.00056 8.883e-06 -3.988e-06 1 6.694e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007056 Epoch 7238 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01237 0.9935 0.9881 2.688e-06 -1.207e-06 -0.006343 2.026e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00324 -0.003029 -0.008988 0.006898 0.9698 0.9742 0.006174 0.8411 0.8297 0.01951 ] Network output: [ 0.9997 0.001139 0.001504 -3.291e-05 1.478e-05 -0.002196 -2.48e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1872 -0.03086 -0.1906 0.197 0.9836 0.9933 0.2091 0.454 0.8755 0.7212 ] Network output: [ -0.01145 1.001 1.01 1.325e-06 -5.949e-07 0.0117 9.987e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005521 0.0004669 0.004366 0.004139 0.9889 0.992 0.005623 0.8704 0.8994 0.01413 ] Network output: [ -0.000901 0.003524 1.003 -0.0001087 4.878e-05 0.9952 -8.189e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1981 0.09333 0.3274 0.1536 0.9851 0.994 0.1987 0.4586 0.882 0.7158 ] Network output: [ 0.007447 -0.03661 0.9964 6.371e-05 -2.86e-05 1.026 4.801e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09803 0.0866 0.1791 0.2058 0.9873 0.992 0.09809 0.7813 0.874 0.3078 ] Network output: [ -0.007428 0.03774 1.002 6.503e-05 -2.919e-05 0.9758 4.901e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09087 0.08896 0.1661 0.1963 0.9855 0.9914 0.09089 0.7091 0.8523 0.2433 ] Network output: [ 0.0002513 0.9997 -0.0005664 8.878e-06 -3.986e-06 1 6.691e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007054 Epoch 7239 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01237 0.9935 0.9881 2.682e-06 -1.204e-06 -0.006356 2.021e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00324 -0.003029 -0.008987 0.006897 0.9698 0.9742 0.006174 0.8411 0.8297 0.01951 ] Network output: [ 0.9997 0.001223 0.001499 -3.29e-05 1.477e-05 -0.002265 -2.479e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1872 -0.03086 -0.1906 0.1969 0.9836 0.9933 0.2091 0.454 0.8755 0.7212 ] Network output: [ -0.01144 1.001 1.01 1.322e-06 -5.936e-07 0.01169 9.965e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005522 0.0004668 0.004366 0.004137 0.9889 0.992 0.005624 0.8703 0.8994 0.01413 ] Network output: [ -0.0009083 0.003638 1.003 -0.0001086 4.875e-05 0.9951 -8.184e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1981 0.09333 0.3274 0.1536 0.9851 0.994 0.1987 0.4586 0.882 0.7158 ] Network output: [ 0.007445 -0.03658 0.9964 6.366e-05 -2.858e-05 1.026 4.797e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09804 0.08661 0.1791 0.2058 0.9873 0.992 0.0981 0.7813 0.874 0.3078 ] Network output: [ -0.007425 0.03771 1.002 6.498e-05 -2.917e-05 0.9758 4.897e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09087 0.08895 0.166 0.1963 0.9855 0.9914 0.09088 0.7091 0.8523 0.2433 ] Network output: [ 0.0002462 0.9997 -0.000559 8.87e-06 -3.982e-06 1 6.685e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007047 Epoch 7240 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01237 0.9935 0.9881 2.679e-06 -1.203e-06 -0.006348 2.019e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00324 -0.003029 -0.008985 0.006896 0.9698 0.9742 0.006174 0.8411 0.8296 0.0195 ] Network output: [ 0.9997 0.001138 0.001502 -3.287e-05 1.476e-05 -0.002195 -2.477e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1873 -0.03087 -0.1905 0.1969 0.9836 0.9933 0.2091 0.454 0.8755 0.7211 ] Network output: [ -0.01144 1.001 1.01 1.32e-06 -5.927e-07 0.01169 9.949e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005522 0.0004667 0.004366 0.004137 0.9889 0.992 0.005624 0.8703 0.8993 0.01413 ] Network output: [ -0.0009003 0.003524 1.003 -0.0001085 4.871e-05 0.9952 -8.176e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1981 0.09333 0.3274 0.1536 0.9851 0.994 0.1988 0.4586 0.882 0.7158 ] Network output: [ 0.007441 -0.03658 0.9964 6.361e-05 -2.856e-05 1.026 4.794e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09804 0.08661 0.1791 0.2058 0.9873 0.992 0.0981 0.7813 0.874 0.3078 ] Network output: [ -0.007422 0.03771 1.002 6.493e-05 -2.915e-05 0.9758 4.894e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09087 0.08895 0.166 0.1963 0.9855 0.9914 0.09088 0.709 0.8523 0.2433 ] Network output: [ 0.0002509 0.9997 -0.0005652 8.865e-06 -3.98e-06 1 6.681e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007045 Epoch 7241 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01237 0.9935 0.9881 2.673e-06 -1.2e-06 -0.00636 2.015e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00324 -0.00303 -0.008984 0.006895 0.9698 0.9742 0.006175 0.8411 0.8296 0.0195 ] Network output: [ 0.9997 0.001219 0.001497 -3.285e-05 1.475e-05 -0.00226 -2.476e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1873 -0.03087 -0.1905 0.1969 0.9836 0.9933 0.2091 0.454 0.8754 0.7211 ] Network output: [ -0.01144 1.001 1.01 1.317e-06 -5.913e-07 0.01169 9.927e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005523 0.0004667 0.004366 0.004136 0.9889 0.992 0.005625 0.8703 0.8993 0.01413 ] Network output: [ -0.0009074 0.003633 1.003 -0.0001084 4.867e-05 0.9951 -8.171e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1981 0.09333 0.3274 0.1535 0.9851 0.994 0.1988 0.4586 0.882 0.7158 ] Network output: [ 0.00744 -0.03655 0.9964 6.356e-05 -2.854e-05 1.026 4.79e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09804 0.08661 0.1791 0.2058 0.9873 0.992 0.09811 0.7812 0.874 0.3078 ] Network output: [ -0.007418 0.03768 1.002 6.489e-05 -2.913e-05 0.9758 4.89e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09086 0.08895 0.166 0.1963 0.9855 0.9914 0.09088 0.709 0.8523 0.2433 ] Network output: [ 0.000246 0.9997 -0.0005581 8.857e-06 -3.976e-06 1 6.675e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007038 Epoch 7242 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01237 0.9935 0.9881 2.669e-06 -1.198e-06 -0.006353 2.012e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003241 -0.00303 -0.008982 0.006894 0.9698 0.9742 0.006175 0.8411 0.8296 0.0195 ] Network output: [ 0.9997 0.001137 0.0015 -3.282e-05 1.474e-05 -0.002193 -2.474e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1873 -0.03088 -0.1905 0.1969 0.9836 0.9933 0.2091 0.454 0.8754 0.7211 ] Network output: [ -0.01144 1.001 1.01 1.315e-06 -5.904e-07 0.01169 9.911e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005524 0.0004666 0.004366 0.004136 0.9889 0.992 0.005625 0.8703 0.8993 0.01413 ] Network output: [ -0.0008996 0.003524 1.003 -0.0001083 4.863e-05 0.9952 -8.164e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1981 0.09333 0.3275 0.1535 0.9851 0.994 0.1988 0.4586 0.8819 0.7158 ] Network output: [ 0.007436 -0.03656 0.9964 6.351e-05 -2.851e-05 1.026 4.787e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09805 0.08662 0.1791 0.2058 0.9873 0.992 0.09811 0.7812 0.874 0.3078 ] Network output: [ -0.007416 0.03767 1.002 6.484e-05 -2.911e-05 0.9758 4.887e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09086 0.08895 0.166 0.1963 0.9855 0.9914 0.09087 0.709 0.8523 0.2433 ] Network output: [ 0.0002505 0.9997 -0.000564 8.852e-06 -3.974e-06 1 6.671e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007035 Epoch 7243 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01236 0.9935 0.9881 2.664e-06 -1.196e-06 -0.006365 2.008e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003241 -0.00303 -0.008981 0.006893 0.9698 0.9742 0.006175 0.8411 0.8296 0.0195 ] Network output: [ 0.9997 0.001215 0.001495 -3.281e-05 1.473e-05 -0.002256 -2.472e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1873 -0.03088 -0.1905 0.1969 0.9836 0.9933 0.2091 0.4539 0.8754 0.7211 ] Network output: [ -0.01144 1.001 1.01 1.312e-06 -5.891e-07 0.01168 9.889e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005524 0.0004666 0.004366 0.004134 0.9889 0.992 0.005626 0.8703 0.8993 0.01413 ] Network output: [ -0.0009064 0.003629 1.003 -0.0001082 4.86e-05 0.9951 -8.158e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1982 0.09333 0.3275 0.1535 0.9851 0.994 0.1988 0.4586 0.8819 0.7158 ] Network output: [ 0.007435 -0.03653 0.9964 6.347e-05 -2.849e-05 1.026 4.783e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09805 0.08662 0.1791 0.2058 0.9873 0.992 0.09812 0.7812 0.874 0.3078 ] Network output: [ -0.007412 0.03765 1.002 6.479e-05 -2.909e-05 0.9758 4.883e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09086 0.08894 0.166 0.1963 0.9855 0.9914 0.09087 0.7089 0.8523 0.2433 ] Network output: [ 0.0002459 0.9997 -0.0005571 8.844e-06 -3.97e-06 1 6.665e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007029 Epoch 7244 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01236 0.9935 0.9881 2.66e-06 -1.194e-06 -0.006357 2.005e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003241 -0.00303 -0.008979 0.006892 0.9698 0.9742 0.006176 0.8411 0.8296 0.0195 ] Network output: [ 0.9997 0.001136 0.001499 -3.278e-05 1.472e-05 -0.002191 -2.47e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1873 -0.03089 -0.1905 0.1969 0.9836 0.9933 0.2091 0.4539 0.8754 0.7211 ] Network output: [ -0.01144 1.001 1.01 1.31e-06 -5.882e-07 0.01168 9.873e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005525 0.0004665 0.004367 0.004134 0.9889 0.992 0.005627 0.8703 0.8993 0.01412 ] Network output: [ -0.000899 0.003524 1.003 -0.0001082 4.855e-05 0.9952 -8.151e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1982 0.09333 0.3275 0.1535 0.9851 0.994 0.1988 0.4585 0.8819 0.7158 ] Network output: [ 0.007431 -0.03653 0.9964 6.342e-05 -2.847e-05 1.026 4.779e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09806 0.08662 0.1791 0.2058 0.9873 0.992 0.09812 0.7812 0.874 0.3078 ] Network output: [ -0.00741 0.03764 1.002 6.475e-05 -2.907e-05 0.9758 4.88e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09086 0.08894 0.166 0.1963 0.9855 0.9914 0.09087 0.7089 0.8523 0.2433 ] Network output: [ 0.0002502 0.9997 -0.0005629 8.839e-06 -3.968e-06 1 6.661e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007026 Epoch 7245 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01236 0.9935 0.9881 2.655e-06 -1.192e-06 -0.006369 2.001e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003241 -0.00303 -0.008978 0.006891 0.9698 0.9742 0.006176 0.841 0.8296 0.01949 ] Network output: [ 0.9997 0.001212 0.001493 -3.276e-05 1.471e-05 -0.002252 -2.469e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1873 -0.03089 -0.1904 0.1969 0.9836 0.9933 0.2091 0.4539 0.8754 0.7211 ] Network output: [ -0.01144 1.001 1.01 1.307e-06 -5.869e-07 0.01167 9.852e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005525 0.0004665 0.004367 0.004133 0.9889 0.992 0.005627 0.8703 0.8993 0.01412 ] Network output: [ -0.0009055 0.003625 1.003 -0.0001081 4.852e-05 0.9951 -8.145e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1982 0.09333 0.3275 0.1535 0.9851 0.994 0.1988 0.4585 0.8819 0.7158 ] Network output: [ 0.007429 -0.0365 0.9964 6.337e-05 -2.845e-05 1.026 4.776e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09806 0.08663 0.1791 0.2058 0.9873 0.992 0.09812 0.7811 0.8739 0.3078 ] Network output: [ -0.007406 0.03762 1.002 6.47e-05 -2.905e-05 0.9758 4.876e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09085 0.08894 0.166 0.1963 0.9855 0.9914 0.09087 0.7089 0.8522 0.2433 ] Network output: [ 0.0002457 0.9997 -0.0005562 8.831e-06 -3.965e-06 1 6.656e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000702 Epoch 7246 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01236 0.9935 0.9881 2.651e-06 -1.19e-06 -0.006362 1.998e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003241 -0.00303 -0.008976 0.00689 0.9698 0.9742 0.006176 0.841 0.8296 0.01949 ] Network output: [ 0.9997 0.001136 0.001497 -3.274e-05 1.47e-05 -0.002189 -2.467e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1873 -0.0309 -0.1904 0.1969 0.9836 0.9933 0.2092 0.4539 0.8754 0.7211 ] Network output: [ -0.01144 1.001 1.01 1.305e-06 -5.859e-07 0.01167 9.836e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005526 0.0004664 0.004367 0.004133 0.9889 0.992 0.005628 0.8703 0.8993 0.01412 ] Network output: [ -0.0008983 0.003523 1.003 -0.000108 4.848e-05 0.9952 -8.138e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1982 0.09334 0.3275 0.1535 0.9851 0.994 0.1988 0.4585 0.8819 0.7157 ] Network output: [ 0.007426 -0.03651 0.9964 6.332e-05 -2.843e-05 1.026 4.772e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09807 0.08663 0.1791 0.2058 0.9873 0.992 0.09813 0.7811 0.8739 0.3078 ] Network output: [ -0.007403 0.03761 1.002 6.465e-05 -2.903e-05 0.9758 4.873e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09085 0.08894 0.166 0.1963 0.9855 0.9914 0.09086 0.7088 0.8522 0.2433 ] Network output: [ 0.0002498 0.9997 -0.0005617 8.826e-06 -3.962e-06 1 6.652e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007017 Epoch 7247 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01235 0.9935 0.9881 2.646e-06 -1.188e-06 -0.006373 1.994e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003241 -0.003031 -0.008975 0.006889 0.9698 0.9742 0.006177 0.841 0.8296 0.01949 ] Network output: [ 0.9997 0.001208 0.001492 -3.272e-05 1.469e-05 -0.002248 -2.466e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1873 -0.0309 -0.1904 0.1969 0.9836 0.9933 0.2092 0.4539 0.8754 0.7211 ] Network output: [ -0.01143 1.001 1.01 1.302e-06 -5.846e-07 0.01166 9.814e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005527 0.0004664 0.004367 0.004132 0.9889 0.992 0.005628 0.8703 0.8993 0.01412 ] Network output: [ -0.0009046 0.003621 1.003 -0.0001079 4.844e-05 0.9951 -8.132e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1982 0.09334 0.3275 0.1535 0.9851 0.994 0.1988 0.4585 0.8819 0.7157 ] Network output: [ 0.007424 -0.03648 0.9964 6.327e-05 -2.841e-05 1.026 4.768e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09807 0.08663 0.1791 0.2058 0.9873 0.992 0.09813 0.7811 0.8739 0.3078 ] Network output: [ -0.0074 0.03759 1.002 6.461e-05 -2.901e-05 0.9758 4.869e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09085 0.08893 0.166 0.1963 0.9855 0.9914 0.09086 0.7088 0.8522 0.2433 ] Network output: [ 0.0002455 0.9997 -0.0005552 8.818e-06 -3.959e-06 1 6.646e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007011 Epoch 7248 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01235 0.9935 0.9881 2.642e-06 -1.186e-06 -0.006366 1.991e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003241 -0.003031 -0.008973 0.006888 0.9698 0.9742 0.006177 0.841 0.8296 0.01949 ] Network output: [ 0.9997 0.001135 0.001495 -3.269e-05 1.468e-05 -0.002188 -2.464e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1873 -0.03091 -0.1904 0.1969 0.9836 0.9933 0.2092 0.4539 0.8754 0.7211 ] Network output: [ -0.01143 1.001 1.01 1.3e-06 -5.837e-07 0.01166 9.798e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005527 0.0004663 0.004367 0.004131 0.9889 0.992 0.005629 0.8703 0.8993 0.01412 ] Network output: [ -0.0008976 0.003523 1.003 -0.0001078 4.84e-05 0.9952 -8.125e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1982 0.09334 0.3276 0.1535 0.9851 0.994 0.1988 0.4585 0.8819 0.7157 ] Network output: [ 0.00742 -0.03648 0.9964 6.323e-05 -2.838e-05 1.026 4.765e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09807 0.08664 0.1791 0.2057 0.9873 0.992 0.09814 0.7811 0.8739 0.3078 ] Network output: [ -0.007397 0.03758 1.002 6.456e-05 -2.898e-05 0.9758 4.866e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09085 0.08893 0.166 0.1963 0.9855 0.9914 0.09086 0.7088 0.8522 0.2433 ] Network output: [ 0.0002495 0.9997 -0.0005605 8.813e-06 -3.957e-06 1 6.642e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007008 Epoch 7249 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01235 0.9935 0.9881 2.637e-06 -1.184e-06 -0.006377 1.987e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003242 -0.003031 -0.008972 0.006887 0.9698 0.9742 0.006177 0.841 0.8296 0.01949 ] Network output: [ 0.9997 0.001204 0.00149 -3.267e-05 1.467e-05 -0.002244 -2.462e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1874 -0.03091 -0.1904 0.1968 0.9836 0.9933 0.2092 0.4538 0.8754 0.7211 ] Network output: [ -0.01143 1.001 1.01 1.297e-06 -5.824e-07 0.01165 9.776e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005528 0.0004663 0.004367 0.00413 0.9889 0.992 0.00563 0.8702 0.8993 0.01412 ] Network output: [ -0.0009036 0.003617 1.003 -0.0001077 4.837e-05 0.9951 -8.119e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1982 0.09334 0.3276 0.1534 0.9851 0.994 0.1989 0.4585 0.8819 0.7157 ] Network output: [ 0.007419 -0.03645 0.9964 6.318e-05 -2.836e-05 1.026 4.761e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09808 0.08664 0.1791 0.2057 0.9873 0.992 0.09814 0.781 0.8739 0.3078 ] Network output: [ -0.007393 0.03755 1.002 6.452e-05 -2.896e-05 0.9758 4.862e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09084 0.08893 0.166 0.1963 0.9855 0.9914 0.09086 0.7087 0.8522 0.2433 ] Network output: [ 0.0002453 0.9997 -0.0005543 8.806e-06 -3.953e-06 1 6.636e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0007002 Epoch 7250 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01235 0.9935 0.9881 2.633e-06 -1.182e-06 -0.006371 1.984e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003242 -0.003031 -0.008971 0.006886 0.9698 0.9742 0.006177 0.841 0.8296 0.01948 ] Network output: [ 0.9997 0.001134 0.001493 -3.265e-05 1.466e-05 -0.002186 -2.46e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1874 -0.03092 -0.1903 0.1968 0.9836 0.9933 0.2092 0.4538 0.8754 0.7211 ] Network output: [ -0.01143 1.001 1.01 1.295e-06 -5.814e-07 0.01165 9.76e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005528 0.0004662 0.004368 0.00413 0.9889 0.992 0.00563 0.8702 0.8993 0.01412 ] Network output: [ -0.0008969 0.003523 1.003 -0.0001076 4.832e-05 0.9952 -8.112e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1982 0.09334 0.3276 0.1534 0.9851 0.994 0.1989 0.4584 0.8819 0.7157 ] Network output: [ 0.007415 -0.03646 0.9964 6.313e-05 -2.834e-05 1.026 4.758e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09808 0.08664 0.1791 0.2057 0.9873 0.992 0.09815 0.781 0.8739 0.3078 ] Network output: [ -0.007391 0.03754 1.002 6.447e-05 -2.894e-05 0.9759 4.859e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09084 0.08892 0.166 0.1963 0.9855 0.9914 0.09085 0.7087 0.8522 0.2433 ] Network output: [ 0.0002491 0.9997 -0.0005593 8.8e-06 -3.951e-06 1 6.632e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006999 Epoch 7251 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01234 0.9936 0.9882 2.627e-06 -1.18e-06 -0.006381 1.98e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003242 -0.003031 -0.008969 0.006885 0.9698 0.9742 0.006178 0.841 0.8296 0.01948 ] Network output: [ 0.9997 0.001201 0.001488 -3.263e-05 1.465e-05 -0.00224 -2.459e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1874 -0.03092 -0.1903 0.1968 0.9836 0.9933 0.2092 0.4538 0.8754 0.7211 ] Network output: [ -0.01143 1.001 1.01 1.292e-06 -5.801e-07 0.01165 9.739e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005529 0.0004661 0.004368 0.004129 0.9889 0.992 0.005631 0.8702 0.8993 0.01411 ] Network output: [ -0.0009027 0.003613 1.003 -0.0001076 4.829e-05 0.9951 -8.106e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1983 0.09334 0.3276 0.1534 0.9851 0.994 0.1989 0.4584 0.8819 0.7157 ] Network output: [ 0.007414 -0.03643 0.9964 6.308e-05 -2.832e-05 1.026 4.754e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09809 0.08665 0.1791 0.2057 0.9873 0.992 0.09815 0.781 0.8739 0.3078 ] Network output: [ -0.007387 0.03752 1.002 6.442e-05 -2.892e-05 0.9759 4.855e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09084 0.08892 0.166 0.1963 0.9855 0.9914 0.09085 0.7087 0.8522 0.2433 ] Network output: [ 0.0002451 0.9997 -0.0005533 8.793e-06 -3.947e-06 1 6.627e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006993 Epoch 7252 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01234 0.9935 0.9882 2.624e-06 -1.178e-06 -0.006375 1.977e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003242 -0.003031 -0.008968 0.006884 0.9698 0.9742 0.006178 0.841 0.8296 0.01948 ] Network output: [ 0.9997 0.001133 0.001491 -3.26e-05 1.464e-05 -0.002184 -2.457e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1874 -0.03093 -0.1903 0.1968 0.9836 0.9933 0.2092 0.4538 0.8754 0.7211 ] Network output: [ -0.01143 1.001 1.01 1.29e-06 -5.792e-07 0.01164 9.722e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00553 0.0004661 0.004368 0.004128 0.9889 0.992 0.005631 0.8702 0.8993 0.01411 ] Network output: [ -0.0008962 0.003522 1.003 -0.0001075 4.825e-05 0.9952 -8.099e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1983 0.09334 0.3276 0.1534 0.9851 0.994 0.1989 0.4584 0.8819 0.7157 ] Network output: [ 0.00741 -0.03643 0.9964 6.303e-05 -2.83e-05 1.026 4.75e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09809 0.08665 0.1791 0.2057 0.9873 0.992 0.09815 0.7809 0.8739 0.3078 ] Network output: [ -0.007384 0.03751 1.002 6.438e-05 -2.89e-05 0.9759 4.852e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09084 0.08892 0.166 0.1963 0.9855 0.9914 0.09085 0.7086 0.8521 0.2433 ] Network output: [ 0.0002488 0.9997 -0.0005582 8.787e-06 -3.945e-06 1 6.622e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000699 Epoch 7253 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01234 0.9936 0.9882 2.618e-06 -1.175e-06 -0.006386 1.973e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003242 -0.003032 -0.008966 0.006883 0.9698 0.9742 0.006178 0.841 0.8296 0.01948 ] Network output: [ 0.9997 0.001197 0.001486 -3.258e-05 1.463e-05 -0.002236 -2.456e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1874 -0.03093 -0.1903 0.1968 0.9836 0.9933 0.2093 0.4538 0.8754 0.7211 ] Network output: [ -0.01143 1.001 1.01 1.287e-06 -5.779e-07 0.01164 9.701e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00553 0.000466 0.004368 0.004127 0.9889 0.992 0.005632 0.8702 0.8993 0.01411 ] Network output: [ -0.0009018 0.003609 1.003 -0.0001074 4.821e-05 0.9952 -8.093e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1983 0.09335 0.3276 0.1534 0.9851 0.994 0.1989 0.4584 0.8819 0.7157 ] Network output: [ 0.007408 -0.0364 0.9963 6.298e-05 -2.828e-05 1.025 4.747e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0981 0.08665 0.1791 0.2057 0.9873 0.992 0.09816 0.7809 0.8739 0.3078 ] Network output: [ -0.007381 0.03749 1.002 6.433e-05 -2.888e-05 0.9759 4.848e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09083 0.08892 0.166 0.1963 0.9855 0.9914 0.09084 0.7086 0.8521 0.2433 ] Network output: [ 0.0002449 0.9997 -0.0005524 8.78e-06 -3.942e-06 1 6.617e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006984 Epoch 7254 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01234 0.9936 0.9882 2.614e-06 -1.174e-06 -0.00638 1.97e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003242 -0.003032 -0.008965 0.006882 0.9698 0.9742 0.006179 0.841 0.8296 0.01948 ] Network output: [ 0.9997 0.001132 0.001489 -3.256e-05 1.462e-05 -0.002182 -2.454e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1874 -0.03094 -0.1903 0.1968 0.9836 0.9933 0.2093 0.4538 0.8754 0.7211 ] Network output: [ -0.01143 1.001 1.01 1.285e-06 -5.769e-07 0.01164 9.685e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005531 0.000466 0.004368 0.004127 0.9889 0.992 0.005633 0.8702 0.8993 0.01411 ] Network output: [ -0.0008955 0.003522 1.003 -0.0001073 4.817e-05 0.9952 -8.086e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1983 0.09335 0.3277 0.1534 0.9851 0.994 0.1989 0.4584 0.8819 0.7157 ] Network output: [ 0.007405 -0.03641 0.9963 6.294e-05 -2.825e-05 1.026 4.743e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0981 0.08666 0.1791 0.2057 0.9873 0.992 0.09816 0.7809 0.8739 0.3078 ] Network output: [ -0.007378 0.03748 1.002 6.429e-05 -2.886e-05 0.9759 4.845e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09083 0.08891 0.166 0.1963 0.9855 0.9914 0.09084 0.7086 0.8521 0.2433 ] Network output: [ 0.0002484 0.9997 -0.000557 8.774e-06 -3.939e-06 1 6.613e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006981 Epoch 7255 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01233 0.9936 0.9882 2.609e-06 -1.171e-06 -0.00639 1.966e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003242 -0.003032 -0.008963 0.006881 0.9698 0.9742 0.006179 0.841 0.8296 0.01947 ] Network output: [ 0.9997 0.001194 0.001485 -3.254e-05 1.461e-05 -0.002232 -2.452e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1874 -0.03094 -0.1902 0.1968 0.9836 0.9933 0.2093 0.4537 0.8754 0.7211 ] Network output: [ -0.01143 1.001 1.01 1.282e-06 -5.757e-07 0.01163 9.663e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005531 0.0004659 0.004368 0.004126 0.9889 0.992 0.005633 0.8702 0.8993 0.01411 ] Network output: [ -0.0009009 0.003606 1.003 -0.0001072 4.813e-05 0.9952 -8.08e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1983 0.09335 0.3277 0.1534 0.9851 0.994 0.1989 0.4584 0.8819 0.7157 ] Network output: [ 0.007403 -0.03638 0.9963 6.289e-05 -2.823e-05 1.025 4.739e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0981 0.08666 0.1791 0.2057 0.9873 0.992 0.09817 0.7809 0.8738 0.3078 ] Network output: [ -0.007375 0.03746 1.002 6.424e-05 -2.884e-05 0.9759 4.841e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09083 0.08891 0.166 0.1963 0.9855 0.9914 0.09084 0.7085 0.8521 0.2433 ] Network output: [ 0.0002447 0.9997 -0.0005514 8.767e-06 -3.936e-06 1 6.607e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006975 Epoch 7256 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01233 0.9936 0.9882 2.605e-06 -1.17e-06 -0.006384 1.963e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003242 -0.003032 -0.008962 0.00688 0.9698 0.9742 0.006179 0.8409 0.8295 0.01947 ] Network output: [ 0.9997 0.001131 0.001487 -3.251e-05 1.46e-05 -0.00218 -2.45e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1874 -0.03095 -0.1902 0.1968 0.9836 0.9933 0.2093 0.4537 0.8754 0.7211 ] Network output: [ -0.01142 1.001 1.01 1.28e-06 -5.747e-07 0.01163 9.647e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005532 0.0004659 0.004369 0.004125 0.9889 0.992 0.005634 0.8702 0.8993 0.01411 ] Network output: [ -0.0008948 0.003521 1.003 -0.0001071 4.809e-05 0.9952 -8.074e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1983 0.09335 0.3277 0.1534 0.9851 0.994 0.1989 0.4583 0.8819 0.7157 ] Network output: [ 0.0074 -0.03638 0.9963 6.284e-05 -2.821e-05 1.025 4.736e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09811 0.08666 0.1792 0.2057 0.9873 0.992 0.09817 0.7808 0.8738 0.3078 ] Network output: [ -0.007372 0.03745 1.002 6.419e-05 -2.882e-05 0.9759 4.838e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09083 0.08891 0.166 0.1963 0.9855 0.9914 0.09084 0.7085 0.8521 0.2433 ] Network output: [ 0.0002481 0.9997 -0.0005559 8.762e-06 -3.933e-06 1 6.603e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006972 Epoch 7257 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01233 0.9936 0.9882 2.6e-06 -1.167e-06 -0.006394 1.96e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003243 -0.003032 -0.00896 0.006879 0.9698 0.9742 0.00618 0.8409 0.8295 0.01947 ] Network output: [ 0.9997 0.00119 0.001483 -3.249e-05 1.459e-05 -0.002228 -2.449e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1874 -0.03095 -0.1902 0.1968 0.9836 0.9933 0.2093 0.4537 0.8754 0.7211 ] Network output: [ -0.01142 1.001 1.01 1.277e-06 -5.734e-07 0.01162 9.626e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005533 0.0004658 0.004369 0.004124 0.9889 0.992 0.005634 0.8702 0.8993 0.0141 ] Network output: [ -0.0009 0.003602 1.003 -0.000107 4.806e-05 0.9952 -8.068e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1983 0.09335 0.3277 0.1533 0.9851 0.994 0.199 0.4583 0.8819 0.7157 ] Network output: [ 0.007398 -0.03635 0.9963 6.279e-05 -2.819e-05 1.025 4.732e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09811 0.08667 0.1792 0.2057 0.9873 0.992 0.09818 0.7808 0.8738 0.3078 ] Network output: [ -0.007368 0.03743 1.002 6.415e-05 -2.88e-05 0.9759 4.834e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09082 0.08891 0.166 0.1963 0.9855 0.9914 0.09083 0.7085 0.8521 0.2433 ] Network output: [ 0.0002445 0.9997 -0.0005504 8.754e-06 -3.93e-06 1 6.597e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006966 Epoch 7258 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01233 0.9936 0.9882 2.596e-06 -1.166e-06 -0.006389 1.957e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003243 -0.003032 -0.008959 0.006878 0.9698 0.9742 0.00618 0.8409 0.8295 0.01947 ] Network output: [ 0.9997 0.00113 0.001485 -3.247e-05 1.458e-05 -0.002178 -2.447e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1875 -0.03096 -0.1902 0.1968 0.9836 0.9933 0.2093 0.4537 0.8753 0.7211 ] Network output: [ -0.01142 1.001 1.01 1.275e-06 -5.724e-07 0.01162 9.609e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005533 0.0004658 0.004369 0.004124 0.9889 0.992 0.005635 0.8702 0.8993 0.0141 ] Network output: [ -0.0008941 0.003521 1.003 -0.000107 4.802e-05 0.9952 -8.061e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1983 0.09335 0.3277 0.1533 0.9851 0.994 0.199 0.4583 0.8819 0.7157 ] Network output: [ 0.007394 -0.03636 0.9963 6.275e-05 -2.817e-05 1.025 4.729e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09812 0.08667 0.1792 0.2057 0.9873 0.992 0.09818 0.7808 0.8738 0.3078 ] Network output: [ -0.007366 0.03741 1.002 6.41e-05 -2.878e-05 0.9759 4.831e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09082 0.0889 0.166 0.1963 0.9855 0.9914 0.09083 0.7084 0.8521 0.2433 ] Network output: [ 0.0002478 0.9997 -0.0005547 8.749e-06 -3.928e-06 1 6.593e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006963 Epoch 7259 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01232 0.9936 0.9882 2.591e-06 -1.163e-06 -0.006398 1.953e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003243 -0.003033 -0.008957 0.006877 0.9698 0.9742 0.00618 0.8409 0.8295 0.01947 ] Network output: [ 0.9997 0.001187 0.001481 -3.245e-05 1.457e-05 -0.002225 -2.445e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1875 -0.03096 -0.1902 0.1967 0.9836 0.9933 0.2093 0.4537 0.8753 0.7211 ] Network output: [ -0.01142 1.001 1.01 1.272e-06 -5.712e-07 0.01161 9.588e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005534 0.0004657 0.004369 0.004123 0.9889 0.992 0.005636 0.8701 0.8992 0.0141 ] Network output: [ -0.000899 0.003598 1.003 -0.0001069 4.798e-05 0.9952 -8.055e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1984 0.09335 0.3277 0.1533 0.9851 0.994 0.199 0.4583 0.8819 0.7157 ] Network output: [ 0.007393 -0.03633 0.9963 6.27e-05 -2.815e-05 1.025 4.725e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09812 0.08667 0.1792 0.2057 0.9873 0.992 0.09818 0.7808 0.8738 0.3078 ] Network output: [ -0.007362 0.03739 1.002 6.405e-05 -2.876e-05 0.9759 4.827e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09082 0.0889 0.166 0.1963 0.9855 0.9914 0.09083 0.7084 0.8521 0.2433 ] Network output: [ 0.0002443 0.9997 -0.0005495 8.741e-06 -3.924e-06 1 6.588e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006958 Epoch 7260 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01232 0.9936 0.9882 2.587e-06 -1.161e-06 -0.006393 1.95e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003243 -0.003033 -0.008956 0.006876 0.9698 0.9742 0.00618 0.8409 0.8295 0.01946 ] Network output: [ 0.9997 0.001129 0.001483 -3.242e-05 1.456e-05 -0.002176 -2.443e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1875 -0.03097 -0.1901 0.1967 0.9836 0.9933 0.2093 0.4537 0.8753 0.7211 ] Network output: [ -0.01142 1.001 1.01 1.27e-06 -5.702e-07 0.01161 9.572e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005534 0.0004657 0.004369 0.004122 0.9889 0.992 0.005636 0.8701 0.8992 0.0141 ] Network output: [ -0.0008934 0.00352 1.003 -0.0001068 4.794e-05 0.9952 -8.048e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1984 0.09336 0.3278 0.1533 0.9851 0.994 0.199 0.4583 0.8818 0.7157 ] Network output: [ 0.007389 -0.03633 0.9963 6.265e-05 -2.813e-05 1.025 4.721e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09813 0.08668 0.1792 0.2056 0.9873 0.992 0.09819 0.7807 0.8738 0.3078 ] Network output: [ -0.007359 0.03738 1.002 6.401e-05 -2.874e-05 0.9759 4.824e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09081 0.0889 0.166 0.1963 0.9855 0.9914 0.09083 0.7084 0.852 0.2433 ] Network output: [ 0.0002474 0.9997 -0.0005536 8.736e-06 -3.922e-06 1 6.584e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006954 Epoch 7261 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01232 0.9936 0.9882 2.582e-06 -1.159e-06 -0.006402 1.946e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003243 -0.003033 -0.008954 0.006875 0.9698 0.9742 0.006181 0.8409 0.8295 0.01946 ] Network output: [ 0.9997 0.001184 0.001479 -3.24e-05 1.455e-05 -0.002221 -2.442e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1875 -0.03097 -0.1901 0.1967 0.9836 0.9933 0.2094 0.4536 0.8753 0.7211 ] Network output: [ -0.01142 1.001 1.01 1.267e-06 -5.689e-07 0.01161 9.551e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005535 0.0004656 0.004369 0.004121 0.9889 0.992 0.005637 0.8701 0.8992 0.0141 ] Network output: [ -0.0008981 0.003595 1.003 -0.0001067 4.791e-05 0.9952 -8.042e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1984 0.09336 0.3278 0.1533 0.9851 0.994 0.199 0.4583 0.8818 0.7157 ] Network output: [ 0.007387 -0.03631 0.9963 6.26e-05 -2.81e-05 1.025 4.718e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09813 0.08668 0.1792 0.2056 0.9873 0.992 0.09819 0.7807 0.8738 0.3077 ] Network output: [ -0.007356 0.03736 1.002 6.396e-05 -2.872e-05 0.9759 4.82e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09081 0.08889 0.166 0.1963 0.9855 0.9914 0.09082 0.7083 0.852 0.2433 ] Network output: [ 0.0002441 0.9997 -0.0005485 8.729e-06 -3.919e-06 1 6.578e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006949 Epoch 7262 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01232 0.9936 0.9882 2.578e-06 -1.157e-06 -0.006398 1.943e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003243 -0.003033 -0.008953 0.006874 0.9698 0.9742 0.006181 0.8409 0.8295 0.01946 ] Network output: [ 0.9997 0.001127 0.001481 -3.238e-05 1.454e-05 -0.002174 -2.44e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1875 -0.03098 -0.1901 0.1967 0.9836 0.9933 0.2094 0.4536 0.8753 0.7211 ] Network output: [ -0.01142 1.001 1.01 1.265e-06 -5.68e-07 0.0116 9.534e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005535 0.0004656 0.004369 0.004121 0.9889 0.992 0.005637 0.8701 0.8992 0.0141 ] Network output: [ -0.0008927 0.003519 1.003 -0.0001066 4.786e-05 0.9952 -8.035e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1984 0.09336 0.3278 0.1533 0.9851 0.994 0.199 0.4582 0.8818 0.7157 ] Network output: [ 0.007384 -0.03631 0.9963 6.255e-05 -2.808e-05 1.025 4.714e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09814 0.08668 0.1792 0.2056 0.9873 0.992 0.0982 0.7807 0.8738 0.3077 ] Network output: [ -0.007353 0.03735 1.002 6.392e-05 -2.869e-05 0.9759 4.817e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09081 0.08889 0.166 0.1962 0.9855 0.9914 0.09082 0.7083 0.852 0.2433 ] Network output: [ 0.0002471 0.9997 -0.0005524 8.723e-06 -3.916e-06 1 6.574e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006945 Epoch 7263 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01232 0.9936 0.9882 2.573e-06 -1.155e-06 -0.006407 1.939e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003243 -0.003033 -0.008951 0.006873 0.9698 0.9742 0.006181 0.8409 0.8295 0.01946 ] Network output: [ 0.9997 0.00118 0.001477 -3.236e-05 1.453e-05 -0.002217 -2.439e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1875 -0.03098 -0.1901 0.1967 0.9836 0.9933 0.2094 0.4536 0.8753 0.7211 ] Network output: [ -0.01142 1.001 1.01 1.262e-06 -5.667e-07 0.0116 9.514e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005536 0.0004655 0.004369 0.00412 0.9889 0.992 0.005638 0.8701 0.8992 0.0141 ] Network output: [ -0.0008972 0.003591 1.003 -0.0001065 4.783e-05 0.9952 -8.029e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1984 0.09336 0.3278 0.1533 0.9851 0.994 0.199 0.4582 0.8818 0.7157 ] Network output: [ 0.007382 -0.03628 0.9963 6.251e-05 -2.806e-05 1.025 4.711e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09814 0.08669 0.1792 0.2056 0.9873 0.992 0.0982 0.7806 0.8738 0.3077 ] Network output: [ -0.00735 0.03733 1.002 6.387e-05 -2.867e-05 0.976 4.813e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09081 0.08889 0.166 0.1962 0.9855 0.9914 0.09082 0.7083 0.852 0.2433 ] Network output: [ 0.0002438 0.9997 -0.0005475 8.716e-06 -3.913e-06 1 6.568e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000694 Epoch 7264 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01231 0.9936 0.9882 2.569e-06 -1.153e-06 -0.006402 1.936e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003243 -0.003033 -0.00895 0.006872 0.9698 0.9742 0.006182 0.8409 0.8295 0.01946 ] Network output: [ 0.9997 0.001126 0.001479 -3.233e-05 1.452e-05 -0.002172 -2.437e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1875 -0.03099 -0.1901 0.1967 0.9836 0.9933 0.2094 0.4536 0.8753 0.721 ] Network output: [ -0.01141 1.001 1.01 1.26e-06 -5.657e-07 0.0116 9.497e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005537 0.0004655 0.00437 0.004119 0.9889 0.992 0.005639 0.8701 0.8992 0.01409 ] Network output: [ -0.000892 0.003519 1.003 -0.0001064 4.779e-05 0.9952 -8.022e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1984 0.09336 0.3278 0.1533 0.9851 0.994 0.199 0.4582 0.8818 0.7157 ] Network output: [ 0.007379 -0.03628 0.9963 6.246e-05 -2.804e-05 1.025 4.707e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09814 0.08669 0.1792 0.2056 0.9873 0.992 0.09821 0.7806 0.8738 0.3077 ] Network output: [ -0.007347 0.03732 1.002 6.382e-05 -2.865e-05 0.976 4.81e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0908 0.08889 0.166 0.1962 0.9855 0.9914 0.09082 0.7082 0.852 0.2433 ] Network output: [ 0.0002468 0.9997 -0.0005513 8.71e-06 -3.91e-06 1 6.564e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006936 Epoch 7265 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01231 0.9936 0.9882 2.564e-06 -1.151e-06 -0.006411 1.932e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003244 -0.003034 -0.008948 0.006871 0.9698 0.9742 0.006182 0.8409 0.8295 0.01945 ] Network output: [ 0.9997 0.001177 0.001476 -3.231e-05 1.451e-05 -0.002213 -2.435e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1875 -0.03099 -0.1901 0.1967 0.9836 0.9933 0.2094 0.4536 0.8753 0.721 ] Network output: [ -0.01141 1.001 1.01 1.257e-06 -5.645e-07 0.01159 9.476e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005537 0.0004654 0.00437 0.004118 0.9889 0.992 0.005639 0.8701 0.8992 0.01409 ] Network output: [ -0.0008963 0.003587 1.003 -0.0001064 4.775e-05 0.9952 -8.016e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1984 0.09336 0.3278 0.1532 0.9851 0.994 0.1991 0.4582 0.8818 0.7157 ] Network output: [ 0.007377 -0.03626 0.9963 6.241e-05 -2.802e-05 1.025 4.703e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09815 0.08669 0.1792 0.2056 0.9873 0.992 0.09821 0.7806 0.8737 0.3077 ] Network output: [ -0.007344 0.0373 1.002 6.378e-05 -2.863e-05 0.976 4.807e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0908 0.08888 0.166 0.1962 0.9855 0.9914 0.09081 0.7082 0.852 0.2434 ] Network output: [ 0.0002436 0.9997 -0.0005465 8.703e-06 -3.907e-06 1 6.559e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006931 Epoch 7266 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01231 0.9936 0.9882 2.56e-06 -1.149e-06 -0.006407 1.929e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003244 -0.003034 -0.008947 0.00687 0.9698 0.9742 0.006182 0.8409 0.8295 0.01945 ] Network output: [ 0.9997 0.001125 0.001477 -3.229e-05 1.45e-05 -0.00217 -2.433e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1875 -0.03099 -0.19 0.1967 0.9836 0.9933 0.2094 0.4536 0.8753 0.721 ] Network output: [ -0.01141 1.001 1.01 1.255e-06 -5.635e-07 0.01159 9.459e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005538 0.0004654 0.00437 0.004118 0.9889 0.992 0.00564 0.8701 0.8992 0.01409 ] Network output: [ -0.0008913 0.003518 1.003 -0.0001063 4.771e-05 0.9952 -8.009e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1984 0.09336 0.3279 0.1532 0.9851 0.994 0.1991 0.4582 0.8818 0.7157 ] Network output: [ 0.007374 -0.03626 0.9963 6.236e-05 -2.8e-05 1.025 4.7e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09815 0.0867 0.1792 0.2056 0.9873 0.992 0.09822 0.7806 0.8737 0.3077 ] Network output: [ -0.007341 0.03729 1.002 6.373e-05 -2.861e-05 0.976 4.803e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0908 0.08888 0.166 0.1962 0.9855 0.9914 0.09081 0.7082 0.852 0.2434 ] Network output: [ 0.0002464 0.9997 -0.0005501 8.697e-06 -3.905e-06 1 6.555e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006928 Epoch 7267 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01231 0.9936 0.9882 2.555e-06 -1.147e-06 -0.006415 1.926e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003244 -0.003034 -0.008945 0.006869 0.9698 0.9742 0.006183 0.8409 0.8295 0.01945 ] Network output: [ 0.9997 0.001174 0.001474 -3.227e-05 1.449e-05 -0.002209 -2.432e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1876 -0.031 -0.19 0.1967 0.9836 0.9933 0.2094 0.4535 0.8753 0.721 ] Network output: [ -0.01141 1.001 1.01 1.252e-06 -5.623e-07 0.01158 9.439e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005538 0.0004653 0.00437 0.004117 0.9889 0.992 0.00564 0.8701 0.8992 0.01409 ] Network output: [ -0.0008954 0.003584 1.003 -0.0001062 4.768e-05 0.9952 -8.003e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1985 0.09337 0.3279 0.1532 0.9851 0.994 0.1991 0.4582 0.8818 0.7156 ] Network output: [ 0.007372 -0.03623 0.9963 6.231e-05 -2.797e-05 1.025 4.696e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09816 0.0867 0.1792 0.2056 0.9873 0.992 0.09822 0.7805 0.8737 0.3077 ] Network output: [ -0.007337 0.03726 1.002 6.369e-05 -2.859e-05 0.976 4.8e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0908 0.08888 0.166 0.1962 0.9855 0.9914 0.09081 0.7081 0.852 0.2434 ] Network output: [ 0.0002434 0.9997 -0.0005456 8.69e-06 -3.901e-06 1 6.549e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006922 Epoch 7268 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0123 0.9936 0.9882 2.551e-06 -1.145e-06 -0.006411 1.923e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003244 -0.003034 -0.008944 0.006868 0.9698 0.9742 0.006183 0.8408 0.8295 0.01945 ] Network output: [ 0.9997 0.001124 0.001476 -3.224e-05 1.448e-05 -0.002168 -2.43e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1876 -0.031 -0.19 0.1967 0.9836 0.9933 0.2094 0.4535 0.8753 0.721 ] Network output: [ -0.01141 1.001 1.01 1.25e-06 -5.613e-07 0.01158 9.422e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005539 0.0004653 0.00437 0.004116 0.9889 0.992 0.005641 0.8701 0.8992 0.01409 ] Network output: [ -0.0008905 0.003517 1.003 -0.0001061 4.764e-05 0.9953 -7.997e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1985 0.09337 0.3279 0.1532 0.9851 0.994 0.1991 0.4581 0.8818 0.7156 ] Network output: [ 0.007368 -0.03623 0.9963 6.227e-05 -2.795e-05 1.025 4.693e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09816 0.0867 0.1792 0.2056 0.9873 0.992 0.09822 0.7805 0.8737 0.3077 ] Network output: [ -0.007335 0.03725 1.002 6.364e-05 -2.857e-05 0.976 4.796e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09079 0.08888 0.166 0.1962 0.9855 0.9914 0.09081 0.7081 0.8519 0.2434 ] Network output: [ 0.0002461 0.9997 -0.000549 8.684e-06 -3.899e-06 1 6.545e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006919 Epoch 7269 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0123 0.9936 0.9882 2.546e-06 -1.143e-06 -0.006419 1.919e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003244 -0.003034 -0.008943 0.006867 0.9698 0.9742 0.006183 0.8408 0.8295 0.01945 ] Network output: [ 0.9997 0.001171 0.001472 -3.222e-05 1.447e-05 -0.002206 -2.429e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1876 -0.03101 -0.19 0.1966 0.9836 0.9933 0.2095 0.4535 0.8753 0.721 ] Network output: [ -0.01141 1.001 1.01 1.247e-06 -5.6e-07 0.01157 9.401e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00554 0.0004653 0.00437 0.004116 0.9889 0.992 0.005642 0.87 0.8992 0.01409 ] Network output: [ -0.0008945 0.003581 1.003 -0.000106 4.76e-05 0.9952 -7.991e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1985 0.09337 0.3279 0.1532 0.9851 0.994 0.1991 0.4581 0.8818 0.7156 ] Network output: [ 0.007366 -0.03621 0.9963 6.222e-05 -2.793e-05 1.025 4.689e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09817 0.08671 0.1792 0.2056 0.9873 0.992 0.09823 0.7805 0.8737 0.3077 ] Network output: [ -0.007331 0.03723 1.002 6.359e-05 -2.855e-05 0.976 4.793e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09079 0.08887 0.166 0.1962 0.9855 0.9914 0.0908 0.7081 0.8519 0.2434 ] Network output: [ 0.0002432 0.9997 -0.0005446 8.677e-06 -3.896e-06 1 6.54e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006913 Epoch 7270 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0123 0.9936 0.9882 2.542e-06 -1.141e-06 -0.006415 1.916e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003244 -0.003034 -0.008941 0.006866 0.9698 0.9742 0.006184 0.8408 0.8295 0.01944 ] Network output: [ 0.9997 0.001123 0.001474 -3.22e-05 1.446e-05 -0.002166 -2.427e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1876 -0.03101 -0.19 0.1966 0.9836 0.9933 0.2095 0.4535 0.8753 0.721 ] Network output: [ -0.01141 1.001 1.01 1.245e-06 -5.59e-07 0.01157 9.384e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00554 0.0004652 0.004371 0.004115 0.9889 0.992 0.005642 0.87 0.8992 0.01409 ] Network output: [ -0.0008898 0.003516 1.003 -0.0001059 4.756e-05 0.9953 -7.984e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1985 0.09337 0.3279 0.1532 0.9851 0.994 0.1991 0.4581 0.8818 0.7156 ] Network output: [ 0.007363 -0.03621 0.9963 6.217e-05 -2.791e-05 1.025 4.685e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09817 0.08671 0.1792 0.2056 0.9873 0.992 0.09823 0.7805 0.8737 0.3077 ] Network output: [ -0.007328 0.03722 1.002 6.355e-05 -2.853e-05 0.976 4.789e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09079 0.08887 0.166 0.1962 0.9855 0.9914 0.0908 0.7081 0.8519 0.2434 ] Network output: [ 0.0002458 0.9997 -0.0005479 8.672e-06 -3.893e-06 1 6.535e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000691 Epoch 7271 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0123 0.9936 0.9882 2.537e-06 -1.139e-06 -0.006423 1.912e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003244 -0.003035 -0.00894 0.006865 0.9698 0.9742 0.006184 0.8408 0.8295 0.01944 ] Network output: [ 0.9997 0.001168 0.00147 -3.218e-05 1.445e-05 -0.002202 -2.425e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1876 -0.03102 -0.1899 0.1966 0.9836 0.9933 0.2095 0.4535 0.8753 0.721 ] Network output: [ -0.01141 1.001 1.01 1.243e-06 -5.578e-07 0.01157 9.364e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005541 0.0004652 0.004371 0.004114 0.9889 0.992 0.005643 0.87 0.8992 0.01408 ] Network output: [ -0.0008936 0.003577 1.003 -0.0001059 4.752e-05 0.9952 -7.978e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1985 0.09337 0.3279 0.1532 0.9851 0.994 0.1991 0.4581 0.8818 0.7156 ] Network output: [ 0.007361 -0.03618 0.9963 6.212e-05 -2.789e-05 1.025 4.682e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09817 0.08671 0.1792 0.2056 0.9873 0.992 0.09824 0.7804 0.8737 0.3077 ] Network output: [ -0.007325 0.0372 1.002 6.35e-05 -2.851e-05 0.976 4.786e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09079 0.08887 0.166 0.1962 0.9855 0.9914 0.0908 0.708 0.8519 0.2434 ] Network output: [ 0.000243 0.9997 -0.0005436 8.665e-06 -3.89e-06 1 6.53e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006904 Epoch 7272 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0123 0.9936 0.9882 2.533e-06 -1.137e-06 -0.00642 1.909e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003244 -0.003035 -0.008938 0.006864 0.9698 0.9742 0.006184 0.8408 0.8294 0.01944 ] Network output: [ 0.9997 0.001121 0.001472 -3.215e-05 1.444e-05 -0.002163 -2.423e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1876 -0.03102 -0.1899 0.1966 0.9836 0.9933 0.2095 0.4535 0.8753 0.721 ] Network output: [ -0.0114 1.001 1.01 1.24e-06 -5.568e-07 0.01156 9.347e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005541 0.0004651 0.004371 0.004114 0.9889 0.992 0.005644 0.87 0.8992 0.01408 ] Network output: [ -0.0008891 0.003515 1.003 -0.0001058 4.748e-05 0.9953 -7.971e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1985 0.09337 0.328 0.1532 0.9851 0.994 0.1991 0.4581 0.8818 0.7156 ] Network output: [ 0.007358 -0.03618 0.9963 6.208e-05 -2.787e-05 1.025 4.678e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09818 0.08672 0.1792 0.2055 0.9873 0.992 0.09824 0.7804 0.8737 0.3077 ] Network output: [ -0.007322 0.03719 1.002 6.346e-05 -2.849e-05 0.976 4.782e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09078 0.08887 0.166 0.1962 0.9855 0.9914 0.0908 0.708 0.8519 0.2434 ] Network output: [ 0.0002455 0.9997 -0.0005467 8.659e-06 -3.887e-06 1 6.526e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006901 Epoch 7273 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01229 0.9936 0.9882 2.528e-06 -1.135e-06 -0.006427 1.905e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003245 -0.003035 -0.008937 0.006863 0.9698 0.9742 0.006184 0.8408 0.8294 0.01944 ] Network output: [ 0.9997 0.001165 0.001468 -3.213e-05 1.443e-05 -0.002198 -2.422e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1876 -0.03103 -0.1899 0.1966 0.9836 0.9933 0.2095 0.4534 0.8753 0.721 ] Network output: [ -0.0114 1.001 1.01 1.238e-06 -5.556e-07 0.01156 9.327e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005542 0.0004651 0.004371 0.004113 0.9889 0.992 0.005644 0.87 0.8992 0.01408 ] Network output: [ -0.0008927 0.003574 1.003 -0.0001057 4.745e-05 0.9952 -7.965e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1985 0.09338 0.328 0.1531 0.9851 0.994 0.1992 0.4581 0.8818 0.7156 ] Network output: [ 0.007356 -0.03616 0.9963 6.203e-05 -2.785e-05 1.025 4.675e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09818 0.08672 0.1792 0.2055 0.9873 0.992 0.09825 0.7804 0.8737 0.3077 ] Network output: [ -0.007319 0.03717 1.002 6.341e-05 -2.847e-05 0.976 4.779e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09078 0.08886 0.166 0.1962 0.9855 0.9914 0.09079 0.708 0.8519 0.2434 ] Network output: [ 0.0002428 0.9997 -0.0005426 8.652e-06 -3.884e-06 1 6.52e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006895 Epoch 7274 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01229 0.9936 0.9882 2.524e-06 -1.133e-06 -0.006424 1.902e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003245 -0.003035 -0.008935 0.006862 0.9698 0.9742 0.006185 0.8408 0.8294 0.01944 ] Network output: [ 0.9997 0.00112 0.00147 -3.211e-05 1.442e-05 -0.002161 -2.42e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1876 -0.03103 -0.1899 0.1966 0.9836 0.9933 0.2095 0.4534 0.8753 0.721 ] Network output: [ -0.0114 1.001 1.01 1.235e-06 -5.546e-07 0.01156 9.31e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005543 0.000465 0.004371 0.004112 0.9889 0.992 0.005645 0.87 0.8992 0.01408 ] Network output: [ -0.0008883 0.003514 1.003 -0.0001056 4.741e-05 0.9953 -7.958e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1985 0.09338 0.328 0.1531 0.9851 0.994 0.1992 0.458 0.8818 0.7156 ] Network output: [ 0.007353 -0.03616 0.9963 6.198e-05 -2.783e-05 1.025 4.671e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09819 0.08672 0.1792 0.2055 0.9873 0.992 0.09825 0.7803 0.8737 0.3077 ] Network output: [ -0.007316 0.03716 1.002 6.336e-05 -2.845e-05 0.976 4.775e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09078 0.08886 0.166 0.1962 0.9855 0.9914 0.09079 0.7079 0.8519 0.2434 ] Network output: [ 0.0002451 0.9997 -0.0005456 8.646e-06 -3.881e-06 1 6.516e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006892 Epoch 7275 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01229 0.9936 0.9882 2.519e-06 -1.131e-06 -0.006432 1.899e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003245 -0.003035 -0.008934 0.006861 0.9698 0.9742 0.006185 0.8408 0.8294 0.01943 ] Network output: [ 0.9997 0.001162 0.001467 -3.209e-05 1.441e-05 -0.002195 -2.418e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1876 -0.03104 -0.1899 0.1966 0.9836 0.9933 0.2095 0.4534 0.8753 0.721 ] Network output: [ -0.0114 1.001 1.01 1.233e-06 -5.534e-07 0.01155 9.29e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005543 0.000465 0.004371 0.004111 0.9889 0.992 0.005645 0.87 0.8992 0.01408 ] Network output: [ -0.0008918 0.00357 1.003 -0.0001055 4.737e-05 0.9952 -7.952e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1986 0.09338 0.328 0.1531 0.9851 0.994 0.1992 0.458 0.8818 0.7156 ] Network output: [ 0.007351 -0.03613 0.9963 6.193e-05 -2.78e-05 1.025 4.667e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09819 0.08673 0.1792 0.2055 0.9873 0.992 0.09826 0.7803 0.8736 0.3077 ] Network output: [ -0.007313 0.03714 1.002 6.332e-05 -2.843e-05 0.9761 4.772e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09078 0.08886 0.166 0.1962 0.9855 0.9914 0.09079 0.7079 0.8519 0.2434 ] Network output: [ 0.0002425 0.9997 -0.0005417 8.639e-06 -3.878e-06 1 6.511e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006886 Epoch 7276 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01229 0.9936 0.9882 2.515e-06 -1.129e-06 -0.006429 1.896e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003245 -0.003035 -0.008932 0.00686 0.9698 0.9742 0.006185 0.8408 0.8294 0.01943 ] Network output: [ 0.9997 0.001119 0.001468 -3.206e-05 1.44e-05 -0.002159 -2.417e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1877 -0.03104 -0.1898 0.1966 0.9836 0.9933 0.2095 0.4534 0.8752 0.721 ] Network output: [ -0.0114 1.001 1.01 1.23e-06 -5.524e-07 0.01155 9.272e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005544 0.0004649 0.004372 0.004111 0.9889 0.992 0.005646 0.87 0.8992 0.01408 ] Network output: [ -0.0008876 0.003513 1.003 -0.0001054 4.733e-05 0.9953 -7.946e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1986 0.09338 0.328 0.1531 0.9851 0.994 0.1992 0.458 0.8818 0.7156 ] Network output: [ 0.007348 -0.03613 0.9963 6.188e-05 -2.778e-05 1.025 4.664e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0982 0.08673 0.1792 0.2055 0.9873 0.992 0.09826 0.7803 0.8736 0.3077 ] Network output: [ -0.00731 0.03712 1.002 6.327e-05 -2.84e-05 0.9761 4.768e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09078 0.08886 0.166 0.1962 0.9855 0.9914 0.09079 0.7079 0.8518 0.2434 ] Network output: [ 0.0002448 0.9997 -0.0005445 8.633e-06 -3.876e-06 1 6.506e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006883 Epoch 7277 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01228 0.9936 0.9882 2.51e-06 -1.127e-06 -0.006436 1.892e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003245 -0.003036 -0.008931 0.006859 0.9698 0.9742 0.006186 0.8408 0.8294 0.01943 ] Network output: [ 0.9997 0.001159 0.001465 -3.204e-05 1.439e-05 -0.002191 -2.415e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1877 -0.03105 -0.1898 0.1966 0.9836 0.9933 0.2096 0.4534 0.8752 0.721 ] Network output: [ -0.0114 1.001 1.01 1.228e-06 -5.512e-07 0.01154 9.252e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005544 0.0004649 0.004372 0.00411 0.9889 0.992 0.005647 0.87 0.8991 0.01407 ] Network output: [ -0.0008909 0.003567 1.003 -0.0001054 4.73e-05 0.9952 -7.94e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1986 0.09338 0.328 0.1531 0.9851 0.994 0.1992 0.458 0.8818 0.7156 ] Network output: [ 0.007346 -0.03611 0.9963 6.184e-05 -2.776e-05 1.025 4.66e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0982 0.08673 0.1792 0.2055 0.9873 0.992 0.09826 0.7803 0.8736 0.3077 ] Network output: [ -0.007307 0.0371 1.002 6.323e-05 -2.838e-05 0.9761 4.765e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09077 0.08885 0.166 0.1962 0.9855 0.9914 0.09078 0.7078 0.8518 0.2434 ] Network output: [ 0.0002423 0.9997 -0.0005407 8.626e-06 -3.873e-06 1 6.501e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006877 Epoch 7278 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01228 0.9936 0.9882 2.506e-06 -1.125e-06 -0.006433 1.889e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003245 -0.003036 -0.008929 0.006858 0.9698 0.9742 0.006186 0.8408 0.8294 0.01943 ] Network output: [ 0.9997 0.001117 0.001466 -3.202e-05 1.437e-05 -0.002157 -2.413e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1877 -0.03105 -0.1898 0.1966 0.9836 0.9933 0.2096 0.4534 0.8752 0.721 ] Network output: [ -0.0114 1.001 1.01 1.225e-06 -5.501e-07 0.01154 9.235e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005545 0.0004648 0.004372 0.004109 0.9889 0.992 0.005647 0.87 0.8991 0.01407 ] Network output: [ -0.0008868 0.003512 1.003 -0.0001053 4.726e-05 0.9953 -7.933e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1986 0.09338 0.3281 0.1531 0.9851 0.994 0.1992 0.458 0.8817 0.7156 ] Network output: [ 0.007342 -0.03611 0.9963 6.179e-05 -2.774e-05 1.025 4.657e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09821 0.08674 0.1792 0.2055 0.9873 0.992 0.09827 0.7802 0.8736 0.3077 ] Network output: [ -0.007304 0.03709 1.002 6.318e-05 -2.836e-05 0.9761 4.761e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09077 0.08885 0.166 0.1962 0.9855 0.9914 0.09078 0.7078 0.8518 0.2434 ] Network output: [ 0.0002445 0.9997 -0.0005434 8.62e-06 -3.87e-06 1 6.497e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006874 Epoch 7279 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01228 0.9936 0.9883 2.501e-06 -1.123e-06 -0.00644 1.885e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003245 -0.003036 -0.008928 0.006857 0.9698 0.9742 0.006186 0.8407 0.8294 0.01943 ] Network output: [ 0.9997 0.001156 0.001463 -3.2e-05 1.437e-05 -0.002188 -2.412e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1877 -0.03106 -0.1898 0.1966 0.9836 0.9933 0.2096 0.4533 0.8752 0.721 ] Network output: [ -0.0114 1.001 1.01 1.223e-06 -5.489e-07 0.01153 9.215e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005546 0.0004648 0.004372 0.004108 0.9889 0.992 0.005648 0.8699 0.8991 0.01407 ] Network output: [ -0.00089 0.003564 1.003 -0.0001052 4.722e-05 0.9952 -7.927e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1986 0.09339 0.3281 0.1531 0.9851 0.994 0.1992 0.458 0.8817 0.7156 ] Network output: [ 0.00734 -0.03608 0.9963 6.174e-05 -2.772e-05 1.025 4.653e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09821 0.08674 0.1792 0.2055 0.9873 0.992 0.09827 0.7802 0.8736 0.3077 ] Network output: [ -0.007301 0.03707 1.002 6.313e-05 -2.834e-05 0.9761 4.758e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09077 0.08885 0.1659 0.1962 0.9855 0.9914 0.09078 0.7078 0.8518 0.2434 ] Network output: [ 0.0002421 0.9997 -0.0005397 8.613e-06 -3.867e-06 1 6.491e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006869 Epoch 7280 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01228 0.9936 0.9883 2.497e-06 -1.121e-06 -0.006437 1.882e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003246 -0.003036 -0.008926 0.006856 0.9698 0.9742 0.006187 0.8407 0.8294 0.01942 ] Network output: [ 0.9997 0.001116 0.001464 -3.198e-05 1.435e-05 -0.002154 -2.41e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1877 -0.03106 -0.1898 0.1965 0.9836 0.9933 0.2096 0.4533 0.8752 0.721 ] Network output: [ -0.01139 1.001 1.01 1.22e-06 -5.479e-07 0.01153 9.198e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005546 0.0004648 0.004372 0.004108 0.9889 0.992 0.005648 0.8699 0.8991 0.01407 ] Network output: [ -0.0008861 0.003511 1.003 -0.0001051 4.718e-05 0.9953 -7.92e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1986 0.09339 0.3281 0.1531 0.9851 0.994 0.1992 0.4579 0.8817 0.7156 ] Network output: [ 0.007337 -0.03608 0.9963 6.169e-05 -2.77e-05 1.025 4.649e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09822 0.08674 0.1792 0.2055 0.9873 0.992 0.09828 0.7802 0.8736 0.3077 ] Network output: [ -0.007298 0.03706 1.002 6.309e-05 -2.832e-05 0.9761 4.755e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09077 0.08885 0.1659 0.1962 0.9855 0.9914 0.09078 0.7077 0.8518 0.2434 ] Network output: [ 0.0002442 0.9997 -0.0005423 8.608e-06 -3.864e-06 1 6.487e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006865 Epoch 7281 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01227 0.9936 0.9883 2.493e-06 -1.119e-06 -0.006444 1.878e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003246 -0.003036 -0.008925 0.006855 0.9698 0.9742 0.006187 0.8407 0.8294 0.01942 ] Network output: [ 0.9997 0.001153 0.001461 -3.196e-05 1.435e-05 -0.002184 -2.408e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1877 -0.03106 -0.1897 0.1965 0.9836 0.9933 0.2096 0.4533 0.8752 0.721 ] Network output: [ -0.01139 1.001 1.01 1.218e-06 -5.467e-07 0.01153 9.178e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005547 0.0004647 0.004372 0.004107 0.9889 0.992 0.005649 0.8699 0.8991 0.01407 ] Network output: [ -0.0008891 0.003561 1.003 -0.000105 4.714e-05 0.9952 -7.914e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1986 0.09339 0.3281 0.153 0.9851 0.994 0.1993 0.4579 0.8817 0.7156 ] Network output: [ 0.007335 -0.03606 0.9963 6.165e-05 -2.768e-05 1.025 4.646e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09822 0.08675 0.1792 0.2055 0.9873 0.992 0.09828 0.7801 0.8736 0.3077 ] Network output: [ -0.007294 0.03704 1.002 6.304e-05 -2.83e-05 0.9761 4.751e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09076 0.08884 0.1659 0.1962 0.9855 0.9914 0.09078 0.7077 0.8518 0.2434 ] Network output: [ 0.0002419 0.9997 -0.0005387 8.601e-06 -3.861e-06 1 6.482e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000686 Epoch 7282 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01227 0.9936 0.9883 2.488e-06 -1.117e-06 -0.006442 1.875e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003246 -0.003036 -0.008923 0.006854 0.9698 0.9742 0.006187 0.8407 0.8294 0.01942 ] Network output: [ 0.9997 0.001114 0.001462 -3.193e-05 1.433e-05 -0.002152 -2.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1877 -0.03107 -0.1897 0.1965 0.9836 0.9933 0.2096 0.4533 0.8752 0.721 ] Network output: [ -0.01139 1.001 1.01 1.216e-06 -5.457e-07 0.01152 9.161e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005547 0.0004647 0.004373 0.004106 0.9889 0.992 0.00565 0.8699 0.8991 0.01407 ] Network output: [ -0.0008853 0.00351 1.003 -0.0001049 4.711e-05 0.9953 -7.908e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1986 0.09339 0.3281 0.153 0.9851 0.994 0.1993 0.4579 0.8817 0.7156 ] Network output: [ 0.007332 -0.03606 0.9963 6.16e-05 -2.765e-05 1.025 4.642e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09822 0.08675 0.1792 0.2055 0.9873 0.992 0.09829 0.7801 0.8736 0.3077 ] Network output: [ -0.007292 0.03703 1.002 6.3e-05 -2.828e-05 0.9761 4.748e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09076 0.08884 0.1659 0.1962 0.9855 0.9914 0.09077 0.7077 0.8518 0.2434 ] Network output: [ 0.0002439 0.9997 -0.0005412 8.595e-06 -3.859e-06 1 6.477e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006856 Epoch 7283 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01227 0.9937 0.9883 2.484e-06 -1.115e-06 -0.006448 1.872e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003246 -0.003037 -0.008922 0.006853 0.9698 0.9742 0.006188 0.8407 0.8294 0.01942 ] Network output: [ 0.9997 0.00115 0.001459 -3.191e-05 1.433e-05 -0.00218 -2.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1877 -0.03107 -0.1897 0.1965 0.9836 0.9933 0.2096 0.4533 0.8752 0.721 ] Network output: [ -0.01139 1.001 1.01 1.213e-06 -5.445e-07 0.01152 9.141e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005548 0.0004646 0.004373 0.004105 0.9889 0.992 0.00565 0.8699 0.8991 0.01407 ] Network output: [ -0.0008882 0.003558 1.003 -0.0001048 4.707e-05 0.9952 -7.901e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1986 0.09339 0.3281 0.153 0.9851 0.994 0.1993 0.4579 0.8817 0.7156 ] Network output: [ 0.00733 -0.03603 0.9963 6.155e-05 -2.763e-05 1.025 4.639e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09823 0.08675 0.1792 0.2055 0.9873 0.992 0.09829 0.7801 0.8736 0.3077 ] Network output: [ -0.007288 0.03701 1.002 6.295e-05 -2.826e-05 0.9761 4.744e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09076 0.08884 0.1659 0.1962 0.9855 0.9914 0.09077 0.7076 0.8518 0.2434 ] Network output: [ 0.0002416 0.9997 -0.0005377 8.588e-06 -3.855e-06 1 6.472e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006851 Epoch 7284 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01227 0.9937 0.9883 2.48e-06 -1.113e-06 -0.006446 1.869e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003246 -0.003037 -0.008921 0.006852 0.9698 0.9742 0.006188 0.8407 0.8294 0.01942 ] Network output: [ 0.9997 0.001113 0.00146 -3.189e-05 1.431e-05 -0.00215 -2.403e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1877 -0.03108 -0.1897 0.1965 0.9836 0.9933 0.2097 0.4533 0.8752 0.721 ] Network output: [ -0.01139 1.001 1.01 1.211e-06 -5.435e-07 0.01152 9.124e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005549 0.0004646 0.004373 0.004105 0.9889 0.992 0.005651 0.8699 0.8991 0.01406 ] Network output: [ -0.0008845 0.003509 1.003 -0.0001048 4.703e-05 0.9953 -7.895e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1987 0.09339 0.3282 0.153 0.9851 0.994 0.1993 0.4579 0.8817 0.7156 ] Network output: [ 0.007327 -0.03603 0.9963 6.15e-05 -2.761e-05 1.025 4.635e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09823 0.08676 0.1792 0.2055 0.9873 0.992 0.0983 0.7801 0.8736 0.3077 ] Network output: [ -0.007285 0.037 1.002 6.29e-05 -2.824e-05 0.9761 4.741e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09076 0.08884 0.1659 0.1962 0.9855 0.9914 0.09077 0.7076 0.8517 0.2434 ] Network output: [ 0.0002436 0.9997 -0.0005401 8.582e-06 -3.853e-06 1 6.468e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006847 Epoch 7285 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01226 0.9937 0.9883 2.475e-06 -1.111e-06 -0.006452 1.865e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003246 -0.003037 -0.008919 0.006851 0.9698 0.9742 0.006188 0.8407 0.8294 0.01941 ] Network output: [ 0.9997 0.001147 0.001458 -3.187e-05 1.431e-05 -0.002177 -2.401e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1878 -0.03108 -0.1897 0.1965 0.9836 0.9933 0.2097 0.4532 0.8752 0.721 ] Network output: [ -0.01139 1.001 1.01 1.208e-06 -5.423e-07 0.01151 9.104e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005549 0.0004645 0.004373 0.004104 0.9889 0.992 0.005651 0.8699 0.8991 0.01406 ] Network output: [ -0.0008873 0.003554 1.003 -0.0001047 4.699e-05 0.9952 -7.889e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1987 0.0934 0.3282 0.153 0.9851 0.994 0.1993 0.4578 0.8817 0.7156 ] Network output: [ 0.007325 -0.03601 0.9963 6.146e-05 -2.759e-05 1.025 4.632e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09824 0.08676 0.1792 0.2054 0.9873 0.992 0.0983 0.78 0.8735 0.3076 ] Network output: [ -0.007282 0.03698 1.002 6.286e-05 -2.822e-05 0.9761 4.737e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09075 0.08884 0.1659 0.1962 0.9855 0.9914 0.09077 0.7076 0.8517 0.2434 ] Network output: [ 0.0002414 0.9997 -0.0005367 8.575e-06 -3.85e-06 1 6.462e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006842 Epoch 7286 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01226 0.9937 0.9883 2.471e-06 -1.109e-06 -0.00645 1.862e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003246 -0.003037 -0.008918 0.00685 0.9698 0.9742 0.006188 0.8407 0.8294 0.01941 ] Network output: [ 0.9997 0.001112 0.001459 -3.184e-05 1.429e-05 -0.002147 -2.4e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1878 -0.03109 -0.1896 0.1965 0.9836 0.9933 0.2097 0.4532 0.8752 0.721 ] Network output: [ -0.01139 1.001 1.01 1.206e-06 -5.413e-07 0.01151 9.087e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00555 0.0004645 0.004373 0.004103 0.9889 0.992 0.005652 0.8699 0.8991 0.01406 ] Network output: [ -0.0008838 0.003507 1.003 -0.0001046 4.695e-05 0.9953 -7.882e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1987 0.0934 0.3282 0.153 0.9851 0.994 0.1993 0.4578 0.8817 0.7156 ] Network output: [ 0.007322 -0.03601 0.9963 6.141e-05 -2.757e-05 1.025 4.628e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09824 0.08676 0.1792 0.2054 0.9873 0.992 0.09831 0.78 0.8735 0.3076 ] Network output: [ -0.007279 0.03696 1.002 6.281e-05 -2.82e-05 0.9761 4.734e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09075 0.08883 0.1659 0.1962 0.9855 0.9914 0.09076 0.7075 0.8517 0.2434 ] Network output: [ 0.0002433 0.9997 -0.000539 8.569e-06 -3.847e-06 1 6.458e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006838 Epoch 7287 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01226 0.9937 0.9883 2.466e-06 -1.107e-06 -0.006456 1.858e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003246 -0.003037 -0.008916 0.006849 0.9698 0.9742 0.006189 0.8407 0.8294 0.01941 ] Network output: [ 0.9997 0.001144 0.001456 -3.182e-05 1.429e-05 -0.002173 -2.398e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1878 -0.03109 -0.1896 0.1965 0.9836 0.9933 0.2097 0.4532 0.8752 0.7209 ] Network output: [ -0.01139 1.001 1.01 1.203e-06 -5.401e-07 0.0115 9.067e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00555 0.0004644 0.004373 0.004103 0.9889 0.992 0.005653 0.8699 0.8991 0.01406 ] Network output: [ -0.0008864 0.003551 1.003 -0.0001045 4.692e-05 0.9952 -7.876e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1987 0.0934 0.3282 0.153 0.9851 0.994 0.1993 0.4578 0.8817 0.7156 ] Network output: [ 0.00732 -0.03598 0.9963 6.136e-05 -2.755e-05 1.025 4.624e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09825 0.08677 0.1792 0.2054 0.9873 0.992 0.09831 0.78 0.8735 0.3076 ] Network output: [ -0.007276 0.03694 1.002 6.277e-05 -2.818e-05 0.9762 4.73e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09075 0.08883 0.1659 0.1962 0.9855 0.9914 0.09076 0.7075 0.8517 0.2434 ] Network output: [ 0.0002412 0.9997 -0.0005357 8.562e-06 -3.844e-06 1 6.453e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006833 Epoch 7288 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01226 0.9937 0.9883 2.462e-06 -1.105e-06 -0.006454 1.855e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003247 -0.003037 -0.008915 0.006848 0.9698 0.9742 0.006189 0.8407 0.8293 0.01941 ] Network output: [ 0.9997 0.00111 0.001457 -3.18e-05 1.427e-05 -0.002145 -2.396e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1878 -0.0311 -0.1896 0.1965 0.9836 0.9933 0.2097 0.4532 0.8752 0.7209 ] Network output: [ -0.01138 1.001 1.01 1.201e-06 -5.391e-07 0.0115 9.049e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005551 0.0004644 0.004374 0.004102 0.9889 0.992 0.005653 0.8699 0.8991 0.01406 ] Network output: [ -0.000883 0.003506 1.003 -0.0001044 4.688e-05 0.9953 -7.869e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1987 0.0934 0.3282 0.153 0.9851 0.994 0.1993 0.4578 0.8817 0.7156 ] Network output: [ 0.007317 -0.03598 0.9963 6.131e-05 -2.753e-05 1.025 4.621e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09825 0.08677 0.1792 0.2054 0.9873 0.992 0.09831 0.78 0.8735 0.3076 ] Network output: [ -0.007273 0.03693 1.002 6.272e-05 -2.816e-05 0.9762 4.727e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09075 0.08883 0.1659 0.1962 0.9855 0.9914 0.09076 0.7075 0.8517 0.2434 ] Network output: [ 0.000243 0.9997 -0.0005379 8.556e-06 -3.841e-06 1 6.448e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000683 Epoch 7289 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01226 0.9937 0.9883 2.457e-06 -1.103e-06 -0.00646 1.852e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003247 -0.003037 -0.008913 0.006847 0.9698 0.9742 0.006189 0.8407 0.8293 0.01941 ] Network output: [ 0.9997 0.001141 0.001454 -3.178e-05 1.427e-05 -0.00217 -2.395e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1878 -0.0311 -0.1896 0.1965 0.9836 0.9933 0.2097 0.4532 0.8752 0.7209 ] Network output: [ -0.01138 1.001 1.01 1.198e-06 -5.379e-07 0.01149 9.03e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005552 0.0004644 0.004374 0.004101 0.9889 0.992 0.005654 0.8698 0.8991 0.01406 ] Network output: [ -0.0008855 0.003548 1.003 -0.0001043 4.684e-05 0.9953 -7.863e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1987 0.0934 0.3282 0.1529 0.9851 0.994 0.1994 0.4578 0.8817 0.7155 ] Network output: [ 0.007314 -0.03596 0.9963 6.127e-05 -2.75e-05 1.025 4.617e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09826 0.08677 0.1792 0.2054 0.9873 0.992 0.09832 0.7799 0.8735 0.3076 ] Network output: [ -0.00727 0.03691 1.002 6.268e-05 -2.814e-05 0.9762 4.723e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09074 0.08883 0.1659 0.1962 0.9855 0.9914 0.09076 0.7074 0.8517 0.2434 ] Network output: [ 0.000241 0.9997 -0.0005348 8.55e-06 -3.838e-06 1 6.443e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006824 Epoch 7290 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01225 0.9937 0.9883 2.453e-06 -1.101e-06 -0.006459 1.849e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003247 -0.003038 -0.008912 0.006847 0.9698 0.9742 0.00619 0.8407 0.8293 0.0194 ] Network output: [ 0.9997 0.001109 0.001455 -3.175e-05 1.425e-05 -0.002143 -2.393e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1878 -0.03111 -0.1896 0.1965 0.9836 0.9933 0.2097 0.4532 0.8752 0.7209 ] Network output: [ -0.01138 1.001 1.01 1.196e-06 -5.369e-07 0.01149 9.012e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005552 0.0004643 0.004374 0.004101 0.9889 0.992 0.005654 0.8698 0.8991 0.01405 ] Network output: [ -0.0008822 0.003505 1.003 -0.0001043 4.68e-05 0.9953 -7.857e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1987 0.0934 0.3282 0.1529 0.9851 0.994 0.1994 0.4578 0.8817 0.7155 ] Network output: [ 0.007311 -0.03596 0.9963 6.122e-05 -2.748e-05 1.025 4.614e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09826 0.08678 0.1792 0.2054 0.9873 0.992 0.09832 0.7799 0.8735 0.3076 ] Network output: [ -0.007267 0.0369 1.002 6.263e-05 -2.812e-05 0.9762 4.72e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09074 0.08882 0.1659 0.1962 0.9855 0.9914 0.09075 0.7074 0.8517 0.2434 ] Network output: [ 0.0002426 0.9997 -0.0005368 8.544e-06 -3.836e-06 1 6.439e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006821 Epoch 7291 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01225 0.9937 0.9883 2.448e-06 -1.099e-06 -0.006465 1.845e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003247 -0.003038 -0.00891 0.006845 0.9698 0.9742 0.00619 0.8406 0.8293 0.0194 ] Network output: [ 0.9997 0.001138 0.001452 -3.173e-05 1.425e-05 -0.002166 -2.391e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1878 -0.03111 -0.1895 0.1964 0.9836 0.9933 0.2097 0.4532 0.8752 0.7209 ] Network output: [ -0.01138 1.001 1.01 1.193e-06 -5.357e-07 0.01149 8.993e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005553 0.0004643 0.004374 0.0041 0.9889 0.992 0.005655 0.8698 0.8991 0.01405 ] Network output: [ -0.0008846 0.003545 1.003 -0.0001042 4.677e-05 0.9953 -7.851e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1987 0.09341 0.3283 0.1529 0.9851 0.994 0.1994 0.4577 0.8817 0.7155 ] Network output: [ 0.007309 -0.03593 0.9962 6.117e-05 -2.746e-05 1.025 4.61e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09826 0.08678 0.1792 0.2054 0.9873 0.992 0.09833 0.7799 0.8735 0.3076 ] Network output: [ -0.007264 0.03688 1.002 6.258e-05 -2.81e-05 0.9762 4.717e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09074 0.08882 0.1659 0.1962 0.9855 0.9914 0.09075 0.7074 0.8517 0.2434 ] Network output: [ 0.0002407 0.9997 -0.0005338 8.537e-06 -3.833e-06 1 6.434e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006816 Epoch 7292 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01225 0.9937 0.9883 2.444e-06 -1.097e-06 -0.006463 1.842e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003247 -0.003038 -0.008909 0.006845 0.9698 0.9742 0.00619 0.8406 0.8293 0.0194 ] Network output: [ 0.9997 0.001107 0.001453 -3.171e-05 1.423e-05 -0.00214 -2.39e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1878 -0.03112 -0.1895 0.1964 0.9836 0.9933 0.2098 0.4531 0.8752 0.7209 ] Network output: [ -0.01138 1.001 1.01 1.191e-06 -5.347e-07 0.01148 8.975e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005553 0.0004642 0.004374 0.004099 0.9889 0.992 0.005656 0.8698 0.8991 0.01405 ] Network output: [ -0.0008815 0.003504 1.003 -0.0001041 4.673e-05 0.9953 -7.844e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1988 0.09341 0.3283 0.1529 0.9851 0.994 0.1994 0.4577 0.8817 0.7155 ] Network output: [ 0.007306 -0.03593 0.9962 6.112e-05 -2.744e-05 1.025 4.607e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09827 0.08678 0.1792 0.2054 0.9873 0.992 0.09833 0.7798 0.8735 0.3076 ] Network output: [ -0.007261 0.03687 1.002 6.254e-05 -2.808e-05 0.9762 4.713e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09074 0.08882 0.1659 0.1962 0.9855 0.9914 0.09075 0.7073 0.8516 0.2434 ] Network output: [ 0.0002423 0.9997 -0.0005357 8.531e-06 -3.83e-06 1 6.429e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006812 Epoch 7293 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01225 0.9937 0.9883 2.44e-06 -1.095e-06 -0.006469 1.839e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003247 -0.003038 -0.008907 0.006844 0.9698 0.9742 0.006191 0.8406 0.8293 0.0194 ] Network output: [ 0.9997 0.001136 0.00145 -3.169e-05 1.423e-05 -0.002163 -2.388e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1878 -0.03112 -0.1895 0.1964 0.9836 0.9933 0.2098 0.4531 0.8752 0.7209 ] Network output: [ -0.01138 1.001 1.01 1.188e-06 -5.335e-07 0.01148 8.956e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005554 0.0004642 0.004374 0.004098 0.9889 0.992 0.005656 0.8698 0.8991 0.01405 ] Network output: [ -0.0008838 0.003542 1.003 -0.000104 4.669e-05 0.9953 -7.838e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1988 0.09341 0.3283 0.1529 0.9851 0.994 0.1994 0.4577 0.8817 0.7155 ] Network output: [ 0.007304 -0.03591 0.9962 6.108e-05 -2.742e-05 1.025 4.603e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09827 0.08679 0.1792 0.2054 0.9873 0.992 0.09834 0.7798 0.8735 0.3076 ] Network output: [ -0.007258 0.03685 1.002 6.249e-05 -2.806e-05 0.9762 4.71e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09074 0.08882 0.1659 0.1962 0.9855 0.9914 0.09075 0.7073 0.8516 0.2434 ] Network output: [ 0.0002405 0.9997 -0.0005328 8.524e-06 -3.827e-06 1 6.424e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006807 Epoch 7294 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01224 0.9937 0.9883 2.435e-06 -1.093e-06 -0.006467 1.835e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003247 -0.003038 -0.008906 0.006843 0.9698 0.9742 0.006191 0.8406 0.8293 0.0194 ] Network output: [ 0.9997 0.001105 0.001451 -3.166e-05 1.421e-05 -0.002138 -2.386e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1879 -0.03113 -0.1895 0.1964 0.9836 0.9933 0.2098 0.4531 0.8751 0.7209 ] Network output: [ -0.01138 1.001 1.01 1.186e-06 -5.325e-07 0.01148 8.938e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005555 0.0004642 0.004374 0.004098 0.9889 0.992 0.005657 0.8698 0.8991 0.01405 ] Network output: [ -0.0008807 0.003502 1.003 -0.0001039 4.665e-05 0.9953 -7.832e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1988 0.09341 0.3283 0.1529 0.9851 0.994 0.1994 0.4577 0.8817 0.7155 ] Network output: [ 0.007301 -0.03591 0.9962 6.103e-05 -2.74e-05 1.025 4.599e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09828 0.08679 0.1793 0.2054 0.9873 0.992 0.09834 0.7798 0.8735 0.3076 ] Network output: [ -0.007255 0.03683 1.002 6.245e-05 -2.803e-05 0.9762 4.706e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09073 0.08881 0.1659 0.1962 0.9855 0.9914 0.09075 0.7073 0.8516 0.2434 ] Network output: [ 0.000242 0.9997 -0.0005346 8.518e-06 -3.824e-06 1 6.42e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006803 Epoch 7295 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01224 0.9937 0.9883 2.431e-06 -1.091e-06 -0.006473 1.832e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003247 -0.003038 -0.008905 0.006842 0.9698 0.9742 0.006191 0.8406 0.8293 0.01939 ] Network output: [ 0.9997 0.001133 0.001449 -3.164e-05 1.42e-05 -0.00216 -2.385e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1879 -0.03113 -0.1895 0.1964 0.9836 0.9933 0.2098 0.4531 0.8751 0.7209 ] Network output: [ -0.01138 1.001 1.01 1.183e-06 -5.313e-07 0.01147 8.919e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005555 0.0004641 0.004375 0.004097 0.9889 0.992 0.005658 0.8698 0.8991 0.01405 ] Network output: [ -0.0008829 0.003539 1.003 -0.0001038 4.662e-05 0.9953 -7.825e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1988 0.09341 0.3283 0.1529 0.9851 0.994 0.1994 0.4577 0.8817 0.7155 ] Network output: [ 0.007299 -0.03589 0.9962 6.098e-05 -2.738e-05 1.025 4.596e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09828 0.08679 0.1793 0.2054 0.9873 0.992 0.09835 0.7798 0.8735 0.3076 ] Network output: [ -0.007252 0.03682 1.002 6.24e-05 -2.801e-05 0.9762 4.703e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09073 0.08881 0.1659 0.1962 0.9855 0.9914 0.09074 0.7072 0.8516 0.2434 ] Network output: [ 0.0002403 0.9997 -0.0005318 8.511e-06 -3.821e-06 1 6.415e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006798 Epoch 7296 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01224 0.9937 0.9883 2.427e-06 -1.089e-06 -0.006471 1.829e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003248 -0.003039 -0.008903 0.006841 0.9698 0.9742 0.006191 0.8406 0.8293 0.01939 ] Network output: [ 0.9997 0.001104 0.001449 -3.162e-05 1.419e-05 -0.002135 -2.383e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1879 -0.03113 -0.1895 0.1964 0.9836 0.9933 0.2098 0.4531 0.8751 0.7209 ] Network output: [ -0.01137 1.001 1.01 1.181e-06 -5.303e-07 0.01147 8.902e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005556 0.0004641 0.004375 0.004096 0.9889 0.992 0.005658 0.8698 0.899 0.01405 ] Network output: [ -0.0008799 0.003501 1.003 -0.0001037 4.658e-05 0.9953 -7.819e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1988 0.09341 0.3283 0.1529 0.9851 0.994 0.1994 0.4577 0.8816 0.7155 ] Network output: [ 0.007296 -0.03588 0.9962 6.094e-05 -2.736e-05 1.025 4.592e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09829 0.0868 0.1793 0.2054 0.9873 0.992 0.09835 0.7797 0.8734 0.3076 ] Network output: [ -0.007249 0.0368 1.002 6.235e-05 -2.799e-05 0.9762 4.699e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09073 0.08881 0.1659 0.1962 0.9855 0.9914 0.09074 0.7072 0.8516 0.2434 ] Network output: [ 0.0002417 0.9997 -0.0005335 8.505e-06 -3.818e-06 1 6.41e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006794 Epoch 7297 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01224 0.9937 0.9883 2.422e-06 -1.087e-06 -0.006477 1.825e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003248 -0.003039 -0.008902 0.00684 0.9698 0.9742 0.006192 0.8406 0.8293 0.01939 ] Network output: [ 0.9997 0.00113 0.001447 -3.16e-05 1.418e-05 -0.002156 -2.381e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1879 -0.03114 -0.1894 0.1964 0.9836 0.9933 0.2098 0.4531 0.8751 0.7209 ] Network output: [ -0.01137 1.001 1.01 1.179e-06 -5.291e-07 0.01146 8.882e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005556 0.000464 0.004375 0.004096 0.9889 0.992 0.005659 0.8698 0.899 0.01404 ] Network output: [ -0.000882 0.003536 1.003 -0.0001037 4.654e-05 0.9953 -7.813e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1988 0.09342 0.3284 0.1528 0.9851 0.994 0.1995 0.4576 0.8816 0.7155 ] Network output: [ 0.007294 -0.03586 0.9962 6.089e-05 -2.733e-05 1.025 4.589e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09829 0.0868 0.1793 0.2053 0.9873 0.992 0.09836 0.7797 0.8734 0.3076 ] Network output: [ -0.007246 0.03678 1.002 6.231e-05 -2.797e-05 0.9762 4.696e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09073 0.08881 0.1659 0.1962 0.9855 0.9914 0.09074 0.7072 0.8516 0.2434 ] Network output: [ 0.00024 0.9997 -0.0005308 8.499e-06 -3.815e-06 1 6.405e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006789 Epoch 7298 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01224 0.9937 0.9883 2.418e-06 -1.085e-06 -0.006476 1.822e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003248 -0.003039 -0.0089 0.006839 0.9698 0.9742 0.006192 0.8406 0.8293 0.01939 ] Network output: [ 0.9997 0.001102 0.001447 -3.157e-05 1.417e-05 -0.002133 -2.379e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1879 -0.03114 -0.1894 0.1964 0.9836 0.9933 0.2098 0.453 0.8751 0.7209 ] Network output: [ -0.01137 1.001 1.01 1.176e-06 -5.281e-07 0.01146 8.865e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005557 0.000464 0.004375 0.004095 0.9889 0.992 0.005659 0.8698 0.899 0.01404 ] Network output: [ -0.0008791 0.003499 1.003 -0.0001036 4.65e-05 0.9953 -7.806e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1988 0.09342 0.3284 0.1528 0.9851 0.994 0.1995 0.4576 0.8816 0.7155 ] Network output: [ 0.007291 -0.03586 0.9962 6.084e-05 -2.731e-05 1.025 4.585e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0983 0.08681 0.1793 0.2053 0.9873 0.992 0.09836 0.7797 0.8734 0.3076 ] Network output: [ -0.007243 0.03677 1.002 6.226e-05 -2.795e-05 0.9762 4.692e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09072 0.0888 0.1659 0.1962 0.9855 0.9914 0.09074 0.7071 0.8516 0.2434 ] Network output: [ 0.0002414 0.9997 -0.0005324 8.493e-06 -3.813e-06 1 6.4e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006785 Epoch 7299 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01223 0.9937 0.9883 2.413e-06 -1.083e-06 -0.006481 1.819e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003248 -0.003039 -0.008899 0.006838 0.9698 0.9742 0.006192 0.8406 0.8293 0.01939 ] Network output: [ 0.9997 0.001127 0.001445 -3.155e-05 1.416e-05 -0.002153 -2.378e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1879 -0.03115 -0.1894 0.1964 0.9836 0.9933 0.2098 0.453 0.8751 0.7209 ] Network output: [ -0.01137 1.001 1.01 1.174e-06 -5.269e-07 0.01146 8.845e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005558 0.000464 0.004375 0.004094 0.9889 0.992 0.00566 0.8697 0.899 0.01404 ] Network output: [ -0.0008811 0.003533 1.003 -0.0001035 4.647e-05 0.9953 -7.8e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1988 0.09342 0.3284 0.1528 0.9851 0.994 0.1995 0.4576 0.8816 0.7155 ] Network output: [ 0.007289 -0.03584 0.9962 6.079e-05 -2.729e-05 1.025 4.582e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0983 0.08681 0.1793 0.2053 0.9873 0.992 0.09836 0.7797 0.8734 0.3076 ] Network output: [ -0.00724 0.03675 1.002 6.222e-05 -2.793e-05 0.9762 4.689e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09072 0.0888 0.1659 0.1961 0.9855 0.9914 0.09073 0.7071 0.8516 0.2434 ] Network output: [ 0.0002398 0.9997 -0.0005298 8.486e-06 -3.81e-06 1 6.395e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000678 Epoch 7300 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01223 0.9937 0.9883 2.409e-06 -1.082e-06 -0.00648 1.816e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003248 -0.003039 -0.008897 0.006837 0.9698 0.9742 0.006193 0.8406 0.8293 0.01938 ] Network output: [ 0.9997 0.001101 0.001445 -3.153e-05 1.415e-05 -0.00213 -2.376e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1879 -0.03115 -0.1894 0.1964 0.9836 0.9933 0.2099 0.453 0.8751 0.7209 ] Network output: [ -0.01137 1.001 1.01 1.171e-06 -5.259e-07 0.01145 8.828e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005558 0.0004639 0.004375 0.004094 0.9889 0.992 0.005661 0.8697 0.899 0.01404 ] Network output: [ -0.0008784 0.003498 1.003 -0.0001034 4.643e-05 0.9953 -7.794e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1989 0.09342 0.3284 0.1528 0.9851 0.994 0.1995 0.4576 0.8816 0.7155 ] Network output: [ 0.007286 -0.03583 0.9962 6.075e-05 -2.727e-05 1.025 4.578e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09831 0.08681 0.1793 0.2053 0.9873 0.992 0.09837 0.7796 0.8734 0.3076 ] Network output: [ -0.007237 0.03674 1.002 6.217e-05 -2.791e-05 0.9763 4.685e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09072 0.0888 0.1659 0.1961 0.9855 0.9914 0.09073 0.7071 0.8515 0.2434 ] Network output: [ 0.0002411 0.9997 -0.0005314 8.48e-06 -3.807e-06 1 6.391e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006777 Epoch 7301 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01223 0.9937 0.9883 2.405e-06 -1.079e-06 -0.006485 1.812e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003248 -0.003039 -0.008896 0.006836 0.9698 0.9742 0.006193 0.8406 0.8293 0.01938 ] Network output: [ 0.9997 0.001125 0.001443 -3.151e-05 1.414e-05 -0.002149 -2.374e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1879 -0.03116 -0.1894 0.1963 0.9836 0.9933 0.2099 0.453 0.8751 0.7209 ] Network output: [ -0.01137 1.001 1.01 1.169e-06 -5.247e-07 0.01145 8.809e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005559 0.0004639 0.004375 0.004093 0.9889 0.992 0.005661 0.8697 0.899 0.01404 ] Network output: [ -0.0008802 0.003531 1.003 -0.0001033 4.639e-05 0.9953 -7.788e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1989 0.09342 0.3284 0.1528 0.9851 0.994 0.1995 0.4576 0.8816 0.7155 ] Network output: [ 0.007284 -0.03581 0.9962 6.07e-05 -2.725e-05 1.025 4.574e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09831 0.08682 0.1793 0.2053 0.9873 0.992 0.09837 0.7796 0.8734 0.3076 ] Network output: [ -0.007234 0.03672 1.002 6.213e-05 -2.789e-05 0.9763 4.682e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09072 0.0888 0.1659 0.1961 0.9855 0.9914 0.09073 0.707 0.8515 0.2434 ] Network output: [ 0.0002396 0.9997 -0.0005288 8.473e-06 -3.804e-06 1 6.386e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006772 Epoch 7302 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01223 0.9937 0.9883 2.4e-06 -1.078e-06 -0.006484 1.809e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003248 -0.00304 -0.008894 0.006835 0.9698 0.9742 0.006193 0.8406 0.8293 0.01938 ] Network output: [ 0.9997 0.001099 0.001444 -3.148e-05 1.413e-05 -0.002128 -2.373e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1879 -0.03116 -0.1893 0.1963 0.9836 0.9933 0.2099 0.453 0.8751 0.7209 ] Network output: [ -0.01137 1.001 1.01 1.166e-06 -5.237e-07 0.01145 8.791e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005559 0.0004638 0.004376 0.004092 0.9889 0.992 0.005662 0.8697 0.899 0.01404 ] Network output: [ -0.0008776 0.003497 1.003 -0.0001032 4.635e-05 0.9953 -7.781e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1989 0.09343 0.3284 0.1528 0.9851 0.994 0.1995 0.4576 0.8816 0.7155 ] Network output: [ 0.007281 -0.0358 0.9962 6.065e-05 -2.723e-05 1.025 4.571e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09832 0.08682 0.1793 0.2053 0.9873 0.992 0.09838 0.7796 0.8734 0.3076 ] Network output: [ -0.007231 0.03671 1.002 6.208e-05 -2.787e-05 0.9763 4.679e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09072 0.0888 0.1659 0.1961 0.9855 0.9914 0.09073 0.707 0.8515 0.2434 ] Network output: [ 0.0002409 0.9997 -0.0005303 8.467e-06 -3.801e-06 1 6.381e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006768 Epoch 7303 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01222 0.9937 0.9883 2.396e-06 -1.076e-06 -0.006489 1.806e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003249 -0.00304 -0.008893 0.006834 0.9698 0.9742 0.006194 0.8405 0.8293 0.01938 ] Network output: [ 0.9997 0.001122 0.001441 -3.146e-05 1.412e-05 -0.002146 -2.371e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.188 -0.03117 -0.1893 0.1963 0.9836 0.9933 0.2099 0.453 0.8751 0.7209 ] Network output: [ -0.01137 1.001 1.01 1.164e-06 -5.225e-07 0.01144 8.772e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00556 0.0004638 0.004376 0.004091 0.9889 0.992 0.005662 0.8697 0.899 0.01404 ] Network output: [ -0.0008793 0.003528 1.003 -0.0001032 4.631e-05 0.9953 -7.775e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1989 0.09343 0.3285 0.1528 0.9851 0.994 0.1995 0.4575 0.8816 0.7155 ] Network output: [ 0.007278 -0.03579 0.9962 6.06e-05 -2.721e-05 1.025 4.567e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09832 0.08682 0.1793 0.2053 0.9873 0.992 0.09838 0.7795 0.8734 0.3076 ] Network output: [ -0.007227 0.03669 1.002 6.204e-05 -2.785e-05 0.9763 4.675e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09071 0.08879 0.1659 0.1961 0.9855 0.9914 0.09073 0.707 0.8515 0.2434 ] Network output: [ 0.0002393 0.9997 -0.0005278 8.461e-06 -3.798e-06 1 6.376e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006763 Epoch 7304 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01222 0.9937 0.9883 2.392e-06 -1.074e-06 -0.006488 1.802e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003249 -0.00304 -0.008891 0.006833 0.9698 0.9742 0.006194 0.8405 0.8292 0.01938 ] Network output: [ 0.9997 0.001098 0.001442 -3.144e-05 1.411e-05 -0.002125 -2.369e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.188 -0.03117 -0.1893 0.1963 0.9836 0.9933 0.2099 0.4529 0.8751 0.7209 ] Network output: [ -0.01136 1.001 1.01 1.162e-06 -5.215e-07 0.01144 8.754e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005561 0.0004638 0.004376 0.004091 0.9889 0.992 0.005663 0.8697 0.899 0.01403 ] Network output: [ -0.0008768 0.003495 1.003 -0.0001031 4.628e-05 0.9953 -7.768e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1989 0.09343 0.3285 0.1528 0.9851 0.994 0.1995 0.4575 0.8816 0.7155 ] Network output: [ 0.007276 -0.03578 0.9962 6.056e-05 -2.719e-05 1.025 4.564e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09832 0.08683 0.1793 0.2053 0.9873 0.992 0.09839 0.7795 0.8734 0.3076 ] Network output: [ -0.007225 0.03667 1.002 6.199e-05 -2.783e-05 0.9763 4.672e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09071 0.08879 0.1659 0.1961 0.9855 0.9914 0.09072 0.707 0.8515 0.2434 ] Network output: [ 0.0002406 0.9997 -0.0005292 8.455e-06 -3.796e-06 1 6.372e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006759 Epoch 7305 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01222 0.9937 0.9883 2.387e-06 -1.072e-06 -0.006493 1.799e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003249 -0.00304 -0.00889 0.006832 0.9698 0.9742 0.006194 0.8405 0.8292 0.01937 ] Network output: [ 0.9997 0.001119 0.001439 -3.142e-05 1.41e-05 -0.002143 -2.368e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.188 -0.03118 -0.1893 0.1963 0.9836 0.9933 0.2099 0.4529 0.8751 0.7209 ] Network output: [ -0.01136 1.001 1.01 1.159e-06 -5.203e-07 0.01143 8.735e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005561 0.0004637 0.004376 0.00409 0.9889 0.992 0.005664 0.8697 0.899 0.01403 ] Network output: [ -0.0008785 0.003525 1.003 -0.000103 4.624e-05 0.9953 -7.762e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1989 0.09343 0.3285 0.1527 0.9851 0.994 0.1996 0.4575 0.8816 0.7155 ] Network output: [ 0.007273 -0.03576 0.9962 6.051e-05 -2.716e-05 1.025 4.56e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09833 0.08683 0.1793 0.2053 0.9873 0.992 0.09839 0.7795 0.8734 0.3076 ] Network output: [ -0.007221 0.03666 1.002 6.194e-05 -2.781e-05 0.9763 4.668e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09071 0.08879 0.1659 0.1961 0.9855 0.9914 0.09072 0.7069 0.8515 0.2434 ] Network output: [ 0.0002391 0.9997 -0.0005268 8.448e-06 -3.793e-06 1 6.367e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006754 Epoch 7306 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01222 0.9937 0.9883 2.383e-06 -1.07e-06 -0.006492 1.796e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003249 -0.00304 -0.008889 0.006831 0.9698 0.9742 0.006195 0.8405 0.8292 0.01937 ] Network output: [ 0.9997 0.001096 0.00144 -3.139e-05 1.409e-05 -0.002123 -2.366e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.188 -0.03118 -0.1893 0.1963 0.9836 0.9933 0.2099 0.4529 0.8751 0.7209 ] Network output: [ -0.01136 1.001 1.01 1.157e-06 -5.193e-07 0.01143 8.718e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005562 0.0004637 0.004376 0.004089 0.9889 0.992 0.005664 0.8697 0.899 0.01403 ] Network output: [ -0.000876 0.003493 1.003 -0.0001029 4.62e-05 0.9953 -7.756e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1989 0.09343 0.3285 0.1527 0.9851 0.994 0.1996 0.4575 0.8816 0.7155 ] Network output: [ 0.00727 -0.03575 0.9962 6.046e-05 -2.714e-05 1.025 4.557e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09833 0.08683 0.1793 0.2053 0.9873 0.992 0.0984 0.7795 0.8733 0.3076 ] Network output: [ -0.007219 0.03664 1.002 6.19e-05 -2.779e-05 0.9763 4.665e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09071 0.08879 0.1659 0.1961 0.9855 0.9914 0.09072 0.7069 0.8515 0.2434 ] Network output: [ 0.0002403 0.9997 -0.0005281 8.442e-06 -3.79e-06 1 6.362e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000675 Epoch 7307 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01221 0.9937 0.9883 2.378e-06 -1.068e-06 -0.006497 1.792e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003249 -0.00304 -0.008887 0.00683 0.9698 0.9742 0.006195 0.8405 0.8292 0.01937 ] Network output: [ 0.9997 0.001117 0.001438 -3.137e-05 1.408e-05 -0.002139 -2.364e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.188 -0.03118 -0.1892 0.1963 0.9836 0.9933 0.2099 0.4529 0.8751 0.7209 ] Network output: [ -0.01136 1.001 1.01 1.154e-06 -5.182e-07 0.01142 8.698e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005562 0.0004637 0.004376 0.004088 0.9889 0.992 0.005665 0.8697 0.899 0.01403 ] Network output: [ -0.0008776 0.003522 1.003 -0.0001028 4.616e-05 0.9953 -7.75e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1989 0.09344 0.3285 0.1527 0.9851 0.994 0.1996 0.4575 0.8816 0.7155 ] Network output: [ 0.007268 -0.03574 0.9962 6.042e-05 -2.712e-05 1.025 4.553e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09834 0.08684 0.1793 0.2053 0.9873 0.992 0.0984 0.7794 0.8733 0.3076 ] Network output: [ -0.007215 0.03662 1.002 6.185e-05 -2.777e-05 0.9763 4.661e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0907 0.08878 0.1659 0.1961 0.9855 0.9914 0.09072 0.7069 0.8515 0.2434 ] Network output: [ 0.0002389 0.9997 -0.0005258 8.435e-06 -3.787e-06 1 6.357e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006745 Epoch 7308 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01221 0.9937 0.9883 2.374e-06 -1.066e-06 -0.006497 1.789e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003249 -0.003041 -0.008886 0.006829 0.9698 0.9742 0.006195 0.8405 0.8292 0.01937 ] Network output: [ 0.9997 0.001094 0.001438 -3.135e-05 1.407e-05 -0.00212 -2.363e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.188 -0.03119 -0.1892 0.1963 0.9836 0.9933 0.21 0.4529 0.8751 0.7209 ] Network output: [ -0.01136 1.001 1.01 1.152e-06 -5.171e-07 0.01142 8.681e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005563 0.0004636 0.004377 0.004088 0.9889 0.992 0.005666 0.8697 0.899 0.01403 ] Network output: [ -0.0008752 0.003492 1.003 -0.0001027 4.613e-05 0.9953 -7.743e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.199 0.09344 0.3285 0.1527 0.9851 0.994 0.1996 0.4575 0.8816 0.7155 ] Network output: [ 0.007265 -0.03573 0.9962 6.037e-05 -2.71e-05 1.025 4.55e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09834 0.08684 0.1793 0.2053 0.9873 0.992 0.09841 0.7794 0.8733 0.3076 ] Network output: [ -0.007213 0.03661 1.002 6.181e-05 -2.775e-05 0.9763 4.658e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0907 0.08878 0.1659 0.1961 0.9855 0.9914 0.09071 0.7068 0.8514 0.2434 ] Network output: [ 0.00024 0.9997 -0.0005271 8.429e-06 -3.784e-06 1 6.353e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006741 Epoch 7309 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01221 0.9937 0.9884 2.37e-06 -1.064e-06 -0.006501 1.786e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003249 -0.003041 -0.008884 0.006828 0.9698 0.9742 0.006195 0.8405 0.8292 0.01937 ] Network output: [ 0.9997 0.001114 0.001436 -3.133e-05 1.406e-05 -0.002136 -2.361e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.188 -0.03119 -0.1892 0.1963 0.9836 0.9933 0.21 0.4529 0.8751 0.7209 ] Network output: [ -0.01136 1.001 1.01 1.149e-06 -5.16e-07 0.01142 8.662e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005564 0.0004636 0.004377 0.004087 0.9889 0.992 0.005666 0.8696 0.899 0.01403 ] Network output: [ -0.0008767 0.003519 1.003 -0.0001027 4.609e-05 0.9953 -7.737e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.199 0.09344 0.3286 0.1527 0.9851 0.994 0.1996 0.4574 0.8816 0.7155 ] Network output: [ 0.007263 -0.03571 0.9962 6.032e-05 -2.708e-05 1.025 4.546e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09835 0.08684 0.1793 0.2052 0.9873 0.992 0.09841 0.7794 0.8733 0.3076 ] Network output: [ -0.007209 0.03659 1.002 6.176e-05 -2.773e-05 0.9763 4.655e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0907 0.08878 0.1659 0.1961 0.9855 0.9914 0.09071 0.7068 0.8514 0.2434 ] Network output: [ 0.0002386 0.9997 -0.0005248 8.423e-06 -3.781e-06 1 6.348e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006737 Epoch 7310 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01221 0.9937 0.9884 2.366e-06 -1.062e-06 -0.006501 1.783e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003249 -0.003041 -0.008883 0.006827 0.9698 0.9742 0.006196 0.8405 0.8292 0.01936 ] Network output: [ 0.9997 0.001093 0.001436 -3.13e-05 1.405e-05 -0.002118 -2.359e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.188 -0.0312 -0.1892 0.1963 0.9836 0.9933 0.21 0.4528 0.8751 0.7208 ] Network output: [ -0.01136 1.001 1.01 1.147e-06 -5.149e-07 0.01141 8.644e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005564 0.0004635 0.004377 0.004086 0.9889 0.992 0.005667 0.8696 0.899 0.01403 ] Network output: [ -0.0008744 0.00349 1.003 -0.0001026 4.605e-05 0.9953 -7.731e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.199 0.09344 0.3286 0.1527 0.9851 0.994 0.1996 0.4574 0.8816 0.7154 ] Network output: [ 0.00726 -0.0357 0.9962 6.027e-05 -2.706e-05 1.025 4.542e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09835 0.08685 0.1793 0.2052 0.9873 0.992 0.09842 0.7794 0.8733 0.3075 ] Network output: [ -0.007206 0.03658 1.002 6.172e-05 -2.771e-05 0.9763 4.651e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0907 0.08878 0.1659 0.1961 0.9855 0.9914 0.09071 0.7068 0.8514 0.2434 ] Network output: [ 0.0002397 0.9997 -0.000526 8.417e-06 -3.778e-06 1 6.343e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006733 Epoch 7311 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01221 0.9937 0.9884 2.361e-06 -1.06e-06 -0.006505 1.779e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00325 -0.003041 -0.008881 0.006826 0.9698 0.9742 0.006196 0.8405 0.8292 0.01936 ] Network output: [ 0.9997 0.001112 0.001434 -3.128e-05 1.404e-05 -0.002133 -2.358e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.188 -0.0312 -0.1892 0.1962 0.9836 0.9933 0.21 0.4528 0.8751 0.7208 ] Network output: [ -0.01136 1.001 1.01 1.144e-06 -5.138e-07 0.01141 8.625e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005565 0.0004635 0.004377 0.004086 0.9889 0.992 0.005667 0.8696 0.899 0.01402 ] Network output: [ -0.0008758 0.003516 1.003 -0.0001025 4.602e-05 0.9953 -7.725e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.199 0.09344 0.3286 0.1527 0.9851 0.994 0.1996 0.4574 0.8816 0.7154 ] Network output: [ 0.007258 -0.03569 0.9962 6.023e-05 -2.704e-05 1.025 4.539e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09836 0.08685 0.1793 0.2052 0.9873 0.992 0.09842 0.7793 0.8733 0.3075 ] Network output: [ -0.007203 0.03656 1.002 6.167e-05 -2.769e-05 0.9763 4.648e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0907 0.08878 0.1659 0.1961 0.9855 0.9914 0.09071 0.7067 0.8514 0.2434 ] Network output: [ 0.0002384 0.9997 -0.0005238 8.41e-06 -3.776e-06 1 6.338e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006728 Epoch 7312 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0122 0.9937 0.9884 2.357e-06 -1.058e-06 -0.006505 1.776e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00325 -0.003041 -0.00888 0.006825 0.9698 0.9742 0.006196 0.8405 0.8292 0.01936 ] Network output: [ 0.9997 0.001091 0.001434 -3.126e-05 1.403e-05 -0.002115 -2.356e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1881 -0.03121 -0.1891 0.1962 0.9836 0.9933 0.21 0.4528 0.875 0.7208 ] Network output: [ -0.01135 1.001 1.01 1.142e-06 -5.128e-07 0.01141 8.608e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005565 0.0004635 0.004377 0.004085 0.9889 0.992 0.005668 0.8696 0.899 0.01402 ] Network output: [ -0.0008736 0.003489 1.003 -0.0001024 4.598e-05 0.9953 -7.718e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.199 0.09345 0.3286 0.1527 0.9851 0.994 0.1996 0.4574 0.8816 0.7154 ] Network output: [ 0.007255 -0.03568 0.9962 6.018e-05 -2.702e-05 1.025 4.535e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09836 0.08686 0.1793 0.2052 0.9873 0.992 0.09842 0.7793 0.8733 0.3075 ] Network output: [ -0.0072 0.03655 1.002 6.162e-05 -2.767e-05 0.9763 4.644e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09069 0.08877 0.1659 0.1961 0.9855 0.9914 0.09071 0.7067 0.8514 0.2434 ] Network output: [ 0.0002394 0.9997 -0.0005249 8.404e-06 -3.773e-06 1 6.333e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006724 Epoch 7313 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0122 0.9938 0.9884 2.353e-06 -1.056e-06 -0.006509 1.773e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00325 -0.003041 -0.008878 0.006824 0.9698 0.9742 0.006197 0.8405 0.8292 0.01936 ] Network output: [ 0.9997 0.001109 0.001432 -3.124e-05 1.402e-05 -0.002129 -2.354e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1881 -0.03121 -0.1891 0.1962 0.9836 0.9933 0.21 0.4528 0.875 0.7208 ] Network output: [ -0.01135 1.001 1.01 1.14e-06 -5.116e-07 0.0114 8.589e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005566 0.0004634 0.004377 0.004084 0.9889 0.992 0.005669 0.8696 0.899 0.01402 ] Network output: [ -0.0008749 0.003514 1.003 -0.0001023 4.594e-05 0.9953 -7.712e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.199 0.09345 0.3286 0.1526 0.9851 0.994 0.1997 0.4574 0.8816 0.7154 ] Network output: [ 0.007253 -0.03566 0.9962 6.013e-05 -2.7e-05 1.025 4.532e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09837 0.08686 0.1793 0.2052 0.9873 0.992 0.09843 0.7793 0.8733 0.3075 ] Network output: [ -0.007197 0.03653 1.002 6.158e-05 -2.765e-05 0.9764 4.641e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09069 0.08877 0.1659 0.1961 0.9855 0.9914 0.0907 0.7067 0.8514 0.2434 ] Network output: [ 0.0002381 0.9997 -0.0005228 8.397e-06 -3.77e-06 1 6.328e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006719 Epoch 7314 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0122 0.9938 0.9884 2.348e-06 -1.054e-06 -0.006509 1.77e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00325 -0.003042 -0.008877 0.006823 0.9698 0.9742 0.006197 0.8404 0.8292 0.01936 ] Network output: [ 0.9997 0.001089 0.001432 -3.121e-05 1.401e-05 -0.002112 -2.352e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1881 -0.03122 -0.1891 0.1962 0.9836 0.9933 0.21 0.4528 0.875 0.7208 ] Network output: [ -0.01135 1.001 1.01 1.137e-06 -5.106e-07 0.0114 8.571e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005567 0.0004634 0.004378 0.004084 0.9889 0.992 0.005669 0.8696 0.899 0.01402 ] Network output: [ -0.0008728 0.003487 1.003 -0.0001022 4.59e-05 0.9953 -7.706e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.199 0.09345 0.3286 0.1526 0.9851 0.994 0.1997 0.4574 0.8815 0.7154 ] Network output: [ 0.00725 -0.03565 0.9962 6.009e-05 -2.697e-05 1.025 4.528e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09837 0.08686 0.1793 0.2052 0.9873 0.992 0.09843 0.7792 0.8733 0.3075 ] Network output: [ -0.007194 0.03651 1.002 6.153e-05 -2.762e-05 0.9764 4.637e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09069 0.08877 0.1659 0.1961 0.9855 0.9914 0.0907 0.7066 0.8514 0.2434 ] Network output: [ 0.0002391 0.9997 -0.0005239 8.391e-06 -3.767e-06 1 6.324e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006715 Epoch 7315 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0122 0.9938 0.9884 2.344e-06 -1.052e-06 -0.006513 1.766e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00325 -0.003042 -0.008876 0.006822 0.9698 0.9742 0.006197 0.8404 0.8292 0.01935 ] Network output: [ 0.9997 0.001107 0.00143 -3.119e-05 1.4e-05 -0.002126 -2.351e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1881 -0.03122 -0.1891 0.1962 0.9836 0.9933 0.2101 0.4528 0.875 0.7208 ] Network output: [ -0.01135 1.001 1.01 1.135e-06 -5.095e-07 0.01139 8.552e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005567 0.0004634 0.004378 0.004083 0.9889 0.992 0.00567 0.8696 0.8989 0.01402 ] Network output: [ -0.0008741 0.003511 1.002 -0.0001022 4.587e-05 0.9953 -7.699e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.199 0.09345 0.3287 0.1526 0.9851 0.994 0.1997 0.4573 0.8815 0.7154 ] Network output: [ 0.007248 -0.03564 0.9962 6.004e-05 -2.695e-05 1.025 4.525e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09838 0.08687 0.1793 0.2052 0.9873 0.992 0.09844 0.7792 0.8733 0.3075 ] Network output: [ -0.007191 0.0365 1.002 6.149e-05 -2.76e-05 0.9764 4.634e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09069 0.08877 0.1659 0.1961 0.9855 0.9914 0.0907 0.7066 0.8514 0.2434 ] Network output: [ 0.0002379 0.9997 -0.0005218 8.385e-06 -3.764e-06 1 6.319e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000671 Epoch 7316 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01219 0.9938 0.9884 2.34e-06 -1.05e-06 -0.006513 1.763e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00325 -0.003042 -0.008874 0.006821 0.9698 0.9742 0.006198 0.8404 0.8292 0.01935 ] Network output: [ 0.9997 0.001087 0.001431 -3.117e-05 1.399e-05 -0.00211 -2.349e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1881 -0.03123 -0.1891 0.1962 0.9836 0.9933 0.2101 0.4527 0.875 0.7208 ] Network output: [ -0.01135 1.001 1.01 1.132e-06 -5.084e-07 0.01139 8.535e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005568 0.0004633 0.004378 0.004082 0.9889 0.992 0.00567 0.8696 0.8989 0.01402 ] Network output: [ -0.000872 0.003485 1.002 -0.0001021 4.583e-05 0.9953 -7.693e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1991 0.09345 0.3287 0.1526 0.9851 0.994 0.1997 0.4573 0.8815 0.7154 ] Network output: [ 0.007245 -0.03563 0.9962 5.999e-05 -2.693e-05 1.025 4.521e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09838 0.08687 0.1793 0.2052 0.9873 0.992 0.09844 0.7792 0.8732 0.3075 ] Network output: [ -0.007188 0.03648 1.002 6.144e-05 -2.758e-05 0.9764 4.631e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09069 0.08877 0.1659 0.1961 0.9855 0.9914 0.0907 0.7066 0.8513 0.2434 ] Network output: [ 0.0002388 0.9997 -0.0005228 8.379e-06 -3.761e-06 1 6.314e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006706 Epoch 7317 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01219 0.9938 0.9884 2.335e-06 -1.048e-06 -0.006517 1.76e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00325 -0.003042 -0.008873 0.00682 0.9698 0.9742 0.006198 0.8404 0.8292 0.01935 ] Network output: [ 0.9997 0.001104 0.001429 -3.115e-05 1.398e-05 -0.002123 -2.347e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1881 -0.03123 -0.189 0.1962 0.9836 0.9933 0.2101 0.4527 0.875 0.7208 ] Network output: [ -0.01135 1.001 1.01 1.13e-06 -5.073e-07 0.01139 8.516e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005568 0.0004633 0.004378 0.004081 0.9889 0.992 0.005671 0.8696 0.8989 0.01401 ] Network output: [ -0.0008732 0.003508 1.002 -0.000102 4.579e-05 0.9953 -7.687e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1991 0.09346 0.3287 0.1526 0.9851 0.994 0.1997 0.4573 0.8815 0.7154 ] Network output: [ 0.007243 -0.03561 0.9962 5.994e-05 -2.691e-05 1.025 4.518e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09839 0.08687 0.1793 0.2052 0.9873 0.992 0.09845 0.7792 0.8732 0.3075 ] Network output: [ -0.007185 0.03647 1.002 6.14e-05 -2.756e-05 0.9764 4.627e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09068 0.08876 0.1659 0.1961 0.9855 0.9914 0.0907 0.7065 0.8513 0.2434 ] Network output: [ 0.0002377 0.9997 -0.0005209 8.372e-06 -3.758e-06 1 6.309e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006702 Epoch 7318 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01219 0.9938 0.9884 2.331e-06 -1.047e-06 -0.006517 1.757e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00325 -0.003042 -0.008871 0.006819 0.9698 0.9742 0.006198 0.8404 0.8292 0.01935 ] Network output: [ 0.9997 0.001086 0.001429 -3.113e-05 1.397e-05 -0.002107 -2.346e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1881 -0.03123 -0.189 0.1962 0.9836 0.9933 0.2101 0.4527 0.875 0.7208 ] Network output: [ -0.01135 1.001 1.01 1.128e-06 -5.062e-07 0.01138 8.498e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005569 0.0004633 0.004378 0.004081 0.9889 0.992 0.005672 0.8696 0.8989 0.01401 ] Network output: [ -0.0008712 0.003484 1.002 -0.0001019 4.575e-05 0.9953 -7.681e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1991 0.09346 0.3287 0.1526 0.9851 0.994 0.1997 0.4573 0.8815 0.7154 ] Network output: [ 0.00724 -0.0356 0.9962 5.99e-05 -2.689e-05 1.025 4.514e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09839 0.08688 0.1793 0.2052 0.9873 0.992 0.09845 0.7791 0.8732 0.3075 ] Network output: [ -0.007182 0.03645 1.002 6.135e-05 -2.754e-05 0.9764 4.624e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09068 0.08876 0.1659 0.1961 0.9855 0.9914 0.09069 0.7065 0.8513 0.2434 ] Network output: [ 0.0002385 0.9997 -0.0005218 8.366e-06 -3.756e-06 1 6.305e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006698 Epoch 7319 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01219 0.9938 0.9884 2.327e-06 -1.045e-06 -0.006521 1.754e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003251 -0.003042 -0.00887 0.006818 0.9698 0.9742 0.006199 0.8404 0.8292 0.01935 ] Network output: [ 0.9997 0.001102 0.001427 -3.11e-05 1.396e-05 -0.00212 -2.344e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1881 -0.03124 -0.189 0.1962 0.9836 0.9933 0.2101 0.4527 0.875 0.7208 ] Network output: [ -0.01135 1.001 1.01 1.125e-06 -5.051e-07 0.01138 8.479e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00557 0.0004632 0.004378 0.00408 0.9889 0.992 0.005672 0.8695 0.8989 0.01401 ] Network output: [ -0.0008723 0.003506 1.002 -0.0001018 4.572e-05 0.9953 -7.674e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1991 0.09346 0.3287 0.1526 0.9851 0.994 0.1997 0.4573 0.8815 0.7154 ] Network output: [ 0.007238 -0.03559 0.9962 5.985e-05 -2.687e-05 1.025 4.511e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09839 0.08688 0.1793 0.2052 0.9873 0.992 0.09846 0.7791 0.8732 0.3075 ] Network output: [ -0.007179 0.03643 1.002 6.131e-05 -2.752e-05 0.9764 4.62e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09068 0.08876 0.1659 0.1961 0.9855 0.9914 0.09069 0.7065 0.8513 0.2434 ] Network output: [ 0.0002374 0.9997 -0.0005199 8.359e-06 -3.753e-06 1 6.3e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006693 Epoch 7320 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01219 0.9938 0.9884 2.323e-06 -1.043e-06 -0.006521 1.75e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003251 -0.003043 -0.008868 0.006817 0.9698 0.9742 0.006199 0.8404 0.8291 0.01934 ] Network output: [ 0.9997 0.001084 0.001427 -3.108e-05 1.395e-05 -0.002104 -2.342e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1881 -0.03124 -0.189 0.1962 0.9836 0.9933 0.2101 0.4527 0.875 0.7208 ] Network output: [ -0.01134 1.001 1.01 1.123e-06 -5.041e-07 0.01137 8.462e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00557 0.0004632 0.004378 0.004079 0.9889 0.992 0.005673 0.8695 0.8989 0.01401 ] Network output: [ -0.0008704 0.003482 1.002 -0.0001017 4.568e-05 0.9954 -7.668e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1991 0.09346 0.3287 0.1526 0.9851 0.994 0.1997 0.4573 0.8815 0.7154 ] Network output: [ 0.007235 -0.03558 0.9962 5.98e-05 -2.685e-05 1.025 4.507e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0984 0.08688 0.1793 0.2052 0.9873 0.992 0.09846 0.7791 0.8732 0.3075 ] Network output: [ -0.007176 0.03642 1.002 6.126e-05 -2.75e-05 0.9764 4.617e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09068 0.08876 0.1659 0.1961 0.9855 0.9914 0.09069 0.7064 0.8513 0.2434 ] Network output: [ 0.0002382 0.9997 -0.0005207 8.353e-06 -3.75e-06 1 6.295e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006689 Epoch 7321 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01218 0.9938 0.9884 2.318e-06 -1.041e-06 -0.006525 1.747e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003251 -0.003043 -0.008867 0.006816 0.9698 0.9742 0.006199 0.8404 0.8291 0.01934 ] Network output: [ 0.9997 0.001099 0.001425 -3.106e-05 1.394e-05 -0.002116 -2.341e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1882 -0.03125 -0.189 0.1962 0.9836 0.9933 0.2101 0.4527 0.875 0.7208 ] Network output: [ -0.01134 1.001 1.01 1.12e-06 -5.03e-07 0.01137 8.443e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005571 0.0004632 0.004379 0.004079 0.9889 0.992 0.005674 0.8695 0.8989 0.01401 ] Network output: [ -0.0008714 0.003503 1.002 -0.0001017 4.564e-05 0.9953 -7.662e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1991 0.09346 0.3287 0.1525 0.9851 0.994 0.1998 0.4572 0.8815 0.7154 ] Network output: [ 0.007233 -0.03556 0.9962 5.976e-05 -2.683e-05 1.025 4.503e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0984 0.08689 0.1793 0.2052 0.9873 0.992 0.09847 0.7791 0.8732 0.3075 ] Network output: [ -0.007173 0.0364 1.002 6.122e-05 -2.748e-05 0.9764 4.613e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09068 0.08875 0.1659 0.1961 0.9855 0.9914 0.09069 0.7064 0.8513 0.2434 ] Network output: [ 0.0002372 0.9997 -0.0005189 8.347e-06 -3.747e-06 1 6.29e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006684 Epoch 7322 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01218 0.9938 0.9884 2.314e-06 -1.039e-06 -0.006525 1.744e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003251 -0.003043 -0.008865 0.006815 0.9698 0.9742 0.006199 0.8404 0.8291 0.01934 ] Network output: [ 0.9997 0.001082 0.001425 -3.104e-05 1.393e-05 -0.002102 -2.339e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1882 -0.03125 -0.189 0.1961 0.9836 0.9933 0.2101 0.4526 0.875 0.7208 ] Network output: [ -0.01134 1.001 1.01 1.118e-06 -5.019e-07 0.01137 8.426e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005571 0.0004631 0.004379 0.004078 0.9889 0.992 0.005674 0.8695 0.8989 0.01401 ] Network output: [ -0.0008696 0.00348 1.002 -0.0001016 4.56e-05 0.9954 -7.656e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1991 0.09347 0.3288 0.1525 0.9851 0.994 0.1998 0.4572 0.8815 0.7154 ] Network output: [ 0.00723 -0.03555 0.9962 5.971e-05 -2.681e-05 1.025 4.5e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09841 0.08689 0.1793 0.2051 0.9873 0.992 0.09847 0.779 0.8732 0.3075 ] Network output: [ -0.007171 0.03639 1.002 6.117e-05 -2.746e-05 0.9764 4.61e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09067 0.08875 0.1659 0.1961 0.9855 0.9914 0.09069 0.7064 0.8513 0.2434 ] Network output: [ 0.000238 0.9997 -0.0005197 8.341e-06 -3.744e-06 1 6.286e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000668 Epoch 7323 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01218 0.9938 0.9884 2.31e-06 -1.037e-06 -0.006529 1.741e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003251 -0.003043 -0.008864 0.006814 0.9698 0.9742 0.0062 0.8404 0.8291 0.01934 ] Network output: [ 0.9997 0.001097 0.001423 -3.101e-05 1.392e-05 -0.002113 -2.337e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1882 -0.03126 -0.1889 0.1961 0.9836 0.9933 0.2102 0.4526 0.875 0.7208 ] Network output: [ -0.01134 1.001 1.01 1.116e-06 -5.008e-07 0.01136 8.407e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005572 0.0004631 0.004379 0.004077 0.9889 0.992 0.005675 0.8695 0.8989 0.01401 ] Network output: [ -0.0008706 0.0035 1.002 -0.0001015 4.557e-05 0.9953 -7.649e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1991 0.09347 0.3288 0.1525 0.9851 0.994 0.1998 0.4572 0.8815 0.7154 ] Network output: [ 0.007227 -0.03554 0.9962 5.966e-05 -2.678e-05 1.025 4.496e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09841 0.0869 0.1793 0.2051 0.9873 0.992 0.09848 0.779 0.8732 0.3075 ] Network output: [ -0.007167 0.03637 1.002 6.112e-05 -2.744e-05 0.9764 4.607e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09067 0.08875 0.1658 0.1961 0.9855 0.9914 0.09068 0.7063 0.8513 0.2434 ] Network output: [ 0.0002369 0.9997 -0.0005179 8.334e-06 -3.741e-06 1 6.281e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006676 Epoch 7324 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01218 0.9938 0.9884 2.306e-06 -1.035e-06 -0.006529 1.738e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003251 -0.003043 -0.008863 0.006814 0.9698 0.9742 0.0062 0.8404 0.8291 0.01934 ] Network output: [ 0.9997 0.001081 0.001423 -3.099e-05 1.391e-05 -0.002099 -2.336e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1882 -0.03126 -0.1889 0.1961 0.9836 0.9933 0.2102 0.4526 0.875 0.7208 ] Network output: [ -0.01134 1.001 1.01 1.113e-06 -4.997e-07 0.01136 8.389e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005573 0.0004631 0.004379 0.004077 0.9889 0.992 0.005675 0.8695 0.8989 0.014 ] Network output: [ -0.0008687 0.003479 1.002 -0.0001014 4.553e-05 0.9954 -7.643e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1992 0.09347 0.3288 0.1525 0.9851 0.994 0.1998 0.4572 0.8815 0.7154 ] Network output: [ 0.007225 -0.03553 0.9962 5.962e-05 -2.676e-05 1.025 4.493e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09842 0.0869 0.1793 0.2051 0.9873 0.992 0.09848 0.779 0.8732 0.3075 ] Network output: [ -0.007165 0.03636 1.002 6.108e-05 -2.742e-05 0.9764 4.603e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09067 0.08875 0.1658 0.1961 0.9855 0.9914 0.09068 0.7063 0.8512 0.2434 ] Network output: [ 0.0002377 0.9997 -0.0005186 8.328e-06 -3.739e-06 1 6.276e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006672 Epoch 7325 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01217 0.9938 0.9884 2.301e-06 -1.033e-06 -0.006533 1.734e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003251 -0.003043 -0.008861 0.006813 0.9698 0.9742 0.0062 0.8404 0.8291 0.01933 ] Network output: [ 0.9997 0.001095 0.001421 -3.097e-05 1.39e-05 -0.00211 -2.334e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1882 -0.03127 -0.1889 0.1961 0.9836 0.9933 0.2102 0.4526 0.875 0.7208 ] Network output: [ -0.01134 1.001 1.01 1.111e-06 -4.986e-07 0.01135 8.371e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005573 0.000463 0.004379 0.004076 0.9889 0.992 0.005676 0.8695 0.8989 0.014 ] Network output: [ -0.0008697 0.003498 1.002 -0.0001013 4.549e-05 0.9953 -7.637e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1992 0.09347 0.3288 0.1525 0.9851 0.994 0.1998 0.4572 0.8815 0.7154 ] Network output: [ 0.007222 -0.03551 0.9962 5.957e-05 -2.674e-05 1.025 4.489e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09842 0.0869 0.1793 0.2051 0.9873 0.992 0.09849 0.779 0.8732 0.3075 ] Network output: [ -0.007161 0.03634 1.002 6.103e-05 -2.74e-05 0.9765 4.6e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09067 0.08875 0.1658 0.1961 0.9855 0.9914 0.09068 0.7063 0.8512 0.2434 ] Network output: [ 0.0002367 0.9997 -0.0005169 8.321e-06 -3.736e-06 1 6.271e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006667 Epoch 7326 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01217 0.9938 0.9884 2.297e-06 -1.031e-06 -0.006534 1.731e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003252 -0.003044 -0.00886 0.006812 0.9698 0.9742 0.006201 0.8403 0.8291 0.01933 ] Network output: [ 0.9997 0.001079 0.001421 -3.095e-05 1.389e-05 -0.002096 -2.332e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1882 -0.03127 -0.1889 0.1961 0.9836 0.9933 0.2102 0.4526 0.875 0.7208 ] Network output: [ -0.01134 1.001 1.01 1.108e-06 -4.976e-07 0.01135 8.353e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005574 0.000463 0.004379 0.004075 0.9889 0.992 0.005677 0.8695 0.8989 0.014 ] Network output: [ -0.0008679 0.003477 1.002 -0.0001013 4.546e-05 0.9954 -7.631e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1992 0.09347 0.3288 0.1525 0.9851 0.994 0.1998 0.4572 0.8815 0.7154 ] Network output: [ 0.00722 -0.0355 0.9962 5.952e-05 -2.672e-05 1.025 4.486e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09843 0.08691 0.1793 0.2051 0.9873 0.992 0.09849 0.7789 0.8732 0.3075 ] Network output: [ -0.007159 0.03632 1.002 6.099e-05 -2.738e-05 0.9765 4.596e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09067 0.08874 0.1658 0.1961 0.9855 0.9914 0.09068 0.7062 0.8512 0.2434 ] Network output: [ 0.0002374 0.9997 -0.0005176 8.315e-06 -3.733e-06 1 6.267e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006663 Epoch 7327 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01217 0.9938 0.9884 2.293e-06 -1.029e-06 -0.006537 1.728e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003252 -0.003044 -0.008858 0.006811 0.9698 0.9742 0.006201 0.8403 0.8291 0.01933 ] Network output: [ 0.9997 0.001092 0.00142 -3.093e-05 1.388e-05 -0.002107 -2.331e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1882 -0.03127 -0.1889 0.1961 0.9836 0.9933 0.2102 0.4526 0.875 0.7208 ] Network output: [ -0.01134 1.001 1.01 1.106e-06 -4.965e-07 0.01135 8.334e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005574 0.000463 0.004379 0.004074 0.9889 0.992 0.005677 0.8695 0.8989 0.014 ] Network output: [ -0.0008688 0.003495 1.002 -0.0001012 4.542e-05 0.9954 -7.624e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1992 0.09348 0.3288 0.1525 0.9851 0.994 0.1998 0.4571 0.8815 0.7154 ] Network output: [ 0.007217 -0.03549 0.9962 5.948e-05 -2.67e-05 1.025 4.482e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09843 0.08691 0.1793 0.2051 0.9873 0.992 0.0985 0.7789 0.8731 0.3075 ] Network output: [ -0.007156 0.03631 1.002 6.094e-05 -2.736e-05 0.9765 4.593e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09066 0.08874 0.1658 0.1961 0.9855 0.9914 0.09068 0.7062 0.8512 0.2434 ] Network output: [ 0.0002364 0.9997 -0.0005159 8.309e-06 -3.73e-06 1 6.262e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006658 Epoch 7328 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01217 0.9938 0.9884 2.289e-06 -1.027e-06 -0.006538 1.725e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003252 -0.003044 -0.008857 0.00681 0.9698 0.9742 0.006201 0.8403 0.8291 0.01933 ] Network output: [ 0.9997 0.001077 0.001419 -3.09e-05 1.387e-05 -0.002094 -2.329e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1882 -0.03128 -0.1888 0.1961 0.9836 0.9933 0.2102 0.4525 0.875 0.7208 ] Network output: [ -0.01133 1.001 1.01 1.104e-06 -4.954e-07 0.01134 8.317e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005575 0.0004629 0.00438 0.004074 0.9889 0.992 0.005678 0.8695 0.8989 0.014 ] Network output: [ -0.0008671 0.003475 1.002 -0.0001011 4.538e-05 0.9954 -7.618e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1992 0.09348 0.3289 0.1525 0.9851 0.994 0.1998 0.4571 0.8815 0.7154 ] Network output: [ 0.007215 -0.03548 0.9962 5.943e-05 -2.668e-05 1.025 4.479e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09844 0.08691 0.1793 0.2051 0.9873 0.992 0.0985 0.7789 0.8731 0.3075 ] Network output: [ -0.007153 0.03629 1.002 6.09e-05 -2.734e-05 0.9765 4.589e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09066 0.08874 0.1658 0.1961 0.9855 0.9914 0.09067 0.7062 0.8512 0.2434 ] Network output: [ 0.0002371 0.9997 -0.0005165 8.303e-06 -3.727e-06 1 6.257e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006654 Epoch 7329 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01216 0.9938 0.9884 2.284e-06 -1.025e-06 -0.006541 1.721e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003252 -0.003044 -0.008855 0.006809 0.9698 0.9742 0.006202 0.8403 0.8291 0.01933 ] Network output: [ 0.9997 0.00109 0.001418 -3.088e-05 1.386e-05 -0.002103 -2.327e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1882 -0.03128 -0.1888 0.1961 0.9836 0.9933 0.2102 0.4525 0.875 0.7208 ] Network output: [ -0.01133 1.001 1.01 1.101e-06 -4.943e-07 0.01134 8.298e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005576 0.0004629 0.00438 0.004073 0.9889 0.992 0.005678 0.8694 0.8989 0.014 ] Network output: [ -0.0008679 0.003492 1.002 -0.000101 4.534e-05 0.9954 -7.612e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1992 0.09348 0.3289 0.1525 0.9851 0.994 0.1999 0.4571 0.8815 0.7154 ] Network output: [ 0.007212 -0.03546 0.9962 5.938e-05 -2.666e-05 1.025 4.475e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09844 0.08692 0.1793 0.2051 0.9873 0.992 0.09851 0.7788 0.8731 0.3075 ] Network output: [ -0.00715 0.03627 1.002 6.085e-05 -2.732e-05 0.9765 4.586e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09066 0.08874 0.1658 0.1961 0.9855 0.9914 0.09067 0.7062 0.8512 0.2434 ] Network output: [ 0.0002362 0.9997 -0.0005149 8.296e-06 -3.724e-06 1 6.252e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006649 Epoch 7330 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01216 0.9938 0.9884 2.28e-06 -1.024e-06 -0.006542 1.718e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003252 -0.003044 -0.008854 0.006808 0.9698 0.9742 0.006202 0.8403 0.8291 0.01932 ] Network output: [ 0.9997 0.001075 0.001418 -3.086e-05 1.385e-05 -0.002091 -2.326e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1883 -0.03129 -0.1888 0.1961 0.9836 0.9933 0.2102 0.4525 0.8749 0.7208 ] Network output: [ -0.01133 1.001 1.01 1.099e-06 -4.933e-07 0.01134 8.281e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005576 0.0004629 0.00438 0.004072 0.9889 0.992 0.005679 0.8694 0.8989 0.014 ] Network output: [ -0.0008663 0.003473 1.002 -0.0001009 4.531e-05 0.9954 -7.606e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1992 0.09348 0.3289 0.1524 0.9851 0.994 0.1999 0.4571 0.8815 0.7154 ] Network output: [ 0.00721 -0.03545 0.9961 5.934e-05 -2.664e-05 1.025 4.472e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09845 0.08692 0.1793 0.2051 0.9873 0.992 0.09851 0.7788 0.8731 0.3075 ] Network output: [ -0.007147 0.03626 1.002 6.081e-05 -2.73e-05 0.9765 4.583e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09066 0.08874 0.1658 0.1961 0.9855 0.9914 0.09067 0.7061 0.8512 0.2434 ] Network output: [ 0.0002368 0.9997 -0.0005155 8.29e-06 -3.722e-06 1 6.248e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006645 Epoch 7331 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01216 0.9938 0.9884 2.276e-06 -1.022e-06 -0.006545 1.715e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003252 -0.003044 -0.008853 0.006807 0.9698 0.9742 0.006202 0.8403 0.8291 0.01932 ] Network output: [ 0.9997 0.001087 0.001416 -3.084e-05 1.384e-05 -0.0021 -2.324e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1883 -0.03129 -0.1888 0.1961 0.9836 0.9933 0.2103 0.4525 0.8749 0.7208 ] Network output: [ -0.01133 1.001 1.01 1.096e-06 -4.922e-07 0.01133 8.262e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005577 0.0004628 0.00438 0.004072 0.9889 0.992 0.00568 0.8694 0.8989 0.01399 ] Network output: [ -0.0008671 0.00349 1.002 -0.0001008 4.527e-05 0.9954 -7.6e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1992 0.09349 0.3289 0.1524 0.9851 0.994 0.1999 0.4571 0.8815 0.7153 ] Network output: [ 0.007207 -0.03544 0.9961 5.929e-05 -2.662e-05 1.025 4.468e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09845 0.08693 0.1794 0.2051 0.9873 0.992 0.09851 0.7788 0.8731 0.3075 ] Network output: [ -0.007144 0.03624 1.002 6.076e-05 -2.728e-05 0.9765 4.579e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09066 0.08873 0.1658 0.1961 0.9855 0.9914 0.09067 0.7061 0.8512 0.2434 ] Network output: [ 0.0002359 0.9997 -0.0005139 8.284e-06 -3.719e-06 1 6.243e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006641 Epoch 7332 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01216 0.9938 0.9884 2.272e-06 -1.02e-06 -0.006546 1.712e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003252 -0.003045 -0.008851 0.006806 0.9698 0.9742 0.006203 0.8403 0.8291 0.01932 ] Network output: [ 0.9997 0.001074 0.001416 -3.081e-05 1.383e-05 -0.002088 -2.322e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1883 -0.0313 -0.1888 0.1961 0.9836 0.9933 0.2103 0.4525 0.8749 0.7208 ] Network output: [ -0.01133 1.001 1.01 1.094e-06 -4.911e-07 0.01133 8.245e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005577 0.0004628 0.00438 0.004071 0.9889 0.992 0.00568 0.8694 0.8989 0.01399 ] Network output: [ -0.0008655 0.003471 1.002 -0.0001008 4.523e-05 0.9954 -7.593e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1993 0.09349 0.3289 0.1524 0.9851 0.994 0.1999 0.4571 0.8815 0.7153 ] Network output: [ 0.007204 -0.03543 0.9961 5.924e-05 -2.66e-05 1.025 4.465e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09846 0.08693 0.1794 0.2051 0.9873 0.992 0.09852 0.7788 0.8731 0.3075 ] Network output: [ -0.007141 0.03623 1.002 6.072e-05 -2.726e-05 0.9765 4.576e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09065 0.08873 0.1658 0.1961 0.9855 0.9914 0.09067 0.7061 0.8511 0.2434 ] Network output: [ 0.0002366 0.9997 -0.0005144 8.277e-06 -3.716e-06 1 6.238e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006637 Epoch 7333 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01216 0.9938 0.9884 2.267e-06 -1.018e-06 -0.006549 1.709e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003252 -0.003045 -0.00885 0.006805 0.9698 0.9742 0.006203 0.8403 0.8291 0.01932 ] Network output: [ 0.9997 0.001085 0.001414 -3.079e-05 1.382e-05 -0.002097 -2.321e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1883 -0.0313 -0.1887 0.196 0.9836 0.9933 0.2103 0.4525 0.8749 0.7207 ] Network output: [ -0.01133 1.001 1.01 1.092e-06 -4.9e-07 0.01132 8.226e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005578 0.0004628 0.00438 0.00407 0.9889 0.992 0.005681 0.8694 0.8989 0.01399 ] Network output: [ -0.0008662 0.003487 1.002 -0.0001007 4.52e-05 0.9954 -7.587e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1993 0.09349 0.3289 0.1524 0.9851 0.994 0.1999 0.457 0.8814 0.7153 ] Network output: [ 0.007202 -0.03541 0.9961 5.92e-05 -2.658e-05 1.025 4.461e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09846 0.08693 0.1794 0.2051 0.9873 0.992 0.09852 0.7787 0.8731 0.3075 ] Network output: [ -0.007138 0.03621 1.002 6.067e-05 -2.724e-05 0.9765 4.572e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09065 0.08873 0.1658 0.1961 0.9855 0.9914 0.09066 0.706 0.8511 0.2435 ] Network output: [ 0.0002357 0.9997 -0.0005129 8.271e-06 -3.713e-06 1 6.233e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006632 Epoch 7334 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01215 0.9938 0.9884 2.263e-06 -1.016e-06 -0.00655 1.706e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003253 -0.003045 -0.008848 0.006804 0.9698 0.9742 0.006203 0.8403 0.8291 0.01932 ] Network output: [ 0.9997 0.001072 0.001414 -3.077e-05 1.381e-05 -0.002086 -2.319e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1883 -0.03131 -0.1887 0.196 0.9836 0.9933 0.2103 0.4524 0.8749 0.7207 ] Network output: [ -0.01133 1.001 1.01 1.089e-06 -4.89e-07 0.01132 8.208e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005579 0.0004627 0.00438 0.00407 0.9889 0.992 0.005682 0.8694 0.8988 0.01399 ] Network output: [ -0.0008646 0.00347 1.002 -0.0001006 4.516e-05 0.9954 -7.581e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1993 0.09349 0.329 0.1524 0.9851 0.994 0.1999 0.457 0.8814 0.7153 ] Network output: [ 0.007199 -0.0354 0.9961 5.915e-05 -2.655e-05 1.025 4.458e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09847 0.08694 0.1794 0.2051 0.9873 0.992 0.09853 0.7787 0.8731 0.3075 ] Network output: [ -0.007135 0.0362 1.002 6.063e-05 -2.722e-05 0.9765 4.569e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09065 0.08873 0.1658 0.1961 0.9855 0.9914 0.09066 0.706 0.8511 0.2435 ] Network output: [ 0.0002363 0.9997 -0.0005134 8.265e-06 -3.71e-06 1 6.229e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006628 Epoch 7335 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01215 0.9938 0.9884 2.259e-06 -1.014e-06 -0.006553 1.702e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003253 -0.003045 -0.008847 0.006803 0.9698 0.9742 0.006204 0.8403 0.8291 0.01931 ] Network output: [ 0.9997 0.001083 0.001412 -3.075e-05 1.38e-05 -0.002094 -2.317e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1883 -0.03131 -0.1887 0.196 0.9836 0.9933 0.2103 0.4524 0.8749 0.7207 ] Network output: [ -0.01133 1.001 1.01 1.087e-06 -4.879e-07 0.01132 8.19e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005579 0.0004627 0.004381 0.004069 0.9889 0.992 0.005682 0.8694 0.8988 0.01399 ] Network output: [ -0.0008653 0.003485 1.002 -0.0001005 4.512e-05 0.9954 -7.575e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1993 0.09349 0.329 0.1524 0.9851 0.994 0.1999 0.457 0.8814 0.7153 ] Network output: [ 0.007197 -0.03539 0.9961 5.91e-05 -2.653e-05 1.025 4.454e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09847 0.08694 0.1794 0.205 0.9873 0.992 0.09853 0.7787 0.8731 0.3075 ] Network output: [ -0.007132 0.03618 1.002 6.058e-05 -2.72e-05 0.9765 4.566e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09065 0.08873 0.1658 0.1961 0.9855 0.9914 0.09066 0.706 0.8511 0.2435 ] Network output: [ 0.0002355 0.9997 -0.0005119 8.258e-06 -3.708e-06 1 6.224e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006624 Epoch 7336 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01215 0.9938 0.9884 2.255e-06 -1.012e-06 -0.006554 1.699e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003253 -0.003045 -0.008845 0.006802 0.9698 0.9742 0.006204 0.8403 0.829 0.01931 ] Network output: [ 0.9997 0.00107 0.001412 -3.072e-05 1.379e-05 -0.002083 -2.315e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1883 -0.03131 -0.1887 0.196 0.9836 0.9933 0.2103 0.4524 0.8749 0.7207 ] Network output: [ -0.01133 1.001 1.01 1.084e-06 -4.868e-07 0.01131 8.172e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00558 0.0004627 0.004381 0.004068 0.9889 0.992 0.005683 0.8694 0.8988 0.01399 ] Network output: [ -0.0008638 0.003468 1.002 -0.0001004 4.508e-05 0.9954 -7.568e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1993 0.0935 0.329 0.1524 0.9851 0.994 0.1999 0.457 0.8814 0.7153 ] Network output: [ 0.007194 -0.03538 0.9961 5.906e-05 -2.651e-05 1.025 4.451e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09848 0.08694 0.1794 0.205 0.9873 0.992 0.09854 0.7787 0.8731 0.3074 ] Network output: [ -0.007129 0.03616 1.002 6.054e-05 -2.718e-05 0.9765 4.562e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09065 0.08872 0.1658 0.1961 0.9855 0.9914 0.09066 0.7059 0.8511 0.2435 ] Network output: [ 0.000236 0.9997 -0.0005124 8.252e-06 -3.705e-06 1 6.219e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006619 Epoch 7337 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01215 0.9938 0.9884 2.25e-06 -1.01e-06 -0.006557 1.696e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003253 -0.003045 -0.008844 0.006801 0.9698 0.9742 0.006204 0.8402 0.829 0.01931 ] Network output: [ 0.9997 0.00108 0.001411 -3.07e-05 1.378e-05 -0.002091 -2.314e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1883 -0.03132 -0.1887 0.196 0.9836 0.9933 0.2103 0.4524 0.8749 0.7207 ] Network output: [ -0.01132 1.001 1.01 1.082e-06 -4.857e-07 0.01131 8.154e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00558 0.0004627 0.004381 0.004068 0.9889 0.992 0.005683 0.8694 0.8988 0.01399 ] Network output: [ -0.0008645 0.003482 1.002 -0.0001003 4.505e-05 0.9954 -7.562e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1993 0.0935 0.329 0.1524 0.9851 0.994 0.2 0.457 0.8814 0.7153 ] Network output: [ 0.007192 -0.03536 0.9961 5.901e-05 -2.649e-05 1.025 4.447e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09848 0.08695 0.1794 0.205 0.9873 0.992 0.09854 0.7786 0.873 0.3074 ] Network output: [ -0.007126 0.03615 1.002 6.049e-05 -2.716e-05 0.9765 4.559e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09064 0.08872 0.1658 0.1961 0.9855 0.9914 0.09066 0.7059 0.8511 0.2435 ] Network output: [ 0.0002352 0.9997 -0.0005109 8.246e-06 -3.702e-06 1 6.214e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006615 Epoch 7338 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01214 0.9938 0.9885 2.246e-06 -1.008e-06 -0.006558 1.693e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003253 -0.003046 -0.008843 0.0068 0.9698 0.9742 0.006204 0.8402 0.829 0.01931 ] Network output: [ 0.9997 0.001068 0.00141 -3.068e-05 1.377e-05 -0.00208 -2.312e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1883 -0.03132 -0.1886 0.196 0.9836 0.9933 0.2104 0.4524 0.8749 0.7207 ] Network output: [ -0.01132 1.001 1.01 1.08e-06 -4.847e-07 0.01131 8.136e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005581 0.0004626 0.004381 0.004067 0.9889 0.992 0.005684 0.8694 0.8988 0.01398 ] Network output: [ -0.000863 0.003466 1.002 -0.0001003 4.501e-05 0.9954 -7.556e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1993 0.0935 0.329 0.1524 0.9851 0.994 0.2 0.457 0.8814 0.7153 ] Network output: [ 0.007189 -0.03535 0.9961 5.896e-05 -2.647e-05 1.025 4.444e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09848 0.08695 0.1794 0.205 0.9873 0.992 0.09855 0.7786 0.873 0.3074 ] Network output: [ -0.007123 0.03613 1.002 6.045e-05 -2.714e-05 0.9766 4.555e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09064 0.08872 0.1658 0.1961 0.9855 0.9914 0.09065 0.7059 0.8511 0.2435 ] Network output: [ 0.0002357 0.9997 -0.0005113 8.24e-06 -3.699e-06 1 6.21e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006611 Epoch 7339 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01214 0.9938 0.9885 2.242e-06 -1.006e-06 -0.006561 1.69e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003253 -0.003046 -0.008841 0.006799 0.9698 0.9742 0.006205 0.8402 0.829 0.01931 ] Network output: [ 0.9997 0.001078 0.001409 -3.066e-05 1.376e-05 -0.002088 -2.31e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1884 -0.03133 -0.1886 0.196 0.9836 0.9933 0.2104 0.4524 0.8749 0.7207 ] Network output: [ -0.01132 1.001 1.01 1.077e-06 -4.836e-07 0.0113 8.118e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005582 0.0004626 0.004381 0.004066 0.9889 0.992 0.005685 0.8693 0.8988 0.01398 ] Network output: [ -0.0008636 0.00348 1.002 -0.0001002 4.497e-05 0.9954 -7.55e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1993 0.0935 0.329 0.1523 0.9851 0.994 0.2 0.4569 0.8814 0.7153 ] Network output: [ 0.007187 -0.03534 0.9961 5.892e-05 -2.645e-05 1.025 4.44e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09849 0.08696 0.1794 0.205 0.9873 0.992 0.09855 0.7786 0.873 0.3074 ] Network output: [ -0.00712 0.03612 1.002 6.04e-05 -2.712e-05 0.9766 4.552e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09064 0.08872 0.1658 0.1961 0.9855 0.9914 0.09065 0.7058 0.8511 0.2435 ] Network output: [ 0.000235 0.9997 -0.0005099 8.233e-06 -3.696e-06 1 6.205e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006606 Epoch 7340 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01214 0.9938 0.9885 2.238e-06 -1.005e-06 -0.006562 1.687e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003253 -0.003046 -0.00884 0.006798 0.9698 0.9742 0.006205 0.8402 0.829 0.01931 ] Network output: [ 0.9997 0.001066 0.001408 -3.063e-05 1.375e-05 -0.002077 -2.309e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1884 -0.03133 -0.1886 0.196 0.9836 0.9933 0.2104 0.4523 0.8749 0.7207 ] Network output: [ -0.01132 1.001 1.01 1.075e-06 -4.825e-07 0.0113 8.101e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005582 0.0004626 0.004381 0.004066 0.9889 0.992 0.005685 0.8693 0.8988 0.01398 ] Network output: [ -0.0008622 0.003464 1.002 -0.0001001 4.494e-05 0.9954 -7.544e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1994 0.09351 0.3291 0.1523 0.9851 0.994 0.2 0.4569 0.8814 0.7153 ] Network output: [ 0.007184 -0.03533 0.9961 5.887e-05 -2.643e-05 1.025 4.437e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09849 0.08696 0.1794 0.205 0.9873 0.992 0.09856 0.7785 0.873 0.3074 ] Network output: [ -0.007117 0.0361 1.002 6.035e-05 -2.71e-05 0.9766 4.549e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09064 0.08872 0.1658 0.1961 0.9855 0.9914 0.09065 0.7058 0.8511 0.2435 ] Network output: [ 0.0002355 0.9997 -0.0005103 8.227e-06 -3.693e-06 1 6.2e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006602 Epoch 7341 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01214 0.9938 0.9885 2.234e-06 -1.003e-06 -0.006565 1.683e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003254 -0.003046 -0.008838 0.006797 0.9698 0.9742 0.006205 0.8402 0.829 0.0193 ] Network output: [ 0.9997 0.001076 0.001407 -3.061e-05 1.374e-05 -0.002084 -2.307e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1884 -0.03134 -0.1886 0.196 0.9836 0.9933 0.2104 0.4523 0.8749 0.7207 ] Network output: [ -0.01132 1.001 1.01 1.072e-06 -4.815e-07 0.01129 8.082e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005583 0.0004625 0.004381 0.004065 0.9889 0.992 0.005686 0.8693 0.8988 0.01398 ] Network output: [ -0.0008627 0.003477 1.002 -0.0001 4.49e-05 0.9954 -7.537e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1994 0.09351 0.3291 0.1523 0.9851 0.994 0.2 0.4569 0.8814 0.7153 ] Network output: [ 0.007182 -0.03531 0.9961 5.882e-05 -2.641e-05 1.025 4.433e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0985 0.08696 0.1794 0.205 0.9873 0.992 0.09856 0.7785 0.873 0.3074 ] Network output: [ -0.007114 0.03608 1.002 6.031e-05 -2.708e-05 0.9766 4.545e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09064 0.08871 0.1658 0.196 0.9855 0.9914 0.09065 0.7058 0.851 0.2435 ] Network output: [ 0.0002347 0.9997 -0.0005089 8.221e-06 -3.691e-06 1 6.195e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006598 Epoch 7342 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01214 0.9938 0.9885 2.229e-06 -1.001e-06 -0.006566 1.68e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003254 -0.003046 -0.008837 0.006796 0.9698 0.9742 0.006206 0.8402 0.829 0.0193 ] Network output: [ 0.9997 0.001065 0.001407 -3.059e-05 1.373e-05 -0.002075 -2.305e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1884 -0.03134 -0.1886 0.196 0.9836 0.9933 0.2104 0.4523 0.8749 0.7207 ] Network output: [ -0.01132 1.001 1.01 1.07e-06 -4.804e-07 0.01129 8.065e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005583 0.0004625 0.004382 0.004064 0.9889 0.992 0.005686 0.8693 0.8988 0.01398 ] Network output: [ -0.0008613 0.003462 1.002 -9.993e-05 4.486e-05 0.9954 -7.531e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1994 0.09351 0.3291 0.1523 0.9851 0.994 0.2 0.4569 0.8814 0.7153 ] Network output: [ 0.007179 -0.0353 0.9961 5.878e-05 -2.639e-05 1.025 4.43e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0985 0.08697 0.1794 0.205 0.9873 0.992 0.09857 0.7785 0.873 0.3074 ] Network output: [ -0.007111 0.03607 1.002 6.026e-05 -2.705e-05 0.9766 4.542e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09063 0.08871 0.1658 0.196 0.9855 0.9914 0.09065 0.7057 0.851 0.2435 ] Network output: [ 0.0002352 0.9997 -0.0005093 8.215e-06 -3.688e-06 1 6.191e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006594 Epoch 7343 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01213 0.9938 0.9885 2.225e-06 -9.99e-07 -0.006569 1.677e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003254 -0.003046 -0.008835 0.006795 0.9698 0.9742 0.006206 0.8402 0.829 0.0193 ] Network output: [ 0.9997 0.001074 0.001405 -3.057e-05 1.372e-05 -0.002081 -2.304e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1884 -0.03134 -0.1885 0.196 0.9836 0.9933 0.2104 0.4523 0.8749 0.7207 ] Network output: [ -0.01132 1.001 1.01 1.068e-06 -4.793e-07 0.01129 8.046e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005584 0.0004625 0.004382 0.004063 0.9889 0.992 0.005687 0.8693 0.8988 0.01398 ] Network output: [ -0.0008618 0.003474 1.002 -9.985e-05 4.483e-05 0.9954 -7.525e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1994 0.09351 0.3291 0.1523 0.9851 0.994 0.2 0.4569 0.8814 0.7153 ] Network output: [ 0.007177 -0.03529 0.9961 5.873e-05 -2.637e-05 1.025 4.426e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09851 0.08697 0.1794 0.205 0.9873 0.992 0.09857 0.7785 0.873 0.3074 ] Network output: [ -0.007108 0.03605 1.002 6.022e-05 -2.703e-05 0.9766 4.538e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09063 0.08871 0.1658 0.196 0.9855 0.9914 0.09064 0.7057 0.851 0.2435 ] Network output: [ 0.0002345 0.9997 -0.0005079 8.208e-06 -3.685e-06 1 6.186e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006589 Epoch 7344 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01213 0.9938 0.9885 2.221e-06 -9.971e-07 -0.00657 1.674e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003254 -0.003047 -0.008834 0.006794 0.9698 0.9742 0.006206 0.8402 0.829 0.0193 ] Network output: [ 0.9997 0.001063 0.001405 -3.054e-05 1.371e-05 -0.002072 -2.302e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1884 -0.03135 -0.1885 0.1959 0.9836 0.9933 0.2104 0.4523 0.8749 0.7207 ] Network output: [ -0.01132 1.001 1.01 1.065e-06 -4.783e-07 0.01128 8.029e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005585 0.0004625 0.004382 0.004063 0.9889 0.992 0.005688 0.8693 0.8988 0.01397 ] Network output: [ -0.0008605 0.00346 1.002 -9.977e-05 4.479e-05 0.9954 -7.519e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1994 0.09352 0.3291 0.1523 0.9851 0.994 0.2 0.4569 0.8814 0.7153 ] Network output: [ 0.007174 -0.03528 0.9961 5.868e-05 -2.635e-05 1.025 4.423e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09851 0.08697 0.1794 0.205 0.9873 0.992 0.09858 0.7784 0.873 0.3074 ] Network output: [ -0.007105 0.03604 1.002 6.017e-05 -2.701e-05 0.9766 4.535e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09063 0.08871 0.1658 0.196 0.9855 0.9914 0.09064 0.7057 0.851 0.2435 ] Network output: [ 0.0002349 0.9997 -0.0005082 8.202e-06 -3.682e-06 1 6.181e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006585 Epoch 7345 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01213 0.9938 0.9885 2.217e-06 -9.952e-07 -0.006573 1.671e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003254 -0.003047 -0.008833 0.006793 0.9698 0.9742 0.006207 0.8402 0.829 0.0193 ] Network output: [ 0.9997 0.001071 0.001403 -3.052e-05 1.37e-05 -0.002078 -2.3e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1884 -0.03135 -0.1885 0.1959 0.9836 0.9933 0.2104 0.4523 0.8749 0.7207 ] Network output: [ -0.01131 1.001 1.01 1.063e-06 -4.772e-07 0.01128 8.011e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005585 0.0004624 0.004382 0.004062 0.9889 0.992 0.005688 0.8693 0.8988 0.01397 ] Network output: [ -0.000861 0.003472 1.002 -9.969e-05 4.475e-05 0.9954 -7.513e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1994 0.09352 0.3291 0.1523 0.9851 0.994 0.2001 0.4568 0.8814 0.7153 ] Network output: [ 0.007172 -0.03526 0.9961 5.864e-05 -2.632e-05 1.025 4.419e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09852 0.08698 0.1794 0.205 0.9873 0.992 0.09858 0.7784 0.873 0.3074 ] Network output: [ -0.007102 0.03602 1.002 6.013e-05 -2.699e-05 0.9766 4.532e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09063 0.08871 0.1658 0.196 0.9855 0.9914 0.09064 0.7056 0.851 0.2435 ] Network output: [ 0.0002342 0.9997 -0.000507 8.196e-06 -3.679e-06 1 6.176e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000658 Epoch 7346 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01213 0.9939 0.9885 2.213e-06 -9.934e-07 -0.006574 1.668e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003254 -0.003047 -0.008831 0.006792 0.9698 0.9742 0.006207 0.8402 0.829 0.01929 ] Network output: [ 0.9997 0.001061 0.001403 -3.05e-05 1.369e-05 -0.002069 -2.299e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1884 -0.03136 -0.1885 0.1959 0.9836 0.9933 0.2105 0.4523 0.8749 0.7207 ] Network output: [ -0.01131 1.001 1.01 1.061e-06 -4.761e-07 0.01128 7.993e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005586 0.0004624 0.004382 0.004061 0.9889 0.992 0.005689 0.8693 0.8988 0.01397 ] Network output: [ -0.0008597 0.003458 1.002 -9.96e-05 4.472e-05 0.9954 -7.506e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1994 0.09352 0.3291 0.1523 0.9851 0.994 0.2001 0.4568 0.8814 0.7153 ] Network output: [ 0.007169 -0.03525 0.9961 5.859e-05 -2.63e-05 1.025 4.416e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09852 0.08698 0.1794 0.205 0.9873 0.992 0.09859 0.7784 0.873 0.3074 ] Network output: [ -0.007099 0.03601 1.002 6.008e-05 -2.697e-05 0.9766 4.528e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09063 0.08871 0.1658 0.196 0.9855 0.9914 0.09064 0.7056 0.851 0.2435 ] Network output: [ 0.0002346 0.9998 -0.0005072 8.189e-06 -3.677e-06 1 6.172e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006576 Epoch 7347 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01212 0.9939 0.9885 2.208e-06 -9.915e-07 -0.006577 1.664e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003254 -0.003047 -0.00883 0.006792 0.9698 0.9742 0.006207 0.8402 0.829 0.01929 ] Network output: [ 0.9997 0.001069 0.001402 -3.048e-05 1.368e-05 -0.002075 -2.297e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1884 -0.03136 -0.1885 0.1959 0.9836 0.9933 0.2105 0.4522 0.8749 0.7207 ] Network output: [ -0.01131 1.001 1.01 1.058e-06 -4.751e-07 0.01127 7.975e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005586 0.0004624 0.004382 0.004061 0.9889 0.992 0.00569 0.8693 0.8988 0.01397 ] Network output: [ -0.0008601 0.00347 1.002 -9.952e-05 4.468e-05 0.9954 -7.5e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1994 0.09352 0.3292 0.1522 0.9851 0.994 0.2001 0.4568 0.8814 0.7153 ] Network output: [ 0.007167 -0.03524 0.9961 5.854e-05 -2.628e-05 1.025 4.412e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09853 0.08699 0.1794 0.205 0.9873 0.992 0.09859 0.7784 0.873 0.3074 ] Network output: [ -0.007096 0.03599 1.002 6.004e-05 -2.695e-05 0.9766 4.525e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09063 0.0887 0.1658 0.196 0.9855 0.9914 0.09064 0.7056 0.851 0.2435 ] Network output: [ 0.000234 0.9998 -0.000506 8.183e-06 -3.674e-06 1 6.167e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006572 Epoch 7348 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01212 0.9939 0.9885 2.204e-06 -9.896e-07 -0.006577 1.661e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003254 -0.003047 -0.008828 0.006791 0.9698 0.9742 0.006208 0.8402 0.829 0.01929 ] Network output: [ 0.9997 0.001059 0.001401 -3.046e-05 1.367e-05 -0.002066 -2.295e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1885 -0.03137 -0.1885 0.1959 0.9836 0.9933 0.2105 0.4522 0.8748 0.7207 ] Network output: [ -0.01131 1.001 1.01 1.056e-06 -4.74e-07 0.01127 7.957e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005587 0.0004623 0.004383 0.00406 0.9889 0.992 0.00569 0.8693 0.8988 0.01397 ] Network output: [ -0.0008589 0.003456 1.002 -9.944e-05 4.464e-05 0.9954 -7.494e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1995 0.09352 0.3292 0.1522 0.9851 0.994 0.2001 0.4568 0.8814 0.7153 ] Network output: [ 0.007164 -0.03523 0.9961 5.85e-05 -2.626e-05 1.025 4.409e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09853 0.08699 0.1794 0.2049 0.9873 0.992 0.0986 0.7783 0.8729 0.3074 ] Network output: [ -0.007094 0.03597 1.002 5.999e-05 -2.693e-05 0.9766 4.521e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09062 0.0887 0.1658 0.196 0.9855 0.9914 0.09064 0.7056 0.851 0.2435 ] Network output: [ 0.0002344 0.9998 -0.0005062 8.177e-06 -3.671e-06 1 6.162e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006568 Epoch 7349 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01212 0.9939 0.9885 2.2e-06 -9.877e-07 -0.006581 1.658e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003255 -0.003047 -0.008827 0.00679 0.9698 0.9742 0.006208 0.8401 0.829 0.01929 ] Network output: [ 0.9997 0.001067 0.0014 -3.043e-05 1.366e-05 -0.002072 -2.294e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1885 -0.03137 -0.1884 0.1959 0.9836 0.9933 0.2105 0.4522 0.8748 0.7207 ] Network output: [ -0.01131 1.001 1.01 1.053e-06 -4.729e-07 0.01126 7.939e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005588 0.0004623 0.004383 0.004059 0.9889 0.992 0.005691 0.8692 0.8988 0.01397 ] Network output: [ -0.0008592 0.003467 1.002 -9.936e-05 4.461e-05 0.9954 -7.488e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1995 0.09353 0.3292 0.1522 0.9851 0.994 0.2001 0.4568 0.8814 0.7153 ] Network output: [ 0.007162 -0.03521 0.9961 5.845e-05 -2.624e-05 1.025 4.405e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09854 0.08699 0.1794 0.2049 0.9873 0.992 0.0986 0.7783 0.8729 0.3074 ] Network output: [ -0.007091 0.03596 1.002 5.995e-05 -2.691e-05 0.9766 4.518e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09062 0.0887 0.1658 0.196 0.9855 0.9914 0.09063 0.7055 0.8509 0.2435 ] Network output: [ 0.0002337 0.9998 -0.000505 8.171e-06 -3.668e-06 1 6.158e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006563 Epoch 7350 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01212 0.9939 0.9885 2.196e-06 -9.859e-07 -0.006581 1.655e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003255 -0.003048 -0.008825 0.006789 0.9698 0.9742 0.006208 0.8401 0.829 0.01929 ] Network output: [ 0.9997 0.001057 0.001399 -3.041e-05 1.365e-05 -0.002063 -2.292e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1885 -0.03137 -0.1884 0.1959 0.9836 0.9933 0.2105 0.4522 0.8748 0.7207 ] Network output: [ -0.01131 1.001 1.01 1.051e-06 -4.719e-07 0.01126 7.922e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005588 0.0004623 0.004383 0.004059 0.9889 0.992 0.005691 0.8692 0.8988 0.01397 ] Network output: [ -0.000858 0.003454 1.002 -9.928e-05 4.457e-05 0.9954 -7.482e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1995 0.09353 0.3292 0.1522 0.9851 0.994 0.2001 0.4568 0.8814 0.7153 ] Network output: [ 0.007159 -0.0352 0.9961 5.84e-05 -2.622e-05 1.025 4.402e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09854 0.087 0.1794 0.2049 0.9873 0.992 0.09861 0.7783 0.8729 0.3074 ] Network output: [ -0.007088 0.03594 1.002 5.99e-05 -2.689e-05 0.9766 4.515e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09062 0.0887 0.1658 0.196 0.9855 0.9914 0.09063 0.7055 0.8509 0.2435 ] Network output: [ 0.0002341 0.9998 -0.0005052 8.164e-06 -3.665e-06 1 6.153e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006559 Epoch 7351 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01212 0.9939 0.9885 2.192e-06 -9.84e-07 -0.006584 1.652e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003255 -0.003048 -0.008824 0.006788 0.9698 0.9742 0.006209 0.8401 0.829 0.01928 ] Network output: [ 0.9997 0.001065 0.001398 -3.039e-05 1.364e-05 -0.002069 -2.29e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1885 -0.03138 -0.1884 0.1959 0.9836 0.9933 0.2105 0.4522 0.8748 0.7207 ] Network output: [ -0.01131 1.001 1.01 1.049e-06 -4.708e-07 0.01126 7.904e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005589 0.0004623 0.004383 0.004058 0.9889 0.992 0.005692 0.8692 0.8988 0.01396 ] Network output: [ -0.0008584 0.003465 1.002 -9.919e-05 4.453e-05 0.9954 -7.476e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1995 0.09353 0.3292 0.1522 0.9851 0.994 0.2001 0.4567 0.8813 0.7153 ] Network output: [ 0.007157 -0.03519 0.9961 5.836e-05 -2.62e-05 1.025 4.398e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09855 0.087 0.1794 0.2049 0.9873 0.992 0.09861 0.7782 0.8729 0.3074 ] Network output: [ -0.007085 0.03593 1.002 5.986e-05 -2.687e-05 0.9767 4.511e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09062 0.0887 0.1658 0.196 0.9855 0.9914 0.09063 0.7055 0.8509 0.2435 ] Network output: [ 0.0002335 0.9998 -0.000504 8.158e-06 -3.662e-06 1 6.148e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006555 Epoch 7352 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01211 0.9939 0.9885 2.188e-06 -9.822e-07 -0.006585 1.649e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003255 -0.003048 -0.008823 0.006787 0.9698 0.9742 0.006209 0.8401 0.8289 0.01928 ] Network output: [ 0.9997 0.001055 0.001398 -3.037e-05 1.363e-05 -0.002061 -2.288e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1885 -0.03138 -0.1884 0.1959 0.9836 0.9933 0.2105 0.4522 0.8748 0.7207 ] Network output: [ -0.01131 1.001 1.01 1.046e-06 -4.698e-07 0.01125 7.886e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005589 0.0004622 0.004383 0.004057 0.9889 0.992 0.005693 0.8692 0.8988 0.01396 ] Network output: [ -0.0008572 0.003453 1.002 -9.911e-05 4.45e-05 0.9954 -7.469e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1995 0.09353 0.3292 0.1522 0.9851 0.994 0.2001 0.4567 0.8813 0.7152 ] Network output: [ 0.007154 -0.03518 0.9961 5.831e-05 -2.618e-05 1.025 4.395e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09855 0.08701 0.1794 0.2049 0.9873 0.992 0.09862 0.7782 0.8729 0.3074 ] Network output: [ -0.007082 0.03591 1.002 5.981e-05 -2.685e-05 0.9767 4.508e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09062 0.08869 0.1658 0.196 0.9855 0.9914 0.09063 0.7054 0.8509 0.2435 ] Network output: [ 0.0002338 0.9998 -0.0005042 8.152e-06 -3.66e-06 1 6.144e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006551 Epoch 7353 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01211 0.9939 0.9885 2.184e-06 -9.803e-07 -0.006588 1.646e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003255 -0.003048 -0.008821 0.006786 0.9698 0.9742 0.006209 0.8401 0.8289 0.01928 ] Network output: [ 0.9997 0.001062 0.001396 -3.034e-05 1.362e-05 -0.002066 -2.287e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1885 -0.03139 -0.1884 0.1959 0.9836 0.9933 0.2105 0.4521 0.8748 0.7207 ] Network output: [ -0.0113 1.001 1.01 1.044e-06 -4.687e-07 0.01125 7.868e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00559 0.0004622 0.004383 0.004057 0.9889 0.992 0.005693 0.8692 0.8987 0.01396 ] Network output: [ -0.0008575 0.003462 1.002 -9.903e-05 4.446e-05 0.9954 -7.463e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1995 0.09354 0.3293 0.1522 0.9851 0.994 0.2002 0.4567 0.8813 0.7152 ] Network output: [ 0.007152 -0.03517 0.9961 5.827e-05 -2.616e-05 1.025 4.391e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09856 0.08701 0.1794 0.2049 0.9873 0.992 0.09862 0.7782 0.8729 0.3074 ] Network output: [ -0.007079 0.0359 1.002 5.977e-05 -2.683e-05 0.9767 4.504e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09061 0.08869 0.1658 0.196 0.9855 0.9914 0.09063 0.7054 0.8509 0.2435 ] Network output: [ 0.0002332 0.9998 -0.000503 8.146e-06 -3.657e-06 1 6.139e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006546 Epoch 7354 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01211 0.9939 0.9885 2.18e-06 -9.785e-07 -0.006589 1.643e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003255 -0.003048 -0.00882 0.006785 0.9698 0.9742 0.006209 0.8401 0.8289 0.01928 ] Network output: [ 0.9997 0.001054 0.001396 -3.032e-05 1.361e-05 -0.002058 -2.285e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1885 -0.03139 -0.1883 0.1959 0.9836 0.9933 0.2106 0.4521 0.8748 0.7207 ] Network output: [ -0.0113 1.001 1.01 1.042e-06 -4.676e-07 0.01124 7.85e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005591 0.0004622 0.004383 0.004056 0.9889 0.992 0.005694 0.8692 0.8987 0.01396 ] Network output: [ -0.0008563 0.003451 1.002 -9.895e-05 4.442e-05 0.9954 -7.457e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1995 0.09354 0.3293 0.1522 0.9851 0.994 0.2002 0.4567 0.8813 0.7152 ] Network output: [ 0.007149 -0.03515 0.9961 5.822e-05 -2.614e-05 1.025 4.388e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09856 0.08701 0.1794 0.2049 0.9873 0.992 0.09863 0.7782 0.8729 0.3074 ] Network output: [ -0.007076 0.03588 1.002 5.972e-05 -2.681e-05 0.9767 4.501e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09061 0.08869 0.1658 0.196 0.9855 0.9914 0.09062 0.7054 0.8509 0.2435 ] Network output: [ 0.0002336 0.9998 -0.0005031 8.139e-06 -3.654e-06 1 6.134e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006542 Epoch 7355 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01211 0.9939 0.9885 2.175e-06 -9.766e-07 -0.006592 1.639e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003255 -0.003048 -0.008818 0.006784 0.9698 0.9742 0.00621 0.8401 0.8289 0.01928 ] Network output: [ 0.9997 0.00106 0.001394 -3.03e-05 1.36e-05 -0.002063 -2.283e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1885 -0.0314 -0.1883 0.1958 0.9836 0.9933 0.2106 0.4521 0.8748 0.7206 ] Network output: [ -0.0113 1.001 1.01 1.039e-06 -4.666e-07 0.01124 7.832e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005591 0.0004622 0.004383 0.004055 0.9889 0.992 0.005694 0.8692 0.8987 0.01396 ] Network output: [ -0.0008566 0.00346 1.002 -9.887e-05 4.439e-05 0.9954 -7.451e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1995 0.09354 0.3293 0.1522 0.9851 0.994 0.2002 0.4567 0.8813 0.7152 ] Network output: [ 0.007147 -0.03514 0.9961 5.817e-05 -2.612e-05 1.025 4.384e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09857 0.08702 0.1794 0.2049 0.9873 0.992 0.09863 0.7781 0.8729 0.3074 ] Network output: [ -0.007073 0.03586 1.002 5.968e-05 -2.679e-05 0.9767 4.498e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09061 0.08869 0.1658 0.196 0.9855 0.9914 0.09062 0.7053 0.8509 0.2435 ] Network output: [ 0.000233 0.9998 -0.000502 8.133e-06 -3.651e-06 1 6.129e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006538 Epoch 7356 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0121 0.9939 0.9885 2.171e-06 -9.748e-07 -0.006593 1.636e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003256 -0.003049 -0.008817 0.006783 0.9698 0.9742 0.00621 0.8401 0.8289 0.01927 ] Network output: [ 0.9997 0.001052 0.001394 -3.028e-05 1.359e-05 -0.002055 -2.282e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1885 -0.0314 -0.1883 0.1958 0.9836 0.9933 0.2106 0.4521 0.8748 0.7206 ] Network output: [ -0.0113 1.001 1.01 1.037e-06 -4.655e-07 0.01124 7.815e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005592 0.0004621 0.004384 0.004055 0.9889 0.992 0.005695 0.8692 0.8987 0.01396 ] Network output: [ -0.0008555 0.003449 1.002 -9.879e-05 4.435e-05 0.9954 -7.445e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1996 0.09354 0.3293 0.1521 0.9851 0.994 0.2002 0.4567 0.8813 0.7152 ] Network output: [ 0.007144 -0.03513 0.9961 5.813e-05 -2.61e-05 1.025 4.381e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09857 0.08702 0.1794 0.2049 0.9873 0.992 0.09864 0.7781 0.8729 0.3074 ] Network output: [ -0.00707 0.03585 1.002 5.963e-05 -2.677e-05 0.9767 4.494e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09061 0.08869 0.1658 0.196 0.9855 0.9914 0.09062 0.7053 0.8509 0.2435 ] Network output: [ 0.0002333 0.9998 -0.0005021 8.127e-06 -3.648e-06 1 6.125e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006533 Epoch 7357 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0121 0.9939 0.9885 2.167e-06 -9.729e-07 -0.006596 1.633e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003256 -0.003049 -0.008815 0.006782 0.9698 0.9742 0.00621 0.8401 0.8289 0.01927 ] Network output: [ 0.9997 0.001058 0.001393 -3.026e-05 1.358e-05 -0.00206 -2.28e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1886 -0.0314 -0.1883 0.1958 0.9836 0.9933 0.2106 0.4521 0.8748 0.7206 ] Network output: [ -0.0113 1.001 1.01 1.035e-06 -4.645e-07 0.01123 7.797e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005592 0.0004621 0.004384 0.004054 0.9889 0.992 0.005696 0.8692 0.8987 0.01396 ] Network output: [ -0.0008557 0.003457 1.002 -9.87e-05 4.431e-05 0.9954 -7.439e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1996 0.09355 0.3293 0.1521 0.9851 0.994 0.2002 0.4566 0.8813 0.7152 ] Network output: [ 0.007142 -0.03512 0.9961 5.808e-05 -2.607e-05 1.025 4.377e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09858 0.08702 0.1794 0.2049 0.9873 0.992 0.09864 0.7781 0.8729 0.3074 ] Network output: [ -0.007067 0.03583 1.002 5.959e-05 -2.675e-05 0.9767 4.491e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09061 0.08869 0.1658 0.196 0.9855 0.9913 0.09062 0.7053 0.8508 0.2435 ] Network output: [ 0.0002327 0.9998 -0.000501 8.121e-06 -3.646e-06 1 6.12e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006529 Epoch 7358 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0121 0.9939 0.9885 2.163e-06 -9.71e-07 -0.006597 1.63e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003256 -0.003049 -0.008814 0.006781 0.9698 0.9742 0.006211 0.8401 0.8289 0.01927 ] Network output: [ 0.9997 0.00105 0.001392 -3.023e-05 1.357e-05 -0.002052 -2.278e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1886 -0.03141 -0.1883 0.1958 0.9836 0.9933 0.2106 0.4521 0.8748 0.7206 ] Network output: [ -0.0113 1.001 1.01 1.032e-06 -4.634e-07 0.01123 7.779e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005593 0.0004621 0.004384 0.004053 0.9889 0.992 0.005696 0.8692 0.8987 0.01395 ] Network output: [ -0.0008547 0.003447 1.002 -9.862e-05 4.428e-05 0.9954 -7.432e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1996 0.09355 0.3293 0.1521 0.9851 0.994 0.2002 0.4566 0.8813 0.7152 ] Network output: [ 0.007139 -0.0351 0.9961 5.803e-05 -2.605e-05 1.025 4.374e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09858 0.08703 0.1794 0.2049 0.9873 0.992 0.09865 0.7781 0.8728 0.3074 ] Network output: [ -0.007064 0.03582 1.002 5.954e-05 -2.673e-05 0.9767 4.487e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09061 0.08868 0.1658 0.196 0.9855 0.9913 0.09062 0.7052 0.8508 0.2435 ] Network output: [ 0.000233 0.9998 -0.0005011 8.114e-06 -3.643e-06 1 6.115e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006525 Epoch 7359 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0121 0.9939 0.9885 2.159e-06 -9.692e-07 -0.0066 1.627e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003256 -0.003049 -0.008813 0.00678 0.9698 0.9742 0.006211 0.8401 0.8289 0.01927 ] Network output: [ 0.9997 0.001056 0.001391 -3.021e-05 1.356e-05 -0.002056 -2.277e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1886 -0.03141 -0.1882 0.1958 0.9836 0.9933 0.2106 0.452 0.8748 0.7206 ] Network output: [ -0.0113 1.001 1.01 1.03e-06 -4.623e-07 0.01123 7.761e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005594 0.0004621 0.004384 0.004052 0.9889 0.992 0.005697 0.8691 0.8987 0.01395 ] Network output: [ -0.0008549 0.003455 1.002 -9.854e-05 4.424e-05 0.9954 -7.426e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1996 0.09355 0.3294 0.1521 0.9851 0.994 0.2002 0.4566 0.8813 0.7152 ] Network output: [ 0.007137 -0.03509 0.9961 5.799e-05 -2.603e-05 1.025 4.37e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09859 0.08703 0.1794 0.2049 0.9873 0.992 0.09865 0.778 0.8728 0.3074 ] Network output: [ -0.007061 0.0358 1.002 5.95e-05 -2.671e-05 0.9767 4.484e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0906 0.08868 0.1658 0.196 0.9855 0.9913 0.09062 0.7052 0.8508 0.2435 ] Network output: [ 0.0002325 0.9998 -0.0005 8.108e-06 -3.64e-06 1 6.11e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000652 Epoch 7360 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0121 0.9939 0.9885 2.155e-06 -9.674e-07 -0.006601 1.624e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003256 -0.003049 -0.008811 0.006779 0.9698 0.9742 0.006211 0.84 0.8289 0.01927 ] Network output: [ 0.9997 0.001048 0.00139 -3.019e-05 1.355e-05 -0.002049 -2.275e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1886 -0.03142 -0.1882 0.1958 0.9836 0.9933 0.2106 0.452 0.8748 0.7206 ] Network output: [ -0.0113 1.001 1.01 1.028e-06 -4.613e-07 0.01122 7.744e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005594 0.000462 0.004384 0.004052 0.9889 0.992 0.005698 0.8691 0.8987 0.01395 ] Network output: [ -0.0008538 0.003445 1.002 -9.846e-05 4.42e-05 0.9954 -7.42e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1996 0.09355 0.3294 0.1521 0.9851 0.994 0.2002 0.4566 0.8813 0.7152 ] Network output: [ 0.007134 -0.03508 0.9961 5.794e-05 -2.601e-05 1.025 4.367e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09859 0.08704 0.1794 0.2049 0.9873 0.992 0.09866 0.778 0.8728 0.3074 ] Network output: [ -0.007058 0.03579 1.002 5.945e-05 -2.669e-05 0.9767 4.481e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0906 0.08868 0.1658 0.196 0.9855 0.9913 0.09061 0.7052 0.8508 0.2435 ] Network output: [ 0.0002328 0.9998 -0.0005001 8.102e-06 -3.637e-06 1 6.106e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006516 Epoch 7361 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01209 0.9939 0.9885 2.151e-06 -9.655e-07 -0.006604 1.621e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003256 -0.003049 -0.00881 0.006778 0.9698 0.9742 0.006212 0.84 0.8289 0.01926 ] Network output: [ 0.9997 0.001054 0.001389 -3.017e-05 1.354e-05 -0.002053 -2.273e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1886 -0.03142 -0.1882 0.1958 0.9836 0.9933 0.2107 0.452 0.8748 0.7206 ] Network output: [ -0.01129 1.001 1.01 1.025e-06 -4.602e-07 0.01122 7.726e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005595 0.000462 0.004384 0.004051 0.9889 0.992 0.005698 0.8691 0.8987 0.01395 ] Network output: [ -0.000854 0.003452 1.002 -9.838e-05 4.417e-05 0.9954 -7.414e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1996 0.09356 0.3294 0.1521 0.9851 0.994 0.2002 0.4566 0.8813 0.7152 ] Network output: [ 0.007132 -0.03507 0.9961 5.79e-05 -2.599e-05 1.025 4.363e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0986 0.08704 0.1794 0.2048 0.9873 0.992 0.09866 0.778 0.8728 0.3074 ] Network output: [ -0.007056 0.03577 1.002 5.941e-05 -2.667e-05 0.9767 4.477e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0906 0.08868 0.1658 0.196 0.9855 0.9913 0.09061 0.7051 0.8508 0.2435 ] Network output: [ 0.0002323 0.9998 -0.0004991 8.096e-06 -3.634e-06 1 6.101e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006512 Epoch 7362 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01209 0.9939 0.9885 2.147e-06 -9.637e-07 -0.006605 1.618e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003256 -0.003049 -0.008808 0.006777 0.9698 0.9742 0.006212 0.84 0.8289 0.01926 ] Network output: [ 0.9997 0.001046 0.001389 -3.014e-05 1.353e-05 -0.002047 -2.272e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1886 -0.03143 -0.1882 0.1958 0.9836 0.9933 0.2107 0.452 0.8748 0.7206 ] Network output: [ -0.01129 1.001 1.01 1.023e-06 -4.592e-07 0.01121 7.709e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005595 0.000462 0.004385 0.00405 0.9889 0.992 0.005699 0.8691 0.8987 0.01395 ] Network output: [ -0.000853 0.003443 1.002 -9.83e-05 4.413e-05 0.9954 -7.408e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1996 0.09356 0.3294 0.1521 0.9851 0.994 0.2003 0.4566 0.8813 0.7152 ] Network output: [ 0.007129 -0.03506 0.9961 5.785e-05 -2.597e-05 1.025 4.36e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0986 0.08704 0.1794 0.2048 0.9873 0.992 0.09867 0.778 0.8728 0.3074 ] Network output: [ -0.007053 0.03575 1.002 5.936e-05 -2.665e-05 0.9767 4.474e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0906 0.08868 0.1658 0.196 0.9855 0.9913 0.09061 0.7051 0.8508 0.2435 ] Network output: [ 0.0002325 0.9998 -0.0004991 8.089e-06 -3.632e-06 1 6.096e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006508 Epoch 7363 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01209 0.9939 0.9885 2.142e-06 -9.618e-07 -0.006607 1.615e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003256 -0.00305 -0.008807 0.006776 0.9698 0.9742 0.006212 0.84 0.8289 0.01926 ] Network output: [ 0.9997 0.001052 0.001387 -3.012e-05 1.352e-05 -0.00205 -2.27e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1886 -0.03143 -0.1882 0.1958 0.9836 0.9933 0.2107 0.452 0.8748 0.7206 ] Network output: [ -0.01129 1.001 1.01 1.02e-06 -4.581e-07 0.01121 7.691e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005596 0.000462 0.004385 0.00405 0.9889 0.992 0.005699 0.8691 0.8987 0.01395 ] Network output: [ -0.0008531 0.00345 1.002 -9.822e-05 4.409e-05 0.9954 -7.402e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1996 0.09356 0.3294 0.1521 0.9851 0.994 0.2003 0.4565 0.8813 0.7152 ] Network output: [ 0.007127 -0.03504 0.9961 5.78e-05 -2.595e-05 1.025 4.356e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09861 0.08705 0.1794 0.2048 0.9873 0.992 0.09867 0.7779 0.8728 0.3073 ] Network output: [ -0.00705 0.03574 1.002 5.932e-05 -2.663e-05 0.9768 4.47e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0906 0.08867 0.1658 0.196 0.9855 0.9913 0.09061 0.7051 0.8508 0.2435 ] Network output: [ 0.000232 0.9998 -0.0004981 8.083e-06 -3.629e-06 1 6.092e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006503 Epoch 7364 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01209 0.9939 0.9885 2.138e-06 -9.6e-07 -0.006609 1.612e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003257 -0.00305 -0.008806 0.006775 0.9698 0.9742 0.006213 0.84 0.8289 0.01926 ] Network output: [ 0.9997 0.001044 0.001387 -3.01e-05 1.351e-05 -0.002044 -2.268e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1886 -0.03144 -0.1881 0.1958 0.9836 0.9933 0.2107 0.452 0.8748 0.7206 ] Network output: [ -0.01129 1.001 1.01 1.018e-06 -4.571e-07 0.01121 7.673e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005597 0.0004619 0.004385 0.004049 0.9889 0.992 0.0057 0.8691 0.8987 0.01395 ] Network output: [ -0.0008521 0.003441 1.002 -9.813e-05 4.406e-05 0.9955 -7.396e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1997 0.09356 0.3294 0.1521 0.9851 0.994 0.2003 0.4565 0.8813 0.7152 ] Network output: [ 0.007124 -0.03503 0.9961 5.776e-05 -2.593e-05 1.025 4.353e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09861 0.08705 0.1794 0.2048 0.9873 0.992 0.09867 0.7779 0.8728 0.3073 ] Network output: [ -0.007047 0.03572 1.002 5.927e-05 -2.661e-05 0.9768 4.467e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0906 0.08867 0.1658 0.196 0.9855 0.9913 0.09061 0.705 0.8508 0.2435 ] Network output: [ 0.0002322 0.9998 -0.0004981 8.077e-06 -3.626e-06 1 6.087e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006499 Epoch 7365 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01208 0.9939 0.9885 2.134e-06 -9.581e-07 -0.006611 1.608e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003257 -0.00305 -0.008804 0.006774 0.9698 0.9742 0.006213 0.84 0.8289 0.01926 ] Network output: [ 0.9997 0.001049 0.001385 -3.008e-05 1.35e-05 -0.002047 -2.267e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1886 -0.03144 -0.1881 0.1958 0.9836 0.9933 0.2107 0.4519 0.8748 0.7206 ] Network output: [ -0.01129 1.001 1.01 1.016e-06 -4.56e-07 0.0112 7.655e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005597 0.0004619 0.004385 0.004048 0.9889 0.992 0.005701 0.8691 0.8987 0.01394 ] Network output: [ -0.0008523 0.003448 1.002 -9.805e-05 4.402e-05 0.9954 -7.39e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1997 0.09357 0.3294 0.152 0.9851 0.994 0.2003 0.4565 0.8813 0.7152 ] Network output: [ 0.007122 -0.03502 0.9961 5.771e-05 -2.591e-05 1.025 4.349e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09862 0.08706 0.1794 0.2048 0.9873 0.992 0.09868 0.7779 0.8728 0.3073 ] Network output: [ -0.007044 0.03571 1.002 5.923e-05 -2.659e-05 0.9768 4.464e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09059 0.08867 0.1658 0.196 0.9855 0.9913 0.09061 0.705 0.8508 0.2435 ] Network output: [ 0.0002318 0.9998 -0.0004971 8.071e-06 -3.623e-06 1 6.082e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006495 Epoch 7366 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01208 0.9939 0.9885 2.13e-06 -9.563e-07 -0.006612 1.605e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003257 -0.00305 -0.008803 0.006774 0.9698 0.9742 0.006213 0.84 0.8289 0.01926 ] Network output: [ 0.9997 0.001043 0.001385 -3.005e-05 1.349e-05 -0.002041 -2.265e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1887 -0.03144 -0.1881 0.1957 0.9836 0.9933 0.2107 0.4519 0.8747 0.7206 ] Network output: [ -0.01129 1.001 1.01 1.014e-06 -4.55e-07 0.0112 7.638e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005598 0.0004619 0.004385 0.004048 0.9889 0.992 0.005701 0.8691 0.8987 0.01394 ] Network output: [ -0.0008513 0.003439 1.002 -9.797e-05 4.398e-05 0.9955 -7.383e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1997 0.09357 0.3295 0.152 0.9851 0.994 0.2003 0.4565 0.8813 0.7152 ] Network output: [ 0.007119 -0.03501 0.9961 5.766e-05 -2.589e-05 1.025 4.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09862 0.08706 0.1794 0.2048 0.9873 0.992 0.09868 0.7778 0.8728 0.3073 ] Network output: [ -0.007041 0.03569 1.002 5.918e-05 -2.657e-05 0.9768 4.46e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09059 0.08867 0.1658 0.196 0.9855 0.9913 0.0906 0.705 0.8507 0.2435 ] Network output: [ 0.000232 0.9998 -0.0004971 8.064e-06 -3.62e-06 1 6.078e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006491 Epoch 7367 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01208 0.9939 0.9885 2.126e-06 -9.545e-07 -0.006615 1.602e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003257 -0.00305 -0.008801 0.006773 0.9698 0.9742 0.006214 0.84 0.8289 0.01925 ] Network output: [ 0.9997 0.001047 0.001384 -3.003e-05 1.348e-05 -0.002044 -2.263e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1887 -0.03145 -0.1881 0.1957 0.9836 0.9933 0.2107 0.4519 0.8747 0.7206 ] Network output: [ -0.01129 1.001 1.01 1.011e-06 -4.539e-07 0.0112 7.62e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005598 0.0004619 0.004385 0.004047 0.9889 0.992 0.005702 0.8691 0.8987 0.01394 ] Network output: [ -0.0008514 0.003445 1.002 -9.789e-05 4.395e-05 0.9955 -7.377e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1997 0.09357 0.3295 0.152 0.9851 0.994 0.2003 0.4565 0.8813 0.7152 ] Network output: [ 0.007117 -0.03499 0.9961 5.762e-05 -2.587e-05 1.025 4.342e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09863 0.08706 0.1795 0.2048 0.9873 0.992 0.09869 0.7778 0.8728 0.3073 ] Network output: [ -0.007038 0.03568 1.002 5.914e-05 -2.655e-05 0.9768 4.457e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09059 0.08867 0.1658 0.196 0.9855 0.9913 0.0906 0.705 0.8507 0.2435 ] Network output: [ 0.0002315 0.9998 -0.0004961 8.058e-06 -3.618e-06 1 6.073e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006486 Epoch 7368 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01208 0.9939 0.9885 2.122e-06 -9.526e-07 -0.006616 1.599e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003257 -0.00305 -0.0088 0.006772 0.9698 0.9742 0.006214 0.84 0.8288 0.01925 ] Network output: [ 0.9997 0.001041 0.001383 -3.001e-05 1.347e-05 -0.002038 -2.262e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1887 -0.03145 -0.1881 0.1957 0.9836 0.9933 0.2107 0.4519 0.8747 0.7206 ] Network output: [ -0.01129 1.001 1.01 1.009e-06 -4.529e-07 0.01119 7.603e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005599 0.0004619 0.004385 0.004046 0.9889 0.992 0.005703 0.8691 0.8987 0.01394 ] Network output: [ -0.0008505 0.003437 1.002 -9.781e-05 4.391e-05 0.9955 -7.371e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1997 0.09357 0.3295 0.152 0.9851 0.994 0.2003 0.4565 0.8813 0.7152 ] Network output: [ 0.007114 -0.03498 0.9961 5.757e-05 -2.585e-05 1.025 4.339e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09863 0.08707 0.1795 0.2048 0.9873 0.992 0.09869 0.7778 0.8728 0.3073 ] Network output: [ -0.007035 0.03566 1.002 5.909e-05 -2.653e-05 0.9768 4.454e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09059 0.08867 0.1658 0.196 0.9855 0.9913 0.0906 0.7049 0.8507 0.2435 ] Network output: [ 0.0002317 0.9998 -0.0004961 8.052e-06 -3.615e-06 1 6.068e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006482 Epoch 7369 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01208 0.9939 0.9886 2.118e-06 -9.508e-07 -0.006619 1.596e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003257 -0.003051 -0.008799 0.006771 0.9698 0.9742 0.006214 0.84 0.8288 0.01925 ] Network output: [ 0.9997 0.001045 0.001382 -2.999e-05 1.346e-05 -0.002041 -2.26e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1887 -0.03146 -0.1881 0.1957 0.9836 0.9933 0.2108 0.4519 0.8747 0.7206 ] Network output: [ -0.01128 1.001 1.01 1.006e-06 -4.518e-07 0.01119 7.585e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0056 0.0004618 0.004385 0.004046 0.9889 0.992 0.005703 0.869 0.8987 0.01394 ] Network output: [ -0.0008505 0.003443 1.002 -9.773e-05 4.387e-05 0.9955 -7.365e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1997 0.09358 0.3295 0.152 0.9851 0.994 0.2003 0.4564 0.8813 0.7152 ] Network output: [ 0.007112 -0.03497 0.9961 5.753e-05 -2.583e-05 1.025 4.335e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09864 0.08707 0.1795 0.2048 0.9873 0.992 0.0987 0.7778 0.8727 0.3073 ] Network output: [ -0.007032 0.03564 1.002 5.905e-05 -2.651e-05 0.9768 4.45e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09059 0.08866 0.1658 0.196 0.9855 0.9913 0.0906 0.7049 0.8507 0.2435 ] Network output: [ 0.0002313 0.9998 -0.0004951 8.046e-06 -3.612e-06 1 6.063e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006478 Epoch 7370 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01207 0.9939 0.9886 2.114e-06 -9.49e-07 -0.00662 1.593e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003257 -0.003051 -0.008797 0.00677 0.9698 0.9742 0.006214 0.84 0.8288 0.01925 ] Network output: [ 0.9997 0.001039 0.001381 -2.997e-05 1.345e-05 -0.002035 -2.258e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1887 -0.03146 -0.188 0.1957 0.9836 0.9933 0.2108 0.4519 0.8747 0.7206 ] Network output: [ -0.01128 1.001 1.01 1.004e-06 -4.508e-07 0.01118 7.568e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0056 0.0004618 0.004386 0.004045 0.9889 0.992 0.005704 0.869 0.8987 0.01394 ] Network output: [ -0.0008496 0.003434 1.002 -9.765e-05 4.384e-05 0.9955 -7.359e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1997 0.09358 0.3295 0.152 0.9851 0.994 0.2004 0.4564 0.8812 0.7152 ] Network output: [ 0.00711 -0.03496 0.9961 5.748e-05 -2.581e-05 1.025 4.332e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09864 0.08708 0.1795 0.2048 0.9873 0.992 0.0987 0.7777 0.8727 0.3073 ] Network output: [ -0.007029 0.03563 1.002 5.901e-05 -2.649e-05 0.9768 4.447e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09059 0.08866 0.1658 0.196 0.9855 0.9913 0.0906 0.7049 0.8507 0.2435 ] Network output: [ 0.0002314 0.9998 -0.0004951 8.04e-06 -3.609e-06 1 6.059e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006474 Epoch 7371 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01207 0.9939 0.9886 2.11e-06 -9.471e-07 -0.006623 1.59e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003258 -0.003051 -0.008796 0.006769 0.9698 0.9742 0.006215 0.84 0.8288 0.01925 ] Network output: [ 0.9997 0.001043 0.00138 -2.994e-05 1.344e-05 -0.002038 -2.257e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1887 -0.03146 -0.188 0.1957 0.9836 0.9933 0.2108 0.4518 0.8747 0.7206 ] Network output: [ -0.01128 1.001 1.01 1.002e-06 -4.497e-07 0.01118 7.55e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005601 0.0004618 0.004386 0.004044 0.9889 0.992 0.005704 0.869 0.8987 0.01394 ] Network output: [ -0.0008497 0.00344 1.002 -9.757e-05 4.38e-05 0.9955 -7.353e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1997 0.09358 0.3295 0.152 0.9851 0.994 0.2004 0.4564 0.8812 0.7152 ] Network output: [ 0.007107 -0.03494 0.996 5.743e-05 -2.578e-05 1.025 4.328e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09865 0.08708 0.1795 0.2048 0.9873 0.992 0.09871 0.7777 0.8727 0.3073 ] Network output: [ -0.007027 0.03561 1.002 5.896e-05 -2.647e-05 0.9768 4.443e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09058 0.08866 0.1657 0.196 0.9855 0.9913 0.0906 0.7048 0.8507 0.2435 ] Network output: [ 0.000231 0.9998 -0.0004941 8.033e-06 -3.606e-06 1 6.054e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006469 Epoch 7372 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01207 0.9939 0.9886 2.106e-06 -9.453e-07 -0.006624 1.587e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003258 -0.003051 -0.008794 0.006768 0.9698 0.9742 0.006215 0.8399 0.8288 0.01924 ] Network output: [ 0.9997 0.001037 0.00138 -2.992e-05 1.343e-05 -0.002032 -2.255e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1887 -0.03147 -0.188 0.1957 0.9836 0.9933 0.2108 0.4518 0.8747 0.7206 ] Network output: [ -0.01128 1.001 1.01 9.995e-07 -4.487e-07 0.01118 7.533e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005602 0.0004618 0.004386 0.004044 0.9889 0.992 0.005705 0.869 0.8987 0.01393 ] Network output: [ -0.0008488 0.003432 1.002 -9.748e-05 4.376e-05 0.9955 -7.347e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1997 0.09358 0.3296 0.152 0.9851 0.994 0.2004 0.4564 0.8812 0.7151 ] Network output: [ 0.007105 -0.03493 0.996 5.739e-05 -2.576e-05 1.025 4.325e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09865 0.08708 0.1795 0.2048 0.9873 0.992 0.09871 0.7777 0.8727 0.3073 ] Network output: [ -0.007024 0.0356 1.002 5.892e-05 -2.645e-05 0.9768 4.44e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09058 0.08866 0.1657 0.196 0.9855 0.9913 0.09059 0.7048 0.8507 0.2435 ] Network output: [ 0.0002312 0.9998 -0.0004941 8.027e-06 -3.604e-06 1 6.049e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006465 Epoch 7373 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01207 0.9939 0.9886 2.102e-06 -9.435e-07 -0.006626 1.584e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003258 -0.003051 -0.008793 0.006767 0.9698 0.9742 0.006215 0.8399 0.8288 0.01924 ] Network output: [ 0.9997 0.001041 0.001378 -2.99e-05 1.342e-05 -0.002035 -2.253e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1887 -0.03147 -0.188 0.1957 0.9836 0.9933 0.2108 0.4518 0.8747 0.7206 ] Network output: [ -0.01128 1.001 1.01 9.972e-07 -4.477e-07 0.01117 7.515e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005602 0.0004618 0.004386 0.004043 0.9889 0.992 0.005706 0.869 0.8986 0.01393 ] Network output: [ -0.0008488 0.003438 1.002 -9.74e-05 4.373e-05 0.9955 -7.341e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1998 0.09359 0.3296 0.1519 0.9851 0.994 0.2004 0.4564 0.8812 0.7151 ] Network output: [ 0.007102 -0.03492 0.996 5.734e-05 -2.574e-05 1.025 4.322e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09866 0.08709 0.1795 0.2048 0.9873 0.992 0.09872 0.7777 0.8727 0.3073 ] Network output: [ -0.007021 0.03558 1.002 5.887e-05 -2.643e-05 0.9768 4.437e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09058 0.08866 0.1657 0.196 0.9855 0.9913 0.09059 0.7048 0.8507 0.2435 ] Network output: [ 0.0002308 0.9998 -0.0004932 8.021e-06 -3.601e-06 1 6.045e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006461 Epoch 7374 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01207 0.9939 0.9886 2.098e-06 -9.417e-07 -0.006628 1.581e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003258 -0.003051 -0.008791 0.006766 0.9698 0.9742 0.006216 0.8399 0.8288 0.01924 ] Network output: [ 0.9997 0.001035 0.001378 -2.988e-05 1.341e-05 -0.00203 -2.252e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1887 -0.03148 -0.188 0.1957 0.9836 0.9933 0.2108 0.4518 0.8747 0.7206 ] Network output: [ -0.01128 1.001 1.01 9.949e-07 -4.466e-07 0.01117 7.498e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005603 0.0004617 0.004386 0.004042 0.9889 0.992 0.005706 0.869 0.8986 0.01393 ] Network output: [ -0.0008479 0.00343 1.002 -9.732e-05 4.369e-05 0.9955 -7.334e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1998 0.09359 0.3296 0.1519 0.9851 0.994 0.2004 0.4564 0.8812 0.7151 ] Network output: [ 0.0071 -0.03491 0.996 5.73e-05 -2.572e-05 1.025 4.318e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09866 0.08709 0.1795 0.2048 0.9873 0.992 0.09872 0.7776 0.8727 0.3073 ] Network output: [ -0.007018 0.03557 1.002 5.883e-05 -2.641e-05 0.9768 4.433e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09058 0.08866 0.1657 0.196 0.9855 0.9913 0.09059 0.7047 0.8506 0.2435 ] Network output: [ 0.0002309 0.9998 -0.0004931 8.015e-06 -3.598e-06 1 6.04e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006457 Epoch 7375 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01206 0.9939 0.9886 2.094e-06 -9.399e-07 -0.00663 1.578e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003258 -0.003052 -0.00879 0.006765 0.9698 0.9742 0.006216 0.8399 0.8288 0.01924 ] Network output: [ 0.9997 0.001039 0.001377 -2.985e-05 1.34e-05 -0.002032 -2.25e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1888 -0.03148 -0.1879 0.1957 0.9836 0.9933 0.2108 0.4518 0.8747 0.7206 ] Network output: [ -0.01128 1.001 1.01 9.925e-07 -4.456e-07 0.01117 7.48e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005603 0.0004617 0.004386 0.004042 0.9889 0.992 0.005707 0.869 0.8986 0.01393 ] Network output: [ -0.0008479 0.003436 1.002 -9.724e-05 4.366e-05 0.9955 -7.328e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1998 0.09359 0.3296 0.1519 0.9851 0.994 0.2004 0.4563 0.8812 0.7151 ] Network output: [ 0.007097 -0.03489 0.996 5.725e-05 -2.57e-05 1.025 4.315e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09867 0.0871 0.1795 0.2047 0.9873 0.992 0.09873 0.7776 0.8727 0.3073 ] Network output: [ -0.007015 0.03555 1.002 5.878e-05 -2.639e-05 0.9768 4.43e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09058 0.08865 0.1657 0.196 0.9855 0.9913 0.09059 0.7047 0.8506 0.2435 ] Network output: [ 0.0002305 0.9998 -0.0004922 8.008e-06 -3.595e-06 1 6.035e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006452 Epoch 7376 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01206 0.9939 0.9886 2.09e-06 -9.381e-07 -0.006632 1.575e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003258 -0.003052 -0.008789 0.006764 0.9698 0.9742 0.006216 0.8399 0.8288 0.01924 ] Network output: [ 0.9997 0.001033 0.001376 -2.983e-05 1.339e-05 -0.002027 -2.248e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1888 -0.03149 -0.1879 0.1957 0.9836 0.9933 0.2108 0.4518 0.8747 0.7206 ] Network output: [ -0.01128 1.001 1.01 9.902e-07 -4.445e-07 0.01116 7.463e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005604 0.0004617 0.004386 0.004041 0.9889 0.992 0.005707 0.869 0.8986 0.01393 ] Network output: [ -0.0008471 0.003428 1.002 -9.716e-05 4.362e-05 0.9955 -7.322e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1998 0.09359 0.3296 0.1519 0.9851 0.994 0.2004 0.4563 0.8812 0.7151 ] Network output: [ 0.007095 -0.03488 0.996 5.72e-05 -2.568e-05 1.025 4.311e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09867 0.0871 0.1795 0.2047 0.9873 0.992 0.09873 0.7776 0.8727 0.3073 ] Network output: [ -0.007012 0.03553 1.002 5.874e-05 -2.637e-05 0.9769 4.427e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09058 0.08865 0.1657 0.196 0.9855 0.9913 0.09059 0.7047 0.8506 0.2435 ] Network output: [ 0.0002307 0.9998 -0.0004921 8.002e-06 -3.592e-06 1 6.031e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006448 Epoch 7377 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01206 0.9939 0.9886 2.085e-06 -9.362e-07 -0.006634 1.572e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003258 -0.003052 -0.008787 0.006763 0.9698 0.9742 0.006217 0.8399 0.8288 0.01923 ] Network output: [ 0.9997 0.001037 0.001375 -2.981e-05 1.338e-05 -0.002029 -2.247e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1888 -0.03149 -0.1879 0.1956 0.9836 0.9933 0.2109 0.4517 0.8747 0.7205 ] Network output: [ -0.01127 1.001 1.01 9.879e-07 -4.435e-07 0.01116 7.445e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005605 0.0004617 0.004387 0.00404 0.9889 0.992 0.005708 0.869 0.8986 0.01393 ] Network output: [ -0.0008471 0.003433 1.002 -9.708e-05 4.358e-05 0.9955 -7.316e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1998 0.0936 0.3296 0.1519 0.9851 0.994 0.2004 0.4563 0.8812 0.7151 ] Network output: [ 0.007092 -0.03487 0.996 5.716e-05 -2.566e-05 1.025 4.308e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09868 0.0871 0.1795 0.2047 0.9873 0.992 0.09874 0.7776 0.8727 0.3073 ] Network output: [ -0.007009 0.03552 1.002 5.869e-05 -2.635e-05 0.9769 4.423e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09057 0.08865 0.1657 0.196 0.9855 0.9913 0.09059 0.7046 0.8506 0.2435 ] Network output: [ 0.0002303 0.9998 -0.0004912 7.996e-06 -3.59e-06 1 6.026e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006444 Epoch 7378 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01206 0.9939 0.9886 2.081e-06 -9.344e-07 -0.006635 1.569e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003259 -0.003052 -0.008786 0.006762 0.9698 0.9742 0.006217 0.8399 0.8288 0.01923 ] Network output: [ 0.9997 0.001031 0.001374 -2.979e-05 1.337e-05 -0.002024 -2.245e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1888 -0.03149 -0.1879 0.1956 0.9836 0.9933 0.2109 0.4517 0.8747 0.7205 ] Network output: [ -0.01127 1.001 1.01 9.856e-07 -4.425e-07 0.01116 7.428e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005605 0.0004616 0.004387 0.00404 0.9889 0.992 0.005709 0.869 0.8986 0.01393 ] Network output: [ -0.0008462 0.003426 1.002 -9.7e-05 4.355e-05 0.9955 -7.31e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1998 0.0936 0.3296 0.1519 0.9851 0.994 0.2005 0.4563 0.8812 0.7151 ] Network output: [ 0.00709 -0.03486 0.996 5.711e-05 -2.564e-05 1.025 4.304e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09868 0.08711 0.1795 0.2047 0.9873 0.992 0.09874 0.7775 0.8727 0.3073 ] Network output: [ -0.007006 0.0355 1.002 5.865e-05 -2.633e-05 0.9769 4.42e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09057 0.08865 0.1657 0.196 0.9855 0.9913 0.09058 0.7046 0.8506 0.2435 ] Network output: [ 0.0002304 0.9998 -0.0004911 7.99e-06 -3.587e-06 1 6.021e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000644 Epoch 7379 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01205 0.994 0.9886 2.077e-06 -9.326e-07 -0.006638 1.566e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003259 -0.003052 -0.008784 0.006761 0.9698 0.9742 0.006217 0.8399 0.8288 0.01923 ] Network output: [ 0.9997 0.001035 0.001373 -2.977e-05 1.336e-05 -0.002026 -2.243e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1888 -0.0315 -0.1879 0.1956 0.9836 0.9933 0.2109 0.4517 0.8747 0.7205 ] Network output: [ -0.01127 1.001 1.01 9.832e-07 -4.414e-07 0.01115 7.41e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005606 0.0004616 0.004387 0.004039 0.9889 0.992 0.005709 0.8689 0.8986 0.01392 ] Network output: [ -0.0008462 0.003431 1.002 -9.692e-05 4.351e-05 0.9955 -7.304e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1998 0.0936 0.3297 0.1519 0.9851 0.994 0.2005 0.4563 0.8812 0.7151 ] Network output: [ 0.007087 -0.03484 0.996 5.707e-05 -2.562e-05 1.025 4.301e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09869 0.08711 0.1795 0.2047 0.9873 0.992 0.09875 0.7775 0.8727 0.3073 ] Network output: [ -0.007003 0.03549 1.002 5.86e-05 -2.631e-05 0.9769 4.417e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09057 0.08865 0.1657 0.196 0.9855 0.9913 0.09058 0.7046 0.8506 0.2435 ] Network output: [ 0.00023 0.9998 -0.0004902 7.984e-06 -3.584e-06 1 6.017e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006436 Epoch 7380 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01205 0.994 0.9886 2.073e-06 -9.308e-07 -0.006639 1.563e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003259 -0.003052 -0.008783 0.00676 0.9698 0.9742 0.006218 0.8399 0.8288 0.01923 ] Network output: [ 0.9997 0.00103 0.001372 -2.974e-05 1.335e-05 -0.002021 -2.242e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1888 -0.0315 -0.1878 0.1956 0.9836 0.9933 0.2109 0.4517 0.8747 0.7205 ] Network output: [ -0.01127 1.001 1.01 9.809e-07 -4.404e-07 0.01115 7.393e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005606 0.0004616 0.004387 0.004038 0.9889 0.992 0.00571 0.8689 0.8986 0.01392 ] Network output: [ -0.0008454 0.003424 1.002 -9.684e-05 4.347e-05 0.9955 -7.298e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1998 0.0936 0.3297 0.1519 0.9851 0.994 0.2005 0.4563 0.8812 0.7151 ] Network output: [ 0.007085 -0.03483 0.996 5.702e-05 -2.56e-05 1.025 4.297e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09869 0.08712 0.1795 0.2047 0.9873 0.992 0.09875 0.7775 0.8726 0.3073 ] Network output: [ -0.007001 0.03547 1.002 5.856e-05 -2.629e-05 0.9769 4.413e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09057 0.08865 0.1657 0.196 0.9855 0.9913 0.09058 0.7045 0.8506 0.2435 ] Network output: [ 0.0002301 0.9998 -0.0004901 7.977e-06 -3.581e-06 1 6.012e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006431 Epoch 7381 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01205 0.994 0.9886 2.069e-06 -9.29e-07 -0.006642 1.56e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003259 -0.003053 -0.008782 0.006759 0.9698 0.9742 0.006218 0.8399 0.8288 0.01923 ] Network output: [ 0.9997 0.001033 0.001371 -2.972e-05 1.334e-05 -0.002023 -2.24e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1888 -0.03151 -0.1878 0.1956 0.9836 0.9933 0.2109 0.4517 0.8747 0.7205 ] Network output: [ -0.01127 1.001 1.01 9.786e-07 -4.393e-07 0.01114 7.375e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005607 0.0004616 0.004387 0.004038 0.9889 0.992 0.005711 0.8689 0.8986 0.01392 ] Network output: [ -0.0008453 0.003429 1.002 -9.676e-05 4.344e-05 0.9955 -7.292e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1999 0.09361 0.3297 0.1519 0.9851 0.994 0.2005 0.4562 0.8812 0.7151 ] Network output: [ 0.007082 -0.03482 0.996 5.698e-05 -2.558e-05 1.025 4.294e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0987 0.08712 0.1795 0.2047 0.9873 0.992 0.09876 0.7774 0.8726 0.3073 ] Network output: [ -0.006998 0.03546 1.002 5.851e-05 -2.627e-05 0.9769 4.41e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09057 0.08864 0.1657 0.196 0.9855 0.9913 0.09058 0.7045 0.8506 0.2435 ] Network output: [ 0.0002298 0.9998 -0.0004892 7.971e-06 -3.579e-06 1 6.007e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006427 Epoch 7382 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01205 0.994 0.9886 2.065e-06 -9.272e-07 -0.006643 1.557e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003259 -0.003053 -0.00878 0.006759 0.9698 0.9742 0.006218 0.8399 0.8288 0.01922 ] Network output: [ 0.9997 0.001028 0.001371 -2.97e-05 1.333e-05 -0.002018 -2.238e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1888 -0.03151 -0.1878 0.1956 0.9836 0.9933 0.2109 0.4517 0.8747 0.7205 ] Network output: [ -0.01127 1.001 1.01 9.763e-07 -4.383e-07 0.01114 7.358e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005608 0.0004616 0.004387 0.004037 0.9889 0.992 0.005711 0.8689 0.8986 0.01392 ] Network output: [ -0.0008445 0.003422 1.002 -9.668e-05 4.34e-05 0.9955 -7.286e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1999 0.09361 0.3297 0.1518 0.9851 0.994 0.2005 0.4562 0.8812 0.7151 ] Network output: [ 0.00708 -0.03481 0.996 5.693e-05 -2.556e-05 1.025 4.29e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0987 0.08712 0.1795 0.2047 0.9873 0.992 0.09876 0.7774 0.8726 0.3073 ] Network output: [ -0.006995 0.03544 1.002 5.847e-05 -2.625e-05 0.9769 4.406e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09057 0.08864 0.1657 0.196 0.9855 0.9913 0.09058 0.7045 0.8505 0.2435 ] Network output: [ 0.0002299 0.9998 -0.0004891 7.965e-06 -3.576e-06 1 6.003e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006423 Epoch 7383 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01205 0.994 0.9886 2.061e-06 -9.254e-07 -0.006645 1.553e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003259 -0.003053 -0.008779 0.006758 0.9698 0.9742 0.006219 0.8399 0.8288 0.01922 ] Network output: [ 0.9997 0.001031 0.001369 -2.968e-05 1.332e-05 -0.00202 -2.236e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1889 -0.03152 -0.1878 0.1956 0.9836 0.9933 0.2109 0.4517 0.8747 0.7205 ] Network output: [ -0.01127 1.001 1.01 9.74e-07 -4.373e-07 0.01114 7.34e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005608 0.0004616 0.004387 0.004036 0.9889 0.992 0.005712 0.8689 0.8986 0.01392 ] Network output: [ -0.0008444 0.003426 1.002 -9.66e-05 4.337e-05 0.9955 -7.28e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1999 0.09361 0.3297 0.1518 0.9851 0.994 0.2005 0.4562 0.8812 0.7151 ] Network output: [ 0.007077 -0.03479 0.996 5.688e-05 -2.554e-05 1.025 4.287e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09871 0.08713 0.1795 0.2047 0.9873 0.992 0.09877 0.7774 0.8726 0.3073 ] Network output: [ -0.006992 0.03542 1.002 5.842e-05 -2.623e-05 0.9769 4.403e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09056 0.08864 0.1657 0.196 0.9855 0.9913 0.09058 0.7045 0.8505 0.2435 ] Network output: [ 0.0002295 0.9998 -0.0004883 7.959e-06 -3.573e-06 1 5.998e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006419 Epoch 7384 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01204 0.994 0.9886 2.057e-06 -9.236e-07 -0.006647 1.55e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003259 -0.003053 -0.008777 0.006757 0.9698 0.9742 0.006219 0.8398 0.8288 0.01922 ] Network output: [ 0.9997 0.001026 0.001369 -2.965e-05 1.331e-05 -0.002015 -2.235e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1889 -0.03152 -0.1878 0.1956 0.9836 0.9933 0.2109 0.4516 0.8747 0.7205 ] Network output: [ -0.01127 1.001 1.01 9.717e-07 -4.362e-07 0.01113 7.323e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005609 0.0004615 0.004388 0.004036 0.9889 0.992 0.005712 0.8689 0.8986 0.01392 ] Network output: [ -0.0008437 0.00342 1.002 -9.651e-05 4.333e-05 0.9955 -7.274e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1999 0.09362 0.3297 0.1518 0.9851 0.994 0.2005 0.4562 0.8812 0.7151 ] Network output: [ 0.007075 -0.03478 0.996 5.684e-05 -2.552e-05 1.025 4.284e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09871 0.08713 0.1795 0.2047 0.9873 0.992 0.09877 0.7774 0.8726 0.3073 ] Network output: [ -0.006989 0.03541 1.002 5.838e-05 -2.621e-05 0.9769 4.4e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09056 0.08864 0.1657 0.196 0.9855 0.9913 0.09058 0.7044 0.8505 0.2435 ] Network output: [ 0.0002296 0.9998 -0.0004881 7.953e-06 -3.57e-06 1 5.993e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006415 Epoch 7385 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01204 0.994 0.9886 2.053e-06 -9.218e-07 -0.006649 1.547e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003259 -0.003053 -0.008776 0.006756 0.9698 0.9742 0.006219 0.8398 0.8287 0.01922 ] Network output: [ 0.9997 0.001029 0.001368 -2.963e-05 1.33e-05 -0.002017 -2.233e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1889 -0.03152 -0.1877 0.1956 0.9836 0.9933 0.211 0.4516 0.8746 0.7205 ] Network output: [ -0.01126 1.001 1.01 9.694e-07 -4.352e-07 0.01113 7.306e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005609 0.0004615 0.004388 0.004035 0.9889 0.992 0.005713 0.8689 0.8986 0.01392 ] Network output: [ -0.0008436 0.003424 1.002 -9.643e-05 4.329e-05 0.9955 -7.268e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1999 0.09362 0.3298 0.1518 0.9851 0.994 0.2005 0.4562 0.8812 0.7151 ] Network output: [ 0.007073 -0.03477 0.996 5.679e-05 -2.55e-05 1.025 4.28e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09872 0.08714 0.1795 0.2047 0.9873 0.992 0.09878 0.7773 0.8726 0.3073 ] Network output: [ -0.006986 0.03539 1.002 5.834e-05 -2.619e-05 0.9769 4.396e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09056 0.08864 0.1657 0.196 0.9855 0.9913 0.09057 0.7044 0.8505 0.2435 ] Network output: [ 0.0002293 0.9998 -0.0004873 7.946e-06 -3.567e-06 1 5.989e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000641 Epoch 7386 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01204 0.994 0.9886 2.049e-06 -9.2e-07 -0.00665 1.544e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00326 -0.003053 -0.008775 0.006755 0.9698 0.9742 0.006219 0.8398 0.8287 0.01922 ] Network output: [ 0.9998 0.001024 0.001367 -2.961e-05 1.329e-05 -0.002012 -2.231e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1889 -0.03153 -0.1877 0.1956 0.9836 0.9933 0.211 0.4516 0.8746 0.7205 ] Network output: [ -0.01126 1.001 1.01 9.671e-07 -4.342e-07 0.01113 7.288e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00561 0.0004615 0.004388 0.004034 0.9889 0.992 0.005714 0.8689 0.8986 0.01391 ] Network output: [ -0.0008428 0.003418 1.002 -9.635e-05 4.326e-05 0.9955 -7.261e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1999 0.09362 0.3298 0.1518 0.9851 0.994 0.2006 0.4562 0.8812 0.7151 ] Network output: [ 0.00707 -0.03476 0.996 5.675e-05 -2.548e-05 1.025 4.277e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09872 0.08714 0.1795 0.2047 0.9873 0.992 0.09878 0.7773 0.8726 0.3073 ] Network output: [ -0.006983 0.03538 1.002 5.829e-05 -2.617e-05 0.9769 4.393e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09056 0.08864 0.1657 0.196 0.9855 0.9913 0.09057 0.7044 0.8505 0.2435 ] Network output: [ 0.0002294 0.9998 -0.0004871 7.94e-06 -3.565e-06 1 5.984e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006406 Epoch 7387 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01204 0.994 0.9886 2.045e-06 -9.182e-07 -0.006653 1.541e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00326 -0.003054 -0.008773 0.006754 0.9698 0.9742 0.00622 0.8398 0.8287 0.01921 ] Network output: [ 0.9998 0.001027 0.001366 -2.959e-05 1.328e-05 -0.002014 -2.23e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1889 -0.03153 -0.1877 0.1956 0.9836 0.9933 0.211 0.4516 0.8746 0.7205 ] Network output: [ -0.01126 1.001 1.01 9.648e-07 -4.331e-07 0.01112 7.271e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005611 0.0004615 0.004388 0.004034 0.9889 0.992 0.005714 0.8689 0.8986 0.01391 ] Network output: [ -0.0008427 0.003422 1.002 -9.627e-05 4.322e-05 0.9955 -7.255e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1999 0.09362 0.3298 0.1518 0.9851 0.994 0.2006 0.4561 0.8812 0.7151 ] Network output: [ 0.007068 -0.03474 0.996 5.67e-05 -2.546e-05 1.025 4.273e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09873 0.08714 0.1795 0.2047 0.9873 0.992 0.09879 0.7773 0.8726 0.3073 ] Network output: [ -0.00698 0.03536 1.002 5.825e-05 -2.615e-05 0.9769 4.39e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09056 0.08864 0.1657 0.1959 0.9855 0.9913 0.09057 0.7043 0.8505 0.2435 ] Network output: [ 0.000229 0.9998 -0.0004863 7.934e-06 -3.562e-06 1 5.979e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006402 Epoch 7388 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01203 0.994 0.9886 2.041e-06 -9.164e-07 -0.006654 1.538e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00326 -0.003054 -0.008772 0.006753 0.9698 0.9742 0.00622 0.8398 0.8287 0.01921 ] Network output: [ 0.9998 0.001022 0.001365 -2.957e-05 1.327e-05 -0.00201 -2.228e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1889 -0.03154 -0.1877 0.1955 0.9836 0.9933 0.211 0.4516 0.8746 0.7205 ] Network output: [ -0.01126 1.001 1.01 9.625e-07 -4.321e-07 0.01112 7.254e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005611 0.0004615 0.004388 0.004033 0.9889 0.992 0.005715 0.8689 0.8986 0.01391 ] Network output: [ -0.000842 0.003416 1.002 -9.619e-05 4.318e-05 0.9955 -7.249e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.1999 0.09363 0.3298 0.1518 0.9851 0.994 0.2006 0.4561 0.8812 0.7151 ] Network output: [ 0.007065 -0.03473 0.996 5.666e-05 -2.543e-05 1.025 4.27e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09873 0.08715 0.1795 0.2046 0.9873 0.992 0.09879 0.7773 0.8726 0.3073 ] Network output: [ -0.006978 0.03535 1.002 5.82e-05 -2.613e-05 0.9769 4.386e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09056 0.08863 0.1657 0.1959 0.9855 0.9913 0.09057 0.7043 0.8505 0.2435 ] Network output: [ 0.0002291 0.9998 -0.0004861 7.928e-06 -3.559e-06 1 5.975e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006398 Epoch 7389 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01203 0.994 0.9886 2.037e-06 -9.146e-07 -0.006656 1.535e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00326 -0.003054 -0.00877 0.006752 0.9698 0.9742 0.00622 0.8398 0.8287 0.01921 ] Network output: [ 0.9998 0.001025 0.001364 -2.954e-05 1.326e-05 -0.002011 -2.226e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1889 -0.03154 -0.1877 0.1955 0.9836 0.9933 0.211 0.4516 0.8746 0.7205 ] Network output: [ -0.01126 1.001 1.01 9.602e-07 -4.311e-07 0.01111 7.236e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005612 0.0004614 0.004388 0.004032 0.9889 0.992 0.005716 0.8688 0.8986 0.01391 ] Network output: [ -0.0008418 0.003419 1.002 -9.611e-05 4.315e-05 0.9955 -7.243e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2 0.09363 0.3298 0.1518 0.9851 0.994 0.2006 0.4561 0.8811 0.7151 ] Network output: [ 0.007063 -0.03472 0.996 5.661e-05 -2.541e-05 1.025 4.266e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09874 0.08715 0.1795 0.2046 0.9873 0.992 0.0988 0.7772 0.8726 0.3073 ] Network output: [ -0.006975 0.03533 1.002 5.816e-05 -2.611e-05 0.977 4.383e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09056 0.08863 0.1657 0.1959 0.9855 0.9913 0.09057 0.7043 0.8505 0.2435 ] Network output: [ 0.0002288 0.9998 -0.0004853 7.922e-06 -3.556e-06 1 5.97e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006394 Epoch 7390 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01203 0.994 0.9886 2.033e-06 -9.129e-07 -0.006658 1.532e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00326 -0.003054 -0.008769 0.006751 0.9698 0.9742 0.006221 0.8398 0.8287 0.01921 ] Network output: [ 0.9998 0.00102 0.001363 -2.952e-05 1.325e-05 -0.002007 -2.225e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1889 -0.03154 -0.1877 0.1955 0.9836 0.9933 0.211 0.4515 0.8746 0.7205 ] Network output: [ -0.01126 1.001 1.01 9.579e-07 -4.3e-07 0.01111 7.219e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005612 0.0004614 0.004388 0.004032 0.9889 0.992 0.005716 0.8688 0.8986 0.01391 ] Network output: [ -0.0008411 0.003414 1.002 -9.603e-05 4.311e-05 0.9955 -7.237e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2 0.09363 0.3298 0.1518 0.9851 0.994 0.2006 0.4561 0.8811 0.7151 ] Network output: [ 0.00706 -0.03471 0.996 5.656e-05 -2.539e-05 1.025 4.263e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09874 0.08716 0.1795 0.2046 0.9873 0.992 0.0988 0.7772 0.8726 0.3073 ] Network output: [ -0.006972 0.03532 1.002 5.811e-05 -2.609e-05 0.977 4.38e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09055 0.08863 0.1657 0.1959 0.9855 0.9913 0.09057 0.7042 0.8505 0.2436 ] Network output: [ 0.0002288 0.9998 -0.0004851 7.915e-06 -3.554e-06 1 5.965e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006389 Epoch 7391 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01203 0.994 0.9886 2.029e-06 -9.111e-07 -0.00666 1.529e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00326 -0.003054 -0.008768 0.00675 0.9698 0.9742 0.006221 0.8398 0.8287 0.01921 ] Network output: [ 0.9998 0.001023 0.001362 -2.95e-05 1.324e-05 -0.002008 -2.223e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1889 -0.03155 -0.1876 0.1955 0.9836 0.9933 0.211 0.4515 0.8746 0.7205 ] Network output: [ -0.01126 1.001 1.01 9.556e-07 -4.29e-07 0.01111 7.202e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005613 0.0004614 0.004389 0.004031 0.9889 0.992 0.005717 0.8688 0.8986 0.01391 ] Network output: [ -0.000841 0.003417 1.002 -9.595e-05 4.308e-05 0.9955 -7.231e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2 0.09363 0.3298 0.1517 0.9851 0.994 0.2006 0.4561 0.8811 0.7151 ] Network output: [ 0.007058 -0.03469 0.996 5.652e-05 -2.537e-05 1.025 4.259e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09875 0.08716 0.1795 0.2046 0.9873 0.992 0.09881 0.7772 0.8725 0.3072 ] Network output: [ -0.006969 0.0353 1.002 5.807e-05 -2.607e-05 0.977 4.376e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09055 0.08863 0.1657 0.1959 0.9855 0.9913 0.09056 0.7042 0.8504 0.2436 ] Network output: [ 0.0002285 0.9998 -0.0004844 7.909e-06 -3.551e-06 1 5.961e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006385 Epoch 7392 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01203 0.994 0.9886 2.025e-06 -9.093e-07 -0.006662 1.526e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00326 -0.003054 -0.008766 0.006749 0.9698 0.9742 0.006221 0.8398 0.8287 0.01921 ] Network output: [ 0.9998 0.001018 0.001362 -2.948e-05 1.323e-05 -0.002004 -2.221e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.189 -0.03155 -0.1876 0.1955 0.9836 0.9933 0.2111 0.4515 0.8746 0.7205 ] Network output: [ -0.01126 1.001 1.01 9.533e-07 -4.28e-07 0.0111 7.185e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005614 0.0004614 0.004389 0.00403 0.9889 0.992 0.005717 0.8688 0.8985 0.01391 ] Network output: [ -0.0008402 0.003412 1.002 -9.587e-05 4.304e-05 0.9955 -7.225e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2 0.09364 0.3299 0.1517 0.9851 0.994 0.2006 0.4561 0.8811 0.7151 ] Network output: [ 0.007055 -0.03468 0.996 5.647e-05 -2.535e-05 1.025 4.256e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09875 0.08716 0.1795 0.2046 0.9873 0.992 0.09882 0.7772 0.8725 0.3072 ] Network output: [ -0.006966 0.03528 1.002 5.802e-05 -2.605e-05 0.977 4.373e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09055 0.08863 0.1657 0.1959 0.9855 0.9913 0.09056 0.7042 0.8504 0.2436 ] Network output: [ 0.0002286 0.9998 -0.0004841 7.903e-06 -3.548e-06 1 5.956e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006381 Epoch 7393 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01202 0.994 0.9886 2.021e-06 -9.075e-07 -0.006664 1.523e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003261 -0.003055 -0.008765 0.006748 0.9698 0.9742 0.006222 0.8398 0.8287 0.0192 ] Network output: [ 0.9998 0.001021 0.001361 -2.945e-05 1.322e-05 -0.002005 -2.22e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.189 -0.03156 -0.1876 0.1955 0.9836 0.9933 0.2111 0.4515 0.8746 0.7205 ] Network output: [ -0.01125 1.001 1.01 9.51e-07 -4.269e-07 0.0111 7.167e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005614 0.0004614 0.004389 0.00403 0.9889 0.992 0.005718 0.8688 0.8985 0.0139 ] Network output: [ -0.0008401 0.003415 1.002 -9.579e-05 4.3e-05 0.9955 -7.219e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2 0.09364 0.3299 0.1517 0.9851 0.994 0.2006 0.456 0.8811 0.715 ] Network output: [ 0.007053 -0.03467 0.996 5.643e-05 -2.533e-05 1.025 4.253e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09876 0.08717 0.1795 0.2046 0.9873 0.992 0.09882 0.7771 0.8725 0.3072 ] Network output: [ -0.006963 0.03527 1.002 5.798e-05 -2.603e-05 0.977 4.37e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09055 0.08863 0.1657 0.1959 0.9855 0.9913 0.09056 0.7041 0.8504 0.2436 ] Network output: [ 0.0002283 0.9998 -0.0004834 7.897e-06 -3.545e-06 1 5.951e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006377 Epoch 7394 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01202 0.994 0.9886 2.017e-06 -9.057e-07 -0.006665 1.52e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003261 -0.003055 -0.008763 0.006747 0.9698 0.9742 0.006222 0.8398 0.8287 0.0192 ] Network output: [ 0.9998 0.001017 0.00136 -2.943e-05 1.321e-05 -0.002001 -2.218e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.189 -0.03156 -0.1876 0.1955 0.9836 0.9933 0.2111 0.4515 0.8746 0.7205 ] Network output: [ -0.01125 1.001 1.01 9.487e-07 -4.259e-07 0.0111 7.15e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005615 0.0004614 0.004389 0.004029 0.9889 0.992 0.005719 0.8688 0.8985 0.0139 ] Network output: [ -0.0008394 0.003409 1.002 -9.571e-05 4.297e-05 0.9955 -7.213e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2 0.09364 0.3299 0.1517 0.9851 0.994 0.2007 0.456 0.8811 0.715 ] Network output: [ 0.00705 -0.03466 0.996 5.638e-05 -2.531e-05 1.025 4.249e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09876 0.08717 0.1795 0.2046 0.9873 0.992 0.09883 0.7771 0.8725 0.3072 ] Network output: [ -0.006961 0.03525 1.002 5.793e-05 -2.601e-05 0.977 4.366e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09055 0.08862 0.1657 0.1959 0.9855 0.9913 0.09056 0.7041 0.8504 0.2436 ] Network output: [ 0.0002283 0.9998 -0.0004832 7.891e-06 -3.542e-06 1 5.947e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006373 Epoch 7395 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01202 0.994 0.9886 2.013e-06 -9.039e-07 -0.006667 1.517e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003261 -0.003055 -0.008762 0.006746 0.9698 0.9742 0.006222 0.8397 0.8287 0.0192 ] Network output: [ 0.9998 0.001019 0.001359 -2.941e-05 1.32e-05 -0.002002 -2.216e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.189 -0.03157 -0.1876 0.1955 0.9836 0.9933 0.2111 0.4515 0.8746 0.7205 ] Network output: [ -0.01125 1.001 1.01 9.465e-07 -4.249e-07 0.01109 7.133e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005615 0.0004613 0.004389 0.004028 0.9889 0.992 0.005719 0.8688 0.8985 0.0139 ] Network output: [ -0.0008392 0.003412 1.002 -9.563e-05 4.293e-05 0.9955 -7.207e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2 0.09365 0.3299 0.1517 0.9851 0.994 0.2007 0.456 0.8811 0.715 ] Network output: [ 0.007048 -0.03464 0.996 5.634e-05 -2.529e-05 1.025 4.246e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09877 0.08718 0.1795 0.2046 0.9873 0.992 0.09883 0.7771 0.8725 0.3072 ] Network output: [ -0.006958 0.03524 1.002 5.789e-05 -2.599e-05 0.977 4.363e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09055 0.08862 0.1657 0.1959 0.9855 0.9913 0.09056 0.7041 0.8504 0.2436 ] Network output: [ 0.000228 0.9998 -0.0004824 7.885e-06 -3.54e-06 1 5.942e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006368 Epoch 7396 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01202 0.994 0.9886 2.01e-06 -9.022e-07 -0.006669 1.514e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003261 -0.003055 -0.008761 0.006745 0.9698 0.9742 0.006223 0.8397 0.8287 0.0192 ] Network output: [ 0.9998 0.001015 0.001358 -2.939e-05 1.319e-05 -0.001998 -2.215e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.189 -0.03157 -0.1875 0.1955 0.9836 0.9933 0.2111 0.4514 0.8746 0.7205 ] Network output: [ -0.01125 1.001 1.01 9.442e-07 -4.239e-07 0.01109 7.116e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005616 0.0004613 0.004389 0.004028 0.9889 0.992 0.00572 0.8688 0.8985 0.0139 ] Network output: [ -0.0008385 0.003407 1.002 -9.555e-05 4.29e-05 0.9955 -7.201e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2 0.09365 0.3299 0.1517 0.9851 0.994 0.2007 0.456 0.8811 0.715 ] Network output: [ 0.007046 -0.03463 0.996 5.629e-05 -2.527e-05 1.025 4.242e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09877 0.08718 0.1795 0.2046 0.9873 0.992 0.09884 0.777 0.8725 0.3072 ] Network output: [ -0.006955 0.03522 1.002 5.785e-05 -2.597e-05 0.977 4.359e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09055 0.08862 0.1657 0.1959 0.9855 0.9913 0.09056 0.704 0.8504 0.2436 ] Network output: [ 0.0002281 0.9998 -0.0004822 7.878e-06 -3.537e-06 1 5.937e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006364 Epoch 7397 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01201 0.994 0.9886 2.006e-06 -9.004e-07 -0.006671 1.511e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003261 -0.003055 -0.008759 0.006745 0.9698 0.9742 0.006223 0.8397 0.8287 0.0192 ] Network output: [ 0.9998 0.001017 0.001357 -2.937e-05 1.318e-05 -0.001999 -2.213e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.189 -0.03157 -0.1875 0.1955 0.9836 0.9933 0.2111 0.4514 0.8746 0.7205 ] Network output: [ -0.01125 1.001 1.01 9.419e-07 -4.228e-07 0.01109 7.098e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005617 0.0004613 0.004389 0.004027 0.9889 0.992 0.005721 0.8688 0.8985 0.0139 ] Network output: [ -0.0008384 0.00341 1.002 -9.547e-05 4.286e-05 0.9955 -7.195e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2001 0.09365 0.3299 0.1517 0.9851 0.994 0.2007 0.456 0.8811 0.715 ] Network output: [ 0.007043 -0.03462 0.996 5.625e-05 -2.525e-05 1.025 4.239e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09878 0.08718 0.1795 0.2046 0.9873 0.992 0.09884 0.777 0.8725 0.3072 ] Network output: [ -0.006952 0.03521 1.002 5.78e-05 -2.595e-05 0.977 4.356e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09054 0.08862 0.1657 0.1959 0.9855 0.9913 0.09056 0.704 0.8504 0.2436 ] Network output: [ 0.0002278 0.9998 -0.0004815 7.872e-06 -3.534e-06 1 5.933e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000636 Epoch 7398 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01201 0.994 0.9886 2.002e-06 -8.986e-07 -0.006673 1.509e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003261 -0.003055 -0.008758 0.006744 0.9698 0.9742 0.006223 0.8397 0.8287 0.01919 ] Network output: [ 0.9998 0.001013 0.001356 -2.934e-05 1.317e-05 -0.001995 -2.211e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.189 -0.03158 -0.1875 0.1955 0.9836 0.9933 0.2111 0.4514 0.8746 0.7205 ] Network output: [ -0.01125 1.001 1.01 9.396e-07 -4.218e-07 0.01108 7.081e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005617 0.0004613 0.00439 0.004026 0.9889 0.992 0.005721 0.8688 0.8985 0.0139 ] Network output: [ -0.0008377 0.003405 1.002 -9.539e-05 4.282e-05 0.9955 -7.189e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2001 0.09365 0.33 0.1517 0.9851 0.994 0.2007 0.456 0.8811 0.715 ] Network output: [ 0.007041 -0.03461 0.996 5.62e-05 -2.523e-05 1.025 4.235e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09878 0.08719 0.1795 0.2046 0.9873 0.992 0.09885 0.777 0.8725 0.3072 ] Network output: [ -0.006949 0.03519 1.002 5.776e-05 -2.593e-05 0.977 4.353e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09054 0.08862 0.1657 0.1959 0.9855 0.9913 0.09055 0.704 0.8504 0.2436 ] Network output: [ 0.0002278 0.9998 -0.0004812 7.866e-06 -3.531e-06 1 5.928e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006356 Epoch 7399 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01201 0.994 0.9887 1.998e-06 -8.968e-07 -0.006675 1.506e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003261 -0.003056 -0.008756 0.006743 0.9698 0.9742 0.006224 0.8397 0.8287 0.01919 ] Network output: [ 0.9998 0.001015 0.001355 -2.932e-05 1.316e-05 -0.001996 -2.21e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.189 -0.03158 -0.1875 0.1954 0.9836 0.9933 0.2111 0.4514 0.8746 0.7204 ] Network output: [ -0.01125 1.001 1.01 9.373e-07 -4.208e-07 0.01108 7.064e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005618 0.0004613 0.00439 0.004026 0.9889 0.992 0.005722 0.8687 0.8985 0.0139 ] Network output: [ -0.0008375 0.003408 1.002 -9.531e-05 4.279e-05 0.9955 -7.183e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2001 0.09366 0.33 0.1517 0.9851 0.994 0.2007 0.4559 0.8811 0.715 ] Network output: [ 0.007038 -0.03459 0.996 5.615e-05 -2.521e-05 1.025 4.232e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09879 0.08719 0.1795 0.2046 0.9873 0.992 0.09885 0.777 0.8725 0.3072 ] Network output: [ -0.006946 0.03518 1.002 5.771e-05 -2.591e-05 0.977 4.349e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09054 0.08862 0.1657 0.1959 0.9855 0.9913 0.09055 0.704 0.8503 0.2436 ] Network output: [ 0.0002275 0.9998 -0.0004805 7.86e-06 -3.529e-06 1 5.923e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006352 Epoch 7400 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01201 0.994 0.9887 1.994e-06 -8.951e-07 -0.006676 1.503e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003261 -0.003056 -0.008755 0.006742 0.9698 0.9742 0.006224 0.8397 0.8287 0.01919 ] Network output: [ 0.9998 0.001011 0.001355 -2.93e-05 1.315e-05 -0.001992 -2.208e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.189 -0.03159 -0.1875 0.1954 0.9836 0.9933 0.2112 0.4514 0.8746 0.7204 ] Network output: [ -0.01125 1.001 1.01 9.351e-07 -4.198e-07 0.01107 7.047e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005619 0.0004613 0.00439 0.004025 0.9889 0.992 0.005722 0.8687 0.8985 0.01389 ] Network output: [ -0.0008368 0.003403 1.002 -9.523e-05 4.275e-05 0.9955 -7.177e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2001 0.09366 0.33 0.1516 0.9851 0.994 0.2007 0.4559 0.8811 0.715 ] Network output: [ 0.007036 -0.03458 0.996 5.611e-05 -2.519e-05 1.025 4.229e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09879 0.0872 0.1795 0.2046 0.9873 0.992 0.09886 0.7769 0.8725 0.3072 ] Network output: [ -0.006943 0.03516 1.002 5.767e-05 -2.589e-05 0.977 4.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09054 0.08862 0.1657 0.1959 0.9855 0.9913 0.09055 0.7039 0.8503 0.2436 ] Network output: [ 0.0002276 0.9998 -0.0004802 7.854e-06 -3.526e-06 1 5.919e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006348 Epoch 7401 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01201 0.994 0.9887 1.99e-06 -8.933e-07 -0.006678 1.5e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003262 -0.003056 -0.008754 0.006741 0.9698 0.9742 0.006224 0.8397 0.8286 0.01919 ] Network output: [ 0.9998 0.001013 0.001353 -2.928e-05 1.314e-05 -0.001993 -2.206e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1891 -0.03159 -0.1874 0.1954 0.9836 0.9933 0.2112 0.4514 0.8746 0.7204 ] Network output: [ -0.01124 1.001 1.01 9.328e-07 -4.188e-07 0.01107 7.03e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005619 0.0004613 0.00439 0.004024 0.9889 0.992 0.005723 0.8687 0.8985 0.01389 ] Network output: [ -0.0008366 0.003405 1.002 -9.515e-05 4.272e-05 0.9955 -7.171e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2001 0.09366 0.33 0.1516 0.9851 0.994 0.2007 0.4559 0.8811 0.715 ] Network output: [ 0.007033 -0.03457 0.996 5.606e-05 -2.517e-05 1.025 4.225e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0988 0.0872 0.1795 0.2046 0.9873 0.992 0.09886 0.7769 0.8724 0.3072 ] Network output: [ -0.006941 0.03514 1.002 5.762e-05 -2.587e-05 0.977 4.343e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09054 0.08861 0.1657 0.1959 0.9855 0.9913 0.09055 0.7039 0.8503 0.2436 ] Network output: [ 0.0002273 0.9998 -0.0004795 7.848e-06 -3.523e-06 1 5.914e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006343 Epoch 7402 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.012 0.994 0.9887 1.986e-06 -8.915e-07 -0.00668 1.497e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003262 -0.003056 -0.008752 0.00674 0.9698 0.9742 0.006225 0.8397 0.8286 0.01919 ] Network output: [ 0.9998 0.001009 0.001353 -2.925e-05 1.313e-05 -0.001989 -2.205e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1891 -0.03159 -0.1874 0.1954 0.9836 0.9933 0.2112 0.4513 0.8746 0.7204 ] Network output: [ -0.01124 1.001 1.01 9.305e-07 -4.177e-07 0.01107 7.013e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00562 0.0004612 0.00439 0.004024 0.9889 0.992 0.005724 0.8687 0.8985 0.01389 ] Network output: [ -0.000836 0.003401 1.002 -9.507e-05 4.268e-05 0.9955 -7.165e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2001 0.09367 0.33 0.1516 0.9851 0.994 0.2008 0.4559 0.8811 0.715 ] Network output: [ 0.007031 -0.03456 0.996 5.602e-05 -2.515e-05 1.025 4.222e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0988 0.08721 0.1795 0.2045 0.9873 0.992 0.09887 0.7769 0.8724 0.3072 ] Network output: [ -0.006938 0.03513 1.002 5.758e-05 -2.585e-05 0.9771 4.339e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09054 0.08861 0.1657 0.1959 0.9855 0.9913 0.09055 0.7039 0.8503 0.2436 ] Network output: [ 0.0002273 0.9998 -0.0004792 7.841e-06 -3.52e-06 1 5.91e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006339 Epoch 7403 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.012 0.994 0.9887 1.982e-06 -8.898e-07 -0.006682 1.494e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003262 -0.003056 -0.008751 0.006739 0.9698 0.9742 0.006225 0.8397 0.8286 0.01918 ] Network output: [ 0.9998 0.001011 0.001352 -2.923e-05 1.312e-05 -0.00199 -2.203e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1891 -0.0316 -0.1874 0.1954 0.9836 0.9933 0.2112 0.4513 0.8745 0.7204 ] Network output: [ -0.01124 1.001 1.01 9.282e-07 -4.167e-07 0.01106 6.995e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00562 0.0004612 0.00439 0.004023 0.9889 0.992 0.005724 0.8687 0.8985 0.01389 ] Network output: [ -0.0008358 0.003403 1.002 -9.499e-05 4.264e-05 0.9955 -7.159e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2001 0.09367 0.33 0.1516 0.9851 0.994 0.2008 0.4559 0.8811 0.715 ] Network output: [ 0.007028 -0.03455 0.996 5.597e-05 -2.513e-05 1.025 4.218e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09881 0.08721 0.1796 0.2045 0.9873 0.992 0.09887 0.7769 0.8724 0.3072 ] Network output: [ -0.006935 0.03511 1.002 5.754e-05 -2.583e-05 0.9771 4.336e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09054 0.08861 0.1657 0.1959 0.9855 0.9913 0.09055 0.7038 0.8503 0.2436 ] Network output: [ 0.000227 0.9998 -0.0004786 7.835e-06 -3.518e-06 1 5.905e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006335 Epoch 7404 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.012 0.994 0.9887 1.978e-06 -8.88e-07 -0.006684 1.491e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003262 -0.003056 -0.00875 0.006738 0.9698 0.9742 0.006225 0.8397 0.8286 0.01918 ] Network output: [ 0.9998 0.001007 0.001351 -2.921e-05 1.311e-05 -0.001987 -2.201e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1891 -0.0316 -0.1874 0.1954 0.9836 0.9933 0.2112 0.4513 0.8745 0.7204 ] Network output: [ -0.01124 1.001 1.01 9.26e-07 -4.157e-07 0.01106 6.978e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005621 0.0004612 0.00439 0.004022 0.9889 0.992 0.005725 0.8687 0.8985 0.01389 ] Network output: [ -0.0008351 0.003399 1.002 -9.491e-05 4.261e-05 0.9955 -7.153e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2001 0.09367 0.33 0.1516 0.9851 0.994 0.2008 0.4559 0.8811 0.715 ] Network output: [ 0.007026 -0.03453 0.996 5.593e-05 -2.511e-05 1.025 4.215e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09881 0.08721 0.1796 0.2045 0.9873 0.992 0.09888 0.7768 0.8724 0.3072 ] Network output: [ -0.006932 0.0351 1.002 5.749e-05 -2.581e-05 0.9771 4.333e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09053 0.08861 0.1657 0.1959 0.9855 0.9913 0.09055 0.7038 0.8503 0.2436 ] Network output: [ 0.0002271 0.9998 -0.0004783 7.829e-06 -3.515e-06 1 5.9e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006331 Epoch 7405 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.012 0.994 0.9887 1.974e-06 -8.863e-07 -0.006686 1.488e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003262 -0.003057 -0.008748 0.006737 0.9698 0.9742 0.006225 0.8397 0.8286 0.01918 ] Network output: [ 0.9998 0.001009 0.00135 -2.919e-05 1.31e-05 -0.001987 -2.2e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1891 -0.03161 -0.1874 0.1954 0.9836 0.9933 0.2112 0.4513 0.8745 0.7204 ] Network output: [ -0.01124 1.001 1.01 9.237e-07 -4.147e-07 0.01106 6.961e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005622 0.0004612 0.00439 0.004022 0.9889 0.992 0.005726 0.8687 0.8985 0.01389 ] Network output: [ -0.0008349 0.003401 1.002 -9.483e-05 4.257e-05 0.9955 -7.147e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2002 0.09367 0.3301 0.1516 0.9851 0.994 0.2008 0.4558 0.8811 0.715 ] Network output: [ 0.007024 -0.03452 0.996 5.588e-05 -2.509e-05 1.025 4.211e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09882 0.08722 0.1796 0.2045 0.9873 0.992 0.09888 0.7768 0.8724 0.3072 ] Network output: [ -0.006929 0.03508 1.002 5.745e-05 -2.579e-05 0.9771 4.329e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09053 0.08861 0.1657 0.1959 0.9855 0.9913 0.09054 0.7038 0.8503 0.2436 ] Network output: [ 0.0002268 0.9998 -0.0004776 7.823e-06 -3.512e-06 1 5.896e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006327 Epoch 7406 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.012 0.994 0.9887 1.97e-06 -8.845e-07 -0.006687 1.485e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003262 -0.003057 -0.008747 0.006736 0.9698 0.9742 0.006226 0.8397 0.8286 0.01918 ] Network output: [ 0.9998 0.001006 0.001349 -2.917e-05 1.309e-05 -0.001984 -2.198e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1891 -0.03161 -0.1874 0.1954 0.9836 0.9933 0.2112 0.4513 0.8745 0.7204 ] Network output: [ -0.01124 1.001 1.01 9.214e-07 -4.137e-07 0.01105 6.944e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005622 0.0004612 0.004391 0.004021 0.9889 0.992 0.005726 0.8687 0.8985 0.01389 ] Network output: [ -0.0008342 0.003397 1.002 -9.475e-05 4.254e-05 0.9955 -7.141e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2002 0.09368 0.3301 0.1516 0.9851 0.994 0.2008 0.4558 0.8811 0.715 ] Network output: [ 0.007021 -0.03451 0.996 5.584e-05 -2.507e-05 1.025 4.208e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09882 0.08722 0.1796 0.2045 0.9873 0.992 0.09889 0.7768 0.8724 0.3072 ] Network output: [ -0.006926 0.03507 1.002 5.74e-05 -2.577e-05 0.9771 4.326e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09053 0.08861 0.1657 0.1959 0.9855 0.9913 0.09054 0.7037 0.8503 0.2436 ] Network output: [ 0.0002268 0.9998 -0.0004773 7.817e-06 -3.509e-06 1 5.891e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006323 Epoch 7407 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01199 0.994 0.9887 1.966e-06 -8.827e-07 -0.006689 1.482e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003262 -0.003057 -0.008745 0.006735 0.9698 0.9742 0.006226 0.8396 0.8286 0.01918 ] Network output: [ 0.9998 0.001007 0.001348 -2.914e-05 1.308e-05 -0.001984 -2.196e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1891 -0.03161 -0.1873 0.1954 0.9836 0.9933 0.2112 0.4513 0.8745 0.7204 ] Network output: [ -0.01124 1.001 1.01 9.192e-07 -4.127e-07 0.01105 6.927e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005623 0.0004612 0.004391 0.00402 0.9889 0.992 0.005727 0.8687 0.8985 0.01388 ] Network output: [ -0.000834 0.003398 1.002 -9.467e-05 4.25e-05 0.9955 -7.135e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2002 0.09368 0.3301 0.1516 0.9851 0.994 0.2008 0.4558 0.8811 0.715 ] Network output: [ 0.007019 -0.0345 0.996 5.579e-05 -2.505e-05 1.025 4.205e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09883 0.08723 0.1796 0.2045 0.9873 0.992 0.09889 0.7768 0.8724 0.3072 ] Network output: [ -0.006924 0.03505 1.002 5.736e-05 -2.575e-05 0.9771 4.323e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09053 0.08861 0.1657 0.1959 0.9855 0.9913 0.09054 0.7037 0.8503 0.2436 ] Network output: [ 0.0002266 0.9998 -0.0004766 7.811e-06 -3.506e-06 1 5.886e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006318 Epoch 7408 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01199 0.994 0.9887 1.962e-06 -8.81e-07 -0.006691 1.479e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003263 -0.003057 -0.008744 0.006734 0.9698 0.9742 0.006226 0.8396 0.8286 0.01918 ] Network output: [ 0.9998 0.001004 0.001347 -2.912e-05 1.307e-05 -0.001981 -2.195e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1891 -0.03162 -0.1873 0.1954 0.9836 0.9933 0.2113 0.4513 0.8745 0.7204 ] Network output: [ -0.01124 1.001 1.01 9.169e-07 -4.116e-07 0.01105 6.91e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005623 0.0004612 0.004391 0.00402 0.9889 0.992 0.005727 0.8687 0.8985 0.01388 ] Network output: [ -0.0008334 0.003395 1.002 -9.459e-05 4.247e-05 0.9956 -7.129e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2002 0.09368 0.3301 0.1516 0.9851 0.994 0.2008 0.4558 0.881 0.715 ] Network output: [ 0.007016 -0.03448 0.996 5.575e-05 -2.503e-05 1.025 4.201e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09883 0.08723 0.1796 0.2045 0.9873 0.992 0.0989 0.7767 0.8724 0.3072 ] Network output: [ -0.006921 0.03504 1.002 5.731e-05 -2.573e-05 0.9771 4.319e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09053 0.0886 0.1657 0.1959 0.9855 0.9913 0.09054 0.7037 0.8502 0.2436 ] Network output: [ 0.0002266 0.9998 -0.0004763 7.804e-06 -3.504e-06 1 5.882e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006314 Epoch 7409 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01199 0.994 0.9887 1.958e-06 -8.792e-07 -0.006693 1.476e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003263 -0.003057 -0.008743 0.006733 0.9698 0.9742 0.006227 0.8396 0.8286 0.01917 ] Network output: [ 0.9998 0.001005 0.001346 -2.91e-05 1.306e-05 -0.001981 -2.193e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1891 -0.03162 -0.1873 0.1954 0.9836 0.9933 0.2113 0.4512 0.8745 0.7204 ] Network output: [ -0.01124 1.001 1.01 9.147e-07 -4.106e-07 0.01104 6.893e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005624 0.0004611 0.004391 0.004019 0.9889 0.992 0.005728 0.8687 0.8985 0.01388 ] Network output: [ -0.0008331 0.003396 1.002 -9.451e-05 4.243e-05 0.9956 -7.123e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2002 0.09369 0.3301 0.1516 0.9851 0.994 0.2008 0.4558 0.881 0.715 ] Network output: [ 0.007014 -0.03447 0.996 5.57e-05 -2.501e-05 1.025 4.198e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09884 0.08723 0.1796 0.2045 0.9873 0.992 0.0989 0.7767 0.8724 0.3072 ] Network output: [ -0.006918 0.03502 1.002 5.727e-05 -2.571e-05 0.9771 4.316e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09053 0.0886 0.1657 0.1959 0.9855 0.9913 0.09054 0.7036 0.8502 0.2436 ] Network output: [ 0.0002263 0.9998 -0.0004757 7.798e-06 -3.501e-06 1 5.877e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000631 Epoch 7410 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01199 0.994 0.9887 1.955e-06 -8.775e-07 -0.006695 1.473e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003263 -0.003057 -0.008741 0.006733 0.9698 0.9742 0.006227 0.8396 0.8286 0.01917 ] Network output: [ 0.9998 0.001002 0.001346 -2.908e-05 1.305e-05 -0.001978 -2.191e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1892 -0.03163 -0.1873 0.1953 0.9836 0.9933 0.2113 0.4512 0.8745 0.7204 ] Network output: [ -0.01123 1.001 1.01 9.124e-07 -4.096e-07 0.01104 6.876e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005625 0.0004611 0.004391 0.004018 0.9889 0.992 0.005729 0.8686 0.8985 0.01388 ] Network output: [ -0.0008325 0.003392 1.002 -9.443e-05 4.239e-05 0.9956 -7.117e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2002 0.09369 0.3301 0.1515 0.9851 0.994 0.2009 0.4558 0.881 0.715 ] Network output: [ 0.007011 -0.03446 0.996 5.566e-05 -2.499e-05 1.025 4.194e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09884 0.08724 0.1796 0.2045 0.9873 0.992 0.09891 0.7767 0.8724 0.3072 ] Network output: [ -0.006915 0.03501 1.002 5.723e-05 -2.569e-05 0.9771 4.313e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09053 0.0886 0.1657 0.1959 0.9855 0.9913 0.09054 0.7036 0.8502 0.2436 ] Network output: [ 0.0002263 0.9998 -0.0004754 7.792e-06 -3.498e-06 1 5.872e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006306 Epoch 7411 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01198 0.994 0.9887 1.951e-06 -8.757e-07 -0.006697 1.47e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003263 -0.003058 -0.00874 0.006732 0.9698 0.9742 0.006227 0.8396 0.8286 0.01917 ] Network output: [ 0.9998 0.001003 0.001345 -2.906e-05 1.304e-05 -0.001978 -2.19e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1892 -0.03163 -0.1873 0.1953 0.9836 0.9933 0.2113 0.4512 0.8745 0.7204 ] Network output: [ -0.01123 1.001 1.01 9.101e-07 -4.086e-07 0.01103 6.859e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005625 0.0004611 0.004391 0.004018 0.9889 0.992 0.005729 0.8686 0.8985 0.01388 ] Network output: [ -0.0008323 0.003394 1.002 -9.435e-05 4.236e-05 0.9956 -7.111e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2002 0.09369 0.3302 0.1515 0.9851 0.994 0.2009 0.4557 0.881 0.715 ] Network output: [ 0.007009 -0.03445 0.996 5.561e-05 -2.497e-05 1.025 4.191e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09885 0.08724 0.1796 0.2045 0.9873 0.992 0.09891 0.7766 0.8724 0.3072 ] Network output: [ -0.006912 0.03499 1.002 5.718e-05 -2.567e-05 0.9771 4.309e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09052 0.0886 0.1657 0.1959 0.9855 0.9913 0.09054 0.7036 0.8502 0.2436 ] Network output: [ 0.0002261 0.9998 -0.0004747 7.786e-06 -3.495e-06 1 5.868e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006302 Epoch 7412 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01198 0.994 0.9887 1.947e-06 -8.74e-07 -0.006698 1.467e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003263 -0.003058 -0.008738 0.006731 0.9698 0.9742 0.006228 0.8396 0.8286 0.01917 ] Network output: [ 0.9998 0.001 0.001344 -2.903e-05 1.303e-05 -0.001975 -2.188e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1892 -0.03163 -0.1872 0.1953 0.9836 0.9933 0.2113 0.4512 0.8745 0.7204 ] Network output: [ -0.01123 1.001 1.01 9.079e-07 -4.076e-07 0.01103 6.842e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005626 0.0004611 0.004391 0.004017 0.9889 0.992 0.00573 0.8686 0.8984 0.01388 ] Network output: [ -0.0008317 0.00339 1.002 -9.427e-05 4.232e-05 0.9956 -7.105e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2002 0.09369 0.3302 0.1515 0.9851 0.994 0.2009 0.4557 0.881 0.715 ] Network output: [ 0.007007 -0.03443 0.996 5.556e-05 -2.495e-05 1.025 4.188e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09885 0.08725 0.1796 0.2045 0.9873 0.992 0.09892 0.7766 0.8723 0.3072 ] Network output: [ -0.00691 0.03497 1.002 5.714e-05 -2.565e-05 0.9771 4.306e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09052 0.0886 0.1657 0.1959 0.9855 0.9913 0.09054 0.7036 0.8502 0.2436 ] Network output: [ 0.000226 0.9998 -0.0004744 7.78e-06 -3.493e-06 1 5.863e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006298 Epoch 7413 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01198 0.9941 0.9887 1.943e-06 -8.723e-07 -0.0067 1.464e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003263 -0.003058 -0.008737 0.00673 0.9698 0.9742 0.006228 0.8396 0.8286 0.01917 ] Network output: [ 0.9998 0.001001 0.001343 -2.901e-05 1.302e-05 -0.001975 -2.186e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1892 -0.03164 -0.1872 0.1953 0.9836 0.9933 0.2113 0.4512 0.8745 0.7204 ] Network output: [ -0.01123 1.001 1.01 9.056e-07 -4.066e-07 0.01103 6.825e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005626 0.0004611 0.004392 0.004016 0.9889 0.992 0.00573 0.8686 0.8984 0.01388 ] Network output: [ -0.0008314 0.003392 1.002 -9.419e-05 4.229e-05 0.9956 -7.099e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2003 0.0937 0.3302 0.1515 0.9851 0.994 0.2009 0.4557 0.881 0.7149 ] Network output: [ 0.007004 -0.03442 0.996 5.552e-05 -2.492e-05 1.025 4.184e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09886 0.08725 0.1796 0.2045 0.9873 0.992 0.09892 0.7766 0.8723 0.3072 ] Network output: [ -0.006907 0.03496 1.002 5.709e-05 -2.563e-05 0.9771 4.303e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09052 0.0886 0.1657 0.1959 0.9855 0.9913 0.09053 0.7035 0.8502 0.2436 ] Network output: [ 0.0002258 0.9998 -0.0004738 7.774e-06 -3.49e-06 1 5.859e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006294 Epoch 7414 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01198 0.9941 0.9887 1.939e-06 -8.705e-07 -0.006702 1.461e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003263 -0.003058 -0.008736 0.006729 0.9698 0.9742 0.006228 0.8396 0.8286 0.01916 ] Network output: [ 0.9998 0.0009982 0.001342 -2.899e-05 1.301e-05 -0.001972 -2.185e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1892 -0.03164 -0.1872 0.1953 0.9836 0.9933 0.2113 0.4512 0.8745 0.7204 ] Network output: [ -0.01123 1.001 1.01 9.034e-07 -4.056e-07 0.01102 6.808e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005627 0.0004611 0.004392 0.004016 0.9889 0.992 0.005731 0.8686 0.8984 0.01387 ] Network output: [ -0.0008308 0.003388 1.002 -9.411e-05 4.225e-05 0.9956 -7.093e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2003 0.0937 0.3302 0.1515 0.9851 0.994 0.2009 0.4557 0.881 0.7149 ] Network output: [ 0.007002 -0.03441 0.9959 5.547e-05 -2.49e-05 1.025 4.181e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09886 0.08726 0.1796 0.2045 0.9873 0.992 0.09893 0.7766 0.8723 0.3072 ] Network output: [ -0.006904 0.03494 1.002 5.705e-05 -2.561e-05 0.9771 4.299e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09052 0.0886 0.1657 0.1959 0.9855 0.9913 0.09053 0.7035 0.8502 0.2436 ] Network output: [ 0.0002258 0.9998 -0.0004734 7.768e-06 -3.487e-06 1 5.854e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006289 Epoch 7415 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01198 0.9941 0.9887 1.935e-06 -8.688e-07 -0.006704 1.458e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003264 -0.003058 -0.008734 0.006728 0.9698 0.9742 0.006229 0.8396 0.8286 0.01916 ] Network output: [ 0.9998 0.000999 0.001341 -2.897e-05 1.3e-05 -0.001972 -2.183e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1892 -0.03165 -0.1872 0.1953 0.9836 0.9933 0.2114 0.4511 0.8745 0.7204 ] Network output: [ -0.01123 1.001 1.01 9.011e-07 -4.046e-07 0.01102 6.791e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005628 0.0004611 0.004392 0.004015 0.9889 0.992 0.005732 0.8686 0.8984 0.01387 ] Network output: [ -0.0008305 0.003389 1.002 -9.404e-05 4.222e-05 0.9956 -7.087e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2003 0.0937 0.3302 0.1515 0.9851 0.994 0.2009 0.4557 0.881 0.7149 ] Network output: [ 0.006999 -0.0344 0.9959 5.543e-05 -2.488e-05 1.025 4.177e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09887 0.08726 0.1796 0.2044 0.9873 0.992 0.09893 0.7765 0.8723 0.3072 ] Network output: [ -0.006901 0.03493 1.002 5.701e-05 -2.559e-05 0.9772 4.296e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09052 0.08859 0.1657 0.1959 0.9855 0.9913 0.09053 0.7035 0.8502 0.2436 ] Network output: [ 0.0002256 0.9998 -0.0004728 7.761e-06 -3.484e-06 1 5.849e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006285 Epoch 7416 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01197 0.9941 0.9887 1.931e-06 -8.67e-07 -0.006705 1.456e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003264 -0.003058 -0.008733 0.006727 0.9698 0.9742 0.006229 0.8396 0.8286 0.01916 ] Network output: [ 0.9998 0.0009963 0.00134 -2.894e-05 1.299e-05 -0.001969 -2.181e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1892 -0.03165 -0.1872 0.1953 0.9836 0.9933 0.2114 0.4511 0.8745 0.7204 ] Network output: [ -0.01123 1.001 1.01 8.989e-07 -4.036e-07 0.01102 6.774e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005628 0.0004611 0.004392 0.004015 0.9889 0.992 0.005732 0.8686 0.8984 0.01387 ] Network output: [ -0.0008299 0.003386 1.002 -9.396e-05 4.218e-05 0.9956 -7.081e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2003 0.09371 0.3302 0.1515 0.9851 0.994 0.2009 0.4557 0.881 0.7149 ] Network output: [ 0.006997 -0.03438 0.9959 5.538e-05 -2.486e-05 1.025 4.174e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09887 0.08726 0.1796 0.2044 0.9873 0.992 0.09894 0.7765 0.8723 0.3072 ] Network output: [ -0.006898 0.03491 1.002 5.696e-05 -2.557e-05 0.9772 4.293e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09052 0.08859 0.1657 0.1959 0.9855 0.9913 0.09053 0.7034 0.8501 0.2436 ] Network output: [ 0.0002255 0.9998 -0.0004725 7.755e-06 -3.482e-06 1 5.845e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006281 Epoch 7417 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01197 0.9941 0.9887 1.927e-06 -8.653e-07 -0.006707 1.453e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003264 -0.003059 -0.008732 0.006726 0.9698 0.9742 0.006229 0.8396 0.8286 0.01916 ] Network output: [ 0.9998 0.0009971 0.001339 -2.892e-05 1.298e-05 -0.001969 -2.18e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1892 -0.03165 -0.1871 0.1953 0.9836 0.9933 0.2114 0.4511 0.8745 0.7204 ] Network output: [ -0.01123 1.001 1.01 8.967e-07 -4.025e-07 0.01101 6.758e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005629 0.0004611 0.004392 0.004014 0.9889 0.992 0.005733 0.8686 0.8984 0.01387 ] Network output: [ -0.0008297 0.003387 1.002 -9.388e-05 4.214e-05 0.9956 -7.075e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2003 0.09371 0.3302 0.1515 0.9851 0.994 0.2009 0.4556 0.881 0.7149 ] Network output: [ 0.006994 -0.03437 0.9959 5.534e-05 -2.484e-05 1.025 4.171e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09888 0.08727 0.1796 0.2044 0.9873 0.992 0.09894 0.7765 0.8723 0.3072 ] Network output: [ -0.006895 0.0349 1.002 5.692e-05 -2.555e-05 0.9772 4.289e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09052 0.08859 0.1657 0.1959 0.9855 0.9913 0.09053 0.7034 0.8501 0.2436 ] Network output: [ 0.0002253 0.9998 -0.0004718 7.749e-06 -3.479e-06 1 5.84e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006277 Epoch 7418 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01197 0.9941 0.9887 1.924e-06 -8.636e-07 -0.006709 1.45e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003264 -0.003059 -0.00873 0.006725 0.9698 0.9742 0.00623 0.8395 0.8285 0.01916 ] Network output: [ 0.9998 0.0009945 0.001339 -2.89e-05 1.297e-05 -0.001966 -2.178e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1892 -0.03166 -0.1871 0.1953 0.9836 0.9933 0.2114 0.4511 0.8745 0.7204 ] Network output: [ -0.01122 1.001 1.01 8.944e-07 -4.015e-07 0.01101 6.741e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005629 0.000461 0.004392 0.004013 0.9889 0.992 0.005734 0.8686 0.8984 0.01387 ] Network output: [ -0.0008291 0.003384 1.002 -9.38e-05 4.211e-05 0.9956 -7.069e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2003 0.09371 0.3303 0.1515 0.9851 0.994 0.201 0.4556 0.881 0.7149 ] Network output: [ 0.006992 -0.03436 0.9959 5.529e-05 -2.482e-05 1.025 4.167e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09888 0.08727 0.1796 0.2044 0.9873 0.992 0.09895 0.7765 0.8723 0.3072 ] Network output: [ -0.006893 0.03488 1.002 5.687e-05 -2.553e-05 0.9772 4.286e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09052 0.08859 0.1657 0.1959 0.9855 0.9913 0.09053 0.7034 0.8501 0.2436 ] Network output: [ 0.0002253 0.9998 -0.0004715 7.743e-06 -3.476e-06 1 5.835e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006273 Epoch 7419 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01197 0.9941 0.9887 1.92e-06 -8.618e-07 -0.006711 1.447e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003264 -0.003059 -0.008729 0.006724 0.9698 0.9742 0.00623 0.8395 0.8285 0.01915 ] Network output: [ 0.9998 0.0009952 0.001338 -2.888e-05 1.296e-05 -0.001966 -2.176e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1893 -0.03166 -0.1871 0.1953 0.9836 0.9933 0.2114 0.4511 0.8745 0.7204 ] Network output: [ -0.01122 1.001 1.01 8.922e-07 -4.005e-07 0.01101 6.724e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00563 0.000461 0.004392 0.004013 0.9889 0.992 0.005734 0.8686 0.8984 0.01387 ] Network output: [ -0.0008288 0.003385 1.002 -9.372e-05 4.207e-05 0.9956 -7.063e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2003 0.09371 0.3303 0.1514 0.9851 0.994 0.201 0.4556 0.881 0.7149 ] Network output: [ 0.00699 -0.03435 0.9959 5.525e-05 -2.48e-05 1.025 4.164e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09889 0.08728 0.1796 0.2044 0.9873 0.992 0.09895 0.7764 0.8723 0.3072 ] Network output: [ -0.00689 0.03487 1.002 5.683e-05 -2.551e-05 0.9772 4.283e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09051 0.08859 0.1657 0.1959 0.9855 0.9913 0.09053 0.7033 0.8501 0.2436 ] Network output: [ 0.0002251 0.9998 -0.0004709 7.737e-06 -3.473e-06 1 5.831e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006269 Epoch 7420 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01197 0.9941 0.9887 1.916e-06 -8.601e-07 -0.006713 1.444e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003264 -0.003059 -0.008727 0.006723 0.9698 0.9742 0.00623 0.8395 0.8285 0.01915 ] Network output: [ 0.9998 0.0009927 0.001337 -2.886e-05 1.295e-05 -0.001963 -2.175e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1893 -0.03167 -0.1871 0.1953 0.9836 0.9933 0.2114 0.4511 0.8745 0.7204 ] Network output: [ -0.01122 1.001 1.01 8.899e-07 -3.995e-07 0.011 6.707e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005631 0.000461 0.004392 0.004012 0.9889 0.992 0.005735 0.8685 0.8984 0.01387 ] Network output: [ -0.0008282 0.003382 1.002 -9.364e-05 4.204e-05 0.9956 -7.057e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2003 0.09372 0.3303 0.1514 0.9851 0.994 0.201 0.4556 0.881 0.7149 ] Network output: [ 0.006987 -0.03434 0.9959 5.52e-05 -2.478e-05 1.025 4.16e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09889 0.08728 0.1796 0.2044 0.9873 0.992 0.09896 0.7764 0.8723 0.3071 ] Network output: [ -0.006887 0.03485 1.002 5.678e-05 -2.549e-05 0.9772 4.279e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09051 0.08859 0.1657 0.1959 0.9855 0.9913 0.09053 0.7033 0.8501 0.2436 ] Network output: [ 0.000225 0.9998 -0.0004705 7.731e-06 -3.471e-06 1 5.826e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006265 Epoch 7421 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01196 0.9941 0.9887 1.912e-06 -8.584e-07 -0.006715 1.441e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003264 -0.003059 -0.008726 0.006722 0.9698 0.9742 0.006231 0.8395 0.8285 0.01915 ] Network output: [ 0.9998 0.0009933 0.001336 -2.883e-05 1.294e-05 -0.001963 -2.173e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1893 -0.03167 -0.1871 0.1952 0.9836 0.9933 0.2114 0.451 0.8745 0.7203 ] Network output: [ -0.01122 1.001 1.01 8.877e-07 -3.985e-07 0.011 6.69e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005631 0.000461 0.004393 0.004011 0.9889 0.992 0.005735 0.8685 0.8984 0.01386 ] Network output: [ -0.0008279 0.003382 1.002 -9.356e-05 4.2e-05 0.9956 -7.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2004 0.09372 0.3303 0.1514 0.9851 0.994 0.201 0.4556 0.881 0.7149 ] Network output: [ 0.006985 -0.03432 0.9959 5.516e-05 -2.476e-05 1.025 4.157e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0989 0.08728 0.1796 0.2044 0.9873 0.992 0.09896 0.7764 0.8723 0.3071 ] Network output: [ -0.006884 0.03484 1.002 5.674e-05 -2.547e-05 0.9772 4.276e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09051 0.08859 0.1657 0.1959 0.9855 0.9913 0.09052 0.7033 0.8501 0.2436 ] Network output: [ 0.0002248 0.9998 -0.0004699 7.725e-06 -3.468e-06 1 5.822e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006261 Epoch 7422 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01196 0.9941 0.9887 1.908e-06 -8.567e-07 -0.006716 1.438e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003264 -0.003059 -0.008725 0.006722 0.9698 0.9742 0.006231 0.8395 0.8285 0.01915 ] Network output: [ 0.9998 0.0009908 0.001335 -2.881e-05 1.293e-05 -0.001961 -2.171e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1893 -0.03168 -0.187 0.1952 0.9836 0.9933 0.2114 0.451 0.8744 0.7203 ] Network output: [ -0.01122 1.001 1.01 8.855e-07 -3.975e-07 0.01099 6.673e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005632 0.000461 0.004393 0.004011 0.9889 0.992 0.005736 0.8685 0.8984 0.01386 ] Network output: [ -0.0008274 0.003379 1.002 -9.348e-05 4.197e-05 0.9956 -7.045e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2004 0.09372 0.3303 0.1514 0.9851 0.994 0.201 0.4556 0.881 0.7149 ] Network output: [ 0.006982 -0.03431 0.9959 5.511e-05 -2.474e-05 1.025 4.154e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09891 0.08729 0.1796 0.2044 0.9873 0.992 0.09897 0.7764 0.8723 0.3071 ] Network output: [ -0.006881 0.03482 1.002 5.67e-05 -2.545e-05 0.9772 4.273e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09051 0.08859 0.1657 0.1959 0.9855 0.9913 0.09052 0.7032 0.8501 0.2436 ] Network output: [ 0.0002248 0.9998 -0.0004696 7.719e-06 -3.465e-06 1 5.817e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006256 Epoch 7423 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01196 0.9941 0.9887 1.904e-06 -8.549e-07 -0.006718 1.435e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003265 -0.00306 -0.008723 0.006721 0.9698 0.9742 0.006231 0.8395 0.8285 0.01915 ] Network output: [ 0.9998 0.0009914 0.001334 -2.879e-05 1.293e-05 -0.00196 -2.17e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1893 -0.03168 -0.187 0.1952 0.9836 0.9933 0.2115 0.451 0.8744 0.7203 ] Network output: [ -0.01122 1.001 1.01 8.832e-07 -3.965e-07 0.01099 6.656e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005633 0.000461 0.004393 0.00401 0.9889 0.992 0.005737 0.8685 0.8984 0.01386 ] Network output: [ -0.0008271 0.00338 1.002 -9.34e-05 4.193e-05 0.9956 -7.039e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2004 0.09373 0.3303 0.1514 0.9851 0.994 0.201 0.4555 0.881 0.7149 ] Network output: [ 0.00698 -0.0343 0.9959 5.507e-05 -2.472e-05 1.025 4.15e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09891 0.08729 0.1796 0.2044 0.9873 0.992 0.09897 0.7763 0.8722 0.3071 ] Network output: [ -0.006879 0.0348 1.002 5.665e-05 -2.543e-05 0.9772 4.27e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09051 0.08858 0.1657 0.1959 0.9855 0.9913 0.09052 0.7032 0.8501 0.2436 ] Network output: [ 0.0002246 0.9998 -0.000469 7.713e-06 -3.462e-06 1 5.812e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006252 Epoch 7424 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01196 0.9941 0.9887 1.901e-06 -8.532e-07 -0.00672 1.432e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003265 -0.00306 -0.008722 0.00672 0.9698 0.9742 0.006232 0.8395 0.8285 0.01915 ] Network output: [ 0.9998 0.000989 0.001333 -2.877e-05 1.292e-05 -0.001958 -2.168e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1893 -0.03168 -0.187 0.1952 0.9836 0.9933 0.2115 0.451 0.8744 0.7203 ] Network output: [ -0.01122 1.001 1.01 8.81e-07 -3.955e-07 0.01099 6.64e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005633 0.000461 0.004393 0.004009 0.9889 0.992 0.005737 0.8685 0.8984 0.01386 ] Network output: [ -0.0008265 0.003377 1.002 -9.332e-05 4.19e-05 0.9956 -7.033e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2004 0.09373 0.3304 0.1514 0.9851 0.994 0.201 0.4555 0.881 0.7149 ] Network output: [ 0.006978 -0.03429 0.9959 5.502e-05 -2.47e-05 1.025 4.147e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09892 0.0873 0.1796 0.2044 0.9873 0.992 0.09898 0.7763 0.8722 0.3071 ] Network output: [ -0.006876 0.03479 1.002 5.661e-05 -2.541e-05 0.9772 4.266e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09051 0.08858 0.1657 0.1959 0.9855 0.9913 0.09052 0.7032 0.8501 0.2436 ] Network output: [ 0.0002245 0.9998 -0.0004686 7.706e-06 -3.46e-06 1 5.808e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006248 Epoch 7425 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01195 0.9941 0.9887 1.897e-06 -8.515e-07 -0.006722 1.429e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003265 -0.00306 -0.00872 0.006719 0.9698 0.9742 0.006232 0.8395 0.8285 0.01914 ] Network output: [ 0.9998 0.0009895 0.001332 -2.875e-05 1.291e-05 -0.001957 -2.166e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1893 -0.03169 -0.187 0.1952 0.9836 0.9933 0.2115 0.451 0.8744 0.7203 ] Network output: [ -0.01122 1.001 1.01 8.788e-07 -3.945e-07 0.01098 6.623e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005634 0.000461 0.004393 0.004009 0.9889 0.992 0.005738 0.8685 0.8984 0.01386 ] Network output: [ -0.0008262 0.003378 1.002 -9.324e-05 4.186e-05 0.9956 -7.027e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2004 0.09373 0.3304 0.1514 0.9851 0.994 0.201 0.4555 0.881 0.7149 ] Network output: [ 0.006975 -0.03427 0.9959 5.498e-05 -2.468e-05 1.025 4.143e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09892 0.0873 0.1796 0.2044 0.9873 0.992 0.09898 0.7763 0.8722 0.3071 ] Network output: [ -0.006873 0.03477 1.002 5.656e-05 -2.539e-05 0.9772 4.263e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09051 0.08858 0.1657 0.1959 0.9855 0.9913 0.09052 0.7032 0.85 0.2436 ] Network output: [ 0.0002243 0.9998 -0.000468 7.7e-06 -3.457e-06 1 5.803e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006244 Epoch 7426 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01195 0.9941 0.9887 1.893e-06 -8.498e-07 -0.006723 1.427e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003265 -0.00306 -0.008719 0.006718 0.9698 0.9742 0.006232 0.8395 0.8285 0.01914 ] Network output: [ 0.9998 0.0009872 0.001332 -2.872e-05 1.29e-05 -0.001955 -2.165e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1893 -0.03169 -0.187 0.1952 0.9836 0.9933 0.2115 0.451 0.8744 0.7203 ] Network output: [ -0.01121 1.001 1.01 8.766e-07 -3.935e-07 0.01098 6.606e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005634 0.000461 0.004393 0.004008 0.9889 0.992 0.005739 0.8685 0.8984 0.01386 ] Network output: [ -0.0008256 0.003375 1.002 -9.316e-05 4.182e-05 0.9956 -7.021e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2004 0.09374 0.3304 0.1514 0.9851 0.994 0.2011 0.4555 0.881 0.7149 ] Network output: [ 0.006973 -0.03426 0.9959 5.493e-05 -2.466e-05 1.025 4.14e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09893 0.08731 0.1796 0.2044 0.9873 0.992 0.09899 0.7763 0.8722 0.3071 ] Network output: [ -0.00687 0.03476 1.002 5.652e-05 -2.537e-05 0.9772 4.26e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09051 0.08858 0.1656 0.1959 0.9855 0.9913 0.09052 0.7031 0.85 0.2436 ] Network output: [ 0.0002243 0.9998 -0.0004677 7.694e-06 -3.454e-06 1 5.799e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000624 Epoch 7427 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01195 0.9941 0.9887 1.889e-06 -8.481e-07 -0.006725 1.424e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003265 -0.00306 -0.008718 0.006717 0.9698 0.9742 0.006232 0.8395 0.8285 0.01914 ] Network output: [ 0.9998 0.0009876 0.001331 -2.87e-05 1.289e-05 -0.001954 -2.163e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1893 -0.03169 -0.187 0.1952 0.9836 0.9933 0.2115 0.451 0.8744 0.7203 ] Network output: [ -0.01121 1.001 1.01 8.743e-07 -3.925e-07 0.01098 6.589e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005635 0.000461 0.004393 0.004007 0.9889 0.992 0.005739 0.8685 0.8984 0.01386 ] Network output: [ -0.0008253 0.003376 1.002 -9.309e-05 4.179e-05 0.9956 -7.015e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2004 0.09374 0.3304 0.1514 0.9851 0.994 0.2011 0.4555 0.881 0.7149 ] Network output: [ 0.00697 -0.03425 0.9959 5.489e-05 -2.464e-05 1.025 4.137e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09893 0.08731 0.1796 0.2044 0.9873 0.992 0.099 0.7762 0.8722 0.3071 ] Network output: [ -0.006867 0.03474 1.002 5.648e-05 -2.535e-05 0.9772 4.256e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0905 0.08858 0.1656 0.1959 0.9855 0.9913 0.09052 0.7031 0.85 0.2436 ] Network output: [ 0.0002241 0.9998 -0.0004671 7.688e-06 -3.451e-06 1 5.794e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006236 Epoch 7428 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01195 0.9941 0.9887 1.885e-06 -8.464e-07 -0.006727 1.421e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003265 -0.00306 -0.008716 0.006716 0.9698 0.9742 0.006233 0.8395 0.8285 0.01914 ] Network output: [ 0.9998 0.0009854 0.00133 -2.868e-05 1.288e-05 -0.001952 -2.161e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1894 -0.0317 -0.1869 0.1952 0.9836 0.9933 0.2115 0.4509 0.8744 0.7203 ] Network output: [ -0.01121 1.001 1.01 8.721e-07 -3.915e-07 0.01097 6.573e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005636 0.0004609 0.004393 0.004007 0.9889 0.992 0.00574 0.8685 0.8984 0.01385 ] Network output: [ -0.0008248 0.003373 1.002 -9.301e-05 4.175e-05 0.9956 -7.009e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2004 0.09374 0.3304 0.1513 0.9851 0.994 0.2011 0.4555 0.8809 0.7149 ] Network output: [ 0.006968 -0.03424 0.9959 5.484e-05 -2.462e-05 1.025 4.133e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09894 0.08731 0.1796 0.2044 0.9873 0.992 0.099 0.7762 0.8722 0.3071 ] Network output: [ -0.006865 0.03473 1.002 5.643e-05 -2.533e-05 0.9773 4.253e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0905 0.08858 0.1656 0.1959 0.9855 0.9913 0.09052 0.7031 0.85 0.2436 ] Network output: [ 0.000224 0.9998 -0.0004667 7.682e-06 -3.449e-06 1 5.789e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006232 Epoch 7429 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01195 0.9941 0.9887 1.881e-06 -8.446e-07 -0.006729 1.418e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003265 -0.003061 -0.008715 0.006715 0.9698 0.9742 0.006233 0.8395 0.8285 0.01914 ] Network output: [ 0.9998 0.0009857 0.001329 -2.866e-05 1.287e-05 -0.001951 -2.16e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1894 -0.0317 -0.1869 0.1952 0.9836 0.9933 0.2115 0.4509 0.8744 0.7203 ] Network output: [ -0.01121 1.001 1.01 8.699e-07 -3.905e-07 0.01097 6.556e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005636 0.0004609 0.004394 0.004006 0.9889 0.992 0.00574 0.8685 0.8984 0.01385 ] Network output: [ -0.0008244 0.003373 1.002 -9.293e-05 4.172e-05 0.9956 -7.003e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2005 0.09374 0.3304 0.1513 0.9851 0.994 0.2011 0.4555 0.8809 0.7149 ] Network output: [ 0.006965 -0.03422 0.9959 5.48e-05 -2.46e-05 1.025 4.13e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09894 0.08732 0.1796 0.2044 0.9873 0.992 0.09901 0.7762 0.8722 0.3071 ] Network output: [ -0.006862 0.03471 1.002 5.639e-05 -2.532e-05 0.9773 4.25e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0905 0.08858 0.1656 0.1959 0.9855 0.9913 0.09051 0.703 0.85 0.2436 ] Network output: [ 0.0002238 0.9998 -0.0004661 7.676e-06 -3.446e-06 1 5.785e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006228 Epoch 7430 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01194 0.9941 0.9887 1.878e-06 -8.429e-07 -0.00673 1.415e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003266 -0.003061 -0.008714 0.006714 0.9698 0.9742 0.006233 0.8394 0.8285 0.01913 ] Network output: [ 0.9998 0.0009836 0.001328 -2.864e-05 1.286e-05 -0.001949 -2.158e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1894 -0.03171 -0.1869 0.1952 0.9836 0.9933 0.2115 0.4509 0.8744 0.7203 ] Network output: [ -0.01121 1.001 1.01 8.677e-07 -3.895e-07 0.01097 6.539e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005637 0.0004609 0.004394 0.004005 0.9889 0.992 0.005741 0.8684 0.8984 0.01385 ] Network output: [ -0.0008239 0.003371 1.002 -9.285e-05 4.168e-05 0.9956 -6.997e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2005 0.09375 0.3304 0.1513 0.9851 0.994 0.2011 0.4554 0.8809 0.7149 ] Network output: [ 0.006963 -0.03421 0.9959 5.475e-05 -2.458e-05 1.025 4.126e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09895 0.08732 0.1796 0.2043 0.9873 0.992 0.09901 0.7761 0.8722 0.3071 ] Network output: [ -0.006859 0.0347 1.002 5.635e-05 -2.53e-05 0.9773 4.246e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0905 0.08858 0.1656 0.1959 0.9855 0.9913 0.09051 0.703 0.85 0.2436 ] Network output: [ 0.0002238 0.9998 -0.0004657 7.67e-06 -3.443e-06 1 5.78e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006224 Epoch 7431 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01194 0.9941 0.9888 1.874e-06 -8.412e-07 -0.006732 1.412e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003266 -0.003061 -0.008712 0.006713 0.9698 0.9742 0.006234 0.8394 0.8285 0.01913 ] Network output: [ 0.9998 0.0009838 0.001327 -2.861e-05 1.285e-05 -0.001948 -2.156e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1894 -0.03171 -0.1869 0.1952 0.9836 0.9933 0.2116 0.4509 0.8744 0.7203 ] Network output: [ -0.01121 1.001 1.01 8.655e-07 -3.885e-07 0.01096 6.522e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005637 0.0004609 0.004394 0.004005 0.9889 0.992 0.005742 0.8684 0.8984 0.01385 ] Network output: [ -0.0008236 0.003371 1.002 -9.277e-05 4.165e-05 0.9956 -6.991e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2005 0.09375 0.3305 0.1513 0.9851 0.994 0.2011 0.4554 0.8809 0.7149 ] Network output: [ 0.006961 -0.0342 0.9959 5.471e-05 -2.456e-05 1.025 4.123e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09895 0.08733 0.1796 0.2043 0.9873 0.992 0.09902 0.7761 0.8722 0.3071 ] Network output: [ -0.006856 0.03468 1.002 5.63e-05 -2.528e-05 0.9773 4.243e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0905 0.08857 0.1656 0.1959 0.9855 0.9913 0.09051 0.703 0.85 0.2436 ] Network output: [ 0.0002236 0.9998 -0.0004652 7.664e-06 -3.441e-06 1 5.776e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006219 Epoch 7432 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01194 0.9941 0.9888 1.87e-06 -8.395e-07 -0.006734 1.409e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003266 -0.003061 -0.008711 0.006712 0.9698 0.9742 0.006234 0.8394 0.8285 0.01913 ] Network output: [ 0.9998 0.0009817 0.001326 -2.859e-05 1.284e-05 -0.001946 -2.155e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1894 -0.03171 -0.1869 0.1951 0.9836 0.9933 0.2116 0.4509 0.8744 0.7203 ] Network output: [ -0.01121 1.001 1.01 8.633e-07 -3.876e-07 0.01096 6.506e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005638 0.0004609 0.004394 0.004004 0.9889 0.992 0.005742 0.8684 0.8983 0.01385 ] Network output: [ -0.000823 0.003368 1.002 -9.269e-05 4.161e-05 0.9956 -6.986e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2005 0.09375 0.3305 0.1513 0.9851 0.994 0.2011 0.4554 0.8809 0.7149 ] Network output: [ 0.006958 -0.03419 0.9959 5.466e-05 -2.454e-05 1.025 4.12e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09896 0.08733 0.1796 0.2043 0.9873 0.992 0.09902 0.7761 0.8722 0.3071 ] Network output: [ -0.006854 0.03467 1.002 5.626e-05 -2.526e-05 0.9773 4.24e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0905 0.08857 0.1656 0.1959 0.9855 0.9913 0.09051 0.7029 0.85 0.2436 ] Network output: [ 0.0002235 0.9998 -0.0004648 7.658e-06 -3.438e-06 1 5.771e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006215 Epoch 7433 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01194 0.9941 0.9888 1.866e-06 -8.378e-07 -0.006736 1.406e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003266 -0.003061 -0.008709 0.006711 0.9698 0.9742 0.006234 0.8394 0.8285 0.01913 ] Network output: [ 0.9998 0.0009819 0.001325 -2.857e-05 1.283e-05 -0.001946 -2.153e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1894 -0.03172 -0.1868 0.1951 0.9836 0.9933 0.2116 0.4509 0.8744 0.7203 ] Network output: [ -0.01121 1.001 1.01 8.61e-07 -3.866e-07 0.01096 6.489e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005639 0.0004609 0.004394 0.004003 0.9889 0.992 0.005743 0.8684 0.8983 0.01385 ] Network output: [ -0.0008227 0.003369 1.002 -9.261e-05 4.158e-05 0.9956 -6.98e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2005 0.09376 0.3305 0.1513 0.9851 0.994 0.2011 0.4554 0.8809 0.7148 ] Network output: [ 0.006956 -0.03417 0.9959 5.462e-05 -2.452e-05 1.025 4.116e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09896 0.08734 0.1796 0.2043 0.9873 0.992 0.09903 0.7761 0.8722 0.3071 ] Network output: [ -0.006851 0.03465 1.002 5.621e-05 -2.524e-05 0.9773 4.236e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0905 0.08857 0.1656 0.1959 0.9855 0.9913 0.09051 0.7029 0.85 0.2436 ] Network output: [ 0.0002234 0.9998 -0.0004642 7.652e-06 -3.435e-06 1 5.766e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006211 Epoch 7434 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01194 0.9941 0.9888 1.862e-06 -8.361e-07 -0.006737 1.404e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003266 -0.003061 -0.008708 0.006711 0.9698 0.9742 0.006235 0.8394 0.8284 0.01913 ] Network output: [ 0.9998 0.0009799 0.001325 -2.855e-05 1.282e-05 -0.001943 -2.151e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1894 -0.03172 -0.1868 0.1951 0.9836 0.9933 0.2116 0.4508 0.8744 0.7203 ] Network output: [ -0.0112 1.001 1.01 8.588e-07 -3.856e-07 0.01095 6.473e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005639 0.0004609 0.004394 0.004003 0.9889 0.992 0.005744 0.8684 0.8983 0.01385 ] Network output: [ -0.0008222 0.003366 1.002 -9.253e-05 4.154e-05 0.9956 -6.974e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2005 0.09376 0.3305 0.1513 0.9851 0.994 0.2012 0.4554 0.8809 0.7148 ] Network output: [ 0.006953 -0.03416 0.9959 5.458e-05 -2.45e-05 1.025 4.113e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09897 0.08734 0.1796 0.2043 0.9873 0.992 0.09903 0.776 0.8721 0.3071 ] Network output: [ -0.006848 0.03464 1.002 5.617e-05 -2.522e-05 0.9773 4.233e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0905 0.08857 0.1656 0.1959 0.9855 0.9913 0.09051 0.7029 0.8499 0.2436 ] Network output: [ 0.0002233 0.9998 -0.0004638 7.645e-06 -3.432e-06 1 5.762e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006207 Epoch 7435 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01193 0.9941 0.9888 1.859e-06 -8.344e-07 -0.006739 1.401e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003266 -0.003062 -0.008707 0.00671 0.9698 0.9742 0.006235 0.8394 0.8284 0.01912 ] Network output: [ 0.9998 0.0009801 0.001324 -2.853e-05 1.281e-05 -0.001943 -2.15e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1894 -0.03173 -0.1868 0.1951 0.9836 0.9933 0.2116 0.4508 0.8744 0.7203 ] Network output: [ -0.0112 1.001 1.01 8.566e-07 -3.846e-07 0.01095 6.456e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00564 0.0004609 0.004394 0.004002 0.9889 0.992 0.005744 0.8684 0.8983 0.01384 ] Network output: [ -0.0008218 0.003367 1.002 -9.246e-05 4.151e-05 0.9956 -6.968e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2005 0.09376 0.3305 0.1513 0.9851 0.994 0.2012 0.4554 0.8809 0.7148 ] Network output: [ 0.006951 -0.03415 0.9959 5.453e-05 -2.448e-05 1.025 4.11e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09897 0.08734 0.1796 0.2043 0.9873 0.992 0.09904 0.776 0.8721 0.3071 ] Network output: [ -0.006845 0.03462 1.002 5.613e-05 -2.52e-05 0.9773 4.23e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09049 0.08857 0.1656 0.1959 0.9855 0.9913 0.09051 0.7028 0.8499 0.2436 ] Network output: [ 0.0002231 0.9998 -0.0004633 7.639e-06 -3.43e-06 1 5.757e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006203 Epoch 7436 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01193 0.9941 0.9888 1.855e-06 -8.327e-07 -0.006741 1.398e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003266 -0.003062 -0.008705 0.006709 0.9698 0.9742 0.006235 0.8394 0.8284 0.01912 ] Network output: [ 0.9998 0.0009781 0.001323 -2.85e-05 1.28e-05 -0.00194 -2.148e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1894 -0.03173 -0.1868 0.1951 0.9836 0.9933 0.2116 0.4508 0.8744 0.7203 ] Network output: [ -0.0112 1.001 1.01 8.544e-07 -3.836e-07 0.01094 6.439e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00564 0.0004609 0.004395 0.004002 0.9889 0.992 0.005745 0.8684 0.8983 0.01384 ] Network output: [ -0.0008213 0.003364 1.002 -9.238e-05 4.147e-05 0.9956 -6.962e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2005 0.09377 0.3305 0.1513 0.9851 0.994 0.2012 0.4553 0.8809 0.7148 ] Network output: [ 0.006949 -0.03414 0.9959 5.449e-05 -2.446e-05 1.025 4.106e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09898 0.08735 0.1796 0.2043 0.9873 0.992 0.09904 0.776 0.8721 0.3071 ] Network output: [ -0.006842 0.03461 1.002 5.608e-05 -2.518e-05 0.9773 4.227e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09049 0.08857 0.1656 0.1959 0.9855 0.9913 0.09051 0.7028 0.8499 0.2436 ] Network output: [ 0.0002231 0.9998 -0.0004629 7.633e-06 -3.427e-06 1 5.753e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006199 Epoch 7437 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01193 0.9941 0.9888 1.851e-06 -8.31e-07 -0.006743 1.395e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003267 -0.003062 -0.008704 0.006708 0.9698 0.9742 0.006236 0.8394 0.8284 0.01912 ] Network output: [ 0.9998 0.0009782 0.001322 -2.848e-05 1.279e-05 -0.00194 -2.146e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1895 -0.03173 -0.1868 0.1951 0.9836 0.9933 0.2116 0.4508 0.8744 0.7203 ] Network output: [ -0.0112 1.001 1.01 8.522e-07 -3.826e-07 0.01094 6.423e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005641 0.0004609 0.004395 0.004001 0.9889 0.992 0.005745 0.8684 0.8983 0.01384 ] Network output: [ -0.000821 0.003364 1.002 -9.23e-05 4.144e-05 0.9956 -6.956e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2006 0.09377 0.3305 0.1513 0.9851 0.994 0.2012 0.4553 0.8809 0.7148 ] Network output: [ 0.006946 -0.03413 0.9959 5.444e-05 -2.444e-05 1.025 4.103e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09898 0.08735 0.1796 0.2043 0.9873 0.992 0.09905 0.776 0.8721 0.3071 ] Network output: [ -0.00684 0.03459 1.002 5.604e-05 -2.516e-05 0.9773 4.223e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09049 0.08857 0.1656 0.1959 0.9855 0.9913 0.0905 0.7028 0.8499 0.2436 ] Network output: [ 0.0002229 0.9998 -0.0004623 7.627e-06 -3.424e-06 1 5.748e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006195 Epoch 7438 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01193 0.9941 0.9888 1.847e-06 -8.293e-07 -0.006744 1.392e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003267 -0.003062 -0.008703 0.006707 0.9698 0.9742 0.006236 0.8394 0.8284 0.01912 ] Network output: [ 0.9998 0.0009763 0.001321 -2.846e-05 1.278e-05 -0.001937 -2.145e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1895 -0.03174 -0.1867 0.1951 0.9836 0.9933 0.2117 0.4508 0.8744 0.7203 ] Network output: [ -0.0112 1.001 1.01 8.5e-07 -3.816e-07 0.01094 6.406e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005642 0.0004609 0.004395 0.004 0.9889 0.992 0.005746 0.8684 0.8983 0.01384 ] Network output: [ -0.0008204 0.003362 1.002 -9.222e-05 4.14e-05 0.9956 -6.95e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2006 0.09377 0.3306 0.1512 0.9851 0.994 0.2012 0.4553 0.8809 0.7148 ] Network output: [ 0.006944 -0.03411 0.9959 5.44e-05 -2.442e-05 1.025 4.099e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09899 0.08736 0.1796 0.2043 0.9873 0.992 0.09905 0.7759 0.8721 0.3071 ] Network output: [ -0.006837 0.03457 1.002 5.599e-05 -2.514e-05 0.9773 4.22e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09049 0.08857 0.1656 0.1959 0.9855 0.9913 0.0905 0.7028 0.8499 0.2436 ] Network output: [ 0.0002228 0.9998 -0.0004619 7.621e-06 -3.421e-06 1 5.744e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006191 Epoch 7439 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01192 0.9941 0.9888 1.844e-06 -8.276e-07 -0.006746 1.389e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003267 -0.003062 -0.008701 0.006706 0.9698 0.9742 0.006236 0.8394 0.8284 0.01912 ] Network output: [ 0.9998 0.0009763 0.00132 -2.844e-05 1.277e-05 -0.001937 -2.143e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1895 -0.03174 -0.1867 0.1951 0.9836 0.9933 0.2117 0.4508 0.8744 0.7203 ] Network output: [ -0.0112 1.001 1.01 8.478e-07 -3.806e-07 0.01093 6.389e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005642 0.0004609 0.004395 0.004 0.9889 0.992 0.005747 0.8684 0.8983 0.01384 ] Network output: [ -0.0008201 0.003362 1.002 -9.214e-05 4.137e-05 0.9956 -6.944e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2006 0.09378 0.3306 0.1512 0.9851 0.994 0.2012 0.4553 0.8809 0.7148 ] Network output: [ 0.006941 -0.0341 0.9959 5.435e-05 -2.44e-05 1.025 4.096e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09899 0.08736 0.1797 0.2043 0.9873 0.992 0.09906 0.7759 0.8721 0.3071 ] Network output: [ -0.006834 0.03456 1.002 5.595e-05 -2.512e-05 0.9773 4.217e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09049 0.08856 0.1656 0.1959 0.9855 0.9913 0.0905 0.7027 0.8499 0.2436 ] Network output: [ 0.0002226 0.9998 -0.0004614 7.615e-06 -3.419e-06 1 5.739e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006187 Epoch 7440 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01192 0.9941 0.9888 1.84e-06 -8.259e-07 -0.006748 1.387e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003267 -0.003062 -0.0087 0.006705 0.9698 0.9742 0.006237 0.8394 0.8284 0.01912 ] Network output: [ 0.9998 0.0009745 0.001319 -2.842e-05 1.276e-05 -0.001934 -2.142e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1895 -0.03175 -0.1867 0.1951 0.9836 0.9933 0.2117 0.4507 0.8744 0.7203 ] Network output: [ -0.0112 1.001 1.01 8.456e-07 -3.796e-07 0.01093 6.373e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005643 0.0004609 0.004395 0.003999 0.9889 0.992 0.005747 0.8683 0.8983 0.01384 ] Network output: [ -0.0008196 0.00336 1.002 -9.206e-05 4.133e-05 0.9956 -6.938e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2006 0.09378 0.3306 0.1512 0.9851 0.994 0.2012 0.4553 0.8809 0.7148 ] Network output: [ 0.006939 -0.03409 0.9959 5.431e-05 -2.438e-05 1.025 4.093e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.099 0.08737 0.1797 0.2043 0.9873 0.992 0.09906 0.7759 0.8721 0.3071 ] Network output: [ -0.006831 0.03454 1.002 5.591e-05 -2.51e-05 0.9773 4.213e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09049 0.08856 0.1656 0.1958 0.9855 0.9913 0.0905 0.7027 0.8499 0.2436 ] Network output: [ 0.0002226 0.9998 -0.000461 7.609e-06 -3.416e-06 1 5.734e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006183 Epoch 7441 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01192 0.9941 0.9888 1.836e-06 -8.242e-07 -0.00675 1.384e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003267 -0.003063 -0.008699 0.006704 0.9698 0.9742 0.006237 0.8394 0.8284 0.01911 ] Network output: [ 0.9998 0.0009745 0.001319 -2.839e-05 1.275e-05 -0.001934 -2.14e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1895 -0.03175 -0.1867 0.1951 0.9836 0.9933 0.2117 0.4507 0.8743 0.7203 ] Network output: [ -0.0112 1.001 1.01 8.434e-07 -3.786e-07 0.01093 6.356e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005643 0.0004609 0.004395 0.003998 0.9889 0.992 0.005748 0.8683 0.8983 0.01384 ] Network output: [ -0.0008192 0.00336 1.002 -9.198e-05 4.13e-05 0.9956 -6.932e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2006 0.09378 0.3306 0.1512 0.9851 0.994 0.2012 0.4553 0.8809 0.7148 ] Network output: [ 0.006937 -0.03408 0.9959 5.426e-05 -2.436e-05 1.025 4.089e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.099 0.08737 0.1797 0.2043 0.9873 0.992 0.09907 0.7759 0.8721 0.3071 ] Network output: [ -0.006829 0.03453 1.002 5.586e-05 -2.508e-05 0.9774 4.21e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09049 0.08856 0.1656 0.1958 0.9855 0.9913 0.0905 0.7027 0.8499 0.2436 ] Network output: [ 0.0002224 0.9998 -0.0004604 7.603e-06 -3.413e-06 1 5.73e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006179 Epoch 7442 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01192 0.9941 0.9888 1.832e-06 -8.226e-07 -0.006751 1.381e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003267 -0.003063 -0.008697 0.006703 0.9698 0.9742 0.006237 0.8393 0.8284 0.01911 ] Network output: [ 0.9998 0.0009727 0.001318 -2.837e-05 1.274e-05 -0.001932 -2.138e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1895 -0.03175 -0.1867 0.1951 0.9836 0.9933 0.2117 0.4507 0.8743 0.7202 ] Network output: [ -0.01119 1.001 1.01 8.412e-07 -3.777e-07 0.01092 6.34e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005644 0.0004608 0.004395 0.003998 0.9889 0.992 0.005749 0.8683 0.8983 0.01383 ] Network output: [ -0.0008187 0.003358 1.002 -9.191e-05 4.126e-05 0.9956 -6.926e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2006 0.09379 0.3306 0.1512 0.9851 0.994 0.2013 0.4552 0.8809 0.7148 ] Network output: [ 0.006934 -0.03406 0.9959 5.422e-05 -2.434e-05 1.025 4.086e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09901 0.08737 0.1797 0.2043 0.9873 0.992 0.09907 0.7758 0.8721 0.3071 ] Network output: [ -0.006826 0.03451 1.002 5.582e-05 -2.506e-05 0.9774 4.207e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09049 0.08856 0.1656 0.1958 0.9855 0.9913 0.0905 0.7026 0.8499 0.2436 ] Network output: [ 0.0002223 0.9998 -0.0004601 7.597e-06 -3.411e-06 1 5.725e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006175 Epoch 7443 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01192 0.9941 0.9888 1.828e-06 -8.209e-07 -0.006753 1.378e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003267 -0.003063 -0.008696 0.006702 0.9698 0.9742 0.006238 0.8393 0.8284 0.01911 ] Network output: [ 0.9998 0.0009726 0.001317 -2.835e-05 1.273e-05 -0.001931 -2.137e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1895 -0.03176 -0.1867 0.1951 0.9836 0.9933 0.2117 0.4507 0.8743 0.7202 ] Network output: [ -0.01119 1.001 1.01 8.391e-07 -3.767e-07 0.01092 6.323e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005645 0.0004608 0.004395 0.003997 0.9889 0.992 0.005749 0.8683 0.8983 0.01383 ] Network output: [ -0.0008184 0.003358 1.002 -9.183e-05 4.123e-05 0.9956 -6.92e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2006 0.09379 0.3306 0.1512 0.9851 0.994 0.2013 0.4552 0.8809 0.7148 ] Network output: [ 0.006932 -0.03405 0.9959 5.417e-05 -2.432e-05 1.025 4.083e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09902 0.08738 0.1797 0.2043 0.9873 0.992 0.09908 0.7758 0.8721 0.3071 ] Network output: [ -0.006823 0.0345 1.002 5.578e-05 -2.504e-05 0.9774 4.203e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09049 0.08856 0.1656 0.1958 0.9855 0.9913 0.0905 0.7026 0.8498 0.2437 ] Network output: [ 0.0002221 0.9998 -0.0004595 7.591e-06 -3.408e-06 1 5.721e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006171 Epoch 7444 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01191 0.9941 0.9888 1.825e-06 -8.192e-07 -0.006755 1.375e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003267 -0.003063 -0.008694 0.006701 0.9698 0.9742 0.006238 0.8393 0.8284 0.01911 ] Network output: [ 0.9998 0.0009709 0.001316 -2.833e-05 1.272e-05 -0.001929 -2.135e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1895 -0.03176 -0.1866 0.195 0.9836 0.9933 0.2117 0.4507 0.8743 0.7202 ] Network output: [ -0.01119 1.001 1.01 8.369e-07 -3.757e-07 0.01092 6.307e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005645 0.0004608 0.004396 0.003996 0.9889 0.992 0.00575 0.8683 0.8983 0.01383 ] Network output: [ -0.0008178 0.003355 1.002 -9.175e-05 4.119e-05 0.9956 -6.915e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2006 0.09379 0.3307 0.1512 0.9851 0.994 0.2013 0.4552 0.8809 0.7148 ] Network output: [ 0.006929 -0.03404 0.9959 5.413e-05 -2.43e-05 1.025 4.079e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09902 0.08738 0.1797 0.2042 0.9873 0.992 0.09908 0.7758 0.8721 0.3071 ] Network output: [ -0.00682 0.03448 1.002 5.573e-05 -2.502e-05 0.9774 4.2e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09048 0.08856 0.1656 0.1958 0.9855 0.9913 0.0905 0.7026 0.8498 0.2437 ] Network output: [ 0.0002221 0.9998 -0.0004591 7.585e-06 -3.405e-06 1 5.716e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006167 Epoch 7445 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01191 0.9941 0.9888 1.821e-06 -8.175e-07 -0.006757 1.372e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003268 -0.003063 -0.008693 0.006701 0.9698 0.9742 0.006238 0.8393 0.8284 0.01911 ] Network output: [ 0.9998 0.0009708 0.001315 -2.831e-05 1.271e-05 -0.001928 -2.133e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1895 -0.03177 -0.1866 0.195 0.9836 0.9933 0.2117 0.4507 0.8743 0.7202 ] Network output: [ -0.01119 1.001 1.01 8.347e-07 -3.747e-07 0.01091 6.29e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005646 0.0004608 0.004396 0.003996 0.9889 0.992 0.00575 0.8683 0.8983 0.01383 ] Network output: [ -0.0008175 0.003355 1.002 -9.167e-05 4.115e-05 0.9956 -6.909e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2007 0.09379 0.3307 0.1512 0.9851 0.994 0.2013 0.4552 0.8809 0.7148 ] Network output: [ 0.006927 -0.03403 0.9959 5.408e-05 -2.428e-05 1.025 4.076e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09903 0.08739 0.1797 0.2042 0.9873 0.992 0.09909 0.7758 0.8721 0.3071 ] Network output: [ -0.006818 0.03447 1.002 5.569e-05 -2.5e-05 0.9774 4.197e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09048 0.08856 0.1656 0.1958 0.9855 0.9913 0.0905 0.7025 0.8498 0.2437 ] Network output: [ 0.0002219 0.9998 -0.0004586 7.579e-06 -3.402e-06 1 5.712e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006162 Epoch 7446 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01191 0.9941 0.9888 1.817e-06 -8.158e-07 -0.006758 1.37e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003268 -0.003063 -0.008692 0.0067 0.9698 0.9742 0.006238 0.8393 0.8284 0.0191 ] Network output: [ 0.9998 0.0009691 0.001314 -2.828e-05 1.27e-05 -0.001926 -2.132e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1896 -0.03177 -0.1866 0.195 0.9836 0.9933 0.2118 0.4507 0.8743 0.7202 ] Network output: [ -0.01119 1.001 1.01 8.325e-07 -3.737e-07 0.01091 6.274e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005647 0.0004608 0.004396 0.003995 0.9889 0.992 0.005751 0.8683 0.8983 0.01383 ] Network output: [ -0.000817 0.003353 1.002 -9.159e-05 4.112e-05 0.9956 -6.903e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2007 0.0938 0.3307 0.1512 0.9851 0.994 0.2013 0.4552 0.8809 0.7148 ] Network output: [ 0.006925 -0.03401 0.9959 5.404e-05 -2.426e-05 1.025 4.073e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09903 0.08739 0.1797 0.2042 0.9873 0.992 0.0991 0.7757 0.872 0.3071 ] Network output: [ -0.006815 0.03445 1.002 5.565e-05 -2.498e-05 0.9774 4.194e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09048 0.08856 0.1656 0.1958 0.9855 0.9913 0.09049 0.7025 0.8498 0.2437 ] Network output: [ 0.0002218 0.9998 -0.0004582 7.573e-06 -3.4e-06 1 5.707e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006158 Epoch 7447 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01191 0.9942 0.9888 1.814e-06 -8.142e-07 -0.00676 1.367e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003268 -0.003064 -0.00869 0.006699 0.9698 0.9742 0.006239 0.8393 0.8284 0.0191 ] Network output: [ 0.9998 0.0009689 0.001313 -2.826e-05 1.269e-05 -0.001925 -2.13e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1896 -0.03177 -0.1866 0.195 0.9836 0.9933 0.2118 0.4506 0.8743 0.7202 ] Network output: [ -0.01119 1.001 1.01 8.303e-07 -3.728e-07 0.01091 6.258e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005647 0.0004608 0.004396 0.003995 0.9889 0.992 0.005752 0.8683 0.8983 0.01383 ] Network output: [ -0.0008166 0.003353 1.002 -9.152e-05 4.108e-05 0.9956 -6.897e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2007 0.0938 0.3307 0.1511 0.9851 0.994 0.2013 0.4552 0.8808 0.7148 ] Network output: [ 0.006922 -0.034 0.9959 5.399e-05 -2.424e-05 1.025 4.069e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09904 0.0874 0.1797 0.2042 0.9873 0.992 0.0991 0.7757 0.872 0.3071 ] Network output: [ -0.006812 0.03444 1.002 5.56e-05 -2.496e-05 0.9774 4.19e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09048 0.08856 0.1656 0.1958 0.9855 0.9913 0.09049 0.7025 0.8498 0.2437 ] Network output: [ 0.0002217 0.9998 -0.0004576 7.567e-06 -3.397e-06 1 5.702e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006154 Epoch 7448 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01191 0.9942 0.9888 1.81e-06 -8.125e-07 -0.006762 1.364e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003268 -0.003064 -0.008689 0.006698 0.9698 0.9742 0.006239 0.8393 0.8284 0.0191 ] Network output: [ 0.9998 0.0009673 0.001313 -2.824e-05 1.268e-05 -0.001923 -2.128e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1896 -0.03178 -0.1866 0.195 0.9836 0.9933 0.2118 0.4506 0.8743 0.7202 ] Network output: [ -0.01119 1.001 1.01 8.281e-07 -3.718e-07 0.0109 6.241e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005648 0.0004608 0.004396 0.003994 0.9889 0.992 0.005752 0.8683 0.8983 0.01383 ] Network output: [ -0.0008161 0.003351 1.002 -9.144e-05 4.105e-05 0.9956 -6.891e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2007 0.0938 0.3307 0.1511 0.9851 0.994 0.2013 0.4551 0.8808 0.7148 ] Network output: [ 0.00692 -0.03399 0.9959 5.395e-05 -2.422e-05 1.024 4.066e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09904 0.0874 0.1797 0.2042 0.9873 0.992 0.09911 0.7757 0.872 0.3071 ] Network output: [ -0.006809 0.03442 1.002 5.556e-05 -2.494e-05 0.9774 4.187e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09048 0.08855 0.1656 0.1958 0.9855 0.9913 0.09049 0.7025 0.8498 0.2437 ] Network output: [ 0.0002216 0.9998 -0.0004572 7.56e-06 -3.394e-06 1 5.698e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000615 Epoch 7449 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0119 0.9942 0.9888 1.806e-06 -8.108e-07 -0.006763 1.361e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003268 -0.003064 -0.008688 0.006697 0.9698 0.9742 0.006239 0.8393 0.8284 0.0191 ] Network output: [ 0.9998 0.0009671 0.001312 -2.822e-05 1.267e-05 -0.001922 -2.127e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1896 -0.03178 -0.1865 0.195 0.9836 0.9933 0.2118 0.4506 0.8743 0.7202 ] Network output: [ -0.01119 1.001 1.01 8.26e-07 -3.708e-07 0.0109 6.225e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005648 0.0004608 0.004396 0.003993 0.9889 0.992 0.005753 0.8683 0.8983 0.01382 ] Network output: [ -0.0008157 0.003351 1.002 -9.136e-05 4.101e-05 0.9956 -6.885e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2007 0.09381 0.3307 0.1511 0.9851 0.994 0.2013 0.4551 0.8808 0.7148 ] Network output: [ 0.006918 -0.03398 0.9959 5.391e-05 -2.42e-05 1.024 4.063e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09905 0.08741 0.1797 0.2042 0.9873 0.992 0.09911 0.7756 0.872 0.3071 ] Network output: [ -0.006807 0.03441 1.002 5.551e-05 -2.492e-05 0.9774 4.184e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09048 0.08855 0.1656 0.1958 0.9855 0.9913 0.09049 0.7024 0.8498 0.2437 ] Network output: [ 0.0002214 0.9998 -0.0004567 7.554e-06 -3.391e-06 1 5.693e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006146 Epoch 7450 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0119 0.9942 0.9888 1.802e-06 -8.092e-07 -0.006765 1.358e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003268 -0.003064 -0.008686 0.006696 0.9698 0.9742 0.00624 0.8393 0.8284 0.0191 ] Network output: [ 0.9998 0.0009655 0.001311 -2.82e-05 1.266e-05 -0.00192 -2.125e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1896 -0.03179 -0.1865 0.195 0.9836 0.9933 0.2118 0.4506 0.8743 0.7202 ] Network output: [ -0.01118 1.001 1.01 8.238e-07 -3.698e-07 0.0109 6.208e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005649 0.0004608 0.004396 0.003993 0.9889 0.992 0.005754 0.8682 0.8983 0.01382 ] Network output: [ -0.0008152 0.003349 1.002 -9.128e-05 4.098e-05 0.9956 -6.879e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2007 0.09381 0.3307 0.1511 0.9851 0.994 0.2014 0.4551 0.8808 0.7148 ] Network output: [ 0.006915 -0.03397 0.9959 5.386e-05 -2.418e-05 1.024 4.059e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09905 0.08741 0.1797 0.2042 0.9873 0.992 0.09912 0.7756 0.872 0.307 ] Network output: [ -0.006804 0.03439 1.002 5.547e-05 -2.49e-05 0.9774 4.18e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09048 0.08855 0.1656 0.1958 0.9855 0.9913 0.09049 0.7024 0.8498 0.2437 ] Network output: [ 0.0002213 0.9998 -0.0004563 7.548e-06 -3.389e-06 1 5.689e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006142 Epoch 7451 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0119 0.9942 0.9888 1.799e-06 -8.075e-07 -0.006767 1.356e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003268 -0.003064 -0.008685 0.006695 0.9698 0.9742 0.00624 0.8393 0.8283 0.01909 ] Network output: [ 0.9998 0.0009653 0.00131 -2.817e-05 1.265e-05 -0.001919 -2.123e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1896 -0.03179 -0.1865 0.195 0.9836 0.9933 0.2118 0.4506 0.8743 0.7202 ] Network output: [ -0.01118 1.001 1.01 8.216e-07 -3.689e-07 0.01089 6.192e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00565 0.0004608 0.004396 0.003992 0.9889 0.992 0.005754 0.8682 0.8983 0.01382 ] Network output: [ -0.0008149 0.003349 1.002 -9.12e-05 4.094e-05 0.9956 -6.873e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2007 0.09381 0.3308 0.1511 0.9851 0.994 0.2014 0.4551 0.8808 0.7148 ] Network output: [ 0.006913 -0.03395 0.9959 5.382e-05 -2.416e-05 1.024 4.056e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09906 0.08741 0.1797 0.2042 0.9873 0.992 0.09912 0.7756 0.872 0.307 ] Network output: [ -0.006801 0.03438 1.002 5.543e-05 -2.488e-05 0.9774 4.177e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09048 0.08855 0.1656 0.1958 0.9855 0.9913 0.09049 0.7024 0.8497 0.2437 ] Network output: [ 0.0002212 0.9998 -0.0004558 7.542e-06 -3.386e-06 1 5.684e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006138 Epoch 7452 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0119 0.9942 0.9888 1.795e-06 -8.058e-07 -0.006768 1.353e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003269 -0.003064 -0.008684 0.006694 0.9698 0.9742 0.00624 0.8393 0.8283 0.01909 ] Network output: [ 0.9998 0.0009637 0.001309 -2.815e-05 1.264e-05 -0.001917 -2.122e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1896 -0.03179 -0.1865 0.195 0.9836 0.9933 0.2118 0.4506 0.8743 0.7202 ] Network output: [ -0.01118 1.001 1.01 8.194e-07 -3.679e-07 0.01089 6.176e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00565 0.0004608 0.004397 0.003991 0.9889 0.992 0.005755 0.8682 0.8982 0.01382 ] Network output: [ -0.0008144 0.003347 1.002 -9.113e-05 4.091e-05 0.9957 -6.868e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2007 0.09382 0.3308 0.1511 0.9851 0.994 0.2014 0.4551 0.8808 0.7148 ] Network output: [ 0.00691 -0.03394 0.9959 5.377e-05 -2.414e-05 1.024 4.052e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09906 0.08742 0.1797 0.2042 0.9873 0.992 0.09913 0.7756 0.872 0.307 ] Network output: [ -0.006798 0.03436 1.002 5.538e-05 -2.486e-05 0.9774 4.174e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09048 0.08855 0.1656 0.1958 0.9855 0.9913 0.09049 0.7023 0.8497 0.2437 ] Network output: [ 0.0002211 0.9998 -0.0004553 7.536e-06 -3.383e-06 1 5.68e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006134 Epoch 7453 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0119 0.9942 0.9888 1.791e-06 -8.042e-07 -0.00677 1.35e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003269 -0.003064 -0.008682 0.006693 0.9698 0.9742 0.006241 0.8392 0.8283 0.01909 ] Network output: [ 0.9998 0.0009635 0.001308 -2.813e-05 1.263e-05 -0.001916 -2.12e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1896 -0.0318 -0.1865 0.195 0.9836 0.9933 0.2118 0.4505 0.8743 0.7202 ] Network output: [ -0.01118 1.001 1.01 8.173e-07 -3.669e-07 0.01089 6.159e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005651 0.0004608 0.004397 0.003991 0.9889 0.992 0.005755 0.8682 0.8982 0.01382 ] Network output: [ -0.000814 0.003346 1.002 -9.105e-05 4.087e-05 0.9957 -6.862e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2008 0.09382 0.3308 0.1511 0.9851 0.994 0.2014 0.4551 0.8808 0.7147 ] Network output: [ 0.006908 -0.03393 0.9959 5.373e-05 -2.412e-05 1.024 4.049e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09907 0.08742 0.1797 0.2042 0.9873 0.992 0.09913 0.7755 0.872 0.307 ] Network output: [ -0.006796 0.03435 1.002 5.534e-05 -2.484e-05 0.9774 4.171e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09047 0.08855 0.1656 0.1958 0.9855 0.9913 0.09049 0.7023 0.8497 0.2437 ] Network output: [ 0.0002209 0.9998 -0.0004548 7.53e-06 -3.381e-06 1 5.675e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000613 Epoch 7454 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01189 0.9942 0.9888 1.788e-06 -8.025e-07 -0.006772 1.347e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003269 -0.003065 -0.008681 0.006692 0.9698 0.9742 0.006241 0.8392 0.8283 0.01909 ] Network output: [ 0.9998 0.0009619 0.001307 -2.811e-05 1.262e-05 -0.001914 -2.118e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1896 -0.0318 -0.1864 0.195 0.9836 0.9933 0.2119 0.4505 0.8743 0.7202 ] Network output: [ -0.01118 1.001 1.01 8.151e-07 -3.659e-07 0.01088 6.143e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005651 0.0004608 0.004397 0.00399 0.9889 0.992 0.005756 0.8682 0.8982 0.01382 ] Network output: [ -0.0008135 0.003344 1.002 -9.097e-05 4.084e-05 0.9957 -6.856e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2008 0.09382 0.3308 0.1511 0.9851 0.994 0.2014 0.455 0.8808 0.7147 ] Network output: [ 0.006906 -0.03392 0.9959 5.368e-05 -2.41e-05 1.024 4.046e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09907 0.08743 0.1797 0.2042 0.9873 0.992 0.09914 0.7755 0.872 0.307 ] Network output: [ -0.006793 0.03433 1.002 5.53e-05 -2.482e-05 0.9775 4.167e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09047 0.08855 0.1656 0.1958 0.9855 0.9913 0.09049 0.7023 0.8497 0.2437 ] Network output: [ 0.0002208 0.9998 -0.0004544 7.524e-06 -3.378e-06 1 5.67e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006126 Epoch 7455 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01189 0.9942 0.9888 1.784e-06 -8.008e-07 -0.006774 1.344e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003269 -0.003065 -0.008679 0.006692 0.9698 0.9742 0.006241 0.8392 0.8283 0.01909 ] Network output: [ 0.9998 0.0009616 0.001306 -2.809e-05 1.261e-05 -0.001913 -2.117e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1897 -0.03181 -0.1864 0.1949 0.9836 0.9933 0.2119 0.4505 0.8743 0.7202 ] Network output: [ -0.01118 1.001 1.01 8.129e-07 -3.65e-07 0.01088 6.127e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005652 0.0004608 0.004397 0.003989 0.9889 0.992 0.005757 0.8682 0.8982 0.01382 ] Network output: [ -0.0008131 0.003344 1.002 -9.089e-05 4.08e-05 0.9957 -6.85e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2008 0.09383 0.3308 0.1511 0.9851 0.994 0.2014 0.455 0.8808 0.7147 ] Network output: [ 0.006903 -0.0339 0.9959 5.364e-05 -2.408e-05 1.024 4.042e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09908 0.08743 0.1797 0.2042 0.9873 0.992 0.09914 0.7755 0.872 0.307 ] Network output: [ -0.00679 0.03431 1.002 5.525e-05 -2.481e-05 0.9775 4.164e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09047 0.08855 0.1656 0.1958 0.9855 0.9913 0.09048 0.7022 0.8497 0.2437 ] Network output: [ 0.0002207 0.9998 -0.0004539 7.518e-06 -3.375e-06 1 5.666e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006122 Epoch 7456 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01189 0.9942 0.9888 1.78e-06 -7.992e-07 -0.006775 1.342e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003269 -0.003065 -0.008678 0.006691 0.9698 0.9742 0.006242 0.8392 0.8283 0.01909 ] Network output: [ 0.9998 0.0009601 0.001306 -2.806e-05 1.26e-05 -0.001911 -2.115e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1897 -0.03181 -0.1864 0.1949 0.9836 0.9933 0.2119 0.4505 0.8743 0.7202 ] Network output: [ -0.01118 1.001 1.01 8.108e-07 -3.64e-07 0.01087 6.11e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005653 0.0004608 0.004397 0.003989 0.9889 0.992 0.005757 0.8682 0.8982 0.01381 ] Network output: [ -0.0008126 0.003342 1.002 -9.081e-05 4.077e-05 0.9957 -6.844e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2008 0.09383 0.3308 0.1511 0.9851 0.994 0.2014 0.455 0.8808 0.7147 ] Network output: [ 0.006901 -0.03389 0.9959 5.36e-05 -2.406e-05 1.024 4.039e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09908 0.08744 0.1797 0.2042 0.9873 0.992 0.09915 0.7755 0.872 0.307 ] Network output: [ -0.006787 0.0343 1.002 5.521e-05 -2.479e-05 0.9775 4.161e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09047 0.08855 0.1656 0.1958 0.9855 0.9913 0.09048 0.7022 0.8497 0.2437 ] Network output: [ 0.0002206 0.9998 -0.0004535 7.512e-06 -3.372e-06 1 5.661e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006118 Epoch 7457 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01189 0.9942 0.9888 1.776e-06 -7.975e-07 -0.006777 1.339e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003269 -0.003065 -0.008677 0.00669 0.9698 0.9742 0.006242 0.8392 0.8283 0.01908 ] Network output: [ 0.9998 0.0009598 0.001305 -2.804e-05 1.259e-05 -0.00191 -2.113e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1897 -0.03181 -0.1864 0.1949 0.9836 0.9933 0.2119 0.4505 0.8743 0.7202 ] Network output: [ -0.01118 1.001 1.01 8.086e-07 -3.63e-07 0.01087 6.094e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005653 0.0004608 0.004397 0.003988 0.9889 0.992 0.005758 0.8682 0.8982 0.01381 ] Network output: [ -0.0008123 0.003342 1.002 -9.074e-05 4.073e-05 0.9957 -6.838e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2008 0.09383 0.3308 0.151 0.9851 0.994 0.2014 0.455 0.8808 0.7147 ] Network output: [ 0.006899 -0.03388 0.9959 5.355e-05 -2.404e-05 1.024 4.036e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09909 0.08744 0.1797 0.2042 0.9873 0.992 0.09915 0.7754 0.8719 0.307 ] Network output: [ -0.006785 0.03428 1.002 5.517e-05 -2.477e-05 0.9775 4.158e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09047 0.08854 0.1656 0.1958 0.9855 0.9913 0.09048 0.7022 0.8497 0.2437 ] Network output: [ 0.0002204 0.9998 -0.000453 7.506e-06 -3.37e-06 1 5.657e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006114 Epoch 7458 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01188 0.9942 0.9888 1.773e-06 -7.959e-07 -0.006779 1.336e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003269 -0.003065 -0.008675 0.006689 0.9698 0.9742 0.006242 0.8392 0.8283 0.01908 ] Network output: [ 0.9998 0.0009584 0.001304 -2.802e-05 1.258e-05 -0.001908 -2.112e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1897 -0.03182 -0.1864 0.1949 0.9836 0.9933 0.2119 0.4505 0.8743 0.7202 ] Network output: [ -0.01117 1.001 1.01 8.065e-07 -3.62e-07 0.01087 6.078e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005654 0.0004608 0.004397 0.003988 0.9889 0.992 0.005759 0.8682 0.8982 0.01381 ] Network output: [ -0.0008118 0.00334 1.002 -9.066e-05 4.07e-05 0.9957 -6.832e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2008 0.09384 0.3309 0.151 0.9851 0.994 0.2015 0.455 0.8808 0.7147 ] Network output: [ 0.006896 -0.03387 0.9958 5.351e-05 -2.402e-05 1.024 4.032e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09909 0.08744 0.1797 0.2041 0.9873 0.992 0.09916 0.7754 0.8719 0.307 ] Network output: [ -0.006782 0.03427 1.002 5.512e-05 -2.475e-05 0.9775 4.154e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09047 0.08854 0.1656 0.1958 0.9855 0.9913 0.09048 0.7021 0.8497 0.2437 ] Network output: [ 0.0002204 0.9998 -0.0004525 7.5e-06 -3.367e-06 1 5.652e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000611 Epoch 7459 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01188 0.9942 0.9888 1.769e-06 -7.942e-07 -0.00678 1.333e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00327 -0.003065 -0.008674 0.006688 0.9698 0.9742 0.006243 0.8392 0.8283 0.01908 ] Network output: [ 0.9998 0.000958 0.001303 -2.8e-05 1.257e-05 -0.001908 -2.11e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1897 -0.03182 -0.1864 0.1949 0.9836 0.9933 0.2119 0.4504 0.8743 0.7202 ] Network output: [ -0.01117 1.001 1.01 8.043e-07 -3.611e-07 0.01086 6.061e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005655 0.0004608 0.004397 0.003987 0.9889 0.992 0.005759 0.8682 0.8982 0.01381 ] Network output: [ -0.0008114 0.00334 1.002 -9.058e-05 4.067e-05 0.9957 -6.826e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2008 0.09384 0.3309 0.151 0.9851 0.994 0.2015 0.455 0.8808 0.7147 ] Network output: [ 0.006894 -0.03386 0.9958 5.346e-05 -2.4e-05 1.024 4.029e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0991 0.08745 0.1797 0.2041 0.9873 0.992 0.09916 0.7754 0.8719 0.307 ] Network output: [ -0.006779 0.03425 1.002 5.508e-05 -2.473e-05 0.9775 4.151e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09047 0.08854 0.1656 0.1958 0.9855 0.9913 0.09048 0.7021 0.8497 0.2437 ] Network output: [ 0.0002202 0.9998 -0.000452 7.494e-06 -3.364e-06 1 5.648e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006106 Epoch 7460 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01188 0.9942 0.9888 1.765e-06 -7.926e-07 -0.006782 1.33e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00327 -0.003066 -0.008673 0.006687 0.9698 0.9742 0.006243 0.8392 0.8283 0.01908 ] Network output: [ 0.9998 0.0009566 0.001302 -2.798e-05 1.256e-05 -0.001906 -2.108e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1897 -0.03182 -0.1863 0.1949 0.9836 0.9933 0.2119 0.4504 0.8743 0.7202 ] Network output: [ -0.01117 1.001 1.01 8.021e-07 -3.601e-07 0.01086 6.045e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005655 0.0004608 0.004398 0.003986 0.9889 0.992 0.00576 0.8681 0.8982 0.01381 ] Network output: [ -0.0008109 0.003338 1.002 -9.05e-05 4.063e-05 0.9957 -6.821e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2008 0.09384 0.3309 0.151 0.9851 0.994 0.2015 0.455 0.8808 0.7147 ] Network output: [ 0.006891 -0.03384 0.9958 5.342e-05 -2.398e-05 1.024 4.026e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09911 0.08745 0.1797 0.2041 0.9873 0.992 0.09917 0.7754 0.8719 0.307 ] Network output: [ -0.006776 0.03424 1.002 5.504e-05 -2.471e-05 0.9775 4.148e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09047 0.08854 0.1656 0.1958 0.9855 0.9913 0.09048 0.7021 0.8496 0.2437 ] Network output: [ 0.0002201 0.9998 -0.0004516 7.488e-06 -3.362e-06 1 5.643e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006102 Epoch 7461 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01188 0.9942 0.9888 1.762e-06 -7.909e-07 -0.006784 1.328e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00327 -0.003066 -0.008671 0.006686 0.9698 0.9742 0.006243 0.8392 0.8283 0.01908 ] Network output: [ 0.9998 0.0009562 0.001301 -2.796e-05 1.255e-05 -0.001905 -2.107e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1897 -0.03183 -0.1863 0.1949 0.9836 0.9933 0.2119 0.4504 0.8742 0.7202 ] Network output: [ -0.01117 1.001 1.01 8e-07 -3.591e-07 0.01086 6.029e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005656 0.0004608 0.004398 0.003986 0.9889 0.992 0.00576 0.8681 0.8982 0.01381 ] Network output: [ -0.0008105 0.003337 1.002 -9.043e-05 4.06e-05 0.9957 -6.815e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2009 0.09385 0.3309 0.151 0.9851 0.994 0.2015 0.4549 0.8808 0.7147 ] Network output: [ 0.006889 -0.03383 0.9958 5.337e-05 -2.396e-05 1.024 4.022e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09911 0.08746 0.1797 0.2041 0.9873 0.992 0.09918 0.7753 0.8719 0.307 ] Network output: [ -0.006774 0.03422 1.002 5.499e-05 -2.469e-05 0.9775 4.144e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09047 0.08854 0.1656 0.1958 0.9855 0.9913 0.09048 0.7021 0.8496 0.2437 ] Network output: [ 0.00022 0.9998 -0.0004511 7.482e-06 -3.359e-06 1 5.639e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006098 Epoch 7462 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01188 0.9942 0.9888 1.758e-06 -7.893e-07 -0.006785 1.325e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00327 -0.003066 -0.00867 0.006685 0.9698 0.9742 0.006244 0.8392 0.8283 0.01907 ] Network output: [ 0.9998 0.0009548 0.0013 -2.793e-05 1.254e-05 -0.001903 -2.105e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1897 -0.03183 -0.1863 0.1949 0.9836 0.9933 0.212 0.4504 0.8742 0.7202 ] Network output: [ -0.01117 1.001 1.01 7.978e-07 -3.582e-07 0.01085 6.013e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005656 0.0004608 0.004398 0.003985 0.9889 0.992 0.005761 0.8681 0.8982 0.01381 ] Network output: [ -0.00081 0.003336 1.002 -9.035e-05 4.056e-05 0.9957 -6.809e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2009 0.09385 0.3309 0.151 0.9851 0.994 0.2015 0.4549 0.8808 0.7147 ] Network output: [ 0.006887 -0.03382 0.9958 5.333e-05 -2.394e-05 1.024 4.019e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09912 0.08746 0.1797 0.2041 0.9873 0.992 0.09918 0.7753 0.8719 0.307 ] Network output: [ -0.006771 0.03421 1.002 5.495e-05 -2.467e-05 0.9775 4.141e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09047 0.08854 0.1656 0.1958 0.9855 0.9913 0.09048 0.702 0.8496 0.2437 ] Network output: [ 0.0002199 0.9998 -0.0004507 7.476e-06 -3.356e-06 1 5.634e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006094 Epoch 7463 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01187 0.9942 0.9889 1.754e-06 -7.876e-07 -0.006787 1.322e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00327 -0.003066 -0.008669 0.006684 0.9698 0.9742 0.006244 0.8392 0.8283 0.01907 ] Network output: [ 0.9998 0.0009544 0.0013 -2.791e-05 1.253e-05 -0.001902 -2.104e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1897 -0.03184 -0.1863 0.1949 0.9836 0.9933 0.212 0.4504 0.8742 0.7202 ] Network output: [ -0.01117 1.001 1.01 7.957e-07 -3.572e-07 0.01085 5.997e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005657 0.0004608 0.004398 0.003984 0.9889 0.992 0.005762 0.8681 0.8982 0.0138 ] Network output: [ -0.0008096 0.003335 1.002 -9.027e-05 4.053e-05 0.9957 -6.803e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2009 0.09385 0.3309 0.151 0.9851 0.994 0.2015 0.4549 0.8808 0.7147 ] Network output: [ 0.006884 -0.03381 0.9958 5.329e-05 -2.392e-05 1.024 4.016e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09912 0.08747 0.1797 0.2041 0.9873 0.992 0.09919 0.7753 0.8719 0.307 ] Network output: [ -0.006768 0.03419 1.002 5.491e-05 -2.465e-05 0.9775 4.138e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08854 0.1656 0.1958 0.9855 0.9913 0.09048 0.702 0.8496 0.2437 ] Network output: [ 0.0002197 0.9998 -0.0004502 7.47e-06 -3.354e-06 1 5.63e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000609 Epoch 7464 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01187 0.9942 0.9889 1.751e-06 -7.86e-07 -0.006789 1.319e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00327 -0.003066 -0.008667 0.006683 0.9698 0.9742 0.006244 0.8392 0.8283 0.01907 ] Network output: [ 0.9998 0.000953 0.001299 -2.789e-05 1.252e-05 -0.0019 -2.102e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1898 -0.03184 -0.1863 0.1949 0.9836 0.9933 0.212 0.4504 0.8742 0.7201 ] Network output: [ -0.01117 1.001 1.01 7.936e-07 -3.563e-07 0.01085 5.98e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005658 0.0004608 0.004398 0.003984 0.9889 0.992 0.005762 0.8681 0.8982 0.0138 ] Network output: [ -0.0008092 0.003333 1.002 -9.019e-05 4.049e-05 0.9957 -6.797e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2009 0.09386 0.3309 0.151 0.9851 0.994 0.2015 0.4549 0.8808 0.7147 ] Network output: [ 0.006882 -0.03379 0.9958 5.324e-05 -2.39e-05 1.024 4.012e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09913 0.08747 0.1797 0.2041 0.9873 0.992 0.09919 0.7753 0.8719 0.307 ] Network output: [ -0.006765 0.03418 1.002 5.486e-05 -2.463e-05 0.9775 4.135e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08854 0.1656 0.1958 0.9855 0.9913 0.09048 0.702 0.8496 0.2437 ] Network output: [ 0.0002196 0.9998 -0.0004497 7.464e-06 -3.351e-06 1 5.625e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006086 Epoch 7465 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01187 0.9942 0.9889 1.747e-06 -7.843e-07 -0.00679 1.317e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00327 -0.003066 -0.008666 0.006683 0.9698 0.9742 0.006245 0.8391 0.8283 0.01907 ] Network output: [ 0.9998 0.0009526 0.001298 -2.787e-05 1.251e-05 -0.001899 -2.1e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1898 -0.03184 -0.1862 0.1949 0.9836 0.9933 0.212 0.4504 0.8742 0.7201 ] Network output: [ -0.01117 1.001 1.01 7.914e-07 -3.553e-07 0.01084 5.964e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005658 0.0004608 0.004398 0.003983 0.9889 0.992 0.005763 0.8681 0.8982 0.0138 ] Network output: [ -0.0008088 0.003333 1.002 -9.012e-05 4.046e-05 0.9957 -6.791e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2009 0.09386 0.331 0.151 0.9851 0.994 0.2015 0.4549 0.8808 0.7147 ] Network output: [ 0.00688 -0.03378 0.9958 5.32e-05 -2.388e-05 1.024 4.009e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09913 0.08748 0.1797 0.2041 0.9873 0.992 0.0992 0.7752 0.8719 0.307 ] Network output: [ -0.006763 0.03416 1.002 5.482e-05 -2.461e-05 0.9775 4.131e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08854 0.1656 0.1958 0.9855 0.9913 0.09047 0.7019 0.8496 0.2437 ] Network output: [ 0.0002195 0.9998 -0.0004492 7.458e-06 -3.348e-06 1 5.62e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006082 Epoch 7466 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01187 0.9942 0.9889 1.743e-06 -7.827e-07 -0.006792 1.314e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00327 -0.003067 -0.008665 0.006682 0.9698 0.9742 0.006245 0.8391 0.8283 0.01907 ] Network output: [ 0.9998 0.0009513 0.001297 -2.785e-05 1.25e-05 -0.001897 -2.099e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1898 -0.03185 -0.1862 0.1949 0.9836 0.9933 0.212 0.4503 0.8742 0.7201 ] Network output: [ -0.01116 1.001 1.01 7.893e-07 -3.543e-07 0.01084 5.948e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005659 0.0004608 0.004398 0.003982 0.9889 0.992 0.005764 0.8681 0.8982 0.0138 ] Network output: [ -0.0008083 0.003331 1.002 -9.004e-05 4.042e-05 0.9957 -6.786e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2009 0.09386 0.331 0.151 0.9851 0.994 0.2016 0.4549 0.8808 0.7147 ] Network output: [ 0.006877 -0.03377 0.9958 5.315e-05 -2.386e-05 1.024 4.006e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09914 0.08748 0.1797 0.2041 0.9873 0.992 0.0992 0.7752 0.8719 0.307 ] Network output: [ -0.00676 0.03415 1.002 5.478e-05 -2.459e-05 0.9775 4.128e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08854 0.1656 0.1958 0.9855 0.9913 0.09047 0.7019 0.8496 0.2437 ] Network output: [ 0.0002194 0.9998 -0.0004488 7.452e-06 -3.345e-06 1 5.616e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006078 Epoch 7467 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01187 0.9942 0.9889 1.74e-06 -7.811e-07 -0.006794 1.311e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003271 -0.003067 -0.008663 0.006681 0.9698 0.9742 0.006245 0.8391 0.8283 0.01907 ] Network output: [ 0.9998 0.0009508 0.001296 -2.782e-05 1.249e-05 -0.001896 -2.097e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1898 -0.03185 -0.1862 0.1948 0.9836 0.9933 0.212 0.4503 0.8742 0.7201 ] Network output: [ -0.01116 1.001 1.01 7.871e-07 -3.534e-07 0.01084 5.932e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005659 0.0004608 0.004398 0.003982 0.9889 0.992 0.005764 0.8681 0.8982 0.0138 ] Network output: [ -0.0008079 0.003331 1.002 -8.996e-05 4.039e-05 0.9957 -6.78e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2009 0.09387 0.331 0.1509 0.9851 0.994 0.2016 0.4548 0.8807 0.7147 ] Network output: [ 0.006875 -0.03376 0.9958 5.311e-05 -2.384e-05 1.024 4.002e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09914 0.08748 0.1797 0.2041 0.9873 0.992 0.09921 0.7752 0.8719 0.307 ] Network output: [ -0.006757 0.03413 1.002 5.473e-05 -2.457e-05 0.9776 4.125e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08853 0.1656 0.1958 0.9855 0.9913 0.09047 0.7019 0.8496 0.2437 ] Network output: [ 0.0002192 0.9998 -0.0004483 7.446e-06 -3.343e-06 1 5.611e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006074 Epoch 7468 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01186 0.9942 0.9889 1.736e-06 -7.794e-07 -0.006795 1.308e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003271 -0.003067 -0.008662 0.00668 0.9698 0.9742 0.006246 0.8391 0.8282 0.01906 ] Network output: [ 0.9998 0.0009495 0.001295 -2.78e-05 1.248e-05 -0.001894 -2.095e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1898 -0.03186 -0.1862 0.1948 0.9836 0.9933 0.212 0.4503 0.8742 0.7201 ] Network output: [ -0.01116 1.001 1.01 7.85e-07 -3.524e-07 0.01083 5.916e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00566 0.0004608 0.004399 0.003981 0.9889 0.992 0.005765 0.8681 0.8982 0.0138 ] Network output: [ -0.0008074 0.003329 1.002 -8.988e-05 4.035e-05 0.9957 -6.774e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2009 0.09387 0.331 0.1509 0.9851 0.994 0.2016 0.4548 0.8807 0.7147 ] Network output: [ 0.006872 -0.03374 0.9958 5.306e-05 -2.382e-05 1.024 3.999e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09915 0.08749 0.1797 0.2041 0.9873 0.992 0.09921 0.7752 0.8718 0.307 ] Network output: [ -0.006755 0.03412 1.002 5.469e-05 -2.455e-05 0.9776 4.122e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08853 0.1656 0.1958 0.9855 0.9913 0.09047 0.7018 0.8496 0.2437 ] Network output: [ 0.0002191 0.9998 -0.0004479 7.44e-06 -3.34e-06 1 5.607e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000607 Epoch 7469 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01186 0.9942 0.9889 1.733e-06 -7.778e-07 -0.006797 1.306e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003271 -0.003067 -0.00866 0.006679 0.9698 0.9742 0.006246 0.8391 0.8282 0.01906 ] Network output: [ 0.9998 0.0009491 0.001294 -2.778e-05 1.247e-05 -0.001893 -2.094e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1898 -0.03186 -0.1862 0.1948 0.9836 0.9933 0.2121 0.4503 0.8742 0.7201 ] Network output: [ -0.01116 1.001 1.01 7.829e-07 -3.515e-07 0.01083 5.9e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005661 0.0004608 0.004399 0.003981 0.9889 0.992 0.005765 0.8681 0.8982 0.0138 ] Network output: [ -0.000807 0.003328 1.002 -8.981e-05 4.032e-05 0.9957 -6.768e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.201 0.09387 0.331 0.1509 0.9851 0.994 0.2016 0.4548 0.8807 0.7147 ] Network output: [ 0.00687 -0.03373 0.9958 5.302e-05 -2.38e-05 1.024 3.996e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09915 0.08749 0.1797 0.2041 0.9873 0.992 0.09922 0.7751 0.8718 0.307 ] Network output: [ -0.006752 0.0341 1.002 5.465e-05 -2.453e-05 0.9776 4.118e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08853 0.1656 0.1958 0.9855 0.9913 0.09047 0.7018 0.8495 0.2437 ] Network output: [ 0.000219 0.9998 -0.0004474 7.434e-06 -3.337e-06 1 5.602e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006066 Epoch 7470 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01186 0.9942 0.9889 1.729e-06 -7.762e-07 -0.006799 1.303e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003271 -0.003067 -0.008659 0.006678 0.9698 0.9742 0.006246 0.8391 0.8282 0.01906 ] Network output: [ 0.9998 0.0009478 0.001294 -2.776e-05 1.246e-05 -0.001891 -2.092e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1898 -0.03186 -0.1861 0.1948 0.9836 0.9933 0.2121 0.4503 0.8742 0.7201 ] Network output: [ -0.01116 1.001 1.01 7.807e-07 -3.505e-07 0.01083 5.884e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005661 0.0004608 0.004399 0.00398 0.9889 0.992 0.005766 0.8681 0.8982 0.0138 ] Network output: [ -0.0008066 0.003327 1.002 -8.973e-05 4.028e-05 0.9957 -6.762e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.201 0.09388 0.331 0.1509 0.9851 0.994 0.2016 0.4548 0.8807 0.7147 ] Network output: [ 0.006868 -0.03372 0.9958 5.298e-05 -2.378e-05 1.024 3.992e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09916 0.0875 0.1797 0.2041 0.9873 0.992 0.09922 0.7751 0.8718 0.307 ] Network output: [ -0.006749 0.03409 1.002 5.46e-05 -2.451e-05 0.9776 4.115e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08853 0.1656 0.1958 0.9855 0.9913 0.09047 0.7018 0.8495 0.2437 ] Network output: [ 0.0002189 0.9998 -0.000447 7.428e-06 -3.335e-06 1 5.598e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006062 Epoch 7471 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01186 0.9942 0.9889 1.725e-06 -7.745e-07 -0.0068 1.3e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003271 -0.003067 -0.008658 0.006677 0.9698 0.9742 0.006246 0.8391 0.8282 0.01906 ] Network output: [ 0.9998 0.0009473 0.001293 -2.774e-05 1.245e-05 -0.00189 -2.09e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1898 -0.03187 -0.1861 0.1948 0.9836 0.9933 0.2121 0.4503 0.8742 0.7201 ] Network output: [ -0.01116 1.001 1.01 7.786e-07 -3.495e-07 0.01082 5.868e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005662 0.0004608 0.004399 0.003979 0.9889 0.992 0.005767 0.868 0.8982 0.01379 ] Network output: [ -0.0008062 0.003326 1.002 -8.965e-05 4.025e-05 0.9957 -6.757e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.201 0.09388 0.3311 0.1509 0.9851 0.994 0.2016 0.4548 0.8807 0.7147 ] Network output: [ 0.006865 -0.03371 0.9958 5.293e-05 -2.376e-05 1.024 3.989e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09916 0.0875 0.1797 0.2041 0.9873 0.992 0.09923 0.7751 0.8718 0.307 ] Network output: [ -0.006746 0.03407 1.002 5.456e-05 -2.449e-05 0.9776 4.112e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08853 0.1656 0.1958 0.9855 0.9913 0.09047 0.7018 0.8495 0.2437 ] Network output: [ 0.0002188 0.9998 -0.0004465 7.422e-06 -3.332e-06 1 5.593e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006058 Epoch 7472 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01186 0.9942 0.9889 1.722e-06 -7.729e-07 -0.006802 1.297e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003271 -0.003068 -0.008656 0.006676 0.9698 0.9742 0.006247 0.8391 0.8282 0.01906 ] Network output: [ 0.9998 0.000946 0.001292 -2.772e-05 1.244e-05 -0.001888 -2.089e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1898 -0.03187 -0.1861 0.1948 0.9836 0.9933 0.2121 0.4502 0.8742 0.7201 ] Network output: [ -0.01116 1.001 1.01 7.765e-07 -3.486e-07 0.01082 5.852e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005662 0.0004608 0.004399 0.003979 0.9889 0.992 0.005767 0.868 0.8981 0.01379 ] Network output: [ -0.0008057 0.003324 1.002 -8.958e-05 4.021e-05 0.9957 -6.751e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.201 0.09388 0.3311 0.1509 0.9851 0.994 0.2016 0.4548 0.8807 0.7146 ] Network output: [ 0.006863 -0.0337 0.9958 5.289e-05 -2.374e-05 1.024 3.986e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09917 0.08751 0.1797 0.2041 0.9873 0.992 0.09923 0.775 0.8718 0.307 ] Network output: [ -0.006744 0.03406 1.002 5.452e-05 -2.447e-05 0.9776 4.109e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08853 0.1656 0.1958 0.9855 0.9913 0.09047 0.7017 0.8495 0.2437 ] Network output: [ 0.0002187 0.9998 -0.000446 7.416e-06 -3.329e-06 1 5.589e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006054 Epoch 7473 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01185 0.9942 0.9889 1.718e-06 -7.713e-07 -0.006804 1.295e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003271 -0.003068 -0.008655 0.006675 0.9698 0.9742 0.006247 0.8391 0.8282 0.01905 ] Network output: [ 0.9998 0.0009455 0.001291 -2.769e-05 1.243e-05 -0.001887 -2.087e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1899 -0.03187 -0.1861 0.1948 0.9836 0.9933 0.2121 0.4502 0.8742 0.7201 ] Network output: [ -0.01116 1.001 1.01 7.743e-07 -3.476e-07 0.01082 5.836e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005663 0.0004608 0.004399 0.003978 0.9889 0.992 0.005768 0.868 0.8981 0.01379 ] Network output: [ -0.0008053 0.003324 1.002 -8.95e-05 4.018e-05 0.9957 -6.745e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.201 0.09389 0.3311 0.1509 0.9851 0.994 0.2016 0.4547 0.8807 0.7146 ] Network output: [ 0.006861 -0.03368 0.9958 5.284e-05 -2.372e-05 1.024 3.983e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09918 0.08751 0.1797 0.204 0.9873 0.992 0.09924 0.775 0.8718 0.307 ] Network output: [ -0.006741 0.03404 1.002 5.447e-05 -2.446e-05 0.9776 4.105e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08853 0.1656 0.1958 0.9855 0.9913 0.09047 0.7017 0.8495 0.2437 ] Network output: [ 0.0002185 0.9998 -0.0004456 7.41e-06 -3.327e-06 1 5.584e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000605 Epoch 7474 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01185 0.9942 0.9889 1.714e-06 -7.697e-07 -0.006805 1.292e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003272 -0.003068 -0.008654 0.006674 0.9698 0.9742 0.006247 0.8391 0.8282 0.01905 ] Network output: [ 0.9998 0.0009442 0.00129 -2.767e-05 1.242e-05 -0.001885 -2.085e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1899 -0.03188 -0.1861 0.1948 0.9836 0.9933 0.2121 0.4502 0.8742 0.7201 ] Network output: [ -0.01116 1.001 1.01 7.722e-07 -3.467e-07 0.01081 5.82e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005664 0.0004608 0.004399 0.003977 0.9889 0.992 0.005769 0.868 0.8981 0.01379 ] Network output: [ -0.0008048 0.003322 1.002 -8.942e-05 4.014e-05 0.9957 -6.739e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.201 0.09389 0.3311 0.1509 0.9851 0.994 0.2017 0.4547 0.8807 0.7146 ] Network output: [ 0.006858 -0.03367 0.9958 5.28e-05 -2.37e-05 1.024 3.979e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09918 0.08752 0.1798 0.204 0.9873 0.992 0.09925 0.775 0.8718 0.307 ] Network output: [ -0.006738 0.03403 1.002 5.443e-05 -2.444e-05 0.9776 4.102e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08853 0.1656 0.1958 0.9855 0.9913 0.09047 0.7017 0.8495 0.2437 ] Network output: [ 0.0002184 0.9998 -0.0004451 7.404e-06 -3.324e-06 1 5.58e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006046 Epoch 7475 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01185 0.9942 0.9889 1.711e-06 -7.68e-07 -0.006807 1.289e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003272 -0.003068 -0.008652 0.006674 0.9698 0.9742 0.006248 0.8391 0.8282 0.01905 ] Network output: [ 0.9998 0.0009437 0.001289 -2.765e-05 1.241e-05 -0.001884 -2.084e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1899 -0.03188 -0.1861 0.1948 0.9836 0.9933 0.2121 0.4502 0.8742 0.7201 ] Network output: [ -0.01115 1.001 1.01 7.701e-07 -3.457e-07 0.01081 5.804e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005664 0.0004608 0.004399 0.003977 0.9889 0.992 0.005769 0.868 0.8981 0.01379 ] Network output: [ -0.0008044 0.003322 1.002 -8.934e-05 4.011e-05 0.9957 -6.733e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.201 0.09389 0.3311 0.1509 0.9851 0.994 0.2017 0.4547 0.8807 0.7146 ] Network output: [ 0.006856 -0.03366 0.9958 5.276e-05 -2.368e-05 1.024 3.976e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09919 0.08752 0.1798 0.204 0.9873 0.992 0.09925 0.775 0.8718 0.307 ] Network output: [ -0.006736 0.03401 1.002 5.439e-05 -2.442e-05 0.9776 4.099e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08853 0.1656 0.1958 0.9855 0.9913 0.09047 0.7016 0.8495 0.2437 ] Network output: [ 0.0002183 0.9998 -0.0004446 7.398e-06 -3.321e-06 1 5.575e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006042 Epoch 7476 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01185 0.9942 0.9889 1.707e-06 -7.664e-07 -0.006809 1.287e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003272 -0.003068 -0.008651 0.006673 0.9698 0.9742 0.006248 0.839 0.8282 0.01905 ] Network output: [ 0.9998 0.0009425 0.001288 -2.763e-05 1.24e-05 -0.001883 -2.082e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1899 -0.03189 -0.186 0.1948 0.9836 0.9933 0.2121 0.4502 0.8742 0.7201 ] Network output: [ -0.01115 1.001 1.01 7.68e-07 -3.448e-07 0.01081 5.788e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005665 0.0004608 0.0044 0.003976 0.9889 0.992 0.00577 0.868 0.8981 0.01379 ] Network output: [ -0.000804 0.00332 1.002 -8.927e-05 4.008e-05 0.9957 -6.728e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.201 0.0939 0.3311 0.1509 0.9851 0.994 0.2017 0.4547 0.8807 0.7146 ] Network output: [ 0.006854 -0.03365 0.9958 5.271e-05 -2.366e-05 1.024 3.973e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09919 0.08752 0.1798 0.204 0.9873 0.992 0.09926 0.7749 0.8718 0.307 ] Network output: [ -0.006733 0.034 1.002 5.434e-05 -2.44e-05 0.9776 4.096e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08853 0.1656 0.1958 0.9855 0.9913 0.09046 0.7016 0.8495 0.2437 ] Network output: [ 0.0002182 0.9998 -0.0004442 7.392e-06 -3.318e-06 1 5.571e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006038 Epoch 7477 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01185 0.9942 0.9889 1.704e-06 -7.648e-07 -0.00681 1.284e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003272 -0.003068 -0.00865 0.006672 0.9698 0.9742 0.006248 0.839 0.8282 0.01905 ] Network output: [ 0.9998 0.000942 0.001288 -2.761e-05 1.239e-05 -0.001881 -2.08e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1899 -0.03189 -0.186 0.1948 0.9836 0.9933 0.2122 0.4502 0.8742 0.7201 ] Network output: [ -0.01115 1.001 1.01 7.659e-07 -3.438e-07 0.0108 5.772e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005666 0.0004608 0.0044 0.003976 0.9889 0.992 0.005771 0.868 0.8981 0.01379 ] Network output: [ -0.0008036 0.003319 1.002 -8.919e-05 4.004e-05 0.9957 -6.722e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2011 0.0939 0.3311 0.1508 0.9851 0.994 0.2017 0.4547 0.8807 0.7146 ] Network output: [ 0.006851 -0.03363 0.9958 5.267e-05 -2.365e-05 1.024 3.969e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0992 0.08753 0.1798 0.204 0.9873 0.992 0.09926 0.7749 0.8718 0.307 ] Network output: [ -0.00673 0.03398 1.002 5.43e-05 -2.438e-05 0.9776 4.092e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08853 0.1656 0.1958 0.9855 0.9913 0.09046 0.7016 0.8495 0.2437 ] Network output: [ 0.000218 0.9998 -0.0004437 7.386e-06 -3.316e-06 1 5.566e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006034 Epoch 7478 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01184 0.9942 0.9889 1.7e-06 -7.632e-07 -0.006812 1.281e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003272 -0.003069 -0.008648 0.006671 0.9698 0.9742 0.006249 0.839 0.8282 0.01905 ] Network output: [ 0.9998 0.0009408 0.001287 -2.758e-05 1.238e-05 -0.00188 -2.079e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1899 -0.03189 -0.186 0.1947 0.9836 0.9933 0.2122 0.4502 0.8742 0.7201 ] Network output: [ -0.01115 1.001 1.01 7.637e-07 -3.429e-07 0.0108 5.756e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005666 0.0004608 0.0044 0.003975 0.9889 0.992 0.005771 0.868 0.8981 0.01378 ] Network output: [ -0.0008031 0.003318 1.002 -8.911e-05 4.001e-05 0.9957 -6.716e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2011 0.0939 0.3312 0.1508 0.9851 0.994 0.2017 0.4547 0.8807 0.7146 ] Network output: [ 0.006849 -0.03362 0.9958 5.263e-05 -2.363e-05 1.024 3.966e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0992 0.08753 0.1798 0.204 0.9873 0.992 0.09927 0.7749 0.8718 0.307 ] Network output: [ -0.006727 0.03397 1.002 5.426e-05 -2.436e-05 0.9776 4.089e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08852 0.1656 0.1958 0.9855 0.9913 0.09046 0.7015 0.8494 0.2437 ] Network output: [ 0.0002179 0.9998 -0.0004433 7.38e-06 -3.313e-06 1 5.562e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000603 Epoch 7479 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01184 0.9942 0.9889 1.696e-06 -7.616e-07 -0.006814 1.278e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003272 -0.003069 -0.008647 0.00667 0.9698 0.9742 0.006249 0.839 0.8282 0.01904 ] Network output: [ 0.9998 0.0009402 0.001286 -2.756e-05 1.237e-05 -0.001879 -2.077e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1899 -0.0319 -0.186 0.1947 0.9836 0.9933 0.2122 0.4501 0.8742 0.7201 ] Network output: [ -0.01115 1.001 1.01 7.616e-07 -3.419e-07 0.0108 5.74e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005667 0.0004608 0.0044 0.003974 0.9889 0.992 0.005772 0.868 0.8981 0.01378 ] Network output: [ -0.0008027 0.003317 1.002 -8.904e-05 3.997e-05 0.9957 -6.71e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2011 0.09391 0.3312 0.1508 0.9851 0.994 0.2017 0.4546 0.8807 0.7146 ] Network output: [ 0.006847 -0.03361 0.9958 5.258e-05 -2.361e-05 1.024 3.963e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09921 0.08754 0.1798 0.204 0.9873 0.992 0.09927 0.7749 0.8718 0.307 ] Network output: [ -0.006725 0.03395 1.002 5.421e-05 -2.434e-05 0.9776 4.086e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08852 0.1656 0.1958 0.9855 0.9913 0.09046 0.7015 0.8494 0.2437 ] Network output: [ 0.0002178 0.9998 -0.0004428 7.374e-06 -3.31e-06 1 5.557e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006026 Epoch 7480 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01184 0.9942 0.9889 1.693e-06 -7.6e-07 -0.006815 1.276e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003272 -0.003069 -0.008646 0.006669 0.9698 0.9742 0.006249 0.839 0.8282 0.01904 ] Network output: [ 0.9998 0.000939 0.001285 -2.754e-05 1.236e-05 -0.001877 -2.076e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1899 -0.0319 -0.186 0.1947 0.9836 0.9933 0.2122 0.4501 0.8741 0.7201 ] Network output: [ -0.01115 1.001 1.01 7.595e-07 -3.41e-07 0.01079 5.724e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005667 0.0004608 0.0044 0.003974 0.9889 0.992 0.005772 0.868 0.8981 0.01378 ] Network output: [ -0.0008022 0.003316 1.002 -8.896e-05 3.994e-05 0.9957 -6.704e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2011 0.09391 0.3312 0.1508 0.9851 0.994 0.2017 0.4546 0.8807 0.7146 ] Network output: [ 0.006844 -0.0336 0.9958 5.254e-05 -2.359e-05 1.024 3.959e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09921 0.08754 0.1798 0.204 0.9873 0.992 0.09928 0.7748 0.8717 0.307 ] Network output: [ -0.006722 0.03394 1.002 5.417e-05 -2.432e-05 0.9776 4.083e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08852 0.1656 0.1958 0.9855 0.9913 0.09046 0.7015 0.8494 0.2437 ] Network output: [ 0.0002177 0.9998 -0.0004424 7.368e-06 -3.308e-06 1 5.553e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006022 Epoch 7481 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01184 0.9942 0.9889 1.689e-06 -7.584e-07 -0.006817 1.273e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003273 -0.003069 -0.008644 0.006668 0.9698 0.9742 0.00625 0.839 0.8282 0.01904 ] Network output: [ 0.9998 0.0009384 0.001284 -2.752e-05 1.235e-05 -0.001876 -2.074e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1899 -0.0319 -0.1859 0.1947 0.9836 0.9933 0.2122 0.4501 0.8741 0.7201 ] Network output: [ -0.01115 1.001 1.01 7.574e-07 -3.4e-07 0.01079 5.708e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005668 0.0004608 0.0044 0.003973 0.9889 0.992 0.005773 0.8679 0.8981 0.01378 ] Network output: [ -0.0008018 0.003315 1.002 -8.888e-05 3.99e-05 0.9957 -6.699e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2011 0.09391 0.3312 0.1508 0.9851 0.994 0.2017 0.4546 0.8807 0.7146 ] Network output: [ 0.006842 -0.03359 0.9958 5.249e-05 -2.357e-05 1.024 3.956e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09922 0.08755 0.1798 0.204 0.9873 0.992 0.09928 0.7748 0.8717 0.307 ] Network output: [ -0.006719 0.03392 1.002 5.413e-05 -2.43e-05 0.9777 4.079e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08852 0.1656 0.1958 0.9855 0.9913 0.09046 0.7015 0.8494 0.2437 ] Network output: [ 0.0002176 0.9998 -0.0004419 7.362e-06 -3.305e-06 1 5.548e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006018 Epoch 7482 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01183 0.9942 0.9889 1.686e-06 -7.568e-07 -0.006819 1.27e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003273 -0.003069 -0.008643 0.006667 0.9698 0.9742 0.00625 0.839 0.8282 0.01904 ] Network output: [ 0.9998 0.0009373 0.001283 -2.75e-05 1.234e-05 -0.001874 -2.072e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.19 -0.03191 -0.1859 0.1947 0.9836 0.9933 0.2122 0.4501 0.8741 0.7201 ] Network output: [ -0.01115 1.001 1.01 7.553e-07 -3.391e-07 0.01078 5.692e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005669 0.0004608 0.0044 0.003972 0.9889 0.992 0.005774 0.8679 0.8981 0.01378 ] Network output: [ -0.0008013 0.003313 1.002 -8.881e-05 3.987e-05 0.9957 -6.693e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2011 0.09392 0.3312 0.1508 0.9851 0.994 0.2018 0.4546 0.8807 0.7146 ] Network output: [ 0.006839 -0.03357 0.9958 5.245e-05 -2.355e-05 1.024 3.953e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09922 0.08755 0.1798 0.204 0.9873 0.992 0.09929 0.7748 0.8717 0.3069 ] Network output: [ -0.006717 0.03391 1.002 5.409e-05 -2.428e-05 0.9777 4.076e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08852 0.1656 0.1958 0.9855 0.9913 0.09046 0.7014 0.8494 0.2437 ] Network output: [ 0.0002175 0.9998 -0.0004414 7.356e-06 -3.302e-06 1 5.544e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006014 Epoch 7483 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01183 0.9943 0.9889 1.682e-06 -7.551e-07 -0.00682 1.268e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003273 -0.003069 -0.008642 0.006666 0.9698 0.9742 0.00625 0.839 0.8282 0.01904 ] Network output: [ 0.9998 0.0009367 0.001283 -2.748e-05 1.233e-05 -0.001873 -2.071e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.19 -0.03191 -0.1859 0.1947 0.9836 0.9933 0.2122 0.4501 0.8741 0.7201 ] Network output: [ -0.01114 1.001 1.01 7.532e-07 -3.381e-07 0.01078 5.676e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005669 0.0004608 0.0044 0.003972 0.9889 0.992 0.005774 0.8679 0.8981 0.01378 ] Network output: [ -0.0008009 0.003313 1.002 -8.873e-05 3.983e-05 0.9957 -6.687e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2011 0.09392 0.3312 0.1508 0.9851 0.994 0.2018 0.4546 0.8807 0.7146 ] Network output: [ 0.006837 -0.03356 0.9958 5.241e-05 -2.353e-05 1.024 3.949e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09923 0.08756 0.1798 0.204 0.9873 0.992 0.09929 0.7748 0.8717 0.3069 ] Network output: [ -0.006714 0.03389 1.002 5.404e-05 -2.426e-05 0.9777 4.073e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08852 0.1656 0.1958 0.9855 0.9913 0.09046 0.7014 0.8494 0.2437 ] Network output: [ 0.0002173 0.9998 -0.000441 7.35e-06 -3.3e-06 1 5.539e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000601 Epoch 7484 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01183 0.9943 0.9889 1.679e-06 -7.535e-07 -0.006822 1.265e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003273 -0.00307 -0.00864 0.006666 0.9698 0.9742 0.006251 0.839 0.8282 0.01903 ] Network output: [ 0.9998 0.0009355 0.001282 -2.745e-05 1.232e-05 -0.001871 -2.069e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.19 -0.03192 -0.1859 0.1947 0.9836 0.9933 0.2122 0.4501 0.8741 0.7201 ] Network output: [ -0.01114 1.001 1.01 7.511e-07 -3.372e-07 0.01078 5.661e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00567 0.0004608 0.0044 0.003971 0.9889 0.992 0.005775 0.8679 0.8981 0.01378 ] Network output: [ -0.0008005 0.003311 1.002 -8.865e-05 3.98e-05 0.9957 -6.681e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2011 0.09392 0.3312 0.1508 0.9851 0.994 0.2018 0.4546 0.8807 0.7146 ] Network output: [ 0.006835 -0.03355 0.9958 5.236e-05 -2.351e-05 1.024 3.946e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09924 0.08756 0.1798 0.204 0.9873 0.992 0.0993 0.7747 0.8717 0.3069 ] Network output: [ -0.006711 0.03388 1.002 5.4e-05 -2.424e-05 0.9777 4.07e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08852 0.1656 0.1958 0.9855 0.9913 0.09046 0.7014 0.8494 0.2437 ] Network output: [ 0.0002172 0.9998 -0.0004405 7.344e-06 -3.297e-06 1 5.535e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006006 Epoch 7485 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01183 0.9943 0.9889 1.675e-06 -7.519e-07 -0.006823 1.262e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003273 -0.00307 -0.008639 0.006665 0.9698 0.9742 0.006251 0.839 0.8281 0.01903 ] Network output: [ 0.9998 0.0009349 0.001281 -2.743e-05 1.232e-05 -0.00187 -2.067e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.19 -0.03192 -0.1859 0.1947 0.9836 0.9933 0.2123 0.45 0.8741 0.72 ] Network output: [ -0.01114 1.001 1.01 7.49e-07 -3.363e-07 0.01077 5.645e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00567 0.0004608 0.004401 0.003971 0.9889 0.992 0.005776 0.8679 0.8981 0.01377 ] Network output: [ -0.0008001 0.00331 1.002 -8.858e-05 3.977e-05 0.9957 -6.675e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2012 0.09393 0.3313 0.1508 0.9851 0.994 0.2018 0.4545 0.8807 0.7146 ] Network output: [ 0.006832 -0.03354 0.9958 5.232e-05 -2.349e-05 1.024 3.943e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09924 0.08757 0.1798 0.204 0.9873 0.992 0.0993 0.7747 0.8717 0.3069 ] Network output: [ -0.006709 0.03386 1.002 5.396e-05 -2.422e-05 0.9777 4.066e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08852 0.1656 0.1958 0.9855 0.9913 0.09046 0.7013 0.8494 0.2437 ] Network output: [ 0.0002171 0.9998 -0.0004401 7.338e-06 -3.294e-06 1 5.53e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0006002 Epoch 7486 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01183 0.9943 0.9889 1.671e-06 -7.503e-07 -0.006825 1.26e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003273 -0.00307 -0.008638 0.006664 0.9698 0.9742 0.006251 0.839 0.8281 0.01903 ] Network output: [ 0.9998 0.0009338 0.00128 -2.741e-05 1.231e-05 -0.001868 -2.066e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.19 -0.03192 -0.1859 0.1947 0.9836 0.9933 0.2123 0.45 0.8741 0.72 ] Network output: [ -0.01114 1.001 1.01 7.469e-07 -3.353e-07 0.01077 5.629e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005671 0.0004608 0.004401 0.00397 0.9889 0.992 0.005776 0.8679 0.8981 0.01377 ] Network output: [ -0.0007996 0.003309 1.002 -8.85e-05 3.973e-05 0.9957 -6.67e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2012 0.09393 0.3313 0.1508 0.9851 0.994 0.2018 0.4545 0.8807 0.7146 ] Network output: [ 0.00683 -0.03353 0.9958 5.227e-05 -2.347e-05 1.024 3.94e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09925 0.08757 0.1798 0.204 0.9873 0.992 0.09931 0.7747 0.8717 0.3069 ] Network output: [ -0.006706 0.03385 1.002 5.391e-05 -2.42e-05 0.9777 4.063e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08852 0.1656 0.1958 0.9855 0.9913 0.09046 0.7013 0.8494 0.2437 ] Network output: [ 0.000217 0.9998 -0.0004396 7.332e-06 -3.292e-06 1 5.526e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005999 Epoch 7487 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01182 0.9943 0.9889 1.668e-06 -7.487e-07 -0.006827 1.257e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003273 -0.00307 -0.008636 0.006663 0.9698 0.9742 0.006252 0.839 0.8281 0.01903 ] Network output: [ 0.9998 0.0009332 0.001279 -2.739e-05 1.23e-05 -0.001867 -2.064e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.19 -0.03193 -0.1858 0.1947 0.9836 0.9933 0.2123 0.45 0.8741 0.72 ] Network output: [ -0.01114 1.001 1.01 7.448e-07 -3.344e-07 0.01077 5.613e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005672 0.0004608 0.004401 0.003969 0.9889 0.992 0.005777 0.8679 0.8981 0.01377 ] Network output: [ -0.0007992 0.003308 1.002 -8.842e-05 3.97e-05 0.9957 -6.664e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2012 0.09394 0.3313 0.1507 0.9851 0.994 0.2018 0.4545 0.8806 0.7146 ] Network output: [ 0.006828 -0.03351 0.9958 5.223e-05 -2.345e-05 1.024 3.936e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09925 0.08757 0.1798 0.204 0.9873 0.992 0.09932 0.7747 0.8717 0.3069 ] Network output: [ -0.006703 0.03383 1.002 5.387e-05 -2.418e-05 0.9777 4.06e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08852 0.1656 0.1958 0.9855 0.9913 0.09046 0.7013 0.8493 0.2437 ] Network output: [ 0.0002168 0.9998 -0.0004391 7.326e-06 -3.289e-06 1 5.521e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005995 Epoch 7488 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01182 0.9943 0.9889 1.664e-06 -7.472e-07 -0.006828 1.254e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003274 -0.00307 -0.008635 0.006662 0.9698 0.9742 0.006252 0.8389 0.8281 0.01903 ] Network output: [ 0.9998 0.0009321 0.001278 -2.737e-05 1.229e-05 -0.001865 -2.062e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.19 -0.03193 -0.1858 0.1947 0.9836 0.9933 0.2123 0.45 0.8741 0.72 ] Network output: [ -0.01114 1.001 1.01 7.427e-07 -3.334e-07 0.01076 5.597e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005672 0.0004608 0.004401 0.003969 0.9889 0.992 0.005777 0.8679 0.8981 0.01377 ] Network output: [ -0.0007987 0.003307 1.002 -8.835e-05 3.966e-05 0.9957 -6.658e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2012 0.09394 0.3313 0.1507 0.9851 0.994 0.2018 0.4545 0.8806 0.7146 ] Network output: [ 0.006825 -0.0335 0.9958 5.219e-05 -2.343e-05 1.024 3.933e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09926 0.08758 0.1798 0.2039 0.9873 0.992 0.09932 0.7746 0.8717 0.3069 ] Network output: [ -0.006701 0.03382 1.002 5.383e-05 -2.417e-05 0.9777 4.057e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08852 0.1655 0.1958 0.9855 0.9913 0.09045 0.7012 0.8493 0.2437 ] Network output: [ 0.0002167 0.9998 -0.0004387 7.32e-06 -3.286e-06 1 5.517e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005991 Epoch 7489 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01182 0.9943 0.9889 1.661e-06 -7.456e-07 -0.00683 1.252e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003274 -0.00307 -0.008634 0.006661 0.9698 0.9742 0.006252 0.8389 0.8281 0.01903 ] Network output: [ 0.9998 0.0009314 0.001277 -2.734e-05 1.228e-05 -0.001864 -2.061e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.19 -0.03193 -0.1858 0.1947 0.9836 0.9933 0.2123 0.45 0.8741 0.72 ] Network output: [ -0.01114 1.001 1.01 7.406e-07 -3.325e-07 0.01076 5.582e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005673 0.0004608 0.004401 0.003968 0.9889 0.992 0.005778 0.8679 0.8981 0.01377 ] Network output: [ -0.0007983 0.003306 1.002 -8.827e-05 3.963e-05 0.9957 -6.652e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2012 0.09394 0.3313 0.1507 0.9851 0.994 0.2018 0.4545 0.8806 0.7146 ] Network output: [ 0.006823 -0.03349 0.9958 5.214e-05 -2.341e-05 1.024 3.93e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09926 0.08758 0.1798 0.2039 0.9873 0.992 0.09933 0.7746 0.8717 0.3069 ] Network output: [ -0.006698 0.0338 1.002 5.379e-05 -2.415e-05 0.9777 4.053e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08851 0.1655 0.1958 0.9855 0.9913 0.09045 0.7012 0.8493 0.2437 ] Network output: [ 0.0002166 0.9998 -0.0004382 7.314e-06 -3.284e-06 1 5.512e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005987 Epoch 7490 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01182 0.9943 0.9889 1.657e-06 -7.44e-07 -0.006832 1.249e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003274 -0.003071 -0.008632 0.00666 0.9698 0.9742 0.006253 0.8389 0.8281 0.01902 ] Network output: [ 0.9998 0.0009303 0.001277 -2.732e-05 1.227e-05 -0.001863 -2.059e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.19 -0.03194 -0.1858 0.1946 0.9836 0.9933 0.2123 0.45 0.8741 0.72 ] Network output: [ -0.01114 1.001 1.01 7.385e-07 -3.316e-07 0.01076 5.566e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005674 0.0004608 0.004401 0.003967 0.9889 0.992 0.005779 0.8679 0.8981 0.01377 ] Network output: [ -0.0007979 0.003305 1.002 -8.82e-05 3.959e-05 0.9957 -6.647e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2012 0.09395 0.3313 0.1507 0.9851 0.994 0.2019 0.4545 0.8806 0.7146 ] Network output: [ 0.006821 -0.03348 0.9958 5.21e-05 -2.339e-05 1.024 3.926e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09927 0.08759 0.1798 0.2039 0.9873 0.992 0.09933 0.7746 0.8717 0.3069 ] Network output: [ -0.006695 0.03379 1.002 5.374e-05 -2.413e-05 0.9777 4.05e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08851 0.1655 0.1958 0.9855 0.9913 0.09045 0.7012 0.8493 0.2437 ] Network output: [ 0.0002165 0.9998 -0.0004378 7.308e-06 -3.281e-06 1 5.508e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005983 Epoch 7491 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01182 0.9943 0.9889 1.654e-06 -7.424e-07 -0.006833 1.246e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003274 -0.003071 -0.008631 0.006659 0.9698 0.9742 0.006253 0.8389 0.8281 0.01902 ] Network output: [ 0.9998 0.0009297 0.001276 -2.73e-05 1.226e-05 -0.001861 -2.058e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1901 -0.03194 -0.1858 0.1946 0.9836 0.9933 0.2123 0.4499 0.8741 0.72 ] Network output: [ -0.01113 1.001 1.01 7.364e-07 -3.306e-07 0.01075 5.55e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005674 0.0004608 0.004401 0.003967 0.9889 0.992 0.005779 0.8678 0.8981 0.01377 ] Network output: [ -0.0007975 0.003304 1.002 -8.812e-05 3.956e-05 0.9957 -6.641e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2012 0.09395 0.3313 0.1507 0.9851 0.994 0.2019 0.4545 0.8806 0.7146 ] Network output: [ 0.006818 -0.03346 0.9958 5.206e-05 -2.337e-05 1.024 3.923e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09927 0.08759 0.1798 0.2039 0.9873 0.992 0.09934 0.7746 0.8716 0.3069 ] Network output: [ -0.006693 0.03377 1.002 5.37e-05 -2.411e-05 0.9777 4.047e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08851 0.1655 0.1958 0.9855 0.9913 0.09045 0.7012 0.8493 0.2438 ] Network output: [ 0.0002164 0.9998 -0.0004373 7.302e-06 -3.278e-06 1 5.503e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005979 Epoch 7492 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01181 0.9943 0.9889 1.65e-06 -7.408e-07 -0.006835 1.244e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003274 -0.003071 -0.00863 0.006658 0.9698 0.9742 0.006253 0.8389 0.8281 0.01902 ] Network output: [ 0.9998 0.0009286 0.001275 -2.728e-05 1.225e-05 -0.00186 -2.056e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1901 -0.03195 -0.1857 0.1946 0.9836 0.9933 0.2124 0.4499 0.8741 0.72 ] Network output: [ -0.01113 1.001 1.01 7.344e-07 -3.297e-07 0.01075 5.534e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005675 0.0004608 0.004401 0.003966 0.9889 0.992 0.00578 0.8678 0.8981 0.01376 ] Network output: [ -0.000797 0.003302 1.002 -8.804e-05 3.953e-05 0.9957 -6.635e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2012 0.09395 0.3314 0.1507 0.9851 0.994 0.2019 0.4544 0.8806 0.7145 ] Network output: [ 0.006816 -0.03345 0.9958 5.201e-05 -2.335e-05 1.024 3.92e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09928 0.0876 0.1798 0.2039 0.9873 0.992 0.09934 0.7745 0.8716 0.3069 ] Network output: [ -0.00669 0.03376 1.002 5.366e-05 -2.409e-05 0.9777 4.044e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08851 0.1655 0.1958 0.9855 0.9913 0.09045 0.7011 0.8493 0.2438 ] Network output: [ 0.0002163 0.9998 -0.0004369 7.296e-06 -3.276e-06 1 5.499e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005975 Epoch 7493 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01181 0.9943 0.9889 1.647e-06 -7.392e-07 -0.006836 1.241e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003274 -0.003071 -0.008628 0.006658 0.9698 0.9742 0.006254 0.8389 0.8281 0.01902 ] Network output: [ 0.9998 0.000928 0.001274 -2.726e-05 1.224e-05 -0.001858 -2.054e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1901 -0.03195 -0.1857 0.1946 0.9836 0.9933 0.2124 0.4499 0.8741 0.72 ] Network output: [ -0.01113 1.001 1.01 7.323e-07 -3.287e-07 0.01075 5.519e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005675 0.0004608 0.004402 0.003966 0.9889 0.992 0.005781 0.8678 0.898 0.01376 ] Network output: [ -0.0007966 0.003302 1.002 -8.797e-05 3.949e-05 0.9957 -6.629e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2012 0.09396 0.3314 0.1507 0.9851 0.994 0.2019 0.4544 0.8806 0.7145 ] Network output: [ 0.006814 -0.03344 0.9958 5.197e-05 -2.333e-05 1.024 3.917e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09928 0.0876 0.1798 0.2039 0.9873 0.992 0.09935 0.7745 0.8716 0.3069 ] Network output: [ -0.006687 0.03374 1.002 5.361e-05 -2.407e-05 0.9777 4.04e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08851 0.1655 0.1958 0.9855 0.9913 0.09045 0.7011 0.8493 0.2438 ] Network output: [ 0.0002161 0.9998 -0.0004364 7.29e-06 -3.273e-06 1 5.494e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005971 Epoch 7494 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01181 0.9943 0.9889 1.643e-06 -7.376e-07 -0.006838 1.238e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003274 -0.003071 -0.008627 0.006657 0.9698 0.9742 0.006254 0.8389 0.8281 0.01902 ] Network output: [ 0.9998 0.0009269 0.001273 -2.724e-05 1.223e-05 -0.001857 -2.053e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1901 -0.03195 -0.1857 0.1946 0.9836 0.9933 0.2124 0.4499 0.8741 0.72 ] Network output: [ -0.01113 1.001 1.01 7.302e-07 -3.278e-07 0.01074 5.503e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005676 0.0004608 0.004402 0.003965 0.9889 0.992 0.005781 0.8678 0.898 0.01376 ] Network output: [ -0.0007961 0.0033 1.002 -8.789e-05 3.946e-05 0.9957 -6.624e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2013 0.09396 0.3314 0.1507 0.9851 0.994 0.2019 0.4544 0.8806 0.7145 ] Network output: [ 0.006811 -0.03343 0.9958 5.193e-05 -2.331e-05 1.024 3.913e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09929 0.08761 0.1798 0.2039 0.9873 0.992 0.09935 0.7745 0.8716 0.3069 ] Network output: [ -0.006685 0.03373 1.002 5.357e-05 -2.405e-05 0.9778 4.037e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08851 0.1655 0.1958 0.9855 0.9913 0.09045 0.7011 0.8493 0.2438 ] Network output: [ 0.000216 0.9998 -0.000436 7.284e-06 -3.27e-06 1 5.49e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005967 Epoch 7495 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01181 0.9943 0.9889 1.64e-06 -7.36e-07 -0.00684 1.236e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003274 -0.003071 -0.008626 0.006656 0.9698 0.9742 0.006254 0.8389 0.8281 0.01901 ] Network output: [ 0.9998 0.0009262 0.001272 -2.721e-05 1.222e-05 -0.001856 -2.051e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1901 -0.03196 -0.1857 0.1946 0.9836 0.9933 0.2124 0.4499 0.8741 0.72 ] Network output: [ -0.01113 1.001 1.01 7.281e-07 -3.269e-07 0.01074 5.487e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005677 0.0004608 0.004402 0.003964 0.9889 0.992 0.005782 0.8678 0.898 0.01376 ] Network output: [ -0.0007957 0.003299 1.002 -8.781e-05 3.942e-05 0.9958 -6.618e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2013 0.09396 0.3314 0.1507 0.9851 0.994 0.2019 0.4544 0.8806 0.7145 ] Network output: [ 0.006809 -0.03342 0.9958 5.188e-05 -2.329e-05 1.024 3.91e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0993 0.08761 0.1798 0.2039 0.9873 0.992 0.09936 0.7745 0.8716 0.3069 ] Network output: [ -0.006682 0.03371 1.002 5.353e-05 -2.403e-05 0.9778 4.034e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08851 0.1655 0.1958 0.9855 0.9913 0.09045 0.701 0.8493 0.2438 ] Network output: [ 0.0002159 0.9998 -0.0004355 7.279e-06 -3.268e-06 1 5.485e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005963 Epoch 7496 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01181 0.9943 0.9889 1.636e-06 -7.345e-07 -0.006841 1.233e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003275 -0.003072 -0.008624 0.006655 0.9698 0.9742 0.006254 0.8389 0.8281 0.01901 ] Network output: [ 0.9998 0.0009252 0.001272 -2.719e-05 1.221e-05 -0.001854 -2.049e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1901 -0.03196 -0.1857 0.1946 0.9836 0.9933 0.2124 0.4499 0.8741 0.72 ] Network output: [ -0.01113 1.001 1.01 7.26e-07 -3.259e-07 0.01074 5.472e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005677 0.0004608 0.004402 0.003964 0.9889 0.992 0.005782 0.8678 0.898 0.01376 ] Network output: [ -0.0007953 0.003298 1.002 -8.774e-05 3.939e-05 0.9958 -6.612e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2013 0.09397 0.3314 0.1507 0.9851 0.994 0.2019 0.4544 0.8806 0.7145 ] Network output: [ 0.006807 -0.0334 0.9958 5.184e-05 -2.327e-05 1.024 3.907e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0993 0.08762 0.1798 0.2039 0.9873 0.992 0.09936 0.7744 0.8716 0.3069 ] Network output: [ -0.006679 0.0337 1.002 5.349e-05 -2.401e-05 0.9778 4.031e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08851 0.1655 0.1958 0.9855 0.9913 0.09045 0.701 0.8492 0.2438 ] Network output: [ 0.0002158 0.9998 -0.0004351 7.273e-06 -3.265e-06 1 5.481e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005959 Epoch 7497 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0118 0.9943 0.989 1.632e-06 -7.329e-07 -0.006843 1.23e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003275 -0.003072 -0.008623 0.006654 0.9698 0.9742 0.006255 0.8389 0.8281 0.01901 ] Network output: [ 0.9998 0.0009245 0.001271 -2.717e-05 1.22e-05 -0.001853 -2.048e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1901 -0.03196 -0.1856 0.1946 0.9836 0.9933 0.2124 0.4499 0.8741 0.72 ] Network output: [ -0.01113 1.001 1.01 7.24e-07 -3.25e-07 0.01073 5.456e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005678 0.0004608 0.004402 0.003963 0.9889 0.992 0.005783 0.8678 0.898 0.01376 ] Network output: [ -0.0007948 0.003297 1.002 -8.766e-05 3.935e-05 0.9958 -6.606e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2013 0.09397 0.3314 0.1506 0.9851 0.994 0.2019 0.4544 0.8806 0.7145 ] Network output: [ 0.006804 -0.03339 0.9958 5.18e-05 -2.325e-05 1.024 3.903e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09931 0.08762 0.1798 0.2039 0.9873 0.992 0.09937 0.7744 0.8716 0.3069 ] Network output: [ -0.006677 0.03368 1.002 5.344e-05 -2.399e-05 0.9778 4.028e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08851 0.1655 0.1958 0.9855 0.9913 0.09045 0.701 0.8492 0.2438 ] Network output: [ 0.0002157 0.9998 -0.0004346 7.267e-06 -3.262e-06 1 5.476e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005955 Epoch 7498 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0118 0.9943 0.989 1.629e-06 -7.313e-07 -0.006844 1.228e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003275 -0.003072 -0.008622 0.006653 0.9698 0.9742 0.006255 0.8389 0.8281 0.01901 ] Network output: [ 0.9998 0.0009235 0.00127 -2.715e-05 1.219e-05 -0.001851 -2.046e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1901 -0.03197 -0.1856 0.1946 0.9836 0.9933 0.2124 0.4498 0.8741 0.72 ] Network output: [ -0.01113 1.001 1.01 7.219e-07 -3.241e-07 0.01073 5.44e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005678 0.0004608 0.004402 0.003962 0.9889 0.992 0.005784 0.8678 0.898 0.01376 ] Network output: [ -0.0007944 0.003296 1.002 -8.759e-05 3.932e-05 0.9958 -6.601e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2013 0.09397 0.3314 0.1506 0.9851 0.994 0.202 0.4543 0.8806 0.7145 ] Network output: [ 0.006802 -0.03338 0.9958 5.175e-05 -2.323e-05 1.024 3.9e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09931 0.08762 0.1798 0.2039 0.9873 0.992 0.09938 0.7744 0.8716 0.3069 ] Network output: [ -0.006674 0.03367 1.002 5.34e-05 -2.397e-05 0.9778 4.024e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.08851 0.1655 0.1958 0.9855 0.9913 0.09045 0.7009 0.8492 0.2438 ] Network output: [ 0.0002156 0.9998 -0.0004342 7.261e-06 -3.26e-06 1 5.472e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005951 Epoch 7499 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0118 0.9943 0.989 1.625e-06 -7.297e-07 -0.006846 1.225e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003275 -0.003072 -0.00862 0.006652 0.9698 0.9742 0.006255 0.8389 0.8281 0.01901 ] Network output: [ 0.9998 0.0009228 0.001269 -2.713e-05 1.218e-05 -0.00185 -2.044e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1901 -0.03197 -0.1856 0.1946 0.9836 0.9933 0.2124 0.4498 0.8741 0.72 ] Network output: [ -0.01112 1.001 1.01 7.198e-07 -3.232e-07 0.01073 5.425e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005679 0.0004609 0.004402 0.003962 0.9889 0.992 0.005784 0.8678 0.898 0.01376 ] Network output: [ -0.000794 0.003295 1.002 -8.751e-05 3.929e-05 0.9958 -6.595e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2013 0.09398 0.3315 0.1506 0.9851 0.994 0.202 0.4543 0.8806 0.7145 ] Network output: [ 0.0068 -0.03337 0.9958 5.171e-05 -2.321e-05 1.024 3.897e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09932 0.08763 0.1798 0.2039 0.9873 0.992 0.09938 0.7744 0.8716 0.3069 ] Network output: [ -0.006671 0.03365 1.002 5.336e-05 -2.395e-05 0.9778 4.021e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.08851 0.1655 0.1958 0.9855 0.9913 0.09045 0.7009 0.8492 0.2438 ] Network output: [ 0.0002154 0.9998 -0.0004337 7.255e-06 -3.257e-06 1 5.467e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005947 Epoch 7500 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0118 0.9943 0.989 1.622e-06 -7.282e-07 -0.006848 1.222e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003275 -0.003072 -0.008619 0.006651 0.9698 0.9742 0.006256 0.8388 0.8281 0.01901 ] Network output: [ 0.9998 0.0009218 0.001268 -2.711e-05 1.217e-05 -0.001848 -2.043e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1902 -0.03198 -0.1856 0.1946 0.9836 0.9933 0.2125 0.4498 0.874 0.72 ] Network output: [ -0.01112 1.001 1.01 7.178e-07 -3.222e-07 0.01072 5.409e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00568 0.0004609 0.004402 0.003961 0.9889 0.992 0.005785 0.8678 0.898 0.01375 ] Network output: [ -0.0007935 0.003294 1.002 -8.743e-05 3.925e-05 0.9958 -6.589e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2013 0.09398 0.3315 0.1506 0.9851 0.994 0.202 0.4543 0.8806 0.7145 ] Network output: [ 0.006797 -0.03335 0.9958 5.167e-05 -2.319e-05 1.024 3.894e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09932 0.08763 0.1798 0.2039 0.9873 0.992 0.09939 0.7743 0.8716 0.3069 ] Network output: [ -0.006669 0.03364 1.002 5.331e-05 -2.393e-05 0.9778 4.018e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.08851 0.1655 0.1958 0.9855 0.9913 0.09045 0.7009 0.8492 0.2438 ] Network output: [ 0.0002153 0.9998 -0.0004333 7.249e-06 -3.254e-06 1 5.463e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005944 Epoch 7501 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0118 0.9943 0.989 1.618e-06 -7.266e-07 -0.006849 1.22e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003275 -0.003072 -0.008618 0.00665 0.9698 0.9742 0.006256 0.8388 0.8281 0.019 ] Network output: [ 0.9998 0.0009211 0.001267 -2.708e-05 1.216e-05 -0.001847 -2.041e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1902 -0.03198 -0.1856 0.1945 0.9836 0.9933 0.2125 0.4498 0.874 0.72 ] Network output: [ -0.01112 1.001 1.01 7.157e-07 -3.213e-07 0.01072 5.394e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00568 0.0004609 0.004403 0.003961 0.9889 0.992 0.005786 0.8677 0.898 0.01375 ] Network output: [ -0.0007931 0.003293 1.002 -8.736e-05 3.922e-05 0.9958 -6.584e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2013 0.09398 0.3315 0.1506 0.9851 0.994 0.202 0.4543 0.8806 0.7145 ] Network output: [ 0.006795 -0.03334 0.9958 5.162e-05 -2.317e-05 1.024 3.89e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09933 0.08764 0.1798 0.2039 0.9873 0.992 0.09939 0.7743 0.8716 0.3069 ] Network output: [ -0.006666 0.03362 1.002 5.327e-05 -2.392e-05 0.9778 4.015e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.08851 0.1655 0.1957 0.9855 0.9913 0.09044 0.7009 0.8492 0.2438 ] Network output: [ 0.0002152 0.9998 -0.0004328 7.243e-06 -3.252e-06 1 5.458e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000594 Epoch 7502 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01179 0.9943 0.989 1.615e-06 -7.25e-07 -0.006851 1.217e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003275 -0.003073 -0.008616 0.00665 0.9698 0.9742 0.006256 0.8388 0.828 0.019 ] Network output: [ 0.9998 0.0009201 0.001267 -2.706e-05 1.215e-05 -0.001845 -2.04e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1902 -0.03198 -0.1856 0.1945 0.9836 0.9933 0.2125 0.4498 0.874 0.72 ] Network output: [ -0.01112 1.001 1.01 7.136e-07 -3.204e-07 0.01072 5.378e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005681 0.0004609 0.004403 0.00396 0.9889 0.992 0.005786 0.8677 0.898 0.01375 ] Network output: [ -0.0007927 0.003291 1.002 -8.728e-05 3.918e-05 0.9958 -6.578e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2014 0.09399 0.3315 0.1506 0.9851 0.994 0.202 0.4543 0.8806 0.7145 ] Network output: [ 0.006793 -0.03333 0.9958 5.158e-05 -2.316e-05 1.024 3.887e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09933 0.08764 0.1798 0.2039 0.9873 0.992 0.0994 0.7743 0.8716 0.3069 ] Network output: [ -0.006663 0.03361 1.002 5.323e-05 -2.39e-05 0.9778 4.012e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.0885 0.1655 0.1957 0.9855 0.9913 0.09044 0.7008 0.8492 0.2438 ] Network output: [ 0.0002151 0.9998 -0.0004324 7.237e-06 -3.249e-06 1 5.454e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005936 Epoch 7503 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01179 0.9943 0.989 1.612e-06 -7.235e-07 -0.006852 1.214e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003276 -0.003073 -0.008615 0.006649 0.9698 0.9742 0.006257 0.8388 0.828 0.019 ] Network output: [ 0.9998 0.0009194 0.001266 -2.704e-05 1.214e-05 -0.001844 -2.038e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1902 -0.03199 -0.1855 0.1945 0.9836 0.9933 0.2125 0.4498 0.874 0.72 ] Network output: [ -0.01112 1.001 1.01 7.116e-07 -3.195e-07 0.01071 5.363e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005681 0.0004609 0.004403 0.003959 0.9889 0.992 0.005787 0.8677 0.898 0.01375 ] Network output: [ -0.0007922 0.00329 1.002 -8.721e-05 3.915e-05 0.9958 -6.572e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2014 0.09399 0.3315 0.1506 0.9851 0.994 0.202 0.4543 0.8806 0.7145 ] Network output: [ 0.00679 -0.03332 0.9958 5.154e-05 -2.314e-05 1.024 3.884e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09934 0.08765 0.1798 0.2038 0.9873 0.992 0.0994 0.7742 0.8715 0.3069 ] Network output: [ -0.006661 0.03359 1.002 5.319e-05 -2.388e-05 0.9778 4.008e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.0885 0.1655 0.1957 0.9855 0.9913 0.09044 0.7008 0.8492 0.2438 ] Network output: [ 0.000215 0.9998 -0.0004319 7.231e-06 -3.246e-06 1 5.45e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005932 Epoch 7504 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01179 0.9943 0.989 1.608e-06 -7.219e-07 -0.006854 1.212e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003276 -0.003073 -0.008614 0.006648 0.9698 0.9742 0.006257 0.8388 0.828 0.019 ] Network output: [ 0.9998 0.0009184 0.001265 -2.702e-05 1.213e-05 -0.001843 -2.036e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1902 -0.03199 -0.1855 0.1945 0.9836 0.9933 0.2125 0.4497 0.874 0.72 ] Network output: [ -0.01112 1.001 1.01 7.095e-07 -3.185e-07 0.01071 5.347e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005682 0.0004609 0.004403 0.003959 0.9889 0.992 0.005787 0.8677 0.898 0.01375 ] Network output: [ -0.0007918 0.003289 1.002 -8.713e-05 3.912e-05 0.9958 -6.566e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2014 0.094 0.3315 0.1506 0.9851 0.994 0.202 0.4542 0.8806 0.7145 ] Network output: [ 0.006788 -0.03331 0.9958 5.149e-05 -2.312e-05 1.024 3.881e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09934 0.08765 0.1798 0.2038 0.9873 0.992 0.09941 0.7742 0.8715 0.3069 ] Network output: [ -0.006658 0.03358 1.002 5.314e-05 -2.386e-05 0.9778 4.005e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.0885 0.1655 0.1957 0.9855 0.9913 0.09044 0.7008 0.8492 0.2438 ] Network output: [ 0.0002148 0.9998 -0.0004315 7.225e-06 -3.244e-06 1 5.445e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005928 Epoch 7505 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01179 0.9943 0.989 1.605e-06 -7.203e-07 -0.006856 1.209e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003276 -0.003073 -0.008612 0.006647 0.9698 0.9742 0.006257 0.8388 0.828 0.019 ] Network output: [ 0.9998 0.0009177 0.001264 -2.7e-05 1.212e-05 -0.001841 -2.035e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1902 -0.03199 -0.1855 0.1945 0.9836 0.9933 0.2125 0.4497 0.874 0.72 ] Network output: [ -0.01112 1.001 1.01 7.075e-07 -3.176e-07 0.01071 5.332e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005683 0.0004609 0.004403 0.003958 0.9889 0.992 0.005788 0.8677 0.898 0.01375 ] Network output: [ -0.0007914 0.003288 1.002 -8.705e-05 3.908e-05 0.9958 -6.561e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2014 0.094 0.3315 0.1506 0.9851 0.994 0.202 0.4542 0.8806 0.7145 ] Network output: [ 0.006786 -0.03329 0.9957 5.145e-05 -2.31e-05 1.024 3.877e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09935 0.08766 0.1798 0.2038 0.9873 0.992 0.09941 0.7742 0.8715 0.3069 ] Network output: [ -0.006655 0.03356 1.002 5.31e-05 -2.384e-05 0.9778 4.002e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.0885 0.1655 0.1957 0.9855 0.9913 0.09044 0.7007 0.8491 0.2438 ] Network output: [ 0.0002147 0.9998 -0.000431 7.219e-06 -3.241e-06 1 5.441e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005924 Epoch 7506 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01179 0.9943 0.989 1.601e-06 -7.188e-07 -0.006857 1.207e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003276 -0.003073 -0.008611 0.006646 0.9698 0.9742 0.006258 0.8388 0.828 0.01899 ] Network output: [ 0.9998 0.0009167 0.001263 -2.698e-05 1.211e-05 -0.00184 -2.033e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1902 -0.032 -0.1855 0.1945 0.9836 0.9933 0.2125 0.4497 0.874 0.7199 ] Network output: [ -0.01112 1.001 1.01 7.054e-07 -3.167e-07 0.0107 5.316e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005683 0.0004609 0.004403 0.003958 0.9889 0.992 0.005789 0.8677 0.898 0.01375 ] Network output: [ -0.0007909 0.003287 1.002 -8.698e-05 3.905e-05 0.9958 -6.555e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2014 0.094 0.3316 0.1506 0.9851 0.994 0.2021 0.4542 0.8806 0.7145 ] Network output: [ 0.006783 -0.03328 0.9957 5.141e-05 -2.308e-05 1.024 3.874e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09936 0.08766 0.1798 0.2038 0.9873 0.992 0.09942 0.7742 0.8715 0.3069 ] Network output: [ -0.006653 0.03355 1.002 5.306e-05 -2.382e-05 0.9778 3.999e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.0885 0.1655 0.1957 0.9855 0.9913 0.09044 0.7007 0.8491 0.2438 ] Network output: [ 0.0002146 0.9998 -0.0004306 7.213e-06 -3.238e-06 1 5.436e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000592 Epoch 7507 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01178 0.9943 0.989 1.598e-06 -7.172e-07 -0.006859 1.204e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003276 -0.003073 -0.00861 0.006645 0.9698 0.9742 0.006258 0.8388 0.828 0.01899 ] Network output: [ 0.9998 0.000916 0.001262 -2.696e-05 1.21e-05 -0.001838 -2.031e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1902 -0.032 -0.1855 0.1945 0.9836 0.9933 0.2125 0.4497 0.874 0.7199 ] Network output: [ -0.01111 1.001 1.01 7.034e-07 -3.158e-07 0.0107 5.301e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005684 0.0004609 0.004403 0.003957 0.9889 0.992 0.005789 0.8677 0.898 0.01374 ] Network output: [ -0.0007905 0.003286 1.002 -8.69e-05 3.901e-05 0.9958 -6.549e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2014 0.09401 0.3316 0.1505 0.9851 0.994 0.2021 0.4542 0.8805 0.7145 ] Network output: [ 0.006781 -0.03327 0.9957 5.136e-05 -2.306e-05 1.024 3.871e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09936 0.08767 0.1798 0.2038 0.9873 0.992 0.09943 0.7741 0.8715 0.3069 ] Network output: [ -0.00665 0.03353 1.002 5.302e-05 -2.38e-05 0.9779 3.995e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.0885 0.1655 0.1957 0.9855 0.9913 0.09044 0.7007 0.8491 0.2438 ] Network output: [ 0.0002145 0.9998 -0.0004301 7.207e-06 -3.236e-06 1 5.432e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005916 Epoch 7508 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01178 0.9943 0.989 1.594e-06 -7.157e-07 -0.00686 1.201e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003276 -0.003074 -0.008608 0.006644 0.9698 0.9742 0.006258 0.8388 0.828 0.01899 ] Network output: [ 0.9998 0.000915 0.001261 -2.693e-05 1.209e-05 -0.001837 -2.03e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1902 -0.032 -0.1854 0.1945 0.9836 0.9933 0.2126 0.4497 0.874 0.7199 ] Network output: [ -0.01111 1.001 1.01 7.013e-07 -3.148e-07 0.0107 5.285e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005685 0.0004609 0.004403 0.003956 0.9889 0.992 0.00579 0.8677 0.898 0.01374 ] Network output: [ -0.0007901 0.003285 1.002 -8.683e-05 3.898e-05 0.9958 -6.544e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2014 0.09401 0.3316 0.1505 0.9851 0.994 0.2021 0.4542 0.8805 0.7145 ] Network output: [ 0.006779 -0.03326 0.9957 5.132e-05 -2.304e-05 1.024 3.868e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09937 0.08767 0.1798 0.2038 0.9873 0.992 0.09943 0.7741 0.8715 0.3069 ] Network output: [ -0.006647 0.03352 1.002 5.297e-05 -2.378e-05 0.9779 3.992e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.0885 0.1655 0.1957 0.9855 0.9913 0.09044 0.7006 0.8491 0.2438 ] Network output: [ 0.0002144 0.9998 -0.0004297 7.201e-06 -3.233e-06 1 5.427e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005912 Epoch 7509 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01178 0.9943 0.989 1.591e-06 -7.141e-07 -0.006862 1.199e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003276 -0.003074 -0.008607 0.006643 0.9698 0.9742 0.006259 0.8388 0.828 0.01899 ] Network output: [ 0.9998 0.0009143 0.001261 -2.691e-05 1.208e-05 -0.001836 -2.028e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1903 -0.03201 -0.1854 0.1945 0.9836 0.9933 0.2126 0.4497 0.874 0.7199 ] Network output: [ -0.01111 1.001 1.01 6.993e-07 -3.139e-07 0.01069 5.27e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005685 0.0004609 0.004403 0.003956 0.9889 0.992 0.005791 0.8677 0.898 0.01374 ] Network output: [ -0.0007896 0.003284 1.002 -8.675e-05 3.895e-05 0.9958 -6.538e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2014 0.09401 0.3316 0.1505 0.9851 0.994 0.2021 0.4542 0.8805 0.7145 ] Network output: [ 0.006777 -0.03325 0.9957 5.128e-05 -2.302e-05 1.024 3.864e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09937 0.08768 0.1799 0.2038 0.9873 0.992 0.09944 0.7741 0.8715 0.3069 ] Network output: [ -0.006645 0.0335 1.002 5.293e-05 -2.376e-05 0.9779 3.989e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.0885 0.1655 0.1957 0.9855 0.9913 0.09044 0.7006 0.8491 0.2438 ] Network output: [ 0.0002142 0.9998 -0.0004292 7.196e-06 -3.23e-06 1 5.423e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005909 Epoch 7510 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01178 0.9943 0.989 1.587e-06 -7.126e-07 -0.006863 1.196e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003277 -0.003074 -0.008606 0.006643 0.9698 0.9742 0.006259 0.8388 0.828 0.01899 ] Network output: [ 0.9998 0.0009133 0.00126 -2.689e-05 1.207e-05 -0.001834 -2.027e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1903 -0.03201 -0.1854 0.1945 0.9836 0.9933 0.2126 0.4497 0.874 0.7199 ] Network output: [ -0.01111 1.001 1.01 6.972e-07 -3.13e-07 0.01069 5.255e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005686 0.0004609 0.004404 0.003955 0.9889 0.992 0.005791 0.8677 0.898 0.01374 ] Network output: [ -0.0007892 0.003282 1.002 -8.668e-05 3.891e-05 0.9958 -6.532e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2015 0.09402 0.3316 0.1505 0.9851 0.994 0.2021 0.4542 0.8805 0.7145 ] Network output: [ 0.006774 -0.03323 0.9957 5.123e-05 -2.3e-05 1.024 3.861e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09938 0.08768 0.1799 0.2038 0.9873 0.992 0.09944 0.7741 0.8715 0.3069 ] Network output: [ -0.006642 0.03349 1.002 5.289e-05 -2.374e-05 0.9779 3.986e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.0885 0.1655 0.1957 0.9855 0.9913 0.09044 0.7006 0.8491 0.2438 ] Network output: [ 0.0002141 0.9998 -0.0004288 7.19e-06 -3.228e-06 1 5.418e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005905 Epoch 7511 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01178 0.9943 0.989 1.584e-06 -7.11e-07 -0.006865 1.194e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003277 -0.003074 -0.008604 0.006642 0.9698 0.9742 0.006259 0.8388 0.828 0.01899 ] Network output: [ 0.9998 0.0009126 0.001259 -2.687e-05 1.206e-05 -0.001833 -2.025e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1903 -0.03202 -0.1854 0.1945 0.9836 0.9933 0.2126 0.4496 0.874 0.7199 ] Network output: [ -0.01111 1.001 1.01 6.952e-07 -3.121e-07 0.01069 5.239e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005686 0.0004609 0.004404 0.003954 0.9889 0.992 0.005792 0.8676 0.898 0.01374 ] Network output: [ -0.0007888 0.003282 1.002 -8.66e-05 3.888e-05 0.9958 -6.527e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2015 0.09402 0.3316 0.1505 0.9851 0.994 0.2021 0.4541 0.8805 0.7144 ] Network output: [ 0.006772 -0.03322 0.9957 5.119e-05 -2.298e-05 1.024 3.858e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09938 0.08768 0.1799 0.2038 0.9873 0.992 0.09945 0.774 0.8715 0.3069 ] Network output: [ -0.006639 0.03347 1.002 5.285e-05 -2.372e-05 0.9779 3.983e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.0885 0.1655 0.1957 0.9855 0.9913 0.09044 0.7006 0.8491 0.2438 ] Network output: [ 0.000214 0.9998 -0.0004283 7.184e-06 -3.225e-06 1 5.414e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005901 Epoch 7512 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01177 0.9943 0.989 1.58e-06 -7.095e-07 -0.006867 1.191e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003277 -0.003074 -0.008603 0.006641 0.9698 0.9742 0.00626 0.8387 0.828 0.01898 ] Network output: [ 0.9998 0.0009116 0.001258 -2.685e-05 1.205e-05 -0.001831 -2.023e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1903 -0.03202 -0.1854 0.1945 0.9836 0.9933 0.2126 0.4496 0.874 0.7199 ] Network output: [ -0.01111 1.001 1.01 6.931e-07 -3.112e-07 0.01068 5.224e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005687 0.0004609 0.004404 0.003954 0.9889 0.992 0.005792 0.8676 0.898 0.01374 ] Network output: [ -0.0007883 0.00328 1.002 -8.653e-05 3.884e-05 0.9958 -6.521e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2015 0.09402 0.3316 0.1505 0.9851 0.994 0.2021 0.4541 0.8805 0.7144 ] Network output: [ 0.00677 -0.03321 0.9957 5.115e-05 -2.296e-05 1.024 3.855e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09939 0.08769 0.1799 0.2038 0.9873 0.992 0.09945 0.774 0.8715 0.3069 ] Network output: [ -0.006637 0.03346 1.002 5.28e-05 -2.371e-05 0.9779 3.979e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.0885 0.1655 0.1957 0.9855 0.9913 0.09044 0.7005 0.8491 0.2438 ] Network output: [ 0.0002139 0.9998 -0.0004279 7.178e-06 -3.222e-06 1 5.409e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005897 Epoch 7513 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01177 0.9943 0.989 1.577e-06 -7.079e-07 -0.006868 1.188e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003277 -0.003074 -0.008602 0.00664 0.9698 0.9742 0.00626 0.8387 0.828 0.01898 ] Network output: [ 0.9998 0.0009109 0.001257 -2.683e-05 1.204e-05 -0.00183 -2.022e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1903 -0.03202 -0.1854 0.1944 0.9836 0.9933 0.2126 0.4496 0.874 0.7199 ] Network output: [ -0.01111 1.001 1.01 6.911e-07 -3.103e-07 0.01068 5.208e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005688 0.000461 0.004404 0.003953 0.9889 0.992 0.005793 0.8676 0.8979 0.01374 ] Network output: [ -0.0007879 0.003279 1.002 -8.645e-05 3.881e-05 0.9958 -6.515e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2015 0.09403 0.3317 0.1505 0.9851 0.994 0.2021 0.4541 0.8805 0.7144 ] Network output: [ 0.006767 -0.0332 0.9957 5.11e-05 -2.294e-05 1.024 3.851e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09939 0.08769 0.1799 0.2038 0.9873 0.992 0.09946 0.774 0.8715 0.3069 ] Network output: [ -0.006634 0.03344 1.002 5.276e-05 -2.369e-05 0.9779 3.976e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.0885 0.1655 0.1957 0.9855 0.9913 0.09044 0.7005 0.8491 0.2438 ] Network output: [ 0.0002138 0.9998 -0.0004274 7.172e-06 -3.22e-06 1 5.405e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005893 Epoch 7514 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01177 0.9943 0.989 1.573e-06 -7.064e-07 -0.00687 1.186e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003277 -0.003075 -0.0086 0.006639 0.9698 0.9742 0.00626 0.8387 0.828 0.01898 ] Network output: [ 0.9998 0.0009099 0.001256 -2.68e-05 1.203e-05 -0.001828 -2.02e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1903 -0.03203 -0.1853 0.1944 0.9836 0.9933 0.2126 0.4496 0.874 0.7199 ] Network output: [ -0.01111 1.001 1.01 6.891e-07 -3.094e-07 0.01068 5.193e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005688 0.000461 0.004404 0.003953 0.9889 0.992 0.005794 0.8676 0.8979 0.01373 ] Network output: [ -0.0007874 0.003278 1.002 -8.638e-05 3.878e-05 0.9958 -6.51e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2015 0.09403 0.3317 0.1505 0.9851 0.994 0.2022 0.4541 0.8805 0.7144 ] Network output: [ 0.006765 -0.03318 0.9957 5.106e-05 -2.292e-05 1.024 3.848e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0994 0.0877 0.1799 0.2038 0.9873 0.992 0.09946 0.774 0.8714 0.3069 ] Network output: [ -0.006631 0.03343 1.002 5.272e-05 -2.367e-05 0.9779 3.973e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.0885 0.1655 0.1957 0.9855 0.9913 0.09044 0.7005 0.849 0.2438 ] Network output: [ 0.0002137 0.9998 -0.000427 7.166e-06 -3.217e-06 1 5.401e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005889 Epoch 7515 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01177 0.9943 0.989 1.57e-06 -7.048e-07 -0.006871 1.183e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003277 -0.003075 -0.008599 0.006638 0.9698 0.9742 0.006261 0.8387 0.828 0.01898 ] Network output: [ 0.9998 0.0009092 0.001256 -2.678e-05 1.202e-05 -0.001827 -2.018e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1903 -0.03203 -0.1853 0.1944 0.9836 0.9933 0.2126 0.4496 0.874 0.7199 ] Network output: [ -0.0111 1.001 1.01 6.87e-07 -3.084e-07 0.01067 5.178e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005689 0.000461 0.004404 0.003952 0.9889 0.992 0.005794 0.8676 0.8979 0.01373 ] Network output: [ -0.000787 0.003277 1.002 -8.63e-05 3.874e-05 0.9958 -6.504e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2015 0.09404 0.3317 0.1505 0.9851 0.994 0.2022 0.4541 0.8805 0.7144 ] Network output: [ 0.006763 -0.03317 0.9957 5.102e-05 -2.29e-05 1.024 3.845e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09941 0.0877 0.1799 0.2038 0.9873 0.992 0.09947 0.7739 0.8714 0.3068 ] Network output: [ -0.006629 0.03341 1.002 5.268e-05 -2.365e-05 0.9779 3.97e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.0885 0.1655 0.1957 0.9855 0.9913 0.09044 0.7004 0.849 0.2438 ] Network output: [ 0.0002135 0.9998 -0.0004265 7.16e-06 -3.214e-06 1 5.396e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005885 Epoch 7516 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01176 0.9943 0.989 1.567e-06 -7.033e-07 -0.006873 1.181e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003277 -0.003075 -0.008598 0.006637 0.9698 0.9742 0.006261 0.8387 0.828 0.01898 ] Network output: [ 0.9998 0.0009082 0.001255 -2.676e-05 1.201e-05 -0.001825 -2.017e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1903 -0.03203 -0.1853 0.1944 0.9836 0.9933 0.2127 0.4496 0.874 0.7199 ] Network output: [ -0.0111 1.001 1.01 6.85e-07 -3.075e-07 0.01067 5.162e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005689 0.000461 0.004404 0.003951 0.9889 0.992 0.005795 0.8676 0.8979 0.01373 ] Network output: [ -0.0007866 0.003276 1.002 -8.622e-05 3.871e-05 0.9958 -6.498e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2015 0.09404 0.3317 0.1505 0.9851 0.994 0.2022 0.4541 0.8805 0.7144 ] Network output: [ 0.00676 -0.03316 0.9957 5.097e-05 -2.288e-05 1.024 3.842e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09941 0.08771 0.1799 0.2038 0.9873 0.992 0.09948 0.7739 0.8714 0.3068 ] Network output: [ -0.006626 0.0334 1.002 5.263e-05 -2.363e-05 0.9779 3.967e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1655 0.1957 0.9854 0.9913 0.09043 0.7004 0.849 0.2438 ] Network output: [ 0.0002134 0.9998 -0.0004261 7.154e-06 -3.212e-06 1 5.392e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005881 Epoch 7517 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01176 0.9943 0.989 1.563e-06 -7.018e-07 -0.006874 1.178e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003278 -0.003075 -0.008596 0.006636 0.9698 0.9742 0.006261 0.8387 0.828 0.01897 ] Network output: [ 0.9998 0.0009075 0.001254 -2.674e-05 1.2e-05 -0.001824 -2.015e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1903 -0.03204 -0.1853 0.1944 0.9836 0.9933 0.2127 0.4495 0.874 0.7199 ] Network output: [ -0.0111 1.001 1.01 6.83e-07 -3.066e-07 0.01067 5.147e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00569 0.000461 0.004404 0.003951 0.9889 0.992 0.005796 0.8676 0.8979 0.01373 ] Network output: [ -0.0007862 0.003275 1.002 -8.615e-05 3.868e-05 0.9958 -6.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2015 0.09404 0.3317 0.1504 0.9851 0.994 0.2022 0.454 0.8805 0.7144 ] Network output: [ 0.006758 -0.03315 0.9957 5.093e-05 -2.286e-05 1.024 3.838e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09942 0.08771 0.1799 0.2038 0.9873 0.992 0.09948 0.7739 0.8714 0.3068 ] Network output: [ -0.006623 0.03338 1.002 5.259e-05 -2.361e-05 0.9779 3.963e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1655 0.1957 0.9854 0.9913 0.09043 0.7004 0.849 0.2438 ] Network output: [ 0.0002133 0.9998 -0.0004256 7.148e-06 -3.209e-06 1 5.387e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005878 Epoch 7518 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01176 0.9943 0.989 1.56e-06 -7.002e-07 -0.006876 1.175e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003278 -0.003075 -0.008595 0.006636 0.9698 0.9742 0.006262 0.8387 0.828 0.01897 ] Network output: [ 0.9998 0.0009066 0.001253 -2.672e-05 1.199e-05 -0.001823 -2.014e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1904 -0.03204 -0.1853 0.1944 0.9836 0.9933 0.2127 0.4495 0.874 0.7199 ] Network output: [ -0.0111 1.001 1.01 6.81e-07 -3.057e-07 0.01066 5.132e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005691 0.000461 0.004404 0.00395 0.9889 0.992 0.005796 0.8676 0.8979 0.01373 ] Network output: [ -0.0007857 0.003274 1.002 -8.607e-05 3.864e-05 0.9958 -6.487e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2016 0.09405 0.3317 0.1504 0.9851 0.994 0.2022 0.454 0.8805 0.7144 ] Network output: [ 0.006756 -0.03314 0.9957 5.089e-05 -2.285e-05 1.024 3.835e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09942 0.08772 0.1799 0.2037 0.9873 0.992 0.09949 0.7739 0.8714 0.3068 ] Network output: [ -0.006621 0.03337 1.002 5.255e-05 -2.359e-05 0.9779 3.96e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1655 0.1957 0.9854 0.9913 0.09043 0.7004 0.849 0.2438 ] Network output: [ 0.0002132 0.9998 -0.0004252 7.142e-06 -3.207e-06 1 5.383e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005874 Epoch 7519 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01176 0.9943 0.989 1.556e-06 -6.987e-07 -0.006877 1.173e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003278 -0.003075 -0.008594 0.006635 0.9698 0.9742 0.006262 0.8387 0.8279 0.01897 ] Network output: [ 0.9998 0.0009058 0.001252 -2.67e-05 1.198e-05 -0.001821 -2.012e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1904 -0.03204 -0.1852 0.1944 0.9836 0.9933 0.2127 0.4495 0.8739 0.7199 ] Network output: [ -0.0111 1.001 1.01 6.789e-07 -3.048e-07 0.01066 5.117e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005691 0.000461 0.004405 0.00395 0.9889 0.992 0.005797 0.8676 0.8979 0.01373 ] Network output: [ -0.0007853 0.003273 1.002 -8.6e-05 3.861e-05 0.9958 -6.481e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2016 0.09405 0.3317 0.1504 0.9851 0.994 0.2022 0.454 0.8805 0.7144 ] Network output: [ 0.006753 -0.03312 0.9957 5.084e-05 -2.283e-05 1.024 3.832e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09943 0.08772 0.1799 0.2037 0.9873 0.992 0.09949 0.7738 0.8714 0.3068 ] Network output: [ -0.006618 0.03335 1.002 5.251e-05 -2.357e-05 0.9779 3.957e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1655 0.1957 0.9854 0.9913 0.09043 0.7003 0.849 0.2438 ] Network output: [ 0.0002131 0.9998 -0.0004247 7.137e-06 -3.204e-06 1 5.378e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000587 Epoch 7520 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01176 0.9944 0.989 1.553e-06 -6.972e-07 -0.006879 1.17e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003278 -0.003076 -0.008592 0.006634 0.9698 0.9742 0.006262 0.8387 0.8279 0.01897 ] Network output: [ 0.9998 0.0009049 0.001251 -2.667e-05 1.198e-05 -0.00182 -2.01e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1904 -0.03205 -0.1852 0.1944 0.9836 0.9933 0.2127 0.4495 0.8739 0.7199 ] Network output: [ -0.0111 1.001 1.01 6.769e-07 -3.039e-07 0.01066 5.101e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005692 0.000461 0.004405 0.003949 0.9889 0.992 0.005798 0.8676 0.8979 0.01373 ] Network output: [ -0.0007848 0.003271 1.002 -8.592e-05 3.857e-05 0.9958 -6.475e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2016 0.09405 0.3318 0.1504 0.9851 0.994 0.2022 0.454 0.8805 0.7144 ] Network output: [ 0.006751 -0.03311 0.9957 5.08e-05 -2.281e-05 1.024 3.829e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09943 0.08773 0.1799 0.2037 0.9873 0.992 0.0995 0.7738 0.8714 0.3068 ] Network output: [ -0.006616 0.03334 1.002 5.246e-05 -2.355e-05 0.9779 3.954e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1655 0.1957 0.9854 0.9913 0.09043 0.7003 0.849 0.2438 ] Network output: [ 0.000213 0.9998 -0.0004243 7.131e-06 -3.201e-06 1 5.374e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005866 Epoch 7521 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01175 0.9944 0.989 1.55e-06 -6.956e-07 -0.00688 1.168e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003278 -0.003076 -0.008591 0.006633 0.9698 0.9742 0.006263 0.8387 0.8279 0.01897 ] Network output: [ 0.9998 0.0009041 0.001251 -2.665e-05 1.197e-05 -0.001818 -2.009e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1904 -0.03205 -0.1852 0.1944 0.9836 0.9933 0.2127 0.4495 0.8739 0.7199 ] Network output: [ -0.0111 1.001 1.01 6.749e-07 -3.03e-07 0.01065 5.086e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005693 0.000461 0.004405 0.003948 0.9889 0.992 0.005798 0.8676 0.8979 0.01373 ] Network output: [ -0.0007844 0.00327 1.002 -8.585e-05 3.854e-05 0.9958 -6.47e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2016 0.09406 0.3318 0.1504 0.9851 0.994 0.2022 0.454 0.8805 0.7144 ] Network output: [ 0.006749 -0.0331 0.9957 5.076e-05 -2.279e-05 1.024 3.825e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09944 0.08773 0.1799 0.2037 0.9873 0.992 0.0995 0.7738 0.8714 0.3068 ] Network output: [ -0.006613 0.03332 1.002 5.242e-05 -2.353e-05 0.978 3.951e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1655 0.1957 0.9854 0.9913 0.09043 0.7003 0.849 0.2438 ] Network output: [ 0.0002128 0.9998 -0.0004239 7.125e-06 -3.199e-06 1 5.37e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005862 Epoch 7522 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01175 0.9944 0.989 1.546e-06 -6.941e-07 -0.006882 1.165e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003278 -0.003076 -0.00859 0.006632 0.9698 0.9742 0.006263 0.8387 0.8279 0.01897 ] Network output: [ 0.9998 0.0009032 0.00125 -2.663e-05 1.196e-05 -0.001817 -2.007e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1904 -0.03206 -0.1852 0.1944 0.9836 0.9933 0.2127 0.4495 0.8739 0.7199 ] Network output: [ -0.0111 1.001 1.01 6.729e-07 -3.021e-07 0.01065 5.071e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005693 0.000461 0.004405 0.003948 0.9889 0.992 0.005799 0.8675 0.8979 0.01372 ] Network output: [ -0.000784 0.003269 1.002 -8.577e-05 3.851e-05 0.9958 -6.464e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2016 0.09406 0.3318 0.1504 0.9851 0.994 0.2023 0.454 0.8805 0.7144 ] Network output: [ 0.006746 -0.03309 0.9957 5.072e-05 -2.277e-05 1.024 3.822e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09944 0.08774 0.1799 0.2037 0.9873 0.992 0.09951 0.7738 0.8714 0.3068 ] Network output: [ -0.00661 0.03331 1.002 5.238e-05 -2.351e-05 0.978 3.947e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1655 0.1957 0.9854 0.9913 0.09043 0.7002 0.849 0.2438 ] Network output: [ 0.0002127 0.9998 -0.0004234 7.119e-06 -3.196e-06 1 5.365e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005858 Epoch 7523 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01175 0.9944 0.989 1.543e-06 -6.926e-07 -0.006884 1.163e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003278 -0.003076 -0.008588 0.006631 0.9698 0.9742 0.006263 0.8386 0.8279 0.01896 ] Network output: [ 0.9998 0.0009025 0.001249 -2.661e-05 1.195e-05 -0.001816 -2.005e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1904 -0.03206 -0.1852 0.1944 0.9836 0.9933 0.2128 0.4495 0.8739 0.7199 ] Network output: [ -0.0111 1.001 1.01 6.709e-07 -3.012e-07 0.01065 5.056e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005694 0.000461 0.004405 0.003947 0.9889 0.992 0.005799 0.8675 0.8979 0.01372 ] Network output: [ -0.0007836 0.003268 1.002 -8.57e-05 3.847e-05 0.9958 -6.459e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2016 0.09407 0.3318 0.1504 0.9851 0.994 0.2023 0.4539 0.8805 0.7144 ] Network output: [ 0.006744 -0.03308 0.9957 5.067e-05 -2.275e-05 1.024 3.819e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09945 0.08774 0.1799 0.2037 0.9873 0.992 0.09951 0.7737 0.8714 0.3068 ] Network output: [ -0.006608 0.03329 1.002 5.234e-05 -2.35e-05 0.978 3.944e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1655 0.1957 0.9854 0.9913 0.09043 0.7002 0.8489 0.2438 ] Network output: [ 0.0002126 0.9998 -0.000423 7.113e-06 -3.193e-06 1 5.361e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005854 Epoch 7524 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01175 0.9944 0.989 1.539e-06 -6.911e-07 -0.006885 1.16e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003278 -0.003076 -0.008587 0.00663 0.9698 0.9742 0.006263 0.8386 0.8279 0.01896 ] Network output: [ 0.9998 0.0009016 0.001248 -2.659e-05 1.194e-05 -0.001814 -2.004e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1904 -0.03206 -0.1851 0.1944 0.9836 0.9933 0.2128 0.4494 0.8739 0.7199 ] Network output: [ -0.01109 1.001 1.01 6.689e-07 -3.003e-07 0.01064 5.041e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005694 0.0004611 0.004405 0.003947 0.9889 0.992 0.0058 0.8675 0.8979 0.01372 ] Network output: [ -0.0007831 0.003267 1.002 -8.562e-05 3.844e-05 0.9958 -6.453e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2016 0.09407 0.3318 0.1504 0.9851 0.994 0.2023 0.4539 0.8805 0.7144 ] Network output: [ 0.006742 -0.03306 0.9957 5.063e-05 -2.273e-05 1.024 3.816e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09946 0.08775 0.1799 0.2037 0.9873 0.992 0.09952 0.7737 0.8714 0.3068 ] Network output: [ -0.006605 0.03328 1.002 5.229e-05 -2.348e-05 0.978 3.941e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1655 0.1957 0.9854 0.9913 0.09043 0.7002 0.8489 0.2438 ] Network output: [ 0.0002125 0.9998 -0.0004225 7.107e-06 -3.191e-06 1 5.356e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005851 Epoch 7525 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01175 0.9944 0.989 1.536e-06 -6.895e-07 -0.006887 1.158e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003279 -0.003076 -0.008586 0.006629 0.9698 0.9742 0.006264 0.8386 0.8279 0.01896 ] Network output: [ 0.9998 0.0009008 0.001247 -2.657e-05 1.193e-05 -0.001813 -2.002e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1904 -0.03207 -0.1851 0.1943 0.9836 0.9933 0.2128 0.4494 0.8739 0.7199 ] Network output: [ -0.01109 1.001 1.01 6.668e-07 -2.994e-07 0.01064 5.026e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005695 0.0004611 0.004405 0.003946 0.9889 0.992 0.005801 0.8675 0.8979 0.01372 ] Network output: [ -0.0007827 0.003266 1.002 -8.555e-05 3.841e-05 0.9958 -6.447e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2016 0.09407 0.3318 0.1504 0.9851 0.994 0.2023 0.4539 0.8805 0.7144 ] Network output: [ 0.00674 -0.03305 0.9957 5.059e-05 -2.271e-05 1.024 3.812e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09946 0.08775 0.1799 0.2037 0.9873 0.992 0.09953 0.7737 0.8714 0.3068 ] Network output: [ -0.006602 0.03327 1.002 5.225e-05 -2.346e-05 0.978 3.938e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1655 0.1957 0.9854 0.9913 0.09043 0.7001 0.8489 0.2438 ] Network output: [ 0.0002124 0.9998 -0.0004221 7.101e-06 -3.188e-06 1 5.352e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005847 Epoch 7526 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01174 0.9944 0.989 1.533e-06 -6.88e-07 -0.006888 1.155e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003279 -0.003077 -0.008584 0.006629 0.9698 0.9742 0.006264 0.8386 0.8279 0.01896 ] Network output: [ 0.9998 0.0008999 0.001247 -2.655e-05 1.192e-05 -0.001811 -2.001e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1904 -0.03207 -0.1851 0.1943 0.9836 0.9933 0.2128 0.4494 0.8739 0.7198 ] Network output: [ -0.01109 1.001 1.01 6.648e-07 -2.985e-07 0.01064 5.01e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005696 0.0004611 0.004405 0.003945 0.9889 0.992 0.005801 0.8675 0.8979 0.01372 ] Network output: [ -0.0007822 0.003265 1.002 -8.547e-05 3.837e-05 0.9958 -6.442e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2017 0.09408 0.3318 0.1504 0.9851 0.994 0.2023 0.4539 0.8805 0.7144 ] Network output: [ 0.006737 -0.03304 0.9957 5.054e-05 -2.269e-05 1.024 3.809e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09947 0.08775 0.1799 0.2037 0.9873 0.992 0.09953 0.7737 0.8713 0.3068 ] Network output: [ -0.0066 0.03325 1.002 5.221e-05 -2.344e-05 0.978 3.935e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1655 0.1957 0.9854 0.9913 0.09043 0.7001 0.8489 0.2438 ] Network output: [ 0.0002123 0.9998 -0.0004216 7.095e-06 -3.185e-06 1 5.347e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005843 Epoch 7527 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01174 0.9944 0.989 1.529e-06 -6.865e-07 -0.00689 1.152e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003279 -0.003077 -0.008583 0.006628 0.9698 0.9742 0.006264 0.8386 0.8279 0.01896 ] Network output: [ 0.9998 0.0008991 0.001246 -2.652e-05 1.191e-05 -0.00181 -1.999e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1905 -0.03207 -0.1851 0.1943 0.9836 0.9933 0.2128 0.4494 0.8739 0.7198 ] Network output: [ -0.01109 1.001 1.01 6.628e-07 -2.976e-07 0.01063 4.995e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005696 0.0004611 0.004405 0.003945 0.9889 0.992 0.005802 0.8675 0.8979 0.01372 ] Network output: [ -0.0007818 0.003264 1.002 -8.54e-05 3.834e-05 0.9958 -6.436e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2017 0.09408 0.3319 0.1504 0.9851 0.994 0.2023 0.4539 0.8805 0.7144 ] Network output: [ 0.006735 -0.03303 0.9957 5.05e-05 -2.267e-05 1.024 3.806e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09947 0.08776 0.1799 0.2037 0.9873 0.992 0.09954 0.7736 0.8713 0.3068 ] Network output: [ -0.006597 0.03324 1.002 5.217e-05 -2.342e-05 0.978 3.932e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1655 0.1957 0.9854 0.9913 0.09043 0.7001 0.8489 0.2438 ] Network output: [ 0.0002121 0.9998 -0.0004212 7.09e-06 -3.183e-06 1 5.343e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005839 Epoch 7528 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01174 0.9944 0.989 1.526e-06 -6.85e-07 -0.006891 1.15e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003279 -0.003077 -0.008582 0.006627 0.9698 0.9742 0.006265 0.8386 0.8279 0.01896 ] Network output: [ 0.9998 0.0008982 0.001245 -2.65e-05 1.19e-05 -0.001808 -1.997e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1905 -0.03208 -0.1851 0.1943 0.9836 0.9933 0.2128 0.4494 0.8739 0.7198 ] Network output: [ -0.01109 1.001 1.01 6.608e-07 -2.967e-07 0.01063 4.98e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005697 0.0004611 0.004406 0.003944 0.9889 0.992 0.005803 0.8675 0.8979 0.01372 ] Network output: [ -0.0007814 0.003263 1.002 -8.532e-05 3.831e-05 0.9958 -6.43e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2017 0.09408 0.3319 0.1503 0.9851 0.994 0.2023 0.4539 0.8804 0.7144 ] Network output: [ 0.006733 -0.03302 0.9957 5.046e-05 -2.265e-05 1.024 3.803e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09948 0.08776 0.1799 0.2037 0.9873 0.992 0.09954 0.7736 0.8713 0.3068 ] Network output: [ -0.006595 0.03322 1.002 5.213e-05 -2.34e-05 0.978 3.928e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1655 0.1957 0.9854 0.9913 0.09043 0.7001 0.8489 0.2438 ] Network output: [ 0.000212 0.9998 -0.0004208 7.084e-06 -3.18e-06 1 5.339e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005835 Epoch 7529 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01174 0.9944 0.989 1.522e-06 -6.835e-07 -0.006893 1.147e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003279 -0.003077 -0.008581 0.006626 0.9698 0.9742 0.006265 0.8386 0.8279 0.01895 ] Network output: [ 0.9998 0.0008975 0.001244 -2.648e-05 1.189e-05 -0.001807 -1.996e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1905 -0.03208 -0.1851 0.1943 0.9836 0.9933 0.2128 0.4494 0.8739 0.7198 ] Network output: [ -0.01109 1.001 1.01 6.588e-07 -2.958e-07 0.01063 4.965e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005697 0.0004611 0.004406 0.003944 0.9889 0.992 0.005803 0.8675 0.8979 0.01371 ] Network output: [ -0.0007809 0.003262 1.002 -8.525e-05 3.827e-05 0.9958 -6.425e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2017 0.09409 0.3319 0.1503 0.9851 0.994 0.2023 0.4539 0.8804 0.7144 ] Network output: [ 0.00673 -0.033 0.9957 5.042e-05 -2.263e-05 1.024 3.799e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09948 0.08777 0.1799 0.2037 0.9873 0.992 0.09955 0.7736 0.8713 0.3068 ] Network output: [ -0.006592 0.03321 1.002 5.208e-05 -2.338e-05 0.978 3.925e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08849 0.1655 0.1957 0.9854 0.9913 0.09043 0.7 0.8489 0.2438 ] Network output: [ 0.0002119 0.9998 -0.0004203 7.078e-06 -3.178e-06 1 5.334e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005831 Epoch 7530 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01174 0.9944 0.9891 1.519e-06 -6.82e-07 -0.006894 1.145e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003279 -0.003077 -0.008579 0.006625 0.9698 0.9742 0.006265 0.8386 0.8279 0.01895 ] Network output: [ 0.9998 0.0008966 0.001243 -2.646e-05 1.188e-05 -0.001806 -1.994e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1905 -0.03208 -0.185 0.1943 0.9836 0.9933 0.2128 0.4493 0.8739 0.7198 ] Network output: [ -0.01109 1.001 1.01 6.568e-07 -2.949e-07 0.01062 4.95e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005698 0.0004611 0.004406 0.003943 0.9889 0.992 0.005804 0.8675 0.8979 0.01371 ] Network output: [ -0.0007805 0.00326 1.002 -8.518e-05 3.824e-05 0.9958 -6.419e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2017 0.09409 0.3319 0.1503 0.9851 0.994 0.2024 0.4538 0.8804 0.7143 ] Network output: [ 0.006728 -0.03299 0.9957 5.037e-05 -2.261e-05 1.024 3.796e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09949 0.08777 0.1799 0.2037 0.9873 0.992 0.09955 0.7736 0.8713 0.3068 ] Network output: [ -0.006589 0.03319 1.002 5.204e-05 -2.336e-05 0.978 3.922e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08849 0.1655 0.1957 0.9854 0.9913 0.09043 0.7 0.8489 0.2438 ] Network output: [ 0.0002118 0.9998 -0.0004199 7.072e-06 -3.175e-06 1 5.33e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005828 Epoch 7531 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01173 0.9944 0.9891 1.516e-06 -6.804e-07 -0.006896 1.142e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003279 -0.003077 -0.008578 0.006624 0.9698 0.9742 0.006266 0.8386 0.8279 0.01895 ] Network output: [ 0.9998 0.0008958 0.001242 -2.644e-05 1.187e-05 -0.001804 -1.992e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1905 -0.03209 -0.185 0.1943 0.9836 0.9933 0.2129 0.4493 0.8739 0.7198 ] Network output: [ -0.01109 1.001 1.01 6.548e-07 -2.94e-07 0.01062 4.935e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005699 0.0004611 0.004406 0.003942 0.9889 0.992 0.005804 0.8675 0.8979 0.01371 ] Network output: [ -0.0007801 0.003259 1.002 -8.51e-05 3.82e-05 0.9958 -6.413e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2017 0.0941 0.3319 0.1503 0.9851 0.994 0.2024 0.4538 0.8804 0.7143 ] Network output: [ 0.006726 -0.03298 0.9957 5.033e-05 -2.26e-05 1.024 3.793e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09949 0.08778 0.1799 0.2037 0.9873 0.992 0.09956 0.7735 0.8713 0.3068 ] Network output: [ -0.006587 0.03318 1.002 5.2e-05 -2.334e-05 0.978 3.919e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08849 0.1655 0.1957 0.9854 0.9913 0.09043 0.7 0.8489 0.2438 ] Network output: [ 0.0002117 0.9998 -0.0004194 7.066e-06 -3.172e-06 1 5.325e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005824 Epoch 7532 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01173 0.9944 0.9891 1.512e-06 -6.789e-07 -0.006897 1.14e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00328 -0.003077 -0.008577 0.006623 0.9698 0.9742 0.006266 0.8386 0.8279 0.01895 ] Network output: [ 0.9998 0.0008949 0.001242 -2.642e-05 1.186e-05 -0.001803 -1.991e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1905 -0.03209 -0.185 0.1943 0.9836 0.9933 0.2129 0.4493 0.8739 0.7198 ] Network output: [ -0.01108 1.001 1.01 6.528e-07 -2.931e-07 0.01062 4.92e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005699 0.0004611 0.004406 0.003942 0.9889 0.992 0.005805 0.8674 0.8979 0.01371 ] Network output: [ -0.0007796 0.003258 1.002 -8.503e-05 3.817e-05 0.9958 -6.408e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2017 0.0941 0.3319 0.1503 0.9851 0.994 0.2024 0.4538 0.8804 0.7143 ] Network output: [ 0.006723 -0.03297 0.9957 5.029e-05 -2.258e-05 1.024 3.79e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0995 0.08778 0.1799 0.2037 0.9873 0.992 0.09956 0.7735 0.8713 0.3068 ] Network output: [ -0.006584 0.03316 1.002 5.196e-05 -2.333e-05 0.978 3.916e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1655 0.1957 0.9854 0.9913 0.09043 0.6999 0.8489 0.2438 ] Network output: [ 0.0002116 0.9998 -0.000419 7.06e-06 -3.17e-06 1 5.321e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000582 Epoch 7533 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01173 0.9944 0.9891 1.509e-06 -6.774e-07 -0.006899 1.137e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00328 -0.003078 -0.008575 0.006623 0.9698 0.9742 0.006266 0.8386 0.8279 0.01895 ] Network output: [ 0.9998 0.0008942 0.001241 -2.64e-05 1.185e-05 -0.001801 -1.989e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1905 -0.03209 -0.185 0.1943 0.9836 0.9933 0.2129 0.4493 0.8739 0.7198 ] Network output: [ -0.01108 1.001 1.01 6.509e-07 -2.922e-07 0.01061 4.905e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0057 0.0004612 0.004406 0.003941 0.9889 0.992 0.005806 0.8674 0.8979 0.01371 ] Network output: [ -0.0007792 0.003257 1.002 -8.495e-05 3.814e-05 0.9958 -6.402e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2017 0.0941 0.3319 0.1503 0.9851 0.994 0.2024 0.4538 0.8804 0.7143 ] Network output: [ 0.006721 -0.03296 0.9957 5.024e-05 -2.256e-05 1.024 3.787e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09951 0.08779 0.1799 0.2036 0.9873 0.992 0.09957 0.7735 0.8713 0.3068 ] Network output: [ -0.006582 0.03315 1.002 5.191e-05 -2.331e-05 0.978 3.912e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1655 0.1957 0.9854 0.9913 0.09042 0.6999 0.8488 0.2438 ] Network output: [ 0.0002115 0.9998 -0.0004186 7.055e-06 -3.167e-06 1 5.316e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005816 Epoch 7534 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01173 0.9944 0.9891 1.506e-06 -6.759e-07 -0.0069 1.135e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00328 -0.003078 -0.008574 0.006622 0.9698 0.9742 0.006267 0.8386 0.8279 0.01894 ] Network output: [ 0.9998 0.0008933 0.00124 -2.637e-05 1.184e-05 -0.0018 -1.988e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1905 -0.0321 -0.185 0.1943 0.9836 0.9933 0.2129 0.4493 0.8739 0.7198 ] Network output: [ -0.01108 1.001 1.01 6.489e-07 -2.913e-07 0.01061 4.89e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005701 0.0004612 0.004406 0.003941 0.9889 0.992 0.005806 0.8674 0.8978 0.01371 ] Network output: [ -0.0007788 0.003256 1.002 -8.488e-05 3.81e-05 0.9958 -6.397e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2018 0.09411 0.332 0.1503 0.9851 0.994 0.2024 0.4538 0.8804 0.7143 ] Network output: [ 0.006719 -0.03294 0.9957 5.02e-05 -2.254e-05 1.024 3.783e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09951 0.08779 0.1799 0.2036 0.9873 0.992 0.09958 0.7735 0.8713 0.3068 ] Network output: [ -0.006579 0.03313 1.002 5.187e-05 -2.329e-05 0.9781 3.909e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1655 0.1957 0.9854 0.9913 0.09042 0.6999 0.8488 0.2438 ] Network output: [ 0.0002113 0.9998 -0.0004181 7.049e-06 -3.164e-06 1 5.312e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005812 Epoch 7535 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01173 0.9944 0.9891 1.502e-06 -6.744e-07 -0.006902 1.132e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00328 -0.003078 -0.008573 0.006621 0.9698 0.9742 0.006267 0.8385 0.8279 0.01894 ] Network output: [ 0.9998 0.0008925 0.001239 -2.635e-05 1.183e-05 -0.001799 -1.986e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1905 -0.0321 -0.1849 0.1943 0.9836 0.9933 0.2129 0.4493 0.8739 0.7198 ] Network output: [ -0.01108 1.001 1.01 6.469e-07 -2.904e-07 0.01061 4.875e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005701 0.0004612 0.004406 0.00394 0.9889 0.992 0.005807 0.8674 0.8978 0.01371 ] Network output: [ -0.0007783 0.003255 1.002 -8.48e-05 3.807e-05 0.9958 -6.391e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2018 0.09411 0.332 0.1503 0.9851 0.994 0.2024 0.4538 0.8804 0.7143 ] Network output: [ 0.006717 -0.03293 0.9957 5.016e-05 -2.252e-05 1.024 3.78e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09952 0.0878 0.1799 0.2036 0.9873 0.992 0.09958 0.7734 0.8713 0.3068 ] Network output: [ -0.006576 0.03312 1.002 5.183e-05 -2.327e-05 0.9781 3.906e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1655 0.1957 0.9854 0.9913 0.09042 0.6999 0.8488 0.2438 ] Network output: [ 0.0002112 0.9998 -0.0004177 7.043e-06 -3.162e-06 1 5.308e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005809 Epoch 7536 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01172 0.9944 0.9891 1.499e-06 -6.729e-07 -0.006903 1.13e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00328 -0.003078 -0.008571 0.00662 0.9698 0.9742 0.006267 0.8385 0.8278 0.01894 ] Network output: [ 0.9998 0.0008916 0.001238 -2.633e-05 1.182e-05 -0.001797 -1.984e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1906 -0.03211 -0.1849 0.1943 0.9836 0.9933 0.2129 0.4493 0.8739 0.7198 ] Network output: [ -0.01108 1.001 1.01 6.449e-07 -2.895e-07 0.0106 4.86e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005702 0.0004612 0.004407 0.003939 0.9889 0.992 0.005808 0.8674 0.8978 0.0137 ] Network output: [ -0.0007779 0.003254 1.002 -8.473e-05 3.804e-05 0.9958 -6.385e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2018 0.09411 0.332 0.1503 0.9851 0.994 0.2024 0.4537 0.8804 0.7143 ] Network output: [ 0.006714 -0.03292 0.9957 5.012e-05 -2.25e-05 1.024 3.777e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09952 0.0878 0.1799 0.2036 0.9873 0.992 0.09959 0.7734 0.8713 0.3068 ] Network output: [ -0.006574 0.0331 1.002 5.179e-05 -2.325e-05 0.9781 3.903e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1655 0.1957 0.9854 0.9913 0.09042 0.6998 0.8488 0.2438 ] Network output: [ 0.0002111 0.9998 -0.0004172 7.037e-06 -3.159e-06 1 5.303e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005805 Epoch 7537 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01172 0.9944 0.9891 1.496e-06 -6.714e-07 -0.006905 1.127e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00328 -0.003078 -0.00857 0.006619 0.9698 0.9742 0.006268 0.8385 0.8278 0.01894 ] Network output: [ 0.9998 0.0008909 0.001237 -2.631e-05 1.181e-05 -0.001796 -1.983e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1906 -0.03211 -0.1849 0.1942 0.9836 0.9933 0.2129 0.4492 0.8739 0.7198 ] Network output: [ -0.01108 1.001 1.01 6.429e-07 -2.886e-07 0.0106 4.845e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005702 0.0004612 0.004407 0.003939 0.9889 0.992 0.005808 0.8674 0.8978 0.0137 ] Network output: [ -0.0007775 0.003253 1.002 -8.465e-05 3.8e-05 0.9958 -6.38e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2018 0.09412 0.332 0.1503 0.9851 0.994 0.2024 0.4537 0.8804 0.7143 ] Network output: [ 0.006712 -0.03291 0.9957 5.007e-05 -2.248e-05 1.024 3.774e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09953 0.08781 0.1799 0.2036 0.9873 0.992 0.09959 0.7734 0.8713 0.3068 ] Network output: [ -0.006571 0.03309 1.002 5.175e-05 -2.323e-05 0.9781 3.9e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1655 0.1957 0.9854 0.9913 0.09042 0.6998 0.8488 0.2439 ] Network output: [ 0.000211 0.9998 -0.0004168 7.031e-06 -3.157e-06 1 5.299e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005801 Epoch 7538 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01172 0.9944 0.9891 1.492e-06 -6.699e-07 -0.006906 1.125e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00328 -0.003078 -0.008569 0.006618 0.9698 0.9742 0.006268 0.8385 0.8278 0.01894 ] Network output: [ 0.9998 0.00089 0.001237 -2.629e-05 1.18e-05 -0.001794 -1.981e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1906 -0.03211 -0.1849 0.1942 0.9836 0.9933 0.2129 0.4492 0.8739 0.7198 ] Network output: [ -0.01108 1.001 1.01 6.409e-07 -2.877e-07 0.0106 4.83e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005703 0.0004612 0.004407 0.003938 0.9889 0.992 0.005809 0.8674 0.8978 0.0137 ] Network output: [ -0.000777 0.003252 1.002 -8.458e-05 3.797e-05 0.9958 -6.374e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2018 0.09412 0.332 0.1502 0.9851 0.994 0.2025 0.4537 0.8804 0.7143 ] Network output: [ 0.00671 -0.03289 0.9957 5.003e-05 -2.246e-05 1.024 3.771e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09953 0.08781 0.1799 0.2036 0.9873 0.992 0.0996 0.7734 0.8712 0.3068 ] Network output: [ -0.006569 0.03307 1.002 5.17e-05 -2.321e-05 0.9781 3.897e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1655 0.1957 0.9854 0.9913 0.09042 0.6998 0.8488 0.2439 ] Network output: [ 0.0002109 0.9998 -0.0004164 7.025e-06 -3.154e-06 1 5.294e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005797 Epoch 7539 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01172 0.9944 0.9891 1.489e-06 -6.684e-07 -0.006908 1.122e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003281 -0.003079 -0.008567 0.006617 0.9698 0.9742 0.006268 0.8385 0.8278 0.01894 ] Network output: [ 0.9998 0.0008892 0.001236 -2.627e-05 1.179e-05 -0.001793 -1.98e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1906 -0.03212 -0.1849 0.1942 0.9836 0.9933 0.213 0.4492 0.8739 0.7198 ] Network output: [ -0.01108 1.001 1.01 6.39e-07 -2.869e-07 0.01059 4.815e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005704 0.0004612 0.004407 0.003938 0.9889 0.992 0.005809 0.8674 0.8978 0.0137 ] Network output: [ -0.0007766 0.003251 1.002 -8.45e-05 3.794e-05 0.9959 -6.369e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2018 0.09413 0.332 0.1502 0.9851 0.994 0.2025 0.4537 0.8804 0.7143 ] Network output: [ 0.006707 -0.03288 0.9957 4.999e-05 -2.244e-05 1.024 3.767e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09954 0.08782 0.1799 0.2036 0.9873 0.992 0.0996 0.7733 0.8712 0.3068 ] Network output: [ -0.006566 0.03306 1.002 5.166e-05 -2.319e-05 0.9781 3.893e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1655 0.1957 0.9854 0.9913 0.09042 0.6997 0.8488 0.2439 ] Network output: [ 0.0002108 0.9998 -0.0004159 7.019e-06 -3.151e-06 1 5.29e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005793 Epoch 7540 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01172 0.9944 0.9891 1.486e-06 -6.669e-07 -0.006909 1.12e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003281 -0.003079 -0.008566 0.006616 0.9698 0.9742 0.006269 0.8385 0.8278 0.01893 ] Network output: [ 0.9998 0.0008884 0.001235 -2.625e-05 1.178e-05 -0.001792 -1.978e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1906 -0.03212 -0.1849 0.1942 0.9836 0.9933 0.213 0.4492 0.8738 0.7198 ] Network output: [ -0.01107 1.001 1.01 6.37e-07 -2.86e-07 0.01059 4.8e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005704 0.0004612 0.004407 0.003937 0.9889 0.992 0.00581 0.8674 0.8978 0.0137 ] Network output: [ -0.0007762 0.003249 1.002 -8.443e-05 3.79e-05 0.9959 -6.363e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2018 0.09413 0.332 0.1502 0.9851 0.994 0.2025 0.4537 0.8804 0.7143 ] Network output: [ 0.006705 -0.03287 0.9957 4.995e-05 -2.242e-05 1.024 3.764e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09955 0.08782 0.1799 0.2036 0.9873 0.992 0.09961 0.7733 0.8712 0.3068 ] Network output: [ -0.006563 0.03304 1.002 5.162e-05 -2.317e-05 0.9781 3.89e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1655 0.1957 0.9854 0.9913 0.09042 0.6997 0.8488 0.2439 ] Network output: [ 0.0002106 0.9998 -0.0004155 7.014e-06 -3.149e-06 1 5.286e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000579 Epoch 7541 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01171 0.9944 0.9891 1.482e-06 -6.654e-07 -0.006911 1.117e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003281 -0.003079 -0.008565 0.006616 0.9698 0.9742 0.006269 0.8385 0.8278 0.01893 ] Network output: [ 0.9998 0.0008876 0.001234 -2.622e-05 1.177e-05 -0.00179 -1.976e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1906 -0.03212 -0.1848 0.1942 0.9836 0.9933 0.213 0.4492 0.8738 0.7198 ] Network output: [ -0.01107 1.001 1.01 6.35e-07 -2.851e-07 0.01059 4.786e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005705 0.0004613 0.004407 0.003936 0.9889 0.992 0.005811 0.8674 0.8978 0.0137 ] Network output: [ -0.0007757 0.003248 1.002 -8.436e-05 3.787e-05 0.9959 -6.357e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2018 0.09413 0.3321 0.1502 0.9851 0.994 0.2025 0.4537 0.8804 0.7143 ] Network output: [ 0.006703 -0.03286 0.9957 4.99e-05 -2.24e-05 1.024 3.761e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09955 0.08783 0.1799 0.2036 0.9873 0.992 0.09962 0.7733 0.8712 0.3068 ] Network output: [ -0.006561 0.03303 1.002 5.158e-05 -2.316e-05 0.9781 3.887e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1655 0.1957 0.9854 0.9913 0.09042 0.6997 0.8488 0.2439 ] Network output: [ 0.0002105 0.9998 -0.0004151 7.008e-06 -3.146e-06 1 5.281e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005786 Epoch 7542 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01171 0.9944 0.9891 1.479e-06 -6.64e-07 -0.006912 1.115e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003281 -0.003079 -0.008563 0.006615 0.9698 0.9742 0.006269 0.8385 0.8278 0.01893 ] Network output: [ 0.9998 0.0008867 0.001233 -2.62e-05 1.176e-05 -0.001789 -1.975e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1906 -0.03213 -0.1848 0.1942 0.9836 0.9933 0.213 0.4492 0.8738 0.7198 ] Network output: [ -0.01107 1.001 1.01 6.33e-07 -2.842e-07 0.01059 4.771e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005705 0.0004613 0.004407 0.003936 0.9889 0.992 0.005811 0.8673 0.8978 0.0137 ] Network output: [ -0.0007753 0.003247 1.002 -8.428e-05 3.784e-05 0.9959 -6.352e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2019 0.09414 0.3321 0.1502 0.9851 0.994 0.2025 0.4536 0.8804 0.7143 ] Network output: [ 0.006701 -0.03285 0.9957 4.986e-05 -2.238e-05 1.024 3.758e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09956 0.08783 0.1799 0.2036 0.9873 0.992 0.09962 0.7733 0.8712 0.3068 ] Network output: [ -0.006558 0.03301 1.002 5.154e-05 -2.314e-05 0.9781 3.884e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1655 0.1957 0.9854 0.9913 0.09042 0.6996 0.8487 0.2439 ] Network output: [ 0.0002104 0.9998 -0.0004146 7.002e-06 -3.143e-06 1 5.277e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005782 Epoch 7543 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01171 0.9944 0.9891 1.476e-06 -6.625e-07 -0.006914 1.112e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003281 -0.003079 -0.008562 0.006614 0.9698 0.9742 0.00627 0.8385 0.8278 0.01893 ] Network output: [ 0.9998 0.000886 0.001233 -2.618e-05 1.175e-05 -0.001787 -1.973e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1906 -0.03213 -0.1848 0.1942 0.9836 0.9933 0.213 0.4492 0.8738 0.7198 ] Network output: [ -0.01107 1.001 1.01 6.311e-07 -2.833e-07 0.01058 4.756e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005706 0.0004613 0.004407 0.003935 0.9889 0.992 0.005812 0.8673 0.8978 0.0137 ] Network output: [ -0.0007749 0.003246 1.002 -8.421e-05 3.78e-05 0.9959 -6.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2019 0.09414 0.3321 0.1502 0.9851 0.994 0.2025 0.4536 0.8804 0.7143 ] Network output: [ 0.006698 -0.03283 0.9957 4.982e-05 -2.237e-05 1.024 3.755e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09956 0.08784 0.18 0.2036 0.9873 0.992 0.09963 0.7732 0.8712 0.3068 ] Network output: [ -0.006556 0.033 1.002 5.149e-05 -2.312e-05 0.9781 3.881e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1655 0.1957 0.9854 0.9913 0.09042 0.6996 0.8487 0.2439 ] Network output: [ 0.0002103 0.9998 -0.0004142 6.996e-06 -3.141e-06 1 5.273e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005778 Epoch 7544 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01171 0.9944 0.9891 1.472e-06 -6.61e-07 -0.006915 1.11e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003281 -0.003079 -0.008561 0.006613 0.9698 0.9742 0.00627 0.8385 0.8278 0.01893 ] Network output: [ 0.9998 0.0008851 0.001232 -2.616e-05 1.174e-05 -0.001786 -1.972e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1906 -0.03213 -0.1848 0.1942 0.9836 0.9933 0.213 0.4491 0.8738 0.7198 ] Network output: [ -0.01107 1.001 1.01 6.291e-07 -2.824e-07 0.01058 4.741e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005707 0.0004613 0.004407 0.003935 0.9889 0.992 0.005813 0.8673 0.8978 0.01369 ] Network output: [ -0.0007744 0.003245 1.002 -8.413e-05 3.777e-05 0.9959 -6.341e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2019 0.09415 0.3321 0.1502 0.9851 0.994 0.2025 0.4536 0.8804 0.7143 ] Network output: [ 0.006696 -0.03282 0.9957 4.978e-05 -2.235e-05 1.024 3.751e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09957 0.08784 0.18 0.2036 0.9873 0.992 0.09963 0.7732 0.8712 0.3068 ] Network output: [ -0.006553 0.03299 1.002 5.145e-05 -2.31e-05 0.9781 3.878e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1655 0.1957 0.9854 0.9913 0.09042 0.6996 0.8487 0.2439 ] Network output: [ 0.0002102 0.9998 -0.0004137 6.99e-06 -3.138e-06 1 5.268e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005774 Epoch 7545 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01171 0.9944 0.9891 1.469e-06 -6.595e-07 -0.006917 1.107e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003281 -0.00308 -0.00856 0.006612 0.9698 0.9742 0.00627 0.8385 0.8278 0.01892 ] Network output: [ 0.9998 0.0008843 0.001231 -2.614e-05 1.173e-05 -0.001785 -1.97e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1907 -0.03214 -0.1848 0.1942 0.9836 0.9933 0.213 0.4491 0.8738 0.7198 ] Network output: [ -0.01107 1.001 1.01 6.271e-07 -2.815e-07 0.01058 4.726e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005707 0.0004613 0.004407 0.003934 0.9889 0.992 0.005813 0.8673 0.8978 0.01369 ] Network output: [ -0.000774 0.003244 1.002 -8.406e-05 3.774e-05 0.9959 -6.335e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2019 0.09415 0.3321 0.1502 0.9851 0.994 0.2025 0.4536 0.8804 0.7143 ] Network output: [ 0.006694 -0.03281 0.9957 4.973e-05 -2.233e-05 1.024 3.748e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09957 0.08784 0.18 0.2036 0.9873 0.992 0.09964 0.7732 0.8712 0.3068 ] Network output: [ -0.00655 0.03297 1.002 5.141e-05 -2.308e-05 0.9781 3.874e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1655 0.1957 0.9854 0.9913 0.09042 0.6996 0.8487 0.2439 ] Network output: [ 0.0002101 0.9998 -0.0004133 6.985e-06 -3.136e-06 1 5.264e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005771 Epoch 7546 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0117 0.9944 0.9891 1.466e-06 -6.58e-07 -0.006918 1.105e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003282 -0.00308 -0.008558 0.006611 0.9698 0.9742 0.006271 0.8385 0.8278 0.01892 ] Network output: [ 0.9998 0.0008835 0.00123 -2.612e-05 1.173e-05 -0.001783 -1.968e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1907 -0.03214 -0.1847 0.1942 0.9836 0.9933 0.213 0.4491 0.8738 0.7198 ] Network output: [ -0.01107 1.001 1.01 6.252e-07 -2.807e-07 0.01057 4.712e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005708 0.0004613 0.004408 0.003933 0.9889 0.992 0.005814 0.8673 0.8978 0.01369 ] Network output: [ -0.0007736 0.003243 1.002 -8.399e-05 3.77e-05 0.9959 -6.329e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2019 0.09415 0.3321 0.1502 0.9851 0.994 0.2026 0.4536 0.8804 0.7143 ] Network output: [ 0.006692 -0.0328 0.9957 4.969e-05 -2.231e-05 1.024 3.745e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09958 0.08785 0.18 0.2036 0.9873 0.992 0.09964 0.7731 0.8712 0.3068 ] Network output: [ -0.006548 0.03296 1.002 5.137e-05 -2.306e-05 0.9781 3.871e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1655 0.1957 0.9854 0.9913 0.09042 0.6995 0.8487 0.2439 ] Network output: [ 0.00021 0.9998 -0.0004129 6.979e-06 -3.133e-06 1 5.259e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005767 Epoch 7547 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0117 0.9944 0.9891 1.462e-06 -6.565e-07 -0.00692 1.102e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003282 -0.00308 -0.008557 0.00661 0.9698 0.9742 0.006271 0.8384 0.8278 0.01892 ] Network output: [ 0.9998 0.0008827 0.001229 -2.61e-05 1.172e-05 -0.001782 -1.967e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1907 -0.03214 -0.1847 0.1942 0.9836 0.9933 0.2131 0.4491 0.8738 0.7197 ] Network output: [ -0.01107 1.001 1.01 6.232e-07 -2.798e-07 0.01057 4.697e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005709 0.0004613 0.004408 0.003933 0.9889 0.992 0.005815 0.8673 0.8978 0.01369 ] Network output: [ -0.0007731 0.003242 1.002 -8.391e-05 3.767e-05 0.9959 -6.324e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2019 0.09416 0.3321 0.1502 0.9851 0.994 0.2026 0.4536 0.8804 0.7143 ] Network output: [ 0.006689 -0.03279 0.9957 4.965e-05 -2.229e-05 1.024 3.742e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09958 0.08785 0.18 0.2036 0.9873 0.992 0.09965 0.7731 0.8712 0.3068 ] Network output: [ -0.006545 0.03294 1.002 5.133e-05 -2.304e-05 0.9781 3.868e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1655 0.1957 0.9854 0.9913 0.09042 0.6995 0.8487 0.2439 ] Network output: [ 0.0002098 0.9998 -0.0004124 6.973e-06 -3.13e-06 1 5.255e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005763 Epoch 7548 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0117 0.9944 0.9891 1.459e-06 -6.551e-07 -0.006921 1.1e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003282 -0.00308 -0.008556 0.00661 0.9698 0.9742 0.006271 0.8384 0.8278 0.01892 ] Network output: [ 0.9998 0.0008818 0.001228 -2.607e-05 1.171e-05 -0.00178 -1.965e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1907 -0.03215 -0.1847 0.1942 0.9836 0.9933 0.2131 0.4491 0.8738 0.7197 ] Network output: [ -0.01106 1.001 1.01 6.213e-07 -2.789e-07 0.01057 4.682e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005709 0.0004614 0.004408 0.003932 0.9889 0.992 0.005815 0.8673 0.8978 0.01369 ] Network output: [ -0.0007727 0.00324 1.002 -8.384e-05 3.764e-05 0.9959 -6.318e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2019 0.09416 0.3322 0.1502 0.9851 0.994 0.2026 0.4536 0.8803 0.7143 ] Network output: [ 0.006687 -0.03277 0.9957 4.961e-05 -2.227e-05 1.024 3.739e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09959 0.08786 0.18 0.2036 0.9873 0.992 0.09966 0.7731 0.8712 0.3068 ] Network output: [ -0.006543 0.03293 1.002 5.128e-05 -2.302e-05 0.9782 3.865e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1655 0.1957 0.9854 0.9913 0.09042 0.6995 0.8487 0.2439 ] Network output: [ 0.0002097 0.9998 -0.000412 6.967e-06 -3.128e-06 1 5.251e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005759 Epoch 7549 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0117 0.9944 0.9891 1.456e-06 -6.536e-07 -0.006923 1.097e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003282 -0.00308 -0.008554 0.006609 0.9698 0.9742 0.006272 0.8384 0.8278 0.01892 ] Network output: [ 0.9998 0.0008811 0.001228 -2.605e-05 1.17e-05 -0.001779 -1.963e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1907 -0.03215 -0.1847 0.1941 0.9836 0.9933 0.2131 0.4491 0.8738 0.7197 ] Network output: [ -0.01106 1.001 1.01 6.193e-07 -2.78e-07 0.01056 4.667e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00571 0.0004614 0.004408 0.003932 0.9889 0.992 0.005816 0.8673 0.8978 0.01369 ] Network output: [ -0.0007723 0.003239 1.002 -8.376e-05 3.76e-05 0.9959 -6.313e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2019 0.09416 0.3322 0.1501 0.9851 0.994 0.2026 0.4535 0.8803 0.7142 ] Network output: [ 0.006685 -0.03276 0.9957 4.956e-05 -2.225e-05 1.024 3.735e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0996 0.08786 0.18 0.2035 0.9873 0.992 0.09966 0.7731 0.8711 0.3067 ] Network output: [ -0.00654 0.03291 1.002 5.124e-05 -2.3e-05 0.9782 3.862e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08848 0.1655 0.1957 0.9854 0.9913 0.09042 0.6994 0.8487 0.2439 ] Network output: [ 0.0002096 0.9998 -0.0004116 6.961e-06 -3.125e-06 1 5.246e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005756 Epoch 7550 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0117 0.9944 0.9891 1.453e-06 -6.521e-07 -0.006924 1.095e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003282 -0.00308 -0.008553 0.006608 0.9698 0.9742 0.006272 0.8384 0.8278 0.01892 ] Network output: [ 0.9998 0.0008802 0.001227 -2.603e-05 1.169e-05 -0.001778 -1.962e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1907 -0.03216 -0.1847 0.1941 0.9836 0.9933 0.2131 0.449 0.8738 0.7197 ] Network output: [ -0.01106 1.001 1.01 6.174e-07 -2.772e-07 0.01056 4.653e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00571 0.0004614 0.004408 0.003931 0.9889 0.992 0.005816 0.8673 0.8978 0.01369 ] Network output: [ -0.0007718 0.003238 1.002 -8.369e-05 3.757e-05 0.9959 -6.307e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.202 0.09417 0.3322 0.1501 0.9851 0.994 0.2026 0.4535 0.8803 0.7142 ] Network output: [ 0.006682 -0.03275 0.9957 4.952e-05 -2.223e-05 1.024 3.732e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0996 0.08787 0.18 0.2035 0.9873 0.992 0.09967 0.773 0.8711 0.3067 ] Network output: [ -0.006537 0.0329 1.002 5.12e-05 -2.299e-05 0.9782 3.859e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08848 0.1655 0.1957 0.9854 0.9913 0.09042 0.6994 0.8487 0.2439 ] Network output: [ 0.0002095 0.9998 -0.0004111 6.955e-06 -3.123e-06 1 5.242e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005752 Epoch 7551 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01169 0.9944 0.9891 1.449e-06 -6.507e-07 -0.006926 1.092e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003282 -0.003081 -0.008552 0.006607 0.9698 0.9742 0.006272 0.8384 0.8278 0.01891 ] Network output: [ 0.9998 0.0008795 0.001226 -2.601e-05 1.168e-05 -0.001776 -1.96e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1907 -0.03216 -0.1847 0.1941 0.9836 0.9933 0.2131 0.449 0.8738 0.7197 ] Network output: [ -0.01106 1.001 1.01 6.154e-07 -2.763e-07 0.01056 4.638e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005711 0.0004614 0.004408 0.00393 0.9889 0.992 0.005817 0.8673 0.8978 0.01368 ] Network output: [ -0.0007714 0.003237 1.002 -8.362e-05 3.754e-05 0.9959 -6.302e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.202 0.09417 0.3322 0.1501 0.9851 0.994 0.2026 0.4535 0.8803 0.7142 ] Network output: [ 0.00668 -0.03274 0.9957 4.948e-05 -2.221e-05 1.024 3.729e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09961 0.08787 0.18 0.2035 0.9873 0.992 0.09967 0.773 0.8711 0.3067 ] Network output: [ -0.006535 0.03288 1.002 5.116e-05 -2.297e-05 0.9782 3.856e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08848 0.1655 0.1957 0.9854 0.9913 0.09042 0.6994 0.8486 0.2439 ] Network output: [ 0.0002094 0.9998 -0.0004107 6.95e-06 -3.12e-06 1 5.238e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005748 Epoch 7552 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01169 0.9944 0.9891 1.446e-06 -6.492e-07 -0.006927 1.09e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003282 -0.003081 -0.00855 0.006606 0.9698 0.9742 0.006273 0.8384 0.8278 0.01891 ] Network output: [ 0.9998 0.0008786 0.001225 -2.599e-05 1.167e-05 -0.001775 -1.959e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1907 -0.03216 -0.1846 0.1941 0.9836 0.9933 0.2131 0.449 0.8738 0.7197 ] Network output: [ -0.01106 1.001 1.01 6.135e-07 -2.754e-07 0.01055 4.623e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005712 0.0004614 0.004408 0.00393 0.9889 0.992 0.005818 0.8673 0.8978 0.01368 ] Network output: [ -0.000771 0.003236 1.002 -8.354e-05 3.751e-05 0.9959 -6.296e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.202 0.09418 0.3322 0.1501 0.9851 0.994 0.2026 0.4535 0.8803 0.7142 ] Network output: [ 0.006678 -0.03273 0.9957 4.944e-05 -2.219e-05 1.024 3.726e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09961 0.08788 0.18 0.2035 0.9873 0.992 0.09968 0.773 0.8711 0.3067 ] Network output: [ -0.006532 0.03287 1.002 5.112e-05 -2.295e-05 0.9782 3.852e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1655 0.1957 0.9854 0.9913 0.09042 0.6994 0.8486 0.2439 ] Network output: [ 0.0002093 0.9998 -0.0004103 6.944e-06 -3.117e-06 1 5.233e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005744 Epoch 7553 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01169 0.9944 0.9891 1.443e-06 -6.477e-07 -0.006929 1.087e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003283 -0.003081 -0.008549 0.006605 0.9698 0.9742 0.006273 0.8384 0.8278 0.01891 ] Network output: [ 0.9998 0.0008778 0.001224 -2.597e-05 1.166e-05 -0.001773 -1.957e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1907 -0.03217 -0.1846 0.1941 0.9836 0.9933 0.2131 0.449 0.8738 0.7197 ] Network output: [ -0.01106 1.001 1.01 6.115e-07 -2.745e-07 0.01055 4.609e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005712 0.0004614 0.004408 0.003929 0.9889 0.992 0.005818 0.8672 0.8978 0.01368 ] Network output: [ -0.0007705 0.003235 1.002 -8.347e-05 3.747e-05 0.9959 -6.291e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.202 0.09418 0.3322 0.1501 0.9851 0.994 0.2026 0.4535 0.8803 0.7142 ] Network output: [ 0.006676 -0.03271 0.9956 4.94e-05 -2.218e-05 1.024 3.723e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09962 0.08788 0.18 0.2035 0.9873 0.992 0.09968 0.773 0.8711 0.3067 ] Network output: [ -0.00653 0.03285 1.002 5.108e-05 -2.293e-05 0.9782 3.849e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1655 0.1957 0.9854 0.9913 0.09042 0.6993 0.8486 0.2439 ] Network output: [ 0.0002092 0.9998 -0.0004098 6.938e-06 -3.115e-06 1 5.229e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000574 Epoch 7554 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01169 0.9944 0.9891 1.44e-06 -6.463e-07 -0.00693 1.085e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003283 -0.003081 -0.008548 0.006604 0.9698 0.9742 0.006273 0.8384 0.8277 0.01891 ] Network output: [ 0.9998 0.000877 0.001224 -2.595e-05 1.165e-05 -0.001772 -1.955e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1908 -0.03217 -0.1846 0.1941 0.9836 0.9933 0.2131 0.449 0.8738 0.7197 ] Network output: [ -0.01106 1.001 1.01 6.096e-07 -2.737e-07 0.01055 4.594e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005713 0.0004615 0.004408 0.003929 0.9889 0.992 0.005819 0.8672 0.8978 0.01368 ] Network output: [ -0.0007701 0.003234 1.002 -8.34e-05 3.744e-05 0.9959 -6.285e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.202 0.09418 0.3322 0.1501 0.9851 0.994 0.2027 0.4535 0.8803 0.7142 ] Network output: [ 0.006673 -0.0327 0.9956 4.935e-05 -2.216e-05 1.024 3.719e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09962 0.08789 0.18 0.2035 0.9873 0.992 0.09969 0.7729 0.8711 0.3067 ] Network output: [ -0.006527 0.03284 1.002 5.103e-05 -2.291e-05 0.9782 3.846e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1655 0.1957 0.9854 0.9913 0.09042 0.6993 0.8486 0.2439 ] Network output: [ 0.000209 0.9998 -0.0004094 6.932e-06 -3.112e-06 1 5.224e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005737 Epoch 7555 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01169 0.9944 0.9891 1.436e-06 -6.448e-07 -0.006932 1.082e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003283 -0.003081 -0.008547 0.006604 0.9698 0.9742 0.006273 0.8384 0.8277 0.01891 ] Network output: [ 0.9998 0.0008762 0.001223 -2.593e-05 1.164e-05 -0.001771 -1.954e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1908 -0.03217 -0.1846 0.1941 0.9836 0.9933 0.2132 0.449 0.8738 0.7197 ] Network output: [ -0.01106 1.001 1.01 6.076e-07 -2.728e-07 0.01054 4.579e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005713 0.0004615 0.004409 0.003928 0.9889 0.992 0.00582 0.8672 0.8977 0.01368 ] Network output: [ -0.0007697 0.003233 1.002 -8.332e-05 3.741e-05 0.9959 -6.279e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.202 0.09419 0.3323 0.1501 0.9851 0.994 0.2027 0.4534 0.8803 0.7142 ] Network output: [ 0.006671 -0.03269 0.9956 4.931e-05 -2.214e-05 1.024 3.716e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09963 0.08789 0.18 0.2035 0.9873 0.992 0.09969 0.7729 0.8711 0.3067 ] Network output: [ -0.006525 0.03282 1.002 5.099e-05 -2.289e-05 0.9782 3.843e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1655 0.1957 0.9854 0.9913 0.09041 0.6993 0.8486 0.2439 ] Network output: [ 0.0002089 0.9998 -0.000409 6.927e-06 -3.11e-06 1 5.22e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005733 Epoch 7556 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01168 0.9944 0.9891 1.433e-06 -6.433e-07 -0.006933 1.08e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003283 -0.003081 -0.008545 0.006603 0.9698 0.9742 0.006274 0.8384 0.8277 0.01891 ] Network output: [ 0.9998 0.0008754 0.001222 -2.59e-05 1.163e-05 -0.001769 -1.952e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1908 -0.03218 -0.1846 0.1941 0.9836 0.9933 0.2132 0.449 0.8738 0.7197 ] Network output: [ -0.01105 1.001 1.01 6.057e-07 -2.719e-07 0.01054 4.565e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005714 0.0004615 0.004409 0.003927 0.9889 0.992 0.00582 0.8672 0.8977 0.01368 ] Network output: [ -0.0007692 0.003232 1.002 -8.325e-05 3.737e-05 0.9959 -6.274e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.202 0.09419 0.3323 0.1501 0.9851 0.994 0.2027 0.4534 0.8803 0.7142 ] Network output: [ 0.006669 -0.03268 0.9956 4.927e-05 -2.212e-05 1.024 3.713e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09964 0.0879 0.18 0.2035 0.9873 0.992 0.0997 0.7729 0.8711 0.3067 ] Network output: [ -0.006522 0.03281 1.002 5.095e-05 -2.287e-05 0.9782 3.84e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1655 0.1957 0.9854 0.9913 0.09041 0.6992 0.8486 0.2439 ] Network output: [ 0.0002088 0.9998 -0.0004085 6.921e-06 -3.107e-06 1 5.216e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005729 Epoch 7557 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01168 0.9944 0.9891 1.43e-06 -6.419e-07 -0.006934 1.078e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003283 -0.003082 -0.008544 0.006602 0.9698 0.9742 0.006274 0.8384 0.8277 0.0189 ] Network output: [ 0.9998 0.0008746 0.001221 -2.588e-05 1.162e-05 -0.001768 -1.951e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1908 -0.03218 -0.1845 0.1941 0.9836 0.9933 0.2132 0.4489 0.8738 0.7197 ] Network output: [ -0.01105 1.001 1.01 6.038e-07 -2.711e-07 0.01054 4.55e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005715 0.0004615 0.004409 0.003927 0.9889 0.992 0.005821 0.8672 0.8977 0.01368 ] Network output: [ -0.0007688 0.003231 1.002 -8.317e-05 3.734e-05 0.9959 -6.268e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.202 0.0942 0.3323 0.1501 0.9851 0.994 0.2027 0.4534 0.8803 0.7142 ] Network output: [ 0.006667 -0.03267 0.9956 4.923e-05 -2.21e-05 1.024 3.71e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09964 0.0879 0.18 0.2035 0.9873 0.992 0.09971 0.7729 0.8711 0.3067 ] Network output: [ -0.006519 0.0328 1.002 5.091e-05 -2.285e-05 0.9782 3.837e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1655 0.1957 0.9854 0.9913 0.09041 0.6992 0.8486 0.2439 ] Network output: [ 0.0002087 0.9998 -0.0004081 6.915e-06 -3.104e-06 1 5.211e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005725 Epoch 7558 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01168 0.9945 0.9891 1.427e-06 -6.404e-07 -0.006936 1.075e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003283 -0.003082 -0.008543 0.006601 0.9698 0.9742 0.006274 0.8384 0.8277 0.0189 ] Network output: [ 0.9998 0.0008738 0.00122 -2.586e-05 1.161e-05 -0.001766 -1.949e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1908 -0.03218 -0.1845 0.1941 0.9836 0.9933 0.2132 0.4489 0.8738 0.7197 ] Network output: [ -0.01105 1.001 1.01 6.018e-07 -2.702e-07 0.01053 4.536e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005715 0.0004615 0.004409 0.003926 0.9889 0.992 0.005821 0.8672 0.8977 0.01368 ] Network output: [ -0.0007684 0.003229 1.002 -8.31e-05 3.731e-05 0.9959 -6.263e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2021 0.0942 0.3323 0.1501 0.9851 0.994 0.2027 0.4534 0.8803 0.7142 ] Network output: [ 0.006664 -0.03265 0.9956 4.918e-05 -2.208e-05 1.024 3.707e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09965 0.08791 0.18 0.2035 0.9873 0.992 0.09971 0.7728 0.8711 0.3067 ] Network output: [ -0.006517 0.03278 1.002 5.087e-05 -2.284e-05 0.9782 3.834e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1655 0.1957 0.9854 0.9913 0.09041 0.6992 0.8486 0.2439 ] Network output: [ 0.0002086 0.9998 -0.0004077 6.909e-06 -3.102e-06 1 5.207e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005722 Epoch 7559 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01168 0.9945 0.9891 1.423e-06 -6.39e-07 -0.006937 1.073e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003283 -0.003082 -0.008541 0.0066 0.9698 0.9742 0.006275 0.8383 0.8277 0.0189 ] Network output: [ 0.9998 0.000873 0.001219 -2.584e-05 1.16e-05 -0.001765 -1.947e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1908 -0.03219 -0.1845 0.1941 0.9836 0.9933 0.2132 0.4489 0.8738 0.7197 ] Network output: [ -0.01105 1.001 1.01 5.999e-07 -2.693e-07 0.01053 4.521e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005716 0.0004615 0.004409 0.003926 0.9889 0.992 0.005822 0.8672 0.8977 0.01367 ] Network output: [ -0.000768 0.003228 1.002 -8.303e-05 3.727e-05 0.9959 -6.257e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2021 0.0942 0.3323 0.1501 0.9851 0.994 0.2027 0.4534 0.8803 0.7142 ] Network output: [ 0.006662 -0.03264 0.9956 4.914e-05 -2.206e-05 1.024 3.704e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09965 0.08791 0.18 0.2035 0.9873 0.992 0.09972 0.7728 0.8711 0.3067 ] Network output: [ -0.006514 0.03277 1.002 5.083e-05 -2.282e-05 0.9782 3.83e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1655 0.1957 0.9854 0.9913 0.09041 0.6992 0.8486 0.2439 ] Network output: [ 0.0002085 0.9998 -0.0004072 6.903e-06 -3.099e-06 1 5.203e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005718 Epoch 7560 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01168 0.9945 0.9891 1.42e-06 -6.375e-07 -0.006939 1.07e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003283 -0.003082 -0.00854 0.006599 0.9698 0.9742 0.006275 0.8383 0.8277 0.0189 ] Network output: [ 0.9998 0.0008722 0.001219 -2.582e-05 1.159e-05 -0.001764 -1.946e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1908 -0.03219 -0.1845 0.1941 0.9836 0.9933 0.2132 0.4489 0.8737 0.7197 ] Network output: [ -0.01105 1.001 1.01 5.98e-07 -2.685e-07 0.01053 4.507e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005717 0.0004616 0.004409 0.003925 0.9889 0.992 0.005823 0.8672 0.8977 0.01367 ] Network output: [ -0.0007675 0.003227 1.002 -8.295e-05 3.724e-05 0.9959 -6.252e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2021 0.09421 0.3323 0.15 0.9851 0.994 0.2027 0.4534 0.8803 0.7142 ] Network output: [ 0.00666 -0.03263 0.9956 4.91e-05 -2.204e-05 1.024 3.7e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09966 0.08792 0.18 0.2035 0.9873 0.992 0.09972 0.7728 0.8711 0.3067 ] Network output: [ -0.006512 0.03275 1.002 5.078e-05 -2.28e-05 0.9782 3.827e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1655 0.1957 0.9854 0.9913 0.09041 0.6991 0.8486 0.2439 ] Network output: [ 0.0002084 0.9998 -0.0004068 6.898e-06 -3.097e-06 1 5.198e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005714 Epoch 7561 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01167 0.9945 0.9891 1.417e-06 -6.361e-07 -0.00694 1.068e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003284 -0.003082 -0.008539 0.006598 0.9698 0.9742 0.006275 0.8383 0.8277 0.0189 ] Network output: [ 0.9998 0.0008714 0.001218 -2.58e-05 1.158e-05 -0.001762 -1.944e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1908 -0.03219 -0.1845 0.194 0.9836 0.9933 0.2132 0.4489 0.8737 0.7197 ] Network output: [ -0.01105 1.001 1.01 5.961e-07 -2.676e-07 0.01052 4.492e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005717 0.0004616 0.004409 0.003924 0.9889 0.992 0.005823 0.8672 0.8977 0.01367 ] Network output: [ -0.0007671 0.003226 1.002 -8.288e-05 3.721e-05 0.9959 -6.246e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2021 0.09421 0.3323 0.15 0.9851 0.994 0.2027 0.4534 0.8803 0.7142 ] Network output: [ 0.006657 -0.03262 0.9956 4.906e-05 -2.202e-05 1.024 3.697e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09966 0.08792 0.18 0.2035 0.9873 0.992 0.09973 0.7728 0.871 0.3067 ] Network output: [ -0.006509 0.03274 1.002 5.074e-05 -2.278e-05 0.9782 3.824e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1655 0.1957 0.9854 0.9913 0.09041 0.6991 0.8485 0.2439 ] Network output: [ 0.0002082 0.9998 -0.0004064 6.892e-06 -3.094e-06 1 5.194e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000571 Epoch 7562 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01167 0.9945 0.9891 1.414e-06 -6.346e-07 -0.006942 1.065e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003284 -0.003082 -0.008537 0.006598 0.9698 0.9742 0.006276 0.8383 0.8277 0.01889 ] Network output: [ 0.9998 0.0008706 0.001217 -2.578e-05 1.157e-05 -0.001761 -1.943e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1908 -0.0322 -0.1845 0.194 0.9836 0.9933 0.2133 0.4489 0.8737 0.7197 ] Network output: [ -0.01105 1.001 1.01 5.941e-07 -2.667e-07 0.01052 4.478e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005718 0.0004616 0.004409 0.003924 0.9889 0.992 0.005824 0.8672 0.8977 0.01367 ] Network output: [ -0.0007667 0.003225 1.002 -8.281e-05 3.718e-05 0.9959 -6.241e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2021 0.09422 0.3324 0.15 0.9851 0.994 0.2028 0.4533 0.8803 0.7142 ] Network output: [ 0.006655 -0.03261 0.9956 4.902e-05 -2.201e-05 1.024 3.694e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09967 0.08793 0.18 0.2035 0.9873 0.992 0.09973 0.7727 0.871 0.3067 ] Network output: [ -0.006507 0.03272 1.002 5.07e-05 -2.276e-05 0.9783 3.821e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1654 0.1957 0.9854 0.9913 0.09041 0.6991 0.8485 0.2439 ] Network output: [ 0.0002081 0.9998 -0.000406 6.886e-06 -3.091e-06 1 5.19e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005707 Epoch 7563 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01167 0.9945 0.9891 1.41e-06 -6.332e-07 -0.006943 1.063e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003284 -0.003083 -0.008536 0.006597 0.9698 0.9742 0.006276 0.8383 0.8277 0.01889 ] Network output: [ 0.9998 0.0008698 0.001216 -2.576e-05 1.156e-05 -0.001759 -1.941e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1909 -0.0322 -0.1844 0.194 0.9836 0.9933 0.2133 0.4488 0.8737 0.7197 ] Network output: [ -0.01105 1.001 1.01 5.922e-07 -2.659e-07 0.01052 4.463e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005718 0.0004616 0.004409 0.003923 0.9889 0.992 0.005825 0.8671 0.8977 0.01367 ] Network output: [ -0.0007662 0.003224 1.002 -8.273e-05 3.714e-05 0.9959 -6.235e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2021 0.09422 0.3324 0.15 0.9851 0.994 0.2028 0.4533 0.8803 0.7142 ] Network output: [ 0.006653 -0.03259 0.9956 4.897e-05 -2.199e-05 1.024 3.691e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09968 0.08793 0.18 0.2035 0.9873 0.992 0.09974 0.7727 0.871 0.3067 ] Network output: [ -0.006504 0.03271 1.002 5.066e-05 -2.274e-05 0.9783 3.818e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1654 0.1957 0.9854 0.9913 0.09041 0.699 0.8485 0.2439 ] Network output: [ 0.000208 0.9998 -0.0004055 6.88e-06 -3.089e-06 1 5.185e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005703 Epoch 7564 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01167 0.9945 0.9891 1.407e-06 -6.317e-07 -0.006945 1.06e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003284 -0.003083 -0.008535 0.006596 0.9698 0.9742 0.006276 0.8383 0.8277 0.01889 ] Network output: [ 0.9998 0.000869 0.001215 -2.573e-05 1.155e-05 -0.001758 -1.939e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1909 -0.0322 -0.1844 0.194 0.9836 0.9933 0.2133 0.4488 0.8737 0.7197 ] Network output: [ -0.01104 1.001 1.01 5.903e-07 -2.65e-07 0.01051 4.449e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005719 0.0004616 0.00441 0.003923 0.9889 0.992 0.005825 0.8671 0.8977 0.01367 ] Network output: [ -0.0007658 0.003223 1.002 -8.266e-05 3.711e-05 0.9959 -6.23e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2021 0.09422 0.3324 0.15 0.9851 0.994 0.2028 0.4533 0.8803 0.7142 ] Network output: [ 0.006651 -0.03258 0.9956 4.893e-05 -2.197e-05 1.024 3.688e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09968 0.08794 0.18 0.2035 0.9873 0.992 0.09975 0.7727 0.871 0.3067 ] Network output: [ -0.006501 0.03269 1.002 5.062e-05 -2.272e-05 0.9783 3.815e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1654 0.1957 0.9854 0.9913 0.09041 0.699 0.8485 0.2439 ] Network output: [ 0.0002079 0.9998 -0.0004051 6.875e-06 -3.086e-06 1 5.181e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005699 Epoch 7565 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01167 0.9945 0.9892 1.404e-06 -6.303e-07 -0.006946 1.058e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003284 -0.003083 -0.008534 0.006595 0.9698 0.9742 0.006277 0.8383 0.8277 0.01889 ] Network output: [ 0.9998 0.0008682 0.001215 -2.571e-05 1.154e-05 -0.001757 -1.938e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1909 -0.03221 -0.1844 0.194 0.9836 0.9933 0.2133 0.4488 0.8737 0.7197 ] Network output: [ -0.01104 1.001 1.01 5.884e-07 -2.641e-07 0.01051 4.434e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00572 0.0004616 0.00441 0.003922 0.9889 0.992 0.005826 0.8671 0.8977 0.01367 ] Network output: [ -0.0007654 0.003222 1.002 -8.259e-05 3.708e-05 0.9959 -6.224e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2021 0.09423 0.3324 0.15 0.9851 0.994 0.2028 0.4533 0.8803 0.7142 ] Network output: [ 0.006648 -0.03257 0.9956 4.889e-05 -2.195e-05 1.024 3.685e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09969 0.08794 0.18 0.2034 0.9873 0.992 0.09975 0.7727 0.871 0.3067 ] Network output: [ -0.006499 0.03268 1.002 5.058e-05 -2.271e-05 0.9783 3.812e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1654 0.1957 0.9854 0.9913 0.09041 0.699 0.8485 0.2439 ] Network output: [ 0.0002078 0.9998 -0.0004047 6.869e-06 -3.084e-06 1 5.177e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005696 Epoch 7566 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01166 0.9945 0.9892 1.401e-06 -6.288e-07 -0.006948 1.056e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003284 -0.003083 -0.008532 0.006594 0.9698 0.9742 0.006277 0.8383 0.8277 0.01889 ] Network output: [ 0.9998 0.0008674 0.001214 -2.569e-05 1.153e-05 -0.001755 -1.936e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1909 -0.03221 -0.1844 0.194 0.9836 0.9933 0.2133 0.4488 0.8737 0.7197 ] Network output: [ -0.01104 1.001 1.01 5.865e-07 -2.633e-07 0.01051 4.42e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00572 0.0004617 0.00441 0.003921 0.9889 0.992 0.005826 0.8671 0.8977 0.01366 ] Network output: [ -0.0007649 0.003221 1.002 -8.252e-05 3.704e-05 0.9959 -6.219e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2022 0.09423 0.3324 0.15 0.9851 0.994 0.2028 0.4533 0.8803 0.7142 ] Network output: [ 0.006646 -0.03256 0.9956 4.885e-05 -2.193e-05 1.024 3.681e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09969 0.08795 0.18 0.2034 0.9873 0.992 0.09976 0.7726 0.871 0.3067 ] Network output: [ -0.006496 0.03266 1.002 5.053e-05 -2.269e-05 0.9783 3.808e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1654 0.1957 0.9854 0.9913 0.09041 0.699 0.8485 0.2439 ] Network output: [ 0.0002077 0.9998 -0.0004042 6.863e-06 -3.081e-06 1 5.172e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005692 Epoch 7567 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01166 0.9945 0.9892 1.397e-06 -6.274e-07 -0.006949 1.053e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003284 -0.003083 -0.008531 0.006593 0.9698 0.9742 0.006277 0.8383 0.8277 0.01889 ] Network output: [ 0.9998 0.0008666 0.001213 -2.567e-05 1.152e-05 -0.001754 -1.935e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1909 -0.03221 -0.1844 0.194 0.9836 0.9933 0.2133 0.4488 0.8737 0.7196 ] Network output: [ -0.01104 1.001 1.01 5.845e-07 -2.624e-07 0.0105 4.405e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005721 0.0004617 0.00441 0.003921 0.9889 0.992 0.005827 0.8671 0.8977 0.01366 ] Network output: [ -0.0007645 0.00322 1.002 -8.244e-05 3.701e-05 0.9959 -6.213e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2022 0.09424 0.3324 0.15 0.9851 0.994 0.2028 0.4533 0.8803 0.7142 ] Network output: [ 0.006644 -0.03255 0.9956 4.881e-05 -2.191e-05 1.024 3.678e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0997 0.08795 0.18 0.2034 0.9873 0.992 0.09976 0.7726 0.871 0.3067 ] Network output: [ -0.006494 0.03265 1.002 5.049e-05 -2.267e-05 0.9783 3.805e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1654 0.1957 0.9854 0.9913 0.09041 0.6989 0.8485 0.2439 ] Network output: [ 0.0002076 0.9998 -0.0004038 6.857e-06 -3.078e-06 1 5.168e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005688 Epoch 7568 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01166 0.9945 0.9892 1.394e-06 -6.259e-07 -0.00695 1.051e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003285 -0.003083 -0.00853 0.006592 0.9698 0.9742 0.006278 0.8383 0.8277 0.01888 ] Network output: [ 0.9998 0.0008658 0.001212 -2.565e-05 1.152e-05 -0.001752 -1.933e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1909 -0.03222 -0.1843 0.194 0.9836 0.9933 0.2133 0.4488 0.8737 0.7196 ] Network output: [ -0.01104 1.001 1.01 5.826e-07 -2.616e-07 0.0105 4.391e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005721 0.0004617 0.00441 0.00392 0.9889 0.992 0.005828 0.8671 0.8977 0.01366 ] Network output: [ -0.0007641 0.003218 1.002 -8.237e-05 3.698e-05 0.9959 -6.208e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2022 0.09424 0.3324 0.15 0.9851 0.994 0.2028 0.4532 0.8803 0.7141 ] Network output: [ 0.006642 -0.03254 0.9956 4.876e-05 -2.189e-05 1.024 3.675e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0997 0.08796 0.18 0.2034 0.9873 0.992 0.09977 0.7726 0.871 0.3067 ] Network output: [ -0.006491 0.03264 1.002 5.045e-05 -2.265e-05 0.9783 3.802e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1654 0.1957 0.9854 0.9913 0.09041 0.6989 0.8485 0.2439 ] Network output: [ 0.0002074 0.9998 -0.0004034 6.851e-06 -3.076e-06 1 5.163e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005684 Epoch 7569 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01166 0.9945 0.9892 1.391e-06 -6.245e-07 -0.006952 1.048e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003285 -0.003084 -0.008528 0.006592 0.9698 0.9742 0.006278 0.8383 0.8277 0.01888 ] Network output: [ 0.9998 0.000865 0.001211 -2.563e-05 1.151e-05 -0.001751 -1.931e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1909 -0.03222 -0.1843 0.194 0.9836 0.9933 0.2133 0.4488 0.8737 0.7196 ] Network output: [ -0.01104 1.001 1.01 5.807e-07 -2.607e-07 0.0105 4.377e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005722 0.0004617 0.00441 0.00392 0.9889 0.992 0.005828 0.8671 0.8977 0.01366 ] Network output: [ -0.0007636 0.003217 1.002 -8.23e-05 3.695e-05 0.9959 -6.202e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2022 0.09424 0.3325 0.15 0.9851 0.994 0.2028 0.4532 0.8802 0.7141 ] Network output: [ 0.006639 -0.03252 0.9956 4.872e-05 -2.187e-05 1.024 3.672e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09971 0.08796 0.18 0.2034 0.9873 0.992 0.09977 0.7726 0.871 0.3067 ] Network output: [ -0.006489 0.03262 1.002 5.041e-05 -2.263e-05 0.9783 3.799e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1654 0.1957 0.9854 0.9913 0.09041 0.6989 0.8485 0.2439 ] Network output: [ 0.0002073 0.9998 -0.000403 6.846e-06 -3.073e-06 1 5.159e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005681 Epoch 7570 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01166 0.9945 0.9892 1.388e-06 -6.231e-07 -0.006953 1.046e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003285 -0.003084 -0.008527 0.006591 0.9698 0.9742 0.006278 0.8382 0.8277 0.01888 ] Network output: [ 0.9998 0.0008642 0.001211 -2.561e-05 1.15e-05 -0.00175 -1.93e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1909 -0.03222 -0.1843 0.194 0.9836 0.9933 0.2134 0.4487 0.8737 0.7196 ] Network output: [ -0.01104 1.001 1.01 5.788e-07 -2.599e-07 0.0105 4.362e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005723 0.0004617 0.00441 0.003919 0.9889 0.992 0.005829 0.8671 0.8977 0.01366 ] Network output: [ -0.0007632 0.003216 1.002 -8.222e-05 3.691e-05 0.9959 -6.197e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2022 0.09425 0.3325 0.15 0.9851 0.994 0.2028 0.4532 0.8802 0.7141 ] Network output: [ 0.006637 -0.03251 0.9956 4.868e-05 -2.185e-05 1.024 3.669e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09972 0.08797 0.18 0.2034 0.9873 0.992 0.09978 0.7725 0.871 0.3067 ] Network output: [ -0.006486 0.03261 1.002 5.037e-05 -2.261e-05 0.9783 3.796e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1654 0.1957 0.9854 0.9913 0.09041 0.6988 0.8484 0.2439 ] Network output: [ 0.0002072 0.9998 -0.0004025 6.84e-06 -3.071e-06 1 5.155e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005677 Epoch 7571 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01165 0.9945 0.9892 1.385e-06 -6.216e-07 -0.006955 1.044e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003285 -0.003084 -0.008526 0.00659 0.9698 0.9742 0.006279 0.8382 0.8276 0.01888 ] Network output: [ 0.9998 0.0008634 0.00121 -2.559e-05 1.149e-05 -0.001748 -1.928e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1909 -0.03223 -0.1843 0.194 0.9836 0.9933 0.2134 0.4487 0.8737 0.7196 ] Network output: [ -0.01104 1.001 1.01 5.769e-07 -2.59e-07 0.01049 4.348e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005723 0.0004617 0.00441 0.003919 0.9889 0.992 0.00583 0.8671 0.8977 0.01366 ] Network output: [ -0.0007628 0.003215 1.002 -8.215e-05 3.688e-05 0.9959 -6.191e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2022 0.09425 0.3325 0.1499 0.9851 0.994 0.2029 0.4532 0.8802 0.7141 ] Network output: [ 0.006635 -0.0325 0.9956 4.864e-05 -2.184e-05 1.024 3.666e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09972 0.08797 0.18 0.2034 0.9873 0.992 0.09979 0.7725 0.871 0.3067 ] Network output: [ -0.006484 0.03259 1.002 5.033e-05 -2.259e-05 0.9783 3.793e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1654 0.1957 0.9854 0.9913 0.09041 0.6988 0.8484 0.2439 ] Network output: [ 0.0002071 0.9999 -0.0004021 6.834e-06 -3.068e-06 1 5.15e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005673 Epoch 7572 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01165 0.9945 0.9892 1.382e-06 -6.202e-07 -0.006956 1.041e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003285 -0.003084 -0.008524 0.006589 0.9698 0.9742 0.006279 0.8382 0.8276 0.01888 ] Network output: [ 0.9998 0.0008626 0.001209 -2.557e-05 1.148e-05 -0.001747 -1.927e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.191 -0.03223 -0.1843 0.194 0.9836 0.9933 0.2134 0.4487 0.8737 0.7196 ] Network output: [ -0.01104 1.001 1.01 5.75e-07 -2.582e-07 0.01049 4.334e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005724 0.0004618 0.00441 0.003918 0.9889 0.992 0.00583 0.8671 0.8977 0.01366 ] Network output: [ -0.0007623 0.003214 1.002 -8.208e-05 3.685e-05 0.9959 -6.186e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2022 0.09426 0.3325 0.1499 0.9851 0.994 0.2029 0.4532 0.8802 0.7141 ] Network output: [ 0.006633 -0.03249 0.9956 4.86e-05 -2.182e-05 1.024 3.662e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09973 0.08797 0.18 0.2034 0.9873 0.992 0.09979 0.7725 0.871 0.3067 ] Network output: [ -0.006481 0.03258 1.002 5.029e-05 -2.258e-05 0.9783 3.79e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1654 0.1957 0.9854 0.9913 0.09041 0.6988 0.8484 0.2439 ] Network output: [ 0.000207 0.9999 -0.0004017 6.828e-06 -3.066e-06 1 5.146e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000567 Epoch 7573 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01165 0.9945 0.9892 1.378e-06 -6.188e-07 -0.006958 1.039e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003285 -0.003084 -0.008523 0.006588 0.9698 0.9742 0.006279 0.8382 0.8276 0.01888 ] Network output: [ 0.9998 0.0008619 0.001208 -2.554e-05 1.147e-05 -0.001746 -1.925e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.191 -0.03223 -0.1843 0.1939 0.9836 0.9933 0.2134 0.4487 0.8737 0.7196 ] Network output: [ -0.01103 1.001 1.01 5.731e-07 -2.573e-07 0.01049 4.319e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005725 0.0004618 0.00441 0.003917 0.9889 0.992 0.005831 0.867 0.8977 0.01366 ] Network output: [ -0.0007619 0.003213 1.002 -8.2e-05 3.681e-05 0.9959 -6.18e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2022 0.09426 0.3325 0.1499 0.9851 0.994 0.2029 0.4532 0.8802 0.7141 ] Network output: [ 0.00663 -0.03248 0.9956 4.855e-05 -2.18e-05 1.024 3.659e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09973 0.08798 0.18 0.2034 0.9873 0.992 0.0998 0.7725 0.8709 0.3067 ] Network output: [ -0.006478 0.03256 1.002 5.024e-05 -2.256e-05 0.9783 3.787e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1654 0.1957 0.9854 0.9913 0.09041 0.6987 0.8484 0.2439 ] Network output: [ 0.0002069 0.9999 -0.0004013 6.823e-06 -3.063e-06 1 5.142e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005666 Epoch 7574 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01165 0.9945 0.9892 1.375e-06 -6.174e-07 -0.006959 1.036e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003285 -0.003084 -0.008522 0.006587 0.9698 0.9742 0.00628 0.8382 0.8276 0.01887 ] Network output: [ 0.9998 0.0008611 0.001207 -2.552e-05 1.146e-05 -0.001744 -1.924e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.191 -0.03224 -0.1842 0.1939 0.9836 0.9933 0.2134 0.4487 0.8737 0.7196 ] Network output: [ -0.01103 1.001 1.01 5.712e-07 -2.564e-07 0.01048 4.305e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005725 0.0004618 0.004411 0.003917 0.9889 0.992 0.005832 0.867 0.8977 0.01365 ] Network output: [ -0.0007615 0.003212 1.002 -8.193e-05 3.678e-05 0.9959 -6.175e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2022 0.09426 0.3325 0.1499 0.9851 0.994 0.2029 0.4532 0.8802 0.7141 ] Network output: [ 0.006628 -0.03246 0.9956 4.851e-05 -2.178e-05 1.024 3.656e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09974 0.08798 0.18 0.2034 0.9873 0.992 0.0998 0.7724 0.8709 0.3067 ] Network output: [ -0.006476 0.03255 1.002 5.02e-05 -2.254e-05 0.9783 3.783e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1654 0.1957 0.9854 0.9913 0.09041 0.6987 0.8484 0.2439 ] Network output: [ 0.0002068 0.9999 -0.0004008 6.817e-06 -3.06e-06 1 5.138e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005662 Epoch 7575 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01165 0.9945 0.9892 1.372e-06 -6.159e-07 -0.00696 1.034e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003286 -0.003085 -0.008521 0.006586 0.9698 0.9742 0.00628 0.8382 0.8276 0.01887 ] Network output: [ 0.9998 0.0008603 0.001207 -2.55e-05 1.145e-05 -0.001743 -1.922e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.191 -0.03224 -0.1842 0.1939 0.9836 0.9933 0.2134 0.4487 0.8737 0.7196 ] Network output: [ -0.01103 1.002 1.01 5.693e-07 -2.556e-07 0.01048 4.291e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005726 0.0004618 0.004411 0.003916 0.9889 0.992 0.005832 0.867 0.8977 0.01365 ] Network output: [ -0.000761 0.003211 1.002 -8.186e-05 3.675e-05 0.9959 -6.169e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2023 0.09427 0.3325 0.1499 0.9851 0.994 0.2029 0.4531 0.8802 0.7141 ] Network output: [ 0.006626 -0.03245 0.9956 4.847e-05 -2.176e-05 1.024 3.653e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09974 0.08799 0.18 0.2034 0.9873 0.992 0.09981 0.7724 0.8709 0.3067 ] Network output: [ -0.006473 0.03253 1.002 5.016e-05 -2.252e-05 0.9783 3.78e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1654 0.1956 0.9854 0.9913 0.09041 0.6987 0.8484 0.2439 ] Network output: [ 0.0002067 0.9999 -0.0004004 6.811e-06 -3.058e-06 1 5.133e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005658 Epoch 7576 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01164 0.9945 0.9892 1.369e-06 -6.145e-07 -0.006962 1.032e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003286 -0.003085 -0.008519 0.006586 0.9698 0.9742 0.00628 0.8382 0.8276 0.01887 ] Network output: [ 0.9998 0.0008595 0.001206 -2.548e-05 1.144e-05 -0.001741 -1.92e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.191 -0.03224 -0.1842 0.1939 0.9836 0.9933 0.2134 0.4487 0.8737 0.7196 ] Network output: [ -0.01103 1.002 1.01 5.675e-07 -2.547e-07 0.01048 4.276e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005726 0.0004618 0.004411 0.003916 0.9889 0.992 0.005833 0.867 0.8977 0.01365 ] Network output: [ -0.0007606 0.00321 1.002 -8.179e-05 3.672e-05 0.9959 -6.164e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2023 0.09427 0.3325 0.1499 0.9851 0.994 0.2029 0.4531 0.8802 0.7141 ] Network output: [ 0.006624 -0.03244 0.9956 4.843e-05 -2.174e-05 1.024 3.65e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09975 0.08799 0.18 0.2034 0.9873 0.992 0.09981 0.7724 0.8709 0.3067 ] Network output: [ -0.006471 0.03252 1.002 5.012e-05 -2.25e-05 0.9784 3.777e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1654 0.1956 0.9854 0.9913 0.09041 0.6987 0.8484 0.2439 ] Network output: [ 0.0002065 0.9999 -0.0004 6.806e-06 -3.055e-06 1 5.129e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005655 Epoch 7577 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01164 0.9945 0.9892 1.366e-06 -6.131e-07 -0.006963 1.029e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003286 -0.003085 -0.008518 0.006585 0.9698 0.9742 0.006281 0.8382 0.8276 0.01887 ] Network output: [ 0.9998 0.0008587 0.001205 -2.546e-05 1.143e-05 -0.00174 -1.919e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.191 -0.03225 -0.1842 0.1939 0.9836 0.9933 0.2134 0.4486 0.8737 0.7196 ] Network output: [ -0.01103 1.002 1.01 5.656e-07 -2.539e-07 0.01047 4.262e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005727 0.0004619 0.004411 0.003915 0.9889 0.992 0.005833 0.867 0.8976 0.01365 ] Network output: [ -0.0007602 0.003209 1.002 -8.171e-05 3.668e-05 0.9959 -6.158e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2023 0.09428 0.3326 0.1499 0.9851 0.994 0.2029 0.4531 0.8802 0.7141 ] Network output: [ 0.006621 -0.03243 0.9956 4.839e-05 -2.172e-05 1.024 3.647e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09976 0.088 0.18 0.2034 0.9873 0.992 0.09982 0.7724 0.8709 0.3067 ] Network output: [ -0.006468 0.03251 1.002 5.008e-05 -2.248e-05 0.9784 3.774e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1654 0.1956 0.9854 0.9913 0.09041 0.6986 0.8484 0.2439 ] Network output: [ 0.0002064 0.9999 -0.0003996 6.8e-06 -3.053e-06 1 5.125e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005651 Epoch 7578 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01164 0.9945 0.9892 1.362e-06 -6.117e-07 -0.006965 1.027e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003286 -0.003085 -0.008517 0.006584 0.9698 0.9742 0.006281 0.8382 0.8276 0.01887 ] Network output: [ 0.9998 0.0008579 0.001204 -2.544e-05 1.142e-05 -0.001739 -1.917e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.191 -0.03225 -0.1842 0.1939 0.9836 0.9933 0.2135 0.4486 0.8737 0.7196 ] Network output: [ -0.01103 1.002 1.01 5.637e-07 -2.531e-07 0.01047 4.248e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005728 0.0004619 0.004411 0.003914 0.9889 0.992 0.005834 0.867 0.8976 0.01365 ] Network output: [ -0.0007598 0.003207 1.002 -8.164e-05 3.665e-05 0.9959 -6.153e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2023 0.09428 0.3326 0.1499 0.9851 0.994 0.2029 0.4531 0.8802 0.7141 ] Network output: [ 0.006619 -0.03242 0.9956 4.835e-05 -2.17e-05 1.024 3.644e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09976 0.088 0.1801 0.2034 0.9873 0.992 0.09983 0.7723 0.8709 0.3067 ] Network output: [ -0.006466 0.03249 1.002 5.004e-05 -2.246e-05 0.9784 3.771e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08847 0.1654 0.1956 0.9854 0.9913 0.09041 0.6986 0.8484 0.2439 ] Network output: [ 0.0002063 0.9999 -0.0003991 6.794e-06 -3.05e-06 1 5.12e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005647 Epoch 7579 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01164 0.9945 0.9892 1.359e-06 -6.103e-07 -0.006966 1.024e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003286 -0.003085 -0.008515 0.006583 0.9698 0.9742 0.006281 0.8382 0.8276 0.01887 ] Network output: [ 0.9998 0.0008571 0.001203 -2.542e-05 1.141e-05 -0.001737 -1.916e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.191 -0.03226 -0.1841 0.1939 0.9836 0.9933 0.2135 0.4486 0.8737 0.7196 ] Network output: [ -0.01103 1.002 1.01 5.618e-07 -2.522e-07 0.01047 4.234e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005728 0.0004619 0.004411 0.003914 0.9889 0.992 0.005835 0.867 0.8976 0.01365 ] Network output: [ -0.0007593 0.003206 1.002 -8.157e-05 3.662e-05 0.9959 -6.147e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2023 0.09428 0.3326 0.1499 0.9851 0.994 0.203 0.4531 0.8802 0.7141 ] Network output: [ 0.006617 -0.0324 0.9956 4.83e-05 -2.169e-05 1.024 3.64e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09977 0.08801 0.1801 0.2034 0.9873 0.992 0.09983 0.7723 0.8709 0.3067 ] Network output: [ -0.006463 0.03248 1.002 5e-05 -2.245e-05 0.9784 3.768e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.09041 0.6986 0.8484 0.2439 ] Network output: [ 0.0002062 0.9999 -0.0003987 6.788e-06 -3.048e-06 1 5.116e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005644 Epoch 7580 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01164 0.9945 0.9892 1.356e-06 -6.088e-07 -0.006967 1.022e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003286 -0.003085 -0.008514 0.006582 0.9698 0.9742 0.006282 0.8382 0.8276 0.01886 ] Network output: [ 0.9998 0.0008563 0.001203 -2.54e-05 1.14e-05 -0.001736 -1.914e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.191 -0.03226 -0.1841 0.1939 0.9836 0.9933 0.2135 0.4486 0.8736 0.7196 ] Network output: [ -0.01103 1.002 1.01 5.599e-07 -2.514e-07 0.01046 4.22e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005729 0.0004619 0.004411 0.003913 0.9889 0.992 0.005835 0.867 0.8976 0.01365 ] Network output: [ -0.0007589 0.003205 1.002 -8.15e-05 3.659e-05 0.9959 -6.142e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2023 0.09429 0.3326 0.1499 0.9851 0.994 0.203 0.4531 0.8802 0.7141 ] Network output: [ 0.006615 -0.03239 0.9956 4.826e-05 -2.167e-05 1.024 3.637e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09977 0.08801 0.1801 0.2034 0.9873 0.992 0.09984 0.7723 0.8709 0.3067 ] Network output: [ -0.006461 0.03246 1.002 4.996e-05 -2.243e-05 0.9784 3.765e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.09041 0.6985 0.8483 0.2439 ] Network output: [ 0.0002061 0.9999 -0.0003983 6.783e-06 -3.045e-06 1 5.112e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000564 Epoch 7581 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01163 0.9945 0.9892 1.353e-06 -6.074e-07 -0.006969 1.02e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003286 -0.003086 -0.008513 0.006581 0.9698 0.9742 0.006282 0.8382 0.8276 0.01886 ] Network output: [ 0.9998 0.0008556 0.001202 -2.538e-05 1.139e-05 -0.001734 -1.912e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1911 -0.03226 -0.1841 0.1939 0.9836 0.9933 0.2135 0.4486 0.8736 0.7196 ] Network output: [ -0.01102 1.002 1.01 5.58e-07 -2.505e-07 0.01046 4.206e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005729 0.0004619 0.004411 0.003913 0.9889 0.992 0.005836 0.867 0.8976 0.01364 ] Network output: [ -0.0007585 0.003204 1.002 -8.142e-05 3.655e-05 0.9959 -6.136e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2023 0.09429 0.3326 0.1499 0.9851 0.994 0.203 0.453 0.8802 0.7141 ] Network output: [ 0.006612 -0.03238 0.9956 4.822e-05 -2.165e-05 1.024 3.634e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09978 0.08802 0.1801 0.2033 0.9873 0.992 0.09984 0.7723 0.8709 0.3067 ] Network output: [ -0.006458 0.03245 1.002 4.991e-05 -2.241e-05 0.9784 3.762e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.09041 0.6985 0.8483 0.244 ] Network output: [ 0.000206 0.9999 -0.0003979 6.777e-06 -3.042e-06 1 5.107e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005636 Epoch 7582 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01163 0.9945 0.9892 1.35e-06 -6.06e-07 -0.00697 1.017e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003287 -0.003086 -0.008512 0.00658 0.9698 0.9742 0.006282 0.8381 0.8276 0.01886 ] Network output: [ 0.9998 0.0008548 0.001201 -2.535e-05 1.138e-05 -0.001733 -1.911e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1911 -0.03227 -0.1841 0.1939 0.9836 0.9933 0.2135 0.4486 0.8736 0.7196 ] Network output: [ -0.01102 1.002 1.01 5.562e-07 -2.497e-07 0.01046 4.191e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00573 0.000462 0.004411 0.003912 0.9889 0.992 0.005837 0.867 0.8976 0.01364 ] Network output: [ -0.000758 0.003203 1.002 -8.135e-05 3.652e-05 0.9959 -6.131e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2023 0.0943 0.3326 0.1498 0.9851 0.994 0.203 0.453 0.8802 0.7141 ] Network output: [ 0.00661 -0.03237 0.9956 4.818e-05 -2.163e-05 1.024 3.631e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09978 0.08802 0.1801 0.2033 0.9873 0.992 0.09985 0.7722 0.8709 0.3067 ] Network output: [ -0.006456 0.03243 1.002 4.987e-05 -2.239e-05 0.9784 3.759e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.09041 0.6985 0.8483 0.244 ] Network output: [ 0.0002059 0.9999 -0.0003974 6.771e-06 -3.04e-06 1 5.103e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005633 Epoch 7583 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01163 0.9945 0.9892 1.347e-06 -6.046e-07 -0.006972 1.015e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003287 -0.003086 -0.00851 0.00658 0.9698 0.9742 0.006283 0.8381 0.8276 0.01886 ] Network output: [ 0.9998 0.000854 0.0012 -2.533e-05 1.137e-05 -0.001732 -1.909e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1911 -0.03227 -0.1841 0.1939 0.9836 0.9933 0.2135 0.4485 0.8736 0.7196 ] Network output: [ -0.01102 1.002 1.01 5.543e-07 -2.488e-07 0.01045 4.177e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005731 0.000462 0.004412 0.003912 0.9889 0.992 0.005837 0.867 0.8976 0.01364 ] Network output: [ -0.0007576 0.003202 1.002 -8.128e-05 3.649e-05 0.9959 -6.125e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2024 0.0943 0.3326 0.1498 0.9851 0.994 0.203 0.453 0.8802 0.7141 ] Network output: [ 0.006608 -0.03236 0.9956 4.814e-05 -2.161e-05 1.024 3.628e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09979 0.08803 0.1801 0.2033 0.9873 0.992 0.09986 0.7722 0.8709 0.3067 ] Network output: [ -0.006453 0.03242 1.002 4.983e-05 -2.237e-05 0.9784 3.755e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.09041 0.6985 0.8483 0.244 ] Network output: [ 0.0002058 0.9999 -0.000397 6.765e-06 -3.037e-06 1 5.099e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005629 Epoch 7584 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01163 0.9945 0.9892 1.344e-06 -6.032e-07 -0.006973 1.013e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003287 -0.003086 -0.008509 0.006579 0.9698 0.9742 0.006283 0.8381 0.8276 0.01886 ] Network output: [ 0.9998 0.0008532 0.001199 -2.531e-05 1.136e-05 -0.00173 -1.908e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1911 -0.03227 -0.1841 0.1939 0.9836 0.9933 0.2135 0.4485 0.8736 0.7196 ] Network output: [ -0.01102 1.002 1.01 5.524e-07 -2.48e-07 0.01045 4.163e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005731 0.000462 0.004412 0.003911 0.9889 0.992 0.005838 0.8669 0.8976 0.01364 ] Network output: [ -0.0007572 0.003201 1.002 -8.121e-05 3.646e-05 0.996 -6.12e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2024 0.09431 0.3327 0.1498 0.9851 0.994 0.203 0.453 0.8802 0.7141 ] Network output: [ 0.006606 -0.03234 0.9956 4.81e-05 -2.159e-05 1.024 3.625e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0998 0.08803 0.1801 0.2033 0.9873 0.992 0.09986 0.7722 0.8709 0.3067 ] Network output: [ -0.006451 0.0324 1.002 4.979e-05 -2.235e-05 0.9784 3.752e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.09041 0.6984 0.8483 0.244 ] Network output: [ 0.0002056 0.9999 -0.0003966 6.76e-06 -3.035e-06 1 5.094e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005625 Epoch 7585 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01163 0.9945 0.9892 1.341e-06 -6.018e-07 -0.006974 1.01e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003287 -0.003086 -0.008508 0.006578 0.9698 0.9742 0.006283 0.8381 0.8276 0.01885 ] Network output: [ 0.9998 0.0008525 0.001199 -2.529e-05 1.135e-05 -0.001729 -1.906e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1911 -0.03228 -0.184 0.1938 0.9836 0.9933 0.2135 0.4485 0.8736 0.7196 ] Network output: [ -0.01102 1.002 1.01 5.505e-07 -2.472e-07 0.01045 4.149e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005732 0.000462 0.004412 0.00391 0.9889 0.992 0.005838 0.8669 0.8976 0.01364 ] Network output: [ -0.0007567 0.0032 1.002 -8.113e-05 3.642e-05 0.996 -6.114e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2024 0.09431 0.3327 0.1498 0.9851 0.994 0.203 0.453 0.8802 0.7141 ] Network output: [ 0.006603 -0.03233 0.9956 4.805e-05 -2.157e-05 1.024 3.622e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0998 0.08804 0.1801 0.2033 0.9873 0.992 0.09987 0.7722 0.8709 0.3067 ] Network output: [ -0.006448 0.03239 1.002 4.975e-05 -2.233e-05 0.9784 3.749e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.09041 0.6984 0.8483 0.244 ] Network output: [ 0.0002055 0.9999 -0.0003962 6.754e-06 -3.032e-06 1 5.09e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005622 Epoch 7586 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01162 0.9945 0.9892 1.337e-06 -6.004e-07 -0.006976 1.008e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003287 -0.003086 -0.008506 0.006577 0.9698 0.9742 0.006283 0.8381 0.8276 0.01885 ] Network output: [ 0.9998 0.0008517 0.001198 -2.527e-05 1.134e-05 -0.001728 -1.904e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1911 -0.03228 -0.184 0.1938 0.9836 0.9933 0.2136 0.4485 0.8736 0.7196 ] Network output: [ -0.01102 1.002 1.01 5.487e-07 -2.463e-07 0.01044 4.135e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005733 0.000462 0.004412 0.00391 0.9889 0.992 0.005839 0.8669 0.8976 0.01364 ] Network output: [ -0.0007563 0.003199 1.002 -8.106e-05 3.639e-05 0.996 -6.109e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2024 0.09431 0.3327 0.1498 0.9851 0.994 0.203 0.453 0.8802 0.7141 ] Network output: [ 0.006601 -0.03232 0.9956 4.801e-05 -2.156e-05 1.024 3.618e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09981 0.08804 0.1801 0.2033 0.9873 0.992 0.09987 0.7721 0.8708 0.3066 ] Network output: [ -0.006446 0.03238 1.002 4.971e-05 -2.232e-05 0.9784 3.746e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.09041 0.6984 0.8483 0.244 ] Network output: [ 0.0002054 0.9999 -0.0003958 6.748e-06 -3.03e-06 1 5.086e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005618 Epoch 7587 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01162 0.9945 0.9892 1.334e-06 -5.99e-07 -0.006977 1.006e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003287 -0.003087 -0.008505 0.006576 0.9698 0.9742 0.006284 0.8381 0.8276 0.01885 ] Network output: [ 0.9998 0.0008509 0.001197 -2.525e-05 1.134e-05 -0.001726 -1.903e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1911 -0.03228 -0.184 0.1938 0.9836 0.9933 0.2136 0.4485 0.8736 0.7195 ] Network output: [ -0.01102 1.002 1.01 5.468e-07 -2.455e-07 0.01044 4.121e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005733 0.0004621 0.004412 0.003909 0.9889 0.992 0.00584 0.8669 0.8976 0.01364 ] Network output: [ -0.0007559 0.003198 1.002 -8.099e-05 3.636e-05 0.996 -6.104e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2024 0.09432 0.3327 0.1498 0.9851 0.994 0.2031 0.453 0.8802 0.714 ] Network output: [ 0.006599 -0.03231 0.9956 4.797e-05 -2.154e-05 1.024 3.615e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09981 0.08805 0.1801 0.2033 0.9873 0.9919 0.09988 0.7721 0.8708 0.3066 ] Network output: [ -0.006443 0.03236 1.002 4.967e-05 -2.23e-05 0.9784 3.743e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6983 0.8483 0.244 ] Network output: [ 0.0002053 0.9999 -0.0003953 6.743e-06 -3.027e-06 1 5.081e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005614 Epoch 7588 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01162 0.9945 0.9892 1.331e-06 -5.976e-07 -0.006979 1.003e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003287 -0.003087 -0.008504 0.006575 0.9698 0.9742 0.006284 0.8381 0.8276 0.01885 ] Network output: [ 0.9998 0.0008501 0.001196 -2.523e-05 1.133e-05 -0.001725 -1.901e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1911 -0.03229 -0.184 0.1938 0.9836 0.9933 0.2136 0.4485 0.8736 0.7195 ] Network output: [ -0.01102 1.002 1.01 5.45e-07 -2.447e-07 0.01044 4.107e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005734 0.0004621 0.004412 0.003909 0.9889 0.992 0.00584 0.8669 0.8976 0.01364 ] Network output: [ -0.0007554 0.003197 1.002 -8.092e-05 3.633e-05 0.996 -6.098e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2024 0.09432 0.3327 0.1498 0.9851 0.994 0.2031 0.4529 0.8802 0.714 ] Network output: [ 0.006597 -0.0323 0.9956 4.793e-05 -2.152e-05 1.024 3.612e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09982 0.08805 0.1801 0.2033 0.9873 0.9919 0.09988 0.7721 0.8708 0.3066 ] Network output: [ -0.00644 0.03235 1.002 4.963e-05 -2.228e-05 0.9784 3.74e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6983 0.8483 0.244 ] Network output: [ 0.0002052 0.9999 -0.0003949 6.737e-06 -3.024e-06 1 5.077e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005611 Epoch 7589 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01162 0.9945 0.9892 1.328e-06 -5.962e-07 -0.00698 1.001e-06 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003288 -0.003087 -0.008503 0.006575 0.9698 0.9742 0.006284 0.8381 0.8275 0.01885 ] Network output: [ 0.9998 0.0008493 0.001195 -2.521e-05 1.132e-05 -0.001723 -1.9e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1911 -0.03229 -0.184 0.1938 0.9836 0.9933 0.2136 0.4485 0.8736 0.7195 ] Network output: [ -0.01101 1.002 1.01 5.431e-07 -2.438e-07 0.01044 4.093e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005734 0.0004621 0.004412 0.003908 0.9889 0.992 0.005841 0.8669 0.8976 0.01363 ] Network output: [ -0.000755 0.003195 1.002 -8.084e-05 3.629e-05 0.996 -6.093e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2024 0.09433 0.3327 0.1498 0.9851 0.994 0.2031 0.4529 0.8802 0.714 ] Network output: [ 0.006595 -0.03229 0.9956 4.789e-05 -2.15e-05 1.024 3.609e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09982 0.08806 0.1801 0.2033 0.9873 0.9919 0.09989 0.7721 0.8708 0.3066 ] Network output: [ -0.006438 0.03233 1.002 4.959e-05 -2.226e-05 0.9784 3.737e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6983 0.8482 0.244 ] Network output: [ 0.0002051 0.9999 -0.0003945 6.731e-06 -3.022e-06 1 5.073e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005607 Epoch 7590 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01162 0.9945 0.9892 1.325e-06 -5.948e-07 -0.006981 9.985e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003288 -0.003087 -0.008501 0.006574 0.9698 0.9742 0.006285 0.8381 0.8275 0.01885 ] Network output: [ 0.9998 0.0008486 0.001195 -2.519e-05 1.131e-05 -0.001722 -1.898e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1912 -0.03229 -0.1839 0.1938 0.9836 0.9933 0.2136 0.4484 0.8736 0.7195 ] Network output: [ -0.01101 1.002 1.01 5.412e-07 -2.43e-07 0.01043 4.079e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005735 0.0004621 0.004412 0.003907 0.9889 0.992 0.005842 0.8669 0.8976 0.01363 ] Network output: [ -0.0007546 0.003194 1.002 -8.077e-05 3.626e-05 0.996 -6.087e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2024 0.09433 0.3327 0.1498 0.9851 0.994 0.2031 0.4529 0.8801 0.714 ] Network output: [ 0.006592 -0.03227 0.9956 4.785e-05 -2.148e-05 1.024 3.606e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09983 0.08806 0.1801 0.2033 0.9873 0.9919 0.0999 0.772 0.8708 0.3066 ] Network output: [ -0.006435 0.03232 1.002 4.954e-05 -2.224e-05 0.9785 3.734e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6983 0.8482 0.244 ] Network output: [ 0.000205 0.9999 -0.0003941 6.726e-06 -3.019e-06 1 5.069e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005603 Epoch 7591 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01162 0.9945 0.9892 1.322e-06 -5.934e-07 -0.006983 9.962e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003288 -0.003087 -0.0085 0.006573 0.9698 0.9742 0.006285 0.8381 0.8275 0.01884 ] Network output: [ 0.9998 0.0008478 0.001194 -2.517e-05 1.13e-05 -0.001721 -1.897e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1912 -0.0323 -0.1839 0.1938 0.9836 0.9933 0.2136 0.4484 0.8736 0.7195 ] Network output: [ -0.01101 1.002 1.01 5.394e-07 -2.422e-07 0.01043 4.065e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005736 0.0004622 0.004412 0.003907 0.9889 0.992 0.005842 0.8669 0.8976 0.01363 ] Network output: [ -0.0007542 0.003193 1.002 -8.07e-05 3.623e-05 0.996 -6.082e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2025 0.09433 0.3328 0.1498 0.9851 0.994 0.2031 0.4529 0.8801 0.714 ] Network output: [ 0.00659 -0.03226 0.9956 4.781e-05 -2.146e-05 1.024 3.603e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09984 0.08807 0.1801 0.2033 0.9873 0.9919 0.0999 0.772 0.8708 0.3066 ] Network output: [ -0.006433 0.0323 1.002 4.95e-05 -2.222e-05 0.9785 3.731e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6982 0.8482 0.244 ] Network output: [ 0.0002049 0.9999 -0.0003937 6.72e-06 -3.017e-06 1 5.064e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00056 Epoch 7592 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01161 0.9945 0.9892 1.319e-06 -5.92e-07 -0.006984 9.938e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003288 -0.003087 -0.008499 0.006572 0.9698 0.9742 0.006285 0.8381 0.8275 0.01884 ] Network output: [ 0.9998 0.000847 0.001193 -2.514e-05 1.129e-05 -0.001719 -1.895e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1912 -0.0323 -0.1839 0.1938 0.9836 0.9933 0.2136 0.4484 0.8736 0.7195 ] Network output: [ -0.01101 1.002 1.01 5.375e-07 -2.413e-07 0.01043 4.051e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005736 0.0004622 0.004412 0.003906 0.9889 0.992 0.005843 0.8669 0.8976 0.01363 ] Network output: [ -0.0007537 0.003192 1.002 -8.063e-05 3.62e-05 0.996 -6.076e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2025 0.09434 0.3328 0.1498 0.9851 0.994 0.2031 0.4529 0.8801 0.714 ] Network output: [ 0.006588 -0.03225 0.9956 4.776e-05 -2.144e-05 1.024 3.6e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09984 0.08807 0.1801 0.2033 0.9873 0.9919 0.09991 0.772 0.8708 0.3066 ] Network output: [ -0.00643 0.03229 1.002 4.946e-05 -2.221e-05 0.9785 3.728e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6982 0.8482 0.244 ] Network output: [ 0.0002047 0.9999 -0.0003932 6.714e-06 -3.014e-06 1 5.06e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005596 Epoch 7593 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01161 0.9945 0.9892 1.316e-06 -5.906e-07 -0.006985 9.915e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003288 -0.003087 -0.008498 0.006571 0.9698 0.9742 0.006286 0.8381 0.8275 0.01884 ] Network output: [ 0.9998 0.0008463 0.001192 -2.512e-05 1.128e-05 -0.001718 -1.893e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1912 -0.0323 -0.1839 0.1938 0.9836 0.9933 0.2136 0.4484 0.8736 0.7195 ] Network output: [ -0.01101 1.002 1.01 5.357e-07 -2.405e-07 0.01042 4.037e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005737 0.0004622 0.004413 0.003906 0.9889 0.992 0.005843 0.8669 0.8976 0.01363 ] Network output: [ -0.0007533 0.003191 1.002 -8.056e-05 3.616e-05 0.996 -6.071e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2025 0.09434 0.3328 0.1497 0.9851 0.994 0.2031 0.4529 0.8801 0.714 ] Network output: [ 0.006586 -0.03224 0.9956 4.772e-05 -2.142e-05 1.024 3.597e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09985 0.08808 0.1801 0.2033 0.9873 0.9919 0.09991 0.772 0.8708 0.3066 ] Network output: [ -0.006428 0.03228 1.002 4.942e-05 -2.219e-05 0.9785 3.725e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6982 0.8482 0.244 ] Network output: [ 0.0002046 0.9999 -0.0003928 6.708e-06 -3.012e-06 1 5.056e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005592 Epoch 7594 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01161 0.9945 0.9892 1.313e-06 -5.892e-07 -0.006987 9.892e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003288 -0.003088 -0.008496 0.00657 0.9698 0.9742 0.006286 0.838 0.8275 0.01884 ] Network output: [ 0.9998 0.0008455 0.001191 -2.51e-05 1.127e-05 -0.001717 -1.892e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1912 -0.03231 -0.1839 0.1938 0.9836 0.9933 0.2137 0.4484 0.8736 0.7195 ] Network output: [ -0.01101 1.002 1.01 5.338e-07 -2.397e-07 0.01042 4.023e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005737 0.0004622 0.004413 0.003905 0.9889 0.992 0.005844 0.8668 0.8976 0.01363 ] Network output: [ -0.0007529 0.00319 1.002 -8.048e-05 3.613e-05 0.996 -6.066e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2025 0.09435 0.3328 0.1497 0.9851 0.994 0.2031 0.4528 0.8801 0.714 ] Network output: [ 0.006583 -0.03223 0.9956 4.768e-05 -2.141e-05 1.024 3.593e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09985 0.08808 0.1801 0.2033 0.9873 0.9919 0.09992 0.7719 0.8708 0.3066 ] Network output: [ -0.006425 0.03226 1.002 4.938e-05 -2.217e-05 0.9785 3.721e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6981 0.8482 0.244 ] Network output: [ 0.0002045 0.9999 -0.0003924 6.703e-06 -3.009e-06 1 5.051e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005589 Epoch 7595 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01161 0.9945 0.9892 1.309e-06 -5.879e-07 -0.006988 9.868e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003288 -0.003088 -0.008495 0.00657 0.9698 0.9742 0.006286 0.838 0.8275 0.01884 ] Network output: [ 0.9998 0.0008447 0.001191 -2.508e-05 1.126e-05 -0.001715 -1.89e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1912 -0.03231 -0.1839 0.1938 0.9836 0.9933 0.2137 0.4484 0.8736 0.7195 ] Network output: [ -0.01101 1.002 1.01 5.32e-07 -2.388e-07 0.01042 4.009e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005738 0.0004622 0.004413 0.003905 0.9889 0.992 0.005845 0.8668 0.8976 0.01363 ] Network output: [ -0.0007524 0.003189 1.002 -8.041e-05 3.61e-05 0.996 -6.06e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2025 0.09435 0.3328 0.1497 0.9851 0.994 0.2032 0.4528 0.8801 0.714 ] Network output: [ 0.006581 -0.03221 0.9956 4.764e-05 -2.139e-05 1.024 3.59e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09986 0.08809 0.1801 0.2033 0.9873 0.9919 0.09992 0.7719 0.8708 0.3066 ] Network output: [ -0.006423 0.03225 1.002 4.934e-05 -2.215e-05 0.9785 3.718e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6981 0.8482 0.244 ] Network output: [ 0.0002044 0.9999 -0.000392 6.697e-06 -3.007e-06 1 5.047e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005585 Epoch 7596 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01161 0.9945 0.9892 1.306e-06 -5.865e-07 -0.00699 9.845e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003288 -0.003088 -0.008494 0.006569 0.9698 0.9742 0.006287 0.838 0.8275 0.01884 ] Network output: [ 0.9998 0.0008439 0.00119 -2.506e-05 1.125e-05 -0.001714 -1.889e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1912 -0.03231 -0.1838 0.1938 0.9836 0.9933 0.2137 0.4484 0.8736 0.7195 ] Network output: [ -0.01101 1.002 1.01 5.302e-07 -2.38e-07 0.01041 3.995e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005739 0.0004623 0.004413 0.003904 0.9889 0.992 0.005845 0.8668 0.8976 0.01362 ] Network output: [ -0.000752 0.003188 1.002 -8.034e-05 3.607e-05 0.996 -6.055e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2025 0.09435 0.3328 0.1497 0.9851 0.994 0.2032 0.4528 0.8801 0.714 ] Network output: [ 0.006579 -0.0322 0.9956 4.76e-05 -2.137e-05 1.024 3.587e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09987 0.08809 0.1801 0.2033 0.9873 0.9919 0.09993 0.7719 0.8708 0.3066 ] Network output: [ -0.00642 0.03223 1.002 4.93e-05 -2.213e-05 0.9785 3.715e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6981 0.8482 0.244 ] Network output: [ 0.0002043 0.9999 -0.0003916 6.691e-06 -3.004e-06 1 5.043e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005581 Epoch 7597 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0116 0.9945 0.9892 1.303e-06 -5.851e-07 -0.006991 9.822e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003289 -0.003088 -0.008492 0.006568 0.9698 0.9742 0.006287 0.838 0.8275 0.01883 ] Network output: [ 0.9998 0.0008432 0.001189 -2.504e-05 1.124e-05 -0.001712 -1.887e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1912 -0.03232 -0.1838 0.1937 0.9836 0.9933 0.2137 0.4483 0.8736 0.7195 ] Network output: [ -0.011 1.002 1.01 5.283e-07 -2.372e-07 0.01041 3.982e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005739 0.0004623 0.004413 0.003903 0.9889 0.992 0.005846 0.8668 0.8976 0.01362 ] Network output: [ -0.0007516 0.003187 1.002 -8.027e-05 3.604e-05 0.996 -6.049e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2025 0.09436 0.3328 0.1497 0.9851 0.994 0.2032 0.4528 0.8801 0.714 ] Network output: [ 0.006577 -0.03219 0.9956 4.756e-05 -2.135e-05 1.024 3.584e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09987 0.0881 0.1801 0.2033 0.9873 0.9919 0.09994 0.7719 0.8708 0.3066 ] Network output: [ -0.006418 0.03222 1.002 4.926e-05 -2.211e-05 0.9785 3.712e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6981 0.8482 0.244 ] Network output: [ 0.0002042 0.9999 -0.0003912 6.686e-06 -3.001e-06 1 5.039e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005578 Epoch 7598 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0116 0.9946 0.9892 1.3e-06 -5.837e-07 -0.006992 9.799e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003289 -0.003088 -0.008491 0.006567 0.9698 0.9742 0.006287 0.838 0.8275 0.01883 ] Network output: [ 0.9998 0.0008424 0.001188 -2.502e-05 1.123e-05 -0.001711 -1.885e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1912 -0.03232 -0.1838 0.1937 0.9836 0.9933 0.2137 0.4483 0.8736 0.7195 ] Network output: [ -0.011 1.002 1.01 5.265e-07 -2.364e-07 0.01041 3.968e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00574 0.0004623 0.004413 0.003903 0.9889 0.992 0.005847 0.8668 0.8975 0.01362 ] Network output: [ -0.0007512 0.003186 1.002 -8.02e-05 3.6e-05 0.996 -6.044e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2025 0.09436 0.3328 0.1497 0.9851 0.994 0.2032 0.4528 0.8801 0.714 ] Network output: [ 0.006574 -0.03218 0.9956 4.752e-05 -2.133e-05 1.024 3.581e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09988 0.0881 0.1801 0.2032 0.9873 0.9919 0.09994 0.7718 0.8707 0.3066 ] Network output: [ -0.006415 0.0322 1.002 4.922e-05 -2.21e-05 0.9785 3.709e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.698 0.8482 0.244 ] Network output: [ 0.0002041 0.9999 -0.0003907 6.68e-06 -2.999e-06 1 5.034e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005574 Epoch 7599 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0116 0.9946 0.9892 1.297e-06 -5.823e-07 -0.006994 9.776e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003289 -0.003088 -0.00849 0.006566 0.9698 0.9742 0.006288 0.838 0.8275 0.01883 ] Network output: [ 0.9998 0.0008416 0.001188 -2.5e-05 1.122e-05 -0.00171 -1.884e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1913 -0.03232 -0.1838 0.1937 0.9836 0.9933 0.2137 0.4483 0.8736 0.7195 ] Network output: [ -0.011 1.002 1.01 5.246e-07 -2.355e-07 0.0104 3.954e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005741 0.0004623 0.004413 0.003902 0.9889 0.992 0.005847 0.8668 0.8975 0.01362 ] Network output: [ -0.0007507 0.003184 1.002 -8.013e-05 3.597e-05 0.996 -6.039e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2026 0.09437 0.3329 0.1497 0.9851 0.994 0.2032 0.4528 0.8801 0.714 ] Network output: [ 0.006572 -0.03217 0.9956 4.748e-05 -2.131e-05 1.024 3.578e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09988 0.08811 0.1801 0.2032 0.9873 0.9919 0.09995 0.7718 0.8707 0.3066 ] Network output: [ -0.006413 0.03219 1.002 4.918e-05 -2.208e-05 0.9785 3.706e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.698 0.8481 0.244 ] Network output: [ 0.000204 0.9999 -0.0003903 6.674e-06 -2.996e-06 1 5.03e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000557 Epoch 7600 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0116 0.9946 0.9892 1.294e-06 -5.81e-07 -0.006995 9.753e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003289 -0.003089 -0.008489 0.006565 0.9698 0.9742 0.006288 0.838 0.8275 0.01883 ] Network output: [ 0.9998 0.0008409 0.001187 -2.498e-05 1.121e-05 -0.001708 -1.882e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1913 -0.03233 -0.1838 0.1937 0.9836 0.9933 0.2137 0.4483 0.8736 0.7195 ] Network output: [ -0.011 1.002 1.01 5.228e-07 -2.347e-07 0.0104 3.94e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005741 0.0004624 0.004413 0.003902 0.9889 0.992 0.005848 0.8668 0.8975 0.01362 ] Network output: [ -0.0007503 0.003183 1.002 -8.005e-05 3.594e-05 0.996 -6.033e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2026 0.09437 0.3329 0.1497 0.9851 0.994 0.2032 0.4528 0.8801 0.714 ] Network output: [ 0.00657 -0.03215 0.9956 4.743e-05 -2.13e-05 1.024 3.575e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09989 0.08811 0.1801 0.2032 0.9873 0.9919 0.09995 0.7718 0.8707 0.3066 ] Network output: [ -0.00641 0.03218 1.002 4.913e-05 -2.206e-05 0.9785 3.703e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.698 0.8481 0.244 ] Network output: [ 0.0002039 0.9999 -0.0003899 6.669e-06 -2.994e-06 1 5.026e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005567 Epoch 7601 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0116 0.9946 0.9893 1.291e-06 -5.796e-07 -0.006996 9.73e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003289 -0.003089 -0.008487 0.006564 0.9698 0.9742 0.006288 0.838 0.8275 0.01883 ] Network output: [ 0.9998 0.0008401 0.001186 -2.496e-05 1.12e-05 -0.001707 -1.881e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1913 -0.03233 -0.1837 0.1937 0.9836 0.9933 0.2138 0.4483 0.8735 0.7195 ] Network output: [ -0.011 1.002 1.01 5.21e-07 -2.339e-07 0.0104 3.926e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005742 0.0004624 0.004413 0.003901 0.9889 0.992 0.005849 0.8668 0.8975 0.01362 ] Network output: [ -0.0007499 0.003182 1.002 -7.998e-05 3.591e-05 0.996 -6.028e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2026 0.09438 0.3329 0.1497 0.9851 0.994 0.2032 0.4527 0.8801 0.714 ] Network output: [ 0.006568 -0.03214 0.9956 4.739e-05 -2.128e-05 1.024 3.572e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09989 0.08812 0.1801 0.2032 0.9873 0.9919 0.09996 0.7718 0.8707 0.3066 ] Network output: [ -0.006408 0.03216 1.002 4.909e-05 -2.204e-05 0.9785 3.7e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6979 0.8481 0.244 ] Network output: [ 0.0002037 0.9999 -0.0003895 6.663e-06 -2.991e-06 1 5.022e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005563 Epoch 7602 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01159 0.9946 0.9893 1.288e-06 -5.782e-07 -0.006998 9.707e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003289 -0.003089 -0.008486 0.006564 0.9698 0.9742 0.006289 0.838 0.8275 0.01883 ] Network output: [ 0.9998 0.0008393 0.001185 -2.493e-05 1.119e-05 -0.001706 -1.879e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1913 -0.03233 -0.1837 0.1937 0.9836 0.9933 0.2138 0.4483 0.8735 0.7195 ] Network output: [ -0.011 1.002 1.01 5.191e-07 -2.331e-07 0.0104 3.912e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005742 0.0004624 0.004413 0.003901 0.9889 0.992 0.005849 0.8668 0.8975 0.01362 ] Network output: [ -0.0007494 0.003181 1.002 -7.991e-05 3.587e-05 0.996 -6.022e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2026 0.09438 0.3329 0.1497 0.9851 0.994 0.2032 0.4527 0.8801 0.714 ] Network output: [ 0.006566 -0.03213 0.9956 4.735e-05 -2.126e-05 1.024 3.569e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.0999 0.08812 0.1801 0.2032 0.9873 0.9919 0.09997 0.7717 0.8707 0.3066 ] Network output: [ -0.006405 0.03215 1.002 4.905e-05 -2.202e-05 0.9785 3.697e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6979 0.8481 0.244 ] Network output: [ 0.0002036 0.9999 -0.0003891 6.657e-06 -2.989e-06 1 5.017e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000556 Epoch 7603 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01159 0.9946 0.9893 1.285e-06 -5.768e-07 -0.006999 9.684e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003289 -0.003089 -0.008485 0.006563 0.9698 0.9742 0.006289 0.838 0.8275 0.01882 ] Network output: [ 0.9998 0.0008386 0.001184 -2.491e-05 1.118e-05 -0.001704 -1.878e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1913 -0.03234 -0.1837 0.1937 0.9836 0.9933 0.2138 0.4483 0.8735 0.7195 ] Network output: [ -0.011 1.002 1.01 5.173e-07 -2.322e-07 0.01039 3.899e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005743 0.0004624 0.004414 0.0039 0.9889 0.992 0.00585 0.8668 0.8975 0.01362 ] Network output: [ -0.000749 0.00318 1.002 -7.984e-05 3.584e-05 0.996 -6.017e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2026 0.09438 0.3329 0.1497 0.9851 0.994 0.2033 0.4527 0.8801 0.714 ] Network output: [ 0.006563 -0.03212 0.9956 4.731e-05 -2.124e-05 1.024 3.566e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09991 0.08813 0.1801 0.2032 0.9873 0.9919 0.09997 0.7717 0.8707 0.3066 ] Network output: [ -0.006403 0.03213 1.002 4.901e-05 -2.2e-05 0.9785 3.694e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6979 0.8481 0.244 ] Network output: [ 0.0002035 0.9999 -0.0003887 6.652e-06 -2.986e-06 1 5.013e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005556 Epoch 7604 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01159 0.9946 0.9893 1.282e-06 -5.755e-07 -0.007 9.661e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00329 -0.003089 -0.008484 0.006562 0.9698 0.9742 0.006289 0.838 0.8275 0.01882 ] Network output: [ 0.9998 0.0008378 0.001184 -2.489e-05 1.118e-05 -0.001703 -1.876e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1913 -0.03234 -0.1837 0.1937 0.9836 0.9933 0.2138 0.4482 0.8735 0.7195 ] Network output: [ -0.011 1.002 1.01 5.155e-07 -2.314e-07 0.01039 3.885e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005744 0.0004625 0.004414 0.003899 0.9889 0.992 0.00585 0.8668 0.8975 0.01361 ] Network output: [ -0.0007486 0.003179 1.002 -7.977e-05 3.581e-05 0.996 -6.012e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2026 0.09439 0.3329 0.1496 0.9851 0.994 0.2033 0.4527 0.8801 0.714 ] Network output: [ 0.006561 -0.03211 0.9955 4.727e-05 -2.122e-05 1.024 3.562e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09991 0.08813 0.1801 0.2032 0.9873 0.9919 0.09998 0.7717 0.8707 0.3066 ] Network output: [ -0.0064 0.03212 1.002 4.897e-05 -2.199e-05 0.9786 3.691e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6979 0.8481 0.244 ] Network output: [ 0.0002034 0.9999 -0.0003882 6.646e-06 -2.984e-06 1 5.009e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005552 Epoch 7605 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01159 0.9946 0.9893 1.279e-06 -5.741e-07 -0.007002 9.638e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00329 -0.003089 -0.008482 0.006561 0.9698 0.9742 0.00629 0.838 0.8275 0.01882 ] Network output: [ 0.9998 0.0008371 0.001183 -2.487e-05 1.117e-05 -0.001702 -1.874e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1913 -0.03234 -0.1837 0.1937 0.9836 0.9933 0.2138 0.4482 0.8735 0.7195 ] Network output: [ -0.011 1.002 1.01 5.137e-07 -2.306e-07 0.01039 3.871e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005744 0.0004625 0.004414 0.003899 0.9889 0.992 0.005851 0.8667 0.8975 0.01361 ] Network output: [ -0.0007482 0.003178 1.002 -7.97e-05 3.578e-05 0.996 -6.006e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2026 0.09439 0.3329 0.1496 0.9851 0.994 0.2033 0.4527 0.8801 0.714 ] Network output: [ 0.006559 -0.0321 0.9955 4.723e-05 -2.12e-05 1.024 3.559e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09992 0.08814 0.1801 0.2032 0.9873 0.9919 0.09998 0.7717 0.8707 0.3066 ] Network output: [ -0.006398 0.0321 1.002 4.893e-05 -2.197e-05 0.9786 3.688e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6978 0.8481 0.244 ] Network output: [ 0.0002033 0.9999 -0.0003878 6.64e-06 -2.981e-06 1 5.004e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005549 Epoch 7606 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01159 0.9946 0.9893 1.276e-06 -5.727e-07 -0.007003 9.615e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00329 -0.00309 -0.008481 0.00656 0.9698 0.9742 0.00629 0.8379 0.8274 0.01882 ] Network output: [ 0.9998 0.0008363 0.001182 -2.485e-05 1.116e-05 -0.0017 -1.873e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1913 -0.03235 -0.1837 0.1937 0.9836 0.9933 0.2138 0.4482 0.8735 0.7195 ] Network output: [ -0.01099 1.002 1.01 5.119e-07 -2.298e-07 0.01038 3.858e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005745 0.0004625 0.004414 0.003898 0.9889 0.992 0.005852 0.8667 0.8975 0.01361 ] Network output: [ -0.0007477 0.003177 1.002 -7.962e-05 3.575e-05 0.996 -6.001e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2026 0.0944 0.333 0.1496 0.9851 0.994 0.2033 0.4527 0.8801 0.7139 ] Network output: [ 0.006557 -0.03208 0.9955 4.719e-05 -2.118e-05 1.024 3.556e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09992 0.08814 0.1801 0.2032 0.9873 0.9919 0.09999 0.7716 0.8707 0.3066 ] Network output: [ -0.006395 0.03209 1.002 4.889e-05 -2.195e-05 0.9786 3.684e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6978 0.8481 0.244 ] Network output: [ 0.0002032 0.9999 -0.0003874 6.635e-06 -2.979e-06 1 5e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005545 Epoch 7607 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01158 0.9946 0.9893 1.273e-06 -5.714e-07 -0.007005 9.592e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00329 -0.00309 -0.00848 0.006559 0.9698 0.9742 0.00629 0.8379 0.8274 0.01882 ] Network output: [ 0.9998 0.0008355 0.001181 -2.483e-05 1.115e-05 -0.001699 -1.871e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1913 -0.03235 -0.1836 0.1937 0.9836 0.9933 0.2138 0.4482 0.8735 0.7194 ] Network output: [ -0.01099 1.002 1.01 5.1e-07 -2.29e-07 0.01038 3.844e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005745 0.0004625 0.004414 0.003898 0.9889 0.992 0.005852 0.8667 0.8975 0.01361 ] Network output: [ -0.0007473 0.003176 1.002 -7.955e-05 3.571e-05 0.996 -5.995e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2027 0.0944 0.333 0.1496 0.9851 0.994 0.2033 0.4526 0.8801 0.7139 ] Network output: [ 0.006554 -0.03207 0.9955 4.715e-05 -2.117e-05 1.024 3.553e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09993 0.08815 0.1801 0.2032 0.9873 0.9919 0.09999 0.7716 0.8707 0.3066 ] Network output: [ -0.006393 0.03208 1.002 4.885e-05 -2.193e-05 0.9786 3.681e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6978 0.8481 0.244 ] Network output: [ 0.0002031 0.9999 -0.000387 6.629e-06 -2.976e-06 1 4.996e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005541 Epoch 7608 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01158 0.9946 0.9893 1.27e-06 -5.7e-07 -0.007006 9.569e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00329 -0.00309 -0.008478 0.006559 0.9698 0.9742 0.006291 0.8379 0.8274 0.01881 ] Network output: [ 0.9998 0.0008348 0.00118 -2.481e-05 1.114e-05 -0.001697 -1.87e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1914 -0.03235 -0.1836 0.1937 0.9836 0.9933 0.2138 0.4482 0.8735 0.7194 ] Network output: [ -0.01099 1.002 1.01 5.082e-07 -2.282e-07 0.01038 3.83e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005746 0.0004626 0.004414 0.003897 0.9889 0.992 0.005853 0.8667 0.8975 0.01361 ] Network output: [ -0.0007469 0.003175 1.002 -7.948e-05 3.568e-05 0.996 -5.99e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2027 0.09441 0.333 0.1496 0.9851 0.994 0.2033 0.4526 0.8801 0.7139 ] Network output: [ 0.006552 -0.03206 0.9955 4.711e-05 -2.115e-05 1.024 3.55e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09994 0.08815 0.1801 0.2032 0.9873 0.9919 0.1 0.7716 0.8707 0.3066 ] Network output: [ -0.00639 0.03206 1.002 4.881e-05 -2.191e-05 0.9786 3.678e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6977 0.8481 0.244 ] Network output: [ 0.000203 0.9999 -0.0003866 6.623e-06 -2.974e-06 1 4.992e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005538 Epoch 7609 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01158 0.9946 0.9893 1.267e-06 -5.687e-07 -0.007007 9.546e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00329 -0.00309 -0.008477 0.006558 0.9698 0.9742 0.006291 0.8379 0.8274 0.01881 ] Network output: [ 0.9998 0.000834 0.00118 -2.479e-05 1.113e-05 -0.001696 -1.868e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1914 -0.03236 -0.1836 0.1936 0.9836 0.9933 0.2139 0.4482 0.8735 0.7194 ] Network output: [ -0.01099 1.002 1.01 5.064e-07 -2.274e-07 0.01037 3.817e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005747 0.0004626 0.004414 0.003897 0.9889 0.992 0.005854 0.8667 0.8975 0.01361 ] Network output: [ -0.0007464 0.003174 1.002 -7.941e-05 3.565e-05 0.996 -5.985e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2027 0.09441 0.333 0.1496 0.9851 0.994 0.2033 0.4526 0.8801 0.7139 ] Network output: [ 0.00655 -0.03205 0.9955 4.706e-05 -2.113e-05 1.024 3.547e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09994 0.08816 0.1801 0.2032 0.9873 0.9919 0.1 0.7716 0.8707 0.3066 ] Network output: [ -0.006388 0.03205 1.002 4.877e-05 -2.189e-05 0.9786 3.675e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6977 0.848 0.244 ] Network output: [ 0.0002029 0.9999 -0.0003862 6.618e-06 -2.971e-06 1 4.987e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005534 Epoch 7610 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01158 0.9946 0.9893 1.264e-06 -5.673e-07 -0.007009 9.523e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00329 -0.00309 -0.008476 0.006557 0.9698 0.9742 0.006291 0.8379 0.8274 0.01881 ] Network output: [ 0.9998 0.0008332 0.001179 -2.477e-05 1.112e-05 -0.001695 -1.867e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1914 -0.03236 -0.1836 0.1936 0.9836 0.9933 0.2139 0.4481 0.8735 0.7194 ] Network output: [ -0.01099 1.002 1.01 5.046e-07 -2.265e-07 0.01037 3.803e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005747 0.0004626 0.004414 0.003896 0.9889 0.992 0.005854 0.8667 0.8975 0.01361 ] Network output: [ -0.000746 0.003172 1.002 -7.934e-05 3.562e-05 0.996 -5.979e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2027 0.09441 0.333 0.1496 0.9851 0.994 0.2033 0.4526 0.8801 0.7139 ] Network output: [ 0.006548 -0.03204 0.9955 4.702e-05 -2.111e-05 1.024 3.544e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09995 0.08816 0.1801 0.2032 0.9873 0.9919 0.1 0.7715 0.8706 0.3066 ] Network output: [ -0.006385 0.03203 1.002 4.873e-05 -2.188e-05 0.9786 3.672e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6977 0.848 0.244 ] Network output: [ 0.0002028 0.9999 -0.0003858 6.612e-06 -2.968e-06 1 4.983e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005531 Epoch 7611 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01158 0.9946 0.9893 1.261e-06 -5.66e-07 -0.00701 9.501e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003291 -0.00309 -0.008475 0.006556 0.9698 0.9742 0.006292 0.8379 0.8274 0.01881 ] Network output: [ 0.9998 0.0008325 0.001178 -2.475e-05 1.111e-05 -0.001693 -1.865e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1914 -0.03236 -0.1836 0.1936 0.9836 0.9933 0.2139 0.4481 0.8735 0.7194 ] Network output: [ -0.01099 1.002 1.01 5.028e-07 -2.257e-07 0.01037 3.789e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005748 0.0004626 0.004414 0.003895 0.9889 0.992 0.005855 0.8667 0.8975 0.0136 ] Network output: [ -0.0007456 0.003171 1.002 -7.927e-05 3.559e-05 0.996 -5.974e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2027 0.09442 0.333 0.1496 0.9851 0.994 0.2034 0.4526 0.8801 0.7139 ] Network output: [ 0.006546 -0.03202 0.9955 4.698e-05 -2.109e-05 1.024 3.541e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09995 0.08817 0.1801 0.2032 0.9873 0.9919 0.1 0.7715 0.8706 0.3066 ] Network output: [ -0.006383 0.03202 1.002 4.869e-05 -2.186e-05 0.9786 3.669e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6977 0.848 0.244 ] Network output: [ 0.0002026 0.9999 -0.0003854 6.607e-06 -2.966e-06 1 4.979e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005527 Epoch 7612 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01157 0.9946 0.9893 1.258e-06 -5.646e-07 -0.007011 9.478e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003291 -0.003091 -0.008473 0.006555 0.9698 0.9742 0.006292 0.8379 0.8274 0.01881 ] Network output: [ 0.9998 0.0008317 0.001177 -2.473e-05 1.11e-05 -0.001692 -1.863e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1914 -0.03237 -0.1836 0.1936 0.9836 0.9933 0.2139 0.4481 0.8735 0.7194 ] Network output: [ -0.01099 1.002 1.01 5.01e-07 -2.249e-07 0.01036 3.776e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005749 0.0004627 0.004414 0.003895 0.9889 0.992 0.005855 0.8667 0.8975 0.0136 ] Network output: [ -0.0007452 0.00317 1.002 -7.92e-05 3.555e-05 0.996 -5.969e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2027 0.09442 0.333 0.1496 0.9851 0.994 0.2034 0.4526 0.88 0.7139 ] Network output: [ 0.006543 -0.03201 0.9955 4.694e-05 -2.107e-05 1.024 3.538e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09996 0.08817 0.1802 0.2032 0.9873 0.9919 0.1 0.7715 0.8706 0.3066 ] Network output: [ -0.00638 0.032 1.002 4.865e-05 -2.184e-05 0.9786 3.666e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6976 0.848 0.244 ] Network output: [ 0.0002025 0.9999 -0.0003849 6.601e-06 -2.963e-06 1 4.975e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005523 Epoch 7613 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01157 0.9946 0.9893 1.255e-06 -5.632e-07 -0.007013 9.455e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003291 -0.003091 -0.008472 0.006554 0.9698 0.9742 0.006292 0.8379 0.8274 0.01881 ] Network output: [ 0.9998 0.000831 0.001176 -2.471e-05 1.109e-05 -0.001691 -1.862e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1914 -0.03237 -0.1835 0.1936 0.9836 0.9933 0.2139 0.4481 0.8735 0.7194 ] Network output: [ -0.01099 1.002 1.01 4.992e-07 -2.241e-07 0.01036 3.762e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005749 0.0004627 0.004415 0.003894 0.9889 0.992 0.005856 0.8667 0.8975 0.0136 ] Network output: [ -0.0007447 0.003169 1.002 -7.913e-05 3.552e-05 0.996 -5.963e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2027 0.09443 0.3331 0.1496 0.9851 0.994 0.2034 0.4526 0.88 0.7139 ] Network output: [ 0.006541 -0.032 0.9955 4.69e-05 -2.106e-05 1.024 3.535e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09996 0.08818 0.1802 0.2032 0.9873 0.9919 0.1 0.7715 0.8706 0.3066 ] Network output: [ -0.006378 0.03199 1.002 4.861e-05 -2.182e-05 0.9786 3.663e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6976 0.848 0.244 ] Network output: [ 0.0002024 0.9999 -0.0003845 6.595e-06 -2.961e-06 1 4.97e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000552 Epoch 7614 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01157 0.9946 0.9893 1.252e-06 -5.619e-07 -0.007014 9.433e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003291 -0.003091 -0.008471 0.006554 0.9698 0.9742 0.006293 0.8379 0.8274 0.0188 ] Network output: [ 0.9998 0.0008302 0.001176 -2.468e-05 1.108e-05 -0.001689 -1.86e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1914 -0.03237 -0.1835 0.1936 0.9836 0.9933 0.2139 0.4481 0.8735 0.7194 ] Network output: [ -0.01098 1.002 1.01 4.974e-07 -2.233e-07 0.01036 3.749e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00575 0.0004627 0.004415 0.003894 0.9889 0.992 0.005857 0.8667 0.8975 0.0136 ] Network output: [ -0.0007443 0.003168 1.002 -7.905e-05 3.549e-05 0.996 -5.958e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2027 0.09443 0.3331 0.1496 0.9851 0.994 0.2034 0.4525 0.88 0.7139 ] Network output: [ 0.006539 -0.03199 0.9955 4.686e-05 -2.104e-05 1.024 3.532e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09997 0.08818 0.1802 0.2031 0.9873 0.9919 0.1 0.7714 0.8706 0.3066 ] Network output: [ -0.006375 0.03198 1.002 4.856e-05 -2.18e-05 0.9786 3.66e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6976 0.848 0.244 ] Network output: [ 0.0002023 0.9999 -0.0003841 6.59e-06 -2.958e-06 1 4.966e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005516 Epoch 7615 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01157 0.9946 0.9893 1.249e-06 -5.606e-07 -0.007015 9.41e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003291 -0.003091 -0.00847 0.006553 0.9698 0.9742 0.006293 0.8379 0.8274 0.0188 ] Network output: [ 0.9998 0.0008295 0.001175 -2.466e-05 1.107e-05 -0.001688 -1.859e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1914 -0.03238 -0.1835 0.1936 0.9836 0.9933 0.2139 0.4481 0.8735 0.7194 ] Network output: [ -0.01098 1.002 1.01 4.956e-07 -2.225e-07 0.01036 3.735e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00575 0.0004627 0.004415 0.003893 0.9889 0.992 0.005857 0.8666 0.8975 0.0136 ] Network output: [ -0.0007439 0.003167 1.002 -7.898e-05 3.546e-05 0.996 -5.952e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2028 0.09444 0.3331 0.1496 0.9851 0.994 0.2034 0.4525 0.88 0.7139 ] Network output: [ 0.006537 -0.03198 0.9955 4.682e-05 -2.102e-05 1.024 3.528e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09998 0.08819 0.1802 0.2031 0.9873 0.9919 0.1 0.7714 0.8706 0.3066 ] Network output: [ -0.006373 0.03196 1.002 4.852e-05 -2.178e-05 0.9786 3.657e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6975 0.848 0.244 ] Network output: [ 0.0002022 0.9999 -0.0003837 6.584e-06 -2.956e-06 1 4.962e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005513 Epoch 7616 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01157 0.9946 0.9893 1.246e-06 -5.592e-07 -0.007017 9.387e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003291 -0.003091 -0.008468 0.006552 0.9698 0.9742 0.006293 0.8379 0.8274 0.0188 ] Network output: [ 0.9998 0.0008287 0.001174 -2.464e-05 1.106e-05 -0.001687 -1.857e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1914 -0.03238 -0.1835 0.1936 0.9836 0.9933 0.2139 0.4481 0.8735 0.7194 ] Network output: [ -0.01098 1.002 1.01 4.938e-07 -2.217e-07 0.01035 3.722e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005751 0.0004628 0.004415 0.003893 0.9889 0.992 0.005858 0.8666 0.8975 0.0136 ] Network output: [ -0.0007434 0.003166 1.002 -7.891e-05 3.543e-05 0.996 -5.947e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2028 0.09444 0.3331 0.1495 0.9851 0.994 0.2034 0.4525 0.88 0.7139 ] Network output: [ 0.006535 -0.03197 0.9955 4.678e-05 -2.1e-05 1.024 3.525e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09998 0.08819 0.1802 0.2031 0.9873 0.9919 0.1 0.7714 0.8706 0.3066 ] Network output: [ -0.00637 0.03195 1.002 4.848e-05 -2.177e-05 0.9786 3.654e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6975 0.848 0.244 ] Network output: [ 0.0002021 0.9999 -0.0003833 6.578e-06 -2.953e-06 1 4.958e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005509 Epoch 7617 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01156 0.9946 0.9893 1.243e-06 -5.579e-07 -0.007018 9.365e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003291 -0.003091 -0.008467 0.006551 0.9698 0.9742 0.006294 0.8379 0.8274 0.0188 ] Network output: [ 0.9998 0.000828 0.001173 -2.462e-05 1.105e-05 -0.001685 -1.856e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1915 -0.03238 -0.1835 0.1936 0.9836 0.9933 0.214 0.448 0.8735 0.7194 ] Network output: [ -0.01098 1.002 1.01 4.92e-07 -2.209e-07 0.01035 3.708e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005752 0.0004628 0.004415 0.003892 0.9889 0.992 0.005859 0.8666 0.8975 0.0136 ] Network output: [ -0.000743 0.003165 1.002 -7.884e-05 3.539e-05 0.996 -5.942e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2028 0.09444 0.3331 0.1495 0.9851 0.994 0.2034 0.4525 0.88 0.7139 ] Network output: [ 0.006532 -0.03195 0.9955 4.674e-05 -2.098e-05 1.024 3.522e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09999 0.0882 0.1802 0.2031 0.9873 0.9919 0.1001 0.7714 0.8706 0.3066 ] Network output: [ -0.006368 0.03193 1.002 4.844e-05 -2.175e-05 0.9786 3.651e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6975 0.848 0.244 ] Network output: [ 0.000202 0.9999 -0.0003829 6.573e-06 -2.951e-06 1 4.953e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005506 Epoch 7618 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01156 0.9946 0.9893 1.24e-06 -5.565e-07 -0.007019 9.342e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003292 -0.003092 -0.008466 0.00655 0.9698 0.9742 0.006294 0.8378 0.8274 0.0188 ] Network output: [ 0.9998 0.0008272 0.001173 -2.46e-05 1.104e-05 -0.001684 -1.854e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1915 -0.03238 -0.1834 0.1936 0.9836 0.9933 0.214 0.448 0.8735 0.7194 ] Network output: [ -0.01098 1.002 1.01 4.902e-07 -2.201e-07 0.01035 3.695e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005752 0.0004628 0.004415 0.003891 0.9889 0.992 0.005859 0.8666 0.8975 0.0136 ] Network output: [ -0.0007426 0.003164 1.002 -7.877e-05 3.536e-05 0.996 -5.936e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2028 0.09445 0.3331 0.1495 0.9851 0.994 0.2034 0.4525 0.88 0.7139 ] Network output: [ 0.00653 -0.03194 0.9955 4.67e-05 -2.096e-05 1.024 3.519e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.09999 0.0882 0.1802 0.2031 0.9873 0.9919 0.1001 0.7713 0.8706 0.3066 ] Network output: [ -0.006366 0.03192 1.002 4.84e-05 -2.173e-05 0.9787 3.648e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6975 0.848 0.244 ] Network output: [ 0.0002019 0.9999 -0.0003825 6.567e-06 -2.948e-06 1 4.949e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005502 Epoch 7619 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01156 0.9946 0.9893 1.237e-06 -5.552e-07 -0.00702 9.32e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003292 -0.003092 -0.008464 0.006549 0.9698 0.9742 0.006294 0.8378 0.8274 0.0188 ] Network output: [ 0.9998 0.0008265 0.001172 -2.458e-05 1.104e-05 -0.001683 -1.852e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1915 -0.03239 -0.1834 0.1936 0.9836 0.9933 0.214 0.448 0.8735 0.7194 ] Network output: [ -0.01098 1.002 1.01 4.884e-07 -2.193e-07 0.01034 3.681e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005753 0.0004628 0.004415 0.003891 0.9889 0.992 0.00586 0.8666 0.8975 0.01359 ] Network output: [ -0.0007422 0.003163 1.002 -7.87e-05 3.533e-05 0.996 -5.931e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2028 0.09445 0.3331 0.1495 0.9851 0.994 0.2035 0.4525 0.88 0.7139 ] Network output: [ 0.006528 -0.03193 0.9955 4.666e-05 -2.095e-05 1.024 3.516e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1 0.08821 0.1802 0.2031 0.9873 0.9919 0.1001 0.7713 0.8706 0.3066 ] Network output: [ -0.006363 0.03191 1.002 4.836e-05 -2.171e-05 0.9787 3.645e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6974 0.8479 0.244 ] Network output: [ 0.0002018 0.9999 -0.0003821 6.562e-06 -2.946e-06 1 4.945e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005498 Epoch 7620 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01156 0.9946 0.9893 1.234e-06 -5.538e-07 -0.007022 9.297e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003292 -0.003092 -0.008463 0.006549 0.9698 0.9742 0.006294 0.8378 0.8274 0.01879 ] Network output: [ 0.9998 0.0008257 0.001171 -2.456e-05 1.103e-05 -0.001681 -1.851e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1915 -0.03239 -0.1834 0.1936 0.9836 0.9933 0.214 0.448 0.8735 0.7194 ] Network output: [ -0.01098 1.002 1.01 4.867e-07 -2.185e-07 0.01034 3.668e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005753 0.0004629 0.004415 0.00389 0.9889 0.992 0.005861 0.8666 0.8974 0.01359 ] Network output: [ -0.0007417 0.003162 1.002 -7.863e-05 3.53e-05 0.996 -5.926e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2028 0.09446 0.3331 0.1495 0.9851 0.994 0.2035 0.4525 0.88 0.7139 ] Network output: [ 0.006526 -0.03192 0.9955 4.662e-05 -2.093e-05 1.024 3.513e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1 0.08821 0.1802 0.2031 0.9873 0.9919 0.1001 0.7713 0.8706 0.3066 ] Network output: [ -0.006361 0.03189 1.002 4.832e-05 -2.169e-05 0.9787 3.642e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6974 0.8479 0.244 ] Network output: [ 0.0002017 0.9999 -0.0003817 6.556e-06 -2.943e-06 1 4.941e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005495 Epoch 7621 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01156 0.9946 0.9893 1.231e-06 -5.525e-07 -0.007023 9.275e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003292 -0.003092 -0.008462 0.006548 0.9698 0.9742 0.006295 0.8378 0.8274 0.01879 ] Network output: [ 0.9998 0.000825 0.00117 -2.454e-05 1.102e-05 -0.00168 -1.849e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1915 -0.03239 -0.1834 0.1935 0.9836 0.9933 0.214 0.448 0.8735 0.7194 ] Network output: [ -0.01098 1.002 1.01 4.849e-07 -2.177e-07 0.01034 3.654e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005754 0.0004629 0.004415 0.00389 0.9889 0.992 0.005861 0.8666 0.8974 0.01359 ] Network output: [ -0.0007413 0.00316 1.002 -7.856e-05 3.527e-05 0.996 -5.92e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2028 0.09446 0.3332 0.1495 0.9851 0.994 0.2035 0.4524 0.88 0.7139 ] Network output: [ 0.006524 -0.03191 0.9955 4.657e-05 -2.091e-05 1.024 3.51e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1 0.08822 0.1802 0.2031 0.9873 0.9919 0.1001 0.7713 0.8706 0.3066 ] Network output: [ -0.006358 0.03188 1.002 4.828e-05 -2.168e-05 0.9787 3.639e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6974 0.8479 0.244 ] Network output: [ 0.0002015 0.9999 -0.0003813 6.55e-06 -2.941e-06 1 4.937e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005491 Epoch 7622 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01155 0.9946 0.9893 1.228e-06 -5.512e-07 -0.007024 9.253e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003292 -0.003092 -0.008461 0.006547 0.9698 0.9742 0.006295 0.8378 0.8274 0.01879 ] Network output: [ 0.9998 0.0008242 0.001169 -2.452e-05 1.101e-05 -0.001678 -1.848e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1915 -0.0324 -0.1834 0.1935 0.9836 0.9933 0.214 0.448 0.8734 0.7194 ] Network output: [ -0.01097 1.002 1.01 4.831e-07 -2.169e-07 0.01033 3.641e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005755 0.0004629 0.004415 0.003889 0.9889 0.992 0.005862 0.8666 0.8974 0.01359 ] Network output: [ -0.0007409 0.003159 1.002 -7.849e-05 3.524e-05 0.996 -5.915e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2028 0.09447 0.3332 0.1495 0.9851 0.994 0.2035 0.4524 0.88 0.7139 ] Network output: [ 0.006521 -0.0319 0.9955 4.653e-05 -2.089e-05 1.024 3.507e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1 0.08822 0.1802 0.2031 0.9873 0.9919 0.1001 0.7712 0.8705 0.3066 ] Network output: [ -0.006356 0.03186 1.002 4.824e-05 -2.166e-05 0.9787 3.636e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6974 0.8479 0.244 ] Network output: [ 0.0002014 0.9999 -0.0003808 6.545e-06 -2.938e-06 1 4.932e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005488 Epoch 7623 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01155 0.9946 0.9893 1.225e-06 -5.498e-07 -0.007026 9.23e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003292 -0.003092 -0.008459 0.006546 0.9698 0.9742 0.006295 0.8378 0.8274 0.01879 ] Network output: [ 0.9998 0.0008235 0.001169 -2.45e-05 1.1e-05 -0.001677 -1.846e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1915 -0.0324 -0.1834 0.1935 0.9836 0.9933 0.214 0.448 0.8734 0.7194 ] Network output: [ -0.01097 1.002 1.01 4.813e-07 -2.161e-07 0.01033 3.627e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005755 0.0004629 0.004415 0.003889 0.9889 0.992 0.005862 0.8666 0.8974 0.01359 ] Network output: [ -0.0007405 0.003158 1.002 -7.842e-05 3.52e-05 0.996 -5.91e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2028 0.09447 0.3332 0.1495 0.9851 0.994 0.2035 0.4524 0.88 0.7139 ] Network output: [ 0.006519 -0.03188 0.9955 4.649e-05 -2.087e-05 1.024 3.504e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1 0.08823 0.1802 0.2031 0.9873 0.9919 0.1001 0.7712 0.8705 0.3066 ] Network output: [ -0.006353 0.03185 1.002 4.82e-05 -2.164e-05 0.9787 3.633e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6973 0.8479 0.244 ] Network output: [ 0.0002013 0.9999 -0.0003804 6.539e-06 -2.936e-06 1 4.928e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005484 Epoch 7624 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01155 0.9946 0.9893 1.222e-06 -5.485e-07 -0.007027 9.208e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003292 -0.003093 -0.008458 0.006545 0.9698 0.9742 0.006296 0.8378 0.8273 0.01879 ] Network output: [ 0.9998 0.0008227 0.001168 -2.448e-05 1.099e-05 -0.001676 -1.845e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1915 -0.0324 -0.1833 0.1935 0.9836 0.9933 0.214 0.4479 0.8734 0.7194 ] Network output: [ -0.01097 1.002 1.01 4.795e-07 -2.153e-07 0.01033 3.614e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005756 0.000463 0.004416 0.003888 0.9889 0.992 0.005863 0.8666 0.8974 0.01359 ] Network output: [ -0.00074 0.003157 1.002 -7.835e-05 3.517e-05 0.996 -5.904e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2029 0.09447 0.3332 0.1495 0.9851 0.994 0.2035 0.4524 0.88 0.7138 ] Network output: [ 0.006517 -0.03187 0.9955 4.645e-05 -2.085e-05 1.024 3.501e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1 0.08823 0.1802 0.2031 0.9873 0.9919 0.1001 0.7712 0.8705 0.3066 ] Network output: [ -0.006351 0.03183 1.002 4.816e-05 -2.162e-05 0.9787 3.629e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6973 0.8479 0.2441 ] Network output: [ 0.0002012 0.9999 -0.00038 6.534e-06 -2.933e-06 1 4.924e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000548 Epoch 7625 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01155 0.9946 0.9893 1.219e-06 -5.472e-07 -0.007028 9.186e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003293 -0.003093 -0.008457 0.006544 0.9698 0.9742 0.006296 0.8378 0.8273 0.01879 ] Network output: [ 0.9998 0.000822 0.001167 -2.446e-05 1.098e-05 -0.001674 -1.843e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1915 -0.03241 -0.1833 0.1935 0.9836 0.9933 0.2141 0.4479 0.8734 0.7194 ] Network output: [ -0.01097 1.002 1.01 4.778e-07 -2.145e-07 0.01032 3.601e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005757 0.000463 0.004416 0.003887 0.9889 0.992 0.005864 0.8666 0.8974 0.01359 ] Network output: [ -0.0007396 0.003156 1.002 -7.828e-05 3.514e-05 0.996 -5.899e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2029 0.09448 0.3332 0.1495 0.9851 0.994 0.2035 0.4524 0.88 0.7138 ] Network output: [ 0.006515 -0.03186 0.9955 4.641e-05 -2.084e-05 1.024 3.498e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1 0.08824 0.1802 0.2031 0.9873 0.9919 0.1001 0.7712 0.8705 0.3065 ] Network output: [ -0.006348 0.03182 1.002 4.812e-05 -2.16e-05 0.9787 3.626e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6973 0.8479 0.2441 ] Network output: [ 0.0002011 0.9999 -0.0003796 6.528e-06 -2.931e-06 1 4.92e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005477 Epoch 7626 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01155 0.9946 0.9893 1.216e-06 -5.459e-07 -0.00703 9.163e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003293 -0.003093 -0.008456 0.006544 0.9698 0.9742 0.006296 0.8378 0.8273 0.01878 ] Network output: [ 0.9998 0.0008212 0.001166 -2.444e-05 1.097e-05 -0.001673 -1.842e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1916 -0.03241 -0.1833 0.1935 0.9836 0.9933 0.2141 0.4479 0.8734 0.7194 ] Network output: [ -0.01097 1.002 1.01 4.76e-07 -2.137e-07 0.01032 3.587e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005757 0.000463 0.004416 0.003887 0.9889 0.992 0.005864 0.8665 0.8974 0.01359 ] Network output: [ -0.0007392 0.003155 1.002 -7.821e-05 3.511e-05 0.996 -5.894e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2029 0.09448 0.3332 0.1495 0.9851 0.994 0.2035 0.4524 0.88 0.7138 ] Network output: [ 0.006513 -0.03185 0.9955 4.637e-05 -2.082e-05 1.024 3.495e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1 0.08824 0.1802 0.2031 0.9873 0.9919 0.1001 0.7711 0.8705 0.3065 ] Network output: [ -0.006346 0.03181 1.002 4.808e-05 -2.158e-05 0.9787 3.623e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6972 0.8479 0.2441 ] Network output: [ 0.000201 0.9999 -0.0003792 6.522e-06 -2.928e-06 1 4.915e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005473 Epoch 7627 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01155 0.9946 0.9893 1.213e-06 -5.445e-07 -0.007031 9.141e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003293 -0.003093 -0.008454 0.006543 0.9698 0.9742 0.006297 0.8378 0.8273 0.01878 ] Network output: [ 0.9998 0.0008205 0.001166 -2.441e-05 1.096e-05 -0.001672 -1.84e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1916 -0.03241 -0.1833 0.1935 0.9836 0.9933 0.2141 0.4479 0.8734 0.7193 ] Network output: [ -0.01097 1.002 1.01 4.742e-07 -2.129e-07 0.01032 3.574e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005758 0.0004631 0.004416 0.003886 0.9889 0.992 0.005865 0.8665 0.8974 0.01358 ] Network output: [ -0.0007387 0.003154 1.002 -7.813e-05 3.508e-05 0.996 -5.888e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2029 0.09449 0.3332 0.1494 0.9851 0.994 0.2036 0.4523 0.88 0.7138 ] Network output: [ 0.00651 -0.03184 0.9955 4.633e-05 -2.08e-05 1.023 3.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1 0.08825 0.1802 0.2031 0.9873 0.9919 0.1001 0.7711 0.8705 0.3065 ] Network output: [ -0.006343 0.03179 1.002 4.804e-05 -2.157e-05 0.9787 3.62e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6972 0.8479 0.2441 ] Network output: [ 0.0002009 0.9999 -0.0003788 6.517e-06 -2.926e-06 1 4.911e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000547 Epoch 7628 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01154 0.9946 0.9893 1.21e-06 -5.432e-07 -0.007032 9.119e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003293 -0.003093 -0.008453 0.006542 0.9698 0.9742 0.006297 0.8378 0.8273 0.01878 ] Network output: [ 0.9998 0.0008197 0.001165 -2.439e-05 1.095e-05 -0.00167 -1.838e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1916 -0.03242 -0.1833 0.1935 0.9836 0.9933 0.2141 0.4479 0.8734 0.7193 ] Network output: [ -0.01097 1.002 1.01 4.725e-07 -2.121e-07 0.01032 3.561e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005758 0.0004631 0.004416 0.003886 0.9889 0.992 0.005866 0.8665 0.8974 0.01358 ] Network output: [ -0.0007383 0.003153 1.002 -7.806e-05 3.505e-05 0.996 -5.883e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2029 0.09449 0.3333 0.1494 0.9851 0.994 0.2036 0.4523 0.88 0.7138 ] Network output: [ 0.006508 -0.03182 0.9955 4.629e-05 -2.078e-05 1.023 3.489e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1001 0.08825 0.1802 0.2031 0.9873 0.9919 0.1001 0.7711 0.8705 0.3065 ] Network output: [ -0.006341 0.03178 1.002 4.8e-05 -2.155e-05 0.9787 3.617e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6972 0.8478 0.2441 ] Network output: [ 0.0002008 0.9999 -0.0003784 6.511e-06 -2.923e-06 1 4.907e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005466 Epoch 7629 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01154 0.9946 0.9893 1.207e-06 -5.419e-07 -0.007034 9.097e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003293 -0.003093 -0.008452 0.006541 0.9698 0.9742 0.006297 0.8378 0.8273 0.01878 ] Network output: [ 0.9998 0.000819 0.001164 -2.437e-05 1.094e-05 -0.001669 -1.837e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1916 -0.03242 -0.1832 0.1935 0.9836 0.9933 0.2141 0.4479 0.8734 0.7193 ] Network output: [ -0.01097 1.002 1.01 4.707e-07 -2.113e-07 0.01031 3.547e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005759 0.0004631 0.004416 0.003885 0.9889 0.992 0.005866 0.8665 0.8974 0.01358 ] Network output: [ -0.0007379 0.003152 1.002 -7.799e-05 3.501e-05 0.996 -5.878e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2029 0.0945 0.3333 0.1494 0.9851 0.994 0.2036 0.4523 0.88 0.7138 ] Network output: [ 0.006506 -0.03181 0.9955 4.625e-05 -2.076e-05 1.023 3.486e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1001 0.08826 0.1802 0.2031 0.9873 0.9919 0.1001 0.7711 0.8705 0.3065 ] Network output: [ -0.006338 0.03176 1.002 4.796e-05 -2.153e-05 0.9787 3.614e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6972 0.8478 0.2441 ] Network output: [ 0.0002007 0.9999 -0.000378 6.506e-06 -2.921e-06 1 4.903e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005463 Epoch 7630 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01154 0.9946 0.9893 1.204e-06 -5.406e-07 -0.007035 9.075e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003293 -0.003094 -0.008451 0.00654 0.9698 0.9742 0.006298 0.8377 0.8273 0.01878 ] Network output: [ 0.9998 0.0008183 0.001163 -2.435e-05 1.093e-05 -0.001668 -1.835e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1916 -0.03242 -0.1832 0.1935 0.9836 0.9933 0.2141 0.4479 0.8734 0.7193 ] Network output: [ -0.01096 1.002 1.01 4.689e-07 -2.105e-07 0.01031 3.534e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00576 0.0004631 0.004416 0.003885 0.9889 0.992 0.005867 0.8665 0.8974 0.01358 ] Network output: [ -0.0007375 0.003151 1.002 -7.792e-05 3.498e-05 0.9961 -5.873e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2029 0.0945 0.3333 0.1494 0.9851 0.994 0.2036 0.4523 0.88 0.7138 ] Network output: [ 0.006504 -0.0318 0.9955 4.621e-05 -2.074e-05 1.023 3.482e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1001 0.08826 0.1802 0.2031 0.9873 0.9919 0.1001 0.771 0.8705 0.3065 ] Network output: [ -0.006336 0.03175 1.002 4.792e-05 -2.151e-05 0.9787 3.611e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6971 0.8478 0.2441 ] Network output: [ 0.0002006 0.9999 -0.0003776 6.5e-06 -2.918e-06 1 4.899e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005459 Epoch 7631 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01154 0.9946 0.9893 1.201e-06 -5.393e-07 -0.007036 9.053e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003293 -0.003094 -0.008449 0.006539 0.9698 0.9742 0.006298 0.8377 0.8273 0.01878 ] Network output: [ 0.9998 0.0008175 0.001163 -2.433e-05 1.092e-05 -0.001666 -1.834e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1916 -0.03243 -0.1832 0.1935 0.9836 0.9933 0.2141 0.4478 0.8734 0.7193 ] Network output: [ -0.01096 1.002 1.01 4.672e-07 -2.097e-07 0.01031 3.521e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00576 0.0004632 0.004416 0.003884 0.9889 0.992 0.005867 0.8665 0.8974 0.01358 ] Network output: [ -0.000737 0.00315 1.002 -7.785e-05 3.495e-05 0.9961 -5.867e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2029 0.0945 0.3333 0.1494 0.9851 0.994 0.2036 0.4523 0.88 0.7138 ] Network output: [ 0.006502 -0.03179 0.9955 4.617e-05 -2.073e-05 1.023 3.479e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1001 0.08827 0.1802 0.203 0.9873 0.9919 0.1001 0.771 0.8705 0.3065 ] Network output: [ -0.006333 0.03174 1.002 4.788e-05 -2.149e-05 0.9787 3.608e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6971 0.8478 0.2441 ] Network output: [ 0.0002005 0.9999 -0.0003772 6.494e-06 -2.916e-06 1 4.894e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005456 Epoch 7632 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01154 0.9946 0.9893 1.198e-06 -5.379e-07 -0.007037 9.03e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003294 -0.003094 -0.008448 0.006539 0.9698 0.9742 0.006298 0.8377 0.8273 0.01877 ] Network output: [ 0.9998 0.0008168 0.001162 -2.431e-05 1.091e-05 -0.001665 -1.832e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1916 -0.03243 -0.1832 0.1935 0.9836 0.9933 0.2141 0.4478 0.8734 0.7193 ] Network output: [ -0.01096 1.002 1.01 4.654e-07 -2.089e-07 0.0103 3.508e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005761 0.0004632 0.004416 0.003883 0.9889 0.992 0.005868 0.8665 0.8974 0.01358 ] Network output: [ -0.0007366 0.003148 1.002 -7.778e-05 3.492e-05 0.9961 -5.862e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.203 0.09451 0.3333 0.1494 0.9851 0.994 0.2036 0.4523 0.88 0.7138 ] Network output: [ 0.006499 -0.03178 0.9955 4.613e-05 -2.071e-05 1.023 3.476e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1001 0.08827 0.1802 0.203 0.9873 0.9919 0.1001 0.771 0.8705 0.3065 ] Network output: [ -0.006331 0.03172 1.002 4.784e-05 -2.148e-05 0.9788 3.605e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6971 0.8478 0.2441 ] Network output: [ 0.0002003 0.9999 -0.0003768 6.489e-06 -2.913e-06 1 4.89e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005452 Epoch 7633 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01153 0.9946 0.9893 1.195e-06 -5.366e-07 -0.007039 9.008e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003294 -0.003094 -0.008447 0.006538 0.9698 0.9742 0.006299 0.8377 0.8273 0.01877 ] Network output: [ 0.9998 0.000816 0.001161 -2.429e-05 1.091e-05 -0.001664 -1.831e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1916 -0.03243 -0.1832 0.1935 0.9836 0.9933 0.2142 0.4478 0.8734 0.7193 ] Network output: [ -0.01096 1.002 1.01 4.637e-07 -2.082e-07 0.0103 3.494e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005762 0.0004632 0.004416 0.003883 0.9889 0.992 0.005869 0.8665 0.8974 0.01358 ] Network output: [ -0.0007362 0.003147 1.002 -7.771e-05 3.489e-05 0.9961 -5.857e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.203 0.09451 0.3333 0.1494 0.9851 0.994 0.2036 0.4523 0.88 0.7138 ] Network output: [ 0.006497 -0.03177 0.9955 4.609e-05 -2.069e-05 1.023 3.473e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1001 0.08828 0.1802 0.203 0.9873 0.9919 0.1001 0.771 0.8705 0.3065 ] Network output: [ -0.006329 0.03171 1.002 4.78e-05 -2.146e-05 0.9788 3.602e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.697 0.8478 0.2441 ] Network output: [ 0.0002002 0.9999 -0.0003764 6.483e-06 -2.911e-06 1 4.886e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005449 Epoch 7634 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01153 0.9946 0.9893 1.192e-06 -5.353e-07 -0.00704 8.986e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003294 -0.003094 -0.008446 0.006537 0.9698 0.9742 0.006299 0.8377 0.8273 0.01877 ] Network output: [ 0.9998 0.0008153 0.00116 -2.427e-05 1.09e-05 -0.001662 -1.829e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1916 -0.03244 -0.1832 0.1934 0.9836 0.9933 0.2142 0.4478 0.8734 0.7193 ] Network output: [ -0.01096 1.002 1.01 4.619e-07 -2.074e-07 0.0103 3.481e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005762 0.0004633 0.004417 0.003882 0.9889 0.992 0.005869 0.8665 0.8974 0.01357 ] Network output: [ -0.0007358 0.003146 1.002 -7.764e-05 3.486e-05 0.9961 -5.851e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.203 0.09452 0.3333 0.1494 0.9851 0.994 0.2036 0.4522 0.8799 0.7138 ] Network output: [ 0.006495 -0.03175 0.9955 4.605e-05 -2.067e-05 1.023 3.47e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1001 0.08828 0.1802 0.203 0.9873 0.9919 0.1002 0.7709 0.8705 0.3065 ] Network output: [ -0.006326 0.03169 1.002 4.776e-05 -2.144e-05 0.9788 3.599e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.697 0.8478 0.2441 ] Network output: [ 0.0002001 0.9999 -0.000376 6.478e-06 -2.908e-06 1 4.882e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005445 Epoch 7635 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01153 0.9946 0.9893 1.189e-06 -5.34e-07 -0.007041 8.964e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003294 -0.003094 -0.008444 0.006536 0.9698 0.9742 0.006299 0.8377 0.8273 0.01877 ] Network output: [ 0.9998 0.0008146 0.001159 -2.425e-05 1.089e-05 -0.001661 -1.828e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1916 -0.03244 -0.1831 0.1934 0.9836 0.9933 0.2142 0.4478 0.8734 0.7193 ] Network output: [ -0.01096 1.002 1.01 4.602e-07 -2.066e-07 0.01029 3.468e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005763 0.0004633 0.004417 0.003882 0.9889 0.992 0.00587 0.8665 0.8974 0.01357 ] Network output: [ -0.0007353 0.003145 1.002 -7.757e-05 3.482e-05 0.9961 -5.846e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.203 0.09452 0.3333 0.1494 0.9851 0.994 0.2036 0.4522 0.8799 0.7138 ] Network output: [ 0.006493 -0.03174 0.9955 4.601e-05 -2.065e-05 1.023 3.467e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1001 0.08829 0.1802 0.203 0.9873 0.9919 0.1002 0.7709 0.8704 0.3065 ] Network output: [ -0.006324 0.03168 1.002 4.772e-05 -2.142e-05 0.9788 3.596e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.697 0.8478 0.2441 ] Network output: [ 0.0002 0.9999 -0.0003756 6.472e-06 -2.906e-06 1 4.878e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005441 Epoch 7636 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01153 0.9946 0.9893 1.187e-06 -5.327e-07 -0.007043 8.942e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003294 -0.003095 -0.008443 0.006535 0.9698 0.9742 0.0063 0.8377 0.8273 0.01877 ] Network output: [ 0.9998 0.0008138 0.001159 -2.423e-05 1.088e-05 -0.00166 -1.826e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1917 -0.03244 -0.1831 0.1934 0.9836 0.9933 0.2142 0.4478 0.8734 0.7193 ] Network output: [ -0.01096 1.002 1.01 4.584e-07 -2.058e-07 0.01029 3.455e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005763 0.0004633 0.004417 0.003881 0.9889 0.992 0.005871 0.8664 0.8974 0.01357 ] Network output: [ -0.0007349 0.003144 1.002 -7.75e-05 3.479e-05 0.9961 -5.841e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.203 0.09453 0.3334 0.1494 0.9851 0.994 0.2037 0.4522 0.8799 0.7138 ] Network output: [ 0.006491 -0.03173 0.9955 4.597e-05 -2.064e-05 1.023 3.464e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1001 0.08829 0.1802 0.203 0.9873 0.9919 0.1002 0.7709 0.8704 0.3065 ] Network output: [ -0.006321 0.03167 1.002 4.768e-05 -2.14e-05 0.9788 3.593e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.697 0.8478 0.2441 ] Network output: [ 0.0001999 0.9999 -0.0003752 6.467e-06 -2.903e-06 1 4.873e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005438 Epoch 7637 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01153 0.9946 0.9894 1.184e-06 -5.314e-07 -0.007044 8.921e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003294 -0.003095 -0.008442 0.006535 0.9698 0.9742 0.0063 0.8377 0.8273 0.01877 ] Network output: [ 0.9998 0.0008131 0.001158 -2.421e-05 1.087e-05 -0.001658 -1.824e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1917 -0.03245 -0.1831 0.1934 0.9836 0.9933 0.2142 0.4478 0.8734 0.7193 ] Network output: [ -0.01096 1.002 1.01 4.567e-07 -2.05e-07 0.01029 3.442e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005764 0.0004633 0.004417 0.003881 0.9889 0.992 0.005871 0.8664 0.8974 0.01357 ] Network output: [ -0.0007345 0.003143 1.002 -7.743e-05 3.476e-05 0.9961 -5.836e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.203 0.09453 0.3334 0.1494 0.9851 0.994 0.2037 0.4522 0.8799 0.7138 ] Network output: [ 0.006488 -0.03172 0.9955 4.593e-05 -2.062e-05 1.023 3.461e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1001 0.0883 0.1802 0.203 0.9873 0.9919 0.1002 0.7709 0.8704 0.3065 ] Network output: [ -0.006319 0.03165 1.002 4.764e-05 -2.139e-05 0.9788 3.59e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6969 0.8478 0.2441 ] Network output: [ 0.0001998 0.9999 -0.0003748 6.461e-06 -2.901e-06 1 4.869e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005434 Epoch 7638 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01152 0.9946 0.9894 1.181e-06 -5.301e-07 -0.007045 8.899e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003294 -0.003095 -0.008441 0.006534 0.9698 0.9742 0.0063 0.8377 0.8273 0.01876 ] Network output: [ 0.9998 0.0008124 0.001157 -2.419e-05 1.086e-05 -0.001657 -1.823e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1917 -0.03245 -0.1831 0.1934 0.9836 0.9933 0.2142 0.4477 0.8734 0.7193 ] Network output: [ -0.01096 1.002 1.01 4.55e-07 -2.042e-07 0.01029 3.429e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005765 0.0004634 0.004417 0.00388 0.9889 0.992 0.005872 0.8664 0.8974 0.01357 ] Network output: [ -0.0007341 0.003142 1.002 -7.736e-05 3.473e-05 0.9961 -5.83e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.203 0.09454 0.3334 0.1494 0.9851 0.994 0.2037 0.4522 0.8799 0.7138 ] Network output: [ 0.006486 -0.03171 0.9955 4.589e-05 -2.06e-05 1.023 3.458e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1001 0.0883 0.1802 0.203 0.9873 0.9919 0.1002 0.7708 0.8704 0.3065 ] Network output: [ -0.006316 0.03164 1.002 4.76e-05 -2.137e-05 0.9788 3.587e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6969 0.8477 0.2441 ] Network output: [ 0.0001997 0.9999 -0.0003744 6.455e-06 -2.898e-06 1 4.865e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005431 Epoch 7639 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01152 0.9947 0.9894 1.178e-06 -5.288e-07 -0.007046 8.877e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003294 -0.003095 -0.008439 0.006533 0.9698 0.9742 0.006301 0.8377 0.8273 0.01876 ] Network output: [ 0.9998 0.0008116 0.001156 -2.417e-05 1.085e-05 -0.001656 -1.821e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1917 -0.03245 -0.1831 0.1934 0.9836 0.9933 0.2142 0.4477 0.8734 0.7193 ] Network output: [ -0.01095 1.002 1.01 4.532e-07 -2.035e-07 0.01028 3.416e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005765 0.0004634 0.004417 0.00388 0.9889 0.992 0.005873 0.8664 0.8974 0.01357 ] Network output: [ -0.0007336 0.003141 1.002 -7.729e-05 3.47e-05 0.9961 -5.825e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.203 0.09454 0.3334 0.1493 0.9851 0.994 0.2037 0.4522 0.8799 0.7138 ] Network output: [ 0.006484 -0.0317 0.9955 4.585e-05 -2.058e-05 1.023 3.455e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1001 0.08831 0.1802 0.203 0.9873 0.9919 0.1002 0.7708 0.8704 0.3065 ] Network output: [ -0.006314 0.03162 1.002 4.756e-05 -2.135e-05 0.9788 3.584e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6969 0.8477 0.2441 ] Network output: [ 0.0001996 0.9999 -0.000374 6.45e-06 -2.896e-06 1 4.861e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005427 Epoch 7640 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01152 0.9947 0.9894 1.175e-06 -5.275e-07 -0.007048 8.855e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003295 -0.003095 -0.008438 0.006532 0.9698 0.9742 0.006301 0.8377 0.8273 0.01876 ] Network output: [ 0.9998 0.0008109 0.001156 -2.415e-05 1.084e-05 -0.001654 -1.82e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1917 -0.03246 -0.183 0.1934 0.9836 0.9933 0.2142 0.4477 0.8734 0.7193 ] Network output: [ -0.01095 1.002 1.01 4.515e-07 -2.027e-07 0.01028 3.402e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005766 0.0004634 0.004417 0.003879 0.9889 0.992 0.005873 0.8664 0.8974 0.01357 ] Network output: [ -0.0007332 0.00314 1.002 -7.722e-05 3.467e-05 0.9961 -5.82e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2031 0.09454 0.3334 0.1493 0.9851 0.994 0.2037 0.4521 0.8799 0.7138 ] Network output: [ 0.006482 -0.03168 0.9955 4.581e-05 -2.056e-05 1.023 3.452e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1001 0.08831 0.1802 0.203 0.9873 0.9919 0.1002 0.7708 0.8704 0.3065 ] Network output: [ -0.006311 0.03161 1.002 4.752e-05 -2.133e-05 0.9788 3.581e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6968 0.8477 0.2441 ] Network output: [ 0.0001995 0.9999 -0.0003736 6.444e-06 -2.893e-06 1 4.857e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005424 Epoch 7641 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01152 0.9947 0.9894 1.172e-06 -5.262e-07 -0.007049 8.833e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003295 -0.003095 -0.008437 0.006531 0.9698 0.9742 0.006301 0.8377 0.8273 0.01876 ] Network output: [ 0.9998 0.0008102 0.001155 -2.413e-05 1.083e-05 -0.001653 -1.818e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1917 -0.03246 -0.183 0.1934 0.9836 0.9933 0.2143 0.4477 0.8734 0.7193 ] Network output: [ -0.01095 1.002 1.01 4.497e-07 -2.019e-07 0.01028 3.389e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005766 0.0004635 0.004417 0.003878 0.9889 0.992 0.005874 0.8664 0.8974 0.01357 ] Network output: [ -0.0007328 0.003139 1.002 -7.715e-05 3.464e-05 0.9961 -5.814e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2031 0.09455 0.3334 0.1493 0.9851 0.994 0.2037 0.4521 0.8799 0.7138 ] Network output: [ 0.00648 -0.03167 0.9955 4.576e-05 -2.055e-05 1.023 3.449e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1001 0.08832 0.1802 0.203 0.9873 0.9919 0.1002 0.7708 0.8704 0.3065 ] Network output: [ -0.006309 0.0316 1.002 4.748e-05 -2.131e-05 0.9788 3.578e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6968 0.8477 0.2441 ] Network output: [ 0.0001994 0.9999 -0.0003732 6.439e-06 -2.891e-06 1 4.852e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000542 Epoch 7642 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01152 0.9947 0.9894 1.169e-06 -5.249e-07 -0.00705 8.811e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003295 -0.003095 -0.008436 0.00653 0.9698 0.9742 0.006302 0.8376 0.8272 0.01876 ] Network output: [ 0.9998 0.0008094 0.001154 -2.411e-05 1.082e-05 -0.001652 -1.817e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1917 -0.03246 -0.183 0.1934 0.9836 0.9933 0.2143 0.4477 0.8734 0.7193 ] Network output: [ -0.01095 1.002 1.01 4.48e-07 -2.011e-07 0.01027 3.376e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005767 0.0004635 0.004417 0.003878 0.9889 0.992 0.005874 0.8664 0.8973 0.01356 ] Network output: [ -0.0007324 0.003138 1.002 -7.708e-05 3.461e-05 0.9961 -5.809e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2031 0.09455 0.3334 0.1493 0.9851 0.994 0.2037 0.4521 0.8799 0.7137 ] Network output: [ 0.006478 -0.03166 0.9955 4.572e-05 -2.053e-05 1.023 3.446e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1001 0.08832 0.1802 0.203 0.9873 0.9919 0.1002 0.7707 0.8704 0.3065 ] Network output: [ -0.006307 0.03158 1.002 4.744e-05 -2.13e-05 0.9788 3.575e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6968 0.8477 0.2441 ] Network output: [ 0.0001993 0.9999 -0.0003728 6.433e-06 -2.888e-06 1 4.848e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005417 Epoch 7643 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01151 0.9947 0.9894 1.166e-06 -5.236e-07 -0.007051 8.79e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003295 -0.003096 -0.008434 0.00653 0.9698 0.9742 0.006302 0.8376 0.8272 0.01875 ] Network output: [ 0.9998 0.0008087 0.001153 -2.409e-05 1.081e-05 -0.00165 -1.815e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1917 -0.03247 -0.183 0.1934 0.9836 0.9933 0.2143 0.4477 0.8734 0.7193 ] Network output: [ -0.01095 1.002 1.01 4.463e-07 -2.004e-07 0.01027 3.363e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005768 0.0004635 0.004417 0.003877 0.9889 0.992 0.005875 0.8664 0.8973 0.01356 ] Network output: [ -0.0007319 0.003136 1.002 -7.701e-05 3.457e-05 0.9961 -5.804e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2031 0.09456 0.3335 0.1493 0.9851 0.994 0.2037 0.4521 0.8799 0.7137 ] Network output: [ 0.006475 -0.03165 0.9955 4.568e-05 -2.051e-05 1.023 3.443e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1001 0.08833 0.1802 0.203 0.9873 0.9919 0.1002 0.7707 0.8704 0.3065 ] Network output: [ -0.006304 0.03157 1.002 4.74e-05 -2.128e-05 0.9788 3.572e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6968 0.8477 0.2441 ] Network output: [ 0.0001992 0.9999 -0.0003724 6.428e-06 -2.886e-06 1 4.844e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005413 Epoch 7644 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01151 0.9947 0.9894 1.163e-06 -5.223e-07 -0.007053 8.768e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003295 -0.003096 -0.008433 0.006529 0.9698 0.9742 0.006302 0.8376 0.8272 0.01875 ] Network output: [ 0.9998 0.000808 0.001153 -2.406e-05 1.08e-05 -0.001649 -1.814e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1917 -0.03247 -0.183 0.1934 0.9836 0.9933 0.2143 0.4476 0.8733 0.7193 ] Network output: [ -0.01095 1.002 1.01 4.446e-07 -1.996e-07 0.01027 3.35e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005768 0.0004636 0.004417 0.003877 0.9889 0.992 0.005876 0.8664 0.8973 0.01356 ] Network output: [ -0.0007315 0.003135 1.002 -7.694e-05 3.454e-05 0.9961 -5.799e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2031 0.09456 0.3335 0.1493 0.9851 0.994 0.2038 0.4521 0.8799 0.7137 ] Network output: [ 0.006473 -0.03164 0.9955 4.564e-05 -2.049e-05 1.023 3.44e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1001 0.08833 0.1802 0.203 0.9873 0.9919 0.1002 0.7707 0.8704 0.3065 ] Network output: [ -0.006302 0.03155 1.002 4.736e-05 -2.126e-05 0.9788 3.569e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6967 0.8477 0.2441 ] Network output: [ 0.000199 0.9999 -0.0003719 6.422e-06 -2.883e-06 1 4.84e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000541 Epoch 7645 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01151 0.9947 0.9894 1.161e-06 -5.21e-07 -0.007054 8.746e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003295 -0.003096 -0.008432 0.006528 0.9698 0.9742 0.006303 0.8376 0.8272 0.01875 ] Network output: [ 0.9998 0.0008072 0.001152 -2.404e-05 1.079e-05 -0.001648 -1.812e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1918 -0.03247 -0.183 0.1934 0.9836 0.9933 0.2143 0.4476 0.8733 0.7193 ] Network output: [ -0.01095 1.002 1.01 4.428e-07 -1.988e-07 0.01026 3.337e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005769 0.0004636 0.004418 0.003876 0.9889 0.992 0.005876 0.8664 0.8973 0.01356 ] Network output: [ -0.0007311 0.003134 1.002 -7.687e-05 3.451e-05 0.9961 -5.793e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2031 0.09457 0.3335 0.1493 0.9851 0.994 0.2038 0.4521 0.8799 0.7137 ] Network output: [ 0.006471 -0.03163 0.9955 4.56e-05 -2.047e-05 1.023 3.437e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1002 0.08834 0.1802 0.203 0.9873 0.9919 0.1002 0.7707 0.8704 0.3065 ] Network output: [ -0.006299 0.03154 1.002 4.732e-05 -2.124e-05 0.9788 3.566e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6967 0.8477 0.2441 ] Network output: [ 0.0001989 0.9999 -0.0003715 6.417e-06 -2.881e-06 1 4.836e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005406 Epoch 7646 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01151 0.9947 0.9894 1.158e-06 -5.197e-07 -0.007055 8.725e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003295 -0.003096 -0.008431 0.006527 0.9698 0.9742 0.006303 0.8376 0.8272 0.01875 ] Network output: [ 0.9998 0.0008065 0.001151 -2.402e-05 1.079e-05 -0.001646 -1.81e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1918 -0.03247 -0.1829 0.1933 0.9836 0.9933 0.2143 0.4476 0.8733 0.7193 ] Network output: [ -0.01095 1.002 1.01 4.411e-07 -1.98e-07 0.01026 3.324e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00577 0.0004636 0.004418 0.003876 0.9889 0.992 0.005877 0.8664 0.8973 0.01356 ] Network output: [ -0.0007307 0.003133 1.002 -7.68e-05 3.448e-05 0.9961 -5.788e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2031 0.09457 0.3335 0.1493 0.9851 0.994 0.2038 0.4521 0.8799 0.7137 ] Network output: [ 0.006469 -0.03161 0.9955 4.556e-05 -2.046e-05 1.023 3.434e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1002 0.08834 0.1803 0.203 0.9873 0.9919 0.1002 0.7706 0.8704 0.3065 ] Network output: [ -0.006297 0.03153 1.002 4.728e-05 -2.122e-05 0.9788 3.563e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6967 0.8477 0.2441 ] Network output: [ 0.0001988 0.9999 -0.0003711 6.411e-06 -2.878e-06 1 4.832e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005403 Epoch 7647 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01151 0.9947 0.9894 1.155e-06 -5.184e-07 -0.007056 8.703e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003296 -0.003096 -0.008429 0.006526 0.9698 0.9742 0.006303 0.8376 0.8272 0.01875 ] Network output: [ 0.9998 0.0008058 0.00115 -2.4e-05 1.078e-05 -0.001645 -1.809e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1918 -0.03248 -0.1829 0.1933 0.9836 0.9933 0.2143 0.4476 0.8733 0.7192 ] Network output: [ -0.01094 1.002 1.01 4.394e-07 -1.973e-07 0.01026 3.311e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00577 0.0004636 0.004418 0.003875 0.9889 0.992 0.005878 0.8663 0.8973 0.01356 ] Network output: [ -0.0007302 0.003132 1.002 -7.673e-05 3.445e-05 0.9961 -5.783e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2031 0.09457 0.3335 0.1493 0.9851 0.994 0.2038 0.452 0.8799 0.7137 ] Network output: [ 0.006467 -0.0316 0.9955 4.552e-05 -2.044e-05 1.023 3.431e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1002 0.08835 0.1803 0.203 0.9873 0.9919 0.1002 0.7706 0.8703 0.3065 ] Network output: [ -0.006294 0.03151 1.002 4.724e-05 -2.121e-05 0.9789 3.56e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6966 0.8477 0.2441 ] Network output: [ 0.0001987 0.9999 -0.0003708 6.405e-06 -2.876e-06 1 4.827e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005399 Epoch 7648 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01151 0.9947 0.9894 1.152e-06 -5.172e-07 -0.007058 8.682e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003296 -0.003096 -0.008428 0.006525 0.9698 0.9742 0.006304 0.8376 0.8272 0.01875 ] Network output: [ 0.9998 0.0008051 0.001149 -2.398e-05 1.077e-05 -0.001644 -1.807e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1918 -0.03248 -0.1829 0.1933 0.9836 0.9933 0.2143 0.4476 0.8733 0.7192 ] Network output: [ -0.01094 1.002 1.01 4.377e-07 -1.965e-07 0.01026 3.298e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005771 0.0004637 0.004418 0.003874 0.9889 0.992 0.005878 0.8663 0.8973 0.01356 ] Network output: [ -0.0007298 0.003131 1.002 -7.666e-05 3.442e-05 0.9961 -5.778e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2032 0.09458 0.3335 0.1493 0.9851 0.994 0.2038 0.452 0.8799 0.7137 ] Network output: [ 0.006464 -0.03159 0.9955 4.548e-05 -2.042e-05 1.023 3.428e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1002 0.08835 0.1803 0.203 0.9873 0.9919 0.1002 0.7706 0.8703 0.3065 ] Network output: [ -0.006292 0.0315 1.002 4.72e-05 -2.119e-05 0.9789 3.557e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6966 0.8476 0.2441 ] Network output: [ 0.0001986 0.9999 -0.0003704 6.4e-06 -2.873e-06 1 4.823e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005396 Epoch 7649 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0115 0.9947 0.9894 1.149e-06 -5.159e-07 -0.007059 8.66e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003296 -0.003097 -0.008427 0.006525 0.9698 0.9742 0.006304 0.8376 0.8272 0.01874 ] Network output: [ 0.9998 0.0008043 0.001149 -2.396e-05 1.076e-05 -0.001642 -1.806e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1918 -0.03248 -0.1829 0.1933 0.9836 0.9933 0.2144 0.4476 0.8733 0.7192 ] Network output: [ -0.01094 1.002 1.01 4.36e-07 -1.957e-07 0.01025 3.286e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005771 0.0004637 0.004418 0.003874 0.9889 0.992 0.005879 0.8663 0.8973 0.01356 ] Network output: [ -0.0007294 0.00313 1.002 -7.659e-05 3.439e-05 0.9961 -5.772e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2032 0.09458 0.3335 0.1493 0.9851 0.994 0.2038 0.452 0.8799 0.7137 ] Network output: [ 0.006462 -0.03158 0.9955 4.544e-05 -2.04e-05 1.023 3.425e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1002 0.08836 0.1803 0.2029 0.9873 0.9919 0.1002 0.7706 0.8703 0.3065 ] Network output: [ -0.006289 0.03148 1.002 4.716e-05 -2.117e-05 0.9789 3.554e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6966 0.8476 0.2441 ] Network output: [ 0.0001985 0.9999 -0.00037 6.394e-06 -2.871e-06 1 4.819e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005392 Epoch 7650 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0115 0.9947 0.9894 1.146e-06 -5.146e-07 -0.00706 8.639e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003296 -0.003097 -0.008426 0.006524 0.9698 0.9742 0.006304 0.8376 0.8272 0.01874 ] Network output: [ 0.9998 0.0008036 0.001148 -2.394e-05 1.075e-05 -0.001641 -1.804e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1918 -0.03249 -0.1829 0.1933 0.9836 0.9933 0.2144 0.4476 0.8733 0.7192 ] Network output: [ -0.01094 1.002 1.01 4.342e-07 -1.949e-07 0.01025 3.273e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005772 0.0004637 0.004418 0.003873 0.9889 0.992 0.005879 0.8663 0.8973 0.01355 ] Network output: [ -0.000729 0.003129 1.002 -7.653e-05 3.435e-05 0.9961 -5.767e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2032 0.09459 0.3335 0.1493 0.9851 0.994 0.2038 0.452 0.8799 0.7137 ] Network output: [ 0.00646 -0.03157 0.9955 4.54e-05 -2.038e-05 1.023 3.422e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1002 0.08837 0.1803 0.2029 0.9873 0.9919 0.1002 0.7706 0.8703 0.3065 ] Network output: [ -0.006287 0.03147 1.002 4.712e-05 -2.115e-05 0.9789 3.551e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6966 0.8476 0.2441 ] Network output: [ 0.0001984 0.9999 -0.0003696 6.389e-06 -2.868e-06 1 4.815e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005389 Epoch 7651 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0115 0.9947 0.9894 1.143e-06 -5.133e-07 -0.007061 8.617e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003296 -0.003097 -0.008424 0.006523 0.9698 0.9742 0.006305 0.8376 0.8272 0.01874 ] Network output: [ 0.9998 0.0008029 0.001147 -2.392e-05 1.074e-05 -0.00164 -1.803e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1918 -0.03249 -0.1829 0.1933 0.9836 0.9933 0.2144 0.4475 0.8733 0.7192 ] Network output: [ -0.01094 1.002 1.01 4.325e-07 -1.942e-07 0.01025 3.26e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005773 0.0004638 0.004418 0.003873 0.9889 0.992 0.00588 0.8663 0.8973 0.01355 ] Network output: [ -0.0007285 0.003128 1.002 -7.646e-05 3.432e-05 0.9961 -5.762e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2032 0.09459 0.3336 0.1492 0.9851 0.994 0.2038 0.452 0.8799 0.7137 ] Network output: [ 0.006458 -0.03156 0.9955 4.536e-05 -2.037e-05 1.023 3.419e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1002 0.08837 0.1803 0.2029 0.9873 0.9919 0.1003 0.7705 0.8703 0.3065 ] Network output: [ -0.006285 0.03146 1.002 4.708e-05 -2.113e-05 0.9789 3.548e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6965 0.8476 0.2441 ] Network output: [ 0.0001983 0.9999 -0.0003692 6.383e-06 -2.866e-06 1 4.811e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005385 Epoch 7652 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0115 0.9947 0.9894 1.141e-06 -5.12e-07 -0.007063 8.596e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003296 -0.003097 -0.008423 0.006522 0.9698 0.9742 0.006305 0.8376 0.8272 0.01874 ] Network output: [ 0.9998 0.0008022 0.001146 -2.39e-05 1.073e-05 -0.001638 -1.801e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1918 -0.03249 -0.1828 0.1933 0.9836 0.9933 0.2144 0.4475 0.8733 0.7192 ] Network output: [ -0.01094 1.002 1.01 4.308e-07 -1.934e-07 0.01024 3.247e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005773 0.0004638 0.004418 0.003872 0.9889 0.992 0.005881 0.8663 0.8973 0.01355 ] Network output: [ -0.0007281 0.003127 1.002 -7.639e-05 3.429e-05 0.9961 -5.757e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2032 0.0946 0.3336 0.1492 0.9851 0.994 0.2039 0.452 0.8799 0.7137 ] Network output: [ 0.006456 -0.03154 0.9955 4.532e-05 -2.035e-05 1.023 3.416e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1002 0.08838 0.1803 0.2029 0.9873 0.9919 0.1003 0.7705 0.8703 0.3065 ] Network output: [ -0.006282 0.03144 1.002 4.704e-05 -2.112e-05 0.9789 3.545e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1654 0.1956 0.9854 0.9913 0.0904 0.6965 0.8476 0.2441 ] Network output: [ 0.0001982 0.9999 -0.0003688 6.378e-06 -2.863e-06 1 4.807e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005382 Epoch 7653 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0115 0.9947 0.9894 1.138e-06 -5.108e-07 -0.007064 8.574e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003296 -0.003097 -0.008422 0.006521 0.9698 0.9742 0.006305 0.8376 0.8272 0.01874 ] Network output: [ 0.9998 0.0008014 0.001146 -2.388e-05 1.072e-05 -0.001637 -1.8e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1918 -0.0325 -0.1828 0.1933 0.9836 0.9933 0.2144 0.4475 0.8733 0.7192 ] Network output: [ -0.01094 1.002 1.01 4.291e-07 -1.927e-07 0.01024 3.234e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005774 0.0004638 0.004418 0.003872 0.9889 0.992 0.005881 0.8663 0.8973 0.01355 ] Network output: [ -0.0007277 0.003126 1.002 -7.632e-05 3.426e-05 0.9961 -5.751e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2032 0.0946 0.3336 0.1492 0.9851 0.994 0.2039 0.452 0.8799 0.7137 ] Network output: [ 0.006454 -0.03153 0.9955 4.528e-05 -2.033e-05 1.023 3.413e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1002 0.08838 0.1803 0.2029 0.9873 0.9919 0.1003 0.7705 0.8703 0.3065 ] Network output: [ -0.00628 0.03143 1.002 4.7e-05 -2.11e-05 0.9789 3.542e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1956 0.9854 0.9913 0.0904 0.6965 0.8476 0.2441 ] Network output: [ 0.0001981 0.9999 -0.0003684 6.372e-06 -2.861e-06 1 4.802e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005378 Epoch 7654 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01149 0.9947 0.9894 1.135e-06 -5.095e-07 -0.007065 8.553e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003297 -0.003097 -0.008421 0.006521 0.9698 0.9742 0.006306 0.8375 0.8272 0.01874 ] Network output: [ 0.9998 0.0008007 0.001145 -2.386e-05 1.071e-05 -0.001636 -1.798e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1919 -0.0325 -0.1828 0.1933 0.9836 0.9933 0.2144 0.4475 0.8733 0.7192 ] Network output: [ -0.01094 1.002 1.01 4.274e-07 -1.919e-07 0.01024 3.221e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005774 0.0004639 0.004418 0.003871 0.9889 0.992 0.005882 0.8663 0.8973 0.01355 ] Network output: [ -0.0007273 0.003125 1.002 -7.625e-05 3.423e-05 0.9961 -5.746e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2032 0.09461 0.3336 0.1492 0.9851 0.994 0.2039 0.4519 0.8799 0.7137 ] Network output: [ 0.006451 -0.03152 0.9955 4.524e-05 -2.031e-05 1.023 3.41e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1002 0.08839 0.1803 0.2029 0.9873 0.9919 0.1003 0.7705 0.8703 0.3065 ] Network output: [ -0.006277 0.03141 1.002 4.696e-05 -2.108e-05 0.9789 3.539e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1956 0.9854 0.9913 0.0904 0.6965 0.8476 0.2441 ] Network output: [ 0.000198 0.9999 -0.000368 6.367e-06 -2.858e-06 1 4.798e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005375 Epoch 7655 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01149 0.9947 0.9894 1.132e-06 -5.082e-07 -0.007066 8.532e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003297 -0.003098 -0.008419 0.00652 0.9698 0.9742 0.006306 0.8375 0.8272 0.01873 ] Network output: [ 0.9998 0.0008 0.001144 -2.384e-05 1.07e-05 -0.001634 -1.797e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1919 -0.0325 -0.1828 0.1933 0.9836 0.9933 0.2144 0.4475 0.8733 0.7192 ] Network output: [ -0.01093 1.002 1.01 4.257e-07 -1.911e-07 0.01024 3.208e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005775 0.0004639 0.004418 0.003871 0.9889 0.992 0.005883 0.8663 0.8973 0.01355 ] Network output: [ -0.0007268 0.003123 1.002 -7.618e-05 3.42e-05 0.9961 -5.741e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2032 0.09461 0.3336 0.1492 0.9851 0.994 0.2039 0.4519 0.8799 0.7137 ] Network output: [ 0.006449 -0.03151 0.9955 4.52e-05 -2.029e-05 1.023 3.407e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1002 0.08839 0.1803 0.2029 0.9873 0.9919 0.1003 0.7704 0.8703 0.3065 ] Network output: [ -0.006275 0.0314 1.002 4.692e-05 -2.106e-05 0.9789 3.536e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1956 0.9854 0.9913 0.0904 0.6964 0.8476 0.2441 ] Network output: [ 0.0001979 0.9999 -0.0003676 6.361e-06 -2.856e-06 1 4.794e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005371 Epoch 7656 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01149 0.9947 0.9894 1.129e-06 -5.07e-07 -0.007068 8.51e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003297 -0.003098 -0.008418 0.006519 0.9698 0.9742 0.006306 0.8375 0.8272 0.01873 ] Network output: [ 0.9998 0.0007993 0.001143 -2.382e-05 1.069e-05 -0.001633 -1.795e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1919 -0.03251 -0.1828 0.1933 0.9836 0.9933 0.2145 0.4475 0.8733 0.7192 ] Network output: [ -0.01093 1.002 1.01 4.24e-07 -1.904e-07 0.01023 3.196e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005776 0.0004639 0.004419 0.00387 0.9889 0.992 0.005883 0.8663 0.8973 0.01355 ] Network output: [ -0.0007264 0.003122 1.002 -7.611e-05 3.417e-05 0.9961 -5.736e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2033 0.09462 0.3336 0.1492 0.9851 0.994 0.2039 0.4519 0.8798 0.7137 ] Network output: [ 0.006447 -0.0315 0.9955 4.516e-05 -2.028e-05 1.023 3.404e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1002 0.0884 0.1803 0.2029 0.9873 0.9919 0.1003 0.7704 0.8703 0.3065 ] Network output: [ -0.006273 0.03139 1.002 4.688e-05 -2.105e-05 0.9789 3.533e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1956 0.9854 0.9913 0.0904 0.6964 0.8476 0.2441 ] Network output: [ 0.0001978 0.9999 -0.0003672 6.356e-06 -2.853e-06 1 4.79e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005368 Epoch 7657 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01149 0.9947 0.9894 1.126e-06 -5.057e-07 -0.007069 8.489e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003297 -0.003098 -0.008417 0.006518 0.9698 0.9742 0.006306 0.8375 0.8272 0.01873 ] Network output: [ 0.9998 0.0007986 0.001143 -2.38e-05 1.068e-05 -0.001632 -1.794e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1919 -0.03251 -0.1827 0.1933 0.9836 0.9933 0.2145 0.4475 0.8733 0.7192 ] Network output: [ -0.01093 1.002 1.01 4.223e-07 -1.896e-07 0.01023 3.183e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005776 0.000464 0.004419 0.003869 0.9889 0.992 0.005884 0.8662 0.8973 0.01354 ] Network output: [ -0.000726 0.003121 1.002 -7.604e-05 3.414e-05 0.9961 -5.731e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2033 0.09462 0.3336 0.1492 0.9851 0.994 0.2039 0.4519 0.8798 0.7137 ] Network output: [ 0.006445 -0.03149 0.9954 4.512e-05 -2.026e-05 1.023 3.401e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1002 0.0884 0.1803 0.2029 0.9873 0.9919 0.1003 0.7704 0.8703 0.3065 ] Network output: [ -0.00627 0.03137 1.002 4.684e-05 -2.103e-05 0.9789 3.53e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1956 0.9854 0.9913 0.0904 0.6964 0.8476 0.2441 ] Network output: [ 0.0001976 0.9999 -0.0003668 6.35e-06 -2.851e-06 1 4.786e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005364 Epoch 7658 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01149 0.9947 0.9894 1.124e-06 -5.044e-07 -0.00707 8.468e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003297 -0.003098 -0.008416 0.006517 0.9698 0.9742 0.006307 0.8375 0.8272 0.01873 ] Network output: [ 0.9998 0.0007978 0.001142 -2.378e-05 1.067e-05 -0.00163 -1.792e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1919 -0.03251 -0.1827 0.1933 0.9836 0.9933 0.2145 0.4474 0.8733 0.7192 ] Network output: [ -0.01093 1.002 1.01 4.206e-07 -1.888e-07 0.01023 3.17e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005777 0.000464 0.004419 0.003869 0.9889 0.992 0.005884 0.8662 0.8973 0.01354 ] Network output: [ -0.0007256 0.00312 1.002 -7.597e-05 3.411e-05 0.9961 -5.725e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2033 0.09462 0.3336 0.1492 0.9851 0.994 0.2039 0.4519 0.8798 0.7137 ] Network output: [ 0.006443 -0.03147 0.9954 4.508e-05 -2.024e-05 1.023 3.398e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1002 0.08841 0.1803 0.2029 0.9873 0.9919 0.1003 0.7704 0.8703 0.3065 ] Network output: [ -0.006268 0.03136 1.002 4.68e-05 -2.101e-05 0.9789 3.527e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1956 0.9854 0.9913 0.0904 0.6963 0.8475 0.2441 ] Network output: [ 0.0001975 0.9999 -0.0003664 6.345e-06 -2.848e-06 1 4.782e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005361 Epoch 7659 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01148 0.9947 0.9894 1.121e-06 -5.032e-07 -0.007071 8.446e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003297 -0.003098 -0.008414 0.006516 0.9698 0.9742 0.006307 0.8375 0.8272 0.01873 ] Network output: [ 0.9998 0.0007971 0.001141 -2.376e-05 1.067e-05 -0.001629 -1.79e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1919 -0.03252 -0.1827 0.1932 0.9836 0.9933 0.2145 0.4474 0.8733 0.7192 ] Network output: [ -0.01093 1.002 1.01 4.189e-07 -1.881e-07 0.01022 3.157e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005778 0.000464 0.004419 0.003868 0.9889 0.992 0.005885 0.8662 0.8973 0.01354 ] Network output: [ -0.0007252 0.003119 1.002 -7.59e-05 3.407e-05 0.9961 -5.72e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2033 0.09463 0.3337 0.1492 0.9851 0.994 0.2039 0.4519 0.8798 0.7137 ] Network output: [ 0.006441 -0.03146 0.9954 4.504e-05 -2.022e-05 1.023 3.395e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1002 0.08841 0.1803 0.2029 0.9873 0.9919 0.1003 0.7703 0.8703 0.3065 ] Network output: [ -0.006265 0.03135 1.002 4.676e-05 -2.099e-05 0.9789 3.524e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1956 0.9854 0.9913 0.0904 0.6963 0.8475 0.2441 ] Network output: [ 0.0001974 0.9999 -0.000366 6.339e-06 -2.846e-06 1 4.777e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005358 Epoch 7660 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01148 0.9947 0.9894 1.118e-06 -5.019e-07 -0.007073 8.425e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003297 -0.003098 -0.008413 0.006516 0.9698 0.9742 0.006307 0.8375 0.8271 0.01873 ] Network output: [ 0.9998 0.0007964 0.00114 -2.374e-05 1.066e-05 -0.001628 -1.789e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1919 -0.03252 -0.1827 0.1932 0.9836 0.9933 0.2145 0.4474 0.8733 0.7192 ] Network output: [ -0.01093 1.002 1.01 4.173e-07 -1.873e-07 0.01022 3.145e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005778 0.0004641 0.004419 0.003868 0.9889 0.992 0.005886 0.8662 0.8973 0.01354 ] Network output: [ -0.0007247 0.003118 1.002 -7.583e-05 3.404e-05 0.9961 -5.715e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2033 0.09463 0.3337 0.1492 0.9851 0.994 0.204 0.4519 0.8798 0.7137 ] Network output: [ 0.006438 -0.03145 0.9954 4.5e-05 -2.02e-05 1.023 3.392e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1002 0.08842 0.1803 0.2029 0.9873 0.9919 0.1003 0.7703 0.8702 0.3065 ] Network output: [ -0.006263 0.03133 1.002 4.672e-05 -2.097e-05 0.9789 3.521e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1956 0.9854 0.9913 0.0904 0.6963 0.8475 0.2441 ] Network output: [ 0.0001973 0.9999 -0.0003656 6.334e-06 -2.843e-06 1 4.773e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005354 Epoch 7661 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01148 0.9947 0.9894 1.115e-06 -5.006e-07 -0.007074 8.404e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003298 -0.003099 -0.008412 0.006515 0.9698 0.9742 0.006308 0.8375 0.8271 0.01872 ] Network output: [ 0.9998 0.0007957 0.00114 -2.372e-05 1.065e-05 -0.001626 -1.787e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1919 -0.03252 -0.1827 0.1932 0.9836 0.9933 0.2145 0.4474 0.8733 0.7192 ] Network output: [ -0.01093 1.002 1.01 4.156e-07 -1.866e-07 0.01022 3.132e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005779 0.0004641 0.004419 0.003867 0.9889 0.992 0.005886 0.8662 0.8973 0.01354 ] Network output: [ -0.0007243 0.003117 1.002 -7.576e-05 3.401e-05 0.9961 -5.71e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2033 0.09464 0.3337 0.1492 0.9851 0.994 0.204 0.4518 0.8798 0.7136 ] Network output: [ 0.006436 -0.03144 0.9954 4.496e-05 -2.019e-05 1.023 3.389e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1002 0.08842 0.1803 0.2029 0.9873 0.9919 0.1003 0.7703 0.8702 0.3065 ] Network output: [ -0.00626 0.03132 1.002 4.668e-05 -2.096e-05 0.979 3.518e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1956 0.9854 0.9913 0.0904 0.6963 0.8475 0.2441 ] Network output: [ 0.0001972 0.9999 -0.0003652 6.328e-06 -2.841e-06 1 4.769e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005351 Epoch 7662 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01148 0.9947 0.9894 1.112e-06 -4.994e-07 -0.007075 8.383e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003298 -0.003099 -0.008411 0.006514 0.9698 0.9742 0.006308 0.8375 0.8271 0.01872 ] Network output: [ 0.9998 0.000795 0.001139 -2.37e-05 1.064e-05 -0.001625 -1.786e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1919 -0.03253 -0.1827 0.1932 0.9836 0.9933 0.2145 0.4474 0.8733 0.7192 ] Network output: [ -0.01093 1.002 1.01 4.139e-07 -1.858e-07 0.01021 3.119e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005779 0.0004641 0.004419 0.003867 0.9889 0.992 0.005887 0.8662 0.8973 0.01354 ] Network output: [ -0.0007239 0.003116 1.002 -7.569e-05 3.398e-05 0.9961 -5.705e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2033 0.09464 0.3337 0.1492 0.9851 0.994 0.204 0.4518 0.8798 0.7136 ] Network output: [ 0.006434 -0.03143 0.9954 4.492e-05 -2.017e-05 1.023 3.386e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1003 0.08843 0.1803 0.2029 0.9873 0.9919 0.1003 0.7703 0.8702 0.3065 ] Network output: [ -0.006258 0.0313 1.002 4.664e-05 -2.094e-05 0.979 3.515e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1956 0.9854 0.9913 0.0904 0.6962 0.8475 0.2441 ] Network output: [ 0.0001971 0.9999 -0.0003648 6.323e-06 -2.839e-06 1 4.765e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005347 Epoch 7663 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01148 0.9947 0.9894 1.11e-06 -4.981e-07 -0.007076 8.362e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003298 -0.003099 -0.008409 0.006513 0.9698 0.9742 0.006308 0.8375 0.8271 0.01872 ] Network output: [ 0.9998 0.0007942 0.001138 -2.368e-05 1.063e-05 -0.001624 -1.784e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.192 -0.03253 -0.1826 0.1932 0.9836 0.9933 0.2145 0.4474 0.8733 0.7192 ] Network output: [ -0.01092 1.002 1.01 4.122e-07 -1.851e-07 0.01021 3.107e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00578 0.0004642 0.004419 0.003866 0.9889 0.992 0.005888 0.8662 0.8973 0.01354 ] Network output: [ -0.0007235 0.003115 1.002 -7.563e-05 3.395e-05 0.9961 -5.699e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2033 0.09465 0.3337 0.1491 0.9851 0.994 0.204 0.4518 0.8798 0.7136 ] Network output: [ 0.006432 -0.03142 0.9954 4.488e-05 -2.015e-05 1.023 3.383e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1003 0.08843 0.1803 0.2029 0.9873 0.9919 0.1003 0.7702 0.8702 0.3065 ] Network output: [ -0.006256 0.03129 1.002 4.66e-05 -2.092e-05 0.979 3.512e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1956 0.9854 0.9913 0.0904 0.6962 0.8475 0.2441 ] Network output: [ 0.000197 0.9999 -0.0003644 6.317e-06 -2.836e-06 1 4.761e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005344 Epoch 7664 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01147 0.9947 0.9894 1.107e-06 -4.969e-07 -0.007078 8.341e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003298 -0.003099 -0.008408 0.006512 0.9698 0.9742 0.006309 0.8375 0.8271 0.01872 ] Network output: [ 0.9998 0.0007935 0.001137 -2.366e-05 1.062e-05 -0.001622 -1.783e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.192 -0.03253 -0.1826 0.1932 0.9836 0.9933 0.2146 0.4474 0.8733 0.7192 ] Network output: [ -0.01092 1.002 1.01 4.105e-07 -1.843e-07 0.01021 3.094e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005781 0.0004642 0.004419 0.003866 0.9889 0.992 0.005888 0.8662 0.8973 0.01354 ] Network output: [ -0.000723 0.003114 1.002 -7.556e-05 3.392e-05 0.9961 -5.694e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2033 0.09465 0.3337 0.1491 0.9851 0.994 0.204 0.4518 0.8798 0.7136 ] Network output: [ 0.00643 -0.03141 0.9954 4.485e-05 -2.013e-05 1.023 3.38e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1003 0.08844 0.1803 0.2029 0.9873 0.9919 0.1003 0.7702 0.8702 0.3065 ] Network output: [ -0.006253 0.03128 1.002 4.656e-05 -2.09e-05 0.979 3.509e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1956 0.9854 0.9913 0.0904 0.6962 0.8475 0.2441 ] Network output: [ 0.0001969 0.9999 -0.000364 6.312e-06 -2.834e-06 1 4.757e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000534 Epoch 7665 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01147 0.9947 0.9894 1.104e-06 -4.956e-07 -0.007079 8.32e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003298 -0.003099 -0.008407 0.006512 0.9698 0.9742 0.006309 0.8375 0.8271 0.01872 ] Network output: [ 0.9998 0.0007928 0.001137 -2.364e-05 1.061e-05 -0.001621 -1.781e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.192 -0.03253 -0.1826 0.1932 0.9836 0.9933 0.2146 0.4473 0.8732 0.7192 ] Network output: [ -0.01092 1.002 1.01 4.089e-07 -1.836e-07 0.01021 3.081e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005781 0.0004642 0.004419 0.003865 0.9889 0.992 0.005889 0.8662 0.8972 0.01353 ] Network output: [ -0.0007226 0.003113 1.002 -7.549e-05 3.389e-05 0.9961 -5.689e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2034 0.09466 0.3337 0.1491 0.9851 0.994 0.204 0.4518 0.8798 0.7136 ] Network output: [ 0.006428 -0.03139 0.9954 4.481e-05 -2.011e-05 1.023 3.377e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1003 0.08844 0.1803 0.2029 0.9873 0.9919 0.1003 0.7702 0.8702 0.3065 ] Network output: [ -0.006251 0.03126 1.002 4.652e-05 -2.089e-05 0.979 3.506e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1956 0.9854 0.9913 0.0904 0.6961 0.8475 0.2442 ] Network output: [ 0.0001968 0.9999 -0.0003636 6.306e-06 -2.831e-06 1 4.753e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005337 Epoch 7666 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01147 0.9947 0.9894 1.101e-06 -4.944e-07 -0.00708 8.299e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003298 -0.003099 -0.008406 0.006511 0.9698 0.9742 0.006309 0.8374 0.8271 0.01872 ] Network output: [ 0.9998 0.0007921 0.001136 -2.361e-05 1.06e-05 -0.00162 -1.78e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.192 -0.03254 -0.1826 0.1932 0.9836 0.9933 0.2146 0.4473 0.8732 0.7191 ] Network output: [ -0.01092 1.002 1.01 4.072e-07 -1.828e-07 0.0102 3.069e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005782 0.0004643 0.004419 0.003864 0.9889 0.992 0.00589 0.8662 0.8972 0.01353 ] Network output: [ -0.0007222 0.003112 1.002 -7.542e-05 3.386e-05 0.9961 -5.684e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2034 0.09466 0.3338 0.1491 0.9851 0.994 0.204 0.4518 0.8798 0.7136 ] Network output: [ 0.006426 -0.03138 0.9954 4.477e-05 -2.01e-05 1.023 3.374e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1003 0.08845 0.1803 0.2028 0.9873 0.9919 0.1003 0.7702 0.8702 0.3064 ] Network output: [ -0.006248 0.03125 1.002 4.648e-05 -2.087e-05 0.979 3.503e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1956 0.9854 0.9913 0.0904 0.6961 0.8475 0.2442 ] Network output: [ 0.0001967 0.9999 -0.0003632 6.301e-06 -2.829e-06 1 4.749e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005333 Epoch 7667 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01147 0.9947 0.9894 1.098e-06 -4.931e-07 -0.007081 8.278e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003298 -0.0031 -0.008404 0.00651 0.9698 0.9742 0.00631 0.8374 0.8271 0.01871 ] Network output: [ 0.9998 0.0007914 0.001135 -2.359e-05 1.059e-05 -0.001618 -1.778e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.192 -0.03254 -0.1826 0.1932 0.9836 0.9933 0.2146 0.4473 0.8732 0.7191 ] Network output: [ -0.01092 1.002 1.01 4.055e-07 -1.821e-07 0.0102 3.056e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005782 0.0004643 0.00442 0.003864 0.9889 0.992 0.00589 0.8662 0.8972 0.01353 ] Network output: [ -0.0007218 0.003111 1.002 -7.535e-05 3.383e-05 0.9961 -5.679e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2034 0.09466 0.3338 0.1491 0.9851 0.994 0.204 0.4517 0.8798 0.7136 ] Network output: [ 0.006423 -0.03137 0.9954 4.473e-05 -2.008e-05 1.023 3.371e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1003 0.08845 0.1803 0.2028 0.9873 0.9919 0.1003 0.7701 0.8702 0.3064 ] Network output: [ -0.006246 0.03123 1.002 4.644e-05 -2.085e-05 0.979 3.5e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1956 0.9854 0.9913 0.0904 0.6961 0.8475 0.2442 ] Network output: [ 0.0001966 0.9999 -0.0003628 6.295e-06 -2.826e-06 1 4.744e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000533 Epoch 7668 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01147 0.9947 0.9894 1.096e-06 -4.919e-07 -0.007082 8.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003299 -0.0031 -0.008403 0.006509 0.9698 0.9742 0.00631 0.8374 0.8271 0.01871 ] Network output: [ 0.9998 0.0007907 0.001134 -2.357e-05 1.058e-05 -0.001617 -1.777e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.192 -0.03254 -0.1826 0.1932 0.9836 0.9933 0.2146 0.4473 0.8732 0.7191 ] Network output: [ -0.01092 1.002 1.01 4.038e-07 -1.813e-07 0.0102 3.044e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005783 0.0004643 0.00442 0.003863 0.9889 0.992 0.005891 0.8661 0.8972 0.01353 ] Network output: [ -0.0007214 0.003109 1.002 -7.528e-05 3.38e-05 0.9961 -5.673e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2034 0.09467 0.3338 0.1491 0.9851 0.994 0.2041 0.4517 0.8798 0.7136 ] Network output: [ 0.006421 -0.03136 0.9954 4.469e-05 -2.006e-05 1.023 3.368e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1003 0.08846 0.1803 0.2028 0.9873 0.9919 0.1004 0.7701 0.8702 0.3064 ] Network output: [ -0.006244 0.03122 1.002 4.64e-05 -2.083e-05 0.979 3.497e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1956 0.9854 0.9913 0.0904 0.6961 0.8475 0.2442 ] Network output: [ 0.0001965 0.9999 -0.0003624 6.29e-06 -2.824e-06 1 4.74e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005326 Epoch 7669 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01147 0.9947 0.9894 1.093e-06 -4.906e-07 -0.007084 8.236e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003299 -0.0031 -0.008402 0.006508 0.9698 0.9742 0.00631 0.8374 0.8271 0.01871 ] Network output: [ 0.9998 0.00079 0.001134 -2.355e-05 1.057e-05 -0.001616 -1.775e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.192 -0.03255 -0.1825 0.1932 0.9836 0.9933 0.2146 0.4473 0.8732 0.7191 ] Network output: [ -0.01092 1.002 1.01 4.022e-07 -1.806e-07 0.01019 3.031e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005784 0.0004644 0.00442 0.003863 0.9889 0.992 0.005891 0.8661 0.8972 0.01353 ] Network output: [ -0.0007209 0.003108 1.002 -7.521e-05 3.377e-05 0.9961 -5.668e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2034 0.09467 0.3338 0.1491 0.9851 0.994 0.2041 0.4517 0.8798 0.7136 ] Network output: [ 0.006419 -0.03135 0.9954 4.465e-05 -2.004e-05 1.023 3.365e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1003 0.08846 0.1803 0.2028 0.9873 0.9919 0.1004 0.7701 0.8702 0.3064 ] Network output: [ -0.006241 0.03121 1.002 4.636e-05 -2.081e-05 0.979 3.494e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1956 0.9854 0.9913 0.09041 0.696 0.8474 0.2442 ] Network output: [ 0.0001964 0.9999 -0.0003621 6.284e-06 -2.821e-06 1 4.736e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005323 Epoch 7670 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01146 0.9947 0.9894 1.09e-06 -4.894e-07 -0.007085 8.215e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003299 -0.0031 -0.008401 0.006508 0.9698 0.9742 0.006311 0.8374 0.8271 0.01871 ] Network output: [ 0.9998 0.0007893 0.001133 -2.353e-05 1.057e-05 -0.001614 -1.774e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.192 -0.03255 -0.1825 0.1932 0.9836 0.9933 0.2146 0.4473 0.8732 0.7191 ] Network output: [ -0.01092 1.002 1.01 4.005e-07 -1.798e-07 0.01019 3.018e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005784 0.0004644 0.00442 0.003862 0.9889 0.992 0.005892 0.8661 0.8972 0.01353 ] Network output: [ -0.0007205 0.003107 1.002 -7.514e-05 3.374e-05 0.9961 -5.663e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2034 0.09468 0.3338 0.1491 0.9851 0.994 0.2041 0.4517 0.8798 0.7136 ] Network output: [ 0.006417 -0.03134 0.9954 4.461e-05 -2.003e-05 1.023 3.362e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1003 0.08847 0.1803 0.2028 0.9873 0.9919 0.1004 0.7701 0.8702 0.3064 ] Network output: [ -0.006239 0.03119 1.002 4.632e-05 -2.08e-05 0.979 3.491e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1956 0.9854 0.9913 0.09041 0.696 0.8474 0.2442 ] Network output: [ 0.0001963 0.9999 -0.0003617 6.279e-06 -2.819e-06 1 4.732e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000532 Epoch 7671 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01146 0.9947 0.9894 1.087e-06 -4.881e-07 -0.007086 8.194e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003299 -0.0031 -0.0084 0.006507 0.9698 0.9742 0.006311 0.8374 0.8271 0.01871 ] Network output: [ 0.9998 0.0007886 0.001132 -2.351e-05 1.056e-05 -0.001613 -1.772e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.192 -0.03255 -0.1825 0.1932 0.9836 0.9933 0.2146 0.4473 0.8732 0.7191 ] Network output: [ -0.01092 1.002 1.01 3.989e-07 -1.791e-07 0.01019 3.006e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005785 0.0004644 0.00442 0.003862 0.9889 0.992 0.005893 0.8661 0.8972 0.01353 ] Network output: [ -0.0007201 0.003106 1.002 -7.508e-05 3.37e-05 0.9961 -5.658e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2034 0.09468 0.3338 0.1491 0.9851 0.994 0.2041 0.4517 0.8798 0.7136 ] Network output: [ 0.006415 -0.03132 0.9954 4.457e-05 -2.001e-05 1.023 3.359e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1003 0.08847 0.1803 0.2028 0.9873 0.9919 0.1004 0.77 0.8702 0.3064 ] Network output: [ -0.006236 0.03118 1.002 4.628e-05 -2.078e-05 0.979 3.488e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1956 0.9854 0.9913 0.09041 0.696 0.8474 0.2442 ] Network output: [ 0.0001962 0.9999 -0.0003613 6.273e-06 -2.816e-06 1 4.728e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005316 Epoch 7672 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01146 0.9947 0.9894 1.085e-06 -4.869e-07 -0.007087 8.173e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003299 -0.0031 -0.008398 0.006506 0.9698 0.9742 0.006311 0.8374 0.8271 0.01871 ] Network output: [ 0.9998 0.0007878 0.001131 -2.349e-05 1.055e-05 -0.001612 -1.771e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1921 -0.03256 -0.1825 0.1931 0.9836 0.9933 0.2147 0.4472 0.8732 0.7191 ] Network output: [ -0.01091 1.002 1.01 3.972e-07 -1.783e-07 0.01019 2.993e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005786 0.0004645 0.00442 0.003861 0.9889 0.992 0.005893 0.8661 0.8972 0.01353 ] Network output: [ -0.0007197 0.003105 1.002 -7.501e-05 3.367e-05 0.9961 -5.653e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2034 0.09469 0.3338 0.1491 0.9851 0.994 0.2041 0.4517 0.8798 0.7136 ] Network output: [ 0.006413 -0.03131 0.9954 4.453e-05 -1.999e-05 1.023 3.356e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1003 0.08848 0.1803 0.2028 0.9873 0.9919 0.1004 0.77 0.8702 0.3064 ] Network output: [ -0.006234 0.03117 1.002 4.624e-05 -2.076e-05 0.979 3.485e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1956 0.9854 0.9913 0.09041 0.696 0.8474 0.2442 ] Network output: [ 0.0001961 0.9999 -0.0003609 6.268e-06 -2.814e-06 1 4.724e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005313 Epoch 7673 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01146 0.9947 0.9894 1.082e-06 -4.857e-07 -0.007088 8.153e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003299 -0.003101 -0.008397 0.006505 0.9698 0.9742 0.006312 0.8374 0.8271 0.0187 ] Network output: [ 0.9998 0.0007871 0.001131 -2.347e-05 1.054e-05 -0.001611 -1.769e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1921 -0.03256 -0.1825 0.1931 0.9836 0.9933 0.2147 0.4472 0.8732 0.7191 ] Network output: [ -0.01091 1.002 1.01 3.955e-07 -1.776e-07 0.01018 2.981e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005786 0.0004645 0.00442 0.003861 0.9889 0.992 0.005894 0.8661 0.8972 0.01352 ] Network output: [ -0.0007192 0.003104 1.002 -7.494e-05 3.364e-05 0.9961 -5.648e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2035 0.09469 0.3338 0.1491 0.9851 0.994 0.2041 0.4517 0.8798 0.7136 ] Network output: [ 0.00641 -0.0313 0.9954 4.449e-05 -1.997e-05 1.023 3.353e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1003 0.08848 0.1803 0.2028 0.9873 0.9919 0.1004 0.77 0.8701 0.3064 ] Network output: [ -0.006232 0.03115 1.002 4.621e-05 -2.074e-05 0.979 3.482e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1955 0.9854 0.9913 0.09041 0.6959 0.8474 0.2442 ] Network output: [ 0.0001959 0.9999 -0.0003605 6.263e-06 -2.811e-06 1 4.72e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005309 Epoch 7674 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01146 0.9947 0.9894 1.079e-06 -4.844e-07 -0.00709 8.132e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003299 -0.003101 -0.008396 0.006504 0.9698 0.9742 0.006312 0.8374 0.8271 0.0187 ] Network output: [ 0.9998 0.0007864 0.00113 -2.345e-05 1.053e-05 -0.001609 -1.767e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1921 -0.03256 -0.1824 0.1931 0.9836 0.9933 0.2147 0.4472 0.8732 0.7191 ] Network output: [ -0.01091 1.002 1.01 3.939e-07 -1.768e-07 0.01018 2.968e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005787 0.0004645 0.00442 0.00386 0.9889 0.992 0.005895 0.8661 0.8972 0.01352 ] Network output: [ -0.0007188 0.003103 1.002 -7.487e-05 3.361e-05 0.9961 -5.642e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2035 0.0947 0.3339 0.1491 0.9851 0.994 0.2041 0.4516 0.8798 0.7136 ] Network output: [ 0.006408 -0.03129 0.9954 4.445e-05 -1.995e-05 1.023 3.35e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1003 0.08849 0.1803 0.2028 0.9873 0.9919 0.1004 0.77 0.8701 0.3064 ] Network output: [ -0.006229 0.03114 1.002 4.617e-05 -2.073e-05 0.979 3.479e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1955 0.9854 0.9913 0.09041 0.6959 0.8474 0.2442 ] Network output: [ 0.0001958 0.9999 -0.0003601 6.257e-06 -2.809e-06 1 4.716e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005306 Epoch 7675 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01145 0.9947 0.9895 1.076e-06 -4.832e-07 -0.007091 8.111e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0033 -0.003101 -0.008395 0.006503 0.9698 0.9742 0.006312 0.8374 0.8271 0.0187 ] Network output: [ 0.9998 0.0007857 0.001129 -2.343e-05 1.052e-05 -0.001608 -1.766e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1921 -0.03257 -0.1824 0.1931 0.9836 0.9933 0.2147 0.4472 0.8732 0.7191 ] Network output: [ -0.01091 1.002 1.01 3.922e-07 -1.761e-07 0.01018 2.956e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005787 0.0004646 0.00442 0.00386 0.9889 0.992 0.005895 0.8661 0.8972 0.01352 ] Network output: [ -0.0007184 0.003102 1.002 -7.48e-05 3.358e-05 0.9961 -5.637e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2035 0.0947 0.3339 0.149 0.9851 0.994 0.2041 0.4516 0.8798 0.7136 ] Network output: [ 0.006406 -0.03128 0.9954 4.441e-05 -1.994e-05 1.023 3.347e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1003 0.08849 0.1803 0.2028 0.9873 0.9919 0.1004 0.7699 0.8701 0.3064 ] Network output: [ -0.006227 0.03112 1.002 4.613e-05 -2.071e-05 0.979 3.476e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1955 0.9854 0.9913 0.09041 0.6959 0.8474 0.2442 ] Network output: [ 0.0001957 0.9999 -0.0003597 6.252e-06 -2.807e-06 1 4.711e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005302 Epoch 7676 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01145 0.9947 0.9895 1.074e-06 -4.819e-07 -0.007092 8.09e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0033 -0.003101 -0.008393 0.006503 0.9698 0.9742 0.006313 0.8374 0.8271 0.0187 ] Network output: [ 0.9998 0.000785 0.001128 -2.341e-05 1.051e-05 -0.001607 -1.764e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1921 -0.03257 -0.1824 0.1931 0.9836 0.9933 0.2147 0.4472 0.8732 0.7191 ] Network output: [ -0.01091 1.002 1.01 3.906e-07 -1.753e-07 0.01017 2.944e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005788 0.0004646 0.00442 0.003859 0.9889 0.992 0.005896 0.8661 0.8972 0.01352 ] Network output: [ -0.000718 0.003101 1.002 -7.473e-05 3.355e-05 0.9961 -5.632e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2035 0.09471 0.3339 0.149 0.9851 0.994 0.2041 0.4516 0.8798 0.7136 ] Network output: [ 0.006404 -0.03127 0.9954 4.437e-05 -1.992e-05 1.023 3.344e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1003 0.0885 0.1803 0.2028 0.9873 0.9919 0.1004 0.7699 0.8701 0.3064 ] Network output: [ -0.006224 0.03111 1.002 4.609e-05 -2.069e-05 0.9791 3.473e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1955 0.9854 0.9913 0.09041 0.6958 0.8474 0.2442 ] Network output: [ 0.0001956 0.9999 -0.0003593 6.246e-06 -2.804e-06 1 4.707e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005299 Epoch 7677 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01145 0.9947 0.9895 1.071e-06 -4.807e-07 -0.007093 8.07e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0033 -0.003101 -0.008392 0.006502 0.9698 0.9742 0.006313 0.8374 0.8271 0.0187 ] Network output: [ 0.9998 0.0007843 0.001128 -2.339e-05 1.05e-05 -0.001605 -1.763e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1921 -0.03257 -0.1824 0.1931 0.9836 0.9933 0.2147 0.4472 0.8732 0.7191 ] Network output: [ -0.01091 1.002 1.01 3.889e-07 -1.746e-07 0.01017 2.931e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005789 0.0004646 0.00442 0.003858 0.9889 0.992 0.005896 0.8661 0.8972 0.01352 ] Network output: [ -0.0007176 0.0031 1.002 -7.467e-05 3.352e-05 0.9962 -5.627e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2035 0.09471 0.3339 0.149 0.9851 0.994 0.2042 0.4516 0.8798 0.7136 ] Network output: [ 0.006402 -0.03126 0.9954 4.433e-05 -1.99e-05 1.023 3.341e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1003 0.0885 0.1803 0.2028 0.9873 0.9919 0.1004 0.7699 0.8701 0.3064 ] Network output: [ -0.006222 0.0311 1.002 4.605e-05 -2.067e-05 0.9791 3.47e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1955 0.9854 0.9913 0.09041 0.6958 0.8474 0.2442 ] Network output: [ 0.0001955 0.9999 -0.0003589 6.241e-06 -2.802e-06 1 4.703e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005296 Epoch 7678 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01145 0.9947 0.9895 1.068e-06 -4.795e-07 -0.007094 8.049e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0033 -0.003101 -0.008391 0.006501 0.9698 0.9742 0.006313 0.8373 0.8271 0.0187 ] Network output: [ 0.9998 0.0007836 0.001127 -2.337e-05 1.049e-05 -0.001604 -1.761e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1921 -0.03257 -0.1824 0.1931 0.9836 0.9933 0.2147 0.4472 0.8732 0.7191 ] Network output: [ -0.01091 1.002 1.01 3.873e-07 -1.739e-07 0.01017 2.919e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005789 0.0004647 0.004421 0.003858 0.9889 0.992 0.005897 0.8661 0.8972 0.01352 ] Network output: [ -0.0007171 0.003099 1.002 -7.46e-05 3.349e-05 0.9962 -5.622e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2035 0.09471 0.3339 0.149 0.9851 0.994 0.2042 0.4516 0.8797 0.7136 ] Network output: [ 0.0064 -0.03124 0.9954 4.429e-05 -1.988e-05 1.023 3.338e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1003 0.08851 0.1803 0.2028 0.9873 0.9919 0.1004 0.7699 0.8701 0.3064 ] Network output: [ -0.00622 0.03108 1.002 4.601e-05 -2.065e-05 0.9791 3.467e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09039 0.08846 0.1653 0.1955 0.9854 0.9913 0.09041 0.6958 0.8474 0.2442 ] Network output: [ 0.0001954 0.9999 -0.0003585 6.235e-06 -2.799e-06 1 4.699e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005292 Epoch 7679 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01145 0.9947 0.9895 1.065e-06 -4.783e-07 -0.007096 8.029e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0033 -0.003101 -0.00839 0.0065 0.9698 0.9742 0.006314 0.8373 0.827 0.01869 ] Network output: [ 0.9998 0.0007829 0.001126 -2.335e-05 1.048e-05 -0.001603 -1.76e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1921 -0.03258 -0.1824 0.1931 0.9836 0.9933 0.2147 0.4471 0.8732 0.7191 ] Network output: [ -0.01091 1.002 1.01 3.857e-07 -1.731e-07 0.01017 2.906e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00579 0.0004647 0.004421 0.003857 0.9889 0.992 0.005898 0.866 0.8972 0.01352 ] Network output: [ -0.0007167 0.003098 1.002 -7.453e-05 3.346e-05 0.9962 -5.617e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2035 0.09472 0.3339 0.149 0.9851 0.994 0.2042 0.4516 0.8797 0.7135 ] Network output: [ 0.006398 -0.03123 0.9954 4.425e-05 -1.987e-05 1.023 3.335e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1004 0.08852 0.1803 0.2028 0.9873 0.9919 0.1004 0.7698 0.8701 0.3064 ] Network output: [ -0.006217 0.03107 1.002 4.597e-05 -2.064e-05 0.9791 3.464e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08846 0.1653 0.1955 0.9854 0.9913 0.09041 0.6958 0.8473 0.2442 ] Network output: [ 0.0001953 0.9999 -0.0003582 6.23e-06 -2.797e-06 1 4.695e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005289 Epoch 7680 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01144 0.9947 0.9895 1.063e-06 -4.77e-07 -0.007097 8.008e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0033 -0.003102 -0.008388 0.006499 0.9698 0.9742 0.006314 0.8373 0.827 0.01869 ] Network output: [ 0.9998 0.0007822 0.001125 -2.333e-05 1.047e-05 -0.001601 -1.758e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1921 -0.03258 -0.1823 0.1931 0.9836 0.9933 0.2148 0.4471 0.8732 0.7191 ] Network output: [ -0.0109 1.002 1.01 3.84e-07 -1.724e-07 0.01016 2.894e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00579 0.0004647 0.004421 0.003857 0.9889 0.992 0.005898 0.866 0.8972 0.01352 ] Network output: [ -0.0007163 0.003097 1.002 -7.446e-05 3.343e-05 0.9962 -5.612e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2035 0.09472 0.3339 0.149 0.9851 0.994 0.2042 0.4516 0.8797 0.7135 ] Network output: [ 0.006395 -0.03122 0.9954 4.421e-05 -1.985e-05 1.023 3.332e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1004 0.08852 0.1804 0.2028 0.9873 0.9919 0.1004 0.7698 0.8701 0.3064 ] Network output: [ -0.006215 0.03106 1.002 4.593e-05 -2.062e-05 0.9791 3.461e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08846 0.1653 0.1955 0.9854 0.9913 0.09041 0.6957 0.8473 0.2442 ] Network output: [ 0.0001952 0.9999 -0.0003578 6.224e-06 -2.794e-06 1 4.691e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005285 Epoch 7681 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01144 0.9947 0.9895 1.06e-06 -4.758e-07 -0.007098 7.987e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0033 -0.003102 -0.008387 0.006499 0.9698 0.9742 0.006314 0.8373 0.827 0.01869 ] Network output: [ 0.9998 0.0007815 0.001125 -2.331e-05 1.047e-05 -0.0016 -1.757e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1921 -0.03258 -0.1823 0.1931 0.9836 0.9933 0.2148 0.4471 0.8732 0.7191 ] Network output: [ -0.0109 1.002 1.01 3.824e-07 -1.717e-07 0.01016 2.882e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005791 0.0004648 0.004421 0.003856 0.9889 0.992 0.005899 0.866 0.8972 0.01351 ] Network output: [ -0.0007159 0.003095 1.002 -7.439e-05 3.34e-05 0.9962 -5.606e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2036 0.09473 0.3339 0.149 0.9851 0.994 0.2042 0.4515 0.8797 0.7135 ] Network output: [ 0.006393 -0.03121 0.9954 4.417e-05 -1.983e-05 1.023 3.329e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1004 0.08853 0.1804 0.2028 0.9873 0.9919 0.1004 0.7698 0.8701 0.3064 ] Network output: [ -0.006212 0.03104 1.002 4.589e-05 -2.06e-05 0.9791 3.458e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08846 0.1653 0.1955 0.9854 0.9913 0.09041 0.6957 0.8473 0.2442 ] Network output: [ 0.0001951 0.9999 -0.0003574 6.219e-06 -2.792e-06 1 4.687e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005282 Epoch 7682 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01144 0.9948 0.9895 1.057e-06 -4.746e-07 -0.007099 7.967e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0033 -0.003102 -0.008386 0.006498 0.9698 0.9742 0.006315 0.8373 0.827 0.01869 ] Network output: [ 0.9998 0.0007808 0.001124 -2.329e-05 1.046e-05 -0.001599 -1.755e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1922 -0.03259 -0.1823 0.1931 0.9836 0.9933 0.2148 0.4471 0.8732 0.7191 ] Network output: [ -0.0109 1.002 1.01 3.807e-07 -1.709e-07 0.01016 2.869e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005792 0.0004648 0.004421 0.003856 0.9889 0.992 0.0059 0.866 0.8972 0.01351 ] Network output: [ -0.0007155 0.003094 1.002 -7.432e-05 3.337e-05 0.9962 -5.601e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2036 0.09473 0.334 0.149 0.9851 0.994 0.2042 0.4515 0.8797 0.7135 ] Network output: [ 0.006391 -0.0312 0.9954 4.413e-05 -1.981e-05 1.023 3.326e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1004 0.08853 0.1804 0.2028 0.9873 0.9919 0.1004 0.7698 0.8701 0.3064 ] Network output: [ -0.00621 0.03103 1.002 4.585e-05 -2.058e-05 0.9791 3.456e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08846 0.1653 0.1955 0.9854 0.9913 0.09041 0.6957 0.8473 0.2442 ] Network output: [ 0.000195 0.9999 -0.000357 6.214e-06 -2.789e-06 1 4.683e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005279 Epoch 7683 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01144 0.9948 0.9895 1.054e-06 -4.734e-07 -0.0071 7.947e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003301 -0.003102 -0.008385 0.006497 0.9698 0.9742 0.006315 0.8373 0.827 0.01869 ] Network output: [ 0.9998 0.0007801 0.001123 -2.327e-05 1.045e-05 -0.001597 -1.754e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1922 -0.03259 -0.1823 0.1931 0.9836 0.9933 0.2148 0.4471 0.8732 0.7191 ] Network output: [ -0.0109 1.002 1.01 3.791e-07 -1.702e-07 0.01015 2.857e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005792 0.0004649 0.004421 0.003855 0.9889 0.992 0.0059 0.866 0.8972 0.01351 ] Network output: [ -0.000715 0.003093 1.002 -7.426e-05 3.334e-05 0.9962 -5.596e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2036 0.09474 0.334 0.149 0.9851 0.994 0.2042 0.4515 0.8797 0.7135 ] Network output: [ 0.006389 -0.03119 0.9954 4.409e-05 -1.98e-05 1.023 3.323e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1004 0.08854 0.1804 0.2028 0.9873 0.9919 0.1004 0.7697 0.8701 0.3064 ] Network output: [ -0.006208 0.03101 1.002 4.581e-05 -2.057e-05 0.9791 3.453e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08846 0.1653 0.1955 0.9854 0.9913 0.09041 0.6957 0.8473 0.2442 ] Network output: [ 0.0001949 0.9999 -0.0003566 6.208e-06 -2.787e-06 1 4.679e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005275 Epoch 7684 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01144 0.9948 0.9895 1.052e-06 -4.722e-07 -0.007101 7.926e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003301 -0.003102 -0.008384 0.006496 0.9698 0.9742 0.006315 0.8373 0.827 0.01869 ] Network output: [ 0.9998 0.0007794 0.001122 -2.325e-05 1.044e-05 -0.001596 -1.752e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1922 -0.03259 -0.1823 0.193 0.9836 0.9933 0.2148 0.4471 0.8732 0.7191 ] Network output: [ -0.0109 1.002 1.01 3.775e-07 -1.695e-07 0.01015 2.845e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005793 0.0004649 0.004421 0.003855 0.9889 0.992 0.005901 0.866 0.8972 0.01351 ] Network output: [ -0.0007146 0.003092 1.002 -7.419e-05 3.331e-05 0.9962 -5.591e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2036 0.09474 0.334 0.149 0.9851 0.994 0.2042 0.4515 0.8797 0.7135 ] Network output: [ 0.006387 -0.03117 0.9954 4.406e-05 -1.978e-05 1.023 3.32e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1004 0.08854 0.1804 0.2027 0.9873 0.9919 0.1005 0.7697 0.8701 0.3064 ] Network output: [ -0.006205 0.031 1.002 4.577e-05 -2.055e-05 0.9791 3.45e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08846 0.1653 0.1955 0.9854 0.9913 0.09041 0.6956 0.8473 0.2442 ] Network output: [ 0.0001948 0.9999 -0.0003562 6.203e-06 -2.785e-06 1 4.675e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005272 Epoch 7685 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01144 0.9948 0.9895 1.049e-06 -4.709e-07 -0.007103 7.906e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003301 -0.003102 -0.008382 0.006495 0.9698 0.9742 0.006316 0.8373 0.827 0.01868 ] Network output: [ 0.9998 0.0007787 0.001122 -2.323e-05 1.043e-05 -0.001595 -1.751e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1922 -0.0326 -0.1823 0.193 0.9836 0.9933 0.2148 0.4471 0.8732 0.719 ] Network output: [ -0.0109 1.002 1.01 3.759e-07 -1.687e-07 0.01015 2.833e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005794 0.0004649 0.004421 0.003854 0.9889 0.992 0.005902 0.866 0.8972 0.01351 ] Network output: [ -0.0007142 0.003091 1.002 -7.412e-05 3.328e-05 0.9962 -5.586e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2036 0.09475 0.334 0.149 0.9851 0.994 0.2043 0.4515 0.8797 0.7135 ] Network output: [ 0.006385 -0.03116 0.9954 4.402e-05 -1.976e-05 1.023 3.317e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1004 0.08855 0.1804 0.2027 0.9873 0.9919 0.1005 0.7697 0.8701 0.3064 ] Network output: [ -0.006203 0.03099 1.002 4.573e-05 -2.053e-05 0.9791 3.447e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08846 0.1653 0.1955 0.9854 0.9913 0.09041 0.6956 0.8473 0.2442 ] Network output: [ 0.0001947 0.9999 -0.0003558 6.197e-06 -2.782e-06 1 4.67e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005268 Epoch 7686 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01143 0.9948 0.9895 1.046e-06 -4.697e-07 -0.007104 7.885e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003301 -0.003103 -0.008381 0.006495 0.9698 0.9742 0.006316 0.8373 0.827 0.01868 ] Network output: [ 0.9998 0.000778 0.001121 -2.321e-05 1.042e-05 -0.001594 -1.749e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1922 -0.0326 -0.1822 0.193 0.9836 0.9933 0.2148 0.447 0.8732 0.719 ] Network output: [ -0.0109 1.002 1.01 3.742e-07 -1.68e-07 0.01014 2.82e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005794 0.000465 0.004421 0.003854 0.9889 0.992 0.005902 0.866 0.8972 0.01351 ] Network output: [ -0.0007138 0.00309 1.002 -7.405e-05 3.324e-05 0.9962 -5.581e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2036 0.09475 0.334 0.149 0.9851 0.994 0.2043 0.4515 0.8797 0.7135 ] Network output: [ 0.006383 -0.03115 0.9954 4.398e-05 -1.974e-05 1.023 3.314e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1004 0.08855 0.1804 0.2027 0.9873 0.9919 0.1005 0.7697 0.87 0.3064 ] Network output: [ -0.006201 0.03097 1.002 4.57e-05 -2.051e-05 0.9791 3.444e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09041 0.6956 0.8473 0.2442 ] Network output: [ 0.0001946 0.9999 -0.0003554 6.192e-06 -2.78e-06 1 4.666e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005265 Epoch 7687 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01143 0.9948 0.9895 1.044e-06 -4.685e-07 -0.007105 7.865e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003301 -0.003103 -0.00838 0.006494 0.9698 0.9742 0.006316 0.8373 0.827 0.01868 ] Network output: [ 0.9998 0.0007773 0.00112 -2.319e-05 1.041e-05 -0.001592 -1.748e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1922 -0.0326 -0.1822 0.193 0.9836 0.9933 0.2148 0.447 0.8731 0.719 ] Network output: [ -0.0109 1.002 1.01 3.726e-07 -1.673e-07 0.01014 2.808e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005795 0.000465 0.004421 0.003853 0.9889 0.992 0.005903 0.866 0.8971 0.01351 ] Network output: [ -0.0007134 0.003089 1.002 -7.398e-05 3.321e-05 0.9962 -5.576e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2036 0.09476 0.334 0.149 0.9851 0.994 0.2043 0.4515 0.8797 0.7135 ] Network output: [ 0.00638 -0.03114 0.9954 4.394e-05 -1.973e-05 1.023 3.311e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1004 0.08856 0.1804 0.2027 0.9873 0.9919 0.1005 0.7696 0.87 0.3064 ] Network output: [ -0.006198 0.03096 1.002 4.566e-05 -2.05e-05 0.9791 3.441e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09041 0.6955 0.8473 0.2442 ] Network output: [ 0.0001945 0.9999 -0.0003551 6.186e-06 -2.777e-06 1 4.662e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005262 Epoch 7688 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01143 0.9948 0.9895 1.041e-06 -4.673e-07 -0.007106 7.845e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003301 -0.003103 -0.008379 0.006493 0.9698 0.9742 0.006317 0.8373 0.827 0.01868 ] Network output: [ 0.9998 0.0007766 0.001119 -2.317e-05 1.04e-05 -0.001591 -1.746e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1922 -0.0326 -0.1822 0.193 0.9836 0.9933 0.2149 0.447 0.8731 0.719 ] Network output: [ -0.01089 1.002 1.01 3.71e-07 -1.666e-07 0.01014 2.796e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005795 0.000465 0.004421 0.003852 0.9889 0.992 0.005903 0.866 0.8971 0.01351 ] Network output: [ -0.0007129 0.003088 1.002 -7.392e-05 3.318e-05 0.9962 -5.571e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2036 0.09476 0.334 0.1489 0.9851 0.994 0.2043 0.4514 0.8797 0.7135 ] Network output: [ 0.006378 -0.03113 0.9954 4.39e-05 -1.971e-05 1.023 3.308e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1004 0.08856 0.1804 0.2027 0.9873 0.9919 0.1005 0.7696 0.87 0.3064 ] Network output: [ -0.006196 0.03095 1.002 4.562e-05 -2.048e-05 0.9791 3.438e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09041 0.6955 0.8473 0.2442 ] Network output: [ 0.0001944 0.9999 -0.0003547 6.181e-06 -2.775e-06 1 4.658e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005258 Epoch 7689 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01143 0.9948 0.9895 1.038e-06 -4.661e-07 -0.007107 7.824e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003301 -0.003103 -0.008377 0.006492 0.9698 0.9742 0.006317 0.8373 0.827 0.01868 ] Network output: [ 0.9998 0.0007759 0.001119 -2.315e-05 1.039e-05 -0.00159 -1.745e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1922 -0.03261 -0.1822 0.193 0.9836 0.9933 0.2149 0.447 0.8731 0.719 ] Network output: [ -0.01089 1.002 1.01 3.694e-07 -1.658e-07 0.01014 2.784e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005796 0.0004651 0.004421 0.003852 0.9889 0.992 0.005904 0.8659 0.8971 0.0135 ] Network output: [ -0.0007125 0.003087 1.002 -7.385e-05 3.315e-05 0.9962 -5.565e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2037 0.09477 0.334 0.1489 0.9851 0.994 0.2043 0.4514 0.8797 0.7135 ] Network output: [ 0.006376 -0.03112 0.9954 4.386e-05 -1.969e-05 1.023 3.305e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1004 0.08857 0.1804 0.2027 0.9873 0.9919 0.1005 0.7696 0.87 0.3064 ] Network output: [ -0.006193 0.03093 1.002 4.558e-05 -2.046e-05 0.9791 3.435e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09041 0.6955 0.8472 0.2442 ] Network output: [ 0.0001943 0.9999 -0.0003543 6.176e-06 -2.772e-06 1 4.654e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005255 Epoch 7690 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01143 0.9948 0.9895 1.036e-06 -4.649e-07 -0.007108 7.804e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003302 -0.003103 -0.008376 0.006491 0.9698 0.9742 0.006317 0.8372 0.827 0.01868 ] Network output: [ 0.9998 0.0007752 0.001118 -2.313e-05 1.038e-05 -0.001588 -1.743e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1922 -0.03261 -0.1822 0.193 0.9836 0.9933 0.2149 0.447 0.8731 0.719 ] Network output: [ -0.01089 1.002 1.01 3.678e-07 -1.651e-07 0.01013 2.772e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005797 0.0004651 0.004422 0.003851 0.9889 0.992 0.005905 0.8659 0.8971 0.0135 ] Network output: [ -0.0007121 0.003086 1.002 -7.378e-05 3.312e-05 0.9962 -5.56e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2037 0.09477 0.3341 0.1489 0.9851 0.994 0.2043 0.4514 0.8797 0.7135 ] Network output: [ 0.006374 -0.03111 0.9954 4.382e-05 -1.967e-05 1.023 3.302e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1004 0.08857 0.1804 0.2027 0.9873 0.9919 0.1005 0.7696 0.87 0.3064 ] Network output: [ -0.006191 0.03092 1.002 4.554e-05 -2.044e-05 0.9791 3.432e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09041 0.6955 0.8472 0.2442 ] Network output: [ 0.0001942 0.9999 -0.0003539 6.17e-06 -2.77e-06 1 4.65e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005251 Epoch 7691 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01142 0.9948 0.9895 1.033e-06 -4.637e-07 -0.00711 7.784e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003302 -0.003103 -0.008375 0.006491 0.9698 0.9742 0.006317 0.8372 0.827 0.01867 ] Network output: [ 0.9998 0.0007745 0.001117 -2.311e-05 1.037e-05 -0.001587 -1.742e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1923 -0.03261 -0.1821 0.193 0.9836 0.9933 0.2149 0.447 0.8731 0.719 ] Network output: [ -0.01089 1.002 1.01 3.661e-07 -1.644e-07 0.01013 2.759e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005797 0.0004651 0.004422 0.003851 0.9889 0.992 0.005905 0.8659 0.8971 0.0135 ] Network output: [ -0.0007117 0.003085 1.002 -7.371e-05 3.309e-05 0.9962 -5.555e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2037 0.09478 0.3341 0.1489 0.9851 0.994 0.2043 0.4514 0.8797 0.7135 ] Network output: [ 0.006372 -0.03109 0.9954 4.378e-05 -1.965e-05 1.023 3.299e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1004 0.08858 0.1804 0.2027 0.9873 0.9919 0.1005 0.7696 0.87 0.3064 ] Network output: [ -0.006189 0.03091 1.003 4.55e-05 -2.043e-05 0.9792 3.429e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09041 0.6954 0.8472 0.2442 ] Network output: [ 0.0001941 0.9999 -0.0003535 6.165e-06 -2.768e-06 1 4.646e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005248 Epoch 7692 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01142 0.9948 0.9895 1.03e-06 -4.625e-07 -0.007111 7.764e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003302 -0.003104 -0.008374 0.00649 0.9698 0.9742 0.006318 0.8372 0.827 0.01867 ] Network output: [ 0.9998 0.0007738 0.001116 -2.309e-05 1.037e-05 -0.001586 -1.74e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1923 -0.03262 -0.1821 0.193 0.9836 0.9933 0.2149 0.447 0.8731 0.719 ] Network output: [ -0.01089 1.002 1.01 3.645e-07 -1.637e-07 0.01013 2.747e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005798 0.0004652 0.004422 0.00385 0.9889 0.992 0.005906 0.8659 0.8971 0.0135 ] Network output: [ -0.0007113 0.003084 1.002 -7.365e-05 3.306e-05 0.9962 -5.55e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2037 0.09478 0.3341 0.1489 0.9851 0.994 0.2043 0.4514 0.8797 0.7135 ] Network output: [ 0.00637 -0.03108 0.9954 4.374e-05 -1.964e-05 1.023 3.297e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1004 0.08858 0.1804 0.2027 0.9873 0.9919 0.1005 0.7695 0.87 0.3064 ] Network output: [ -0.006186 0.03089 1.003 4.546e-05 -2.041e-05 0.9792 3.426e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09041 0.6954 0.8472 0.2442 ] Network output: [ 0.0001939 0.9999 -0.0003531 6.159e-06 -2.765e-06 1 4.642e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005245 Epoch 7693 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01142 0.9948 0.9895 1.027e-06 -4.613e-07 -0.007112 7.743e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003302 -0.003104 -0.008373 0.006489 0.9698 0.9742 0.006318 0.8372 0.827 0.01867 ] Network output: [ 0.9998 0.0007732 0.001116 -2.307e-05 1.036e-05 -0.001584 -1.739e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1923 -0.03262 -0.1821 0.193 0.9836 0.9933 0.2149 0.4469 0.8731 0.719 ] Network output: [ -0.01089 1.002 1.01 3.629e-07 -1.629e-07 0.01012 2.735e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005798 0.0004652 0.004422 0.00385 0.9889 0.992 0.005907 0.8659 0.8971 0.0135 ] Network output: [ -0.0007108 0.003083 1.002 -7.358e-05 3.303e-05 0.9962 -5.545e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2037 0.09478 0.3341 0.1489 0.9851 0.994 0.2044 0.4514 0.8797 0.7135 ] Network output: [ 0.006368 -0.03107 0.9954 4.37e-05 -1.962e-05 1.023 3.294e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1004 0.08859 0.1804 0.2027 0.9873 0.9919 0.1005 0.7695 0.87 0.3064 ] Network output: [ -0.006184 0.03088 1.003 4.542e-05 -2.039e-05 0.9792 3.423e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09041 0.6954 0.8472 0.2442 ] Network output: [ 0.0001938 0.9999 -0.0003528 6.154e-06 -2.763e-06 1 4.638e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005241 Epoch 7694 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01142 0.9948 0.9895 1.025e-06 -4.601e-07 -0.007113 7.723e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003302 -0.003104 -0.008371 0.006488 0.9698 0.9742 0.006318 0.8372 0.827 0.01867 ] Network output: [ 0.9998 0.0007725 0.001115 -2.305e-05 1.035e-05 -0.001583 -1.737e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1923 -0.03262 -0.1821 0.193 0.9836 0.9933 0.2149 0.4469 0.8731 0.719 ] Network output: [ -0.01089 1.002 1.01 3.613e-07 -1.622e-07 0.01012 2.723e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005799 0.0004653 0.004422 0.003849 0.9889 0.992 0.005907 0.8659 0.8971 0.0135 ] Network output: [ -0.0007104 0.003081 1.002 -7.351e-05 3.3e-05 0.9962 -5.54e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2037 0.09479 0.3341 0.1489 0.9851 0.994 0.2044 0.4513 0.8797 0.7135 ] Network output: [ 0.006365 -0.03106 0.9954 4.366e-05 -1.96e-05 1.023 3.291e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1004 0.08859 0.1804 0.2027 0.9873 0.9919 0.1005 0.7695 0.87 0.3064 ] Network output: [ -0.006182 0.03086 1.003 4.538e-05 -2.037e-05 0.9792 3.42e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09041 0.6954 0.8472 0.2442 ] Network output: [ 0.0001937 0.9999 -0.0003524 6.149e-06 -2.76e-06 1 4.634e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005238 Epoch 7695 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01142 0.9948 0.9895 1.022e-06 -4.589e-07 -0.007114 7.703e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003302 -0.003104 -0.00837 0.006487 0.9698 0.9742 0.006319 0.8372 0.827 0.01867 ] Network output: [ 0.9998 0.0007718 0.001114 -2.303e-05 1.034e-05 -0.001582 -1.736e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1923 -0.03263 -0.1821 0.193 0.9836 0.9933 0.2149 0.4469 0.8731 0.719 ] Network output: [ -0.01089 1.002 1.01 3.597e-07 -1.615e-07 0.01012 2.711e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0058 0.0004653 0.004422 0.003849 0.9889 0.992 0.005908 0.8659 0.8971 0.0135 ] Network output: [ -0.00071 0.00308 1.002 -7.344e-05 3.297e-05 0.9962 -5.535e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2037 0.09479 0.3341 0.1489 0.9851 0.994 0.2044 0.4513 0.8797 0.7135 ] Network output: [ 0.006363 -0.03105 0.9954 4.362e-05 -1.958e-05 1.023 3.288e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1005 0.0886 0.1804 0.2027 0.9873 0.9919 0.1005 0.7695 0.87 0.3064 ] Network output: [ -0.006179 0.03085 1.003 4.534e-05 -2.036e-05 0.9792 3.417e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09041 0.6953 0.8472 0.2442 ] Network output: [ 0.0001936 0.9999 -0.000352 6.143e-06 -2.758e-06 1 4.63e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005235 Epoch 7696 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01142 0.9948 0.9895 1.019e-06 -4.577e-07 -0.007115 7.683e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003302 -0.003104 -0.008369 0.006487 0.9698 0.9742 0.006319 0.8372 0.827 0.01867 ] Network output: [ 0.9998 0.0007711 0.001113 -2.301e-05 1.033e-05 -0.001581 -1.734e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1923 -0.03263 -0.1821 0.193 0.9836 0.9933 0.215 0.4469 0.8731 0.719 ] Network output: [ -0.01089 1.002 1.01 3.581e-07 -1.608e-07 0.01012 2.699e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0058 0.0004653 0.004422 0.003848 0.9889 0.992 0.005908 0.8659 0.8971 0.0135 ] Network output: [ -0.0007096 0.003079 1.002 -7.338e-05 3.294e-05 0.9962 -5.53e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2037 0.0948 0.3341 0.1489 0.9851 0.994 0.2044 0.4513 0.8797 0.7135 ] Network output: [ 0.006361 -0.03104 0.9954 4.359e-05 -1.957e-05 1.023 3.285e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1005 0.0886 0.1804 0.2027 0.9873 0.9919 0.1005 0.7694 0.87 0.3064 ] Network output: [ -0.006177 0.03084 1.003 4.53e-05 -2.034e-05 0.9792 3.414e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09041 0.6953 0.8472 0.2442 ] Network output: [ 0.0001935 0.9999 -0.0003516 6.138e-06 -2.755e-06 1 4.626e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005231 Epoch 7697 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01141 0.9948 0.9895 1.017e-06 -4.565e-07 -0.007117 7.663e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003303 -0.003104 -0.008368 0.006486 0.9698 0.9742 0.006319 0.8372 0.8269 0.01866 ] Network output: [ 0.9998 0.0007704 0.001113 -2.299e-05 1.032e-05 -0.001579 -1.733e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1923 -0.03263 -0.182 0.1929 0.9836 0.9933 0.215 0.4469 0.8731 0.719 ] Network output: [ -0.01088 1.002 1.01 3.565e-07 -1.601e-07 0.01011 2.687e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005801 0.0004654 0.004422 0.003848 0.9889 0.992 0.005909 0.8659 0.8971 0.01349 ] Network output: [ -0.0007092 0.003078 1.002 -7.331e-05 3.291e-05 0.9962 -5.525e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2037 0.0948 0.3342 0.1489 0.9851 0.994 0.2044 0.4513 0.8797 0.7134 ] Network output: [ 0.006359 -0.03103 0.9954 4.355e-05 -1.955e-05 1.023 3.282e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1005 0.08861 0.1804 0.2027 0.9873 0.9919 0.1005 0.7694 0.87 0.3064 ] Network output: [ -0.006174 0.03082 1.003 4.527e-05 -2.032e-05 0.9792 3.411e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09041 0.6953 0.8472 0.2442 ] Network output: [ 0.0001934 0.9999 -0.0003512 6.132e-06 -2.753e-06 1 4.622e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005228 Epoch 7698 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01141 0.9948 0.9895 1.014e-06 -4.553e-07 -0.007118 7.643e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003303 -0.003105 -0.008366 0.006485 0.9698 0.9742 0.00632 0.8372 0.8269 0.01866 ] Network output: [ 0.9998 0.0007697 0.001112 -2.297e-05 1.031e-05 -0.001578 -1.731e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1923 -0.03264 -0.182 0.1929 0.9836 0.9933 0.215 0.4469 0.8731 0.719 ] Network output: [ -0.01088 1.002 1.01 3.549e-07 -1.593e-07 0.01011 2.675e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005802 0.0004654 0.004422 0.003847 0.9889 0.992 0.00591 0.8659 0.8971 0.01349 ] Network output: [ -0.0007088 0.003077 1.002 -7.324e-05 3.288e-05 0.9962 -5.52e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2038 0.09481 0.3342 0.1489 0.9851 0.994 0.2044 0.4513 0.8797 0.7134 ] Network output: [ 0.006357 -0.03101 0.9954 4.351e-05 -1.953e-05 1.023 3.279e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1005 0.08861 0.1804 0.2027 0.9873 0.9919 0.1005 0.7694 0.87 0.3064 ] Network output: [ -0.006172 0.03081 1.003 4.523e-05 -2.03e-05 0.9792 3.408e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09041 0.6952 0.8472 0.2442 ] Network output: [ 0.0001933 0.9999 -0.0003508 6.127e-06 -2.751e-06 1 4.618e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005224 Epoch 7699 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01141 0.9948 0.9895 1.012e-06 -4.541e-07 -0.007119 7.623e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003303 -0.003105 -0.008365 0.006484 0.9698 0.9742 0.00632 0.8372 0.8269 0.01866 ] Network output: [ 0.9998 0.000769 0.001111 -2.295e-05 1.03e-05 -0.001577 -1.73e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1923 -0.03264 -0.182 0.1929 0.9836 0.9933 0.215 0.4469 0.8731 0.719 ] Network output: [ -0.01088 1.002 1.01 3.533e-07 -1.586e-07 0.01011 2.663e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005802 0.0004655 0.004422 0.003847 0.9889 0.992 0.00591 0.8659 0.8971 0.01349 ] Network output: [ -0.0007083 0.003076 1.002 -7.317e-05 3.285e-05 0.9962 -5.515e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2038 0.09481 0.3342 0.1489 0.9851 0.994 0.2044 0.4513 0.8797 0.7134 ] Network output: [ 0.006355 -0.031 0.9954 4.347e-05 -1.951e-05 1.023 3.276e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1005 0.08862 0.1804 0.2027 0.9873 0.9919 0.1005 0.7694 0.8699 0.3064 ] Network output: [ -0.00617 0.0308 1.003 4.519e-05 -2.029e-05 0.9792 3.406e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09041 0.6952 0.8471 0.2442 ] Network output: [ 0.0001932 0.9999 -0.0003505 6.122e-06 -2.748e-06 1 4.613e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005221 Epoch 7700 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01141 0.9948 0.9895 1.009e-06 -4.529e-07 -0.00712 7.603e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003303 -0.003105 -0.008364 0.006483 0.9698 0.9742 0.00632 0.8372 0.8269 0.01866 ] Network output: [ 0.9998 0.0007683 0.00111 -2.293e-05 1.029e-05 -0.001575 -1.728e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1924 -0.03264 -0.182 0.1929 0.9836 0.9933 0.215 0.4468 0.8731 0.719 ] Network output: [ -0.01088 1.002 1.01 3.517e-07 -1.579e-07 0.01011 2.651e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005803 0.0004655 0.004422 0.003846 0.9889 0.992 0.005911 0.8658 0.8971 0.01349 ] Network output: [ -0.0007079 0.003075 1.002 -7.311e-05 3.282e-05 0.9962 -5.51e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2038 0.09482 0.3342 0.1488 0.9851 0.994 0.2044 0.4513 0.8797 0.7134 ] Network output: [ 0.006353 -0.03099 0.9954 4.343e-05 -1.95e-05 1.023 3.273e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1005 0.08862 0.1804 0.2027 0.9873 0.9919 0.1005 0.7693 0.8699 0.3064 ] Network output: [ -0.006167 0.03078 1.003 4.515e-05 -2.027e-05 0.9792 3.403e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09041 0.6952 0.8471 0.2442 ] Network output: [ 0.0001931 0.9999 -0.0003501 6.116e-06 -2.746e-06 1 4.609e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005218 Epoch 7701 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01141 0.9948 0.9895 1.006e-06 -4.517e-07 -0.007121 7.583e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003303 -0.003105 -0.008363 0.006483 0.9698 0.9742 0.006321 0.8372 0.8269 0.01866 ] Network output: [ 0.9998 0.0007676 0.00111 -2.291e-05 1.028e-05 -0.001574 -1.726e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1924 -0.03264 -0.182 0.1929 0.9836 0.9933 0.215 0.4468 0.8731 0.719 ] Network output: [ -0.01088 1.002 1.01 3.502e-07 -1.572e-07 0.0101 2.639e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005803 0.0004655 0.004422 0.003845 0.9889 0.992 0.005912 0.8658 0.8971 0.01349 ] Network output: [ -0.0007075 0.003074 1.002 -7.304e-05 3.279e-05 0.9962 -5.504e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2038 0.09482 0.3342 0.1488 0.9851 0.994 0.2045 0.4512 0.8796 0.7134 ] Network output: [ 0.006351 -0.03098 0.9954 4.339e-05 -1.948e-05 1.023 3.27e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1005 0.08863 0.1804 0.2027 0.9873 0.9919 0.1006 0.7693 0.8699 0.3064 ] Network output: [ -0.006165 0.03077 1.003 4.511e-05 -2.025e-05 0.9792 3.4e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09041 0.6952 0.8471 0.2442 ] Network output: [ 0.000193 0.9999 -0.0003497 6.111e-06 -2.743e-06 1 4.605e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005214 Epoch 7702 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0114 0.9948 0.9895 1.004e-06 -4.505e-07 -0.007122 7.563e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003303 -0.003105 -0.008362 0.006482 0.9698 0.9742 0.006321 0.8371 0.8269 0.01866 ] Network output: [ 0.9998 0.000767 0.001109 -2.289e-05 1.028e-05 -0.001573 -1.725e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1924 -0.03265 -0.182 0.1929 0.9836 0.9933 0.215 0.4468 0.8731 0.719 ] Network output: [ -0.01088 1.002 1.01 3.486e-07 -1.565e-07 0.0101 2.627e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005804 0.0004656 0.004423 0.003845 0.9889 0.992 0.005912 0.8658 0.8971 0.01349 ] Network output: [ -0.0007071 0.003073 1.002 -7.297e-05 3.276e-05 0.9962 -5.499e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2038 0.09483 0.3342 0.1488 0.9851 0.994 0.2045 0.4512 0.8796 0.7134 ] Network output: [ 0.006348 -0.03097 0.9954 4.335e-05 -1.946e-05 1.023 3.267e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1005 0.08864 0.1804 0.2026 0.9873 0.9919 0.1006 0.7693 0.8699 0.3064 ] Network output: [ -0.006163 0.03076 1.003 4.507e-05 -2.023e-05 0.9792 3.397e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09041 0.6951 0.8471 0.2442 ] Network output: [ 0.0001929 0.9999 -0.0003493 6.106e-06 -2.741e-06 1 4.601e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005211 Epoch 7703 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0114 0.9948 0.9895 1.001e-06 -4.494e-07 -0.007123 7.543e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003303 -0.003105 -0.00836 0.006481 0.9698 0.9742 0.006321 0.8371 0.8269 0.01865 ] Network output: [ 0.9998 0.0007663 0.001108 -2.287e-05 1.027e-05 -0.001572 -1.723e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1924 -0.03265 -0.1819 0.1929 0.9836 0.9933 0.215 0.4468 0.8731 0.719 ] Network output: [ -0.01088 1.002 1.01 3.47e-07 -1.558e-07 0.0101 2.615e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005805 0.0004656 0.004423 0.003844 0.9889 0.992 0.005913 0.8658 0.8971 0.01349 ] Network output: [ -0.0007067 0.003072 1.002 -7.29e-05 3.273e-05 0.9962 -5.494e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2038 0.09483 0.3342 0.1488 0.9851 0.994 0.2045 0.4512 0.8796 0.7134 ] Network output: [ 0.006346 -0.03096 0.9954 4.331e-05 -1.945e-05 1.023 3.264e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1005 0.08864 0.1804 0.2026 0.9873 0.9919 0.1006 0.7693 0.8699 0.3064 ] Network output: [ -0.00616 0.03074 1.003 4.503e-05 -2.022e-05 0.9792 3.394e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09042 0.6951 0.8471 0.2442 ] Network output: [ 0.0001928 0.9999 -0.0003489 6.1e-06 -2.739e-06 1 4.597e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005208 Epoch 7704 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0114 0.9948 0.9895 9.983e-07 -4.482e-07 -0.007125 7.524e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003304 -0.003106 -0.008359 0.00648 0.9698 0.9742 0.006322 0.8371 0.8269 0.01865 ] Network output: [ 0.9998 0.0007656 0.001108 -2.285e-05 1.026e-05 -0.00157 -1.722e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1924 -0.03265 -0.1819 0.1929 0.9836 0.9933 0.2151 0.4468 0.8731 0.719 ] Network output: [ -0.01088 1.002 1.01 3.454e-07 -1.551e-07 0.01009 2.603e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005805 0.0004656 0.004423 0.003844 0.9889 0.992 0.005913 0.8658 0.8971 0.01348 ] Network output: [ -0.0007062 0.003071 1.002 -7.284e-05 3.27e-05 0.9962 -5.489e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2038 0.09484 0.3342 0.1488 0.9851 0.994 0.2045 0.4512 0.8796 0.7134 ] Network output: [ 0.006344 -0.03095 0.9954 4.327e-05 -1.943e-05 1.023 3.261e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1005 0.08865 0.1804 0.2026 0.9873 0.9919 0.1006 0.7692 0.8699 0.3064 ] Network output: [ -0.006158 0.03073 1.003 4.499e-05 -2.02e-05 0.9792 3.391e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09042 0.6951 0.8471 0.2442 ] Network output: [ 0.0001927 0.9999 -0.0003486 6.095e-06 -2.736e-06 1 4.593e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005204 Epoch 7705 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0114 0.9948 0.9895 9.957e-07 -4.47e-07 -0.007126 7.504e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003304 -0.003106 -0.008358 0.006479 0.9698 0.9742 0.006322 0.8371 0.8269 0.01865 ] Network output: [ 0.9998 0.0007649 0.001107 -2.283e-05 1.025e-05 -0.001569 -1.72e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1924 -0.03266 -0.1819 0.1929 0.9836 0.9933 0.2151 0.4468 0.8731 0.7189 ] Network output: [ -0.01087 1.002 1.01 3.438e-07 -1.544e-07 0.01009 2.591e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005806 0.0004657 0.004423 0.003843 0.9889 0.992 0.005914 0.8658 0.8971 0.01348 ] Network output: [ -0.0007058 0.00307 1.002 -7.277e-05 3.267e-05 0.9962 -5.484e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2038 0.09484 0.3343 0.1488 0.9851 0.994 0.2045 0.4512 0.8796 0.7134 ] Network output: [ 0.006342 -0.03093 0.9954 4.324e-05 -1.941e-05 1.023 3.258e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1005 0.08865 0.1804 0.2026 0.9873 0.9919 0.1006 0.7692 0.8699 0.3064 ] Network output: [ -0.006156 0.03071 1.003 4.496e-05 -2.018e-05 0.9792 3.388e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09042 0.6951 0.8471 0.2442 ] Network output: [ 0.0001926 0.9999 -0.0003482 6.089e-06 -2.734e-06 1 4.589e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005201 Epoch 7706 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0114 0.9948 0.9895 9.931e-07 -4.458e-07 -0.007127 7.484e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003304 -0.003106 -0.008357 0.006479 0.9698 0.9742 0.006322 0.8371 0.8269 0.01865 ] Network output: [ 0.9998 0.0007642 0.001106 -2.281e-05 1.024e-05 -0.001568 -1.719e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1924 -0.03266 -0.1819 0.1929 0.9836 0.9933 0.2151 0.4468 0.8731 0.7189 ] Network output: [ -0.01087 1.002 1.01 3.423e-07 -1.536e-07 0.01009 2.579e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005806 0.0004657 0.004423 0.003843 0.9889 0.992 0.005915 0.8658 0.8971 0.01348 ] Network output: [ -0.0007054 0.003069 1.002 -7.27e-05 3.264e-05 0.9962 -5.479e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2039 0.09485 0.3343 0.1488 0.9851 0.994 0.2045 0.4512 0.8796 0.7134 ] Network output: [ 0.00634 -0.03092 0.9954 4.32e-05 -1.939e-05 1.023 3.255e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1005 0.08866 0.1804 0.2026 0.9873 0.9919 0.1006 0.7692 0.8699 0.3064 ] Network output: [ -0.006153 0.0307 1.003 4.492e-05 -2.016e-05 0.9793 3.385e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09042 0.695 0.8471 0.2443 ] Network output: [ 0.0001925 0.9999 -0.0003478 6.084e-06 -2.731e-06 1 4.585e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005198 Epoch 7707 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01139 0.9948 0.9895 9.904e-07 -4.446e-07 -0.007128 7.464e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003304 -0.003106 -0.008355 0.006478 0.9698 0.9742 0.006323 0.8371 0.8269 0.01865 ] Network output: [ 0.9998 0.0007635 0.001105 -2.279e-05 1.023e-05 -0.001566 -1.717e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1924 -0.03266 -0.1819 0.1929 0.9836 0.9933 0.2151 0.4467 0.8731 0.7189 ] Network output: [ -0.01087 1.002 1.01 3.407e-07 -1.529e-07 0.01009 2.567e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005807 0.0004658 0.004423 0.003842 0.9889 0.992 0.005915 0.8658 0.8971 0.01348 ] Network output: [ -0.000705 0.003068 1.002 -7.264e-05 3.261e-05 0.9962 -5.474e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2039 0.09485 0.3343 0.1488 0.9851 0.994 0.2045 0.4512 0.8796 0.7134 ] Network output: [ 0.006338 -0.03091 0.9954 4.316e-05 -1.938e-05 1.023 3.253e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1005 0.08866 0.1804 0.2026 0.9873 0.9919 0.1006 0.7692 0.8699 0.3064 ] Network output: [ -0.006151 0.03069 1.003 4.488e-05 -2.015e-05 0.9793 3.382e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09042 0.695 0.8471 0.2443 ] Network output: [ 0.0001924 0.9999 -0.0003474 6.079e-06 -2.729e-06 1 4.581e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005194 Epoch 7708 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01139 0.9948 0.9895 9.878e-07 -4.435e-07 -0.007129 7.445e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003304 -0.003106 -0.008354 0.006477 0.9698 0.9742 0.006323 0.8371 0.8269 0.01865 ] Network output: [ 0.9998 0.0007629 0.001105 -2.277e-05 1.022e-05 -0.001565 -1.716e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1924 -0.03266 -0.1819 0.1929 0.9836 0.9933 0.2151 0.4467 0.8731 0.7189 ] Network output: [ -0.01087 1.002 1.01 3.391e-07 -1.522e-07 0.01008 2.556e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005808 0.0004658 0.004423 0.003842 0.9889 0.992 0.005916 0.8658 0.8971 0.01348 ] Network output: [ -0.0007046 0.003067 1.002 -7.257e-05 3.258e-05 0.9962 -5.469e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2039 0.09485 0.3343 0.1488 0.9851 0.994 0.2045 0.4511 0.8796 0.7134 ] Network output: [ 0.006336 -0.0309 0.9954 4.312e-05 -1.936e-05 1.023 3.25e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1005 0.08867 0.1804 0.2026 0.9873 0.9919 0.1006 0.7691 0.8699 0.3064 ] Network output: [ -0.006149 0.03067 1.003 4.484e-05 -2.013e-05 0.9793 3.379e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09042 0.695 0.8471 0.2443 ] Network output: [ 0.0001923 0.9999 -0.000347 6.073e-06 -2.727e-06 1 4.577e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005191 Epoch 7709 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01139 0.9948 0.9895 9.852e-07 -4.423e-07 -0.00713 7.425e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003304 -0.003106 -0.008353 0.006476 0.9698 0.9742 0.006323 0.8371 0.8269 0.01864 ] Network output: [ 0.9998 0.0007622 0.001104 -2.275e-05 1.021e-05 -0.001564 -1.714e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1925 -0.03267 -0.1818 0.1929 0.9836 0.9933 0.2151 0.4467 0.873 0.7189 ] Network output: [ -0.01087 1.002 1.01 3.375e-07 -1.515e-07 0.01008 2.544e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005808 0.0004658 0.004423 0.003841 0.9889 0.992 0.005917 0.8658 0.8971 0.01348 ] Network output: [ -0.0007042 0.003065 1.002 -7.25e-05 3.255e-05 0.9962 -5.464e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2039 0.09486 0.3343 0.1488 0.9851 0.994 0.2045 0.4511 0.8796 0.7134 ] Network output: [ 0.006334 -0.03089 0.9954 4.308e-05 -1.934e-05 1.023 3.247e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1005 0.08867 0.1804 0.2026 0.9873 0.9919 0.1006 0.7691 0.8699 0.3064 ] Network output: [ -0.006146 0.03066 1.003 4.48e-05 -2.011e-05 0.9793 3.376e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0904 0.08847 0.1653 0.1955 0.9854 0.9913 0.09042 0.6949 0.8471 0.2443 ] Network output: [ 0.0001922 0.9999 -0.0003467 6.068e-06 -2.724e-06 1 4.573e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005188 Epoch 7710 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01139 0.9948 0.9895 9.826e-07 -4.411e-07 -0.007131 7.405e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003304 -0.003107 -0.008352 0.006475 0.9698 0.9742 0.006324 0.8371 0.8269 0.01864 ] Network output: [ 0.9998 0.0007615 0.001103 -2.273e-05 1.02e-05 -0.001563 -1.713e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1925 -0.03267 -0.1818 0.1928 0.9836 0.9933 0.2151 0.4467 0.873 0.7189 ] Network output: [ -0.01087 1.002 1.01 3.36e-07 -1.508e-07 0.01008 2.532e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005809 0.0004659 0.004423 0.003841 0.9889 0.992 0.005917 0.8658 0.897 0.01348 ] Network output: [ -0.0007037 0.003064 1.002 -7.244e-05 3.252e-05 0.9962 -5.459e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2039 0.09486 0.3343 0.1488 0.9851 0.994 0.2046 0.4511 0.8796 0.7134 ] Network output: [ 0.006331 -0.03088 0.9954 4.304e-05 -1.932e-05 1.023 3.244e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1005 0.08868 0.1804 0.2026 0.9873 0.9919 0.1006 0.7691 0.8699 0.3064 ] Network output: [ -0.006144 0.03065 1.003 4.476e-05 -2.01e-05 0.9793 3.373e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08847 0.1653 0.1955 0.9854 0.9913 0.09042 0.6949 0.847 0.2443 ] Network output: [ 0.0001921 0.9999 -0.0003463 6.063e-06 -2.722e-06 1 4.569e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005184 Epoch 7711 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01139 0.9948 0.9895 9.8e-07 -4.4e-07 -0.007132 7.386e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003305 -0.003107 -0.008351 0.006475 0.9698 0.9742 0.006324 0.8371 0.8269 0.01864 ] Network output: [ 0.9998 0.0007608 0.001102 -2.271e-05 1.02e-05 -0.001561 -1.711e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1925 -0.03267 -0.1818 0.1928 0.9836 0.9933 0.2151 0.4467 0.873 0.7189 ] Network output: [ -0.01087 1.002 1.01 3.344e-07 -1.501e-07 0.01007 2.52e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00581 0.0004659 0.004423 0.00384 0.9889 0.992 0.005918 0.8657 0.897 0.01348 ] Network output: [ -0.0007033 0.003063 1.002 -7.237e-05 3.249e-05 0.9962 -5.454e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2039 0.09487 0.3343 0.1488 0.9851 0.994 0.2046 0.4511 0.8796 0.7134 ] Network output: [ 0.006329 -0.03087 0.9954 4.3e-05 -1.931e-05 1.023 3.241e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1005 0.08868 0.1804 0.2026 0.9873 0.9919 0.1006 0.7691 0.8699 0.3063 ] Network output: [ -0.006142 0.03063 1.003 4.472e-05 -2.008e-05 0.9793 3.371e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08847 0.1653 0.1955 0.9854 0.9913 0.09042 0.6949 0.847 0.2443 ] Network output: [ 0.000192 0.9999 -0.0003459 6.057e-06 -2.719e-06 1 4.565e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005181 Epoch 7712 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01139 0.9948 0.9895 9.774e-07 -4.388e-07 -0.007134 7.366e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003305 -0.003107 -0.008349 0.006474 0.9698 0.9742 0.006324 0.8371 0.8269 0.01864 ] Network output: [ 0.9998 0.0007601 0.001102 -2.269e-05 1.019e-05 -0.00156 -1.71e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1925 -0.03268 -0.1818 0.1928 0.9836 0.9933 0.2152 0.4467 0.873 0.7189 ] Network output: [ -0.01087 1.002 1.01 3.328e-07 -1.494e-07 0.01007 2.508e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00581 0.000466 0.004423 0.00384 0.9889 0.992 0.005919 0.8657 0.897 0.01347 ] Network output: [ -0.0007029 0.003062 1.002 -7.23e-05 3.246e-05 0.9962 -5.449e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2039 0.09487 0.3343 0.1488 0.9851 0.994 0.2046 0.4511 0.8796 0.7134 ] Network output: [ 0.006327 -0.03085 0.9954 4.297e-05 -1.929e-05 1.023 3.238e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1006 0.08869 0.1804 0.2026 0.9873 0.9919 0.1006 0.769 0.8698 0.3063 ] Network output: [ -0.006139 0.03062 1.003 4.468e-05 -2.006e-05 0.9793 3.368e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08847 0.1653 0.1955 0.9854 0.9913 0.09042 0.6949 0.847 0.2443 ] Network output: [ 0.0001919 0.9999 -0.0003455 6.052e-06 -2.717e-06 1 4.561e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005178 Epoch 7713 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01138 0.9948 0.9895 9.748e-07 -4.376e-07 -0.007135 7.347e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003305 -0.003107 -0.008348 0.006473 0.9698 0.9742 0.006325 0.8371 0.8269 0.01864 ] Network output: [ 0.9998 0.0007595 0.001101 -2.267e-05 1.018e-05 -0.001559 -1.708e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1925 -0.03268 -0.1818 0.1928 0.9836 0.9933 0.2152 0.4467 0.873 0.7189 ] Network output: [ -0.01086 1.002 1.01 3.313e-07 -1.487e-07 0.01007 2.497e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005811 0.000466 0.004423 0.003839 0.9889 0.992 0.005919 0.8657 0.897 0.01347 ] Network output: [ -0.0007025 0.003061 1.002 -7.224e-05 3.243e-05 0.9962 -5.444e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2039 0.09488 0.3344 0.1487 0.9851 0.994 0.2046 0.4511 0.8796 0.7134 ] Network output: [ 0.006325 -0.03084 0.9953 4.293e-05 -1.927e-05 1.023 3.235e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1006 0.08869 0.1804 0.2026 0.9873 0.9919 0.1006 0.769 0.8698 0.3063 ] Network output: [ -0.006137 0.03061 1.003 4.465e-05 -2.004e-05 0.9793 3.365e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08847 0.1653 0.1955 0.9854 0.9913 0.09042 0.6948 0.847 0.2443 ] Network output: [ 0.0001918 0.9999 -0.0003452 6.047e-06 -2.715e-06 1 4.557e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005174 Epoch 7714 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01138 0.9948 0.9896 9.722e-07 -4.365e-07 -0.007136 7.327e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003305 -0.003107 -0.008347 0.006472 0.9698 0.9742 0.006325 0.837 0.8269 0.01864 ] Network output: [ 0.9998 0.0007588 0.0011 -2.265e-05 1.017e-05 -0.001557 -1.707e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1925 -0.03268 -0.1817 0.1928 0.9836 0.9933 0.2152 0.4466 0.873 0.7189 ] Network output: [ -0.01086 1.002 1.01 3.297e-07 -1.48e-07 0.01007 2.485e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005811 0.000466 0.004424 0.003838 0.9889 0.992 0.00592 0.8657 0.897 0.01347 ] Network output: [ -0.0007021 0.00306 1.002 -7.217e-05 3.24e-05 0.9962 -5.439e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.204 0.09488 0.3344 0.1487 0.9851 0.994 0.2046 0.4511 0.8796 0.7133 ] Network output: [ 0.006323 -0.03083 0.9953 4.289e-05 -1.925e-05 1.023 3.232e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1006 0.0887 0.1804 0.2026 0.9873 0.9919 0.1006 0.769 0.8698 0.3063 ] Network output: [ -0.006134 0.03059 1.003 4.461e-05 -2.003e-05 0.9793 3.362e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08847 0.1653 0.1955 0.9854 0.9913 0.09042 0.6948 0.847 0.2443 ] Network output: [ 0.0001917 0.9999 -0.0003448 6.041e-06 -2.712e-06 1 4.553e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005171 Epoch 7715 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01138 0.9948 0.9896 9.696e-07 -4.353e-07 -0.007137 7.307e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003305 -0.003107 -0.008346 0.006471 0.9698 0.9742 0.006325 0.837 0.8268 0.01863 ] Network output: [ 0.9998 0.0007581 0.001099 -2.263e-05 1.016e-05 -0.001556 -1.705e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1925 -0.03269 -0.1817 0.1928 0.9836 0.9933 0.2152 0.4466 0.873 0.7189 ] Network output: [ -0.01086 1.002 1.01 3.282e-07 -1.473e-07 0.01006 2.473e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005812 0.0004661 0.004424 0.003838 0.9889 0.992 0.00592 0.8657 0.897 0.01347 ] Network output: [ -0.0007017 0.003059 1.002 -7.21e-05 3.237e-05 0.9962 -5.434e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.204 0.09489 0.3344 0.1487 0.9851 0.994 0.2046 0.451 0.8796 0.7133 ] Network output: [ 0.006321 -0.03082 0.9953 4.285e-05 -1.924e-05 1.023 3.229e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1006 0.0887 0.1805 0.2026 0.9873 0.9919 0.1006 0.769 0.8698 0.3063 ] Network output: [ -0.006132 0.03058 1.003 4.457e-05 -2.001e-05 0.9793 3.359e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08847 0.1653 0.1955 0.9854 0.9913 0.09042 0.6948 0.847 0.2443 ] Network output: [ 0.0001916 0.9999 -0.0003444 6.036e-06 -2.71e-06 1 4.549e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005168 Epoch 7716 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01138 0.9948 0.9896 9.671e-07 -4.341e-07 -0.007138 7.288e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003305 -0.003107 -0.008345 0.006471 0.9698 0.9742 0.006326 0.837 0.8268 0.01863 ] Network output: [ 0.9998 0.0007574 0.001099 -2.261e-05 1.015e-05 -0.001555 -1.704e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1925 -0.03269 -0.1817 0.1928 0.9836 0.9933 0.2152 0.4466 0.873 0.7189 ] Network output: [ -0.01086 1.002 1.01 3.266e-07 -1.466e-07 0.01006 2.462e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005813 0.0004661 0.004424 0.003837 0.9889 0.992 0.005921 0.8657 0.897 0.01347 ] Network output: [ -0.0007013 0.003058 1.002 -7.204e-05 3.234e-05 0.9962 -5.429e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.204 0.09489 0.3344 0.1487 0.9851 0.994 0.2046 0.451 0.8796 0.7133 ] Network output: [ 0.006319 -0.03081 0.9953 4.281e-05 -1.922e-05 1.023 3.226e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1006 0.08871 0.1805 0.2026 0.9873 0.9919 0.1006 0.7689 0.8698 0.3063 ] Network output: [ -0.00613 0.03057 1.003 4.453e-05 -1.999e-05 0.9793 3.356e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1653 0.1955 0.9854 0.9913 0.09042 0.6948 0.847 0.2443 ] Network output: [ 0.0001915 0.9999 -0.000344 6.031e-06 -2.707e-06 1 4.545e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005164 Epoch 7717 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01138 0.9948 0.9896 9.645e-07 -4.33e-07 -0.007139 7.269e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003305 -0.003108 -0.008343 0.00647 0.9698 0.9742 0.006326 0.837 0.8268 0.01863 ] Network output: [ 0.9998 0.0007568 0.001098 -2.259e-05 1.014e-05 -0.001554 -1.702e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1925 -0.03269 -0.1817 0.1928 0.9836 0.9933 0.2152 0.4466 0.873 0.7189 ] Network output: [ -0.01086 1.002 1.01 3.251e-07 -1.459e-07 0.01006 2.45e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005813 0.0004662 0.004424 0.003837 0.9889 0.992 0.005922 0.8657 0.897 0.01347 ] Network output: [ -0.0007008 0.003057 1.002 -7.197e-05 3.231e-05 0.9962 -5.424e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.204 0.0949 0.3344 0.1487 0.9851 0.994 0.2046 0.451 0.8796 0.7133 ] Network output: [ 0.006317 -0.0308 0.9953 4.277e-05 -1.92e-05 1.023 3.223e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1006 0.08871 0.1805 0.2026 0.9873 0.9919 0.1007 0.7689 0.8698 0.3063 ] Network output: [ -0.006127 0.03055 1.003 4.449e-05 -1.997e-05 0.9793 3.353e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1653 0.1955 0.9854 0.9913 0.09042 0.6947 0.847 0.2443 ] Network output: [ 0.0001914 0.9999 -0.0003437 6.025e-06 -2.705e-06 1 4.541e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005161 Epoch 7718 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01137 0.9948 0.9896 9.619e-07 -4.318e-07 -0.00714 7.249e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003306 -0.003108 -0.008342 0.006469 0.9698 0.9742 0.006326 0.837 0.8268 0.01863 ] Network output: [ 0.9998 0.0007561 0.001097 -2.257e-05 1.013e-05 -0.001552 -1.701e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1925 -0.03269 -0.1817 0.1928 0.9836 0.9933 0.2152 0.4466 0.873 0.7189 ] Network output: [ -0.01086 1.002 1.01 3.235e-07 -1.452e-07 0.01005 2.438e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005814 0.0004662 0.004424 0.003836 0.9889 0.992 0.005922 0.8657 0.897 0.01347 ] Network output: [ -0.0007004 0.003056 1.002 -7.19e-05 3.228e-05 0.9962 -5.419e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.204 0.0949 0.3344 0.1487 0.9851 0.994 0.2047 0.451 0.8796 0.7133 ] Network output: [ 0.006315 -0.03079 0.9953 4.273e-05 -1.918e-05 1.023 3.221e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1006 0.08872 0.1805 0.2026 0.9873 0.9919 0.1007 0.7689 0.8698 0.3063 ] Network output: [ -0.006125 0.03054 1.003 4.445e-05 -1.996e-05 0.9793 3.35e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1653 0.1955 0.9854 0.9913 0.09042 0.6947 0.847 0.2443 ] Network output: [ 0.0001913 0.9999 -0.0003433 6.02e-06 -2.703e-06 1 4.537e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005158 Epoch 7719 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01137 0.9948 0.9896 9.593e-07 -4.307e-07 -0.007141 7.23e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003306 -0.003108 -0.008341 0.006468 0.9698 0.9742 0.006327 0.837 0.8268 0.01863 ] Network output: [ 0.9998 0.0007554 0.001097 -2.255e-05 1.012e-05 -0.001551 -1.699e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1926 -0.0327 -0.1817 0.1928 0.9836 0.9933 0.2152 0.4466 0.873 0.7189 ] Network output: [ -0.01086 1.002 1.01 3.22e-07 -1.445e-07 0.01005 2.427e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005814 0.0004662 0.004424 0.003836 0.9889 0.992 0.005923 0.8657 0.897 0.01347 ] Network output: [ -0.0007 0.003055 1.002 -7.184e-05 3.225e-05 0.9962 -5.414e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.204 0.09491 0.3344 0.1487 0.9851 0.994 0.2047 0.451 0.8796 0.7133 ] Network output: [ 0.006312 -0.03077 0.9953 4.27e-05 -1.917e-05 1.023 3.218e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1006 0.08873 0.1805 0.2026 0.9873 0.9919 0.1007 0.7689 0.8698 0.3063 ] Network output: [ -0.006123 0.03053 1.003 4.442e-05 -1.994e-05 0.9793 3.347e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1653 0.1955 0.9854 0.9913 0.09042 0.6947 0.847 0.2443 ] Network output: [ 0.0001912 0.9999 -0.0003429 6.015e-06 -2.7e-06 1 4.533e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005154 Epoch 7720 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01137 0.9948 0.9896 9.568e-07 -4.295e-07 -0.007142 7.21e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003306 -0.003108 -0.00834 0.006467 0.9698 0.9742 0.006327 0.837 0.8268 0.01863 ] Network output: [ 0.9998 0.0007547 0.001096 -2.253e-05 1.011e-05 -0.00155 -1.698e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1926 -0.0327 -0.1816 0.1928 0.9836 0.9933 0.2153 0.4466 0.873 0.7189 ] Network output: [ -0.01086 1.002 1.01 3.204e-07 -1.439e-07 0.01005 2.415e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005815 0.0004663 0.004424 0.003835 0.9889 0.992 0.005924 0.8657 0.897 0.01346 ] Network output: [ -0.0006996 0.003054 1.002 -7.177e-05 3.222e-05 0.9962 -5.409e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.204 0.09491 0.3344 0.1487 0.9851 0.994 0.2047 0.451 0.8796 0.7133 ] Network output: [ 0.00631 -0.03076 0.9953 4.266e-05 -1.915e-05 1.023 3.215e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1006 0.08873 0.1805 0.2026 0.9873 0.9919 0.1007 0.7689 0.8698 0.3063 ] Network output: [ -0.00612 0.03051 1.003 4.438e-05 -1.992e-05 0.9793 3.344e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1653 0.1955 0.9854 0.9913 0.09042 0.6946 0.8469 0.2443 ] Network output: [ 0.000191 0.9999 -0.0003425 6.009e-06 -2.698e-06 1 4.529e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005151 Epoch 7721 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01137 0.9948 0.9896 9.542e-07 -4.284e-07 -0.007143 7.191e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003306 -0.003108 -0.008339 0.006467 0.9698 0.9742 0.006327 0.837 0.8268 0.01862 ] Network output: [ 0.9998 0.0007541 0.001095 -2.251e-05 1.011e-05 -0.001548 -1.696e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1926 -0.0327 -0.1816 0.1928 0.9836 0.9933 0.2153 0.4465 0.873 0.7189 ] Network output: [ -0.01086 1.002 1.01 3.189e-07 -1.432e-07 0.01005 2.403e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005816 0.0004663 0.004424 0.003835 0.9889 0.992 0.005924 0.8656 0.897 0.01346 ] Network output: [ -0.0006992 0.003053 1.002 -7.17e-05 3.219e-05 0.9962 -5.404e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.204 0.09492 0.3345 0.1487 0.9851 0.994 0.2047 0.451 0.8796 0.7133 ] Network output: [ 0.006308 -0.03075 0.9953 4.262e-05 -1.913e-05 1.023 3.212e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1006 0.08874 0.1805 0.2025 0.9873 0.9919 0.1007 0.7688 0.8698 0.3063 ] Network output: [ -0.006118 0.0305 1.003 4.434e-05 -1.991e-05 0.9794 3.342e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1653 0.1955 0.9854 0.9913 0.09042 0.6946 0.8469 0.2443 ] Network output: [ 0.0001909 0.9999 -0.0003422 6.004e-06 -2.695e-06 1 4.525e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005148 Epoch 7722 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01137 0.9948 0.9896 9.516e-07 -4.272e-07 -0.007145 7.172e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003306 -0.003108 -0.008337 0.006466 0.9698 0.9742 0.006328 0.837 0.8268 0.01862 ] Network output: [ 0.9998 0.0007534 0.001094 -2.249e-05 1.01e-05 -0.001547 -1.695e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1926 -0.03271 -0.1816 0.1928 0.9836 0.9933 0.2153 0.4465 0.873 0.7189 ] Network output: [ -0.01085 1.002 1.01 3.174e-07 -1.425e-07 0.01004 2.392e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005816 0.0004664 0.004424 0.003834 0.9889 0.992 0.005925 0.8656 0.897 0.01346 ] Network output: [ -0.0006988 0.003052 1.002 -7.164e-05 3.216e-05 0.9962 -5.399e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.204 0.09492 0.3345 0.1487 0.9851 0.994 0.2047 0.4509 0.8796 0.7133 ] Network output: [ 0.006306 -0.03074 0.9953 4.258e-05 -1.912e-05 1.023 3.209e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1006 0.08874 0.1805 0.2025 0.9873 0.9919 0.1007 0.7688 0.8698 0.3063 ] Network output: [ -0.006116 0.03049 1.003 4.43e-05 -1.989e-05 0.9794 3.339e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1653 0.1955 0.9854 0.9913 0.09042 0.6946 0.8469 0.2443 ] Network output: [ 0.0001908 0.9999 -0.0003418 5.999e-06 -2.693e-06 1 4.521e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005145 Epoch 7723 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01137 0.9948 0.9896 9.491e-07 -4.261e-07 -0.007146 7.153e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003306 -0.003109 -0.008336 0.006465 0.9698 0.9742 0.006328 0.837 0.8268 0.01862 ] Network output: [ 0.9998 0.0007527 0.001094 -2.247e-05 1.009e-05 -0.001546 -1.694e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1926 -0.03271 -0.1816 0.1927 0.9836 0.9933 0.2153 0.4465 0.873 0.7189 ] Network output: [ -0.01085 1.002 1.01 3.158e-07 -1.418e-07 0.01004 2.38e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005817 0.0004664 0.004424 0.003834 0.9889 0.992 0.005925 0.8656 0.897 0.01346 ] Network output: [ -0.0006983 0.003051 1.002 -7.157e-05 3.213e-05 0.9962 -5.394e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2041 0.09493 0.3345 0.1487 0.9851 0.994 0.2047 0.4509 0.8796 0.7133 ] Network output: [ 0.006304 -0.03073 0.9953 4.254e-05 -1.91e-05 1.023 3.206e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1006 0.08875 0.1805 0.2025 0.9873 0.9919 0.1007 0.7688 0.8698 0.3063 ] Network output: [ -0.006113 0.03047 1.003 4.426e-05 -1.987e-05 0.9794 3.336e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1653 0.1955 0.9854 0.9913 0.09042 0.6946 0.8469 0.2443 ] Network output: [ 0.0001907 0.9999 -0.0003414 5.993e-06 -2.691e-06 1 4.517e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005141 Epoch 7724 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01136 0.9948 0.9896 9.465e-07 -4.249e-07 -0.007147 7.133e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003306 -0.003109 -0.008335 0.006464 0.9698 0.9742 0.006328 0.837 0.8268 0.01862 ] Network output: [ 0.9998 0.0007521 0.001093 -2.245e-05 1.008e-05 -0.001545 -1.692e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1926 -0.03271 -0.1816 0.1927 0.9836 0.9933 0.2153 0.4465 0.873 0.7188 ] Network output: [ -0.01085 1.002 1.01 3.143e-07 -1.411e-07 0.01004 2.369e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005818 0.0004665 0.004424 0.003833 0.9889 0.992 0.005926 0.8656 0.897 0.01346 ] Network output: [ -0.0006979 0.003049 1.002 -7.15e-05 3.21e-05 0.9962 -5.389e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2041 0.09493 0.3345 0.1487 0.9851 0.994 0.2047 0.4509 0.8795 0.7133 ] Network output: [ 0.006302 -0.03072 0.9953 4.25e-05 -1.908e-05 1.023 3.203e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1006 0.08875 0.1805 0.2025 0.9873 0.9919 0.1007 0.7688 0.8698 0.3063 ] Network output: [ -0.006111 0.03046 1.003 4.422e-05 -1.985e-05 0.9794 3.333e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1653 0.1955 0.9854 0.9913 0.09042 0.6945 0.8469 0.2443 ] Network output: [ 0.0001906 0.9999 -0.000341 5.988e-06 -2.688e-06 1 4.513e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005138 Epoch 7725 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01136 0.9948 0.9896 9.44e-07 -4.238e-07 -0.007148 7.114e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003306 -0.003109 -0.008334 0.006464 0.9698 0.9742 0.006329 0.837 0.8268 0.01862 ] Network output: [ 0.9998 0.0007514 0.001092 -2.243e-05 1.007e-05 -0.001543 -1.691e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1926 -0.03271 -0.1816 0.1927 0.9836 0.9933 0.2153 0.4465 0.873 0.7188 ] Network output: [ -0.01085 1.002 1.01 3.128e-07 -1.404e-07 0.01003 2.357e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005818 0.0004665 0.004424 0.003833 0.9889 0.992 0.005927 0.8656 0.897 0.01346 ] Network output: [ -0.0006975 0.003048 1.002 -7.144e-05 3.207e-05 0.9962 -5.384e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2041 0.09494 0.3345 0.1487 0.9851 0.994 0.2047 0.4509 0.8795 0.7133 ] Network output: [ 0.0063 -0.03071 0.9953 4.247e-05 -1.906e-05 1.023 3.2e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1006 0.08876 0.1805 0.2025 0.9873 0.9919 0.1007 0.7687 0.8697 0.3063 ] Network output: [ -0.006109 0.03045 1.003 4.419e-05 -1.984e-05 0.9794 3.33e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1653 0.1955 0.9854 0.9913 0.09042 0.6945 0.8469 0.2443 ] Network output: [ 0.0001905 0.9999 -0.0003407 5.983e-06 -2.686e-06 1 4.509e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005135 Epoch 7726 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01136 0.9949 0.9896 9.414e-07 -4.226e-07 -0.007149 7.095e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003307 -0.003109 -0.008333 0.006463 0.9698 0.9742 0.006329 0.8369 0.8268 0.01862 ] Network output: [ 0.9998 0.0007507 0.001092 -2.241e-05 1.006e-05 -0.001542 -1.689e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1926 -0.03272 -0.1815 0.1927 0.9836 0.9933 0.2153 0.4465 0.873 0.7188 ] Network output: [ -0.01085 1.002 1.01 3.112e-07 -1.397e-07 0.01003 2.346e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005819 0.0004665 0.004424 0.003832 0.9889 0.992 0.005927 0.8656 0.897 0.01346 ] Network output: [ -0.0006971 0.003047 1.002 -7.137e-05 3.204e-05 0.9963 -5.379e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2041 0.09494 0.3345 0.1486 0.9851 0.994 0.2048 0.4509 0.8795 0.7133 ] Network output: [ 0.006298 -0.03069 0.9953 4.243e-05 -1.905e-05 1.023 3.197e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1006 0.08876 0.1805 0.2025 0.9873 0.9919 0.1007 0.7687 0.8697 0.3063 ] Network output: [ -0.006107 0.03043 1.003 4.415e-05 -1.982e-05 0.9794 3.327e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1653 0.1955 0.9854 0.9913 0.09042 0.6945 0.8469 0.2443 ] Network output: [ 0.0001904 0.9999 -0.0003403 5.977e-06 -2.683e-06 1 4.505e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005131 Epoch 7727 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01136 0.9949 0.9896 9.389e-07 -4.215e-07 -0.00715 7.076e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003307 -0.003109 -0.008331 0.006462 0.9698 0.9742 0.006329 0.8369 0.8268 0.01861 ] Network output: [ 0.9998 0.0007501 0.001091 -2.239e-05 1.005e-05 -0.001541 -1.688e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1926 -0.03272 -0.1815 0.1927 0.9836 0.9933 0.2153 0.4465 0.873 0.7188 ] Network output: [ -0.01085 1.002 1.01 3.097e-07 -1.39e-07 0.01003 2.334e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005819 0.0004666 0.004425 0.003832 0.9889 0.992 0.005928 0.8656 0.897 0.01346 ] Network output: [ -0.0006967 0.003046 1.002 -7.131e-05 3.201e-05 0.9963 -5.374e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2041 0.09495 0.3345 0.1486 0.9851 0.994 0.2048 0.4509 0.8795 0.7133 ] Network output: [ 0.006296 -0.03068 0.9953 4.239e-05 -1.903e-05 1.023 3.195e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1006 0.08877 0.1805 0.2025 0.9873 0.9919 0.1007 0.7687 0.8697 0.3063 ] Network output: [ -0.006104 0.03042 1.003 4.411e-05 -1.98e-05 0.9794 3.324e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1653 0.1955 0.9854 0.9913 0.09043 0.6945 0.8469 0.2443 ] Network output: [ 0.0001903 0.9999 -0.0003399 5.972e-06 -2.681e-06 1 4.501e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005128 Epoch 7728 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01136 0.9949 0.9896 9.364e-07 -4.204e-07 -0.007151 7.057e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003307 -0.003109 -0.00833 0.006461 0.9698 0.9742 0.006329 0.8369 0.8268 0.01861 ] Network output: [ 0.9998 0.0007494 0.00109 -2.237e-05 1.004e-05 -0.00154 -1.686e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1927 -0.03272 -0.1815 0.1927 0.9836 0.9933 0.2154 0.4464 0.873 0.7188 ] Network output: [ -0.01085 1.002 1.01 3.082e-07 -1.384e-07 0.01003 2.323e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00582 0.0004666 0.004425 0.003831 0.9889 0.992 0.005929 0.8656 0.897 0.01345 ] Network output: [ -0.0006963 0.003045 1.002 -7.124e-05 3.198e-05 0.9963 -5.369e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2041 0.09495 0.3345 0.1486 0.9851 0.994 0.2048 0.4509 0.8795 0.7133 ] Network output: [ 0.006293 -0.03067 0.9953 4.235e-05 -1.901e-05 1.023 3.192e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1007 0.08877 0.1805 0.2025 0.9873 0.9919 0.1007 0.7687 0.8697 0.3063 ] Network output: [ -0.006102 0.03041 1.003 4.407e-05 -1.978e-05 0.9794 3.321e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1653 0.1955 0.9854 0.9913 0.09043 0.6944 0.8469 0.2443 ] Network output: [ 0.0001902 0.9999 -0.0003395 5.967e-06 -2.679e-06 1 4.497e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005125 Epoch 7729 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01135 0.9949 0.9896 9.338e-07 -4.192e-07 -0.007152 7.038e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003307 -0.00311 -0.008329 0.00646 0.9698 0.9742 0.00633 0.8369 0.8268 0.01861 ] Network output: [ 0.9998 0.0007487 0.001089 -2.235e-05 1.003e-05 -0.001538 -1.685e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1927 -0.03273 -0.1815 0.1927 0.9836 0.9933 0.2154 0.4464 0.873 0.7188 ] Network output: [ -0.01085 1.002 1.01 3.067e-07 -1.377e-07 0.01002 2.311e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005821 0.0004667 0.004425 0.003831 0.9889 0.992 0.005929 0.8656 0.897 0.01345 ] Network output: [ -0.0006959 0.003044 1.002 -7.117e-05 3.195e-05 0.9963 -5.364e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2041 0.09495 0.3346 0.1486 0.9851 0.994 0.2048 0.4508 0.8795 0.7133 ] Network output: [ 0.006291 -0.03066 0.9953 4.231e-05 -1.9e-05 1.023 3.189e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1007 0.08878 0.1805 0.2025 0.9873 0.9919 0.1007 0.7686 0.8697 0.3063 ] Network output: [ -0.0061 0.03039 1.003 4.403e-05 -1.977e-05 0.9794 3.318e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1653 0.1955 0.9854 0.9913 0.09043 0.6944 0.8469 0.2443 ] Network output: [ 0.0001901 0.9999 -0.0003392 5.962e-06 -2.676e-06 1 4.493e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005122 Epoch 7730 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01135 0.9949 0.9896 9.313e-07 -4.181e-07 -0.007153 7.019e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003307 -0.00311 -0.008328 0.00646 0.9698 0.9742 0.00633 0.8369 0.8268 0.01861 ] Network output: [ 0.9998 0.0007481 0.001089 -2.233e-05 1.003e-05 -0.001537 -1.683e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1927 -0.03273 -0.1815 0.1927 0.9836 0.9933 0.2154 0.4464 0.873 0.7188 ] Network output: [ -0.01084 1.002 1.01 3.051e-07 -1.37e-07 0.01002 2.3e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005821 0.0004667 0.004425 0.00383 0.9889 0.992 0.00593 0.8656 0.897 0.01345 ] Network output: [ -0.0006954 0.003043 1.002 -7.111e-05 3.192e-05 0.9963 -5.359e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2041 0.09496 0.3346 0.1486 0.985 0.994 0.2048 0.4508 0.8795 0.7133 ] Network output: [ 0.006289 -0.03065 0.9953 4.227e-05 -1.898e-05 1.023 3.186e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1007 0.08878 0.1805 0.2025 0.9873 0.9919 0.1007 0.7686 0.8697 0.3063 ] Network output: [ -0.006097 0.03038 1.003 4.399e-05 -1.975e-05 0.9794 3.316e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1653 0.1955 0.9854 0.9913 0.09043 0.6944 0.8469 0.2443 ] Network output: [ 0.00019 0.9999 -0.0003388 5.956e-06 -2.674e-06 1 4.489e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005118 Epoch 7731 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01135 0.9949 0.9896 9.288e-07 -4.17e-07 -0.007154 7e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003307 -0.00311 -0.008326 0.006459 0.9698 0.9742 0.00633 0.8369 0.8268 0.01861 ] Network output: [ 0.9998 0.0007474 0.001088 -2.231e-05 1.002e-05 -0.001536 -1.682e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1927 -0.03273 -0.1815 0.1927 0.9836 0.9933 0.2154 0.4464 0.873 0.7188 ] Network output: [ -0.01084 1.002 1.01 3.036e-07 -1.363e-07 0.01002 2.288e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005822 0.0004668 0.004425 0.003829 0.9889 0.992 0.00593 0.8656 0.897 0.01345 ] Network output: [ -0.000695 0.003042 1.002 -7.104e-05 3.189e-05 0.9963 -5.354e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2042 0.09496 0.3346 0.1486 0.985 0.994 0.2048 0.4508 0.8795 0.7133 ] Network output: [ 0.006287 -0.03064 0.9953 4.224e-05 -1.896e-05 1.023 3.183e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1007 0.08879 0.1805 0.2025 0.9873 0.9919 0.1007 0.7686 0.8697 0.3063 ] Network output: [ -0.006095 0.03037 1.003 4.396e-05 -1.973e-05 0.9794 3.313e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09041 0.08848 0.1653 0.1955 0.9854 0.9913 0.09043 0.6943 0.8468 0.2443 ] Network output: [ 0.0001899 0.9999 -0.0003384 5.951e-06 -2.672e-06 1 4.485e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005115 Epoch 7732 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01135 0.9949 0.9896 9.263e-07 -4.158e-07 -0.007155 6.981e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003307 -0.00311 -0.008325 0.006458 0.9698 0.9742 0.006331 0.8369 0.8268 0.01861 ] Network output: [ 0.9998 0.0007467 0.001087 -2.229e-05 1.001e-05 -0.001534 -1.68e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1927 -0.03274 -0.1814 0.1927 0.9836 0.9933 0.2154 0.4464 0.8729 0.7188 ] Network output: [ -0.01084 1.002 1.01 3.021e-07 -1.356e-07 0.01002 2.277e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005822 0.0004668 0.004425 0.003829 0.9889 0.992 0.005931 0.8655 0.897 0.01345 ] Network output: [ -0.0006946 0.003041 1.002 -7.098e-05 3.186e-05 0.9963 -5.349e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2042 0.09497 0.3346 0.1486 0.985 0.994 0.2048 0.4508 0.8795 0.7132 ] Network output: [ 0.006285 -0.03063 0.9953 4.22e-05 -1.894e-05 1.023 3.18e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1007 0.08879 0.1805 0.2025 0.9873 0.9919 0.1007 0.7686 0.8697 0.3063 ] Network output: [ -0.006093 0.03035 1.003 4.392e-05 -1.972e-05 0.9794 3.31e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08848 0.1653 0.1955 0.9854 0.9913 0.09043 0.6943 0.8468 0.2443 ] Network output: [ 0.0001898 0.9999 -0.0003381 5.946e-06 -2.669e-06 1 4.481e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005112 Epoch 7733 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01135 0.9949 0.9896 9.237e-07 -4.147e-07 -0.007157 6.962e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003308 -0.00311 -0.008324 0.006457 0.9698 0.9742 0.006331 0.8369 0.8268 0.0186 ] Network output: [ 0.9998 0.0007461 0.001086 -2.227e-05 1e-05 -0.001533 -1.679e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1927 -0.03274 -0.1814 0.1927 0.9835 0.9933 0.2154 0.4464 0.8729 0.7188 ] Network output: [ -0.01084 1.002 1.01 3.006e-07 -1.35e-07 0.01001 2.265e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005823 0.0004668 0.004425 0.003828 0.9889 0.992 0.005932 0.8655 0.897 0.01345 ] Network output: [ -0.0006942 0.00304 1.002 -7.091e-05 3.183e-05 0.9963 -5.344e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2042 0.09497 0.3346 0.1486 0.985 0.994 0.2048 0.4508 0.8795 0.7132 ] Network output: [ 0.006283 -0.03062 0.9953 4.216e-05 -1.893e-05 1.023 3.177e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1007 0.0888 0.1805 0.2025 0.9873 0.9919 0.1007 0.7685 0.8697 0.3063 ] Network output: [ -0.00609 0.03034 1.003 4.388e-05 -1.97e-05 0.9794 3.307e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08848 0.1653 0.1955 0.9854 0.9913 0.09043 0.6943 0.8468 0.2443 ] Network output: [ 0.0001897 0.9999 -0.0003377 5.94e-06 -2.667e-06 1 4.477e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005108 Epoch 7734 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01135 0.9949 0.9896 9.212e-07 -4.136e-07 -0.007158 6.943e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003308 -0.00311 -0.008323 0.006456 0.9698 0.9742 0.006331 0.8369 0.8267 0.0186 ] Network output: [ 0.9998 0.0007454 0.001086 -2.225e-05 9.991e-06 -0.001532 -1.677e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1927 -0.03274 -0.1814 0.1927 0.9835 0.9933 0.2154 0.4464 0.8729 0.7188 ] Network output: [ -0.01084 1.002 1.01 2.991e-07 -1.343e-07 0.01001 2.254e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005824 0.0004669 0.004425 0.003828 0.9889 0.992 0.005932 0.8655 0.8969 0.01345 ] Network output: [ -0.0006938 0.003039 1.002 -7.084e-05 3.18e-05 0.9963 -5.339e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2042 0.09498 0.3346 0.1486 0.985 0.994 0.2049 0.4508 0.8795 0.7132 ] Network output: [ 0.006281 -0.0306 0.9953 4.212e-05 -1.891e-05 1.023 3.174e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1007 0.08881 0.1805 0.2025 0.9873 0.9919 0.1008 0.7685 0.8697 0.3063 ] Network output: [ -0.006088 0.03033 1.003 4.384e-05 -1.968e-05 0.9794 3.304e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08848 0.1653 0.1955 0.9854 0.9913 0.09043 0.6943 0.8468 0.2443 ] Network output: [ 0.0001896 0.9999 -0.0003373 5.935e-06 -2.664e-06 1 4.473e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005105 Epoch 7735 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01134 0.9949 0.9896 9.187e-07 -4.125e-07 -0.007159 6.924e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003308 -0.003111 -0.008322 0.006456 0.9698 0.9742 0.006332 0.8369 0.8267 0.0186 ] Network output: [ 0.9998 0.0007447 0.001085 -2.223e-05 9.982e-06 -0.001531 -1.676e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1927 -0.03274 -0.1814 0.1927 0.9835 0.9933 0.2154 0.4463 0.8729 0.7188 ] Network output: [ -0.01084 1.002 1.01 2.976e-07 -1.336e-07 0.01001 2.243e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005824 0.0004669 0.004425 0.003827 0.9889 0.992 0.005933 0.8655 0.8969 0.01345 ] Network output: [ -0.0006934 0.003038 1.002 -7.078e-05 3.178e-05 0.9963 -5.334e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2042 0.09498 0.3346 0.1486 0.985 0.994 0.2049 0.4508 0.8795 0.7132 ] Network output: [ 0.006279 -0.03059 0.9953 4.208e-05 -1.889e-05 1.023 3.172e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1007 0.08881 0.1805 0.2025 0.9873 0.9919 0.1008 0.7685 0.8697 0.3063 ] Network output: [ -0.006086 0.03031 1.003 4.38e-05 -1.966e-05 0.9794 3.301e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08848 0.1653 0.1955 0.9854 0.9913 0.09043 0.6942 0.8468 0.2443 ] Network output: [ 0.0001895 0.9999 -0.000337 5.93e-06 -2.662e-06 1 4.469e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005102 Epoch 7736 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01134 0.9949 0.9896 9.162e-07 -4.113e-07 -0.00716 6.905e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003308 -0.003111 -0.00832 0.006455 0.9698 0.9742 0.006332 0.8369 0.8267 0.0186 ] Network output: [ 0.9998 0.0007441 0.001084 -2.221e-05 9.973e-06 -0.001529 -1.674e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1927 -0.03275 -0.1814 0.1926 0.9835 0.9933 0.2155 0.4463 0.8729 0.7188 ] Network output: [ -0.01084 1.002 1.01 2.961e-07 -1.329e-07 0.01 2.231e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005825 0.000467 0.004425 0.003827 0.9889 0.992 0.005934 0.8655 0.8969 0.01344 ] Network output: [ -0.000693 0.003037 1.002 -7.071e-05 3.175e-05 0.9963 -5.329e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2042 0.09499 0.3346 0.1486 0.985 0.994 0.2049 0.4507 0.8795 0.7132 ] Network output: [ 0.006277 -0.03058 0.9953 4.205e-05 -1.888e-05 1.023 3.169e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1007 0.08882 0.1805 0.2025 0.9873 0.9919 0.1008 0.7685 0.8697 0.3063 ] Network output: [ -0.006083 0.0303 1.003 4.377e-05 -1.965e-05 0.9795 3.298e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08848 0.1653 0.1955 0.9854 0.9913 0.09043 0.6942 0.8468 0.2443 ] Network output: [ 0.0001894 0.9999 -0.0003366 5.925e-06 -2.66e-06 1 4.465e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005099 Epoch 7737 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01134 0.9949 0.9896 9.137e-07 -4.102e-07 -0.007161 6.886e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003308 -0.003111 -0.008319 0.006454 0.9698 0.9742 0.006332 0.8369 0.8267 0.0186 ] Network output: [ 0.9998 0.0007434 0.001084 -2.22e-05 9.964e-06 -0.001528 -1.673e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1928 -0.03275 -0.1813 0.1926 0.9835 0.9933 0.2155 0.4463 0.8729 0.7188 ] Network output: [ -0.01084 1.002 1.01 2.946e-07 -1.322e-07 0.01 2.22e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005826 0.000467 0.004425 0.003826 0.9889 0.992 0.005934 0.8655 0.8969 0.01344 ] Network output: [ -0.0006926 0.003036 1.002 -7.065e-05 3.172e-05 0.9963 -5.324e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2042 0.09499 0.3347 0.1486 0.985 0.994 0.2049 0.4507 0.8795 0.7132 ] Network output: [ 0.006275 -0.03057 0.9953 4.201e-05 -1.886e-05 1.023 3.166e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1007 0.08882 0.1805 0.2025 0.9873 0.9919 0.1008 0.7684 0.8697 0.3063 ] Network output: [ -0.006081 0.03029 1.003 4.373e-05 -1.963e-05 0.9795 3.295e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1653 0.1955 0.9854 0.9913 0.09043 0.6942 0.8468 0.2443 ] Network output: [ 0.0001893 0.9999 -0.0003362 5.919e-06 -2.657e-06 1 4.461e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005095 Epoch 7738 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01134 0.9949 0.9896 9.112e-07 -4.091e-07 -0.007162 6.867e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003308 -0.003111 -0.008318 0.006453 0.9698 0.9742 0.006333 0.8368 0.8267 0.0186 ] Network output: [ 0.9998 0.0007428 0.001083 -2.218e-05 9.955e-06 -0.001527 -1.671e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1928 -0.03275 -0.1813 0.1926 0.9835 0.9933 0.2155 0.4463 0.8729 0.7188 ] Network output: [ -0.01083 1.002 1.01 2.931e-07 -1.316e-07 0.009999 2.209e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005826 0.0004671 0.004425 0.003826 0.9889 0.992 0.005935 0.8655 0.8969 0.01344 ] Network output: [ -0.0006921 0.003035 1.002 -7.058e-05 3.169e-05 0.9963 -5.319e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2042 0.095 0.3347 0.1486 0.985 0.994 0.2049 0.4507 0.8795 0.7132 ] Network output: [ 0.006273 -0.03056 0.9953 4.197e-05 -1.884e-05 1.023 3.163e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1007 0.08883 0.1805 0.2025 0.9873 0.9919 0.1008 0.7684 0.8696 0.3063 ] Network output: [ -0.006079 0.03027 1.003 4.369e-05 -1.961e-05 0.9795 3.293e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1653 0.1955 0.9854 0.9913 0.09043 0.6942 0.8468 0.2443 ] Network output: [ 0.0001892 0.9999 -0.0003358 5.914e-06 -2.655e-06 1 4.457e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005092 Epoch 7739 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01134 0.9949 0.9896 9.087e-07 -4.08e-07 -0.007163 6.849e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003308 -0.003111 -0.008317 0.006453 0.9698 0.9742 0.006333 0.8368 0.8267 0.01859 ] Network output: [ 0.9998 0.0007421 0.001082 -2.216e-05 9.947e-06 -0.001526 -1.67e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1928 -0.03276 -0.1813 0.1926 0.9835 0.9933 0.2155 0.4463 0.8729 0.7188 ] Network output: [ -0.01083 1.002 1.01 2.916e-07 -1.309e-07 0.009996 2.197e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005827 0.0004671 0.004425 0.003825 0.9889 0.992 0.005936 0.8655 0.8969 0.01344 ] Network output: [ -0.0006917 0.003034 1.002 -7.052e-05 3.166e-05 0.9963 -5.314e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2043 0.095 0.3347 0.1485 0.985 0.994 0.2049 0.4507 0.8795 0.7132 ] Network output: [ 0.00627 -0.03055 0.9953 4.193e-05 -1.882e-05 1.023 3.16e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1007 0.08883 0.1805 0.2025 0.9873 0.9919 0.1008 0.7684 0.8696 0.3063 ] Network output: [ -0.006076 0.03026 1.003 4.365e-05 -1.96e-05 0.9795 3.29e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1653 0.1955 0.9854 0.9913 0.09043 0.6941 0.8468 0.2443 ] Network output: [ 0.0001891 0.9999 -0.0003355 5.909e-06 -2.653e-06 1 4.453e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005089 Epoch 7740 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01133 0.9949 0.9896 9.063e-07 -4.068e-07 -0.007164 6.83e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003309 -0.003111 -0.008316 0.006452 0.9698 0.9742 0.006333 0.8368 0.8267 0.01859 ] Network output: [ 0.9998 0.0007414 0.001081 -2.214e-05 9.938e-06 -0.001524 -1.668e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1928 -0.03276 -0.1813 0.1926 0.9835 0.9933 0.2155 0.4463 0.8729 0.7188 ] Network output: [ -0.01083 1.002 1.01 2.901e-07 -1.302e-07 0.009994 2.186e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005827 0.0004671 0.004426 0.003825 0.9889 0.992 0.005936 0.8655 0.8969 0.01344 ] Network output: [ -0.0006913 0.003032 1.002 -7.045e-05 3.163e-05 0.9963 -5.309e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2043 0.09501 0.3347 0.1485 0.985 0.994 0.2049 0.4507 0.8795 0.7132 ] Network output: [ 0.006268 -0.03054 0.9953 4.189e-05 -1.881e-05 1.023 3.157e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1007 0.08884 0.1805 0.2024 0.9873 0.9919 0.1008 0.7684 0.8696 0.3063 ] Network output: [ -0.006074 0.03025 1.003 4.361e-05 -1.958e-05 0.9795 3.287e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1653 0.1955 0.9854 0.9913 0.09043 0.6941 0.8468 0.2443 ] Network output: [ 0.000189 0.9999 -0.0003351 5.903e-06 -2.65e-06 1 4.449e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005086 Epoch 7741 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01133 0.9949 0.9896 9.038e-07 -4.057e-07 -0.007165 6.811e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003309 -0.003112 -0.008315 0.006451 0.9698 0.9742 0.006334 0.8368 0.8267 0.01859 ] Network output: [ 0.9998 0.0007408 0.001081 -2.212e-05 9.929e-06 -0.001523 -1.667e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1928 -0.03276 -0.1813 0.1926 0.9835 0.9933 0.2155 0.4463 0.8729 0.7188 ] Network output: [ -0.01083 1.002 1.01 2.886e-07 -1.296e-07 0.009991 2.175e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005828 0.0004672 0.004426 0.003824 0.9889 0.992 0.005937 0.8655 0.8969 0.01344 ] Network output: [ -0.0006909 0.003031 1.002 -7.038e-05 3.16e-05 0.9963 -5.304e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2043 0.09501 0.3347 0.1485 0.985 0.994 0.2049 0.4507 0.8795 0.7132 ] Network output: [ 0.006266 -0.03053 0.9953 4.186e-05 -1.879e-05 1.023 3.154e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1007 0.08884 0.1805 0.2024 0.9873 0.9919 0.1008 0.7684 0.8696 0.3063 ] Network output: [ -0.006072 0.03023 1.003 4.358e-05 -1.956e-05 0.9795 3.284e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1653 0.1955 0.9854 0.9913 0.09043 0.6941 0.8467 0.2443 ] Network output: [ 0.0001889 0.9999 -0.0003347 5.898e-06 -2.648e-06 1 4.445e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005082 Epoch 7742 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01133 0.9949 0.9896 9.013e-07 -4.046e-07 -0.007166 6.792e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003309 -0.003112 -0.008313 0.00645 0.9698 0.9742 0.006334 0.8368 0.8267 0.01859 ] Network output: [ 0.9998 0.0007401 0.00108 -2.21e-05 9.92e-06 -0.001522 -1.665e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1928 -0.03276 -0.1813 0.1926 0.9835 0.9933 0.2155 0.4463 0.8729 0.7188 ] Network output: [ -0.01083 1.002 1.01 2.871e-07 -1.289e-07 0.009988 2.164e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005829 0.0004672 0.004426 0.003824 0.9889 0.992 0.005937 0.8655 0.8969 0.01344 ] Network output: [ -0.0006905 0.00303 1.002 -7.032e-05 3.157e-05 0.9963 -5.299e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2043 0.09502 0.3347 0.1485 0.985 0.994 0.2049 0.4507 0.8795 0.7132 ] Network output: [ 0.006264 -0.03051 0.9953 4.182e-05 -1.877e-05 1.023 3.152e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1007 0.08885 0.1805 0.2024 0.9873 0.9919 0.1008 0.7683 0.8696 0.3063 ] Network output: [ -0.006069 0.03022 1.003 4.354e-05 -1.955e-05 0.9795 3.281e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1653 0.1955 0.9854 0.9913 0.09043 0.6941 0.8467 0.2443 ] Network output: [ 0.0001888 0.9999 -0.0003344 5.893e-06 -2.646e-06 1 4.441e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005079 Epoch 7743 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01133 0.9949 0.9896 8.988e-07 -4.035e-07 -0.007167 6.774e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003309 -0.003112 -0.008312 0.006449 0.9698 0.9742 0.006334 0.8368 0.8267 0.01859 ] Network output: [ 0.9998 0.0007395 0.001079 -2.208e-05 9.911e-06 -0.001521 -1.664e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1928 -0.03277 -0.1812 0.1926 0.9835 0.9933 0.2155 0.4462 0.8729 0.7187 ] Network output: [ -0.01083 1.002 1.01 2.856e-07 -1.282e-07 0.009985 2.153e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005829 0.0004673 0.004426 0.003823 0.9889 0.992 0.005938 0.8654 0.8969 0.01344 ] Network output: [ -0.0006901 0.003029 1.002 -7.025e-05 3.154e-05 0.9963 -5.295e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2043 0.09502 0.3347 0.1485 0.985 0.994 0.205 0.4506 0.8795 0.7132 ] Network output: [ 0.006262 -0.0305 0.9953 4.178e-05 -1.876e-05 1.023 3.149e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1007 0.08885 0.1805 0.2024 0.9873 0.9919 0.1008 0.7683 0.8696 0.3063 ] Network output: [ -0.006067 0.03021 1.003 4.35e-05 -1.953e-05 0.9795 3.278e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1653 0.1955 0.9854 0.9913 0.09043 0.694 0.8467 0.2443 ] Network output: [ 0.0001887 0.9999 -0.000334 5.888e-06 -2.643e-06 1 4.437e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005076 Epoch 7744 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01133 0.9949 0.9896 8.963e-07 -4.024e-07 -0.007168 6.755e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003309 -0.003112 -0.008311 0.006449 0.9698 0.9742 0.006335 0.8368 0.8267 0.01859 ] Network output: [ 0.9998 0.0007388 0.001079 -2.206e-05 9.903e-06 -0.001519 -1.662e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1928 -0.03277 -0.1812 0.1926 0.9835 0.9933 0.2156 0.4462 0.8729 0.7187 ] Network output: [ -0.01083 1.002 1.01 2.841e-07 -1.276e-07 0.009983 2.141e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00583 0.0004673 0.004426 0.003823 0.9889 0.992 0.005939 0.8654 0.8969 0.01343 ] Network output: [ -0.0006897 0.003028 1.002 -7.019e-05 3.151e-05 0.9963 -5.29e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2043 0.09503 0.3347 0.1485 0.985 0.994 0.205 0.4506 0.8795 0.7132 ] Network output: [ 0.00626 -0.03049 0.9953 4.174e-05 -1.874e-05 1.023 3.146e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1007 0.08886 0.1805 0.2024 0.9873 0.9919 0.1008 0.7683 0.8696 0.3063 ] Network output: [ -0.006065 0.03019 1.003 4.346e-05 -1.951e-05 0.9795 3.275e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1653 0.1955 0.9854 0.9913 0.09043 0.694 0.8467 0.2443 ] Network output: [ 0.0001886 0.9999 -0.0003336 5.882e-06 -2.641e-06 1 4.433e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005073 Epoch 7745 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01133 0.9949 0.9896 8.939e-07 -4.013e-07 -0.007169 6.736e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003309 -0.003112 -0.00831 0.006448 0.9698 0.9742 0.006335 0.8368 0.8267 0.01859 ] Network output: [ 0.9998 0.0007382 0.001078 -2.204e-05 9.894e-06 -0.001518 -1.661e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1928 -0.03277 -0.1812 0.1926 0.9835 0.9933 0.2156 0.4462 0.8729 0.7187 ] Network output: [ -0.01083 1.002 1.01 2.827e-07 -1.269e-07 0.00998 2.13e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00583 0.0004674 0.004426 0.003822 0.9889 0.992 0.005939 0.8654 0.8969 0.01343 ] Network output: [ -0.0006893 0.003027 1.002 -7.012e-05 3.148e-05 0.9963 -5.285e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2043 0.09503 0.3348 0.1485 0.985 0.994 0.205 0.4506 0.8795 0.7132 ] Network output: [ 0.006258 -0.03048 0.9953 4.17e-05 -1.872e-05 1.023 3.143e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1008 0.08886 0.1805 0.2024 0.9873 0.9919 0.1008 0.7683 0.8696 0.3063 ] Network output: [ -0.006063 0.03018 1.003 4.342e-05 -1.949e-05 0.9795 3.273e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1653 0.1955 0.9854 0.9913 0.09043 0.694 0.8467 0.2444 ] Network output: [ 0.0001885 0.9999 -0.0003333 5.877e-06 -2.639e-06 1 4.429e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000507 Epoch 7746 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01132 0.9949 0.9896 8.914e-07 -4.002e-07 -0.00717 6.718e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003309 -0.003112 -0.008309 0.006447 0.9698 0.9742 0.006335 0.8368 0.8267 0.01858 ] Network output: [ 0.9998 0.0007375 0.001077 -2.202e-05 9.885e-06 -0.001517 -1.659e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1928 -0.03278 -0.1812 0.1926 0.9835 0.9933 0.2156 0.4462 0.8729 0.7187 ] Network output: [ -0.01083 1.002 1.01 2.812e-07 -1.262e-07 0.009977 2.119e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005831 0.0004674 0.004426 0.003822 0.9889 0.992 0.00594 0.8654 0.8969 0.01343 ] Network output: [ -0.0006889 0.003026 1.002 -7.006e-05 3.145e-05 0.9963 -5.28e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2043 0.09504 0.3348 0.1485 0.985 0.994 0.205 0.4506 0.8795 0.7132 ] Network output: [ 0.006256 -0.03047 0.9953 4.167e-05 -1.871e-05 1.023 3.14e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1008 0.08887 0.1805 0.2024 0.9873 0.9919 0.1008 0.7682 0.8696 0.3063 ] Network output: [ -0.00606 0.03017 1.003 4.339e-05 -1.948e-05 0.9795 3.27e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1653 0.1955 0.9854 0.9913 0.09044 0.6939 0.8467 0.2444 ] Network output: [ 0.0001884 0.9999 -0.0003329 5.872e-06 -2.636e-06 1 4.425e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005066 Epoch 7747 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01132 0.9949 0.9896 8.889e-07 -3.991e-07 -0.007171 6.699e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00331 -0.003112 -0.008307 0.006446 0.9698 0.9742 0.006336 0.8368 0.8267 0.01858 ] Network output: [ 0.9998 0.0007369 0.001076 -2.2e-05 9.876e-06 -0.001516 -1.658e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1929 -0.03278 -0.1812 0.1926 0.9835 0.9933 0.2156 0.4462 0.8729 0.7187 ] Network output: [ -0.01082 1.002 1.01 2.797e-07 -1.256e-07 0.009975 2.108e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005832 0.0004675 0.004426 0.003821 0.9889 0.992 0.005941 0.8654 0.8969 0.01343 ] Network output: [ -0.0006884 0.003025 1.002 -6.999e-05 3.142e-05 0.9963 -5.275e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2043 0.09504 0.3348 0.1485 0.985 0.994 0.205 0.4506 0.8794 0.7132 ] Network output: [ 0.006254 -0.03046 0.9953 4.163e-05 -1.869e-05 1.023 3.137e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1008 0.08887 0.1805 0.2024 0.9873 0.9919 0.1008 0.7682 0.8696 0.3063 ] Network output: [ -0.006058 0.03015 1.003 4.335e-05 -1.946e-05 0.9795 3.267e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1653 0.1955 0.9854 0.9913 0.09044 0.6939 0.8467 0.2444 ] Network output: [ 0.0001883 0.9999 -0.0003326 5.867e-06 -2.634e-06 1 4.421e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005063 Epoch 7748 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01132 0.9949 0.9896 8.865e-07 -3.98e-07 -0.007172 6.681e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00331 -0.003113 -0.008306 0.006445 0.9698 0.9742 0.006336 0.8368 0.8267 0.01858 ] Network output: [ 0.9998 0.0007362 0.001076 -2.198e-05 9.868e-06 -0.001514 -1.656e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1929 -0.03278 -0.1812 0.1926 0.9835 0.9933 0.2156 0.4462 0.8729 0.7187 ] Network output: [ -0.01082 1.002 1.01 2.782e-07 -1.249e-07 0.009972 2.097e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005832 0.0004675 0.004426 0.003821 0.9889 0.992 0.005941 0.8654 0.8969 0.01343 ] Network output: [ -0.000688 0.003024 1.002 -6.993e-05 3.139e-05 0.9963 -5.27e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2044 0.09505 0.3348 0.1485 0.985 0.994 0.205 0.4506 0.8794 0.7132 ] Network output: [ 0.006252 -0.03045 0.9953 4.159e-05 -1.867e-05 1.023 3.134e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1008 0.08888 0.1805 0.2024 0.9873 0.9919 0.1008 0.7682 0.8696 0.3063 ] Network output: [ -0.006056 0.03014 1.003 4.331e-05 -1.944e-05 0.9795 3.264e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1653 0.1955 0.9854 0.9913 0.09044 0.6939 0.8467 0.2444 ] Network output: [ 0.0001882 0.9999 -0.0003322 5.862e-06 -2.631e-06 1 4.417e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000506 Epoch 7749 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01132 0.9949 0.9896 8.84e-07 -3.969e-07 -0.007173 6.662e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00331 -0.003113 -0.008305 0.006445 0.9698 0.9742 0.006336 0.8368 0.8267 0.01858 ] Network output: [ 0.9998 0.0007356 0.001075 -2.196e-05 9.859e-06 -0.001513 -1.655e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1929 -0.03278 -0.1811 0.1925 0.9835 0.9933 0.2156 0.4462 0.8729 0.7187 ] Network output: [ -0.01082 1.002 1.01 2.768e-07 -1.242e-07 0.009969 2.086e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005833 0.0004675 0.004426 0.00382 0.9889 0.992 0.005942 0.8654 0.8969 0.01343 ] Network output: [ -0.0006876 0.003023 1.002 -6.986e-05 3.136e-05 0.9963 -5.265e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2044 0.09505 0.3348 0.1485 0.985 0.994 0.205 0.4506 0.8794 0.7132 ] Network output: [ 0.00625 -0.03044 0.9953 4.155e-05 -1.865e-05 1.023 3.132e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1008 0.08889 0.1806 0.2024 0.9873 0.9919 0.1008 0.7682 0.8696 0.3063 ] Network output: [ -0.006053 0.03013 1.003 4.327e-05 -1.943e-05 0.9795 3.261e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09042 0.08849 0.1653 0.1955 0.9854 0.9913 0.09044 0.6939 0.8467 0.2444 ] Network output: [ 0.0001881 0.9999 -0.0003318 5.856e-06 -2.629e-06 1 4.413e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005057 Epoch 7750 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01132 0.9949 0.9896 8.816e-07 -3.958e-07 -0.007174 6.644e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00331 -0.003113 -0.008304 0.006444 0.9698 0.9742 0.006337 0.8368 0.8267 0.01858 ] Network output: [ 0.9998 0.0007349 0.001074 -2.194e-05 9.85e-06 -0.001512 -1.654e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1929 -0.03279 -0.1811 0.1925 0.9835 0.9933 0.2156 0.4461 0.8729 0.7187 ] Network output: [ -0.01082 1.002 1.01 2.753e-07 -1.236e-07 0.009966 2.075e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005834 0.0004676 0.004426 0.003819 0.9889 0.992 0.005942 0.8654 0.8969 0.01343 ] Network output: [ -0.0006872 0.003022 1.002 -6.98e-05 3.133e-05 0.9963 -5.26e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2044 0.09506 0.3348 0.1485 0.985 0.994 0.205 0.4505 0.8794 0.7131 ] Network output: [ 0.006247 -0.03042 0.9953 4.152e-05 -1.864e-05 1.023 3.129e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1008 0.08889 0.1806 0.2024 0.9873 0.9919 0.1009 0.7681 0.8696 0.3063 ] Network output: [ -0.006051 0.03011 1.003 4.323e-05 -1.941e-05 0.9795 3.258e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.08849 0.1653 0.1955 0.9854 0.9913 0.09044 0.6938 0.8467 0.2444 ] Network output: [ 0.000188 0.9999 -0.0003315 5.851e-06 -2.627e-06 1 4.41e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005053 Epoch 7751 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01131 0.9949 0.9896 8.791e-07 -3.947e-07 -0.007176 6.625e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00331 -0.003113 -0.008303 0.006443 0.9698 0.9742 0.006337 0.8367 0.8267 0.01858 ] Network output: [ 0.9998 0.0007343 0.001074 -2.192e-05 9.841e-06 -0.001511 -1.652e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1929 -0.03279 -0.1811 0.1925 0.9835 0.9933 0.2156 0.4461 0.8729 0.7187 ] Network output: [ -0.01082 1.002 1.01 2.738e-07 -1.229e-07 0.009964 2.064e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005834 0.0004676 0.004426 0.003819 0.9889 0.992 0.005943 0.8654 0.8969 0.01343 ] Network output: [ -0.0006868 0.003021 1.002 -6.973e-05 3.131e-05 0.9963 -5.255e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2044 0.09506 0.3348 0.1485 0.985 0.994 0.2051 0.4505 0.8794 0.7131 ] Network output: [ 0.006245 -0.03041 0.9953 4.148e-05 -1.862e-05 1.023 3.126e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1008 0.0889 0.1806 0.2024 0.9873 0.9919 0.1009 0.7681 0.8696 0.3063 ] Network output: [ -0.006049 0.0301 1.003 4.32e-05 -1.939e-05 0.9796 3.255e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.08849 0.1653 0.1955 0.9854 0.9913 0.09044 0.6938 0.8467 0.2444 ] Network output: [ 0.0001879 0.9999 -0.0003311 5.846e-06 -2.624e-06 1 4.406e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000505 Epoch 7752 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01131 0.9949 0.9896 8.767e-07 -3.936e-07 -0.007177 6.607e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00331 -0.003113 -0.008301 0.006442 0.9698 0.9742 0.006337 0.8367 0.8267 0.01857 ] Network output: [ 0.9998 0.0007336 0.001073 -2.19e-05 9.833e-06 -0.001509 -1.651e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1929 -0.03279 -0.1811 0.1925 0.9835 0.9933 0.2157 0.4461 0.8729 0.7187 ] Network output: [ -0.01082 1.002 1.01 2.723e-07 -1.223e-07 0.009961 2.052e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005835 0.0004677 0.004426 0.003818 0.9889 0.992 0.005944 0.8654 0.8969 0.01342 ] Network output: [ -0.0006864 0.00302 1.002 -6.967e-05 3.128e-05 0.9963 -5.25e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2044 0.09507 0.3348 0.1484 0.985 0.994 0.2051 0.4505 0.8794 0.7131 ] Network output: [ 0.006243 -0.0304 0.9953 4.144e-05 -1.86e-05 1.023 3.123e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1008 0.0889 0.1806 0.2024 0.9873 0.9919 0.1009 0.7681 0.8695 0.3063 ] Network output: [ -0.006047 0.03009 1.003 4.316e-05 -1.938e-05 0.9796 3.253e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.08849 0.1653 0.1955 0.9854 0.9913 0.09044 0.6938 0.8466 0.2444 ] Network output: [ 0.0001878 0.9999 -0.0003307 5.841e-06 -2.622e-06 1 4.402e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005047 Epoch 7753 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01131 0.9949 0.9897 8.743e-07 -3.925e-07 -0.007178 6.589e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00331 -0.003113 -0.0083 0.006442 0.9698 0.9742 0.006338 0.8367 0.8266 0.01857 ] Network output: [ 0.9998 0.000733 0.001072 -2.188e-05 9.824e-06 -0.001508 -1.649e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1929 -0.0328 -0.1811 0.1925 0.9835 0.9933 0.2157 0.4461 0.8729 0.7187 ] Network output: [ -0.01082 1.002 1.01 2.709e-07 -1.216e-07 0.009958 2.041e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005835 0.0004677 0.004427 0.003818 0.9889 0.992 0.005944 0.8654 0.8969 0.01342 ] Network output: [ -0.000686 0.003019 1.002 -6.96e-05 3.125e-05 0.9963 -5.245e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2044 0.09507 0.3349 0.1484 0.985 0.994 0.2051 0.4505 0.8794 0.7131 ] Network output: [ 0.006241 -0.03039 0.9953 4.14e-05 -1.859e-05 1.023 3.12e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1008 0.08891 0.1806 0.2024 0.9873 0.9919 0.1009 0.7681 0.8695 0.3063 ] Network output: [ -0.006044 0.03007 1.003 4.312e-05 -1.936e-05 0.9796 3.25e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.08849 0.1653 0.1955 0.9854 0.9913 0.09044 0.6938 0.8466 0.2444 ] Network output: [ 0.0001877 0.9999 -0.0003304 5.835e-06 -2.62e-06 1 4.398e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005044 Epoch 7754 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01131 0.9949 0.9897 8.718e-07 -3.914e-07 -0.007179 6.57e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003311 -0.003114 -0.008299 0.006441 0.9698 0.9742 0.006338 0.8367 0.8266 0.01857 ] Network output: [ 0.9998 0.0007323 0.001072 -2.186e-05 9.815e-06 -0.001507 -1.648e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1929 -0.0328 -0.1811 0.1925 0.9835 0.9933 0.2157 0.4461 0.8728 0.7187 ] Network output: [ -0.01082 1.002 1.01 2.694e-07 -1.21e-07 0.009956 2.03e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005836 0.0004678 0.004427 0.003817 0.9889 0.992 0.005945 0.8653 0.8969 0.01342 ] Network output: [ -0.0006856 0.003018 1.002 -6.954e-05 3.122e-05 0.9963 -5.241e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2044 0.09508 0.3349 0.1484 0.985 0.994 0.2051 0.4505 0.8794 0.7131 ] Network output: [ 0.006239 -0.03038 0.9953 4.137e-05 -1.857e-05 1.023 3.117e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1008 0.08891 0.1806 0.2024 0.9873 0.9919 0.1009 0.768 0.8695 0.3063 ] Network output: [ -0.006042 0.03006 1.003 4.308e-05 -1.934e-05 0.9796 3.247e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.08849 0.1653 0.1955 0.9854 0.9913 0.09044 0.6937 0.8466 0.2444 ] Network output: [ 0.0001876 0.9999 -0.00033 5.83e-06 -2.617e-06 1 4.394e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005041 Epoch 7755 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01131 0.9949 0.9897 8.694e-07 -3.903e-07 -0.00718 6.552e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003311 -0.003114 -0.008298 0.00644 0.9698 0.9742 0.006338 0.8367 0.8266 0.01857 ] Network output: [ 0.9998 0.0007317 0.001071 -2.184e-05 9.806e-06 -0.001506 -1.646e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1929 -0.0328 -0.181 0.1925 0.9835 0.9933 0.2157 0.4461 0.8728 0.7187 ] Network output: [ -0.01081 1.002 1.01 2.68e-07 -1.203e-07 0.009953 2.019e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005837 0.0004678 0.004427 0.003817 0.9889 0.992 0.005946 0.8653 0.8969 0.01342 ] Network output: [ -0.0006852 0.003017 1.002 -6.947e-05 3.119e-05 0.9963 -5.236e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2044 0.09508 0.3349 0.1484 0.985 0.994 0.2051 0.4505 0.8794 0.7131 ] Network output: [ 0.006237 -0.03037 0.9953 4.133e-05 -1.855e-05 1.023 3.115e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1008 0.08892 0.1806 0.2024 0.9873 0.9919 0.1009 0.768 0.8695 0.3063 ] Network output: [ -0.00604 0.03005 1.003 4.305e-05 -1.932e-05 0.9796 3.244e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.08849 0.1653 0.1955 0.9854 0.9913 0.09044 0.6937 0.8466 0.2444 ] Network output: [ 0.0001875 0.9999 -0.0003296 5.825e-06 -2.615e-06 1 4.39e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005037 Epoch 7756 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01131 0.9949 0.9897 8.67e-07 -3.892e-07 -0.007181 6.534e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003311 -0.003114 -0.008297 0.006439 0.9698 0.9742 0.006339 0.8367 0.8266 0.01857 ] Network output: [ 0.9998 0.000731 0.00107 -2.182e-05 9.798e-06 -0.001504 -1.645e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.193 -0.0328 -0.181 0.1925 0.9835 0.9933 0.2157 0.4461 0.8728 0.7187 ] Network output: [ -0.01081 1.002 1.01 2.665e-07 -1.196e-07 0.00995 2.008e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005837 0.0004679 0.004427 0.003816 0.9889 0.992 0.005946 0.8653 0.8969 0.01342 ] Network output: [ -0.0006848 0.003016 1.002 -6.941e-05 3.116e-05 0.9963 -5.231e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2045 0.09509 0.3349 0.1484 0.985 0.994 0.2051 0.4505 0.8794 0.7131 ] Network output: [ 0.006235 -0.03036 0.9953 4.129e-05 -1.854e-05 1.023 3.112e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1008 0.08892 0.1806 0.2024 0.9873 0.9919 0.1009 0.768 0.8695 0.3063 ] Network output: [ -0.006037 0.03003 1.003 4.301e-05 -1.931e-05 0.9796 3.241e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.0885 0.1653 0.1955 0.9854 0.9913 0.09044 0.6937 0.8466 0.2444 ] Network output: [ 0.0001874 0.9999 -0.0003293 5.82e-06 -2.613e-06 1 4.386e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005034 Epoch 7757 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0113 0.9949 0.9897 8.646e-07 -3.881e-07 -0.007182 6.516e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003311 -0.003114 -0.008295 0.006438 0.9698 0.9742 0.006339 0.8367 0.8266 0.01857 ] Network output: [ 0.9998 0.0007304 0.001069 -2.18e-05 9.789e-06 -0.001503 -1.643e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.193 -0.03281 -0.181 0.1925 0.9835 0.9933 0.2157 0.446 0.8728 0.7187 ] Network output: [ -0.01081 1.002 1.01 2.651e-07 -1.19e-07 0.009947 1.998e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005838 0.0004679 0.004427 0.003816 0.9889 0.992 0.005947 0.8653 0.8968 0.01342 ] Network output: [ -0.0006843 0.003015 1.002 -6.934e-05 3.113e-05 0.9963 -5.226e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2045 0.09509 0.3349 0.1484 0.985 0.994 0.2051 0.4504 0.8794 0.7131 ] Network output: [ 0.006233 -0.03035 0.9953 4.125e-05 -1.852e-05 1.023 3.109e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1008 0.08893 0.1806 0.2024 0.9873 0.9919 0.1009 0.768 0.8695 0.3063 ] Network output: [ -0.006035 0.03002 1.003 4.297e-05 -1.929e-05 0.9796 3.238e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.0885 0.1653 0.1955 0.9854 0.9913 0.09044 0.6937 0.8466 0.2444 ] Network output: [ 0.0001873 0.9999 -0.0003289 5.815e-06 -2.61e-06 1 4.382e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005031 Epoch 7758 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0113 0.9949 0.9897 8.621e-07 -3.87e-07 -0.007183 6.497e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003311 -0.003114 -0.008294 0.006438 0.9698 0.9742 0.006339 0.8367 0.8266 0.01856 ] Network output: [ 0.9998 0.0007297 0.001069 -2.179e-05 9.78e-06 -0.001502 -1.642e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.193 -0.03281 -0.181 0.1925 0.9835 0.9933 0.2157 0.446 0.8728 0.7187 ] Network output: [ -0.01081 1.002 1.01 2.636e-07 -1.183e-07 0.009945 1.987e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005838 0.000468 0.004427 0.003815 0.9889 0.9919 0.005947 0.8653 0.8968 0.01342 ] Network output: [ -0.0006839 0.003013 1.002 -6.928e-05 3.11e-05 0.9963 -5.221e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2045 0.0951 0.3349 0.1484 0.985 0.994 0.2051 0.4504 0.8794 0.7131 ] Network output: [ 0.006231 -0.03033 0.9953 4.122e-05 -1.85e-05 1.023 3.106e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1008 0.08893 0.1806 0.2024 0.9873 0.9919 0.1009 0.7679 0.8695 0.3063 ] Network output: [ -0.006033 0.03001 1.003 4.293e-05 -1.927e-05 0.9796 3.236e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.0885 0.1653 0.1955 0.9854 0.9913 0.09044 0.6936 0.8466 0.2444 ] Network output: [ 0.0001872 0.9999 -0.0003286 5.809e-06 -2.608e-06 1 4.378e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005028 Epoch 7759 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0113 0.9949 0.9897 8.597e-07 -3.86e-07 -0.007184 6.479e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003311 -0.003114 -0.008293 0.006437 0.9698 0.9742 0.00634 0.8367 0.8266 0.01856 ] Network output: [ 0.9998 0.0007291 0.001068 -2.177e-05 9.771e-06 -0.001501 -1.64e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.193 -0.03281 -0.181 0.1925 0.9835 0.9933 0.2157 0.446 0.8728 0.7187 ] Network output: [ -0.01081 1.002 1.01 2.622e-07 -1.177e-07 0.009942 1.976e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005839 0.000468 0.004427 0.003815 0.9889 0.9919 0.005948 0.8653 0.8968 0.01342 ] Network output: [ -0.0006835 0.003012 1.002 -6.921e-05 3.107e-05 0.9963 -5.216e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2045 0.0951 0.3349 0.1484 0.985 0.994 0.2052 0.4504 0.8794 0.7131 ] Network output: [ 0.006229 -0.03032 0.9953 4.118e-05 -1.849e-05 1.023 3.103e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1008 0.08894 0.1806 0.2023 0.9873 0.9919 0.1009 0.7679 0.8695 0.3062 ] Network output: [ -0.006031 0.02999 1.003 4.29e-05 -1.926e-05 0.9796 3.233e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.0885 0.1653 0.1955 0.9854 0.9913 0.09044 0.6936 0.8466 0.2444 ] Network output: [ 0.0001871 0.9999 -0.0003282 5.804e-06 -2.606e-06 1 4.374e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005025 Epoch 7760 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0113 0.9949 0.9897 8.573e-07 -3.849e-07 -0.007185 6.461e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003311 -0.003115 -0.008292 0.006436 0.9698 0.9742 0.00634 0.8367 0.8266 0.01856 ] Network output: [ 0.9998 0.0007284 0.001067 -2.175e-05 9.763e-06 -0.001499 -1.639e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.193 -0.03282 -0.181 0.1925 0.9835 0.9933 0.2158 0.446 0.8728 0.7187 ] Network output: [ -0.01081 1.002 1.01 2.607e-07 -1.17e-07 0.009939 1.965e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00584 0.000468 0.004427 0.003814 0.9889 0.9919 0.005949 0.8653 0.8968 0.01341 ] Network output: [ -0.0006831 0.003011 1.002 -6.915e-05 3.104e-05 0.9963 -5.211e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2045 0.09511 0.3349 0.1484 0.985 0.994 0.2052 0.4504 0.8794 0.7131 ] Network output: [ 0.006227 -0.03031 0.9953 4.114e-05 -1.847e-05 1.023 3.1e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1008 0.08895 0.1806 0.2023 0.9873 0.9919 0.1009 0.7679 0.8695 0.3062 ] Network output: [ -0.006028 0.02998 1.003 4.286e-05 -1.924e-05 0.9796 3.23e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.0885 0.1653 0.1955 0.9854 0.9913 0.09044 0.6936 0.8466 0.2444 ] Network output: [ 0.000187 0.9999 -0.0003278 5.799e-06 -2.603e-06 1 4.37e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005021 Epoch 7761 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0113 0.9949 0.9897 8.549e-07 -3.838e-07 -0.007186 6.443e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003311 -0.003115 -0.008291 0.006435 0.9698 0.9742 0.00634 0.8367 0.8266 0.01856 ] Network output: [ 0.9998 0.0007278 0.001067 -2.173e-05 9.754e-06 -0.001498 -1.637e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.193 -0.03282 -0.1809 0.1925 0.9835 0.9933 0.2158 0.446 0.8728 0.7187 ] Network output: [ -0.01081 1.002 1.01 2.593e-07 -1.164e-07 0.009937 1.954e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00584 0.0004681 0.004427 0.003814 0.9889 0.9919 0.005949 0.8653 0.8968 0.01341 ] Network output: [ -0.0006827 0.00301 1.002 -6.908e-05 3.101e-05 0.9963 -5.206e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2045 0.09511 0.3349 0.1484 0.985 0.994 0.2052 0.4504 0.8794 0.7131 ] Network output: [ 0.006225 -0.0303 0.9953 4.11e-05 -1.845e-05 1.023 3.098e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1009 0.08895 0.1806 0.2023 0.9873 0.9919 0.1009 0.7679 0.8695 0.3062 ] Network output: [ -0.006026 0.02997 1.003 4.282e-05 -1.922e-05 0.9796 3.227e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.0885 0.1653 0.1955 0.9854 0.9913 0.09044 0.6935 0.8466 0.2444 ] Network output: [ 0.0001869 0.9999 -0.0003275 5.794e-06 -2.601e-06 1 4.366e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005018 Epoch 7762 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01129 0.9949 0.9897 8.525e-07 -3.827e-07 -0.007187 6.425e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003312 -0.003115 -0.00829 0.006435 0.9698 0.9742 0.00634 0.8367 0.8266 0.01856 ] Network output: [ 0.9998 0.0007271 0.001066 -2.171e-05 9.745e-06 -0.001497 -1.636e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.193 -0.03282 -0.1809 0.1924 0.9835 0.9933 0.2158 0.446 0.8728 0.7186 ] Network output: [ -0.01081 1.002 1.01 2.578e-07 -1.157e-07 0.009934 1.943e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005841 0.0004681 0.004427 0.003813 0.9889 0.9919 0.00595 0.8653 0.8968 0.01341 ] Network output: [ -0.0006823 0.003009 1.002 -6.902e-05 3.099e-05 0.9963 -5.202e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2045 0.09512 0.335 0.1484 0.985 0.994 0.2052 0.4504 0.8794 0.7131 ] Network output: [ 0.006223 -0.03029 0.9953 4.107e-05 -1.844e-05 1.023 3.095e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1009 0.08896 0.1806 0.2023 0.9873 0.9919 0.1009 0.7679 0.8695 0.3062 ] Network output: [ -0.006024 0.02995 1.003 4.278e-05 -1.921e-05 0.9796 3.224e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.0885 0.1653 0.1955 0.9854 0.9913 0.09044 0.6935 0.8466 0.2444 ] Network output: [ 0.0001868 0.9999 -0.0003271 5.789e-06 -2.599e-06 1 4.362e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005015 Epoch 7763 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01129 0.9949 0.9897 8.501e-07 -3.816e-07 -0.007188 6.407e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003312 -0.003115 -0.008288 0.006434 0.9698 0.9742 0.006341 0.8366 0.8266 0.01856 ] Network output: [ 0.9998 0.0007265 0.001065 -2.169e-05 9.737e-06 -0.001496 -1.634e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.193 -0.03282 -0.1809 0.1924 0.9835 0.9933 0.2158 0.446 0.8728 0.7186 ] Network output: [ -0.01081 1.002 1.01 2.564e-07 -1.151e-07 0.009931 1.932e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005842 0.0004682 0.004427 0.003813 0.9889 0.9919 0.005951 0.8653 0.8968 0.01341 ] Network output: [ -0.0006819 0.003008 1.002 -6.896e-05 3.096e-05 0.9963 -5.197e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2045 0.09512 0.335 0.1484 0.985 0.994 0.2052 0.4504 0.8794 0.7131 ] Network output: [ 0.00622 -0.03028 0.9953 4.103e-05 -1.842e-05 1.023 3.092e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1009 0.08896 0.1806 0.2023 0.9873 0.9919 0.1009 0.7678 0.8695 0.3062 ] Network output: [ -0.006021 0.02994 1.003 4.275e-05 -1.919e-05 0.9796 3.221e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.0885 0.1653 0.1955 0.9854 0.9913 0.09045 0.6935 0.8465 0.2444 ] Network output: [ 0.0001867 0.9999 -0.0003268 5.783e-06 -2.596e-06 1 4.359e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005012 Epoch 7764 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01129 0.9949 0.9897 8.477e-07 -3.806e-07 -0.007189 6.389e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003312 -0.003115 -0.008287 0.006433 0.9698 0.9742 0.006341 0.8366 0.8266 0.01855 ] Network output: [ 0.9998 0.0007259 0.001064 -2.167e-05 9.728e-06 -0.001494 -1.633e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.193 -0.03283 -0.1809 0.1924 0.9835 0.9933 0.2158 0.4459 0.8728 0.7186 ] Network output: [ -0.0108 1.002 1.01 2.549e-07 -1.145e-07 0.009929 1.921e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005842 0.0004682 0.004427 0.003812 0.9889 0.9919 0.005951 0.8653 0.8968 0.01341 ] Network output: [ -0.0006815 0.003007 1.002 -6.889e-05 3.093e-05 0.9963 -5.192e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2046 0.09513 0.335 0.1484 0.985 0.994 0.2052 0.4503 0.8794 0.7131 ] Network output: [ 0.006218 -0.03027 0.9953 4.099e-05 -1.84e-05 1.023 3.089e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1009 0.08897 0.1806 0.2023 0.9873 0.9919 0.1009 0.7678 0.8695 0.3062 ] Network output: [ -0.006019 0.02993 1.003 4.271e-05 -1.917e-05 0.9796 3.219e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.0885 0.1653 0.1955 0.9854 0.9913 0.09045 0.6935 0.8465 0.2444 ] Network output: [ 0.0001866 0.9999 -0.0003264 5.778e-06 -2.594e-06 1 4.355e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005009 Epoch 7765 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01129 0.9949 0.9897 8.453e-07 -3.795e-07 -0.00719 6.371e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003312 -0.003115 -0.008286 0.006432 0.9698 0.9742 0.006341 0.8366 0.8266 0.01855 ] Network output: [ 0.9998 0.0007252 0.001064 -2.165e-05 9.719e-06 -0.001493 -1.632e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1931 -0.03283 -0.1809 0.1924 0.9835 0.9933 0.2158 0.4459 0.8728 0.7186 ] Network output: [ -0.0108 1.002 1.01 2.535e-07 -1.138e-07 0.009926 1.911e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005843 0.0004683 0.004427 0.003812 0.9889 0.9919 0.005952 0.8652 0.8968 0.01341 ] Network output: [ -0.0006811 0.003006 1.002 -6.883e-05 3.09e-05 0.9963 -5.187e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2046 0.09513 0.335 0.1484 0.985 0.994 0.2052 0.4503 0.8794 0.7131 ] Network output: [ 0.006216 -0.03026 0.9953 4.095e-05 -1.839e-05 1.023 3.086e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1009 0.08897 0.1806 0.2023 0.9873 0.9919 0.1009 0.7678 0.8694 0.3062 ] Network output: [ -0.006017 0.02991 1.003 4.267e-05 -1.916e-05 0.9796 3.216e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.0885 0.1653 0.1955 0.9854 0.9913 0.09045 0.6934 0.8465 0.2444 ] Network output: [ 0.0001865 0.9999 -0.000326 5.773e-06 -2.592e-06 1 4.351e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005005 Epoch 7766 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01129 0.9949 0.9897 8.429e-07 -3.784e-07 -0.007191 6.353e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003312 -0.003116 -0.008285 0.006431 0.9698 0.9742 0.006342 0.8366 0.8266 0.01855 ] Network output: [ 0.9998 0.0007246 0.001063 -2.163e-05 9.71e-06 -0.001492 -1.63e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1931 -0.03283 -0.1808 0.1924 0.9835 0.9933 0.2158 0.4459 0.8728 0.7186 ] Network output: [ -0.0108 1.002 1.01 2.521e-07 -1.132e-07 0.009923 1.9e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005843 0.0004683 0.004427 0.003811 0.9889 0.9919 0.005952 0.8652 0.8968 0.01341 ] Network output: [ -0.0006807 0.003005 1.002 -6.876e-05 3.087e-05 0.9963 -5.182e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2046 0.09514 0.335 0.1483 0.985 0.994 0.2052 0.4503 0.8794 0.7131 ] Network output: [ 0.006214 -0.03024 0.9953 4.092e-05 -1.837e-05 1.023 3.084e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1009 0.08898 0.1806 0.2023 0.9873 0.9919 0.101 0.7678 0.8694 0.3062 ] Network output: [ -0.006015 0.0299 1.003 4.263e-05 -1.914e-05 0.9797 3.213e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09043 0.0885 0.1653 0.1955 0.9854 0.9913 0.09045 0.6934 0.8465 0.2444 ] Network output: [ 0.0001864 0.9999 -0.0003257 5.768e-06 -2.589e-06 1 4.347e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0005002 Epoch 7767 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01129 0.9949 0.9897 8.406e-07 -3.774e-07 -0.007192 6.335e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003312 -0.003116 -0.008284 0.006431 0.9698 0.9742 0.006342 0.8366 0.8266 0.01855 ] Network output: [ 0.9998 0.0007239 0.001062 -2.161e-05 9.702e-06 -0.001491 -1.629e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1931 -0.03283 -0.1808 0.1924 0.9835 0.9933 0.2158 0.4459 0.8728 0.7186 ] Network output: [ -0.0108 1.002 1.01 2.506e-07 -1.125e-07 0.009921 1.889e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005844 0.0004684 0.004428 0.003811 0.9889 0.9919 0.005953 0.8652 0.8968 0.01341 ] Network output: [ -0.0006803 0.003004 1.002 -6.87e-05 3.084e-05 0.9963 -5.177e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2046 0.09514 0.335 0.1483 0.985 0.994 0.2052 0.4503 0.8794 0.713 ] Network output: [ 0.006212 -0.03023 0.9953 4.088e-05 -1.835e-05 1.023 3.081e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1009 0.08898 0.1806 0.2023 0.9873 0.9919 0.101 0.7677 0.8694 0.3062 ] Network output: [ -0.006012 0.02989 1.003 4.26e-05 -1.912e-05 0.9797 3.21e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.0885 0.1653 0.1955 0.9854 0.9913 0.09045 0.6934 0.8465 0.2444 ] Network output: [ 0.0001863 0.9999 -0.0003253 5.763e-06 -2.587e-06 1 4.343e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004999 Epoch 7768 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01128 0.9949 0.9897 8.382e-07 -3.763e-07 -0.007193 6.317e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003312 -0.003116 -0.008282 0.00643 0.9698 0.9742 0.006342 0.8366 0.8266 0.01855 ] Network output: [ 0.9998 0.0007233 0.001062 -2.159e-05 9.693e-06 -0.001489 -1.627e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1931 -0.03284 -0.1808 0.1924 0.9835 0.9933 0.2159 0.4459 0.8728 0.7186 ] Network output: [ -0.0108 1.002 1.01 2.492e-07 -1.119e-07 0.009918 1.878e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005845 0.0004684 0.004428 0.00381 0.9889 0.9919 0.005954 0.8652 0.8968 0.01341 ] Network output: [ -0.0006798 0.003003 1.002 -6.863e-05 3.081e-05 0.9963 -5.172e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2046 0.09515 0.335 0.1483 0.985 0.994 0.2053 0.4503 0.8794 0.713 ] Network output: [ 0.00621 -0.03022 0.9953 4.084e-05 -1.834e-05 1.023 3.078e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1009 0.08899 0.1806 0.2023 0.9873 0.9919 0.101 0.7677 0.8694 0.3062 ] Network output: [ -0.00601 0.02988 1.003 4.256e-05 -1.911e-05 0.9797 3.207e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.0885 0.1653 0.1955 0.9854 0.9913 0.09045 0.6934 0.8465 0.2444 ] Network output: [ 0.0001862 0.9999 -0.000325 5.757e-06 -2.585e-06 1 4.339e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004996 Epoch 7769 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01128 0.9949 0.9897 8.358e-07 -3.752e-07 -0.007194 6.299e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003313 -0.003116 -0.008281 0.006429 0.9698 0.9742 0.006343 0.8366 0.8266 0.01855 ] Network output: [ 0.9998 0.0007227 0.001061 -2.157e-05 9.684e-06 -0.001488 -1.626e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1931 -0.03284 -0.1808 0.1924 0.9835 0.9933 0.2159 0.4459 0.8728 0.7186 ] Network output: [ -0.0108 1.002 1.01 2.478e-07 -1.112e-07 0.009915 1.867e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005845 0.0004685 0.004428 0.00381 0.9889 0.9919 0.005954 0.8652 0.8968 0.0134 ] Network output: [ -0.0006794 0.003002 1.002 -6.857e-05 3.078e-05 0.9963 -5.168e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2046 0.09515 0.335 0.1483 0.985 0.994 0.2053 0.4503 0.8794 0.713 ] Network output: [ 0.006208 -0.03021 0.9953 4.08e-05 -1.832e-05 1.023 3.075e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1009 0.08899 0.1806 0.2023 0.9873 0.9919 0.101 0.7677 0.8694 0.3062 ] Network output: [ -0.006008 0.02986 1.003 4.252e-05 -1.909e-05 0.9797 3.204e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.0885 0.1653 0.1955 0.9854 0.9913 0.09045 0.6933 0.8465 0.2444 ] Network output: [ 0.0001861 0.9999 -0.0003246 5.752e-06 -2.582e-06 1 4.335e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004993 Epoch 7770 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01128 0.9949 0.9897 8.334e-07 -3.742e-07 -0.007195 6.281e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003313 -0.003116 -0.00828 0.006428 0.9698 0.9742 0.006343 0.8366 0.8266 0.01854 ] Network output: [ 0.9998 0.000722 0.00106 -2.155e-05 9.676e-06 -0.001487 -1.624e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1931 -0.03284 -0.1808 0.1924 0.9835 0.9933 0.2159 0.4459 0.8728 0.7186 ] Network output: [ -0.0108 1.002 1.01 2.464e-07 -1.106e-07 0.009913 1.857e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005846 0.0004685 0.004428 0.003809 0.9889 0.9919 0.005955 0.8652 0.8968 0.0134 ] Network output: [ -0.000679 0.003001 1.002 -6.85e-05 3.075e-05 0.9963 -5.163e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2046 0.09516 0.3351 0.1483 0.985 0.994 0.2053 0.4503 0.8794 0.713 ] Network output: [ 0.006206 -0.0302 0.9953 4.077e-05 -1.83e-05 1.023 3.072e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1009 0.089 0.1806 0.2023 0.9873 0.9919 0.101 0.7677 0.8694 0.3062 ] Network output: [ -0.006006 0.02985 1.003 4.248e-05 -1.907e-05 0.9797 3.202e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.0885 0.1653 0.1955 0.9854 0.9913 0.09045 0.6933 0.8465 0.2444 ] Network output: [ 0.000186 0.9999 -0.0003242 5.747e-06 -2.58e-06 1 4.331e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000499 Epoch 7771 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01128 0.9949 0.9897 8.311e-07 -3.731e-07 -0.007196 6.263e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003313 -0.003116 -0.008279 0.006428 0.9698 0.9742 0.006343 0.8366 0.8266 0.01854 ] Network output: [ 0.9998 0.0007214 0.00106 -2.153e-05 9.667e-06 -0.001486 -1.623e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1931 -0.03285 -0.1808 0.1924 0.9835 0.9933 0.2159 0.4459 0.8728 0.7186 ] Network output: [ -0.0108 1.002 1.01 2.449e-07 -1.1e-07 0.00991 1.846e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005846 0.0004686 0.004428 0.003809 0.9889 0.9919 0.005956 0.8652 0.8968 0.0134 ] Network output: [ -0.0006786 0.003 1.002 -6.844e-05 3.073e-05 0.9963 -5.158e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2046 0.09516 0.3351 0.1483 0.985 0.994 0.2053 0.4502 0.8793 0.713 ] Network output: [ 0.006204 -0.03019 0.9953 4.073e-05 -1.829e-05 1.023 3.07e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1009 0.089 0.1806 0.2023 0.9873 0.9919 0.101 0.7676 0.8694 0.3062 ] Network output: [ -0.006003 0.02984 1.003 4.245e-05 -1.906e-05 0.9797 3.199e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.0885 0.1653 0.1955 0.9854 0.9913 0.09045 0.6933 0.8465 0.2444 ] Network output: [ 0.0001859 0.9999 -0.0003239 5.742e-06 -2.578e-06 1 4.327e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004986 Epoch 7772 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01128 0.9949 0.9897 8.287e-07 -3.72e-07 -0.007197 6.245e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003313 -0.003116 -0.008278 0.006427 0.9698 0.9742 0.006344 0.8366 0.8265 0.01854 ] Network output: [ 0.9998 0.0007208 0.001059 -2.151e-05 9.658e-06 -0.001484 -1.621e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1931 -0.03285 -0.1807 0.1924 0.9835 0.9933 0.2159 0.4458 0.8728 0.7186 ] Network output: [ -0.01079 1.002 1.01 2.435e-07 -1.093e-07 0.009907 1.835e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005847 0.0004686 0.004428 0.003808 0.9889 0.9919 0.005956 0.8652 0.8968 0.0134 ] Network output: [ -0.0006782 0.002999 1.002 -6.838e-05 3.07e-05 0.9963 -5.153e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2046 0.09517 0.3351 0.1483 0.985 0.994 0.2053 0.4502 0.8793 0.713 ] Network output: [ 0.006202 -0.03018 0.9952 4.069e-05 -1.827e-05 1.023 3.067e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1009 0.08901 0.1806 0.2023 0.9873 0.9919 0.101 0.7676 0.8694 0.3062 ] Network output: [ -0.006001 0.02982 1.003 4.241e-05 -1.904e-05 0.9797 3.196e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08851 0.1652 0.1955 0.9854 0.9913 0.09045 0.6933 0.8465 0.2444 ] Network output: [ 0.0001858 0.9999 -0.0003235 5.737e-06 -2.575e-06 1 4.323e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004983 Epoch 7773 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01128 0.995 0.9897 8.263e-07 -3.71e-07 -0.007198 6.228e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003313 -0.003117 -0.008277 0.006426 0.9698 0.9742 0.006344 0.8366 0.8265 0.01854 ] Network output: [ 0.9998 0.0007201 0.001058 -2.149e-05 9.65e-06 -0.001483 -1.62e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1931 -0.03285 -0.1807 0.1924 0.9835 0.9933 0.2159 0.4458 0.8728 0.7186 ] Network output: [ -0.01079 1.002 1.01 2.421e-07 -1.087e-07 0.009905 1.825e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005848 0.0004687 0.004428 0.003808 0.9889 0.9919 0.005957 0.8652 0.8968 0.0134 ] Network output: [ -0.0006778 0.002998 1.002 -6.831e-05 3.067e-05 0.9963 -5.148e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2047 0.09517 0.3351 0.1483 0.985 0.994 0.2053 0.4502 0.8793 0.713 ] Network output: [ 0.0062 -0.03017 0.9952 4.066e-05 -1.825e-05 1.023 3.064e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1009 0.08902 0.1806 0.2023 0.9873 0.9919 0.101 0.7676 0.8694 0.3062 ] Network output: [ -0.005999 0.02981 1.003 4.237e-05 -1.902e-05 0.9797 3.193e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08851 0.1652 0.1955 0.9854 0.9913 0.09045 0.6932 0.8465 0.2444 ] Network output: [ 0.0001857 0.9999 -0.0003232 5.732e-06 -2.573e-06 1 4.32e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000498 Epoch 7774 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01127 0.995 0.9897 8.24e-07 -3.699e-07 -0.007199 6.21e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003313 -0.003117 -0.008275 0.006425 0.9698 0.9742 0.006344 0.8366 0.8265 0.01854 ] Network output: [ 0.9998 0.0007195 0.001057 -2.148e-05 9.641e-06 -0.001482 -1.618e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1931 -0.03285 -0.1807 0.1924 0.9835 0.9933 0.2159 0.4458 0.8728 0.7186 ] Network output: [ -0.01079 1.002 1.01 2.407e-07 -1.081e-07 0.009902 1.814e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005848 0.0004687 0.004428 0.003807 0.9889 0.9919 0.005958 0.8652 0.8968 0.0134 ] Network output: [ -0.0006774 0.002997 1.002 -6.825e-05 3.064e-05 0.9963 -5.143e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2047 0.09518 0.3351 0.1483 0.985 0.994 0.2053 0.4502 0.8793 0.713 ] Network output: [ 0.006198 -0.03016 0.9952 4.062e-05 -1.823e-05 1.023 3.061e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1009 0.08902 0.1806 0.2023 0.9873 0.9919 0.101 0.7676 0.8694 0.3062 ] Network output: [ -0.005996 0.0298 1.003 4.233e-05 -1.901e-05 0.9797 3.19e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08851 0.1652 0.1955 0.9854 0.9913 0.09045 0.6932 0.8464 0.2444 ] Network output: [ 0.0001856 0.9999 -0.0003228 5.726e-06 -2.571e-06 1 4.316e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004977 Epoch 7775 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01127 0.995 0.9897 8.216e-07 -3.689e-07 -0.0072 6.192e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003313 -0.003117 -0.008274 0.006425 0.9698 0.9742 0.006345 0.8365 0.8265 0.01854 ] Network output: [ 0.9998 0.0007189 0.001057 -2.146e-05 9.633e-06 -0.001481 -1.617e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1932 -0.03286 -0.1807 0.1923 0.9835 0.9933 0.2159 0.4458 0.8728 0.7186 ] Network output: [ -0.01079 1.002 1.01 2.393e-07 -1.074e-07 0.009899 1.803e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005849 0.0004688 0.004428 0.003807 0.9889 0.9919 0.005958 0.8651 0.8968 0.0134 ] Network output: [ -0.000677 0.002996 1.002 -6.818e-05 3.061e-05 0.9964 -5.139e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2047 0.09518 0.3351 0.1483 0.985 0.994 0.2053 0.4502 0.8793 0.713 ] Network output: [ 0.006196 -0.03014 0.9952 4.058e-05 -1.822e-05 1.023 3.058e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1009 0.08903 0.1806 0.2023 0.9873 0.9919 0.101 0.7675 0.8694 0.3062 ] Network output: [ -0.005994 0.02978 1.003 4.23e-05 -1.899e-05 0.9797 3.188e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08851 0.1652 0.1955 0.9854 0.9913 0.09045 0.6932 0.8464 0.2444 ] Network output: [ 0.0001855 0.9999 -0.0003225 5.721e-06 -2.568e-06 1 4.312e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004974 Epoch 7776 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01127 0.995 0.9897 8.193e-07 -3.678e-07 -0.007201 6.174e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003314 -0.003117 -0.008273 0.006424 0.9698 0.9742 0.006345 0.8365 0.8265 0.01854 ] Network output: [ 0.9998 0.0007182 0.001056 -2.144e-05 9.624e-06 -0.00148 -1.616e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1932 -0.03286 -0.1807 0.1923 0.9835 0.9933 0.216 0.4458 0.8728 0.7186 ] Network output: [ -0.01079 1.002 1.01 2.379e-07 -1.068e-07 0.009897 1.793e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005849 0.0004688 0.004428 0.003806 0.9889 0.9919 0.005959 0.8651 0.8968 0.0134 ] Network output: [ -0.0006766 0.002995 1.002 -6.812e-05 3.058e-05 0.9964 -5.134e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2047 0.09519 0.3351 0.1483 0.985 0.994 0.2054 0.4502 0.8793 0.713 ] Network output: [ 0.006194 -0.03013 0.9952 4.054e-05 -1.82e-05 1.023 3.056e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1009 0.08903 0.1806 0.2023 0.9873 0.9919 0.101 0.7675 0.8694 0.3062 ] Network output: [ -0.005992 0.02977 1.003 4.226e-05 -1.897e-05 0.9797 3.185e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08851 0.1652 0.1955 0.9854 0.9913 0.09045 0.6932 0.8464 0.2444 ] Network output: [ 0.0001854 0.9999 -0.0003221 5.716e-06 -2.566e-06 1 4.308e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004971 Epoch 7777 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01127 0.995 0.9897 8.169e-07 -3.668e-07 -0.007202 6.157e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003314 -0.003117 -0.008272 0.006423 0.9698 0.9742 0.006345 0.8365 0.8265 0.01853 ] Network output: [ 0.9998 0.0007176 0.001055 -2.142e-05 9.615e-06 -0.001478 -1.614e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1932 -0.03286 -0.1807 0.1923 0.9835 0.9933 0.216 0.4458 0.8728 0.7186 ] Network output: [ -0.01079 1.002 1.01 2.365e-07 -1.062e-07 0.009894 1.782e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00585 0.0004689 0.004428 0.003806 0.9889 0.9919 0.005959 0.8651 0.8968 0.01339 ] Network output: [ -0.0006762 0.002994 1.002 -6.806e-05 3.055e-05 0.9964 -5.129e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2047 0.09519 0.3351 0.1483 0.985 0.994 0.2054 0.4502 0.8793 0.713 ] Network output: [ 0.006192 -0.03012 0.9952 4.051e-05 -1.819e-05 1.023 3.053e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.101 0.08904 0.1806 0.2023 0.9873 0.9919 0.101 0.7675 0.8694 0.3062 ] Network output: [ -0.00599 0.02976 1.003 4.222e-05 -1.896e-05 0.9797 3.182e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08851 0.1652 0.1955 0.9854 0.9913 0.09045 0.6931 0.8464 0.2444 ] Network output: [ 0.0001853 0.9999 -0.0003218 5.711e-06 -2.564e-06 1 4.304e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004967 Epoch 7778 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01127 0.995 0.9897 8.146e-07 -3.657e-07 -0.007203 6.139e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003314 -0.003117 -0.008271 0.006422 0.9698 0.9742 0.006346 0.8365 0.8265 0.01853 ] Network output: [ 0.9998 0.000717 0.001055 -2.14e-05 9.607e-06 -0.001477 -1.613e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1932 -0.03287 -0.1806 0.1923 0.9835 0.9933 0.216 0.4458 0.8727 0.7186 ] Network output: [ -0.01079 1.002 1.01 2.351e-07 -1.055e-07 0.009891 1.772e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005851 0.0004689 0.004428 0.003805 0.9889 0.9919 0.00596 0.8651 0.8968 0.01339 ] Network output: [ -0.0006758 0.002992 1.002 -6.799e-05 3.052e-05 0.9964 -5.124e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2047 0.0952 0.3352 0.1483 0.985 0.994 0.2054 0.4501 0.8793 0.713 ] Network output: [ 0.00619 -0.03011 0.9952 4.047e-05 -1.817e-05 1.023 3.05e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.101 0.08904 0.1806 0.2022 0.9873 0.9919 0.101 0.7675 0.8694 0.3062 ] Network output: [ -0.005987 0.02974 1.003 4.219e-05 -1.894e-05 0.9797 3.179e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08851 0.1652 0.1955 0.9854 0.9913 0.09046 0.6931 0.8464 0.2444 ] Network output: [ 0.0001852 0.9999 -0.0003214 5.706e-06 -2.562e-06 1 4.3e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004964 Epoch 7779 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01126 0.995 0.9897 8.123e-07 -3.647e-07 -0.007204 6.121e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003314 -0.003118 -0.008269 0.006421 0.9698 0.9742 0.006346 0.8365 0.8265 0.01853 ] Network output: [ 0.9998 0.0007163 0.001054 -2.138e-05 9.598e-06 -0.001476 -1.611e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1932 -0.03287 -0.1806 0.1923 0.9835 0.9933 0.216 0.4457 0.8727 0.7186 ] Network output: [ -0.01079 1.002 1.01 2.337e-07 -1.049e-07 0.009889 1.761e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005851 0.0004689 0.004428 0.003804 0.9889 0.9919 0.005961 0.8651 0.8968 0.01339 ] Network output: [ -0.0006754 0.002991 1.002 -6.793e-05 3.05e-05 0.9964 -5.119e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2047 0.0952 0.3352 0.1483 0.985 0.994 0.2054 0.4501 0.8793 0.713 ] Network output: [ 0.006187 -0.0301 0.9952 4.043e-05 -1.815e-05 1.023 3.047e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.101 0.08905 0.1806 0.2022 0.9873 0.9919 0.101 0.7675 0.8693 0.3062 ] Network output: [ -0.005985 0.02973 1.003 4.215e-05 -1.892e-05 0.9797 3.176e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08851 0.1652 0.1955 0.9854 0.9913 0.09046 0.6931 0.8464 0.2444 ] Network output: [ 0.0001851 0.9999 -0.000321 5.701e-06 -2.559e-06 1 4.296e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004961 Epoch 7780 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01126 0.995 0.9897 8.099e-07 -3.636e-07 -0.007205 6.104e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003314 -0.003118 -0.008268 0.006421 0.9698 0.9742 0.006346 0.8365 0.8265 0.01853 ] Network output: [ 0.9998 0.0007157 0.001053 -2.136e-05 9.589e-06 -0.001475 -1.61e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1932 -0.03287 -0.1806 0.1923 0.9835 0.9933 0.216 0.4457 0.8727 0.7185 ] Network output: [ -0.01078 1.002 1.01 2.323e-07 -1.043e-07 0.009886 1.75e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005852 0.000469 0.004428 0.003804 0.9889 0.9919 0.005961 0.8651 0.8968 0.01339 ] Network output: [ -0.000675 0.00299 1.002 -6.787e-05 3.047e-05 0.9964 -5.115e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2047 0.09521 0.3352 0.1482 0.985 0.994 0.2054 0.4501 0.8793 0.713 ] Network output: [ 0.006185 -0.03009 0.9952 4.04e-05 -1.814e-05 1.023 3.044e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.101 0.08905 0.1806 0.2022 0.9873 0.9919 0.101 0.7674 0.8693 0.3062 ] Network output: [ -0.005983 0.02972 1.003 4.211e-05 -1.891e-05 0.9797 3.174e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08851 0.1652 0.1955 0.9854 0.9913 0.09046 0.693 0.8464 0.2444 ] Network output: [ 0.000185 0.9999 -0.0003207 5.696e-06 -2.557e-06 1 4.292e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004958 Epoch 7781 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01126 0.995 0.9897 8.076e-07 -3.626e-07 -0.007206 6.086e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003314 -0.003118 -0.008267 0.00642 0.9698 0.9742 0.006347 0.8365 0.8265 0.01853 ] Network output: [ 0.9998 0.0007151 0.001053 -2.134e-05 9.581e-06 -0.001473 -1.608e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1932 -0.03287 -0.1806 0.1923 0.9835 0.9933 0.216 0.4457 0.8727 0.7185 ] Network output: [ -0.01078 1.002 1.01 2.309e-07 -1.036e-07 0.009884 1.74e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005853 0.000469 0.004429 0.003803 0.9889 0.9919 0.005962 0.8651 0.8967 0.01339 ] Network output: [ -0.0006746 0.002989 1.002 -6.78e-05 3.044e-05 0.9964 -5.11e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2048 0.09521 0.3352 0.1482 0.985 0.994 0.2054 0.4501 0.8793 0.713 ] Network output: [ 0.006183 -0.03008 0.9952 4.036e-05 -1.812e-05 1.023 3.042e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.101 0.08906 0.1806 0.2022 0.9873 0.9919 0.101 0.7674 0.8693 0.3062 ] Network output: [ -0.005981 0.02971 1.003 4.207e-05 -1.889e-05 0.9797 3.171e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09044 0.08851 0.1652 0.1955 0.9854 0.9913 0.09046 0.693 0.8464 0.2444 ] Network output: [ 0.0001849 0.9999 -0.0003203 5.69e-06 -2.555e-06 1 4.288e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004955 Epoch 7782 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01126 0.995 0.9897 8.053e-07 -3.615e-07 -0.007207 6.069e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003314 -0.003118 -0.008266 0.006419 0.9698 0.9742 0.006347 0.8365 0.8265 0.01853 ] Network output: [ 0.9998 0.0007144 0.001052 -2.132e-05 9.572e-06 -0.001472 -1.607e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1932 -0.03288 -0.1806 0.1923 0.9835 0.9933 0.216 0.4457 0.8727 0.7185 ] Network output: [ -0.01078 1.002 1.01 2.295e-07 -1.03e-07 0.009881 1.729e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005853 0.0004691 0.004429 0.003803 0.9889 0.9919 0.005963 0.8651 0.8967 0.01339 ] Network output: [ -0.0006742 0.002988 1.002 -6.774e-05 3.041e-05 0.9964 -5.105e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2048 0.09522 0.3352 0.1482 0.985 0.994 0.2054 0.4501 0.8793 0.713 ] Network output: [ 0.006181 -0.03007 0.9952 4.032e-05 -1.81e-05 1.023 3.039e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.101 0.08906 0.1806 0.2022 0.9873 0.9919 0.101 0.7674 0.8693 0.3062 ] Network output: [ -0.005978 0.02969 1.003 4.204e-05 -1.887e-05 0.9798 3.168e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08851 0.1652 0.1955 0.9854 0.9913 0.09046 0.693 0.8464 0.2444 ] Network output: [ 0.0001848 0.9999 -0.00032 5.685e-06 -2.552e-06 1 4.285e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004952 Epoch 7783 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01126 0.995 0.9897 8.029e-07 -3.605e-07 -0.007208 6.051e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003315 -0.003118 -0.008265 0.006418 0.9698 0.9742 0.006347 0.8365 0.8265 0.01852 ] Network output: [ 0.9998 0.0007138 0.001051 -2.13e-05 9.564e-06 -0.001471 -1.605e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1932 -0.03288 -0.1806 0.1923 0.9835 0.9933 0.216 0.4457 0.8727 0.7185 ] Network output: [ -0.01078 1.002 1.01 2.281e-07 -1.024e-07 0.009878 1.719e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005854 0.0004691 0.004429 0.003802 0.9889 0.9919 0.005963 0.8651 0.8967 0.01339 ] Network output: [ -0.0006738 0.002987 1.002 -6.767e-05 3.038e-05 0.9964 -5.1e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2048 0.09522 0.3352 0.1482 0.985 0.994 0.2054 0.4501 0.8793 0.713 ] Network output: [ 0.006179 -0.03006 0.9952 4.028e-05 -1.809e-05 1.023 3.036e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.101 0.08907 0.1807 0.2022 0.9873 0.9919 0.1011 0.7674 0.8693 0.3062 ] Network output: [ -0.005976 0.02968 1.003 4.2e-05 -1.886e-05 0.9798 3.165e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08851 0.1652 0.1955 0.9854 0.9913 0.09046 0.693 0.8464 0.2444 ] Network output: [ 0.0001847 0.9999 -0.0003196 5.68e-06 -2.55e-06 1 4.281e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004949 Epoch 7784 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01126 0.995 0.9897 8.006e-07 -3.594e-07 -0.007209 6.034e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003315 -0.003118 -0.008264 0.006418 0.9698 0.9742 0.006348 0.8365 0.8265 0.01852 ] Network output: [ 0.9998 0.0007132 0.001051 -2.128e-05 9.555e-06 -0.00147 -1.604e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1933 -0.03288 -0.1805 0.1923 0.9835 0.9933 0.2161 0.4457 0.8727 0.7185 ] Network output: [ -0.01078 1.002 1.01 2.267e-07 -1.018e-07 0.009876 1.708e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005854 0.0004692 0.004429 0.003802 0.9889 0.9919 0.005964 0.8651 0.8967 0.01339 ] Network output: [ -0.0006733 0.002986 1.002 -6.761e-05 3.035e-05 0.9964 -5.095e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2048 0.09523 0.3352 0.1482 0.985 0.994 0.2055 0.4501 0.8793 0.713 ] Network output: [ 0.006177 -0.03004 0.9952 4.025e-05 -1.807e-05 1.023 3.033e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.101 0.08908 0.1807 0.2022 0.9873 0.9919 0.1011 0.7673 0.8693 0.3062 ] Network output: [ -0.005974 0.02967 1.003 4.196e-05 -1.884e-05 0.9798 3.162e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08851 0.1652 0.1955 0.9854 0.9913 0.09046 0.6929 0.8464 0.2445 ] Network output: [ 0.0001846 0.9999 -0.0003193 5.675e-06 -2.548e-06 1 4.277e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004945 Epoch 7785 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01125 0.995 0.9897 7.983e-07 -3.584e-07 -0.00721 6.016e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003315 -0.003119 -0.008262 0.006417 0.9698 0.9742 0.006348 0.8365 0.8265 0.01852 ] Network output: [ 0.9998 0.0007126 0.00105 -2.126e-05 9.546e-06 -0.001469 -1.603e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1933 -0.03288 -0.1805 0.1923 0.9835 0.9933 0.2161 0.4457 0.8727 0.7185 ] Network output: [ -0.01078 1.002 1.01 2.253e-07 -1.011e-07 0.009873 1.698e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005855 0.0004692 0.004429 0.003801 0.9889 0.9919 0.005964 0.8651 0.8967 0.01338 ] Network output: [ -0.0006729 0.002985 1.002 -6.755e-05 3.032e-05 0.9964 -5.091e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2048 0.09523 0.3352 0.1482 0.985 0.994 0.2055 0.45 0.8793 0.7129 ] Network output: [ 0.006175 -0.03003 0.9952 4.021e-05 -1.805e-05 1.023 3.03e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.101 0.08908 0.1807 0.2022 0.9873 0.9919 0.1011 0.7673 0.8693 0.3062 ] Network output: [ -0.005972 0.02965 1.003 4.193e-05 -1.882e-05 0.9798 3.16e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08851 0.1652 0.1955 0.9854 0.9913 0.09046 0.6929 0.8463 0.2445 ] Network output: [ 0.0001845 0.9999 -0.0003189 5.67e-06 -2.545e-06 1 4.273e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004942 Epoch 7786 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01125 0.995 0.9897 7.96e-07 -3.574e-07 -0.007211 5.999e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003315 -0.003119 -0.008261 0.006416 0.9698 0.9742 0.006348 0.8365 0.8265 0.01852 ] Network output: [ 0.9998 0.0007119 0.001049 -2.125e-05 9.538e-06 -0.001467 -1.601e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1933 -0.03289 -0.1805 0.1923 0.9835 0.9933 0.2161 0.4456 0.8727 0.7185 ] Network output: [ -0.01078 1.002 1.01 2.239e-07 -1.005e-07 0.00987 1.688e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005856 0.0004693 0.004429 0.003801 0.9889 0.9919 0.005965 0.865 0.8967 0.01338 ] Network output: [ -0.0006725 0.002984 1.002 -6.748e-05 3.03e-05 0.9964 -5.086e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2048 0.09524 0.3353 0.1482 0.985 0.994 0.2055 0.45 0.8793 0.7129 ] Network output: [ 0.006173 -0.03002 0.9952 4.017e-05 -1.804e-05 1.023 3.028e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.101 0.08909 0.1807 0.2022 0.9873 0.9919 0.1011 0.7673 0.8693 0.3062 ] Network output: [ -0.005969 0.02964 1.003 4.189e-05 -1.881e-05 0.9798 3.157e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08851 0.1652 0.1955 0.9854 0.9913 0.09046 0.6929 0.8463 0.2445 ] Network output: [ 0.0001844 0.9999 -0.0003186 5.665e-06 -2.543e-06 1 4.269e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004939 Epoch 7787 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01125 0.995 0.9897 7.937e-07 -3.563e-07 -0.007212 5.982e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003315 -0.003119 -0.00826 0.006415 0.9698 0.9742 0.006349 0.8365 0.8265 0.01852 ] Network output: [ 0.9998 0.0007113 0.001048 -2.123e-05 9.529e-06 -0.001466 -1.6e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1933 -0.03289 -0.1805 0.1923 0.9835 0.9933 0.2161 0.4456 0.8727 0.7185 ] Network output: [ -0.01078 1.002 1.01 2.225e-07 -9.991e-08 0.009868 1.677e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005856 0.0004693 0.004429 0.0038 0.9889 0.9919 0.005966 0.865 0.8967 0.01338 ] Network output: [ -0.0006721 0.002983 1.002 -6.742e-05 3.027e-05 0.9964 -5.081e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2048 0.09524 0.3353 0.1482 0.985 0.994 0.2055 0.45 0.8793 0.7129 ] Network output: [ 0.006171 -0.03001 0.9952 4.014e-05 -1.802e-05 1.023 3.025e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.101 0.08909 0.1807 0.2022 0.9873 0.9919 0.1011 0.7673 0.8693 0.3062 ] Network output: [ -0.005967 0.02963 1.003 4.185e-05 -1.879e-05 0.9798 3.154e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08852 0.1652 0.1955 0.9854 0.9913 0.09046 0.6929 0.8463 0.2445 ] Network output: [ 0.0001843 0.9999 -0.0003182 5.66e-06 -2.541e-06 1 4.265e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004936 Epoch 7788 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01125 0.995 0.9897 7.914e-07 -3.553e-07 -0.007212 5.964e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003315 -0.003119 -0.008259 0.006415 0.9698 0.9742 0.006349 0.8364 0.8265 0.01852 ] Network output: [ 0.9998 0.0007107 0.001048 -2.121e-05 9.521e-06 -0.001465 -1.598e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1933 -0.03289 -0.1805 0.1923 0.9835 0.9933 0.2161 0.4456 0.8727 0.7185 ] Network output: [ -0.01078 1.002 1.01 2.212e-07 -9.929e-08 0.009865 1.667e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005857 0.0004694 0.004429 0.0038 0.9889 0.9919 0.005966 0.865 0.8967 0.01338 ] Network output: [ -0.0006717 0.002982 1.002 -6.736e-05 3.024e-05 0.9964 -5.076e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2048 0.09525 0.3353 0.1482 0.985 0.994 0.2055 0.45 0.8793 0.7129 ] Network output: [ 0.006169 -0.03 0.9952 4.01e-05 -1.8e-05 1.023 3.022e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.101 0.0891 0.1807 0.2022 0.9873 0.9919 0.1011 0.7672 0.8693 0.3062 ] Network output: [ -0.005965 0.02961 1.003 4.181e-05 -1.877e-05 0.9798 3.151e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08852 0.1652 0.1955 0.9854 0.9913 0.09046 0.6928 0.8463 0.2445 ] Network output: [ 0.0001842 0.9999 -0.0003179 5.655e-06 -2.539e-06 1 4.261e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004933 Epoch 7789 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01125 0.995 0.9897 7.891e-07 -3.543e-07 -0.007213 5.947e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003315 -0.003119 -0.008258 0.006414 0.9698 0.9742 0.006349 0.8364 0.8265 0.01851 ] Network output: [ 0.9998 0.0007101 0.001047 -2.119e-05 9.512e-06 -0.001464 -1.597e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1933 -0.0329 -0.1805 0.1922 0.9835 0.9933 0.2161 0.4456 0.8727 0.7185 ] Network output: [ -0.01077 1.002 1.01 2.198e-07 -9.867e-08 0.009862 1.656e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005857 0.0004694 0.004429 0.003799 0.9889 0.9919 0.005967 0.865 0.8967 0.01338 ] Network output: [ -0.0006713 0.002981 1.002 -6.729e-05 3.021e-05 0.9964 -5.071e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2048 0.09525 0.3353 0.1482 0.985 0.994 0.2055 0.45 0.8793 0.7129 ] Network output: [ 0.006167 -0.02999 0.9952 4.006e-05 -1.799e-05 1.023 3.019e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.101 0.0891 0.1807 0.2022 0.9873 0.9919 0.1011 0.7672 0.8693 0.3062 ] Network output: [ -0.005963 0.0296 1.003 4.178e-05 -1.876e-05 0.9798 3.148e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08852 0.1652 0.1955 0.9854 0.9913 0.09046 0.6928 0.8463 0.2445 ] Network output: [ 0.0001841 0.9999 -0.0003175 5.649e-06 -2.536e-06 1 4.258e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000493 Epoch 7790 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01124 0.995 0.9897 7.868e-07 -3.532e-07 -0.007214 5.93e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003316 -0.003119 -0.008257 0.006413 0.9698 0.9742 0.00635 0.8364 0.8265 0.01851 ] Network output: [ 0.9998 0.0007094 0.001046 -2.117e-05 9.503e-06 -0.001462 -1.595e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1933 -0.0329 -0.1804 0.1922 0.9835 0.9933 0.2161 0.4456 0.8727 0.7185 ] Network output: [ -0.01077 1.002 1.01 2.184e-07 -9.805e-08 0.00986 1.646e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005858 0.0004695 0.004429 0.003799 0.9889 0.9919 0.005968 0.865 0.8967 0.01338 ] Network output: [ -0.0006709 0.00298 1.002 -6.723e-05 3.018e-05 0.9964 -5.067e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2049 0.09526 0.3353 0.1482 0.985 0.994 0.2055 0.45 0.8793 0.7129 ] Network output: [ 0.006165 -0.02998 0.9952 4.003e-05 -1.797e-05 1.023 3.017e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.101 0.08911 0.1807 0.2022 0.9873 0.9919 0.1011 0.7672 0.8693 0.3062 ] Network output: [ -0.00596 0.02959 1.003 4.174e-05 -1.874e-05 0.9798 3.146e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08852 0.1652 0.1955 0.9854 0.9913 0.09046 0.6928 0.8463 0.2445 ] Network output: [ 0.000184 0.9999 -0.0003172 5.644e-06 -2.534e-06 1 4.254e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004927 Epoch 7791 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01124 0.995 0.9897 7.845e-07 -3.522e-07 -0.007215 5.912e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003316 -0.00312 -0.008255 0.006412 0.9698 0.9742 0.00635 0.8364 0.8264 0.01851 ] Network output: [ 0.9998 0.0007088 0.001046 -2.115e-05 9.495e-06 -0.001461 -1.594e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1933 -0.0329 -0.1804 0.1922 0.9835 0.9933 0.2161 0.4456 0.8727 0.7185 ] Network output: [ -0.01077 1.002 1.01 2.17e-07 -9.743e-08 0.009857 1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005859 0.0004695 0.004429 0.003798 0.9889 0.9919 0.005968 0.865 0.8967 0.01338 ] Network output: [ -0.0006705 0.002979 1.002 -6.717e-05 3.015e-05 0.9964 -5.062e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2049 0.09526 0.3353 0.1482 0.985 0.994 0.2055 0.45 0.8793 0.7129 ] Network output: [ 0.006163 -0.02997 0.9952 3.999e-05 -1.795e-05 1.023 3.014e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.101 0.08911 0.1807 0.2022 0.9873 0.9919 0.1011 0.7672 0.8693 0.3062 ] Network output: [ -0.005958 0.02958 1.003 4.17e-05 -1.872e-05 0.9798 3.143e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08852 0.1652 0.1955 0.9854 0.9913 0.09046 0.6928 0.8463 0.2445 ] Network output: [ 0.0001839 0.9999 -0.0003168 5.639e-06 -2.532e-06 1 4.25e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004924 Epoch 7792 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01124 0.995 0.9897 7.822e-07 -3.512e-07 -0.007216 5.895e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003316 -0.00312 -0.008254 0.006411 0.9698 0.9742 0.00635 0.8364 0.8264 0.01851 ] Network output: [ 0.9998 0.0007082 0.001045 -2.113e-05 9.486e-06 -0.00146 -1.592e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1933 -0.0329 -0.1804 0.1922 0.9835 0.9933 0.2161 0.4456 0.8727 0.7185 ] Network output: [ -0.01077 1.002 1.01 2.157e-07 -9.682e-08 0.009855 1.625e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005859 0.0004696 0.004429 0.003798 0.9889 0.9919 0.005969 0.865 0.8967 0.01338 ] Network output: [ -0.0006701 0.002978 1.002 -6.71e-05 3.013e-05 0.9964 -5.057e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2049 0.09527 0.3353 0.1482 0.985 0.994 0.2055 0.4499 0.8793 0.7129 ] Network output: [ 0.006161 -0.02996 0.9952 3.995e-05 -1.794e-05 1.023 3.011e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.101 0.08912 0.1807 0.2022 0.9873 0.9919 0.1011 0.7671 0.8693 0.3062 ] Network output: [ -0.005956 0.02956 1.003 4.167e-05 -1.871e-05 0.9798 3.14e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08852 0.1652 0.1955 0.9854 0.9913 0.09047 0.6927 0.8463 0.2445 ] Network output: [ 0.0001838 0.9999 -0.0003165 5.634e-06 -2.529e-06 1 4.246e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000492 Epoch 7793 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01124 0.995 0.9897 7.799e-07 -3.501e-07 -0.007217 5.878e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003316 -0.00312 -0.008253 0.006411 0.9698 0.9742 0.006351 0.8364 0.8264 0.01851 ] Network output: [ 0.9998 0.0007076 0.001044 -2.111e-05 9.478e-06 -0.001459 -1.591e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1934 -0.03291 -0.1804 0.1922 0.9835 0.9933 0.2162 0.4455 0.8727 0.7185 ] Network output: [ -0.01077 1.002 1.01 2.143e-07 -9.62e-08 0.009852 1.615e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00586 0.0004696 0.004429 0.003797 0.9889 0.9919 0.005969 0.865 0.8967 0.01337 ] Network output: [ -0.0006697 0.002977 1.002 -6.704e-05 3.01e-05 0.9964 -5.052e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2049 0.09527 0.3353 0.1482 0.985 0.994 0.2056 0.4499 0.8793 0.7129 ] Network output: [ 0.006159 -0.02994 0.9952 3.992e-05 -1.792e-05 1.023 3.008e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1011 0.08912 0.1807 0.2022 0.9873 0.9919 0.1011 0.7671 0.8692 0.3062 ] Network output: [ -0.005954 0.02955 1.003 4.163e-05 -1.869e-05 0.9798 3.137e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08852 0.1652 0.1955 0.9854 0.9913 0.09047 0.6927 0.8463 0.2445 ] Network output: [ 0.0001837 0.9999 -0.0003161 5.629e-06 -2.527e-06 1 4.242e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004917 Epoch 7794 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01124 0.995 0.9898 7.777e-07 -3.491e-07 -0.007218 5.861e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003316 -0.00312 -0.008252 0.00641 0.9698 0.9742 0.006351 0.8364 0.8264 0.01851 ] Network output: [ 0.9998 0.0007069 0.001044 -2.109e-05 9.469e-06 -0.001458 -1.59e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1934 -0.03291 -0.1804 0.1922 0.9835 0.9933 0.2162 0.4455 0.8727 0.7185 ] Network output: [ -0.01077 1.002 1.01 2.129e-07 -9.559e-08 0.009849 1.605e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005861 0.0004697 0.004429 0.003797 0.9889 0.9919 0.00597 0.865 0.8967 0.01337 ] Network output: [ -0.0006693 0.002976 1.002 -6.698e-05 3.007e-05 0.9964 -5.048e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2049 0.09528 0.3353 0.1481 0.985 0.994 0.2056 0.4499 0.8793 0.7129 ] Network output: [ 0.006157 -0.02993 0.9952 3.988e-05 -1.79e-05 1.023 3.006e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1011 0.08913 0.1807 0.2022 0.9873 0.9919 0.1011 0.7671 0.8692 0.3062 ] Network output: [ -0.005951 0.02954 1.003 4.159e-05 -1.867e-05 0.9798 3.135e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09045 0.08852 0.1652 0.1955 0.9854 0.9913 0.09047 0.6927 0.8463 0.2445 ] Network output: [ 0.0001836 0.9999 -0.0003158 5.624e-06 -2.525e-06 1 4.238e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004914 Epoch 7795 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01124 0.995 0.9898 7.754e-07 -3.481e-07 -0.007219 5.844e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003316 -0.00312 -0.008251 0.006409 0.9698 0.9742 0.006351 0.8364 0.8264 0.0185 ] Network output: [ 0.9998 0.0007063 0.001043 -2.107e-05 9.461e-06 -0.001456 -1.588e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1934 -0.03291 -0.1804 0.1922 0.9835 0.9933 0.2162 0.4455 0.8727 0.7185 ] Network output: [ -0.01077 1.002 1.01 2.116e-07 -9.498e-08 0.009847 1.594e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005861 0.0004697 0.00443 0.003796 0.9889 0.9919 0.005971 0.865 0.8967 0.01337 ] Network output: [ -0.0006689 0.002975 1.002 -6.691e-05 3.004e-05 0.9964 -5.043e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2049 0.09528 0.3354 0.1481 0.985 0.994 0.2056 0.4499 0.8792 0.7129 ] Network output: [ 0.006155 -0.02992 0.9952 3.984e-05 -1.789e-05 1.023 3.003e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1011 0.08914 0.1807 0.2022 0.9873 0.9919 0.1011 0.7671 0.8692 0.3062 ] Network output: [ -0.005949 0.02952 1.003 4.156e-05 -1.866e-05 0.9798 3.132e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08852 0.1652 0.1955 0.9854 0.9913 0.09047 0.6927 0.8463 0.2445 ] Network output: [ 0.0001835 0.9999 -0.0003154 5.619e-06 -2.523e-06 1 4.235e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004911 Epoch 7796 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01123 0.995 0.9898 7.731e-07 -3.471e-07 -0.00722 5.826e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003316 -0.00312 -0.00825 0.006408 0.9698 0.9742 0.006351 0.8364 0.8264 0.0185 ] Network output: [ 0.9998 0.0007057 0.001042 -2.105e-05 9.452e-06 -0.001455 -1.587e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1934 -0.03291 -0.1803 0.1922 0.9835 0.9933 0.2162 0.4455 0.8727 0.7185 ] Network output: [ -0.01077 1.002 1.01 2.102e-07 -9.437e-08 0.009844 1.584e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005862 0.0004698 0.00443 0.003796 0.9889 0.9919 0.005971 0.865 0.8967 0.01337 ] Network output: [ -0.0006685 0.002974 1.002 -6.685e-05 3.001e-05 0.9964 -5.038e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2049 0.09529 0.3354 0.1481 0.985 0.994 0.2056 0.4499 0.8792 0.7129 ] Network output: [ 0.006153 -0.02991 0.9952 3.981e-05 -1.787e-05 1.023 3e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1011 0.08914 0.1807 0.2022 0.9873 0.9919 0.1011 0.7671 0.8692 0.3062 ] Network output: [ -0.005947 0.02951 1.003 4.152e-05 -1.864e-05 0.9798 3.129e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08852 0.1652 0.1955 0.9854 0.9913 0.09047 0.6926 0.8462 0.2445 ] Network output: [ 0.0001834 0.9999 -0.0003151 5.614e-06 -2.52e-06 1 4.231e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004908 Epoch 7797 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01123 0.995 0.9898 7.708e-07 -3.461e-07 -0.007221 5.809e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003316 -0.00312 -0.008248 0.006408 0.9698 0.9742 0.006352 0.8364 0.8264 0.0185 ] Network output: [ 0.9998 0.0007051 0.001042 -2.104e-05 9.444e-06 -0.001454 -1.585e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1934 -0.03292 -0.1803 0.1922 0.9835 0.9933 0.2162 0.4455 0.8727 0.7185 ] Network output: [ -0.01076 1.002 1.01 2.088e-07 -9.376e-08 0.009842 1.574e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005862 0.0004698 0.00443 0.003795 0.9889 0.9919 0.005972 0.8649 0.8967 0.01337 ] Network output: [ -0.0006681 0.002973 1.002 -6.679e-05 2.998e-05 0.9964 -5.033e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2049 0.09529 0.3354 0.1481 0.985 0.994 0.2056 0.4499 0.8792 0.7129 ] Network output: [ 0.006151 -0.0299 0.9952 3.977e-05 -1.785e-05 1.023 2.997e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1011 0.08915 0.1807 0.2022 0.9873 0.9919 0.1011 0.767 0.8692 0.3062 ] Network output: [ -0.005945 0.0295 1.003 4.148e-05 -1.862e-05 0.9798 3.126e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08852 0.1652 0.1955 0.9854 0.9913 0.09047 0.6926 0.8462 0.2445 ] Network output: [ 0.0001833 0.9999 -0.0003147 5.609e-06 -2.518e-06 1 4.227e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004905 Epoch 7798 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01123 0.995 0.9898 7.686e-07 -3.45e-07 -0.007222 5.792e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003317 -0.003121 -0.008247 0.006407 0.9698 0.9742 0.006352 0.8364 0.8264 0.0185 ] Network output: [ 0.9998 0.0007045 0.001041 -2.102e-05 9.435e-06 -0.001453 -1.584e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1934 -0.03292 -0.1803 0.1922 0.9835 0.9933 0.2162 0.4455 0.8727 0.7185 ] Network output: [ -0.01076 1.002 1.01 2.075e-07 -9.315e-08 0.009839 1.564e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005863 0.0004699 0.00443 0.003795 0.9889 0.9919 0.005973 0.8649 0.8967 0.01337 ] Network output: [ -0.0006677 0.002972 1.002 -6.673e-05 2.996e-05 0.9964 -5.029e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.205 0.0953 0.3354 0.1481 0.985 0.994 0.2056 0.4499 0.8792 0.7129 ] Network output: [ 0.006149 -0.02989 0.9952 3.973e-05 -1.784e-05 1.023 2.994e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1011 0.08915 0.1807 0.2021 0.9873 0.9919 0.1011 0.767 0.8692 0.3062 ] Network output: [ -0.005943 0.02949 1.003 4.145e-05 -1.861e-05 0.9799 3.124e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08852 0.1652 0.1955 0.9854 0.9913 0.09047 0.6926 0.8462 0.2445 ] Network output: [ 0.0001832 0.9999 -0.0003144 5.604e-06 -2.516e-06 1 4.223e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004902 Epoch 7799 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01123 0.995 0.9898 7.663e-07 -3.44e-07 -0.007223 5.775e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003317 -0.003121 -0.008246 0.006406 0.9698 0.9742 0.006352 0.8364 0.8264 0.0185 ] Network output: [ 0.9998 0.0007038 0.00104 -2.1e-05 9.426e-06 -0.001452 -1.582e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1934 -0.03292 -0.1803 0.1922 0.9835 0.9933 0.2162 0.4455 0.8727 0.7184 ] Network output: [ -0.01076 1.002 1.01 2.061e-07 -9.254e-08 0.009836 1.553e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005864 0.0004699 0.00443 0.003794 0.9889 0.9919 0.005973 0.8649 0.8967 0.01337 ] Network output: [ -0.0006673 0.002971 1.002 -6.666e-05 2.993e-05 0.9964 -5.024e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.205 0.0953 0.3354 0.1481 0.985 0.994 0.2056 0.4498 0.8792 0.7129 ] Network output: [ 0.006146 -0.02988 0.9952 3.97e-05 -1.782e-05 1.023 2.992e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1011 0.08916 0.1807 0.2021 0.9873 0.9919 0.1012 0.767 0.8692 0.3062 ] Network output: [ -0.00594 0.02947 1.003 4.141e-05 -1.859e-05 0.9799 3.121e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08852 0.1652 0.1955 0.9854 0.9913 0.09047 0.6925 0.8462 0.2445 ] Network output: [ 0.0001831 0.9999 -0.000314 5.599e-06 -2.513e-06 1 4.219e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004899 Epoch 7800 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01123 0.995 0.9898 7.641e-07 -3.43e-07 -0.007224 5.758e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003317 -0.003121 -0.008245 0.006405 0.9698 0.9742 0.006353 0.8363 0.8264 0.0185 ] Network output: [ 0.9998 0.0007032 0.00104 -2.098e-05 9.418e-06 -0.00145 -1.581e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1934 -0.03293 -0.1803 0.1922 0.9835 0.9933 0.2162 0.4455 0.8727 0.7184 ] Network output: [ -0.01076 1.002 1.01 2.048e-07 -9.193e-08 0.009834 1.543e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005864 0.00047 0.00443 0.003794 0.9889 0.9919 0.005974 0.8649 0.8967 0.01337 ] Network output: [ -0.0006669 0.00297 1.002 -6.66e-05 2.99e-05 0.9964 -5.019e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.205 0.09531 0.3354 0.1481 0.985 0.994 0.2056 0.4498 0.8792 0.7129 ] Network output: [ 0.006144 -0.02987 0.9952 3.966e-05 -1.781e-05 1.023 2.989e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1011 0.08916 0.1807 0.2021 0.9873 0.9919 0.1012 0.767 0.8692 0.3062 ] Network output: [ -0.005938 0.02946 1.003 4.137e-05 -1.857e-05 0.9799 3.118e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08853 0.1652 0.1955 0.9854 0.9913 0.09047 0.6925 0.8462 0.2445 ] Network output: [ 0.0001831 0.9999 -0.0003137 5.593e-06 -2.511e-06 1 4.215e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004896 Epoch 7801 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01123 0.995 0.9898 7.618e-07 -3.42e-07 -0.007225 5.741e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003317 -0.003121 -0.008244 0.006405 0.9698 0.9742 0.006353 0.8363 0.8264 0.0185 ] Network output: [ 0.9998 0.0007026 0.001039 -2.096e-05 9.409e-06 -0.001449 -1.58e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1934 -0.03293 -0.1803 0.1922 0.9835 0.9933 0.2163 0.4454 0.8726 0.7184 ] Network output: [ -0.01076 1.002 1.01 2.034e-07 -9.133e-08 0.009831 1.533e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005865 0.00047 0.00443 0.003793 0.9889 0.9919 0.005974 0.8649 0.8967 0.01336 ] Network output: [ -0.0006665 0.002968 1.002 -6.654e-05 2.987e-05 0.9964 -5.014e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.205 0.09531 0.3354 0.1481 0.985 0.994 0.2057 0.4498 0.8792 0.7129 ] Network output: [ 0.006142 -0.02986 0.9952 3.962e-05 -1.779e-05 1.023 2.986e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1011 0.08917 0.1807 0.2021 0.9873 0.9919 0.1012 0.7669 0.8692 0.3062 ] Network output: [ -0.005936 0.02945 1.003 4.134e-05 -1.856e-05 0.9799 3.115e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08853 0.1652 0.1955 0.9854 0.9913 0.09047 0.6925 0.8462 0.2445 ] Network output: [ 0.000183 0.9999 -0.0003133 5.588e-06 -2.509e-06 1 4.212e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004893 Epoch 7802 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01122 0.995 0.9898 7.596e-07 -3.41e-07 -0.007226 5.724e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003317 -0.003121 -0.008243 0.006404 0.9698 0.9742 0.006353 0.8363 0.8264 0.01849 ] Network output: [ 0.9998 0.000702 0.001038 -2.094e-05 9.401e-06 -0.001448 -1.578e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1934 -0.03293 -0.1802 0.1921 0.9835 0.9933 0.2163 0.4454 0.8726 0.7184 ] Network output: [ -0.01076 1.002 1.01 2.021e-07 -9.072e-08 0.009829 1.523e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005865 0.0004701 0.00443 0.003793 0.9889 0.9919 0.005975 0.8649 0.8967 0.01336 ] Network output: [ -0.0006661 0.002967 1.002 -6.647e-05 2.984e-05 0.9964 -5.01e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.205 0.09532 0.3354 0.1481 0.985 0.994 0.2057 0.4498 0.8792 0.7128 ] Network output: [ 0.00614 -0.02985 0.9952 3.959e-05 -1.777e-05 1.023 2.983e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1011 0.08917 0.1807 0.2021 0.9873 0.9919 0.1012 0.7669 0.8692 0.3062 ] Network output: [ -0.005934 0.02943 1.003 4.13e-05 -1.854e-05 0.9799 3.112e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08853 0.1652 0.1955 0.9854 0.9913 0.09047 0.6925 0.8462 0.2445 ] Network output: [ 0.0001829 0.9999 -0.000313 5.583e-06 -2.507e-06 1 4.208e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004889 Epoch 7803 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01122 0.995 0.9898 7.573e-07 -3.4e-07 -0.007227 5.707e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003317 -0.003121 -0.008241 0.006403 0.9698 0.9742 0.006354 0.8363 0.8264 0.01849 ] Network output: [ 0.9998 0.0007014 0.001038 -2.092e-05 9.392e-06 -0.001447 -1.577e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1935 -0.03293 -0.1802 0.1921 0.9835 0.9933 0.2163 0.4454 0.8726 0.7184 ] Network output: [ -0.01076 1.002 1.01 2.007e-07 -9.012e-08 0.009826 1.513e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005866 0.0004702 0.00443 0.003792 0.9889 0.9919 0.005976 0.8649 0.8967 0.01336 ] Network output: [ -0.0006657 0.002966 1.002 -6.641e-05 2.981e-05 0.9964 -5.005e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.205 0.09532 0.3355 0.1481 0.985 0.994 0.2057 0.4498 0.8792 0.7128 ] Network output: [ 0.006138 -0.02983 0.9952 3.955e-05 -1.776e-05 1.023 2.981e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1011 0.08918 0.1807 0.2021 0.9873 0.9919 0.1012 0.7669 0.8692 0.3062 ] Network output: [ -0.005931 0.02942 1.003 4.126e-05 -1.852e-05 0.9799 3.11e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08853 0.1652 0.1955 0.9854 0.9913 0.09047 0.6924 0.8462 0.2445 ] Network output: [ 0.0001828 0.9999 -0.0003126 5.578e-06 -2.504e-06 1 4.204e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004886 Epoch 7804 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01122 0.995 0.9898 7.551e-07 -3.39e-07 -0.007228 5.69e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003317 -0.003122 -0.00824 0.006402 0.9698 0.9742 0.006354 0.8363 0.8264 0.01849 ] Network output: [ 0.9998 0.0007008 0.001037 -2.09e-05 9.384e-06 -0.001445 -1.575e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1935 -0.03294 -0.1802 0.1921 0.9835 0.9933 0.2163 0.4454 0.8726 0.7184 ] Network output: [ -0.01076 1.002 1.01 1.994e-07 -8.951e-08 0.009823 1.503e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005867 0.0004702 0.00443 0.003792 0.9889 0.9919 0.005976 0.8649 0.8967 0.01336 ] Network output: [ -0.0006653 0.002965 1.002 -6.635e-05 2.979e-05 0.9964 -5e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.205 0.09533 0.3355 0.1481 0.985 0.994 0.2057 0.4498 0.8792 0.7128 ] Network output: [ 0.006136 -0.02982 0.9952 3.952e-05 -1.774e-05 1.023 2.978e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1011 0.08918 0.1807 0.2021 0.9873 0.9919 0.1012 0.7669 0.8692 0.3062 ] Network output: [ -0.005929 0.02941 1.003 4.123e-05 -1.851e-05 0.9799 3.107e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08853 0.1652 0.1955 0.9854 0.9913 0.09047 0.6924 0.8462 0.2445 ] Network output: [ 0.0001827 0.9999 -0.0003123 5.573e-06 -2.502e-06 1 4.2e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004883 Epoch 7805 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01122 0.995 0.9898 7.528e-07 -3.38e-07 -0.007229 5.674e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003318 -0.003122 -0.008239 0.006402 0.9698 0.9742 0.006354 0.8363 0.8264 0.01849 ] Network output: [ 0.9998 0.0007001 0.001036 -2.088e-05 9.375e-06 -0.001444 -1.574e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1935 -0.03294 -0.1802 0.1921 0.9835 0.9933 0.2163 0.4454 0.8726 0.7184 ] Network output: [ -0.01076 1.002 1.01 1.981e-07 -8.891e-08 0.009821 1.493e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005867 0.0004703 0.00443 0.003791 0.9889 0.9919 0.005977 0.8649 0.8967 0.01336 ] Network output: [ -0.0006649 0.002964 1.002 -6.629e-05 2.976e-05 0.9964 -4.996e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.205 0.09533 0.3355 0.1481 0.985 0.994 0.2057 0.4498 0.8792 0.7128 ] Network output: [ 0.006134 -0.02981 0.9952 3.948e-05 -1.772e-05 1.023 2.975e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1011 0.08919 0.1807 0.2021 0.9873 0.9919 0.1012 0.7668 0.8692 0.3062 ] Network output: [ -0.005927 0.0294 1.003 4.119e-05 -1.849e-05 0.9799 3.104e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08853 0.1652 0.1955 0.9854 0.9913 0.09048 0.6924 0.8462 0.2445 ] Network output: [ 0.0001826 0.9999 -0.000312 5.568e-06 -2.5e-06 1 4.196e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000488 Epoch 7806 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01122 0.995 0.9898 7.506e-07 -3.37e-07 -0.00723 5.657e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003318 -0.003122 -0.008238 0.006401 0.9698 0.9742 0.006355 0.8363 0.8264 0.01849 ] Network output: [ 0.9998 0.0006995 0.001035 -2.086e-05 9.367e-06 -0.001443 -1.572e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1935 -0.03294 -0.1802 0.1921 0.9835 0.9933 0.2163 0.4454 0.8726 0.7184 ] Network output: [ -0.01075 1.002 1.01 1.967e-07 -8.831e-08 0.009818 1.482e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005868 0.0004703 0.00443 0.003791 0.9889 0.9919 0.005978 0.8649 0.8966 0.01336 ] Network output: [ -0.0006645 0.002963 1.002 -6.622e-05 2.973e-05 0.9964 -4.991e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.205 0.09534 0.3355 0.1481 0.985 0.994 0.2057 0.4498 0.8792 0.7128 ] Network output: [ 0.006132 -0.0298 0.9952 3.944e-05 -1.771e-05 1.023 2.973e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1011 0.0892 0.1807 0.2021 0.9873 0.9919 0.1012 0.7668 0.8692 0.3062 ] Network output: [ -0.005925 0.02938 1.003 4.115e-05 -1.848e-05 0.9799 3.101e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08853 0.1652 0.1955 0.9854 0.9913 0.09048 0.6924 0.8462 0.2445 ] Network output: [ 0.0001825 0.9999 -0.0003116 5.563e-06 -2.497e-06 1 4.193e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004877 Epoch 7807 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01122 0.995 0.9898 7.484e-07 -3.36e-07 -0.00723 5.64e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003318 -0.003122 -0.008237 0.0064 0.9698 0.9742 0.006355 0.8363 0.8264 0.01849 ] Network output: [ 0.9998 0.0006989 0.001035 -2.085e-05 9.358e-06 -0.001442 -1.571e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1935 -0.03294 -0.1802 0.1921 0.9835 0.9933 0.2163 0.4454 0.8726 0.7184 ] Network output: [ -0.01075 1.002 1.01 1.954e-07 -8.771e-08 0.009816 1.472e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005868 0.0004704 0.00443 0.00379 0.9889 0.9919 0.005978 0.8649 0.8966 0.01336 ] Network output: [ -0.0006641 0.002962 1.002 -6.616e-05 2.97e-05 0.9964 -4.986e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2051 0.09534 0.3355 0.1481 0.985 0.994 0.2057 0.4497 0.8792 0.7128 ] Network output: [ 0.00613 -0.02979 0.9952 3.941e-05 -1.769e-05 1.022 2.97e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1011 0.0892 0.1807 0.2021 0.9873 0.9919 0.1012 0.7668 0.8691 0.3062 ] Network output: [ -0.005923 0.02937 1.003 4.112e-05 -1.846e-05 0.9799 3.099e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09046 0.08853 0.1652 0.1955 0.9854 0.9913 0.09048 0.6923 0.8461 0.2445 ] Network output: [ 0.0001824 0.9999 -0.0003113 5.558e-06 -2.495e-06 1 4.189e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004874 Epoch 7808 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01121 0.995 0.9898 7.461e-07 -3.35e-07 -0.007231 5.623e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003318 -0.003122 -0.008236 0.006399 0.9698 0.9742 0.006355 0.8363 0.8264 0.01848 ] Network output: [ 0.9998 0.0006983 0.001034 -2.083e-05 9.35e-06 -0.001441 -1.57e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1935 -0.03295 -0.1801 0.1921 0.9835 0.9933 0.2163 0.4453 0.8726 0.7184 ] Network output: [ -0.01075 1.002 1.01 1.94e-07 -8.711e-08 0.009813 1.462e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005869 0.0004704 0.00443 0.00379 0.9889 0.9919 0.005979 0.8648 0.8966 0.01336 ] Network output: [ -0.0006637 0.002961 1.002 -6.61e-05 2.967e-05 0.9964 -4.981e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2051 0.09535 0.3355 0.148 0.985 0.994 0.2057 0.4497 0.8792 0.7128 ] Network output: [ 0.006128 -0.02978 0.9952 3.937e-05 -1.767e-05 1.022 2.967e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1011 0.08921 0.1807 0.2021 0.9873 0.9919 0.1012 0.7668 0.8691 0.3062 ] Network output: [ -0.00592 0.02936 1.003 4.108e-05 -1.844e-05 0.9799 3.096e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09047 0.08853 0.1652 0.1955 0.9854 0.9913 0.09048 0.6923 0.8461 0.2445 ] Network output: [ 0.0001823 0.9999 -0.0003109 5.553e-06 -2.493e-06 1 4.185e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004871 Epoch 7809 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01121 0.995 0.9898 7.439e-07 -3.34e-07 -0.007232 5.606e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003318 -0.003122 -0.008234 0.006399 0.9698 0.9742 0.006356 0.8363 0.8264 0.01848 ] Network output: [ 0.9998 0.0006977 0.001033 -2.081e-05 9.341e-06 -0.001439 -1.568e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1935 -0.03295 -0.1801 0.1921 0.9835 0.9933 0.2164 0.4453 0.8726 0.7184 ] Network output: [ -0.01075 1.002 1.01 1.927e-07 -8.652e-08 0.009811 1.452e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00587 0.0004705 0.00443 0.003789 0.9889 0.9919 0.005979 0.8648 0.8966 0.01335 ] Network output: [ -0.0006633 0.00296 1.002 -6.604e-05 2.965e-05 0.9964 -4.977e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2051 0.09535 0.3355 0.148 0.985 0.994 0.2057 0.4497 0.8792 0.7128 ] Network output: [ 0.006126 -0.02977 0.9952 3.933e-05 -1.766e-05 1.022 2.964e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1012 0.08921 0.1807 0.2021 0.9873 0.9919 0.1012 0.7668 0.8691 0.3062 ] Network output: [ -0.005918 0.02934 1.003 4.104e-05 -1.843e-05 0.9799 3.093e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09047 0.08853 0.1652 0.1955 0.9854 0.9913 0.09048 0.6923 0.8461 0.2445 ] Network output: [ 0.0001822 0.9999 -0.0003106 5.548e-06 -2.491e-06 1 4.181e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004868 Epoch 7810 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01121 0.995 0.9898 7.417e-07 -3.33e-07 -0.007233 5.59e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003318 -0.003123 -0.008233 0.006398 0.9698 0.9742 0.006356 0.8363 0.8263 0.01848 ] Network output: [ 0.9998 0.0006971 0.001033 -2.079e-05 9.333e-06 -0.001438 -1.567e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1935 -0.03295 -0.1801 0.1921 0.9835 0.9933 0.2164 0.4453 0.8726 0.7184 ] Network output: [ -0.01075 1.002 1.01 1.914e-07 -8.592e-08 0.009808 1.442e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00587 0.0004705 0.004431 0.003789 0.9889 0.9919 0.00598 0.8648 0.8966 0.01335 ] Network output: [ -0.0006629 0.002959 1.002 -6.597e-05 2.962e-05 0.9964 -4.972e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2051 0.09536 0.3355 0.148 0.985 0.994 0.2058 0.4497 0.8792 0.7128 ] Network output: [ 0.006124 -0.02976 0.9952 3.93e-05 -1.764e-05 1.022 2.962e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1012 0.08922 0.1807 0.2021 0.9873 0.9919 0.1012 0.7667 0.8691 0.3062 ] Network output: [ -0.005916 0.02933 1.003 4.101e-05 -1.841e-05 0.9799 3.09e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09047 0.08853 0.1652 0.1955 0.9854 0.9913 0.09048 0.6923 0.8461 0.2445 ] Network output: [ 0.0001821 0.9999 -0.0003102 5.543e-06 -2.488e-06 1 4.177e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004865 Epoch 7811 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01121 0.995 0.9898 7.395e-07 -3.32e-07 -0.007234 5.573e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003318 -0.003123 -0.008232 0.006397 0.9698 0.9742 0.006356 0.8363 0.8263 0.01848 ] Network output: [ 0.9998 0.0006965 0.001032 -2.077e-05 9.324e-06 -0.001437 -1.565e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1935 -0.03295 -0.1801 0.1921 0.9835 0.9933 0.2164 0.4453 0.8726 0.7184 ] Network output: [ -0.01075 1.002 1.01 1.901e-07 -8.532e-08 0.009805 1.432e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005871 0.0004706 0.004431 0.003788 0.9889 0.9919 0.005981 0.8648 0.8966 0.01335 ] Network output: [ -0.0006625 0.002958 1.002 -6.591e-05 2.959e-05 0.9964 -4.967e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2051 0.09536 0.3355 0.148 0.985 0.994 0.2058 0.4497 0.8792 0.7128 ] Network output: [ 0.006122 -0.02975 0.9952 3.926e-05 -1.763e-05 1.022 2.959e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1012 0.08922 0.1807 0.2021 0.9873 0.9919 0.1012 0.7667 0.8691 0.3061 ] Network output: [ -0.005914 0.02932 1.003 4.097e-05 -1.839e-05 0.9799 3.088e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09047 0.08853 0.1652 0.1955 0.9854 0.9913 0.09048 0.6922 0.8461 0.2445 ] Network output: [ 0.000182 0.9999 -0.0003099 5.538e-06 -2.486e-06 1 4.173e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004862 Epoch 7812 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01121 0.995 0.9898 7.373e-07 -3.31e-07 -0.007235 5.556e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003319 -0.003123 -0.008231 0.006396 0.9698 0.9742 0.006357 0.8362 0.8263 0.01848 ] Network output: [ 0.9998 0.0006959 0.001031 -2.075e-05 9.316e-06 -0.001436 -1.564e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1936 -0.03296 -0.1801 0.1921 0.9835 0.9933 0.2164 0.4453 0.8726 0.7184 ] Network output: [ -0.01075 1.002 1.01 1.887e-07 -8.473e-08 0.009803 1.422e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005872 0.0004706 0.004431 0.003788 0.9889 0.9919 0.005981 0.8648 0.8966 0.01335 ] Network output: [ -0.0006621 0.002957 1.002 -6.585e-05 2.956e-05 0.9964 -4.963e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2051 0.09537 0.3356 0.148 0.985 0.994 0.2058 0.4497 0.8792 0.7128 ] Network output: [ 0.00612 -0.02974 0.9952 3.922e-05 -1.761e-05 1.022 2.956e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1012 0.08923 0.1807 0.2021 0.9873 0.9919 0.1012 0.7667 0.8691 0.3061 ] Network output: [ -0.005911 0.02931 1.003 4.093e-05 -1.838e-05 0.9799 3.085e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09047 0.08853 0.1652 0.1955 0.9854 0.9913 0.09048 0.6922 0.8461 0.2445 ] Network output: [ 0.0001819 0.9999 -0.0003095 5.533e-06 -2.484e-06 1 4.17e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004859 Epoch 7813 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0112 0.995 0.9898 7.351e-07 -3.3e-07 -0.007236 5.54e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003319 -0.003123 -0.00823 0.006395 0.9698 0.9742 0.006357 0.8362 0.8263 0.01848 ] Network output: [ 0.9998 0.0006953 0.001031 -2.073e-05 9.308e-06 -0.001435 -1.562e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1936 -0.03296 -0.1801 0.1921 0.9835 0.9933 0.2164 0.4453 0.8726 0.7184 ] Network output: [ -0.01075 1.002 1.01 1.874e-07 -8.414e-08 0.0098 1.412e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005872 0.0004707 0.004431 0.003787 0.9889 0.9919 0.005982 0.8648 0.8966 0.01335 ] Network output: [ -0.0006617 0.002956 1.002 -6.579e-05 2.953e-05 0.9964 -4.958e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2051 0.09537 0.3356 0.148 0.985 0.994 0.2058 0.4497 0.8792 0.7128 ] Network output: [ 0.006118 -0.02972 0.9952 3.919e-05 -1.759e-05 1.022 2.953e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1012 0.08923 0.1807 0.2021 0.9873 0.9919 0.1012 0.7667 0.8691 0.3061 ] Network output: [ -0.005909 0.02929 1.003 4.09e-05 -1.836e-05 0.9799 3.082e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09047 0.08854 0.1652 0.1955 0.9854 0.9913 0.09048 0.6922 0.8461 0.2445 ] Network output: [ 0.0001818 0.9999 -0.0003092 5.528e-06 -2.482e-06 1 4.166e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004856 Epoch 7814 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0112 0.995 0.9898 7.328e-07 -3.29e-07 -0.007237 5.523e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003319 -0.003123 -0.008229 0.006395 0.9698 0.9742 0.006357 0.8362 0.8263 0.01847 ] Network output: [ 0.9998 0.0006946 0.00103 -2.071e-05 9.299e-06 -0.001433 -1.561e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1936 -0.03296 -0.18 0.1921 0.9835 0.9933 0.2164 0.4453 0.8726 0.7184 ] Network output: [ -0.01074 1.002 1.01 1.861e-07 -8.354e-08 0.009798 1.402e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005873 0.0004707 0.004431 0.003787 0.9889 0.9919 0.005983 0.8648 0.8966 0.01335 ] Network output: [ -0.0006613 0.002955 1.002 -6.572e-05 2.951e-05 0.9964 -4.953e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2051 0.09538 0.3356 0.148 0.985 0.994 0.2058 0.4496 0.8792 0.7128 ] Network output: [ 0.006116 -0.02971 0.9952 3.915e-05 -1.758e-05 1.022 2.951e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1012 0.08924 0.1807 0.2021 0.9873 0.9919 0.1012 0.7666 0.8691 0.3061 ] Network output: [ -0.005907 0.02928 1.003 4.086e-05 -1.834e-05 0.98 3.079e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09047 0.08854 0.1652 0.1955 0.9854 0.9913 0.09048 0.6922 0.8461 0.2445 ] Network output: [ 0.0001817 0.9999 -0.0003089 5.523e-06 -2.479e-06 1 4.162e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004853 Epoch 7815 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0112 0.995 0.9898 7.306e-07 -3.28e-07 -0.007238 5.506e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003319 -0.003123 -0.008227 0.006394 0.9698 0.9742 0.006358 0.8362 0.8263 0.01847 ] Network output: [ 0.9998 0.000694 0.001029 -2.069e-05 9.291e-06 -0.001432 -1.56e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1936 -0.03297 -0.18 0.1921 0.9835 0.9933 0.2164 0.4453 0.8726 0.7184 ] Network output: [ -0.01074 1.002 1.01 1.848e-07 -8.295e-08 0.009795 1.393e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005873 0.0004708 0.004431 0.003786 0.9889 0.9919 0.005983 0.8648 0.8966 0.01335 ] Network output: [ -0.0006609 0.002954 1.002 -6.566e-05 2.948e-05 0.9964 -4.948e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2052 0.09538 0.3356 0.148 0.985 0.994 0.2058 0.4496 0.8792 0.7128 ] Network output: [ 0.006114 -0.0297 0.9952 3.912e-05 -1.756e-05 1.022 2.948e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1012 0.08925 0.1807 0.2021 0.9873 0.9919 0.1013 0.7666 0.8691 0.3061 ] Network output: [ -0.005905 0.02927 1.003 4.082e-05 -1.833e-05 0.98 3.077e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09047 0.08854 0.1652 0.1955 0.9854 0.9913 0.09048 0.6921 0.8461 0.2445 ] Network output: [ 0.0001816 0.9999 -0.0003085 5.518e-06 -2.477e-06 1 4.158e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000485 Epoch 7816 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0112 0.995 0.9898 7.284e-07 -3.27e-07 -0.007239 5.49e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003319 -0.003124 -0.008226 0.006393 0.9698 0.9742 0.006358 0.8362 0.8263 0.01847 ] Network output: [ 0.9998 0.0006934 0.001029 -2.068e-05 9.282e-06 -0.001431 -1.558e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1936 -0.03297 -0.18 0.192 0.9835 0.9932 0.2164 0.4452 0.8726 0.7184 ] Network output: [ -0.01074 1.002 1.01 1.835e-07 -8.236e-08 0.009793 1.383e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005874 0.0004708 0.004431 0.003786 0.9889 0.9919 0.005984 0.8648 0.8966 0.01335 ] Network output: [ -0.0006605 0.002953 1.002 -6.56e-05 2.945e-05 0.9964 -4.944e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2052 0.09539 0.3356 0.148 0.985 0.994 0.2058 0.4496 0.8792 0.7128 ] Network output: [ 0.006112 -0.02969 0.9952 3.908e-05 -1.754e-05 1.022 2.945e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1012 0.08925 0.1807 0.2021 0.9873 0.9919 0.1013 0.7666 0.8691 0.3061 ] Network output: [ -0.005903 0.02925 1.003 4.079e-05 -1.831e-05 0.98 3.074e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09047 0.08854 0.1652 0.1955 0.9854 0.9913 0.09048 0.6921 0.8461 0.2445 ] Network output: [ 0.0001815 0.9999 -0.0003082 5.513e-06 -2.475e-06 1 4.155e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004847 Epoch 7817 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0112 0.995 0.9898 7.263e-07 -3.26e-07 -0.00724 5.473e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003319 -0.003124 -0.008225 0.006392 0.9698 0.9742 0.006358 0.8362 0.8263 0.01847 ] Network output: [ 0.9998 0.0006928 0.001028 -2.066e-05 9.274e-06 -0.00143 -1.557e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1936 -0.03297 -0.18 0.192 0.9835 0.9932 0.2165 0.4452 0.8726 0.7184 ] Network output: [ -0.01074 1.002 1.01 1.821e-07 -8.177e-08 0.00979 1.373e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005875 0.0004709 0.004431 0.003785 0.9889 0.9919 0.005984 0.8648 0.8966 0.01335 ] Network output: [ -0.0006601 0.002952 1.002 -6.554e-05 2.942e-05 0.9964 -4.939e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2052 0.09539 0.3356 0.148 0.985 0.994 0.2058 0.4496 0.8792 0.7128 ] Network output: [ 0.00611 -0.02968 0.9952 3.904e-05 -1.753e-05 1.022 2.942e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1012 0.08926 0.1808 0.2021 0.9873 0.9919 0.1013 0.7666 0.8691 0.3061 ] Network output: [ -0.0059 0.02924 1.003 4.075e-05 -1.83e-05 0.98 3.071e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09047 0.08854 0.1652 0.1955 0.9854 0.9913 0.09049 0.6921 0.8461 0.2445 ] Network output: [ 0.0001814 0.9999 -0.0003078 5.508e-06 -2.473e-06 1 4.151e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004844 Epoch 7818 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0112 0.995 0.9898 7.241e-07 -3.251e-07 -0.007241 5.457e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003319 -0.003124 -0.008224 0.006392 0.9698 0.9742 0.006359 0.8362 0.8263 0.01847 ] Network output: [ 0.9998 0.0006922 0.001027 -2.064e-05 9.265e-06 -0.001429 -1.555e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1936 -0.03297 -0.18 0.192 0.9835 0.9932 0.2165 0.4452 0.8726 0.7183 ] Network output: [ -0.01074 1.002 1.01 1.808e-07 -8.118e-08 0.009788 1.363e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005875 0.0004709 0.004431 0.003785 0.9889 0.9919 0.005985 0.8648 0.8966 0.01334 ] Network output: [ -0.0006597 0.002951 1.002 -6.548e-05 2.939e-05 0.9964 -4.934e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2052 0.0954 0.3356 0.148 0.985 0.994 0.2059 0.4496 0.8792 0.7128 ] Network output: [ 0.006108 -0.02967 0.9952 3.901e-05 -1.751e-05 1.022 2.94e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1012 0.08926 0.1808 0.2021 0.9873 0.9919 0.1013 0.7665 0.8691 0.3061 ] Network output: [ -0.005898 0.02923 1.003 4.072e-05 -1.828e-05 0.98 3.068e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09047 0.08854 0.1652 0.1955 0.9854 0.9913 0.09049 0.6921 0.846 0.2445 ] Network output: [ 0.0001813 0.9999 -0.0003075 5.503e-06 -2.47e-06 1 4.147e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000484 Epoch 7819 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01119 0.995 0.9898 7.219e-07 -3.241e-07 -0.007241 5.44e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00332 -0.003124 -0.008223 0.006391 0.9698 0.9742 0.006359 0.8362 0.8263 0.01847 ] Network output: [ 0.9998 0.0006916 0.001027 -2.062e-05 9.257e-06 -0.001427 -1.554e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1936 -0.03298 -0.1799 0.192 0.9835 0.9932 0.2165 0.4452 0.8726 0.7183 ] Network output: [ -0.01074 1.002 1.01 1.795e-07 -8.06e-08 0.009785 1.353e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005876 0.000471 0.004431 0.003784 0.9889 0.9919 0.005986 0.8647 0.8966 0.01334 ] Network output: [ -0.0006593 0.00295 1.002 -6.541e-05 2.937e-05 0.9964 -4.93e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2052 0.0954 0.3356 0.148 0.985 0.994 0.2059 0.4496 0.8791 0.7128 ] Network output: [ 0.006106 -0.02966 0.9952 3.897e-05 -1.75e-05 1.022 2.937e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1012 0.08927 0.1808 0.202 0.9873 0.9919 0.1013 0.7665 0.8691 0.3061 ] Network output: [ -0.005896 0.02922 1.003 4.068e-05 -1.826e-05 0.98 3.066e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09047 0.08854 0.1652 0.1955 0.9854 0.9913 0.09049 0.692 0.846 0.2445 ] Network output: [ 0.0001812 0.9999 -0.0003071 5.498e-06 -2.468e-06 1 4.143e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004837 Epoch 7820 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01119 0.995 0.9898 7.197e-07 -3.231e-07 -0.007242 5.424e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00332 -0.003124 -0.008222 0.00639 0.9698 0.9742 0.006359 0.8362 0.8263 0.01847 ] Network output: [ 0.9998 0.000691 0.001026 -2.06e-05 9.248e-06 -0.001426 -1.553e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1936 -0.03298 -0.1799 0.192 0.9835 0.9932 0.2165 0.4452 0.8726 0.7183 ] Network output: [ -0.01074 1.002 1.01 1.782e-07 -8.001e-08 0.009782 1.343e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005876 0.000471 0.004431 0.003784 0.9889 0.9919 0.005986 0.8647 0.8966 0.01334 ] Network output: [ -0.0006589 0.002949 1.002 -6.535e-05 2.934e-05 0.9964 -4.925e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2052 0.09541 0.3357 0.148 0.985 0.994 0.2059 0.4496 0.8791 0.7127 ] Network output: [ 0.006104 -0.02965 0.9952 3.894e-05 -1.748e-05 1.022 2.934e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1012 0.08927 0.1808 0.202 0.9873 0.9919 0.1013 0.7665 0.8691 0.3061 ] Network output: [ -0.005894 0.0292 1.003 4.064e-05 -1.825e-05 0.98 3.063e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09048 0.08854 0.1652 0.1955 0.9854 0.9913 0.09049 0.692 0.846 0.2445 ] Network output: [ 0.0001811 0.9999 -0.0003068 5.493e-06 -2.466e-06 1 4.139e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004834 Epoch 7821 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01119 0.995 0.9898 7.175e-07 -3.221e-07 -0.007243 5.407e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00332 -0.003124 -0.008221 0.006389 0.9698 0.9742 0.00636 0.8362 0.8263 0.01846 ] Network output: [ 0.9998 0.0006904 0.001025 -2.058e-05 9.24e-06 -0.001425 -1.551e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1936 -0.03298 -0.1799 0.192 0.9835 0.9932 0.2165 0.4452 0.8726 0.7183 ] Network output: [ -0.01074 1.002 1.01 1.769e-07 -7.942e-08 0.00978 1.333e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005877 0.0004711 0.004431 0.003783 0.9889 0.9919 0.005987 0.8647 0.8966 0.01334 ] Network output: [ -0.0006585 0.002948 1.002 -6.529e-05 2.931e-05 0.9964 -4.92e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2052 0.09541 0.3357 0.148 0.985 0.994 0.2059 0.4495 0.8791 0.7127 ] Network output: [ 0.006102 -0.02964 0.9952 3.89e-05 -1.746e-05 1.022 2.932e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1012 0.08928 0.1808 0.202 0.9873 0.9919 0.1013 0.7665 0.869 0.3061 ] Network output: [ -0.005892 0.02919 1.003 4.061e-05 -1.823e-05 0.98 3.06e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09048 0.08854 0.1652 0.1955 0.9854 0.9913 0.09049 0.692 0.846 0.2445 ] Network output: [ 0.000181 0.9999 -0.0003065 5.488e-06 -2.464e-06 1 4.136e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004831 Epoch 7822 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01119 0.9951 0.9898 7.153e-07 -3.211e-07 -0.007244 5.391e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00332 -0.003124 -0.008219 0.006389 0.9698 0.9742 0.00636 0.8362 0.8263 0.01846 ] Network output: [ 0.9998 0.0006898 0.001025 -2.056e-05 9.232e-06 -0.001424 -1.55e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1937 -0.03298 -0.1799 0.192 0.9835 0.9932 0.2165 0.4452 0.8726 0.7183 ] Network output: [ -0.01074 1.002 1.01 1.756e-07 -7.884e-08 0.009777 1.323e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005878 0.0004712 0.004431 0.003783 0.9889 0.9919 0.005988 0.8647 0.8966 0.01334 ] Network output: [ -0.0006581 0.002947 1.002 -6.523e-05 2.928e-05 0.9964 -4.916e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2052 0.09542 0.3357 0.1479 0.985 0.994 0.2059 0.4495 0.8791 0.7127 ] Network output: [ 0.0061 -0.02963 0.9952 3.886e-05 -1.745e-05 1.022 2.929e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1012 0.08928 0.1808 0.202 0.9873 0.9919 0.1013 0.7664 0.869 0.3061 ] Network output: [ -0.005889 0.02918 1.003 4.057e-05 -1.821e-05 0.98 3.058e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09048 0.08854 0.1652 0.1955 0.9854 0.9913 0.09049 0.692 0.846 0.2445 ] Network output: [ 0.0001809 0.9999 -0.0003061 5.483e-06 -2.461e-06 1 4.132e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004828 Epoch 7823 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01119 0.9951 0.9898 7.132e-07 -3.202e-07 -0.007245 5.375e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00332 -0.003125 -0.008218 0.006388 0.9698 0.9742 0.00636 0.8362 0.8263 0.01846 ] Network output: [ 0.9998 0.0006892 0.001024 -2.054e-05 9.223e-06 -0.001423 -1.548e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1937 -0.03299 -0.1799 0.192 0.9835 0.9932 0.2165 0.4451 0.8726 0.7183 ] Network output: [ -0.01073 1.002 1.01 1.743e-07 -7.826e-08 0.009775 1.314e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005878 0.0004712 0.004431 0.003782 0.9889 0.9919 0.005988 0.8647 0.8966 0.01334 ] Network output: [ -0.0006577 0.002946 1.002 -6.517e-05 2.926e-05 0.9964 -4.911e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2052 0.09542 0.3357 0.1479 0.985 0.994 0.2059 0.4495 0.8791 0.7127 ] Network output: [ 0.006098 -0.02962 0.9952 3.883e-05 -1.743e-05 1.022 2.926e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1012 0.08929 0.1808 0.202 0.9873 0.9919 0.1013 0.7664 0.869 0.3061 ] Network output: [ -0.005887 0.02917 1.003 4.053e-05 -1.82e-05 0.98 3.055e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09048 0.08854 0.1652 0.1955 0.9854 0.9913 0.09049 0.6919 0.846 0.2446 ] Network output: [ 0.0001808 0.9999 -0.0003058 5.478e-06 -2.459e-06 1 4.128e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004825 Epoch 7824 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01119 0.9951 0.9898 7.11e-07 -3.192e-07 -0.007246 5.358e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00332 -0.003125 -0.008217 0.006387 0.9698 0.9742 0.006361 0.8362 0.8263 0.01846 ] Network output: [ 0.9998 0.0006886 0.001023 -2.053e-05 9.215e-06 -0.001422 -1.547e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1937 -0.03299 -0.1799 0.192 0.9835 0.9932 0.2165 0.4451 0.8726 0.7183 ] Network output: [ -0.01073 1.002 1.01 1.73e-07 -7.767e-08 0.009772 1.304e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005879 0.0004713 0.004431 0.003782 0.9889 0.9919 0.005989 0.8647 0.8966 0.01334 ] Network output: [ -0.0006573 0.002945 1.002 -6.51e-05 2.923e-05 0.9964 -4.906e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2053 0.09543 0.3357 0.1479 0.985 0.994 0.2059 0.4495 0.8791 0.7127 ] Network output: [ 0.006096 -0.0296 0.9952 3.879e-05 -1.742e-05 1.022 2.923e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1012 0.08929 0.1808 0.202 0.9873 0.9919 0.1013 0.7664 0.869 0.3061 ] Network output: [ -0.005885 0.02915 1.003 4.05e-05 -1.818e-05 0.98 3.052e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09048 0.08854 0.1652 0.1955 0.9854 0.9913 0.09049 0.6919 0.846 0.2446 ] Network output: [ 0.0001807 0.9999 -0.0003054 5.473e-06 -2.457e-06 1 4.124e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004822 Epoch 7825 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01118 0.9951 0.9898 7.088e-07 -3.182e-07 -0.007247 5.342e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00332 -0.003125 -0.008216 0.006386 0.9698 0.9742 0.006361 0.8361 0.8263 0.01846 ] Network output: [ 0.9998 0.000688 0.001023 -2.051e-05 9.206e-06 -0.00142 -1.545e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1937 -0.03299 -0.1798 0.192 0.9835 0.9932 0.2166 0.4451 0.8725 0.7183 ] Network output: [ -0.01073 1.002 1.01 1.717e-07 -7.709e-08 0.00977 1.294e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00588 0.0004713 0.004431 0.003781 0.9889 0.9919 0.005989 0.8647 0.8966 0.01334 ] Network output: [ -0.0006569 0.002944 1.002 -6.504e-05 2.92e-05 0.9964 -4.902e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2053 0.09543 0.3357 0.1479 0.985 0.994 0.2059 0.4495 0.8791 0.7127 ] Network output: [ 0.006094 -0.02959 0.9952 3.876e-05 -1.74e-05 1.022 2.921e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1013 0.0893 0.1808 0.202 0.9873 0.9919 0.1013 0.7664 0.869 0.3061 ] Network output: [ -0.005883 0.02914 1.003 4.046e-05 -1.817e-05 0.98 3.049e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09048 0.08855 0.1652 0.1955 0.9854 0.9913 0.09049 0.6919 0.846 0.2446 ] Network output: [ 0.0001806 0.9999 -0.0003051 5.468e-06 -2.455e-06 1 4.121e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004819 Epoch 7826 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01118 0.9951 0.9898 7.067e-07 -3.172e-07 -0.007248 5.326e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00332 -0.003125 -0.008215 0.006386 0.9698 0.9742 0.006361 0.8361 0.8263 0.01846 ] Network output: [ 0.9998 0.0006874 0.001022 -2.049e-05 9.198e-06 -0.001419 -1.544e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1937 -0.03299 -0.1798 0.192 0.9835 0.9932 0.2166 0.4451 0.8725 0.7183 ] Network output: [ -0.01073 1.002 1.01 1.704e-07 -7.651e-08 0.009767 1.284e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00588 0.0004714 0.004432 0.003781 0.9889 0.9919 0.00599 0.8647 0.8966 0.01333 ] Network output: [ -0.0006565 0.002943 1.002 -6.498e-05 2.917e-05 0.9964 -4.897e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2053 0.09544 0.3357 0.1479 0.985 0.994 0.206 0.4495 0.8791 0.7127 ] Network output: [ 0.006092 -0.02958 0.9952 3.872e-05 -1.738e-05 1.022 2.918e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1013 0.08931 0.1808 0.202 0.9873 0.9919 0.1013 0.7664 0.869 0.3061 ] Network output: [ -0.005881 0.02913 1.003 4.043e-05 -1.815e-05 0.98 3.047e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09048 0.08855 0.1652 0.1955 0.9854 0.9913 0.09049 0.6918 0.846 0.2446 ] Network output: [ 0.0001805 0.9999 -0.0003048 5.463e-06 -2.452e-06 1 4.117e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004816 Epoch 7827 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01118 0.9951 0.9898 7.045e-07 -3.163e-07 -0.007249 5.309e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003321 -0.003125 -0.008214 0.006385 0.9698 0.9742 0.006361 0.8361 0.8263 0.01845 ] Network output: [ 0.9998 0.0006868 0.001021 -2.047e-05 9.19e-06 -0.001418 -1.543e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1937 -0.033 -0.1798 0.192 0.9835 0.9932 0.2166 0.4451 0.8725 0.7183 ] Network output: [ -0.01073 1.002 1.01 1.691e-07 -7.593e-08 0.009765 1.275e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005881 0.0004714 0.004432 0.00378 0.9889 0.9919 0.005991 0.8647 0.8966 0.01333 ] Network output: [ -0.0006561 0.002942 1.002 -6.492e-05 2.914e-05 0.9965 -4.892e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2053 0.09544 0.3357 0.1479 0.985 0.994 0.206 0.4495 0.8791 0.7127 ] Network output: [ 0.00609 -0.02957 0.9952 3.868e-05 -1.737e-05 1.022 2.915e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1013 0.08931 0.1808 0.202 0.9873 0.9919 0.1013 0.7663 0.869 0.3061 ] Network output: [ -0.005878 0.02911 1.003 4.039e-05 -1.813e-05 0.98 3.044e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09048 0.08855 0.1652 0.1955 0.9854 0.9913 0.09049 0.6918 0.846 0.2446 ] Network output: [ 0.0001804 0.9999 -0.0003044 5.458e-06 -2.45e-06 1 4.113e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004813 Epoch 7828 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01118 0.9951 0.9898 7.024e-07 -3.153e-07 -0.007249 5.293e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003321 -0.003125 -0.008212 0.006384 0.9698 0.9742 0.006362 0.8361 0.8263 0.01845 ] Network output: [ 0.9998 0.0006862 0.001021 -2.045e-05 9.181e-06 -0.001417 -1.541e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1937 -0.033 -0.1798 0.192 0.9835 0.9932 0.2166 0.4451 0.8725 0.7183 ] Network output: [ -0.01073 1.002 1.01 1.679e-07 -7.536e-08 0.009762 1.265e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005881 0.0004715 0.004432 0.00378 0.9889 0.9919 0.005991 0.8647 0.8966 0.01333 ] Network output: [ -0.0006557 0.002941 1.002 -6.486e-05 2.912e-05 0.9965 -4.888e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2053 0.09545 0.3357 0.1479 0.985 0.994 0.206 0.4494 0.8791 0.7127 ] Network output: [ 0.006088 -0.02956 0.9952 3.865e-05 -1.735e-05 1.022 2.913e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1013 0.08932 0.1808 0.202 0.9873 0.9919 0.1013 0.7663 0.869 0.3061 ] Network output: [ -0.005876 0.0291 1.003 4.035e-05 -1.812e-05 0.98 3.041e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09048 0.08855 0.1652 0.1955 0.9854 0.9913 0.09049 0.6918 0.846 0.2446 ] Network output: [ 0.0001804 0.9999 -0.0003041 5.453e-06 -2.448e-06 1 4.109e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000481 Epoch 7829 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01118 0.9951 0.9898 7.002e-07 -3.143e-07 -0.00725 5.277e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003321 -0.003126 -0.008211 0.006383 0.9698 0.9742 0.006362 0.8361 0.8263 0.01845 ] Network output: [ 0.9998 0.0006856 0.00102 -2.043e-05 9.173e-06 -0.001416 -1.54e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1937 -0.033 -0.1798 0.1919 0.9835 0.9932 0.2166 0.4451 0.8725 0.7183 ] Network output: [ -0.01073 1.002 1.01 1.666e-07 -7.478e-08 0.00976 1.255e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005882 0.0004715 0.004432 0.003779 0.9889 0.9919 0.005992 0.8647 0.8966 0.01333 ] Network output: [ -0.0006553 0.00294 1.002 -6.48e-05 2.909e-05 0.9965 -4.883e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2053 0.09545 0.3358 0.1479 0.985 0.994 0.206 0.4494 0.8791 0.7127 ] Network output: [ 0.006086 -0.02955 0.9952 3.861e-05 -1.733e-05 1.022 2.91e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1013 0.08932 0.1808 0.202 0.9873 0.9919 0.1013 0.7663 0.869 0.3061 ] Network output: [ -0.005874 0.02909 1.003 4.032e-05 -1.81e-05 0.98 3.038e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09048 0.08855 0.1652 0.1955 0.9854 0.9913 0.0905 0.6918 0.8459 0.2446 ] Network output: [ 0.0001803 0.9999 -0.0003038 5.448e-06 -2.446e-06 1 4.105e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004807 Epoch 7830 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01117 0.9951 0.9898 6.981e-07 -3.134e-07 -0.007251 5.261e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003321 -0.003126 -0.00821 0.006383 0.9698 0.9742 0.006362 0.8361 0.8262 0.01845 ] Network output: [ 0.9998 0.000685 0.001019 -2.041e-05 9.164e-06 -0.001414 -1.538e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1937 -0.03301 -0.1798 0.1919 0.9835 0.9932 0.2166 0.4451 0.8725 0.7183 ] Network output: [ -0.01073 1.002 1.01 1.653e-07 -7.42e-08 0.009757 1.246e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005883 0.0004716 0.004432 0.003779 0.9889 0.9919 0.005993 0.8646 0.8965 0.01333 ] Network output: [ -0.0006549 0.002938 1.002 -6.473e-05 2.906e-05 0.9965 -4.879e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2053 0.09546 0.3358 0.1479 0.985 0.994 0.206 0.4494 0.8791 0.7127 ] Network output: [ 0.006084 -0.02954 0.9952 3.858e-05 -1.732e-05 1.022 2.907e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1013 0.08933 0.1808 0.202 0.9873 0.9919 0.1013 0.7663 0.869 0.3061 ] Network output: [ -0.005872 0.02908 1.003 4.028e-05 -1.808e-05 0.9801 3.036e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09048 0.08855 0.1652 0.1955 0.9854 0.9913 0.0905 0.6917 0.8459 0.2446 ] Network output: [ 0.0001802 0.9999 -0.0003034 5.443e-06 -2.443e-06 1 4.102e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004804 Epoch 7831 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01117 0.9951 0.9898 6.959e-07 -3.124e-07 -0.007252 5.245e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003321 -0.003126 -0.008209 0.006382 0.9698 0.9742 0.006363 0.8361 0.8262 0.01845 ] Network output: [ 0.9998 0.0006844 0.001019 -2.039e-05 9.156e-06 -0.001413 -1.537e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1938 -0.03301 -0.1797 0.1919 0.9835 0.9932 0.2166 0.445 0.8725 0.7183 ] Network output: [ -0.01072 1.002 1.01 1.64e-07 -7.363e-08 0.009755 1.236e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005883 0.0004716 0.004432 0.003778 0.9889 0.9919 0.005993 0.8646 0.8965 0.01333 ] Network output: [ -0.0006545 0.002937 1.002 -6.467e-05 2.903e-05 0.9965 -4.874e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2053 0.09546 0.3358 0.1479 0.985 0.994 0.206 0.4494 0.8791 0.7127 ] Network output: [ 0.006082 -0.02953 0.9952 3.854e-05 -1.73e-05 1.022 2.905e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1013 0.08933 0.1808 0.202 0.9873 0.9919 0.1014 0.7662 0.869 0.3061 ] Network output: [ -0.00587 0.02906 1.003 4.025e-05 -1.807e-05 0.9801 3.033e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09049 0.08855 0.1652 0.1955 0.9854 0.9913 0.0905 0.6917 0.8459 0.2446 ] Network output: [ 0.0001801 0.9999 -0.0003031 5.438e-06 -2.441e-06 1 4.098e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004801 Epoch 7832 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01117 0.9951 0.9898 6.938e-07 -3.115e-07 -0.007253 5.229e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003321 -0.003126 -0.008208 0.006381 0.9698 0.9742 0.006363 0.8361 0.8262 0.01845 ] Network output: [ 0.9998 0.0006838 0.001018 -2.038e-05 9.148e-06 -0.001412 -1.536e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1938 -0.03301 -0.1797 0.1919 0.9835 0.9932 0.2166 0.445 0.8725 0.7183 ] Network output: [ -0.01072 1.002 1.01 1.627e-07 -7.305e-08 0.009752 1.226e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005884 0.0004717 0.004432 0.003778 0.9889 0.9919 0.005994 0.8646 0.8965 0.01333 ] Network output: [ -0.0006541 0.002936 1.002 -6.461e-05 2.901e-05 0.9965 -4.869e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2054 0.09547 0.3358 0.1479 0.985 0.994 0.206 0.4494 0.8791 0.7127 ] Network output: [ 0.00608 -0.02952 0.9952 3.851e-05 -1.729e-05 1.022 2.902e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1013 0.08934 0.1808 0.202 0.9873 0.9919 0.1014 0.7662 0.869 0.3061 ] Network output: [ -0.005867 0.02905 1.003 4.021e-05 -1.805e-05 0.9801 3.03e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09049 0.08855 0.1652 0.1955 0.9854 0.9913 0.0905 0.6917 0.8459 0.2446 ] Network output: [ 0.00018 0.9999 -0.0003027 5.433e-06 -2.439e-06 1 4.094e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004798 Epoch 7833 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01117 0.9951 0.9898 6.916e-07 -3.105e-07 -0.007254 5.212e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003321 -0.003126 -0.008207 0.00638 0.9698 0.9742 0.006363 0.8361 0.8262 0.01844 ] Network output: [ 0.9998 0.0006832 0.001017 -2.036e-05 9.139e-06 -0.001411 -1.534e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1938 -0.03301 -0.1797 0.1919 0.9835 0.9932 0.2167 0.445 0.8725 0.7183 ] Network output: [ -0.01072 1.002 1.01 1.615e-07 -7.248e-08 0.009749 1.217e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005884 0.0004717 0.004432 0.003777 0.9889 0.9919 0.005994 0.8646 0.8965 0.01333 ] Network output: [ -0.0006537 0.002935 1.002 -6.455e-05 2.898e-05 0.9965 -4.865e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2054 0.09548 0.3358 0.1479 0.985 0.994 0.206 0.4494 0.8791 0.7127 ] Network output: [ 0.006078 -0.02951 0.9952 3.847e-05 -1.727e-05 1.022 2.899e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1013 0.08934 0.1808 0.202 0.9873 0.9919 0.1014 0.7662 0.869 0.3061 ] Network output: [ -0.005865 0.02904 1.003 4.017e-05 -1.804e-05 0.9801 3.028e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09049 0.08855 0.1652 0.1955 0.9854 0.9913 0.0905 0.6917 0.8459 0.2446 ] Network output: [ 0.0001799 0.9999 -0.0003024 5.428e-06 -2.437e-06 1 4.09e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004795 Epoch 7834 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01117 0.9951 0.9898 6.895e-07 -3.095e-07 -0.007255 5.196e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003322 -0.003126 -0.008206 0.00638 0.9698 0.9742 0.006364 0.8361 0.8262 0.01844 ] Network output: [ 0.9998 0.0006826 0.001017 -2.034e-05 9.131e-06 -0.00141 -1.533e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1938 -0.03302 -0.1797 0.1919 0.9835 0.9932 0.2167 0.445 0.8725 0.7183 ] Network output: [ -0.01072 1.002 1.01 1.602e-07 -7.191e-08 0.009747 1.207e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005885 0.0004718 0.004432 0.003777 0.9889 0.9919 0.005995 0.8646 0.8965 0.01332 ] Network output: [ -0.0006533 0.002934 1.002 -6.449e-05 2.895e-05 0.9965 -4.86e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2054 0.09548 0.3358 0.1479 0.985 0.994 0.206 0.4494 0.8791 0.7127 ] Network output: [ 0.006076 -0.0295 0.9952 3.843e-05 -1.725e-05 1.022 2.897e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1013 0.08935 0.1808 0.202 0.9873 0.9919 0.1014 0.7662 0.869 0.3061 ] Network output: [ -0.005863 0.02903 1.003 4.014e-05 -1.802e-05 0.9801 3.025e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09049 0.08855 0.1652 0.1955 0.9854 0.9913 0.0905 0.6916 0.8459 0.2446 ] Network output: [ 0.0001798 0.9999 -0.0003021 5.423e-06 -2.434e-06 1 4.087e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004792 Epoch 7835 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01117 0.9951 0.9898 6.874e-07 -3.086e-07 -0.007256 5.18e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003322 -0.003127 -0.008204 0.006379 0.9698 0.9742 0.006364 0.8361 0.8262 0.01844 ] Network output: [ 0.9998 0.000682 0.001016 -2.032e-05 9.123e-06 -0.001409 -1.531e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1938 -0.03302 -0.1797 0.1919 0.9835 0.9932 0.2167 0.445 0.8725 0.7183 ] Network output: [ -0.01072 1.002 1.01 1.589e-07 -7.134e-08 0.009744 1.198e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005886 0.0004719 0.004432 0.003776 0.9889 0.9919 0.005996 0.8646 0.8965 0.01332 ] Network output: [ -0.0006529 0.002933 1.002 -6.443e-05 2.892e-05 0.9965 -4.855e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2054 0.09549 0.3358 0.1479 0.985 0.994 0.2061 0.4494 0.8791 0.7127 ] Network output: [ 0.006074 -0.02948 0.9951 3.84e-05 -1.724e-05 1.022 2.894e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1013 0.08936 0.1808 0.202 0.9873 0.9919 0.1014 0.7661 0.8689 0.3061 ] Network output: [ -0.005861 0.02901 1.003 4.01e-05 -1.8e-05 0.9801 3.022e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09049 0.08855 0.1652 0.1954 0.9854 0.9913 0.0905 0.6916 0.8459 0.2446 ] Network output: [ 0.0001797 0.9999 -0.0003017 5.418e-06 -2.432e-06 1 4.083e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004789 Epoch 7836 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01116 0.9951 0.9899 6.853e-07 -3.076e-07 -0.007257 5.164e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003322 -0.003127 -0.008203 0.006378 0.9698 0.9742 0.006364 0.8361 0.8262 0.01844 ] Network output: [ 0.9998 0.0006814 0.001015 -2.03e-05 9.114e-06 -0.001407 -1.53e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1938 -0.03302 -0.1797 0.1919 0.9835 0.9932 0.2167 0.445 0.8725 0.7182 ] Network output: [ -0.01072 1.002 1.01 1.576e-07 -7.077e-08 0.009742 1.188e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005886 0.0004719 0.004432 0.003776 0.9889 0.9919 0.005996 0.8646 0.8965 0.01332 ] Network output: [ -0.0006525 0.002932 1.002 -6.437e-05 2.89e-05 0.9965 -4.851e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2054 0.09549 0.3358 0.1479 0.985 0.994 0.2061 0.4493 0.8791 0.7127 ] Network output: [ 0.006072 -0.02947 0.9951 3.836e-05 -1.722e-05 1.022 2.891e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1013 0.08936 0.1808 0.202 0.9873 0.9919 0.1014 0.7661 0.8689 0.3061 ] Network output: [ -0.005859 0.029 1.003 4.007e-05 -1.799e-05 0.9801 3.02e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09049 0.08856 0.1652 0.1954 0.9854 0.9913 0.0905 0.6916 0.8459 0.2446 ] Network output: [ 0.0001796 0.9999 -0.0003014 5.413e-06 -2.43e-06 1 4.079e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004786 Epoch 7837 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01116 0.9951 0.9899 6.831e-07 -3.067e-07 -0.007257 5.148e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003322 -0.003127 -0.008202 0.006377 0.9698 0.9742 0.006365 0.836 0.8262 0.01844 ] Network output: [ 0.9998 0.0006808 0.001015 -2.028e-05 9.106e-06 -0.001406 -1.529e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1938 -0.03302 -0.1796 0.1919 0.9835 0.9932 0.2167 0.445 0.8725 0.7182 ] Network output: [ -0.01072 1.002 1.01 1.564e-07 -7.02e-08 0.009739 1.178e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005887 0.000472 0.004432 0.003775 0.9889 0.9919 0.005997 0.8646 0.8965 0.01332 ] Network output: [ -0.0006521 0.002931 1.002 -6.43e-05 2.887e-05 0.9965 -4.846e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2054 0.0955 0.3359 0.1478 0.985 0.994 0.2061 0.4493 0.8791 0.7126 ] Network output: [ 0.00607 -0.02946 0.9951 3.833e-05 -1.721e-05 1.022 2.888e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1013 0.08937 0.1808 0.202 0.9873 0.9919 0.1014 0.7661 0.8689 0.3061 ] Network output: [ -0.005856 0.02899 1.003 4.003e-05 -1.797e-05 0.9801 3.017e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09049 0.08856 0.1652 0.1954 0.9854 0.9913 0.0905 0.6916 0.8459 0.2446 ] Network output: [ 0.0001795 0.9999 -0.0003011 5.408e-06 -2.428e-06 1 4.076e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004783 Epoch 7838 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01116 0.9951 0.9899 6.81e-07 -3.057e-07 -0.007258 5.132e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003322 -0.003127 -0.008201 0.006377 0.9698 0.9742 0.006365 0.836 0.8262 0.01844 ] Network output: [ 0.9998 0.0006802 0.001014 -2.026e-05 9.098e-06 -0.001405 -1.527e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1938 -0.03303 -0.1796 0.1919 0.9835 0.9932 0.2167 0.4449 0.8725 0.7182 ] Network output: [ -0.01072 1.002 1.01 1.551e-07 -6.963e-08 0.009737 1.169e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005887 0.000472 0.004432 0.003775 0.9889 0.9919 0.005998 0.8646 0.8965 0.01332 ] Network output: [ -0.0006517 0.00293 1.002 -6.424e-05 2.884e-05 0.9965 -4.842e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2054 0.0955 0.3359 0.1478 0.985 0.994 0.2061 0.4493 0.8791 0.7126 ] Network output: [ 0.006068 -0.02945 0.9951 3.829e-05 -1.719e-05 1.022 2.886e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1013 0.08937 0.1808 0.202 0.9873 0.9919 0.1014 0.7661 0.8689 0.3061 ] Network output: [ -0.005854 0.02898 1.003 3.999e-05 -1.795e-05 0.9801 3.014e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09049 0.08856 0.1652 0.1954 0.9854 0.9913 0.0905 0.6915 0.8459 0.2446 ] Network output: [ 0.0001794 0.9999 -0.0003007 5.403e-06 -2.426e-06 1 4.072e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000478 Epoch 7839 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01116 0.9951 0.9899 6.789e-07 -3.048e-07 -0.007259 5.116e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003322 -0.003127 -0.0082 0.006376 0.9698 0.9742 0.006365 0.836 0.8262 0.01844 ] Network output: [ 0.9998 0.0006796 0.001013 -2.025e-05 9.089e-06 -0.001404 -1.526e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1938 -0.03303 -0.1796 0.1919 0.9835 0.9932 0.2167 0.4449 0.8725 0.7182 ] Network output: [ -0.01072 1.002 1.01 1.538e-07 -6.907e-08 0.009734 1.159e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005888 0.0004721 0.004432 0.003774 0.9889 0.9919 0.005998 0.8646 0.8965 0.01332 ] Network output: [ -0.0006513 0.002929 1.002 -6.418e-05 2.881e-05 0.9965 -4.837e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2054 0.09551 0.3359 0.1478 0.985 0.994 0.2061 0.4493 0.8791 0.7126 ] Network output: [ 0.006066 -0.02944 0.9951 3.826e-05 -1.717e-05 1.022 2.883e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1013 0.08938 0.1808 0.202 0.9873 0.9919 0.1014 0.7661 0.8689 0.3061 ] Network output: [ -0.005852 0.02896 1.003 3.996e-05 -1.794e-05 0.9801 3.011e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09049 0.08856 0.1652 0.1954 0.9854 0.9913 0.0905 0.6915 0.8459 0.2446 ] Network output: [ 0.0001793 0.9999 -0.0003004 5.398e-06 -2.423e-06 1 4.068e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004777 Epoch 7840 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01116 0.9951 0.9899 6.768e-07 -3.038e-07 -0.00726 5.101e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003322 -0.003127 -0.008199 0.006375 0.9698 0.9742 0.006366 0.836 0.8262 0.01843 ] Network output: [ 0.9998 0.0006791 0.001013 -2.023e-05 9.081e-06 -0.001403 -1.524e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1938 -0.03303 -0.1796 0.1919 0.9835 0.9932 0.2167 0.4449 0.8725 0.7182 ] Network output: [ -0.01071 1.002 1.01 1.526e-07 -6.85e-08 0.009732 1.15e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005889 0.0004721 0.004432 0.003774 0.9889 0.9919 0.005999 0.8646 0.8965 0.01332 ] Network output: [ -0.0006509 0.002928 1.002 -6.412e-05 2.879e-05 0.9965 -4.832e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2054 0.09551 0.3359 0.1478 0.985 0.994 0.2061 0.4493 0.8791 0.7126 ] Network output: [ 0.006064 -0.02943 0.9951 3.822e-05 -1.716e-05 1.022 2.88e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1013 0.08938 0.1808 0.2019 0.9873 0.9919 0.1014 0.766 0.8689 0.3061 ] Network output: [ -0.00585 0.02895 1.003 3.992e-05 -1.792e-05 0.9801 3.009e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09049 0.08856 0.1652 0.1954 0.9854 0.9913 0.09051 0.6915 0.8458 0.2446 ] Network output: [ 0.0001792 0.9999 -0.0003001 5.393e-06 -2.421e-06 1 4.064e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004774 Epoch 7841 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01116 0.9951 0.9899 6.747e-07 -3.029e-07 -0.007261 5.085e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003323 -0.003127 -0.008198 0.006374 0.9698 0.9742 0.006366 0.836 0.8262 0.01843 ] Network output: [ 0.9998 0.0006785 0.001012 -2.021e-05 9.073e-06 -0.001401 -1.523e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1939 -0.03303 -0.1796 0.1919 0.9835 0.9932 0.2167 0.4449 0.8725 0.7182 ] Network output: [ -0.01071 1.002 1.01 1.513e-07 -6.794e-08 0.009729 1.14e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005889 0.0004722 0.004432 0.003773 0.9889 0.9919 0.005999 0.8645 0.8965 0.01332 ] Network output: [ -0.0006505 0.002927 1.002 -6.406e-05 2.876e-05 0.9965 -4.828e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2055 0.09552 0.3359 0.1478 0.985 0.994 0.2061 0.4493 0.8791 0.7126 ] Network output: [ 0.006062 -0.02942 0.9951 3.818e-05 -1.714e-05 1.022 2.878e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1013 0.08939 0.1808 0.2019 0.9873 0.9919 0.1014 0.766 0.8689 0.3061 ] Network output: [ -0.005848 0.02894 1.003 3.989e-05 -1.791e-05 0.9801 3.006e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09049 0.08856 0.1652 0.1954 0.9854 0.9913 0.09051 0.6915 0.8458 0.2446 ] Network output: [ 0.0001791 0.9999 -0.0002997 5.388e-06 -2.419e-06 1 4.061e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004771 Epoch 7842 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01115 0.9951 0.9899 6.726e-07 -3.019e-07 -0.007262 5.069e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003323 -0.003128 -0.008196 0.006374 0.9698 0.9742 0.006366 0.836 0.8262 0.01843 ] Network output: [ 0.9998 0.0006779 0.001011 -2.019e-05 9.064e-06 -0.0014 -1.522e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1939 -0.03304 -0.1796 0.1919 0.9835 0.9932 0.2168 0.4449 0.8725 0.7182 ] Network output: [ -0.01071 1.002 1.01 1.501e-07 -6.737e-08 0.009727 1.131e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00589 0.0004722 0.004433 0.003773 0.9889 0.9919 0.006 0.8645 0.8965 0.01332 ] Network output: [ -0.0006501 0.002926 1.002 -6.4e-05 2.873e-05 0.9965 -4.823e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2055 0.09552 0.3359 0.1478 0.985 0.994 0.2061 0.4493 0.8791 0.7126 ] Network output: [ 0.00606 -0.02941 0.9951 3.815e-05 -1.713e-05 1.022 2.875e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1014 0.08939 0.1808 0.2019 0.9873 0.9919 0.1014 0.766 0.8689 0.3061 ] Network output: [ -0.005846 0.02893 1.003 3.985e-05 -1.789e-05 0.9801 3.003e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0905 0.08856 0.1652 0.1954 0.9854 0.9913 0.09051 0.6914 0.8458 0.2446 ] Network output: [ 0.000179 0.9999 -0.0002994 5.383e-06 -2.417e-06 1 4.057e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004768 Epoch 7843 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01115 0.9951 0.9899 6.705e-07 -3.01e-07 -0.007263 5.053e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003323 -0.003128 -0.008195 0.006373 0.9698 0.9742 0.006367 0.836 0.8262 0.01843 ] Network output: [ 0.9998 0.0006773 0.001011 -2.017e-05 9.056e-06 -0.001399 -1.52e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1939 -0.03304 -0.1795 0.1918 0.9835 0.9932 0.2168 0.4449 0.8725 0.7182 ] Network output: [ -0.01071 1.002 1.01 1.488e-07 -6.681e-08 0.009724 1.122e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005891 0.0004723 0.004433 0.003772 0.9889 0.9919 0.006001 0.8645 0.8965 0.01331 ] Network output: [ -0.0006497 0.002925 1.002 -6.394e-05 2.87e-05 0.9965 -4.819e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2055 0.09553 0.3359 0.1478 0.985 0.994 0.2062 0.4492 0.8791 0.7126 ] Network output: [ 0.006057 -0.0294 0.9951 3.811e-05 -1.711e-05 1.022 2.872e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1014 0.0894 0.1808 0.2019 0.9873 0.9919 0.1014 0.766 0.8689 0.3061 ] Network output: [ -0.005843 0.02891 1.003 3.982e-05 -1.787e-05 0.9801 3.001e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0905 0.08856 0.1652 0.1954 0.9854 0.9913 0.09051 0.6914 0.8458 0.2446 ] Network output: [ 0.0001789 0.9999 -0.0002991 5.378e-06 -2.414e-06 1 4.053e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004765 Epoch 7844 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01115 0.9951 0.9899 6.684e-07 -3.001e-07 -0.007263 5.037e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003323 -0.003128 -0.008194 0.006372 0.9698 0.9742 0.006367 0.836 0.8262 0.01843 ] Network output: [ 0.9998 0.0006767 0.00101 -2.015e-05 9.048e-06 -0.001398 -1.519e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1939 -0.03304 -0.1795 0.1918 0.9835 0.9932 0.2168 0.4449 0.8725 0.7182 ] Network output: [ -0.01071 1.002 1.01 1.476e-07 -6.625e-08 0.009722 1.112e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005891 0.0004724 0.004433 0.003772 0.9889 0.9919 0.006001 0.8645 0.8965 0.01331 ] Network output: [ -0.0006493 0.002924 1.002 -6.388e-05 2.868e-05 0.9965 -4.814e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2055 0.09553 0.3359 0.1478 0.985 0.994 0.2062 0.4492 0.879 0.7126 ] Network output: [ 0.006055 -0.02939 0.9951 3.808e-05 -1.709e-05 1.022 2.87e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1014 0.08941 0.1808 0.2019 0.9873 0.9919 0.1014 0.7659 0.8689 0.3061 ] Network output: [ -0.005841 0.0289 1.003 3.978e-05 -1.786e-05 0.9801 2.998e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0905 0.08856 0.1652 0.1954 0.9854 0.9913 0.09051 0.6914 0.8458 0.2446 ] Network output: [ 0.0001788 0.9999 -0.0002987 5.373e-06 -2.412e-06 1 4.049e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004762 Epoch 7845 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01115 0.9951 0.9899 6.663e-07 -2.991e-07 -0.007264 5.021e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003323 -0.003128 -0.008193 0.006371 0.9698 0.9742 0.006367 0.836 0.8262 0.01843 ] Network output: [ 0.9998 0.0006761 0.001009 -2.013e-05 9.039e-06 -0.001397 -1.517e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1939 -0.03304 -0.1795 0.1918 0.9835 0.9932 0.2168 0.4449 0.8725 0.7182 ] Network output: [ -0.01071 1.002 1.01 1.463e-07 -6.569e-08 0.009719 1.103e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005892 0.0004724 0.004433 0.003771 0.9889 0.9919 0.006002 0.8645 0.8965 0.01331 ] Network output: [ -0.0006489 0.002923 1.002 -6.382e-05 2.865e-05 0.9965 -4.809e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2055 0.09554 0.3359 0.1478 0.985 0.994 0.2062 0.4492 0.879 0.7126 ] Network output: [ 0.006053 -0.02938 0.9951 3.804e-05 -1.708e-05 1.022 2.867e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1014 0.08941 0.1808 0.2019 0.9873 0.9919 0.1014 0.7659 0.8689 0.3061 ] Network output: [ -0.005839 0.02889 1.003 3.974e-05 -1.784e-05 0.9801 2.995e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0905 0.08856 0.1652 0.1954 0.9854 0.9913 0.09051 0.6914 0.8458 0.2446 ] Network output: [ 0.0001787 0.9999 -0.0002984 5.368e-06 -2.41e-06 1 4.046e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004759 Epoch 7846 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01115 0.9951 0.9899 6.642e-07 -2.982e-07 -0.007265 5.006e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003323 -0.003128 -0.008192 0.006371 0.9698 0.9742 0.006368 0.836 0.8262 0.01842 ] Network output: [ 0.9998 0.0006755 0.001009 -2.012e-05 9.031e-06 -0.001396 -1.516e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1939 -0.03305 -0.1795 0.1918 0.9835 0.9932 0.2168 0.4448 0.8725 0.7182 ] Network output: [ -0.01071 1.002 1.01 1.451e-07 -6.513e-08 0.009717 1.093e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005892 0.0004725 0.004433 0.003771 0.9889 0.9919 0.006003 0.8645 0.8965 0.01331 ] Network output: [ -0.0006486 0.002922 1.002 -6.375e-05 2.862e-05 0.9965 -4.805e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2055 0.09554 0.336 0.1478 0.985 0.994 0.2062 0.4492 0.879 0.7126 ] Network output: [ 0.006051 -0.02937 0.9951 3.801e-05 -1.706e-05 1.022 2.864e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1014 0.08942 0.1808 0.2019 0.9873 0.9919 0.1014 0.7659 0.8689 0.3061 ] Network output: [ -0.005837 0.02887 1.003 3.971e-05 -1.783e-05 0.9802 2.993e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0905 0.08856 0.1652 0.1954 0.9854 0.9913 0.09051 0.6913 0.8458 0.2446 ] Network output: [ 0.0001786 0.9999 -0.0002981 5.363e-06 -2.408e-06 1 4.042e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004756 Epoch 7847 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01115 0.9951 0.9899 6.621e-07 -2.973e-07 -0.007266 4.99e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003323 -0.003128 -0.008191 0.00637 0.9698 0.9742 0.006368 0.836 0.8262 0.01842 ] Network output: [ 0.9998 0.0006749 0.001008 -2.01e-05 9.023e-06 -0.001394 -1.515e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1939 -0.03305 -0.1795 0.1918 0.9835 0.9932 0.2168 0.4448 0.8725 0.7182 ] Network output: [ -0.01071 1.002 1.01 1.438e-07 -6.457e-08 0.009714 1.084e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005893 0.0004725 0.004433 0.00377 0.9889 0.9919 0.006003 0.8645 0.8965 0.01331 ] Network output: [ -0.0006482 0.002921 1.002 -6.369e-05 2.859e-05 0.9965 -4.8e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2055 0.09555 0.336 0.1478 0.985 0.994 0.2062 0.4492 0.879 0.7126 ] Network output: [ 0.006049 -0.02935 0.9951 3.797e-05 -1.705e-05 1.022 2.862e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1014 0.08942 0.1808 0.2019 0.9873 0.9919 0.1015 0.7659 0.8689 0.3061 ] Network output: [ -0.005835 0.02886 1.003 3.967e-05 -1.781e-05 0.9802 2.99e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0905 0.08857 0.1652 0.1954 0.9854 0.9913 0.09051 0.6913 0.8458 0.2446 ] Network output: [ 0.0001785 0.9999 -0.0002977 5.358e-06 -2.406e-06 1 4.038e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004753 Epoch 7848 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01114 0.9951 0.9899 6.6e-07 -2.963e-07 -0.007267 4.974e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003324 -0.003129 -0.00819 0.006369 0.9698 0.9742 0.006368 0.836 0.8262 0.01842 ] Network output: [ 0.9998 0.0006743 0.001007 -2.008e-05 9.014e-06 -0.001393 -1.513e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1939 -0.03305 -0.1795 0.1918 0.9835 0.9932 0.2168 0.4448 0.8725 0.7182 ] Network output: [ -0.0107 1.002 1.01 1.426e-07 -6.401e-08 0.009712 1.075e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005894 0.0004726 0.004433 0.00377 0.9889 0.9919 0.006004 0.8645 0.8965 0.01331 ] Network output: [ -0.0006478 0.00292 1.002 -6.363e-05 2.857e-05 0.9965 -4.796e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2055 0.09555 0.336 0.1478 0.985 0.994 0.2062 0.4492 0.879 0.7126 ] Network output: [ 0.006047 -0.02934 0.9951 3.794e-05 -1.703e-05 1.022 2.859e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1014 0.08943 0.1808 0.2019 0.9873 0.9919 0.1015 0.7659 0.8689 0.3061 ] Network output: [ -0.005832 0.02885 1.003 3.964e-05 -1.779e-05 0.9802 2.987e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0905 0.08857 0.1652 0.1954 0.9854 0.9913 0.09051 0.6913 0.8458 0.2446 ] Network output: [ 0.0001785 0.9999 -0.0002974 5.353e-06 -2.403e-06 1 4.035e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000475 Epoch 7849 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01114 0.9951 0.9899 6.58e-07 -2.954e-07 -0.007268 4.959e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003324 -0.003129 -0.008188 0.006368 0.9698 0.9742 0.006369 0.836 0.8262 0.01842 ] Network output: [ 0.9998 0.0006738 0.001007 -2.006e-05 9.006e-06 -0.001392 -1.512e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1939 -0.03305 -0.1794 0.1918 0.9835 0.9932 0.2168 0.4448 0.8724 0.7182 ] Network output: [ -0.0107 1.002 1.01 1.413e-07 -6.346e-08 0.009709 1.065e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005894 0.0004726 0.004433 0.003769 0.9889 0.9919 0.006004 0.8645 0.8965 0.01331 ] Network output: [ -0.0006474 0.002919 1.002 -6.357e-05 2.854e-05 0.9965 -4.791e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2056 0.09556 0.336 0.1478 0.985 0.994 0.2062 0.4492 0.879 0.7126 ] Network output: [ 0.006045 -0.02933 0.9951 3.79e-05 -1.702e-05 1.022 2.856e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1014 0.08943 0.1808 0.2019 0.9873 0.9919 0.1015 0.7658 0.8688 0.3061 ] Network output: [ -0.00583 0.02884 1.003 3.96e-05 -1.778e-05 0.9802 2.984e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0905 0.08857 0.1652 0.1954 0.9854 0.9913 0.09051 0.6913 0.8458 0.2446 ] Network output: [ 0.0001784 0.9999 -0.0002971 5.349e-06 -2.401e-06 1 4.031e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004747 Epoch 7850 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01114 0.9951 0.9899 6.559e-07 -2.945e-07 -0.007269 4.943e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003324 -0.003129 -0.008187 0.006368 0.9698 0.9742 0.006369 0.8359 0.8261 0.01842 ] Network output: [ 0.9998 0.0006732 0.001006 -2.004e-05 8.998e-06 -0.001391 -1.51e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1939 -0.03306 -0.1794 0.1918 0.9835 0.9932 0.2169 0.4448 0.8724 0.7182 ] Network output: [ -0.0107 1.002 1.01 1.401e-07 -6.29e-08 0.009707 1.056e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005895 0.0004727 0.004433 0.003769 0.9889 0.9919 0.006005 0.8645 0.8965 0.01331 ] Network output: [ -0.000647 0.002918 1.002 -6.351e-05 2.851e-05 0.9965 -4.786e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2056 0.09556 0.336 0.1478 0.985 0.994 0.2062 0.4491 0.879 0.7126 ] Network output: [ 0.006043 -0.02932 0.9951 3.787e-05 -1.7e-05 1.022 2.854e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1014 0.08944 0.1808 0.2019 0.9873 0.9919 0.1015 0.7658 0.8688 0.3061 ] Network output: [ -0.005828 0.02882 1.003 3.957e-05 -1.776e-05 0.9802 2.982e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0905 0.08857 0.1652 0.1954 0.9854 0.9913 0.09052 0.6912 0.8458 0.2446 ] Network output: [ 0.0001783 0.9999 -0.0002967 5.344e-06 -2.399e-06 1 4.027e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004744 Epoch 7851 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01114 0.9951 0.9899 6.538e-07 -2.935e-07 -0.007269 4.927e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003324 -0.003129 -0.008186 0.006367 0.9698 0.9742 0.006369 0.8359 0.8261 0.01842 ] Network output: [ 0.9998 0.0006726 0.001005 -2.002e-05 8.99e-06 -0.00139 -1.509e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.194 -0.03306 -0.1794 0.1918 0.9835 0.9932 0.2169 0.4448 0.8724 0.7182 ] Network output: [ -0.0107 1.002 1.01 1.389e-07 -6.235e-08 0.009705 1.047e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005895 0.0004728 0.004433 0.003768 0.9889 0.9919 0.006006 0.8645 0.8965 0.0133 ] Network output: [ -0.0006466 0.002917 1.002 -6.345e-05 2.849e-05 0.9965 -4.782e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2056 0.09557 0.336 0.1478 0.985 0.994 0.2062 0.4491 0.879 0.7126 ] Network output: [ 0.006041 -0.02931 0.9951 3.783e-05 -1.698e-05 1.022 2.851e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1014 0.08944 0.1809 0.2019 0.9873 0.9919 0.1015 0.7658 0.8688 0.3061 ] Network output: [ -0.005826 0.02881 1.003 3.953e-05 -1.775e-05 0.9802 2.979e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0905 0.08857 0.1652 0.1954 0.9854 0.9913 0.09052 0.6912 0.8458 0.2446 ] Network output: [ 0.0001782 0.9999 -0.0002964 5.339e-06 -2.397e-06 1 4.023e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004741 Epoch 7852 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01114 0.9951 0.9899 6.517e-07 -2.926e-07 -0.00727 4.912e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003324 -0.003129 -0.008185 0.006366 0.9698 0.9742 0.00637 0.8359 0.8261 0.01841 ] Network output: [ 0.9998 0.000672 0.001005 -2.001e-05 8.981e-06 -0.001389 -1.508e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.194 -0.03306 -0.1794 0.1918 0.9835 0.9932 0.2169 0.4448 0.8724 0.7182 ] Network output: [ -0.0107 1.002 1.01 1.376e-07 -6.179e-08 0.009702 1.037e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005896 0.0004728 0.004433 0.003768 0.9889 0.9919 0.006006 0.8644 0.8965 0.0133 ] Network output: [ -0.0006462 0.002916 1.002 -6.339e-05 2.846e-05 0.9965 -4.777e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2056 0.09557 0.336 0.1477 0.985 0.994 0.2063 0.4491 0.879 0.7126 ] Network output: [ 0.006039 -0.0293 0.9951 3.78e-05 -1.697e-05 1.022 2.848e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1014 0.08945 0.1809 0.2019 0.9873 0.9919 0.1015 0.7658 0.8688 0.3061 ] Network output: [ -0.005824 0.0288 1.003 3.949e-05 -1.773e-05 0.9802 2.976e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0905 0.08857 0.1652 0.1954 0.9854 0.9913 0.09052 0.6912 0.8457 0.2446 ] Network output: [ 0.0001781 0.9999 -0.0002961 5.334e-06 -2.395e-06 1 4.02e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004738 Epoch 7853 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01114 0.9951 0.9899 6.497e-07 -2.917e-07 -0.007271 4.896e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003324 -0.003129 -0.008184 0.006365 0.9698 0.9742 0.00637 0.8359 0.8261 0.01841 ] Network output: [ 0.9998 0.0006714 0.001004 -1.999e-05 8.973e-06 -0.001387 -1.506e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.194 -0.03306 -0.1794 0.1918 0.9835 0.9932 0.2169 0.4447 0.8724 0.7182 ] Network output: [ -0.0107 1.002 1.01 1.364e-07 -6.124e-08 0.0097 1.028e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005897 0.0004729 0.004433 0.003768 0.9889 0.9919 0.006007 0.8644 0.8965 0.0133 ] Network output: [ -0.0006458 0.002915 1.002 -6.333e-05 2.843e-05 0.9965 -4.773e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2056 0.09558 0.336 0.1477 0.985 0.994 0.2063 0.4491 0.879 0.7126 ] Network output: [ 0.006037 -0.02929 0.9951 3.776e-05 -1.695e-05 1.022 2.846e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1014 0.08946 0.1809 0.2019 0.9873 0.9919 0.1015 0.7657 0.8688 0.3061 ] Network output: [ -0.005822 0.02879 1.003 3.946e-05 -1.771e-05 0.9802 2.974e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09051 0.08857 0.1652 0.1954 0.9854 0.9913 0.09052 0.6912 0.8457 0.2446 ] Network output: [ 0.000178 0.9999 -0.0002957 5.329e-06 -2.392e-06 1 4.016e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004735 Epoch 7854 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01113 0.9951 0.9899 6.476e-07 -2.907e-07 -0.007272 4.881e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003324 -0.00313 -0.008183 0.006365 0.9698 0.9742 0.00637 0.8359 0.8261 0.01841 ] Network output: [ 0.9998 0.0006708 0.001003 -1.997e-05 8.965e-06 -0.001386 -1.505e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.194 -0.03307 -0.1794 0.1918 0.9835 0.9932 0.2169 0.4447 0.8724 0.7181 ] Network output: [ -0.0107 1.002 1.01 1.352e-07 -6.069e-08 0.009697 1.019e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005897 0.0004729 0.004433 0.003767 0.9889 0.9919 0.006008 0.8644 0.8965 0.0133 ] Network output: [ -0.0006454 0.002914 1.002 -6.327e-05 2.84e-05 0.9965 -4.768e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2056 0.09558 0.336 0.1477 0.985 0.994 0.2063 0.4491 0.879 0.7125 ] Network output: [ 0.006035 -0.02928 0.9951 3.772e-05 -1.694e-05 1.022 2.843e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1014 0.08946 0.1809 0.2019 0.9873 0.9919 0.1015 0.7657 0.8688 0.3061 ] Network output: [ -0.005819 0.02877 1.003 3.942e-05 -1.77e-05 0.9802 2.971e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09051 0.08857 0.1652 0.1954 0.9854 0.9913 0.09052 0.6911 0.8457 0.2446 ] Network output: [ 0.0001779 0.9999 -0.0002954 5.324e-06 -2.39e-06 1 4.012e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004733 Epoch 7855 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01113 0.9951 0.9899 6.456e-07 -2.898e-07 -0.007273 4.865e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003325 -0.00313 -0.008182 0.006364 0.9698 0.9742 0.00637 0.8359 0.8261 0.01841 ] Network output: [ 0.9998 0.0006702 0.001003 -1.995e-05 8.957e-06 -0.001385 -1.504e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.194 -0.03307 -0.1793 0.1918 0.9835 0.9932 0.2169 0.4447 0.8724 0.7181 ] Network output: [ -0.0107 1.002 1.01 1.34e-07 -6.014e-08 0.009695 1.01e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005898 0.000473 0.004433 0.003767 0.9889 0.9919 0.006008 0.8644 0.8964 0.0133 ] Network output: [ -0.000645 0.002913 1.002 -6.321e-05 2.838e-05 0.9965 -4.764e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2056 0.09559 0.3361 0.1477 0.985 0.994 0.2063 0.4491 0.879 0.7125 ] Network output: [ 0.006033 -0.02927 0.9951 3.769e-05 -1.692e-05 1.022 2.84e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1014 0.08947 0.1809 0.2019 0.9873 0.9919 0.1015 0.7657 0.8688 0.3061 ] Network output: [ -0.005817 0.02876 1.003 3.939e-05 -1.768e-05 0.9802 2.968e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09051 0.08857 0.1652 0.1954 0.9854 0.9913 0.09052 0.6911 0.8457 0.2446 ] Network output: [ 0.0001778 0.9999 -0.0002951 5.319e-06 -2.388e-06 1 4.009e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000473 Epoch 7856 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01113 0.9951 0.9899 6.435e-07 -2.889e-07 -0.007274 4.85e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003325 -0.00313 -0.00818 0.006363 0.9698 0.9742 0.006371 0.8359 0.8261 0.01841 ] Network output: [ 0.9998 0.0006697 0.001002 -1.993e-05 8.948e-06 -0.001384 -1.502e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.194 -0.03307 -0.1793 0.1918 0.9835 0.9932 0.2169 0.4447 0.8724 0.7181 ] Network output: [ -0.0107 1.002 1.01 1.327e-07 -5.959e-08 0.009692 1e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005898 0.000473 0.004433 0.003766 0.9889 0.9919 0.006009 0.8644 0.8964 0.0133 ] Network output: [ -0.0006446 0.002912 1.002 -6.315e-05 2.835e-05 0.9965 -4.759e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2056 0.0956 0.3361 0.1477 0.985 0.994 0.2063 0.4491 0.879 0.7125 ] Network output: [ 0.006031 -0.02926 0.9951 3.765e-05 -1.69e-05 1.022 2.838e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1014 0.08947 0.1809 0.2019 0.9873 0.9919 0.1015 0.7657 0.8688 0.3061 ] Network output: [ -0.005815 0.02875 1.003 3.935e-05 -1.767e-05 0.9802 2.966e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09051 0.08857 0.1652 0.1954 0.9854 0.9913 0.09052 0.6911 0.8457 0.2446 ] Network output: [ 0.0001777 0.9999 -0.0002947 5.314e-06 -2.386e-06 1 4.005e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004727 Epoch 7857 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01113 0.9951 0.9899 6.415e-07 -2.88e-07 -0.007274 4.834e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003325 -0.00313 -0.008179 0.006363 0.9698 0.9742 0.006371 0.8359 0.8261 0.01841 ] Network output: [ 0.9998 0.0006691 0.001002 -1.991e-05 8.94e-06 -0.001383 -1.501e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.194 -0.03307 -0.1793 0.1917 0.9835 0.9932 0.2169 0.4447 0.8724 0.7181 ] Network output: [ -0.01069 1.002 1.01 1.315e-07 -5.904e-08 0.00969 9.911e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005899 0.0004731 0.004433 0.003766 0.9889 0.9919 0.006009 0.8644 0.8964 0.0133 ] Network output: [ -0.0006442 0.002911 1.002 -6.309e-05 2.832e-05 0.9965 -4.755e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2056 0.0956 0.3361 0.1477 0.985 0.994 0.2063 0.4491 0.879 0.7125 ] Network output: [ 0.00603 -0.02925 0.9951 3.762e-05 -1.689e-05 1.022 2.835e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1015 0.08948 0.1809 0.2019 0.9873 0.9919 0.1015 0.7656 0.8688 0.3061 ] Network output: [ -0.005813 0.02874 1.003 3.932e-05 -1.765e-05 0.9802 2.963e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09051 0.08858 0.1652 0.1954 0.9854 0.9913 0.09052 0.6911 0.8457 0.2446 ] Network output: [ 0.0001776 0.9999 -0.0002944 5.309e-06 -2.384e-06 1 4.001e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004724 Epoch 7858 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01113 0.9951 0.9899 6.394e-07 -2.871e-07 -0.007275 4.819e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003325 -0.00313 -0.008178 0.006362 0.9698 0.9742 0.006371 0.8359 0.8261 0.01841 ] Network output: [ 0.9998 0.0006685 0.001001 -1.99e-05 8.932e-06 -0.001382 -1.499e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.194 -0.03308 -0.1793 0.1917 0.9835 0.9932 0.217 0.4447 0.8724 0.7181 ] Network output: [ -0.01069 1.002 1.01 1.303e-07 -5.849e-08 0.009687 9.819e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0059 0.0004732 0.004433 0.003765 0.9889 0.9919 0.00601 0.8644 0.8964 0.0133 ] Network output: [ -0.0006438 0.00291 1.002 -6.303e-05 2.83e-05 0.9965 -4.75e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2057 0.09561 0.3361 0.1477 0.985 0.994 0.2063 0.449 0.879 0.7125 ] Network output: [ 0.006028 -0.02924 0.9951 3.758e-05 -1.687e-05 1.022 2.832e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1015 0.08948 0.1809 0.2019 0.9873 0.9919 0.1015 0.7656 0.8688 0.3061 ] Network output: [ -0.005811 0.02872 1.003 3.928e-05 -1.763e-05 0.9802 2.96e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09051 0.08858 0.1652 0.1954 0.9854 0.9913 0.09052 0.691 0.8457 0.2446 ] Network output: [ 0.0001775 0.9999 -0.0002941 5.304e-06 -2.381e-06 1 3.998e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004721 Epoch 7859 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01113 0.9951 0.9899 6.374e-07 -2.861e-07 -0.007276 4.803e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003325 -0.00313 -0.008177 0.006361 0.9698 0.9742 0.006372 0.8359 0.8261 0.0184 ] Network output: [ 0.9998 0.0006679 0.001 -1.988e-05 8.924e-06 -0.00138 -1.498e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.194 -0.03308 -0.1793 0.1917 0.9835 0.9932 0.217 0.4447 0.8724 0.7181 ] Network output: [ -0.01069 1.002 1.01 1.291e-07 -5.795e-08 0.009685 9.728e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0059 0.0004732 0.004434 0.003765 0.9889 0.9919 0.006011 0.8644 0.8964 0.01329 ] Network output: [ -0.0006434 0.002909 1.002 -6.297e-05 2.827e-05 0.9965 -4.745e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2057 0.09561 0.3361 0.1477 0.985 0.994 0.2063 0.449 0.879 0.7125 ] Network output: [ 0.006026 -0.02922 0.9951 3.755e-05 -1.686e-05 1.022 2.83e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1015 0.08949 0.1809 0.2019 0.9873 0.9919 0.1015 0.7656 0.8688 0.3061 ] Network output: [ -0.005809 0.02871 1.003 3.925e-05 -1.762e-05 0.9802 2.958e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09051 0.08858 0.1652 0.1954 0.9854 0.9913 0.09052 0.691 0.8457 0.2446 ] Network output: [ 0.0001774 0.9999 -0.0002938 5.299e-06 -2.379e-06 1 3.994e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004718 Epoch 7860 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01112 0.9951 0.9899 6.353e-07 -2.852e-07 -0.007277 4.788e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003325 -0.00313 -0.008176 0.00636 0.9698 0.9742 0.006372 0.8359 0.8261 0.0184 ] Network output: [ 0.9998 0.0006673 0.0009995 -1.986e-05 8.915e-06 -0.001379 -1.497e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1941 -0.03308 -0.1793 0.1917 0.9835 0.9932 0.217 0.4447 0.8724 0.7181 ] Network output: [ -0.01069 1.002 1.01 1.279e-07 -5.74e-08 0.009682 9.636e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005901 0.0004733 0.004434 0.003764 0.9889 0.9919 0.006011 0.8644 0.8964 0.01329 ] Network output: [ -0.000643 0.002908 1.002 -6.291e-05 2.824e-05 0.9965 -4.741e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2057 0.09562 0.3361 0.1477 0.985 0.994 0.2064 0.449 0.879 0.7125 ] Network output: [ 0.006024 -0.02921 0.9951 3.751e-05 -1.684e-05 1.022 2.827e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1015 0.08949 0.1809 0.2019 0.9873 0.9919 0.1015 0.7656 0.8688 0.3061 ] Network output: [ -0.005806 0.0287 1.003 3.921e-05 -1.76e-05 0.9802 2.955e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09051 0.08858 0.1652 0.1954 0.9854 0.9913 0.09053 0.691 0.8457 0.2446 ] Network output: [ 0.0001773 0.9999 -0.0002934 5.295e-06 -2.377e-06 1 3.99e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004715 Epoch 7861 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01112 0.9951 0.9899 6.333e-07 -2.843e-07 -0.007278 4.773e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003325 -0.003131 -0.008175 0.00636 0.9698 0.9742 0.006372 0.8359 0.8261 0.0184 ] Network output: [ 0.9998 0.0006668 0.0009989 -1.984e-05 8.907e-06 -0.001378 -1.495e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1941 -0.03309 -0.1792 0.1917 0.9835 0.9932 0.217 0.4446 0.8724 0.7181 ] Network output: [ -0.01069 1.002 1.01 1.266e-07 -5.686e-08 0.00968 9.545e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005901 0.0004733 0.004434 0.003764 0.9889 0.9919 0.006012 0.8644 0.8964 0.01329 ] Network output: [ -0.0006427 0.002907 1.002 -6.285e-05 2.821e-05 0.9965 -4.736e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2057 0.09562 0.3361 0.1477 0.985 0.994 0.2064 0.449 0.879 0.7125 ] Network output: [ 0.006022 -0.0292 0.9951 3.748e-05 -1.683e-05 1.022 2.825e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1015 0.0895 0.1809 0.2018 0.9873 0.9919 0.1015 0.7656 0.8688 0.3061 ] Network output: [ -0.005804 0.02869 1.003 3.918e-05 -1.759e-05 0.9802 2.952e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09051 0.08858 0.1652 0.1954 0.9854 0.9913 0.09053 0.691 0.8457 0.2447 ] Network output: [ 0.0001772 0.9999 -0.0002931 5.29e-06 -2.375e-06 1 3.987e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004712 Epoch 7862 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01112 0.9951 0.9899 6.313e-07 -2.834e-07 -0.007279 4.757e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003325 -0.003131 -0.008174 0.006359 0.9698 0.9742 0.006373 0.8358 0.8261 0.0184 ] Network output: [ 0.9998 0.0006662 0.0009982 -1.982e-05 8.899e-06 -0.001377 -1.494e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1941 -0.03309 -0.1792 0.1917 0.9835 0.9932 0.217 0.4446 0.8724 0.7181 ] Network output: [ -0.01069 1.002 1.01 1.254e-07 -5.631e-08 0.009677 9.453e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005902 0.0004734 0.004434 0.003763 0.9889 0.9919 0.006013 0.8644 0.8964 0.01329 ] Network output: [ -0.0006423 0.002906 1.002 -6.279e-05 2.819e-05 0.9965 -4.732e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2057 0.09563 0.3361 0.1477 0.985 0.994 0.2064 0.449 0.879 0.7125 ] Network output: [ 0.00602 -0.02919 0.9951 3.744e-05 -1.681e-05 1.022 2.822e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1015 0.08951 0.1809 0.2018 0.9873 0.9919 0.1015 0.7655 0.8688 0.3061 ] Network output: [ -0.005802 0.02867 1.003 3.914e-05 -1.757e-05 0.9803 2.95e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09051 0.08858 0.1652 0.1954 0.9854 0.9913 0.09053 0.6909 0.8457 0.2447 ] Network output: [ 0.0001771 0.9999 -0.0002928 5.285e-06 -2.373e-06 1 3.983e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004709 Epoch 7863 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01112 0.9951 0.9899 6.292e-07 -2.825e-07 -0.007279 4.742e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003326 -0.003131 -0.008172 0.006358 0.9698 0.9742 0.006373 0.8358 0.8261 0.0184 ] Network output: [ 0.9998 0.0006656 0.0009976 -1.98e-05 8.891e-06 -0.001376 -1.492e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1941 -0.03309 -0.1792 0.1917 0.9835 0.9932 0.217 0.4446 0.8724 0.7181 ] Network output: [ -0.01069 1.002 1.01 1.242e-07 -5.577e-08 0.009675 9.362e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005903 0.0004734 0.004434 0.003763 0.9889 0.9919 0.006013 0.8644 0.8964 0.01329 ] Network output: [ -0.0006419 0.002905 1.002 -6.273e-05 2.816e-05 0.9965 -4.727e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2057 0.09563 0.3362 0.1477 0.985 0.994 0.2064 0.449 0.879 0.7125 ] Network output: [ 0.006018 -0.02918 0.9951 3.741e-05 -1.679e-05 1.022 2.819e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1015 0.08951 0.1809 0.2018 0.9873 0.9919 0.1016 0.7655 0.8687 0.3061 ] Network output: [ -0.0058 0.02866 1.003 3.91e-05 -1.756e-05 0.9803 2.947e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09052 0.08858 0.1652 0.1954 0.9854 0.9913 0.09053 0.6909 0.8456 0.2447 ] Network output: [ 0.000177 0.9999 -0.0002925 5.28e-06 -2.37e-06 1 3.979e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004706 Epoch 7864 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01112 0.9951 0.9899 6.272e-07 -2.816e-07 -0.00728 4.727e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003326 -0.003131 -0.008171 0.006357 0.9698 0.9742 0.006373 0.8358 0.8261 0.0184 ] Network output: [ 0.9998 0.000665 0.0009969 -1.979e-05 8.882e-06 -0.001375 -1.491e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1941 -0.03309 -0.1792 0.1917 0.9835 0.9932 0.217 0.4446 0.8724 0.7181 ] Network output: [ -0.01069 1.002 1.01 1.23e-07 -5.523e-08 0.009672 9.272e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005903 0.0004735 0.004434 0.003762 0.9889 0.9919 0.006014 0.8643 0.8964 0.01329 ] Network output: [ -0.0006415 0.002904 1.002 -6.267e-05 2.813e-05 0.9965 -4.723e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2057 0.09564 0.3362 0.1477 0.985 0.994 0.2064 0.449 0.879 0.7125 ] Network output: [ 0.006016 -0.02917 0.9951 3.737e-05 -1.678e-05 1.022 2.817e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1015 0.08952 0.1809 0.2018 0.9873 0.9919 0.1016 0.7655 0.8687 0.3061 ] Network output: [ -0.005798 0.02865 1.003 3.907e-05 -1.754e-05 0.9803 2.944e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09052 0.08858 0.1652 0.1954 0.9854 0.9913 0.09053 0.6909 0.8456 0.2447 ] Network output: [ 0.000177 0.9999 -0.0002921 5.275e-06 -2.368e-06 1 3.975e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004703 Epoch 7865 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01111 0.9951 0.9899 6.252e-07 -2.807e-07 -0.007281 4.712e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003326 -0.003131 -0.00817 0.006357 0.9698 0.9742 0.006374 0.8358 0.8261 0.01839 ] Network output: [ 0.9998 0.0006645 0.0009963 -1.977e-05 8.874e-06 -0.001374 -1.49e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1941 -0.0331 -0.1792 0.1917 0.9835 0.9932 0.217 0.4446 0.8724 0.7181 ] Network output: [ -0.01068 1.002 1.01 1.218e-07 -5.469e-08 0.00967 9.181e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005904 0.0004736 0.004434 0.003762 0.9889 0.9919 0.006014 0.8643 0.8964 0.01329 ] Network output: [ -0.0006411 0.002903 1.002 -6.261e-05 2.811e-05 0.9965 -4.718e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2057 0.09564 0.3362 0.1477 0.985 0.994 0.2064 0.4489 0.879 0.7125 ] Network output: [ 0.006014 -0.02916 0.9951 3.734e-05 -1.676e-05 1.022 2.814e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1015 0.08952 0.1809 0.2018 0.9873 0.9919 0.1016 0.7655 0.8687 0.3061 ] Network output: [ -0.005796 0.02864 1.003 3.903e-05 -1.752e-05 0.9803 2.942e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09052 0.08858 0.1652 0.1954 0.9854 0.9913 0.09053 0.6909 0.8456 0.2447 ] Network output: [ 0.0001769 0.9999 -0.0002918 5.27e-06 -2.366e-06 1 3.972e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00047 Epoch 7866 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01111 0.9951 0.9899 6.232e-07 -2.798e-07 -0.007282 4.696e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003326 -0.003131 -0.008169 0.006356 0.9698 0.9742 0.006374 0.8358 0.8261 0.01839 ] Network output: [ 0.9998 0.0006639 0.0009956 -1.975e-05 8.866e-06 -0.001372 -1.488e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1941 -0.0331 -0.1792 0.1917 0.9835 0.9932 0.2171 0.4446 0.8724 0.7181 ] Network output: [ -0.01068 1.002 1.01 1.206e-07 -5.415e-08 0.009668 9.09e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005905 0.0004736 0.004434 0.003761 0.9889 0.9919 0.006015 0.8643 0.8964 0.01329 ] Network output: [ -0.0006407 0.002902 1.002 -6.255e-05 2.808e-05 0.9965 -4.714e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2058 0.09565 0.3362 0.1477 0.985 0.994 0.2064 0.4489 0.879 0.7125 ] Network output: [ 0.006012 -0.02915 0.9951 3.73e-05 -1.675e-05 1.022 2.811e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1015 0.08953 0.1809 0.2018 0.9873 0.9919 0.1016 0.7654 0.8687 0.3061 ] Network output: [ -0.005794 0.02863 1.003 3.9e-05 -1.751e-05 0.9803 2.939e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09052 0.08858 0.1652 0.1954 0.9854 0.9913 0.09053 0.6908 0.8456 0.2447 ] Network output: [ 0.0001768 0.9999 -0.0002915 5.265e-06 -2.364e-06 1 3.968e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004697 Epoch 7867 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01111 0.9951 0.9899 6.212e-07 -2.789e-07 -0.007283 4.681e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003326 -0.003132 -0.008168 0.006355 0.9698 0.9742 0.006374 0.8358 0.8261 0.01839 ] Network output: [ 0.9998 0.0006633 0.000995 -1.973e-05 8.858e-06 -0.001371 -1.487e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1941 -0.0331 -0.1791 0.1917 0.9835 0.9932 0.2171 0.4446 0.8724 0.7181 ] Network output: [ -0.01068 1.002 1.01 1.194e-07 -5.361e-08 0.009665 9e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005905 0.0004737 0.004434 0.003761 0.9889 0.9919 0.006016 0.8643 0.8964 0.01329 ] Network output: [ -0.0006403 0.002901 1.002 -6.249e-05 2.805e-05 0.9965 -4.709e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2058 0.09565 0.3362 0.1476 0.985 0.994 0.2064 0.4489 0.879 0.7125 ] Network output: [ 0.00601 -0.02914 0.9951 3.727e-05 -1.673e-05 1.022 2.809e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1015 0.08953 0.1809 0.2018 0.9873 0.9919 0.1016 0.7654 0.8687 0.3061 ] Network output: [ -0.005791 0.02861 1.003 3.896e-05 -1.749e-05 0.9803 2.936e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09052 0.08859 0.1652 0.1954 0.9854 0.9913 0.09053 0.6908 0.8456 0.2447 ] Network output: [ 0.0001767 0.9999 -0.0002911 5.26e-06 -2.362e-06 1 3.964e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004694 Epoch 7868 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01111 0.9951 0.9899 6.192e-07 -2.78e-07 -0.007283 4.666e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003326 -0.003132 -0.008167 0.006354 0.9698 0.9742 0.006375 0.8358 0.8261 0.01839 ] Network output: [ 0.9998 0.0006627 0.0009944 -1.971e-05 8.85e-06 -0.00137 -1.486e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1941 -0.0331 -0.1791 0.1917 0.9835 0.9932 0.2171 0.4445 0.8724 0.7181 ] Network output: [ -0.01068 1.002 1.01 1.182e-07 -5.308e-08 0.009663 8.91e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005906 0.0004737 0.004434 0.00376 0.9889 0.9919 0.006016 0.8643 0.8964 0.01328 ] Network output: [ -0.0006399 0.0029 1.002 -6.243e-05 2.803e-05 0.9965 -4.705e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2058 0.09566 0.3362 0.1476 0.985 0.994 0.2064 0.4489 0.879 0.7125 ] Network output: [ 0.006008 -0.02913 0.9951 3.723e-05 -1.672e-05 1.022 2.806e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1015 0.08954 0.1809 0.2018 0.9873 0.9919 0.1016 0.7654 0.8687 0.3061 ] Network output: [ -0.005789 0.0286 1.003 3.893e-05 -1.748e-05 0.9803 2.934e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09052 0.08859 0.1652 0.1954 0.9854 0.9913 0.09053 0.6908 0.8456 0.2447 ] Network output: [ 0.0001766 0.9999 -0.0002908 5.256e-06 -2.359e-06 1 3.961e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004691 Epoch 7869 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01111 0.9951 0.9899 6.171e-07 -2.771e-07 -0.007284 4.651e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003326 -0.003132 -0.008166 0.006354 0.9698 0.9742 0.006375 0.8358 0.826 0.01839 ] Network output: [ 0.9998 0.0006621 0.0009937 -1.969e-05 8.841e-06 -0.001369 -1.484e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1941 -0.03311 -0.1791 0.1917 0.9835 0.9932 0.2171 0.4445 0.8724 0.7181 ] Network output: [ -0.01068 1.002 1.01 1.17e-07 -5.254e-08 0.00966 8.82e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005906 0.0004738 0.004434 0.00376 0.9889 0.9919 0.006017 0.8643 0.8964 0.01328 ] Network output: [ -0.0006395 0.002899 1.002 -6.237e-05 2.8e-05 0.9965 -4.7e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2058 0.09566 0.3362 0.1476 0.985 0.994 0.2065 0.4489 0.8789 0.7125 ] Network output: [ 0.006006 -0.02912 0.9951 3.72e-05 -1.67e-05 1.022 2.803e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1015 0.08955 0.1809 0.2018 0.9873 0.9919 0.1016 0.7654 0.8687 0.306 ] Network output: [ -0.005787 0.02859 1.003 3.889e-05 -1.746e-05 0.9803 2.931e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09052 0.08859 0.1652 0.1954 0.9854 0.9913 0.09053 0.6908 0.8456 0.2447 ] Network output: [ 0.0001765 0.9999 -0.0002905 5.251e-06 -2.357e-06 1 3.957e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004689 Epoch 7870 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01111 0.9951 0.9899 6.151e-07 -2.762e-07 -0.007285 4.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003327 -0.003132 -0.008165 0.006353 0.9698 0.9742 0.006375 0.8358 0.826 0.01839 ] Network output: [ 0.9998 0.0006616 0.0009931 -1.968e-05 8.833e-06 -0.001368 -1.483e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1942 -0.03311 -0.1791 0.1917 0.9835 0.9932 0.2171 0.4445 0.8724 0.7181 ] Network output: [ -0.01068 1.002 1.01 1.158e-07 -5.2e-08 0.009658 8.73e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005907 0.0004739 0.004434 0.003759 0.9889 0.9919 0.006018 0.8643 0.8964 0.01328 ] Network output: [ -0.0006391 0.002898 1.002 -6.231e-05 2.797e-05 0.9965 -4.696e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2058 0.09567 0.3362 0.1476 0.985 0.994 0.2065 0.4489 0.8789 0.7125 ] Network output: [ 0.006004 -0.02911 0.9951 3.716e-05 -1.668e-05 1.022 2.801e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1015 0.08955 0.1809 0.2018 0.9873 0.9919 0.1016 0.7653 0.8687 0.306 ] Network output: [ -0.005785 0.02858 1.003 3.886e-05 -1.744e-05 0.9803 2.928e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09052 0.08859 0.1652 0.1954 0.9854 0.9913 0.09054 0.6907 0.8456 0.2447 ] Network output: [ 0.0001764 0.9999 -0.0002902 5.246e-06 -2.355e-06 1 3.953e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004686 Epoch 7871 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0111 0.9951 0.9899 6.131e-07 -2.753e-07 -0.007286 4.621e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003327 -0.003132 -0.008163 0.006352 0.9698 0.9742 0.006376 0.8358 0.826 0.01839 ] Network output: [ 0.9998 0.000661 0.0009924 -1.966e-05 8.825e-06 -0.001367 -1.481e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1942 -0.03311 -0.1791 0.1916 0.9835 0.9932 0.2171 0.4445 0.8724 0.7181 ] Network output: [ -0.01068 1.002 1.01 1.146e-07 -5.147e-08 0.009655 8.64e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005908 0.0004739 0.004434 0.003759 0.9889 0.9919 0.006018 0.8643 0.8964 0.01328 ] Network output: [ -0.0006387 0.002897 1.002 -6.225e-05 2.795e-05 0.9965 -4.691e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2058 0.09567 0.3362 0.1476 0.985 0.994 0.2065 0.4489 0.8789 0.7124 ] Network output: [ 0.006002 -0.0291 0.9951 3.713e-05 -1.667e-05 1.022 2.798e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1015 0.08956 0.1809 0.2018 0.9873 0.9919 0.1016 0.7653 0.8687 0.306 ] Network output: [ -0.005783 0.02856 1.003 3.882e-05 -1.743e-05 0.9803 2.926e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09052 0.08859 0.1652 0.1954 0.9854 0.9913 0.09054 0.6907 0.8456 0.2447 ] Network output: [ 0.0001763 0.9999 -0.0002898 5.241e-06 -2.353e-06 1 3.95e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004683 Epoch 7872 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0111 0.9951 0.9899 6.111e-07 -2.744e-07 -0.007287 4.606e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003327 -0.003132 -0.008162 0.006351 0.9698 0.9742 0.006376 0.8358 0.826 0.01838 ] Network output: [ 0.9998 0.0006604 0.0009918 -1.964e-05 8.817e-06 -0.001365 -1.48e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1942 -0.03311 -0.1791 0.1916 0.9835 0.9932 0.2171 0.4445 0.8724 0.7181 ] Network output: [ -0.01068 1.002 1.01 1.135e-07 -5.094e-08 0.009653 8.551e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005908 0.000474 0.004434 0.003758 0.9889 0.9919 0.006019 0.8643 0.8964 0.01328 ] Network output: [ -0.0006384 0.002895 1.002 -6.219e-05 2.792e-05 0.9965 -4.687e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2058 0.09568 0.3363 0.1476 0.985 0.994 0.2065 0.4488 0.8789 0.7124 ] Network output: [ 0.006 -0.02909 0.9951 3.71e-05 -1.665e-05 1.022 2.796e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1015 0.08956 0.1809 0.2018 0.9873 0.9919 0.1016 0.7653 0.8687 0.306 ] Network output: [ -0.005781 0.02855 1.003 3.879e-05 -1.741e-05 0.9803 2.923e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09053 0.08859 0.1652 0.1954 0.9854 0.9913 0.09054 0.6907 0.8456 0.2447 ] Network output: [ 0.0001762 0.9999 -0.0002895 5.236e-06 -2.351e-06 1 3.946e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000468 Epoch 7873 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0111 0.9952 0.9899 6.092e-07 -2.735e-07 -0.007288 4.591e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003327 -0.003133 -0.008161 0.006351 0.9698 0.9742 0.006376 0.8358 0.826 0.01838 ] Network output: [ 0.9998 0.0006599 0.0009911 -1.962e-05 8.809e-06 -0.001364 -1.479e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1942 -0.03312 -0.179 0.1916 0.9835 0.9932 0.2171 0.4445 0.8724 0.718 ] Network output: [ -0.01068 1.002 1.01 1.123e-07 -5.04e-08 0.009651 8.461e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005909 0.000474 0.004434 0.003758 0.9889 0.9919 0.006019 0.8643 0.8964 0.01328 ] Network output: [ -0.000638 0.002894 1.002 -6.213e-05 2.789e-05 0.9965 -4.682e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2058 0.09568 0.3363 0.1476 0.985 0.994 0.2065 0.4488 0.8789 0.7124 ] Network output: [ 0.005998 -0.02907 0.9951 3.706e-05 -1.664e-05 1.022 2.793e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1016 0.08957 0.1809 0.2018 0.9873 0.9919 0.1016 0.7653 0.8687 0.306 ] Network output: [ -0.005779 0.02854 1.003 3.875e-05 -1.74e-05 0.9803 2.921e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09053 0.08859 0.1652 0.1954 0.9854 0.9913 0.09054 0.6906 0.8456 0.2447 ] Network output: [ 0.0001761 0.9999 -0.0002892 5.231e-06 -2.349e-06 1 3.943e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004677 Epoch 7874 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0111 0.9952 0.9899 6.072e-07 -2.726e-07 -0.007288 4.576e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003327 -0.003133 -0.00816 0.00635 0.9698 0.9742 0.006377 0.8358 0.826 0.01838 ] Network output: [ 0.9998 0.0006593 0.0009905 -1.96e-05 8.801e-06 -0.001363 -1.477e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1942 -0.03312 -0.179 0.1916 0.9835 0.9932 0.2171 0.4445 0.8723 0.718 ] Network output: [ -0.01067 1.002 1.01 1.111e-07 -4.987e-08 0.009648 8.372e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005909 0.0004741 0.004434 0.003757 0.9889 0.9919 0.00602 0.8643 0.8964 0.01328 ] Network output: [ -0.0006376 0.002893 1.002 -6.207e-05 2.786e-05 0.9965 -4.678e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2058 0.09569 0.3363 0.1476 0.985 0.994 0.2065 0.4488 0.8789 0.7124 ] Network output: [ 0.005996 -0.02906 0.9951 3.703e-05 -1.662e-05 1.022 2.79e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1016 0.08957 0.1809 0.2018 0.9873 0.9919 0.1016 0.7653 0.8687 0.306 ] Network output: [ -0.005776 0.02853 1.003 3.872e-05 -1.738e-05 0.9803 2.918e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09053 0.08859 0.1652 0.1954 0.9854 0.9913 0.09054 0.6906 0.8456 0.2447 ] Network output: [ 0.000176 0.9999 -0.0002889 5.227e-06 -2.346e-06 1 3.939e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004674 Epoch 7875 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0111 0.9952 0.9899 6.052e-07 -2.717e-07 -0.007289 4.561e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003327 -0.003133 -0.008159 0.006349 0.9698 0.9742 0.006377 0.8357 0.826 0.01838 ] Network output: [ 0.9998 0.0006587 0.0009898 -1.959e-05 8.792e-06 -0.001362 -1.476e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1942 -0.03312 -0.179 0.1916 0.9835 0.9932 0.2172 0.4445 0.8723 0.718 ] Network output: [ -0.01067 1.002 1.01 1.099e-07 -4.934e-08 0.009646 8.283e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00591 0.0004741 0.004434 0.003757 0.9889 0.9919 0.006021 0.8642 0.8964 0.01328 ] Network output: [ -0.0006372 0.002892 1.002 -6.201e-05 2.784e-05 0.9965 -4.673e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2059 0.0957 0.3363 0.1476 0.985 0.994 0.2065 0.4488 0.8789 0.7124 ] Network output: [ 0.005994 -0.02905 0.9951 3.699e-05 -1.661e-05 1.022 2.788e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1016 0.08958 0.1809 0.2018 0.9873 0.9919 0.1016 0.7652 0.8687 0.306 ] Network output: [ -0.005774 0.02851 1.003 3.868e-05 -1.737e-05 0.9803 2.915e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09053 0.08859 0.1652 0.1954 0.9854 0.9913 0.09054 0.6906 0.8455 0.2447 ] Network output: [ 0.0001759 0.9999 -0.0002886 5.222e-06 -2.344e-06 1 3.935e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004671 Epoch 7876 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0111 0.9952 0.9899 6.032e-07 -2.708e-07 -0.00729 4.546e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003327 -0.003133 -0.008158 0.006348 0.9698 0.9742 0.006377 0.8357 0.826 0.01838 ] Network output: [ 0.9998 0.0006581 0.0009892 -1.957e-05 8.784e-06 -0.001361 -1.475e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1942 -0.03312 -0.179 0.1916 0.9835 0.9932 0.2172 0.4444 0.8723 0.718 ] Network output: [ -0.01067 1.002 1.01 1.087e-07 -4.881e-08 0.009643 8.194e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005911 0.0004742 0.004435 0.003756 0.9889 0.9919 0.006021 0.8642 0.8964 0.01327 ] Network output: [ -0.0006368 0.002891 1.002 -6.195e-05 2.781e-05 0.9965 -4.669e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2059 0.0957 0.3363 0.1476 0.985 0.994 0.2065 0.4488 0.8789 0.7124 ] Network output: [ 0.005992 -0.02904 0.9951 3.696e-05 -1.659e-05 1.022 2.785e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1016 0.08958 0.1809 0.2018 0.9873 0.9919 0.1016 0.7652 0.8687 0.306 ] Network output: [ -0.005772 0.0285 1.003 3.865e-05 -1.735e-05 0.9803 2.913e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09053 0.08859 0.1652 0.1954 0.9854 0.9913 0.09054 0.6906 0.8455 0.2447 ] Network output: [ 0.0001758 0.9999 -0.0002882 5.217e-06 -2.342e-06 1 3.932e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004668 Epoch 7877 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01109 0.9952 0.9899 6.012e-07 -2.699e-07 -0.007291 4.531e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003328 -0.003133 -0.008157 0.006348 0.9698 0.9742 0.006378 0.8357 0.826 0.01838 ] Network output: [ 0.9998 0.0006576 0.0009886 -1.955e-05 8.776e-06 -0.00136 -1.473e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1942 -0.03313 -0.179 0.1916 0.9835 0.9932 0.2172 0.4444 0.8723 0.718 ] Network output: [ -0.01067 1.002 1.01 1.076e-07 -4.829e-08 0.009641 8.106e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005911 0.0004743 0.004435 0.003756 0.9889 0.9919 0.006022 0.8642 0.8964 0.01327 ] Network output: [ -0.0006364 0.00289 1.002 -6.189e-05 2.778e-05 0.9965 -4.664e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2059 0.09571 0.3363 0.1476 0.985 0.994 0.2066 0.4488 0.8789 0.7124 ] Network output: [ 0.00599 -0.02903 0.9951 3.692e-05 -1.658e-05 1.022 2.783e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1016 0.08959 0.1809 0.2018 0.9873 0.9919 0.1016 0.7652 0.8687 0.306 ] Network output: [ -0.00577 0.02849 1.003 3.861e-05 -1.733e-05 0.9803 2.91e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09053 0.0886 0.1652 0.1954 0.9854 0.9913 0.09054 0.6905 0.8455 0.2447 ] Network output: [ 0.0001757 0.9999 -0.0002879 5.212e-06 -2.34e-06 1 3.928e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004665 Epoch 7878 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01109 0.9952 0.9899 5.992e-07 -2.69e-07 -0.007292 4.516e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003328 -0.003133 -0.008156 0.006347 0.9698 0.9742 0.006378 0.8357 0.826 0.01837 ] Network output: [ 0.9998 0.000657 0.0009879 -1.953e-05 8.768e-06 -0.001359 -1.472e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1942 -0.03313 -0.179 0.1916 0.9835 0.9932 0.2172 0.4444 0.8723 0.718 ] Network output: [ -0.01067 1.002 1.01 1.064e-07 -4.776e-08 0.009638 8.017e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005912 0.0004743 0.004435 0.003755 0.9889 0.9919 0.006023 0.8642 0.8964 0.01327 ] Network output: [ -0.000636 0.002889 1.002 -6.183e-05 2.776e-05 0.9965 -4.66e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2059 0.09571 0.3363 0.1476 0.985 0.994 0.2066 0.4488 0.8789 0.7124 ] Network output: [ 0.005988 -0.02902 0.9951 3.689e-05 -1.656e-05 1.022 2.78e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1016 0.0896 0.1809 0.2018 0.9873 0.9919 0.1016 0.7652 0.8686 0.306 ] Network output: [ -0.005768 0.02848 1.003 3.858e-05 -1.732e-05 0.9803 2.907e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09053 0.0886 0.1652 0.1954 0.9854 0.9913 0.09054 0.6905 0.8455 0.2447 ] Network output: [ 0.0001757 0.9999 -0.0002876 5.207e-06 -2.338e-06 1 3.924e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004662 Epoch 7879 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01109 0.9952 0.9899 5.973e-07 -2.681e-07 -0.007292 4.501e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003328 -0.003133 -0.008154 0.006346 0.9698 0.9742 0.006378 0.8357 0.826 0.01837 ] Network output: [ 0.9998 0.0006564 0.0009873 -1.951e-05 8.76e-06 -0.001357 -1.471e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1943 -0.03313 -0.179 0.1916 0.9835 0.9932 0.2172 0.4444 0.8723 0.718 ] Network output: [ -0.01067 1.002 1.01 1.052e-07 -4.723e-08 0.009636 7.929e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005912 0.0004744 0.004435 0.003755 0.9889 0.9919 0.006023 0.8642 0.8964 0.01327 ] Network output: [ -0.0006356 0.002888 1.002 -6.177e-05 2.773e-05 0.9965 -4.655e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2059 0.09572 0.3363 0.1476 0.985 0.994 0.2066 0.4488 0.8789 0.7124 ] Network output: [ 0.005986 -0.02901 0.9951 3.685e-05 -1.654e-05 1.022 2.777e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1016 0.0896 0.1809 0.2018 0.9873 0.9919 0.1017 0.7651 0.8686 0.306 ] Network output: [ -0.005766 0.02846 1.003 3.854e-05 -1.73e-05 0.9804 2.905e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09053 0.0886 0.1652 0.1954 0.9854 0.9913 0.09054 0.6905 0.8455 0.2447 ] Network output: [ 0.0001756 0.9999 -0.0002873 5.202e-06 -2.336e-06 1 3.921e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004659 Epoch 7880 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01109 0.9952 0.99 5.953e-07 -2.673e-07 -0.007293 4.486e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003328 -0.003134 -0.008153 0.006346 0.9698 0.9742 0.006379 0.8357 0.826 0.01837 ] Network output: [ 0.9998 0.0006559 0.0009866 -1.949e-05 8.752e-06 -0.001356 -1.469e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1943 -0.03313 -0.1789 0.1916 0.9835 0.9932 0.2172 0.4444 0.8723 0.718 ] Network output: [ -0.01067 1.002 1.01 1.04e-07 -4.671e-08 0.009634 7.841e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005913 0.0004744 0.004435 0.003754 0.9889 0.9919 0.006024 0.8642 0.8963 0.01327 ] Network output: [ -0.0006352 0.002887 1.002 -6.171e-05 2.77e-05 0.9966 -4.651e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2059 0.09572 0.3363 0.1476 0.985 0.994 0.2066 0.4487 0.8789 0.7124 ] Network output: [ 0.005984 -0.029 0.9951 3.682e-05 -1.653e-05 1.022 2.775e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1016 0.08961 0.1809 0.2018 0.9873 0.9919 0.1017 0.7651 0.8686 0.306 ] Network output: [ -0.005764 0.02845 1.003 3.851e-05 -1.729e-05 0.9804 2.902e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09053 0.0886 0.1652 0.1954 0.9854 0.9913 0.09055 0.6905 0.8455 0.2447 ] Network output: [ 0.0001755 0.9999 -0.0002869 5.198e-06 -2.333e-06 1 3.917e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004657 Epoch 7881 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01109 0.9952 0.99 5.933e-07 -2.664e-07 -0.007294 4.472e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003328 -0.003134 -0.008152 0.006345 0.9698 0.9742 0.006379 0.8357 0.826 0.01837 ] Network output: [ 0.9998 0.0006553 0.000986 -1.948e-05 8.744e-06 -0.001355 -1.468e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1943 -0.03314 -0.1789 0.1916 0.9835 0.9932 0.2172 0.4444 0.8723 0.718 ] Network output: [ -0.01067 1.002 1.01 1.029e-07 -4.618e-08 0.009631 7.753e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005914 0.0004745 0.004435 0.003754 0.9889 0.9919 0.006024 0.8642 0.8963 0.01327 ] Network output: [ -0.0006349 0.002886 1.002 -6.165e-05 2.768e-05 0.9966 -4.646e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2059 0.09573 0.3364 0.1476 0.985 0.994 0.2066 0.4487 0.8789 0.7124 ] Network output: [ 0.005982 -0.02899 0.9951 3.678e-05 -1.651e-05 1.022 2.772e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1016 0.08961 0.1809 0.2018 0.9873 0.9919 0.1017 0.7651 0.8686 0.306 ] Network output: [ -0.005761 0.02844 1.003 3.847e-05 -1.727e-05 0.9804 2.899e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09053 0.0886 0.1652 0.1954 0.9854 0.9913 0.09055 0.6904 0.8455 0.2447 ] Network output: [ 0.0001754 0.9999 -0.0002866 5.193e-06 -2.331e-06 1 3.913e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004654 Epoch 7882 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01109 0.9952 0.99 5.914e-07 -2.655e-07 -0.007295 4.457e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003328 -0.003134 -0.008151 0.006344 0.9698 0.9742 0.006379 0.8357 0.826 0.01837 ] Network output: [ 0.9998 0.0006547 0.0009853 -1.946e-05 8.735e-06 -0.001354 -1.466e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1943 -0.03314 -0.1789 0.1916 0.9835 0.9932 0.2172 0.4444 0.8723 0.718 ] Network output: [ -0.01066 1.002 1.01 1.017e-07 -4.566e-08 0.009629 7.665e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005914 0.0004746 0.004435 0.003753 0.9889 0.9919 0.006025 0.8642 0.8963 0.01327 ] Network output: [ -0.0006345 0.002885 1.002 -6.159e-05 2.765e-05 0.9966 -4.642e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2059 0.09573 0.3364 0.1475 0.985 0.994 0.2066 0.4487 0.8789 0.7124 ] Network output: [ 0.00598 -0.02898 0.9951 3.675e-05 -1.65e-05 1.022 2.769e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1016 0.08962 0.1809 0.2018 0.9873 0.9919 0.1017 0.7651 0.8686 0.306 ] Network output: [ -0.005759 0.02843 1.003 3.844e-05 -1.726e-05 0.9804 2.897e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09054 0.0886 0.1652 0.1954 0.9854 0.9913 0.09055 0.6904 0.8455 0.2447 ] Network output: [ 0.0001753 1 -0.0002863 5.188e-06 -2.329e-06 1 3.91e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004651 Epoch 7883 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01108 0.9952 0.99 5.894e-07 -2.646e-07 -0.007295 4.442e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003328 -0.003134 -0.00815 0.006343 0.9698 0.9742 0.006379 0.8357 0.826 0.01837 ] Network output: [ 0.9998 0.0006542 0.0009847 -1.944e-05 8.727e-06 -0.001353 -1.465e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1943 -0.03314 -0.1789 0.1916 0.9835 0.9932 0.2173 0.4444 0.8723 0.718 ] Network output: [ -0.01066 1.002 1.01 1.005e-07 -4.514e-08 0.009626 7.577e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005915 0.0004746 0.004435 0.003753 0.9889 0.9919 0.006026 0.8642 0.8963 0.01327 ] Network output: [ -0.0006341 0.002884 1.002 -6.153e-05 2.762e-05 0.9966 -4.637e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.206 0.09574 0.3364 0.1475 0.985 0.994 0.2066 0.4487 0.8789 0.7124 ] Network output: [ 0.005978 -0.02897 0.9951 3.671e-05 -1.648e-05 1.022 2.767e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1016 0.08962 0.1809 0.2017 0.9873 0.9919 0.1017 0.7651 0.8686 0.306 ] Network output: [ -0.005757 0.02841 1.003 3.84e-05 -1.724e-05 0.9804 2.894e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09054 0.0886 0.1652 0.1954 0.9854 0.9913 0.09055 0.6904 0.8455 0.2447 ] Network output: [ 0.0001752 1 -0.000286 5.183e-06 -2.327e-06 1 3.906e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004648 Epoch 7884 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01108 0.9952 0.99 5.875e-07 -2.637e-07 -0.007296 4.427e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003328 -0.003134 -0.008149 0.006343 0.9698 0.9742 0.00638 0.8357 0.826 0.01837 ] Network output: [ 0.9998 0.0006536 0.0009841 -1.942e-05 8.719e-06 -0.001352 -1.464e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1943 -0.03314 -0.1789 0.1916 0.9835 0.9932 0.2173 0.4443 0.8723 0.718 ] Network output: [ -0.01066 1.002 1.01 9.938e-08 -4.462e-08 0.009624 7.49e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005915 0.0004747 0.004435 0.003753 0.9889 0.9919 0.006026 0.8642 0.8963 0.01327 ] Network output: [ -0.0006337 0.002883 1.002 -6.148e-05 2.76e-05 0.9966 -4.633e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.206 0.09574 0.3364 0.1475 0.985 0.994 0.2066 0.4487 0.8789 0.7124 ] Network output: [ 0.005976 -0.02896 0.9951 3.668e-05 -1.647e-05 1.022 2.764e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1016 0.08963 0.1809 0.2017 0.9873 0.9919 0.1017 0.765 0.8686 0.306 ] Network output: [ -0.005755 0.0284 1.003 3.837e-05 -1.722e-05 0.9804 2.892e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09054 0.0886 0.1652 0.1954 0.9854 0.9913 0.09055 0.6904 0.8455 0.2447 ] Network output: [ 0.0001751 1 -0.0002857 5.178e-06 -2.325e-06 1 3.902e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004645 Epoch 7885 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01108 0.9952 0.99 5.855e-07 -2.629e-07 -0.007297 4.413e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003329 -0.003134 -0.008148 0.006342 0.9698 0.9742 0.00638 0.8357 0.826 0.01836 ] Network output: [ 0.9998 0.000653 0.0009834 -1.94e-05 8.711e-06 -0.001351 -1.462e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1943 -0.03315 -0.1789 0.1915 0.9835 0.9932 0.2173 0.4443 0.8723 0.718 ] Network output: [ -0.01066 1.002 1.01 9.822e-08 -4.41e-08 0.009622 7.402e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005916 0.0004747 0.004435 0.003752 0.9889 0.9919 0.006027 0.8642 0.8963 0.01326 ] Network output: [ -0.0006333 0.002882 1.002 -6.142e-05 2.757e-05 0.9966 -4.628e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.206 0.09575 0.3364 0.1475 0.985 0.994 0.2066 0.4487 0.8789 0.7124 ] Network output: [ 0.005974 -0.02895 0.9951 3.665e-05 -1.645e-05 1.022 2.762e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1016 0.08963 0.181 0.2017 0.9873 0.9919 0.1017 0.765 0.8686 0.306 ] Network output: [ -0.005753 0.02839 1.003 3.833e-05 -1.721e-05 0.9804 2.889e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09054 0.0886 0.1652 0.1954 0.9854 0.9913 0.09055 0.6903 0.8455 0.2447 ] Network output: [ 0.000175 1 -0.0002853 5.173e-06 -2.323e-06 1 3.899e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004642 Epoch 7886 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01108 0.9952 0.99 5.836e-07 -2.62e-07 -0.007298 4.398e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003329 -0.003135 -0.008147 0.006341 0.9698 0.9742 0.00638 0.8357 0.826 0.01836 ] Network output: [ 0.9998 0.0006525 0.0009828 -1.939e-05 8.703e-06 -0.001349 -1.461e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1943 -0.03315 -0.1788 0.1915 0.9835 0.9932 0.2173 0.4443 0.8723 0.718 ] Network output: [ -0.01066 1.002 1.01 9.706e-08 -4.358e-08 0.009619 7.315e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005917 0.0004748 0.004435 0.003752 0.9889 0.9919 0.006028 0.8641 0.8963 0.01326 ] Network output: [ -0.0006329 0.002881 1.002 -6.136e-05 2.755e-05 0.9966 -4.624e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.206 0.09575 0.3364 0.1475 0.985 0.994 0.2067 0.4487 0.8789 0.7124 ] Network output: [ 0.005972 -0.02894 0.9951 3.661e-05 -1.644e-05 1.022 2.759e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1016 0.08964 0.181 0.2017 0.9873 0.9919 0.1017 0.765 0.8686 0.306 ] Network output: [ -0.005751 0.02838 1.003 3.83e-05 -1.719e-05 0.9804 2.886e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09054 0.08861 0.1652 0.1954 0.9854 0.9913 0.09055 0.6903 0.8455 0.2447 ] Network output: [ 0.0001749 1 -0.000285 5.169e-06 -2.32e-06 1 3.895e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004639 Epoch 7887 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01108 0.9952 0.99 5.816e-07 -2.611e-07 -0.007299 4.383e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003329 -0.003135 -0.008145 0.00634 0.9698 0.9742 0.006381 0.8357 0.826 0.01836 ] Network output: [ 0.9998 0.0006519 0.0009822 -1.937e-05 8.695e-06 -0.001348 -1.46e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1943 -0.03315 -0.1788 0.1915 0.9835 0.9932 0.2173 0.4443 0.8723 0.718 ] Network output: [ -0.01066 1.002 1.01 9.591e-08 -4.306e-08 0.009617 7.228e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005917 0.0004749 0.004435 0.003751 0.9889 0.9919 0.006028 0.8641 0.8963 0.01326 ] Network output: [ -0.0006325 0.00288 1.002 -6.13e-05 2.752e-05 0.9966 -4.62e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.206 0.09576 0.3364 0.1475 0.985 0.994 0.2067 0.4486 0.8789 0.7124 ] Network output: [ 0.00597 -0.02893 0.9951 3.658e-05 -1.642e-05 1.022 2.757e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1016 0.08965 0.181 0.2017 0.9873 0.9919 0.1017 0.765 0.8686 0.306 ] Network output: [ -0.005749 0.02837 1.003 3.826e-05 -1.718e-05 0.9804 2.884e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09054 0.08861 0.1652 0.1954 0.9854 0.9913 0.09055 0.6903 0.8454 0.2447 ] Network output: [ 0.0001748 1 -0.0002847 5.164e-06 -2.318e-06 1 3.892e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004636 Epoch 7888 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01108 0.9952 0.99 5.797e-07 -2.602e-07 -0.007299 4.369e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003329 -0.003135 -0.008144 0.00634 0.9698 0.9742 0.006381 0.8356 0.826 0.01836 ] Network output: [ 0.9998 0.0006513 0.0009815 -1.935e-05 8.687e-06 -0.001347 -1.458e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1943 -0.03315 -0.1788 0.1915 0.9835 0.9932 0.2173 0.4443 0.8723 0.718 ] Network output: [ -0.01066 1.002 1.01 9.476e-08 -4.254e-08 0.009614 7.141e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005918 0.0004749 0.004435 0.003751 0.9889 0.9919 0.006029 0.8641 0.8963 0.01326 ] Network output: [ -0.0006321 0.002879 1.002 -6.124e-05 2.749e-05 0.9966 -4.615e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.206 0.09576 0.3364 0.1475 0.985 0.994 0.2067 0.4486 0.8789 0.7123 ] Network output: [ 0.005968 -0.02891 0.9951 3.654e-05 -1.64e-05 1.022 2.754e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1016 0.08965 0.181 0.2017 0.9873 0.9919 0.1017 0.7649 0.8686 0.306 ] Network output: [ -0.005746 0.02835 1.003 3.823e-05 -1.716e-05 0.9804 2.881e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09054 0.08861 0.1652 0.1954 0.9854 0.9913 0.09055 0.6903 0.8454 0.2447 ] Network output: [ 0.0001747 1 -0.0002844 5.159e-06 -2.316e-06 1 3.888e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004634 Epoch 7889 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01107 0.9952 0.99 5.777e-07 -2.594e-07 -0.0073 4.354e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003329 -0.003135 -0.008143 0.006339 0.9698 0.9742 0.006381 0.8356 0.826 0.01836 ] Network output: [ 0.9998 0.0006508 0.0009809 -1.933e-05 8.679e-06 -0.001346 -1.457e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1944 -0.03316 -0.1788 0.1915 0.9835 0.9932 0.2173 0.4443 0.8723 0.718 ] Network output: [ -0.01066 1.002 1.01 9.361e-08 -4.202e-08 0.009612 7.055e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005919 0.000475 0.004435 0.00375 0.9889 0.9919 0.006029 0.8641 0.8963 0.01326 ] Network output: [ -0.0006318 0.002878 1.002 -6.118e-05 2.747e-05 0.9966 -4.611e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.206 0.09577 0.3364 0.1475 0.985 0.994 0.2067 0.4486 0.8789 0.7123 ] Network output: [ 0.005966 -0.0289 0.9951 3.651e-05 -1.639e-05 1.022 2.751e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1017 0.08966 0.181 0.2017 0.9873 0.9919 0.1017 0.7649 0.8686 0.306 ] Network output: [ -0.005744 0.02834 1.003 3.819e-05 -1.715e-05 0.9804 2.878e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09054 0.08861 0.1652 0.1954 0.9854 0.9913 0.09056 0.6902 0.8454 0.2447 ] Network output: [ 0.0001746 1 -0.0002841 5.154e-06 -2.314e-06 1 3.884e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004631 Epoch 7890 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01107 0.9952 0.99 5.758e-07 -2.585e-07 -0.007301 4.339e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003329 -0.003135 -0.008142 0.006338 0.9698 0.9742 0.006382 0.8356 0.8259 0.01836 ] Network output: [ 0.9998 0.0006502 0.0009802 -1.931e-05 8.671e-06 -0.001345 -1.456e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1944 -0.03316 -0.1788 0.1915 0.9835 0.9932 0.2173 0.4443 0.8723 0.718 ] Network output: [ -0.01066 1.002 1.01 9.246e-08 -4.151e-08 0.00961 6.968e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005919 0.000475 0.004435 0.00375 0.9889 0.9919 0.00603 0.8641 0.8963 0.01326 ] Network output: [ -0.0006314 0.002877 1.002 -6.112e-05 2.744e-05 0.9966 -4.606e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.206 0.09578 0.3365 0.1475 0.985 0.994 0.2067 0.4486 0.8789 0.7123 ] Network output: [ 0.005964 -0.02889 0.9951 3.647e-05 -1.637e-05 1.022 2.749e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1017 0.08966 0.181 0.2017 0.9873 0.9919 0.1017 0.7649 0.8686 0.306 ] Network output: [ -0.005742 0.02833 1.003 3.816e-05 -1.713e-05 0.9804 2.876e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09054 0.08861 0.1652 0.1954 0.9854 0.9913 0.09056 0.6902 0.8454 0.2447 ] Network output: [ 0.0001746 1 -0.0002837 5.149e-06 -2.312e-06 1 3.881e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004628 Epoch 7891 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01107 0.9952 0.99 5.739e-07 -2.576e-07 -0.007302 4.325e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003329 -0.003135 -0.008141 0.006338 0.9698 0.9742 0.006382 0.8356 0.8259 0.01835 ] Network output: [ 0.9998 0.0006497 0.0009796 -1.93e-05 8.663e-06 -0.001344 -1.454e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1944 -0.03316 -0.1788 0.1915 0.9835 0.9932 0.2174 0.4442 0.8723 0.7179 ] Network output: [ -0.01065 1.002 1.01 9.131e-08 -4.099e-08 0.009607 6.882e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00592 0.0004751 0.004435 0.003749 0.9889 0.9919 0.006031 0.8641 0.8963 0.01326 ] Network output: [ -0.000631 0.002876 1.002 -6.106e-05 2.741e-05 0.9966 -4.602e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.206 0.09578 0.3365 0.1475 0.985 0.994 0.2067 0.4486 0.8789 0.7123 ] Network output: [ 0.005962 -0.02888 0.9951 3.644e-05 -1.636e-05 1.022 2.746e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1017 0.08967 0.181 0.2017 0.9873 0.9919 0.1017 0.7649 0.8686 0.306 ] Network output: [ -0.00574 0.02832 1.003 3.812e-05 -1.712e-05 0.9804 2.873e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09055 0.08861 0.1652 0.1954 0.9854 0.9913 0.09056 0.6902 0.8454 0.2447 ] Network output: [ 0.0001745 1 -0.0002834 5.145e-06 -2.31e-06 1 3.877e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004625 Epoch 7892 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01107 0.9952 0.99 5.719e-07 -2.568e-07 -0.007302 4.31e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00333 -0.003136 -0.00814 0.006337 0.9698 0.9742 0.006382 0.8356 0.8259 0.01835 ] Network output: [ 0.9998 0.0006491 0.000979 -1.928e-05 8.655e-06 -0.001343 -1.453e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1944 -0.03316 -0.1787 0.1915 0.9835 0.9932 0.2174 0.4442 0.8723 0.7179 ] Network output: [ -0.01065 1.002 1.01 9.017e-08 -4.048e-08 0.009605 6.796e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00592 0.0004752 0.004435 0.003749 0.9889 0.9919 0.006031 0.8641 0.8963 0.01326 ] Network output: [ -0.0006306 0.002875 1.002 -6.1e-05 2.739e-05 0.9966 -4.597e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2061 0.09579 0.3365 0.1475 0.985 0.994 0.2067 0.4486 0.8789 0.7123 ] Network output: [ 0.00596 -0.02887 0.9951 3.64e-05 -1.634e-05 1.022 2.744e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1017 0.08967 0.181 0.2017 0.9873 0.9919 0.1017 0.7648 0.8685 0.306 ] Network output: [ -0.005738 0.0283 1.003 3.809e-05 -1.71e-05 0.9804 2.871e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09055 0.08861 0.1652 0.1954 0.9854 0.9913 0.09056 0.6902 0.8454 0.2447 ] Network output: [ 0.0001744 1 -0.0002831 5.14e-06 -2.307e-06 1 3.874e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004622 Epoch 7893 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01107 0.9952 0.99 5.7e-07 -2.559e-07 -0.007303 4.296e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00333 -0.003136 -0.008139 0.006336 0.9698 0.9742 0.006383 0.8356 0.8259 0.01835 ] Network output: [ 0.9998 0.0006485 0.0009783 -1.926e-05 8.646e-06 -0.001342 -1.451e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1944 -0.03317 -0.1787 0.1915 0.9835 0.9932 0.2174 0.4442 0.8723 0.7179 ] Network output: [ -0.01065 1.002 1.01 8.903e-08 -3.997e-08 0.009602 6.71e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005921 0.0004752 0.004435 0.003748 0.9889 0.9919 0.006032 0.8641 0.8963 0.01325 ] Network output: [ -0.0006302 0.002874 1.002 -6.094e-05 2.736e-05 0.9966 -4.593e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2061 0.09579 0.3365 0.1475 0.985 0.994 0.2067 0.4486 0.8789 0.7123 ] Network output: [ 0.005958 -0.02886 0.9951 3.637e-05 -1.633e-05 1.022 2.741e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1017 0.08968 0.181 0.2017 0.9873 0.9919 0.1017 0.7648 0.8685 0.306 ] Network output: [ -0.005736 0.02829 1.003 3.806e-05 -1.708e-05 0.9804 2.868e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09055 0.08861 0.1652 0.1954 0.9854 0.9913 0.09056 0.6901 0.8454 0.2447 ] Network output: [ 0.0001743 1 -0.0002828 5.135e-06 -2.305e-06 1 3.87e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004619 Epoch 7894 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01107 0.9952 0.99 5.681e-07 -2.55e-07 -0.007304 4.281e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00333 -0.003136 -0.008138 0.006335 0.9698 0.9742 0.006383 0.8356 0.8259 0.01835 ] Network output: [ 0.9998 0.000648 0.0009777 -1.924e-05 8.638e-06 -0.00134 -1.45e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1944 -0.03317 -0.1787 0.1915 0.9835 0.9932 0.2174 0.4442 0.8723 0.7179 ] Network output: [ -0.01065 1.002 1.01 8.789e-08 -3.946e-08 0.0096 6.624e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005922 0.0004753 0.004435 0.003748 0.9889 0.9919 0.006033 0.8641 0.8963 0.01325 ] Network output: [ -0.0006298 0.002873 1.002 -6.089e-05 2.733e-05 0.9966 -4.589e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2061 0.0958 0.3365 0.1475 0.985 0.994 0.2067 0.4486 0.8789 0.7123 ] Network output: [ 0.005956 -0.02885 0.9951 3.634e-05 -1.631e-05 1.022 2.738e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1017 0.08969 0.181 0.2017 0.9873 0.9919 0.1017 0.7648 0.8685 0.306 ] Network output: [ -0.005734 0.02828 1.003 3.802e-05 -1.707e-05 0.9804 2.865e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09055 0.08861 0.1652 0.1954 0.9854 0.9913 0.09056 0.6901 0.8454 0.2447 ] Network output: [ 0.0001742 1 -0.0002825 5.13e-06 -2.303e-06 1 3.866e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004616 Epoch 7895 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01106 0.9952 0.99 5.662e-07 -2.542e-07 -0.007305 4.267e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00333 -0.003136 -0.008137 0.006335 0.9698 0.9742 0.006383 0.8356 0.8259 0.01835 ] Network output: [ 0.9998 0.0006474 0.0009771 -1.922e-05 8.63e-06 -0.001339 -1.449e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1944 -0.03317 -0.1787 0.1915 0.9835 0.9932 0.2174 0.4442 0.8723 0.7179 ] Network output: [ -0.01065 1.002 1.01 8.675e-08 -3.895e-08 0.009598 6.538e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005922 0.0004754 0.004436 0.003747 0.9889 0.9919 0.006033 0.8641 0.8963 0.01325 ] Network output: [ -0.0006294 0.002872 1.002 -6.083e-05 2.731e-05 0.9966 -4.584e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2061 0.0958 0.3365 0.1475 0.985 0.994 0.2068 0.4485 0.8788 0.7123 ] Network output: [ 0.005954 -0.02884 0.9951 3.63e-05 -1.63e-05 1.022 2.736e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1017 0.08969 0.181 0.2017 0.9873 0.9919 0.1018 0.7648 0.8685 0.306 ] Network output: [ -0.005732 0.02827 1.003 3.799e-05 -1.705e-05 0.9804 2.863e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09055 0.08862 0.1652 0.1954 0.9854 0.9913 0.09056 0.6901 0.8454 0.2447 ] Network output: [ 0.0001741 1 -0.0002822 5.126e-06 -2.301e-06 1 3.863e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004614 Epoch 7896 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01106 0.9952 0.99 5.643e-07 -2.533e-07 -0.007306 4.253e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00333 -0.003136 -0.008135 0.006334 0.9698 0.9742 0.006384 0.8356 0.8259 0.01835 ] Network output: [ 0.9998 0.0006469 0.0009764 -1.921e-05 8.622e-06 -0.001338 -1.447e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1944 -0.03317 -0.1787 0.1915 0.9835 0.9932 0.2174 0.4442 0.8723 0.7179 ] Network output: [ -0.01065 1.002 1.01 8.562e-08 -3.844e-08 0.009595 6.452e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005923 0.0004754 0.004436 0.003747 0.9889 0.9919 0.006034 0.8641 0.8963 0.01325 ] Network output: [ -0.000629 0.002871 1.002 -6.077e-05 2.728e-05 0.9966 -4.58e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2061 0.09581 0.3365 0.1475 0.985 0.994 0.2068 0.4485 0.8788 0.7123 ] Network output: [ 0.005952 -0.02883 0.9951 3.627e-05 -1.628e-05 1.022 2.733e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1017 0.0897 0.181 0.2017 0.9873 0.9919 0.1018 0.7648 0.8685 0.306 ] Network output: [ -0.005729 0.02826 1.003 3.795e-05 -1.704e-05 0.9805 2.86e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09055 0.08862 0.1652 0.1954 0.9854 0.9913 0.09056 0.6901 0.8454 0.2447 ] Network output: [ 0.000174 1 -0.0002818 5.121e-06 -2.299e-06 1 3.859e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004611 Epoch 7897 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01106 0.9952 0.99 5.624e-07 -2.525e-07 -0.007306 4.238e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00333 -0.003136 -0.008134 0.006333 0.9698 0.9742 0.006384 0.8356 0.8259 0.01835 ] Network output: [ 0.9998 0.0006463 0.0009758 -1.919e-05 8.614e-06 -0.001337 -1.446e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1944 -0.03317 -0.1787 0.1915 0.9835 0.9932 0.2174 0.4442 0.8723 0.7179 ] Network output: [ -0.01065 1.002 1.01 8.449e-08 -3.793e-08 0.009593 6.367e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005923 0.0004755 0.004436 0.003746 0.9889 0.9919 0.006034 0.864 0.8963 0.01325 ] Network output: [ -0.0006287 0.00287 1.002 -6.071e-05 2.725e-05 0.9966 -4.575e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2061 0.09581 0.3365 0.1475 0.985 0.994 0.2068 0.4485 0.8788 0.7123 ] Network output: [ 0.00595 -0.02882 0.9951 3.623e-05 -1.627e-05 1.022 2.731e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1017 0.0897 0.181 0.2017 0.9873 0.9919 0.1018 0.7647 0.8685 0.306 ] Network output: [ -0.005727 0.02824 1.003 3.792e-05 -1.702e-05 0.9805 2.858e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09055 0.08862 0.1652 0.1954 0.9854 0.9913 0.09056 0.69 0.8454 0.2447 ] Network output: [ 0.0001739 1 -0.0002815 5.116e-06 -2.297e-06 1 3.856e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004608 Epoch 7898 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01106 0.9952 0.99 5.605e-07 -2.516e-07 -0.007307 4.224e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00333 -0.003136 -0.008133 0.006332 0.9698 0.9742 0.006384 0.8356 0.8259 0.01834 ] Network output: [ 0.9998 0.0006457 0.0009752 -1.917e-05 8.606e-06 -0.001336 -1.445e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1944 -0.03318 -0.1786 0.1915 0.9835 0.9932 0.2174 0.4442 0.8723 0.7179 ] Network output: [ -0.01065 1.002 1.01 8.336e-08 -3.742e-08 0.00959 6.282e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005924 0.0004755 0.004436 0.003746 0.9889 0.9919 0.006035 0.864 0.8963 0.01325 ] Network output: [ -0.0006283 0.002869 1.002 -6.065e-05 2.723e-05 0.9966 -4.571e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2061 0.09582 0.3365 0.1474 0.985 0.994 0.2068 0.4485 0.8788 0.7123 ] Network output: [ 0.005948 -0.02881 0.9951 3.62e-05 -1.625e-05 1.022 2.728e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1017 0.08971 0.181 0.2017 0.9873 0.9919 0.1018 0.7647 0.8685 0.306 ] Network output: [ -0.005725 0.02823 1.003 3.788e-05 -1.701e-05 0.9805 2.855e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09055 0.08862 0.1652 0.1954 0.9854 0.9913 0.09057 0.69 0.8454 0.2448 ] Network output: [ 0.0001738 1 -0.0002812 5.111e-06 -2.295e-06 1 3.852e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004605 Epoch 7899 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01106 0.9952 0.99 5.585e-07 -2.508e-07 -0.007308 4.209e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003331 -0.003137 -0.008132 0.006332 0.9698 0.9742 0.006385 0.8356 0.8259 0.01834 ] Network output: [ 0.9998 0.0006452 0.0009745 -1.915e-05 8.598e-06 -0.001335 -1.443e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1945 -0.03318 -0.1786 0.1914 0.9835 0.9932 0.2175 0.4441 0.8722 0.7179 ] Network output: [ -0.01065 1.002 1.01 8.223e-08 -3.691e-08 0.009588 6.197e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005925 0.0004756 0.004436 0.003745 0.9889 0.9919 0.006036 0.864 0.8963 0.01325 ] Network output: [ -0.0006279 0.002868 1.002 -6.059e-05 2.72e-05 0.9966 -4.566e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2061 0.09582 0.3366 0.1474 0.985 0.994 0.2068 0.4485 0.8788 0.7123 ] Network output: [ 0.005946 -0.0288 0.9951 3.617e-05 -1.624e-05 1.022 2.726e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1017 0.08971 0.181 0.2017 0.9873 0.9919 0.1018 0.7647 0.8685 0.306 ] Network output: [ -0.005723 0.02822 1.003 3.785e-05 -1.699e-05 0.9805 2.852e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09055 0.08862 0.1652 0.1954 0.9854 0.9913 0.09057 0.69 0.8453 0.2448 ] Network output: [ 0.0001737 1 -0.0002809 5.106e-06 -2.292e-06 1 3.848e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004602 Epoch 7900 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01106 0.9952 0.99 5.566e-07 -2.499e-07 -0.007309 4.195e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003331 -0.003137 -0.008131 0.006331 0.9698 0.9742 0.006385 0.8355 0.8259 0.01834 ] Network output: [ 0.9998 0.0006446 0.0009739 -1.913e-05 8.59e-06 -0.001334 -1.442e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1945 -0.03318 -0.1786 0.1914 0.9835 0.9932 0.2175 0.4441 0.8722 0.7179 ] Network output: [ -0.01064 1.002 1.01 8.11e-08 -3.641e-08 0.009586 6.112e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005925 0.0004757 0.004436 0.003745 0.9889 0.9919 0.006036 0.864 0.8963 0.01325 ] Network output: [ -0.0006275 0.002867 1.002 -6.053e-05 2.718e-05 0.9966 -4.562e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2061 0.09583 0.3366 0.1474 0.985 0.994 0.2068 0.4485 0.8788 0.7123 ] Network output: [ 0.005944 -0.02879 0.9951 3.613e-05 -1.622e-05 1.022 2.723e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1017 0.08972 0.181 0.2017 0.9873 0.9919 0.1018 0.7647 0.8685 0.306 ] Network output: [ -0.005721 0.02821 1.003 3.781e-05 -1.698e-05 0.9805 2.85e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09056 0.08862 0.1652 0.1954 0.9854 0.9913 0.09057 0.69 0.8453 0.2448 ] Network output: [ 0.0001736 1 -0.0002806 5.102e-06 -2.29e-06 1 3.845e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004599 Epoch 7901 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01105 0.9952 0.99 5.548e-07 -2.49e-07 -0.007309 4.181e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003331 -0.003137 -0.00813 0.00633 0.9698 0.9742 0.006385 0.8355 0.8259 0.01834 ] Network output: [ 0.9998 0.0006441 0.0009733 -1.912e-05 8.582e-06 -0.001333 -1.441e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1945 -0.03318 -0.1786 0.1914 0.9835 0.9932 0.2175 0.4441 0.8722 0.7179 ] Network output: [ -0.01064 1.002 1.01 7.998e-08 -3.59e-08 0.009583 6.027e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005926 0.0004757 0.004436 0.003744 0.9889 0.9919 0.006037 0.864 0.8963 0.01325 ] Network output: [ -0.0006271 0.002866 1.002 -6.048e-05 2.715e-05 0.9966 -4.558e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2062 0.09583 0.3366 0.1474 0.985 0.994 0.2068 0.4485 0.8788 0.7123 ] Network output: [ 0.005942 -0.02878 0.995 3.61e-05 -1.621e-05 1.022 2.72e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1017 0.08972 0.181 0.2017 0.9873 0.9919 0.1018 0.7646 0.8685 0.306 ] Network output: [ -0.005719 0.02819 1.003 3.778e-05 -1.696e-05 0.9805 2.847e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09056 0.08862 0.1652 0.1954 0.9854 0.9913 0.09057 0.6899 0.8453 0.2448 ] Network output: [ 0.0001735 1 -0.0002803 5.097e-06 -2.288e-06 1 3.841e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004596 Epoch 7902 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01105 0.9952 0.99 5.529e-07 -2.482e-07 -0.00731 4.167e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003331 -0.003137 -0.008129 0.00633 0.9698 0.9742 0.006386 0.8355 0.8259 0.01834 ] Network output: [ 0.9998 0.0006435 0.0009727 -1.91e-05 8.574e-06 -0.001331 -1.439e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1945 -0.03319 -0.1786 0.1914 0.9835 0.9932 0.2175 0.4441 0.8722 0.7179 ] Network output: [ -0.01064 1.002 1.01 7.886e-08 -3.54e-08 0.009581 5.943e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005926 0.0004758 0.004436 0.003744 0.9889 0.9919 0.006038 0.864 0.8963 0.01324 ] Network output: [ -0.0006267 0.002865 1.002 -6.042e-05 2.712e-05 0.9966 -4.553e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2062 0.09584 0.3366 0.1474 0.985 0.994 0.2068 0.4484 0.8788 0.7123 ] Network output: [ 0.005941 -0.02877 0.995 3.606e-05 -1.619e-05 1.022 2.718e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1017 0.08973 0.181 0.2017 0.9873 0.9919 0.1018 0.7646 0.8685 0.306 ] Network output: [ -0.005717 0.02818 1.003 3.774e-05 -1.694e-05 0.9805 2.845e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09056 0.08862 0.1652 0.1954 0.9854 0.9913 0.09057 0.6899 0.8453 0.2448 ] Network output: [ 0.0001735 1 -0.0002799 5.092e-06 -2.286e-06 1 3.838e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004594 Epoch 7903 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01105 0.9952 0.99 5.51e-07 -2.473e-07 -0.007311 4.152e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003331 -0.003137 -0.008128 0.006329 0.9698 0.9742 0.006386 0.8355 0.8259 0.01834 ] Network output: [ 0.9998 0.000643 0.000972 -1.908e-05 8.566e-06 -0.00133 -1.438e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1945 -0.03319 -0.1786 0.1914 0.9835 0.9932 0.2175 0.4441 0.8722 0.7179 ] Network output: [ -0.01064 1.002 1.01 7.774e-08 -3.49e-08 0.009579 5.858e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005927 0.0004758 0.004436 0.003743 0.9889 0.9919 0.006038 0.864 0.8963 0.01324 ] Network output: [ -0.0006264 0.002864 1.002 -6.036e-05 2.71e-05 0.9966 -4.549e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2062 0.09584 0.3366 0.1474 0.985 0.994 0.2069 0.4484 0.8788 0.7123 ] Network output: [ 0.005939 -0.02876 0.995 3.603e-05 -1.617e-05 1.022 2.715e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1017 0.08974 0.181 0.2017 0.9873 0.9919 0.1018 0.7646 0.8685 0.306 ] Network output: [ -0.005715 0.02817 1.003 3.771e-05 -1.693e-05 0.9805 2.842e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09056 0.08862 0.1652 0.1954 0.9854 0.9913 0.09057 0.6899 0.8453 0.2448 ] Network output: [ 0.0001734 1 -0.0002796 5.087e-06 -2.284e-06 1 3.834e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004591 Epoch 7904 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01105 0.9952 0.99 5.491e-07 -2.465e-07 -0.007312 4.138e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003331 -0.003137 -0.008126 0.006328 0.9698 0.9742 0.006386 0.8355 0.8259 0.01833 ] Network output: [ 0.9998 0.0006424 0.0009714 -1.906e-05 8.558e-06 -0.001329 -1.437e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1945 -0.03319 -0.1785 0.1914 0.9835 0.9932 0.2175 0.4441 0.8722 0.7179 ] Network output: [ -0.01064 1.002 1.01 7.662e-08 -3.44e-08 0.009576 5.774e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005928 0.0004759 0.004436 0.003743 0.9889 0.9919 0.006039 0.864 0.8963 0.01324 ] Network output: [ -0.000626 0.002863 1.002 -6.03e-05 2.707e-05 0.9966 -4.544e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2062 0.09585 0.3366 0.1474 0.985 0.994 0.2069 0.4484 0.8788 0.7123 ] Network output: [ 0.005937 -0.02874 0.995 3.599e-05 -1.616e-05 1.022 2.713e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1017 0.08974 0.181 0.2017 0.9873 0.9919 0.1018 0.7646 0.8685 0.306 ] Network output: [ -0.005713 0.02816 1.003 3.768e-05 -1.691e-05 0.9805 2.839e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09056 0.08863 0.1652 0.1954 0.9854 0.9913 0.09057 0.6899 0.8453 0.2448 ] Network output: [ 0.0001733 1 -0.0002793 5.083e-06 -2.282e-06 1 3.83e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004588 Epoch 7905 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01105 0.9952 0.99 5.472e-07 -2.457e-07 -0.007312 4.124e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003331 -0.003138 -0.008125 0.006327 0.9698 0.9742 0.006387 0.8355 0.8259 0.01833 ] Network output: [ 0.9998 0.0006419 0.0009708 -1.905e-05 8.55e-06 -0.001328 -1.435e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1945 -0.03319 -0.1785 0.1914 0.9835 0.9932 0.2175 0.4441 0.8722 0.7179 ] Network output: [ -0.01064 1.002 1.01 7.55e-08 -3.39e-08 0.009574 5.69e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005928 0.000476 0.004436 0.003743 0.9889 0.9919 0.006039 0.864 0.8963 0.01324 ] Network output: [ -0.0006256 0.002862 1.002 -6.024e-05 2.704e-05 0.9966 -4.54e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2062 0.09586 0.3366 0.1474 0.985 0.994 0.2069 0.4484 0.8788 0.7122 ] Network output: [ 0.005935 -0.02873 0.995 3.596e-05 -1.614e-05 1.022 2.71e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1018 0.08975 0.181 0.2016 0.9873 0.9919 0.1018 0.7646 0.8685 0.306 ] Network output: [ -0.00571 0.02815 1.003 3.764e-05 -1.69e-05 0.9805 2.837e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09056 0.08863 0.1652 0.1954 0.9854 0.9913 0.09057 0.6898 0.8453 0.2448 ] Network output: [ 0.0001732 1 -0.000279 5.078e-06 -2.28e-06 1 3.827e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004585 Epoch 7906 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01105 0.9952 0.99 5.453e-07 -2.448e-07 -0.007313 4.11e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003332 -0.003138 -0.008124 0.006327 0.9698 0.9742 0.006387 0.8355 0.8259 0.01833 ] Network output: [ 0.9998 0.0006413 0.0009701 -1.903e-05 8.542e-06 -0.001327 -1.434e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1945 -0.0332 -0.1785 0.1914 0.9835 0.9932 0.2175 0.4441 0.8722 0.7179 ] Network output: [ -0.01064 1.002 1.01 7.439e-08 -3.34e-08 0.009572 5.606e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005929 0.000476 0.004436 0.003742 0.9889 0.9919 0.00604 0.864 0.8962 0.01324 ] Network output: [ -0.0006252 0.002861 1.002 -6.018e-05 2.702e-05 0.9966 -4.536e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2062 0.09586 0.3366 0.1474 0.985 0.994 0.2069 0.4484 0.8788 0.7122 ] Network output: [ 0.005933 -0.02872 0.995 3.593e-05 -1.613e-05 1.022 2.708e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1018 0.08975 0.181 0.2016 0.9873 0.9919 0.1018 0.7645 0.8685 0.306 ] Network output: [ -0.005708 0.02813 1.003 3.761e-05 -1.688e-05 0.9805 2.834e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09056 0.08863 0.1652 0.1954 0.9854 0.9913 0.09058 0.6898 0.8453 0.2448 ] Network output: [ 0.0001731 1 -0.0002787 5.073e-06 -2.278e-06 1 3.823e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004582 Epoch 7907 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01104 0.9952 0.99 5.434e-07 -2.44e-07 -0.007314 4.096e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003332 -0.003138 -0.008123 0.006326 0.9698 0.9742 0.006387 0.8355 0.8259 0.01833 ] Network output: [ 0.9998 0.0006407 0.0009695 -1.901e-05 8.534e-06 -0.001326 -1.433e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1945 -0.0332 -0.1785 0.1914 0.9835 0.9932 0.2175 0.444 0.8722 0.7179 ] Network output: [ -0.01064 1.002 1.01 7.328e-08 -3.29e-08 0.009569 5.523e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005929 0.0004761 0.004436 0.003742 0.9889 0.9919 0.006041 0.864 0.8962 0.01324 ] Network output: [ -0.0006248 0.00286 1.002 -6.013e-05 2.699e-05 0.9966 -4.531e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2062 0.09587 0.3366 0.1474 0.985 0.994 0.2069 0.4484 0.8788 0.7122 ] Network output: [ 0.005931 -0.02871 0.995 3.589e-05 -1.611e-05 1.022 2.705e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1018 0.08976 0.181 0.2016 0.9873 0.9919 0.1018 0.7645 0.8684 0.306 ] Network output: [ -0.005706 0.02812 1.003 3.757e-05 -1.687e-05 0.9805 2.832e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09056 0.08863 0.1652 0.1954 0.9854 0.9913 0.09058 0.6898 0.8453 0.2448 ] Network output: [ 0.000173 1 -0.0002784 5.068e-06 -2.275e-06 1 3.82e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004579 Epoch 7908 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01104 0.9952 0.99 5.416e-07 -2.431e-07 -0.007315 4.081e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003332 -0.003138 -0.008122 0.006325 0.9698 0.9742 0.006387 0.8355 0.8259 0.01833 ] Network output: [ 0.9998 0.0006402 0.0009689 -1.899e-05 8.526e-06 -0.001325 -1.431e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1946 -0.0332 -0.1785 0.1914 0.9835 0.9932 0.2176 0.444 0.8722 0.7179 ] Network output: [ -0.01063 1.002 1.01 7.217e-08 -3.24e-08 0.009567 5.439e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00593 0.0004762 0.004436 0.003741 0.9889 0.9919 0.006041 0.8639 0.8962 0.01324 ] Network output: [ -0.0006244 0.002859 1.002 -6.007e-05 2.697e-05 0.9966 -4.527e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2062 0.09587 0.3367 0.1474 0.985 0.994 0.2069 0.4484 0.8788 0.7122 ] Network output: [ 0.005929 -0.0287 0.995 3.586e-05 -1.61e-05 1.022 2.702e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1018 0.08976 0.181 0.2016 0.9873 0.9919 0.1018 0.7645 0.8684 0.306 ] Network output: [ -0.005704 0.02811 1.003 3.754e-05 -1.685e-05 0.9805 2.829e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09056 0.08863 0.1652 0.1954 0.9854 0.9913 0.09058 0.6898 0.8453 0.2448 ] Network output: [ 0.0001729 1 -0.0002781 5.064e-06 -2.273e-06 1 3.816e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004577 Epoch 7909 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01104 0.9952 0.99 5.397e-07 -2.423e-07 -0.007315 4.067e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003332 -0.003138 -0.008121 0.006324 0.9698 0.9742 0.006388 0.8355 0.8259 0.01833 ] Network output: [ 0.9998 0.0006396 0.0009683 -1.897e-05 8.518e-06 -0.001324 -1.43e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1946 -0.0332 -0.1785 0.1914 0.9835 0.9932 0.2176 0.444 0.8722 0.7178 ] Network output: [ -0.01063 1.002 1.01 7.107e-08 -3.19e-08 0.009564 5.356e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005931 0.0004762 0.004436 0.003741 0.9889 0.9919 0.006042 0.8639 0.8962 0.01324 ] Network output: [ -0.0006241 0.002858 1.002 -6.001e-05 2.694e-05 0.9966 -4.523e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2063 0.09588 0.3367 0.1474 0.985 0.994 0.2069 0.4484 0.8788 0.7122 ] Network output: [ 0.005927 -0.02869 0.995 3.582e-05 -1.608e-05 1.022 2.7e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1018 0.08977 0.181 0.2016 0.9873 0.9919 0.1018 0.7645 0.8684 0.306 ] Network output: [ -0.005702 0.0281 1.003 3.75e-05 -1.684e-05 0.9805 2.826e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09057 0.08863 0.1652 0.1954 0.9854 0.9913 0.09058 0.6897 0.8453 0.2448 ] Network output: [ 0.0001728 1 -0.0002778 5.059e-06 -2.271e-06 1 3.813e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004574 Epoch 7910 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01104 0.9952 0.99 5.378e-07 -2.415e-07 -0.007316 4.053e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003332 -0.003138 -0.00812 0.006324 0.9698 0.9742 0.006388 0.8355 0.8258 0.01833 ] Network output: [ 0.9998 0.0006391 0.0009676 -1.896e-05 8.51e-06 -0.001322 -1.429e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1946 -0.03321 -0.1784 0.1914 0.9835 0.9932 0.2176 0.444 0.8722 0.7178 ] Network output: [ -0.01063 1.002 1.01 6.996e-08 -3.141e-08 0.009562 5.273e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005931 0.0004763 0.004436 0.00374 0.9889 0.9919 0.006042 0.8639 0.8962 0.01323 ] Network output: [ -0.0006237 0.002857 1.002 -5.995e-05 2.691e-05 0.9966 -4.518e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2063 0.09588 0.3367 0.1474 0.985 0.994 0.2069 0.4483 0.8788 0.7122 ] Network output: [ 0.005925 -0.02868 0.995 3.579e-05 -1.607e-05 1.022 2.697e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1018 0.08978 0.181 0.2016 0.9873 0.9919 0.1019 0.7644 0.8684 0.306 ] Network output: [ -0.0057 0.02808 1.003 3.747e-05 -1.682e-05 0.9805 2.824e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09057 0.08863 0.1652 0.1954 0.9854 0.9913 0.09058 0.6897 0.8452 0.2448 ] Network output: [ 0.0001727 1 -0.0002774 5.054e-06 -2.269e-06 1 3.809e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004571 Epoch 7911 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01104 0.9952 0.99 5.36e-07 -2.406e-07 -0.007317 4.039e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003332 -0.003138 -0.008119 0.006323 0.9698 0.9742 0.006388 0.8355 0.8258 0.01832 ] Network output: [ 0.9998 0.0006385 0.000967 -1.894e-05 8.502e-06 -0.001321 -1.427e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1946 -0.03321 -0.1784 0.1914 0.9835 0.9932 0.2176 0.444 0.8722 0.7178 ] Network output: [ -0.01063 1.002 1.01 6.886e-08 -3.091e-08 0.00956 5.189e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005932 0.0004763 0.004436 0.00374 0.9889 0.9919 0.006043 0.8639 0.8962 0.01323 ] Network output: [ -0.0006233 0.002856 1.002 -5.989e-05 2.689e-05 0.9966 -4.514e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2063 0.09589 0.3367 0.1474 0.985 0.994 0.2069 0.4483 0.8788 0.7122 ] Network output: [ 0.005923 -0.02867 0.995 3.576e-05 -1.605e-05 1.022 2.695e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1018 0.08978 0.181 0.2016 0.9873 0.9919 0.1019 0.7644 0.8684 0.306 ] Network output: [ -0.005698 0.02807 1.003 3.744e-05 -1.681e-05 0.9805 2.821e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09057 0.08863 0.1652 0.1954 0.9854 0.9913 0.09058 0.6897 0.8452 0.2448 ] Network output: [ 0.0001726 1 -0.0002771 5.05e-06 -2.267e-06 1 3.805e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004568 Epoch 7912 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01104 0.9952 0.99 5.341e-07 -2.398e-07 -0.007318 4.025e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003332 -0.003139 -0.008118 0.006322 0.9699 0.9742 0.006389 0.8355 0.8258 0.01832 ] Network output: [ 0.9998 0.000638 0.0009664 -1.892e-05 8.494e-06 -0.00132 -1.426e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1946 -0.03321 -0.1784 0.1914 0.9835 0.9932 0.2176 0.444 0.8722 0.7178 ] Network output: [ -0.01063 1.002 1.01 6.776e-08 -3.042e-08 0.009557 5.107e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005933 0.0004764 0.004436 0.003739 0.9889 0.9919 0.006044 0.8639 0.8962 0.01323 ] Network output: [ -0.0006229 0.002855 1.002 -5.984e-05 2.686e-05 0.9966 -4.509e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2063 0.09589 0.3367 0.1474 0.985 0.994 0.207 0.4483 0.8788 0.7122 ] Network output: [ 0.005921 -0.02866 0.995 3.572e-05 -1.604e-05 1.022 2.692e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1018 0.08979 0.181 0.2016 0.9873 0.9919 0.1019 0.7644 0.8684 0.306 ] Network output: [ -0.005696 0.02806 1.003 3.74e-05 -1.679e-05 0.9805 2.819e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09057 0.08863 0.1652 0.1954 0.9854 0.9913 0.09058 0.6897 0.8452 0.2448 ] Network output: [ 0.0001726 1 -0.0002768 5.045e-06 -2.265e-06 1 3.802e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004565 Epoch 7913 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01103 0.9952 0.99 5.323e-07 -2.389e-07 -0.007318 4.011e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003332 -0.003139 -0.008116 0.006322 0.9699 0.9742 0.006389 0.8354 0.8258 0.01832 ] Network output: [ 0.9998 0.0006374 0.0009658 -1.89e-05 8.486e-06 -0.001319 -1.425e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1946 -0.03321 -0.1784 0.1913 0.9835 0.9932 0.2176 0.444 0.8722 0.7178 ] Network output: [ -0.01063 1.002 1.01 6.666e-08 -2.993e-08 0.009555 5.024e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005933 0.0004765 0.004436 0.003739 0.9889 0.9919 0.006044 0.8639 0.8962 0.01323 ] Network output: [ -0.0006225 0.002854 1.002 -5.978e-05 2.684e-05 0.9966 -4.505e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2063 0.0959 0.3367 0.1473 0.985 0.994 0.207 0.4483 0.8788 0.7122 ] Network output: [ 0.005919 -0.02865 0.995 3.569e-05 -1.602e-05 1.022 2.69e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1018 0.08979 0.181 0.2016 0.9873 0.9919 0.1019 0.7644 0.8684 0.306 ] Network output: [ -0.005694 0.02805 1.003 3.737e-05 -1.678e-05 0.9806 2.816e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09057 0.08864 0.1652 0.1954 0.9854 0.9913 0.09058 0.6896 0.8452 0.2448 ] Network output: [ 0.0001725 1 -0.0002765 5.04e-06 -2.263e-06 1 3.798e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004563 Epoch 7914 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01103 0.9952 0.99 5.304e-07 -2.381e-07 -0.007319 3.997e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003333 -0.003139 -0.008115 0.006321 0.9699 0.9742 0.006389 0.8354 0.8258 0.01832 ] Network output: [ 0.9998 0.0006369 0.0009651 -1.889e-05 8.478e-06 -0.001318 -1.423e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1946 -0.03322 -0.1784 0.1913 0.9835 0.9932 0.2176 0.4439 0.8722 0.7178 ] Network output: [ -0.01063 1.002 1.01 6.557e-08 -2.943e-08 0.009553 4.941e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005934 0.0004765 0.004437 0.003738 0.9889 0.9919 0.006045 0.8639 0.8962 0.01323 ] Network output: [ -0.0006221 0.002853 1.002 -5.972e-05 2.681e-05 0.9966 -4.501e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2063 0.0959 0.3367 0.1473 0.985 0.994 0.207 0.4483 0.8788 0.7122 ] Network output: [ 0.005917 -0.02864 0.995 3.566e-05 -1.601e-05 1.022 2.687e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1018 0.0898 0.181 0.2016 0.9873 0.9919 0.1019 0.7644 0.8684 0.306 ] Network output: [ -0.005692 0.02804 1.003 3.733e-05 -1.676e-05 0.9806 2.814e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09057 0.08864 0.1652 0.1954 0.9854 0.9913 0.09058 0.6896 0.8452 0.2448 ] Network output: [ 0.0001724 1 -0.0002762 5.035e-06 -2.261e-06 1 3.795e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000456 Epoch 7915 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01103 0.9952 0.99 5.286e-07 -2.373e-07 -0.00732 3.983e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003333 -0.003139 -0.008114 0.00632 0.9699 0.9742 0.00639 0.8354 0.8258 0.01832 ] Network output: [ 0.9998 0.0006363 0.0009645 -1.887e-05 8.47e-06 -0.001317 -1.422e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1946 -0.03322 -0.1784 0.1913 0.9835 0.9932 0.2176 0.4439 0.8722 0.7178 ] Network output: [ -0.01063 1.002 1.01 6.447e-08 -2.894e-08 0.00955 4.859e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005934 0.0004766 0.004437 0.003738 0.9889 0.9919 0.006046 0.8639 0.8962 0.01323 ] Network output: [ -0.0006218 0.002852 1.002 -5.966e-05 2.678e-05 0.9966 -4.496e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2063 0.09591 0.3367 0.1473 0.985 0.994 0.207 0.4483 0.8788 0.7122 ] Network output: [ 0.005915 -0.02863 0.995 3.562e-05 -1.599e-05 1.022 2.685e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1018 0.0898 0.181 0.2016 0.9873 0.9919 0.1019 0.7643 0.8684 0.306 ] Network output: [ -0.005689 0.02802 1.003 3.73e-05 -1.674e-05 0.9806 2.811e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09057 0.08864 0.1652 0.1954 0.9854 0.9913 0.09059 0.6896 0.8452 0.2448 ] Network output: [ 0.0001723 1 -0.0002759 5.031e-06 -2.258e-06 1 3.791e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004557 Epoch 7916 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01103 0.9952 0.99 5.267e-07 -2.365e-07 -0.00732 3.969e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003333 -0.003139 -0.008113 0.006319 0.9699 0.9742 0.00639 0.8354 0.8258 0.01832 ] Network output: [ 0.9998 0.0006358 0.0009639 -1.885e-05 8.462e-06 -0.001316 -1.421e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1946 -0.03322 -0.1783 0.1913 0.9835 0.9932 0.2177 0.4439 0.8722 0.7178 ] Network output: [ -0.01063 1.002 1.01 6.338e-08 -2.845e-08 0.009548 4.777e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005935 0.0004767 0.004437 0.003737 0.9889 0.9919 0.006046 0.8639 0.8962 0.01323 ] Network output: [ -0.0006214 0.002851 1.002 -5.96e-05 2.676e-05 0.9966 -4.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2063 0.09592 0.3367 0.1473 0.985 0.994 0.207 0.4483 0.8788 0.7122 ] Network output: [ 0.005913 -0.02862 0.995 3.559e-05 -1.598e-05 1.022 2.682e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1018 0.08981 0.181 0.2016 0.9873 0.9919 0.1019 0.7643 0.8684 0.306 ] Network output: [ -0.005687 0.02801 1.003 3.726e-05 -1.673e-05 0.9806 2.808e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09057 0.08864 0.1652 0.1954 0.9854 0.9912 0.09059 0.6896 0.8452 0.2448 ] Network output: [ 0.0001722 1 -0.0002756 5.026e-06 -2.256e-06 1 3.788e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004554 Epoch 7917 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01103 0.9952 0.99 5.249e-07 -2.356e-07 -0.007321 3.956e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003333 -0.003139 -0.008112 0.006319 0.9699 0.9742 0.00639 0.8354 0.8258 0.01832 ] Network output: [ 0.9998 0.0006353 0.0009633 -1.883e-05 8.454e-06 -0.001315 -1.419e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1946 -0.03322 -0.1783 0.1913 0.9835 0.9932 0.2177 0.4439 0.8722 0.7178 ] Network output: [ -0.01062 1.002 1.01 6.229e-08 -2.797e-08 0.009546 4.695e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005936 0.0004767 0.004437 0.003737 0.9889 0.9919 0.006047 0.8639 0.8962 0.01323 ] Network output: [ -0.000621 0.00285 1.002 -5.955e-05 2.673e-05 0.9966 -4.488e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2063 0.09592 0.3368 0.1473 0.985 0.994 0.207 0.4482 0.8788 0.7122 ] Network output: [ 0.005911 -0.02861 0.995 3.555e-05 -1.596e-05 1.022 2.68e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1018 0.08982 0.181 0.2016 0.9873 0.9919 0.1019 0.7643 0.8684 0.306 ] Network output: [ -0.005685 0.028 1.003 3.723e-05 -1.671e-05 0.9806 2.806e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09058 0.08864 0.1652 0.1954 0.9854 0.9912 0.09059 0.6896 0.8452 0.2448 ] Network output: [ 0.0001721 1 -0.0002753 5.021e-06 -2.254e-06 1 3.784e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004551 Epoch 7918 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01103 0.9952 0.99 5.23e-07 -2.348e-07 -0.007322 3.942e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003333 -0.00314 -0.008111 0.006318 0.9699 0.9742 0.006391 0.8354 0.8258 0.01831 ] Network output: [ 0.9998 0.0006347 0.0009626 -1.881e-05 8.446e-06 -0.001314 -1.418e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1947 -0.03323 -0.1783 0.1913 0.9835 0.9932 0.2177 0.4439 0.8722 0.7178 ] Network output: [ -0.01062 1.002 1.01 6.121e-08 -2.748e-08 0.009543 4.613e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005936 0.0004768 0.004437 0.003736 0.9889 0.9919 0.006047 0.8639 0.8962 0.01323 ] Network output: [ -0.0006206 0.002849 1.002 -5.949e-05 2.671e-05 0.9966 -4.483e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2064 0.09593 0.3368 0.1473 0.985 0.994 0.207 0.4482 0.8788 0.7122 ] Network output: [ 0.005909 -0.0286 0.995 3.552e-05 -1.595e-05 1.022 2.677e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1018 0.08982 0.181 0.2016 0.9873 0.9919 0.1019 0.7643 0.8684 0.306 ] Network output: [ -0.005683 0.02799 1.003 3.72e-05 -1.67e-05 0.9806 2.803e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09058 0.08864 0.1652 0.1954 0.9854 0.9912 0.09059 0.6895 0.8452 0.2448 ] Network output: [ 0.000172 1 -0.000275 5.017e-06 -2.252e-06 1 3.781e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004549 Epoch 7919 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01102 0.9952 0.99 5.212e-07 -2.34e-07 -0.007323 3.928e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003333 -0.00314 -0.00811 0.006317 0.9699 0.9742 0.006391 0.8354 0.8258 0.01831 ] Network output: [ 0.9998 0.0006342 0.000962 -1.88e-05 8.439e-06 -0.001312 -1.417e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1947 -0.03323 -0.1783 0.1913 0.9835 0.9932 0.2177 0.4439 0.8722 0.7178 ] Network output: [ -0.01062 1.002 1.01 6.012e-08 -2.699e-08 0.009541 4.531e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005937 0.0004769 0.004437 0.003736 0.9889 0.9919 0.006048 0.8639 0.8962 0.01322 ] Network output: [ -0.0006202 0.002848 1.002 -5.943e-05 2.668e-05 0.9966 -4.479e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2064 0.09593 0.3368 0.1473 0.985 0.994 0.207 0.4482 0.8788 0.7122 ] Network output: [ 0.005907 -0.02859 0.995 3.549e-05 -1.593e-05 1.022 2.674e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1018 0.08983 0.181 0.2016 0.9873 0.9919 0.1019 0.7642 0.8684 0.306 ] Network output: [ -0.005681 0.02798 1.003 3.716e-05 -1.668e-05 0.9806 2.801e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09058 0.08864 0.1652 0.1954 0.9854 0.9912 0.09059 0.6895 0.8452 0.2448 ] Network output: [ 0.0001719 1 -0.0002746 5.012e-06 -2.25e-06 1 3.777e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004546 Epoch 7920 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01102 0.9952 0.99 5.194e-07 -2.332e-07 -0.007323 3.914e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003333 -0.00314 -0.008109 0.006316 0.9699 0.9742 0.006391 0.8354 0.8258 0.01831 ] Network output: [ 0.9998 0.0006336 0.0009614 -1.878e-05 8.431e-06 -0.001311 -1.415e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1947 -0.03323 -0.1783 0.1913 0.9835 0.9932 0.2177 0.4439 0.8722 0.7178 ] Network output: [ -0.01062 1.002 1.01 5.904e-08 -2.65e-08 0.009539 4.449e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005937 0.0004769 0.004437 0.003735 0.9889 0.9919 0.006049 0.8638 0.8962 0.01322 ] Network output: [ -0.0006198 0.002847 1.002 -5.937e-05 2.665e-05 0.9966 -4.475e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2064 0.09594 0.3368 0.1473 0.985 0.994 0.2071 0.4482 0.8788 0.7122 ] Network output: [ 0.005905 -0.02858 0.995 3.545e-05 -1.592e-05 1.022 2.672e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1018 0.08983 0.1811 0.2016 0.9873 0.9919 0.1019 0.7642 0.8684 0.306 ] Network output: [ -0.005679 0.02796 1.003 3.713e-05 -1.667e-05 0.9806 2.798e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09058 0.08864 0.1652 0.1954 0.9854 0.9912 0.09059 0.6895 0.8452 0.2448 ] Network output: [ 0.0001718 1 -0.0002743 5.007e-06 -2.248e-06 1 3.774e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004543 Epoch 7921 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01102 0.9952 0.99 5.175e-07 -2.323e-07 -0.007324 3.9e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003334 -0.00314 -0.008108 0.006316 0.9699 0.9742 0.006392 0.8354 0.8258 0.01831 ] Network output: [ 0.9998 0.0006331 0.0009608 -1.876e-05 8.423e-06 -0.00131 -1.414e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1947 -0.03323 -0.1783 0.1913 0.9835 0.9932 0.2177 0.4439 0.8722 0.7178 ] Network output: [ -0.01062 1.002 1.01 5.796e-08 -2.602e-08 0.009536 4.368e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005938 0.000477 0.004437 0.003735 0.9889 0.9919 0.006049 0.8638 0.8962 0.01322 ] Network output: [ -0.0006195 0.002846 1.002 -5.932e-05 2.663e-05 0.9966 -4.47e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2064 0.09594 0.3368 0.1473 0.985 0.994 0.2071 0.4482 0.8787 0.7122 ] Network output: [ 0.005903 -0.02857 0.995 3.542e-05 -1.59e-05 1.022 2.669e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1019 0.08984 0.1811 0.2016 0.9873 0.9919 0.1019 0.7642 0.8684 0.306 ] Network output: [ -0.005677 0.02795 1.003 3.709e-05 -1.665e-05 0.9806 2.796e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09058 0.08865 0.1652 0.1954 0.9854 0.9912 0.09059 0.6895 0.8452 0.2448 ] Network output: [ 0.0001717 1 -0.000274 5.002e-06 -2.246e-06 1 3.77e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000454 Epoch 7922 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01102 0.9952 0.99 5.157e-07 -2.315e-07 -0.007325 3.886e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003334 -0.00314 -0.008107 0.006315 0.9699 0.9742 0.006392 0.8354 0.8258 0.01831 ] Network output: [ 0.9998 0.0006325 0.0009602 -1.874e-05 8.415e-06 -0.001309 -1.413e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1947 -0.03324 -0.1782 0.1913 0.9835 0.9932 0.2177 0.4438 0.8722 0.7178 ] Network output: [ -0.01062 1.002 1.01 5.688e-08 -2.553e-08 0.009534 4.287e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005939 0.000477 0.004437 0.003735 0.9889 0.9919 0.00605 0.8638 0.8962 0.01322 ] Network output: [ -0.0006191 0.002845 1.002 -5.926e-05 2.66e-05 0.9966 -4.466e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2064 0.09595 0.3368 0.1473 0.985 0.994 0.2071 0.4482 0.8787 0.7121 ] Network output: [ 0.005901 -0.02856 0.995 3.539e-05 -1.589e-05 1.022 2.667e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1019 0.08984 0.1811 0.2016 0.9873 0.9919 0.1019 0.7642 0.8683 0.306 ] Network output: [ -0.005675 0.02794 1.003 3.706e-05 -1.664e-05 0.9806 2.793e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09058 0.08865 0.1652 0.1954 0.9854 0.9912 0.09059 0.6894 0.8451 0.2448 ] Network output: [ 0.0001717 1 -0.0002737 4.998e-06 -2.244e-06 1 3.766e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004537 Epoch 7923 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01102 0.9952 0.99 5.139e-07 -2.307e-07 -0.007325 3.873e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003334 -0.00314 -0.008105 0.006314 0.9699 0.9742 0.006392 0.8354 0.8258 0.01831 ] Network output: [ 0.9998 0.000632 0.0009595 -1.873e-05 8.407e-06 -0.001308 -1.411e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1947 -0.03324 -0.1782 0.1913 0.9835 0.9932 0.2177 0.4438 0.8722 0.7178 ] Network output: [ -0.01062 1.002 1.01 5.58e-08 -2.505e-08 0.009532 4.205e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005939 0.0004771 0.004437 0.003734 0.9889 0.9919 0.006051 0.8638 0.8962 0.01322 ] Network output: [ -0.0006187 0.002844 1.002 -5.92e-05 2.658e-05 0.9966 -4.462e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2064 0.09595 0.3368 0.1473 0.985 0.994 0.2071 0.4482 0.8787 0.7121 ] Network output: [ 0.0059 -0.02854 0.995 3.535e-05 -1.587e-05 1.022 2.664e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1019 0.08985 0.1811 0.2016 0.9873 0.9919 0.1019 0.7642 0.8683 0.306 ] Network output: [ -0.005673 0.02793 1.003 3.703e-05 -1.662e-05 0.9806 2.79e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09058 0.08865 0.1652 0.1954 0.9854 0.9912 0.0906 0.6894 0.8451 0.2448 ] Network output: [ 0.0001716 1 -0.0002734 4.993e-06 -2.242e-06 1 3.763e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004535 Epoch 7924 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01102 0.9952 0.9901 5.121e-07 -2.299e-07 -0.007326 3.859e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003334 -0.003141 -0.008104 0.006314 0.9699 0.9742 0.006393 0.8354 0.8258 0.0183 ] Network output: [ 0.9998 0.0006314 0.0009589 -1.871e-05 8.399e-06 -0.001307 -1.41e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1947 -0.03324 -0.1782 0.1913 0.9835 0.9932 0.2178 0.4438 0.8722 0.7178 ] Network output: [ -0.01062 1.002 1.01 5.473e-08 -2.457e-08 0.009529 4.124e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00594 0.0004772 0.004437 0.003734 0.9889 0.9919 0.006051 0.8638 0.8962 0.01322 ] Network output: [ -0.0006183 0.002843 1.002 -5.914e-05 2.655e-05 0.9966 -4.457e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2064 0.09596 0.3368 0.1473 0.985 0.994 0.2071 0.4482 0.8787 0.7121 ] Network output: [ 0.005898 -0.02853 0.995 3.532e-05 -1.586e-05 1.022 2.662e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1019 0.08985 0.1811 0.2016 0.9873 0.9919 0.1019 0.7641 0.8683 0.306 ] Network output: [ -0.005671 0.02792 1.003 3.699e-05 -1.661e-05 0.9806 2.788e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09058 0.08865 0.1652 0.1954 0.9854 0.9912 0.0906 0.6894 0.8451 0.2448 ] Network output: [ 0.0001715 1 -0.0002731 4.988e-06 -2.239e-06 1 3.759e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004532 Epoch 7925 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01101 0.9952 0.9901 5.102e-07 -2.291e-07 -0.007327 3.845e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003334 -0.003141 -0.008103 0.006313 0.9699 0.9742 0.006393 0.8354 0.8258 0.0183 ] Network output: [ 0.9998 0.0006309 0.0009583 -1.869e-05 8.391e-06 -0.001306 -1.409e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1947 -0.03324 -0.1782 0.1913 0.9835 0.9932 0.2178 0.4438 0.8721 0.7178 ] Network output: [ -0.01061 1.002 1.01 5.366e-08 -2.409e-08 0.009527 4.044e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00594 0.0004772 0.004437 0.003733 0.9889 0.9919 0.006052 0.8638 0.8962 0.01322 ] Network output: [ -0.0006179 0.002842 1.002 -5.909e-05 2.653e-05 0.9966 -4.453e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2064 0.09596 0.3368 0.1473 0.985 0.994 0.2071 0.4481 0.8787 0.7121 ] Network output: [ 0.005896 -0.02852 0.995 3.529e-05 -1.584e-05 1.022 2.659e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1019 0.08986 0.1811 0.2016 0.9873 0.9919 0.1019 0.7641 0.8683 0.306 ] Network output: [ -0.005669 0.0279 1.003 3.696e-05 -1.659e-05 0.9806 2.785e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09059 0.08865 0.1652 0.1954 0.9854 0.9912 0.0906 0.6894 0.8451 0.2448 ] Network output: [ 0.0001714 1 -0.0002728 4.984e-06 -2.237e-06 1 3.756e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004529 Epoch 7926 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01101 0.9953 0.9901 5.084e-07 -2.283e-07 -0.007328 3.832e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003334 -0.003141 -0.008102 0.006312 0.9699 0.9742 0.006393 0.8353 0.8258 0.0183 ] Network output: [ 0.9998 0.0006304 0.0009577 -1.867e-05 8.383e-06 -0.001305 -1.407e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1947 -0.03324 -0.1782 0.1913 0.9835 0.9932 0.2178 0.4438 0.8721 0.7178 ] Network output: [ -0.01061 1.002 1.01 5.259e-08 -2.361e-08 0.009525 3.963e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005941 0.0004773 0.004437 0.003733 0.9889 0.9919 0.006052 0.8638 0.8962 0.01322 ] Network output: [ -0.0006176 0.002841 1.002 -5.903e-05 2.65e-05 0.9966 -4.449e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2064 0.09597 0.3369 0.1473 0.985 0.994 0.2071 0.4481 0.8787 0.7121 ] Network output: [ 0.005894 -0.02851 0.995 3.525e-05 -1.583e-05 1.022 2.657e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1019 0.08987 0.1811 0.2016 0.9873 0.9919 0.102 0.7641 0.8683 0.306 ] Network output: [ -0.005666 0.02789 1.003 3.692e-05 -1.658e-05 0.9806 2.783e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09059 0.08865 0.1652 0.1954 0.9854 0.9912 0.0906 0.6893 0.8451 0.2448 ] Network output: [ 0.0001713 1 -0.0002725 4.979e-06 -2.235e-06 1 3.752e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004526 Epoch 7927 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01101 0.9953 0.9901 5.066e-07 -2.274e-07 -0.007328 3.818e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003334 -0.003141 -0.008101 0.006311 0.9699 0.9742 0.006394 0.8353 0.8258 0.0183 ] Network output: [ 0.9998 0.0006298 0.0009571 -1.866e-05 8.375e-06 -0.001304 -1.406e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1947 -0.03325 -0.1782 0.1913 0.9835 0.9932 0.2178 0.4438 0.8721 0.7177 ] Network output: [ -0.01061 1.002 1.01 5.152e-08 -2.313e-08 0.009522 3.883e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005942 0.0004774 0.004437 0.003732 0.9889 0.9919 0.006053 0.8638 0.8962 0.01321 ] Network output: [ -0.0006172 0.00284 1.002 -5.897e-05 2.647e-05 0.9966 -4.444e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2065 0.09597 0.3369 0.1473 0.985 0.994 0.2071 0.4481 0.8787 0.7121 ] Network output: [ 0.005892 -0.0285 0.995 3.522e-05 -1.581e-05 1.022 2.654e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1019 0.08987 0.1811 0.2015 0.9873 0.9919 0.102 0.7641 0.8683 0.306 ] Network output: [ -0.005664 0.02788 1.003 3.689e-05 -1.656e-05 0.9806 2.78e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09059 0.08865 0.1652 0.1954 0.9854 0.9912 0.0906 0.6893 0.8451 0.2448 ] Network output: [ 0.0001712 1 -0.0002722 4.974e-06 -2.233e-06 1 3.749e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004523 Epoch 7928 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01101 0.9953 0.9901 5.048e-07 -2.266e-07 -0.007329 3.804e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003335 -0.003141 -0.0081 0.006311 0.9699 0.9742 0.006394 0.8353 0.8258 0.0183 ] Network output: [ 0.9998 0.0006293 0.0009565 -1.864e-05 8.367e-06 -0.001303 -1.405e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1948 -0.03325 -0.1782 0.1912 0.9835 0.9932 0.2178 0.4438 0.8721 0.7177 ] Network output: [ -0.01061 1.002 1.01 5.045e-08 -2.265e-08 0.00952 3.802e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005942 0.0004774 0.004437 0.003732 0.9889 0.9919 0.006054 0.8638 0.8962 0.01321 ] Network output: [ -0.0006168 0.002839 1.002 -5.891e-05 2.645e-05 0.9966 -4.44e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2065 0.09598 0.3369 0.1473 0.985 0.994 0.2071 0.4481 0.8787 0.7121 ] Network output: [ 0.00589 -0.02849 0.995 3.519e-05 -1.58e-05 1.022 2.652e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1019 0.08988 0.1811 0.2015 0.9873 0.9919 0.102 0.764 0.8683 0.306 ] Network output: [ -0.005662 0.02787 1.003 3.686e-05 -1.655e-05 0.9806 2.778e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09059 0.08865 0.1652 0.1954 0.9854 0.9912 0.0906 0.6893 0.8451 0.2448 ] Network output: [ 0.0001711 1 -0.0002719 4.97e-06 -2.231e-06 1 3.745e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004521 Epoch 7929 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01101 0.9953 0.9901 5.03e-07 -2.258e-07 -0.00733 3.791e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003335 -0.003141 -0.008099 0.00631 0.9699 0.9742 0.006394 0.8353 0.8258 0.0183 ] Network output: [ 0.9998 0.0006287 0.0009558 -1.862e-05 8.36e-06 -0.001301 -1.403e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1948 -0.03325 -0.1781 0.1912 0.9835 0.9932 0.2178 0.4438 0.8721 0.7177 ] Network output: [ -0.01061 1.002 1.01 4.939e-08 -2.217e-08 0.009518 3.722e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005943 0.0004775 0.004437 0.003731 0.9889 0.9919 0.006054 0.8638 0.8962 0.01321 ] Network output: [ -0.0006164 0.002838 1.002 -5.886e-05 2.642e-05 0.9966 -4.436e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2065 0.09599 0.3369 0.1473 0.985 0.994 0.2072 0.4481 0.8787 0.7121 ] Network output: [ 0.005888 -0.02848 0.995 3.515e-05 -1.578e-05 1.022 2.649e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1019 0.08988 0.1811 0.2015 0.9873 0.9919 0.102 0.764 0.8683 0.306 ] Network output: [ -0.00566 0.02786 1.003 3.682e-05 -1.653e-05 0.9806 2.775e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09059 0.08865 0.1652 0.1954 0.9854 0.9912 0.0906 0.6893 0.8451 0.2448 ] Network output: [ 0.000171 1 -0.0002716 4.965e-06 -2.229e-06 1 3.742e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004518 Epoch 7930 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01101 0.9953 0.9901 5.012e-07 -2.25e-07 -0.00733 3.777e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003335 -0.003141 -0.008098 0.006309 0.9699 0.9742 0.006394 0.8353 0.8257 0.0183 ] Network output: [ 0.9998 0.0006282 0.0009552 -1.86e-05 8.352e-06 -0.0013 -1.402e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1948 -0.03325 -0.1781 0.1912 0.9835 0.9932 0.2178 0.4437 0.8721 0.7177 ] Network output: [ -0.01061 1.002 1.01 4.833e-08 -2.17e-08 0.009516 3.642e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005943 0.0004776 0.004437 0.003731 0.9889 0.9919 0.006055 0.8638 0.8962 0.01321 ] Network output: [ -0.000616 0.002837 1.002 -5.88e-05 2.64e-05 0.9966 -4.431e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2065 0.09599 0.3369 0.1472 0.985 0.994 0.2072 0.4481 0.8787 0.7121 ] Network output: [ 0.005886 -0.02847 0.995 3.512e-05 -1.577e-05 1.022 2.647e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1019 0.08989 0.1811 0.2015 0.9873 0.9919 0.102 0.764 0.8683 0.306 ] Network output: [ -0.005658 0.02784 1.003 3.679e-05 -1.652e-05 0.9807 2.773e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09059 0.08866 0.1652 0.1954 0.9854 0.9912 0.0906 0.6892 0.8451 0.2448 ] Network output: [ 0.0001709 1 -0.0002713 4.96e-06 -2.227e-06 1 3.738e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004515 Epoch 7931 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.011 0.9953 0.9901 4.994e-07 -2.242e-07 -0.007331 3.764e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003335 -0.003142 -0.008097 0.006309 0.9699 0.9742 0.006395 0.8353 0.8257 0.01829 ] Network output: [ 0.9998 0.0006276 0.0009546 -1.859e-05 8.344e-06 -0.001299 -1.401e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1948 -0.03326 -0.1781 0.1912 0.9835 0.9932 0.2178 0.4437 0.8721 0.7177 ] Network output: [ -0.01061 1.002 1.01 4.727e-08 -2.122e-08 0.009513 3.562e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005944 0.0004776 0.004437 0.00373 0.9889 0.9919 0.006056 0.8637 0.8962 0.01321 ] Network output: [ -0.0006157 0.002836 1.002 -5.874e-05 2.637e-05 0.9966 -4.427e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2065 0.096 0.3369 0.1472 0.985 0.994 0.2072 0.4481 0.8787 0.7121 ] Network output: [ 0.005884 -0.02846 0.995 3.509e-05 -1.575e-05 1.022 2.644e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1019 0.08989 0.1811 0.2015 0.9873 0.9919 0.102 0.764 0.8683 0.306 ] Network output: [ -0.005656 0.02783 1.003 3.676e-05 -1.65e-05 0.9807 2.77e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09059 0.08866 0.1652 0.1954 0.9854 0.9912 0.09061 0.6892 0.8451 0.2448 ] Network output: [ 0.0001708 1 -0.000271 4.956e-06 -2.225e-06 1 3.735e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004512 Epoch 7932 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.011 0.9953 0.9901 4.976e-07 -2.234e-07 -0.007332 3.75e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003335 -0.003142 -0.008096 0.006308 0.9699 0.9742 0.006395 0.8353 0.8257 0.01829 ] Network output: [ 0.9998 0.0006271 0.000954 -1.857e-05 8.336e-06 -0.001298 -1.399e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1948 -0.03326 -0.1781 0.1912 0.9835 0.9932 0.2178 0.4437 0.8721 0.7177 ] Network output: [ -0.01061 1.002 1.01 4.621e-08 -2.075e-08 0.009511 3.483e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005945 0.0004777 0.004437 0.00373 0.9889 0.9919 0.006056 0.8637 0.8961 0.01321 ] Network output: [ -0.0006153 0.002835 1.002 -5.869e-05 2.635e-05 0.9966 -4.423e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2065 0.096 0.3369 0.1472 0.985 0.994 0.2072 0.4481 0.8787 0.7121 ] Network output: [ 0.005882 -0.02845 0.995 3.505e-05 -1.574e-05 1.022 2.642e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1019 0.0899 0.1811 0.2015 0.9873 0.9919 0.102 0.764 0.8683 0.306 ] Network output: [ -0.005654 0.02782 1.003 3.672e-05 -1.649e-05 0.9807 2.767e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09059 0.08866 0.1652 0.1954 0.9854 0.9912 0.09061 0.6892 0.8451 0.2448 ] Network output: [ 0.0001708 1 -0.0002707 4.951e-06 -2.223e-06 1 3.731e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000451 Epoch 7933 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.011 0.9953 0.9901 4.958e-07 -2.226e-07 -0.007333 3.737e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003335 -0.003142 -0.008094 0.006307 0.9699 0.9742 0.006395 0.8353 0.8257 0.01829 ] Network output: [ 0.9998 0.0006266 0.0009534 -1.855e-05 8.328e-06 -0.001297 -1.398e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1948 -0.03326 -0.1781 0.1912 0.9835 0.9932 0.2179 0.4437 0.8721 0.7177 ] Network output: [ -0.01061 1.002 1.01 4.516e-08 -2.027e-08 0.009509 3.403e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005945 0.0004778 0.004437 0.003729 0.9889 0.9919 0.006057 0.8637 0.8961 0.01321 ] Network output: [ -0.0006149 0.002834 1.002 -5.863e-05 2.632e-05 0.9966 -4.418e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2065 0.09601 0.3369 0.1472 0.985 0.994 0.2072 0.448 0.8787 0.7121 ] Network output: [ 0.00588 -0.02844 0.995 3.502e-05 -1.572e-05 1.022 2.639e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1019 0.08991 0.1811 0.2015 0.9873 0.9919 0.102 0.7639 0.8683 0.306 ] Network output: [ -0.005652 0.02781 1.003 3.669e-05 -1.647e-05 0.9807 2.765e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09059 0.08866 0.1652 0.1954 0.9854 0.9912 0.09061 0.6892 0.8451 0.2448 ] Network output: [ 0.0001707 1 -0.0002703 4.946e-06 -2.221e-06 1 3.728e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004507 Epoch 7934 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.011 0.9953 0.9901 4.94e-07 -2.218e-07 -0.007333 3.723e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003335 -0.003142 -0.008093 0.006306 0.9699 0.9742 0.006396 0.8353 0.8257 0.01829 ] Network output: [ 0.9998 0.000626 0.0009528 -1.853e-05 8.32e-06 -0.001296 -1.397e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1948 -0.03326 -0.1781 0.1912 0.9835 0.9932 0.2179 0.4437 0.8721 0.7177 ] Network output: [ -0.0106 1.002 1.01 4.41e-08 -1.98e-08 0.009506 3.324e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005946 0.0004778 0.004437 0.003729 0.9889 0.9919 0.006057 0.8637 0.8961 0.01321 ] Network output: [ -0.0006145 0.002833 1.002 -5.857e-05 2.629e-05 0.9966 -4.414e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2065 0.09601 0.3369 0.1472 0.985 0.994 0.2072 0.448 0.8787 0.7121 ] Network output: [ 0.005878 -0.02843 0.995 3.499e-05 -1.571e-05 1.022 2.637e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1019 0.08991 0.1811 0.2015 0.9873 0.9919 0.102 0.7639 0.8683 0.3059 ] Network output: [ -0.00565 0.0278 1.003 3.665e-05 -1.646e-05 0.9807 2.762e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0906 0.08866 0.1652 0.1954 0.9854 0.9912 0.09061 0.6891 0.845 0.2448 ] Network output: [ 0.0001706 1 -0.00027 4.942e-06 -2.218e-06 1 3.724e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004504 Epoch 7935 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.011 0.9953 0.9901 4.923e-07 -2.21e-07 -0.007334 3.71e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003335 -0.003142 -0.008092 0.006306 0.9699 0.9742 0.006396 0.8353 0.8257 0.01829 ] Network output: [ 0.9998 0.0006255 0.0009522 -1.852e-05 8.312e-06 -0.001295 -1.395e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1948 -0.03327 -0.178 0.1912 0.9835 0.9932 0.2179 0.4437 0.8721 0.7177 ] Network output: [ -0.0106 1.002 1.01 4.305e-08 -1.933e-08 0.009504 3.245e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005946 0.0004779 0.004438 0.003728 0.9889 0.9919 0.006058 0.8637 0.8961 0.01321 ] Network output: [ -0.0006142 0.002832 1.002 -5.851e-05 2.627e-05 0.9967 -4.41e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2066 0.09602 0.337 0.1472 0.985 0.994 0.2072 0.448 0.8787 0.7121 ] Network output: [ 0.005876 -0.02842 0.995 3.495e-05 -1.569e-05 1.022 2.634e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1019 0.08992 0.1811 0.2015 0.9873 0.9919 0.102 0.7639 0.8683 0.3059 ] Network output: [ -0.005648 0.02778 1.003 3.662e-05 -1.644e-05 0.9807 2.76e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0906 0.08866 0.1652 0.1954 0.9854 0.9912 0.09061 0.6891 0.845 0.2449 ] Network output: [ 0.0001705 1 -0.0002697 4.937e-06 -2.216e-06 1 3.721e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004501 Epoch 7936 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.011 0.9953 0.9901 4.905e-07 -2.202e-07 -0.007335 3.696e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003336 -0.003142 -0.008091 0.006305 0.9699 0.9742 0.006396 0.8353 0.8257 0.01829 ] Network output: [ 0.9998 0.000625 0.0009515 -1.85e-05 8.304e-06 -0.001294 -1.394e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1948 -0.03327 -0.178 0.1912 0.9835 0.9932 0.2179 0.4437 0.8721 0.7177 ] Network output: [ -0.0106 1.002 1.01 4.201e-08 -1.886e-08 0.009502 3.166e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005947 0.0004779 0.004438 0.003728 0.9889 0.9919 0.006059 0.8637 0.8961 0.0132 ] Network output: [ -0.0006138 0.002831 1.002 -5.846e-05 2.624e-05 0.9967 -4.406e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2066 0.09602 0.337 0.1472 0.985 0.994 0.2072 0.448 0.8787 0.7121 ] Network output: [ 0.005874 -0.02841 0.995 3.492e-05 -1.568e-05 1.022 2.632e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1019 0.08992 0.1811 0.2015 0.9873 0.9919 0.102 0.7639 0.8683 0.3059 ] Network output: [ -0.005646 0.02777 1.003 3.659e-05 -1.643e-05 0.9807 2.757e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0906 0.08866 0.1652 0.1954 0.9854 0.9912 0.09061 0.6891 0.845 0.2449 ] Network output: [ 0.0001704 1 -0.0002694 4.932e-06 -2.214e-06 1 3.717e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004499 Epoch 7937 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01099 0.9953 0.9901 4.887e-07 -2.194e-07 -0.007335 3.683e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003336 -0.003143 -0.00809 0.006304 0.9699 0.9742 0.006397 0.8353 0.8257 0.01828 ] Network output: [ 0.9998 0.0006244 0.0009509 -1.848e-05 8.297e-06 -0.001293 -1.393e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1948 -0.03327 -0.178 0.1912 0.9835 0.9932 0.2179 0.4437 0.8721 0.7177 ] Network output: [ -0.0106 1.002 1.01 4.096e-08 -1.839e-08 0.009499 3.087e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005948 0.000478 0.004438 0.003728 0.9889 0.9919 0.006059 0.8637 0.8961 0.0132 ] Network output: [ -0.0006134 0.00283 1.002 -5.84e-05 2.622e-05 0.9967 -4.401e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2066 0.09603 0.337 0.1472 0.985 0.994 0.2072 0.448 0.8787 0.7121 ] Network output: [ 0.005872 -0.0284 0.995 3.489e-05 -1.566e-05 1.022 2.629e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.102 0.08993 0.1811 0.2015 0.9873 0.9919 0.102 0.7638 0.8682 0.3059 ] Network output: [ -0.005644 0.02776 1.003 3.655e-05 -1.641e-05 0.9807 2.755e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0906 0.08866 0.1652 0.1954 0.9854 0.9912 0.09061 0.6891 0.845 0.2449 ] Network output: [ 0.0001703 1 -0.0002691 4.928e-06 -2.212e-06 1 3.714e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004496 Epoch 7938 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01099 0.9953 0.9901 4.869e-07 -2.186e-07 -0.007336 3.67e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003336 -0.003143 -0.008089 0.006304 0.9699 0.9742 0.006397 0.8353 0.8257 0.01828 ] Network output: [ 0.9998 0.0006239 0.0009503 -1.846e-05 8.289e-06 -0.001292 -1.391e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1949 -0.03327 -0.178 0.1912 0.9835 0.9932 0.2179 0.4436 0.8721 0.7177 ] Network output: [ -0.0106 1.002 1.01 3.991e-08 -1.792e-08 0.009497 3.008e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005948 0.0004781 0.004438 0.003727 0.9889 0.9919 0.00606 0.8637 0.8961 0.0132 ] Network output: [ -0.000613 0.002829 1.002 -5.834e-05 2.619e-05 0.9967 -4.397e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2066 0.09603 0.337 0.1472 0.985 0.994 0.2073 0.448 0.8787 0.7121 ] Network output: [ 0.00587 -0.02839 0.995 3.485e-05 -1.565e-05 1.022 2.627e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.102 0.08993 0.1811 0.2015 0.9873 0.9919 0.102 0.7638 0.8682 0.3059 ] Network output: [ -0.005641 0.02775 1.003 3.652e-05 -1.639e-05 0.9807 2.752e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0906 0.08867 0.1652 0.1954 0.9854 0.9912 0.09061 0.689 0.845 0.2449 ] Network output: [ 0.0001702 1 -0.0002688 4.923e-06 -2.21e-06 1 3.71e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004493 Epoch 7939 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01099 0.9953 0.9901 4.852e-07 -2.178e-07 -0.007337 3.656e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003336 -0.003143 -0.008088 0.006303 0.9699 0.9742 0.006397 0.8352 0.8257 0.01828 ] Network output: [ 0.9998 0.0006234 0.0009497 -1.845e-05 8.281e-06 -0.001291 -1.39e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1949 -0.03328 -0.178 0.1912 0.9835 0.9932 0.2179 0.4436 0.8721 0.7177 ] Network output: [ -0.0106 1.002 1.01 3.887e-08 -1.745e-08 0.009495 2.93e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005949 0.0004781 0.004438 0.003727 0.9889 0.9919 0.00606 0.8637 0.8961 0.0132 ] Network output: [ -0.0006126 0.002828 1.002 -5.829e-05 2.617e-05 0.9967 -4.393e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2066 0.09604 0.337 0.1472 0.985 0.994 0.2073 0.448 0.8787 0.712 ] Network output: [ 0.005868 -0.02838 0.995 3.482e-05 -1.563e-05 1.022 2.624e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.102 0.08994 0.1811 0.2015 0.9873 0.9919 0.102 0.7638 0.8682 0.3059 ] Network output: [ -0.005639 0.02774 1.003 3.649e-05 -1.638e-05 0.9807 2.75e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0906 0.08867 0.1652 0.1954 0.9854 0.9912 0.09062 0.689 0.845 0.2449 ] Network output: [ 0.0001701 1 -0.0002685 4.918e-06 -2.208e-06 1 3.707e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000449 Epoch 7940 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01099 0.9953 0.9901 4.834e-07 -2.17e-07 -0.007337 3.643e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003336 -0.003143 -0.008087 0.006302 0.9699 0.9742 0.006398 0.8352 0.8257 0.01828 ] Network output: [ 0.9998 0.0006228 0.0009491 -1.843e-05 8.273e-06 -0.001289 -1.389e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1949 -0.03328 -0.178 0.1912 0.9835 0.9932 0.2179 0.4436 0.8721 0.7177 ] Network output: [ -0.0106 1.002 1.01 3.783e-08 -1.698e-08 0.009493 2.851e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005949 0.0004782 0.004438 0.003726 0.9889 0.9919 0.006061 0.8637 0.8961 0.0132 ] Network output: [ -0.0006123 0.002827 1.002 -5.823e-05 2.614e-05 0.9967 -4.388e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2066 0.09605 0.337 0.1472 0.985 0.994 0.2073 0.4479 0.8787 0.712 ] Network output: [ 0.005867 -0.02837 0.995 3.479e-05 -1.562e-05 1.022 2.622e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.102 0.08995 0.1811 0.2015 0.9873 0.9919 0.102 0.7638 0.8682 0.3059 ] Network output: [ -0.005637 0.02772 1.003 3.645e-05 -1.636e-05 0.9807 2.747e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0906 0.08867 0.1652 0.1954 0.9854 0.9912 0.09062 0.689 0.845 0.2449 ] Network output: [ 0.0001701 1 -0.0002682 4.914e-06 -2.206e-06 1 3.703e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004488 Epoch 7941 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01099 0.9953 0.9901 4.816e-07 -2.162e-07 -0.007338 3.63e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003336 -0.003143 -0.008086 0.006301 0.9699 0.9742 0.006398 0.8352 0.8257 0.01828 ] Network output: [ 0.9998 0.0006223 0.0009485 -1.841e-05 8.265e-06 -0.001288 -1.388e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1949 -0.03328 -0.1779 0.1912 0.9835 0.9932 0.218 0.4436 0.8721 0.7177 ] Network output: [ -0.0106 1.002 1.01 3.679e-08 -1.652e-08 0.00949 2.773e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00595 0.0004783 0.004438 0.003726 0.9889 0.9919 0.006062 0.8637 0.8961 0.0132 ] Network output: [ -0.0006119 0.002826 1.002 -5.817e-05 2.612e-05 0.9967 -4.384e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2066 0.09605 0.337 0.1472 0.985 0.994 0.2073 0.4479 0.8787 0.712 ] Network output: [ 0.005865 -0.02836 0.995 3.475e-05 -1.56e-05 1.022 2.619e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.102 0.08995 0.1811 0.2015 0.9873 0.9919 0.102 0.7638 0.8682 0.3059 ] Network output: [ -0.005635 0.02771 1.003 3.642e-05 -1.635e-05 0.9807 2.745e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09061 0.08867 0.1652 0.1954 0.9854 0.9912 0.09062 0.689 0.845 0.2449 ] Network output: [ 0.00017 1 -0.0002679 4.909e-06 -2.204e-06 1 3.7e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004485 Epoch 7942 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01099 0.9953 0.9901 4.799e-07 -2.154e-07 -0.007339 3.616e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003336 -0.003143 -0.008085 0.006301 0.9699 0.9742 0.006398 0.8352 0.8257 0.01828 ] Network output: [ 0.9998 0.0006218 0.0009479 -1.839e-05 8.258e-06 -0.001287 -1.386e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1949 -0.03328 -0.1779 0.1911 0.9835 0.9932 0.218 0.4436 0.8721 0.7177 ] Network output: [ -0.0106 1.002 1.01 3.576e-08 -1.605e-08 0.009488 2.695e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005951 0.0004783 0.004438 0.003725 0.9889 0.9919 0.006062 0.8636 0.8961 0.0132 ] Network output: [ -0.0006115 0.002825 1.001 -5.812e-05 2.609e-05 0.9967 -4.38e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2066 0.09606 0.337 0.1472 0.985 0.994 0.2073 0.4479 0.8787 0.712 ] Network output: [ 0.005863 -0.02835 0.995 3.472e-05 -1.559e-05 1.022 2.617e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.102 0.08996 0.1811 0.2015 0.9873 0.9919 0.1021 0.7637 0.8682 0.3059 ] Network output: [ -0.005633 0.0277 1.003 3.639e-05 -1.633e-05 0.9807 2.742e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09061 0.08867 0.1652 0.1954 0.9854 0.9912 0.09062 0.6889 0.845 0.2449 ] Network output: [ 0.0001699 1 -0.0002676 4.905e-06 -2.202e-06 1 3.696e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004482 Epoch 7943 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01098 0.9953 0.9901 4.781e-07 -2.146e-07 -0.007339 3.603e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003337 -0.003143 -0.008083 0.0063 0.9699 0.9742 0.006399 0.8352 0.8257 0.01828 ] Network output: [ 0.9998 0.0006212 0.0009473 -1.838e-05 8.25e-06 -0.001286 -1.385e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1949 -0.03328 -0.1779 0.1911 0.9835 0.9932 0.218 0.4436 0.8721 0.7177 ] Network output: [ -0.01059 1.002 1.01 3.472e-08 -1.559e-08 0.009486 2.617e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005951 0.0004784 0.004438 0.003725 0.9889 0.9919 0.006063 0.8636 0.8961 0.0132 ] Network output: [ -0.0006111 0.002824 1.001 -5.806e-05 2.607e-05 0.9967 -4.376e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2066 0.09606 0.337 0.1472 0.985 0.994 0.2073 0.4479 0.8787 0.712 ] Network output: [ 0.005861 -0.02834 0.995 3.469e-05 -1.557e-05 1.022 2.614e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.102 0.08996 0.1811 0.2015 0.9873 0.9919 0.1021 0.7637 0.8682 0.3059 ] Network output: [ -0.005631 0.02769 1.003 3.635e-05 -1.632e-05 0.9807 2.74e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09061 0.08867 0.1652 0.1954 0.9854 0.9912 0.09062 0.6889 0.845 0.2449 ] Network output: [ 0.0001698 1 -0.0002673 4.9e-06 -2.2e-06 1 3.693e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004479 Epoch 7944 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01098 0.9953 0.9901 4.764e-07 -2.139e-07 -0.00734 3.59e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003337 -0.003144 -0.008082 0.006299 0.9699 0.9742 0.006399 0.8352 0.8257 0.01827 ] Network output: [ 0.9998 0.0006207 0.0009467 -1.836e-05 8.242e-06 -0.001285 -1.384e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1949 -0.03329 -0.1779 0.1911 0.9835 0.9932 0.218 0.4436 0.8721 0.7177 ] Network output: [ -0.01059 1.002 1.01 3.369e-08 -1.513e-08 0.009483 2.539e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005952 0.0004785 0.004438 0.003724 0.9889 0.9919 0.006064 0.8636 0.8961 0.0132 ] Network output: [ -0.0006108 0.002823 1.001 -5.8e-05 2.604e-05 0.9967 -4.371e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2067 0.09607 0.3371 0.1472 0.985 0.994 0.2073 0.4479 0.8787 0.712 ] Network output: [ 0.005859 -0.02833 0.995 3.465e-05 -1.556e-05 1.022 2.612e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.102 0.08997 0.1811 0.2015 0.9873 0.9919 0.1021 0.7637 0.8682 0.3059 ] Network output: [ -0.005629 0.02768 1.003 3.632e-05 -1.63e-05 0.9807 2.737e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09061 0.08867 0.1652 0.1954 0.9854 0.9912 0.09062 0.6889 0.845 0.2449 ] Network output: [ 0.0001697 1 -0.000267 4.895e-06 -2.198e-06 1 3.689e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004477 Epoch 7945 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01098 0.9953 0.9901 4.746e-07 -2.131e-07 -0.007341 3.577e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003337 -0.003144 -0.008081 0.006299 0.9699 0.9742 0.006399 0.8352 0.8257 0.01827 ] Network output: [ 0.9998 0.0006202 0.0009461 -1.834e-05 8.234e-06 -0.001284 -1.382e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1949 -0.03329 -0.1779 0.1911 0.9835 0.9932 0.218 0.4436 0.8721 0.7176 ] Network output: [ -0.01059 1.002 1.01 3.266e-08 -1.466e-08 0.009481 2.462e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005952 0.0004785 0.004438 0.003724 0.9889 0.9919 0.006064 0.8636 0.8961 0.01319 ] Network output: [ -0.0006104 0.002822 1.001 -5.795e-05 2.601e-05 0.9967 -4.367e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2067 0.09607 0.3371 0.1472 0.985 0.994 0.2073 0.4479 0.8787 0.712 ] Network output: [ 0.005857 -0.02831 0.995 3.462e-05 -1.554e-05 1.022 2.609e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.102 0.08997 0.1811 0.2015 0.9873 0.9919 0.1021 0.7637 0.8682 0.3059 ] Network output: [ -0.005627 0.02766 1.003 3.628e-05 -1.629e-05 0.9807 2.735e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09061 0.08867 0.1652 0.1954 0.9854 0.9912 0.09062 0.6889 0.845 0.2449 ] Network output: [ 0.0001696 1 -0.0002667 4.891e-06 -2.196e-06 1 3.686e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004474 Epoch 7946 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01098 0.9953 0.9901 4.729e-07 -2.123e-07 -0.007342 3.564e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003337 -0.003144 -0.00808 0.006298 0.9699 0.9742 0.0064 0.8352 0.8257 0.01827 ] Network output: [ 0.9998 0.0006196 0.0009454 -1.832e-05 8.226e-06 -0.001283 -1.381e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1949 -0.03329 -0.1779 0.1911 0.9835 0.9932 0.218 0.4435 0.8721 0.7176 ] Network output: [ -0.01059 1.002 1.01 3.164e-08 -1.42e-08 0.009479 2.384e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005953 0.0004786 0.004438 0.003723 0.9889 0.9919 0.006065 0.8636 0.8961 0.01319 ] Network output: [ -0.00061 0.002821 1.001 -5.789e-05 2.599e-05 0.9967 -4.363e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2067 0.09608 0.3371 0.1471 0.985 0.994 0.2074 0.4479 0.8787 0.712 ] Network output: [ 0.005855 -0.0283 0.995 3.459e-05 -1.553e-05 1.022 2.607e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.102 0.08998 0.1811 0.2015 0.9873 0.9919 0.1021 0.7636 0.8682 0.3059 ] Network output: [ -0.005625 0.02765 1.003 3.625e-05 -1.627e-05 0.9807 2.732e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09061 0.08868 0.1652 0.1954 0.9854 0.9912 0.09062 0.6888 0.845 0.2449 ] Network output: [ 0.0001695 1 -0.0002664 4.886e-06 -2.194e-06 1 3.682e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004471 Epoch 7947 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01098 0.9953 0.9901 4.711e-07 -2.115e-07 -0.007342 3.55e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003337 -0.003144 -0.008079 0.006297 0.9699 0.9742 0.0064 0.8352 0.8257 0.01827 ] Network output: [ 0.9998 0.0006191 0.0009448 -1.831e-05 8.219e-06 -0.001282 -1.38e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1949 -0.03329 -0.1778 0.1911 0.9835 0.9932 0.218 0.4435 0.8721 0.7176 ] Network output: [ -0.01059 1.002 1.01 3.061e-08 -1.374e-08 0.009477 2.307e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005954 0.0004787 0.004438 0.003723 0.9889 0.9919 0.006065 0.8636 0.8961 0.01319 ] Network output: [ -0.0006096 0.00282 1.001 -5.783e-05 2.596e-05 0.9967 -4.359e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2067 0.09608 0.3371 0.1471 0.985 0.994 0.2074 0.4479 0.8787 0.712 ] Network output: [ 0.005853 -0.02829 0.995 3.455e-05 -1.551e-05 1.022 2.604e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.102 0.08999 0.1811 0.2015 0.9873 0.9919 0.1021 0.7636 0.8682 0.3059 ] Network output: [ -0.005623 0.02764 1.003 3.622e-05 -1.626e-05 0.9808 2.729e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09061 0.08868 0.1652 0.1954 0.9854 0.9912 0.09063 0.6888 0.8449 0.2449 ] Network output: [ 0.0001694 1 -0.0002661 4.881e-06 -2.191e-06 1 3.679e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004468 Epoch 7948 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01098 0.9953 0.9901 4.694e-07 -2.107e-07 -0.007343 3.537e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003337 -0.003144 -0.008078 0.006296 0.9699 0.9742 0.0064 0.8352 0.8257 0.01827 ] Network output: [ 0.9998 0.0006186 0.0009442 -1.829e-05 8.211e-06 -0.001281 -1.378e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.195 -0.0333 -0.1778 0.1911 0.9835 0.9932 0.218 0.4435 0.8721 0.7176 ] Network output: [ -0.01059 1.002 1.01 2.959e-08 -1.328e-08 0.009474 2.23e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005954 0.0004787 0.004438 0.003722 0.9889 0.9919 0.006066 0.8636 0.8961 0.01319 ] Network output: [ -0.0006093 0.002819 1.001 -5.778e-05 2.594e-05 0.9967 -4.354e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2067 0.09609 0.3371 0.1471 0.985 0.994 0.2074 0.4478 0.8786 0.712 ] Network output: [ 0.005851 -0.02828 0.995 3.452e-05 -1.55e-05 1.022 2.602e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.102 0.08999 0.1811 0.2015 0.9873 0.9919 0.1021 0.7636 0.8682 0.3059 ] Network output: [ -0.005621 0.02763 1.003 3.618e-05 -1.624e-05 0.9808 2.727e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09061 0.08868 0.1652 0.1954 0.9854 0.9912 0.09063 0.6888 0.8449 0.2449 ] Network output: [ 0.0001693 1 -0.0002658 4.877e-06 -2.189e-06 1 3.675e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004466 Epoch 7949 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01097 0.9953 0.9901 4.676e-07 -2.099e-07 -0.007344 3.524e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003337 -0.003144 -0.008077 0.006296 0.9699 0.9742 0.006401 0.8352 0.8257 0.01827 ] Network output: [ 0.9998 0.000618 0.0009436 -1.827e-05 8.203e-06 -0.00128 -1.377e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.195 -0.0333 -0.1778 0.1911 0.9835 0.9932 0.218 0.4435 0.8721 0.7176 ] Network output: [ -0.01059 1.002 1.01 2.857e-08 -1.282e-08 0.009472 2.153e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005955 0.0004788 0.004438 0.003722 0.9889 0.9919 0.006067 0.8636 0.8961 0.01319 ] Network output: [ -0.0006089 0.002818 1.001 -5.772e-05 2.591e-05 0.9967 -4.35e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2067 0.09609 0.3371 0.1471 0.985 0.994 0.2074 0.4478 0.8786 0.712 ] Network output: [ 0.005849 -0.02827 0.995 3.449e-05 -1.548e-05 1.022 2.599e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.102 0.09 0.1811 0.2015 0.9873 0.9919 0.1021 0.7636 0.8682 0.3059 ] Network output: [ -0.005619 0.02762 1.003 3.615e-05 -1.623e-05 0.9808 2.724e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09062 0.08868 0.1652 0.1954 0.9854 0.9912 0.09063 0.6888 0.8449 0.2449 ] Network output: [ 0.0001693 1 -0.0002655 4.872e-06 -2.187e-06 1 3.672e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004463 Epoch 7950 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01097 0.9953 0.9901 4.659e-07 -2.092e-07 -0.007344 3.511e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003337 -0.003145 -0.008076 0.006295 0.9699 0.9742 0.006401 0.8352 0.8257 0.01827 ] Network output: [ 0.9998 0.0006175 0.000943 -1.825e-05 8.195e-06 -0.001279 -1.376e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.195 -0.0333 -0.1778 0.1911 0.9835 0.9932 0.2181 0.4435 0.872 0.7176 ] Network output: [ -0.01059 1.002 1.01 2.755e-08 -1.237e-08 0.00947 2.076e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005956 0.0004789 0.004438 0.003722 0.9889 0.9919 0.006067 0.8636 0.8961 0.01319 ] Network output: [ -0.0006085 0.002817 1.001 -5.767e-05 2.589e-05 0.9967 -4.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2067 0.0961 0.3371 0.1471 0.985 0.994 0.2074 0.4478 0.8786 0.712 ] Network output: [ 0.005847 -0.02826 0.995 3.446e-05 -1.547e-05 1.022 2.597e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.102 0.09 0.1811 0.2014 0.9873 0.9919 0.1021 0.7636 0.8682 0.3059 ] Network output: [ -0.005617 0.02761 1.003 3.612e-05 -1.621e-05 0.9808 2.722e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09062 0.08868 0.1652 0.1954 0.9854 0.9912 0.09063 0.6887 0.8449 0.2449 ] Network output: [ 0.0001692 1 -0.0002652 4.868e-06 -2.185e-06 1 3.668e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000446 Epoch 7951 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01097 0.9953 0.9901 4.642e-07 -2.084e-07 -0.007345 3.498e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003338 -0.003145 -0.008075 0.006294 0.9699 0.9742 0.006401 0.8352 0.8256 0.01826 ] Network output: [ 0.9998 0.000617 0.0009424 -1.824e-05 8.187e-06 -0.001277 -1.374e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.195 -0.0333 -0.1778 0.1911 0.9835 0.9932 0.2181 0.4435 0.872 0.7176 ] Network output: [ -0.01058 1.002 1.01 2.653e-08 -1.191e-08 0.009467 1.999e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005956 0.0004789 0.004438 0.003721 0.9889 0.9919 0.006068 0.8636 0.8961 0.01319 ] Network output: [ -0.0006081 0.002816 1.001 -5.761e-05 2.586e-05 0.9967 -4.342e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2067 0.09611 0.3371 0.1471 0.985 0.994 0.2074 0.4478 0.8786 0.712 ] Network output: [ 0.005845 -0.02825 0.995 3.442e-05 -1.545e-05 1.022 2.594e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.102 0.09001 0.1811 0.2014 0.9873 0.9919 0.1021 0.7635 0.8682 0.3059 ] Network output: [ -0.005615 0.02759 1.003 3.608e-05 -1.62e-05 0.9808 2.719e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09062 0.08868 0.1651 0.1954 0.9854 0.9912 0.09063 0.6887 0.8449 0.2449 ] Network output: [ 0.0001691 1 -0.0002649 4.863e-06 -2.183e-06 1 3.665e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004457 Epoch 7952 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01097 0.9953 0.9901 4.624e-07 -2.076e-07 -0.007346 3.485e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003338 -0.003145 -0.008074 0.006294 0.9699 0.9742 0.006402 0.8351 0.8256 0.01826 ] Network output: [ 0.9998 0.0006164 0.0009418 -1.822e-05 8.18e-06 -0.001276 -1.373e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.195 -0.03331 -0.1778 0.1911 0.9835 0.9932 0.2181 0.4435 0.872 0.7176 ] Network output: [ -0.01058 1.002 1.01 2.551e-08 -1.145e-08 0.009465 1.923e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005957 0.000479 0.004438 0.003721 0.9889 0.9919 0.006069 0.8636 0.8961 0.01319 ] Network output: [ -0.0006077 0.002815 1.001 -5.755e-05 2.584e-05 0.9967 -4.337e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2067 0.09611 0.3371 0.1471 0.985 0.994 0.2074 0.4478 0.8786 0.712 ] Network output: [ 0.005843 -0.02824 0.995 3.439e-05 -1.544e-05 1.022 2.592e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1021 0.09001 0.1811 0.2014 0.9873 0.9919 0.1021 0.7635 0.8681 0.3059 ] Network output: [ -0.005613 0.02758 1.003 3.605e-05 -1.618e-05 0.9808 2.717e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09062 0.08868 0.1651 0.1954 0.9854 0.9912 0.09063 0.6887 0.8449 0.2449 ] Network output: [ 0.000169 1 -0.0002646 4.858e-06 -2.181e-06 1 3.661e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004455 Epoch 7953 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01097 0.9953 0.9901 4.607e-07 -2.068e-07 -0.007346 3.472e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003338 -0.003145 -0.008073 0.006293 0.9699 0.9742 0.006402 0.8351 0.8256 0.01826 ] Network output: [ 0.9998 0.0006159 0.0009412 -1.82e-05 8.172e-06 -0.001275 -1.372e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.195 -0.03331 -0.1777 0.1911 0.9835 0.9932 0.2181 0.4435 0.872 0.7176 ] Network output: [ -0.01058 1.002 1.01 2.45e-08 -1.1e-08 0.009463 1.847e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005957 0.0004791 0.004438 0.00372 0.9889 0.9919 0.006069 0.8636 0.8961 0.01318 ] Network output: [ -0.0006074 0.002814 1.001 -5.75e-05 2.581e-05 0.9967 -4.333e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2068 0.09612 0.3372 0.1471 0.985 0.994 0.2074 0.4478 0.8786 0.712 ] Network output: [ 0.005841 -0.02823 0.995 3.436e-05 -1.542e-05 1.022 2.589e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1021 0.09002 0.1811 0.2014 0.9873 0.9919 0.1021 0.7635 0.8681 0.3059 ] Network output: [ -0.00561 0.02757 1.003 3.602e-05 -1.617e-05 0.9808 2.714e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09062 0.08868 0.1651 0.1954 0.9854 0.9912 0.09063 0.6887 0.8449 0.2449 ] Network output: [ 0.0001689 1 -0.0002643 4.854e-06 -2.179e-06 1 3.658e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004452 Epoch 7954 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01097 0.9953 0.9901 4.59e-07 -2.061e-07 -0.007347 3.459e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003338 -0.003145 -0.008071 0.006292 0.9699 0.9742 0.006402 0.8351 0.8256 0.01826 ] Network output: [ 0.9998 0.0006154 0.0009406 -1.819e-05 8.164e-06 -0.001274 -1.371e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.195 -0.03331 -0.1777 0.1911 0.9835 0.9932 0.2181 0.4434 0.872 0.7176 ] Network output: [ -0.01058 1.002 1.01 2.349e-08 -1.055e-08 0.009461 1.77e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005958 0.0004791 0.004438 0.00372 0.9889 0.9919 0.00607 0.8635 0.8961 0.01318 ] Network output: [ -0.000607 0.002813 1.001 -5.744e-05 2.579e-05 0.9967 -4.329e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2068 0.09612 0.3372 0.1471 0.985 0.994 0.2074 0.4478 0.8786 0.712 ] Network output: [ 0.00584 -0.02822 0.995 3.432e-05 -1.541e-05 1.022 2.587e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1021 0.09003 0.1812 0.2014 0.9873 0.9919 0.1021 0.7635 0.8681 0.3059 ] Network output: [ -0.005608 0.02756 1.003 3.598e-05 -1.615e-05 0.9808 2.712e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09062 0.08869 0.1651 0.1954 0.9854 0.9912 0.09063 0.6886 0.8449 0.2449 ] Network output: [ 0.0001688 1 -0.000264 4.849e-06 -2.177e-06 1 3.655e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004449 Epoch 7955 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01096 0.9953 0.9901 4.573e-07 -2.053e-07 -0.007348 3.446e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003338 -0.003145 -0.00807 0.006292 0.9699 0.9742 0.006402 0.8351 0.8256 0.01826 ] Network output: [ 0.9998 0.0006149 0.00094 -1.817e-05 8.156e-06 -0.001273 -1.369e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.195 -0.03331 -0.1777 0.1911 0.9835 0.9932 0.2181 0.4434 0.872 0.7176 ] Network output: [ -0.01058 1.002 1.01 2.248e-08 -1.009e-08 0.009458 1.694e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005959 0.0004792 0.004438 0.003719 0.9889 0.9919 0.00607 0.8635 0.8961 0.01318 ] Network output: [ -0.0006066 0.002812 1.001 -5.738e-05 2.576e-05 0.9967 -4.325e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2068 0.09613 0.3372 0.1471 0.985 0.994 0.2075 0.4478 0.8786 0.712 ] Network output: [ 0.005838 -0.02821 0.995 3.429e-05 -1.539e-05 1.022 2.584e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1021 0.09003 0.1812 0.2014 0.9873 0.9919 0.1021 0.7634 0.8681 0.3059 ] Network output: [ -0.005606 0.02755 1.003 3.595e-05 -1.614e-05 0.9808 2.709e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09062 0.08869 0.1651 0.1954 0.9854 0.9912 0.09064 0.6886 0.8449 0.2449 ] Network output: [ 0.0001687 1 -0.0002637 4.845e-06 -2.175e-06 1 3.651e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004447 Epoch 7956 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01096 0.9953 0.9901 4.556e-07 -2.045e-07 -0.007348 3.433e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003338 -0.003145 -0.008069 0.006291 0.9699 0.9742 0.006403 0.8351 0.8256 0.01826 ] Network output: [ 0.9998 0.0006143 0.0009394 -1.815e-05 8.149e-06 -0.001272 -1.368e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.195 -0.03332 -0.1777 0.191 0.9835 0.9932 0.2181 0.4434 0.872 0.7176 ] Network output: [ -0.01058 1.002 1.01 2.148e-08 -9.641e-09 0.009456 1.618e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005959 0.0004793 0.004438 0.003719 0.9889 0.9919 0.006071 0.8635 0.8961 0.01318 ] Network output: [ -0.0006063 0.002811 1.001 -5.733e-05 2.574e-05 0.9967 -4.32e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2068 0.09613 0.3372 0.1471 0.985 0.994 0.2075 0.4477 0.8786 0.7119 ] Network output: [ 0.005836 -0.0282 0.995 3.426e-05 -1.538e-05 1.022 2.582e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1021 0.09004 0.1812 0.2014 0.9873 0.9919 0.1021 0.7634 0.8681 0.3059 ] Network output: [ -0.005604 0.02753 1.003 3.592e-05 -1.612e-05 0.9808 2.707e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09062 0.08869 0.1651 0.1954 0.9854 0.9912 0.09064 0.6886 0.8449 0.2449 ] Network output: [ 0.0001686 1 -0.0002634 4.84e-06 -2.173e-06 1 3.648e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004444 Epoch 7957 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01096 0.9953 0.9901 4.538e-07 -2.037e-07 -0.007349 3.42e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003338 -0.003146 -0.008068 0.00629 0.9699 0.9742 0.006403 0.8351 0.8256 0.01825 ] Network output: [ 0.9998 0.0006138 0.0009388 -1.813e-05 8.141e-06 -0.001271 -1.367e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1951 -0.03332 -0.1777 0.191 0.9835 0.9932 0.2181 0.4434 0.872 0.7176 ] Network output: [ -0.01058 1.002 1.01 2.047e-08 -9.19e-09 0.009454 1.543e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00596 0.0004793 0.004439 0.003718 0.9889 0.9919 0.006072 0.8635 0.8961 0.01318 ] Network output: [ -0.0006059 0.00281 1.001 -5.727e-05 2.571e-05 0.9967 -4.316e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2068 0.09614 0.3372 0.1471 0.985 0.994 0.2075 0.4477 0.8786 0.7119 ] Network output: [ 0.005834 -0.02819 0.995 3.423e-05 -1.537e-05 1.022 2.579e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1021 0.09004 0.1812 0.2014 0.9873 0.9919 0.1021 0.7634 0.8681 0.3059 ] Network output: [ -0.005602 0.02752 1.003 3.588e-05 -1.611e-05 0.9808 2.704e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09063 0.08869 0.1651 0.1954 0.9854 0.9912 0.09064 0.6886 0.8449 0.2449 ] Network output: [ 0.0001686 1 -0.0002631 4.836e-06 -2.171e-06 1 3.644e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004441 Epoch 7958 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01096 0.9953 0.9901 4.521e-07 -2.03e-07 -0.00735 3.407e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003339 -0.003146 -0.008067 0.006289 0.9699 0.9742 0.006403 0.8351 0.8256 0.01825 ] Network output: [ 0.9998 0.0006133 0.0009382 -1.812e-05 8.133e-06 -0.00127 -1.365e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1951 -0.03332 -0.1777 0.191 0.9835 0.9932 0.2182 0.4434 0.872 0.7176 ] Network output: [ -0.01058 1.002 1.01 1.947e-08 -8.74e-09 0.009452 1.467e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00596 0.0004794 0.004439 0.003718 0.9889 0.9919 0.006072 0.8635 0.8961 0.01318 ] Network output: [ -0.0006055 0.002809 1.001 -5.722e-05 2.569e-05 0.9967 -4.312e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2068 0.09614 0.3372 0.1471 0.985 0.994 0.2075 0.4477 0.8786 0.7119 ] Network output: [ 0.005832 -0.02818 0.995 3.419e-05 -1.535e-05 1.022 2.577e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1021 0.09005 0.1812 0.2014 0.9873 0.9919 0.1022 0.7634 0.8681 0.3059 ] Network output: [ -0.0056 0.02751 1.003 3.585e-05 -1.609e-05 0.9808 2.702e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09063 0.08869 0.1651 0.1954 0.9854 0.9912 0.09064 0.6885 0.8449 0.2449 ] Network output: [ 0.0001685 1 -0.0002628 4.831e-06 -2.169e-06 1 3.641e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004438 Epoch 7959 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01096 0.9953 0.9901 4.504e-07 -2.022e-07 -0.00735 3.395e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003339 -0.003146 -0.008066 0.006289 0.9699 0.9742 0.006404 0.8351 0.8256 0.01825 ] Network output: [ 0.9998 0.0006128 0.0009376 -1.81e-05 8.125e-06 -0.001269 -1.364e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1951 -0.03332 -0.1777 0.191 0.9835 0.9932 0.2182 0.4434 0.872 0.7176 ] Network output: [ -0.01058 1.002 1.01 1.847e-08 -8.291e-09 0.009449 1.392e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005961 0.0004795 0.004439 0.003717 0.9889 0.9919 0.006073 0.8635 0.896 0.01318 ] Network output: [ -0.0006051 0.002808 1.001 -5.716e-05 2.566e-05 0.9967 -4.308e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2068 0.09615 0.3372 0.1471 0.985 0.994 0.2075 0.4477 0.8786 0.7119 ] Network output: [ 0.00583 -0.02817 0.995 3.416e-05 -1.534e-05 1.022 2.574e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1021 0.09005 0.1812 0.2014 0.9873 0.9919 0.1022 0.7634 0.8681 0.3059 ] Network output: [ -0.005598 0.0275 1.003 3.582e-05 -1.608e-05 0.9808 2.699e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09063 0.08869 0.1651 0.1954 0.9854 0.9912 0.09064 0.6885 0.8448 0.2449 ] Network output: [ 0.0001684 1 -0.0002625 4.826e-06 -2.167e-06 1 3.637e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004436 Epoch 7960 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01096 0.9953 0.9901 4.487e-07 -2.014e-07 -0.007351 3.382e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003339 -0.003146 -0.008065 0.006288 0.9699 0.9742 0.006404 0.8351 0.8256 0.01825 ] Network output: [ 0.9998 0.0006122 0.000937 -1.808e-05 8.118e-06 -0.001268 -1.363e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1951 -0.03332 -0.1776 0.191 0.9835 0.9932 0.2182 0.4434 0.872 0.7176 ] Network output: [ -0.01057 1.002 1.01 1.747e-08 -7.843e-09 0.009447 1.317e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005962 0.0004795 0.004439 0.003717 0.9889 0.9919 0.006073 0.8635 0.896 0.01318 ] Network output: [ -0.0006048 0.002807 1.001 -5.71e-05 2.564e-05 0.9967 -4.304e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2068 0.09616 0.3372 0.1471 0.985 0.994 0.2075 0.4477 0.8786 0.7119 ] Network output: [ 0.005828 -0.02816 0.995 3.413e-05 -1.532e-05 1.022 2.572e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1021 0.09006 0.1812 0.2014 0.9873 0.9919 0.1022 0.7633 0.8681 0.3059 ] Network output: [ -0.005596 0.02749 1.003 3.578e-05 -1.607e-05 0.9808 2.697e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09063 0.08869 0.1651 0.1954 0.9854 0.9912 0.09064 0.6885 0.8448 0.2449 ] Network output: [ 0.0001683 1 -0.0002622 4.822e-06 -2.165e-06 1 3.634e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004433 Epoch 7961 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01095 0.9953 0.9901 4.47e-07 -2.007e-07 -0.007351 3.369e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003339 -0.003146 -0.008064 0.006287 0.9699 0.9742 0.006404 0.8351 0.8256 0.01825 ] Network output: [ 0.9998 0.0006117 0.0009364 -1.806e-05 8.11e-06 -0.001267 -1.361e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1951 -0.03333 -0.1776 0.191 0.9835 0.9932 0.2182 0.4434 0.872 0.7176 ] Network output: [ -0.01057 1.002 1.01 1.647e-08 -7.396e-09 0.009445 1.242e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005962 0.0004796 0.004439 0.003716 0.9889 0.9919 0.006074 0.8635 0.896 0.01318 ] Network output: [ -0.0006044 0.002806 1.001 -5.705e-05 2.561e-05 0.9967 -4.299e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2069 0.09616 0.3372 0.1471 0.985 0.994 0.2075 0.4477 0.8786 0.7119 ] Network output: [ 0.005826 -0.02815 0.995 3.41e-05 -1.531e-05 1.022 2.57e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1021 0.09007 0.1812 0.2014 0.9873 0.9919 0.1022 0.7633 0.8681 0.3059 ] Network output: [ -0.005594 0.02748 1.003 3.575e-05 -1.605e-05 0.9808 2.694e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09063 0.0887 0.1651 0.1954 0.9854 0.9912 0.09064 0.6885 0.8448 0.2449 ] Network output: [ 0.0001682 1 -0.0002619 4.817e-06 -2.163e-06 1 3.63e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000443 Epoch 7962 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01095 0.9953 0.9901 4.453e-07 -1.999e-07 -0.007352 3.356e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003339 -0.003146 -0.008063 0.006287 0.9699 0.9742 0.006405 0.8351 0.8256 0.01825 ] Network output: [ 0.9998 0.0006112 0.0009358 -1.805e-05 8.102e-06 -0.001266 -1.36e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1951 -0.03333 -0.1776 0.191 0.9835 0.9932 0.2182 0.4433 0.872 0.7176 ] Network output: [ -0.01057 1.002 1.01 1.548e-08 -6.949e-09 0.009443 1.167e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005963 0.0004797 0.004439 0.003716 0.9889 0.9919 0.006075 0.8635 0.896 0.01317 ] Network output: [ -0.000604 0.002805 1.001 -5.699e-05 2.559e-05 0.9967 -4.295e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2069 0.09617 0.3372 0.1471 0.985 0.994 0.2075 0.4477 0.8786 0.7119 ] Network output: [ 0.005824 -0.02814 0.995 3.406e-05 -1.529e-05 1.022 2.567e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1021 0.09007 0.1812 0.2014 0.9873 0.9919 0.1022 0.7633 0.8681 0.3059 ] Network output: [ -0.005592 0.02746 1.003 3.572e-05 -1.604e-05 0.9808 2.692e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09063 0.0887 0.1651 0.1954 0.9854 0.9912 0.09064 0.6885 0.8448 0.2449 ] Network output: [ 0.0001681 1 -0.0002616 4.813e-06 -2.161e-06 1 3.627e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004428 Epoch 7963 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01095 0.9953 0.9901 4.436e-07 -1.992e-07 -0.007353 3.343e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003339 -0.003147 -0.008062 0.006286 0.9699 0.9742 0.006405 0.8351 0.8256 0.01825 ] Network output: [ 0.9998 0.0006107 0.0009352 -1.803e-05 8.095e-06 -0.001265 -1.359e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1951 -0.03333 -0.1776 0.191 0.9835 0.9932 0.2182 0.4433 0.872 0.7175 ] Network output: [ -0.01057 1.002 1.01 1.449e-08 -6.504e-09 0.00944 1.092e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005963 0.0004797 0.004439 0.003716 0.9889 0.9919 0.006075 0.8635 0.896 0.01317 ] Network output: [ -0.0006036 0.002804 1.001 -5.694e-05 2.556e-05 0.9967 -4.291e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2069 0.09617 0.3373 0.147 0.985 0.994 0.2075 0.4476 0.8786 0.7119 ] Network output: [ 0.005822 -0.02813 0.995 3.403e-05 -1.528e-05 1.022 2.565e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1021 0.09008 0.1812 0.2014 0.9873 0.9919 0.1022 0.7633 0.8681 0.3059 ] Network output: [ -0.00559 0.02745 1.003 3.569e-05 -1.602e-05 0.9808 2.689e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09063 0.0887 0.1651 0.1954 0.9854 0.9912 0.09065 0.6884 0.8448 0.2449 ] Network output: [ 0.000168 1 -0.0002613 4.808e-06 -2.159e-06 1 3.624e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004425 Epoch 7964 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01095 0.9953 0.9901 4.419e-07 -1.984e-07 -0.007353 3.331e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003339 -0.003147 -0.008061 0.006285 0.9699 0.9742 0.006405 0.8351 0.8256 0.01824 ] Network output: [ 0.9998 0.0006101 0.0009346 -1.801e-05 8.087e-06 -0.001264 -1.358e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1951 -0.03333 -0.1776 0.191 0.9835 0.9932 0.2182 0.4433 0.872 0.7175 ] Network output: [ -0.01057 1.002 1.01 1.35e-08 -6.059e-09 0.009438 1.017e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005964 0.0004798 0.004439 0.003715 0.9889 0.9919 0.006076 0.8635 0.896 0.01317 ] Network output: [ -0.0006033 0.002803 1.001 -5.688e-05 2.554e-05 0.9967 -4.287e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2069 0.09618 0.3373 0.147 0.985 0.994 0.2076 0.4476 0.8786 0.7119 ] Network output: [ 0.00582 -0.02812 0.995 3.4e-05 -1.526e-05 1.022 2.562e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1021 0.09008 0.1812 0.2014 0.9873 0.9919 0.1022 0.7632 0.8681 0.3059 ] Network output: [ -0.005588 0.02744 1.003 3.565e-05 -1.601e-05 0.9809 2.687e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09063 0.0887 0.1651 0.1954 0.9854 0.9912 0.09065 0.6884 0.8448 0.2449 ] Network output: [ 0.0001679 1 -0.000261 4.804e-06 -2.156e-06 1 3.62e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004422 Epoch 7965 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01095 0.9953 0.9901 4.403e-07 -1.976e-07 -0.007354 3.318e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00334 -0.003147 -0.00806 0.006284 0.9699 0.9742 0.006406 0.835 0.8256 0.01824 ] Network output: [ 0.9998 0.0006096 0.000934 -1.8e-05 8.079e-06 -0.001262 -1.356e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1951 -0.03334 -0.1776 0.191 0.9835 0.9932 0.2182 0.4433 0.872 0.7175 ] Network output: [ -0.01057 1.002 1.01 1.251e-08 -5.616e-09 0.009436 9.427e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005965 0.0004799 0.004439 0.003715 0.9889 0.9919 0.006077 0.8634 0.896 0.01317 ] Network output: [ -0.0006029 0.002802 1.001 -5.683e-05 2.551e-05 0.9967 -4.283e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2069 0.09618 0.3373 0.147 0.985 0.994 0.2076 0.4476 0.8786 0.7119 ] Network output: [ 0.005818 -0.02811 0.995 3.396e-05 -1.525e-05 1.022 2.56e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1021 0.09009 0.1812 0.2014 0.9873 0.9919 0.1022 0.7632 0.8681 0.3059 ] Network output: [ -0.005586 0.02743 1.003 3.562e-05 -1.599e-05 0.9809 2.684e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09064 0.0887 0.1651 0.1954 0.9854 0.9912 0.09065 0.6884 0.8448 0.2449 ] Network output: [ 0.0001679 1 -0.0002607 4.799e-06 -2.154e-06 1 3.617e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000442 Epoch 7966 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01095 0.9953 0.9901 4.386e-07 -1.969e-07 -0.007355 3.305e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00334 -0.003147 -0.008058 0.006284 0.9699 0.9742 0.006406 0.835 0.8256 0.01824 ] Network output: [ 0.9998 0.0006091 0.0009334 -1.798e-05 8.072e-06 -0.001261 -1.355e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1951 -0.03334 -0.1775 0.191 0.9835 0.9932 0.2182 0.4433 0.872 0.7175 ] Network output: [ -0.01057 1.002 1.01 1.152e-08 -5.173e-09 0.009434 8.684e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005965 0.0004799 0.004439 0.003714 0.9889 0.9919 0.006077 0.8634 0.896 0.01317 ] Network output: [ -0.0006025 0.002801 1.001 -5.677e-05 2.549e-05 0.9967 -4.278e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2069 0.09619 0.3373 0.147 0.985 0.994 0.2076 0.4476 0.8786 0.7119 ] Network output: [ 0.005817 -0.0281 0.995 3.393e-05 -1.523e-05 1.022 2.557e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1021 0.09009 0.1812 0.2014 0.9873 0.9919 0.1022 0.7632 0.8681 0.3059 ] Network output: [ -0.005584 0.02742 1.003 3.559e-05 -1.598e-05 0.9809 2.682e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09064 0.0887 0.1651 0.1954 0.9854 0.9912 0.09065 0.6884 0.8448 0.2449 ] Network output: [ 0.0001678 1 -0.0002604 4.794e-06 -2.152e-06 1 3.613e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004417 Epoch 7967 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01095 0.9953 0.9901 4.369e-07 -1.961e-07 -0.007355 3.293e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00334 -0.003147 -0.008057 0.006283 0.9699 0.9742 0.006406 0.835 0.8256 0.01824 ] Network output: [ 0.9998 0.0006086 0.0009328 -1.796e-05 8.064e-06 -0.00126 -1.354e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1952 -0.03334 -0.1775 0.191 0.9835 0.9932 0.2183 0.4433 0.872 0.7175 ] Network output: [ -0.01057 1.002 1.01 1.054e-08 -4.732e-09 0.009431 7.943e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005966 0.00048 0.004439 0.003714 0.9889 0.9919 0.006078 0.8634 0.896 0.01317 ] Network output: [ -0.0006021 0.0028 1.001 -5.671e-05 2.546e-05 0.9967 -4.274e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2069 0.09619 0.3373 0.147 0.985 0.994 0.2076 0.4476 0.8786 0.7119 ] Network output: [ 0.005815 -0.02809 0.995 3.39e-05 -1.522e-05 1.022 2.555e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1021 0.0901 0.1812 0.2014 0.9873 0.9919 0.1022 0.7632 0.8681 0.3059 ] Network output: [ -0.005582 0.0274 1.003 3.555e-05 -1.596e-05 0.9809 2.679e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09064 0.0887 0.1651 0.1954 0.9854 0.9912 0.09065 0.6883 0.8448 0.2449 ] Network output: [ 0.0001677 1 -0.0002602 4.79e-06 -2.15e-06 1 3.61e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004414 Epoch 7968 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01094 0.9953 0.9901 4.352e-07 -1.954e-07 -0.007356 3.28e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00334 -0.003147 -0.008056 0.006282 0.9699 0.9742 0.006407 0.835 0.8256 0.01824 ] Network output: [ 0.9998 0.0006081 0.0009322 -1.794e-05 8.056e-06 -0.001259 -1.352e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1952 -0.03334 -0.1775 0.191 0.9835 0.9932 0.2183 0.4433 0.872 0.7175 ] Network output: [ -0.01057 1.002 1.01 9.558e-09 -4.291e-09 0.009429 7.203e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005966 0.0004801 0.004439 0.003713 0.9889 0.9919 0.006078 0.8634 0.896 0.01317 ] Network output: [ -0.0006018 0.002799 1.001 -5.666e-05 2.544e-05 0.9967 -4.27e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2069 0.0962 0.3373 0.147 0.985 0.994 0.2076 0.4476 0.8786 0.7119 ] Network output: [ 0.005813 -0.02808 0.995 3.387e-05 -1.52e-05 1.022 2.552e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1022 0.09011 0.1812 0.2014 0.9873 0.9919 0.1022 0.7632 0.868 0.3059 ] Network output: [ -0.00558 0.02739 1.003 3.552e-05 -1.595e-05 0.9809 2.677e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09064 0.0887 0.1651 0.1954 0.9854 0.9912 0.09065 0.6883 0.8448 0.2449 ] Network output: [ 0.0001676 1 -0.0002599 4.785e-06 -2.148e-06 1 3.606e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004411 Epoch 7969 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01094 0.9953 0.9901 4.335e-07 -1.946e-07 -0.007357 3.267e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00334 -0.003147 -0.008055 0.006282 0.9699 0.9742 0.006407 0.835 0.8256 0.01824 ] Network output: [ 0.9998 0.0006075 0.0009316 -1.793e-05 8.048e-06 -0.001258 -1.351e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1952 -0.03334 -0.1775 0.191 0.9835 0.9932 0.2183 0.4433 0.872 0.7175 ] Network output: [ -0.01056 1.002 1.01 8.578e-09 -3.851e-09 0.009427 6.465e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005967 0.0004801 0.004439 0.003713 0.9889 0.9919 0.006079 0.8634 0.896 0.01317 ] Network output: [ -0.0006014 0.002798 1.001 -5.66e-05 2.541e-05 0.9967 -4.266e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2069 0.09621 0.3373 0.147 0.985 0.994 0.2076 0.4476 0.8786 0.7119 ] Network output: [ 0.005811 -0.02807 0.995 3.383e-05 -1.519e-05 1.022 2.55e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1022 0.09011 0.1812 0.2014 0.9873 0.9919 0.1022 0.7631 0.868 0.3059 ] Network output: [ -0.005578 0.02738 1.003 3.549e-05 -1.593e-05 0.9809 2.674e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09064 0.08871 0.1651 0.1954 0.9854 0.9912 0.09065 0.6883 0.8448 0.2449 ] Network output: [ 0.0001675 1 -0.0002596 4.781e-06 -2.146e-06 1 3.603e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004409 Epoch 7970 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01094 0.9953 0.9902 4.319e-07 -1.939e-07 -0.007357 3.255e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00334 -0.003148 -0.008054 0.006281 0.9699 0.9742 0.006407 0.835 0.8256 0.01824 ] Network output: [ 0.9998 0.000607 0.000931 -1.791e-05 8.041e-06 -0.001257 -1.35e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1952 -0.03335 -0.1775 0.191 0.9835 0.9932 0.2183 0.4432 0.872 0.7175 ] Network output: [ -0.01056 1.002 1.01 7.6e-09 -3.412e-09 0.009425 5.728e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005968 0.0004802 0.004439 0.003712 0.9889 0.9919 0.00608 0.8634 0.896 0.01317 ] Network output: [ -0.000601 0.002797 1.001 -5.655e-05 2.539e-05 0.9967 -4.262e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.207 0.09621 0.3373 0.147 0.985 0.994 0.2076 0.4476 0.8786 0.7119 ] Network output: [ 0.005809 -0.02806 0.995 3.38e-05 -1.518e-05 1.022 2.547e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1022 0.09012 0.1812 0.2014 0.9873 0.9919 0.1022 0.7631 0.868 0.3059 ] Network output: [ -0.005576 0.02737 1.003 3.545e-05 -1.592e-05 0.9809 2.672e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09064 0.08871 0.1651 0.1954 0.9854 0.9912 0.09066 0.6883 0.8448 0.2449 ] Network output: [ 0.0001674 1 -0.0002593 4.776e-06 -2.144e-06 1 3.6e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004406 Epoch 7971 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01094 0.9953 0.9902 4.302e-07 -1.931e-07 -0.007358 3.242e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00334 -0.003148 -0.008053 0.00628 0.9699 0.9742 0.006408 0.835 0.8256 0.01823 ] Network output: [ 0.9998 0.0006065 0.0009304 -1.789e-05 8.033e-06 -0.001256 -1.349e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1952 -0.03335 -0.1775 0.1909 0.9835 0.9932 0.2183 0.4432 0.872 0.7175 ] Network output: [ -0.01056 1.002 1.01 6.624e-09 -2.974e-09 0.009423 4.992e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005968 0.0004803 0.004439 0.003712 0.9889 0.9919 0.00608 0.8634 0.896 0.01316 ] Network output: [ -0.0006007 0.002796 1.001 -5.649e-05 2.536e-05 0.9967 -4.257e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.207 0.09622 0.3373 0.147 0.985 0.994 0.2076 0.4475 0.8786 0.7119 ] Network output: [ 0.005807 -0.02805 0.995 3.377e-05 -1.516e-05 1.022 2.545e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1022 0.09012 0.1812 0.2014 0.9873 0.9919 0.1022 0.7631 0.868 0.3059 ] Network output: [ -0.005574 0.02736 1.003 3.542e-05 -1.59e-05 0.9809 2.669e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09064 0.08871 0.1651 0.1954 0.9854 0.9912 0.09066 0.6882 0.8447 0.2449 ] Network output: [ 0.0001673 1 -0.000259 4.772e-06 -2.142e-06 1 3.596e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004403 Epoch 7972 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01094 0.9953 0.9902 4.285e-07 -1.924e-07 -0.007359 3.23e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00334 -0.003148 -0.008052 0.00628 0.9699 0.9742 0.006408 0.835 0.8255 0.01823 ] Network output: [ 0.9998 0.000606 0.0009298 -1.788e-05 8.025e-06 -0.001255 -1.347e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1952 -0.03335 -0.1774 0.1909 0.9835 0.9932 0.2183 0.4432 0.872 0.7175 ] Network output: [ -0.01056 1.002 1.01 5.651e-09 -2.537e-09 0.00942 4.259e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005969 0.0004803 0.004439 0.003711 0.9889 0.9919 0.006081 0.8634 0.896 0.01316 ] Network output: [ -0.0006003 0.002795 1.001 -5.644e-05 2.534e-05 0.9967 -4.253e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.207 0.09622 0.3374 0.147 0.985 0.994 0.2076 0.4475 0.8786 0.7118 ] Network output: [ 0.005805 -0.02804 0.9949 3.374e-05 -1.515e-05 1.022 2.543e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1022 0.09013 0.1812 0.2014 0.9873 0.9919 0.1022 0.7631 0.868 0.3059 ] Network output: [ -0.005572 0.02735 1.003 3.539e-05 -1.589e-05 0.9809 2.667e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09065 0.08871 0.1651 0.1954 0.9854 0.9912 0.09066 0.6882 0.8447 0.245 ] Network output: [ 0.0001672 1 -0.0002587 4.767e-06 -2.14e-06 1 3.593e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004401 Epoch 7973 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01094 0.9953 0.9902 4.269e-07 -1.916e-07 -0.007359 3.217e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003341 -0.003148 -0.008051 0.006279 0.9699 0.9742 0.006408 0.835 0.8255 0.01823 ] Network output: [ 0.9998 0.0006055 0.0009292 -1.786e-05 8.018e-06 -0.001254 -1.346e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1952 -0.03335 -0.1774 0.1909 0.9835 0.9932 0.2183 0.4432 0.872 0.7175 ] Network output: [ -0.01056 1.002 1.01 4.679e-09 -2.101e-09 0.009418 3.526e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005969 0.0004804 0.004439 0.003711 0.9889 0.9919 0.006081 0.8634 0.896 0.01316 ] Network output: [ -0.0005999 0.002795 1.001 -5.638e-05 2.531e-05 0.9967 -4.249e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.207 0.09623 0.3374 0.147 0.985 0.994 0.2077 0.4475 0.8786 0.7118 ] Network output: [ 0.005803 -0.02803 0.9949 3.371e-05 -1.513e-05 1.022 2.54e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1022 0.09013 0.1812 0.2014 0.9873 0.9919 0.1023 0.763 0.868 0.3059 ] Network output: [ -0.005569 0.02733 1.003 3.536e-05 -1.587e-05 0.9809 2.665e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09065 0.08871 0.1651 0.1954 0.9854 0.9912 0.09066 0.6882 0.8447 0.245 ] Network output: [ 0.0001672 1 -0.0002584 4.763e-06 -2.138e-06 1 3.589e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004398 Epoch 7974 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01093 0.9953 0.9902 4.252e-07 -1.909e-07 -0.00736 3.205e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003341 -0.003148 -0.00805 0.006278 0.9699 0.9742 0.006408 0.835 0.8255 0.01823 ] Network output: [ 0.9998 0.000605 0.0009286 -1.784e-05 8.01e-06 -0.001253 -1.345e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1952 -0.03336 -0.1774 0.1909 0.9835 0.9932 0.2183 0.4432 0.872 0.7175 ] Network output: [ -0.01056 1.002 1.01 3.71e-09 -1.665e-09 0.009416 2.796e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00597 0.0004805 0.004439 0.003711 0.9889 0.9919 0.006082 0.8634 0.896 0.01316 ] Network output: [ -0.0005995 0.002794 1.001 -5.633e-05 2.529e-05 0.9967 -4.245e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.207 0.09623 0.3374 0.147 0.985 0.994 0.2077 0.4475 0.8786 0.7118 ] Network output: [ 0.005801 -0.02802 0.9949 3.367e-05 -1.512e-05 1.022 2.538e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1022 0.09014 0.1812 0.2013 0.9873 0.9919 0.1023 0.763 0.868 0.3059 ] Network output: [ -0.005567 0.02732 1.003 3.532e-05 -1.586e-05 0.9809 2.662e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09065 0.08871 0.1651 0.1954 0.9854 0.9912 0.09066 0.6882 0.8447 0.245 ] Network output: [ 0.0001671 1 -0.0002581 4.758e-06 -2.136e-06 1 3.586e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004395 Epoch 7975 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01093 0.9953 0.9902 4.236e-07 -1.902e-07 -0.00736 3.192e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003341 -0.003148 -0.008049 0.006277 0.9699 0.9742 0.006409 0.835 0.8255 0.01823 ] Network output: [ 0.9998 0.0006044 0.000928 -1.783e-05 8.003e-06 -0.001252 -1.343e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1952 -0.03336 -0.1774 0.1909 0.9835 0.9932 0.2184 0.4432 0.872 0.7175 ] Network output: [ -0.01056 1.002 1.01 2.742e-09 -1.231e-09 0.009414 2.067e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005971 0.0004805 0.004439 0.00371 0.9889 0.9919 0.006083 0.8634 0.896 0.01316 ] Network output: [ -0.0005992 0.002793 1.001 -5.627e-05 2.526e-05 0.9967 -4.241e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.207 0.09624 0.3374 0.147 0.985 0.994 0.2077 0.4475 0.8785 0.7118 ] Network output: [ 0.005799 -0.028 0.9949 3.364e-05 -1.51e-05 1.022 2.535e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1022 0.09015 0.1812 0.2013 0.9873 0.9919 0.1023 0.763 0.868 0.3059 ] Network output: [ -0.005565 0.02731 1.003 3.529e-05 -1.584e-05 0.9809 2.66e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09065 0.08871 0.1651 0.1954 0.9854 0.9912 0.09066 0.6881 0.8447 0.245 ] Network output: [ 0.000167 1 -0.0002578 4.754e-06 -2.134e-06 1 3.582e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004393 Epoch 7976 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01093 0.9953 0.9902 4.219e-07 -1.894e-07 -0.007361 3.18e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003341 -0.003149 -0.008048 0.006277 0.9699 0.9742 0.006409 0.835 0.8255 0.01823 ] Network output: [ 0.9998 0.0006039 0.0009274 -1.781e-05 7.995e-06 -0.001251 -1.342e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1952 -0.03336 -0.1774 0.1909 0.9835 0.9932 0.2184 0.4432 0.872 0.7175 ] Network output: [ -0.01056 1.002 1.01 1.777e-09 -7.976e-10 0.009411 1.339e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005971 0.0004806 0.004439 0.00371 0.9889 0.9919 0.006083 0.8634 0.896 0.01316 ] Network output: [ -0.0005988 0.002792 1.001 -5.622e-05 2.524e-05 0.9967 -4.237e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.207 0.09624 0.3374 0.147 0.985 0.994 0.2077 0.4475 0.8785 0.7118 ] Network output: [ 0.005797 -0.02799 0.9949 3.361e-05 -1.509e-05 1.022 2.533e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1022 0.09015 0.1812 0.2013 0.9873 0.9919 0.1023 0.763 0.868 0.3059 ] Network output: [ -0.005563 0.0273 1.003 3.526e-05 -1.583e-05 0.9809 2.657e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09065 0.08872 0.1651 0.1954 0.9854 0.9912 0.09066 0.6881 0.8447 0.245 ] Network output: [ 0.0001669 1 -0.0002575 4.749e-06 -2.132e-06 1 3.579e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000439 Epoch 7977 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01093 0.9953 0.9902 4.203e-07 -1.887e-07 -0.007362 3.167e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003341 -0.003149 -0.008047 0.006276 0.9699 0.9742 0.006409 0.835 0.8255 0.01823 ] Network output: [ 0.9998 0.0006034 0.0009269 -1.779e-05 7.987e-06 -0.00125 -1.341e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1953 -0.03336 -0.1774 0.1909 0.9835 0.9932 0.2184 0.4432 0.8719 0.7175 ] Network output: [ -0.01055 1.002 1.01 8.133e-10 -3.651e-10 0.009409 6.129e-10 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005972 0.0004807 0.004439 0.003709 0.9889 0.9919 0.006084 0.8633 0.896 0.01316 ] Network output: [ -0.0005984 0.002791 1.001 -5.616e-05 2.521e-05 0.9967 -4.232e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.207 0.09625 0.3374 0.147 0.985 0.994 0.2077 0.4475 0.8785 0.7118 ] Network output: [ 0.005795 -0.02798 0.9949 3.358e-05 -1.507e-05 1.022 2.53e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1022 0.09016 0.1812 0.2013 0.9873 0.9919 0.1023 0.763 0.868 0.3059 ] Network output: [ -0.005561 0.02729 1.003 3.522e-05 -1.581e-05 0.9809 2.655e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09065 0.08872 0.1651 0.1954 0.9854 0.9912 0.09067 0.6881 0.8447 0.245 ] Network output: [ 0.0001668 1 -0.0002572 4.745e-06 -2.13e-06 1 3.576e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004387 Epoch 7978 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01093 0.9953 0.9902 4.186e-07 -1.879e-07 -0.007362 3.155e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003341 -0.003149 -0.008045 0.006275 0.9699 0.9742 0.00641 0.8349 0.8255 0.01822 ] Network output: [ 0.9998 0.0006029 0.0009263 -1.777e-05 7.98e-06 -0.001249 -1.34e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1953 -0.03337 -0.1773 0.1909 0.9835 0.9932 0.2184 0.4431 0.8719 0.7175 ] Network output: [ -0.01055 1.002 1.01 -1.481e-10 6.647e-11 0.009407 -1.116e-10 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005972 0.0004808 0.004439 0.003709 0.9889 0.9919 0.006085 0.8633 0.896 0.01316 ] Network output: [ -0.0005981 0.00279 1.001 -5.611e-05 2.519e-05 0.9967 -4.228e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.207 0.09625 0.3374 0.147 0.985 0.994 0.2077 0.4475 0.8785 0.7118 ] Network output: [ 0.005794 -0.02797 0.9949 3.354e-05 -1.506e-05 1.022 2.528e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1022 0.09016 0.1812 0.2013 0.9873 0.9919 0.1023 0.7629 0.868 0.3059 ] Network output: [ -0.005559 0.02728 1.003 3.519e-05 -1.58e-05 0.9809 2.652e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09065 0.08872 0.1651 0.1954 0.9854 0.9912 0.09067 0.6881 0.8447 0.245 ] Network output: [ 0.0001667 1 -0.0002569 4.74e-06 -2.128e-06 1 3.572e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004385 Epoch 7979 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01093 0.9953 0.9902 4.17e-07 -1.872e-07 -0.007363 3.142e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003341 -0.003149 -0.008044 0.006275 0.9699 0.9742 0.00641 0.8349 0.8255 0.01822 ] Network output: [ 0.9998 0.0006024 0.0009257 -1.776e-05 7.972e-06 -0.001248 -1.338e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1953 -0.03337 -0.1773 0.1909 0.9835 0.9932 0.2184 0.4431 0.8719 0.7175 ] Network output: [ -0.01055 1.002 1.01 -1.107e-09 4.972e-10 0.009405 -8.346e-10 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005973 0.0004808 0.004439 0.003708 0.9889 0.9919 0.006085 0.8633 0.896 0.01315 ] Network output: [ -0.0005977 0.002789 1.001 -5.605e-05 2.516e-05 0.9967 -4.224e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2071 0.09626 0.3374 0.147 0.985 0.994 0.2077 0.4474 0.8785 0.7118 ] Network output: [ 0.005792 -0.02796 0.9949 3.351e-05 -1.504e-05 1.022 2.526e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1022 0.09017 0.1812 0.2013 0.9873 0.9919 0.1023 0.7629 0.868 0.3059 ] Network output: [ -0.005557 0.02726 1.003 3.516e-05 -1.578e-05 0.9809 2.65e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09066 0.08872 0.1651 0.1954 0.9854 0.9912 0.09067 0.688 0.8447 0.245 ] Network output: [ 0.0001666 1 -0.0002566 4.736e-06 -2.126e-06 1 3.569e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004382 Epoch 7980 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01092 0.9953 0.9902 4.153e-07 -1.865e-07 -0.007364 3.13e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003342 -0.003149 -0.008043 0.006274 0.9699 0.9742 0.00641 0.8349 0.8255 0.01822 ] Network output: [ 0.9998 0.0006019 0.0009251 -1.774e-05 7.964e-06 -0.001247 -1.337e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1953 -0.03337 -0.1773 0.1909 0.9835 0.9932 0.2184 0.4431 0.8719 0.7175 ] Network output: [ -0.01055 1.002 1.01 -2.065e-09 9.269e-10 0.009403 -1.556e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005974 0.0004809 0.00444 0.003708 0.9889 0.9919 0.006086 0.8633 0.896 0.01315 ] Network output: [ -0.0005973 0.002788 1.001 -5.6e-05 2.514e-05 0.9967 -4.22e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2071 0.09627 0.3374 0.1469 0.985 0.994 0.2077 0.4474 0.8785 0.7118 ] Network output: [ 0.00579 -0.02795 0.9949 3.348e-05 -1.503e-05 1.022 2.523e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1022 0.09017 0.1812 0.2013 0.9873 0.9919 0.1023 0.7629 0.868 0.3059 ] Network output: [ -0.005555 0.02725 1.003 3.513e-05 -1.577e-05 0.9809 2.647e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09066 0.08872 0.1651 0.1954 0.9854 0.9912 0.09067 0.688 0.8447 0.245 ] Network output: [ 0.0001666 1 -0.0002563 4.731e-06 -2.124e-06 1 3.565e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004379 Epoch 7981 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01092 0.9953 0.9902 4.137e-07 -1.857e-07 -0.007364 3.118e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003342 -0.003149 -0.008042 0.006273 0.9699 0.9742 0.006411 0.8349 0.8255 0.01822 ] Network output: [ 0.9998 0.0006014 0.0009245 -1.772e-05 7.957e-06 -0.001245 -1.336e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1953 -0.03337 -0.1773 0.1909 0.9835 0.9932 0.2184 0.4431 0.8719 0.7174 ] Network output: [ -0.01055 1.002 1.01 -3.02e-09 1.356e-09 0.0094 -2.276e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005974 0.000481 0.00444 0.003707 0.9889 0.9919 0.006086 0.8633 0.896 0.01315 ] Network output: [ -0.000597 0.002787 1.001 -5.594e-05 2.511e-05 0.9967 -4.216e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2071 0.09627 0.3374 0.1469 0.985 0.994 0.2078 0.4474 0.8785 0.7118 ] Network output: [ 0.005788 -0.02794 0.9949 3.345e-05 -1.502e-05 1.022 2.521e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1022 0.09018 0.1812 0.2013 0.9873 0.9919 0.1023 0.7629 0.868 0.3059 ] Network output: [ -0.005553 0.02724 1.003 3.509e-05 -1.575e-05 0.9809 2.645e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09066 0.08872 0.1651 0.1954 0.9854 0.9912 0.09067 0.688 0.8447 0.245 ] Network output: [ 0.0001665 1 -0.0002561 4.727e-06 -2.122e-06 1 3.562e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004377 Epoch 7982 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01092 0.9953 0.9902 4.121e-07 -1.85e-07 -0.007365 3.105e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003342 -0.00315 -0.008041 0.006272 0.9699 0.9742 0.006411 0.8349 0.8255 0.01822 ] Network output: [ 0.9998 0.0006008 0.0009239 -1.771e-05 7.949e-06 -0.001244 -1.334e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1953 -0.03337 -0.1773 0.1909 0.9835 0.9932 0.2184 0.4431 0.8719 0.7174 ] Network output: [ -0.01055 1.002 1.01 -3.973e-09 1.784e-09 0.009398 -2.994e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005975 0.000481 0.00444 0.003707 0.9889 0.9919 0.006087 0.8633 0.896 0.01315 ] Network output: [ -0.0005966 0.002786 1.001 -5.589e-05 2.509e-05 0.9967 -4.212e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2071 0.09628 0.3375 0.1469 0.985 0.994 0.2078 0.4474 0.8785 0.7118 ] Network output: [ 0.005786 -0.02793 0.9949 3.341e-05 -1.5e-05 1.022 2.518e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1022 0.09019 0.1812 0.2013 0.9873 0.9919 0.1023 0.7628 0.868 0.3059 ] Network output: [ -0.005551 0.02723 1.003 3.506e-05 -1.574e-05 0.981 2.642e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09066 0.08872 0.1651 0.1954 0.9854 0.9912 0.09067 0.688 0.8447 0.245 ] Network output: [ 0.0001664 1 -0.0002558 4.722e-06 -2.12e-06 1 3.559e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004374 Epoch 7983 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01092 0.9954 0.9902 4.104e-07 -1.843e-07 -0.007365 3.093e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003342 -0.00315 -0.00804 0.006272 0.9699 0.9742 0.006411 0.8349 0.8255 0.01822 ] Network output: [ 0.9998 0.0006003 0.0009233 -1.769e-05 7.942e-06 -0.001243 -1.333e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1953 -0.03338 -0.1773 0.1909 0.9835 0.9932 0.2184 0.4431 0.8719 0.7174 ] Network output: [ -0.01055 1.002 1.01 -4.925e-09 2.211e-09 0.009396 -3.711e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005975 0.0004811 0.00444 0.003706 0.9889 0.9919 0.006088 0.8633 0.896 0.01315 ] Network output: [ -0.0005962 0.002785 1.001 -5.583e-05 2.506e-05 0.9967 -4.208e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2071 0.09628 0.3375 0.1469 0.985 0.994 0.2078 0.4474 0.8785 0.7118 ] Network output: [ 0.005784 -0.02792 0.9949 3.338e-05 -1.499e-05 1.022 2.516e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1022 0.09019 0.1812 0.2013 0.9873 0.9919 0.1023 0.7628 0.8679 0.3059 ] Network output: [ -0.005549 0.02722 1.003 3.503e-05 -1.573e-05 0.981 2.64e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09066 0.08873 0.1651 0.1954 0.9854 0.9912 0.09067 0.6879 0.8447 0.245 ] Network output: [ 0.0001663 1 -0.0002555 4.718e-06 -2.118e-06 1 3.555e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004371 Epoch 7984 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01092 0.9954 0.9902 4.088e-07 -1.835e-07 -0.007366 3.081e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003342 -0.00315 -0.008039 0.006271 0.9699 0.9742 0.006412 0.8349 0.8255 0.01821 ] Network output: [ 0.9998 0.0005998 0.0009227 -1.767e-05 7.934e-06 -0.001242 -1.332e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1953 -0.03338 -0.1773 0.1909 0.9835 0.9932 0.2185 0.4431 0.8719 0.7174 ] Network output: [ -0.01055 1.002 1.01 -5.874e-09 2.637e-09 0.009394 -4.427e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005976 0.0004812 0.00444 0.003706 0.9889 0.9919 0.006088 0.8633 0.896 0.01315 ] Network output: [ -0.0005958 0.002784 1.001 -5.578e-05 2.504e-05 0.9967 -4.203e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2071 0.09629 0.3375 0.1469 0.985 0.994 0.2078 0.4474 0.8785 0.7118 ] Network output: [ 0.005782 -0.02791 0.9949 3.335e-05 -1.497e-05 1.022 2.513e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1023 0.0902 0.1812 0.2013 0.9873 0.9919 0.1023 0.7628 0.8679 0.3059 ] Network output: [ -0.005547 0.02721 1.003 3.5e-05 -1.571e-05 0.981 2.637e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09066 0.08873 0.1651 0.1954 0.9854 0.9912 0.09067 0.6879 0.8446 0.245 ] Network output: [ 0.0001662 1 -0.0002552 4.713e-06 -2.116e-06 1 3.552e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004369 Epoch 7985 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01092 0.9954 0.9902 4.072e-07 -1.828e-07 -0.007367 3.069e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003342 -0.00315 -0.008038 0.00627 0.9699 0.9742 0.006412 0.8349 0.8255 0.01821 ] Network output: [ 0.9998 0.0005993 0.0009221 -1.766e-05 7.926e-06 -0.001241 -1.331e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1953 -0.03338 -0.1772 0.1909 0.9835 0.9932 0.2185 0.4431 0.8719 0.7174 ] Network output: [ -0.01055 1.002 1.01 -6.821e-09 3.062e-09 0.009392 -5.14e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005977 0.0004812 0.00444 0.003706 0.9889 0.9919 0.006089 0.8633 0.896 0.01315 ] Network output: [ -0.0005955 0.002783 1.001 -5.572e-05 2.502e-05 0.9967 -4.199e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2071 0.09629 0.3375 0.1469 0.985 0.994 0.2078 0.4474 0.8785 0.7118 ] Network output: [ 0.00578 -0.0279 0.9949 3.332e-05 -1.496e-05 1.022 2.511e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1023 0.0902 0.1812 0.2013 0.9873 0.9919 0.1023 0.7628 0.8679 0.3059 ] Network output: [ -0.005545 0.02719 1.003 3.496e-05 -1.57e-05 0.981 2.635e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09066 0.08873 0.1651 0.1954 0.9854 0.9912 0.09068 0.6879 0.8446 0.245 ] Network output: [ 0.0001661 1 -0.0002549 4.709e-06 -2.114e-06 1 3.549e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004366 Epoch 7986 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01091 0.9954 0.9902 4.056e-07 -1.821e-07 -0.007367 3.056e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003342 -0.00315 -0.008037 0.00627 0.9699 0.9742 0.006412 0.8349 0.8255 0.01821 ] Network output: [ 0.9998 0.0005988 0.0009215 -1.764e-05 7.919e-06 -0.00124 -1.329e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1953 -0.03338 -0.1772 0.1908 0.9835 0.9932 0.2185 0.443 0.8719 0.7174 ] Network output: [ -0.01054 1.002 1.01 -7.766e-09 3.486e-09 0.009389 -5.853e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005977 0.0004813 0.00444 0.003705 0.9889 0.9919 0.006089 0.8633 0.8959 0.01315 ] Network output: [ -0.0005951 0.002782 1.001 -5.567e-05 2.499e-05 0.9967 -4.195e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2071 0.0963 0.3375 0.1469 0.985 0.994 0.2078 0.4474 0.8785 0.7118 ] Network output: [ 0.005778 -0.02789 0.9949 3.329e-05 -1.494e-05 1.022 2.509e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1023 0.09021 0.1812 0.2013 0.9873 0.9919 0.1023 0.7628 0.8679 0.3059 ] Network output: [ -0.005543 0.02718 1.003 3.493e-05 -1.568e-05 0.981 2.632e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09067 0.08873 0.1651 0.1954 0.9854 0.9912 0.09068 0.6879 0.8446 0.245 ] Network output: [ 0.000166 1 -0.0002546 4.704e-06 -2.112e-06 1 3.545e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004364 Epoch 7987 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01091 0.9954 0.9902 4.039e-07 -1.813e-07 -0.007368 3.044e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003343 -0.00315 -0.008036 0.006269 0.9699 0.9742 0.006413 0.8349 0.8255 0.01821 ] Network output: [ 0.9998 0.0005983 0.000921 -1.762e-05 7.911e-06 -0.001239 -1.328e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1954 -0.03339 -0.1772 0.1908 0.9835 0.9932 0.2185 0.443 0.8719 0.7174 ] Network output: [ -0.01054 1.002 1.01 -8.709e-09 3.91e-09 0.009387 -6.564e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005978 0.0004814 0.00444 0.003705 0.9889 0.9919 0.00609 0.8633 0.8959 0.01315 ] Network output: [ -0.0005947 0.002781 1.001 -5.561e-05 2.497e-05 0.9967 -4.191e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2071 0.0963 0.3375 0.1469 0.985 0.994 0.2078 0.4473 0.8785 0.7118 ] Network output: [ 0.005776 -0.02788 0.9949 3.325e-05 -1.493e-05 1.022 2.506e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1023 0.09021 0.1812 0.2013 0.9873 0.9919 0.1023 0.7627 0.8679 0.3059 ] Network output: [ -0.005541 0.02717 1.003 3.49e-05 -1.567e-05 0.981 2.63e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09067 0.08873 0.1651 0.1954 0.9854 0.9912 0.09068 0.6878 0.8446 0.245 ] Network output: [ 0.000166 1 -0.0002543 4.7e-06 -2.11e-06 1 3.542e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004361 Epoch 7988 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01091 0.9954 0.9902 4.023e-07 -1.806e-07 -0.007369 3.032e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003343 -0.00315 -0.008035 0.006268 0.9699 0.9742 0.006413 0.8349 0.8255 0.01821 ] Network output: [ 0.9998 0.0005978 0.0009204 -1.761e-05 7.904e-06 -0.001238 -1.327e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1954 -0.03339 -0.1772 0.1908 0.9835 0.9932 0.2185 0.443 0.8719 0.7174 ] Network output: [ -0.01054 1.002 1.01 -9.65e-09 4.332e-09 0.009385 -7.273e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005978 0.0004814 0.00444 0.003704 0.9889 0.9919 0.006091 0.8632 0.8959 0.01314 ] Network output: [ -0.0005944 0.00278 1.001 -5.556e-05 2.494e-05 0.9967 -4.187e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2072 0.09631 0.3375 0.1469 0.985 0.994 0.2078 0.4473 0.8785 0.7118 ] Network output: [ 0.005775 -0.02787 0.9949 3.322e-05 -1.491e-05 1.022 2.504e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1023 0.09022 0.1812 0.2013 0.9873 0.9919 0.1023 0.7627 0.8679 0.3059 ] Network output: [ -0.005539 0.02716 1.003 3.487e-05 -1.565e-05 0.981 2.628e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09067 0.08873 0.1651 0.1954 0.9854 0.9912 0.09068 0.6878 0.8446 0.245 ] Network output: [ 0.0001659 1 -0.000254 4.695e-06 -2.108e-06 1 3.538e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004358 Epoch 7989 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01091 0.9954 0.9902 4.007e-07 -1.799e-07 -0.007369 3.02e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003343 -0.003151 -0.008034 0.006268 0.9699 0.9742 0.006413 0.8349 0.8255 0.01821 ] Network output: [ 0.9998 0.0005973 0.0009198 -1.759e-05 7.896e-06 -0.001237 -1.326e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1954 -0.03339 -0.1772 0.1908 0.9835 0.9932 0.2185 0.443 0.8719 0.7174 ] Network output: [ -0.01054 1.002 1.01 -1.059e-08 4.754e-09 0.009383 -7.981e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005979 0.0004815 0.00444 0.003704 0.9889 0.9919 0.006091 0.8632 0.8959 0.01314 ] Network output: [ -0.000594 0.002779 1.001 -5.55e-05 2.492e-05 0.9967 -4.183e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2072 0.09632 0.3375 0.1469 0.985 0.994 0.2078 0.4473 0.8785 0.7117 ] Network output: [ 0.005773 -0.02786 0.9949 3.319e-05 -1.49e-05 1.022 2.501e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1023 0.09023 0.1813 0.2013 0.9873 0.9919 0.1024 0.7627 0.8679 0.3059 ] Network output: [ -0.005537 0.02715 1.003 3.483e-05 -1.564e-05 0.981 2.625e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09067 0.08873 0.1651 0.1954 0.9854 0.9912 0.09068 0.6878 0.8446 0.245 ] Network output: [ 0.0001658 1 -0.0002537 4.691e-06 -2.106e-06 1 3.535e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004356 Epoch 7990 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01091 0.9954 0.9902 3.991e-07 -1.792e-07 -0.00737 3.008e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003343 -0.003151 -0.008033 0.006267 0.9699 0.9742 0.006414 0.8349 0.8255 0.01821 ] Network output: [ 0.9998 0.0005968 0.0009192 -1.757e-05 7.888e-06 -0.001236 -1.324e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1954 -0.03339 -0.1772 0.1908 0.9835 0.9932 0.2185 0.443 0.8719 0.7174 ] Network output: [ -0.01054 1.002 1.01 -1.153e-08 5.175e-09 0.009381 -8.687e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00598 0.0004816 0.00444 0.003703 0.9889 0.9919 0.006092 0.8632 0.8959 0.01314 ] Network output: [ -0.0005936 0.002778 1.001 -5.545e-05 2.489e-05 0.9967 -4.179e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2072 0.09632 0.3375 0.1469 0.985 0.994 0.2079 0.4473 0.8785 0.7117 ] Network output: [ 0.005771 -0.02785 0.9949 3.316e-05 -1.489e-05 1.022 2.499e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1023 0.09023 0.1813 0.2013 0.9873 0.9919 0.1024 0.7627 0.8679 0.3059 ] Network output: [ -0.005535 0.02714 1.003 3.48e-05 -1.562e-05 0.981 2.623e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09067 0.08873 0.1651 0.1954 0.9854 0.9912 0.09068 0.6878 0.8446 0.245 ] Network output: [ 0.0001657 1 -0.0002534 4.686e-06 -2.104e-06 1 3.532e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004353 Epoch 7991 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01091 0.9954 0.9902 3.975e-07 -1.784e-07 -0.00737 2.996e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003343 -0.003151 -0.008032 0.006266 0.9699 0.9742 0.006414 0.8348 0.8255 0.0182 ] Network output: [ 0.9998 0.0005962 0.0009186 -1.755e-05 7.881e-06 -0.001235 -1.323e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1954 -0.03339 -0.1771 0.1908 0.9835 0.9932 0.2185 0.443 0.8719 0.7174 ] Network output: [ -0.01054 1.002 1.01 -1.246e-08 5.595e-09 0.009378 -9.392e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00598 0.0004817 0.00444 0.003703 0.9889 0.9919 0.006093 0.8632 0.8959 0.01314 ] Network output: [ -0.0005933 0.002777 1.001 -5.539e-05 2.487e-05 0.9967 -4.175e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2072 0.09633 0.3376 0.1469 0.985 0.994 0.2079 0.4473 0.8785 0.7117 ] Network output: [ 0.005769 -0.02784 0.9949 3.313e-05 -1.487e-05 1.022 2.497e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1023 0.09024 0.1813 0.2013 0.9873 0.9919 0.1024 0.7627 0.8679 0.3059 ] Network output: [ -0.005533 0.02712 1.003 3.477e-05 -1.561e-05 0.981 2.62e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09067 0.08874 0.1651 0.1954 0.9854 0.9912 0.09068 0.6878 0.8446 0.245 ] Network output: [ 0.0001656 1 -0.0002532 4.682e-06 -2.102e-06 1 3.528e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000435 Epoch 7992 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0109 0.9954 0.9902 3.959e-07 -1.777e-07 -0.007371 2.984e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003343 -0.003151 -0.00803 0.006266 0.9699 0.9742 0.006414 0.8348 0.8255 0.0182 ] Network output: [ 0.9998 0.0005957 0.000918 -1.754e-05 7.873e-06 -0.001234 -1.322e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1954 -0.0334 -0.1771 0.1908 0.9835 0.9932 0.2186 0.443 0.8719 0.7174 ] Network output: [ -0.01054 1.002 1.01 -1.339e-08 6.013e-09 0.009376 -1.009e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005981 0.0004817 0.00444 0.003702 0.9889 0.9919 0.006093 0.8632 0.8959 0.01314 ] Network output: [ -0.0005929 0.002776 1.001 -5.534e-05 2.484e-05 0.9967 -4.171e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2072 0.09633 0.3376 0.1469 0.985 0.994 0.2079 0.4473 0.8785 0.7117 ] Network output: [ 0.005767 -0.02783 0.9949 3.309e-05 -1.486e-05 1.022 2.494e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1023 0.09024 0.1813 0.2013 0.9873 0.9919 0.1024 0.7626 0.8679 0.3059 ] Network output: [ -0.005531 0.02711 1.003 3.474e-05 -1.559e-05 0.981 2.618e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09067 0.08874 0.1651 0.1954 0.9854 0.9912 0.09069 0.6877 0.8446 0.245 ] Network output: [ 0.0001655 1 -0.0002529 4.677e-06 -2.1e-06 1 3.525e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004348 Epoch 7993 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0109 0.9954 0.9902 3.943e-07 -1.77e-07 -0.007372 2.971e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003343 -0.003151 -0.008029 0.006265 0.9699 0.9742 0.006414 0.8348 0.8254 0.0182 ] Network output: [ 0.9998 0.0005952 0.0009174 -1.752e-05 7.866e-06 -0.001233 -1.32e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1954 -0.0334 -0.1771 0.1908 0.9835 0.9932 0.2186 0.443 0.8719 0.7174 ] Network output: [ -0.01054 1.002 1.01 -1.433e-08 6.431e-09 0.009374 -1.08e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005981 0.0004818 0.00444 0.003702 0.9889 0.9919 0.006094 0.8632 0.8959 0.01314 ] Network output: [ -0.0005925 0.002775 1.001 -5.528e-05 2.482e-05 0.9968 -4.166e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2072 0.09634 0.3376 0.1469 0.985 0.994 0.2079 0.4473 0.8785 0.7117 ] Network output: [ 0.005765 -0.02782 0.9949 3.306e-05 -1.484e-05 1.022 2.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1023 0.09025 0.1813 0.2013 0.9873 0.9919 0.1024 0.7626 0.8679 0.3059 ] Network output: [ -0.005529 0.0271 1.003 3.47e-05 -1.558e-05 0.981 2.615e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09067 0.08874 0.1651 0.1954 0.9854 0.9912 0.09069 0.6877 0.8446 0.245 ] Network output: [ 0.0001654 1 -0.0002526 4.673e-06 -2.098e-06 1 3.522e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004345 Epoch 7994 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0109 0.9954 0.9902 3.927e-07 -1.763e-07 -0.007372 2.959e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003343 -0.003151 -0.008028 0.006264 0.9699 0.9742 0.006415 0.8348 0.8254 0.0182 ] Network output: [ 0.9998 0.0005947 0.0009169 -1.75e-05 7.858e-06 -0.001232 -1.319e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1954 -0.0334 -0.1771 0.1908 0.9835 0.9932 0.2186 0.4429 0.8719 0.7174 ] Network output: [ -0.01054 1.002 1.01 -1.526e-08 6.849e-09 0.009372 -1.15e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005982 0.0004819 0.00444 0.003702 0.9889 0.9919 0.006094 0.8632 0.8959 0.01314 ] Network output: [ -0.0005922 0.002774 1.001 -5.523e-05 2.479e-05 0.9968 -4.162e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2072 0.09634 0.3376 0.1469 0.985 0.994 0.2079 0.4473 0.8785 0.7117 ] Network output: [ 0.005763 -0.02781 0.9949 3.303e-05 -1.483e-05 1.021 2.489e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1023 0.09025 0.1813 0.2013 0.9873 0.9919 0.1024 0.7626 0.8679 0.3059 ] Network output: [ -0.005527 0.02709 1.003 3.467e-05 -1.557e-05 0.981 2.613e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09068 0.08874 0.1651 0.1954 0.9854 0.9912 0.09069 0.6877 0.8446 0.245 ] Network output: [ 0.0001654 1 -0.0002523 4.668e-06 -2.096e-06 1 3.518e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004342 Epoch 7995 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0109 0.9954 0.9902 3.911e-07 -1.756e-07 -0.007373 2.947e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003344 -0.003152 -0.008027 0.006263 0.9699 0.9742 0.006415 0.8348 0.8254 0.0182 ] Network output: [ 0.9998 0.0005942 0.0009163 -1.749e-05 7.851e-06 -0.001231 -1.318e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1954 -0.0334 -0.1771 0.1908 0.9835 0.9932 0.2186 0.4429 0.8719 0.7174 ] Network output: [ -0.01053 1.002 1.01 -1.618e-08 7.265e-09 0.00937 -1.22e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005983 0.0004819 0.00444 0.003701 0.9889 0.9919 0.006095 0.8632 0.8959 0.01314 ] Network output: [ -0.0005918 0.002773 1.001 -5.518e-05 2.477e-05 0.9968 -4.158e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2072 0.09635 0.3376 0.1469 0.985 0.994 0.2079 0.4472 0.8785 0.7117 ] Network output: [ 0.005761 -0.0278 0.9949 3.3e-05 -1.481e-05 1.021 2.487e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1023 0.09026 0.1813 0.2013 0.9873 0.9919 0.1024 0.7626 0.8679 0.3059 ] Network output: [ -0.005525 0.02708 1.003 3.464e-05 -1.555e-05 0.981 2.61e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09068 0.08874 0.1651 0.1954 0.9854 0.9912 0.09069 0.6877 0.8446 0.245 ] Network output: [ 0.0001653 1 -0.000252 4.664e-06 -2.094e-06 1 3.515e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000434 Epoch 7996 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0109 0.9954 0.9902 3.895e-07 -1.749e-07 -0.007373 2.935e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003344 -0.003152 -0.008026 0.006263 0.9699 0.9742 0.006415 0.8348 0.8254 0.0182 ] Network output: [ 0.9998 0.0005937 0.0009157 -1.747e-05 7.843e-06 -0.00123 -1.317e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1954 -0.03341 -0.1771 0.1908 0.9835 0.9932 0.2186 0.4429 0.8719 0.7174 ] Network output: [ -0.01053 1.002 1.01 -1.711e-08 7.68e-09 0.009368 -1.289e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005983 0.000482 0.00444 0.003701 0.9889 0.9919 0.006096 0.8632 0.8959 0.01314 ] Network output: [ -0.0005914 0.002772 1.001 -5.512e-05 2.475e-05 0.9968 -4.154e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2072 0.09636 0.3376 0.1469 0.985 0.994 0.2079 0.4472 0.8785 0.7117 ] Network output: [ 0.005759 -0.02779 0.9949 3.297e-05 -1.48e-05 1.021 2.485e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1023 0.09027 0.1813 0.2013 0.9873 0.9919 0.1024 0.7625 0.8679 0.3059 ] Network output: [ -0.005523 0.02707 1.003 3.461e-05 -1.554e-05 0.981 2.608e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09068 0.08874 0.1651 0.1954 0.9854 0.9912 0.09069 0.6876 0.8445 0.245 ] Network output: [ 0.0001652 1 -0.0002517 4.659e-06 -2.092e-06 1 3.511e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004337 Epoch 7997 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0109 0.9954 0.9902 3.879e-07 -1.742e-07 -0.007374 2.923e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003344 -0.003152 -0.008025 0.006262 0.9699 0.9742 0.006416 0.8348 0.8254 0.0182 ] Network output: [ 0.9998 0.0005932 0.0009151 -1.745e-05 7.836e-06 -0.001229 -1.315e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1955 -0.03341 -0.177 0.1908 0.9835 0.9932 0.2186 0.4429 0.8719 0.7174 ] Network output: [ -0.01053 1.002 1.01 -1.803e-08 8.095e-09 0.009365 -1.359e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005984 0.0004821 0.00444 0.0037 0.9889 0.9919 0.006096 0.8632 0.8959 0.01313 ] Network output: [ -0.0005911 0.002771 1.001 -5.507e-05 2.472e-05 0.9968 -4.15e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2073 0.09636 0.3376 0.1468 0.985 0.994 0.2079 0.4472 0.8785 0.7117 ] Network output: [ 0.005757 -0.02778 0.9949 3.294e-05 -1.479e-05 1.021 2.482e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1023 0.09027 0.1813 0.2013 0.9873 0.9919 0.1024 0.7625 0.8679 0.3059 ] Network output: [ -0.005521 0.02705 1.003 3.457e-05 -1.552e-05 0.981 2.606e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09068 0.08874 0.1651 0.1954 0.9854 0.9912 0.09069 0.6876 0.8445 0.245 ] Network output: [ 0.0001651 1 -0.0002514 4.655e-06 -2.09e-06 1 3.508e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004335 Epoch 7998 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0109 0.9954 0.9902 3.863e-07 -1.734e-07 -0.007375 2.912e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003344 -0.003152 -0.008024 0.006261 0.9699 0.9742 0.006416 0.8348 0.8254 0.01819 ] Network output: [ 0.9998 0.0005927 0.0009145 -1.744e-05 7.828e-06 -0.001228 -1.314e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1955 -0.03341 -0.177 0.1908 0.9835 0.9932 0.2186 0.4429 0.8719 0.7174 ] Network output: [ -0.01053 1.002 1.01 -1.895e-08 8.508e-09 0.009363 -1.428e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005984 0.0004821 0.00444 0.0037 0.9889 0.9919 0.006097 0.8632 0.8959 0.01313 ] Network output: [ -0.0005907 0.00277 1.001 -5.501e-05 2.47e-05 0.9968 -4.146e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2073 0.09637 0.3376 0.1468 0.985 0.994 0.2079 0.4472 0.8785 0.7117 ] Network output: [ 0.005756 -0.02777 0.9949 3.29e-05 -1.477e-05 1.021 2.48e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1023 0.09028 0.1813 0.2012 0.9873 0.9919 0.1024 0.7625 0.8679 0.3059 ] Network output: [ -0.005519 0.02704 1.003 3.454e-05 -1.551e-05 0.981 2.603e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09068 0.08875 0.1651 0.1954 0.9854 0.9912 0.09069 0.6876 0.8445 0.245 ] Network output: [ 0.000165 1 -0.0002512 4.65e-06 -2.088e-06 1 3.505e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004332 Epoch 7999 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01089 0.9954 0.9902 3.848e-07 -1.727e-07 -0.007375 2.9e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003344 -0.003152 -0.008023 0.006261 0.9699 0.9742 0.006416 0.8348 0.8254 0.01819 ] Network output: [ 0.9998 0.0005922 0.0009139 -1.742e-05 7.821e-06 -0.001227 -1.313e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1955 -0.03341 -0.177 0.1908 0.9835 0.9932 0.2186 0.4429 0.8719 0.7173 ] Network output: [ -0.01053 1.002 1.01 -1.987e-08 8.921e-09 0.009361 -1.498e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005985 0.0004822 0.00444 0.003699 0.9889 0.9919 0.006097 0.8632 0.8959 0.01313 ] Network output: [ -0.0005903 0.002769 1.001 -5.496e-05 2.467e-05 0.9968 -4.142e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2073 0.09637 0.3376 0.1468 0.985 0.994 0.208 0.4472 0.8785 0.7117 ] Network output: [ 0.005754 -0.02776 0.9949 3.287e-05 -1.476e-05 1.021 2.477e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1023 0.09028 0.1813 0.2012 0.9873 0.9919 0.1024 0.7625 0.8678 0.3059 ] Network output: [ -0.005517 0.02703 1.003 3.451e-05 -1.549e-05 0.981 2.601e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09068 0.08875 0.1651 0.1954 0.9854 0.9912 0.0907 0.6876 0.8445 0.245 ] Network output: [ 0.0001649 1 -0.0002509 4.646e-06 -2.086e-06 1 3.501e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004329 Epoch 8000 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01089 0.9954 0.9902 3.832e-07 -1.72e-07 -0.007376 2.888e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003344 -0.003152 -0.008022 0.00626 0.9699 0.9742 0.006417 0.8348 0.8254 0.01819 ] Network output: [ 0.9998 0.0005917 0.0009134 -1.74e-05 7.813e-06 -0.001226 -1.312e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1955 -0.03341 -0.177 0.1908 0.9835 0.9932 0.2186 0.4429 0.8719 0.7173 ] Network output: [ -0.01053 1.002 1.01 -2.079e-08 9.332e-09 0.009359 -1.567e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005986 0.0004823 0.00444 0.003699 0.9889 0.9919 0.006098 0.8631 0.8959 0.01313 ] Network output: [ -0.00059 0.002768 1.001 -5.49e-05 2.465e-05 0.9968 -4.138e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2073 0.09638 0.3376 0.1468 0.985 0.994 0.208 0.4472 0.8785 0.7117 ] Network output: [ 0.005752 -0.02775 0.9949 3.284e-05 -1.474e-05 1.021 2.475e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1024 0.09029 0.1813 0.2012 0.9873 0.9919 0.1024 0.7625 0.8678 0.3059 ] Network output: [ -0.005515 0.02702 1.003 3.448e-05 -1.548e-05 0.9811 2.598e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09068 0.08875 0.1651 0.1954 0.9854 0.9912 0.0907 0.6875 0.8445 0.245 ] Network output: [ 0.0001648 1 -0.0002506 4.642e-06 -2.084e-06 1 3.498e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004327 Epoch 8001 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01089 0.9954 0.9902 3.816e-07 -1.713e-07 -0.007376 2.876e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003344 -0.003152 -0.008021 0.006259 0.9699 0.9742 0.006417 0.8348 0.8254 0.01819 ] Network output: [ 0.9998 0.0005912 0.0009128 -1.739e-05 7.806e-06 -0.001225 -1.31e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1955 -0.03342 -0.177 0.1907 0.9835 0.9932 0.2187 0.4429 0.8719 0.7173 ] Network output: [ -0.01053 1.002 1.01 -2.17e-08 9.743e-09 0.009357 -1.636e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005986 0.0004824 0.00444 0.003698 0.9889 0.9919 0.006099 0.8631 0.8959 0.01313 ] Network output: [ -0.0005896 0.002767 1.001 -5.485e-05 2.462e-05 0.9968 -4.134e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2073 0.09638 0.3377 0.1468 0.985 0.994 0.208 0.4472 0.8785 0.7117 ] Network output: [ 0.00575 -0.02774 0.9949 3.281e-05 -1.473e-05 1.021 2.473e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1024 0.09029 0.1813 0.2012 0.9873 0.9919 0.1024 0.7624 0.8678 0.3059 ] Network output: [ -0.005513 0.02701 1.003 3.444e-05 -1.546e-05 0.9811 2.596e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09069 0.08875 0.1651 0.1954 0.9854 0.9912 0.0907 0.6875 0.8445 0.245 ] Network output: [ 0.0001648 1 -0.0002503 4.637e-06 -2.082e-06 1 3.495e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004324 Epoch 8002 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01089 0.9954 0.9902 3.8e-07 -1.706e-07 -0.007377 2.864e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003345 -0.003153 -0.00802 0.006259 0.9699 0.9742 0.006417 0.8348 0.8254 0.01819 ] Network output: [ 0.9998 0.0005907 0.0009122 -1.737e-05 7.798e-06 -0.001224 -1.309e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1955 -0.03342 -0.177 0.1907 0.9835 0.9932 0.2187 0.4428 0.8719 0.7173 ] Network output: [ -0.01053 1.002 1.01 -2.262e-08 1.015e-08 0.009355 -1.704e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005987 0.0004824 0.00444 0.003698 0.9889 0.9919 0.006099 0.8631 0.8959 0.01313 ] Network output: [ -0.0005892 0.002766 1.001 -5.48e-05 2.46e-05 0.9968 -4.13e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2073 0.09639 0.3377 0.1468 0.985 0.994 0.208 0.4472 0.8785 0.7117 ] Network output: [ 0.005748 -0.02773 0.9949 3.278e-05 -1.471e-05 1.021 2.47e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1024 0.0903 0.1813 0.2012 0.9873 0.9919 0.1024 0.7624 0.8678 0.3059 ] Network output: [ -0.005511 0.027 1.003 3.441e-05 -1.545e-05 0.9811 2.593e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09069 0.08875 0.1651 0.1954 0.9854 0.9912 0.0907 0.6875 0.8445 0.245 ] Network output: [ 0.0001647 1 -0.00025 4.633e-06 -2.08e-06 1 3.491e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004322 Epoch 8003 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01089 0.9954 0.9902 3.785e-07 -1.699e-07 -0.007377 2.852e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003345 -0.003153 -0.008019 0.006258 0.9699 0.9742 0.006418 0.8348 0.8254 0.01819 ] Network output: [ 0.9998 0.0005902 0.0009116 -1.735e-05 7.791e-06 -0.001222 -1.308e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1955 -0.03342 -0.177 0.1907 0.9835 0.9932 0.2187 0.4428 0.8719 0.7173 ] Network output: [ -0.01053 1.002 1.01 -2.353e-08 1.056e-08 0.009352 -1.773e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005987 0.0004825 0.00444 0.003698 0.9889 0.9919 0.0061 0.8631 0.8959 0.01313 ] Network output: [ -0.0005889 0.002765 1.001 -5.474e-05 2.458e-05 0.9968 -4.126e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2073 0.09639 0.3377 0.1468 0.985 0.994 0.208 0.4471 0.8784 0.7117 ] Network output: [ 0.005746 -0.02772 0.9949 3.274e-05 -1.47e-05 1.021 2.468e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1024 0.09031 0.1813 0.2012 0.9873 0.9919 0.1024 0.7624 0.8678 0.3059 ] Network output: [ -0.005509 0.02699 1.003 3.438e-05 -1.543e-05 0.9811 2.591e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09069 0.08875 0.1651 0.1954 0.9854 0.9912 0.0907 0.6875 0.8445 0.245 ] Network output: [ 0.0001646 1 -0.0002497 4.628e-06 -2.078e-06 1 3.488e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004319 Epoch 8004 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01089 0.9954 0.9902 3.769e-07 -1.692e-07 -0.007378 2.84e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003345 -0.003153 -0.008018 0.006257 0.9699 0.9742 0.006418 0.8347 0.8254 0.01819 ] Network output: [ 0.9998 0.0005897 0.000911 -1.734e-05 7.783e-06 -0.001221 -1.307e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1955 -0.03342 -0.1769 0.1907 0.9835 0.9932 0.2187 0.4428 0.8718 0.7173 ] Network output: [ -0.01052 1.002 1.01 -2.444e-08 1.097e-08 0.00935 -1.842e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005988 0.0004826 0.00444 0.003697 0.9889 0.9919 0.006101 0.8631 0.8959 0.01313 ] Network output: [ -0.0005885 0.002764 1.001 -5.469e-05 2.455e-05 0.9968 -4.121e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2073 0.0964 0.3377 0.1468 0.985 0.994 0.208 0.4471 0.8784 0.7117 ] Network output: [ 0.005744 -0.02771 0.9949 3.271e-05 -1.469e-05 1.021 2.465e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1024 0.09031 0.1813 0.2012 0.9873 0.9919 0.1024 0.7624 0.8678 0.3059 ] Network output: [ -0.005507 0.02697 1.003 3.435e-05 -1.542e-05 0.9811 2.589e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09069 0.08875 0.1651 0.1954 0.9854 0.9912 0.0907 0.6874 0.8445 0.245 ] Network output: [ 0.0001645 1 -0.0002494 4.624e-06 -2.076e-06 1 3.485e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004316 Epoch 8005 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01088 0.9954 0.9902 3.753e-07 -1.685e-07 -0.007379 2.829e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003345 -0.003153 -0.008017 0.006256 0.9699 0.9742 0.006418 0.8347 0.8254 0.01818 ] Network output: [ 0.9998 0.0005892 0.0009105 -1.732e-05 7.776e-06 -0.00122 -1.305e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1955 -0.03343 -0.1769 0.1907 0.9835 0.9932 0.2187 0.4428 0.8718 0.7173 ] Network output: [ -0.01052 1.002 1.01 -2.534e-08 1.138e-08 0.009348 -1.91e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005989 0.0004826 0.00444 0.003697 0.9889 0.9919 0.006101 0.8631 0.8959 0.01313 ] Network output: [ -0.0005881 0.002763 1.001 -5.463e-05 2.453e-05 0.9968 -4.117e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2073 0.09641 0.3377 0.1468 0.985 0.994 0.208 0.4471 0.8784 0.7117 ] Network output: [ 0.005742 -0.0277 0.9949 3.268e-05 -1.467e-05 1.021 2.463e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1024 0.09032 0.1813 0.2012 0.9873 0.9919 0.1025 0.7623 0.8678 0.3059 ] Network output: [ -0.005505 0.02696 1.003 3.432e-05 -1.541e-05 0.9811 2.586e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09069 0.08876 0.1651 0.1954 0.9854 0.9912 0.0907 0.6874 0.8445 0.245 ] Network output: [ 0.0001644 1 -0.0002492 4.619e-06 -2.074e-06 1 3.481e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004314 Epoch 8006 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01088 0.9954 0.9902 3.738e-07 -1.678e-07 -0.007379 2.817e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003345 -0.003153 -0.008015 0.006256 0.9699 0.9742 0.006419 0.8347 0.8254 0.01818 ] Network output: [ 0.9998 0.0005887 0.0009099 -1.73e-05 7.768e-06 -0.001219 -1.304e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1955 -0.03343 -0.1769 0.1907 0.9835 0.9932 0.2187 0.4428 0.8718 0.7173 ] Network output: [ -0.01052 1.002 1.01 -2.625e-08 1.178e-08 0.009346 -1.978e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005989 0.0004827 0.004441 0.003696 0.9889 0.9919 0.006102 0.8631 0.8959 0.01312 ] Network output: [ -0.0005878 0.002762 1.001 -5.458e-05 2.45e-05 0.9968 -4.113e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2074 0.09641 0.3377 0.1468 0.985 0.994 0.208 0.4471 0.8784 0.7116 ] Network output: [ 0.00574 -0.02769 0.9949 3.265e-05 -1.466e-05 1.021 2.461e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1024 0.09032 0.1813 0.2012 0.9873 0.9919 0.1025 0.7623 0.8678 0.3059 ] Network output: [ -0.005503 0.02695 1.003 3.428e-05 -1.539e-05 0.9811 2.584e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09069 0.08876 0.1651 0.1954 0.9854 0.9912 0.09071 0.6874 0.8445 0.245 ] Network output: [ 0.0001643 1 -0.0002489 4.615e-06 -2.072e-06 1 3.478e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004311 Epoch 8007 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01088 0.9954 0.9902 3.722e-07 -1.671e-07 -0.00738 2.805e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003345 -0.003153 -0.008014 0.006255 0.9699 0.9742 0.006419 0.8347 0.8254 0.01818 ] Network output: [ 0.9998 0.0005882 0.0009093 -1.729e-05 7.761e-06 -0.001218 -1.303e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1956 -0.03343 -0.1769 0.1907 0.9835 0.9932 0.2187 0.4428 0.8718 0.7173 ] Network output: [ -0.01052 1.002 1.01 -2.715e-08 1.219e-08 0.009344 -2.046e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00599 0.0004828 0.004441 0.003696 0.9889 0.9919 0.006102 0.8631 0.8959 0.01312 ] Network output: [ -0.0005874 0.002761 1.001 -5.453e-05 2.448e-05 0.9968 -4.109e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2074 0.09642 0.3377 0.1468 0.985 0.994 0.208 0.4471 0.8784 0.7116 ] Network output: [ 0.005739 -0.02768 0.9949 3.262e-05 -1.464e-05 1.021 2.458e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1024 0.09033 0.1813 0.2012 0.9873 0.9919 0.1025 0.7623 0.8678 0.3059 ] Network output: [ -0.005501 0.02694 1.003 3.425e-05 -1.538e-05 0.9811 2.581e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0907 0.08876 0.1651 0.1954 0.9854 0.9912 0.09071 0.6874 0.8445 0.245 ] Network output: [ 0.0001643 1 -0.0002486 4.611e-06 -2.07e-06 1 3.475e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004308 Epoch 8008 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01088 0.9954 0.9902 3.707e-07 -1.664e-07 -0.00738 2.793e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003345 -0.003154 -0.008013 0.006254 0.9699 0.9742 0.006419 0.8347 0.8254 0.01818 ] Network output: [ 0.9998 0.0005877 0.0009087 -1.727e-05 7.753e-06 -0.001217 -1.302e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1956 -0.03343 -0.1769 0.1907 0.9835 0.9932 0.2187 0.4428 0.8718 0.7173 ] Network output: [ -0.01052 1.002 1.01 -2.805e-08 1.259e-08 0.009342 -2.114e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00599 0.0004829 0.004441 0.003695 0.9889 0.9919 0.006103 0.8631 0.8959 0.01312 ] Network output: [ -0.000587 0.00276 1.001 -5.447e-05 2.445e-05 0.9968 -4.105e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2074 0.09642 0.3377 0.1468 0.985 0.994 0.2081 0.4471 0.8784 0.7116 ] Network output: [ 0.005737 -0.02767 0.9949 3.259e-05 -1.463e-05 1.021 2.456e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1024 0.09033 0.1813 0.2012 0.9873 0.9919 0.1025 0.7623 0.8678 0.3058 ] Network output: [ -0.005499 0.02693 1.003 3.422e-05 -1.536e-05 0.9811 2.579e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0907 0.08876 0.1651 0.1954 0.9854 0.9912 0.09071 0.6873 0.8445 0.245 ] Network output: [ 0.0001642 1 -0.0002483 4.606e-06 -2.068e-06 1 3.471e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004306 Epoch 8009 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01088 0.9954 0.9902 3.691e-07 -1.657e-07 -0.007381 2.782e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003345 -0.003154 -0.008012 0.006254 0.9699 0.9742 0.00642 0.8347 0.8254 0.01818 ] Network output: [ 0.9998 0.0005872 0.0009082 -1.725e-05 7.746e-06 -0.001216 -1.3e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1956 -0.03343 -0.1769 0.1907 0.9835 0.9932 0.2188 0.4428 0.8718 0.7173 ] Network output: [ -0.01052 1.002 1.01 -2.895e-08 1.3e-08 0.009339 -2.182e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005991 0.0004829 0.004441 0.003695 0.9889 0.9919 0.006104 0.8631 0.8959 0.01312 ] Network output: [ -0.0005867 0.002759 1.001 -5.442e-05 2.443e-05 0.9968 -4.101e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2074 0.09643 0.3377 0.1468 0.985 0.994 0.2081 0.4471 0.8784 0.7116 ] Network output: [ 0.005735 -0.02766 0.9949 3.256e-05 -1.462e-05 1.021 2.453e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1024 0.09034 0.1813 0.2012 0.9873 0.9919 0.1025 0.7623 0.8678 0.3058 ] Network output: [ -0.005497 0.02692 1.003 3.419e-05 -1.535e-05 0.9811 2.576e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0907 0.08876 0.1651 0.1954 0.9854 0.9912 0.09071 0.6873 0.8444 0.2451 ] Network output: [ 0.0001641 1 -0.000248 4.602e-06 -2.066e-06 1 3.468e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004303 Epoch 8010 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01088 0.9954 0.9902 3.676e-07 -1.65e-07 -0.007382 2.77e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003346 -0.003154 -0.008011 0.006253 0.9699 0.9742 0.00642 0.8347 0.8254 0.01818 ] Network output: [ 0.9998 0.0005867 0.0009076 -1.724e-05 7.738e-06 -0.001215 -1.299e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1956 -0.03344 -0.1768 0.1907 0.9835 0.9932 0.2188 0.4427 0.8718 0.7173 ] Network output: [ -0.01052 1.002 1.01 -2.985e-08 1.34e-08 0.009337 -2.25e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005992 0.000483 0.004441 0.003694 0.9889 0.9919 0.006104 0.8631 0.8959 0.01312 ] Network output: [ -0.0005863 0.002758 1.001 -5.437e-05 2.441e-05 0.9968 -4.097e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2074 0.09643 0.3378 0.1468 0.985 0.994 0.2081 0.4471 0.8784 0.7116 ] Network output: [ 0.005733 -0.02765 0.9949 3.252e-05 -1.46e-05 1.021 2.451e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1024 0.09035 0.1813 0.2012 0.9873 0.9919 0.1025 0.7622 0.8678 0.3058 ] Network output: [ -0.005495 0.02691 1.003 3.416e-05 -1.533e-05 0.9811 2.574e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0907 0.08876 0.1651 0.1954 0.9854 0.9912 0.09071 0.6873 0.8444 0.2451 ] Network output: [ 0.000164 1 -0.0002477 4.597e-06 -2.064e-06 1 3.465e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004301 Epoch 8011 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01087 0.9954 0.9902 3.66e-07 -1.643e-07 -0.007382 2.758e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003346 -0.003154 -0.00801 0.006252 0.9699 0.9742 0.00642 0.8347 0.8254 0.01818 ] Network output: [ 0.9998 0.0005862 0.000907 -1.722e-05 7.731e-06 -0.001214 -1.298e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1956 -0.03344 -0.1768 0.1907 0.9835 0.9932 0.2188 0.4427 0.8718 0.7173 ] Network output: [ -0.01052 1.002 1.01 -3.075e-08 1.38e-08 0.009335 -2.317e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005992 0.0004831 0.004441 0.003694 0.9889 0.9919 0.006105 0.863 0.8959 0.01312 ] Network output: [ -0.000586 0.002757 1.001 -5.431e-05 2.438e-05 0.9968 -4.093e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2074 0.09644 0.3378 0.1468 0.985 0.994 0.2081 0.447 0.8784 0.7116 ] Network output: [ 0.005731 -0.02764 0.9949 3.249e-05 -1.459e-05 1.021 2.449e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1024 0.09035 0.1813 0.2012 0.9873 0.9919 0.1025 0.7622 0.8678 0.3058 ] Network output: [ -0.005493 0.02689 1.003 3.412e-05 -1.532e-05 0.9811 2.572e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0907 0.08876 0.1651 0.1954 0.9854 0.9912 0.09071 0.6873 0.8444 0.2451 ] Network output: [ 0.0001639 1 -0.0002475 4.593e-06 -2.062e-06 1 3.461e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004298 Epoch 8012 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01087 0.9954 0.9902 3.645e-07 -1.636e-07 -0.007383 2.747e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003346 -0.003154 -0.008009 0.006252 0.9699 0.9742 0.00642 0.8347 0.8254 0.01817 ] Network output: [ 0.9998 0.0005857 0.0009064 -1.72e-05 7.723e-06 -0.001213 -1.297e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1956 -0.03344 -0.1768 0.1907 0.9835 0.9932 0.2188 0.4427 0.8718 0.7173 ] Network output: [ -0.01051 1.002 1.01 -3.164e-08 1.42e-08 0.009333 -2.384e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005993 0.0004831 0.004441 0.003694 0.9889 0.9919 0.006105 0.863 0.8959 0.01312 ] Network output: [ -0.0005856 0.002756 1.001 -5.426e-05 2.436e-05 0.9968 -4.089e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2074 0.09644 0.3378 0.1468 0.985 0.994 0.2081 0.447 0.8784 0.7116 ] Network output: [ 0.005729 -0.02763 0.9949 3.246e-05 -1.457e-05 1.021 2.446e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1024 0.09036 0.1813 0.2012 0.9873 0.9919 0.1025 0.7622 0.8678 0.3058 ] Network output: [ -0.005491 0.02688 1.003 3.409e-05 -1.53e-05 0.9811 2.569e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0907 0.08877 0.1651 0.1954 0.9854 0.9912 0.09072 0.6873 0.8444 0.2451 ] Network output: [ 0.0001638 1 -0.0002472 4.589e-06 -2.06e-06 1 3.458e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004296 Epoch 8013 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01087 0.9954 0.9902 3.629e-07 -1.629e-07 -0.007383 2.735e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003346 -0.003154 -0.008008 0.006251 0.9699 0.9742 0.006421 0.8347 0.8254 0.01817 ] Network output: [ 0.9998 0.0005852 0.0009059 -1.719e-05 7.716e-06 -0.001212 -1.295e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1956 -0.03344 -0.1768 0.1907 0.9835 0.9932 0.2188 0.4427 0.8718 0.7173 ] Network output: [ -0.01051 1.002 1.01 -3.253e-08 1.46e-08 0.009331 -2.452e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005993 0.0004832 0.004441 0.003693 0.9889 0.9919 0.006106 0.863 0.8958 0.01312 ] Network output: [ -0.0005852 0.002755 1.001 -5.42e-05 2.433e-05 0.9968 -4.085e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2074 0.09645 0.3378 0.1468 0.985 0.994 0.2081 0.447 0.8784 0.7116 ] Network output: [ 0.005727 -0.02762 0.9949 3.243e-05 -1.456e-05 1.021 2.444e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1024 0.09036 0.1813 0.2012 0.9873 0.9919 0.1025 0.7622 0.8678 0.3058 ] Network output: [ -0.005489 0.02687 1.003 3.406e-05 -1.529e-05 0.9811 2.567e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0907 0.08877 0.1651 0.1954 0.9854 0.9912 0.09072 0.6872 0.8444 0.2451 ] Network output: [ 0.0001638 1 -0.0002469 4.584e-06 -2.058e-06 1 3.455e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004293 Epoch 8014 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01087 0.9954 0.9902 3.614e-07 -1.622e-07 -0.007384 2.724e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003346 -0.003154 -0.008007 0.00625 0.9699 0.9742 0.006421 0.8347 0.8253 0.01817 ] Network output: [ 0.9998 0.0005847 0.0009053 -1.717e-05 7.708e-06 -0.001211 -1.294e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1956 -0.03345 -0.1768 0.1907 0.9835 0.9932 0.2188 0.4427 0.8718 0.7173 ] Network output: [ -0.01051 1.002 1.01 -3.342e-08 1.5e-08 0.009329 -2.519e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005994 0.0004833 0.004441 0.003693 0.9889 0.9919 0.006107 0.863 0.8958 0.01312 ] Network output: [ -0.0005849 0.002754 1.001 -5.415e-05 2.431e-05 0.9968 -4.081e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2075 0.09646 0.3378 0.1468 0.985 0.994 0.2081 0.447 0.8784 0.7116 ] Network output: [ 0.005725 -0.02761 0.9949 3.24e-05 -1.454e-05 1.021 2.442e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1024 0.09037 0.1813 0.2012 0.9873 0.9919 0.1025 0.7622 0.8678 0.3058 ] Network output: [ -0.005487 0.02686 1.003 3.403e-05 -1.528e-05 0.9811 2.564e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09071 0.08877 0.1651 0.1954 0.9854 0.9912 0.09072 0.6872 0.8444 0.2451 ] Network output: [ 0.0001637 1 -0.0002466 4.58e-06 -2.056e-06 1 3.451e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000429 Epoch 8015 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01087 0.9954 0.9902 3.599e-07 -1.616e-07 -0.007384 2.712e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003346 -0.003155 -0.008006 0.00625 0.9699 0.9742 0.006421 0.8347 0.8253 0.01817 ] Network output: [ 0.9998 0.0005842 0.0009047 -1.715e-05 7.701e-06 -0.00121 -1.293e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1956 -0.03345 -0.1768 0.1906 0.9835 0.9932 0.2188 0.4427 0.8718 0.7173 ] Network output: [ -0.01051 1.002 1.01 -3.431e-08 1.54e-08 0.009327 -2.586e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005995 0.0004834 0.004441 0.003692 0.9889 0.9919 0.006107 0.863 0.8958 0.01311 ] Network output: [ -0.0005845 0.002754 1.001 -5.41e-05 2.429e-05 0.9968 -4.077e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2075 0.09646 0.3378 0.1467 0.985 0.994 0.2081 0.447 0.8784 0.7116 ] Network output: [ 0.005723 -0.0276 0.9949 3.237e-05 -1.453e-05 1.021 2.439e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1025 0.09037 0.1813 0.2012 0.9873 0.9919 0.1025 0.7621 0.8677 0.3058 ] Network output: [ -0.005485 0.02685 1.003 3.4e-05 -1.526e-05 0.9811 2.562e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09071 0.08877 0.1651 0.1954 0.9854 0.9912 0.09072 0.6872 0.8444 0.2451 ] Network output: [ 0.0001636 1 -0.0002463 4.575e-06 -2.054e-06 1 3.448e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004288 Epoch 8016 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01087 0.9954 0.9902 3.583e-07 -1.609e-07 -0.007385 2.7e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003346 -0.003155 -0.008005 0.006249 0.9699 0.9742 0.006422 0.8347 0.8253 0.01817 ] Network output: [ 0.9998 0.0005837 0.0009041 -1.714e-05 7.694e-06 -0.001209 -1.292e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1956 -0.03345 -0.1767 0.1906 0.9835 0.9932 0.2188 0.4427 0.8718 0.7172 ] Network output: [ -0.01051 1.002 1.01 -3.519e-08 1.58e-08 0.009324 -2.652e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005995 0.0004834 0.004441 0.003692 0.9889 0.9919 0.006108 0.863 0.8958 0.01311 ] Network output: [ -0.0005841 0.002753 1.001 -5.404e-05 2.426e-05 0.9968 -4.073e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2075 0.09647 0.3378 0.1467 0.985 0.994 0.2081 0.447 0.8784 0.7116 ] Network output: [ 0.005722 -0.02759 0.9949 3.234e-05 -1.452e-05 1.021 2.437e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1025 0.09038 0.1813 0.2012 0.9873 0.9919 0.1025 0.7621 0.8677 0.3058 ] Network output: [ -0.005483 0.02684 1.003 3.396e-05 -1.525e-05 0.9811 2.56e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09071 0.08877 0.1651 0.1954 0.9854 0.9912 0.09072 0.6872 0.8444 0.2451 ] Network output: [ 0.0001635 1 -0.0002461 4.571e-06 -2.052e-06 1 3.445e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004285 Epoch 8017 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01086 0.9954 0.9902 3.568e-07 -1.602e-07 -0.007385 2.689e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003347 -0.003155 -0.008004 0.006248 0.9699 0.9742 0.006422 0.8346 0.8253 0.01817 ] Network output: [ 0.9998 0.0005832 0.0009036 -1.712e-05 7.686e-06 -0.001208 -1.29e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1957 -0.03345 -0.1767 0.1906 0.9835 0.9932 0.2188 0.4427 0.8718 0.7172 ] Network output: [ -0.01051 1.002 1.01 -3.608e-08 1.62e-08 0.009322 -2.719e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005996 0.0004835 0.004441 0.003691 0.9889 0.9919 0.006109 0.863 0.8958 0.01311 ] Network output: [ -0.0005838 0.002752 1.001 -5.399e-05 2.424e-05 0.9968 -4.069e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2075 0.09647 0.3378 0.1467 0.985 0.994 0.2082 0.447 0.8784 0.7116 ] Network output: [ 0.00572 -0.02758 0.9949 3.23e-05 -1.45e-05 1.021 2.435e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1025 0.09039 0.1813 0.2012 0.9873 0.9919 0.1025 0.7621 0.8677 0.3058 ] Network output: [ -0.005481 0.02683 1.003 3.393e-05 -1.523e-05 0.9811 2.557e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09071 0.08877 0.1651 0.1954 0.9854 0.9912 0.09072 0.6871 0.8444 0.2451 ] Network output: [ 0.0001634 1 -0.0002458 4.567e-06 -2.05e-06 1 3.442e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004283 Epoch 8018 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01086 0.9954 0.9903 3.553e-07 -1.595e-07 -0.007386 2.677e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003347 -0.003155 -0.008003 0.006248 0.9699 0.9742 0.006422 0.8346 0.8253 0.01816 ] Network output: [ 0.9998 0.0005827 0.000903 -1.71e-05 7.679e-06 -0.001207 -1.289e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1957 -0.03345 -0.1767 0.1906 0.9835 0.9932 0.2189 0.4426 0.8718 0.7172 ] Network output: [ -0.01051 1.002 1.01 -3.696e-08 1.659e-08 0.00932 -2.785e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005996 0.0004836 0.004441 0.003691 0.9889 0.9919 0.006109 0.863 0.8958 0.01311 ] Network output: [ -0.0005834 0.002751 1.001 -5.394e-05 2.421e-05 0.9968 -4.065e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2075 0.09648 0.3378 0.1467 0.985 0.994 0.2082 0.447 0.8784 0.7116 ] Network output: [ 0.005718 -0.02757 0.9949 3.227e-05 -1.449e-05 1.021 2.432e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1025 0.09039 0.1813 0.2012 0.9873 0.9919 0.1025 0.7621 0.8677 0.3058 ] Network output: [ -0.005479 0.02681 1.003 3.39e-05 -1.522e-05 0.9812 2.555e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09071 0.08878 0.1651 0.1954 0.9854 0.9912 0.09072 0.6871 0.8444 0.2451 ] Network output: [ 0.0001633 1 -0.0002455 4.562e-06 -2.048e-06 1 3.438e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000428 Epoch 8019 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01086 0.9954 0.9903 3.538e-07 -1.588e-07 -0.007387 2.666e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003347 -0.003155 -0.008002 0.006247 0.9699 0.9742 0.006423 0.8346 0.8253 0.01816 ] Network output: [ 0.9998 0.0005822 0.0009024 -1.709e-05 7.671e-06 -0.001206 -1.288e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1957 -0.03346 -0.1767 0.1906 0.9835 0.9932 0.2189 0.4426 0.8718 0.7172 ] Network output: [ -0.01051 1.002 1.01 -3.784e-08 1.699e-08 0.009318 -2.852e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005997 0.0004836 0.004441 0.00369 0.9889 0.9919 0.00611 0.863 0.8958 0.01311 ] Network output: [ -0.000583 0.00275 1.001 -5.388e-05 2.419e-05 0.9968 -4.061e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2075 0.09648 0.3378 0.1467 0.985 0.994 0.2082 0.4469 0.8784 0.7116 ] Network output: [ 0.005716 -0.02756 0.9949 3.224e-05 -1.447e-05 1.021 2.43e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1025 0.0904 0.1813 0.2012 0.9873 0.9919 0.1025 0.762 0.8677 0.3058 ] Network output: [ -0.005477 0.0268 1.003 3.387e-05 -1.52e-05 0.9812 2.552e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09071 0.08878 0.1651 0.1954 0.9854 0.9912 0.09073 0.6871 0.8444 0.2451 ] Network output: [ 0.0001632 1 -0.0002452 4.558e-06 -2.046e-06 1 3.435e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004277 Epoch 8020 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01086 0.9954 0.9903 3.522e-07 -1.581e-07 -0.007387 2.655e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003347 -0.003155 -0.008001 0.006246 0.9699 0.9742 0.006423 0.8346 0.8253 0.01816 ] Network output: [ 0.9998 0.0005817 0.0009018 -1.707e-05 7.664e-06 -0.001205 -1.287e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1957 -0.03346 -0.1767 0.1906 0.9835 0.9932 0.2189 0.4426 0.8718 0.7172 ] Network output: [ -0.01051 1.002 1.01 -3.872e-08 1.738e-08 0.009316 -2.918e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005998 0.0004837 0.004441 0.00369 0.9889 0.9919 0.00611 0.863 0.8958 0.01311 ] Network output: [ -0.0005827 0.002749 1.001 -5.383e-05 2.417e-05 0.9968 -4.057e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2075 0.09649 0.3379 0.1467 0.985 0.994 0.2082 0.4469 0.8784 0.7116 ] Network output: [ 0.005714 -0.02755 0.9949 3.221e-05 -1.446e-05 1.021 2.427e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1025 0.0904 0.1813 0.2012 0.9873 0.9919 0.1026 0.762 0.8677 0.3058 ] Network output: [ -0.005475 0.02679 1.003 3.384e-05 -1.519e-05 0.9812 2.55e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09071 0.08878 0.1651 0.1954 0.9854 0.9912 0.09073 0.6871 0.8444 0.2451 ] Network output: [ 0.0001632 1 -0.0002449 4.553e-06 -2.044e-06 1 3.432e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004275 Epoch 8021 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01086 0.9954 0.9903 3.507e-07 -1.575e-07 -0.007388 2.643e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003347 -0.003155 -0.008 0.006245 0.9699 0.9742 0.006423 0.8346 0.8253 0.01816 ] Network output: [ 0.9998 0.0005812 0.0009013 -1.705e-05 7.657e-06 -0.001204 -1.285e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1957 -0.03346 -0.1767 0.1906 0.9835 0.9932 0.2189 0.4426 0.8718 0.7172 ] Network output: [ -0.0105 1.002 1.01 -3.959e-08 1.777e-08 0.009314 -2.984e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005998 0.0004838 0.004441 0.00369 0.9889 0.9919 0.006111 0.863 0.8958 0.01311 ] Network output: [ -0.0005823 0.002748 1.001 -5.378e-05 2.414e-05 0.9968 -4.053e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2075 0.0965 0.3379 0.1467 0.985 0.994 0.2082 0.4469 0.8784 0.7116 ] Network output: [ 0.005712 -0.02754 0.9949 3.218e-05 -1.445e-05 1.021 2.425e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1025 0.09041 0.1813 0.2012 0.9873 0.9919 0.1026 0.762 0.8677 0.3058 ] Network output: [ -0.005473 0.02678 1.003 3.38e-05 -1.518e-05 0.9812 2.548e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09072 0.08878 0.1651 0.1954 0.9854 0.9912 0.09073 0.687 0.8444 0.2451 ] Network output: [ 0.0001631 1 -0.0002447 4.549e-06 -2.042e-06 1 3.428e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004272 Epoch 8022 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01086 0.9954 0.9903 3.492e-07 -1.568e-07 -0.007388 2.632e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003347 -0.003156 -0.007999 0.006245 0.9699 0.9742 0.006424 0.8346 0.8253 0.01816 ] Network output: [ 0.9998 0.0005807 0.0009007 -1.704e-05 7.649e-06 -0.001203 -1.284e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1957 -0.03346 -0.1767 0.1906 0.9835 0.9932 0.2189 0.4426 0.8718 0.7172 ] Network output: [ -0.0105 1.002 1.01 -4.047e-08 1.817e-08 0.009312 -3.05e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005999 0.0004839 0.004441 0.003689 0.9889 0.9919 0.006112 0.863 0.8958 0.01311 ] Network output: [ -0.000582 0.002747 1.001 -5.372e-05 2.412e-05 0.9968 -4.049e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2075 0.0965 0.3379 0.1467 0.985 0.994 0.2082 0.4469 0.8784 0.7115 ] Network output: [ 0.00571 -0.02753 0.9949 3.215e-05 -1.443e-05 1.021 2.423e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1025 0.09041 0.1813 0.2012 0.9873 0.9919 0.1026 0.762 0.8677 0.3058 ] Network output: [ -0.005471 0.02677 1.003 3.377e-05 -1.516e-05 0.9812 2.545e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09072 0.08878 0.1651 0.1954 0.9854 0.9912 0.09073 0.687 0.8443 0.2451 ] Network output: [ 0.000163 1 -0.0002444 4.545e-06 -2.04e-06 1 3.425e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000427 Epoch 8023 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01086 0.9954 0.9903 3.477e-07 -1.561e-07 -0.007389 2.62e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003347 -0.003156 -0.007997 0.006244 0.9699 0.9742 0.006424 0.8346 0.8253 0.01816 ] Network output: [ 0.9998 0.0005802 0.0009001 -1.702e-05 7.642e-06 -0.001202 -1.283e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1957 -0.03346 -0.1766 0.1906 0.9835 0.9932 0.2189 0.4426 0.8718 0.7172 ] Network output: [ -0.0105 1.002 1.01 -4.134e-08 1.856e-08 0.009309 -3.115e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.005999 0.0004839 0.004441 0.003689 0.9889 0.9919 0.006112 0.8629 0.8958 0.01311 ] Network output: [ -0.0005816 0.002746 1.001 -5.367e-05 2.409e-05 0.9968 -4.045e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2076 0.09651 0.3379 0.1467 0.985 0.994 0.2082 0.4469 0.8784 0.7115 ] Network output: [ 0.005708 -0.02752 0.9949 3.212e-05 -1.442e-05 1.021 2.42e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1025 0.09042 0.1813 0.2011 0.9873 0.9919 0.1026 0.762 0.8677 0.3058 ] Network output: [ -0.005469 0.02676 1.003 3.374e-05 -1.515e-05 0.9812 2.543e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09072 0.08878 0.1651 0.1954 0.9854 0.9912 0.09073 0.687 0.8443 0.2451 ] Network output: [ 0.0001629 1 -0.0002441 4.54e-06 -2.038e-06 1 3.422e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004267 Epoch 8024 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01085 0.9954 0.9903 3.462e-07 -1.554e-07 -0.007389 2.609e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003347 -0.003156 -0.007996 0.006243 0.9699 0.9742 0.006424 0.8346 0.8253 0.01816 ] Network output: [ 0.9998 0.0005797 0.0008996 -1.701e-05 7.634e-06 -0.001201 -1.282e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1957 -0.03347 -0.1766 0.1906 0.9835 0.9932 0.2189 0.4426 0.8718 0.7172 ] Network output: [ -0.0105 1.002 1.01 -4.221e-08 1.895e-08 0.009307 -3.181e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006 0.000484 0.004441 0.003688 0.9889 0.9919 0.006113 0.8629 0.8958 0.0131 ] Network output: [ -0.0005812 0.002745 1.001 -5.362e-05 2.407e-05 0.9968 -4.041e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2076 0.09651 0.3379 0.1467 0.985 0.994 0.2082 0.4469 0.8784 0.7115 ] Network output: [ 0.005707 -0.02751 0.9949 3.209e-05 -1.44e-05 1.021 2.418e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1025 0.09043 0.1814 0.2011 0.9873 0.9919 0.1026 0.7619 0.8677 0.3058 ] Network output: [ -0.005467 0.02675 1.003 3.371e-05 -1.513e-05 0.9812 2.54e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09072 0.08878 0.1651 0.1954 0.9854 0.9912 0.09073 0.687 0.8443 0.2451 ] Network output: [ 0.0001628 1 -0.0002438 4.536e-06 -2.036e-06 1 3.418e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004265 Epoch 8025 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01085 0.9954 0.9903 3.447e-07 -1.547e-07 -0.00739 2.598e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003348 -0.003156 -0.007995 0.006243 0.9699 0.9742 0.006425 0.8346 0.8253 0.01815 ] Network output: [ 0.9998 0.0005793 0.000899 -1.699e-05 7.627e-06 -0.0012 -1.28e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1957 -0.03347 -0.1766 0.1906 0.9835 0.9932 0.2189 0.4426 0.8718 0.7172 ] Network output: [ -0.0105 1.002 1.01 -4.308e-08 1.934e-08 0.009305 -3.246e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006001 0.0004841 0.004441 0.003688 0.9889 0.9919 0.006113 0.8629 0.8958 0.0131 ] Network output: [ -0.0005809 0.002744 1.001 -5.356e-05 2.405e-05 0.9968 -4.037e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2076 0.09652 0.3379 0.1467 0.985 0.994 0.2083 0.4469 0.8784 0.7115 ] Network output: [ 0.005705 -0.0275 0.9949 3.205e-05 -1.439e-05 1.021 2.416e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1025 0.09043 0.1814 0.2011 0.9873 0.9919 0.1026 0.7619 0.8677 0.3058 ] Network output: [ -0.005465 0.02673 1.003 3.368e-05 -1.512e-05 0.9812 2.538e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09072 0.08879 0.1651 0.1954 0.9854 0.9912 0.09073 0.6869 0.8443 0.2451 ] Network output: [ 0.0001627 1 -0.0002435 4.532e-06 -2.034e-06 1 3.415e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004262 Epoch 8026 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01085 0.9954 0.9903 3.432e-07 -1.541e-07 -0.00739 2.586e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003348 -0.003156 -0.007994 0.006242 0.9699 0.9742 0.006425 0.8346 0.8253 0.01815 ] Network output: [ 0.9998 0.0005788 0.0008984 -1.697e-05 7.62e-06 -0.001199 -1.279e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1957 -0.03347 -0.1766 0.1906 0.9835 0.9932 0.219 0.4426 0.8718 0.7172 ] Network output: [ -0.0105 1.002 1.01 -4.394e-08 1.973e-08 0.009303 -3.312e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006001 0.0004841 0.004441 0.003687 0.9889 0.9919 0.006114 0.8629 0.8958 0.0131 ] Network output: [ -0.0005805 0.002743 1.001 -5.351e-05 2.402e-05 0.9968 -4.033e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2076 0.09652 0.3379 0.1467 0.985 0.994 0.2083 0.4469 0.8784 0.7115 ] Network output: [ 0.005703 -0.02749 0.9949 3.202e-05 -1.438e-05 1.021 2.413e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1025 0.09044 0.1814 0.2011 0.9873 0.9919 0.1026 0.7619 0.8677 0.3058 ] Network output: [ -0.005463 0.02672 1.003 3.365e-05 -1.51e-05 0.9812 2.536e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09072 0.08879 0.1651 0.1954 0.9854 0.9912 0.09074 0.6869 0.8443 0.2451 ] Network output: [ 0.0001627 1 -0.0002433 4.527e-06 -2.032e-06 1 3.412e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000426 Epoch 8027 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01085 0.9954 0.9903 3.417e-07 -1.534e-07 -0.007391 2.575e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003348 -0.003156 -0.007993 0.006241 0.9699 0.9742 0.006425 0.8346 0.8253 0.01815 ] Network output: [ 0.9998 0.0005783 0.0008979 -1.696e-05 7.612e-06 -0.001198 -1.278e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1958 -0.03347 -0.1766 0.1906 0.9835 0.9932 0.219 0.4425 0.8718 0.7172 ] Network output: [ -0.0105 1.002 1.01 -4.481e-08 2.012e-08 0.009301 -3.377e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006002 0.0004842 0.004441 0.003687 0.9889 0.9919 0.006115 0.8629 0.8958 0.0131 ] Network output: [ -0.0005802 0.002742 1.001 -5.346e-05 2.4e-05 0.9968 -4.029e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2076 0.09653 0.3379 0.1467 0.985 0.994 0.2083 0.4468 0.8784 0.7115 ] Network output: [ 0.005701 -0.02748 0.9949 3.199e-05 -1.436e-05 1.021 2.411e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1025 0.09044 0.1814 0.2011 0.9873 0.9919 0.1026 0.7619 0.8677 0.3058 ] Network output: [ -0.005461 0.02671 1.003 3.361e-05 -1.509e-05 0.9812 2.533e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09072 0.08879 0.1651 0.1954 0.9854 0.9912 0.09074 0.6869 0.8443 0.2451 ] Network output: [ 0.0001626 1 -0.000243 4.523e-06 -2.03e-06 1 3.409e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004257 Epoch 8028 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01085 0.9954 0.9903 3.402e-07 -1.527e-07 -0.007392 2.564e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003348 -0.003157 -0.007992 0.006241 0.9699 0.9742 0.006426 0.8346 0.8253 0.01815 ] Network output: [ 0.9998 0.0005778 0.0008973 -1.694e-05 7.605e-06 -0.001197 -1.277e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1958 -0.03348 -0.1766 0.1906 0.9835 0.9932 0.219 0.4425 0.8718 0.7172 ] Network output: [ -0.0105 1.002 1.01 -4.567e-08 2.05e-08 0.009299 -3.442e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006002 0.0004843 0.004441 0.003686 0.9889 0.9919 0.006115 0.8629 0.8958 0.0131 ] Network output: [ -0.0005798 0.002741 1.001 -5.34e-05 2.398e-05 0.9968 -4.025e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2076 0.09653 0.3379 0.1467 0.985 0.994 0.2083 0.4468 0.8784 0.7115 ] Network output: [ 0.005699 -0.02747 0.9949 3.196e-05 -1.435e-05 1.021 2.409e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1025 0.09045 0.1814 0.2011 0.9873 0.9919 0.1026 0.7619 0.8677 0.3058 ] Network output: [ -0.005459 0.0267 1.003 3.358e-05 -1.508e-05 0.9812 2.531e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09073 0.08879 0.1651 0.1954 0.9854 0.9912 0.09074 0.6869 0.8443 0.2451 ] Network output: [ 0.0001625 1 -0.0002427 4.518e-06 -2.029e-06 1 3.405e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004254 Epoch 8029 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01085 0.9954 0.9903 3.387e-07 -1.521e-07 -0.007392 2.553e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003348 -0.003157 -0.007991 0.00624 0.9699 0.9742 0.006426 0.8346 0.8253 0.01815 ] Network output: [ 0.9998 0.0005773 0.0008967 -1.692e-05 7.597e-06 -0.001196 -1.275e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1958 -0.03348 -0.1765 0.1906 0.9835 0.9932 0.219 0.4425 0.8718 0.7172 ] Network output: [ -0.0105 1.002 1.01 -4.653e-08 2.089e-08 0.009297 -3.507e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006003 0.0004844 0.004441 0.003686 0.9889 0.9919 0.006116 0.8629 0.8958 0.0131 ] Network output: [ -0.0005794 0.00274 1.001 -5.335e-05 2.395e-05 0.9968 -4.021e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2076 0.09654 0.3379 0.1467 0.985 0.994 0.2083 0.4468 0.8784 0.7115 ] Network output: [ 0.005697 -0.02746 0.9949 3.193e-05 -1.433e-05 1.021 2.406e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1025 0.09046 0.1814 0.2011 0.9873 0.9919 0.1026 0.7618 0.8677 0.3058 ] Network output: [ -0.005457 0.02669 1.003 3.355e-05 -1.506e-05 0.9812 2.529e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09073 0.08879 0.1651 0.1954 0.9854 0.9912 0.09074 0.6869 0.8443 0.2451 ] Network output: [ 0.0001624 1 -0.0002424 4.514e-06 -2.027e-06 1 3.402e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004252 Epoch 8030 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01084 0.9954 0.9903 3.372e-07 -1.514e-07 -0.007393 2.541e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003348 -0.003157 -0.00799 0.006239 0.9699 0.9742 0.006426 0.8345 0.8253 0.01815 ] Network output: [ 0.9998 0.0005768 0.0008962 -1.691e-05 7.59e-06 -0.001195 -1.274e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1958 -0.03348 -0.1765 0.1905 0.9835 0.9932 0.219 0.4425 0.8718 0.7172 ] Network output: [ -0.01049 1.002 1.01 -4.739e-08 2.127e-08 0.009295 -3.571e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006004 0.0004844 0.004441 0.003686 0.9889 0.9919 0.006117 0.8629 0.8958 0.0131 ] Network output: [ -0.0005791 0.002739 1.001 -5.33e-05 2.393e-05 0.9968 -4.017e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2076 0.09655 0.338 0.1467 0.985 0.994 0.2083 0.4468 0.8784 0.7115 ] Network output: [ 0.005695 -0.02745 0.9949 3.19e-05 -1.432e-05 1.021 2.404e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1025 0.09046 0.1814 0.2011 0.9873 0.9919 0.1026 0.7618 0.8676 0.3058 ] Network output: [ -0.005455 0.02668 1.003 3.352e-05 -1.505e-05 0.9812 2.526e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09073 0.08879 0.1651 0.1954 0.9854 0.9912 0.09074 0.6868 0.8443 0.2451 ] Network output: [ 0.0001623 1 -0.0002422 4.51e-06 -2.025e-06 0.9999 3.399e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004249 Epoch 8031 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01084 0.9954 0.9903 3.357e-07 -1.507e-07 -0.007393 2.53e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003348 -0.003157 -0.007989 0.006239 0.9699 0.9742 0.006426 0.8345 0.8253 0.01815 ] Network output: [ 0.9998 0.0005763 0.0008956 -1.689e-05 7.583e-06 -0.001194 -1.273e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1958 -0.03348 -0.1765 0.1905 0.9835 0.9932 0.219 0.4425 0.8717 0.7172 ] Network output: [ -0.01049 1.002 1.01 -4.824e-08 2.166e-08 0.009293 -3.636e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006004 0.0004845 0.004441 0.003685 0.9889 0.9919 0.006117 0.8629 0.8958 0.0131 ] Network output: [ -0.0005787 0.002738 1.001 -5.325e-05 2.39e-05 0.9968 -4.013e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2076 0.09655 0.338 0.1467 0.985 0.994 0.2083 0.4468 0.8783 0.7115 ] Network output: [ 0.005693 -0.02744 0.9949 3.187e-05 -1.431e-05 1.021 2.402e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1026 0.09047 0.1814 0.2011 0.9873 0.9919 0.1026 0.7618 0.8676 0.3058 ] Network output: [ -0.005453 0.02667 1.003 3.349e-05 -1.503e-05 0.9812 2.524e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09073 0.08879 0.1651 0.1954 0.9854 0.9912 0.09074 0.6868 0.8443 0.2451 ] Network output: [ 0.0001622 1 -0.0002419 4.505e-06 -2.023e-06 0.9999 3.395e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004247 Epoch 8032 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01084 0.9954 0.9903 3.342e-07 -1.501e-07 -0.007394 2.519e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003349 -0.003157 -0.007988 0.006238 0.9699 0.9742 0.006427 0.8345 0.8253 0.01814 ] Network output: [ 0.9998 0.0005758 0.000895 -1.687e-05 7.575e-06 -0.001193 -1.272e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1958 -0.03348 -0.1765 0.1905 0.9835 0.9932 0.219 0.4425 0.8717 0.7172 ] Network output: [ -0.01049 1.002 1.01 -4.91e-08 2.204e-08 0.00929 -3.7e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006005 0.0004846 0.004441 0.003685 0.9889 0.9919 0.006118 0.8629 0.8958 0.01309 ] Network output: [ -0.0005784 0.002737 1.001 -5.319e-05 2.388e-05 0.9968 -4.009e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2077 0.09656 0.338 0.1467 0.985 0.994 0.2083 0.4468 0.8783 0.7115 ] Network output: [ 0.005692 -0.02743 0.9949 3.184e-05 -1.429e-05 1.021 2.399e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1026 0.09047 0.1814 0.2011 0.9873 0.9919 0.1026 0.7618 0.8676 0.3058 ] Network output: [ -0.005451 0.02665 1.003 3.346e-05 -1.502e-05 0.9812 2.521e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09073 0.0888 0.1651 0.1954 0.9854 0.9912 0.09074 0.6868 0.8443 0.2451 ] Network output: [ 0.0001622 1 -0.0002416 4.501e-06 -2.021e-06 0.9999 3.392e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004244 Epoch 8033 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01084 0.9954 0.9903 3.328e-07 -1.494e-07 -0.007394 2.508e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003349 -0.003157 -0.007987 0.006237 0.9699 0.9742 0.006427 0.8345 0.8253 0.01814 ] Network output: [ 0.9998 0.0005753 0.0008945 -1.686e-05 7.568e-06 -0.001192 -1.27e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1958 -0.03349 -0.1765 0.1905 0.9835 0.9932 0.219 0.4425 0.8717 0.7172 ] Network output: [ -0.01049 1.002 1.01 -4.995e-08 2.242e-08 0.009288 -3.764e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006005 0.0004847 0.004441 0.003684 0.9889 0.9919 0.006118 0.8629 0.8958 0.01309 ] Network output: [ -0.000578 0.002736 1.001 -5.314e-05 2.386e-05 0.9968 -4.005e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2077 0.09656 0.338 0.1466 0.985 0.994 0.2083 0.4468 0.8783 0.7115 ] Network output: [ 0.00569 -0.02742 0.9949 3.181e-05 -1.428e-05 1.021 2.397e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1026 0.09048 0.1814 0.2011 0.9873 0.9919 0.1026 0.7617 0.8676 0.3058 ] Network output: [ -0.005449 0.02664 1.003 3.342e-05 -1.501e-05 0.9812 2.519e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09073 0.0888 0.1651 0.1954 0.9854 0.9912 0.09075 0.6868 0.8443 0.2451 ] Network output: [ 0.0001621 1 -0.0002413 4.497e-06 -2.019e-06 0.9999 3.389e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004242 Epoch 8034 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01084 0.9954 0.9903 3.313e-07 -1.487e-07 -0.007395 2.497e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003349 -0.003157 -0.007986 0.006237 0.9699 0.9742 0.006427 0.8345 0.8253 0.01814 ] Network output: [ 0.9998 0.0005748 0.0008939 -1.684e-05 7.561e-06 -0.001191 -1.269e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1958 -0.03349 -0.1765 0.1905 0.9835 0.9932 0.219 0.4425 0.8717 0.7171 ] Network output: [ -0.01049 1.002 1.01 -5.08e-08 2.281e-08 0.009286 -3.829e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006006 0.0004847 0.004442 0.003684 0.9889 0.9919 0.006119 0.8629 0.8958 0.01309 ] Network output: [ -0.0005776 0.002735 1.001 -5.309e-05 2.383e-05 0.9968 -4.001e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2077 0.09657 0.338 0.1466 0.985 0.994 0.2084 0.4468 0.8783 0.7115 ] Network output: [ 0.005688 -0.02741 0.9949 3.178e-05 -1.427e-05 1.021 2.395e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1026 0.09048 0.1814 0.2011 0.9873 0.9919 0.1026 0.7617 0.8676 0.3058 ] Network output: [ -0.005447 0.02663 1.003 3.339e-05 -1.499e-05 0.9812 2.517e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09074 0.0888 0.1651 0.1954 0.9854 0.9912 0.09075 0.6867 0.8443 0.2451 ] Network output: [ 0.000162 1 -0.0002411 4.492e-06 -2.017e-06 0.9999 3.386e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004239 Epoch 8035 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01084 0.9954 0.9903 3.298e-07 -1.481e-07 -0.007395 2.486e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003349 -0.003158 -0.007985 0.006236 0.9699 0.9742 0.006428 0.8345 0.8252 0.01814 ] Network output: [ 0.9998 0.0005744 0.0008933 -1.683e-05 7.553e-06 -0.00119 -1.268e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1958 -0.03349 -0.1764 0.1905 0.9835 0.9932 0.2191 0.4424 0.8717 0.7171 ] Network output: [ -0.01049 1.002 1.01 -5.165e-08 2.319e-08 0.009284 -3.893e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006007 0.0004848 0.004442 0.003683 0.9889 0.9919 0.00612 0.8628 0.8958 0.01309 ] Network output: [ -0.0005773 0.002734 1.001 -5.304e-05 2.381e-05 0.9968 -3.997e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2077 0.09657 0.338 0.1466 0.985 0.994 0.2084 0.4467 0.8783 0.7115 ] Network output: [ 0.005686 -0.0274 0.9949 3.174e-05 -1.425e-05 1.021 2.392e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1026 0.09049 0.1814 0.2011 0.9873 0.9919 0.1026 0.7617 0.8676 0.3058 ] Network output: [ -0.005445 0.02662 1.003 3.336e-05 -1.498e-05 0.9812 2.514e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09074 0.0888 0.1651 0.1954 0.9854 0.9912 0.09075 0.6867 0.8442 0.2451 ] Network output: [ 0.0001619 1 -0.0002408 4.488e-06 -2.015e-06 0.9999 3.382e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004237 Epoch 8036 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01083 0.9954 0.9903 3.283e-07 -1.474e-07 -0.007396 2.474e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003349 -0.003158 -0.007984 0.006235 0.9699 0.9742 0.006428 0.8345 0.8252 0.01814 ] Network output: [ 0.9998 0.0005739 0.0008928 -1.681e-05 7.546e-06 -0.001189 -1.267e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1958 -0.03349 -0.1764 0.1905 0.9835 0.9932 0.2191 0.4424 0.8717 0.7171 ] Network output: [ -0.01049 1.002 1.01 -5.25e-08 2.357e-08 0.009282 -3.956e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006007 0.0004849 0.004442 0.003683 0.9889 0.9919 0.00612 0.8628 0.8958 0.01309 ] Network output: [ -0.0005769 0.002733 1.001 -5.298e-05 2.379e-05 0.9968 -3.993e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2077 0.09658 0.338 0.1466 0.985 0.994 0.2084 0.4467 0.8783 0.7115 ] Network output: [ 0.005684 -0.02739 0.9949 3.171e-05 -1.424e-05 1.021 2.39e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1026 0.0905 0.1814 0.2011 0.9873 0.9919 0.1027 0.7617 0.8676 0.3058 ] Network output: [ -0.005443 0.02661 1.003 3.333e-05 -1.496e-05 0.9813 2.512e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09074 0.0888 0.1651 0.1954 0.9854 0.9912 0.09075 0.6867 0.8442 0.2451 ] Network output: [ 0.0001618 1 -0.0002405 4.484e-06 -2.013e-06 0.9999 3.379e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004234 Epoch 8037 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01083 0.9954 0.9903 3.269e-07 -1.467e-07 -0.007396 2.463e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003349 -0.003158 -0.007983 0.006234 0.9699 0.9742 0.006428 0.8345 0.8252 0.01814 ] Network output: [ 0.9998 0.0005734 0.0008922 -1.679e-05 7.539e-06 -0.001188 -1.266e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1959 -0.03349 -0.1764 0.1905 0.9835 0.9932 0.2191 0.4424 0.8717 0.7171 ] Network output: [ -0.01049 1.002 1.01 -5.334e-08 2.395e-08 0.00928 -4.02e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006008 0.0004849 0.004442 0.003683 0.9889 0.9919 0.006121 0.8628 0.8958 0.01309 ] Network output: [ -0.0005766 0.002732 1.001 -5.293e-05 2.376e-05 0.9968 -3.989e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2077 0.09659 0.338 0.1466 0.985 0.994 0.2084 0.4467 0.8783 0.7115 ] Network output: [ 0.005682 -0.02738 0.9949 3.168e-05 -1.422e-05 1.021 2.388e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1026 0.0905 0.1814 0.2011 0.9873 0.9919 0.1027 0.7617 0.8676 0.3058 ] Network output: [ -0.005441 0.0266 1.003 3.33e-05 -1.495e-05 0.9813 2.51e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09074 0.0888 0.1651 0.1954 0.9854 0.9912 0.09075 0.6867 0.8442 0.2451 ] Network output: [ 0.0001617 1 -0.0002402 4.479e-06 -2.011e-06 0.9999 3.376e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004232 Epoch 8038 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01083 0.9954 0.9903 3.254e-07 -1.461e-07 -0.007397 2.452e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003349 -0.003158 -0.007982 0.006234 0.9699 0.9742 0.006429 0.8345 0.8252 0.01814 ] Network output: [ 0.9998 0.0005729 0.0008916 -1.678e-05 7.531e-06 -0.001187 -1.264e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1959 -0.0335 -0.1764 0.1905 0.9835 0.9932 0.2191 0.4424 0.8717 0.7171 ] Network output: [ -0.01049 1.002 1.01 -5.419e-08 2.433e-08 0.009278 -4.084e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006008 0.000485 0.004442 0.003682 0.9889 0.9919 0.006121 0.8628 0.8958 0.01309 ] Network output: [ -0.0005762 0.002731 1.001 -5.288e-05 2.374e-05 0.9968 -3.985e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2077 0.09659 0.338 0.1466 0.985 0.994 0.2084 0.4467 0.8783 0.7115 ] Network output: [ 0.00568 -0.02737 0.9949 3.165e-05 -1.421e-05 1.021 2.385e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1026 0.09051 0.1814 0.2011 0.9873 0.9919 0.1027 0.7616 0.8676 0.3058 ] Network output: [ -0.005439 0.02659 1.003 3.327e-05 -1.493e-05 0.9813 2.507e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09074 0.08881 0.1651 0.1954 0.9854 0.9912 0.09075 0.6866 0.8442 0.2451 ] Network output: [ 0.0001617 1 -0.00024 4.475e-06 -2.009e-06 0.9999 3.373e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004229 Epoch 8039 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01083 0.9954 0.9903 3.239e-07 -1.454e-07 -0.007398 2.441e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003349 -0.003158 -0.007981 0.006233 0.9699 0.9742 0.006429 0.8345 0.8252 0.01813 ] Network output: [ 0.9998 0.0005724 0.0008911 -1.676e-05 7.524e-06 -0.001186 -1.263e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1959 -0.0335 -0.1764 0.1905 0.9835 0.9932 0.2191 0.4424 0.8717 0.7171 ] Network output: [ -0.01048 1.002 1.01 -5.503e-08 2.47e-08 0.009276 -4.147e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006009 0.0004851 0.004442 0.003682 0.9889 0.9919 0.006122 0.8628 0.8958 0.01309 ] Network output: [ -0.0005758 0.00273 1.001 -5.282e-05 2.371e-05 0.9968 -3.981e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2077 0.0966 0.338 0.1466 0.985 0.994 0.2084 0.4467 0.8783 0.7114 ] Network output: [ 0.005678 -0.02736 0.9949 3.162e-05 -1.42e-05 1.021 2.383e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1026 0.09051 0.1814 0.2011 0.9873 0.9919 0.1027 0.7616 0.8676 0.3058 ] Network output: [ -0.005437 0.02658 1.003 3.324e-05 -1.492e-05 0.9813 2.505e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09074 0.08881 0.1651 0.1954 0.9854 0.9912 0.09076 0.6866 0.8442 0.2451 ] Network output: [ 0.0001616 1 -0.0002397 4.471e-06 -2.007e-06 0.9999 3.369e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004226 Epoch 8040 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01083 0.9954 0.9903 3.225e-07 -1.448e-07 -0.007398 2.43e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00335 -0.003158 -0.00798 0.006232 0.9699 0.9742 0.006429 0.8345 0.8252 0.01813 ] Network output: [ 0.9998 0.0005719 0.0008905 -1.674e-05 7.517e-06 -0.001185 -1.262e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1959 -0.0335 -0.1764 0.1905 0.9835 0.9932 0.2191 0.4424 0.8717 0.7171 ] Network output: [ -0.01048 1.002 1.01 -5.587e-08 2.508e-08 0.009274 -4.21e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00601 0.0004852 0.004442 0.003681 0.9889 0.9919 0.006123 0.8628 0.8958 0.01309 ] Network output: [ -0.0005755 0.002729 1.001 -5.277e-05 2.369e-05 0.9968 -3.977e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2077 0.0966 0.3381 0.1466 0.985 0.994 0.2084 0.4467 0.8783 0.7114 ] Network output: [ 0.005677 -0.02735 0.9949 3.159e-05 -1.418e-05 1.021 2.381e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1026 0.09052 0.1814 0.2011 0.9873 0.9919 0.1027 0.7616 0.8676 0.3058 ] Network output: [ -0.005435 0.02656 1.003 3.32e-05 -1.491e-05 0.9813 2.502e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09074 0.08881 0.1651 0.1954 0.9854 0.9912 0.09076 0.6866 0.8442 0.2451 ] Network output: [ 0.0001615 1 -0.0002394 4.466e-06 -2.005e-06 0.9999 3.366e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004224 Epoch 8041 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01083 0.9954 0.9903 3.21e-07 -1.441e-07 -0.007399 2.419e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00335 -0.003159 -0.007979 0.006232 0.9699 0.9742 0.00643 0.8345 0.8252 0.01813 ] Network output: [ 0.9998 0.0005714 0.00089 -1.673e-05 7.51e-06 -0.001184 -1.261e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1959 -0.0335 -0.1764 0.1905 0.9835 0.9932 0.2191 0.4424 0.8717 0.7171 ] Network output: [ -0.01048 1.002 1.01 -5.67e-08 2.546e-08 0.009272 -4.273e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00601 0.0004852 0.004442 0.003681 0.9889 0.9919 0.006123 0.8628 0.8957 0.01308 ] Network output: [ -0.0005751 0.002728 1.001 -5.272e-05 2.367e-05 0.9968 -3.973e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2078 0.09661 0.3381 0.1466 0.985 0.994 0.2084 0.4467 0.8783 0.7114 ] Network output: [ 0.005675 -0.02734 0.9949 3.156e-05 -1.417e-05 1.021 2.378e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1026 0.09052 0.1814 0.2011 0.9873 0.9919 0.1027 0.7616 0.8676 0.3058 ] Network output: [ -0.005433 0.02655 1.003 3.317e-05 -1.489e-05 0.9813 2.5e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09075 0.08881 0.1651 0.1954 0.9854 0.9912 0.09076 0.6866 0.8442 0.2451 ] Network output: [ 0.0001614 1 -0.0002391 4.462e-06 -2.003e-06 0.9999 3.363e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004221 Epoch 8042 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01083 0.9954 0.9903 3.196e-07 -1.435e-07 -0.007399 2.408e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00335 -0.003159 -0.007977 0.006231 0.9699 0.9742 0.00643 0.8345 0.8252 0.01813 ] Network output: [ 0.9998 0.000571 0.0008894 -1.671e-05 7.502e-06 -0.001183 -1.259e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1959 -0.03351 -0.1763 0.1905 0.9835 0.9932 0.2191 0.4424 0.8717 0.7171 ] Network output: [ -0.01048 1.002 1.01 -5.754e-08 2.583e-08 0.009269 -4.336e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006011 0.0004853 0.004442 0.00368 0.9889 0.9919 0.006124 0.8628 0.8957 0.01308 ] Network output: [ -0.0005748 0.002727 1.001 -5.267e-05 2.364e-05 0.9968 -3.969e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2078 0.09661 0.3381 0.1466 0.985 0.994 0.2084 0.4467 0.8783 0.7114 ] Network output: [ 0.005673 -0.02733 0.9949 3.153e-05 -1.415e-05 1.021 2.376e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1026 0.09053 0.1814 0.2011 0.9873 0.9919 0.1027 0.7616 0.8676 0.3058 ] Network output: [ -0.005431 0.02654 1.003 3.314e-05 -1.488e-05 0.9813 2.498e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09075 0.08881 0.1651 0.1954 0.9854 0.9912 0.09076 0.6865 0.8442 0.2451 ] Network output: [ 0.0001613 1 -0.0002389 4.458e-06 -2.001e-06 0.9999 3.36e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004219 Epoch 8043 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01082 0.9955 0.9903 3.181e-07 -1.428e-07 -0.0074 2.397e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00335 -0.003159 -0.007976 0.00623 0.9699 0.9742 0.00643 0.8344 0.8252 0.01813 ] Network output: [ 0.9998 0.0005705 0.0008888 -1.669e-05 7.495e-06 -0.001182 -1.258e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1959 -0.03351 -0.1763 0.1905 0.9835 0.9932 0.2191 0.4423 0.8717 0.7171 ] Network output: [ -0.01048 1.002 1.01 -5.837e-08 2.621e-08 0.009267 -4.399e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006011 0.0004854 0.004442 0.00368 0.9889 0.9919 0.006124 0.8628 0.8957 0.01308 ] Network output: [ -0.0005744 0.002727 1.001 -5.262e-05 2.362e-05 0.9968 -3.965e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2078 0.09662 0.3381 0.1466 0.985 0.994 0.2085 0.4466 0.8783 0.7114 ] Network output: [ 0.005671 -0.02732 0.9949 3.15e-05 -1.414e-05 1.021 2.374e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1026 0.09054 0.1814 0.2011 0.9873 0.9919 0.1027 0.7615 0.8676 0.3058 ] Network output: [ -0.005429 0.02653 1.003 3.311e-05 -1.486e-05 0.9813 2.495e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09075 0.08881 0.1651 0.1954 0.9854 0.9912 0.09076 0.6865 0.8442 0.2451 ] Network output: [ 0.0001613 1 -0.0002386 4.454e-06 -1.999e-06 0.9999 3.356e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004216 Epoch 8044 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01082 0.9955 0.9903 3.167e-07 -1.422e-07 -0.0074 2.386e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00335 -0.003159 -0.007975 0.00623 0.9699 0.9742 0.006431 0.8344 0.8252 0.01813 ] Network output: [ 0.9998 0.00057 0.0008883 -1.668e-05 7.488e-06 -0.001181 -1.257e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1959 -0.03351 -0.1763 0.1905 0.9835 0.9932 0.2192 0.4423 0.8717 0.7171 ] Network output: [ -0.01048 1.002 1.01 -5.92e-08 2.658e-08 0.009265 -4.462e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006012 0.0004855 0.004442 0.003679 0.9889 0.9919 0.006125 0.8628 0.8957 0.01308 ] Network output: [ -0.000574 0.002726 1.001 -5.256e-05 2.36e-05 0.9968 -3.961e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2078 0.09662 0.3381 0.1466 0.985 0.994 0.2085 0.4466 0.8783 0.7114 ] Network output: [ 0.005669 -0.02731 0.9949 3.147e-05 -1.413e-05 1.021 2.371e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1026 0.09054 0.1814 0.2011 0.9873 0.9919 0.1027 0.7615 0.8676 0.3058 ] Network output: [ -0.005427 0.02652 1.003 3.308e-05 -1.485e-05 0.9813 2.493e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09075 0.08881 0.1651 0.1954 0.9854 0.9912 0.09076 0.6865 0.8442 0.2451 ] Network output: [ 0.0001612 1 -0.0002383 4.449e-06 -1.997e-06 0.9999 3.353e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004214 Epoch 8045 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01082 0.9955 0.9903 3.152e-07 -1.415e-07 -0.007401 2.376e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00335 -0.003159 -0.007974 0.006229 0.9699 0.9742 0.006431 0.8344 0.8252 0.01813 ] Network output: [ 0.9998 0.0005695 0.0008877 -1.666e-05 7.48e-06 -0.00118 -1.256e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1959 -0.03351 -0.1763 0.1905 0.9835 0.9932 0.2192 0.4423 0.8717 0.7171 ] Network output: [ -0.01048 1.002 1.01 -6.003e-08 2.695e-08 0.009263 -4.524e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006013 0.0004855 0.004442 0.003679 0.9889 0.9919 0.006126 0.8628 0.8957 0.01308 ] Network output: [ -0.0005737 0.002725 1.001 -5.251e-05 2.357e-05 0.9968 -3.957e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2078 0.09663 0.3381 0.1466 0.985 0.994 0.2085 0.4466 0.8783 0.7114 ] Network output: [ 0.005667 -0.0273 0.9949 3.144e-05 -1.411e-05 1.021 2.369e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1026 0.09055 0.1814 0.2011 0.9873 0.9919 0.1027 0.7615 0.8676 0.3058 ] Network output: [ -0.005425 0.02651 1.003 3.305e-05 -1.484e-05 0.9813 2.491e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09075 0.08882 0.1651 0.1954 0.9854 0.9912 0.09076 0.6865 0.8442 0.2451 ] Network output: [ 0.0001611 1 -0.0002381 4.445e-06 -1.996e-06 0.9999 3.35e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004211 Epoch 8046 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01082 0.9955 0.9903 3.138e-07 -1.409e-07 -0.007401 2.365e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00335 -0.003159 -0.007973 0.006228 0.9699 0.9742 0.006431 0.8344 0.8252 0.01812 ] Network output: [ 0.9998 0.000569 0.0008871 -1.665e-05 7.473e-06 -0.001179 -1.255e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1959 -0.03351 -0.1763 0.1904 0.9835 0.9932 0.2192 0.4423 0.8717 0.7171 ] Network output: [ -0.01048 1.002 1.01 -6.086e-08 2.732e-08 0.009261 -4.587e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006013 0.0004856 0.004442 0.003679 0.9889 0.9919 0.006126 0.8627 0.8957 0.01308 ] Network output: [ -0.0005733 0.002724 1.001 -5.246e-05 2.355e-05 0.9968 -3.953e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2078 0.09664 0.3381 0.1466 0.985 0.994 0.2085 0.4466 0.8783 0.7114 ] Network output: [ 0.005665 -0.02729 0.9949 3.141e-05 -1.41e-05 1.021 2.367e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1026 0.09055 0.1814 0.2011 0.9873 0.9919 0.1027 0.7615 0.8676 0.3058 ] Network output: [ -0.005423 0.0265 1.003 3.302e-05 -1.482e-05 0.9813 2.488e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09075 0.08882 0.1651 0.1954 0.9854 0.9912 0.09077 0.6865 0.8442 0.2452 ] Network output: [ 0.000161 1 -0.0002378 4.441e-06 -1.994e-06 0.9999 3.347e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004209 Epoch 8047 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01082 0.9955 0.9903 3.123e-07 -1.402e-07 -0.007402 2.354e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003351 -0.003159 -0.007972 0.006228 0.9699 0.9742 0.006432 0.8344 0.8252 0.01812 ] Network output: [ 0.9998 0.0005686 0.0008866 -1.663e-05 7.466e-06 -0.001178 -1.253e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.196 -0.03352 -0.1763 0.1904 0.9835 0.9932 0.2192 0.4423 0.8717 0.7171 ] Network output: [ -0.01048 1.002 1.01 -6.169e-08 2.769e-08 0.009259 -4.649e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006014 0.0004857 0.004442 0.003678 0.9889 0.9919 0.006127 0.8627 0.8957 0.01308 ] Network output: [ -0.000573 0.002723 1.001 -5.241e-05 2.353e-05 0.9968 -3.949e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2078 0.09664 0.3381 0.1466 0.985 0.994 0.2085 0.4466 0.8783 0.7114 ] Network output: [ 0.005664 -0.02728 0.9949 3.138e-05 -1.409e-05 1.021 2.365e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1027 0.09056 0.1814 0.2011 0.9873 0.9919 0.1027 0.7614 0.8675 0.3058 ] Network output: [ -0.005421 0.02648 1.003 3.299e-05 -1.481e-05 0.9813 2.486e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09076 0.08882 0.1651 0.1954 0.9854 0.9912 0.09077 0.6864 0.8442 0.2452 ] Network output: [ 0.0001609 1 -0.0002375 4.436e-06 -1.992e-06 0.9999 3.343e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004206 Epoch 8048 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01082 0.9955 0.9903 3.109e-07 -1.396e-07 -0.007402 2.343e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003351 -0.00316 -0.007971 0.006227 0.9699 0.9742 0.006432 0.8344 0.8252 0.01812 ] Network output: [ 0.9998 0.0005681 0.000886 -1.661e-05 7.459e-06 -0.001177 -1.252e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.196 -0.03352 -0.1762 0.1904 0.9835 0.9932 0.2192 0.4423 0.8717 0.7171 ] Network output: [ -0.01047 1.002 1.01 -6.251e-08 2.806e-08 0.009257 -4.711e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006014 0.0004858 0.004442 0.003678 0.9889 0.9919 0.006128 0.8627 0.8957 0.01308 ] Network output: [ -0.0005726 0.002722 1.001 -5.235e-05 2.35e-05 0.9968 -3.946e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2078 0.09665 0.3381 0.1466 0.985 0.994 0.2085 0.4466 0.8783 0.7114 ] Network output: [ 0.005662 -0.02727 0.9949 3.134e-05 -1.407e-05 1.021 2.362e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1027 0.09056 0.1814 0.201 0.9873 0.9919 0.1027 0.7614 0.8675 0.3058 ] Network output: [ -0.005419 0.02647 1.003 3.295e-05 -1.479e-05 0.9813 2.484e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09076 0.08882 0.1651 0.1954 0.9854 0.9912 0.09077 0.6864 0.8441 0.2452 ] Network output: [ 0.0001608 1 -0.0002372 4.432e-06 -1.99e-06 0.9999 3.34e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004204 Epoch 8049 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01081 0.9955 0.9903 3.095e-07 -1.389e-07 -0.007403 2.332e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003351 -0.00316 -0.00797 0.006226 0.9699 0.9742 0.006432 0.8344 0.8252 0.01812 ] Network output: [ 0.9998 0.0005676 0.0008855 -1.66e-05 7.451e-06 -0.001176 -1.251e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.196 -0.03352 -0.1762 0.1904 0.9835 0.9932 0.2192 0.4423 0.8717 0.7171 ] Network output: [ -0.01047 1.002 1.01 -6.333e-08 2.843e-08 0.009255 -4.773e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006015 0.0004858 0.004442 0.003677 0.9889 0.9919 0.006128 0.8627 0.8957 0.01308 ] Network output: [ -0.0005723 0.002721 1.001 -5.23e-05 2.348e-05 0.9968 -3.942e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2078 0.09665 0.3382 0.1466 0.985 0.994 0.2085 0.4466 0.8783 0.7114 ] Network output: [ 0.00566 -0.02726 0.9948 3.131e-05 -1.406e-05 1.021 2.36e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1027 0.09057 0.1814 0.201 0.9873 0.9919 0.1027 0.7614 0.8675 0.3058 ] Network output: [ -0.005417 0.02646 1.003 3.292e-05 -1.478e-05 0.9813 2.481e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09076 0.08882 0.1651 0.1954 0.9854 0.9912 0.09077 0.6864 0.8441 0.2452 ] Network output: [ 0.0001608 1 -0.000237 4.428e-06 -1.988e-06 0.9999 3.337e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004201 Epoch 8050 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01081 0.9955 0.9903 3.08e-07 -1.383e-07 -0.007403 2.321e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003351 -0.00316 -0.007969 0.006226 0.9699 0.9742 0.006432 0.8344 0.8252 0.01812 ] Network output: [ 0.9998 0.0005671 0.0008849 -1.658e-05 7.444e-06 -0.001175 -1.25e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.196 -0.03352 -0.1762 0.1904 0.9835 0.9932 0.2192 0.4423 0.8717 0.7171 ] Network output: [ -0.01047 1.002 1.01 -6.415e-08 2.88e-08 0.009253 -4.835e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006016 0.0004859 0.004442 0.003677 0.9889 0.9919 0.006129 0.8627 0.8957 0.01307 ] Network output: [ -0.0005719 0.00272 1.001 -5.225e-05 2.346e-05 0.9968 -3.938e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2079 0.09666 0.3382 0.1466 0.985 0.994 0.2085 0.4466 0.8783 0.7114 ] Network output: [ 0.005658 -0.02725 0.9948 3.128e-05 -1.404e-05 1.021 2.358e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1027 0.09058 0.1814 0.201 0.9873 0.9919 0.1027 0.7614 0.8675 0.3058 ] Network output: [ -0.005415 0.02645 1.003 3.289e-05 -1.477e-05 0.9813 2.479e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09076 0.08882 0.1651 0.1954 0.9854 0.9912 0.09077 0.6864 0.8441 0.2452 ] Network output: [ 0.0001607 1 -0.0002367 4.424e-06 -1.986e-06 0.9999 3.334e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004199 Epoch 8051 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01081 0.9955 0.9903 3.066e-07 -1.376e-07 -0.007404 2.311e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003351 -0.00316 -0.007968 0.006225 0.9699 0.9742 0.006433 0.8344 0.8252 0.01812 ] Network output: [ 0.9998 0.0005666 0.0008844 -1.657e-05 7.437e-06 -0.001174 -1.248e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.196 -0.03352 -0.1762 0.1904 0.9835 0.9932 0.2192 0.4422 0.8717 0.7171 ] Network output: [ -0.01047 1.002 1.01 -6.497e-08 2.917e-08 0.009251 -4.897e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006016 0.000486 0.004442 0.003676 0.9889 0.9919 0.006129 0.8627 0.8957 0.01307 ] Network output: [ -0.0005716 0.002719 1.001 -5.22e-05 2.343e-05 0.9968 -3.934e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2079 0.09666 0.3382 0.1465 0.985 0.994 0.2085 0.4465 0.8783 0.7114 ] Network output: [ 0.005656 -0.02724 0.9948 3.125e-05 -1.403e-05 1.021 2.355e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1027 0.09058 0.1814 0.201 0.9873 0.9919 0.1027 0.7614 0.8675 0.3058 ] Network output: [ -0.005413 0.02644 1.003 3.286e-05 -1.475e-05 0.9813 2.477e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09076 0.08883 0.1651 0.1954 0.9854 0.9912 0.09077 0.6863 0.8441 0.2452 ] Network output: [ 0.0001606 1 -0.0002364 4.419e-06 -1.984e-06 0.9999 3.33e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004196 Epoch 8052 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01081 0.9955 0.9903 3.052e-07 -1.37e-07 -0.007404 2.3e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003351 -0.00316 -0.007967 0.006224 0.9699 0.9742 0.006433 0.8344 0.8252 0.01812 ] Network output: [ 0.9998 0.0005662 0.0008838 -1.655e-05 7.43e-06 -0.001173 -1.247e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.196 -0.03353 -0.1762 0.1904 0.9835 0.9932 0.2193 0.4422 0.8717 0.717 ] Network output: [ -0.01047 1.002 1.01 -6.579e-08 2.954e-08 0.009249 -4.958e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006017 0.0004861 0.004442 0.003676 0.9889 0.9919 0.00613 0.8627 0.8957 0.01307 ] Network output: [ -0.0005712 0.002718 1.001 -5.215e-05 2.341e-05 0.9968 -3.93e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2079 0.09667 0.3382 0.1465 0.985 0.994 0.2086 0.4465 0.8783 0.7114 ] Network output: [ 0.005654 -0.02723 0.9948 3.122e-05 -1.402e-05 1.021 2.353e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1027 0.09059 0.1814 0.201 0.9873 0.9919 0.1028 0.7613 0.8675 0.3058 ] Network output: [ -0.005411 0.02643 1.003 3.283e-05 -1.474e-05 0.9813 2.474e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09076 0.08883 0.1651 0.1954 0.9854 0.9912 0.09078 0.6863 0.8441 0.2452 ] Network output: [ 0.0001605 1 -0.0002362 4.415e-06 -1.982e-06 0.9999 3.327e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004194 Epoch 8053 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01081 0.9955 0.9903 3.038e-07 -1.364e-07 -0.007405 2.289e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003351 -0.00316 -0.007966 0.006224 0.9699 0.9742 0.006433 0.8344 0.8252 0.01811 ] Network output: [ 0.9998 0.0005657 0.0008832 -1.653e-05 7.422e-06 -0.001172 -1.246e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.196 -0.03353 -0.1762 0.1904 0.9835 0.9932 0.2193 0.4422 0.8717 0.717 ] Network output: [ -0.01047 1.002 1.01 -6.66e-08 2.99e-08 0.009247 -5.019e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006017 0.0004861 0.004442 0.003676 0.9889 0.9919 0.006131 0.8627 0.8957 0.01307 ] Network output: [ -0.0005708 0.002717 1.001 -5.209e-05 2.339e-05 0.9969 -3.926e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2079 0.09668 0.3382 0.1465 0.985 0.994 0.2086 0.4465 0.8783 0.7114 ] Network output: [ 0.005652 -0.02722 0.9948 3.119e-05 -1.4e-05 1.021 2.351e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1027 0.09059 0.1814 0.201 0.9873 0.9919 0.1028 0.7613 0.8675 0.3058 ] Network output: [ -0.005409 0.02642 1.003 3.28e-05 -1.472e-05 0.9813 2.472e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09076 0.08883 0.1651 0.1954 0.9854 0.9912 0.09078 0.6863 0.8441 0.2452 ] Network output: [ 0.0001604 1 -0.0002359 4.411e-06 -1.98e-06 0.9999 3.324e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004191 Epoch 8054 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01081 0.9955 0.9903 3.023e-07 -1.357e-07 -0.007405 2.278e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003351 -0.003161 -0.007965 0.006223 0.9699 0.9742 0.006434 0.8344 0.8252 0.01811 ] Network output: [ 0.9998 0.0005652 0.0008827 -1.652e-05 7.415e-06 -0.001171 -1.245e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.196 -0.03353 -0.1762 0.1904 0.9835 0.9932 0.2193 0.4422 0.8717 0.717 ] Network output: [ -0.01047 1.002 1.01 -6.742e-08 3.027e-08 0.009245 -5.081e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006018 0.0004862 0.004442 0.003675 0.9889 0.9919 0.006131 0.8627 0.8957 0.01307 ] Network output: [ -0.0005705 0.002716 1.001 -5.204e-05 2.336e-05 0.9969 -3.922e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2079 0.09668 0.3382 0.1465 0.985 0.994 0.2086 0.4465 0.8783 0.7114 ] Network output: [ 0.005651 -0.02721 0.9948 3.116e-05 -1.399e-05 1.021 2.348e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1027 0.0906 0.1814 0.201 0.9873 0.9919 0.1028 0.7613 0.8675 0.3058 ] Network output: [ -0.005408 0.02641 1.003 3.277e-05 -1.471e-05 0.9813 2.469e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09077 0.08883 0.1651 0.1954 0.9854 0.9912 0.09078 0.6863 0.8441 0.2452 ] Network output: [ 0.0001604 1 -0.0002356 4.406e-06 -1.978e-06 0.9999 3.321e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004189 Epoch 8055 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01081 0.9955 0.9903 3.009e-07 -1.351e-07 -0.007406 2.268e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003352 -0.003161 -0.007964 0.006222 0.9699 0.9742 0.006434 0.8344 0.8252 0.01811 ] Network output: [ 0.9998 0.0005647 0.0008821 -1.65e-05 7.408e-06 -0.00117 -1.244e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.196 -0.03353 -0.1761 0.1904 0.9835 0.9932 0.2193 0.4422 0.8717 0.717 ] Network output: [ -0.01047 1.002 1.01 -6.823e-08 3.063e-08 0.009242 -5.142e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006019 0.0004863 0.004442 0.003675 0.9889 0.9919 0.006132 0.8627 0.8957 0.01307 ] Network output: [ -0.0005701 0.002715 1.001 -5.199e-05 2.334e-05 0.9969 -3.918e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2079 0.09669 0.3382 0.1465 0.985 0.994 0.2086 0.4465 0.8783 0.7113 ] Network output: [ 0.005649 -0.0272 0.9948 3.113e-05 -1.398e-05 1.021 2.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1027 0.0906 0.1814 0.201 0.9873 0.9919 0.1028 0.7613 0.8675 0.3058 ] Network output: [ -0.005406 0.0264 1.003 3.274e-05 -1.47e-05 0.9814 2.467e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09077 0.08883 0.1651 0.1954 0.9854 0.9912 0.09078 0.6862 0.8441 0.2452 ] Network output: [ 0.0001603 1 -0.0002353 4.402e-06 -1.976e-06 0.9999 3.318e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004186 Epoch 8056 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0108 0.9955 0.9903 2.995e-07 -1.345e-07 -0.007406 2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003352 -0.003161 -0.007963 0.006222 0.9699 0.9742 0.006434 0.8343 0.8252 0.01811 ] Network output: [ 0.9998 0.0005642 0.0008816 -1.648e-05 7.401e-06 -0.001169 -1.242e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.196 -0.03354 -0.1761 0.1904 0.9835 0.9932 0.2193 0.4422 0.8717 0.717 ] Network output: [ -0.01046 1.002 1.01 -6.904e-08 3.099e-08 0.00924 -5.203e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006019 0.0004864 0.004442 0.003674 0.9889 0.9919 0.006132 0.8627 0.8957 0.01307 ] Network output: [ -0.0005698 0.002714 1.001 -5.194e-05 2.332e-05 0.9969 -3.914e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2079 0.09669 0.3382 0.1465 0.985 0.994 0.2086 0.4465 0.8783 0.7113 ] Network output: [ 0.005647 -0.02719 0.9948 3.11e-05 -1.396e-05 1.021 2.344e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1027 0.09061 0.1814 0.201 0.9873 0.9919 0.1028 0.7613 0.8675 0.3058 ] Network output: [ -0.005404 0.02638 1.003 3.271e-05 -1.468e-05 0.9814 2.465e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09077 0.08883 0.1651 0.1954 0.9853 0.9912 0.09078 0.6862 0.8441 0.2452 ] Network output: [ 0.0001602 1 -0.0002351 4.398e-06 -1.974e-06 0.9999 3.314e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004184 Epoch 8057 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0108 0.9955 0.9903 2.981e-07 -1.338e-07 -0.007407 2.246e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003352 -0.003161 -0.007962 0.006221 0.9699 0.9742 0.006435 0.8343 0.8251 0.01811 ] Network output: [ 0.9998 0.0005638 0.000881 -1.647e-05 7.393e-06 -0.001168 -1.241e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1961 -0.03354 -0.1761 0.1904 0.9835 0.9932 0.2193 0.4422 0.8717 0.717 ] Network output: [ -0.01046 1.002 1.01 -6.984e-08 3.136e-08 0.009238 -5.264e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00602 0.0004864 0.004442 0.003674 0.9889 0.9919 0.006133 0.8627 0.8957 0.01307 ] Network output: [ -0.0005694 0.002713 1.001 -5.189e-05 2.329e-05 0.9969 -3.91e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2079 0.0967 0.3382 0.1465 0.985 0.994 0.2086 0.4465 0.8783 0.7113 ] Network output: [ 0.005645 -0.02718 0.9948 3.107e-05 -1.395e-05 1.021 2.342e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1027 0.09062 0.1814 0.201 0.9873 0.9919 0.1028 0.7612 0.8675 0.3058 ] Network output: [ -0.005402 0.02637 1.003 3.267e-05 -1.467e-05 0.9814 2.462e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09077 0.08883 0.1651 0.1954 0.9853 0.9912 0.09078 0.6862 0.8441 0.2452 ] Network output: [ 0.0001601 1 -0.0002348 4.394e-06 -1.972e-06 0.9999 3.311e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004181 Epoch 8058 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0108 0.9955 0.9903 2.967e-07 -1.332e-07 -0.007407 2.236e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003352 -0.003161 -0.007961 0.00622 0.9699 0.9742 0.006435 0.8343 0.8251 0.01811 ] Network output: [ 0.9998 0.0005633 0.0008805 -1.645e-05 7.386e-06 -0.001167 -1.24e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1961 -0.03354 -0.1761 0.1904 0.9835 0.9932 0.2193 0.4422 0.8717 0.717 ] Network output: [ -0.01046 1.002 1.01 -7.065e-08 3.172e-08 0.009236 -5.324e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00602 0.0004865 0.004442 0.003673 0.9889 0.9919 0.006134 0.8626 0.8957 0.01307 ] Network output: [ -0.0005691 0.002712 1.001 -5.183e-05 2.327e-05 0.9969 -3.906e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2079 0.0967 0.3382 0.1465 0.985 0.994 0.2086 0.4465 0.8783 0.7113 ] Network output: [ 0.005643 -0.02717 0.9948 3.104e-05 -1.393e-05 1.021 2.339e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1027 0.09062 0.1814 0.201 0.9873 0.9919 0.1028 0.7612 0.8675 0.3058 ] Network output: [ -0.0054 0.02636 1.003 3.264e-05 -1.466e-05 0.9814 2.46e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09077 0.08884 0.1651 0.1954 0.9853 0.9912 0.09079 0.6862 0.8441 0.2452 ] Network output: [ 0.00016 1 -0.0002345 4.389e-06 -1.971e-06 0.9999 3.308e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004179 Epoch 8059 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0108 0.9955 0.9903 2.953e-07 -1.326e-07 -0.007408 2.225e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003352 -0.003161 -0.00796 0.00622 0.9699 0.9742 0.006435 0.8343 0.8251 0.01811 ] Network output: [ 0.9998 0.0005628 0.0008799 -1.644e-05 7.379e-06 -0.001166 -1.239e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1961 -0.03354 -0.1761 0.1904 0.9835 0.9932 0.2193 0.4422 0.8716 0.717 ] Network output: [ -0.01046 1.002 1.01 -7.145e-08 3.208e-08 0.009234 -5.385e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006021 0.0004866 0.004442 0.003673 0.9889 0.9919 0.006134 0.8626 0.8957 0.01306 ] Network output: [ -0.0005687 0.002711 1.001 -5.178e-05 2.325e-05 0.9969 -3.903e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.208 0.09671 0.3383 0.1465 0.985 0.994 0.2086 0.4464 0.8783 0.7113 ] Network output: [ 0.005641 -0.02716 0.9948 3.101e-05 -1.392e-05 1.021 2.337e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1027 0.09063 0.1815 0.201 0.9873 0.9919 0.1028 0.7612 0.8675 0.3058 ] Network output: [ -0.005398 0.02635 1.003 3.261e-05 -1.464e-05 0.9814 2.458e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09077 0.08884 0.1651 0.1954 0.9853 0.9912 0.09079 0.6862 0.8441 0.2452 ] Network output: [ 0.0001599 1 -0.0002343 4.385e-06 -1.969e-06 0.9999 3.305e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004176 Epoch 8060 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0108 0.9955 0.9903 2.939e-07 -1.319e-07 -0.007408 2.215e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003352 -0.003161 -0.007959 0.006219 0.9699 0.9742 0.006436 0.8343 0.8251 0.0181 ] Network output: [ 0.9998 0.0005623 0.0008794 -1.642e-05 7.372e-06 -0.001165 -1.238e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1961 -0.03354 -0.1761 0.1904 0.9835 0.9932 0.2193 0.4421 0.8716 0.717 ] Network output: [ -0.01046 1.002 1.01 -7.225e-08 3.244e-08 0.009232 -5.445e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006022 0.0004867 0.004442 0.003673 0.9889 0.9919 0.006135 0.8626 0.8957 0.01306 ] Network output: [ -0.0005684 0.00271 1.001 -5.173e-05 2.322e-05 0.9969 -3.899e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.208 0.09672 0.3383 0.1465 0.985 0.994 0.2086 0.4464 0.8782 0.7113 ] Network output: [ 0.005639 -0.02715 0.9948 3.098e-05 -1.391e-05 1.021 2.335e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1027 0.09063 0.1815 0.201 0.9873 0.9919 0.1028 0.7612 0.8675 0.3058 ] Network output: [ -0.005396 0.02634 1.003 3.258e-05 -1.463e-05 0.9814 2.455e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09078 0.08884 0.1651 0.1954 0.9853 0.9912 0.09079 0.6861 0.8441 0.2452 ] Network output: [ 0.0001599 1 -0.000234 4.381e-06 -1.967e-06 0.9999 3.302e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004174 Epoch 8061 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0108 0.9955 0.9903 2.925e-07 -1.313e-07 -0.007409 2.204e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003352 -0.003162 -0.007958 0.006218 0.9699 0.9742 0.006436 0.8343 0.8251 0.0181 ] Network output: [ 0.9998 0.0005619 0.0008788 -1.64e-05 7.365e-06 -0.001164 -1.236e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1961 -0.03355 -0.176 0.1903 0.9835 0.9932 0.2194 0.4421 0.8716 0.717 ] Network output: [ -0.01046 1.002 1.01 -7.305e-08 3.28e-08 0.00923 -5.506e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006022 0.0004867 0.004442 0.003672 0.9889 0.9919 0.006135 0.8626 0.8957 0.01306 ] Network output: [ -0.000568 0.002709 1.001 -5.168e-05 2.32e-05 0.9969 -3.895e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.208 0.09672 0.3383 0.1465 0.985 0.994 0.2087 0.4464 0.8782 0.7113 ] Network output: [ 0.005638 -0.02714 0.9948 3.095e-05 -1.389e-05 1.021 2.332e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1027 0.09064 0.1815 0.201 0.9873 0.9919 0.1028 0.7611 0.8675 0.3058 ] Network output: [ -0.005394 0.02633 1.003 3.255e-05 -1.461e-05 0.9814 2.453e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09078 0.08884 0.1651 0.1954 0.9853 0.9912 0.09079 0.6861 0.844 0.2452 ] Network output: [ 0.0001598 1 -0.0002337 4.377e-06 -1.965e-06 0.9999 3.298e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004171 Epoch 8062 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01079 0.9955 0.9903 2.911e-07 -1.307e-07 -0.007409 2.194e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003353 -0.003162 -0.007957 0.006218 0.9699 0.9742 0.006436 0.8343 0.8251 0.0181 ] Network output: [ 0.9998 0.0005614 0.0008783 -1.639e-05 7.357e-06 -0.001163 -1.235e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1961 -0.03355 -0.176 0.1903 0.9835 0.9932 0.2194 0.4421 0.8716 0.717 ] Network output: [ -0.01046 1.002 1.01 -7.385e-08 3.315e-08 0.009228 -5.566e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006023 0.0004868 0.004442 0.003672 0.9889 0.9919 0.006136 0.8626 0.8957 0.01306 ] Network output: [ -0.0005676 0.002708 1.001 -5.163e-05 2.318e-05 0.9969 -3.891e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.208 0.09673 0.3383 0.1465 0.985 0.994 0.2087 0.4464 0.8782 0.7113 ] Network output: [ 0.005636 -0.02713 0.9948 3.092e-05 -1.388e-05 1.021 2.33e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1028 0.09065 0.1815 0.201 0.9873 0.9919 0.1028 0.7611 0.8675 0.3058 ] Network output: [ -0.005392 0.02632 1.003 3.252e-05 -1.46e-05 0.9814 2.451e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09078 0.08884 0.1651 0.1954 0.9853 0.9912 0.09079 0.6861 0.844 0.2452 ] Network output: [ 0.0001597 1 -0.0002335 4.372e-06 -1.963e-06 0.9999 3.295e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004169 Epoch 8063 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01079 0.9955 0.9903 2.897e-07 -1.3e-07 -0.00741 2.183e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003353 -0.003162 -0.007956 0.006217 0.9699 0.9742 0.006437 0.8343 0.8251 0.0181 ] Network output: [ 0.9998 0.0005609 0.0008777 -1.637e-05 7.35e-06 -0.001162 -1.234e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1961 -0.03355 -0.176 0.1903 0.9835 0.9932 0.2194 0.4421 0.8716 0.717 ] Network output: [ -0.01046 1.002 1.01 -7.465e-08 3.351e-08 0.009226 -5.626e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006023 0.0004869 0.004442 0.003671 0.9889 0.9919 0.006137 0.8626 0.8957 0.01306 ] Network output: [ -0.0005673 0.002707 1.001 -5.158e-05 2.315e-05 0.9969 -3.887e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.208 0.09673 0.3383 0.1465 0.985 0.994 0.2087 0.4464 0.8782 0.7113 ] Network output: [ 0.005634 -0.02712 0.9948 3.089e-05 -1.387e-05 1.021 2.328e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1028 0.09065 0.1815 0.201 0.9873 0.9919 0.1028 0.7611 0.8674 0.3058 ] Network output: [ -0.00539 0.02631 1.003 3.249e-05 -1.459e-05 0.9814 2.449e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09078 0.08884 0.1651 0.1954 0.9853 0.9912 0.09079 0.6861 0.844 0.2452 ] Network output: [ 0.0001596 1 -0.0002332 4.368e-06 -1.961e-06 0.9999 3.292e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004166 Epoch 8064 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01079 0.9955 0.9903 2.883e-07 -1.294e-07 -0.00741 2.173e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003353 -0.003162 -0.007955 0.006216 0.9699 0.9742 0.006437 0.8343 0.8251 0.0181 ] Network output: [ 0.9998 0.0005604 0.0008772 -1.636e-05 7.343e-06 -0.001161 -1.233e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1961 -0.03355 -0.176 0.1903 0.9835 0.9932 0.2194 0.4421 0.8716 0.717 ] Network output: [ -0.01046 1.002 1.01 -7.544e-08 3.387e-08 0.009224 -5.686e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006024 0.000487 0.004442 0.003671 0.9889 0.9919 0.006137 0.8626 0.8957 0.01306 ] Network output: [ -0.0005669 0.002706 1.001 -5.153e-05 2.313e-05 0.9969 -3.883e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.208 0.09674 0.3383 0.1465 0.985 0.994 0.2087 0.4464 0.8782 0.7113 ] Network output: [ 0.005632 -0.02711 0.9948 3.086e-05 -1.385e-05 1.021 2.326e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1028 0.09066 0.1815 0.201 0.9873 0.9919 0.1028 0.7611 0.8674 0.3058 ] Network output: [ -0.005388 0.02629 1.003 3.246e-05 -1.457e-05 0.9814 2.446e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09078 0.08885 0.1651 0.1954 0.9853 0.9912 0.09079 0.686 0.844 0.2452 ] Network output: [ 0.0001595 1 -0.0002329 4.364e-06 -1.959e-06 0.9999 3.289e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004164 Epoch 8065 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01079 0.9955 0.9903 2.869e-07 -1.288e-07 -0.007411 2.162e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003353 -0.003162 -0.007953 0.006216 0.9699 0.9742 0.006437 0.8343 0.8251 0.0181 ] Network output: [ 0.9998 0.00056 0.0008766 -1.634e-05 7.336e-06 -0.00116 -1.231e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1961 -0.03355 -0.176 0.1903 0.9835 0.9932 0.2194 0.4421 0.8716 0.717 ] Network output: [ -0.01045 1.002 1.01 -7.623e-08 3.422e-08 0.009222 -5.745e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006025 0.000487 0.004442 0.00367 0.9889 0.9919 0.006138 0.8626 0.8957 0.01306 ] Network output: [ -0.0005666 0.002705 1.001 -5.147e-05 2.311e-05 0.9969 -3.879e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.208 0.09674 0.3383 0.1465 0.985 0.994 0.2087 0.4464 0.8782 0.7113 ] Network output: [ 0.00563 -0.0271 0.9948 3.083e-05 -1.384e-05 1.021 2.323e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1028 0.09066 0.1815 0.201 0.9873 0.9919 0.1028 0.7611 0.8674 0.3058 ] Network output: [ -0.005386 0.02628 1.003 3.243e-05 -1.456e-05 0.9814 2.444e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09078 0.08885 0.1651 0.1954 0.9853 0.9912 0.0908 0.686 0.844 0.2452 ] Network output: [ 0.0001595 1 -0.0002327 4.36e-06 -1.957e-06 0.9999 3.286e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004161 Epoch 8066 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01079 0.9955 0.9903 2.855e-07 -1.282e-07 -0.007411 2.152e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003353 -0.003162 -0.007952 0.006215 0.9699 0.9742 0.006437 0.8343 0.8251 0.0181 ] Network output: [ 0.9998 0.0005595 0.0008761 -1.632e-05 7.329e-06 -0.001159 -1.23e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1961 -0.03356 -0.176 0.1903 0.9835 0.9932 0.2194 0.4421 0.8716 0.717 ] Network output: [ -0.01045 1.002 1.01 -7.703e-08 3.458e-08 0.00922 -5.805e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006025 0.0004871 0.004442 0.00367 0.9889 0.9919 0.006139 0.8626 0.8957 0.01306 ] Network output: [ -0.0005662 0.002705 1.001 -5.142e-05 2.309e-05 0.9969 -3.875e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.208 0.09675 0.3383 0.1465 0.985 0.994 0.2087 0.4464 0.8782 0.7113 ] Network output: [ 0.005628 -0.02709 0.9948 3.08e-05 -1.383e-05 1.021 2.321e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1028 0.09067 0.1815 0.201 0.9873 0.9919 0.1028 0.761 0.8674 0.3058 ] Network output: [ -0.005384 0.02627 1.003 3.24e-05 -1.454e-05 0.9814 2.442e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09079 0.08885 0.1651 0.1954 0.9853 0.9912 0.0908 0.686 0.844 0.2452 ] Network output: [ 0.0001594 1 -0.0002324 4.355e-06 -1.955e-06 0.9999 3.282e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004159 Epoch 8067 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01079 0.9955 0.9904 2.841e-07 -1.276e-07 -0.007412 2.141e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003353 -0.003162 -0.007951 0.006214 0.9699 0.9742 0.006438 0.8343 0.8251 0.01809 ] Network output: [ 0.9998 0.000559 0.0008755 -1.631e-05 7.322e-06 -0.001158 -1.229e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1962 -0.03356 -0.176 0.1903 0.9835 0.9932 0.2194 0.4421 0.8716 0.717 ] Network output: [ -0.01045 1.002 1.01 -7.781e-08 3.493e-08 0.009218 -5.864e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006026 0.0004872 0.004443 0.00367 0.9889 0.9919 0.006139 0.8626 0.8957 0.01306 ] Network output: [ -0.0005659 0.002704 1.001 -5.137e-05 2.306e-05 0.9969 -3.871e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.208 0.09676 0.3383 0.1465 0.985 0.994 0.2087 0.4463 0.8782 0.7113 ] Network output: [ 0.005626 -0.02708 0.9948 3.077e-05 -1.381e-05 1.021 2.319e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1028 0.09067 0.1815 0.201 0.9873 0.9919 0.1029 0.761 0.8674 0.3058 ] Network output: [ -0.005382 0.02626 1.003 3.237e-05 -1.453e-05 0.9814 2.439e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09079 0.08885 0.1651 0.1954 0.9853 0.9912 0.0908 0.686 0.844 0.2452 ] Network output: [ 0.0001593 1 -0.0002321 4.351e-06 -1.953e-06 0.9999 3.279e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004156 Epoch 8068 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01078 0.9955 0.9904 2.827e-07 -1.269e-07 -0.007412 2.131e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003353 -0.003163 -0.00795 0.006213 0.9699 0.9742 0.006438 0.8343 0.8251 0.01809 ] Network output: [ 0.9998 0.0005585 0.000875 -1.629e-05 7.314e-06 -0.001157 -1.228e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1962 -0.03356 -0.1759 0.1903 0.9835 0.9932 0.2194 0.442 0.8716 0.717 ] Network output: [ -0.01045 1.002 1.01 -7.86e-08 3.529e-08 0.009216 -5.924e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006026 0.0004873 0.004443 0.003669 0.9889 0.9919 0.00614 0.8626 0.8957 0.01305 ] Network output: [ -0.0005655 0.002703 1.001 -5.132e-05 2.304e-05 0.9969 -3.868e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2081 0.09676 0.3383 0.1465 0.985 0.994 0.2087 0.4463 0.8782 0.7113 ] Network output: [ 0.005625 -0.02707 0.9948 3.074e-05 -1.38e-05 1.021 2.316e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1028 0.09068 0.1815 0.201 0.9873 0.9919 0.1029 0.761 0.8674 0.3058 ] Network output: [ -0.00538 0.02625 1.003 3.234e-05 -1.452e-05 0.9814 2.437e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09079 0.08885 0.1651 0.1954 0.9853 0.9912 0.0908 0.6859 0.844 0.2452 ] Network output: [ 0.0001592 1 -0.0002319 4.347e-06 -1.951e-06 0.9999 3.276e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004154 Epoch 8069 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01078 0.9955 0.9904 2.814e-07 -1.263e-07 -0.007413 2.12e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003353 -0.003163 -0.007949 0.006213 0.9699 0.9742 0.006438 0.8343 0.8251 0.01809 ] Network output: [ 0.9998 0.0005581 0.0008744 -1.628e-05 7.307e-06 -0.001156 -1.227e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1962 -0.03356 -0.1759 0.1903 0.9835 0.9932 0.2194 0.442 0.8716 0.7169 ] Network output: [ -0.01045 1.002 1.01 -7.939e-08 3.564e-08 0.009214 -5.983e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006027 0.0004873 0.004443 0.003669 0.9889 0.9919 0.00614 0.8626 0.8956 0.01305 ] Network output: [ -0.0005652 0.002702 1.001 -5.127e-05 2.302e-05 0.9969 -3.864e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2081 0.09677 0.3384 0.1465 0.985 0.994 0.2087 0.4463 0.8782 0.7113 ] Network output: [ 0.005623 -0.02706 0.9948 3.071e-05 -1.379e-05 1.021 2.314e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1028 0.09069 0.1815 0.201 0.9873 0.9919 0.1029 0.761 0.8674 0.3058 ] Network output: [ -0.005378 0.02624 1.003 3.23e-05 -1.45e-05 0.9814 2.435e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09079 0.08885 0.1651 0.1954 0.9853 0.9912 0.0908 0.6859 0.844 0.2452 ] Network output: [ 0.0001591 1 -0.0002316 4.343e-06 -1.95e-06 0.9999 3.273e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004152 Epoch 8070 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01078 0.9955 0.9904 2.8e-07 -1.257e-07 -0.007413 2.11e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003354 -0.003163 -0.007948 0.006212 0.9699 0.9742 0.006439 0.8342 0.8251 0.01809 ] Network output: [ 0.9998 0.0005576 0.0008739 -1.626e-05 7.3e-06 -0.001155 -1.225e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1962 -0.03356 -0.1759 0.1903 0.9835 0.9932 0.2195 0.442 0.8716 0.7169 ] Network output: [ -0.01045 1.002 1.01 -8.017e-08 3.599e-08 0.009212 -6.042e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006027 0.0004874 0.004443 0.003668 0.9889 0.9919 0.006141 0.8625 0.8956 0.01305 ] Network output: [ -0.0005648 0.002701 1.001 -5.122e-05 2.299e-05 0.9969 -3.86e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2081 0.09677 0.3384 0.1464 0.985 0.994 0.2088 0.4463 0.8782 0.7113 ] Network output: [ 0.005621 -0.02705 0.9948 3.068e-05 -1.377e-05 1.021 2.312e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1028 0.09069 0.1815 0.201 0.9873 0.9919 0.1029 0.761 0.8674 0.3058 ] Network output: [ -0.005376 0.02623 1.003 3.227e-05 -1.449e-05 0.9814 2.432e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09079 0.08886 0.1651 0.1954 0.9853 0.9912 0.0908 0.6859 0.844 0.2452 ] Network output: [ 0.0001591 1 -0.0002313 4.338e-06 -1.948e-06 0.9999 3.27e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004149 Epoch 8071 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01078 0.9955 0.9904 2.786e-07 -1.251e-07 -0.007414 2.1e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003354 -0.003163 -0.007947 0.006211 0.9699 0.9742 0.006439 0.8342 0.8251 0.01809 ] Network output: [ 0.9998 0.0005571 0.0008733 -1.624e-05 7.293e-06 -0.001154 -1.224e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1962 -0.03357 -0.1759 0.1903 0.9835 0.9932 0.2195 0.442 0.8716 0.7169 ] Network output: [ -0.01045 1.002 1.01 -8.095e-08 3.634e-08 0.00921 -6.101e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006028 0.0004875 0.004443 0.003668 0.9889 0.9919 0.006142 0.8625 0.8956 0.01305 ] Network output: [ -0.0005645 0.0027 1.001 -5.117e-05 2.297e-05 0.9969 -3.856e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2081 0.09678 0.3384 0.1464 0.985 0.994 0.2088 0.4463 0.8782 0.7113 ] Network output: [ 0.005619 -0.02704 0.9948 3.065e-05 -1.376e-05 1.021 2.31e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1028 0.0907 0.1815 0.201 0.9873 0.9919 0.1029 0.7609 0.8674 0.3058 ] Network output: [ -0.005374 0.02622 1.003 3.224e-05 -1.448e-05 0.9814 2.43e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09079 0.08886 0.1651 0.1954 0.9853 0.9912 0.09081 0.6859 0.844 0.2452 ] Network output: [ 0.000159 1 -0.0002311 4.334e-06 -1.946e-06 0.9999 3.266e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004147 Epoch 8072 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01078 0.9955 0.9904 2.772e-07 -1.245e-07 -0.007414 2.089e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003354 -0.003163 -0.007946 0.006211 0.9699 0.9742 0.006439 0.8342 0.8251 0.01809 ] Network output: [ 0.9998 0.0005567 0.0008728 -1.623e-05 7.286e-06 -0.001153 -1.223e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1962 -0.03357 -0.1759 0.1903 0.9835 0.9932 0.2195 0.442 0.8716 0.7169 ] Network output: [ -0.01045 1.002 1.01 -8.173e-08 3.669e-08 0.009208 -6.159e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006029 0.0004876 0.004443 0.003667 0.9889 0.9919 0.006142 0.8625 0.8956 0.01305 ] Network output: [ -0.0005641 0.002699 1.001 -5.111e-05 2.295e-05 0.9969 -3.852e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2081 0.09678 0.3384 0.1464 0.985 0.994 0.2088 0.4463 0.8782 0.7112 ] Network output: [ 0.005617 -0.02703 0.9948 3.062e-05 -1.375e-05 1.021 2.307e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1028 0.0907 0.1815 0.201 0.9873 0.9919 0.1029 0.7609 0.8674 0.3058 ] Network output: [ -0.005372 0.02621 1.003 3.221e-05 -1.446e-05 0.9814 2.428e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0908 0.08886 0.1651 0.1954 0.9853 0.9912 0.09081 0.6859 0.844 0.2452 ] Network output: [ 0.0001589 1 -0.0002308 4.33e-06 -1.944e-06 0.9999 3.263e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004144 Epoch 8073 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01078 0.9955 0.9904 2.759e-07 -1.239e-07 -0.007415 2.079e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003354 -0.003163 -0.007945 0.00621 0.9699 0.9742 0.00644 0.8342 0.8251 0.01809 ] Network output: [ 0.9998 0.0005562 0.0008722 -1.621e-05 7.279e-06 -0.001152 -1.222e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1962 -0.03357 -0.1759 0.1903 0.9835 0.9932 0.2195 0.442 0.8716 0.7169 ] Network output: [ -0.01045 1.002 1.01 -8.251e-08 3.704e-08 0.009206 -6.218e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006029 0.0004876 0.004443 0.003667 0.9889 0.9919 0.006143 0.8625 0.8956 0.01305 ] Network output: [ -0.0005638 0.002698 1.001 -5.106e-05 2.292e-05 0.9969 -3.848e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2081 0.09679 0.3384 0.1464 0.985 0.994 0.2088 0.4463 0.8782 0.7112 ] Network output: [ 0.005615 -0.02702 0.9948 3.059e-05 -1.373e-05 1.021 2.305e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1028 0.09071 0.1815 0.201 0.9873 0.9919 0.1029 0.7609 0.8674 0.3058 ] Network output: [ -0.00537 0.02619 1.003 3.218e-05 -1.445e-05 0.9814 2.425e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0908 0.08886 0.1651 0.1954 0.9853 0.9912 0.09081 0.6858 0.844 0.2452 ] Network output: [ 0.0001588 1 -0.0002305 4.326e-06 -1.942e-06 0.9999 3.26e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004142 Epoch 8074 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01078 0.9955 0.9904 2.745e-07 -1.232e-07 -0.007415 2.069e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003354 -0.003164 -0.007944 0.006209 0.9699 0.9742 0.00644 0.8342 0.8251 0.01808 ] Network output: [ 0.9998 0.0005557 0.0008717 -1.62e-05 7.272e-06 -0.001151 -1.221e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1962 -0.03357 -0.1758 0.1903 0.9835 0.9932 0.2195 0.442 0.8716 0.7169 ] Network output: [ -0.01044 1.002 1.01 -8.328e-08 3.739e-08 0.009204 -6.276e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00603 0.0004877 0.004443 0.003667 0.9889 0.9919 0.006143 0.8625 0.8956 0.01305 ] Network output: [ -0.0005634 0.002697 1.001 -5.101e-05 2.29e-05 0.9969 -3.844e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2081 0.0968 0.3384 0.1464 0.985 0.994 0.2088 0.4463 0.8782 0.7112 ] Network output: [ 0.005613 -0.02701 0.9948 3.056e-05 -1.372e-05 1.021 2.303e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1028 0.09071 0.1815 0.2009 0.9873 0.9919 0.1029 0.7609 0.8674 0.3058 ] Network output: [ -0.005368 0.02618 1.003 3.215e-05 -1.443e-05 0.9815 2.423e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0908 0.08886 0.1651 0.1954 0.9853 0.9912 0.09081 0.6858 0.8439 0.2452 ] Network output: [ 0.0001587 1 -0.0002303 4.322e-06 -1.94e-06 0.9999 3.257e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004139 Epoch 8075 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01077 0.9955 0.9904 2.732e-07 -1.226e-07 -0.007416 2.059e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003354 -0.003164 -0.007943 0.006209 0.9699 0.9742 0.00644 0.8342 0.8251 0.01808 ] Network output: [ 0.9998 0.0005553 0.0008711 -1.618e-05 7.264e-06 -0.00115 -1.219e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1962 -0.03357 -0.1758 0.1903 0.9835 0.9932 0.2195 0.442 0.8716 0.7169 ] Network output: [ -0.01044 1.002 1.01 -8.406e-08 3.774e-08 0.009201 -6.335e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00603 0.0004878 0.004443 0.003666 0.9889 0.9919 0.006144 0.8625 0.8956 0.01305 ] Network output: [ -0.0005631 0.002696 1.001 -5.096e-05 2.288e-05 0.9969 -3.841e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2081 0.0968 0.3384 0.1464 0.985 0.994 0.2088 0.4462 0.8782 0.7112 ] Network output: [ 0.005612 -0.027 0.9948 3.053e-05 -1.37e-05 1.021 2.301e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1028 0.09072 0.1815 0.2009 0.9873 0.9919 0.1029 0.7608 0.8674 0.3058 ] Network output: [ -0.005366 0.02617 1.003 3.212e-05 -1.442e-05 0.9815 2.421e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0908 0.08886 0.1651 0.1954 0.9853 0.9912 0.09081 0.6858 0.8439 0.2452 ] Network output: [ 0.0001586 1 -0.00023 4.317e-06 -1.938e-06 0.9999 3.254e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004137 Epoch 8076 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01077 0.9955 0.9904 2.718e-07 -1.22e-07 -0.007416 2.048e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003354 -0.003164 -0.007942 0.006208 0.9699 0.9742 0.006441 0.8342 0.8251 0.01808 ] Network output: [ 0.9998 0.0005548 0.0008706 -1.617e-05 7.257e-06 -0.001149 -1.218e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1962 -0.03358 -0.1758 0.1902 0.9835 0.9932 0.2195 0.442 0.8716 0.7169 ] Network output: [ -0.01044 1.002 1.01 -8.483e-08 3.808e-08 0.009199 -6.393e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006031 0.0004879 0.004443 0.003666 0.9889 0.9919 0.006145 0.8625 0.8956 0.01305 ] Network output: [ -0.0005627 0.002695 1.001 -5.091e-05 2.286e-05 0.9969 -3.837e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2081 0.09681 0.3384 0.1464 0.985 0.994 0.2088 0.4462 0.8782 0.7112 ] Network output: [ 0.00561 -0.02699 0.9948 3.05e-05 -1.369e-05 1.021 2.298e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1028 0.09073 0.1815 0.2009 0.9873 0.9919 0.1029 0.7608 0.8674 0.3058 ] Network output: [ -0.005365 0.02616 1.003 3.209e-05 -1.441e-05 0.9815 2.418e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0908 0.08887 0.1651 0.1954 0.9853 0.9912 0.09081 0.6858 0.8439 0.2452 ] Network output: [ 0.0001586 1 -0.0002298 4.313e-06 -1.936e-06 0.9999 3.251e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004134 Epoch 8077 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01077 0.9955 0.9904 2.704e-07 -1.214e-07 -0.007417 2.038e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003355 -0.003164 -0.007941 0.006207 0.9699 0.9742 0.006441 0.8342 0.8251 0.01808 ] Network output: [ 0.9998 0.0005543 0.00087 -1.615e-05 7.25e-06 -0.001148 -1.217e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1963 -0.03358 -0.1758 0.1902 0.9835 0.9932 0.2195 0.4419 0.8716 0.7169 ] Network output: [ -0.01044 1.002 1.01 -8.56e-08 3.843e-08 0.009197 -6.451e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006032 0.0004879 0.004443 0.003665 0.9889 0.9919 0.006145 0.8625 0.8956 0.01305 ] Network output: [ -0.0005624 0.002694 1.001 -5.086e-05 2.283e-05 0.9969 -3.833e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2082 0.09681 0.3384 0.1464 0.985 0.994 0.2088 0.4462 0.8782 0.7112 ] Network output: [ 0.005608 -0.02698 0.9948 3.047e-05 -1.368e-05 1.021 2.296e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1028 0.09073 0.1815 0.2009 0.9873 0.9919 0.1029 0.7608 0.8674 0.3058 ] Network output: [ -0.005363 0.02615 1.003 3.206e-05 -1.439e-05 0.9815 2.416e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0908 0.08887 0.1651 0.1954 0.9853 0.9912 0.09082 0.6857 0.8439 0.2452 ] Network output: [ 0.0001585 1 -0.0002295 4.309e-06 -1.934e-06 0.9999 3.247e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004132 Epoch 8078 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01077 0.9955 0.9904 2.691e-07 -1.208e-07 -0.007417 2.028e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003355 -0.003164 -0.00794 0.006207 0.9699 0.9742 0.006441 0.8342 0.8251 0.01808 ] Network output: [ 0.9998 0.0005538 0.0008695 -1.613e-05 7.243e-06 -0.001147 -1.216e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1963 -0.03358 -0.1758 0.1902 0.9835 0.9932 0.2196 0.4419 0.8716 0.7169 ] Network output: [ -0.01044 1.002 1.01 -8.637e-08 3.877e-08 0.009195 -6.509e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006032 0.000488 0.004443 0.003665 0.9889 0.9919 0.006146 0.8625 0.8956 0.01304 ] Network output: [ -0.000562 0.002693 1.001 -5.081e-05 2.281e-05 0.9969 -3.829e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2082 0.09682 0.3384 0.1464 0.985 0.994 0.2088 0.4462 0.8782 0.7112 ] Network output: [ 0.005606 -0.02697 0.9948 3.044e-05 -1.366e-05 1.021 2.294e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1029 0.09074 0.1815 0.2009 0.9873 0.9919 0.1029 0.7608 0.8674 0.3058 ] Network output: [ -0.005361 0.02614 1.003 3.203e-05 -1.438e-05 0.9815 2.414e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0908 0.08887 0.1651 0.1954 0.9853 0.9912 0.09082 0.6857 0.8439 0.2452 ] Network output: [ 0.0001584 1 -0.0002292 4.305e-06 -1.933e-06 0.9999 3.244e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004129 Epoch 8079 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01077 0.9955 0.9904 2.677e-07 -1.202e-07 -0.007417 2.018e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003355 -0.003164 -0.007939 0.006206 0.9699 0.9742 0.006442 0.8342 0.825 0.01808 ] Network output: [ 0.9998 0.0005534 0.000869 -1.612e-05 7.236e-06 -0.001146 -1.215e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1963 -0.03358 -0.1758 0.1902 0.9835 0.9932 0.2196 0.4419 0.8716 0.7169 ] Network output: [ -0.01044 1.002 1.01 -8.713e-08 3.912e-08 0.009193 -6.567e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006033 0.0004881 0.004443 0.003664 0.9889 0.9919 0.006146 0.8625 0.8956 0.01304 ] Network output: [ -0.0005617 0.002692 1.001 -5.076e-05 2.279e-05 0.9969 -3.825e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2082 0.09682 0.3384 0.1464 0.985 0.994 0.2089 0.4462 0.8782 0.7112 ] Network output: [ 0.005604 -0.02696 0.9948 3.041e-05 -1.365e-05 1.021 2.292e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1029 0.09074 0.1815 0.2009 0.9873 0.9919 0.1029 0.7608 0.8673 0.3058 ] Network output: [ -0.005359 0.02613 1.003 3.2e-05 -1.437e-05 0.9815 2.412e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09081 0.08887 0.1651 0.1954 0.9853 0.9912 0.09082 0.6857 0.8439 0.2452 ] Network output: [ 0.0001583 1 -0.000229 4.301e-06 -1.931e-06 0.9999 3.241e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004127 Epoch 8080 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01077 0.9955 0.9904 2.664e-07 -1.196e-07 -0.007418 2.008e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003355 -0.003164 -0.007938 0.006205 0.9699 0.9742 0.006442 0.8342 0.825 0.01808 ] Network output: [ 0.9998 0.0005529 0.0008684 -1.61e-05 7.229e-06 -0.001145 -1.214e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1963 -0.03358 -0.1758 0.1902 0.9835 0.9932 0.2196 0.4419 0.8716 0.7169 ] Network output: [ -0.01044 1.002 1.01 -8.79e-08 3.946e-08 0.009191 -6.624e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006033 0.0004882 0.004443 0.003664 0.9889 0.9919 0.006147 0.8625 0.8956 0.01304 ] Network output: [ -0.0005613 0.002691 1.001 -5.071e-05 2.276e-05 0.9969 -3.821e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2082 0.09683 0.3385 0.1464 0.985 0.994 0.2089 0.4462 0.8782 0.7112 ] Network output: [ 0.005602 -0.02695 0.9948 3.038e-05 -1.364e-05 1.021 2.289e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1029 0.09075 0.1815 0.2009 0.9873 0.9919 0.1029 0.7607 0.8673 0.3058 ] Network output: [ -0.005357 0.02612 1.003 3.197e-05 -1.435e-05 0.9815 2.409e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09081 0.08887 0.1651 0.1954 0.9853 0.9912 0.09082 0.6857 0.8439 0.2452 ] Network output: [ 0.0001582 1 -0.0002287 4.296e-06 -1.929e-06 0.9999 3.238e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004124 Epoch 8081 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01076 0.9955 0.9904 2.65e-07 -1.19e-07 -0.007418 1.997e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003355 -0.003165 -0.007937 0.006205 0.9699 0.9742 0.006442 0.8342 0.825 0.01807 ] Network output: [ 0.9998 0.0005524 0.0008679 -1.609e-05 7.222e-06 -0.001144 -1.212e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1963 -0.03359 -0.1757 0.1902 0.9835 0.9932 0.2196 0.4419 0.8716 0.7169 ] Network output: [ -0.01044 1.002 1.01 -8.866e-08 3.98e-08 0.009189 -6.682e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006034 0.0004882 0.004443 0.003664 0.9889 0.9919 0.006148 0.8625 0.8956 0.01304 ] Network output: [ -0.000561 0.00269 1.001 -5.066e-05 2.274e-05 0.9969 -3.818e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2082 0.09684 0.3385 0.1464 0.985 0.994 0.2089 0.4462 0.8782 0.7112 ] Network output: [ 0.005601 -0.02694 0.9948 3.035e-05 -1.362e-05 1.021 2.287e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1029 0.09076 0.1815 0.2009 0.9873 0.9919 0.1029 0.7607 0.8673 0.3058 ] Network output: [ -0.005355 0.02611 1.003 3.194e-05 -1.434e-05 0.9815 2.407e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09081 0.08887 0.1651 0.1954 0.9853 0.9912 0.09082 0.6856 0.8439 0.2452 ] Network output: [ 0.0001582 1 -0.0002284 4.292e-06 -1.927e-06 0.9999 3.235e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004122 Epoch 8082 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01076 0.9955 0.9904 2.637e-07 -1.184e-07 -0.007419 1.987e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003355 -0.003165 -0.007936 0.006204 0.9699 0.9742 0.006442 0.8342 0.825 0.01807 ] Network output: [ 0.9998 0.000552 0.0008673 -1.607e-05 7.215e-06 -0.001143 -1.211e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1963 -0.03359 -0.1757 0.1902 0.9835 0.9932 0.2196 0.4419 0.8716 0.7169 ] Network output: [ -0.01044 1.002 1.01 -8.942e-08 4.015e-08 0.009187 -6.739e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006035 0.0004883 0.004443 0.003663 0.9889 0.9919 0.006148 0.8624 0.8956 0.01304 ] Network output: [ -0.0005606 0.002689 1.001 -5.061e-05 2.272e-05 0.9969 -3.814e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2082 0.09684 0.3385 0.1464 0.985 0.994 0.2089 0.4462 0.8782 0.7112 ] Network output: [ 0.005599 -0.02693 0.9948 3.032e-05 -1.361e-05 1.021 2.285e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1029 0.09076 0.1815 0.2009 0.9873 0.9919 0.1029 0.7607 0.8673 0.3058 ] Network output: [ -0.005353 0.0261 1.003 3.191e-05 -1.432e-05 0.9815 2.405e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09081 0.08888 0.1651 0.1954 0.9853 0.9912 0.09082 0.6856 0.8439 0.2453 ] Network output: [ 0.0001581 1 -0.0002282 4.288e-06 -1.925e-06 0.9999 3.232e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000412 Epoch 8083 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01076 0.9955 0.9904 2.624e-07 -1.178e-07 -0.007419 1.977e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003355 -0.003165 -0.007935 0.006203 0.9699 0.9742 0.006443 0.8341 0.825 0.01807 ] Network output: [ 0.9998 0.0005515 0.0008668 -1.605e-05 7.208e-06 -0.001142 -1.21e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1963 -0.03359 -0.1757 0.1902 0.9835 0.9932 0.2196 0.4419 0.8716 0.7169 ] Network output: [ -0.01043 1.002 1.01 -9.018e-08 4.049e-08 0.009185 -6.797e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006035 0.0004884 0.004443 0.003663 0.9889 0.9919 0.006149 0.8624 0.8956 0.01304 ] Network output: [ -0.0005603 0.002688 1.001 -5.056e-05 2.27e-05 0.9969 -3.81e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2082 0.09685 0.3385 0.1464 0.985 0.994 0.2089 0.4462 0.8782 0.7112 ] Network output: [ 0.005597 -0.02692 0.9948 3.029e-05 -1.36e-05 1.021 2.283e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1029 0.09077 0.1815 0.2009 0.9873 0.9919 0.103 0.7607 0.8673 0.3058 ] Network output: [ -0.005351 0.02608 1.003 3.188e-05 -1.431e-05 0.9815 2.402e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09081 0.08888 0.1651 0.1954 0.9853 0.9912 0.09083 0.6856 0.8439 0.2453 ] Network output: [ 0.000158 1 -0.0002279 4.284e-06 -1.923e-06 0.9999 3.228e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004117 Epoch 8084 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01076 0.9955 0.9904 2.61e-07 -1.172e-07 -0.00742 1.967e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003355 -0.003165 -0.007934 0.006203 0.9699 0.9742 0.006443 0.8341 0.825 0.01807 ] Network output: [ 0.9998 0.0005511 0.0008662 -1.604e-05 7.201e-06 -0.001141 -1.209e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1963 -0.03359 -0.1757 0.1902 0.9835 0.9932 0.2196 0.4419 0.8716 0.7169 ] Network output: [ -0.01043 1.002 1.01 -9.094e-08 4.083e-08 0.009183 -6.854e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006036 0.0004885 0.004443 0.003662 0.9889 0.9919 0.006149 0.8624 0.8956 0.01304 ] Network output: [ -0.0005599 0.002687 1.001 -5.05e-05 2.267e-05 0.9969 -3.806e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2082 0.09685 0.3385 0.1464 0.985 0.994 0.2089 0.4461 0.8782 0.7112 ] Network output: [ 0.005595 -0.02691 0.9948 3.026e-05 -1.358e-05 1.021 2.28e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1029 0.09077 0.1815 0.2009 0.9873 0.9919 0.103 0.7607 0.8673 0.3058 ] Network output: [ -0.005349 0.02607 1.003 3.185e-05 -1.43e-05 0.9815 2.4e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09081 0.08888 0.1651 0.1954 0.9853 0.9912 0.09083 0.6856 0.8439 0.2453 ] Network output: [ 0.0001579 1 -0.0002277 4.28e-06 -1.921e-06 0.9999 3.225e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004115 Epoch 8085 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01076 0.9955 0.9904 2.597e-07 -1.166e-07 -0.00742 1.957e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003356 -0.003165 -0.007933 0.006202 0.9699 0.9742 0.006443 0.8341 0.825 0.01807 ] Network output: [ 0.9998 0.0005506 0.0008657 -1.602e-05 7.194e-06 -0.00114 -1.208e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1963 -0.03359 -0.1757 0.1902 0.9835 0.9932 0.2196 0.4418 0.8716 0.7169 ] Network output: [ -0.01043 1.002 1.01 -9.17e-08 4.117e-08 0.009181 -6.911e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006036 0.0004885 0.004443 0.003662 0.9889 0.9919 0.00615 0.8624 0.8956 0.01304 ] Network output: [ -0.0005596 0.002687 1.001 -5.045e-05 2.265e-05 0.9969 -3.802e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2082 0.09686 0.3385 0.1464 0.985 0.994 0.2089 0.4461 0.8782 0.7112 ] Network output: [ 0.005593 -0.0269 0.9948 3.023e-05 -1.357e-05 1.021 2.278e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1029 0.09078 0.1815 0.2009 0.9873 0.9919 0.103 0.7606 0.8673 0.3058 ] Network output: [ -0.005347 0.02606 1.003 3.182e-05 -1.428e-05 0.9815 2.398e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09082 0.08888 0.1651 0.1954 0.9853 0.9912 0.09083 0.6856 0.8439 0.2453 ] Network output: [ 0.0001578 1 -0.0002274 4.276e-06 -1.919e-06 0.9999 3.222e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004112 Epoch 8086 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01076 0.9955 0.9904 2.584e-07 -1.16e-07 -0.007421 1.947e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003356 -0.003165 -0.007932 0.006201 0.9699 0.9742 0.006444 0.8341 0.825 0.01807 ] Network output: [ 0.9998 0.0005501 0.0008652 -1.601e-05 7.186e-06 -0.001139 -1.206e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1963 -0.0336 -0.1757 0.1902 0.9835 0.9932 0.2196 0.4418 0.8716 0.7169 ] Network output: [ -0.01043 1.002 1.01 -9.245e-08 4.15e-08 0.009179 -6.967e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006037 0.0004886 0.004443 0.003661 0.9889 0.9919 0.006151 0.8624 0.8956 0.01304 ] Network output: [ -0.0005592 0.002686 1.001 -5.04e-05 2.263e-05 0.9969 -3.799e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2083 0.09686 0.3385 0.1464 0.985 0.994 0.2089 0.4461 0.8782 0.7112 ] Network output: [ 0.005591 -0.02689 0.9948 3.02e-05 -1.356e-05 1.021 2.276e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1029 0.09078 0.1815 0.2009 0.9873 0.9919 0.103 0.7606 0.8673 0.3058 ] Network output: [ -0.005345 0.02605 1.003 3.179e-05 -1.427e-05 0.9815 2.395e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09082 0.08888 0.1651 0.1954 0.9853 0.9912 0.09083 0.6855 0.8439 0.2453 ] Network output: [ 0.0001578 1 -0.0002271 4.271e-06 -1.918e-06 0.9999 3.219e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000411 Epoch 8087 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01076 0.9955 0.9904 2.57e-07 -1.154e-07 -0.007421 1.937e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003356 -0.003166 -0.007931 0.006201 0.9699 0.9742 0.006444 0.8341 0.825 0.01807 ] Network output: [ 0.9998 0.0005497 0.0008646 -1.599e-05 7.179e-06 -0.001138 -1.205e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1963 -0.0336 -0.1756 0.1902 0.9835 0.9932 0.2197 0.4418 0.8716 0.7168 ] Network output: [ -0.01043 1.002 1.01 -9.32e-08 4.184e-08 0.009177 -7.024e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006038 0.0004887 0.004443 0.003661 0.9889 0.9919 0.006151 0.8624 0.8956 0.01303 ] Network output: [ -0.0005589 0.002685 1.001 -5.035e-05 2.261e-05 0.9969 -3.795e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2083 0.09687 0.3385 0.1464 0.985 0.994 0.2089 0.4461 0.8782 0.7112 ] Network output: [ 0.005589 -0.02688 0.9948 3.017e-05 -1.354e-05 1.021 2.274e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1029 0.09079 0.1815 0.2009 0.9873 0.9919 0.103 0.7606 0.8673 0.3058 ] Network output: [ -0.005343 0.02604 1.003 3.176e-05 -1.426e-05 0.9815 2.393e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09082 0.08888 0.1651 0.1954 0.9853 0.9912 0.09083 0.6855 0.8439 0.2453 ] Network output: [ 0.0001577 1 -0.0002269 4.267e-06 -1.916e-06 0.9999 3.216e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004107 Epoch 8088 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01075 0.9955 0.9904 2.557e-07 -1.148e-07 -0.007422 1.927e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003356 -0.003166 -0.00793 0.0062 0.9699 0.9742 0.006444 0.8341 0.825 0.01806 ] Network output: [ 0.9998 0.0005492 0.0008641 -1.598e-05 7.172e-06 -0.001138 -1.204e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1964 -0.0336 -0.1756 0.1902 0.9835 0.9932 0.2197 0.4418 0.8715 0.7168 ] Network output: [ -0.01043 1.002 1.01 -9.395e-08 4.218e-08 0.009175 -7.081e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006038 0.0004888 0.004443 0.003661 0.9889 0.9919 0.006152 0.8624 0.8956 0.01303 ] Network output: [ -0.0005585 0.002684 1.001 -5.03e-05 2.258e-05 0.9969 -3.791e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2083 0.09688 0.3385 0.1464 0.985 0.994 0.209 0.4461 0.8782 0.7111 ] Network output: [ 0.005588 -0.02687 0.9948 3.014e-05 -1.353e-05 1.021 2.271e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1029 0.0908 0.1815 0.2009 0.9873 0.9919 0.103 0.7606 0.8673 0.3058 ] Network output: [ -0.005341 0.02603 1.003 3.173e-05 -1.424e-05 0.9815 2.391e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09082 0.08888 0.1651 0.1954 0.9853 0.9912 0.09083 0.6855 0.8438 0.2453 ] Network output: [ 0.0001576 1 -0.0002266 4.263e-06 -1.914e-06 0.9999 3.213e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004105 Epoch 8089 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01075 0.9955 0.9904 2.544e-07 -1.142e-07 -0.007422 1.917e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003356 -0.003166 -0.007929 0.006199 0.9699 0.9742 0.006445 0.8341 0.825 0.01806 ] Network output: [ 0.9998 0.0005487 0.0008635 -1.596e-05 7.165e-06 -0.001137 -1.203e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1964 -0.0336 -0.1756 0.1902 0.9835 0.9932 0.2197 0.4418 0.8715 0.7168 ] Network output: [ -0.01043 1.002 1.01 -9.47e-08 4.252e-08 0.009173 -7.137e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006039 0.0004889 0.004443 0.00366 0.9889 0.9919 0.006153 0.8624 0.8956 0.01303 ] Network output: [ -0.0005582 0.002683 1.001 -5.025e-05 2.256e-05 0.9969 -3.787e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2083 0.09688 0.3385 0.1463 0.985 0.994 0.209 0.4461 0.8782 0.7111 ] Network output: [ 0.005586 -0.02686 0.9948 3.011e-05 -1.352e-05 1.021 2.269e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1029 0.0908 0.1815 0.2009 0.9873 0.9919 0.103 0.7606 0.8673 0.3058 ] Network output: [ -0.005339 0.02602 1.003 3.169e-05 -1.423e-05 0.9815 2.389e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09082 0.08889 0.1651 0.1954 0.9853 0.9912 0.09084 0.6855 0.8438 0.2453 ] Network output: [ 0.0001575 1 -0.0002263 4.259e-06 -1.912e-06 0.9999 3.21e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004103 Epoch 8090 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01075 0.9955 0.9904 2.531e-07 -1.136e-07 -0.007423 1.907e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003356 -0.003166 -0.007928 0.006199 0.9699 0.9742 0.006445 0.8341 0.825 0.01806 ] Network output: [ 0.9998 0.0005483 0.000863 -1.594e-05 7.158e-06 -0.001136 -1.202e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1964 -0.03361 -0.1756 0.1902 0.9835 0.9932 0.2197 0.4418 0.8715 0.7168 ] Network output: [ -0.01043 1.002 1.01 -9.545e-08 4.285e-08 0.009171 -7.193e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006039 0.0004889 0.004443 0.00366 0.9889 0.9919 0.006153 0.8624 0.8956 0.01303 ] Network output: [ -0.0005578 0.002682 1.001 -5.02e-05 2.254e-05 0.9969 -3.783e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2083 0.09689 0.3386 0.1463 0.985 0.994 0.209 0.4461 0.8781 0.7111 ] Network output: [ 0.005584 -0.02685 0.9948 3.008e-05 -1.35e-05 1.021 2.267e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1029 0.09081 0.1815 0.2009 0.9873 0.9919 0.103 0.7605 0.8673 0.3058 ] Network output: [ -0.005337 0.02601 1.003 3.166e-05 -1.422e-05 0.9815 2.386e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09082 0.08889 0.1651 0.1954 0.9853 0.9912 0.09084 0.6854 0.8438 0.2453 ] Network output: [ 0.0001574 1 -0.0002261 4.255e-06 -1.91e-06 0.9999 3.206e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00041 Epoch 8091 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01075 0.9955 0.9904 2.517e-07 -1.13e-07 -0.007423 1.897e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003356 -0.003166 -0.007927 0.006198 0.9699 0.9742 0.006445 0.8341 0.825 0.01806 ] Network output: [ 0.9998 0.0005478 0.0008625 -1.593e-05 7.151e-06 -0.001135 -1.2e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1964 -0.03361 -0.1756 0.1902 0.9835 0.9932 0.2197 0.4418 0.8715 0.7168 ] Network output: [ -0.01043 1.002 1.01 -9.619e-08 4.318e-08 0.009169 -7.249e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00604 0.000489 0.004443 0.003659 0.9889 0.9919 0.006154 0.8624 0.8956 0.01303 ] Network output: [ -0.0005575 0.002681 1.001 -5.015e-05 2.251e-05 0.9969 -3.78e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2083 0.09689 0.3386 0.1463 0.985 0.994 0.209 0.4461 0.8781 0.7111 ] Network output: [ 0.005582 -0.02684 0.9948 3.005e-05 -1.349e-05 1.021 2.265e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1029 0.09081 0.1815 0.2009 0.9873 0.9919 0.103 0.7605 0.8673 0.3058 ] Network output: [ -0.005336 0.026 1.003 3.163e-05 -1.42e-05 0.9815 2.384e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09083 0.08889 0.1651 0.1954 0.9853 0.9912 0.09084 0.6854 0.8438 0.2453 ] Network output: [ 0.0001574 1 -0.0002258 4.251e-06 -1.908e-06 0.9999 3.203e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004098 Epoch 8092 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01075 0.9955 0.9904 2.504e-07 -1.124e-07 -0.007423 1.887e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003357 -0.003166 -0.007926 0.006197 0.9699 0.9742 0.006446 0.8341 0.825 0.01806 ] Network output: [ 0.9998 0.0005473 0.0008619 -1.591e-05 7.144e-06 -0.001134 -1.199e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1964 -0.03361 -0.1756 0.1901 0.9835 0.9932 0.2197 0.4418 0.8715 0.7168 ] Network output: [ -0.01042 1.002 1.01 -9.694e-08 4.352e-08 0.009167 -7.305e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006041 0.0004891 0.004443 0.003659 0.9889 0.9919 0.006154 0.8624 0.8956 0.01303 ] Network output: [ -0.0005571 0.00268 1.001 -5.01e-05 2.249e-05 0.9969 -3.776e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2083 0.0969 0.3386 0.1463 0.985 0.994 0.209 0.446 0.8781 0.7111 ] Network output: [ 0.00558 -0.02683 0.9948 3.002e-05 -1.348e-05 1.021 2.262e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1029 0.09082 0.1815 0.2009 0.9873 0.9919 0.103 0.7605 0.8673 0.3058 ] Network output: [ -0.005334 0.02599 1.003 3.16e-05 -1.419e-05 0.9816 2.382e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09083 0.08889 0.1651 0.1954 0.9853 0.9912 0.09084 0.6854 0.8438 0.2453 ] Network output: [ 0.0001573 1 -0.0002256 4.246e-06 -1.906e-06 0.9999 3.2e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004095 Epoch 8093 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01075 0.9955 0.9904 2.491e-07 -1.118e-07 -0.007424 1.877e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003357 -0.003166 -0.007925 0.006197 0.9699 0.9742 0.006446 0.8341 0.825 0.01806 ] Network output: [ 0.9998 0.0005469 0.0008614 -1.59e-05 7.137e-06 -0.001133 -1.198e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1964 -0.03361 -0.1756 0.1901 0.9835 0.9932 0.2197 0.4418 0.8715 0.7168 ] Network output: [ -0.01042 1.002 1.01 -9.768e-08 4.385e-08 0.009165 -7.361e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006041 0.0004892 0.004443 0.003658 0.9889 0.9919 0.006155 0.8624 0.8956 0.01303 ] Network output: [ -0.0005568 0.002679 1.001 -5.005e-05 2.247e-05 0.9969 -3.772e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2083 0.0969 0.3386 0.1463 0.985 0.994 0.209 0.446 0.8781 0.7111 ] Network output: [ 0.005578 -0.02682 0.9948 2.999e-05 -1.346e-05 1.021 2.26e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1029 0.09082 0.1815 0.2009 0.9873 0.9919 0.103 0.7605 0.8673 0.3058 ] Network output: [ -0.005332 0.02597 1.003 3.157e-05 -1.417e-05 0.9816 2.38e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09083 0.08889 0.1651 0.1954 0.9853 0.9912 0.09084 0.6854 0.8438 0.2453 ] Network output: [ 0.0001572 1 -0.0002253 4.242e-06 -1.905e-06 0.9999 3.197e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004093 Epoch 8094 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01074 0.9955 0.9904 2.478e-07 -1.112e-07 -0.007424 1.868e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003357 -0.003167 -0.007924 0.006196 0.9699 0.9742 0.006446 0.8341 0.825 0.01806 ] Network output: [ 0.9998 0.0005464 0.0008608 -1.588e-05 7.13e-06 -0.001132 -1.197e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1964 -0.03361 -0.1755 0.1901 0.9835 0.9932 0.2197 0.4417 0.8715 0.7168 ] Network output: [ -0.01042 1.002 1.01 -9.842e-08 4.418e-08 0.009163 -7.417e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006042 0.0004892 0.004443 0.003658 0.9889 0.9919 0.006156 0.8623 0.8956 0.01303 ] Network output: [ -0.0005564 0.002678 1.001 -5e-05 2.245e-05 0.9969 -3.768e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2083 0.09691 0.3386 0.1463 0.985 0.994 0.209 0.446 0.8781 0.7111 ] Network output: [ 0.005577 -0.02681 0.9948 2.996e-05 -1.345e-05 1.021 2.258e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.103 0.09083 0.1816 0.2009 0.9873 0.9919 0.103 0.7604 0.8673 0.3058 ] Network output: [ -0.00533 0.02596 1.003 3.154e-05 -1.416e-05 0.9816 2.377e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09083 0.08889 0.1651 0.1954 0.9853 0.9912 0.09084 0.6853 0.8438 0.2453 ] Network output: [ 0.0001571 1 -0.0002251 4.238e-06 -1.903e-06 0.9999 3.194e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000409 Epoch 8095 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01074 0.9955 0.9904 2.465e-07 -1.107e-07 -0.007425 1.858e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003357 -0.003167 -0.007923 0.006195 0.9699 0.9742 0.006446 0.8341 0.825 0.01805 ] Network output: [ 0.9998 0.000546 0.0008603 -1.587e-05 7.123e-06 -0.001131 -1.196e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1964 -0.03362 -0.1755 0.1901 0.9835 0.9932 0.2197 0.4417 0.8715 0.7168 ] Network output: [ -0.01042 1.002 1.01 -9.916e-08 4.451e-08 0.009161 -7.473e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006042 0.0004893 0.004443 0.003658 0.9889 0.9919 0.006156 0.8623 0.8956 0.01303 ] Network output: [ -0.0005561 0.002677 1.001 -4.995e-05 2.242e-05 0.9969 -3.764e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2084 0.09692 0.3386 0.1463 0.985 0.994 0.209 0.446 0.8781 0.7111 ] Network output: [ 0.005575 -0.0268 0.9948 2.993e-05 -1.344e-05 1.021 2.256e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.103 0.09084 0.1816 0.2009 0.9873 0.9919 0.103 0.7604 0.8673 0.3057 ] Network output: [ -0.005328 0.02595 1.003 3.151e-05 -1.415e-05 0.9816 2.375e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09083 0.0889 0.1651 0.1954 0.9853 0.9912 0.09085 0.6853 0.8438 0.2453 ] Network output: [ 0.000157 1 -0.0002248 4.234e-06 -1.901e-06 0.9999 3.191e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004088 Epoch 8096 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01074 0.9955 0.9904 2.452e-07 -1.101e-07 -0.007425 1.848e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003357 -0.003167 -0.007922 0.006195 0.9699 0.9742 0.006447 0.834 0.825 0.01805 ] Network output: [ 0.9998 0.0005455 0.0008598 -1.585e-05 7.116e-06 -0.00113 -1.195e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1964 -0.03362 -0.1755 0.1901 0.9835 0.9932 0.2198 0.4417 0.8715 0.7168 ] Network output: [ -0.01042 1.002 1.01 -9.989e-08 4.485e-08 0.009159 -7.528e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006043 0.0004894 0.004443 0.003657 0.9889 0.9919 0.006157 0.8623 0.8956 0.01302 ] Network output: [ -0.0005557 0.002676 1.001 -4.99e-05 2.24e-05 0.9969 -3.761e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2084 0.09692 0.3386 0.1463 0.985 0.994 0.209 0.446 0.8781 0.7111 ] Network output: [ 0.005573 -0.02679 0.9948 2.99e-05 -1.342e-05 1.021 2.254e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.103 0.09084 0.1816 0.2009 0.9873 0.9919 0.103 0.7604 0.8672 0.3057 ] Network output: [ -0.005326 0.02594 1.003 3.148e-05 -1.413e-05 0.9816 2.373e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09083 0.0889 0.1651 0.1954 0.9853 0.9912 0.09085 0.6853 0.8438 0.2453 ] Network output: [ 0.000157 1 -0.0002245 4.23e-06 -1.899e-06 0.9999 3.188e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004086 Epoch 8097 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01074 0.9955 0.9904 2.439e-07 -1.095e-07 -0.007426 1.838e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003357 -0.003167 -0.007921 0.006194 0.9699 0.9742 0.006447 0.834 0.825 0.01805 ] Network output: [ 0.9998 0.000545 0.0008592 -1.584e-05 7.109e-06 -0.001129 -1.193e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1964 -0.03362 -0.1755 0.1901 0.9835 0.9932 0.2198 0.4417 0.8715 0.7168 ] Network output: [ -0.01042 1.002 1.01 -1.006e-07 4.517e-08 0.009157 -7.584e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006044 0.0004895 0.004443 0.003657 0.9889 0.9919 0.006157 0.8623 0.8956 0.01302 ] Network output: [ -0.0005554 0.002675 1.001 -4.985e-05 2.238e-05 0.9969 -3.757e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2084 0.09693 0.3386 0.1463 0.985 0.994 0.2091 0.446 0.8781 0.7111 ] Network output: [ 0.005571 -0.02678 0.9948 2.987e-05 -1.341e-05 1.021 2.251e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.103 0.09085 0.1816 0.2009 0.9873 0.9919 0.103 0.7604 0.8672 0.3057 ] Network output: [ -0.005324 0.02593 1.003 3.145e-05 -1.412e-05 0.9816 2.37e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09084 0.0889 0.1651 0.1954 0.9853 0.9912 0.09085 0.6853 0.8438 0.2453 ] Network output: [ 0.0001569 1 -0.0002243 4.226e-06 -1.897e-06 0.9999 3.185e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004083 Epoch 8098 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01074 0.9955 0.9904 2.426e-07 -1.089e-07 -0.007426 1.828e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003357 -0.003167 -0.007919 0.006193 0.9699 0.9742 0.006447 0.834 0.825 0.01805 ] Network output: [ 0.9998 0.0005446 0.0008587 -1.582e-05 7.102e-06 -0.001128 -1.192e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1965 -0.03362 -0.1755 0.1901 0.9835 0.9932 0.2198 0.4417 0.8715 0.7168 ] Network output: [ -0.01042 1.002 1.01 -1.014e-07 4.55e-08 0.009155 -7.639e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006044 0.0004895 0.004443 0.003656 0.9889 0.9919 0.006158 0.8623 0.8955 0.01302 ] Network output: [ -0.000555 0.002674 1.001 -4.98e-05 2.236e-05 0.9969 -3.753e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2084 0.09693 0.3386 0.1463 0.985 0.994 0.2091 0.446 0.8781 0.7111 ] Network output: [ 0.005569 -0.02677 0.9948 2.984e-05 -1.34e-05 1.021 2.249e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.103 0.09085 0.1816 0.2009 0.9873 0.9919 0.103 0.7604 0.8672 0.3057 ] Network output: [ -0.005322 0.02592 1.003 3.142e-05 -1.411e-05 0.9816 2.368e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09084 0.0889 0.1651 0.1954 0.9853 0.9912 0.09085 0.6853 0.8438 0.2453 ] Network output: [ 0.0001568 1 -0.000224 4.222e-06 -1.895e-06 0.9999 3.182e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004081 Epoch 8099 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01074 0.9955 0.9904 2.413e-07 -1.083e-07 -0.007427 1.818e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003357 -0.003167 -0.007918 0.006193 0.9699 0.9742 0.006448 0.834 0.825 0.01805 ] Network output: [ 0.9998 0.0005441 0.0008582 -1.58e-05 7.095e-06 -0.001127 -1.191e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1965 -0.03362 -0.1755 0.1901 0.9835 0.9932 0.2198 0.4417 0.8715 0.7168 ] Network output: [ -0.01042 1.002 1.01 -1.021e-07 4.583e-08 0.009153 -7.694e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006045 0.0004896 0.004443 0.003656 0.9889 0.9919 0.006159 0.8623 0.8955 0.01302 ] Network output: [ -0.0005547 0.002673 1.001 -4.975e-05 2.233e-05 0.9969 -3.749e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2084 0.09694 0.3386 0.1463 0.985 0.994 0.2091 0.446 0.8781 0.7111 ] Network output: [ 0.005567 -0.02676 0.9948 2.981e-05 -1.338e-05 1.021 2.247e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.103 0.09086 0.1816 0.2009 0.9873 0.9919 0.1031 0.7603 0.8672 0.3057 ] Network output: [ -0.00532 0.02591 1.003 3.139e-05 -1.409e-05 0.9816 2.366e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09084 0.0889 0.1651 0.1954 0.9853 0.9912 0.09085 0.6852 0.8438 0.2453 ] Network output: [ 0.0001567 1 -0.0002238 4.217e-06 -1.893e-06 0.9999 3.178e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004078 Epoch 8100 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01074 0.9955 0.9904 2.4e-07 -1.077e-07 -0.007427 1.809e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003358 -0.003167 -0.007917 0.006192 0.9699 0.9742 0.006448 0.834 0.825 0.01805 ] Network output: [ 0.9998 0.0005437 0.0008576 -1.579e-05 7.088e-06 -0.001126 -1.19e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1965 -0.03363 -0.1754 0.1901 0.9835 0.9932 0.2198 0.4417 0.8715 0.7168 ] Network output: [ -0.01042 1.002 1.01 -1.028e-07 4.616e-08 0.009151 -7.749e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006045 0.0004897 0.004443 0.003655 0.9889 0.9919 0.006159 0.8623 0.8955 0.01302 ] Network output: [ -0.0005543 0.002672 1.001 -4.97e-05 2.231e-05 0.9969 -3.746e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2084 0.09694 0.3387 0.1463 0.985 0.994 0.2091 0.4459 0.8781 0.7111 ] Network output: [ 0.005566 -0.02675 0.9948 2.979e-05 -1.337e-05 1.021 2.245e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.103 0.09087 0.1816 0.2009 0.9873 0.9919 0.1031 0.7603 0.8672 0.3057 ] Network output: [ -0.005318 0.0259 1.003 3.136e-05 -1.408e-05 0.9816 2.364e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09084 0.0889 0.1651 0.1954 0.9853 0.9912 0.09085 0.6852 0.8438 0.2453 ] Network output: [ 0.0001567 1 -0.0002235 4.213e-06 -1.892e-06 0.9999 3.175e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004076 Epoch 8101 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01073 0.9955 0.9904 2.387e-07 -1.072e-07 -0.007427 1.799e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003358 -0.003168 -0.007916 0.006191 0.9699 0.9742 0.006448 0.834 0.8249 0.01805 ] Network output: [ 0.9998 0.0005432 0.0008571 -1.577e-05 7.081e-06 -0.001125 -1.189e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1965 -0.03363 -0.1754 0.1901 0.9835 0.9932 0.2198 0.4417 0.8715 0.7168 ] Network output: [ -0.01041 1.002 1.01 -1.035e-07 4.649e-08 0.009149 -7.804e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006046 0.0004898 0.004443 0.003655 0.9889 0.9919 0.00616 0.8623 0.8955 0.01302 ] Network output: [ -0.000554 0.002671 1.001 -4.965e-05 2.229e-05 0.9969 -3.742e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2084 0.09695 0.3387 0.1463 0.985 0.994 0.2091 0.4459 0.8781 0.7111 ] Network output: [ 0.005564 -0.02674 0.9948 2.976e-05 -1.336e-05 1.021 2.242e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.103 0.09087 0.1816 0.2008 0.9873 0.9919 0.1031 0.7603 0.8672 0.3057 ] Network output: [ -0.005316 0.02589 1.003 3.133e-05 -1.407e-05 0.9816 2.361e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09084 0.08891 0.1651 0.1954 0.9853 0.9912 0.09086 0.6852 0.8437 0.2453 ] Network output: [ 0.0001566 1 -0.0002233 4.209e-06 -1.89e-06 0.9999 3.172e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004074 Epoch 8102 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01073 0.9955 0.9904 2.374e-07 -1.066e-07 -0.007428 1.789e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003358 -0.003168 -0.007915 0.006191 0.9699 0.9742 0.006449 0.834 0.8249 0.01804 ] Network output: [ 0.9998 0.0005428 0.0008566 -1.576e-05 7.074e-06 -0.001124 -1.188e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1965 -0.03363 -0.1754 0.1901 0.9835 0.9932 0.2198 0.4416 0.8715 0.7168 ] Network output: [ -0.01041 1.002 1.01 -1.043e-07 4.681e-08 0.009147 -7.858e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006046 0.0004899 0.004443 0.003655 0.9889 0.9919 0.00616 0.8623 0.8955 0.01302 ] Network output: [ -0.0005536 0.00267 1.001 -4.96e-05 2.227e-05 0.9969 -3.738e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2084 0.09696 0.3387 0.1463 0.985 0.994 0.2091 0.4459 0.8781 0.7111 ] Network output: [ 0.005562 -0.02673 0.9948 2.973e-05 -1.335e-05 1.021 2.24e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.103 0.09088 0.1816 0.2008 0.9873 0.9919 0.1031 0.7603 0.8672 0.3057 ] Network output: [ -0.005314 0.02588 1.003 3.13e-05 -1.405e-05 0.9816 2.359e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09084 0.08891 0.1651 0.1954 0.9853 0.9912 0.09086 0.6852 0.8437 0.2453 ] Network output: [ 0.0001565 1 -0.000223 4.205e-06 -1.888e-06 0.9999 3.169e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004071 Epoch 8103 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01073 0.9955 0.9904 2.361e-07 -1.06e-07 -0.007428 1.78e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003358 -0.003168 -0.007914 0.00619 0.9699 0.9742 0.006449 0.834 0.8249 0.01804 ] Network output: [ 0.9998 0.0005423 0.000856 -1.574e-05 7.067e-06 -0.001123 -1.186e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1965 -0.03363 -0.1754 0.1901 0.9835 0.9932 0.2198 0.4416 0.8715 0.7168 ] Network output: [ -0.01041 1.002 1.01 -1.05e-07 4.714e-08 0.009145 -7.913e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006047 0.0004899 0.004443 0.003654 0.9889 0.9919 0.006161 0.8623 0.8955 0.01302 ] Network output: [ -0.0005533 0.00267 1.001 -4.955e-05 2.224e-05 0.9969 -3.734e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2084 0.09696 0.3387 0.1463 0.985 0.994 0.2091 0.4459 0.8781 0.7111 ] Network output: [ 0.00556 -0.02672 0.9948 2.97e-05 -1.333e-05 1.021 2.238e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.103 0.09088 0.1816 0.2008 0.9873 0.9919 0.1031 0.7603 0.8672 0.3057 ] Network output: [ -0.005312 0.02586 1.003 3.127e-05 -1.404e-05 0.9816 2.357e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09085 0.08891 0.1651 0.1954 0.9853 0.9912 0.09086 0.6851 0.8437 0.2453 ] Network output: [ 0.0001564 1 -0.0002227 4.201e-06 -1.886e-06 0.9999 3.166e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004069 Epoch 8104 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01073 0.9955 0.9904 2.349e-07 -1.054e-07 -0.007429 1.77e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003358 -0.003168 -0.007913 0.006189 0.9699 0.9742 0.006449 0.834 0.8249 0.01804 ] Network output: [ 0.9998 0.0005418 0.0008555 -1.573e-05 7.06e-06 -0.001122 -1.185e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1965 -0.03363 -0.1754 0.1901 0.9835 0.9932 0.2198 0.4416 0.8715 0.7167 ] Network output: [ -0.01041 1.002 1.01 -1.057e-07 4.746e-08 0.009143 -7.967e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006048 0.00049 0.004444 0.003654 0.9889 0.9919 0.006162 0.8623 0.8955 0.01302 ] Network output: [ -0.0005529 0.002669 1.001 -4.95e-05 2.222e-05 0.9969 -3.73e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2085 0.09697 0.3387 0.1463 0.985 0.994 0.2091 0.4459 0.8781 0.7111 ] Network output: [ 0.005558 -0.02671 0.9948 2.967e-05 -1.332e-05 1.021 2.236e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.103 0.09089 0.1816 0.2008 0.9873 0.9919 0.1031 0.7602 0.8672 0.3057 ] Network output: [ -0.00531 0.02585 1.003 3.124e-05 -1.403e-05 0.9816 2.355e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09085 0.08891 0.1651 0.1954 0.9853 0.9912 0.09086 0.6851 0.8437 0.2453 ] Network output: [ 0.0001563 1 -0.0002225 4.197e-06 -1.884e-06 0.9999 3.163e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004066 Epoch 8105 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01073 0.9955 0.9904 2.336e-07 -1.049e-07 -0.007429 1.76e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003358 -0.003168 -0.007912 0.006189 0.9699 0.9742 0.00645 0.834 0.8249 0.01804 ] Network output: [ 0.9998 0.0005414 0.000855 -1.571e-05 7.053e-06 -0.001121 -1.184e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1965 -0.03364 -0.1754 0.1901 0.9835 0.9932 0.2199 0.4416 0.8715 0.7167 ] Network output: [ -0.01041 1.002 1.01 -1.064e-07 4.778e-08 0.009141 -8.021e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006048 0.0004901 0.004444 0.003653 0.9889 0.9919 0.006162 0.8623 0.8955 0.01301 ] Network output: [ -0.0005526 0.002668 1.001 -4.945e-05 2.22e-05 0.9969 -3.727e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2085 0.09697 0.3387 0.1463 0.985 0.994 0.2091 0.4459 0.8781 0.711 ] Network output: [ 0.005556 -0.02671 0.9948 2.964e-05 -1.331e-05 1.021 2.234e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.103 0.09089 0.1816 0.2008 0.9873 0.9919 0.1031 0.7602 0.8672 0.3057 ] Network output: [ -0.005309 0.02584 1.003 3.121e-05 -1.401e-05 0.9816 2.352e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09085 0.08891 0.1651 0.1954 0.9853 0.9912 0.09086 0.6851 0.8437 0.2453 ] Network output: [ 0.0001563 1 -0.0002222 4.193e-06 -1.882e-06 0.9999 3.16e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004064 Epoch 8106 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01073 0.9955 0.9904 2.323e-07 -1.043e-07 -0.00743 1.751e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003358 -0.003168 -0.007911 0.006188 0.9699 0.9742 0.00645 0.834 0.8249 0.01804 ] Network output: [ 0.9998 0.0005409 0.0008544 -1.57e-05 7.046e-06 -0.00112 -1.183e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1965 -0.03364 -0.1754 0.1901 0.9835 0.9932 0.2199 0.4416 0.8715 0.7167 ] Network output: [ -0.01041 1.002 1.01 -1.072e-07 4.811e-08 0.009139 -8.076e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006049 0.0004902 0.004444 0.003653 0.9889 0.9919 0.006163 0.8622 0.8955 0.01301 ] Network output: [ -0.0005523 0.002667 1.001 -4.94e-05 2.218e-05 0.9969 -3.723e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2085 0.09698 0.3387 0.1463 0.985 0.994 0.2092 0.4459 0.8781 0.711 ] Network output: [ 0.005555 -0.0267 0.9948 2.961e-05 -1.329e-05 1.021 2.231e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.103 0.0909 0.1816 0.2008 0.9873 0.9919 0.1031 0.7602 0.8672 0.3057 ] Network output: [ -0.005307 0.02583 1.003 3.118e-05 -1.4e-05 0.9816 2.35e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09085 0.08892 0.1651 0.1954 0.9853 0.9912 0.09086 0.6851 0.8437 0.2453 ] Network output: [ 0.0001562 1 -0.000222 4.189e-06 -1.88e-06 0.9999 3.157e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004062 Epoch 8107 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01073 0.9956 0.9904 2.31e-07 -1.037e-07 -0.00743 1.741e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003358 -0.003169 -0.00791 0.006187 0.9699 0.9742 0.00645 0.834 0.8249 0.01804 ] Network output: [ 0.9998 0.0005405 0.0008539 -1.568e-05 7.039e-06 -0.001119 -1.182e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1965 -0.03364 -0.1753 0.19 0.9835 0.9932 0.2199 0.4416 0.8715 0.7167 ] Network output: [ -0.01041 1.002 1.01 -1.079e-07 4.843e-08 0.009137 -8.13e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006049 0.0004902 0.004444 0.003653 0.9889 0.9919 0.006163 0.8622 0.8955 0.01301 ] Network output: [ -0.0005519 0.002666 1.001 -4.935e-05 2.216e-05 0.9969 -3.719e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2085 0.09698 0.3387 0.1463 0.985 0.994 0.2092 0.4459 0.8781 0.711 ] Network output: [ 0.005553 -0.02669 0.9948 2.958e-05 -1.328e-05 1.021 2.229e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.103 0.09091 0.1816 0.2008 0.9873 0.9919 0.1031 0.7602 0.8672 0.3057 ] Network output: [ -0.005305 0.02582 1.003 3.115e-05 -1.399e-05 0.9816 2.348e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09085 0.08892 0.1651 0.1954 0.9853 0.9912 0.09087 0.6851 0.8437 0.2453 ] Network output: [ 0.0001561 1 -0.0002217 4.185e-06 -1.879e-06 0.9999 3.154e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004059 Epoch 8108 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01072 0.9956 0.9904 2.297e-07 -1.031e-07 -0.007431 1.731e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003359 -0.003169 -0.007909 0.006187 0.9699 0.9742 0.006451 0.834 0.8249 0.01804 ] Network output: [ 0.9998 0.00054 0.0008534 -1.566e-05 7.032e-06 -0.001118 -1.181e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1966 -0.03364 -0.1753 0.19 0.9835 0.9932 0.2199 0.4416 0.8715 0.7167 ] Network output: [ -0.01041 1.002 1.01 -1.086e-07 4.875e-08 0.009136 -8.183e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00605 0.0004903 0.004444 0.003652 0.9889 0.9919 0.006164 0.8622 0.8955 0.01301 ] Network output: [ -0.0005516 0.002665 1.001 -4.93e-05 2.213e-05 0.9969 -3.715e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2085 0.09699 0.3387 0.1463 0.985 0.994 0.2092 0.4459 0.8781 0.711 ] Network output: [ 0.005551 -0.02668 0.9948 2.955e-05 -1.327e-05 1.021 2.227e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.103 0.09091 0.1816 0.2008 0.9873 0.9919 0.1031 0.7602 0.8672 0.3057 ] Network output: [ -0.005303 0.02581 1.003 3.112e-05 -1.397e-05 0.9816 2.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09086 0.08892 0.1651 0.1954 0.9853 0.9912 0.09087 0.685 0.8437 0.2453 ] Network output: [ 0.000156 1 -0.0002215 4.18e-06 -1.877e-06 0.9999 3.151e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004057 Epoch 8109 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01072 0.9956 0.9904 2.285e-07 -1.026e-07 -0.007431 1.722e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003359 -0.003169 -0.007908 0.006186 0.9699 0.9742 0.006451 0.834 0.8249 0.01803 ] Network output: [ 0.9998 0.0005396 0.0008528 -1.565e-05 7.025e-06 -0.001117 -1.179e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1966 -0.03364 -0.1753 0.19 0.9835 0.9932 0.2199 0.4416 0.8715 0.7167 ] Network output: [ -0.01041 1.002 1.01 -1.093e-07 4.907e-08 0.009134 -8.237e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006051 0.0004904 0.004444 0.003652 0.9889 0.9919 0.006165 0.8622 0.8955 0.01301 ] Network output: [ -0.0005512 0.002664 1.001 -4.925e-05 2.211e-05 0.9969 -3.712e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2085 0.097 0.3387 0.1462 0.985 0.994 0.2092 0.4458 0.8781 0.711 ] Network output: [ 0.005549 -0.02667 0.9948 2.952e-05 -1.325e-05 1.021 2.225e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1031 0.09092 0.1816 0.2008 0.9873 0.9919 0.1031 0.7601 0.8672 0.3057 ] Network output: [ -0.005301 0.0258 1.003 3.109e-05 -1.396e-05 0.9816 2.343e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09086 0.08892 0.1651 0.1954 0.9853 0.9912 0.09087 0.685 0.8437 0.2453 ] Network output: [ 0.0001559 1 -0.0002212 4.176e-06 -1.875e-06 0.9999 3.147e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004054 Epoch 8110 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01072 0.9956 0.9904 2.272e-07 -1.02e-07 -0.007431 1.712e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003359 -0.003169 -0.007907 0.006185 0.9699 0.9742 0.006451 0.8339 0.8249 0.01803 ] Network output: [ 0.9998 0.0005391 0.0008523 -1.563e-05 7.018e-06 -0.001116 -1.178e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1966 -0.03365 -0.1753 0.19 0.9835 0.9932 0.2199 0.4416 0.8715 0.7167 ] Network output: [ -0.0104 1.002 1.01 -1.1e-07 4.939e-08 0.009132 -8.291e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006051 0.0004905 0.004444 0.003651 0.9889 0.9919 0.006165 0.8622 0.8955 0.01301 ] Network output: [ -0.0005509 0.002663 1.001 -4.92e-05 2.209e-05 0.9969 -3.708e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2085 0.097 0.3388 0.1462 0.985 0.994 0.2092 0.4458 0.8781 0.711 ] Network output: [ 0.005547 -0.02666 0.9948 2.949e-05 -1.324e-05 1.021 2.223e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1031 0.09092 0.1816 0.2008 0.9873 0.9919 0.1031 0.7601 0.8672 0.3057 ] Network output: [ -0.005299 0.02579 1.003 3.106e-05 -1.395e-05 0.9816 2.341e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09086 0.08892 0.1651 0.1954 0.9853 0.9912 0.09087 0.685 0.8437 0.2453 ] Network output: [ 0.0001559 1 -0.000221 4.172e-06 -1.873e-06 0.9999 3.144e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004052 Epoch 8111 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01072 0.9956 0.9904 2.259e-07 -1.014e-07 -0.007432 1.703e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003359 -0.003169 -0.007906 0.006185 0.9699 0.9742 0.006451 0.8339 0.8249 0.01803 ] Network output: [ 0.9998 0.0005387 0.0008518 -1.562e-05 7.012e-06 -0.001116 -1.177e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1966 -0.03365 -0.1753 0.19 0.9835 0.9932 0.2199 0.4415 0.8715 0.7167 ] Network output: [ -0.0104 1.002 1.01 -1.107e-07 4.971e-08 0.00913 -8.344e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006052 0.0004906 0.004444 0.003651 0.9889 0.9919 0.006166 0.8622 0.8955 0.01301 ] Network output: [ -0.0005505 0.002662 1.001 -4.915e-05 2.207e-05 0.9969 -3.704e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2085 0.09701 0.3388 0.1462 0.985 0.994 0.2092 0.4458 0.8781 0.711 ] Network output: [ 0.005546 -0.02665 0.9948 2.946e-05 -1.323e-05 1.021 2.22e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1031 0.09093 0.1816 0.2008 0.9873 0.9919 0.1031 0.7601 0.8672 0.3057 ] Network output: [ -0.005297 0.02578 1.003 3.103e-05 -1.393e-05 0.9816 2.339e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09086 0.08892 0.1651 0.1954 0.9853 0.9912 0.09087 0.685 0.8437 0.2453 ] Network output: [ 0.0001558 1 -0.0002207 4.168e-06 -1.871e-06 0.9999 3.141e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000405 Epoch 8112 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01072 0.9956 0.9904 2.247e-07 -1.009e-07 -0.007432 1.693e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003359 -0.003169 -0.007905 0.006184 0.9699 0.9742 0.006452 0.8339 0.8249 0.01803 ] Network output: [ 0.9998 0.0005382 0.0008513 -1.56e-05 7.005e-06 -0.001115 -1.176e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1966 -0.03365 -0.1753 0.19 0.9835 0.9932 0.2199 0.4415 0.8715 0.7167 ] Network output: [ -0.0104 1.002 1.01 -1.114e-07 5.002e-08 0.009128 -8.398e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006052 0.0004906 0.004444 0.00365 0.9889 0.9919 0.006166 0.8622 0.8955 0.01301 ] Network output: [ -0.0005502 0.002661 1.001 -4.91e-05 2.204e-05 0.9969 -3.701e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2085 0.09701 0.3388 0.1462 0.985 0.994 0.2092 0.4458 0.8781 0.711 ] Network output: [ 0.005544 -0.02664 0.9948 2.943e-05 -1.321e-05 1.021 2.218e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1031 0.09093 0.1816 0.2008 0.9873 0.9919 0.1031 0.7601 0.8672 0.3057 ] Network output: [ -0.005295 0.02577 1.003 3.1e-05 -1.392e-05 0.9817 2.337e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09086 0.08893 0.1651 0.1954 0.9853 0.9912 0.09087 0.6849 0.8437 0.2453 ] Network output: [ 0.0001557 1 -0.0002204 4.164e-06 -1.869e-06 0.9999 3.138e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004047 Epoch 8113 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01072 0.9956 0.9904 2.234e-07 -1.003e-07 -0.007433 1.684e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003359 -0.003169 -0.007904 0.006183 0.9699 0.9742 0.006452 0.8339 0.8249 0.01803 ] Network output: [ 0.9998 0.0005378 0.0008507 -1.559e-05 6.998e-06 -0.001114 -1.175e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1966 -0.03365 -0.1752 0.19 0.9835 0.9932 0.22 0.4415 0.8715 0.7167 ] Network output: [ -0.0104 1.002 1.01 -1.121e-07 5.034e-08 0.009126 -8.451e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006053 0.0004907 0.004444 0.00365 0.9889 0.9919 0.006167 0.8622 0.8955 0.01301 ] Network output: [ -0.0005498 0.00266 1.001 -4.905e-05 2.202e-05 0.9969 -3.697e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2086 0.09702 0.3388 0.1462 0.985 0.994 0.2092 0.4458 0.8781 0.711 ] Network output: [ 0.005542 -0.02663 0.9948 2.94e-05 -1.32e-05 1.021 2.216e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1031 0.09094 0.1816 0.2008 0.9873 0.9919 0.1031 0.7601 0.8671 0.3057 ] Network output: [ -0.005293 0.02576 1.003 3.097e-05 -1.391e-05 0.9817 2.334e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09086 0.08893 0.1651 0.1954 0.9853 0.9912 0.09088 0.6849 0.8437 0.2453 ] Network output: [ 0.0001556 1 -0.0002202 4.16e-06 -1.868e-06 0.9999 3.135e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004045 Epoch 8114 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01071 0.9956 0.9904 2.222e-07 -9.974e-08 -0.007433 1.674e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003359 -0.00317 -0.007903 0.006183 0.9699 0.9742 0.006452 0.8339 0.8249 0.01803 ] Network output: [ 0.9998 0.0005373 0.0008502 -1.557e-05 6.991e-06 -0.001113 -1.174e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1966 -0.03365 -0.1752 0.19 0.9835 0.9932 0.22 0.4415 0.8715 0.7167 ] Network output: [ -0.0104 1.002 1.01 -1.128e-07 5.066e-08 0.009124 -8.504e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006054 0.0004908 0.004444 0.00365 0.9889 0.9919 0.006168 0.8622 0.8955 0.013 ] Network output: [ -0.0005495 0.002659 1.001 -4.9e-05 2.2e-05 0.9969 -3.693e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2086 0.09703 0.3388 0.1462 0.985 0.994 0.2092 0.4458 0.8781 0.711 ] Network output: [ 0.00554 -0.02662 0.9948 2.938e-05 -1.319e-05 1.021 2.214e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1031 0.09095 0.1816 0.2008 0.9873 0.9919 0.1032 0.76 0.8671 0.3057 ] Network output: [ -0.005291 0.02575 1.003 3.094e-05 -1.389e-05 0.9817 2.332e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09087 0.08893 0.1651 0.1954 0.9853 0.9912 0.09088 0.6849 0.8437 0.2453 ] Network output: [ 0.0001556 1 -0.0002199 4.156e-06 -1.866e-06 0.9999 3.132e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004043 Epoch 8115 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01071 0.9956 0.9904 2.209e-07 -9.918e-08 -0.007433 1.665e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00336 -0.00317 -0.007902 0.006182 0.9699 0.9742 0.006453 0.8339 0.8249 0.01803 ] Network output: [ 0.9998 0.0005368 0.0008497 -1.556e-05 6.984e-06 -0.001112 -1.172e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1966 -0.03366 -0.1752 0.19 0.9835 0.9932 0.22 0.4415 0.8715 0.7167 ] Network output: [ -0.0104 1.002 1.01 -1.135e-07 5.097e-08 0.009122 -8.557e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006054 0.0004909 0.004444 0.003649 0.9889 0.9919 0.006168 0.8622 0.8955 0.013 ] Network output: [ -0.0005492 0.002658 1.001 -4.895e-05 2.198e-05 0.9969 -3.689e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2086 0.09703 0.3388 0.1462 0.985 0.994 0.2093 0.4458 0.8781 0.711 ] Network output: [ 0.005538 -0.02661 0.9948 2.935e-05 -1.317e-05 1.021 2.212e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1031 0.09095 0.1816 0.2008 0.9873 0.9919 0.1032 0.76 0.8671 0.3057 ] Network output: [ -0.005289 0.02573 1.003 3.092e-05 -1.388e-05 0.9817 2.33e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09087 0.08893 0.1651 0.1954 0.9853 0.9912 0.09088 0.6849 0.8436 0.2453 ] Network output: [ 0.0001555 1 -0.0002197 4.152e-06 -1.864e-06 0.9999 3.129e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000404 Epoch 8116 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01071 0.9956 0.9904 2.197e-07 -9.862e-08 -0.007434 1.655e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00336 -0.00317 -0.007901 0.006182 0.9699 0.9742 0.006453 0.8339 0.8249 0.01802 ] Network output: [ 0.9998 0.0005364 0.0008491 -1.554e-05 6.977e-06 -0.001111 -1.171e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1966 -0.03366 -0.1752 0.19 0.9835 0.9932 0.22 0.4415 0.8715 0.7167 ] Network output: [ -0.0104 1.002 1.01 -1.142e-07 5.129e-08 0.00912 -8.61e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006055 0.000491 0.004444 0.003649 0.9889 0.9919 0.006169 0.8622 0.8955 0.013 ] Network output: [ -0.0005488 0.002657 1.001 -4.891e-05 2.196e-05 0.997 -3.686e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2086 0.09704 0.3388 0.1462 0.985 0.994 0.2093 0.4458 0.8781 0.711 ] Network output: [ 0.005536 -0.0266 0.9948 2.932e-05 -1.316e-05 1.021 2.209e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1031 0.09096 0.1816 0.2008 0.9873 0.9919 0.1032 0.76 0.8671 0.3057 ] Network output: [ -0.005288 0.02572 1.003 3.089e-05 -1.387e-05 0.9817 2.328e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09087 0.08893 0.1651 0.1954 0.9853 0.9912 0.09088 0.6848 0.8436 0.2453 ] Network output: [ 0.0001554 1 -0.0002194 4.148e-06 -1.862e-06 0.9999 3.126e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004038 Epoch 8117 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01071 0.9956 0.9904 2.184e-07 -9.805e-08 -0.007434 1.646e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00336 -0.00317 -0.0079 0.006181 0.9699 0.9742 0.006453 0.8339 0.8249 0.01802 ] Network output: [ 0.9998 0.0005359 0.0008486 -1.553e-05 6.97e-06 -0.00111 -1.17e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1966 -0.03366 -0.1752 0.19 0.9835 0.9932 0.22 0.4415 0.8714 0.7167 ] Network output: [ -0.0104 1.002 1.01 -1.149e-07 5.16e-08 0.009118 -8.662e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006055 0.000491 0.004444 0.003648 0.9889 0.9919 0.006169 0.8622 0.8955 0.013 ] Network output: [ -0.0005485 0.002656 1.001 -4.886e-05 2.193e-05 0.997 -3.682e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2086 0.09704 0.3388 0.1462 0.985 0.994 0.2093 0.4457 0.8781 0.711 ] Network output: [ 0.005535 -0.02659 0.9948 2.929e-05 -1.315e-05 1.021 2.207e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1031 0.09096 0.1816 0.2008 0.9873 0.9919 0.1032 0.76 0.8671 0.3057 ] Network output: [ -0.005286 0.02571 1.003 3.086e-05 -1.385e-05 0.9817 2.325e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09087 0.08893 0.1651 0.1954 0.9853 0.9912 0.09088 0.6848 0.8436 0.2453 ] Network output: [ 0.0001553 1 -0.0002192 4.144e-06 -1.86e-06 0.9999 3.123e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004035 Epoch 8118 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01071 0.9956 0.9905 2.172e-07 -9.75e-08 -0.007435 1.637e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00336 -0.00317 -0.007899 0.00618 0.9699 0.9742 0.006454 0.8339 0.8249 0.01802 ] Network output: [ 0.9998 0.0005355 0.0008481 -1.551e-05 6.963e-06 -0.001109 -1.169e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1967 -0.03366 -0.1752 0.19 0.9835 0.9932 0.22 0.4415 0.8714 0.7167 ] Network output: [ -0.0104 1.002 1.01 -1.156e-07 5.191e-08 0.009116 -8.715e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006056 0.0004911 0.004444 0.003648 0.9889 0.9919 0.00617 0.8621 0.8955 0.013 ] Network output: [ -0.0005481 0.002655 1.001 -4.881e-05 2.191e-05 0.997 -3.678e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2086 0.09705 0.3388 0.1462 0.985 0.994 0.2093 0.4457 0.8781 0.711 ] Network output: [ 0.005533 -0.02658 0.9948 2.926e-05 -1.314e-05 1.021 2.205e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1031 0.09097 0.1816 0.2008 0.9873 0.9919 0.1032 0.7599 0.8671 0.3057 ] Network output: [ -0.005284 0.0257 1.003 3.083e-05 -1.384e-05 0.9817 2.323e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09087 0.08894 0.1651 0.1954 0.9853 0.9912 0.09088 0.6848 0.8436 0.2453 ] Network output: [ 0.0001552 1 -0.0002189 4.14e-06 -1.858e-06 0.9999 3.12e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004033 Epoch 8119 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01071 0.9956 0.9905 2.159e-07 -9.694e-08 -0.007435 1.627e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00336 -0.00317 -0.007898 0.00618 0.9699 0.9742 0.006454 0.8339 0.8249 0.01802 ] Network output: [ 0.9998 0.000535 0.0008476 -1.549e-05 6.956e-06 -0.001108 -1.168e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1967 -0.03366 -0.1752 0.19 0.9835 0.9932 0.22 0.4414 0.8714 0.7167 ] Network output: [ -0.01039 1.002 1.01 -1.163e-07 5.223e-08 0.009114 -8.767e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006057 0.0004912 0.004444 0.003648 0.9889 0.9919 0.006171 0.8621 0.8955 0.013 ] Network output: [ -0.0005478 0.002655 1.001 -4.876e-05 2.189e-05 0.997 -3.675e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2086 0.09705 0.3388 0.1462 0.985 0.994 0.2093 0.4457 0.8781 0.711 ] Network output: [ 0.005531 -0.02657 0.9948 2.923e-05 -1.312e-05 1.021 2.203e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1031 0.09098 0.1816 0.2008 0.9873 0.9919 0.1032 0.7599 0.8671 0.3057 ] Network output: [ -0.005282 0.02569 1.003 3.08e-05 -1.383e-05 0.9817 2.321e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09087 0.08894 0.1651 0.1954 0.9853 0.9912 0.09089 0.6848 0.8436 0.2454 ] Network output: [ 0.0001552 1 -0.0002187 4.136e-06 -1.857e-06 0.9999 3.117e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004031 Epoch 8120 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01071 0.9956 0.9905 2.147e-07 -9.638e-08 -0.007436 1.618e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00336 -0.00317 -0.007897 0.006179 0.9699 0.9742 0.006454 0.8339 0.8249 0.01802 ] Network output: [ 0.9998 0.0005346 0.000847 -1.548e-05 6.949e-06 -0.001107 -1.167e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1967 -0.03367 -0.1751 0.19 0.9835 0.9932 0.22 0.4414 0.8714 0.7167 ] Network output: [ -0.01039 1.002 1.01 -1.17e-07 5.254e-08 0.009112 -8.82e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006057 0.0004913 0.004444 0.003647 0.9889 0.9919 0.006171 0.8621 0.8955 0.013 ] Network output: [ -0.0005474 0.002654 1.001 -4.871e-05 2.187e-05 0.997 -3.671e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2086 0.09706 0.3389 0.1462 0.985 0.994 0.2093 0.4457 0.878 0.711 ] Network output: [ 0.005529 -0.02656 0.9948 2.92e-05 -1.311e-05 1.021 2.201e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1031 0.09098 0.1816 0.2008 0.9873 0.9919 0.1032 0.7599 0.8671 0.3057 ] Network output: [ -0.00528 0.02568 1.003 3.077e-05 -1.381e-05 0.9817 2.319e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09088 0.08894 0.1651 0.1954 0.9853 0.9912 0.09089 0.6848 0.8436 0.2454 ] Network output: [ 0.0001551 1 -0.0002184 4.131e-06 -1.855e-06 0.9999 3.114e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004028 Epoch 8121 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0107 0.9956 0.9905 2.134e-07 -9.582e-08 -0.007436 1.609e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00336 -0.003171 -0.007896 0.006178 0.9699 0.9742 0.006455 0.8339 0.8249 0.01802 ] Network output: [ 0.9998 0.0005341 0.0008465 -1.546e-05 6.942e-06 -0.001106 -1.165e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1967 -0.03367 -0.1751 0.19 0.9835 0.9932 0.22 0.4414 0.8714 0.7167 ] Network output: [ -0.01039 1.002 1.01 -1.177e-07 5.285e-08 0.00911 -8.872e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006058 0.0004913 0.004444 0.003647 0.9889 0.9919 0.006172 0.8621 0.8955 0.013 ] Network output: [ -0.0005471 0.002653 1.001 -4.866e-05 2.184e-05 0.997 -3.667e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2086 0.09707 0.3389 0.1462 0.985 0.994 0.2093 0.4457 0.878 0.7109 ] Network output: [ 0.005527 -0.02655 0.9948 2.917e-05 -1.31e-05 1.021 2.199e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1031 0.09099 0.1816 0.2008 0.9873 0.9919 0.1032 0.7599 0.8671 0.3057 ] Network output: [ -0.005278 0.02567 1.003 3.074e-05 -1.38e-05 0.9817 2.316e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09088 0.08894 0.1651 0.1954 0.9853 0.9912 0.09089 0.6847 0.8436 0.2454 ] Network output: [ 0.000155 1 -0.0002182 4.127e-06 -1.853e-06 0.9999 3.111e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004026 Epoch 8122 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0107 0.9956 0.9905 2.122e-07 -9.527e-08 -0.007436 1.599e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00336 -0.003171 -0.007895 0.006178 0.9699 0.9742 0.006455 0.8339 0.8249 0.01802 ] Network output: [ 0.9998 0.0005337 0.000846 -1.545e-05 6.936e-06 -0.001105 -1.164e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1967 -0.03367 -0.1751 0.19 0.9835 0.9932 0.2201 0.4414 0.8714 0.7166 ] Network output: [ -0.01039 1.002 1.01 -1.184e-07 5.316e-08 0.009108 -8.924e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006058 0.0004914 0.004444 0.003646 0.9889 0.9919 0.006172 0.8621 0.8955 0.013 ] Network output: [ -0.0005468 0.002652 1.001 -4.861e-05 2.182e-05 0.997 -3.663e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2087 0.09707 0.3389 0.1462 0.985 0.994 0.2093 0.4457 0.878 0.7109 ] Network output: [ 0.005525 -0.02654 0.9948 2.914e-05 -1.308e-05 1.021 2.196e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1031 0.09099 0.1816 0.2008 0.9873 0.9919 0.1032 0.7599 0.8671 0.3057 ] Network output: [ -0.005276 0.02566 1.003 3.071e-05 -1.379e-05 0.9817 2.314e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09088 0.08894 0.1651 0.1954 0.9853 0.9912 0.09089 0.6847 0.8436 0.2454 ] Network output: [ 0.0001549 1 -0.0002179 4.123e-06 -1.851e-06 0.9999 3.107e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004024 Epoch 8123 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0107 0.9956 0.9905 2.11e-07 -9.471e-08 -0.007437 1.59e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003361 -0.003171 -0.007894 0.006177 0.9699 0.9742 0.006455 0.8338 0.8248 0.01801 ] Network output: [ 0.9998 0.0005333 0.0008455 -1.543e-05 6.929e-06 -0.001104 -1.163e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1967 -0.03367 -0.1751 0.1899 0.9835 0.9932 0.2201 0.4414 0.8714 0.7166 ] Network output: [ -0.01039 1.002 1.01 -1.191e-07 5.347e-08 0.009106 -8.976e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006059 0.0004915 0.004444 0.003646 0.9889 0.9919 0.006173 0.8621 0.8955 0.01299 ] Network output: [ -0.0005464 0.002651 1.001 -4.856e-05 2.18e-05 0.997 -3.66e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2087 0.09708 0.3389 0.1462 0.985 0.994 0.2093 0.4457 0.878 0.7109 ] Network output: [ 0.005524 -0.02653 0.9948 2.912e-05 -1.307e-05 1.021 2.194e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1031 0.091 0.1816 0.2008 0.9873 0.9919 0.1032 0.7598 0.8671 0.3057 ] Network output: [ -0.005274 0.02565 1.003 3.068e-05 -1.377e-05 0.9817 2.312e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09088 0.08894 0.1651 0.1954 0.9853 0.9912 0.09089 0.6847 0.8436 0.2454 ] Network output: [ 0.0001548 1 -0.0002177 4.119e-06 -1.849e-06 0.9999 3.104e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004021 Epoch 8124 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0107 0.9956 0.9905 2.097e-07 -9.416e-08 -0.007437 1.581e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003361 -0.003171 -0.007893 0.006176 0.9699 0.9742 0.006455 0.8338 0.8248 0.01801 ] Network output: [ 0.9998 0.0005328 0.0008449 -1.542e-05 6.922e-06 -0.001103 -1.162e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1967 -0.03367 -0.1751 0.1899 0.9835 0.9932 0.2201 0.4414 0.8714 0.7166 ] Network output: [ -0.01039 1.002 1.01 -1.198e-07 5.378e-08 0.009104 -9.028e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006059 0.0004916 0.004444 0.003645 0.9889 0.9919 0.006174 0.8621 0.8955 0.01299 ] Network output: [ -0.0005461 0.00265 1.001 -4.851e-05 2.178e-05 0.997 -3.656e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2087 0.09708 0.3389 0.1462 0.985 0.994 0.2094 0.4457 0.878 0.7109 ] Network output: [ 0.005522 -0.02652 0.9948 2.909e-05 -1.306e-05 1.021 2.192e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1031 0.091 0.1816 0.2008 0.9873 0.9919 0.1032 0.7598 0.8671 0.3057 ] Network output: [ -0.005272 0.02564 1.003 3.065e-05 -1.376e-05 0.9817 2.31e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09088 0.08895 0.1651 0.1954 0.9853 0.9912 0.0909 0.6847 0.8436 0.2454 ] Network output: [ 0.0001548 1 -0.0002174 4.115e-06 -1.847e-06 0.9999 3.101e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004019 Epoch 8125 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0107 0.9956 0.9905 2.085e-07 -9.361e-08 -0.007438 1.571e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003361 -0.003171 -0.007892 0.006176 0.9699 0.9742 0.006456 0.8338 0.8248 0.01801 ] Network output: [ 0.9998 0.0005324 0.0008444 -1.54e-05 6.915e-06 -0.001102 -1.161e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1967 -0.03367 -0.1751 0.1899 0.9835 0.9932 0.2201 0.4414 0.8714 0.7166 ] Network output: [ -0.01039 1.002 1.01 -1.205e-07 5.409e-08 0.009102 -9.079e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00606 0.0004917 0.004444 0.003645 0.9889 0.9919 0.006174 0.8621 0.8955 0.01299 ] Network output: [ -0.0005457 0.002649 1.001 -4.846e-05 2.176e-05 0.997 -3.652e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2087 0.09709 0.3389 0.1462 0.985 0.994 0.2094 0.4456 0.878 0.7109 ] Network output: [ 0.00552 -0.02651 0.9948 2.906e-05 -1.305e-05 1.021 2.19e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1032 0.09101 0.1816 0.2008 0.9873 0.9919 0.1032 0.7598 0.8671 0.3057 ] Network output: [ -0.00527 0.02563 1.003 3.062e-05 -1.375e-05 0.9817 2.308e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09088 0.08895 0.1651 0.1954 0.9853 0.9912 0.0909 0.6846 0.8436 0.2454 ] Network output: [ 0.0001547 1 -0.0002172 4.111e-06 -1.846e-06 0.9999 3.098e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004016 Epoch 8126 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0107 0.9956 0.9905 2.073e-07 -9.306e-08 -0.007438 1.562e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003361 -0.003171 -0.007891 0.006175 0.9699 0.9742 0.006456 0.8338 0.8248 0.01801 ] Network output: [ 0.9998 0.0005319 0.0008439 -1.539e-05 6.908e-06 -0.001101 -1.16e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1967 -0.03368 -0.1751 0.1899 0.9835 0.9932 0.2201 0.4414 0.8714 0.7166 ] Network output: [ -0.01039 1.002 1.01 -1.212e-07 5.439e-08 0.0091 -9.131e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006061 0.0004917 0.004444 0.003645 0.9889 0.9919 0.006175 0.8621 0.8955 0.01299 ] Network output: [ -0.0005454 0.002648 1.001 -4.841e-05 2.173e-05 0.997 -3.649e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2087 0.09709 0.3389 0.1462 0.985 0.994 0.2094 0.4456 0.878 0.7109 ] Network output: [ 0.005518 -0.0265 0.9948 2.903e-05 -1.303e-05 1.021 2.188e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1032 0.09102 0.1816 0.2008 0.9873 0.9919 0.1032 0.7598 0.8671 0.3057 ] Network output: [ -0.005268 0.02562 1.003 3.059e-05 -1.373e-05 0.9817 2.305e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09089 0.08895 0.1651 0.1954 0.9853 0.9912 0.0909 0.6846 0.8436 0.2454 ] Network output: [ 0.0001546 1 -0.0002169 4.107e-06 -1.844e-06 0.9999 3.095e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004014 Epoch 8127 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01069 0.9956 0.9905 2.061e-07 -9.251e-08 -0.007438 1.553e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003361 -0.003172 -0.00789 0.006174 0.9699 0.9742 0.006456 0.8338 0.8248 0.01801 ] Network output: [ 0.9998 0.0005315 0.0008434 -1.537e-05 6.901e-06 -0.001101 -1.159e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1967 -0.03368 -0.175 0.1899 0.9835 0.9932 0.2201 0.4414 0.8714 0.7166 ] Network output: [ -0.01039 1.002 1.01 -1.218e-07 5.47e-08 0.009098 -9.182e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006061 0.0004918 0.004444 0.003644 0.9889 0.9919 0.006176 0.8621 0.8954 0.01299 ] Network output: [ -0.000545 0.002647 1.001 -4.837e-05 2.171e-05 0.997 -3.645e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2087 0.0971 0.3389 0.1462 0.985 0.994 0.2094 0.4456 0.878 0.7109 ] Network output: [ 0.005516 -0.02649 0.9948 2.9e-05 -1.302e-05 1.021 2.186e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1032 0.09102 0.1816 0.2008 0.9873 0.9919 0.1032 0.7598 0.8671 0.3057 ] Network output: [ -0.005267 0.0256 1.003 3.056e-05 -1.372e-05 0.9817 2.303e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09089 0.08895 0.1651 0.1954 0.9853 0.9912 0.0909 0.6846 0.8436 0.2454 ] Network output: [ 0.0001545 1 -0.0002167 4.103e-06 -1.842e-06 0.9999 3.092e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004012 Epoch 8128 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01069 0.9956 0.9905 2.048e-07 -9.196e-08 -0.007439 1.544e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003361 -0.003172 -0.007889 0.006174 0.9699 0.9742 0.006457 0.8338 0.8248 0.01801 ] Network output: [ 0.9998 0.000531 0.0008428 -1.536e-05 6.894e-06 -0.0011 -1.157e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1967 -0.03368 -0.175 0.1899 0.9835 0.9932 0.2201 0.4413 0.8714 0.7166 ] Network output: [ -0.01038 1.002 1.01 -1.225e-07 5.5e-08 0.009096 -9.233e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006062 0.0004919 0.004444 0.003644 0.9889 0.9919 0.006176 0.8621 0.8954 0.01299 ] Network output: [ -0.0005447 0.002646 1.001 -4.832e-05 2.169e-05 0.997 -3.641e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2087 0.09711 0.3389 0.1462 0.985 0.994 0.2094 0.4456 0.878 0.7109 ] Network output: [ 0.005515 -0.02648 0.9948 2.897e-05 -1.301e-05 1.021 2.183e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1032 0.09103 0.1816 0.2007 0.9873 0.9919 0.1032 0.7597 0.8671 0.3057 ] Network output: [ -0.005265 0.02559 1.003 3.053e-05 -1.371e-05 0.9817 2.301e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09089 0.08895 0.1651 0.1954 0.9853 0.9912 0.0909 0.6846 0.8436 0.2454 ] Network output: [ 0.0001545 1 -0.0002164 4.099e-06 -1.84e-06 0.9999 3.089e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004009 Epoch 8129 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01069 0.9956 0.9905 2.036e-07 -9.142e-08 -0.007439 1.535e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003361 -0.003172 -0.007888 0.006173 0.9699 0.9742 0.006457 0.8338 0.8248 0.01801 ] Network output: [ 0.9998 0.0005306 0.0008423 -1.534e-05 6.888e-06 -0.001099 -1.156e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1968 -0.03368 -0.175 0.1899 0.9835 0.9932 0.2201 0.4413 0.8714 0.7166 ] Network output: [ -0.01038 1.002 1.01 -1.232e-07 5.531e-08 0.009095 -9.285e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006062 0.000492 0.004444 0.003643 0.9889 0.9919 0.006177 0.8621 0.8954 0.01299 ] Network output: [ -0.0005444 0.002645 1.001 -4.827e-05 2.167e-05 0.997 -3.638e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2087 0.09711 0.3389 0.1461 0.985 0.994 0.2094 0.4456 0.878 0.7109 ] Network output: [ 0.005513 -0.02647 0.9948 2.894e-05 -1.299e-05 1.021 2.181e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1032 0.09103 0.1816 0.2007 0.9873 0.9919 0.1032 0.7597 0.8671 0.3057 ] Network output: [ -0.005263 0.02558 1.003 3.05e-05 -1.369e-05 0.9817 2.299e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09089 0.08895 0.1651 0.1954 0.9853 0.9912 0.0909 0.6846 0.8435 0.2454 ] Network output: [ 0.0001544 1 -0.0002162 4.095e-06 -1.838e-06 0.9999 3.086e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004007 Epoch 8130 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01069 0.9956 0.9905 2.024e-07 -9.087e-08 -0.00744 1.525e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003362 -0.003172 -0.007887 0.006172 0.9699 0.9742 0.006457 0.8338 0.8248 0.018 ] Network output: [ 0.9998 0.0005301 0.0008418 -1.533e-05 6.881e-06 -0.001098 -1.155e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1968 -0.03368 -0.175 0.1899 0.9835 0.9932 0.2201 0.4413 0.8714 0.7166 ] Network output: [ -0.01038 1.002 1.01 -1.239e-07 5.561e-08 0.009093 -9.336e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006063 0.0004921 0.004444 0.003643 0.9889 0.9919 0.006177 0.862 0.8954 0.01299 ] Network output: [ -0.000544 0.002644 1.001 -4.822e-05 2.165e-05 0.997 -3.634e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2087 0.09712 0.3389 0.1461 0.985 0.994 0.2094 0.4456 0.878 0.7109 ] Network output: [ 0.005511 -0.02646 0.9948 2.891e-05 -1.298e-05 1.021 2.179e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1032 0.09104 0.1817 0.2007 0.9873 0.9919 0.1033 0.7597 0.867 0.3057 ] Network output: [ -0.005261 0.02557 1.003 3.047e-05 -1.368e-05 0.9817 2.296e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09089 0.08896 0.1651 0.1954 0.9853 0.9912 0.09091 0.6845 0.8435 0.2454 ] Network output: [ 0.0001543 1 -0.0002159 4.091e-06 -1.837e-06 0.9999 3.083e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004005 Epoch 8131 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01069 0.9956 0.9905 2.012e-07 -9.033e-08 -0.00744 1.516e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003362 -0.003172 -0.007886 0.006172 0.9699 0.9742 0.006458 0.8338 0.8248 0.018 ] Network output: [ 0.9998 0.0005297 0.0008413 -1.531e-05 6.874e-06 -0.001097 -1.154e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1968 -0.03369 -0.175 0.1899 0.9835 0.9932 0.2202 0.4413 0.8714 0.7166 ] Network output: [ -0.01038 1.002 1.01 -1.246e-07 5.592e-08 0.009091 -9.386e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006064 0.0004921 0.004444 0.003643 0.9889 0.9919 0.006178 0.862 0.8954 0.01299 ] Network output: [ -0.0005437 0.002643 1.001 -4.817e-05 2.163e-05 0.997 -3.63e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2087 0.09712 0.339 0.1461 0.985 0.994 0.2094 0.4456 0.878 0.7109 ] Network output: [ 0.005509 -0.02645 0.9948 2.889e-05 -1.297e-05 1.021 2.177e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1032 0.09104 0.1817 0.2007 0.9873 0.9919 0.1033 0.7597 0.867 0.3057 ] Network output: [ -0.005259 0.02556 1.003 3.044e-05 -1.367e-05 0.9818 2.294e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09089 0.08896 0.1651 0.1954 0.9853 0.9912 0.09091 0.6845 0.8435 0.2454 ] Network output: [ 0.0001542 1 -0.0002157 4.087e-06 -1.835e-06 0.9999 3.08e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004002 Epoch 8132 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01069 0.9956 0.9905 2e-07 -8.978e-08 -0.00744 1.507e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003362 -0.003172 -0.007885 0.006171 0.9699 0.9742 0.006458 0.8338 0.8248 0.018 ] Network output: [ 0.9998 0.0005292 0.0008408 -1.53e-05 6.867e-06 -0.001096 -1.153e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1968 -0.03369 -0.175 0.1899 0.9835 0.9932 0.2202 0.4413 0.8714 0.7166 ] Network output: [ -0.01038 1.002 1.01 -1.252e-07 5.622e-08 0.009089 -9.437e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006064 0.0004922 0.004444 0.003642 0.9889 0.9919 0.006179 0.862 0.8954 0.01299 ] Network output: [ -0.0005433 0.002642 1.001 -4.812e-05 2.16e-05 0.997 -3.627e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2088 0.09713 0.339 0.1461 0.985 0.994 0.2094 0.4456 0.878 0.7109 ] Network output: [ 0.005507 -0.02644 0.9947 2.886e-05 -1.295e-05 1.021 2.175e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1032 0.09105 0.1817 0.2007 0.9873 0.9919 0.1033 0.7597 0.867 0.3057 ] Network output: [ -0.005257 0.02555 1.003 3.041e-05 -1.365e-05 0.9818 2.292e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0909 0.08896 0.1651 0.1954 0.9853 0.9912 0.09091 0.6845 0.8435 0.2454 ] Network output: [ 0.0001542 1 -0.0002154 4.083e-06 -1.833e-06 0.9999 3.077e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0004 Epoch 8133 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01069 0.9956 0.9905 1.988e-07 -8.924e-08 -0.007441 1.498e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003362 -0.003172 -0.007884 0.00617 0.9699 0.9742 0.006458 0.8338 0.8248 0.018 ] Network output: [ 0.9998 0.0005288 0.0008402 -1.528e-05 6.86e-06 -0.001095 -1.152e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1968 -0.03369 -0.1749 0.1899 0.9835 0.9932 0.2202 0.4413 0.8714 0.7166 ] Network output: [ -0.01038 1.002 1.01 -1.259e-07 5.652e-08 0.009087 -9.488e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006065 0.0004923 0.004444 0.003642 0.9889 0.9919 0.006179 0.862 0.8954 0.01298 ] Network output: [ -0.000543 0.002641 1.001 -4.807e-05 2.158e-05 0.997 -3.623e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2088 0.09713 0.339 0.1461 0.985 0.994 0.2095 0.4456 0.878 0.7109 ] Network output: [ 0.005506 -0.02643 0.9947 2.883e-05 -1.294e-05 1.021 2.173e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1032 0.09106 0.1817 0.2007 0.9873 0.9919 0.1033 0.7596 0.867 0.3057 ] Network output: [ -0.005255 0.02554 1.003 3.038e-05 -1.364e-05 0.9818 2.29e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0909 0.08896 0.1651 0.1954 0.9853 0.9912 0.09091 0.6845 0.8435 0.2454 ] Network output: [ 0.0001541 1 -0.0002152 4.079e-06 -1.831e-06 0.9999 3.074e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003998 Epoch 8134 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01068 0.9956 0.9905 1.976e-07 -8.87e-08 -0.007441 1.489e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003362 -0.003173 -0.007883 0.00617 0.9699 0.9742 0.006459 0.8338 0.8248 0.018 ] Network output: [ 0.9998 0.0005283 0.0008397 -1.527e-05 6.853e-06 -0.001094 -1.15e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1968 -0.03369 -0.1749 0.1899 0.9835 0.9932 0.2202 0.4413 0.8714 0.7166 ] Network output: [ -0.01038 1.002 1.01 -1.266e-07 5.682e-08 0.009085 -9.538e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006065 0.0004924 0.004444 0.003641 0.9889 0.9919 0.00618 0.862 0.8954 0.01298 ] Network output: [ -0.0005427 0.002641 1.001 -4.802e-05 2.156e-05 0.997 -3.619e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2088 0.09714 0.339 0.1461 0.985 0.994 0.2095 0.4455 0.878 0.7109 ] Network output: [ 0.005504 -0.02642 0.9947 2.88e-05 -1.293e-05 1.021 2.17e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1032 0.09106 0.1817 0.2007 0.9873 0.9919 0.1033 0.7596 0.867 0.3057 ] Network output: [ -0.005253 0.02553 1.003 3.035e-05 -1.363e-05 0.9818 2.288e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0909 0.08896 0.1651 0.1954 0.9853 0.9912 0.09091 0.6844 0.8435 0.2454 ] Network output: [ 0.000154 1 -0.0002149 4.075e-06 -1.829e-06 0.9999 3.071e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003995 Epoch 8135 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01068 0.9956 0.9905 1.964e-07 -8.816e-08 -0.007442 1.48e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003362 -0.003173 -0.007882 0.006169 0.9699 0.9742 0.006459 0.8338 0.8248 0.018 ] Network output: [ 0.9998 0.0005279 0.0008392 -1.525e-05 6.847e-06 -0.001093 -1.149e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1968 -0.03369 -0.1749 0.1899 0.9835 0.9932 0.2202 0.4413 0.8714 0.7166 ] Network output: [ -0.01038 1.002 1.01 -1.272e-07 5.712e-08 0.009083 -9.589e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006066 0.0004925 0.004444 0.003641 0.9889 0.9919 0.00618 0.862 0.8954 0.01298 ] Network output: [ -0.0005423 0.00264 1.001 -4.798e-05 2.154e-05 0.997 -3.616e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2088 0.09715 0.339 0.1461 0.985 0.994 0.2095 0.4455 0.878 0.7109 ] Network output: [ 0.005502 -0.02641 0.9947 2.877e-05 -1.292e-05 1.021 2.168e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1032 0.09107 0.1817 0.2007 0.9873 0.9919 0.1033 0.7596 0.867 0.3057 ] Network output: [ -0.005251 0.02552 1.003 3.033e-05 -1.361e-05 0.9818 2.285e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0909 0.08897 0.1651 0.1954 0.9853 0.9912 0.09091 0.6844 0.8435 0.2454 ] Network output: [ 0.0001539 1 -0.0002147 4.071e-06 -1.828e-06 0.9999 3.068e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003993 Epoch 8136 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01068 0.9956 0.9905 1.952e-07 -8.762e-08 -0.007442 1.471e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003362 -0.003173 -0.007881 0.006168 0.9699 0.9742 0.006459 0.8338 0.8248 0.018 ] Network output: [ 0.9998 0.0005275 0.0008387 -1.524e-05 6.84e-06 -0.001092 -1.148e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1968 -0.0337 -0.1749 0.1899 0.9835 0.9932 0.2202 0.4413 0.8714 0.7166 ] Network output: [ -0.01038 1.002 1.01 -1.279e-07 5.742e-08 0.009081 -9.639e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006067 0.0004925 0.004444 0.00364 0.9889 0.9919 0.006181 0.862 0.8954 0.01298 ] Network output: [ -0.000542 0.002639 1.001 -4.793e-05 2.152e-05 0.997 -3.612e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2088 0.09715 0.339 0.1461 0.985 0.994 0.2095 0.4455 0.878 0.7109 ] Network output: [ 0.0055 -0.02641 0.9947 2.874e-05 -1.29e-05 1.021 2.166e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1032 0.09107 0.1817 0.2007 0.9873 0.9919 0.1033 0.7596 0.867 0.3057 ] Network output: [ -0.00525 0.02551 1.003 3.03e-05 -1.36e-05 0.9818 2.283e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0909 0.08897 0.1651 0.1954 0.9853 0.9912 0.09092 0.6844 0.8435 0.2454 ] Network output: [ 0.0001538 1 -0.0002144 4.067e-06 -1.826e-06 0.9999 3.065e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003991 Epoch 8137 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01068 0.9956 0.9905 1.94e-07 -8.708e-08 -0.007442 1.462e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003362 -0.003173 -0.00788 0.006168 0.9699 0.9742 0.00646 0.8337 0.8248 0.018 ] Network output: [ 0.9998 0.000527 0.0008382 -1.522e-05 6.833e-06 -0.001091 -1.147e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1968 -0.0337 -0.1749 0.1899 0.9835 0.9932 0.2202 0.4412 0.8714 0.7166 ] Network output: [ -0.01037 1.002 1.01 -1.286e-07 5.772e-08 0.009079 -9.689e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006067 0.0004926 0.004444 0.00364 0.9889 0.9919 0.006182 0.862 0.8954 0.01298 ] Network output: [ -0.0005416 0.002638 1.001 -4.788e-05 2.149e-05 0.997 -3.608e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2088 0.09716 0.339 0.1461 0.985 0.994 0.2095 0.4455 0.878 0.7109 ] Network output: [ 0.005498 -0.0264 0.9947 2.871e-05 -1.289e-05 1.021 2.164e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1032 0.09108 0.1817 0.2007 0.9873 0.9919 0.1033 0.7596 0.867 0.3057 ] Network output: [ -0.005248 0.0255 1.003 3.027e-05 -1.359e-05 0.9818 2.281e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09091 0.08897 0.1651 0.1954 0.9853 0.9912 0.09092 0.6844 0.8435 0.2454 ] Network output: [ 0.0001538 1 -0.0002142 4.063e-06 -1.824e-06 0.9999 3.062e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003988 Epoch 8138 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01068 0.9956 0.9905 1.928e-07 -8.655e-08 -0.007443 1.453e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003363 -0.003173 -0.007879 0.006167 0.9699 0.9742 0.00646 0.8337 0.8248 0.01799 ] Network output: [ 0.9998 0.0005266 0.0008376 -1.521e-05 6.826e-06 -0.00109 -1.146e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1968 -0.0337 -0.1749 0.1899 0.9835 0.9932 0.2202 0.4412 0.8714 0.7166 ] Network output: [ -0.01037 1.002 1.01 -1.292e-07 5.802e-08 0.009077 -9.739e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006068 0.0004927 0.004444 0.00364 0.9889 0.9919 0.006182 0.862 0.8954 0.01298 ] Network output: [ -0.0005413 0.002637 1.001 -4.783e-05 2.147e-05 0.997 -3.605e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2088 0.09716 0.339 0.1461 0.985 0.994 0.2095 0.4455 0.878 0.7108 ] Network output: [ 0.005496 -0.02639 0.9947 2.868e-05 -1.288e-05 1.021 2.162e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1032 0.09109 0.1817 0.2007 0.9873 0.9919 0.1033 0.7595 0.867 0.3057 ] Network output: [ -0.005246 0.02549 1.003 3.024e-05 -1.357e-05 0.9818 2.279e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09091 0.08897 0.1651 0.1954 0.9853 0.9912 0.09092 0.6844 0.8435 0.2454 ] Network output: [ 0.0001537 1 -0.0002139 4.059e-06 -1.822e-06 0.9999 3.059e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003986 Epoch 8139 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01068 0.9956 0.9905 1.916e-07 -8.601e-08 -0.007443 1.444e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003363 -0.003173 -0.007878 0.006166 0.9699 0.9742 0.00646 0.8337 0.8248 0.01799 ] Network output: [ 0.9998 0.0005261 0.0008371 -1.519e-05 6.82e-06 -0.001089 -1.145e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1969 -0.0337 -0.1749 0.1898 0.9835 0.9932 0.2202 0.4412 0.8714 0.7166 ] Network output: [ -0.01037 1.002 1.01 -1.299e-07 5.831e-08 0.009075 -9.789e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006068 0.0004928 0.004444 0.003639 0.9889 0.9919 0.006183 0.862 0.8954 0.01298 ] Network output: [ -0.000541 0.002636 1.001 -4.778e-05 2.145e-05 0.997 -3.601e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2088 0.09717 0.339 0.1461 0.985 0.994 0.2095 0.4455 0.878 0.7108 ] Network output: [ 0.005495 -0.02638 0.9947 2.866e-05 -1.286e-05 1.021 2.16e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1032 0.09109 0.1817 0.2007 0.9873 0.9919 0.1033 0.7595 0.867 0.3057 ] Network output: [ -0.005244 0.02548 1.003 3.021e-05 -1.356e-05 0.9818 2.277e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09091 0.08897 0.1651 0.1954 0.9853 0.9912 0.09092 0.6843 0.8435 0.2454 ] Network output: [ 0.0001536 1 -0.0002137 4.055e-06 -1.82e-06 0.9999 3.056e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003984 Epoch 8140 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01068 0.9956 0.9905 1.904e-07 -8.548e-08 -0.007444 1.435e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003363 -0.003173 -0.007877 0.006166 0.9699 0.9742 0.00646 0.8337 0.8248 0.01799 ] Network output: [ 0.9998 0.0005257 0.0008366 -1.518e-05 6.813e-06 -0.001088 -1.144e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1969 -0.0337 -0.1748 0.1898 0.9835 0.9932 0.2203 0.4412 0.8714 0.7165 ] Network output: [ -0.01037 1.002 1.01 -1.305e-07 5.861e-08 0.009073 -9.839e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006069 0.0004929 0.004444 0.003639 0.9889 0.9919 0.006183 0.862 0.8954 0.01298 ] Network output: [ -0.0005406 0.002635 1.001 -4.773e-05 2.143e-05 0.997 -3.597e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2088 0.09718 0.339 0.1461 0.985 0.994 0.2095 0.4455 0.878 0.7108 ] Network output: [ 0.005493 -0.02637 0.9947 2.863e-05 -1.285e-05 1.021 2.157e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1032 0.0911 0.1817 0.2007 0.9873 0.9919 0.1033 0.7595 0.867 0.3057 ] Network output: [ -0.005242 0.02547 1.003 3.018e-05 -1.355e-05 0.9818 2.274e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09091 0.08897 0.1651 0.1954 0.9853 0.9912 0.09092 0.6843 0.8435 0.2454 ] Network output: [ 0.0001535 1 -0.0002134 4.051e-06 -1.819e-06 0.9999 3.053e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003981 Epoch 8141 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01067 0.9956 0.9905 1.892e-07 -8.494e-08 -0.007444 1.426e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003363 -0.003174 -0.007876 0.006165 0.9699 0.9742 0.006461 0.8337 0.8248 0.01799 ] Network output: [ 0.9998 0.0005252 0.0008361 -1.516e-05 6.806e-06 -0.001088 -1.143e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1969 -0.03371 -0.1748 0.1898 0.9835 0.9932 0.2203 0.4412 0.8714 0.7165 ] Network output: [ -0.01037 1.002 1.01 -1.312e-07 5.89e-08 0.009071 -9.888e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006069 0.0004929 0.004444 0.003638 0.9889 0.9919 0.006184 0.862 0.8954 0.01298 ] Network output: [ -0.0005403 0.002634 1.001 -4.769e-05 2.141e-05 0.997 -3.594e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2089 0.09718 0.3391 0.1461 0.985 0.994 0.2095 0.4455 0.878 0.7108 ] Network output: [ 0.005491 -0.02636 0.9947 2.86e-05 -1.284e-05 1.021 2.155e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1033 0.0911 0.1817 0.2007 0.9873 0.9919 0.1033 0.7595 0.867 0.3057 ] Network output: [ -0.00524 0.02545 1.003 3.015e-05 -1.354e-05 0.9818 2.272e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09091 0.08898 0.1651 0.1954 0.9853 0.9912 0.09093 0.6843 0.8435 0.2454 ] Network output: [ 0.0001535 1 -0.0002132 4.047e-06 -1.817e-06 0.9999 3.05e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003979 Epoch 8142 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01067 0.9956 0.9905 1.88e-07 -8.441e-08 -0.007444 1.417e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003363 -0.003174 -0.007875 0.006164 0.9699 0.9742 0.006461 0.8337 0.8248 0.01799 ] Network output: [ 0.9998 0.0005248 0.0008356 -1.515e-05 6.799e-06 -0.001087 -1.141e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1969 -0.03371 -0.1748 0.1898 0.9835 0.9932 0.2203 0.4412 0.8714 0.7165 ] Network output: [ -0.01037 1.002 1.01 -1.319e-07 5.92e-08 0.00907 -9.938e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00607 0.000493 0.004444 0.003638 0.9889 0.9919 0.006185 0.8619 0.8954 0.01297 ] Network output: [ -0.0005399 0.002633 1.001 -4.764e-05 2.139e-05 0.997 -3.59e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2089 0.09719 0.3391 0.1461 0.985 0.994 0.2096 0.4454 0.878 0.7108 ] Network output: [ 0.005489 -0.02635 0.9947 2.857e-05 -1.283e-05 1.021 2.153e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1033 0.09111 0.1817 0.2007 0.9873 0.9919 0.1033 0.7594 0.867 0.3057 ] Network output: [ -0.005238 0.02544 1.003 3.012e-05 -1.352e-05 0.9818 2.27e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09091 0.08898 0.1651 0.1954 0.9853 0.9912 0.09093 0.6843 0.8434 0.2454 ] Network output: [ 0.0001534 1 -0.000213 4.043e-06 -1.815e-06 0.9999 3.047e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003977 Epoch 8143 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01067 0.9956 0.9905 1.868e-07 -8.388e-08 -0.007445 1.408e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003363 -0.003174 -0.007874 0.006164 0.9699 0.9742 0.006461 0.8337 0.8248 0.01799 ] Network output: [ 0.9998 0.0005244 0.0008351 -1.513e-05 6.792e-06 -0.001086 -1.14e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1969 -0.03371 -0.1748 0.1898 0.9835 0.9932 0.2203 0.4412 0.8714 0.7165 ] Network output: [ -0.01037 1.002 1.01 -1.325e-07 5.949e-08 0.009068 -9.987e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006071 0.0004931 0.004444 0.003638 0.9889 0.9919 0.006185 0.8619 0.8954 0.01297 ] Network output: [ -0.0005396 0.002632 1.001 -4.759e-05 2.136e-05 0.997 -3.586e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2089 0.09719 0.3391 0.1461 0.985 0.994 0.2096 0.4454 0.878 0.7108 ] Network output: [ 0.005487 -0.02634 0.9947 2.854e-05 -1.281e-05 1.021 2.151e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1033 0.09111 0.1817 0.2007 0.9873 0.9919 0.1033 0.7594 0.867 0.3057 ] Network output: [ -0.005236 0.02543 1.003 3.009e-05 -1.351e-05 0.9818 2.268e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09092 0.08898 0.1651 0.1954 0.9853 0.9912 0.09093 0.6842 0.8434 0.2454 ] Network output: [ 0.0001533 1 -0.0002127 4.039e-06 -1.813e-06 0.9999 3.044e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003974 Epoch 8144 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01067 0.9956 0.9905 1.857e-07 -8.335e-08 -0.007445 1.399e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003363 -0.003174 -0.007873 0.006163 0.9699 0.9742 0.006462 0.8337 0.8248 0.01799 ] Network output: [ 0.9998 0.0005239 0.0008346 -1.511e-05 6.786e-06 -0.001085 -1.139e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1969 -0.03371 -0.1748 0.1898 0.9835 0.9932 0.2203 0.4412 0.8714 0.7165 ] Network output: [ -0.01037 1.002 1.01 -1.332e-07 5.979e-08 0.009066 -1.004e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006071 0.0004932 0.004444 0.003637 0.9889 0.9919 0.006186 0.8619 0.8954 0.01297 ] Network output: [ -0.0005393 0.002631 1.001 -4.754e-05 2.134e-05 0.997 -3.583e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2089 0.0972 0.3391 0.1461 0.985 0.994 0.2096 0.4454 0.878 0.7108 ] Network output: [ 0.005486 -0.02633 0.9947 2.851e-05 -1.28e-05 1.021 2.149e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1033 0.09112 0.1817 0.2007 0.9873 0.9919 0.1033 0.7594 0.867 0.3057 ] Network output: [ -0.005234 0.02542 1.003 3.006e-05 -1.35e-05 0.9818 2.266e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09092 0.08898 0.1651 0.1954 0.9853 0.9912 0.09093 0.6842 0.8434 0.2454 ] Network output: [ 0.0001532 1 -0.0002125 4.035e-06 -1.811e-06 0.9999 3.041e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003972 Epoch 8145 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01067 0.9956 0.9905 1.845e-07 -8.282e-08 -0.007445 1.39e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003363 -0.003174 -0.007872 0.006163 0.9699 0.9742 0.006462 0.8337 0.8248 0.01798 ] Network output: [ 0.9998 0.0005235 0.000834 -1.51e-05 6.779e-06 -0.001084 -1.138e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1969 -0.03371 -0.1748 0.1898 0.9835 0.9932 0.2203 0.4411 0.8714 0.7165 ] Network output: [ -0.01037 1.002 1.01 -1.338e-07 6.008e-08 0.009064 -1.009e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006072 0.0004933 0.004444 0.003637 0.9889 0.9919 0.006186 0.8619 0.8954 0.01297 ] Network output: [ -0.0005389 0.00263 1.001 -4.749e-05 2.132e-05 0.997 -3.579e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2089 0.0972 0.3391 0.1461 0.985 0.994 0.2096 0.4454 0.878 0.7108 ] Network output: [ 0.005484 -0.02632 0.9947 2.849e-05 -1.279e-05 1.021 2.147e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1033 0.09113 0.1817 0.2007 0.9873 0.9919 0.1033 0.7594 0.867 0.3057 ] Network output: [ -0.005233 0.02541 1.003 3.003e-05 -1.348e-05 0.9818 2.263e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09092 0.08898 0.1651 0.1954 0.9853 0.9912 0.09093 0.6842 0.8434 0.2454 ] Network output: [ 0.0001531 1 -0.0002122 4.031e-06 -1.81e-06 0.9999 3.038e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000397 Epoch 8146 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01067 0.9956 0.9905 1.833e-07 -8.23e-08 -0.007446 1.382e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003364 -0.003174 -0.007871 0.006162 0.9699 0.9742 0.006462 0.8337 0.8247 0.01798 ] Network output: [ 0.9998 0.000523 0.0008335 -1.508e-05 6.772e-06 -0.001083 -1.137e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1969 -0.03372 -0.1747 0.1898 0.9835 0.9932 0.2203 0.4411 0.8713 0.7165 ] Network output: [ -0.01036 1.002 1.01 -1.345e-07 6.037e-08 0.009062 -1.013e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006072 0.0004933 0.004444 0.003636 0.9889 0.9919 0.006187 0.8619 0.8954 0.01297 ] Network output: [ -0.0005386 0.002629 1.001 -4.744e-05 2.13e-05 0.997 -3.576e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2089 0.09721 0.3391 0.1461 0.985 0.994 0.2096 0.4454 0.878 0.7108 ] Network output: [ 0.005482 -0.02631 0.9947 2.846e-05 -1.278e-05 1.021 2.145e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1033 0.09113 0.1817 0.2007 0.9873 0.9919 0.1034 0.7594 0.867 0.3057 ] Network output: [ -0.005231 0.0254 1.003 3.001e-05 -1.347e-05 0.9818 2.261e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09092 0.08898 0.1651 0.1954 0.9853 0.9912 0.09093 0.6842 0.8434 0.2454 ] Network output: [ 0.0001531 1 -0.000212 4.027e-06 -1.808e-06 0.9999 3.035e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003967 Epoch 8147 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01066 0.9956 0.9905 1.821e-07 -8.177e-08 -0.007446 1.373e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003364 -0.003174 -0.00787 0.006161 0.9699 0.9742 0.006463 0.8337 0.8247 0.01798 ] Network output: [ 0.9998 0.0005226 0.000833 -1.507e-05 6.765e-06 -0.001082 -1.136e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1969 -0.03372 -0.1747 0.1898 0.9835 0.9932 0.2203 0.4411 0.8713 0.7165 ] Network output: [ -0.01036 1.002 1.01 -1.351e-07 6.066e-08 0.00906 -1.018e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006073 0.0004934 0.004444 0.003636 0.9889 0.9919 0.006188 0.8619 0.8954 0.01297 ] Network output: [ -0.0005383 0.002628 1.001 -4.74e-05 2.128e-05 0.997 -3.572e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2089 0.09722 0.3391 0.1461 0.985 0.994 0.2096 0.4454 0.878 0.7108 ] Network output: [ 0.00548 -0.0263 0.9947 2.843e-05 -1.276e-05 1.021 2.143e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1033 0.09114 0.1817 0.2007 0.9873 0.9919 0.1034 0.7593 0.8669 0.3057 ] Network output: [ -0.005229 0.02539 1.003 2.998e-05 -1.346e-05 0.9818 2.259e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09092 0.08899 0.1651 0.1954 0.9853 0.9912 0.09094 0.6842 0.8434 0.2454 ] Network output: [ 0.000153 1 -0.0002117 4.023e-06 -1.806e-06 0.9999 3.032e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003965 Epoch 8148 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01066 0.9956 0.9905 1.81e-07 -8.124e-08 -0.007447 1.364e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003364 -0.003175 -0.007869 0.006161 0.9699 0.9742 0.006463 0.8337 0.8247 0.01798 ] Network output: [ 0.9998 0.0005222 0.0008325 -1.505e-05 6.759e-06 -0.001081 -1.135e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1969 -0.03372 -0.1747 0.1898 0.9835 0.9932 0.2203 0.4411 0.8713 0.7165 ] Network output: [ -0.01036 1.002 1.01 -1.358e-07 6.095e-08 0.009058 -1.023e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006074 0.0004935 0.004444 0.003636 0.9889 0.9919 0.006188 0.8619 0.8954 0.01297 ] Network output: [ -0.0005379 0.002628 1.001 -4.735e-05 2.126e-05 0.997 -3.568e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2089 0.09722 0.3391 0.1461 0.985 0.994 0.2096 0.4454 0.878 0.7108 ] Network output: [ 0.005478 -0.02629 0.9947 2.84e-05 -1.275e-05 1.021 2.14e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1033 0.09114 0.1817 0.2007 0.9873 0.9919 0.1034 0.7593 0.8669 0.3057 ] Network output: [ -0.005227 0.02538 1.003 2.995e-05 -1.344e-05 0.9818 2.257e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09092 0.08899 0.1651 0.1954 0.9853 0.9912 0.09094 0.6841 0.8434 0.2454 ] Network output: [ 0.0001529 1 -0.0002115 4.019e-06 -1.804e-06 0.9999 3.029e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003963 Epoch 8149 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01066 0.9956 0.9905 1.798e-07 -8.072e-08 -0.007447 1.355e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003364 -0.003175 -0.007868 0.00616 0.9699 0.9742 0.006463 0.8337 0.8247 0.01798 ] Network output: [ 0.9998 0.0005217 0.000832 -1.504e-05 6.752e-06 -0.00108 -1.133e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1969 -0.03372 -0.1747 0.1898 0.9835 0.9932 0.2204 0.4411 0.8713 0.7165 ] Network output: [ -0.01036 1.002 1.01 -1.364e-07 6.124e-08 0.009056 -1.028e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006074 0.0004936 0.004444 0.003635 0.9889 0.9919 0.006189 0.8619 0.8954 0.01297 ] Network output: [ -0.0005376 0.002627 1.001 -4.73e-05 2.123e-05 0.997 -3.565e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2089 0.09723 0.3391 0.146 0.985 0.994 0.2096 0.4454 0.878 0.7108 ] Network output: [ 0.005477 -0.02628 0.9947 2.837e-05 -1.274e-05 1.021 2.138e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1033 0.09115 0.1817 0.2007 0.9873 0.9919 0.1034 0.7593 0.8669 0.3057 ] Network output: [ -0.005225 0.02537 1.003 2.992e-05 -1.343e-05 0.9818 2.255e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09093 0.08899 0.1651 0.1954 0.9853 0.9912 0.09094 0.6841 0.8434 0.2454 ] Network output: [ 0.0001528 1 -0.0002112 4.015e-06 -1.802e-06 0.9999 3.026e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000396 Epoch 8150 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01066 0.9956 0.9905 1.786e-07 -8.02e-08 -0.007447 1.346e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003364 -0.003175 -0.007867 0.006159 0.9699 0.9742 0.006464 0.8337 0.8247 0.01798 ] Network output: [ 0.9998 0.0005213 0.0008315 -1.502e-05 6.745e-06 -0.001079 -1.132e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.197 -0.03372 -0.1747 0.1898 0.9835 0.9932 0.2204 0.4411 0.8713 0.7165 ] Network output: [ -0.01036 1.002 1.01 -1.371e-07 6.153e-08 0.009054 -1.033e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006075 0.0004937 0.004444 0.003635 0.9889 0.9919 0.006189 0.8619 0.8954 0.01297 ] Network output: [ -0.0005372 0.002626 1.001 -4.725e-05 2.121e-05 0.997 -3.561e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.209 0.09723 0.3391 0.146 0.985 0.994 0.2096 0.4454 0.878 0.7108 ] Network output: [ 0.005475 -0.02627 0.9947 2.834e-05 -1.272e-05 1.021 2.136e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1033 0.09115 0.1817 0.2007 0.9873 0.9919 0.1034 0.7593 0.8669 0.3057 ] Network output: [ -0.005223 0.02536 1.003 2.989e-05 -1.342e-05 0.9818 2.253e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09093 0.08899 0.1651 0.1954 0.9853 0.9912 0.09094 0.6841 0.8434 0.2454 ] Network output: [ 0.0001528 1 -0.000211 4.011e-06 -1.801e-06 0.9999 3.023e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003958 Epoch 8151 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01066 0.9956 0.9905 1.775e-07 -7.968e-08 -0.007448 1.338e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003364 -0.003175 -0.007866 0.006159 0.9699 0.9742 0.006464 0.8336 0.8247 0.01798 ] Network output: [ 0.9998 0.0005209 0.000831 -1.501e-05 6.738e-06 -0.001078 -1.131e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.197 -0.03373 -0.1747 0.1898 0.9835 0.9932 0.2204 0.4411 0.8713 0.7165 ] Network output: [ -0.01036 1.002 1.01 -1.377e-07 6.182e-08 0.009052 -1.038e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006075 0.0004937 0.004445 0.003634 0.9889 0.9919 0.00619 0.8619 0.8954 0.01296 ] Network output: [ -0.0005369 0.002625 1.001 -4.72e-05 2.119e-05 0.997 -3.557e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.209 0.09724 0.3391 0.146 0.985 0.994 0.2097 0.4453 0.8779 0.7108 ] Network output: [ 0.005473 -0.02626 0.9947 2.832e-05 -1.271e-05 1.021 2.134e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1033 0.09116 0.1817 0.2007 0.9873 0.9919 0.1034 0.7593 0.8669 0.3057 ] Network output: [ -0.005221 0.02535 1.003 2.986e-05 -1.341e-05 0.9819 2.25e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09093 0.08899 0.1651 0.1954 0.9853 0.9912 0.09094 0.6841 0.8434 0.2454 ] Network output: [ 0.0001527 1 -0.0002108 4.007e-06 -1.799e-06 0.9999 3.02e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003956 Epoch 8152 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01066 0.9956 0.9905 1.763e-07 -7.916e-08 -0.007448 1.329e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003364 -0.003175 -0.007865 0.006158 0.9699 0.9742 0.006464 0.8336 0.8247 0.01797 ] Network output: [ 0.9998 0.0005204 0.0008304 -1.499e-05 6.732e-06 -0.001077 -1.13e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.197 -0.03373 -0.1747 0.1898 0.9835 0.9932 0.2204 0.4411 0.8713 0.7165 ] Network output: [ -0.01036 1.002 1.01 -1.383e-07 6.21e-08 0.00905 -1.043e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006076 0.0004938 0.004445 0.003634 0.9889 0.9919 0.006191 0.8619 0.8954 0.01296 ] Network output: [ -0.0005366 0.002624 1.001 -4.716e-05 2.117e-05 0.997 -3.554e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.209 0.09724 0.3392 0.146 0.985 0.994 0.2097 0.4453 0.8779 0.7108 ] Network output: [ 0.005471 -0.02625 0.9947 2.829e-05 -1.27e-05 1.021 2.132e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1033 0.09117 0.1817 0.2007 0.9873 0.9919 0.1034 0.7592 0.8669 0.3057 ] Network output: [ -0.005219 0.02534 1.003 2.983e-05 -1.339e-05 0.9819 2.248e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09093 0.089 0.1651 0.1954 0.9853 0.9912 0.09094 0.684 0.8434 0.2454 ] Network output: [ 0.0001526 1 -0.0002105 4.003e-06 -1.797e-06 0.9999 3.017e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003954 Epoch 8153 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01066 0.9956 0.9905 1.752e-07 -7.864e-08 -0.007448 1.32e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003365 -0.003175 -0.007864 0.006157 0.9699 0.9742 0.006464 0.8336 0.8247 0.01797 ] Network output: [ 0.9998 0.00052 0.0008299 -1.498e-05 6.725e-06 -0.001077 -1.129e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.197 -0.03373 -0.1746 0.1898 0.9835 0.9932 0.2204 0.4411 0.8713 0.7165 ] Network output: [ -0.01036 1.002 1.01 -1.39e-07 6.239e-08 0.009049 -1.047e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006077 0.0004939 0.004445 0.003633 0.9889 0.9919 0.006191 0.8619 0.8954 0.01296 ] Network output: [ -0.0005362 0.002623 1.001 -4.711e-05 2.115e-05 0.997 -3.55e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.209 0.09725 0.3392 0.146 0.985 0.994 0.2097 0.4453 0.8779 0.7108 ] Network output: [ 0.005469 -0.02624 0.9947 2.826e-05 -1.269e-05 1.021 2.13e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1033 0.09117 0.1817 0.2007 0.9873 0.9919 0.1034 0.7592 0.8669 0.3057 ] Network output: [ -0.005217 0.02533 1.003 2.98e-05 -1.338e-05 0.9819 2.246e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09093 0.089 0.1651 0.1954 0.9853 0.9912 0.09095 0.684 0.8434 0.2454 ] Network output: [ 0.0001525 1 -0.0002103 3.999e-06 -1.795e-06 0.9999 3.014e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003951 Epoch 8154 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01065 0.9956 0.9905 1.74e-07 -7.812e-08 -0.007449 1.311e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003365 -0.003176 -0.007863 0.006157 0.9699 0.9742 0.006465 0.8336 0.8247 0.01797 ] Network output: [ 0.9998 0.0005195 0.0008294 -1.496e-05 6.718e-06 -0.001076 -1.128e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.197 -0.03373 -0.1746 0.1898 0.9835 0.9932 0.2204 0.441 0.8713 0.7165 ] Network output: [ -0.01036 1.002 1.01 -1.396e-07 6.268e-08 0.009047 -1.052e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006077 0.000494 0.004445 0.003633 0.9889 0.9919 0.006192 0.8618 0.8954 0.01296 ] Network output: [ -0.0005359 0.002622 1.001 -4.706e-05 2.113e-05 0.997 -3.547e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.209 0.09726 0.3392 0.146 0.985 0.994 0.2097 0.4453 0.8779 0.7107 ] Network output: [ 0.005468 -0.02623 0.9947 2.823e-05 -1.267e-05 1.021 2.128e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1033 0.09118 0.1817 0.2007 0.9873 0.9919 0.1034 0.7592 0.8669 0.3057 ] Network output: [ -0.005216 0.02532 1.003 2.977e-05 -1.337e-05 0.9819 2.244e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09094 0.089 0.1651 0.1954 0.9853 0.9912 0.09095 0.684 0.8434 0.2454 ] Network output: [ 0.0001525 1 -0.00021 3.995e-06 -1.794e-06 0.9999 3.011e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003949 Epoch 8155 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01065 0.9956 0.9905 1.729e-07 -7.76e-08 -0.007449 1.303e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003365 -0.003176 -0.007862 0.006156 0.9699 0.9742 0.006465 0.8336 0.8247 0.01797 ] Network output: [ 0.9998 0.0005191 0.0008289 -1.495e-05 6.712e-06 -0.001075 -1.127e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.197 -0.03373 -0.1746 0.1897 0.9835 0.9932 0.2204 0.441 0.8713 0.7165 ] Network output: [ -0.01035 1.002 1.01 -1.402e-07 6.296e-08 0.009045 -1.057e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006078 0.0004941 0.004445 0.003633 0.9889 0.9919 0.006192 0.8618 0.8954 0.01296 ] Network output: [ -0.0005356 0.002621 1.001 -4.701e-05 2.111e-05 0.997 -3.543e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.209 0.09726 0.3392 0.146 0.985 0.994 0.2097 0.4453 0.8779 0.7107 ] Network output: [ 0.005466 -0.02622 0.9947 2.82e-05 -1.266e-05 1.021 2.125e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1033 0.09118 0.1817 0.2007 0.9873 0.9919 0.1034 0.7592 0.8669 0.3057 ] Network output: [ -0.005214 0.02531 1.003 2.974e-05 -1.335e-05 0.9819 2.242e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09094 0.089 0.1651 0.1954 0.9853 0.9912 0.09095 0.684 0.8434 0.2455 ] Network output: [ 0.0001524 1 -0.0002098 3.991e-06 -1.792e-06 0.9999 3.008e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003947 Epoch 8156 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01065 0.9956 0.9905 1.717e-07 -7.708e-08 -0.00745 1.294e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003365 -0.003176 -0.007861 0.006155 0.9699 0.9742 0.006465 0.8336 0.8247 0.01797 ] Network output: [ 0.9998 0.0005187 0.0008284 -1.494e-05 6.705e-06 -0.001074 -1.126e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.197 -0.03373 -0.1746 0.1897 0.9835 0.9932 0.2204 0.441 0.8713 0.7165 ] Network output: [ -0.01035 1.002 1.01 -1.409e-07 6.325e-08 0.009043 -1.062e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006078 0.0004941 0.004445 0.003632 0.9889 0.9919 0.006193 0.8618 0.8954 0.01296 ] Network output: [ -0.0005352 0.00262 1.001 -4.696e-05 2.108e-05 0.997 -3.539e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.209 0.09727 0.3392 0.146 0.985 0.994 0.2097 0.4453 0.8779 0.7107 ] Network output: [ 0.005464 -0.02621 0.9947 2.818e-05 -1.265e-05 1.021 2.123e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1033 0.09119 0.1817 0.2006 0.9873 0.9919 0.1034 0.7592 0.8669 0.3057 ] Network output: [ -0.005212 0.0253 1.003 2.972e-05 -1.334e-05 0.9819 2.24e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09094 0.089 0.1651 0.1954 0.9853 0.9912 0.09095 0.684 0.8433 0.2455 ] Network output: [ 0.0001523 1 -0.0002095 3.987e-06 -1.79e-06 0.9999 3.005e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003944 Epoch 8157 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01065 0.9956 0.9905 1.706e-07 -7.657e-08 -0.00745 1.285e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003365 -0.003176 -0.00786 0.006155 0.9699 0.9742 0.006466 0.8336 0.8247 0.01797 ] Network output: [ 0.9998 0.0005182 0.0008279 -1.492e-05 6.698e-06 -0.001073 -1.124e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.197 -0.03374 -0.1746 0.1897 0.9835 0.9932 0.2204 0.441 0.8713 0.7164 ] Network output: [ -0.01035 1.002 1.01 -1.415e-07 6.353e-08 0.009041 -1.066e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006079 0.0004942 0.004445 0.003632 0.9889 0.9919 0.006194 0.8618 0.8953 0.01296 ] Network output: [ -0.0005349 0.002619 1.001 -4.692e-05 2.106e-05 0.997 -3.536e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.209 0.09727 0.3392 0.146 0.985 0.994 0.2097 0.4453 0.8779 0.7107 ] Network output: [ 0.005462 -0.0262 0.9947 2.815e-05 -1.264e-05 1.021 2.121e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1034 0.0912 0.1817 0.2006 0.9873 0.9919 0.1034 0.7591 0.8669 0.3057 ] Network output: [ -0.00521 0.02528 1.003 2.969e-05 -1.333e-05 0.9819 2.237e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09094 0.089 0.1651 0.1954 0.9853 0.9912 0.09095 0.6839 0.8433 0.2455 ] Network output: [ 0.0001522 1 -0.0002093 3.983e-06 -1.788e-06 0.9999 3.002e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003942 Epoch 8158 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01065 0.9956 0.9905 1.694e-07 -7.605e-08 -0.00745 1.277e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003365 -0.003176 -0.007859 0.006154 0.9699 0.9742 0.006466 0.8336 0.8247 0.01797 ] Network output: [ 0.9998 0.0005178 0.0008274 -1.491e-05 6.692e-06 -0.001072 -1.123e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.197 -0.03374 -0.1746 0.1897 0.9835 0.9932 0.2205 0.441 0.8713 0.7164 ] Network output: [ -0.01035 1.002 1.01 -1.421e-07 6.381e-08 0.009039 -1.071e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006079 0.0004943 0.004445 0.003631 0.9889 0.9919 0.006194 0.8618 0.8953 0.01296 ] Network output: [ -0.0005345 0.002618 1.001 -4.687e-05 2.104e-05 0.997 -3.532e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.209 0.09728 0.3392 0.146 0.985 0.994 0.2097 0.4453 0.8779 0.7107 ] Network output: [ 0.00546 -0.02619 0.9947 2.812e-05 -1.262e-05 1.021 2.119e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1034 0.0912 0.1817 0.2006 0.9873 0.9919 0.1034 0.7591 0.8669 0.3057 ] Network output: [ -0.005208 0.02527 1.003 2.966e-05 -1.331e-05 0.9819 2.235e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09094 0.08901 0.1651 0.1954 0.9853 0.9912 0.09096 0.6839 0.8433 0.2455 ] Network output: [ 0.0001522 1 -0.0002091 3.979e-06 -1.786e-06 0.9999 2.999e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000394 Epoch 8159 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01065 0.9956 0.9905 1.683e-07 -7.554e-08 -0.007451 1.268e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003365 -0.003176 -0.007858 0.006153 0.9699 0.9742 0.006466 0.8336 0.8247 0.01796 ] Network output: [ 0.9998 0.0005174 0.0008269 -1.489e-05 6.685e-06 -0.001071 -1.122e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.197 -0.03374 -0.1746 0.1897 0.9835 0.9932 0.2205 0.441 0.8713 0.7164 ] Network output: [ -0.01035 1.002 1.01 -1.428e-07 6.41e-08 0.009037 -1.076e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00608 0.0004944 0.004445 0.003631 0.9889 0.9919 0.006195 0.8618 0.8953 0.01296 ] Network output: [ -0.0005342 0.002617 1.001 -4.682e-05 2.102e-05 0.997 -3.529e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2091 0.09729 0.3392 0.146 0.985 0.994 0.2097 0.4452 0.8779 0.7107 ] Network output: [ 0.005459 -0.02619 0.9947 2.809e-05 -1.261e-05 1.021 2.117e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1034 0.09121 0.1817 0.2006 0.9873 0.9919 0.1034 0.7591 0.8669 0.3057 ] Network output: [ -0.005206 0.02526 1.003 2.963e-05 -1.33e-05 0.9819 2.233e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09094 0.08901 0.1651 0.1954 0.9853 0.9912 0.09096 0.6839 0.8433 0.2455 ] Network output: [ 0.0001521 1 -0.0002088 3.975e-06 -1.785e-06 0.9999 2.996e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003937 Epoch 8160 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01065 0.9956 0.9905 1.671e-07 -7.503e-08 -0.007451 1.26e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003365 -0.003176 -0.007857 0.006153 0.9699 0.9742 0.006467 0.8336 0.8247 0.01796 ] Network output: [ 0.9998 0.0005169 0.0008264 -1.488e-05 6.678e-06 -0.00107 -1.121e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1971 -0.03374 -0.1745 0.1897 0.9835 0.9932 0.2205 0.441 0.8713 0.7164 ] Network output: [ -0.01035 1.002 1.01 -1.434e-07 6.438e-08 0.009035 -1.081e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006081 0.0004945 0.004445 0.003631 0.9889 0.9919 0.006195 0.8618 0.8953 0.01295 ] Network output: [ -0.0005339 0.002616 1.001 -4.677e-05 2.1e-05 0.997 -3.525e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2091 0.09729 0.3392 0.146 0.985 0.994 0.2097 0.4452 0.8779 0.7107 ] Network output: [ 0.005457 -0.02618 0.9947 2.806e-05 -1.26e-05 1.021 2.115e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1034 0.09121 0.1817 0.2006 0.9873 0.9919 0.1034 0.7591 0.8669 0.3057 ] Network output: [ -0.005204 0.02525 1.003 2.96e-05 -1.329e-05 0.9819 2.231e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09095 0.08901 0.1651 0.1954 0.9853 0.9912 0.09096 0.6839 0.8433 0.2455 ] Network output: [ 0.000152 1 -0.0002086 3.971e-06 -1.783e-06 0.9999 2.993e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003935 Epoch 8161 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01064 0.9956 0.9905 1.66e-07 -7.452e-08 -0.007451 1.251e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003366 -0.003177 -0.007856 0.006152 0.9699 0.9742 0.006467 0.8336 0.8247 0.01796 ] Network output: [ 0.9998 0.0005165 0.0008259 -1.486e-05 6.671e-06 -0.001069 -1.12e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1971 -0.03374 -0.1745 0.1897 0.9835 0.9932 0.2205 0.441 0.8713 0.7164 ] Network output: [ -0.01035 1.002 1.01 -1.44e-07 6.466e-08 0.009033 -1.085e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006081 0.0004945 0.004445 0.00363 0.9889 0.9919 0.006196 0.8618 0.8953 0.01295 ] Network output: [ -0.0005335 0.002616 1.001 -4.673e-05 2.098e-05 0.997 -3.521e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2091 0.0973 0.3392 0.146 0.985 0.994 0.2098 0.4452 0.8779 0.7107 ] Network output: [ 0.005455 -0.02617 0.9947 2.803e-05 -1.259e-05 1.021 2.113e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1034 0.09122 0.1817 0.2006 0.9873 0.9919 0.1035 0.7591 0.8669 0.3057 ] Network output: [ -0.005202 0.02524 1.003 2.957e-05 -1.328e-05 0.9819 2.229e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09095 0.08901 0.1651 0.1954 0.9853 0.9912 0.09096 0.6838 0.8433 0.2455 ] Network output: [ 0.0001519 1 -0.0002083 3.968e-06 -1.781e-06 0.9999 2.99e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003933 Epoch 8162 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01064 0.9956 0.9905 1.649e-07 -7.401e-08 -0.007452 1.242e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003366 -0.003177 -0.007855 0.006152 0.9699 0.9742 0.006467 0.8336 0.8247 0.01796 ] Network output: [ 0.9998 0.0005161 0.0008254 -1.485e-05 6.665e-06 -0.001068 -1.119e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1971 -0.03375 -0.1745 0.1897 0.9835 0.9932 0.2205 0.441 0.8713 0.7164 ] Network output: [ -0.01035 1.002 1.01 -1.446e-07 6.494e-08 0.009032 -1.09e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006082 0.0004946 0.004445 0.00363 0.9889 0.9919 0.006197 0.8618 0.8953 0.01295 ] Network output: [ -0.0005332 0.002615 1.001 -4.668e-05 2.096e-05 0.997 -3.518e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2091 0.0973 0.3392 0.146 0.985 0.994 0.2098 0.4452 0.8779 0.7107 ] Network output: [ 0.005453 -0.02616 0.9947 2.801e-05 -1.257e-05 1.021 2.111e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1034 0.09122 0.1817 0.2006 0.9873 0.9919 0.1035 0.759 0.8669 0.3057 ] Network output: [ -0.005201 0.02523 1.003 2.954e-05 -1.326e-05 0.9819 2.227e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09095 0.08901 0.1651 0.1954 0.9853 0.9912 0.09096 0.6838 0.8433 0.2455 ] Network output: [ 0.0001519 1 -0.0002081 3.964e-06 -1.779e-06 0.9999 2.987e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003931 Epoch 8163 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01064 0.9956 0.9905 1.637e-07 -7.35e-08 -0.007452 1.234e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003366 -0.003177 -0.007854 0.006151 0.9699 0.9742 0.006468 0.8336 0.8247 0.01796 ] Network output: [ 0.9998 0.0005156 0.0008248 -1.483e-05 6.658e-06 -0.001067 -1.118e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1971 -0.03375 -0.1745 0.1897 0.9835 0.9932 0.2205 0.4409 0.8713 0.7164 ] Network output: [ -0.01035 1.002 1.01 -1.453e-07 6.522e-08 0.00903 -1.095e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006082 0.0004947 0.004445 0.003629 0.9889 0.9919 0.006197 0.8618 0.8953 0.01295 ] Network output: [ -0.0005329 0.002614 1.001 -4.663e-05 2.093e-05 0.997 -3.514e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2091 0.09731 0.3393 0.146 0.985 0.994 0.2098 0.4452 0.8779 0.7107 ] Network output: [ 0.005451 -0.02615 0.9947 2.798e-05 -1.256e-05 1.021 2.109e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1034 0.09123 0.1817 0.2006 0.9873 0.9919 0.1035 0.759 0.8669 0.3057 ] Network output: [ -0.005199 0.02522 1.003 2.952e-05 -1.325e-05 0.9819 2.224e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09095 0.08902 0.1651 0.1954 0.9853 0.9912 0.09096 0.6838 0.8433 0.2455 ] Network output: [ 0.0001518 1 -0.0002079 3.96e-06 -1.778e-06 0.9999 2.984e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003928 Epoch 8164 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01064 0.9956 0.9905 1.626e-07 -7.299e-08 -0.007452 1.225e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003366 -0.003177 -0.007853 0.00615 0.9699 0.9742 0.006468 0.8335 0.8247 0.01796 ] Network output: [ 0.9998 0.0005152 0.0008243 -1.482e-05 6.651e-06 -0.001067 -1.117e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1971 -0.03375 -0.1745 0.1897 0.9835 0.9932 0.2205 0.4409 0.8713 0.7164 ] Network output: [ -0.01034 1.002 1.01 -1.459e-07 6.549e-08 0.009028 -1.099e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006083 0.0004948 0.004445 0.003629 0.9889 0.9919 0.006198 0.8618 0.8953 0.01295 ] Network output: [ -0.0005325 0.002613 1.001 -4.658e-05 2.091e-05 0.997 -3.511e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2091 0.09731 0.3393 0.146 0.985 0.994 0.2098 0.4452 0.8779 0.7107 ] Network output: [ 0.00545 -0.02614 0.9947 2.795e-05 -1.255e-05 1.021 2.106e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1034 0.09124 0.1817 0.2006 0.9873 0.9919 0.1035 0.759 0.8668 0.3057 ] Network output: [ -0.005197 0.02521 1.003 2.949e-05 -1.324e-05 0.9819 2.222e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09095 0.08902 0.1651 0.1954 0.9853 0.9912 0.09097 0.6838 0.8433 0.2455 ] Network output: [ 0.0001517 1 -0.0002076 3.956e-06 -1.776e-06 0.9999 2.981e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003926 Epoch 8165 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01064 0.9956 0.9905 1.615e-07 -7.249e-08 -0.007453 1.217e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003366 -0.003177 -0.007852 0.00615 0.9699 0.9742 0.006468 0.8335 0.8247 0.01796 ] Network output: [ 0.9998 0.0005148 0.0008238 -1.48e-05 6.645e-06 -0.001066 -1.115e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1971 -0.03375 -0.1745 0.1897 0.9835 0.9932 0.2205 0.4409 0.8713 0.7164 ] Network output: [ -0.01034 1.002 1.01 -1.465e-07 6.577e-08 0.009026 -1.104e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006084 0.0004949 0.004445 0.003629 0.9889 0.9919 0.006198 0.8618 0.8953 0.01295 ] Network output: [ -0.0005322 0.002612 1.001 -4.654e-05 2.089e-05 0.997 -3.507e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2091 0.09732 0.3393 0.146 0.985 0.994 0.2098 0.4452 0.8779 0.7107 ] Network output: [ 0.005448 -0.02613 0.9947 2.792e-05 -1.254e-05 1.021 2.104e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1034 0.09124 0.1817 0.2006 0.9873 0.9919 0.1035 0.759 0.8668 0.3057 ] Network output: [ -0.005195 0.0252 1.003 2.946e-05 -1.322e-05 0.9819 2.22e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09096 0.08902 0.1651 0.1954 0.9853 0.9912 0.09097 0.6838 0.8433 0.2455 ] Network output: [ 0.0001516 1 -0.0002074 3.952e-06 -1.774e-06 0.9999 2.978e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003924 Epoch 8166 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01064 0.9956 0.9905 1.603e-07 -7.198e-08 -0.007453 1.208e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003366 -0.003177 -0.007851 0.006149 0.9699 0.9742 0.006468 0.8335 0.8247 0.01795 ] Network output: [ 0.9998 0.0005143 0.0008233 -1.479e-05 6.638e-06 -0.001065 -1.114e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1971 -0.03375 -0.1745 0.1897 0.9835 0.9932 0.2205 0.4409 0.8713 0.7164 ] Network output: [ -0.01034 1.002 1.01 -1.471e-07 6.605e-08 0.009024 -1.109e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006084 0.000495 0.004445 0.003628 0.9889 0.9919 0.006199 0.8617 0.8953 0.01295 ] Network output: [ -0.0005319 0.002611 1.001 -4.649e-05 2.087e-05 0.997 -3.504e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2091 0.09733 0.3393 0.146 0.985 0.994 0.2098 0.4452 0.8779 0.7107 ] Network output: [ 0.005446 -0.02612 0.9947 2.79e-05 -1.252e-05 1.021 2.102e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1034 0.09125 0.1818 0.2006 0.9873 0.9919 0.1035 0.759 0.8668 0.3057 ] Network output: [ -0.005193 0.02519 1.003 2.943e-05 -1.321e-05 0.9819 2.218e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09096 0.08902 0.1651 0.1954 0.9853 0.9912 0.09097 0.6837 0.8433 0.2455 ] Network output: [ 0.0001516 1 -0.0002071 3.948e-06 -1.772e-06 0.9999 2.975e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003921 Epoch 8167 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01063 0.9956 0.9905 1.592e-07 -7.148e-08 -0.007453 1.2e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003366 -0.003177 -0.00785 0.006148 0.9699 0.9742 0.006469 0.8335 0.8247 0.01795 ] Network output: [ 0.9998 0.0005139 0.0008228 -1.477e-05 6.632e-06 -0.001064 -1.113e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1971 -0.03376 -0.1744 0.1897 0.9835 0.9932 0.2206 0.4409 0.8713 0.7164 ] Network output: [ -0.01034 1.002 1.01 -1.477e-07 6.633e-08 0.009022 -1.113e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006085 0.000495 0.004445 0.003628 0.9889 0.9919 0.0062 0.8617 0.8953 0.01295 ] Network output: [ -0.0005315 0.00261 1.001 -4.644e-05 2.085e-05 0.997 -3.5e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2091 0.09733 0.3393 0.146 0.985 0.994 0.2098 0.4452 0.8779 0.7107 ] Network output: [ 0.005444 -0.02611 0.9947 2.787e-05 -1.251e-05 1.021 2.1e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1034 0.09125 0.1818 0.2006 0.9873 0.9919 0.1035 0.7589 0.8668 0.3057 ] Network output: [ -0.005191 0.02518 1.003 2.94e-05 -1.32e-05 0.9819 2.216e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09096 0.08902 0.1651 0.1954 0.9853 0.9912 0.09097 0.6837 0.8433 0.2455 ] Network output: [ 0.0001515 1 -0.0002069 3.944e-06 -1.771e-06 0.9999 2.972e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003919 Epoch 8168 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01063 0.9956 0.9905 1.581e-07 -7.097e-08 -0.007454 1.191e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003366 -0.003178 -0.007849 0.006148 0.9699 0.9742 0.006469 0.8335 0.8247 0.01795 ] Network output: [ 0.9998 0.0005135 0.0008223 -1.476e-05 6.625e-06 -0.001063 -1.112e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1971 -0.03376 -0.1744 0.1897 0.9835 0.9932 0.2206 0.4409 0.8713 0.7164 ] Network output: [ -0.01034 1.002 1.01 -1.484e-07 6.66e-08 0.00902 -1.118e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006085 0.0004951 0.004445 0.003627 0.9889 0.9919 0.0062 0.8617 0.8953 0.01295 ] Network output: [ -0.0005312 0.002609 1.001 -4.64e-05 2.083e-05 0.997 -3.497e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2092 0.09734 0.3393 0.146 0.985 0.994 0.2098 0.4451 0.8779 0.7107 ] Network output: [ 0.005442 -0.0261 0.9947 2.784e-05 -1.25e-05 1.021 2.098e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1034 0.09126 0.1818 0.2006 0.9873 0.9919 0.1035 0.7589 0.8668 0.3057 ] Network output: [ -0.005189 0.02517 1.003 2.937e-05 -1.319e-05 0.9819 2.214e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09096 0.08902 0.1651 0.1954 0.9853 0.9912 0.09097 0.6837 0.8433 0.2455 ] Network output: [ 0.0001514 1 -0.0002067 3.94e-06 -1.769e-06 0.9999 2.969e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003917 Epoch 8169 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01063 0.9956 0.9905 1.57e-07 -7.047e-08 -0.007454 1.183e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003367 -0.003178 -0.007848 0.006147 0.9699 0.9742 0.006469 0.8335 0.8246 0.01795 ] Network output: [ 0.9998 0.0005131 0.0008218 -1.474e-05 6.618e-06 -0.001062 -1.111e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1971 -0.03376 -0.1744 0.1897 0.9835 0.9932 0.2206 0.4409 0.8713 0.7164 ] Network output: [ -0.01034 1.002 1.009 -1.49e-07 6.688e-08 0.009018 -1.123e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006086 0.0004952 0.004445 0.003627 0.9889 0.9919 0.006201 0.8617 0.8953 0.01295 ] Network output: [ -0.0005309 0.002608 1.001 -4.635e-05 2.081e-05 0.997 -3.493e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2092 0.09734 0.3393 0.146 0.985 0.994 0.2098 0.4451 0.8779 0.7107 ] Network output: [ 0.005441 -0.02609 0.9947 2.781e-05 -1.249e-05 1.021 2.096e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1034 0.09126 0.1818 0.2006 0.9873 0.9919 0.1035 0.7589 0.8668 0.3057 ] Network output: [ -0.005187 0.02516 1.003 2.934e-05 -1.317e-05 0.9819 2.211e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09096 0.08903 0.1651 0.1954 0.9853 0.9912 0.09098 0.6837 0.8433 0.2455 ] Network output: [ 0.0001513 1 -0.0002064 3.936e-06 -1.767e-06 0.9999 2.966e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003915 Epoch 8170 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01063 0.9956 0.9906 1.559e-07 -6.997e-08 -0.007454 1.175e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003367 -0.003178 -0.007847 0.006146 0.9699 0.9742 0.00647 0.8335 0.8246 0.01795 ] Network output: [ 0.9998 0.0005126 0.0008213 -1.473e-05 6.612e-06 -0.001061 -1.11e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1972 -0.03376 -0.1744 0.1897 0.9835 0.9932 0.2206 0.4409 0.8713 0.7164 ] Network output: [ -0.01034 1.002 1.009 -1.496e-07 6.715e-08 0.009017 -1.127e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006087 0.0004953 0.004445 0.003627 0.9889 0.9919 0.006201 0.8617 0.8953 0.01294 ] Network output: [ -0.0005305 0.002607 1.001 -4.63e-05 2.079e-05 0.997 -3.489e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2092 0.09735 0.3393 0.1459 0.985 0.994 0.2099 0.4451 0.8779 0.7106 ] Network output: [ 0.005439 -0.02608 0.9947 2.778e-05 -1.247e-05 1.021 2.094e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1034 0.09127 0.1818 0.2006 0.9873 0.9919 0.1035 0.7589 0.8668 0.3057 ] Network output: [ -0.005186 0.02515 1.003 2.932e-05 -1.316e-05 0.982 2.209e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09096 0.08903 0.1651 0.1954 0.9853 0.9912 0.09098 0.6836 0.8433 0.2455 ] Network output: [ 0.0001513 1 -0.0002062 3.932e-06 -1.765e-06 0.9999 2.963e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003912 Epoch 8171 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01063 0.9956 0.9906 1.547e-07 -6.947e-08 -0.007455 1.166e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003367 -0.003178 -0.007846 0.006146 0.9699 0.9742 0.00647 0.8335 0.8246 0.01795 ] Network output: [ 0.9998 0.0005122 0.0008208 -1.471e-05 6.605e-06 -0.00106 -1.109e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1972 -0.03376 -0.1744 0.1896 0.9835 0.9932 0.2206 0.4409 0.8713 0.7164 ] Network output: [ -0.01034 1.002 1.009 -1.502e-07 6.742e-08 0.009015 -1.132e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006087 0.0004954 0.004445 0.003626 0.9889 0.9919 0.006202 0.8617 0.8953 0.01294 ] Network output: [ -0.0005302 0.002606 1.001 -4.625e-05 2.077e-05 0.997 -3.486e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2092 0.09736 0.3393 0.1459 0.985 0.994 0.2099 0.4451 0.8779 0.7106 ] Network output: [ 0.005437 -0.02607 0.9947 2.776e-05 -1.246e-05 1.021 2.092e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1034 0.09128 0.1818 0.2006 0.9873 0.9919 0.1035 0.7589 0.8668 0.3057 ] Network output: [ -0.005184 0.02514 1.003 2.929e-05 -1.315e-05 0.982 2.207e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09097 0.08903 0.1651 0.1954 0.9853 0.9912 0.09098 0.6836 0.8432 0.2455 ] Network output: [ 0.0001512 1 -0.0002059 3.928e-06 -1.764e-06 0.9999 2.961e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000391 Epoch 8172 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01063 0.9956 0.9906 1.536e-07 -6.897e-08 -0.007455 1.158e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003367 -0.003178 -0.007845 0.006145 0.9699 0.9742 0.00647 0.8335 0.8246 0.01795 ] Network output: [ 0.9998 0.0005118 0.0008203 -1.47e-05 6.598e-06 -0.001059 -1.108e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1972 -0.03377 -0.1744 0.1896 0.9835 0.9932 0.2206 0.4408 0.8713 0.7164 ] Network output: [ -0.01034 1.002 1.009 -1.508e-07 6.77e-08 0.009013 -1.136e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006088 0.0004954 0.004445 0.003626 0.9889 0.9919 0.006203 0.8617 0.8953 0.01294 ] Network output: [ -0.0005299 0.002605 1.001 -4.621e-05 2.074e-05 0.997 -3.482e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2092 0.09736 0.3393 0.1459 0.985 0.994 0.2099 0.4451 0.8779 0.7106 ] Network output: [ 0.005435 -0.02606 0.9947 2.773e-05 -1.245e-05 1.021 2.09e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1035 0.09128 0.1818 0.2006 0.9873 0.9919 0.1035 0.7588 0.8668 0.3057 ] Network output: [ -0.005182 0.02513 1.003 2.926e-05 -1.314e-05 0.982 2.205e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09097 0.08903 0.1651 0.1954 0.9853 0.9912 0.09098 0.6836 0.8432 0.2455 ] Network output: [ 0.0001511 1 -0.0002057 3.924e-06 -1.762e-06 0.9999 2.958e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003908 Epoch 8173 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01063 0.9956 0.9906 1.525e-07 -6.847e-08 -0.007456 1.149e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003367 -0.003178 -0.007844 0.006144 0.9699 0.9742 0.006471 0.8335 0.8246 0.01795 ] Network output: [ 0.9998 0.0005113 0.0008198 -1.468e-05 6.592e-06 -0.001058 -1.107e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1972 -0.03377 -0.1743 0.1896 0.9835 0.9932 0.2206 0.4408 0.8713 0.7164 ] Network output: [ -0.01033 1.002 1.009 -1.514e-07 6.797e-08 0.009011 -1.141e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006088 0.0004955 0.004445 0.003625 0.9889 0.9919 0.006203 0.8617 0.8953 0.01294 ] Network output: [ -0.0005295 0.002605 1.001 -4.616e-05 2.072e-05 0.997 -3.479e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2092 0.09737 0.3394 0.1459 0.985 0.994 0.2099 0.4451 0.8779 0.7106 ] Network output: [ 0.005433 -0.02605 0.9947 2.77e-05 -1.244e-05 1.021 2.088e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1035 0.09129 0.1818 0.2006 0.9873 0.9919 0.1035 0.7588 0.8668 0.3057 ] Network output: [ -0.00518 0.02512 1.003 2.923e-05 -1.312e-05 0.982 2.203e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09097 0.08903 0.1651 0.1954 0.9853 0.9912 0.09098 0.6836 0.8432 0.2455 ] Network output: [ 0.000151 1 -0.0002055 3.921e-06 -1.76e-06 0.9999 2.955e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003906 Epoch 8174 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01062 0.9956 0.9906 1.514e-07 -6.798e-08 -0.007456 1.141e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003367 -0.003178 -0.007843 0.006144 0.9699 0.9742 0.006471 0.8335 0.8246 0.01794 ] Network output: [ 0.9998 0.0005109 0.0008193 -1.467e-05 6.585e-06 -0.001058 -1.105e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1972 -0.03377 -0.1743 0.1896 0.9835 0.9932 0.2206 0.4408 0.8713 0.7163 ] Network output: [ -0.01033 1.002 1.009 -1.52e-07 6.824e-08 0.009009 -1.146e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006089 0.0004956 0.004445 0.003625 0.9889 0.9919 0.006204 0.8617 0.8953 0.01294 ] Network output: [ -0.0005292 0.002604 1.001 -4.611e-05 2.07e-05 0.997 -3.475e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2092 0.09737 0.3394 0.1459 0.985 0.994 0.2099 0.4451 0.8779 0.7106 ] Network output: [ 0.005432 -0.02604 0.9947 2.767e-05 -1.242e-05 1.021 2.086e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1035 0.09129 0.1818 0.2006 0.9873 0.9919 0.1035 0.7588 0.8668 0.3057 ] Network output: [ -0.005178 0.02511 1.003 2.92e-05 -1.311e-05 0.982 2.201e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09097 0.08904 0.1651 0.1954 0.9853 0.9912 0.09098 0.6836 0.8432 0.2455 ] Network output: [ 0.000151 1 -0.0002052 3.917e-06 -1.758e-06 0.9999 2.952e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003903 Epoch 8175 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01062 0.9957 0.9906 1.503e-07 -6.748e-08 -0.007456 1.133e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003367 -0.003179 -0.007842 0.006143 0.9699 0.9743 0.006471 0.8335 0.8246 0.01794 ] Network output: [ 0.9998 0.0005105 0.0008188 -1.465e-05 6.579e-06 -0.001057 -1.104e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1972 -0.03377 -0.1743 0.1896 0.9835 0.9932 0.2206 0.4408 0.8713 0.7163 ] Network output: [ -0.01033 1.002 1.009 -1.526e-07 6.851e-08 0.009007 -1.15e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006089 0.0004957 0.004445 0.003625 0.9889 0.9919 0.006204 0.8617 0.8953 0.01294 ] Network output: [ -0.0005289 0.002603 1.001 -4.607e-05 2.068e-05 0.997 -3.472e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2092 0.09738 0.3394 0.1459 0.985 0.994 0.2099 0.4451 0.8779 0.7106 ] Network output: [ 0.00543 -0.02603 0.9947 2.765e-05 -1.241e-05 1.021 2.083e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1035 0.0913 0.1818 0.2006 0.9873 0.9919 0.1035 0.7588 0.8668 0.3057 ] Network output: [ -0.005176 0.02509 1.003 2.917e-05 -1.31e-05 0.982 2.199e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09097 0.08904 0.1651 0.1954 0.9853 0.9912 0.09099 0.6835 0.8432 0.2455 ] Network output: [ 0.0001509 1 -0.000205 3.913e-06 -1.757e-06 0.9999 2.949e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003901 Epoch 8176 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01062 0.9957 0.9906 1.492e-07 -6.699e-08 -0.007457 1.125e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003367 -0.003179 -0.007841 0.006142 0.9699 0.9743 0.006471 0.8335 0.8246 0.01794 ] Network output: [ 0.9998 0.0005101 0.0008183 -1.464e-05 6.572e-06 -0.001056 -1.103e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1972 -0.03377 -0.1743 0.1896 0.9835 0.9932 0.2207 0.4408 0.8713 0.7163 ] Network output: [ -0.01033 1.002 1.009 -1.532e-07 6.878e-08 0.009005 -1.155e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00609 0.0004958 0.004445 0.003624 0.9889 0.9919 0.006205 0.8617 0.8953 0.01294 ] Network output: [ -0.0005285 0.002602 1.001 -4.602e-05 2.066e-05 0.997 -3.468e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2092 0.09738 0.3394 0.1459 0.985 0.994 0.2099 0.4451 0.8779 0.7106 ] Network output: [ 0.005428 -0.02602 0.9947 2.762e-05 -1.24e-05 1.021 2.081e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1035 0.09131 0.1818 0.2006 0.9873 0.9919 0.1035 0.7588 0.8668 0.3057 ] Network output: [ -0.005174 0.02508 1.003 2.914e-05 -1.308e-05 0.982 2.196e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09098 0.08904 0.1651 0.1954 0.9853 0.9912 0.09099 0.6835 0.8432 0.2455 ] Network output: [ 0.0001508 1 -0.0002048 3.909e-06 -1.755e-06 0.9999 2.946e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003899 Epoch 8177 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01062 0.9957 0.9906 1.481e-07 -6.649e-08 -0.007457 1.116e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003368 -0.003179 -0.00784 0.006142 0.9699 0.9743 0.006472 0.8335 0.8246 0.01794 ] Network output: [ 0.9998 0.0005096 0.0008178 -1.462e-05 6.565e-06 -0.001055 -1.102e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1972 -0.03377 -0.1743 0.1896 0.9835 0.9932 0.2207 0.4408 0.8712 0.7163 ] Network output: [ -0.01033 1.002 1.009 -1.538e-07 6.905e-08 0.009004 -1.159e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006091 0.0004959 0.004445 0.003624 0.9889 0.9919 0.006206 0.8617 0.8953 0.01294 ] Network output: [ -0.0005282 0.002601 1.001 -4.597e-05 2.064e-05 0.997 -3.465e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2092 0.09739 0.3394 0.1459 0.985 0.994 0.2099 0.445 0.8779 0.7106 ] Network output: [ 0.005426 -0.02601 0.9947 2.759e-05 -1.239e-05 1.021 2.079e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1035 0.09131 0.1818 0.2006 0.9873 0.9919 0.1036 0.7587 0.8668 0.3057 ] Network output: [ -0.005173 0.02507 1.003 2.912e-05 -1.307e-05 0.982 2.194e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09098 0.08904 0.1651 0.1954 0.9853 0.9912 0.09099 0.6835 0.8432 0.2455 ] Network output: [ 0.0001507 1 -0.0002045 3.905e-06 -1.753e-06 0.9999 2.943e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003896 Epoch 8178 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01062 0.9957 0.9906 1.47e-07 -6.6e-08 -0.007457 1.108e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003368 -0.003179 -0.007839 0.006141 0.9699 0.9743 0.006472 0.8334 0.8246 0.01794 ] Network output: [ 0.9998 0.0005092 0.0008173 -1.461e-05 6.559e-06 -0.001054 -1.101e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1972 -0.03378 -0.1743 0.1896 0.9835 0.9932 0.2207 0.4408 0.8712 0.7163 ] Network output: [ -0.01033 1.002 1.009 -1.544e-07 6.932e-08 0.009002 -1.164e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006091 0.0004959 0.004445 0.003623 0.9889 0.9919 0.006206 0.8616 0.8953 0.01294 ] Network output: [ -0.0005279 0.0026 1.001 -4.593e-05 2.062e-05 0.997 -3.461e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2093 0.0974 0.3394 0.1459 0.985 0.994 0.2099 0.445 0.8779 0.7106 ] Network output: [ 0.005424 -0.026 0.9947 2.756e-05 -1.237e-05 1.021 2.077e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1035 0.09132 0.1818 0.2006 0.9873 0.9919 0.1036 0.7587 0.8668 0.3057 ] Network output: [ -0.005171 0.02506 1.003 2.909e-05 -1.306e-05 0.982 2.192e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09098 0.08904 0.1651 0.1954 0.9853 0.9912 0.09099 0.6835 0.8432 0.2455 ] Network output: [ 0.0001507 1 -0.0002043 3.901e-06 -1.751e-06 0.9999 2.94e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003894 Epoch 8179 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01062 0.9957 0.9906 1.459e-07 -6.551e-08 -0.007458 1.1e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003368 -0.003179 -0.007838 0.006141 0.9699 0.9743 0.006472 0.8334 0.8246 0.01794 ] Network output: [ 0.9998 0.0005088 0.0008168 -1.46e-05 6.552e-06 -0.001053 -1.1e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1972 -0.03378 -0.1743 0.1896 0.9835 0.9932 0.2207 0.4408 0.8712 0.7163 ] Network output: [ -0.01033 1.002 1.009 -1.55e-07 6.958e-08 0.009 -1.168e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006092 0.000496 0.004445 0.003623 0.9889 0.9919 0.006207 0.8616 0.8953 0.01293 ] Network output: [ -0.0005275 0.002599 1.001 -4.588e-05 2.06e-05 0.997 -3.458e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2093 0.0974 0.3394 0.1459 0.985 0.994 0.21 0.445 0.8779 0.7106 ] Network output: [ 0.005423 -0.026 0.9947 2.753e-05 -1.236e-05 1.021 2.075e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1035 0.09132 0.1818 0.2006 0.9873 0.9919 0.1036 0.7587 0.8668 0.3057 ] Network output: [ -0.005169 0.02505 1.003 2.906e-05 -1.305e-05 0.982 2.19e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09098 0.08904 0.1651 0.1954 0.9853 0.9912 0.09099 0.6834 0.8432 0.2455 ] Network output: [ 0.0001506 1 -0.000204 3.897e-06 -1.75e-06 0.9999 2.937e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003892 Epoch 8180 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01062 0.9957 0.9906 1.448e-07 -6.502e-08 -0.007458 1.092e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003368 -0.003179 -0.007837 0.00614 0.9699 0.9743 0.006473 0.8334 0.8246 0.01794 ] Network output: [ 0.9998 0.0005084 0.0008163 -1.458e-05 6.546e-06 -0.001052 -1.099e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1972 -0.03378 -0.1742 0.1896 0.9835 0.9932 0.2207 0.4408 0.8712 0.7163 ] Network output: [ -0.01033 1.002 1.009 -1.556e-07 6.985e-08 0.008998 -1.173e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006092 0.0004961 0.004445 0.003623 0.9889 0.9919 0.006207 0.8616 0.8953 0.01293 ] Network output: [ -0.0005272 0.002598 1.001 -4.583e-05 2.058e-05 0.997 -3.454e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2093 0.09741 0.3394 0.1459 0.985 0.994 0.21 0.445 0.8779 0.7106 ] Network output: [ 0.005421 -0.02599 0.9947 2.751e-05 -1.235e-05 1.021 2.073e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1035 0.09133 0.1818 0.2006 0.9873 0.9919 0.1036 0.7587 0.8668 0.3057 ] Network output: [ -0.005167 0.02504 1.003 2.903e-05 -1.303e-05 0.982 2.188e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09098 0.08905 0.1651 0.1954 0.9853 0.9912 0.091 0.6834 0.8432 0.2455 ] Network output: [ 0.0001505 1 -0.0002038 3.893e-06 -1.748e-06 0.9999 2.934e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000389 Epoch 8181 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01061 0.9957 0.9906 1.437e-07 -6.453e-08 -0.007458 1.083e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003368 -0.00318 -0.007836 0.006139 0.9699 0.9743 0.006473 0.8334 0.8246 0.01793 ] Network output: [ 0.9998 0.0005079 0.0008158 -1.457e-05 6.539e-06 -0.001051 -1.098e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1973 -0.03378 -0.1742 0.1896 0.9835 0.9932 0.2207 0.4407 0.8712 0.7163 ] Network output: [ -0.01033 1.002 1.009 -1.562e-07 7.012e-08 0.008996 -1.177e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006093 0.0004962 0.004445 0.003622 0.9889 0.9919 0.006208 0.8616 0.8953 0.01293 ] Network output: [ -0.0005269 0.002597 1.001 -4.578e-05 2.055e-05 0.997 -3.45e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2093 0.09741 0.3394 0.1459 0.985 0.994 0.21 0.445 0.8779 0.7106 ] Network output: [ 0.005419 -0.02598 0.9947 2.748e-05 -1.234e-05 1.021 2.071e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1035 0.09133 0.1818 0.2006 0.9873 0.9919 0.1036 0.7586 0.8668 0.3057 ] Network output: [ -0.005165 0.02503 1.003 2.9e-05 -1.302e-05 0.982 2.186e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09098 0.08905 0.1651 0.1954 0.9853 0.9912 0.091 0.6834 0.8432 0.2455 ] Network output: [ 0.0001504 1 -0.0002036 3.889e-06 -1.746e-06 0.9999 2.931e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003887 Epoch 8182 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01061 0.9957 0.9906 1.427e-07 -6.404e-08 -0.007459 1.075e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003368 -0.00318 -0.007835 0.006139 0.9699 0.9743 0.006473 0.8334 0.8246 0.01793 ] Network output: [ 0.9998 0.0005075 0.0008153 -1.455e-05 6.533e-06 -0.00105 -1.097e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1973 -0.03378 -0.1742 0.1896 0.9835 0.9932 0.2207 0.4407 0.8712 0.7163 ] Network output: [ -0.01032 1.002 1.009 -1.568e-07 7.038e-08 0.008994 -1.182e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006094 0.0004963 0.004445 0.003622 0.9889 0.9919 0.006209 0.8616 0.8953 0.01293 ] Network output: [ -0.0005266 0.002596 1.001 -4.574e-05 2.053e-05 0.9971 -3.447e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2093 0.09742 0.3394 0.1459 0.985 0.994 0.21 0.445 0.8778 0.7106 ] Network output: [ 0.005417 -0.02597 0.9947 2.745e-05 -1.232e-05 1.021 2.069e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1035 0.09134 0.1818 0.2006 0.9873 0.9919 0.1036 0.7586 0.8667 0.3057 ] Network output: [ -0.005163 0.02502 1.003 2.897e-05 -1.301e-05 0.982 2.184e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09099 0.08905 0.1651 0.1954 0.9853 0.9912 0.091 0.6834 0.8432 0.2455 ] Network output: [ 0.0001504 1 -0.0002033 3.886e-06 -1.744e-06 0.9999 2.928e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003885 Epoch 8183 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01061 0.9957 0.9906 1.416e-07 -6.356e-08 -0.007459 1.067e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003368 -0.00318 -0.007834 0.006138 0.9699 0.9743 0.006474 0.8334 0.8246 0.01793 ] Network output: [ 0.9998 0.0005071 0.0008148 -1.454e-05 6.526e-06 -0.00105 -1.096e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1973 -0.03379 -0.1742 0.1896 0.9835 0.9932 0.2207 0.4407 0.8712 0.7163 ] Network output: [ -0.01032 1.002 1.009 -1.574e-07 7.065e-08 0.008993 -1.186e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006094 0.0004963 0.004445 0.003621 0.9889 0.9919 0.006209 0.8616 0.8953 0.01293 ] Network output: [ -0.0005262 0.002595 1.001 -4.569e-05 2.051e-05 0.9971 -3.443e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2093 0.09743 0.3394 0.1459 0.985 0.994 0.21 0.445 0.8778 0.7106 ] Network output: [ 0.005416 -0.02596 0.9947 2.742e-05 -1.231e-05 1.021 2.067e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1035 0.09135 0.1818 0.2006 0.9873 0.9919 0.1036 0.7586 0.8667 0.3057 ] Network output: [ -0.005161 0.02501 1.003 2.895e-05 -1.3e-05 0.982 2.182e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09099 0.08905 0.1651 0.1954 0.9853 0.9912 0.091 0.6834 0.8432 0.2455 ] Network output: [ 0.0001503 1 -0.0002031 3.882e-06 -1.743e-06 0.9999 2.925e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003883 Epoch 8184 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01061 0.9957 0.9906 1.405e-07 -6.307e-08 -0.007459 1.059e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003369 -0.00318 -0.007833 0.006137 0.9699 0.9743 0.006474 0.8334 0.8246 0.01793 ] Network output: [ 0.9998 0.0005067 0.0008143 -1.452e-05 6.519e-06 -0.001049 -1.094e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1973 -0.03379 -0.1742 0.1896 0.9835 0.9932 0.2207 0.4407 0.8712 0.7163 ] Network output: [ -0.01032 1.002 1.009 -1.58e-07 7.091e-08 0.008991 -1.19e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006095 0.0004964 0.004445 0.003621 0.9889 0.9919 0.00621 0.8616 0.8953 0.01293 ] Network output: [ -0.0005259 0.002594 1.001 -4.564e-05 2.049e-05 0.9971 -3.44e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2093 0.09743 0.3395 0.1459 0.985 0.994 0.21 0.445 0.8778 0.7106 ] Network output: [ 0.005414 -0.02595 0.9947 2.74e-05 -1.23e-05 1.021 2.065e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1035 0.09135 0.1818 0.2006 0.9873 0.9919 0.1036 0.7586 0.8667 0.3057 ] Network output: [ -0.00516 0.025 1.003 2.892e-05 -1.298e-05 0.982 2.179e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09099 0.08905 0.1651 0.1954 0.9853 0.9912 0.091 0.6833 0.8432 0.2455 ] Network output: [ 0.0001502 1 -0.0002029 3.878e-06 -1.741e-06 0.9999 2.922e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003881 Epoch 8185 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01061 0.9957 0.9906 1.394e-07 -6.259e-08 -0.00746 1.051e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003369 -0.00318 -0.007832 0.006137 0.9699 0.9743 0.006474 0.8334 0.8246 0.01793 ] Network output: [ 0.9998 0.0005062 0.0008138 -1.451e-05 6.513e-06 -0.001048 -1.093e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1973 -0.03379 -0.1742 0.1896 0.9835 0.9932 0.2208 0.4407 0.8712 0.7163 ] Network output: [ -0.01032 1.002 1.009 -1.585e-07 7.117e-08 0.008989 -1.195e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006095 0.0004965 0.004445 0.003621 0.9889 0.9919 0.00621 0.8616 0.8953 0.01293 ] Network output: [ -0.0005256 0.002594 1.001 -4.56e-05 2.047e-05 0.9971 -3.436e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2093 0.09744 0.3395 0.1459 0.985 0.994 0.21 0.4449 0.8778 0.7106 ] Network output: [ 0.005412 -0.02594 0.9947 2.737e-05 -1.229e-05 1.021 2.063e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1035 0.09136 0.1818 0.2005 0.9873 0.9919 0.1036 0.7586 0.8667 0.3057 ] Network output: [ -0.005158 0.02499 1.003 2.889e-05 -1.297e-05 0.982 2.177e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09099 0.08906 0.1651 0.1954 0.9853 0.9912 0.09101 0.6833 0.8431 0.2455 ] Network output: [ 0.0001501 1 -0.0002026 3.874e-06 -1.739e-06 0.9999 2.92e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003878 Epoch 8186 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01061 0.9957 0.9906 1.383e-07 -6.21e-08 -0.00746 1.043e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003369 -0.00318 -0.007831 0.006136 0.9699 0.9743 0.006475 0.8334 0.8246 0.01793 ] Network output: [ 0.9998 0.0005058 0.0008133 -1.449e-05 6.506e-06 -0.001047 -1.092e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1973 -0.03379 -0.1742 0.1896 0.9835 0.9932 0.2208 0.4407 0.8712 0.7163 ] Network output: [ -0.01032 1.002 1.009 -1.591e-07 7.144e-08 0.008987 -1.199e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006096 0.0004966 0.004445 0.00362 0.9889 0.9919 0.006211 0.8616 0.8953 0.01293 ] Network output: [ -0.0005252 0.002593 1.001 -4.555e-05 2.045e-05 0.9971 -3.433e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2093 0.09744 0.3395 0.1459 0.985 0.994 0.21 0.4449 0.8778 0.7106 ] Network output: [ 0.00541 -0.02593 0.9947 2.734e-05 -1.227e-05 1.021 2.061e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1035 0.09136 0.1818 0.2005 0.9873 0.9919 0.1036 0.7585 0.8667 0.3057 ] Network output: [ -0.005156 0.02498 1.003 2.886e-05 -1.296e-05 0.982 2.175e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09099 0.08906 0.1651 0.1954 0.9853 0.9912 0.09101 0.6833 0.8431 0.2455 ] Network output: [ 0.0001501 1 -0.0002024 3.87e-06 -1.737e-06 0.9999 2.917e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003876 Epoch 8187 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01061 0.9957 0.9906 1.373e-07 -6.162e-08 -0.00746 1.034e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003369 -0.00318 -0.00783 0.006135 0.9699 0.9743 0.006475 0.8334 0.8246 0.01793 ] Network output: [ 0.9998 0.0005054 0.0008128 -1.448e-05 6.5e-06 -0.001046 -1.091e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1973 -0.03379 -0.1741 0.1895 0.9835 0.9932 0.2208 0.4407 0.8712 0.7163 ] Network output: [ -0.01032 1.002 1.009 -1.597e-07 7.17e-08 0.008985 -1.204e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006096 0.0004967 0.004445 0.00362 0.9889 0.9919 0.006212 0.8616 0.8952 0.01293 ] Network output: [ -0.0005249 0.002592 1.001 -4.551e-05 2.043e-05 0.9971 -3.429e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2094 0.09745 0.3395 0.1459 0.985 0.994 0.21 0.4449 0.8778 0.7105 ] Network output: [ 0.005408 -0.02592 0.9947 2.731e-05 -1.226e-05 1.021 2.059e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1035 0.09137 0.1818 0.2005 0.9873 0.9919 0.1036 0.7585 0.8667 0.3057 ] Network output: [ -0.005154 0.02497 1.003 2.883e-05 -1.294e-05 0.982 2.173e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.091 0.08906 0.1651 0.1954 0.9853 0.9912 0.09101 0.6833 0.8431 0.2455 ] Network output: [ 0.00015 1 -0.0002022 3.866e-06 -1.736e-06 0.9999 2.914e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003874 Epoch 8188 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0106 0.9957 0.9906 1.362e-07 -6.114e-08 -0.00746 1.026e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003369 -0.003181 -0.007829 0.006135 0.9699 0.9743 0.006475 0.8334 0.8246 0.01792 ] Network output: [ 0.9998 0.000505 0.0008123 -1.446e-05 6.493e-06 -0.001045 -1.09e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1973 -0.03379 -0.1741 0.1895 0.9835 0.9932 0.2208 0.4407 0.8712 0.7163 ] Network output: [ -0.01032 1.002 1.009 -1.603e-07 7.196e-08 0.008983 -1.208e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006097 0.0004968 0.004445 0.003619 0.9889 0.9919 0.006212 0.8616 0.8952 0.01293 ] Network output: [ -0.0005246 0.002591 1.001 -4.546e-05 2.041e-05 0.9971 -3.426e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2094 0.09745 0.3395 0.1459 0.985 0.994 0.2101 0.4449 0.8778 0.7105 ] Network output: [ 0.005407 -0.02591 0.9947 2.729e-05 -1.225e-05 1.021 2.056e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1036 0.09138 0.1818 0.2005 0.9873 0.9919 0.1036 0.7585 0.8667 0.3057 ] Network output: [ -0.005152 0.02496 1.003 2.881e-05 -1.293e-05 0.982 2.171e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.091 0.08906 0.1651 0.1954 0.9853 0.9912 0.09101 0.6833 0.8431 0.2455 ] Network output: [ 0.0001499 1 -0.0002019 3.862e-06 -1.734e-06 0.9999 2.911e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003872 Epoch 8189 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0106 0.9957 0.9906 1.351e-07 -6.066e-08 -0.007461 1.018e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003369 -0.003181 -0.007828 0.006134 0.9699 0.9743 0.006475 0.8334 0.8246 0.01792 ] Network output: [ 0.9998 0.0005045 0.0008118 -1.445e-05 6.487e-06 -0.001044 -1.089e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1973 -0.0338 -0.1741 0.1895 0.9835 0.9932 0.2208 0.4407 0.8712 0.7163 ] Network output: [ -0.01032 1.002 1.009 -1.609e-07 7.222e-08 0.008981 -1.212e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006098 0.0004968 0.004445 0.003619 0.9889 0.9919 0.006213 0.8616 0.8952 0.01292 ] Network output: [ -0.0005242 0.00259 1.001 -4.541e-05 2.039e-05 0.9971 -3.422e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2094 0.09746 0.3395 0.1459 0.985 0.994 0.2101 0.4449 0.8778 0.7105 ] Network output: [ 0.005405 -0.0259 0.9947 2.726e-05 -1.224e-05 1.021 2.054e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1036 0.09138 0.1818 0.2005 0.9873 0.9919 0.1036 0.7585 0.8667 0.3057 ] Network output: [ -0.00515 0.02495 1.003 2.878e-05 -1.292e-05 0.982 2.169e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.091 0.08906 0.1651 0.1954 0.9853 0.9912 0.09101 0.6832 0.8431 0.2455 ] Network output: [ 0.0001498 1 -0.0002017 3.859e-06 -1.732e-06 0.9999 2.908e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003869 Epoch 8190 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0106 0.9957 0.9906 1.34e-07 -6.018e-08 -0.007461 1.01e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003369 -0.003181 -0.007827 0.006134 0.9699 0.9743 0.006476 0.8334 0.8246 0.01792 ] Network output: [ 0.9998 0.0005041 0.0008113 -1.443e-05 6.48e-06 -0.001043 -1.088e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1973 -0.0338 -0.1741 0.1895 0.9835 0.9932 0.2208 0.4406 0.8712 0.7163 ] Network output: [ -0.01032 1.002 1.009 -1.614e-07 7.248e-08 0.00898 -1.217e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006098 0.0004969 0.004445 0.003619 0.9889 0.9919 0.006213 0.8615 0.8952 0.01292 ] Network output: [ -0.0005239 0.002589 1.001 -4.537e-05 2.037e-05 0.9971 -3.419e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2094 0.09747 0.3395 0.1459 0.985 0.994 0.2101 0.4449 0.8778 0.7105 ] Network output: [ 0.005403 -0.02589 0.9947 2.723e-05 -1.223e-05 1.021 2.052e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1036 0.09139 0.1818 0.2005 0.9873 0.9919 0.1036 0.7585 0.8667 0.3057 ] Network output: [ -0.005148 0.02494 1.003 2.875e-05 -1.291e-05 0.982 2.167e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.091 0.08906 0.1651 0.1954 0.9853 0.9912 0.09101 0.6832 0.8431 0.2455 ] Network output: [ 0.0001498 1 -0.0002015 3.855e-06 -1.731e-06 0.9999 2.905e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003867 Epoch 8191 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0106 0.9957 0.9906 1.33e-07 -5.97e-08 -0.007461 1.002e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003369 -0.003181 -0.007826 0.006133 0.9699 0.9743 0.006476 0.8334 0.8246 0.01792 ] Network output: [ 0.9998 0.0005037 0.0008108 -1.442e-05 6.474e-06 -0.001042 -1.087e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1974 -0.0338 -0.1741 0.1895 0.9835 0.9932 0.2208 0.4406 0.8712 0.7163 ] Network output: [ -0.01031 1.002 1.009 -1.62e-07 7.274e-08 0.008978 -1.221e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006099 0.000497 0.004445 0.003618 0.9889 0.9919 0.006214 0.8615 0.8952 0.01292 ] Network output: [ -0.0005236 0.002588 1.001 -4.532e-05 2.035e-05 0.9971 -3.415e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2094 0.09747 0.3395 0.1459 0.985 0.994 0.2101 0.4449 0.8778 0.7105 ] Network output: [ 0.005401 -0.02588 0.9947 2.721e-05 -1.221e-05 1.021 2.05e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1036 0.09139 0.1818 0.2005 0.9873 0.9919 0.1036 0.7584 0.8667 0.3057 ] Network output: [ -0.005147 0.02493 1.003 2.872e-05 -1.289e-05 0.9821 2.165e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.091 0.08907 0.1651 0.1954 0.9853 0.9912 0.09102 0.6832 0.8431 0.2456 ] Network output: [ 0.0001497 1 -0.0002012 3.851e-06 -1.729e-06 0.9999 2.902e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003865 Epoch 8192 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0106 0.9957 0.9906 1.319e-07 -5.922e-08 -0.007462 9.942e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00337 -0.003181 -0.007825 0.006132 0.9699 0.9743 0.006476 0.8333 0.8245 0.01792 ] Network output: [ 0.9998 0.0005033 0.0008103 -1.441e-05 6.467e-06 -0.001042 -1.086e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1974 -0.0338 -0.1741 0.1895 0.9835 0.9932 0.2208 0.4406 0.8712 0.7162 ] Network output: [ -0.01031 1.002 1.009 -1.626e-07 7.3e-08 0.008976 -1.225e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006099 0.0004971 0.004445 0.003618 0.9889 0.9919 0.006215 0.8615 0.8952 0.01292 ] Network output: [ -0.0005233 0.002587 1.001 -4.527e-05 2.032e-05 0.9971 -3.412e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2094 0.09748 0.3395 0.1458 0.985 0.994 0.2101 0.4449 0.8778 0.7105 ] Network output: [ 0.005399 -0.02587 0.9947 2.718e-05 -1.22e-05 1.021 2.048e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1036 0.0914 0.1818 0.2005 0.9873 0.9919 0.1036 0.7584 0.8667 0.3057 ] Network output: [ -0.005145 0.02492 1.003 2.869e-05 -1.288e-05 0.9821 2.162e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09101 0.08907 0.1651 0.1954 0.9853 0.9912 0.09102 0.6832 0.8431 0.2456 ] Network output: [ 0.0001496 1 -0.000201 3.847e-06 -1.727e-06 0.9999 2.899e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003863 Epoch 8193 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0106 0.9957 0.9906 1.309e-07 -5.875e-08 -0.007462 9.862e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00337 -0.003181 -0.007824 0.006132 0.9699 0.9743 0.006477 0.8333 0.8245 0.01792 ] Network output: [ 0.9998 0.0005028 0.0008098 -1.439e-05 6.461e-06 -0.001041 -1.085e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1974 -0.0338 -0.1741 0.1895 0.9835 0.9932 0.2208 0.4406 0.8712 0.7162 ] Network output: [ -0.01031 1.002 1.009 -1.632e-07 7.325e-08 0.008974 -1.23e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0061 0.0004972 0.004445 0.003617 0.9889 0.9919 0.006215 0.8615 0.8952 0.01292 ] Network output: [ -0.0005229 0.002586 1.001 -4.523e-05 2.03e-05 0.9971 -3.408e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2094 0.09748 0.3395 0.1458 0.985 0.994 0.2101 0.4449 0.8778 0.7105 ] Network output: [ 0.005398 -0.02586 0.9947 2.715e-05 -1.219e-05 1.02 2.046e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1036 0.0914 0.1818 0.2005 0.9873 0.9919 0.1037 0.7584 0.8667 0.3057 ] Network output: [ -0.005143 0.02491 1.003 2.867e-05 -1.287e-05 0.9821 2.16e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09101 0.08907 0.1651 0.1954 0.9853 0.9912 0.09102 0.6831 0.8431 0.2456 ] Network output: [ 0.0001495 1 -0.0002008 3.843e-06 -1.725e-06 0.9999 2.896e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003861 Epoch 8194 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01059 0.9957 0.9906 1.298e-07 -5.827e-08 -0.007462 9.782e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00337 -0.003181 -0.007823 0.006131 0.9699 0.9743 0.006477 0.8333 0.8245 0.01792 ] Network output: [ 0.9998 0.0005024 0.0008093 -1.438e-05 6.454e-06 -0.00104 -1.083e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1974 -0.03381 -0.174 0.1895 0.9835 0.9932 0.2209 0.4406 0.8712 0.7162 ] Network output: [ -0.01031 1.002 1.009 -1.637e-07 7.351e-08 0.008972 -1.234e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006101 0.0004973 0.004445 0.003617 0.9889 0.9919 0.006216 0.8615 0.8952 0.01292 ] Network output: [ -0.0005226 0.002585 1.001 -4.518e-05 2.028e-05 0.9971 -3.405e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2094 0.09749 0.3395 0.1458 0.985 0.994 0.2101 0.4448 0.8778 0.7105 ] Network output: [ 0.005396 -0.02585 0.9947 2.712e-05 -1.218e-05 1.02 2.044e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1036 0.09141 0.1818 0.2005 0.9873 0.9919 0.1037 0.7584 0.8667 0.3057 ] Network output: [ -0.005141 0.0249 1.003 2.864e-05 -1.286e-05 0.9821 2.158e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09101 0.08907 0.1651 0.1954 0.9853 0.9912 0.09102 0.6831 0.8431 0.2456 ] Network output: [ 0.0001495 1 -0.0002005 3.839e-06 -1.724e-06 0.9999 2.893e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003858 Epoch 8195 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01059 0.9957 0.9906 1.287e-07 -5.78e-08 -0.007463 9.702e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00337 -0.003182 -0.007822 0.00613 0.9699 0.9743 0.006477 0.8333 0.8245 0.01792 ] Network output: [ 0.9998 0.000502 0.0008089 -1.436e-05 6.448e-06 -0.001039 -1.082e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1974 -0.03381 -0.174 0.1895 0.9835 0.9932 0.2209 0.4406 0.8712 0.7162 ] Network output: [ -0.01031 1.002 1.009 -1.643e-07 7.377e-08 0.00897 -1.238e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006101 0.0004973 0.004445 0.003617 0.9889 0.9919 0.006216 0.8615 0.8952 0.01292 ] Network output: [ -0.0005223 0.002584 1.001 -4.513e-05 2.026e-05 0.9971 -3.402e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2094 0.0975 0.3396 0.1458 0.985 0.994 0.2101 0.4448 0.8778 0.7105 ] Network output: [ 0.005394 -0.02584 0.9947 2.71e-05 -1.216e-05 1.02 2.042e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1036 0.09142 0.1818 0.2005 0.9873 0.9919 0.1037 0.7584 0.8667 0.3057 ] Network output: [ -0.005139 0.02489 1.003 2.861e-05 -1.284e-05 0.9821 2.156e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09101 0.08907 0.1651 0.1954 0.9853 0.9912 0.09102 0.6831 0.8431 0.2456 ] Network output: [ 0.0001494 1 -0.0002003 3.836e-06 -1.722e-06 0.9999 2.891e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003856 Epoch 8196 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01059 0.9957 0.9906 1.277e-07 -5.732e-08 -0.007463 9.623e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00337 -0.003182 -0.007821 0.00613 0.9699 0.9743 0.006478 0.8333 0.8245 0.01791 ] Network output: [ 0.9998 0.0005016 0.0008084 -1.435e-05 6.441e-06 -0.001038 -1.081e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1974 -0.03381 -0.174 0.1895 0.9835 0.9932 0.2209 0.4406 0.8712 0.7162 ] Network output: [ -0.01031 1.002 1.009 -1.649e-07 7.402e-08 0.008969 -1.243e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006102 0.0004974 0.004445 0.003616 0.9889 0.9919 0.006217 0.8615 0.8952 0.01292 ] Network output: [ -0.0005219 0.002583 1.001 -4.509e-05 2.024e-05 0.9971 -3.398e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2095 0.0975 0.3396 0.1458 0.985 0.994 0.2101 0.4448 0.8778 0.7105 ] Network output: [ 0.005392 -0.02584 0.9947 2.707e-05 -1.215e-05 1.02 2.04e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1036 0.09142 0.1818 0.2005 0.9873 0.9919 0.1037 0.7583 0.8667 0.3057 ] Network output: [ -0.005137 0.02488 1.003 2.858e-05 -1.283e-05 0.9821 2.154e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09101 0.08908 0.1651 0.1954 0.9853 0.9912 0.09103 0.6831 0.8431 0.2456 ] Network output: [ 0.0001493 1 -0.0002001 3.832e-06 -1.72e-06 0.9999 2.888e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003854 Epoch 8197 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01059 0.9957 0.9906 1.266e-07 -5.685e-08 -0.007463 9.543e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00337 -0.003182 -0.00782 0.006129 0.9699 0.9743 0.006478 0.8333 0.8245 0.01791 ] Network output: [ 0.9998 0.0005012 0.0008079 -1.433e-05 6.435e-06 -0.001037 -1.08e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1974 -0.03381 -0.174 0.1895 0.9835 0.9932 0.2209 0.4406 0.8712 0.7162 ] Network output: [ -0.01031 1.002 1.009 -1.655e-07 7.428e-08 0.008967 -1.247e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006102 0.0004975 0.004445 0.003616 0.9889 0.9919 0.006218 0.8615 0.8952 0.01292 ] Network output: [ -0.0005216 0.002583 1.001 -4.504e-05 2.022e-05 0.9971 -3.395e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2095 0.09751 0.3396 0.1458 0.985 0.994 0.2101 0.4448 0.8778 0.7105 ] Network output: [ 0.005391 -0.02583 0.9947 2.704e-05 -1.214e-05 1.02 2.038e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1036 0.09143 0.1818 0.2005 0.9873 0.9919 0.1037 0.7583 0.8667 0.3057 ] Network output: [ -0.005136 0.02487 1.003 2.855e-05 -1.282e-05 0.9821 2.152e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09101 0.08908 0.1651 0.1954 0.9853 0.9912 0.09103 0.6831 0.8431 0.2456 ] Network output: [ 0.0001492 1 -0.0001998 3.828e-06 -1.718e-06 0.9999 2.885e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003852 Epoch 8198 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01059 0.9957 0.9906 1.256e-07 -5.638e-08 -0.007464 9.464e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00337 -0.003182 -0.007819 0.006128 0.9699 0.9743 0.006478 0.8333 0.8245 0.01791 ] Network output: [ 0.9998 0.0005007 0.0008074 -1.432e-05 6.428e-06 -0.001036 -1.079e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1974 -0.03381 -0.174 0.1895 0.9835 0.9932 0.2209 0.4406 0.8712 0.7162 ] Network output: [ -0.01031 1.002 1.009 -1.66e-07 7.453e-08 0.008965 -1.251e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006103 0.0004976 0.004445 0.003615 0.9889 0.9919 0.006218 0.8615 0.8952 0.01291 ] Network output: [ -0.0005213 0.002582 1.001 -4.5e-05 2.02e-05 0.9971 -3.391e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2095 0.09751 0.3396 0.1458 0.985 0.994 0.2102 0.4448 0.8778 0.7105 ] Network output: [ 0.005389 -0.02582 0.9947 2.701e-05 -1.213e-05 1.02 2.036e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1036 0.09143 0.1818 0.2005 0.9873 0.9919 0.1037 0.7583 0.8667 0.3057 ] Network output: [ -0.005134 0.02486 1.003 2.853e-05 -1.281e-05 0.9821 2.15e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09102 0.08908 0.1651 0.1954 0.9853 0.9912 0.09103 0.683 0.8431 0.2456 ] Network output: [ 0.0001492 1 -0.0001996 3.824e-06 -1.717e-06 0.9999 2.882e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003849 Epoch 8199 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01059 0.9957 0.9906 1.245e-07 -5.591e-08 -0.007464 9.385e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00337 -0.003182 -0.007818 0.006128 0.9699 0.9743 0.006479 0.8333 0.8245 0.01791 ] Network output: [ 0.9998 0.0005003 0.0008069 -1.43e-05 6.422e-06 -0.001035 -1.078e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1974 -0.03382 -0.174 0.1895 0.9835 0.9932 0.2209 0.4405 0.8712 0.7162 ] Network output: [ -0.01031 1.002 1.009 -1.666e-07 7.479e-08 0.008963 -1.255e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006103 0.0004977 0.004445 0.003615 0.9889 0.9919 0.006219 0.8615 0.8952 0.01291 ] Network output: [ -0.000521 0.002581 1.001 -4.495e-05 2.018e-05 0.9971 -3.388e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2095 0.09752 0.3396 0.1458 0.985 0.994 0.2102 0.4448 0.8778 0.7105 ] Network output: [ 0.005387 -0.02581 0.9947 2.699e-05 -1.212e-05 1.02 2.034e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1036 0.09144 0.1818 0.2005 0.9873 0.9919 0.1037 0.7583 0.8666 0.3057 ] Network output: [ -0.005132 0.02484 1.003 2.85e-05 -1.279e-05 0.9821 2.148e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09102 0.08908 0.1651 0.1954 0.9853 0.9912 0.09103 0.683 0.843 0.2456 ] Network output: [ 0.0001491 1 -0.0001994 3.82e-06 -1.715e-06 0.9999 2.879e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003847 Epoch 8200 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01059 0.9957 0.9906 1.235e-07 -5.544e-08 -0.007464 9.307e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003371 -0.003182 -0.007817 0.006127 0.9699 0.9743 0.006479 0.8333 0.8245 0.01791 ] Network output: [ 0.9998 0.0004999 0.0008064 -1.429e-05 6.415e-06 -0.001035 -1.077e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1974 -0.03382 -0.1739 0.1895 0.9835 0.9932 0.2209 0.4405 0.8712 0.7162 ] Network output: [ -0.0103 1.002 1.009 -1.671e-07 7.504e-08 0.008961 -1.26e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006104 0.0004978 0.004445 0.003615 0.9889 0.9919 0.006219 0.8615 0.8952 0.01291 ] Network output: [ -0.0005206 0.00258 1.001 -4.49e-05 2.016e-05 0.9971 -3.384e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2095 0.09752 0.3396 0.1458 0.985 0.994 0.2102 0.4448 0.8778 0.7105 ] Network output: [ 0.005385 -0.0258 0.9947 2.696e-05 -1.21e-05 1.02 2.032e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1036 0.09144 0.1818 0.2005 0.9873 0.9919 0.1037 0.7583 0.8666 0.3057 ] Network output: [ -0.00513 0.02483 1.003 2.847e-05 -1.278e-05 0.9821 2.146e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09102 0.08908 0.1651 0.1954 0.9853 0.9912 0.09103 0.683 0.843 0.2456 ] Network output: [ 0.000149 1 -0.0001991 3.816e-06 -1.713e-06 0.9999 2.876e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003845 Epoch 8201 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01058 0.9957 0.9906 1.224e-07 -5.497e-08 -0.007465 9.228e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003371 -0.003182 -0.007816 0.006127 0.9699 0.9743 0.006479 0.8333 0.8245 0.01791 ] Network output: [ 0.9998 0.0004995 0.0008059 -1.428e-05 6.409e-06 -0.001034 -1.076e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1974 -0.03382 -0.1739 0.1895 0.9835 0.9932 0.2209 0.4405 0.8712 0.7162 ] Network output: [ -0.0103 1.002 1.009 -1.677e-07 7.529e-08 0.00896 -1.264e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006105 0.0004978 0.004445 0.003614 0.9889 0.9919 0.00622 0.8615 0.8952 0.01291 ] Network output: [ -0.0005203 0.002579 1.001 -4.486e-05 2.014e-05 0.9971 -3.381e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2095 0.09753 0.3396 0.1458 0.985 0.994 0.2102 0.4448 0.8778 0.7105 ] Network output: [ 0.005383 -0.02579 0.9947 2.693e-05 -1.209e-05 1.02 2.03e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1036 0.09145 0.1818 0.2005 0.9873 0.9919 0.1037 0.7582 0.8666 0.3057 ] Network output: [ -0.005128 0.02482 1.003 2.844e-05 -1.277e-05 0.9821 2.143e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09102 0.08909 0.1651 0.1954 0.9853 0.9912 0.09104 0.683 0.843 0.2456 ] Network output: [ 0.0001489 1 -0.0001989 3.813e-06 -1.712e-06 0.9999 2.873e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003843 Epoch 8202 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01058 0.9957 0.9906 1.214e-07 -5.45e-08 -0.007465 9.15e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003371 -0.003183 -0.007815 0.006126 0.9699 0.9743 0.006479 0.8333 0.8245 0.01791 ] Network output: [ 0.9998 0.0004991 0.0008054 -1.426e-05 6.402e-06 -0.001033 -1.075e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1975 -0.03382 -0.1739 0.1895 0.9835 0.9932 0.2209 0.4405 0.8712 0.7162 ] Network output: [ -0.0103 1.002 1.009 -1.683e-07 7.554e-08 0.008958 -1.268e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006105 0.0004979 0.004445 0.003614 0.9889 0.9919 0.006221 0.8615 0.8952 0.01291 ] Network output: [ -0.00052 0.002578 1.001 -4.481e-05 2.012e-05 0.9971 -3.377e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2095 0.09754 0.3396 0.1458 0.985 0.994 0.2102 0.4448 0.8778 0.7105 ] Network output: [ 0.005382 -0.02578 0.9947 2.691e-05 -1.208e-05 1.02 2.028e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1036 0.09146 0.1819 0.2005 0.9873 0.9919 0.1037 0.7582 0.8666 0.3056 ] Network output: [ -0.005126 0.02481 1.003 2.841e-05 -1.276e-05 0.9821 2.141e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09102 0.08909 0.1651 0.1954 0.9853 0.9912 0.09104 0.6829 0.843 0.2456 ] Network output: [ 0.0001489 1 -0.0001987 3.809e-06 -1.71e-06 0.9999 2.87e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003841 Epoch 8203 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01058 0.9957 0.9906 1.204e-07 -5.404e-08 -0.007465 9.072e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003371 -0.003183 -0.007814 0.006125 0.9699 0.9743 0.00648 0.8333 0.8245 0.0179 ] Network output: [ 0.9998 0.0004987 0.0008049 -1.425e-05 6.396e-06 -0.001032 -1.074e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1975 -0.03382 -0.1739 0.1894 0.9835 0.9932 0.221 0.4405 0.8712 0.7162 ] Network output: [ -0.0103 1.002 1.009 -1.688e-07 7.579e-08 0.008956 -1.272e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006106 0.000498 0.004445 0.003613 0.9889 0.9919 0.006221 0.8614 0.8952 0.01291 ] Network output: [ -0.0005196 0.002577 1.001 -4.477e-05 2.01e-05 0.9971 -3.374e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2095 0.09754 0.3396 0.1458 0.985 0.994 0.2102 0.4447 0.8778 0.7104 ] Network output: [ 0.00538 -0.02577 0.9947 2.688e-05 -1.207e-05 1.02 2.026e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1036 0.09146 0.1819 0.2005 0.9873 0.9919 0.1037 0.7582 0.8666 0.3056 ] Network output: [ -0.005124 0.0248 1.003 2.839e-05 -1.274e-05 0.9821 2.139e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09103 0.08909 0.1651 0.1954 0.9853 0.9912 0.09104 0.6829 0.843 0.2456 ] Network output: [ 0.0001488 1 -0.0001985 3.805e-06 -1.708e-06 0.9999 2.868e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003838 Epoch 8204 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01058 0.9957 0.9906 1.193e-07 -5.357e-08 -0.007465 8.993e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003371 -0.003183 -0.007813 0.006125 0.9699 0.9743 0.00648 0.8333 0.8245 0.0179 ] Network output: [ 0.9998 0.0004982 0.0008044 -1.423e-05 6.389e-06 -0.001031 -1.073e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1975 -0.03382 -0.1739 0.1894 0.9835 0.9932 0.221 0.4405 0.8712 0.7162 ] Network output: [ -0.0103 1.002 1.009 -1.694e-07 7.604e-08 0.008954 -1.277e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006106 0.0004981 0.004445 0.003613 0.9889 0.9919 0.006222 0.8614 0.8952 0.01291 ] Network output: [ -0.0005193 0.002576 1.001 -4.472e-05 2.008e-05 0.9971 -3.37e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2095 0.09755 0.3396 0.1458 0.985 0.994 0.2102 0.4447 0.8778 0.7104 ] Network output: [ 0.005378 -0.02576 0.9947 2.685e-05 -1.205e-05 1.02 2.024e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1037 0.09147 0.1819 0.2005 0.9873 0.9919 0.1037 0.7582 0.8666 0.3056 ] Network output: [ -0.005123 0.02479 1.003 2.836e-05 -1.273e-05 0.9821 2.137e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09103 0.08909 0.1651 0.1954 0.9853 0.9912 0.09104 0.6829 0.843 0.2456 ] Network output: [ 0.0001487 1 -0.0001982 3.801e-06 -1.706e-06 0.9999 2.865e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003836 Epoch 8205 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01058 0.9957 0.9906 1.183e-07 -5.311e-08 -0.007466 8.916e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003371 -0.003183 -0.007812 0.006124 0.9699 0.9743 0.00648 0.8333 0.8245 0.0179 ] Network output: [ 0.9998 0.0004978 0.0008039 -1.422e-05 6.383e-06 -0.00103 -1.072e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1975 -0.03383 -0.1739 0.1894 0.9835 0.9932 0.221 0.4405 0.8712 0.7162 ] Network output: [ -0.0103 1.002 1.009 -1.699e-07 7.629e-08 0.008952 -1.281e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006107 0.0004982 0.004445 0.003613 0.9889 0.9919 0.006222 0.8614 0.8952 0.01291 ] Network output: [ -0.000519 0.002575 1.001 -4.468e-05 2.006e-05 0.9971 -3.367e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2095 0.09755 0.3396 0.1458 0.985 0.994 0.2102 0.4447 0.8778 0.7104 ] Network output: [ 0.005376 -0.02575 0.9947 2.682e-05 -1.204e-05 1.02 2.022e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1037 0.09147 0.1819 0.2005 0.9873 0.9919 0.1037 0.7582 0.8666 0.3056 ] Network output: [ -0.005121 0.02478 1.003 2.833e-05 -1.272e-05 0.9821 2.135e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09103 0.08909 0.1651 0.1954 0.9853 0.9912 0.09104 0.6829 0.843 0.2456 ] Network output: [ 0.0001487 1 -0.000198 3.797e-06 -1.705e-06 0.9999 2.862e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003834 Epoch 8206 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01058 0.9957 0.9906 1.173e-07 -5.265e-08 -0.007466 8.838e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003371 -0.003183 -0.007811 0.006123 0.9699 0.9743 0.006481 0.8332 0.8245 0.0179 ] Network output: [ 0.9998 0.0004974 0.0008035 -1.42e-05 6.377e-06 -0.001029 -1.07e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1975 -0.03383 -0.1739 0.1894 0.9835 0.9932 0.221 0.4405 0.8712 0.7162 ] Network output: [ -0.0103 1.002 1.009 -1.705e-07 7.654e-08 0.00895 -1.285e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006108 0.0004983 0.004445 0.003612 0.9889 0.9919 0.006223 0.8614 0.8952 0.01291 ] Network output: [ -0.0005187 0.002574 1.001 -4.463e-05 2.004e-05 0.9971 -3.363e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2096 0.09756 0.3397 0.1458 0.985 0.994 0.2102 0.4447 0.8778 0.7104 ] Network output: [ 0.005375 -0.02574 0.9947 2.68e-05 -1.203e-05 1.02 2.02e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1037 0.09148 0.1819 0.2005 0.9873 0.9919 0.1037 0.7581 0.8666 0.3056 ] Network output: [ -0.005119 0.02477 1.003 2.83e-05 -1.271e-05 0.9821 2.133e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09103 0.08909 0.1651 0.1954 0.9853 0.9912 0.09104 0.6829 0.843 0.2456 ] Network output: [ 0.0001486 1 -0.0001978 3.794e-06 -1.703e-06 0.9999 2.859e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003832 Epoch 8207 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01058 0.9957 0.9906 1.162e-07 -5.219e-08 -0.007466 8.76e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003371 -0.003183 -0.00781 0.006123 0.9699 0.9743 0.006481 0.8332 0.8245 0.0179 ] Network output: [ 0.9998 0.000497 0.000803 -1.419e-05 6.37e-06 -0.001028 -1.069e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1975 -0.03383 -0.1738 0.1894 0.9835 0.9932 0.221 0.4405 0.8712 0.7162 ] Network output: [ -0.0103 1.002 1.009 -1.71e-07 7.679e-08 0.008949 -1.289e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006108 0.0004983 0.004445 0.003612 0.9889 0.9919 0.006224 0.8614 0.8952 0.0129 ] Network output: [ -0.0005183 0.002573 1.001 -4.458e-05 2.002e-05 0.9971 -3.36e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2096 0.09757 0.3397 0.1458 0.985 0.994 0.2103 0.4447 0.8778 0.7104 ] Network output: [ 0.005373 -0.02573 0.9947 2.677e-05 -1.202e-05 1.02 2.018e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1037 0.09149 0.1819 0.2005 0.9873 0.9919 0.1037 0.7581 0.8666 0.3056 ] Network output: [ -0.005117 0.02476 1.003 2.828e-05 -1.269e-05 0.9821 2.131e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09103 0.0891 0.1651 0.1954 0.9853 0.9912 0.09105 0.6828 0.843 0.2456 ] Network output: [ 0.0001485 1 -0.0001975 3.79e-06 -1.701e-06 0.9999 2.856e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003829 Epoch 8208 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01057 0.9957 0.9906 1.152e-07 -5.172e-08 -0.007467 8.683e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003372 -0.003183 -0.00781 0.006122 0.9699 0.9743 0.006481 0.8332 0.8245 0.0179 ] Network output: [ 0.9998 0.0004966 0.0008025 -1.417e-05 6.364e-06 -0.001028 -1.068e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1975 -0.03383 -0.1738 0.1894 0.9835 0.9932 0.221 0.4404 0.8711 0.7162 ] Network output: [ -0.0103 1.002 1.009 -1.716e-07 7.703e-08 0.008947 -1.293e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006109 0.0004984 0.004445 0.003611 0.9889 0.9919 0.006224 0.8614 0.8952 0.0129 ] Network output: [ -0.000518 0.002573 1.001 -4.454e-05 1.999e-05 0.9971 -3.357e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2096 0.09757 0.3397 0.1458 0.985 0.994 0.2103 0.4447 0.8778 0.7104 ] Network output: [ 0.005371 -0.02572 0.9947 2.674e-05 -1.201e-05 1.02 2.016e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1037 0.09149 0.1819 0.2005 0.9873 0.9919 0.1037 0.7581 0.8666 0.3056 ] Network output: [ -0.005115 0.02475 1.003 2.825e-05 -1.268e-05 0.9821 2.129e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09104 0.0891 0.1651 0.1954 0.9853 0.9912 0.09105 0.6828 0.843 0.2456 ] Network output: [ 0.0001484 1 -0.0001973 3.786e-06 -1.7e-06 0.9999 2.853e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003827 Epoch 8209 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01057 0.9957 0.9906 1.142e-07 -5.126e-08 -0.007467 8.606e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003372 -0.003184 -0.007809 0.006121 0.9699 0.9743 0.006482 0.8332 0.8245 0.0179 ] Network output: [ 0.9998 0.0004962 0.000802 -1.416e-05 6.357e-06 -0.001027 -1.067e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1975 -0.03383 -0.1738 0.1894 0.9835 0.9932 0.221 0.4404 0.8711 0.7161 ] Network output: [ -0.01029 1.002 1.009 -1.721e-07 7.728e-08 0.008945 -1.297e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006109 0.0004985 0.004445 0.003611 0.9889 0.9919 0.006225 0.8614 0.8952 0.0129 ] Network output: [ -0.0005177 0.002572 1.001 -4.449e-05 1.997e-05 0.9971 -3.353e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2096 0.09758 0.3397 0.1458 0.985 0.994 0.2103 0.4447 0.8778 0.7104 ] Network output: [ 0.005369 -0.02571 0.9947 2.672e-05 -1.199e-05 1.02 2.013e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1037 0.0915 0.1819 0.2005 0.9873 0.9919 0.1038 0.7581 0.8666 0.3056 ] Network output: [ -0.005113 0.02474 1.003 2.822e-05 -1.267e-05 0.9821 2.127e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09104 0.0891 0.1651 0.1954 0.9853 0.9912 0.09105 0.6828 0.843 0.2456 ] Network output: [ 0.0001484 1 -0.0001971 3.782e-06 -1.698e-06 0.9999 2.85e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003825 Epoch 8210 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01057 0.9957 0.9906 1.132e-07 -5.081e-08 -0.007467 8.529e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003372 -0.003184 -0.007808 0.006121 0.9699 0.9743 0.006482 0.8332 0.8245 0.01789 ] Network output: [ 0.9998 0.0004957 0.0008015 -1.415e-05 6.351e-06 -0.001026 -1.066e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1975 -0.03384 -0.1738 0.1894 0.9835 0.9932 0.221 0.4404 0.8711 0.7161 ] Network output: [ -0.01029 1.002 1.009 -1.727e-07 7.753e-08 0.008943 -1.301e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00611 0.0004986 0.004445 0.003611 0.9889 0.9919 0.006225 0.8614 0.8952 0.0129 ] Network output: [ -0.0005174 0.002571 1.001 -4.445e-05 1.995e-05 0.9971 -3.35e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2096 0.09758 0.3397 0.1458 0.985 0.994 0.2103 0.4447 0.8778 0.7104 ] Network output: [ 0.005367 -0.0257 0.9947 2.669e-05 -1.198e-05 1.02 2.011e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1037 0.0915 0.1819 0.2005 0.9873 0.9919 0.1038 0.7581 0.8666 0.3056 ] Network output: [ -0.005112 0.02473 1.003 2.819e-05 -1.266e-05 0.9821 2.125e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09104 0.0891 0.1651 0.1954 0.9853 0.9912 0.09105 0.6828 0.843 0.2456 ] Network output: [ 0.0001483 1 -0.0001969 3.778e-06 -1.696e-06 0.9999 2.848e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003823 Epoch 8211 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01057 0.9957 0.9906 1.121e-07 -5.035e-08 -0.007468 8.452e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003372 -0.003184 -0.007807 0.00612 0.9699 0.9743 0.006482 0.8332 0.8245 0.01789 ] Network output: [ 0.9998 0.0004953 0.000801 -1.413e-05 6.344e-06 -0.001025 -1.065e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1975 -0.03384 -0.1738 0.1894 0.9835 0.9932 0.221 0.4404 0.8711 0.7161 ] Network output: [ -0.01029 1.002 1.009 -1.732e-07 7.777e-08 0.008941 -1.306e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00611 0.0004987 0.004445 0.00361 0.9889 0.9919 0.006226 0.8614 0.8952 0.0129 ] Network output: [ -0.000517 0.00257 1.001 -4.44e-05 1.993e-05 0.9971 -3.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2096 0.09759 0.3397 0.1458 0.985 0.994 0.2103 0.4447 0.8778 0.7104 ] Network output: [ 0.005366 -0.02569 0.9947 2.666e-05 -1.197e-05 1.02 2.009e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1037 0.09151 0.1819 0.2005 0.9873 0.9919 0.1038 0.758 0.8666 0.3056 ] Network output: [ -0.00511 0.02472 1.003 2.817e-05 -1.264e-05 0.9822 2.123e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09104 0.0891 0.1651 0.1954 0.9853 0.9912 0.09105 0.6828 0.843 0.2456 ] Network output: [ 0.0001482 1 -0.0001966 3.775e-06 -1.695e-06 0.9999 2.845e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003821 Epoch 8212 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01057 0.9957 0.9906 1.111e-07 -4.989e-08 -0.007468 8.375e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003372 -0.003184 -0.007806 0.00612 0.9699 0.9743 0.006482 0.8332 0.8245 0.01789 ] Network output: [ 0.9998 0.0004949 0.0008005 -1.412e-05 6.338e-06 -0.001024 -1.064e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1975 -0.03384 -0.1738 0.1894 0.9835 0.9932 0.2211 0.4404 0.8711 0.7161 ] Network output: [ -0.01029 1.002 1.009 -1.738e-07 7.802e-08 0.00894 -1.31e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006111 0.0004988 0.004445 0.00361 0.9889 0.9919 0.006226 0.8614 0.8952 0.0129 ] Network output: [ -0.0005167 0.002569 1.001 -4.436e-05 1.991e-05 0.9971 -3.343e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2096 0.09759 0.3397 0.1458 0.985 0.994 0.2103 0.4446 0.8778 0.7104 ] Network output: [ 0.005364 -0.02569 0.9947 2.664e-05 -1.196e-05 1.02 2.007e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1037 0.09151 0.1819 0.2005 0.9873 0.9919 0.1038 0.758 0.8666 0.3056 ] Network output: [ -0.005108 0.02471 1.003 2.814e-05 -1.263e-05 0.9822 2.121e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09104 0.08911 0.1651 0.1954 0.9853 0.9912 0.09106 0.6827 0.843 0.2456 ] Network output: [ 0.0001481 1 -0.0001964 3.771e-06 -1.693e-06 0.9999 2.842e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003818 Epoch 8213 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01057 0.9957 0.9906 1.101e-07 -4.944e-08 -0.007468 8.299e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003372 -0.003184 -0.007805 0.006119 0.9699 0.9743 0.006483 0.8332 0.8245 0.01789 ] Network output: [ 0.9998 0.0004945 0.0008 -1.41e-05 6.332e-06 -0.001023 -1.063e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1976 -0.03384 -0.1738 0.1894 0.9835 0.9932 0.2211 0.4404 0.8711 0.7161 ] Network output: [ -0.01029 1.002 1.009 -1.743e-07 7.826e-08 0.008938 -1.314e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006112 0.0004988 0.004445 0.003609 0.9889 0.9919 0.006227 0.8614 0.8952 0.0129 ] Network output: [ -0.0005164 0.002568 1.001 -4.431e-05 1.989e-05 0.9971 -3.339e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2096 0.0976 0.3397 0.1458 0.985 0.994 0.2103 0.4446 0.8778 0.7104 ] Network output: [ 0.005362 -0.02568 0.9947 2.661e-05 -1.195e-05 1.02 2.005e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1037 0.09152 0.1819 0.2005 0.9873 0.9919 0.1038 0.758 0.8666 0.3056 ] Network output: [ -0.005106 0.0247 1.003 2.811e-05 -1.262e-05 0.9822 2.118e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09104 0.08911 0.1651 0.1954 0.9853 0.9912 0.09106 0.6827 0.843 0.2456 ] Network output: [ 0.0001481 1 -0.0001962 3.767e-06 -1.691e-06 0.9999 2.839e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003816 Epoch 8214 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01057 0.9957 0.9906 1.091e-07 -4.898e-08 -0.007468 8.222e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003372 -0.003184 -0.007804 0.006118 0.9699 0.9743 0.006483 0.8332 0.8245 0.01789 ] Network output: [ 0.9998 0.0004941 0.0007996 -1.409e-05 6.325e-06 -0.001022 -1.062e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1976 -0.03384 -0.1737 0.1894 0.9835 0.9932 0.2211 0.4404 0.8711 0.7161 ] Network output: [ -0.01029 1.002 1.009 -1.749e-07 7.85e-08 0.008936 -1.318e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006112 0.0004989 0.004445 0.003609 0.9889 0.9919 0.006228 0.8614 0.8952 0.0129 ] Network output: [ -0.0005161 0.002567 1.001 -4.427e-05 1.987e-05 0.9971 -3.336e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2096 0.09761 0.3397 0.1457 0.985 0.994 0.2103 0.4446 0.8777 0.7104 ] Network output: [ 0.00536 -0.02567 0.9947 2.658e-05 -1.193e-05 1.02 2.003e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1037 0.09153 0.1819 0.2005 0.9873 0.9919 0.1038 0.758 0.8666 0.3056 ] Network output: [ -0.005104 0.02469 1.003 2.808e-05 -1.261e-05 0.9822 2.116e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09105 0.08911 0.1651 0.1954 0.9853 0.9912 0.09106 0.6827 0.8429 0.2456 ] Network output: [ 0.000148 1 -0.0001959 3.763e-06 -1.689e-06 0.9999 2.836e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003814 Epoch 8215 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01056 0.9957 0.9906 1.081e-07 -4.853e-08 -0.007469 8.146e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003373 -0.003184 -0.007803 0.006118 0.9699 0.9743 0.006483 0.8332 0.8244 0.01789 ] Network output: [ 0.9998 0.0004937 0.0007991 -1.407e-05 6.319e-06 -0.001022 -1.061e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1976 -0.03384 -0.1737 0.1894 0.9835 0.9932 0.2211 0.4404 0.8711 0.7161 ] Network output: [ -0.01029 1.002 1.009 -1.754e-07 7.875e-08 0.008934 -1.322e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006113 0.000499 0.004445 0.003609 0.9889 0.9919 0.006228 0.8613 0.8952 0.0129 ] Network output: [ -0.0005157 0.002566 1.001 -4.422e-05 1.985e-05 0.9971 -3.333e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2097 0.09761 0.3397 0.1457 0.985 0.994 0.2103 0.4446 0.8777 0.7104 ] Network output: [ 0.005359 -0.02566 0.9947 2.656e-05 -1.192e-05 1.02 2.001e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1037 0.09153 0.1819 0.2004 0.9873 0.9919 0.1038 0.758 0.8666 0.3056 ] Network output: [ -0.005102 0.02468 1.003 2.805e-05 -1.259e-05 0.9822 2.114e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09105 0.08911 0.1651 0.1954 0.9853 0.9912 0.09106 0.6827 0.8429 0.2456 ] Network output: [ 0.0001479 1 -0.0001957 3.76e-06 -1.688e-06 0.9999 2.833e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003812 Epoch 8216 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01056 0.9957 0.9906 1.071e-07 -4.807e-08 -0.007469 8.07e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003373 -0.003185 -0.007802 0.006117 0.9699 0.9743 0.006484 0.8332 0.8244 0.01789 ] Network output: [ 0.9998 0.0004933 0.0007986 -1.406e-05 6.312e-06 -0.001021 -1.06e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1976 -0.03385 -0.1737 0.1894 0.9835 0.9932 0.2211 0.4404 0.8711 0.7161 ] Network output: [ -0.01029 1.002 1.009 -1.759e-07 7.899e-08 0.008932 -1.326e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006113 0.0004991 0.004445 0.003608 0.9889 0.9919 0.006229 0.8613 0.8952 0.0129 ] Network output: [ -0.0005154 0.002565 1.001 -4.417e-05 1.983e-05 0.9971 -3.329e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2097 0.09762 0.3397 0.1457 0.985 0.994 0.2104 0.4446 0.8777 0.7104 ] Network output: [ 0.005357 -0.02565 0.9947 2.653e-05 -1.191e-05 1.02 1.999e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1037 0.09154 0.1819 0.2004 0.9873 0.9919 0.1038 0.7579 0.8666 0.3056 ] Network output: [ -0.005101 0.02467 1.003 2.803e-05 -1.258e-05 0.9822 2.112e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09105 0.08911 0.1651 0.1954 0.9853 0.9912 0.09106 0.6826 0.8429 0.2456 ] Network output: [ 0.0001478 1 -0.0001955 3.756e-06 -1.686e-06 0.9999 2.83e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000381 Epoch 8217 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01056 0.9957 0.9906 1.061e-07 -4.762e-08 -0.007469 7.995e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003373 -0.003185 -0.007801 0.006116 0.9699 0.9743 0.006484 0.8332 0.8244 0.01789 ] Network output: [ 0.9998 0.0004929 0.0007981 -1.405e-05 6.306e-06 -0.00102 -1.059e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1976 -0.03385 -0.1737 0.1894 0.9835 0.9932 0.2211 0.4403 0.8711 0.7161 ] Network output: [ -0.01029 1.002 1.009 -1.765e-07 7.923e-08 0.008931 -1.33e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006114 0.0004992 0.004445 0.003608 0.9889 0.9919 0.006229 0.8613 0.8952 0.01289 ] Network output: [ -0.0005151 0.002564 1.001 -4.413e-05 1.981e-05 0.9971 -3.326e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2097 0.09762 0.3398 0.1457 0.985 0.994 0.2104 0.4446 0.8777 0.7104 ] Network output: [ 0.005355 -0.02564 0.9947 2.65e-05 -1.19e-05 1.02 1.997e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1037 0.09154 0.1819 0.2004 0.9873 0.9919 0.1038 0.7579 0.8665 0.3056 ] Network output: [ -0.005099 0.02466 1.003 2.8e-05 -1.257e-05 0.9822 2.11e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09105 0.08912 0.1651 0.1954 0.9853 0.9912 0.09107 0.6826 0.8429 0.2456 ] Network output: [ 0.0001478 1 -0.0001953 3.752e-06 -1.684e-06 0.9999 2.828e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003808 Epoch 8218 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01056 0.9957 0.9906 1.051e-07 -4.717e-08 -0.00747 7.919e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003373 -0.003185 -0.0078 0.006116 0.9699 0.9743 0.006484 0.8332 0.8244 0.01788 ] Network output: [ 0.9998 0.0004924 0.0007976 -1.403e-05 6.3e-06 -0.001019 -1.058e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1976 -0.03385 -0.1737 0.1894 0.9835 0.9932 0.2211 0.4403 0.8711 0.7161 ] Network output: [ -0.01028 1.002 1.009 -1.77e-07 7.947e-08 0.008929 -1.334e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006115 0.0004993 0.004445 0.003607 0.9889 0.9919 0.00623 0.8613 0.8951 0.01289 ] Network output: [ -0.0005148 0.002564 1.001 -4.408e-05 1.979e-05 0.9971 -3.322e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2097 0.09763 0.3398 0.1457 0.985 0.994 0.2104 0.4446 0.8777 0.7104 ] Network output: [ 0.005353 -0.02563 0.9947 2.648e-05 -1.189e-05 1.02 1.995e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1037 0.09155 0.1819 0.2004 0.9873 0.9919 0.1038 0.7579 0.8665 0.3056 ] Network output: [ -0.005097 0.02465 1.003 2.797e-05 -1.256e-05 0.9822 2.108e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09105 0.08912 0.1651 0.1954 0.9853 0.9912 0.09107 0.6826 0.8429 0.2456 ] Network output: [ 0.0001477 1 -0.000195 3.748e-06 -1.683e-06 0.9999 2.825e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003805 Epoch 8219 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01056 0.9957 0.9906 1.041e-07 -4.672e-08 -0.00747 7.843e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003373 -0.003185 -0.007799 0.006115 0.9699 0.9743 0.006485 0.8331 0.8244 0.01788 ] Network output: [ 0.9998 0.000492 0.0007971 -1.402e-05 6.293e-06 -0.001018 -1.056e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1976 -0.03385 -0.1737 0.1893 0.9835 0.9932 0.2211 0.4403 0.8711 0.7161 ] Network output: [ -0.01028 1.002 1.009 -1.776e-07 7.971e-08 0.008927 -1.338e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006115 0.0004993 0.004445 0.003607 0.9889 0.9919 0.006231 0.8613 0.8951 0.01289 ] Network output: [ -0.0005144 0.002563 1.001 -4.404e-05 1.977e-05 0.9971 -3.319e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2097 0.09764 0.3398 0.1457 0.985 0.994 0.2104 0.4446 0.8777 0.7103 ] Network output: [ 0.005352 -0.02562 0.9947 2.645e-05 -1.187e-05 1.02 1.993e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1037 0.09155 0.1819 0.2004 0.9873 0.9919 0.1038 0.7579 0.8665 0.3056 ] Network output: [ -0.005095 0.02464 1.003 2.794e-05 -1.255e-05 0.9822 2.106e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09106 0.08912 0.1651 0.1954 0.9853 0.9912 0.09107 0.6826 0.8429 0.2456 ] Network output: [ 0.0001476 1 -0.0001948 3.745e-06 -1.681e-06 0.9999 2.822e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003803 Epoch 8220 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01056 0.9957 0.9906 1.031e-07 -4.627e-08 -0.00747 7.768e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003373 -0.003185 -0.007798 0.006115 0.9699 0.9743 0.006485 0.8331 0.8244 0.01788 ] Network output: [ 0.9998 0.0004916 0.0007967 -1.4e-05 6.287e-06 -0.001017 -1.055e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1976 -0.03385 -0.1737 0.1893 0.9835 0.9932 0.2211 0.4403 0.8711 0.7161 ] Network output: [ -0.01028 1.002 1.009 -1.781e-07 7.995e-08 0.008925 -1.342e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006116 0.0004994 0.004445 0.003607 0.9889 0.9919 0.006231 0.8613 0.8951 0.01289 ] Network output: [ -0.0005141 0.002562 1.001 -4.399e-05 1.975e-05 0.9971 -3.315e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2097 0.09764 0.3398 0.1457 0.985 0.994 0.2104 0.4445 0.8777 0.7103 ] Network output: [ 0.00535 -0.02561 0.9947 2.642e-05 -1.186e-05 1.02 1.991e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1038 0.09156 0.1819 0.2004 0.9873 0.9919 0.1038 0.7579 0.8665 0.3056 ] Network output: [ -0.005093 0.02463 1.003 2.792e-05 -1.253e-05 0.9822 2.104e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09106 0.08912 0.1651 0.1954 0.9853 0.9912 0.09107 0.6826 0.8429 0.2456 ] Network output: [ 0.0001476 1 -0.0001946 3.741e-06 -1.679e-06 0.9999 2.819e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003801 Epoch 8221 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01056 0.9957 0.9906 1.021e-07 -4.583e-08 -0.00747 7.693e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003373 -0.003185 -0.007797 0.006114 0.9699 0.9743 0.006485 0.8331 0.8244 0.01788 ] Network output: [ 0.9998 0.0004912 0.0007962 -1.399e-05 6.28e-06 -0.001016 -1.054e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1976 -0.03386 -0.1736 0.1893 0.9835 0.9932 0.2212 0.4403 0.8711 0.7161 ] Network output: [ -0.01028 1.002 1.009 -1.786e-07 8.019e-08 0.008924 -1.346e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006116 0.0004995 0.004445 0.003606 0.9889 0.9919 0.006232 0.8613 0.8951 0.01289 ] Network output: [ -0.0005138 0.002561 1.001 -4.395e-05 1.973e-05 0.9971 -3.312e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2097 0.09765 0.3398 0.1457 0.985 0.994 0.2104 0.4445 0.8777 0.7103 ] Network output: [ 0.005348 -0.0256 0.9947 2.64e-05 -1.185e-05 1.02 1.989e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1038 0.09157 0.1819 0.2004 0.9873 0.9919 0.1038 0.7578 0.8665 0.3056 ] Network output: [ -0.005091 0.02462 1.003 2.789e-05 -1.252e-05 0.9822 2.102e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09106 0.08912 0.1651 0.1954 0.9853 0.9912 0.09107 0.6825 0.8429 0.2456 ] Network output: [ 0.0001475 1 -0.0001944 3.737e-06 -1.678e-06 0.9999 2.816e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003799 Epoch 8222 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01055 0.9957 0.9906 1.011e-07 -4.538e-08 -0.007471 7.618e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003373 -0.003186 -0.007796 0.006113 0.9699 0.9743 0.006486 0.8331 0.8244 0.01788 ] Network output: [ 0.9998 0.0004908 0.0007957 -1.398e-05 6.274e-06 -0.001015 -1.053e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1976 -0.03386 -0.1736 0.1893 0.9835 0.9932 0.2212 0.4403 0.8711 0.7161 ] Network output: [ -0.01028 1.002 1.009 -1.791e-07 8.042e-08 0.008922 -1.35e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006117 0.0004996 0.004446 0.003606 0.9889 0.9919 0.006232 0.8613 0.8951 0.01289 ] Network output: [ -0.0005135 0.00256 1.001 -4.39e-05 1.971e-05 0.9971 -3.309e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2097 0.09765 0.3398 0.1457 0.985 0.994 0.2104 0.4445 0.8777 0.7103 ] Network output: [ 0.005346 -0.02559 0.9947 2.637e-05 -1.184e-05 1.02 1.987e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1038 0.09157 0.1819 0.2004 0.9873 0.9919 0.1038 0.7578 0.8665 0.3056 ] Network output: [ -0.00509 0.02461 1.003 2.786e-05 -1.251e-05 0.9822 2.1e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09106 0.08913 0.1651 0.1954 0.9853 0.9912 0.09108 0.6825 0.8429 0.2456 ] Network output: [ 0.0001474 1 -0.0001941 3.733e-06 -1.676e-06 0.9999 2.814e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003797 Epoch 8223 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01055 0.9957 0.9906 1.001e-07 -4.493e-08 -0.007471 7.543e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003374 -0.003186 -0.007795 0.006113 0.9699 0.9743 0.006486 0.8331 0.8244 0.01788 ] Network output: [ 0.9998 0.0004904 0.0007952 -1.396e-05 6.268e-06 -0.001015 -1.052e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1977 -0.03386 -0.1736 0.1893 0.9835 0.9932 0.2212 0.4403 0.8711 0.7161 ] Network output: [ -0.01028 1.002 1.009 -1.797e-07 8.066e-08 0.00892 -1.354e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006117 0.0004997 0.004446 0.003605 0.9889 0.9919 0.006233 0.8613 0.8951 0.01289 ] Network output: [ -0.0005131 0.002559 1.001 -4.386e-05 1.969e-05 0.9971 -3.305e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2097 0.09766 0.3398 0.1457 0.985 0.994 0.2104 0.4445 0.8777 0.7103 ] Network output: [ 0.005344 -0.02558 0.9946 2.634e-05 -1.183e-05 1.02 1.985e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1038 0.09158 0.1819 0.2004 0.9873 0.9919 0.1038 0.7578 0.8665 0.3056 ] Network output: [ -0.005088 0.0246 1.003 2.784e-05 -1.25e-05 0.9822 2.098e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09106 0.08913 0.1651 0.1954 0.9853 0.9912 0.09108 0.6825 0.8429 0.2456 ] Network output: [ 0.0001473 1 -0.0001939 3.73e-06 -1.674e-06 0.9999 2.811e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003794 Epoch 8224 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01055 0.9957 0.9907 9.91e-08 -4.449e-08 -0.007471 7.469e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003374 -0.003186 -0.007794 0.006112 0.9699 0.9743 0.006486 0.8331 0.8244 0.01788 ] Network output: [ 0.9998 0.00049 0.0007947 -1.395e-05 6.261e-06 -0.001014 -1.051e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1977 -0.03386 -0.1736 0.1893 0.9835 0.9932 0.2212 0.4403 0.8711 0.7161 ] Network output: [ -0.01028 1.002 1.009 -1.802e-07 8.09e-08 0.008918 -1.358e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006118 0.0004998 0.004446 0.003605 0.9889 0.9919 0.006234 0.8613 0.8951 0.01289 ] Network output: [ -0.0005128 0.002558 1.001 -4.381e-05 1.967e-05 0.9971 -3.302e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2098 0.09767 0.3398 0.1457 0.985 0.994 0.2104 0.4445 0.8777 0.7103 ] Network output: [ 0.005343 -0.02557 0.9946 2.632e-05 -1.181e-05 1.02 1.983e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1038 0.09158 0.1819 0.2004 0.9873 0.9919 0.1039 0.7578 0.8665 0.3056 ] Network output: [ -0.005086 0.02459 1.003 2.781e-05 -1.248e-05 0.9822 2.096e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09107 0.08913 0.1651 0.1954 0.9853 0.9912 0.09108 0.6825 0.8429 0.2456 ] Network output: [ 0.0001473 1 -0.0001937 3.726e-06 -1.673e-06 0.9999 2.808e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003792 Epoch 8225 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01055 0.9957 0.9907 9.811e-08 -4.405e-08 -0.007472 7.394e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003374 -0.003186 -0.007793 0.006111 0.9699 0.9743 0.006486 0.8331 0.8244 0.01787 ] Network output: [ 0.9998 0.0004896 0.0007942 -1.393e-05 6.255e-06 -0.001013 -1.05e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1977 -0.03386 -0.1736 0.1893 0.9835 0.9932 0.2212 0.4403 0.8711 0.7161 ] Network output: [ -0.01028 1.002 1.009 -1.807e-07 8.113e-08 0.008916 -1.362e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006119 0.0004999 0.004446 0.003605 0.9889 0.9919 0.006234 0.8613 0.8951 0.01289 ] Network output: [ -0.0005125 0.002557 1.001 -4.377e-05 1.965e-05 0.9971 -3.298e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2098 0.09767 0.3398 0.1457 0.985 0.994 0.2105 0.4445 0.8777 0.7103 ] Network output: [ 0.005341 -0.02556 0.9946 2.629e-05 -1.18e-05 1.02 1.981e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1038 0.09159 0.1819 0.2004 0.9873 0.9919 0.1039 0.7578 0.8665 0.3056 ] Network output: [ -0.005084 0.02458 1.003 2.778e-05 -1.247e-05 0.9822 2.094e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09107 0.08913 0.1651 0.1954 0.9853 0.9912 0.09108 0.6824 0.8429 0.2456 ] Network output: [ 0.0001472 1 -0.0001935 3.722e-06 -1.671e-06 0.9999 2.805e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000379 Epoch 8226 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01055 0.9957 0.9907 9.713e-08 -4.36e-08 -0.007472 7.32e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003374 -0.003186 -0.007792 0.006111 0.9699 0.9743 0.006487 0.8331 0.8244 0.01787 ] Network output: [ 0.9998 0.0004892 0.0007938 -1.392e-05 6.249e-06 -0.001012 -1.049e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1977 -0.03386 -0.1736 0.1893 0.9835 0.9932 0.2212 0.4402 0.8711 0.7161 ] Network output: [ -0.01028 1.002 1.009 -1.812e-07 8.137e-08 0.008915 -1.366e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006119 0.0004999 0.004446 0.003604 0.9889 0.9919 0.006235 0.8613 0.8951 0.01289 ] Network output: [ -0.0005122 0.002556 1.001 -4.372e-05 1.963e-05 0.9971 -3.295e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2098 0.09768 0.3398 0.1457 0.985 0.994 0.2105 0.4445 0.8777 0.7103 ] Network output: [ 0.005339 -0.02556 0.9946 2.626e-05 -1.179e-05 1.02 1.979e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1038 0.0916 0.1819 0.2004 0.9873 0.9919 0.1039 0.7577 0.8665 0.3056 ] Network output: [ -0.005082 0.02457 1.003 2.775e-05 -1.246e-05 0.9822 2.092e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09107 0.08913 0.1651 0.1954 0.9853 0.9912 0.09108 0.6824 0.8429 0.2456 ] Network output: [ 0.0001471 1 -0.0001932 3.718e-06 -1.669e-06 0.9999 2.802e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003788 Epoch 8227 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01055 0.9957 0.9907 9.614e-08 -4.316e-08 -0.007472 7.246e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003374 -0.003186 -0.007791 0.00611 0.9699 0.9743 0.006487 0.8331 0.8244 0.01787 ] Network output: [ 0.9998 0.0004888 0.0007933 -1.39e-05 6.242e-06 -0.001011 -1.048e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1977 -0.03387 -0.1736 0.1893 0.9835 0.9932 0.2212 0.4402 0.8711 0.716 ] Network output: [ -0.01027 1.002 1.009 -1.818e-07 8.16e-08 0.008913 -1.37e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00612 0.0005 0.004446 0.003604 0.9889 0.9919 0.006235 0.8612 0.8951 0.01288 ] Network output: [ -0.0005118 0.002555 1.001 -4.368e-05 1.961e-05 0.9971 -3.292e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2098 0.09768 0.3398 0.1457 0.985 0.994 0.2105 0.4445 0.8777 0.7103 ] Network output: [ 0.005337 -0.02555 0.9946 2.624e-05 -1.178e-05 1.02 1.977e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1038 0.0916 0.1819 0.2004 0.9873 0.9919 0.1039 0.7577 0.8665 0.3056 ] Network output: [ -0.00508 0.02456 1.003 2.773e-05 -1.245e-05 0.9822 2.09e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09107 0.08913 0.1651 0.1954 0.9853 0.9912 0.09108 0.6824 0.8429 0.2456 ] Network output: [ 0.000147 1 -0.000193 3.715e-06 -1.668e-06 0.9999 2.799e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003786 Epoch 8228 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01055 0.9957 0.9907 9.516e-08 -4.272e-08 -0.007472 7.172e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003374 -0.003186 -0.00779 0.006109 0.9699 0.9743 0.006487 0.8331 0.8244 0.01787 ] Network output: [ 0.9998 0.0004883 0.0007928 -1.389e-05 6.236e-06 -0.00101 -1.047e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1977 -0.03387 -0.1735 0.1893 0.9835 0.9932 0.2212 0.4402 0.8711 0.716 ] Network output: [ -0.01027 1.002 1.009 -1.823e-07 8.184e-08 0.008911 -1.374e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00612 0.0005001 0.004446 0.003603 0.9889 0.9919 0.006236 0.8612 0.8951 0.01288 ] Network output: [ -0.0005115 0.002554 1.001 -4.363e-05 1.959e-05 0.9971 -3.288e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2098 0.09769 0.3399 0.1457 0.985 0.994 0.2105 0.4445 0.8777 0.7103 ] Network output: [ 0.005336 -0.02554 0.9946 2.621e-05 -1.177e-05 1.02 1.975e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1038 0.09161 0.1819 0.2004 0.9873 0.9919 0.1039 0.7577 0.8665 0.3056 ] Network output: [ -0.005079 0.02455 1.003 2.77e-05 -1.244e-05 0.9822 2.087e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09107 0.08914 0.1651 0.1954 0.9853 0.9912 0.09109 0.6824 0.8428 0.2457 ] Network output: [ 0.000147 1 -0.0001928 3.711e-06 -1.666e-06 0.9999 2.797e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003784 Epoch 8229 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01054 0.9957 0.9907 9.418e-08 -4.228e-08 -0.007473 7.098e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003374 -0.003187 -0.007789 0.006109 0.9699 0.9743 0.006488 0.8331 0.8244 0.01787 ] Network output: [ 0.9998 0.0004879 0.0007923 -1.388e-05 6.23e-06 -0.001009 -1.046e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1977 -0.03387 -0.1735 0.1893 0.9835 0.9932 0.2212 0.4402 0.8711 0.716 ] Network output: [ -0.01027 1.002 1.009 -1.828e-07 8.207e-08 0.008909 -1.378e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006121 0.0005002 0.004446 0.003603 0.9889 0.9919 0.006237 0.8612 0.8951 0.01288 ] Network output: [ -0.0005112 0.002554 1.001 -4.359e-05 1.957e-05 0.9971 -3.285e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2098 0.09769 0.3399 0.1457 0.985 0.994 0.2105 0.4444 0.8777 0.7103 ] Network output: [ 0.005334 -0.02553 0.9946 2.618e-05 -1.175e-05 1.02 1.973e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1038 0.09161 0.1819 0.2004 0.9873 0.9919 0.1039 0.7577 0.8665 0.3056 ] Network output: [ -0.005077 0.02454 1.003 2.767e-05 -1.242e-05 0.9822 2.085e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09108 0.08914 0.1651 0.1954 0.9853 0.9912 0.09109 0.6824 0.8428 0.2457 ] Network output: [ 0.0001469 1 -0.0001926 3.707e-06 -1.664e-06 0.9999 2.794e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003781 Epoch 8230 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01054 0.9957 0.9907 9.321e-08 -4.184e-08 -0.007473 7.024e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003374 -0.003187 -0.007788 0.006108 0.9699 0.9743 0.006488 0.8331 0.8244 0.01787 ] Network output: [ 0.9998 0.0004875 0.0007918 -1.386e-05 6.223e-06 -0.001009 -1.045e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1977 -0.03387 -0.1735 0.1893 0.9835 0.9932 0.2213 0.4402 0.8711 0.716 ] Network output: [ -0.01027 1.002 1.009 -1.833e-07 8.23e-08 0.008908 -1.382e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006121 0.0005003 0.004446 0.003603 0.9889 0.9919 0.006237 0.8612 0.8951 0.01288 ] Network output: [ -0.0005109 0.002553 1.001 -4.354e-05 1.955e-05 0.9971 -3.282e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2098 0.0977 0.3399 0.1457 0.985 0.994 0.2105 0.4444 0.8777 0.7103 ] Network output: [ 0.005332 -0.02552 0.9946 2.616e-05 -1.174e-05 1.02 1.971e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1038 0.09162 0.1819 0.2004 0.9873 0.9919 0.1039 0.7577 0.8665 0.3056 ] Network output: [ -0.005075 0.02453 1.003 2.764e-05 -1.241e-05 0.9822 2.083e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09108 0.08914 0.1651 0.1954 0.9853 0.9912 0.09109 0.6823 0.8428 0.2457 ] Network output: [ 0.0001468 1 -0.0001923 3.703e-06 -1.663e-06 0.9999 2.791e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003779 Epoch 8231 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01054 0.9957 0.9907 9.223e-08 -4.141e-08 -0.007473 6.951e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003375 -0.003187 -0.007787 0.006108 0.9699 0.9743 0.006488 0.8331 0.8244 0.01787 ] Network output: [ 0.9998 0.0004871 0.0007914 -1.385e-05 6.217e-06 -0.001008 -1.044e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1977 -0.03387 -0.1735 0.1893 0.9835 0.9932 0.2213 0.4402 0.8711 0.716 ] Network output: [ -0.01027 1.002 1.009 -1.838e-07 8.253e-08 0.008906 -1.385e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006122 0.0005004 0.004446 0.003602 0.9889 0.9919 0.006238 0.8612 0.8951 0.01288 ] Network output: [ -0.0005106 0.002552 1.001 -4.35e-05 1.953e-05 0.9971 -3.278e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2098 0.09771 0.3399 0.1457 0.985 0.994 0.2105 0.4444 0.8777 0.7103 ] Network output: [ 0.00533 -0.02551 0.9946 2.613e-05 -1.173e-05 1.02 1.969e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1038 0.09162 0.1819 0.2004 0.9873 0.9919 0.1039 0.7576 0.8665 0.3056 ] Network output: [ -0.005073 0.02452 1.003 2.762e-05 -1.24e-05 0.9822 2.081e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09108 0.08914 0.1651 0.1954 0.9853 0.9912 0.09109 0.6823 0.8428 0.2457 ] Network output: [ 0.0001468 1 -0.0001921 3.7e-06 -1.661e-06 0.9999 2.788e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003777 Epoch 8232 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01054 0.9957 0.9907 9.126e-08 -4.097e-08 -0.007473 6.878e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003375 -0.003187 -0.007786 0.006107 0.9699 0.9743 0.006489 0.8331 0.8244 0.01786 ] Network output: [ 0.9998 0.0004867 0.0007909 -1.383e-05 6.211e-06 -0.001007 -1.043e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1977 -0.03387 -0.1735 0.1893 0.9835 0.9932 0.2213 0.4402 0.8711 0.716 ] Network output: [ -0.01027 1.002 1.009 -1.844e-07 8.276e-08 0.008904 -1.389e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006123 0.0005004 0.004446 0.003602 0.9889 0.9919 0.006238 0.8612 0.8951 0.01288 ] Network output: [ -0.0005102 0.002551 1.001 -4.345e-05 1.951e-05 0.9971 -3.275e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2098 0.09771 0.3399 0.1457 0.985 0.994 0.2105 0.4444 0.8777 0.7103 ] Network output: [ 0.005329 -0.0255 0.9946 2.61e-05 -1.172e-05 1.02 1.967e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1038 0.09163 0.1819 0.2004 0.9873 0.9919 0.1039 0.7576 0.8665 0.3056 ] Network output: [ -0.005071 0.02451 1.003 2.759e-05 -1.239e-05 0.9823 2.079e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09108 0.08914 0.1651 0.1954 0.9853 0.9912 0.09109 0.6823 0.8428 0.2457 ] Network output: [ 0.0001467 1 -0.0001919 3.696e-06 -1.659e-06 0.9999 2.785e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003775 Epoch 8233 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01054 0.9957 0.9907 9.029e-08 -4.053e-08 -0.007474 6.804e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003375 -0.003187 -0.007785 0.006106 0.9699 0.9743 0.006489 0.833 0.8244 0.01786 ] Network output: [ 0.9998 0.0004863 0.0007904 -1.382e-05 6.205e-06 -0.001006 -1.042e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1977 -0.03388 -0.1735 0.1893 0.9835 0.9932 0.2213 0.4402 0.8711 0.716 ] Network output: [ -0.01027 1.002 1.009 -1.849e-07 8.299e-08 0.008902 -1.393e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006123 0.0005005 0.004446 0.003601 0.9889 0.9919 0.006239 0.8612 0.8951 0.01288 ] Network output: [ -0.0005099 0.00255 1.001 -4.341e-05 1.949e-05 0.9971 -3.271e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2098 0.09772 0.3399 0.1457 0.985 0.994 0.2105 0.4444 0.8777 0.7103 ] Network output: [ 0.005327 -0.02549 0.9946 2.608e-05 -1.171e-05 1.02 1.965e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1038 0.09164 0.1819 0.2004 0.9873 0.9919 0.1039 0.7576 0.8665 0.3056 ] Network output: [ -0.00507 0.0245 1.003 2.756e-05 -1.237e-05 0.9823 2.077e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09108 0.08915 0.1651 0.1954 0.9853 0.9912 0.0911 0.6823 0.8428 0.2457 ] Network output: [ 0.0001466 1 -0.0001917 3.692e-06 -1.658e-06 0.9999 2.783e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003773 Epoch 8234 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01054 0.9957 0.9907 8.932e-08 -4.01e-08 -0.007474 6.731e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003375 -0.003187 -0.007784 0.006106 0.9699 0.9743 0.006489 0.833 0.8244 0.01786 ] Network output: [ 0.9998 0.0004859 0.0007899 -1.381e-05 6.198e-06 -0.001005 -1.04e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1978 -0.03388 -0.1735 0.1893 0.9835 0.9932 0.2213 0.4402 0.8711 0.716 ] Network output: [ -0.01027 1.002 1.009 -1.854e-07 8.322e-08 0.0089 -1.397e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006124 0.0005006 0.004446 0.003601 0.9889 0.9919 0.00624 0.8612 0.8951 0.01288 ] Network output: [ -0.0005096 0.002549 1.001 -4.336e-05 1.947e-05 0.9971 -3.268e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2099 0.09772 0.3399 0.1457 0.985 0.994 0.2105 0.4444 0.8777 0.7103 ] Network output: [ 0.005325 -0.02548 0.9946 2.605e-05 -1.17e-05 1.02 1.963e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1038 0.09164 0.1819 0.2004 0.9873 0.9919 0.1039 0.7576 0.8665 0.3056 ] Network output: [ -0.005068 0.02449 1.003 2.754e-05 -1.236e-05 0.9823 2.075e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09109 0.08915 0.1651 0.1954 0.9853 0.9912 0.0911 0.6823 0.8428 0.2457 ] Network output: [ 0.0001465 1 -0.0001914 3.689e-06 -1.656e-06 0.9999 2.78e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003771 Epoch 8235 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01054 0.9957 0.9907 8.835e-08 -3.967e-08 -0.007474 6.659e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003375 -0.003187 -0.007783 0.006105 0.9699 0.9743 0.006489 0.833 0.8244 0.01786 ] Network output: [ 0.9998 0.0004855 0.0007895 -1.379e-05 6.192e-06 -0.001004 -1.039e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1978 -0.03388 -0.1734 0.1893 0.9835 0.9932 0.2213 0.4401 0.8711 0.716 ] Network output: [ -0.01027 1.002 1.009 -1.859e-07 8.345e-08 0.008899 -1.401e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006124 0.0005007 0.004446 0.003601 0.9889 0.9919 0.00624 0.8612 0.8951 0.01288 ] Network output: [ -0.0005093 0.002548 1.001 -4.332e-05 1.945e-05 0.9971 -3.265e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2099 0.09773 0.3399 0.1457 0.985 0.994 0.2106 0.4444 0.8777 0.7103 ] Network output: [ 0.005323 -0.02547 0.9946 2.602e-05 -1.168e-05 1.02 1.961e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1039 0.09165 0.1819 0.2004 0.9873 0.9919 0.1039 0.7576 0.8664 0.3056 ] Network output: [ -0.005066 0.02448 1.003 2.751e-05 -1.235e-05 0.9823 2.073e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09109 0.08915 0.1651 0.1954 0.9853 0.9912 0.0911 0.6822 0.8428 0.2457 ] Network output: [ 0.0001465 1 -0.0001912 3.685e-06 -1.654e-06 0.9999 2.777e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003768 Epoch 8236 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01053 0.9957 0.9907 8.739e-08 -3.923e-08 -0.007474 6.586e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003375 -0.003188 -0.007782 0.006104 0.9699 0.9743 0.00649 0.833 0.8244 0.01786 ] Network output: [ 0.9998 0.0004851 0.000789 -1.378e-05 6.186e-06 -0.001004 -1.038e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1978 -0.03388 -0.1734 0.1892 0.9835 0.9932 0.2213 0.4401 0.8711 0.716 ] Network output: [ -0.01027 1.002 1.009 -1.864e-07 8.368e-08 0.008897 -1.405e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006125 0.0005008 0.004446 0.0036 0.9889 0.9919 0.006241 0.8612 0.8951 0.01287 ] Network output: [ -0.000509 0.002547 1.001 -4.328e-05 1.943e-05 0.9971 -3.261e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2099 0.09774 0.3399 0.1456 0.985 0.994 0.2106 0.4444 0.8777 0.7102 ] Network output: [ 0.005322 -0.02546 0.9946 2.6e-05 -1.167e-05 1.02 1.959e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1039 0.09165 0.1819 0.2004 0.9873 0.9919 0.1039 0.7575 0.8664 0.3056 ] Network output: [ -0.005064 0.02447 1.004 2.748e-05 -1.234e-05 0.9823 2.071e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09109 0.08915 0.1651 0.1955 0.9853 0.9912 0.0911 0.6822 0.8428 0.2457 ] Network output: [ 0.0001464 1 -0.000191 3.681e-06 -1.653e-06 0.9999 2.774e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003766 Epoch 8237 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01053 0.9957 0.9907 8.643e-08 -3.88e-08 -0.007475 6.514e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003375 -0.003188 -0.007781 0.006104 0.9699 0.9743 0.00649 0.833 0.8244 0.01786 ] Network output: [ 0.9998 0.0004847 0.0007885 -1.376e-05 6.179e-06 -0.001003 -1.037e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1978 -0.03388 -0.1734 0.1892 0.9835 0.9932 0.2213 0.4401 0.8711 0.716 ] Network output: [ -0.01026 1.002 1.009 -1.869e-07 8.391e-08 0.008895 -1.409e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006126 0.0005009 0.004446 0.0036 0.9889 0.9919 0.006241 0.8612 0.8951 0.01287 ] Network output: [ -0.0005086 0.002546 1.001 -4.323e-05 1.941e-05 0.9971 -3.258e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2099 0.09774 0.3399 0.1456 0.985 0.994 0.2106 0.4444 0.8777 0.7102 ] Network output: [ 0.00532 -0.02545 0.9946 2.597e-05 -1.166e-05 1.02 1.957e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1039 0.09166 0.1819 0.2004 0.9873 0.9919 0.1039 0.7575 0.8664 0.3056 ] Network output: [ -0.005062 0.02446 1.004 2.745e-05 -1.233e-05 0.9823 2.069e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09109 0.08915 0.1651 0.1955 0.9853 0.9912 0.0911 0.6822 0.8428 0.2457 ] Network output: [ 0.0001463 1 -0.0001908 3.677e-06 -1.651e-06 0.9999 2.771e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003764 Epoch 8238 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01053 0.9957 0.9907 8.547e-08 -3.837e-08 -0.007475 6.441e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003375 -0.003188 -0.00778 0.006103 0.9699 0.9743 0.00649 0.833 0.8244 0.01786 ] Network output: [ 0.9998 0.0004843 0.000788 -1.375e-05 6.173e-06 -0.001002 -1.036e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1978 -0.03389 -0.1734 0.1892 0.9835 0.9932 0.2213 0.4401 0.8711 0.716 ] Network output: [ -0.01026 1.002 1.009 -1.874e-07 8.414e-08 0.008893 -1.412e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006126 0.000501 0.004446 0.0036 0.9889 0.9919 0.006242 0.8612 0.8951 0.01287 ] Network output: [ -0.0005083 0.002545 1.001 -4.319e-05 1.939e-05 0.9971 -3.255e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2099 0.09775 0.3399 0.1456 0.985 0.994 0.2106 0.4443 0.8777 0.7102 ] Network output: [ 0.005318 -0.02544 0.9946 2.595e-05 -1.165e-05 1.02 1.955e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1039 0.09166 0.182 0.2004 0.9873 0.9919 0.1039 0.7575 0.8664 0.3056 ] Network output: [ -0.00506 0.02445 1.004 2.743e-05 -1.231e-05 0.9823 2.067e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09109 0.08916 0.1651 0.1955 0.9853 0.9912 0.09111 0.6822 0.8428 0.2457 ] Network output: [ 0.0001463 1 -0.0001906 3.674e-06 -1.649e-06 0.9999 2.769e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003762 Epoch 8239 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01053 0.9957 0.9907 8.451e-08 -3.794e-08 -0.007475 6.369e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003376 -0.003188 -0.007779 0.006103 0.9699 0.9743 0.006491 0.833 0.8243 0.01786 ] Network output: [ 0.9998 0.0004839 0.0007876 -1.374e-05 6.167e-06 -0.001001 -1.035e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1978 -0.03389 -0.1734 0.1892 0.9835 0.9932 0.2214 0.4401 0.871 0.716 ] Network output: [ -0.01026 1.002 1.009 -1.879e-07 8.436e-08 0.008892 -1.416e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006127 0.000501 0.004446 0.003599 0.9889 0.9919 0.006243 0.8612 0.8951 0.01287 ] Network output: [ -0.000508 0.002545 1.001 -4.314e-05 1.937e-05 0.9971 -3.251e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2099 0.09775 0.34 0.1456 0.985 0.994 0.2106 0.4443 0.8777 0.7102 ] Network output: [ 0.005316 -0.02544 0.9946 2.592e-05 -1.164e-05 1.02 1.953e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1039 0.09167 0.182 0.2004 0.9873 0.9919 0.1039 0.7575 0.8664 0.3056 ] Network output: [ -0.005059 0.02444 1.004 2.74e-05 -1.23e-05 0.9823 2.065e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0911 0.08916 0.1651 0.1955 0.9853 0.9912 0.09111 0.6821 0.8428 0.2457 ] Network output: [ 0.0001462 1 -0.0001903 3.67e-06 -1.648e-06 0.9999 2.766e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000376 Epoch 8240 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01053 0.9957 0.9907 8.356e-08 -3.751e-08 -0.007476 6.297e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003376 -0.003188 -0.007778 0.006102 0.9699 0.9743 0.006491 0.833 0.8243 0.01785 ] Network output: [ 0.9998 0.0004835 0.0007871 -1.372e-05 6.161e-06 -0.001 -1.034e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1978 -0.03389 -0.1734 0.1892 0.9835 0.9932 0.2214 0.4401 0.871 0.716 ] Network output: [ -0.01026 1.002 1.009 -1.884e-07 8.459e-08 0.00889 -1.42e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006127 0.0005011 0.004446 0.003599 0.9889 0.9919 0.006243 0.8611 0.8951 0.01287 ] Network output: [ -0.0005077 0.002544 1.001 -4.31e-05 1.935e-05 0.9971 -3.248e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2099 0.09776 0.34 0.1456 0.985 0.994 0.2106 0.4443 0.8777 0.7102 ] Network output: [ 0.005315 -0.02543 0.9946 2.589e-05 -1.162e-05 1.02 1.951e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1039 0.09168 0.182 0.2004 0.9873 0.9919 0.104 0.7575 0.8664 0.3056 ] Network output: [ -0.005057 0.02443 1.004 2.737e-05 -1.229e-05 0.9823 2.063e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0911 0.08916 0.1651 0.1955 0.9853 0.9912 0.09111 0.6821 0.8428 0.2457 ] Network output: [ 0.0001461 1 -0.0001901 3.666e-06 -1.646e-06 0.9999 2.763e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003758 Epoch 8241 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01053 0.9957 0.9907 8.26e-08 -3.708e-08 -0.007476 6.225e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003376 -0.003188 -0.007777 0.006101 0.9699 0.9743 0.006491 0.833 0.8243 0.01785 ] Network output: [ 0.9998 0.0004831 0.0007866 -1.371e-05 6.154e-06 -0.0009993 -1.033e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1978 -0.03389 -0.1733 0.1892 0.9835 0.9932 0.2214 0.4401 0.871 0.716 ] Network output: [ -0.01026 1.002 1.009 -1.889e-07 8.481e-08 0.008888 -1.424e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006128 0.0005012 0.004446 0.003598 0.9889 0.9919 0.006244 0.8611 0.8951 0.01287 ] Network output: [ -0.0005074 0.002543 1.001 -4.305e-05 1.933e-05 0.9971 -3.245e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2099 0.09777 0.34 0.1456 0.985 0.994 0.2106 0.4443 0.8777 0.7102 ] Network output: [ 0.005313 -0.02542 0.9946 2.587e-05 -1.161e-05 1.02 1.949e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1039 0.09168 0.182 0.2004 0.9873 0.9919 0.104 0.7574 0.8664 0.3056 ] Network output: [ -0.005055 0.02441 1.004 2.735e-05 -1.228e-05 0.9823 2.061e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0911 0.08916 0.1651 0.1955 0.9853 0.9912 0.09111 0.6821 0.8428 0.2457 ] Network output: [ 0.000146 1 -0.0001899 3.663e-06 -1.644e-06 0.9999 2.76e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003756 Epoch 8242 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01053 0.9957 0.9907 8.165e-08 -3.666e-08 -0.007476 6.154e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003376 -0.003188 -0.007777 0.006101 0.9699 0.9743 0.006492 0.833 0.8243 0.01785 ] Network output: [ 0.9998 0.0004827 0.0007861 -1.369e-05 6.148e-06 -0.0009985 -1.032e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1978 -0.03389 -0.1733 0.1892 0.9835 0.9932 0.2214 0.4401 0.871 0.716 ] Network output: [ -0.01026 1.002 1.009 -1.894e-07 8.504e-08 0.008886 -1.428e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006128 0.0005013 0.004446 0.003598 0.9889 0.9919 0.006244 0.8611 0.8951 0.01287 ] Network output: [ -0.000507 0.002542 1.001 -4.301e-05 1.931e-05 0.9971 -3.241e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2099 0.09777 0.34 0.1456 0.985 0.994 0.2106 0.4443 0.8777 0.7102 ] Network output: [ 0.005311 -0.02541 0.9946 2.584e-05 -1.16e-05 1.02 1.947e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1039 0.09169 0.182 0.2004 0.9873 0.9919 0.104 0.7574 0.8664 0.3056 ] Network output: [ -0.005053 0.0244 1.004 2.732e-05 -1.226e-05 0.9823 2.059e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0911 0.08916 0.1651 0.1955 0.9853 0.9912 0.09111 0.6821 0.8428 0.2457 ] Network output: [ 0.000146 1 -0.0001897 3.659e-06 -1.643e-06 0.9999 2.757e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003753 Epoch 8243 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01052 0.9957 0.9907 8.071e-08 -3.623e-08 -0.007476 6.082e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003376 -0.003189 -0.007776 0.0061 0.9699 0.9743 0.006492 0.833 0.8243 0.01785 ] Network output: [ 0.9998 0.0004823 0.0007857 -1.368e-05 6.142e-06 -0.0009976 -1.031e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1978 -0.03389 -0.1733 0.1892 0.9835 0.9932 0.2214 0.4401 0.871 0.716 ] Network output: [ -0.01026 1.002 1.009 -1.899e-07 8.526e-08 0.008885 -1.431e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006129 0.0005014 0.004446 0.003598 0.9889 0.9919 0.006245 0.8611 0.8951 0.01287 ] Network output: [ -0.0005067 0.002541 1.001 -4.296e-05 1.929e-05 0.9971 -3.238e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.21 0.09778 0.34 0.1456 0.985 0.994 0.2106 0.4443 0.8777 0.7102 ] Network output: [ 0.005309 -0.0254 0.9946 2.581e-05 -1.159e-05 1.02 1.945e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1039 0.09169 0.182 0.2004 0.9873 0.9919 0.104 0.7574 0.8664 0.3056 ] Network output: [ -0.005051 0.02439 1.004 2.729e-05 -1.225e-05 0.9823 2.057e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0911 0.08917 0.1651 0.1955 0.9853 0.9912 0.09112 0.6821 0.8427 0.2457 ] Network output: [ 0.0001459 1 -0.0001895 3.655e-06 -1.641e-06 0.9999 2.755e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003751 Epoch 8244 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01052 0.9957 0.9907 7.976e-08 -3.581e-08 -0.007477 6.011e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003376 -0.003189 -0.007775 0.006099 0.9699 0.9743 0.006492 0.833 0.8243 0.01785 ] Network output: [ 0.9998 0.0004819 0.0007852 -1.367e-05 6.136e-06 -0.0009968 -1.03e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1979 -0.0339 -0.1733 0.1892 0.9835 0.9932 0.2214 0.44 0.871 0.7159 ] Network output: [ -0.01026 1.002 1.009 -1.904e-07 8.548e-08 0.008883 -1.435e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00613 0.0005015 0.004446 0.003597 0.9889 0.9919 0.006245 0.8611 0.8951 0.01287 ] Network output: [ -0.0005064 0.00254 1.001 -4.292e-05 1.927e-05 0.9971 -3.235e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.21 0.09778 0.34 0.1456 0.985 0.994 0.2107 0.4443 0.8777 0.7102 ] Network output: [ 0.005307 -0.02539 0.9946 2.579e-05 -1.158e-05 1.02 1.943e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1039 0.0917 0.182 0.2004 0.9873 0.9919 0.104 0.7574 0.8664 0.3056 ] Network output: [ -0.00505 0.02438 1.004 2.727e-05 -1.224e-05 0.9823 2.055e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0911 0.08917 0.1651 0.1955 0.9853 0.9912 0.09112 0.682 0.8427 0.2457 ] Network output: [ 0.0001458 1 -0.0001892 3.652e-06 -1.639e-06 0.9999 2.752e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003749 Epoch 8245 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01052 0.9957 0.9907 7.881e-08 -3.538e-08 -0.007477 5.94e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003376 -0.003189 -0.007774 0.006099 0.9699 0.9743 0.006492 0.833 0.8243 0.01785 ] Network output: [ 0.9998 0.0004815 0.0007847 -1.365e-05 6.129e-06 -0.0009959 -1.029e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1979 -0.0339 -0.1733 0.1892 0.9835 0.9932 0.2214 0.44 0.871 0.7159 ] Network output: [ -0.01026 1.002 1.009 -1.909e-07 8.571e-08 0.008881 -1.439e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00613 0.0005016 0.004446 0.003597 0.9889 0.9919 0.006246 0.8611 0.8951 0.01287 ] Network output: [ -0.0005061 0.002539 1.001 -4.288e-05 1.925e-05 0.9971 -3.231e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.21 0.09779 0.34 0.1456 0.985 0.994 0.2107 0.4443 0.8777 0.7102 ] Network output: [ 0.005306 -0.02538 0.9946 2.576e-05 -1.157e-05 1.02 1.942e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1039 0.09171 0.182 0.2003 0.9873 0.9919 0.104 0.7574 0.8664 0.3056 ] Network output: [ -0.005048 0.02437 1.004 2.724e-05 -1.223e-05 0.9823 2.053e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09111 0.08917 0.1651 0.1955 0.9853 0.9912 0.09112 0.682 0.8427 0.2457 ] Network output: [ 0.0001458 1 -0.000189 3.648e-06 -1.638e-06 0.9999 2.749e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003747 Epoch 8246 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01052 0.9957 0.9907 7.787e-08 -3.496e-08 -0.007477 5.869e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003376 -0.003189 -0.007773 0.006098 0.9699 0.9743 0.006493 0.833 0.8243 0.01785 ] Network output: [ 0.9998 0.0004811 0.0007842 -1.364e-05 6.123e-06 -0.0009951 -1.028e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1979 -0.0339 -0.1733 0.1892 0.9835 0.9932 0.2214 0.44 0.871 0.7159 ] Network output: [ -0.01025 1.002 1.009 -1.914e-07 8.593e-08 0.008879 -1.442e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006131 0.0005016 0.004446 0.003596 0.9889 0.9919 0.006247 0.8611 0.8951 0.01286 ] Network output: [ -0.0005058 0.002538 1.001 -4.283e-05 1.923e-05 0.9971 -3.228e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.21 0.09779 0.34 0.1456 0.985 0.994 0.2107 0.4443 0.8777 0.7102 ] Network output: [ 0.005304 -0.02537 0.9946 2.574e-05 -1.155e-05 1.02 1.94e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1039 0.09171 0.182 0.2003 0.9873 0.9919 0.104 0.7573 0.8664 0.3056 ] Network output: [ -0.005046 0.02436 1.004 2.721e-05 -1.222e-05 0.9823 2.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09111 0.08917 0.1651 0.1955 0.9853 0.9912 0.09112 0.682 0.8427 0.2457 ] Network output: [ 0.0001457 1 -0.0001888 3.644e-06 -1.636e-06 0.9999 2.746e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003745 Epoch 8247 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01052 0.9957 0.9907 7.693e-08 -3.454e-08 -0.007477 5.798e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003377 -0.003189 -0.007772 0.006098 0.9699 0.9743 0.006493 0.8329 0.8243 0.01784 ] Network output: [ 0.9998 0.0004807 0.0007838 -1.363e-05 6.117e-06 -0.0009943 -1.027e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1979 -0.0339 -0.1733 0.1892 0.9835 0.9932 0.2214 0.44 0.871 0.7159 ] Network output: [ -0.01025 1.002 1.009 -1.919e-07 8.615e-08 0.008878 -1.446e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006131 0.0005017 0.004446 0.003596 0.9889 0.9919 0.006247 0.8611 0.8951 0.01286 ] Network output: [ -0.0005054 0.002537 1.001 -4.279e-05 1.921e-05 0.9971 -3.225e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.21 0.0978 0.34 0.1456 0.985 0.994 0.2107 0.4442 0.8776 0.7102 ] Network output: [ 0.005302 -0.02536 0.9946 2.571e-05 -1.154e-05 1.02 1.938e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1039 0.09172 0.182 0.2003 0.9873 0.9919 0.104 0.7573 0.8664 0.3056 ] Network output: [ -0.005044 0.02435 1.004 2.719e-05 -1.22e-05 0.9823 2.049e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09111 0.08917 0.1651 0.1955 0.9853 0.9912 0.09112 0.682 0.8427 0.2457 ] Network output: [ 0.0001456 1 -0.0001886 3.641e-06 -1.634e-06 0.9999 2.744e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003743 Epoch 8248 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01052 0.9958 0.9907 7.6e-08 -3.412e-08 -0.007478 5.727e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003377 -0.003189 -0.007771 0.006097 0.9699 0.9743 0.006493 0.8329 0.8243 0.01784 ] Network output: [ 0.9998 0.0004803 0.0007833 -1.361e-05 6.111e-06 -0.0009934 -1.026e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1979 -0.0339 -0.1732 0.1892 0.9835 0.9932 0.2215 0.44 0.871 0.7159 ] Network output: [ -0.01025 1.002 1.009 -1.924e-07 8.637e-08 0.008876 -1.45e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006132 0.0005018 0.004446 0.003596 0.9889 0.9919 0.006248 0.8611 0.8951 0.01286 ] Network output: [ -0.0005051 0.002537 1.001 -4.274e-05 1.919e-05 0.9971 -3.221e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.21 0.09781 0.34 0.1456 0.985 0.994 0.2107 0.4442 0.8776 0.7102 ] Network output: [ 0.0053 -0.02535 0.9946 2.568e-05 -1.153e-05 1.02 1.936e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1039 0.09172 0.182 0.2003 0.9873 0.9919 0.104 0.7573 0.8664 0.3056 ] Network output: [ -0.005042 0.02434 1.004 2.716e-05 -1.219e-05 0.9823 2.047e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09111 0.08918 0.1651 0.1955 0.9853 0.9912 0.09113 0.682 0.8427 0.2457 ] Network output: [ 0.0001455 1 -0.0001884 3.637e-06 -1.633e-06 0.9999 2.741e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003741 Epoch 8249 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01052 0.9958 0.9907 7.506e-08 -3.37e-08 -0.007478 5.657e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003377 -0.003189 -0.00777 0.006096 0.9699 0.9743 0.006494 0.8329 0.8243 0.01784 ] Network output: [ 0.9998 0.0004798 0.0007828 -1.36e-05 6.104e-06 -0.0009926 -1.025e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1979 -0.0339 -0.1732 0.1892 0.9835 0.9932 0.2215 0.44 0.871 0.7159 ] Network output: [ -0.01025 1.002 1.009 -1.929e-07 8.659e-08 0.008874 -1.454e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006132 0.0005019 0.004446 0.003595 0.9889 0.9919 0.006248 0.8611 0.8951 0.01286 ] Network output: [ -0.0005048 0.002536 1.001 -4.27e-05 1.917e-05 0.9971 -3.218e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.21 0.09781 0.34 0.1456 0.985 0.994 0.2107 0.4442 0.8776 0.7102 ] Network output: [ 0.005299 -0.02534 0.9946 2.566e-05 -1.152e-05 1.02 1.934e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1039 0.09173 0.182 0.2003 0.9873 0.9919 0.104 0.7573 0.8664 0.3056 ] Network output: [ -0.005041 0.02433 1.004 2.713e-05 -1.218e-05 0.9823 2.045e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09111 0.08918 0.1651 0.1955 0.9853 0.9912 0.09113 0.6819 0.8427 0.2457 ] Network output: [ 0.0001455 1 -0.0001881 3.633e-06 -1.631e-06 0.9999 2.738e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003738 Epoch 8250 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01051 0.9958 0.9907 7.413e-08 -3.328e-08 -0.007478 5.586e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003377 -0.00319 -0.007769 0.006096 0.9699 0.9743 0.006494 0.8329 0.8243 0.01784 ] Network output: [ 0.9998 0.0004794 0.0007824 -1.358e-05 6.098e-06 -0.0009917 -1.024e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1979 -0.03391 -0.1732 0.1892 0.9835 0.9932 0.2215 0.44 0.871 0.7159 ] Network output: [ -0.01025 1.002 1.009 -1.934e-07 8.681e-08 0.008872 -1.457e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006133 0.000502 0.004446 0.003595 0.9889 0.9919 0.006249 0.8611 0.895 0.01286 ] Network output: [ -0.0005045 0.002535 1.001 -4.266e-05 1.915e-05 0.9971 -3.215e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.21 0.09782 0.3401 0.1456 0.985 0.994 0.2107 0.4442 0.8776 0.7102 ] Network output: [ 0.005297 -0.02533 0.9946 2.563e-05 -1.151e-05 1.02 1.932e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1039 0.09173 0.182 0.2003 0.9873 0.9919 0.104 0.7573 0.8664 0.3056 ] Network output: [ -0.005039 0.02432 1.004 2.71e-05 -1.217e-05 0.9823 2.043e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09112 0.08918 0.1651 0.1955 0.9853 0.9912 0.09113 0.6819 0.8427 0.2457 ] Network output: [ 0.0001454 1 -0.0001879 3.63e-06 -1.629e-06 0.9999 2.735e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003736 Epoch 8251 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01051 0.9958 0.9907 7.32e-08 -3.286e-08 -0.007478 5.516e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003377 -0.00319 -0.007768 0.006095 0.9699 0.9743 0.006494 0.8329 0.8243 0.01784 ] Network output: [ 0.9998 0.000479 0.0007819 -1.357e-05 6.092e-06 -0.0009909 -1.023e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1979 -0.03391 -0.1732 0.1892 0.9835 0.9932 0.2215 0.44 0.871 0.7159 ] Network output: [ -0.01025 1.002 1.009 -1.939e-07 8.703e-08 0.008871 -1.461e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006134 0.0005021 0.004446 0.003594 0.9889 0.9919 0.00625 0.8611 0.895 0.01286 ] Network output: [ -0.0005042 0.002534 1.001 -4.261e-05 1.913e-05 0.9971 -3.211e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.21 0.09782 0.3401 0.1456 0.985 0.994 0.2107 0.4442 0.8776 0.7102 ] Network output: [ 0.005295 -0.02532 0.9946 2.561e-05 -1.15e-05 1.02 1.93e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.104 0.09174 0.182 0.2003 0.9873 0.9919 0.104 0.7572 0.8664 0.3056 ] Network output: [ -0.005037 0.02431 1.004 2.708e-05 -1.216e-05 0.9823 2.041e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09112 0.08918 0.1651 0.1955 0.9853 0.9912 0.09113 0.6819 0.8427 0.2457 ] Network output: [ 0.0001453 1 -0.0001877 3.626e-06 -1.628e-06 0.9999 2.733e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003734 Epoch 8252 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01051 0.9958 0.9907 7.227e-08 -3.244e-08 -0.007479 5.446e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003377 -0.00319 -0.007767 0.006094 0.9699 0.9743 0.006495 0.8329 0.8243 0.01784 ] Network output: [ 0.9998 0.0004786 0.0007814 -1.356e-05 6.086e-06 -0.0009901 -1.022e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1979 -0.03391 -0.1732 0.1892 0.9835 0.9932 0.2215 0.44 0.871 0.7159 ] Network output: [ -0.01025 1.002 1.009 -1.943e-07 8.725e-08 0.008869 -1.465e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006134 0.0005021 0.004446 0.003594 0.9889 0.9919 0.00625 0.861 0.895 0.01286 ] Network output: [ -0.0005038 0.002533 1.001 -4.257e-05 1.911e-05 0.9972 -3.208e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.21 0.09783 0.3401 0.1456 0.985 0.994 0.2107 0.4442 0.8776 0.7101 ] Network output: [ 0.005293 -0.02532 0.9946 2.558e-05 -1.148e-05 1.02 1.928e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.104 0.09175 0.182 0.2003 0.9873 0.9919 0.104 0.7572 0.8664 0.3056 ] Network output: [ -0.005035 0.0243 1.004 2.705e-05 -1.214e-05 0.9824 2.039e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09112 0.08918 0.1651 0.1955 0.9853 0.9912 0.09113 0.6819 0.8427 0.2457 ] Network output: [ 0.0001453 1 -0.0001875 3.622e-06 -1.626e-06 0.9999 2.73e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003732 Epoch 8253 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01051 0.9958 0.9907 7.134e-08 -3.203e-08 -0.007479 5.376e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003377 -0.00319 -0.007766 0.006094 0.9699 0.9743 0.006495 0.8329 0.8243 0.01784 ] Network output: [ 0.9998 0.0004782 0.0007809 -1.354e-05 6.08e-06 -0.0009892 -1.021e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1979 -0.03391 -0.1732 0.1891 0.9835 0.9932 0.2215 0.4399 0.871 0.7159 ] Network output: [ -0.01025 1.002 1.009 -1.948e-07 8.746e-08 0.008867 -1.468e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006135 0.0005022 0.004446 0.003594 0.9889 0.9919 0.006251 0.861 0.895 0.01286 ] Network output: [ -0.0005035 0.002532 1.001 -4.252e-05 1.909e-05 0.9972 -3.205e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2101 0.09784 0.3401 0.1456 0.985 0.994 0.2107 0.4442 0.8776 0.7101 ] Network output: [ 0.005292 -0.02531 0.9946 2.555e-05 -1.147e-05 1.02 1.926e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.104 0.09175 0.182 0.2003 0.9873 0.9919 0.104 0.7572 0.8664 0.3056 ] Network output: [ -0.005033 0.02429 1.004 2.702e-05 -1.213e-05 0.9824 2.037e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09112 0.08919 0.1651 0.1955 0.9853 0.9912 0.09114 0.6819 0.8427 0.2457 ] Network output: [ 0.0001452 1 -0.0001873 3.619e-06 -1.624e-06 0.9999 2.727e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000373 Epoch 8254 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01051 0.9958 0.9907 7.041e-08 -3.161e-08 -0.007479 5.307e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003377 -0.00319 -0.007765 0.006093 0.9699 0.9743 0.006495 0.8329 0.8243 0.01784 ] Network output: [ 0.9998 0.0004778 0.0007805 -1.353e-05 6.073e-06 -0.0009884 -1.02e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1979 -0.03391 -0.1732 0.1891 0.9835 0.9932 0.2215 0.4399 0.871 0.7159 ] Network output: [ -0.01025 1.002 1.009 -1.953e-07 8.768e-08 0.008865 -1.472e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006135 0.0005023 0.004446 0.003593 0.9889 0.9919 0.006251 0.861 0.895 0.01286 ] Network output: [ -0.0005032 0.002531 1.001 -4.248e-05 1.907e-05 0.9972 -3.201e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2101 0.09784 0.3401 0.1456 0.985 0.994 0.2108 0.4442 0.8776 0.7101 ] Network output: [ 0.00529 -0.0253 0.9946 2.553e-05 -1.146e-05 1.02 1.924e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.104 0.09176 0.182 0.2003 0.9873 0.9919 0.104 0.7572 0.8663 0.3056 ] Network output: [ -0.005032 0.02428 1.004 2.7e-05 -1.212e-05 0.9824 2.035e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09112 0.08919 0.1651 0.1955 0.9853 0.9912 0.09114 0.6818 0.8427 0.2457 ] Network output: [ 0.0001451 1 -0.000187 3.615e-06 -1.623e-06 0.9999 2.724e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003728 Epoch 8255 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01051 0.9958 0.9907 6.949e-08 -3.12e-08 -0.007479 5.237e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003378 -0.00319 -0.007764 0.006093 0.9699 0.9743 0.006495 0.8329 0.8243 0.01783 ] Network output: [ 0.9998 0.0004775 0.00078 -1.351e-05 6.067e-06 -0.0009876 -1.018e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.198 -0.03392 -0.1731 0.1891 0.9835 0.9932 0.2215 0.4399 0.871 0.7159 ] Network output: [ -0.01024 1.002 1.009 -1.958e-07 8.79e-08 0.008864 -1.476e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006136 0.0005024 0.004446 0.003593 0.9889 0.9919 0.006252 0.861 0.895 0.01285 ] Network output: [ -0.0005029 0.00253 1.001 -4.244e-05 1.905e-05 0.9972 -3.198e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2101 0.09785 0.3401 0.1456 0.985 0.994 0.2108 0.4442 0.8776 0.7101 ] Network output: [ 0.005288 -0.02529 0.9946 2.55e-05 -1.145e-05 1.02 1.922e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.104 0.09176 0.182 0.2003 0.9873 0.9919 0.104 0.7572 0.8663 0.3056 ] Network output: [ -0.00503 0.02427 1.004 2.697e-05 -1.211e-05 0.9824 2.033e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09113 0.08919 0.1651 0.1955 0.9853 0.9912 0.09114 0.6818 0.8427 0.2457 ] Network output: [ 0.000145 1 -0.0001868 3.611e-06 -1.621e-06 0.9999 2.722e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003726 Epoch 8256 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01051 0.9958 0.9907 6.857e-08 -3.078e-08 -0.007479 5.168e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003378 -0.00319 -0.007763 0.006092 0.9699 0.9743 0.006496 0.8329 0.8243 0.01783 ] Network output: [ 0.9998 0.0004771 0.0007795 -1.35e-05 6.061e-06 -0.0009867 -1.017e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.198 -0.03392 -0.1731 0.1891 0.9835 0.9932 0.2215 0.4399 0.871 0.7159 ] Network output: [ -0.01024 1.002 1.009 -1.963e-07 8.811e-08 0.008862 -1.479e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006137 0.0005025 0.004446 0.003593 0.9889 0.9919 0.006253 0.861 0.895 0.01285 ] Network output: [ -0.0005026 0.002529 1.001 -4.239e-05 1.903e-05 0.9972 -3.195e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2101 0.09785 0.3401 0.1456 0.985 0.994 0.2108 0.4441 0.8776 0.7101 ] Network output: [ 0.005286 -0.02528 0.9946 2.548e-05 -1.144e-05 1.02 1.92e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.104 0.09177 0.182 0.2003 0.9873 0.9919 0.1041 0.7571 0.8663 0.3056 ] Network output: [ -0.005028 0.02426 1.004 2.694e-05 -1.21e-05 0.9824 2.031e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09113 0.08919 0.1651 0.1955 0.9853 0.9912 0.09114 0.6818 0.8427 0.2457 ] Network output: [ 0.000145 1 -0.0001866 3.608e-06 -1.62e-06 0.9999 2.719e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003723 Epoch 8257 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0105 0.9958 0.9907 6.765e-08 -3.037e-08 -0.00748 5.099e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003378 -0.003191 -0.007762 0.006091 0.9699 0.9743 0.006496 0.8329 0.8243 0.01783 ] Network output: [ 0.9998 0.0004767 0.0007791 -1.349e-05 6.055e-06 -0.0009859 -1.016e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.198 -0.03392 -0.1731 0.1891 0.9835 0.9932 0.2216 0.4399 0.871 0.7159 ] Network output: [ -0.01024 1.002 1.009 -1.967e-07 8.833e-08 0.00886 -1.483e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006137 0.0005026 0.004446 0.003592 0.9889 0.9919 0.006253 0.861 0.895 0.01285 ] Network output: [ -0.0005023 0.002528 1.001 -4.235e-05 1.901e-05 0.9972 -3.191e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2101 0.09786 0.3401 0.1456 0.985 0.994 0.2108 0.4441 0.8776 0.7101 ] Network output: [ 0.005285 -0.02527 0.9946 2.545e-05 -1.143e-05 1.02 1.918e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.104 0.09177 0.182 0.2003 0.9873 0.9919 0.1041 0.7571 0.8663 0.3056 ] Network output: [ -0.005026 0.02425 1.004 2.692e-05 -1.208e-05 0.9824 2.029e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09113 0.08919 0.1651 0.1955 0.9853 0.9912 0.09114 0.6818 0.8427 0.2457 ] Network output: [ 0.0001449 1 -0.0001864 3.604e-06 -1.618e-06 0.9999 2.716e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003721 Epoch 8258 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0105 0.9958 0.9907 6.674e-08 -2.996e-08 -0.00748 5.029e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003378 -0.003191 -0.007761 0.006091 0.9699 0.9743 0.006496 0.8329 0.8243 0.01783 ] Network output: [ 0.9998 0.0004763 0.0007786 -1.347e-05 6.049e-06 -0.0009851 -1.015e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.198 -0.03392 -0.1731 0.1891 0.9835 0.9932 0.2216 0.4399 0.871 0.7159 ] Network output: [ -0.01024 1.002 1.009 -1.972e-07 8.854e-08 0.008859 -1.486e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006138 0.0005027 0.004446 0.003592 0.9889 0.9919 0.006254 0.861 0.895 0.01285 ] Network output: [ -0.0005019 0.002528 1.001 -4.23e-05 1.899e-05 0.9972 -3.188e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2101 0.09787 0.3401 0.1456 0.985 0.994 0.2108 0.4441 0.8776 0.7101 ] Network output: [ 0.005283 -0.02526 0.9946 2.542e-05 -1.141e-05 1.02 1.916e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.104 0.09178 0.182 0.2003 0.9873 0.9919 0.1041 0.7571 0.8663 0.3056 ] Network output: [ -0.005024 0.02424 1.004 2.689e-05 -1.207e-05 0.9824 2.027e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09113 0.0892 0.1651 0.1955 0.9853 0.9912 0.09115 0.6817 0.8426 0.2457 ] Network output: [ 0.0001448 1 -0.0001862 3.6e-06 -1.616e-06 0.9999 2.713e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003719 Epoch 8259 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0105 0.9958 0.9907 6.582e-08 -2.955e-08 -0.00748 4.961e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003378 -0.003191 -0.00776 0.00609 0.9699 0.9743 0.006497 0.8329 0.8243 0.01783 ] Network output: [ 0.9998 0.0004759 0.0007781 -1.346e-05 6.042e-06 -0.0009842 -1.014e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.198 -0.03392 -0.1731 0.1891 0.9835 0.9932 0.2216 0.4399 0.871 0.7159 ] Network output: [ -0.01024 1.002 1.009 -1.977e-07 8.875e-08 0.008857 -1.49e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006138 0.0005028 0.004446 0.003591 0.9889 0.9919 0.006254 0.861 0.895 0.01285 ] Network output: [ -0.0005016 0.002527 1.001 -4.226e-05 1.897e-05 0.9972 -3.185e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2101 0.09787 0.3401 0.1455 0.985 0.994 0.2108 0.4441 0.8776 0.7101 ] Network output: [ 0.005281 -0.02525 0.9946 2.54e-05 -1.14e-05 1.02 1.914e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.104 0.09179 0.182 0.2003 0.9873 0.9919 0.1041 0.7571 0.8663 0.3056 ] Network output: [ -0.005022 0.02423 1.004 2.686e-05 -1.206e-05 0.9824 2.025e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09113 0.0892 0.1651 0.1955 0.9853 0.9912 0.09115 0.6817 0.8426 0.2457 ] Network output: [ 0.0001448 1 -0.000186 3.597e-06 -1.615e-06 0.9999 2.711e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003717 Epoch 8260 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0105 0.9958 0.9907 6.491e-08 -2.914e-08 -0.00748 4.892e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003378 -0.003191 -0.007759 0.00609 0.9699 0.9743 0.006497 0.8329 0.8243 0.01783 ] Network output: [ 0.9998 0.0004755 0.0007777 -1.345e-05 6.036e-06 -0.0009834 -1.013e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.198 -0.03392 -0.1731 0.1891 0.9835 0.9932 0.2216 0.4399 0.871 0.7159 ] Network output: [ -0.01024 1.002 1.009 -1.982e-07 8.897e-08 0.008855 -1.493e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006139 0.0005028 0.004446 0.003591 0.9889 0.9919 0.006255 0.861 0.895 0.01285 ] Network output: [ -0.0005013 0.002526 1.001 -4.222e-05 1.895e-05 0.9972 -3.182e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2101 0.09788 0.3401 0.1455 0.985 0.994 0.2108 0.4441 0.8776 0.7101 ] Network output: [ 0.005279 -0.02524 0.9946 2.537e-05 -1.139e-05 1.02 1.912e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.104 0.09179 0.182 0.2003 0.9873 0.9919 0.1041 0.7571 0.8663 0.3056 ] Network output: [ -0.005021 0.02422 1.004 2.684e-05 -1.205e-05 0.9824 2.023e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09114 0.0892 0.1651 0.1955 0.9853 0.9912 0.09115 0.6817 0.8426 0.2457 ] Network output: [ 0.0001447 1 -0.0001857 3.593e-06 -1.613e-06 0.9999 2.708e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003715 Epoch 8261 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0105 0.9958 0.9907 6.4e-08 -2.873e-08 -0.007481 4.823e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003378 -0.003191 -0.007758 0.006089 0.9699 0.9743 0.006497 0.8329 0.8243 0.01783 ] Network output: [ 0.9998 0.0004751 0.0007772 -1.343e-05 6.03e-06 -0.0009826 -1.012e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.198 -0.03393 -0.1731 0.1891 0.9835 0.9932 0.2216 0.4399 0.871 0.7158 ] Network output: [ -0.01024 1.002 1.009 -1.986e-07 8.918e-08 0.008853 -1.497e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006139 0.0005029 0.004446 0.003591 0.9889 0.9919 0.006256 0.861 0.895 0.01285 ] Network output: [ -0.000501 0.002525 1.001 -4.217e-05 1.893e-05 0.9972 -3.178e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2101 0.09788 0.3401 0.1455 0.985 0.994 0.2108 0.4441 0.8776 0.7101 ] Network output: [ 0.005278 -0.02523 0.9946 2.535e-05 -1.138e-05 1.02 1.91e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.104 0.0918 0.182 0.2003 0.9873 0.9919 0.1041 0.757 0.8663 0.3056 ] Network output: [ -0.005019 0.02421 1.004 2.681e-05 -1.204e-05 0.9824 2.021e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09114 0.0892 0.1651 0.1955 0.9853 0.9912 0.09115 0.6817 0.8426 0.2457 ] Network output: [ 0.0001446 1 -0.0001855 3.589e-06 -1.611e-06 0.9999 2.705e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003713 Epoch 8262 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0105 0.9958 0.9907 6.309e-08 -2.832e-08 -0.007481 4.755e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003378 -0.003191 -0.007757 0.006088 0.9699 0.9743 0.006498 0.8328 0.8242 0.01782 ] Network output: [ 0.9998 0.0004747 0.0007767 -1.342e-05 6.024e-06 -0.0009818 -1.011e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.198 -0.03393 -0.173 0.1891 0.9835 0.9932 0.2216 0.4399 0.871 0.7158 ] Network output: [ -0.01024 1.002 1.009 -1.991e-07 8.939e-08 0.008852 -1.501e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00614 0.000503 0.004446 0.00359 0.9889 0.9919 0.006256 0.861 0.895 0.01285 ] Network output: [ -0.0005007 0.002524 1.001 -4.213e-05 1.891e-05 0.9972 -3.175e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2102 0.09789 0.3402 0.1455 0.985 0.994 0.2108 0.4441 0.8776 0.7101 ] Network output: [ 0.005276 -0.02522 0.9946 2.532e-05 -1.137e-05 1.02 1.908e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.104 0.0918 0.182 0.2003 0.9873 0.9919 0.1041 0.757 0.8663 0.3056 ] Network output: [ -0.005017 0.0242 1.004 2.678e-05 -1.202e-05 0.9824 2.019e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09114 0.0892 0.1651 0.1955 0.9853 0.9912 0.09115 0.6817 0.8426 0.2457 ] Network output: [ 0.0001446 1 -0.0001853 3.586e-06 -1.61e-06 0.9999 2.702e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003711 Epoch 8263 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0105 0.9958 0.9907 6.219e-08 -2.792e-08 -0.007481 4.687e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003379 -0.003191 -0.007756 0.006088 0.9699 0.9743 0.006498 0.8328 0.8242 0.01782 ] Network output: [ 0.9998 0.0004743 0.0007763 -1.34e-05 6.018e-06 -0.0009809 -1.01e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.198 -0.03393 -0.173 0.1891 0.9835 0.9932 0.2216 0.4398 0.871 0.7158 ] Network output: [ -0.01024 1.002 1.009 -1.996e-07 8.96e-08 0.00885 -1.504e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006141 0.0005031 0.004446 0.00359 0.9889 0.9919 0.006257 0.861 0.895 0.01285 ] Network output: [ -0.0005004 0.002523 1.001 -4.209e-05 1.889e-05 0.9972 -3.172e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2102 0.09789 0.3402 0.1455 0.985 0.994 0.2109 0.4441 0.8776 0.7101 ] Network output: [ 0.005274 -0.02521 0.9946 2.53e-05 -1.136e-05 1.02 1.906e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.104 0.09181 0.182 0.2003 0.9873 0.9919 0.1041 0.757 0.8663 0.3056 ] Network output: [ -0.005015 0.02419 1.004 2.676e-05 -1.201e-05 0.9824 2.017e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09114 0.08921 0.1651 0.1955 0.9853 0.9912 0.09116 0.6816 0.8426 0.2457 ] Network output: [ 0.0001445 1 -0.0001851 3.582e-06 -1.608e-06 0.9999 2.7e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003709 Epoch 8264 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01049 0.9958 0.9907 6.128e-08 -2.751e-08 -0.007481 4.618e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003379 -0.003192 -0.007755 0.006087 0.9699 0.9743 0.006498 0.8328 0.8242 0.01782 ] Network output: [ 0.9998 0.0004739 0.0007758 -1.339e-05 6.012e-06 -0.0009801 -1.009e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.198 -0.03393 -0.173 0.1891 0.9835 0.9932 0.2216 0.4398 0.871 0.7158 ] Network output: [ -0.01023 1.002 1.009 -2.001e-07 8.981e-08 0.008848 -1.508e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006141 0.0005032 0.004446 0.003589 0.9889 0.9919 0.006257 0.861 0.895 0.01285 ] Network output: [ -0.0005 0.002522 1.001 -4.204e-05 1.887e-05 0.9972 -3.168e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2102 0.0979 0.3402 0.1455 0.985 0.994 0.2109 0.4441 0.8776 0.7101 ] Network output: [ 0.005272 -0.02521 0.9946 2.527e-05 -1.134e-05 1.02 1.904e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.104 0.09182 0.182 0.2003 0.9873 0.9919 0.1041 0.757 0.8663 0.3056 ] Network output: [ -0.005014 0.02418 1.004 2.673e-05 -1.2e-05 0.9824 2.015e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09114 0.08921 0.1651 0.1955 0.9853 0.9912 0.09116 0.6816 0.8426 0.2458 ] Network output: [ 0.0001444 1 -0.0001849 3.579e-06 -1.607e-06 0.9999 2.697e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003707 Epoch 8265 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01049 0.9958 0.9907 6.038e-08 -2.711e-08 -0.007482 4.551e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003379 -0.003192 -0.007754 0.006086 0.9699 0.9743 0.006498 0.8328 0.8242 0.01782 ] Network output: [ 0.9998 0.0004735 0.0007753 -1.338e-05 6.005e-06 -0.0009793 -1.008e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.198 -0.03393 -0.173 0.1891 0.9835 0.9932 0.2216 0.4398 0.871 0.7158 ] Network output: [ -0.01023 1.002 1.009 -2.005e-07 9.002e-08 0.008847 -1.511e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006142 0.0005033 0.004446 0.003589 0.9889 0.9919 0.006258 0.8609 0.895 0.01284 ] Network output: [ -0.0004997 0.002521 1.001 -4.2e-05 1.886e-05 0.9972 -3.165e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2102 0.09791 0.3402 0.1455 0.985 0.994 0.2109 0.444 0.8776 0.7101 ] Network output: [ 0.005271 -0.0252 0.9946 2.524e-05 -1.133e-05 1.02 1.902e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.104 0.09182 0.182 0.2003 0.9873 0.9919 0.1041 0.757 0.8663 0.3056 ] Network output: [ -0.005012 0.02417 1.004 2.671e-05 -1.199e-05 0.9824 2.013e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09115 0.08921 0.1651 0.1955 0.9853 0.9912 0.09116 0.6816 0.8426 0.2458 ] Network output: [ 0.0001443 1 -0.0001847 3.575e-06 -1.605e-06 0.9999 2.694e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003704 Epoch 8266 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01049 0.9958 0.9907 5.948e-08 -2.67e-08 -0.007482 4.483e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003379 -0.003192 -0.007754 0.006086 0.9699 0.9743 0.006499 0.8328 0.8242 0.01782 ] Network output: [ 0.9998 0.0004731 0.0007749 -1.336e-05 5.999e-06 -0.0009785 -1.007e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1981 -0.03393 -0.173 0.1891 0.9835 0.9932 0.2217 0.4398 0.871 0.7158 ] Network output: [ -0.01023 1.002 1.009 -2.01e-07 9.023e-08 0.008845 -1.515e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006142 0.0005034 0.004446 0.003589 0.9889 0.9919 0.006258 0.8609 0.895 0.01284 ] Network output: [ -0.0004994 0.00252 1.001 -4.196e-05 1.884e-05 0.9972 -3.162e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2102 0.09791 0.3402 0.1455 0.985 0.994 0.2109 0.444 0.8776 0.7101 ] Network output: [ 0.005269 -0.02519 0.9946 2.522e-05 -1.132e-05 1.02 1.901e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.104 0.09183 0.182 0.2003 0.9873 0.9919 0.1041 0.7569 0.8663 0.3056 ] Network output: [ -0.00501 0.02416 1.004 2.668e-05 -1.198e-05 0.9824 2.011e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09115 0.08921 0.1651 0.1955 0.9853 0.9912 0.09116 0.6816 0.8426 0.2458 ] Network output: [ 0.0001443 1 -0.0001845 3.571e-06 -1.603e-06 0.9999 2.691e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003702 Epoch 8267 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01049 0.9958 0.9907 5.858e-08 -2.63e-08 -0.007482 4.415e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003379 -0.003192 -0.007753 0.006085 0.9699 0.9743 0.006499 0.8328 0.8242 0.01782 ] Network output: [ 0.9998 0.0004727 0.0007744 -1.335e-05 5.993e-06 -0.0009776 -1.006e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1981 -0.03394 -0.173 0.1891 0.9835 0.9932 0.2217 0.4398 0.871 0.7158 ] Network output: [ -0.01023 1.002 1.009 -2.014e-07 9.044e-08 0.008843 -1.518e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006143 0.0005034 0.004446 0.003588 0.9889 0.9919 0.006259 0.8609 0.895 0.01284 ] Network output: [ -0.0004991 0.00252 1.001 -4.191e-05 1.882e-05 0.9972 -3.159e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2102 0.09792 0.3402 0.1455 0.985 0.994 0.2109 0.444 0.8776 0.7101 ] Network output: [ 0.005267 -0.02518 0.9946 2.519e-05 -1.131e-05 1.02 1.899e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1041 0.09183 0.182 0.2003 0.9873 0.9919 0.1041 0.7569 0.8663 0.3056 ] Network output: [ -0.005008 0.02415 1.004 2.665e-05 -1.197e-05 0.9824 2.009e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09115 0.08921 0.1651 0.1955 0.9853 0.9912 0.09116 0.6816 0.8426 0.2458 ] Network output: [ 0.0001442 1 -0.0001842 3.568e-06 -1.602e-06 0.9999 2.689e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00037 Epoch 8268 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01049 0.9958 0.9907 5.769e-08 -2.59e-08 -0.007482 4.348e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003379 -0.003192 -0.007752 0.006085 0.9699 0.9743 0.006499 0.8328 0.8242 0.01782 ] Network output: [ 0.9998 0.0004723 0.0007739 -1.334e-05 5.987e-06 -0.0009768 -1.005e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1981 -0.03394 -0.173 0.1891 0.9835 0.9932 0.2217 0.4398 0.871 0.7158 ] Network output: [ -0.01023 1.002 1.009 -2.019e-07 9.065e-08 0.008841 -1.522e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006143 0.0005035 0.004446 0.003588 0.9889 0.9919 0.00626 0.8609 0.895 0.01284 ] Network output: [ -0.0004988 0.002519 1.001 -4.187e-05 1.88e-05 0.9972 -3.155e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2102 0.09792 0.3402 0.1455 0.985 0.994 0.2109 0.444 0.8776 0.71 ] Network output: [ 0.005265 -0.02517 0.9946 2.517e-05 -1.13e-05 1.02 1.897e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1041 0.09184 0.182 0.2003 0.9873 0.9919 0.1041 0.7569 0.8663 0.3056 ] Network output: [ -0.005006 0.02414 1.004 2.663e-05 -1.195e-05 0.9824 2.007e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09115 0.08922 0.1651 0.1955 0.9853 0.9912 0.09117 0.6815 0.8426 0.2458 ] Network output: [ 0.0001441 1 -0.000184 3.564e-06 -1.6e-06 0.9999 2.686e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003698 Epoch 8269 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01049 0.9958 0.9907 5.68e-08 -2.55e-08 -0.007482 4.28e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003379 -0.003192 -0.007751 0.006084 0.9699 0.9743 0.0065 0.8328 0.8242 0.01782 ] Network output: [ 0.9998 0.0004719 0.0007735 -1.332e-05 5.981e-06 -0.000976 -1.004e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1981 -0.03394 -0.1729 0.189 0.9835 0.9932 0.2217 0.4398 0.871 0.7158 ] Network output: [ -0.01023 1.002 1.009 -2.024e-07 9.085e-08 0.00884 -1.525e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006144 0.0005036 0.004446 0.003587 0.9889 0.9919 0.00626 0.8609 0.895 0.01284 ] Network output: [ -0.0004985 0.002518 1.001 -4.183e-05 1.878e-05 0.9972 -3.152e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2102 0.09793 0.3402 0.1455 0.985 0.994 0.2109 0.444 0.8776 0.71 ] Network output: [ 0.005264 -0.02516 0.9946 2.514e-05 -1.129e-05 1.02 1.895e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1041 0.09184 0.182 0.2003 0.9873 0.9919 0.1041 0.7569 0.8663 0.3056 ] Network output: [ -0.005005 0.02413 1.004 2.66e-05 -1.194e-05 0.9824 2.005e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09115 0.08922 0.1651 0.1955 0.9853 0.9912 0.09117 0.6815 0.8426 0.2458 ] Network output: [ 0.0001441 1 -0.0001838 3.56e-06 -1.598e-06 0.9999 2.683e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003696 Epoch 8270 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01049 0.9958 0.9907 5.591e-08 -2.51e-08 -0.007483 4.213e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003379 -0.003192 -0.00775 0.006083 0.9699 0.9743 0.0065 0.8328 0.8242 0.01781 ] Network output: [ 0.9998 0.0004715 0.000773 -1.331e-05 5.975e-06 -0.0009752 -1.003e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1981 -0.03394 -0.1729 0.189 0.9835 0.9932 0.2217 0.4398 0.871 0.7158 ] Network output: [ -0.01023 1.002 1.009 -2.028e-07 9.106e-08 0.008838 -1.529e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006145 0.0005037 0.004446 0.003587 0.9889 0.9919 0.006261 0.8609 0.895 0.01284 ] Network output: [ -0.0004982 0.002517 1.001 -4.178e-05 1.876e-05 0.9972 -3.149e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2102 0.09794 0.3402 0.1455 0.985 0.994 0.2109 0.444 0.8776 0.71 ] Network output: [ 0.005262 -0.02515 0.9946 2.512e-05 -1.128e-05 1.02 1.893e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1041 0.09185 0.182 0.2003 0.9873 0.9919 0.1041 0.7569 0.8663 0.3056 ] Network output: [ -0.005003 0.02412 1.004 2.657e-05 -1.193e-05 0.9824 2.003e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09116 0.08922 0.1651 0.1955 0.9853 0.9912 0.09117 0.6815 0.8426 0.2458 ] Network output: [ 0.000144 1 -0.0001836 3.557e-06 -1.597e-06 0.9999 2.681e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003694 Epoch 8271 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01048 0.9958 0.9907 5.502e-08 -2.47e-08 -0.007483 4.146e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00338 -0.003193 -0.007749 0.006083 0.9699 0.9743 0.0065 0.8328 0.8242 0.01781 ] Network output: [ 0.9998 0.0004711 0.0007726 -1.33e-05 5.969e-06 -0.0009743 -1.002e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1981 -0.03394 -0.1729 0.189 0.9835 0.9932 0.2217 0.4398 0.871 0.7158 ] Network output: [ -0.01023 1.002 1.009 -2.033e-07 9.127e-08 0.008836 -1.532e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006145 0.0005038 0.004446 0.003587 0.9889 0.9919 0.006261 0.8609 0.895 0.01284 ] Network output: [ -0.0004978 0.002516 1.001 -4.174e-05 1.874e-05 0.9972 -3.146e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2102 0.09794 0.3402 0.1455 0.985 0.994 0.2109 0.444 0.8776 0.71 ] Network output: [ 0.00526 -0.02514 0.9946 2.509e-05 -1.126e-05 1.02 1.891e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1041 0.09186 0.182 0.2003 0.9873 0.9919 0.1041 0.7568 0.8663 0.3056 ] Network output: [ -0.005001 0.02411 1.004 2.655e-05 -1.192e-05 0.9824 2.001e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09116 0.08922 0.1651 0.1955 0.9853 0.9912 0.09117 0.6815 0.8426 0.2458 ] Network output: [ 0.0001439 1 -0.0001834 3.553e-06 -1.595e-06 0.9999 2.678e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003692 Epoch 8272 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01048 0.9958 0.9907 5.413e-08 -2.43e-08 -0.007483 4.079e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00338 -0.003193 -0.007748 0.006082 0.9699 0.9743 0.006501 0.8328 0.8242 0.01781 ] Network output: [ 0.9998 0.0004707 0.0007721 -1.328e-05 5.963e-06 -0.0009735 -1.001e-05 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1981 -0.03394 -0.1729 0.189 0.9835 0.9932 0.2217 0.4397 0.8709 0.7158 ] Network output: [ -0.01023 1.002 1.009 -2.037e-07 9.147e-08 0.008835 -1.536e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006146 0.0005039 0.004446 0.003586 0.9889 0.9919 0.006262 0.8609 0.895 0.01284 ] Network output: [ -0.0004975 0.002515 1.001 -4.17e-05 1.872e-05 0.9972 -3.142e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2103 0.09795 0.3402 0.1455 0.985 0.994 0.2109 0.444 0.8776 0.71 ] Network output: [ 0.005258 -0.02513 0.9946 2.506e-05 -1.125e-05 1.02 1.889e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1041 0.09186 0.182 0.2003 0.9873 0.9919 0.1042 0.7568 0.8662 0.3056 ] Network output: [ -0.004999 0.0241 1.004 2.652e-05 -1.191e-05 0.9824 1.999e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09116 0.08922 0.1651 0.1955 0.9853 0.9912 0.09117 0.6815 0.8426 0.2458 ] Network output: [ 0.0001439 1 -0.0001832 3.55e-06 -1.594e-06 0.9999 2.675e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000369 Epoch 8273 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01048 0.9958 0.9907 5.324e-08 -2.39e-08 -0.007483 4.013e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00338 -0.003193 -0.007747 0.006081 0.9699 0.9743 0.006501 0.8328 0.8242 0.01781 ] Network output: [ 0.9998 0.0004703 0.0007716 -1.327e-05 5.957e-06 -0.0009727 -9.999e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1981 -0.03395 -0.1729 0.189 0.9835 0.9932 0.2217 0.4397 0.8709 0.7158 ] Network output: [ -0.01022 1.002 1.009 -2.042e-07 9.167e-08 0.008833 -1.539e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006146 0.000504 0.004446 0.003586 0.9889 0.9919 0.006263 0.8609 0.895 0.01284 ] Network output: [ -0.0004972 0.002514 1.001 -4.165e-05 1.87e-05 0.9972 -3.139e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2103 0.09795 0.3403 0.1455 0.985 0.994 0.211 0.444 0.8776 0.71 ] Network output: [ 0.005257 -0.02512 0.9946 2.504e-05 -1.124e-05 1.02 1.887e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1041 0.09187 0.182 0.2003 0.9873 0.9919 0.1042 0.7568 0.8662 0.3056 ] Network output: [ -0.004997 0.02409 1.004 2.649e-05 -1.189e-05 0.9825 1.997e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09116 0.08923 0.1651 0.1955 0.9853 0.9912 0.09118 0.6814 0.8425 0.2458 ] Network output: [ 0.0001438 1 -0.000183 3.546e-06 -1.592e-06 0.9999 2.672e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003688 Epoch 8274 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01048 0.9958 0.9907 5.236e-08 -2.351e-08 -0.007484 3.946e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00338 -0.003193 -0.007746 0.006081 0.9699 0.9743 0.006501 0.8328 0.8242 0.01781 ] Network output: [ 0.9998 0.0004699 0.0007712 -1.325e-05 5.95e-06 -0.0009719 -9.989e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1981 -0.03395 -0.1729 0.189 0.9835 0.9932 0.2217 0.4397 0.8709 0.7158 ] Network output: [ -0.01022 1.002 1.009 -2.047e-07 9.188e-08 0.008831 -1.542e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006147 0.000504 0.004446 0.003586 0.9889 0.9919 0.006263 0.8609 0.895 0.01284 ] Network output: [ -0.0004969 0.002513 1.001 -4.161e-05 1.868e-05 0.9972 -3.136e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2103 0.09796 0.3403 0.1455 0.985 0.994 0.211 0.4439 0.8776 0.71 ] Network output: [ 0.005255 -0.02511 0.9946 2.501e-05 -1.123e-05 1.02 1.885e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1041 0.09187 0.182 0.2003 0.9873 0.9919 0.1042 0.7568 0.8662 0.3056 ] Network output: [ -0.004996 0.02408 1.004 2.647e-05 -1.188e-05 0.9825 1.995e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09116 0.08923 0.1651 0.1955 0.9853 0.9912 0.09118 0.6814 0.8425 0.2458 ] Network output: [ 0.0001437 1 -0.0001827 3.542e-06 -1.59e-06 0.9999 2.67e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003686 Epoch 8275 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01048 0.9958 0.9907 5.148e-08 -2.311e-08 -0.007484 3.88e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00338 -0.003193 -0.007745 0.00608 0.9699 0.9743 0.006501 0.8328 0.8242 0.01781 ] Network output: [ 0.9998 0.0004695 0.0007707 -1.324e-05 5.944e-06 -0.0009711 -9.979e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1981 -0.03395 -0.1729 0.189 0.9835 0.9932 0.2217 0.4397 0.8709 0.7158 ] Network output: [ -0.01022 1.002 1.009 -2.051e-07 9.208e-08 0.008829 -1.546e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006147 0.0005041 0.004446 0.003585 0.9889 0.9919 0.006264 0.8609 0.895 0.01283 ] Network output: [ -0.0004966 0.002512 1.001 -4.157e-05 1.866e-05 0.9972 -3.133e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2103 0.09797 0.3403 0.1455 0.985 0.994 0.211 0.4439 0.8776 0.71 ] Network output: [ 0.005253 -0.02511 0.9946 2.499e-05 -1.122e-05 1.02 1.883e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1041 0.09188 0.1821 0.2003 0.9873 0.9919 0.1042 0.7568 0.8662 0.3056 ] Network output: [ -0.004994 0.02407 1.004 2.644e-05 -1.187e-05 0.9825 1.993e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09117 0.08923 0.1651 0.1955 0.9853 0.9912 0.09118 0.6814 0.8425 0.2458 ] Network output: [ 0.0001436 1 -0.0001825 3.539e-06 -1.589e-06 0.9999 2.667e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003683 Epoch 8276 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01048 0.9958 0.9907 5.06e-08 -2.272e-08 -0.007484 3.814e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00338 -0.003193 -0.007744 0.00608 0.9699 0.9743 0.006502 0.8327 0.8242 0.01781 ] Network output: [ 0.9998 0.0004692 0.0007703 -1.323e-05 5.938e-06 -0.0009702 -9.969e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1981 -0.03395 -0.1728 0.189 0.9835 0.9932 0.2218 0.4397 0.8709 0.7158 ] Network output: [ -0.01022 1.002 1.009 -2.056e-07 9.229e-08 0.008828 -1.549e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006148 0.0005042 0.004446 0.003585 0.9889 0.9919 0.006264 0.8609 0.895 0.01283 ] Network output: [ -0.0004963 0.002512 1.001 -4.152e-05 1.864e-05 0.9972 -3.129e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2103 0.09797 0.3403 0.1455 0.985 0.994 0.211 0.4439 0.8776 0.71 ] Network output: [ 0.005252 -0.0251 0.9946 2.496e-05 -1.121e-05 1.02 1.881e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1041 0.09188 0.1821 0.2003 0.9873 0.9919 0.1042 0.7568 0.8662 0.3056 ] Network output: [ -0.004992 0.02406 1.004 2.642e-05 -1.186e-05 0.9825 1.991e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09117 0.08923 0.1651 0.1955 0.9853 0.9912 0.09118 0.6814 0.8425 0.2458 ] Network output: [ 0.0001436 1 -0.0001823 3.535e-06 -1.587e-06 0.9999 2.664e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003681 Epoch 8277 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01048 0.9958 0.9907 4.973e-08 -2.232e-08 -0.007484 3.747e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00338 -0.003193 -0.007743 0.006079 0.9699 0.9743 0.006502 0.8327 0.8242 0.0178 ] Network output: [ 0.9998 0.0004688 0.0007698 -1.321e-05 5.932e-06 -0.0009694 -9.958e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1982 -0.03395 -0.1728 0.189 0.9835 0.9932 0.2218 0.4397 0.8709 0.7158 ] Network output: [ -0.01022 1.002 1.009 -2.06e-07 9.249e-08 0.008826 -1.553e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006149 0.0005043 0.004446 0.003584 0.9889 0.9919 0.006265 0.8608 0.895 0.01283 ] Network output: [ -0.000496 0.002511 1.001 -4.148e-05 1.862e-05 0.9972 -3.126e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2103 0.09798 0.3403 0.1455 0.985 0.994 0.211 0.4439 0.8776 0.71 ] Network output: [ 0.00525 -0.02509 0.9946 2.494e-05 -1.12e-05 1.02 1.879e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1041 0.09189 0.1821 0.2002 0.9873 0.9919 0.1042 0.7567 0.8662 0.3056 ] Network output: [ -0.00499 0.02405 1.004 2.639e-05 -1.185e-05 0.9825 1.989e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09117 0.08923 0.1651 0.1955 0.9853 0.9912 0.09118 0.6813 0.8425 0.2458 ] Network output: [ 0.0001435 1 -0.0001821 3.532e-06 -1.586e-06 0.9999 2.662e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003679 Epoch 8278 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01047 0.9958 0.9907 4.885e-08 -2.193e-08 -0.007484 3.682e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00338 -0.003194 -0.007742 0.006078 0.9699 0.9743 0.006502 0.8327 0.8242 0.0178 ] Network output: [ 0.9998 0.0004684 0.0007693 -1.32e-05 5.926e-06 -0.0009686 -9.948e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1982 -0.03396 -0.1728 0.189 0.9835 0.9932 0.2218 0.4397 0.8709 0.7158 ] Network output: [ -0.01022 1.002 1.009 -2.065e-07 9.269e-08 0.008824 -1.556e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006149 0.0005044 0.004446 0.003584 0.9889 0.9919 0.006266 0.8608 0.895 0.01283 ] Network output: [ -0.0004956 0.00251 1.001 -4.144e-05 1.86e-05 0.9972 -3.123e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2103 0.09798 0.3403 0.1455 0.985 0.994 0.211 0.4439 0.8776 0.71 ] Network output: [ 0.005248 -0.02508 0.9946 2.491e-05 -1.118e-05 1.02 1.877e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1041 0.0919 0.1821 0.2002 0.9873 0.9919 0.1042 0.7567 0.8662 0.3056 ] Network output: [ -0.004988 0.02404 1.004 2.636e-05 -1.184e-05 0.9825 1.987e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09117 0.08924 0.1651 0.1955 0.9853 0.9912 0.09119 0.6813 0.8425 0.2458 ] Network output: [ 0.0001434 1 -0.0001819 3.528e-06 -1.584e-06 0.9999 2.659e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003677 Epoch 8279 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01047 0.9958 0.9907 4.798e-08 -2.154e-08 -0.007485 3.616e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003381 -0.003194 -0.007741 0.006078 0.9699 0.9743 0.006503 0.8327 0.8242 0.0178 ] Network output: [ 0.9998 0.000468 0.0007689 -1.319e-05 5.92e-06 -0.0009678 -9.938e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1982 -0.03396 -0.1728 0.189 0.9835 0.9932 0.2218 0.4397 0.8709 0.7157 ] Network output: [ -0.01022 1.002 1.009 -2.069e-07 9.289e-08 0.008823 -1.559e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00615 0.0005045 0.004446 0.003584 0.9889 0.9919 0.006266 0.8608 0.895 0.01283 ] Network output: [ -0.0004953 0.002509 1.001 -4.139e-05 1.858e-05 0.9972 -3.12e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2103 0.09799 0.3403 0.1455 0.985 0.994 0.211 0.4439 0.8776 0.71 ] Network output: [ 0.005246 -0.02507 0.9946 2.489e-05 -1.117e-05 1.02 1.875e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1041 0.0919 0.1821 0.2002 0.9873 0.9919 0.1042 0.7567 0.8662 0.3056 ] Network output: [ -0.004987 0.02403 1.004 2.634e-05 -1.182e-05 0.9825 1.985e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09117 0.08924 0.1651 0.1955 0.9853 0.9912 0.09119 0.6813 0.8425 0.2458 ] Network output: [ 0.0001434 1 -0.0001817 3.525e-06 -1.582e-06 0.9999 2.656e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003675 Epoch 8280 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01047 0.9958 0.9908 4.711e-08 -2.115e-08 -0.007485 3.55e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003381 -0.003194 -0.00774 0.006077 0.9699 0.9743 0.006503 0.8327 0.8242 0.0178 ] Network output: [ 0.9998 0.0004676 0.0007684 -1.317e-05 5.914e-06 -0.000967 -9.928e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1982 -0.03396 -0.1728 0.189 0.9835 0.9932 0.2218 0.4397 0.8709 0.7157 ] Network output: [ -0.01022 1.002 1.009 -2.074e-07 9.309e-08 0.008821 -1.563e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00615 0.0005046 0.004446 0.003583 0.9889 0.9919 0.006267 0.8608 0.895 0.01283 ] Network output: [ -0.000495 0.002508 1.001 -4.135e-05 1.856e-05 0.9972 -3.116e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2103 0.09799 0.3403 0.1455 0.985 0.994 0.211 0.4439 0.8776 0.71 ] Network output: [ 0.005245 -0.02506 0.9946 2.486e-05 -1.116e-05 1.02 1.874e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1041 0.09191 0.1821 0.2002 0.9873 0.9919 0.1042 0.7567 0.8662 0.3056 ] Network output: [ -0.004985 0.02402 1.004 2.631e-05 -1.181e-05 0.9825 1.983e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09118 0.08924 0.1651 0.1955 0.9853 0.9912 0.09119 0.6813 0.8425 0.2458 ] Network output: [ 0.0001433 1 -0.0001815 3.521e-06 -1.581e-06 0.9999 2.654e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003673 Epoch 8281 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01047 0.9958 0.9908 4.624e-08 -2.076e-08 -0.007485 3.485e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003381 -0.003194 -0.007739 0.006077 0.9699 0.9743 0.006503 0.8327 0.8242 0.0178 ] Network output: [ 0.9998 0.0004672 0.000768 -1.316e-05 5.908e-06 -0.0009662 -9.918e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1982 -0.03396 -0.1728 0.189 0.9835 0.9932 0.2218 0.4396 0.8709 0.7157 ] Network output: [ -0.01022 1.002 1.009 -2.078e-07 9.329e-08 0.008819 -1.566e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006151 0.0005047 0.004446 0.003583 0.9889 0.9919 0.006267 0.8608 0.895 0.01283 ] Network output: [ -0.0004947 0.002507 1.001 -4.131e-05 1.855e-05 0.9972 -3.113e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2104 0.098 0.3403 0.1455 0.985 0.994 0.211 0.4439 0.8775 0.71 ] Network output: [ 0.005243 -0.02505 0.9946 2.484e-05 -1.115e-05 1.02 1.872e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1041 0.09191 0.1821 0.2002 0.9873 0.9919 0.1042 0.7567 0.8662 0.3056 ] Network output: [ -0.004983 0.02401 1.004 2.628e-05 -1.18e-05 0.9825 1.981e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09118 0.08924 0.1651 0.1955 0.9853 0.9912 0.09119 0.6813 0.8425 0.2458 ] Network output: [ 0.0001432 1 -0.0001813 3.517e-06 -1.579e-06 0.9999 2.651e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003671 Epoch 8282 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01047 0.9958 0.9908 4.537e-08 -2.037e-08 -0.007485 3.419e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003381 -0.003194 -0.007738 0.006076 0.9699 0.9743 0.006504 0.8327 0.8242 0.0178 ] Network output: [ 0.9998 0.0004668 0.0007675 -1.315e-05 5.902e-06 -0.0009653 -9.907e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1982 -0.03396 -0.1728 0.189 0.9835 0.9932 0.2218 0.4396 0.8709 0.7157 ] Network output: [ -0.01022 1.002 1.009 -2.082e-07 9.349e-08 0.008818 -1.569e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006151 0.0005047 0.004446 0.003583 0.9889 0.9919 0.006268 0.8608 0.8949 0.01283 ] Network output: [ -0.0004944 0.002506 1.001 -4.127e-05 1.853e-05 0.9972 -3.11e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2104 0.09801 0.3403 0.1454 0.985 0.994 0.2111 0.4439 0.8775 0.71 ] Network output: [ 0.005241 -0.02504 0.9946 2.481e-05 -1.114e-05 1.02 1.87e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1041 0.09192 0.1821 0.2002 0.9873 0.9919 0.1042 0.7566 0.8662 0.3056 ] Network output: [ -0.004981 0.024 1.004 2.626e-05 -1.179e-05 0.9825 1.979e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09118 0.08924 0.1651 0.1955 0.9853 0.9912 0.09119 0.6812 0.8425 0.2458 ] Network output: [ 0.0001432 1 -0.0001811 3.514e-06 -1.577e-06 0.9999 2.648e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003669 Epoch 8283 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01047 0.9958 0.9908 4.451e-08 -1.998e-08 -0.007485 3.354e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003381 -0.003194 -0.007737 0.006075 0.9699 0.9743 0.006504 0.8327 0.8242 0.0178 ] Network output: [ 0.9998 0.0004664 0.000767 -1.313e-05 5.896e-06 -0.0009645 -9.897e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1982 -0.03396 -0.1727 0.189 0.9835 0.9932 0.2218 0.4396 0.8709 0.7157 ] Network output: [ -0.01021 1.002 1.009 -2.087e-07 9.369e-08 0.008816 -1.573e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006152 0.0005048 0.004446 0.003582 0.9889 0.9919 0.006269 0.8608 0.8949 0.01283 ] Network output: [ -0.0004941 0.002505 1.001 -4.122e-05 1.851e-05 0.9972 -3.107e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2104 0.09801 0.3403 0.1454 0.985 0.994 0.2111 0.4438 0.8775 0.71 ] Network output: [ 0.005239 -0.02503 0.9946 2.478e-05 -1.113e-05 1.02 1.868e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1042 0.09193 0.1821 0.2002 0.9873 0.9919 0.1042 0.7566 0.8662 0.3056 ] Network output: [ -0.004979 0.02399 1.004 2.623e-05 -1.178e-05 0.9825 1.977e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09118 0.08925 0.1651 0.1955 0.9853 0.9912 0.0912 0.6812 0.8425 0.2458 ] Network output: [ 0.0001431 1 -0.0001808 3.51e-06 -1.576e-06 0.9999 2.645e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003667 Epoch 8284 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01047 0.9958 0.9908 4.365e-08 -1.959e-08 -0.007486 3.289e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003381 -0.003194 -0.007736 0.006075 0.9699 0.9743 0.006504 0.8327 0.8242 0.0178 ] Network output: [ 0.9998 0.000466 0.0007666 -1.312e-05 5.89e-06 -0.0009637 -9.887e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1982 -0.03397 -0.1727 0.189 0.9835 0.9932 0.2218 0.4396 0.8709 0.7157 ] Network output: [ -0.01021 1.002 1.009 -2.091e-07 9.388e-08 0.008814 -1.576e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006153 0.0005049 0.004446 0.003582 0.9889 0.9919 0.006269 0.8608 0.8949 0.01282 ] Network output: [ -0.0004938 0.002504 1.001 -4.118e-05 1.849e-05 0.9972 -3.104e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2104 0.09802 0.3403 0.1454 0.985 0.994 0.2111 0.4438 0.8775 0.71 ] Network output: [ 0.005238 -0.02502 0.9946 2.476e-05 -1.112e-05 1.02 1.866e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1042 0.09193 0.1821 0.2002 0.9873 0.9919 0.1042 0.7566 0.8662 0.3056 ] Network output: [ -0.004978 0.02398 1.004 2.621e-05 -1.177e-05 0.9825 1.975e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09118 0.08925 0.1651 0.1955 0.9853 0.9912 0.0912 0.6812 0.8425 0.2458 ] Network output: [ 0.000143 1 -0.0001806 3.507e-06 -1.574e-06 0.9999 2.643e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003665 Epoch 8285 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01046 0.9958 0.9908 4.279e-08 -1.921e-08 -0.007486 3.225e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003381 -0.003195 -0.007735 0.006074 0.9699 0.9743 0.006504 0.8327 0.8242 0.01779 ] Network output: [ 0.9998 0.0004656 0.0007661 -1.311e-05 5.884e-06 -0.0009629 -9.877e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1982 -0.03397 -0.1727 0.189 0.9835 0.9932 0.2219 0.4396 0.8709 0.7157 ] Network output: [ -0.01021 1.002 1.009 -2.096e-07 9.408e-08 0.008812 -1.579e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006153 0.000505 0.004446 0.003581 0.9889 0.9919 0.00627 0.8608 0.8949 0.01282 ] Network output: [ -0.0004935 0.002504 1.001 -4.114e-05 1.847e-05 0.9972 -3.1e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2104 0.09802 0.3404 0.1454 0.985 0.994 0.2111 0.4438 0.8775 0.7099 ] Network output: [ 0.005236 -0.02501 0.9946 2.473e-05 -1.11e-05 1.02 1.864e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1042 0.09194 0.1821 0.2002 0.9873 0.9919 0.1042 0.7566 0.8662 0.3056 ] Network output: [ -0.004976 0.02397 1.004 2.618e-05 -1.175e-05 0.9825 1.973e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09119 0.08925 0.1651 0.1955 0.9853 0.9912 0.0912 0.6812 0.8425 0.2458 ] Network output: [ 0.0001429 1 -0.0001804 3.503e-06 -1.573e-06 0.9999 2.64e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003663 Epoch 8286 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01046 0.9958 0.9908 4.193e-08 -1.882e-08 -0.007486 3.16e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003381 -0.003195 -0.007734 0.006074 0.9699 0.9743 0.006505 0.8327 0.8242 0.01779 ] Network output: [ 0.9998 0.0004653 0.0007657 -1.309e-05 5.878e-06 -0.0009621 -9.867e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1982 -0.03397 -0.1727 0.1889 0.9835 0.9932 0.2219 0.4396 0.8709 0.7157 ] Network output: [ -0.01021 1.002 1.009 -2.1e-07 9.428e-08 0.008811 -1.583e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006154 0.0005051 0.004446 0.003581 0.9889 0.9919 0.00627 0.8608 0.8949 0.01282 ] Network output: [ -0.0004931 0.002503 1.001 -4.11e-05 1.845e-05 0.9972 -3.097e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2104 0.09803 0.3404 0.1454 0.985 0.994 0.2111 0.4438 0.8775 0.7099 ] Network output: [ 0.005234 -0.02501 0.9946 2.471e-05 -1.109e-05 1.02 1.862e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1042 0.09194 0.1821 0.2002 0.9873 0.9919 0.1042 0.7566 0.8662 0.3056 ] Network output: [ -0.004974 0.02396 1.004 2.615e-05 -1.174e-05 0.9825 1.971e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09119 0.08925 0.1651 0.1955 0.9853 0.9912 0.0912 0.6812 0.8425 0.2458 ] Network output: [ 0.0001429 1 -0.0001802 3.5e-06 -1.571e-06 0.9999 2.637e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003661 Epoch 8287 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01046 0.9958 0.9908 4.107e-08 -1.844e-08 -0.007486 3.095e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003382 -0.003195 -0.007734 0.006073 0.9699 0.9743 0.006505 0.8327 0.8241 0.01779 ] Network output: [ 0.9998 0.0004649 0.0007652 -1.308e-05 5.872e-06 -0.0009613 -9.857e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1982 -0.03397 -0.1727 0.1889 0.9835 0.9932 0.2219 0.4396 0.8709 0.7157 ] Network output: [ -0.01021 1.002 1.009 -2.104e-07 9.447e-08 0.008809 -1.586e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006154 0.0005052 0.004446 0.003581 0.9889 0.9919 0.006271 0.8608 0.8949 0.01282 ] Network output: [ -0.0004928 0.002502 1.001 -4.105e-05 1.843e-05 0.9972 -3.094e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2104 0.09804 0.3404 0.1454 0.985 0.994 0.2111 0.4438 0.8775 0.7099 ] Network output: [ 0.005232 -0.025 0.9946 2.468e-05 -1.108e-05 1.02 1.86e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1042 0.09195 0.1821 0.2002 0.9873 0.9919 0.1042 0.7565 0.8662 0.3056 ] Network output: [ -0.004972 0.02395 1.004 2.613e-05 -1.173e-05 0.9825 1.969e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09119 0.08925 0.1651 0.1955 0.9853 0.9912 0.0912 0.6811 0.8425 0.2458 ] Network output: [ 0.0001428 1 -0.00018 3.496e-06 -1.57e-06 0.9999 2.635e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003658 Epoch 8288 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01046 0.9958 0.9908 4.022e-08 -1.806e-08 -0.007486 3.031e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003382 -0.003195 -0.007733 0.006072 0.9699 0.9743 0.006505 0.8327 0.8241 0.01779 ] Network output: [ 0.9998 0.0004645 0.0007648 -1.307e-05 5.866e-06 -0.0009605 -9.847e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1983 -0.03397 -0.1727 0.1889 0.9835 0.9932 0.2219 0.4396 0.8709 0.7157 ] Network output: [ -0.01021 1.002 1.009 -2.109e-07 9.467e-08 0.008807 -1.589e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006155 0.0005053 0.004446 0.00358 0.9889 0.9919 0.006271 0.8608 0.8949 0.01282 ] Network output: [ -0.0004925 0.002501 1.001 -4.101e-05 1.841e-05 0.9972 -3.091e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2104 0.09804 0.3404 0.1454 0.985 0.994 0.2111 0.4438 0.8775 0.7099 ] Network output: [ 0.005231 -0.02499 0.9946 2.466e-05 -1.107e-05 1.02 1.858e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1042 0.09195 0.1821 0.2002 0.9873 0.9919 0.1043 0.7565 0.8662 0.3056 ] Network output: [ -0.004971 0.02394 1.004 2.61e-05 -1.172e-05 0.9825 1.967e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09119 0.08926 0.1651 0.1955 0.9853 0.9912 0.09121 0.6811 0.8425 0.2458 ] Network output: [ 0.0001427 1 -0.0001798 3.493e-06 -1.568e-06 0.9999 2.632e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003656 Epoch 8289 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01046 0.9958 0.9908 3.937e-08 -1.767e-08 -0.007487 2.967e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003382 -0.003195 -0.007732 0.006072 0.9699 0.9743 0.006506 0.8327 0.8241 0.01779 ] Network output: [ 0.9998 0.0004641 0.0007643 -1.305e-05 5.86e-06 -0.0009597 -9.837e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1983 -0.03397 -0.1727 0.1889 0.9835 0.9932 0.2219 0.4396 0.8709 0.7157 ] Network output: [ -0.01021 1.002 1.009 -2.113e-07 9.486e-08 0.008806 -1.593e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006156 0.0005053 0.004446 0.00358 0.9889 0.9919 0.006272 0.8608 0.8949 0.01282 ] Network output: [ -0.0004922 0.0025 1.001 -4.097e-05 1.839e-05 0.9972 -3.087e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2104 0.09805 0.3404 0.1454 0.985 0.994 0.2111 0.4438 0.8775 0.7099 ] Network output: [ 0.005229 -0.02498 0.9946 2.463e-05 -1.106e-05 1.02 1.856e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1042 0.09196 0.1821 0.2002 0.9873 0.9919 0.1043 0.7565 0.8662 0.3056 ] Network output: [ -0.004969 0.02394 1.004 2.608e-05 -1.171e-05 0.9825 1.965e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09119 0.08926 0.1651 0.1955 0.9853 0.9912 0.09121 0.6811 0.8424 0.2458 ] Network output: [ 0.0001427 1 -0.0001796 3.489e-06 -1.566e-06 0.9999 2.629e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003654 Epoch 8290 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01046 0.9958 0.9908 3.852e-08 -1.729e-08 -0.007487 2.903e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003382 -0.003195 -0.007731 0.006071 0.9699 0.9743 0.006506 0.8326 0.8241 0.01779 ] Network output: [ 0.9998 0.0004637 0.0007638 -1.304e-05 5.854e-06 -0.0009589 -9.826e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1983 -0.03398 -0.1726 0.1889 0.9835 0.9932 0.2219 0.4396 0.8709 0.7157 ] Network output: [ -0.01021 1.002 1.009 -2.117e-07 9.506e-08 0.008804 -1.596e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006156 0.0005054 0.004446 0.003579 0.9889 0.9919 0.006273 0.8607 0.8949 0.01282 ] Network output: [ -0.0004919 0.002499 1.001 -4.092e-05 1.837e-05 0.9972 -3.084e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2104 0.09805 0.3404 0.1454 0.985 0.994 0.2111 0.4438 0.8775 0.7099 ] Network output: [ 0.005227 -0.02497 0.9946 2.461e-05 -1.105e-05 1.02 1.855e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1042 0.09197 0.1821 0.2002 0.9873 0.9919 0.1043 0.7565 0.8662 0.3056 ] Network output: [ -0.004967 0.02393 1.004 2.605e-05 -1.17e-05 0.9825 1.963e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0912 0.08926 0.1651 0.1955 0.9853 0.9912 0.09121 0.6811 0.8424 0.2458 ] Network output: [ 0.0001426 1 -0.0001794 3.485e-06 -1.565e-06 0.9999 2.627e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003652 Epoch 8291 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01046 0.9958 0.9908 3.767e-08 -1.691e-08 -0.007487 2.839e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003382 -0.003195 -0.00773 0.00607 0.9699 0.9743 0.006506 0.8326 0.8241 0.01779 ] Network output: [ 0.9998 0.0004633 0.0007634 -1.303e-05 5.848e-06 -0.0009581 -9.816e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1983 -0.03398 -0.1726 0.1889 0.9835 0.9932 0.2219 0.4395 0.8709 0.7157 ] Network output: [ -0.01021 1.002 1.009 -2.122e-07 9.525e-08 0.008802 -1.599e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006157 0.0005055 0.004446 0.003579 0.9889 0.9919 0.006273 0.8607 0.8949 0.01282 ] Network output: [ -0.0004916 0.002498 1.001 -4.088e-05 1.835e-05 0.9972 -3.081e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2105 0.09806 0.3404 0.1454 0.985 0.994 0.2111 0.4438 0.8775 0.7099 ] Network output: [ 0.005225 -0.02496 0.9946 2.458e-05 -1.104e-05 1.02 1.853e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1042 0.09197 0.1821 0.2002 0.9873 0.9919 0.1043 0.7565 0.8661 0.3056 ] Network output: [ -0.004965 0.02392 1.004 2.602e-05 -1.168e-05 0.9825 1.961e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0912 0.08926 0.1651 0.1955 0.9853 0.9912 0.09121 0.6811 0.8424 0.2458 ] Network output: [ 0.0001425 1 -0.0001792 3.482e-06 -1.563e-06 0.9999 2.624e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000365 Epoch 8292 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01045 0.9958 0.9908 3.682e-08 -1.653e-08 -0.007487 2.775e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003382 -0.003196 -0.007729 0.00607 0.9699 0.9743 0.006507 0.8326 0.8241 0.01778 ] Network output: [ 0.9998 0.0004629 0.0007629 -1.301e-05 5.842e-06 -0.0009572 -9.806e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1983 -0.03398 -0.1726 0.1889 0.9835 0.9932 0.2219 0.4395 0.8709 0.7157 ] Network output: [ -0.0102 1.002 1.009 -2.126e-07 9.545e-08 0.008801 -1.602e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006157 0.0005056 0.004446 0.003579 0.9889 0.9919 0.006274 0.8607 0.8949 0.01282 ] Network output: [ -0.0004913 0.002497 1.001 -4.084e-05 1.833e-05 0.9972 -3.078e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2105 0.09807 0.3404 0.1454 0.985 0.994 0.2112 0.4438 0.8775 0.7099 ] Network output: [ 0.005224 -0.02495 0.9946 2.456e-05 -1.102e-05 1.02 1.851e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1042 0.09198 0.1821 0.2002 0.9873 0.9919 0.1043 0.7564 0.8661 0.3056 ] Network output: [ -0.004963 0.02391 1.004 2.6e-05 -1.167e-05 0.9825 1.959e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0912 0.08926 0.1651 0.1955 0.9853 0.9912 0.09121 0.681 0.8424 0.2458 ] Network output: [ 0.0001425 1 -0.000179 3.478e-06 -1.562e-06 0.9999 2.621e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003648 Epoch 8293 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01045 0.9958 0.9908 3.598e-08 -1.615e-08 -0.007487 2.711e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003382 -0.003196 -0.007728 0.006069 0.9699 0.9743 0.006507 0.8326 0.8241 0.01778 ] Network output: [ 0.9998 0.0004625 0.0007625 -1.3e-05 5.836e-06 -0.0009564 -9.796e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1983 -0.03398 -0.1726 0.1889 0.9835 0.9932 0.2219 0.4395 0.8709 0.7157 ] Network output: [ -0.0102 1.002 1.009 -2.13e-07 9.564e-08 0.008799 -1.605e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006158 0.0005057 0.004446 0.003578 0.9889 0.9919 0.006274 0.8607 0.8949 0.01282 ] Network output: [ -0.000491 0.002497 1.001 -4.08e-05 1.832e-05 0.9972 -3.075e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2105 0.09807 0.3404 0.1454 0.985 0.994 0.2112 0.4437 0.8775 0.7099 ] Network output: [ 0.005222 -0.02494 0.9946 2.453e-05 -1.101e-05 1.02 1.849e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1042 0.09198 0.1821 0.2002 0.9873 0.9919 0.1043 0.7564 0.8661 0.3056 ] Network output: [ -0.004962 0.0239 1.004 2.597e-05 -1.166e-05 0.9825 1.957e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0912 0.08927 0.1651 0.1955 0.9853 0.9912 0.09122 0.681 0.8424 0.2458 ] Network output: [ 0.0001424 1 -0.0001788 3.475e-06 -1.56e-06 0.9999 2.619e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003646 Epoch 8294 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01045 0.9958 0.9908 3.514e-08 -1.577e-08 -0.007488 2.648e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003382 -0.003196 -0.007727 0.006069 0.9699 0.9743 0.006507 0.8326 0.8241 0.01778 ] Network output: [ 0.9998 0.0004622 0.000762 -1.299e-05 5.83e-06 -0.0009556 -9.786e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1983 -0.03398 -0.1726 0.1889 0.9835 0.9932 0.222 0.4395 0.8709 0.7157 ] Network output: [ -0.0102 1.002 1.009 -2.135e-07 9.583e-08 0.008797 -1.609e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006158 0.0005058 0.004446 0.003578 0.9889 0.9919 0.006275 0.8607 0.8949 0.01281 ] Network output: [ -0.0004907 0.002496 1.001 -4.076e-05 1.83e-05 0.9972 -3.071e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2105 0.09808 0.3404 0.1454 0.985 0.994 0.2112 0.4437 0.8775 0.7099 ] Network output: [ 0.00522 -0.02493 0.9946 2.451e-05 -1.1e-05 1.02 1.847e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1042 0.09199 0.1821 0.2002 0.9873 0.9919 0.1043 0.7564 0.8661 0.3056 ] Network output: [ -0.00496 0.02389 1.004 2.595e-05 -1.165e-05 0.9825 1.955e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0912 0.08927 0.1651 0.1955 0.9853 0.9912 0.09122 0.681 0.8424 0.2458 ] Network output: [ 0.0001423 1 -0.0001785 3.471e-06 -1.558e-06 0.9999 2.616e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003644 Epoch 8295 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01045 0.9958 0.9908 3.43e-08 -1.54e-08 -0.007488 2.585e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003383 -0.003196 -0.007726 0.006068 0.9699 0.9743 0.006507 0.8326 0.8241 0.01778 ] Network output: [ 0.9998 0.0004618 0.0007616 -1.297e-05 5.824e-06 -0.0009548 -9.776e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1983 -0.03398 -0.1726 0.1889 0.9835 0.9932 0.222 0.4395 0.8709 0.7157 ] Network output: [ -0.0102 1.002 1.009 -2.139e-07 9.602e-08 0.008796 -1.612e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006159 0.0005059 0.004446 0.003578 0.9889 0.9919 0.006276 0.8607 0.8949 0.01281 ] Network output: [ -0.0004903 0.002495 1.001 -4.071e-05 1.828e-05 0.9972 -3.068e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2105 0.09808 0.3404 0.1454 0.985 0.994 0.2112 0.4437 0.8775 0.7099 ] Network output: [ 0.005219 -0.02492 0.9946 2.448e-05 -1.099e-05 1.02 1.845e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1042 0.09199 0.1821 0.2002 0.9873 0.9919 0.1043 0.7564 0.8661 0.3056 ] Network output: [ -0.004958 0.02388 1.004 2.592e-05 -1.164e-05 0.9826 1.954e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09121 0.08927 0.1651 0.1955 0.9853 0.9912 0.09122 0.681 0.8424 0.2458 ] Network output: [ 0.0001423 1 -0.0001783 3.468e-06 -1.557e-06 0.9999 2.613e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003642 Epoch 8296 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01045 0.9958 0.9908 3.346e-08 -1.502e-08 -0.007488 2.521e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003383 -0.003196 -0.007725 0.006067 0.9699 0.9743 0.006508 0.8326 0.8241 0.01778 ] Network output: [ 0.9998 0.0004614 0.0007611 -1.296e-05 5.818e-06 -0.000954 -9.766e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1983 -0.03399 -0.1726 0.1889 0.9835 0.9932 0.222 0.4395 0.8709 0.7156 ] Network output: [ -0.0102 1.002 1.009 -2.143e-07 9.621e-08 0.008794 -1.615e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00616 0.000506 0.004446 0.003577 0.9889 0.9919 0.006276 0.8607 0.8949 0.01281 ] Network output: [ -0.00049 0.002494 1.001 -4.067e-05 1.826e-05 0.9972 -3.065e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2105 0.09809 0.3405 0.1454 0.985 0.994 0.2112 0.4437 0.8775 0.7099 ] Network output: [ 0.005217 -0.02492 0.9946 2.446e-05 -1.098e-05 1.02 1.843e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1042 0.092 0.1821 0.2002 0.9873 0.9919 0.1043 0.7564 0.8661 0.3056 ] Network output: [ -0.004956 0.02387 1.004 2.59e-05 -1.163e-05 0.9826 1.952e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09121 0.08927 0.1651 0.1955 0.9853 0.9912 0.09122 0.6809 0.8424 0.2458 ] Network output: [ 0.0001422 1 -0.0001781 3.464e-06 -1.555e-06 0.9999 2.611e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000364 Epoch 8297 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01045 0.9958 0.9908 3.262e-08 -1.464e-08 -0.007488 2.458e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003383 -0.003196 -0.007724 0.006067 0.9699 0.9743 0.006508 0.8326 0.8241 0.01778 ] Network output: [ 0.9998 0.000461 0.0007607 -1.295e-05 5.812e-06 -0.0009532 -9.756e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1983 -0.03399 -0.1725 0.1889 0.9835 0.9932 0.222 0.4395 0.8709 0.7156 ] Network output: [ -0.0102 1.002 1.009 -2.147e-07 9.64e-08 0.008792 -1.618e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00616 0.000506 0.004446 0.003577 0.9889 0.9919 0.006277 0.8607 0.8949 0.01281 ] Network output: [ -0.0004897 0.002493 1.001 -4.063e-05 1.824e-05 0.9972 -3.062e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2105 0.09809 0.3405 0.1454 0.985 0.994 0.2112 0.4437 0.8775 0.7099 ] Network output: [ 0.005215 -0.02491 0.9946 2.443e-05 -1.097e-05 1.02 1.841e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1042 0.09201 0.1821 0.2002 0.9873 0.9919 0.1043 0.7563 0.8661 0.3056 ] Network output: [ -0.004955 0.02386 1.004 2.587e-05 -1.161e-05 0.9826 1.95e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09121 0.08927 0.1651 0.1955 0.9853 0.9912 0.09122 0.6809 0.8424 0.2458 ] Network output: [ 0.0001421 1 -0.0001779 3.461e-06 -1.554e-06 0.9999 2.608e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003638 Epoch 8298 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01045 0.9958 0.9908 3.179e-08 -1.427e-08 -0.007488 2.396e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003383 -0.003196 -0.007723 0.006066 0.9699 0.9743 0.006508 0.8326 0.8241 0.01778 ] Network output: [ 0.9998 0.0004606 0.0007602 -1.293e-05 5.806e-06 -0.0009524 -9.746e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1984 -0.03399 -0.1725 0.1889 0.9835 0.9932 0.222 0.4395 0.8709 0.7156 ] Network output: [ -0.0102 1.002 1.009 -2.152e-07 9.659e-08 0.008791 -1.621e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006161 0.0005061 0.004446 0.003576 0.9889 0.9919 0.006277 0.8607 0.8949 0.01281 ] Network output: [ -0.0004894 0.002492 1.001 -4.059e-05 1.822e-05 0.9972 -3.059e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2105 0.0981 0.3405 0.1454 0.985 0.994 0.2112 0.4437 0.8775 0.7099 ] Network output: [ 0.005213 -0.0249 0.9946 2.441e-05 -1.096e-05 1.02 1.839e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1042 0.09201 0.1821 0.2002 0.9873 0.9919 0.1043 0.7563 0.8661 0.3056 ] Network output: [ -0.004953 0.02385 1.004 2.584e-05 -1.16e-05 0.9826 1.948e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09121 0.08928 0.1651 0.1955 0.9853 0.9912 0.09123 0.6809 0.8424 0.2458 ] Network output: [ 0.000142 1 -0.0001777 3.457e-06 -1.552e-06 0.9999 2.605e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003636 Epoch 8299 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01044 0.9958 0.9908 3.096e-08 -1.39e-08 -0.007489 2.333e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003383 -0.003197 -0.007722 0.006066 0.9699 0.9743 0.006509 0.8326 0.8241 0.01778 ] Network output: [ 0.9998 0.0004602 0.0007598 -1.292e-05 5.8e-06 -0.0009516 -9.736e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1984 -0.03399 -0.1725 0.1889 0.9835 0.9932 0.222 0.4395 0.8709 0.7156 ] Network output: [ -0.0102 1.002 1.009 -2.156e-07 9.678e-08 0.008789 -1.625e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006161 0.0005062 0.004446 0.003576 0.9889 0.9919 0.006278 0.8607 0.8949 0.01281 ] Network output: [ -0.0004891 0.002491 1.001 -4.054e-05 1.82e-05 0.9972 -3.056e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2105 0.09811 0.3405 0.1454 0.985 0.994 0.2112 0.4437 0.8775 0.7099 ] Network output: [ 0.005212 -0.02489 0.9946 2.438e-05 -1.095e-05 1.02 1.838e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1043 0.09202 0.1821 0.2002 0.9873 0.9919 0.1043 0.7563 0.8661 0.3056 ] Network output: [ -0.004951 0.02384 1.004 2.582e-05 -1.159e-05 0.9826 1.946e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09122 0.08928 0.1651 0.1955 0.9853 0.9912 0.09123 0.6809 0.8424 0.2458 ] Network output: [ 0.000142 1 -0.0001775 3.454e-06 -1.55e-06 0.9999 2.603e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003634 Epoch 8300 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01044 0.9958 0.9908 3.012e-08 -1.352e-08 -0.007489 2.27e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003383 -0.003197 -0.007721 0.006065 0.9699 0.9743 0.006509 0.8326 0.8241 0.01777 ] Network output: [ 0.9998 0.0004598 0.0007593 -1.291e-05 5.794e-06 -0.0009508 -9.726e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1984 -0.03399 -0.1725 0.1889 0.9835 0.9932 0.222 0.4394 0.8709 0.7156 ] Network output: [ -0.0102 1.002 1.009 -2.16e-07 9.697e-08 0.008787 -1.628e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006162 0.0005063 0.004446 0.003576 0.9889 0.9919 0.006278 0.8607 0.8949 0.01281 ] Network output: [ -0.0004888 0.00249 1.001 -4.05e-05 1.818e-05 0.9972 -3.052e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2106 0.09811 0.3405 0.1454 0.985 0.994 0.2112 0.4437 0.8775 0.7099 ] Network output: [ 0.00521 -0.02488 0.9946 2.436e-05 -1.093e-05 1.02 1.836e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1043 0.09202 0.1821 0.2002 0.9873 0.9919 0.1043 0.7563 0.8661 0.3056 ] Network output: [ -0.004949 0.02383 1.004 2.579e-05 -1.158e-05 0.9826 1.944e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09122 0.08928 0.1651 0.1955 0.9853 0.9912 0.09123 0.6809 0.8424 0.2459 ] Network output: [ 0.0001419 1 -0.0001773 3.45e-06 -1.549e-06 0.9999 2.6e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003632 Epoch 8301 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01044 0.9958 0.9908 2.93e-08 -1.315e-08 -0.007489 2.208e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003383 -0.003197 -0.00772 0.006064 0.9699 0.9743 0.006509 0.8326 0.8241 0.01777 ] Network output: [ 0.9998 0.0004595 0.0007589 -1.289e-05 5.788e-06 -0.00095 -9.716e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1984 -0.03399 -0.1725 0.1889 0.9835 0.9932 0.222 0.4394 0.8709 0.7156 ] Network output: [ -0.01019 1.002 1.009 -2.164e-07 9.715e-08 0.008786 -1.631e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006162 0.0005064 0.004446 0.003575 0.9889 0.9919 0.006279 0.8607 0.8949 0.01281 ] Network output: [ -0.0004885 0.002489 1.001 -4.046e-05 1.816e-05 0.9972 -3.049e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2106 0.09812 0.3405 0.1454 0.985 0.994 0.2113 0.4437 0.8775 0.7098 ] Network output: [ 0.005208 -0.02487 0.9946 2.433e-05 -1.092e-05 1.02 1.834e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1043 0.09203 0.1821 0.2002 0.9873 0.9919 0.1043 0.7563 0.8661 0.3056 ] Network output: [ -0.004947 0.02382 1.004 2.577e-05 -1.157e-05 0.9826 1.942e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09122 0.08928 0.1651 0.1955 0.9853 0.9912 0.09123 0.6808 0.8424 0.2459 ] Network output: [ 0.0001418 1 -0.0001771 3.447e-06 -1.547e-06 0.9999 2.597e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000363 Epoch 8302 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01044 0.9958 0.9908 2.847e-08 -1.278e-08 -0.007489 2.146e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003383 -0.003197 -0.007719 0.006064 0.9699 0.9743 0.00651 0.8326 0.8241 0.01777 ] Network output: [ 0.9998 0.0004591 0.0007584 -1.288e-05 5.782e-06 -0.0009492 -9.706e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1984 -0.034 -0.1725 0.1889 0.9835 0.9932 0.222 0.4394 0.8709 0.7156 ] Network output: [ -0.01019 1.002 1.009 -2.168e-07 9.734e-08 0.008784 -1.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006163 0.0005065 0.004446 0.003575 0.9889 0.9919 0.00628 0.8607 0.8949 0.01281 ] Network output: [ -0.0004882 0.002489 1.001 -4.042e-05 1.814e-05 0.9972 -3.046e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2106 0.09812 0.3405 0.1454 0.985 0.994 0.2113 0.4436 0.8775 0.7098 ] Network output: [ 0.005206 -0.02486 0.9946 2.431e-05 -1.091e-05 1.02 1.832e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1043 0.09203 0.1821 0.2002 0.9873 0.9919 0.1043 0.7562 0.8661 0.3056 ] Network output: [ -0.004946 0.02381 1.004 2.574e-05 -1.156e-05 0.9826 1.94e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09122 0.08928 0.1651 0.1955 0.9853 0.9912 0.09123 0.6808 0.8424 0.2459 ] Network output: [ 0.0001418 1 -0.0001769 3.443e-06 -1.546e-06 0.9999 2.595e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003628 Epoch 8303 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01044 0.9958 0.9908 2.765e-08 -1.241e-08 -0.007489 2.084e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003384 -0.003197 -0.007718 0.006063 0.9699 0.9743 0.00651 0.8326 0.8241 0.01777 ] Network output: [ 0.9998 0.0004587 0.000758 -1.287e-05 5.776e-06 -0.0009484 -9.696e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1984 -0.034 -0.1725 0.1888 0.9835 0.9932 0.2221 0.4394 0.8709 0.7156 ] Network output: [ -0.01019 1.002 1.009 -2.172e-07 9.753e-08 0.008782 -1.637e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006164 0.0005066 0.004446 0.003575 0.9889 0.9919 0.00628 0.8606 0.8949 0.01281 ] Network output: [ -0.0004879 0.002488 1.001 -4.038e-05 1.813e-05 0.9972 -3.043e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2106 0.09813 0.3405 0.1454 0.985 0.994 0.2113 0.4436 0.8775 0.7098 ] Network output: [ 0.005205 -0.02485 0.9946 2.428e-05 -1.09e-05 1.02 1.83e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1043 0.09204 0.1821 0.2002 0.9873 0.9919 0.1043 0.7562 0.8661 0.3056 ] Network output: [ -0.004944 0.0238 1.004 2.572e-05 -1.154e-05 0.9826 1.938e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09122 0.08929 0.1651 0.1955 0.9853 0.9912 0.09124 0.6808 0.8424 0.2459 ] Network output: [ 0.0001417 1 -0.0001767 3.44e-06 -1.544e-06 0.9999 2.592e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003626 Epoch 8304 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01044 0.9958 0.9908 2.682e-08 -1.204e-08 -0.00749 2.022e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003384 -0.003197 -0.007718 0.006063 0.9699 0.9743 0.00651 0.8325 0.8241 0.01777 ] Network output: [ 0.9998 0.0004583 0.0007575 -1.285e-05 5.77e-06 -0.0009476 -9.686e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1984 -0.034 -0.1724 0.1888 0.9835 0.9932 0.2221 0.4394 0.8709 0.7156 ] Network output: [ -0.01019 1.002 1.009 -2.177e-07 9.771e-08 0.008781 -1.64e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006164 0.0005067 0.004446 0.003574 0.9889 0.9919 0.006281 0.8606 0.8949 0.0128 ] Network output: [ -0.0004876 0.002487 1.001 -4.033e-05 1.811e-05 0.9972 -3.04e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2106 0.09814 0.3405 0.1454 0.985 0.994 0.2113 0.4436 0.8775 0.7098 ] Network output: [ 0.005203 -0.02484 0.9946 2.426e-05 -1.089e-05 1.02 1.828e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1043 0.09205 0.1821 0.2002 0.9873 0.9919 0.1044 0.7562 0.8661 0.3056 ] Network output: [ -0.004942 0.02379 1.004 2.569e-05 -1.153e-05 0.9826 1.936e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09123 0.08929 0.1651 0.1955 0.9853 0.9912 0.09124 0.6808 0.8423 0.2459 ] Network output: [ 0.0001416 1 -0.0001765 3.436e-06 -1.543e-06 0.9999 2.59e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003624 Epoch 8305 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01044 0.9958 0.9908 2.6e-08 -1.167e-08 -0.00749 1.96e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003384 -0.003197 -0.007717 0.006062 0.9699 0.9743 0.00651 0.8325 0.8241 0.01777 ] Network output: [ 0.9998 0.0004579 0.0007571 -1.284e-05 5.764e-06 -0.0009468 -9.676e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1984 -0.034 -0.1724 0.1888 0.9835 0.9932 0.2221 0.4394 0.8708 0.7156 ] Network output: [ -0.01019 1.002 1.009 -2.181e-07 9.79e-08 0.008779 -1.643e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006165 0.0005067 0.004446 0.003574 0.9889 0.9919 0.006281 0.8606 0.8949 0.0128 ] Network output: [ -0.0004873 0.002486 1.001 -4.029e-05 1.809e-05 0.9972 -3.037e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2106 0.09814 0.3405 0.1454 0.985 0.994 0.2113 0.4436 0.8775 0.7098 ] Network output: [ 0.005201 -0.02483 0.9946 2.423e-05 -1.088e-05 1.02 1.826e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1043 0.09205 0.1821 0.2002 0.9873 0.9919 0.1044 0.7562 0.8661 0.3056 ] Network output: [ -0.00494 0.02378 1.004 2.566e-05 -1.152e-05 0.9826 1.934e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09123 0.08929 0.1651 0.1955 0.9853 0.9912 0.09124 0.6808 0.8423 0.2459 ] Network output: [ 0.0001416 1 -0.0001763 3.433e-06 -1.541e-06 0.9999 2.587e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003621 Epoch 8306 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01043 0.9958 0.9908 2.519e-08 -1.131e-08 -0.00749 1.898e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003384 -0.003198 -0.007716 0.006061 0.9699 0.9743 0.006511 0.8325 0.8241 0.01777 ] Network output: [ 0.9998 0.0004575 0.0007566 -1.283e-05 5.758e-06 -0.000946 -9.666e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1984 -0.034 -0.1724 0.1888 0.9835 0.9932 0.2221 0.4394 0.8708 0.7156 ] Network output: [ -0.01019 1.002 1.009 -2.185e-07 9.808e-08 0.008777 -1.647e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006165 0.0005068 0.004446 0.003573 0.9889 0.9919 0.006282 0.8606 0.8949 0.0128 ] Network output: [ -0.0004869 0.002485 1.001 -4.025e-05 1.807e-05 0.9972 -3.033e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2106 0.09815 0.3405 0.1453 0.985 0.994 0.2113 0.4436 0.8775 0.7098 ] Network output: [ 0.005199 -0.02483 0.9946 2.421e-05 -1.087e-05 1.02 1.824e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1043 0.09206 0.1821 0.2002 0.9873 0.9919 0.1044 0.7562 0.8661 0.3056 ] Network output: [ -0.004939 0.02377 1.004 2.564e-05 -1.151e-05 0.9826 1.932e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09123 0.08929 0.1651 0.1955 0.9853 0.9912 0.09124 0.6807 0.8423 0.2459 ] Network output: [ 0.0001415 1 -0.0001761 3.429e-06 -1.539e-06 0.9999 2.584e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003619 Epoch 8307 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01043 0.9958 0.9908 2.437e-08 -1.094e-08 -0.00749 1.837e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003384 -0.003198 -0.007715 0.006061 0.9699 0.9743 0.006511 0.8325 0.8241 0.01777 ] Network output: [ 0.9998 0.0004572 0.0007562 -1.281e-05 5.752e-06 -0.0009452 -9.656e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1984 -0.034 -0.1724 0.1888 0.9835 0.9932 0.2221 0.4394 0.8708 0.7156 ] Network output: [ -0.01019 1.002 1.009 -2.189e-07 9.827e-08 0.008776 -1.65e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006166 0.0005069 0.004446 0.003573 0.9889 0.9919 0.006283 0.8606 0.8949 0.0128 ] Network output: [ -0.0004866 0.002484 1.001 -4.021e-05 1.805e-05 0.9972 -3.03e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2106 0.09815 0.3405 0.1453 0.985 0.994 0.2113 0.4436 0.8775 0.7098 ] Network output: [ 0.005198 -0.02482 0.9946 2.418e-05 -1.086e-05 1.02 1.823e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1043 0.09206 0.1821 0.2002 0.9873 0.9919 0.1044 0.7561 0.8661 0.3056 ] Network output: [ -0.004937 0.02376 1.004 2.561e-05 -1.15e-05 0.9826 1.93e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09123 0.08929 0.1651 0.1955 0.9853 0.9912 0.09124 0.6807 0.8423 0.2459 ] Network output: [ 0.0001414 1 -0.0001759 3.426e-06 -1.538e-06 0.9999 2.582e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003617 Epoch 8308 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01043 0.9958 0.9908 2.356e-08 -1.057e-08 -0.00749 1.775e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003384 -0.003198 -0.007714 0.00606 0.9699 0.9743 0.006511 0.8325 0.8241 0.01776 ] Network output: [ 0.9998 0.0004568 0.0007557 -1.28e-05 5.746e-06 -0.0009444 -9.646e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1984 -0.03401 -0.1724 0.1888 0.9835 0.9932 0.2221 0.4394 0.8708 0.7156 ] Network output: [ -0.01019 1.002 1.009 -2.193e-07 9.845e-08 0.008774 -1.653e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006166 0.000507 0.004446 0.003573 0.9889 0.9919 0.006283 0.8606 0.8949 0.0128 ] Network output: [ -0.0004863 0.002483 1.001 -4.017e-05 1.803e-05 0.9972 -3.027e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2106 0.09816 0.3406 0.1453 0.985 0.994 0.2113 0.4436 0.8775 0.7098 ] Network output: [ 0.005196 -0.02481 0.9946 2.416e-05 -1.085e-05 1.02 1.821e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1043 0.09207 0.1821 0.2002 0.9873 0.9919 0.1044 0.7561 0.8661 0.3056 ] Network output: [ -0.004935 0.02375 1.004 2.559e-05 -1.149e-05 0.9826 1.928e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09123 0.0893 0.1651 0.1955 0.9853 0.9912 0.09125 0.6807 0.8423 0.2459 ] Network output: [ 0.0001414 1 -0.0001757 3.422e-06 -1.536e-06 0.9999 2.579e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003615 Epoch 8309 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01043 0.9958 0.9908 2.274e-08 -1.021e-08 -0.00749 1.714e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003384 -0.003198 -0.007713 0.006059 0.9699 0.9743 0.006512 0.8325 0.8241 0.01776 ] Network output: [ 0.9998 0.0004564 0.0007553 -1.279e-05 5.74e-06 -0.0009437 -9.636e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1985 -0.03401 -0.1724 0.1888 0.9835 0.9932 0.2221 0.4394 0.8708 0.7156 ] Network output: [ -0.01019 1.002 1.009 -2.197e-07 9.863e-08 0.008772 -1.656e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006167 0.0005071 0.004446 0.003572 0.9889 0.9919 0.006284 0.8606 0.8949 0.0128 ] Network output: [ -0.000486 0.002482 1.001 -4.012e-05 1.801e-05 0.9972 -3.024e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2106 0.09817 0.3406 0.1453 0.985 0.994 0.2113 0.4436 0.8775 0.7098 ] Network output: [ 0.005194 -0.0248 0.9946 2.413e-05 -1.083e-05 1.02 1.819e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1043 0.09208 0.1821 0.2001 0.9873 0.9919 0.1044 0.7561 0.8661 0.3056 ] Network output: [ -0.004933 0.02374 1.004 2.556e-05 -1.148e-05 0.9826 1.926e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09124 0.0893 0.1651 0.1955 0.9853 0.9912 0.09125 0.6807 0.8423 0.2459 ] Network output: [ 0.0001413 1 -0.0001755 3.419e-06 -1.535e-06 0.9999 2.576e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003613 Epoch 8310 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01043 0.9958 0.9908 2.193e-08 -9.846e-09 -0.007491 1.653e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003384 -0.003198 -0.007712 0.006059 0.9699 0.9743 0.006512 0.8325 0.8241 0.01776 ] Network output: [ 0.9998 0.000456 0.0007548 -1.277e-05 5.734e-06 -0.0009429 -9.626e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1985 -0.03401 -0.1724 0.1888 0.9835 0.9932 0.2221 0.4393 0.8708 0.7156 ] Network output: [ -0.01018 1.002 1.009 -2.201e-07 9.882e-08 0.008771 -1.659e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006168 0.0005072 0.004446 0.003572 0.9889 0.9919 0.006284 0.8606 0.8949 0.0128 ] Network output: [ -0.0004857 0.002482 1.001 -4.008e-05 1.799e-05 0.9972 -3.021e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2107 0.09817 0.3406 0.1453 0.985 0.994 0.2113 0.4436 0.8775 0.7098 ] Network output: [ 0.005193 -0.02479 0.9946 2.411e-05 -1.082e-05 1.02 1.817e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1043 0.09208 0.1821 0.2001 0.9873 0.9919 0.1044 0.7561 0.866 0.3056 ] Network output: [ -0.004932 0.02373 1.004 2.554e-05 -1.146e-05 0.9826 1.924e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09124 0.0893 0.1651 0.1955 0.9853 0.9912 0.09125 0.6807 0.8423 0.2459 ] Network output: [ 0.0001412 1 -0.0001752 3.415e-06 -1.533e-06 0.9999 2.574e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003611 Epoch 8311 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01043 0.9958 0.9908 2.112e-08 -9.483e-09 -0.007491 1.592e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003385 -0.003198 -0.007711 0.006058 0.9699 0.9743 0.006512 0.8325 0.824 0.01776 ] Network output: [ 0.9998 0.0004556 0.0007544 -1.276e-05 5.728e-06 -0.0009421 -9.616e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1985 -0.03401 -0.1723 0.1888 0.9835 0.9932 0.2221 0.4393 0.8708 0.7156 ] Network output: [ -0.01018 1.002 1.009 -2.205e-07 9.9e-08 0.008769 -1.662e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006168 0.0005073 0.004446 0.003572 0.9889 0.9919 0.006285 0.8606 0.8949 0.0128 ] Network output: [ -0.0004854 0.002481 1.001 -4.004e-05 1.798e-05 0.9972 -3.018e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2107 0.09818 0.3406 0.1453 0.985 0.994 0.2114 0.4435 0.8775 0.7098 ] Network output: [ 0.005191 -0.02478 0.9946 2.408e-05 -1.081e-05 1.02 1.815e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1043 0.09209 0.1821 0.2001 0.9873 0.9919 0.1044 0.7561 0.866 0.3056 ] Network output: [ -0.00493 0.02372 1.004 2.551e-05 -1.145e-05 0.9826 1.923e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09124 0.0893 0.1651 0.1955 0.9853 0.9912 0.09125 0.6806 0.8423 0.2459 ] Network output: [ 0.0001412 1 -0.000175 3.412e-06 -1.532e-06 0.9999 2.571e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003609 Epoch 8312 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01043 0.9958 0.9908 2.032e-08 -9.121e-09 -0.007491 1.531e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003385 -0.003198 -0.00771 0.006058 0.9699 0.9743 0.006513 0.8325 0.824 0.01776 ] Network output: [ 0.9998 0.0004553 0.0007539 -1.275e-05 5.723e-06 -0.0009413 -9.606e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1985 -0.03401 -0.1723 0.1888 0.9835 0.9932 0.2221 0.4393 0.8708 0.7156 ] Network output: [ -0.01018 1.002 1.009 -2.209e-07 9.918e-08 0.008767 -1.665e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006169 0.0005074 0.004446 0.003571 0.9889 0.9919 0.006286 0.8606 0.8949 0.0128 ] Network output: [ -0.0004851 0.00248 1.001 -4e-05 1.796e-05 0.9972 -3.014e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2107 0.09818 0.3406 0.1453 0.985 0.994 0.2114 0.4435 0.8775 0.7098 ] Network output: [ 0.005189 -0.02477 0.9946 2.406e-05 -1.08e-05 1.02 1.813e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1043 0.09209 0.1821 0.2001 0.9873 0.9919 0.1044 0.756 0.866 0.3056 ] Network output: [ -0.004928 0.02371 1.004 2.549e-05 -1.144e-05 0.9826 1.921e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09124 0.0893 0.1651 0.1955 0.9853 0.9912 0.09125 0.6806 0.8423 0.2459 ] Network output: [ 0.0001411 1 -0.0001748 3.408e-06 -1.53e-06 0.9999 2.569e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003607 Epoch 8313 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01042 0.9958 0.9908 1.951e-08 -8.76e-09 -0.007491 1.471e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003385 -0.003199 -0.007709 0.006057 0.9699 0.9743 0.006513 0.8325 0.824 0.01776 ] Network output: [ 0.9998 0.0004549 0.0007535 -1.273e-05 5.717e-06 -0.0009405 -9.597e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1985 -0.03401 -0.1723 0.1888 0.9835 0.9932 0.2222 0.4393 0.8708 0.7156 ] Network output: [ -0.01018 1.002 1.009 -2.213e-07 9.936e-08 0.008766 -1.668e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006169 0.0005075 0.004446 0.003571 0.9889 0.9919 0.006286 0.8606 0.8949 0.0128 ] Network output: [ -0.0004848 0.002479 1.001 -3.996e-05 1.794e-05 0.9972 -3.011e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2107 0.09819 0.3406 0.1453 0.985 0.994 0.2114 0.4435 0.8775 0.7098 ] Network output: [ 0.005187 -0.02476 0.9946 2.403e-05 -1.079e-05 1.02 1.811e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1043 0.0921 0.1822 0.2001 0.9873 0.9919 0.1044 0.756 0.866 0.3056 ] Network output: [ -0.004926 0.0237 1.004 2.546e-05 -1.143e-05 0.9826 1.919e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09124 0.08931 0.1651 0.1955 0.9853 0.9912 0.09126 0.6806 0.8423 0.2459 ] Network output: [ 0.000141 1 -0.0001746 3.405e-06 -1.528e-06 0.9999 2.566e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003605 Epoch 8314 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01042 0.9958 0.9908 1.871e-08 -8.4e-09 -0.007491 1.41e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003385 -0.003199 -0.007708 0.006056 0.9699 0.9743 0.006513 0.8325 0.824 0.01776 ] Network output: [ 0.9998 0.0004545 0.000753 -1.272e-05 5.711e-06 -0.0009397 -9.587e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1985 -0.03402 -0.1723 0.1888 0.9835 0.9932 0.2222 0.4393 0.8708 0.7155 ] Network output: [ -0.01018 1.002 1.009 -2.217e-07 9.954e-08 0.008764 -1.671e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00617 0.0005075 0.004446 0.00357 0.9889 0.9919 0.006287 0.8606 0.8949 0.01279 ] Network output: [ -0.0004845 0.002478 1.001 -3.992e-05 1.792e-05 0.9972 -3.008e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2107 0.0982 0.3406 0.1453 0.985 0.994 0.2114 0.4435 0.8775 0.7098 ] Network output: [ 0.005186 -0.02475 0.9946 2.401e-05 -1.078e-05 1.02 1.809e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1043 0.0921 0.1822 0.2001 0.9873 0.9919 0.1044 0.756 0.866 0.3056 ] Network output: [ -0.004924 0.02369 1.004 2.543e-05 -1.142e-05 0.9826 1.917e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09125 0.08931 0.1651 0.1955 0.9853 0.9912 0.09126 0.6806 0.8423 0.2459 ] Network output: [ 0.000141 1 -0.0001744 3.401e-06 -1.527e-06 0.9999 2.563e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003603 Epoch 8315 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01042 0.9958 0.9908 1.791e-08 -8.04e-09 -0.007492 1.35e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003385 -0.003199 -0.007707 0.006056 0.9699 0.9743 0.006513 0.8325 0.824 0.01775 ] Network output: [ 0.9998 0.0004541 0.0007526 -1.271e-05 5.705e-06 -0.0009389 -9.577e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1985 -0.03402 -0.1723 0.1888 0.9835 0.9932 0.2222 0.4393 0.8708 0.7155 ] Network output: [ -0.01018 1.002 1.009 -2.221e-07 9.972e-08 0.008762 -1.674e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00617 0.0005076 0.004446 0.00357 0.9889 0.9919 0.006287 0.8605 0.8948 0.01279 ] Network output: [ -0.0004842 0.002477 1.001 -3.987e-05 1.79e-05 0.9972 -3.005e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2107 0.0982 0.3406 0.1453 0.985 0.994 0.2114 0.4435 0.8774 0.7098 ] Network output: [ 0.005184 -0.02474 0.9946 2.399e-05 -1.077e-05 1.02 1.808e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1044 0.09211 0.1822 0.2001 0.9873 0.9919 0.1044 0.756 0.866 0.3056 ] Network output: [ -0.004923 0.02368 1.004 2.541e-05 -1.141e-05 0.9826 1.915e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09125 0.08931 0.1651 0.1955 0.9853 0.9912 0.09126 0.6806 0.8423 0.2459 ] Network output: [ 0.0001409 1 -0.0001742 3.398e-06 -1.525e-06 0.9999 2.561e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003601 Epoch 8316 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01042 0.9958 0.9908 1.711e-08 -7.682e-09 -0.007492 1.29e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003385 -0.003199 -0.007706 0.006055 0.9699 0.9743 0.006514 0.8325 0.824 0.01775 ] Network output: [ 0.9998 0.0004537 0.0007521 -1.269e-05 5.699e-06 -0.0009381 -9.567e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1985 -0.03402 -0.1723 0.1888 0.9835 0.9932 0.2222 0.4393 0.8708 0.7155 ] Network output: [ -0.01018 1.002 1.009 -2.225e-07 9.99e-08 0.008761 -1.677e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006171 0.0005077 0.004446 0.00357 0.9889 0.9919 0.006288 0.8605 0.8948 0.01279 ] Network output: [ -0.0004839 0.002476 1.001 -3.983e-05 1.788e-05 0.9972 -3.002e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2107 0.09821 0.3406 0.1453 0.985 0.994 0.2114 0.4435 0.8774 0.7098 ] Network output: [ 0.005182 -0.02474 0.9946 2.396e-05 -1.076e-05 1.02 1.806e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1044 0.09212 0.1822 0.2001 0.9873 0.9919 0.1044 0.756 0.866 0.3056 ] Network output: [ -0.004921 0.02367 1.004 2.538e-05 -1.14e-05 0.9827 1.913e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09125 0.08931 0.1651 0.1955 0.9853 0.9912 0.09126 0.6805 0.8423 0.2459 ] Network output: [ 0.0001408 1 -0.000174 3.394e-06 -1.524e-06 0.9999 2.558e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003599 Epoch 8317 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01042 0.9958 0.9908 1.631e-08 -7.324e-09 -0.007492 1.229e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003385 -0.003199 -0.007705 0.006055 0.9699 0.9743 0.006514 0.8325 0.824 0.01775 ] Network output: [ 0.9998 0.0004534 0.0007517 -1.268e-05 5.693e-06 -0.0009373 -9.557e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1985 -0.03402 -0.1723 0.1888 0.9835 0.9932 0.2222 0.4393 0.8708 0.7155 ] Network output: [ -0.01018 1.002 1.009 -2.229e-07 1.001e-07 0.008759 -1.68e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006172 0.0005078 0.004446 0.003569 0.9889 0.9919 0.006288 0.8605 0.8948 0.01279 ] Network output: [ -0.0004836 0.002475 1.001 -3.979e-05 1.786e-05 0.9972 -2.999e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2107 0.09821 0.3406 0.1453 0.985 0.994 0.2114 0.4435 0.8774 0.7097 ] Network output: [ 0.00518 -0.02473 0.9946 2.394e-05 -1.075e-05 1.02 1.804e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1044 0.09212 0.1822 0.2001 0.9873 0.9919 0.1044 0.756 0.866 0.3056 ] Network output: [ -0.004919 0.02366 1.004 2.536e-05 -1.138e-05 0.9827 1.911e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09125 0.08932 0.1651 0.1955 0.9853 0.9912 0.09127 0.6805 0.8423 0.2459 ] Network output: [ 0.0001408 1 -0.0001738 3.391e-06 -1.522e-06 0.9999 2.555e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003597 Epoch 8318 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01042 0.9958 0.9908 1.552e-08 -6.967e-09 -0.007492 1.17e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003385 -0.003199 -0.007704 0.006054 0.9699 0.9743 0.006514 0.8324 0.824 0.01775 ] Network output: [ 0.9998 0.000453 0.0007513 -1.267e-05 5.687e-06 -0.0009365 -9.547e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1985 -0.03402 -0.1722 0.1888 0.9835 0.9932 0.2222 0.4393 0.8708 0.7155 ] Network output: [ -0.01018 1.002 1.009 -2.233e-07 1.003e-07 0.008758 -1.683e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006172 0.0005079 0.004446 0.003569 0.9889 0.9919 0.006289 0.8605 0.8948 0.01279 ] Network output: [ -0.0004833 0.002475 1.001 -3.975e-05 1.784e-05 0.9972 -2.996e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2107 0.09822 0.3406 0.1453 0.985 0.994 0.2114 0.4435 0.8774 0.7097 ] Network output: [ 0.005179 -0.02472 0.9946 2.391e-05 -1.073e-05 1.02 1.802e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1044 0.09213 0.1822 0.2001 0.9873 0.9919 0.1044 0.7559 0.866 0.3056 ] Network output: [ -0.004917 0.02365 1.004 2.533e-05 -1.137e-05 0.9827 1.909e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09125 0.08932 0.1651 0.1955 0.9853 0.9912 0.09127 0.6805 0.8423 0.2459 ] Network output: [ 0.0001407 1 -0.0001736 3.387e-06 -1.521e-06 0.9999 2.553e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003595 Epoch 8319 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01042 0.9958 0.9908 1.473e-08 -6.611e-09 -0.007492 1.11e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003386 -0.003199 -0.007703 0.006053 0.9699 0.9743 0.006515 0.8324 0.824 0.01775 ] Network output: [ 0.9998 0.0004526 0.0007508 -1.266e-05 5.681e-06 -0.0009358 -9.537e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1985 -0.03402 -0.1722 0.1888 0.9835 0.9932 0.2222 0.4392 0.8708 0.7155 ] Network output: [ -0.01018 1.002 1.009 -2.237e-07 1.004e-07 0.008756 -1.686e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006173 0.000508 0.004446 0.003569 0.9889 0.9919 0.00629 0.8605 0.8948 0.01279 ] Network output: [ -0.000483 0.002474 1.001 -3.971e-05 1.783e-05 0.9972 -2.993e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2107 0.09822 0.3406 0.1453 0.985 0.994 0.2114 0.4435 0.8774 0.7097 ] Network output: [ 0.005177 -0.02471 0.9946 2.389e-05 -1.072e-05 1.02 1.8e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1044 0.09213 0.1822 0.2001 0.9873 0.9919 0.1045 0.7559 0.866 0.3056 ] Network output: [ -0.004916 0.02364 1.004 2.531e-05 -1.136e-05 0.9827 1.907e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09126 0.08932 0.1651 0.1955 0.9853 0.9912 0.09127 0.6805 0.8423 0.2459 ] Network output: [ 0.0001406 1 -0.0001734 3.384e-06 -1.519e-06 0.9999 2.55e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003593 Epoch 8320 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01041 0.9958 0.9908 1.393e-08 -6.256e-09 -0.007492 1.05e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003386 -0.0032 -0.007703 0.006053 0.9699 0.9743 0.006515 0.8324 0.824 0.01775 ] Network output: [ 0.9998 0.0004522 0.0007504 -1.264e-05 5.675e-06 -0.000935 -9.527e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1986 -0.03403 -0.1722 0.1888 0.9835 0.9932 0.2222 0.4392 0.8708 0.7155 ] Network output: [ -0.01017 1.002 1.009 -2.241e-07 1.006e-07 0.008754 -1.689e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006173 0.0005081 0.004446 0.003568 0.9889 0.9919 0.00629 0.8605 0.8948 0.01279 ] Network output: [ -0.0004827 0.002473 1.001 -3.967e-05 1.781e-05 0.9972 -2.989e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2108 0.09823 0.3407 0.1453 0.985 0.994 0.2115 0.4434 0.8774 0.7097 ] Network output: [ 0.005175 -0.0247 0.9946 2.386e-05 -1.071e-05 1.02 1.798e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1044 0.09214 0.1822 0.2001 0.9873 0.9919 0.1045 0.7559 0.866 0.3056 ] Network output: [ -0.004914 0.02363 1.004 2.528e-05 -1.135e-05 0.9827 1.905e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09126 0.08932 0.1651 0.1955 0.9853 0.9912 0.09127 0.6805 0.8422 0.2459 ] Network output: [ 0.0001406 1 -0.0001732 3.38e-06 -1.518e-06 0.9999 2.548e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003591 Epoch 8321 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01041 0.9958 0.9908 1.315e-08 -5.902e-09 -0.007493 9.907e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003386 -0.0032 -0.007702 0.006052 0.9699 0.9743 0.006515 0.8324 0.824 0.01775 ] Network output: [ 0.9998 0.0004519 0.0007499 -1.263e-05 5.67e-06 -0.0009342 -9.518e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1986 -0.03403 -0.1722 0.1887 0.9835 0.9932 0.2222 0.4392 0.8708 0.7155 ] Network output: [ -0.01017 1.002 1.009 -2.245e-07 1.008e-07 0.008753 -1.692e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006174 0.0005082 0.004446 0.003568 0.9889 0.9919 0.006291 0.8605 0.8948 0.01279 ] Network output: [ -0.0004823 0.002472 1.001 -3.963e-05 1.779e-05 0.9972 -2.986e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2108 0.09824 0.3407 0.1453 0.985 0.994 0.2115 0.4434 0.8774 0.7097 ] Network output: [ 0.005174 -0.02469 0.9946 2.384e-05 -1.07e-05 1.02 1.797e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1044 0.09214 0.1822 0.2001 0.9873 0.9919 0.1045 0.7559 0.866 0.3056 ] Network output: [ -0.004912 0.02362 1.004 2.526e-05 -1.134e-05 0.9827 1.903e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09126 0.08932 0.1651 0.1955 0.9853 0.9912 0.09127 0.6804 0.8422 0.2459 ] Network output: [ 0.0001405 1 -0.000173 3.377e-06 -1.516e-06 0.9999 2.545e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003589 Epoch 8322 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01041 0.9958 0.9908 1.236e-08 -5.548e-09 -0.007493 9.314e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003386 -0.0032 -0.007701 0.006052 0.9699 0.9743 0.006516 0.8324 0.824 0.01775 ] Network output: [ 0.9998 0.0004515 0.0007495 -1.262e-05 5.664e-06 -0.0009334 -9.508e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1986 -0.03403 -0.1722 0.1887 0.9835 0.9932 0.2223 0.4392 0.8708 0.7155 ] Network output: [ -0.01017 1.002 1.009 -2.249e-07 1.01e-07 0.008751 -1.695e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006174 0.0005082 0.004446 0.003567 0.9889 0.9919 0.006291 0.8605 0.8948 0.01279 ] Network output: [ -0.000482 0.002471 1.001 -3.958e-05 1.777e-05 0.9972 -2.983e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2108 0.09824 0.3407 0.1453 0.985 0.994 0.2115 0.4434 0.8774 0.7097 ] Network output: [ 0.005172 -0.02468 0.9946 2.381e-05 -1.069e-05 1.02 1.795e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1044 0.09215 0.1822 0.2001 0.9873 0.9919 0.1045 0.7559 0.866 0.3056 ] Network output: [ -0.00491 0.02361 1.004 2.523e-05 -1.133e-05 0.9827 1.902e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09126 0.08933 0.1651 0.1955 0.9853 0.9912 0.09128 0.6804 0.8422 0.2459 ] Network output: [ 0.0001404 1 -0.0001728 3.374e-06 -1.515e-06 0.9999 2.542e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003587 Epoch 8323 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01041 0.9958 0.9908 1.157e-08 -5.196e-09 -0.007493 8.722e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003386 -0.0032 -0.0077 0.006051 0.9699 0.9743 0.006516 0.8324 0.824 0.01774 ] Network output: [ 0.9998 0.0004511 0.000749 -1.26e-05 5.658e-06 -0.0009326 -9.498e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1986 -0.03403 -0.1722 0.1887 0.9835 0.9932 0.2223 0.4392 0.8708 0.7155 ] Network output: [ -0.01017 1.002 1.009 -2.253e-07 1.011e-07 0.008749 -1.698e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006175 0.0005083 0.004446 0.003567 0.9889 0.9919 0.006292 0.8605 0.8948 0.01278 ] Network output: [ -0.0004817 0.00247 1.001 -3.954e-05 1.775e-05 0.9972 -2.98e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2108 0.09825 0.3407 0.1453 0.985 0.994 0.2115 0.4434 0.8774 0.7097 ] Network output: [ 0.00517 -0.02467 0.9946 2.379e-05 -1.068e-05 1.02 1.793e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1044 0.09216 0.1822 0.2001 0.9873 0.9919 0.1045 0.7558 0.866 0.3056 ] Network output: [ -0.004909 0.0236 1.004 2.521e-05 -1.132e-05 0.9827 1.9e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09126 0.08933 0.1651 0.1955 0.9853 0.9912 0.09128 0.6804 0.8422 0.2459 ] Network output: [ 0.0001404 1 -0.0001726 3.37e-06 -1.513e-06 0.9999 2.54e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003585 Epoch 8324 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01041 0.9958 0.9908 1.079e-08 -4.844e-09 -0.007493 8.132e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003386 -0.0032 -0.007699 0.00605 0.9699 0.9743 0.006516 0.8324 0.824 0.01774 ] Network output: [ 0.9998 0.0004507 0.0007486 -1.259e-05 5.652e-06 -0.0009318 -9.488e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1986 -0.03403 -0.1722 0.1887 0.9835 0.9932 0.2223 0.4392 0.8708 0.7155 ] Network output: [ -0.01017 1.002 1.009 -2.257e-07 1.013e-07 0.008748 -1.701e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006176 0.0005084 0.004446 0.003567 0.9889 0.9919 0.006293 0.8605 0.8948 0.01278 ] Network output: [ -0.0004814 0.002469 1.001 -3.95e-05 1.773e-05 0.9972 -2.977e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2108 0.09825 0.3407 0.1453 0.985 0.994 0.2115 0.4434 0.8774 0.7097 ] Network output: [ 0.005168 -0.02466 0.9946 2.376e-05 -1.067e-05 1.02 1.791e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1044 0.09216 0.1822 0.2001 0.9873 0.9919 0.1045 0.7558 0.866 0.3056 ] Network output: [ -0.004907 0.02359 1.004 2.518e-05 -1.13e-05 0.9827 1.898e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09127 0.08933 0.1651 0.1955 0.9853 0.9912 0.09128 0.6804 0.8422 0.2459 ] Network output: [ 0.0001403 1 -0.0001724 3.367e-06 -1.511e-06 0.9999 2.537e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003583 Epoch 8325 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01041 0.9958 0.9908 1.001e-08 -4.493e-09 -0.007493 7.543e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003386 -0.0032 -0.007698 0.00605 0.9699 0.9743 0.006516 0.8324 0.824 0.01774 ] Network output: [ 0.9998 0.0004504 0.0007482 -1.258e-05 5.646e-06 -0.000931 -9.478e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1986 -0.03403 -0.1722 0.1887 0.9835 0.9932 0.2223 0.4392 0.8708 0.7155 ] Network output: [ -0.01017 1.002 1.009 -2.26e-07 1.015e-07 0.008746 -1.704e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006176 0.0005085 0.004446 0.003566 0.9889 0.9919 0.006293 0.8605 0.8948 0.01278 ] Network output: [ -0.0004811 0.002468 1.001 -3.946e-05 1.772e-05 0.9972 -2.974e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2108 0.09826 0.3407 0.1453 0.985 0.994 0.2115 0.4434 0.8774 0.7097 ] Network output: [ 0.005167 -0.02466 0.9945 2.374e-05 -1.066e-05 1.02 1.789e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1044 0.09217 0.1822 0.2001 0.9873 0.9919 0.1045 0.7558 0.866 0.3056 ] Network output: [ -0.004905 0.02358 1.004 2.516e-05 -1.129e-05 0.9827 1.896e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09127 0.08933 0.1651 0.1955 0.9853 0.9912 0.09128 0.6803 0.8422 0.2459 ] Network output: [ 0.0001402 1 -0.0001722 3.363e-06 -1.51e-06 0.9999 2.535e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003581 Epoch 8326 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01041 0.9959 0.9908 9.229e-09 -4.143e-09 -0.007493 6.955e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003386 -0.0032 -0.007697 0.006049 0.9699 0.9743 0.006517 0.8324 0.824 0.01774 ] Network output: [ 0.9998 0.00045 0.0007477 -1.256e-05 5.64e-06 -0.0009303 -9.468e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1986 -0.03404 -0.1721 0.1887 0.9835 0.9932 0.2223 0.4392 0.8708 0.7155 ] Network output: [ -0.01017 1.002 1.009 -2.264e-07 1.017e-07 0.008744 -1.706e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006177 0.0005086 0.004446 0.003566 0.9889 0.9919 0.006294 0.8605 0.8948 0.01278 ] Network output: [ -0.0004808 0.002468 1.001 -3.942e-05 1.77e-05 0.9973 -2.971e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2108 0.09827 0.3407 0.1453 0.985 0.994 0.2115 0.4434 0.8774 0.7097 ] Network output: [ 0.005165 -0.02465 0.9945 2.372e-05 -1.065e-05 1.02 1.787e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1044 0.09217 0.1822 0.2001 0.9873 0.9919 0.1045 0.7558 0.866 0.3056 ] Network output: [ -0.004903 0.02357 1.004 2.513e-05 -1.128e-05 0.9827 1.894e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09127 0.08933 0.1651 0.1955 0.9853 0.9912 0.09128 0.6803 0.8422 0.2459 ] Network output: [ 0.0001401 1 -0.000172 3.36e-06 -1.508e-06 0.9999 2.532e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003579 Epoch 8327 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01041 0.9959 0.9908 8.451e-09 -3.794e-09 -0.007493 6.369e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003387 -0.003201 -0.007696 0.006049 0.9699 0.9743 0.006517 0.8324 0.824 0.01774 ] Network output: [ 0.9998 0.0004496 0.0007473 -1.255e-05 5.635e-06 -0.0009295 -9.459e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1986 -0.03404 -0.1721 0.1887 0.9835 0.9932 0.2223 0.4392 0.8708 0.7155 ] Network output: [ -0.01017 1.002 1.009 -2.268e-07 1.018e-07 0.008743 -1.709e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006177 0.0005087 0.004446 0.003566 0.9889 0.9919 0.006294 0.8605 0.8948 0.01278 ] Network output: [ -0.0004805 0.002467 1.001 -3.938e-05 1.768e-05 0.9973 -2.968e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2108 0.09827 0.3407 0.1453 0.985 0.994 0.2115 0.4434 0.8774 0.7097 ] Network output: [ 0.005163 -0.02464 0.9945 2.369e-05 -1.064e-05 1.02 1.785e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1044 0.09218 0.1822 0.2001 0.9873 0.9919 0.1045 0.7558 0.866 0.3056 ] Network output: [ -0.004902 0.02356 1.004 2.511e-05 -1.127e-05 0.9827 1.892e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09127 0.08934 0.1651 0.1955 0.9853 0.9912 0.09129 0.6803 0.8422 0.2459 ] Network output: [ 0.0001401 1 -0.0001718 3.356e-06 -1.507e-06 0.9999 2.529e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003577 Epoch 8328 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0104 0.9959 0.9908 7.676e-09 -3.446e-09 -0.007494 5.785e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003387 -0.003201 -0.007695 0.006048 0.9699 0.9743 0.006517 0.8324 0.824 0.01774 ] Network output: [ 0.9998 0.0004492 0.0007468 -1.254e-05 5.629e-06 -0.0009287 -9.449e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1986 -0.03404 -0.1721 0.1887 0.9835 0.9932 0.2223 0.4392 0.8708 0.7155 ] Network output: [ -0.01017 1.002 1.009 -2.272e-07 1.02e-07 0.008741 -1.712e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006178 0.0005088 0.004446 0.003565 0.9889 0.9919 0.006295 0.8604 0.8948 0.01278 ] Network output: [ -0.0004802 0.002466 1.001 -3.934e-05 1.766e-05 0.9973 -2.965e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2108 0.09828 0.3407 0.1453 0.985 0.994 0.2115 0.4434 0.8774 0.7097 ] Network output: [ 0.005162 -0.02463 0.9945 2.367e-05 -1.063e-05 1.02 1.784e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1044 0.09218 0.1822 0.2001 0.9873 0.9919 0.1045 0.7557 0.866 0.3056 ] Network output: [ -0.0049 0.02355 1.004 2.508e-05 -1.126e-05 0.9827 1.89e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09128 0.08934 0.1651 0.1955 0.9853 0.9912 0.09129 0.6803 0.8422 0.2459 ] Network output: [ 0.00014 1 -0.0001716 3.353e-06 -1.505e-06 0.9999 2.527e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003575 Epoch 8329 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0104 0.9959 0.9908 6.902e-09 -3.098e-09 -0.007494 5.201e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003387 -0.003201 -0.007694 0.006047 0.9699 0.9743 0.006518 0.8324 0.824 0.01774 ] Network output: [ 0.9998 0.0004488 0.0007464 -1.252e-05 5.623e-06 -0.0009279 -9.439e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1986 -0.03404 -0.1721 0.1887 0.9835 0.9932 0.2223 0.4391 0.8708 0.7155 ] Network output: [ -0.01016 1.002 1.009 -2.276e-07 1.022e-07 0.00874 -1.715e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006178 0.0005089 0.004446 0.003565 0.9889 0.9919 0.006295 0.8604 0.8948 0.01278 ] Network output: [ -0.0004799 0.002465 1.001 -3.93e-05 1.764e-05 0.9973 -2.961e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2109 0.09828 0.3407 0.1453 0.985 0.994 0.2115 0.4434 0.8774 0.7097 ] Network output: [ 0.00516 -0.02462 0.9945 2.364e-05 -1.061e-05 1.02 1.782e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1044 0.09219 0.1822 0.2001 0.9873 0.9919 0.1045 0.7557 0.8659 0.3056 ] Network output: [ -0.004898 0.02354 1.004 2.506e-05 -1.125e-05 0.9827 1.888e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09128 0.08934 0.1651 0.1955 0.9853 0.9912 0.09129 0.6803 0.8422 0.2459 ] Network output: [ 0.0001399 1 -0.0001714 3.349e-06 -1.504e-06 0.9999 2.524e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003573 Epoch 8330 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0104 0.9959 0.9908 6.13e-09 -2.752e-09 -0.007494 4.62e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003387 -0.003201 -0.007693 0.006047 0.9699 0.9743 0.006518 0.8324 0.824 0.01773 ] Network output: [ 0.9998 0.0004485 0.000746 -1.251e-05 5.617e-06 -0.0009271 -9.429e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1986 -0.03404 -0.1721 0.1887 0.9835 0.9932 0.2223 0.4391 0.8708 0.7155 ] Network output: [ -0.01016 1.002 1.009 -2.28e-07 1.023e-07 0.008738 -1.718e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006179 0.000509 0.004446 0.003564 0.9889 0.9919 0.006296 0.8604 0.8948 0.01278 ] Network output: [ -0.0004796 0.002464 1.001 -3.925e-05 1.762e-05 0.9973 -2.958e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2109 0.09829 0.3407 0.1453 0.985 0.994 0.2116 0.4433 0.8774 0.7097 ] Network output: [ 0.005158 -0.02461 0.9945 2.362e-05 -1.06e-05 1.02 1.78e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1044 0.0922 0.1822 0.2001 0.9873 0.9919 0.1045 0.7557 0.8659 0.3056 ] Network output: [ -0.004896 0.02353 1.004 2.503e-05 -1.124e-05 0.9827 1.886e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09128 0.08934 0.1651 0.1955 0.9853 0.9912 0.09129 0.6802 0.8422 0.2459 ] Network output: [ 0.0001399 1 -0.0001712 3.346e-06 -1.502e-06 0.9999 2.522e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003571 Epoch 8331 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0104 0.9959 0.9908 5.36e-09 -2.406e-09 -0.007494 4.039e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003387 -0.003201 -0.007692 0.006046 0.9699 0.9743 0.006518 0.8324 0.824 0.01773 ] Network output: [ 0.9998 0.0004481 0.0007455 -1.25e-05 5.611e-06 -0.0009264 -9.42e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1987 -0.03404 -0.1721 0.1887 0.9835 0.9932 0.2223 0.4391 0.8708 0.7154 ] Network output: [ -0.01016 1.002 1.009 -2.283e-07 1.025e-07 0.008736 -1.721e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006179 0.000509 0.004446 0.003564 0.9889 0.9919 0.006297 0.8604 0.8948 0.01278 ] Network output: [ -0.0004793 0.002463 1.001 -3.921e-05 1.76e-05 0.9973 -2.955e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2109 0.0983 0.3408 0.1452 0.985 0.994 0.2116 0.4433 0.8774 0.7097 ] Network output: [ 0.005156 -0.0246 0.9945 2.359e-05 -1.059e-05 1.02 1.778e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1045 0.0922 0.1822 0.2001 0.9873 0.9919 0.1045 0.7557 0.8659 0.3056 ] Network output: [ -0.004895 0.02352 1.004 2.5e-05 -1.123e-05 0.9827 1.884e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09128 0.08934 0.1651 0.1955 0.9853 0.9912 0.09129 0.6802 0.8422 0.2459 ] Network output: [ 0.0001398 1 -0.000171 3.343e-06 -1.501e-06 0.9999 2.519e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003569 Epoch 8332 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0104 0.9959 0.9908 4.591e-09 -2.061e-09 -0.007494 3.46e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003387 -0.003201 -0.007691 0.006046 0.9699 0.9743 0.006518 0.8324 0.824 0.01773 ] Network output: [ 0.9998 0.0004477 0.0007451 -1.249e-05 5.605e-06 -0.0009256 -9.41e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1987 -0.03405 -0.1721 0.1887 0.9835 0.9932 0.2224 0.4391 0.8708 0.7154 ] Network output: [ -0.01016 1.002 1.009 -2.287e-07 1.027e-07 0.008735 -1.724e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00618 0.0005091 0.004446 0.003564 0.9889 0.9919 0.006297 0.8604 0.8948 0.01278 ] Network output: [ -0.000479 0.002462 1.001 -3.917e-05 1.759e-05 0.9973 -2.952e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2109 0.0983 0.3408 0.1452 0.985 0.994 0.2116 0.4433 0.8774 0.7097 ] Network output: [ 0.005155 -0.02459 0.9945 2.357e-05 -1.058e-05 1.02 1.776e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1045 0.09221 0.1822 0.2001 0.9873 0.9919 0.1045 0.7557 0.8659 0.3056 ] Network output: [ -0.004893 0.02352 1.004 2.498e-05 -1.121e-05 0.9827 1.883e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09128 0.08935 0.1651 0.1955 0.9853 0.9912 0.0913 0.6802 0.8422 0.2459 ] Network output: [ 0.0001397 1 -0.0001708 3.339e-06 -1.499e-06 0.9999 2.517e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003567 Epoch 8333 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0104 0.9959 0.9908 3.825e-09 -1.717e-09 -0.007494 2.883e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003387 -0.003201 -0.00769 0.006045 0.9699 0.9743 0.006519 0.8323 0.824 0.01773 ] Network output: [ 0.9998 0.0004474 0.0007446 -1.247e-05 5.6e-06 -0.0009248 -9.4e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1987 -0.03405 -0.172 0.1887 0.9835 0.9932 0.2224 0.4391 0.8708 0.7154 ] Network output: [ -0.01016 1.002 1.009 -2.291e-07 1.028e-07 0.008733 -1.726e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006181 0.0005092 0.004446 0.003563 0.9889 0.9919 0.006298 0.8604 0.8948 0.01277 ] Network output: [ -0.0004787 0.002461 1.001 -3.913e-05 1.757e-05 0.9973 -2.949e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2109 0.09831 0.3408 0.1452 0.985 0.994 0.2116 0.4433 0.8774 0.7096 ] Network output: [ 0.005153 -0.02458 0.9945 2.355e-05 -1.057e-05 1.02 1.774e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1045 0.09221 0.1822 0.2001 0.9873 0.9919 0.1045 0.7556 0.8659 0.3056 ] Network output: [ -0.004891 0.02351 1.004 2.495e-05 -1.12e-05 0.9827 1.881e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09129 0.08935 0.1651 0.1955 0.9853 0.9912 0.0913 0.6802 0.8422 0.2459 ] Network output: [ 0.0001397 1 -0.0001706 3.336e-06 -1.498e-06 0.9999 2.514e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003565 Epoch 8334 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0104 0.9959 0.9908 3.061e-09 -1.374e-09 -0.007495 2.307e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003387 -0.003202 -0.007689 0.006044 0.9699 0.9743 0.006519 0.8323 0.824 0.01773 ] Network output: [ 0.9998 0.000447 0.0007442 -1.246e-05 5.594e-06 -0.000924 -9.39e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1987 -0.03405 -0.172 0.1887 0.9835 0.9932 0.2224 0.4391 0.8708 0.7154 ] Network output: [ -0.01016 1.002 1.009 -2.295e-07 1.03e-07 0.008731 -1.729e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006181 0.0005093 0.004446 0.003563 0.9889 0.9919 0.006298 0.8604 0.8948 0.01277 ] Network output: [ -0.0004784 0.002461 1.001 -3.909e-05 1.755e-05 0.9973 -2.946e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2109 0.09831 0.3408 0.1452 0.985 0.994 0.2116 0.4433 0.8774 0.7096 ] Network output: [ 0.005151 -0.02457 0.9945 2.352e-05 -1.056e-05 1.02 1.773e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1045 0.09222 0.1822 0.2001 0.9873 0.9919 0.1045 0.7556 0.8659 0.3056 ] Network output: [ -0.004889 0.0235 1.004 2.493e-05 -1.119e-05 0.9827 1.879e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09129 0.08935 0.1651 0.1955 0.9853 0.9912 0.0913 0.6802 0.8422 0.2459 ] Network output: [ 0.0001396 1 -0.0001704 3.332e-06 -1.496e-06 0.9999 2.511e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003563 Epoch 8335 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01039 0.9959 0.9908 2.298e-09 -1.032e-09 -0.007495 1.732e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003388 -0.003202 -0.007689 0.006044 0.9699 0.9743 0.006519 0.8323 0.824 0.01773 ] Network output: [ 0.9998 0.0004466 0.0007438 -1.245e-05 5.588e-06 -0.0009233 -9.381e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1987 -0.03405 -0.172 0.1887 0.9835 0.9932 0.2224 0.4391 0.8708 0.7154 ] Network output: [ -0.01016 1.002 1.009 -2.298e-07 1.032e-07 0.00873 -1.732e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006182 0.0005094 0.004446 0.003563 0.9889 0.9919 0.006299 0.8604 0.8948 0.01277 ] Network output: [ -0.0004781 0.00246 1.001 -3.905e-05 1.753e-05 0.9973 -2.943e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2109 0.09832 0.3408 0.1452 0.985 0.994 0.2116 0.4433 0.8774 0.7096 ] Network output: [ 0.00515 -0.02457 0.9945 2.35e-05 -1.055e-05 1.02 1.771e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1045 0.09222 0.1822 0.2001 0.9873 0.9919 0.1046 0.7556 0.8659 0.3056 ] Network output: [ -0.004888 0.02349 1.004 2.49e-05 -1.118e-05 0.9827 1.877e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09129 0.08935 0.1651 0.1955 0.9853 0.9912 0.0913 0.6801 0.8421 0.2459 ] Network output: [ 0.0001395 1 -0.0001702 3.329e-06 -1.494e-06 0.9999 2.509e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003561 Epoch 8336 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01039 0.9959 0.9908 1.537e-09 -6.902e-10 -0.007495 1.159e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003388 -0.003202 -0.007688 0.006043 0.9699 0.9743 0.00652 0.8323 0.8239 0.01773 ] Network output: [ 0.9998 0.0004462 0.0007433 -1.243e-05 5.582e-06 -0.0009225 -9.371e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1987 -0.03405 -0.172 0.1887 0.9835 0.9932 0.2224 0.4391 0.8708 0.7154 ] Network output: [ -0.01016 1.002 1.009 -2.302e-07 1.034e-07 0.008728 -1.735e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006182 0.0005095 0.004446 0.003562 0.9889 0.9919 0.0063 0.8604 0.8948 0.01277 ] Network output: [ -0.0004778 0.002459 1.001 -3.901e-05 1.751e-05 0.9973 -2.94e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2109 0.09833 0.3408 0.1452 0.985 0.994 0.2116 0.4433 0.8774 0.7096 ] Network output: [ 0.005148 -0.02456 0.9945 2.347e-05 -1.054e-05 1.02 1.769e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1045 0.09223 0.1822 0.2001 0.9873 0.9919 0.1046 0.7556 0.8659 0.3056 ] Network output: [ -0.004886 0.02348 1.004 2.488e-05 -1.117e-05 0.9827 1.875e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09129 0.08935 0.1651 0.1955 0.9853 0.9912 0.0913 0.6801 0.8421 0.246 ] Network output: [ 0.0001395 1 -0.00017 3.326e-06 -1.493e-06 0.9999 2.506e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003559 Epoch 8337 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01039 0.9959 0.9908 7.786e-10 -3.495e-10 -0.007495 5.868e-10 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003388 -0.003202 -0.007687 0.006043 0.9699 0.9743 0.00652 0.8323 0.8239 0.01773 ] Network output: [ 0.9998 0.0004459 0.0007429 -1.242e-05 5.576e-06 -0.0009217 -9.361e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1987 -0.03405 -0.172 0.1887 0.9835 0.9932 0.2224 0.4391 0.8708 0.7154 ] Network output: [ -0.01016 1.002 1.009 -2.306e-07 1.035e-07 0.008727 -1.738e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006183 0.0005096 0.004446 0.003562 0.9889 0.9919 0.0063 0.8604 0.8948 0.01277 ] Network output: [ -0.0004775 0.002458 1.001 -3.897e-05 1.749e-05 0.9973 -2.937e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2109 0.09833 0.3408 0.1452 0.985 0.994 0.2116 0.4433 0.8774 0.7096 ] Network output: [ 0.005146 -0.02455 0.9945 2.345e-05 -1.053e-05 1.02 1.767e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1045 0.09224 0.1822 0.2001 0.9873 0.9919 0.1046 0.7556 0.8659 0.3056 ] Network output: [ -0.004884 0.02347 1.004 2.485e-05 -1.116e-05 0.9827 1.873e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09129 0.08936 0.1651 0.1955 0.9853 0.9912 0.09131 0.6801 0.8421 0.246 ] Network output: [ 0.0001394 1 -0.0001698 3.322e-06 -1.491e-06 0.9999 2.504e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003557 Epoch 8338 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01039 0.9959 0.9909 2.164e-11 -9.715e-12 -0.007495 1.631e-11 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003388 -0.003202 -0.007686 0.006042 0.9699 0.9743 0.00652 0.8323 0.8239 0.01772 ] Network output: [ 0.9998 0.0004455 0.0007424 -1.241e-05 5.571e-06 -0.0009209 -9.352e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1987 -0.03405 -0.172 0.1886 0.9835 0.9932 0.2224 0.4391 0.8708 0.7154 ] Network output: [ -0.01015 1.002 1.009 -2.31e-07 1.037e-07 0.008725 -1.741e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006183 0.0005097 0.004446 0.003561 0.9889 0.9919 0.006301 0.8604 0.8948 0.01277 ] Network output: [ -0.0004772 0.002457 1.001 -3.893e-05 1.748e-05 0.9973 -2.934e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2109 0.09834 0.3408 0.1452 0.985 0.994 0.2116 0.4433 0.8774 0.7096 ] Network output: [ 0.005144 -0.02454 0.9945 2.342e-05 -1.052e-05 1.02 1.765e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1045 0.09224 0.1822 0.2001 0.9873 0.9919 0.1046 0.7555 0.8659 0.3056 ] Network output: [ -0.004882 0.02346 1.004 2.483e-05 -1.115e-05 0.9828 1.871e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0913 0.08936 0.1651 0.1955 0.9853 0.9912 0.09131 0.6801 0.8421 0.246 ] Network output: [ 0.0001393 1 -0.0001696 3.319e-06 -1.49e-06 0.9999 2.501e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003555 Epoch 8339 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01039 0.9959 0.9909 -7.334e-10 3.293e-10 -0.007495 -5.527e-10 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003388 -0.003202 -0.007685 0.006041 0.9699 0.9743 0.006521 0.8323 0.8239 0.01772 ] Network output: [ 0.9998 0.0004451 0.000742 -1.24e-05 5.565e-06 -0.0009202 -9.342e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1987 -0.03406 -0.172 0.1886 0.9835 0.9932 0.2224 0.439 0.8707 0.7154 ] Network output: [ -0.01015 1.002 1.009 -2.313e-07 1.038e-07 0.008723 -1.743e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006184 0.0005098 0.004446 0.003561 0.9889 0.9919 0.006301 0.8604 0.8948 0.01277 ] Network output: [ -0.0004769 0.002456 1.001 -3.889e-05 1.746e-05 0.9973 -2.931e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.211 0.09834 0.3408 0.1452 0.985 0.994 0.2117 0.4432 0.8774 0.7096 ] Network output: [ 0.005143 -0.02453 0.9945 2.34e-05 -1.051e-05 1.02 1.764e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1045 0.09225 0.1822 0.2001 0.9873 0.9919 0.1046 0.7555 0.8659 0.3056 ] Network output: [ -0.004881 0.02345 1.004 2.48e-05 -1.114e-05 0.9828 1.869e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0913 0.08936 0.1651 0.1955 0.9853 0.9912 0.09131 0.6801 0.8421 0.246 ] Network output: [ 0.0001393 1 -0.0001694 3.315e-06 -1.488e-06 0.9999 2.499e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003553 Epoch 8340 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01039 0.9959 0.9909 -1.487e-09 6.674e-10 -0.007495 -1.12e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003388 -0.003202 -0.007684 0.006041 0.9699 0.9743 0.006521 0.8323 0.8239 0.01772 ] Network output: [ 0.9998 0.0004447 0.0007416 -1.238e-05 5.559e-06 -0.0009194 -9.332e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1987 -0.03406 -0.1719 0.1886 0.9835 0.9932 0.2224 0.439 0.8707 0.7154 ] Network output: [ -0.01015 1.002 1.009 -2.317e-07 1.04e-07 0.008722 -1.746e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006185 0.0005098 0.004446 0.003561 0.9889 0.9919 0.006302 0.8604 0.8948 0.01277 ] Network output: [ -0.0004766 0.002455 1.001 -3.885e-05 1.744e-05 0.9973 -2.928e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.211 0.09835 0.3408 0.1452 0.985 0.994 0.2117 0.4432 0.8774 0.7096 ] Network output: [ 0.005141 -0.02452 0.9945 2.338e-05 -1.049e-05 1.02 1.762e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1045 0.09225 0.1822 0.2001 0.9873 0.9919 0.1046 0.7555 0.8659 0.3056 ] Network output: [ -0.004879 0.02344 1.004 2.478e-05 -1.112e-05 0.9828 1.867e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0913 0.08936 0.1651 0.1955 0.9853 0.9912 0.09131 0.68 0.8421 0.246 ] Network output: [ 0.0001392 1 -0.0001692 3.312e-06 -1.487e-06 0.9999 2.496e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003551 Epoch 8341 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01039 0.9959 0.9909 -2.238e-09 1.005e-09 -0.007496 -1.687e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003388 -0.003203 -0.007683 0.00604 0.9699 0.9743 0.006521 0.8323 0.8239 0.01772 ] Network output: [ 0.9998 0.0004444 0.0007411 -1.237e-05 5.553e-06 -0.0009186 -9.323e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1987 -0.03406 -0.1719 0.1886 0.9835 0.9932 0.2225 0.439 0.8707 0.7154 ] Network output: [ -0.01015 1.002 1.009 -2.321e-07 1.042e-07 0.00872 -1.749e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006185 0.0005099 0.004446 0.00356 0.9889 0.9919 0.006302 0.8603 0.8948 0.01277 ] Network output: [ -0.0004763 0.002454 1.001 -3.881e-05 1.742e-05 0.9973 -2.924e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.211 0.09835 0.3408 0.1452 0.985 0.994 0.2117 0.4432 0.8774 0.7096 ] Network output: [ 0.005139 -0.02451 0.9945 2.335e-05 -1.048e-05 1.02 1.76e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1045 0.09226 0.1822 0.2001 0.9873 0.9919 0.1046 0.7555 0.8659 0.3056 ] Network output: [ -0.004877 0.02343 1.004 2.476e-05 -1.111e-05 0.9828 1.866e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0913 0.08937 0.1651 0.1955 0.9853 0.9912 0.09132 0.68 0.8421 0.246 ] Network output: [ 0.0001391 1 -0.000169 3.309e-06 -1.485e-06 0.9999 2.493e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003549 Epoch 8342 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01038 0.9959 0.9909 -2.988e-09 1.341e-09 -0.007496 -2.251e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003388 -0.003203 -0.007682 0.00604 0.9699 0.9743 0.006521 0.8323 0.8239 0.01772 ] Network output: [ 0.9998 0.000444 0.0007407 -1.236e-05 5.548e-06 -0.0009178 -9.313e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1988 -0.03406 -0.1719 0.1886 0.9835 0.9932 0.2225 0.439 0.8707 0.7154 ] Network output: [ -0.01015 1.002 1.009 -2.324e-07 1.043e-07 0.008719 -1.752e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006186 0.00051 0.004446 0.00356 0.9889 0.9919 0.006303 0.8603 0.8948 0.01277 ] Network output: [ -0.000476 0.002454 1.001 -3.876e-05 1.74e-05 0.9973 -2.921e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.211 0.09836 0.3408 0.1452 0.985 0.994 0.2117 0.4432 0.8774 0.7096 ] Network output: [ 0.005138 -0.0245 0.9945 2.333e-05 -1.047e-05 1.02 1.758e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1045 0.09226 0.1822 0.2 0.9873 0.9919 0.1046 0.7555 0.8659 0.3056 ] Network output: [ -0.004875 0.02342 1.004 2.473e-05 -1.11e-05 0.9828 1.864e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0913 0.08937 0.1651 0.1955 0.9853 0.9912 0.09132 0.68 0.8421 0.246 ] Network output: [ 0.0001391 1 -0.0001688 3.305e-06 -1.484e-06 0.9999 2.491e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003547 Epoch 8343 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01038 0.9959 0.9909 -3.735e-09 1.677e-09 -0.007496 -2.815e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003389 -0.003203 -0.007681 0.006039 0.9699 0.9743 0.006522 0.8323 0.8239 0.01772 ] Network output: [ 0.9998 0.0004436 0.0007403 -1.234e-05 5.542e-06 -0.0009171 -9.303e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1988 -0.03406 -0.1719 0.1886 0.9835 0.9932 0.2225 0.439 0.8707 0.7154 ] Network output: [ -0.01015 1.002 1.009 -2.328e-07 1.045e-07 0.008717 -1.754e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006186 0.0005101 0.004446 0.00356 0.9889 0.9919 0.006304 0.8603 0.8948 0.01276 ] Network output: [ -0.0004757 0.002453 1.001 -3.872e-05 1.738e-05 0.9973 -2.918e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.211 0.09837 0.3409 0.1452 0.985 0.994 0.2117 0.4432 0.8774 0.7096 ] Network output: [ 0.005136 -0.02449 0.9945 2.33e-05 -1.046e-05 1.02 1.756e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1045 0.09227 0.1822 0.2 0.9873 0.9919 0.1046 0.7555 0.8659 0.3055 ] Network output: [ -0.004874 0.02341 1.004 2.471e-05 -1.109e-05 0.9828 1.862e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09131 0.08937 0.1651 0.1955 0.9853 0.9912 0.09132 0.68 0.8421 0.246 ] Network output: [ 0.000139 1 -0.0001686 3.302e-06 -1.482e-06 0.9999 2.488e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003545 Epoch 8344 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01038 0.9959 0.9909 -4.481e-09 2.012e-09 -0.007496 -3.377e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003389 -0.003203 -0.00768 0.006038 0.9699 0.9743 0.006522 0.8323 0.8239 0.01772 ] Network output: [ 0.9998 0.0004433 0.0007398 -1.233e-05 5.536e-06 -0.0009163 -9.294e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1988 -0.03406 -0.1719 0.1886 0.9835 0.9932 0.2225 0.439 0.8707 0.7154 ] Network output: [ -0.01015 1.002 1.009 -2.331e-07 1.047e-07 0.008715 -1.757e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006187 0.0005102 0.004446 0.003559 0.9889 0.9919 0.006304 0.8603 0.8948 0.01276 ] Network output: [ -0.0004754 0.002452 1.001 -3.868e-05 1.737e-05 0.9973 -2.915e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.211 0.09837 0.3409 0.1452 0.985 0.994 0.2117 0.4432 0.8774 0.7096 ] Network output: [ 0.005134 -0.02449 0.9945 2.328e-05 -1.045e-05 1.02 1.754e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1045 0.09228 0.1822 0.2 0.9873 0.9919 0.1046 0.7554 0.8659 0.3055 ] Network output: [ -0.004872 0.0234 1.004 2.468e-05 -1.108e-05 0.9828 1.86e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09131 0.08937 0.1651 0.1955 0.9853 0.9912 0.09132 0.68 0.8421 0.246 ] Network output: [ 0.0001389 1 -0.0001684 3.298e-06 -1.481e-06 0.9999 2.486e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003543 Epoch 8345 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01038 0.9959 0.9909 -5.225e-09 2.346e-09 -0.007496 -3.938e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003389 -0.003203 -0.007679 0.006038 0.9699 0.9743 0.006522 0.8323 0.8239 0.01772 ] Network output: [ 0.9998 0.0004429 0.0007394 -1.232e-05 5.53e-06 -0.0009155 -9.284e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1988 -0.03407 -0.1719 0.1886 0.9835 0.9932 0.2225 0.439 0.8707 0.7154 ] Network output: [ -0.01015 1.002 1.009 -2.335e-07 1.048e-07 0.008714 -1.76e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006187 0.0005103 0.004446 0.003559 0.9889 0.9919 0.006305 0.8603 0.8948 0.01276 ] Network output: [ -0.0004751 0.002451 1.001 -3.864e-05 1.735e-05 0.9973 -2.912e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.211 0.09838 0.3409 0.1452 0.985 0.994 0.2117 0.4432 0.8774 0.7096 ] Network output: [ 0.005132 -0.02448 0.9945 2.326e-05 -1.044e-05 1.02 1.753e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1045 0.09228 0.1822 0.2 0.9873 0.9919 0.1046 0.7554 0.8659 0.3055 ] Network output: [ -0.00487 0.02339 1.004 2.466e-05 -1.107e-05 0.9828 1.858e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09131 0.08937 0.1651 0.1955 0.9853 0.9912 0.09132 0.6799 0.8421 0.246 ] Network output: [ 0.0001389 1 -0.0001682 3.295e-06 -1.479e-06 0.9999 2.483e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003541 Epoch 8346 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01038 0.9959 0.9909 -5.967e-09 2.679e-09 -0.007496 -4.497e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003389 -0.003203 -0.007678 0.006037 0.9699 0.9743 0.006523 0.8323 0.8239 0.01771 ] Network output: [ 0.9998 0.0004425 0.000739 -1.231e-05 5.525e-06 -0.0009148 -9.274e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1988 -0.03407 -0.1719 0.1886 0.9835 0.9932 0.2225 0.439 0.8707 0.7154 ] Network output: [ -0.01015 1.002 1.009 -2.339e-07 1.05e-07 0.008712 -1.763e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006188 0.0005104 0.004446 0.003558 0.9889 0.9919 0.006305 0.8603 0.8948 0.01276 ] Network output: [ -0.0004748 0.00245 1.001 -3.86e-05 1.733e-05 0.9973 -2.909e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.211 0.09838 0.3409 0.1452 0.985 0.994 0.2117 0.4432 0.8774 0.7096 ] Network output: [ 0.005131 -0.02447 0.9945 2.323e-05 -1.043e-05 1.02 1.751e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1045 0.09229 0.1822 0.2 0.9873 0.9919 0.1046 0.7554 0.8659 0.3055 ] Network output: [ -0.004868 0.02338 1.004 2.463e-05 -1.106e-05 0.9828 1.856e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09131 0.08938 0.1651 0.1955 0.9853 0.9912 0.09133 0.6799 0.8421 0.246 ] Network output: [ 0.0001388 1 -0.000168 3.292e-06 -1.478e-06 0.9999 2.481e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003539 Epoch 8347 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01038 0.9959 0.9909 -6.707e-09 3.011e-09 -0.007496 -5.055e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003389 -0.003203 -0.007677 0.006037 0.9699 0.9743 0.006523 0.8322 0.8239 0.01771 ] Network output: [ 0.9998 0.0004422 0.0007385 -1.229e-05 5.519e-06 -0.000914 -9.265e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1988 -0.03407 -0.1718 0.1886 0.9835 0.9932 0.2225 0.439 0.8707 0.7154 ] Network output: [ -0.01015 1.002 1.009 -2.342e-07 1.052e-07 0.00871 -1.765e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006189 0.0005105 0.004446 0.003558 0.9889 0.9919 0.006306 0.8603 0.8948 0.01276 ] Network output: [ -0.0004745 0.002449 1.001 -3.856e-05 1.731e-05 0.9973 -2.906e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.211 0.09839 0.3409 0.1452 0.985 0.994 0.2117 0.4432 0.8774 0.7096 ] Network output: [ 0.005129 -0.02446 0.9945 2.321e-05 -1.042e-05 1.02 1.749e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1046 0.09229 0.1822 0.2 0.9873 0.9919 0.1046 0.7554 0.8659 0.3055 ] Network output: [ -0.004867 0.02337 1.004 2.461e-05 -1.105e-05 0.9828 1.854e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09131 0.08938 0.1651 0.1955 0.9853 0.9912 0.09133 0.6799 0.8421 0.246 ] Network output: [ 0.0001387 1 -0.0001679 3.288e-06 -1.476e-06 0.9999 2.478e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003537 Epoch 8348 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01038 0.9959 0.9909 -7.446e-09 3.343e-09 -0.007497 -5.611e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003389 -0.003204 -0.007677 0.006036 0.9699 0.9743 0.006523 0.8322 0.8239 0.01771 ] Network output: [ 0.9998 0.0004418 0.0007381 -1.228e-05 5.513e-06 -0.0009132 -9.255e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1988 -0.03407 -0.1718 0.1886 0.9835 0.9932 0.2225 0.4389 0.8707 0.7153 ] Network output: [ -0.01014 1.002 1.009 -2.346e-07 1.053e-07 0.008709 -1.768e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006189 0.0005106 0.004446 0.003558 0.9889 0.9919 0.006306 0.8603 0.8947 0.01276 ] Network output: [ -0.0004742 0.002448 1.001 -3.852e-05 1.729e-05 0.9973 -2.903e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.211 0.0984 0.3409 0.1452 0.985 0.994 0.2117 0.4432 0.8774 0.7096 ] Network output: [ 0.005127 -0.02445 0.9945 2.318e-05 -1.041e-05 1.02 1.747e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1046 0.0923 0.1822 0.2 0.9873 0.9919 0.1046 0.7554 0.8658 0.3055 ] Network output: [ -0.004865 0.02336 1.004 2.458e-05 -1.104e-05 0.9828 1.853e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09132 0.08938 0.1651 0.1955 0.9853 0.9912 0.09133 0.6799 0.8421 0.246 ] Network output: [ 0.0001387 1 -0.0001677 3.285e-06 -1.475e-06 0.9999 2.476e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003535 Epoch 8349 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01037 0.9959 0.9909 -8.182e-09 3.673e-09 -0.007497 -6.166e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003389 -0.003204 -0.007676 0.006035 0.9699 0.9743 0.006523 0.8322 0.8239 0.01771 ] Network output: [ 0.9998 0.0004414 0.0007377 -1.227e-05 5.507e-06 -0.0009125 -9.245e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1988 -0.03407 -0.1718 0.1886 0.9835 0.9932 0.2225 0.4389 0.8707 0.7153 ] Network output: [ -0.01014 1.002 1.009 -2.349e-07 1.055e-07 0.008707 -1.771e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00619 0.0005106 0.004446 0.003557 0.9889 0.9919 0.006307 0.8603 0.8947 0.01276 ] Network output: [ -0.0004739 0.002448 1.001 -3.848e-05 1.728e-05 0.9973 -2.9e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2111 0.0984 0.3409 0.1452 0.985 0.994 0.2118 0.4431 0.8774 0.7096 ] Network output: [ 0.005126 -0.02444 0.9945 2.316e-05 -1.04e-05 1.02 1.745e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1046 0.09231 0.1822 0.2 0.9873 0.9919 0.1046 0.7553 0.8658 0.3055 ] Network output: [ -0.004863 0.02335 1.004 2.456e-05 -1.102e-05 0.9828 1.851e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09132 0.08938 0.1651 0.1955 0.9853 0.9912 0.09133 0.6799 0.8421 0.246 ] Network output: [ 0.0001386 1 -0.0001675 3.281e-06 -1.473e-06 0.9999 2.473e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003533 Epoch 8350 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01037 0.9959 0.9909 -8.917e-09 4.003e-09 -0.007497 -6.72e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003389 -0.003204 -0.007675 0.006035 0.9699 0.9743 0.006524 0.8322 0.8239 0.01771 ] Network output: [ 0.9998 0.000441 0.0007372 -1.226e-05 5.502e-06 -0.0009117 -9.236e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1988 -0.03407 -0.1718 0.1886 0.9835 0.9932 0.2226 0.4389 0.8707 0.7153 ] Network output: [ -0.01014 1.002 1.009 -2.353e-07 1.056e-07 0.008706 -1.773e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00619 0.0005107 0.004446 0.003557 0.9889 0.9919 0.006308 0.8603 0.8947 0.01276 ] Network output: [ -0.0004736 0.002447 1.001 -3.844e-05 1.726e-05 0.9973 -2.897e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2111 0.09841 0.3409 0.1452 0.985 0.994 0.2118 0.4431 0.8774 0.7095 ] Network output: [ 0.005124 -0.02443 0.9945 2.314e-05 -1.039e-05 1.02 1.744e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1046 0.09231 0.1822 0.2 0.9873 0.9919 0.1046 0.7553 0.8658 0.3055 ] Network output: [ -0.004861 0.02334 1.004 2.453e-05 -1.101e-05 0.9828 1.849e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09132 0.08938 0.1651 0.1955 0.9853 0.9912 0.09133 0.6798 0.8421 0.246 ] Network output: [ 0.0001385 1 -0.0001673 3.278e-06 -1.472e-06 0.9999 2.47e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003531 Epoch 8351 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01037 0.9959 0.9909 -9.65e-09 4.332e-09 -0.007497 -7.273e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00339 -0.003204 -0.007674 0.006034 0.9699 0.9743 0.006524 0.8322 0.8239 0.01771 ] Network output: [ 0.9998 0.0004407 0.0007368 -1.224e-05 5.496e-06 -0.0009109 -9.226e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1988 -0.03408 -0.1718 0.1886 0.9835 0.9932 0.2226 0.4389 0.8707 0.7153 ] Network output: [ -0.01014 1.002 1.009 -2.357e-07 1.058e-07 0.008704 -1.776e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006191 0.0005108 0.004446 0.003557 0.9889 0.9919 0.006308 0.8603 0.8947 0.01276 ] Network output: [ -0.0004733 0.002446 1.001 -3.84e-05 1.724e-05 0.9973 -2.894e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2111 0.09841 0.3409 0.1452 0.985 0.994 0.2118 0.4431 0.8773 0.7095 ] Network output: [ 0.005122 -0.02442 0.9945 2.311e-05 -1.038e-05 1.02 1.742e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1046 0.09232 0.1823 0.2 0.9873 0.9919 0.1047 0.7553 0.8658 0.3055 ] Network output: [ -0.00486 0.02333 1.004 2.451e-05 -1.1e-05 0.9828 1.847e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09132 0.08939 0.1651 0.1955 0.9853 0.9912 0.09134 0.6798 0.842 0.246 ] Network output: [ 0.0001385 1 -0.0001671 3.275e-06 -1.47e-06 0.9999 2.468e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003529 Epoch 8352 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01037 0.9959 0.9909 -1.038e-08 4.66e-09 -0.007497 -7.824e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00339 -0.003204 -0.007673 0.006034 0.9699 0.9743 0.006524 0.8322 0.8239 0.01771 ] Network output: [ 0.9998 0.0004403 0.0007364 -1.223e-05 5.49e-06 -0.0009102 -9.217e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1988 -0.03408 -0.1718 0.1886 0.9835 0.9932 0.2226 0.4389 0.8707 0.7153 ] Network output: [ -0.01014 1.002 1.009 -2.36e-07 1.06e-07 0.008702 -1.779e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006191 0.0005109 0.004446 0.003556 0.9889 0.9919 0.006309 0.8603 0.8947 0.01276 ] Network output: [ -0.000473 0.002445 1.001 -3.836e-05 1.722e-05 0.9973 -2.891e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2111 0.09842 0.3409 0.1452 0.985 0.994 0.2118 0.4431 0.8773 0.7095 ] Network output: [ 0.00512 -0.02442 0.9945 2.309e-05 -1.037e-05 1.02 1.74e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1046 0.09232 0.1823 0.2 0.9873 0.9919 0.1047 0.7553 0.8658 0.3055 ] Network output: [ -0.004858 0.02332 1.004 2.448e-05 -1.099e-05 0.9828 1.845e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09133 0.08939 0.1651 0.1955 0.9853 0.9912 0.09134 0.6798 0.842 0.246 ] Network output: [ 0.0001384 1 -0.0001669 3.271e-06 -1.469e-06 0.9999 2.465e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003527 Epoch 8353 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01037 0.9959 0.9909 -1.111e-08 4.988e-09 -0.007497 -8.373e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00339 -0.003204 -0.007672 0.006033 0.9699 0.9743 0.006525 0.8322 0.8239 0.01771 ] Network output: [ 0.9998 0.0004399 0.0007359 -1.222e-05 5.485e-06 -0.0009094 -9.207e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1989 -0.03408 -0.1718 0.1886 0.9835 0.9932 0.2226 0.4389 0.8707 0.7153 ] Network output: [ -0.01014 1.002 1.009 -2.364e-07 1.061e-07 0.008701 -1.781e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006192 0.000511 0.004446 0.003556 0.9889 0.9919 0.006309 0.8603 0.8947 0.01275 ] Network output: [ -0.0004727 0.002444 1.001 -3.832e-05 1.72e-05 0.9973 -2.888e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2111 0.09843 0.3409 0.1452 0.985 0.994 0.2118 0.4431 0.8773 0.7095 ] Network output: [ 0.005119 -0.02441 0.9945 2.306e-05 -1.035e-05 1.02 1.738e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1046 0.09233 0.1823 0.2 0.9873 0.9919 0.1047 0.7553 0.8658 0.3055 ] Network output: [ -0.004856 0.02331 1.004 2.446e-05 -1.098e-05 0.9828 1.843e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09133 0.08939 0.1651 0.1955 0.9853 0.9912 0.09134 0.6798 0.842 0.246 ] Network output: [ 0.0001384 1 -0.0001667 3.268e-06 -1.467e-06 0.9999 2.463e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003525 Epoch 8354 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01037 0.9959 0.9909 -1.184e-08 5.314e-09 -0.007497 -8.921e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00339 -0.003204 -0.007671 0.006032 0.9699 0.9743 0.006525 0.8322 0.8239 0.0177 ] Network output: [ 0.9998 0.0004396 0.0007355 -1.22e-05 5.479e-06 -0.0009087 -9.198e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1989 -0.03408 -0.1717 0.1886 0.9835 0.9932 0.2226 0.4389 0.8707 0.7153 ] Network output: [ -0.01014 1.002 1.009 -2.367e-07 1.063e-07 0.008699 -1.784e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006193 0.0005111 0.004446 0.003556 0.9889 0.9919 0.00631 0.8602 0.8947 0.01275 ] Network output: [ -0.0004724 0.002443 1.001 -3.828e-05 1.719e-05 0.9973 -2.885e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2111 0.09843 0.3409 0.1452 0.985 0.994 0.2118 0.4431 0.8773 0.7095 ] Network output: [ 0.005117 -0.0244 0.9945 2.304e-05 -1.034e-05 1.02 1.736e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1046 0.09233 0.1823 0.2 0.9873 0.9919 0.1047 0.7552 0.8658 0.3055 ] Network output: [ -0.004854 0.0233 1.004 2.443e-05 -1.097e-05 0.9828 1.841e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09133 0.08939 0.1651 0.1955 0.9853 0.9912 0.09134 0.6798 0.842 0.246 ] Network output: [ 0.0001383 1 -0.0001665 3.265e-06 -1.466e-06 0.9999 2.46e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003523 Epoch 8355 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01037 0.9959 0.9909 -1.256e-08 5.64e-09 -0.007497 -9.468e-09 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00339 -0.003205 -0.00767 0.006032 0.9699 0.9743 0.006525 0.8322 0.8239 0.0177 ] Network output: [ 0.9998 0.0004392 0.0007351 -1.219e-05 5.473e-06 -0.0009079 -9.188e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1989 -0.03408 -0.1717 0.1885 0.9835 0.9932 0.2226 0.4389 0.8707 0.7153 ] Network output: [ -0.01014 1.002 1.009 -2.371e-07 1.064e-07 0.008698 -1.787e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006193 0.0005112 0.004446 0.003555 0.9889 0.9919 0.006311 0.8602 0.8947 0.01275 ] Network output: [ -0.0004721 0.002442 1.001 -3.824e-05 1.717e-05 0.9973 -2.882e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2111 0.09844 0.341 0.1452 0.985 0.994 0.2118 0.4431 0.8773 0.7095 ] Network output: [ 0.005115 -0.02439 0.9945 2.302e-05 -1.033e-05 1.02 1.735e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1046 0.09234 0.1823 0.2 0.9873 0.9919 0.1047 0.7552 0.8658 0.3055 ] Network output: [ -0.004853 0.02329 1.004 2.441e-05 -1.096e-05 0.9828 1.84e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09133 0.08939 0.1651 0.1955 0.9853 0.9912 0.09134 0.6797 0.842 0.246 ] Network output: [ 0.0001382 1 -0.0001663 3.261e-06 -1.464e-06 0.9999 2.458e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003521 Epoch 8356 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01037 0.9959 0.9909 -1.329e-08 5.965e-09 -0.007498 -1.001e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00339 -0.003205 -0.007669 0.006031 0.9699 0.9743 0.006526 0.8322 0.8239 0.0177 ] Network output: [ 0.9998 0.0004388 0.0007346 -1.218e-05 5.468e-06 -0.0009071 -9.178e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1989 -0.03408 -0.1717 0.1885 0.9835 0.9932 0.2226 0.4389 0.8707 0.7153 ] Network output: [ -0.01014 1.002 1.009 -2.374e-07 1.066e-07 0.008696 -1.789e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006194 0.0005113 0.004446 0.003555 0.9889 0.9919 0.006311 0.8602 0.8947 0.01275 ] Network output: [ -0.0004718 0.002441 1.001 -3.82e-05 1.715e-05 0.9973 -2.879e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2111 0.09844 0.341 0.1452 0.985 0.994 0.2118 0.4431 0.8773 0.7095 ] Network output: [ 0.005114 -0.02438 0.9945 2.299e-05 -1.032e-05 1.02 1.733e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1046 0.09235 0.1823 0.2 0.9873 0.9919 0.1047 0.7552 0.8658 0.3055 ] Network output: [ -0.004851 0.02328 1.004 2.438e-05 -1.095e-05 0.9828 1.838e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09133 0.0894 0.1651 0.1955 0.9853 0.9912 0.09135 0.6797 0.842 0.246 ] Network output: [ 0.0001382 1 -0.0001661 3.258e-06 -1.463e-06 0.9999 2.455e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003519 Epoch 8357 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01036 0.9959 0.9909 -1.401e-08 6.289e-09 -0.007498 -1.056e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00339 -0.003205 -0.007668 0.006031 0.9699 0.9743 0.006526 0.8322 0.8239 0.0177 ] Network output: [ 0.9998 0.0004385 0.0007342 -1.217e-05 5.462e-06 -0.0009064 -9.169e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1989 -0.03409 -0.1717 0.1885 0.9835 0.9932 0.2226 0.4389 0.8707 0.7153 ] Network output: [ -0.01013 1.002 1.009 -2.377e-07 1.067e-07 0.008695 -1.792e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006194 0.0005114 0.004446 0.003554 0.9889 0.9919 0.006312 0.8602 0.8947 0.01275 ] Network output: [ -0.0004715 0.002441 1.001 -3.816e-05 1.713e-05 0.9973 -2.876e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2111 0.09845 0.341 0.1451 0.985 0.994 0.2118 0.4431 0.8773 0.7095 ] Network output: [ 0.005112 -0.02437 0.9945 2.297e-05 -1.031e-05 1.02 1.731e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1046 0.09235 0.1823 0.2 0.9873 0.9919 0.1047 0.7552 0.8658 0.3055 ] Network output: [ -0.004849 0.02328 1.004 2.436e-05 -1.094e-05 0.9828 1.836e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09134 0.0894 0.1651 0.1955 0.9853 0.9912 0.09135 0.6797 0.842 0.246 ] Network output: [ 0.0001381 1 -0.0001659 3.255e-06 -1.461e-06 0.9999 2.453e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003517 Epoch 8358 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01036 0.9959 0.9909 -1.473e-08 6.613e-09 -0.007498 -1.11e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00339 -0.003205 -0.007667 0.00603 0.9699 0.9743 0.006526 0.8322 0.8239 0.0177 ] Network output: [ 0.9998 0.0004381 0.0007338 -1.215e-05 5.456e-06 -0.0009056 -9.159e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1989 -0.03409 -0.1717 0.1885 0.9835 0.9932 0.2226 0.4388 0.8707 0.7153 ] Network output: [ -0.01013 1.002 1.009 -2.381e-07 1.069e-07 0.008693 -1.794e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006195 0.0005115 0.004446 0.003554 0.9889 0.9919 0.006312 0.8602 0.8947 0.01275 ] Network output: [ -0.0004712 0.00244 1.001 -3.812e-05 1.711e-05 0.9973 -2.873e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2112 0.09846 0.341 0.1451 0.985 0.994 0.2118 0.443 0.8773 0.7095 ] Network output: [ 0.00511 -0.02436 0.9945 2.295e-05 -1.03e-05 1.02 1.729e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1046 0.09236 0.1823 0.2 0.9873 0.9919 0.1047 0.7552 0.8658 0.3055 ] Network output: [ -0.004847 0.02327 1.004 2.434e-05 -1.092e-05 0.9828 1.834e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09134 0.0894 0.1651 0.1955 0.9853 0.9912 0.09135 0.6797 0.842 0.246 ] Network output: [ 0.000138 1 -0.0001657 3.251e-06 -1.46e-06 0.9999 2.45e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003515 Epoch 8359 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01036 0.9959 0.9909 -1.545e-08 6.935e-09 -0.007498 -1.164e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003391 -0.003205 -0.007666 0.006029 0.9699 0.9743 0.006526 0.8322 0.8239 0.0177 ] Network output: [ 0.9998 0.0004377 0.0007334 -1.214e-05 5.451e-06 -0.0009049 -9.15e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1989 -0.03409 -0.1717 0.1885 0.9835 0.9932 0.2226 0.4388 0.8707 0.7153 ] Network output: [ -0.01013 1.002 1.009 -2.384e-07 1.07e-07 0.008691 -1.797e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006195 0.0005115 0.004446 0.003554 0.9889 0.9919 0.006313 0.8602 0.8947 0.01275 ] Network output: [ -0.0004709 0.002439 1.001 -3.808e-05 1.71e-05 0.9973 -2.87e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2112 0.09846 0.341 0.1451 0.985 0.994 0.2119 0.443 0.8773 0.7095 ] Network output: [ 0.005108 -0.02435 0.9945 2.292e-05 -1.029e-05 1.02 1.727e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1046 0.09236 0.1823 0.2 0.9873 0.9919 0.1047 0.7551 0.8658 0.3055 ] Network output: [ -0.004846 0.02326 1.004 2.431e-05 -1.091e-05 0.9828 1.832e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09134 0.0894 0.1651 0.1955 0.9853 0.9912 0.09135 0.6797 0.842 0.246 ] Network output: [ 0.000138 1 -0.0001655 3.248e-06 -1.458e-06 0.9999 2.448e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003513 Epoch 8360 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01036 0.9959 0.9909 -1.616e-08 7.257e-09 -0.007498 -1.218e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003391 -0.003205 -0.007665 0.006029 0.9699 0.9743 0.006527 0.8322 0.8239 0.0177 ] Network output: [ 0.9998 0.0004374 0.0007329 -1.213e-05 5.445e-06 -0.0009041 -9.14e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1989 -0.03409 -0.1717 0.1885 0.9835 0.9932 0.2227 0.4388 0.8707 0.7153 ] Network output: [ -0.01013 1.002 1.009 -2.388e-07 1.072e-07 0.00869 -1.8e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006196 0.0005116 0.004446 0.003553 0.9889 0.9919 0.006313 0.8602 0.8947 0.01275 ] Network output: [ -0.0004706 0.002438 1.001 -3.804e-05 1.708e-05 0.9973 -2.867e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2112 0.09847 0.341 0.1451 0.985 0.994 0.2119 0.443 0.8773 0.7095 ] Network output: [ 0.005107 -0.02434 0.9945 2.29e-05 -1.028e-05 1.02 1.726e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1046 0.09237 0.1823 0.2 0.9873 0.9919 0.1047 0.7551 0.8658 0.3055 ] Network output: [ -0.004844 0.02325 1.004 2.429e-05 -1.09e-05 0.9828 1.83e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09134 0.08941 0.1651 0.1955 0.9853 0.9912 0.09136 0.6796 0.842 0.246 ] Network output: [ 0.0001379 1 -0.0001653 3.245e-06 -1.457e-06 0.9999 2.445e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003511 Epoch 8361 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01036 0.9959 0.9909 -1.688e-08 7.578e-09 -0.007498 -1.272e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003391 -0.003205 -0.007665 0.006028 0.9699 0.9743 0.006527 0.8322 0.8238 0.01769 ] Network output: [ 0.9998 0.000437 0.0007325 -1.212e-05 5.439e-06 -0.0009033 -9.131e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1989 -0.03409 -0.1716 0.1885 0.9835 0.9932 0.2227 0.4388 0.8707 0.7153 ] Network output: [ -0.01013 1.002 1.009 -2.391e-07 1.074e-07 0.008688 -1.802e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006197 0.0005117 0.004446 0.003553 0.9889 0.9919 0.006314 0.8602 0.8947 0.01275 ] Network output: [ -0.0004703 0.002437 1.001 -3.8e-05 1.706e-05 0.9973 -2.864e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2112 0.09847 0.341 0.1451 0.985 0.994 0.2119 0.443 0.8773 0.7095 ] Network output: [ 0.005105 -0.02434 0.9945 2.287e-05 -1.027e-05 1.02 1.724e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1046 0.09237 0.1823 0.2 0.9873 0.9919 0.1047 0.7551 0.8658 0.3055 ] Network output: [ -0.004842 0.02324 1.004 2.426e-05 -1.089e-05 0.9829 1.828e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09134 0.08941 0.1651 0.1955 0.9853 0.9912 0.09136 0.6796 0.842 0.246 ] Network output: [ 0.0001378 1 -0.0001651 3.241e-06 -1.455e-06 0.9999 2.443e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003509 Epoch 8362 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01036 0.9959 0.9909 -1.759e-08 7.898e-09 -0.007498 -1.326e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003391 -0.003206 -0.007664 0.006028 0.9699 0.9743 0.006527 0.8321 0.8238 0.01769 ] Network output: [ 0.9998 0.0004366 0.0007321 -1.21e-05 5.434e-06 -0.0009026 -9.121e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1989 -0.03409 -0.1716 0.1885 0.9835 0.9932 0.2227 0.4388 0.8707 0.7153 ] Network output: [ -0.01013 1.002 1.009 -2.395e-07 1.075e-07 0.008687 -1.805e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006197 0.0005118 0.004446 0.003553 0.9889 0.9919 0.006315 0.8602 0.8947 0.01275 ] Network output: [ -0.00047 0.002436 1.001 -3.796e-05 1.704e-05 0.9973 -2.861e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2112 0.09848 0.341 0.1451 0.985 0.994 0.2119 0.443 0.8773 0.7095 ] Network output: [ 0.005103 -0.02433 0.9945 2.285e-05 -1.026e-05 1.02 1.722e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1046 0.09238 0.1823 0.2 0.9873 0.9919 0.1047 0.7551 0.8658 0.3055 ] Network output: [ -0.00484 0.02323 1.004 2.424e-05 -1.088e-05 0.9829 1.827e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09135 0.08941 0.1651 0.1955 0.9853 0.9912 0.09136 0.6796 0.842 0.246 ] Network output: [ 0.0001378 1 -0.0001649 3.238e-06 -1.454e-06 0.9999 2.44e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003508 Epoch 8363 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01036 0.9959 0.9909 -1.83e-08 8.217e-09 -0.007498 -1.379e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003391 -0.003206 -0.007663 0.006027 0.9699 0.9743 0.006528 0.8321 0.8238 0.01769 ] Network output: [ 0.9998 0.0004363 0.0007316 -1.209e-05 5.428e-06 -0.0009018 -9.112e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1989 -0.0341 -0.1716 0.1885 0.9835 0.9932 0.2227 0.4388 0.8707 0.7153 ] Network output: [ -0.01013 1.002 1.009 -2.398e-07 1.077e-07 0.008685 -1.807e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006198 0.0005119 0.004446 0.003552 0.9889 0.9919 0.006315 0.8602 0.8947 0.01274 ] Network output: [ -0.0004697 0.002435 1.001 -3.792e-05 1.702e-05 0.9973 -2.858e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2112 0.09849 0.341 0.1451 0.985 0.994 0.2119 0.443 0.8773 0.7095 ] Network output: [ 0.005102 -0.02432 0.9945 2.283e-05 -1.025e-05 1.02 1.72e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1047 0.09239 0.1823 0.2 0.9873 0.9919 0.1047 0.7551 0.8658 0.3055 ] Network output: [ -0.004839 0.02322 1.004 2.421e-05 -1.087e-05 0.9829 1.825e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09135 0.08941 0.1651 0.1955 0.9853 0.9912 0.09136 0.6796 0.842 0.246 ] Network output: [ 0.0001377 1 -0.0001647 3.235e-06 -1.452e-06 0.9999 2.438e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003506 Epoch 8364 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01035 0.9959 0.9909 -1.901e-08 8.536e-09 -0.007499 -1.433e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003391 -0.003206 -0.007662 0.006026 0.9699 0.9743 0.006528 0.8321 0.8238 0.01769 ] Network output: [ 0.9998 0.0004359 0.0007312 -1.208e-05 5.422e-06 -0.0009011 -9.102e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.199 -0.0341 -0.1716 0.1885 0.9835 0.9932 0.2227 0.4388 0.8707 0.7153 ] Network output: [ -0.01013 1.002 1.009 -2.401e-07 1.078e-07 0.008683 -1.81e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006198 0.000512 0.004446 0.003552 0.9889 0.9919 0.006316 0.8602 0.8947 0.01274 ] Network output: [ -0.0004694 0.002435 1.001 -3.788e-05 1.701e-05 0.9973 -2.855e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2112 0.09849 0.341 0.1451 0.985 0.994 0.2119 0.443 0.8773 0.7095 ] Network output: [ 0.0051 -0.02431 0.9945 2.28e-05 -1.024e-05 1.02 1.719e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1047 0.09239 0.1823 0.2 0.9873 0.9919 0.1047 0.7551 0.8658 0.3055 ] Network output: [ -0.004837 0.02321 1.004 2.419e-05 -1.086e-05 0.9829 1.823e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09135 0.08941 0.1651 0.1955 0.9853 0.9912 0.09136 0.6796 0.842 0.246 ] Network output: [ 0.0001376 1 -0.0001646 3.231e-06 -1.451e-06 0.9999 2.435e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003504 Epoch 8365 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01035 0.9959 0.9909 -1.972e-08 8.853e-09 -0.007499 -1.486e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003391 -0.003206 -0.007661 0.006026 0.9699 0.9743 0.006528 0.8321 0.8238 0.01769 ] Network output: [ 0.9998 0.0004356 0.0007308 -1.207e-05 5.417e-06 -0.0009003 -9.093e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.199 -0.0341 -0.1716 0.1885 0.9835 0.9932 0.2227 0.4388 0.8707 0.7153 ] Network output: [ -0.01013 1.002 1.009 -2.405e-07 1.08e-07 0.008682 -1.812e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006199 0.0005121 0.004446 0.003551 0.9889 0.9919 0.006316 0.8602 0.8947 0.01274 ] Network output: [ -0.0004691 0.002434 1.001 -3.784e-05 1.699e-05 0.9973 -2.852e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2112 0.0985 0.341 0.1451 0.985 0.994 0.2119 0.443 0.8773 0.7095 ] Network output: [ 0.005098 -0.0243 0.9945 2.278e-05 -1.023e-05 1.02 1.717e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1047 0.0924 0.1823 0.2 0.9873 0.9919 0.1047 0.755 0.8658 0.3055 ] Network output: [ -0.004835 0.0232 1.004 2.416e-05 -1.085e-05 0.9829 1.821e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09135 0.08942 0.1651 0.1955 0.9853 0.9912 0.09137 0.6795 0.842 0.246 ] Network output: [ 0.0001376 1 -0.0001644 3.228e-06 -1.449e-06 0.9999 2.433e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003502 Epoch 8366 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01035 0.9959 0.9909 -2.043e-08 9.17e-09 -0.007499 -1.539e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003391 -0.003206 -0.00766 0.006025 0.9699 0.9743 0.006528 0.8321 0.8238 0.01769 ] Network output: [ 0.9998 0.0004352 0.0007304 -1.205e-05 5.411e-06 -0.0008996 -9.083e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.199 -0.0341 -0.1716 0.1885 0.9835 0.9932 0.2227 0.4388 0.8707 0.7152 ] Network output: [ -0.01013 1.002 1.009 -2.408e-07 1.081e-07 0.00868 -1.815e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006199 0.0005122 0.004446 0.003551 0.9889 0.9919 0.006317 0.8602 0.8947 0.01274 ] Network output: [ -0.0004688 0.002433 1.001 -3.78e-05 1.697e-05 0.9973 -2.849e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2112 0.0985 0.341 0.1451 0.985 0.994 0.2119 0.443 0.8773 0.7094 ] Network output: [ 0.005097 -0.02429 0.9945 2.276e-05 -1.022e-05 1.02 1.715e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1047 0.0924 0.1823 0.2 0.9873 0.9919 0.1047 0.755 0.8658 0.3055 ] Network output: [ -0.004834 0.02319 1.004 2.414e-05 -1.084e-05 0.9829 1.819e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09136 0.08942 0.1651 0.1955 0.9853 0.9912 0.09137 0.6795 0.842 0.246 ] Network output: [ 0.0001375 1 -0.0001642 3.225e-06 -1.448e-06 0.9999 2.43e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00035 Epoch 8367 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01035 0.9959 0.9909 -2.113e-08 9.486e-09 -0.007499 -1.592e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003392 -0.003206 -0.007659 0.006025 0.9699 0.9743 0.006529 0.8321 0.8238 0.01769 ] Network output: [ 0.9998 0.0004348 0.0007299 -1.204e-05 5.405e-06 -0.0008988 -9.074e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.199 -0.0341 -0.1716 0.1885 0.9835 0.9932 0.2227 0.4388 0.8707 0.7152 ] Network output: [ -0.01012 1.002 1.009 -2.412e-07 1.083e-07 0.008679 -1.817e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0062 0.0005123 0.004446 0.003551 0.9889 0.9919 0.006317 0.8601 0.8947 0.01274 ] Network output: [ -0.0004685 0.002432 1.001 -3.776e-05 1.695e-05 0.9973 -2.846e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2112 0.09851 0.3411 0.1451 0.985 0.994 0.2119 0.443 0.8773 0.7094 ] Network output: [ 0.005095 -0.02428 0.9945 2.273e-05 -1.021e-05 1.02 1.713e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1047 0.09241 0.1823 0.2 0.9873 0.9919 0.1048 0.755 0.8658 0.3055 ] Network output: [ -0.004832 0.02318 1.004 2.412e-05 -1.083e-05 0.9829 1.817e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09136 0.08942 0.1651 0.1955 0.9853 0.9912 0.09137 0.6795 0.8419 0.246 ] Network output: [ 0.0001374 1 -0.000164 3.221e-06 -1.446e-06 0.9999 2.428e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003498 Epoch 8368 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01035 0.9959 0.9909 -2.183e-08 9.802e-09 -0.007499 -1.645e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003392 -0.003206 -0.007658 0.006024 0.9699 0.9743 0.006529 0.8321 0.8238 0.01769 ] Network output: [ 0.9998 0.0004345 0.0007295 -1.203e-05 5.4e-06 -0.0008981 -9.064e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.199 -0.0341 -0.1716 0.1885 0.9835 0.9932 0.2227 0.4387 0.8707 0.7152 ] Network output: [ -0.01012 1.002 1.009 -2.415e-07 1.084e-07 0.008677 -1.82e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0062 0.0005124 0.004446 0.00355 0.9889 0.9919 0.006318 0.8601 0.8947 0.01274 ] Network output: [ -0.0004682 0.002431 1.001 -3.772e-05 1.693e-05 0.9973 -2.843e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2113 0.09851 0.3411 0.1451 0.985 0.994 0.2119 0.4429 0.8773 0.7094 ] Network output: [ 0.005093 -0.02427 0.9945 2.271e-05 -1.02e-05 1.02 1.711e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1047 0.09241 0.1823 0.2 0.9873 0.9919 0.1048 0.755 0.8657 0.3055 ] Network output: [ -0.00483 0.02317 1.004 2.409e-05 -1.082e-05 0.9829 1.816e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09136 0.08942 0.1651 0.1955 0.9853 0.9912 0.09137 0.6795 0.8419 0.246 ] Network output: [ 0.0001374 1 -0.0001638 3.218e-06 -1.445e-06 0.9999 2.425e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003496 Epoch 8369 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01035 0.9959 0.9909 -2.253e-08 1.012e-08 -0.007499 -1.698e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003392 -0.003207 -0.007657 0.006023 0.9699 0.9743 0.006529 0.8321 0.8238 0.01768 ] Network output: [ 0.9998 0.0004341 0.0007291 -1.202e-05 5.394e-06 -0.0008973 -9.055e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.199 -0.0341 -0.1715 0.1885 0.9835 0.9932 0.2228 0.4387 0.8707 0.7152 ] Network output: [ -0.01012 1.002 1.009 -2.418e-07 1.086e-07 0.008676 -1.822e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006201 0.0005124 0.004446 0.00355 0.9889 0.9919 0.006319 0.8601 0.8947 0.01274 ] Network output: [ -0.0004679 0.00243 1.001 -3.768e-05 1.692e-05 0.9973 -2.84e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2113 0.09852 0.3411 0.1451 0.985 0.994 0.212 0.4429 0.8773 0.7094 ] Network output: [ 0.005091 -0.02427 0.9945 2.269e-05 -1.018e-05 1.02 1.71e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1047 0.09242 0.1823 0.2 0.9873 0.9919 0.1048 0.755 0.8657 0.3055 ] Network output: [ -0.004828 0.02316 1.004 2.407e-05 -1.08e-05 0.9829 1.814e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09136 0.08942 0.1651 0.1955 0.9853 0.9912 0.09137 0.6795 0.8419 0.246 ] Network output: [ 0.0001373 1 -0.0001636 3.215e-06 -1.443e-06 0.9999 2.423e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003494 Epoch 8370 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01035 0.9959 0.9909 -2.323e-08 1.043e-08 -0.007499 -1.751e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003392 -0.003207 -0.007656 0.006023 0.9699 0.9743 0.00653 0.8321 0.8238 0.01768 ] Network output: [ 0.9998 0.0004337 0.0007287 -1.2e-05 5.388e-06 -0.0008966 -9.046e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.199 -0.03411 -0.1715 0.1885 0.9835 0.9932 0.2228 0.4387 0.8707 0.7152 ] Network output: [ -0.01012 1.002 1.009 -2.421e-07 1.087e-07 0.008674 -1.825e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006202 0.0005125 0.004446 0.00355 0.9889 0.9919 0.006319 0.8601 0.8947 0.01274 ] Network output: [ -0.0004676 0.002429 1.001 -3.764e-05 1.69e-05 0.9973 -2.837e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2113 0.09853 0.3411 0.1451 0.985 0.994 0.212 0.4429 0.8773 0.7094 ] Network output: [ 0.00509 -0.02426 0.9945 2.266e-05 -1.017e-05 1.02 1.708e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1047 0.09243 0.1823 0.2 0.9873 0.9919 0.1048 0.7549 0.8657 0.3055 ] Network output: [ -0.004827 0.02315 1.004 2.404e-05 -1.079e-05 0.9829 1.812e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09136 0.08943 0.1651 0.1955 0.9853 0.9912 0.09138 0.6794 0.8419 0.246 ] Network output: [ 0.0001372 1 -0.0001634 3.211e-06 -1.442e-06 0.9999 2.42e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003492 Epoch 8371 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01034 0.9959 0.9909 -2.393e-08 1.074e-08 -0.007499 -1.803e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003392 -0.003207 -0.007655 0.006022 0.9699 0.9743 0.00653 0.8321 0.8238 0.01768 ] Network output: [ 0.9998 0.0004334 0.0007282 -1.199e-05 5.383e-06 -0.0008958 -9.036e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.199 -0.03411 -0.1715 0.1885 0.9835 0.9932 0.2228 0.4387 0.8707 0.7152 ] Network output: [ -0.01012 1.002 1.009 -2.425e-07 1.089e-07 0.008672 -1.827e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006202 0.0005126 0.004446 0.003549 0.9889 0.9919 0.00632 0.8601 0.8947 0.01274 ] Network output: [ -0.0004673 0.002429 1.001 -3.76e-05 1.688e-05 0.9973 -2.834e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2113 0.09853 0.3411 0.1451 0.985 0.994 0.212 0.4429 0.8773 0.7094 ] Network output: [ 0.005088 -0.02425 0.9945 2.264e-05 -1.016e-05 1.02 1.706e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1047 0.09243 0.1823 0.2 0.9873 0.9919 0.1048 0.7549 0.8657 0.3055 ] Network output: [ -0.004825 0.02314 1.004 2.402e-05 -1.078e-05 0.9829 1.81e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09137 0.08943 0.1651 0.1955 0.9853 0.9912 0.09138 0.6794 0.8419 0.246 ] Network output: [ 0.0001372 1 -0.0001632 3.208e-06 -1.44e-06 0.9999 2.418e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000349 Epoch 8372 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01034 0.9959 0.9909 -2.462e-08 1.105e-08 -0.007499 -1.856e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003392 -0.003207 -0.007654 0.006022 0.9699 0.9743 0.00653 0.8321 0.8238 0.01768 ] Network output: [ 0.9998 0.000433 0.0007278 -1.198e-05 5.377e-06 -0.0008951 -9.027e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.199 -0.03411 -0.1715 0.1885 0.9835 0.9932 0.2228 0.4387 0.8707 0.7152 ] Network output: [ -0.01012 1.002 1.009 -2.428e-07 1.09e-07 0.008671 -1.83e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006203 0.0005127 0.004446 0.003549 0.9889 0.9919 0.00632 0.8601 0.8947 0.01274 ] Network output: [ -0.000467 0.002428 1.001 -3.756e-05 1.686e-05 0.9973 -2.831e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2113 0.09854 0.3411 0.1451 0.985 0.994 0.212 0.4429 0.8773 0.7094 ] Network output: [ 0.005086 -0.02424 0.9945 2.262e-05 -1.015e-05 1.02 1.704e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1047 0.09244 0.1823 0.2 0.9873 0.9919 0.1048 0.7549 0.8657 0.3055 ] Network output: [ -0.004823 0.02313 1.004 2.399e-05 -1.077e-05 0.9829 1.808e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09137 0.08943 0.1651 0.1955 0.9853 0.9912 0.09138 0.6794 0.8419 0.246 ] Network output: [ 0.0001371 1 -0.000163 3.205e-06 -1.439e-06 0.9999 2.415e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003488 Epoch 8373 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01034 0.9959 0.9909 -2.532e-08 1.137e-08 -0.0075 -1.908e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003392 -0.003207 -0.007654 0.006021 0.9699 0.9743 0.006531 0.8321 0.8238 0.01768 ] Network output: [ 0.9998 0.0004327 0.0007274 -1.197e-05 5.372e-06 -0.0008943 -9.017e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.199 -0.03411 -0.1715 0.1884 0.9835 0.9932 0.2228 0.4387 0.8706 0.7152 ] Network output: [ -0.01012 1.002 1.009 -2.431e-07 1.092e-07 0.008669 -1.832e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006203 0.0005128 0.004446 0.003549 0.9889 0.9919 0.006321 0.8601 0.8947 0.01273 ] Network output: [ -0.0004667 0.002427 1.001 -3.752e-05 1.685e-05 0.9973 -2.828e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2113 0.09854 0.3411 0.1451 0.985 0.994 0.212 0.4429 0.8773 0.7094 ] Network output: [ 0.005085 -0.02423 0.9945 2.259e-05 -1.014e-05 1.02 1.703e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1047 0.09244 0.1823 0.2 0.9873 0.9919 0.1048 0.7549 0.8657 0.3055 ] Network output: [ -0.004821 0.02312 1.004 2.397e-05 -1.076e-05 0.9829 1.806e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09137 0.08943 0.1651 0.1955 0.9853 0.9912 0.09138 0.6794 0.8419 0.2461 ] Network output: [ 0.000137 1 -0.0001628 3.201e-06 -1.437e-06 0.9999 2.413e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003486 Epoch 8374 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01034 0.9959 0.9909 -2.601e-08 1.168e-08 -0.0075 -1.96e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003392 -0.003207 -0.007653 0.006021 0.9699 0.9743 0.006531 0.8321 0.8238 0.01768 ] Network output: [ 0.9998 0.0004323 0.000727 -1.195e-05 5.366e-06 -0.0008936 -9.008e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.199 -0.03411 -0.1715 0.1884 0.9835 0.9932 0.2228 0.4387 0.8706 0.7152 ] Network output: [ -0.01012 1.002 1.009 -2.435e-07 1.093e-07 0.008668 -1.835e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006204 0.0005129 0.004446 0.003548 0.9889 0.9919 0.006322 0.8601 0.8947 0.01273 ] Network output: [ -0.0004664 0.002426 1.001 -3.748e-05 1.683e-05 0.9973 -2.825e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2113 0.09855 0.3411 0.1451 0.985 0.994 0.212 0.4429 0.8773 0.7094 ] Network output: [ 0.005083 -0.02422 0.9945 2.257e-05 -1.013e-05 1.02 1.701e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1047 0.09245 0.1823 0.2 0.9873 0.9919 0.1048 0.7549 0.8657 0.3055 ] Network output: [ -0.00482 0.02311 1.004 2.395e-05 -1.075e-05 0.9829 1.805e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09137 0.08944 0.1651 0.1955 0.9853 0.9912 0.09139 0.6794 0.8419 0.2461 ] Network output: [ 0.000137 1 -0.0001626 3.198e-06 -1.436e-06 0.9999 2.41e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003484 Epoch 8375 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01034 0.9959 0.9909 -2.67e-08 1.199e-08 -0.0075 -2.012e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003392 -0.003207 -0.007652 0.00602 0.9699 0.9743 0.006531 0.8321 0.8238 0.01768 ] Network output: [ 0.9998 0.0004319 0.0007265 -1.194e-05 5.36e-06 -0.0008928 -8.998e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1991 -0.03411 -0.1715 0.1884 0.9835 0.9932 0.2228 0.4387 0.8706 0.7152 ] Network output: [ -0.01012 1.002 1.009 -2.438e-07 1.094e-07 0.008666 -1.837e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006204 0.000513 0.004446 0.003548 0.9889 0.9919 0.006322 0.8601 0.8947 0.01273 ] Network output: [ -0.0004661 0.002425 1.001 -3.744e-05 1.681e-05 0.9973 -2.822e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2113 0.09856 0.3411 0.1451 0.985 0.994 0.212 0.4429 0.8773 0.7094 ] Network output: [ 0.005081 -0.02421 0.9945 2.255e-05 -1.012e-05 1.02 1.699e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1047 0.09245 0.1823 0.2 0.9873 0.9919 0.1048 0.7548 0.8657 0.3055 ] Network output: [ -0.004818 0.0231 1.004 2.392e-05 -1.074e-05 0.9829 1.803e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09137 0.08944 0.1651 0.1955 0.9853 0.9912 0.09139 0.6793 0.8419 0.2461 ] Network output: [ 0.0001369 1 -0.0001625 3.195e-06 -1.434e-06 0.9999 2.408e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003482 Epoch 8376 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01034 0.9959 0.9909 -2.739e-08 1.23e-08 -0.0075 -2.064e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003393 -0.003207 -0.007651 0.006019 0.9699 0.9743 0.006531 0.832 0.8238 0.01768 ] Network output: [ 0.9998 0.0004316 0.0007261 -1.193e-05 5.355e-06 -0.0008921 -8.989e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1991 -0.03412 -0.1714 0.1884 0.9835 0.9932 0.2228 0.4387 0.8706 0.7152 ] Network output: [ -0.01011 1.002 1.009 -2.441e-07 1.096e-07 0.008665 -1.84e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006205 0.0005131 0.004446 0.003547 0.9889 0.9919 0.006323 0.8601 0.8947 0.01273 ] Network output: [ -0.0004658 0.002424 1.001 -3.741e-05 1.679e-05 0.9973 -2.819e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2113 0.09856 0.3411 0.1451 0.985 0.994 0.212 0.4429 0.8773 0.7094 ] Network output: [ 0.00508 -0.0242 0.9945 2.252e-05 -1.011e-05 1.02 1.697e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1047 0.09246 0.1823 0.1999 0.9873 0.9919 0.1048 0.7548 0.8657 0.3055 ] Network output: [ -0.004816 0.02309 1.004 2.39e-05 -1.073e-05 0.9829 1.801e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09138 0.08944 0.1651 0.1955 0.9853 0.9912 0.09139 0.6793 0.8419 0.2461 ] Network output: [ 0.0001368 1 -0.0001623 3.192e-06 -1.433e-06 0.9999 2.405e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000348 Epoch 8377 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01034 0.9959 0.9909 -2.807e-08 1.26e-08 -0.0075 -2.116e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003393 -0.003208 -0.00765 0.006019 0.9699 0.9743 0.006532 0.832 0.8238 0.01767 ] Network output: [ 0.9998 0.0004312 0.0007257 -1.192e-05 5.349e-06 -0.0008913 -8.98e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1991 -0.03412 -0.1714 0.1884 0.9835 0.9932 0.2228 0.4387 0.8706 0.7152 ] Network output: [ -0.01011 1.002 1.009 -2.444e-07 1.097e-07 0.008663 -1.842e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006206 0.0005132 0.004446 0.003547 0.9889 0.9919 0.006323 0.8601 0.8947 0.01273 ] Network output: [ -0.0004655 0.002423 1.001 -3.737e-05 1.678e-05 0.9973 -2.816e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2113 0.09857 0.3411 0.1451 0.985 0.994 0.212 0.4428 0.8773 0.7094 ] Network output: [ 0.005078 -0.0242 0.9945 2.25e-05 -1.01e-05 1.02 1.696e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1047 0.09247 0.1823 0.1999 0.9873 0.9919 0.1048 0.7548 0.8657 0.3055 ] Network output: [ -0.004815 0.02309 1.004 2.387e-05 -1.072e-05 0.9829 1.799e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09138 0.08944 0.1651 0.1955 0.9853 0.9912 0.09139 0.6793 0.8419 0.2461 ] Network output: [ 0.0001368 1 -0.0001621 3.188e-06 -1.431e-06 0.9999 2.403e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003478 Epoch 8378 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01034 0.9959 0.9909 -2.876e-08 1.291e-08 -0.0075 -2.167e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003393 -0.003208 -0.007649 0.006018 0.9699 0.9743 0.006532 0.832 0.8238 0.01767 ] Network output: [ 0.9998 0.0004308 0.0007253 -1.19e-05 5.344e-06 -0.0008906 -8.97e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1991 -0.03412 -0.1714 0.1884 0.9835 0.9932 0.2228 0.4386 0.8706 0.7152 ] Network output: [ -0.01011 1.002 1.009 -2.448e-07 1.099e-07 0.008661 -1.845e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006206 0.0005133 0.004446 0.003547 0.9889 0.9919 0.006324 0.8601 0.8947 0.01273 ] Network output: [ -0.0004652 0.002422 1.001 -3.733e-05 1.676e-05 0.9973 -2.813e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2114 0.09857 0.3411 0.1451 0.985 0.994 0.2121 0.4428 0.8773 0.7094 ] Network output: [ 0.005076 -0.02419 0.9945 2.248e-05 -1.009e-05 1.02 1.694e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1047 0.09247 0.1823 0.1999 0.9873 0.9919 0.1048 0.7548 0.8657 0.3055 ] Network output: [ -0.004813 0.02308 1.004 2.385e-05 -1.071e-05 0.9829 1.797e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09138 0.08944 0.1651 0.1955 0.9853 0.9912 0.09139 0.6793 0.8419 0.2461 ] Network output: [ 0.0001367 1 -0.0001619 3.185e-06 -1.43e-06 0.9999 2.4e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003476 Epoch 8379 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01033 0.9959 0.9909 -2.944e-08 1.322e-08 -0.0075 -2.219e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003393 -0.003208 -0.007648 0.006018 0.9699 0.9743 0.006532 0.832 0.8238 0.01767 ] Network output: [ 0.9998 0.0004305 0.0007248 -1.189e-05 5.338e-06 -0.0008898 -8.961e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1991 -0.03412 -0.1714 0.1884 0.9835 0.9932 0.2229 0.4386 0.8706 0.7152 ] Network output: [ -0.01011 1.002 1.009 -2.451e-07 1.1e-07 0.00866 -1.847e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006207 0.0005133 0.004446 0.003546 0.9889 0.9919 0.006324 0.8601 0.8947 0.01273 ] Network output: [ -0.0004649 0.002422 1.001 -3.729e-05 1.674e-05 0.9973 -2.81e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2114 0.09858 0.3411 0.1451 0.985 0.994 0.2121 0.4428 0.8773 0.7094 ] Network output: [ 0.005074 -0.02418 0.9945 2.245e-05 -1.008e-05 1.02 1.692e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1048 0.09248 0.1823 0.1999 0.9873 0.9919 0.1048 0.7548 0.8657 0.3055 ] Network output: [ -0.004811 0.02307 1.004 2.382e-05 -1.07e-05 0.9829 1.795e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09138 0.08945 0.1651 0.1955 0.9853 0.9912 0.0914 0.6793 0.8419 0.2461 ] Network output: [ 0.0001367 1 -0.0001617 3.182e-06 -1.428e-06 0.9999 2.398e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003474 Epoch 8380 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01033 0.9959 0.9909 -3.012e-08 1.352e-08 -0.0075 -2.27e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003393 -0.003208 -0.007647 0.006017 0.9699 0.9743 0.006533 0.832 0.8238 0.01767 ] Network output: [ 0.9998 0.0004301 0.0007244 -1.188e-05 5.332e-06 -0.0008891 -8.952e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1991 -0.03412 -0.1714 0.1884 0.9835 0.9932 0.2229 0.4386 0.8706 0.7152 ] Network output: [ -0.01011 1.002 1.009 -2.454e-07 1.102e-07 0.008658 -1.849e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006207 0.0005134 0.004446 0.003546 0.9889 0.9919 0.006325 0.86 0.8947 0.01273 ] Network output: [ -0.0004646 0.002421 1.001 -3.725e-05 1.672e-05 0.9973 -2.807e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2114 0.09859 0.3412 0.1451 0.985 0.994 0.2121 0.4428 0.8773 0.7094 ] Network output: [ 0.005073 -0.02417 0.9945 2.243e-05 -1.007e-05 1.02 1.69e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1048 0.09248 0.1823 0.1999 0.9873 0.9919 0.1048 0.7547 0.8657 0.3055 ] Network output: [ -0.004809 0.02306 1.004 2.38e-05 -1.068e-05 0.9829 1.794e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09139 0.08945 0.1651 0.1955 0.9853 0.9912 0.0914 0.6792 0.8419 0.2461 ] Network output: [ 0.0001366 1 -0.0001615 3.178e-06 -1.427e-06 0.9999 2.395e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003473 Epoch 8381 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01033 0.9959 0.9909 -3.08e-08 1.383e-08 -0.0075 -2.321e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003393 -0.003208 -0.007646 0.006016 0.9699 0.9743 0.006533 0.832 0.8238 0.01767 ] Network output: [ 0.9998 0.0004298 0.000724 -1.187e-05 5.327e-06 -0.0008883 -8.942e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1991 -0.03412 -0.1714 0.1884 0.9835 0.9932 0.2229 0.4386 0.8706 0.7152 ] Network output: [ -0.01011 1.002 1.009 -2.457e-07 1.103e-07 0.008657 -1.852e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006208 0.0005135 0.004446 0.003546 0.9889 0.9919 0.006326 0.86 0.8947 0.01273 ] Network output: [ -0.0004644 0.00242 1.001 -3.721e-05 1.67e-05 0.9973 -2.804e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2114 0.09859 0.3412 0.1451 0.985 0.994 0.2121 0.4428 0.8773 0.7094 ] Network output: [ 0.005071 -0.02416 0.9945 2.241e-05 -1.006e-05 1.02 1.689e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1048 0.09249 0.1823 0.1999 0.9873 0.9919 0.1048 0.7547 0.8657 0.3055 ] Network output: [ -0.004808 0.02305 1.004 2.378e-05 -1.067e-05 0.9829 1.792e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09139 0.08945 0.1651 0.1955 0.9853 0.9912 0.0914 0.6792 0.8419 0.2461 ] Network output: [ 0.0001365 1 -0.0001613 3.175e-06 -1.425e-06 0.9999 2.393e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003471 Epoch 8382 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01033 0.9959 0.9909 -3.148e-08 1.413e-08 -0.007501 -2.372e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003393 -0.003208 -0.007645 0.006016 0.9699 0.9743 0.006533 0.832 0.8238 0.01767 ] Network output: [ 0.9998 0.0004294 0.0007236 -1.185e-05 5.321e-06 -0.0008876 -8.933e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1991 -0.03413 -0.1714 0.1884 0.9835 0.9932 0.2229 0.4386 0.8706 0.7152 ] Network output: [ -0.01011 1.002 1.009 -2.46e-07 1.105e-07 0.008655 -1.854e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006208 0.0005136 0.004446 0.003545 0.9889 0.9919 0.006326 0.86 0.8946 0.01273 ] Network output: [ -0.0004641 0.002419 1.001 -3.717e-05 1.669e-05 0.9973 -2.801e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2114 0.0986 0.3412 0.1451 0.985 0.994 0.2121 0.4428 0.8773 0.7093 ] Network output: [ 0.005069 -0.02415 0.9945 2.238e-05 -1.005e-05 1.02 1.687e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1048 0.09249 0.1823 0.1999 0.9873 0.9919 0.1048 0.7547 0.8657 0.3055 ] Network output: [ -0.004806 0.02304 1.004 2.375e-05 -1.066e-05 0.9829 1.79e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09139 0.08945 0.1651 0.1955 0.9853 0.9912 0.0914 0.6792 0.8419 0.2461 ] Network output: [ 0.0001365 1 -0.0001611 3.172e-06 -1.424e-06 0.9999 2.39e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003469 Epoch 8383 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01033 0.9959 0.9909 -3.216e-08 1.444e-08 -0.007501 -2.423e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003393 -0.003208 -0.007644 0.006015 0.9699 0.9743 0.006533 0.832 0.8238 0.01767 ] Network output: [ 0.9998 0.0004291 0.0007232 -1.184e-05 5.316e-06 -0.0008869 -8.924e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1991 -0.03413 -0.1713 0.1884 0.9835 0.9932 0.2229 0.4386 0.8706 0.7151 ] Network output: [ -0.01011 1.002 1.009 -2.463e-07 1.106e-07 0.008654 -1.857e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006209 0.0005137 0.004446 0.003545 0.9889 0.9919 0.006327 0.86 0.8946 0.01272 ] Network output: [ -0.0004638 0.002418 1.001 -3.713e-05 1.667e-05 0.9973 -2.798e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2114 0.0986 0.3412 0.145 0.985 0.994 0.2121 0.4428 0.8773 0.7093 ] Network output: [ 0.005068 -0.02414 0.9945 2.236e-05 -1.004e-05 1.02 1.685e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1048 0.0925 0.1823 0.1999 0.9873 0.9919 0.1048 0.7547 0.8657 0.3055 ] Network output: [ -0.004804 0.02303 1.004 2.373e-05 -1.065e-05 0.983 1.788e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09139 0.08945 0.1651 0.1955 0.9853 0.9912 0.0914 0.6792 0.8419 0.2461 ] Network output: [ 0.0001364 1 -0.0001609 3.169e-06 -1.422e-06 0.9999 2.388e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003467 Epoch 8384 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01033 0.9959 0.9909 -3.283e-08 1.474e-08 -0.007501 -2.474e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003394 -0.003209 -0.007644 0.006015 0.9699 0.9743 0.006534 0.832 0.8238 0.01767 ] Network output: [ 0.9998 0.0004287 0.0007227 -1.183e-05 5.31e-06 -0.0008861 -8.914e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1991 -0.03413 -0.1713 0.1884 0.9835 0.9932 0.2229 0.4386 0.8706 0.7151 ] Network output: [ -0.01011 1.002 1.009 -2.467e-07 1.107e-07 0.008652 -1.859e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006209 0.0005138 0.004446 0.003545 0.9889 0.9919 0.006327 0.86 0.8946 0.01272 ] Network output: [ -0.0004635 0.002417 1.001 -3.709e-05 1.665e-05 0.9973 -2.795e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2114 0.09861 0.3412 0.145 0.985 0.994 0.2121 0.4428 0.8773 0.7093 ] Network output: [ 0.005066 -0.02413 0.9945 2.234e-05 -1.003e-05 1.02 1.683e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1048 0.09251 0.1823 0.1999 0.9873 0.9919 0.1049 0.7547 0.8657 0.3055 ] Network output: [ -0.004802 0.02302 1.004 2.37e-05 -1.064e-05 0.983 1.786e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09139 0.08946 0.1651 0.1955 0.9853 0.9912 0.09141 0.6792 0.8418 0.2461 ] Network output: [ 0.0001363 1 -0.0001608 3.165e-06 -1.421e-06 0.9999 2.385e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003465 Epoch 8385 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01033 0.9959 0.9909 -3.35e-08 1.504e-08 -0.007501 -2.525e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003394 -0.003209 -0.007643 0.006014 0.9699 0.9743 0.006534 0.832 0.8238 0.01766 ] Network output: [ 0.9998 0.0004283 0.0007223 -1.182e-05 5.305e-06 -0.0008854 -8.905e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1991 -0.03413 -0.1713 0.1884 0.9835 0.9932 0.2229 0.4386 0.8706 0.7151 ] Network output: [ -0.01011 1.002 1.009 -2.47e-07 1.109e-07 0.008651 -1.861e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00621 0.0005139 0.004446 0.003544 0.9889 0.9919 0.006328 0.86 0.8946 0.01272 ] Network output: [ -0.0004632 0.002416 1.001 -3.705e-05 1.663e-05 0.9973 -2.792e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2114 0.09862 0.3412 0.145 0.985 0.994 0.2121 0.4428 0.8773 0.7093 ] Network output: [ 0.005064 -0.02413 0.9945 2.231e-05 -1.002e-05 1.02 1.682e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1048 0.09251 0.1823 0.1999 0.9873 0.9919 0.1049 0.7547 0.8657 0.3055 ] Network output: [ -0.004801 0.02301 1.004 2.368e-05 -1.063e-05 0.983 1.785e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0914 0.08946 0.1651 0.1955 0.9853 0.9912 0.09141 0.6791 0.8418 0.2461 ] Network output: [ 0.0001363 1 -0.0001606 3.162e-06 -1.42e-06 0.9999 2.383e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003463 Epoch 8386 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01032 0.9959 0.9909 -3.417e-08 1.534e-08 -0.007501 -2.575e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003394 -0.003209 -0.007642 0.006013 0.9699 0.9743 0.006534 0.832 0.8237 0.01766 ] Network output: [ 0.9998 0.000428 0.0007219 -1.18e-05 5.299e-06 -0.0008846 -8.896e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1991 -0.03413 -0.1713 0.1884 0.9835 0.9932 0.2229 0.4386 0.8706 0.7151 ] Network output: [ -0.0101 1.002 1.009 -2.473e-07 1.11e-07 0.008649 -1.864e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006211 0.000514 0.004446 0.003544 0.9889 0.9919 0.006328 0.86 0.8946 0.01272 ] Network output: [ -0.0004629 0.002416 1.001 -3.701e-05 1.662e-05 0.9973 -2.789e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2114 0.09862 0.3412 0.145 0.985 0.994 0.2121 0.4428 0.8773 0.7093 ] Network output: [ 0.005063 -0.02412 0.9945 2.229e-05 -1.001e-05 1.02 1.68e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1048 0.09252 0.1823 0.1999 0.9873 0.9919 0.1049 0.7546 0.8657 0.3055 ] Network output: [ -0.004799 0.023 1.004 2.366e-05 -1.062e-05 0.983 1.783e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0914 0.08946 0.1651 0.1955 0.9853 0.9912 0.09141 0.6791 0.8418 0.2461 ] Network output: [ 0.0001362 1 -0.0001604 3.159e-06 -1.418e-06 0.9999 2.381e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003461 Epoch 8387 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01032 0.9959 0.9909 -3.484e-08 1.564e-08 -0.007501 -2.626e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003394 -0.003209 -0.007641 0.006013 0.9699 0.9743 0.006535 0.832 0.8237 0.01766 ] Network output: [ 0.9998 0.0004276 0.0007215 -1.179e-05 5.294e-06 -0.0008839 -8.886e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1992 -0.03413 -0.1713 0.1884 0.9835 0.9932 0.2229 0.4386 0.8706 0.7151 ] Network output: [ -0.0101 1.002 1.009 -2.476e-07 1.112e-07 0.008648 -1.866e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006211 0.0005141 0.004446 0.003543 0.9889 0.9919 0.006329 0.86 0.8946 0.01272 ] Network output: [ -0.0004626 0.002415 1.001 -3.697e-05 1.66e-05 0.9973 -2.787e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2114 0.09863 0.3412 0.145 0.985 0.994 0.2121 0.4427 0.8772 0.7093 ] Network output: [ 0.005061 -0.02411 0.9945 2.227e-05 -9.996e-06 1.02 1.678e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1048 0.09252 0.1823 0.1999 0.9873 0.9919 0.1049 0.7546 0.8657 0.3055 ] Network output: [ -0.004797 0.02299 1.004 2.363e-05 -1.061e-05 0.983 1.781e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0914 0.08946 0.1651 0.1955 0.9853 0.9912 0.09141 0.6791 0.8418 0.2461 ] Network output: [ 0.0001361 1 -0.0001602 3.155e-06 -1.417e-06 0.9999 2.378e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003459 Epoch 8388 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01032 0.9959 0.9909 -3.551e-08 1.594e-08 -0.007501 -2.676e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003394 -0.003209 -0.00764 0.006012 0.9699 0.9743 0.006535 0.832 0.8237 0.01766 ] Network output: [ 0.9998 0.0004273 0.0007211 -1.178e-05 5.288e-06 -0.0008832 -8.877e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1992 -0.03413 -0.1713 0.1884 0.9835 0.9932 0.223 0.4385 0.8706 0.7151 ] Network output: [ -0.0101 1.002 1.009 -2.479e-07 1.113e-07 0.008646 -1.868e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006212 0.0005142 0.004446 0.003543 0.9889 0.9919 0.00633 0.86 0.8946 0.01272 ] Network output: [ -0.0004623 0.002414 1.001 -3.694e-05 1.658e-05 0.9973 -2.784e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2115 0.09863 0.3412 0.145 0.985 0.994 0.2122 0.4427 0.8772 0.7093 ] Network output: [ 0.005059 -0.0241 0.9945 2.224e-05 -9.986e-06 1.02 1.676e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1048 0.09253 0.1823 0.1999 0.9873 0.9919 0.1049 0.7546 0.8656 0.3055 ] Network output: [ -0.004796 0.02298 1.004 2.361e-05 -1.06e-05 0.983 1.779e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0914 0.08947 0.1651 0.1955 0.9853 0.9912 0.09142 0.6791 0.8418 0.2461 ] Network output: [ 0.0001361 1 -0.00016 3.152e-06 -1.415e-06 0.9999 2.376e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003457 Epoch 8389 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01032 0.9959 0.9909 -3.618e-08 1.624e-08 -0.007501 -2.726e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003394 -0.003209 -0.007639 0.006012 0.9699 0.9743 0.006535 0.832 0.8237 0.01766 ] Network output: [ 0.9998 0.0004269 0.0007206 -1.177e-05 5.283e-06 -0.0008824 -8.868e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1992 -0.03414 -0.1713 0.1884 0.9835 0.9932 0.223 0.4385 0.8706 0.7151 ] Network output: [ -0.0101 1.002 1.009 -2.482e-07 1.114e-07 0.008644 -1.871e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006212 0.0005142 0.004446 0.003543 0.9889 0.9919 0.00633 0.86 0.8946 0.01272 ] Network output: [ -0.000462 0.002413 1.001 -3.69e-05 1.656e-05 0.9973 -2.781e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2115 0.09864 0.3412 0.145 0.985 0.994 0.2122 0.4427 0.8772 0.7093 ] Network output: [ 0.005057 -0.02409 0.9945 2.222e-05 -9.975e-06 1.02 1.675e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1048 0.09253 0.1824 0.1999 0.9873 0.9919 0.1049 0.7546 0.8656 0.3055 ] Network output: [ -0.004794 0.02297 1.004 2.358e-05 -1.059e-05 0.983 1.777e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0914 0.08947 0.1651 0.1955 0.9853 0.9912 0.09142 0.6791 0.8418 0.2461 ] Network output: [ 0.000136 1 -0.0001598 3.149e-06 -1.414e-06 0.9999 2.373e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003455 Epoch 8390 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01032 0.9959 0.9909 -3.684e-08 1.654e-08 -0.007501 -2.776e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003394 -0.003209 -0.007638 0.006011 0.9699 0.9743 0.006536 0.832 0.8237 0.01766 ] Network output: [ 0.9998 0.0004265 0.0007202 -1.175e-05 5.277e-06 -0.0008817 -8.859e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1992 -0.03414 -0.1712 0.1884 0.9835 0.9932 0.223 0.4385 0.8706 0.7151 ] Network output: [ -0.0101 1.002 1.009 -2.485e-07 1.116e-07 0.008643 -1.873e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006213 0.0005143 0.004446 0.003542 0.9889 0.9919 0.006331 0.86 0.8946 0.01272 ] Network output: [ -0.0004617 0.002412 1.001 -3.686e-05 1.655e-05 0.9973 -2.778e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2115 0.09864 0.3412 0.145 0.985 0.994 0.2122 0.4427 0.8772 0.7093 ] Network output: [ 0.005056 -0.02408 0.9945 2.22e-05 -9.965e-06 1.02 1.673e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1048 0.09254 0.1824 0.1999 0.9873 0.9919 0.1049 0.7546 0.8656 0.3055 ] Network output: [ -0.004792 0.02296 1.004 2.356e-05 -1.058e-05 0.983 1.776e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09141 0.08947 0.1651 0.1955 0.9853 0.9912 0.09142 0.679 0.8418 0.2461 ] Network output: [ 0.0001359 1 -0.0001596 3.146e-06 -1.412e-06 0.9999 2.371e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003453 Epoch 8391 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01032 0.9959 0.9909 -3.75e-08 1.684e-08 -0.007501 -2.826e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003394 -0.00321 -0.007637 0.006011 0.9699 0.9743 0.006536 0.8319 0.8237 0.01766 ] Network output: [ 0.9998 0.0004262 0.0007198 -1.174e-05 5.271e-06 -0.0008809 -8.849e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1992 -0.03414 -0.1712 0.1883 0.9835 0.9932 0.223 0.4385 0.8706 0.7151 ] Network output: [ -0.0101 1.002 1.009 -2.488e-07 1.117e-07 0.008641 -1.875e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006213 0.0005144 0.004446 0.003542 0.9889 0.9919 0.006331 0.86 0.8946 0.01272 ] Network output: [ -0.0004614 0.002411 1.001 -3.682e-05 1.653e-05 0.9973 -2.775e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2115 0.09865 0.3412 0.145 0.985 0.994 0.2122 0.4427 0.8772 0.7093 ] Network output: [ 0.005054 -0.02407 0.9945 2.217e-05 -9.955e-06 1.02 1.671e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1048 0.09255 0.1824 0.1999 0.9873 0.9919 0.1049 0.7545 0.8656 0.3055 ] Network output: [ -0.00479 0.02295 1.004 2.354e-05 -1.057e-05 0.983 1.774e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09141 0.08947 0.1651 0.1955 0.9853 0.9912 0.09142 0.679 0.8418 0.2461 ] Network output: [ 0.0001359 1 -0.0001595 3.142e-06 -1.411e-06 0.9999 2.368e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003451 Epoch 8392 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01032 0.9959 0.9909 -3.816e-08 1.713e-08 -0.007501 -2.876e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003395 -0.00321 -0.007636 0.00601 0.9699 0.9743 0.006536 0.8319 0.8237 0.01765 ] Network output: [ 0.9998 0.0004258 0.0007194 -1.173e-05 5.266e-06 -0.0008802 -8.84e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1992 -0.03414 -0.1712 0.1883 0.9835 0.9932 0.223 0.4385 0.8706 0.7151 ] Network output: [ -0.0101 1.002 1.009 -2.491e-07 1.118e-07 0.00864 -1.878e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006214 0.0005145 0.004446 0.003542 0.9889 0.9919 0.006332 0.86 0.8946 0.01272 ] Network output: [ -0.0004611 0.00241 1.001 -3.678e-05 1.651e-05 0.9973 -2.772e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2115 0.09866 0.3413 0.145 0.985 0.994 0.2122 0.4427 0.8772 0.7093 ] Network output: [ 0.005052 -0.02406 0.9945 2.215e-05 -9.944e-06 1.02 1.669e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1048 0.09255 0.1824 0.1999 0.9873 0.9919 0.1049 0.7545 0.8656 0.3055 ] Network output: [ -0.004789 0.02294 1.004 2.351e-05 -1.056e-05 0.983 1.772e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09141 0.08947 0.1651 0.1955 0.9853 0.9912 0.09142 0.679 0.8418 0.2461 ] Network output: [ 0.0001358 1 -0.0001593 3.139e-06 -1.409e-06 0.9999 2.366e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000345 Epoch 8393 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01031 0.9959 0.9909 -3.882e-08 1.743e-08 -0.007502 -2.926e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003395 -0.00321 -0.007635 0.006009 0.9699 0.9743 0.006536 0.8319 0.8237 0.01765 ] Network output: [ 0.9998 0.0004255 0.000719 -1.172e-05 5.26e-06 -0.0008795 -8.831e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1992 -0.03414 -0.1712 0.1883 0.9835 0.9932 0.223 0.4385 0.8706 0.7151 ] Network output: [ -0.0101 1.002 1.009 -2.494e-07 1.12e-07 0.008638 -1.88e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006215 0.0005146 0.004446 0.003541 0.9889 0.9919 0.006332 0.8599 0.8946 0.01271 ] Network output: [ -0.0004608 0.00241 1.001 -3.674e-05 1.649e-05 0.9973 -2.769e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2115 0.09866 0.3413 0.145 0.985 0.994 0.2122 0.4427 0.8772 0.7093 ] Network output: [ 0.005051 -0.02406 0.9945 2.213e-05 -9.934e-06 1.02 1.668e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1048 0.09256 0.1824 0.1999 0.9873 0.9919 0.1049 0.7545 0.8656 0.3055 ] Network output: [ -0.004787 0.02294 1.004 2.349e-05 -1.055e-05 0.983 1.77e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09141 0.08948 0.1651 0.1955 0.9853 0.9912 0.09143 0.679 0.8418 0.2461 ] Network output: [ 0.0001358 1 -0.0001591 3.136e-06 -1.408e-06 0.9999 2.363e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003448 Epoch 8394 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01031 0.9959 0.9909 -3.948e-08 1.772e-08 -0.007502 -2.975e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003395 -0.00321 -0.007634 0.006009 0.9699 0.9743 0.006537 0.8319 0.8237 0.01765 ] Network output: [ 0.9998 0.0004251 0.0007185 -1.171e-05 5.255e-06 -0.0008787 -8.822e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1992 -0.03414 -0.1712 0.1883 0.9835 0.9932 0.223 0.4385 0.8706 0.7151 ] Network output: [ -0.0101 1.002 1.009 -2.497e-07 1.121e-07 0.008637 -1.882e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006215 0.0005147 0.004446 0.003541 0.9889 0.9919 0.006333 0.8599 0.8946 0.01271 ] Network output: [ -0.0004605 0.002409 1.001 -3.67e-05 1.648e-05 0.9973 -2.766e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2115 0.09867 0.3413 0.145 0.985 0.994 0.2122 0.4427 0.8772 0.7093 ] Network output: [ 0.005049 -0.02405 0.9945 2.21e-05 -9.924e-06 1.02 1.666e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1048 0.09256 0.1824 0.1999 0.9873 0.9919 0.1049 0.7545 0.8656 0.3055 ] Network output: [ -0.004785 0.02293 1.004 2.347e-05 -1.053e-05 0.983 1.768e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09142 0.08948 0.1651 0.1955 0.9853 0.9912 0.09143 0.679 0.8418 0.2461 ] Network output: [ 0.0001357 1 -0.0001589 3.133e-06 -1.406e-06 0.9999 2.361e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003446 Epoch 8395 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01031 0.9959 0.9909 -4.013e-08 1.802e-08 -0.007502 -3.025e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003395 -0.00321 -0.007634 0.006008 0.9699 0.9743 0.006537 0.8319 0.8237 0.01765 ] Network output: [ 0.9998 0.0004248 0.0007181 -1.169e-05 5.249e-06 -0.000878 -8.812e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1992 -0.03415 -0.1712 0.1883 0.9835 0.9932 0.223 0.4385 0.8706 0.7151 ] Network output: [ -0.01009 1.002 1.009 -2.5e-07 1.123e-07 0.008635 -1.884e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006216 0.0005148 0.004446 0.003541 0.9889 0.9919 0.006334 0.8599 0.8946 0.01271 ] Network output: [ -0.0004602 0.002408 1.001 -3.666e-05 1.646e-05 0.9973 -2.763e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2115 0.09867 0.3413 0.145 0.985 0.994 0.2122 0.4427 0.8772 0.7093 ] Network output: [ 0.005047 -0.02404 0.9945 2.208e-05 -9.913e-06 1.02 1.664e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1049 0.09257 0.1824 0.1999 0.9873 0.9919 0.1049 0.7545 0.8656 0.3055 ] Network output: [ -0.004784 0.02292 1.004 2.344e-05 -1.052e-05 0.983 1.767e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09142 0.08948 0.1651 0.1955 0.9853 0.9912 0.09143 0.6789 0.8418 0.2461 ] Network output: [ 0.0001356 1 -0.0001587 3.129e-06 -1.405e-06 0.9999 2.358e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003444 Epoch 8396 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01031 0.9959 0.9909 -4.079e-08 1.831e-08 -0.007502 -3.074e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003395 -0.00321 -0.007633 0.006008 0.9699 0.9743 0.006537 0.8319 0.8237 0.01765 ] Network output: [ 0.9998 0.0004244 0.0007177 -1.168e-05 5.244e-06 -0.0008773 -8.803e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1992 -0.03415 -0.1712 0.1883 0.9835 0.9932 0.223 0.4385 0.8706 0.7151 ] Network output: [ -0.01009 1.002 1.009 -2.503e-07 1.124e-07 0.008634 -1.887e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006216 0.0005149 0.004446 0.00354 0.9889 0.9919 0.006334 0.8599 0.8946 0.01271 ] Network output: [ -0.00046 0.002407 1.001 -3.663e-05 1.644e-05 0.9973 -2.76e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2115 0.09868 0.3413 0.145 0.985 0.994 0.2122 0.4427 0.8772 0.7093 ] Network output: [ 0.005046 -0.02403 0.9945 2.206e-05 -9.903e-06 1.02 1.662e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1049 0.09257 0.1824 0.1999 0.9873 0.9919 0.1049 0.7544 0.8656 0.3055 ] Network output: [ -0.004782 0.02291 1.004 2.342e-05 -1.051e-05 0.983 1.765e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09142 0.08948 0.1651 0.1955 0.9853 0.9912 0.09143 0.6789 0.8418 0.2461 ] Network output: [ 0.0001356 1 -0.0001585 3.126e-06 -1.403e-06 0.9999 2.356e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003442 Epoch 8397 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01031 0.9959 0.9909 -4.144e-08 1.86e-08 -0.007502 -3.123e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003395 -0.00321 -0.007632 0.006007 0.9699 0.9743 0.006538 0.8319 0.8237 0.01765 ] Network output: [ 0.9998 0.0004241 0.0007173 -1.167e-05 5.238e-06 -0.0008765 -8.794e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1992 -0.03415 -0.1712 0.1883 0.9835 0.9932 0.223 0.4385 0.8706 0.7151 ] Network output: [ -0.01009 1.002 1.009 -2.506e-07 1.125e-07 0.008632 -1.889e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006217 0.000515 0.004446 0.00354 0.9889 0.9919 0.006335 0.8599 0.8946 0.01271 ] Network output: [ -0.0004597 0.002406 1.001 -3.659e-05 1.643e-05 0.9973 -2.757e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2115 0.09869 0.3413 0.145 0.985 0.994 0.2122 0.4426 0.8772 0.7093 ] Network output: [ 0.005044 -0.02402 0.9945 2.204e-05 -9.893e-06 1.02 1.661e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1049 0.09258 0.1824 0.1999 0.9873 0.9919 0.1049 0.7544 0.8656 0.3055 ] Network output: [ -0.00478 0.0229 1.004 2.339e-05 -1.05e-05 0.983 1.763e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09142 0.08948 0.1651 0.1955 0.9853 0.9912 0.09143 0.6789 0.8418 0.2461 ] Network output: [ 0.0001355 1 -0.0001583 3.123e-06 -1.402e-06 0.9998 2.354e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000344 Epoch 8398 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01031 0.9959 0.991 -4.209e-08 1.89e-08 -0.007502 -3.172e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003395 -0.003211 -0.007631 0.006006 0.9699 0.9743 0.006538 0.8319 0.8237 0.01765 ] Network output: [ 0.9998 0.0004237 0.0007169 -1.166e-05 5.233e-06 -0.0008758 -8.785e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1993 -0.03415 -0.1711 0.1883 0.9835 0.9932 0.2231 0.4384 0.8706 0.7151 ] Network output: [ -0.01009 1.002 1.009 -2.509e-07 1.127e-07 0.008631 -1.891e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006217 0.0005151 0.004446 0.00354 0.9889 0.9919 0.006335 0.8599 0.8946 0.01271 ] Network output: [ -0.0004594 0.002405 1.001 -3.655e-05 1.641e-05 0.9973 -2.754e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2116 0.09869 0.3413 0.145 0.985 0.994 0.2123 0.4426 0.8772 0.7093 ] Network output: [ 0.005042 -0.02401 0.9945 2.201e-05 -9.882e-06 1.02 1.659e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1049 0.09259 0.1824 0.1999 0.9873 0.9919 0.1049 0.7544 0.8656 0.3055 ] Network output: [ -0.004778 0.02289 1.004 2.337e-05 -1.049e-05 0.983 1.761e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09142 0.08949 0.1651 0.1955 0.9853 0.9912 0.09144 0.6789 0.8418 0.2461 ] Network output: [ 0.0001354 1 -0.0001582 3.12e-06 -1.401e-06 0.9998 2.351e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003438 Epoch 8399 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01031 0.9959 0.991 -4.274e-08 1.919e-08 -0.007502 -3.221e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003395 -0.003211 -0.00763 0.006006 0.9699 0.9743 0.006538 0.8319 0.8237 0.01765 ] Network output: [ 0.9998 0.0004233 0.0007165 -1.164e-05 5.227e-06 -0.0008751 -8.775e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1993 -0.03415 -0.1711 0.1883 0.9835 0.9932 0.2231 0.4384 0.8706 0.7151 ] Network output: [ -0.01009 1.002 1.009 -2.512e-07 1.128e-07 0.008629 -1.893e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006218 0.0005152 0.004446 0.003539 0.9889 0.9919 0.006336 0.8599 0.8946 0.01271 ] Network output: [ -0.0004591 0.002404 1.001 -3.651e-05 1.639e-05 0.9973 -2.751e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2116 0.0987 0.3413 0.145 0.985 0.994 0.2123 0.4426 0.8772 0.7092 ] Network output: [ 0.005041 -0.024 0.9945 2.199e-05 -9.872e-06 1.02 1.657e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1049 0.09259 0.1824 0.1999 0.9873 0.9919 0.1049 0.7544 0.8656 0.3055 ] Network output: [ -0.004777 0.02288 1.004 2.335e-05 -1.048e-05 0.983 1.759e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09143 0.08949 0.1651 0.1955 0.9853 0.9912 0.09144 0.6789 0.8418 0.2461 ] Network output: [ 0.0001354 1 -0.000158 3.117e-06 -1.399e-06 0.9998 2.349e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003436 Epoch 8400 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01031 0.9959 0.991 -4.339e-08 1.948e-08 -0.007502 -3.27e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003396 -0.003211 -0.007629 0.006005 0.9699 0.9743 0.006538 0.8319 0.8237 0.01764 ] Network output: [ 0.9998 0.000423 0.0007161 -1.163e-05 5.222e-06 -0.0008743 -8.766e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1993 -0.03415 -0.1711 0.1883 0.9835 0.9932 0.2231 0.4384 0.8706 0.7151 ] Network output: [ -0.01009 1.002 1.009 -2.515e-07 1.129e-07 0.008627 -1.896e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006218 0.0005152 0.004445 0.003539 0.9889 0.9919 0.006337 0.8599 0.8946 0.01271 ] Network output: [ -0.0004588 0.002404 1.001 -3.647e-05 1.637e-05 0.9973 -2.749e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2116 0.0987 0.3413 0.145 0.985 0.994 0.2123 0.4426 0.8772 0.7092 ] Network output: [ 0.005039 -0.024 0.9945 2.197e-05 -9.862e-06 1.02 1.656e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1049 0.0926 0.1824 0.1999 0.9873 0.9919 0.105 0.7544 0.8656 0.3055 ] Network output: [ -0.004775 0.02287 1.004 2.332e-05 -1.047e-05 0.983 1.758e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09143 0.08949 0.1651 0.1955 0.9853 0.9912 0.09144 0.6788 0.8417 0.2461 ] Network output: [ 0.0001353 1 -0.0001578 3.113e-06 -1.398e-06 0.9998 2.346e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003434 Epoch 8401 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0103 0.9959 0.991 -4.403e-08 1.977e-08 -0.007502 -3.318e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003396 -0.003211 -0.007628 0.006005 0.9699 0.9743 0.006539 0.8319 0.8237 0.01764 ] Network output: [ 0.9998 0.0004226 0.0007156 -1.162e-05 5.217e-06 -0.0008736 -8.757e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1993 -0.03415 -0.1711 0.1883 0.9835 0.9932 0.2231 0.4384 0.8706 0.715 ] Network output: [ -0.01009 1.002 1.009 -2.518e-07 1.131e-07 0.008626 -1.898e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006219 0.0005153 0.004445 0.003538 0.9889 0.9919 0.006337 0.8599 0.8946 0.01271 ] Network output: [ -0.0004585 0.002403 1.001 -3.643e-05 1.636e-05 0.9973 -2.746e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2116 0.09871 0.3413 0.145 0.985 0.994 0.2123 0.4426 0.8772 0.7092 ] Network output: [ 0.005037 -0.02399 0.9945 2.194e-05 -9.852e-06 1.02 1.654e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1049 0.0926 0.1824 0.1999 0.9873 0.9919 0.105 0.7544 0.8656 0.3055 ] Network output: [ -0.004773 0.02286 1.004 2.33e-05 -1.046e-05 0.983 1.756e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09143 0.08949 0.1651 0.1955 0.9853 0.9912 0.09144 0.6788 0.8417 0.2461 ] Network output: [ 0.0001352 1 -0.0001576 3.11e-06 -1.396e-06 0.9998 2.344e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003432 Epoch 8402 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0103 0.9959 0.991 -4.467e-08 2.006e-08 -0.007502 -3.367e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003396 -0.003211 -0.007627 0.006004 0.9699 0.9743 0.006539 0.8319 0.8237 0.01764 ] Network output: [ 0.9998 0.0004223 0.0007152 -1.161e-05 5.211e-06 -0.0008729 -8.748e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1993 -0.03416 -0.1711 0.1883 0.9835 0.9932 0.2231 0.4384 0.8706 0.715 ] Network output: [ -0.01009 1.002 1.009 -2.521e-07 1.132e-07 0.008624 -1.9e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00622 0.0005154 0.004445 0.003538 0.9889 0.9919 0.006338 0.8599 0.8946 0.01271 ] Network output: [ -0.0004582 0.002402 1.001 -3.639e-05 1.634e-05 0.9973 -2.743e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2116 0.09872 0.3413 0.145 0.985 0.994 0.2123 0.4426 0.8772 0.7092 ] Network output: [ 0.005036 -0.02398 0.9945 2.192e-05 -9.841e-06 1.02 1.652e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1049 0.09261 0.1824 0.1999 0.9873 0.9919 0.105 0.7543 0.8656 0.3055 ] Network output: [ -0.004772 0.02285 1.004 2.328e-05 -1.045e-05 0.983 1.754e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09143 0.0895 0.1651 0.1955 0.9853 0.9912 0.09145 0.6788 0.8417 0.2461 ] Network output: [ 0.0001352 1 -0.0001574 3.107e-06 -1.395e-06 0.9998 2.341e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000343 Epoch 8403 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0103 0.9959 0.991 -4.532e-08 2.034e-08 -0.007502 -3.415e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003396 -0.003211 -0.007626 0.006003 0.9699 0.9743 0.006539 0.8319 0.8237 0.01764 ] Network output: [ 0.9998 0.0004219 0.0007148 -1.16e-05 5.206e-06 -0.0008722 -8.739e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1993 -0.03416 -0.1711 0.1883 0.9835 0.9932 0.2231 0.4384 0.8706 0.715 ] Network output: [ -0.01009 1.002 1.009 -2.524e-07 1.133e-07 0.008623 -1.902e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00622 0.0005155 0.004445 0.003538 0.9889 0.9919 0.006338 0.8599 0.8946 0.0127 ] Network output: [ -0.0004579 0.002401 1.001 -3.636e-05 1.632e-05 0.9973 -2.74e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2116 0.09872 0.3413 0.145 0.985 0.994 0.2123 0.4426 0.8772 0.7092 ] Network output: [ 0.005034 -0.02397 0.9945 2.19e-05 -9.831e-06 1.02 1.65e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1049 0.09261 0.1824 0.1999 0.9873 0.9919 0.105 0.7543 0.8656 0.3055 ] Network output: [ -0.00477 0.02284 1.004 2.325e-05 -1.044e-05 0.983 1.752e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09143 0.0895 0.1651 0.1955 0.9853 0.9912 0.09145 0.6788 0.8417 0.2461 ] Network output: [ 0.0001351 1 -0.0001572 3.104e-06 -1.393e-06 0.9998 2.339e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003429 Epoch 8404 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0103 0.9959 0.991 -4.596e-08 2.063e-08 -0.007502 -3.463e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003396 -0.003211 -0.007625 0.006003 0.9699 0.9743 0.00654 0.8319 0.8237 0.01764 ] Network output: [ 0.9998 0.0004216 0.0007144 -1.158e-05 5.2e-06 -0.0008714 -8.73e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1993 -0.03416 -0.1711 0.1883 0.9835 0.9932 0.2231 0.4384 0.8706 0.715 ] Network output: [ -0.01008 1.002 1.009 -2.527e-07 1.135e-07 0.008621 -1.905e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006221 0.0005156 0.004445 0.003537 0.9889 0.9919 0.006339 0.8599 0.8946 0.0127 ] Network output: [ -0.0004576 0.0024 1.001 -3.632e-05 1.63e-05 0.9974 -2.737e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2116 0.09873 0.3414 0.145 0.985 0.994 0.2123 0.4426 0.8772 0.7092 ] Network output: [ 0.005032 -0.02396 0.9945 2.188e-05 -9.821e-06 1.02 1.649e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1049 0.09262 0.1824 0.1999 0.9873 0.9919 0.105 0.7543 0.8656 0.3055 ] Network output: [ -0.004768 0.02283 1.004 2.323e-05 -1.043e-05 0.983 1.751e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09144 0.0895 0.1651 0.1955 0.9853 0.9912 0.09145 0.6788 0.8417 0.2461 ] Network output: [ 0.0001351 1 -0.0001571 3.1e-06 -1.392e-06 0.9998 2.337e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003427 Epoch 8405 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0103 0.9959 0.991 -4.66e-08 2.092e-08 -0.007503 -3.512e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003396 -0.003212 -0.007624 0.006002 0.9699 0.9743 0.00654 0.8319 0.8237 0.01764 ] Network output: [ 0.9998 0.0004212 0.000714 -1.157e-05 5.195e-06 -0.0008707 -8.72e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1993 -0.03416 -0.171 0.1883 0.9835 0.9932 0.2231 0.4384 0.8706 0.715 ] Network output: [ -0.01008 1.002 1.009 -2.53e-07 1.136e-07 0.00862 -1.907e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006221 0.0005157 0.004445 0.003537 0.9889 0.9919 0.006339 0.8599 0.8946 0.0127 ] Network output: [ -0.0004573 0.002399 1.001 -3.628e-05 1.629e-05 0.9974 -2.734e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2116 0.09873 0.3414 0.145 0.985 0.994 0.2123 0.4426 0.8772 0.7092 ] Network output: [ 0.00503 -0.02395 0.9945 2.185e-05 -9.811e-06 1.02 1.647e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1049 0.09263 0.1824 0.1999 0.9873 0.9919 0.105 0.7543 0.8656 0.3055 ] Network output: [ -0.004766 0.02282 1.004 2.32e-05 -1.042e-05 0.983 1.749e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09144 0.0895 0.1651 0.1955 0.9853 0.9912 0.09145 0.6787 0.8417 0.2461 ] Network output: [ 0.000135 1 -0.0001569 3.097e-06 -1.39e-06 0.9998 2.334e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003425 Epoch 8406 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0103 0.9959 0.991 -4.723e-08 2.12e-08 -0.007503 -3.56e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003396 -0.003212 -0.007624 0.006002 0.9699 0.9743 0.00654 0.8318 0.8237 0.01764 ] Network output: [ 0.9998 0.0004209 0.0007136 -1.156e-05 5.189e-06 -0.00087 -8.711e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1993 -0.03416 -0.171 0.1883 0.9835 0.9932 0.2231 0.4384 0.8706 0.715 ] Network output: [ -0.01008 1.002 1.009 -2.533e-07 1.137e-07 0.008618 -1.909e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006222 0.0005158 0.004445 0.003537 0.9889 0.9919 0.00634 0.8598 0.8946 0.0127 ] Network output: [ -0.000457 0.002399 1.001 -3.624e-05 1.627e-05 0.9974 -2.731e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2116 0.09874 0.3414 0.145 0.985 0.994 0.2123 0.4425 0.8772 0.7092 ] Network output: [ 0.005029 -0.02394 0.9945 2.183e-05 -9.8e-06 1.019 1.645e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1049 0.09263 0.1824 0.1999 0.9873 0.9919 0.105 0.7543 0.8656 0.3055 ] Network output: [ -0.004765 0.02281 1.004 2.318e-05 -1.041e-05 0.9831 1.747e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09144 0.0895 0.1651 0.1955 0.9853 0.9912 0.09145 0.6787 0.8417 0.2461 ] Network output: [ 0.0001349 1 -0.0001567 3.094e-06 -1.389e-06 0.9998 2.332e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003423 Epoch 8407 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0103 0.9959 0.991 -4.787e-08 2.149e-08 -0.007503 -3.607e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003396 -0.003212 -0.007623 0.006001 0.9699 0.9743 0.00654 0.8318 0.8237 0.01764 ] Network output: [ 0.9998 0.0004205 0.0007132 -1.155e-05 5.184e-06 -0.0008693 -8.702e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1993 -0.03416 -0.171 0.1883 0.9835 0.9932 0.2232 0.4384 0.8706 0.715 ] Network output: [ -0.01008 1.002 1.009 -2.536e-07 1.138e-07 0.008617 -1.911e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006222 0.0005159 0.004445 0.003536 0.9889 0.9919 0.006341 0.8598 0.8946 0.0127 ] Network output: [ -0.0004567 0.002398 1.001 -3.62e-05 1.625e-05 0.9974 -2.728e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2117 0.09875 0.3414 0.145 0.985 0.994 0.2123 0.4425 0.8772 0.7092 ] Network output: [ 0.005027 -0.02393 0.9945 2.181e-05 -9.79e-06 1.019 1.643e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1049 0.09264 0.1824 0.1999 0.9873 0.9919 0.105 0.7542 0.8656 0.3055 ] Network output: [ -0.004763 0.0228 1.004 2.316e-05 -1.04e-05 0.9831 1.745e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09144 0.08951 0.1651 0.1955 0.9853 0.9912 0.09146 0.6787 0.8417 0.2461 ] Network output: [ 0.0001349 1 -0.0001565 3.091e-06 -1.388e-06 0.9998 2.329e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003421 Epoch 8408 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01029 0.9959 0.991 -4.85e-08 2.177e-08 -0.007503 -3.655e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003396 -0.003212 -0.007622 0.006001 0.9699 0.9743 0.006541 0.8318 0.8237 0.01763 ] Network output: [ 0.9998 0.0004202 0.0007127 -1.153e-05 5.178e-06 -0.0008685 -8.693e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1993 -0.03417 -0.171 0.1882 0.9835 0.9932 0.2232 0.4383 0.8706 0.715 ] Network output: [ -0.01008 1.002 1.009 -2.539e-07 1.14e-07 0.008615 -1.913e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006223 0.000516 0.004445 0.003536 0.9889 0.9919 0.006341 0.8598 0.8946 0.0127 ] Network output: [ -0.0004565 0.002397 1.001 -3.616e-05 1.624e-05 0.9974 -2.725e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2117 0.09875 0.3414 0.145 0.985 0.994 0.2124 0.4425 0.8772 0.7092 ] Network output: [ 0.005025 -0.02393 0.9945 2.178e-05 -9.78e-06 1.019 1.642e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1049 0.09264 0.1824 0.1999 0.9873 0.9919 0.105 0.7542 0.8655 0.3055 ] Network output: [ -0.004761 0.0228 1.004 2.313e-05 -1.039e-05 0.9831 1.743e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09145 0.08951 0.1651 0.1955 0.9853 0.9912 0.09146 0.6787 0.8417 0.2461 ] Network output: [ 0.0001348 1 -0.0001563 3.088e-06 -1.386e-06 0.9998 2.327e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003419 Epoch 8409 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01029 0.9959 0.991 -4.913e-08 2.206e-08 -0.007503 -3.703e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003397 -0.003212 -0.007621 0.006 0.9699 0.9743 0.006541 0.8318 0.8237 0.01763 ] Network output: [ 0.9998 0.0004198 0.0007123 -1.152e-05 5.173e-06 -0.0008678 -8.684e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1994 -0.03417 -0.171 0.1882 0.9835 0.9932 0.2232 0.4383 0.8705 0.715 ] Network output: [ -0.01008 1.002 1.009 -2.542e-07 1.141e-07 0.008614 -1.915e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006224 0.0005161 0.004445 0.003536 0.9889 0.9919 0.006342 0.8598 0.8946 0.0127 ] Network output: [ -0.0004562 0.002396 1.001 -3.613e-05 1.622e-05 0.9974 -2.723e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2117 0.09876 0.3414 0.1449 0.985 0.994 0.2124 0.4425 0.8772 0.7092 ] Network output: [ 0.005024 -0.02392 0.9945 2.176e-05 -9.77e-06 1.019 1.64e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1049 0.09265 0.1824 0.1999 0.9873 0.9919 0.105 0.7542 0.8655 0.3055 ] Network output: [ -0.00476 0.02279 1.004 2.311e-05 -1.038e-05 0.9831 1.742e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09145 0.08951 0.1651 0.1955 0.9853 0.9912 0.09146 0.6787 0.8417 0.2462 ] Network output: [ 0.0001347 1 -0.0001561 3.084e-06 -1.385e-06 0.9998 2.324e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003417 Epoch 8410 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01029 0.9959 0.991 -4.976e-08 2.234e-08 -0.007503 -3.75e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003397 -0.003212 -0.00762 0.005999 0.9699 0.9743 0.006541 0.8318 0.8237 0.01763 ] Network output: [ 0.9998 0.0004195 0.0007119 -1.151e-05 5.168e-06 -0.0008671 -8.675e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1994 -0.03417 -0.171 0.1882 0.9835 0.9932 0.2232 0.4383 0.8705 0.715 ] Network output: [ -0.01008 1.002 1.009 -2.544e-07 1.142e-07 0.008612 -1.918e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006224 0.0005162 0.004445 0.003535 0.9889 0.9919 0.006342 0.8598 0.8946 0.0127 ] Network output: [ -0.0004559 0.002395 1.001 -3.609e-05 1.62e-05 0.9974 -2.72e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2117 0.09876 0.3414 0.1449 0.985 0.994 0.2124 0.4425 0.8772 0.7092 ] Network output: [ 0.005022 -0.02391 0.9945 2.174e-05 -9.76e-06 1.019 1.638e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1049 0.09265 0.1824 0.1999 0.9873 0.9919 0.105 0.7542 0.8655 0.3055 ] Network output: [ -0.004758 0.02278 1.004 2.309e-05 -1.036e-05 0.9831 1.74e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09145 0.08951 0.1651 0.1955 0.9853 0.9912 0.09146 0.6786 0.8417 0.2462 ] Network output: [ 0.0001347 1 -0.000156 3.081e-06 -1.383e-06 0.9998 2.322e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003415 Epoch 8411 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01029 0.996 0.991 -5.039e-08 2.262e-08 -0.007503 -3.798e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003397 -0.003212 -0.007619 0.005999 0.9699 0.9743 0.006542 0.8318 0.8236 0.01763 ] Network output: [ 0.9998 0.0004191 0.0007115 -1.15e-05 5.162e-06 -0.0008664 -8.666e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1994 -0.03417 -0.171 0.1882 0.9835 0.9932 0.2232 0.4383 0.8705 0.715 ] Network output: [ -0.01008 1.002 1.009 -2.547e-07 1.144e-07 0.008611 -1.92e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006225 0.0005162 0.004445 0.003535 0.9889 0.9919 0.006343 0.8598 0.8946 0.0127 ] Network output: [ -0.0004556 0.002394 1.001 -3.605e-05 1.618e-05 0.9974 -2.717e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2117 0.09877 0.3414 0.1449 0.985 0.994 0.2124 0.4425 0.8772 0.7092 ] Network output: [ 0.00502 -0.0239 0.9945 2.172e-05 -9.749e-06 1.019 1.637e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.105 0.09266 0.1824 0.1998 0.9873 0.9919 0.105 0.7542 0.8655 0.3055 ] Network output: [ -0.004756 0.02277 1.004 2.306e-05 -1.035e-05 0.9831 1.738e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09145 0.08952 0.1651 0.1955 0.9853 0.9912 0.09147 0.6786 0.8417 0.2462 ] Network output: [ 0.0001346 1 -0.0001558 3.078e-06 -1.382e-06 0.9998 2.32e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003413 Epoch 8412 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01029 0.996 0.991 -5.102e-08 2.29e-08 -0.007503 -3.845e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003397 -0.003212 -0.007618 0.005998 0.9699 0.9743 0.006542 0.8318 0.8236 0.01763 ] Network output: [ 0.9998 0.0004188 0.0007111 -1.149e-05 5.157e-06 -0.0008656 -8.657e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1994 -0.03417 -0.1709 0.1882 0.9835 0.9932 0.2232 0.4383 0.8705 0.715 ] Network output: [ -0.01008 1.002 1.009 -2.55e-07 1.145e-07 0.008609 -1.922e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006225 0.0005163 0.004445 0.003534 0.9889 0.9919 0.006343 0.8598 0.8946 0.0127 ] Network output: [ -0.0004553 0.002393 1.001 -3.601e-05 1.617e-05 0.9974 -2.714e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2117 0.09878 0.3414 0.1449 0.985 0.994 0.2124 0.4425 0.8772 0.7092 ] Network output: [ 0.005019 -0.02389 0.9945 2.169e-05 -9.739e-06 1.019 1.635e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.105 0.09267 0.1824 0.1998 0.9873 0.9919 0.105 0.7541 0.8655 0.3055 ] Network output: [ -0.004754 0.02276 1.004 2.304e-05 -1.034e-05 0.9831 1.736e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09145 0.08952 0.1651 0.1955 0.9853 0.9912 0.09147 0.6786 0.8417 0.2462 ] Network output: [ 0.0001345 1 -0.0001556 3.075e-06 -1.38e-06 0.9998 2.317e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003412 Epoch 8413 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01029 0.996 0.991 -5.164e-08 2.318e-08 -0.007503 -3.892e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003397 -0.003213 -0.007617 0.005998 0.9699 0.9743 0.006542 0.8318 0.8236 0.01763 ] Network output: [ 0.9998 0.0004184 0.0007107 -1.147e-05 5.151e-06 -0.0008649 -8.647e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1994 -0.03417 -0.1709 0.1882 0.9835 0.9932 0.2232 0.4383 0.8705 0.715 ] Network output: [ -0.01008 1.002 1.009 -2.553e-07 1.146e-07 0.008608 -1.924e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006226 0.0005164 0.004445 0.003534 0.9889 0.9919 0.006344 0.8598 0.8946 0.01269 ] Network output: [ -0.000455 0.002393 1.001 -3.597e-05 1.615e-05 0.9974 -2.711e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2117 0.09878 0.3414 0.1449 0.985 0.994 0.2124 0.4425 0.8772 0.7092 ] Network output: [ 0.005017 -0.02388 0.9945 2.167e-05 -9.729e-06 1.019 1.633e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.105 0.09267 0.1824 0.1998 0.9873 0.9919 0.105 0.7541 0.8655 0.3055 ] Network output: [ -0.004753 0.02275 1.004 2.302e-05 -1.033e-05 0.9831 1.735e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09146 0.08952 0.1651 0.1955 0.9853 0.9912 0.09147 0.6786 0.8417 0.2462 ] Network output: [ 0.0001345 1 -0.0001554 3.072e-06 -1.379e-06 0.9998 2.315e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000341 Epoch 8414 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01029 0.996 0.991 -5.227e-08 2.346e-08 -0.007503 -3.939e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003397 -0.003213 -0.007616 0.005997 0.9699 0.9743 0.006542 0.8318 0.8236 0.01763 ] Network output: [ 0.9998 0.0004181 0.0007103 -1.146e-05 5.146e-06 -0.0008642 -8.638e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1994 -0.03417 -0.1709 0.1882 0.9835 0.9932 0.2232 0.4383 0.8705 0.715 ] Network output: [ -0.01007 1.002 1.009 -2.556e-07 1.147e-07 0.008606 -1.926e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006226 0.0005165 0.004445 0.003534 0.9889 0.9919 0.006345 0.8598 0.8946 0.01269 ] Network output: [ -0.0004547 0.002392 1.001 -3.594e-05 1.613e-05 0.9974 -2.708e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2117 0.09879 0.3414 0.1449 0.985 0.994 0.2124 0.4425 0.8772 0.7092 ] Network output: [ 0.005015 -0.02387 0.9945 2.165e-05 -9.719e-06 1.019 1.632e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.105 0.09268 0.1824 0.1998 0.9873 0.9919 0.105 0.7541 0.8655 0.3055 ] Network output: [ -0.004751 0.02274 1.004 2.299e-05 -1.032e-05 0.9831 1.733e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09146 0.08952 0.1651 0.1955 0.9853 0.9912 0.09147 0.6786 0.8417 0.2462 ] Network output: [ 0.0001344 1 -0.0001552 3.068e-06 -1.378e-06 0.9998 2.312e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003408 Epoch 8415 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01029 0.996 0.991 -5.289e-08 2.374e-08 -0.007503 -3.986e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003397 -0.003213 -0.007615 0.005996 0.9699 0.9743 0.006543 0.8318 0.8236 0.01763 ] Network output: [ 0.9998 0.0004177 0.0007099 -1.145e-05 5.14e-06 -0.0008635 -8.629e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1994 -0.03418 -0.1709 0.1882 0.9835 0.9932 0.2232 0.4383 0.8705 0.715 ] Network output: [ -0.01007 1.002 1.009 -2.559e-07 1.149e-07 0.008605 -1.928e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006227 0.0005166 0.004445 0.003533 0.9889 0.9919 0.006345 0.8598 0.8946 0.01269 ] Network output: [ -0.0004544 0.002391 1.001 -3.59e-05 1.612e-05 0.9974 -2.705e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2117 0.09879 0.3414 0.1449 0.985 0.994 0.2124 0.4425 0.8772 0.7091 ] Network output: [ 0.005014 -0.02387 0.9945 2.163e-05 -9.709e-06 1.019 1.63e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.105 0.09268 0.1824 0.1998 0.9873 0.9919 0.105 0.7541 0.8655 0.3055 ] Network output: [ -0.004749 0.02273 1.004 2.297e-05 -1.031e-05 0.9831 1.731e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09146 0.08952 0.1651 0.1955 0.9853 0.9912 0.09147 0.6785 0.8417 0.2462 ] Network output: [ 0.0001344 1 -0.000155 3.065e-06 -1.376e-06 0.9998 2.31e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003406 Epoch 8416 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01028 0.996 0.991 -5.351e-08 2.402e-08 -0.007503 -4.033e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003397 -0.003213 -0.007615 0.005996 0.9699 0.9743 0.006543 0.8318 0.8236 0.01762 ] Network output: [ 0.9998 0.0004174 0.0007095 -1.144e-05 5.135e-06 -0.0008628 -8.62e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1994 -0.03418 -0.1709 0.1882 0.9835 0.9932 0.2232 0.4383 0.8705 0.715 ] Network output: [ -0.01007 1.002 1.009 -2.561e-07 1.15e-07 0.008603 -1.93e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006227 0.0005167 0.004445 0.003533 0.9889 0.9919 0.006346 0.8598 0.8946 0.01269 ] Network output: [ -0.0004541 0.00239 1.001 -3.586e-05 1.61e-05 0.9974 -2.702e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2117 0.0988 0.3414 0.1449 0.985 0.994 0.2124 0.4424 0.8772 0.7091 ] Network output: [ 0.005012 -0.02386 0.9945 2.16e-05 -9.699e-06 1.019 1.628e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.105 0.09269 0.1824 0.1998 0.9873 0.9919 0.1051 0.7541 0.8655 0.3055 ] Network output: [ -0.004748 0.02272 1.004 2.295e-05 -1.03e-05 0.9831 1.729e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09146 0.08953 0.1651 0.1955 0.9853 0.9912 0.09148 0.6785 0.8417 0.2462 ] Network output: [ 0.0001343 1 -0.0001549 3.062e-06 -1.375e-06 0.9998 2.308e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003404 Epoch 8417 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01028 0.996 0.991 -5.413e-08 2.43e-08 -0.007503 -4.079e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003398 -0.003213 -0.007614 0.005995 0.9699 0.9743 0.006543 0.8318 0.8236 0.01762 ] Network output: [ 0.9998 0.000417 0.0007091 -1.143e-05 5.13e-06 -0.000862 -8.611e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1994 -0.03418 -0.1709 0.1882 0.9835 0.9932 0.2233 0.4383 0.8705 0.715 ] Network output: [ -0.01007 1.002 1.009 -2.564e-07 1.151e-07 0.008602 -1.932e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006228 0.0005168 0.004445 0.003533 0.9889 0.9919 0.006346 0.8598 0.8945 0.01269 ] Network output: [ -0.0004539 0.002389 1.001 -3.582e-05 1.608e-05 0.9974 -2.7e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2118 0.0988 0.3415 0.1449 0.985 0.994 0.2124 0.4424 0.8772 0.7091 ] Network output: [ 0.00501 -0.02385 0.9945 2.158e-05 -9.689e-06 1.019 1.626e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.105 0.09269 0.1824 0.1998 0.9873 0.9919 0.1051 0.7541 0.8655 0.3055 ] Network output: [ -0.004746 0.02271 1.004 2.292e-05 -1.029e-05 0.9831 1.728e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09147 0.08953 0.1651 0.1955 0.9853 0.9912 0.09148 0.6785 0.8416 0.2462 ] Network output: [ 0.0001342 1 -0.0001547 3.059e-06 -1.373e-06 0.9998 2.305e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003402 Epoch 8418 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01028 0.996 0.991 -5.475e-08 2.458e-08 -0.007503 -4.126e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003398 -0.003213 -0.007613 0.005995 0.9699 0.9743 0.006544 0.8318 0.8236 0.01762 ] Network output: [ 0.9998 0.0004167 0.0007086 -1.141e-05 5.124e-06 -0.0008613 -8.602e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1994 -0.03418 -0.1709 0.1882 0.9835 0.9932 0.2233 0.4382 0.8705 0.7149 ] Network output: [ -0.01007 1.002 1.009 -2.567e-07 1.152e-07 0.0086 -1.934e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006229 0.0005169 0.004445 0.003532 0.9889 0.9919 0.006347 0.8598 0.8945 0.01269 ] Network output: [ -0.0004536 0.002388 1.001 -3.578e-05 1.606e-05 0.9974 -2.697e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2118 0.09881 0.3415 0.1449 0.985 0.994 0.2125 0.4424 0.8772 0.7091 ] Network output: [ 0.005009 -0.02384 0.9945 2.156e-05 -9.678e-06 1.019 1.625e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.105 0.0927 0.1824 0.1998 0.9873 0.9919 0.1051 0.754 0.8655 0.3055 ] Network output: [ -0.004744 0.0227 1.004 2.29e-05 -1.028e-05 0.9831 1.726e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09147 0.08953 0.1651 0.1955 0.9853 0.9912 0.09148 0.6785 0.8416 0.2462 ] Network output: [ 0.0001342 1 -0.0001545 3.056e-06 -1.372e-06 0.9998 2.303e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00034 Epoch 8419 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01028 0.996 0.991 -5.536e-08 2.485e-08 -0.007504 -4.172e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003398 -0.003213 -0.007612 0.005994 0.9699 0.9743 0.006544 0.8318 0.8236 0.01762 ] Network output: [ 0.9998 0.0004163 0.0007082 -1.14e-05 5.119e-06 -0.0008606 -8.593e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1994 -0.03418 -0.1709 0.1882 0.9835 0.9932 0.2233 0.4382 0.8705 0.7149 ] Network output: [ -0.01007 1.002 1.009 -2.57e-07 1.154e-07 0.008599 -1.937e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006229 0.000517 0.004445 0.003532 0.9889 0.9919 0.006347 0.8597 0.8945 0.01269 ] Network output: [ -0.0004533 0.002387 1.001 -3.575e-05 1.605e-05 0.9974 -2.694e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2118 0.09882 0.3415 0.1449 0.985 0.994 0.2125 0.4424 0.8772 0.7091 ] Network output: [ 0.005007 -0.02383 0.9945 2.154e-05 -9.668e-06 1.019 1.623e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.105 0.09271 0.1824 0.1998 0.9873 0.9919 0.1051 0.754 0.8655 0.3055 ] Network output: [ -0.004743 0.02269 1.004 2.288e-05 -1.027e-05 0.9831 1.724e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09147 0.08953 0.1651 0.1955 0.9853 0.9912 0.09148 0.6785 0.8416 0.2462 ] Network output: [ 0.0001341 1 -0.0001543 3.053e-06 -1.37e-06 0.9998 2.3e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003398 Epoch 8420 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01028 0.996 0.991 -5.598e-08 2.513e-08 -0.007504 -4.218e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003398 -0.003214 -0.007611 0.005994 0.9699 0.9743 0.006544 0.8317 0.8236 0.01762 ] Network output: [ 0.9998 0.000416 0.0007078 -1.139e-05 5.113e-06 -0.0008599 -8.584e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1995 -0.03418 -0.1708 0.1882 0.9835 0.9932 0.2233 0.4382 0.8705 0.7149 ] Network output: [ -0.01007 1.002 1.009 -2.572e-07 1.155e-07 0.008597 -1.939e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00623 0.0005171 0.004445 0.003532 0.9889 0.9919 0.006348 0.8597 0.8945 0.01269 ] Network output: [ -0.000453 0.002387 1.001 -3.571e-05 1.603e-05 0.9974 -2.691e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2118 0.09882 0.3415 0.1449 0.985 0.994 0.2125 0.4424 0.8772 0.7091 ] Network output: [ 0.005005 -0.02382 0.9945 2.151e-05 -9.658e-06 1.019 1.621e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.105 0.09271 0.1824 0.1998 0.9873 0.9919 0.1051 0.754 0.8655 0.3055 ] Network output: [ -0.004741 0.02268 1.004 2.285e-05 -1.026e-05 0.9831 1.722e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09147 0.08953 0.1651 0.1955 0.9853 0.9912 0.09149 0.6784 0.8416 0.2462 ] Network output: [ 0.000134 1 -0.0001541 3.049e-06 -1.369e-06 0.9998 2.298e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003397 Epoch 8421 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01028 0.996 0.991 -5.659e-08 2.54e-08 -0.007504 -4.265e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003398 -0.003214 -0.00761 0.005993 0.9699 0.9743 0.006545 0.8317 0.8236 0.01762 ] Network output: [ 0.9998 0.0004156 0.0007074 -1.138e-05 5.108e-06 -0.0008592 -8.575e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1995 -0.03419 -0.1708 0.1882 0.9835 0.9932 0.2233 0.4382 0.8705 0.7149 ] Network output: [ -0.01007 1.002 1.009 -2.575e-07 1.156e-07 0.008596 -1.941e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00623 0.0005172 0.004445 0.003531 0.9889 0.9919 0.006349 0.8597 0.8945 0.01269 ] Network output: [ -0.0004527 0.002386 1.001 -3.567e-05 1.601e-05 0.9974 -2.688e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2118 0.09883 0.3415 0.1449 0.985 0.994 0.2125 0.4424 0.8772 0.7091 ] Network output: [ 0.005003 -0.02381 0.9945 2.149e-05 -9.648e-06 1.019 1.62e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.105 0.09272 0.1824 0.1998 0.9873 0.9919 0.1051 0.754 0.8655 0.3055 ] Network output: [ -0.004739 0.02267 1.004 2.283e-05 -1.025e-05 0.9831 1.721e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09147 0.08954 0.1651 0.1955 0.9853 0.9912 0.09149 0.6784 0.8416 0.2462 ] Network output: [ 0.000134 1 -0.000154 3.046e-06 -1.368e-06 0.9998 2.296e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003395 Epoch 8422 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01028 0.996 0.991 -5.72e-08 2.568e-08 -0.007504 -4.311e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003398 -0.003214 -0.007609 0.005992 0.9699 0.9743 0.006545 0.8317 0.8236 0.01762 ] Network output: [ 0.9998 0.0004153 0.000707 -1.137e-05 5.103e-06 -0.0008584 -8.566e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1995 -0.03419 -0.1708 0.1882 0.9835 0.9932 0.2233 0.4382 0.8705 0.7149 ] Network output: [ -0.01007 1.002 1.009 -2.578e-07 1.157e-07 0.008594 -1.943e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006231 0.0005173 0.004445 0.003531 0.9889 0.9919 0.006349 0.8597 0.8945 0.01269 ] Network output: [ -0.0004524 0.002385 1.001 -3.563e-05 1.6e-05 0.9974 -2.685e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2118 0.09883 0.3415 0.1449 0.985 0.994 0.2125 0.4424 0.8772 0.7091 ] Network output: [ 0.005002 -0.0238 0.9945 2.147e-05 -9.638e-06 1.019 1.618e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.105 0.09272 0.1824 0.1998 0.9873 0.9919 0.1051 0.754 0.8655 0.3055 ] Network output: [ -0.004737 0.02267 1.004 2.281e-05 -1.024e-05 0.9831 1.719e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09148 0.08954 0.1651 0.1955 0.9853 0.9912 0.09149 0.6784 0.8416 0.2462 ] Network output: [ 0.0001339 1 -0.0001538 3.043e-06 -1.366e-06 0.9998 2.293e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003393 Epoch 8423 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01027 0.996 0.991 -5.781e-08 2.595e-08 -0.007504 -4.357e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003398 -0.003214 -0.007608 0.005992 0.9699 0.9743 0.006545 0.8317 0.8236 0.01762 ] Network output: [ 0.9998 0.0004149 0.0007066 -1.135e-05 5.097e-06 -0.0008577 -8.557e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1995 -0.03419 -0.1708 0.1882 0.9835 0.9932 0.2233 0.4382 0.8705 0.7149 ] Network output: [ -0.01006 1.002 1.009 -2.581e-07 1.158e-07 0.008593 -1.945e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006231 0.0005173 0.004445 0.003531 0.9889 0.9919 0.00635 0.8597 0.8945 0.01268 ] Network output: [ -0.0004521 0.002384 1.001 -3.559e-05 1.598e-05 0.9974 -2.682e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2118 0.09884 0.3415 0.1449 0.985 0.994 0.2125 0.4424 0.8772 0.7091 ] Network output: [ 0.005 -0.0238 0.9945 2.145e-05 -9.628e-06 1.019 1.616e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.105 0.09273 0.1824 0.1998 0.9873 0.9919 0.1051 0.7539 0.8655 0.3055 ] Network output: [ -0.004736 0.02266 1.004 2.278e-05 -1.023e-05 0.9831 1.717e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09148 0.08954 0.1651 0.1955 0.9853 0.9912 0.09149 0.6784 0.8416 0.2462 ] Network output: [ 0.0001339 1 -0.0001536 3.04e-06 -1.365e-06 0.9998 2.291e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003391 Epoch 8424 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01027 0.996 0.991 -5.841e-08 2.622e-08 -0.007504 -4.402e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003398 -0.003214 -0.007607 0.005991 0.9699 0.9743 0.006545 0.8317 0.8236 0.01761 ] Network output: [ 0.9998 0.0004146 0.0007062 -1.134e-05 5.092e-06 -0.000857 -8.548e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1995 -0.03419 -0.1708 0.1882 0.9835 0.9932 0.2233 0.4382 0.8705 0.7149 ] Network output: [ -0.01006 1.002 1.009 -2.583e-07 1.16e-07 0.008591 -1.947e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006232 0.0005174 0.004445 0.00353 0.9889 0.9919 0.00635 0.8597 0.8945 0.01268 ] Network output: [ -0.0004518 0.002383 1.001 -3.556e-05 1.596e-05 0.9974 -2.68e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2118 0.09885 0.3415 0.1449 0.985 0.994 0.2125 0.4424 0.8772 0.7091 ] Network output: [ 0.004998 -0.02379 0.9945 2.142e-05 -9.618e-06 1.019 1.615e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.105 0.09273 0.1824 0.1998 0.9873 0.9919 0.1051 0.7539 0.8655 0.3055 ] Network output: [ -0.004734 0.02265 1.004 2.276e-05 -1.022e-05 0.9831 1.715e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09148 0.08954 0.1651 0.1955 0.9853 0.9912 0.09149 0.6784 0.8416 0.2462 ] Network output: [ 0.0001338 1 -0.0001534 3.037e-06 -1.363e-06 0.9998 2.289e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003389 Epoch 8425 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01027 0.996 0.991 -5.902e-08 2.65e-08 -0.007504 -4.448e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003399 -0.003214 -0.007606 0.005991 0.9699 0.9743 0.006546 0.8317 0.8236 0.01761 ] Network output: [ 0.9998 0.0004142 0.0007058 -1.133e-05 5.087e-06 -0.0008563 -8.539e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1995 -0.03419 -0.1708 0.1882 0.9835 0.9932 0.2233 0.4382 0.8705 0.7149 ] Network output: [ -0.01006 1.002 1.009 -2.586e-07 1.161e-07 0.00859 -1.949e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006232 0.0005175 0.004445 0.00353 0.9889 0.9919 0.006351 0.8597 0.8945 0.01268 ] Network output: [ -0.0004516 0.002382 1.001 -3.552e-05 1.595e-05 0.9974 -2.677e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2118 0.09885 0.3415 0.1449 0.985 0.994 0.2125 0.4424 0.8771 0.7091 ] Network output: [ 0.004997 -0.02378 0.9945 2.14e-05 -9.608e-06 1.019 1.613e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.105 0.09274 0.1824 0.1998 0.9873 0.9919 0.1051 0.7539 0.8655 0.3055 ] Network output: [ -0.004732 0.02264 1.004 2.274e-05 -1.021e-05 0.9831 1.714e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09148 0.08955 0.1651 0.1955 0.9853 0.9912 0.0915 0.6783 0.8416 0.2462 ] Network output: [ 0.0001337 1 -0.0001532 3.034e-06 -1.362e-06 0.9998 2.286e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003387 Epoch 8426 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01027 0.996 0.991 -5.962e-08 2.677e-08 -0.007504 -4.494e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003399 -0.003214 -0.007606 0.00599 0.9699 0.9743 0.006546 0.8317 0.8236 0.01761 ] Network output: [ 0.9998 0.0004139 0.0007054 -1.132e-05 5.081e-06 -0.0008556 -8.53e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1995 -0.03419 -0.1708 0.1881 0.9835 0.9932 0.2233 0.4382 0.8705 0.7149 ] Network output: [ -0.01006 1.002 1.009 -2.589e-07 1.162e-07 0.008588 -1.951e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006233 0.0005176 0.004445 0.00353 0.9889 0.9919 0.006351 0.8597 0.8945 0.01268 ] Network output: [ -0.0004513 0.002382 1.001 -3.548e-05 1.593e-05 0.9974 -2.674e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2118 0.09886 0.3415 0.1449 0.985 0.994 0.2125 0.4423 0.8771 0.7091 ] Network output: [ 0.004995 -0.02377 0.9945 2.138e-05 -9.598e-06 1.019 1.611e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.105 0.09275 0.1824 0.1998 0.9873 0.9919 0.1051 0.7539 0.8655 0.3055 ] Network output: [ -0.004731 0.02263 1.004 2.271e-05 -1.02e-05 0.9831 1.712e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09149 0.08955 0.1651 0.1955 0.9853 0.9912 0.0915 0.6783 0.8416 0.2462 ] Network output: [ 0.0001337 1 -0.0001531 3.03e-06 -1.36e-06 0.9998 2.284e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003385 Epoch 8427 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01027 0.996 0.991 -6.023e-08 2.704e-08 -0.007504 -4.539e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003399 -0.003215 -0.007605 0.00599 0.9699 0.9743 0.006546 0.8317 0.8236 0.01761 ] Network output: [ 0.9998 0.0004135 0.000705 -1.131e-05 5.076e-06 -0.0008549 -8.521e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1995 -0.03419 -0.1707 0.1881 0.9835 0.9932 0.2234 0.4382 0.8705 0.7149 ] Network output: [ -0.01006 1.002 1.009 -2.591e-07 1.163e-07 0.008587 -1.953e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006234 0.0005177 0.004445 0.003529 0.9889 0.9919 0.006352 0.8597 0.8945 0.01268 ] Network output: [ -0.000451 0.002381 1.001 -3.544e-05 1.591e-05 0.9974 -2.671e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2119 0.09886 0.3415 0.1449 0.985 0.994 0.2126 0.4423 0.8771 0.7091 ] Network output: [ 0.004993 -0.02376 0.9945 2.136e-05 -9.588e-06 1.019 1.61e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1051 0.09275 0.1824 0.1998 0.9873 0.9919 0.1051 0.7539 0.8655 0.3055 ] Network output: [ -0.004729 0.02262 1.004 2.269e-05 -1.019e-05 0.9831 1.71e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09149 0.08955 0.1651 0.1955 0.9853 0.9912 0.0915 0.6783 0.8416 0.2462 ] Network output: [ 0.0001336 1 -0.0001529 3.027e-06 -1.359e-06 0.9998 2.281e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003383 Epoch 8428 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01027 0.996 0.991 -6.083e-08 2.731e-08 -0.007504 -4.584e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003399 -0.003215 -0.007604 0.005989 0.9699 0.9743 0.006547 0.8317 0.8236 0.01761 ] Network output: [ 0.9998 0.0004132 0.0007046 -1.129e-05 5.071e-06 -0.0008542 -8.512e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1995 -0.0342 -0.1707 0.1881 0.9835 0.9932 0.2234 0.4381 0.8705 0.7149 ] Network output: [ -0.01006 1.002 1.009 -2.594e-07 1.165e-07 0.008585 -1.955e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006234 0.0005178 0.004445 0.003529 0.9889 0.9919 0.006353 0.8597 0.8945 0.01268 ] Network output: [ -0.0004507 0.00238 1.001 -3.541e-05 1.59e-05 0.9974 -2.668e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2119 0.09887 0.3415 0.1449 0.985 0.994 0.2126 0.4423 0.8771 0.7091 ] Network output: [ 0.004992 -0.02375 0.9945 2.133e-05 -9.578e-06 1.019 1.608e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1051 0.09276 0.1825 0.1998 0.9873 0.9919 0.1051 0.7538 0.8654 0.3055 ] Network output: [ -0.004727 0.02261 1.004 2.267e-05 -1.018e-05 0.9831 1.708e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09149 0.08955 0.1651 0.1955 0.9853 0.9912 0.0915 0.6783 0.8416 0.2462 ] Network output: [ 0.0001335 1 -0.0001527 3.024e-06 -1.358e-06 0.9998 2.279e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003382 Epoch 8429 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01027 0.996 0.991 -6.143e-08 2.758e-08 -0.007504 -4.629e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003399 -0.003215 -0.007603 0.005988 0.9699 0.9743 0.006547 0.8317 0.8236 0.01761 ] Network output: [ 0.9998 0.0004128 0.0007042 -1.128e-05 5.065e-06 -0.0008535 -8.503e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1995 -0.0342 -0.1707 0.1881 0.9835 0.9932 0.2234 0.4381 0.8705 0.7149 ] Network output: [ -0.01006 1.002 1.009 -2.597e-07 1.166e-07 0.008584 -1.957e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006235 0.0005179 0.004445 0.003528 0.9889 0.9919 0.006353 0.8597 0.8945 0.01268 ] Network output: [ -0.0004504 0.002379 1.001 -3.537e-05 1.588e-05 0.9974 -2.665e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2119 0.09888 0.3416 0.1449 0.985 0.994 0.2126 0.4423 0.8771 0.7091 ] Network output: [ 0.00499 -0.02374 0.9945 2.131e-05 -9.568e-06 1.019 1.606e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1051 0.09276 0.1825 0.1998 0.9873 0.9919 0.1051 0.7538 0.8654 0.3055 ] Network output: [ -0.004726 0.0226 1.004 2.264e-05 -1.017e-05 0.9832 1.707e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09149 0.08955 0.1651 0.1955 0.9853 0.9912 0.0915 0.6783 0.8416 0.2462 ] Network output: [ 0.0001335 1 -0.0001525 3.021e-06 -1.356e-06 0.9998 2.277e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000338 Epoch 8430 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01027 0.996 0.991 -6.203e-08 2.785e-08 -0.007504 -4.674e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003399 -0.003215 -0.007602 0.005988 0.9699 0.9743 0.006547 0.8317 0.8236 0.01761 ] Network output: [ 0.9998 0.0004125 0.0007038 -1.127e-05 5.06e-06 -0.0008527 -8.494e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1995 -0.0342 -0.1707 0.1881 0.9835 0.9932 0.2234 0.4381 0.8705 0.7149 ] Network output: [ -0.01006 1.002 1.009 -2.599e-07 1.167e-07 0.008582 -1.959e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006235 0.000518 0.004445 0.003528 0.9889 0.9919 0.006354 0.8597 0.8945 0.01268 ] Network output: [ -0.0004501 0.002378 1.001 -3.533e-05 1.586e-05 0.9974 -2.663e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2119 0.09888 0.3416 0.1449 0.985 0.994 0.2126 0.4423 0.8771 0.7091 ] Network output: [ 0.004988 -0.02374 0.9945 2.129e-05 -9.558e-06 1.019 1.604e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1051 0.09277 0.1825 0.1998 0.9873 0.9919 0.1051 0.7538 0.8654 0.3055 ] Network output: [ -0.004724 0.02259 1.004 2.262e-05 -1.016e-05 0.9832 1.705e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09149 0.08956 0.1651 0.1955 0.9853 0.9912 0.09151 0.6782 0.8416 0.2462 ] Network output: [ 0.0001334 1 -0.0001524 3.018e-06 -1.355e-06 0.9998 2.274e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003378 Epoch 8431 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01026 0.996 0.991 -6.262e-08 2.811e-08 -0.007504 -4.719e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003399 -0.003215 -0.007601 0.005987 0.9699 0.9743 0.006547 0.8317 0.8236 0.01761 ] Network output: [ 0.9998 0.0004122 0.0007034 -1.126e-05 5.055e-06 -0.000852 -8.485e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1995 -0.0342 -0.1707 0.1881 0.9835 0.9932 0.2234 0.4381 0.8705 0.7149 ] Network output: [ -0.01006 1.002 1.009 -2.602e-07 1.168e-07 0.008581 -1.961e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006236 0.0005181 0.004445 0.003528 0.9889 0.9919 0.006354 0.8597 0.8945 0.01268 ] Network output: [ -0.0004498 0.002377 1.001 -3.529e-05 1.584e-05 0.9974 -2.66e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2119 0.09889 0.3416 0.1449 0.985 0.994 0.2126 0.4423 0.8771 0.709 ] Network output: [ 0.004987 -0.02373 0.9945 2.127e-05 -9.548e-06 1.019 1.603e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1051 0.09277 0.1825 0.1998 0.9873 0.9919 0.1051 0.7538 0.8654 0.3055 ] Network output: [ -0.004722 0.02258 1.004 2.26e-05 -1.015e-05 0.9832 1.703e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0915 0.08956 0.1651 0.1955 0.9853 0.9912 0.09151 0.6782 0.8416 0.2462 ] Network output: [ 0.0001334 1 -0.0001522 3.015e-06 -1.353e-06 0.9998 2.272e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003376 Epoch 8432 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01026 0.996 0.991 -6.322e-08 2.838e-08 -0.007504 -4.764e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003399 -0.003215 -0.0076 0.005987 0.9699 0.9743 0.006548 0.8317 0.8236 0.0176 ] Network output: [ 0.9998 0.0004118 0.000703 -1.125e-05 5.049e-06 -0.0008513 -8.476e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1996 -0.0342 -0.1707 0.1881 0.9835 0.9932 0.2234 0.4381 0.8705 0.7149 ] Network output: [ -0.01006 1.002 1.009 -2.604e-07 1.169e-07 0.008579 -1.963e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006236 0.0005182 0.004445 0.003527 0.9889 0.9919 0.006355 0.8596 0.8945 0.01268 ] Network output: [ -0.0004495 0.002376 1.001 -3.526e-05 1.583e-05 0.9974 -2.657e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2119 0.09889 0.3416 0.1449 0.985 0.994 0.2126 0.4423 0.8771 0.709 ] Network output: [ 0.004985 -0.02372 0.9945 2.125e-05 -9.538e-06 1.019 1.601e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1051 0.09278 0.1825 0.1998 0.9873 0.9919 0.1052 0.7538 0.8654 0.3055 ] Network output: [ -0.00472 0.02257 1.004 2.258e-05 -1.013e-05 0.9832 1.701e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0915 0.08956 0.1651 0.1955 0.9853 0.9912 0.09151 0.6782 0.8416 0.2462 ] Network output: [ 0.0001333 1 -0.000152 3.012e-06 -1.352e-06 0.9998 2.27e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003374 Epoch 8433 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01026 0.996 0.991 -6.381e-08 2.865e-08 -0.007504 -4.809e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003399 -0.003215 -0.007599 0.005986 0.9699 0.9743 0.006548 0.8317 0.8236 0.0176 ] Network output: [ 0.9998 0.0004115 0.0007026 -1.124e-05 5.044e-06 -0.0008506 -8.467e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1996 -0.0342 -0.1707 0.1881 0.9835 0.9932 0.2234 0.4381 0.8705 0.7149 ] Network output: [ -0.01005 1.002 1.009 -2.607e-07 1.17e-07 0.008578 -1.965e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006237 0.0005183 0.004445 0.003527 0.9889 0.9919 0.006355 0.8596 0.8945 0.01268 ] Network output: [ -0.0004493 0.002376 1.001 -3.522e-05 1.581e-05 0.9974 -2.654e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2119 0.0989 0.3416 0.1449 0.985 0.994 0.2126 0.4423 0.8771 0.709 ] Network output: [ 0.004983 -0.02371 0.9945 2.122e-05 -9.528e-06 1.019 1.599e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1051 0.09278 0.1825 0.1998 0.9873 0.9919 0.1052 0.7538 0.8654 0.3055 ] Network output: [ -0.004719 0.02256 1.004 2.255e-05 -1.012e-05 0.9832 1.7e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0915 0.08956 0.1651 0.1955 0.9853 0.9912 0.09151 0.6782 0.8416 0.2462 ] Network output: [ 0.0001332 1 -0.0001518 3.008e-06 -1.351e-06 0.9998 2.267e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003372 Epoch 8434 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01026 0.996 0.991 -6.44e-08 2.891e-08 -0.007504 -4.853e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0034 -0.003216 -0.007598 0.005985 0.9699 0.9743 0.006548 0.8317 0.8236 0.0176 ] Network output: [ 0.9998 0.0004111 0.0007022 -1.122e-05 5.039e-06 -0.0008499 -8.458e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1996 -0.0342 -0.1706 0.1881 0.9835 0.9932 0.2234 0.4381 0.8705 0.7149 ] Network output: [ -0.01005 1.002 1.009 -2.61e-07 1.172e-07 0.008576 -1.967e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006237 0.0005184 0.004445 0.003527 0.9889 0.9919 0.006356 0.8596 0.8945 0.01267 ] Network output: [ -0.000449 0.002375 1.001 -3.518e-05 1.579e-05 0.9974 -2.651e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2119 0.09891 0.3416 0.1449 0.985 0.994 0.2126 0.4423 0.8771 0.709 ] Network output: [ 0.004982 -0.0237 0.9945 2.12e-05 -9.518e-06 1.019 1.598e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1051 0.09279 0.1825 0.1998 0.9873 0.9919 0.1052 0.7537 0.8654 0.3055 ] Network output: [ -0.004717 0.02255 1.004 2.253e-05 -1.011e-05 0.9832 1.698e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0915 0.08957 0.1651 0.1955 0.9853 0.9912 0.09152 0.6782 0.8415 0.2462 ] Network output: [ 0.0001332 1 -0.0001516 3.005e-06 -1.349e-06 0.9998 2.265e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000337 Epoch 8435 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01026 0.996 0.991 -6.499e-08 2.918e-08 -0.007504 -4.898e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0034 -0.003216 -0.007598 0.005985 0.9699 0.9743 0.006549 0.8316 0.8236 0.0176 ] Network output: [ 0.9998 0.0004108 0.0007017 -1.121e-05 5.033e-06 -0.0008492 -8.449e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1996 -0.03421 -0.1706 0.1881 0.9835 0.9932 0.2234 0.4381 0.8705 0.7148 ] Network output: [ -0.01005 1.002 1.009 -2.612e-07 1.173e-07 0.008575 -1.969e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006238 0.0005184 0.004445 0.003526 0.9889 0.9919 0.006357 0.8596 0.8945 0.01267 ] Network output: [ -0.0004487 0.002374 1.001 -3.514e-05 1.578e-05 0.9974 -2.649e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2119 0.09891 0.3416 0.1449 0.985 0.994 0.2126 0.4423 0.8771 0.709 ] Network output: [ 0.00498 -0.02369 0.9945 2.118e-05 -9.508e-06 1.019 1.596e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1051 0.0928 0.1825 0.1998 0.9873 0.9919 0.1052 0.7537 0.8654 0.3055 ] Network output: [ -0.004715 0.02255 1.004 2.251e-05 -1.01e-05 0.9832 1.696e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09151 0.08957 0.1651 0.1955 0.9853 0.9912 0.09152 0.6781 0.8415 0.2462 ] Network output: [ 0.0001331 1 -0.0001515 3.002e-06 -1.348e-06 0.9998 2.263e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003369 Epoch 8436 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01026 0.996 0.991 -6.558e-08 2.944e-08 -0.007504 -4.942e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0034 -0.003216 -0.007597 0.005984 0.9699 0.9743 0.006549 0.8316 0.8236 0.0176 ] Network output: [ 0.9998 0.0004104 0.0007013 -1.12e-05 5.028e-06 -0.0008485 -8.441e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1996 -0.03421 -0.1706 0.1881 0.9835 0.9932 0.2235 0.4381 0.8705 0.7148 ] Network output: [ -0.01005 1.002 1.009 -2.615e-07 1.174e-07 0.008573 -1.971e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006239 0.0005185 0.004445 0.003526 0.9889 0.9919 0.006357 0.8596 0.8945 0.01267 ] Network output: [ -0.0004484 0.002373 1.001 -3.511e-05 1.576e-05 0.9974 -2.646e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2119 0.09892 0.3416 0.1449 0.985 0.994 0.2126 0.4422 0.8771 0.709 ] Network output: [ 0.004978 -0.02368 0.9945 2.116e-05 -9.498e-06 1.019 1.594e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1051 0.0928 0.1825 0.1998 0.9873 0.9919 0.1052 0.7537 0.8654 0.3055 ] Network output: [ -0.004714 0.02254 1.004 2.248e-05 -1.009e-05 0.9832 1.694e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09151 0.08957 0.1651 0.1955 0.9853 0.9912 0.09152 0.6781 0.8415 0.2462 ] Network output: [ 0.000133 1 -0.0001513 2.999e-06 -1.346e-06 0.9998 2.26e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003367 Epoch 8437 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01026 0.996 0.991 -6.616e-08 2.97e-08 -0.007504 -4.986e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0034 -0.003216 -0.007596 0.005984 0.9699 0.9743 0.006549 0.8316 0.8235 0.0176 ] Network output: [ 0.9998 0.0004101 0.0007009 -1.119e-05 5.023e-06 -0.0008478 -8.432e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1996 -0.03421 -0.1706 0.1881 0.9835 0.9932 0.2235 0.4381 0.8705 0.7148 ] Network output: [ -0.01005 1.002 1.009 -2.617e-07 1.175e-07 0.008572 -1.973e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006239 0.0005186 0.004445 0.003526 0.9889 0.9919 0.006358 0.8596 0.8945 0.01267 ] Network output: [ -0.0004481 0.002372 1.001 -3.507e-05 1.574e-05 0.9974 -2.643e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.212 0.09892 0.3416 0.1448 0.985 0.994 0.2127 0.4422 0.8771 0.709 ] Network output: [ 0.004977 -0.02368 0.9945 2.113e-05 -9.488e-06 1.019 1.593e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1051 0.09281 0.1825 0.1998 0.9873 0.9919 0.1052 0.7537 0.8654 0.3055 ] Network output: [ -0.004712 0.02253 1.004 2.246e-05 -1.008e-05 0.9832 1.693e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09151 0.08957 0.1651 0.1955 0.9853 0.9912 0.09152 0.6781 0.8415 0.2462 ] Network output: [ 0.000133 1 -0.0001511 2.996e-06 -1.345e-06 0.9998 2.258e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003365 Epoch 8438 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01025 0.996 0.991 -6.675e-08 2.997e-08 -0.007505 -5.03e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0034 -0.003216 -0.007595 0.005983 0.9699 0.9743 0.006549 0.8316 0.8235 0.0176 ] Network output: [ 0.9998 0.0004097 0.0007005 -1.118e-05 5.017e-06 -0.0008471 -8.423e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1996 -0.03421 -0.1706 0.1881 0.9835 0.9932 0.2235 0.438 0.8705 0.7148 ] Network output: [ -0.01005 1.002 1.009 -2.62e-07 1.176e-07 0.00857 -1.974e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00624 0.0005187 0.004445 0.003525 0.9889 0.9919 0.006358 0.8596 0.8945 0.01267 ] Network output: [ -0.0004478 0.002371 1.001 -3.503e-05 1.573e-05 0.9974 -2.64e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.212 0.09893 0.3416 0.1448 0.985 0.994 0.2127 0.4422 0.8771 0.709 ] Network output: [ 0.004975 -0.02367 0.9945 2.111e-05 -9.478e-06 1.019 1.591e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1051 0.09281 0.1825 0.1998 0.9873 0.9919 0.1052 0.7537 0.8654 0.3055 ] Network output: [ -0.00471 0.02252 1.004 2.244e-05 -1.007e-05 0.9832 1.691e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09151 0.08957 0.1651 0.1955 0.9853 0.9912 0.09152 0.6781 0.8415 0.2462 ] Network output: [ 0.0001329 1 -0.0001509 2.993e-06 -1.344e-06 0.9998 2.255e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003363 Epoch 8439 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01025 0.996 0.991 -6.733e-08 3.023e-08 -0.007505 -5.074e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0034 -0.003216 -0.007594 0.005983 0.9699 0.9743 0.00655 0.8316 0.8235 0.0176 ] Network output: [ 0.9998 0.0004094 0.0007001 -1.116e-05 5.012e-06 -0.0008464 -8.414e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1996 -0.03421 -0.1706 0.1881 0.9835 0.9932 0.2235 0.438 0.8705 0.7148 ] Network output: [ -0.01005 1.002 1.009 -2.622e-07 1.177e-07 0.008569 -1.976e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00624 0.0005188 0.004445 0.003525 0.9889 0.9919 0.006359 0.8596 0.8945 0.01267 ] Network output: [ -0.0004475 0.002371 1.001 -3.5e-05 1.571e-05 0.9974 -2.637e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.212 0.09893 0.3416 0.1448 0.985 0.994 0.2127 0.4422 0.8771 0.709 ] Network output: [ 0.004973 -0.02366 0.9945 2.109e-05 -9.468e-06 1.019 1.589e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1051 0.09282 0.1825 0.1998 0.9873 0.9919 0.1052 0.7536 0.8654 0.3055 ] Network output: [ -0.004709 0.02251 1.004 2.241e-05 -1.006e-05 0.9832 1.689e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09151 0.08958 0.1651 0.1955 0.9853 0.9912 0.09153 0.6781 0.8415 0.2462 ] Network output: [ 0.0001329 1 -0.0001508 2.99e-06 -1.342e-06 0.9998 2.253e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003361 Epoch 8440 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01025 0.996 0.991 -6.792e-08 3.049e-08 -0.007505 -5.118e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0034 -0.003216 -0.007593 0.005982 0.9699 0.9743 0.00655 0.8316 0.8235 0.01759 ] Network output: [ 0.9998 0.0004091 0.0006997 -1.115e-05 5.007e-06 -0.0008457 -8.405e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1996 -0.03421 -0.1706 0.1881 0.9835 0.9932 0.2235 0.438 0.8705 0.7148 ] Network output: [ -0.01005 1.002 1.009 -2.625e-07 1.178e-07 0.008567 -1.978e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006241 0.0005189 0.004445 0.003525 0.9889 0.9919 0.006359 0.8596 0.8945 0.01267 ] Network output: [ -0.0004473 0.00237 1.001 -3.496e-05 1.569e-05 0.9974 -2.635e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.212 0.09894 0.3416 0.1448 0.985 0.994 0.2127 0.4422 0.8771 0.709 ] Network output: [ 0.004972 -0.02365 0.9945 2.107e-05 -9.458e-06 1.019 1.588e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1051 0.09282 0.1825 0.1998 0.9873 0.9919 0.1052 0.7536 0.8654 0.3055 ] Network output: [ -0.004707 0.0225 1.004 2.239e-05 -1.005e-05 0.9832 1.687e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09152 0.08958 0.1651 0.1955 0.9853 0.9912 0.09153 0.678 0.8415 0.2462 ] Network output: [ 0.0001328 1 -0.0001506 2.987e-06 -1.341e-06 0.9998 2.251e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003359 Epoch 8441 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01025 0.996 0.991 -6.85e-08 3.075e-08 -0.007505 -5.162e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0034 -0.003216 -0.007592 0.005981 0.9699 0.9743 0.00655 0.8316 0.8235 0.01759 ] Network output: [ 0.9998 0.0004087 0.0006993 -1.114e-05 5.002e-06 -0.000845 -8.396e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1996 -0.03422 -0.1706 0.1881 0.9835 0.9932 0.2235 0.438 0.8705 0.7148 ] Network output: [ -0.01005 1.002 1.009 -2.628e-07 1.18e-07 0.008566 -1.98e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006241 0.000519 0.004445 0.003524 0.9889 0.9919 0.00636 0.8596 0.8945 0.01267 ] Network output: [ -0.000447 0.002369 1.001 -3.492e-05 1.568e-05 0.9974 -2.632e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.212 0.09895 0.3416 0.1448 0.985 0.994 0.2127 0.4422 0.8771 0.709 ] Network output: [ 0.00497 -0.02364 0.9945 2.105e-05 -9.448e-06 1.019 1.586e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1051 0.09283 0.1825 0.1998 0.9873 0.9919 0.1052 0.7536 0.8654 0.3055 ] Network output: [ -0.004705 0.02249 1.004 2.237e-05 -1.004e-05 0.9832 1.686e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09152 0.08958 0.1651 0.1955 0.9853 0.9912 0.09153 0.678 0.8415 0.2462 ] Network output: [ 0.0001327 1 -0.0001504 2.983e-06 -1.339e-06 0.9998 2.248e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003357 Epoch 8442 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01025 0.996 0.991 -6.907e-08 3.101e-08 -0.007505 -5.206e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003401 -0.003217 -0.007591 0.005981 0.9699 0.9743 0.006551 0.8316 0.8235 0.01759 ] Network output: [ 0.9998 0.0004084 0.0006989 -1.113e-05 4.996e-06 -0.0008443 -8.387e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1996 -0.03422 -0.1705 0.1881 0.9835 0.9932 0.2235 0.438 0.8705 0.7148 ] Network output: [ -0.01005 1.002 1.009 -2.63e-07 1.181e-07 0.008564 -1.982e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006242 0.0005191 0.004445 0.003524 0.9889 0.9919 0.006361 0.8596 0.8945 0.01267 ] Network output: [ -0.0004467 0.002368 1.001 -3.488e-05 1.566e-05 0.9974 -2.629e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.212 0.09895 0.3417 0.1448 0.985 0.994 0.2127 0.4422 0.8771 0.709 ] Network output: [ 0.004968 -0.02363 0.9944 2.102e-05 -9.438e-06 1.019 1.584e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1051 0.09284 0.1825 0.1998 0.9873 0.9919 0.1052 0.7536 0.8654 0.3055 ] Network output: [ -0.004704 0.02248 1.004 2.235e-05 -1.003e-05 0.9832 1.684e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09152 0.08958 0.1651 0.1955 0.9853 0.9912 0.09153 0.678 0.8415 0.2462 ] Network output: [ 0.0001327 1 -0.0001502 2.98e-06 -1.338e-06 0.9998 2.246e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003356 Epoch 8443 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01025 0.996 0.991 -6.965e-08 3.127e-08 -0.007505 -5.249e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003401 -0.003217 -0.00759 0.00598 0.9699 0.9743 0.006551 0.8316 0.8235 0.01759 ] Network output: [ 0.9998 0.000408 0.0006985 -1.112e-05 4.991e-06 -0.0008436 -8.378e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1997 -0.03422 -0.1705 0.1881 0.9835 0.9932 0.2235 0.438 0.8705 0.7148 ] Network output: [ -0.01004 1.002 1.009 -2.633e-07 1.182e-07 0.008563 -1.984e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006242 0.0005192 0.004445 0.003523 0.9889 0.9919 0.006361 0.8596 0.8945 0.01267 ] Network output: [ -0.0004464 0.002367 1.001 -3.485e-05 1.564e-05 0.9974 -2.626e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.212 0.09896 0.3417 0.1448 0.985 0.994 0.2127 0.4422 0.8771 0.709 ] Network output: [ 0.004967 -0.02362 0.9944 2.1e-05 -9.429e-06 1.019 1.583e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1051 0.09284 0.1825 0.1998 0.9873 0.9919 0.1052 0.7536 0.8654 0.3055 ] Network output: [ -0.004702 0.02247 1.004 2.232e-05 -1.002e-05 0.9832 1.682e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09152 0.08959 0.1651 0.1955 0.9853 0.9912 0.09154 0.678 0.8415 0.2462 ] Network output: [ 0.0001326 1 -0.0001501 2.977e-06 -1.337e-06 0.9998 2.244e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003354 Epoch 8444 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01025 0.996 0.991 -7.023e-08 3.153e-08 -0.007505 -5.293e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003401 -0.003217 -0.00759 0.00598 0.9699 0.9743 0.006551 0.8316 0.8235 0.01759 ] Network output: [ 0.9998 0.0004077 0.0006981 -1.111e-05 4.986e-06 -0.0008429 -8.37e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1997 -0.03422 -0.1705 0.188 0.9835 0.9932 0.2235 0.438 0.8705 0.7148 ] Network output: [ -0.01004 1.002 1.009 -2.635e-07 1.183e-07 0.008561 -1.986e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006243 0.0005193 0.004445 0.003523 0.9889 0.9919 0.006362 0.8596 0.8945 0.01266 ] Network output: [ -0.0004461 0.002366 1.001 -3.481e-05 1.563e-05 0.9974 -2.623e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.212 0.09896 0.3417 0.1448 0.985 0.994 0.2127 0.4422 0.8771 0.709 ] Network output: [ 0.004965 -0.02362 0.9944 2.098e-05 -9.419e-06 1.019 1.581e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1052 0.09285 0.1825 0.1998 0.9873 0.9919 0.1052 0.7536 0.8654 0.3055 ] Network output: [ -0.0047 0.02246 1.004 2.23e-05 -1.001e-05 0.9832 1.681e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09152 0.08959 0.1651 0.1955 0.9853 0.9912 0.09154 0.678 0.8415 0.2462 ] Network output: [ 0.0001326 1 -0.0001499 2.974e-06 -1.335e-06 0.9998 2.241e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003352 Epoch 8445 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01025 0.996 0.991 -7.08e-08 3.179e-08 -0.007505 -5.336e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003401 -0.003217 -0.007589 0.005979 0.9699 0.9743 0.006551 0.8316 0.8235 0.01759 ] Network output: [ 0.9998 0.0004073 0.0006977 -1.109e-05 4.98e-06 -0.0008422 -8.361e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1997 -0.03422 -0.1705 0.188 0.9835 0.9932 0.2235 0.438 0.8704 0.7148 ] Network output: [ -0.01004 1.002 1.009 -2.638e-07 1.184e-07 0.00856 -1.988e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006244 0.0005194 0.004445 0.003523 0.9889 0.9919 0.006362 0.8596 0.8945 0.01266 ] Network output: [ -0.0004458 0.002365 1.001 -3.477e-05 1.561e-05 0.9974 -2.621e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.212 0.09897 0.3417 0.1448 0.985 0.994 0.2127 0.4422 0.8771 0.709 ] Network output: [ 0.004963 -0.02361 0.9944 2.096e-05 -9.409e-06 1.019 1.579e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1052 0.09285 0.1825 0.1998 0.9873 0.9919 0.1052 0.7535 0.8654 0.3055 ] Network output: [ -0.004698 0.02245 1.004 2.228e-05 -1e-05 0.9832 1.679e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09153 0.08959 0.1651 0.1955 0.9853 0.9912 0.09154 0.6779 0.8415 0.2462 ] Network output: [ 0.0001325 1 -0.0001497 2.971e-06 -1.334e-06 0.9998 2.239e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000335 Epoch 8446 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01024 0.996 0.991 -7.137e-08 3.204e-08 -0.007505 -5.379e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003401 -0.003217 -0.007588 0.005979 0.9699 0.9743 0.006552 0.8316 0.8235 0.01759 ] Network output: [ 0.9998 0.000407 0.0006973 -1.108e-05 4.975e-06 -0.0008415 -8.352e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1997 -0.03422 -0.1705 0.188 0.9835 0.9932 0.2236 0.438 0.8704 0.7148 ] Network output: [ -0.01004 1.002 1.009 -2.64e-07 1.185e-07 0.008558 -1.99e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006244 0.0005195 0.004445 0.003522 0.9889 0.9919 0.006363 0.8595 0.8945 0.01266 ] Network output: [ -0.0004456 0.002365 1.001 -3.474e-05 1.559e-05 0.9974 -2.618e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.212 0.09898 0.3417 0.1448 0.985 0.994 0.2127 0.4421 0.8771 0.709 ] Network output: [ 0.004962 -0.0236 0.9944 2.094e-05 -9.399e-06 1.019 1.578e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1052 0.09286 0.1825 0.1998 0.9873 0.9919 0.1052 0.7535 0.8654 0.3055 ] Network output: [ -0.004697 0.02245 1.004 2.225e-05 -9.991e-06 0.9832 1.677e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09153 0.08959 0.1651 0.1955 0.9853 0.9912 0.09154 0.6779 0.8415 0.2463 ] Network output: [ 0.0001324 1 -0.0001495 2.968e-06 -1.332e-06 0.9998 2.237e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003348 Epoch 8447 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01024 0.996 0.991 -7.194e-08 3.23e-08 -0.007505 -5.422e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003401 -0.003217 -0.007587 0.005978 0.9699 0.9743 0.006552 0.8316 0.8235 0.01759 ] Network output: [ 0.9998 0.0004067 0.0006969 -1.107e-05 4.97e-06 -0.0008408 -8.343e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1997 -0.03422 -0.1705 0.188 0.9835 0.9932 0.2236 0.438 0.8704 0.7148 ] Network output: [ -0.01004 1.002 1.009 -2.642e-07 1.186e-07 0.008557 -1.991e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006245 0.0005195 0.004445 0.003522 0.9889 0.9919 0.006363 0.8595 0.8945 0.01266 ] Network output: [ -0.0004453 0.002364 1.001 -3.47e-05 1.558e-05 0.9974 -2.615e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2121 0.09898 0.3417 0.1448 0.985 0.994 0.2128 0.4421 0.8771 0.709 ] Network output: [ 0.00496 -0.02359 0.9944 2.091e-05 -9.389e-06 1.019 1.576e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1052 0.09286 0.1825 0.1997 0.9873 0.9919 0.1052 0.7535 0.8654 0.3055 ] Network output: [ -0.004695 0.02244 1.004 2.223e-05 -9.981e-06 0.9832 1.675e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09153 0.08959 0.1651 0.1955 0.9853 0.9912 0.09154 0.6779 0.8415 0.2463 ] Network output: [ 0.0001324 1 -0.0001494 2.965e-06 -1.331e-06 0.9998 2.234e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003346 Epoch 8448 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01024 0.996 0.991 -7.251e-08 3.255e-08 -0.007505 -5.465e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003401 -0.003217 -0.007586 0.005977 0.9699 0.9743 0.006552 0.8316 0.8235 0.01758 ] Network output: [ 0.9998 0.0004063 0.0006965 -1.106e-05 4.965e-06 -0.0008401 -8.334e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1997 -0.03423 -0.1705 0.188 0.9835 0.9932 0.2236 0.4379 0.8704 0.7148 ] Network output: [ -0.01004 1.002 1.009 -2.645e-07 1.187e-07 0.008555 -1.993e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006245 0.0005196 0.004445 0.003522 0.9889 0.9919 0.006364 0.8595 0.8945 0.01266 ] Network output: [ -0.000445 0.002363 1.001 -3.466e-05 1.556e-05 0.9974 -2.612e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2121 0.09899 0.3417 0.1448 0.985 0.994 0.2128 0.4421 0.8771 0.7089 ] Network output: [ 0.004958 -0.02358 0.9944 2.089e-05 -9.379e-06 1.019 1.574e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1052 0.09287 0.1825 0.1997 0.9873 0.9919 0.1053 0.7535 0.8654 0.3055 ] Network output: [ -0.004693 0.02243 1.004 2.221e-05 -9.97e-06 0.9832 1.674e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09153 0.0896 0.1651 0.1955 0.9853 0.9912 0.09155 0.6779 0.8415 0.2463 ] Network output: [ 0.0001323 1 -0.0001492 2.962e-06 -1.33e-06 0.9998 2.232e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003345 Epoch 8449 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01024 0.996 0.991 -7.308e-08 3.281e-08 -0.007505 -5.508e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003401 -0.003218 -0.007585 0.005977 0.9699 0.9743 0.006553 0.8316 0.8235 0.01758 ] Network output: [ 0.9998 0.000406 0.0006961 -1.105e-05 4.959e-06 -0.0008394 -8.326e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1997 -0.03423 -0.1704 0.188 0.9835 0.9932 0.2236 0.4379 0.8704 0.7148 ] Network output: [ -0.01004 1.002 1.009 -2.647e-07 1.188e-07 0.008554 -1.995e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006246 0.0005197 0.004445 0.003521 0.9889 0.9919 0.006364 0.8595 0.8945 0.01266 ] Network output: [ -0.0004447 0.002362 1.001 -3.463e-05 1.554e-05 0.9974 -2.609e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2121 0.09899 0.3417 0.1448 0.985 0.994 0.2128 0.4421 0.8771 0.7089 ] Network output: [ 0.004957 -0.02357 0.9944 2.087e-05 -9.369e-06 1.019 1.573e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1052 0.09288 0.1825 0.1997 0.9873 0.9919 0.1053 0.7535 0.8653 0.3055 ] Network output: [ -0.004692 0.02242 1.004 2.219e-05 -9.96e-06 0.9832 1.672e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09154 0.0896 0.1651 0.1955 0.9853 0.9912 0.09155 0.6779 0.8415 0.2463 ] Network output: [ 0.0001322 1 -0.000149 2.959e-06 -1.328e-06 0.9998 2.23e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003343 Epoch 8450 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01024 0.996 0.991 -7.365e-08 3.306e-08 -0.007505 -5.55e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003401 -0.003218 -0.007584 0.005976 0.9699 0.9743 0.006553 0.8315 0.8235 0.01758 ] Network output: [ 0.9998 0.0004056 0.0006957 -1.104e-05 4.954e-06 -0.0008387 -8.317e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1997 -0.03423 -0.1704 0.188 0.9835 0.9932 0.2236 0.4379 0.8704 0.7148 ] Network output: [ -0.01004 1.002 1.009 -2.65e-07 1.19e-07 0.008553 -1.997e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006246 0.0005198 0.004445 0.003521 0.9889 0.9919 0.006365 0.8595 0.8945 0.01266 ] Network output: [ -0.0004444 0.002361 1.001 -3.459e-05 1.553e-05 0.9974 -2.607e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2121 0.099 0.3417 0.1448 0.985 0.994 0.2128 0.4421 0.8771 0.7089 ] Network output: [ 0.004955 -0.02356 0.9944 2.085e-05 -9.36e-06 1.019 1.571e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1052 0.09288 0.1825 0.1997 0.9873 0.9919 0.1053 0.7534 0.8653 0.3055 ] Network output: [ -0.00469 0.02241 1.004 2.216e-05 -9.95e-06 0.9832 1.67e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09154 0.0896 0.1651 0.1955 0.9853 0.9912 0.09155 0.6779 0.8414 0.2463 ] Network output: [ 0.0001322 1 -0.0001488 2.956e-06 -1.327e-06 0.9998 2.227e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003341 Epoch 8451 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01024 0.996 0.991 -7.421e-08 3.332e-08 -0.007505 -5.593e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003402 -0.003218 -0.007583 0.005976 0.9699 0.9743 0.006553 0.8315 0.8235 0.01758 ] Network output: [ 0.9998 0.0004053 0.0006953 -1.102e-05 4.949e-06 -0.000838 -8.308e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1997 -0.03423 -0.1704 0.188 0.9835 0.9932 0.2236 0.4379 0.8704 0.7148 ] Network output: [ -0.01004 1.002 1.009 -2.652e-07 1.191e-07 0.008551 -1.999e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006247 0.0005199 0.004445 0.003521 0.9889 0.9919 0.006366 0.8595 0.8945 0.01266 ] Network output: [ -0.0004441 0.00236 1.001 -3.455e-05 1.551e-05 0.9974 -2.604e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2121 0.09901 0.3417 0.1448 0.985 0.994 0.2128 0.4421 0.8771 0.7089 ] Network output: [ 0.004953 -0.02356 0.9944 2.083e-05 -9.35e-06 1.019 1.57e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1052 0.09289 0.1825 0.1997 0.9873 0.9919 0.1053 0.7534 0.8653 0.3055 ] Network output: [ -0.004688 0.0224 1.004 2.214e-05 -9.94e-06 0.9832 1.669e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09154 0.0896 0.1651 0.1955 0.9853 0.9912 0.09155 0.6778 0.8414 0.2463 ] Network output: [ 0.0001321 1 -0.0001487 2.952e-06 -1.325e-06 0.9998 2.225e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003339 Epoch 8452 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01024 0.996 0.991 -7.478e-08 3.357e-08 -0.007505 -5.635e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003402 -0.003218 -0.007582 0.005975 0.9699 0.9743 0.006553 0.8315 0.8235 0.01758 ] Network output: [ 0.9998 0.000405 0.000695 -1.101e-05 4.944e-06 -0.0008373 -8.299e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1997 -0.03423 -0.1704 0.188 0.9835 0.9932 0.2236 0.4379 0.8704 0.7148 ] Network output: [ -0.01003 1.002 1.009 -2.655e-07 1.192e-07 0.00855 -2.001e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006247 0.00052 0.004445 0.00352 0.9889 0.9919 0.006366 0.8595 0.8944 0.01266 ] Network output: [ -0.0004439 0.00236 1.001 -3.451e-05 1.55e-05 0.9974 -2.601e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2121 0.09901 0.3417 0.1448 0.985 0.994 0.2128 0.4421 0.8771 0.7089 ] Network output: [ 0.004952 -0.02355 0.9944 2.08e-05 -9.34e-06 1.019 1.568e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1052 0.09289 0.1825 0.1997 0.9873 0.9919 0.1053 0.7534 0.8653 0.3055 ] Network output: [ -0.004687 0.02239 1.004 2.212e-05 -9.93e-06 0.9833 1.667e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09154 0.08961 0.1651 0.1955 0.9853 0.9912 0.09156 0.6778 0.8414 0.2463 ] Network output: [ 0.0001321 1 -0.0001485 2.949e-06 -1.324e-06 0.9998 2.223e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003337 Epoch 8453 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01023 0.996 0.991 -7.534e-08 3.382e-08 -0.007505 -5.678e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003402 -0.003218 -0.007582 0.005975 0.9699 0.9743 0.006554 0.8315 0.8235 0.01758 ] Network output: [ 0.9998 0.0004046 0.0006946 -1.1e-05 4.939e-06 -0.0008366 -8.29e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1997 -0.03423 -0.1704 0.188 0.9835 0.9932 0.2236 0.4379 0.8704 0.7147 ] Network output: [ -0.01003 1.002 1.009 -2.657e-07 1.193e-07 0.008548 -2.002e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006248 0.0005201 0.004445 0.00352 0.9889 0.9919 0.006367 0.8595 0.8944 0.01266 ] Network output: [ -0.0004436 0.002359 1.001 -3.448e-05 1.548e-05 0.9974 -2.598e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2121 0.09902 0.3417 0.1448 0.985 0.994 0.2128 0.4421 0.8771 0.7089 ] Network output: [ 0.00495 -0.02354 0.9944 2.078e-05 -9.33e-06 1.019 1.566e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1052 0.0929 0.1825 0.1997 0.9873 0.9919 0.1053 0.7534 0.8653 0.3055 ] Network output: [ -0.004685 0.02238 1.004 2.21e-05 -9.919e-06 0.9833 1.665e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09154 0.08961 0.1651 0.1955 0.9853 0.9912 0.09156 0.6778 0.8414 0.2463 ] Network output: [ 0.000132 1 -0.0001483 2.946e-06 -1.323e-06 0.9998 2.22e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003335 Epoch 8454 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01023 0.996 0.991 -7.59e-08 3.407e-08 -0.007505 -5.72e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003402 -0.003218 -0.007581 0.005974 0.9699 0.9743 0.006554 0.8315 0.8235 0.01758 ] Network output: [ 0.9998 0.0004043 0.0006942 -1.099e-05 4.933e-06 -0.0008359 -8.282e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1998 -0.03423 -0.1704 0.188 0.9835 0.9932 0.2236 0.4379 0.8704 0.7147 ] Network output: [ -0.01003 1.002 1.009 -2.659e-07 1.194e-07 0.008547 -2.004e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006249 0.0005202 0.004445 0.00352 0.9889 0.9919 0.006367 0.8595 0.8944 0.01265 ] Network output: [ -0.0004433 0.002358 1.001 -3.444e-05 1.546e-05 0.9974 -2.596e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2121 0.09902 0.3417 0.1448 0.985 0.994 0.2128 0.4421 0.8771 0.7089 ] Network output: [ 0.004948 -0.02353 0.9944 2.076e-05 -9.32e-06 1.019 1.565e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1052 0.0929 0.1825 0.1997 0.9873 0.9919 0.1053 0.7534 0.8653 0.3055 ] Network output: [ -0.004683 0.02237 1.004 2.207e-05 -9.909e-06 0.9833 1.663e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09155 0.08961 0.1651 0.1955 0.9853 0.9912 0.09156 0.6778 0.8414 0.2463 ] Network output: [ 0.0001319 1 -0.0001482 2.943e-06 -1.321e-06 0.9998 2.218e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003334 Epoch 8455 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01023 0.996 0.991 -7.646e-08 3.432e-08 -0.007505 -5.762e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003402 -0.003218 -0.00758 0.005973 0.9699 0.9743 0.006554 0.8315 0.8235 0.01758 ] Network output: [ 0.9998 0.0004039 0.0006938 -1.098e-05 4.928e-06 -0.0008352 -8.273e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1998 -0.03424 -0.1704 0.188 0.9835 0.9932 0.2236 0.4379 0.8704 0.7147 ] Network output: [ -0.01003 1.002 1.009 -2.662e-07 1.195e-07 0.008545 -2.006e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006249 0.0005203 0.004445 0.003519 0.9889 0.9919 0.006368 0.8595 0.8944 0.01265 ] Network output: [ -0.000443 0.002357 1.001 -3.44e-05 1.545e-05 0.9974 -2.593e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2121 0.09903 0.3418 0.1448 0.985 0.994 0.2128 0.4421 0.8771 0.7089 ] Network output: [ 0.004947 -0.02352 0.9944 2.074e-05 -9.311e-06 1.019 1.563e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1052 0.09291 0.1825 0.1997 0.9873 0.9919 0.1053 0.7534 0.8653 0.3055 ] Network output: [ -0.004682 0.02236 1.004 2.205e-05 -9.899e-06 0.9833 1.662e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09155 0.08961 0.1651 0.1955 0.9853 0.9912 0.09156 0.6778 0.8414 0.2463 ] Network output: [ 0.0001319 1 -0.000148 2.94e-06 -1.32e-06 0.9998 2.216e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003332 Epoch 8456 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01023 0.996 0.991 -7.701e-08 3.457e-08 -0.007505 -5.804e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003402 -0.003219 -0.007579 0.005973 0.9699 0.9743 0.006555 0.8315 0.8235 0.01757 ] Network output: [ 0.9998 0.0004036 0.0006934 -1.097e-05 4.923e-06 -0.0008345 -8.264e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1998 -0.03424 -0.1704 0.188 0.9835 0.9932 0.2237 0.4379 0.8704 0.7147 ] Network output: [ -0.01003 1.002 1.009 -2.664e-07 1.196e-07 0.008544 -2.008e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00625 0.0005204 0.004445 0.003519 0.9889 0.9919 0.006368 0.8595 0.8944 0.01265 ] Network output: [ -0.0004427 0.002356 1.001 -3.437e-05 1.543e-05 0.9974 -2.59e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2121 0.09904 0.3418 0.1448 0.985 0.994 0.2128 0.442 0.8771 0.7089 ] Network output: [ 0.004945 -0.02351 0.9944 2.072e-05 -9.301e-06 1.019 1.561e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1052 0.09292 0.1825 0.1997 0.9873 0.9919 0.1053 0.7533 0.8653 0.3055 ] Network output: [ -0.00468 0.02235 1.004 2.203e-05 -9.889e-06 0.9833 1.66e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09155 0.08961 0.1651 0.1955 0.9853 0.9912 0.09156 0.6777 0.8414 0.2463 ] Network output: [ 0.0001318 1 -0.0001478 2.937e-06 -1.319e-06 0.9998 2.213e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000333 Epoch 8457 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01023 0.996 0.991 -7.757e-08 3.482e-08 -0.007505 -5.846e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003402 -0.003219 -0.007578 0.005972 0.9699 0.9743 0.006555 0.8315 0.8235 0.01757 ] Network output: [ 0.9998 0.0004033 0.000693 -1.095e-05 4.918e-06 -0.0008338 -8.255e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1998 -0.03424 -0.1703 0.188 0.9835 0.9932 0.2237 0.4379 0.8704 0.7147 ] Network output: [ -0.01003 1.002 1.009 -2.666e-07 1.197e-07 0.008542 -2.01e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00625 0.0005205 0.004445 0.003519 0.9889 0.9919 0.006369 0.8595 0.8944 0.01265 ] Network output: [ -0.0004424 0.002355 1.001 -3.433e-05 1.541e-05 0.9974 -2.587e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2122 0.09904 0.3418 0.1448 0.985 0.994 0.2129 0.442 0.8771 0.7089 ] Network output: [ 0.004943 -0.02351 0.9944 2.07e-05 -9.291e-06 1.019 1.56e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1052 0.09292 0.1825 0.1997 0.9873 0.9919 0.1053 0.7533 0.8653 0.3055 ] Network output: [ -0.004678 0.02234 1.004 2.2e-05 -9.879e-06 0.9833 1.658e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09155 0.08962 0.1651 0.1955 0.9853 0.9912 0.09157 0.6777 0.8414 0.2463 ] Network output: [ 0.0001318 1 -0.0001476 2.934e-06 -1.317e-06 0.9998 2.211e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003328 Epoch 8458 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01023 0.996 0.991 -7.812e-08 3.507e-08 -0.007505 -5.888e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003402 -0.003219 -0.007577 0.005972 0.9699 0.9743 0.006555 0.8315 0.8235 0.01757 ] Network output: [ 0.9998 0.0004029 0.0006926 -1.094e-05 4.912e-06 -0.0008331 -8.247e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1998 -0.03424 -0.1703 0.188 0.9835 0.9932 0.2237 0.4378 0.8704 0.7147 ] Network output: [ -0.01003 1.002 1.009 -2.669e-07 1.198e-07 0.008541 -2.011e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006251 0.0005206 0.004445 0.003518 0.9889 0.9919 0.00637 0.8595 0.8944 0.01265 ] Network output: [ -0.0004422 0.002355 1.001 -3.43e-05 1.54e-05 0.9974 -2.585e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2122 0.09905 0.3418 0.1448 0.985 0.994 0.2129 0.442 0.8771 0.7089 ] Network output: [ 0.004942 -0.0235 0.9944 2.067e-05 -9.281e-06 1.019 1.558e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1052 0.09293 0.1825 0.1997 0.9873 0.9919 0.1053 0.7533 0.8653 0.3055 ] Network output: [ -0.004677 0.02234 1.004 2.198e-05 -9.869e-06 0.9833 1.657e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09156 0.08962 0.1651 0.1955 0.9853 0.9912 0.09157 0.6777 0.8414 0.2463 ] Network output: [ 0.0001317 1 -0.0001475 2.931e-06 -1.316e-06 0.9998 2.209e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003326 Epoch 8459 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01023 0.996 0.991 -7.868e-08 3.532e-08 -0.007505 -5.929e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003403 -0.003219 -0.007576 0.005971 0.9699 0.9743 0.006555 0.8315 0.8235 0.01757 ] Network output: [ 0.9998 0.0004026 0.0006922 -1.093e-05 4.907e-06 -0.0008324 -8.238e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1998 -0.03424 -0.1703 0.188 0.9835 0.9932 0.2237 0.4378 0.8704 0.7147 ] Network output: [ -0.01003 1.002 1.009 -2.671e-07 1.199e-07 0.008539 -2.013e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006251 0.0005207 0.004445 0.003518 0.9889 0.9919 0.00637 0.8594 0.8944 0.01265 ] Network output: [ -0.0004419 0.002354 1.001 -3.426e-05 1.538e-05 0.9974 -2.582e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2122 0.09905 0.3418 0.1448 0.985 0.994 0.2129 0.442 0.8771 0.7089 ] Network output: [ 0.00494 -0.02349 0.9944 2.065e-05 -9.271e-06 1.019 1.556e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1052 0.09293 0.1825 0.1997 0.9873 0.9919 0.1053 0.7533 0.8653 0.3055 ] Network output: [ -0.004675 0.02233 1.004 2.196e-05 -9.859e-06 0.9833 1.655e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09156 0.08962 0.1651 0.1956 0.9853 0.9912 0.09157 0.6777 0.8414 0.2463 ] Network output: [ 0.0001316 1 -0.0001473 2.928e-06 -1.314e-06 0.9998 2.207e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003324 Epoch 8460 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01023 0.996 0.9911 -7.923e-08 3.557e-08 -0.007505 -5.971e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003403 -0.003219 -0.007575 0.005971 0.9699 0.9743 0.006556 0.8315 0.8235 0.01757 ] Network output: [ 0.9998 0.0004023 0.0006918 -1.092e-05 4.902e-06 -0.0008317 -8.229e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1998 -0.03424 -0.1703 0.188 0.9835 0.9932 0.2237 0.4378 0.8704 0.7147 ] Network output: [ -0.01003 1.002 1.009 -2.673e-07 1.2e-07 0.008538 -2.015e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006252 0.0005207 0.004445 0.003518 0.9889 0.9919 0.006371 0.8594 0.8944 0.01265 ] Network output: [ -0.0004416 0.002353 1.001 -3.422e-05 1.536e-05 0.9974 -2.579e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2122 0.09906 0.3418 0.1448 0.985 0.994 0.2129 0.442 0.8771 0.7089 ] Network output: [ 0.004938 -0.02348 0.9944 2.063e-05 -9.262e-06 1.019 1.555e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1053 0.09294 0.1825 0.1997 0.9873 0.9919 0.1053 0.7533 0.8653 0.3055 ] Network output: [ -0.004673 0.02232 1.004 2.194e-05 -9.849e-06 0.9833 1.653e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09156 0.08962 0.1651 0.1956 0.9853 0.9912 0.09157 0.6777 0.8414 0.2463 ] Network output: [ 0.0001316 1 -0.0001471 2.925e-06 -1.313e-06 0.9998 2.204e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003323 Epoch 8461 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01022 0.996 0.9911 -7.978e-08 3.581e-08 -0.007505 -6.012e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003403 -0.003219 -0.007574 0.00597 0.9699 0.9743 0.006556 0.8315 0.8235 0.01757 ] Network output: [ 0.9998 0.0004019 0.0006914 -1.091e-05 4.897e-06 -0.000831 -8.22e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1998 -0.03424 -0.1703 0.188 0.9835 0.9932 0.2237 0.4378 0.8704 0.7147 ] Network output: [ -0.01003 1.002 1.009 -2.676e-07 1.201e-07 0.008536 -2.017e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006252 0.0005208 0.004445 0.003517 0.9889 0.9919 0.006371 0.8594 0.8944 0.01265 ] Network output: [ -0.0004413 0.002352 1.001 -3.419e-05 1.535e-05 0.9974 -2.576e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2122 0.09907 0.3418 0.1448 0.985 0.994 0.2129 0.442 0.8771 0.7089 ] Network output: [ 0.004937 -0.02347 0.9944 2.061e-05 -9.252e-06 1.019 1.553e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1053 0.09294 0.1825 0.1997 0.9873 0.9919 0.1053 0.7532 0.8653 0.3055 ] Network output: [ -0.004672 0.02231 1.004 2.191e-05 -9.838e-06 0.9833 1.652e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09156 0.08963 0.1651 0.1956 0.9853 0.9912 0.09158 0.6776 0.8414 0.2463 ] Network output: [ 0.0001315 1 -0.0001469 2.922e-06 -1.312e-06 0.9998 2.202e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003321 Epoch 8462 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01022 0.996 0.9911 -8.032e-08 3.606e-08 -0.007505 -6.054e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003403 -0.003219 -0.007574 0.005969 0.9699 0.9743 0.006556 0.8315 0.8235 0.01757 ] Network output: [ 0.9998 0.0004016 0.000691 -1.09e-05 4.892e-06 -0.0008304 -8.212e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1998 -0.03425 -0.1703 0.188 0.9835 0.9932 0.2237 0.4378 0.8704 0.7147 ] Network output: [ -0.01002 1.002 1.009 -2.678e-07 1.202e-07 0.008535 -2.018e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006253 0.0005209 0.004445 0.003517 0.9889 0.9919 0.006372 0.8594 0.8944 0.01265 ] Network output: [ -0.000441 0.002351 1.001 -3.415e-05 1.533e-05 0.9974 -2.574e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2122 0.09907 0.3418 0.1448 0.985 0.994 0.2129 0.442 0.8771 0.7089 ] Network output: [ 0.004935 -0.02346 0.9944 2.059e-05 -9.242e-06 1.019 1.552e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1053 0.09295 0.1825 0.1997 0.9873 0.9919 0.1053 0.7532 0.8653 0.3055 ] Network output: [ -0.00467 0.0223 1.004 2.189e-05 -9.828e-06 0.9833 1.65e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09156 0.08963 0.1651 0.1956 0.9853 0.9912 0.09158 0.6776 0.8414 0.2463 ] Network output: [ 0.0001314 1 -0.0001468 2.919e-06 -1.31e-06 0.9998 2.2e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003319 Epoch 8463 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01022 0.996 0.9911 -8.087e-08 3.631e-08 -0.007505 -6.095e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003403 -0.003219 -0.007573 0.005969 0.9699 0.9743 0.006557 0.8315 0.8235 0.01757 ] Network output: [ 0.9998 0.0004012 0.0006906 -1.088e-05 4.887e-06 -0.0008297 -8.203e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1998 -0.03425 -0.1703 0.1879 0.9835 0.9932 0.2237 0.4378 0.8704 0.7147 ] Network output: [ -0.01002 1.002 1.009 -2.68e-07 1.203e-07 0.008534 -2.02e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006254 0.000521 0.004445 0.003516 0.9889 0.9919 0.006372 0.8594 0.8944 0.01265 ] Network output: [ -0.0004408 0.00235 1.001 -3.411e-05 1.531e-05 0.9974 -2.571e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2122 0.09908 0.3418 0.1448 0.985 0.994 0.2129 0.442 0.877 0.7089 ] Network output: [ 0.004933 -0.02345 0.9944 2.057e-05 -9.233e-06 1.019 1.55e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1053 0.09295 0.1825 0.1997 0.9873 0.9919 0.1053 0.7532 0.8653 0.3055 ] Network output: [ -0.004668 0.02229 1.004 2.187e-05 -9.818e-06 0.9833 1.648e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09157 0.08963 0.1651 0.1956 0.9853 0.9912 0.09158 0.6776 0.8414 0.2463 ] Network output: [ 0.0001314 1 -0.0001466 2.916e-06 -1.309e-06 0.9998 2.197e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003317 Epoch 8464 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01022 0.996 0.9911 -8.142e-08 3.655e-08 -0.007505 -6.136e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003403 -0.00322 -0.007572 0.005968 0.9699 0.9743 0.006557 0.8315 0.8234 0.01756 ] Network output: [ 0.9998 0.0004009 0.0006902 -1.087e-05 4.881e-06 -0.000829 -8.194e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1998 -0.03425 -0.1702 0.1879 0.9835 0.9932 0.2237 0.4378 0.8704 0.7147 ] Network output: [ -0.01002 1.002 1.009 -2.683e-07 1.204e-07 0.008532 -2.022e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006254 0.0005211 0.004445 0.003516 0.9889 0.9919 0.006373 0.8594 0.8944 0.01264 ] Network output: [ -0.0004405 0.002349 1.001 -3.408e-05 1.53e-05 0.9974 -2.568e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2122 0.09908 0.3418 0.1448 0.985 0.994 0.2129 0.442 0.877 0.7088 ] Network output: [ 0.004932 -0.02345 0.9944 2.054e-05 -9.223e-06 1.019 1.548e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1053 0.09296 0.1825 0.1997 0.9873 0.9919 0.1053 0.7532 0.8653 0.3055 ] Network output: [ -0.004666 0.02228 1.004 2.185e-05 -9.808e-06 0.9833 1.647e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09157 0.08963 0.1651 0.1956 0.9853 0.9912 0.09158 0.6776 0.8414 0.2463 ] Network output: [ 0.0001313 1 -0.0001464 2.913e-06 -1.308e-06 0.9998 2.195e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003315 Epoch 8465 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01022 0.996 0.9911 -8.196e-08 3.679e-08 -0.007505 -6.177e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003403 -0.00322 -0.007571 0.005968 0.9699 0.9743 0.006557 0.8314 0.8234 0.01756 ] Network output: [ 0.9998 0.0004006 0.0006898 -1.086e-05 4.876e-06 -0.0008283 -8.186e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1998 -0.03425 -0.1702 0.1879 0.9835 0.9932 0.2237 0.4378 0.8704 0.7147 ] Network output: [ -0.01002 1.002 1.009 -2.685e-07 1.205e-07 0.008531 -2.023e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006255 0.0005212 0.004445 0.003516 0.9889 0.9919 0.006374 0.8594 0.8944 0.01264 ] Network output: [ -0.0004402 0.002349 1.001 -3.404e-05 1.528e-05 0.9974 -2.565e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2122 0.09909 0.3418 0.1447 0.985 0.994 0.2129 0.442 0.877 0.7088 ] Network output: [ 0.00493 -0.02344 0.9944 2.052e-05 -9.213e-06 1.019 1.547e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1053 0.09297 0.1825 0.1997 0.9873 0.9919 0.1054 0.7532 0.8653 0.3055 ] Network output: [ -0.004665 0.02227 1.004 2.183e-05 -9.798e-06 0.9833 1.645e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09157 0.08963 0.1651 0.1956 0.9853 0.9912 0.09158 0.6776 0.8414 0.2463 ] Network output: [ 0.0001313 1 -0.0001463 2.91e-06 -1.306e-06 0.9998 2.193e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003314 Epoch 8466 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01022 0.996 0.9911 -8.25e-08 3.704e-08 -0.007505 -6.218e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003403 -0.00322 -0.00757 0.005967 0.9699 0.9743 0.006557 0.8314 0.8234 0.01756 ] Network output: [ 0.9998 0.0004002 0.0006894 -1.085e-05 4.871e-06 -0.0008276 -8.177e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1999 -0.03425 -0.1702 0.1879 0.9835 0.9932 0.2238 0.4378 0.8704 0.7147 ] Network output: [ -0.01002 1.002 1.009 -2.687e-07 1.206e-07 0.008529 -2.025e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006255 0.0005213 0.004445 0.003515 0.9889 0.9919 0.006374 0.8594 0.8944 0.01264 ] Network output: [ -0.0004399 0.002348 1.001 -3.4e-05 1.527e-05 0.9974 -2.563e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2122 0.09909 0.3418 0.1447 0.985 0.994 0.2129 0.4419 0.877 0.7088 ] Network output: [ 0.004928 -0.02343 0.9944 2.05e-05 -9.203e-06 1.019 1.545e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1053 0.09297 0.1825 0.1997 0.9873 0.9919 0.1054 0.7531 0.8653 0.3055 ] Network output: [ -0.004663 0.02226 1.004 2.18e-05 -9.788e-06 0.9833 1.643e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09157 0.08964 0.1651 0.1956 0.9853 0.9912 0.09159 0.6775 0.8414 0.2463 ] Network output: [ 0.0001312 1 -0.0001461 2.906e-06 -1.305e-06 0.9998 2.19e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003312 Epoch 8467 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01022 0.996 0.9911 -8.304e-08 3.728e-08 -0.007505 -6.258e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003403 -0.00322 -0.007569 0.005967 0.9699 0.9743 0.006558 0.8314 0.8234 0.01756 ] Network output: [ 0.9998 0.0003999 0.000689 -1.084e-05 4.866e-06 -0.0008269 -8.168e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1999 -0.03425 -0.1702 0.1879 0.9835 0.9932 0.2238 0.4378 0.8704 0.7147 ] Network output: [ -0.01002 1.002 1.009 -2.689e-07 1.207e-07 0.008528 -2.027e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006256 0.0005214 0.004445 0.003515 0.9889 0.9919 0.006375 0.8594 0.8944 0.01264 ] Network output: [ -0.0004396 0.002347 1.001 -3.397e-05 1.525e-05 0.9974 -2.56e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2123 0.0991 0.3419 0.1447 0.985 0.994 0.213 0.4419 0.877 0.7088 ] Network output: [ 0.004927 -0.02342 0.9944 2.048e-05 -9.194e-06 1.019 1.543e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1053 0.09298 0.1826 0.1997 0.9873 0.9919 0.1054 0.7531 0.8653 0.3055 ] Network output: [ -0.004661 0.02225 1.004 2.178e-05 -9.778e-06 0.9833 1.641e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09158 0.08964 0.1651 0.1956 0.9853 0.9912 0.09159 0.6775 0.8414 0.2463 ] Network output: [ 0.0001311 1 -0.0001459 2.903e-06 -1.303e-06 0.9998 2.188e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000331 Epoch 8468 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01021 0.996 0.9911 -8.358e-08 3.752e-08 -0.007505 -6.299e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003404 -0.00322 -0.007568 0.005966 0.9699 0.9743 0.006558 0.8314 0.8234 0.01756 ] Network output: [ 0.9998 0.0003996 0.0006886 -1.083e-05 4.861e-06 -0.0008262 -8.16e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1999 -0.03426 -0.1702 0.1879 0.9835 0.9932 0.2238 0.4378 0.8704 0.7147 ] Network output: [ -0.01002 1.002 1.009 -2.692e-07 1.208e-07 0.008526 -2.029e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006256 0.0005215 0.004445 0.003515 0.9889 0.9919 0.006375 0.8594 0.8944 0.01264 ] Network output: [ -0.0004394 0.002346 1.001 -3.393e-05 1.523e-05 0.9974 -2.557e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2123 0.09911 0.3419 0.1447 0.985 0.994 0.213 0.4419 0.877 0.7088 ] Network output: [ 0.004925 -0.02341 0.9944 2.046e-05 -9.184e-06 1.019 1.542e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1053 0.09298 0.1826 0.1997 0.9873 0.9919 0.1054 0.7531 0.8653 0.3055 ] Network output: [ -0.00466 0.02225 1.004 2.176e-05 -9.768e-06 0.9833 1.64e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09158 0.08964 0.1651 0.1956 0.9853 0.9912 0.09159 0.6775 0.8413 0.2463 ] Network output: [ 0.0001311 1 -0.0001458 2.9e-06 -1.302e-06 0.9998 2.186e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003308 Epoch 8469 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01021 0.996 0.9911 -8.412e-08 3.776e-08 -0.007505 -6.339e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003404 -0.00322 -0.007567 0.005965 0.9699 0.9743 0.006558 0.8314 0.8234 0.01756 ] Network output: [ 0.9998 0.0003992 0.0006882 -1.082e-05 4.856e-06 -0.0008255 -8.151e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1999 -0.03426 -0.1702 0.1879 0.9835 0.9932 0.2238 0.4377 0.8704 0.7147 ] Network output: [ -0.01002 1.002 1.009 -2.694e-07 1.209e-07 0.008525 -2.03e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006257 0.0005216 0.004445 0.003514 0.9889 0.9919 0.006376 0.8594 0.8944 0.01264 ] Network output: [ -0.0004391 0.002345 1.001 -3.39e-05 1.522e-05 0.9974 -2.554e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2123 0.09911 0.3419 0.1447 0.985 0.994 0.213 0.4419 0.877 0.7088 ] Network output: [ 0.004923 -0.0234 0.9944 2.044e-05 -9.174e-06 1.019 1.54e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1053 0.09299 0.1826 0.1997 0.9873 0.9919 0.1054 0.7531 0.8653 0.3055 ] Network output: [ -0.004658 0.02224 1.004 2.174e-05 -9.758e-06 0.9833 1.638e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09158 0.08964 0.1651 0.1956 0.9853 0.9912 0.09159 0.6775 0.8413 0.2463 ] Network output: [ 0.000131 1 -0.0001456 2.897e-06 -1.301e-06 0.9998 2.184e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003306 Epoch 8470 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01021 0.996 0.9911 -8.465e-08 3.8e-08 -0.007505 -6.38e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003404 -0.00322 -0.007567 0.005965 0.9699 0.9743 0.006559 0.8314 0.8234 0.01756 ] Network output: [ 0.9998 0.0003989 0.0006879 -1.08e-05 4.85e-06 -0.0008249 -8.142e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1999 -0.03426 -0.1702 0.1879 0.9835 0.9932 0.2238 0.4377 0.8704 0.7146 ] Network output: [ -0.01002 1.002 1.009 -2.696e-07 1.21e-07 0.008523 -2.032e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006257 0.0005217 0.004445 0.003514 0.9889 0.9919 0.006376 0.8594 0.8944 0.01264 ] Network output: [ -0.0004388 0.002344 1.001 -3.386e-05 1.52e-05 0.9974 -2.552e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2123 0.09912 0.3419 0.1447 0.985 0.994 0.213 0.4419 0.877 0.7088 ] Network output: [ 0.004922 -0.0234 0.9944 2.041e-05 -9.165e-06 1.019 1.539e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1053 0.09299 0.1826 0.1997 0.9873 0.9919 0.1054 0.7531 0.8652 0.3055 ] Network output: [ -0.004656 0.02223 1.004 2.171e-05 -9.748e-06 0.9833 1.636e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09158 0.08965 0.1651 0.1956 0.9853 0.9912 0.0916 0.6775 0.8413 0.2463 ] Network output: [ 0.000131 1 -0.0001454 2.894e-06 -1.299e-06 0.9998 2.181e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003305 Epoch 8471 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01021 0.996 0.9911 -8.519e-08 3.824e-08 -0.007505 -6.42e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003404 -0.003221 -0.007566 0.005964 0.9699 0.9743 0.006559 0.8314 0.8234 0.01756 ] Network output: [ 0.9998 0.0003985 0.0006875 -1.079e-05 4.845e-06 -0.0008242 -8.134e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1999 -0.03426 -0.1702 0.1879 0.9835 0.9932 0.2238 0.4377 0.8704 0.7146 ] Network output: [ -0.01001 1.002 1.009 -2.698e-07 1.211e-07 0.008522 -2.034e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006258 0.0005218 0.004445 0.003514 0.9889 0.9919 0.006377 0.8594 0.8944 0.01264 ] Network output: [ -0.0004385 0.002344 1.001 -3.382e-05 1.518e-05 0.9974 -2.549e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2123 0.09912 0.3419 0.1447 0.985 0.994 0.213 0.4419 0.877 0.7088 ] Network output: [ 0.00492 -0.02339 0.9944 2.039e-05 -9.155e-06 1.019 1.537e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1053 0.093 0.1826 0.1997 0.9873 0.9919 0.1054 0.7531 0.8652 0.3055 ] Network output: [ -0.004655 0.02222 1.004 2.169e-05 -9.738e-06 0.9833 1.635e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09159 0.08965 0.1651 0.1956 0.9853 0.9912 0.0916 0.6774 0.8413 0.2463 ] Network output: [ 0.0001309 1 -0.0001452 2.891e-06 -1.298e-06 0.9998 2.179e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003303 Epoch 8472 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01021 0.996 0.9911 -8.572e-08 3.848e-08 -0.007505 -6.46e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003404 -0.003221 -0.007565 0.005964 0.9699 0.9743 0.006559 0.8314 0.8234 0.01755 ] Network output: [ 0.9998 0.0003982 0.0006871 -1.078e-05 4.84e-06 -0.0008235 -8.125e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1999 -0.03426 -0.1701 0.1879 0.9835 0.9932 0.2238 0.4377 0.8704 0.7146 ] Network output: [ -0.01001 1.002 1.009 -2.701e-07 1.212e-07 0.008521 -2.035e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006259 0.0005219 0.004445 0.003513 0.9889 0.9919 0.006378 0.8593 0.8944 0.01264 ] Network output: [ -0.0004382 0.002343 1.001 -3.379e-05 1.517e-05 0.9974 -2.546e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2123 0.09913 0.3419 0.1447 0.985 0.994 0.213 0.4419 0.877 0.7088 ] Network output: [ 0.004918 -0.02338 0.9944 2.037e-05 -9.146e-06 1.019 1.535e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1053 0.09301 0.1826 0.1997 0.9873 0.9919 0.1054 0.753 0.8652 0.3055 ] Network output: [ -0.004653 0.02221 1.004 2.167e-05 -9.728e-06 0.9833 1.633e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09159 0.08965 0.1651 0.1956 0.9853 0.9912 0.0916 0.6774 0.8413 0.2463 ] Network output: [ 0.0001308 1 -0.0001451 2.888e-06 -1.297e-06 0.9998 2.177e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003301 Epoch 8473 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01021 0.996 0.9911 -8.625e-08 3.872e-08 -0.007506 -6.5e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003404 -0.003221 -0.007564 0.005963 0.9699 0.9743 0.006559 0.8314 0.8234 0.01755 ] Network output: [ 0.9998 0.0003979 0.0006867 -1.077e-05 4.835e-06 -0.0008228 -8.117e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1999 -0.03426 -0.1701 0.1879 0.9835 0.9932 0.2238 0.4377 0.8704 0.7146 ] Network output: [ -0.01001 1.002 1.009 -2.703e-07 1.213e-07 0.008519 -2.037e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006259 0.0005219 0.004445 0.003513 0.9889 0.9919 0.006378 0.8593 0.8944 0.01264 ] Network output: [ -0.000438 0.002342 1.001 -3.375e-05 1.515e-05 0.9974 -2.544e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2123 0.09914 0.3419 0.1447 0.985 0.994 0.213 0.4419 0.877 0.7088 ] Network output: [ 0.004917 -0.02337 0.9944 2.035e-05 -9.136e-06 1.019 1.534e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1053 0.09301 0.1826 0.1997 0.9873 0.9919 0.1054 0.753 0.8652 0.3055 ] Network output: [ -0.004651 0.0222 1.004 2.165e-05 -9.718e-06 0.9833 1.631e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09159 0.08965 0.1651 0.1956 0.9853 0.9912 0.0916 0.6774 0.8413 0.2463 ] Network output: [ 0.0001308 1 -0.0001449 2.885e-06 -1.295e-06 0.9998 2.174e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003299 Epoch 8474 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01021 0.996 0.9911 -8.678e-08 3.896e-08 -0.007506 -6.54e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003404 -0.003221 -0.007563 0.005963 0.9699 0.9743 0.00656 0.8314 0.8234 0.01755 ] Network output: [ 0.9998 0.0003975 0.0006863 -1.076e-05 4.83e-06 -0.0008221 -8.108e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1999 -0.03426 -0.1701 0.1879 0.9835 0.9932 0.2238 0.4377 0.8704 0.7146 ] Network output: [ -0.01001 1.002 1.009 -2.705e-07 1.214e-07 0.008518 -2.039e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00626 0.000522 0.004445 0.003513 0.9889 0.9919 0.006379 0.8593 0.8944 0.01264 ] Network output: [ -0.0004377 0.002341 1.001 -3.372e-05 1.514e-05 0.9974 -2.541e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2123 0.09914 0.3419 0.1447 0.985 0.994 0.213 0.4419 0.877 0.7088 ] Network output: [ 0.004915 -0.02336 0.9944 2.033e-05 -9.126e-06 1.019 1.532e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1053 0.09302 0.1826 0.1997 0.9873 0.9919 0.1054 0.753 0.8652 0.3055 ] Network output: [ -0.00465 0.02219 1.004 2.162e-05 -9.708e-06 0.9833 1.63e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09159 0.08965 0.165 0.1956 0.9853 0.9912 0.0916 0.6774 0.8413 0.2463 ] Network output: [ 0.0001307 1 -0.0001447 2.882e-06 -1.294e-06 0.9998 2.172e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003297 Epoch 8475 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01021 0.996 0.9911 -8.731e-08 3.92e-08 -0.007506 -6.58e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003404 -0.003221 -0.007562 0.005962 0.9699 0.9743 0.00656 0.8314 0.8234 0.01755 ] Network output: [ 0.9998 0.0003972 0.0006859 -1.075e-05 4.825e-06 -0.0008214 -8.099e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1999 -0.03427 -0.1701 0.1879 0.9835 0.9932 0.2239 0.4377 0.8704 0.7146 ] Network output: [ -0.01001 1.002 1.009 -2.707e-07 1.215e-07 0.008516 -2.04e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00626 0.0005221 0.004444 0.003512 0.9889 0.9919 0.006379 0.8593 0.8944 0.01263 ] Network output: [ -0.0004374 0.00234 1.001 -3.368e-05 1.512e-05 0.9974 -2.538e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2123 0.09915 0.3419 0.1447 0.985 0.994 0.213 0.4419 0.877 0.7088 ] Network output: [ 0.004913 -0.02335 0.9944 2.031e-05 -9.117e-06 1.019 1.53e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1053 0.09302 0.1826 0.1997 0.9873 0.9919 0.1054 0.753 0.8652 0.3055 ] Network output: [ -0.004648 0.02218 1.004 2.16e-05 -9.698e-06 0.9834 1.628e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09159 0.08966 0.165 0.1956 0.9853 0.9912 0.09161 0.6774 0.8413 0.2463 ] Network output: [ 0.0001307 1 -0.0001446 2.879e-06 -1.293e-06 0.9998 2.17e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003296 Epoch 8476 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0102 0.996 0.9911 -8.784e-08 3.943e-08 -0.007506 -6.62e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003405 -0.003221 -0.007561 0.005961 0.9699 0.9743 0.00656 0.8314 0.8234 0.01755 ] Network output: [ 0.9998 0.0003969 0.0006855 -1.074e-05 4.82e-06 -0.0008208 -8.091e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.1999 -0.03427 -0.1701 0.1879 0.9835 0.9932 0.2239 0.4377 0.8704 0.7146 ] Network output: [ -0.01001 1.002 1.009 -2.709e-07 1.216e-07 0.008515 -2.042e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006261 0.0005222 0.004444 0.003512 0.9889 0.9919 0.00638 0.8593 0.8944 0.01263 ] Network output: [ -0.0004371 0.002339 1.001 -3.364e-05 1.51e-05 0.9974 -2.535e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2123 0.09915 0.3419 0.1447 0.985 0.994 0.213 0.4418 0.877 0.7088 ] Network output: [ 0.004912 -0.02334 0.9944 2.029e-05 -9.107e-06 1.019 1.529e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1054 0.09303 0.1826 0.1997 0.9873 0.9919 0.1054 0.753 0.8652 0.3055 ] Network output: [ -0.004646 0.02217 1.004 2.158e-05 -9.688e-06 0.9834 1.626e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0916 0.08966 0.165 0.1956 0.9853 0.9912 0.09161 0.6774 0.8413 0.2463 ] Network output: [ 0.0001306 1 -0.0001444 2.876e-06 -1.291e-06 0.9998 2.168e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003294 Epoch 8477 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0102 0.996 0.9911 -8.836e-08 3.967e-08 -0.007506 -6.659e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003405 -0.003221 -0.00756 0.005961 0.9699 0.9743 0.006561 0.8314 0.8234 0.01755 ] Network output: [ 0.9998 0.0003965 0.0006851 -1.072e-05 4.815e-06 -0.0008201 -8.082e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2 -0.03427 -0.1701 0.1879 0.9835 0.9932 0.2239 0.4377 0.8704 0.7146 ] Network output: [ -0.01001 1.002 1.009 -2.711e-07 1.217e-07 0.008513 -2.043e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006261 0.0005223 0.004444 0.003512 0.9889 0.9919 0.00638 0.8593 0.8944 0.01263 ] Network output: [ -0.0004368 0.002339 1.001 -3.361e-05 1.509e-05 0.9974 -2.533e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2124 0.09916 0.3419 0.1447 0.985 0.994 0.2131 0.4418 0.877 0.7088 ] Network output: [ 0.00491 -0.02334 0.9944 2.026e-05 -9.097e-06 1.019 1.527e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1054 0.09303 0.1826 0.1997 0.9873 0.9919 0.1054 0.7529 0.8652 0.3055 ] Network output: [ -0.004645 0.02216 1.004 2.156e-05 -9.678e-06 0.9834 1.625e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0916 0.08966 0.165 0.1956 0.9853 0.9912 0.09161 0.6773 0.8413 0.2463 ] Network output: [ 0.0001305 1 -0.0001442 2.873e-06 -1.29e-06 0.9998 2.165e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003292 Epoch 8478 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0102 0.996 0.9911 -8.889e-08 3.991e-08 -0.007506 -6.699e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003405 -0.003222 -0.007559 0.00596 0.9699 0.9743 0.006561 0.8314 0.8234 0.01755 ] Network output: [ 0.9998 0.0003962 0.0006847 -1.071e-05 4.809e-06 -0.0008194 -8.074e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2 -0.03427 -0.1701 0.1879 0.9835 0.9932 0.2239 0.4377 0.8704 0.7146 ] Network output: [ -0.01001 1.002 1.009 -2.714e-07 1.218e-07 0.008512 -2.045e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006262 0.0005224 0.004444 0.003511 0.9889 0.9919 0.006381 0.8593 0.8944 0.01263 ] Network output: [ -0.0004366 0.002338 1.001 -3.357e-05 1.507e-05 0.9974 -2.53e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2124 0.09917 0.3419 0.1447 0.985 0.994 0.2131 0.4418 0.877 0.7088 ] Network output: [ 0.004908 -0.02333 0.9944 2.024e-05 -9.088e-06 1.019 1.526e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1054 0.09304 0.1826 0.1997 0.9873 0.9919 0.1054 0.7529 0.8652 0.3055 ] Network output: [ -0.004643 0.02216 1.004 2.154e-05 -9.668e-06 0.9834 1.623e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0916 0.08966 0.165 0.1956 0.9853 0.9912 0.09161 0.6773 0.8413 0.2463 ] Network output: [ 0.0001305 1 -0.0001441 2.87e-06 -1.289e-06 0.9998 2.163e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000329 Epoch 8479 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0102 0.996 0.9911 -8.941e-08 4.014e-08 -0.007506 -6.738e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003405 -0.003222 -0.007559 0.00596 0.9699 0.9743 0.006561 0.8314 0.8234 0.01755 ] Network output: [ 0.9998 0.0003959 0.0006844 -1.07e-05 4.804e-06 -0.0008187 -8.065e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2 -0.03427 -0.17 0.1879 0.9835 0.9932 0.2239 0.4376 0.8704 0.7146 ] Network output: [ -0.01001 1.002 1.009 -2.716e-07 1.219e-07 0.00851 -2.047e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006262 0.0005225 0.004444 0.003511 0.9889 0.9919 0.006381 0.8593 0.8944 0.01263 ] Network output: [ -0.0004363 0.002337 1.001 -3.354e-05 1.506e-05 0.9974 -2.527e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2124 0.09917 0.3419 0.1447 0.985 0.994 0.2131 0.4418 0.877 0.7088 ] Network output: [ 0.004907 -0.02332 0.9944 2.022e-05 -9.078e-06 1.019 1.524e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1054 0.09305 0.1826 0.1997 0.9873 0.9919 0.1054 0.7529 0.8652 0.3055 ] Network output: [ -0.004641 0.02215 1.004 2.151e-05 -9.658e-06 0.9834 1.621e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0916 0.08967 0.165 0.1956 0.9853 0.9912 0.09162 0.6773 0.8413 0.2463 ] Network output: [ 0.0001304 1 -0.0001439 2.867e-06 -1.287e-06 0.9998 2.161e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003288 Epoch 8480 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0102 0.996 0.9911 -8.993e-08 4.037e-08 -0.007506 -6.778e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003405 -0.003222 -0.007558 0.005959 0.9699 0.9743 0.006561 0.8313 0.8234 0.01754 ] Network output: [ 0.9998 0.0003955 0.000684 -1.069e-05 4.799e-06 -0.000818 -8.056e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2 -0.03427 -0.17 0.1879 0.9835 0.9932 0.2239 0.4376 0.8704 0.7146 ] Network output: [ -0.01001 1.002 1.009 -2.718e-07 1.22e-07 0.008509 -2.048e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006263 0.0005226 0.004444 0.003511 0.9889 0.9919 0.006382 0.8593 0.8944 0.01263 ] Network output: [ -0.000436 0.002336 1.001 -3.35e-05 1.504e-05 0.9974 -2.525e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2124 0.09918 0.342 0.1447 0.985 0.994 0.2131 0.4418 0.877 0.7088 ] Network output: [ 0.004905 -0.02331 0.9944 2.02e-05 -9.069e-06 1.019 1.522e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1054 0.09305 0.1826 0.1997 0.9873 0.9919 0.1054 0.7529 0.8652 0.3055 ] Network output: [ -0.00464 0.02214 1.004 2.149e-05 -9.648e-06 0.9834 1.62e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09161 0.08967 0.165 0.1956 0.9853 0.9912 0.09162 0.6773 0.8413 0.2463 ] Network output: [ 0.0001304 1 -0.0001437 2.864e-06 -1.286e-06 0.9998 2.159e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003287 Epoch 8481 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0102 0.996 0.9911 -9.045e-08 4.061e-08 -0.007506 -6.817e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003405 -0.003222 -0.007557 0.005959 0.9699 0.9743 0.006562 0.8313 0.8234 0.01754 ] Network output: [ 0.9998 0.0003952 0.0006836 -1.068e-05 4.794e-06 -0.0008174 -8.048e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2 -0.03427 -0.17 0.1878 0.9835 0.9932 0.2239 0.4376 0.8704 0.7146 ] Network output: [ -0.01 1.002 1.009 -2.72e-07 1.221e-07 0.008508 -2.05e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006263 0.0005227 0.004444 0.00351 0.9889 0.9919 0.006383 0.8593 0.8944 0.01263 ] Network output: [ -0.0004357 0.002335 1.001 -3.346e-05 1.502e-05 0.9974 -2.522e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2124 0.09918 0.342 0.1447 0.985 0.994 0.2131 0.4418 0.877 0.7087 ] Network output: [ 0.004903 -0.0233 0.9944 2.018e-05 -9.059e-06 1.019 1.521e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1054 0.09306 0.1826 0.1997 0.9873 0.9919 0.1055 0.7529 0.8652 0.3055 ] Network output: [ -0.004638 0.02213 1.004 2.147e-05 -9.638e-06 0.9834 1.618e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09161 0.08967 0.165 0.1956 0.9853 0.9912 0.09162 0.6773 0.8413 0.2463 ] Network output: [ 0.0001303 1 -0.0001436 2.861e-06 -1.284e-06 0.9998 2.156e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003285 Epoch 8482 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0102 0.996 0.9911 -9.097e-08 4.084e-08 -0.007506 -6.856e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003405 -0.003222 -0.007556 0.005958 0.9699 0.9743 0.006562 0.8313 0.8234 0.01754 ] Network output: [ 0.9998 0.0003949 0.0006832 -1.067e-05 4.789e-06 -0.0008167 -8.039e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2 -0.03428 -0.17 0.1878 0.9835 0.9932 0.2239 0.4376 0.8704 0.7146 ] Network output: [ -0.01 1.002 1.009 -2.722e-07 1.222e-07 0.008506 -2.051e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006264 0.0005228 0.004444 0.00351 0.9889 0.9919 0.006383 0.8593 0.8944 0.01263 ] Network output: [ -0.0004355 0.002334 1.001 -3.343e-05 1.501e-05 0.9974 -2.519e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2124 0.09919 0.342 0.1447 0.985 0.994 0.2131 0.4418 0.877 0.7087 ] Network output: [ 0.004902 -0.02329 0.9944 2.016e-05 -9.05e-06 1.019 1.519e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1054 0.09306 0.1826 0.1997 0.9873 0.9919 0.1055 0.7529 0.8652 0.3055 ] Network output: [ -0.004636 0.02212 1.004 2.145e-05 -9.628e-06 0.9834 1.616e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09161 0.08967 0.165 0.1956 0.9853 0.9912 0.09162 0.6772 0.8413 0.2464 ] Network output: [ 0.0001302 1 -0.0001434 2.858e-06 -1.283e-06 0.9998 2.154e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003283 Epoch 8483 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0102 0.996 0.9911 -9.149e-08 4.107e-08 -0.007506 -6.895e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003405 -0.003222 -0.007555 0.005957 0.9699 0.9743 0.006562 0.8313 0.8234 0.01754 ] Network output: [ 0.9998 0.0003945 0.0006828 -1.066e-05 4.784e-06 -0.000816 -8.031e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2 -0.03428 -0.17 0.1878 0.9835 0.9932 0.2239 0.4376 0.8703 0.7146 ] Network output: [ -0.01 1.002 1.009 -2.724e-07 1.223e-07 0.008505 -2.053e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006265 0.0005229 0.004444 0.00351 0.9889 0.9919 0.006384 0.8593 0.8944 0.01263 ] Network output: [ -0.0004352 0.002334 1.001 -3.339e-05 1.499e-05 0.9974 -2.517e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2124 0.0992 0.342 0.1447 0.985 0.994 0.2131 0.4418 0.877 0.7087 ] Network output: [ 0.0049 -0.02329 0.9944 2.014e-05 -9.04e-06 1.019 1.518e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1054 0.09307 0.1826 0.1997 0.9873 0.9919 0.1055 0.7528 0.8652 0.3055 ] Network output: [ -0.004635 0.02211 1.004 2.142e-05 -9.618e-06 0.9834 1.615e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09161 0.08967 0.165 0.1956 0.9853 0.9912 0.09163 0.6772 0.8413 0.2464 ] Network output: [ 0.0001302 1 -0.0001432 2.855e-06 -1.282e-06 0.9998 2.152e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003281 Epoch 8484 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01019 0.996 0.9911 -9.2e-08 4.13e-08 -0.007506 -6.934e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003405 -0.003222 -0.007554 0.005957 0.9699 0.9743 0.006563 0.8313 0.8234 0.01754 ] Network output: [ 0.9998 0.0003942 0.0006824 -1.064e-05 4.779e-06 -0.0008153 -8.022e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2 -0.03428 -0.17 0.1878 0.9835 0.9932 0.2239 0.4376 0.8703 0.7146 ] Network output: [ -0.01 1.002 1.009 -2.726e-07 1.224e-07 0.008503 -2.055e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006265 0.000523 0.004444 0.003509 0.9889 0.9919 0.006384 0.8593 0.8944 0.01263 ] Network output: [ -0.0004349 0.002333 1.001 -3.336e-05 1.498e-05 0.9974 -2.514e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2124 0.0992 0.342 0.1447 0.985 0.994 0.2131 0.4418 0.877 0.7087 ] Network output: [ 0.004898 -0.02328 0.9944 2.012e-05 -9.031e-06 1.019 1.516e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1054 0.09307 0.1826 0.1996 0.9873 0.9919 0.1055 0.7528 0.8652 0.3055 ] Network output: [ -0.004633 0.0221 1.004 2.14e-05 -9.609e-06 0.9834 1.613e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09161 0.08968 0.165 0.1956 0.9853 0.9912 0.09163 0.6772 0.8413 0.2464 ] Network output: [ 0.0001301 1 -0.0001431 2.852e-06 -1.28e-06 0.9998 2.149e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003279 Epoch 8485 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01019 0.996 0.9911 -9.252e-08 4.153e-08 -0.007506 -6.972e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003406 -0.003222 -0.007553 0.005956 0.9699 0.9743 0.006563 0.8313 0.8234 0.01754 ] Network output: [ 0.9998 0.0003939 0.000682 -1.063e-05 4.774e-06 -0.0008147 -8.014e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2 -0.03428 -0.17 0.1878 0.9835 0.9932 0.224 0.4376 0.8703 0.7146 ] Network output: [ -0.01 1.002 1.009 -2.728e-07 1.225e-07 0.008502 -2.056e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006266 0.0005231 0.004444 0.003509 0.9889 0.9919 0.006385 0.8593 0.8944 0.01262 ] Network output: [ -0.0004346 0.002332 1.001 -3.332e-05 1.496e-05 0.9974 -2.511e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2124 0.09921 0.342 0.1447 0.985 0.994 0.2131 0.4418 0.877 0.7087 ] Network output: [ 0.004897 -0.02327 0.9944 2.009e-05 -9.021e-06 1.019 1.514e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1054 0.09308 0.1826 0.1996 0.9873 0.9919 0.1055 0.7528 0.8652 0.3055 ] Network output: [ -0.004631 0.02209 1.004 2.138e-05 -9.599e-06 0.9834 1.611e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09162 0.08968 0.165 0.1956 0.9853 0.9912 0.09163 0.6772 0.8412 0.2464 ] Network output: [ 0.0001301 1 -0.0001429 2.849e-06 -1.279e-06 0.9998 2.147e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003278 Epoch 8486 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01019 0.996 0.9911 -9.303e-08 4.176e-08 -0.007506 -7.011e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003406 -0.003223 -0.007552 0.005956 0.9699 0.9743 0.006563 0.8313 0.8234 0.01754 ] Network output: [ 0.9998 0.0003936 0.0006816 -1.062e-05 4.769e-06 -0.000814 -8.005e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2 -0.03428 -0.17 0.1878 0.9835 0.9932 0.224 0.4376 0.8703 0.7146 ] Network output: [ -0.009999 1.002 1.009 -2.73e-07 1.226e-07 0.0085 -2.058e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006266 0.0005232 0.004444 0.003509 0.9889 0.9919 0.006385 0.8592 0.8944 0.01262 ] Network output: [ -0.0004343 0.002331 1.001 -3.329e-05 1.494e-05 0.9974 -2.509e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2124 0.09921 0.342 0.1447 0.985 0.994 0.2131 0.4417 0.877 0.7087 ] Network output: [ 0.004895 -0.02326 0.9944 2.007e-05 -9.012e-06 1.019 1.513e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1054 0.09308 0.1826 0.1996 0.9873 0.9919 0.1055 0.7528 0.8652 0.3055 ] Network output: [ -0.00463 0.02208 1.004 2.136e-05 -9.589e-06 0.9834 1.61e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09162 0.08968 0.165 0.1956 0.9853 0.9912 0.09163 0.6772 0.8412 0.2464 ] Network output: [ 0.00013 1 -0.0001427 2.846e-06 -1.278e-06 0.9998 2.145e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003276 Epoch 8487 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01019 0.996 0.9911 -9.354e-08 4.199e-08 -0.007506 -7.05e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003406 -0.003223 -0.007552 0.005955 0.9699 0.9743 0.006563 0.8313 0.8234 0.01754 ] Network output: [ 0.9998 0.0003932 0.0006813 -1.061e-05 4.764e-06 -0.0008133 -7.997e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2 -0.03428 -0.1699 0.1878 0.9835 0.9932 0.224 0.4376 0.8703 0.7146 ] Network output: [ -0.009998 1.002 1.009 -2.733e-07 1.227e-07 0.008499 -2.059e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006267 0.0005232 0.004444 0.003508 0.9889 0.9919 0.006386 0.8592 0.8944 0.01262 ] Network output: [ -0.0004341 0.00233 1.001 -3.325e-05 1.493e-05 0.9974 -2.506e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2124 0.09922 0.342 0.1447 0.985 0.994 0.2132 0.4417 0.877 0.7087 ] Network output: [ 0.004893 -0.02325 0.9944 2.005e-05 -9.002e-06 1.019 1.511e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1054 0.09309 0.1826 0.1996 0.9873 0.9919 0.1055 0.7528 0.8652 0.3055 ] Network output: [ -0.004628 0.02207 1.004 2.134e-05 -9.579e-06 0.9834 1.608e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09162 0.08968 0.165 0.1956 0.9853 0.9912 0.09163 0.6771 0.8412 0.2464 ] Network output: [ 0.0001299 1 -0.0001426 2.843e-06 -1.276e-06 0.9998 2.143e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003274 Epoch 8488 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01019 0.996 0.9911 -9.405e-08 4.222e-08 -0.007506 -7.088e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003406 -0.003223 -0.007551 0.005955 0.9699 0.9743 0.006564 0.8313 0.8234 0.01753 ] Network output: [ 0.9998 0.0003929 0.0006809 -1.06e-05 4.759e-06 -0.0008126 -7.988e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2 -0.03428 -0.1699 0.1878 0.9835 0.9932 0.224 0.4376 0.8703 0.7145 ] Network output: [ -0.009997 1.002 1.009 -2.735e-07 1.228e-07 0.008498 -2.061e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006267 0.0005233 0.004444 0.003508 0.9889 0.9919 0.006387 0.8592 0.8944 0.01262 ] Network output: [ -0.0004338 0.002329 1.001 -3.321e-05 1.491e-05 0.9975 -2.503e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2125 0.09922 0.342 0.1447 0.985 0.994 0.2132 0.4417 0.877 0.7087 ] Network output: [ 0.004892 -0.02324 0.9944 2.003e-05 -8.993e-06 1.019 1.51e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1054 0.0931 0.1826 0.1996 0.9873 0.9919 0.1055 0.7527 0.8652 0.3055 ] Network output: [ -0.004626 0.02207 1.004 2.131e-05 -9.569e-06 0.9834 1.606e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09162 0.08969 0.165 0.1956 0.9853 0.9912 0.09164 0.6771 0.8412 0.2464 ] Network output: [ 0.0001299 1 -0.0001424 2.84e-06 -1.275e-06 0.9998 2.14e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003272 Epoch 8489 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01019 0.996 0.9911 -9.456e-08 4.245e-08 -0.007506 -7.126e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003406 -0.003223 -0.00755 0.005954 0.9699 0.9743 0.006564 0.8313 0.8234 0.01753 ] Network output: [ 0.9998 0.0003926 0.0006805 -1.059e-05 4.753e-06 -0.000812 -7.98e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2001 -0.03429 -0.1699 0.1878 0.9835 0.9932 0.224 0.4376 0.8703 0.7145 ] Network output: [ -0.009996 1.002 1.009 -2.737e-07 1.229e-07 0.008496 -2.062e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006268 0.0005234 0.004444 0.003507 0.9889 0.9919 0.006387 0.8592 0.8943 0.01262 ] Network output: [ -0.0004335 0.002329 1.001 -3.318e-05 1.49e-05 0.9975 -2.5e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2125 0.09923 0.342 0.1447 0.985 0.994 0.2132 0.4417 0.877 0.7087 ] Network output: [ 0.00489 -0.02323 0.9944 2.001e-05 -8.983e-06 1.019 1.508e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1054 0.0931 0.1826 0.1996 0.9873 0.9919 0.1055 0.7527 0.8652 0.3055 ] Network output: [ -0.004625 0.02206 1.004 2.129e-05 -9.559e-06 0.9834 1.605e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09163 0.08969 0.165 0.1956 0.9853 0.9912 0.09164 0.6771 0.8412 0.2464 ] Network output: [ 0.0001298 1 -0.0001422 2.837e-06 -1.274e-06 0.9998 2.138e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000327 Epoch 8490 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01019 0.996 0.9911 -9.507e-08 4.268e-08 -0.007506 -7.164e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003406 -0.003223 -0.007549 0.005953 0.9699 0.9743 0.006564 0.8313 0.8233 0.01753 ] Network output: [ 0.9998 0.0003922 0.0006801 -1.058e-05 4.748e-06 -0.0008113 -7.971e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2001 -0.03429 -0.1699 0.1878 0.9835 0.9932 0.224 0.4375 0.8703 0.7145 ] Network output: [ -0.009995 1.002 1.009 -2.739e-07 1.229e-07 0.008495 -2.064e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006268 0.0005235 0.004444 0.003507 0.9889 0.9919 0.006388 0.8592 0.8943 0.01262 ] Network output: [ -0.0004332 0.002328 1.001 -3.314e-05 1.488e-05 0.9975 -2.498e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2125 0.09924 0.342 0.1447 0.985 0.994 0.2132 0.4417 0.877 0.7087 ] Network output: [ 0.004888 -0.02323 0.9944 1.999e-05 -8.974e-06 1.019 1.506e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1054 0.09311 0.1826 0.1996 0.9873 0.9919 0.1055 0.7527 0.8652 0.3055 ] Network output: [ -0.004623 0.02205 1.004 2.127e-05 -9.549e-06 0.9834 1.603e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09163 0.08969 0.165 0.1956 0.9853 0.9912 0.09164 0.6771 0.8412 0.2464 ] Network output: [ 0.0001298 1 -0.0001421 2.834e-06 -1.272e-06 0.9998 2.136e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003269 Epoch 8491 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01018 0.996 0.9911 -9.557e-08 4.291e-08 -0.007506 -7.203e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003406 -0.003223 -0.007548 0.005953 0.9699 0.9743 0.006565 0.8313 0.8233 0.01753 ] Network output: [ 0.9998 0.0003919 0.0006797 -1.057e-05 4.743e-06 -0.0008106 -7.963e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2001 -0.03429 -0.1699 0.1878 0.9835 0.9932 0.224 0.4375 0.8703 0.7145 ] Network output: [ -0.009994 1.002 1.009 -2.741e-07 1.23e-07 0.008493 -2.065e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006269 0.0005236 0.004444 0.003507 0.9889 0.9919 0.006388 0.8592 0.8943 0.01262 ] Network output: [ -0.000433 0.002327 1.001 -3.311e-05 1.486e-05 0.9975 -2.495e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2125 0.09924 0.342 0.1447 0.985 0.994 0.2132 0.4417 0.877 0.7087 ] Network output: [ 0.004887 -0.02322 0.9944 1.997e-05 -8.964e-06 1.019 1.505e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1054 0.09311 0.1826 0.1996 0.9873 0.9919 0.1055 0.7527 0.8651 0.3055 ] Network output: [ -0.004621 0.02204 1.004 2.125e-05 -9.54e-06 0.9834 1.601e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09163 0.08969 0.165 0.1956 0.9853 0.9912 0.09164 0.6771 0.8412 0.2464 ] Network output: [ 0.0001297 1 -0.0001419 2.831e-06 -1.271e-06 0.9998 2.134e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003267 Epoch 8492 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01018 0.996 0.9911 -9.607e-08 4.313e-08 -0.007506 -7.24e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003406 -0.003223 -0.007547 0.005952 0.9699 0.9743 0.006565 0.8313 0.8233 0.01753 ] Network output: [ 0.9998 0.0003916 0.0006793 -1.055e-05 4.738e-06 -0.00081 -7.954e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2001 -0.03429 -0.1699 0.1878 0.9835 0.9932 0.224 0.4375 0.8703 0.7145 ] Network output: [ -0.009993 1.002 1.009 -2.743e-07 1.231e-07 0.008492 -2.067e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00627 0.0005237 0.004444 0.003506 0.9889 0.9919 0.006389 0.8592 0.8943 0.01262 ] Network output: [ -0.0004327 0.002326 1.001 -3.307e-05 1.485e-05 0.9975 -2.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2125 0.09925 0.342 0.1447 0.985 0.994 0.2132 0.4417 0.877 0.7087 ] Network output: [ 0.004885 -0.02321 0.9944 1.995e-05 -8.955e-06 1.019 1.503e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1054 0.09312 0.1826 0.1996 0.9873 0.9919 0.1055 0.7527 0.8651 0.3055 ] Network output: [ -0.00462 0.02203 1.004 2.123e-05 -9.53e-06 0.9834 1.6e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09163 0.0897 0.165 0.1956 0.9853 0.9912 0.09165 0.677 0.8412 0.2464 ] Network output: [ 0.0001296 1 -0.0001417 2.828e-06 -1.27e-06 0.9998 2.131e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003265 Epoch 8493 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01018 0.996 0.9911 -9.658e-08 4.336e-08 -0.007506 -7.278e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003407 -0.003224 -0.007546 0.005952 0.9699 0.9743 0.006565 0.8313 0.8233 0.01753 ] Network output: [ 0.9998 0.0003912 0.0006789 -1.054e-05 4.733e-06 -0.0008093 -7.946e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2001 -0.03429 -0.1699 0.1878 0.9835 0.9932 0.224 0.4375 0.8703 0.7145 ] Network output: [ -0.009992 1.002 1.009 -2.745e-07 1.232e-07 0.00849 -2.068e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00627 0.0005238 0.004444 0.003506 0.9889 0.9919 0.006389 0.8592 0.8943 0.01262 ] Network output: [ -0.0004324 0.002325 1.001 -3.304e-05 1.483e-05 0.9975 -2.49e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2125 0.09925 0.3421 0.1447 0.985 0.994 0.2132 0.4417 0.877 0.7087 ] Network output: [ 0.004883 -0.0232 0.9944 1.993e-05 -8.945e-06 1.019 1.502e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1055 0.09312 0.1826 0.1996 0.9873 0.9919 0.1055 0.7527 0.8651 0.3055 ] Network output: [ -0.004618 0.02202 1.004 2.121e-05 -9.52e-06 0.9834 1.598e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09163 0.0897 0.165 0.1956 0.9853 0.9912 0.09165 0.677 0.8412 0.2464 ] Network output: [ 0.0001296 1 -0.0001416 2.825e-06 -1.268e-06 0.9998 2.129e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003263 Epoch 8494 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01018 0.996 0.9911 -9.708e-08 4.358e-08 -0.007506 -7.316e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003407 -0.003224 -0.007545 0.005951 0.9699 0.9743 0.006565 0.8313 0.8233 0.01753 ] Network output: [ 0.9998 0.0003909 0.0006786 -1.053e-05 4.728e-06 -0.0008086 -7.937e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2001 -0.03429 -0.1698 0.1878 0.9835 0.9932 0.224 0.4375 0.8703 0.7145 ] Network output: [ -0.009991 1.002 1.009 -2.747e-07 1.233e-07 0.008489 -2.07e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006271 0.0005239 0.004444 0.003506 0.9889 0.9919 0.00639 0.8592 0.8943 0.01262 ] Network output: [ -0.0004321 0.002324 1.001 -3.3e-05 1.482e-05 0.9975 -2.487e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2125 0.09926 0.3421 0.1446 0.985 0.994 0.2132 0.4417 0.877 0.7087 ] Network output: [ 0.004882 -0.02319 0.9944 1.99e-05 -8.936e-06 1.019 1.5e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1055 0.09313 0.1826 0.1996 0.9873 0.9919 0.1055 0.7526 0.8651 0.3055 ] Network output: [ -0.004616 0.02201 1.004 2.118e-05 -9.51e-06 0.9834 1.596e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09164 0.0897 0.165 0.1956 0.9853 0.9912 0.09165 0.677 0.8412 0.2464 ] Network output: [ 0.0001295 1 -0.0001414 2.822e-06 -1.267e-06 0.9998 2.127e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003262 Epoch 8495 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01018 0.996 0.9911 -9.758e-08 4.381e-08 -0.007506 -7.354e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003407 -0.003224 -0.007545 0.005951 0.9699 0.9743 0.006566 0.8312 0.8233 0.01753 ] Network output: [ 0.9998 0.0003906 0.0006782 -1.052e-05 4.723e-06 -0.000808 -7.929e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2001 -0.03429 -0.1698 0.1878 0.9835 0.9932 0.2241 0.4375 0.8703 0.7145 ] Network output: [ -0.00999 1.002 1.009 -2.749e-07 1.234e-07 0.008488 -2.071e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006271 0.000524 0.004444 0.003505 0.9889 0.9919 0.006391 0.8592 0.8943 0.01261 ] Network output: [ -0.0004319 0.002324 1.001 -3.297e-05 1.48e-05 0.9975 -2.484e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2125 0.09927 0.3421 0.1446 0.985 0.994 0.2132 0.4417 0.877 0.7087 ] Network output: [ 0.00488 -0.02318 0.9944 1.988e-05 -8.926e-06 1.019 1.498e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1055 0.09314 0.1826 0.1996 0.9873 0.9919 0.1055 0.7526 0.8651 0.3055 ] Network output: [ -0.004615 0.022 1.004 2.116e-05 -9.5e-06 0.9834 1.595e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09164 0.0897 0.165 0.1956 0.9853 0.9912 0.09165 0.677 0.8412 0.2464 ] Network output: [ 0.0001295 1 -0.0001412 2.819e-06 -1.266e-06 0.9998 2.125e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000326 Epoch 8496 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01018 0.996 0.9911 -9.808e-08 4.403e-08 -0.007506 -7.391e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003407 -0.003224 -0.007544 0.00595 0.9699 0.9743 0.006566 0.8312 0.8233 0.01752 ] Network output: [ 0.9998 0.0003903 0.0006778 -1.051e-05 4.718e-06 -0.0008073 -7.92e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2001 -0.0343 -0.1698 0.1878 0.9835 0.9932 0.2241 0.4375 0.8703 0.7145 ] Network output: [ -0.009989 1.002 1.009 -2.751e-07 1.235e-07 0.008486 -2.073e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006272 0.0005241 0.004444 0.003505 0.9889 0.9919 0.006391 0.8592 0.8943 0.01261 ] Network output: [ -0.0004316 0.002323 1.001 -3.293e-05 1.478e-05 0.9975 -2.482e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2125 0.09927 0.3421 0.1446 0.985 0.994 0.2132 0.4417 0.877 0.7087 ] Network output: [ 0.004878 -0.02318 0.9944 1.986e-05 -8.917e-06 1.019 1.497e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1055 0.09314 0.1826 0.1996 0.9873 0.9919 0.1055 0.7526 0.8651 0.3055 ] Network output: [ -0.004613 0.02199 1.004 2.114e-05 -9.49e-06 0.9834 1.593e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09164 0.0897 0.165 0.1956 0.9853 0.9912 0.09165 0.677 0.8412 0.2464 ] Network output: [ 0.0001294 1 -0.0001411 2.816e-06 -1.264e-06 0.9998 2.123e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003258 Epoch 8497 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01018 0.996 0.9911 -9.857e-08 4.425e-08 -0.007506 -7.429e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003407 -0.003224 -0.007543 0.00595 0.9699 0.9743 0.006566 0.8312 0.8233 0.01752 ] Network output: [ 0.9998 0.0003899 0.0006774 -1.05e-05 4.713e-06 -0.0008066 -7.912e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2001 -0.0343 -0.1698 0.1878 0.9835 0.9932 0.2241 0.4375 0.8703 0.7145 ] Network output: [ -0.009988 1.002 1.009 -2.753e-07 1.236e-07 0.008485 -2.074e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006272 0.0005242 0.004444 0.003505 0.9889 0.9919 0.006392 0.8592 0.8943 0.01261 ] Network output: [ -0.0004313 0.002322 1.001 -3.29e-05 1.477e-05 0.9975 -2.479e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2125 0.09928 0.3421 0.1446 0.985 0.994 0.2133 0.4416 0.877 0.7086 ] Network output: [ 0.004877 -0.02317 0.9944 1.984e-05 -8.907e-06 1.019 1.495e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1055 0.09315 0.1826 0.1996 0.9873 0.9919 0.1056 0.7526 0.8651 0.3055 ] Network output: [ -0.004611 0.02199 1.004 2.112e-05 -9.481e-06 0.9834 1.592e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09164 0.08971 0.165 0.1956 0.9853 0.9912 0.09166 0.6769 0.8412 0.2464 ] Network output: [ 0.0001293 1 -0.0001409 2.813e-06 -1.263e-06 0.9998 2.12e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003256 Epoch 8498 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01018 0.996 0.9911 -9.907e-08 4.448e-08 -0.007506 -7.466e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003407 -0.003224 -0.007542 0.005949 0.9699 0.9743 0.006567 0.8312 0.8233 0.01752 ] Network output: [ 0.9998 0.0003896 0.000677 -1.049e-05 4.708e-06 -0.0008059 -7.904e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2001 -0.0343 -0.1698 0.1878 0.9835 0.9932 0.2241 0.4375 0.8703 0.7145 ] Network output: [ -0.009987 1.002 1.009 -2.755e-07 1.237e-07 0.008483 -2.076e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006273 0.0005243 0.004444 0.003504 0.9889 0.9919 0.006392 0.8592 0.8943 0.01261 ] Network output: [ -0.000431 0.002321 1.001 -3.286e-05 1.475e-05 0.9975 -2.477e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2126 0.09928 0.3421 0.1446 0.985 0.994 0.2133 0.4416 0.877 0.7086 ] Network output: [ 0.004875 -0.02316 0.9944 1.982e-05 -8.898e-06 1.019 1.494e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1055 0.09315 0.1826 0.1996 0.9873 0.9919 0.1056 0.7526 0.8651 0.3055 ] Network output: [ -0.00461 0.02198 1.004 2.11e-05 -9.471e-06 0.9834 1.59e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09165 0.08971 0.165 0.1956 0.9853 0.9912 0.09166 0.6769 0.8412 0.2464 ] Network output: [ 0.0001293 1 -0.0001407 2.811e-06 -1.262e-06 0.9998 2.118e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003254 Epoch 8499 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01017 0.996 0.9911 -9.956e-08 4.47e-08 -0.007506 -7.503e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003407 -0.003224 -0.007541 0.005948 0.9699 0.9743 0.006567 0.8312 0.8233 0.01752 ] Network output: [ 0.9998 0.0003893 0.0006767 -1.048e-05 4.703e-06 -0.0008053 -7.895e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2001 -0.0343 -0.1698 0.1878 0.9835 0.9932 0.2241 0.4375 0.8703 0.7145 ] Network output: [ -0.009986 1.002 1.009 -2.757e-07 1.238e-07 0.008482 -2.077e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006273 0.0005244 0.004444 0.003504 0.9889 0.9919 0.006393 0.8591 0.8943 0.01261 ] Network output: [ -0.0004308 0.00232 1.001 -3.283e-05 1.474e-05 0.9975 -2.474e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2126 0.09929 0.3421 0.1446 0.985 0.994 0.2133 0.4416 0.877 0.7086 ] Network output: [ 0.004873 -0.02315 0.9944 1.98e-05 -8.889e-06 1.019 1.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1055 0.09316 0.1826 0.1996 0.9873 0.9919 0.1056 0.7525 0.8651 0.3055 ] Network output: [ -0.004608 0.02197 1.004 2.107e-05 -9.461e-06 0.9835 1.588e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09165 0.08971 0.165 0.1956 0.9853 0.9912 0.09166 0.6769 0.8412 0.2464 ] Network output: [ 0.0001292 1 -0.0001406 2.808e-06 -1.26e-06 0.9998 2.116e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003253 Epoch 8500 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01017 0.996 0.9911 -1.001e-07 4.492e-08 -0.007505 -7.54e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003407 -0.003225 -0.00754 0.005948 0.9699 0.9743 0.006567 0.8312 0.8233 0.01752 ] Network output: [ 0.9998 0.0003889 0.0006763 -1.047e-05 4.698e-06 -0.0008046 -7.887e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2002 -0.0343 -0.1698 0.1877 0.9835 0.9932 0.2241 0.4374 0.8703 0.7145 ] Network output: [ -0.009985 1.002 1.009 -2.758e-07 1.238e-07 0.008481 -2.079e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006274 0.0005245 0.004444 0.003504 0.9889 0.9919 0.006393 0.8591 0.8943 0.01261 ] Network output: [ -0.0004305 0.002319 1.001 -3.279e-05 1.472e-05 0.9975 -2.471e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2126 0.0993 0.3421 0.1446 0.985 0.994 0.2133 0.4416 0.877 0.7086 ] Network output: [ 0.004872 -0.02314 0.9944 1.978e-05 -8.879e-06 1.019 1.491e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1055 0.09316 0.1826 0.1996 0.9873 0.9919 0.1056 0.7525 0.8651 0.3055 ] Network output: [ -0.004606 0.02196 1.004 2.105e-05 -9.451e-06 0.9835 1.587e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09165 0.08971 0.165 0.1956 0.9853 0.9912 0.09166 0.6769 0.8412 0.2464 ] Network output: [ 0.0001292 1 -0.0001404 2.805e-06 -1.259e-06 0.9998 2.114e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003251 Epoch 8501 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01017 0.996 0.9911 -1.005e-07 4.514e-08 -0.007505 -7.577e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003407 -0.003225 -0.007539 0.005947 0.9699 0.9743 0.006567 0.8312 0.8233 0.01752 ] Network output: [ 0.9998 0.0003886 0.0006759 -1.045e-05 4.693e-06 -0.000804 -7.878e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2002 -0.0343 -0.1698 0.1877 0.9835 0.9932 0.2241 0.4374 0.8703 0.7145 ] Network output: [ -0.009984 1.002 1.009 -2.76e-07 1.239e-07 0.008479 -2.08e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006274 0.0005245 0.004444 0.003503 0.9889 0.9919 0.006394 0.8591 0.8943 0.01261 ] Network output: [ -0.0004302 0.002319 1.001 -3.276e-05 1.471e-05 0.9975 -2.469e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2126 0.0993 0.3421 0.1446 0.985 0.994 0.2133 0.4416 0.877 0.7086 ] Network output: [ 0.00487 -0.02313 0.9944 1.976e-05 -8.87e-06 1.019 1.489e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1055 0.09317 0.1826 0.1996 0.9873 0.9919 0.1056 0.7525 0.8651 0.3055 ] Network output: [ -0.004605 0.02195 1.004 2.103e-05 -9.442e-06 0.9835 1.585e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09165 0.08972 0.165 0.1956 0.9853 0.9912 0.09167 0.6769 0.8412 0.2464 ] Network output: [ 0.0001291 1 -0.0001403 2.802e-06 -1.258e-06 0.9998 2.111e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003249 Epoch 8502 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01017 0.996 0.9911 -1.01e-07 4.536e-08 -0.007505 -7.614e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003408 -0.003225 -0.007539 0.005947 0.9699 0.9743 0.006568 0.8312 0.8233 0.01752 ] Network output: [ 0.9998 0.0003883 0.0006755 -1.044e-05 4.688e-06 -0.0008033 -7.87e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2002 -0.0343 -0.1697 0.1877 0.9835 0.9932 0.2241 0.4374 0.8703 0.7145 ] Network output: [ -0.009983 1.002 1.009 -2.762e-07 1.24e-07 0.008478 -2.082e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006275 0.0005246 0.004444 0.003503 0.9889 0.9919 0.006394 0.8591 0.8943 0.01261 ] Network output: [ -0.0004299 0.002318 1.001 -3.272e-05 1.469e-05 0.9975 -2.466e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2126 0.09931 0.3421 0.1446 0.985 0.994 0.2133 0.4416 0.877 0.7086 ] Network output: [ 0.004868 -0.02313 0.9944 1.974e-05 -8.86e-06 1.019 1.487e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1055 0.09317 0.1826 0.1996 0.9873 0.9919 0.1056 0.7525 0.8651 0.3055 ] Network output: [ -0.004603 0.02194 1.004 2.101e-05 -9.432e-06 0.9835 1.583e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09165 0.08972 0.165 0.1956 0.9853 0.9912 0.09167 0.6769 0.8412 0.2464 ] Network output: [ 0.0001291 1 -0.0001401 2.799e-06 -1.256e-06 0.9998 2.109e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003247 Epoch 8503 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01017 0.996 0.9911 -1.015e-07 4.558e-08 -0.007505 -7.651e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003408 -0.003225 -0.007538 0.005946 0.9699 0.9743 0.006568 0.8312 0.8233 0.01752 ] Network output: [ 0.9998 0.000388 0.0006751 -1.043e-05 4.683e-06 -0.0008026 -7.862e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2002 -0.03431 -0.1697 0.1877 0.9835 0.9932 0.2241 0.4374 0.8703 0.7145 ] Network output: [ -0.009982 1.002 1.009 -2.764e-07 1.241e-07 0.008476 -2.083e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006276 0.0005247 0.004444 0.003503 0.9889 0.9919 0.006395 0.8591 0.8943 0.01261 ] Network output: [ -0.0004297 0.002317 1.001 -3.269e-05 1.467e-05 0.9975 -2.463e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2126 0.09931 0.3421 0.1446 0.985 0.994 0.2133 0.4416 0.8769 0.7086 ] Network output: [ 0.004867 -0.02312 0.9944 1.972e-05 -8.851e-06 1.019 1.486e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1055 0.09318 0.1826 0.1996 0.9873 0.9919 0.1056 0.7525 0.8651 0.3055 ] Network output: [ -0.004601 0.02193 1.004 2.099e-05 -9.422e-06 0.9835 1.582e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09166 0.08972 0.165 0.1956 0.9853 0.9912 0.09167 0.6768 0.8411 0.2464 ] Network output: [ 0.000129 1 -0.0001399 2.796e-06 -1.255e-06 0.9998 2.107e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003246 Epoch 8504 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01017 0.9961 0.9911 -1.02e-07 4.58e-08 -0.007505 -7.688e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003408 -0.003225 -0.007537 0.005946 0.9699 0.9743 0.006568 0.8312 0.8233 0.01751 ] Network output: [ 0.9998 0.0003876 0.0006747 -1.042e-05 4.678e-06 -0.000802 -7.853e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2002 -0.03431 -0.1697 0.1877 0.9835 0.9932 0.2241 0.4374 0.8703 0.7145 ] Network output: [ -0.009981 1.002 1.009 -2.766e-07 1.242e-07 0.008475 -2.085e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006276 0.0005248 0.004444 0.003502 0.9889 0.9919 0.006396 0.8591 0.8943 0.01261 ] Network output: [ -0.0004294 0.002316 1.001 -3.265e-05 1.466e-05 0.9975 -2.461e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2126 0.09932 0.3421 0.1446 0.985 0.994 0.2133 0.4416 0.8769 0.7086 ] Network output: [ 0.004865 -0.02311 0.9944 1.969e-05 -8.842e-06 1.019 1.484e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1055 0.09319 0.1826 0.1996 0.9873 0.9919 0.1056 0.7525 0.8651 0.3055 ] Network output: [ -0.0046 0.02192 1.004 2.097e-05 -9.412e-06 0.9835 1.58e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09166 0.08972 0.165 0.1956 0.9853 0.9912 0.09167 0.6768 0.8411 0.2464 ] Network output: [ 0.0001289 1 -0.0001398 2.793e-06 -1.254e-06 0.9998 2.105e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003244 Epoch 8505 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01017 0.9961 0.9911 -1.025e-07 4.601e-08 -0.007505 -7.724e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003408 -0.003225 -0.007536 0.005945 0.9699 0.9743 0.006569 0.8312 0.8233 0.01751 ] Network output: [ 0.9998 0.0003873 0.0006744 -1.041e-05 4.673e-06 -0.0008013 -7.845e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2002 -0.03431 -0.1697 0.1877 0.9835 0.9932 0.2242 0.4374 0.8703 0.7145 ] Network output: [ -0.00998 1.002 1.009 -2.768e-07 1.243e-07 0.008474 -2.086e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006277 0.0005249 0.004444 0.003502 0.9889 0.9919 0.006396 0.8591 0.8943 0.01261 ] Network output: [ -0.0004291 0.002315 1.001 -3.262e-05 1.464e-05 0.9975 -2.458e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2126 0.09933 0.3421 0.1446 0.985 0.994 0.2133 0.4416 0.8769 0.7086 ] Network output: [ 0.004863 -0.0231 0.9944 1.967e-05 -8.832e-06 1.019 1.483e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1055 0.09319 0.1826 0.1996 0.9873 0.9919 0.1056 0.7524 0.8651 0.3055 ] Network output: [ -0.004598 0.02191 1.004 2.094e-05 -9.403e-06 0.9835 1.578e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09166 0.08972 0.165 0.1956 0.9853 0.9912 0.09167 0.6768 0.8411 0.2464 ] Network output: [ 0.0001289 1 -0.0001396 2.79e-06 -1.252e-06 0.9998 2.103e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003242 Epoch 8506 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01017 0.9961 0.9911 -1.03e-07 4.623e-08 -0.007505 -7.761e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003408 -0.003225 -0.007535 0.005944 0.9699 0.9743 0.006569 0.8312 0.8233 0.01751 ] Network output: [ 0.9998 0.000387 0.000674 -1.04e-05 4.668e-06 -0.0008006 -7.836e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2002 -0.03431 -0.1697 0.1877 0.9835 0.9932 0.2242 0.4374 0.8703 0.7144 ] Network output: [ -0.009979 1.002 1.009 -2.77e-07 1.244e-07 0.008472 -2.087e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006277 0.000525 0.004444 0.003502 0.9889 0.9919 0.006397 0.8591 0.8943 0.0126 ] Network output: [ -0.0004288 0.002314 1.001 -3.258e-05 1.463e-05 0.9975 -2.455e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2126 0.09933 0.3422 0.1446 0.985 0.994 0.2133 0.4416 0.8769 0.7086 ] Network output: [ 0.004862 -0.02309 0.9944 1.965e-05 -8.823e-06 1.019 1.481e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1055 0.0932 0.1826 0.1996 0.9873 0.9919 0.1056 0.7524 0.8651 0.3055 ] Network output: [ -0.004596 0.02191 1.004 2.092e-05 -9.393e-06 0.9835 1.577e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09166 0.08973 0.165 0.1956 0.9853 0.9912 0.09168 0.6768 0.8411 0.2464 ] Network output: [ 0.0001288 1 -0.0001394 2.787e-06 -1.251e-06 0.9998 2.1e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000324 Epoch 8507 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01016 0.9961 0.9911 -1.035e-07 4.645e-08 -0.007505 -7.797e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003408 -0.003225 -0.007534 0.005944 0.9699 0.9743 0.006569 0.8312 0.8233 0.01751 ] Network output: [ 0.9998 0.0003867 0.0006736 -1.039e-05 4.663e-06 -0.0008 -7.828e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2002 -0.03431 -0.1697 0.1877 0.9835 0.9932 0.2242 0.4374 0.8703 0.7144 ] Network output: [ -0.009978 1.002 1.009 -2.772e-07 1.244e-07 0.008471 -2.089e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006278 0.0005251 0.004444 0.003501 0.9889 0.9919 0.006397 0.8591 0.8943 0.0126 ] Network output: [ -0.0004286 0.002314 1.001 -3.255e-05 1.461e-05 0.9975 -2.453e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2126 0.09934 0.3422 0.1446 0.985 0.994 0.2134 0.4415 0.8769 0.7086 ] Network output: [ 0.00486 -0.02308 0.9944 1.963e-05 -8.814e-06 1.019 1.48e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1055 0.0932 0.1827 0.1996 0.9873 0.9919 0.1056 0.7524 0.8651 0.3055 ] Network output: [ -0.004595 0.0219 1.004 2.09e-05 -9.383e-06 0.9835 1.575e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09167 0.08973 0.165 0.1956 0.9853 0.9912 0.09168 0.6768 0.8411 0.2464 ] Network output: [ 0.0001288 1 -0.0001393 2.784e-06 -1.25e-06 0.9998 2.098e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003239 Epoch 8508 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01016 0.9961 0.9911 -1.039e-07 4.666e-08 -0.007505 -7.833e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003408 -0.003226 -0.007533 0.005943 0.9699 0.9743 0.006569 0.8312 0.8233 0.01751 ] Network output: [ 0.9998 0.0003863 0.0006732 -1.038e-05 4.658e-06 -0.0007993 -7.82e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2002 -0.03431 -0.1697 0.1877 0.9835 0.9932 0.2242 0.4374 0.8703 0.7144 ] Network output: [ -0.009977 1.002 1.009 -2.774e-07 1.245e-07 0.008469 -2.09e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006278 0.0005252 0.004444 0.003501 0.9889 0.9919 0.006398 0.8591 0.8943 0.0126 ] Network output: [ -0.0004283 0.002313 1.001 -3.251e-05 1.46e-05 0.9975 -2.45e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2127 0.09934 0.3422 0.1446 0.985 0.994 0.2134 0.4415 0.8769 0.7086 ] Network output: [ 0.004859 -0.02308 0.9944 1.961e-05 -8.804e-06 1.019 1.478e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1055 0.09321 0.1827 0.1996 0.9873 0.9919 0.1056 0.7524 0.8651 0.3055 ] Network output: [ -0.004593 0.02189 1.004 2.088e-05 -9.374e-06 0.9835 1.574e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09167 0.08973 0.165 0.1956 0.9853 0.9912 0.09168 0.6767 0.8411 0.2464 ] Network output: [ 0.0001287 1 -0.0001391 2.781e-06 -1.249e-06 0.9998 2.096e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003237 Epoch 8509 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01016 0.9961 0.9911 -1.044e-07 4.688e-08 -0.007505 -7.87e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003408 -0.003226 -0.007532 0.005943 0.9699 0.9743 0.00657 0.8312 0.8233 0.01751 ] Network output: [ 0.9998 0.000386 0.0006728 -1.037e-05 4.653e-06 -0.0007987 -7.811e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2002 -0.03431 -0.1696 0.1877 0.9835 0.9932 0.2242 0.4374 0.8703 0.7144 ] Network output: [ -0.009976 1.002 1.009 -2.775e-07 1.246e-07 0.008468 -2.092e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006279 0.0005253 0.004444 0.003501 0.9889 0.9919 0.006398 0.8591 0.8943 0.0126 ] Network output: [ -0.000428 0.002312 1.001 -3.248e-05 1.458e-05 0.9975 -2.448e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2127 0.09935 0.3422 0.1446 0.985 0.994 0.2134 0.4415 0.8769 0.7086 ] Network output: [ 0.004857 -0.02307 0.9944 1.959e-05 -8.795e-06 1.019 1.476e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1056 0.09321 0.1827 0.1996 0.9873 0.9919 0.1056 0.7524 0.8651 0.3055 ] Network output: [ -0.004591 0.02188 1.004 2.086e-05 -9.364e-06 0.9835 1.572e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09167 0.08973 0.165 0.1956 0.9853 0.9912 0.09168 0.6767 0.8411 0.2464 ] Network output: [ 0.0001286 1 -0.000139 2.778e-06 -1.247e-06 0.9998 2.094e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003235 Epoch 8510 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01016 0.9961 0.9911 -1.049e-07 4.709e-08 -0.007505 -7.906e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003409 -0.003226 -0.007532 0.005942 0.9699 0.9743 0.00657 0.8312 0.8233 0.01751 ] Network output: [ 0.9998 0.0003857 0.0006725 -1.035e-05 4.648e-06 -0.000798 -7.803e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2002 -0.03432 -0.1696 0.1877 0.9835 0.9932 0.2242 0.4374 0.8703 0.7144 ] Network output: [ -0.009974 1.002 1.009 -2.777e-07 1.247e-07 0.008467 -2.093e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006279 0.0005254 0.004444 0.0035 0.9889 0.9919 0.006399 0.8591 0.8943 0.0126 ] Network output: [ -0.0004277 0.002311 1.001 -3.244e-05 1.456e-05 0.9975 -2.445e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2127 0.09935 0.3422 0.1446 0.985 0.994 0.2134 0.4415 0.8769 0.7086 ] Network output: [ 0.004855 -0.02306 0.9944 1.957e-05 -8.786e-06 1.019 1.475e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1056 0.09322 0.1827 0.1996 0.9873 0.9919 0.1056 0.7524 0.8651 0.3055 ] Network output: [ -0.00459 0.02187 1.004 2.084e-05 -9.354e-06 0.9835 1.57e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09167 0.08974 0.165 0.1956 0.9853 0.9912 0.09169 0.6767 0.8411 0.2464 ] Network output: [ 0.0001286 1 -0.0001388 2.775e-06 -1.246e-06 0.9998 2.091e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003233 Epoch 8511 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01016 0.9961 0.9911 -1.054e-07 4.731e-08 -0.007505 -7.942e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003409 -0.003226 -0.007531 0.005942 0.9699 0.9743 0.00657 0.8311 0.8233 0.01751 ] Network output: [ 0.9998 0.0003853 0.0006721 -1.034e-05 4.643e-06 -0.0007973 -7.795e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2002 -0.03432 -0.1696 0.1877 0.9835 0.9932 0.2242 0.4373 0.8703 0.7144 ] Network output: [ -0.009973 1.002 1.009 -2.779e-07 1.248e-07 0.008465 -2.094e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00628 0.0005255 0.004444 0.0035 0.9889 0.9919 0.006399 0.8591 0.8943 0.0126 ] Network output: [ -0.0004275 0.00231 1.001 -3.241e-05 1.455e-05 0.9975 -2.442e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2127 0.09936 0.3422 0.1446 0.985 0.994 0.2134 0.4415 0.8769 0.7086 ] Network output: [ 0.004854 -0.02305 0.9944 1.955e-05 -8.776e-06 1.019 1.473e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1056 0.09323 0.1827 0.1996 0.9873 0.9919 0.1056 0.7523 0.8651 0.3055 ] Network output: [ -0.004588 0.02186 1.004 2.081e-05 -9.345e-06 0.9835 1.569e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09168 0.08974 0.165 0.1956 0.9853 0.9912 0.09169 0.6767 0.8411 0.2464 ] Network output: [ 0.0001285 1 -0.0001386 2.772e-06 -1.245e-06 0.9998 2.089e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003232 Epoch 8512 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01016 0.9961 0.9911 -1.059e-07 4.752e-08 -0.007505 -7.978e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003409 -0.003226 -0.00753 0.005941 0.9699 0.9743 0.00657 0.8311 0.8233 0.0175 ] Network output: [ 0.9998 0.000385 0.0006717 -1.033e-05 4.638e-06 -0.0007967 -7.786e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2003 -0.03432 -0.1696 0.1877 0.9835 0.9932 0.2242 0.4373 0.8703 0.7144 ] Network output: [ -0.009972 1.002 1.009 -2.781e-07 1.248e-07 0.008464 -2.096e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00628 0.0005256 0.004444 0.0035 0.9889 0.9919 0.0064 0.8591 0.8943 0.0126 ] Network output: [ -0.0004272 0.00231 1.001 -3.237e-05 1.453e-05 0.9975 -2.44e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2127 0.09937 0.3422 0.1446 0.985 0.994 0.2134 0.4415 0.8769 0.7086 ] Network output: [ 0.004852 -0.02304 0.9944 1.953e-05 -8.767e-06 1.019 1.472e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1056 0.09323 0.1827 0.1996 0.9873 0.9919 0.1056 0.7523 0.8651 0.3055 ] Network output: [ -0.004587 0.02185 1.004 2.079e-05 -9.335e-06 0.9835 1.567e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09168 0.08974 0.165 0.1956 0.9853 0.9912 0.09169 0.6767 0.8411 0.2464 ] Network output: [ 0.0001285 1 -0.0001385 2.769e-06 -1.243e-06 0.9998 2.087e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000323 Epoch 8513 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01016 0.9961 0.9911 -1.063e-07 4.773e-08 -0.007505 -8.013e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003409 -0.003226 -0.007529 0.005941 0.9699 0.9743 0.006571 0.8311 0.8233 0.0175 ] Network output: [ 0.9998 0.0003847 0.0006713 -1.032e-05 4.633e-06 -0.000796 -7.778e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2003 -0.03432 -0.1696 0.1877 0.9835 0.9932 0.2242 0.4373 0.8703 0.7144 ] Network output: [ -0.009971 1.002 1.009 -2.783e-07 1.249e-07 0.008462 -2.097e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006281 0.0005257 0.004444 0.003499 0.9889 0.9919 0.006401 0.859 0.8943 0.0126 ] Network output: [ -0.0004269 0.002309 1.001 -3.234e-05 1.452e-05 0.9975 -2.437e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2127 0.09937 0.3422 0.1446 0.985 0.994 0.2134 0.4415 0.8769 0.7086 ] Network output: [ 0.00485 -0.02303 0.9944 1.951e-05 -8.758e-06 1.019 1.47e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1056 0.09324 0.1827 0.1996 0.9873 0.9919 0.1056 0.7523 0.865 0.3055 ] Network output: [ -0.004585 0.02184 1.004 2.077e-05 -9.325e-06 0.9835 1.565e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09168 0.08974 0.165 0.1956 0.9853 0.9912 0.09169 0.6766 0.8411 0.2464 ] Network output: [ 0.0001284 1 -0.0001383 2.766e-06 -1.242e-06 0.9998 2.085e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003228 Epoch 8514 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01015 0.9961 0.9911 -1.068e-07 4.795e-08 -0.007505 -8.049e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003409 -0.003226 -0.007528 0.00594 0.9699 0.9743 0.006571 0.8311 0.8233 0.0175 ] Network output: [ 0.9998 0.0003844 0.000671 -1.031e-05 4.628e-06 -0.0007954 -7.77e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2003 -0.03432 -0.1696 0.1877 0.9835 0.9932 0.2242 0.4373 0.8703 0.7144 ] Network output: [ -0.00997 1.002 1.009 -2.785e-07 1.25e-07 0.008461 -2.099e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006282 0.0005258 0.004444 0.003499 0.9889 0.9919 0.006401 0.859 0.8943 0.0126 ] Network output: [ -0.0004266 0.002308 1.001 -3.23e-05 1.45e-05 0.9975 -2.434e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2127 0.09938 0.3422 0.1446 0.985 0.994 0.2134 0.4415 0.8769 0.7085 ] Network output: [ 0.004849 -0.02303 0.9944 1.949e-05 -8.749e-06 1.019 1.469e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1056 0.09324 0.1827 0.1996 0.9873 0.9919 0.1057 0.7523 0.865 0.3055 ] Network output: [ -0.004583 0.02183 1.004 2.075e-05 -9.316e-06 0.9835 1.564e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09168 0.08974 0.165 0.1956 0.9853 0.9912 0.0917 0.6766 0.8411 0.2464 ] Network output: [ 0.0001283 1 -0.0001381 2.764e-06 -1.241e-06 0.9998 2.083e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003226 Epoch 8515 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01015 0.9961 0.9911 -1.073e-07 4.816e-08 -0.007505 -8.084e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003409 -0.003227 -0.007527 0.005939 0.9699 0.9743 0.006571 0.8311 0.8233 0.0175 ] Network output: [ 0.9998 0.0003841 0.0006706 -1.03e-05 4.624e-06 -0.0007947 -7.762e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2003 -0.03432 -0.1696 0.1877 0.9835 0.9932 0.2243 0.4373 0.8703 0.7144 ] Network output: [ -0.009969 1.002 1.009 -2.786e-07 1.251e-07 0.00846 -2.1e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006282 0.0005259 0.004444 0.003499 0.9889 0.9919 0.006402 0.859 0.8943 0.0126 ] Network output: [ -0.0004264 0.002307 1.001 -3.227e-05 1.449e-05 0.9975 -2.432e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2127 0.09938 0.3422 0.1446 0.985 0.994 0.2134 0.4415 0.8769 0.7085 ] Network output: [ 0.004847 -0.02302 0.9944 1.947e-05 -8.739e-06 1.019 1.467e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1056 0.09325 0.1827 0.1996 0.9873 0.9919 0.1057 0.7523 0.865 0.3055 ] Network output: [ -0.004582 0.02183 1.004 2.073e-05 -9.306e-06 0.9835 1.562e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09168 0.08975 0.165 0.1956 0.9853 0.9912 0.0917 0.6766 0.8411 0.2464 ] Network output: [ 0.0001283 1 -0.000138 2.761e-06 -1.239e-06 0.9998 2.08e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003225 Epoch 8516 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01015 0.9961 0.9911 -1.077e-07 4.837e-08 -0.007505 -8.12e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003409 -0.003227 -0.007526 0.005939 0.9699 0.9743 0.006572 0.8311 0.8233 0.0175 ] Network output: [ 0.9998 0.0003837 0.0006702 -1.029e-05 4.619e-06 -0.000794 -7.753e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2003 -0.03432 -0.1696 0.1877 0.9835 0.9932 0.2243 0.4373 0.8703 0.7144 ] Network output: [ -0.009968 1.002 1.009 -2.788e-07 1.252e-07 0.008458 -2.101e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006283 0.0005259 0.004444 0.003498 0.9889 0.9919 0.006402 0.859 0.8943 0.01259 ] Network output: [ -0.0004261 0.002306 1.001 -3.223e-05 1.447e-05 0.9975 -2.429e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2127 0.09939 0.3422 0.1446 0.985 0.994 0.2134 0.4415 0.8769 0.7085 ] Network output: [ 0.004845 -0.02301 0.9944 1.945e-05 -8.73e-06 1.019 1.466e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1056 0.09325 0.1827 0.1996 0.9873 0.9919 0.1057 0.7522 0.865 0.3055 ] Network output: [ -0.00458 0.02182 1.004 2.071e-05 -9.296e-06 0.9835 1.561e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09169 0.08975 0.165 0.1956 0.9853 0.9912 0.0917 0.6766 0.8411 0.2464 ] Network output: [ 0.0001282 1 -0.0001378 2.758e-06 -1.238e-06 0.9998 2.078e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003223 Epoch 8517 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01015 0.9961 0.9911 -1.082e-07 4.858e-08 -0.007505 -8.155e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003409 -0.003227 -0.007526 0.005938 0.9699 0.9743 0.006572 0.8311 0.8232 0.0175 ] Network output: [ 0.9998 0.0003834 0.0006698 -1.028e-05 4.614e-06 -0.0007934 -7.745e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2003 -0.03433 -0.1695 0.1877 0.9835 0.9932 0.2243 0.4373 0.8703 0.7144 ] Network output: [ -0.009967 1.002 1.009 -2.79e-07 1.253e-07 0.008457 -2.103e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006283 0.000526 0.004444 0.003498 0.9889 0.9919 0.006403 0.859 0.8943 0.01259 ] Network output: [ -0.0004258 0.002305 1.001 -3.22e-05 1.446e-05 0.9975 -2.427e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2127 0.0994 0.3422 0.1446 0.985 0.994 0.2134 0.4414 0.8769 0.7085 ] Network output: [ 0.004844 -0.023 0.9944 1.943e-05 -8.721e-06 1.019 1.464e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1056 0.09326 0.1827 0.1996 0.9873 0.9919 0.1057 0.7522 0.865 0.3055 ] Network output: [ -0.004578 0.02181 1.004 2.069e-05 -9.287e-06 0.9835 1.559e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09169 0.08975 0.165 0.1956 0.9853 0.9912 0.0917 0.6766 0.8411 0.2464 ] Network output: [ 0.0001282 1 -0.0001377 2.755e-06 -1.237e-06 0.9998 2.076e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003221 Epoch 8518 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01015 0.9961 0.9911 -1.087e-07 4.879e-08 -0.007505 -8.19e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003409 -0.003227 -0.007525 0.005938 0.9699 0.9743 0.006572 0.8311 0.8232 0.0175 ] Network output: [ 0.9998 0.0003831 0.0006695 -1.027e-05 4.609e-06 -0.0007927 -7.737e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2003 -0.03433 -0.1695 0.1876 0.9835 0.9932 0.2243 0.4373 0.8703 0.7144 ] Network output: [ -0.009966 1.002 1.009 -2.792e-07 1.253e-07 0.008455 -2.104e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006284 0.0005261 0.004444 0.003498 0.9889 0.9919 0.006403 0.859 0.8943 0.01259 ] Network output: [ -0.0004256 0.002305 1.001 -3.216e-05 1.444e-05 0.9975 -2.424e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2128 0.0994 0.3422 0.1446 0.985 0.994 0.2135 0.4414 0.8769 0.7085 ] Network output: [ 0.004842 -0.02299 0.9944 1.94e-05 -8.712e-06 1.019 1.462e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1056 0.09326 0.1827 0.1996 0.9873 0.9919 0.1057 0.7522 0.865 0.3055 ] Network output: [ -0.004577 0.0218 1.004 2.066e-05 -9.277e-06 0.9835 1.557e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09169 0.08975 0.165 0.1956 0.9853 0.9912 0.0917 0.6766 0.8411 0.2464 ] Network output: [ 0.0001281 1 -0.0001375 2.752e-06 -1.235e-06 0.9998 2.074e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003219 Epoch 8519 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01015 0.9961 0.9911 -1.091e-07 4.9e-08 -0.007505 -8.226e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00341 -0.003227 -0.007524 0.005937 0.9699 0.9743 0.006572 0.8311 0.8232 0.0175 ] Network output: [ 0.9998 0.0003828 0.0006691 -1.025e-05 4.604e-06 -0.0007921 -7.728e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2003 -0.03433 -0.1695 0.1876 0.9835 0.9932 0.2243 0.4373 0.8703 0.7144 ] Network output: [ -0.009965 1.002 1.009 -2.794e-07 1.254e-07 0.008454 -2.105e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006284 0.0005262 0.004444 0.003497 0.9889 0.9919 0.006404 0.859 0.8943 0.01259 ] Network output: [ -0.0004253 0.002304 1.001 -3.213e-05 1.442e-05 0.9975 -2.421e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2128 0.09941 0.3422 0.1446 0.985 0.994 0.2135 0.4414 0.8769 0.7085 ] Network output: [ 0.00484 -0.02298 0.9944 1.938e-05 -8.702e-06 1.019 1.461e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1056 0.09327 0.1827 0.1996 0.9873 0.9919 0.1057 0.7522 0.865 0.3055 ] Network output: [ -0.004575 0.02179 1.004 2.064e-05 -9.267e-06 0.9835 1.556e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09169 0.08976 0.165 0.1956 0.9853 0.9912 0.09171 0.6765 0.8411 0.2465 ] Network output: [ 0.0001281 1 -0.0001373 2.749e-06 -1.234e-06 0.9998 2.072e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003218 Epoch 8520 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01015 0.9961 0.9911 -1.096e-07 4.921e-08 -0.007505 -8.261e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00341 -0.003227 -0.007523 0.005937 0.9699 0.9743 0.006573 0.8311 0.8232 0.01749 ] Network output: [ 0.9998 0.0003824 0.0006687 -1.024e-05 4.599e-06 -0.0007914 -7.72e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2003 -0.03433 -0.1695 0.1876 0.9835 0.9932 0.2243 0.4373 0.8703 0.7144 ] Network output: [ -0.009964 1.002 1.009 -2.795e-07 1.255e-07 0.008453 -2.107e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006285 0.0005263 0.004444 0.003497 0.9889 0.9919 0.006405 0.859 0.8943 0.01259 ] Network output: [ -0.000425 0.002303 1.001 -3.209e-05 1.441e-05 0.9975 -2.419e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2128 0.09941 0.3423 0.1446 0.985 0.994 0.2135 0.4414 0.8769 0.7085 ] Network output: [ 0.004839 -0.02298 0.9944 1.936e-05 -8.693e-06 1.019 1.459e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1056 0.09328 0.1827 0.1996 0.9873 0.9919 0.1057 0.7522 0.865 0.3055 ] Network output: [ -0.004573 0.02178 1.004 2.062e-05 -9.258e-06 0.9835 1.554e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0917 0.08976 0.165 0.1956 0.9853 0.9912 0.09171 0.6765 0.841 0.2465 ] Network output: [ 0.000128 1 -0.0001372 2.746e-06 -1.233e-06 0.9998 2.07e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003216 Epoch 8521 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01015 0.9961 0.9911 -1.101e-07 4.942e-08 -0.007505 -8.296e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00341 -0.003227 -0.007522 0.005936 0.9699 0.9743 0.006573 0.8311 0.8232 0.01749 ] Network output: [ 0.9998 0.0003821 0.0006683 -1.023e-05 4.594e-06 -0.0007908 -7.712e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2003 -0.03433 -0.1695 0.1876 0.9835 0.9932 0.2243 0.4372 0.8702 0.7144 ] Network output: [ -0.009963 1.002 1.009 -2.797e-07 1.256e-07 0.008451 -2.108e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006285 0.0005264 0.004444 0.003497 0.9889 0.9919 0.006405 0.859 0.8943 0.01259 ] Network output: [ -0.0004247 0.002302 1.001 -3.206e-05 1.439e-05 0.9975 -2.416e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2128 0.09942 0.3423 0.1446 0.985 0.994 0.2135 0.4414 0.8769 0.7085 ] Network output: [ 0.004837 -0.02297 0.9944 1.934e-05 -8.684e-06 1.019 1.458e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1056 0.09328 0.1827 0.1996 0.9873 0.9919 0.1057 0.7522 0.865 0.3055 ] Network output: [ -0.004572 0.02177 1.004 2.06e-05 -9.248e-06 0.9835 1.553e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0917 0.08976 0.165 0.1956 0.9853 0.9912 0.09171 0.6765 0.841 0.2465 ] Network output: [ 0.0001279 1 -0.000137 2.743e-06 -1.232e-06 0.9998 2.067e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003214 Epoch 8522 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01014 0.9961 0.9911 -1.105e-07 4.962e-08 -0.007505 -8.33e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00341 -0.003227 -0.007521 0.005936 0.9699 0.9743 0.006573 0.8311 0.8232 0.01749 ] Network output: [ 0.9998 0.0003818 0.000668 -1.022e-05 4.589e-06 -0.0007901 -7.704e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2003 -0.03433 -0.1695 0.1876 0.9835 0.9932 0.2243 0.4372 0.8702 0.7144 ] Network output: [ -0.009962 1.002 1.009 -2.799e-07 1.256e-07 0.00845 -2.109e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006286 0.0005265 0.004444 0.003496 0.9889 0.9919 0.006406 0.859 0.8943 0.01259 ] Network output: [ -0.0004245 0.002301 1.001 -3.203e-05 1.438e-05 0.9975 -2.414e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2128 0.09943 0.3423 0.1446 0.985 0.994 0.2135 0.4414 0.8769 0.7085 ] Network output: [ 0.004835 -0.02296 0.9944 1.932e-05 -8.675e-06 1.019 1.456e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1056 0.09329 0.1827 0.1996 0.9873 0.9919 0.1057 0.7521 0.865 0.3055 ] Network output: [ -0.00457 0.02176 1.004 2.058e-05 -9.239e-06 0.9835 1.551e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0917 0.08976 0.165 0.1956 0.9853 0.9912 0.09171 0.6765 0.841 0.2465 ] Network output: [ 0.0001279 1 -0.0001369 2.74e-06 -1.23e-06 0.9998 2.065e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003212 Epoch 8523 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01014 0.9961 0.9911 -1.11e-07 4.983e-08 -0.007505 -8.365e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00341 -0.003228 -0.00752 0.005935 0.9699 0.9743 0.006574 0.8311 0.8232 0.01749 ] Network output: [ 0.9998 0.0003815 0.0006676 -1.021e-05 4.584e-06 -0.0007895 -7.695e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2003 -0.03433 -0.1695 0.1876 0.9835 0.9932 0.2243 0.4372 0.8702 0.7143 ] Network output: [ -0.009961 1.002 1.009 -2.8e-07 1.257e-07 0.008448 -2.111e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006287 0.0005266 0.004444 0.003496 0.9889 0.9919 0.006406 0.859 0.8943 0.01259 ] Network output: [ -0.0004242 0.0023 1.001 -3.199e-05 1.436e-05 0.9975 -2.411e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2128 0.09943 0.3423 0.1446 0.985 0.994 0.2135 0.4414 0.8769 0.7085 ] Network output: [ 0.004834 -0.02295 0.9944 1.93e-05 -8.666e-06 1.019 1.455e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1056 0.09329 0.1827 0.1995 0.9873 0.9919 0.1057 0.7521 0.865 0.3055 ] Network output: [ -0.004568 0.02176 1.004 2.056e-05 -9.229e-06 0.9836 1.549e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0917 0.08977 0.165 0.1956 0.9853 0.9912 0.09172 0.6765 0.841 0.2465 ] Network output: [ 0.0001278 1 -0.0001367 2.737e-06 -1.229e-06 0.9998 2.063e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003211 Epoch 8524 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01014 0.9961 0.9911 -1.115e-07 5.004e-08 -0.007505 -8.4e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00341 -0.003228 -0.007519 0.005934 0.9699 0.9743 0.006574 0.8311 0.8232 0.01749 ] Network output: [ 0.9998 0.0003811 0.0006672 -1.02e-05 4.579e-06 -0.0007888 -7.687e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2004 -0.03434 -0.1695 0.1876 0.9835 0.9932 0.2243 0.4372 0.8702 0.7143 ] Network output: [ -0.00996 1.002 1.009 -2.802e-07 1.258e-07 0.008447 -2.112e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006287 0.0005267 0.004444 0.003495 0.9889 0.9919 0.006407 0.859 0.8943 0.01259 ] Network output: [ -0.0004239 0.0023 1.001 -3.196e-05 1.435e-05 0.9975 -2.408e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2128 0.09944 0.3423 0.1445 0.985 0.994 0.2135 0.4414 0.8769 0.7085 ] Network output: [ 0.004832 -0.02294 0.9944 1.928e-05 -8.656e-06 1.019 1.453e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1056 0.0933 0.1827 0.1995 0.9873 0.9919 0.1057 0.7521 0.865 0.3055 ] Network output: [ -0.004567 0.02175 1.004 2.054e-05 -9.22e-06 0.9836 1.548e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09171 0.08977 0.165 0.1956 0.9853 0.9912 0.09172 0.6764 0.841 0.2465 ] Network output: [ 0.0001278 1 -0.0001366 2.735e-06 -1.228e-06 0.9998 2.061e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003209 Epoch 8525 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01014 0.9961 0.9912 -1.119e-07 5.024e-08 -0.007505 -8.434e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00341 -0.003228 -0.007519 0.005934 0.9699 0.9743 0.006574 0.8311 0.8232 0.01749 ] Network output: [ 0.9998 0.0003808 0.0006668 -1.019e-05 4.574e-06 -0.0007882 -7.679e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2004 -0.03434 -0.1694 0.1876 0.9835 0.9932 0.2244 0.4372 0.8702 0.7143 ] Network output: [ -0.009959 1.002 1.009 -2.804e-07 1.259e-07 0.008446 -2.113e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006288 0.0005268 0.004444 0.003495 0.9889 0.9919 0.006407 0.859 0.8943 0.01259 ] Network output: [ -0.0004237 0.002299 1.001 -3.192e-05 1.433e-05 0.9975 -2.406e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2128 0.09944 0.3423 0.1445 0.985 0.994 0.2135 0.4414 0.8769 0.7085 ] Network output: [ 0.00483 -0.02293 0.9944 1.926e-05 -8.647e-06 1.019 1.452e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1056 0.0933 0.1827 0.1995 0.9873 0.9919 0.1057 0.7521 0.865 0.3055 ] Network output: [ -0.004565 0.02174 1.004 2.052e-05 -9.21e-06 0.9836 1.546e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09171 0.08977 0.165 0.1956 0.9853 0.9912 0.09172 0.6764 0.841 0.2465 ] Network output: [ 0.0001277 1 -0.0001364 2.732e-06 -1.226e-06 0.9998 2.059e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003207 Epoch 8526 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01014 0.9961 0.9912 -1.124e-07 5.045e-08 -0.007505 -8.469e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00341 -0.003228 -0.007518 0.005933 0.9699 0.9743 0.006574 0.831 0.8232 0.01749 ] Network output: [ 0.9998 0.0003805 0.0006665 -1.018e-05 4.569e-06 -0.0007875 -7.671e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2004 -0.03434 -0.1694 0.1876 0.9835 0.9932 0.2244 0.4372 0.8702 0.7143 ] Network output: [ -0.009958 1.002 1.009 -2.806e-07 1.26e-07 0.008444 -2.114e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006288 0.0005269 0.004444 0.003495 0.9889 0.9919 0.006408 0.8589 0.8942 0.01258 ] Network output: [ -0.0004234 0.002298 1.001 -3.189e-05 1.432e-05 0.9975 -2.403e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2128 0.09945 0.3423 0.1445 0.985 0.994 0.2135 0.4414 0.8769 0.7085 ] Network output: [ 0.004829 -0.02293 0.9944 1.924e-05 -8.638e-06 1.019 1.45e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1057 0.09331 0.1827 0.1995 0.9873 0.9919 0.1057 0.7521 0.865 0.3055 ] Network output: [ -0.004563 0.02173 1.004 2.049e-05 -9.201e-06 0.9836 1.544e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09171 0.08977 0.165 0.1956 0.9853 0.9912 0.09172 0.6764 0.841 0.2465 ] Network output: [ 0.0001276 1 -0.0001362 2.729e-06 -1.225e-06 0.9998 2.056e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003205 Epoch 8527 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01014 0.9961 0.9912 -1.128e-07 5.065e-08 -0.007505 -8.503e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00341 -0.003228 -0.007517 0.005933 0.9699 0.9743 0.006575 0.831 0.8232 0.01749 ] Network output: [ 0.9998 0.0003802 0.0006661 -1.017e-05 4.565e-06 -0.0007869 -7.663e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2004 -0.03434 -0.1694 0.1876 0.9835 0.9932 0.2244 0.4372 0.8702 0.7143 ] Network output: [ -0.009957 1.002 1.009 -2.807e-07 1.26e-07 0.008443 -2.116e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006289 0.000527 0.004443 0.003494 0.9889 0.9919 0.006408 0.8589 0.8942 0.01258 ] Network output: [ -0.0004231 0.002297 1.001 -3.185e-05 1.43e-05 0.9975 -2.401e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2128 0.09945 0.3423 0.1445 0.985 0.994 0.2135 0.4414 0.8769 0.7085 ] Network output: [ 0.004827 -0.02292 0.9944 1.922e-05 -8.629e-06 1.019 1.449e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1057 0.09331 0.1827 0.1995 0.9873 0.9919 0.1057 0.752 0.865 0.3055 ] Network output: [ -0.004562 0.02172 1.004 2.047e-05 -9.191e-06 0.9836 1.543e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09171 0.08977 0.165 0.1956 0.9853 0.9912 0.09172 0.6764 0.841 0.2465 ] Network output: [ 0.0001276 1 -0.0001361 2.726e-06 -1.224e-06 0.9998 2.054e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003204 Epoch 8528 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01014 0.9961 0.9912 -1.133e-07 5.086e-08 -0.007505 -8.537e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003411 -0.003228 -0.007516 0.005932 0.9699 0.9743 0.006575 0.831 0.8232 0.01749 ] Network output: [ 0.9998 0.0003799 0.0006657 -1.016e-05 4.56e-06 -0.0007862 -7.654e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2004 -0.03434 -0.1694 0.1876 0.9835 0.9932 0.2244 0.4372 0.8702 0.7143 ] Network output: [ -0.009956 1.002 1.009 -2.809e-07 1.261e-07 0.008442 -2.117e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006289 0.0005271 0.004443 0.003494 0.9889 0.9919 0.006409 0.8589 0.8942 0.01258 ] Network output: [ -0.0004228 0.002296 1.001 -3.182e-05 1.429e-05 0.9975 -2.398e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2129 0.09946 0.3423 0.1445 0.985 0.994 0.2136 0.4413 0.8769 0.7085 ] Network output: [ 0.004826 -0.02291 0.9944 1.92e-05 -8.62e-06 1.019 1.447e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1057 0.09332 0.1827 0.1995 0.9873 0.9919 0.1057 0.752 0.865 0.3055 ] Network output: [ -0.00456 0.02171 1.004 2.045e-05 -9.181e-06 0.9836 1.541e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09171 0.08978 0.165 0.1956 0.9853 0.9912 0.09173 0.6764 0.841 0.2465 ] Network output: [ 0.0001275 1 -0.0001359 2.723e-06 -1.222e-06 0.9998 2.052e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003202 Epoch 8529 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01014 0.9961 0.9912 -1.137e-07 5.106e-08 -0.007505 -8.571e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003411 -0.003228 -0.007515 0.005932 0.9699 0.9743 0.006575 0.831 0.8232 0.01748 ] Network output: [ 0.9998 0.0003795 0.0006653 -1.015e-05 4.555e-06 -0.0007856 -7.646e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2004 -0.03434 -0.1694 0.1876 0.9835 0.9932 0.2244 0.4372 0.8702 0.7143 ] Network output: [ -0.009955 1.002 1.009 -2.811e-07 1.262e-07 0.00844 -2.118e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00629 0.0005272 0.004443 0.003494 0.9889 0.9919 0.00641 0.8589 0.8942 0.01258 ] Network output: [ -0.0004226 0.002295 1.001 -3.179e-05 1.427e-05 0.9975 -2.396e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2129 0.09947 0.3423 0.1445 0.985 0.994 0.2136 0.4413 0.8769 0.7085 ] Network output: [ 0.004824 -0.0229 0.9944 1.918e-05 -8.611e-06 1.019 1.445e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1057 0.09333 0.1827 0.1995 0.9873 0.9919 0.1057 0.752 0.865 0.3055 ] Network output: [ -0.004558 0.0217 1.004 2.043e-05 -9.172e-06 0.9836 1.54e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09172 0.08978 0.165 0.1956 0.9853 0.9912 0.09173 0.6763 0.841 0.2465 ] Network output: [ 0.0001275 1 -0.0001358 2.72e-06 -1.221e-06 0.9998 2.05e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00032 Epoch 8530 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01013 0.9961 0.9912 -1.142e-07 5.126e-08 -0.007505 -8.605e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003411 -0.003229 -0.007514 0.005931 0.9699 0.9743 0.006576 0.831 0.8232 0.01748 ] Network output: [ 0.9998 0.0003792 0.000665 -1.013e-05 4.55e-06 -0.0007849 -7.638e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2004 -0.03434 -0.1694 0.1876 0.9835 0.9932 0.2244 0.4372 0.8702 0.7143 ] Network output: [ -0.009954 1.002 1.009 -2.812e-07 1.263e-07 0.008439 -2.119e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00629 0.0005273 0.004443 0.003493 0.9889 0.9919 0.00641 0.8589 0.8942 0.01258 ] Network output: [ -0.0004223 0.002295 1.001 -3.175e-05 1.425e-05 0.9975 -2.393e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2129 0.09947 0.3423 0.1445 0.985 0.994 0.2136 0.4413 0.8769 0.7084 ] Network output: [ 0.004822 -0.02289 0.9944 1.916e-05 -8.601e-06 1.019 1.444e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1057 0.09333 0.1827 0.1995 0.9873 0.9919 0.1057 0.752 0.865 0.3055 ] Network output: [ -0.004557 0.02169 1.004 2.041e-05 -9.162e-06 0.9836 1.538e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09172 0.08978 0.165 0.1956 0.9853 0.9912 0.09173 0.6763 0.841 0.2465 ] Network output: [ 0.0001274 1 -0.0001356 2.717e-06 -1.22e-06 0.9998 2.048e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003198 Epoch 8531 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01013 0.9961 0.9912 -1.146e-07 5.146e-08 -0.007505 -8.639e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003411 -0.003229 -0.007513 0.00593 0.9699 0.9743 0.006576 0.831 0.8232 0.01748 ] Network output: [ 0.9998 0.0003789 0.0006646 -1.012e-05 4.545e-06 -0.0007843 -7.63e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2004 -0.03434 -0.1694 0.1876 0.9835 0.9932 0.2244 0.4372 0.8702 0.7143 ] Network output: [ -0.009953 1.002 1.009 -2.814e-07 1.263e-07 0.008437 -2.121e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006291 0.0005273 0.004443 0.003493 0.9889 0.9919 0.006411 0.8589 0.8942 0.01258 ] Network output: [ -0.000422 0.002294 1.001 -3.172e-05 1.424e-05 0.9975 -2.39e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2129 0.09948 0.3423 0.1445 0.985 0.994 0.2136 0.4413 0.8769 0.7084 ] Network output: [ 0.004821 -0.02288 0.9944 1.914e-05 -8.592e-06 1.019 1.442e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1057 0.09334 0.1827 0.1995 0.9873 0.9919 0.1058 0.752 0.865 0.3055 ] Network output: [ -0.004555 0.02168 1.004 2.039e-05 -9.153e-06 0.9836 1.537e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09172 0.08978 0.165 0.1956 0.9853 0.9912 0.09173 0.6763 0.841 0.2465 ] Network output: [ 0.0001274 1 -0.0001354 2.714e-06 -1.219e-06 0.9998 2.046e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003197 Epoch 8532 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01013 0.9961 0.9912 -1.151e-07 5.166e-08 -0.007505 -8.673e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003411 -0.003229 -0.007513 0.00593 0.9699 0.9743 0.006576 0.831 0.8232 0.01748 ] Network output: [ 0.9998 0.0003786 0.0006642 -1.011e-05 4.54e-06 -0.0007836 -7.622e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2004 -0.03435 -0.1693 0.1876 0.9835 0.9932 0.2244 0.4371 0.8702 0.7143 ] Network output: [ -0.009952 1.002 1.009 -2.816e-07 1.264e-07 0.008436 -2.122e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006291 0.0005274 0.004443 0.003493 0.9889 0.9919 0.006411 0.8589 0.8942 0.01258 ] Network output: [ -0.0004218 0.002293 1.001 -3.168e-05 1.422e-05 0.9975 -2.388e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2129 0.09948 0.3423 0.1445 0.985 0.994 0.2136 0.4413 0.8769 0.7084 ] Network output: [ 0.004819 -0.02288 0.9944 1.912e-05 -8.583e-06 1.019 1.441e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1057 0.09334 0.1827 0.1995 0.9873 0.9919 0.1058 0.752 0.865 0.3055 ] Network output: [ -0.004554 0.02168 1.004 2.037e-05 -9.143e-06 0.9836 1.535e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09172 0.08979 0.165 0.1956 0.9853 0.9912 0.09174 0.6763 0.841 0.2465 ] Network output: [ 0.0001273 1 -0.0001353 2.712e-06 -1.217e-06 0.9998 2.043e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003195 Epoch 8533 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01013 0.9961 0.9912 -1.155e-07 5.187e-08 -0.007505 -8.707e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003411 -0.003229 -0.007512 0.005929 0.9699 0.9743 0.006576 0.831 0.8232 0.01748 ] Network output: [ 0.9998 0.0003783 0.0006638 -1.01e-05 4.535e-06 -0.000783 -7.613e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2004 -0.03435 -0.1693 0.1876 0.9835 0.9932 0.2244 0.4371 0.8702 0.7143 ] Network output: [ -0.009951 1.002 1.009 -2.817e-07 1.265e-07 0.008435 -2.123e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006292 0.0005275 0.004443 0.003492 0.9889 0.9919 0.006412 0.8589 0.8942 0.01258 ] Network output: [ -0.0004215 0.002292 1.001 -3.165e-05 1.421e-05 0.9975 -2.385e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2129 0.09949 0.3424 0.1445 0.985 0.994 0.2136 0.4413 0.8769 0.7084 ] Network output: [ 0.004817 -0.02287 0.9944 1.91e-05 -8.574e-06 1.019 1.439e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1057 0.09335 0.1827 0.1995 0.9873 0.9919 0.1058 0.7519 0.865 0.3055 ] Network output: [ -0.004552 0.02167 1.004 2.035e-05 -9.134e-06 0.9836 1.533e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09173 0.08979 0.165 0.1956 0.9853 0.9912 0.09174 0.6763 0.841 0.2465 ] Network output: [ 0.0001272 1 -0.0001351 2.709e-06 -1.216e-06 0.9998 2.041e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003193 Epoch 8534 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01013 0.9961 0.9912 -1.16e-07 5.207e-08 -0.007505 -8.74e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003411 -0.003229 -0.007511 0.005929 0.9699 0.9743 0.006577 0.831 0.8232 0.01748 ] Network output: [ 0.9998 0.0003779 0.0006635 -1.009e-05 4.53e-06 -0.0007824 -7.605e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2004 -0.03435 -0.1693 0.1876 0.9835 0.9932 0.2244 0.4371 0.8702 0.7143 ] Network output: [ -0.00995 1.002 1.009 -2.819e-07 1.266e-07 0.008433 -2.124e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006292 0.0005276 0.004443 0.003492 0.9889 0.9919 0.006412 0.8589 0.8942 0.01258 ] Network output: [ -0.0004212 0.002291 1.001 -3.162e-05 1.419e-05 0.9975 -2.383e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2129 0.0995 0.3424 0.1445 0.985 0.994 0.2136 0.4413 0.8769 0.7084 ] Network output: [ 0.004816 -0.02286 0.9944 1.908e-05 -8.565e-06 1.019 1.438e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1057 0.09335 0.1827 0.1995 0.9873 0.9919 0.1058 0.7519 0.8649 0.3055 ] Network output: [ -0.00455 0.02166 1.004 2.032e-05 -9.124e-06 0.9836 1.532e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09173 0.08979 0.165 0.1956 0.9853 0.9912 0.09174 0.6763 0.841 0.2465 ] Network output: [ 0.0001272 1 -0.000135 2.706e-06 -1.215e-06 0.9998 2.039e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003191 Epoch 8535 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01013 0.9961 0.9912 -1.164e-07 5.227e-08 -0.007504 -8.774e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003411 -0.003229 -0.00751 0.005928 0.9699 0.9743 0.006577 0.831 0.8232 0.01748 ] Network output: [ 0.9998 0.0003776 0.0006631 -1.008e-05 4.526e-06 -0.0007817 -7.597e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2005 -0.03435 -0.1693 0.1876 0.9835 0.9932 0.2245 0.4371 0.8702 0.7143 ] Network output: [ -0.009949 1.002 1.009 -2.821e-07 1.266e-07 0.008432 -2.126e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006293 0.0005277 0.004443 0.003492 0.9889 0.9919 0.006413 0.8589 0.8942 0.01258 ] Network output: [ -0.000421 0.002291 1.001 -3.158e-05 1.418e-05 0.9975 -2.38e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2129 0.0995 0.3424 0.1445 0.985 0.994 0.2136 0.4413 0.8769 0.7084 ] Network output: [ 0.004814 -0.02285 0.9944 1.906e-05 -8.556e-06 1.019 1.436e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1057 0.09336 0.1827 0.1995 0.9873 0.9919 0.1058 0.7519 0.8649 0.3055 ] Network output: [ -0.004549 0.02165 1.004 2.03e-05 -9.115e-06 0.9836 1.53e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09173 0.08979 0.165 0.1956 0.9853 0.9912 0.09174 0.6762 0.841 0.2465 ] Network output: [ 0.0001271 1 -0.0001348 2.703e-06 -1.213e-06 0.9998 2.037e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000319 Epoch 8536 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01013 0.9961 0.9912 -1.169e-07 5.246e-08 -0.007504 -8.807e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003412 -0.003229 -0.007509 0.005928 0.9699 0.9743 0.006577 0.831 0.8232 0.01748 ] Network output: [ 0.9999 0.0003773 0.0006627 -1.007e-05 4.521e-06 -0.0007811 -7.589e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2005 -0.03435 -0.1693 0.1876 0.9835 0.9932 0.2245 0.4371 0.8702 0.7143 ] Network output: [ -0.009948 1.002 1.009 -2.822e-07 1.267e-07 0.008431 -2.127e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006294 0.0005278 0.004443 0.003491 0.9889 0.9919 0.006413 0.8589 0.8942 0.01258 ] Network output: [ -0.0004207 0.00229 1.001 -3.155e-05 1.416e-05 0.9975 -2.378e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2129 0.09951 0.3424 0.1445 0.985 0.994 0.2136 0.4413 0.8769 0.7084 ] Network output: [ 0.004812 -0.02284 0.9944 1.904e-05 -8.547e-06 1.019 1.435e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1057 0.09336 0.1827 0.1995 0.9873 0.9919 0.1058 0.7519 0.8649 0.3055 ] Network output: [ -0.004547 0.02164 1.004 2.028e-05 -9.106e-06 0.9836 1.529e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09173 0.0898 0.165 0.1956 0.9853 0.9912 0.09175 0.6762 0.841 0.2465 ] Network output: [ 0.0001271 1 -0.0001347 2.7e-06 -1.212e-06 0.9998 2.035e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003188 Epoch 8537 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01013 0.9961 0.9912 -1.173e-07 5.266e-08 -0.007504 -8.84e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003412 -0.003229 -0.007508 0.005927 0.9699 0.9743 0.006578 0.831 0.8232 0.01747 ] Network output: [ 0.9999 0.000377 0.0006624 -1.006e-05 4.516e-06 -0.0007804 -7.581e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2005 -0.03435 -0.1693 0.1875 0.9835 0.9932 0.2245 0.4371 0.8702 0.7143 ] Network output: [ -0.009947 1.002 1.009 -2.824e-07 1.268e-07 0.008429 -2.128e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006294 0.0005279 0.004443 0.003491 0.9889 0.9919 0.006414 0.8589 0.8942 0.01257 ] Network output: [ -0.0004204 0.002289 1.001 -3.151e-05 1.415e-05 0.9975 -2.375e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2129 0.09951 0.3424 0.1445 0.985 0.994 0.2136 0.4413 0.8769 0.7084 ] Network output: [ 0.004811 -0.02283 0.9944 1.902e-05 -8.538e-06 1.019 1.433e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1057 0.09337 0.1827 0.1995 0.9873 0.9919 0.1058 0.7519 0.8649 0.3055 ] Network output: [ -0.004545 0.02163 1.004 2.026e-05 -9.096e-06 0.9836 1.527e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09173 0.0898 0.165 0.1956 0.9853 0.9912 0.09175 0.6762 0.841 0.2465 ] Network output: [ 0.000127 1 -0.0001345 2.697e-06 -1.211e-06 0.9998 2.033e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003186 Epoch 8538 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01012 0.9961 0.9912 -1.177e-07 5.286e-08 -0.007504 -8.874e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003412 -0.00323 -0.007507 0.005927 0.9699 0.9743 0.006578 0.831 0.8232 0.01747 ] Network output: [ 0.9999 0.0003767 0.000662 -1.005e-05 4.511e-06 -0.0007798 -7.573e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2005 -0.03435 -0.1693 0.1875 0.9835 0.9932 0.2245 0.4371 0.8702 0.7143 ] Network output: [ -0.009946 1.002 1.009 -2.825e-07 1.268e-07 0.008428 -2.129e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006295 0.000528 0.004443 0.003491 0.9889 0.9919 0.006415 0.8589 0.8942 0.01257 ] Network output: [ -0.0004202 0.002288 1.001 -3.148e-05 1.413e-05 0.9975 -2.372e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2129 0.09952 0.3424 0.1445 0.985 0.994 0.2137 0.4412 0.8769 0.7084 ] Network output: [ 0.004809 -0.02283 0.9944 1.9e-05 -8.529e-06 1.019 1.432e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1057 0.09338 0.1827 0.1995 0.9873 0.9919 0.1058 0.7518 0.8649 0.3055 ] Network output: [ -0.004544 0.02162 1.004 2.024e-05 -9.087e-06 0.9836 1.525e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09174 0.0898 0.165 0.1956 0.9853 0.9912 0.09175 0.6762 0.8409 0.2465 ] Network output: [ 0.000127 1 -0.0001343 2.694e-06 -1.21e-06 0.9998 2.031e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003185 Epoch 8539 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01012 0.9961 0.9912 -1.182e-07 5.306e-08 -0.007504 -8.907e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003412 -0.00323 -0.007507 0.005926 0.9699 0.9743 0.006578 0.831 0.8232 0.01747 ] Network output: [ 0.9999 0.0003763 0.0006616 -1.004e-05 4.506e-06 -0.0007791 -7.565e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2005 -0.03436 -0.1693 0.1875 0.9835 0.9932 0.2245 0.4371 0.8702 0.7143 ] Network output: [ -0.009945 1.002 1.009 -2.827e-07 1.269e-07 0.008427 -2.13e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006295 0.0005281 0.004443 0.00349 0.9889 0.9919 0.006415 0.8589 0.8942 0.01257 ] Network output: [ -0.0004199 0.002287 1.001 -3.145e-05 1.412e-05 0.9975 -2.37e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.213 0.09953 0.3424 0.1445 0.985 0.994 0.2137 0.4412 0.8769 0.7084 ] Network output: [ 0.004807 -0.02282 0.9944 1.898e-05 -8.519e-06 1.019 1.43e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1057 0.09338 0.1827 0.1995 0.9873 0.9919 0.1058 0.7518 0.8649 0.3055 ] Network output: [ -0.004542 0.02161 1.004 2.022e-05 -9.077e-06 0.9836 1.524e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09174 0.0898 0.165 0.1956 0.9853 0.9912 0.09175 0.6762 0.8409 0.2465 ] Network output: [ 0.0001269 1 -0.0001342 2.692e-06 -1.208e-06 0.9998 2.028e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003183 Epoch 8540 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01012 0.9961 0.9912 -1.186e-07 5.325e-08 -0.007504 -8.94e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003412 -0.00323 -0.007506 0.005926 0.9699 0.9743 0.006578 0.831 0.8232 0.01747 ] Network output: [ 0.9999 0.000376 0.0006613 -1.003e-05 4.501e-06 -0.0007785 -7.557e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2005 -0.03436 -0.1692 0.1875 0.9835 0.9932 0.2245 0.4371 0.8702 0.7143 ] Network output: [ -0.009944 1.002 1.009 -2.829e-07 1.27e-07 0.008425 -2.132e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006296 0.0005282 0.004443 0.00349 0.9889 0.9919 0.006416 0.8588 0.8942 0.01257 ] Network output: [ -0.0004196 0.002286 1.001 -3.141e-05 1.41e-05 0.9975 -2.367e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.213 0.09953 0.3424 0.1445 0.985 0.994 0.2137 0.4412 0.8769 0.7084 ] Network output: [ 0.004806 -0.02281 0.9944 1.896e-05 -8.51e-06 1.019 1.429e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1057 0.09339 0.1827 0.1995 0.9873 0.9919 0.1058 0.7518 0.8649 0.3055 ] Network output: [ -0.00454 0.02161 1.004 2.02e-05 -9.068e-06 0.9836 1.522e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09174 0.0898 0.165 0.1956 0.9853 0.9912 0.09175 0.6761 0.8409 0.2465 ] Network output: [ 0.0001268 1 -0.000134 2.689e-06 -1.207e-06 0.9998 2.026e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003181 Epoch 8541 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01012 0.9961 0.9912 -1.191e-07 5.345e-08 -0.007504 -8.973e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003412 -0.00323 -0.007505 0.005925 0.9699 0.9743 0.006579 0.8309 0.8232 0.01747 ] Network output: [ 0.9999 0.0003757 0.0006609 -1.002e-05 4.497e-06 -0.0007779 -7.548e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2005 -0.03436 -0.1692 0.1875 0.9835 0.9932 0.2245 0.4371 0.8702 0.7142 ] Network output: [ -0.009943 1.002 1.009 -2.83e-07 1.271e-07 0.008424 -2.133e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006296 0.0005283 0.004443 0.00349 0.9889 0.9919 0.006416 0.8588 0.8942 0.01257 ] Network output: [ -0.0004193 0.002286 1.001 -3.138e-05 1.409e-05 0.9975 -2.365e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.213 0.09954 0.3424 0.1445 0.985 0.994 0.2137 0.4412 0.8769 0.7084 ] Network output: [ 0.004804 -0.0228 0.9944 1.894e-05 -8.501e-06 1.019 1.427e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1057 0.09339 0.1827 0.1995 0.9873 0.9919 0.1058 0.7518 0.8649 0.3055 ] Network output: [ -0.004539 0.0216 1.004 2.018e-05 -9.058e-06 0.9836 1.521e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09174 0.08981 0.165 0.1956 0.9853 0.9912 0.09176 0.6761 0.8409 0.2465 ] Network output: [ 0.0001268 1 -0.0001339 2.686e-06 -1.206e-06 0.9998 2.024e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003179 Epoch 8542 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01012 0.9961 0.9912 -1.195e-07 5.365e-08 -0.007504 -9.006e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003412 -0.00323 -0.007504 0.005924 0.9699 0.9743 0.006579 0.8309 0.8232 0.01747 ] Network output: [ 0.9999 0.0003754 0.0006605 -1.001e-05 4.492e-06 -0.0007772 -7.54e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2005 -0.03436 -0.1692 0.1875 0.9835 0.9932 0.2245 0.4371 0.8702 0.7142 ] Network output: [ -0.009942 1.002 1.009 -2.832e-07 1.271e-07 0.008422 -2.134e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006297 0.0005284 0.004443 0.003489 0.9889 0.9919 0.006417 0.8588 0.8942 0.01257 ] Network output: [ -0.0004191 0.002285 1.001 -3.134e-05 1.407e-05 0.9975 -2.362e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.213 0.09954 0.3424 0.1445 0.985 0.994 0.2137 0.4412 0.8769 0.7084 ] Network output: [ 0.004803 -0.02279 0.9944 1.892e-05 -8.492e-06 1.019 1.426e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1058 0.0934 0.1827 0.1995 0.9873 0.9919 0.1058 0.7518 0.8649 0.3055 ] Network output: [ -0.004537 0.02159 1.004 2.016e-05 -9.049e-06 0.9836 1.519e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09175 0.08981 0.165 0.1956 0.9853 0.9912 0.09176 0.6761 0.8409 0.2465 ] Network output: [ 0.0001267 1 -0.0001337 2.683e-06 -1.204e-06 0.9998 2.022e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003178 Epoch 8543 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01012 0.9961 0.9912 -1.199e-07 5.384e-08 -0.007504 -9.038e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003412 -0.00323 -0.007503 0.005924 0.9699 0.9743 0.006579 0.8309 0.8232 0.01747 ] Network output: [ 0.9999 0.0003751 0.0006601 -9.995e-06 4.487e-06 -0.0007766 -7.532e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2005 -0.03436 -0.1692 0.1875 0.9835 0.9932 0.2245 0.437 0.8702 0.7142 ] Network output: [ -0.009941 1.002 1.009 -2.833e-07 1.272e-07 0.008421 -2.135e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006297 0.0005285 0.004443 0.003489 0.9889 0.9919 0.006417 0.8588 0.8942 0.01257 ] Network output: [ -0.0004188 0.002284 1.001 -3.131e-05 1.406e-05 0.9975 -2.36e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.213 0.09955 0.3424 0.1445 0.985 0.994 0.2137 0.4412 0.8768 0.7084 ] Network output: [ 0.004801 -0.02278 0.9944 1.89e-05 -8.483e-06 1.019 1.424e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1058 0.0934 0.1827 0.1995 0.9873 0.9919 0.1058 0.7518 0.8649 0.3055 ] Network output: [ -0.004535 0.02158 1.004 2.014e-05 -9.04e-06 0.9836 1.517e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09175 0.08981 0.165 0.1956 0.9853 0.9912 0.09176 0.6761 0.8409 0.2465 ] Network output: [ 0.0001267 1 -0.0001336 2.68e-06 -1.203e-06 0.9998 2.02e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003176 Epoch 8544 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01012 0.9961 0.9912 -1.204e-07 5.403e-08 -0.007504 -9.071e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003412 -0.00323 -0.007502 0.005923 0.9699 0.9743 0.006579 0.8309 0.8231 0.01747 ] Network output: [ 0.9999 0.0003748 0.0006598 -9.984e-06 4.482e-06 -0.0007759 -7.524e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2005 -0.03436 -0.1692 0.1875 0.9835 0.9932 0.2245 0.437 0.8702 0.7142 ] Network output: [ -0.00994 1.002 1.009 -2.835e-07 1.273e-07 0.00842 -2.136e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006298 0.0005286 0.004443 0.003489 0.9889 0.9919 0.006418 0.8588 0.8942 0.01257 ] Network output: [ -0.0004185 0.002283 1.001 -3.128e-05 1.404e-05 0.9975 -2.357e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.213 0.09955 0.3424 0.1445 0.985 0.994 0.2137 0.4412 0.8768 0.7084 ] Network output: [ 0.004799 -0.02278 0.9944 1.888e-05 -8.474e-06 1.019 1.423e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1058 0.09341 0.1827 0.1995 0.9873 0.9919 0.1058 0.7517 0.8649 0.3055 ] Network output: [ -0.004534 0.02157 1.004 2.011e-05 -9.03e-06 0.9836 1.516e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09175 0.08981 0.165 0.1956 0.9853 0.9912 0.09176 0.6761 0.8409 0.2465 ] Network output: [ 0.0001266 1 -0.0001334 2.677e-06 -1.202e-06 0.9998 2.018e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003174 Epoch 8545 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01011 0.9961 0.9912 -1.208e-07 5.423e-08 -0.007504 -9.103e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003413 -0.003231 -0.007501 0.005923 0.9699 0.9743 0.00658 0.8309 0.8231 0.01746 ] Network output: [ 0.9999 0.0003744 0.0006594 -9.973e-06 4.477e-06 -0.0007753 -7.516e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2005 -0.03436 -0.1692 0.1875 0.9835 0.9932 0.2246 0.437 0.8702 0.7142 ] Network output: [ -0.009939 1.002 1.009 -2.836e-07 1.273e-07 0.008418 -2.138e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006298 0.0005287 0.004443 0.003488 0.9889 0.9919 0.006418 0.8588 0.8942 0.01257 ] Network output: [ -0.0004183 0.002282 1.001 -3.124e-05 1.403e-05 0.9975 -2.355e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.213 0.09956 0.3424 0.1445 0.985 0.994 0.2137 0.4412 0.8768 0.7084 ] Network output: [ 0.004798 -0.02277 0.9944 1.886e-05 -8.465e-06 1.019 1.421e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1058 0.09342 0.1827 0.1995 0.9873 0.9919 0.1058 0.7517 0.8649 0.3055 ] Network output: [ -0.004532 0.02156 1.004 2.009e-05 -9.021e-06 0.9836 1.514e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09175 0.08982 0.165 0.1956 0.9853 0.9912 0.09177 0.676 0.8409 0.2465 ] Network output: [ 0.0001266 1 -0.0001333 2.674e-06 -1.201e-06 0.9998 2.016e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003173 Epoch 8546 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01011 0.9961 0.9912 -1.212e-07 5.442e-08 -0.007504 -9.136e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003413 -0.003231 -0.007501 0.005922 0.9699 0.9743 0.00658 0.8309 0.8231 0.01746 ] Network output: [ 0.9999 0.0003741 0.000659 -9.963e-06 4.473e-06 -0.0007747 -7.508e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2005 -0.03437 -0.1692 0.1875 0.9835 0.9932 0.2246 0.437 0.8702 0.7142 ] Network output: [ -0.009937 1.002 1.009 -2.838e-07 1.274e-07 0.008417 -2.139e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006299 0.0005288 0.004443 0.003488 0.9889 0.9919 0.006419 0.8588 0.8942 0.01257 ] Network output: [ -0.000418 0.002282 1.001 -3.121e-05 1.401e-05 0.9975 -2.352e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.213 0.09957 0.3425 0.1445 0.985 0.994 0.2137 0.4412 0.8768 0.7084 ] Network output: [ 0.004796 -0.02276 0.9944 1.884e-05 -8.456e-06 1.019 1.42e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1058 0.09342 0.1827 0.1995 0.9873 0.9919 0.1058 0.7517 0.8649 0.3055 ] Network output: [ -0.004531 0.02155 1.004 2.007e-05 -9.011e-06 0.9836 1.513e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09176 0.08982 0.165 0.1956 0.9853 0.9912 0.09177 0.676 0.8409 0.2465 ] Network output: [ 0.0001265 1 -0.0001331 2.672e-06 -1.199e-06 0.9998 2.013e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003171 Epoch 8547 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01011 0.9961 0.9912 -1.217e-07 5.461e-08 -0.007504 -9.168e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003413 -0.003231 -0.0075 0.005922 0.9699 0.9743 0.00658 0.8309 0.8231 0.01746 ] Network output: [ 0.9999 0.0003738 0.0006587 -9.952e-06 4.468e-06 -0.000774 -7.5e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2006 -0.03437 -0.1692 0.1875 0.9835 0.9932 0.2246 0.437 0.8702 0.7142 ] Network output: [ -0.009936 1.002 1.009 -2.839e-07 1.275e-07 0.008416 -2.14e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0063 0.0005288 0.004443 0.003488 0.9889 0.9919 0.00642 0.8588 0.8942 0.01256 ] Network output: [ -0.0004177 0.002281 1.001 -3.118e-05 1.4e-05 0.9975 -2.35e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.213 0.09957 0.3425 0.1445 0.985 0.994 0.2137 0.4412 0.8768 0.7083 ] Network output: [ 0.004794 -0.02275 0.9944 1.882e-05 -8.447e-06 1.019 1.418e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1058 0.09343 0.1827 0.1995 0.9873 0.9919 0.1059 0.7517 0.8649 0.3055 ] Network output: [ -0.004529 0.02155 1.004 2.005e-05 -9.002e-06 0.9836 1.511e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09176 0.08982 0.165 0.1956 0.9853 0.9912 0.09177 0.676 0.8409 0.2465 ] Network output: [ 0.0001264 1 -0.000133 2.669e-06 -1.198e-06 0.9998 2.011e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003169 Epoch 8548 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01011 0.9961 0.9912 -1.221e-07 5.481e-08 -0.007504 -9.2e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003413 -0.003231 -0.007499 0.005921 0.9699 0.9743 0.006581 0.8309 0.8231 0.01746 ] Network output: [ 0.9999 0.0003735 0.0006583 -9.941e-06 4.463e-06 -0.0007734 -7.492e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2006 -0.03437 -0.1691 0.1875 0.9835 0.9932 0.2246 0.437 0.8702 0.7142 ] Network output: [ -0.009935 1.002 1.009 -2.841e-07 1.275e-07 0.008414 -2.141e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0063 0.0005289 0.004443 0.003487 0.9889 0.9919 0.00642 0.8588 0.8942 0.01256 ] Network output: [ -0.0004175 0.00228 1.001 -3.114e-05 1.398e-05 0.9975 -2.347e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.213 0.09958 0.3425 0.1445 0.985 0.994 0.2138 0.4412 0.8768 0.7083 ] Network output: [ 0.004793 -0.02274 0.9944 1.88e-05 -8.438e-06 1.019 1.417e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1058 0.09343 0.1828 0.1995 0.9873 0.9919 0.1059 0.7517 0.8649 0.3055 ] Network output: [ -0.004527 0.02154 1.004 2.003e-05 -8.993e-06 0.9837 1.51e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09176 0.08982 0.165 0.1956 0.9853 0.9912 0.09177 0.676 0.8409 0.2465 ] Network output: [ 0.0001264 1 -0.0001328 2.666e-06 -1.197e-06 0.9998 2.009e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003167 Epoch 8549 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01011 0.9961 0.9912 -1.225e-07 5.5e-08 -0.007504 -9.232e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003413 -0.003231 -0.007498 0.005921 0.9699 0.9743 0.006581 0.8309 0.8231 0.01746 ] Network output: [ 0.9999 0.0003732 0.0006579 -9.93e-06 4.458e-06 -0.0007728 -7.484e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2006 -0.03437 -0.1691 0.1875 0.9835 0.9932 0.2246 0.437 0.8702 0.7142 ] Network output: [ -0.009934 1.002 1.009 -2.842e-07 1.276e-07 0.008413 -2.142e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006301 0.000529 0.004443 0.003487 0.9889 0.9919 0.006421 0.8588 0.8942 0.01256 ] Network output: [ -0.0004172 0.002279 1.001 -3.111e-05 1.397e-05 0.9975 -2.344e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2131 0.09958 0.3425 0.1445 0.985 0.994 0.2138 0.4411 0.8768 0.7083 ] Network output: [ 0.004791 -0.02274 0.9944 1.878e-05 -8.429e-06 1.019 1.415e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1058 0.09344 0.1828 0.1995 0.9873 0.9919 0.1059 0.7517 0.8649 0.3055 ] Network output: [ -0.004526 0.02153 1.004 2.001e-05 -8.983e-06 0.9837 1.508e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09176 0.08982 0.165 0.1956 0.9853 0.9912 0.09178 0.676 0.8409 0.2465 ] Network output: [ 0.0001263 1 -0.0001326 2.663e-06 -1.196e-06 0.9998 2.007e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003166 Epoch 8550 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01011 0.9961 0.9912 -1.229e-07 5.519e-08 -0.007504 -9.264e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003413 -0.003231 -0.007497 0.00592 0.9699 0.9743 0.006581 0.8309 0.8231 0.01746 ] Network output: [ 0.9999 0.0003729 0.0006576 -9.92e-06 4.453e-06 -0.0007721 -7.476e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2006 -0.03437 -0.1691 0.1875 0.9835 0.9932 0.2246 0.437 0.8702 0.7142 ] Network output: [ -0.009933 1.002 1.009 -2.844e-07 1.277e-07 0.008412 -2.143e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006301 0.0005291 0.004443 0.003487 0.9889 0.9919 0.006421 0.8588 0.8942 0.01256 ] Network output: [ -0.0004169 0.002278 1.001 -3.108e-05 1.395e-05 0.9975 -2.342e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2131 0.09959 0.3425 0.1445 0.985 0.994 0.2138 0.4411 0.8768 0.7083 ] Network output: [ 0.004789 -0.02273 0.9944 1.876e-05 -8.42e-06 1.019 1.414e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1058 0.09344 0.1828 0.1995 0.9873 0.9919 0.1059 0.7516 0.8649 0.3055 ] Network output: [ -0.004524 0.02152 1.004 1.999e-05 -8.974e-06 0.9837 1.506e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09176 0.08983 0.165 0.1956 0.9853 0.9912 0.09178 0.676 0.8409 0.2465 ] Network output: [ 0.0001263 1 -0.0001325 2.66e-06 -1.194e-06 0.9998 2.005e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003164 Epoch 8551 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01011 0.9961 0.9912 -1.234e-07 5.538e-08 -0.007504 -9.296e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003413 -0.003231 -0.007496 0.005919 0.9699 0.9743 0.006581 0.8309 0.8231 0.01746 ] Network output: [ 0.9999 0.0003725 0.0006572 -9.909e-06 4.449e-06 -0.0007715 -7.468e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2006 -0.03437 -0.1691 0.1875 0.9835 0.9932 0.2246 0.437 0.8702 0.7142 ] Network output: [ -0.009932 1.002 1.009 -2.845e-07 1.277e-07 0.00841 -2.144e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006302 0.0005292 0.004443 0.003486 0.9889 0.9919 0.006422 0.8588 0.8942 0.01256 ] Network output: [ -0.0004167 0.002277 1.001 -3.104e-05 1.394e-05 0.9975 -2.339e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2131 0.0996 0.3425 0.1445 0.985 0.994 0.2138 0.4411 0.8768 0.7083 ] Network output: [ 0.004788 -0.02272 0.9944 1.874e-05 -8.411e-06 1.019 1.412e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1058 0.09345 0.1828 0.1995 0.9873 0.9919 0.1059 0.7516 0.8649 0.3055 ] Network output: [ -0.004522 0.02151 1.004 1.997e-05 -8.965e-06 0.9837 1.505e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09177 0.08983 0.165 0.1956 0.9853 0.9912 0.09178 0.6759 0.8409 0.2465 ] Network output: [ 0.0001262 1 -0.0001323 2.658e-06 -1.193e-06 0.9998 2.003e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003162 Epoch 8552 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01011 0.9961 0.9912 -1.238e-07 5.557e-08 -0.007504 -9.328e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003413 -0.003231 -0.007495 0.005919 0.9699 0.9743 0.006582 0.8309 0.8231 0.01746 ] Network output: [ 0.9999 0.0003722 0.0006568 -9.899e-06 4.444e-06 -0.0007708 -7.46e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2006 -0.03437 -0.1691 0.1875 0.9835 0.9932 0.2246 0.437 0.8702 0.7142 ] Network output: [ -0.009931 1.002 1.009 -2.847e-07 1.278e-07 0.008409 -2.145e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006302 0.0005293 0.004443 0.003486 0.9889 0.9919 0.006422 0.8588 0.8942 0.01256 ] Network output: [ -0.0004164 0.002277 1.001 -3.101e-05 1.392e-05 0.9975 -2.337e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2131 0.0996 0.3425 0.1445 0.985 0.994 0.2138 0.4411 0.8768 0.7083 ] Network output: [ 0.004786 -0.02271 0.9944 1.872e-05 -8.402e-06 1.019 1.411e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1058 0.09345 0.1828 0.1995 0.9873 0.9919 0.1059 0.7516 0.8649 0.3055 ] Network output: [ -0.004521 0.0215 1.004 1.995e-05 -8.955e-06 0.9837 1.503e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09177 0.08983 0.165 0.1956 0.9853 0.9912 0.09178 0.6759 0.8409 0.2465 ] Network output: [ 0.0001262 1 -0.0001322 2.655e-06 -1.192e-06 0.9998 2.001e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003161 Epoch 8553 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0101 0.9961 0.9912 -1.242e-07 5.576e-08 -0.007504 -9.36e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003413 -0.003232 -0.007495 0.005918 0.9699 0.9743 0.006582 0.8309 0.8231 0.01745 ] Network output: [ 0.9999 0.0003719 0.0006565 -9.888e-06 4.439e-06 -0.0007702 -7.452e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2006 -0.03438 -0.1691 0.1875 0.9835 0.9932 0.2246 0.437 0.8702 0.7142 ] Network output: [ -0.00993 1.002 1.009 -2.848e-07 1.279e-07 0.008408 -2.147e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006303 0.0005294 0.004443 0.003486 0.9889 0.9919 0.006423 0.8588 0.8942 0.01256 ] Network output: [ -0.0004161 0.002276 1.001 -3.098e-05 1.391e-05 0.9975 -2.334e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2131 0.09961 0.3425 0.1445 0.985 0.994 0.2138 0.4411 0.8768 0.7083 ] Network output: [ 0.004785 -0.0227 0.9944 1.87e-05 -8.393e-06 1.019 1.409e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1058 0.09346 0.1828 0.1995 0.9873 0.9919 0.1059 0.7516 0.8649 0.3055 ] Network output: [ -0.004519 0.02149 1.004 1.993e-05 -8.946e-06 0.9837 1.502e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09177 0.08983 0.165 0.1956 0.9853 0.9912 0.09178 0.6759 0.8409 0.2465 ] Network output: [ 0.0001261 1 -0.000132 2.652e-06 -1.191e-06 0.9998 1.999e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003159 Epoch 8554 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0101 0.9961 0.9912 -1.246e-07 5.595e-08 -0.007504 -9.392e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003414 -0.003232 -0.007494 0.005918 0.9699 0.9743 0.006582 0.8309 0.8231 0.01745 ] Network output: [ 0.9999 0.0003716 0.0006561 -9.877e-06 4.434e-06 -0.0007696 -7.444e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2006 -0.03438 -0.1691 0.1875 0.9835 0.9932 0.2246 0.4369 0.8702 0.7142 ] Network output: [ -0.009929 1.002 1.009 -2.85e-07 1.279e-07 0.008406 -2.148e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006303 0.0005295 0.004443 0.003485 0.9889 0.9919 0.006423 0.8587 0.8942 0.01256 ] Network output: [ -0.0004159 0.002275 1.001 -3.094e-05 1.389e-05 0.9975 -2.332e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2131 0.09961 0.3425 0.1445 0.985 0.994 0.2138 0.4411 0.8768 0.7083 ] Network output: [ 0.004783 -0.02269 0.9944 1.868e-05 -8.385e-06 1.019 1.408e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1058 0.09347 0.1828 0.1995 0.9873 0.9919 0.1059 0.7516 0.8649 0.3055 ] Network output: [ -0.004517 0.02148 1.004 1.991e-05 -8.937e-06 0.9837 1.5e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09177 0.08984 0.165 0.1956 0.9853 0.9912 0.09179 0.6759 0.8409 0.2465 ] Network output: [ 0.000126 1 -0.0001319 2.649e-06 -1.189e-06 0.9998 1.996e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003157 Epoch 8555 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0101 0.9961 0.9912 -1.25e-07 5.613e-08 -0.007503 -9.423e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003414 -0.003232 -0.007493 0.005917 0.9699 0.9743 0.006583 0.8309 0.8231 0.01745 ] Network output: [ 0.9999 0.0003713 0.0006558 -9.867e-06 4.43e-06 -0.0007689 -7.436e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2006 -0.03438 -0.169 0.1875 0.9835 0.9932 0.2247 0.4369 0.8702 0.7142 ] Network output: [ -0.009928 1.002 1.009 -2.851e-07 1.28e-07 0.008405 -2.149e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006304 0.0005296 0.004443 0.003485 0.9889 0.9919 0.006424 0.8587 0.8942 0.01256 ] Network output: [ -0.0004156 0.002274 1.001 -3.091e-05 1.388e-05 0.9975 -2.329e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2131 0.09962 0.3425 0.1444 0.985 0.994 0.2138 0.4411 0.8768 0.7083 ] Network output: [ 0.004781 -0.02269 0.9944 1.866e-05 -8.376e-06 1.019 1.406e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1058 0.09347 0.1828 0.1995 0.9873 0.9919 0.1059 0.7515 0.8649 0.3054 ] Network output: [ -0.004516 0.02148 1.004 1.989e-05 -8.927e-06 0.9837 1.499e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09178 0.08984 0.165 0.1956 0.9853 0.9912 0.09179 0.6759 0.8409 0.2465 ] Network output: [ 0.000126 1 -0.0001317 2.646e-06 -1.188e-06 0.9998 1.994e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003155 Epoch 8556 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0101 0.9961 0.9912 -1.255e-07 5.632e-08 -0.007503 -9.455e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003414 -0.003232 -0.007492 0.005917 0.9699 0.9743 0.006583 0.8309 0.8231 0.01745 ] Network output: [ 0.9999 0.000371 0.0006554 -9.856e-06 4.425e-06 -0.0007683 -7.428e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2006 -0.03438 -0.169 0.1874 0.9835 0.9932 0.2247 0.4369 0.8702 0.7142 ] Network output: [ -0.009927 1.002 1.009 -2.853e-07 1.281e-07 0.008403 -2.15e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006304 0.0005297 0.004443 0.003485 0.9889 0.9919 0.006425 0.8587 0.8942 0.01256 ] Network output: [ -0.0004153 0.002273 1.001 -3.087e-05 1.386e-05 0.9975 -2.327e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2131 0.09963 0.3425 0.1444 0.985 0.994 0.2138 0.4411 0.8768 0.7083 ] Network output: [ 0.00478 -0.02268 0.9944 1.864e-05 -8.367e-06 1.019 1.405e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1058 0.09348 0.1828 0.1995 0.9873 0.9919 0.1059 0.7515 0.8648 0.3054 ] Network output: [ -0.004514 0.02147 1.004 1.987e-05 -8.918e-06 0.9837 1.497e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09178 0.08984 0.165 0.1956 0.9853 0.9912 0.09179 0.6758 0.8408 0.2466 ] Network output: [ 0.0001259 1 -0.0001316 2.643e-06 -1.187e-06 0.9998 1.992e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003154 Epoch 8557 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0101 0.9961 0.9912 -1.259e-07 5.651e-08 -0.007503 -9.486e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003414 -0.003232 -0.007491 0.005916 0.9699 0.9743 0.006583 0.8308 0.8231 0.01745 ] Network output: [ 0.9999 0.0003706 0.000655 -9.845e-06 4.42e-06 -0.0007677 -7.42e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2006 -0.03438 -0.169 0.1874 0.9835 0.9932 0.2247 0.4369 0.8702 0.7142 ] Network output: [ -0.009926 1.002 1.009 -2.854e-07 1.281e-07 0.008402 -2.151e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006305 0.0005298 0.004443 0.003484 0.9889 0.9919 0.006425 0.8587 0.8942 0.01256 ] Network output: [ -0.0004151 0.002273 1.001 -3.084e-05 1.385e-05 0.9975 -2.324e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2131 0.09963 0.3425 0.1444 0.985 0.994 0.2138 0.4411 0.8768 0.7083 ] Network output: [ 0.004778 -0.02267 0.9944 1.862e-05 -8.358e-06 1.019 1.403e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1058 0.09348 0.1828 0.1995 0.9873 0.9919 0.1059 0.7515 0.8648 0.3054 ] Network output: [ -0.004513 0.02146 1.004 1.984e-05 -8.909e-06 0.9837 1.496e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09178 0.08984 0.165 0.1956 0.9853 0.9912 0.09179 0.6758 0.8408 0.2466 ] Network output: [ 0.0001259 1 -0.0001314 2.641e-06 -1.185e-06 0.9998 1.99e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003152 Epoch 8558 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0101 0.9961 0.9912 -1.263e-07 5.669e-08 -0.007503 -9.517e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003414 -0.003232 -0.00749 0.005916 0.9699 0.9743 0.006583 0.8308 0.8231 0.01745 ] Network output: [ 0.9999 0.0003703 0.0006547 -9.835e-06 4.415e-06 -0.0007671 -7.412e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2006 -0.03438 -0.169 0.1874 0.9835 0.9932 0.2247 0.4369 0.8702 0.7141 ] Network output: [ -0.009925 1.002 1.009 -2.855e-07 1.282e-07 0.008401 -2.152e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006306 0.0005299 0.004443 0.003484 0.9889 0.9919 0.006426 0.8587 0.8942 0.01255 ] Network output: [ -0.0004148 0.002272 1.001 -3.081e-05 1.383e-05 0.9975 -2.322e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2131 0.09964 0.3425 0.1444 0.985 0.994 0.2138 0.4411 0.8768 0.7083 ] Network output: [ 0.004776 -0.02266 0.9944 1.86e-05 -8.349e-06 1.019 1.402e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1058 0.09349 0.1828 0.1995 0.9873 0.9919 0.1059 0.7515 0.8648 0.3054 ] Network output: [ -0.004511 0.02145 1.004 1.982e-05 -8.9e-06 0.9837 1.494e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09178 0.08985 0.165 0.1956 0.9853 0.9912 0.0918 0.6758 0.8408 0.2466 ] Network output: [ 0.0001258 1 -0.0001313 2.638e-06 -1.184e-06 0.9998 1.988e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000315 Epoch 8559 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0101 0.9961 0.9912 -1.267e-07 5.688e-08 -0.007503 -9.548e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003414 -0.003232 -0.007489 0.005915 0.9699 0.9743 0.006584 0.8308 0.8231 0.01745 ] Network output: [ 0.9999 0.00037 0.0006543 -9.824e-06 4.41e-06 -0.0007664 -7.404e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2007 -0.03438 -0.169 0.1874 0.9835 0.9932 0.2247 0.4369 0.8702 0.7141 ] Network output: [ -0.009924 1.002 1.009 -2.857e-07 1.283e-07 0.008399 -2.153e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006306 0.00053 0.004443 0.003484 0.9889 0.9919 0.006426 0.8587 0.8942 0.01255 ] Network output: [ -0.0004145 0.002271 1.001 -3.078e-05 1.382e-05 0.9975 -2.319e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2132 0.09964 0.3425 0.1444 0.985 0.994 0.2139 0.441 0.8768 0.7083 ] Network output: [ 0.004775 -0.02265 0.9944 1.858e-05 -8.34e-06 1.019 1.4e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1059 0.09349 0.1828 0.1995 0.9873 0.9919 0.1059 0.7515 0.8648 0.3054 ] Network output: [ -0.004509 0.02144 1.004 1.98e-05 -8.89e-06 0.9837 1.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09179 0.08985 0.165 0.1956 0.9853 0.9912 0.0918 0.6758 0.8408 0.2466 ] Network output: [ 0.0001258 1 -0.0001311 2.635e-06 -1.183e-06 0.9998 1.986e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003149 Epoch 8560 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0101 0.9961 0.9912 -1.271e-07 5.706e-08 -0.007503 -9.579e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003414 -0.003233 -0.007489 0.005914 0.9699 0.9743 0.006584 0.8308 0.8231 0.01745 ] Network output: [ 0.9999 0.0003697 0.0006539 -9.814e-06 4.406e-06 -0.0007658 -7.396e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2007 -0.03438 -0.169 0.1874 0.9835 0.9932 0.2247 0.4369 0.8702 0.7141 ] Network output: [ -0.009923 1.002 1.009 -2.858e-07 1.283e-07 0.008398 -2.154e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006307 0.0005301 0.004443 0.003483 0.9889 0.9919 0.006427 0.8587 0.8942 0.01255 ] Network output: [ -0.0004143 0.00227 1.001 -3.074e-05 1.38e-05 0.9975 -2.317e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2132 0.09965 0.3426 0.1444 0.985 0.994 0.2139 0.441 0.8768 0.7083 ] Network output: [ 0.004773 -0.02264 0.9944 1.856e-05 -8.331e-06 1.019 1.399e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1059 0.0935 0.1828 0.1995 0.9873 0.9919 0.1059 0.7515 0.8648 0.3054 ] Network output: [ -0.004508 0.02143 1.004 1.978e-05 -8.881e-06 0.9837 1.491e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09179 0.08985 0.165 0.1956 0.9853 0.9912 0.0918 0.6758 0.8408 0.2466 ] Network output: [ 0.0001257 1 -0.000131 2.632e-06 -1.182e-06 0.9998 1.984e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003147 Epoch 8561 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01009 0.9961 0.9912 -1.275e-07 5.725e-08 -0.007503 -9.61e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003414 -0.003233 -0.007488 0.005914 0.9699 0.9743 0.006584 0.8308 0.8231 0.01744 ] Network output: [ 0.9999 0.0003694 0.0006536 -9.803e-06 4.401e-06 -0.0007652 -7.388e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2007 -0.03439 -0.169 0.1874 0.9835 0.9932 0.2247 0.4369 0.8701 0.7141 ] Network output: [ -0.009922 1.002 1.009 -2.86e-07 1.284e-07 0.008397 -2.155e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006307 0.0005302 0.004443 0.003483 0.9889 0.9919 0.006427 0.8587 0.8942 0.01255 ] Network output: [ -0.000414 0.002269 1.001 -3.071e-05 1.379e-05 0.9975 -2.314e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2132 0.09965 0.3426 0.1444 0.985 0.994 0.2139 0.441 0.8768 0.7083 ] Network output: [ 0.004771 -0.02264 0.9944 1.854e-05 -8.322e-06 1.019 1.397e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1059 0.0935 0.1828 0.1995 0.9873 0.9919 0.1059 0.7514 0.8648 0.3054 ] Network output: [ -0.004506 0.02142 1.004 1.976e-05 -8.872e-06 0.9837 1.489e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09179 0.08985 0.165 0.1956 0.9853 0.9912 0.0918 0.6757 0.8408 0.2466 ] Network output: [ 0.0001256 1 -0.0001308 2.629e-06 -1.18e-06 0.9998 1.982e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003145 Epoch 8562 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01009 0.9961 0.9912 -1.279e-07 5.743e-08 -0.007503 -9.641e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003414 -0.003233 -0.007487 0.005913 0.9699 0.9743 0.006584 0.8308 0.8231 0.01744 ] Network output: [ 0.9999 0.0003691 0.0006532 -9.793e-06 4.396e-06 -0.0007645 -7.38e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2007 -0.03439 -0.169 0.1874 0.9835 0.9932 0.2247 0.4369 0.8701 0.7141 ] Network output: [ -0.009921 1.002 1.009 -2.861e-07 1.284e-07 0.008395 -2.156e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006308 0.0005303 0.004443 0.003483 0.9889 0.9919 0.006428 0.8587 0.8942 0.01255 ] Network output: [ -0.0004137 0.002268 1.001 -3.068e-05 1.377e-05 0.9975 -2.312e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2132 0.09966 0.3426 0.1444 0.985 0.994 0.2139 0.441 0.8768 0.7083 ] Network output: [ 0.00477 -0.02263 0.9944 1.852e-05 -8.313e-06 1.019 1.396e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1059 0.09351 0.1828 0.1994 0.9873 0.9919 0.1059 0.7514 0.8648 0.3054 ] Network output: [ -0.004504 0.02142 1.004 1.974e-05 -8.863e-06 0.9837 1.488e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09179 0.08985 0.165 0.1956 0.9853 0.9912 0.09181 0.6757 0.8408 0.2466 ] Network output: [ 0.0001256 1 -0.0001307 2.627e-06 -1.179e-06 0.9998 1.98e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003144 Epoch 8563 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01009 0.9961 0.9912 -1.283e-07 5.762e-08 -0.007503 -9.672e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003415 -0.003233 -0.007486 0.005913 0.9699 0.9743 0.006585 0.8308 0.8231 0.01744 ] Network output: [ 0.9999 0.0003688 0.0006528 -9.782e-06 4.392e-06 -0.0007639 -7.372e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2007 -0.03439 -0.1689 0.1874 0.9835 0.9932 0.2247 0.4369 0.8701 0.7141 ] Network output: [ -0.00992 1.002 1.009 -2.862e-07 1.285e-07 0.008394 -2.157e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006308 0.0005303 0.004443 0.003482 0.9889 0.9919 0.006428 0.8587 0.8941 0.01255 ] Network output: [ -0.0004135 0.002268 1.001 -3.064e-05 1.376e-05 0.9975 -2.309e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2132 0.09967 0.3426 0.1444 0.985 0.994 0.2139 0.441 0.8768 0.7082 ] Network output: [ 0.004768 -0.02262 0.9944 1.85e-05 -8.304e-06 1.019 1.394e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1059 0.09352 0.1828 0.1994 0.9873 0.9919 0.1059 0.7514 0.8648 0.3054 ] Network output: [ -0.004503 0.02141 1.004 1.972e-05 -8.853e-06 0.9837 1.486e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09179 0.08986 0.165 0.1956 0.9853 0.9912 0.09181 0.6757 0.8408 0.2466 ] Network output: [ 0.0001255 1 -0.0001305 2.624e-06 -1.178e-06 0.9998 1.977e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003142 Epoch 8564 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01009 0.9961 0.9912 -1.287e-07 5.78e-08 -0.007503 -9.703e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003415 -0.003233 -0.007485 0.005912 0.9699 0.9743 0.006585 0.8308 0.8231 0.01744 ] Network output: [ 0.9999 0.0003685 0.0006525 -9.772e-06 4.387e-06 -0.0007633 -7.364e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2007 -0.03439 -0.1689 0.1874 0.9835 0.9932 0.2247 0.4369 0.8701 0.7141 ] Network output: [ -0.009919 1.002 1.009 -2.864e-07 1.286e-07 0.008393 -2.158e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006309 0.0005304 0.004443 0.003482 0.9889 0.9919 0.006429 0.8587 0.8941 0.01255 ] Network output: [ -0.0004132 0.002267 1.001 -3.061e-05 1.374e-05 0.9975 -2.307e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2132 0.09967 0.3426 0.1444 0.985 0.994 0.2139 0.441 0.8768 0.7082 ] Network output: [ 0.004767 -0.02261 0.9944 1.848e-05 -8.296e-06 1.019 1.393e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1059 0.09352 0.1828 0.1994 0.9873 0.9919 0.106 0.7514 0.8648 0.3054 ] Network output: [ -0.004501 0.0214 1.004 1.97e-05 -8.844e-06 0.9837 1.485e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0918 0.08986 0.165 0.1956 0.9853 0.9912 0.09181 0.6757 0.8408 0.2466 ] Network output: [ 0.0001255 1 -0.0001304 2.621e-06 -1.177e-06 0.9998 1.975e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000314 Epoch 8565 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01009 0.9961 0.9912 -1.292e-07 5.798e-08 -0.007503 -9.733e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003415 -0.003233 -0.007484 0.005912 0.9699 0.9743 0.006585 0.8308 0.8231 0.01744 ] Network output: [ 0.9999 0.0003681 0.0006521 -9.761e-06 4.382e-06 -0.0007627 -7.356e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2007 -0.03439 -0.1689 0.1874 0.9835 0.9932 0.2248 0.4368 0.8701 0.7141 ] Network output: [ -0.009918 1.002 1.009 -2.865e-07 1.286e-07 0.008391 -2.159e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006309 0.0005305 0.004443 0.003482 0.9889 0.9919 0.00643 0.8587 0.8941 0.01255 ] Network output: [ -0.000413 0.002266 1.001 -3.058e-05 1.373e-05 0.9975 -2.304e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2132 0.09968 0.3426 0.1444 0.985 0.994 0.2139 0.441 0.8768 0.7082 ] Network output: [ 0.004765 -0.0226 0.9944 1.846e-05 -8.287e-06 1.019 1.391e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1059 0.09353 0.1828 0.1994 0.9873 0.9919 0.106 0.7514 0.8648 0.3054 ] Network output: [ -0.0045 0.02139 1.004 1.968e-05 -8.835e-06 0.9837 1.483e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0918 0.08986 0.165 0.1956 0.9853 0.9912 0.09181 0.6757 0.8408 0.2466 ] Network output: [ 0.0001254 1 -0.0001302 2.618e-06 -1.175e-06 0.9998 1.973e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003139 Epoch 8566 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01009 0.9961 0.9912 -1.296e-07 5.816e-08 -0.007503 -9.764e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003415 -0.003233 -0.007484 0.005911 0.9699 0.9743 0.006586 0.8308 0.8231 0.01744 ] Network output: [ 0.9999 0.0003678 0.0006518 -9.751e-06 4.377e-06 -0.000762 -7.348e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2007 -0.03439 -0.1689 0.1874 0.9835 0.9932 0.2248 0.4368 0.8701 0.7141 ] Network output: [ -0.009917 1.002 1.009 -2.867e-07 1.287e-07 0.00839 -2.16e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00631 0.0005306 0.004443 0.003481 0.9889 0.9919 0.00643 0.8587 0.8941 0.01255 ] Network output: [ -0.0004127 0.002265 1.001 -3.054e-05 1.371e-05 0.9975 -2.302e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2132 0.09968 0.3426 0.1444 0.985 0.994 0.2139 0.441 0.8768 0.7082 ] Network output: [ 0.004763 -0.0226 0.9944 1.844e-05 -8.278e-06 1.019 1.39e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1059 0.09353 0.1828 0.1994 0.9873 0.9919 0.106 0.7514 0.8648 0.3054 ] Network output: [ -0.004498 0.02138 1.004 1.966e-05 -8.826e-06 0.9837 1.482e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0918 0.08986 0.165 0.1956 0.9853 0.9912 0.09181 0.6757 0.8408 0.2466 ] Network output: [ 0.0001254 1 -0.0001301 2.616e-06 -1.174e-06 0.9998 1.971e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003137 Epoch 8567 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01009 0.9961 0.9912 -1.3e-07 5.834e-08 -0.007503 -9.794e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003415 -0.003233 -0.007483 0.005911 0.9699 0.9743 0.006586 0.8308 0.8231 0.01744 ] Network output: [ 0.9999 0.0003675 0.0006514 -9.74e-06 4.373e-06 -0.0007614 -7.34e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2007 -0.03439 -0.1689 0.1874 0.9835 0.9932 0.2248 0.4368 0.8701 0.7141 ] Network output: [ -0.009916 1.002 1.009 -2.868e-07 1.288e-07 0.008389 -2.161e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00631 0.0005307 0.004443 0.003481 0.9889 0.9919 0.006431 0.8587 0.8941 0.01255 ] Network output: [ -0.0004124 0.002264 1.001 -3.051e-05 1.37e-05 0.9975 -2.299e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2132 0.09969 0.3426 0.1444 0.985 0.994 0.2139 0.441 0.8768 0.7082 ] Network output: [ 0.004762 -0.02259 0.9944 1.842e-05 -8.269e-06 1.019 1.388e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1059 0.09354 0.1828 0.1994 0.9873 0.9919 0.106 0.7513 0.8648 0.3054 ] Network output: [ -0.004496 0.02137 1.004 1.964e-05 -8.816e-06 0.9837 1.48e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0918 0.08987 0.165 0.1956 0.9853 0.9912 0.09182 0.6756 0.8408 0.2466 ] Network output: [ 0.0001253 1 -0.0001299 2.613e-06 -1.173e-06 0.9998 1.969e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003135 Epoch 8568 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01009 0.9961 0.9912 -1.304e-07 5.852e-08 -0.007503 -9.825e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003415 -0.003234 -0.007482 0.00591 0.9699 0.9743 0.006586 0.8308 0.8231 0.01744 ] Network output: [ 0.9999 0.0003672 0.000651 -9.73e-06 4.368e-06 -0.0007608 -7.333e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2007 -0.0344 -0.1689 0.1874 0.9835 0.9932 0.2248 0.4368 0.8701 0.7141 ] Network output: [ -0.009915 1.002 1.009 -2.869e-07 1.288e-07 0.008387 -2.162e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006311 0.0005308 0.004443 0.003481 0.9889 0.9919 0.006431 0.8586 0.8941 0.01254 ] Network output: [ -0.0004122 0.002264 1.001 -3.048e-05 1.368e-05 0.9975 -2.297e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2132 0.0997 0.3426 0.1444 0.985 0.994 0.2139 0.441 0.8768 0.7082 ] Network output: [ 0.00476 -0.02258 0.9944 1.84e-05 -8.26e-06 1.019 1.387e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1059 0.09354 0.1828 0.1994 0.9873 0.9919 0.106 0.7513 0.8648 0.3054 ] Network output: [ -0.004495 0.02136 1.004 1.962e-05 -8.807e-06 0.9837 1.478e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09181 0.08987 0.165 0.1956 0.9853 0.9912 0.09182 0.6756 0.8408 0.2466 ] Network output: [ 0.0001253 1 -0.0001298 2.61e-06 -1.172e-06 0.9998 1.967e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003133 Epoch 8569 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01008 0.9961 0.9912 -1.308e-07 5.87e-08 -0.007503 -9.855e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003415 -0.003234 -0.007481 0.00591 0.9699 0.9743 0.006586 0.8308 0.8231 0.01744 ] Network output: [ 0.9999 0.0003669 0.0006507 -9.719e-06 4.363e-06 -0.0007602 -7.325e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2007 -0.0344 -0.1689 0.1874 0.9835 0.9932 0.2248 0.4368 0.8701 0.7141 ] Network output: [ -0.009914 1.002 1.009 -2.871e-07 1.289e-07 0.008386 -2.163e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006311 0.0005309 0.004442 0.00348 0.9889 0.9919 0.006432 0.8586 0.8941 0.01254 ] Network output: [ -0.0004119 0.002263 1.001 -3.044e-05 1.367e-05 0.9975 -2.294e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2132 0.0997 0.3426 0.1444 0.985 0.994 0.214 0.441 0.8768 0.7082 ] Network output: [ 0.004758 -0.02257 0.9944 1.838e-05 -8.251e-06 1.019 1.385e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1059 0.09355 0.1828 0.1994 0.9873 0.9919 0.106 0.7513 0.8648 0.3054 ] Network output: [ -0.004493 0.02135 1.004 1.96e-05 -8.798e-06 0.9837 1.477e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09181 0.08987 0.165 0.1956 0.9853 0.9912 0.09182 0.6756 0.8408 0.2466 ] Network output: [ 0.0001252 1 -0.0001296 2.607e-06 -1.17e-06 0.9998 1.965e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003132 Epoch 8570 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01008 0.9961 0.9912 -1.312e-07 5.888e-08 -0.007502 -9.885e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003415 -0.003234 -0.00748 0.005909 0.9699 0.9743 0.006587 0.8308 0.8231 0.01743 ] Network output: [ 0.9999 0.0003666 0.0006503 -9.709e-06 4.359e-06 -0.0007595 -7.317e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2007 -0.0344 -0.1689 0.1874 0.9835 0.9932 0.2248 0.4368 0.8701 0.7141 ] Network output: [ -0.009913 1.002 1.009 -2.872e-07 1.289e-07 0.008385 -2.164e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006312 0.000531 0.004442 0.00348 0.9889 0.9919 0.006432 0.8586 0.8941 0.01254 ] Network output: [ -0.0004116 0.002262 1.001 -3.041e-05 1.365e-05 0.9975 -2.292e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2133 0.09971 0.3426 0.1444 0.985 0.994 0.214 0.4409 0.8768 0.7082 ] Network output: [ 0.004757 -0.02256 0.9944 1.836e-05 -8.243e-06 1.019 1.384e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1059 0.09355 0.1828 0.1994 0.9873 0.9919 0.106 0.7513 0.8648 0.3054 ] Network output: [ -0.004491 0.02135 1.004 1.958e-05 -8.789e-06 0.9837 1.475e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09181 0.08987 0.165 0.1956 0.9853 0.9912 0.09182 0.6756 0.8408 0.2466 ] Network output: [ 0.0001251 1 -0.0001294 2.604e-06 -1.169e-06 0.9998 1.963e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000313 Epoch 8571 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01008 0.9961 0.9912 -1.316e-07 5.906e-08 -0.007502 -9.915e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003416 -0.003234 -0.007479 0.005908 0.9699 0.9743 0.006587 0.8308 0.8231 0.01743 ] Network output: [ 0.9999 0.0003663 0.00065 -9.698e-06 4.354e-06 -0.0007589 -7.309e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2008 -0.0344 -0.1688 0.1874 0.9835 0.9932 0.2248 0.4368 0.8701 0.7141 ] Network output: [ -0.009912 1.002 1.009 -2.873e-07 1.29e-07 0.008383 -2.165e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006313 0.0005311 0.004442 0.00348 0.9889 0.9919 0.006433 0.8586 0.8941 0.01254 ] Network output: [ -0.0004114 0.002261 1.001 -3.038e-05 1.364e-05 0.9975 -2.289e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2133 0.09971 0.3426 0.1444 0.985 0.994 0.214 0.4409 0.8768 0.7082 ] Network output: [ 0.004755 -0.02255 0.9944 1.834e-05 -8.234e-06 1.019 1.382e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1059 0.09356 0.1828 0.1994 0.9873 0.9919 0.106 0.7513 0.8648 0.3054 ] Network output: [ -0.00449 0.02134 1.004 1.956e-05 -8.78e-06 0.9837 1.474e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09181 0.08988 0.165 0.1956 0.9853 0.9912 0.09183 0.6756 0.8408 0.2466 ] Network output: [ 0.0001251 1 -0.0001293 2.602e-06 -1.168e-06 0.9998 1.961e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003128 Epoch 8572 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01008 0.9961 0.9912 -1.32e-07 5.924e-08 -0.007502 -9.945e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003416 -0.003234 -0.007478 0.005908 0.9699 0.9743 0.006587 0.8307 0.823 0.01743 ] Network output: [ 0.9999 0.000366 0.0006496 -9.688e-06 4.349e-06 -0.0007583 -7.301e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2008 -0.0344 -0.1688 0.1874 0.9835 0.9932 0.2248 0.4368 0.8701 0.7141 ] Network output: [ -0.009911 1.002 1.009 -2.875e-07 1.29e-07 0.008382 -2.166e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006313 0.0005312 0.004442 0.003479 0.9889 0.9919 0.006433 0.8586 0.8941 0.01254 ] Network output: [ -0.0004111 0.00226 1.001 -3.035e-05 1.362e-05 0.9975 -2.287e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2133 0.09972 0.3426 0.1444 0.985 0.994 0.214 0.4409 0.8768 0.7082 ] Network output: [ 0.004753 -0.02255 0.9944 1.832e-05 -8.225e-06 1.019 1.381e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1059 0.09356 0.1828 0.1994 0.9873 0.9919 0.106 0.7512 0.8648 0.3054 ] Network output: [ -0.004488 0.02133 1.004 1.954e-05 -8.771e-06 0.9838 1.472e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09182 0.08988 0.165 0.1956 0.9853 0.9912 0.09183 0.6755 0.8408 0.2466 ] Network output: [ 0.000125 1 -0.0001291 2.599e-06 -1.167e-06 0.9998 1.959e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003127 Epoch 8573 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01008 0.9961 0.9912 -1.324e-07 5.942e-08 -0.007502 -9.975e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003416 -0.003234 -0.007478 0.005907 0.9699 0.9743 0.006588 0.8307 0.823 0.01743 ] Network output: [ 0.9999 0.0003656 0.0006492 -9.677e-06 4.345e-06 -0.0007577 -7.293e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2008 -0.0344 -0.1688 0.1874 0.9835 0.9932 0.2248 0.4368 0.8701 0.7141 ] Network output: [ -0.00991 1.002 1.009 -2.876e-07 1.291e-07 0.008381 -2.167e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006314 0.0005313 0.004442 0.003479 0.9889 0.9919 0.006434 0.8586 0.8941 0.01254 ] Network output: [ -0.0004108 0.002259 1.001 -3.031e-05 1.361e-05 0.9975 -2.284e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2133 0.09973 0.3426 0.1444 0.985 0.994 0.214 0.4409 0.8768 0.7082 ] Network output: [ 0.004752 -0.02254 0.9944 1.83e-05 -8.216e-06 1.019 1.379e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1059 0.09357 0.1828 0.1994 0.9873 0.9919 0.106 0.7512 0.8648 0.3054 ] Network output: [ -0.004487 0.02132 1.004 1.952e-05 -8.761e-06 0.9838 1.471e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09182 0.08988 0.165 0.1956 0.9853 0.9912 0.09183 0.6755 0.8408 0.2466 ] Network output: [ 0.000125 1 -0.000129 2.596e-06 -1.166e-06 0.9998 1.957e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003125 Epoch 8574 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01008 0.9961 0.9912 -1.328e-07 5.96e-08 -0.007502 -1e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003416 -0.003234 -0.007477 0.005907 0.9699 0.9743 0.006588 0.8307 0.823 0.01743 ] Network output: [ 0.9999 0.0003653 0.0006489 -9.667e-06 4.34e-06 -0.000757 -7.285e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2008 -0.0344 -0.1688 0.1874 0.9835 0.9932 0.2248 0.4368 0.8701 0.7141 ] Network output: [ -0.009909 1.002 1.009 -2.877e-07 1.292e-07 0.008379 -2.168e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006314 0.0005314 0.004442 0.003479 0.9889 0.9919 0.006435 0.8586 0.8941 0.01254 ] Network output: [ -0.0004106 0.002259 1.001 -3.028e-05 1.359e-05 0.9975 -2.282e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2133 0.09973 0.3427 0.1444 0.985 0.994 0.214 0.4409 0.8768 0.7082 ] Network output: [ 0.00475 -0.02253 0.9944 1.828e-05 -8.207e-06 1.019 1.378e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1059 0.09358 0.1828 0.1994 0.9873 0.9919 0.106 0.7512 0.8648 0.3054 ] Network output: [ -0.004485 0.02131 1.004 1.95e-05 -8.752e-06 0.9838 1.469e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09182 0.08988 0.165 0.1956 0.9853 0.9912 0.09183 0.6755 0.8408 0.2466 ] Network output: [ 0.0001249 1 -0.0001289 2.593e-06 -1.164e-06 0.9998 1.954e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003123 Epoch 8575 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01008 0.9961 0.9912 -1.331e-07 5.977e-08 -0.007502 -1.003e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003416 -0.003234 -0.007476 0.005906 0.9699 0.9743 0.006588 0.8307 0.823 0.01743 ] Network output: [ 0.9999 0.000365 0.0006485 -9.656e-06 4.335e-06 -0.0007564 -7.277e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2008 -0.03441 -0.1688 0.1873 0.9835 0.9932 0.2249 0.4368 0.8701 0.7141 ] Network output: [ -0.009908 1.002 1.009 -2.878e-07 1.292e-07 0.008378 -2.169e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006315 0.0005315 0.004442 0.003478 0.9889 0.9919 0.006435 0.8586 0.8941 0.01254 ] Network output: [ -0.0004103 0.002258 1.001 -3.025e-05 1.358e-05 0.9975 -2.28e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2133 0.09974 0.3427 0.1444 0.985 0.994 0.214 0.4409 0.8768 0.7082 ] Network output: [ 0.004749 -0.02252 0.9944 1.826e-05 -8.199e-06 1.019 1.376e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1059 0.09358 0.1828 0.1994 0.9873 0.9919 0.106 0.7512 0.8648 0.3054 ] Network output: [ -0.004483 0.0213 1.004 1.948e-05 -8.743e-06 0.9838 1.468e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09182 0.08988 0.165 0.1956 0.9853 0.9912 0.09184 0.6755 0.8407 0.2466 ] Network output: [ 0.0001249 1 -0.0001287 2.591e-06 -1.163e-06 0.9998 1.952e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003122 Epoch 8576 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01008 0.9961 0.9912 -1.335e-07 5.995e-08 -0.007502 -1.006e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003416 -0.003235 -0.007475 0.005906 0.9699 0.9743 0.006588 0.8307 0.823 0.01743 ] Network output: [ 0.9999 0.0003647 0.0006482 -9.646e-06 4.33e-06 -0.0007558 -7.27e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2008 -0.03441 -0.1688 0.1873 0.9835 0.9932 0.2249 0.4367 0.8701 0.714 ] Network output: [ -0.009907 1.002 1.009 -2.88e-07 1.293e-07 0.008377 -2.17e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006315 0.0005316 0.004442 0.003478 0.9889 0.9919 0.006436 0.8586 0.8941 0.01254 ] Network output: [ -0.0004101 0.002257 1.001 -3.021e-05 1.356e-05 0.9976 -2.277e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2133 0.09974 0.3427 0.1444 0.985 0.994 0.214 0.4409 0.8768 0.7082 ] Network output: [ 0.004747 -0.02251 0.9944 1.824e-05 -8.19e-06 1.019 1.375e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.106 0.09359 0.1828 0.1994 0.9873 0.9919 0.106 0.7512 0.8648 0.3054 ] Network output: [ -0.004482 0.02129 1.004 1.945e-05 -8.734e-06 0.9838 1.466e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09182 0.08989 0.165 0.1956 0.9853 0.9912 0.09184 0.6755 0.8407 0.2466 ] Network output: [ 0.0001248 1 -0.0001286 2.588e-06 -1.162e-06 0.9998 1.95e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000312 Epoch 8577 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01007 0.9961 0.9912 -1.339e-07 6.012e-08 -0.007502 -1.009e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003416 -0.003235 -0.007474 0.005905 0.9699 0.9743 0.006589 0.8307 0.823 0.01743 ] Network output: [ 0.9999 0.0003644 0.0006478 -9.636e-06 4.326e-06 -0.0007552 -7.262e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2008 -0.03441 -0.1688 0.1873 0.9835 0.9932 0.2249 0.4367 0.8701 0.714 ] Network output: [ -0.009906 1.002 1.009 -2.881e-07 1.293e-07 0.008375 -2.171e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006316 0.0005317 0.004442 0.003478 0.9889 0.9919 0.006436 0.8586 0.8941 0.01254 ] Network output: [ -0.0004098 0.002256 1.001 -3.018e-05 1.355e-05 0.9976 -2.275e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2133 0.09975 0.3427 0.1444 0.985 0.994 0.214 0.4409 0.8768 0.7082 ] Network output: [ 0.004745 -0.02251 0.9944 1.822e-05 -8.181e-06 1.019 1.373e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.106 0.09359 0.1828 0.1994 0.9873 0.9919 0.106 0.7512 0.8648 0.3054 ] Network output: [ -0.00448 0.02129 1.004 1.943e-05 -8.725e-06 0.9838 1.465e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09183 0.08989 0.165 0.1956 0.9853 0.9912 0.09184 0.6755 0.8407 0.2466 ] Network output: [ 0.0001247 1 -0.0001284 2.585e-06 -1.161e-06 0.9998 1.948e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003118 Epoch 8578 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01007 0.9961 0.9912 -1.343e-07 6.03e-08 -0.007502 -1.012e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003416 -0.003235 -0.007473 0.005905 0.9699 0.9743 0.006589 0.8307 0.823 0.01742 ] Network output: [ 0.9999 0.0003641 0.0006474 -9.625e-06 4.321e-06 -0.0007546 -7.254e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2008 -0.03441 -0.1688 0.1873 0.9835 0.9932 0.2249 0.4367 0.8701 0.714 ] Network output: [ -0.009905 1.002 1.009 -2.882e-07 1.294e-07 0.008374 -2.172e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006316 0.0005318 0.004442 0.003477 0.9889 0.9919 0.006437 0.8586 0.8941 0.01254 ] Network output: [ -0.0004095 0.002255 1.001 -3.015e-05 1.354e-05 0.9976 -2.272e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2133 0.09975 0.3427 0.1444 0.985 0.994 0.214 0.4409 0.8768 0.7082 ] Network output: [ 0.004744 -0.0225 0.9944 1.82e-05 -8.172e-06 1.019 1.372e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.106 0.0936 0.1828 0.1994 0.9873 0.9919 0.106 0.7511 0.8648 0.3054 ] Network output: [ -0.004478 0.02128 1.004 1.941e-05 -8.716e-06 0.9838 1.463e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09183 0.08989 0.165 0.1956 0.9853 0.9912 0.09184 0.6754 0.8407 0.2466 ] Network output: [ 0.0001247 1 -0.0001283 2.582e-06 -1.159e-06 0.9998 1.946e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003117 Epoch 8579 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01007 0.9961 0.9912 -1.347e-07 6.047e-08 -0.007502 -1.015e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003416 -0.003235 -0.007473 0.005904 0.9699 0.9743 0.006589 0.8307 0.823 0.01742 ] Network output: [ 0.9999 0.0003638 0.0006471 -9.615e-06 4.316e-06 -0.000754 -7.246e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2008 -0.03441 -0.1687 0.1873 0.9835 0.9932 0.2249 0.4367 0.8701 0.714 ] Network output: [ -0.009904 1.002 1.009 -2.884e-07 1.295e-07 0.008373 -2.173e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006317 0.0005319 0.004442 0.003477 0.9889 0.9919 0.006437 0.8586 0.8941 0.01253 ] Network output: [ -0.0004093 0.002255 1.001 -3.012e-05 1.352e-05 0.9976 -2.27e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2133 0.09976 0.3427 0.1444 0.985 0.994 0.2141 0.4409 0.8768 0.7082 ] Network output: [ 0.004742 -0.02249 0.9944 1.818e-05 -8.164e-06 1.019 1.37e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.106 0.0936 0.1828 0.1994 0.9873 0.9919 0.106 0.7511 0.8647 0.3054 ] Network output: [ -0.004477 0.02127 1.004 1.939e-05 -8.707e-06 0.9838 1.462e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09183 0.08989 0.165 0.1956 0.9853 0.9912 0.09184 0.6754 0.8407 0.2466 ] Network output: [ 0.0001246 1 -0.0001281 2.58e-06 -1.158e-06 0.9998 1.944e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003115 Epoch 8580 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01007 0.9961 0.9912 -1.351e-07 6.065e-08 -0.007502 -1.018e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003417 -0.003235 -0.007472 0.005903 0.9699 0.9743 0.006589 0.8307 0.823 0.01742 ] Network output: [ 0.9999 0.0003635 0.0006467 -9.605e-06 4.312e-06 -0.0007533 -7.238e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2008 -0.03441 -0.1687 0.1873 0.9835 0.9932 0.2249 0.4367 0.8701 0.714 ] Network output: [ -0.009903 1.002 1.009 -2.885e-07 1.295e-07 0.008372 -2.174e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006317 0.0005319 0.004442 0.003477 0.9889 0.9919 0.006438 0.8586 0.8941 0.01253 ] Network output: [ -0.000409 0.002254 1.001 -3.008e-05 1.351e-05 0.9976 -2.267e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2134 0.09977 0.3427 0.1444 0.985 0.994 0.2141 0.4409 0.8768 0.7081 ] Network output: [ 0.00474 -0.02248 0.9944 1.817e-05 -8.155e-06 1.019 1.369e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.106 0.09361 0.1828 0.1994 0.9873 0.9919 0.106 0.7511 0.8647 0.3054 ] Network output: [ -0.004475 0.02126 1.004 1.937e-05 -8.698e-06 0.9838 1.46e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09183 0.0899 0.165 0.1956 0.9853 0.9912 0.09185 0.6754 0.8407 0.2466 ] Network output: [ 0.0001246 1 -0.000128 2.577e-06 -1.157e-06 0.9998 1.942e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003113 Epoch 8581 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01007 0.9961 0.9912 -1.355e-07 6.082e-08 -0.007502 -1.021e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003417 -0.003235 -0.007471 0.005903 0.9699 0.9743 0.00659 0.8307 0.823 0.01742 ] Network output: [ 0.9999 0.0003632 0.0006464 -9.594e-06 4.307e-06 -0.0007527 -7.23e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2008 -0.03441 -0.1687 0.1873 0.9835 0.9932 0.2249 0.4367 0.8701 0.714 ] Network output: [ -0.009902 1.002 1.009 -2.886e-07 1.296e-07 0.00837 -2.175e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006318 0.000532 0.004442 0.003476 0.9889 0.9919 0.006438 0.8586 0.8941 0.01253 ] Network output: [ -0.0004087 0.002253 1.001 -3.005e-05 1.349e-05 0.9976 -2.265e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2134 0.09977 0.3427 0.1444 0.985 0.994 0.2141 0.4408 0.8768 0.7081 ] Network output: [ 0.004739 -0.02247 0.9944 1.815e-05 -8.146e-06 1.019 1.368e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.106 0.09361 0.1828 0.1994 0.9873 0.9919 0.1061 0.7511 0.8647 0.3054 ] Network output: [ -0.004474 0.02125 1.004 1.935e-05 -8.688e-06 0.9838 1.459e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09184 0.0899 0.165 0.1956 0.9853 0.9912 0.09185 0.6754 0.8407 0.2466 ] Network output: [ 0.0001245 1 -0.0001278 2.574e-06 -1.156e-06 0.9998 1.94e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003112 Epoch 8582 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01007 0.9961 0.9912 -1.359e-07 6.099e-08 -0.007502 -1.024e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003417 -0.003235 -0.00747 0.005902 0.9699 0.9743 0.00659 0.8307 0.823 0.01742 ] Network output: [ 0.9999 0.0003629 0.000646 -9.584e-06 4.303e-06 -0.0007521 -7.223e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2008 -0.03441 -0.1687 0.1873 0.9835 0.9932 0.2249 0.4367 0.8701 0.714 ] Network output: [ -0.009901 1.002 1.009 -2.887e-07 1.296e-07 0.008369 -2.176e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006318 0.0005321 0.004442 0.003476 0.9889 0.9919 0.006439 0.8585 0.8941 0.01253 ] Network output: [ -0.0004085 0.002252 1.001 -3.002e-05 1.348e-05 0.9976 -2.262e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2134 0.09978 0.3427 0.1444 0.985 0.994 0.2141 0.4408 0.8768 0.7081 ] Network output: [ 0.004737 -0.02246 0.9944 1.813e-05 -8.138e-06 1.019 1.366e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.106 0.09362 0.1828 0.1994 0.9873 0.9919 0.1061 0.7511 0.8647 0.3054 ] Network output: [ -0.004472 0.02124 1.004 1.933e-05 -8.679e-06 0.9838 1.457e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09184 0.0899 0.165 0.1956 0.9853 0.9912 0.09185 0.6754 0.8407 0.2466 ] Network output: [ 0.0001245 1 -0.0001277 2.571e-06 -1.154e-06 0.9998 1.938e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000311 Epoch 8583 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01007 0.9961 0.9912 -1.362e-07 6.117e-08 -0.007501 -1.027e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003417 -0.003236 -0.007469 0.005902 0.9699 0.9743 0.00659 0.8307 0.823 0.01742 ] Network output: [ 0.9999 0.0003625 0.0006457 -9.574e-06 4.298e-06 -0.0007515 -7.215e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2009 -0.03442 -0.1687 0.1873 0.9835 0.9932 0.2249 0.4367 0.8701 0.714 ] Network output: [ -0.0099 1.002 1.009 -2.888e-07 1.297e-07 0.008368 -2.177e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006319 0.0005322 0.004442 0.003476 0.9889 0.9919 0.00644 0.8585 0.8941 0.01253 ] Network output: [ -0.0004082 0.002251 1.001 -2.999e-05 1.346e-05 0.9976 -2.26e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2134 0.09978 0.3427 0.1444 0.985 0.994 0.2141 0.4408 0.8768 0.7081 ] Network output: [ 0.004736 -0.02246 0.9943 1.811e-05 -8.129e-06 1.019 1.365e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.106 0.09363 0.1828 0.1994 0.9873 0.9919 0.1061 0.7511 0.8647 0.3054 ] Network output: [ -0.00447 0.02123 1.004 1.931e-05 -8.67e-06 0.9838 1.455e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09184 0.0899 0.165 0.1956 0.9853 0.9912 0.09185 0.6753 0.8407 0.2466 ] Network output: [ 0.0001244 1 -0.0001275 2.569e-06 -1.153e-06 0.9998 1.936e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003108 Epoch 8584 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01007 0.9961 0.9912 -1.366e-07 6.134e-08 -0.007501 -1.03e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003417 -0.003236 -0.007468 0.005901 0.9699 0.9743 0.006591 0.8307 0.823 0.01742 ] Network output: [ 0.9999 0.0003622 0.0006453 -9.563e-06 4.293e-06 -0.0007509 -7.207e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2009 -0.03442 -0.1687 0.1873 0.9835 0.9932 0.2249 0.4367 0.8701 0.714 ] Network output: [ -0.009899 1.002 1.009 -2.89e-07 1.297e-07 0.008366 -2.178e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00632 0.0005323 0.004442 0.003475 0.9889 0.9919 0.00644 0.8585 0.8941 0.01253 ] Network output: [ -0.000408 0.002251 1.001 -2.995e-05 1.345e-05 0.9976 -2.257e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2134 0.09979 0.3427 0.1444 0.985 0.994 0.2141 0.4408 0.8768 0.7081 ] Network output: [ 0.004734 -0.02245 0.9943 1.809e-05 -8.12e-06 1.019 1.363e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.106 0.09363 0.1828 0.1994 0.9873 0.9919 0.1061 0.751 0.8647 0.3054 ] Network output: [ -0.004469 0.02123 1.004 1.929e-05 -8.661e-06 0.9838 1.454e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09184 0.08991 0.165 0.1956 0.9853 0.9912 0.09186 0.6753 0.8407 0.2466 ] Network output: [ 0.0001244 1 -0.0001274 2.566e-06 -1.152e-06 0.9998 1.934e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003107 Epoch 8585 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01006 0.9961 0.9912 -1.37e-07 6.151e-08 -0.007501 -1.033e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003417 -0.003236 -0.007467 0.005901 0.9699 0.9743 0.006591 0.8307 0.823 0.01742 ] Network output: [ 0.9999 0.0003619 0.0006449 -9.553e-06 4.289e-06 -0.0007503 -7.199e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2009 -0.03442 -0.1687 0.1873 0.9835 0.9932 0.225 0.4367 0.8701 0.714 ] Network output: [ -0.009898 1.002 1.009 -2.891e-07 1.298e-07 0.008365 -2.179e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00632 0.0005324 0.004442 0.003475 0.9889 0.9919 0.006441 0.8585 0.8941 0.01253 ] Network output: [ -0.0004077 0.00225 1.001 -2.992e-05 1.343e-05 0.9976 -2.255e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2134 0.0998 0.3427 0.1444 0.985 0.994 0.2141 0.4408 0.8767 0.7081 ] Network output: [ 0.004732 -0.02244 0.9943 1.807e-05 -8.111e-06 1.019 1.362e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.106 0.09364 0.1828 0.1994 0.9873 0.9919 0.1061 0.751 0.8647 0.3054 ] Network output: [ -0.004467 0.02122 1.004 1.927e-05 -8.652e-06 0.9838 1.452e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09185 0.08991 0.165 0.1956 0.9853 0.9912 0.09186 0.6753 0.8407 0.2466 ] Network output: [ 0.0001243 1 -0.0001272 2.563e-06 -1.151e-06 0.9998 1.932e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003105 Epoch 8586 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01006 0.9961 0.9912 -1.374e-07 6.168e-08 -0.007501 -1.035e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003417 -0.003236 -0.007467 0.0059 0.9699 0.9743 0.006591 0.8307 0.823 0.01741 ] Network output: [ 0.9999 0.0003616 0.0006446 -9.543e-06 4.284e-06 -0.0007496 -7.192e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2009 -0.03442 -0.1686 0.1873 0.9835 0.9932 0.225 0.4367 0.8701 0.714 ] Network output: [ -0.009897 1.002 1.009 -2.892e-07 1.298e-07 0.008364 -2.18e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006321 0.0005325 0.004442 0.003475 0.9889 0.9919 0.006441 0.8585 0.8941 0.01253 ] Network output: [ -0.0004074 0.002249 1.001 -2.989e-05 1.342e-05 0.9976 -2.253e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2134 0.0998 0.3427 0.1444 0.985 0.994 0.2141 0.4408 0.8767 0.7081 ] Network output: [ 0.004731 -0.02243 0.9943 1.805e-05 -8.103e-06 1.019 1.36e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.106 0.09364 0.1828 0.1994 0.9873 0.9919 0.1061 0.751 0.8647 0.3054 ] Network output: [ -0.004465 0.02121 1.004 1.925e-05 -8.643e-06 0.9838 1.451e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09185 0.08991 0.165 0.1956 0.9853 0.9912 0.09186 0.6753 0.8407 0.2466 ] Network output: [ 0.0001242 1 -0.0001271 2.56e-06 -1.149e-06 0.9998 1.93e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003103 Epoch 8587 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01006 0.9961 0.9912 -1.378e-07 6.185e-08 -0.007501 -1.038e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003417 -0.003236 -0.007466 0.0059 0.9699 0.9743 0.006591 0.8307 0.823 0.01741 ] Network output: [ 0.9999 0.0003613 0.0006442 -9.532e-06 4.279e-06 -0.000749 -7.184e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2009 -0.03442 -0.1686 0.1873 0.9835 0.9932 0.225 0.4366 0.8701 0.714 ] Network output: [ -0.009896 1.002 1.009 -2.893e-07 1.299e-07 0.008362 -2.18e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006321 0.0005326 0.004442 0.003474 0.9889 0.9919 0.006442 0.8585 0.8941 0.01253 ] Network output: [ -0.0004072 0.002248 1.001 -2.986e-05 1.34e-05 0.9976 -2.25e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2134 0.09981 0.3428 0.1443 0.985 0.994 0.2141 0.4408 0.8767 0.7081 ] Network output: [ 0.004729 -0.02242 0.9943 1.803e-05 -8.094e-06 1.019 1.359e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.106 0.09365 0.1828 0.1994 0.9873 0.9919 0.1061 0.751 0.8647 0.3054 ] Network output: [ -0.004464 0.0212 1.004 1.923e-05 -8.634e-06 0.9838 1.449e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09185 0.08991 0.165 0.1956 0.9853 0.9912 0.09186 0.6753 0.8407 0.2466 ] Network output: [ 0.0001242 1 -0.0001269 2.558e-06 -1.148e-06 0.9998 1.928e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003102 Epoch 8588 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01006 0.9961 0.9912 -1.381e-07 6.202e-08 -0.007501 -1.041e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003417 -0.003236 -0.007465 0.005899 0.9699 0.9743 0.006592 0.8306 0.823 0.01741 ] Network output: [ 0.9999 0.000361 0.0006439 -9.522e-06 4.275e-06 -0.0007484 -7.176e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2009 -0.03442 -0.1686 0.1873 0.9835 0.9932 0.225 0.4366 0.8701 0.714 ] Network output: [ -0.009895 1.002 1.009 -2.894e-07 1.299e-07 0.008361 -2.181e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006322 0.0005327 0.004442 0.003474 0.9889 0.9919 0.006442 0.8585 0.8941 0.01253 ] Network output: [ -0.0004069 0.002247 1.001 -2.982e-05 1.339e-05 0.9976 -2.248e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2134 0.09981 0.3428 0.1443 0.985 0.994 0.2141 0.4408 0.8767 0.7081 ] Network output: [ 0.004727 -0.02242 0.9943 1.801e-05 -8.085e-06 1.019 1.357e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.106 0.09365 0.1828 0.1994 0.9873 0.9919 0.1061 0.751 0.8647 0.3054 ] Network output: [ -0.004462 0.02119 1.004 1.921e-05 -8.625e-06 0.9838 1.448e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09185 0.08992 0.165 0.1956 0.9853 0.9912 0.09187 0.6753 0.8407 0.2466 ] Network output: [ 0.0001241 1 -0.0001268 2.555e-06 -1.147e-06 0.9998 1.926e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00031 Epoch 8589 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01006 0.9961 0.9912 -1.385e-07 6.219e-08 -0.007501 -1.044e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003418 -0.003236 -0.007464 0.005899 0.9699 0.9743 0.006592 0.8306 0.823 0.01741 ] Network output: [ 0.9999 0.0003607 0.0006435 -9.512e-06 4.27e-06 -0.0007478 -7.168e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2009 -0.03442 -0.1686 0.1873 0.9835 0.9932 0.225 0.4366 0.8701 0.714 ] Network output: [ -0.009894 1.002 1.009 -2.896e-07 1.3e-07 0.00836 -2.182e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006322 0.0005328 0.004442 0.003474 0.9889 0.9919 0.006443 0.8585 0.8941 0.01253 ] Network output: [ -0.0004066 0.002246 1.001 -2.979e-05 1.337e-05 0.9976 -2.245e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2134 0.09982 0.3428 0.1443 0.985 0.994 0.2141 0.4408 0.8767 0.7081 ] Network output: [ 0.004726 -0.02241 0.9943 1.799e-05 -8.077e-06 1.019 1.356e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.106 0.09366 0.1829 0.1994 0.9873 0.9919 0.1061 0.751 0.8647 0.3054 ] Network output: [ -0.004461 0.02118 1.004 1.919e-05 -8.616e-06 0.9838 1.446e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09185 0.08992 0.165 0.1956 0.9853 0.9912 0.09187 0.6752 0.8407 0.2466 ] Network output: [ 0.0001241 1 -0.0001266 2.552e-06 -1.146e-06 0.9998 1.923e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003098 Epoch 8590 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01006 0.9961 0.9912 -1.389e-07 6.236e-08 -0.007501 -1.047e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003418 -0.003236 -0.007463 0.005898 0.9699 0.9743 0.006592 0.8306 0.823 0.01741 ] Network output: [ 0.9999 0.0003604 0.0006432 -9.501e-06 4.266e-06 -0.0007472 -7.161e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2009 -0.03443 -0.1686 0.1873 0.9835 0.9932 0.225 0.4366 0.8701 0.714 ] Network output: [ -0.009893 1.002 1.009 -2.897e-07 1.3e-07 0.008358 -2.183e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006323 0.0005329 0.004442 0.003473 0.9889 0.9919 0.006443 0.8585 0.8941 0.01252 ] Network output: [ -0.0004064 0.002246 1.001 -2.976e-05 1.336e-05 0.9976 -2.243e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2135 0.09983 0.3428 0.1443 0.985 0.994 0.2142 0.4408 0.8767 0.7081 ] Network output: [ 0.004724 -0.0224 0.9943 1.797e-05 -8.068e-06 1.019 1.354e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.106 0.09366 0.1829 0.1994 0.9873 0.9919 0.1061 0.7509 0.8647 0.3054 ] Network output: [ -0.004459 0.02117 1.004 1.917e-05 -8.607e-06 0.9838 1.445e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09186 0.08992 0.165 0.1956 0.9853 0.9912 0.09187 0.6752 0.8407 0.2466 ] Network output: [ 0.000124 1 -0.0001265 2.55e-06 -1.145e-06 0.9998 1.921e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003097 Epoch 8591 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01006 0.9961 0.9913 -1.393e-07 6.253e-08 -0.007501 -1.05e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003418 -0.003237 -0.007462 0.005897 0.9699 0.9743 0.006593 0.8306 0.823 0.01741 ] Network output: [ 0.9999 0.0003601 0.0006428 -9.491e-06 4.261e-06 -0.0007466 -7.153e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2009 -0.03443 -0.1686 0.1873 0.9835 0.9932 0.225 0.4366 0.8701 0.714 ] Network output: [ -0.009892 1.002 1.009 -2.898e-07 1.301e-07 0.008357 -2.184e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006323 0.000533 0.004442 0.003473 0.9889 0.9919 0.006444 0.8585 0.8941 0.01252 ] Network output: [ -0.0004061 0.002245 1.001 -2.973e-05 1.335e-05 0.9976 -2.24e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2135 0.09983 0.3428 0.1443 0.985 0.994 0.2142 0.4407 0.8767 0.7081 ] Network output: [ 0.004723 -0.02239 0.9943 1.795e-05 -8.059e-06 1.019 1.353e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.106 0.09367 0.1829 0.1994 0.9873 0.9919 0.1061 0.7509 0.8647 0.3054 ] Network output: [ -0.004457 0.02117 1.004 1.915e-05 -8.598e-06 0.9838 1.443e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09186 0.08992 0.165 0.1956 0.9853 0.9912 0.09187 0.6752 0.8407 0.2466 ] Network output: [ 0.000124 1 -0.0001263 2.547e-06 -1.143e-06 0.9998 1.919e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003095 Epoch 8592 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01006 0.9961 0.9913 -1.396e-07 6.269e-08 -0.007501 -1.052e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003418 -0.003237 -0.007462 0.005897 0.9699 0.9743 0.006593 0.8306 0.823 0.01741 ] Network output: [ 0.9999 0.0003598 0.0006425 -9.481e-06 4.256e-06 -0.000746 -7.145e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2009 -0.03443 -0.1686 0.1873 0.9835 0.9932 0.225 0.4366 0.8701 0.714 ] Network output: [ -0.009891 1.002 1.009 -2.899e-07 1.302e-07 0.008356 -2.185e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006324 0.0005331 0.004442 0.003473 0.9889 0.9919 0.006444 0.8585 0.8941 0.01252 ] Network output: [ -0.0004059 0.002244 1.001 -2.97e-05 1.333e-05 0.9976 -2.238e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2135 0.09984 0.3428 0.1443 0.985 0.994 0.2142 0.4407 0.8767 0.7081 ] Network output: [ 0.004721 -0.02238 0.9943 1.793e-05 -8.051e-06 1.019 1.351e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.106 0.09368 0.1829 0.1994 0.9873 0.9919 0.1061 0.7509 0.8647 0.3054 ] Network output: [ -0.004456 0.02116 1.004 1.913e-05 -8.589e-06 0.9838 1.442e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09186 0.08992 0.165 0.1956 0.9853 0.9912 0.09187 0.6752 0.8407 0.2466 ] Network output: [ 0.0001239 1 -0.0001262 2.544e-06 -1.142e-06 0.9998 1.917e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003093 Epoch 8593 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01005 0.9961 0.9913 -1.4e-07 6.286e-08 -0.007501 -1.055e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003418 -0.003237 -0.007461 0.005896 0.9699 0.9743 0.006593 0.8306 0.823 0.01741 ] Network output: [ 0.9999 0.0003595 0.0006421 -9.471e-06 4.252e-06 -0.0007454 -7.137e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2009 -0.03443 -0.1686 0.1873 0.9835 0.9932 0.225 0.4366 0.8701 0.714 ] Network output: [ -0.00989 1.002 1.009 -2.9e-07 1.302e-07 0.008354 -2.186e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006324 0.0005332 0.004442 0.003472 0.9889 0.9919 0.006445 0.8585 0.8941 0.01252 ] Network output: [ -0.0004056 0.002243 1.001 -2.966e-05 1.332e-05 0.9976 -2.236e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2135 0.09984 0.3428 0.1443 0.985 0.994 0.2142 0.4407 0.8767 0.7081 ] Network output: [ 0.004719 -0.02238 0.9943 1.791e-05 -8.042e-06 1.019 1.35e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1061 0.09368 0.1829 0.1994 0.9873 0.9919 0.1061 0.7509 0.8647 0.3054 ] Network output: [ -0.004454 0.02115 1.004 1.911e-05 -8.58e-06 0.9838 1.44e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09186 0.08993 0.165 0.1956 0.9853 0.9912 0.09188 0.6752 0.8406 0.2467 ] Network output: [ 0.0001239 1 -0.000126 2.541e-06 -1.141e-06 0.9998 1.915e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003092 Epoch 8594 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01005 0.9961 0.9913 -1.404e-07 6.303e-08 -0.0075 -1.058e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003418 -0.003237 -0.00746 0.005896 0.9699 0.9743 0.006593 0.8306 0.823 0.01741 ] Network output: [ 0.9999 0.0003592 0.0006418 -9.46e-06 4.247e-06 -0.0007447 -7.13e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2009 -0.03443 -0.1685 0.1873 0.9835 0.9932 0.225 0.4366 0.8701 0.7139 ] Network output: [ -0.009889 1.002 1.009 -2.901e-07 1.303e-07 0.008353 -2.187e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006325 0.0005333 0.004442 0.003472 0.9889 0.9919 0.006446 0.8585 0.8941 0.01252 ] Network output: [ -0.0004053 0.002242 1.001 -2.963e-05 1.33e-05 0.9976 -2.233e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2135 0.09985 0.3428 0.1443 0.985 0.994 0.2142 0.4407 0.8767 0.7081 ] Network output: [ 0.004718 -0.02237 0.9943 1.789e-05 -8.034e-06 1.019 1.349e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1061 0.09369 0.1829 0.1994 0.9873 0.9919 0.1061 0.7509 0.8647 0.3054 ] Network output: [ -0.004453 0.02114 1.004 1.909e-05 -8.571e-06 0.9838 1.439e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09187 0.08993 0.165 0.1956 0.9853 0.9912 0.09188 0.6751 0.8406 0.2467 ] Network output: [ 0.0001238 1 -0.0001259 2.539e-06 -1.14e-06 0.9998 1.913e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000309 Epoch 8595 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01005 0.9961 0.9913 -1.408e-07 6.319e-08 -0.0075 -1.061e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003418 -0.003237 -0.007459 0.005895 0.9699 0.9743 0.006594 0.8306 0.823 0.0174 ] Network output: [ 0.9999 0.0003589 0.0006414 -9.45e-06 4.243e-06 -0.0007441 -7.122e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.201 -0.03443 -0.1685 0.1872 0.9835 0.9932 0.2251 0.4366 0.8701 0.7139 ] Network output: [ -0.009888 1.002 1.009 -2.903e-07 1.303e-07 0.008352 -2.187e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006325 0.0005334 0.004442 0.003472 0.9889 0.9919 0.006446 0.8585 0.8941 0.01252 ] Network output: [ -0.0004051 0.002242 1.001 -2.96e-05 1.329e-05 0.9976 -2.231e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2135 0.09985 0.3428 0.1443 0.985 0.994 0.2142 0.4407 0.8767 0.7081 ] Network output: [ 0.004716 -0.02236 0.9943 1.788e-05 -8.025e-06 1.019 1.347e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1061 0.09369 0.1829 0.1994 0.9873 0.9919 0.1061 0.7508 0.8647 0.3054 ] Network output: [ -0.004451 0.02113 1.004 1.907e-05 -8.562e-06 0.9838 1.437e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09187 0.08993 0.165 0.1956 0.9853 0.9912 0.09188 0.6751 0.8406 0.2467 ] Network output: [ 0.0001237 1 -0.0001258 2.536e-06 -1.139e-06 0.9998 1.911e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003088 Epoch 8596 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01005 0.9961 0.9913 -1.411e-07 6.336e-08 -0.0075 -1.064e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003418 -0.003237 -0.007458 0.005895 0.9699 0.9743 0.006594 0.8306 0.823 0.0174 ] Network output: [ 0.9999 0.0003586 0.000641 -9.44e-06 4.238e-06 -0.0007435 -7.114e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.201 -0.03443 -0.1685 0.1872 0.9835 0.9932 0.2251 0.4366 0.8701 0.7139 ] Network output: [ -0.009887 1.002 1.009 -2.904e-07 1.304e-07 0.008351 -2.188e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006326 0.0005335 0.004442 0.003471 0.9889 0.9919 0.006447 0.8584 0.8941 0.01252 ] Network output: [ -0.0004048 0.002241 1.001 -2.957e-05 1.327e-05 0.9976 -2.228e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2135 0.09986 0.3428 0.1443 0.985 0.994 0.2142 0.4407 0.8767 0.708 ] Network output: [ 0.004714 -0.02235 0.9943 1.786e-05 -8.016e-06 1.019 1.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1061 0.0937 0.1829 0.1994 0.9873 0.9919 0.1061 0.7508 0.8647 0.3054 ] Network output: [ -0.004449 0.02112 1.004 1.905e-05 -8.553e-06 0.9838 1.436e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09187 0.08993 0.165 0.1956 0.9853 0.9912 0.09188 0.6751 0.8406 0.2467 ] Network output: [ 0.0001237 1 -0.0001256 2.533e-06 -1.137e-06 0.9998 1.909e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003087 Epoch 8597 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01005 0.9961 0.9913 -1.415e-07 6.352e-08 -0.0075 -1.066e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003418 -0.003237 -0.007457 0.005894 0.9699 0.9743 0.006594 0.8306 0.823 0.0174 ] Network output: [ 0.9999 0.0003582 0.0006407 -9.43e-06 4.233e-06 -0.0007429 -7.107e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.201 -0.03443 -0.1685 0.1872 0.9835 0.9932 0.2251 0.4366 0.8701 0.7139 ] Network output: [ -0.009886 1.002 1.009 -2.905e-07 1.304e-07 0.008349 -2.189e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006327 0.0005335 0.004442 0.003471 0.9889 0.9919 0.006447 0.8584 0.8941 0.01252 ] Network output: [ -0.0004046 0.00224 1.001 -2.954e-05 1.326e-05 0.9976 -2.226e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2135 0.09987 0.3428 0.1443 0.985 0.994 0.2142 0.4407 0.8767 0.708 ] Network output: [ 0.004713 -0.02234 0.9943 1.784e-05 -8.008e-06 1.019 1.344e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1061 0.0937 0.1829 0.1994 0.9873 0.9919 0.1062 0.7508 0.8647 0.3054 ] Network output: [ -0.004448 0.02112 1.004 1.903e-05 -8.544e-06 0.9839 1.434e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09187 0.08994 0.165 0.1956 0.9853 0.9912 0.09189 0.6751 0.8406 0.2467 ] Network output: [ 0.0001236 1 -0.0001255 2.531e-06 -1.136e-06 0.9998 1.907e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003085 Epoch 8598 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01005 0.9961 0.9913 -1.419e-07 6.369e-08 -0.0075 -1.069e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003419 -0.003238 -0.007457 0.005894 0.9699 0.9743 0.006594 0.8306 0.823 0.0174 ] Network output: [ 0.9999 0.0003579 0.0006403 -9.42e-06 4.229e-06 -0.0007423 -7.099e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.201 -0.03444 -0.1685 0.1872 0.9835 0.9932 0.2251 0.4365 0.8701 0.7139 ] Network output: [ -0.009885 1.002 1.009 -2.906e-07 1.305e-07 0.008348 -2.19e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006327 0.0005336 0.004442 0.003471 0.9889 0.9919 0.006448 0.8584 0.8941 0.01252 ] Network output: [ -0.0004043 0.002239 1.001 -2.95e-05 1.325e-05 0.9976 -2.223e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2135 0.09987 0.3428 0.1443 0.985 0.994 0.2142 0.4407 0.8767 0.708 ] Network output: [ 0.004711 -0.02233 0.9943 1.782e-05 -7.999e-06 1.019 1.343e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1061 0.09371 0.1829 0.1994 0.9873 0.9919 0.1062 0.7508 0.8647 0.3054 ] Network output: [ -0.004446 0.02111 1.004 1.901e-05 -8.535e-06 0.9839 1.433e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09188 0.08994 0.165 0.1956 0.9853 0.9912 0.09189 0.6751 0.8406 0.2467 ] Network output: [ 0.0001236 1 -0.0001253 2.528e-06 -1.135e-06 0.9998 1.905e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003083 Epoch 8599 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01005 0.9961 0.9913 -1.422e-07 6.385e-08 -0.0075 -1.072e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003419 -0.003238 -0.007456 0.005893 0.9699 0.9743 0.006595 0.8306 0.8229 0.0174 ] Network output: [ 0.9999 0.0003576 0.00064 -9.409e-06 4.224e-06 -0.0007417 -7.091e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.201 -0.03444 -0.1685 0.1872 0.9835 0.9932 0.2251 0.4365 0.8701 0.7139 ] Network output: [ -0.009884 1.002 1.009 -2.907e-07 1.305e-07 0.008347 -2.191e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006328 0.0005337 0.004442 0.00347 0.9889 0.9919 0.006448 0.8584 0.8941 0.01252 ] Network output: [ -0.000404 0.002238 1.001 -2.947e-05 1.323e-05 0.9976 -2.221e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2135 0.09988 0.3428 0.1443 0.985 0.994 0.2142 0.4407 0.8767 0.708 ] Network output: [ 0.00471 -0.02233 0.9943 1.78e-05 -7.991e-06 1.019 1.341e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1061 0.09371 0.1829 0.1994 0.9873 0.9919 0.1062 0.7508 0.8647 0.3054 ] Network output: [ -0.004444 0.0211 1.004 1.899e-05 -8.526e-06 0.9839 1.431e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09188 0.08994 0.165 0.1956 0.9853 0.9912 0.09189 0.6751 0.8406 0.2467 ] Network output: [ 0.0001235 1 -0.0001252 2.525e-06 -1.134e-06 0.9998 1.903e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003082 Epoch 8600 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01005 0.9961 0.9913 -1.426e-07 6.401e-08 -0.0075 -1.075e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003419 -0.003238 -0.007455 0.005893 0.9699 0.9743 0.006595 0.8306 0.8229 0.0174 ] Network output: [ 0.9999 0.0003573 0.0006396 -9.399e-06 4.22e-06 -0.0007411 -7.084e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.201 -0.03444 -0.1685 0.1872 0.9835 0.9932 0.2251 0.4365 0.8701 0.7139 ] Network output: [ -0.009883 1.002 1.009 -2.908e-07 1.306e-07 0.008345 -2.192e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006328 0.0005338 0.004442 0.00347 0.9889 0.9919 0.006449 0.8584 0.8941 0.01251 ] Network output: [ -0.0004038 0.002238 1.001 -2.944e-05 1.322e-05 0.9976 -2.219e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2135 0.09988 0.3428 0.1443 0.985 0.994 0.2143 0.4407 0.8767 0.708 ] Network output: [ 0.004708 -0.02232 0.9943 1.778e-05 -7.982e-06 1.019 1.34e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1061 0.09372 0.1829 0.1994 0.9873 0.9919 0.1062 0.7508 0.8647 0.3054 ] Network output: [ -0.004443 0.02109 1.004 1.897e-05 -8.517e-06 0.9839 1.43e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09188 0.08994 0.165 0.1956 0.9853 0.9912 0.09189 0.675 0.8406 0.2467 ] Network output: [ 0.0001235 1 -0.000125 2.523e-06 -1.132e-06 0.9998 1.901e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000308 Epoch 8601 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01004 0.9961 0.9913 -1.43e-07 6.418e-08 -0.0075 -1.077e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003419 -0.003238 -0.007454 0.005892 0.9699 0.9743 0.006595 0.8306 0.8229 0.0174 ] Network output: [ 0.9999 0.000357 0.0006393 -9.389e-06 4.215e-06 -0.0007405 -7.076e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.201 -0.03444 -0.1685 0.1872 0.9835 0.9932 0.2251 0.4365 0.8701 0.7139 ] Network output: [ -0.009882 1.002 1.009 -2.909e-07 1.306e-07 0.008344 -2.192e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006329 0.0005339 0.004442 0.00347 0.9889 0.9919 0.006449 0.8584 0.8941 0.01251 ] Network output: [ -0.0004035 0.002237 1.001 -2.941e-05 1.32e-05 0.9976 -2.216e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2136 0.09989 0.3429 0.1443 0.985 0.994 0.2143 0.4407 0.8767 0.708 ] Network output: [ 0.004706 -0.02231 0.9943 1.776e-05 -7.974e-06 1.019 1.339e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1061 0.09372 0.1829 0.1994 0.9873 0.9919 0.1062 0.7507 0.8647 0.3054 ] Network output: [ -0.004441 0.02108 1.004 1.895e-05 -8.508e-06 0.9839 1.428e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09188 0.08995 0.165 0.1956 0.9853 0.9912 0.0919 0.675 0.8406 0.2467 ] Network output: [ 0.0001234 1 -0.0001249 2.52e-06 -1.131e-06 0.9998 1.899e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003078 Epoch 8602 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01004 0.9961 0.9913 -1.433e-07 6.434e-08 -0.0075 -1.08e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003419 -0.003238 -0.007453 0.005892 0.9699 0.9743 0.006596 0.8306 0.8229 0.0174 ] Network output: [ 0.9999 0.0003567 0.0006389 -9.379e-06 4.211e-06 -0.0007399 -7.068e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.201 -0.03444 -0.1684 0.1872 0.9835 0.9932 0.2251 0.4365 0.87 0.7139 ] Network output: [ -0.00988 1.002 1.009 -2.91e-07 1.306e-07 0.008343 -2.193e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006329 0.000534 0.004442 0.003469 0.9889 0.9919 0.00645 0.8584 0.894 0.01251 ] Network output: [ -0.0004033 0.002236 1.001 -2.938e-05 1.319e-05 0.9976 -2.214e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2136 0.0999 0.3429 0.1443 0.985 0.994 0.2143 0.4406 0.8767 0.708 ] Network output: [ 0.004705 -0.0223 0.9943 1.774e-05 -7.965e-06 1.019 1.337e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1061 0.09373 0.1829 0.1994 0.9873 0.9919 0.1062 0.7507 0.8646 0.3054 ] Network output: [ -0.00444 0.02107 1.004 1.893e-05 -8.5e-06 0.9839 1.427e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09189 0.08995 0.165 0.1956 0.9853 0.9912 0.0919 0.675 0.8406 0.2467 ] Network output: [ 0.0001234 1 -0.0001247 2.517e-06 -1.13e-06 0.9998 1.897e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003077 Epoch 8603 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01004 0.9961 0.9913 -1.437e-07 6.45e-08 -0.0075 -1.083e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003419 -0.003238 -0.007452 0.005891 0.9699 0.9743 0.006596 0.8305 0.8229 0.01739 ] Network output: [ 0.9999 0.0003564 0.0006386 -9.369e-06 4.206e-06 -0.0007393 -7.061e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.201 -0.03444 -0.1684 0.1872 0.9835 0.9932 0.2251 0.4365 0.87 0.7139 ] Network output: [ -0.009879 1.002 1.009 -2.911e-07 1.307e-07 0.008341 -2.194e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00633 0.0005341 0.004442 0.003469 0.9889 0.9919 0.00645 0.8584 0.894 0.01251 ] Network output: [ -0.000403 0.002235 1.001 -2.934e-05 1.317e-05 0.9976 -2.211e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2136 0.0999 0.3429 0.1443 0.985 0.994 0.2143 0.4406 0.8767 0.708 ] Network output: [ 0.004703 -0.02229 0.9943 1.772e-05 -7.956e-06 1.019 1.336e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1061 0.09374 0.1829 0.1993 0.9873 0.9919 0.1062 0.7507 0.8646 0.3054 ] Network output: [ -0.004438 0.02106 1.004 1.891e-05 -8.491e-06 0.9839 1.425e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09189 0.08995 0.165 0.1956 0.9853 0.9912 0.0919 0.675 0.8406 0.2467 ] Network output: [ 0.0001233 1 -0.0001246 2.514e-06 -1.129e-06 0.9998 1.895e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003075 Epoch 8604 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01004 0.9961 0.9913 -1.44e-07 6.466e-08 -0.0075 -1.085e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003419 -0.003238 -0.007451 0.00589 0.9699 0.9743 0.006596 0.8305 0.8229 0.01739 ] Network output: [ 0.9999 0.0003561 0.0006382 -9.359e-06 4.201e-06 -0.0007387 -7.053e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.201 -0.03444 -0.1684 0.1872 0.9835 0.9932 0.2251 0.4365 0.87 0.7139 ] Network output: [ -0.009878 1.002 1.009 -2.912e-07 1.307e-07 0.00834 -2.195e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00633 0.0005342 0.004442 0.003469 0.9889 0.9919 0.006451 0.8584 0.894 0.01251 ] Network output: [ -0.0004027 0.002234 1.001 -2.931e-05 1.316e-05 0.9976 -2.209e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2136 0.09991 0.3429 0.1443 0.985 0.994 0.2143 0.4406 0.8767 0.708 ] Network output: [ 0.004701 -0.02229 0.9943 1.77e-05 -7.948e-06 1.019 1.334e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1061 0.09374 0.1829 0.1993 0.9873 0.9919 0.1062 0.7507 0.8646 0.3054 ] Network output: [ -0.004436 0.02106 1.004 1.889e-05 -8.482e-06 0.9839 1.424e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09189 0.08995 0.165 0.1956 0.9853 0.9912 0.0919 0.675 0.8406 0.2467 ] Network output: [ 0.0001233 1 -0.0001245 2.512e-06 -1.128e-06 0.9998 1.893e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003073 Epoch 8605 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01004 0.9961 0.9913 -1.444e-07 6.482e-08 -0.007499 -1.088e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003419 -0.003238 -0.007451 0.00589 0.9699 0.9743 0.006596 0.8305 0.8229 0.01739 ] Network output: [ 0.9999 0.0003558 0.0006379 -9.348e-06 4.197e-06 -0.0007381 -7.045e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.201 -0.03445 -0.1684 0.1872 0.9835 0.9932 0.2252 0.4365 0.87 0.7139 ] Network output: [ -0.009877 1.002 1.009 -2.913e-07 1.308e-07 0.008339 -2.196e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006331 0.0005343 0.004441 0.003469 0.9889 0.9919 0.006452 0.8584 0.894 0.01251 ] Network output: [ -0.0004025 0.002234 1.001 -2.928e-05 1.314e-05 0.9976 -2.207e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2136 0.09991 0.3429 0.1443 0.985 0.994 0.2143 0.4406 0.8767 0.708 ] Network output: [ 0.0047 -0.02228 0.9943 1.768e-05 -7.939e-06 1.019 1.333e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1061 0.09375 0.1829 0.1993 0.9873 0.9919 0.1062 0.7507 0.8646 0.3054 ] Network output: [ -0.004435 0.02105 1.004 1.887e-05 -8.473e-06 0.9839 1.422e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09189 0.08995 0.165 0.1956 0.9853 0.9912 0.09191 0.6749 0.8406 0.2467 ] Network output: [ 0.0001232 1 -0.0001243 2.509e-06 -1.126e-06 0.9998 1.891e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003072 Epoch 8606 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01004 0.9962 0.9913 -1.447e-07 6.498e-08 -0.007499 -1.091e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003419 -0.003239 -0.00745 0.005889 0.9699 0.9743 0.006597 0.8305 0.8229 0.01739 ] Network output: [ 0.9999 0.0003555 0.0006375 -9.338e-06 4.192e-06 -0.0007375 -7.038e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.201 -0.03445 -0.1684 0.1872 0.9835 0.9932 0.2252 0.4365 0.87 0.7139 ] Network output: [ -0.009876 1.002 1.009 -2.914e-07 1.308e-07 0.008338 -2.196e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006331 0.0005344 0.004441 0.003468 0.9889 0.9919 0.006452 0.8584 0.894 0.01251 ] Network output: [ -0.0004022 0.002233 1.001 -2.925e-05 1.313e-05 0.9976 -2.204e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2136 0.09992 0.3429 0.1443 0.985 0.994 0.2143 0.4406 0.8767 0.708 ] Network output: [ 0.004698 -0.02227 0.9943 1.767e-05 -7.931e-06 1.019 1.331e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1061 0.09375 0.1829 0.1993 0.9873 0.9919 0.1062 0.7507 0.8646 0.3054 ] Network output: [ -0.004433 0.02104 1.004 1.885e-05 -8.464e-06 0.9839 1.421e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09189 0.08996 0.165 0.1956 0.9853 0.9912 0.09191 0.6749 0.8406 0.2467 ] Network output: [ 0.0001231 1 -0.0001242 2.506e-06 -1.125e-06 0.9998 1.889e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000307 Epoch 8607 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01004 0.9962 0.9913 -1.451e-07 6.514e-08 -0.007499 -1.094e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00342 -0.003239 -0.007449 0.005889 0.9699 0.9743 0.006597 0.8305 0.8229 0.01739 ] Network output: [ 0.9999 0.0003552 0.0006372 -9.328e-06 4.188e-06 -0.0007369 -7.03e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2011 -0.03445 -0.1684 0.1872 0.9835 0.9932 0.2252 0.4365 0.87 0.7139 ] Network output: [ -0.009875 1.002 1.009 -2.915e-07 1.309e-07 0.008336 -2.197e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006332 0.0005345 0.004441 0.003468 0.9889 0.9919 0.006453 0.8584 0.894 0.01251 ] Network output: [ -0.000402 0.002232 1.001 -2.922e-05 1.312e-05 0.9976 -2.202e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2136 0.09992 0.3429 0.1443 0.985 0.994 0.2143 0.4406 0.8767 0.708 ] Network output: [ 0.004697 -0.02226 0.9943 1.765e-05 -7.922e-06 1.019 1.33e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1061 0.09376 0.1829 0.1993 0.9873 0.9919 0.1062 0.7506 0.8646 0.3054 ] Network output: [ -0.004432 0.02103 1.004 1.883e-05 -8.455e-06 0.9839 1.419e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0919 0.08996 0.165 0.1956 0.9853 0.9912 0.09191 0.6749 0.8406 0.2467 ] Network output: [ 0.0001231 1 -0.000124 2.504e-06 -1.124e-06 0.9998 1.887e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003069 Epoch 8608 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01004 0.9962 0.9913 -1.455e-07 6.53e-08 -0.007499 -1.096e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00342 -0.003239 -0.007448 0.005888 0.9699 0.9743 0.006597 0.8305 0.8229 0.01739 ] Network output: [ 0.9999 0.0003549 0.0006368 -9.318e-06 4.183e-06 -0.0007363 -7.022e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2011 -0.03445 -0.1684 0.1872 0.9835 0.9932 0.2252 0.4365 0.87 0.7139 ] Network output: [ -0.009874 1.002 1.009 -2.916e-07 1.309e-07 0.008335 -2.198e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006332 0.0005346 0.004441 0.003468 0.9889 0.9919 0.006453 0.8584 0.894 0.01251 ] Network output: [ -0.0004017 0.002231 1.001 -2.918e-05 1.31e-05 0.9976 -2.199e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2136 0.09993 0.3429 0.1443 0.985 0.994 0.2143 0.4406 0.8767 0.708 ] Network output: [ 0.004695 -0.02225 0.9943 1.763e-05 -7.914e-06 1.019 1.329e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1061 0.09376 0.1829 0.1993 0.9873 0.9919 0.1062 0.7506 0.8646 0.3054 ] Network output: [ -0.00443 0.02102 1.004 1.881e-05 -8.446e-06 0.9839 1.418e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0919 0.08996 0.165 0.1956 0.9853 0.9912 0.09191 0.6749 0.8406 0.2467 ] Network output: [ 0.000123 1 -0.0001239 2.501e-06 -1.123e-06 0.9998 1.885e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003067 Epoch 8609 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01003 0.9962 0.9913 -1.458e-07 6.546e-08 -0.007499 -1.099e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00342 -0.003239 -0.007447 0.005888 0.9699 0.9743 0.006597 0.8305 0.8229 0.01739 ] Network output: [ 0.9999 0.0003546 0.0006365 -9.308e-06 4.179e-06 -0.0007357 -7.015e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2011 -0.03445 -0.1684 0.1872 0.9835 0.9932 0.2252 0.4364 0.87 0.7139 ] Network output: [ -0.009873 1.002 1.009 -2.918e-07 1.31e-07 0.008334 -2.199e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006333 0.0005347 0.004441 0.003467 0.9889 0.9919 0.006454 0.8583 0.894 0.01251 ] Network output: [ -0.0004015 0.00223 1.001 -2.915e-05 1.309e-05 0.9976 -2.197e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2136 0.09994 0.3429 0.1443 0.985 0.994 0.2143 0.4406 0.8767 0.708 ] Network output: [ 0.004693 -0.02225 0.9943 1.761e-05 -7.905e-06 1.019 1.327e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1061 0.09377 0.1829 0.1993 0.9873 0.9919 0.1062 0.7506 0.8646 0.3054 ] Network output: [ -0.004428 0.02101 1.004 1.879e-05 -8.437e-06 0.9839 1.416e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0919 0.08996 0.165 0.1956 0.9853 0.9912 0.09191 0.6749 0.8406 0.2467 ] Network output: [ 0.000123 1 -0.0001237 2.498e-06 -1.122e-06 0.9998 1.883e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003065 Epoch 8610 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01003 0.9962 0.9913 -1.462e-07 6.562e-08 -0.007499 -1.102e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00342 -0.003239 -0.007446 0.005887 0.9699 0.9743 0.006598 0.8305 0.8229 0.01739 ] Network output: [ 0.9999 0.0003543 0.0006361 -9.298e-06 4.174e-06 -0.0007351 -7.007e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2011 -0.03445 -0.1683 0.1872 0.9835 0.9932 0.2252 0.4364 0.87 0.7139 ] Network output: [ -0.009872 1.002 1.009 -2.919e-07 1.31e-07 0.008332 -2.199e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006333 0.0005348 0.004441 0.003467 0.9889 0.9919 0.006454 0.8583 0.894 0.01251 ] Network output: [ -0.0004012 0.002229 1.001 -2.912e-05 1.307e-05 0.9976 -2.195e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2136 0.09994 0.3429 0.1443 0.985 0.994 0.2144 0.4406 0.8767 0.708 ] Network output: [ 0.004692 -0.02224 0.9943 1.759e-05 -7.897e-06 1.019 1.326e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1062 0.09377 0.1829 0.1993 0.9873 0.9919 0.1062 0.7506 0.8646 0.3054 ] Network output: [ -0.004427 0.021 1.004 1.877e-05 -8.428e-06 0.9839 1.415e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0919 0.08997 0.165 0.1956 0.9853 0.9912 0.09192 0.6749 0.8406 0.2467 ] Network output: [ 0.0001229 1 -0.0001236 2.496e-06 -1.12e-06 0.9998 1.881e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003064 Epoch 8611 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01003 0.9962 0.9913 -1.465e-07 6.577e-08 -0.007499 -1.104e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00342 -0.003239 -0.007446 0.005887 0.9699 0.9743 0.006598 0.8305 0.8229 0.01738 ] Network output: [ 0.9999 0.000354 0.0006358 -9.288e-06 4.17e-06 -0.0007345 -7e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2011 -0.03445 -0.1683 0.1872 0.9835 0.9932 0.2252 0.4364 0.87 0.7139 ] Network output: [ -0.009871 1.002 1.009 -2.92e-07 1.311e-07 0.008331 -2.2e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006334 0.0005349 0.004441 0.003467 0.9889 0.9919 0.006455 0.8583 0.894 0.0125 ] Network output: [ -0.0004009 0.002229 1.001 -2.909e-05 1.306e-05 0.9976 -2.192e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2137 0.09995 0.3429 0.1443 0.985 0.994 0.2144 0.4406 0.8767 0.708 ] Network output: [ 0.00469 -0.02223 0.9943 1.757e-05 -7.888e-06 1.019 1.324e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1062 0.09378 0.1829 0.1993 0.9873 0.9919 0.1062 0.7506 0.8646 0.3054 ] Network output: [ -0.004425 0.021 1.004 1.875e-05 -8.42e-06 0.9839 1.413e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09191 0.08997 0.165 0.1956 0.9853 0.9912 0.09192 0.6748 0.8406 0.2467 ] Network output: [ 0.0001229 1 -0.0001235 2.493e-06 -1.119e-06 0.9998 1.879e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003062 Epoch 8612 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01003 0.9962 0.9913 -1.469e-07 6.593e-08 -0.007499 -1.107e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00342 -0.003239 -0.007445 0.005886 0.9699 0.9743 0.006598 0.8305 0.8229 0.01738 ] Network output: [ 0.9999 0.0003537 0.0006354 -9.278e-06 4.165e-06 -0.0007339 -6.992e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2011 -0.03445 -0.1683 0.1872 0.9835 0.9932 0.2252 0.4364 0.87 0.7138 ] Network output: [ -0.00987 1.002 1.009 -2.921e-07 1.311e-07 0.00833 -2.201e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006335 0.000535 0.004441 0.003466 0.9889 0.9919 0.006455 0.8583 0.894 0.0125 ] Network output: [ -0.0004007 0.002228 1.001 -2.906e-05 1.305e-05 0.9976 -2.19e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2137 0.09995 0.3429 0.1443 0.985 0.994 0.2144 0.4406 0.8767 0.708 ] Network output: [ 0.004688 -0.02222 0.9943 1.755e-05 -7.88e-06 1.019 1.323e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1062 0.09379 0.1829 0.1993 0.9873 0.9919 0.1062 0.7506 0.8646 0.3054 ] Network output: [ -0.004424 0.02099 1.004 1.873e-05 -8.411e-06 0.9839 1.412e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09191 0.08997 0.165 0.1956 0.9853 0.9912 0.09192 0.6748 0.8405 0.2467 ] Network output: [ 0.0001228 1 -0.0001233 2.49e-06 -1.118e-06 0.9998 1.877e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000306 Epoch 8613 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01003 0.9962 0.9913 -1.472e-07 6.609e-08 -0.007499 -1.109e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00342 -0.003239 -0.007444 0.005886 0.9699 0.9743 0.006599 0.8305 0.8229 0.01738 ] Network output: [ 0.9999 0.0003534 0.0006351 -9.268e-06 4.161e-06 -0.0007333 -6.984e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2011 -0.03446 -0.1683 0.1872 0.9835 0.9932 0.2252 0.4364 0.87 0.7138 ] Network output: [ -0.009869 1.002 1.009 -2.922e-07 1.312e-07 0.008329 -2.202e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006335 0.0005351 0.004441 0.003466 0.9889 0.9919 0.006456 0.8583 0.894 0.0125 ] Network output: [ -0.0004004 0.002227 1.001 -2.903e-05 1.303e-05 0.9976 -2.188e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2137 0.09996 0.3429 0.1443 0.985 0.994 0.2144 0.4405 0.8767 0.7079 ] Network output: [ 0.004687 -0.02221 0.9943 1.753e-05 -7.871e-06 1.019 1.321e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1062 0.09379 0.1829 0.1993 0.9873 0.9919 0.1062 0.7505 0.8646 0.3054 ] Network output: [ -0.004422 0.02098 1.004 1.872e-05 -8.402e-06 0.9839 1.41e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09191 0.08997 0.165 0.1956 0.9853 0.9912 0.09192 0.6748 0.8405 0.2467 ] Network output: [ 0.0001228 1 -0.0001232 2.488e-06 -1.117e-06 0.9998 1.875e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003059 Epoch 8614 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01003 0.9962 0.9913 -1.476e-07 6.624e-08 -0.007498 -1.112e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00342 -0.00324 -0.007443 0.005885 0.9699 0.9743 0.006599 0.8305 0.8229 0.01738 ] Network output: [ 0.9999 0.0003531 0.0006347 -9.258e-06 4.156e-06 -0.0007327 -6.977e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2011 -0.03446 -0.1683 0.1871 0.9835 0.9932 0.2252 0.4364 0.87 0.7138 ] Network output: [ -0.009868 1.002 1.009 -2.923e-07 1.312e-07 0.008327 -2.202e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006336 0.0005352 0.004441 0.003466 0.9889 0.9919 0.006457 0.8583 0.894 0.0125 ] Network output: [ -0.0004002 0.002226 1.001 -2.9e-05 1.302e-05 0.9976 -2.185e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2137 0.09997 0.3429 0.1443 0.985 0.994 0.2144 0.4405 0.8767 0.7079 ] Network output: [ 0.004685 -0.02221 0.9943 1.751e-05 -7.863e-06 1.019 1.32e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1062 0.0938 0.1829 0.1993 0.9873 0.9919 0.1063 0.7505 0.8646 0.3054 ] Network output: [ -0.00442 0.02097 1.004 1.87e-05 -8.393e-06 0.9839 1.409e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09191 0.08998 0.165 0.1956 0.9853 0.9912 0.09193 0.6748 0.8405 0.2467 ] Network output: [ 0.0001227 1 -0.000123 2.485e-06 -1.116e-06 0.9998 1.873e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003057 Epoch 8615 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01003 0.9962 0.9913 -1.479e-07 6.64e-08 -0.007498 -1.115e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00342 -0.00324 -0.007442 0.005884 0.9699 0.9743 0.006599 0.8305 0.8229 0.01738 ] Network output: [ 0.9999 0.0003528 0.0006344 -9.248e-06 4.152e-06 -0.0007321 -6.969e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2011 -0.03446 -0.1683 0.1871 0.9835 0.9932 0.2253 0.4364 0.87 0.7138 ] Network output: [ -0.009867 1.002 1.009 -2.923e-07 1.312e-07 0.008326 -2.203e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006336 0.0005352 0.004441 0.003465 0.9889 0.9919 0.006457 0.8583 0.894 0.0125 ] Network output: [ -0.0003999 0.002225 1.001 -2.896e-05 1.3e-05 0.9976 -2.183e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2137 0.09997 0.343 0.1443 0.985 0.994 0.2144 0.4405 0.8767 0.7079 ] Network output: [ 0.004684 -0.0222 0.9943 1.75e-05 -7.855e-06 1.019 1.319e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1062 0.0938 0.1829 0.1993 0.9873 0.9919 0.1063 0.7505 0.8646 0.3054 ] Network output: [ -0.004419 0.02096 1.004 1.868e-05 -8.384e-06 0.9839 1.407e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09192 0.08998 0.165 0.1956 0.9853 0.9912 0.09193 0.6748 0.8405 0.2467 ] Network output: [ 0.0001227 1 -0.0001229 2.482e-06 -1.114e-06 0.9998 1.871e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003055 Epoch 8616 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01003 0.9962 0.9913 -1.482e-07 6.655e-08 -0.007498 -1.117e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003421 -0.00324 -0.007441 0.005884 0.9699 0.9743 0.006599 0.8305 0.8229 0.01738 ] Network output: [ 0.9999 0.0003525 0.0006341 -9.238e-06 4.147e-06 -0.0007315 -6.962e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2011 -0.03446 -0.1683 0.1871 0.9835 0.9932 0.2253 0.4364 0.87 0.7138 ] Network output: [ -0.009866 1.002 1.009 -2.924e-07 1.313e-07 0.008325 -2.204e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006337 0.0005353 0.004441 0.003465 0.9889 0.9919 0.006458 0.8583 0.894 0.0125 ] Network output: [ -0.0003997 0.002225 1.001 -2.893e-05 1.299e-05 0.9976 -2.18e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2137 0.09998 0.343 0.1443 0.985 0.994 0.2144 0.4405 0.8767 0.7079 ] Network output: [ 0.004682 -0.02219 0.9943 1.748e-05 -7.846e-06 1.019 1.317e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1062 0.09381 0.1829 0.1993 0.9873 0.9919 0.1063 0.7505 0.8646 0.3054 ] Network output: [ -0.004417 0.02095 1.004 1.866e-05 -8.376e-06 0.9839 1.406e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09192 0.08998 0.165 0.1956 0.9853 0.9912 0.09193 0.6747 0.8405 0.2467 ] Network output: [ 0.0001226 1 -0.0001227 2.48e-06 -1.113e-06 0.9998 1.869e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003054 Epoch 8617 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01002 0.9962 0.9913 -1.486e-07 6.671e-08 -0.007498 -1.12e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003421 -0.00324 -0.007441 0.005883 0.9699 0.9743 0.0066 0.8305 0.8229 0.01738 ] Network output: [ 0.9999 0.0003522 0.0006337 -9.228e-06 4.143e-06 -0.0007309 -6.954e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2011 -0.03446 -0.1683 0.1871 0.9835 0.9932 0.2253 0.4364 0.87 0.7138 ] Network output: [ -0.009865 1.002 1.009 -2.925e-07 1.313e-07 0.008323 -2.205e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006337 0.0005354 0.004441 0.003465 0.9889 0.9919 0.006458 0.8583 0.894 0.0125 ] Network output: [ -0.0003994 0.002224 1.001 -2.89e-05 1.297e-05 0.9976 -2.178e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2137 0.09998 0.343 0.1443 0.985 0.994 0.2144 0.4405 0.8767 0.7079 ] Network output: [ 0.00468 -0.02218 0.9943 1.746e-05 -7.838e-06 1.019 1.316e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1062 0.09381 0.1829 0.1993 0.9873 0.9919 0.1063 0.7505 0.8646 0.3054 ] Network output: [ -0.004415 0.02095 1.004 1.864e-05 -8.367e-06 0.9839 1.405e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09192 0.08998 0.165 0.1956 0.9853 0.9912 0.09193 0.6747 0.8405 0.2467 ] Network output: [ 0.0001225 1 -0.0001226 2.477e-06 -1.112e-06 0.9998 1.867e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003052 Epoch 8618 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01002 0.9962 0.9913 -1.489e-07 6.686e-08 -0.007498 -1.122e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003421 -0.00324 -0.00744 0.005883 0.9699 0.9743 0.0066 0.8305 0.8229 0.01738 ] Network output: [ 0.9999 0.0003519 0.0006334 -9.218e-06 4.138e-06 -0.0007303 -6.947e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2011 -0.03446 -0.1682 0.1871 0.9835 0.9932 0.2253 0.4364 0.87 0.7138 ] Network output: [ -0.009864 1.002 1.009 -2.926e-07 1.314e-07 0.008322 -2.205e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006338 0.0005355 0.004441 0.003464 0.9889 0.9919 0.006459 0.8583 0.894 0.0125 ] Network output: [ -0.0003991 0.002223 1.001 -2.887e-05 1.296e-05 0.9976 -2.176e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2137 0.09999 0.343 0.1443 0.985 0.994 0.2144 0.4405 0.8767 0.7079 ] Network output: [ 0.004679 -0.02217 0.9943 1.744e-05 -7.829e-06 1.019 1.314e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1062 0.09382 0.1829 0.1993 0.9873 0.9919 0.1063 0.7504 0.8646 0.3054 ] Network output: [ -0.004414 0.02094 1.004 1.862e-05 -8.358e-06 0.9839 1.403e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09192 0.08999 0.165 0.1956 0.9853 0.9912 0.09194 0.6747 0.8405 0.2467 ] Network output: [ 0.0001225 1 -0.0001225 2.474e-06 -1.111e-06 0.9998 1.865e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003051 Epoch 8619 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01002 0.9962 0.9913 -1.493e-07 6.702e-08 -0.007498 -1.125e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003421 -0.00324 -0.007439 0.005882 0.9699 0.9743 0.0066 0.8304 0.8229 0.01738 ] Network output: [ 0.9999 0.0003516 0.000633 -9.208e-06 4.134e-06 -0.0007297 -6.939e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2012 -0.03446 -0.1682 0.1871 0.9835 0.9932 0.2253 0.4364 0.87 0.7138 ] Network output: [ -0.009863 1.002 1.009 -2.927e-07 1.314e-07 0.008321 -2.206e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006338 0.0005356 0.004441 0.003464 0.9889 0.9919 0.006459 0.8583 0.894 0.0125 ] Network output: [ -0.0003989 0.002222 1.001 -2.884e-05 1.295e-05 0.9976 -2.173e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2137 0.09999 0.343 0.1443 0.985 0.994 0.2144 0.4405 0.8767 0.7079 ] Network output: [ 0.004677 -0.02216 0.9943 1.742e-05 -7.821e-06 1.019 1.313e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1062 0.09382 0.1829 0.1993 0.9873 0.9919 0.1063 0.7504 0.8646 0.3054 ] Network output: [ -0.004412 0.02093 1.004 1.86e-05 -8.349e-06 0.9839 1.402e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09192 0.08999 0.165 0.1957 0.9853 0.9912 0.09194 0.6747 0.8405 0.2467 ] Network output: [ 0.0001224 1 -0.0001223 2.472e-06 -1.11e-06 0.9998 1.863e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003049 Epoch 8620 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01002 0.9962 0.9913 -1.496e-07 6.717e-08 -0.007498 -1.128e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003421 -0.00324 -0.007438 0.005882 0.9699 0.9743 0.0066 0.8304 0.8229 0.01737 ] Network output: [ 0.9999 0.0003513 0.0006327 -9.198e-06 4.129e-06 -0.0007291 -6.932e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2012 -0.03446 -0.1682 0.1871 0.9835 0.9932 0.2253 0.4363 0.87 0.7138 ] Network output: [ -0.009862 1.002 1.009 -2.928e-07 1.315e-07 0.00832 -2.207e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006339 0.0005357 0.004441 0.003464 0.9889 0.9919 0.00646 0.8583 0.894 0.0125 ] Network output: [ -0.0003986 0.002221 1.001 -2.881e-05 1.293e-05 0.9976 -2.171e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2137 0.1 0.343 0.1442 0.985 0.994 0.2144 0.4405 0.8767 0.7079 ] Network output: [ 0.004676 -0.02216 0.9943 1.74e-05 -7.812e-06 1.019 1.311e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1062 0.09383 0.1829 0.1993 0.9873 0.9919 0.1063 0.7504 0.8646 0.3054 ] Network output: [ -0.004411 0.02092 1.004 1.858e-05 -8.34e-06 0.9839 1.4e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09193 0.08999 0.165 0.1957 0.9853 0.9912 0.09194 0.6747 0.8405 0.2467 ] Network output: [ 0.0001224 1 -0.0001222 2.469e-06 -1.108e-06 0.9998 1.861e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003047 Epoch 8621 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01002 0.9962 0.9913 -1.5e-07 6.732e-08 -0.007498 -1.13e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003421 -0.00324 -0.007437 0.005881 0.9699 0.9743 0.006601 0.8304 0.8229 0.01737 ] Network output: [ 0.9999 0.000351 0.0006323 -9.188e-06 4.125e-06 -0.0007285 -6.924e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2012 -0.03447 -0.1682 0.1871 0.9835 0.9932 0.2253 0.4363 0.87 0.7138 ] Network output: [ -0.009861 1.002 1.009 -2.929e-07 1.315e-07 0.008318 -2.208e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006339 0.0005358 0.004441 0.003463 0.9889 0.9919 0.00646 0.8583 0.894 0.0125 ] Network output: [ -0.0003984 0.002221 1.001 -2.878e-05 1.292e-05 0.9976 -2.169e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2137 0.1 0.343 0.1442 0.985 0.994 0.2145 0.4405 0.8767 0.7079 ] Network output: [ 0.004674 -0.02215 0.9943 1.738e-05 -7.804e-06 1.019 1.31e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1062 0.09383 0.1829 0.1993 0.9873 0.9919 0.1063 0.7504 0.8646 0.3054 ] Network output: [ -0.004409 0.02091 1.004 1.856e-05 -8.332e-06 0.9839 1.399e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09193 0.08999 0.165 0.1957 0.9853 0.9912 0.09194 0.6747 0.8405 0.2467 ] Network output: [ 0.0001223 1 -0.000122 2.467e-06 -1.107e-06 0.9998 1.859e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003046 Epoch 8622 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01002 0.9962 0.9913 -1.503e-07 6.747e-08 -0.007498 -1.133e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003421 -0.003241 -0.007436 0.005881 0.9699 0.9743 0.006601 0.8304 0.8229 0.01737 ] Network output: [ 0.9999 0.0003507 0.000632 -9.178e-06 4.12e-06 -0.0007279 -6.917e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2012 -0.03447 -0.1682 0.1871 0.9835 0.9932 0.2253 0.4363 0.87 0.7138 ] Network output: [ -0.00986 1.002 1.009 -2.93e-07 1.315e-07 0.008317 -2.208e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00634 0.0005359 0.004441 0.003463 0.9889 0.9919 0.006461 0.8583 0.894 0.01249 ] Network output: [ -0.0003981 0.00222 1.001 -2.874e-05 1.29e-05 0.9976 -2.166e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2138 0.1 0.343 0.1442 0.985 0.994 0.2145 0.4405 0.8767 0.7079 ] Network output: [ 0.004672 -0.02214 0.9943 1.736e-05 -7.796e-06 1.019 1.309e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1062 0.09384 0.1829 0.1993 0.9873 0.9919 0.1063 0.7504 0.8646 0.3054 ] Network output: [ -0.004407 0.0209 1.004 1.854e-05 -8.323e-06 0.984 1.397e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09193 0.08999 0.165 0.1957 0.9853 0.9912 0.09194 0.6746 0.8405 0.2467 ] Network output: [ 0.0001223 1 -0.0001219 2.464e-06 -1.106e-06 0.9998 1.857e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003044 Epoch 8623 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01002 0.9962 0.9913 -1.506e-07 6.762e-08 -0.007497 -1.135e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003421 -0.003241 -0.007436 0.00588 0.9699 0.9743 0.006601 0.8304 0.8229 0.01737 ] Network output: [ 0.9999 0.0003504 0.0006316 -9.168e-06 4.116e-06 -0.0007273 -6.909e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2012 -0.03447 -0.1682 0.1871 0.9835 0.9932 0.2253 0.4363 0.87 0.7138 ] Network output: [ -0.009859 1.002 1.009 -2.931e-07 1.316e-07 0.008316 -2.209e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00634 0.000536 0.004441 0.003463 0.9889 0.9919 0.006461 0.8583 0.894 0.01249 ] Network output: [ -0.0003979 0.002219 1.001 -2.871e-05 1.289e-05 0.9976 -2.164e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2138 0.1 0.343 0.1442 0.985 0.994 0.2145 0.4405 0.8767 0.7079 ] Network output: [ 0.004671 -0.02213 0.9943 1.735e-05 -7.787e-06 1.019 1.307e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1062 0.09385 0.1829 0.1993 0.9873 0.9919 0.1063 0.7504 0.8646 0.3054 ] Network output: [ -0.004406 0.0209 1.004 1.852e-05 -8.314e-06 0.984 1.396e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09193 0.09 0.165 0.1957 0.9853 0.9912 0.09195 0.6746 0.8405 0.2467 ] Network output: [ 0.0001222 1 -0.0001218 2.461e-06 -1.105e-06 0.9998 1.855e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003042 Epoch 8624 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01002 0.9962 0.9913 -1.51e-07 6.778e-08 -0.007497 -1.138e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003421 -0.003241 -0.007435 0.00588 0.9699 0.9743 0.006602 0.8304 0.8229 0.01737 ] Network output: [ 0.9999 0.0003501 0.0006313 -9.158e-06 4.111e-06 -0.0007267 -6.902e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2012 -0.03447 -0.1682 0.1871 0.9835 0.9932 0.2253 0.4363 0.87 0.7138 ] Network output: [ -0.009858 1.002 1.009 -2.932e-07 1.316e-07 0.008314 -2.21e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006341 0.0005361 0.004441 0.003462 0.9889 0.9919 0.006462 0.8582 0.894 0.01249 ] Network output: [ -0.0003976 0.002218 1.001 -2.868e-05 1.288e-05 0.9976 -2.162e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2138 0.1 0.343 0.1442 0.985 0.994 0.2145 0.4404 0.8767 0.7079 ] Network output: [ 0.004669 -0.02212 0.9943 1.733e-05 -7.779e-06 1.019 1.306e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1062 0.09385 0.1829 0.1993 0.9873 0.9919 0.1063 0.7503 0.8646 0.3054 ] Network output: [ -0.004404 0.02089 1.004 1.85e-05 -8.305e-06 0.984 1.394e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09194 0.09 0.165 0.1957 0.9853 0.9912 0.09195 0.6746 0.8405 0.2467 ] Network output: [ 0.0001222 1 -0.0001216 2.459e-06 -1.104e-06 0.9998 1.853e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003041 Epoch 8625 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01001 0.9962 0.9913 -1.513e-07 6.793e-08 -0.007497 -1.14e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003422 -0.003241 -0.007434 0.005879 0.9699 0.9743 0.006602 0.8304 0.8229 0.01737 ] Network output: [ 0.9999 0.0003498 0.0006309 -9.148e-06 4.107e-06 -0.0007261 -6.894e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2012 -0.03447 -0.1682 0.1871 0.9835 0.9932 0.2253 0.4363 0.87 0.7138 ] Network output: [ -0.009857 1.002 1.009 -2.933e-07 1.317e-07 0.008313 -2.21e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006341 0.0005362 0.004441 0.003462 0.9889 0.9919 0.006463 0.8582 0.894 0.01249 ] Network output: [ -0.0003973 0.002217 1.001 -2.865e-05 1.286e-05 0.9976 -2.159e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2138 0.1 0.343 0.1442 0.985 0.994 0.2145 0.4404 0.8767 0.7079 ] Network output: [ 0.004667 -0.02212 0.9943 1.731e-05 -7.771e-06 1.019 1.304e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1062 0.09386 0.1829 0.1993 0.9873 0.9919 0.1063 0.7503 0.8645 0.3054 ] Network output: [ -0.004403 0.02088 1.004 1.848e-05 -8.297e-06 0.984 1.393e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09194 0.09 0.165 0.1957 0.9853 0.9912 0.09195 0.6746 0.8405 0.2467 ] Network output: [ 0.0001221 1 -0.0001215 2.456e-06 -1.103e-06 0.9998 1.851e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003039 Epoch 8626 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01001 0.9962 0.9913 -1.516e-07 6.808e-08 -0.007497 -1.143e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003422 -0.003241 -0.007433 0.005879 0.9699 0.9743 0.006602 0.8304 0.8229 0.01737 ] Network output: [ 0.9999 0.0003495 0.0006306 -9.138e-06 4.102e-06 -0.0007255 -6.887e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2012 -0.03447 -0.1681 0.1871 0.9835 0.9932 0.2254 0.4363 0.87 0.7138 ] Network output: [ -0.009856 1.002 1.009 -2.934e-07 1.317e-07 0.008312 -2.211e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006342 0.0005363 0.004441 0.003462 0.9889 0.9919 0.006463 0.8582 0.894 0.01249 ] Network output: [ -0.0003971 0.002217 1.001 -2.862e-05 1.285e-05 0.9976 -2.157e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2138 0.1 0.343 0.1442 0.985 0.994 0.2145 0.4404 0.8767 0.7079 ] Network output: [ 0.004666 -0.02211 0.9943 1.729e-05 -7.762e-06 1.019 1.303e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1062 0.09386 0.1829 0.1993 0.9873 0.9919 0.1063 0.7503 0.8645 0.3054 ] Network output: [ -0.004401 0.02087 1.004 1.846e-05 -8.288e-06 0.984 1.391e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09194 0.09 0.165 0.1957 0.9853 0.9912 0.09195 0.6746 0.8405 0.2467 ] Network output: [ 0.0001221 1 -0.0001213 2.453e-06 -1.101e-06 0.9998 1.849e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003038 Epoch 8627 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01001 0.9962 0.9913 -1.52e-07 6.822e-08 -0.007497 -1.145e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003422 -0.003241 -0.007432 0.005878 0.9699 0.9743 0.006602 0.8304 0.8229 0.01737 ] Network output: [ 0.9999 0.0003492 0.0006303 -9.128e-06 4.098e-06 -0.000725 -6.879e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2012 -0.03447 -0.1681 0.1871 0.9835 0.9932 0.2254 0.4363 0.87 0.7138 ] Network output: [ -0.009855 1.002 1.009 -2.935e-07 1.317e-07 0.008311 -2.212e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006343 0.0005364 0.004441 0.003461 0.9889 0.9919 0.006464 0.8582 0.894 0.01249 ] Network output: [ -0.0003968 0.002216 1.001 -2.859e-05 1.283e-05 0.9976 -2.154e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2138 0.1 0.343 0.1442 0.985 0.994 0.2145 0.4404 0.8767 0.7079 ] Network output: [ 0.004664 -0.0221 0.9943 1.727e-05 -7.754e-06 1.019 1.302e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1063 0.09387 0.1829 0.1993 0.9873 0.9919 0.1063 0.7503 0.8645 0.3054 ] Network output: [ -0.004399 0.02086 1.004 1.844e-05 -8.279e-06 0.984 1.39e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09194 0.09001 0.165 0.1957 0.9853 0.9912 0.09196 0.6745 0.8405 0.2467 ] Network output: [ 0.000122 1 -0.0001212 2.451e-06 -1.1e-06 0.9998 1.847e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003036 Epoch 8628 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01001 0.9962 0.9913 -1.523e-07 6.837e-08 -0.007497 -1.148e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003422 -0.003241 -0.007431 0.005878 0.9699 0.9743 0.006603 0.8304 0.8228 0.01736 ] Network output: [ 0.9999 0.0003489 0.0006299 -9.118e-06 4.093e-06 -0.0007244 -6.872e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2012 -0.03448 -0.1681 0.1871 0.9835 0.9932 0.2254 0.4363 0.87 0.7138 ] Network output: [ -0.009854 1.002 1.009 -2.936e-07 1.318e-07 0.008309 -2.212e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006343 0.0005365 0.004441 0.003461 0.9889 0.9919 0.006464 0.8582 0.894 0.01249 ] Network output: [ -0.0003966 0.002215 1.001 -2.856e-05 1.282e-05 0.9976 -2.152e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2138 0.1 0.343 0.1442 0.985 0.994 0.2145 0.4404 0.8767 0.7079 ] Network output: [ 0.004663 -0.02209 0.9943 1.725e-05 -7.745e-06 1.019 1.3e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1063 0.09387 0.1829 0.1993 0.9873 0.9919 0.1063 0.7503 0.8645 0.3054 ] Network output: [ -0.004398 0.02085 1.004 1.842e-05 -8.27e-06 0.984 1.388e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09195 0.09001 0.165 0.1957 0.9853 0.9912 0.09196 0.6745 0.8405 0.2467 ] Network output: [ 0.000122 1 -0.0001211 2.448e-06 -1.099e-06 0.9998 1.845e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003034 Epoch 8629 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01001 0.9962 0.9913 -1.526e-07 6.852e-08 -0.007497 -1.15e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003422 -0.003242 -0.007431 0.005877 0.9699 0.9743 0.006603 0.8304 0.8228 0.01736 ] Network output: [ 0.9999 0.0003486 0.0006296 -9.108e-06 4.089e-06 -0.0007238 -6.864e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2012 -0.03448 -0.1681 0.1871 0.9835 0.9932 0.2254 0.4363 0.87 0.7137 ] Network output: [ -0.009853 1.002 1.009 -2.936e-07 1.318e-07 0.008308 -2.213e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006344 0.0005366 0.004441 0.003461 0.9889 0.9919 0.006465 0.8582 0.894 0.01249 ] Network output: [ -0.0003963 0.002214 1.001 -2.853e-05 1.281e-05 0.9976 -2.15e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2138 0.1001 0.3431 0.1442 0.985 0.994 0.2145 0.4404 0.8766 0.7079 ] Network output: [ 0.004661 -0.02208 0.9943 1.723e-05 -7.737e-06 1.019 1.299e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1063 0.09388 0.1829 0.1993 0.9873 0.9919 0.1063 0.7503 0.8645 0.3054 ] Network output: [ -0.004396 0.02084 1.004 1.84e-05 -8.262e-06 0.984 1.387e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09195 0.09001 0.165 0.1957 0.9853 0.9912 0.09196 0.6745 0.8405 0.2467 ] Network output: [ 0.0001219 1 -0.0001209 2.445e-06 -1.098e-06 0.9998 1.843e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003033 Epoch 8630 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01001 0.9962 0.9913 -1.53e-07 6.867e-08 -0.007497 -1.153e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003422 -0.003242 -0.00743 0.005876 0.9699 0.9743 0.006603 0.8304 0.8228 0.01736 ] Network output: [ 0.9999 0.0003483 0.0006292 -9.098e-06 4.085e-06 -0.0007232 -6.857e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2012 -0.03448 -0.1681 0.1871 0.9835 0.9932 0.2254 0.4363 0.87 0.7137 ] Network output: [ -0.009852 1.002 1.009 -2.937e-07 1.319e-07 0.008307 -2.214e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006344 0.0005367 0.004441 0.00346 0.9889 0.9919 0.006465 0.8582 0.894 0.01249 ] Network output: [ -0.0003961 0.002213 1.001 -2.849e-05 1.279e-05 0.9976 -2.147e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2138 0.1001 0.3431 0.1442 0.985 0.994 0.2145 0.4404 0.8766 0.7078 ] Network output: [ 0.004659 -0.02208 0.9943 1.722e-05 -7.729e-06 1.019 1.297e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1063 0.09388 0.1829 0.1993 0.9873 0.9919 0.1063 0.7502 0.8645 0.3054 ] Network output: [ -0.004395 0.02084 1.004 1.838e-05 -8.253e-06 0.984 1.385e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09195 0.09001 0.165 0.1957 0.9853 0.9912 0.09196 0.6745 0.8405 0.2468 ] Network output: [ 0.0001218 1 -0.0001208 2.443e-06 -1.097e-06 0.9998 1.841e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003031 Epoch 8631 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01001 0.9962 0.9913 -1.533e-07 6.882e-08 -0.007497 -1.155e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003422 -0.003242 -0.007429 0.005876 0.9699 0.9743 0.006603 0.8304 0.8228 0.01736 ] Network output: [ 0.9999 0.000348 0.0006289 -9.088e-06 4.08e-06 -0.0007226 -6.849e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2013 -0.03448 -0.1681 0.1871 0.9835 0.9932 0.2254 0.4362 0.87 0.7137 ] Network output: [ -0.009851 1.002 1.009 -2.938e-07 1.319e-07 0.008306 -2.214e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006345 0.0005368 0.004441 0.00346 0.9889 0.9919 0.006466 0.8582 0.894 0.01249 ] Network output: [ -0.0003958 0.002213 1.001 -2.846e-05 1.278e-05 0.9976 -2.145e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2138 0.1001 0.3431 0.1442 0.985 0.994 0.2146 0.4404 0.8766 0.7078 ] Network output: [ 0.004658 -0.02207 0.9943 1.72e-05 -7.72e-06 1.019 1.296e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1063 0.09389 0.183 0.1993 0.9873 0.9919 0.1064 0.7502 0.8645 0.3054 ] Network output: [ -0.004393 0.02083 1.004 1.836e-05 -8.244e-06 0.984 1.384e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09195 0.09002 0.165 0.1957 0.9853 0.9912 0.09197 0.6745 0.8404 0.2468 ] Network output: [ 0.0001218 1 -0.0001206 2.44e-06 -1.096e-06 0.9998 1.839e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003029 Epoch 8632 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01001 0.9962 0.9913 -1.536e-07 6.896e-08 -0.007496 -1.158e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003422 -0.003242 -0.007428 0.005875 0.9699 0.9743 0.006604 0.8304 0.8228 0.01736 ] Network output: [ 0.9999 0.0003477 0.0006285 -9.079e-06 4.076e-06 -0.000722 -6.842e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2013 -0.03448 -0.1681 0.1871 0.9835 0.9932 0.2254 0.4362 0.87 0.7137 ] Network output: [ -0.00985 1.002 1.009 -2.939e-07 1.319e-07 0.008304 -2.215e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006345 0.0005369 0.004441 0.00346 0.9889 0.9919 0.006466 0.8582 0.894 0.01249 ] Network output: [ -0.0003956 0.002212 1.001 -2.843e-05 1.276e-05 0.9976 -2.143e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2139 0.1001 0.3431 0.1442 0.985 0.994 0.2146 0.4404 0.8766 0.7078 ] Network output: [ 0.004656 -0.02206 0.9943 1.718e-05 -7.712e-06 1.018 1.295e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1063 0.09389 0.183 0.1993 0.9873 0.9919 0.1064 0.7502 0.8645 0.3054 ] Network output: [ -0.004391 0.02082 1.004 1.834e-05 -8.236e-06 0.984 1.383e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09196 0.09002 0.165 0.1957 0.9853 0.9912 0.09197 0.6745 0.8404 0.2468 ] Network output: [ 0.0001217 1 -0.0001205 2.438e-06 -1.094e-06 0.9998 1.837e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003028 Epoch 8633 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01 0.9962 0.9913 -1.539e-07 6.911e-08 -0.007496 -1.16e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003422 -0.003242 -0.007427 0.005875 0.9699 0.9743 0.006604 0.8304 0.8228 0.01736 ] Network output: [ 0.9999 0.0003474 0.0006282 -9.069e-06 4.071e-06 -0.0007214 -6.834e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2013 -0.03448 -0.1681 0.1871 0.9835 0.9932 0.2254 0.4362 0.87 0.7137 ] Network output: [ -0.009849 1.002 1.009 -2.94e-07 1.32e-07 0.008303 -2.216e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006346 0.0005369 0.004441 0.003459 0.9889 0.9919 0.006467 0.8582 0.894 0.01248 ] Network output: [ -0.0003953 0.002211 1.001 -2.84e-05 1.275e-05 0.9976 -2.14e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2139 0.1001 0.3431 0.1442 0.985 0.994 0.2146 0.4404 0.8766 0.7078 ] Network output: [ 0.004654 -0.02205 0.9943 1.716e-05 -7.704e-06 1.018 1.293e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1063 0.0939 0.183 0.1993 0.9873 0.9919 0.1064 0.7502 0.8645 0.3054 ] Network output: [ -0.00439 0.02081 1.004 1.833e-05 -8.227e-06 0.984 1.381e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09196 0.09002 0.165 0.1957 0.9853 0.9912 0.09197 0.6744 0.8404 0.2468 ] Network output: [ 0.0001217 1 -0.0001204 2.435e-06 -1.093e-06 0.9998 1.835e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003026 Epoch 8634 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01 0.9962 0.9913 -1.543e-07 6.926e-08 -0.007496 -1.163e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003423 -0.003242 -0.007427 0.005874 0.9699 0.9743 0.006604 0.8304 0.8228 0.01736 ] Network output: [ 0.9999 0.0003471 0.0006279 -9.059e-06 4.067e-06 -0.0007208 -6.827e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2013 -0.03448 -0.168 0.187 0.9835 0.9932 0.2254 0.4362 0.87 0.7137 ] Network output: [ -0.009848 1.002 1.009 -2.941e-07 1.32e-07 0.008302 -2.216e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006346 0.000537 0.004441 0.003459 0.9889 0.9919 0.006467 0.8582 0.894 0.01248 ] Network output: [ -0.0003951 0.00221 1.001 -2.837e-05 1.274e-05 0.9976 -2.138e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2139 0.1001 0.3431 0.1442 0.985 0.994 0.2146 0.4404 0.8766 0.7078 ] Network output: [ 0.004653 -0.02204 0.9943 1.714e-05 -7.696e-06 1.018 1.292e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1063 0.09391 0.183 0.1993 0.9873 0.9919 0.1064 0.7502 0.8645 0.3054 ] Network output: [ -0.004388 0.0208 1.004 1.831e-05 -8.218e-06 0.984 1.38e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09196 0.09002 0.165 0.1957 0.9853 0.9912 0.09197 0.6744 0.8404 0.2468 ] Network output: [ 0.0001216 1 -0.0001202 2.432e-06 -1.092e-06 0.9998 1.833e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003025 Epoch 8635 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01 0.9962 0.9913 -1.546e-07 6.94e-08 -0.007496 -1.165e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003423 -0.003242 -0.007426 0.005874 0.9699 0.9743 0.006605 0.8303 0.8228 0.01736 ] Network output: [ 0.9999 0.0003468 0.0006275 -9.049e-06 4.062e-06 -0.0007203 -6.82e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2013 -0.03448 -0.168 0.187 0.9835 0.9932 0.2254 0.4362 0.87 0.7137 ] Network output: [ -0.009847 1.002 1.009 -2.942e-07 1.321e-07 0.0083 -2.217e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006347 0.0005371 0.004441 0.003459 0.9889 0.9919 0.006468 0.8582 0.894 0.01248 ] Network output: [ -0.0003948 0.002209 1.001 -2.834e-05 1.272e-05 0.9976 -2.136e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2139 0.1001 0.3431 0.1442 0.985 0.994 0.2146 0.4403 0.8766 0.7078 ] Network output: [ 0.004651 -0.02204 0.9943 1.712e-05 -7.687e-06 1.018 1.29e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1063 0.09391 0.183 0.1993 0.9873 0.9919 0.1064 0.7502 0.8645 0.3054 ] Network output: [ -0.004387 0.02079 1.004 1.829e-05 -8.21e-06 0.984 1.378e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09196 0.09002 0.165 0.1957 0.9853 0.9912 0.09198 0.6744 0.8404 0.2468 ] Network output: [ 0.0001216 1 -0.0001201 2.43e-06 -1.091e-06 0.9998 1.831e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003023 Epoch 8636 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.01 0.9962 0.9913 -1.549e-07 6.955e-08 -0.007496 -1.167e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003423 -0.003242 -0.007425 0.005873 0.9699 0.9743 0.006605 0.8303 0.8228 0.01736 ] Network output: [ 0.9999 0.0003465 0.0006272 -9.039e-06 4.058e-06 -0.0007197 -6.812e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2013 -0.03449 -0.168 0.187 0.9835 0.9932 0.2255 0.4362 0.87 0.7137 ] Network output: [ -0.009846 1.002 1.009 -2.942e-07 1.321e-07 0.008299 -2.217e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006347 0.0005372 0.004441 0.003458 0.9889 0.9919 0.006469 0.8582 0.894 0.01248 ] Network output: [ -0.0003945 0.002209 1.001 -2.831e-05 1.271e-05 0.9976 -2.133e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2139 0.1001 0.3431 0.1442 0.985 0.994 0.2146 0.4403 0.8766 0.7078 ] Network output: [ 0.00465 -0.02203 0.9943 1.71e-05 -7.679e-06 1.018 1.289e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1063 0.09392 0.183 0.1993 0.9873 0.9919 0.1064 0.7501 0.8645 0.3054 ] Network output: [ -0.004385 0.02079 1.004 1.827e-05 -8.201e-06 0.984 1.377e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09196 0.09003 0.165 0.1957 0.9853 0.9912 0.09198 0.6744 0.8404 0.2468 ] Network output: [ 0.0001215 1 -0.0001199 2.427e-06 -1.09e-06 0.9998 1.829e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003021 Epoch 8637 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009999 0.9962 0.9913 -1.552e-07 6.969e-08 -0.007496 -1.17e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003423 -0.003243 -0.007424 0.005873 0.9699 0.9743 0.006605 0.8303 0.8228 0.01735 ] Network output: [ 0.9999 0.0003462 0.0006268 -9.029e-06 4.054e-06 -0.0007191 -6.805e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2013 -0.03449 -0.168 0.187 0.9835 0.9932 0.2255 0.4362 0.87 0.7137 ] Network output: [ -0.009845 1.002 1.009 -2.943e-07 1.321e-07 0.008298 -2.218e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006348 0.0005373 0.004441 0.003458 0.9889 0.9919 0.006469 0.8582 0.894 0.01248 ] Network output: [ -0.0003943 0.002208 1.001 -2.828e-05 1.27e-05 0.9976 -2.131e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2139 0.1001 0.3431 0.1442 0.985 0.994 0.2146 0.4403 0.8766 0.7078 ] Network output: [ 0.004648 -0.02202 0.9943 1.709e-05 -7.671e-06 1.018 1.288e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1063 0.09392 0.183 0.1993 0.9873 0.9919 0.1064 0.7501 0.8645 0.3054 ] Network output: [ -0.004383 0.02078 1.004 1.825e-05 -8.192e-06 0.984 1.375e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09197 0.09003 0.165 0.1957 0.9853 0.9912 0.09198 0.6744 0.8404 0.2468 ] Network output: [ 0.0001215 1 -0.0001198 2.425e-06 -1.088e-06 0.9998 1.827e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000302 Epoch 8638 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009998 0.9962 0.9913 -1.556e-07 6.984e-08 -0.007496 -1.172e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003423 -0.003243 -0.007423 0.005872 0.9699 0.9743 0.006605 0.8303 0.8228 0.01735 ] Network output: [ 0.9999 0.0003459 0.0006265 -9.02e-06 4.049e-06 -0.0007185 -6.797e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2013 -0.03449 -0.168 0.187 0.9835 0.9932 0.2255 0.4362 0.87 0.7137 ] Network output: [ -0.009844 1.002 1.009 -2.944e-07 1.322e-07 0.008297 -2.219e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006348 0.0005374 0.00444 0.003458 0.9889 0.9919 0.00647 0.8581 0.894 0.01248 ] Network output: [ -0.000394 0.002207 1.001 -2.825e-05 1.268e-05 0.9976 -2.129e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2139 0.1001 0.3431 0.1442 0.985 0.994 0.2146 0.4403 0.8766 0.7078 ] Network output: [ 0.004646 -0.02201 0.9943 1.707e-05 -7.662e-06 1.018 1.286e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1063 0.09393 0.183 0.1993 0.9873 0.9919 0.1064 0.7501 0.8645 0.3054 ] Network output: [ -0.004382 0.02077 1.004 1.823e-05 -8.184e-06 0.984 1.374e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09197 0.09003 0.165 0.1957 0.9853 0.9912 0.09198 0.6744 0.8404 0.2468 ] Network output: [ 0.0001214 1 -0.0001197 2.422e-06 -1.087e-06 0.9998 1.825e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003018 Epoch 8639 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009997 0.9962 0.9913 -1.559e-07 6.998e-08 -0.007496 -1.175e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003423 -0.003243 -0.007422 0.005872 0.9699 0.9743 0.006606 0.8303 0.8228 0.01735 ] Network output: [ 0.9999 0.0003456 0.0006261 -9.01e-06 4.045e-06 -0.0007179 -6.79e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2013 -0.03449 -0.168 0.187 0.9835 0.9932 0.2255 0.4362 0.87 0.7137 ] Network output: [ -0.009843 1.002 1.009 -2.945e-07 1.322e-07 0.008295 -2.219e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006349 0.0005375 0.00444 0.003457 0.9889 0.9919 0.00647 0.8581 0.894 0.01248 ] Network output: [ -0.0003938 0.002206 1.001 -2.822e-05 1.267e-05 0.9976 -2.126e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2139 0.1001 0.3431 0.1442 0.985 0.994 0.2146 0.4403 0.8766 0.7078 ] Network output: [ 0.004645 -0.022 0.9943 1.705e-05 -7.654e-06 1.018 1.285e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1063 0.09393 0.183 0.1993 0.9873 0.9919 0.1064 0.7501 0.8645 0.3054 ] Network output: [ -0.00438 0.02076 1.004 1.821e-05 -8.175e-06 0.984 1.372e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09197 0.09003 0.165 0.1957 0.9853 0.9912 0.09198 0.6743 0.8404 0.2468 ] Network output: [ 0.0001214 1 -0.0001195 2.419e-06 -1.086e-06 0.9998 1.823e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003017 Epoch 8640 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009996 0.9962 0.9913 -1.562e-07 7.012e-08 -0.007495 -1.177e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003423 -0.003243 -0.007422 0.005871 0.9699 0.9743 0.006606 0.8303 0.8228 0.01735 ] Network output: [ 0.9999 0.0003453 0.0006258 -9e-06 4.04e-06 -0.0007173 -6.783e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2013 -0.03449 -0.168 0.187 0.9835 0.9932 0.2255 0.4362 0.87 0.7137 ] Network output: [ -0.009842 1.002 1.009 -2.946e-07 1.322e-07 0.008294 -2.22e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006349 0.0005376 0.00444 0.003457 0.9889 0.9919 0.006471 0.8581 0.894 0.01248 ] Network output: [ -0.0003935 0.002205 1.001 -2.819e-05 1.265e-05 0.9976 -2.124e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2139 0.1001 0.3431 0.1442 0.985 0.994 0.2146 0.4403 0.8766 0.7078 ] Network output: [ 0.004643 -0.022 0.9943 1.703e-05 -7.646e-06 1.018 1.284e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1063 0.09394 0.183 0.1993 0.9873 0.9919 0.1064 0.7501 0.8645 0.3054 ] Network output: [ -0.004379 0.02075 1.004 1.819e-05 -8.167e-06 0.984 1.371e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09197 0.09004 0.165 0.1957 0.9853 0.9912 0.09199 0.6743 0.8404 0.2468 ] Network output: [ 0.0001213 1 -0.0001194 2.417e-06 -1.085e-06 0.9998 1.821e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003015 Epoch 8641 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009994 0.9962 0.9913 -1.565e-07 7.026e-08 -0.007495 -1.18e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003423 -0.003243 -0.007421 0.005871 0.9699 0.9743 0.006606 0.8303 0.8228 0.01735 ] Network output: [ 0.9999 0.000345 0.0006255 -8.99e-06 4.036e-06 -0.0007167 -6.775e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2013 -0.03449 -0.168 0.187 0.9835 0.9932 0.2255 0.4362 0.87 0.7137 ] Network output: [ -0.009841 1.002 1.009 -2.946e-07 1.323e-07 0.008293 -2.22e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00635 0.0005377 0.00444 0.003457 0.9889 0.9919 0.006471 0.8581 0.894 0.01248 ] Network output: [ -0.0003933 0.002205 1.001 -2.816e-05 1.264e-05 0.9976 -2.122e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2139 0.1001 0.3431 0.1442 0.985 0.994 0.2146 0.4403 0.8766 0.7078 ] Network output: [ 0.004642 -0.02199 0.9943 1.701e-05 -7.638e-06 1.018 1.282e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1063 0.09394 0.183 0.1993 0.9873 0.9919 0.1064 0.7501 0.8645 0.3054 ] Network output: [ -0.004377 0.02074 1.004 1.817e-05 -8.158e-06 0.984 1.369e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09198 0.09004 0.165 0.1957 0.9853 0.9912 0.09199 0.6743 0.8404 0.2468 ] Network output: [ 0.0001213 1 -0.0001193 2.414e-06 -1.084e-06 0.9998 1.819e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003013 Epoch 8642 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009993 0.9962 0.9913 -1.568e-07 7.041e-08 -0.007495 -1.182e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003423 -0.003243 -0.00742 0.00587 0.9699 0.9743 0.006606 0.8303 0.8228 0.01735 ] Network output: [ 0.9999 0.0003447 0.0006251 -8.98e-06 4.032e-06 -0.0007162 -6.768e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2013 -0.03449 -0.1679 0.187 0.9835 0.9932 0.2255 0.4362 0.87 0.7137 ] Network output: [ -0.00984 1.002 1.009 -2.947e-07 1.323e-07 0.008292 -2.221e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00635 0.0005378 0.00444 0.003457 0.9889 0.9919 0.006472 0.8581 0.8939 0.01248 ] Network output: [ -0.000393 0.002204 1.001 -2.812e-05 1.263e-05 0.9976 -2.12e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2139 0.1001 0.3431 0.1442 0.985 0.994 0.2147 0.4403 0.8766 0.7078 ] Network output: [ 0.00464 -0.02198 0.9943 1.699e-05 -7.629e-06 1.018 1.281e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1063 0.09395 0.183 0.1993 0.9873 0.9919 0.1064 0.75 0.8645 0.3054 ] Network output: [ -0.004375 0.02074 1.004 1.815e-05 -8.149e-06 0.984 1.368e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09198 0.09004 0.165 0.1957 0.9853 0.9912 0.09199 0.6743 0.8404 0.2468 ] Network output: [ 0.0001212 1 -0.0001191 2.412e-06 -1.083e-06 0.9998 1.817e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003012 Epoch 8643 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009992 0.9962 0.9913 -1.571e-07 7.055e-08 -0.007495 -1.184e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003424 -0.003243 -0.007419 0.005869 0.9699 0.9743 0.006607 0.8303 0.8228 0.01735 ] Network output: [ 0.9999 0.0003444 0.0006248 -8.971e-06 4.027e-06 -0.0007156 -6.76e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2014 -0.03449 -0.1679 0.187 0.9835 0.9932 0.2255 0.4361 0.87 0.7137 ] Network output: [ -0.009839 1.002 1.009 -2.948e-07 1.323e-07 0.00829 -2.222e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006351 0.0005379 0.00444 0.003456 0.9889 0.9919 0.006472 0.8581 0.8939 0.01247 ] Network output: [ -0.0003928 0.002203 1.001 -2.809e-05 1.261e-05 0.9976 -2.117e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.214 0.1001 0.3432 0.1442 0.985 0.994 0.2147 0.4403 0.8766 0.7078 ] Network output: [ 0.004638 -0.02197 0.9943 1.698e-05 -7.621e-06 1.018 1.279e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1063 0.09395 0.183 0.1993 0.9873 0.9919 0.1064 0.75 0.8645 0.3054 ] Network output: [ -0.004374 0.02073 1.004 1.813e-05 -8.141e-06 0.984 1.367e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09198 0.09004 0.165 0.1957 0.9853 0.9912 0.09199 0.6743 0.8404 0.2468 ] Network output: [ 0.0001212 1 -0.000119 2.409e-06 -1.081e-06 0.9998 1.816e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000301 Epoch 8644 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009991 0.9962 0.9913 -1.575e-07 7.069e-08 -0.007495 -1.187e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003424 -0.003243 -0.007418 0.005869 0.9699 0.9743 0.006607 0.8303 0.8228 0.01735 ] Network output: [ 0.9999 0.0003441 0.0006244 -8.961e-06 4.023e-06 -0.000715 -6.753e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2014 -0.0345 -0.1679 0.187 0.9835 0.9932 0.2255 0.4361 0.8699 0.7137 ] Network output: [ -0.009838 1.002 1.009 -2.949e-07 1.324e-07 0.008289 -2.222e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006352 0.000538 0.00444 0.003456 0.9889 0.9919 0.006473 0.8581 0.8939 0.01247 ] Network output: [ -0.0003925 0.002202 1.001 -2.806e-05 1.26e-05 0.9976 -2.115e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.214 0.1001 0.3432 0.1442 0.985 0.994 0.2147 0.4403 0.8766 0.7078 ] Network output: [ 0.004637 -0.02196 0.9943 1.696e-05 -7.613e-06 1.018 1.278e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1064 0.09396 0.183 0.1993 0.9873 0.9919 0.1064 0.75 0.8645 0.3054 ] Network output: [ -0.004372 0.02072 1.004 1.811e-05 -8.132e-06 0.984 1.365e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09198 0.09005 0.165 0.1957 0.9853 0.9912 0.092 0.6742 0.8404 0.2468 ] Network output: [ 0.0001211 1 -0.0001188 2.406e-06 -1.08e-06 0.9998 1.814e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003008 Epoch 8645 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009989 0.9962 0.9913 -1.578e-07 7.083e-08 -0.007495 -1.189e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003424 -0.003244 -0.007417 0.005868 0.9699 0.9743 0.006607 0.8303 0.8228 0.01734 ] Network output: [ 0.9999 0.0003438 0.0006241 -8.951e-06 4.018e-06 -0.0007144 -6.746e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2014 -0.0345 -0.1679 0.187 0.9835 0.9932 0.2255 0.4361 0.8699 0.7137 ] Network output: [ -0.009837 1.002 1.009 -2.949e-07 1.324e-07 0.008288 -2.223e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006352 0.0005381 0.00444 0.003456 0.9889 0.9919 0.006473 0.8581 0.8939 0.01247 ] Network output: [ -0.0003923 0.002201 1.001 -2.803e-05 1.258e-05 0.9976 -2.113e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.214 0.1001 0.3432 0.1442 0.985 0.994 0.2147 0.4403 0.8766 0.7078 ] Network output: [ 0.004635 -0.02196 0.9943 1.694e-05 -7.605e-06 1.018 1.277e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1064 0.09397 0.183 0.1993 0.9873 0.9919 0.1064 0.75 0.8645 0.3054 ] Network output: [ -0.004371 0.02071 1.004 1.81e-05 -8.124e-06 0.984 1.364e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09199 0.09005 0.165 0.1957 0.9853 0.9912 0.092 0.6742 0.8404 0.2468 ] Network output: [ 0.000121 1 -0.0001187 2.404e-06 -1.079e-06 0.9998 1.812e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003007 Epoch 8646 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009988 0.9962 0.9913 -1.581e-07 7.097e-08 -0.007495 -1.191e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003424 -0.003244 -0.007417 0.005868 0.9699 0.9743 0.006608 0.8303 0.8228 0.01734 ] Network output: [ 0.9999 0.0003435 0.0006238 -8.941e-06 4.014e-06 -0.0007138 -6.738e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2014 -0.0345 -0.1679 0.187 0.9835 0.9932 0.2256 0.4361 0.8699 0.7137 ] Network output: [ -0.009836 1.002 1.009 -2.95e-07 1.324e-07 0.008287 -2.223e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006353 0.0005382 0.00444 0.003455 0.9889 0.9919 0.006474 0.8581 0.8939 0.01247 ] Network output: [ -0.000392 0.002201 1.001 -2.8e-05 1.257e-05 0.9976 -2.11e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.214 0.1002 0.3432 0.1442 0.985 0.994 0.2147 0.4402 0.8766 0.7078 ] Network output: [ 0.004634 -0.02195 0.9943 1.692e-05 -7.597e-06 1.018 1.275e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1064 0.09397 0.183 0.1992 0.9873 0.9919 0.1064 0.75 0.8645 0.3054 ] Network output: [ -0.004369 0.0207 1.004 1.808e-05 -8.115e-06 0.984 1.362e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09199 0.09005 0.165 0.1957 0.9853 0.9912 0.092 0.6742 0.8404 0.2468 ] Network output: [ 0.000121 1 -0.0001186 2.401e-06 -1.078e-06 0.9998 1.81e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003005 Epoch 8647 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009987 0.9962 0.9913 -1.584e-07 7.111e-08 -0.007495 -1.194e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003424 -0.003244 -0.007416 0.005867 0.9699 0.9743 0.006608 0.8303 0.8228 0.01734 ] Network output: [ 0.9999 0.0003432 0.0006234 -8.932e-06 4.01e-06 -0.0007133 -6.731e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2014 -0.0345 -0.1679 0.187 0.9835 0.9932 0.2256 0.4361 0.8699 0.7136 ] Network output: [ -0.009835 1.002 1.009 -2.951e-07 1.325e-07 0.008285 -2.224e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006353 0.0005383 0.00444 0.003455 0.9889 0.9919 0.006474 0.8581 0.8939 0.01247 ] Network output: [ -0.0003918 0.0022 1.001 -2.797e-05 1.256e-05 0.9976 -2.108e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.214 0.1002 0.3432 0.1442 0.985 0.994 0.2147 0.4402 0.8766 0.7077 ] Network output: [ 0.004632 -0.02194 0.9943 1.69e-05 -7.588e-06 1.018 1.274e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1064 0.09398 0.183 0.1992 0.9873 0.9919 0.1064 0.7499 0.8645 0.3054 ] Network output: [ -0.004367 0.02069 1.004 1.806e-05 -8.106e-06 0.984 1.361e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09199 0.09005 0.165 0.1957 0.9853 0.9912 0.092 0.6742 0.8404 0.2468 ] Network output: [ 0.0001209 1 -0.0001184 2.399e-06 -1.077e-06 0.9998 1.808e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003004 Epoch 8648 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009986 0.9962 0.9913 -1.587e-07 7.125e-08 -0.007494 -1.196e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003424 -0.003244 -0.007415 0.005867 0.9699 0.9743 0.006608 0.8303 0.8228 0.01734 ] Network output: [ 0.9999 0.0003429 0.0006231 -8.922e-06 4.005e-06 -0.0007127 -6.724e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2014 -0.0345 -0.1679 0.187 0.9835 0.9932 0.2256 0.4361 0.8699 0.7136 ] Network output: [ -0.009834 1.002 1.009 -2.952e-07 1.325e-07 0.008284 -2.224e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006354 0.0005384 0.00444 0.003455 0.9889 0.9919 0.006475 0.8581 0.8939 0.01247 ] Network output: [ -0.0003915 0.002199 1.001 -2.794e-05 1.254e-05 0.9976 -2.106e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.214 0.1002 0.3432 0.1442 0.985 0.994 0.2147 0.4402 0.8766 0.7077 ] Network output: [ 0.00463 -0.02193 0.9943 1.688e-05 -7.58e-06 1.018 1.272e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1064 0.09398 0.183 0.1992 0.9873 0.9919 0.1065 0.7499 0.8644 0.3054 ] Network output: [ -0.004366 0.02069 1.004 1.804e-05 -8.098e-06 0.9841 1.359e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09199 0.09006 0.165 0.1957 0.9853 0.9912 0.09201 0.6742 0.8404 0.2468 ] Network output: [ 0.0001209 1 -0.0001183 2.396e-06 -1.076e-06 0.9998 1.806e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003002 Epoch 8649 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009985 0.9962 0.9913 -1.59e-07 7.139e-08 -0.007494 -1.198e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003424 -0.003244 -0.007414 0.005866 0.9699 0.9743 0.006608 0.8303 0.8228 0.01734 ] Network output: [ 0.9999 0.0003426 0.0006228 -8.912e-06 4.001e-06 -0.0007121 -6.716e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2014 -0.0345 -0.1679 0.187 0.9835 0.9932 0.2256 0.4361 0.8699 0.7136 ] Network output: [ -0.009833 1.002 1.009 -2.952e-07 1.325e-07 0.008283 -2.225e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006354 0.0005385 0.00444 0.003454 0.9889 0.9919 0.006476 0.8581 0.8939 0.01247 ] Network output: [ -0.0003913 0.002198 1.001 -2.791e-05 1.253e-05 0.9976 -2.103e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.214 0.1002 0.3432 0.1442 0.985 0.994 0.2147 0.4402 0.8766 0.7077 ] Network output: [ 0.004629 -0.02192 0.9943 1.687e-05 -7.572e-06 1.018 1.271e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1064 0.09399 0.183 0.1992 0.9873 0.9919 0.1065 0.7499 0.8644 0.3054 ] Network output: [ -0.004364 0.02068 1.004 1.802e-05 -8.089e-06 0.9841 1.358e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.092 0.09006 0.165 0.1957 0.9853 0.9912 0.09201 0.6742 0.8404 0.2468 ] Network output: [ 0.0001208 1 -0.0001182 2.394e-06 -1.075e-06 0.9998 1.804e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0003 Epoch 8650 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009983 0.9962 0.9913 -1.593e-07 7.152e-08 -0.007494 -1.201e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003424 -0.003244 -0.007413 0.005866 0.9699 0.9743 0.006609 0.8303 0.8228 0.01734 ] Network output: [ 0.9999 0.0003424 0.0006224 -8.902e-06 3.997e-06 -0.0007115 -6.709e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2014 -0.0345 -0.1678 0.187 0.9835 0.9932 0.2256 0.4361 0.8699 0.7136 ] Network output: [ -0.009832 1.002 1.009 -2.953e-07 1.326e-07 0.008282 -2.226e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006355 0.0005386 0.00444 0.003454 0.9889 0.9919 0.006476 0.8581 0.8939 0.01247 ] Network output: [ -0.000391 0.002197 1.001 -2.788e-05 1.252e-05 0.9976 -2.101e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.214 0.1002 0.3432 0.1442 0.985 0.994 0.2147 0.4402 0.8766 0.7077 ] Network output: [ 0.004627 -0.02192 0.9943 1.685e-05 -7.564e-06 1.018 1.27e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1064 0.09399 0.183 0.1992 0.9873 0.9919 0.1065 0.7499 0.8644 0.3054 ] Network output: [ -0.004363 0.02067 1.004 1.8e-05 -8.081e-06 0.9841 1.357e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.092 0.09006 0.165 0.1957 0.9853 0.9912 0.09201 0.6741 0.8403 0.2468 ] Network output: [ 0.0001208 1 -0.000118 2.391e-06 -1.073e-06 0.9998 1.802e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002999 Epoch 8651 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009982 0.9962 0.9913 -1.596e-07 7.166e-08 -0.007494 -1.203e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003424 -0.003244 -0.007412 0.005865 0.9699 0.9743 0.006609 0.8302 0.8228 0.01734 ] Network output: [ 0.9999 0.0003421 0.0006221 -8.893e-06 3.992e-06 -0.0007109 -6.702e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2014 -0.03451 -0.1678 0.187 0.9835 0.9932 0.2256 0.4361 0.8699 0.7136 ] Network output: [ -0.009831 1.002 1.009 -2.954e-07 1.326e-07 0.00828 -2.226e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006355 0.0005387 0.00444 0.003454 0.9889 0.9919 0.006477 0.8581 0.8939 0.01247 ] Network output: [ -0.0003907 0.002197 1.001 -2.785e-05 1.25e-05 0.9976 -2.099e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.214 0.1002 0.3432 0.1442 0.985 0.994 0.2147 0.4402 0.8766 0.7077 ] Network output: [ 0.004625 -0.02191 0.9943 1.683e-05 -7.556e-06 1.018 1.268e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1064 0.094 0.183 0.1992 0.9873 0.9919 0.1065 0.7499 0.8644 0.3054 ] Network output: [ -0.004361 0.02066 1.004 1.798e-05 -8.072e-06 0.9841 1.355e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.092 0.09006 0.165 0.1957 0.9853 0.9912 0.09201 0.6741 0.8403 0.2468 ] Network output: [ 0.0001207 1 -0.0001179 2.388e-06 -1.072e-06 0.9998 1.8e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002997 Epoch 8652 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009981 0.9962 0.9913 -1.599e-07 7.18e-08 -0.007494 -1.205e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003425 -0.003244 -0.007412 0.005865 0.9699 0.9743 0.006609 0.8302 0.8228 0.01734 ] Network output: [ 0.9999 0.0003418 0.0006217 -8.883e-06 3.988e-06 -0.0007104 -6.695e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2014 -0.03451 -0.1678 0.187 0.9835 0.9932 0.2256 0.4361 0.8699 0.7136 ] Network output: [ -0.00983 1.002 1.009 -2.955e-07 1.326e-07 0.008279 -2.227e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006356 0.0005387 0.00444 0.003453 0.9889 0.9919 0.006477 0.858 0.8939 0.01247 ] Network output: [ -0.0003905 0.002196 1.001 -2.782e-05 1.249e-05 0.9976 -2.097e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.214 0.1002 0.3432 0.1442 0.985 0.994 0.2148 0.4402 0.8766 0.7077 ] Network output: [ 0.004624 -0.0219 0.9943 1.681e-05 -7.548e-06 1.018 1.267e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1064 0.094 0.183 0.1992 0.9873 0.9919 0.1065 0.7499 0.8644 0.3054 ] Network output: [ -0.00436 0.02065 1.004 1.796e-05 -8.064e-06 0.9841 1.354e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.092 0.09006 0.165 0.1957 0.9853 0.9912 0.09202 0.6741 0.8403 0.2468 ] Network output: [ 0.0001207 1 -0.0001178 2.386e-06 -1.071e-06 0.9998 1.798e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002996 Epoch 8653 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00998 0.9962 0.9913 -1.602e-07 7.194e-08 -0.007494 -1.208e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003425 -0.003245 -0.007411 0.005864 0.9699 0.9743 0.006609 0.8302 0.8228 0.01734 ] Network output: [ 0.9999 0.0003415 0.0006214 -8.873e-06 3.984e-06 -0.0007098 -6.687e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2014 -0.03451 -0.1678 0.187 0.9835 0.9932 0.2256 0.4361 0.8699 0.7136 ] Network output: [ -0.009829 1.002 1.009 -2.955e-07 1.327e-07 0.008278 -2.227e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006356 0.0005388 0.00444 0.003453 0.9889 0.9919 0.006478 0.858 0.8939 0.01247 ] Network output: [ -0.0003902 0.002195 1.001 -2.779e-05 1.248e-05 0.9976 -2.094e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.214 0.1002 0.3432 0.1442 0.985 0.994 0.2148 0.4402 0.8766 0.7077 ] Network output: [ 0.004622 -0.02189 0.9943 1.679e-05 -7.539e-06 1.018 1.266e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1064 0.09401 0.183 0.1992 0.9873 0.9919 0.1065 0.7498 0.8644 0.3054 ] Network output: [ -0.004358 0.02064 1.004 1.794e-05 -8.055e-06 0.9841 1.352e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.092 0.09007 0.165 0.1957 0.9853 0.9912 0.09202 0.6741 0.8403 0.2468 ] Network output: [ 0.0001206 1 -0.0001176 2.383e-06 -1.07e-06 0.9998 1.796e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002994 Epoch 8654 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009978 0.9962 0.9913 -1.605e-07 7.207e-08 -0.007494 -1.21e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003425 -0.003245 -0.00741 0.005864 0.9699 0.9743 0.00661 0.8302 0.8228 0.01733 ] Network output: [ 0.9999 0.0003412 0.0006211 -8.864e-06 3.979e-06 -0.0007092 -6.68e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2014 -0.03451 -0.1678 0.1869 0.9835 0.9932 0.2256 0.436 0.8699 0.7136 ] Network output: [ -0.009828 1.002 1.009 -2.956e-07 1.327e-07 0.008277 -2.228e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006357 0.0005389 0.00444 0.003453 0.9889 0.9919 0.006478 0.858 0.8939 0.01246 ] Network output: [ -0.00039 0.002194 1.001 -2.776e-05 1.246e-05 0.9976 -2.092e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2141 0.1002 0.3432 0.1441 0.985 0.994 0.2148 0.4402 0.8766 0.7077 ] Network output: [ 0.004621 -0.02188 0.9943 1.678e-05 -7.531e-06 1.018 1.264e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1064 0.09401 0.183 0.1992 0.9873 0.9919 0.1065 0.7498 0.8644 0.3054 ] Network output: [ -0.004356 0.02064 1.004 1.792e-05 -8.047e-06 0.9841 1.351e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09201 0.09007 0.165 0.1957 0.9853 0.9912 0.09202 0.6741 0.8403 0.2468 ] Network output: [ 0.0001206 1 -0.0001175 2.381e-06 -1.069e-06 0.9998 1.794e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002993 Epoch 8655 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009977 0.9962 0.9913 -1.608e-07 7.221e-08 -0.007493 -1.212e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003425 -0.003245 -0.007409 0.005863 0.9699 0.9743 0.00661 0.8302 0.8228 0.01733 ] Network output: [ 0.9999 0.0003409 0.0006207 -8.854e-06 3.975e-06 -0.0007086 -6.673e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2015 -0.03451 -0.1678 0.1869 0.9835 0.9932 0.2256 0.436 0.8699 0.7136 ] Network output: [ -0.009827 1.002 1.009 -2.957e-07 1.327e-07 0.008275 -2.228e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006357 0.000539 0.00444 0.003452 0.9889 0.9919 0.006479 0.858 0.8939 0.01246 ] Network output: [ -0.0003897 0.002193 1.001 -2.773e-05 1.245e-05 0.9976 -2.09e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2141 0.1002 0.3432 0.1441 0.985 0.994 0.2148 0.4402 0.8766 0.7077 ] Network output: [ 0.004619 -0.02188 0.9943 1.676e-05 -7.523e-06 1.018 1.263e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1064 0.09402 0.183 0.1992 0.9873 0.9919 0.1065 0.7498 0.8644 0.3054 ] Network output: [ -0.004355 0.02063 1.004 1.791e-05 -8.038e-06 0.9841 1.349e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09201 0.09007 0.165 0.1957 0.9853 0.9912 0.09202 0.674 0.8403 0.2468 ] Network output: [ 0.0001205 1 -0.0001174 2.378e-06 -1.068e-06 0.9998 1.792e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002991 Epoch 8656 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009976 0.9962 0.9913 -1.611e-07 7.234e-08 -0.007493 -1.214e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003425 -0.003245 -0.007408 0.005863 0.9699 0.9743 0.00661 0.8302 0.8227 0.01733 ] Network output: [ 0.9999 0.0003406 0.0006204 -8.844e-06 3.971e-06 -0.0007081 -6.665e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2015 -0.03451 -0.1678 0.1869 0.9835 0.9932 0.2257 0.436 0.8699 0.7136 ] Network output: [ -0.009826 1.002 1.009 -2.957e-07 1.328e-07 0.008274 -2.229e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006358 0.0005391 0.00444 0.003452 0.9889 0.9919 0.006479 0.858 0.8939 0.01246 ] Network output: [ -0.0003895 0.002193 1.001 -2.77e-05 1.243e-05 0.9976 -2.087e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2141 0.1002 0.3432 0.1441 0.985 0.994 0.2148 0.4402 0.8766 0.7077 ] Network output: [ 0.004617 -0.02187 0.9943 1.674e-05 -7.515e-06 1.018 1.262e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1064 0.09403 0.183 0.1992 0.9873 0.9919 0.1065 0.7498 0.8644 0.3054 ] Network output: [ -0.004353 0.02062 1.004 1.789e-05 -8.03e-06 0.9841 1.348e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09201 0.09007 0.165 0.1957 0.9853 0.9912 0.09202 0.674 0.8403 0.2468 ] Network output: [ 0.0001205 1 -0.0001172 2.376e-06 -1.066e-06 0.9998 1.79e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002989 Epoch 8657 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009975 0.9962 0.9913 -1.614e-07 7.248e-08 -0.007493 -1.217e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003425 -0.003245 -0.007408 0.005862 0.9699 0.9743 0.006611 0.8302 0.8227 0.01733 ] Network output: [ 0.9999 0.0003403 0.0006201 -8.835e-06 3.966e-06 -0.0007075 -6.658e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2015 -0.03451 -0.1678 0.1869 0.9835 0.9932 0.2257 0.436 0.8699 0.7136 ] Network output: [ -0.009825 1.002 1.009 -2.958e-07 1.328e-07 0.008273 -2.229e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006358 0.0005392 0.00444 0.003452 0.9889 0.9919 0.00648 0.858 0.8939 0.01246 ] Network output: [ -0.0003892 0.002192 1.001 -2.767e-05 1.242e-05 0.9976 -2.085e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2141 0.1002 0.3432 0.1441 0.985 0.994 0.2148 0.4401 0.8766 0.7077 ] Network output: [ 0.004616 -0.02186 0.9943 1.672e-05 -7.507e-06 1.018 1.26e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1064 0.09403 0.183 0.1992 0.9873 0.9919 0.1065 0.7498 0.8644 0.3054 ] Network output: [ -0.004352 0.02061 1.004 1.787e-05 -8.021e-06 0.9841 1.347e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09201 0.09008 0.165 0.1957 0.9853 0.9912 0.09203 0.674 0.8403 0.2468 ] Network output: [ 0.0001204 1 -0.0001171 2.373e-06 -1.065e-06 0.9998 1.788e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002988 Epoch 8658 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009974 0.9962 0.9913 -1.617e-07 7.261e-08 -0.007493 -1.219e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003425 -0.003245 -0.007407 0.005861 0.9699 0.9743 0.006611 0.8302 0.8227 0.01733 ] Network output: [ 0.9999 0.00034 0.0006197 -8.825e-06 3.962e-06 -0.0007069 -6.651e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2015 -0.03451 -0.1677 0.1869 0.9835 0.9932 0.2257 0.436 0.8699 0.7136 ] Network output: [ -0.009824 1.002 1.009 -2.959e-07 1.328e-07 0.008272 -2.23e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006359 0.0005393 0.00444 0.003451 0.9889 0.9919 0.00648 0.858 0.8939 0.01246 ] Network output: [ -0.000389 0.002191 1.001 -2.764e-05 1.241e-05 0.9976 -2.083e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2141 0.1002 0.3433 0.1441 0.985 0.994 0.2148 0.4401 0.8766 0.7077 ] Network output: [ 0.004614 -0.02185 0.9943 1.67e-05 -7.499e-06 1.018 1.259e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1064 0.09404 0.183 0.1992 0.9873 0.9919 0.1065 0.7498 0.8644 0.3054 ] Network output: [ -0.00435 0.0206 1.004 1.785e-05 -8.013e-06 0.9841 1.345e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09202 0.09008 0.165 0.1957 0.9853 0.9912 0.09203 0.674 0.8403 0.2468 ] Network output: [ 0.0001204 1 -0.000117 2.37e-06 -1.064e-06 0.9998 1.786e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002986 Epoch 8659 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009972 0.9962 0.9913 -1.62e-07 7.275e-08 -0.007493 -1.221e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003425 -0.003245 -0.007406 0.005861 0.9699 0.9743 0.006611 0.8302 0.8227 0.01733 ] Network output: [ 0.9999 0.0003397 0.0006194 -8.815e-06 3.958e-06 -0.0007063 -6.644e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2015 -0.03452 -0.1677 0.1869 0.9835 0.9932 0.2257 0.436 0.8699 0.7136 ] Network output: [ -0.009823 1.002 1.009 -2.959e-07 1.329e-07 0.00827 -2.23e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006359 0.0005394 0.00444 0.003451 0.9889 0.9919 0.006481 0.858 0.8939 0.01246 ] Network output: [ -0.0003887 0.00219 1.001 -2.761e-05 1.239e-05 0.9976 -2.081e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2141 0.1002 0.3433 0.1441 0.985 0.994 0.2148 0.4401 0.8766 0.7077 ] Network output: [ 0.004613 -0.02184 0.9943 1.669e-05 -7.491e-06 1.018 1.257e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1064 0.09404 0.183 0.1992 0.9873 0.9919 0.1065 0.7497 0.8644 0.3054 ] Network output: [ -0.004348 0.02059 1.004 1.783e-05 -8.004e-06 0.9841 1.344e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09202 0.09008 0.165 0.1957 0.9853 0.9912 0.09203 0.674 0.8403 0.2468 ] Network output: [ 0.0001203 1 -0.0001168 2.368e-06 -1.063e-06 0.9998 1.785e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002985 Epoch 8660 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009971 0.9962 0.9913 -1.623e-07 7.288e-08 -0.007493 -1.223e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003425 -0.003246 -0.007405 0.00586 0.9699 0.9743 0.006611 0.8302 0.8227 0.01733 ] Network output: [ 0.9999 0.0003394 0.0006191 -8.806e-06 3.953e-06 -0.0007058 -6.636e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2015 -0.03452 -0.1677 0.1869 0.9835 0.9932 0.2257 0.436 0.8699 0.7136 ] Network output: [ -0.009822 1.002 1.009 -2.96e-07 1.329e-07 0.008269 -2.231e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00636 0.0005395 0.00444 0.003451 0.9889 0.9919 0.006482 0.858 0.8939 0.01246 ] Network output: [ -0.0003885 0.002189 1.001 -2.758e-05 1.238e-05 0.9976 -2.078e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2141 0.1002 0.3433 0.1441 0.985 0.994 0.2148 0.4401 0.8766 0.7077 ] Network output: [ 0.004611 -0.02184 0.9943 1.667e-05 -7.483e-06 1.018 1.256e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1064 0.09405 0.183 0.1992 0.9873 0.9919 0.1065 0.7497 0.8644 0.3054 ] Network output: [ -0.004347 0.02059 1.004 1.781e-05 -7.996e-06 0.9841 1.342e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09202 0.09008 0.165 0.1957 0.9853 0.9912 0.09203 0.674 0.8403 0.2468 ] Network output: [ 0.0001203 1 -0.0001167 2.365e-06 -1.062e-06 0.9998 1.783e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002983 Epoch 8661 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00997 0.9962 0.9914 -1.626e-07 7.301e-08 -0.007493 -1.226e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003426 -0.003246 -0.007404 0.00586 0.9699 0.9743 0.006612 0.8302 0.8227 0.01733 ] Network output: [ 0.9999 0.0003391 0.0006187 -8.796e-06 3.949e-06 -0.0007052 -6.629e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2015 -0.03452 -0.1677 0.1869 0.9835 0.9932 0.2257 0.436 0.8699 0.7136 ] Network output: [ -0.009821 1.002 1.009 -2.961e-07 1.329e-07 0.008268 -2.231e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006361 0.0005396 0.00444 0.00345 0.9889 0.9919 0.006482 0.858 0.8939 0.01246 ] Network output: [ -0.0003882 0.002189 1.001 -2.755e-05 1.237e-05 0.9976 -2.076e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2141 0.1002 0.3433 0.1441 0.985 0.994 0.2148 0.4401 0.8766 0.7077 ] Network output: [ 0.004609 -0.02183 0.9943 1.665e-05 -7.474e-06 1.018 1.255e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1065 0.09405 0.183 0.1992 0.9873 0.9919 0.1065 0.7497 0.8644 0.3054 ] Network output: [ -0.004345 0.02058 1.004 1.779e-05 -7.987e-06 0.9841 1.341e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09202 0.09009 0.165 0.1957 0.9853 0.9912 0.09204 0.6739 0.8403 0.2468 ] Network output: [ 0.0001202 1 -0.0001166 2.363e-06 -1.061e-06 0.9998 1.781e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002981 Epoch 8662 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009969 0.9962 0.9914 -1.629e-07 7.315e-08 -0.007492 -1.228e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003426 -0.003246 -0.007403 0.005859 0.9699 0.9743 0.006612 0.8302 0.8227 0.01732 ] Network output: [ 0.9999 0.0003388 0.0006184 -8.787e-06 3.945e-06 -0.0007046 -6.622e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2015 -0.03452 -0.1677 0.1869 0.9835 0.9932 0.2257 0.436 0.8699 0.7136 ] Network output: [ -0.00982 1.002 1.009 -2.961e-07 1.329e-07 0.008267 -2.232e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006361 0.0005397 0.00444 0.00345 0.9889 0.9919 0.006483 0.858 0.8939 0.01246 ] Network output: [ -0.000388 0.002188 1.001 -2.752e-05 1.235e-05 0.9976 -2.074e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2141 0.1002 0.3433 0.1441 0.985 0.994 0.2148 0.4401 0.8766 0.7077 ] Network output: [ 0.004608 -0.02182 0.9943 1.663e-05 -7.466e-06 1.018 1.253e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1065 0.09406 0.183 0.1992 0.9873 0.9919 0.1065 0.7497 0.8644 0.3054 ] Network output: [ -0.004344 0.02057 1.004 1.777e-05 -7.979e-06 0.9841 1.339e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09203 0.09009 0.165 0.1957 0.9853 0.9912 0.09204 0.6739 0.8403 0.2468 ] Network output: [ 0.0001202 1 -0.0001164 2.36e-06 -1.06e-06 0.9998 1.779e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000298 Epoch 8663 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009967 0.9962 0.9914 -1.632e-07 7.328e-08 -0.007492 -1.23e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003426 -0.003246 -0.007403 0.005859 0.9699 0.9743 0.006612 0.8302 0.8227 0.01732 ] Network output: [ 0.9999 0.0003385 0.000618 -8.777e-06 3.94e-06 -0.0007041 -6.615e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2015 -0.03452 -0.1677 0.1869 0.9835 0.9932 0.2257 0.436 0.8699 0.7136 ] Network output: [ -0.009819 1.002 1.009 -2.962e-07 1.33e-07 0.008266 -2.232e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006362 0.0005398 0.00444 0.00345 0.9889 0.9919 0.006483 0.858 0.8939 0.01246 ] Network output: [ -0.0003877 0.002187 1.001 -2.749e-05 1.234e-05 0.9976 -2.071e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2141 0.1003 0.3433 0.1441 0.985 0.994 0.2149 0.4401 0.8766 0.7076 ] Network output: [ 0.004606 -0.02181 0.9943 1.661e-05 -7.458e-06 1.018 1.252e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1065 0.09406 0.183 0.1992 0.9873 0.9919 0.1065 0.7497 0.8644 0.3054 ] Network output: [ -0.004342 0.02056 1.004 1.775e-05 -7.971e-06 0.9841 1.338e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09203 0.09009 0.165 0.1957 0.9853 0.9912 0.09204 0.6739 0.8403 0.2468 ] Network output: [ 0.0001201 1 -0.0001163 2.358e-06 -1.058e-06 0.9998 1.777e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002978 Epoch 8664 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009966 0.9962 0.9914 -1.635e-07 7.341e-08 -0.007492 -1.232e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003426 -0.003246 -0.007402 0.005858 0.9699 0.9743 0.006612 0.8302 0.8227 0.01732 ] Network output: [ 0.9999 0.0003383 0.0006177 -8.767e-06 3.936e-06 -0.0007035 -6.607e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2015 -0.03452 -0.1677 0.1869 0.9835 0.9932 0.2257 0.436 0.8699 0.7136 ] Network output: [ -0.009818 1.002 1.009 -2.963e-07 1.33e-07 0.008264 -2.233e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006362 0.0005399 0.00444 0.003449 0.9889 0.9919 0.006484 0.858 0.8939 0.01246 ] Network output: [ -0.0003875 0.002186 1.001 -2.746e-05 1.233e-05 0.9976 -2.069e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2142 0.1003 0.3433 0.1441 0.985 0.994 0.2149 0.4401 0.8766 0.7076 ] Network output: [ 0.004605 -0.0218 0.9943 1.66e-05 -7.45e-06 1.018 1.251e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1065 0.09407 0.183 0.1992 0.9873 0.9919 0.1065 0.7497 0.8644 0.3054 ] Network output: [ -0.00434 0.02055 1.004 1.774e-05 -7.962e-06 0.9841 1.337e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09203 0.09009 0.165 0.1957 0.9853 0.9912 0.09204 0.6739 0.8403 0.2468 ] Network output: [ 0.00012 1 -0.0001162 2.355e-06 -1.057e-06 0.9998 1.775e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002977 Epoch 8665 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009965 0.9962 0.9914 -1.638e-07 7.354e-08 -0.007492 -1.235e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003426 -0.003246 -0.007401 0.005858 0.9699 0.9743 0.006613 0.8302 0.8227 0.01732 ] Network output: [ 0.9999 0.000338 0.0006174 -8.758e-06 3.932e-06 -0.0007029 -6.6e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2015 -0.03452 -0.1677 0.1869 0.9835 0.9932 0.2257 0.436 0.8699 0.7135 ] Network output: [ -0.009817 1.002 1.009 -2.963e-07 1.33e-07 0.008263 -2.233e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006363 0.00054 0.00444 0.003449 0.9889 0.9919 0.006484 0.858 0.8939 0.01245 ] Network output: [ -0.0003872 0.002185 1.001 -2.743e-05 1.231e-05 0.9976 -2.067e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2142 0.1003 0.3433 0.1441 0.985 0.994 0.2149 0.4401 0.8766 0.7076 ] Network output: [ 0.004603 -0.0218 0.9943 1.658e-05 -7.442e-06 1.018 1.249e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1065 0.09407 0.183 0.1992 0.9873 0.9919 0.1066 0.7496 0.8644 0.3054 ] Network output: [ -0.004339 0.02055 1.004 1.772e-05 -7.954e-06 0.9841 1.335e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09203 0.0901 0.165 0.1957 0.9853 0.9912 0.09205 0.6739 0.8403 0.2468 ] Network output: [ 0.00012 1 -0.000116 2.353e-06 -1.056e-06 0.9998 1.773e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002975 Epoch 8666 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009964 0.9962 0.9914 -1.641e-07 7.367e-08 -0.007492 -1.237e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003426 -0.003246 -0.0074 0.005857 0.9699 0.9743 0.006613 0.8302 0.8227 0.01732 ] Network output: [ 0.9999 0.0003377 0.000617 -8.748e-06 3.927e-06 -0.0007023 -6.593e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2015 -0.03452 -0.1676 0.1869 0.9835 0.9932 0.2257 0.4359 0.8699 0.7135 ] Network output: [ -0.009816 1.002 1.009 -2.964e-07 1.331e-07 0.008262 -2.234e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006363 0.0005401 0.00444 0.003449 0.9889 0.9919 0.006485 0.8579 0.8939 0.01245 ] Network output: [ -0.000387 0.002185 1.001 -2.74e-05 1.23e-05 0.9976 -2.065e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2142 0.1003 0.3433 0.1441 0.985 0.994 0.2149 0.4401 0.8766 0.7076 ] Network output: [ 0.004601 -0.02179 0.9943 1.656e-05 -7.434e-06 1.018 1.248e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1065 0.09408 0.183 0.1992 0.9873 0.9919 0.1066 0.7496 0.8644 0.3054 ] Network output: [ -0.004337 0.02054 1.004 1.77e-05 -7.945e-06 0.9841 1.334e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09204 0.0901 0.165 0.1957 0.9853 0.9912 0.09205 0.6739 0.8403 0.2468 ] Network output: [ 0.0001199 1 -0.0001159 2.35e-06 -1.055e-06 0.9998 1.771e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002973 Epoch 8667 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009962 0.9962 0.9914 -1.644e-07 7.38e-08 -0.007492 -1.239e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003426 -0.003246 -0.007399 0.005857 0.9699 0.9743 0.006613 0.8301 0.8227 0.01732 ] Network output: [ 0.9999 0.0003374 0.0006167 -8.739e-06 3.923e-06 -0.0007018 -6.586e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2016 -0.03453 -0.1676 0.1869 0.9835 0.9932 0.2258 0.4359 0.8699 0.7135 ] Network output: [ -0.009815 1.002 1.009 -2.964e-07 1.331e-07 0.008261 -2.234e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006364 0.0005402 0.00444 0.003449 0.9889 0.9919 0.006485 0.8579 0.8939 0.01245 ] Network output: [ -0.0003867 0.002184 1.001 -2.737e-05 1.229e-05 0.9976 -2.062e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2142 0.1003 0.3433 0.1441 0.985 0.994 0.2149 0.4401 0.8766 0.7076 ] Network output: [ 0.0046 -0.02178 0.9943 1.654e-05 -7.426e-06 1.018 1.247e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1065 0.09408 0.183 0.1992 0.9873 0.9919 0.1066 0.7496 0.8644 0.3054 ] Network output: [ -0.004336 0.02053 1.004 1.768e-05 -7.937e-06 0.9841 1.332e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09204 0.0901 0.165 0.1957 0.9853 0.9912 0.09205 0.6738 0.8403 0.2468 ] Network output: [ 0.0001199 1 -0.0001158 2.348e-06 -1.054e-06 0.9998 1.769e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002972 Epoch 8668 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009961 0.9962 0.9914 -1.647e-07 7.393e-08 -0.007492 -1.241e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003426 -0.003247 -0.007399 0.005856 0.9699 0.9743 0.006613 0.8301 0.8227 0.01732 ] Network output: [ 0.9999 0.0003371 0.0006164 -8.729e-06 3.919e-06 -0.0007012 -6.579e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2016 -0.03453 -0.1676 0.1869 0.9835 0.9932 0.2258 0.4359 0.8699 0.7135 ] Network output: [ -0.009814 1.002 1.009 -2.965e-07 1.331e-07 0.008259 -2.235e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006364 0.0005403 0.00444 0.003448 0.9889 0.9919 0.006486 0.8579 0.8939 0.01245 ] Network output: [ -0.0003865 0.002183 1.001 -2.734e-05 1.227e-05 0.9976 -2.06e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2142 0.1003 0.3433 0.1441 0.985 0.994 0.2149 0.4401 0.8766 0.7076 ] Network output: [ 0.004598 -0.02177 0.9943 1.652e-05 -7.418e-06 1.018 1.245e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1065 0.09409 0.183 0.1992 0.9873 0.9919 0.1066 0.7496 0.8644 0.3054 ] Network output: [ -0.004334 0.02052 1.004 1.766e-05 -7.928e-06 0.9841 1.331e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09204 0.0901 0.165 0.1957 0.9853 0.9912 0.09205 0.6738 0.8403 0.2469 ] Network output: [ 0.0001198 1 -0.0001156 2.345e-06 -1.053e-06 0.9998 1.767e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000297 Epoch 8669 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00996 0.9962 0.9914 -1.65e-07 7.406e-08 -0.007491 -1.243e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003426 -0.003247 -0.007398 0.005856 0.9699 0.9743 0.006614 0.8301 0.8227 0.01732 ] Network output: [ 0.9999 0.0003368 0.000616 -8.72e-06 3.915e-06 -0.0007006 -6.571e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2016 -0.03453 -0.1676 0.1869 0.9835 0.9932 0.2258 0.4359 0.8699 0.7135 ] Network output: [ -0.009813 1.002 1.009 -2.966e-07 1.331e-07 0.008258 -2.235e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006365 0.0005404 0.004439 0.003448 0.9889 0.9919 0.006486 0.8579 0.8939 0.01245 ] Network output: [ -0.0003862 0.002182 1.001 -2.731e-05 1.226e-05 0.9976 -2.058e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2142 0.1003 0.3433 0.1441 0.985 0.994 0.2149 0.44 0.8766 0.7076 ] Network output: [ 0.004597 -0.02176 0.9943 1.651e-05 -7.41e-06 1.018 1.244e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1065 0.0941 0.183 0.1992 0.9873 0.9919 0.1066 0.7496 0.8644 0.3054 ] Network output: [ -0.004333 0.02051 1.004 1.764e-05 -7.92e-06 0.9841 1.33e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09204 0.0901 0.165 0.1957 0.9853 0.9912 0.09206 0.6738 0.8403 0.2469 ] Network output: [ 0.0001198 1 -0.0001155 2.343e-06 -1.052e-06 0.9998 1.765e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002969 Epoch 8670 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009959 0.9962 0.9914 -1.653e-07 7.419e-08 -0.007491 -1.245e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003426 -0.003247 -0.007397 0.005855 0.9699 0.9743 0.006614 0.8301 0.8227 0.01732 ] Network output: [ 0.9999 0.0003365 0.0006157 -8.71e-06 3.91e-06 -0.0007001 -6.564e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2016 -0.03453 -0.1676 0.1869 0.9835 0.9932 0.2258 0.4359 0.8699 0.7135 ] Network output: [ -0.009812 1.002 1.009 -2.966e-07 1.332e-07 0.008257 -2.235e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006365 0.0005405 0.004439 0.003448 0.9889 0.9919 0.006487 0.8579 0.8939 0.01245 ] Network output: [ -0.000386 0.002181 1.001 -2.728e-05 1.225e-05 0.9976 -2.056e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2142 0.1003 0.3433 0.1441 0.985 0.994 0.2149 0.44 0.8766 0.7076 ] Network output: [ 0.004595 -0.02176 0.9943 1.649e-05 -7.402e-06 1.018 1.243e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1065 0.0941 0.183 0.1992 0.9873 0.9919 0.1066 0.7496 0.8644 0.3054 ] Network output: [ -0.004331 0.0205 1.004 1.762e-05 -7.912e-06 0.9841 1.328e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09204 0.09011 0.165 0.1957 0.9853 0.9912 0.09206 0.6738 0.8402 0.2469 ] Network output: [ 0.0001197 1 -0.0001154 2.34e-06 -1.051e-06 0.9998 1.764e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002967 Epoch 8671 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009958 0.9962 0.9914 -1.655e-07 7.432e-08 -0.007491 -1.248e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003427 -0.003247 -0.007396 0.005855 0.9699 0.9743 0.006614 0.8301 0.8227 0.01731 ] Network output: [ 0.9999 0.0003362 0.0006154 -8.701e-06 3.906e-06 -0.0006995 -6.557e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2016 -0.03453 -0.1676 0.1869 0.9835 0.9932 0.2258 0.4359 0.8699 0.7135 ] Network output: [ -0.009811 1.002 1.009 -2.967e-07 1.332e-07 0.008256 -2.236e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006366 0.0005405 0.004439 0.003447 0.9889 0.9919 0.006487 0.8579 0.8939 0.01245 ] Network output: [ -0.0003857 0.002181 1.001 -2.725e-05 1.223e-05 0.9977 -2.053e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2142 0.1003 0.3433 0.1441 0.985 0.994 0.2149 0.44 0.8766 0.7076 ] Network output: [ 0.004593 -0.02175 0.9943 1.647e-05 -7.394e-06 1.018 1.241e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1065 0.09411 0.183 0.1992 0.9873 0.9919 0.1066 0.7495 0.8644 0.3054 ] Network output: [ -0.004329 0.0205 1.004 1.76e-05 -7.903e-06 0.9841 1.327e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09205 0.09011 0.165 0.1957 0.9853 0.9912 0.09206 0.6738 0.8402 0.2469 ] Network output: [ 0.0001197 1 -0.0001152 2.338e-06 -1.049e-06 0.9998 1.762e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002966 Epoch 8672 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009956 0.9962 0.9914 -1.658e-07 7.444e-08 -0.007491 -1.25e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003427 -0.003247 -0.007395 0.005854 0.9699 0.9743 0.006615 0.8301 0.8227 0.01731 ] Network output: [ 0.9999 0.0003359 0.0006151 -8.691e-06 3.902e-06 -0.0006989 -6.55e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2016 -0.03453 -0.1676 0.1869 0.9835 0.9932 0.2258 0.4359 0.8699 0.7135 ] Network output: [ -0.00981 1.002 1.009 -2.967e-07 1.332e-07 0.008254 -2.236e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006366 0.0005406 0.004439 0.003447 0.9889 0.9919 0.006488 0.8579 0.8939 0.01245 ] Network output: [ -0.0003855 0.00218 1.001 -2.722e-05 1.222e-05 0.9977 -2.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2142 0.1003 0.3434 0.1441 0.985 0.994 0.2149 0.44 0.8766 0.7076 ] Network output: [ 0.004592 -0.02174 0.9943 1.645e-05 -7.386e-06 1.018 1.24e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1065 0.09411 0.183 0.1992 0.9873 0.9919 0.1066 0.7495 0.8643 0.3054 ] Network output: [ -0.004328 0.02049 1.004 1.759e-05 -7.895e-06 0.9841 1.325e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09205 0.09011 0.165 0.1957 0.9853 0.9912 0.09206 0.6737 0.8402 0.2469 ] Network output: [ 0.0001196 1 -0.0001151 2.335e-06 -1.048e-06 0.9998 1.76e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002964 Epoch 8673 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009955 0.9962 0.9914 -1.661e-07 7.457e-08 -0.007491 -1.252e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003427 -0.003247 -0.007394 0.005854 0.9699 0.9743 0.006615 0.8301 0.8227 0.01731 ] Network output: [ 0.9999 0.0003356 0.0006147 -8.682e-06 3.898e-06 -0.0006984 -6.543e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2016 -0.03453 -0.1676 0.1869 0.9835 0.9932 0.2258 0.4359 0.8699 0.7135 ] Network output: [ -0.009809 1.002 1.009 -2.968e-07 1.332e-07 0.008253 -2.237e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006367 0.0005407 0.004439 0.003447 0.9889 0.9919 0.006489 0.8579 0.8939 0.01245 ] Network output: [ -0.0003852 0.002179 1.001 -2.719e-05 1.221e-05 0.9977 -2.049e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2142 0.1003 0.3434 0.1441 0.985 0.994 0.2149 0.44 0.8765 0.7076 ] Network output: [ 0.00459 -0.02173 0.9943 1.643e-05 -7.378e-06 1.018 1.239e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1065 0.09412 0.183 0.1992 0.9873 0.9919 0.1066 0.7495 0.8643 0.3054 ] Network output: [ -0.004326 0.02048 1.004 1.757e-05 -7.887e-06 0.9842 1.324e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09205 0.09011 0.165 0.1957 0.9853 0.9912 0.09206 0.6737 0.8402 0.2469 ] Network output: [ 0.0001196 1 -0.000115 2.333e-06 -1.047e-06 0.9998 1.758e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002962 Epoch 8674 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009954 0.9962 0.9914 -1.664e-07 7.47e-08 -0.007491 -1.254e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003427 -0.003247 -0.007394 0.005853 0.9699 0.9743 0.006615 0.8301 0.8227 0.01731 ] Network output: [ 0.9999 0.0003353 0.0006144 -8.672e-06 3.893e-06 -0.0006978 -6.536e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2016 -0.03453 -0.1675 0.1868 0.9835 0.9932 0.2258 0.4359 0.8699 0.7135 ] Network output: [ -0.009808 1.002 1.009 -2.969e-07 1.333e-07 0.008252 -2.237e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006367 0.0005408 0.004439 0.003446 0.9889 0.9919 0.006489 0.8579 0.8939 0.01245 ] Network output: [ -0.000385 0.002178 1.001 -2.716e-05 1.219e-05 0.9977 -2.047e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2142 0.1003 0.3434 0.1441 0.985 0.994 0.215 0.44 0.8765 0.7076 ] Network output: [ 0.004589 -0.02173 0.9943 1.642e-05 -7.37e-06 1.018 1.237e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1065 0.09412 0.1831 0.1992 0.9873 0.9919 0.1066 0.7495 0.8643 0.3054 ] Network output: [ -0.004325 0.02047 1.004 1.755e-05 -7.878e-06 0.9842 1.323e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09205 0.09012 0.165 0.1957 0.9853 0.9912 0.09207 0.6737 0.8402 0.2469 ] Network output: [ 0.0001195 1 -0.0001148 2.33e-06 -1.046e-06 0.9998 1.756e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002961 Epoch 8675 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009953 0.9962 0.9914 -1.667e-07 7.483e-08 -0.00749 -1.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003427 -0.003247 -0.007393 0.005853 0.9699 0.9743 0.006615 0.8301 0.8227 0.01731 ] Network output: [ 0.9999 0.0003351 0.0006141 -8.663e-06 3.889e-06 -0.0006972 -6.528e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2016 -0.03454 -0.1675 0.1868 0.9835 0.9932 0.2258 0.4359 0.8699 0.7135 ] Network output: [ -0.009807 1.002 1.009 -2.969e-07 1.333e-07 0.008251 -2.238e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006368 0.0005409 0.004439 0.003446 0.9889 0.9919 0.00649 0.8579 0.8939 0.01245 ] Network output: [ -0.0003847 0.002177 1.001 -2.713e-05 1.218e-05 0.9977 -2.044e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2143 0.1003 0.3434 0.1441 0.985 0.994 0.215 0.44 0.8765 0.7076 ] Network output: [ 0.004587 -0.02172 0.9943 1.64e-05 -7.362e-06 1.018 1.236e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1065 0.09413 0.1831 0.1992 0.9873 0.9919 0.1066 0.7495 0.8643 0.3054 ] Network output: [ -0.004323 0.02046 1.004 1.753e-05 -7.87e-06 0.9842 1.321e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09206 0.09012 0.165 0.1957 0.9853 0.9912 0.09207 0.6737 0.8402 0.2469 ] Network output: [ 0.0001195 1 -0.0001147 2.327e-06 -1.045e-06 0.9998 1.754e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002959 Epoch 8676 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009951 0.9962 0.9914 -1.67e-07 7.495e-08 -0.00749 -1.258e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003427 -0.003248 -0.007392 0.005852 0.9699 0.9743 0.006616 0.8301 0.8227 0.01731 ] Network output: [ 0.9999 0.0003348 0.0006137 -8.653e-06 3.885e-06 -0.0006967 -6.521e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2016 -0.03454 -0.1675 0.1868 0.9835 0.9932 0.2258 0.4359 0.8699 0.7135 ] Network output: [ -0.009806 1.002 1.009 -2.97e-07 1.333e-07 0.00825 -2.238e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006368 0.000541 0.004439 0.003446 0.9889 0.9919 0.00649 0.8579 0.8939 0.01244 ] Network output: [ -0.0003845 0.002177 1.001 -2.71e-05 1.217e-05 0.9977 -2.042e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2143 0.1003 0.3434 0.1441 0.985 0.994 0.215 0.44 0.8765 0.7076 ] Network output: [ 0.004585 -0.02171 0.9943 1.638e-05 -7.354e-06 1.018 1.235e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1065 0.09413 0.1831 0.1992 0.9873 0.9919 0.1066 0.7495 0.8643 0.3054 ] Network output: [ -0.004321 0.02045 1.004 1.751e-05 -7.862e-06 0.9842 1.32e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09206 0.09012 0.165 0.1957 0.9853 0.9912 0.09207 0.6737 0.8402 0.2469 ] Network output: [ 0.0001194 1 -0.0001146 2.325e-06 -1.044e-06 0.9998 1.752e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002958 Epoch 8677 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00995 0.9962 0.9914 -1.672e-07 7.508e-08 -0.00749 -1.26e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003427 -0.003248 -0.007391 0.005851 0.9699 0.9743 0.006616 0.8301 0.8227 0.01731 ] Network output: [ 0.9999 0.0003345 0.0006134 -8.644e-06 3.881e-06 -0.0006961 -6.514e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2016 -0.03454 -0.1675 0.1868 0.9835 0.9932 0.2259 0.4358 0.8699 0.7135 ] Network output: [ -0.009805 1.002 1.009 -2.97e-07 1.333e-07 0.008248 -2.238e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006369 0.0005411 0.004439 0.003445 0.9889 0.9919 0.006491 0.8579 0.8939 0.01244 ] Network output: [ -0.0003842 0.002176 1.001 -2.707e-05 1.215e-05 0.9977 -2.04e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2143 0.1003 0.3434 0.1441 0.985 0.994 0.215 0.44 0.8765 0.7076 ] Network output: [ 0.004584 -0.0217 0.9943 1.636e-05 -7.346e-06 1.018 1.233e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1065 0.09414 0.1831 0.1992 0.9873 0.9919 0.1066 0.7494 0.8643 0.3054 ] Network output: [ -0.00432 0.02045 1.004 1.749e-05 -7.853e-06 0.9842 1.318e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09206 0.09012 0.165 0.1957 0.9853 0.9912 0.09207 0.6737 0.8402 0.2469 ] Network output: [ 0.0001194 1 -0.0001144 2.322e-06 -1.043e-06 0.9998 1.75e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002956 Epoch 8678 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009949 0.9962 0.9914 -1.675e-07 7.52e-08 -0.00749 -1.262e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003427 -0.003248 -0.00739 0.005851 0.9699 0.9743 0.006616 0.8301 0.8227 0.01731 ] Network output: [ 0.9999 0.0003342 0.0006131 -8.634e-06 3.876e-06 -0.0006956 -6.507e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2016 -0.03454 -0.1675 0.1868 0.9835 0.9932 0.2259 0.4358 0.8699 0.7135 ] Network output: [ -0.009804 1.002 1.009 -2.971e-07 1.334e-07 0.008247 -2.239e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006369 0.0005412 0.004439 0.003445 0.9889 0.9919 0.006491 0.8579 0.8939 0.01244 ] Network output: [ -0.000384 0.002175 1.001 -2.704e-05 1.214e-05 0.9977 -2.038e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2143 0.1003 0.3434 0.1441 0.985 0.994 0.215 0.44 0.8765 0.7076 ] Network output: [ 0.004582 -0.02169 0.9943 1.635e-05 -7.338e-06 1.018 1.232e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1066 0.09414 0.1831 0.1992 0.9873 0.9919 0.1066 0.7494 0.8643 0.3054 ] Network output: [ -0.004318 0.02044 1.004 1.747e-05 -7.845e-06 0.9842 1.317e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09206 0.09013 0.165 0.1957 0.9853 0.9912 0.09208 0.6736 0.8402 0.2469 ] Network output: [ 0.0001193 1 -0.0001143 2.32e-06 -1.042e-06 0.9998 1.748e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002955 Epoch 8679 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009948 0.9962 0.9914 -1.678e-07 7.533e-08 -0.00749 -1.265e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003427 -0.003248 -0.00739 0.00585 0.9699 0.9743 0.006616 0.8301 0.8227 0.0173 ] Network output: [ 0.9999 0.0003339 0.0006127 -8.625e-06 3.872e-06 -0.000695 -6.5e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2017 -0.03454 -0.1675 0.1868 0.9835 0.9932 0.2259 0.4358 0.8699 0.7135 ] Network output: [ -0.009803 1.002 1.009 -2.971e-07 1.334e-07 0.008246 -2.239e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00637 0.0005413 0.004439 0.003445 0.9889 0.9919 0.006492 0.8579 0.8939 0.01244 ] Network output: [ -0.0003838 0.002174 1.001 -2.701e-05 1.213e-05 0.9977 -2.036e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2143 0.1003 0.3434 0.1441 0.985 0.994 0.215 0.44 0.8765 0.7076 ] Network output: [ 0.004581 -0.02169 0.9943 1.633e-05 -7.33e-06 1.018 1.231e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1066 0.09415 0.1831 0.1992 0.9873 0.9919 0.1066 0.7494 0.8643 0.3054 ] Network output: [ -0.004317 0.02043 1.004 1.746e-05 -7.837e-06 0.9842 1.316e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09207 0.09013 0.165 0.1957 0.9853 0.9912 0.09208 0.6736 0.8402 0.2469 ] Network output: [ 0.0001193 1 -0.0001142 2.317e-06 -1.04e-06 0.9998 1.747e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002953 Epoch 8680 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009947 0.9962 0.9914 -1.681e-07 7.545e-08 -0.00749 -1.267e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003428 -0.003248 -0.007389 0.00585 0.9699 0.9743 0.006617 0.8301 0.8227 0.0173 ] Network output: [ 0.9999 0.0003336 0.0006124 -8.615e-06 3.868e-06 -0.0006944 -6.493e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2017 -0.03454 -0.1675 0.1868 0.9835 0.9932 0.2259 0.4358 0.8699 0.7135 ] Network output: [ -0.009802 1.002 1.009 -2.972e-07 1.334e-07 0.008245 -2.24e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006371 0.0005414 0.004439 0.003444 0.9889 0.9919 0.006492 0.8578 0.8939 0.01244 ] Network output: [ -0.0003835 0.002173 1.001 -2.698e-05 1.211e-05 0.9977 -2.033e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2143 0.1003 0.3434 0.1441 0.985 0.994 0.215 0.4399 0.8765 0.7075 ] Network output: [ 0.004579 -0.02168 0.9943 1.631e-05 -7.322e-06 1.018 1.229e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1066 0.09415 0.1831 0.1992 0.9873 0.9919 0.1066 0.7494 0.8643 0.3054 ] Network output: [ -0.004315 0.02042 1.004 1.744e-05 -7.828e-06 0.9842 1.314e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09207 0.09013 0.165 0.1957 0.9853 0.9912 0.09208 0.6736 0.8402 0.2469 ] Network output: [ 0.0001192 1 -0.000114 2.315e-06 -1.039e-06 0.9998 1.745e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002951 Epoch 8681 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009945 0.9962 0.9914 -1.683e-07 7.557e-08 -0.00749 -1.269e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003428 -0.003248 -0.007388 0.005849 0.9699 0.9743 0.006617 0.8301 0.8227 0.0173 ] Network output: [ 0.9999 0.0003333 0.0006121 -8.606e-06 3.864e-06 -0.0006939 -6.486e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2017 -0.03454 -0.1675 0.1868 0.9835 0.9932 0.2259 0.4358 0.8699 0.7135 ] Network output: [ -0.009801 1.002 1.009 -2.972e-07 1.334e-07 0.008243 -2.24e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006371 0.0005415 0.004439 0.003444 0.9889 0.9919 0.006493 0.8578 0.8939 0.01244 ] Network output: [ -0.0003833 0.002173 1.001 -2.695e-05 1.21e-05 0.9977 -2.031e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2143 0.1004 0.3434 0.1441 0.985 0.994 0.215 0.4399 0.8765 0.7075 ] Network output: [ 0.004577 -0.02167 0.9943 1.629e-05 -7.314e-06 1.018 1.228e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1066 0.09416 0.1831 0.1992 0.9873 0.9919 0.1066 0.7494 0.8643 0.3054 ] Network output: [ -0.004314 0.02041 1.004 1.742e-05 -7.82e-06 0.9842 1.313e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09207 0.09013 0.165 0.1957 0.9853 0.9912 0.09208 0.6736 0.8402 0.2469 ] Network output: [ 0.0001192 1 -0.0001139 2.312e-06 -1.038e-06 0.9998 1.743e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000295 Epoch 8682 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009944 0.9962 0.9914 -1.686e-07 7.57e-08 -0.007489 -1.271e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003428 -0.003248 -0.007387 0.005849 0.9699 0.9743 0.006617 0.8301 0.8227 0.0173 ] Network output: [ 0.9999 0.000333 0.0006117 -8.597e-06 3.859e-06 -0.0006933 -6.479e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2017 -0.03454 -0.1674 0.1868 0.9835 0.9932 0.2259 0.4358 0.8699 0.7135 ] Network output: [ -0.0098 1.002 1.009 -2.973e-07 1.335e-07 0.008242 -2.24e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006372 0.0005416 0.004439 0.003444 0.9889 0.9919 0.006493 0.8578 0.8938 0.01244 ] Network output: [ -0.000383 0.002172 1.001 -2.692e-05 1.209e-05 0.9977 -2.029e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2143 0.1004 0.3434 0.1441 0.985 0.994 0.215 0.4399 0.8765 0.7075 ] Network output: [ 0.004576 -0.02166 0.9943 1.627e-05 -7.306e-06 1.018 1.227e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1066 0.09417 0.1831 0.1992 0.9873 0.9919 0.1066 0.7494 0.8643 0.3054 ] Network output: [ -0.004312 0.02041 1.004 1.74e-05 -7.812e-06 0.9842 1.311e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09207 0.09014 0.165 0.1957 0.9853 0.9912 0.09209 0.6736 0.8402 0.2469 ] Network output: [ 0.0001191 1 -0.0001138 2.31e-06 -1.037e-06 0.9998 1.741e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002948 Epoch 8683 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009943 0.9962 0.9914 -1.689e-07 7.582e-08 -0.007489 -1.273e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003428 -0.003248 -0.007386 0.005848 0.9699 0.9743 0.006617 0.83 0.8227 0.0173 ] Network output: [ 0.9999 0.0003327 0.0006114 -8.587e-06 3.855e-06 -0.0006928 -6.472e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2017 -0.03455 -0.1674 0.1868 0.9835 0.9932 0.2259 0.4358 0.8699 0.7134 ] Network output: [ -0.009799 1.002 1.009 -2.973e-07 1.335e-07 0.008241 -2.241e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006372 0.0005417 0.004439 0.003443 0.9889 0.9919 0.006494 0.8578 0.8938 0.01244 ] Network output: [ -0.0003828 0.002171 1.001 -2.689e-05 1.207e-05 0.9977 -2.027e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2143 0.1004 0.3434 0.1441 0.985 0.994 0.215 0.4399 0.8765 0.7075 ] Network output: [ 0.004574 -0.02165 0.9943 1.626e-05 -7.298e-06 1.018 1.225e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1066 0.09417 0.1831 0.1992 0.9873 0.9919 0.1067 0.7493 0.8643 0.3054 ] Network output: [ -0.00431 0.0204 1.004 1.738e-05 -7.803e-06 0.9842 1.31e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09208 0.09014 0.165 0.1957 0.9853 0.9912 0.09209 0.6736 0.8402 0.2469 ] Network output: [ 0.0001191 1 -0.0001137 2.307e-06 -1.036e-06 0.9998 1.739e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002947 Epoch 8684 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009942 0.9962 0.9914 -1.692e-07 7.594e-08 -0.007489 -1.275e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003428 -0.003249 -0.007385 0.005848 0.9699 0.9743 0.006618 0.83 0.8227 0.0173 ] Network output: [ 0.9999 0.0003325 0.0006111 -8.578e-06 3.851e-06 -0.0006922 -6.465e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2017 -0.03455 -0.1674 0.1868 0.9835 0.9932 0.2259 0.4358 0.8699 0.7134 ] Network output: [ -0.009798 1.002 1.009 -2.974e-07 1.335e-07 0.00824 -2.241e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006373 0.0005418 0.004439 0.003443 0.9889 0.9919 0.006494 0.8578 0.8938 0.01244 ] Network output: [ -0.0003825 0.00217 1.001 -2.686e-05 1.206e-05 0.9977 -2.024e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2143 0.1004 0.3434 0.1441 0.985 0.994 0.2151 0.4399 0.8765 0.7075 ] Network output: [ 0.004573 -0.02165 0.9943 1.624e-05 -7.291e-06 1.018 1.224e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1066 0.09418 0.1831 0.1992 0.9873 0.9919 0.1067 0.7493 0.8643 0.3054 ] Network output: [ -0.004309 0.02039 1.004 1.736e-05 -7.795e-06 0.9842 1.309e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09208 0.09014 0.165 0.1957 0.9853 0.9912 0.09209 0.6735 0.8402 0.2469 ] Network output: [ 0.000119 1 -0.0001135 2.305e-06 -1.035e-06 0.9998 1.737e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002945 Epoch 8685 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009941 0.9962 0.9914 -1.694e-07 7.607e-08 -0.007489 -1.277e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003428 -0.003249 -0.007385 0.005847 0.9699 0.9743 0.006618 0.83 0.8226 0.0173 ] Network output: [ 0.9999 0.0003322 0.0006108 -8.568e-06 3.847e-06 -0.0006916 -6.457e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2017 -0.03455 -0.1674 0.1868 0.9835 0.9932 0.2259 0.4358 0.8699 0.7134 ] Network output: [ -0.009797 1.002 1.009 -2.974e-07 1.335e-07 0.008239 -2.242e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006373 0.0005419 0.004439 0.003443 0.9889 0.9919 0.006495 0.8578 0.8938 0.01244 ] Network output: [ -0.0003823 0.00217 1.001 -2.683e-05 1.205e-05 0.9977 -2.022e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2143 0.1004 0.3434 0.1441 0.985 0.994 0.2151 0.4399 0.8765 0.7075 ] Network output: [ 0.004571 -0.02164 0.9943 1.622e-05 -7.283e-06 1.018 1.223e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1066 0.09418 0.1831 0.1992 0.9873 0.9919 0.1067 0.7493 0.8643 0.3054 ] Network output: [ -0.004307 0.02038 1.004 1.735e-05 -7.787e-06 0.9842 1.307e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09208 0.09014 0.165 0.1957 0.9853 0.9912 0.09209 0.6735 0.8402 0.2469 ] Network output: [ 0.000119 1 -0.0001134 2.302e-06 -1.034e-06 0.9998 1.735e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002944 Epoch 8686 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009939 0.9962 0.9914 -1.697e-07 7.619e-08 -0.007489 -1.279e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003428 -0.003249 -0.007384 0.005847 0.9699 0.9743 0.006618 0.83 0.8226 0.0173 ] Network output: [ 0.9999 0.0003319 0.0006104 -8.559e-06 3.842e-06 -0.0006911 -6.45e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2017 -0.03455 -0.1674 0.1868 0.9835 0.9932 0.2259 0.4358 0.8699 0.7134 ] Network output: [ -0.009796 1.002 1.009 -2.975e-07 1.336e-07 0.008237 -2.242e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006374 0.000542 0.004439 0.003442 0.9889 0.9919 0.006496 0.8578 0.8938 0.01244 ] Network output: [ -0.000382 0.002169 1.001 -2.68e-05 1.203e-05 0.9977 -2.02e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2144 0.1004 0.3434 0.1441 0.985 0.994 0.2151 0.4399 0.8765 0.7075 ] Network output: [ 0.004569 -0.02163 0.9943 1.62e-05 -7.275e-06 1.018 1.221e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1066 0.09419 0.1831 0.1992 0.9873 0.9919 0.1067 0.7493 0.8643 0.3054 ] Network output: [ -0.004306 0.02037 1.004 1.733e-05 -7.779e-06 0.9842 1.306e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09208 0.09015 0.165 0.1957 0.9853 0.9912 0.0921 0.6735 0.8402 0.2469 ] Network output: [ 0.0001189 1 -0.0001133 2.3e-06 -1.033e-06 0.9998 1.733e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002942 Epoch 8687 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009938 0.9962 0.9914 -1.7e-07 7.631e-08 -0.007489 -1.281e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003428 -0.003249 -0.007383 0.005846 0.9699 0.9743 0.006619 0.83 0.8226 0.0173 ] Network output: [ 0.9999 0.0003316 0.0006101 -8.55e-06 3.838e-06 -0.0006905 -6.443e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2017 -0.03455 -0.1674 0.1868 0.9835 0.9932 0.2259 0.4358 0.8698 0.7134 ] Network output: [ -0.009795 1.002 1.009 -2.975e-07 1.336e-07 0.008236 -2.242e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006374 0.0005421 0.004439 0.003442 0.9889 0.9919 0.006496 0.8578 0.8938 0.01243 ] Network output: [ -0.0003818 0.002168 1.001 -2.677e-05 1.202e-05 0.9977 -2.018e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2144 0.1004 0.3435 0.1441 0.985 0.994 0.2151 0.4399 0.8765 0.7075 ] Network output: [ 0.004568 -0.02162 0.9943 1.619e-05 -7.267e-06 1.018 1.22e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1066 0.09419 0.1831 0.1992 0.9873 0.9919 0.1067 0.7493 0.8643 0.3054 ] Network output: [ -0.004304 0.02036 1.004 1.731e-05 -7.77e-06 0.9842 1.304e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09208 0.09015 0.165 0.1957 0.9853 0.9912 0.0921 0.6735 0.8402 0.2469 ] Network output: [ 0.0001189 1 -0.0001131 2.298e-06 -1.031e-06 0.9998 1.731e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002941 Epoch 8688 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009937 0.9962 0.9914 -1.702e-07 7.643e-08 -0.007488 -1.283e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003428 -0.003249 -0.007382 0.005846 0.9699 0.9743 0.006619 0.83 0.8226 0.01729 ] Network output: [ 0.9999 0.0003313 0.0006098 -8.54e-06 3.834e-06 -0.00069 -6.436e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2017 -0.03455 -0.1674 0.1868 0.9835 0.9932 0.226 0.4358 0.8698 0.7134 ] Network output: [ -0.009794 1.002 1.009 -2.976e-07 1.336e-07 0.008235 -2.243e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006375 0.0005422 0.004439 0.003442 0.9889 0.9919 0.006497 0.8578 0.8938 0.01243 ] Network output: [ -0.0003815 0.002167 1.001 -2.674e-05 1.201e-05 0.9977 -2.016e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2144 0.1004 0.3435 0.1441 0.985 0.994 0.2151 0.4399 0.8765 0.7075 ] Network output: [ 0.004566 -0.02161 0.9943 1.617e-05 -7.259e-06 1.018 1.219e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1066 0.0942 0.1831 0.1992 0.9873 0.9919 0.1067 0.7493 0.8643 0.3054 ] Network output: [ -0.004303 0.02036 1.004 1.729e-05 -7.762e-06 0.9842 1.303e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09209 0.09015 0.165 0.1957 0.9853 0.9912 0.0921 0.6735 0.8402 0.2469 ] Network output: [ 0.0001188 1 -0.000113 2.295e-06 -1.03e-06 0.9998 1.73e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002939 Epoch 8689 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009936 0.9962 0.9914 -1.705e-07 7.655e-08 -0.007488 -1.285e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003429 -0.003249 -0.007381 0.005845 0.9699 0.9743 0.006619 0.83 0.8226 0.01729 ] Network output: [ 0.9999 0.000331 0.0006095 -8.531e-06 3.83e-06 -0.0006894 -6.429e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2017 -0.03455 -0.1674 0.1868 0.9835 0.9932 0.226 0.4357 0.8698 0.7134 ] Network output: [ -0.009793 1.002 1.009 -2.976e-07 1.336e-07 0.008234 -2.243e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006375 0.0005422 0.004439 0.003442 0.9889 0.9919 0.006497 0.8578 0.8938 0.01243 ] Network output: [ -0.0003813 0.002166 1.001 -2.671e-05 1.199e-05 0.9977 -2.013e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2144 0.1004 0.3435 0.144 0.985 0.994 0.2151 0.4399 0.8765 0.7075 ] Network output: [ 0.004565 -0.02161 0.9943 1.615e-05 -7.251e-06 1.018 1.217e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1066 0.0942 0.1831 0.1991 0.9873 0.9919 0.1067 0.7492 0.8643 0.3054 ] Network output: [ -0.004301 0.02035 1.004 1.727e-05 -7.754e-06 0.9842 1.302e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09209 0.09015 0.165 0.1957 0.9853 0.9912 0.0921 0.6735 0.8402 0.2469 ] Network output: [ 0.0001188 1 -0.0001129 2.293e-06 -1.029e-06 0.9998 1.728e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002937 Epoch 8690 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009934 0.9962 0.9914 -1.708e-07 7.667e-08 -0.007488 -1.287e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003429 -0.003249 -0.007381 0.005845 0.9699 0.9743 0.006619 0.83 0.8226 0.01729 ] Network output: [ 0.9999 0.0003307 0.0006091 -8.522e-06 3.826e-06 -0.0006888 -6.422e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2017 -0.03455 -0.1673 0.1868 0.9835 0.9932 0.226 0.4357 0.8698 0.7134 ] Network output: [ -0.009792 1.002 1.009 -2.977e-07 1.336e-07 0.008233 -2.243e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006376 0.0005423 0.004439 0.003441 0.9889 0.9919 0.006498 0.8578 0.8938 0.01243 ] Network output: [ -0.000381 0.002166 1.001 -2.669e-05 1.198e-05 0.9977 -2.011e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2144 0.1004 0.3435 0.144 0.985 0.994 0.2151 0.4399 0.8765 0.7075 ] Network output: [ 0.004563 -0.0216 0.9943 1.613e-05 -7.243e-06 1.018 1.216e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1066 0.09421 0.1831 0.1991 0.9873 0.9919 0.1067 0.7492 0.8643 0.3054 ] Network output: [ -0.004299 0.02034 1.004 1.725e-05 -7.746e-06 0.9842 1.3e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09209 0.09015 0.165 0.1957 0.9853 0.9912 0.09211 0.6734 0.8401 0.2469 ] Network output: [ 0.0001187 1 -0.0001128 2.29e-06 -1.028e-06 0.9998 1.726e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002936 Epoch 8691 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009933 0.9962 0.9914 -1.71e-07 7.679e-08 -0.007488 -1.289e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003429 -0.003249 -0.00738 0.005844 0.9699 0.9743 0.00662 0.83 0.8226 0.01729 ] Network output: [ 0.9999 0.0003304 0.0006088 -8.512e-06 3.821e-06 -0.0006883 -6.415e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2017 -0.03456 -0.1673 0.1868 0.9835 0.9932 0.226 0.4357 0.8698 0.7134 ] Network output: [ -0.009791 1.002 1.009 -2.977e-07 1.337e-07 0.008231 -2.244e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006376 0.0005424 0.004439 0.003441 0.9889 0.9919 0.006498 0.8578 0.8938 0.01243 ] Network output: [ -0.0003808 0.002165 1.001 -2.666e-05 1.197e-05 0.9977 -2.009e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2144 0.1004 0.3435 0.144 0.985 0.994 0.2151 0.4398 0.8765 0.7075 ] Network output: [ 0.004561 -0.02159 0.9943 1.612e-05 -7.235e-06 1.018 1.215e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1066 0.09421 0.1831 0.1991 0.9873 0.9919 0.1067 0.7492 0.8643 0.3054 ] Network output: [ -0.004298 0.02033 1.004 1.724e-05 -7.737e-06 0.9842 1.299e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09209 0.09016 0.165 0.1957 0.9853 0.9912 0.09211 0.6734 0.8401 0.2469 ] Network output: [ 0.0001187 1 -0.0001126 2.288e-06 -1.027e-06 0.9998 1.724e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002934 Epoch 8692 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009932 0.9962 0.9914 -1.713e-07 7.691e-08 -0.007488 -1.291e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003429 -0.00325 -0.007379 0.005844 0.9699 0.9743 0.00662 0.83 0.8226 0.01729 ] Network output: [ 0.9999 0.0003302 0.0006085 -8.503e-06 3.817e-06 -0.0006877 -6.408e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2018 -0.03456 -0.1673 0.1868 0.9835 0.9932 0.226 0.4357 0.8698 0.7134 ] Network output: [ -0.00979 1.002 1.009 -2.978e-07 1.337e-07 0.00823 -2.244e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006377 0.0005425 0.004439 0.003441 0.9889 0.9919 0.006499 0.8578 0.8938 0.01243 ] Network output: [ -0.0003805 0.002164 1.001 -2.663e-05 1.195e-05 0.9977 -2.007e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2144 0.1004 0.3435 0.144 0.985 0.994 0.2151 0.4398 0.8765 0.7075 ] Network output: [ 0.00456 -0.02158 0.9943 1.61e-05 -7.228e-06 1.018 1.213e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1066 0.09422 0.1831 0.1991 0.9873 0.9919 0.1067 0.7492 0.8643 0.3054 ] Network output: [ -0.004296 0.02032 1.004 1.722e-05 -7.729e-06 0.9842 1.298e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0921 0.09016 0.165 0.1957 0.9853 0.9912 0.09211 0.6734 0.8401 0.2469 ] Network output: [ 0.0001186 1 -0.0001125 2.285e-06 -1.026e-06 0.9998 1.722e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002933 Epoch 8693 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009931 0.9962 0.9914 -1.716e-07 7.703e-08 -0.007488 -1.293e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003429 -0.00325 -0.007378 0.005843 0.9699 0.9743 0.00662 0.83 0.8226 0.01729 ] Network output: [ 0.9999 0.0003299 0.0006081 -8.494e-06 3.813e-06 -0.0006872 -6.401e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2018 -0.03456 -0.1673 0.1868 0.9835 0.9932 0.226 0.4357 0.8698 0.7134 ] Network output: [ -0.009789 1.002 1.009 -2.978e-07 1.337e-07 0.008229 -2.244e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006377 0.0005426 0.004439 0.00344 0.9889 0.9919 0.006499 0.8578 0.8938 0.01243 ] Network output: [ -0.0003803 0.002163 1.001 -2.66e-05 1.194e-05 0.9977 -2.004e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2144 0.1004 0.3435 0.144 0.985 0.994 0.2151 0.4398 0.8765 0.7075 ] Network output: [ 0.004558 -0.02158 0.9943 1.608e-05 -7.22e-06 1.018 1.212e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1066 0.09422 0.1831 0.1991 0.9873 0.9919 0.1067 0.7492 0.8643 0.3054 ] Network output: [ -0.004295 0.02032 1.004 1.72e-05 -7.721e-06 0.9842 1.296e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0921 0.09016 0.165 0.1957 0.9853 0.9912 0.09211 0.6734 0.8401 0.2469 ] Network output: [ 0.0001186 1 -0.0001124 2.283e-06 -1.025e-06 0.9998 1.72e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002931 Epoch 8694 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00993 0.9962 0.9914 -1.718e-07 7.714e-08 -0.007487 -1.295e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003429 -0.00325 -0.007377 0.005843 0.9699 0.9743 0.00662 0.83 0.8226 0.01729 ] Network output: [ 0.9999 0.0003296 0.0006078 -8.484e-06 3.809e-06 -0.0006866 -6.394e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2018 -0.03456 -0.1673 0.1867 0.9835 0.9932 0.226 0.4357 0.8698 0.7134 ] Network output: [ -0.009788 1.002 1.009 -2.979e-07 1.337e-07 0.008228 -2.245e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006378 0.0005427 0.004439 0.00344 0.9889 0.9919 0.0065 0.8578 0.8938 0.01243 ] Network output: [ -0.0003801 0.002162 1.001 -2.657e-05 1.193e-05 0.9977 -2.002e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2144 0.1004 0.3435 0.144 0.985 0.994 0.2151 0.4398 0.8765 0.7075 ] Network output: [ 0.004557 -0.02157 0.9943 1.606e-05 -7.212e-06 1.018 1.211e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1066 0.09423 0.1831 0.1991 0.9873 0.9919 0.1067 0.7492 0.8643 0.3054 ] Network output: [ -0.004293 0.02031 1.004 1.718e-05 -7.713e-06 0.9842 1.295e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0921 0.09016 0.165 0.1957 0.9853 0.9912 0.09211 0.6734 0.8401 0.2469 ] Network output: [ 0.0001185 1 -0.0001122 2.28e-06 -1.024e-06 0.9998 1.718e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000293 Epoch 8695 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009928 0.9962 0.9914 -1.721e-07 7.726e-08 -0.007487 -1.297e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003429 -0.00325 -0.007377 0.005842 0.9699 0.9743 0.006621 0.83 0.8226 0.01729 ] Network output: [ 0.9999 0.0003293 0.0006075 -8.475e-06 3.805e-06 -0.0006861 -6.387e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2018 -0.03456 -0.1673 0.1867 0.9835 0.9932 0.226 0.4357 0.8698 0.7134 ] Network output: [ -0.009787 1.002 1.009 -2.979e-07 1.337e-07 0.008227 -2.245e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006378 0.0005428 0.004439 0.00344 0.9889 0.9919 0.0065 0.8577 0.8938 0.01243 ] Network output: [ -0.0003798 0.002162 1.001 -2.654e-05 1.191e-05 0.9977 -2e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2144 0.1004 0.3435 0.144 0.985 0.994 0.2152 0.4398 0.8765 0.7075 ] Network output: [ 0.004555 -0.02156 0.9943 1.605e-05 -7.204e-06 1.018 1.209e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1067 0.09424 0.1831 0.1991 0.9873 0.9919 0.1067 0.7491 0.8643 0.3054 ] Network output: [ -0.004291 0.0203 1.004 1.716e-05 -7.705e-06 0.9842 1.293e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0921 0.09017 0.165 0.1957 0.9853 0.9912 0.09212 0.6733 0.8401 0.2469 ] Network output: [ 0.0001184 1 -0.0001121 2.278e-06 -1.023e-06 0.9998 1.717e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002928 Epoch 8696 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009927 0.9962 0.9914 -1.724e-07 7.738e-08 -0.007487 -1.299e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003429 -0.00325 -0.007376 0.005841 0.9699 0.9743 0.006621 0.83 0.8226 0.01728 ] Network output: [ 0.9999 0.000329 0.0006072 -8.466e-06 3.801e-06 -0.0006855 -6.38e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2018 -0.03456 -0.1673 0.1867 0.9835 0.9932 0.226 0.4357 0.8698 0.7134 ] Network output: [ -0.009786 1.002 1.009 -2.979e-07 1.338e-07 0.008225 -2.245e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006379 0.0005429 0.004439 0.003439 0.9889 0.9919 0.006501 0.8577 0.8938 0.01243 ] Network output: [ -0.0003796 0.002161 1.001 -2.651e-05 1.19e-05 0.9977 -1.998e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2145 0.1004 0.3435 0.144 0.985 0.994 0.2152 0.4398 0.8765 0.7075 ] Network output: [ 0.004553 -0.02155 0.9943 1.603e-05 -7.196e-06 1.018 1.208e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1067 0.09424 0.1831 0.1991 0.9873 0.9919 0.1067 0.7491 0.8642 0.3054 ] Network output: [ -0.00429 0.02029 1.004 1.714e-05 -7.696e-06 0.9842 1.292e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09211 0.09017 0.165 0.1957 0.9853 0.9912 0.09212 0.6733 0.8401 0.2469 ] Network output: [ 0.0001184 1 -0.000112 2.275e-06 -1.021e-06 0.9998 1.715e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002927 Epoch 8697 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009926 0.9962 0.9914 -1.726e-07 7.749e-08 -0.007487 -1.301e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003429 -0.00325 -0.007375 0.005841 0.9699 0.9743 0.006621 0.83 0.8226 0.01728 ] Network output: [ 0.9999 0.0003287 0.0006068 -8.456e-06 3.796e-06 -0.000685 -6.373e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2018 -0.03456 -0.1673 0.1867 0.9835 0.9932 0.226 0.4357 0.8698 0.7134 ] Network output: [ -0.009785 1.002 1.009 -2.98e-07 1.338e-07 0.008224 -2.246e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006379 0.000543 0.004438 0.003439 0.9889 0.9919 0.006501 0.8577 0.8938 0.01243 ] Network output: [ -0.0003793 0.00216 1.001 -2.648e-05 1.189e-05 0.9977 -1.996e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2145 0.1004 0.3435 0.144 0.985 0.994 0.2152 0.4398 0.8765 0.7074 ] Network output: [ 0.004552 -0.02154 0.9943 1.601e-05 -7.188e-06 1.018 1.207e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1067 0.09425 0.1831 0.1991 0.9873 0.9919 0.1067 0.7491 0.8642 0.3054 ] Network output: [ -0.004288 0.02028 1.004 1.713e-05 -7.688e-06 0.9842 1.291e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09211 0.09017 0.165 0.1957 0.9853 0.9912 0.09212 0.6733 0.8401 0.2469 ] Network output: [ 0.0001183 1 -0.0001119 2.273e-06 -1.02e-06 0.9998 1.713e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002925 Epoch 8698 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009925 0.9962 0.9914 -1.729e-07 7.761e-08 -0.007487 -1.303e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00343 -0.00325 -0.007374 0.00584 0.9699 0.9743 0.006621 0.83 0.8226 0.01728 ] Network output: [ 0.9999 0.0003284 0.0006065 -8.447e-06 3.792e-06 -0.0006844 -6.366e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2018 -0.03456 -0.1672 0.1867 0.9835 0.9932 0.2261 0.4357 0.8698 0.7134 ] Network output: [ -0.009784 1.002 1.009 -2.98e-07 1.338e-07 0.008223 -2.246e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00638 0.0005431 0.004438 0.003439 0.9889 0.9919 0.006502 0.8577 0.8938 0.01242 ] Network output: [ -0.0003791 0.002159 1.001 -2.645e-05 1.188e-05 0.9977 -1.994e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2145 0.1005 0.3435 0.144 0.985 0.994 0.2152 0.4398 0.8765 0.7074 ] Network output: [ 0.00455 -0.02154 0.9943 1.599e-05 -7.181e-06 1.018 1.205e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1067 0.09425 0.1831 0.1991 0.9873 0.9919 0.1067 0.7491 0.8642 0.3054 ] Network output: [ -0.004287 0.02027 1.004 1.711e-05 -7.68e-06 0.9842 1.289e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09211 0.09017 0.165 0.1957 0.9853 0.9912 0.09212 0.6733 0.8401 0.2469 ] Network output: [ 0.0001183 1 -0.0001117 2.27e-06 -1.019e-06 0.9998 1.711e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002923 Epoch 8699 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009924 0.9962 0.9914 -1.731e-07 7.773e-08 -0.007487 -1.305e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00343 -0.00325 -0.007373 0.00584 0.9699 0.9743 0.006622 0.8299 0.8226 0.01728 ] Network output: [ 0.9999 0.0003282 0.0006062 -8.438e-06 3.788e-06 -0.0006839 -6.359e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2018 -0.03457 -0.1672 0.1867 0.9835 0.9932 0.2261 0.4357 0.8698 0.7134 ] Network output: [ -0.009783 1.002 1.009 -2.981e-07 1.338e-07 0.008222 -2.246e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00638 0.0005432 0.004438 0.003438 0.9889 0.9919 0.006502 0.8577 0.8938 0.01242 ] Network output: [ -0.0003788 0.002158 1.001 -2.642e-05 1.186e-05 0.9977 -1.991e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2145 0.1005 0.3435 0.144 0.985 0.994 0.2152 0.4398 0.8765 0.7074 ] Network output: [ 0.004549 -0.02153 0.9943 1.598e-05 -7.173e-06 1.018 1.204e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1067 0.09426 0.1831 0.1991 0.9873 0.9919 0.1067 0.7491 0.8642 0.3054 ] Network output: [ -0.004285 0.02027 1.004 1.709e-05 -7.672e-06 0.9843 1.288e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09211 0.09018 0.165 0.1957 0.9853 0.9912 0.09213 0.6733 0.8401 0.2469 ] Network output: [ 0.0001182 1 -0.0001116 2.268e-06 -1.018e-06 0.9998 1.709e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002922 Epoch 8700 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009922 0.9962 0.9914 -1.734e-07 7.784e-08 -0.007486 -1.307e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00343 -0.003251 -0.007373 0.005839 0.9699 0.9743 0.006622 0.8299 0.8226 0.01728 ] Network output: [ 0.9999 0.0003279 0.0006059 -8.429e-06 3.784e-06 -0.0006833 -6.352e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2018 -0.03457 -0.1672 0.1867 0.9835 0.9932 0.2261 0.4356 0.8698 0.7134 ] Network output: [ -0.009782 1.002 1.009 -2.981e-07 1.338e-07 0.008221 -2.247e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006381 0.0005433 0.004438 0.003438 0.9889 0.9919 0.006503 0.8577 0.8938 0.01242 ] Network output: [ -0.0003786 0.002158 1.001 -2.639e-05 1.185e-05 0.9977 -1.989e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2145 0.1005 0.3435 0.144 0.985 0.994 0.2152 0.4398 0.8765 0.7074 ] Network output: [ 0.004547 -0.02152 0.9943 1.596e-05 -7.165e-06 1.018 1.203e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1067 0.09426 0.1831 0.1991 0.9873 0.9919 0.1068 0.7491 0.8642 0.3054 ] Network output: [ -0.004284 0.02026 1.004 1.707e-05 -7.664e-06 0.9843 1.287e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09212 0.09018 0.165 0.1957 0.9853 0.9912 0.09213 0.6733 0.8401 0.2469 ] Network output: [ 0.0001182 1 -0.0001115 2.265e-06 -1.017e-06 0.9998 1.707e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000292 Epoch 8701 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009921 0.9962 0.9914 -1.736e-07 7.796e-08 -0.007486 -1.309e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00343 -0.003251 -0.007372 0.005839 0.9699 0.9743 0.006622 0.8299 0.8226 0.01728 ] Network output: [ 0.9999 0.0003276 0.0006055 -8.419e-06 3.78e-06 -0.0006828 -6.345e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2018 -0.03457 -0.1672 0.1867 0.9835 0.9932 0.2261 0.4356 0.8698 0.7133 ] Network output: [ -0.009781 1.002 1.009 -2.982e-07 1.339e-07 0.008219 -2.247e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006382 0.0005434 0.004438 0.003438 0.9889 0.9919 0.006504 0.8577 0.8938 0.01242 ] Network output: [ -0.0003783 0.002157 1.001 -2.637e-05 1.184e-05 0.9977 -1.987e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2145 0.1005 0.3436 0.144 0.985 0.994 0.2152 0.4398 0.8765 0.7074 ] Network output: [ 0.004545 -0.02151 0.9943 1.594e-05 -7.157e-06 1.018 1.201e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1067 0.09427 0.1831 0.1991 0.9873 0.9919 0.1068 0.749 0.8642 0.3054 ] Network output: [ -0.004282 0.02025 1.004 1.705e-05 -7.656e-06 0.9843 1.285e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09212 0.09018 0.165 0.1957 0.9853 0.9912 0.09213 0.6732 0.8401 0.2469 ] Network output: [ 0.0001181 1 -0.0001113 2.263e-06 -1.016e-06 0.9998 1.705e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002919 Epoch 8702 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00992 0.9962 0.9914 -1.739e-07 7.807e-08 -0.007486 -1.311e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00343 -0.003251 -0.007371 0.005838 0.9699 0.9743 0.006623 0.8299 0.8226 0.01728 ] Network output: [ 0.9999 0.0003273 0.0006052 -8.41e-06 3.776e-06 -0.0006822 -6.338e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2018 -0.03457 -0.1672 0.1867 0.9835 0.9932 0.2261 0.4356 0.8698 0.7133 ] Network output: [ -0.00978 1.002 1.009 -2.982e-07 1.339e-07 0.008218 -2.247e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006382 0.0005435 0.004438 0.003437 0.9889 0.9919 0.006504 0.8577 0.8938 0.01242 ] Network output: [ -0.0003781 0.002156 1.001 -2.634e-05 1.182e-05 0.9977 -1.985e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2145 0.1005 0.3436 0.144 0.985 0.994 0.2152 0.4398 0.8765 0.7074 ] Network output: [ 0.004544 -0.0215 0.9943 1.593e-05 -7.149e-06 1.018 1.2e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1067 0.09427 0.1831 0.1991 0.9873 0.9919 0.1068 0.749 0.8642 0.3054 ] Network output: [ -0.00428 0.02024 1.004 1.703e-05 -7.647e-06 0.9843 1.284e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09212 0.09018 0.165 0.1957 0.9853 0.9912 0.09213 0.6732 0.8401 0.2469 ] Network output: [ 0.0001181 1 -0.0001112 2.261e-06 -1.015e-06 0.9998 1.704e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002917 Epoch 8703 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009919 0.9962 0.9914 -1.742e-07 7.819e-08 -0.007486 -1.313e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00343 -0.003251 -0.00737 0.005838 0.9699 0.9743 0.006623 0.8299 0.8226 0.01728 ] Network output: [ 0.9999 0.000327 0.0006049 -8.401e-06 3.772e-06 -0.0006817 -6.331e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2018 -0.03457 -0.1672 0.1867 0.9835 0.9932 0.2261 0.4356 0.8698 0.7133 ] Network output: [ -0.009779 1.002 1.009 -2.982e-07 1.339e-07 0.008217 -2.248e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006383 0.0005436 0.004438 0.003437 0.9889 0.9919 0.006505 0.8577 0.8938 0.01242 ] Network output: [ -0.0003778 0.002155 1.001 -2.631e-05 1.181e-05 0.9977 -1.983e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2145 0.1005 0.3436 0.144 0.985 0.994 0.2152 0.4397 0.8765 0.7074 ] Network output: [ 0.004542 -0.0215 0.9943 1.591e-05 -7.142e-06 1.018 1.199e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1067 0.09428 0.1831 0.1991 0.9873 0.9919 0.1068 0.749 0.8642 0.3054 ] Network output: [ -0.004279 0.02023 1.004 1.702e-05 -7.639e-06 0.9843 1.282e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09212 0.09019 0.165 0.1957 0.9853 0.9912 0.09214 0.6732 0.8401 0.2469 ] Network output: [ 0.000118 1 -0.0001111 2.258e-06 -1.014e-06 0.9998 1.702e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002916 Epoch 8704 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009918 0.9962 0.9914 -1.744e-07 7.83e-08 -0.007486 -1.314e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00343 -0.003251 -0.007369 0.005837 0.9699 0.9743 0.006623 0.8299 0.8226 0.01728 ] Network output: [ 0.9999 0.0003267 0.0006046 -8.392e-06 3.767e-06 -0.0006811 -6.324e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2019 -0.03457 -0.1672 0.1867 0.9835 0.9932 0.2261 0.4356 0.8698 0.7133 ] Network output: [ -0.009778 1.002 1.009 -2.983e-07 1.339e-07 0.008216 -2.248e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006383 0.0005437 0.004438 0.003437 0.9889 0.9919 0.006505 0.8577 0.8938 0.01242 ] Network output: [ -0.0003776 0.002154 1.001 -2.628e-05 1.18e-05 0.9977 -1.98e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2145 0.1005 0.3436 0.144 0.985 0.994 0.2152 0.4397 0.8765 0.7074 ] Network output: [ 0.004541 -0.02149 0.9943 1.589e-05 -7.134e-06 1.018 1.198e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1067 0.09428 0.1831 0.1991 0.9873 0.9919 0.1068 0.749 0.8642 0.3054 ] Network output: [ -0.004277 0.02023 1.004 1.7e-05 -7.631e-06 0.9843 1.281e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09213 0.09019 0.165 0.1957 0.9853 0.9912 0.09214 0.6732 0.8401 0.2469 ] Network output: [ 0.000118 1 -0.000111 2.256e-06 -1.013e-06 0.9998 1.7e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002914 Epoch 8705 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009916 0.9962 0.9914 -1.747e-07 7.841e-08 -0.007486 -1.316e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00343 -0.003251 -0.007368 0.005837 0.9699 0.9743 0.006623 0.8299 0.8226 0.01727 ] Network output: [ 0.9999 0.0003265 0.0006042 -8.383e-06 3.763e-06 -0.0006806 -6.317e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2019 -0.03457 -0.1672 0.1867 0.9835 0.9932 0.2261 0.4356 0.8698 0.7133 ] Network output: [ -0.009777 1.002 1.009 -2.983e-07 1.339e-07 0.008215 -2.248e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006384 0.0005438 0.004438 0.003436 0.9889 0.9919 0.006506 0.8577 0.8938 0.01242 ] Network output: [ -0.0003774 0.002154 1.001 -2.625e-05 1.178e-05 0.9977 -1.978e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2145 0.1005 0.3436 0.144 0.985 0.994 0.2152 0.4397 0.8765 0.7074 ] Network output: [ 0.004539 -0.02148 0.9943 1.587e-05 -7.126e-06 1.018 1.196e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1067 0.09429 0.1831 0.1991 0.9873 0.9919 0.1068 0.749 0.8642 0.3054 ] Network output: [ -0.004276 0.02022 1.004 1.698e-05 -7.623e-06 0.9843 1.28e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09213 0.09019 0.165 0.1957 0.9853 0.9912 0.09214 0.6732 0.8401 0.247 ] Network output: [ 0.0001179 1 -0.0001108 2.253e-06 -1.012e-06 0.9998 1.698e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002913 Epoch 8706 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009915 0.9962 0.9914 -1.749e-07 7.853e-08 -0.007485 -1.318e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00343 -0.003251 -0.007368 0.005836 0.9699 0.9743 0.006624 0.8299 0.8226 0.01727 ] Network output: [ 0.9999 0.0003262 0.0006039 -8.373e-06 3.759e-06 -0.00068 -6.31e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2019 -0.03457 -0.1671 0.1867 0.9835 0.9932 0.2261 0.4356 0.8698 0.7133 ] Network output: [ -0.009776 1.002 1.009 -2.983e-07 1.339e-07 0.008213 -2.248e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006384 0.0005439 0.004438 0.003436 0.9889 0.9919 0.006506 0.8577 0.8938 0.01242 ] Network output: [ -0.0003771 0.002153 1.001 -2.622e-05 1.177e-05 0.9977 -1.976e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2145 0.1005 0.3436 0.144 0.985 0.994 0.2153 0.4397 0.8765 0.7074 ] Network output: [ 0.004537 -0.02147 0.9943 1.586e-05 -7.118e-06 1.018 1.195e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1067 0.09429 0.1831 0.1991 0.9873 0.9919 0.1068 0.749 0.8642 0.3054 ] Network output: [ -0.004274 0.02021 1.004 1.696e-05 -7.615e-06 0.9843 1.278e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09213 0.09019 0.165 0.1957 0.9853 0.9912 0.09214 0.6732 0.8401 0.247 ] Network output: [ 0.0001179 1 -0.0001107 2.251e-06 -1.01e-06 0.9998 1.696e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002911 Epoch 8707 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009914 0.9962 0.9914 -1.752e-07 7.864e-08 -0.007485 -1.32e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00343 -0.003251 -0.007367 0.005836 0.9699 0.9743 0.006624 0.8299 0.8226 0.01727 ] Network output: [ 0.9999 0.0003259 0.0006036 -8.364e-06 3.755e-06 -0.0006795 -6.303e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2019 -0.03458 -0.1671 0.1867 0.9835 0.9932 0.2261 0.4356 0.8698 0.7133 ] Network output: [ -0.009775 1.002 1.009 -2.984e-07 1.34e-07 0.008212 -2.249e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006385 0.000544 0.004438 0.003436 0.9889 0.9919 0.006507 0.8577 0.8938 0.01242 ] Network output: [ -0.0003769 0.002152 1.001 -2.619e-05 1.176e-05 0.9977 -1.974e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2146 0.1005 0.3436 0.144 0.985 0.994 0.2153 0.4397 0.8765 0.7074 ] Network output: [ 0.004536 -0.02146 0.9943 1.584e-05 -7.111e-06 1.018 1.194e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1067 0.0943 0.1831 0.1991 0.9873 0.9919 0.1068 0.7489 0.8642 0.3054 ] Network output: [ -0.004273 0.0202 1.004 1.694e-05 -7.607e-06 0.9843 1.277e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09213 0.09019 0.165 0.1957 0.9853 0.9912 0.09215 0.6731 0.8401 0.247 ] Network output: [ 0.0001178 1 -0.0001106 2.248e-06 -1.009e-06 0.9998 1.694e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000291 Epoch 8708 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009913 0.9962 0.9914 -1.754e-07 7.875e-08 -0.007485 -1.322e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003431 -0.003252 -0.007366 0.005835 0.9699 0.9743 0.006624 0.8299 0.8226 0.01727 ] Network output: [ 0.9999 0.0003256 0.0006033 -8.355e-06 3.751e-06 -0.0006789 -6.297e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2019 -0.03458 -0.1671 0.1867 0.9835 0.9932 0.2261 0.4356 0.8698 0.7133 ] Network output: [ -0.009774 1.002 1.009 -2.984e-07 1.34e-07 0.008211 -2.249e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006385 0.000544 0.004438 0.003436 0.9889 0.9919 0.006507 0.8577 0.8938 0.01242 ] Network output: [ -0.0003766 0.002151 1.001 -2.616e-05 1.175e-05 0.9977 -1.972e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2146 0.1005 0.3436 0.144 0.985 0.994 0.2153 0.4397 0.8765 0.7074 ] Network output: [ 0.004534 -0.02146 0.9943 1.582e-05 -7.103e-06 1.018 1.192e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1067 0.09431 0.1831 0.1991 0.9873 0.9919 0.1068 0.7489 0.8642 0.3054 ] Network output: [ -0.004271 0.02019 1.004 1.693e-05 -7.599e-06 0.9843 1.276e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09213 0.0902 0.165 0.1957 0.9853 0.9912 0.09215 0.6731 0.8401 0.247 ] Network output: [ 0.0001178 1 -0.0001105 2.246e-06 -1.008e-06 0.9998 1.693e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002908 Epoch 8709 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009911 0.9962 0.9914 -1.757e-07 7.886e-08 -0.007485 -1.324e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003431 -0.003252 -0.007365 0.005835 0.9699 0.9743 0.006624 0.8299 0.8226 0.01727 ] Network output: [ 0.9999 0.0003253 0.000603 -8.346e-06 3.747e-06 -0.0006784 -6.29e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2019 -0.03458 -0.1671 0.1867 0.9835 0.9932 0.2262 0.4356 0.8698 0.7133 ] Network output: [ -0.009773 1.002 1.009 -2.984e-07 1.34e-07 0.00821 -2.249e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006386 0.0005441 0.004438 0.003435 0.9889 0.9919 0.006508 0.8576 0.8938 0.01241 ] Network output: [ -0.0003764 0.002151 1.001 -2.613e-05 1.173e-05 0.9977 -1.97e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2146 0.1005 0.3436 0.144 0.985 0.994 0.2153 0.4397 0.8765 0.7074 ] Network output: [ 0.004533 -0.02145 0.9943 1.58e-05 -7.095e-06 1.018 1.191e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1067 0.09431 0.1831 0.1991 0.9873 0.9919 0.1068 0.7489 0.8642 0.3054 ] Network output: [ -0.004269 0.02019 1.004 1.691e-05 -7.591e-06 0.9843 1.274e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09214 0.0902 0.165 0.1957 0.9853 0.9912 0.09215 0.6731 0.8401 0.247 ] Network output: [ 0.0001177 1 -0.0001103 2.244e-06 -1.007e-06 0.9998 1.691e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002906 Epoch 8710 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00991 0.9962 0.9914 -1.759e-07 7.897e-08 -0.007485 -1.326e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003431 -0.003252 -0.007364 0.005834 0.9699 0.9743 0.006625 0.8299 0.8226 0.01727 ] Network output: [ 0.9999 0.000325 0.0006026 -8.337e-06 3.743e-06 -0.0006778 -6.283e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2019 -0.03458 -0.1671 0.1867 0.9835 0.9932 0.2262 0.4356 0.8698 0.7133 ] Network output: [ -0.009772 1.002 1.009 -2.985e-07 1.34e-07 0.008209 -2.249e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006386 0.0005442 0.004438 0.003435 0.9889 0.9919 0.006508 0.8576 0.8938 0.01241 ] Network output: [ -0.0003761 0.00215 1.001 -2.611e-05 1.172e-05 0.9977 -1.967e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2146 0.1005 0.3436 0.144 0.985 0.994 0.2153 0.4397 0.8765 0.7074 ] Network output: [ 0.004531 -0.02144 0.9943 1.579e-05 -7.087e-06 1.018 1.19e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1067 0.09432 0.1831 0.1991 0.9873 0.9919 0.1068 0.7489 0.8642 0.3054 ] Network output: [ -0.004268 0.02018 1.004 1.689e-05 -7.583e-06 0.9843 1.273e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09214 0.0902 0.165 0.1957 0.9853 0.9912 0.09215 0.6731 0.84 0.247 ] Network output: [ 0.0001177 1 -0.0001102 2.241e-06 -1.006e-06 0.9998 1.689e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002905 Epoch 8711 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009909 0.9962 0.9914 -1.762e-07 7.908e-08 -0.007485 -1.328e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003431 -0.003252 -0.007364 0.005834 0.9699 0.9743 0.006625 0.8299 0.8226 0.01727 ] Network output: [ 0.9999 0.0003248 0.0006023 -8.327e-06 3.739e-06 -0.0006773 -6.276e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2019 -0.03458 -0.1671 0.1867 0.9835 0.9932 0.2262 0.4356 0.8698 0.7133 ] Network output: [ -0.009771 1.002 1.009 -2.985e-07 1.34e-07 0.008207 -2.25e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006387 0.0005443 0.004438 0.003435 0.9889 0.9919 0.006509 0.8576 0.8938 0.01241 ] Network output: [ -0.0003759 0.002149 1.001 -2.608e-05 1.171e-05 0.9977 -1.965e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2146 0.1005 0.3436 0.144 0.985 0.994 0.2153 0.4397 0.8765 0.7074 ] Network output: [ 0.004529 -0.02143 0.9943 1.577e-05 -7.08e-06 1.018 1.188e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1067 0.09432 0.1831 0.1991 0.9873 0.9919 0.1068 0.7489 0.8642 0.3054 ] Network output: [ -0.004266 0.02017 1.004 1.687e-05 -7.575e-06 0.9843 1.272e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09214 0.0902 0.165 0.1957 0.9853 0.9912 0.09216 0.6731 0.84 0.247 ] Network output: [ 0.0001176 1 -0.0001101 2.239e-06 -1.005e-06 0.9998 1.687e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002903 Epoch 8712 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009908 0.9962 0.9914 -1.764e-07 7.919e-08 -0.007484 -1.329e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003431 -0.003252 -0.007363 0.005833 0.9699 0.9743 0.006625 0.8299 0.8226 0.01727 ] Network output: [ 0.9999 0.0003245 0.000602 -8.318e-06 3.734e-06 -0.0006767 -6.269e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2019 -0.03458 -0.1671 0.1867 0.9835 0.9932 0.2262 0.4355 0.8698 0.7133 ] Network output: [ -0.00977 1.002 1.009 -2.985e-07 1.34e-07 0.008206 -2.25e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006387 0.0005444 0.004438 0.003434 0.9889 0.9919 0.006509 0.8576 0.8938 0.01241 ] Network output: [ -0.0003756 0.002148 1.001 -2.605e-05 1.169e-05 0.9977 -1.963e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2146 0.1005 0.3436 0.144 0.985 0.994 0.2153 0.4397 0.8765 0.7074 ] Network output: [ 0.004528 -0.02143 0.9943 1.575e-05 -7.072e-06 1.018 1.187e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1067 0.09433 0.1831 0.1991 0.9873 0.9919 0.1068 0.7489 0.8642 0.3054 ] Network output: [ -0.004265 0.02016 1.004 1.685e-05 -7.567e-06 0.9843 1.27e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09214 0.09021 0.165 0.1957 0.9853 0.9912 0.09216 0.6731 0.84 0.247 ] Network output: [ 0.0001176 1 -0.00011 2.236e-06 -1.004e-06 0.9998 1.685e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002902 Epoch 8713 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009907 0.9962 0.9914 -1.766e-07 7.93e-08 -0.007484 -1.331e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003431 -0.003252 -0.007362 0.005833 0.9699 0.9743 0.006625 0.8299 0.8226 0.01727 ] Network output: [ 0.9999 0.0003242 0.0006017 -8.309e-06 3.73e-06 -0.0006762 -6.262e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2019 -0.03458 -0.1671 0.1867 0.9835 0.9932 0.2262 0.4355 0.8698 0.7133 ] Network output: [ -0.009769 1.002 1.009 -2.986e-07 1.34e-07 0.008205 -2.25e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006388 0.0005445 0.004438 0.003434 0.9889 0.9919 0.00651 0.8576 0.8938 0.01241 ] Network output: [ -0.0003754 0.002147 1.001 -2.602e-05 1.168e-05 0.9977 -1.961e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2146 0.1005 0.3436 0.144 0.985 0.994 0.2153 0.4397 0.8765 0.7074 ] Network output: [ 0.004526 -0.02142 0.9943 1.574e-05 -7.064e-06 1.018 1.186e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1068 0.09433 0.1831 0.1991 0.9873 0.9919 0.1068 0.7488 0.8642 0.3054 ] Network output: [ -0.004263 0.02015 1.004 1.684e-05 -7.558e-06 0.9843 1.269e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09215 0.09021 0.165 0.1957 0.9853 0.9912 0.09216 0.673 0.84 0.247 ] Network output: [ 0.0001175 1 -0.0001098 2.234e-06 -1.003e-06 0.9998 1.683e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00029 Epoch 8714 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009905 0.9962 0.9914 -1.769e-07 7.941e-08 -0.007484 -1.333e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003431 -0.003252 -0.007361 0.005832 0.9699 0.9743 0.006626 0.8299 0.8225 0.01726 ] Network output: [ 0.9999 0.0003239 0.0006014 -8.3e-06 3.726e-06 -0.0006756 -6.255e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2019 -0.03458 -0.167 0.1866 0.9835 0.9932 0.2262 0.4355 0.8698 0.7133 ] Network output: [ -0.009768 1.002 1.009 -2.986e-07 1.341e-07 0.008204 -2.25e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006388 0.0005446 0.004438 0.003434 0.9889 0.9919 0.006511 0.8576 0.8938 0.01241 ] Network output: [ -0.0003752 0.002147 1.001 -2.599e-05 1.167e-05 0.9977 -1.959e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2146 0.1005 0.3436 0.144 0.985 0.994 0.2153 0.4396 0.8765 0.7073 ] Network output: [ 0.004525 -0.02141 0.9943 1.572e-05 -7.057e-06 1.018 1.185e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1068 0.09434 0.1831 0.1991 0.9873 0.9919 0.1068 0.7488 0.8642 0.3054 ] Network output: [ -0.004262 0.02014 1.004 1.682e-05 -7.55e-06 0.9843 1.267e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09215 0.09021 0.165 0.1957 0.9853 0.9912 0.09216 0.673 0.84 0.247 ] Network output: [ 0.0001175 1 -0.0001097 2.231e-06 -1.002e-06 0.9998 1.682e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002899 Epoch 8715 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009904 0.9962 0.9914 -1.771e-07 7.952e-08 -0.007484 -1.335e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003431 -0.003252 -0.00736 0.005832 0.9699 0.9743 0.006626 0.8298 0.8225 0.01726 ] Network output: [ 0.9999 0.0003236 0.000601 -8.291e-06 3.722e-06 -0.0006751 -6.248e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2019 -0.03459 -0.167 0.1866 0.9835 0.9932 0.2262 0.4355 0.8698 0.7133 ] Network output: [ -0.009767 1.002 1.009 -2.986e-07 1.341e-07 0.008203 -2.251e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006389 0.0005447 0.004438 0.003433 0.9889 0.9919 0.006511 0.8576 0.8938 0.01241 ] Network output: [ -0.0003749 0.002146 1.001 -2.596e-05 1.166e-05 0.9977 -1.957e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2146 0.1006 0.3436 0.144 0.985 0.994 0.2153 0.4396 0.8765 0.7073 ] Network output: [ 0.004523 -0.0214 0.9943 1.57e-05 -7.049e-06 1.018 1.183e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1068 0.09434 0.1831 0.1991 0.9873 0.9919 0.1068 0.7488 0.8642 0.3054 ] Network output: [ -0.00426 0.02014 1.004 1.68e-05 -7.542e-06 0.9843 1.266e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09215 0.09021 0.165 0.1957 0.9853 0.9912 0.09216 0.673 0.84 0.247 ] Network output: [ 0.0001174 1 -0.0001096 2.229e-06 -1.001e-06 0.9998 1.68e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002897 Epoch 8716 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009903 0.9962 0.9914 -1.774e-07 7.963e-08 -0.007484 -1.337e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003431 -0.003253 -0.00736 0.005831 0.9699 0.9743 0.006626 0.8298 0.8225 0.01726 ] Network output: [ 0.9999 0.0003233 0.0006007 -8.282e-06 3.718e-06 -0.0006746 -6.241e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.202 -0.03459 -0.167 0.1866 0.9835 0.9932 0.2262 0.4355 0.8698 0.7133 ] Network output: [ -0.009766 1.002 1.009 -2.987e-07 1.341e-07 0.008201 -2.251e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006389 0.0005448 0.004438 0.003433 0.9889 0.9919 0.006512 0.8576 0.8938 0.01241 ] Network output: [ -0.0003747 0.002145 1.001 -2.593e-05 1.164e-05 0.9977 -1.954e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2146 0.1006 0.3437 0.144 0.985 0.994 0.2154 0.4396 0.8765 0.7073 ] Network output: [ 0.004521 -0.02139 0.9943 1.568e-05 -7.041e-06 1.018 1.182e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1068 0.09435 0.1831 0.1991 0.9873 0.9919 0.1068 0.7488 0.8642 0.3054 ] Network output: [ -0.004259 0.02013 1.004 1.678e-05 -7.534e-06 0.9843 1.265e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09215 0.09022 0.165 0.1957 0.9853 0.9912 0.09217 0.673 0.84 0.247 ] Network output: [ 0.0001174 1 -0.0001095 2.227e-06 -9.996e-07 0.9998 1.678e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002896 Epoch 8717 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009902 0.9962 0.9914 -1.776e-07 7.974e-08 -0.007483 -1.339e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003432 -0.003253 -0.007359 0.005831 0.9699 0.9743 0.006627 0.8298 0.8225 0.01726 ] Network output: [ 0.9999 0.0003231 0.0006004 -8.273e-06 3.714e-06 -0.000674 -6.235e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.202 -0.03459 -0.167 0.1866 0.9835 0.9932 0.2262 0.4355 0.8698 0.7133 ] Network output: [ -0.009765 1.002 1.009 -2.987e-07 1.341e-07 0.0082 -2.251e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00639 0.0005449 0.004438 0.003433 0.9889 0.9919 0.006512 0.8576 0.8938 0.01241 ] Network output: [ -0.0003744 0.002144 1.001 -2.591e-05 1.163e-05 0.9977 -1.952e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2146 0.1006 0.3437 0.144 0.985 0.994 0.2154 0.4396 0.8765 0.7073 ] Network output: [ 0.00452 -0.02139 0.9943 1.567e-05 -7.034e-06 1.018 1.181e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1068 0.09435 0.1831 0.1991 0.9873 0.9919 0.1069 0.7488 0.8642 0.3054 ] Network output: [ -0.004257 0.02012 1.004 1.676e-05 -7.526e-06 0.9843 1.263e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09216 0.09022 0.165 0.1957 0.9853 0.9912 0.09217 0.673 0.84 0.247 ] Network output: [ 0.0001173 1 -0.0001093 2.224e-06 -9.985e-07 0.9998 1.676e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002894 Epoch 8718 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009901 0.9963 0.9914 -1.779e-07 7.985e-08 -0.007483 -1.34e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003432 -0.003253 -0.007358 0.00583 0.9699 0.9743 0.006627 0.8298 0.8225 0.01726 ] Network output: [ 0.9999 0.0003228 0.0006001 -8.264e-06 3.71e-06 -0.0006735 -6.228e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.202 -0.03459 -0.167 0.1866 0.9835 0.9932 0.2262 0.4355 0.8698 0.7133 ] Network output: [ -0.009764 1.002 1.009 -2.987e-07 1.341e-07 0.008199 -2.251e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00639 0.000545 0.004438 0.003432 0.9889 0.9919 0.006513 0.8576 0.8938 0.01241 ] Network output: [ -0.0003742 0.002143 1.001 -2.588e-05 1.162e-05 0.9977 -1.95e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2147 0.1006 0.3437 0.144 0.985 0.994 0.2154 0.4396 0.8765 0.7073 ] Network output: [ 0.004518 -0.02138 0.9943 1.565e-05 -7.026e-06 1.018 1.179e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1068 0.09436 0.1832 0.1991 0.9873 0.9919 0.1069 0.7488 0.8642 0.3054 ] Network output: [ -0.004255 0.02011 1.004 1.675e-05 -7.518e-06 0.9843 1.262e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09216 0.09022 0.165 0.1957 0.9853 0.9912 0.09217 0.6729 0.84 0.247 ] Network output: [ 0.0001173 1 -0.0001092 2.222e-06 -9.974e-07 0.9998 1.674e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002893 Epoch 8719 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009899 0.9963 0.9914 -1.781e-07 7.995e-08 -0.007483 -1.342e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003432 -0.003253 -0.007357 0.005829 0.9699 0.9743 0.006627 0.8298 0.8225 0.01726 ] Network output: [ 0.9999 0.0003225 0.0005997 -8.255e-06 3.706e-06 -0.0006729 -6.221e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.202 -0.03459 -0.167 0.1866 0.9835 0.9932 0.2263 0.4355 0.8698 0.7132 ] Network output: [ -0.009764 1.002 1.009 -2.988e-07 1.341e-07 0.008198 -2.252e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006391 0.0005451 0.004438 0.003432 0.9889 0.9919 0.006513 0.8576 0.8938 0.01241 ] Network output: [ -0.0003739 0.002143 1.001 -2.585e-05 1.16e-05 0.9977 -1.948e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2147 0.1006 0.3437 0.144 0.985 0.994 0.2154 0.4396 0.8764 0.7073 ] Network output: [ 0.004517 -0.02137 0.9943 1.563e-05 -7.018e-06 1.018 1.178e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1068 0.09436 0.1832 0.1991 0.9873 0.9919 0.1069 0.7487 0.8642 0.3054 ] Network output: [ -0.004254 0.0201 1.004 1.673e-05 -7.51e-06 0.9843 1.261e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09216 0.09022 0.165 0.1957 0.9853 0.9912 0.09217 0.6729 0.84 0.247 ] Network output: [ 0.0001172 1 -0.0001091 2.219e-06 -9.963e-07 0.9998 1.673e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002891 Epoch 8720 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009898 0.9963 0.9914 -1.783e-07 8.006e-08 -0.007483 -1.344e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003432 -0.003253 -0.007356 0.005829 0.9699 0.9743 0.006627 0.8298 0.8225 0.01726 ] Network output: [ 0.9999 0.0003222 0.0005994 -8.245e-06 3.702e-06 -0.0006724 -6.214e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.202 -0.03459 -0.167 0.1866 0.9835 0.9932 0.2263 0.4355 0.8698 0.7132 ] Network output: [ -0.009763 1.002 1.009 -2.988e-07 1.341e-07 0.008197 -2.252e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006391 0.0005452 0.004438 0.003432 0.9889 0.9919 0.006514 0.8576 0.8938 0.0124 ] Network output: [ -0.0003737 0.002142 1.001 -2.582e-05 1.159e-05 0.9977 -1.946e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2147 0.1006 0.3437 0.144 0.985 0.994 0.2154 0.4396 0.8764 0.7073 ] Network output: [ 0.004515 -0.02136 0.9943 1.562e-05 -7.011e-06 1.018 1.177e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1068 0.09437 0.1832 0.1991 0.9873 0.9919 0.1069 0.7487 0.8641 0.3054 ] Network output: [ -0.004252 0.0201 1.004 1.671e-05 -7.502e-06 0.9843 1.259e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09216 0.09023 0.165 0.1957 0.9853 0.9912 0.09218 0.6729 0.84 0.247 ] Network output: [ 0.0001172 1 -0.000109 2.217e-06 -9.953e-07 0.9998 1.671e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000289 Epoch 8721 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009897 0.9963 0.9914 -1.786e-07 8.017e-08 -0.007483 -1.346e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003432 -0.003253 -0.007356 0.005828 0.9699 0.9743 0.006628 0.8298 0.8225 0.01726 ] Network output: [ 0.9999 0.0003219 0.0005991 -8.236e-06 3.698e-06 -0.0006719 -6.207e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.202 -0.03459 -0.167 0.1866 0.9835 0.9932 0.2263 0.4355 0.8698 0.7132 ] Network output: [ -0.009762 1.002 1.009 -2.988e-07 1.342e-07 0.008196 -2.252e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006392 0.0005453 0.004438 0.003431 0.9889 0.9919 0.006514 0.8576 0.8938 0.0124 ] Network output: [ -0.0003735 0.002141 1.001 -2.579e-05 1.158e-05 0.9977 -1.944e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2147 0.1006 0.3437 0.144 0.985 0.994 0.2154 0.4396 0.8764 0.7073 ] Network output: [ 0.004513 -0.02136 0.9943 1.56e-05 -7.003e-06 1.018 1.176e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1068 0.09437 0.1832 0.1991 0.9873 0.9919 0.1069 0.7487 0.8641 0.3054 ] Network output: [ -0.004251 0.02009 1.004 1.669e-05 -7.494e-06 0.9843 1.258e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09217 0.09023 0.165 0.1957 0.9853 0.9912 0.09218 0.6729 0.84 0.247 ] Network output: [ 0.0001171 1 -0.0001088 2.215e-06 -9.942e-07 0.9998 1.669e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002888 Epoch 8722 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009896 0.9963 0.9914 -1.788e-07 8.027e-08 -0.007482 -1.348e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003432 -0.003253 -0.007355 0.005828 0.9699 0.9743 0.006628 0.8298 0.8225 0.01725 ] Network output: [ 0.9999 0.0003217 0.0005988 -8.227e-06 3.694e-06 -0.0006713 -6.2e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.202 -0.03459 -0.167 0.1866 0.9835 0.9932 0.2263 0.4355 0.8698 0.7132 ] Network output: [ -0.009761 1.002 1.009 -2.988e-07 1.342e-07 0.008194 -2.252e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006392 0.0005454 0.004438 0.003431 0.9889 0.9919 0.006515 0.8576 0.8938 0.0124 ] Network output: [ -0.0003732 0.00214 1.001 -2.576e-05 1.157e-05 0.9977 -1.942e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2147 0.1006 0.3437 0.144 0.985 0.994 0.2154 0.4396 0.8764 0.7073 ] Network output: [ 0.004512 -0.02135 0.9943 1.558e-05 -6.995e-06 1.018 1.174e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1068 0.09438 0.1832 0.1991 0.9873 0.9919 0.1069 0.7487 0.8641 0.3054 ] Network output: [ -0.004249 0.02008 1.004 1.668e-05 -7.486e-06 0.9843 1.257e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09217 0.09023 0.165 0.1957 0.9853 0.9912 0.09218 0.6729 0.84 0.247 ] Network output: [ 0.0001171 1 -0.0001087 2.212e-06 -9.931e-07 0.9998 1.667e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002887 Epoch 8723 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009895 0.9963 0.9914 -1.79e-07 8.038e-08 -0.007482 -1.349e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003432 -0.003253 -0.007354 0.005827 0.9699 0.9743 0.006628 0.8298 0.8225 0.01725 ] Network output: [ 0.9999 0.0003214 0.0005985 -8.218e-06 3.689e-06 -0.0006708 -6.194e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.202 -0.0346 -0.1669 0.1866 0.9835 0.9932 0.2263 0.4355 0.8698 0.7132 ] Network output: [ -0.00976 1.002 1.009 -2.989e-07 1.342e-07 0.008193 -2.252e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006393 0.0005455 0.004437 0.003431 0.9889 0.9919 0.006515 0.8576 0.8938 0.0124 ] Network output: [ -0.000373 0.00214 1.001 -2.573e-05 1.155e-05 0.9977 -1.939e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2147 0.1006 0.3437 0.144 0.985 0.994 0.2154 0.4396 0.8764 0.7073 ] Network output: [ 0.00451 -0.02134 0.9943 1.557e-05 -6.988e-06 1.018 1.173e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1068 0.09439 0.1832 0.1991 0.9873 0.9919 0.1069 0.7487 0.8641 0.3054 ] Network output: [ -0.004248 0.02007 1.004 1.666e-05 -7.478e-06 0.9843 1.255e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09217 0.09023 0.165 0.1957 0.9853 0.9912 0.09218 0.6729 0.84 0.247 ] Network output: [ 0.000117 1 -0.0001086 2.21e-06 -9.92e-07 0.9998 1.665e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002885 Epoch 8724 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009893 0.9963 0.9914 -1.793e-07 8.048e-08 -0.007482 -1.351e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003432 -0.003254 -0.007353 0.005827 0.9699 0.9743 0.006628 0.8298 0.8225 0.01725 ] Network output: [ 0.9999 0.0003211 0.0005982 -8.209e-06 3.685e-06 -0.0006702 -6.187e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.202 -0.0346 -0.1669 0.1866 0.9835 0.9932 0.2263 0.4354 0.8698 0.7132 ] Network output: [ -0.009759 1.002 1.009 -2.989e-07 1.342e-07 0.008192 -2.253e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006393 0.0005456 0.004437 0.003431 0.9889 0.9919 0.006516 0.8575 0.8937 0.0124 ] Network output: [ -0.0003727 0.002139 1.001 -2.571e-05 1.154e-05 0.9977 -1.937e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2147 0.1006 0.3437 0.144 0.985 0.994 0.2154 0.4396 0.8764 0.7073 ] Network output: [ 0.004509 -0.02133 0.9943 1.555e-05 -6.98e-06 1.018 1.172e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1068 0.09439 0.1832 0.1991 0.9873 0.9919 0.1069 0.7487 0.8641 0.3054 ] Network output: [ -0.004246 0.02006 1.004 1.664e-05 -7.47e-06 0.9843 1.254e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09217 0.09024 0.165 0.1957 0.9853 0.9912 0.09219 0.6728 0.84 0.247 ] Network output: [ 0.000117 1 -0.0001085 2.207e-06 -9.909e-07 0.9998 1.664e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002884 Epoch 8725 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009892 0.9963 0.9914 -1.795e-07 8.059e-08 -0.007482 -1.353e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003432 -0.003254 -0.007352 0.005826 0.9699 0.9743 0.006629 0.8298 0.8225 0.01725 ] Network output: [ 0.9999 0.0003208 0.0005978 -8.2e-06 3.681e-06 -0.0006697 -6.18e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.202 -0.0346 -0.1669 0.1866 0.9835 0.9932 0.2263 0.4354 0.8698 0.7132 ] Network output: [ -0.009758 1.002 1.009 -2.989e-07 1.342e-07 0.008191 -2.253e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006394 0.0005457 0.004437 0.00343 0.9889 0.9919 0.006516 0.8575 0.8937 0.0124 ] Network output: [ -0.0003725 0.002138 1.001 -2.568e-05 1.153e-05 0.9977 -1.935e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2147 0.1006 0.3437 0.1439 0.985 0.994 0.2154 0.4396 0.8764 0.7073 ] Network output: [ 0.004507 -0.02132 0.9943 1.553e-05 -6.973e-06 1.018 1.17e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1068 0.0944 0.1832 0.1991 0.9873 0.9919 0.1069 0.7486 0.8641 0.3054 ] Network output: [ -0.004244 0.02006 1.004 1.662e-05 -7.462e-06 0.9844 1.253e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09217 0.09024 0.165 0.1957 0.9853 0.9912 0.09219 0.6728 0.84 0.247 ] Network output: [ 0.0001169 1 -0.0001084 2.205e-06 -9.899e-07 0.9998 1.662e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002882 Epoch 8726 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009891 0.9963 0.9914 -1.797e-07 8.069e-08 -0.007482 -1.355e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003432 -0.003254 -0.007352 0.005826 0.9699 0.9743 0.006629 0.8298 0.8225 0.01725 ] Network output: [ 0.9999 0.0003205 0.0005975 -8.191e-06 3.677e-06 -0.0006692 -6.173e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.202 -0.0346 -0.1669 0.1866 0.9835 0.9932 0.2263 0.4354 0.8698 0.7132 ] Network output: [ -0.009757 1.002 1.009 -2.99e-07 1.342e-07 0.00819 -2.253e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006395 0.0005458 0.004437 0.00343 0.9889 0.9919 0.006517 0.8575 0.8937 0.0124 ] Network output: [ -0.0003723 0.002137 1.001 -2.565e-05 1.152e-05 0.9977 -1.933e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2147 0.1006 0.3437 0.1439 0.985 0.994 0.2154 0.4395 0.8764 0.7073 ] Network output: [ 0.004506 -0.02132 0.9943 1.551e-05 -6.965e-06 1.018 1.169e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1068 0.0944 0.1832 0.1991 0.9873 0.9919 0.1069 0.7486 0.8641 0.3054 ] Network output: [ -0.004243 0.02005 1.004 1.66e-05 -7.454e-06 0.9844 1.251e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09218 0.09024 0.165 0.1957 0.9853 0.9912 0.09219 0.6728 0.84 0.247 ] Network output: [ 0.0001169 1 -0.0001082 2.203e-06 -9.888e-07 0.9998 1.66e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000288 Epoch 8727 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00989 0.9963 0.9914 -1.8e-07 8.08e-08 -0.007482 -1.356e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003433 -0.003254 -0.007351 0.005825 0.9699 0.9743 0.006629 0.8298 0.8225 0.01725 ] Network output: [ 0.9999 0.0003203 0.0005972 -8.182e-06 3.673e-06 -0.0006686 -6.166e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.202 -0.0346 -0.1669 0.1866 0.9835 0.9932 0.2263 0.4354 0.8698 0.7132 ] Network output: [ -0.009756 1.002 1.009 -2.99e-07 1.342e-07 0.008188 -2.253e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006395 0.0005458 0.004437 0.00343 0.9889 0.9919 0.006517 0.8575 0.8937 0.0124 ] Network output: [ -0.000372 0.002136 1.001 -2.562e-05 1.15e-05 0.9977 -1.931e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2147 0.1006 0.3437 0.1439 0.985 0.994 0.2155 0.4395 0.8764 0.7073 ] Network output: [ 0.004504 -0.02131 0.9943 1.55e-05 -6.957e-06 1.018 1.168e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1068 0.09441 0.1832 0.1991 0.9873 0.9919 0.1069 0.7486 0.8641 0.3054 ] Network output: [ -0.004241 0.02004 1.004 1.659e-05 -7.446e-06 0.9844 1.25e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09218 0.09024 0.165 0.1957 0.9853 0.9912 0.09219 0.6728 0.84 0.247 ] Network output: [ 0.0001168 1 -0.0001081 2.2e-06 -9.877e-07 0.9998 1.658e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002879 Epoch 8728 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009889 0.9963 0.9914 -1.802e-07 8.09e-08 -0.007481 -1.358e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003433 -0.003254 -0.00735 0.005825 0.9699 0.9743 0.006629 0.8298 0.8225 0.01725 ] Network output: [ 0.9999 0.00032 0.0005969 -8.173e-06 3.669e-06 -0.0006681 -6.16e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.202 -0.0346 -0.1669 0.1866 0.9835 0.9932 0.2263 0.4354 0.8698 0.7132 ] Network output: [ -0.009755 1.002 1.009 -2.99e-07 1.342e-07 0.008187 -2.253e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006396 0.0005459 0.004437 0.003429 0.9889 0.9919 0.006518 0.8575 0.8937 0.0124 ] Network output: [ -0.0003718 0.002136 1.001 -2.559e-05 1.149e-05 0.9977 -1.929e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2147 0.1006 0.3437 0.1439 0.985 0.994 0.2155 0.4395 0.8764 0.7073 ] Network output: [ 0.004502 -0.0213 0.9943 1.548e-05 -6.95e-06 1.018 1.167e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1068 0.09441 0.1832 0.1991 0.9873 0.9919 0.1069 0.7486 0.8641 0.3054 ] Network output: [ -0.00424 0.02003 1.004 1.657e-05 -7.439e-06 0.9844 1.249e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09218 0.09024 0.165 0.1957 0.9853 0.9912 0.0922 0.6728 0.84 0.247 ] Network output: [ 0.0001168 1 -0.000108 2.198e-06 -9.867e-07 0.9998 1.656e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002877 Epoch 8729 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009887 0.9963 0.9914 -1.804e-07 8.1e-08 -0.007481 -1.36e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003433 -0.003254 -0.007349 0.005824 0.9699 0.9743 0.00663 0.8298 0.8225 0.01725 ] Network output: [ 0.9999 0.0003197 0.0005966 -8.164e-06 3.665e-06 -0.0006675 -6.153e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2021 -0.0346 -0.1669 0.1866 0.9835 0.9932 0.2263 0.4354 0.8698 0.7132 ] Network output: [ -0.009754 1.002 1.009 -2.99e-07 1.342e-07 0.008186 -2.254e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006396 0.000546 0.004437 0.003429 0.9889 0.9919 0.006518 0.8575 0.8937 0.0124 ] Network output: [ -0.0003715 0.002135 1.001 -2.557e-05 1.148e-05 0.9977 -1.927e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2148 0.1006 0.3437 0.1439 0.985 0.994 0.2155 0.4395 0.8764 0.7073 ] Network output: [ 0.004501 -0.02129 0.9943 1.546e-05 -6.942e-06 1.018 1.165e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1068 0.09442 0.1832 0.1991 0.9873 0.9919 0.1069 0.7486 0.8641 0.3054 ] Network output: [ -0.004238 0.02002 1.004 1.655e-05 -7.431e-06 0.9844 1.247e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09218 0.09025 0.165 0.1957 0.9853 0.9912 0.0922 0.6728 0.84 0.247 ] Network output: [ 0.0001167 1 -0.0001079 2.195e-06 -9.856e-07 0.9998 1.655e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002876 Epoch 8730 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009886 0.9963 0.9914 -1.807e-07 8.111e-08 -0.007481 -1.362e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003433 -0.003254 -0.007348 0.005824 0.9699 0.9743 0.00663 0.8298 0.8225 0.01725 ] Network output: [ 0.9999 0.0003194 0.0005962 -8.155e-06 3.661e-06 -0.000667 -6.146e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2021 -0.0346 -0.1669 0.1866 0.9835 0.9932 0.2264 0.4354 0.8698 0.7132 ] Network output: [ -0.009753 1.002 1.009 -2.99e-07 1.343e-07 0.008185 -2.254e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006397 0.0005461 0.004437 0.003429 0.9889 0.9919 0.006519 0.8575 0.8937 0.0124 ] Network output: [ -0.0003713 0.002134 1.001 -2.554e-05 1.146e-05 0.9977 -1.925e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2148 0.1006 0.3437 0.1439 0.985 0.994 0.2155 0.4395 0.8764 0.7073 ] Network output: [ 0.004499 -0.02128 0.9943 1.545e-05 -6.935e-06 1.018 1.164e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1069 0.09442 0.1832 0.1991 0.9873 0.9919 0.1069 0.7486 0.8641 0.3054 ] Network output: [ -0.004237 0.02002 1.004 1.653e-05 -7.423e-06 0.9844 1.246e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09219 0.09025 0.165 0.1957 0.9853 0.9912 0.0922 0.6727 0.8399 0.247 ] Network output: [ 0.0001167 1 -0.0001077 2.193e-06 -9.845e-07 0.9998 1.653e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002874 Epoch 8731 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009885 0.9963 0.9914 -1.809e-07 8.121e-08 -0.007481 -1.363e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003433 -0.003254 -0.007348 0.005823 0.9699 0.9743 0.00663 0.8297 0.8225 0.01724 ] Network output: [ 0.9999 0.0003191 0.0005959 -8.146e-06 3.657e-06 -0.0006665 -6.139e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2021 -0.0346 -0.1668 0.1866 0.9835 0.9932 0.2264 0.4354 0.8698 0.7132 ] Network output: [ -0.009752 1.002 1.009 -2.991e-07 1.343e-07 0.008184 -2.254e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006397 0.0005462 0.004437 0.003428 0.9889 0.9919 0.00652 0.8575 0.8937 0.01239 ] Network output: [ -0.000371 0.002133 1.001 -2.551e-05 1.145e-05 0.9977 -1.922e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2148 0.1006 0.3438 0.1439 0.985 0.994 0.2155 0.4395 0.8764 0.7072 ] Network output: [ 0.004498 -0.02128 0.9943 1.543e-05 -6.927e-06 1.018 1.163e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1069 0.09443 0.1832 0.1991 0.9873 0.9919 0.1069 0.7485 0.8641 0.3054 ] Network output: [ -0.004235 0.02001 1.004 1.652e-05 -7.415e-06 0.9844 1.245e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09219 0.09025 0.165 0.1957 0.9853 0.9912 0.0922 0.6727 0.8399 0.247 ] Network output: [ 0.0001166 1 -0.0001076 2.191e-06 -9.834e-07 0.9998 1.651e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002873 Epoch 8732 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009884 0.9963 0.9915 -1.811e-07 8.131e-08 -0.007481 -1.365e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003433 -0.003255 -0.007347 0.005823 0.9699 0.9743 0.006631 0.8297 0.8225 0.01724 ] Network output: [ 0.9999 0.0003189 0.0005956 -8.137e-06 3.653e-06 -0.0006659 -6.132e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2021 -0.03461 -0.1668 0.1866 0.9835 0.9932 0.2264 0.4354 0.8697 0.7132 ] Network output: [ -0.009751 1.002 1.009 -2.991e-07 1.343e-07 0.008183 -2.254e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006398 0.0005463 0.004437 0.003428 0.9889 0.9919 0.00652 0.8575 0.8937 0.01239 ] Network output: [ -0.0003708 0.002132 1.001 -2.548e-05 1.144e-05 0.9977 -1.92e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2148 0.1007 0.3438 0.1439 0.985 0.994 0.2155 0.4395 0.8764 0.7072 ] Network output: [ 0.004496 -0.02127 0.9943 1.541e-05 -6.92e-06 1.018 1.162e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1069 0.09443 0.1832 0.1991 0.9873 0.9919 0.1069 0.7485 0.8641 0.3054 ] Network output: [ -0.004234 0.02 1.004 1.65e-05 -7.407e-06 0.9844 1.243e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09219 0.09025 0.165 0.1957 0.9853 0.9912 0.0922 0.6727 0.8399 0.247 ] Network output: [ 0.0001166 1 -0.0001075 2.188e-06 -9.824e-07 0.9998 1.649e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002871 Epoch 8733 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009883 0.9963 0.9915 -1.813e-07 8.141e-08 -0.00748 -1.367e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003433 -0.003255 -0.007346 0.005822 0.9699 0.9743 0.006631 0.8297 0.8225 0.01724 ] Network output: [ 0.9999 0.0003186 0.0005953 -8.128e-06 3.649e-06 -0.0006654 -6.126e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2021 -0.03461 -0.1668 0.1866 0.9835 0.9932 0.2264 0.4354 0.8697 0.7132 ] Network output: [ -0.00975 1.002 1.009 -2.991e-07 1.343e-07 0.008181 -2.254e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006398 0.0005464 0.004437 0.003428 0.9889 0.9919 0.006521 0.8575 0.8937 0.01239 ] Network output: [ -0.0003706 0.002132 1.001 -2.545e-05 1.143e-05 0.9977 -1.918e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2148 0.1007 0.3438 0.1439 0.985 0.994 0.2155 0.4395 0.8764 0.7072 ] Network output: [ 0.004494 -0.02126 0.9943 1.54e-05 -6.912e-06 1.018 1.16e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1069 0.09444 0.1832 0.1991 0.9873 0.9919 0.1069 0.7485 0.8641 0.3054 ] Network output: [ -0.004232 0.01999 1.004 1.648e-05 -7.399e-06 0.9844 1.242e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09219 0.09026 0.165 0.1957 0.9853 0.9912 0.09221 0.6727 0.8399 0.247 ] Network output: [ 0.0001165 1 -0.0001074 2.186e-06 -9.813e-07 0.9998 1.647e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000287 Epoch 8734 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009881 0.9963 0.9915 -1.816e-07 8.151e-08 -0.00748 -1.368e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003433 -0.003255 -0.007345 0.005822 0.9699 0.9743 0.006631 0.8297 0.8225 0.01724 ] Network output: [ 0.9999 0.0003183 0.000595 -8.119e-06 3.645e-06 -0.0006649 -6.119e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2021 -0.03461 -0.1668 0.1866 0.9835 0.9932 0.2264 0.4354 0.8697 0.7132 ] Network output: [ -0.009749 1.002 1.009 -2.991e-07 1.343e-07 0.00818 -2.254e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006399 0.0005465 0.004437 0.003427 0.9889 0.9919 0.006521 0.8575 0.8937 0.01239 ] Network output: [ -0.0003703 0.002131 1.001 -2.542e-05 1.141e-05 0.9977 -1.916e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2148 0.1007 0.3438 0.1439 0.985 0.994 0.2155 0.4395 0.8764 0.7072 ] Network output: [ 0.004493 -0.02125 0.9943 1.538e-05 -6.904e-06 1.018 1.159e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1069 0.09444 0.1832 0.199 0.9873 0.9919 0.1069 0.7485 0.8641 0.3054 ] Network output: [ -0.00423 0.01998 1.004 1.646e-05 -7.391e-06 0.9844 1.241e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0922 0.09026 0.165 0.1957 0.9853 0.9912 0.09221 0.6727 0.8399 0.247 ] Network output: [ 0.0001165 1 -0.0001073 2.183e-06 -9.802e-07 0.9998 1.646e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002868 Epoch 8735 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00988 0.9963 0.9915 -1.818e-07 8.162e-08 -0.00748 -1.37e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003433 -0.003255 -0.007344 0.005821 0.9699 0.9743 0.006631 0.8297 0.8225 0.01724 ] Network output: [ 0.9999 0.000318 0.0005947 -8.11e-06 3.641e-06 -0.0006643 -6.112e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2021 -0.03461 -0.1668 0.1865 0.9835 0.9932 0.2264 0.4354 0.8697 0.7132 ] Network output: [ -0.009748 1.002 1.009 -2.991e-07 1.343e-07 0.008179 -2.254e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006399 0.0005466 0.004437 0.003427 0.9889 0.9919 0.006522 0.8575 0.8937 0.01239 ] Network output: [ -0.0003701 0.00213 1.001 -2.54e-05 1.14e-05 0.9977 -1.914e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2148 0.1007 0.3438 0.1439 0.985 0.994 0.2155 0.4395 0.8764 0.7072 ] Network output: [ 0.004491 -0.02125 0.9943 1.536e-05 -6.897e-06 1.018 1.158e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1069 0.09445 0.1832 0.199 0.9873 0.9919 0.107 0.7485 0.8641 0.3054 ] Network output: [ -0.004229 0.01998 1.004 1.645e-05 -7.383e-06 0.9844 1.239e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0922 0.09026 0.165 0.1957 0.9853 0.9912 0.09221 0.6727 0.8399 0.247 ] Network output: [ 0.0001164 1 -0.0001071 2.181e-06 -9.792e-07 0.9998 1.644e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002867 Epoch 8736 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009879 0.9963 0.9915 -1.82e-07 8.172e-08 -0.00748 -1.372e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003434 -0.003255 -0.007344 0.005821 0.9699 0.9743 0.006632 0.8297 0.8225 0.01724 ] Network output: [ 0.9999 0.0003177 0.0005944 -8.101e-06 3.637e-06 -0.0006638 -6.106e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2021 -0.03461 -0.1668 0.1865 0.9835 0.9932 0.2264 0.4353 0.8697 0.7132 ] Network output: [ -0.009747 1.002 1.009 -2.992e-07 1.343e-07 0.008178 -2.255e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0064 0.0005467 0.004437 0.003427 0.9889 0.9919 0.006522 0.8575 0.8937 0.01239 ] Network output: [ -0.0003698 0.002129 1.001 -2.537e-05 1.139e-05 0.9977 -1.912e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2148 0.1007 0.3438 0.1439 0.985 0.994 0.2155 0.4395 0.8764 0.7072 ] Network output: [ 0.00449 -0.02124 0.9943 1.535e-05 -6.889e-06 1.018 1.157e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1069 0.09445 0.1832 0.199 0.9873 0.9919 0.107 0.7485 0.8641 0.3054 ] Network output: [ -0.004227 0.01997 1.004 1.643e-05 -7.375e-06 0.9844 1.238e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0922 0.09026 0.165 0.1957 0.9853 0.9912 0.09221 0.6726 0.8399 0.247 ] Network output: [ 0.0001164 1 -0.000107 2.179e-06 -9.781e-07 0.9998 1.642e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002865 Epoch 8737 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009878 0.9963 0.9915 -1.822e-07 8.182e-08 -0.00748 -1.373e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003434 -0.003255 -0.007343 0.00582 0.9699 0.9743 0.006632 0.8297 0.8225 0.01724 ] Network output: [ 0.9999 0.0003175 0.000594 -8.093e-06 3.633e-06 -0.0006633 -6.099e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2021 -0.03461 -0.1668 0.1865 0.9835 0.9932 0.2264 0.4353 0.8697 0.7132 ] Network output: [ -0.009746 1.002 1.009 -2.992e-07 1.343e-07 0.008177 -2.255e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0064 0.0005468 0.004437 0.003427 0.9889 0.9919 0.006523 0.8575 0.8937 0.01239 ] Network output: [ -0.0003696 0.002129 1.001 -2.534e-05 1.138e-05 0.9977 -1.91e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2148 0.1007 0.3438 0.1439 0.985 0.994 0.2155 0.4394 0.8764 0.7072 ] Network output: [ 0.004488 -0.02123 0.9943 1.533e-05 -6.882e-06 1.018 1.155e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1069 0.09446 0.1832 0.199 0.9873 0.9919 0.107 0.7484 0.8641 0.3054 ] Network output: [ -0.004226 0.01996 1.004 1.641e-05 -7.367e-06 0.9844 1.237e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0922 0.09027 0.165 0.1957 0.9853 0.9912 0.09222 0.6726 0.8399 0.247 ] Network output: [ 0.0001163 1 -0.0001069 2.176e-06 -9.771e-07 0.9998 1.64e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002864 Epoch 8738 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009877 0.9963 0.9915 -1.825e-07 8.192e-08 -0.007479 -1.375e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003434 -0.003255 -0.007342 0.00582 0.9699 0.9743 0.006632 0.8297 0.8225 0.01724 ] Network output: [ 0.9999 0.0003172 0.0005937 -8.084e-06 3.629e-06 -0.0006627 -6.092e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2021 -0.03461 -0.1668 0.1865 0.9835 0.9932 0.2264 0.4353 0.8697 0.7131 ] Network output: [ -0.009745 1.002 1.009 -2.992e-07 1.343e-07 0.008176 -2.255e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006401 0.0005469 0.004437 0.003426 0.9889 0.9919 0.006523 0.8574 0.8937 0.01239 ] Network output: [ -0.0003694 0.002128 1.001 -2.531e-05 1.136e-05 0.9977 -1.908e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2148 0.1007 0.3438 0.1439 0.985 0.994 0.2156 0.4394 0.8764 0.7072 ] Network output: [ 0.004486 -0.02122 0.9943 1.531e-05 -6.874e-06 1.018 1.154e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1069 0.09447 0.1832 0.199 0.9873 0.9919 0.107 0.7484 0.8641 0.3054 ] Network output: [ -0.004224 0.01995 1.004 1.639e-05 -7.359e-06 0.9844 1.235e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09221 0.09027 0.165 0.1957 0.9853 0.9912 0.09222 0.6726 0.8399 0.247 ] Network output: [ 0.0001163 1 -0.0001068 2.174e-06 -9.76e-07 0.9998 1.638e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002862 Epoch 8739 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009875 0.9963 0.9915 -1.827e-07 8.201e-08 -0.007479 -1.377e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003434 -0.003255 -0.007341 0.005819 0.9699 0.9743 0.006632 0.8297 0.8225 0.01724 ] Network output: [ 0.9999 0.0003169 0.0005934 -8.075e-06 3.625e-06 -0.0006622 -6.085e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2021 -0.03461 -0.1667 0.1865 0.9835 0.9932 0.2264 0.4353 0.8697 0.7131 ] Network output: [ -0.009744 1.002 1.009 -2.992e-07 1.343e-07 0.008174 -2.255e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006401 0.000547 0.004437 0.003426 0.9889 0.9919 0.006524 0.8574 0.8937 0.01239 ] Network output: [ -0.0003691 0.002127 1.001 -2.528e-05 1.135e-05 0.9977 -1.906e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2148 0.1007 0.3438 0.1439 0.985 0.994 0.2156 0.4394 0.8764 0.7072 ] Network output: [ 0.004485 -0.02121 0.9943 1.53e-05 -6.867e-06 1.018 1.153e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1069 0.09447 0.1832 0.199 0.9873 0.9919 0.107 0.7484 0.8641 0.3054 ] Network output: [ -0.004223 0.01994 1.004 1.638e-05 -7.352e-06 0.9844 1.234e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09221 0.09027 0.165 0.1957 0.9853 0.9912 0.09222 0.6726 0.8399 0.247 ] Network output: [ 0.0001162 1 -0.0001066 2.172e-06 -9.749e-07 0.9998 1.637e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002861 Epoch 8740 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009874 0.9963 0.9915 -1.829e-07 8.211e-08 -0.007479 -1.378e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003434 -0.003256 -0.00734 0.005819 0.9699 0.9743 0.006633 0.8297 0.8225 0.01723 ] Network output: [ 0.9999 0.0003166 0.0005931 -8.066e-06 3.621e-06 -0.0006617 -6.079e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2021 -0.03462 -0.1667 0.1865 0.9835 0.9932 0.2265 0.4353 0.8697 0.7131 ] Network output: [ -0.009743 1.002 1.009 -2.992e-07 1.343e-07 0.008173 -2.255e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006402 0.0005471 0.004437 0.003426 0.9889 0.9919 0.006524 0.8574 0.8937 0.01239 ] Network output: [ -0.0003689 0.002126 1.001 -2.526e-05 1.134e-05 0.9977 -1.903e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2149 0.1007 0.3438 0.1439 0.985 0.994 0.2156 0.4394 0.8764 0.7072 ] Network output: [ 0.004483 -0.02121 0.9943 1.528e-05 -6.859e-06 1.018 1.151e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1069 0.09448 0.1832 0.199 0.9873 0.9919 0.107 0.7484 0.8641 0.3054 ] Network output: [ -0.004221 0.01994 1.004 1.636e-05 -7.344e-06 0.9844 1.233e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09221 0.09027 0.165 0.1957 0.9853 0.9912 0.09222 0.6726 0.8399 0.247 ] Network output: [ 0.0001162 1 -0.0001065 2.169e-06 -9.739e-07 0.9998 1.635e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002859 Epoch 8741 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009873 0.9963 0.9915 -1.831e-07 8.221e-08 -0.007479 -1.38e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003434 -0.003256 -0.00734 0.005818 0.9699 0.9743 0.006633 0.8297 0.8225 0.01723 ] Network output: [ 0.9999 0.0003164 0.0005928 -8.057e-06 3.617e-06 -0.0006612 -6.072e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2022 -0.03462 -0.1667 0.1865 0.9835 0.9932 0.2265 0.4353 0.8697 0.7131 ] Network output: [ -0.009742 1.002 1.009 -2.993e-07 1.343e-07 0.008172 -2.255e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006402 0.0005472 0.004437 0.003425 0.9889 0.9919 0.006525 0.8574 0.8937 0.01239 ] Network output: [ -0.0003686 0.002125 1.001 -2.523e-05 1.133e-05 0.9977 -1.901e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2149 0.1007 0.3438 0.1439 0.985 0.994 0.2156 0.4394 0.8764 0.7072 ] Network output: [ 0.004482 -0.0212 0.9943 1.526e-05 -6.852e-06 1.018 1.15e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1069 0.09448 0.1832 0.199 0.9873 0.9919 0.107 0.7484 0.8641 0.3054 ] Network output: [ -0.004219 0.01993 1.004 1.634e-05 -7.336e-06 0.9844 1.231e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09221 0.09028 0.165 0.1957 0.9853 0.9912 0.09223 0.6726 0.8399 0.247 ] Network output: [ 0.0001161 1 -0.0001064 2.167e-06 -9.728e-07 0.9998 1.633e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002858 Epoch 8742 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009872 0.9963 0.9915 -1.833e-07 8.231e-08 -0.007479 -1.382e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003434 -0.003256 -0.007339 0.005818 0.9699 0.9743 0.006633 0.8297 0.8225 0.01723 ] Network output: [ 0.9999 0.0003161 0.0005925 -8.048e-06 3.613e-06 -0.0006606 -6.065e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2022 -0.03462 -0.1667 0.1865 0.9835 0.9932 0.2265 0.4353 0.8697 0.7131 ] Network output: [ -0.009741 1.002 1.009 -2.993e-07 1.344e-07 0.008171 -2.255e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006403 0.0005473 0.004437 0.003425 0.9889 0.9919 0.006525 0.8574 0.8937 0.01238 ] Network output: [ -0.0003684 0.002125 1.001 -2.52e-05 1.131e-05 0.9977 -1.899e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2149 0.1007 0.3438 0.1439 0.985 0.994 0.2156 0.4394 0.8764 0.7072 ] Network output: [ 0.00448 -0.02119 0.9943 1.525e-05 -6.844e-06 1.018 1.149e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1069 0.09449 0.1832 0.199 0.9873 0.9919 0.107 0.7484 0.8641 0.3054 ] Network output: [ -0.004218 0.01992 1.004 1.632e-05 -7.328e-06 0.9844 1.23e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09222 0.09028 0.165 0.1957 0.9853 0.9912 0.09223 0.6725 0.8399 0.247 ] Network output: [ 0.0001161 1 -0.0001063 2.165e-06 -9.718e-07 0.9998 1.631e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002856 Epoch 8743 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009871 0.9963 0.9915 -1.836e-07 8.241e-08 -0.007478 -1.383e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003434 -0.003256 -0.007338 0.005817 0.9699 0.9743 0.006633 0.8297 0.8225 0.01723 ] Network output: [ 0.9999 0.0003158 0.0005922 -8.039e-06 3.609e-06 -0.0006601 -6.059e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2022 -0.03462 -0.1667 0.1865 0.9835 0.9932 0.2265 0.4353 0.8697 0.7131 ] Network output: [ -0.00974 1.002 1.009 -2.993e-07 1.344e-07 0.00817 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006403 0.0005474 0.004437 0.003425 0.9889 0.9919 0.006526 0.8574 0.8937 0.01238 ] Network output: [ -0.0003682 0.002124 1.001 -2.517e-05 1.13e-05 0.9977 -1.897e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2149 0.1007 0.3438 0.1439 0.985 0.994 0.2156 0.4394 0.8764 0.7072 ] Network output: [ 0.004479 -0.02118 0.9943 1.523e-05 -6.837e-06 1.018 1.148e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1069 0.09449 0.1832 0.199 0.9873 0.9919 0.107 0.7483 0.8641 0.3054 ] Network output: [ -0.004216 0.01991 1.004 1.631e-05 -7.32e-06 0.9844 1.229e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09222 0.09028 0.165 0.1957 0.9853 0.9912 0.09223 0.6725 0.8399 0.2471 ] Network output: [ 0.000116 1 -0.0001062 2.162e-06 -9.707e-07 0.9998 1.63e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002855 Epoch 8744 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00987 0.9963 0.9915 -1.838e-07 8.251e-08 -0.007478 -1.385e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003434 -0.003256 -0.007337 0.005817 0.9699 0.9743 0.006634 0.8297 0.8224 0.01723 ] Network output: [ 0.9999 0.0003155 0.0005918 -8.03e-06 3.605e-06 -0.0006596 -6.052e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2022 -0.03462 -0.1667 0.1865 0.9835 0.9932 0.2265 0.4353 0.8697 0.7131 ] Network output: [ -0.009739 1.002 1.009 -2.993e-07 1.344e-07 0.008169 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006404 0.0005475 0.004437 0.003424 0.9889 0.9919 0.006526 0.8574 0.8937 0.01238 ] Network output: [ -0.0003679 0.002123 1.001 -2.515e-05 1.129e-05 0.9977 -1.895e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2149 0.1007 0.3438 0.1439 0.985 0.994 0.2156 0.4394 0.8764 0.7072 ] Network output: [ 0.004477 -0.02118 0.9943 1.521e-05 -6.829e-06 1.018 1.146e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1069 0.0945 0.1832 0.199 0.9873 0.9919 0.107 0.7483 0.8641 0.3054 ] Network output: [ -0.004215 0.0199 1.004 1.629e-05 -7.312e-06 0.9844 1.228e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09222 0.09028 0.165 0.1957 0.9853 0.9912 0.09223 0.6725 0.8399 0.2471 ] Network output: [ 0.000116 1 -0.000106 2.16e-06 -9.696e-07 0.9998 1.628e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002853 Epoch 8745 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009868 0.9963 0.9915 -1.84e-07 8.26e-08 -0.007478 -1.387e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003434 -0.003256 -0.007336 0.005816 0.9699 0.9743 0.006634 0.8297 0.8224 0.01723 ] Network output: [ 0.9999 0.0003153 0.0005915 -8.021e-06 3.601e-06 -0.000659 -6.045e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2022 -0.03462 -0.1667 0.1865 0.9835 0.9932 0.2265 0.4353 0.8697 0.7131 ] Network output: [ -0.009738 1.002 1.009 -2.993e-07 1.344e-07 0.008167 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006404 0.0005476 0.004437 0.003424 0.9889 0.9919 0.006527 0.8574 0.8937 0.01238 ] Network output: [ -0.0003677 0.002122 1.001 -2.512e-05 1.128e-05 0.9977 -1.893e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2149 0.1007 0.3438 0.1439 0.985 0.994 0.2156 0.4394 0.8764 0.7072 ] Network output: [ 0.004475 -0.02117 0.9943 1.52e-05 -6.822e-06 1.018 1.145e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1069 0.0945 0.1832 0.199 0.9873 0.9919 0.107 0.7483 0.864 0.3054 ] Network output: [ -0.004213 0.0199 1.004 1.627e-05 -7.305e-06 0.9844 1.226e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09222 0.09029 0.165 0.1957 0.9853 0.9912 0.09224 0.6725 0.8399 0.2471 ] Network output: [ 0.0001159 1 -0.0001059 2.158e-06 -9.686e-07 0.9998 1.626e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002852 Epoch 8746 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009867 0.9963 0.9915 -1.842e-07 8.27e-08 -0.007478 -1.388e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003435 -0.003256 -0.007336 0.005816 0.9699 0.9743 0.006634 0.8297 0.8224 0.01723 ] Network output: [ 0.9999 0.000315 0.0005912 -8.013e-06 3.597e-06 -0.0006585 -6.039e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2022 -0.03462 -0.1667 0.1865 0.9835 0.9932 0.2265 0.4353 0.8697 0.7131 ] Network output: [ -0.009737 1.002 1.009 -2.993e-07 1.344e-07 0.008166 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006405 0.0005476 0.004437 0.003424 0.9889 0.9919 0.006527 0.8574 0.8937 0.01238 ] Network output: [ -0.0003675 0.002122 1.001 -2.509e-05 1.126e-05 0.9977 -1.891e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2149 0.1007 0.3439 0.1439 0.985 0.994 0.2156 0.4394 0.8764 0.7072 ] Network output: [ 0.004474 -0.02116 0.9943 1.518e-05 -6.815e-06 1.018 1.144e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1069 0.09451 0.1832 0.199 0.9873 0.9919 0.107 0.7483 0.864 0.3054 ] Network output: [ -0.004212 0.01989 1.004 1.625e-05 -7.297e-06 0.9844 1.225e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09222 0.09029 0.165 0.1957 0.9853 0.9912 0.09224 0.6725 0.8399 0.2471 ] Network output: [ 0.0001159 1 -0.0001058 2.155e-06 -9.675e-07 0.9998 1.624e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000285 Epoch 8747 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009866 0.9963 0.9915 -1.844e-07 8.279e-08 -0.007478 -1.39e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003435 -0.003256 -0.007335 0.005815 0.9699 0.9743 0.006634 0.8297 0.8224 0.01723 ] Network output: [ 0.9999 0.0003147 0.0005909 -8.004e-06 3.593e-06 -0.000658 -6.032e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2022 -0.03462 -0.1666 0.1865 0.9835 0.9932 0.2265 0.4353 0.8697 0.7131 ] Network output: [ -0.009736 1.002 1.009 -2.993e-07 1.344e-07 0.008165 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006405 0.0005477 0.004437 0.003423 0.9889 0.9919 0.006528 0.8574 0.8937 0.01238 ] Network output: [ -0.0003672 0.002121 1.001 -2.506e-05 1.125e-05 0.9977 -1.889e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2149 0.1007 0.3439 0.1439 0.985 0.994 0.2156 0.4394 0.8764 0.7072 ] Network output: [ 0.004472 -0.02115 0.9943 1.516e-05 -6.807e-06 1.018 1.143e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1069 0.09451 0.1832 0.199 0.9873 0.9919 0.107 0.7483 0.864 0.3054 ] Network output: [ -0.00421 0.01988 1.004 1.624e-05 -7.289e-06 0.9844 1.224e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09223 0.09029 0.165 0.1957 0.9853 0.9912 0.09224 0.6725 0.8399 0.2471 ] Network output: [ 0.0001158 1 -0.0001057 2.153e-06 -9.665e-07 0.9998 1.622e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002849 Epoch 8748 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009865 0.9963 0.9915 -1.846e-07 8.289e-08 -0.007477 -1.391e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003435 -0.003257 -0.007334 0.005814 0.9699 0.9743 0.006635 0.8296 0.8224 0.01722 ] Network output: [ 0.9999 0.0003144 0.0005906 -7.995e-06 3.589e-06 -0.0006575 -6.025e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2022 -0.03463 -0.1666 0.1865 0.9835 0.9932 0.2265 0.4352 0.8697 0.7131 ] Network output: [ -0.009735 1.002 1.009 -2.994e-07 1.344e-07 0.008164 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006406 0.0005478 0.004436 0.003423 0.9889 0.9919 0.006529 0.8574 0.8937 0.01238 ] Network output: [ -0.000367 0.00212 1.001 -2.503e-05 1.124e-05 0.9977 -1.887e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2149 0.1007 0.3439 0.1439 0.985 0.994 0.2156 0.4394 0.8764 0.7071 ] Network output: [ 0.004471 -0.02114 0.9943 1.515e-05 -6.8e-06 1.018 1.141e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.107 0.09452 0.1832 0.199 0.9873 0.9919 0.107 0.7483 0.864 0.3054 ] Network output: [ -0.004209 0.01987 1.004 1.622e-05 -7.281e-06 0.9844 1.222e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09223 0.09029 0.165 0.1957 0.9853 0.9912 0.09224 0.6724 0.8399 0.2471 ] Network output: [ 0.0001158 1 -0.0001056 2.151e-06 -9.654e-07 0.9998 1.621e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002847 Epoch 8749 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009864 0.9963 0.9915 -1.848e-07 8.299e-08 -0.007477 -1.393e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003435 -0.003257 -0.007333 0.005814 0.9699 0.9743 0.006635 0.8296 0.8224 0.01722 ] Network output: [ 0.9999 0.0003142 0.0005903 -7.986e-06 3.585e-06 -0.0006569 -6.019e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2022 -0.03463 -0.1666 0.1865 0.9835 0.9932 0.2265 0.4352 0.8697 0.7131 ] Network output: [ -0.009734 1.002 1.009 -2.994e-07 1.344e-07 0.008163 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006406 0.0005479 0.004436 0.003423 0.9889 0.9919 0.006529 0.8574 0.8937 0.01238 ] Network output: [ -0.0003667 0.002119 1.001 -2.501e-05 1.123e-05 0.9977 -1.885e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2149 0.1007 0.3439 0.1439 0.985 0.994 0.2157 0.4393 0.8764 0.7071 ] Network output: [ 0.004469 -0.02114 0.9943 1.513e-05 -6.792e-06 1.018 1.14e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.107 0.09452 0.1832 0.199 0.9873 0.9919 0.107 0.7482 0.864 0.3054 ] Network output: [ -0.004207 0.01986 1.004 1.62e-05 -7.273e-06 0.9844 1.221e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09223 0.09029 0.165 0.1957 0.9853 0.9912 0.09225 0.6724 0.8399 0.2471 ] Network output: [ 0.0001157 1 -0.0001055 2.148e-06 -9.644e-07 0.9998 1.619e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002846 Epoch 8750 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009862 0.9963 0.9915 -1.851e-07 8.308e-08 -0.007477 -1.395e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003435 -0.003257 -0.007332 0.005813 0.9699 0.9743 0.006635 0.8296 0.8224 0.01722 ] Network output: [ 0.9999 0.0003139 0.00059 -7.977e-06 3.581e-06 -0.0006564 -6.012e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2022 -0.03463 -0.1666 0.1865 0.9835 0.9932 0.2265 0.4352 0.8697 0.7131 ] Network output: [ -0.009733 1.002 1.009 -2.994e-07 1.344e-07 0.008162 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006407 0.000548 0.004436 0.003423 0.9889 0.9919 0.00653 0.8574 0.8937 0.01238 ] Network output: [ -0.0003665 0.002118 1.001 -2.498e-05 1.121e-05 0.9977 -1.883e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2149 0.1008 0.3439 0.1439 0.985 0.994 0.2157 0.4393 0.8764 0.7071 ] Network output: [ 0.004467 -0.02113 0.9943 1.511e-05 -6.785e-06 1.018 1.139e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.107 0.09453 0.1832 0.199 0.9873 0.9919 0.107 0.7482 0.864 0.3054 ] Network output: [ -0.004206 0.01986 1.004 1.618e-05 -7.266e-06 0.9844 1.22e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09223 0.0903 0.165 0.1957 0.9853 0.9912 0.09225 0.6724 0.8398 0.2471 ] Network output: [ 0.0001157 1 -0.0001053 2.146e-06 -9.633e-07 0.9998 1.617e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002844 Epoch 8751 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009861 0.9963 0.9915 -1.853e-07 8.317e-08 -0.007477 -1.396e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003435 -0.003257 -0.007332 0.005813 0.9699 0.9743 0.006635 0.8296 0.8224 0.01722 ] Network output: [ 0.9999 0.0003136 0.0005896 -7.968e-06 3.577e-06 -0.0006559 -6.005e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2022 -0.03463 -0.1666 0.1865 0.9835 0.9932 0.2266 0.4352 0.8697 0.7131 ] Network output: [ -0.009732 1.002 1.009 -2.994e-07 1.344e-07 0.008161 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006407 0.0005481 0.004436 0.003422 0.9889 0.9919 0.00653 0.8574 0.8937 0.01238 ] Network output: [ -0.0003663 0.002118 1.001 -2.495e-05 1.12e-05 0.9977 -1.88e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.215 0.1008 0.3439 0.1439 0.985 0.994 0.2157 0.4393 0.8764 0.7071 ] Network output: [ 0.004466 -0.02112 0.9943 1.51e-05 -6.777e-06 1.018 1.138e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.107 0.09453 0.1832 0.199 0.9873 0.9919 0.107 0.7482 0.864 0.3054 ] Network output: [ -0.004204 0.01985 1.004 1.617e-05 -7.258e-06 0.9844 1.218e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09224 0.0903 0.165 0.1957 0.9853 0.9912 0.09225 0.6724 0.8398 0.2471 ] Network output: [ 0.0001157 1 -0.0001052 2.143e-06 -9.623e-07 0.9998 1.615e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002843 Epoch 8752 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00986 0.9963 0.9915 -1.855e-07 8.327e-08 -0.007477 -1.398e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003435 -0.003257 -0.007331 0.005812 0.9699 0.9743 0.006636 0.8296 0.8224 0.01722 ] Network output: [ 0.9999 0.0003133 0.0005893 -7.96e-06 3.573e-06 -0.0006554 -5.999e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2022 -0.03463 -0.1666 0.1865 0.9835 0.9932 0.2266 0.4352 0.8697 0.7131 ] Network output: [ -0.009731 1.002 1.009 -2.994e-07 1.344e-07 0.008159 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006408 0.0005482 0.004436 0.003422 0.9889 0.9919 0.006531 0.8574 0.8937 0.01238 ] Network output: [ -0.000366 0.002117 1.001 -2.492e-05 1.119e-05 0.9977 -1.878e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.215 0.1008 0.3439 0.1439 0.985 0.994 0.2157 0.4393 0.8764 0.7071 ] Network output: [ 0.004464 -0.02111 0.9943 1.508e-05 -6.77e-06 1.018 1.136e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.107 0.09454 0.1832 0.199 0.9873 0.9919 0.1071 0.7482 0.864 0.3054 ] Network output: [ -0.004202 0.01984 1.004 1.615e-05 -7.25e-06 0.9845 1.217e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09224 0.0903 0.165 0.1957 0.9853 0.9912 0.09225 0.6724 0.8398 0.2471 ] Network output: [ 0.0001156 1 -0.0001051 2.141e-06 -9.612e-07 0.9998 1.614e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002841 Epoch 8753 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009859 0.9963 0.9915 -1.857e-07 8.336e-08 -0.007476 -1.399e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003435 -0.003257 -0.00733 0.005812 0.9699 0.9743 0.006636 0.8296 0.8224 0.01722 ] Network output: [ 0.9999 0.000313 0.000589 -7.951e-06 3.569e-06 -0.0006548 -5.992e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2022 -0.03463 -0.1666 0.1865 0.9835 0.9932 0.2266 0.4352 0.8697 0.7131 ] Network output: [ -0.00973 1.002 1.009 -2.994e-07 1.344e-07 0.008158 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006408 0.0005483 0.004436 0.003422 0.9889 0.9919 0.006531 0.8573 0.8937 0.01237 ] Network output: [ -0.0003658 0.002116 1.001 -2.49e-05 1.118e-05 0.9977 -1.876e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.215 0.1008 0.3439 0.1439 0.985 0.994 0.2157 0.4393 0.8764 0.7071 ] Network output: [ 0.004463 -0.02111 0.9943 1.506e-05 -6.763e-06 1.018 1.135e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.107 0.09454 0.1832 0.199 0.9873 0.9919 0.1071 0.7482 0.864 0.3054 ] Network output: [ -0.004201 0.01983 1.004 1.613e-05 -7.242e-06 0.9845 1.216e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09224 0.0903 0.165 0.1957 0.9853 0.9912 0.09225 0.6723 0.8398 0.2471 ] Network output: [ 0.0001156 1 -0.000105 2.139e-06 -9.602e-07 0.9998 1.612e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000284 Epoch 8754 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009858 0.9963 0.9915 -1.859e-07 8.346e-08 -0.007476 -1.401e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003435 -0.003257 -0.007329 0.005811 0.9699 0.9743 0.006636 0.8296 0.8224 0.01722 ] Network output: [ 0.9999 0.0003128 0.0005887 -7.942e-06 3.565e-06 -0.0006543 -5.985e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2023 -0.03463 -0.1666 0.1865 0.9835 0.9932 0.2266 0.4352 0.8697 0.7131 ] Network output: [ -0.009729 1.002 1.009 -2.994e-07 1.344e-07 0.008157 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006409 0.0005484 0.004436 0.003421 0.9889 0.9919 0.006532 0.8573 0.8937 0.01237 ] Network output: [ -0.0003655 0.002115 1.001 -2.487e-05 1.116e-05 0.9977 -1.874e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.215 0.1008 0.3439 0.1439 0.985 0.994 0.2157 0.4393 0.8764 0.7071 ] Network output: [ 0.004461 -0.0211 0.9943 1.505e-05 -6.755e-06 1.018 1.134e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.107 0.09455 0.1832 0.199 0.9873 0.9919 0.1071 0.7482 0.864 0.3054 ] Network output: [ -0.004199 0.01982 1.004 1.611e-05 -7.235e-06 0.9845 1.214e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09224 0.09031 0.165 0.1957 0.9853 0.9912 0.09226 0.6723 0.8398 0.2471 ] Network output: [ 0.0001155 1 -0.0001049 2.136e-06 -9.592e-07 0.9998 1.61e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002838 Epoch 8755 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009856 0.9963 0.9915 -1.861e-07 8.355e-08 -0.007476 -1.403e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003436 -0.003257 -0.007328 0.005811 0.9699 0.9743 0.006637 0.8296 0.8224 0.01722 ] Network output: [ 0.9999 0.0003125 0.0005884 -7.933e-06 3.562e-06 -0.0006538 -5.979e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2023 -0.03463 -0.1666 0.1864 0.9835 0.9932 0.2266 0.4352 0.8697 0.7131 ] Network output: [ -0.009728 1.002 1.009 -2.994e-07 1.344e-07 0.008156 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00641 0.0005485 0.004436 0.003421 0.9889 0.9919 0.006532 0.8573 0.8937 0.01237 ] Network output: [ -0.0003653 0.002114 1.001 -2.484e-05 1.115e-05 0.9977 -1.872e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.215 0.1008 0.3439 0.1439 0.985 0.994 0.2157 0.4393 0.8764 0.7071 ] Network output: [ 0.00446 -0.02109 0.9943 1.503e-05 -6.748e-06 1.018 1.133e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.107 0.09456 0.1832 0.199 0.9873 0.9919 0.1071 0.7481 0.864 0.3054 ] Network output: [ -0.004198 0.01982 1.004 1.61e-05 -7.227e-06 0.9845 1.213e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09225 0.09031 0.165 0.1957 0.9853 0.9912 0.09226 0.6723 0.8398 0.2471 ] Network output: [ 0.0001155 1 -0.0001047 2.134e-06 -9.581e-07 0.9998 1.608e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002837 Epoch 8756 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009855 0.9963 0.9915 -1.863e-07 8.364e-08 -0.007476 -1.404e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003436 -0.003258 -0.007328 0.00581 0.9699 0.9743 0.006637 0.8296 0.8224 0.01722 ] Network output: [ 0.9999 0.0003122 0.0005881 -7.925e-06 3.558e-06 -0.0006533 -5.972e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2023 -0.03464 -0.1665 0.1864 0.9835 0.9932 0.2266 0.4352 0.8697 0.713 ] Network output: [ -0.009727 1.002 1.009 -2.994e-07 1.344e-07 0.008155 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00641 0.0005486 0.004436 0.003421 0.9889 0.9919 0.006533 0.8573 0.8937 0.01237 ] Network output: [ -0.0003651 0.002114 1.001 -2.481e-05 1.114e-05 0.9977 -1.87e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.215 0.1008 0.3439 0.1439 0.985 0.994 0.2157 0.4393 0.8764 0.7071 ] Network output: [ 0.004458 -0.02108 0.9943 1.501e-05 -6.74e-06 1.018 1.132e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.107 0.09456 0.1832 0.199 0.9873 0.9919 0.1071 0.7481 0.864 0.3054 ] Network output: [ -0.004196 0.01981 1.004 1.608e-05 -7.219e-06 0.9845 1.212e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09225 0.09031 0.165 0.1957 0.9853 0.9912 0.09226 0.6723 0.8398 0.2471 ] Network output: [ 0.0001154 1 -0.0001046 2.132e-06 -9.571e-07 0.9998 1.607e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002835 Epoch 8757 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009854 0.9963 0.9915 -1.865e-07 8.373e-08 -0.007476 -1.406e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003436 -0.003258 -0.007327 0.00581 0.9699 0.9743 0.006637 0.8296 0.8224 0.01721 ] Network output: [ 0.9999 0.000312 0.0005878 -7.916e-06 3.554e-06 -0.0006527 -5.966e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2023 -0.03464 -0.1665 0.1864 0.9835 0.9932 0.2266 0.4352 0.8697 0.713 ] Network output: [ -0.009726 1.002 1.009 -2.994e-07 1.344e-07 0.008154 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006411 0.0005487 0.004436 0.00342 0.9889 0.9919 0.006533 0.8573 0.8937 0.01237 ] Network output: [ -0.0003648 0.002113 1.001 -2.479e-05 1.113e-05 0.9977 -1.868e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.215 0.1008 0.3439 0.1439 0.985 0.994 0.2157 0.4393 0.8764 0.7071 ] Network output: [ 0.004456 -0.02108 0.9943 1.5e-05 -6.733e-06 1.018 1.13e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.107 0.09457 0.1832 0.199 0.9873 0.9919 0.1071 0.7481 0.864 0.3054 ] Network output: [ -0.004195 0.0198 1.004 1.606e-05 -7.211e-06 0.9845 1.211e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09225 0.09031 0.165 0.1957 0.9853 0.9912 0.09226 0.6723 0.8398 0.2471 ] Network output: [ 0.0001154 1 -0.0001045 2.13e-06 -9.56e-07 0.9998 1.605e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002834 Epoch 8758 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009853 0.9963 0.9915 -1.867e-07 8.383e-08 -0.007475 -1.407e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003436 -0.003258 -0.007326 0.005809 0.9699 0.9743 0.006637 0.8296 0.8224 0.01721 ] Network output: [ 0.9999 0.0003117 0.0005875 -7.907e-06 3.55e-06 -0.0006522 -5.959e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2023 -0.03464 -0.1665 0.1864 0.9835 0.9932 0.2266 0.4352 0.8697 0.713 ] Network output: [ -0.009725 1.002 1.009 -2.994e-07 1.344e-07 0.008152 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006411 0.0005488 0.004436 0.00342 0.9889 0.9919 0.006534 0.8573 0.8937 0.01237 ] Network output: [ -0.0003646 0.002112 1.001 -2.476e-05 1.112e-05 0.9977 -1.866e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.215 0.1008 0.3439 0.1439 0.985 0.994 0.2157 0.4393 0.8764 0.7071 ] Network output: [ 0.004455 -0.02107 0.9943 1.498e-05 -6.726e-06 1.018 1.129e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.107 0.09457 0.1832 0.199 0.9873 0.9919 0.1071 0.7481 0.864 0.3054 ] Network output: [ -0.004193 0.01979 1.004 1.605e-05 -7.204e-06 0.9845 1.209e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09225 0.09032 0.165 0.1958 0.9853 0.9912 0.09227 0.6723 0.8398 0.2471 ] Network output: [ 0.0001153 1 -0.0001044 2.127e-06 -9.55e-07 0.9998 1.603e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002832 Epoch 8759 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009852 0.9963 0.9915 -1.869e-07 8.392e-08 -0.007475 -1.409e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003436 -0.003258 -0.007325 0.005809 0.9699 0.9743 0.006638 0.8296 0.8224 0.01721 ] Network output: [ 0.9999 0.0003114 0.0005872 -7.898e-06 3.546e-06 -0.0006517 -5.952e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2023 -0.03464 -0.1665 0.1864 0.9835 0.9932 0.2266 0.4352 0.8697 0.713 ] Network output: [ -0.009724 1.002 1.009 -2.995e-07 1.344e-07 0.008151 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006412 0.0005489 0.004436 0.00342 0.9889 0.9919 0.006534 0.8573 0.8937 0.01237 ] Network output: [ -0.0003644 0.002111 1.001 -2.473e-05 1.11e-05 0.9977 -1.864e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.215 0.1008 0.3439 0.1439 0.985 0.994 0.2157 0.4393 0.8764 0.7071 ] Network output: [ 0.004453 -0.02106 0.9943 1.496e-05 -6.718e-06 1.018 1.128e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.107 0.09458 0.1832 0.199 0.9873 0.9919 0.1071 0.7481 0.864 0.3054 ] Network output: [ -0.004192 0.01978 1.004 1.603e-05 -7.196e-06 0.9845 1.208e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09226 0.09032 0.165 0.1958 0.9853 0.9912 0.09227 0.6722 0.8398 0.2471 ] Network output: [ 0.0001153 1 -0.0001043 2.125e-06 -9.539e-07 0.9998 1.601e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002831 Epoch 8760 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009851 0.9963 0.9915 -1.871e-07 8.401e-08 -0.007475 -1.41e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003436 -0.003258 -0.007325 0.005808 0.9699 0.9743 0.006638 0.8296 0.8224 0.01721 ] Network output: [ 0.9999 0.0003111 0.0005869 -7.89e-06 3.542e-06 -0.0006512 -5.946e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2023 -0.03464 -0.1665 0.1864 0.9835 0.9932 0.2266 0.4351 0.8697 0.713 ] Network output: [ -0.009723 1.002 1.009 -2.995e-07 1.344e-07 0.00815 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006412 0.000549 0.004436 0.003419 0.9889 0.9919 0.006535 0.8573 0.8937 0.01237 ] Network output: [ -0.0003641 0.002111 1.001 -2.47e-05 1.109e-05 0.9977 -1.862e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.215 0.1008 0.3439 0.1439 0.985 0.994 0.2158 0.4393 0.8764 0.7071 ] Network output: [ 0.004452 -0.02105 0.9943 1.495e-05 -6.711e-06 1.018 1.127e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.107 0.09458 0.1832 0.199 0.9873 0.9919 0.1071 0.7481 0.864 0.3054 ] Network output: [ -0.00419 0.01978 1.004 1.601e-05 -7.188e-06 0.9845 1.207e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09226 0.09032 0.165 0.1958 0.9853 0.9912 0.09227 0.6722 0.8398 0.2471 ] Network output: [ 0.0001152 1 -0.0001042 2.123e-06 -9.529e-07 0.9998 1.6e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002829 Epoch 8761 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009849 0.9963 0.9915 -1.873e-07 8.41e-08 -0.007475 -1.412e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003436 -0.003258 -0.007324 0.005808 0.9699 0.9743 0.006638 0.8296 0.8224 0.01721 ] Network output: [ 0.9999 0.0003109 0.0005865 -7.881e-06 3.538e-06 -0.0006507 -5.939e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2023 -0.03464 -0.1665 0.1864 0.9835 0.9932 0.2266 0.4351 0.8697 0.713 ] Network output: [ -0.009722 1.002 1.009 -2.995e-07 1.344e-07 0.008149 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006413 0.0005491 0.004436 0.003419 0.9889 0.9919 0.006535 0.8573 0.8937 0.01237 ] Network output: [ -0.0003639 0.00211 1.001 -2.468e-05 1.108e-05 0.9977 -1.86e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.215 0.1008 0.344 0.1439 0.985 0.994 0.2158 0.4392 0.8764 0.7071 ] Network output: [ 0.00445 -0.02104 0.9943 1.493e-05 -6.704e-06 1.018 1.125e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.107 0.09459 0.1832 0.199 0.9873 0.9919 0.1071 0.748 0.864 0.3054 ] Network output: [ -0.004188 0.01977 1.004 1.599e-05 -7.18e-06 0.9845 1.205e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09226 0.09032 0.165 0.1958 0.9853 0.9912 0.09227 0.6722 0.8398 0.2471 ] Network output: [ 0.0001152 1 -0.000104 2.12e-06 -9.519e-07 0.9998 1.598e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002828 Epoch 8762 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009848 0.9963 0.9915 -1.875e-07 8.419e-08 -0.007474 -1.413e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003436 -0.003258 -0.007323 0.005807 0.9699 0.9743 0.006638 0.8296 0.8224 0.01721 ] Network output: [ 0.9999 0.0003106 0.0005862 -7.872e-06 3.534e-06 -0.0006501 -5.933e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2023 -0.03464 -0.1665 0.1864 0.9835 0.9932 0.2267 0.4351 0.8697 0.713 ] Network output: [ -0.009721 1.002 1.009 -2.995e-07 1.344e-07 0.008148 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006413 0.0005492 0.004436 0.003419 0.9889 0.9919 0.006536 0.8573 0.8937 0.01237 ] Network output: [ -0.0003636 0.002109 1.001 -2.465e-05 1.107e-05 0.9977 -1.858e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2151 0.1008 0.344 0.1438 0.985 0.994 0.2158 0.4392 0.8764 0.7071 ] Network output: [ 0.004448 -0.02104 0.9943 1.492e-05 -6.696e-06 1.018 1.124e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.107 0.09459 0.1832 0.199 0.9873 0.9919 0.1071 0.748 0.864 0.3054 ] Network output: [ -0.004187 0.01976 1.004 1.598e-05 -7.173e-06 0.9845 1.204e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09226 0.09033 0.165 0.1958 0.9853 0.9912 0.09228 0.6722 0.8398 0.2471 ] Network output: [ 0.0001151 1 -0.0001039 2.118e-06 -9.508e-07 0.9998 1.596e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002826 Epoch 8763 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009847 0.9963 0.9915 -1.877e-07 8.428e-08 -0.007474 -1.415e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003436 -0.003258 -0.007322 0.005807 0.9699 0.9743 0.006639 0.8296 0.8224 0.01721 ] Network output: [ 0.9999 0.0003103 0.0005859 -7.863e-06 3.53e-06 -0.0006496 -5.926e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2023 -0.03464 -0.1665 0.1864 0.9835 0.9932 0.2267 0.4351 0.8697 0.713 ] Network output: [ -0.00972 1.002 1.009 -2.995e-07 1.344e-07 0.008147 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006414 0.0005493 0.004436 0.003419 0.9889 0.9919 0.006536 0.8573 0.8937 0.01237 ] Network output: [ -0.0003634 0.002108 1.001 -2.462e-05 1.105e-05 0.9977 -1.856e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2151 0.1008 0.344 0.1438 0.985 0.994 0.2158 0.4392 0.8764 0.7071 ] Network output: [ 0.004447 -0.02103 0.9943 1.49e-05 -6.689e-06 1.018 1.123e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.107 0.0946 0.1833 0.199 0.9873 0.9919 0.1071 0.748 0.864 0.3054 ] Network output: [ -0.004185 0.01975 1.004 1.596e-05 -7.165e-06 0.9845 1.203e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09227 0.09033 0.165 0.1958 0.9853 0.9912 0.09228 0.6722 0.8398 0.2471 ] Network output: [ 0.0001151 1 -0.0001038 2.116e-06 -9.498e-07 0.9998 1.594e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002825 Epoch 8764 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009846 0.9963 0.9915 -1.879e-07 8.437e-08 -0.007474 -1.416e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003436 -0.003259 -0.007321 0.005806 0.9699 0.9743 0.006639 0.8295 0.8224 0.01721 ] Network output: [ 0.9999 0.00031 0.0005856 -7.855e-06 3.526e-06 -0.0006491 -5.92e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2023 -0.03464 -0.1664 0.1864 0.9835 0.9932 0.2267 0.4351 0.8697 0.713 ] Network output: [ -0.009719 1.002 1.009 -2.995e-07 1.344e-07 0.008146 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006414 0.0005494 0.004436 0.003418 0.9889 0.9919 0.006537 0.8573 0.8937 0.01236 ] Network output: [ -0.0003632 0.002107 1.001 -2.46e-05 1.104e-05 0.9977 -1.854e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2151 0.1008 0.344 0.1438 0.985 0.994 0.2158 0.4392 0.8764 0.7071 ] Network output: [ 0.004445 -0.02102 0.9943 1.488e-05 -6.682e-06 1.018 1.122e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.107 0.0946 0.1833 0.199 0.9873 0.9919 0.1071 0.748 0.864 0.3054 ] Network output: [ -0.004184 0.01974 1.004 1.594e-05 -7.157e-06 0.9845 1.202e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09227 0.09033 0.165 0.1958 0.9853 0.9912 0.09228 0.6722 0.8398 0.2471 ] Network output: [ 0.000115 1 -0.0001037 2.113e-06 -9.488e-07 0.9998 1.593e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002823 Epoch 8765 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009845 0.9963 0.9915 -1.881e-07 8.446e-08 -0.007474 -1.418e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003437 -0.003259 -0.007321 0.005806 0.9699 0.9743 0.006639 0.8295 0.8224 0.01721 ] Network output: [ 0.9999 0.0003098 0.0005853 -7.846e-06 3.522e-06 -0.0006486 -5.913e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2023 -0.03465 -0.1664 0.1864 0.9835 0.9932 0.2267 0.4351 0.8697 0.713 ] Network output: [ -0.009718 1.002 1.009 -2.995e-07 1.344e-07 0.008144 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006415 0.0005494 0.004436 0.003418 0.9889 0.9919 0.006538 0.8573 0.8937 0.01236 ] Network output: [ -0.0003629 0.002107 1.001 -2.457e-05 1.103e-05 0.9977 -1.852e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2151 0.1008 0.344 0.1438 0.985 0.994 0.2158 0.4392 0.8764 0.707 ] Network output: [ 0.004444 -0.02101 0.9943 1.487e-05 -6.674e-06 1.018 1.12e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1071 0.09461 0.1833 0.199 0.9873 0.9919 0.1071 0.748 0.864 0.3054 ] Network output: [ -0.004182 0.01974 1.004 1.593e-05 -7.15e-06 0.9845 1.2e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09227 0.09033 0.165 0.1958 0.9853 0.9912 0.09228 0.6721 0.8398 0.2471 ] Network output: [ 0.000115 1 -0.0001036 2.111e-06 -9.477e-07 0.9998 1.591e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002822 Epoch 8766 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009843 0.9963 0.9915 -1.883e-07 8.455e-08 -0.007474 -1.419e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003437 -0.003259 -0.00732 0.005805 0.9699 0.9743 0.006639 0.8295 0.8224 0.0172 ] Network output: [ 0.9999 0.0003095 0.000585 -7.837e-06 3.518e-06 -0.0006481 -5.906e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2023 -0.03465 -0.1664 0.1864 0.9835 0.9932 0.2267 0.4351 0.8697 0.713 ] Network output: [ -0.009717 1.002 1.009 -2.995e-07 1.344e-07 0.008143 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006415 0.0005495 0.004436 0.003418 0.9889 0.9919 0.006538 0.8573 0.8936 0.01236 ] Network output: [ -0.0003627 0.002106 1.001 -2.454e-05 1.102e-05 0.9977 -1.85e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2151 0.1008 0.344 0.1438 0.985 0.994 0.2158 0.4392 0.8764 0.707 ] Network output: [ 0.004442 -0.02101 0.9943 1.485e-05 -6.667e-06 1.018 1.119e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1071 0.09461 0.1833 0.199 0.9873 0.9919 0.1071 0.748 0.864 0.3054 ] Network output: [ -0.004181 0.01973 1.004 1.591e-05 -7.142e-06 0.9845 1.199e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09227 0.09034 0.165 0.1958 0.9853 0.9912 0.09229 0.6721 0.8398 0.2471 ] Network output: [ 0.0001149 1 -0.0001035 2.109e-06 -9.467e-07 0.9998 1.589e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000282 Epoch 8767 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009842 0.9963 0.9915 -1.885e-07 8.463e-08 -0.007473 -1.421e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003437 -0.003259 -0.007319 0.005805 0.9699 0.9743 0.00664 0.8295 0.8224 0.0172 ] Network output: [ 0.9999 0.0003092 0.0005847 -7.829e-06 3.515e-06 -0.0006475 -5.9e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2024 -0.03465 -0.1664 0.1864 0.9835 0.9932 0.2267 0.4351 0.8697 0.713 ] Network output: [ -0.009716 1.002 1.009 -2.995e-07 1.345e-07 0.008142 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006416 0.0005496 0.004436 0.003417 0.9889 0.9919 0.006539 0.8573 0.8936 0.01236 ] Network output: [ -0.0003625 0.002105 1.001 -2.451e-05 1.101e-05 0.9977 -1.847e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2151 0.1009 0.344 0.1438 0.985 0.994 0.2158 0.4392 0.8763 0.707 ] Network output: [ 0.004441 -0.021 0.9943 1.483e-05 -6.66e-06 1.018 1.118e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1071 0.09462 0.1833 0.199 0.9873 0.9919 0.1071 0.7479 0.864 0.3054 ] Network output: [ -0.004179 0.01972 1.004 1.589e-05 -7.134e-06 0.9845 1.198e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09227 0.09034 0.165 0.1958 0.9853 0.9912 0.09229 0.6721 0.8398 0.2471 ] Network output: [ 0.0001149 1 -0.0001033 2.106e-06 -9.457e-07 0.9998 1.587e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002819 Epoch 8768 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009841 0.9963 0.9915 -1.887e-07 8.472e-08 -0.007473 -1.422e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003437 -0.003259 -0.007318 0.005804 0.9699 0.9743 0.00664 0.8295 0.8224 0.0172 ] Network output: [ 0.9999 0.0003089 0.0005844 -7.82e-06 3.511e-06 -0.000647 -5.893e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2024 -0.03465 -0.1664 0.1864 0.9835 0.9932 0.2267 0.4351 0.8697 0.713 ] Network output: [ -0.009715 1.002 1.009 -2.995e-07 1.345e-07 0.008141 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006416 0.0005497 0.004436 0.003417 0.9889 0.9919 0.006539 0.8572 0.8936 0.01236 ] Network output: [ -0.0003622 0.002104 1.001 -2.449e-05 1.099e-05 0.9977 -1.845e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2151 0.1009 0.344 0.1438 0.985 0.994 0.2158 0.4392 0.8763 0.707 ] Network output: [ 0.004439 -0.02099 0.9943 1.482e-05 -6.652e-06 1.018 1.117e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1071 0.09462 0.1833 0.199 0.9873 0.9919 0.1071 0.7479 0.864 0.3054 ] Network output: [ -0.004178 0.01971 1.004 1.587e-05 -7.127e-06 0.9845 1.196e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09228 0.09034 0.165 0.1958 0.9853 0.9912 0.09229 0.6721 0.8398 0.2471 ] Network output: [ 0.0001148 1 -0.0001032 2.104e-06 -9.446e-07 0.9998 1.586e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002817 Epoch 8769 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00984 0.9963 0.9915 -1.889e-07 8.481e-08 -0.007473 -1.424e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003437 -0.003259 -0.007317 0.005804 0.9699 0.9743 0.00664 0.8295 0.8224 0.0172 ] Network output: [ 0.9999 0.0003087 0.0005841 -7.811e-06 3.507e-06 -0.0006465 -5.887e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2024 -0.03465 -0.1664 0.1864 0.9835 0.9932 0.2267 0.4351 0.8697 0.713 ] Network output: [ -0.009714 1.002 1.009 -2.995e-07 1.345e-07 0.00814 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006417 0.0005498 0.004436 0.003417 0.9889 0.9919 0.00654 0.8572 0.8936 0.01236 ] Network output: [ -0.000362 0.002104 1.001 -2.446e-05 1.098e-05 0.9977 -1.843e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2151 0.1009 0.344 0.1438 0.985 0.994 0.2158 0.4392 0.8763 0.707 ] Network output: [ 0.004437 -0.02098 0.9943 1.48e-05 -6.645e-06 1.018 1.116e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1071 0.09463 0.1833 0.199 0.9873 0.9919 0.1071 0.7479 0.864 0.3054 ] Network output: [ -0.004176 0.0197 1.004 1.586e-05 -7.119e-06 0.9845 1.195e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09228 0.09034 0.165 0.1958 0.9853 0.9912 0.09229 0.6721 0.8398 0.2471 ] Network output: [ 0.0001148 1 -0.0001031 2.102e-06 -9.436e-07 0.9998 1.584e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002816 Epoch 8770 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009839 0.9963 0.9915 -1.891e-07 8.49e-08 -0.007473 -1.425e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003437 -0.003259 -0.007317 0.005803 0.9699 0.9743 0.00664 0.8295 0.8224 0.0172 ] Network output: [ 0.9999 0.0003084 0.0005838 -7.803e-06 3.503e-06 -0.000646 -5.88e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2024 -0.03465 -0.1664 0.1864 0.9835 0.9932 0.2267 0.4351 0.8697 0.713 ] Network output: [ -0.009713 1.002 1.009 -2.995e-07 1.345e-07 0.008139 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006417 0.0005499 0.004436 0.003416 0.9889 0.9919 0.00654 0.8572 0.8936 0.01236 ] Network output: [ -0.0003618 0.002103 1.001 -2.443e-05 1.097e-05 0.9977 -1.841e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2151 0.1009 0.344 0.1438 0.985 0.994 0.2158 0.4392 0.8763 0.707 ] Network output: [ 0.004436 -0.02097 0.9943 1.479e-05 -6.638e-06 1.018 1.114e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1071 0.09463 0.1833 0.199 0.9873 0.9919 0.1072 0.7479 0.8639 0.3054 ] Network output: [ -0.004175 0.0197 1.004 1.584e-05 -7.111e-06 0.9845 1.194e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09228 0.09034 0.165 0.1958 0.9853 0.9912 0.0923 0.6721 0.8398 0.2471 ] Network output: [ 0.0001147 1 -0.000103 2.1e-06 -9.426e-07 0.9998 1.582e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002814 Epoch 8771 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009838 0.9963 0.9915 -1.893e-07 8.498e-08 -0.007473 -1.427e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003437 -0.003259 -0.007316 0.005803 0.9699 0.9743 0.006641 0.8295 0.8224 0.0172 ] Network output: [ 0.9999 0.0003081 0.0005835 -7.794e-06 3.499e-06 -0.0006455 -5.874e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2024 -0.03465 -0.1664 0.1864 0.9835 0.9932 0.2267 0.4351 0.8697 0.713 ] Network output: [ -0.009712 1.002 1.009 -2.995e-07 1.345e-07 0.008138 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006418 0.00055 0.004436 0.003416 0.9889 0.9919 0.006541 0.8572 0.8936 0.01236 ] Network output: [ -0.0003615 0.002102 1.001 -2.441e-05 1.096e-05 0.9977 -1.839e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2151 0.1009 0.344 0.1438 0.985 0.994 0.2159 0.4392 0.8763 0.707 ] Network output: [ 0.004434 -0.02097 0.9943 1.477e-05 -6.63e-06 1.018 1.113e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1071 0.09464 0.1833 0.199 0.9873 0.9919 0.1072 0.7479 0.8639 0.3054 ] Network output: [ -0.004173 0.01969 1.004 1.582e-05 -7.104e-06 0.9845 1.193e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09228 0.09035 0.165 0.1958 0.9853 0.9912 0.0923 0.672 0.8397 0.2471 ] Network output: [ 0.0001147 1 -0.0001029 2.097e-06 -9.415e-07 0.9998 1.581e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002813 Epoch 8772 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009836 0.9963 0.9915 -1.895e-07 8.507e-08 -0.007472 -1.428e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003437 -0.00326 -0.007315 0.005802 0.9699 0.9743 0.006641 0.8295 0.8224 0.0172 ] Network output: [ 0.9999 0.0003079 0.0005832 -7.785e-06 3.495e-06 -0.000645 -5.867e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2024 -0.03465 -0.1663 0.1864 0.9835 0.9932 0.2268 0.435 0.8697 0.713 ] Network output: [ -0.009711 1.002 1.009 -2.995e-07 1.345e-07 0.008137 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006418 0.0005501 0.004435 0.003416 0.9889 0.9919 0.006541 0.8572 0.8936 0.01236 ] Network output: [ -0.0003613 0.002101 1.001 -2.438e-05 1.094e-05 0.9977 -1.837e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2151 0.1009 0.344 0.1438 0.985 0.994 0.2159 0.4392 0.8763 0.707 ] Network output: [ 0.004433 -0.02096 0.9942 1.475e-05 -6.623e-06 1.018 1.112e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1071 0.09464 0.1833 0.199 0.9873 0.9919 0.1072 0.7479 0.8639 0.3054 ] Network output: [ -0.004171 0.01968 1.004 1.581e-05 -7.096e-06 0.9845 1.191e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09229 0.09035 0.165 0.1958 0.9853 0.9912 0.0923 0.672 0.8397 0.2471 ] Network output: [ 0.0001146 1 -0.0001028 2.095e-06 -9.405e-07 0.9998 1.579e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002811 Epoch 8773 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009835 0.9963 0.9915 -1.897e-07 8.516e-08 -0.007472 -1.43e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003437 -0.00326 -0.007314 0.005802 0.9699 0.9743 0.006641 0.8295 0.8224 0.0172 ] Network output: [ 0.9999 0.0003076 0.0005829 -7.777e-06 3.491e-06 -0.0006444 -5.861e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2024 -0.03466 -0.1663 0.1864 0.9835 0.9932 0.2268 0.435 0.8697 0.713 ] Network output: [ -0.00971 1.002 1.009 -2.995e-07 1.345e-07 0.008135 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006419 0.0005502 0.004435 0.003415 0.9889 0.9919 0.006542 0.8572 0.8936 0.01236 ] Network output: [ -0.0003611 0.0021 1.001 -2.435e-05 1.093e-05 0.9978 -1.835e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2152 0.1009 0.344 0.1438 0.985 0.994 0.2159 0.4391 0.8763 0.707 ] Network output: [ 0.004431 -0.02095 0.9942 1.474e-05 -6.616e-06 1.018 1.111e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1071 0.09465 0.1833 0.199 0.9873 0.9919 0.1072 0.7479 0.8639 0.3054 ] Network output: [ -0.00417 0.01967 1.004 1.579e-05 -7.089e-06 0.9845 1.19e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09229 0.09035 0.165 0.1958 0.9853 0.9912 0.0923 0.672 0.8397 0.2471 ] Network output: [ 0.0001146 1 -0.0001026 2.093e-06 -9.395e-07 0.9998 1.577e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000281 Epoch 8774 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009834 0.9963 0.9915 -1.899e-07 8.524e-08 -0.007472 -1.431e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003438 -0.00326 -0.007313 0.005801 0.9699 0.9743 0.006641 0.8295 0.8223 0.0172 ] Network output: [ 0.9999 0.0003073 0.0005825 -7.768e-06 3.487e-06 -0.0006439 -5.854e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2024 -0.03466 -0.1663 0.1864 0.9835 0.9932 0.2268 0.435 0.8697 0.7129 ] Network output: [ -0.009709 1.002 1.009 -2.995e-07 1.345e-07 0.008134 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006419 0.0005503 0.004435 0.003415 0.9889 0.9919 0.006542 0.8572 0.8936 0.01236 ] Network output: [ -0.0003608 0.0021 1.001 -2.432e-05 1.092e-05 0.9978 -1.833e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2152 0.1009 0.344 0.1438 0.985 0.994 0.2159 0.4391 0.8763 0.707 ] Network output: [ 0.004429 -0.02094 0.9942 1.472e-05 -6.609e-06 1.018 1.109e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1071 0.09465 0.1833 0.199 0.9873 0.9919 0.1072 0.7478 0.8639 0.3054 ] Network output: [ -0.004168 0.01967 1.004 1.577e-05 -7.081e-06 0.9845 1.189e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09229 0.09035 0.165 0.1958 0.9853 0.9912 0.09231 0.672 0.8397 0.2471 ] Network output: [ 0.0001145 1 -0.0001025 2.09e-06 -9.385e-07 0.9998 1.575e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002809 Epoch 8775 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009833 0.9963 0.9915 -1.901e-07 8.533e-08 -0.007472 -1.432e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003438 -0.00326 -0.007313 0.005801 0.9699 0.9743 0.006642 0.8295 0.8223 0.01719 ] Network output: [ 0.9999 0.000307 0.0005822 -7.76e-06 3.484e-06 -0.0006434 -5.848e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2024 -0.03466 -0.1663 0.1864 0.9835 0.9932 0.2268 0.435 0.8697 0.7129 ] Network output: [ -0.009708 1.002 1.009 -2.995e-07 1.344e-07 0.008133 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00642 0.0005504 0.004435 0.003415 0.9889 0.9919 0.006543 0.8572 0.8936 0.01235 ] Network output: [ -0.0003606 0.002099 1.001 -2.43e-05 1.091e-05 0.9978 -1.831e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2152 0.1009 0.344 0.1438 0.985 0.994 0.2159 0.4391 0.8763 0.707 ] Network output: [ 0.004428 -0.02094 0.9942 1.47e-05 -6.601e-06 1.018 1.108e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1071 0.09466 0.1833 0.199 0.9873 0.9919 0.1072 0.7478 0.8639 0.3054 ] Network output: [ -0.004167 0.01966 1.004 1.576e-05 -7.073e-06 0.9845 1.187e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09229 0.09036 0.165 0.1958 0.9853 0.9912 0.09231 0.672 0.8397 0.2471 ] Network output: [ 0.0001145 1 -0.0001024 2.088e-06 -9.374e-07 0.9998 1.574e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002807 Epoch 8776 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009832 0.9963 0.9915 -1.903e-07 8.541e-08 -0.007471 -1.434e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003438 -0.00326 -0.007312 0.0058 0.9699 0.9743 0.006642 0.8295 0.8223 0.01719 ] Network output: [ 0.9999 0.0003068 0.0005819 -7.751e-06 3.48e-06 -0.0006429 -5.841e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2024 -0.03466 -0.1663 0.1863 0.9835 0.9932 0.2268 0.435 0.8697 0.7129 ] Network output: [ -0.009707 1.002 1.009 -2.995e-07 1.344e-07 0.008132 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00642 0.0005505 0.004435 0.003415 0.9889 0.9919 0.006543 0.8572 0.8936 0.01235 ] Network output: [ -0.0003603 0.002098 1.001 -2.427e-05 1.09e-05 0.9978 -1.829e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2152 0.1009 0.3441 0.1438 0.985 0.994 0.2159 0.4391 0.8763 0.707 ] Network output: [ 0.004426 -0.02093 0.9942 1.469e-05 -6.594e-06 1.018 1.107e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1071 0.09467 0.1833 0.199 0.9873 0.9919 0.1072 0.7478 0.8639 0.3054 ] Network output: [ -0.004165 0.01965 1.004 1.574e-05 -7.066e-06 0.9845 1.186e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0923 0.09036 0.165 0.1958 0.9853 0.9911 0.09231 0.672 0.8397 0.2471 ] Network output: [ 0.0001144 1 -0.0001023 2.086e-06 -9.364e-07 0.9998 1.572e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002806 Epoch 8777 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00983 0.9963 0.9915 -1.904e-07 8.55e-08 -0.007471 -1.435e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003438 -0.00326 -0.007311 0.0058 0.9699 0.9743 0.006642 0.8295 0.8223 0.01719 ] Network output: [ 0.9999 0.0003065 0.0005816 -7.742e-06 3.476e-06 -0.0006424 -5.835e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2024 -0.03466 -0.1663 0.1863 0.9835 0.9932 0.2268 0.435 0.8697 0.7129 ] Network output: [ -0.009707 1.002 1.009 -2.995e-07 1.344e-07 0.008131 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006421 0.0005506 0.004435 0.003414 0.9889 0.9919 0.006544 0.8572 0.8936 0.01235 ] Network output: [ -0.0003601 0.002097 1.001 -2.424e-05 1.088e-05 0.9978 -1.827e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2152 0.1009 0.3441 0.1438 0.985 0.994 0.2159 0.4391 0.8763 0.707 ] Network output: [ 0.004425 -0.02092 0.9942 1.467e-05 -6.587e-06 1.018 1.106e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1071 0.09467 0.1833 0.199 0.9873 0.9919 0.1072 0.7478 0.8639 0.3054 ] Network output: [ -0.004164 0.01964 1.004 1.572e-05 -7.058e-06 0.9845 1.185e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0923 0.09036 0.165 0.1958 0.9853 0.9911 0.09231 0.6719 0.8397 0.2471 ] Network output: [ 0.0001144 1 -0.0001022 2.084e-06 -9.354e-07 0.9998 1.57e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002804 Epoch 8778 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009829 0.9963 0.9915 -1.906e-07 8.558e-08 -0.007471 -1.437e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003438 -0.00326 -0.00731 0.005799 0.9699 0.9743 0.006643 0.8295 0.8223 0.01719 ] Network output: [ 0.9999 0.0003062 0.0005813 -7.734e-06 3.472e-06 -0.0006419 -5.828e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2024 -0.03466 -0.1663 0.1863 0.9835 0.9932 0.2268 0.435 0.8696 0.7129 ] Network output: [ -0.009706 1.002 1.009 -2.995e-07 1.344e-07 0.00813 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006421 0.0005507 0.004435 0.003414 0.9889 0.9919 0.006544 0.8572 0.8936 0.01235 ] Network output: [ -0.0003599 0.002097 1.001 -2.422e-05 1.087e-05 0.9978 -1.825e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2152 0.1009 0.3441 0.1438 0.985 0.994 0.2159 0.4391 0.8763 0.707 ] Network output: [ 0.004423 -0.02091 0.9942 1.466e-05 -6.58e-06 1.018 1.105e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1071 0.09468 0.1833 0.199 0.9873 0.9919 0.1072 0.7478 0.8639 0.3054 ] Network output: [ -0.004162 0.01963 1.004 1.571e-05 -7.051e-06 0.9845 1.184e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0923 0.09036 0.165 0.1958 0.9853 0.9911 0.09231 0.6719 0.8397 0.2471 ] Network output: [ 0.0001143 1 -0.0001021 2.081e-06 -9.344e-07 0.9998 1.569e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002803 Epoch 8779 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009828 0.9963 0.9915 -1.908e-07 8.567e-08 -0.007471 -1.438e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003438 -0.00326 -0.00731 0.005799 0.9699 0.9743 0.006643 0.8295 0.8223 0.01719 ] Network output: [ 0.9999 0.000306 0.000581 -7.725e-06 3.468e-06 -0.0006414 -5.822e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2025 -0.03466 -0.1663 0.1863 0.9835 0.9932 0.2268 0.435 0.8696 0.7129 ] Network output: [ -0.009705 1.002 1.009 -2.995e-07 1.344e-07 0.008129 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006422 0.0005508 0.004435 0.003414 0.9889 0.9919 0.006545 0.8572 0.8936 0.01235 ] Network output: [ -0.0003596 0.002096 1.001 -2.419e-05 1.086e-05 0.9978 -1.823e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2152 0.1009 0.3441 0.1438 0.985 0.994 0.2159 0.4391 0.8763 0.707 ] Network output: [ 0.004422 -0.02091 0.9942 1.464e-05 -6.573e-06 1.018 1.103e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1071 0.09468 0.1833 0.199 0.9873 0.9919 0.1072 0.7478 0.8639 0.3054 ] Network output: [ -0.004161 0.01963 1.004 1.569e-05 -7.043e-06 0.9846 1.182e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0923 0.09037 0.165 0.1958 0.9853 0.9911 0.09232 0.6719 0.8397 0.2471 ] Network output: [ 0.0001143 1 -0.000102 2.079e-06 -9.334e-07 0.9998 1.567e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002801 Epoch 8780 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009827 0.9963 0.9915 -1.91e-07 8.575e-08 -0.007471 -1.439e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003438 -0.003261 -0.007309 0.005798 0.9699 0.9743 0.006643 0.8295 0.8223 0.01719 ] Network output: [ 0.9999 0.0003057 0.0005807 -7.717e-06 3.464e-06 -0.0006409 -5.816e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2025 -0.03466 -0.1663 0.1863 0.9835 0.9932 0.2268 0.435 0.8696 0.7129 ] Network output: [ -0.009704 1.002 1.009 -2.995e-07 1.344e-07 0.008127 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006422 0.0005509 0.004435 0.003413 0.9889 0.9919 0.006545 0.8572 0.8936 0.01235 ] Network output: [ -0.0003594 0.002095 1.001 -2.416e-05 1.085e-05 0.9978 -1.821e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2152 0.1009 0.3441 0.1438 0.985 0.994 0.2159 0.4391 0.8763 0.707 ] Network output: [ 0.00442 -0.0209 0.9942 1.462e-05 -6.565e-06 1.018 1.102e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1071 0.09469 0.1833 0.1989 0.9873 0.9919 0.1072 0.7477 0.8639 0.3054 ] Network output: [ -0.004159 0.01962 1.004 1.567e-05 -7.035e-06 0.9846 1.181e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09231 0.09037 0.165 0.1958 0.9853 0.9911 0.09232 0.6719 0.8397 0.2471 ] Network output: [ 0.0001142 1 -0.0001018 2.077e-06 -9.323e-07 0.9998 1.565e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00028 Epoch 8781 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009826 0.9963 0.9915 -1.912e-07 8.583e-08 -0.00747 -1.441e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003438 -0.003261 -0.007308 0.005798 0.9699 0.9743 0.006643 0.8294 0.8223 0.01719 ] Network output: [ 0.9999 0.0003054 0.0005804 -7.708e-06 3.46e-06 -0.0006404 -5.809e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2025 -0.03466 -0.1662 0.1863 0.9835 0.9932 0.2268 0.435 0.8696 0.7129 ] Network output: [ -0.009703 1.002 1.009 -2.995e-07 1.344e-07 0.008126 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006423 0.000551 0.004435 0.003413 0.9889 0.9919 0.006546 0.8572 0.8936 0.01235 ] Network output: [ -0.0003592 0.002094 1.001 -2.414e-05 1.084e-05 0.9978 -1.819e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2152 0.1009 0.3441 0.1438 0.985 0.994 0.2159 0.4391 0.8763 0.707 ] Network output: [ 0.004418 -0.02089 0.9942 1.461e-05 -6.558e-06 1.018 1.101e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1071 0.09469 0.1833 0.1989 0.9873 0.9919 0.1072 0.7477 0.8639 0.3054 ] Network output: [ -0.004158 0.01961 1.004 1.565e-05 -7.028e-06 0.9846 1.18e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09231 0.09037 0.165 0.1958 0.9853 0.9911 0.09232 0.6719 0.8397 0.2472 ] Network output: [ 0.0001142 1 -0.0001017 2.074e-06 -9.313e-07 0.9998 1.563e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002798 Epoch 8782 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009825 0.9963 0.9915 -1.914e-07 8.591e-08 -0.00747 -1.442e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003438 -0.003261 -0.007307 0.005797 0.9699 0.9743 0.006644 0.8294 0.8223 0.01719 ] Network output: [ 0.9999 0.0003051 0.0005801 -7.7e-06 3.457e-06 -0.0006398 -5.803e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2025 -0.03467 -0.1662 0.1863 0.9835 0.9932 0.2268 0.435 0.8696 0.7129 ] Network output: [ -0.009702 1.002 1.009 -2.995e-07 1.344e-07 0.008125 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006423 0.0005511 0.004435 0.003413 0.9889 0.9919 0.006546 0.8572 0.8936 0.01235 ] Network output: [ -0.0003589 0.002093 1.001 -2.411e-05 1.082e-05 0.9978 -1.817e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2152 0.1009 0.3441 0.1438 0.985 0.994 0.216 0.4391 0.8763 0.7069 ] Network output: [ 0.004417 -0.02088 0.9942 1.459e-05 -6.551e-06 1.018 1.1e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1071 0.0947 0.1833 0.1989 0.9873 0.9919 0.1072 0.7477 0.8639 0.3054 ] Network output: [ -0.004156 0.0196 1.004 1.564e-05 -7.02e-06 0.9846 1.179e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09231 0.09037 0.165 0.1958 0.9853 0.9911 0.09232 0.6719 0.8397 0.2472 ] Network output: [ 0.0001142 1 -0.0001016 2.072e-06 -9.303e-07 0.9998 1.562e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002797 Epoch 8783 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009823 0.9963 0.9915 -1.916e-07 8.6e-08 -0.00747 -1.444e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003438 -0.003261 -0.007306 0.005797 0.9699 0.9743 0.006644 0.8294 0.8223 0.01718 ] Network output: [ 0.9999 0.0003049 0.0005798 -7.691e-06 3.453e-06 -0.0006393 -5.796e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2025 -0.03467 -0.1662 0.1863 0.9835 0.9932 0.2269 0.435 0.8696 0.7129 ] Network output: [ -0.009701 1.002 1.009 -2.995e-07 1.344e-07 0.008124 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006424 0.0005511 0.004435 0.003412 0.9889 0.9919 0.006547 0.8571 0.8936 0.01235 ] Network output: [ -0.0003587 0.002093 1.001 -2.408e-05 1.081e-05 0.9978 -1.815e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2152 0.1009 0.3441 0.1438 0.985 0.994 0.216 0.4391 0.8763 0.7069 ] Network output: [ 0.004415 -0.02087 0.9942 1.458e-05 -6.544e-06 1.018 1.099e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1072 0.0947 0.1833 0.1989 0.9873 0.9919 0.1072 0.7477 0.8639 0.3054 ] Network output: [ -0.004154 0.01959 1.004 1.562e-05 -7.013e-06 0.9846 1.177e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09231 0.09038 0.165 0.1958 0.9853 0.9911 0.09233 0.6718 0.8397 0.2472 ] Network output: [ 0.0001141 1 -0.0001015 2.07e-06 -9.293e-07 0.9998 1.56e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002795 Epoch 8784 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009822 0.9963 0.9915 -1.917e-07 8.608e-08 -0.00747 -1.445e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003439 -0.003261 -0.007306 0.005796 0.9699 0.9743 0.006644 0.8294 0.8223 0.01718 ] Network output: [ 0.9999 0.0003046 0.0005795 -7.683e-06 3.449e-06 -0.0006388 -5.79e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2025 -0.03467 -0.1662 0.1863 0.9835 0.9932 0.2269 0.435 0.8696 0.7129 ] Network output: [ -0.0097 1.002 1.009 -2.994e-07 1.344e-07 0.008123 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006424 0.0005512 0.004435 0.003412 0.9889 0.9919 0.006547 0.8571 0.8936 0.01235 ] Network output: [ -0.0003585 0.002092 1.001 -2.406e-05 1.08e-05 0.9978 -1.813e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2153 0.1009 0.3441 0.1438 0.985 0.994 0.216 0.4391 0.8763 0.7069 ] Network output: [ 0.004414 -0.02087 0.9942 1.456e-05 -6.537e-06 1.018 1.097e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1072 0.09471 0.1833 0.1989 0.9873 0.9919 0.1072 0.7477 0.8639 0.3054 ] Network output: [ -0.004153 0.01959 1.004 1.56e-05 -7.005e-06 0.9846 1.176e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09232 0.09038 0.165 0.1958 0.9853 0.9911 0.09233 0.6718 0.8397 0.2472 ] Network output: [ 0.0001141 1 -0.0001014 2.068e-06 -9.283e-07 0.9998 1.558e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002794 Epoch 8785 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009821 0.9963 0.9915 -1.919e-07 8.616e-08 -0.007469 -1.446e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003439 -0.003261 -0.007305 0.005796 0.9699 0.9743 0.006644 0.8294 0.8223 0.01718 ] Network output: [ 0.9999 0.0003043 0.0005792 -7.674e-06 3.445e-06 -0.0006383 -5.783e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2025 -0.03467 -0.1662 0.1863 0.9835 0.9932 0.2269 0.4349 0.8696 0.7129 ] Network output: [ -0.009699 1.002 1.009 -2.994e-07 1.344e-07 0.008122 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006425 0.0005513 0.004435 0.003412 0.9889 0.9919 0.006548 0.8571 0.8936 0.01235 ] Network output: [ -0.0003582 0.002091 1.001 -2.403e-05 1.079e-05 0.9978 -1.811e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2153 0.101 0.3441 0.1438 0.985 0.994 0.216 0.439 0.8763 0.7069 ] Network output: [ 0.004412 -0.02086 0.9942 1.454e-05 -6.529e-06 1.018 1.096e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1072 0.09471 0.1833 0.1989 0.9873 0.9919 0.1072 0.7477 0.8639 0.3054 ] Network output: [ -0.004151 0.01958 1.004 1.559e-05 -6.998e-06 0.9846 1.175e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09232 0.09038 0.165 0.1958 0.9853 0.9911 0.09233 0.6718 0.8397 0.2472 ] Network output: [ 0.000114 1 -0.0001013 2.065e-06 -9.273e-07 0.9998 1.557e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002792 Epoch 8786 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00982 0.9963 0.9915 -1.921e-07 8.624e-08 -0.007469 -1.448e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003439 -0.003261 -0.007304 0.005795 0.9699 0.9743 0.006645 0.8294 0.8223 0.01718 ] Network output: [ 0.9999 0.0003041 0.0005789 -7.666e-06 3.441e-06 -0.0006378 -5.777e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2025 -0.03467 -0.1662 0.1863 0.9835 0.9932 0.2269 0.4349 0.8696 0.7129 ] Network output: [ -0.009698 1.002 1.009 -2.994e-07 1.344e-07 0.008121 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006425 0.0005514 0.004435 0.003411 0.9889 0.9919 0.006549 0.8571 0.8936 0.01234 ] Network output: [ -0.000358 0.00209 1.001 -2.4e-05 1.078e-05 0.9978 -1.809e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2153 0.101 0.3441 0.1438 0.985 0.994 0.216 0.439 0.8763 0.7069 ] Network output: [ 0.004411 -0.02085 0.9942 1.453e-05 -6.522e-06 1.018 1.095e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1072 0.09472 0.1833 0.1989 0.9873 0.9919 0.1072 0.7476 0.8639 0.3054 ] Network output: [ -0.00415 0.01957 1.004 1.557e-05 -6.99e-06 0.9846 1.173e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09232 0.09038 0.165 0.1958 0.9853 0.9911 0.09233 0.6718 0.8397 0.2472 ] Network output: [ 0.000114 1 -0.0001012 2.063e-06 -9.262e-07 0.9998 1.555e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002791 Epoch 8787 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009819 0.9963 0.9915 -1.923e-07 8.632e-08 -0.007469 -1.449e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003439 -0.003261 -0.007303 0.005795 0.9699 0.9743 0.006645 0.8294 0.8223 0.01718 ] Network output: [ 0.9999 0.0003038 0.0005786 -7.657e-06 3.438e-06 -0.0006373 -5.771e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2025 -0.03467 -0.1662 0.1863 0.9835 0.9932 0.2269 0.4349 0.8696 0.7129 ] Network output: [ -0.009697 1.002 1.009 -2.994e-07 1.344e-07 0.00812 -2.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006426 0.0005515 0.004435 0.003411 0.9889 0.9919 0.006549 0.8571 0.8936 0.01234 ] Network output: [ -0.0003578 0.00209 1.001 -2.398e-05 1.076e-05 0.9978 -1.807e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2153 0.101 0.3441 0.1438 0.985 0.994 0.216 0.439 0.8763 0.7069 ] Network output: [ 0.004409 -0.02084 0.9942 1.451e-05 -6.515e-06 1.018 1.094e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1072 0.09472 0.1833 0.1989 0.9873 0.9919 0.1072 0.7476 0.8639 0.3054 ] Network output: [ -0.004148 0.01956 1.004 1.555e-05 -6.983e-06 0.9846 1.172e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09232 0.09039 0.165 0.1958 0.9853 0.9911 0.09234 0.6718 0.8397 0.2472 ] Network output: [ 0.0001139 1 -0.000101 2.061e-06 -9.252e-07 0.9998 1.553e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002789 Epoch 8788 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009817 0.9963 0.9915 -1.925e-07 8.64e-08 -0.007469 -1.45e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003439 -0.003262 -0.007302 0.005794 0.9699 0.9743 0.006645 0.8294 0.8223 0.01718 ] Network output: [ 0.9999 0.0003035 0.0005783 -7.649e-06 3.434e-06 -0.0006368 -5.764e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2025 -0.03467 -0.1662 0.1863 0.9835 0.9932 0.2269 0.4349 0.8696 0.7129 ] Network output: [ -0.009696 1.002 1.009 -2.994e-07 1.344e-07 0.008119 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006426 0.0005516 0.004435 0.003411 0.9889 0.9919 0.00655 0.8571 0.8936 0.01234 ] Network output: [ -0.0003575 0.002089 1.001 -2.395e-05 1.075e-05 0.9978 -1.805e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2153 0.101 0.3441 0.1438 0.985 0.994 0.216 0.439 0.8763 0.7069 ] Network output: [ 0.004407 -0.02084 0.9942 1.45e-05 -6.508e-06 1.018 1.092e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1072 0.09473 0.1833 0.1989 0.9873 0.9919 0.1073 0.7476 0.8639 0.3054 ] Network output: [ -0.004147 0.01956 1.004 1.554e-05 -6.975e-06 0.9846 1.171e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09233 0.09039 0.165 0.1958 0.9853 0.9911 0.09234 0.6718 0.8397 0.2472 ] Network output: [ 0.0001139 1 -0.0001009 2.059e-06 -9.242e-07 0.9998 1.551e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002788 Epoch 8789 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009816 0.9963 0.9915 -1.926e-07 8.648e-08 -0.007468 -1.452e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003439 -0.003262 -0.007302 0.005794 0.9699 0.9743 0.006645 0.8294 0.8223 0.01718 ] Network output: [ 0.9999 0.0003033 0.000578 -7.64e-06 3.43e-06 -0.0006363 -5.758e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2025 -0.03467 -0.1661 0.1863 0.9835 0.9932 0.2269 0.4349 0.8696 0.7129 ] Network output: [ -0.009695 1.002 1.009 -2.994e-07 1.344e-07 0.008117 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006427 0.0005517 0.004435 0.003411 0.9889 0.9919 0.00655 0.8571 0.8936 0.01234 ] Network output: [ -0.0003573 0.002088 1.001 -2.392e-05 1.074e-05 0.9978 -1.803e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2153 0.101 0.3441 0.1438 0.985 0.994 0.216 0.439 0.8763 0.7069 ] Network output: [ 0.004406 -0.02083 0.9942 1.448e-05 -6.501e-06 1.018 1.091e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1072 0.09473 0.1833 0.1989 0.9873 0.9919 0.1073 0.7476 0.8639 0.3054 ] Network output: [ -0.004145 0.01955 1.004 1.552e-05 -6.968e-06 0.9846 1.17e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09233 0.09039 0.165 0.1958 0.9853 0.9911 0.09234 0.6717 0.8397 0.2472 ] Network output: [ 0.0001138 1 -0.0001008 2.056e-06 -9.232e-07 0.9998 1.55e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002787 Epoch 8790 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009815 0.9963 0.9915 -1.928e-07 8.656e-08 -0.007468 -1.453e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003439 -0.003262 -0.007301 0.005793 0.9699 0.9743 0.006646 0.8294 0.8223 0.01718 ] Network output: [ 0.9999 0.000303 0.0005777 -7.632e-06 3.426e-06 -0.0006358 -5.751e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2025 -0.03468 -0.1661 0.1863 0.9835 0.9932 0.2269 0.4349 0.8696 0.7129 ] Network output: [ -0.009694 1.002 1.009 -2.994e-07 1.344e-07 0.008116 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006427 0.0005518 0.004435 0.00341 0.9889 0.9919 0.006551 0.8571 0.8936 0.01234 ] Network output: [ -0.0003571 0.002087 1.001 -2.39e-05 1.073e-05 0.9978 -1.801e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2153 0.101 0.3441 0.1438 0.985 0.994 0.216 0.439 0.8763 0.7069 ] Network output: [ 0.004404 -0.02082 0.9942 1.446e-05 -6.494e-06 1.018 1.09e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1072 0.09474 0.1833 0.1989 0.9873 0.9919 0.1073 0.7476 0.8639 0.3054 ] Network output: [ -0.004144 0.01954 1.004 1.55e-05 -6.96e-06 0.9846 1.168e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09233 0.09039 0.165 0.1958 0.9853 0.9911 0.09234 0.6717 0.8397 0.2472 ] Network output: [ 0.0001138 1 -0.0001007 2.054e-06 -9.222e-07 0.9998 1.548e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002785 Epoch 8791 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009814 0.9963 0.9915 -1.93e-07 8.664e-08 -0.007468 -1.454e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003439 -0.003262 -0.0073 0.005793 0.9699 0.9743 0.006646 0.8294 0.8223 0.01718 ] Network output: [ 0.9999 0.0003027 0.0005774 -7.623e-06 3.422e-06 -0.0006353 -5.745e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2025 -0.03468 -0.1661 0.1863 0.9835 0.9932 0.2269 0.4349 0.8696 0.7129 ] Network output: [ -0.009693 1.002 1.009 -2.994e-07 1.344e-07 0.008115 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006428 0.0005519 0.004435 0.00341 0.9889 0.9919 0.006551 0.8571 0.8936 0.01234 ] Network output: [ -0.0003568 0.002086 1.001 -2.387e-05 1.072e-05 0.9978 -1.799e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2153 0.101 0.3442 0.1438 0.985 0.994 0.216 0.439 0.8763 0.7069 ] Network output: [ 0.004403 -0.02081 0.9942 1.445e-05 -6.486e-06 1.018 1.089e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1072 0.09474 0.1833 0.1989 0.9873 0.9919 0.1073 0.7476 0.8639 0.3054 ] Network output: [ -0.004142 0.01953 1.004 1.549e-05 -6.953e-06 0.9846 1.167e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09233 0.09039 0.165 0.1958 0.9853 0.9911 0.09235 0.6717 0.8397 0.2472 ] Network output: [ 0.0001137 1 -0.0001006 2.052e-06 -9.212e-07 0.9998 1.546e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002784 Epoch 8792 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009813 0.9963 0.9915 -1.932e-07 8.672e-08 -0.007468 -1.456e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003439 -0.003262 -0.007299 0.005792 0.9699 0.9743 0.006646 0.8294 0.8223 0.01717 ] Network output: [ 0.9999 0.0003025 0.0005771 -7.615e-06 3.419e-06 -0.0006348 -5.739e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2026 -0.03468 -0.1661 0.1863 0.9835 0.9932 0.2269 0.4349 0.8696 0.7129 ] Network output: [ -0.009692 1.002 1.009 -2.994e-07 1.344e-07 0.008114 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006428 0.000552 0.004435 0.00341 0.9889 0.9919 0.006552 0.8571 0.8936 0.01234 ] Network output: [ -0.0003566 0.002086 1.001 -2.384e-05 1.07e-05 0.9978 -1.797e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2153 0.101 0.3442 0.1438 0.985 0.994 0.216 0.439 0.8763 0.7069 ] Network output: [ 0.004401 -0.02081 0.9942 1.443e-05 -6.479e-06 1.018 1.088e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1072 0.09475 0.1833 0.1989 0.9873 0.9919 0.1073 0.7475 0.8639 0.3054 ] Network output: [ -0.004141 0.01952 1.004 1.547e-05 -6.945e-06 0.9846 1.166e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09233 0.0904 0.165 0.1958 0.9853 0.9911 0.09235 0.6717 0.8396 0.2472 ] Network output: [ 0.0001137 1 -0.0001005 2.05e-06 -9.202e-07 0.9998 1.545e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002782 Epoch 8793 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009812 0.9963 0.9915 -1.934e-07 8.68e-08 -0.007468 -1.457e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003439 -0.003262 -0.007299 0.005792 0.9699 0.9743 0.006646 0.8294 0.8223 0.01717 ] Network output: [ 0.9999 0.0003022 0.0005768 -7.606e-06 3.415e-06 -0.0006343 -5.732e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2026 -0.03468 -0.1661 0.1863 0.9835 0.9932 0.2269 0.4349 0.8696 0.7128 ] Network output: [ -0.009691 1.003 1.009 -2.994e-07 1.344e-07 0.008113 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006429 0.0005521 0.004435 0.003409 0.9889 0.9919 0.006552 0.8571 0.8936 0.01234 ] Network output: [ -0.0003564 0.002085 1.001 -2.382e-05 1.069e-05 0.9978 -1.795e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2153 0.101 0.3442 0.1438 0.985 0.994 0.2161 0.439 0.8763 0.7069 ] Network output: [ 0.004399 -0.0208 0.9942 1.442e-05 -6.472e-06 1.018 1.086e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1072 0.09475 0.1833 0.1989 0.9873 0.9919 0.1073 0.7475 0.8639 0.3054 ] Network output: [ -0.004139 0.01952 1.004 1.545e-05 -6.938e-06 0.9846 1.165e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09234 0.0904 0.165 0.1958 0.9853 0.9911 0.09235 0.6717 0.8396 0.2472 ] Network output: [ 0.0001136 1 -0.0001004 2.047e-06 -9.192e-07 0.9998 1.543e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002781 Epoch 8794 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00981 0.9963 0.9915 -1.935e-07 8.688e-08 -0.007467 -1.458e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00344 -0.003262 -0.007298 0.005791 0.9699 0.9743 0.006647 0.8294 0.8223 0.01717 ] Network output: [ 0.9999 0.0003019 0.0005765 -7.598e-06 3.411e-06 -0.0006338 -5.726e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2026 -0.03468 -0.1661 0.1863 0.9835 0.9932 0.227 0.4349 0.8696 0.7128 ] Network output: [ -0.00969 1.003 1.009 -2.994e-07 1.344e-07 0.008112 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006429 0.0005522 0.004435 0.003409 0.9889 0.9919 0.006553 0.8571 0.8936 0.01234 ] Network output: [ -0.0003561 0.002084 1.001 -2.379e-05 1.068e-05 0.9978 -1.793e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2153 0.101 0.3442 0.1438 0.985 0.994 0.2161 0.439 0.8763 0.7069 ] Network output: [ 0.004398 -0.02079 0.9942 1.44e-05 -6.465e-06 1.018 1.085e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1072 0.09476 0.1833 0.1989 0.9873 0.9919 0.1073 0.7475 0.8639 0.3054 ] Network output: [ -0.004137 0.01951 1.004 1.544e-05 -6.93e-06 0.9846 1.163e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09234 0.0904 0.165 0.1958 0.9853 0.9911 0.09235 0.6717 0.8396 0.2472 ] Network output: [ 0.0001136 1 -0.0001003 2.045e-06 -9.182e-07 0.9998 1.541e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002779 Epoch 8795 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009809 0.9963 0.9915 -1.937e-07 8.696e-08 -0.007467 -1.46e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00344 -0.003262 -0.007297 0.005791 0.9699 0.9743 0.006647 0.8294 0.8223 0.01717 ] Network output: [ 0.9999 0.0003017 0.0005762 -7.589e-06 3.407e-06 -0.0006333 -5.72e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2026 -0.03468 -0.1661 0.1863 0.9835 0.9932 0.227 0.4349 0.8696 0.7128 ] Network output: [ -0.009689 1.003 1.009 -2.993e-07 1.344e-07 0.008111 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00643 0.0005523 0.004434 0.003409 0.9889 0.9919 0.006553 0.8571 0.8936 0.01234 ] Network output: [ -0.0003559 0.002083 1.001 -2.376e-05 1.067e-05 0.9978 -1.791e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2154 0.101 0.3442 0.1438 0.985 0.994 0.2161 0.439 0.8763 0.7069 ] Network output: [ 0.004396 -0.02078 0.9942 1.439e-05 -6.458e-06 1.018 1.084e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1072 0.09476 0.1833 0.1989 0.9873 0.9919 0.1073 0.7475 0.8639 0.3054 ] Network output: [ -0.004136 0.0195 1.004 1.542e-05 -6.923e-06 0.9846 1.162e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09234 0.0904 0.165 0.1958 0.9853 0.9911 0.09236 0.6716 0.8396 0.2472 ] Network output: [ 0.0001135 1 -0.0001002 2.043e-06 -9.172e-07 0.9998 1.54e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002778 Epoch 8796 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009808 0.9963 0.9915 -1.939e-07 8.704e-08 -0.007467 -1.461e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00344 -0.003263 -0.007296 0.00579 0.9699 0.9743 0.006647 0.8294 0.8223 0.01717 ] Network output: [ 0.9999 0.0003014 0.0005759 -7.581e-06 3.403e-06 -0.0006328 -5.713e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2026 -0.03468 -0.1661 0.1863 0.9835 0.9932 0.227 0.4349 0.8696 0.7128 ] Network output: [ -0.009688 1.003 1.009 -2.993e-07 1.344e-07 0.00811 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00643 0.0005524 0.004434 0.003408 0.9889 0.9919 0.006554 0.8571 0.8936 0.01234 ] Network output: [ -0.0003557 0.002083 1.001 -2.374e-05 1.066e-05 0.9978 -1.789e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2154 0.101 0.3442 0.1438 0.985 0.994 0.2161 0.439 0.8763 0.7069 ] Network output: [ 0.004395 -0.02078 0.9942 1.437e-05 -6.451e-06 1.018 1.083e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1072 0.09477 0.1833 0.1989 0.9873 0.9919 0.1073 0.7475 0.8638 0.3054 ] Network output: [ -0.004134 0.01949 1.004 1.54e-05 -6.915e-06 0.9846 1.161e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09234 0.09041 0.165 0.1958 0.9853 0.9911 0.09236 0.6716 0.8396 0.2472 ] Network output: [ 0.0001135 1 -0.0001 2.041e-06 -9.162e-07 0.9998 1.538e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002776 Epoch 8797 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009807 0.9963 0.9915 -1.94e-07 8.711e-08 -0.007467 -1.462e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00344 -0.003263 -0.007295 0.00579 0.9699 0.9743 0.006647 0.8293 0.8223 0.01717 ] Network output: [ 0.9999 0.0003011 0.0005756 -7.572e-06 3.4e-06 -0.0006322 -5.707e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2026 -0.03468 -0.166 0.1862 0.9835 0.9932 0.227 0.4348 0.8696 0.7128 ] Network output: [ -0.009687 1.003 1.009 -2.993e-07 1.344e-07 0.008108 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006431 0.0005525 0.004434 0.003408 0.9889 0.9919 0.006554 0.8571 0.8936 0.01234 ] Network output: [ -0.0003554 0.002082 1.001 -2.371e-05 1.065e-05 0.9978 -1.787e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2154 0.101 0.3442 0.1438 0.985 0.994 0.2161 0.4389 0.8763 0.7069 ] Network output: [ 0.004393 -0.02077 0.9942 1.435e-05 -6.444e-06 1.018 1.082e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1072 0.09478 0.1833 0.1989 0.9873 0.9919 0.1073 0.7475 0.8638 0.3054 ] Network output: [ -0.004133 0.01948 1.004 1.539e-05 -6.908e-06 0.9846 1.16e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09235 0.09041 0.165 0.1958 0.9853 0.9911 0.09236 0.6716 0.8396 0.2472 ] Network output: [ 0.0001134 1 -9.993e-05 2.039e-06 -9.152e-07 0.9998 1.536e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002775 Epoch 8798 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009806 0.9963 0.9915 -1.942e-07 8.719e-08 -0.007466 -1.464e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00344 -0.003263 -0.007295 0.005789 0.9699 0.9743 0.006648 0.8293 0.8223 0.01717 ] Network output: [ 0.9999 0.0003009 0.0005753 -7.564e-06 3.396e-06 -0.0006317 -5.701e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2026 -0.03468 -0.166 0.1862 0.9835 0.9932 0.227 0.4348 0.8696 0.7128 ] Network output: [ -0.009686 1.003 1.009 -2.993e-07 1.344e-07 0.008107 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006431 0.0005526 0.004434 0.003408 0.9889 0.9919 0.006555 0.857 0.8936 0.01233 ] Network output: [ -0.0003552 0.002081 1.001 -2.369e-05 1.063e-05 0.9978 -1.785e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2154 0.101 0.3442 0.1438 0.985 0.994 0.2161 0.4389 0.8763 0.7069 ] Network output: [ 0.004392 -0.02076 0.9942 1.434e-05 -6.437e-06 1.018 1.081e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1072 0.09478 0.1833 0.1989 0.9873 0.9919 0.1073 0.7474 0.8638 0.3054 ] Network output: [ -0.004131 0.01948 1.004 1.537e-05 -6.9e-06 0.9846 1.158e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09235 0.09041 0.165 0.1958 0.9853 0.9911 0.09236 0.6716 0.8396 0.2472 ] Network output: [ 0.0001134 1 -9.982e-05 2.036e-06 -9.142e-07 0.9998 1.535e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002773 Epoch 8799 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009805 0.9963 0.9915 -1.944e-07 8.727e-08 -0.007466 -1.465e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00344 -0.003263 -0.007294 0.005788 0.9699 0.9743 0.006648 0.8293 0.8223 0.01717 ] Network output: [ 0.9999 0.0003006 0.000575 -7.556e-06 3.392e-06 -0.0006312 -5.694e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2026 -0.03469 -0.166 0.1862 0.9835 0.9932 0.227 0.4348 0.8696 0.7128 ] Network output: [ -0.009685 1.003 1.009 -2.993e-07 1.344e-07 0.008106 -2.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006432 0.0005527 0.004434 0.003408 0.9889 0.9919 0.006555 0.857 0.8936 0.01233 ] Network output: [ -0.000355 0.00208 1.001 -2.366e-05 1.062e-05 0.9978 -1.783e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2154 0.101 0.3442 0.1438 0.985 0.994 0.2161 0.4389 0.8763 0.7069 ] Network output: [ 0.00439 -0.02075 0.9942 1.432e-05 -6.43e-06 1.018 1.079e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1072 0.09479 0.1833 0.1989 0.9873 0.9919 0.1073 0.7474 0.8638 0.3054 ] Network output: [ -0.00413 0.01947 1.004 1.535e-05 -6.893e-06 0.9846 1.157e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09235 0.09041 0.165 0.1958 0.9853 0.9911 0.09236 0.6716 0.8396 0.2472 ] Network output: [ 0.0001133 1 -9.971e-05 2.034e-06 -9.132e-07 0.9998 1.533e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002772 Epoch 8800 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009803 0.9963 0.9915 -1.946e-07 8.734e-08 -0.007466 -1.466e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00344 -0.003263 -0.007293 0.005788 0.9699 0.9743 0.006648 0.8293 0.8223 0.01717 ] Network output: [ 0.9999 0.0003003 0.0005747 -7.547e-06 3.388e-06 -0.0006307 -5.688e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2026 -0.03469 -0.166 0.1862 0.9835 0.9932 0.227 0.4348 0.8696 0.7128 ] Network output: [ -0.009684 1.003 1.009 -2.993e-07 1.344e-07 0.008105 -2.255e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006433 0.0005528 0.004434 0.003407 0.9889 0.9919 0.006556 0.857 0.8936 0.01233 ] Network output: [ -0.0003547 0.00208 1.001 -2.363e-05 1.061e-05 0.9978 -1.781e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2154 0.101 0.3442 0.1437 0.985 0.994 0.2161 0.4389 0.8763 0.7068 ] Network output: [ 0.004388 -0.02074 0.9942 1.431e-05 -6.423e-06 1.018 1.078e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1072 0.09479 0.1833 0.1989 0.9873 0.9919 0.1073 0.7474 0.8638 0.3054 ] Network output: [ -0.004128 0.01946 1.004 1.534e-05 -6.886e-06 0.9846 1.156e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09235 0.09042 0.165 0.1958 0.9853 0.9911 0.09237 0.6716 0.8396 0.2472 ] Network output: [ 0.0001133 1 -9.96e-05 2.032e-06 -9.122e-07 0.9998 1.531e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000277 Epoch 8801 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009802 0.9963 0.9915 -1.947e-07 8.742e-08 -0.007466 -1.468e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00344 -0.003263 -0.007292 0.005787 0.9699 0.9743 0.006648 0.8293 0.8223 0.01716 ] Network output: [ 0.9999 0.0003001 0.0005744 -7.539e-06 3.384e-06 -0.0006302 -5.682e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2026 -0.03469 -0.166 0.1862 0.9835 0.9932 0.227 0.4348 0.8696 0.7128 ] Network output: [ -0.009683 1.003 1.009 -2.993e-07 1.343e-07 0.008104 -2.255e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006433 0.0005528 0.004434 0.003407 0.9889 0.9919 0.006556 0.857 0.8936 0.01233 ] Network output: [ -0.0003545 0.002079 1.001 -2.361e-05 1.06e-05 0.9978 -1.779e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2154 0.101 0.3442 0.1437 0.985 0.994 0.2161 0.4389 0.8763 0.7068 ] Network output: [ 0.004387 -0.02074 0.9942 1.429e-05 -6.415e-06 1.018 1.077e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1073 0.0948 0.1833 0.1989 0.9873 0.9919 0.1073 0.7474 0.8638 0.3054 ] Network output: [ -0.004127 0.01945 1.004 1.532e-05 -6.878e-06 0.9846 1.155e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09236 0.09042 0.165 0.1958 0.9853 0.9911 0.09237 0.6715 0.8396 0.2472 ] Network output: [ 0.0001133 1 -9.949e-05 2.03e-06 -9.112e-07 0.9998 1.53e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002769 Epoch 8802 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009801 0.9963 0.9915 -1.949e-07 8.75e-08 -0.007465 -1.469e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00344 -0.003263 -0.007292 0.005787 0.9699 0.9743 0.006649 0.8293 0.8223 0.01716 ] Network output: [ 0.9999 0.0002998 0.0005741 -7.531e-06 3.381e-06 -0.0006297 -5.675e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2026 -0.03469 -0.166 0.1862 0.9835 0.9932 0.227 0.4348 0.8696 0.7128 ] Network output: [ -0.009682 1.003 1.009 -2.992e-07 1.343e-07 0.008103 -2.255e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006434 0.0005529 0.004434 0.003407 0.9889 0.9919 0.006557 0.857 0.8936 0.01233 ] Network output: [ -0.0003543 0.002078 1.001 -2.358e-05 1.059e-05 0.9978 -1.777e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2154 0.1011 0.3442 0.1437 0.985 0.994 0.2161 0.4389 0.8763 0.7068 ] Network output: [ 0.004385 -0.02073 0.9942 1.427e-05 -6.408e-06 1.018 1.076e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1073 0.0948 0.1833 0.1989 0.9873 0.9919 0.1073 0.7474 0.8638 0.3054 ] Network output: [ -0.004125 0.01945 1.004 1.53e-05 -6.871e-06 0.9846 1.153e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09236 0.09042 0.165 0.1958 0.9853 0.9911 0.09237 0.6715 0.8396 0.2472 ] Network output: [ 0.0001132 1 -9.937e-05 2.027e-06 -9.102e-07 0.9998 1.528e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002768 Epoch 8803 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0098 0.9963 0.9915 -1.951e-07 8.757e-08 -0.007465 -1.47e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00344 -0.003263 -0.007291 0.005786 0.9699 0.9743 0.006649 0.8293 0.8223 0.01716 ] Network output: [ 0.9999 0.0002995 0.0005738 -7.522e-06 3.377e-06 -0.0006292 -5.669e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2026 -0.03469 -0.166 0.1862 0.9835 0.9932 0.227 0.4348 0.8696 0.7128 ] Network output: [ -0.009681 1.003 1.009 -2.992e-07 1.343e-07 0.008102 -2.255e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006434 0.000553 0.004434 0.003406 0.9889 0.9919 0.006557 0.857 0.8936 0.01233 ] Network output: [ -0.0003541 0.002077 1.001 -2.355e-05 1.057e-05 0.9978 -1.775e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2154 0.1011 0.3442 0.1437 0.985 0.994 0.2161 0.4389 0.8763 0.7068 ] Network output: [ 0.004384 -0.02072 0.9942 1.426e-05 -6.401e-06 1.018 1.075e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1073 0.09481 0.1833 0.1989 0.9873 0.9919 0.1073 0.7474 0.8638 0.3054 ] Network output: [ -0.004124 0.01944 1.004 1.529e-05 -6.863e-06 0.9846 1.152e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09236 0.09042 0.165 0.1958 0.9853 0.9911 0.09237 0.6715 0.8396 0.2472 ] Network output: [ 0.0001132 1 -9.926e-05 2.025e-06 -9.092e-07 0.9998 1.526e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002766 Epoch 8804 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009799 0.9963 0.9915 -1.952e-07 8.765e-08 -0.007465 -1.471e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003441 -0.003264 -0.00729 0.005786 0.9699 0.9743 0.006649 0.8293 0.8223 0.01716 ] Network output: [ 0.9999 0.0002993 0.0005735 -7.514e-06 3.373e-06 -0.0006287 -5.663e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2026 -0.03469 -0.166 0.1862 0.9835 0.9932 0.227 0.4348 0.8696 0.7128 ] Network output: [ -0.00968 1.003 1.009 -2.992e-07 1.343e-07 0.008101 -2.255e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006435 0.0005531 0.004434 0.003406 0.9889 0.9919 0.006558 0.857 0.8936 0.01233 ] Network output: [ -0.0003538 0.002076 1.001 -2.353e-05 1.056e-05 0.9978 -1.773e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2154 0.1011 0.3442 0.1437 0.985 0.994 0.2162 0.4389 0.8763 0.7068 ] Network output: [ 0.004382 -0.02071 0.9942 1.424e-05 -6.394e-06 1.018 1.073e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1073 0.09481 0.1833 0.1989 0.9873 0.9919 0.1073 0.7474 0.8638 0.3054 ] Network output: [ -0.004122 0.01943 1.004 1.527e-05 -6.856e-06 0.9846 1.151e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09236 0.09043 0.165 0.1958 0.9853 0.9911 0.09238 0.6715 0.8396 0.2472 ] Network output: [ 0.0001131 1 -9.915e-05 2.023e-06 -9.082e-07 0.9998 1.525e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002765 Epoch 8805 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009798 0.9963 0.9915 -1.954e-07 8.772e-08 -0.007465 -1.473e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003441 -0.003264 -0.007289 0.005785 0.9699 0.9743 0.006649 0.8293 0.8222 0.01716 ] Network output: [ 0.9999 0.000299 0.0005732 -7.505e-06 3.369e-06 -0.0006282 -5.656e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2027 -0.03469 -0.166 0.1862 0.9835 0.9932 0.2271 0.4348 0.8696 0.7128 ] Network output: [ -0.009679 1.003 1.009 -2.992e-07 1.343e-07 0.0081 -2.255e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006435 0.0005532 0.004434 0.003406 0.9889 0.9919 0.006558 0.857 0.8936 0.01233 ] Network output: [ -0.0003536 0.002076 1.001 -2.35e-05 1.055e-05 0.9978 -1.771e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2154 0.1011 0.3442 0.1437 0.985 0.994 0.2162 0.4389 0.8763 0.7068 ] Network output: [ 0.004381 -0.02071 0.9942 1.423e-05 -6.387e-06 1.018 1.072e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1073 0.09482 0.1833 0.1989 0.9873 0.9919 0.1073 0.7473 0.8638 0.3054 ] Network output: [ -0.004121 0.01942 1.004 1.526e-05 -6.849e-06 0.9846 1.15e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09237 0.09043 0.165 0.1958 0.9853 0.9911 0.09238 0.6715 0.8396 0.2472 ] Network output: [ 0.0001131 1 -9.904e-05 2.021e-06 -9.072e-07 0.9998 1.523e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002763 Epoch 8806 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009796 0.9963 0.9915 -1.956e-07 8.779e-08 -0.007464 -1.474e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003441 -0.003264 -0.007288 0.005785 0.9699 0.9743 0.00665 0.8293 0.8222 0.01716 ] Network output: [ 0.9999 0.0002987 0.0005729 -7.497e-06 3.366e-06 -0.0006277 -5.65e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2027 -0.03469 -0.1659 0.1862 0.9835 0.9932 0.2271 0.4348 0.8696 0.7128 ] Network output: [ -0.009678 1.003 1.009 -2.992e-07 1.343e-07 0.008098 -2.255e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006436 0.0005533 0.004434 0.003405 0.9889 0.9919 0.006559 0.857 0.8936 0.01233 ] Network output: [ -0.0003534 0.002075 1.001 -2.348e-05 1.054e-05 0.9978 -1.769e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2155 0.1011 0.3443 0.1437 0.985 0.994 0.2162 0.4389 0.8763 0.7068 ] Network output: [ 0.004379 -0.0207 0.9942 1.421e-05 -6.38e-06 1.018 1.071e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1073 0.09482 0.1833 0.1989 0.9873 0.9919 0.1074 0.7473 0.8638 0.3054 ] Network output: [ -0.004119 0.01941 1.004 1.524e-05 -6.841e-06 0.9847 1.148e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09237 0.09043 0.165 0.1958 0.9853 0.9911 0.09238 0.6715 0.8396 0.2472 ] Network output: [ 0.000113 1 -9.893e-05 2.019e-06 -9.062e-07 0.9998 1.521e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002762 Epoch 8807 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009795 0.9963 0.9916 -1.957e-07 8.787e-08 -0.007464 -1.475e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003441 -0.003264 -0.007288 0.005784 0.9699 0.9743 0.00665 0.8293 0.8222 0.01716 ] Network output: [ 0.9999 0.0002985 0.0005726 -7.489e-06 3.362e-06 -0.0006272 -5.644e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2027 -0.0347 -0.1659 0.1862 0.9835 0.9932 0.2271 0.4348 0.8696 0.7128 ] Network output: [ -0.009677 1.003 1.009 -2.992e-07 1.343e-07 0.008097 -2.255e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006436 0.0005534 0.004434 0.003405 0.9889 0.9919 0.006559 0.857 0.8936 0.01233 ] Network output: [ -0.0003531 0.002074 1.001 -2.345e-05 1.053e-05 0.9978 -1.767e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2155 0.1011 0.3443 0.1437 0.985 0.994 0.2162 0.4389 0.8763 0.7068 ] Network output: [ 0.004377 -0.02069 0.9942 1.42e-05 -6.373e-06 1.018 1.07e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1073 0.09483 0.1833 0.1989 0.9873 0.9919 0.1074 0.7473 0.8638 0.3054 ] Network output: [ -0.004118 0.01941 1.004 1.522e-05 -6.834e-06 0.9847 1.147e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09237 0.09043 0.165 0.1958 0.9853 0.9911 0.09238 0.6714 0.8396 0.2472 ] Network output: [ 0.000113 1 -9.882e-05 2.016e-06 -9.052e-07 0.9998 1.52e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000276 Epoch 8808 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009794 0.9963 0.9916 -1.959e-07 8.794e-08 -0.007464 -1.476e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003441 -0.003264 -0.007287 0.005784 0.9699 0.9743 0.00665 0.8293 0.8222 0.01716 ] Network output: [ 0.9999 0.0002982 0.0005723 -7.48e-06 3.358e-06 -0.0006268 -5.638e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2027 -0.0347 -0.1659 0.1862 0.9835 0.9932 0.2271 0.4348 0.8696 0.7128 ] Network output: [ -0.009676 1.003 1.009 -2.991e-07 1.343e-07 0.008096 -2.254e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006437 0.0005535 0.004434 0.003405 0.9889 0.9919 0.00656 0.857 0.8936 0.01233 ] Network output: [ -0.0003529 0.002073 1.001 -2.342e-05 1.052e-05 0.9978 -1.765e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2155 0.1011 0.3443 0.1437 0.985 0.994 0.2162 0.4389 0.8763 0.7068 ] Network output: [ 0.004376 -0.02068 0.9942 1.418e-05 -6.366e-06 1.018 1.069e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1073 0.09483 0.1833 0.1989 0.9873 0.9919 0.1074 0.7473 0.8638 0.3054 ] Network output: [ -0.004116 0.0194 1.004 1.521e-05 -6.826e-06 0.9847 1.146e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09237 0.09044 0.165 0.1958 0.9853 0.9911 0.09239 0.6714 0.8396 0.2472 ] Network output: [ 0.0001129 1 -9.871e-05 2.014e-06 -9.042e-07 0.9998 1.518e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002759 Epoch 8809 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009793 0.9963 0.9916 -1.961e-07 8.802e-08 -0.007464 -1.478e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003441 -0.003264 -0.007286 0.005783 0.9699 0.9743 0.006651 0.8293 0.8222 0.01716 ] Network output: [ 0.9999 0.0002979 0.000572 -7.472e-06 3.355e-06 -0.0006263 -5.631e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2027 -0.0347 -0.1659 0.1862 0.9835 0.9932 0.2271 0.4348 0.8696 0.7128 ] Network output: [ -0.009675 1.003 1.009 -2.991e-07 1.343e-07 0.008095 -2.254e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006437 0.0005536 0.004434 0.003405 0.9889 0.9919 0.00656 0.857 0.8936 0.01232 ] Network output: [ -0.0003527 0.002073 1.001 -2.34e-05 1.05e-05 0.9978 -1.763e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2155 0.1011 0.3443 0.1437 0.985 0.994 0.2162 0.4388 0.8763 0.7068 ] Network output: [ 0.004374 -0.02068 0.9942 1.417e-05 -6.359e-06 1.018 1.068e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1073 0.09484 0.1834 0.1989 0.9873 0.9919 0.1074 0.7473 0.8638 0.3054 ] Network output: [ -0.004114 0.01939 1.004 1.519e-05 -6.819e-06 0.9847 1.145e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09238 0.09044 0.165 0.1958 0.9853 0.9911 0.09239 0.6714 0.8396 0.2472 ] Network output: [ 0.0001129 1 -9.861e-05 2.012e-06 -9.032e-07 0.9998 1.516e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002757 Epoch 8810 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009792 0.9963 0.9916 -1.962e-07 8.809e-08 -0.007463 -1.479e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003441 -0.003264 -0.007285 0.005783 0.9699 0.9743 0.006651 0.8293 0.8222 0.01715 ] Network output: [ 0.9999 0.0002977 0.0005717 -7.464e-06 3.351e-06 -0.0006258 -5.625e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2027 -0.0347 -0.1659 0.1862 0.9835 0.9932 0.2271 0.4347 0.8696 0.7128 ] Network output: [ -0.009674 1.003 1.009 -2.991e-07 1.343e-07 0.008094 -2.254e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006438 0.0005537 0.004434 0.003404 0.9889 0.9919 0.006561 0.857 0.8935 0.01232 ] Network output: [ -0.0003524 0.002072 1.001 -2.337e-05 1.049e-05 0.9978 -1.761e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2155 0.1011 0.3443 0.1437 0.985 0.994 0.2162 0.4388 0.8763 0.7068 ] Network output: [ 0.004373 -0.02067 0.9942 1.415e-05 -6.352e-06 1.018 1.066e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1073 0.09484 0.1834 0.1989 0.9873 0.9919 0.1074 0.7473 0.8638 0.3054 ] Network output: [ -0.004113 0.01938 1.004 1.517e-05 -6.812e-06 0.9847 1.143e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09238 0.09044 0.165 0.1958 0.9853 0.9911 0.09239 0.6714 0.8396 0.2472 ] Network output: [ 0.0001128 1 -9.85e-05 2.01e-06 -9.022e-07 0.9998 1.515e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002756 Epoch 8811 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009791 0.9963 0.9916 -1.964e-07 8.816e-08 -0.007463 -1.48e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003441 -0.003264 -0.007285 0.005782 0.9699 0.9743 0.006651 0.8293 0.8222 0.01715 ] Network output: [ 0.9999 0.0002974 0.0005714 -7.456e-06 3.347e-06 -0.0006253 -5.619e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2027 -0.0347 -0.1659 0.1862 0.9835 0.9932 0.2271 0.4347 0.8696 0.7127 ] Network output: [ -0.009673 1.003 1.009 -2.991e-07 1.343e-07 0.008093 -2.254e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006438 0.0005538 0.004434 0.003404 0.9889 0.9919 0.006562 0.857 0.8935 0.01232 ] Network output: [ -0.0003522 0.002071 1.001 -2.335e-05 1.048e-05 0.9978 -1.759e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2155 0.1011 0.3443 0.1437 0.985 0.994 0.2162 0.4388 0.8763 0.7068 ] Network output: [ 0.004371 -0.02066 0.9942 1.413e-05 -6.345e-06 1.018 1.065e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1073 0.09485 0.1834 0.1989 0.9873 0.9919 0.1074 0.7472 0.8638 0.3054 ] Network output: [ -0.004111 0.01938 1.004 1.516e-05 -6.804e-06 0.9847 1.142e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09238 0.09044 0.165 0.1958 0.9853 0.9911 0.09239 0.6714 0.8396 0.2472 ] Network output: [ 0.0001128 1 -9.839e-05 2.007e-06 -9.012e-07 0.9998 1.513e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002755 Epoch 8812 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009789 0.9963 0.9916 -1.965e-07 8.823e-08 -0.007463 -1.481e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003441 -0.003264 -0.007284 0.005782 0.9699 0.9743 0.006651 0.8293 0.8222 0.01715 ] Network output: [ 0.9999 0.0002971 0.0005711 -7.447e-06 3.343e-06 -0.0006248 -5.612e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2027 -0.0347 -0.1659 0.1862 0.9835 0.9932 0.2271 0.4347 0.8696 0.7127 ] Network output: [ -0.009672 1.003 1.009 -2.991e-07 1.343e-07 0.008092 -2.254e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006439 0.0005539 0.004434 0.003404 0.9889 0.9919 0.006562 0.857 0.8935 0.01232 ] Network output: [ -0.000352 0.00207 1.001 -2.332e-05 1.047e-05 0.9978 -1.757e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2155 0.1011 0.3443 0.1437 0.985 0.994 0.2162 0.4388 0.8763 0.7068 ] Network output: [ 0.00437 -0.02065 0.9942 1.412e-05 -6.338e-06 1.018 1.064e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1073 0.09485 0.1834 0.1989 0.9873 0.9919 0.1074 0.7472 0.8638 0.3054 ] Network output: [ -0.00411 0.01937 1.004 1.514e-05 -6.797e-06 0.9847 1.141e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09238 0.09045 0.165 0.1958 0.9853 0.9911 0.0924 0.6714 0.8396 0.2472 ] Network output: [ 0.0001127 1 -9.828e-05 2.005e-06 -9.002e-07 0.9998 1.511e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002753 Epoch 8813 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009788 0.9963 0.9916 -1.967e-07 8.831e-08 -0.007463 -1.482e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003442 -0.003265 -0.007283 0.005781 0.9699 0.9743 0.006652 0.8293 0.8222 0.01715 ] Network output: [ 0.9999 0.0002969 0.0005708 -7.439e-06 3.34e-06 -0.0006243 -5.606e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2027 -0.0347 -0.1659 0.1862 0.9835 0.9932 0.2271 0.4347 0.8696 0.7127 ] Network output: [ -0.009671 1.003 1.009 -2.99e-07 1.343e-07 0.008091 -2.254e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006439 0.000554 0.004434 0.003403 0.9889 0.9919 0.006563 0.8569 0.8935 0.01232 ] Network output: [ -0.0003517 0.002069 1.001 -2.329e-05 1.046e-05 0.9978 -1.756e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2155 0.1011 0.3443 0.1437 0.985 0.994 0.2162 0.4388 0.8763 0.7068 ] Network output: [ 0.004368 -0.02065 0.9942 1.41e-05 -6.331e-06 1.018 1.063e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1073 0.09486 0.1834 0.1989 0.9873 0.9919 0.1074 0.7472 0.8638 0.3054 ] Network output: [ -0.004108 0.01936 1.004 1.512e-05 -6.79e-06 0.9847 1.14e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09238 0.09045 0.165 0.1958 0.9853 0.9911 0.0924 0.6713 0.8396 0.2472 ] Network output: [ 0.0001127 1 -9.817e-05 2.003e-06 -8.993e-07 0.9998 1.51e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002752 Epoch 8814 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009787 0.9963 0.9916 -1.969e-07 8.838e-08 -0.007463 -1.484e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003442 -0.003265 -0.007282 0.005781 0.9699 0.9743 0.006652 0.8292 0.8222 0.01715 ] Network output: [ 0.9999 0.0002966 0.0005705 -7.431e-06 3.336e-06 -0.0006238 -5.6e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2027 -0.0347 -0.1658 0.1862 0.9835 0.9932 0.2271 0.4347 0.8696 0.7127 ] Network output: [ -0.009671 1.003 1.009 -2.99e-07 1.342e-07 0.00809 -2.254e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00644 0.0005541 0.004434 0.003403 0.9889 0.9919 0.006563 0.8569 0.8935 0.01232 ] Network output: [ -0.0003515 0.002069 1.001 -2.327e-05 1.045e-05 0.9978 -1.754e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2155 0.1011 0.3443 0.1437 0.985 0.994 0.2162 0.4388 0.8763 0.7068 ] Network output: [ 0.004366 -0.02064 0.9942 1.409e-05 -6.324e-06 1.018 1.062e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1073 0.09486 0.1834 0.1989 0.9873 0.9919 0.1074 0.7472 0.8638 0.3054 ] Network output: [ -0.004107 0.01935 1.004 1.511e-05 -6.782e-06 0.9847 1.139e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09239 0.09045 0.165 0.1958 0.9853 0.9911 0.0924 0.6713 0.8395 0.2472 ] Network output: [ 0.0001126 1 -9.806e-05 2.001e-06 -8.983e-07 0.9998 1.508e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000275 Epoch 8815 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009786 0.9963 0.9916 -1.97e-07 8.845e-08 -0.007462 -1.485e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003442 -0.003265 -0.007281 0.00578 0.9699 0.9743 0.006652 0.8292 0.8222 0.01715 ] Network output: [ 0.9999 0.0002963 0.0005702 -7.422e-06 3.332e-06 -0.0006233 -5.594e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2027 -0.0347 -0.1658 0.1862 0.9835 0.9932 0.2272 0.4347 0.8696 0.7127 ] Network output: [ -0.00967 1.003 1.009 -2.99e-07 1.342e-07 0.008089 -2.253e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00644 0.0005542 0.004434 0.003403 0.9889 0.9919 0.006564 0.8569 0.8935 0.01232 ] Network output: [ -0.0003513 0.002068 1.001 -2.324e-05 1.043e-05 0.9978 -1.752e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2155 0.1011 0.3443 0.1437 0.985 0.994 0.2163 0.4388 0.8763 0.7068 ] Network output: [ 0.004365 -0.02063 0.9942 1.407e-05 -6.317e-06 1.018 1.06e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1073 0.09487 0.1834 0.1989 0.9873 0.9919 0.1074 0.7472 0.8638 0.3054 ] Network output: [ -0.004105 0.01934 1.004 1.509e-05 -6.775e-06 0.9847 1.137e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09239 0.09045 0.165 0.1958 0.9853 0.9911 0.0924 0.6713 0.8395 0.2472 ] Network output: [ 0.0001126 1 -9.795e-05 1.999e-06 -8.973e-07 0.9998 1.506e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002749 Epoch 8816 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009785 0.9963 0.9916 -1.972e-07 8.852e-08 -0.007462 -1.486e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003442 -0.003265 -0.007281 0.00578 0.9699 0.9743 0.006652 0.8292 0.8222 0.01715 ] Network output: [ 0.9999 0.0002961 0.0005699 -7.414e-06 3.328e-06 -0.0006228 -5.588e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2027 -0.03471 -0.1658 0.1862 0.9835 0.9932 0.2272 0.4347 0.8696 0.7127 ] Network output: [ -0.009669 1.003 1.009 -2.99e-07 1.342e-07 0.008087 -2.253e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006441 0.0005543 0.004434 0.003402 0.9889 0.9919 0.006564 0.8569 0.8935 0.01232 ] Network output: [ -0.0003511 0.002067 1.001 -2.322e-05 1.042e-05 0.9978 -1.75e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2155 0.1011 0.3443 0.1437 0.985 0.994 0.2163 0.4388 0.8763 0.7068 ] Network output: [ 0.004363 -0.02062 0.9942 1.406e-05 -6.31e-06 1.018 1.059e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1073 0.09487 0.1834 0.1989 0.9873 0.9919 0.1074 0.7472 0.8638 0.3054 ] Network output: [ -0.004104 0.01934 1.004 1.508e-05 -6.768e-06 0.9847 1.136e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09239 0.09045 0.165 0.1958 0.9853 0.9911 0.09241 0.6713 0.8395 0.2472 ] Network output: [ 0.0001126 1 -9.784e-05 1.996e-06 -8.963e-07 0.9998 1.505e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002747 Epoch 8817 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009784 0.9963 0.9916 -1.973e-07 8.859e-08 -0.007462 -1.487e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003442 -0.003265 -0.00728 0.005779 0.9699 0.9743 0.006653 0.8292 0.8222 0.01715 ] Network output: [ 0.9999 0.0002958 0.0005696 -7.406e-06 3.325e-06 -0.0006223 -5.581e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2028 -0.03471 -0.1658 0.1862 0.9835 0.9932 0.2272 0.4347 0.8696 0.7127 ] Network output: [ -0.009668 1.003 1.009 -2.99e-07 1.342e-07 0.008086 -2.253e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006441 0.0005544 0.004433 0.003402 0.9889 0.9919 0.006565 0.8569 0.8935 0.01232 ] Network output: [ -0.0003508 0.002066 1.001 -2.319e-05 1.041e-05 0.9978 -1.748e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2156 0.1011 0.3443 0.1437 0.985 0.994 0.2163 0.4388 0.8762 0.7067 ] Network output: [ 0.004362 -0.02061 0.9942 1.404e-05 -6.303e-06 1.018 1.058e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1073 0.09488 0.1834 0.1989 0.9873 0.9919 0.1074 0.7471 0.8638 0.3054 ] Network output: [ -0.004102 0.01933 1.004 1.506e-05 -6.76e-06 0.9847 1.135e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09239 0.09046 0.165 0.1958 0.9853 0.9911 0.09241 0.6713 0.8395 0.2472 ] Network output: [ 0.0001125 1 -9.773e-05 1.994e-06 -8.953e-07 0.9998 1.503e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002746 Epoch 8818 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009782 0.9963 0.9916 -1.975e-07 8.866e-08 -0.007462 -1.488e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003442 -0.003265 -0.007279 0.005779 0.9699 0.9743 0.006653 0.8292 0.8222 0.01715 ] Network output: [ 0.9999 0.0002955 0.0005693 -7.398e-06 3.321e-06 -0.0006218 -5.575e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2028 -0.03471 -0.1658 0.1861 0.9835 0.9932 0.2272 0.4347 0.8696 0.7127 ] Network output: [ -0.009667 1.003 1.009 -2.989e-07 1.342e-07 0.008085 -2.253e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006442 0.0005545 0.004433 0.003402 0.9889 0.9919 0.006565 0.8569 0.8935 0.01232 ] Network output: [ -0.0003506 0.002066 1.001 -2.316e-05 1.04e-05 0.9978 -1.746e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2156 0.1011 0.3443 0.1437 0.985 0.994 0.2163 0.4388 0.8762 0.7067 ] Network output: [ 0.00436 -0.02061 0.9942 1.403e-05 -6.296e-06 1.018 1.057e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1073 0.09488 0.1834 0.1989 0.9873 0.9919 0.1074 0.7471 0.8638 0.3054 ] Network output: [ -0.004101 0.01932 1.004 1.504e-05 -6.753e-06 0.9847 1.134e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0924 0.09046 0.165 0.1958 0.9853 0.9911 0.09241 0.6713 0.8395 0.2472 ] Network output: [ 0.0001125 1 -9.763e-05 1.992e-06 -8.943e-07 0.9998 1.501e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002744 Epoch 8819 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009781 0.9963 0.9916 -1.976e-07 8.873e-08 -0.007461 -1.49e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003442 -0.003265 -0.007278 0.005778 0.9699 0.9743 0.006653 0.8292 0.8222 0.01714 ] Network output: [ 0.9999 0.0002953 0.000569 -7.389e-06 3.317e-06 -0.0006213 -5.569e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2028 -0.03471 -0.1658 0.1861 0.9835 0.9932 0.2272 0.4347 0.8696 0.7127 ] Network output: [ -0.009666 1.003 1.009 -2.989e-07 1.342e-07 0.008084 -2.253e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006442 0.0005545 0.004433 0.003402 0.9889 0.9919 0.006566 0.8569 0.8935 0.01232 ] Network output: [ -0.0003504 0.002065 1.001 -2.314e-05 1.039e-05 0.9978 -1.744e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2156 0.1011 0.3443 0.1437 0.985 0.994 0.2163 0.4388 0.8762 0.7067 ] Network output: [ 0.004359 -0.0206 0.9942 1.401e-05 -6.289e-06 1.018 1.056e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1074 0.09489 0.1834 0.1989 0.9873 0.9919 0.1074 0.7471 0.8638 0.3054 ] Network output: [ -0.004099 0.01931 1.004 1.503e-05 -6.746e-06 0.9847 1.132e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0924 0.09046 0.165 0.1958 0.9853 0.9911 0.09241 0.6712 0.8395 0.2473 ] Network output: [ 0.0001124 1 -9.752e-05 1.99e-06 -8.934e-07 0.9998 1.5e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002743 Epoch 8820 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00978 0.9963 0.9916 -1.978e-07 8.88e-08 -0.007461 -1.491e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003442 -0.003265 -0.007278 0.005778 0.9699 0.9743 0.006653 0.8292 0.8222 0.01714 ] Network output: [ 0.9999 0.000295 0.0005687 -7.381e-06 3.314e-06 -0.0006208 -5.563e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2028 -0.03471 -0.1658 0.1861 0.9835 0.9932 0.2272 0.4347 0.8696 0.7127 ] Network output: [ -0.009665 1.003 1.009 -2.989e-07 1.342e-07 0.008083 -2.253e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006443 0.0005546 0.004433 0.003401 0.9889 0.9919 0.006566 0.8569 0.8935 0.01231 ] Network output: [ -0.0003501 0.002064 1.001 -2.311e-05 1.038e-05 0.9978 -1.742e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2156 0.1012 0.3443 0.1437 0.985 0.994 0.2163 0.4388 0.8762 0.7067 ] Network output: [ 0.004357 -0.02059 0.9942 1.399e-05 -6.283e-06 1.018 1.055e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1074 0.09489 0.1834 0.1989 0.9873 0.9919 0.1074 0.7471 0.8638 0.3054 ] Network output: [ -0.004098 0.01931 1.004 1.501e-05 -6.739e-06 0.9847 1.131e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0924 0.09046 0.165 0.1958 0.9853 0.9911 0.09241 0.6712 0.8395 0.2473 ] Network output: [ 0.0001124 1 -9.741e-05 1.988e-06 -8.924e-07 0.9998 1.498e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002742 Epoch 8821 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009779 0.9963 0.9916 -1.98e-07 8.887e-08 -0.007461 -1.492e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003442 -0.003266 -0.007277 0.005777 0.9699 0.9743 0.006654 0.8292 0.8222 0.01714 ] Network output: [ 0.9999 0.0002948 0.0005684 -7.373e-06 3.31e-06 -0.0006203 -5.556e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2028 -0.03471 -0.1658 0.1861 0.9835 0.9932 0.2272 0.4347 0.8696 0.7127 ] Network output: [ -0.009664 1.003 1.009 -2.989e-07 1.342e-07 0.008082 -2.252e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006443 0.0005547 0.004433 0.003401 0.9889 0.9919 0.006567 0.8569 0.8935 0.01231 ] Network output: [ -0.0003499 0.002063 1.001 -2.309e-05 1.036e-05 0.9978 -1.74e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2156 0.1012 0.3443 0.1437 0.985 0.994 0.2163 0.4387 0.8762 0.7067 ] Network output: [ 0.004355 -0.02058 0.9942 1.398e-05 -6.276e-06 1.018 1.053e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1074 0.0949 0.1834 0.1989 0.9873 0.9919 0.1074 0.7471 0.8638 0.3054 ] Network output: [ -0.004096 0.0193 1.004 1.499e-05 -6.731e-06 0.9847 1.13e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0924 0.09047 0.165 0.1958 0.9853 0.9911 0.09242 0.6712 0.8395 0.2473 ] Network output: [ 0.0001123 1 -9.73e-05 1.986e-06 -8.914e-07 0.9998 1.496e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000274 Epoch 8822 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009778 0.9963 0.9916 -1.981e-07 8.894e-08 -0.007461 -1.493e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003442 -0.003266 -0.007276 0.005777 0.9699 0.9743 0.006654 0.8292 0.8222 0.01714 ] Network output: [ 0.9999 0.0002945 0.0005681 -7.365e-06 3.306e-06 -0.0006198 -5.55e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2028 -0.03471 -0.1658 0.1861 0.9835 0.9932 0.2272 0.4346 0.8696 0.7127 ] Network output: [ -0.009663 1.003 1.009 -2.988e-07 1.342e-07 0.008081 -2.252e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006444 0.0005548 0.004433 0.003401 0.9889 0.9919 0.006567 0.8569 0.8935 0.01231 ] Network output: [ -0.0003497 0.002063 1.001 -2.306e-05 1.035e-05 0.9978 -1.738e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2156 0.1012 0.3444 0.1437 0.985 0.994 0.2163 0.4387 0.8762 0.7067 ] Network output: [ 0.004354 -0.02058 0.9942 1.396e-05 -6.269e-06 1.018 1.052e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1074 0.0949 0.1834 0.1989 0.9873 0.9919 0.1074 0.7471 0.8637 0.3054 ] Network output: [ -0.004095 0.01929 1.004 1.498e-05 -6.724e-06 0.9847 1.129e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09241 0.09047 0.165 0.1958 0.9853 0.9911 0.09242 0.6712 0.8395 0.2473 ] Network output: [ 0.0001123 1 -9.72e-05 1.983e-06 -8.904e-07 0.9998 1.495e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002739 Epoch 8823 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009777 0.9963 0.9916 -1.983e-07 8.901e-08 -0.00746 -1.494e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003443 -0.003266 -0.007275 0.005776 0.9699 0.9743 0.006654 0.8292 0.8222 0.01714 ] Network output: [ 0.9999 0.0002942 0.0005678 -7.357e-06 3.303e-06 -0.0006193 -5.544e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2028 -0.03471 -0.1657 0.1861 0.9835 0.9932 0.2272 0.4346 0.8696 0.7127 ] Network output: [ -0.009662 1.003 1.009 -2.988e-07 1.341e-07 0.00808 -2.252e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006444 0.0005549 0.004433 0.0034 0.9889 0.9919 0.006568 0.8569 0.8935 0.01231 ] Network output: [ -0.0003495 0.002062 1.001 -2.304e-05 1.034e-05 0.9978 -1.736e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2156 0.1012 0.3444 0.1437 0.985 0.994 0.2163 0.4387 0.8762 0.7067 ] Network output: [ 0.004352 -0.02057 0.9942 1.395e-05 -6.262e-06 1.018 1.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1074 0.09491 0.1834 0.1989 0.9873 0.9919 0.1074 0.747 0.8637 0.3054 ] Network output: [ -0.004093 0.01928 1.004 1.496e-05 -6.717e-06 0.9847 1.128e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09241 0.09047 0.165 0.1958 0.9853 0.9911 0.09242 0.6712 0.8395 0.2473 ] Network output: [ 0.0001122 1 -9.709e-05 1.981e-06 -8.894e-07 0.9998 1.493e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002737 Epoch 8824 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009775 0.9963 0.9916 -1.984e-07 8.907e-08 -0.00746 -1.495e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003443 -0.003266 -0.007274 0.005776 0.9699 0.9743 0.006654 0.8292 0.8222 0.01714 ] Network output: [ 0.9999 0.000294 0.0005675 -7.348e-06 3.299e-06 -0.0006189 -5.538e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2028 -0.03472 -0.1657 0.1861 0.9835 0.9932 0.2272 0.4346 0.8696 0.7127 ] Network output: [ -0.009661 1.003 1.009 -2.988e-07 1.341e-07 0.008079 -2.252e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006445 0.000555 0.004433 0.0034 0.9889 0.9919 0.006568 0.8569 0.8935 0.01231 ] Network output: [ -0.0003492 0.002061 1.001 -2.301e-05 1.033e-05 0.9978 -1.734e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2156 0.1012 0.3444 0.1437 0.985 0.994 0.2163 0.4387 0.8762 0.7067 ] Network output: [ 0.004351 -0.02056 0.9942 1.393e-05 -6.255e-06 1.018 1.05e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1074 0.09492 0.1834 0.1989 0.9873 0.9919 0.1075 0.747 0.8637 0.3054 ] Network output: [ -0.004092 0.01927 1.004 1.495e-05 -6.71e-06 0.9847 1.126e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09241 0.09047 0.165 0.1958 0.9853 0.9911 0.09242 0.6712 0.8395 0.2473 ] Network output: [ 0.0001122 1 -9.698e-05 1.979e-06 -8.885e-07 0.9998 1.491e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002736 Epoch 8825 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009774 0.9963 0.9916 -1.986e-07 8.914e-08 -0.00746 -1.496e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003443 -0.003266 -0.007274 0.005775 0.9699 0.9743 0.006655 0.8292 0.8222 0.01714 ] Network output: [ 0.9999 0.0002937 0.0005672 -7.34e-06 3.295e-06 -0.0006184 -5.532e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2028 -0.03472 -0.1657 0.1861 0.9835 0.9932 0.2272 0.4346 0.8695 0.7127 ] Network output: [ -0.00966 1.003 1.009 -2.988e-07 1.341e-07 0.008078 -2.252e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006445 0.0005551 0.004433 0.0034 0.9889 0.9919 0.006569 0.8569 0.8935 0.01231 ] Network output: [ -0.000349 0.00206 1.001 -2.298e-05 1.032e-05 0.9978 -1.732e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2156 0.1012 0.3444 0.1437 0.985 0.994 0.2163 0.4387 0.8762 0.7067 ] Network output: [ 0.004349 -0.02055 0.9942 1.392e-05 -6.248e-06 1.018 1.049e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1074 0.09492 0.1834 0.1989 0.9873 0.9919 0.1075 0.747 0.8637 0.3054 ] Network output: [ -0.00409 0.01927 1.004 1.493e-05 -6.702e-06 0.9847 1.125e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09241 0.09048 0.165 0.1958 0.9853 0.9911 0.09243 0.6711 0.8395 0.2473 ] Network output: [ 0.0001121 1 -9.687e-05 1.977e-06 -8.875e-07 0.9998 1.49e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002734 Epoch 8826 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009773 0.9963 0.9916 -1.987e-07 8.921e-08 -0.00746 -1.498e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003443 -0.003266 -0.007273 0.005775 0.9699 0.9743 0.006655 0.8292 0.8222 0.01714 ] Network output: [ 0.9999 0.0002934 0.0005669 -7.332e-06 3.292e-06 -0.0006179 -5.526e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2028 -0.03472 -0.1657 0.1861 0.9835 0.9932 0.2273 0.4346 0.8695 0.7127 ] Network output: [ -0.009659 1.003 1.009 -2.987e-07 1.341e-07 0.008077 -2.251e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006446 0.0005552 0.004433 0.003399 0.9889 0.9919 0.006569 0.8569 0.8935 0.01231 ] Network output: [ -0.0003488 0.002059 1.001 -2.296e-05 1.031e-05 0.9978 -1.73e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2156 0.1012 0.3444 0.1437 0.985 0.994 0.2164 0.4387 0.8762 0.7067 ] Network output: [ 0.004348 -0.02055 0.9942 1.39e-05 -6.241e-06 1.018 1.048e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1074 0.09493 0.1834 0.1989 0.9873 0.9919 0.1075 0.747 0.8637 0.3054 ] Network output: [ -0.004088 0.01926 1.004 1.491e-05 -6.695e-06 0.9847 1.124e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09242 0.09048 0.165 0.1958 0.9853 0.9911 0.09243 0.6711 0.8395 0.2473 ] Network output: [ 0.0001121 1 -9.677e-05 1.975e-06 -8.865e-07 0.9998 1.488e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002733 Epoch 8827 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009772 0.9963 0.9916 -1.989e-07 8.928e-08 -0.007459 -1.499e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003443 -0.003266 -0.007272 0.005774 0.9699 0.9743 0.006655 0.8292 0.8222 0.01714 ] Network output: [ 0.9999 0.0002932 0.0005666 -7.324e-06 3.288e-06 -0.0006174 -5.519e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2028 -0.03472 -0.1657 0.1861 0.9835 0.9932 0.2273 0.4346 0.8695 0.7127 ] Network output: [ -0.009658 1.003 1.009 -2.987e-07 1.341e-07 0.008075 -2.251e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006446 0.0005553 0.004433 0.003399 0.9889 0.9919 0.00657 0.8569 0.8935 0.01231 ] Network output: [ -0.0003485 0.002059 1.001 -2.293e-05 1.03e-05 0.9978 -1.728e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2156 0.1012 0.3444 0.1437 0.985 0.994 0.2164 0.4387 0.8762 0.7067 ] Network output: [ 0.004346 -0.02054 0.9942 1.389e-05 -6.234e-06 1.018 1.047e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1074 0.09493 0.1834 0.1989 0.9873 0.9919 0.1075 0.747 0.8637 0.3054 ] Network output: [ -0.004087 0.01925 1.004 1.49e-05 -6.688e-06 0.9847 1.123e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09242 0.09048 0.165 0.1958 0.9853 0.9911 0.09243 0.6711 0.8395 0.2473 ] Network output: [ 0.000112 1 -9.666e-05 1.973e-06 -8.855e-07 0.9998 1.487e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002732 Epoch 8828 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009771 0.9963 0.9916 -1.99e-07 8.934e-08 -0.007459 -1.5e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003443 -0.003266 -0.007271 0.005774 0.9699 0.9743 0.006655 0.8292 0.8222 0.01713 ] Network output: [ 0.9999 0.0002929 0.0005664 -7.316e-06 3.284e-06 -0.0006169 -5.513e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2028 -0.03472 -0.1657 0.1861 0.9835 0.9932 0.2273 0.4346 0.8695 0.7127 ] Network output: [ -0.009657 1.003 1.009 -2.987e-07 1.341e-07 0.008074 -2.251e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006447 0.0005554 0.004433 0.003399 0.9889 0.9919 0.00657 0.8568 0.8935 0.01231 ] Network output: [ -0.0003483 0.002058 1.001 -2.291e-05 1.028e-05 0.9978 -1.726e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2157 0.1012 0.3444 0.1437 0.985 0.994 0.2164 0.4387 0.8762 0.7067 ] Network output: [ 0.004344 -0.02053 0.9942 1.387e-05 -6.227e-06 1.018 1.045e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1074 0.09494 0.1834 0.1988 0.9873 0.9919 0.1075 0.747 0.8637 0.3054 ] Network output: [ -0.004085 0.01924 1.004 1.488e-05 -6.681e-06 0.9847 1.121e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09242 0.09048 0.165 0.1958 0.9853 0.9911 0.09243 0.6711 0.8395 0.2473 ] Network output: [ 0.000112 1 -9.655e-05 1.97e-06 -8.846e-07 0.9998 1.485e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000273 Epoch 8829 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00977 0.9963 0.9916 -1.992e-07 8.941e-08 -0.007459 -1.501e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003443 -0.003267 -0.007271 0.005773 0.9699 0.9743 0.006656 0.8292 0.8222 0.01713 ] Network output: [ 0.9999 0.0002927 0.0005661 -7.307e-06 3.281e-06 -0.0006164 -5.507e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2028 -0.03472 -0.1657 0.1861 0.9835 0.9932 0.2273 0.4346 0.8695 0.7126 ] Network output: [ -0.009656 1.003 1.009 -2.987e-07 1.341e-07 0.008073 -2.251e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006447 0.0005555 0.004433 0.003399 0.9889 0.9919 0.006571 0.8568 0.8935 0.01231 ] Network output: [ -0.0003481 0.002057 1.001 -2.288e-05 1.027e-05 0.9978 -1.724e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2157 0.1012 0.3444 0.1437 0.985 0.994 0.2164 0.4387 0.8762 0.7067 ] Network output: [ 0.004343 -0.02052 0.9942 1.386e-05 -6.22e-06 1.018 1.044e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1074 0.09494 0.1834 0.1988 0.9873 0.9919 0.1075 0.747 0.8637 0.3054 ] Network output: [ -0.004084 0.01924 1.004 1.486e-05 -6.673e-06 0.9847 1.12e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09242 0.09049 0.165 0.1958 0.9853 0.9911 0.09244 0.6711 0.8395 0.2473 ] Network output: [ 0.000112 1 -9.645e-05 1.968e-06 -8.836e-07 0.9998 1.483e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002729 Epoch 8830 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009769 0.9963 0.9916 -1.993e-07 8.948e-08 -0.007458 -1.502e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003443 -0.003267 -0.00727 0.005773 0.9699 0.9743 0.006656 0.8292 0.8222 0.01713 ] Network output: [ 0.9999 0.0002924 0.0005658 -7.299e-06 3.277e-06 -0.0006159 -5.501e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2029 -0.03472 -0.1657 0.1861 0.9835 0.9932 0.2273 0.4346 0.8695 0.7126 ] Network output: [ -0.009655 1.003 1.009 -2.986e-07 1.341e-07 0.008072 -2.251e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006448 0.0005556 0.004433 0.003398 0.9889 0.9919 0.006571 0.8568 0.8935 0.01231 ] Network output: [ -0.0003479 0.002056 1.001 -2.286e-05 1.026e-05 0.9978 -1.723e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2157 0.1012 0.3444 0.1437 0.985 0.994 0.2164 0.4387 0.8762 0.7067 ] Network output: [ 0.004341 -0.02052 0.9942 1.384e-05 -6.214e-06 1.018 1.043e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1074 0.09495 0.1834 0.1988 0.9873 0.9919 0.1075 0.7469 0.8637 0.3054 ] Network output: [ -0.004082 0.01923 1.004 1.485e-05 -6.666e-06 0.9847 1.119e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09243 0.09049 0.165 0.1958 0.9853 0.9911 0.09244 0.6711 0.8395 0.2473 ] Network output: [ 0.0001119 1 -9.634e-05 1.966e-06 -8.826e-07 0.9998 1.482e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002727 Epoch 8831 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009767 0.9963 0.9916 -1.995e-07 8.954e-08 -0.007458 -1.503e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003443 -0.003267 -0.007269 0.005772 0.9699 0.9743 0.006656 0.8291 0.8222 0.01713 ] Network output: [ 0.9999 0.0002921 0.0005655 -7.291e-06 3.273e-06 -0.0006154 -5.495e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2029 -0.03472 -0.1656 0.1861 0.9835 0.9932 0.2273 0.4346 0.8695 0.7126 ] Network output: [ -0.009654 1.003 1.009 -2.986e-07 1.34e-07 0.008071 -2.25e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006448 0.0005557 0.004433 0.003398 0.9889 0.9919 0.006572 0.8568 0.8935 0.0123 ] Network output: [ -0.0003476 0.002056 1.001 -2.283e-05 1.025e-05 0.9978 -1.721e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2157 0.1012 0.3444 0.1437 0.985 0.994 0.2164 0.4387 0.8762 0.7067 ] Network output: [ 0.00434 -0.02051 0.9942 1.383e-05 -6.207e-06 1.018 1.042e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1074 0.09495 0.1834 0.1988 0.9873 0.9919 0.1075 0.7469 0.8637 0.3054 ] Network output: [ -0.004081 0.01922 1.004 1.483e-05 -6.659e-06 0.9847 1.118e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09243 0.09049 0.165 0.1958 0.9853 0.9911 0.09244 0.671 0.8395 0.2473 ] Network output: [ 0.0001119 1 -9.624e-05 1.964e-06 -8.817e-07 0.9998 1.48e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002726 Epoch 8832 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009766 0.9963 0.9916 -1.996e-07 8.961e-08 -0.007458 -1.504e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003443 -0.003267 -0.007268 0.005772 0.9699 0.9743 0.006656 0.8291 0.8222 0.01713 ] Network output: [ 0.9999 0.0002919 0.0005652 -7.283e-06 3.27e-06 -0.000615 -5.489e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2029 -0.03472 -0.1656 0.1861 0.9835 0.9932 0.2273 0.4346 0.8695 0.7126 ] Network output: [ -0.009653 1.003 1.009 -2.986e-07 1.34e-07 0.00807 -2.25e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006449 0.0005558 0.004433 0.003398 0.9889 0.9919 0.006572 0.8568 0.8935 0.0123 ] Network output: [ -0.0003474 0.002055 1.001 -2.281e-05 1.024e-05 0.9978 -1.719e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2157 0.1012 0.3444 0.1437 0.985 0.994 0.2164 0.4387 0.8762 0.7067 ] Network output: [ 0.004338 -0.0205 0.9942 1.381e-05 -6.2e-06 1.018 1.041e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1074 0.09496 0.1834 0.1988 0.9873 0.9919 0.1075 0.7469 0.8637 0.3054 ] Network output: [ -0.004079 0.01921 1.004 1.482e-05 -6.652e-06 0.9847 1.117e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09243 0.09049 0.165 0.1958 0.9853 0.9911 0.09244 0.671 0.8395 0.2473 ] Network output: [ 0.0001118 1 -9.613e-05 1.962e-06 -8.807e-07 0.9998 1.478e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002724 Epoch 8833 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009765 0.9963 0.9916 -1.997e-07 8.967e-08 -0.007458 -1.505e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003444 -0.003267 -0.007267 0.005771 0.9699 0.9743 0.006657 0.8291 0.8222 0.01713 ] Network output: [ 0.9999 0.0002916 0.0005649 -7.275e-06 3.266e-06 -0.0006145 -5.483e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2029 -0.03473 -0.1656 0.1861 0.9835 0.9932 0.2273 0.4346 0.8695 0.7126 ] Network output: [ -0.009652 1.003 1.009 -2.985e-07 1.34e-07 0.008069 -2.25e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006449 0.0005559 0.004433 0.003397 0.9889 0.9919 0.006573 0.8568 0.8935 0.0123 ] Network output: [ -0.0003472 0.002054 1.001 -2.278e-05 1.023e-05 0.9978 -1.717e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2157 0.1012 0.3444 0.1437 0.985 0.994 0.2164 0.4387 0.8762 0.7067 ] Network output: [ 0.004337 -0.02049 0.9942 1.379e-05 -6.193e-06 1.018 1.04e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1074 0.09496 0.1834 0.1988 0.9873 0.9919 0.1075 0.7469 0.8637 0.3054 ] Network output: [ -0.004078 0.01921 1.004 1.48e-05 -6.645e-06 0.9848 1.115e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09243 0.0905 0.165 0.1958 0.9853 0.9911 0.09245 0.671 0.8395 0.2473 ] Network output: [ 0.0001118 1 -9.602e-05 1.96e-06 -8.797e-07 0.9998 1.477e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002723 Epoch 8834 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009764 0.9963 0.9916 -1.999e-07 8.974e-08 -0.007457 -1.506e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003444 -0.003267 -0.007267 0.005771 0.9699 0.9743 0.006657 0.8291 0.8222 0.01713 ] Network output: [ 0.9999 0.0002913 0.0005646 -7.267e-06 3.262e-06 -0.000614 -5.477e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2029 -0.03473 -0.1656 0.1861 0.9835 0.9932 0.2273 0.4346 0.8695 0.7126 ] Network output: [ -0.009651 1.003 1.009 -2.985e-07 1.34e-07 0.008068 -2.25e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00645 0.000556 0.004433 0.003397 0.9889 0.9919 0.006573 0.8568 0.8935 0.0123 ] Network output: [ -0.0003469 0.002053 1.001 -2.276e-05 1.022e-05 0.9978 -1.715e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2157 0.1012 0.3444 0.1437 0.985 0.994 0.2164 0.4386 0.8762 0.7066 ] Network output: [ 0.004335 -0.02049 0.9942 1.378e-05 -6.186e-06 1.018 1.038e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1074 0.09497 0.1834 0.1988 0.9873 0.9919 0.1075 0.7469 0.8637 0.3054 ] Network output: [ -0.004076 0.0192 1.004 1.478e-05 -6.637e-06 0.9848 1.114e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09243 0.0905 0.165 0.1958 0.9853 0.9911 0.09245 0.671 0.8395 0.2473 ] Network output: [ 0.0001117 1 -9.592e-05 1.957e-06 -8.788e-07 0.9998 1.475e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002722 Epoch 8835 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009763 0.9963 0.9916 -2e-07 8.98e-08 -0.007457 -1.508e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003444 -0.003267 -0.007266 0.00577 0.9699 0.9743 0.006657 0.8291 0.8222 0.01713 ] Network output: [ 0.9999 0.0002911 0.0005643 -7.259e-06 3.259e-06 -0.0006135 -5.47e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2029 -0.03473 -0.1656 0.1861 0.9835 0.9932 0.2273 0.4345 0.8695 0.7126 ] Network output: [ -0.00965 1.003 1.009 -2.985e-07 1.34e-07 0.008067 -2.249e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00645 0.0005561 0.004433 0.003397 0.9889 0.9919 0.006574 0.8568 0.8935 0.0123 ] Network output: [ -0.0003467 0.002053 1.001 -2.273e-05 1.02e-05 0.9978 -1.713e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2157 0.1012 0.3444 0.1437 0.985 0.994 0.2164 0.4386 0.8762 0.7066 ] Network output: [ 0.004334 -0.02048 0.9942 1.376e-05 -6.179e-06 1.018 1.037e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1074 0.09497 0.1834 0.1988 0.9873 0.9919 0.1075 0.7469 0.8637 0.3054 ] Network output: [ -0.004075 0.01919 1.004 1.477e-05 -6.63e-06 0.9848 1.113e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09244 0.0905 0.165 0.1958 0.9853 0.9911 0.09245 0.671 0.8394 0.2473 ] Network output: [ 0.0001117 1 -9.581e-05 1.955e-06 -8.778e-07 0.9998 1.474e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000272 Epoch 8836 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009762 0.9963 0.9916 -2.002e-07 8.987e-08 -0.007457 -1.509e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003444 -0.003267 -0.007265 0.00577 0.9699 0.9743 0.006657 0.8291 0.8221 0.01713 ] Network output: [ 0.9999 0.0002908 0.000564 -7.251e-06 3.255e-06 -0.000613 -5.464e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2029 -0.03473 -0.1656 0.1861 0.9835 0.9932 0.2273 0.4345 0.8695 0.7126 ] Network output: [ -0.009649 1.003 1.009 -2.984e-07 1.34e-07 0.008066 -2.249e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006451 0.0005561 0.004433 0.003396 0.9889 0.9919 0.006574 0.8568 0.8935 0.0123 ] Network output: [ -0.0003465 0.002052 1.001 -2.27e-05 1.019e-05 0.9978 -1.711e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2157 0.1012 0.3444 0.1437 0.985 0.994 0.2164 0.4386 0.8762 0.7066 ] Network output: [ 0.004332 -0.02047 0.9942 1.375e-05 -6.173e-06 1.018 1.036e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1074 0.09498 0.1834 0.1988 0.9873 0.9919 0.1075 0.7468 0.8637 0.3054 ] Network output: [ -0.004073 0.01918 1.004 1.475e-05 -6.623e-06 0.9848 1.112e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09244 0.0905 0.165 0.1958 0.9853 0.9911 0.09245 0.671 0.8394 0.2473 ] Network output: [ 0.0001116 1 -9.571e-05 1.953e-06 -8.768e-07 0.9998 1.472e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002719 Epoch 8837 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00976 0.9963 0.9916 -2.003e-07 8.993e-08 -0.007457 -1.51e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003444 -0.003267 -0.007264 0.005769 0.9699 0.9743 0.006658 0.8291 0.8221 0.01712 ] Network output: [ 0.9999 0.0002906 0.0005637 -7.243e-06 3.251e-06 -0.0006125 -5.458e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2029 -0.03473 -0.1656 0.1861 0.9835 0.9932 0.2274 0.4345 0.8695 0.7126 ] Network output: [ -0.009648 1.003 1.009 -2.984e-07 1.34e-07 0.008065 -2.249e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006451 0.0005562 0.004433 0.003396 0.9889 0.9919 0.006575 0.8568 0.8935 0.0123 ] Network output: [ -0.0003463 0.002051 1.001 -2.268e-05 1.018e-05 0.9978 -1.709e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2157 0.1012 0.3444 0.1437 0.985 0.994 0.2165 0.4386 0.8762 0.7066 ] Network output: [ 0.00433 -0.02046 0.9942 1.373e-05 -6.166e-06 1.018 1.035e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1075 0.09498 0.1834 0.1988 0.9873 0.9919 0.1075 0.7468 0.8637 0.3054 ] Network output: [ -0.004072 0.01917 1.004 1.474e-05 -6.616e-06 0.9848 1.111e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09244 0.0905 0.165 0.1958 0.9853 0.9911 0.09246 0.6709 0.8394 0.2473 ] Network output: [ 0.0001116 1 -9.56e-05 1.951e-06 -8.759e-07 0.9998 1.47e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002717 Epoch 8838 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009759 0.9963 0.9916 -2.005e-07 8.999e-08 -0.007456 -1.511e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003444 -0.003268 -0.007264 0.005769 0.9699 0.9743 0.006658 0.8291 0.8221 0.01712 ] Network output: [ 0.9999 0.0002903 0.0005634 -7.234e-06 3.248e-06 -0.000612 -5.452e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2029 -0.03473 -0.1656 0.1861 0.9835 0.9932 0.2274 0.4345 0.8695 0.7126 ] Network output: [ -0.009647 1.003 1.009 -2.984e-07 1.34e-07 0.008064 -2.249e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006452 0.0005563 0.004432 0.003396 0.9889 0.9919 0.006575 0.8568 0.8935 0.0123 ] Network output: [ -0.000346 0.00205 1.001 -2.265e-05 1.017e-05 0.9978 -1.707e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2157 0.1013 0.3445 0.1437 0.985 0.994 0.2165 0.4386 0.8762 0.7066 ] Network output: [ 0.004329 -0.02046 0.9942 1.372e-05 -6.159e-06 1.018 1.034e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1075 0.09499 0.1834 0.1988 0.9873 0.9919 0.1075 0.7468 0.8637 0.3054 ] Network output: [ -0.00407 0.01917 1.004 1.472e-05 -6.609e-06 0.9848 1.109e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09244 0.09051 0.165 0.1958 0.9853 0.9911 0.09246 0.6709 0.8394 0.2473 ] Network output: [ 0.0001115 1 -9.55e-05 1.949e-06 -8.749e-07 0.9998 1.469e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002716 Epoch 8839 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009758 0.9963 0.9916 -2.006e-07 9.006e-08 -0.007456 -1.512e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003444 -0.003268 -0.007263 0.005768 0.9699 0.9743 0.006658 0.8291 0.8221 0.01712 ] Network output: [ 0.9999 0.00029 0.0005631 -7.226e-06 3.244e-06 -0.0006116 -5.446e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2029 -0.03473 -0.1656 0.186 0.9835 0.9932 0.2274 0.4345 0.8695 0.7126 ] Network output: [ -0.009646 1.003 1.009 -2.983e-07 1.339e-07 0.008062 -2.248e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006452 0.0005564 0.004432 0.003396 0.9889 0.9919 0.006576 0.8568 0.8935 0.0123 ] Network output: [ -0.0003458 0.002049 1.001 -2.263e-05 1.016e-05 0.9978 -1.705e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2158 0.1013 0.3445 0.1437 0.985 0.994 0.2165 0.4386 0.8762 0.7066 ] Network output: [ 0.004327 -0.02045 0.9942 1.37e-05 -6.152e-06 1.018 1.033e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1075 0.09499 0.1834 0.1988 0.9873 0.9919 0.1075 0.7468 0.8637 0.3054 ] Network output: [ -0.004069 0.01916 1.004 1.47e-05 -6.601e-06 0.9848 1.108e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09245 0.09051 0.165 0.1958 0.9853 0.9911 0.09246 0.6709 0.8394 0.2473 ] Network output: [ 0.0001115 1 -9.539e-05 1.947e-06 -8.739e-07 0.9998 1.467e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002714 Epoch 8840 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009757 0.9963 0.9916 -2.007e-07 9.012e-08 -0.007456 -1.513e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003444 -0.003268 -0.007262 0.005768 0.9699 0.9743 0.006658 0.8291 0.8221 0.01712 ] Network output: [ 0.9999 0.0002898 0.0005628 -7.218e-06 3.241e-06 -0.0006111 -5.44e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2029 -0.03473 -0.1655 0.186 0.9835 0.9932 0.2274 0.4345 0.8695 0.7126 ] Network output: [ -0.009645 1.003 1.009 -2.983e-07 1.339e-07 0.008061 -2.248e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006453 0.0005565 0.004432 0.003395 0.9889 0.9919 0.006577 0.8568 0.8935 0.0123 ] Network output: [ -0.0003456 0.002049 1.001 -2.26e-05 1.015e-05 0.9978 -1.703e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2158 0.1013 0.3445 0.1436 0.985 0.994 0.2165 0.4386 0.8762 0.7066 ] Network output: [ 0.004326 -0.02044 0.9942 1.369e-05 -6.145e-06 1.018 1.032e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1075 0.095 0.1834 0.1988 0.9873 0.9919 0.1075 0.7468 0.8637 0.3054 ] Network output: [ -0.004067 0.01915 1.004 1.469e-05 -6.594e-06 0.9848 1.107e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09245 0.09051 0.165 0.1958 0.9853 0.9911 0.09246 0.6709 0.8394 0.2473 ] Network output: [ 0.0001114 1 -9.529e-05 1.945e-06 -8.73e-07 0.9998 1.465e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002713 Epoch 8841 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009756 0.9963 0.9916 -2.009e-07 9.018e-08 -0.007456 -1.514e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003444 -0.003268 -0.007261 0.005767 0.9699 0.9743 0.006659 0.8291 0.8221 0.01712 ] Network output: [ 0.9999 0.0002895 0.0005626 -7.21e-06 3.237e-06 -0.0006106 -5.434e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2029 -0.03473 -0.1655 0.186 0.9835 0.9932 0.2274 0.4345 0.8695 0.7126 ] Network output: [ -0.009644 1.003 1.009 -2.983e-07 1.339e-07 0.00806 -2.248e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006453 0.0005566 0.004432 0.003395 0.9889 0.9919 0.006577 0.8568 0.8935 0.0123 ] Network output: [ -0.0003454 0.002048 1.001 -2.258e-05 1.014e-05 0.9978 -1.702e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2158 0.1013 0.3445 0.1436 0.985 0.994 0.2165 0.4386 0.8762 0.7066 ] Network output: [ 0.004324 -0.02043 0.9942 1.367e-05 -6.138e-06 1.018 1.03e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1075 0.095 0.1834 0.1988 0.9873 0.9919 0.1075 0.7468 0.8637 0.3054 ] Network output: [ -0.004066 0.01914 1.004 1.467e-05 -6.587e-06 0.9848 1.106e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09245 0.09051 0.165 0.1958 0.9853 0.9911 0.09247 0.6709 0.8394 0.2473 ] Network output: [ 0.0001114 1 -9.518e-05 1.942e-06 -8.72e-07 0.9998 1.464e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002712 Epoch 8842 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009755 0.9963 0.9916 -2.01e-07 9.024e-08 -0.007455 -1.515e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003444 -0.003268 -0.007261 0.005767 0.9699 0.9743 0.006659 0.8291 0.8221 0.01712 ] Network output: [ 0.9999 0.0002893 0.0005623 -7.202e-06 3.233e-06 -0.0006101 -5.428e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2029 -0.03474 -0.1655 0.186 0.9835 0.9932 0.2274 0.4345 0.8695 0.7126 ] Network output: [ -0.009643 1.003 1.009 -2.982e-07 1.339e-07 0.008059 -2.248e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006454 0.0005567 0.004432 0.003395 0.9889 0.9919 0.006578 0.8568 0.8935 0.0123 ] Network output: [ -0.0003451 0.002047 1.001 -2.255e-05 1.012e-05 0.9978 -1.7e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2158 0.1013 0.3445 0.1436 0.985 0.994 0.2165 0.4386 0.8762 0.7066 ] Network output: [ 0.004323 -0.02042 0.9942 1.366e-05 -6.132e-06 1.018 1.029e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1075 0.09501 0.1834 0.1988 0.9873 0.9919 0.1076 0.7467 0.8637 0.3054 ] Network output: [ -0.004064 0.01914 1.004 1.466e-05 -6.58e-06 0.9848 1.105e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09245 0.09052 0.165 0.1958 0.9853 0.9911 0.09247 0.6709 0.8394 0.2473 ] Network output: [ 0.0001114 1 -9.508e-05 1.94e-06 -8.711e-07 0.9998 1.462e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000271 Epoch 8843 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009753 0.9963 0.9916 -2.012e-07 9.031e-08 -0.007455 -1.516e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003445 -0.003268 -0.00726 0.005766 0.9699 0.9743 0.006659 0.8291 0.8221 0.01712 ] Network output: [ 0.9999 0.000289 0.000562 -7.194e-06 3.23e-06 -0.0006096 -5.422e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.203 -0.03474 -0.1655 0.186 0.9835 0.9932 0.2274 0.4345 0.8695 0.7126 ] Network output: [ -0.009642 1.003 1.009 -2.982e-07 1.339e-07 0.008058 -2.247e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006454 0.0005568 0.004432 0.003394 0.9889 0.9919 0.006578 0.8567 0.8935 0.01229 ] Network output: [ -0.0003449 0.002046 1.001 -2.253e-05 1.011e-05 0.9978 -1.698e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2158 0.1013 0.3445 0.1436 0.985 0.994 0.2165 0.4386 0.8762 0.7066 ] Network output: [ 0.004321 -0.02042 0.9942 1.364e-05 -6.125e-06 1.018 1.028e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1075 0.09501 0.1834 0.1988 0.9873 0.9919 0.1076 0.7467 0.8637 0.3054 ] Network output: [ -0.004063 0.01913 1.004 1.464e-05 -6.573e-06 0.9848 1.103e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09246 0.09052 0.165 0.1958 0.9853 0.9911 0.09247 0.6709 0.8394 0.2473 ] Network output: [ 0.0001113 1 -9.497e-05 1.938e-06 -8.701e-07 0.9998 1.461e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002709 Epoch 8844 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009752 0.9963 0.9916 -2.013e-07 9.037e-08 -0.007455 -1.517e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003445 -0.003268 -0.007259 0.005766 0.9699 0.9743 0.006659 0.8291 0.8221 0.01712 ] Network output: [ 0.9999 0.0002887 0.0005617 -7.186e-06 3.226e-06 -0.0006092 -5.416e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.203 -0.03474 -0.1655 0.186 0.9835 0.9932 0.2274 0.4345 0.8695 0.7126 ] Network output: [ -0.009642 1.003 1.009 -2.982e-07 1.339e-07 0.008057 -2.247e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006455 0.0005569 0.004432 0.003394 0.9889 0.9919 0.006579 0.8567 0.8935 0.01229 ] Network output: [ -0.0003447 0.002046 1.001 -2.25e-05 1.01e-05 0.9978 -1.696e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2158 0.1013 0.3445 0.1436 0.985 0.994 0.2165 0.4386 0.8762 0.7066 ] Network output: [ 0.004319 -0.02041 0.9942 1.363e-05 -6.118e-06 1.018 1.027e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1075 0.09502 0.1834 0.1988 0.9873 0.9919 0.1076 0.7467 0.8637 0.3054 ] Network output: [ -0.004061 0.01912 1.004 1.463e-05 -6.566e-06 0.9848 1.102e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09246 0.09052 0.165 0.1958 0.9853 0.9911 0.09247 0.6708 0.8394 0.2473 ] Network output: [ 0.0001113 1 -9.487e-05 1.936e-06 -8.691e-07 0.9998 1.459e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002707 Epoch 8845 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009751 0.9964 0.9916 -2.014e-07 9.043e-08 -0.007455 -1.518e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003445 -0.003268 -0.007258 0.005765 0.9699 0.9743 0.00666 0.8291 0.8221 0.01712 ] Network output: [ 0.9999 0.0002885 0.0005614 -7.178e-06 3.223e-06 -0.0006087 -5.41e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.203 -0.03474 -0.1655 0.186 0.9835 0.9932 0.2274 0.4345 0.8695 0.7126 ] Network output: [ -0.009641 1.003 1.009 -2.981e-07 1.338e-07 0.008056 -2.247e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006455 0.000557 0.004432 0.003394 0.9889 0.9919 0.006579 0.8567 0.8935 0.01229 ] Network output: [ -0.0003444 0.002045 1.001 -2.248e-05 1.009e-05 0.9978 -1.694e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2158 0.1013 0.3445 0.1436 0.985 0.994 0.2165 0.4386 0.8762 0.7066 ] Network output: [ 0.004318 -0.0204 0.9942 1.361e-05 -6.111e-06 1.018 1.026e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1075 0.09502 0.1834 0.1988 0.9873 0.9919 0.1076 0.7467 0.8637 0.3054 ] Network output: [ -0.004059 0.01911 1.004 1.461e-05 -6.559e-06 0.9848 1.101e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09246 0.09052 0.165 0.1958 0.9853 0.9911 0.09247 0.6708 0.8394 0.2473 ] Network output: [ 0.0001112 1 -9.477e-05 1.934e-06 -8.682e-07 0.9998 1.457e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002706 Epoch 8846 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00975 0.9964 0.9916 -2.016e-07 9.049e-08 -0.007454 -1.519e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003445 -0.003269 -0.007257 0.005765 0.9699 0.9743 0.00666 0.8291 0.8221 0.01711 ] Network output: [ 0.9999 0.0002882 0.0005611 -7.17e-06 3.219e-06 -0.0006082 -5.404e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.203 -0.03474 -0.1655 0.186 0.9835 0.9932 0.2274 0.4345 0.8695 0.7126 ] Network output: [ -0.00964 1.003 1.009 -2.981e-07 1.338e-07 0.008055 -2.247e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006456 0.0005571 0.004432 0.003393 0.9889 0.9919 0.00658 0.8567 0.8935 0.01229 ] Network output: [ -0.0003442 0.002044 1.001 -2.245e-05 1.008e-05 0.9978 -1.692e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2158 0.1013 0.3445 0.1436 0.985 0.994 0.2165 0.4385 0.8762 0.7066 ] Network output: [ 0.004316 -0.02039 0.9942 1.36e-05 -6.105e-06 1.018 1.025e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1075 0.09503 0.1834 0.1988 0.9873 0.9919 0.1076 0.7467 0.8637 0.3054 ] Network output: [ -0.004058 0.01911 1.004 1.459e-05 -6.552e-06 0.9848 1.1e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09246 0.09053 0.165 0.1958 0.9853 0.9911 0.09248 0.6708 0.8394 0.2473 ] Network output: [ 0.0001112 1 -9.466e-05 1.932e-06 -8.672e-07 0.9998 1.456e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002705 Epoch 8847 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009749 0.9964 0.9916 -2.017e-07 9.055e-08 -0.007454 -1.52e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003445 -0.003269 -0.007257 0.005764 0.9699 0.9743 0.00666 0.8291 0.8221 0.01711 ] Network output: [ 0.9999 0.000288 0.0005608 -7.162e-06 3.215e-06 -0.0006077 -5.398e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.203 -0.03474 -0.1655 0.186 0.9835 0.9932 0.2274 0.4344 0.8695 0.7126 ] Network output: [ -0.009639 1.003 1.009 -2.981e-07 1.338e-07 0.008054 -2.246e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006456 0.0005572 0.004432 0.003393 0.9889 0.9919 0.00658 0.8567 0.8935 0.01229 ] Network output: [ -0.000344 0.002043 1.001 -2.243e-05 1.007e-05 0.9978 -1.69e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2158 0.1013 0.3445 0.1436 0.985 0.994 0.2165 0.4385 0.8762 0.7066 ] Network output: [ 0.004315 -0.02039 0.9942 1.358e-05 -6.098e-06 1.018 1.024e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1075 0.09503 0.1834 0.1988 0.9873 0.9919 0.1076 0.7467 0.8637 0.3054 ] Network output: [ -0.004056 0.0191 1.004 1.458e-05 -6.544e-06 0.9848 1.099e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09247 0.09053 0.165 0.1958 0.9853 0.9911 0.09248 0.6708 0.8394 0.2473 ] Network output: [ 0.0001111 1 -9.456e-05 1.93e-06 -8.663e-07 0.9998 1.454e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002703 Epoch 8848 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009748 0.9964 0.9916 -2.018e-07 9.061e-08 -0.007454 -1.521e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003445 -0.003269 -0.007256 0.005764 0.9699 0.9743 0.00666 0.829 0.8221 0.01711 ] Network output: [ 0.9999 0.0002877 0.0005605 -7.154e-06 3.212e-06 -0.0006072 -5.392e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.203 -0.03474 -0.1654 0.186 0.9835 0.9932 0.2275 0.4344 0.8695 0.7125 ] Network output: [ -0.009638 1.003 1.009 -2.98e-07 1.338e-07 0.008053 -2.246e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006457 0.0005573 0.004432 0.003393 0.9889 0.9919 0.006581 0.8567 0.8935 0.01229 ] Network output: [ -0.0003438 0.002043 1.001 -2.24e-05 1.006e-05 0.9978 -1.688e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2158 0.1013 0.3445 0.1436 0.985 0.994 0.2166 0.4385 0.8762 0.7066 ] Network output: [ 0.004313 -0.02038 0.9942 1.357e-05 -6.091e-06 1.018 1.023e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1075 0.09504 0.1834 0.1988 0.9873 0.9919 0.1076 0.7467 0.8636 0.3054 ] Network output: [ -0.004055 0.01909 1.004 1.456e-05 -6.537e-06 0.9848 1.097e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09247 0.09053 0.165 0.1958 0.9853 0.9911 0.09248 0.6708 0.8394 0.2473 ] Network output: [ 0.0001111 1 -9.445e-05 1.927e-06 -8.653e-07 0.9998 1.453e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002702 Epoch 8849 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009747 0.9964 0.9916 -2.02e-07 9.067e-08 -0.007454 -1.522e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003445 -0.003269 -0.007255 0.005763 0.9699 0.9743 0.006661 0.829 0.8221 0.01711 ] Network output: [ 0.9999 0.0002874 0.0005602 -7.146e-06 3.208e-06 -0.0006068 -5.386e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.203 -0.03474 -0.1654 0.186 0.9835 0.9932 0.2275 0.4344 0.8695 0.7125 ] Network output: [ -0.009637 1.003 1.009 -2.98e-07 1.338e-07 0.008052 -2.246e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006457 0.0005574 0.004432 0.003393 0.9889 0.9919 0.006581 0.8567 0.8935 0.01229 ] Network output: [ -0.0003435 0.002042 1.001 -2.238e-05 1.005e-05 0.9978 -1.686e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2158 0.1013 0.3445 0.1436 0.985 0.994 0.2166 0.4385 0.8762 0.7066 ] Network output: [ 0.004312 -0.02037 0.9942 1.355e-05 -6.084e-06 1.018 1.021e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1075 0.09504 0.1834 0.1988 0.9873 0.9919 0.1076 0.7466 0.8636 0.3054 ] Network output: [ -0.004053 0.01908 1.004 1.455e-05 -6.53e-06 0.9848 1.096e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09247 0.09053 0.165 0.1958 0.9853 0.9911 0.09248 0.6708 0.8394 0.2473 ] Network output: [ 0.000111 1 -9.435e-05 1.925e-06 -8.644e-07 0.9998 1.451e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00027 Epoch 8850 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009745 0.9964 0.9916 -2.021e-07 9.073e-08 -0.007453 -1.523e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003445 -0.003269 -0.007254 0.005763 0.9699 0.9743 0.006661 0.829 0.8221 0.01711 ] Network output: [ 0.9999 0.0002872 0.0005599 -7.138e-06 3.205e-06 -0.0006063 -5.379e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.203 -0.03474 -0.1654 0.186 0.9835 0.9932 0.2275 0.4344 0.8695 0.7125 ] Network output: [ -0.009636 1.003 1.009 -2.98e-07 1.338e-07 0.008051 -2.246e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006458 0.0005575 0.004432 0.003392 0.9889 0.9919 0.006582 0.8567 0.8935 0.01229 ] Network output: [ -0.0003433 0.002041 1.001 -2.235e-05 1.003e-05 0.9978 -1.685e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2158 0.1013 0.3445 0.1436 0.985 0.994 0.2166 0.4385 0.8762 0.7066 ] Network output: [ 0.00431 -0.02036 0.9942 1.354e-05 -6.078e-06 1.018 1.02e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1075 0.09505 0.1834 0.1988 0.9873 0.9919 0.1076 0.7466 0.8636 0.3054 ] Network output: [ -0.004052 0.01907 1.004 1.453e-05 -6.523e-06 0.9848 1.095e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09247 0.09054 0.165 0.1958 0.9853 0.9911 0.09249 0.6707 0.8394 0.2473 ] Network output: [ 0.000111 1 -9.425e-05 1.923e-06 -8.634e-07 0.9998 1.449e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002699 Epoch 8851 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009744 0.9964 0.9916 -2.022e-07 9.079e-08 -0.007453 -1.524e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003445 -0.003269 -0.007254 0.005762 0.9699 0.9743 0.006661 0.829 0.8221 0.01711 ] Network output: [ 0.9999 0.0002869 0.0005597 -7.13e-06 3.201e-06 -0.0006058 -5.373e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.203 -0.03475 -0.1654 0.186 0.9835 0.9932 0.2275 0.4344 0.8695 0.7125 ] Network output: [ -0.009635 1.003 1.009 -2.979e-07 1.337e-07 0.00805 -2.245e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006458 0.0005576 0.004432 0.003392 0.9889 0.9919 0.006582 0.8567 0.8935 0.01229 ] Network output: [ -0.0003431 0.00204 1.001 -2.233e-05 1.002e-05 0.9978 -1.683e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2159 0.1013 0.3445 0.1436 0.985 0.994 0.2166 0.4385 0.8762 0.7066 ] Network output: [ 0.004308 -0.02036 0.9942 1.352e-05 -6.071e-06 1.018 1.019e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1075 0.09505 0.1834 0.1988 0.9873 0.9919 0.1076 0.7466 0.8636 0.3054 ] Network output: [ -0.00405 0.01907 1.004 1.451e-05 -6.516e-06 0.9848 1.094e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09248 0.09054 0.165 0.1958 0.9853 0.9911 0.09249 0.6707 0.8394 0.2473 ] Network output: [ 0.0001109 1 -9.414e-05 1.921e-06 -8.625e-07 0.9998 1.448e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002697 Epoch 8852 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009743 0.9964 0.9916 -2.024e-07 9.085e-08 -0.007453 -1.525e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003445 -0.003269 -0.007253 0.005762 0.9699 0.9743 0.006661 0.829 0.8221 0.01711 ] Network output: [ 0.9999 0.0002867 0.0005594 -7.122e-06 3.197e-06 -0.0006053 -5.367e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.203 -0.03475 -0.1654 0.186 0.9835 0.9932 0.2275 0.4344 0.8695 0.7125 ] Network output: [ -0.009634 1.003 1.009 -2.979e-07 1.337e-07 0.008049 -2.245e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006459 0.0005576 0.004432 0.003392 0.9889 0.9919 0.006583 0.8567 0.8935 0.01229 ] Network output: [ -0.0003429 0.00204 1.001 -2.23e-05 1.001e-05 0.9978 -1.681e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2159 0.1013 0.3445 0.1436 0.985 0.994 0.2166 0.4385 0.8762 0.7065 ] Network output: [ 0.004307 -0.02035 0.9942 1.351e-05 -6.064e-06 1.018 1.018e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1075 0.09506 0.1834 0.1988 0.9873 0.9919 0.1076 0.7466 0.8636 0.3054 ] Network output: [ -0.004049 0.01906 1.004 1.45e-05 -6.509e-06 0.9848 1.093e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09248 0.09054 0.165 0.1958 0.9853 0.9911 0.09249 0.6707 0.8394 0.2473 ] Network output: [ 0.0001109 1 -9.404e-05 1.919e-06 -8.615e-07 0.9998 1.446e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002696 Epoch 8853 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009742 0.9964 0.9916 -2.025e-07 9.091e-08 -0.007452 -1.526e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003446 -0.003269 -0.007252 0.005761 0.9699 0.9743 0.006662 0.829 0.8221 0.01711 ] Network output: [ 0.9999 0.0002864 0.0005591 -7.114e-06 3.194e-06 -0.0006048 -5.361e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.203 -0.03475 -0.1654 0.186 0.9835 0.9932 0.2275 0.4344 0.8695 0.7125 ] Network output: [ -0.009633 1.003 1.009 -2.978e-07 1.337e-07 0.008047 -2.245e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006459 0.0005577 0.004432 0.003391 0.9889 0.9919 0.006583 0.8567 0.8935 0.01229 ] Network output: [ -0.0003426 0.002039 1.001 -2.228e-05 1e-05 0.9978 -1.679e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2159 0.1013 0.3446 0.1436 0.985 0.994 0.2166 0.4385 0.8762 0.7065 ] Network output: [ 0.004305 -0.02034 0.9942 1.349e-05 -6.058e-06 1.018 1.017e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1075 0.09506 0.1834 0.1988 0.9873 0.9919 0.1076 0.7466 0.8636 0.3054 ] Network output: [ -0.004047 0.01905 1.004 1.448e-05 -6.502e-06 0.9848 1.092e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09248 0.09054 0.165 0.1958 0.9853 0.9911 0.09249 0.6707 0.8394 0.2473 ] Network output: [ 0.0001109 1 -9.394e-05 1.917e-06 -8.606e-07 0.9998 1.445e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002695 Epoch 8854 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009741 0.9964 0.9916 -2.026e-07 9.097e-08 -0.007452 -1.527e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003446 -0.00327 -0.007251 0.005761 0.9699 0.9743 0.006662 0.829 0.8221 0.01711 ] Network output: [ 0.9999 0.0002862 0.0005588 -7.106e-06 3.19e-06 -0.0006044 -5.355e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.203 -0.03475 -0.1654 0.186 0.9835 0.9932 0.2275 0.4344 0.8695 0.7125 ] Network output: [ -0.009632 1.003 1.009 -2.978e-07 1.337e-07 0.008046 -2.244e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00646 0.0005578 0.004432 0.003391 0.9889 0.9919 0.006584 0.8567 0.8935 0.01228 ] Network output: [ -0.0003424 0.002038 1.001 -2.225e-05 9.99e-06 0.9978 -1.677e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2159 0.1013 0.3446 0.1436 0.985 0.994 0.2166 0.4385 0.8762 0.7065 ] Network output: [ 0.004304 -0.02033 0.9942 1.348e-05 -6.051e-06 1.018 1.016e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1075 0.09507 0.1834 0.1988 0.9873 0.9919 0.1076 0.7466 0.8636 0.3054 ] Network output: [ -0.004046 0.01904 1.004 1.447e-05 -6.495e-06 0.9848 1.09e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09248 0.09055 0.165 0.1958 0.9853 0.9911 0.0925 0.6707 0.8394 0.2473 ] Network output: [ 0.0001108 1 -9.384e-05 1.915e-06 -8.596e-07 0.9998 1.443e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002693 Epoch 8855 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00974 0.9964 0.9916 -2.028e-07 9.103e-08 -0.007452 -1.528e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003446 -0.00327 -0.007251 0.00576 0.9699 0.9743 0.006662 0.829 0.8221 0.0171 ] Network output: [ 0.9999 0.0002859 0.0005585 -7.098e-06 3.187e-06 -0.0006039 -5.349e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.203 -0.03475 -0.1654 0.186 0.9835 0.9932 0.2275 0.4344 0.8695 0.7125 ] Network output: [ -0.009631 1.003 1.009 -2.978e-07 1.337e-07 0.008045 -2.244e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00646 0.0005579 0.004432 0.003391 0.9889 0.9919 0.006584 0.8567 0.8934 0.01228 ] Network output: [ -0.0003422 0.002037 1.001 -2.223e-05 9.979e-06 0.9978 -1.675e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2159 0.1014 0.3446 0.1436 0.985 0.994 0.2166 0.4385 0.8762 0.7065 ] Network output: [ 0.004302 -0.02033 0.9942 1.346e-05 -6.044e-06 1.018 1.015e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1076 0.09507 0.1834 0.1988 0.9873 0.9919 0.1076 0.7465 0.8636 0.3054 ] Network output: [ -0.004044 0.01904 1.004 1.445e-05 -6.488e-06 0.9848 1.089e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09249 0.09055 0.165 0.1958 0.9853 0.9911 0.0925 0.6707 0.8394 0.2473 ] Network output: [ 0.0001108 1 -9.373e-05 1.913e-06 -8.587e-07 0.9998 1.441e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002692 Epoch 8856 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009739 0.9964 0.9916 -2.029e-07 9.108e-08 -0.007452 -1.529e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003446 -0.00327 -0.00725 0.00576 0.9699 0.9743 0.006662 0.829 0.8221 0.0171 ] Network output: [ 0.9999 0.0002856 0.0005582 -7.09e-06 3.183e-06 -0.0006034 -5.344e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2031 -0.03475 -0.1654 0.186 0.9835 0.9932 0.2275 0.4344 0.8695 0.7125 ] Network output: [ -0.00963 1.003 1.009 -2.977e-07 1.337e-07 0.008044 -2.244e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006461 0.000558 0.004432 0.003391 0.9889 0.9919 0.006585 0.8567 0.8934 0.01228 ] Network output: [ -0.000342 0.002036 1.001 -2.22e-05 9.968e-06 0.9978 -1.673e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2159 0.1014 0.3446 0.1436 0.985 0.994 0.2166 0.4385 0.8762 0.7065 ] Network output: [ 0.004301 -0.02032 0.9942 1.345e-05 -6.037e-06 1.018 1.014e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1076 0.09508 0.1835 0.1988 0.9873 0.9919 0.1076 0.7465 0.8636 0.3054 ] Network output: [ -0.004043 0.01903 1.004 1.444e-05 -6.481e-06 0.9848 1.088e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09249 0.09055 0.165 0.1958 0.9853 0.9911 0.0925 0.6706 0.8394 0.2473 ] Network output: [ 0.0001107 1 -9.363e-05 1.911e-06 -8.577e-07 0.9998 1.44e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000269 Epoch 8857 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009737 0.9964 0.9916 -2.03e-07 9.114e-08 -0.007451 -1.53e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003446 -0.00327 -0.007249 0.005759 0.9699 0.9743 0.006663 0.829 0.8221 0.0171 ] Network output: [ 0.9999 0.0002854 0.0005579 -7.082e-06 3.18e-06 -0.0006029 -5.338e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2031 -0.03475 -0.1653 0.186 0.9835 0.9932 0.2275 0.4344 0.8695 0.7125 ] Network output: [ -0.009629 1.003 1.009 -2.977e-07 1.336e-07 0.008043 -2.243e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006461 0.0005581 0.004432 0.00339 0.9889 0.9919 0.006585 0.8567 0.8934 0.01228 ] Network output: [ -0.0003417 0.002036 1.001 -2.218e-05 9.957e-06 0.9978 -1.671e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2159 0.1014 0.3446 0.1436 0.985 0.994 0.2166 0.4385 0.8762 0.7065 ] Network output: [ 0.004299 -0.02031 0.9942 1.343e-05 -6.031e-06 1.018 1.012e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1076 0.09508 0.1835 0.1988 0.9873 0.9919 0.1076 0.7465 0.8636 0.3054 ] Network output: [ -0.004041 0.01902 1.004 1.442e-05 -6.474e-06 0.9848 1.087e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09249 0.09055 0.165 0.1958 0.9853 0.9911 0.0925 0.6706 0.8393 0.2474 ] Network output: [ 0.0001107 1 -9.353e-05 1.908e-06 -8.568e-07 0.9998 1.438e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002689 Epoch 8858 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009736 0.9964 0.9916 -2.031e-07 9.12e-08 -0.007451 -1.531e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003446 -0.00327 -0.007248 0.005759 0.9699 0.9743 0.006663 0.829 0.8221 0.0171 ] Network output: [ 0.9999 0.0002851 0.0005576 -7.074e-06 3.176e-06 -0.0006025 -5.332e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2031 -0.03475 -0.1653 0.186 0.9835 0.9932 0.2275 0.4344 0.8695 0.7125 ] Network output: [ -0.009628 1.003 1.009 -2.977e-07 1.336e-07 0.008042 -2.243e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006462 0.0005582 0.004432 0.00339 0.9889 0.9919 0.006586 0.8566 0.8934 0.01228 ] Network output: [ -0.0003415 0.002035 1.001 -2.215e-05 9.945e-06 0.9978 -1.67e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2159 0.1014 0.3446 0.1436 0.985 0.994 0.2166 0.4384 0.8762 0.7065 ] Network output: [ 0.004298 -0.0203 0.9942 1.342e-05 -6.024e-06 1.018 1.011e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1076 0.09509 0.1835 0.1988 0.9873 0.9919 0.1076 0.7465 0.8636 0.3054 ] Network output: [ -0.00404 0.01901 1.004 1.44e-05 -6.467e-06 0.9848 1.086e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09249 0.09056 0.165 0.1958 0.9853 0.9911 0.09251 0.6706 0.8393 0.2474 ] Network output: [ 0.0001106 1 -9.343e-05 1.906e-06 -8.558e-07 0.9998 1.437e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002688 Epoch 8859 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009735 0.9964 0.9916 -2.033e-07 9.125e-08 -0.007451 -1.532e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003446 -0.00327 -0.007248 0.005758 0.9699 0.9743 0.006663 0.829 0.8221 0.0171 ] Network output: [ 0.9999 0.0002849 0.0005574 -7.067e-06 3.172e-06 -0.000602 -5.326e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2031 -0.03476 -0.1653 0.186 0.9835 0.9932 0.2276 0.4344 0.8695 0.7125 ] Network output: [ -0.009627 1.003 1.009 -2.976e-07 1.336e-07 0.008041 -2.243e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006462 0.0005583 0.004431 0.00339 0.9889 0.9919 0.006586 0.8566 0.8934 0.01228 ] Network output: [ -0.0003413 0.002034 1.001 -2.213e-05 9.934e-06 0.9978 -1.668e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2159 0.1014 0.3446 0.1436 0.985 0.994 0.2167 0.4384 0.8762 0.7065 ] Network output: [ 0.004296 -0.0203 0.9942 1.34e-05 -6.017e-06 1.018 1.01e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1076 0.09509 0.1835 0.1988 0.9873 0.9919 0.1076 0.7465 0.8636 0.3054 ] Network output: [ -0.004038 0.01901 1.004 1.439e-05 -6.46e-06 0.9848 1.084e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09249 0.09056 0.165 0.1958 0.9853 0.9911 0.09251 0.6706 0.8393 0.2474 ] Network output: [ 0.0001106 1 -9.333e-05 1.904e-06 -8.549e-07 0.9998 1.435e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002686 Epoch 8860 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009734 0.9964 0.9916 -2.034e-07 9.131e-08 -0.007451 -1.533e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003446 -0.00327 -0.007247 0.005758 0.9699 0.9743 0.006663 0.829 0.8221 0.0171 ] Network output: [ 0.9999 0.0002846 0.0005571 -7.059e-06 3.169e-06 -0.0006015 -5.32e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2031 -0.03476 -0.1653 0.186 0.9835 0.9932 0.2276 0.4343 0.8695 0.7125 ] Network output: [ -0.009626 1.003 1.009 -2.976e-07 1.336e-07 0.00804 -2.243e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006463 0.0005584 0.004431 0.003389 0.9889 0.9919 0.006587 0.8566 0.8934 0.01228 ] Network output: [ -0.0003411 0.002033 1.001 -2.21e-05 9.923e-06 0.9978 -1.666e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2159 0.1014 0.3446 0.1436 0.985 0.994 0.2167 0.4384 0.8762 0.7065 ] Network output: [ 0.004294 -0.02029 0.9942 1.339e-05 -6.011e-06 1.018 1.009e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1076 0.0951 0.1835 0.1988 0.9873 0.9919 0.1077 0.7465 0.8636 0.3054 ] Network output: [ -0.004037 0.019 1.004 1.437e-05 -6.453e-06 0.9849 1.083e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0925 0.09056 0.165 0.1958 0.9853 0.9911 0.09251 0.6706 0.8393 0.2474 ] Network output: [ 0.0001105 1 -9.322e-05 1.902e-06 -8.539e-07 0.9998 1.434e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002685 Epoch 8861 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009733 0.9964 0.9916 -2.035e-07 9.137e-08 -0.00745 -1.534e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003446 -0.00327 -0.007246 0.005758 0.9699 0.9743 0.006664 0.829 0.8221 0.0171 ] Network output: [ 0.9999 0.0002844 0.0005568 -7.051e-06 3.165e-06 -0.000601 -5.314e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2031 -0.03476 -0.1653 0.1859 0.9835 0.9932 0.2276 0.4343 0.8695 0.7125 ] Network output: [ -0.009625 1.003 1.009 -2.975e-07 1.336e-07 0.008039 -2.242e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006463 0.0005585 0.004431 0.003389 0.9889 0.9919 0.006587 0.8566 0.8934 0.01228 ] Network output: [ -0.0003408 0.002033 1.001 -2.208e-05 9.912e-06 0.9978 -1.664e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2159 0.1014 0.3446 0.1436 0.985 0.994 0.2167 0.4384 0.8762 0.7065 ] Network output: [ 0.004293 -0.02028 0.9942 1.337e-05 -6.004e-06 1.018 1.008e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1076 0.0951 0.1835 0.1988 0.9873 0.9919 0.1077 0.7464 0.8636 0.3054 ] Network output: [ -0.004035 0.01899 1.004 1.436e-05 -6.446e-06 0.9849 1.082e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0925 0.09056 0.165 0.1958 0.9853 0.9911 0.09251 0.6706 0.8393 0.2474 ] Network output: [ 0.0001105 1 -9.312e-05 1.9e-06 -8.53e-07 0.9998 1.432e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002683 Epoch 8862 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009732 0.9964 0.9916 -2.036e-07 9.142e-08 -0.00745 -1.535e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003446 -0.00327 -0.007245 0.005757 0.9699 0.9743 0.006664 0.829 0.8221 0.0171 ] Network output: [ 0.9999 0.0002841 0.0005565 -7.043e-06 3.162e-06 -0.0006006 -5.308e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2031 -0.03476 -0.1653 0.1859 0.9835 0.9932 0.2276 0.4343 0.8695 0.7125 ] Network output: [ -0.009624 1.003 1.009 -2.975e-07 1.336e-07 0.008038 -2.242e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006464 0.0005586 0.004431 0.003389 0.9889 0.9919 0.006588 0.8566 0.8934 0.01228 ] Network output: [ -0.0003406 0.002032 1.001 -2.205e-05 9.901e-06 0.9978 -1.662e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.216 0.1014 0.3446 0.1436 0.985 0.994 0.2167 0.4384 0.8762 0.7065 ] Network output: [ 0.004291 -0.02027 0.9942 1.336e-05 -5.997e-06 1.018 1.007e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1076 0.09511 0.1835 0.1988 0.9873 0.9919 0.1077 0.7464 0.8636 0.3054 ] Network output: [ -0.004034 0.01898 1.004 1.434e-05 -6.439e-06 0.9849 1.081e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0925 0.09056 0.165 0.1958 0.9853 0.9911 0.09252 0.6705 0.8393 0.2474 ] Network output: [ 0.0001105 1 -9.302e-05 1.898e-06 -8.521e-07 0.9998 1.43e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002682 Epoch 8863 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00973 0.9964 0.9916 -2.038e-07 9.148e-08 -0.00745 -1.536e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003447 -0.003271 -0.007244 0.005757 0.9699 0.9743 0.006664 0.829 0.8221 0.0171 ] Network output: [ 0.9999 0.0002838 0.0005562 -7.035e-06 3.158e-06 -0.0006001 -5.302e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2031 -0.03476 -0.1653 0.1859 0.9835 0.9932 0.2276 0.4343 0.8695 0.7125 ] Network output: [ -0.009623 1.003 1.009 -2.974e-07 1.335e-07 0.008037 -2.242e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006464 0.0005587 0.004431 0.003388 0.9889 0.9919 0.006588 0.8566 0.8934 0.01228 ] Network output: [ -0.0003404 0.002031 1.001 -2.203e-05 9.89e-06 0.9978 -1.66e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.216 0.1014 0.3446 0.1436 0.985 0.994 0.2167 0.4384 0.8762 0.7065 ] Network output: [ 0.00429 -0.02027 0.9942 1.334e-05 -5.991e-06 1.018 1.006e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1076 0.09512 0.1835 0.1988 0.9873 0.9919 0.1077 0.7464 0.8636 0.3054 ] Network output: [ -0.004032 0.01898 1.004 1.433e-05 -6.432e-06 0.9849 1.08e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0925 0.09057 0.165 0.1958 0.9853 0.9911 0.09252 0.6705 0.8393 0.2474 ] Network output: [ 0.0001104 1 -9.292e-05 1.896e-06 -8.511e-07 0.9998 1.429e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002681 Epoch 8864 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009729 0.9964 0.9916 -2.039e-07 9.153e-08 -0.007449 -1.537e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003447 -0.003271 -0.007244 0.005756 0.9699 0.9743 0.006664 0.829 0.8221 0.01709 ] Network output: [ 0.9999 0.0002836 0.0005559 -7.027e-06 3.155e-06 -0.0005996 -5.296e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2031 -0.03476 -0.1653 0.1859 0.9835 0.9932 0.2276 0.4343 0.8695 0.7125 ] Network output: [ -0.009622 1.003 1.009 -2.974e-07 1.335e-07 0.008036 -2.241e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006465 0.0005588 0.004431 0.003388 0.9889 0.9919 0.006589 0.8566 0.8934 0.01228 ] Network output: [ -0.0003402 0.00203 1.001 -2.201e-05 9.879e-06 0.9978 -1.658e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.216 0.1014 0.3446 0.1436 0.985 0.994 0.2167 0.4384 0.8762 0.7065 ] Network output: [ 0.004288 -0.02026 0.9942 1.333e-05 -5.984e-06 1.018 1.005e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1076 0.09512 0.1835 0.1988 0.9873 0.9919 0.1077 0.7464 0.8636 0.3054 ] Network output: [ -0.004031 0.01897 1.004 1.431e-05 -6.425e-06 0.9849 1.079e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09251 0.09057 0.165 0.1958 0.9853 0.9911 0.09252 0.6705 0.8393 0.2474 ] Network output: [ 0.0001104 1 -9.282e-05 1.894e-06 -8.502e-07 0.9998 1.427e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002679 Epoch 8865 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009728 0.9964 0.9916 -2.04e-07 9.159e-08 -0.007449 -1.538e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003447 -0.003271 -0.007243 0.005756 0.9699 0.9743 0.006665 0.8289 0.8221 0.01709 ] Network output: [ 0.9999 0.0002833 0.0005556 -7.019e-06 3.151e-06 -0.0005992 -5.29e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2031 -0.03476 -0.1653 0.1859 0.9835 0.9932 0.2276 0.4343 0.8695 0.7125 ] Network output: [ -0.009621 1.003 1.009 -2.974e-07 1.335e-07 0.008035 -2.241e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006465 0.0005589 0.004431 0.003388 0.9889 0.9919 0.006589 0.8566 0.8934 0.01228 ] Network output: [ -0.0003399 0.00203 1.001 -2.198e-05 9.868e-06 0.9978 -1.657e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.216 0.1014 0.3446 0.1436 0.985 0.994 0.2167 0.4384 0.8762 0.7065 ] Network output: [ 0.004287 -0.02025 0.9942 1.331e-05 -5.977e-06 1.018 1.003e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1076 0.09513 0.1835 0.1988 0.9873 0.9919 0.1077 0.7464 0.8636 0.3054 ] Network output: [ -0.004029 0.01896 1.004 1.43e-05 -6.418e-06 0.9849 1.077e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09251 0.09057 0.165 0.1958 0.9853 0.9911 0.09252 0.6705 0.8393 0.2474 ] Network output: [ 0.0001103 1 -9.272e-05 1.892e-06 -8.493e-07 0.9998 1.426e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002678 Epoch 8866 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009727 0.9964 0.9916 -2.041e-07 9.164e-08 -0.007449 -1.538e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003447 -0.003271 -0.007242 0.005755 0.9699 0.9743 0.006665 0.8289 0.8221 0.01709 ] Network output: [ 0.9999 0.0002831 0.0005554 -7.011e-06 3.148e-06 -0.0005987 -5.284e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2031 -0.03476 -0.1652 0.1859 0.9835 0.9932 0.2276 0.4343 0.8695 0.7125 ] Network output: [ -0.00962 1.003 1.009 -2.973e-07 1.335e-07 0.008034 -2.241e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006466 0.000559 0.004431 0.003388 0.9889 0.9919 0.00659 0.8566 0.8934 0.01227 ] Network output: [ -0.0003397 0.002029 1.001 -2.196e-05 9.857e-06 0.9978 -1.655e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.216 0.1014 0.3446 0.1436 0.985 0.994 0.2167 0.4384 0.8762 0.7065 ] Network output: [ 0.004285 -0.02024 0.9942 1.33e-05 -5.971e-06 1.018 1.002e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1076 0.09513 0.1835 0.1988 0.9873 0.9919 0.1077 0.7464 0.8636 0.3054 ] Network output: [ -0.004028 0.01895 1.004 1.428e-05 -6.411e-06 0.9849 1.076e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09251 0.09057 0.165 0.1958 0.9853 0.9911 0.09252 0.6705 0.8393 0.2474 ] Network output: [ 0.0001103 1 -9.262e-05 1.89e-06 -8.483e-07 0.9998 1.424e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002676 Epoch 8867 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009726 0.9964 0.9916 -2.043e-07 9.17e-08 -0.007449 -1.539e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003447 -0.003271 -0.007241 0.005755 0.9699 0.9743 0.006665 0.8289 0.822 0.01709 ] Network output: [ 0.9999 0.0002828 0.0005551 -7.004e-06 3.144e-06 -0.0005982 -5.278e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2031 -0.03476 -0.1652 0.1859 0.9835 0.9932 0.2276 0.4343 0.8695 0.7124 ] Network output: [ -0.009619 1.003 1.009 -2.973e-07 1.335e-07 0.008033 -2.24e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006466 0.0005591 0.004431 0.003387 0.9889 0.9919 0.00659 0.8566 0.8934 0.01227 ] Network output: [ -0.0003395 0.002028 1.001 -2.193e-05 9.846e-06 0.9978 -1.653e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.216 0.1014 0.3446 0.1436 0.985 0.994 0.2167 0.4384 0.8762 0.7065 ] Network output: [ 0.004284 -0.02024 0.9942 1.329e-05 -5.964e-06 1.018 1.001e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1076 0.09514 0.1835 0.1988 0.9873 0.9919 0.1077 0.7464 0.8636 0.3054 ] Network output: [ -0.004026 0.01894 1.004 1.426e-05 -6.404e-06 0.9849 1.075e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09251 0.09058 0.165 0.1958 0.9853 0.9911 0.09253 0.6705 0.8393 0.2474 ] Network output: [ 0.0001102 1 -9.252e-05 1.888e-06 -8.474e-07 0.9998 1.423e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002675 Epoch 8868 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009725 0.9964 0.9916 -2.044e-07 9.175e-08 -0.007448 -1.54e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003447 -0.003271 -0.007241 0.005754 0.9699 0.9743 0.006665 0.8289 0.822 0.01709 ] Network output: [ 0.9999 0.0002826 0.0005548 -6.996e-06 3.141e-06 -0.0005978 -5.272e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2031 -0.03477 -0.1652 0.1859 0.9835 0.9932 0.2276 0.4343 0.8695 0.7124 ] Network output: [ -0.009618 1.003 1.009 -2.972e-07 1.334e-07 0.008032 -2.24e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006467 0.0005592 0.004431 0.003387 0.9889 0.9919 0.006591 0.8566 0.8934 0.01227 ] Network output: [ -0.0003393 0.002027 1.001 -2.191e-05 9.835e-06 0.9978 -1.651e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.216 0.1014 0.3446 0.1436 0.985 0.994 0.2167 0.4384 0.8761 0.7065 ] Network output: [ 0.004282 -0.02023 0.9942 1.327e-05 -5.958e-06 1.018 1e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1076 0.09514 0.1835 0.1988 0.9873 0.9919 0.1077 0.7463 0.8636 0.3054 ] Network output: [ -0.004025 0.01894 1.004 1.425e-05 -6.397e-06 0.9849 1.074e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09252 0.09058 0.165 0.1958 0.9853 0.9911 0.09253 0.6704 0.8393 0.2474 ] Network output: [ 0.0001102 1 -9.241e-05 1.885e-06 -8.464e-07 0.9998 1.421e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002674 Epoch 8869 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009724 0.9964 0.9916 -2.045e-07 9.181e-08 -0.007448 -1.541e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003447 -0.003271 -0.00724 0.005754 0.9699 0.9743 0.006666 0.8289 0.822 0.01709 ] Network output: [ 0.9999 0.0002823 0.0005545 -6.988e-06 3.137e-06 -0.0005973 -5.266e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2032 -0.03477 -0.1652 0.1859 0.9835 0.9932 0.2276 0.4343 0.8695 0.7124 ] Network output: [ -0.009617 1.003 1.009 -2.972e-07 1.334e-07 0.008031 -2.24e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006467 0.0005592 0.004431 0.003387 0.9889 0.9919 0.006591 0.8566 0.8934 0.01227 ] Network output: [ -0.0003391 0.002027 1.001 -2.188e-05 9.824e-06 0.9978 -1.649e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.216 0.1014 0.3447 0.1436 0.985 0.994 0.2167 0.4384 0.8761 0.7064 ] Network output: [ 0.00428 -0.02022 0.9942 1.326e-05 -5.951e-06 1.018 9.99e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1076 0.09515 0.1835 0.1988 0.9873 0.9919 0.1077 0.7463 0.8636 0.3054 ] Network output: [ -0.004023 0.01893 1.004 1.423e-05 -6.39e-06 0.9849 1.073e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09252 0.09058 0.165 0.1958 0.9853 0.9911 0.09253 0.6704 0.8393 0.2474 ] Network output: [ 0.0001101 1 -9.231e-05 1.883e-06 -8.455e-07 0.9998 1.419e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002672 Epoch 8870 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009722 0.9964 0.9916 -2.046e-07 9.186e-08 -0.007448 -1.542e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003447 -0.003271 -0.007239 0.005753 0.9699 0.9743 0.006666 0.8289 0.822 0.01709 ] Network output: [ 0.9999 0.000282 0.0005542 -6.98e-06 3.134e-06 -0.0005968 -5.26e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2032 -0.03477 -0.1652 0.1859 0.9835 0.9932 0.2277 0.4343 0.8695 0.7124 ] Network output: [ -0.009617 1.003 1.009 -2.971e-07 1.334e-07 0.008029 -2.239e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006468 0.0005593 0.004431 0.003386 0.9889 0.9919 0.006592 0.8566 0.8934 0.01227 ] Network output: [ -0.0003388 0.002026 1.001 -2.186e-05 9.813e-06 0.9978 -1.647e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.216 0.1014 0.3447 0.1436 0.985 0.994 0.2167 0.4384 0.8761 0.7064 ] Network output: [ 0.004279 -0.02021 0.9942 1.324e-05 -5.944e-06 1.017 9.979e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1076 0.09515 0.1835 0.1988 0.9873 0.9919 0.1077 0.7463 0.8636 0.3054 ] Network output: [ -0.004022 0.01892 1.004 1.422e-05 -6.383e-06 0.9849 1.072e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09252 0.09058 0.165 0.1958 0.9853 0.9911 0.09253 0.6704 0.8393 0.2474 ] Network output: [ 0.0001101 1 -9.221e-05 1.881e-06 -8.446e-07 0.9998 1.418e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002671 Epoch 8871 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009721 0.9964 0.9916 -2.047e-07 9.191e-08 -0.007447 -1.543e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003447 -0.003272 -0.007238 0.005753 0.9699 0.9743 0.006666 0.8289 0.822 0.01709 ] Network output: [ 0.9999 0.0002818 0.0005539 -6.972e-06 3.13e-06 -0.0005963 -5.254e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2032 -0.03477 -0.1652 0.1859 0.9835 0.9932 0.2277 0.4343 0.8695 0.7124 ] Network output: [ -0.009616 1.003 1.009 -2.971e-07 1.334e-07 0.008028 -2.239e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006468 0.0005594 0.004431 0.003386 0.9889 0.9919 0.006592 0.8566 0.8934 0.01227 ] Network output: [ -0.0003386 0.002025 1.001 -2.183e-05 9.802e-06 0.9978 -1.645e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.216 0.1014 0.3447 0.1436 0.985 0.994 0.2168 0.4383 0.8761 0.7064 ] Network output: [ 0.004277 -0.02021 0.9942 1.323e-05 -5.938e-06 1.017 9.968e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1076 0.09516 0.1835 0.1988 0.9873 0.9919 0.1077 0.7463 0.8636 0.3054 ] Network output: [ -0.00402 0.01891 1.004 1.42e-05 -6.376e-06 0.9849 1.07e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09252 0.09059 0.165 0.1958 0.9853 0.9911 0.09254 0.6704 0.8393 0.2474 ] Network output: [ 0.00011 1 -9.211e-05 1.879e-06 -8.436e-07 0.9998 1.416e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002669 Epoch 8872 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00972 0.9964 0.9916 -2.049e-07 9.197e-08 -0.007447 -1.544e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003447 -0.003272 -0.007238 0.005752 0.9699 0.9743 0.006666 0.8289 0.822 0.01709 ] Network output: [ 0.9999 0.0002815 0.0005536 -6.964e-06 3.127e-06 -0.0005959 -5.249e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2032 -0.03477 -0.1652 0.1859 0.9835 0.9932 0.2277 0.4343 0.8695 0.7124 ] Network output: [ -0.009615 1.003 1.009 -2.97e-07 1.334e-07 0.008027 -2.239e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006469 0.0005595 0.004431 0.003386 0.9889 0.9919 0.006593 0.8566 0.8934 0.01227 ] Network output: [ -0.0003384 0.002024 1.001 -2.181e-05 9.791e-06 0.9978 -1.644e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.216 0.1014 0.3447 0.1436 0.985 0.994 0.2168 0.4383 0.8761 0.7064 ] Network output: [ 0.004276 -0.0202 0.9942 1.321e-05 -5.931e-06 1.017 9.957e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1076 0.09516 0.1835 0.1988 0.9873 0.9919 0.1077 0.7463 0.8636 0.3054 ] Network output: [ -0.004019 0.01891 1.004 1.419e-05 -6.369e-06 0.9849 1.069e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09253 0.09059 0.165 0.1958 0.9853 0.9911 0.09254 0.6704 0.8393 0.2474 ] Network output: [ 0.00011 1 -9.201e-05 1.877e-06 -8.427e-07 0.9998 1.415e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002668 Epoch 8873 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009719 0.9964 0.9916 -2.05e-07 9.202e-08 -0.007447 -1.545e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003448 -0.003272 -0.007237 0.005752 0.9699 0.9743 0.006667 0.8289 0.822 0.01708 ] Network output: [ 0.9999 0.0002813 0.0005534 -6.957e-06 3.123e-06 -0.0005954 -5.243e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2032 -0.03477 -0.1652 0.1859 0.9834 0.9932 0.2277 0.4342 0.8695 0.7124 ] Network output: [ -0.009614 1.003 1.009 -2.97e-07 1.333e-07 0.008026 -2.238e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006469 0.0005596 0.004431 0.003386 0.9889 0.9919 0.006593 0.8565 0.8934 0.01227 ] Network output: [ -0.0003382 0.002023 1.001 -2.178e-05 9.78e-06 0.9978 -1.642e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2161 0.1015 0.3447 0.1436 0.985 0.994 0.2168 0.4383 0.8761 0.7064 ] Network output: [ 0.004274 -0.02019 0.9942 1.32e-05 -5.925e-06 1.017 9.946e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1076 0.09517 0.1835 0.1988 0.9873 0.9919 0.1077 0.7463 0.8636 0.3054 ] Network output: [ -0.004017 0.0189 1.004 1.417e-05 -6.362e-06 0.9849 1.068e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09253 0.09059 0.165 0.1958 0.9853 0.9911 0.09254 0.6704 0.8393 0.2474 ] Network output: [ 0.00011 1 -9.191e-05 1.875e-06 -8.418e-07 0.9998 1.413e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002667 Epoch 8874 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009718 0.9964 0.9916 -2.051e-07 9.207e-08 -0.007447 -1.546e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003448 -0.003272 -0.007236 0.005751 0.9699 0.9743 0.006667 0.8289 0.822 0.01708 ] Network output: [ 0.9999 0.000281 0.0005531 -6.949e-06 3.12e-06 -0.0005949 -5.237e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2032 -0.03477 -0.1651 0.1859 0.9834 0.9932 0.2277 0.4342 0.8694 0.7124 ] Network output: [ -0.009613 1.003 1.009 -2.969e-07 1.333e-07 0.008025 -2.238e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00647 0.0005597 0.004431 0.003385 0.9889 0.9919 0.006594 0.8565 0.8934 0.01227 ] Network output: [ -0.0003379 0.002023 1.001 -2.176e-05 9.769e-06 0.9978 -1.64e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2161 0.1015 0.3447 0.1436 0.985 0.994 0.2168 0.4383 0.8761 0.7064 ] Network output: [ 0.004273 -0.02018 0.9942 1.318e-05 -5.918e-06 1.017 9.935e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1077 0.09517 0.1835 0.1988 0.9873 0.9919 0.1077 0.7462 0.8636 0.3054 ] Network output: [ -0.004016 0.01889 1.004 1.416e-05 -6.355e-06 0.9849 1.067e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09253 0.09059 0.165 0.1958 0.9853 0.9911 0.09254 0.6703 0.8393 0.2474 ] Network output: [ 0.0001099 1 -9.181e-05 1.873e-06 -8.409e-07 0.9998 1.412e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002665 Epoch 8875 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009717 0.9964 0.9916 -2.052e-07 9.212e-08 -0.007446 -1.546e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003448 -0.003272 -0.007235 0.005751 0.9699 0.9743 0.006667 0.8289 0.822 0.01708 ] Network output: [ 0.9999 0.0002808 0.0005528 -6.941e-06 3.116e-06 -0.0005945 -5.231e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2032 -0.03477 -0.1651 0.1859 0.9834 0.9932 0.2277 0.4342 0.8694 0.7124 ] Network output: [ -0.009612 1.003 1.009 -2.969e-07 1.333e-07 0.008024 -2.238e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00647 0.0005598 0.004431 0.003385 0.9889 0.9919 0.006594 0.8565 0.8934 0.01227 ] Network output: [ -0.0003377 0.002022 1.001 -2.174e-05 9.758e-06 0.9978 -1.638e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2161 0.1015 0.3447 0.1436 0.985 0.994 0.2168 0.4383 0.8761 0.7064 ] Network output: [ 0.004271 -0.02018 0.9942 1.317e-05 -5.912e-06 1.017 9.924e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1077 0.09518 0.1835 0.1988 0.9873 0.9919 0.1077 0.7462 0.8635 0.3054 ] Network output: [ -0.004014 0.01888 1.004 1.414e-05 -6.348e-06 0.9849 1.066e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09253 0.0906 0.165 0.1958 0.9853 0.9911 0.09255 0.6703 0.8393 0.2474 ] Network output: [ 0.0001099 1 -9.171e-05 1.871e-06 -8.399e-07 0.9998 1.41e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002664 Epoch 8876 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009716 0.9964 0.9916 -2.053e-07 9.218e-08 -0.007446 -1.547e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003448 -0.003272 -0.007235 0.00575 0.9699 0.9743 0.006667 0.8289 0.822 0.01708 ] Network output: [ 0.9999 0.0002805 0.0005525 -6.933e-06 3.113e-06 -0.000594 -5.225e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2032 -0.03477 -0.1651 0.1859 0.9834 0.9932 0.2277 0.4342 0.8694 0.7124 ] Network output: [ -0.009611 1.003 1.009 -2.969e-07 1.333e-07 0.008023 -2.237e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006471 0.0005599 0.004431 0.003385 0.9889 0.9919 0.006595 0.8565 0.8934 0.01227 ] Network output: [ -0.0003375 0.002021 1.001 -2.171e-05 9.747e-06 0.9978 -1.636e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2161 0.1015 0.3447 0.1436 0.985 0.994 0.2168 0.4383 0.8761 0.7064 ] Network output: [ 0.004269 -0.02017 0.9942 1.315e-05 -5.905e-06 1.017 9.913e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1077 0.09518 0.1835 0.1988 0.9873 0.9919 0.1077 0.7462 0.8635 0.3054 ] Network output: [ -0.004012 0.01888 1.004 1.413e-05 -6.342e-06 0.9849 1.065e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09254 0.0906 0.165 0.1958 0.9853 0.9911 0.09255 0.6703 0.8393 0.2474 ] Network output: [ 0.0001098 1 -9.162e-05 1.869e-06 -8.39e-07 0.9998 1.408e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002662 Epoch 8877 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009714 0.9964 0.9916 -2.054e-07 9.223e-08 -0.007446 -1.548e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003448 -0.003272 -0.007234 0.00575 0.9699 0.9743 0.006668 0.8289 0.822 0.01708 ] Network output: [ 0.9999 0.0002803 0.0005522 -6.925e-06 3.109e-06 -0.0005935 -5.219e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2032 -0.03478 -0.1651 0.1859 0.9834 0.9932 0.2277 0.4342 0.8694 0.7124 ] Network output: [ -0.00961 1.003 1.009 -2.968e-07 1.332e-07 0.008022 -2.237e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006471 0.00056 0.004431 0.003384 0.9889 0.9919 0.006595 0.8565 0.8934 0.01226 ] Network output: [ -0.0003373 0.00202 1.001 -2.169e-05 9.736e-06 0.9978 -1.634e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2161 0.1015 0.3447 0.1436 0.985 0.994 0.2168 0.4383 0.8761 0.7064 ] Network output: [ 0.004268 -0.02016 0.9942 1.314e-05 -5.898e-06 1.017 9.902e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1077 0.09519 0.1835 0.1987 0.9873 0.9919 0.1077 0.7462 0.8635 0.3054 ] Network output: [ -0.004011 0.01887 1.004 1.411e-05 -6.335e-06 0.9849 1.063e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09254 0.0906 0.165 0.1958 0.9853 0.9911 0.09255 0.6703 0.8393 0.2474 ] Network output: [ 0.0001098 1 -9.152e-05 1.867e-06 -8.381e-07 0.9998 1.407e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002661 Epoch 8878 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009713 0.9964 0.9916 -2.055e-07 9.228e-08 -0.007445 -1.549e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003448 -0.003272 -0.007233 0.005749 0.9699 0.9743 0.006668 0.8289 0.822 0.01708 ] Network output: [ 0.9999 0.00028 0.0005519 -6.918e-06 3.106e-06 -0.0005931 -5.213e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2032 -0.03478 -0.1651 0.1859 0.9834 0.9932 0.2277 0.4342 0.8694 0.7124 ] Network output: [ -0.009609 1.003 1.009 -2.968e-07 1.332e-07 0.008021 -2.236e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006472 0.0005601 0.004431 0.003384 0.9889 0.9919 0.006596 0.8565 0.8934 0.01226 ] Network output: [ -0.000337 0.00202 1.001 -2.166e-05 9.725e-06 0.9978 -1.633e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2161 0.1015 0.3447 0.1436 0.985 0.994 0.2168 0.4383 0.8761 0.7064 ] Network output: [ 0.004266 -0.02015 0.9942 1.312e-05 -5.892e-06 1.017 9.891e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1077 0.09519 0.1835 0.1987 0.9873 0.9919 0.1077 0.7462 0.8635 0.3054 ] Network output: [ -0.004009 0.01886 1.004 1.41e-05 -6.328e-06 0.9849 1.062e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09254 0.0906 0.165 0.1958 0.9853 0.9911 0.09255 0.6703 0.8393 0.2474 ] Network output: [ 0.0001097 1 -9.142e-05 1.865e-06 -8.371e-07 0.9998 1.405e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000266 Epoch 8879 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009712 0.9964 0.9916 -2.057e-07 9.233e-08 -0.007445 -1.55e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003448 -0.003273 -0.007232 0.005749 0.9699 0.9743 0.006668 0.8289 0.822 0.01708 ] Network output: [ 0.9999 0.0002798 0.0005517 -6.91e-06 3.102e-06 -0.0005926 -5.208e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2032 -0.03478 -0.1651 0.1859 0.9834 0.9932 0.2277 0.4342 0.8694 0.7124 ] Network output: [ -0.009608 1.003 1.009 -2.967e-07 1.332e-07 0.00802 -2.236e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006472 0.0005602 0.00443 0.003384 0.9889 0.9919 0.006596 0.8565 0.8934 0.01226 ] Network output: [ -0.0003368 0.002019 1.001 -2.164e-05 9.714e-06 0.9978 -1.631e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2161 0.1015 0.3447 0.1436 0.985 0.994 0.2168 0.4383 0.8761 0.7064 ] Network output: [ 0.004265 -0.02015 0.9942 1.311e-05 -5.885e-06 1.017 9.88e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1077 0.0952 0.1835 0.1987 0.9873 0.9919 0.1078 0.7462 0.8635 0.3054 ] Network output: [ -0.004008 0.01885 1.004 1.408e-05 -6.321e-06 0.9849 1.061e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09254 0.09061 0.165 0.1958 0.9853 0.9911 0.09256 0.6703 0.8392 0.2474 ] Network output: [ 0.0001097 1 -9.132e-05 1.863e-06 -8.362e-07 0.9998 1.404e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002658 Epoch 8880 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009711 0.9964 0.9916 -2.058e-07 9.238e-08 -0.007445 -1.551e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003448 -0.003273 -0.007232 0.005748 0.9699 0.9743 0.006668 0.8289 0.822 0.01708 ] Network output: [ 0.9999 0.0002795 0.0005514 -6.902e-06 3.099e-06 -0.0005921 -5.202e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2032 -0.03478 -0.1651 0.1859 0.9834 0.9932 0.2277 0.4342 0.8694 0.7124 ] Network output: [ -0.009607 1.003 1.009 -2.967e-07 1.332e-07 0.008019 -2.236e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006473 0.0005603 0.00443 0.003383 0.9889 0.9919 0.006597 0.8565 0.8934 0.01226 ] Network output: [ -0.0003366 0.002018 1.001 -2.161e-05 9.704e-06 0.9978 -1.629e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2161 0.1015 0.3447 0.1436 0.985 0.994 0.2168 0.4383 0.8761 0.7064 ] Network output: [ 0.004263 -0.02014 0.9942 1.309e-05 -5.879e-06 1.017 9.869e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1077 0.0952 0.1835 0.1987 0.9873 0.9919 0.1078 0.7462 0.8635 0.3054 ] Network output: [ -0.004006 0.01885 1.004 1.406e-05 -6.314e-06 0.9849 1.06e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09254 0.09061 0.165 0.1958 0.9853 0.9911 0.09256 0.6703 0.8392 0.2474 ] Network output: [ 0.0001096 1 -9.122e-05 1.861e-06 -8.353e-07 0.9998 1.402e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002657 Epoch 8881 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00971 0.9964 0.9916 -2.059e-07 9.243e-08 -0.007445 -1.552e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003448 -0.003273 -0.007231 0.005748 0.9699 0.9743 0.006669 0.8289 0.822 0.01708 ] Network output: [ 0.9999 0.0002792 0.0005511 -6.894e-06 3.095e-06 -0.0005917 -5.196e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2032 -0.03478 -0.1651 0.1859 0.9834 0.9932 0.2278 0.4342 0.8694 0.7124 ] Network output: [ -0.009606 1.003 1.009 -2.966e-07 1.332e-07 0.008018 -2.235e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006473 0.0005604 0.00443 0.003383 0.9889 0.9919 0.006598 0.8565 0.8934 0.01226 ] Network output: [ -0.0003364 0.002017 1.001 -2.159e-05 9.693e-06 0.9978 -1.627e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2161 0.1015 0.3447 0.1435 0.985 0.994 0.2168 0.4383 0.8761 0.7064 ] Network output: [ 0.004262 -0.02013 0.9942 1.308e-05 -5.872e-06 1.017 9.858e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1077 0.09521 0.1835 0.1987 0.9873 0.9919 0.1078 0.7461 0.8635 0.3054 ] Network output: [ -0.004005 0.01884 1.004 1.405e-05 -6.307e-06 0.9849 1.059e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09255 0.09061 0.165 0.1958 0.9853 0.9911 0.09256 0.6702 0.8392 0.2474 ] Network output: [ 0.0001096 1 -9.112e-05 1.859e-06 -8.344e-07 0.9998 1.401e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002656 Epoch 8882 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009709 0.9964 0.9916 -2.06e-07 9.248e-08 -0.007444 -1.552e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003448 -0.003273 -0.00723 0.005747 0.9699 0.9743 0.006669 0.8288 0.822 0.01707 ] Network output: [ 0.9999 0.000279 0.0005508 -6.887e-06 3.092e-06 -0.0005912 -5.19e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2033 -0.03478 -0.1651 0.1858 0.9834 0.9932 0.2278 0.4342 0.8694 0.7124 ] Network output: [ -0.009605 1.003 1.009 -2.966e-07 1.331e-07 0.008017 -2.235e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006474 0.0005605 0.00443 0.003383 0.9889 0.9919 0.006598 0.8565 0.8934 0.01226 ] Network output: [ -0.0003362 0.002017 1.001 -2.157e-05 9.682e-06 0.9979 -1.625e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2161 0.1015 0.3447 0.1435 0.985 0.994 0.2169 0.4383 0.8761 0.7064 ] Network output: [ 0.00426 -0.02012 0.9942 1.307e-05 -5.866e-06 1.017 9.847e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1077 0.09521 0.1835 0.1987 0.9873 0.9919 0.1078 0.7461 0.8635 0.3054 ] Network output: [ -0.004003 0.01883 1.004 1.403e-05 -6.3e-06 0.9849 1.058e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09255 0.09061 0.165 0.1958 0.9853 0.9911 0.09256 0.6702 0.8392 0.2474 ] Network output: [ 0.0001096 1 -9.102e-05 1.857e-06 -8.335e-07 0.9998 1.399e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002654 Epoch 8883 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009708 0.9964 0.9916 -2.061e-07 9.253e-08 -0.007444 -1.553e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003448 -0.003273 -0.007229 0.005747 0.9699 0.9743 0.006669 0.8288 0.822 0.01707 ] Network output: [ 0.9999 0.0002787 0.0005505 -6.879e-06 3.088e-06 -0.0005908 -5.184e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2033 -0.03478 -0.165 0.1858 0.9834 0.9932 0.2278 0.4342 0.8694 0.7124 ] Network output: [ -0.009604 1.003 1.009 -2.965e-07 1.331e-07 0.008016 -2.235e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006474 0.0005606 0.00443 0.003383 0.9889 0.9919 0.006599 0.8565 0.8934 0.01226 ] Network output: [ -0.0003359 0.002016 1.001 -2.154e-05 9.671e-06 0.9979 -1.623e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2161 0.1015 0.3447 0.1435 0.985 0.994 0.2169 0.4383 0.8761 0.7064 ] Network output: [ 0.004259 -0.02012 0.9942 1.305e-05 -5.859e-06 1.017 9.836e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1077 0.09522 0.1835 0.1987 0.9873 0.9919 0.1078 0.7461 0.8635 0.3054 ] Network output: [ -0.004002 0.01882 1.004 1.402e-05 -6.293e-06 0.9849 1.056e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09255 0.09061 0.165 0.1958 0.9853 0.9911 0.09257 0.6702 0.8392 0.2474 ] Network output: [ 0.0001095 1 -9.092e-05 1.854e-06 -8.325e-07 0.9998 1.398e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002653 Epoch 8884 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009707 0.9964 0.9917 -2.062e-07 9.258e-08 -0.007444 -1.554e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003449 -0.003273 -0.007228 0.005746 0.9699 0.9743 0.006669 0.8288 0.822 0.01707 ] Network output: [ 0.9999 0.0002785 0.0005503 -6.871e-06 3.085e-06 -0.0005903 -5.178e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2033 -0.03478 -0.165 0.1858 0.9834 0.9932 0.2278 0.4342 0.8694 0.7124 ] Network output: [ -0.009603 1.003 1.009 -2.965e-07 1.331e-07 0.008015 -2.234e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006475 0.0005606 0.00443 0.003382 0.9889 0.9919 0.006599 0.8565 0.8934 0.01226 ] Network output: [ -0.0003357 0.002015 1.001 -2.152e-05 9.66e-06 0.9979 -1.622e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2161 0.1015 0.3447 0.1435 0.985 0.994 0.2169 0.4382 0.8761 0.7064 ] Network output: [ 0.004257 -0.02011 0.9942 1.304e-05 -5.853e-06 1.017 9.825e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1077 0.09522 0.1835 0.1987 0.9873 0.9919 0.1078 0.7461 0.8635 0.3054 ] Network output: [ -0.004 0.01882 1.004 1.4e-05 -6.287e-06 0.9849 1.055e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09255 0.09062 0.165 0.1958 0.9853 0.9911 0.09257 0.6702 0.8392 0.2474 ] Network output: [ 0.0001095 1 -9.083e-05 1.852e-06 -8.316e-07 0.9998 1.396e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002651 Epoch 8885 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009705 0.9964 0.9917 -2.063e-07 9.263e-08 -0.007443 -1.555e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003449 -0.003273 -0.007228 0.005746 0.9699 0.9743 0.00667 0.8288 0.822 0.01707 ] Network output: [ 0.9999 0.0002782 0.00055 -6.864e-06 3.081e-06 -0.0005898 -5.173e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2033 -0.03478 -0.165 0.1858 0.9834 0.9932 0.2278 0.4342 0.8694 0.7124 ] Network output: [ -0.009602 1.003 1.009 -2.964e-07 1.331e-07 0.008014 -2.234e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006475 0.0005607 0.00443 0.003382 0.9889 0.9919 0.0066 0.8565 0.8934 0.01226 ] Network output: [ -0.0003355 0.002014 1.001 -2.149e-05 9.649e-06 0.9979 -1.62e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2162 0.1015 0.3448 0.1435 0.985 0.994 0.2169 0.4382 0.8761 0.7064 ] Network output: [ 0.004255 -0.0201 0.9942 1.302e-05 -5.846e-06 1.017 9.814e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1077 0.09523 0.1835 0.1987 0.9873 0.9919 0.1078 0.7461 0.8635 0.3054 ] Network output: [ -0.003999 0.01881 1.004 1.399e-05 -6.28e-06 0.9849 1.054e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09256 0.09062 0.165 0.1959 0.9853 0.9911 0.09257 0.6702 0.8392 0.2474 ] Network output: [ 0.0001094 1 -9.073e-05 1.85e-06 -8.307e-07 0.9998 1.394e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000265 Epoch 8886 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009704 0.9964 0.9917 -2.064e-07 9.268e-08 -0.007443 -1.556e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003449 -0.003273 -0.007227 0.005745 0.9699 0.9743 0.00667 0.8288 0.822 0.01707 ] Network output: [ 0.9999 0.000278 0.0005497 -6.856e-06 3.078e-06 -0.0005894 -5.167e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2033 -0.03479 -0.165 0.1858 0.9834 0.9932 0.2278 0.4341 0.8694 0.7123 ] Network output: [ -0.009601 1.003 1.009 -2.964e-07 1.33e-07 0.008013 -2.233e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006476 0.0005608 0.00443 0.003382 0.9889 0.9919 0.0066 0.8565 0.8934 0.01226 ] Network output: [ -0.0003353 0.002014 1.001 -2.147e-05 9.638e-06 0.9979 -1.618e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2162 0.1015 0.3448 0.1435 0.985 0.994 0.2169 0.4382 0.8761 0.7064 ] Network output: [ 0.004254 -0.02009 0.9942 1.301e-05 -5.84e-06 1.017 9.803e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1077 0.09523 0.1835 0.1987 0.9873 0.9919 0.1078 0.7461 0.8635 0.3054 ] Network output: [ -0.003997 0.0188 1.004 1.397e-05 -6.273e-06 0.9849 1.053e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09256 0.09062 0.165 0.1959 0.9853 0.9911 0.09257 0.6702 0.8392 0.2474 ] Network output: [ 0.0001094 1 -9.063e-05 1.848e-06 -8.298e-07 0.9998 1.393e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002649 Epoch 8887 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009703 0.9964 0.9917 -2.065e-07 9.273e-08 -0.007443 -1.557e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003449 -0.003273 -0.007226 0.005745 0.9699 0.9743 0.00667 0.8288 0.822 0.01707 ] Network output: [ 0.9999 0.0002777 0.0005494 -6.848e-06 3.074e-06 -0.0005889 -5.161e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2033 -0.03479 -0.165 0.1858 0.9834 0.9932 0.2278 0.4341 0.8694 0.7123 ] Network output: [ -0.0096 1.003 1.009 -2.963e-07 1.33e-07 0.008012 -2.233e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006476 0.0005609 0.00443 0.003381 0.9889 0.9919 0.006601 0.8565 0.8934 0.01226 ] Network output: [ -0.0003351 0.002013 1.001 -2.145e-05 9.628e-06 0.9979 -1.616e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2162 0.1015 0.3448 0.1435 0.985 0.994 0.2169 0.4382 0.8761 0.7063 ] Network output: [ 0.004252 -0.02009 0.9942 1.299e-05 -5.833e-06 1.017 9.792e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1077 0.09524 0.1835 0.1987 0.9873 0.9919 0.1078 0.746 0.8635 0.3054 ] Network output: [ -0.003996 0.01879 1.004 1.396e-05 -6.266e-06 0.9849 1.052e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09256 0.09062 0.165 0.1959 0.9853 0.9911 0.09258 0.6701 0.8392 0.2474 ] Network output: [ 0.0001093 1 -9.053e-05 1.846e-06 -8.289e-07 0.9998 1.391e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002647 Epoch 8888 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009702 0.9964 0.9917 -2.067e-07 9.277e-08 -0.007443 -1.557e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003449 -0.003274 -0.007225 0.005744 0.9699 0.9743 0.00667 0.8288 0.822 0.01707 ] Network output: [ 0.9999 0.0002775 0.0005491 -6.84e-06 3.071e-06 -0.0005884 -5.155e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2033 -0.03479 -0.165 0.1858 0.9834 0.9932 0.2278 0.4341 0.8694 0.7123 ] Network output: [ -0.009599 1.003 1.009 -2.962e-07 1.33e-07 0.008011 -2.233e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006477 0.000561 0.00443 0.003381 0.9889 0.9919 0.006601 0.8565 0.8934 0.01226 ] Network output: [ -0.0003348 0.002012 1.001 -2.142e-05 9.617e-06 0.9979 -1.614e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2162 0.1015 0.3448 0.1435 0.985 0.994 0.2169 0.4382 0.8761 0.7063 ] Network output: [ 0.004251 -0.02008 0.9942 1.298e-05 -5.827e-06 1.017 9.781e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1077 0.09524 0.1835 0.1987 0.9873 0.9919 0.1078 0.746 0.8635 0.3054 ] Network output: [ -0.003994 0.01879 1.004 1.394e-05 -6.259e-06 0.985 1.051e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09256 0.09063 0.165 0.1959 0.9853 0.9911 0.09258 0.6701 0.8392 0.2474 ] Network output: [ 0.0001093 1 -9.043e-05 1.844e-06 -8.279e-07 0.9998 1.39e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002646 Epoch 8889 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009701 0.9964 0.9917 -2.068e-07 9.282e-08 -0.007442 -1.558e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003449 -0.003274 -0.007225 0.005744 0.9699 0.9743 0.006671 0.8288 0.822 0.01707 ] Network output: [ 0.9999 0.0002772 0.0005489 -6.833e-06 3.067e-06 -0.000588 -5.149e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2033 -0.03479 -0.165 0.1858 0.9834 0.9932 0.2278 0.4341 0.8694 0.7123 ] Network output: [ -0.009598 1.003 1.009 -2.962e-07 1.33e-07 0.00801 -2.232e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006477 0.0005611 0.00443 0.003381 0.9889 0.9919 0.006602 0.8564 0.8934 0.01225 ] Network output: [ -0.0003346 0.002011 1.001 -2.14e-05 9.606e-06 0.9979 -1.613e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2162 0.1015 0.3448 0.1435 0.985 0.994 0.2169 0.4382 0.8761 0.7063 ] Network output: [ 0.004249 -0.02007 0.9942 1.296e-05 -5.82e-06 1.017 9.77e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1077 0.09525 0.1835 0.1987 0.9873 0.9919 0.1078 0.746 0.8635 0.3054 ] Network output: [ -0.003993 0.01878 1.004 1.393e-05 -6.252e-06 0.985 1.05e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09257 0.09063 0.165 0.1959 0.9853 0.9911 0.09258 0.6701 0.8392 0.2474 ] Network output: [ 0.0001093 1 -9.034e-05 1.842e-06 -8.27e-07 0.9998 1.388e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002645 Epoch 8890 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0097 0.9964 0.9917 -2.069e-07 9.287e-08 -0.007442 -1.559e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003449 -0.003274 -0.007224 0.005743 0.9699 0.9743 0.006671 0.8288 0.822 0.01707 ] Network output: [ 0.9999 0.000277 0.0005486 -6.825e-06 3.064e-06 -0.0005875 -5.144e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2033 -0.03479 -0.165 0.1858 0.9834 0.9932 0.2278 0.4341 0.8694 0.7123 ] Network output: [ -0.009597 1.003 1.009 -2.961e-07 1.329e-07 0.008009 -2.232e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006478 0.0005612 0.00443 0.003381 0.9889 0.9919 0.006602 0.8564 0.8934 0.01225 ] Network output: [ -0.0003344 0.002011 1.001 -2.137e-05 9.595e-06 0.9979 -1.611e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2162 0.1015 0.3448 0.1435 0.985 0.994 0.2169 0.4382 0.8761 0.7063 ] Network output: [ 0.004248 -0.02006 0.9942 1.295e-05 -5.814e-06 1.017 9.76e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1077 0.09525 0.1835 0.1987 0.9873 0.9919 0.1078 0.746 0.8635 0.3054 ] Network output: [ -0.003991 0.01877 1.004 1.391e-05 -6.246e-06 0.985 1.048e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09257 0.09063 0.165 0.1959 0.9853 0.9911 0.09258 0.6701 0.8392 0.2474 ] Network output: [ 0.0001092 1 -9.024e-05 1.84e-06 -8.261e-07 0.9998 1.387e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002643 Epoch 8891 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009699 0.9964 0.9917 -2.07e-07 9.292e-08 -0.007442 -1.56e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003449 -0.003274 -0.007223 0.005743 0.9699 0.9743 0.006671 0.8288 0.822 0.01706 ] Network output: [ 0.9999 0.0002767 0.0005483 -6.817e-06 3.061e-06 -0.0005871 -5.138e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2033 -0.03479 -0.165 0.1858 0.9834 0.9932 0.2278 0.4341 0.8694 0.7123 ] Network output: [ -0.009596 1.003 1.009 -2.961e-07 1.329e-07 0.008008 -2.231e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006478 0.0005613 0.00443 0.00338 0.9889 0.9919 0.006603 0.8564 0.8934 0.01225 ] Network output: [ -0.0003342 0.00201 1.001 -2.135e-05 9.585e-06 0.9979 -1.609e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2162 0.1016 0.3448 0.1435 0.985 0.994 0.2169 0.4382 0.8761 0.7063 ] Network output: [ 0.004246 -0.02006 0.9942 1.294e-05 -5.807e-06 1.017 9.749e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1077 0.09526 0.1835 0.1987 0.9873 0.9919 0.1078 0.746 0.8635 0.3054 ] Network output: [ -0.00399 0.01876 1.004 1.39e-05 -6.239e-06 0.985 1.047e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09257 0.09063 0.165 0.1959 0.9853 0.9911 0.09258 0.6701 0.8392 0.2474 ] Network output: [ 0.0001092 1 -9.014e-05 1.838e-06 -8.252e-07 0.9998 1.385e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002642 Epoch 8892 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009697 0.9964 0.9917 -2.071e-07 9.296e-08 -0.007441 -1.561e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003449 -0.003274 -0.007222 0.005742 0.9699 0.9743 0.006671 0.8288 0.822 0.01706 ] Network output: [ 0.9999 0.0002765 0.000548 -6.81e-06 3.057e-06 -0.0005866 -5.132e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2033 -0.03479 -0.1649 0.1858 0.9834 0.9932 0.2279 0.4341 0.8694 0.7123 ] Network output: [ -0.009595 1.003 1.009 -2.96e-07 1.329e-07 0.008007 -2.231e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006479 0.0005614 0.00443 0.00338 0.9889 0.9919 0.006603 0.8564 0.8934 0.01225 ] Network output: [ -0.0003339 0.002009 1.001 -2.133e-05 9.574e-06 0.9979 -1.607e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2162 0.1016 0.3448 0.1435 0.985 0.994 0.2169 0.4382 0.8761 0.7063 ] Network output: [ 0.004245 -0.02005 0.9942 1.292e-05 -5.801e-06 1.017 9.738e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1078 0.09526 0.1835 0.1987 0.9873 0.9919 0.1078 0.746 0.8635 0.3054 ] Network output: [ -0.003988 0.01876 1.004 1.388e-05 -6.232e-06 0.985 1.046e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09257 0.09064 0.165 0.1959 0.9853 0.9911 0.09259 0.6701 0.8392 0.2474 ] Network output: [ 0.0001091 1 -9.004e-05 1.836e-06 -8.243e-07 0.9998 1.384e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000264 Epoch 8893 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009696 0.9964 0.9917 -2.072e-07 9.301e-08 -0.007441 -1.561e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003449 -0.003274 -0.007222 0.005742 0.9699 0.9743 0.006672 0.8288 0.822 0.01706 ] Network output: [ 0.9999 0.0002762 0.0005477 -6.802e-06 3.054e-06 -0.0005861 -5.126e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2033 -0.03479 -0.1649 0.1858 0.9834 0.9932 0.2279 0.4341 0.8694 0.7123 ] Network output: [ -0.009595 1.003 1.009 -2.96e-07 1.329e-07 0.008006 -2.231e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006479 0.0005615 0.00443 0.00338 0.9889 0.9919 0.006604 0.8564 0.8934 0.01225 ] Network output: [ -0.0003337 0.002008 1.001 -2.13e-05 9.563e-06 0.9979 -1.605e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2162 0.1016 0.3448 0.1435 0.985 0.994 0.217 0.4382 0.8761 0.7063 ] Network output: [ 0.004243 -0.02004 0.9942 1.291e-05 -5.794e-06 1.017 9.727e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1078 0.09527 0.1835 0.1987 0.9873 0.9919 0.1078 0.7459 0.8635 0.3054 ] Network output: [ -0.003987 0.01875 1.004 1.387e-05 -6.225e-06 0.985 1.045e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09258 0.09064 0.165 0.1959 0.9853 0.9911 0.09259 0.67 0.8392 0.2474 ] Network output: [ 0.0001091 1 -8.995e-05 1.834e-06 -8.234e-07 0.9998 1.382e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002639 Epoch 8894 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009695 0.9964 0.9917 -2.073e-07 9.306e-08 -0.007441 -1.562e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00345 -0.003274 -0.007221 0.005741 0.9699 0.9743 0.006672 0.8288 0.822 0.01706 ] Network output: [ 0.9999 0.000276 0.0005475 -6.795e-06 3.05e-06 -0.0005857 -5.121e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2033 -0.03479 -0.1649 0.1858 0.9834 0.9932 0.2279 0.4341 0.8694 0.7123 ] Network output: [ -0.009594 1.003 1.009 -2.959e-07 1.329e-07 0.008004 -2.23e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00648 0.0005616 0.00443 0.003379 0.9889 0.9919 0.006604 0.8564 0.8934 0.01225 ] Network output: [ -0.0003335 0.002007 1.001 -2.128e-05 9.552e-06 0.9979 -1.604e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2162 0.1016 0.3448 0.1435 0.985 0.994 0.217 0.4382 0.8761 0.7063 ] Network output: [ 0.004241 -0.02003 0.9942 1.289e-05 -5.788e-06 1.017 9.716e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1078 0.09527 0.1835 0.1987 0.9873 0.9919 0.1078 0.7459 0.8635 0.3054 ] Network output: [ -0.003985 0.01874 1.004 1.385e-05 -6.218e-06 0.985 1.044e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09258 0.09064 0.165 0.1959 0.9853 0.9911 0.09259 0.67 0.8392 0.2474 ] Network output: [ 0.000109 1 -8.985e-05 1.832e-06 -8.225e-07 0.9998 1.381e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002638 Epoch 8895 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009694 0.9964 0.9917 -2.074e-07 9.31e-08 -0.007441 -1.563e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00345 -0.003274 -0.00722 0.005741 0.9699 0.9743 0.006672 0.8288 0.822 0.01706 ] Network output: [ 0.9999 0.0002757 0.0005472 -6.787e-06 3.047e-06 -0.0005852 -5.115e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2034 -0.0348 -0.1649 0.1858 0.9834 0.9932 0.2279 0.4341 0.8694 0.7123 ] Network output: [ -0.009593 1.003 1.009 -2.959e-07 1.328e-07 0.008003 -2.23e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00648 0.0005617 0.00443 0.003379 0.9889 0.9919 0.006605 0.8564 0.8934 0.01225 ] Network output: [ -0.0003333 0.002007 1.001 -2.125e-05 9.542e-06 0.9979 -1.602e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2162 0.1016 0.3448 0.1435 0.985 0.994 0.217 0.4382 0.8761 0.7063 ] Network output: [ 0.00424 -0.02003 0.9942 1.288e-05 -5.782e-06 1.017 9.705e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1078 0.09528 0.1835 0.1987 0.9873 0.9919 0.1078 0.7459 0.8635 0.3054 ] Network output: [ -0.003984 0.01873 1.004 1.384e-05 -6.212e-06 0.985 1.043e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09258 0.09064 0.165 0.1959 0.9853 0.9911 0.09259 0.67 0.8392 0.2474 ] Network output: [ 0.000109 1 -8.975e-05 1.83e-06 -8.216e-07 0.9998 1.379e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002636 Epoch 8896 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009693 0.9964 0.9917 -2.075e-07 9.315e-08 -0.00744 -1.564e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00345 -0.003275 -0.007219 0.00574 0.9699 0.9743 0.006672 0.8288 0.822 0.01706 ] Network output: [ 0.9999 0.0002755 0.0005469 -6.779e-06 3.043e-06 -0.0005848 -5.109e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2034 -0.0348 -0.1649 0.1858 0.9834 0.9932 0.2279 0.4341 0.8694 0.7123 ] Network output: [ -0.009592 1.003 1.009 -2.958e-07 1.328e-07 0.008002 -2.229e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006481 0.0005618 0.00443 0.003379 0.9889 0.9919 0.006605 0.8564 0.8934 0.01225 ] Network output: [ -0.0003331 0.002006 1.001 -2.123e-05 9.531e-06 0.9979 -1.6e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2163 0.1016 0.3448 0.1435 0.985 0.994 0.217 0.4381 0.8761 0.7063 ] Network output: [ 0.004238 -0.02002 0.9942 1.286e-05 -5.775e-06 1.017 9.695e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1078 0.09528 0.1835 0.1987 0.9873 0.9919 0.1078 0.7459 0.8635 0.3054 ] Network output: [ -0.003982 0.01873 1.004 1.382e-05 -6.205e-06 0.985 1.042e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09258 0.09065 0.165 0.1959 0.9853 0.9911 0.0926 0.67 0.8392 0.2475 ] Network output: [ 0.0001089 1 -8.966e-05 1.828e-06 -8.206e-07 0.9998 1.378e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002635 Epoch 8897 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009692 0.9964 0.9917 -2.076e-07 9.32e-08 -0.00744 -1.564e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00345 -0.003275 -0.007219 0.00574 0.9699 0.9743 0.006673 0.8288 0.822 0.01706 ] Network output: [ 0.9999 0.0002752 0.0005466 -6.772e-06 3.04e-06 -0.0005843 -5.103e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2034 -0.0348 -0.1649 0.1858 0.9834 0.9932 0.2279 0.4341 0.8694 0.7123 ] Network output: [ -0.009591 1.003 1.009 -2.958e-07 1.328e-07 0.008001 -2.229e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006481 0.0005619 0.00443 0.003378 0.9889 0.9919 0.006606 0.8564 0.8934 0.01225 ] Network output: [ -0.0003328 0.002005 1.001 -2.121e-05 9.52e-06 0.9979 -1.598e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2163 0.1016 0.3448 0.1435 0.985 0.994 0.217 0.4381 0.8761 0.7063 ] Network output: [ 0.004237 -0.02001 0.9942 1.285e-05 -5.769e-06 1.017 9.684e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1078 0.09529 0.1835 0.1987 0.9873 0.9919 0.1079 0.7459 0.8635 0.3054 ] Network output: [ -0.003981 0.01872 1.004 1.381e-05 -6.198e-06 0.985 1.04e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09259 0.09065 0.165 0.1959 0.9853 0.9911 0.0926 0.67 0.8392 0.2475 ] Network output: [ 0.0001089 1 -8.956e-05 1.826e-06 -8.197e-07 0.9998 1.376e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002634 Epoch 8898 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009691 0.9964 0.9917 -2.077e-07 9.324e-08 -0.00744 -1.565e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00345 -0.003275 -0.007218 0.005739 0.9699 0.9743 0.006673 0.8288 0.822 0.01706 ] Network output: [ 0.9999 0.000275 0.0005463 -6.764e-06 3.037e-06 -0.0005839 -5.098e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2034 -0.0348 -0.1649 0.1858 0.9834 0.9932 0.2279 0.4341 0.8694 0.7123 ] Network output: [ -0.00959 1.003 1.009 -2.957e-07 1.328e-07 0.008 -2.228e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006482 0.000562 0.004429 0.003378 0.9889 0.9919 0.006606 0.8564 0.8934 0.01225 ] Network output: [ -0.0003326 0.002004 1.001 -2.118e-05 9.509e-06 0.9979 -1.596e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2163 0.1016 0.3448 0.1435 0.985 0.994 0.217 0.4381 0.8761 0.7063 ] Network output: [ 0.004235 -0.02 0.9942 1.284e-05 -5.762e-06 1.017 9.673e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1078 0.09529 0.1835 0.1987 0.9873 0.9919 0.1079 0.7459 0.8635 0.3054 ] Network output: [ -0.003979 0.01871 1.004 1.379e-05 -6.191e-06 0.985 1.039e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09259 0.09065 0.165 0.1959 0.9853 0.9911 0.0926 0.67 0.8392 0.2475 ] Network output: [ 0.0001089 1 -8.947e-05 1.824e-06 -8.188e-07 0.9998 1.375e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002632 Epoch 8899 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009689 0.9964 0.9917 -2.078e-07 9.329e-08 -0.007439 -1.566e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00345 -0.003275 -0.007217 0.005739 0.9699 0.9743 0.006673 0.8287 0.8219 0.01706 ] Network output: [ 0.9999 0.0002747 0.0005461 -6.756e-06 3.033e-06 -0.0005834 -5.092e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2034 -0.0348 -0.1649 0.1858 0.9834 0.9932 0.2279 0.434 0.8694 0.7123 ] Network output: [ -0.009589 1.003 1.009 -2.956e-07 1.327e-07 0.007999 -2.228e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006482 0.000562 0.004429 0.003378 0.9889 0.9919 0.006607 0.8564 0.8934 0.01225 ] Network output: [ -0.0003324 0.002004 1.001 -2.116e-05 9.499e-06 0.9979 -1.595e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2163 0.1016 0.3448 0.1435 0.985 0.994 0.217 0.4381 0.8761 0.7063 ] Network output: [ 0.004234 -0.02 0.9942 1.282e-05 -5.756e-06 1.017 9.662e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1078 0.0953 0.1835 0.1987 0.9873 0.9919 0.1079 0.7459 0.8635 0.3054 ] Network output: [ -0.003978 0.0187 1.004 1.378e-05 -6.185e-06 0.985 1.038e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09259 0.09065 0.165 0.1959 0.9853 0.9911 0.0926 0.6699 0.8392 0.2475 ] Network output: [ 0.0001088 1 -8.937e-05 1.822e-06 -8.179e-07 0.9998 1.373e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002631 Epoch 8900 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009688 0.9964 0.9917 -2.079e-07 9.333e-08 -0.007439 -1.567e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00345 -0.003275 -0.007216 0.005738 0.9699 0.9743 0.006673 0.8287 0.8219 0.01705 ] Network output: [ 0.9999 0.0002745 0.0005458 -6.749e-06 3.03e-06 -0.0005829 -5.086e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2034 -0.0348 -0.1648 0.1858 0.9834 0.9932 0.2279 0.434 0.8694 0.7123 ] Network output: [ -0.009588 1.003 1.009 -2.956e-07 1.327e-07 0.007998 -2.228e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006483 0.0005621 0.004429 0.003378 0.9889 0.9919 0.006607 0.8564 0.8934 0.01224 ] Network output: [ -0.0003322 0.002003 1.001 -2.113e-05 9.488e-06 0.9979 -1.593e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2163 0.1016 0.3448 0.1435 0.985 0.994 0.217 0.4381 0.8761 0.7063 ] Network output: [ 0.004232 -0.01999 0.9942 1.281e-05 -5.749e-06 1.017 9.652e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1078 0.0953 0.1835 0.1987 0.9873 0.9919 0.1079 0.7458 0.8635 0.3054 ] Network output: [ -0.003976 0.01869 1.004 1.376e-05 -6.178e-06 0.985 1.037e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09259 0.09066 0.165 0.1959 0.9853 0.9911 0.09261 0.6699 0.8392 0.2475 ] Network output: [ 0.0001088 1 -8.927e-05 1.82e-06 -8.17e-07 0.9998 1.372e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002629 Epoch 8901 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009687 0.9964 0.9917 -2.08e-07 9.338e-08 -0.007439 -1.568e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00345 -0.003275 -0.007216 0.005738 0.9699 0.9743 0.006674 0.8287 0.8219 0.01705 ] Network output: [ 0.9999 0.0002742 0.0005455 -6.741e-06 3.026e-06 -0.0005825 -5.08e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2034 -0.0348 -0.1648 0.1858 0.9834 0.9932 0.2279 0.434 0.8694 0.7123 ] Network output: [ -0.009587 1.003 1.009 -2.955e-07 1.327e-07 0.007997 -2.227e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006483 0.0005622 0.004429 0.003377 0.9889 0.9919 0.006608 0.8564 0.8933 0.01224 ] Network output: [ -0.000332 0.002002 1.001 -2.111e-05 9.477e-06 0.9979 -1.591e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2163 0.1016 0.3449 0.1435 0.985 0.994 0.217 0.4381 0.8761 0.7063 ] Network output: [ 0.004231 -0.01998 0.9942 1.279e-05 -5.743e-06 1.017 9.641e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1078 0.09531 0.1835 0.1987 0.9873 0.9919 0.1079 0.7458 0.8635 0.3054 ] Network output: [ -0.003975 0.01869 1.004 1.375e-05 -6.171e-06 0.985 1.036e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09259 0.09066 0.165 0.1959 0.9853 0.9911 0.09261 0.6699 0.8392 0.2475 ] Network output: [ 0.0001087 1 -8.918e-05 1.818e-06 -8.161e-07 0.9998 1.37e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002628 Epoch 8902 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009686 0.9964 0.9917 -2.081e-07 9.342e-08 -0.007439 -1.568e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00345 -0.003275 -0.007215 0.005737 0.9699 0.9743 0.006674 0.8287 0.8219 0.01705 ] Network output: [ 0.9999 0.0002739 0.0005452 -6.734e-06 3.023e-06 -0.000582 -5.075e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2034 -0.0348 -0.1648 0.1858 0.9834 0.9932 0.2279 0.434 0.8694 0.7123 ] Network output: [ -0.009586 1.003 1.009 -2.955e-07 1.326e-07 0.007996 -2.227e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006484 0.0005623 0.004429 0.003377 0.9889 0.9919 0.006608 0.8564 0.8933 0.01224 ] Network output: [ -0.0003317 0.002001 1.001 -2.109e-05 9.467e-06 0.9979 -1.589e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2163 0.1016 0.3449 0.1435 0.985 0.994 0.217 0.4381 0.8761 0.7063 ] Network output: [ 0.004229 -0.01997 0.9942 1.278e-05 -5.737e-06 1.017 9.63e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1078 0.09531 0.1835 0.1987 0.9873 0.9919 0.1079 0.7458 0.8635 0.3054 ] Network output: [ -0.003973 0.01868 1.004 1.373e-05 -6.164e-06 0.985 1.035e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0926 0.09066 0.165 0.1959 0.9853 0.9911 0.09261 0.6699 0.8391 0.2475 ] Network output: [ 0.0001087 1 -8.908e-05 1.816e-06 -8.152e-07 0.9998 1.368e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002627 Epoch 8903 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009685 0.9964 0.9917 -2.082e-07 9.346e-08 -0.007438 -1.569e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00345 -0.003275 -0.007214 0.005737 0.9699 0.9743 0.006674 0.8287 0.8219 0.01705 ] Network output: [ 0.9999 0.0002737 0.0005449 -6.726e-06 3.02e-06 -0.0005816 -5.069e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2034 -0.0348 -0.1648 0.1858 0.9834 0.9932 0.228 0.434 0.8694 0.7123 ] Network output: [ -0.009585 1.003 1.009 -2.954e-07 1.326e-07 0.007995 -2.226e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006484 0.0005624 0.004429 0.003377 0.9889 0.9919 0.006609 0.8564 0.8933 0.01224 ] Network output: [ -0.0003315 0.002001 1.001 -2.106e-05 9.456e-06 0.9979 -1.587e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2163 0.1016 0.3449 0.1435 0.985 0.994 0.217 0.4381 0.8761 0.7063 ] Network output: [ 0.004227 -0.01997 0.9942 1.276e-05 -5.73e-06 1.017 9.619e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1078 0.09532 0.1835 0.1987 0.9873 0.9919 0.1079 0.7458 0.8634 0.3054 ] Network output: [ -0.003972 0.01867 1.004 1.372e-05 -6.158e-06 0.985 1.034e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0926 0.09066 0.165 0.1959 0.9853 0.9911 0.09261 0.6699 0.8391 0.2475 ] Network output: [ 0.0001086 1 -8.899e-05 1.814e-06 -8.143e-07 0.9998 1.367e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002625 Epoch 8904 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009684 0.9964 0.9917 -2.083e-07 9.351e-08 -0.007438 -1.57e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003451 -0.003275 -0.007213 0.005736 0.9699 0.9743 0.006674 0.8287 0.8219 0.01705 ] Network output: [ 0.9999 0.0002734 0.0005447 -6.719e-06 3.016e-06 -0.0005811 -5.063e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2034 -0.03481 -0.1648 0.1857 0.9834 0.9932 0.228 0.434 0.8694 0.7122 ] Network output: [ -0.009584 1.003 1.009 -2.954e-07 1.326e-07 0.007994 -2.226e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006485 0.0005625 0.004429 0.003376 0.9889 0.9919 0.006609 0.8563 0.8933 0.01224 ] Network output: [ -0.0003313 0.002 1.001 -2.104e-05 9.446e-06 0.9979 -1.586e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2163 0.1016 0.3449 0.1435 0.985 0.994 0.217 0.4381 0.8761 0.7063 ] Network output: [ 0.004226 -0.01996 0.9942 1.275e-05 -5.724e-06 1.017 9.609e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1078 0.09532 0.1835 0.1987 0.9873 0.9919 0.1079 0.7458 0.8634 0.3054 ] Network output: [ -0.00397 0.01866 1.004 1.37e-05 -6.151e-06 0.985 1.033e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0926 0.09066 0.165 0.1959 0.9853 0.9911 0.09262 0.6699 0.8391 0.2475 ] Network output: [ 0.0001086 1 -8.889e-05 1.812e-06 -8.134e-07 0.9998 1.365e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002624 Epoch 8905 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009683 0.9964 0.9917 -2.084e-07 9.355e-08 -0.007438 -1.57e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003451 -0.003276 -0.007213 0.005736 0.9699 0.9743 0.006675 0.8287 0.8219 0.01705 ] Network output: [ 0.9999 0.0002732 0.0005444 -6.711e-06 3.013e-06 -0.0005807 -5.058e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2034 -0.03481 -0.1648 0.1857 0.9834 0.9932 0.228 0.434 0.8694 0.7122 ] Network output: [ -0.009583 1.003 1.009 -2.953e-07 1.326e-07 0.007993 -2.225e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006485 0.0005626 0.004429 0.003376 0.9889 0.9919 0.00661 0.8563 0.8933 0.01224 ] Network output: [ -0.0003311 0.001999 1.001 -2.102e-05 9.435e-06 0.9979 -1.584e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2163 0.1016 0.3449 0.1435 0.985 0.994 0.2171 0.4381 0.8761 0.7062 ] Network output: [ 0.004224 -0.01995 0.9942 1.274e-05 -5.718e-06 1.017 9.598e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1078 0.09533 0.1836 0.1987 0.9873 0.9919 0.1079 0.7458 0.8634 0.3054 ] Network output: [ -0.003969 0.01866 1.004 1.369e-05 -6.144e-06 0.985 1.031e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0926 0.09067 0.165 0.1959 0.9853 0.9911 0.09262 0.6699 0.8391 0.2475 ] Network output: [ 0.0001086 1 -8.88e-05 1.81e-06 -8.125e-07 0.9998 1.364e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002623 Epoch 8906 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009682 0.9964 0.9917 -2.085e-07 9.359e-08 -0.007437 -1.571e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003451 -0.003276 -0.007212 0.005735 0.9699 0.9743 0.006675 0.8287 0.8219 0.01705 ] Network output: [ 0.9999 0.0002729 0.0005441 -6.703e-06 3.009e-06 -0.0005802 -5.052e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2034 -0.03481 -0.1648 0.1857 0.9834 0.9932 0.228 0.434 0.8694 0.7122 ] Network output: [ -0.009582 1.003 1.009 -2.952e-07 1.325e-07 0.007992 -2.225e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006486 0.0005627 0.004429 0.003376 0.9889 0.9919 0.00661 0.8563 0.8933 0.01224 ] Network output: [ -0.0003309 0.001998 1.001 -2.099e-05 9.424e-06 0.9979 -1.582e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2163 0.1016 0.3449 0.1435 0.985 0.994 0.2171 0.4381 0.8761 0.7062 ] Network output: [ 0.004223 -0.01994 0.9942 1.272e-05 -5.711e-06 1.017 9.587e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1078 0.09533 0.1836 0.1987 0.9873 0.9919 0.1079 0.7457 0.8634 0.3054 ] Network output: [ -0.003967 0.01865 1.004 1.367e-05 -6.138e-06 0.985 1.03e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09261 0.09067 0.165 0.1959 0.9853 0.9911 0.09262 0.6698 0.8391 0.2475 ] Network output: [ 0.0001085 1 -8.87e-05 1.808e-06 -8.116e-07 0.9998 1.362e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002621 Epoch 8907 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00968 0.9964 0.9917 -2.086e-07 9.364e-08 -0.007437 -1.572e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003451 -0.003276 -0.007211 0.005735 0.9699 0.9743 0.006675 0.8287 0.8219 0.01705 ] Network output: [ 0.9999 0.0002727 0.0005438 -6.696e-06 3.006e-06 -0.0005798 -5.046e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2034 -0.03481 -0.1648 0.1857 0.9834 0.9932 0.228 0.434 0.8694 0.7122 ] Network output: [ -0.009581 1.003 1.009 -2.952e-07 1.325e-07 0.007991 -2.225e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006486 0.0005628 0.004429 0.003376 0.9889 0.9919 0.006611 0.8563 0.8933 0.01224 ] Network output: [ -0.0003307 0.001998 1.001 -2.097e-05 9.414e-06 0.9979 -1.58e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2163 0.1016 0.3449 0.1435 0.985 0.994 0.2171 0.4381 0.8761 0.7062 ] Network output: [ 0.004221 -0.01994 0.9942 1.271e-05 -5.705e-06 1.017 9.577e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1078 0.09534 0.1836 0.1987 0.9873 0.9919 0.1079 0.7457 0.8634 0.3054 ] Network output: [ -0.003966 0.01864 1.004 1.366e-05 -6.131e-06 0.985 1.029e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09261 0.09067 0.165 0.1959 0.9853 0.9911 0.09262 0.6698 0.8391 0.2475 ] Network output: [ 0.0001085 1 -8.861e-05 1.806e-06 -8.107e-07 0.9998 1.361e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000262 Epoch 8908 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009679 0.9964 0.9917 -2.087e-07 9.368e-08 -0.007437 -1.573e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003451 -0.003276 -0.00721 0.005734 0.9699 0.9743 0.006675 0.8287 0.8219 0.01705 ] Network output: [ 0.9999 0.0002724 0.0005436 -6.688e-06 3.003e-06 -0.0005793 -5.041e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2034 -0.03481 -0.1648 0.1857 0.9834 0.9932 0.228 0.434 0.8694 0.7122 ] Network output: [ -0.00958 1.003 1.009 -2.951e-07 1.325e-07 0.00799 -2.224e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006487 0.0005629 0.004429 0.003375 0.9889 0.9919 0.006611 0.8563 0.8933 0.01224 ] Network output: [ -0.0003304 0.001997 1.001 -2.095e-05 9.403e-06 0.9979 -1.579e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2164 0.1016 0.3449 0.1435 0.985 0.994 0.2171 0.4381 0.8761 0.7062 ] Network output: [ 0.00422 -0.01993 0.9942 1.269e-05 -5.698e-06 1.017 9.566e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1078 0.09534 0.1836 0.1987 0.9873 0.9919 0.1079 0.7457 0.8634 0.3054 ] Network output: [ -0.003964 0.01863 1.004 1.364e-05 -6.124e-06 0.985 1.028e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09261 0.09067 0.165 0.1959 0.9853 0.9911 0.09263 0.6698 0.8391 0.2475 ] Network output: [ 0.0001084 1 -8.851e-05 1.804e-06 -8.098e-07 0.9998 1.359e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002618 Epoch 8909 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009678 0.9964 0.9917 -2.088e-07 9.372e-08 -0.007436 -1.573e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003451 -0.003276 -0.00721 0.005734 0.9699 0.9743 0.006676 0.8287 0.8219 0.01704 ] Network output: [ 0.9999 0.0002722 0.0005433 -6.681e-06 2.999e-06 -0.0005789 -5.035e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2035 -0.03481 -0.1647 0.1857 0.9834 0.9932 0.228 0.434 0.8694 0.7122 ] Network output: [ -0.009579 1.003 1.009 -2.951e-07 1.325e-07 0.007989 -2.224e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006487 0.000563 0.004429 0.003375 0.9889 0.9919 0.006612 0.8563 0.8933 0.01224 ] Network output: [ -0.0003302 0.001996 1.001 -2.092e-05 9.393e-06 0.9979 -1.577e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2164 0.1017 0.3449 0.1435 0.985 0.994 0.2171 0.438 0.8761 0.7062 ] Network output: [ 0.004218 -0.01992 0.9942 1.268e-05 -5.692e-06 1.017 9.555e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1078 0.09535 0.1836 0.1987 0.9873 0.9919 0.1079 0.7457 0.8634 0.3054 ] Network output: [ -0.003963 0.01863 1.004 1.363e-05 -6.117e-06 0.985 1.027e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09261 0.09068 0.165 0.1959 0.9853 0.9911 0.09263 0.6698 0.8391 0.2475 ] Network output: [ 0.0001084 1 -8.842e-05 1.802e-06 -8.089e-07 0.9998 1.358e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002617 Epoch 8910 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009677 0.9964 0.9917 -2.089e-07 9.376e-08 -0.007436 -1.574e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003451 -0.003276 -0.007209 0.005733 0.9699 0.9743 0.006676 0.8287 0.8219 0.01704 ] Network output: [ 0.9999 0.0002719 0.000543 -6.673e-06 2.996e-06 -0.0005784 -5.029e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2035 -0.03481 -0.1647 0.1857 0.9834 0.9932 0.228 0.434 0.8694 0.7122 ] Network output: [ -0.009578 1.003 1.009 -2.95e-07 1.324e-07 0.007988 -2.223e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006488 0.0005631 0.004429 0.003375 0.9889 0.9919 0.006612 0.8563 0.8933 0.01224 ] Network output: [ -0.00033 0.001995 1.001 -2.09e-05 9.382e-06 0.9979 -1.575e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2164 0.1017 0.3449 0.1435 0.985 0.994 0.2171 0.438 0.8761 0.7062 ] Network output: [ 0.004217 -0.01991 0.9942 1.266e-05 -5.686e-06 1.017 9.545e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1078 0.09535 0.1836 0.1987 0.9873 0.9919 0.1079 0.7457 0.8634 0.3054 ] Network output: [ -0.003961 0.01862 1.004 1.361e-05 -6.111e-06 0.985 1.026e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09262 0.09068 0.165 0.1959 0.9853 0.9911 0.09263 0.6698 0.8391 0.2475 ] Network output: [ 0.0001083 1 -8.832e-05 1.8e-06 -8.08e-07 0.9998 1.356e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002616 Epoch 8911 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009676 0.9964 0.9917 -2.09e-07 9.381e-08 -0.007436 -1.575e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003451 -0.003276 -0.007208 0.005733 0.9699 0.9743 0.006676 0.8287 0.8219 0.01704 ] Network output: [ 0.9999 0.0002717 0.0005427 -6.666e-06 2.993e-06 -0.000578 -5.024e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2035 -0.03481 -0.1647 0.1857 0.9834 0.9932 0.228 0.434 0.8694 0.7122 ] Network output: [ -0.009577 1.003 1.009 -2.949e-07 1.324e-07 0.007987 -2.223e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006488 0.0005632 0.004429 0.003374 0.9889 0.9919 0.006613 0.8563 0.8933 0.01224 ] Network output: [ -0.0003298 0.001995 1.001 -2.088e-05 9.372e-06 0.9979 -1.573e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2164 0.1017 0.3449 0.1435 0.985 0.994 0.2171 0.438 0.8761 0.7062 ] Network output: [ 0.004215 -0.01991 0.9942 1.265e-05 -5.679e-06 1.017 9.534e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1079 0.09536 0.1836 0.1987 0.9873 0.9919 0.1079 0.7457 0.8634 0.3054 ] Network output: [ -0.00396 0.01861 1.004 1.36e-05 -6.104e-06 0.985 1.025e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09262 0.09068 0.165 0.1959 0.9853 0.9911 0.09263 0.6698 0.8391 0.2475 ] Network output: [ 0.0001083 1 -8.823e-05 1.798e-06 -8.071e-07 0.9998 1.355e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002614 Epoch 8912 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009675 0.9964 0.9917 -2.09e-07 9.385e-08 -0.007435 -1.575e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003451 -0.003276 -0.007207 0.005732 0.9699 0.9743 0.006676 0.8287 0.8219 0.01704 ] Network output: [ 0.9999 0.0002715 0.0005425 -6.658e-06 2.989e-06 -0.0005775 -5.018e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2035 -0.03481 -0.1647 0.1857 0.9834 0.9932 0.228 0.4339 0.8694 0.7122 ] Network output: [ -0.009576 1.003 1.009 -2.949e-07 1.324e-07 0.007986 -2.222e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006488 0.0005633 0.004429 0.003374 0.9889 0.9919 0.006613 0.8563 0.8933 0.01223 ] Network output: [ -0.0003296 0.001994 1.001 -2.085e-05 9.361e-06 0.9979 -1.571e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2164 0.1017 0.3449 0.1435 0.985 0.994 0.2171 0.438 0.8761 0.7062 ] Network output: [ 0.004214 -0.0199 0.9942 1.264e-05 -5.673e-06 1.017 9.523e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1079 0.09536 0.1836 0.1987 0.9873 0.9919 0.1079 0.7457 0.8634 0.3054 ] Network output: [ -0.003958 0.0186 1.004 1.358e-05 -6.097e-06 0.985 1.024e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09262 0.09068 0.165 0.1959 0.9853 0.9911 0.09263 0.6697 0.8391 0.2475 ] Network output: [ 0.0001083 1 -8.813e-05 1.796e-06 -8.062e-07 0.9998 1.353e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002613 Epoch 8913 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009674 0.9964 0.9917 -2.091e-07 9.389e-08 -0.007435 -1.576e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003451 -0.003277 -0.007207 0.005732 0.9699 0.9743 0.006677 0.8287 0.8219 0.01704 ] Network output: [ 0.9999 0.0002712 0.0005422 -6.651e-06 2.986e-06 -0.0005771 -5.012e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2035 -0.03482 -0.1647 0.1857 0.9834 0.9932 0.228 0.4339 0.8694 0.7122 ] Network output: [ -0.009575 1.003 1.009 -2.948e-07 1.324e-07 0.007985 -2.222e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006489 0.0005634 0.004429 0.003374 0.9889 0.9919 0.006614 0.8563 0.8933 0.01223 ] Network output: [ -0.0003293 0.001993 1.001 -2.083e-05 9.351e-06 0.9979 -1.57e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2164 0.1017 0.3449 0.1435 0.985 0.994 0.2171 0.438 0.8761 0.7062 ] Network output: [ 0.004212 -0.01989 0.9942 1.262e-05 -5.667e-06 1.017 9.513e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1079 0.09537 0.1836 0.1987 0.9873 0.9919 0.1079 0.7456 0.8634 0.3054 ] Network output: [ -0.003957 0.0186 1.004 1.357e-05 -6.091e-06 0.985 1.022e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09262 0.09069 0.165 0.1959 0.9853 0.9911 0.09264 0.6697 0.8391 0.2475 ] Network output: [ 0.0001082 1 -8.804e-05 1.794e-06 -8.053e-07 0.9998 1.352e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002612 Epoch 8914 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009673 0.9964 0.9917 -2.092e-07 9.393e-08 -0.007435 -1.577e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003452 -0.003277 -0.007206 0.005732 0.9699 0.9743 0.006677 0.8287 0.8219 0.01704 ] Network output: [ 0.9999 0.000271 0.0005419 -6.643e-06 2.982e-06 -0.0005766 -5.007e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2035 -0.03482 -0.1647 0.1857 0.9834 0.9932 0.228 0.4339 0.8694 0.7122 ] Network output: [ -0.009574 1.003 1.009 -2.947e-07 1.323e-07 0.007984 -2.221e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006489 0.0005634 0.004429 0.003374 0.9889 0.9919 0.006614 0.8563 0.8933 0.01223 ] Network output: [ -0.0003291 0.001992 1.001 -2.08e-05 9.34e-06 0.9979 -1.568e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2164 0.1017 0.3449 0.1435 0.985 0.994 0.2171 0.438 0.8761 0.7062 ] Network output: [ 0.00421 -0.01989 0.9942 1.261e-05 -5.66e-06 1.017 9.502e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1079 0.09537 0.1836 0.1987 0.9873 0.9919 0.1079 0.7456 0.8634 0.3054 ] Network output: [ -0.003955 0.01859 1.004 1.355e-05 -6.084e-06 0.985 1.021e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09263 0.09069 0.165 0.1959 0.9853 0.9911 0.09264 0.6697 0.8391 0.2475 ] Network output: [ 0.0001082 1 -8.794e-05 1.792e-06 -8.044e-07 0.9998 1.35e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000261 Epoch 8915 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009671 0.9964 0.9917 -2.093e-07 9.397e-08 -0.007435 -1.577e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003452 -0.003277 -0.007205 0.005731 0.9699 0.9743 0.006677 0.8287 0.8219 0.01704 ] Network output: [ 0.9999 0.0002707 0.0005416 -6.636e-06 2.979e-06 -0.0005762 -5.001e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2035 -0.03482 -0.1647 0.1857 0.9834 0.9932 0.2281 0.4339 0.8694 0.7122 ] Network output: [ -0.009574 1.003 1.009 -2.947e-07 1.323e-07 0.007983 -2.221e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00649 0.0005635 0.004429 0.003373 0.9889 0.9919 0.006615 0.8563 0.8933 0.01223 ] Network output: [ -0.0003289 0.001992 1.001 -2.078e-05 9.329e-06 0.9979 -1.566e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2164 0.1017 0.3449 0.1435 0.985 0.994 0.2171 0.438 0.8761 0.7062 ] Network output: [ 0.004209 -0.01988 0.9942 1.259e-05 -5.654e-06 1.017 9.492e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1079 0.09538 0.1836 0.1987 0.9873 0.9919 0.1079 0.7456 0.8634 0.3054 ] Network output: [ -0.003954 0.01858 1.004 1.354e-05 -6.077e-06 0.985 1.02e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09263 0.09069 0.165 0.1959 0.9853 0.9911 0.09264 0.6697 0.8391 0.2475 ] Network output: [ 0.0001081 1 -8.785e-05 1.79e-06 -8.035e-07 0.9998 1.349e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002609 Epoch 8916 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00967 0.9964 0.9917 -2.094e-07 9.401e-08 -0.007434 -1.578e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003452 -0.003277 -0.007204 0.005731 0.9699 0.9743 0.006677 0.8286 0.8219 0.01704 ] Network output: [ 0.9999 0.0002705 0.0005414 -6.628e-06 2.976e-06 -0.0005757 -4.995e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2035 -0.03482 -0.1647 0.1857 0.9834 0.9932 0.2281 0.4339 0.8694 0.7122 ] Network output: [ -0.009573 1.003 1.009 -2.946e-07 1.323e-07 0.007982 -2.22e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00649 0.0005636 0.004429 0.003373 0.9889 0.9919 0.006615 0.8563 0.8933 0.01223 ] Network output: [ -0.0003287 0.001991 1.001 -2.076e-05 9.319e-06 0.9979 -1.564e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2164 0.1017 0.3449 0.1435 0.985 0.994 0.2172 0.438 0.8761 0.7062 ] Network output: [ 0.004207 -0.01987 0.9942 1.258e-05 -5.648e-06 1.017 9.481e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1079 0.09538 0.1836 0.1987 0.9873 0.9919 0.108 0.7456 0.8634 0.3054 ] Network output: [ -0.003952 0.01857 1.004 1.352e-05 -6.071e-06 0.9851 1.019e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09263 0.09069 0.165 0.1959 0.9853 0.9911 0.09264 0.6697 0.8391 0.2475 ] Network output: [ 0.0001081 1 -8.776e-05 1.788e-06 -8.027e-07 0.9998 1.347e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002608 Epoch 8917 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009669 0.9964 0.9917 -2.095e-07 9.405e-08 -0.007434 -1.579e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003452 -0.003277 -0.007204 0.00573 0.9699 0.9743 0.006678 0.8286 0.8219 0.01704 ] Network output: [ 0.9999 0.0002702 0.0005411 -6.621e-06 2.972e-06 -0.0005753 -4.99e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2035 -0.03482 -0.1647 0.1857 0.9834 0.9932 0.2281 0.4339 0.8694 0.7122 ] Network output: [ -0.009572 1.003 1.009 -2.946e-07 1.322e-07 0.007981 -2.22e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006491 0.0005637 0.004428 0.003373 0.9889 0.9919 0.006616 0.8563 0.8933 0.01223 ] Network output: [ -0.0003285 0.00199 1.001 -2.073e-05 9.309e-06 0.9979 -1.563e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2164 0.1017 0.3449 0.1435 0.985 0.994 0.2172 0.438 0.8761 0.7062 ] Network output: [ 0.004206 -0.01986 0.9942 1.257e-05 -5.642e-06 1.017 9.47e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1079 0.09539 0.1836 0.1987 0.9873 0.9919 0.108 0.7456 0.8634 0.3054 ] Network output: [ -0.003951 0.01857 1.004 1.351e-05 -6.064e-06 0.9851 1.018e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09263 0.0907 0.165 0.1959 0.9853 0.9911 0.09265 0.6697 0.8391 0.2475 ] Network output: [ 0.000108 1 -8.766e-05 1.786e-06 -8.018e-07 0.9998 1.346e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002606 Epoch 8918 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009668 0.9964 0.9917 -2.096e-07 9.409e-08 -0.007434 -1.58e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003452 -0.003277 -0.007203 0.00573 0.9699 0.9743 0.006678 0.8286 0.8219 0.01703 ] Network output: [ 0.9999 0.00027 0.0005408 -6.613e-06 2.969e-06 -0.0005748 -4.984e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2035 -0.03482 -0.1646 0.1857 0.9834 0.9932 0.2281 0.4339 0.8694 0.7122 ] Network output: [ -0.009571 1.003 1.009 -2.945e-07 1.322e-07 0.00798 -2.219e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006491 0.0005638 0.004428 0.003372 0.9889 0.9919 0.006616 0.8563 0.8933 0.01223 ] Network output: [ -0.0003283 0.001989 1.001 -2.071e-05 9.298e-06 0.9979 -1.561e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2164 0.1017 0.345 0.1435 0.985 0.994 0.2172 0.438 0.8761 0.7062 ] Network output: [ 0.004204 -0.01986 0.9942 1.255e-05 -5.635e-06 1.017 9.46e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1079 0.09539 0.1836 0.1987 0.9873 0.9919 0.108 0.7456 0.8634 0.3054 ] Network output: [ -0.003949 0.01856 1.004 1.349e-05 -6.058e-06 0.9851 1.017e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09264 0.0907 0.165 0.1959 0.9853 0.9911 0.09265 0.6696 0.8391 0.2475 ] Network output: [ 0.000108 1 -8.757e-05 1.784e-06 -8.009e-07 0.9998 1.344e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002605 Epoch 8919 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009667 0.9964 0.9917 -2.097e-07 9.413e-08 -0.007433 -1.58e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003452 -0.003277 -0.007202 0.005729 0.9699 0.9743 0.006678 0.8286 0.8219 0.01703 ] Network output: [ 0.9999 0.0002697 0.0005405 -6.606e-06 2.966e-06 -0.0005744 -4.978e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2035 -0.03482 -0.1646 0.1857 0.9834 0.9932 0.2281 0.4339 0.8694 0.7122 ] Network output: [ -0.00957 1.003 1.009 -2.944e-07 1.322e-07 0.007979 -2.219e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006492 0.0005639 0.004428 0.003372 0.9889 0.9919 0.006617 0.8563 0.8933 0.01223 ] Network output: [ -0.000328 0.001989 1.001 -2.069e-05 9.288e-06 0.9979 -1.559e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2165 0.1017 0.345 0.1435 0.985 0.994 0.2172 0.438 0.8761 0.7062 ] Network output: [ 0.004203 -0.01985 0.9942 1.254e-05 -5.629e-06 1.017 9.449e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1079 0.0954 0.1836 0.1987 0.9873 0.9919 0.108 0.7455 0.8634 0.3054 ] Network output: [ -0.003948 0.01855 1.004 1.348e-05 -6.051e-06 0.9851 1.016e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09264 0.0907 0.165 0.1959 0.9853 0.9911 0.09265 0.6696 0.8391 0.2475 ] Network output: [ 0.0001079 1 -8.748e-05 1.782e-06 -8e-07 0.9998 1.343e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002603 Epoch 8920 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009666 0.9964 0.9917 -2.098e-07 9.417e-08 -0.007433 -1.581e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003452 -0.003277 -0.007201 0.005729 0.9699 0.9743 0.006678 0.8286 0.8219 0.01703 ] Network output: [ 0.9999 0.0002695 0.0005403 -6.599e-06 2.962e-06 -0.0005739 -4.973e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2035 -0.03482 -0.1646 0.1857 0.9834 0.9932 0.2281 0.4339 0.8694 0.7122 ] Network output: [ -0.009569 1.003 1.009 -2.944e-07 1.322e-07 0.007978 -2.218e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006492 0.000564 0.004428 0.003372 0.9889 0.9919 0.006617 0.8562 0.8933 0.01223 ] Network output: [ -0.0003278 0.001988 1.001 -2.066e-05 9.277e-06 0.9979 -1.557e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2165 0.1017 0.345 0.1435 0.985 0.994 0.2172 0.438 0.8761 0.7062 ] Network output: [ 0.004201 -0.01984 0.9942 1.252e-05 -5.623e-06 1.017 9.439e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1079 0.0954 0.1836 0.1987 0.9873 0.9919 0.108 0.7455 0.8634 0.3054 ] Network output: [ -0.003946 0.01855 1.004 1.346e-05 -6.044e-06 0.9851 1.015e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09264 0.0907 0.165 0.1959 0.9853 0.9911 0.09265 0.6696 0.8391 0.2475 ] Network output: [ 0.0001079 1 -8.738e-05 1.78e-06 -7.991e-07 0.9998 1.341e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002602 Epoch 8921 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009665 0.9964 0.9917 -2.099e-07 9.421e-08 -0.007433 -1.582e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003452 -0.003277 -0.007201 0.005728 0.9699 0.9743 0.006679 0.8286 0.8219 0.01703 ] Network output: [ 0.9999 0.0002692 0.00054 -6.591e-06 2.959e-06 -0.0005735 -4.967e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2035 -0.03482 -0.1646 0.1857 0.9834 0.9932 0.2281 0.4339 0.8694 0.7122 ] Network output: [ -0.009568 1.003 1.009 -2.943e-07 1.321e-07 0.007977 -2.218e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006493 0.0005641 0.004428 0.003372 0.9889 0.9919 0.006618 0.8562 0.8933 0.01223 ] Network output: [ -0.0003276 0.001987 1.001 -2.064e-05 9.267e-06 0.9979 -1.556e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2165 0.1017 0.345 0.1435 0.985 0.994 0.2172 0.438 0.876 0.7062 ] Network output: [ 0.0042 -0.01983 0.9942 1.251e-05 -5.616e-06 1.017 9.428e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1079 0.09541 0.1836 0.1987 0.9873 0.9919 0.108 0.7455 0.8634 0.3054 ] Network output: [ -0.003945 0.01854 1.004 1.345e-05 -6.038e-06 0.9851 1.014e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09264 0.09071 0.165 0.1959 0.9853 0.9911 0.09266 0.6696 0.8391 0.2475 ] Network output: [ 0.0001079 1 -8.729e-05 1.778e-06 -7.982e-07 0.9998 1.34e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002601 Epoch 8922 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009664 0.9964 0.9917 -2.099e-07 9.425e-08 -0.007432 -1.582e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003452 -0.003278 -0.0072 0.005728 0.9699 0.9743 0.006679 0.8286 0.8219 0.01703 ] Network output: [ 0.9999 0.000269 0.0005397 -6.584e-06 2.956e-06 -0.000573 -4.962e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2036 -0.03483 -0.1646 0.1857 0.9834 0.9932 0.2281 0.4339 0.8694 0.7122 ] Network output: [ -0.009567 1.003 1.009 -2.942e-07 1.321e-07 0.007976 -2.217e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006493 0.0005642 0.004428 0.003371 0.9889 0.9919 0.006618 0.8562 0.8933 0.01223 ] Network output: [ -0.0003274 0.001986 1.001 -2.062e-05 9.256e-06 0.9979 -1.554e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2165 0.1017 0.345 0.1435 0.985 0.994 0.2172 0.4379 0.876 0.7061 ] Network output: [ 0.004198 -0.01983 0.9942 1.25e-05 -5.61e-06 1.017 9.418e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1079 0.09541 0.1836 0.1987 0.9873 0.9919 0.108 0.7455 0.8634 0.3054 ] Network output: [ -0.003943 0.01853 1.004 1.343e-05 -6.031e-06 0.9851 1.012e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09265 0.09071 0.165 0.1959 0.9853 0.9911 0.09266 0.6696 0.8391 0.2475 ] Network output: [ 0.0001078 1 -8.72e-05 1.776e-06 -7.973e-07 0.9998 1.338e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002599 Epoch 8923 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009662 0.9964 0.9917 -2.1e-07 9.429e-08 -0.007432 -1.583e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003452 -0.003278 -0.007199 0.005727 0.9699 0.9743 0.006679 0.8286 0.8219 0.01703 ] Network output: [ 0.9999 0.0002687 0.0005394 -6.576e-06 2.952e-06 -0.0005726 -4.956e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2036 -0.03483 -0.1646 0.1857 0.9834 0.9932 0.2281 0.4339 0.8694 0.7121 ] Network output: [ -0.009566 1.003 1.009 -2.942e-07 1.321e-07 0.007975 -2.217e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006494 0.0005643 0.004428 0.003371 0.9889 0.9919 0.006619 0.8562 0.8933 0.01222 ] Network output: [ -0.0003272 0.001985 1.001 -2.06e-05 9.246e-06 0.9979 -1.552e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2165 0.1017 0.345 0.1435 0.985 0.994 0.2172 0.4379 0.876 0.7061 ] Network output: [ 0.004196 -0.01982 0.9942 1.248e-05 -5.604e-06 1.017 9.407e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1079 0.09542 0.1836 0.1987 0.9873 0.9919 0.108 0.7455 0.8634 0.3054 ] Network output: [ -0.003942 0.01852 1.004 1.342e-05 -6.025e-06 0.9851 1.011e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09265 0.09071 0.165 0.1959 0.9853 0.9911 0.09266 0.6696 0.8391 0.2475 ] Network output: [ 0.0001078 1 -8.711e-05 1.774e-06 -7.964e-07 0.9998 1.337e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002598 Epoch 8924 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009661 0.9964 0.9917 -2.101e-07 9.433e-08 -0.007432 -1.583e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003452 -0.003278 -0.007198 0.005727 0.9699 0.9743 0.006679 0.8286 0.8219 0.01703 ] Network output: [ 0.9999 0.0002685 0.0005392 -6.569e-06 2.949e-06 -0.0005721 -4.95e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2036 -0.03483 -0.1646 0.1857 0.9834 0.9932 0.2281 0.4339 0.8694 0.7121 ] Network output: [ -0.009565 1.003 1.009 -2.941e-07 1.32e-07 0.007974 -2.216e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006494 0.0005644 0.004428 0.003371 0.9889 0.9919 0.006619 0.8562 0.8933 0.01222 ] Network output: [ -0.0003269 0.001985 1.001 -2.057e-05 9.235e-06 0.9979 -1.55e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2165 0.1017 0.345 0.1434 0.985 0.994 0.2172 0.4379 0.876 0.7061 ] Network output: [ 0.004195 -0.01981 0.9942 1.247e-05 -5.598e-06 1.017 9.397e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1079 0.09542 0.1836 0.1987 0.9873 0.9919 0.108 0.7455 0.8634 0.3054 ] Network output: [ -0.00394 0.01852 1.004 1.34e-05 -6.018e-06 0.9851 1.01e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09265 0.09071 0.165 0.1959 0.9853 0.9911 0.09266 0.6696 0.8391 0.2475 ] Network output: [ 0.0001077 1 -8.701e-05 1.772e-06 -7.956e-07 0.9998 1.335e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002597 Epoch 8925 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00966 0.9964 0.9917 -2.102e-07 9.436e-08 -0.007431 -1.584e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003453 -0.003278 -0.007198 0.005726 0.9699 0.9743 0.00668 0.8286 0.8219 0.01703 ] Network output: [ 0.9999 0.0002682 0.0005389 -6.561e-06 2.946e-06 -0.0005717 -4.945e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2036 -0.03483 -0.1646 0.1857 0.9834 0.9932 0.2281 0.4339 0.8693 0.7121 ] Network output: [ -0.009564 1.003 1.009 -2.94e-07 1.32e-07 0.007973 -2.216e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006495 0.0005645 0.004428 0.00337 0.9889 0.9919 0.00662 0.8562 0.8933 0.01222 ] Network output: [ -0.0003267 0.001984 1.001 -2.055e-05 9.225e-06 0.9979 -1.549e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2165 0.1017 0.345 0.1434 0.985 0.994 0.2172 0.4379 0.876 0.7061 ] Network output: [ 0.004193 -0.0198 0.9942 1.245e-05 -5.591e-06 1.017 9.386e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1079 0.09543 0.1836 0.1987 0.9873 0.9919 0.108 0.7455 0.8634 0.3054 ] Network output: [ -0.003939 0.01851 1.004 1.339e-05 -6.011e-06 0.9851 1.009e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09265 0.09071 0.165 0.1959 0.9853 0.9911 0.09267 0.6695 0.839 0.2475 ] Network output: [ 0.0001077 1 -8.692e-05 1.77e-06 -7.947e-07 0.9998 1.334e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002595 Epoch 8926 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009659 0.9964 0.9917 -2.103e-07 9.44e-08 -0.007431 -1.585e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003453 -0.003278 -0.007197 0.005726 0.9699 0.9743 0.00668 0.8286 0.8219 0.01703 ] Network output: [ 0.9999 0.000268 0.0005386 -6.554e-06 2.942e-06 -0.0005713 -4.939e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2036 -0.03483 -0.1646 0.1856 0.9834 0.9932 0.2282 0.4338 0.8693 0.7121 ] Network output: [ -0.009563 1.003 1.009 -2.94e-07 1.32e-07 0.007972 -2.215e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006495 0.0005646 0.004428 0.00337 0.9889 0.9919 0.00662 0.8562 0.8933 0.01222 ] Network output: [ -0.0003265 0.001983 1.001 -2.053e-05 9.215e-06 0.9979 -1.547e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2165 0.1017 0.345 0.1434 0.985 0.994 0.2172 0.4379 0.876 0.7061 ] Network output: [ 0.004192 -0.0198 0.9942 1.244e-05 -5.585e-06 1.017 9.376e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1079 0.09543 0.1836 0.1987 0.9873 0.9919 0.108 0.7454 0.8634 0.3054 ] Network output: [ -0.003937 0.0185 1.004 1.338e-05 -6.005e-06 0.9851 1.008e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09265 0.09072 0.165 0.1959 0.9853 0.9911 0.09267 0.6695 0.839 0.2475 ] Network output: [ 0.0001076 1 -8.683e-05 1.768e-06 -7.938e-07 0.9998 1.333e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002594 Epoch 8927 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009658 0.9964 0.9917 -2.104e-07 9.444e-08 -0.007431 -1.585e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003453 -0.003278 -0.007196 0.005725 0.9699 0.9743 0.00668 0.8286 0.8219 0.01702 ] Network output: [ 0.9999 0.0002677 0.0005383 -6.547e-06 2.939e-06 -0.0005708 -4.934e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2036 -0.03483 -0.1645 0.1856 0.9834 0.9932 0.2282 0.4338 0.8693 0.7121 ] Network output: [ -0.009562 1.003 1.009 -2.939e-07 1.319e-07 0.007971 -2.215e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006496 0.0005647 0.004428 0.00337 0.9889 0.9919 0.006621 0.8562 0.8933 0.01222 ] Network output: [ -0.0003263 0.001982 1.001 -2.05e-05 9.204e-06 0.9979 -1.545e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2165 0.1018 0.345 0.1434 0.985 0.994 0.2172 0.4379 0.876 0.7061 ] Network output: [ 0.00419 -0.01979 0.9942 1.243e-05 -5.579e-06 1.017 9.365e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1079 0.09544 0.1836 0.1986 0.9873 0.9919 0.108 0.7454 0.8634 0.3054 ] Network output: [ -0.003936 0.01849 1.004 1.336e-05 -5.998e-06 0.9851 1.007e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09266 0.09072 0.165 0.1959 0.9853 0.9911 0.09267 0.6695 0.839 0.2475 ] Network output: [ 0.0001076 1 -8.674e-05 1.766e-06 -7.929e-07 0.9998 1.331e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002593 Epoch 8928 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009657 0.9964 0.9917 -2.104e-07 9.448e-08 -0.007431 -1.586e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003453 -0.003278 -0.007195 0.005725 0.9699 0.9743 0.00668 0.8286 0.8219 0.01702 ] Network output: [ 0.9999 0.0002675 0.0005381 -6.539e-06 2.936e-06 -0.0005704 -4.928e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2036 -0.03483 -0.1645 0.1856 0.9834 0.9932 0.2282 0.4338 0.8693 0.7121 ] Network output: [ -0.009561 1.003 1.009 -2.938e-07 1.319e-07 0.00797 -2.214e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006496 0.0005647 0.004428 0.00337 0.9889 0.9919 0.006621 0.8562 0.8933 0.01222 ] Network output: [ -0.0003261 0.001982 1.001 -2.048e-05 9.194e-06 0.9979 -1.543e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2165 0.1018 0.345 0.1434 0.985 0.994 0.2173 0.4379 0.876 0.7061 ] Network output: [ 0.004189 -0.01978 0.9942 1.241e-05 -5.573e-06 1.017 9.355e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1079 0.09544 0.1836 0.1986 0.9873 0.9919 0.108 0.7454 0.8634 0.3054 ] Network output: [ -0.003934 0.01849 1.004 1.335e-05 -5.992e-06 0.9851 1.006e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09266 0.09072 0.165 0.1959 0.9853 0.9911 0.09267 0.6695 0.839 0.2475 ] Network output: [ 0.0001076 1 -8.664e-05 1.764e-06 -7.92e-07 0.9998 1.33e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002591 Epoch 8929 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009656 0.9964 0.9917 -2.105e-07 9.451e-08 -0.00743 -1.587e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003453 -0.003278 -0.007195 0.005724 0.9699 0.9743 0.006681 0.8286 0.8219 0.01702 ] Network output: [ 0.9999 0.0002672 0.0005378 -6.532e-06 2.932e-06 -0.0005699 -4.923e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2036 -0.03483 -0.1645 0.1856 0.9834 0.9932 0.2282 0.4338 0.8693 0.7121 ] Network output: [ -0.00956 1.003 1.009 -2.938e-07 1.319e-07 0.007969 -2.214e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006497 0.0005648 0.004428 0.003369 0.9889 0.9919 0.006622 0.8562 0.8933 0.01222 ] Network output: [ -0.0003259 0.001981 1.001 -2.046e-05 9.184e-06 0.9979 -1.542e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2165 0.1018 0.345 0.1434 0.985 0.994 0.2173 0.4379 0.876 0.7061 ] Network output: [ 0.004187 -0.01977 0.9942 1.24e-05 -5.566e-06 1.017 9.344e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1079 0.09545 0.1836 0.1986 0.9873 0.9919 0.108 0.7454 0.8634 0.3054 ] Network output: [ -0.003933 0.01848 1.004 1.333e-05 -5.985e-06 0.9851 1.005e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09266 0.09072 0.165 0.1959 0.9853 0.9911 0.09268 0.6695 0.839 0.2475 ] Network output: [ 0.0001075 1 -8.655e-05 1.762e-06 -7.911e-07 0.9998 1.328e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000259 Epoch 8930 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009655 0.9964 0.9917 -2.106e-07 9.455e-08 -0.00743 -1.587e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003453 -0.003278 -0.007194 0.005724 0.9699 0.9743 0.006681 0.8286 0.8219 0.01702 ] Network output: [ 0.9999 0.000267 0.0005375 -6.525e-06 2.929e-06 -0.0005695 -4.917e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2036 -0.03483 -0.1645 0.1856 0.9834 0.9932 0.2282 0.4338 0.8693 0.7121 ] Network output: [ -0.009559 1.003 1.009 -2.937e-07 1.319e-07 0.007968 -2.213e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006497 0.0005649 0.004428 0.003369 0.9889 0.9919 0.006622 0.8562 0.8933 0.01222 ] Network output: [ -0.0003256 0.00198 1.001 -2.043e-05 9.173e-06 0.9979 -1.54e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2165 0.1018 0.345 0.1434 0.985 0.994 0.2173 0.4379 0.876 0.7061 ] Network output: [ 0.004186 -0.01977 0.9942 1.239e-05 -5.56e-06 1.017 9.334e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.108 0.09545 0.1836 0.1986 0.9873 0.9919 0.108 0.7454 0.8633 0.3054 ] Network output: [ -0.003931 0.01847 1.004 1.332e-05 -5.979e-06 0.9851 1.004e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09266 0.09073 0.165 0.1959 0.9853 0.9911 0.09268 0.6695 0.839 0.2475 ] Network output: [ 0.0001075 1 -8.646e-05 1.76e-06 -7.903e-07 0.9998 1.327e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002589 Epoch 8931 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009653 0.9964 0.9917 -2.107e-07 9.459e-08 -0.00743 -1.588e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003453 -0.003279 -0.007193 0.005723 0.9699 0.9743 0.006681 0.8286 0.8218 0.01702 ] Network output: [ 0.9999 0.0002667 0.0005373 -6.517e-06 2.926e-06 -0.000569 -4.912e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2036 -0.03484 -0.1645 0.1856 0.9834 0.9932 0.2282 0.4338 0.8693 0.7121 ] Network output: [ -0.009558 1.003 1.009 -2.936e-07 1.318e-07 0.007967 -2.213e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006498 0.000565 0.004428 0.003369 0.9889 0.9919 0.006623 0.8562 0.8933 0.01222 ] Network output: [ -0.0003254 0.001979 1.001 -2.041e-05 9.163e-06 0.9979 -1.538e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2166 0.1018 0.345 0.1434 0.985 0.994 0.2173 0.4379 0.876 0.7061 ] Network output: [ 0.004184 -0.01976 0.9942 1.237e-05 -5.554e-06 1.017 9.324e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.108 0.09546 0.1836 0.1986 0.9873 0.9919 0.108 0.7454 0.8633 0.3053 ] Network output: [ -0.00393 0.01846 1.004 1.33e-05 -5.972e-06 0.9851 1.003e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09267 0.09073 0.165 0.1959 0.9853 0.9911 0.09268 0.6694 0.839 0.2475 ] Network output: [ 0.0001074 1 -8.637e-05 1.758e-06 -7.894e-07 0.9998 1.325e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002587 Epoch 8932 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009652 0.9964 0.9917 -2.108e-07 9.462e-08 -0.007429 -1.588e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003453 -0.003279 -0.007192 0.005723 0.9699 0.9743 0.006681 0.8286 0.8218 0.01702 ] Network output: [ 0.9999 0.0002665 0.000537 -6.51e-06 2.922e-06 -0.0005686 -4.906e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2036 -0.03484 -0.1645 0.1856 0.9834 0.9932 0.2282 0.4338 0.8693 0.7121 ] Network output: [ -0.009557 1.003 1.009 -2.936e-07 1.318e-07 0.007966 -2.212e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006498 0.0005651 0.004428 0.003368 0.9889 0.9919 0.006623 0.8562 0.8933 0.01222 ] Network output: [ -0.0003252 0.001979 1.001 -2.039e-05 9.153e-06 0.9979 -1.536e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2166 0.1018 0.345 0.1434 0.985 0.994 0.2173 0.4379 0.876 0.7061 ] Network output: [ 0.004183 -0.01975 0.9942 1.236e-05 -5.548e-06 1.017 9.313e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.108 0.09546 0.1836 0.1986 0.9873 0.9919 0.108 0.7454 0.8633 0.3053 ] Network output: [ -0.003928 0.01846 1.004 1.329e-05 -5.965e-06 0.9851 1.001e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09267 0.09073 0.165 0.1959 0.9853 0.9911 0.09268 0.6694 0.839 0.2475 ] Network output: [ 0.0001074 1 -8.628e-05 1.756e-06 -7.885e-07 0.9998 1.324e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002586 Epoch 8933 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009651 0.9964 0.9917 -2.108e-07 9.466e-08 -0.007429 -1.589e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003453 -0.003279 -0.007192 0.005722 0.9699 0.9743 0.006682 0.8286 0.8218 0.01702 ] Network output: [ 0.9999 0.0002663 0.0005367 -6.502e-06 2.919e-06 -0.0005682 -4.9e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2036 -0.03484 -0.1645 0.1856 0.9834 0.9932 0.2282 0.4338 0.8693 0.7121 ] Network output: [ -0.009556 1.003 1.009 -2.935e-07 1.318e-07 0.007965 -2.212e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006499 0.0005652 0.004428 0.003368 0.9889 0.9919 0.006624 0.8562 0.8933 0.01222 ] Network output: [ -0.000325 0.001978 1.001 -2.036e-05 9.142e-06 0.9979 -1.535e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2166 0.1018 0.345 0.1434 0.985 0.994 0.2173 0.4379 0.876 0.7061 ] Network output: [ 0.004181 -0.01974 0.9942 1.234e-05 -5.542e-06 1.017 9.303e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.108 0.09547 0.1836 0.1986 0.9873 0.9919 0.108 0.7453 0.8633 0.3053 ] Network output: [ -0.003927 0.01845 1.004 1.327e-05 -5.959e-06 0.9851 1e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09267 0.09073 0.165 0.1959 0.9853 0.9911 0.09268 0.6694 0.839 0.2475 ] Network output: [ 0.0001074 1 -8.619e-05 1.754e-06 -7.876e-07 0.9998 1.322e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002585 Epoch 8934 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00965 0.9964 0.9917 -2.109e-07 9.469e-08 -0.007429 -1.59e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003453 -0.003279 -0.007191 0.005722 0.9699 0.9743 0.006682 0.8285 0.8218 0.01702 ] Network output: [ 0.9999 0.000266 0.0005364 -6.495e-06 2.916e-06 -0.0005677 -4.895e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2036 -0.03484 -0.1645 0.1856 0.9834 0.9932 0.2282 0.4338 0.8693 0.7121 ] Network output: [ -0.009556 1.003 1.009 -2.934e-07 1.317e-07 0.007964 -2.211e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006499 0.0005653 0.004428 0.003368 0.9889 0.9919 0.006624 0.8562 0.8933 0.01222 ] Network output: [ -0.0003248 0.001977 1.001 -2.034e-05 9.132e-06 0.9979 -1.533e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2166 0.1018 0.3451 0.1434 0.985 0.994 0.2173 0.4379 0.876 0.7061 ] Network output: [ 0.004179 -0.01974 0.9942 1.233e-05 -5.535e-06 1.017 9.292e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.108 0.09547 0.1836 0.1986 0.9873 0.9919 0.108 0.7453 0.8633 0.3053 ] Network output: [ -0.003925 0.01844 1.004 1.326e-05 -5.952e-06 0.9851 9.992e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09267 0.09074 0.165 0.1959 0.9853 0.9911 0.09269 0.6694 0.839 0.2475 ] Network output: [ 0.0001073 1 -8.61e-05 1.753e-06 -7.868e-07 0.9998 1.321e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002583 Epoch 8935 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009649 0.9964 0.9917 -2.11e-07 9.473e-08 -0.007428 -1.59e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003454 -0.003279 -0.00719 0.005721 0.9699 0.9743 0.006682 0.8285 0.8218 0.01702 ] Network output: [ 0.9999 0.0002658 0.0005362 -6.488e-06 2.913e-06 -0.0005673 -4.889e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2037 -0.03484 -0.1644 0.1856 0.9834 0.9932 0.2282 0.4338 0.8693 0.7121 ] Network output: [ -0.009555 1.003 1.009 -2.934e-07 1.317e-07 0.007963 -2.211e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0065 0.0005654 0.004428 0.003368 0.9889 0.9919 0.006625 0.8561 0.8933 0.01221 ] Network output: [ -0.0003246 0.001976 1.001 -2.032e-05 9.122e-06 0.9979 -1.531e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2166 0.1018 0.3451 0.1434 0.985 0.994 0.2173 0.4378 0.876 0.7061 ] Network output: [ 0.004178 -0.01973 0.9942 1.232e-05 -5.529e-06 1.017 9.282e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.108 0.09548 0.1836 0.1986 0.9873 0.9919 0.1081 0.7453 0.8633 0.3053 ] Network output: [ -0.003924 0.01843 1.004 1.324e-05 -5.946e-06 0.9851 9.981e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09268 0.09074 0.165 0.1959 0.9853 0.9911 0.09269 0.6694 0.839 0.2476 ] Network output: [ 0.0001073 1 -8.6e-05 1.751e-06 -7.859e-07 0.9998 1.319e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002582 Epoch 8936 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009648 0.9964 0.9917 -2.111e-07 9.476e-08 -0.007428 -1.591e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003454 -0.003279 -0.007189 0.005721 0.9699 0.9743 0.006682 0.8285 0.8218 0.01702 ] Network output: [ 0.9999 0.0002655 0.0005359 -6.48e-06 2.909e-06 -0.0005668 -4.884e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2037 -0.03484 -0.1644 0.1856 0.9834 0.9932 0.2282 0.4338 0.8693 0.7121 ] Network output: [ -0.009554 1.003 1.009 -2.933e-07 1.317e-07 0.007962 -2.21e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0065 0.0005655 0.004427 0.003367 0.9889 0.9919 0.006625 0.8561 0.8933 0.01221 ] Network output: [ -0.0003244 0.001976 1.001 -2.03e-05 9.111e-06 0.9979 -1.53e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2166 0.1018 0.3451 0.1434 0.985 0.994 0.2173 0.4378 0.876 0.7061 ] Network output: [ 0.004176 -0.01972 0.9942 1.23e-05 -5.523e-06 1.017 9.272e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.108 0.09548 0.1836 0.1986 0.9873 0.9919 0.1081 0.7453 0.8633 0.3053 ] Network output: [ -0.003922 0.01843 1.004 1.323e-05 -5.939e-06 0.9851 9.97e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09268 0.09074 0.165 0.1959 0.9853 0.9911 0.09269 0.6694 0.839 0.2476 ] Network output: [ 0.0001072 1 -8.591e-05 1.749e-06 -7.85e-07 0.9998 1.318e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002581 Epoch 8937 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009647 0.9964 0.9917 -2.112e-07 9.48e-08 -0.007428 -1.591e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003454 -0.003279 -0.007189 0.00572 0.9699 0.9743 0.006683 0.8285 0.8218 0.01701 ] Network output: [ 0.9999 0.0002653 0.0005356 -6.473e-06 2.906e-06 -0.0005664 -4.878e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2037 -0.03484 -0.1644 0.1856 0.9834 0.9932 0.2283 0.4338 0.8693 0.7121 ] Network output: [ -0.009553 1.003 1.009 -2.932e-07 1.316e-07 0.007961 -2.21e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006501 0.0005656 0.004427 0.003367 0.9889 0.9919 0.006626 0.8561 0.8933 0.01221 ] Network output: [ -0.0003241 0.001975 1.001 -2.027e-05 9.101e-06 0.9979 -1.528e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2166 0.1018 0.3451 0.1434 0.985 0.994 0.2173 0.4378 0.876 0.7061 ] Network output: [ 0.004175 -0.01971 0.9942 1.229e-05 -5.517e-06 1.017 9.261e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.108 0.09549 0.1836 0.1986 0.9873 0.9919 0.1081 0.7453 0.8633 0.3053 ] Network output: [ -0.003921 0.01842 1.004 1.322e-05 -5.933e-06 0.9851 9.959e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09268 0.09074 0.165 0.1959 0.9853 0.9911 0.09269 0.6693 0.839 0.2476 ] Network output: [ 0.0001072 1 -8.582e-05 1.747e-06 -7.841e-07 0.9998 1.316e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002579 Epoch 8938 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009646 0.9964 0.9917 -2.112e-07 9.483e-08 -0.007427 -1.592e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003454 -0.003279 -0.007188 0.00572 0.9699 0.9743 0.006683 0.8285 0.8218 0.01701 ] Network output: [ 0.9999 0.000265 0.0005354 -6.466e-06 2.903e-06 -0.0005659 -4.873e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2037 -0.03484 -0.1644 0.1856 0.9834 0.9932 0.2283 0.4338 0.8693 0.7121 ] Network output: [ -0.009552 1.003 1.009 -2.932e-07 1.316e-07 0.00796 -2.209e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006501 0.0005657 0.004427 0.003367 0.9889 0.9919 0.006626 0.8561 0.8933 0.01221 ] Network output: [ -0.0003239 0.001974 1.001 -2.025e-05 9.091e-06 0.9979 -1.526e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2166 0.1018 0.3451 0.1434 0.985 0.994 0.2173 0.4378 0.876 0.7061 ] Network output: [ 0.004173 -0.01971 0.9942 1.228e-05 -5.511e-06 1.017 9.251e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.108 0.09549 0.1836 0.1986 0.9873 0.9919 0.1081 0.7453 0.8633 0.3053 ] Network output: [ -0.003919 0.01841 1.004 1.32e-05 -5.926e-06 0.9851 9.949e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09268 0.09075 0.165 0.1959 0.9853 0.9911 0.0927 0.6693 0.839 0.2476 ] Network output: [ 0.0001071 1 -8.573e-05 1.745e-06 -7.833e-07 0.9998 1.315e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002578 Epoch 8939 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009644 0.9964 0.9917 -2.113e-07 9.487e-08 -0.007427 -1.593e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003454 -0.00328 -0.007187 0.005719 0.9699 0.9743 0.006683 0.8285 0.8218 0.01701 ] Network output: [ 0.9999 0.0002648 0.0005351 -6.459e-06 2.899e-06 -0.0005655 -4.867e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2037 -0.03484 -0.1644 0.1856 0.9834 0.9932 0.2283 0.4337 0.8693 0.7121 ] Network output: [ -0.009551 1.003 1.009 -2.931e-07 1.316e-07 0.007959 -2.209e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006502 0.0005658 0.004427 0.003366 0.9889 0.9919 0.006627 0.8561 0.8933 0.01221 ] Network output: [ -0.0003237 0.001973 1.001 -2.023e-05 9.081e-06 0.9979 -1.524e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2166 0.1018 0.3451 0.1434 0.985 0.994 0.2174 0.4378 0.876 0.7061 ] Network output: [ 0.004172 -0.0197 0.9942 1.226e-05 -5.505e-06 1.017 9.241e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.108 0.0955 0.1836 0.1986 0.9873 0.9919 0.1081 0.7452 0.8633 0.3053 ] Network output: [ -0.003918 0.0184 1.004 1.319e-05 -5.92e-06 0.9851 9.938e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09269 0.09075 0.165 0.1959 0.9853 0.9911 0.0927 0.6693 0.839 0.2476 ] Network output: [ 0.0001071 1 -8.564e-05 1.743e-06 -7.824e-07 0.9998 1.313e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002577 Epoch 8940 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009643 0.9964 0.9917 -2.114e-07 9.49e-08 -0.007427 -1.593e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003454 -0.00328 -0.007186 0.005719 0.9699 0.9743 0.006683 0.8285 0.8218 0.01701 ] Network output: [ 0.9999 0.0002645 0.0005348 -6.451e-06 2.896e-06 -0.0005651 -4.862e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2037 -0.03485 -0.1644 0.1856 0.9834 0.9932 0.2283 0.4337 0.8693 0.7121 ] Network output: [ -0.00955 1.003 1.009 -2.93e-07 1.315e-07 0.007958 -2.208e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006502 0.0005659 0.004427 0.003366 0.9889 0.9919 0.006627 0.8561 0.8933 0.01221 ] Network output: [ -0.0003235 0.001973 1.001 -2.02e-05 9.07e-06 0.9979 -1.523e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2166 0.1018 0.3451 0.1434 0.985 0.994 0.2174 0.4378 0.876 0.706 ] Network output: [ 0.00417 -0.01969 0.9942 1.225e-05 -5.498e-06 1.017 9.23e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.108 0.0955 0.1836 0.1986 0.9873 0.9919 0.1081 0.7452 0.8633 0.3053 ] Network output: [ -0.003916 0.0184 1.004 1.317e-05 -5.913e-06 0.9851 9.927e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09269 0.09075 0.165 0.1959 0.9853 0.9911 0.0927 0.6693 0.839 0.2476 ] Network output: [ 0.0001071 1 -8.555e-05 1.741e-06 -7.815e-07 0.9998 1.312e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002575 Epoch 8941 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009642 0.9964 0.9917 -2.115e-07 9.494e-08 -0.007426 -1.594e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003454 -0.00328 -0.007186 0.005718 0.9699 0.9743 0.006684 0.8285 0.8218 0.01701 ] Network output: [ 0.9999 0.0002643 0.0005345 -6.444e-06 2.893e-06 -0.0005646 -4.856e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2037 -0.03485 -0.1644 0.1856 0.9834 0.9932 0.2283 0.4337 0.8693 0.7121 ] Network output: [ -0.009549 1.003 1.009 -2.929e-07 1.315e-07 0.007957 -2.208e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006503 0.000566 0.004427 0.003366 0.9889 0.9919 0.006628 0.8561 0.8933 0.01221 ] Network output: [ -0.0003233 0.001972 1.001 -2.018e-05 9.06e-06 0.9979 -1.521e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2166 0.1018 0.3451 0.1434 0.985 0.994 0.2174 0.4378 0.876 0.706 ] Network output: [ 0.004169 -0.01969 0.9942 1.223e-05 -5.492e-06 1.017 9.22e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.108 0.09551 0.1836 0.1986 0.9873 0.9919 0.1081 0.7452 0.8633 0.3053 ] Network output: [ -0.003915 0.01839 1.004 1.316e-05 -5.907e-06 0.9851 9.916e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09269 0.09075 0.165 0.1959 0.9853 0.9911 0.0927 0.6693 0.839 0.2476 ] Network output: [ 0.000107 1 -8.546e-05 1.739e-06 -7.807e-07 0.9998 1.31e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002574 Epoch 8942 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009641 0.9964 0.9917 -2.115e-07 9.497e-08 -0.007426 -1.594e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003454 -0.00328 -0.007185 0.005718 0.9699 0.9743 0.006684 0.8285 0.8218 0.01701 ] Network output: [ 0.9999 0.000264 0.0005343 -6.437e-06 2.89e-06 -0.0005642 -4.851e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2037 -0.03485 -0.1644 0.1856 0.9834 0.9932 0.2283 0.4337 0.8693 0.7121 ] Network output: [ -0.009548 1.003 1.009 -2.929e-07 1.315e-07 0.007956 -2.207e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006503 0.000566 0.004427 0.003366 0.9889 0.9919 0.006628 0.8561 0.8933 0.01221 ] Network output: [ -0.0003231 0.001971 1.001 -2.016e-05 9.05e-06 0.9979 -1.519e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2167 0.1018 0.3451 0.1434 0.985 0.994 0.2174 0.4378 0.876 0.706 ] Network output: [ 0.004167 -0.01968 0.9942 1.222e-05 -5.486e-06 1.017 9.21e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.108 0.09551 0.1836 0.1986 0.9873 0.9919 0.1081 0.7452 0.8633 0.3053 ] Network output: [ -0.003913 0.01838 1.004 1.314e-05 -5.9e-06 0.9851 9.905e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09269 0.09076 0.165 0.1959 0.9853 0.9911 0.09271 0.6693 0.839 0.2476 ] Network output: [ 0.000107 1 -8.537e-05 1.737e-06 -7.798e-07 0.9998 1.309e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002573 Epoch 8943 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00964 0.9964 0.9917 -2.116e-07 9.5e-08 -0.007426 -1.595e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003454 -0.00328 -0.007184 0.005717 0.9699 0.9743 0.006684 0.8285 0.8218 0.01701 ] Network output: [ 0.9999 0.0002638 0.000534 -6.429e-06 2.886e-06 -0.0005638 -4.845e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2037 -0.03485 -0.1644 0.1856 0.9834 0.9932 0.2283 0.4337 0.8693 0.712 ] Network output: [ -0.009547 1.003 1.009 -2.928e-07 1.314e-07 0.007955 -2.207e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006504 0.0005661 0.004427 0.003365 0.9889 0.9919 0.006629 0.8561 0.8933 0.01221 ] Network output: [ -0.0003228 0.00197 1.001 -2.014e-05 9.04e-06 0.9979 -1.518e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2167 0.1018 0.3451 0.1434 0.985 0.994 0.2174 0.4378 0.876 0.706 ] Network output: [ 0.004165 -0.01967 0.9942 1.221e-05 -5.48e-06 1.017 9.199e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.108 0.09552 0.1836 0.1986 0.9873 0.9919 0.1081 0.7452 0.8633 0.3053 ] Network output: [ -0.003912 0.01837 1.004 1.313e-05 -5.894e-06 0.9851 9.894e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0927 0.09076 0.165 0.1959 0.9853 0.9911 0.09271 0.6693 0.839 0.2476 ] Network output: [ 0.0001069 1 -8.528e-05 1.735e-06 -7.789e-07 0.9998 1.308e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002571 Epoch 8944 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009639 0.9964 0.9917 -2.117e-07 9.504e-08 -0.007425 -1.595e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003454 -0.00328 -0.007183 0.005717 0.9699 0.9743 0.006684 0.8285 0.8218 0.01701 ] Network output: [ 0.9999 0.0002636 0.0005337 -6.422e-06 2.883e-06 -0.0005633 -4.84e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2037 -0.03485 -0.1643 0.1856 0.9834 0.9932 0.2283 0.4337 0.8693 0.712 ] Network output: [ -0.009546 1.003 1.009 -2.927e-07 1.314e-07 0.007954 -2.206e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006504 0.0005662 0.004427 0.003365 0.9889 0.9919 0.006629 0.8561 0.8933 0.01221 ] Network output: [ -0.0003226 0.00197 1.001 -2.011e-05 9.03e-06 0.9979 -1.516e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2167 0.1018 0.3451 0.1434 0.985 0.994 0.2174 0.4378 0.876 0.706 ] Network output: [ 0.004164 -0.01966 0.9942 1.219e-05 -5.474e-06 1.017 9.189e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.108 0.09552 0.1836 0.1986 0.9873 0.9919 0.1081 0.7452 0.8633 0.3053 ] Network output: [ -0.00391 0.01837 1.004 1.311e-05 -5.887e-06 0.9852 9.883e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0927 0.09076 0.165 0.1959 0.9853 0.9911 0.09271 0.6692 0.839 0.2476 ] Network output: [ 0.0001069 1 -8.519e-05 1.733e-06 -7.781e-07 0.9998 1.306e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000257 Epoch 8945 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009638 0.9964 0.9917 -2.118e-07 9.507e-08 -0.007425 -1.596e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003454 -0.00328 -0.007183 0.005717 0.9699 0.9743 0.006685 0.8285 0.8218 0.01701 ] Network output: [ 0.9999 0.0002633 0.0005335 -6.415e-06 2.88e-06 -0.0005629 -4.834e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2037 -0.03485 -0.1643 0.1856 0.9834 0.9932 0.2283 0.4337 0.8693 0.712 ] Network output: [ -0.009545 1.003 1.009 -2.926e-07 1.314e-07 0.007953 -2.205e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006505 0.0005663 0.004427 0.003365 0.9889 0.9919 0.00663 0.8561 0.8933 0.01221 ] Network output: [ -0.0003224 0.001969 1.001 -2.009e-05 9.019e-06 0.9979 -1.514e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2167 0.1019 0.3451 0.1434 0.985 0.994 0.2174 0.4378 0.876 0.706 ] Network output: [ 0.004162 -0.01966 0.9942 1.218e-05 -5.468e-06 1.017 9.179e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.108 0.09553 0.1836 0.1986 0.9873 0.9919 0.1081 0.7452 0.8633 0.3053 ] Network output: [ -0.003909 0.01836 1.004 1.31e-05 -5.881e-06 0.9852 9.872e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0927 0.09076 0.165 0.1959 0.9853 0.9911 0.09271 0.6692 0.839 0.2476 ] Network output: [ 0.0001068 1 -8.51e-05 1.731e-06 -7.772e-07 0.9998 1.305e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002569 Epoch 8946 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009637 0.9964 0.9917 -2.118e-07 9.51e-08 -0.007425 -1.596e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003455 -0.00328 -0.007182 0.005716 0.9699 0.9743 0.006685 0.8285 0.8218 0.017 ] Network output: [ 0.9999 0.0002631 0.0005332 -6.408e-06 2.877e-06 -0.0005624 -4.829e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2037 -0.03485 -0.1643 0.1856 0.9834 0.9932 0.2283 0.4337 0.8693 0.712 ] Network output: [ -0.009544 1.003 1.009 -2.926e-07 1.313e-07 0.007952 -2.205e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006505 0.0005664 0.004427 0.003364 0.9889 0.9919 0.00663 0.8561 0.8933 0.0122 ] Network output: [ -0.0003222 0.001968 1.001 -2.007e-05 9.009e-06 0.9979 -1.512e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2167 0.1019 0.3451 0.1434 0.985 0.994 0.2174 0.4378 0.876 0.706 ] Network output: [ 0.004161 -0.01965 0.9942 1.217e-05 -5.462e-06 1.017 9.168e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.108 0.09553 0.1836 0.1986 0.9873 0.9919 0.1081 0.7451 0.8633 0.3053 ] Network output: [ -0.003907 0.01835 1.004 1.309e-05 -5.875e-06 0.9852 9.862e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0927 0.09076 0.165 0.1959 0.9853 0.9911 0.09272 0.6692 0.839 0.2476 ] Network output: [ 0.0001068 1 -8.501e-05 1.729e-06 -7.763e-07 0.9998 1.303e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002567 Epoch 8947 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009636 0.9964 0.9917 -2.119e-07 9.513e-08 -0.007425 -1.597e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003455 -0.00328 -0.007181 0.005716 0.9699 0.9743 0.006685 0.8285 0.8218 0.017 ] Network output: [ 0.9999 0.0002628 0.0005329 -6.4e-06 2.873e-06 -0.000562 -4.824e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2037 -0.03485 -0.1643 0.1856 0.9834 0.9932 0.2283 0.4337 0.8693 0.712 ] Network output: [ -0.009543 1.003 1.009 -2.925e-07 1.313e-07 0.007951 -2.204e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006506 0.0005665 0.004427 0.003364 0.9889 0.9919 0.006631 0.8561 0.8933 0.0122 ] Network output: [ -0.000322 0.001967 1.001 -2.005e-05 8.999e-06 0.9979 -1.511e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2167 0.1019 0.3451 0.1434 0.985 0.994 0.2174 0.4378 0.876 0.706 ] Network output: [ 0.004159 -0.01964 0.9942 1.215e-05 -5.456e-06 1.017 9.158e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.108 0.09554 0.1836 0.1986 0.9873 0.9919 0.1081 0.7451 0.8633 0.3053 ] Network output: [ -0.003906 0.01834 1.004 1.307e-05 -5.868e-06 0.9852 9.851e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0927 0.09077 0.165 0.1959 0.9853 0.9911 0.09272 0.6692 0.839 0.2476 ] Network output: [ 0.0001068 1 -8.492e-05 1.727e-06 -7.755e-07 0.9998 1.302e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002566 Epoch 8948 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009634 0.9964 0.9917 -2.12e-07 9.517e-08 -0.007424 -1.598e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003455 -0.003281 -0.00718 0.005715 0.9699 0.9743 0.006685 0.8285 0.8218 0.017 ] Network output: [ 0.9999 0.0002626 0.0005327 -6.393e-06 2.87e-06 -0.0005616 -4.818e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2037 -0.03485 -0.1643 0.1856 0.9834 0.9932 0.2284 0.4337 0.8693 0.712 ] Network output: [ -0.009542 1.003 1.009 -2.924e-07 1.313e-07 0.00795 -2.204e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006506 0.0005666 0.004427 0.003364 0.9889 0.9919 0.006631 0.8561 0.8932 0.0122 ] Network output: [ -0.0003218 0.001967 1.001 -2.002e-05 8.989e-06 0.9979 -1.509e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2167 0.1019 0.3451 0.1434 0.985 0.994 0.2174 0.4377 0.876 0.706 ] Network output: [ 0.004158 -0.01963 0.9942 1.214e-05 -5.449e-06 1.017 9.148e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.108 0.09554 0.1836 0.1986 0.9873 0.9919 0.1081 0.7451 0.8633 0.3053 ] Network output: [ -0.003904 0.01834 1.004 1.306e-05 -5.862e-06 0.9852 9.84e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09271 0.09077 0.165 0.1959 0.9853 0.9911 0.09272 0.6692 0.8389 0.2476 ] Network output: [ 0.0001067 1 -8.483e-05 1.725e-06 -7.746e-07 0.9998 1.3e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002565 Epoch 8949 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009633 0.9964 0.9917 -2.121e-07 9.52e-08 -0.007424 -1.598e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003455 -0.003281 -0.00718 0.005715 0.9699 0.9743 0.006686 0.8285 0.8218 0.017 ] Network output: [ 0.9999 0.0002623 0.0005324 -6.386e-06 2.867e-06 -0.0005611 -4.813e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2038 -0.03485 -0.1643 0.1855 0.9834 0.9932 0.2284 0.4337 0.8693 0.712 ] Network output: [ -0.009541 1.003 1.009 -2.924e-07 1.312e-07 0.007949 -2.203e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006507 0.0005667 0.004427 0.003363 0.9889 0.9919 0.006632 0.8561 0.8932 0.0122 ] Network output: [ -0.0003216 0.001966 1.001 -2e-05 8.979e-06 0.9979 -1.507e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2167 0.1019 0.3451 0.1434 0.985 0.994 0.2174 0.4377 0.876 0.706 ] Network output: [ 0.004156 -0.01963 0.9942 1.212e-05 -5.443e-06 1.017 9.138e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1081 0.09555 0.1836 0.1986 0.9873 0.9919 0.1081 0.7451 0.8633 0.3053 ] Network output: [ -0.003903 0.01833 1.004 1.304e-05 -5.855e-06 0.9852 9.829e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09271 0.09077 0.165 0.1959 0.9853 0.9911 0.09272 0.6692 0.8389 0.2476 ] Network output: [ 0.0001067 1 -8.475e-05 1.723e-06 -7.737e-07 0.9998 1.299e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002563 Epoch 8950 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009632 0.9964 0.9917 -2.121e-07 9.523e-08 -0.007424 -1.599e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003455 -0.003281 -0.007179 0.005714 0.9699 0.9743 0.006686 0.8285 0.8218 0.017 ] Network output: [ 0.9999 0.0002621 0.0005321 -6.379e-06 2.864e-06 -0.0005607 -4.807e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2038 -0.03486 -0.1643 0.1855 0.9834 0.9932 0.2284 0.4337 0.8693 0.712 ] Network output: [ -0.00954 1.003 1.009 -2.923e-07 1.312e-07 0.007948 -2.203e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006507 0.0005668 0.004427 0.003363 0.9889 0.9919 0.006632 0.8561 0.8932 0.0122 ] Network output: [ -0.0003213 0.001965 1.001 -1.998e-05 8.969e-06 0.9979 -1.506e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2167 0.1019 0.3452 0.1434 0.985 0.994 0.2174 0.4377 0.876 0.706 ] Network output: [ 0.004155 -0.01962 0.9942 1.211e-05 -5.437e-06 1.017 9.127e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1081 0.09555 0.1836 0.1986 0.9873 0.9919 0.1081 0.7451 0.8633 0.3053 ] Network output: [ -0.003901 0.01832 1.004 1.303e-05 -5.849e-06 0.9852 9.818e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09271 0.09077 0.165 0.1959 0.9853 0.9911 0.09273 0.6691 0.8389 0.2476 ] Network output: [ 0.0001066 1 -8.466e-05 1.722e-06 -7.729e-07 0.9998 1.297e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002562 Epoch 8951 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009631 0.9964 0.9917 -2.122e-07 9.526e-08 -0.007423 -1.599e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003455 -0.003281 -0.007178 0.005714 0.9699 0.9743 0.006686 0.8284 0.8218 0.017 ] Network output: [ 0.9999 0.0002618 0.0005319 -6.372e-06 2.86e-06 -0.0005603 -4.802e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2038 -0.03486 -0.1643 0.1855 0.9834 0.9932 0.2284 0.4337 0.8693 0.712 ] Network output: [ -0.009539 1.003 1.009 -2.922e-07 1.312e-07 0.007947 -2.202e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006508 0.0005669 0.004427 0.003363 0.9889 0.9919 0.006633 0.856 0.8932 0.0122 ] Network output: [ -0.0003211 0.001964 1.001 -1.995e-05 8.958e-06 0.9979 -1.504e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2167 0.1019 0.3452 0.1434 0.985 0.994 0.2175 0.4377 0.876 0.706 ] Network output: [ 0.004153 -0.01961 0.9942 1.21e-05 -5.431e-06 1.017 9.117e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1081 0.09556 0.1836 0.1986 0.9873 0.9919 0.1081 0.7451 0.8633 0.3053 ] Network output: [ -0.0039 0.01831 1.004 1.301e-05 -5.842e-06 0.9852 9.808e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09271 0.09078 0.165 0.1959 0.9853 0.9911 0.09273 0.6691 0.8389 0.2476 ] Network output: [ 0.0001066 1 -8.457e-05 1.72e-06 -7.72e-07 0.9998 1.296e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002561 Epoch 8952 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00963 0.9964 0.9917 -2.123e-07 9.529e-08 -0.007423 -1.6e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003455 -0.003281 -0.007178 0.005713 0.9699 0.9743 0.006686 0.8284 0.8218 0.017 ] Network output: [ 0.9999 0.0002616 0.0005316 -6.364e-06 2.857e-06 -0.0005598 -4.796e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2038 -0.03486 -0.1643 0.1855 0.9834 0.9932 0.2284 0.4337 0.8693 0.712 ] Network output: [ -0.009539 1.003 1.009 -2.921e-07 1.311e-07 0.007946 -2.202e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006508 0.000567 0.004427 0.003363 0.9889 0.9919 0.006633 0.856 0.8932 0.0122 ] Network output: [ -0.0003209 0.001964 1.001 -1.993e-05 8.948e-06 0.9979 -1.502e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2167 0.1019 0.3452 0.1434 0.985 0.994 0.2175 0.4377 0.876 0.706 ] Network output: [ 0.004152 -0.0196 0.9942 1.208e-05 -5.425e-06 1.017 9.107e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1081 0.09556 0.1836 0.1986 0.9873 0.9919 0.1081 0.745 0.8633 0.3053 ] Network output: [ -0.003898 0.01831 1.004 1.3e-05 -5.836e-06 0.9852 9.797e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09272 0.09078 0.165 0.1959 0.9853 0.9911 0.09273 0.6691 0.8389 0.2476 ] Network output: [ 0.0001066 1 -8.448e-05 1.718e-06 -7.712e-07 0.9998 1.295e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002559 Epoch 8953 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009629 0.9964 0.9917 -2.123e-07 9.532e-08 -0.007423 -1.6e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003455 -0.003281 -0.007177 0.005713 0.9699 0.9743 0.006686 0.8284 0.8218 0.017 ] Network output: [ 0.9999 0.0002614 0.0005313 -6.357e-06 2.854e-06 -0.0005594 -4.791e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2038 -0.03486 -0.1642 0.1855 0.9834 0.9932 0.2284 0.4336 0.8693 0.712 ] Network output: [ -0.009538 1.003 1.009 -2.921e-07 1.311e-07 0.007945 -2.201e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006509 0.0005671 0.004427 0.003362 0.9889 0.9919 0.006634 0.856 0.8932 0.0122 ] Network output: [ -0.0003207 0.001963 1.001 -1.991e-05 8.938e-06 0.9979 -1.5e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2167 0.1019 0.3452 0.1434 0.985 0.994 0.2175 0.4377 0.876 0.706 ] Network output: [ 0.00415 -0.0196 0.9942 1.207e-05 -5.419e-06 1.017 9.097e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1081 0.09557 0.1836 0.1986 0.9873 0.9919 0.1081 0.745 0.8633 0.3053 ] Network output: [ -0.003897 0.0183 1.004 1.299e-05 -5.83e-06 0.9852 9.786e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09272 0.09078 0.165 0.1959 0.9853 0.9911 0.09273 0.6691 0.8389 0.2476 ] Network output: [ 0.0001065 1 -8.439e-05 1.716e-06 -7.703e-07 0.9998 1.293e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002558 Epoch 8954 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009628 0.9964 0.9917 -2.124e-07 9.535e-08 -0.007422 -1.601e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003455 -0.003281 -0.007176 0.005712 0.9699 0.9743 0.006687 0.8284 0.8218 0.017 ] Network output: [ 0.9999 0.0002611 0.0005311 -6.35e-06 2.851e-06 -0.000559 -4.786e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2038 -0.03486 -0.1642 0.1855 0.9834 0.9932 0.2284 0.4336 0.8693 0.712 ] Network output: [ -0.009537 1.003 1.009 -2.92e-07 1.311e-07 0.007944 -2.2e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006509 0.0005672 0.004426 0.003362 0.9889 0.9919 0.006634 0.856 0.8932 0.0122 ] Network output: [ -0.0003205 0.001962 1.001 -1.989e-05 8.928e-06 0.9979 -1.499e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2168 0.1019 0.3452 0.1434 0.985 0.994 0.2175 0.4377 0.876 0.706 ] Network output: [ 0.004148 -0.01959 0.9942 1.206e-05 -5.413e-06 1.017 9.087e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1081 0.09557 0.1836 0.1986 0.9873 0.9919 0.1082 0.745 0.8633 0.3053 ] Network output: [ -0.003896 0.01829 1.004 1.297e-05 -5.823e-06 0.9852 9.775e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09272 0.09078 0.165 0.1959 0.9853 0.9911 0.09273 0.6691 0.8389 0.2476 ] Network output: [ 0.0001065 1 -8.43e-05 1.714e-06 -7.694e-07 0.9998 1.292e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002557 Epoch 8955 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009627 0.9964 0.9917 -2.125e-07 9.538e-08 -0.007422 -1.601e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003455 -0.003281 -0.007175 0.005712 0.9699 0.9743 0.006687 0.8284 0.8218 0.01699 ] Network output: [ 0.9999 0.0002609 0.0005308 -6.343e-06 2.848e-06 -0.0005585 -4.78e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2038 -0.03486 -0.1642 0.1855 0.9834 0.9932 0.2284 0.4336 0.8693 0.712 ] Network output: [ -0.009536 1.003 1.009 -2.919e-07 1.31e-07 0.007943 -2.2e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00651 0.0005672 0.004426 0.003362 0.9889 0.9919 0.006635 0.856 0.8932 0.0122 ] Network output: [ -0.0003203 0.001961 1.001 -1.986e-05 8.918e-06 0.9979 -1.497e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2168 0.1019 0.3452 0.1434 0.985 0.994 0.2175 0.4377 0.876 0.706 ] Network output: [ 0.004147 -0.01958 0.9942 1.204e-05 -5.407e-06 1.017 9.077e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1081 0.09558 0.1837 0.1986 0.9873 0.9919 0.1082 0.745 0.8633 0.3053 ] Network output: [ -0.003894 0.01829 1.004 1.296e-05 -5.817e-06 0.9852 9.765e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09272 0.09079 0.165 0.1959 0.9853 0.9911 0.09274 0.6691 0.8389 0.2476 ] Network output: [ 0.0001064 1 -8.421e-05 1.712e-06 -7.686e-07 0.9998 1.29e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002555 Epoch 8956 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009625 0.9964 0.9917 -2.125e-07 9.541e-08 -0.007422 -1.602e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003456 -0.003282 -0.007175 0.005711 0.9699 0.9743 0.006687 0.8284 0.8218 0.01699 ] Network output: [ 0.9999 0.0002606 0.0005305 -6.336e-06 2.844e-06 -0.0005581 -4.775e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2038 -0.03486 -0.1642 0.1855 0.9834 0.9932 0.2284 0.4336 0.8693 0.712 ] Network output: [ -0.009535 1.003 1.009 -2.918e-07 1.31e-07 0.007942 -2.199e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00651 0.0005673 0.004426 0.003362 0.9889 0.9919 0.006635 0.856 0.8932 0.0122 ] Network output: [ -0.0003201 0.001961 1.001 -1.984e-05 8.908e-06 0.9979 -1.495e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2168 0.1019 0.3452 0.1434 0.985 0.994 0.2175 0.4377 0.876 0.706 ] Network output: [ 0.004145 -0.01957 0.9942 1.203e-05 -5.401e-06 1.017 9.066e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1081 0.09558 0.1837 0.1986 0.9873 0.9919 0.1082 0.745 0.8633 0.3053 ] Network output: [ -0.003893 0.01828 1.004 1.294e-05 -5.81e-06 0.9852 9.754e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09273 0.09079 0.165 0.1959 0.9853 0.9911 0.09274 0.6691 0.8389 0.2476 ] Network output: [ 0.0001064 1 -8.413e-05 1.71e-06 -7.677e-07 0.9998 1.289e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002554 Epoch 8957 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009624 0.9964 0.9917 -2.126e-07 9.544e-08 -0.007421 -1.602e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003456 -0.003282 -0.007174 0.005711 0.9699 0.9743 0.006687 0.8284 0.8218 0.01699 ] Network output: [ 0.9999 0.0002604 0.0005303 -6.328e-06 2.841e-06 -0.0005577 -4.769e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2038 -0.03486 -0.1642 0.1855 0.9834 0.9932 0.2284 0.4336 0.8693 0.712 ] Network output: [ -0.009534 1.003 1.009 -2.917e-07 1.31e-07 0.007941 -2.199e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00651 0.0005674 0.004426 0.003361 0.9889 0.9919 0.006636 0.856 0.8932 0.0122 ] Network output: [ -0.0003199 0.00196 1.001 -1.982e-05 8.898e-06 0.9979 -1.494e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2168 0.1019 0.3452 0.1434 0.985 0.994 0.2175 0.4377 0.876 0.706 ] Network output: [ 0.004144 -0.01957 0.9942 1.202e-05 -5.395e-06 1.017 9.056e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1081 0.09559 0.1837 0.1986 0.9873 0.9919 0.1082 0.745 0.8633 0.3053 ] Network output: [ -0.003891 0.01827 1.004 1.293e-05 -5.804e-06 0.9852 9.743e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09273 0.09079 0.165 0.1959 0.9853 0.9911 0.09274 0.669 0.8389 0.2476 ] Network output: [ 0.0001063 1 -8.404e-05 1.708e-06 -7.669e-07 0.9998 1.287e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002553 Epoch 8958 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009623 0.9964 0.9917 -2.127e-07 9.547e-08 -0.007421 -1.603e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003456 -0.003282 -0.007173 0.00571 0.9699 0.9743 0.006688 0.8284 0.8218 0.01699 ] Network output: [ 0.9999 0.0002601 0.00053 -6.321e-06 2.838e-06 -0.0005572 -4.764e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2038 -0.03486 -0.1642 0.1855 0.9834 0.9932 0.2284 0.4336 0.8693 0.712 ] Network output: [ -0.009533 1.003 1.009 -2.917e-07 1.309e-07 0.00794 -2.198e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006511 0.0005675 0.004426 0.003361 0.9889 0.9919 0.006636 0.856 0.8932 0.01219 ] Network output: [ -0.0003196 0.001959 1.001 -1.98e-05 8.888e-06 0.9979 -1.492e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2168 0.1019 0.3452 0.1434 0.985 0.994 0.2175 0.4377 0.876 0.7059 ] Network output: [ 0.004142 -0.01956 0.9942 1.2e-05 -5.389e-06 1.017 9.046e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1081 0.09559 0.1837 0.1986 0.9873 0.9919 0.1082 0.745 0.8633 0.3053 ] Network output: [ -0.00389 0.01826 1.004 1.291e-05 -5.798e-06 0.9852 9.733e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09273 0.09079 0.165 0.1959 0.9853 0.9911 0.09274 0.669 0.8389 0.2476 ] Network output: [ 0.0001063 1 -8.395e-05 1.706e-06 -7.66e-07 0.9998 1.286e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002551 Epoch 8959 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009622 0.9964 0.9917 -2.127e-07 9.55e-08 -0.007421 -1.603e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003456 -0.003282 -0.007172 0.00571 0.9699 0.9743 0.006688 0.8284 0.8218 0.01699 ] Network output: [ 0.9999 0.0002599 0.0005297 -6.314e-06 2.835e-06 -0.0005568 -4.759e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2038 -0.03487 -0.1642 0.1855 0.9834 0.9932 0.2284 0.4336 0.8693 0.712 ] Network output: [ -0.009532 1.003 1.009 -2.916e-07 1.309e-07 0.007939 -2.198e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006511 0.0005676 0.004426 0.003361 0.9889 0.9919 0.006637 0.856 0.8932 0.01219 ] Network output: [ -0.0003194 0.001958 1.001 -1.978e-05 8.878e-06 0.9979 -1.49e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2168 0.1019 0.3452 0.1434 0.985 0.994 0.2175 0.4377 0.876 0.7059 ] Network output: [ 0.004141 -0.01955 0.9942 1.199e-05 -5.383e-06 1.017 9.036e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1081 0.0956 0.1837 0.1986 0.9873 0.9919 0.1082 0.7449 0.8632 0.3053 ] Network output: [ -0.003888 0.01826 1.004 1.29e-05 -5.791e-06 0.9852 9.722e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09273 0.0908 0.165 0.1959 0.9853 0.9911 0.09275 0.669 0.8389 0.2476 ] Network output: [ 0.0001063 1 -8.386e-05 1.704e-06 -7.652e-07 0.9998 1.284e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000255 Epoch 8960 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009621 0.9964 0.9917 -2.128e-07 9.553e-08 -0.00742 -1.604e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003456 -0.003282 -0.007172 0.005709 0.9699 0.9743 0.006688 0.8284 0.8218 0.01699 ] Network output: [ 0.9999 0.0002597 0.0005295 -6.307e-06 2.831e-06 -0.0005564 -4.753e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2038 -0.03487 -0.1642 0.1855 0.9834 0.9932 0.2285 0.4336 0.8693 0.712 ] Network output: [ -0.009531 1.003 1.009 -2.915e-07 1.309e-07 0.007938 -2.197e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006512 0.0005677 0.004426 0.00336 0.9889 0.9919 0.006637 0.856 0.8932 0.01219 ] Network output: [ -0.0003192 0.001958 1.001 -1.975e-05 8.868e-06 0.9979 -1.489e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2168 0.1019 0.3452 0.1434 0.985 0.994 0.2175 0.4377 0.876 0.7059 ] Network output: [ 0.004139 -0.01955 0.9942 1.198e-05 -5.377e-06 1.017 9.026e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1081 0.0956 0.1837 0.1986 0.9873 0.9919 0.1082 0.7449 0.8632 0.3053 ] Network output: [ -0.003887 0.01825 1.004 1.289e-05 -5.785e-06 0.9852 9.711e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09274 0.0908 0.165 0.1959 0.9853 0.9911 0.09275 0.669 0.8389 0.2476 ] Network output: [ 0.0001062 1 -8.377e-05 1.702e-06 -7.643e-07 0.9998 1.283e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002549 Epoch 8961 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00962 0.9964 0.9917 -2.129e-07 9.556e-08 -0.00742 -1.604e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003456 -0.003282 -0.007171 0.005709 0.9699 0.9743 0.006688 0.8284 0.8218 0.01699 ] Network output: [ 0.9999 0.0002594 0.0005292 -6.3e-06 2.828e-06 -0.000556 -4.748e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2038 -0.03487 -0.1642 0.1855 0.9834 0.9932 0.2285 0.4336 0.8693 0.712 ] Network output: [ -0.00953 1.003 1.009 -2.914e-07 1.308e-07 0.007937 -2.196e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006512 0.0005678 0.004426 0.00336 0.9889 0.9919 0.006638 0.856 0.8932 0.01219 ] Network output: [ -0.000319 0.001957 1.001 -1.973e-05 8.858e-06 0.9979 -1.487e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2168 0.1019 0.3452 0.1434 0.985 0.994 0.2175 0.4377 0.876 0.7059 ] Network output: [ 0.004138 -0.01954 0.9942 1.196e-05 -5.371e-06 1.017 9.016e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1081 0.09561 0.1837 0.1986 0.9873 0.9919 0.1082 0.7449 0.8632 0.3053 ] Network output: [ -0.003885 0.01824 1.004 1.287e-05 -5.779e-06 0.9852 9.701e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09274 0.0908 0.165 0.1959 0.9853 0.9911 0.09275 0.669 0.8389 0.2476 ] Network output: [ 0.0001062 1 -8.369e-05 1.701e-06 -7.635e-07 0.9998 1.282e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002547 Epoch 8962 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009619 0.9964 0.9917 -2.129e-07 9.559e-08 -0.00742 -1.605e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003456 -0.003282 -0.00717 0.005708 0.9699 0.9743 0.006689 0.8284 0.8218 0.01699 ] Network output: [ 0.9999 0.0002592 0.0005289 -6.293e-06 2.825e-06 -0.0005555 -4.742e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2039 -0.03487 -0.1641 0.1855 0.9834 0.9932 0.2285 0.4336 0.8693 0.7119 ] Network output: [ -0.009529 1.003 1.009 -2.914e-07 1.308e-07 0.007936 -2.196e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006513 0.0005679 0.004426 0.00336 0.9889 0.9919 0.006638 0.856 0.8932 0.01219 ] Network output: [ -0.0003188 0.001956 1.001 -1.971e-05 8.848e-06 0.9979 -1.485e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2168 0.1019 0.3452 0.1434 0.985 0.994 0.2176 0.4376 0.876 0.7059 ] Network output: [ 0.004136 -0.01953 0.9942 1.195e-05 -5.365e-06 1.017 9.006e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1081 0.09561 0.1837 0.1986 0.9873 0.9919 0.1082 0.7449 0.8632 0.3053 ] Network output: [ -0.003884 0.01823 1.004 1.286e-05 -5.772e-06 0.9852 9.69e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09274 0.0908 0.165 0.1959 0.9853 0.9911 0.09275 0.669 0.8389 0.2476 ] Network output: [ 0.0001061 1 -8.36e-05 1.699e-06 -7.626e-07 0.9998 1.28e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002546 Epoch 8963 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009618 0.9964 0.9917 -2.13e-07 9.562e-08 -0.007419 -1.605e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003456 -0.003282 -0.007169 0.005708 0.9699 0.9743 0.006689 0.8284 0.8218 0.01699 ] Network output: [ 0.9999 0.0002589 0.0005287 -6.286e-06 2.822e-06 -0.0005551 -4.737e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2039 -0.03487 -0.1641 0.1855 0.9834 0.9932 0.2285 0.4336 0.8693 0.7119 ] Network output: [ -0.009528 1.003 1.009 -2.913e-07 1.308e-07 0.007935 -2.195e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006513 0.000568 0.004426 0.00336 0.9889 0.9919 0.006639 0.856 0.8932 0.01219 ] Network output: [ -0.0003186 0.001955 1.001 -1.969e-05 8.838e-06 0.9979 -1.484e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2168 0.102 0.3452 0.1434 0.985 0.994 0.2176 0.4376 0.876 0.7059 ] Network output: [ 0.004135 -0.01952 0.9942 1.194e-05 -5.359e-06 1.017 8.995e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1081 0.09562 0.1837 0.1986 0.9873 0.9919 0.1082 0.7449 0.8632 0.3053 ] Network output: [ -0.003882 0.01823 1.004 1.284e-05 -5.766e-06 0.9852 9.679e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09274 0.09081 0.165 0.1959 0.9853 0.9911 0.09276 0.6689 0.8389 0.2476 ] Network output: [ 0.0001061 1 -8.351e-05 1.697e-06 -7.618e-07 0.9998 1.279e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002545 Epoch 8964 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009617 0.9964 0.9918 -2.13e-07 9.564e-08 -0.007419 -1.606e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003456 -0.003282 -0.007169 0.005707 0.9699 0.9743 0.006689 0.8284 0.8217 0.01698 ] Network output: [ 0.9999 0.0002587 0.0005284 -6.278e-06 2.819e-06 -0.0005547 -4.732e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2039 -0.03487 -0.1641 0.1855 0.9834 0.9932 0.2285 0.4336 0.8693 0.7119 ] Network output: [ -0.009527 1.003 1.009 -2.912e-07 1.307e-07 0.007934 -2.195e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006514 0.0005681 0.004426 0.003359 0.9889 0.9919 0.006639 0.856 0.8932 0.01219 ] Network output: [ -0.0003184 0.001955 1.001 -1.966e-05 8.828e-06 0.9979 -1.482e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2168 0.102 0.3452 0.1434 0.985 0.994 0.2176 0.4376 0.876 0.7059 ] Network output: [ 0.004133 -0.01952 0.9942 1.192e-05 -5.353e-06 1.017 8.985e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1081 0.09562 0.1837 0.1986 0.9873 0.9919 0.1082 0.7449 0.8632 0.3053 ] Network output: [ -0.003881 0.01822 1.004 1.283e-05 -5.76e-06 0.9852 9.669e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09275 0.09081 0.165 0.1959 0.9853 0.9911 0.09276 0.6689 0.8389 0.2476 ] Network output: [ 0.0001061 1 -8.343e-05 1.695e-06 -7.609e-07 0.9998 1.277e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002543 Epoch 8965 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009615 0.9964 0.9918 -2.131e-07 9.567e-08 -0.007419 -1.606e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003456 -0.003283 -0.007168 0.005707 0.9699 0.9743 0.006689 0.8284 0.8217 0.01698 ] Network output: [ 0.9999 0.0002585 0.0005281 -6.271e-06 2.815e-06 -0.0005542 -4.726e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2039 -0.03487 -0.1641 0.1855 0.9834 0.9932 0.2285 0.4336 0.8693 0.7119 ] Network output: [ -0.009526 1.003 1.009 -2.911e-07 1.307e-07 0.007933 -2.194e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006514 0.0005682 0.004426 0.003359 0.9889 0.9919 0.00664 0.856 0.8932 0.01219 ] Network output: [ -0.0003182 0.001954 1.001 -1.964e-05 8.818e-06 0.9979 -1.48e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2169 0.102 0.3452 0.1434 0.985 0.994 0.2176 0.4376 0.876 0.7059 ] Network output: [ 0.004132 -0.01951 0.9942 1.191e-05 -5.347e-06 1.017 8.975e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1081 0.09562 0.1837 0.1986 0.9873 0.9919 0.1082 0.7449 0.8632 0.3053 ] Network output: [ -0.003879 0.01821 1.004 1.282e-05 -5.753e-06 0.9852 9.658e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09275 0.09081 0.165 0.1959 0.9853 0.9911 0.09276 0.6689 0.8389 0.2476 ] Network output: [ 0.000106 1 -8.334e-05 1.693e-06 -7.601e-07 0.9998 1.276e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002542 Epoch 8966 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009614 0.9964 0.9918 -2.132e-07 9.57e-08 -0.007418 -1.607e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003456 -0.003283 -0.007167 0.005706 0.9699 0.9743 0.00669 0.8284 0.8217 0.01698 ] Network output: [ 0.9999 0.0002582 0.0005279 -6.264e-06 2.812e-06 -0.0005538 -4.721e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2039 -0.03487 -0.1641 0.1855 0.9834 0.9932 0.2285 0.4335 0.8693 0.7119 ] Network output: [ -0.009525 1.003 1.009 -2.91e-07 1.307e-07 0.007932 -2.193e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006515 0.0005683 0.004426 0.003359 0.9889 0.9919 0.00664 0.856 0.8932 0.01219 ] Network output: [ -0.0003179 0.001953 1.001 -1.962e-05 8.808e-06 0.9979 -1.479e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2169 0.102 0.3452 0.1434 0.985 0.994 0.2176 0.4376 0.876 0.7059 ] Network output: [ 0.00413 -0.0195 0.9942 1.19e-05 -5.341e-06 1.017 8.965e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1081 0.09563 0.1837 0.1986 0.9873 0.9919 0.1082 0.7448 0.8632 0.3053 ] Network output: [ -0.003878 0.0182 1.004 1.28e-05 -5.747e-06 0.9852 9.647e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09275 0.09081 0.165 0.1959 0.9853 0.9911 0.09276 0.6689 0.8389 0.2476 ] Network output: [ 0.000106 1 -8.325e-05 1.691e-06 -7.592e-07 0.9998 1.274e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002541 Epoch 8967 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009613 0.9964 0.9918 -2.132e-07 9.573e-08 -0.007418 -1.607e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003457 -0.003283 -0.007166 0.005706 0.9699 0.9743 0.00669 0.8284 0.8217 0.01698 ] Network output: [ 0.9999 0.000258 0.0005276 -6.257e-06 2.809e-06 -0.0005534 -4.716e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2039 -0.03487 -0.1641 0.1855 0.9834 0.9932 0.2285 0.4335 0.8693 0.7119 ] Network output: [ -0.009524 1.003 1.009 -2.91e-07 1.306e-07 0.007931 -2.193e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006515 0.0005684 0.004426 0.003358 0.9889 0.9919 0.006641 0.8559 0.8932 0.01219 ] Network output: [ -0.0003177 0.001952 1.001 -1.96e-05 8.798e-06 0.9979 -1.477e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2169 0.102 0.3453 0.1434 0.985 0.994 0.2176 0.4376 0.876 0.7059 ] Network output: [ 0.004128 -0.01949 0.9942 1.188e-05 -5.335e-06 1.017 8.955e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1081 0.09563 0.1837 0.1986 0.9873 0.9919 0.1082 0.7448 0.8632 0.3053 ] Network output: [ -0.003876 0.0182 1.004 1.279e-05 -5.741e-06 0.9852 9.637e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09275 0.09081 0.165 0.1959 0.9853 0.9911 0.09277 0.6689 0.8389 0.2476 ] Network output: [ 0.0001059 1 -8.317e-05 1.689e-06 -7.584e-07 0.9998 1.273e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002539 Epoch 8968 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009612 0.9964 0.9918 -2.133e-07 9.575e-08 -0.007418 -1.607e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003457 -0.003283 -0.007166 0.005705 0.9699 0.9743 0.00669 0.8284 0.8217 0.01698 ] Network output: [ 0.9999 0.0002577 0.0005273 -6.25e-06 2.806e-06 -0.000553 -4.71e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2039 -0.03488 -0.1641 0.1855 0.9834 0.9932 0.2285 0.4335 0.8693 0.7119 ] Network output: [ -0.009523 1.003 1.009 -2.909e-07 1.306e-07 0.00793 -2.192e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006516 0.0005684 0.004426 0.003358 0.9889 0.9919 0.006641 0.8559 0.8932 0.01219 ] Network output: [ -0.0003175 0.001952 1.001 -1.958e-05 8.788e-06 0.9979 -1.475e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2169 0.102 0.3453 0.1433 0.985 0.994 0.2176 0.4376 0.876 0.7059 ] Network output: [ 0.004127 -0.01949 0.9942 1.187e-05 -5.329e-06 1.017 8.945e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.09564 0.1837 0.1986 0.9873 0.9919 0.1082 0.7448 0.8632 0.3053 ] Network output: [ -0.003875 0.01819 1.004 1.277e-05 -5.734e-06 0.9852 9.626e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09275 0.09082 0.165 0.1959 0.9853 0.9911 0.09277 0.6689 0.8389 0.2476 ] Network output: [ 0.0001059 1 -8.308e-05 1.687e-06 -7.575e-07 0.9998 1.272e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002538 Epoch 8969 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009611 0.9964 0.9918 -2.134e-07 9.578e-08 -0.007417 -1.608e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003457 -0.003283 -0.007165 0.005705 0.9699 0.9743 0.00669 0.8283 0.8217 0.01698 ] Network output: [ 0.9999 0.0002575 0.0005271 -6.243e-06 2.803e-06 -0.0005525 -4.705e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2039 -0.03488 -0.1641 0.1855 0.9834 0.9932 0.2285 0.4335 0.8693 0.7119 ] Network output: [ -0.009522 1.003 1.009 -2.908e-07 1.306e-07 0.007929 -2.192e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006516 0.0005685 0.004426 0.003358 0.9889 0.9919 0.006642 0.8559 0.8932 0.01219 ] Network output: [ -0.0003173 0.001951 1.001 -1.955e-05 8.778e-06 0.9979 -1.474e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2169 0.102 0.3453 0.1433 0.985 0.994 0.2176 0.4376 0.876 0.7059 ] Network output: [ 0.004125 -0.01948 0.9942 1.186e-05 -5.323e-06 1.017 8.935e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.09564 0.1837 0.1986 0.9873 0.9919 0.1082 0.7448 0.8632 0.3053 ] Network output: [ -0.003873 0.01818 1.004 1.276e-05 -5.728e-06 0.9852 9.616e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09276 0.09082 0.165 0.1959 0.9853 0.9911 0.09277 0.6689 0.8389 0.2476 ] Network output: [ 0.0001058 1 -8.299e-05 1.685e-06 -7.567e-07 0.9998 1.27e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002537 Epoch 8970 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00961 0.9964 0.9918 -2.134e-07 9.581e-08 -0.007417 -1.608e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003457 -0.003283 -0.007164 0.005705 0.9699 0.9743 0.006691 0.8283 0.8217 0.01698 ] Network output: [ 0.9999 0.0002573 0.0005268 -6.236e-06 2.8e-06 -0.0005521 -4.7e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2039 -0.03488 -0.1641 0.1855 0.9834 0.9932 0.2285 0.4335 0.8693 0.7119 ] Network output: [ -0.009522 1.003 1.009 -2.907e-07 1.305e-07 0.007928 -2.191e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006517 0.0005686 0.004426 0.003358 0.9889 0.9919 0.006642 0.8559 0.8932 0.01218 ] Network output: [ -0.0003171 0.00195 1.001 -1.953e-05 8.768e-06 0.9979 -1.472e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2169 0.102 0.3453 0.1433 0.985 0.994 0.2176 0.4376 0.876 0.7059 ] Network output: [ 0.004124 -0.01947 0.9942 1.184e-05 -5.317e-06 1.017 8.925e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.09565 0.1837 0.1986 0.9873 0.9919 0.1082 0.7448 0.8632 0.3053 ] Network output: [ -0.003872 0.01818 1.004 1.274e-05 -5.722e-06 0.9852 9.605e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09276 0.09082 0.165 0.1959 0.9853 0.9911 0.09277 0.6688 0.8389 0.2476 ] Network output: [ 0.0001058 1 -8.291e-05 1.684e-06 -7.558e-07 0.9998 1.269e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002535 Epoch 8971 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009609 0.9964 0.9918 -2.135e-07 9.583e-08 -0.007417 -1.609e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003457 -0.003283 -0.007163 0.005704 0.9699 0.9743 0.006691 0.8283 0.8217 0.01698 ] Network output: [ 0.9999 0.000257 0.0005265 -6.229e-06 2.796e-06 -0.0005517 -4.694e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2039 -0.03488 -0.164 0.1854 0.9834 0.9932 0.2286 0.4335 0.8693 0.7119 ] Network output: [ -0.009521 1.003 1.009 -2.906e-07 1.305e-07 0.007927 -2.19e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006517 0.0005687 0.004425 0.003357 0.9889 0.9919 0.006643 0.8559 0.8932 0.01218 ] Network output: [ -0.0003169 0.001949 1.001 -1.951e-05 8.758e-06 0.9979 -1.47e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2169 0.102 0.3453 0.1433 0.985 0.994 0.2176 0.4376 0.876 0.7059 ] Network output: [ 0.004122 -0.01946 0.9942 1.183e-05 -5.311e-06 1.017 8.915e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.09565 0.1837 0.1986 0.9873 0.9919 0.1082 0.7448 0.8632 0.3053 ] Network output: [ -0.00387 0.01817 1.004 1.273e-05 -5.715e-06 0.9852 9.594e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09276 0.09082 0.165 0.1959 0.9853 0.9911 0.09278 0.6688 0.8388 0.2476 ] Network output: [ 0.0001058 1 -8.282e-05 1.682e-06 -7.55e-07 0.9998 1.267e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002534 Epoch 8972 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009608 0.9964 0.9918 -2.135e-07 9.586e-08 -0.007416 -1.609e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003457 -0.003283 -0.007163 0.005704 0.9699 0.9743 0.006691 0.8283 0.8217 0.01698 ] Network output: [ 0.9999 0.0002568 0.0005263 -6.222e-06 2.793e-06 -0.0005513 -4.689e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2039 -0.03488 -0.164 0.1854 0.9834 0.9932 0.2286 0.4335 0.8693 0.7119 ] Network output: [ -0.00952 1.003 1.009 -2.906e-07 1.304e-07 0.007926 -2.19e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006518 0.0005688 0.004425 0.003357 0.9889 0.9919 0.006643 0.8559 0.8932 0.01218 ] Network output: [ -0.0003167 0.001949 1.001 -1.949e-05 8.748e-06 0.9979 -1.469e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2169 0.102 0.3453 0.1433 0.985 0.994 0.2176 0.4376 0.876 0.7059 ] Network output: [ 0.004121 -0.01946 0.9942 1.182e-05 -5.305e-06 1.017 8.905e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.09566 0.1837 0.1986 0.9873 0.9919 0.1082 0.7447 0.8632 0.3053 ] Network output: [ -0.003869 0.01816 1.004 1.272e-05 -5.709e-06 0.9853 9.584e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09276 0.09083 0.165 0.1959 0.9853 0.9911 0.09278 0.6688 0.8388 0.2476 ] Network output: [ 0.0001057 1 -8.274e-05 1.68e-06 -7.541e-07 0.9998 1.266e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002533 Epoch 8973 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009607 0.9964 0.9918 -2.136e-07 9.589e-08 -0.007416 -1.61e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003457 -0.003283 -0.007162 0.005703 0.9699 0.9743 0.006691 0.8283 0.8217 0.01697 ] Network output: [ 0.9999 0.0002565 0.000526 -6.215e-06 2.79e-06 -0.0005508 -4.684e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2039 -0.03488 -0.164 0.1854 0.9834 0.9932 0.2286 0.4335 0.8693 0.7119 ] Network output: [ -0.009519 1.003 1.009 -2.905e-07 1.304e-07 0.007925 -2.189e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006518 0.0005689 0.004425 0.003357 0.9889 0.9919 0.006644 0.8559 0.8932 0.01218 ] Network output: [ -0.0003165 0.001948 1.001 -1.946e-05 8.738e-06 0.9979 -1.467e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2169 0.102 0.3453 0.1433 0.985 0.994 0.2176 0.4376 0.876 0.7059 ] Network output: [ 0.004119 -0.01945 0.9942 1.18e-05 -5.299e-06 1.017 8.895e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.09566 0.1837 0.1986 0.9873 0.9919 0.1083 0.7447 0.8632 0.3053 ] Network output: [ -0.003867 0.01815 1.004 1.27e-05 -5.703e-06 0.9853 9.573e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09277 0.09083 0.165 0.1959 0.9852 0.9911 0.09278 0.6688 0.8388 0.2476 ] Network output: [ 0.0001057 1 -8.265e-05 1.678e-06 -7.533e-07 0.9998 1.265e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002531 Epoch 8974 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009605 0.9964 0.9918 -2.136e-07 9.591e-08 -0.007416 -1.61e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003457 -0.003284 -0.007161 0.005703 0.9699 0.9743 0.006692 0.8283 0.8217 0.01697 ] Network output: [ 0.9999 0.0002563 0.0005258 -6.208e-06 2.787e-06 -0.0005504 -4.678e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2039 -0.03488 -0.164 0.1854 0.9834 0.9932 0.2286 0.4335 0.8693 0.7119 ] Network output: [ -0.009518 1.003 1.009 -2.904e-07 1.304e-07 0.007924 -2.189e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006519 0.000569 0.004425 0.003356 0.9889 0.9919 0.006644 0.8559 0.8932 0.01218 ] Network output: [ -0.0003162 0.001947 1.001 -1.944e-05 8.729e-06 0.9979 -1.465e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2169 0.102 0.3453 0.1433 0.985 0.994 0.2177 0.4376 0.876 0.7059 ] Network output: [ 0.004118 -0.01944 0.9942 1.179e-05 -5.293e-06 1.017 8.885e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.09567 0.1837 0.1986 0.9873 0.9919 0.1083 0.7447 0.8632 0.3053 ] Network output: [ -0.003866 0.01815 1.004 1.269e-05 -5.697e-06 0.9853 9.563e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09277 0.09083 0.165 0.1959 0.9852 0.9911 0.09278 0.6688 0.8388 0.2477 ] Network output: [ 0.0001056 1 -8.256e-05 1.676e-06 -7.525e-07 0.9998 1.263e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000253 Epoch 8975 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009604 0.9964 0.9918 -2.137e-07 9.594e-08 -0.007415 -1.611e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003457 -0.003284 -0.007161 0.005702 0.9699 0.9743 0.006692 0.8283 0.8217 0.01697 ] Network output: [ 0.9999 0.000256 0.0005255 -6.201e-06 2.784e-06 -0.00055 -4.673e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2039 -0.03488 -0.164 0.1854 0.9834 0.9932 0.2286 0.4335 0.8693 0.7119 ] Network output: [ -0.009517 1.003 1.009 -2.903e-07 1.303e-07 0.007923 -2.188e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006519 0.0005691 0.004425 0.003356 0.9889 0.9919 0.006645 0.8559 0.8932 0.01218 ] Network output: [ -0.000316 0.001946 1.001 -1.942e-05 8.719e-06 0.9979 -1.464e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2169 0.102 0.3453 0.1433 0.985 0.994 0.2177 0.4375 0.876 0.7059 ] Network output: [ 0.004116 -0.01943 0.9942 1.178e-05 -5.287e-06 1.017 8.875e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.09567 0.1837 0.1986 0.9873 0.9919 0.1083 0.7447 0.8632 0.3053 ] Network output: [ -0.003864 0.01814 1.004 1.267e-05 -5.69e-06 0.9853 9.552e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09277 0.09083 0.165 0.1959 0.9852 0.9911 0.09278 0.6688 0.8388 0.2477 ] Network output: [ 0.0001056 1 -8.248e-05 1.674e-06 -7.516e-07 0.9998 1.262e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002529 Epoch 8976 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009603 0.9964 0.9918 -2.138e-07 9.596e-08 -0.007415 -1.611e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003457 -0.003284 -0.00716 0.005702 0.9699 0.9743 0.006692 0.8283 0.8217 0.01697 ] Network output: [ 0.9999 0.0002558 0.0005252 -6.194e-06 2.781e-06 -0.0005496 -4.668e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.204 -0.03488 -0.164 0.1854 0.9834 0.9932 0.2286 0.4335 0.8693 0.7119 ] Network output: [ -0.009516 1.003 1.009 -2.902e-07 1.303e-07 0.007922 -2.187e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00652 0.0005692 0.004425 0.003356 0.9889 0.9919 0.006645 0.8559 0.8932 0.01218 ] Network output: [ -0.0003158 0.001946 1.001 -1.94e-05 8.709e-06 0.9979 -1.462e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2169 0.102 0.3453 0.1433 0.985 0.994 0.2177 0.4375 0.876 0.7058 ] Network output: [ 0.004115 -0.01943 0.9942 1.176e-05 -5.281e-06 1.017 8.865e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.09568 0.1837 0.1986 0.9873 0.9919 0.1083 0.7447 0.8632 0.3053 ] Network output: [ -0.003863 0.01813 1.004 1.266e-05 -5.684e-06 0.9853 9.542e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09277 0.09084 0.165 0.1959 0.9852 0.9911 0.09279 0.6687 0.8388 0.2477 ] Network output: [ 0.0001056 1 -8.239e-05 1.672e-06 -7.508e-07 0.9998 1.26e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002527 Epoch 8977 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009602 0.9964 0.9918 -2.138e-07 9.599e-08 -0.007415 -1.611e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003457 -0.003284 -0.007159 0.005701 0.9699 0.9743 0.006692 0.8283 0.8217 0.01697 ] Network output: [ 0.9999 0.0002556 0.000525 -6.187e-06 2.777e-06 -0.0005491 -4.662e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.204 -0.03489 -0.164 0.1854 0.9834 0.9932 0.2286 0.4335 0.8693 0.7119 ] Network output: [ -0.009515 1.003 1.009 -2.902e-07 1.303e-07 0.007921 -2.187e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00652 0.0005693 0.004425 0.003356 0.9889 0.9919 0.006646 0.8559 0.8932 0.01218 ] Network output: [ -0.0003156 0.001945 1.001 -1.938e-05 8.699e-06 0.9979 -1.46e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.217 0.102 0.3453 0.1433 0.985 0.994 0.2177 0.4375 0.8759 0.7058 ] Network output: [ 0.004113 -0.01942 0.9942 1.175e-05 -5.275e-06 1.017 8.855e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.09568 0.1837 0.1986 0.9873 0.9919 0.1083 0.7447 0.8632 0.3053 ] Network output: [ -0.003861 0.01812 1.004 1.265e-05 -5.678e-06 0.9853 9.531e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09278 0.09084 0.165 0.1959 0.9852 0.9911 0.09279 0.6687 0.8388 0.2477 ] Network output: [ 0.0001055 1 -8.231e-05 1.67e-06 -7.499e-07 0.9998 1.259e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002526 Epoch 8978 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009601 0.9964 0.9918 -2.139e-07 9.601e-08 -0.007414 -1.612e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003458 -0.003284 -0.007158 0.005701 0.9699 0.9743 0.006693 0.8283 0.8217 0.01697 ] Network output: [ 0.9999 0.0002553 0.0005247 -6.18e-06 2.774e-06 -0.0005487 -4.657e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.204 -0.03489 -0.164 0.1854 0.9834 0.9932 0.2286 0.4335 0.8692 0.7119 ] Network output: [ -0.009514 1.003 1.009 -2.901e-07 1.302e-07 0.00792 -2.186e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006521 0.0005694 0.004425 0.003355 0.9889 0.9919 0.006646 0.8559 0.8932 0.01218 ] Network output: [ -0.0003154 0.001944 1.001 -1.935e-05 8.689e-06 0.9979 -1.459e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.217 0.102 0.3453 0.1433 0.985 0.994 0.2177 0.4375 0.8759 0.7058 ] Network output: [ 0.004111 -0.01941 0.9942 1.174e-05 -5.269e-06 1.017 8.845e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.09569 0.1837 0.1986 0.9873 0.9919 0.1083 0.7447 0.8632 0.3053 ] Network output: [ -0.00386 0.01812 1.004 1.263e-05 -5.671e-06 0.9853 9.521e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09278 0.09084 0.165 0.1959 0.9852 0.9911 0.09279 0.6687 0.8388 0.2477 ] Network output: [ 0.0001055 1 -8.222e-05 1.669e-06 -7.491e-07 0.9998 1.258e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002525 Epoch 8979 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0096 0.9964 0.9918 -2.139e-07 9.604e-08 -0.007414 -1.612e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003458 -0.003284 -0.007158 0.0057 0.9699 0.9743 0.006693 0.8283 0.8217 0.01697 ] Network output: [ 0.9999 0.0002551 0.0005244 -6.173e-06 2.771e-06 -0.0005483 -4.652e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.204 -0.03489 -0.164 0.1854 0.9834 0.9932 0.2286 0.4335 0.8692 0.7119 ] Network output: [ -0.009513 1.003 1.009 -2.9e-07 1.302e-07 0.007919 -2.185e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006521 0.0005695 0.004425 0.003355 0.9889 0.9919 0.006647 0.8559 0.8932 0.01218 ] Network output: [ -0.0003152 0.001943 1.001 -1.933e-05 8.679e-06 0.9979 -1.457e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.217 0.102 0.3453 0.1433 0.985 0.994 0.2177 0.4375 0.8759 0.7058 ] Network output: [ 0.00411 -0.01941 0.9942 1.172e-05 -5.263e-06 1.017 8.835e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.09569 0.1837 0.1985 0.9873 0.9919 0.1083 0.7446 0.8632 0.3053 ] Network output: [ -0.003858 0.01811 1.004 1.262e-05 -5.665e-06 0.9853 9.51e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09278 0.09084 0.165 0.1959 0.9852 0.9911 0.09279 0.6687 0.8388 0.2477 ] Network output: [ 0.0001054 1 -8.214e-05 1.667e-06 -7.483e-07 0.9998 1.256e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002524 Epoch 8980 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009599 0.9964 0.9918 -2.14e-07 9.606e-08 -0.007414 -1.613e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003458 -0.003284 -0.007157 0.0057 0.9699 0.9743 0.006693 0.8283 0.8217 0.01697 ] Network output: [ 0.9999 0.0002549 0.0005242 -6.166e-06 2.768e-06 -0.0005479 -4.647e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.204 -0.03489 -0.1639 0.1854 0.9834 0.9932 0.2286 0.4334 0.8692 0.7119 ] Network output: [ -0.009512 1.003 1.009 -2.899e-07 1.301e-07 0.007918 -2.185e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006522 0.0005696 0.004425 0.003355 0.9889 0.9919 0.006647 0.8559 0.8932 0.01218 ] Network output: [ -0.000315 0.001943 1.001 -1.931e-05 8.669e-06 0.9979 -1.455e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.217 0.102 0.3453 0.1433 0.985 0.994 0.2177 0.4375 0.8759 0.7058 ] Network output: [ 0.004108 -0.0194 0.9942 1.171e-05 -5.257e-06 1.017 8.825e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.0957 0.1837 0.1985 0.9873 0.9919 0.1083 0.7446 0.8632 0.3053 ] Network output: [ -0.003857 0.0181 1.004 1.261e-05 -5.659e-06 0.9853 9.5e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09278 0.09085 0.165 0.1959 0.9852 0.9911 0.0928 0.6687 0.8388 0.2477 ] Network output: [ 0.0001054 1 -8.205e-05 1.665e-06 -7.474e-07 0.9998 1.255e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002522 Epoch 8981 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009598 0.9964 0.9918 -2.14e-07 9.609e-08 -0.007413 -1.613e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003458 -0.003284 -0.007156 0.005699 0.9699 0.9743 0.006693 0.8283 0.8217 0.01697 ] Network output: [ 0.9999 0.0002546 0.0005239 -6.159e-06 2.765e-06 -0.0005474 -4.641e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.204 -0.03489 -0.1639 0.1854 0.9834 0.9932 0.2286 0.4334 0.8692 0.7118 ] Network output: [ -0.009511 1.003 1.009 -2.898e-07 1.301e-07 0.007917 -2.184e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006522 0.0005696 0.004425 0.003354 0.9889 0.9919 0.006648 0.8559 0.8932 0.01218 ] Network output: [ -0.0003148 0.001942 1.001 -1.929e-05 8.66e-06 0.9979 -1.454e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.217 0.102 0.3453 0.1433 0.985 0.994 0.2177 0.4375 0.8759 0.7058 ] Network output: [ 0.004107 -0.01939 0.9942 1.17e-05 -5.251e-06 1.017 8.816e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.0957 0.1837 0.1985 0.9873 0.9919 0.1083 0.7446 0.8632 0.3053 ] Network output: [ -0.003855 0.01809 1.004 1.259e-05 -5.653e-06 0.9853 9.489e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09279 0.09085 0.165 0.1959 0.9852 0.9911 0.0928 0.6687 0.8388 0.2477 ] Network output: [ 0.0001054 1 -8.197e-05 1.663e-06 -7.466e-07 0.9998 1.253e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002521 Epoch 8982 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009597 0.9964 0.9918 -2.141e-07 9.611e-08 -0.007413 -1.613e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003458 -0.003284 -0.007155 0.005699 0.9699 0.9743 0.006693 0.8283 0.8217 0.01697 ] Network output: [ 0.9999 0.0002544 0.0005237 -6.152e-06 2.762e-06 -0.000547 -4.636e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.204 -0.03489 -0.1639 0.1854 0.9834 0.9932 0.2287 0.4334 0.8692 0.7118 ] Network output: [ -0.00951 1.003 1.009 -2.897e-07 1.301e-07 0.007916 -2.184e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006523 0.0005697 0.004425 0.003354 0.9889 0.9919 0.006648 0.8559 0.8932 0.01217 ] Network output: [ -0.0003146 0.001941 1.001 -1.927e-05 8.65e-06 0.9979 -1.452e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.217 0.1021 0.3453 0.1433 0.985 0.994 0.2177 0.4375 0.8759 0.7058 ] Network output: [ 0.004105 -0.01938 0.9942 1.168e-05 -5.246e-06 1.017 8.806e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.09571 0.1837 0.1985 0.9873 0.9919 0.1083 0.7446 0.8632 0.3053 ] Network output: [ -0.003854 0.01809 1.004 1.258e-05 -5.647e-06 0.9853 9.479e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09279 0.09085 0.165 0.1959 0.9852 0.9911 0.0928 0.6687 0.8388 0.2477 ] Network output: [ 0.0001053 1 -8.188e-05 1.661e-06 -7.458e-07 0.9998 1.252e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000252 Epoch 8983 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009596 0.9964 0.9918 -2.141e-07 9.613e-08 -0.007413 -1.614e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003458 -0.003285 -0.007155 0.005698 0.9699 0.9743 0.006694 0.8283 0.8217 0.01696 ] Network output: [ 0.9999 0.0002541 0.0005234 -6.145e-06 2.759e-06 -0.0005466 -4.631e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.204 -0.03489 -0.1639 0.1854 0.9834 0.9932 0.2287 0.4334 0.8692 0.7118 ] Network output: [ -0.009509 1.003 1.009 -2.896e-07 1.3e-07 0.007916 -2.183e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006523 0.0005698 0.004425 0.003354 0.9889 0.9919 0.006649 0.8558 0.8932 0.01217 ] Network output: [ -0.0003143 0.00194 1.001 -1.925e-05 8.64e-06 0.9979 -1.45e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.217 0.1021 0.3453 0.1433 0.985 0.994 0.2177 0.4375 0.8759 0.7058 ] Network output: [ 0.004104 -0.01938 0.9942 1.167e-05 -5.24e-06 1.017 8.796e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.09571 0.1837 0.1985 0.9873 0.9919 0.1083 0.7446 0.8632 0.3053 ] Network output: [ -0.003852 0.01808 1.004 1.256e-05 -5.64e-06 0.9853 9.468e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09279 0.09085 0.165 0.1959 0.9852 0.9911 0.0928 0.6686 0.8388 0.2477 ] Network output: [ 0.0001053 1 -8.18e-05 1.659e-06 -7.449e-07 0.9998 1.251e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002518 Epoch 8984 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009594 0.9964 0.9918 -2.142e-07 9.616e-08 -0.007412 -1.614e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003458 -0.003285 -0.007154 0.005698 0.9699 0.9743 0.006694 0.8283 0.8217 0.01696 ] Network output: [ 0.9999 0.0002539 0.0005231 -6.138e-06 2.755e-06 -0.0005462 -4.626e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.204 -0.03489 -0.1639 0.1854 0.9834 0.9932 0.2287 0.4334 0.8692 0.7118 ] Network output: [ -0.009508 1.003 1.009 -2.896e-07 1.3e-07 0.007915 -2.182e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006524 0.0005699 0.004425 0.003354 0.9889 0.9919 0.006649 0.8558 0.8932 0.01217 ] Network output: [ -0.0003141 0.00194 1.001 -1.922e-05 8.63e-06 0.9979 -1.449e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.217 0.1021 0.3454 0.1433 0.985 0.994 0.2177 0.4375 0.8759 0.7058 ] Network output: [ 0.004102 -0.01937 0.9942 1.166e-05 -5.234e-06 1.017 8.786e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.09572 0.1837 0.1985 0.9873 0.9919 0.1083 0.7446 0.8632 0.3053 ] Network output: [ -0.003851 0.01807 1.004 1.255e-05 -5.634e-06 0.9853 9.458e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09279 0.09086 0.165 0.1959 0.9852 0.9911 0.09281 0.6686 0.8388 0.2477 ] Network output: [ 0.0001052 1 -8.171e-05 1.657e-06 -7.441e-07 0.9998 1.249e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002517 Epoch 8985 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009593 0.9964 0.9918 -2.142e-07 9.618e-08 -0.007412 -1.615e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003458 -0.003285 -0.007153 0.005697 0.9699 0.9743 0.006694 0.8283 0.8217 0.01696 ] Network output: [ 0.9999 0.0002537 0.0005229 -6.131e-06 2.752e-06 -0.0005458 -4.62e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.204 -0.03489 -0.1639 0.1854 0.9834 0.9932 0.2287 0.4334 0.8692 0.7118 ] Network output: [ -0.009507 1.003 1.009 -2.895e-07 1.3e-07 0.007914 -2.182e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006524 0.00057 0.004425 0.003353 0.9889 0.9919 0.00665 0.8558 0.8932 0.01217 ] Network output: [ -0.0003139 0.001939 1.001 -1.92e-05 8.62e-06 0.9979 -1.447e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.217 0.1021 0.3454 0.1433 0.985 0.994 0.2177 0.4375 0.8759 0.7058 ] Network output: [ 0.004101 -0.01936 0.9942 1.164e-05 -5.228e-06 1.017 8.776e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.09572 0.1837 0.1985 0.9873 0.9919 0.1083 0.7446 0.8632 0.3053 ] Network output: [ -0.003849 0.01807 1.004 1.254e-05 -5.628e-06 0.9853 9.448e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09279 0.09086 0.165 0.1959 0.9852 0.9911 0.09281 0.6686 0.8388 0.2477 ] Network output: [ 0.0001052 1 -8.163e-05 1.656e-06 -7.433e-07 0.9998 1.248e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002516 Epoch 8986 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009592 0.9964 0.9918 -2.143e-07 9.62e-08 -0.007412 -1.615e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003458 -0.003285 -0.007152 0.005697 0.9699 0.9743 0.006694 0.8282 0.8217 0.01696 ] Network output: [ 0.9999 0.0002534 0.0005226 -6.124e-06 2.749e-06 -0.0005453 -4.615e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.204 -0.03489 -0.1639 0.1854 0.9834 0.9932 0.2287 0.4334 0.8692 0.7118 ] Network output: [ -0.009507 1.003 1.009 -2.894e-07 1.299e-07 0.007913 -2.181e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006524 0.0005701 0.004425 0.003353 0.9889 0.9919 0.00665 0.8558 0.8932 0.01217 ] Network output: [ -0.0003137 0.001938 1.001 -1.918e-05 8.611e-06 0.9979 -1.445e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.217 0.1021 0.3454 0.1433 0.985 0.994 0.2178 0.4375 0.8759 0.7058 ] Network output: [ 0.004099 -0.01935 0.9942 1.163e-05 -5.222e-06 1.017 8.766e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1082 0.09573 0.1837 0.1985 0.9873 0.9919 0.1083 0.7445 0.8632 0.3053 ] Network output: [ -0.003848 0.01806 1.004 1.252e-05 -5.622e-06 0.9853 9.437e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0928 0.09086 0.165 0.1959 0.9852 0.9911 0.09281 0.6686 0.8388 0.2477 ] Network output: [ 0.0001051 1 -8.155e-05 1.654e-06 -7.424e-07 0.9998 1.246e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002514 Epoch 8987 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009591 0.9964 0.9918 -2.143e-07 9.623e-08 -0.007411 -1.615e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003458 -0.003285 -0.007152 0.005696 0.9699 0.9743 0.006695 0.8282 0.8217 0.01696 ] Network output: [ 0.9999 0.0002532 0.0005223 -6.117e-06 2.746e-06 -0.0005449 -4.61e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.204 -0.0349 -0.1639 0.1854 0.9834 0.9932 0.2287 0.4334 0.8692 0.7118 ] Network output: [ -0.009506 1.003 1.009 -2.893e-07 1.299e-07 0.007912 -2.18e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006525 0.0005702 0.004425 0.003353 0.9889 0.9919 0.006651 0.8558 0.8932 0.01217 ] Network output: [ -0.0003135 0.001937 1.001 -1.916e-05 8.601e-06 0.9979 -1.444e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.217 0.1021 0.3454 0.1433 0.985 0.994 0.2178 0.4375 0.8759 0.7058 ] Network output: [ 0.004098 -0.01935 0.9942 1.162e-05 -5.216e-06 1.017 8.756e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.09573 0.1837 0.1985 0.9873 0.9919 0.1083 0.7445 0.8632 0.3053 ] Network output: [ -0.003847 0.01805 1.004 1.251e-05 -5.616e-06 0.9853 9.427e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0928 0.09086 0.165 0.1959 0.9852 0.9911 0.09281 0.6686 0.8388 0.2477 ] Network output: [ 0.0001051 1 -8.146e-05 1.652e-06 -7.416e-07 0.9998 1.245e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002513 Epoch 8988 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00959 0.9964 0.9918 -2.144e-07 9.625e-08 -0.007411 -1.616e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003459 -0.003285 -0.007151 0.005696 0.9699 0.9743 0.006695 0.8282 0.8217 0.01696 ] Network output: [ 0.9999 0.0002529 0.0005221 -6.11e-06 2.743e-06 -0.0005445 -4.605e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.204 -0.0349 -0.1639 0.1854 0.9834 0.9932 0.2287 0.4334 0.8692 0.7118 ] Network output: [ -0.009505 1.003 1.009 -2.892e-07 1.298e-07 0.007911 -2.18e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006525 0.0005703 0.004425 0.003352 0.9889 0.9919 0.006651 0.8558 0.8932 0.01217 ] Network output: [ -0.0003133 0.001937 1.001 -1.914e-05 8.591e-06 0.9979 -1.442e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.217 0.1021 0.3454 0.1433 0.985 0.994 0.2178 0.4374 0.8759 0.7058 ] Network output: [ 0.004096 -0.01934 0.9942 1.161e-05 -5.21e-06 1.017 8.746e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.09574 0.1837 0.1985 0.9873 0.9919 0.1083 0.7445 0.8631 0.3053 ] Network output: [ -0.003845 0.01804 1.004 1.249e-05 -5.609e-06 0.9853 9.416e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0928 0.09086 0.165 0.1959 0.9852 0.9911 0.09282 0.6686 0.8388 0.2477 ] Network output: [ 0.0001051 1 -8.138e-05 1.65e-06 -7.408e-07 0.9998 1.244e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002512 Epoch 8989 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009589 0.9965 0.9918 -2.144e-07 9.627e-08 -0.007411 -1.616e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003459 -0.003285 -0.00715 0.005696 0.9699 0.9743 0.006695 0.8282 0.8217 0.01696 ] Network output: [ 0.9999 0.0002527 0.0005218 -6.103e-06 2.74e-06 -0.0005441 -4.599e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2041 -0.0349 -0.1638 0.1854 0.9834 0.9932 0.2287 0.4334 0.8692 0.7118 ] Network output: [ -0.009504 1.003 1.009 -2.891e-07 1.298e-07 0.00791 -2.179e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006526 0.0005704 0.004424 0.003352 0.9889 0.9919 0.006652 0.8558 0.8932 0.01217 ] Network output: [ -0.0003131 0.001936 1.001 -1.912e-05 8.581e-06 0.9979 -1.441e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2171 0.1021 0.3454 0.1433 0.985 0.994 0.2178 0.4374 0.8759 0.7058 ] Network output: [ 0.004095 -0.01933 0.9942 1.159e-05 -5.204e-06 1.017 8.737e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.09574 0.1837 0.1985 0.9873 0.9919 0.1083 0.7445 0.8631 0.3053 ] Network output: [ -0.003844 0.01804 1.004 1.248e-05 -5.603e-06 0.9853 9.406e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0928 0.09087 0.165 0.1959 0.9852 0.9911 0.09282 0.6685 0.8388 0.2477 ] Network output: [ 0.000105 1 -8.13e-05 1.648e-06 -7.4e-07 0.9998 1.242e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000251 Epoch 8990 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009588 0.9965 0.9918 -2.145e-07 9.629e-08 -0.00741 -1.616e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003459 -0.003285 -0.00715 0.005695 0.9699 0.9743 0.006695 0.8282 0.8217 0.01696 ] Network output: [ 0.9999 0.0002525 0.0005216 -6.096e-06 2.737e-06 -0.0005437 -4.594e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2041 -0.0349 -0.1638 0.1854 0.9834 0.9932 0.2287 0.4334 0.8692 0.7118 ] Network output: [ -0.009503 1.003 1.009 -2.89e-07 1.298e-07 0.007909 -2.178e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006526 0.0005705 0.004424 0.003352 0.9889 0.9919 0.006652 0.8558 0.8932 0.01217 ] Network output: [ -0.0003129 0.001935 1.001 -1.909e-05 8.572e-06 0.9979 -1.439e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2171 0.1021 0.3454 0.1433 0.985 0.994 0.2178 0.4374 0.8759 0.7058 ] Network output: [ 0.004093 -0.01933 0.9942 1.158e-05 -5.199e-06 1.017 8.727e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.09575 0.1837 0.1985 0.9873 0.9919 0.1083 0.7445 0.8631 0.3053 ] Network output: [ -0.003842 0.01803 1.004 1.247e-05 -5.597e-06 0.9853 9.396e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09281 0.09087 0.165 0.1959 0.9852 0.9911 0.09282 0.6685 0.8388 0.2477 ] Network output: [ 0.000105 1 -8.121e-05 1.646e-06 -7.391e-07 0.9998 1.241e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002509 Epoch 8991 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009587 0.9965 0.9918 -2.145e-07 9.631e-08 -0.00741 -1.617e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003459 -0.003286 -0.007149 0.005695 0.9699 0.9743 0.006696 0.8282 0.8217 0.01696 ] Network output: [ 0.9999 0.0002522 0.0005213 -6.089e-06 2.734e-06 -0.0005432 -4.589e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2041 -0.0349 -0.1638 0.1854 0.9834 0.9932 0.2287 0.4334 0.8692 0.7118 ] Network output: [ -0.009502 1.003 1.009 -2.89e-07 1.297e-07 0.007908 -2.178e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006527 0.0005706 0.004424 0.003352 0.9889 0.9919 0.006653 0.8558 0.8932 0.01217 ] Network output: [ -0.0003127 0.001934 1.001 -1.907e-05 8.562e-06 0.9979 -1.437e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2171 0.1021 0.3454 0.1433 0.985 0.994 0.2178 0.4374 0.8759 0.7058 ] Network output: [ 0.004091 -0.01932 0.9942 1.157e-05 -5.193e-06 1.017 8.717e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.09575 0.1837 0.1985 0.9873 0.9919 0.1083 0.7445 0.8631 0.3053 ] Network output: [ -0.003841 0.01802 1.004 1.245e-05 -5.591e-06 0.9853 9.385e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09281 0.09087 0.165 0.1959 0.9852 0.9911 0.09282 0.6685 0.8388 0.2477 ] Network output: [ 0.0001049 1 -8.113e-05 1.645e-06 -7.383e-07 0.9998 1.239e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002508 Epoch 8992 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009586 0.9965 0.9918 -2.146e-07 9.634e-08 -0.007409 -1.617e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003459 -0.003286 -0.007148 0.005694 0.9699 0.9743 0.006696 0.8282 0.8217 0.01695 ] Network output: [ 0.9999 0.000252 0.000521 -6.082e-06 2.73e-06 -0.0005428 -4.584e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2041 -0.0349 -0.1638 0.1854 0.9834 0.9932 0.2287 0.4334 0.8692 0.7118 ] Network output: [ -0.009501 1.003 1.009 -2.889e-07 1.297e-07 0.007907 -2.177e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006527 0.0005707 0.004424 0.003351 0.9889 0.9919 0.006653 0.8558 0.8932 0.01217 ] Network output: [ -0.0003125 0.001934 1.001 -1.905e-05 8.552e-06 0.9979 -1.436e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2171 0.1021 0.3454 0.1433 0.985 0.994 0.2178 0.4374 0.8759 0.7058 ] Network output: [ 0.00409 -0.01931 0.9942 1.155e-05 -5.187e-06 1.017 8.707e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.09576 0.1837 0.1985 0.9873 0.9919 0.1084 0.7444 0.8631 0.3053 ] Network output: [ -0.003839 0.01801 1.004 1.244e-05 -5.585e-06 0.9853 9.375e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09281 0.09087 0.165 0.1959 0.9852 0.9911 0.09283 0.6685 0.8388 0.2477 ] Network output: [ 0.0001049 1 -8.105e-05 1.643e-06 -7.375e-07 0.9998 1.238e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002507 Epoch 8993 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009585 0.9965 0.9918 -2.146e-07 9.636e-08 -0.007409 -1.618e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003459 -0.003286 -0.007147 0.005694 0.9699 0.9743 0.006696 0.8282 0.8217 0.01695 ] Network output: [ 0.9999 0.0002518 0.0005208 -6.075e-06 2.727e-06 -0.0005424 -4.578e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2041 -0.0349 -0.1638 0.1853 0.9834 0.9932 0.2287 0.4334 0.8692 0.7118 ] Network output: [ -0.0095 1.003 1.009 -2.888e-07 1.296e-07 0.007906 -2.176e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006528 0.0005707 0.004424 0.003351 0.9889 0.9919 0.006654 0.8558 0.8932 0.01216 ] Network output: [ -0.0003122 0.001933 1.001 -1.903e-05 8.543e-06 0.9979 -1.434e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2171 0.1021 0.3454 0.1433 0.985 0.994 0.2178 0.4374 0.8759 0.7058 ] Network output: [ 0.004088 -0.0193 0.9942 1.154e-05 -5.181e-06 1.017 8.697e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.09576 0.1837 0.1985 0.9873 0.9919 0.1084 0.7444 0.8631 0.3053 ] Network output: [ -0.003838 0.01801 1.004 1.243e-05 -5.578e-06 0.9853 9.365e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09281 0.09088 0.165 0.1959 0.9852 0.9911 0.09283 0.6685 0.8388 0.2477 ] Network output: [ 0.0001049 1 -8.096e-05 1.641e-06 -7.367e-07 0.9998 1.237e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002505 Epoch 8994 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009583 0.9965 0.9918 -2.147e-07 9.638e-08 -0.007409 -1.618e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003459 -0.003286 -0.007147 0.005693 0.9699 0.9743 0.006696 0.8282 0.8217 0.01695 ] Network output: [ 0.9999 0.0002515 0.0005205 -6.068e-06 2.724e-06 -0.000542 -4.573e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2041 -0.0349 -0.1638 0.1853 0.9834 0.9932 0.2288 0.4333 0.8692 0.7118 ] Network output: [ -0.009499 1.003 1.009 -2.887e-07 1.296e-07 0.007905 -2.176e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006528 0.0005708 0.004424 0.003351 0.9889 0.9919 0.006654 0.8558 0.8932 0.01216 ] Network output: [ -0.000312 0.001932 1.001 -1.901e-05 8.533e-06 0.9979 -1.432e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2171 0.1021 0.3454 0.1433 0.985 0.994 0.2178 0.4374 0.8759 0.7057 ] Network output: [ 0.004087 -0.0193 0.9942 1.153e-05 -5.175e-06 1.017 8.688e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.09577 0.1837 0.1985 0.9873 0.9919 0.1084 0.7444 0.8631 0.3053 ] Network output: [ -0.003836 0.018 1.004 1.241e-05 -5.572e-06 0.9853 9.354e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09282 0.09088 0.165 0.1959 0.9852 0.9911 0.09283 0.6685 0.8388 0.2477 ] Network output: [ 0.0001048 1 -8.088e-05 1.639e-06 -7.358e-07 0.9998 1.235e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002504 Epoch 8995 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009582 0.9965 0.9918 -2.147e-07 9.64e-08 -0.007408 -1.618e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003459 -0.003286 -0.007146 0.005693 0.9699 0.9743 0.006697 0.8282 0.8217 0.01695 ] Network output: [ 0.9999 0.0002513 0.0005203 -6.061e-06 2.721e-06 -0.0005416 -4.568e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2041 -0.0349 -0.1638 0.1853 0.9834 0.9932 0.2288 0.4333 0.8692 0.7118 ] Network output: [ -0.009498 1.003 1.009 -2.886e-07 1.296e-07 0.007904 -2.175e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006529 0.0005709 0.004424 0.00335 0.9889 0.9919 0.006655 0.8558 0.8932 0.01216 ] Network output: [ -0.0003118 0.001931 1.001 -1.899e-05 8.523e-06 0.9979 -1.431e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2171 0.1021 0.3454 0.1433 0.985 0.994 0.2178 0.4374 0.8759 0.7057 ] Network output: [ 0.004085 -0.01929 0.9942 1.151e-05 -5.169e-06 1.017 8.678e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.09577 0.1837 0.1985 0.9873 0.9919 0.1084 0.7444 0.8631 0.3053 ] Network output: [ -0.003835 0.01799 1.004 1.24e-05 -5.566e-06 0.9853 9.344e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09282 0.09088 0.165 0.1959 0.9852 0.9911 0.09283 0.6685 0.8387 0.2477 ] Network output: [ 0.0001048 1 -8.08e-05 1.637e-06 -7.35e-07 0.9998 1.234e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002503 Epoch 8996 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009581 0.9965 0.9918 -2.148e-07 9.642e-08 -0.007408 -1.619e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003459 -0.003286 -0.007145 0.005692 0.9699 0.9743 0.006697 0.8282 0.8217 0.01695 ] Network output: [ 0.9999 0.000251 0.00052 -6.055e-06 2.718e-06 -0.0005412 -4.563e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2041 -0.03491 -0.1638 0.1853 0.9834 0.9932 0.2288 0.4333 0.8692 0.7118 ] Network output: [ -0.009497 1.003 1.009 -2.885e-07 1.295e-07 0.007903 -2.174e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006529 0.000571 0.004424 0.00335 0.9889 0.9919 0.006655 0.8558 0.8931 0.01216 ] Network output: [ -0.0003116 0.001931 1.001 -1.896e-05 8.514e-06 0.9979 -1.429e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2171 0.1021 0.3454 0.1433 0.985 0.994 0.2178 0.4374 0.8759 0.7057 ] Network output: [ 0.004084 -0.01928 0.9942 1.15e-05 -5.164e-06 1.017 8.668e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.09578 0.1837 0.1985 0.9873 0.9919 0.1084 0.7444 0.8631 0.3053 ] Network output: [ -0.003833 0.01799 1.004 1.238e-05 -5.56e-06 0.9853 9.334e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09282 0.09088 0.165 0.1959 0.9852 0.9911 0.09283 0.6684 0.8387 0.2477 ] Network output: [ 0.0001047 1 -8.071e-05 1.635e-06 -7.342e-07 0.9998 1.232e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002501 Epoch 8997 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00958 0.9965 0.9918 -2.148e-07 9.644e-08 -0.007408 -1.619e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003459 -0.003286 -0.007144 0.005692 0.9699 0.9743 0.006697 0.8282 0.8216 0.01695 ] Network output: [ 0.9999 0.0002508 0.0005197 -6.048e-06 2.715e-06 -0.0005407 -4.558e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2041 -0.03491 -0.1638 0.1853 0.9834 0.9932 0.2288 0.4333 0.8692 0.7118 ] Network output: [ -0.009496 1.003 1.009 -2.884e-07 1.295e-07 0.007902 -2.174e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00653 0.0005711 0.004424 0.00335 0.9889 0.9919 0.006656 0.8558 0.8931 0.01216 ] Network output: [ -0.0003114 0.00193 1.001 -1.894e-05 8.504e-06 0.9979 -1.428e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2171 0.1021 0.3454 0.1433 0.985 0.994 0.2179 0.4374 0.8759 0.7057 ] Network output: [ 0.004082 -0.01927 0.9942 1.149e-05 -5.158e-06 1.017 8.658e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.09578 0.1837 0.1985 0.9873 0.9919 0.1084 0.7444 0.8631 0.3053 ] Network output: [ -0.003832 0.01798 1.004 1.237e-05 -5.554e-06 0.9853 9.323e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09282 0.09089 0.165 0.1959 0.9852 0.9911 0.09284 0.6684 0.8387 0.2477 ] Network output: [ 0.0001047 1 -8.063e-05 1.634e-06 -7.334e-07 0.9998 1.231e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00025 Epoch 8998 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009579 0.9965 0.9918 -2.149e-07 9.646e-08 -0.007407 -1.619e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003459 -0.003286 -0.007144 0.005691 0.9699 0.9743 0.006697 0.8282 0.8216 0.01695 ] Network output: [ 0.9999 0.0002506 0.0005195 -6.041e-06 2.712e-06 -0.0005403 -4.553e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2041 -0.03491 -0.1637 0.1853 0.9834 0.9932 0.2288 0.4333 0.8692 0.7118 ] Network output: [ -0.009495 1.003 1.009 -2.883e-07 1.294e-07 0.007901 -2.173e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00653 0.0005712 0.004424 0.00335 0.9889 0.9919 0.006656 0.8558 0.8931 0.01216 ] Network output: [ -0.0003112 0.001929 1.001 -1.892e-05 8.494e-06 0.9979 -1.426e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2171 0.1021 0.3454 0.1433 0.985 0.994 0.2179 0.4374 0.8759 0.7057 ] Network output: [ 0.004081 -0.01927 0.9942 1.148e-05 -5.152e-06 1.017 8.649e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.09579 0.1837 0.1985 0.9873 0.9919 0.1084 0.7444 0.8631 0.3053 ] Network output: [ -0.00383 0.01797 1.004 1.236e-05 -5.548e-06 0.9853 9.313e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09283 0.09089 0.165 0.1959 0.9852 0.9911 0.09284 0.6684 0.8387 0.2477 ] Network output: [ 0.0001047 1 -8.055e-05 1.632e-06 -7.325e-07 0.9998 1.23e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002499 Epoch 8999 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009578 0.9965 0.9918 -2.149e-07 9.648e-08 -0.007407 -1.62e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00346 -0.003286 -0.007143 0.005691 0.9699 0.9743 0.006698 0.8282 0.8216 0.01695 ] Network output: [ 0.9999 0.0002503 0.0005192 -6.034e-06 2.709e-06 -0.0005399 -4.547e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2041 -0.03491 -0.1637 0.1853 0.9834 0.9932 0.2288 0.4333 0.8692 0.7118 ] Network output: [ -0.009494 1.003 1.009 -2.883e-07 1.294e-07 0.0079 -2.172e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006531 0.0005713 0.004424 0.003349 0.9889 0.9919 0.006657 0.8557 0.8931 0.01216 ] Network output: [ -0.000311 0.001928 1.001 -1.89e-05 8.485e-06 0.9979 -1.424e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2171 0.1021 0.3454 0.1433 0.985 0.994 0.2179 0.4374 0.8759 0.7057 ] Network output: [ 0.004079 -0.01926 0.9942 1.146e-05 -5.146e-06 1.017 8.639e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.09579 0.1837 0.1985 0.9873 0.9919 0.1084 0.7443 0.8631 0.3053 ] Network output: [ -0.003829 0.01796 1.004 1.234e-05 -5.542e-06 0.9853 9.303e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09283 0.09089 0.165 0.1959 0.9852 0.9911 0.09284 0.6684 0.8387 0.2477 ] Network output: [ 0.0001046 1 -8.047e-05 1.63e-06 -7.317e-07 0.9998 1.228e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002497 Epoch 9000 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009577 0.9965 0.9918 -2.15e-07 9.65e-08 -0.007407 -1.62e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00346 -0.003287 -0.007142 0.00569 0.9699 0.9743 0.006698 0.8282 0.8216 0.01695 ] Network output: [ 0.9999 0.0002501 0.000519 -6.027e-06 2.706e-06 -0.0005395 -4.542e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2041 -0.03491 -0.1637 0.1853 0.9834 0.9932 0.2288 0.4333 0.8692 0.7117 ] Network output: [ -0.009493 1.003 1.009 -2.882e-07 1.294e-07 0.007899 -2.172e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006531 0.0005714 0.004424 0.003349 0.9889 0.9919 0.006657 0.8557 0.8931 0.01216 ] Network output: [ -0.0003108 0.001928 1.001 -1.888e-05 8.475e-06 0.998 -1.423e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2171 0.1022 0.3454 0.1433 0.985 0.994 0.2179 0.4374 0.8759 0.7057 ] Network output: [ 0.004078 -0.01925 0.9942 1.145e-05 -5.14e-06 1.017 8.629e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.0958 0.1837 0.1985 0.9873 0.9919 0.1084 0.7443 0.8631 0.3053 ] Network output: [ -0.003827 0.01796 1.004 1.233e-05 -5.536e-06 0.9853 9.293e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09283 0.09089 0.165 0.1959 0.9852 0.9911 0.09284 0.6684 0.8387 0.2477 ] Network output: [ 0.0001046 1 -8.038e-05 1.628e-06 -7.309e-07 0.9998 1.227e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002496 Epoch 9001 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009576 0.9965 0.9918 -2.15e-07 9.652e-08 -0.007406 -1.62e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00346 -0.003287 -0.007142 0.00569 0.9699 0.9743 0.006698 0.8282 0.8216 0.01694 ] Network output: [ 0.9999 0.0002499 0.0005187 -6.02e-06 2.703e-06 -0.0005391 -4.537e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2041 -0.03491 -0.1637 0.1853 0.9834 0.9932 0.2288 0.4333 0.8692 0.7117 ] Network output: [ -0.009492 1.003 1.009 -2.881e-07 1.293e-07 0.007898 -2.171e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006532 0.0005715 0.004424 0.003349 0.9889 0.9919 0.006657 0.8557 0.8931 0.01216 ] Network output: [ -0.0003106 0.001927 1.001 -1.886e-05 8.465e-06 0.998 -1.421e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2172 0.1022 0.3455 0.1433 0.985 0.994 0.2179 0.4374 0.8759 0.7057 ] Network output: [ 0.004076 -0.01924 0.9942 1.144e-05 -5.135e-06 1.017 8.619e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.0958 0.1837 0.1985 0.9873 0.9919 0.1084 0.7443 0.8631 0.3053 ] Network output: [ -0.003826 0.01795 1.004 1.232e-05 -5.529e-06 0.9854 9.282e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09283 0.0909 0.165 0.1959 0.9852 0.9911 0.09285 0.6684 0.8387 0.2477 ] Network output: [ 0.0001045 1 -8.03e-05 1.626e-06 -7.301e-07 0.9998 1.226e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002495 Epoch 9002 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009575 0.9965 0.9918 -2.15e-07 9.654e-08 -0.007406 -1.621e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00346 -0.003287 -0.007141 0.005689 0.9699 0.9743 0.006698 0.8282 0.8216 0.01694 ] Network output: [ 0.9999 0.0002496 0.0005185 -6.013e-06 2.7e-06 -0.0005387 -4.532e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2041 -0.03491 -0.1637 0.1853 0.9834 0.9932 0.2288 0.4333 0.8692 0.7117 ] Network output: [ -0.009492 1.003 1.009 -2.88e-07 1.293e-07 0.007897 -2.17e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006532 0.0005716 0.004424 0.003348 0.9889 0.9919 0.006658 0.8557 0.8931 0.01216 ] Network output: [ -0.0003104 0.001926 1.001 -1.884e-05 8.456e-06 0.998 -1.419e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2172 0.1022 0.3455 0.1433 0.985 0.994 0.2179 0.4373 0.8759 0.7057 ] Network output: [ 0.004075 -0.01924 0.9942 1.142e-05 -5.129e-06 1.017 8.61e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.09581 0.1837 0.1985 0.9873 0.9919 0.1084 0.7443 0.8631 0.3053 ] Network output: [ -0.003824 0.01794 1.004 1.23e-05 -5.523e-06 0.9854 9.272e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09284 0.0909 0.165 0.1959 0.9852 0.9911 0.09285 0.6683 0.8387 0.2477 ] Network output: [ 0.0001045 1 -8.022e-05 1.624e-06 -7.293e-07 0.9998 1.224e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002494 Epoch 9003 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009574 0.9965 0.9918 -2.151e-07 9.656e-08 -0.007406 -1.621e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00346 -0.003287 -0.00714 0.005689 0.9699 0.9743 0.006699 0.8282 0.8216 0.01694 ] Network output: [ 0.9999 0.0002494 0.0005182 -6.007e-06 2.697e-06 -0.0005383 -4.527e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2042 -0.03491 -0.1637 0.1853 0.9834 0.9932 0.2288 0.4333 0.8692 0.7117 ] Network output: [ -0.009491 1.003 1.009 -2.879e-07 1.292e-07 0.007896 -2.17e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006533 0.0005717 0.004424 0.003348 0.9889 0.9919 0.006658 0.8557 0.8931 0.01216 ] Network output: [ -0.0003102 0.001925 1.001 -1.881e-05 8.446e-06 0.998 -1.418e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2172 0.1022 0.3455 0.1433 0.985 0.994 0.2179 0.4373 0.8759 0.7057 ] Network output: [ 0.004073 -0.01923 0.9942 1.141e-05 -5.123e-06 1.017 8.6e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.09581 0.1837 0.1985 0.9873 0.9919 0.1084 0.7443 0.8631 0.3053 ] Network output: [ -0.003823 0.01793 1.004 1.229e-05 -5.517e-06 0.9854 9.262e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09284 0.0909 0.165 0.1959 0.9852 0.9911 0.09285 0.6683 0.8387 0.2477 ] Network output: [ 0.0001045 1 -8.014e-05 1.623e-06 -7.285e-07 0.9998 1.223e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002492 Epoch 9004 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009572 0.9965 0.9918 -2.151e-07 9.658e-08 -0.007405 -1.621e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00346 -0.003287 -0.007139 0.005688 0.9699 0.9743 0.006699 0.8281 0.8216 0.01694 ] Network output: [ 0.9999 0.0002491 0.0005179 -6e-06 2.693e-06 -0.0005378 -4.522e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2042 -0.03491 -0.1637 0.1853 0.9834 0.9932 0.2288 0.4333 0.8692 0.7117 ] Network output: [ -0.00949 1.003 1.009 -2.878e-07 1.292e-07 0.007895 -2.169e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006533 0.0005718 0.004424 0.003348 0.9889 0.9919 0.006659 0.8557 0.8931 0.01216 ] Network output: [ -0.00031 0.001925 1.001 -1.879e-05 8.437e-06 0.998 -1.416e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2172 0.1022 0.3455 0.1433 0.985 0.994 0.2179 0.4373 0.8759 0.7057 ] Network output: [ 0.004072 -0.01922 0.9942 1.14e-05 -5.117e-06 1.017 8.59e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.09581 0.1837 0.1985 0.9873 0.9919 0.1084 0.7443 0.8631 0.3053 ] Network output: [ -0.003821 0.01793 1.004 1.228e-05 -5.511e-06 0.9854 9.252e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09284 0.0909 0.165 0.1959 0.9852 0.9911 0.09285 0.6683 0.8387 0.2477 ] Network output: [ 0.0001044 1 -8.006e-05 1.621e-06 -7.276e-07 0.9998 1.221e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002491 Epoch 9005 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009571 0.9965 0.9918 -2.152e-07 9.66e-08 -0.007405 -1.622e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00346 -0.003287 -0.007139 0.005688 0.9699 0.9743 0.006699 0.8281 0.8216 0.01694 ] Network output: [ 0.9999 0.0002489 0.0005177 -5.993e-06 2.69e-06 -0.0005374 -4.516e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2042 -0.03491 -0.1637 0.1853 0.9834 0.9932 0.2289 0.4333 0.8692 0.7117 ] Network output: [ -0.009489 1.003 1.009 -2.877e-07 1.292e-07 0.007894 -2.168e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006534 0.0005718 0.004424 0.003348 0.9889 0.9919 0.006659 0.8557 0.8931 0.01215 ] Network output: [ -0.0003097 0.001924 1.001 -1.877e-05 8.427e-06 0.998 -1.415e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2172 0.1022 0.3455 0.1433 0.985 0.994 0.2179 0.4373 0.8759 0.7057 ] Network output: [ 0.00407 -0.01922 0.9942 1.139e-05 -5.112e-06 1.017 8.581e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1083 0.09582 0.1837 0.1985 0.9873 0.9919 0.1084 0.7443 0.8631 0.3053 ] Network output: [ -0.00382 0.01792 1.004 1.226e-05 -5.505e-06 0.9854 9.241e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09284 0.0909 0.165 0.196 0.9852 0.9911 0.09286 0.6683 0.8387 0.2477 ] Network output: [ 0.0001044 1 -7.998e-05 1.619e-06 -7.268e-07 0.9998 1.22e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000249 Epoch 9006 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00957 0.9965 0.9918 -2.152e-07 9.661e-08 -0.007405 -1.622e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00346 -0.003287 -0.007138 0.005687 0.9699 0.9743 0.006699 0.8281 0.8216 0.01694 ] Network output: [ 0.9999 0.0002487 0.0005174 -5.986e-06 2.687e-06 -0.000537 -4.511e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2042 -0.03492 -0.1637 0.1853 0.9834 0.9932 0.2289 0.4333 0.8692 0.7117 ] Network output: [ -0.009488 1.003 1.009 -2.876e-07 1.291e-07 0.007893 -2.168e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006534 0.0005719 0.004423 0.003347 0.9889 0.9919 0.00666 0.8557 0.8931 0.01215 ] Network output: [ -0.0003095 0.001923 1.001 -1.875e-05 8.418e-06 0.998 -1.413e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2172 0.1022 0.3455 0.1433 0.985 0.994 0.2179 0.4373 0.8759 0.7057 ] Network output: [ 0.004068 -0.01921 0.9942 1.137e-05 -5.106e-06 1.017 8.571e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.09582 0.1838 0.1985 0.9873 0.9919 0.1084 0.7442 0.8631 0.3053 ] Network output: [ -0.003818 0.01791 1.004 1.225e-05 -5.499e-06 0.9854 9.231e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09284 0.09091 0.165 0.196 0.9852 0.9911 0.09286 0.6683 0.8387 0.2477 ] Network output: [ 0.0001043 1 -7.989e-05 1.617e-06 -7.26e-07 0.9998 1.219e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002488 Epoch 9007 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009569 0.9965 0.9918 -2.152e-07 9.663e-08 -0.007404 -1.622e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00346 -0.003287 -0.007137 0.005687 0.9699 0.9743 0.006699 0.8281 0.8216 0.01694 ] Network output: [ 0.9999 0.0002484 0.0005172 -5.979e-06 2.684e-06 -0.0005366 -4.506e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2042 -0.03492 -0.1636 0.1853 0.9834 0.9932 0.2289 0.4333 0.8692 0.7117 ] Network output: [ -0.009487 1.003 1.009 -2.875e-07 1.291e-07 0.007893 -2.167e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006535 0.000572 0.004423 0.003347 0.9889 0.9919 0.00666 0.8557 0.8931 0.01215 ] Network output: [ -0.0003093 0.001922 1.001 -1.873e-05 8.408e-06 0.998 -1.411e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2172 0.1022 0.3455 0.1433 0.985 0.994 0.2179 0.4373 0.8759 0.7057 ] Network output: [ 0.004067 -0.0192 0.9942 1.136e-05 -5.1e-06 1.017 8.561e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.09583 0.1838 0.1985 0.9873 0.9919 0.1084 0.7442 0.8631 0.3053 ] Network output: [ -0.003817 0.01791 1.004 1.224e-05 -5.493e-06 0.9854 9.221e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09285 0.09091 0.165 0.196 0.9852 0.9911 0.09286 0.6683 0.8387 0.2477 ] Network output: [ 0.0001043 1 -7.981e-05 1.615e-06 -7.252e-07 0.9998 1.217e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002487 Epoch 9008 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009568 0.9965 0.9918 -2.153e-07 9.665e-08 -0.007404 -1.622e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00346 -0.003287 -0.007136 0.005687 0.9699 0.9743 0.0067 0.8281 0.8216 0.01694 ] Network output: [ 0.9999 0.0002482 0.0005169 -5.972e-06 2.681e-06 -0.0005362 -4.501e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2042 -0.03492 -0.1636 0.1853 0.9834 0.9932 0.2289 0.4332 0.8692 0.7117 ] Network output: [ -0.009486 1.003 1.009 -2.874e-07 1.29e-07 0.007892 -2.166e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006535 0.0005721 0.004423 0.003347 0.9889 0.9919 0.006661 0.8557 0.8931 0.01215 ] Network output: [ -0.0003091 0.001922 1.001 -1.871e-05 8.398e-06 0.998 -1.41e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2172 0.1022 0.3455 0.1433 0.985 0.994 0.2179 0.4373 0.8759 0.7057 ] Network output: [ 0.004065 -0.01919 0.9942 1.135e-05 -5.094e-06 1.017 8.552e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.09583 0.1838 0.1985 0.9873 0.9919 0.1084 0.7442 0.8631 0.3053 ] Network output: [ -0.003816 0.0179 1.004 1.222e-05 -5.487e-06 0.9854 9.211e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09285 0.09091 0.165 0.196 0.9852 0.9911 0.09286 0.6683 0.8387 0.2477 ] Network output: [ 0.0001043 1 -7.973e-05 1.614e-06 -7.244e-07 0.9998 1.216e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002486 Epoch 9009 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009567 0.9965 0.9918 -2.153e-07 9.667e-08 -0.007404 -1.623e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00346 -0.003288 -0.007136 0.005686 0.9699 0.9743 0.0067 0.8281 0.8216 0.01694 ] Network output: [ 0.9999 0.000248 0.0005167 -5.966e-06 2.678e-06 -0.0005358 -4.496e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2042 -0.03492 -0.1636 0.1853 0.9834 0.9932 0.2289 0.4332 0.8692 0.7117 ] Network output: [ -0.009485 1.003 1.009 -2.873e-07 1.29e-07 0.007891 -2.166e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006535 0.0005722 0.004423 0.003347 0.9889 0.9919 0.006661 0.8557 0.8931 0.01215 ] Network output: [ -0.0003089 0.001921 1.001 -1.869e-05 8.389e-06 0.998 -1.408e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2172 0.1022 0.3455 0.1433 0.985 0.994 0.218 0.4373 0.8759 0.7057 ] Network output: [ 0.004064 -0.01919 0.9942 1.133e-05 -5.089e-06 1.017 8.542e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.09584 0.1838 0.1985 0.9873 0.9919 0.1084 0.7442 0.8631 0.3053 ] Network output: [ -0.003814 0.01789 1.004 1.221e-05 -5.481e-06 0.9854 9.201e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09285 0.09091 0.165 0.196 0.9852 0.9911 0.09287 0.6682 0.8387 0.2477 ] Network output: [ 0.0001042 1 -7.965e-05 1.612e-06 -7.236e-07 0.9998 1.215e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002485 Epoch 9010 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009566 0.9965 0.9918 -2.154e-07 9.669e-08 -0.007403 -1.623e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003461 -0.003288 -0.007135 0.005686 0.9699 0.9743 0.0067 0.8281 0.8216 0.01694 ] Network output: [ 0.9999 0.0002477 0.0005164 -5.959e-06 2.675e-06 -0.0005354 -4.491e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2042 -0.03492 -0.1636 0.1853 0.9834 0.9932 0.2289 0.4332 0.8692 0.7117 ] Network output: [ -0.009484 1.003 1.009 -2.873e-07 1.29e-07 0.00789 -2.165e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006536 0.0005723 0.004423 0.003346 0.9889 0.9919 0.006662 0.8557 0.8931 0.01215 ] Network output: [ -0.0003087 0.00192 1.001 -1.866e-05 8.379e-06 0.998 -1.407e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2172 0.1022 0.3455 0.1433 0.985 0.994 0.218 0.4373 0.8759 0.7057 ] Network output: [ 0.004062 -0.01918 0.9942 1.132e-05 -5.083e-06 1.017 8.533e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.09584 0.1838 0.1985 0.9873 0.9919 0.1084 0.7442 0.8631 0.3053 ] Network output: [ -0.003813 0.01788 1.004 1.219e-05 -5.475e-06 0.9854 9.19e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09285 0.09092 0.165 0.196 0.9852 0.9911 0.09287 0.6682 0.8387 0.2477 ] Network output: [ 0.0001042 1 -7.957e-05 1.61e-06 -7.228e-07 0.9998 1.213e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002483 Epoch 9011 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009565 0.9965 0.9918 -2.154e-07 9.67e-08 -0.007403 -1.623e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003461 -0.003288 -0.007134 0.005685 0.9699 0.9743 0.0067 0.8281 0.8216 0.01693 ] Network output: [ 0.9999 0.0002475 0.0005161 -5.952e-06 2.672e-06 -0.000535 -4.486e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2042 -0.03492 -0.1636 0.1853 0.9834 0.9932 0.2289 0.4332 0.8692 0.7117 ] Network output: [ -0.009483 1.003 1.009 -2.872e-07 1.289e-07 0.007889 -2.164e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006536 0.0005724 0.004423 0.003346 0.9889 0.9919 0.006662 0.8557 0.8931 0.01215 ] Network output: [ -0.0003085 0.001919 1.001 -1.864e-05 8.37e-06 0.998 -1.405e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2172 0.1022 0.3455 0.1433 0.985 0.994 0.218 0.4373 0.8759 0.7057 ] Network output: [ 0.004061 -0.01917 0.9942 1.131e-05 -5.077e-06 1.017 8.523e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.09585 0.1838 0.1985 0.9873 0.9919 0.1085 0.7442 0.8631 0.3053 ] Network output: [ -0.003811 0.01788 1.004 1.218e-05 -5.469e-06 0.9854 9.18e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09286 0.09092 0.165 0.196 0.9852 0.9911 0.09287 0.6682 0.8387 0.2477 ] Network output: [ 0.0001041 1 -7.949e-05 1.608e-06 -7.22e-07 0.9998 1.212e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002482 Epoch 9012 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009564 0.9965 0.9918 -2.154e-07 9.672e-08 -0.007402 -1.624e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003461 -0.003288 -0.007134 0.005685 0.9699 0.9743 0.006701 0.8281 0.8216 0.01693 ] Network output: [ 0.9999 0.0002473 0.0005159 -5.945e-06 2.669e-06 -0.0005346 -4.481e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2042 -0.03492 -0.1636 0.1853 0.9834 0.9932 0.2289 0.4332 0.8692 0.7117 ] Network output: [ -0.009482 1.003 1.009 -2.871e-07 1.289e-07 0.007888 -2.163e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006537 0.0005725 0.004423 0.003346 0.9889 0.9919 0.006663 0.8557 0.8931 0.01215 ] Network output: [ -0.0003083 0.001919 1.001 -1.862e-05 8.36e-06 0.998 -1.403e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2173 0.1022 0.3455 0.1433 0.985 0.994 0.218 0.4373 0.8759 0.7056 ] Network output: [ 0.004059 -0.01916 0.9942 1.13e-05 -5.071e-06 1.017 8.513e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.09585 0.1838 0.1985 0.9873 0.9919 0.1085 0.7442 0.8631 0.3053 ] Network output: [ -0.00381 0.01787 1.004 1.217e-05 -5.463e-06 0.9854 9.17e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09286 0.09092 0.165 0.196 0.9852 0.9911 0.09287 0.6682 0.8387 0.2477 ] Network output: [ 0.0001041 1 -7.941e-05 1.606e-06 -7.211e-07 0.9998 1.211e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002481 Epoch 9013 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009563 0.9965 0.9918 -2.155e-07 9.674e-08 -0.007402 -1.624e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003461 -0.003288 -0.007133 0.005684 0.9699 0.9743 0.006701 0.8281 0.8216 0.01693 ] Network output: [ 0.9999 0.000247 0.0005156 -5.939e-06 2.666e-06 -0.0005342 -4.476e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2042 -0.03492 -0.1636 0.1853 0.9834 0.9932 0.2289 0.4332 0.8692 0.7117 ] Network output: [ -0.009481 1.003 1.009 -2.87e-07 1.288e-07 0.007887 -2.163e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006537 0.0005726 0.004423 0.003345 0.9889 0.9919 0.006663 0.8557 0.8931 0.01215 ] Network output: [ -0.0003081 0.001918 1.001 -1.86e-05 8.351e-06 0.998 -1.402e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2173 0.1022 0.3455 0.1432 0.985 0.994 0.218 0.4373 0.8759 0.7056 ] Network output: [ 0.004058 -0.01916 0.9942 1.128e-05 -5.066e-06 1.017 8.504e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.09586 0.1838 0.1985 0.9873 0.9919 0.1085 0.7441 0.8631 0.3053 ] Network output: [ -0.003808 0.01786 1.004 1.215e-05 -5.457e-06 0.9854 9.16e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09286 0.09092 0.165 0.196 0.9852 0.9911 0.09287 0.6682 0.8387 0.2477 ] Network output: [ 0.0001041 1 -7.933e-05 1.605e-06 -7.203e-07 0.9998 1.209e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002479 Epoch 9014 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009561 0.9965 0.9918 -2.155e-07 9.675e-08 -0.007402 -1.624e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003461 -0.003288 -0.007132 0.005684 0.9699 0.9743 0.006701 0.8281 0.8216 0.01693 ] Network output: [ 0.9999 0.0002468 0.0005154 -5.932e-06 2.663e-06 -0.0005337 -4.47e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2042 -0.03492 -0.1636 0.1853 0.9834 0.9932 0.2289 0.4332 0.8692 0.7117 ] Network output: [ -0.00948 1.003 1.009 -2.869e-07 1.288e-07 0.007886 -2.162e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006538 0.0005727 0.004423 0.003345 0.9889 0.9919 0.006664 0.8557 0.8931 0.01215 ] Network output: [ -0.0003079 0.001917 1.001 -1.858e-05 8.341e-06 0.998 -1.4e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2173 0.1022 0.3455 0.1432 0.985 0.994 0.218 0.4373 0.8759 0.7056 ] Network output: [ 0.004056 -0.01915 0.9942 1.127e-05 -5.06e-06 1.017 8.494e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.09586 0.1838 0.1985 0.9873 0.9919 0.1085 0.7441 0.8631 0.3053 ] Network output: [ -0.003807 0.01785 1.004 1.214e-05 -5.451e-06 0.9854 9.15e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09286 0.09093 0.165 0.196 0.9852 0.9911 0.09288 0.6682 0.8387 0.2478 ] Network output: [ 0.000104 1 -7.925e-05 1.603e-06 -7.195e-07 0.9998 1.208e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002478 Epoch 9015 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00956 0.9965 0.9918 -2.156e-07 9.677e-08 -0.007401 -1.624e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003461 -0.003288 -0.007131 0.005683 0.9699 0.9743 0.006701 0.8281 0.8216 0.01693 ] Network output: [ 0.9999 0.0002466 0.0005151 -5.925e-06 2.66e-06 -0.0005333 -4.465e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2042 -0.03493 -0.1636 0.1853 0.9834 0.9932 0.2289 0.4332 0.8692 0.7117 ] Network output: [ -0.009479 1.003 1.009 -2.868e-07 1.287e-07 0.007885 -2.161e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006538 0.0005728 0.004423 0.003345 0.9889 0.9919 0.006664 0.8556 0.8931 0.01215 ] Network output: [ -0.0003077 0.001916 1.001 -1.856e-05 8.332e-06 0.998 -1.399e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2173 0.1022 0.3455 0.1432 0.985 0.994 0.218 0.4373 0.8759 0.7056 ] Network output: [ 0.004055 -0.01914 0.9942 1.126e-05 -5.054e-06 1.017 8.485e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.09587 0.1838 0.1985 0.9873 0.9919 0.1085 0.7441 0.8631 0.3053 ] Network output: [ -0.003805 0.01785 1.004 1.213e-05 -5.445e-06 0.9854 9.14e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09287 0.09093 0.165 0.196 0.9852 0.9911 0.09288 0.6682 0.8387 0.2478 ] Network output: [ 0.000104 1 -7.917e-05 1.601e-06 -7.187e-07 0.9998 1.207e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002477 Epoch 9016 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009559 0.9965 0.9918 -2.156e-07 9.679e-08 -0.007401 -1.625e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003461 -0.003288 -0.007131 0.005683 0.9699 0.9743 0.006702 0.8281 0.8216 0.01693 ] Network output: [ 0.9999 0.0002463 0.0005149 -5.918e-06 2.657e-06 -0.0005329 -4.46e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2043 -0.03493 -0.1635 0.1852 0.9834 0.9932 0.2289 0.4332 0.8692 0.7117 ] Network output: [ -0.009478 1.003 1.009 -2.867e-07 1.287e-07 0.007884 -2.161e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006539 0.0005729 0.004423 0.003345 0.9889 0.9919 0.006665 0.8556 0.8931 0.01215 ] Network output: [ -0.0003075 0.001916 1.001 -1.854e-05 8.322e-06 0.998 -1.397e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2173 0.1022 0.3455 0.1432 0.985 0.994 0.218 0.4372 0.8759 0.7056 ] Network output: [ 0.004053 -0.01914 0.9942 1.125e-05 -5.049e-06 1.017 8.475e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.09587 0.1838 0.1985 0.9873 0.9919 0.1085 0.7441 0.8631 0.3053 ] Network output: [ -0.003804 0.01784 1.004 1.211e-05 -5.438e-06 0.9854 9.13e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09287 0.09093 0.165 0.196 0.9852 0.9911 0.09288 0.6681 0.8387 0.2478 ] Network output: [ 0.0001039 1 -7.909e-05 1.599e-06 -7.179e-07 0.9998 1.205e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002476 Epoch 9017 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009558 0.9965 0.9918 -2.156e-07 9.68e-08 -0.007401 -1.625e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003461 -0.003288 -0.00713 0.005682 0.9699 0.9743 0.006702 0.8281 0.8216 0.01693 ] Network output: [ 0.9999 0.0002461 0.0005146 -5.912e-06 2.654e-06 -0.0005325 -4.455e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2043 -0.03493 -0.1635 0.1852 0.9834 0.9932 0.229 0.4332 0.8692 0.7117 ] Network output: [ -0.009478 1.003 1.008 -2.866e-07 1.287e-07 0.007883 -2.16e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006539 0.0005729 0.004423 0.003344 0.9889 0.9919 0.006665 0.8556 0.8931 0.01214 ] Network output: [ -0.0003073 0.001915 1.001 -1.852e-05 8.313e-06 0.998 -1.396e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2173 0.1022 0.3456 0.1432 0.985 0.994 0.218 0.4372 0.8759 0.7056 ] Network output: [ 0.004052 -0.01913 0.9942 1.123e-05 -5.043e-06 1.017 8.465e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.09588 0.1838 0.1985 0.9873 0.9919 0.1085 0.7441 0.863 0.3053 ] Network output: [ -0.003802 0.01783 1.004 1.21e-05 -5.432e-06 0.9854 9.12e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09287 0.09093 0.165 0.196 0.9852 0.9911 0.09288 0.6681 0.8387 0.2478 ] Network output: [ 0.0001039 1 -7.901e-05 1.597e-06 -7.171e-07 0.9998 1.204e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002474 Epoch 9018 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009557 0.9965 0.9918 -2.157e-07 9.682e-08 -0.0074 -1.625e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003461 -0.003289 -0.007129 0.005682 0.9699 0.9743 0.006702 0.8281 0.8216 0.01693 ] Network output: [ 0.9999 0.0002459 0.0005143 -5.905e-06 2.651e-06 -0.0005321 -4.45e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2043 -0.03493 -0.1635 0.1852 0.9834 0.9932 0.229 0.4332 0.8692 0.7117 ] Network output: [ -0.009477 1.003 1.008 -2.865e-07 1.286e-07 0.007882 -2.159e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00654 0.000573 0.004423 0.003344 0.9889 0.9919 0.006666 0.8556 0.8931 0.01214 ] Network output: [ -0.000307 0.001914 1.001 -1.85e-05 8.304e-06 0.998 -1.394e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2173 0.1023 0.3456 0.1432 0.985 0.994 0.218 0.4372 0.8759 0.7056 ] Network output: [ 0.00405 -0.01912 0.9942 1.122e-05 -5.037e-06 1.017 8.456e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.09588 0.1838 0.1985 0.9873 0.9919 0.1085 0.7441 0.863 0.3053 ] Network output: [ -0.003801 0.01783 1.004 1.209e-05 -5.426e-06 0.9854 9.109e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09287 0.09094 0.165 0.196 0.9852 0.9911 0.09289 0.6681 0.8387 0.2478 ] Network output: [ 0.0001039 1 -7.893e-05 1.596e-06 -7.163e-07 0.9998 1.202e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002473 Epoch 9019 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009556 0.9965 0.9918 -2.157e-07 9.683e-08 -0.0074 -1.626e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003461 -0.003289 -0.007128 0.005681 0.9699 0.9743 0.006702 0.8281 0.8216 0.01693 ] Network output: [ 0.9999 0.0002456 0.0005141 -5.898e-06 2.648e-06 -0.0005317 -4.445e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2043 -0.03493 -0.1635 0.1852 0.9834 0.9932 0.229 0.4332 0.8692 0.7117 ] Network output: [ -0.009476 1.003 1.008 -2.864e-07 1.286e-07 0.007881 -2.158e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00654 0.0005731 0.004423 0.003344 0.9889 0.9919 0.006666 0.8556 0.8931 0.01214 ] Network output: [ -0.0003068 0.001913 1.001 -1.847e-05 8.294e-06 0.998 -1.392e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2173 0.1023 0.3456 0.1432 0.985 0.994 0.218 0.4372 0.8759 0.7056 ] Network output: [ 0.004048 -0.01911 0.9942 1.121e-05 -5.031e-06 1.017 8.446e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.09589 0.1838 0.1985 0.9873 0.9919 0.1085 0.744 0.863 0.3053 ] Network output: [ -0.003799 0.01782 1.004 1.207e-05 -5.42e-06 0.9854 9.099e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09288 0.09094 0.165 0.196 0.9852 0.9911 0.09289 0.6681 0.8387 0.2478 ] Network output: [ 0.0001038 1 -7.885e-05 1.594e-06 -7.155e-07 0.9998 1.201e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002472 Epoch 9020 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009555 0.9965 0.9918 -2.157e-07 9.685e-08 -0.0074 -1.626e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003461 -0.003289 -0.007128 0.005681 0.9699 0.9743 0.006703 0.8281 0.8216 0.01692 ] Network output: [ 0.9999 0.0002454 0.0005138 -5.891e-06 2.645e-06 -0.0005313 -4.44e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2043 -0.03493 -0.1635 0.1852 0.9834 0.9932 0.229 0.4332 0.8692 0.7116 ] Network output: [ -0.009475 1.003 1.008 -2.863e-07 1.285e-07 0.00788 -2.158e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006541 0.0005732 0.004423 0.003343 0.9889 0.9919 0.006667 0.8556 0.8931 0.01214 ] Network output: [ -0.0003066 0.001913 1.001 -1.845e-05 8.285e-06 0.998 -1.391e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2173 0.1023 0.3456 0.1432 0.985 0.994 0.218 0.4372 0.8759 0.7056 ] Network output: [ 0.004047 -0.01911 0.9942 1.119e-05 -5.026e-06 1.017 8.437e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.09589 0.1838 0.1985 0.9873 0.9919 0.1085 0.744 0.863 0.3053 ] Network output: [ -0.003798 0.01781 1.004 1.206e-05 -5.414e-06 0.9854 9.089e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09288 0.09094 0.165 0.196 0.9852 0.9911 0.09289 0.6681 0.8386 0.2478 ] Network output: [ 0.0001038 1 -7.877e-05 1.592e-06 -7.147e-07 0.9998 1.2e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000247 Epoch 9021 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009554 0.9965 0.9918 -2.158e-07 9.686e-08 -0.007399 -1.626e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003462 -0.003289 -0.007127 0.00568 0.9699 0.9743 0.006703 0.8281 0.8216 0.01692 ] Network output: [ 0.9999 0.0002452 0.0005136 -5.885e-06 2.642e-06 -0.0005309 -4.435e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2043 -0.03493 -0.1635 0.1852 0.9834 0.9932 0.229 0.4332 0.8692 0.7116 ] Network output: [ -0.009474 1.003 1.008 -2.862e-07 1.285e-07 0.007879 -2.157e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006541 0.0005733 0.004423 0.003343 0.9889 0.9919 0.006667 0.8556 0.8931 0.01214 ] Network output: [ -0.0003064 0.001912 1.001 -1.843e-05 8.275e-06 0.998 -1.389e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2173 0.1023 0.3456 0.1432 0.985 0.994 0.2181 0.4372 0.8759 0.7056 ] Network output: [ 0.004045 -0.0191 0.9942 1.118e-05 -5.02e-06 1.017 8.427e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.0959 0.1838 0.1985 0.9873 0.9919 0.1085 0.744 0.863 0.3053 ] Network output: [ -0.003796 0.0178 1.004 1.205e-05 -5.408e-06 0.9854 9.079e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09288 0.09094 0.165 0.196 0.9852 0.9911 0.09289 0.6681 0.8386 0.2478 ] Network output: [ 0.0001037 1 -7.869e-05 1.59e-06 -7.139e-07 0.9998 1.198e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002469 Epoch 9022 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009553 0.9965 0.9918 -2.158e-07 9.688e-08 -0.007399 -1.626e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003462 -0.003289 -0.007126 0.00568 0.9699 0.9743 0.006703 0.828 0.8216 0.01692 ] Network output: [ 0.9999 0.0002449 0.0005133 -5.878e-06 2.639e-06 -0.0005305 -4.43e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2043 -0.03493 -0.1635 0.1852 0.9834 0.9932 0.229 0.4331 0.8692 0.7116 ] Network output: [ -0.009473 1.003 1.008 -2.861e-07 1.285e-07 0.007878 -2.156e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006542 0.0005734 0.004422 0.003343 0.9889 0.9919 0.006668 0.8556 0.8931 0.01214 ] Network output: [ -0.0003062 0.001911 1.001 -1.841e-05 8.266e-06 0.998 -1.388e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2173 0.1023 0.3456 0.1432 0.985 0.994 0.2181 0.4372 0.8759 0.7056 ] Network output: [ 0.004044 -0.01909 0.9942 1.117e-05 -5.014e-06 1.017 8.418e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.0959 0.1838 0.1985 0.9873 0.9919 0.1085 0.744 0.863 0.3053 ] Network output: [ -0.003795 0.0178 1.004 1.203e-05 -5.403e-06 0.9854 9.069e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09288 0.09095 0.165 0.196 0.9852 0.9911 0.0929 0.668 0.8386 0.2478 ] Network output: [ 0.0001037 1 -7.861e-05 1.588e-06 -7.131e-07 0.9998 1.197e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002468 Epoch 9023 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009552 0.9965 0.9918 -2.158e-07 9.689e-08 -0.007398 -1.627e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003462 -0.003289 -0.007126 0.005679 0.9699 0.9743 0.006703 0.828 0.8216 0.01692 ] Network output: [ 0.9999 0.0002447 0.0005131 -5.871e-06 2.636e-06 -0.0005301 -4.425e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2043 -0.03493 -0.1635 0.1852 0.9834 0.9932 0.229 0.4331 0.8692 0.7116 ] Network output: [ -0.009472 1.003 1.008 -2.86e-07 1.284e-07 0.007877 -2.156e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006542 0.0005735 0.004422 0.003343 0.9889 0.9919 0.006668 0.8556 0.8931 0.01214 ] Network output: [ -0.000306 0.00191 1.001 -1.839e-05 8.256e-06 0.998 -1.386e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2173 0.1023 0.3456 0.1432 0.985 0.994 0.2181 0.4372 0.8759 0.7056 ] Network output: [ 0.004042 -0.01909 0.9942 1.116e-05 -5.009e-06 1.017 8.408e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.09591 0.1838 0.1985 0.9873 0.9919 0.1085 0.744 0.863 0.3053 ] Network output: [ -0.003793 0.01779 1.004 1.202e-05 -5.397e-06 0.9854 9.059e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09288 0.09095 0.165 0.196 0.9852 0.9911 0.0929 0.668 0.8386 0.2478 ] Network output: [ 0.0001037 1 -7.853e-05 1.587e-06 -7.123e-07 0.9998 1.196e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002467 Epoch 9024 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009551 0.9965 0.9918 -2.159e-07 9.691e-08 -0.007398 -1.627e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003462 -0.003289 -0.007125 0.005679 0.9699 0.9743 0.006704 0.828 0.8216 0.01692 ] Network output: [ 0.9999 0.0002445 0.0005128 -5.865e-06 2.633e-06 -0.0005297 -4.42e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2043 -0.03493 -0.1635 0.1852 0.9834 0.9932 0.229 0.4331 0.8692 0.7116 ] Network output: [ -0.009471 1.003 1.008 -2.859e-07 1.284e-07 0.007876 -2.155e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006543 0.0005736 0.004422 0.003342 0.9889 0.9919 0.006669 0.8556 0.8931 0.01214 ] Network output: [ -0.0003058 0.00191 1.001 -1.837e-05 8.247e-06 0.998 -1.384e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2174 0.1023 0.3456 0.1432 0.985 0.994 0.2181 0.4372 0.8759 0.7056 ] Network output: [ 0.004041 -0.01908 0.9942 1.114e-05 -5.003e-06 1.017 8.399e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.09591 0.1838 0.1985 0.9873 0.9919 0.1085 0.744 0.863 0.3053 ] Network output: [ -0.003792 0.01778 1.004 1.201e-05 -5.391e-06 0.9854 9.049e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09289 0.09095 0.165 0.196 0.9852 0.9911 0.0929 0.668 0.8386 0.2478 ] Network output: [ 0.0001036 1 -7.845e-05 1.585e-06 -7.115e-07 0.9998 1.194e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002465 Epoch 9025 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009549 0.9965 0.9918 -2.159e-07 9.692e-08 -0.007398 -1.627e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003462 -0.003289 -0.007124 0.005679 0.9699 0.9743 0.006704 0.828 0.8216 0.01692 ] Network output: [ 0.9999 0.0002442 0.0005126 -5.858e-06 2.63e-06 -0.0005293 -4.415e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2043 -0.03494 -0.1634 0.1852 0.9834 0.9932 0.229 0.4331 0.8692 0.7116 ] Network output: [ -0.00947 1.003 1.008 -2.858e-07 1.283e-07 0.007876 -2.154e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006543 0.0005737 0.004422 0.003342 0.9889 0.9919 0.006669 0.8556 0.8931 0.01214 ] Network output: [ -0.0003056 0.001909 1.001 -1.835e-05 8.238e-06 0.998 -1.383e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2174 0.1023 0.3456 0.1432 0.985 0.994 0.2181 0.4372 0.8759 0.7056 ] Network output: [ 0.004039 -0.01907 0.9942 1.113e-05 -4.998e-06 1.017 8.389e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1084 0.09592 0.1838 0.1985 0.9873 0.9919 0.1085 0.744 0.863 0.3053 ] Network output: [ -0.00379 0.01778 1.004 1.199e-05 -5.385e-06 0.9854 9.039e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09289 0.09095 0.165 0.196 0.9852 0.9911 0.0929 0.668 0.8386 0.2478 ] Network output: [ 0.0001036 1 -7.837e-05 1.583e-06 -7.107e-07 0.9998 1.193e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002464 Epoch 9026 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009548 0.9965 0.9918 -2.159e-07 9.694e-08 -0.007397 -1.627e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003462 -0.003289 -0.007123 0.005678 0.9699 0.9743 0.006704 0.828 0.8216 0.01692 ] Network output: [ 0.9999 0.000244 0.0005123 -5.851e-06 2.627e-06 -0.0005289 -4.41e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2043 -0.03494 -0.1634 0.1852 0.9834 0.9932 0.229 0.4331 0.8692 0.7116 ] Network output: [ -0.009469 1.003 1.008 -2.857e-07 1.283e-07 0.007875 -2.153e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006544 0.0005738 0.004422 0.003342 0.9889 0.9919 0.00667 0.8556 0.8931 0.01214 ] Network output: [ -0.0003054 0.001908 1.001 -1.833e-05 8.228e-06 0.998 -1.381e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2174 0.1023 0.3456 0.1432 0.985 0.994 0.2181 0.4372 0.8759 0.7056 ] Network output: [ 0.004038 -0.01906 0.9942 1.112e-05 -4.992e-06 1.017 8.38e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.09592 0.1838 0.1985 0.9873 0.9919 0.1085 0.7439 0.863 0.3053 ] Network output: [ -0.003789 0.01777 1.004 1.198e-05 -5.379e-06 0.9854 9.029e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09289 0.09095 0.165 0.196 0.9852 0.9911 0.09291 0.668 0.8386 0.2478 ] Network output: [ 0.0001035 1 -7.829e-05 1.581e-06 -7.099e-07 0.9998 1.192e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002463 Epoch 9027 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009547 0.9965 0.9918 -2.16e-07 9.695e-08 -0.007397 -1.628e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003462 -0.00329 -0.007123 0.005678 0.9699 0.9743 0.006704 0.828 0.8216 0.01692 ] Network output: [ 0.9999 0.0002438 0.0005121 -5.845e-06 2.624e-06 -0.0005285 -4.405e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2043 -0.03494 -0.1634 0.1852 0.9834 0.9932 0.229 0.4331 0.8692 0.7116 ] Network output: [ -0.009468 1.003 1.008 -2.856e-07 1.282e-07 0.007874 -2.153e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006544 0.0005739 0.004422 0.003342 0.9889 0.9919 0.00667 0.8556 0.8931 0.01214 ] Network output: [ -0.0003052 0.001907 1.001 -1.831e-05 8.219e-06 0.998 -1.38e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2174 0.1023 0.3456 0.1432 0.985 0.994 0.2181 0.4372 0.8759 0.7056 ] Network output: [ 0.004036 -0.01906 0.9942 1.111e-05 -4.986e-06 1.017 8.37e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.09593 0.1838 0.1985 0.9873 0.9919 0.1085 0.7439 0.863 0.3053 ] Network output: [ -0.003788 0.01776 1.004 1.197e-05 -5.373e-06 0.9854 9.019e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09289 0.09096 0.165 0.196 0.9852 0.9911 0.09291 0.668 0.8386 0.2478 ] Network output: [ 0.0001035 1 -7.822e-05 1.58e-06 -7.091e-07 0.9998 1.19e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002461 Epoch 9028 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009546 0.9965 0.9918 -2.16e-07 9.696e-08 -0.007397 -1.628e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003462 -0.00329 -0.007122 0.005677 0.9699 0.9743 0.006704 0.828 0.8216 0.01692 ] Network output: [ 0.9999 0.0002435 0.0005118 -5.838e-06 2.621e-06 -0.0005281 -4.4e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2043 -0.03494 -0.1634 0.1852 0.9834 0.9932 0.2291 0.4331 0.8692 0.7116 ] Network output: [ -0.009467 1.003 1.008 -2.855e-07 1.282e-07 0.007873 -2.152e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006545 0.0005739 0.004422 0.003341 0.9889 0.9919 0.006671 0.8556 0.8931 0.01214 ] Network output: [ -0.000305 0.001907 1.001 -1.829e-05 8.21e-06 0.998 -1.378e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2174 0.1023 0.3456 0.1432 0.985 0.994 0.2181 0.4372 0.8759 0.7056 ] Network output: [ 0.004035 -0.01905 0.9942 1.109e-05 -4.981e-06 1.017 8.361e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.09593 0.1838 0.1985 0.9873 0.9919 0.1085 0.7439 0.863 0.3053 ] Network output: [ -0.003786 0.01775 1.004 1.195e-05 -5.367e-06 0.9854 9.009e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0929 0.09096 0.165 0.196 0.9852 0.9911 0.09291 0.668 0.8386 0.2478 ] Network output: [ 0.0001035 1 -7.814e-05 1.578e-06 -7.083e-07 0.9998 1.189e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000246 Epoch 9029 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009545 0.9965 0.9918 -2.16e-07 9.698e-08 -0.007396 -1.628e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003462 -0.00329 -0.007121 0.005677 0.9699 0.9743 0.006705 0.828 0.8216 0.01692 ] Network output: [ 0.9999 0.0002433 0.0005116 -5.831e-06 2.618e-06 -0.0005277 -4.395e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2043 -0.03494 -0.1634 0.1852 0.9834 0.9932 0.2291 0.4331 0.8692 0.7116 ] Network output: [ -0.009466 1.003 1.008 -2.855e-07 1.281e-07 0.007872 -2.151e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006545 0.000574 0.004422 0.003341 0.9889 0.9919 0.006671 0.8556 0.8931 0.01213 ] Network output: [ -0.0003048 0.001906 1.001 -1.827e-05 8.2e-06 0.998 -1.377e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2174 0.1023 0.3456 0.1432 0.985 0.994 0.2181 0.4371 0.8759 0.7056 ] Network output: [ 0.004033 -0.01904 0.9942 1.108e-05 -4.975e-06 1.017 8.352e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.09593 0.1838 0.1985 0.9873 0.9919 0.1085 0.7439 0.863 0.3053 ] Network output: [ -0.003785 0.01775 1.004 1.194e-05 -5.361e-06 0.9855 8.999e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0929 0.09096 0.165 0.196 0.9852 0.9911 0.09291 0.6679 0.8386 0.2478 ] Network output: [ 0.0001034 1 -7.806e-05 1.576e-06 -7.075e-07 0.9998 1.188e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002459 Epoch 9030 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009544 0.9965 0.9918 -2.16e-07 9.699e-08 -0.007396 -1.628e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003462 -0.00329 -0.00712 0.005676 0.9699 0.9743 0.006705 0.828 0.8215 0.01691 ] Network output: [ 0.9999 0.0002431 0.0005113 -5.825e-06 2.615e-06 -0.0005273 -4.39e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2044 -0.03494 -0.1634 0.1852 0.9834 0.9932 0.2291 0.4331 0.8692 0.7116 ] Network output: [ -0.009465 1.003 1.008 -2.854e-07 1.281e-07 0.007871 -2.151e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006545 0.0005741 0.004422 0.003341 0.9889 0.9919 0.006672 0.8556 0.8931 0.01213 ] Network output: [ -0.0003046 0.001905 1.001 -1.825e-05 8.191e-06 0.998 -1.375e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2174 0.1023 0.3456 0.1432 0.985 0.994 0.2181 0.4371 0.8759 0.7056 ] Network output: [ 0.004032 -0.01903 0.9942 1.107e-05 -4.969e-06 1.017 8.342e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.09594 0.1838 0.1985 0.9873 0.9919 0.1085 0.7439 0.863 0.3053 ] Network output: [ -0.003783 0.01774 1.004 1.193e-05 -5.355e-06 0.9855 8.989e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0929 0.09096 0.165 0.196 0.9852 0.9911 0.09291 0.6679 0.8386 0.2478 ] Network output: [ 0.0001034 1 -7.798e-05 1.574e-06 -7.067e-07 0.9998 1.186e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002458 Epoch 9031 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009543 0.9965 0.9918 -2.161e-07 9.7e-08 -0.007396 -1.628e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003462 -0.00329 -0.00712 0.005676 0.9699 0.9743 0.006705 0.828 0.8215 0.01691 ] Network output: [ 0.9999 0.0002428 0.000511 -5.818e-06 2.612e-06 -0.0005269 -4.385e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2044 -0.03494 -0.1634 0.1852 0.9834 0.9932 0.2291 0.4331 0.8692 0.7116 ] Network output: [ -0.009465 1.003 1.008 -2.853e-07 1.281e-07 0.00787 -2.15e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006546 0.0005742 0.004422 0.00334 0.9889 0.9919 0.006672 0.8555 0.8931 0.01213 ] Network output: [ -0.0003044 0.001905 1.001 -1.822e-05 8.182e-06 0.998 -1.373e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2174 0.1023 0.3456 0.1432 0.985 0.994 0.2181 0.4371 0.8759 0.7055 ] Network output: [ 0.00403 -0.01903 0.9942 1.106e-05 -4.964e-06 1.017 8.333e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.09594 0.1838 0.1985 0.9873 0.9919 0.1086 0.7439 0.863 0.3053 ] Network output: [ -0.003782 0.01773 1.004 1.191e-05 -5.349e-06 0.9855 8.979e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0929 0.09097 0.165 0.196 0.9852 0.9911 0.09292 0.6679 0.8386 0.2478 ] Network output: [ 0.0001033 1 -7.79e-05 1.572e-06 -7.059e-07 0.9998 1.185e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002456 Epoch 9032 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009542 0.9965 0.9918 -2.161e-07 9.702e-08 -0.007395 -1.629e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003463 -0.00329 -0.007119 0.005675 0.9699 0.9743 0.006705 0.828 0.8215 0.01691 ] Network output: [ 0.9999 0.0002426 0.0005108 -5.811e-06 2.609e-06 -0.0005264 -4.38e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2044 -0.03494 -0.1634 0.1852 0.9834 0.9932 0.2291 0.4331 0.8692 0.7116 ] Network output: [ -0.009464 1.003 1.008 -2.852e-07 1.28e-07 0.007869 -2.149e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006546 0.0005743 0.004422 0.00334 0.9889 0.9919 0.006673 0.8555 0.8931 0.01213 ] Network output: [ -0.0003042 0.001904 1.001 -1.82e-05 8.172e-06 0.998 -1.372e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2174 0.1023 0.3456 0.1432 0.985 0.994 0.2181 0.4371 0.8759 0.7055 ] Network output: [ 0.004029 -0.01902 0.9942 1.104e-05 -4.958e-06 1.017 8.323e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.09595 0.1838 0.1984 0.9873 0.9919 0.1086 0.7439 0.863 0.3053 ] Network output: [ -0.00378 0.01773 1.004 1.19e-05 -5.343e-06 0.9855 8.969e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09291 0.09097 0.165 0.196 0.9852 0.9911 0.09292 0.6679 0.8386 0.2478 ] Network output: [ 0.0001033 1 -7.782e-05 1.571e-06 -7.051e-07 0.9998 1.184e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002455 Epoch 9033 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009541 0.9965 0.9918 -2.161e-07 9.703e-08 -0.007395 -1.629e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003463 -0.00329 -0.007118 0.005675 0.9699 0.9743 0.006706 0.828 0.8215 0.01691 ] Network output: [ 0.9999 0.0002424 0.0005105 -5.805e-06 2.606e-06 -0.000526 -4.375e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2044 -0.03494 -0.1634 0.1852 0.9834 0.9932 0.2291 0.4331 0.8691 0.7116 ] Network output: [ -0.009463 1.003 1.008 -2.851e-07 1.28e-07 0.007868 -2.148e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006547 0.0005744 0.004422 0.00334 0.9889 0.9919 0.006673 0.8555 0.8931 0.01213 ] Network output: [ -0.000304 0.001903 1.001 -1.818e-05 8.163e-06 0.998 -1.37e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2174 0.1023 0.3456 0.1432 0.985 0.994 0.2182 0.4371 0.8759 0.7055 ] Network output: [ 0.004027 -0.01901 0.9942 1.103e-05 -4.953e-06 1.017 8.314e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.09595 0.1838 0.1984 0.9873 0.9919 0.1086 0.7438 0.863 0.3053 ] Network output: [ -0.003779 0.01772 1.004 1.189e-05 -5.337e-06 0.9855 8.959e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09291 0.09097 0.165 0.196 0.9852 0.9911 0.09292 0.6679 0.8386 0.2478 ] Network output: [ 0.0001033 1 -7.775e-05 1.569e-06 -7.043e-07 0.9998 1.182e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002454 Epoch 9034 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00954 0.9965 0.9918 -2.162e-07 9.704e-08 -0.007394 -1.629e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003463 -0.00329 -0.007118 0.005674 0.9699 0.9743 0.006706 0.828 0.8215 0.01691 ] Network output: [ 0.9999 0.0002421 0.0005103 -5.798e-06 2.603e-06 -0.0005256 -4.37e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2044 -0.03495 -0.1633 0.1852 0.9834 0.9932 0.2291 0.4331 0.8691 0.7116 ] Network output: [ -0.009462 1.003 1.008 -2.85e-07 1.279e-07 0.007867 -2.148e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006547 0.0005745 0.004422 0.00334 0.9889 0.9919 0.006674 0.8555 0.8931 0.01213 ] Network output: [ -0.0003038 0.001902 1.001 -1.816e-05 8.154e-06 0.998 -1.369e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2174 0.1023 0.3456 0.1432 0.985 0.994 0.2182 0.4371 0.8759 0.7055 ] Network output: [ 0.004025 -0.01901 0.9942 1.102e-05 -4.947e-06 1.017 8.305e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.09596 0.1838 0.1984 0.9873 0.9919 0.1086 0.7438 0.863 0.3053 ] Network output: [ -0.003777 0.01771 1.004 1.188e-05 -5.331e-06 0.9855 8.949e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09291 0.09097 0.165 0.196 0.9852 0.9911 0.09292 0.6679 0.8386 0.2478 ] Network output: [ 0.0001032 1 -7.767e-05 1.567e-06 -7.036e-07 0.9998 1.181e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002452 Epoch 9035 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009539 0.9965 0.9918 -2.162e-07 9.705e-08 -0.007394 -1.629e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003463 -0.00329 -0.007117 0.005674 0.9699 0.9743 0.006706 0.828 0.8215 0.01691 ] Network output: [ 0.9999 0.0002419 0.00051 -5.791e-06 2.6e-06 -0.0005252 -4.365e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2044 -0.03495 -0.1633 0.1852 0.9834 0.9932 0.2291 0.4331 0.8691 0.7116 ] Network output: [ -0.009461 1.003 1.008 -2.849e-07 1.279e-07 0.007866 -2.147e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006548 0.0005746 0.004422 0.003339 0.9889 0.9919 0.006674 0.8555 0.8931 0.01213 ] Network output: [ -0.0003035 0.001902 1.001 -1.814e-05 8.144e-06 0.998 -1.367e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2174 0.1023 0.3457 0.1432 0.985 0.994 0.2182 0.4371 0.8758 0.7055 ] Network output: [ 0.004024 -0.019 0.9942 1.101e-05 -4.941e-06 1.017 8.295e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.09596 0.1838 0.1984 0.9873 0.9919 0.1086 0.7438 0.863 0.3053 ] Network output: [ -0.003776 0.0177 1.004 1.186e-05 -5.325e-06 0.9855 8.94e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09291 0.09098 0.165 0.196 0.9852 0.9911 0.09293 0.6679 0.8386 0.2478 ] Network output: [ 0.0001032 1 -7.759e-05 1.565e-06 -7.028e-07 0.9998 1.18e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002451 Epoch 9036 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009538 0.9965 0.9918 -2.162e-07 9.707e-08 -0.007394 -1.629e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003463 -0.003291 -0.007116 0.005673 0.9699 0.9743 0.006706 0.828 0.8215 0.01691 ] Network output: [ 0.9999 0.0002417 0.0005098 -5.785e-06 2.597e-06 -0.0005248 -4.36e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2044 -0.03495 -0.1633 0.1852 0.9834 0.9932 0.2291 0.433 0.8691 0.7116 ] Network output: [ -0.00946 1.003 1.008 -2.848e-07 1.278e-07 0.007865 -2.146e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006548 0.0005747 0.004422 0.003339 0.9889 0.9919 0.006675 0.8555 0.8931 0.01213 ] Network output: [ -0.0003033 0.001901 1.001 -1.812e-05 8.135e-06 0.998 -1.366e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2175 0.1023 0.3457 0.1432 0.985 0.994 0.2182 0.4371 0.8758 0.7055 ] Network output: [ 0.004022 -0.01899 0.9942 1.099e-05 -4.936e-06 1.017 8.286e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.09597 0.1838 0.1984 0.9873 0.9919 0.1086 0.7438 0.863 0.3053 ] Network output: [ -0.003774 0.0177 1.004 1.185e-05 -5.319e-06 0.9855 8.93e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09292 0.09098 0.165 0.196 0.9852 0.9911 0.09293 0.6678 0.8386 0.2478 ] Network output: [ 0.0001032 1 -7.751e-05 1.564e-06 -7.02e-07 0.9998 1.178e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000245 Epoch 9037 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009536 0.9965 0.9918 -2.162e-07 9.708e-08 -0.007393 -1.63e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003463 -0.003291 -0.007115 0.005673 0.9699 0.9743 0.006707 0.828 0.8215 0.01691 ] Network output: [ 0.9999 0.0002414 0.0005095 -5.778e-06 2.594e-06 -0.0005244 -4.355e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2044 -0.03495 -0.1633 0.1852 0.9834 0.9932 0.2291 0.433 0.8691 0.7116 ] Network output: [ -0.009459 1.003 1.008 -2.847e-07 1.278e-07 0.007864 -2.145e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006549 0.0005748 0.004422 0.003339 0.9889 0.9919 0.006675 0.8555 0.8931 0.01213 ] Network output: [ -0.0003031 0.0019 1.001 -1.81e-05 8.126e-06 0.998 -1.364e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2175 0.1024 0.3457 0.1432 0.985 0.994 0.2182 0.4371 0.8758 0.7055 ] Network output: [ 0.004021 -0.01898 0.9942 1.098e-05 -4.93e-06 1.017 8.276e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.09597 0.1838 0.1984 0.9873 0.9919 0.1086 0.7438 0.863 0.3053 ] Network output: [ -0.003773 0.01769 1.004 1.184e-05 -5.313e-06 0.9855 8.92e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09292 0.09098 0.165 0.196 0.9852 0.9911 0.09293 0.6678 0.8386 0.2478 ] Network output: [ 0.0001031 1 -7.744e-05 1.562e-06 -7.012e-07 0.9998 1.177e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002449 Epoch 9038 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009535 0.9965 0.9918 -2.163e-07 9.709e-08 -0.007393 -1.63e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003463 -0.003291 -0.007115 0.005672 0.9699 0.9743 0.006707 0.828 0.8215 0.01691 ] Network output: [ 0.9999 0.0002412 0.0005093 -5.772e-06 2.591e-06 -0.000524 -4.35e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2044 -0.03495 -0.1633 0.1852 0.9834 0.9932 0.2291 0.433 0.8691 0.7116 ] Network output: [ -0.009458 1.003 1.008 -2.846e-07 1.278e-07 0.007863 -2.145e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006549 0.0005749 0.004422 0.003338 0.9889 0.9919 0.006676 0.8555 0.8931 0.01213 ] Network output: [ -0.0003029 0.001899 1.001 -1.808e-05 8.117e-06 0.998 -1.363e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2175 0.1024 0.3457 0.1432 0.985 0.994 0.2182 0.4371 0.8758 0.7055 ] Network output: [ 0.004019 -0.01898 0.9942 1.097e-05 -4.925e-06 1.017 8.267e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.09598 0.1838 0.1984 0.9873 0.9919 0.1086 0.7438 0.863 0.3053 ] Network output: [ -0.003771 0.01768 1.004 1.182e-05 -5.308e-06 0.9855 8.91e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09292 0.09098 0.165 0.196 0.9852 0.9911 0.09293 0.6678 0.8386 0.2478 ] Network output: [ 0.0001031 1 -7.736e-05 1.56e-06 -7.004e-07 0.9998 1.176e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002447 Epoch 9039 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009534 0.9965 0.9918 -2.163e-07 9.71e-08 -0.007393 -1.63e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003463 -0.003291 -0.007114 0.005672 0.9699 0.9743 0.006707 0.828 0.8215 0.0169 ] Network output: [ 0.9999 0.000241 0.000509 -5.765e-06 2.588e-06 -0.0005236 -4.345e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2044 -0.03495 -0.1633 0.1851 0.9834 0.9932 0.2291 0.433 0.8691 0.7116 ] Network output: [ -0.009457 1.003 1.008 -2.845e-07 1.277e-07 0.007862 -2.144e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00655 0.0005749 0.004421 0.003338 0.9889 0.9919 0.006676 0.8555 0.8931 0.01213 ] Network output: [ -0.0003027 0.001899 1.001 -1.806e-05 8.107e-06 0.998 -1.361e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2175 0.1024 0.3457 0.1432 0.985 0.994 0.2182 0.4371 0.8758 0.7055 ] Network output: [ 0.004018 -0.01897 0.9942 1.096e-05 -4.919e-06 1.017 8.258e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.09598 0.1838 0.1984 0.9873 0.9919 0.1086 0.7438 0.863 0.3053 ] Network output: [ -0.00377 0.01767 1.004 1.181e-05 -5.302e-06 0.9855 8.9e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09292 0.09099 0.165 0.196 0.9852 0.9911 0.09294 0.6678 0.8386 0.2478 ] Network output: [ 0.000103 1 -7.728e-05 1.558e-06 -6.996e-07 0.9998 1.174e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002446 Epoch 9040 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009533 0.9965 0.9918 -2.163e-07 9.711e-08 -0.007392 -1.63e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003463 -0.003291 -0.007113 0.005672 0.9699 0.9743 0.006707 0.8279 0.8215 0.0169 ] Network output: [ 0.9999 0.0002407 0.0005088 -5.759e-06 2.585e-06 -0.0005232 -4.34e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2044 -0.03495 -0.1633 0.1851 0.9834 0.9932 0.2292 0.433 0.8691 0.7115 ] Network output: [ -0.009456 1.003 1.008 -2.844e-07 1.277e-07 0.007862 -2.143e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00655 0.000575 0.004421 0.003338 0.9889 0.9919 0.006677 0.8555 0.8931 0.01213 ] Network output: [ -0.0003025 0.001898 1.001 -1.804e-05 8.098e-06 0.998 -1.359e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2175 0.1024 0.3457 0.1432 0.985 0.994 0.2182 0.4371 0.8758 0.7055 ] Network output: [ 0.004016 -0.01896 0.9942 1.094e-05 -4.914e-06 1.017 8.248e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.09599 0.1838 0.1984 0.9873 0.9919 0.1086 0.7437 0.863 0.3053 ] Network output: [ -0.003768 0.01767 1.004 1.18e-05 -5.296e-06 0.9855 8.89e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09292 0.09099 0.165 0.196 0.9852 0.9911 0.09294 0.6678 0.8386 0.2478 ] Network output: [ 0.000103 1 -7.721e-05 1.557e-06 -6.988e-07 0.9998 1.173e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002445 Epoch 9041 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009532 0.9965 0.9918 -2.163e-07 9.712e-08 -0.007392 -1.63e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003463 -0.003291 -0.007113 0.005671 0.9699 0.9743 0.006708 0.8279 0.8215 0.0169 ] Network output: [ 0.9999 0.0002405 0.0005085 -5.752e-06 2.582e-06 -0.0005229 -4.335e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2044 -0.03495 -0.1633 0.1851 0.9834 0.9932 0.2292 0.433 0.8691 0.7115 ] Network output: [ -0.009455 1.003 1.008 -2.843e-07 1.276e-07 0.007861 -2.142e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006551 0.0005751 0.004421 0.003338 0.9889 0.9919 0.006677 0.8555 0.8931 0.01212 ] Network output: [ -0.0003023 0.001897 1.001 -1.802e-05 8.089e-06 0.998 -1.358e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2175 0.1024 0.3457 0.1432 0.985 0.994 0.2182 0.4371 0.8758 0.7055 ] Network output: [ 0.004015 -0.01895 0.9942 1.093e-05 -4.908e-06 1.017 8.239e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.09599 0.1838 0.1984 0.9873 0.9919 0.1086 0.7437 0.863 0.3053 ] Network output: [ -0.003767 0.01766 1.004 1.178e-05 -5.29e-06 0.9855 8.88e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09293 0.09099 0.165 0.196 0.9852 0.9911 0.09294 0.6678 0.8386 0.2478 ] Network output: [ 0.000103 1 -7.713e-05 1.555e-06 -6.98e-07 0.9998 1.172e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002444 Epoch 9042 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009531 0.9965 0.9918 -2.164e-07 9.713e-08 -0.007392 -1.631e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003463 -0.003291 -0.007112 0.005671 0.9699 0.9743 0.006708 0.8279 0.8215 0.0169 ] Network output: [ 0.9999 0.0002403 0.0005083 -5.745e-06 2.579e-06 -0.0005225 -4.33e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2044 -0.03495 -0.1633 0.1851 0.9834 0.9932 0.2292 0.433 0.8691 0.7115 ] Network output: [ -0.009454 1.003 1.008 -2.842e-07 1.276e-07 0.00786 -2.142e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006551 0.0005752 0.004421 0.003337 0.9889 0.9919 0.006677 0.8555 0.8931 0.01212 ] Network output: [ -0.0003021 0.001896 1.001 -1.8e-05 8.08e-06 0.998 -1.356e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2175 0.1024 0.3457 0.1432 0.985 0.994 0.2182 0.4371 0.8758 0.7055 ] Network output: [ 0.004013 -0.01895 0.9942 1.092e-05 -4.902e-06 1.017 8.23e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.096 0.1838 0.1984 0.9873 0.9919 0.1086 0.7437 0.863 0.3053 ] Network output: [ -0.003766 0.01765 1.004 1.177e-05 -5.284e-06 0.9855 8.87e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09293 0.09099 0.165 0.196 0.9852 0.9911 0.09294 0.6677 0.8386 0.2478 ] Network output: [ 0.0001029 1 -7.705e-05 1.553e-06 -6.973e-07 0.9998 1.17e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002442 Epoch 9043 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00953 0.9965 0.9918 -2.164e-07 9.715e-08 -0.007391 -1.631e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003464 -0.003291 -0.007111 0.00567 0.9699 0.9743 0.006708 0.8279 0.8215 0.0169 ] Network output: [ 0.9999 0.00024 0.000508 -5.739e-06 2.576e-06 -0.0005221 -4.325e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2044 -0.03495 -0.1632 0.1851 0.9834 0.9932 0.2292 0.433 0.8691 0.7115 ] Network output: [ -0.009453 1.003 1.008 -2.841e-07 1.275e-07 0.007859 -2.141e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006552 0.0005753 0.004421 0.003337 0.9889 0.9919 0.006678 0.8555 0.8931 0.01212 ] Network output: [ -0.0003019 0.001896 1.001 -1.798e-05 8.071e-06 0.998 -1.355e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2175 0.1024 0.3457 0.1432 0.985 0.994 0.2182 0.437 0.8758 0.7055 ] Network output: [ 0.004012 -0.01894 0.9942 1.091e-05 -4.897e-06 1.017 8.22e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.096 0.1838 0.1984 0.9873 0.9919 0.1086 0.7437 0.863 0.3053 ] Network output: [ -0.003764 0.01765 1.004 1.176e-05 -5.278e-06 0.9855 8.861e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09293 0.09099 0.165 0.196 0.9852 0.9911 0.09295 0.6677 0.8386 0.2478 ] Network output: [ 0.0001029 1 -7.698e-05 1.551e-06 -6.965e-07 0.9998 1.169e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002441 Epoch 9044 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009529 0.9965 0.9918 -2.164e-07 9.716e-08 -0.007391 -1.631e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003464 -0.003291 -0.00711 0.00567 0.9699 0.9743 0.006708 0.8279 0.8215 0.0169 ] Network output: [ 0.9999 0.0002398 0.0005078 -5.732e-06 2.573e-06 -0.0005217 -4.32e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2045 -0.03496 -0.1632 0.1851 0.9834 0.9932 0.2292 0.433 0.8691 0.7115 ] Network output: [ -0.009452 1.003 1.008 -2.84e-07 1.275e-07 0.007858 -2.14e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006552 0.0005754 0.004421 0.003337 0.9889 0.9919 0.006678 0.8555 0.8931 0.01212 ] Network output: [ -0.0003017 0.001895 1.001 -1.796e-05 8.061e-06 0.998 -1.353e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2175 0.1024 0.3457 0.1432 0.985 0.994 0.2182 0.437 0.8758 0.7055 ] Network output: [ 0.00401 -0.01893 0.9942 1.09e-05 -4.891e-06 1.017 8.211e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1085 0.09601 0.1838 0.1984 0.9873 0.9919 0.1086 0.7437 0.863 0.3053 ] Network output: [ -0.003763 0.01764 1.004 1.174e-05 -5.272e-06 0.9855 8.851e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09293 0.091 0.165 0.196 0.9852 0.9911 0.09295 0.6677 0.8385 0.2478 ] Network output: [ 0.0001028 1 -7.69e-05 1.55e-06 -6.957e-07 0.9998 1.168e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000244 Epoch 9045 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009528 0.9965 0.9918 -2.164e-07 9.717e-08 -0.00739 -1.631e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003464 -0.003292 -0.00711 0.005669 0.9699 0.9743 0.006708 0.8279 0.8215 0.0169 ] Network output: [ 0.9999 0.0002396 0.0005075 -5.726e-06 2.57e-06 -0.0005213 -4.315e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2045 -0.03496 -0.1632 0.1851 0.9834 0.9932 0.2292 0.433 0.8691 0.7115 ] Network output: [ -0.009452 1.003 1.008 -2.839e-07 1.274e-07 0.007857 -2.139e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006553 0.0005755 0.004421 0.003337 0.9889 0.9919 0.006679 0.8555 0.8931 0.01212 ] Network output: [ -0.0003015 0.001894 1.001 -1.794e-05 8.052e-06 0.998 -1.352e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2175 0.1024 0.3457 0.1432 0.985 0.994 0.2183 0.437 0.8758 0.7055 ] Network output: [ 0.004009 -0.01893 0.9942 1.088e-05 -4.886e-06 1.017 8.202e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.09601 0.1838 0.1984 0.9873 0.9919 0.1086 0.7437 0.863 0.3053 ] Network output: [ -0.003761 0.01763 1.004 1.173e-05 -5.267e-06 0.9855 8.841e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09294 0.091 0.165 0.196 0.9852 0.9911 0.09295 0.6677 0.8385 0.2478 ] Network output: [ 0.0001028 1 -7.682e-05 1.548e-06 -6.949e-07 0.9998 1.167e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002439 Epoch 9046 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009527 0.9965 0.9918 -2.165e-07 9.718e-08 -0.00739 -1.631e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003464 -0.003292 -0.007109 0.005669 0.9699 0.9743 0.006709 0.8279 0.8215 0.0169 ] Network output: [ 0.9999 0.0002394 0.0005073 -5.719e-06 2.568e-06 -0.0005209 -4.31e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2045 -0.03496 -0.1632 0.1851 0.9834 0.9932 0.2292 0.433 0.8691 0.7115 ] Network output: [ -0.009451 1.003 1.008 -2.838e-07 1.274e-07 0.007856 -2.139e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006553 0.0005756 0.004421 0.003336 0.9889 0.9919 0.006679 0.8555 0.893 0.01212 ] Network output: [ -0.0003013 0.001893 1.001 -1.792e-05 8.043e-06 0.998 -1.35e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2175 0.1024 0.3457 0.1432 0.985 0.994 0.2183 0.437 0.8758 0.7055 ] Network output: [ 0.004007 -0.01892 0.9942 1.087e-05 -4.88e-06 1.017 8.193e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.09602 0.1838 0.1984 0.9873 0.9919 0.1086 0.7437 0.863 0.3053 ] Network output: [ -0.00376 0.01762 1.004 1.172e-05 -5.261e-06 0.9855 8.831e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09294 0.091 0.165 0.196 0.9852 0.9911 0.09295 0.6677 0.8385 0.2478 ] Network output: [ 0.0001028 1 -7.675e-05 1.546e-06 -6.941e-07 0.9998 1.165e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002437 Epoch 9047 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009526 0.9965 0.9919 -2.165e-07 9.719e-08 -0.00739 -1.631e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003464 -0.003292 -0.007108 0.005668 0.9699 0.9743 0.006709 0.8279 0.8215 0.0169 ] Network output: [ 0.9999 0.0002391 0.000507 -5.713e-06 2.565e-06 -0.0005205 -4.305e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2045 -0.03496 -0.1632 0.1851 0.9834 0.9932 0.2292 0.433 0.8691 0.7115 ] Network output: [ -0.00945 1.003 1.008 -2.837e-07 1.273e-07 0.007855 -2.138e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006554 0.0005757 0.004421 0.003336 0.9889 0.9919 0.00668 0.8554 0.893 0.01212 ] Network output: [ -0.0003011 0.001893 1.001 -1.79e-05 8.034e-06 0.998 -1.349e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2175 0.1024 0.3457 0.1432 0.985 0.994 0.2183 0.437 0.8758 0.7055 ] Network output: [ 0.004006 -0.01891 0.9942 1.086e-05 -4.875e-06 1.017 8.183e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.09602 0.1838 0.1984 0.9873 0.9919 0.1086 0.7436 0.8629 0.3053 ] Network output: [ -0.003758 0.01762 1.004 1.171e-05 -5.255e-06 0.9855 8.821e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09294 0.091 0.165 0.196 0.9852 0.9911 0.09295 0.6677 0.8385 0.2478 ] Network output: [ 0.0001027 1 -7.667e-05 1.544e-06 -6.933e-07 0.9998 1.164e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002436 Epoch 9048 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009525 0.9965 0.9919 -2.165e-07 9.72e-08 -0.007389 -1.632e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003464 -0.003292 -0.007108 0.005668 0.9699 0.9743 0.006709 0.8279 0.8215 0.01689 ] Network output: [ 0.9999 0.0002389 0.0005068 -5.706e-06 2.562e-06 -0.0005201 -4.3e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2045 -0.03496 -0.1632 0.1851 0.9834 0.9932 0.2292 0.433 0.8691 0.7115 ] Network output: [ -0.009449 1.003 1.008 -2.836e-07 1.273e-07 0.007854 -2.137e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006554 0.0005758 0.004421 0.003336 0.9889 0.9919 0.00668 0.8554 0.893 0.01212 ] Network output: [ -0.0003009 0.001892 1.001 -1.787e-05 8.025e-06 0.998 -1.347e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2176 0.1024 0.3457 0.1432 0.985 0.994 0.2183 0.437 0.8758 0.7055 ] Network output: [ 0.004004 -0.0189 0.9942 1.085e-05 -4.869e-06 1.017 8.174e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.09603 0.1838 0.1984 0.9873 0.9919 0.1086 0.7436 0.8629 0.3053 ] Network output: [ -0.003757 0.01761 1.004 1.169e-05 -5.249e-06 0.9855 8.812e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09294 0.09101 0.165 0.196 0.9852 0.9911 0.09296 0.6677 0.8385 0.2478 ] Network output: [ 0.0001027 1 -7.659e-05 1.543e-06 -6.926e-07 0.9998 1.163e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002435 Epoch 9049 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009523 0.9965 0.9919 -2.165e-07 9.72e-08 -0.007389 -1.632e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003464 -0.003292 -0.007107 0.005667 0.9699 0.9743 0.006709 0.8279 0.8215 0.01689 ] Network output: [ 0.9999 0.0002387 0.0005065 -5.7e-06 2.559e-06 -0.0005197 -4.295e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2045 -0.03496 -0.1632 0.1851 0.9834 0.9932 0.2292 0.433 0.8691 0.7115 ] Network output: [ -0.009448 1.003 1.008 -2.835e-07 1.273e-07 0.007853 -2.136e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006554 0.0005758 0.004421 0.003335 0.9889 0.9919 0.006681 0.8554 0.893 0.01212 ] Network output: [ -0.0003007 0.001891 1.001 -1.785e-05 8.016e-06 0.998 -1.346e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2176 0.1024 0.3457 0.1432 0.985 0.994 0.2183 0.437 0.8758 0.7054 ] Network output: [ 0.004003 -0.0189 0.9942 1.083e-05 -4.864e-06 1.017 8.165e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.09603 0.1838 0.1984 0.9873 0.9919 0.1086 0.7436 0.8629 0.3053 ] Network output: [ -0.003755 0.0176 1.004 1.168e-05 -5.243e-06 0.9855 8.802e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09295 0.09101 0.165 0.196 0.9852 0.9911 0.09296 0.6676 0.8385 0.2478 ] Network output: [ 0.0001026 1 -7.652e-05 1.541e-06 -6.918e-07 0.9998 1.161e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002433 Epoch 9050 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009522 0.9965 0.9919 -2.165e-07 9.721e-08 -0.007389 -1.632e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003464 -0.003292 -0.007106 0.005667 0.9699 0.9743 0.00671 0.8279 0.8215 0.01689 ] Network output: [ 0.9999 0.0002384 0.0005063 -5.693e-06 2.556e-06 -0.0005193 -4.29e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2045 -0.03496 -0.1632 0.1851 0.9834 0.9932 0.2292 0.4329 0.8691 0.7115 ] Network output: [ -0.009447 1.003 1.008 -2.834e-07 1.272e-07 0.007852 -2.135e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006555 0.0005759 0.004421 0.003335 0.9889 0.9919 0.006681 0.8554 0.893 0.01212 ] Network output: [ -0.0003005 0.00189 1.001 -1.783e-05 8.006e-06 0.998 -1.344e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2176 0.1024 0.3457 0.1432 0.985 0.994 0.2183 0.437 0.8758 0.7054 ] Network output: [ 0.004001 -0.01889 0.9942 1.082e-05 -4.858e-06 1.017 8.156e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.09603 0.1838 0.1984 0.9873 0.9919 0.1087 0.7436 0.8629 0.3053 ] Network output: [ -0.003754 0.0176 1.004 1.167e-05 -5.237e-06 0.9855 8.792e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09295 0.09101 0.165 0.196 0.9852 0.9911 0.09296 0.6676 0.8385 0.2478 ] Network output: [ 0.0001026 1 -7.644e-05 1.539e-06 -6.91e-07 0.9998 1.16e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002432 Epoch 9051 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009521 0.9965 0.9919 -2.166e-07 9.722e-08 -0.007388 -1.632e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003464 -0.003292 -0.007105 0.005666 0.9699 0.9743 0.00671 0.8279 0.8215 0.01689 ] Network output: [ 0.9999 0.0002382 0.000506 -5.687e-06 2.553e-06 -0.0005189 -4.286e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2045 -0.03496 -0.1632 0.1851 0.9834 0.9932 0.2292 0.4329 0.8691 0.7115 ] Network output: [ -0.009446 1.003 1.008 -2.832e-07 1.272e-07 0.007851 -2.135e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006555 0.000576 0.004421 0.003335 0.9889 0.9919 0.006682 0.8554 0.893 0.01212 ] Network output: [ -0.0003003 0.00189 1.001 -1.781e-05 7.997e-06 0.998 -1.343e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2176 0.1024 0.3457 0.1432 0.985 0.994 0.2183 0.437 0.8758 0.7054 ] Network output: [ 0.003999 -0.01888 0.9942 1.081e-05 -4.853e-06 1.017 8.146e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.09604 0.1838 0.1984 0.9873 0.9919 0.1087 0.7436 0.8629 0.3053 ] Network output: [ -0.003752 0.01759 1.004 1.165e-05 -5.232e-06 0.9855 8.782e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09295 0.09101 0.165 0.196 0.9852 0.9911 0.09296 0.6676 0.8385 0.2478 ] Network output: [ 0.0001026 1 -7.637e-05 1.537e-06 -6.902e-07 0.9998 1.159e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002431 Epoch 9052 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00952 0.9965 0.9919 -2.166e-07 9.723e-08 -0.007388 -1.632e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003464 -0.003292 -0.007105 0.005666 0.9699 0.9743 0.00671 0.8279 0.8215 0.01689 ] Network output: [ 0.9999 0.000238 0.0005058 -5.68e-06 2.55e-06 -0.0005185 -4.281e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2045 -0.03496 -0.1632 0.1851 0.9834 0.9932 0.2293 0.4329 0.8691 0.7115 ] Network output: [ -0.009445 1.003 1.008 -2.831e-07 1.271e-07 0.00785 -2.134e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006556 0.0005761 0.004421 0.003335 0.9889 0.9919 0.006682 0.8554 0.893 0.01212 ] Network output: [ -0.0003001 0.001889 1.001 -1.779e-05 7.988e-06 0.998 -1.341e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2176 0.1024 0.3458 0.1432 0.985 0.994 0.2183 0.437 0.8758 0.7054 ] Network output: [ 0.003998 -0.01888 0.9942 1.08e-05 -4.847e-06 1.017 8.137e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.09604 0.1838 0.1984 0.9873 0.9919 0.1087 0.7436 0.8629 0.3053 ] Network output: [ -0.003751 0.01758 1.004 1.164e-05 -5.226e-06 0.9855 8.772e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09295 0.09102 0.165 0.196 0.9852 0.9911 0.09297 0.6676 0.8385 0.2478 ] Network output: [ 0.0001025 1 -7.629e-05 1.536e-06 -6.895e-07 0.9998 1.157e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000243 Epoch 9053 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009519 0.9965 0.9919 -2.166e-07 9.724e-08 -0.007387 -1.632e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003464 -0.003292 -0.007104 0.005665 0.9699 0.9743 0.00671 0.8279 0.8215 0.01689 ] Network output: [ 0.9999 0.0002377 0.0005055 -5.674e-06 2.547e-06 -0.0005181 -4.276e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2045 -0.03497 -0.1631 0.1851 0.9834 0.9932 0.2293 0.4329 0.8691 0.7115 ] Network output: [ -0.009444 1.003 1.008 -2.83e-07 1.271e-07 0.00785 -2.133e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006556 0.0005762 0.004421 0.003334 0.9889 0.9919 0.006683 0.8554 0.893 0.01211 ] Network output: [ -0.0002999 0.001888 1.001 -1.777e-05 7.979e-06 0.998 -1.339e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2176 0.1024 0.3458 0.1432 0.985 0.994 0.2183 0.437 0.8758 0.7054 ] Network output: [ 0.003996 -0.01887 0.9942 1.079e-05 -4.842e-06 1.017 8.128e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.09605 0.1838 0.1984 0.9873 0.9919 0.1087 0.7436 0.8629 0.3053 ] Network output: [ -0.003749 0.01757 1.004 1.163e-05 -5.22e-06 0.9855 8.763e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09296 0.09102 0.165 0.196 0.9852 0.9911 0.09297 0.6676 0.8385 0.2478 ] Network output: [ 0.0001025 1 -7.622e-05 1.534e-06 -6.887e-07 0.9998 1.156e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002428 Epoch 9054 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009518 0.9965 0.9919 -2.166e-07 9.725e-08 -0.007387 -1.633e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003465 -0.003293 -0.007103 0.005665 0.9699 0.9743 0.006711 0.8279 0.8215 0.01689 ] Network output: [ 0.9999 0.0002375 0.0005053 -5.667e-06 2.544e-06 -0.0005177 -4.271e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2045 -0.03497 -0.1631 0.1851 0.9834 0.9932 0.2293 0.4329 0.8691 0.7115 ] Network output: [ -0.009443 1.003 1.008 -2.829e-07 1.27e-07 0.007849 -2.132e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006557 0.0005763 0.004421 0.003334 0.9889 0.9919 0.006683 0.8554 0.893 0.01211 ] Network output: [ -0.0002997 0.001887 1.001 -1.775e-05 7.97e-06 0.998 -1.338e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2176 0.1024 0.3458 0.1432 0.985 0.994 0.2183 0.437 0.8758 0.7054 ] Network output: [ 0.003995 -0.01886 0.9942 1.077e-05 -4.836e-06 1.017 8.119e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.09605 0.1838 0.1984 0.9873 0.9919 0.1087 0.7435 0.8629 0.3053 ] Network output: [ -0.003748 0.01757 1.004 1.161e-05 -5.214e-06 0.9855 8.753e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09296 0.09102 0.165 0.196 0.9852 0.9911 0.09297 0.6676 0.8385 0.2479 ] Network output: [ 0.0001025 1 -7.614e-05 1.532e-06 -6.879e-07 0.9998 1.155e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002427 Epoch 9055 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009517 0.9965 0.9919 -2.166e-07 9.726e-08 -0.007387 -1.633e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003465 -0.003293 -0.007103 0.005665 0.9699 0.9743 0.006711 0.8279 0.8215 0.01689 ] Network output: [ 0.9999 0.0002373 0.000505 -5.661e-06 2.541e-06 -0.0005173 -4.266e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2045 -0.03497 -0.1631 0.1851 0.9834 0.9932 0.2293 0.4329 0.8691 0.7115 ] Network output: [ -0.009442 1.003 1.008 -2.828e-07 1.27e-07 0.007848 -2.132e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006557 0.0005764 0.00442 0.003334 0.9889 0.9919 0.006684 0.8554 0.893 0.01211 ] Network output: [ -0.0002995 0.001887 1.001 -1.773e-05 7.961e-06 0.998 -1.336e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2176 0.1024 0.3458 0.1432 0.985 0.994 0.2183 0.437 0.8758 0.7054 ] Network output: [ 0.003993 -0.01885 0.9942 1.076e-05 -4.831e-06 1.017 8.11e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.09606 0.1838 0.1984 0.9873 0.9919 0.1087 0.7435 0.8629 0.3053 ] Network output: [ -0.003747 0.01756 1.004 1.16e-05 -5.208e-06 0.9855 8.743e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09296 0.09102 0.165 0.196 0.9852 0.9911 0.09297 0.6676 0.8385 0.2479 ] Network output: [ 0.0001024 1 -7.607e-05 1.531e-06 -6.871e-07 0.9998 1.153e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002426 Epoch 9056 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009516 0.9965 0.9919 -2.167e-07 9.727e-08 -0.007386 -1.633e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003465 -0.003293 -0.007102 0.005664 0.9699 0.9743 0.006711 0.8279 0.8215 0.01689 ] Network output: [ 0.9999 0.0002371 0.0005048 -5.654e-06 2.538e-06 -0.0005169 -4.261e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2045 -0.03497 -0.1631 0.1851 0.9834 0.9932 0.2293 0.4329 0.8691 0.7115 ] Network output: [ -0.009441 1.003 1.008 -2.827e-07 1.269e-07 0.007847 -2.131e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006558 0.0005765 0.00442 0.003333 0.9889 0.9919 0.006684 0.8554 0.893 0.01211 ] Network output: [ -0.0002993 0.001886 1.001 -1.771e-05 7.952e-06 0.998 -1.335e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2176 0.1025 0.3458 0.1432 0.985 0.994 0.2184 0.437 0.8758 0.7054 ] Network output: [ 0.003992 -0.01885 0.9942 1.075e-05 -4.825e-06 1.017 8.1e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.09606 0.1838 0.1984 0.9873 0.9919 0.1087 0.7435 0.8629 0.3053 ] Network output: [ -0.003745 0.01755 1.004 1.159e-05 -5.203e-06 0.9855 8.734e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09296 0.09103 0.165 0.196 0.9852 0.9911 0.09298 0.6675 0.8385 0.2479 ] Network output: [ 0.0001024 1 -7.599e-05 1.529e-06 -6.864e-07 0.9998 1.152e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002425 Epoch 9057 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009515 0.9965 0.9919 -2.167e-07 9.727e-08 -0.007386 -1.633e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003465 -0.003293 -0.007101 0.005664 0.9699 0.9743 0.006711 0.8279 0.8215 0.01689 ] Network output: [ 0.9999 0.0002368 0.0005045 -5.648e-06 2.535e-06 -0.0005165 -4.256e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2045 -0.03497 -0.1631 0.1851 0.9834 0.9932 0.2293 0.4329 0.8691 0.7115 ] Network output: [ -0.00944 1.003 1.008 -2.826e-07 1.269e-07 0.007846 -2.13e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006558 0.0005766 0.00442 0.003333 0.9889 0.9919 0.006685 0.8554 0.893 0.01211 ] Network output: [ -0.0002991 0.001885 1.001 -1.769e-05 7.943e-06 0.998 -1.333e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2176 0.1025 0.3458 0.1432 0.985 0.994 0.2184 0.4369 0.8758 0.7054 ] Network output: [ 0.00399 -0.01884 0.9942 1.074e-05 -4.82e-06 1.017 8.091e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.09607 0.1838 0.1984 0.9873 0.9919 0.1087 0.7435 0.8629 0.3053 ] Network output: [ -0.003744 0.01755 1.004 1.158e-05 -5.197e-06 0.9855 8.724e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09296 0.09103 0.165 0.196 0.9852 0.9911 0.09298 0.6675 0.8385 0.2479 ] Network output: [ 0.0001023 1 -7.592e-05 1.527e-06 -6.856e-07 0.9998 1.151e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002423 Epoch 9058 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009514 0.9965 0.9919 -2.167e-07 9.728e-08 -0.007386 -1.633e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003465 -0.003293 -0.0071 0.005663 0.9699 0.9743 0.006711 0.8278 0.8215 0.01688 ] Network output: [ 0.9999 0.0002366 0.0005043 -5.641e-06 2.533e-06 -0.0005161 -4.251e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2046 -0.03497 -0.1631 0.1851 0.9834 0.9932 0.2293 0.4329 0.8691 0.7115 ] Network output: [ -0.009439 1.003 1.008 -2.825e-07 1.268e-07 0.007845 -2.129e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006559 0.0005767 0.00442 0.003333 0.9889 0.9919 0.006685 0.8554 0.893 0.01211 ] Network output: [ -0.0002989 0.001884 1.001 -1.767e-05 7.934e-06 0.998 -1.332e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2176 0.1025 0.3458 0.1432 0.985 0.994 0.2184 0.4369 0.8758 0.7054 ] Network output: [ 0.003989 -0.01883 0.9942 1.072e-05 -4.814e-06 1.017 8.082e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.09607 0.1838 0.1984 0.9873 0.9919 0.1087 0.7435 0.8629 0.3053 ] Network output: [ -0.003742 0.01754 1.004 1.156e-05 -5.191e-06 0.9856 8.714e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09297 0.09103 0.165 0.196 0.9852 0.9911 0.09298 0.6675 0.8385 0.2479 ] Network output: [ 0.0001023 1 -7.584e-05 1.525e-06 -6.848e-07 0.9998 1.15e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002422 Epoch 9059 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009513 0.9965 0.9919 -2.167e-07 9.729e-08 -0.007385 -1.633e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003465 -0.003293 -0.0071 0.005663 0.9699 0.9743 0.006712 0.8278 0.8215 0.01688 ] Network output: [ 0.9999 0.0002364 0.000504 -5.635e-06 2.53e-06 -0.0005157 -4.247e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2046 -0.03497 -0.1631 0.1851 0.9834 0.9932 0.2293 0.4329 0.8691 0.7114 ] Network output: [ -0.009439 1.003 1.008 -2.824e-07 1.268e-07 0.007844 -2.128e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006559 0.0005768 0.00442 0.003333 0.9889 0.9919 0.006686 0.8554 0.893 0.01211 ] Network output: [ -0.0002987 0.001884 1.001 -1.765e-05 7.925e-06 0.998 -1.33e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2176 0.1025 0.3458 0.1432 0.985 0.994 0.2184 0.4369 0.8758 0.7054 ] Network output: [ 0.003987 -0.01882 0.9942 1.071e-05 -4.809e-06 1.017 8.073e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.09608 0.1838 0.1984 0.9873 0.9919 0.1087 0.7435 0.8629 0.3053 ] Network output: [ -0.003741 0.01753 1.004 1.155e-05 -5.185e-06 0.9856 8.705e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09297 0.09103 0.165 0.196 0.9852 0.9911 0.09298 0.6675 0.8385 0.2479 ] Network output: [ 0.0001023 1 -7.577e-05 1.524e-06 -6.84e-07 0.9998 1.148e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002421 Epoch 9060 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009512 0.9965 0.9919 -2.167e-07 9.73e-08 -0.007385 -1.633e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003465 -0.003293 -0.007099 0.005662 0.9699 0.9743 0.006712 0.8278 0.8215 0.01688 ] Network output: [ 0.9999 0.0002361 0.0005038 -5.628e-06 2.527e-06 -0.0005154 -4.242e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2046 -0.03497 -0.1631 0.1851 0.9834 0.9932 0.2293 0.4329 0.8691 0.7114 ] Network output: [ -0.009438 1.003 1.008 -2.823e-07 1.267e-07 0.007843 -2.128e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00656 0.0005768 0.00442 0.003332 0.9889 0.9919 0.006686 0.8554 0.893 0.01211 ] Network output: [ -0.0002985 0.001883 1.001 -1.763e-05 7.916e-06 0.998 -1.329e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2177 0.1025 0.3458 0.1431 0.985 0.994 0.2184 0.4369 0.8758 0.7054 ] Network output: [ 0.003986 -0.01882 0.9942 1.07e-05 -4.804e-06 1.017 8.064e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.09608 0.1839 0.1984 0.9873 0.9919 0.1087 0.7435 0.8629 0.3053 ] Network output: [ -0.003739 0.01752 1.004 1.154e-05 -5.179e-06 0.9856 8.695e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09297 0.09103 0.165 0.196 0.9852 0.9911 0.09299 0.6675 0.8385 0.2479 ] Network output: [ 0.0001022 1 -7.569e-05 1.522e-06 -6.833e-07 0.9998 1.147e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000242 Epoch 9061 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00951 0.9965 0.9919 -2.167e-07 9.73e-08 -0.007384 -1.633e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003465 -0.003293 -0.007098 0.005662 0.9699 0.9743 0.006712 0.8278 0.8215 0.01688 ] Network output: [ 0.9999 0.0002359 0.0005035 -5.622e-06 2.524e-06 -0.000515 -4.237e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2046 -0.03497 -0.1631 0.1851 0.9834 0.9932 0.2293 0.4329 0.8691 0.7114 ] Network output: [ -0.009437 1.003 1.008 -2.822e-07 1.267e-07 0.007842 -2.127e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00656 0.0005769 0.00442 0.003332 0.9889 0.9919 0.006687 0.8554 0.893 0.01211 ] Network output: [ -0.0002983 0.001882 1.001 -1.761e-05 7.907e-06 0.998 -1.327e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2177 0.1025 0.3458 0.1431 0.985 0.994 0.2184 0.4369 0.8758 0.7054 ] Network output: [ 0.003984 -0.01881 0.9942 1.069e-05 -4.798e-06 1.017 8.055e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.09609 0.1839 0.1984 0.9873 0.9919 0.1087 0.7434 0.8629 0.3053 ] Network output: [ -0.003738 0.01752 1.004 1.152e-05 -5.174e-06 0.9856 8.685e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09297 0.09104 0.165 0.196 0.9852 0.9911 0.09299 0.6675 0.8385 0.2479 ] Network output: [ 0.0001022 1 -7.562e-05 1.52e-06 -6.825e-07 0.9998 1.146e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002418 Epoch 9062 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009509 0.9965 0.9919 -2.168e-07 9.731e-08 -0.007384 -1.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003465 -0.003293 -0.007098 0.005661 0.9699 0.9743 0.006712 0.8278 0.8215 0.01688 ] Network output: [ 0.9999 0.0002357 0.0005033 -5.615e-06 2.521e-06 -0.0005146 -4.232e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2046 -0.03497 -0.163 0.185 0.9834 0.9932 0.2293 0.4329 0.8691 0.7114 ] Network output: [ -0.009436 1.003 1.008 -2.821e-07 1.266e-07 0.007841 -2.126e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006561 0.000577 0.00442 0.003332 0.9889 0.9919 0.006687 0.8554 0.893 0.01211 ] Network output: [ -0.0002981 0.001882 1.001 -1.759e-05 7.897e-06 0.998 -1.326e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2177 0.1025 0.3458 0.1431 0.985 0.994 0.2184 0.4369 0.8758 0.7054 ] Network output: [ 0.003983 -0.0188 0.9942 1.068e-05 -4.793e-06 1.017 8.046e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.09609 0.1839 0.1984 0.9873 0.9919 0.1087 0.7434 0.8629 0.3053 ] Network output: [ -0.003736 0.01751 1.004 1.151e-05 -5.168e-06 0.9856 8.676e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09298 0.09104 0.165 0.196 0.9852 0.9911 0.09299 0.6674 0.8385 0.2479 ] Network output: [ 0.0001021 1 -7.555e-05 1.519e-06 -6.817e-07 0.9998 1.144e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002417 Epoch 9063 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009508 0.9965 0.9919 -2.168e-07 9.732e-08 -0.007384 -1.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003465 -0.003294 -0.007097 0.005661 0.9699 0.9743 0.006713 0.8278 0.8215 0.01688 ] Network output: [ 0.9999 0.0002355 0.0005031 -5.609e-06 2.518e-06 -0.0005142 -4.227e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2046 -0.03498 -0.163 0.185 0.9834 0.9932 0.2294 0.4329 0.8691 0.7114 ] Network output: [ -0.009435 1.003 1.008 -2.82e-07 1.266e-07 0.00784 -2.125e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006561 0.0005771 0.00442 0.003332 0.9889 0.9919 0.006688 0.8554 0.893 0.01211 ] Network output: [ -0.0002979 0.001881 1.001 -1.757e-05 7.888e-06 0.998 -1.324e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2177 0.1025 0.3458 0.1431 0.985 0.994 0.2184 0.4369 0.8758 0.7054 ] Network output: [ 0.003981 -0.0188 0.9942 1.066e-05 -4.787e-06 1.017 8.036e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.0961 0.1839 0.1984 0.9873 0.9919 0.1087 0.7434 0.8629 0.3053 ] Network output: [ -0.003735 0.0175 1.004 1.15e-05 -5.162e-06 0.9856 8.666e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09298 0.09104 0.165 0.196 0.9852 0.9911 0.09299 0.6674 0.8385 0.2479 ] Network output: [ 0.0001021 1 -7.547e-05 1.517e-06 -6.81e-07 0.9998 1.143e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002416 Epoch 9064 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009507 0.9965 0.9919 -2.168e-07 9.732e-08 -0.007383 -1.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003465 -0.003294 -0.007096 0.00566 0.9699 0.9743 0.006713 0.8278 0.8214 0.01688 ] Network output: [ 0.9999 0.0002352 0.0005028 -5.603e-06 2.515e-06 -0.0005138 -4.222e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2046 -0.03498 -0.163 0.185 0.9834 0.9932 0.2294 0.4328 0.8691 0.7114 ] Network output: [ -0.009434 1.003 1.008 -2.819e-07 1.266e-07 0.007839 -2.124e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006562 0.0005772 0.00442 0.003331 0.9889 0.9919 0.006688 0.8553 0.893 0.01211 ] Network output: [ -0.0002976 0.00188 1.001 -1.755e-05 7.879e-06 0.998 -1.323e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2177 0.1025 0.3458 0.1431 0.985 0.994 0.2184 0.4369 0.8758 0.7054 ] Network output: [ 0.00398 -0.01879 0.9942 1.065e-05 -4.782e-06 1.017 8.027e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1086 0.0961 0.1839 0.1984 0.9873 0.9919 0.1087 0.7434 0.8629 0.3053 ] Network output: [ -0.003733 0.0175 1.004 1.149e-05 -5.157e-06 0.9856 8.656e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09298 0.09104 0.165 0.196 0.9852 0.9911 0.09299 0.6674 0.8385 0.2479 ] Network output: [ 0.0001021 1 -7.54e-05 1.515e-06 -6.802e-07 0.9998 1.142e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002415 Epoch 9065 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009506 0.9965 0.9919 -2.168e-07 9.733e-08 -0.007383 -1.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003465 -0.003294 -0.007095 0.00566 0.9699 0.9743 0.006713 0.8278 0.8214 0.01688 ] Network output: [ 0.9999 0.000235 0.0005026 -5.596e-06 2.512e-06 -0.0005134 -4.217e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2046 -0.03498 -0.163 0.185 0.9834 0.9932 0.2294 0.4328 0.8691 0.7114 ] Network output: [ -0.009433 1.003 1.008 -2.818e-07 1.265e-07 0.007839 -2.124e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006562 0.0005773 0.00442 0.003331 0.9889 0.9919 0.006689 0.8553 0.893 0.0121 ] Network output: [ -0.0002974 0.001879 1.001 -1.753e-05 7.87e-06 0.998 -1.321e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2177 0.1025 0.3458 0.1431 0.985 0.994 0.2184 0.4369 0.8758 0.7054 ] Network output: [ 0.003978 -0.01878 0.9942 1.064e-05 -4.776e-06 1.017 8.018e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.09611 0.1839 0.1984 0.9873 0.9919 0.1087 0.7434 0.8629 0.3053 ] Network output: [ -0.003732 0.01749 1.004 1.147e-05 -5.151e-06 0.9856 8.647e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09298 0.09105 0.165 0.196 0.9852 0.9911 0.093 0.6674 0.8385 0.2479 ] Network output: [ 0.000102 1 -7.532e-05 1.513e-06 -6.794e-07 0.9998 1.141e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002413 Epoch 9066 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009505 0.9965 0.9919 -2.168e-07 9.734e-08 -0.007383 -1.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003466 -0.003294 -0.007095 0.005659 0.9699 0.9743 0.006713 0.8278 0.8214 0.01688 ] Network output: [ 0.9999 0.0002348 0.0005023 -5.59e-06 2.509e-06 -0.000513 -4.213e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2046 -0.03498 -0.163 0.185 0.9834 0.9932 0.2294 0.4328 0.8691 0.7114 ] Network output: [ -0.009432 1.003 1.008 -2.817e-07 1.265e-07 0.007838 -2.123e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006562 0.0005774 0.00442 0.003331 0.9889 0.9919 0.006689 0.8553 0.893 0.0121 ] Network output: [ -0.0002972 0.001879 1.001 -1.751e-05 7.861e-06 0.998 -1.32e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2177 0.1025 0.3458 0.1431 0.985 0.994 0.2184 0.4369 0.8758 0.7054 ] Network output: [ 0.003977 -0.01877 0.9942 1.063e-05 -4.771e-06 1.017 8.009e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.09611 0.1839 0.1984 0.9873 0.9919 0.1087 0.7434 0.8629 0.3053 ] Network output: [ -0.00373 0.01748 1.004 1.146e-05 -5.145e-06 0.9856 8.637e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09299 0.09105 0.165 0.196 0.9852 0.9911 0.093 0.6674 0.8385 0.2479 ] Network output: [ 0.000102 1 -7.525e-05 1.512e-06 -6.787e-07 0.9998 1.139e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002412 Epoch 9067 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009504 0.9965 0.9919 -2.168e-07 9.734e-08 -0.007382 -1.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003466 -0.003294 -0.007094 0.005659 0.9699 0.9743 0.006714 0.8278 0.8214 0.01687 ] Network output: [ 0.9999 0.0002345 0.0005021 -5.583e-06 2.507e-06 -0.0005126 -4.208e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2046 -0.03498 -0.163 0.185 0.9834 0.9932 0.2294 0.4328 0.8691 0.7114 ] Network output: [ -0.009431 1.003 1.008 -2.816e-07 1.264e-07 0.007837 -2.122e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006563 0.0005775 0.00442 0.00333 0.9889 0.9919 0.00669 0.8553 0.893 0.0121 ] Network output: [ -0.000297 0.001878 1.001 -1.749e-05 7.853e-06 0.998 -1.318e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2177 0.1025 0.3458 0.1431 0.985 0.994 0.2184 0.4369 0.8758 0.7054 ] Network output: [ 0.003975 -0.01877 0.9942 1.062e-05 -4.766e-06 1.017 8e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.09611 0.1839 0.1984 0.9873 0.9919 0.1087 0.7434 0.8629 0.3053 ] Network output: [ -0.003729 0.01748 1.004 1.145e-05 -5.139e-06 0.9856 8.627e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09299 0.09105 0.165 0.196 0.9852 0.9911 0.093 0.6674 0.8385 0.2479 ] Network output: [ 0.000102 1 -7.518e-05 1.51e-06 -6.779e-07 0.9998 1.138e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002411 Epoch 9068 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009503 0.9965 0.9919 -2.168e-07 9.735e-08 -0.007382 -1.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003466 -0.003294 -0.007093 0.005658 0.9699 0.9743 0.006714 0.8278 0.8214 0.01687 ] Network output: [ 0.9999 0.0002343 0.0005018 -5.577e-06 2.504e-06 -0.0005122 -4.203e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2046 -0.03498 -0.163 0.185 0.9834 0.9932 0.2294 0.4328 0.8691 0.7114 ] Network output: [ -0.00943 1.003 1.008 -2.815e-07 1.264e-07 0.007836 -2.121e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006563 0.0005776 0.00442 0.00333 0.9889 0.9919 0.00669 0.8553 0.893 0.0121 ] Network output: [ -0.0002968 0.001877 1.001 -1.747e-05 7.844e-06 0.998 -1.317e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2177 0.1025 0.3458 0.1431 0.985 0.994 0.2185 0.4369 0.8758 0.7053 ] Network output: [ 0.003974 -0.01876 0.9942 1.06e-05 -4.76e-06 1.017 7.991e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.09612 0.1839 0.1984 0.9873 0.9919 0.1087 0.7433 0.8629 0.3053 ] Network output: [ -0.003728 0.01747 1.004 1.144e-05 -5.134e-06 0.9856 8.618e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09299 0.09105 0.165 0.196 0.9852 0.9911 0.093 0.6674 0.8385 0.2479 ] Network output: [ 0.0001019 1 -7.51e-05 1.508e-06 -6.771e-07 0.9998 1.137e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000241 Epoch 9069 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009502 0.9965 0.9919 -2.169e-07 9.735e-08 -0.007381 -1.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003466 -0.003294 -0.007093 0.005658 0.9699 0.9743 0.006714 0.8278 0.8214 0.01687 ] Network output: [ 0.9999 0.0002341 0.0005016 -5.571e-06 2.501e-06 -0.0005118 -4.198e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2046 -0.03498 -0.163 0.185 0.9834 0.9932 0.2294 0.4328 0.8691 0.7114 ] Network output: [ -0.009429 1.003 1.008 -2.814e-07 1.263e-07 0.007835 -2.12e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006564 0.0005777 0.00442 0.00333 0.9889 0.9919 0.00669 0.8553 0.893 0.0121 ] Network output: [ -0.0002966 0.001876 1.001 -1.745e-05 7.835e-06 0.998 -1.315e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2177 0.1025 0.3459 0.1431 0.985 0.994 0.2185 0.4369 0.8758 0.7053 ] Network output: [ 0.003972 -0.01875 0.9942 1.059e-05 -4.755e-06 1.017 7.982e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.09612 0.1839 0.1984 0.9873 0.9919 0.1087 0.7433 0.8629 0.3053 ] Network output: [ -0.003726 0.01746 1.004 1.142e-05 -5.128e-06 0.9856 8.608e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09299 0.09106 0.165 0.196 0.9852 0.9911 0.09301 0.6673 0.8384 0.2479 ] Network output: [ 0.0001019 1 -7.503e-05 1.507e-06 -6.764e-07 0.9998 1.135e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002408 Epoch 9070 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009501 0.9965 0.9919 -2.169e-07 9.736e-08 -0.007381 -1.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003466 -0.003294 -0.007092 0.005658 0.9699 0.9743 0.006714 0.8278 0.8214 0.01687 ] Network output: [ 0.9999 0.0002339 0.0005013 -5.564e-06 2.498e-06 -0.0005115 -4.193e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2046 -0.03498 -0.163 0.185 0.9834 0.9932 0.2294 0.4328 0.8691 0.7114 ] Network output: [ -0.009428 1.003 1.008 -2.813e-07 1.263e-07 0.007834 -2.12e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006564 0.0005777 0.004419 0.00333 0.9889 0.9919 0.006691 0.8553 0.893 0.0121 ] Network output: [ -0.0002964 0.001876 1.001 -1.743e-05 7.826e-06 0.998 -1.314e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2177 0.1025 0.3459 0.1431 0.985 0.994 0.2185 0.4369 0.8758 0.7053 ] Network output: [ 0.00397 -0.01875 0.9942 1.058e-05 -4.749e-06 1.017 7.973e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.09613 0.1839 0.1984 0.9873 0.9919 0.1088 0.7433 0.8629 0.3053 ] Network output: [ -0.003725 0.01745 1.004 1.141e-05 -5.122e-06 0.9856 8.599e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.093 0.09106 0.165 0.196 0.9852 0.9911 0.09301 0.6673 0.8384 0.2479 ] Network output: [ 0.0001018 1 -7.496e-05 1.505e-06 -6.756e-07 0.9998 1.134e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002407 Epoch 9071 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0095 0.9965 0.9919 -2.169e-07 9.736e-08 -0.007381 -1.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003466 -0.003294 -0.007091 0.005657 0.9699 0.9743 0.006715 0.8278 0.8214 0.01687 ] Network output: [ 0.9999 0.0002336 0.0005011 -5.558e-06 2.495e-06 -0.0005111 -4.189e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2046 -0.03498 -0.1629 0.185 0.9834 0.9932 0.2294 0.4328 0.8691 0.7114 ] Network output: [ -0.009427 1.003 1.008 -2.811e-07 1.262e-07 0.007833 -2.119e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006565 0.0005778 0.004419 0.003329 0.9889 0.9919 0.006691 0.8553 0.893 0.0121 ] Network output: [ -0.0002962 0.001875 1.001 -1.741e-05 7.817e-06 0.998 -1.312e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2177 0.1025 0.3459 0.1431 0.985 0.994 0.2185 0.4368 0.8758 0.7053 ] Network output: [ 0.003969 -0.01874 0.9942 1.057e-05 -4.744e-06 1.017 7.964e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.09613 0.1839 0.1984 0.9873 0.9919 0.1088 0.7433 0.8629 0.3053 ] Network output: [ -0.003723 0.01745 1.004 1.14e-05 -5.117e-06 0.9856 8.589e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.093 0.09106 0.165 0.196 0.9852 0.9911 0.09301 0.6673 0.8384 0.2479 ] Network output: [ 0.0001018 1 -7.488e-05 1.503e-06 -6.749e-07 0.9998 1.133e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002406 Epoch 9072 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009499 0.9965 0.9919 -2.169e-07 9.737e-08 -0.00738 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003466 -0.003295 -0.00709 0.005657 0.9699 0.9743 0.006715 0.8278 0.8214 0.01687 ] Network output: [ 0.9999 0.0002334 0.0005008 -5.552e-06 2.492e-06 -0.0005107 -4.184e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2047 -0.03499 -0.1629 0.185 0.9834 0.9932 0.2294 0.4328 0.8691 0.7114 ] Network output: [ -0.009427 1.003 1.008 -2.81e-07 1.262e-07 0.007832 -2.118e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006565 0.0005779 0.004419 0.003329 0.9889 0.9919 0.006692 0.8553 0.893 0.0121 ] Network output: [ -0.000296 0.001874 1.001 -1.739e-05 7.808e-06 0.998 -1.311e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2178 0.1025 0.3459 0.1431 0.985 0.994 0.2185 0.4368 0.8758 0.7053 ] Network output: [ 0.003967 -0.01873 0.9942 1.056e-05 -4.739e-06 1.017 7.955e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.09614 0.1839 0.1984 0.9873 0.9919 0.1088 0.7433 0.8629 0.3053 ] Network output: [ -0.003722 0.01744 1.004 1.138e-05 -5.111e-06 0.9856 8.58e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.093 0.09106 0.165 0.196 0.9852 0.9911 0.09301 0.6673 0.8384 0.2479 ] Network output: [ 0.0001018 1 -7.481e-05 1.502e-06 -6.741e-07 0.9998 1.132e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002405 Epoch 9073 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009498 0.9965 0.9919 -2.169e-07 9.737e-08 -0.00738 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003466 -0.003295 -0.00709 0.005656 0.9699 0.9743 0.006715 0.8278 0.8214 0.01687 ] Network output: [ 0.9999 0.0002332 0.0005006 -5.545e-06 2.489e-06 -0.0005103 -4.179e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2047 -0.03499 -0.1629 0.185 0.9834 0.9932 0.2294 0.4328 0.8691 0.7114 ] Network output: [ -0.009426 1.003 1.008 -2.809e-07 1.261e-07 0.007831 -2.117e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006566 0.000578 0.004419 0.003329 0.9889 0.9919 0.006692 0.8553 0.893 0.0121 ] Network output: [ -0.0002958 0.001873 1.001 -1.737e-05 7.799e-06 0.998 -1.309e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2178 0.1025 0.3459 0.1431 0.985 0.994 0.2185 0.4368 0.8758 0.7053 ] Network output: [ 0.003966 -0.01872 0.9942 1.054e-05 -4.733e-06 1.017 7.946e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.09614 0.1839 0.1984 0.9873 0.9919 0.1088 0.7433 0.8629 0.3053 ] Network output: [ -0.00372 0.01743 1.004 1.137e-05 -5.105e-06 0.9856 8.57e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.093 0.09106 0.165 0.196 0.9852 0.9911 0.09302 0.6673 0.8384 0.2479 ] Network output: [ 0.0001017 1 -7.474e-05 1.5e-06 -6.733e-07 0.9998 1.13e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002403 Epoch 9074 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009496 0.9965 0.9919 -2.169e-07 9.738e-08 -0.007379 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003466 -0.003295 -0.007089 0.005656 0.9699 0.9743 0.006715 0.8278 0.8214 0.01687 ] Network output: [ 0.9999 0.000233 0.0005003 -5.539e-06 2.487e-06 -0.0005099 -4.174e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2047 -0.03499 -0.1629 0.185 0.9834 0.9932 0.2294 0.4328 0.8691 0.7114 ] Network output: [ -0.009425 1.003 1.008 -2.808e-07 1.261e-07 0.00783 -2.116e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006566 0.0005781 0.004419 0.003329 0.9889 0.9919 0.006693 0.8553 0.893 0.0121 ] Network output: [ -0.0002956 0.001873 1.001 -1.735e-05 7.79e-06 0.998 -1.308e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2178 0.1026 0.3459 0.1431 0.985 0.994 0.2185 0.4368 0.8758 0.7053 ] Network output: [ 0.003964 -0.01872 0.9942 1.053e-05 -4.728e-06 1.017 7.937e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.09615 0.1839 0.1984 0.9873 0.9919 0.1088 0.7433 0.8629 0.3053 ] Network output: [ -0.003719 0.01743 1.004 1.136e-05 -5.099e-06 0.9856 8.56e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.093 0.09107 0.165 0.196 0.9852 0.9911 0.09302 0.6673 0.8384 0.2479 ] Network output: [ 0.0001017 1 -7.467e-05 1.498e-06 -6.726e-07 0.9998 1.129e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002402 Epoch 9075 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009495 0.9965 0.9919 -2.169e-07 9.738e-08 -0.007379 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003466 -0.003295 -0.007088 0.005655 0.9699 0.9743 0.006715 0.8278 0.8214 0.01687 ] Network output: [ 0.9999 0.0002327 0.0005001 -5.532e-06 2.484e-06 -0.0005095 -4.169e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2047 -0.03499 -0.1629 0.185 0.9834 0.9932 0.2295 0.4328 0.8691 0.7114 ] Network output: [ -0.009424 1.003 1.008 -2.807e-07 1.26e-07 0.007829 -2.116e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006567 0.0005782 0.004419 0.003328 0.9889 0.9919 0.006693 0.8553 0.893 0.0121 ] Network output: [ -0.0002954 0.001872 1.001 -1.733e-05 7.781e-06 0.998 -1.306e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2178 0.1026 0.3459 0.1431 0.985 0.994 0.2185 0.4368 0.8758 0.7053 ] Network output: [ 0.003963 -0.01871 0.9942 1.052e-05 -4.723e-06 1.017 7.928e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.09615 0.1839 0.1984 0.9873 0.9919 0.1088 0.7432 0.8629 0.3053 ] Network output: [ -0.003717 0.01742 1.004 1.135e-05 -5.094e-06 0.9856 8.551e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09301 0.09107 0.165 0.196 0.9852 0.9911 0.09302 0.6673 0.8384 0.2479 ] Network output: [ 0.0001017 1 -7.459e-05 1.496e-06 -6.718e-07 0.9998 1.128e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002401 Epoch 9076 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009494 0.9965 0.9919 -2.169e-07 9.739e-08 -0.007379 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003466 -0.003295 -0.007088 0.005655 0.9699 0.9743 0.006716 0.8277 0.8214 0.01687 ] Network output: [ 0.9999 0.0002325 0.0004999 -5.526e-06 2.481e-06 -0.0005091 -4.165e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2047 -0.03499 -0.1629 0.185 0.9834 0.9932 0.2295 0.4328 0.8691 0.7114 ] Network output: [ -0.009423 1.003 1.008 -2.806e-07 1.26e-07 0.007829 -2.115e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006567 0.0005783 0.004419 0.003328 0.9889 0.9919 0.006694 0.8553 0.893 0.0121 ] Network output: [ -0.0002952 0.001871 1.001 -1.731e-05 7.772e-06 0.998 -1.305e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2178 0.1026 0.3459 0.1431 0.985 0.994 0.2185 0.4368 0.8758 0.7053 ] Network output: [ 0.003961 -0.0187 0.9942 1.051e-05 -4.717e-06 1.017 7.919e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.09616 0.1839 0.1984 0.9873 0.9919 0.1088 0.7432 0.8629 0.3053 ] Network output: [ -0.003716 0.01741 1.004 1.133e-05 -5.088e-06 0.9856 8.541e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09301 0.09107 0.165 0.196 0.9852 0.9911 0.09302 0.6672 0.8384 0.2479 ] Network output: [ 0.0001016 1 -7.452e-05 1.495e-06 -6.711e-07 0.9998 1.127e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00024 Epoch 9077 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009493 0.9965 0.9919 -2.169e-07 9.739e-08 -0.007378 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003467 -0.003295 -0.007087 0.005654 0.9699 0.9743 0.006716 0.8277 0.8214 0.01686 ] Network output: [ 0.9999 0.0002323 0.0004996 -5.52e-06 2.478e-06 -0.0005088 -4.16e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2047 -0.03499 -0.1629 0.185 0.9834 0.9932 0.2295 0.4328 0.8691 0.7114 ] Network output: [ -0.009422 1.003 1.008 -2.805e-07 1.259e-07 0.007828 -2.114e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006568 0.0005784 0.004419 0.003328 0.9889 0.9919 0.006694 0.8553 0.893 0.01209 ] Network output: [ -0.000295 0.00187 1.001 -1.729e-05 7.763e-06 0.998 -1.303e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2178 0.1026 0.3459 0.1431 0.985 0.994 0.2185 0.4368 0.8758 0.7053 ] Network output: [ 0.00396 -0.0187 0.9942 1.05e-05 -4.712e-06 1.017 7.91e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.09616 0.1839 0.1984 0.9873 0.9919 0.1088 0.7432 0.8629 0.3053 ] Network output: [ -0.003714 0.0174 1.004 1.132e-05 -5.082e-06 0.9856 8.532e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09301 0.09107 0.165 0.196 0.9852 0.9911 0.09303 0.6672 0.8384 0.2479 ] Network output: [ 0.0001016 1 -7.445e-05 1.493e-06 -6.703e-07 0.9998 1.125e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002398 Epoch 9078 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009492 0.9965 0.9919 -2.169e-07 9.74e-08 -0.007378 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003467 -0.003295 -0.007086 0.005654 0.9699 0.9743 0.006716 0.8277 0.8214 0.01686 ] Network output: [ 0.9999 0.000232 0.0004994 -5.513e-06 2.475e-06 -0.0005084 -4.155e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2047 -0.03499 -0.1629 0.185 0.9834 0.9932 0.2295 0.4328 0.8691 0.7114 ] Network output: [ -0.009421 1.003 1.008 -2.804e-07 1.259e-07 0.007827 -2.113e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006568 0.0005785 0.004419 0.003327 0.9889 0.9919 0.006695 0.8553 0.893 0.01209 ] Network output: [ -0.0002948 0.00187 1.001 -1.727e-05 7.754e-06 0.998 -1.302e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2178 0.1026 0.3459 0.1431 0.985 0.994 0.2185 0.4368 0.8758 0.7053 ] Network output: [ 0.003958 -0.01869 0.9942 1.048e-05 -4.707e-06 1.017 7.901e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.09617 0.1839 0.1984 0.9873 0.9919 0.1088 0.7432 0.8628 0.3053 ] Network output: [ -0.003713 0.0174 1.004 1.131e-05 -5.077e-06 0.9856 8.522e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09301 0.09108 0.165 0.196 0.9852 0.9911 0.09303 0.6672 0.8384 0.2479 ] Network output: [ 0.0001015 1 -7.438e-05 1.491e-06 -6.695e-07 0.9998 1.124e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002397 Epoch 9079 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009491 0.9965 0.9919 -2.17e-07 9.74e-08 -0.007378 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003467 -0.003295 -0.007085 0.005653 0.9699 0.9743 0.006716 0.8277 0.8214 0.01686 ] Network output: [ 0.9999 0.0002318 0.0004991 -5.507e-06 2.472e-06 -0.000508 -4.15e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2047 -0.03499 -0.1629 0.185 0.9834 0.9932 0.2295 0.4327 0.8691 0.7113 ] Network output: [ -0.00942 1.003 1.008 -2.803e-07 1.258e-07 0.007826 -2.112e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006569 0.0005786 0.004419 0.003327 0.9889 0.9919 0.006695 0.8553 0.893 0.01209 ] Network output: [ -0.0002946 0.001869 1.001 -1.725e-05 7.746e-06 0.998 -1.3e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2178 0.1026 0.3459 0.1431 0.985 0.994 0.2185 0.4368 0.8758 0.7053 ] Network output: [ 0.003957 -0.01868 0.9942 1.047e-05 -4.701e-06 1.017 7.892e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.09617 0.1839 0.1984 0.9873 0.9919 0.1088 0.7432 0.8628 0.3053 ] Network output: [ -0.003712 0.01739 1.004 1.13e-05 -5.071e-06 0.9856 8.513e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09302 0.09108 0.165 0.196 0.9852 0.9911 0.09303 0.6672 0.8384 0.2479 ] Network output: [ 0.0001015 1 -7.43e-05 1.49e-06 -6.688e-07 0.9998 1.123e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002396 Epoch 9080 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00949 0.9965 0.9919 -2.17e-07 9.74e-08 -0.007377 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003467 -0.003295 -0.007085 0.005653 0.9699 0.9743 0.006717 0.8277 0.8214 0.01686 ] Network output: [ 0.9999 0.0002316 0.0004989 -5.501e-06 2.47e-06 -0.0005076 -4.146e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2047 -0.03499 -0.1628 0.185 0.9834 0.9932 0.2295 0.4327 0.8691 0.7113 ] Network output: [ -0.009419 1.003 1.008 -2.802e-07 1.258e-07 0.007825 -2.111e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006569 0.0005786 0.004419 0.003327 0.9889 0.9919 0.006696 0.8552 0.893 0.01209 ] Network output: [ -0.0002944 0.001868 1.001 -1.723e-05 7.737e-06 0.998 -1.299e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2178 0.1026 0.3459 0.1431 0.985 0.994 0.2186 0.4368 0.8758 0.7053 ] Network output: [ 0.003955 -0.01867 0.9942 1.046e-05 -4.696e-06 1.017 7.883e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.09618 0.1839 0.1984 0.9873 0.9919 0.1088 0.7432 0.8628 0.3053 ] Network output: [ -0.00371 0.01738 1.004 1.128e-05 -5.065e-06 0.9856 8.503e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09302 0.09108 0.165 0.196 0.9852 0.9911 0.09303 0.6672 0.8384 0.2479 ] Network output: [ 0.0001015 1 -7.423e-05 1.488e-06 -6.68e-07 0.9998 1.121e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002395 Epoch 9081 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009489 0.9965 0.9919 -2.17e-07 9.741e-08 -0.007377 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003467 -0.003296 -0.007084 0.005652 0.9699 0.9743 0.006717 0.8277 0.8214 0.01686 ] Network output: [ 0.9999 0.0002314 0.0004986 -5.495e-06 2.467e-06 -0.0005072 -4.141e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2047 -0.03499 -0.1628 0.185 0.9834 0.9932 0.2295 0.4327 0.8691 0.7113 ] Network output: [ -0.009418 1.003 1.008 -2.801e-07 1.257e-07 0.007824 -2.111e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006569 0.0005787 0.004419 0.003327 0.9889 0.9919 0.006696 0.8552 0.893 0.01209 ] Network output: [ -0.0002942 0.001867 1.001 -1.721e-05 7.728e-06 0.998 -1.297e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2178 0.1026 0.3459 0.1431 0.985 0.994 0.2186 0.4368 0.8758 0.7053 ] Network output: [ 0.003954 -0.01867 0.9942 1.045e-05 -4.691e-06 1.017 7.874e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.09618 0.1839 0.1984 0.9873 0.9919 0.1088 0.7432 0.8628 0.3053 ] Network output: [ -0.003709 0.01738 1.004 1.127e-05 -5.06e-06 0.9856 8.494e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09302 0.09108 0.165 0.196 0.9852 0.9911 0.09303 0.6672 0.8384 0.2479 ] Network output: [ 0.0001014 1 -7.416e-05 1.486e-06 -6.673e-07 0.9998 1.12e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002393 Epoch 9082 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009488 0.9965 0.9919 -2.17e-07 9.741e-08 -0.007376 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003467 -0.003296 -0.007083 0.005652 0.9699 0.9743 0.006717 0.8277 0.8214 0.01686 ] Network output: [ 0.9999 0.0002311 0.0004984 -5.488e-06 2.464e-06 -0.0005068 -4.136e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2047 -0.035 -0.1628 0.185 0.9834 0.9932 0.2295 0.4327 0.8691 0.7113 ] Network output: [ -0.009417 1.003 1.008 -2.8e-07 1.257e-07 0.007823 -2.11e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00657 0.0005788 0.004419 0.003326 0.9889 0.9919 0.006697 0.8552 0.893 0.01209 ] Network output: [ -0.000294 0.001867 1.001 -1.719e-05 7.719e-06 0.998 -1.296e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2178 0.1026 0.3459 0.1431 0.985 0.994 0.2186 0.4368 0.8758 0.7053 ] Network output: [ 0.003952 -0.01866 0.9942 1.044e-05 -4.685e-06 1.017 7.865e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.09618 0.1839 0.1984 0.9873 0.9919 0.1088 0.7431 0.8628 0.3053 ] Network output: [ -0.003707 0.01737 1.004 1.126e-05 -5.054e-06 0.9856 8.485e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09302 0.09109 0.165 0.196 0.9852 0.9911 0.09304 0.6672 0.8384 0.2479 ] Network output: [ 0.0001014 1 -7.409e-05 1.485e-06 -6.665e-07 0.9998 1.119e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002392 Epoch 9083 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009487 0.9965 0.9919 -2.17e-07 9.741e-08 -0.007376 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003467 -0.003296 -0.007083 0.005652 0.9699 0.9743 0.006717 0.8277 0.8214 0.01686 ] Network output: [ 0.9999 0.0002309 0.0004981 -5.482e-06 2.461e-06 -0.0005065 -4.131e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2047 -0.035 -0.1628 0.185 0.9834 0.9932 0.2295 0.4327 0.8691 0.7113 ] Network output: [ -0.009416 1.003 1.008 -2.798e-07 1.256e-07 0.007822 -2.109e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00657 0.0005789 0.004419 0.003326 0.9889 0.9919 0.006697 0.8552 0.893 0.01209 ] Network output: [ -0.0002938 0.001866 1.001 -1.717e-05 7.71e-06 0.998 -1.294e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2178 0.1026 0.3459 0.1431 0.985 0.994 0.2186 0.4368 0.8758 0.7053 ] Network output: [ 0.003951 -0.01865 0.9942 1.042e-05 -4.68e-06 1.017 7.856e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.09619 0.1839 0.1984 0.9873 0.9919 0.1088 0.7431 0.8628 0.3053 ] Network output: [ -0.003706 0.01736 1.004 1.125e-05 -5.049e-06 0.9856 8.475e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09303 0.09109 0.165 0.196 0.9852 0.9911 0.09304 0.6671 0.8384 0.2479 ] Network output: [ 0.0001013 1 -7.402e-05 1.483e-06 -6.658e-07 0.9998 1.118e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002391 Epoch 9084 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009486 0.9965 0.9919 -2.17e-07 9.741e-08 -0.007376 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003467 -0.003296 -0.007082 0.005651 0.9699 0.9743 0.006718 0.8277 0.8214 0.01686 ] Network output: [ 0.9999 0.0002307 0.0004979 -5.476e-06 2.458e-06 -0.0005061 -4.127e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2047 -0.035 -0.1628 0.185 0.9834 0.9932 0.2295 0.4327 0.8691 0.7113 ] Network output: [ -0.009415 1.003 1.008 -2.797e-07 1.256e-07 0.007821 -2.108e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006571 0.000579 0.004419 0.003326 0.9889 0.9919 0.006698 0.8552 0.893 0.01209 ] Network output: [ -0.0002936 0.001865 1.001 -1.715e-05 7.701e-06 0.998 -1.293e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2179 0.1026 0.3459 0.1431 0.985 0.994 0.2186 0.4368 0.8758 0.7053 ] Network output: [ 0.003949 -0.01865 0.9942 1.041e-05 -4.675e-06 1.017 7.847e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1087 0.09619 0.1839 0.1984 0.9873 0.9919 0.1088 0.7431 0.8628 0.3053 ] Network output: [ -0.003704 0.01735 1.004 1.123e-05 -5.043e-06 0.9856 8.466e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09303 0.09109 0.165 0.196 0.9852 0.9911 0.09304 0.6671 0.8384 0.2479 ] Network output: [ 0.0001013 1 -7.395e-05 1.481e-06 -6.65e-07 0.9998 1.116e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000239 Epoch 9085 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009485 0.9965 0.9919 -2.17e-07 9.742e-08 -0.007375 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003467 -0.003296 -0.007081 0.005651 0.9699 0.9743 0.006718 0.8277 0.8214 0.01686 ] Network output: [ 0.9999 0.0002305 0.0004977 -5.469e-06 2.455e-06 -0.0005057 -4.122e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2047 -0.035 -0.1628 0.185 0.9834 0.9932 0.2295 0.4327 0.8691 0.7113 ] Network output: [ -0.009415 1.003 1.008 -2.796e-07 1.255e-07 0.00782 -2.107e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006571 0.0005791 0.004419 0.003326 0.9889 0.9919 0.006698 0.8552 0.893 0.01209 ] Network output: [ -0.0002934 0.001865 1.001 -1.714e-05 7.693e-06 0.998 -1.291e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2179 0.1026 0.3459 0.1431 0.985 0.994 0.2186 0.4367 0.8758 0.7053 ] Network output: [ 0.003948 -0.01864 0.9942 1.04e-05 -4.669e-06 1.017 7.838e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1088 0.0962 0.1839 0.1984 0.9873 0.9919 0.1088 0.7431 0.8628 0.3053 ] Network output: [ -0.003703 0.01735 1.004 1.122e-05 -5.037e-06 0.9856 8.456e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09303 0.09109 0.165 0.196 0.9852 0.9911 0.09304 0.6671 0.8384 0.2479 ] Network output: [ 0.0001013 1 -7.387e-05 1.48e-06 -6.643e-07 0.9998 1.115e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002388 Epoch 9086 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009484 0.9965 0.9919 -2.17e-07 9.742e-08 -0.007375 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003467 -0.003296 -0.00708 0.00565 0.9699 0.9743 0.006718 0.8277 0.8214 0.01685 ] Network output: [ 0.9999 0.0002302 0.0004974 -5.463e-06 2.453e-06 -0.0005053 -4.117e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2048 -0.035 -0.1628 0.1849 0.9834 0.9932 0.2295 0.4327 0.8691 0.7113 ] Network output: [ -0.009414 1.003 1.008 -2.795e-07 1.255e-07 0.00782 -2.106e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006572 0.0005792 0.004418 0.003325 0.9889 0.9919 0.006699 0.8552 0.893 0.01209 ] Network output: [ -0.0002932 0.001864 1.001 -1.712e-05 7.684e-06 0.998 -1.29e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2179 0.1026 0.346 0.1431 0.985 0.994 0.2186 0.4367 0.8758 0.7052 ] Network output: [ 0.003946 -0.01863 0.9942 1.039e-05 -4.664e-06 1.017 7.829e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1088 0.0962 0.1839 0.1984 0.9873 0.9919 0.1088 0.7431 0.8628 0.3053 ] Network output: [ -0.003701 0.01734 1.004 1.121e-05 -5.032e-06 0.9856 8.447e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09303 0.0911 0.165 0.196 0.9852 0.9911 0.09305 0.6671 0.8384 0.2479 ] Network output: [ 0.0001012 1 -7.38e-05 1.478e-06 -6.635e-07 0.9998 1.114e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002387 Epoch 9087 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009483 0.9965 0.9919 -2.17e-07 9.742e-08 -0.007374 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003467 -0.003296 -0.00708 0.00565 0.9699 0.9743 0.006718 0.8277 0.8214 0.01685 ] Network output: [ 0.9999 0.00023 0.0004972 -5.457e-06 2.45e-06 -0.0005049 -4.112e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2048 -0.035 -0.1628 0.1849 0.9834 0.9932 0.2296 0.4327 0.8691 0.7113 ] Network output: [ -0.009413 1.003 1.008 -2.794e-07 1.254e-07 0.007819 -2.106e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006572 0.0005793 0.004418 0.003325 0.9889 0.9919 0.006699 0.8552 0.893 0.01209 ] Network output: [ -0.000293 0.001863 1.001 -1.71e-05 7.675e-06 0.998 -1.288e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2179 0.1026 0.346 0.1431 0.985 0.994 0.2186 0.4367 0.8758 0.7052 ] Network output: [ 0.003945 -0.01862 0.9942 1.038e-05 -4.659e-06 1.017 7.821e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1088 0.09621 0.1839 0.1983 0.9873 0.9919 0.1088 0.7431 0.8628 0.3053 ] Network output: [ -0.0037 0.01733 1.004 1.12e-05 -5.026e-06 0.9857 8.437e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09303 0.0911 0.165 0.196 0.9852 0.9911 0.09305 0.6671 0.8384 0.2479 ] Network output: [ 0.0001012 1 -7.373e-05 1.476e-06 -6.628e-07 0.9998 1.113e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002386 Epoch 9088 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009481 0.9965 0.9919 -2.17e-07 9.742e-08 -0.007374 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003468 -0.003296 -0.007079 0.005649 0.9699 0.9743 0.006718 0.8277 0.8214 0.01685 ] Network output: [ 0.9999 0.0002298 0.0004969 -5.451e-06 2.447e-06 -0.0005045 -4.108e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2048 -0.035 -0.1628 0.1849 0.9834 0.9932 0.2296 0.4327 0.8691 0.7113 ] Network output: [ -0.009412 1.003 1.008 -2.793e-07 1.254e-07 0.007818 -2.105e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006573 0.0005794 0.004418 0.003325 0.9889 0.9919 0.0067 0.8552 0.893 0.01209 ] Network output: [ -0.0002928 0.001862 1.001 -1.708e-05 7.666e-06 0.998 -1.287e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2179 0.1026 0.346 0.1431 0.985 0.994 0.2186 0.4367 0.8758 0.7052 ] Network output: [ 0.003943 -0.01862 0.9942 1.037e-05 -4.653e-06 1.017 7.812e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1088 0.09621 0.1839 0.1983 0.9873 0.9919 0.1088 0.7431 0.8628 0.3053 ] Network output: [ -0.003698 0.01733 1.004 1.118e-05 -5.021e-06 0.9857 8.428e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09304 0.0911 0.165 0.196 0.9852 0.9911 0.09305 0.6671 0.8384 0.2479 ] Network output: [ 0.0001012 1 -7.366e-05 1.475e-06 -6.62e-07 0.9998 1.111e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002385 Epoch 9089 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00948 0.9965 0.9919 -2.17e-07 9.743e-08 -0.007374 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003468 -0.003296 -0.007078 0.005649 0.9699 0.9743 0.006719 0.8277 0.8214 0.01685 ] Network output: [ 0.9999 0.0002296 0.0004967 -5.444e-06 2.444e-06 -0.0005042 -4.103e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2048 -0.035 -0.1628 0.1849 0.9834 0.9932 0.2296 0.4327 0.8691 0.7113 ] Network output: [ -0.009411 1.003 1.008 -2.792e-07 1.253e-07 0.007817 -2.104e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006573 0.0005794 0.004418 0.003324 0.9889 0.9919 0.0067 0.8552 0.893 0.01208 ] Network output: [ -0.0002926 0.001862 1.001 -1.706e-05 7.657e-06 0.998 -1.285e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2179 0.1026 0.346 0.1431 0.985 0.994 0.2186 0.4367 0.8758 0.7052 ] Network output: [ 0.003942 -0.01861 0.9942 1.035e-05 -4.648e-06 1.017 7.803e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1088 0.09622 0.1839 0.1983 0.9873 0.9919 0.1088 0.743 0.8628 0.3053 ] Network output: [ -0.003697 0.01732 1.004 1.117e-05 -5.015e-06 0.9857 8.419e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09304 0.0911 0.165 0.196 0.9852 0.9911 0.09305 0.6671 0.8384 0.2479 ] Network output: [ 0.0001011 1 -7.359e-05 1.473e-06 -6.613e-07 0.9998 1.11e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002384 Epoch 9090 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009479 0.9965 0.9919 -2.17e-07 9.743e-08 -0.007373 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003468 -0.003297 -0.007078 0.005648 0.9699 0.9743 0.006719 0.8277 0.8214 0.01685 ] Network output: [ 0.9999 0.0002293 0.0004964 -5.438e-06 2.441e-06 -0.0005038 -4.098e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2048 -0.035 -0.1627 0.1849 0.9834 0.9932 0.2296 0.4327 0.869 0.7113 ] Network output: [ -0.00941 1.003 1.008 -2.791e-07 1.253e-07 0.007816 -2.103e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006574 0.0005795 0.004418 0.003324 0.9889 0.9919 0.006701 0.8552 0.893 0.01208 ] Network output: [ -0.0002924 0.001861 1.001 -1.704e-05 7.649e-06 0.998 -1.284e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2179 0.1026 0.346 0.1431 0.985 0.994 0.2186 0.4367 0.8758 0.7052 ] Network output: [ 0.00394 -0.0186 0.9942 1.034e-05 -4.643e-06 1.017 7.794e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1088 0.09622 0.1839 0.1983 0.9873 0.9919 0.1089 0.743 0.8628 0.3053 ] Network output: [ -0.003696 0.01731 1.004 1.116e-05 -5.009e-06 0.9857 8.409e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09304 0.0911 0.165 0.196 0.9852 0.9911 0.09306 0.667 0.8384 0.2479 ] Network output: [ 0.0001011 1 -7.352e-05 1.471e-06 -6.605e-07 0.9998 1.109e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002382 Epoch 9091 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009478 0.9965 0.9919 -2.17e-07 9.743e-08 -0.007373 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003468 -0.003297 -0.007077 0.005648 0.9699 0.9743 0.006719 0.8277 0.8214 0.01685 ] Network output: [ 0.9999 0.0002291 0.0004962 -5.432e-06 2.439e-06 -0.0005034 -4.094e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2048 -0.035 -0.1627 0.1849 0.9834 0.9932 0.2296 0.4327 0.869 0.7113 ] Network output: [ -0.009409 1.003 1.008 -2.79e-07 1.252e-07 0.007815 -2.102e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006574 0.0005796 0.004418 0.003324 0.9889 0.9919 0.006701 0.8552 0.893 0.01208 ] Network output: [ -0.0002922 0.00186 1.001 -1.702e-05 7.64e-06 0.998 -1.283e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2179 0.1026 0.346 0.1431 0.985 0.994 0.2186 0.4367 0.8758 0.7052 ] Network output: [ 0.003938 -0.0186 0.9942 1.033e-05 -4.638e-06 1.017 7.785e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1088 0.09623 0.1839 0.1983 0.9873 0.9919 0.1089 0.743 0.8628 0.3053 ] Network output: [ -0.003694 0.01731 1.004 1.115e-05 -5.004e-06 0.9857 8.4e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09304 0.09111 0.165 0.196 0.9852 0.9911 0.09306 0.667 0.8384 0.2479 ] Network output: [ 0.000101 1 -7.345e-05 1.47e-06 -6.598e-07 0.9998 1.108e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002381 Epoch 9092 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009477 0.9965 0.9919 -2.17e-07 9.743e-08 -0.007372 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003468 -0.003297 -0.007076 0.005647 0.9699 0.9743 0.006719 0.8277 0.8214 0.01685 ] Network output: [ 0.9999 0.0002289 0.000496 -5.426e-06 2.436e-06 -0.000503 -4.089e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2048 -0.03501 -0.1627 0.1849 0.9834 0.9932 0.2296 0.4327 0.869 0.7113 ] Network output: [ -0.009408 1.003 1.008 -2.788e-07 1.252e-07 0.007814 -2.101e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006575 0.0005797 0.004418 0.003324 0.9889 0.9919 0.006701 0.8552 0.893 0.01208 ] Network output: [ -0.000292 0.001859 1.001 -1.7e-05 7.631e-06 0.998 -1.281e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2179 0.1026 0.346 0.1431 0.985 0.994 0.2187 0.4367 0.8758 0.7052 ] Network output: [ 0.003937 -0.01859 0.9942 1.032e-05 -4.632e-06 1.017 7.776e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1088 0.09623 0.1839 0.1983 0.9873 0.9919 0.1089 0.743 0.8628 0.3053 ] Network output: [ -0.003693 0.0173 1.004 1.113e-05 -4.998e-06 0.9857 8.39e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09305 0.09111 0.165 0.196 0.9852 0.9911 0.09306 0.667 0.8384 0.2479 ] Network output: [ 0.000101 1 -7.338e-05 1.468e-06 -6.59e-07 0.9998 1.106e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000238 Epoch 9093 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009476 0.9965 0.9919 -2.17e-07 9.743e-08 -0.007372 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003468 -0.003297 -0.007075 0.005647 0.9699 0.9743 0.00672 0.8277 0.8214 0.01685 ] Network output: [ 0.9999 0.0002287 0.0004957 -5.419e-06 2.433e-06 -0.0005026 -4.084e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2048 -0.03501 -0.1627 0.1849 0.9834 0.9932 0.2296 0.4327 0.869 0.7113 ] Network output: [ -0.009407 1.003 1.008 -2.787e-07 1.251e-07 0.007813 -2.101e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006575 0.0005798 0.004418 0.003323 0.9889 0.9919 0.006702 0.8552 0.893 0.01208 ] Network output: [ -0.0002918 0.001859 1.001 -1.698e-05 7.622e-06 0.998 -1.28e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2179 0.1027 0.346 0.1431 0.985 0.994 0.2187 0.4367 0.8758 0.7052 ] Network output: [ 0.003935 -0.01858 0.9942 1.031e-05 -4.627e-06 1.017 7.767e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1088 0.09624 0.1839 0.1983 0.9873 0.9919 0.1089 0.743 0.8628 0.3053 ] Network output: [ -0.003691 0.01729 1.004 1.112e-05 -4.993e-06 0.9857 8.381e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09305 0.09111 0.165 0.196 0.9852 0.9911 0.09306 0.667 0.8384 0.2479 ] Network output: [ 0.000101 1 -7.331e-05 1.466e-06 -6.583e-07 0.9998 1.105e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002379 Epoch 9094 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009475 0.9965 0.9919 -2.17e-07 9.743e-08 -0.007372 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003468 -0.003297 -0.007075 0.005647 0.9699 0.9743 0.00672 0.8277 0.8214 0.01685 ] Network output: [ 0.9999 0.0002284 0.0004955 -5.413e-06 2.43e-06 -0.0005023 -4.08e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2048 -0.03501 -0.1627 0.1849 0.9834 0.9932 0.2296 0.4326 0.869 0.7113 ] Network output: [ -0.009406 1.003 1.008 -2.786e-07 1.251e-07 0.007812 -2.1e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006576 0.0005799 0.004418 0.003323 0.9889 0.9919 0.006702 0.8552 0.893 0.01208 ] Network output: [ -0.0002916 0.001858 1.001 -1.696e-05 7.614e-06 0.998 -1.278e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2179 0.1027 0.346 0.1431 0.985 0.994 0.2187 0.4367 0.8758 0.7052 ] Network output: [ 0.003934 -0.01857 0.9942 1.029e-05 -4.622e-06 1.017 7.759e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1088 0.09624 0.1839 0.1983 0.9873 0.9919 0.1089 0.743 0.8628 0.3053 ] Network output: [ -0.00369 0.01728 1.004 1.111e-05 -4.987e-06 0.9857 8.372e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09305 0.09111 0.165 0.196 0.9852 0.9911 0.09307 0.667 0.8383 0.248 ] Network output: [ 0.0001009 1 -7.324e-05 1.465e-06 -6.576e-07 0.9998 1.104e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002377 Epoch 9095 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009474 0.9965 0.9919 -2.17e-07 9.743e-08 -0.007371 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003468 -0.003297 -0.007074 0.005646 0.9699 0.9743 0.00672 0.8276 0.8214 0.01685 ] Network output: [ 0.9999 0.0002282 0.0004952 -5.407e-06 2.427e-06 -0.0005019 -4.075e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2048 -0.03501 -0.1627 0.1849 0.9834 0.9932 0.2296 0.4326 0.869 0.7113 ] Network output: [ -0.009405 1.003 1.008 -2.785e-07 1.25e-07 0.007811 -2.099e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006576 0.00058 0.004418 0.003323 0.9889 0.9919 0.006703 0.8552 0.893 0.01208 ] Network output: [ -0.0002914 0.001857 1.001 -1.694e-05 7.605e-06 0.998 -1.277e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2179 0.1027 0.346 0.1431 0.985 0.994 0.2187 0.4367 0.8757 0.7052 ] Network output: [ 0.003932 -0.01857 0.9942 1.028e-05 -4.617e-06 1.017 7.75e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1088 0.09625 0.1839 0.1983 0.9873 0.9919 0.1089 0.743 0.8628 0.3053 ] Network output: [ -0.003688 0.01728 1.004 1.11e-05 -4.981e-06 0.9857 8.362e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09305 0.09112 0.165 0.196 0.9852 0.9911 0.09307 0.667 0.8383 0.248 ] Network output: [ 0.0001009 1 -7.317e-05 1.463e-06 -6.568e-07 0.9998 1.103e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002376 Epoch 9096 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009473 0.9965 0.9919 -2.17e-07 9.743e-08 -0.007371 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003468 -0.003297 -0.007073 0.005646 0.9699 0.9743 0.00672 0.8276 0.8214 0.01684 ] Network output: [ 0.9999 0.000228 0.000495 -5.401e-06 2.425e-06 -0.0005015 -4.07e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2048 -0.03501 -0.1627 0.1849 0.9834 0.9932 0.2296 0.4326 0.869 0.7113 ] Network output: [ -0.009404 1.003 1.008 -2.784e-07 1.25e-07 0.007811 -2.098e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006576 0.0005801 0.004418 0.003323 0.9889 0.9919 0.006703 0.8552 0.893 0.01208 ] Network output: [ -0.0002912 0.001856 1.001 -1.692e-05 7.596e-06 0.998 -1.275e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.218 0.1027 0.346 0.1431 0.985 0.994 0.2187 0.4367 0.8757 0.7052 ] Network output: [ 0.003931 -0.01856 0.9942 1.027e-05 -4.611e-06 1.017 7.741e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1088 0.09625 0.1839 0.1983 0.9873 0.9919 0.1089 0.7429 0.8628 0.3053 ] Network output: [ -0.003687 0.01727 1.004 1.108e-05 -4.976e-06 0.9857 8.353e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09306 0.09112 0.165 0.196 0.9852 0.9911 0.09307 0.6669 0.8383 0.248 ] Network output: [ 0.0001009 1 -7.31e-05 1.461e-06 -6.561e-07 0.9998 1.101e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002375 Epoch 9097 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009472 0.9965 0.9919 -2.17e-07 9.744e-08 -0.00737 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003468 -0.003297 -0.007073 0.005645 0.9699 0.9743 0.006721 0.8276 0.8214 0.01684 ] Network output: [ 0.9999 0.0002278 0.0004948 -5.395e-06 2.422e-06 -0.0005011 -4.066e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2048 -0.03501 -0.1627 0.1849 0.9834 0.9932 0.2296 0.4326 0.869 0.7113 ] Network output: [ -0.009404 1.003 1.008 -2.783e-07 1.249e-07 0.00781 -2.097e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006577 0.0005802 0.004418 0.003322 0.9889 0.9919 0.006704 0.8551 0.893 0.01208 ] Network output: [ -0.000291 0.001856 1.001 -1.69e-05 7.588e-06 0.998 -1.274e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.218 0.1027 0.346 0.1431 0.985 0.994 0.2187 0.4367 0.8757 0.7052 ] Network output: [ 0.003929 -0.01855 0.9942 1.026e-05 -4.606e-06 1.017 7.732e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1088 0.09625 0.1839 0.1983 0.9873 0.9919 0.1089 0.7429 0.8628 0.3053 ] Network output: [ -0.003685 0.01726 1.004 1.107e-05 -4.97e-06 0.9857 8.344e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09306 0.09112 0.165 0.196 0.9852 0.9911 0.09307 0.6669 0.8383 0.248 ] Network output: [ 0.0001008 1 -7.303e-05 1.46e-06 -6.553e-07 0.9998 1.1e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002374 Epoch 9098 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009471 0.9965 0.9919 -2.17e-07 9.744e-08 -0.00737 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003468 -0.003297 -0.007072 0.005645 0.9699 0.9743 0.006721 0.8276 0.8214 0.01684 ] Network output: [ 0.9999 0.0002275 0.0004945 -5.388e-06 2.419e-06 -0.0005008 -4.061e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2048 -0.03501 -0.1627 0.1849 0.9834 0.9932 0.2296 0.4326 0.869 0.7113 ] Network output: [ -0.009403 1.003 1.008 -2.782e-07 1.249e-07 0.007809 -2.096e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006577 0.0005803 0.004418 0.003322 0.9889 0.9919 0.006704 0.8551 0.8929 0.01208 ] Network output: [ -0.0002908 0.001855 1.001 -1.688e-05 7.579e-06 0.998 -1.272e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.218 0.1027 0.346 0.1431 0.985 0.994 0.2187 0.4367 0.8757 0.7052 ] Network output: [ 0.003928 -0.01855 0.9942 1.025e-05 -4.601e-06 1.017 7.723e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1088 0.09626 0.1839 0.1983 0.9873 0.9919 0.1089 0.7429 0.8628 0.3053 ] Network output: [ -0.003684 0.01726 1.004 1.106e-05 -4.965e-06 0.9857 8.334e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09306 0.09112 0.165 0.196 0.9852 0.9911 0.09307 0.6669 0.8383 0.248 ] Network output: [ 0.0001008 1 -7.296e-05 1.458e-06 -6.546e-07 0.9998 1.099e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002372 Epoch 9099 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00947 0.9965 0.9919 -2.17e-07 9.744e-08 -0.00737 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003468 -0.003298 -0.007071 0.005644 0.9699 0.9743 0.006721 0.8276 0.8213 0.01684 ] Network output: [ 0.9999 0.0002273 0.0004943 -5.382e-06 2.416e-06 -0.0005004 -4.056e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2048 -0.03501 -0.1626 0.1849 0.9834 0.9932 0.2297 0.4326 0.869 0.7112 ] Network output: [ -0.009402 1.003 1.008 -2.781e-07 1.248e-07 0.007808 -2.096e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006578 0.0005803 0.004418 0.003322 0.9889 0.9919 0.006705 0.8551 0.8929 0.01208 ] Network output: [ -0.0002906 0.001854 1.001 -1.686e-05 7.57e-06 0.998 -1.271e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.218 0.1027 0.346 0.1431 0.985 0.994 0.2187 0.4367 0.8757 0.7052 ] Network output: [ 0.003926 -0.01854 0.9942 1.024e-05 -4.596e-06 1.017 7.715e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1088 0.09626 0.1839 0.1983 0.9873 0.9919 0.1089 0.7429 0.8628 0.3053 ] Network output: [ -0.003682 0.01725 1.004 1.105e-05 -4.959e-06 0.9857 8.325e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09306 0.09113 0.165 0.196 0.9852 0.9911 0.09308 0.6669 0.8383 0.248 ] Network output: [ 0.0001007 1 -7.289e-05 1.456e-06 -6.538e-07 0.9998 1.098e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002371 Epoch 9100 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009469 0.9965 0.9919 -2.17e-07 9.744e-08 -0.007369 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003469 -0.003298 -0.007071 0.005644 0.9699 0.9743 0.006721 0.8276 0.8213 0.01684 ] Network output: [ 0.9999 0.0002271 0.000494 -5.376e-06 2.413e-06 -0.0005 -4.052e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2049 -0.03501 -0.1626 0.1849 0.9834 0.9932 0.2297 0.4326 0.869 0.7112 ] Network output: [ -0.009401 1.003 1.008 -2.779e-07 1.248e-07 0.007807 -2.095e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006578 0.0005804 0.004418 0.003321 0.9889 0.9919 0.006705 0.8551 0.8929 0.01208 ] Network output: [ -0.0002904 0.001853 1.001 -1.684e-05 7.562e-06 0.998 -1.269e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.218 0.1027 0.346 0.1431 0.985 0.994 0.2187 0.4366 0.8757 0.7052 ] Network output: [ 0.003925 -0.01853 0.9942 1.022e-05 -4.59e-06 1.017 7.706e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1088 0.09627 0.1839 0.1983 0.9873 0.9919 0.1089 0.7429 0.8628 0.3053 ] Network output: [ -0.003681 0.01724 1.004 1.103e-05 -4.954e-06 0.9857 8.316e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09307 0.09113 0.165 0.196 0.9852 0.9911 0.09308 0.6669 0.8383 0.248 ] Network output: [ 0.0001007 1 -7.282e-05 1.455e-06 -6.531e-07 0.9998 1.096e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000237 Epoch 9101 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009468 0.9965 0.9919 -2.17e-07 9.744e-08 -0.007369 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003469 -0.003298 -0.00707 0.005643 0.9699 0.9743 0.006721 0.8276 0.8213 0.01684 ] Network output: [ 0.9999 0.0002269 0.0004938 -5.37e-06 2.411e-06 -0.0004996 -4.047e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2049 -0.03501 -0.1626 0.1849 0.9834 0.9932 0.2297 0.4326 0.869 0.7112 ] Network output: [ -0.0094 1.003 1.008 -2.778e-07 1.247e-07 0.007806 -2.094e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006579 0.0005805 0.004417 0.003321 0.9889 0.9919 0.006706 0.8551 0.8929 0.01207 ] Network output: [ -0.0002902 0.001853 1.001 -1.682e-05 7.553e-06 0.998 -1.268e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.218 0.1027 0.346 0.1431 0.985 0.994 0.2187 0.4366 0.8757 0.7052 ] Network output: [ 0.003923 -0.01852 0.9942 1.021e-05 -4.585e-06 1.017 7.697e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1088 0.09627 0.1839 0.1983 0.9873 0.9919 0.1089 0.7429 0.8628 0.3053 ] Network output: [ -0.00368 0.01723 1.004 1.102e-05 -4.948e-06 0.9857 8.307e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09307 0.09113 0.165 0.196 0.9852 0.9911 0.09308 0.6669 0.8383 0.248 ] Network output: [ 0.0001007 1 -7.275e-05 1.453e-06 -6.524e-07 0.9998 1.095e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002369 Epoch 9102 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009467 0.9965 0.9919 -2.17e-07 9.744e-08 -0.007368 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003469 -0.003298 -0.007069 0.005643 0.9699 0.9743 0.006722 0.8276 0.8213 0.01684 ] Network output: [ 0.9999 0.0002266 0.0004935 -5.364e-06 2.408e-06 -0.0004993 -4.042e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2049 -0.03502 -0.1626 0.1849 0.9834 0.9932 0.2297 0.4326 0.869 0.7112 ] Network output: [ -0.009399 1.003 1.008 -2.777e-07 1.247e-07 0.007805 -2.093e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006579 0.0005806 0.004417 0.003321 0.9889 0.9919 0.006706 0.8551 0.8929 0.01207 ] Network output: [ -0.00029 0.001852 1.001 -1.68e-05 7.544e-06 0.998 -1.266e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.218 0.1027 0.346 0.1431 0.985 0.994 0.2187 0.4366 0.8757 0.7052 ] Network output: [ 0.003922 -0.01852 0.9942 1.02e-05 -4.58e-06 1.017 7.688e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1088 0.09628 0.1839 0.1983 0.9873 0.9919 0.1089 0.7429 0.8628 0.3053 ] Network output: [ -0.003678 0.01723 1.004 1.101e-05 -4.943e-06 0.9857 8.297e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09307 0.09113 0.165 0.196 0.9852 0.9911 0.09308 0.6669 0.8383 0.248 ] Network output: [ 0.0001006 1 -7.268e-05 1.452e-06 -6.516e-07 0.9998 1.094e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002367 Epoch 9103 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009465 0.9965 0.9919 -2.17e-07 9.744e-08 -0.007368 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003469 -0.003298 -0.007068 0.005642 0.9699 0.9743 0.006722 0.8276 0.8213 0.01684 ] Network output: [ 0.9999 0.0002264 0.0004933 -5.357e-06 2.405e-06 -0.0004989 -4.038e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2049 -0.03502 -0.1626 0.1849 0.9834 0.9932 0.2297 0.4326 0.869 0.7112 ] Network output: [ -0.009398 1.003 1.008 -2.776e-07 1.246e-07 0.007804 -2.092e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00658 0.0005807 0.004417 0.003321 0.9889 0.9919 0.006707 0.8551 0.8929 0.01207 ] Network output: [ -0.0002898 0.001851 1.001 -1.679e-05 7.536e-06 0.998 -1.265e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.218 0.1027 0.346 0.1431 0.985 0.994 0.2187 0.4366 0.8757 0.7052 ] Network output: [ 0.00392 -0.01851 0.9942 1.019e-05 -4.575e-06 1.017 7.68e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1088 0.09628 0.1839 0.1983 0.9873 0.9919 0.1089 0.7428 0.8628 0.3053 ] Network output: [ -0.003677 0.01722 1.004 1.1e-05 -4.937e-06 0.9857 8.288e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09307 0.09114 0.165 0.196 0.9852 0.9911 0.09309 0.6668 0.8383 0.248 ] Network output: [ 0.0001006 1 -7.261e-05 1.45e-06 -6.509e-07 0.9998 1.093e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002366 Epoch 9104 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009464 0.9965 0.9919 -2.17e-07 9.743e-08 -0.007368 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003469 -0.003298 -0.007068 0.005642 0.9699 0.9743 0.006722 0.8276 0.8213 0.01684 ] Network output: [ 0.9999 0.0002262 0.0004931 -5.351e-06 2.402e-06 -0.0004985 -4.033e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2049 -0.03502 -0.1626 0.1849 0.9834 0.9932 0.2297 0.4326 0.869 0.7112 ] Network output: [ -0.009397 1.003 1.008 -2.775e-07 1.246e-07 0.007803 -2.091e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00658 0.0005808 0.004417 0.00332 0.9889 0.9919 0.006707 0.8551 0.8929 0.01207 ] Network output: [ -0.0002896 0.001851 1.001 -1.677e-05 7.527e-06 0.998 -1.264e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.218 0.1027 0.3461 0.1431 0.985 0.994 0.2187 0.4366 0.8757 0.7052 ] Network output: [ 0.003919 -0.0185 0.9942 1.018e-05 -4.569e-06 1.017 7.671e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1088 0.09629 0.1839 0.1983 0.9873 0.9919 0.1089 0.7428 0.8628 0.3053 ] Network output: [ -0.003675 0.01721 1.004 1.099e-05 -4.932e-06 0.9857 8.279e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09307 0.09114 0.165 0.196 0.9852 0.9911 0.09309 0.6668 0.8383 0.248 ] Network output: [ 0.0001006 1 -7.254e-05 1.448e-06 -6.502e-07 0.9998 1.091e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002365 Epoch 9105 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009463 0.9965 0.9919 -2.17e-07 9.743e-08 -0.007367 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003469 -0.003298 -0.007067 0.005641 0.9699 0.9743 0.006722 0.8276 0.8213 0.01684 ] Network output: [ 0.9999 0.000226 0.0004928 -5.345e-06 2.4e-06 -0.0004981 -4.028e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2049 -0.03502 -0.1626 0.1849 0.9834 0.9932 0.2297 0.4326 0.869 0.7112 ] Network output: [ -0.009396 1.003 1.008 -2.774e-07 1.245e-07 0.007803 -2.09e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006581 0.0005809 0.004417 0.00332 0.9889 0.9919 0.006708 0.8551 0.8929 0.01207 ] Network output: [ -0.0002895 0.00185 1.001 -1.675e-05 7.518e-06 0.998 -1.262e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.218 0.1027 0.3461 0.1431 0.985 0.994 0.2188 0.4366 0.8757 0.7051 ] Network output: [ 0.003917 -0.0185 0.9942 1.017e-05 -4.564e-06 1.017 7.662e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1088 0.09629 0.1839 0.1983 0.9873 0.9919 0.1089 0.7428 0.8628 0.3053 ] Network output: [ -0.003674 0.01721 1.004 1.097e-05 -4.926e-06 0.9857 8.27e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09308 0.09114 0.165 0.196 0.9852 0.9911 0.09309 0.6668 0.8383 0.248 ] Network output: [ 0.0001005 1 -7.248e-05 1.447e-06 -6.494e-07 0.9998 1.09e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002364 Epoch 9106 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009462 0.9965 0.9919 -2.17e-07 9.743e-08 -0.007367 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003469 -0.003298 -0.007066 0.005641 0.9699 0.9743 0.006723 0.8276 0.8213 0.01683 ] Network output: [ 0.9999 0.0002258 0.0004926 -5.339e-06 2.397e-06 -0.0004978 -4.024e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2049 -0.03502 -0.1626 0.1849 0.9834 0.9932 0.2297 0.4326 0.869 0.7112 ] Network output: [ -0.009395 1.003 1.008 -2.773e-07 1.245e-07 0.007802 -2.09e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006581 0.000581 0.004417 0.00332 0.9889 0.9919 0.006708 0.8551 0.8929 0.01207 ] Network output: [ -0.0002893 0.001849 1.001 -1.673e-05 7.51e-06 0.998 -1.261e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.218 0.1027 0.3461 0.1431 0.985 0.994 0.2188 0.4366 0.8757 0.7051 ] Network output: [ 0.003916 -0.01849 0.9942 1.016e-05 -4.559e-06 1.017 7.653e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.0963 0.1839 0.1983 0.9873 0.9919 0.1089 0.7428 0.8628 0.3053 ] Network output: [ -0.003672 0.0172 1.004 1.096e-05 -4.921e-06 0.9857 8.26e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09308 0.09114 0.165 0.196 0.9852 0.9911 0.09309 0.6668 0.8383 0.248 ] Network output: [ 0.0001005 1 -7.241e-05 1.445e-06 -6.487e-07 0.9998 1.089e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002363 Epoch 9107 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009461 0.9965 0.9919 -2.17e-07 9.743e-08 -0.007366 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003469 -0.003298 -0.007066 0.005641 0.9699 0.9743 0.006723 0.8276 0.8213 0.01683 ] Network output: [ 0.9999 0.0002255 0.0004923 -5.333e-06 2.394e-06 -0.0004974 -4.019e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2049 -0.03502 -0.1626 0.1849 0.9834 0.9932 0.2297 0.4326 0.869 0.7112 ] Network output: [ -0.009394 1.003 1.008 -2.771e-07 1.244e-07 0.007801 -2.089e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006582 0.0005811 0.004417 0.00332 0.9889 0.9919 0.006709 0.8551 0.8929 0.01207 ] Network output: [ -0.0002891 0.001848 1.001 -1.671e-05 7.501e-06 0.998 -1.259e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.218 0.1027 0.3461 0.1431 0.985 0.994 0.2188 0.4366 0.8757 0.7051 ] Network output: [ 0.003914 -0.01848 0.9942 1.014e-05 -4.554e-06 1.017 7.645e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.0963 0.1839 0.1983 0.9873 0.9919 0.1089 0.7428 0.8628 0.3053 ] Network output: [ -0.003671 0.01719 1.004 1.095e-05 -4.915e-06 0.9857 8.251e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09308 0.09114 0.165 0.196 0.9852 0.9911 0.0931 0.6668 0.8383 0.248 ] Network output: [ 0.0001005 1 -7.234e-05 1.443e-06 -6.48e-07 0.9998 1.088e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002361 Epoch 9108 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00946 0.9965 0.9919 -2.17e-07 9.743e-08 -0.007366 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003469 -0.003298 -0.007065 0.00564 0.9699 0.9743 0.006723 0.8276 0.8213 0.01683 ] Network output: [ 0.9999 0.0002253 0.0004921 -5.327e-06 2.391e-06 -0.000497 -4.014e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2049 -0.03502 -0.1625 0.1849 0.9834 0.9932 0.2297 0.4325 0.869 0.7112 ] Network output: [ -0.009393 1.003 1.008 -2.77e-07 1.244e-07 0.0078 -2.088e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006582 0.0005811 0.004417 0.003319 0.9889 0.9919 0.006709 0.8551 0.8929 0.01207 ] Network output: [ -0.0002889 0.001848 1.001 -1.669e-05 7.492e-06 0.998 -1.258e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.218 0.1027 0.3461 0.143 0.985 0.994 0.2188 0.4366 0.8757 0.7051 ] Network output: [ 0.003913 -0.01847 0.9942 1.013e-05 -4.549e-06 1.017 7.636e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.09631 0.1839 0.1983 0.9873 0.9919 0.1089 0.7428 0.8628 0.3053 ] Network output: [ -0.003669 0.01719 1.004 1.094e-05 -4.91e-06 0.9857 8.242e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09308 0.09115 0.165 0.196 0.9852 0.9911 0.0931 0.6668 0.8383 0.248 ] Network output: [ 0.0001004 1 -7.227e-05 1.442e-06 -6.472e-07 0.9998 1.086e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000236 Epoch 9109 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009459 0.9965 0.9919 -2.17e-07 9.743e-08 -0.007366 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003469 -0.003299 -0.007064 0.00564 0.9699 0.9743 0.006723 0.8276 0.8213 0.01683 ] Network output: [ 0.9999 0.0002251 0.0004919 -5.321e-06 2.389e-06 -0.0004966 -4.01e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2049 -0.03502 -0.1625 0.1848 0.9834 0.9932 0.2297 0.4325 0.869 0.7112 ] Network output: [ -0.009393 1.003 1.008 -2.769e-07 1.243e-07 0.007799 -2.087e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006582 0.0005812 0.004417 0.003319 0.9889 0.9919 0.00671 0.8551 0.8929 0.01207 ] Network output: [ -0.0002887 0.001847 1.001 -1.667e-05 7.484e-06 0.998 -1.256e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2181 0.1027 0.3461 0.143 0.985 0.994 0.2188 0.4366 0.8757 0.7051 ] Network output: [ 0.003911 -0.01847 0.9942 1.012e-05 -4.544e-06 1.017 7.627e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.09631 0.1839 0.1983 0.9873 0.9919 0.1089 0.7428 0.8627 0.3053 ] Network output: [ -0.003668 0.01718 1.004 1.092e-05 -4.904e-06 0.9857 8.233e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09309 0.09115 0.165 0.196 0.9852 0.9911 0.0931 0.6668 0.8383 0.248 ] Network output: [ 0.0001004 1 -7.22e-05 1.44e-06 -6.465e-07 0.9998 1.085e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002359 Epoch 9110 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009458 0.9965 0.9919 -2.17e-07 9.743e-08 -0.007365 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003469 -0.003299 -0.007063 0.005639 0.9699 0.9743 0.006723 0.8276 0.8213 0.01683 ] Network output: [ 0.9999 0.0002249 0.0004916 -5.315e-06 2.386e-06 -0.0004963 -4.005e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2049 -0.03502 -0.1625 0.1848 0.9834 0.9932 0.2297 0.4325 0.869 0.7112 ] Network output: [ -0.009392 1.003 1.008 -2.768e-07 1.243e-07 0.007798 -2.086e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006583 0.0005813 0.004417 0.003319 0.9889 0.9919 0.00671 0.8551 0.8929 0.01207 ] Network output: [ -0.0002885 0.001846 1.001 -1.665e-05 7.475e-06 0.998 -1.255e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2181 0.1027 0.3461 0.143 0.985 0.994 0.2188 0.4366 0.8757 0.7051 ] Network output: [ 0.00391 -0.01846 0.9942 1.011e-05 -4.538e-06 1.017 7.619e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.09631 0.1839 0.1983 0.9873 0.9919 0.1089 0.7427 0.8627 0.3053 ] Network output: [ -0.003666 0.01717 1.004 1.091e-05 -4.899e-06 0.9857 8.223e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09309 0.09115 0.165 0.196 0.9852 0.9911 0.0931 0.6667 0.8383 0.248 ] Network output: [ 0.0001003 1 -7.213e-05 1.438e-06 -6.458e-07 0.9998 1.084e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002358 Epoch 9111 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009457 0.9965 0.9919 -2.17e-07 9.742e-08 -0.007365 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00347 -0.003299 -0.007063 0.005639 0.9699 0.9743 0.006724 0.8276 0.8213 0.01683 ] Network output: [ 0.9999 0.0002246 0.0004914 -5.308e-06 2.383e-06 -0.0004959 -4.001e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2049 -0.03503 -0.1625 0.1848 0.9834 0.9932 0.2298 0.4325 0.869 0.7112 ] Network output: [ -0.009391 1.003 1.008 -2.767e-07 1.242e-07 0.007797 -2.085e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006583 0.0005814 0.004417 0.003318 0.9889 0.9919 0.006711 0.8551 0.8929 0.01207 ] Network output: [ -0.0002883 0.001845 1.001 -1.663e-05 7.467e-06 0.998 -1.253e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2181 0.1027 0.3461 0.143 0.985 0.994 0.2188 0.4366 0.8757 0.7051 ] Network output: [ 0.003908 -0.01845 0.9942 1.01e-05 -4.533e-06 1.017 7.61e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.09632 0.1839 0.1983 0.9873 0.9919 0.109 0.7427 0.8627 0.3053 ] Network output: [ -0.003665 0.01716 1.004 1.09e-05 -4.893e-06 0.9857 8.214e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09309 0.09115 0.165 0.196 0.9852 0.9911 0.0931 0.6667 0.8383 0.248 ] Network output: [ 0.0001003 1 -7.207e-05 1.437e-06 -6.45e-07 0.9998 1.083e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002356 Epoch 9112 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009456 0.9965 0.9919 -2.17e-07 9.742e-08 -0.007364 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00347 -0.003299 -0.007062 0.005638 0.9699 0.9743 0.006724 0.8276 0.8213 0.01683 ] Network output: [ 0.9999 0.0002244 0.0004912 -5.302e-06 2.38e-06 -0.0004955 -3.996e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2049 -0.03503 -0.1625 0.1848 0.9834 0.9932 0.2298 0.4325 0.869 0.7112 ] Network output: [ -0.00939 1.003 1.008 -2.766e-07 1.242e-07 0.007796 -2.084e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006584 0.0005815 0.004417 0.003318 0.9889 0.9919 0.006711 0.8551 0.8929 0.01207 ] Network output: [ -0.0002881 0.001845 1.001 -1.661e-05 7.458e-06 0.998 -1.252e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2181 0.1028 0.3461 0.143 0.985 0.994 0.2188 0.4366 0.8757 0.7051 ] Network output: [ 0.003907 -0.01845 0.9942 1.009e-05 -4.528e-06 1.017 7.601e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.09632 0.1839 0.1983 0.9873 0.9919 0.109 0.7427 0.8627 0.3053 ] Network output: [ -0.003664 0.01716 1.004 1.089e-05 -4.888e-06 0.9857 8.205e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09309 0.09116 0.165 0.196 0.9852 0.9911 0.09311 0.6667 0.8383 0.248 ] Network output: [ 0.0001003 1 -7.2e-05 1.435e-06 -6.443e-07 0.9998 1.082e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002355 Epoch 9113 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009455 0.9965 0.9919 -2.17e-07 9.742e-08 -0.007364 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00347 -0.003299 -0.007061 0.005638 0.9699 0.9743 0.006724 0.8275 0.8213 0.01683 ] Network output: [ 0.9999 0.0002242 0.0004909 -5.296e-06 2.378e-06 -0.0004951 -3.991e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2049 -0.03503 -0.1625 0.1848 0.9834 0.9932 0.2298 0.4325 0.869 0.7112 ] Network output: [ -0.009389 1.003 1.008 -2.765e-07 1.241e-07 0.007796 -2.083e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006584 0.0005816 0.004417 0.003318 0.9889 0.9919 0.006711 0.8551 0.8929 0.01206 ] Network output: [ -0.0002879 0.001844 1.001 -1.659e-05 7.45e-06 0.998 -1.251e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2181 0.1028 0.3461 0.143 0.985 0.994 0.2188 0.4366 0.8757 0.7051 ] Network output: [ 0.003905 -0.01844 0.9942 1.007e-05 -4.523e-06 1.017 7.593e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.09633 0.1839 0.1983 0.9873 0.9919 0.109 0.7427 0.8627 0.3053 ] Network output: [ -0.003662 0.01715 1.004 1.088e-05 -4.882e-06 0.9857 8.196e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0931 0.09116 0.165 0.196 0.9852 0.9911 0.09311 0.6667 0.8383 0.248 ] Network output: [ 0.0001002 1 -7.193e-05 1.434e-06 -6.436e-07 0.9998 1.08e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002354 Epoch 9114 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009454 0.9965 0.9919 -2.17e-07 9.742e-08 -0.007364 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00347 -0.003299 -0.007061 0.005637 0.9699 0.9743 0.006724 0.8275 0.8213 0.01683 ] Network output: [ 0.9999 0.000224 0.0004907 -5.29e-06 2.375e-06 -0.0004948 -3.987e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.205 -0.03503 -0.1625 0.1848 0.9834 0.9932 0.2298 0.4325 0.869 0.7112 ] Network output: [ -0.009388 1.003 1.008 -2.763e-07 1.241e-07 0.007795 -2.083e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006585 0.0005817 0.004417 0.003318 0.9889 0.9919 0.006712 0.855 0.8929 0.01206 ] Network output: [ -0.0002877 0.001843 1.001 -1.657e-05 7.441e-06 0.998 -1.249e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2181 0.1028 0.3461 0.143 0.985 0.994 0.2188 0.4365 0.8757 0.7051 ] Network output: [ 0.003903 -0.01843 0.9942 1.006e-05 -4.518e-06 1.017 7.584e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.09633 0.1839 0.1983 0.9873 0.9919 0.109 0.7427 0.8627 0.3053 ] Network output: [ -0.003661 0.01714 1.004 1.086e-05 -4.877e-06 0.9857 8.187e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0931 0.09116 0.165 0.196 0.9852 0.9911 0.09311 0.6667 0.8383 0.248 ] Network output: [ 0.0001002 1 -7.186e-05 1.432e-06 -6.428e-07 0.9998 1.079e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002353 Epoch 9115 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009453 0.9965 0.9919 -2.17e-07 9.742e-08 -0.007363 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00347 -0.003299 -0.00706 0.005637 0.9699 0.9743 0.006725 0.8275 0.8213 0.01682 ] Network output: [ 0.9999 0.0002238 0.0004904 -5.284e-06 2.372e-06 -0.0004944 -3.982e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.205 -0.03503 -0.1625 0.1848 0.9834 0.9932 0.2298 0.4325 0.869 0.7112 ] Network output: [ -0.009387 1.003 1.008 -2.762e-07 1.24e-07 0.007794 -2.082e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006585 0.0005818 0.004417 0.003317 0.9889 0.9919 0.006712 0.855 0.8929 0.01206 ] Network output: [ -0.0002875 0.001842 1.001 -1.656e-05 7.433e-06 0.998 -1.248e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2181 0.1028 0.3461 0.143 0.985 0.994 0.2188 0.4365 0.8757 0.7051 ] Network output: [ 0.003902 -0.01842 0.9942 1.005e-05 -4.513e-06 1.016 7.575e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.09634 0.184 0.1983 0.9873 0.9919 0.109 0.7427 0.8627 0.3053 ] Network output: [ -0.003659 0.01714 1.004 1.085e-05 -4.871e-06 0.9857 8.178e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0931 0.09116 0.165 0.196 0.9852 0.9911 0.09311 0.6667 0.8383 0.248 ] Network output: [ 0.0001002 1 -7.179e-05 1.43e-06 -6.421e-07 0.9998 1.078e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002352 Epoch 9116 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009452 0.9965 0.9919 -2.17e-07 9.741e-08 -0.007363 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00347 -0.003299 -0.007059 0.005636 0.9699 0.9743 0.006725 0.8275 0.8213 0.01682 ] Network output: [ 0.9999 0.0002235 0.0004902 -5.278e-06 2.369e-06 -0.000494 -3.978e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.205 -0.03503 -0.1625 0.1848 0.9834 0.9932 0.2298 0.4325 0.869 0.7112 ] Network output: [ -0.009386 1.003 1.008 -2.761e-07 1.24e-07 0.007793 -2.081e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006586 0.0005819 0.004416 0.003317 0.9889 0.9919 0.006713 0.855 0.8929 0.01206 ] Network output: [ -0.0002873 0.001842 1.001 -1.654e-05 7.424e-06 0.998 -1.246e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2181 0.1028 0.3461 0.143 0.985 0.994 0.2188 0.4365 0.8757 0.7051 ] Network output: [ 0.0039 -0.01842 0.9942 1.004e-05 -4.507e-06 1.016 7.567e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.09634 0.184 0.1983 0.9873 0.9919 0.109 0.7427 0.8627 0.3053 ] Network output: [ -0.003658 0.01713 1.004 1.084e-05 -4.866e-06 0.9858 8.169e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0931 0.09117 0.165 0.196 0.9852 0.9911 0.09312 0.6667 0.8383 0.248 ] Network output: [ 0.0001001 1 -7.173e-05 1.429e-06 -6.414e-07 0.9998 1.077e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000235 Epoch 9117 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009451 0.9965 0.9919 -2.17e-07 9.741e-08 -0.007362 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00347 -0.003299 -0.007059 0.005636 0.9699 0.9743 0.006725 0.8275 0.8213 0.01682 ] Network output: [ 0.9999 0.0002233 0.00049 -5.272e-06 2.367e-06 -0.0004937 -3.973e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.205 -0.03503 -0.1625 0.1848 0.9834 0.9932 0.2298 0.4325 0.869 0.7112 ] Network output: [ -0.009385 1.003 1.008 -2.76e-07 1.239e-07 0.007792 -2.08e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006586 0.0005819 0.004416 0.003317 0.9889 0.9919 0.006713 0.855 0.8929 0.01206 ] Network output: [ -0.0002871 0.001841 1.001 -1.652e-05 7.416e-06 0.998 -1.245e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2181 0.1028 0.3461 0.143 0.985 0.994 0.2189 0.4365 0.8757 0.7051 ] Network output: [ 0.003899 -0.01841 0.9942 1.003e-05 -4.502e-06 1.016 7.558e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.09635 0.184 0.1983 0.9873 0.9919 0.109 0.7426 0.8627 0.3053 ] Network output: [ -0.003656 0.01712 1.004 1.083e-05 -4.861e-06 0.9858 8.159e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0931 0.09117 0.165 0.196 0.9852 0.9911 0.09312 0.6666 0.8383 0.248 ] Network output: [ 0.0001001 1 -7.166e-05 1.427e-06 -6.407e-07 0.9998 1.075e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002349 Epoch 9118 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00945 0.9965 0.9919 -2.17e-07 9.741e-08 -0.007362 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00347 -0.0033 -0.007058 0.005636 0.9699 0.9743 0.006725 0.8275 0.8213 0.01682 ] Network output: [ 0.9999 0.0002231 0.0004897 -5.266e-06 2.364e-06 -0.0004933 -3.969e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.205 -0.03503 -0.1624 0.1848 0.9834 0.9932 0.2298 0.4325 0.869 0.7112 ] Network output: [ -0.009384 1.003 1.008 -2.759e-07 1.238e-07 0.007791 -2.079e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006587 0.000582 0.004416 0.003317 0.9889 0.9919 0.006714 0.855 0.8929 0.01206 ] Network output: [ -0.0002869 0.00184 1.001 -1.65e-05 7.407e-06 0.998 -1.243e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2181 0.1028 0.3461 0.143 0.985 0.994 0.2189 0.4365 0.8757 0.7051 ] Network output: [ 0.003897 -0.0184 0.9942 1.002e-05 -4.497e-06 1.016 7.549e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.09635 0.184 0.1983 0.9873 0.9919 0.109 0.7426 0.8627 0.3053 ] Network output: [ -0.003655 0.01712 1.004 1.081e-05 -4.855e-06 0.9858 8.15e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09311 0.09117 0.165 0.196 0.9852 0.9911 0.09312 0.6666 0.8383 0.248 ] Network output: [ 0.0001 1 -7.159e-05 1.425e-06 -6.399e-07 0.9998 1.074e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002348 Epoch 9119 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009448 0.9965 0.9919 -2.17e-07 9.74e-08 -0.007362 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00347 -0.0033 -0.007057 0.005635 0.9699 0.9743 0.006726 0.8275 0.8213 0.01682 ] Network output: [ 0.9999 0.0002229 0.0004895 -5.26e-06 2.361e-06 -0.0004929 -3.964e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.205 -0.03503 -0.1624 0.1848 0.9834 0.9932 0.2298 0.4325 0.869 0.7111 ] Network output: [ -0.009383 1.003 1.008 -2.758e-07 1.238e-07 0.00779 -2.078e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006587 0.0005821 0.004416 0.003316 0.9889 0.9919 0.006714 0.855 0.8929 0.01206 ] Network output: [ -0.0002867 0.001839 1.001 -1.648e-05 7.399e-06 0.998 -1.242e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2181 0.1028 0.3461 0.143 0.985 0.994 0.2189 0.4365 0.8757 0.7051 ] Network output: [ 0.003896 -0.0184 0.9942 1.001e-05 -4.492e-06 1.016 7.541e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.09636 0.184 0.1983 0.9873 0.9919 0.109 0.7426 0.8627 0.3053 ] Network output: [ -0.003653 0.01711 1.004 1.08e-05 -4.85e-06 0.9858 8.141e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09311 0.09117 0.165 0.196 0.9852 0.9911 0.09312 0.6666 0.8383 0.248 ] Network output: [ 0.0001 1 -7.152e-05 1.424e-06 -6.392e-07 0.9998 1.073e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002347 Epoch 9120 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009447 0.9965 0.9919 -2.17e-07 9.74e-08 -0.007361 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00347 -0.0033 -0.007056 0.005635 0.9699 0.9743 0.006726 0.8275 0.8213 0.01682 ] Network output: [ 0.9999 0.0002226 0.0004892 -5.254e-06 2.359e-06 -0.0004925 -3.959e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.205 -0.03503 -0.1624 0.1848 0.9834 0.9932 0.2298 0.4325 0.869 0.7111 ] Network output: [ -0.009382 1.003 1.008 -2.756e-07 1.237e-07 0.007789 -2.077e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006588 0.0005822 0.004416 0.003316 0.9889 0.9919 0.006715 0.855 0.8929 0.01206 ] Network output: [ -0.0002865 0.001839 1.001 -1.646e-05 7.39e-06 0.998 -1.241e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2181 0.1028 0.3461 0.143 0.985 0.994 0.2189 0.4365 0.8757 0.7051 ] Network output: [ 0.003894 -0.01839 0.9942 9.995e-06 -4.487e-06 1.016 7.532e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.09636 0.184 0.1983 0.9873 0.9919 0.109 0.7426 0.8627 0.3053 ] Network output: [ -0.003652 0.0171 1.004 1.079e-05 -4.844e-06 0.9858 8.132e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09311 0.09117 0.165 0.196 0.9852 0.9911 0.09313 0.6666 0.8382 0.248 ] Network output: [ 9.997e-05 1 -7.146e-05 1.422e-06 -6.385e-07 0.9998 1.072e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002345 Epoch 9121 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009446 0.9965 0.9919 -2.17e-07 9.74e-08 -0.007361 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00347 -0.0033 -0.007056 0.005634 0.9699 0.9743 0.006726 0.8275 0.8213 0.01682 ] Network output: [ 0.9999 0.0002224 0.000489 -5.248e-06 2.356e-06 -0.0004922 -3.955e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.205 -0.03504 -0.1624 0.1848 0.9834 0.9932 0.2298 0.4325 0.869 0.7111 ] Network output: [ -0.009382 1.003 1.008 -2.755e-07 1.237e-07 0.007789 -2.076e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006588 0.0005823 0.004416 0.003316 0.9889 0.9919 0.006715 0.855 0.8929 0.01206 ] Network output: [ -0.0002863 0.001838 1.001 -1.644e-05 7.382e-06 0.998 -1.239e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2182 0.1028 0.3461 0.143 0.985 0.994 0.2189 0.4365 0.8757 0.7051 ] Network output: [ 0.003893 -0.01838 0.9942 9.983e-06 -4.482e-06 1.016 7.524e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.09637 0.184 0.1983 0.9873 0.9919 0.109 0.7426 0.8627 0.3053 ] Network output: [ -0.003651 0.01709 1.004 1.078e-05 -4.839e-06 0.9858 8.123e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09311 0.09118 0.165 0.1961 0.9852 0.9911 0.09313 0.6666 0.8382 0.248 ] Network output: [ 9.994e-05 1 -7.139e-05 1.421e-06 -6.378e-07 0.9998 1.071e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002344 Epoch 9122 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009445 0.9965 0.9919 -2.169e-07 9.739e-08 -0.00736 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00347 -0.0033 -0.007055 0.005634 0.9699 0.9743 0.006726 0.8275 0.8213 0.01682 ] Network output: [ 0.9999 0.0002222 0.0004888 -5.242e-06 2.353e-06 -0.0004918 -3.95e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.205 -0.03504 -0.1624 0.1848 0.9834 0.9932 0.2298 0.4325 0.869 0.7111 ] Network output: [ -0.009381 1.003 1.008 -2.754e-07 1.236e-07 0.007788 -2.076e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006588 0.0005824 0.004416 0.003315 0.9889 0.9919 0.006716 0.855 0.8929 0.01206 ] Network output: [ -0.0002861 0.001837 1.001 -1.642e-05 7.373e-06 0.998 -1.238e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2182 0.1028 0.3462 0.143 0.985 0.994 0.2189 0.4365 0.8757 0.7051 ] Network output: [ 0.003891 -0.01837 0.9942 9.972e-06 -4.477e-06 1.016 7.515e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.09637 0.184 0.1983 0.9873 0.9919 0.109 0.7426 0.8627 0.3053 ] Network output: [ -0.003649 0.01709 1.004 1.077e-05 -4.833e-06 0.9858 8.114e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09312 0.09118 0.165 0.1961 0.9852 0.9911 0.09313 0.6666 0.8382 0.248 ] Network output: [ 9.99e-05 1 -7.132e-05 1.419e-06 -6.37e-07 0.9998 1.069e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002343 Epoch 9123 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009444 0.9965 0.9919 -2.169e-07 9.739e-08 -0.00736 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003471 -0.0033 -0.007054 0.005633 0.9699 0.9743 0.006726 0.8275 0.8213 0.01682 ] Network output: [ 0.9999 0.000222 0.0004885 -5.236e-06 2.35e-06 -0.0004914 -3.946e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.205 -0.03504 -0.1624 0.1848 0.9834 0.9932 0.2299 0.4324 0.869 0.7111 ] Network output: [ -0.00938 1.003 1.008 -2.753e-07 1.236e-07 0.007787 -2.075e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006589 0.0005825 0.004416 0.003315 0.9889 0.9919 0.006716 0.855 0.8929 0.01206 ] Network output: [ -0.0002859 0.001837 1.001 -1.64e-05 7.365e-06 0.998 -1.236e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2182 0.1028 0.3462 0.143 0.985 0.994 0.2189 0.4365 0.8757 0.7051 ] Network output: [ 0.00389 -0.01837 0.9942 9.961e-06 -4.472e-06 1.016 7.507e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.09637 0.184 0.1983 0.9873 0.9919 0.109 0.7426 0.8627 0.3053 ] Network output: [ -0.003648 0.01708 1.004 1.075e-05 -4.828e-06 0.9858 8.105e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09312 0.09118 0.165 0.1961 0.9852 0.9911 0.09313 0.6666 0.8382 0.248 ] Network output: [ 9.987e-05 1 -7.126e-05 1.417e-06 -6.363e-07 0.9998 1.068e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002342 Epoch 9124 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009443 0.9965 0.9919 -2.169e-07 9.739e-08 -0.00736 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003471 -0.0033 -0.007054 0.005633 0.9699 0.9743 0.006727 0.8275 0.8213 0.01682 ] Network output: [ 0.9999 0.0002218 0.0004883 -5.23e-06 2.348e-06 -0.0004911 -3.941e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.205 -0.03504 -0.1624 0.1848 0.9834 0.9932 0.2299 0.4324 0.869 0.7111 ] Network output: [ -0.009379 1.003 1.008 -2.752e-07 1.235e-07 0.007786 -2.074e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006589 0.0005826 0.004416 0.003315 0.9889 0.9919 0.006717 0.855 0.8929 0.01206 ] Network output: [ -0.0002857 0.001836 1.001 -1.639e-05 7.356e-06 0.998 -1.235e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2182 0.1028 0.3462 0.143 0.985 0.994 0.2189 0.4365 0.8757 0.705 ] Network output: [ 0.003888 -0.01836 0.9942 9.949e-06 -4.467e-06 1.016 7.498e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.09638 0.184 0.1983 0.9873 0.9919 0.109 0.7425 0.8627 0.3053 ] Network output: [ -0.003646 0.01707 1.004 1.074e-05 -4.823e-06 0.9858 8.096e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09312 0.09118 0.165 0.1961 0.9852 0.9911 0.09313 0.6665 0.8382 0.248 ] Network output: [ 9.983e-05 1 -7.119e-05 1.416e-06 -6.356e-07 0.9998 1.067e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002341 Epoch 9125 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009442 0.9965 0.9919 -2.169e-07 9.738e-08 -0.007359 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003471 -0.0033 -0.007053 0.005632 0.9699 0.9743 0.006727 0.8275 0.8213 0.01681 ] Network output: [ 0.9999 0.0002215 0.0004881 -5.224e-06 2.345e-06 -0.0004907 -3.937e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.205 -0.03504 -0.1624 0.1848 0.9834 0.9932 0.2299 0.4324 0.869 0.7111 ] Network output: [ -0.009378 1.003 1.008 -2.75e-07 1.235e-07 0.007785 -2.073e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00659 0.0005827 0.004416 0.003315 0.9889 0.9919 0.006717 0.855 0.8929 0.01205 ] Network output: [ -0.0002855 0.001835 1.001 -1.637e-05 7.348e-06 0.998 -1.233e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2182 0.1028 0.3462 0.143 0.985 0.994 0.2189 0.4365 0.8757 0.705 ] Network output: [ 0.003887 -0.01835 0.9942 9.938e-06 -4.461e-06 1.016 7.489e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1089 0.09638 0.184 0.1983 0.9873 0.9919 0.109 0.7425 0.8627 0.3053 ] Network output: [ -0.003645 0.01707 1.004 1.073e-05 -4.817e-06 0.9858 8.087e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09312 0.09119 0.165 0.1961 0.9852 0.9911 0.09314 0.6665 0.8382 0.248 ] Network output: [ 9.979e-05 1 -7.112e-05 1.414e-06 -6.349e-07 0.9998 1.066e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002339 Epoch 9126 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009441 0.9965 0.9919 -2.169e-07 9.738e-08 -0.007359 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003471 -0.0033 -0.007052 0.005632 0.9699 0.9743 0.006727 0.8275 0.8213 0.01681 ] Network output: [ 0.9999 0.0002213 0.0004878 -5.218e-06 2.342e-06 -0.0004903 -3.932e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.205 -0.03504 -0.1624 0.1848 0.9834 0.9932 0.2299 0.4324 0.869 0.7111 ] Network output: [ -0.009377 1.003 1.008 -2.749e-07 1.234e-07 0.007784 -2.072e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00659 0.0005827 0.004416 0.003314 0.9889 0.9919 0.006718 0.855 0.8929 0.01205 ] Network output: [ -0.0002853 0.001834 1.001 -1.635e-05 7.339e-06 0.9981 -1.232e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2182 0.1028 0.3462 0.143 0.985 0.994 0.2189 0.4365 0.8757 0.705 ] Network output: [ 0.003885 -0.01835 0.9942 9.927e-06 -4.456e-06 1.016 7.481e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.09639 0.184 0.1983 0.9873 0.9919 0.109 0.7425 0.8627 0.3053 ] Network output: [ -0.003643 0.01706 1.004 1.072e-05 -4.812e-06 0.9858 8.078e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09313 0.09119 0.165 0.1961 0.9852 0.9911 0.09314 0.6665 0.8382 0.248 ] Network output: [ 9.976e-05 1 -7.106e-05 1.413e-06 -6.342e-07 0.9998 1.065e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002338 Epoch 9127 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00944 0.9965 0.9919 -2.169e-07 9.737e-08 -0.007358 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003471 -0.003301 -0.007052 0.005631 0.9699 0.9743 0.006727 0.8275 0.8213 0.01681 ] Network output: [ 0.9999 0.0002211 0.0004876 -5.212e-06 2.34e-06 -0.00049 -3.928e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.205 -0.03504 -0.1623 0.1848 0.9834 0.9932 0.2299 0.4324 0.869 0.7111 ] Network output: [ -0.009376 1.003 1.008 -2.748e-07 1.234e-07 0.007783 -2.071e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006591 0.0005828 0.004416 0.003314 0.9889 0.9919 0.006718 0.855 0.8929 0.01205 ] Network output: [ -0.0002851 0.001834 1.001 -1.633e-05 7.331e-06 0.9981 -1.231e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2182 0.1028 0.3462 0.143 0.985 0.994 0.2189 0.4365 0.8757 0.705 ] Network output: [ 0.003884 -0.01834 0.9942 9.915e-06 -4.451e-06 1.016 7.472e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.09639 0.184 0.1983 0.9873 0.9919 0.109 0.7425 0.8627 0.3053 ] Network output: [ -0.003642 0.01705 1.004 1.071e-05 -4.806e-06 0.9858 8.069e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09313 0.09119 0.165 0.1961 0.9852 0.9911 0.09314 0.6665 0.8382 0.248 ] Network output: [ 9.972e-05 1 -7.099e-05 1.411e-06 -6.334e-07 0.9998 1.063e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002337 Epoch 9128 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009439 0.9965 0.9919 -2.169e-07 9.737e-08 -0.007358 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003471 -0.003301 -0.007051 0.005631 0.9699 0.9743 0.006728 0.8275 0.8213 0.01681 ] Network output: [ 0.9999 0.0002209 0.0004874 -5.206e-06 2.337e-06 -0.0004896 -3.923e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2051 -0.03504 -0.1623 0.1848 0.9834 0.9932 0.2299 0.4324 0.869 0.7111 ] Network output: [ -0.009375 1.003 1.008 -2.747e-07 1.233e-07 0.007782 -2.07e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006591 0.0005829 0.004416 0.003314 0.9889 0.9919 0.006719 0.855 0.8929 0.01205 ] Network output: [ -0.0002849 0.001833 1.001 -1.631e-05 7.322e-06 0.9981 -1.229e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2182 0.1028 0.3462 0.143 0.985 0.994 0.2189 0.4365 0.8757 0.705 ] Network output: [ 0.003882 -0.01833 0.9942 9.904e-06 -4.446e-06 1.016 7.464e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.0964 0.184 0.1983 0.9873 0.9919 0.109 0.7425 0.8627 0.3053 ] Network output: [ -0.00364 0.01705 1.004 1.069e-05 -4.801e-06 0.9858 8.06e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09313 0.09119 0.165 0.1961 0.9852 0.9911 0.09314 0.6665 0.8382 0.248 ] Network output: [ 9.968e-05 1 -7.093e-05 1.409e-06 -6.327e-07 0.9998 1.062e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002336 Epoch 9129 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009438 0.9965 0.9919 -2.169e-07 9.736e-08 -0.007358 -1.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003471 -0.003301 -0.00705 0.005631 0.9699 0.9743 0.006728 0.8275 0.8213 0.01681 ] Network output: [ 0.9999 0.0002207 0.0004871 -5.2e-06 2.334e-06 -0.0004892 -3.919e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2051 -0.03504 -0.1623 0.1848 0.9834 0.9932 0.2299 0.4324 0.869 0.7111 ] Network output: [ -0.009374 1.003 1.008 -2.746e-07 1.233e-07 0.007782 -2.069e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006592 0.000583 0.004416 0.003314 0.9889 0.9919 0.006719 0.855 0.8929 0.01205 ] Network output: [ -0.0002847 0.001832 1.001 -1.629e-05 7.314e-06 0.9981 -1.228e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2182 0.1028 0.3462 0.143 0.985 0.994 0.219 0.4364 0.8757 0.705 ] Network output: [ 0.003881 -0.01832 0.9942 9.893e-06 -4.441e-06 1.016 7.455e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.0964 0.184 0.1983 0.9873 0.9919 0.109 0.7425 0.8627 0.3053 ] Network output: [ -0.003639 0.01704 1.004 1.068e-05 -4.796e-06 0.9858 8.051e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09313 0.0912 0.165 0.1961 0.9852 0.9911 0.09315 0.6665 0.8382 0.248 ] Network output: [ 9.965e-05 1 -7.086e-05 1.408e-06 -6.32e-07 0.9998 1.061e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002335 Epoch 9130 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009437 0.9965 0.9919 -2.169e-07 9.736e-08 -0.007357 -1.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003471 -0.003301 -0.007049 0.00563 0.9699 0.9743 0.006728 0.8275 0.8213 0.01681 ] Network output: [ 0.9999 0.0002204 0.0004869 -5.194e-06 2.332e-06 -0.0004889 -3.914e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2051 -0.03504 -0.1623 0.1848 0.9834 0.9932 0.2299 0.4324 0.869 0.7111 ] Network output: [ -0.009373 1.003 1.008 -2.745e-07 1.232e-07 0.007781 -2.068e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006592 0.0005831 0.004416 0.003313 0.9889 0.9919 0.006719 0.8549 0.8929 0.01205 ] Network output: [ -0.0002845 0.001831 1.001 -1.627e-05 7.306e-06 0.9981 -1.226e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2182 0.1028 0.3462 0.143 0.985 0.994 0.219 0.4364 0.8757 0.705 ] Network output: [ 0.003879 -0.01832 0.9942 9.881e-06 -4.436e-06 1.016 7.447e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.09641 0.184 0.1983 0.9873 0.9919 0.109 0.7425 0.8627 0.3053 ] Network output: [ -0.003638 0.01703 1.004 1.067e-05 -4.79e-06 0.9858 8.041e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09314 0.0912 0.165 0.1961 0.9852 0.9911 0.09315 0.6665 0.8382 0.248 ] Network output: [ 9.961e-05 1 -7.079e-05 1.406e-06 -6.313e-07 0.9998 1.06e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002333 Epoch 9131 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009436 0.9965 0.9919 -2.169e-07 9.735e-08 -0.007357 -1.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003471 -0.003301 -0.007049 0.00563 0.9699 0.9743 0.006728 0.8275 0.8213 0.01681 ] Network output: [ 0.9999 0.0002202 0.0004866 -5.188e-06 2.329e-06 -0.0004885 -3.91e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2051 -0.03505 -0.1623 0.1848 0.9834 0.9932 0.2299 0.4324 0.869 0.7111 ] Network output: [ -0.009372 1.003 1.008 -2.743e-07 1.232e-07 0.00778 -2.067e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006593 0.0005832 0.004415 0.003313 0.9889 0.9919 0.00672 0.8549 0.8929 0.01205 ] Network output: [ -0.0002843 0.001831 1.001 -1.625e-05 7.297e-06 0.9981 -1.225e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2182 0.1029 0.3462 0.143 0.985 0.994 0.219 0.4364 0.8757 0.705 ] Network output: [ 0.003878 -0.01831 0.9942 9.87e-06 -4.431e-06 1.016 7.438e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.09641 0.184 0.1983 0.9873 0.9919 0.1091 0.7424 0.8627 0.3053 ] Network output: [ -0.003636 0.01703 1.004 1.066e-05 -4.785e-06 0.9858 8.032e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09314 0.0912 0.165 0.1961 0.9852 0.9911 0.09315 0.6664 0.8382 0.248 ] Network output: [ 9.957e-05 1 -7.073e-05 1.405e-06 -6.306e-07 0.9998 1.059e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002332 Epoch 9132 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009435 0.9965 0.9919 -2.168e-07 9.735e-08 -0.007356 -1.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003471 -0.003301 -0.007048 0.005629 0.9699 0.9743 0.006728 0.8274 0.8213 0.01681 ] Network output: [ 0.9999 0.00022 0.0004864 -5.182e-06 2.326e-06 -0.0004881 -3.905e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2051 -0.03505 -0.1623 0.1848 0.9834 0.9932 0.2299 0.4324 0.869 0.7111 ] Network output: [ -0.009371 1.003 1.008 -2.742e-07 1.231e-07 0.007779 -2.067e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006593 0.0005833 0.004415 0.003313 0.9889 0.9919 0.00672 0.8549 0.8929 0.01205 ] Network output: [ -0.0002841 0.00183 1.001 -1.624e-05 7.289e-06 0.9981 -1.224e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2182 0.1029 0.3462 0.143 0.985 0.994 0.219 0.4364 0.8757 0.705 ] Network output: [ 0.003876 -0.0183 0.9942 9.859e-06 -4.426e-06 1.016 7.43e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.09642 0.184 0.1983 0.9873 0.9919 0.1091 0.7424 0.8627 0.3053 ] Network output: [ -0.003635 0.01702 1.004 1.065e-05 -4.78e-06 0.9858 8.024e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09314 0.0912 0.165 0.1961 0.9852 0.9911 0.09315 0.6664 0.8382 0.248 ] Network output: [ 9.954e-05 1 -7.066e-05 1.403e-06 -6.299e-07 0.9998 1.057e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002331 Epoch 9133 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009434 0.9965 0.992 -2.168e-07 9.734e-08 -0.007356 -1.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003471 -0.003301 -0.007047 0.005629 0.9699 0.9743 0.006729 0.8274 0.8213 0.01681 ] Network output: [ 0.9999 0.0002198 0.0004862 -5.176e-06 2.324e-06 -0.0004878 -3.901e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2051 -0.03505 -0.1623 0.1847 0.9834 0.9932 0.2299 0.4324 0.869 0.7111 ] Network output: [ -0.009371 1.003 1.008 -2.741e-07 1.231e-07 0.007778 -2.066e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006593 0.0005834 0.004415 0.003313 0.9889 0.9919 0.006721 0.8549 0.8929 0.01205 ] Network output: [ -0.0002839 0.001829 1.001 -1.622e-05 7.28e-06 0.9981 -1.222e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2183 0.1029 0.3462 0.143 0.985 0.994 0.219 0.4364 0.8757 0.705 ] Network output: [ 0.003875 -0.0183 0.9942 9.848e-06 -4.421e-06 1.016 7.421e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.09642 0.184 0.1983 0.9873 0.9919 0.1091 0.7424 0.8627 0.3053 ] Network output: [ -0.003633 0.01701 1.004 1.063e-05 -4.774e-06 0.9858 8.015e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09314 0.0912 0.165 0.1961 0.9852 0.9911 0.09316 0.6664 0.8382 0.248 ] Network output: [ 9.95e-05 1 -7.06e-05 1.401e-06 -6.291e-07 0.9998 1.056e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000233 Epoch 9134 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009433 0.9965 0.992 -2.168e-07 9.734e-08 -0.007355 -1.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003471 -0.003301 -0.007047 0.005628 0.9699 0.9743 0.006729 0.8274 0.8212 0.0168 ] Network output: [ 0.9999 0.0002196 0.0004859 -5.17e-06 2.321e-06 -0.0004874 -3.896e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2051 -0.03505 -0.1623 0.1847 0.9834 0.9932 0.2299 0.4324 0.869 0.7111 ] Network output: [ -0.00937 1.003 1.008 -2.74e-07 1.23e-07 0.007777 -2.065e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006594 0.0005834 0.004415 0.003312 0.9889 0.9919 0.006721 0.8549 0.8929 0.01205 ] Network output: [ -0.0002838 0.001828 1.001 -1.62e-05 7.272e-06 0.9981 -1.221e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2183 0.1029 0.3462 0.143 0.985 0.994 0.219 0.4364 0.8757 0.705 ] Network output: [ 0.003873 -0.01829 0.9942 9.836e-06 -4.416e-06 1.016 7.413e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.09642 0.184 0.1983 0.9873 0.9919 0.1091 0.7424 0.8627 0.3053 ] Network output: [ -0.003632 0.017 1.004 1.062e-05 -4.769e-06 0.9858 8.006e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09314 0.09121 0.165 0.1961 0.9852 0.9911 0.09316 0.6664 0.8382 0.2481 ] Network output: [ 9.947e-05 1 -7.053e-05 1.4e-06 -6.284e-07 0.9998 1.055e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002329 Epoch 9135 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009432 0.9965 0.992 -2.168e-07 9.733e-08 -0.007355 -1.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003472 -0.003301 -0.007046 0.005628 0.9699 0.9743 0.006729 0.8274 0.8212 0.0168 ] Network output: [ 0.9999 0.0002193 0.0004857 -5.164e-06 2.318e-06 -0.000487 -3.892e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2051 -0.03505 -0.1623 0.1847 0.9834 0.9932 0.23 0.4324 0.869 0.7111 ] Network output: [ -0.009369 1.003 1.008 -2.739e-07 1.229e-07 0.007776 -2.064e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006594 0.0005835 0.004415 0.003312 0.9889 0.9919 0.006722 0.8549 0.8929 0.01205 ] Network output: [ -0.0002836 0.001828 1.001 -1.618e-05 7.264e-06 0.9981 -1.219e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2183 0.1029 0.3462 0.143 0.985 0.994 0.219 0.4364 0.8757 0.705 ] Network output: [ 0.003872 -0.01828 0.9942 9.825e-06 -4.411e-06 1.016 7.405e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.09643 0.184 0.1983 0.9873 0.9919 0.1091 0.7424 0.8627 0.3053 ] Network output: [ -0.00363 0.017 1.004 1.061e-05 -4.764e-06 0.9858 7.997e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09315 0.09121 0.165 0.1961 0.9852 0.9911 0.09316 0.6664 0.8382 0.2481 ] Network output: [ 9.943e-05 1 -7.047e-05 1.398e-06 -6.277e-07 0.9998 1.054e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002327 Epoch 9136 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009431 0.9965 0.992 -2.168e-07 9.732e-08 -0.007355 -1.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003472 -0.003302 -0.007045 0.005627 0.9699 0.9743 0.006729 0.8274 0.8212 0.0168 ] Network output: [ 0.9999 0.0002191 0.0004855 -5.158e-06 2.316e-06 -0.0004867 -3.887e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2051 -0.03505 -0.1623 0.1847 0.9834 0.9932 0.23 0.4324 0.869 0.7111 ] Network output: [ -0.009368 1.003 1.008 -2.737e-07 1.229e-07 0.007775 -2.063e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006595 0.0005836 0.004415 0.003312 0.9889 0.9919 0.006722 0.8549 0.8929 0.01205 ] Network output: [ -0.0002834 0.001827 1.001 -1.616e-05 7.255e-06 0.9981 -1.218e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2183 0.1029 0.3462 0.143 0.985 0.994 0.219 0.4364 0.8757 0.705 ] Network output: [ 0.00387 -0.01827 0.9942 9.814e-06 -4.406e-06 1.016 7.396e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.09643 0.184 0.1983 0.9873 0.9919 0.1091 0.7424 0.8627 0.3053 ] Network output: [ -0.003629 0.01699 1.004 1.06e-05 -4.758e-06 0.9858 7.988e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09315 0.09121 0.165 0.1961 0.9852 0.9911 0.09316 0.6664 0.8382 0.2481 ] Network output: [ 9.939e-05 1 -7.04e-05 1.397e-06 -6.27e-07 0.9998 1.053e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002326 Epoch 9137 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009429 0.9965 0.992 -2.168e-07 9.732e-08 -0.007354 -1.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003472 -0.003302 -0.007045 0.005627 0.9699 0.9743 0.00673 0.8274 0.8212 0.0168 ] Network output: [ 0.9999 0.0002189 0.0004852 -5.152e-06 2.313e-06 -0.0004863 -3.883e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2051 -0.03505 -0.1622 0.1847 0.9834 0.9932 0.23 0.4324 0.869 0.7111 ] Network output: [ -0.009367 1.003 1.008 -2.736e-07 1.228e-07 0.007775 -2.062e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006595 0.0005837 0.004415 0.003311 0.9889 0.9919 0.006723 0.8549 0.8929 0.01204 ] Network output: [ -0.0002832 0.001826 1.001 -1.614e-05 7.247e-06 0.9981 -1.217e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2183 0.1029 0.3462 0.143 0.985 0.994 0.219 0.4364 0.8757 0.705 ] Network output: [ 0.003869 -0.01827 0.9942 9.803e-06 -4.401e-06 1.016 7.388e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.09644 0.184 0.1983 0.9873 0.9919 0.1091 0.7424 0.8627 0.3053 ] Network output: [ -0.003627 0.01698 1.004 1.059e-05 -4.753e-06 0.9858 7.979e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09315 0.09121 0.165 0.1961 0.9852 0.9911 0.09316 0.6664 0.8382 0.2481 ] Network output: [ 9.936e-05 1 -7.034e-05 1.395e-06 -6.263e-07 0.9998 1.051e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002325 Epoch 9138 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009428 0.9965 0.992 -2.168e-07 9.731e-08 -0.007354 -1.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003472 -0.003302 -0.007044 0.005626 0.9699 0.9743 0.00673 0.8274 0.8212 0.0168 ] Network output: [ 0.9999 0.0002187 0.000485 -5.146e-06 2.31e-06 -0.000486 -3.878e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2051 -0.03505 -0.1622 0.1847 0.9834 0.9932 0.23 0.4323 0.869 0.7111 ] Network output: [ -0.009366 1.003 1.008 -2.735e-07 1.228e-07 0.007774 -2.061e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006596 0.0005838 0.004415 0.003311 0.9889 0.9919 0.006723 0.8549 0.8929 0.01204 ] Network output: [ -0.000283 0.001826 1.001 -1.612e-05 7.239e-06 0.9981 -1.215e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2183 0.1029 0.3462 0.143 0.985 0.9939 0.219 0.4364 0.8757 0.705 ] Network output: [ 0.003867 -0.01826 0.9942 9.792e-06 -4.396e-06 1.016 7.379e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.09644 0.184 0.1983 0.9873 0.9919 0.1091 0.7423 0.8627 0.3053 ] Network output: [ -0.003626 0.01698 1.004 1.058e-05 -4.748e-06 0.9858 7.97e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09315 0.09122 0.165 0.1961 0.9852 0.9911 0.09317 0.6663 0.8382 0.2481 ] Network output: [ 9.932e-05 1 -7.027e-05 1.393e-06 -6.256e-07 0.9998 1.05e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002324 Epoch 9139 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009427 0.9965 0.992 -2.167e-07 9.731e-08 -0.007353 -1.633e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003472 -0.003302 -0.007043 0.005626 0.9699 0.9743 0.00673 0.8274 0.8212 0.0168 ] Network output: [ 0.9999 0.0002185 0.0004848 -5.14e-06 2.308e-06 -0.0004856 -3.874e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2051 -0.03505 -0.1622 0.1847 0.9834 0.9932 0.23 0.4323 0.869 0.7111 ] Network output: [ -0.009365 1.003 1.008 -2.734e-07 1.227e-07 0.007773 -2.06e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006596 0.0005839 0.004415 0.003311 0.9889 0.9919 0.006724 0.8549 0.8929 0.01204 ] Network output: [ -0.0002828 0.001825 1.001 -1.611e-05 7.23e-06 0.9981 -1.214e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2183 0.1029 0.3462 0.143 0.985 0.9939 0.219 0.4364 0.8757 0.705 ] Network output: [ 0.003866 -0.01825 0.9942 9.78e-06 -4.391e-06 1.016 7.371e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.09645 0.184 0.1983 0.9873 0.9919 0.1091 0.7423 0.8627 0.3053 ] Network output: [ -0.003625 0.01697 1.004 1.056e-05 -4.742e-06 0.9858 7.961e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09316 0.09122 0.165 0.1961 0.9852 0.9911 0.09317 0.6663 0.8382 0.2481 ] Network output: [ 9.928e-05 1 -7.021e-05 1.392e-06 -6.249e-07 0.9998 1.049e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002322 Epoch 9140 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009426 0.9965 0.992 -2.167e-07 9.73e-08 -0.007353 -1.633e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003472 -0.003302 -0.007042 0.005626 0.9699 0.9743 0.00673 0.8274 0.8212 0.0168 ] Network output: [ 0.9999 0.0002183 0.0004845 -5.134e-06 2.305e-06 -0.0004852 -3.869e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2051 -0.03505 -0.1622 0.1847 0.9834 0.9932 0.23 0.4323 0.869 0.711 ] Network output: [ -0.009364 1.003 1.008 -2.733e-07 1.227e-07 0.007772 -2.059e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006597 0.000584 0.004415 0.003311 0.9889 0.9919 0.006724 0.8549 0.8929 0.01204 ] Network output: [ -0.0002826 0.001824 1.001 -1.609e-05 7.222e-06 0.9981 -1.212e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2183 0.1029 0.3463 0.143 0.985 0.9939 0.219 0.4364 0.8757 0.705 ] Network output: [ 0.003864 -0.01825 0.9942 9.769e-06 -4.386e-06 1.016 7.362e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.09645 0.184 0.1983 0.9873 0.9919 0.1091 0.7423 0.8627 0.3053 ] Network output: [ -0.003623 0.01696 1.004 1.055e-05 -4.737e-06 0.9858 7.952e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09316 0.09122 0.165 0.1961 0.9852 0.9911 0.09317 0.6663 0.8382 0.2481 ] Network output: [ 9.925e-05 1 -7.014e-05 1.39e-06 -6.242e-07 0.9998 1.048e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002321 Epoch 9141 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009425 0.9965 0.992 -2.167e-07 9.729e-08 -0.007353 -1.633e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003472 -0.003302 -0.007042 0.005625 0.9699 0.9743 0.006731 0.8274 0.8212 0.0168 ] Network output: [ 0.9999 0.000218 0.0004843 -5.128e-06 2.302e-06 -0.0004849 -3.865e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2051 -0.03506 -0.1622 0.1847 0.9834 0.9932 0.23 0.4323 0.869 0.711 ] Network output: [ -0.009363 1.003 1.008 -2.731e-07 1.226e-07 0.007771 -2.058e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006597 0.0005841 0.004415 0.00331 0.9889 0.9919 0.006725 0.8549 0.8929 0.01204 ] Network output: [ -0.0002824 0.001823 1.001 -1.607e-05 7.214e-06 0.9981 -1.211e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2183 0.1029 0.3463 0.143 0.985 0.9939 0.2191 0.4364 0.8757 0.705 ] Network output: [ 0.003863 -0.01824 0.9942 9.758e-06 -4.381e-06 1.016 7.354e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.09646 0.184 0.1983 0.9873 0.9919 0.1091 0.7423 0.8626 0.3053 ] Network output: [ -0.003622 0.01696 1.004 1.054e-05 -4.732e-06 0.9858 7.943e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09316 0.09122 0.165 0.1961 0.9852 0.9911 0.09317 0.6663 0.8382 0.2481 ] Network output: [ 9.921e-05 1 -7.008e-05 1.389e-06 -6.235e-07 0.9998 1.047e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000232 Epoch 9142 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009424 0.9965 0.992 -2.167e-07 9.729e-08 -0.007352 -1.633e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003472 -0.003302 -0.007041 0.005625 0.9699 0.9743 0.006731 0.8274 0.8212 0.0168 ] Network output: [ 0.9999 0.0002178 0.0004841 -5.122e-06 2.3e-06 -0.0004845 -3.86e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2052 -0.03506 -0.1622 0.1847 0.9834 0.9932 0.23 0.4323 0.869 0.711 ] Network output: [ -0.009362 1.003 1.008 -2.73e-07 1.226e-07 0.00777 -2.058e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006598 0.0005842 0.004415 0.00331 0.9889 0.9919 0.006725 0.8549 0.8929 0.01204 ] Network output: [ -0.0002822 0.001823 1.001 -1.605e-05 7.205e-06 0.9981 -1.21e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2183 0.1029 0.3463 0.143 0.985 0.9939 0.2191 0.4364 0.8757 0.705 ] Network output: [ 0.003861 -0.01823 0.9942 9.747e-06 -4.376e-06 1.016 7.346e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.09646 0.184 0.1983 0.9873 0.9919 0.1091 0.7423 0.8626 0.3053 ] Network output: [ -0.00362 0.01695 1.004 1.053e-05 -4.726e-06 0.9858 7.934e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09316 0.09123 0.165 0.1961 0.9852 0.9911 0.09318 0.6663 0.8382 0.2481 ] Network output: [ 9.918e-05 1 -7.001e-05 1.387e-06 -6.227e-07 0.9998 1.045e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002319 Epoch 9143 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009423 0.9965 0.992 -2.167e-07 9.728e-08 -0.007352 -1.633e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003472 -0.003302 -0.00704 0.005624 0.9699 0.9743 0.006731 0.8274 0.8212 0.0168 ] Network output: [ 0.9999 0.0002176 0.0004838 -5.116e-06 2.297e-06 -0.0004841 -3.856e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2052 -0.03506 -0.1622 0.1847 0.9834 0.9932 0.23 0.4323 0.869 0.711 ] Network output: [ -0.009361 1.003 1.008 -2.729e-07 1.225e-07 0.007769 -2.057e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006598 0.0005842 0.004415 0.00331 0.9889 0.9919 0.006726 0.8549 0.8929 0.01204 ] Network output: [ -0.000282 0.001822 1.001 -1.603e-05 7.197e-06 0.9981 -1.208e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2183 0.1029 0.3463 0.143 0.985 0.9939 0.2191 0.4364 0.8757 0.7049 ] Network output: [ 0.003859 -0.01823 0.9942 9.736e-06 -4.371e-06 1.016 7.337e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.09647 0.184 0.1982 0.9873 0.9919 0.1091 0.7423 0.8626 0.3053 ] Network output: [ -0.003619 0.01694 1.004 1.052e-05 -4.721e-06 0.9858 7.925e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09317 0.09123 0.165 0.1961 0.9852 0.9911 0.09318 0.6663 0.8382 0.2481 ] Network output: [ 9.914e-05 1 -6.995e-05 1.386e-06 -6.22e-07 0.9998 1.044e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002318 Epoch 9144 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009422 0.9965 0.992 -2.167e-07 9.727e-08 -0.007351 -1.633e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003472 -0.003302 -0.00704 0.005624 0.9699 0.9743 0.006731 0.8274 0.8212 0.01679 ] Network output: [ 0.9999 0.0002174 0.0004836 -5.11e-06 2.294e-06 -0.0004838 -3.851e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2052 -0.03506 -0.1622 0.1847 0.9834 0.9932 0.23 0.4323 0.869 0.711 ] Network output: [ -0.009361 1.003 1.008 -2.728e-07 1.225e-07 0.007769 -2.056e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006599 0.0005843 0.004415 0.00331 0.9889 0.9919 0.006726 0.8549 0.8929 0.01204 ] Network output: [ -0.0002818 0.001821 1.001 -1.601e-05 7.189e-06 0.9981 -1.207e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2183 0.1029 0.3463 0.143 0.985 0.9939 0.2191 0.4363 0.8757 0.7049 ] Network output: [ 0.003858 -0.01822 0.9942 9.725e-06 -4.366e-06 1.016 7.329e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.09647 0.184 0.1982 0.9873 0.9919 0.1091 0.7423 0.8626 0.3053 ] Network output: [ -0.003617 0.01693 1.004 1.05e-05 -4.716e-06 0.9858 7.916e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09317 0.09123 0.165 0.1961 0.9852 0.9911 0.09318 0.6663 0.8382 0.2481 ] Network output: [ 9.911e-05 1 -6.989e-05 1.384e-06 -6.213e-07 0.9998 1.043e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002316 Epoch 9145 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009421 0.9965 0.992 -2.167e-07 9.726e-08 -0.007351 -1.633e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003472 -0.003302 -0.007039 0.005623 0.9699 0.9743 0.006731 0.8274 0.8212 0.01679 ] Network output: [ 0.9999 0.0002172 0.0004834 -5.105e-06 2.292e-06 -0.0004834 -3.847e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2052 -0.03506 -0.1622 0.1847 0.9834 0.9932 0.23 0.4323 0.869 0.711 ] Network output: [ -0.00936 1.003 1.008 -2.726e-07 1.224e-07 0.007768 -2.055e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006599 0.0005844 0.004415 0.003309 0.9889 0.9919 0.006727 0.8549 0.8929 0.01204 ] Network output: [ -0.0002816 0.00182 1.001 -1.599e-05 7.181e-06 0.9981 -1.205e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2184 0.1029 0.3463 0.143 0.985 0.9939 0.2191 0.4363 0.8757 0.7049 ] Network output: [ 0.003856 -0.01821 0.9942 9.714e-06 -4.361e-06 1.016 7.32e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.09647 0.184 0.1982 0.9873 0.9919 0.1091 0.7422 0.8626 0.3053 ] Network output: [ -0.003616 0.01693 1.004 1.049e-05 -4.71e-06 0.9858 7.907e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09317 0.09123 0.165 0.1961 0.9852 0.9911 0.09318 0.6662 0.8382 0.2481 ] Network output: [ 9.907e-05 1 -6.982e-05 1.382e-06 -6.206e-07 0.9998 1.042e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002315 Epoch 9146 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00942 0.9965 0.992 -2.166e-07 9.726e-08 -0.007351 -1.633e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003473 -0.003303 -0.007038 0.005623 0.9699 0.9743 0.006732 0.8274 0.8212 0.01679 ] Network output: [ 0.9999 0.0002169 0.0004831 -5.099e-06 2.289e-06 -0.0004831 -3.842e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2052 -0.03506 -0.1621 0.1847 0.9834 0.9932 0.23 0.4323 0.869 0.711 ] Network output: [ -0.009359 1.003 1.008 -2.725e-07 1.223e-07 0.007767 -2.054e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006599 0.0005845 0.004414 0.003309 0.9889 0.9919 0.006727 0.8549 0.8929 0.01204 ] Network output: [ -0.0002814 0.00182 1.001 -1.598e-05 7.172e-06 0.9981 -1.204e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2184 0.1029 0.3463 0.143 0.985 0.9939 0.2191 0.4363 0.8757 0.7049 ] Network output: [ 0.003855 -0.0182 0.9942 9.702e-06 -4.356e-06 1.016 7.312e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.109 0.09648 0.184 0.1982 0.9873 0.9919 0.1091 0.7422 0.8626 0.3053 ] Network output: [ -0.003615 0.01692 1.004 1.048e-05 -4.705e-06 0.9859 7.899e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09317 0.09123 0.165 0.1961 0.9852 0.9911 0.09319 0.6662 0.8381 0.2481 ] Network output: [ 9.903e-05 1 -6.976e-05 1.381e-06 -6.199e-07 0.9998 1.041e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002314 Epoch 9147 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009419 0.9965 0.992 -2.166e-07 9.725e-08 -0.00735 -1.633e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003473 -0.003303 -0.007038 0.005622 0.9699 0.9743 0.006732 0.8274 0.8212 0.01679 ] Network output: [ 0.9999 0.0002167 0.0004829 -5.093e-06 2.286e-06 -0.0004827 -3.838e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2052 -0.03506 -0.1621 0.1847 0.9834 0.9932 0.2301 0.4323 0.869 0.711 ] Network output: [ -0.009358 1.003 1.008 -2.724e-07 1.223e-07 0.007766 -2.053e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0066 0.0005846 0.004414 0.003309 0.9889 0.9919 0.006727 0.8548 0.8929 0.01204 ] Network output: [ -0.0002812 0.001819 1.001 -1.596e-05 7.164e-06 0.9981 -1.203e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2184 0.1029 0.3463 0.143 0.985 0.9939 0.2191 0.4363 0.8757 0.7049 ] Network output: [ 0.003853 -0.0182 0.9942 9.691e-06 -4.351e-06 1.016 7.304e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.09648 0.184 0.1982 0.9873 0.9919 0.1091 0.7422 0.8626 0.3053 ] Network output: [ -0.003613 0.01691 1.004 1.047e-05 -4.7e-06 0.9859 7.89e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09317 0.09124 0.165 0.1961 0.9852 0.9911 0.09319 0.6662 0.8381 0.2481 ] Network output: [ 9.9e-05 1 -6.969e-05 1.379e-06 -6.192e-07 0.9998 1.039e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002313 Epoch 9148 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009418 0.9965 0.992 -2.166e-07 9.724e-08 -0.00735 -1.632e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003473 -0.003303 -0.007037 0.005622 0.9699 0.9743 0.006732 0.8274 0.8212 0.01679 ] Network output: [ 0.9999 0.0002165 0.0004827 -5.087e-06 2.284e-06 -0.0004823 -3.834e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2052 -0.03506 -0.1621 0.1847 0.9834 0.9932 0.2301 0.4323 0.869 0.711 ] Network output: [ -0.009357 1.003 1.008 -2.723e-07 1.222e-07 0.007765 -2.052e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0066 0.0005847 0.004414 0.003308 0.9889 0.9919 0.006728 0.8548 0.8929 0.01204 ] Network output: [ -0.000281 0.001818 1.001 -1.594e-05 7.156e-06 0.9981 -1.201e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2184 0.1029 0.3463 0.143 0.985 0.9939 0.2191 0.4363 0.8757 0.7049 ] Network output: [ 0.003852 -0.01819 0.9942 9.68e-06 -4.346e-06 1.016 7.295e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.09649 0.184 0.1982 0.9873 0.9919 0.1091 0.7422 0.8626 0.3053 ] Network output: [ -0.003612 0.01691 1.004 1.046e-05 -4.695e-06 0.9859 7.881e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09318 0.09124 0.165 0.1961 0.9852 0.9911 0.09319 0.6662 0.8381 0.2481 ] Network output: [ 9.896e-05 1 -6.963e-05 1.378e-06 -6.185e-07 0.9998 1.038e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002312 Epoch 9149 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009417 0.9965 0.992 -2.166e-07 9.723e-08 -0.007349 -1.632e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003473 -0.003303 -0.007036 0.005621 0.9699 0.9743 0.006732 0.8274 0.8212 0.01679 ] Network output: [ 0.9999 0.0002163 0.0004824 -5.081e-06 2.281e-06 -0.000482 -3.829e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2052 -0.03506 -0.1621 0.1847 0.9834 0.9932 0.2301 0.4323 0.869 0.711 ] Network output: [ -0.009356 1.003 1.008 -2.722e-07 1.222e-07 0.007764 -2.051e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006601 0.0005848 0.004414 0.003308 0.9889 0.9919 0.006728 0.8548 0.8929 0.01203 ] Network output: [ -0.0002808 0.001817 1.001 -1.592e-05 7.148e-06 0.9981 -1.2e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2184 0.1029 0.3463 0.143 0.985 0.9939 0.2191 0.4363 0.8757 0.7049 ] Network output: [ 0.00385 -0.01818 0.9942 9.669e-06 -4.341e-06 1.016 7.287e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.09649 0.184 0.1982 0.9873 0.9919 0.1091 0.7422 0.8626 0.3053 ] Network output: [ -0.00361 0.0169 1.004 1.045e-05 -4.689e-06 0.9859 7.872e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09318 0.09124 0.165 0.1961 0.9852 0.9911 0.09319 0.6662 0.8381 0.2481 ] Network output: [ 9.893e-05 1 -6.957e-05 1.376e-06 -6.178e-07 0.9998 1.037e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000231 Epoch 9150 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009416 0.9965 0.992 -2.166e-07 9.722e-08 -0.007349 -1.632e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003473 -0.003303 -0.007036 0.005621 0.9699 0.9743 0.006733 0.8274 0.8212 0.01679 ] Network output: [ 0.9999 0.0002161 0.0004822 -5.075e-06 2.278e-06 -0.0004816 -3.825e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2052 -0.03506 -0.1621 0.1847 0.9834 0.9932 0.2301 0.4323 0.8689 0.711 ] Network output: [ -0.009355 1.003 1.008 -2.72e-07 1.221e-07 0.007763 -2.05e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006601 0.0005849 0.004414 0.003308 0.9889 0.9919 0.006729 0.8548 0.8928 0.01203 ] Network output: [ -0.0002806 0.001817 1.001 -1.59e-05 7.139e-06 0.9981 -1.198e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2184 0.103 0.3463 0.143 0.985 0.9939 0.2191 0.4363 0.8757 0.7049 ] Network output: [ 0.003849 -0.01818 0.9942 9.658e-06 -4.336e-06 1.016 7.279e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.0965 0.184 0.1982 0.9873 0.9919 0.1091 0.7422 0.8626 0.3053 ] Network output: [ -0.003609 0.01689 1.005 1.043e-05 -4.684e-06 0.9859 7.863e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09318 0.09124 0.165 0.1961 0.9852 0.9911 0.0932 0.6662 0.8381 0.2481 ] Network output: [ 9.889e-05 1 -6.95e-05 1.375e-06 -6.171e-07 0.9998 1.036e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002309 Epoch 9151 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009415 0.9965 0.992 -2.165e-07 9.722e-08 -0.007348 -1.632e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003473 -0.003303 -0.007035 0.005621 0.9699 0.9743 0.006733 0.8273 0.8212 0.01679 ] Network output: [ 0.9999 0.0002159 0.000482 -5.069e-06 2.276e-06 -0.0004813 -3.82e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2052 -0.03507 -0.1621 0.1847 0.9834 0.9932 0.2301 0.4323 0.8689 0.711 ] Network output: [ -0.009354 1.003 1.008 -2.719e-07 1.221e-07 0.007763 -2.049e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006602 0.0005849 0.004414 0.003308 0.9889 0.9919 0.006729 0.8548 0.8928 0.01203 ] Network output: [ -0.0002804 0.001816 1.001 -1.588e-05 7.131e-06 0.9981 -1.197e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2184 0.103 0.3463 0.143 0.985 0.9939 0.2191 0.4363 0.8757 0.7049 ] Network output: [ 0.003847 -0.01817 0.9942 9.647e-06 -4.331e-06 1.016 7.27e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.0965 0.184 0.1982 0.9873 0.9919 0.1091 0.7422 0.8626 0.3053 ] Network output: [ -0.003607 0.01689 1.005 1.042e-05 -4.679e-06 0.9859 7.854e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09318 0.09125 0.165 0.1961 0.9852 0.9911 0.0932 0.6662 0.8381 0.2481 ] Network output: [ 9.885e-05 1 -6.944e-05 1.373e-06 -6.164e-07 0.9998 1.035e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002308 Epoch 9152 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009414 0.9965 0.992 -2.165e-07 9.721e-08 -0.007348 -1.632e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003473 -0.003303 -0.007034 0.00562 0.9699 0.9743 0.006733 0.8273 0.8212 0.01679 ] Network output: [ 0.9999 0.0002156 0.0004817 -5.063e-06 2.273e-06 -0.0004809 -3.816e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2052 -0.03507 -0.1621 0.1847 0.9834 0.9932 0.2301 0.4323 0.8689 0.711 ] Network output: [ -0.009353 1.003 1.008 -2.718e-07 1.22e-07 0.007762 -2.048e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006602 0.000585 0.004414 0.003307 0.9889 0.9919 0.00673 0.8548 0.8928 0.01203 ] Network output: [ -0.0002803 0.001815 1.001 -1.587e-05 7.123e-06 0.9981 -1.196e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2184 0.103 0.3463 0.143 0.985 0.9939 0.2191 0.4363 0.8757 0.7049 ] Network output: [ 0.003846 -0.01816 0.9942 9.636e-06 -4.326e-06 1.016 7.262e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.09651 0.184 0.1982 0.9873 0.9919 0.1092 0.7422 0.8626 0.3053 ] Network output: [ -0.003606 0.01688 1.005 1.041e-05 -4.674e-06 0.9859 7.846e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09319 0.09125 0.165 0.1961 0.9852 0.9911 0.0932 0.6661 0.8381 0.2481 ] Network output: [ 9.882e-05 1 -6.938e-05 1.371e-06 -6.157e-07 0.9998 1.034e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002307 Epoch 9153 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009413 0.9965 0.992 -2.165e-07 9.72e-08 -0.007348 -1.632e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003473 -0.003303 -0.007033 0.00562 0.9699 0.9743 0.006733 0.8273 0.8212 0.01679 ] Network output: [ 0.9999 0.0002154 0.0004815 -5.058e-06 2.271e-06 -0.0004806 -3.812e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2052 -0.03507 -0.1621 0.1847 0.9834 0.9932 0.2301 0.4322 0.8689 0.711 ] Network output: [ -0.009352 1.003 1.008 -2.717e-07 1.22e-07 0.007761 -2.047e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006603 0.0005851 0.004414 0.003307 0.9889 0.9919 0.00673 0.8548 0.8928 0.01203 ] Network output: [ -0.0002801 0.001815 1.001 -1.585e-05 7.115e-06 0.9981 -1.194e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2184 0.103 0.3463 0.143 0.985 0.9939 0.2192 0.4363 0.8757 0.7049 ] Network output: [ 0.003844 -0.01815 0.9942 9.625e-06 -4.321e-06 1.016 7.254e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.09651 0.184 0.1982 0.9873 0.9919 0.1092 0.7421 0.8626 0.3053 ] Network output: [ -0.003604 0.01687 1.005 1.04e-05 -4.668e-06 0.9859 7.837e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09319 0.09125 0.165 0.1961 0.9852 0.9911 0.0932 0.6661 0.8381 0.2481 ] Network output: [ 9.878e-05 1 -6.931e-05 1.37e-06 -6.15e-07 0.9998 1.032e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002306 Epoch 9154 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009412 0.9965 0.992 -2.165e-07 9.719e-08 -0.007347 -1.632e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003473 -0.003303 -0.007033 0.005619 0.9699 0.9743 0.006733 0.8273 0.8212 0.01678 ] Network output: [ 0.9999 0.0002152 0.0004813 -5.052e-06 2.268e-06 -0.0004802 -3.807e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2052 -0.03507 -0.1621 0.1847 0.9834 0.9932 0.2301 0.4322 0.8689 0.711 ] Network output: [ -0.009351 1.003 1.008 -2.715e-07 1.219e-07 0.00776 -2.046e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006603 0.0005852 0.004414 0.003307 0.9889 0.9919 0.006731 0.8548 0.8928 0.01203 ] Network output: [ -0.0002799 0.001814 1.001 -1.583e-05 7.107e-06 0.9981 -1.193e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2184 0.103 0.3463 0.143 0.985 0.9939 0.2192 0.4363 0.8757 0.7049 ] Network output: [ 0.003843 -0.01815 0.9942 9.614e-06 -4.316e-06 1.016 7.245e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.09652 0.184 0.1982 0.9873 0.9919 0.1092 0.7421 0.8626 0.3053 ] Network output: [ -0.003603 0.01687 1.005 1.039e-05 -4.663e-06 0.9859 7.828e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09319 0.09125 0.165 0.1961 0.9852 0.9911 0.0932 0.6661 0.8381 0.2481 ] Network output: [ 9.875e-05 1 -6.925e-05 1.368e-06 -6.143e-07 0.9998 1.031e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002304 Epoch 9155 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009411 0.9965 0.992 -2.165e-07 9.718e-08 -0.007347 -1.631e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003473 -0.003304 -0.007032 0.005619 0.9699 0.9743 0.006734 0.8273 0.8212 0.01678 ] Network output: [ 0.9999 0.000215 0.000481 -5.046e-06 2.265e-06 -0.0004798 -3.803e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2052 -0.03507 -0.1621 0.1847 0.9834 0.9932 0.2301 0.4322 0.8689 0.711 ] Network output: [ -0.009351 1.003 1.008 -2.714e-07 1.219e-07 0.007759 -2.046e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006604 0.0005853 0.004414 0.003307 0.9889 0.9919 0.006731 0.8548 0.8928 0.01203 ] Network output: [ -0.0002797 0.001813 1.001 -1.581e-05 7.098e-06 0.9981 -1.192e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2184 0.103 0.3463 0.143 0.985 0.9939 0.2192 0.4363 0.8757 0.7049 ] Network output: [ 0.003841 -0.01814 0.9942 9.603e-06 -4.311e-06 1.016 7.237e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.09652 0.184 0.1982 0.9873 0.9919 0.1092 0.7421 0.8626 0.3053 ] Network output: [ -0.003602 0.01686 1.005 1.038e-05 -4.658e-06 0.9859 7.819e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09319 0.09126 0.165 0.1961 0.9852 0.9911 0.09321 0.6661 0.8381 0.2481 ] Network output: [ 9.871e-05 1 -6.919e-05 1.367e-06 -6.136e-07 0.9998 1.03e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002303 Epoch 9156 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009409 0.9965 0.992 -2.165e-07 9.717e-08 -0.007346 -1.631e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003473 -0.003304 -0.007031 0.005618 0.9699 0.9743 0.006734 0.8273 0.8212 0.01678 ] Network output: [ 0.9999 0.0002148 0.0004808 -5.04e-06 2.263e-06 -0.0004795 -3.798e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2052 -0.03507 -0.162 0.1847 0.9834 0.9932 0.2301 0.4322 0.8689 0.711 ] Network output: [ -0.00935 1.003 1.008 -2.713e-07 1.218e-07 0.007758 -2.045e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006604 0.0005854 0.004414 0.003306 0.9889 0.9919 0.006732 0.8548 0.8928 0.01203 ] Network output: [ -0.0002795 0.001812 1.001 -1.579e-05 7.09e-06 0.9981 -1.19e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2184 0.103 0.3463 0.143 0.985 0.9939 0.2192 0.4363 0.8757 0.7049 ] Network output: [ 0.00384 -0.01813 0.9942 9.592e-06 -4.306e-06 1.016 7.229e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.09652 0.184 0.1982 0.9873 0.9919 0.1092 0.7421 0.8626 0.3053 ] Network output: [ -0.0036 0.01685 1.005 1.036e-05 -4.653e-06 0.9859 7.81e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0932 0.09126 0.165 0.1961 0.9852 0.9911 0.09321 0.6661 0.8381 0.2481 ] Network output: [ 9.868e-05 1 -6.913e-05 1.365e-06 -6.129e-07 0.9998 1.029e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002302 Epoch 9157 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009408 0.9966 0.992 -2.164e-07 9.716e-08 -0.007346 -1.631e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003473 -0.003304 -0.007031 0.005618 0.9699 0.9743 0.006734 0.8273 0.8212 0.01678 ] Network output: [ 0.9999 0.0002145 0.0004806 -5.034e-06 2.26e-06 -0.0004791 -3.794e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2053 -0.03507 -0.162 0.1846 0.9834 0.9932 0.2301 0.4322 0.8689 0.711 ] Network output: [ -0.009349 1.003 1.008 -2.712e-07 1.217e-07 0.007757 -2.044e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006604 0.0005855 0.004414 0.003306 0.9889 0.9919 0.006732 0.8548 0.8928 0.01203 ] Network output: [ -0.0002793 0.001812 1.001 -1.578e-05 7.082e-06 0.9981 -1.189e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2184 0.103 0.3464 0.143 0.985 0.9939 0.2192 0.4363 0.8757 0.7049 ] Network output: [ 0.003838 -0.01813 0.9942 9.581e-06 -4.301e-06 1.016 7.221e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.09653 0.184 0.1982 0.9873 0.9919 0.1092 0.7421 0.8626 0.3053 ] Network output: [ -0.003599 0.01685 1.005 1.035e-05 -4.647e-06 0.9859 7.802e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0932 0.09126 0.165 0.1961 0.9852 0.9911 0.09321 0.6661 0.8381 0.2481 ] Network output: [ 9.864e-05 1 -6.906e-05 1.364e-06 -6.122e-07 0.9998 1.028e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002301 Epoch 9158 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009407 0.9966 0.992 -2.164e-07 9.715e-08 -0.007345 -1.631e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003474 -0.003304 -0.00703 0.005617 0.9699 0.9743 0.006734 0.8273 0.8212 0.01678 ] Network output: [ 0.9999 0.0002143 0.0004803 -5.028e-06 2.257e-06 -0.0004788 -3.79e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2053 -0.03507 -0.162 0.1846 0.9834 0.9932 0.2301 0.4322 0.8689 0.711 ] Network output: [ -0.009348 1.003 1.008 -2.711e-07 1.217e-07 0.007757 -2.043e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006605 0.0005856 0.004414 0.003306 0.9889 0.9919 0.006733 0.8548 0.8928 0.01203 ] Network output: [ -0.0002791 0.001811 1.001 -1.576e-05 7.074e-06 0.9981 -1.188e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2185 0.103 0.3464 0.1429 0.985 0.9939 0.2192 0.4362 0.8756 0.7049 ] Network output: [ 0.003837 -0.01812 0.9942 9.57e-06 -4.296e-06 1.016 7.212e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.09653 0.184 0.1982 0.9873 0.9919 0.1092 0.7421 0.8626 0.3053 ] Network output: [ -0.003597 0.01684 1.005 1.034e-05 -4.642e-06 0.9859 7.793e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0932 0.09126 0.165 0.1961 0.9852 0.9911 0.09321 0.6661 0.8381 0.2481 ] Network output: [ 9.861e-05 1 -6.9e-05 1.362e-06 -6.115e-07 0.9998 1.027e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00023 Epoch 9159 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009406 0.9966 0.992 -2.164e-07 9.714e-08 -0.007345 -1.631e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003474 -0.003304 -0.007029 0.005617 0.9699 0.9743 0.006735 0.8273 0.8212 0.01678 ] Network output: [ 0.9999 0.0002141 0.0004801 -5.023e-06 2.255e-06 -0.0004784 -3.785e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2053 -0.03507 -0.162 0.1846 0.9834 0.9932 0.2302 0.4322 0.8689 0.711 ] Network output: [ -0.009347 1.003 1.008 -2.709e-07 1.216e-07 0.007756 -2.042e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006605 0.0005856 0.004414 0.003306 0.9889 0.9919 0.006733 0.8548 0.8928 0.01203 ] Network output: [ -0.0002789 0.00181 1.001 -1.574e-05 7.066e-06 0.9981 -1.186e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2185 0.103 0.3464 0.1429 0.985 0.9939 0.2192 0.4362 0.8756 0.7049 ] Network output: [ 0.003835 -0.01811 0.9942 9.559e-06 -4.292e-06 1.016 7.204e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.09654 0.184 0.1982 0.9873 0.9919 0.1092 0.7421 0.8626 0.3053 ] Network output: [ -0.003596 0.01683 1.005 1.033e-05 -4.637e-06 0.9859 7.784e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0932 0.09126 0.165 0.1961 0.9852 0.9911 0.09322 0.666 0.8381 0.2481 ] Network output: [ 9.857e-05 1 -6.894e-05 1.361e-06 -6.108e-07 0.9998 1.025e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002298 Epoch 9160 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009405 0.9966 0.992 -2.164e-07 9.714e-08 -0.007345 -1.631e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003474 -0.003304 -0.007029 0.005617 0.9699 0.9743 0.006735 0.8273 0.8212 0.01678 ] Network output: [ 0.9999 0.0002139 0.0004799 -5.017e-06 2.252e-06 -0.0004781 -3.781e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2053 -0.03507 -0.162 0.1846 0.9834 0.9932 0.2302 0.4322 0.8689 0.7109 ] Network output: [ -0.009346 1.003 1.008 -2.708e-07 1.216e-07 0.007755 -2.041e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006606 0.0005857 0.004414 0.003305 0.9889 0.9919 0.006734 0.8548 0.8928 0.01203 ] Network output: [ -0.0002787 0.001809 1.001 -1.572e-05 7.058e-06 0.9981 -1.185e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2185 0.103 0.3464 0.1429 0.985 0.9939 0.2192 0.4362 0.8756 0.7049 ] Network output: [ 0.003834 -0.01811 0.9942 9.548e-06 -4.287e-06 1.016 7.196e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.09654 0.184 0.1982 0.9873 0.9919 0.1092 0.742 0.8626 0.3053 ] Network output: [ -0.003594 0.01682 1.005 1.032e-05 -4.632e-06 0.9859 7.775e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0932 0.09127 0.165 0.1961 0.9852 0.9911 0.09322 0.666 0.8381 0.2481 ] Network output: [ 9.854e-05 1 -6.888e-05 1.359e-06 -6.101e-07 0.9998 1.024e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002297 Epoch 9161 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009404 0.9966 0.992 -2.163e-07 9.713e-08 -0.007344 -1.63e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003474 -0.003304 -0.007028 0.005616 0.9699 0.9743 0.006735 0.8273 0.8212 0.01678 ] Network output: [ 0.9999 0.0002137 0.0004796 -5.011e-06 2.25e-06 -0.0004777 -3.776e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2053 -0.03508 -0.162 0.1846 0.9834 0.9932 0.2302 0.4322 0.8689 0.7109 ] Network output: [ -0.009345 1.003 1.008 -2.707e-07 1.215e-07 0.007754 -2.04e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006606 0.0005858 0.004413 0.003305 0.9889 0.9919 0.006734 0.8548 0.8928 0.01202 ] Network output: [ -0.0002785 0.001809 1.001 -1.57e-05 7.05e-06 0.9981 -1.183e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2185 0.103 0.3464 0.1429 0.985 0.9939 0.2192 0.4362 0.8756 0.7049 ] Network output: [ 0.003832 -0.0181 0.9942 9.537e-06 -4.282e-06 1.016 7.188e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.09655 0.184 0.1982 0.9873 0.9919 0.1092 0.742 0.8626 0.3053 ] Network output: [ -0.003593 0.01682 1.005 1.031e-05 -4.627e-06 0.9859 7.767e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09321 0.09127 0.165 0.1961 0.9852 0.9911 0.09322 0.666 0.8381 0.2481 ] Network output: [ 9.85e-05 1 -6.881e-05 1.358e-06 -6.094e-07 0.9998 1.023e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002296 Epoch 9162 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009403 0.9966 0.992 -2.163e-07 9.712e-08 -0.007344 -1.63e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003474 -0.003304 -0.007027 0.005616 0.9699 0.9743 0.006735 0.8273 0.8212 0.01678 ] Network output: [ 0.9999 0.0002135 0.0004794 -5.005e-06 2.247e-06 -0.0004773 -3.772e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2053 -0.03508 -0.162 0.1846 0.9834 0.9932 0.2302 0.4322 0.8689 0.7109 ] Network output: [ -0.009344 1.003 1.008 -2.706e-07 1.215e-07 0.007753 -2.039e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006607 0.0005859 0.004413 0.003305 0.9889 0.9919 0.006734 0.8548 0.8928 0.01202 ] Network output: [ -0.0002783 0.001808 1.001 -1.568e-05 7.041e-06 0.9981 -1.182e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2185 0.103 0.3464 0.1429 0.985 0.9939 0.2192 0.4362 0.8756 0.7048 ] Network output: [ 0.003831 -0.01809 0.9942 9.526e-06 -4.277e-06 1.016 7.179e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.09655 0.184 0.1982 0.9873 0.9919 0.1092 0.742 0.8626 0.3053 ] Network output: [ -0.003591 0.01681 1.005 1.029e-05 -4.621e-06 0.9859 7.758e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09321 0.09127 0.165 0.1961 0.9852 0.9911 0.09322 0.666 0.8381 0.2481 ] Network output: [ 9.846e-05 1 -6.875e-05 1.356e-06 -6.087e-07 0.9998 1.022e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002295 Epoch 9163 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009402 0.9966 0.992 -2.163e-07 9.711e-08 -0.007343 -1.63e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003474 -0.003304 -0.007027 0.005615 0.9699 0.9743 0.006735 0.8273 0.8212 0.01678 ] Network output: [ 0.9999 0.0002133 0.0004792 -4.999e-06 2.244e-06 -0.000477 -3.768e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2053 -0.03508 -0.162 0.1846 0.9834 0.9932 0.2302 0.4322 0.8689 0.7109 ] Network output: [ -0.009343 1.003 1.008 -2.704e-07 1.214e-07 0.007752 -2.038e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006607 0.000586 0.004413 0.003304 0.9889 0.9919 0.006735 0.8548 0.8928 0.01202 ] Network output: [ -0.0002781 0.001807 1.001 -1.567e-05 7.033e-06 0.9981 -1.181e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2185 0.103 0.3464 0.1429 0.985 0.9939 0.2192 0.4362 0.8756 0.7048 ] Network output: [ 0.003829 -0.01808 0.9942 9.516e-06 -4.272e-06 1.016 7.171e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.09656 0.184 0.1982 0.9873 0.9919 0.1092 0.742 0.8626 0.3053 ] Network output: [ -0.00359 0.0168 1.005 1.028e-05 -4.616e-06 0.9859 7.749e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09321 0.09127 0.165 0.1961 0.9852 0.9911 0.09322 0.666 0.8381 0.2481 ] Network output: [ 9.843e-05 1 -6.869e-05 1.354e-06 -6.081e-07 0.9998 1.021e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002294 Epoch 9164 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009401 0.9966 0.992 -2.163e-07 9.71e-08 -0.007343 -1.63e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003474 -0.003304 -0.007026 0.005615 0.9699 0.9743 0.006736 0.8273 0.8212 0.01677 ] Network output: [ 0.9999 0.000213 0.000479 -4.994e-06 2.242e-06 -0.0004766 -3.763e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2053 -0.03508 -0.162 0.1846 0.9834 0.9932 0.2302 0.4322 0.8689 0.7109 ] Network output: [ -0.009342 1.003 1.008 -2.703e-07 1.213e-07 0.007751 -2.037e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006608 0.0005861 0.004413 0.003304 0.9889 0.9919 0.006735 0.8547 0.8928 0.01202 ] Network output: [ -0.0002779 0.001807 1.001 -1.565e-05 7.025e-06 0.9981 -1.179e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2185 0.103 0.3464 0.1429 0.985 0.9939 0.2192 0.4362 0.8756 0.7048 ] Network output: [ 0.003828 -0.01808 0.9942 9.505e-06 -4.267e-06 1.016 7.163e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.09656 0.184 0.1982 0.9873 0.9919 0.1092 0.742 0.8626 0.3053 ] Network output: [ -0.003589 0.0168 1.005 1.027e-05 -4.611e-06 0.9859 7.74e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09321 0.09128 0.165 0.1961 0.9852 0.9911 0.09323 0.666 0.8381 0.2481 ] Network output: [ 9.839e-05 1 -6.863e-05 1.353e-06 -6.074e-07 0.9998 1.02e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002293 Epoch 9165 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0094 0.9966 0.992 -2.163e-07 9.708e-08 -0.007342 -1.63e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003474 -0.003305 -0.007025 0.005614 0.9699 0.9743 0.006736 0.8273 0.8212 0.01677 ] Network output: [ 0.9999 0.0002128 0.0004787 -4.988e-06 2.239e-06 -0.0004763 -3.759e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2053 -0.03508 -0.1619 0.1846 0.9834 0.9932 0.2302 0.4322 0.8689 0.7109 ] Network output: [ -0.009341 1.003 1.008 -2.702e-07 1.213e-07 0.007751 -2.036e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006608 0.0005862 0.004413 0.003304 0.9889 0.9919 0.006736 0.8547 0.8928 0.01202 ] Network output: [ -0.0002777 0.001806 1.001 -1.563e-05 7.017e-06 0.9981 -1.178e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2185 0.103 0.3464 0.1429 0.985 0.9939 0.2192 0.4362 0.8756 0.7048 ] Network output: [ 0.003826 -0.01807 0.9942 9.494e-06 -4.262e-06 1.016 7.155e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.09656 0.184 0.1982 0.9873 0.9919 0.1092 0.742 0.8626 0.3053 ] Network output: [ -0.003587 0.01679 1.005 1.026e-05 -4.606e-06 0.9859 7.732e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09322 0.09128 0.165 0.1961 0.9852 0.9911 0.09323 0.666 0.8381 0.2481 ] Network output: [ 9.836e-05 1 -6.857e-05 1.351e-06 -6.067e-07 0.9998 1.018e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002291 Epoch 9166 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009399 0.9966 0.992 -2.162e-07 9.707e-08 -0.007342 -1.63e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003474 -0.003305 -0.007024 0.005614 0.9699 0.9743 0.006736 0.8273 0.8212 0.01677 ] Network output: [ 0.9999 0.0002126 0.0004785 -4.982e-06 2.237e-06 -0.0004759 -3.755e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2053 -0.03508 -0.1619 0.1846 0.9834 0.9932 0.2302 0.4322 0.8689 0.7109 ] Network output: [ -0.009341 1.003 1.008 -2.701e-07 1.212e-07 0.00775 -2.035e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006609 0.0005863 0.004413 0.003304 0.9889 0.9919 0.006736 0.8547 0.8928 0.01202 ] Network output: [ -0.0002776 0.001805 1.001 -1.561e-05 7.009e-06 0.9981 -1.177e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2185 0.103 0.3464 0.1429 0.985 0.9939 0.2193 0.4362 0.8756 0.7048 ] Network output: [ 0.003825 -0.01806 0.9942 9.483e-06 -4.257e-06 1.016 7.147e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.09657 0.184 0.1982 0.9873 0.9919 0.1092 0.742 0.8626 0.3053 ] Network output: [ -0.003586 0.01678 1.005 1.025e-05 -4.601e-06 0.9859 7.723e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09322 0.09128 0.165 0.1961 0.9852 0.9911 0.09323 0.6659 0.8381 0.2481 ] Network output: [ 9.832e-05 1 -6.851e-05 1.35e-06 -6.06e-07 0.9998 1.017e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000229 Epoch 9167 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009398 0.9966 0.992 -2.162e-07 9.706e-08 -0.007342 -1.629e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003474 -0.003305 -0.007024 0.005613 0.9699 0.9743 0.006736 0.8273 0.8212 0.01677 ] Network output: [ 0.9999 0.0002124 0.0004783 -4.976e-06 2.234e-06 -0.0004756 -3.75e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2053 -0.03508 -0.1619 0.1846 0.9834 0.9932 0.2302 0.4322 0.8689 0.7109 ] Network output: [ -0.00934 1.003 1.008 -2.699e-07 1.212e-07 0.007749 -2.034e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006609 0.0005863 0.004413 0.003303 0.9889 0.9919 0.006737 0.8547 0.8928 0.01202 ] Network output: [ -0.0002774 0.001804 1.001 -1.559e-05 7.001e-06 0.9981 -1.175e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2185 0.103 0.3464 0.1429 0.985 0.9939 0.2193 0.4362 0.8756 0.7048 ] Network output: [ 0.003823 -0.01806 0.9942 9.472e-06 -4.252e-06 1.016 7.138e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1091 0.09657 0.184 0.1982 0.9873 0.9919 0.1092 0.7419 0.8626 0.3053 ] Network output: [ -0.003584 0.01678 1.005 1.024e-05 -4.595e-06 0.9859 7.714e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09322 0.09128 0.165 0.1961 0.9852 0.9911 0.09323 0.6659 0.8381 0.2481 ] Network output: [ 9.829e-05 1 -6.844e-05 1.348e-06 -6.053e-07 0.9998 1.016e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002289 Epoch 9168 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009397 0.9966 0.992 -2.162e-07 9.705e-08 -0.007341 -1.629e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003474 -0.003305 -0.007023 0.005613 0.9699 0.9743 0.006737 0.8273 0.8212 0.01677 ] Network output: [ 0.9999 0.0002122 0.000478 -4.971e-06 2.232e-06 -0.0004752 -3.746e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2053 -0.03508 -0.1619 0.1846 0.9834 0.9932 0.2302 0.4322 0.8689 0.7109 ] Network output: [ -0.009339 1.003 1.008 -2.698e-07 1.211e-07 0.007748 -2.033e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006609 0.0005864 0.004413 0.003303 0.9889 0.9919 0.006737 0.8547 0.8928 0.01202 ] Network output: [ -0.0002772 0.001804 1.001 -1.558e-05 6.993e-06 0.9981 -1.174e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2185 0.103 0.3464 0.1429 0.985 0.9939 0.2193 0.4362 0.8756 0.7048 ] Network output: [ 0.003822 -0.01805 0.9942 9.461e-06 -4.248e-06 1.016 7.13e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.09658 0.184 0.1982 0.9873 0.9919 0.1092 0.7419 0.8626 0.3053 ] Network output: [ -0.003583 0.01677 1.005 1.022e-05 -4.59e-06 0.9859 7.706e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09322 0.09129 0.165 0.1961 0.9852 0.9911 0.09324 0.6659 0.8381 0.2481 ] Network output: [ 9.825e-05 1 -6.838e-05 1.347e-06 -6.046e-07 0.9998 1.015e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002288 Epoch 9169 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009396 0.9966 0.992 -2.162e-07 9.704e-08 -0.007341 -1.629e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003474 -0.003305 -0.007022 0.005612 0.9699 0.9743 0.006737 0.8272 0.8212 0.01677 ] Network output: [ 0.9999 0.000212 0.0004778 -4.965e-06 2.229e-06 -0.0004749 -3.742e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2053 -0.03508 -0.1619 0.1846 0.9834 0.9932 0.2302 0.4321 0.8689 0.7109 ] Network output: [ -0.009338 1.003 1.008 -2.697e-07 1.211e-07 0.007747 -2.032e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00661 0.0005865 0.004413 0.003303 0.9889 0.9919 0.006738 0.8547 0.8928 0.01202 ] Network output: [ -0.000277 0.001803 1.001 -1.556e-05 6.985e-06 0.9981 -1.173e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2185 0.103 0.3464 0.1429 0.985 0.9939 0.2193 0.4362 0.8756 0.7048 ] Network output: [ 0.00382 -0.01804 0.9942 9.45e-06 -4.243e-06 1.016 7.122e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.09658 0.184 0.1982 0.9873 0.9919 0.1092 0.7419 0.8626 0.3053 ] Network output: [ -0.003581 0.01676 1.005 1.021e-05 -4.585e-06 0.9859 7.697e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09322 0.09129 0.165 0.1961 0.9852 0.9911 0.09324 0.6659 0.8381 0.2481 ] Network output: [ 9.822e-05 1 -6.832e-05 1.345e-06 -6.039e-07 0.9998 1.014e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002287 Epoch 9170 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009395 0.9966 0.992 -2.161e-07 9.703e-08 -0.00734 -1.629e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003475 -0.003305 -0.007022 0.005612 0.9699 0.9743 0.006737 0.8272 0.8211 0.01677 ] Network output: [ 0.9999 0.0002117 0.0004776 -4.959e-06 2.226e-06 -0.0004745 -3.737e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2053 -0.03508 -0.1619 0.1846 0.9834 0.9932 0.2302 0.4321 0.8689 0.7109 ] Network output: [ -0.009337 1.003 1.008 -2.696e-07 1.21e-07 0.007746 -2.031e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00661 0.0005866 0.004413 0.003303 0.9889 0.9919 0.006738 0.8547 0.8928 0.01202 ] Network output: [ -0.0002768 0.001802 1.001 -1.554e-05 6.977e-06 0.9981 -1.171e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2186 0.1031 0.3464 0.1429 0.985 0.9939 0.2193 0.4362 0.8756 0.7048 ] Network output: [ 0.003819 -0.01803 0.9942 9.44e-06 -4.238e-06 1.016 7.114e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.09659 0.184 0.1982 0.9873 0.9919 0.1092 0.7419 0.8626 0.3053 ] Network output: [ -0.00358 0.01676 1.005 1.02e-05 -4.58e-06 0.9859 7.688e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09323 0.09129 0.165 0.1961 0.9852 0.9911 0.09324 0.6659 0.8381 0.2481 ] Network output: [ 9.818e-05 1 -6.826e-05 1.344e-06 -6.032e-07 0.9998 1.013e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002285 Epoch 9171 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009394 0.9966 0.992 -2.161e-07 9.702e-08 -0.00734 -1.629e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003475 -0.003305 -0.007021 0.005612 0.9699 0.9743 0.006737 0.8272 0.8211 0.01677 ] Network output: [ 0.9999 0.0002115 0.0004773 -4.953e-06 2.224e-06 -0.0004742 -3.733e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2054 -0.03509 -0.1619 0.1846 0.9834 0.9932 0.2303 0.4321 0.8689 0.7109 ] Network output: [ -0.009336 1.003 1.008 -2.694e-07 1.21e-07 0.007746 -2.03e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006611 0.0005867 0.004413 0.003302 0.9889 0.9919 0.006739 0.8547 0.8928 0.01202 ] Network output: [ -0.0002766 0.001801 1.001 -1.552e-05 6.969e-06 0.9981 -1.17e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2186 0.1031 0.3464 0.1429 0.985 0.9939 0.2193 0.4362 0.8756 0.7048 ] Network output: [ 0.003817 -0.01803 0.9942 9.429e-06 -4.233e-06 1.016 7.106e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.09659 0.184 0.1982 0.9873 0.9919 0.1092 0.7419 0.8626 0.3053 ] Network output: [ -0.003579 0.01675 1.005 1.019e-05 -4.575e-06 0.9859 7.68e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09323 0.09129 0.165 0.1961 0.9852 0.9911 0.09324 0.6659 0.8381 0.2481 ] Network output: [ 9.815e-05 1 -6.82e-05 1.342e-06 -6.025e-07 0.9998 1.011e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002284 Epoch 9172 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009393 0.9966 0.992 -2.161e-07 9.701e-08 -0.00734 -1.628e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003475 -0.003305 -0.00702 0.005611 0.9699 0.9743 0.006737 0.8272 0.8211 0.01677 ] Network output: [ 0.9999 0.0002113 0.0004771 -4.948e-06 2.221e-06 -0.0004738 -3.729e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2054 -0.03509 -0.1619 0.1846 0.9834 0.9932 0.2303 0.4321 0.8689 0.7109 ] Network output: [ -0.009335 1.003 1.008 -2.693e-07 1.209e-07 0.007745 -2.03e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006611 0.0005868 0.004413 0.003302 0.9889 0.9919 0.006739 0.8547 0.8928 0.01202 ] Network output: [ -0.0002764 0.001801 1.001 -1.55e-05 6.961e-06 0.9981 -1.168e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2186 0.1031 0.3464 0.1429 0.985 0.9939 0.2193 0.4362 0.8756 0.7048 ] Network output: [ 0.003816 -0.01802 0.9942 9.418e-06 -4.228e-06 1.016 7.098e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.0966 0.184 0.1982 0.9873 0.9919 0.1092 0.7419 0.8626 0.3053 ] Network output: [ -0.003577 0.01674 1.005 1.018e-05 -4.57e-06 0.9859 7.671e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09323 0.09129 0.165 0.1961 0.9852 0.9911 0.09325 0.6659 0.8381 0.2481 ] Network output: [ 9.811e-05 1 -6.814e-05 1.341e-06 -6.019e-07 0.9998 1.01e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002283 Epoch 9173 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009392 0.9966 0.992 -2.161e-07 9.7e-08 -0.007339 -1.628e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003475 -0.003305 -0.00702 0.005611 0.9699 0.9743 0.006738 0.8272 0.8211 0.01676 ] Network output: [ 0.9999 0.0002111 0.0004769 -4.942e-06 2.219e-06 -0.0004735 -3.724e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2054 -0.03509 -0.1619 0.1846 0.9834 0.9932 0.2303 0.4321 0.8689 0.7109 ] Network output: [ -0.009334 1.003 1.008 -2.692e-07 1.208e-07 0.007744 -2.029e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006612 0.0005869 0.004413 0.003302 0.9889 0.9919 0.00674 0.8547 0.8928 0.01202 ] Network output: [ -0.0002762 0.0018 1.001 -1.549e-05 6.953e-06 0.9981 -1.167e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2186 0.1031 0.3464 0.1429 0.985 0.9939 0.2193 0.4361 0.8756 0.7048 ] Network output: [ 0.003814 -0.01801 0.9942 9.407e-06 -4.223e-06 1.016 7.09e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.0966 0.1841 0.1982 0.9873 0.9919 0.1093 0.7419 0.8625 0.3053 ] Network output: [ -0.003576 0.01673 1.005 1.017e-05 -4.565e-06 0.9859 7.663e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09323 0.0913 0.165 0.1961 0.9852 0.9911 0.09325 0.6658 0.838 0.2481 ] Network output: [ 9.808e-05 1 -6.808e-05 1.339e-06 -6.012e-07 0.9998 1.009e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002282 Epoch 9174 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009391 0.9966 0.992 -2.16e-07 9.699e-08 -0.007339 -1.628e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003475 -0.003306 -0.007019 0.00561 0.9699 0.9743 0.006738 0.8272 0.8211 0.01676 ] Network output: [ 0.9999 0.0002109 0.0004767 -4.936e-06 2.216e-06 -0.0004731 -3.72e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2054 -0.03509 -0.1619 0.1846 0.9834 0.9932 0.2303 0.4321 0.8689 0.7109 ] Network output: [ -0.009333 1.003 1.008 -2.69e-07 1.208e-07 0.007743 -2.028e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006612 0.000587 0.004413 0.003302 0.9889 0.9919 0.00674 0.8547 0.8928 0.01201 ] Network output: [ -0.000276 0.001799 1.001 -1.547e-05 6.945e-06 0.9981 -1.166e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2186 0.1031 0.3464 0.1429 0.985 0.9939 0.2193 0.4361 0.8756 0.7048 ] Network output: [ 0.003813 -0.01801 0.9942 9.396e-06 -4.218e-06 1.016 7.081e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.09661 0.1841 0.1982 0.9873 0.9919 0.1093 0.7418 0.8625 0.3053 ] Network output: [ -0.003574 0.01673 1.005 1.016e-05 -4.559e-06 0.9859 7.654e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09324 0.0913 0.165 0.1961 0.9852 0.9911 0.09325 0.6658 0.838 0.2481 ] Network output: [ 9.804e-05 1 -6.802e-05 1.338e-06 -6.005e-07 0.9998 1.008e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002281 Epoch 9175 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00939 0.9966 0.992 -2.16e-07 9.697e-08 -0.007338 -1.628e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003475 -0.003306 -0.007018 0.00561 0.9699 0.9743 0.006738 0.8272 0.8211 0.01676 ] Network output: [ 0.9999 0.0002107 0.0004764 -4.931e-06 2.214e-06 -0.0004728 -3.716e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2054 -0.03509 -0.1618 0.1846 0.9834 0.9932 0.2303 0.4321 0.8689 0.7109 ] Network output: [ -0.009332 1.003 1.008 -2.689e-07 1.207e-07 0.007742 -2.027e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006613 0.000587 0.004412 0.003301 0.9889 0.9919 0.00674 0.8547 0.8928 0.01201 ] Network output: [ -0.0002758 0.001798 1.001 -1.545e-05 6.937e-06 0.9981 -1.164e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2186 0.1031 0.3465 0.1429 0.985 0.9939 0.2193 0.4361 0.8756 0.7048 ] Network output: [ 0.003811 -0.018 0.9942 9.386e-06 -4.214e-06 1.016 7.073e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.09661 0.1841 0.1982 0.9873 0.9919 0.1093 0.7418 0.8625 0.3053 ] Network output: [ -0.003573 0.01672 1.005 1.014e-05 -4.554e-06 0.986 7.645e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09324 0.0913 0.165 0.1961 0.9852 0.9911 0.09325 0.6658 0.838 0.2482 ] Network output: [ 9.801e-05 1 -6.796e-05 1.336e-06 -5.998e-07 0.9998 1.007e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002279 Epoch 9176 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009389 0.9966 0.992 -2.16e-07 9.696e-08 -0.007338 -1.628e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003475 -0.003306 -0.007018 0.005609 0.9699 0.9743 0.006738 0.8272 0.8211 0.01676 ] Network output: [ 0.9999 0.0002105 0.0004762 -4.925e-06 2.211e-06 -0.0004724 -3.712e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2054 -0.03509 -0.1618 0.1846 0.9834 0.9932 0.2303 0.4321 0.8689 0.7109 ] Network output: [ -0.009332 1.003 1.008 -2.688e-07 1.207e-07 0.007741 -2.026e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006613 0.0005871 0.004412 0.003301 0.9889 0.9919 0.006741 0.8547 0.8928 0.01201 ] Network output: [ -0.0002756 0.001798 1.001 -1.543e-05 6.929e-06 0.9981 -1.163e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2186 0.1031 0.3465 0.1429 0.985 0.9939 0.2193 0.4361 0.8756 0.7048 ] Network output: [ 0.00381 -0.01799 0.9942 9.375e-06 -4.209e-06 1.016 7.065e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.09661 0.1841 0.1982 0.9873 0.9919 0.1093 0.7418 0.8625 0.3053 ] Network output: [ -0.003571 0.01671 1.005 1.013e-05 -4.549e-06 0.986 7.637e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09324 0.0913 0.165 0.1961 0.9852 0.9911 0.09325 0.6658 0.838 0.2482 ] Network output: [ 9.797e-05 1 -6.79e-05 1.335e-06 -5.991e-07 0.9998 1.006e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002278 Epoch 9177 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009388 0.9966 0.992 -2.16e-07 9.695e-08 -0.007337 -1.627e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003475 -0.003306 -0.007017 0.005609 0.9699 0.9743 0.006739 0.8272 0.8211 0.01676 ] Network output: [ 0.9999 0.0002102 0.000476 -4.919e-06 2.208e-06 -0.0004721 -3.707e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2054 -0.03509 -0.1618 0.1846 0.9834 0.9932 0.2303 0.4321 0.8689 0.7109 ] Network output: [ -0.009331 1.003 1.008 -2.687e-07 1.206e-07 0.00774 -2.025e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006614 0.0005872 0.004412 0.003301 0.9889 0.9919 0.006741 0.8547 0.8928 0.01201 ] Network output: [ -0.0002754 0.001797 1.001 -1.542e-05 6.921e-06 0.9981 -1.162e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2186 0.1031 0.3465 0.1429 0.985 0.9939 0.2193 0.4361 0.8756 0.7048 ] Network output: [ 0.003808 -0.01799 0.9942 9.364e-06 -4.204e-06 1.016 7.057e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.09662 0.1841 0.1982 0.9873 0.9919 0.1093 0.7418 0.8625 0.3053 ] Network output: [ -0.00357 0.01671 1.005 1.012e-05 -4.544e-06 0.986 7.628e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09324 0.09131 0.165 0.1961 0.9852 0.9911 0.09326 0.6658 0.838 0.2482 ] Network output: [ 9.794e-05 1 -6.784e-05 1.333e-06 -5.984e-07 0.9998 1.005e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002277 Epoch 9178 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009387 0.9966 0.992 -2.159e-07 9.694e-08 -0.007337 -1.627e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003475 -0.003306 -0.007016 0.005608 0.9699 0.9743 0.006739 0.8272 0.8211 0.01676 ] Network output: [ 0.9999 0.00021 0.0004757 -4.913e-06 2.206e-06 -0.0004717 -3.703e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2054 -0.03509 -0.1618 0.1846 0.9834 0.9932 0.2303 0.4321 0.8689 0.7109 ] Network output: [ -0.00933 1.003 1.008 -2.685e-07 1.206e-07 0.00774 -2.024e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006614 0.0005873 0.004412 0.0033 0.9889 0.9919 0.006742 0.8547 0.8928 0.01201 ] Network output: [ -0.0002753 0.001796 1.001 -1.54e-05 6.913e-06 0.9981 -1.16e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2186 0.1031 0.3465 0.1429 0.985 0.9939 0.2194 0.4361 0.8756 0.7048 ] Network output: [ 0.003807 -0.01798 0.9942 9.353e-06 -4.199e-06 1.016 7.049e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.09662 0.1841 0.1982 0.9873 0.9919 0.1093 0.7418 0.8625 0.3053 ] Network output: [ -0.003569 0.0167 1.005 1.011e-05 -4.539e-06 0.986 7.62e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09325 0.09131 0.165 0.1961 0.9852 0.9911 0.09326 0.6658 0.838 0.2482 ] Network output: [ 9.79e-05 1 -6.778e-05 1.332e-06 -5.978e-07 0.9998 1.003e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002276 Epoch 9179 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009385 0.9966 0.992 -2.159e-07 9.692e-08 -0.007336 -1.627e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003475 -0.003306 -0.007015 0.005608 0.9699 0.9743 0.006739 0.8272 0.8211 0.01676 ] Network output: [ 0.9999 0.0002098 0.0004755 -4.908e-06 2.203e-06 -0.0004714 -3.699e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2054 -0.03509 -0.1618 0.1846 0.9834 0.9932 0.2303 0.4321 0.8689 0.7109 ] Network output: [ -0.009329 1.003 1.008 -2.684e-07 1.205e-07 0.007739 -2.023e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006614 0.0005874 0.004412 0.0033 0.9889 0.9919 0.006742 0.8547 0.8928 0.01201 ] Network output: [ -0.0002751 0.001796 1.001 -1.538e-05 6.905e-06 0.9981 -1.159e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2186 0.1031 0.3465 0.1429 0.985 0.9939 0.2194 0.4361 0.8756 0.7048 ] Network output: [ 0.003805 -0.01797 0.9942 9.343e-06 -4.194e-06 1.016 7.041e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.09663 0.1841 0.1982 0.9873 0.9919 0.1093 0.7418 0.8625 0.3053 ] Network output: [ -0.003567 0.01669 1.005 1.01e-05 -4.534e-06 0.986 7.611e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09325 0.09131 0.165 0.1961 0.9852 0.9911 0.09326 0.6658 0.838 0.2482 ] Network output: [ 9.787e-05 1 -6.772e-05 1.33e-06 -5.971e-07 0.9998 1.002e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002275 Epoch 9180 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009384 0.9966 0.992 -2.159e-07 9.691e-08 -0.007336 -1.627e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003475 -0.003306 -0.007015 0.005608 0.9699 0.9743 0.006739 0.8272 0.8211 0.01676 ] Network output: [ 0.9999 0.0002096 0.0004753 -4.902e-06 2.201e-06 -0.000471 -3.694e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2054 -0.03509 -0.1618 0.1846 0.9834 0.9932 0.2303 0.4321 0.8689 0.7109 ] Network output: [ -0.009328 1.003 1.008 -2.683e-07 1.204e-07 0.007738 -2.022e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006615 0.0005875 0.004412 0.0033 0.9889 0.9919 0.006743 0.8547 0.8928 0.01201 ] Network output: [ -0.0002749 0.001795 1.001 -1.536e-05 6.897e-06 0.9981 -1.158e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2186 0.1031 0.3465 0.1429 0.985 0.9939 0.2194 0.4361 0.8756 0.7048 ] Network output: [ 0.003803 -0.01796 0.9942 9.332e-06 -4.19e-06 1.016 7.033e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.09663 0.1841 0.1982 0.9873 0.9919 0.1093 0.7418 0.8625 0.3053 ] Network output: [ -0.003566 0.01669 1.005 1.009e-05 -4.529e-06 0.986 7.602e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09325 0.09131 0.165 0.1961 0.9852 0.9911 0.09326 0.6657 0.838 0.2482 ] Network output: [ 9.783e-05 1 -6.766e-05 1.328e-06 -5.964e-07 0.9998 1.001e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002274 Epoch 9181 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009383 0.9966 0.992 -2.158e-07 9.69e-08 -0.007336 -1.627e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003475 -0.003306 -0.007014 0.005607 0.9699 0.9743 0.006739 0.8272 0.8211 0.01676 ] Network output: [ 0.9999 0.0002094 0.0004751 -4.896e-06 2.198e-06 -0.0004707 -3.69e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2054 -0.0351 -0.1618 0.1845 0.9834 0.9932 0.2303 0.4321 0.8689 0.7108 ] Network output: [ -0.009327 1.003 1.008 -2.682e-07 1.204e-07 0.007737 -2.021e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006615 0.0005876 0.004412 0.0033 0.9889 0.9919 0.006743 0.8546 0.8928 0.01201 ] Network output: [ -0.0002747 0.001794 1.001 -1.534e-05 6.889e-06 0.9981 -1.156e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2186 0.1031 0.3465 0.1429 0.985 0.9939 0.2194 0.4361 0.8756 0.7047 ] Network output: [ 0.003802 -0.01796 0.9942 9.321e-06 -4.185e-06 1.016 7.025e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.09664 0.1841 0.1982 0.9873 0.9919 0.1093 0.7418 0.8625 0.3053 ] Network output: [ -0.003564 0.01668 1.005 1.008e-05 -4.524e-06 0.986 7.594e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09325 0.09132 0.165 0.1961 0.9852 0.9911 0.09327 0.6657 0.838 0.2482 ] Network output: [ 9.78e-05 1 -6.76e-05 1.327e-06 -5.957e-07 0.9998 1e-06 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002272 Epoch 9182 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009382 0.9966 0.992 -2.158e-07 9.689e-08 -0.007335 -1.626e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003476 -0.003306 -0.007013 0.005607 0.9699 0.9743 0.00674 0.8272 0.8211 0.01676 ] Network output: [ 0.9999 0.0002092 0.0004748 -4.891e-06 2.196e-06 -0.0004703 -3.686e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2054 -0.0351 -0.1618 0.1845 0.9834 0.9932 0.2303 0.4321 0.8689 0.7108 ] Network output: [ -0.009326 1.003 1.008 -2.68e-07 1.203e-07 0.007736 -2.02e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006616 0.0005877 0.004412 0.003299 0.9889 0.9919 0.006744 0.8546 0.8928 0.01201 ] Network output: [ -0.0002745 0.001793 1.001 -1.533e-05 6.881e-06 0.9981 -1.155e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2186 0.1031 0.3465 0.1429 0.985 0.9939 0.2194 0.4361 0.8756 0.7047 ] Network output: [ 0.0038 -0.01795 0.9942 9.311e-06 -4.18e-06 1.016 7.017e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.09664 0.1841 0.1982 0.9873 0.9919 0.1093 0.7417 0.8625 0.3053 ] Network output: [ -0.003563 0.01667 1.005 1.006e-05 -4.519e-06 0.986 7.585e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09325 0.09132 0.165 0.1961 0.9852 0.9911 0.09327 0.6657 0.838 0.2482 ] Network output: [ 9.776e-05 1 -6.754e-05 1.325e-06 -5.95e-07 0.9998 9.989e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002271 Epoch 9183 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009381 0.9966 0.992 -2.158e-07 9.687e-08 -0.007335 -1.626e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003476 -0.003306 -0.007013 0.005606 0.9699 0.9743 0.00674 0.8272 0.8211 0.01675 ] Network output: [ 0.9999 0.000209 0.0004746 -4.885e-06 2.193e-06 -0.00047 -3.682e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2054 -0.0351 -0.1618 0.1845 0.9834 0.9932 0.2304 0.4321 0.8689 0.7108 ] Network output: [ -0.009325 1.003 1.008 -2.679e-07 1.203e-07 0.007735 -2.019e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006616 0.0005877 0.004412 0.003299 0.9889 0.9919 0.006744 0.8546 0.8928 0.01201 ] Network output: [ -0.0002743 0.001793 1.001 -1.531e-05 6.873e-06 0.9981 -1.154e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2187 0.1031 0.3465 0.1429 0.985 0.9939 0.2194 0.4361 0.8756 0.7047 ] Network output: [ 0.003799 -0.01794 0.9942 9.3e-06 -4.175e-06 1.016 7.009e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.09665 0.1841 0.1982 0.9873 0.9919 0.1093 0.7417 0.8625 0.3053 ] Network output: [ -0.003561 0.01667 1.005 1.005e-05 -4.513e-06 0.986 7.577e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09326 0.09132 0.165 0.1961 0.9852 0.9911 0.09327 0.6657 0.838 0.2482 ] Network output: [ 9.773e-05 1 -6.748e-05 1.324e-06 -5.944e-07 0.9998 9.978e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000227 Epoch 9184 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00938 0.9966 0.992 -2.158e-07 9.686e-08 -0.007334 -1.626e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003476 -0.003307 -0.007012 0.005606 0.9699 0.9743 0.00674 0.8272 0.8211 0.01675 ] Network output: [ 0.9999 0.0002087 0.0004744 -4.879e-06 2.191e-06 -0.0004696 -3.677e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2054 -0.0351 -0.1617 0.1845 0.9834 0.9932 0.2304 0.432 0.8689 0.7108 ] Network output: [ -0.009324 1.003 1.008 -2.678e-07 1.202e-07 0.007735 -2.018e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006617 0.0005878 0.004412 0.003299 0.9889 0.9919 0.006745 0.8546 0.8928 0.01201 ] Network output: [ -0.0002741 0.001792 1.001 -1.529e-05 6.865e-06 0.9981 -1.152e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2187 0.1031 0.3465 0.1429 0.985 0.9939 0.2194 0.4361 0.8756 0.7047 ] Network output: [ 0.003797 -0.01794 0.9942 9.289e-06 -4.17e-06 1.016 7.001e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.09665 0.1841 0.1982 0.9873 0.9919 0.1093 0.7417 0.8625 0.3053 ] Network output: [ -0.00356 0.01666 1.005 1.004e-05 -4.508e-06 0.986 7.568e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09326 0.09132 0.165 0.1961 0.9852 0.9911 0.09327 0.6657 0.838 0.2482 ] Network output: [ 9.769e-05 1 -6.742e-05 1.322e-06 -5.937e-07 0.9998 9.966e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002269 Epoch 9185 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009379 0.9966 0.992 -2.157e-07 9.685e-08 -0.007334 -1.626e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003476 -0.003307 -0.007011 0.005605 0.9699 0.9743 0.00674 0.8272 0.8211 0.01675 ] Network output: [ 0.9999 0.0002085 0.0004742 -4.874e-06 2.188e-06 -0.0004693 -3.673e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2054 -0.0351 -0.1617 0.1845 0.9834 0.9932 0.2304 0.432 0.8689 0.7108 ] Network output: [ -0.009323 1.003 1.008 -2.676e-07 1.202e-07 0.007734 -2.017e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006617 0.0005879 0.004412 0.003299 0.9889 0.9919 0.006745 0.8546 0.8928 0.01201 ] Network output: [ -0.0002739 0.001791 1.001 -1.527e-05 6.857e-06 0.9981 -1.151e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2187 0.1031 0.3465 0.1429 0.985 0.9939 0.2194 0.4361 0.8756 0.7047 ] Network output: [ 0.003796 -0.01793 0.9942 9.279e-06 -4.166e-06 1.016 6.993e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.09665 0.1841 0.1982 0.9873 0.9919 0.1093 0.7417 0.8625 0.3053 ] Network output: [ -0.003559 0.01665 1.005 1.003e-05 -4.503e-06 0.986 7.56e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09326 0.09132 0.165 0.1961 0.9852 0.9911 0.09328 0.6657 0.838 0.2482 ] Network output: [ 9.766e-05 1 -6.736e-05 1.321e-06 -5.93e-07 0.9998 9.955e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002268 Epoch 9186 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009378 0.9966 0.992 -2.157e-07 9.683e-08 -0.007333 -1.626e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003476 -0.003307 -0.007011 0.005605 0.9699 0.9743 0.006741 0.8272 0.8211 0.01675 ] Network output: [ 0.9999 0.0002083 0.0004739 -4.868e-06 2.185e-06 -0.0004689 -3.669e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2055 -0.0351 -0.1617 0.1845 0.9834 0.9932 0.2304 0.432 0.8689 0.7108 ] Network output: [ -0.009322 1.003 1.008 -2.675e-07 1.201e-07 0.007733 -2.016e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006618 0.000588 0.004412 0.003298 0.9889 0.9919 0.006746 0.8546 0.8928 0.012 ] Network output: [ -0.0002737 0.00179 1.001 -1.526e-05 6.849e-06 0.9981 -1.15e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2187 0.1031 0.3465 0.1429 0.985 0.9939 0.2194 0.4361 0.8756 0.7047 ] Network output: [ 0.003794 -0.01792 0.9942 9.268e-06 -4.161e-06 1.016 6.985e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.09666 0.1841 0.1982 0.9873 0.9919 0.1093 0.7417 0.8625 0.3053 ] Network output: [ -0.003557 0.01665 1.005 1.002e-05 -4.498e-06 0.986 7.551e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09326 0.09133 0.165 0.1961 0.9852 0.9911 0.09328 0.6657 0.838 0.2482 ] Network output: [ 9.762e-05 1 -6.73e-05 1.319e-06 -5.923e-07 0.9998 9.944e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002266 Epoch 9187 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009377 0.9966 0.992 -2.157e-07 9.682e-08 -0.007333 -1.625e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003476 -0.003307 -0.00701 0.005604 0.9699 0.9743 0.006741 0.8272 0.8211 0.01675 ] Network output: [ 0.9999 0.0002081 0.0004737 -4.862e-06 2.183e-06 -0.0004686 -3.665e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2055 -0.0351 -0.1617 0.1845 0.9834 0.9932 0.2304 0.432 0.8689 0.7108 ] Network output: [ -0.009322 1.003 1.008 -2.674e-07 1.2e-07 0.007732 -2.015e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006618 0.0005881 0.004412 0.003298 0.9889 0.9919 0.006746 0.8546 0.8928 0.012 ] Network output: [ -0.0002735 0.00179 1.001 -1.524e-05 6.841e-06 0.9981 -1.148e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2187 0.1031 0.3465 0.1429 0.985 0.9939 0.2194 0.4361 0.8756 0.7047 ] Network output: [ 0.003793 -0.01792 0.9942 9.257e-06 -4.156e-06 1.016 6.977e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.09666 0.1841 0.1982 0.9873 0.9919 0.1093 0.7417 0.8625 0.3053 ] Network output: [ -0.003556 0.01664 1.005 1.001e-05 -4.493e-06 0.986 7.543e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09327 0.09133 0.165 0.1961 0.9852 0.9911 0.09328 0.6657 0.838 0.2482 ] Network output: [ 9.759e-05 1 -6.724e-05 1.318e-06 -5.917e-07 0.9998 9.932e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002265 Epoch 9188 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009376 0.9966 0.992 -2.156e-07 9.681e-08 -0.007333 -1.625e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003476 -0.003307 -0.007009 0.005604 0.9699 0.9743 0.006741 0.8271 0.8211 0.01675 ] Network output: [ 0.9999 0.0002079 0.0004735 -4.857e-06 2.18e-06 -0.0004682 -3.66e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2055 -0.0351 -0.1617 0.1845 0.9834 0.9932 0.2304 0.432 0.8689 0.7108 ] Network output: [ -0.009321 1.003 1.008 -2.673e-07 1.2e-07 0.007731 -2.014e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006618 0.0005882 0.004412 0.003298 0.9889 0.9919 0.006747 0.8546 0.8928 0.012 ] Network output: [ -0.0002733 0.001789 1.001 -1.522e-05 6.833e-06 0.9981 -1.147e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2187 0.1031 0.3465 0.1429 0.985 0.9939 0.2194 0.4361 0.8756 0.7047 ] Network output: [ 0.003791 -0.01791 0.9942 9.247e-06 -4.151e-06 1.016 6.969e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1092 0.09667 0.1841 0.1982 0.9873 0.9919 0.1093 0.7417 0.8625 0.3053 ] Network output: [ -0.003554 0.01663 1.005 9.997e-06 -4.488e-06 0.986 7.534e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09327 0.09133 0.165 0.1961 0.9852 0.9911 0.09328 0.6656 0.838 0.2482 ] Network output: [ 9.755e-05 1 -6.718e-05 1.316e-06 -5.91e-07 0.9998 9.921e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002264 Epoch 9189 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009375 0.9966 0.992 -2.156e-07 9.679e-08 -0.007332 -1.625e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003476 -0.003307 -0.007009 0.005604 0.9699 0.9743 0.006741 0.8271 0.8211 0.01675 ] Network output: [ 0.9999 0.0002077 0.0004732 -4.851e-06 2.178e-06 -0.0004679 -3.656e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2055 -0.0351 -0.1617 0.1845 0.9834 0.9932 0.2304 0.432 0.8689 0.7108 ] Network output: [ -0.00932 1.003 1.008 -2.671e-07 1.199e-07 0.00773 -2.013e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006619 0.0005883 0.004411 0.003298 0.9889 0.9919 0.006747 0.8546 0.8928 0.012 ] Network output: [ -0.0002732 0.001788 1.001 -1.52e-05 6.825e-06 0.9981 -1.146e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2187 0.1032 0.3465 0.1429 0.985 0.9939 0.2194 0.436 0.8756 0.7047 ] Network output: [ 0.00379 -0.0179 0.9942 9.236e-06 -4.146e-06 1.016 6.961e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1093 0.09667 0.1841 0.1982 0.9873 0.9919 0.1093 0.7416 0.8625 0.3053 ] Network output: [ -0.003553 0.01663 1.005 9.986e-06 -4.483e-06 0.986 7.526e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09327 0.09133 0.165 0.1961 0.9852 0.9911 0.09328 0.6656 0.838 0.2482 ] Network output: [ 9.752e-05 1 -6.712e-05 1.315e-06 -5.903e-07 0.9998 9.91e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002263 Epoch 9190 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009374 0.9966 0.992 -2.156e-07 9.678e-08 -0.007332 -1.625e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003476 -0.003307 -0.007008 0.005603 0.9699 0.9743 0.006741 0.8271 0.8211 0.01675 ] Network output: [ 0.9999 0.0002075 0.000473 -4.846e-06 2.175e-06 -0.0004675 -3.652e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2055 -0.0351 -0.1617 0.1845 0.9834 0.9932 0.2304 0.432 0.8689 0.7108 ] Network output: [ -0.009319 1.003 1.008 -2.67e-07 1.199e-07 0.00773 -2.012e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006619 0.0005883 0.004411 0.003297 0.9889 0.9919 0.006747 0.8546 0.8928 0.012 ] Network output: [ -0.000273 0.001788 1.001 -1.519e-05 6.817e-06 0.9981 -1.144e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2187 0.1032 0.3465 0.1429 0.985 0.9939 0.2195 0.436 0.8756 0.7047 ] Network output: [ 0.003788 -0.01789 0.9942 9.226e-06 -4.142e-06 1.016 6.953e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1093 0.09668 0.1841 0.1982 0.9873 0.9919 0.1093 0.7416 0.8625 0.3053 ] Network output: [ -0.003551 0.01662 1.005 9.975e-06 -4.478e-06 0.986 7.517e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09327 0.09134 0.165 0.1961 0.9852 0.9911 0.09329 0.6656 0.838 0.2482 ] Network output: [ 9.749e-05 1 -6.706e-05 1.313e-06 -5.896e-07 0.9998 9.898e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002262 Epoch 9191 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009373 0.9966 0.992 -2.155e-07 9.677e-08 -0.007331 -1.624e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003476 -0.003307 -0.007007 0.005603 0.9699 0.9743 0.006742 0.8271 0.8211 0.01675 ] Network output: [ 0.9999 0.0002072 0.0004728 -4.84e-06 2.173e-06 -0.0004672 -3.648e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2055 -0.03511 -0.1617 0.1845 0.9834 0.9932 0.2304 0.432 0.8689 0.7108 ] Network output: [ -0.009318 1.003 1.008 -2.669e-07 1.198e-07 0.007729 -2.011e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00662 0.0005884 0.004411 0.003297 0.9889 0.9919 0.006748 0.8546 0.8928 0.012 ] Network output: [ -0.0002728 0.001787 1.001 -1.517e-05 6.81e-06 0.9981 -1.143e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2187 0.1032 0.3465 0.1429 0.985 0.9939 0.2195 0.436 0.8756 0.7047 ] Network output: [ 0.003787 -0.01789 0.9942 9.215e-06 -4.137e-06 1.016 6.945e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1093 0.09668 0.1841 0.1982 0.9873 0.9919 0.1093 0.7416 0.8625 0.3053 ] Network output: [ -0.00355 0.01661 1.005 9.963e-06 -4.473e-06 0.986 7.509e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09328 0.09134 0.165 0.1961 0.9852 0.9911 0.09329 0.6656 0.838 0.2482 ] Network output: [ 9.745e-05 1 -6.7e-05 1.312e-06 -5.89e-07 0.9998 9.887e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002261 Epoch 9192 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009372 0.9966 0.992 -2.155e-07 9.675e-08 -0.007331 -1.624e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003476 -0.003307 -0.007007 0.005602 0.9699 0.9743 0.006742 0.8271 0.8211 0.01675 ] Network output: [ 0.9999 0.000207 0.0004726 -4.834e-06 2.17e-06 -0.0004669 -3.643e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2055 -0.03511 -0.1617 0.1845 0.9834 0.9932 0.2304 0.432 0.8689 0.7108 ] Network output: [ -0.009317 1.003 1.008 -2.667e-07 1.198e-07 0.007728 -2.01e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00662 0.0005885 0.004411 0.003297 0.9889 0.9919 0.006748 0.8546 0.8928 0.012 ] Network output: [ -0.0002726 0.001786 1.001 -1.515e-05 6.802e-06 0.9981 -1.142e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2187 0.1032 0.3465 0.1429 0.985 0.9939 0.2195 0.436 0.8756 0.7047 ] Network output: [ 0.003785 -0.01788 0.9942 9.204e-06 -4.132e-06 1.016 6.937e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1093 0.09669 0.1841 0.1982 0.9873 0.9919 0.1093 0.7416 0.8625 0.3053 ] Network output: [ -0.003548 0.0166 1.005 9.952e-06 -4.468e-06 0.986 7.5e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09328 0.09134 0.165 0.1961 0.9852 0.9911 0.09329 0.6656 0.838 0.2482 ] Network output: [ 9.742e-05 1 -6.695e-05 1.31e-06 -5.883e-07 0.9998 9.876e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002259 Epoch 9193 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009371 0.9966 0.992 -2.155e-07 9.674e-08 -0.00733 -1.624e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003476 -0.003308 -0.007006 0.005602 0.9699 0.9743 0.006742 0.8271 0.8211 0.01674 ] Network output: [ 0.9999 0.0002068 0.0004723 -4.829e-06 2.168e-06 -0.0004665 -3.639e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2055 -0.03511 -0.1617 0.1845 0.9834 0.9932 0.2304 0.432 0.8689 0.7108 ] Network output: [ -0.009316 1.003 1.008 -2.666e-07 1.197e-07 0.007727 -2.009e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006621 0.0005886 0.004411 0.003297 0.9889 0.9919 0.006749 0.8546 0.8928 0.012 ] Network output: [ -0.0002724 0.001785 1.001 -1.513e-05 6.794e-06 0.9981 -1.14e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2187 0.1032 0.3465 0.1429 0.985 0.9939 0.2195 0.436 0.8756 0.7047 ] Network output: [ 0.003784 -0.01787 0.9942 9.194e-06 -4.127e-06 1.016 6.929e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1093 0.09669 0.1841 0.1982 0.9873 0.9919 0.1093 0.7416 0.8625 0.3053 ] Network output: [ -0.003547 0.0166 1.005 9.941e-06 -4.463e-06 0.986 7.492e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09328 0.09134 0.165 0.1961 0.9852 0.9911 0.09329 0.6656 0.838 0.2482 ] Network output: [ 9.738e-05 1 -6.689e-05 1.309e-06 -5.876e-07 0.9998 9.864e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002258 Epoch 9194 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00937 0.9966 0.992 -2.154e-07 9.672e-08 -0.00733 -1.624e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003476 -0.003308 -0.007005 0.005601 0.9699 0.9743 0.006742 0.8271 0.8211 0.01674 ] Network output: [ 0.9999 0.0002066 0.0004721 -4.823e-06 2.165e-06 -0.0004662 -3.635e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2055 -0.03511 -0.1616 0.1845 0.9834 0.9932 0.2304 0.432 0.8689 0.7108 ] Network output: [ -0.009315 1.003 1.008 -2.665e-07 1.196e-07 0.007726 -2.008e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006621 0.0005887 0.004411 0.003296 0.9889 0.9919 0.006749 0.8546 0.8928 0.012 ] Network output: [ -0.0002722 0.001785 1.001 -1.512e-05 6.786e-06 0.9981 -1.139e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2187 0.1032 0.3466 0.1429 0.985 0.9939 0.2195 0.436 0.8756 0.7047 ] Network output: [ 0.003782 -0.01787 0.9942 9.183e-06 -4.123e-06 1.016 6.921e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1093 0.09669 0.1841 0.1982 0.9873 0.9919 0.1094 0.7416 0.8625 0.3053 ] Network output: [ -0.003546 0.01659 1.005 9.93e-06 -4.458e-06 0.986 7.483e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09328 0.09134 0.165 0.1961 0.9852 0.9911 0.0933 0.6656 0.838 0.2482 ] Network output: [ 9.735e-05 1 -6.683e-05 1.307e-06 -5.87e-07 0.9998 9.853e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002257 Epoch 9195 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009369 0.9966 0.992 -2.154e-07 9.671e-08 -0.00733 -1.623e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003477 -0.003308 -0.007005 0.005601 0.9699 0.9743 0.006743 0.8271 0.8211 0.01674 ] Network output: [ 0.9999 0.0002064 0.0004719 -4.818e-06 2.163e-06 -0.0004658 -3.631e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2055 -0.03511 -0.1616 0.1845 0.9834 0.9932 0.2305 0.432 0.8689 0.7108 ] Network output: [ -0.009314 1.003 1.008 -2.664e-07 1.196e-07 0.007725 -2.007e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006622 0.0005888 0.004411 0.003296 0.9889 0.9919 0.00675 0.8546 0.8928 0.012 ] Network output: [ -0.000272 0.001784 1.001 -1.51e-05 6.778e-06 0.9981 -1.138e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2188 0.1032 0.3466 0.1429 0.985 0.9939 0.2195 0.436 0.8756 0.7047 ] Network output: [ 0.003781 -0.01786 0.9942 9.173e-06 -4.118e-06 1.016 6.913e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1093 0.0967 0.1841 0.1982 0.9873 0.9919 0.1094 0.7416 0.8625 0.3053 ] Network output: [ -0.003544 0.01658 1.005 9.919e-06 -4.453e-06 0.986 7.475e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09328 0.09135 0.165 0.1961 0.9852 0.9911 0.0933 0.6655 0.838 0.2482 ] Network output: [ 9.731e-05 1 -6.677e-05 1.306e-06 -5.863e-07 0.9998 9.842e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002256 Epoch 9196 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009368 0.9966 0.992 -2.154e-07 9.669e-08 -0.007329 -1.623e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003477 -0.003308 -0.007004 0.0056 0.9699 0.9743 0.006743 0.8271 0.8211 0.01674 ] Network output: [ 0.9999 0.0002062 0.0004717 -4.812e-06 2.16e-06 -0.0004655 -3.626e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2055 -0.03511 -0.1616 0.1845 0.9834 0.9932 0.2305 0.432 0.8689 0.7108 ] Network output: [ -0.009313 1.003 1.008 -2.662e-07 1.195e-07 0.007725 -2.006e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006622 0.0005889 0.004411 0.003296 0.9889 0.9919 0.00675 0.8546 0.8928 0.012 ] Network output: [ -0.0002718 0.001783 1.001 -1.508e-05 6.77e-06 0.9981 -1.137e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2188 0.1032 0.3466 0.1429 0.985 0.9939 0.2195 0.436 0.8756 0.7047 ] Network output: [ 0.003779 -0.01785 0.9942 9.162e-06 -4.113e-06 1.016 6.905e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1093 0.0967 0.1841 0.1982 0.9873 0.9919 0.1094 0.7415 0.8625 0.3053 ] Network output: [ -0.003543 0.01658 1.005 9.907e-06 -4.448e-06 0.986 7.467e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09329 0.09135 0.165 0.1961 0.9852 0.9911 0.0933 0.6655 0.838 0.2482 ] Network output: [ 9.728e-05 1 -6.671e-05 1.304e-06 -5.856e-07 0.9998 9.831e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002255 Epoch 9197 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009367 0.9966 0.992 -2.154e-07 9.668e-08 -0.007329 -1.623e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003477 -0.003308 -0.007003 0.0056 0.9699 0.9743 0.006743 0.8271 0.8211 0.01674 ] Network output: [ 0.9999 0.000206 0.0004714 -4.806e-06 2.158e-06 -0.0004651 -3.622e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2055 -0.03511 -0.1616 0.1845 0.9834 0.9932 0.2305 0.432 0.8689 0.7108 ] Network output: [ -0.009313 1.003 1.008 -2.661e-07 1.195e-07 0.007724 -2.005e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006622 0.000589 0.004411 0.003295 0.9889 0.9919 0.006751 0.8546 0.8928 0.012 ] Network output: [ -0.0002716 0.001782 1.001 -1.506e-05 6.763e-06 0.9981 -1.135e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2188 0.1032 0.3466 0.1429 0.9849 0.9939 0.2195 0.436 0.8756 0.7047 ] Network output: [ 0.003778 -0.01784 0.9942 9.152e-06 -4.109e-06 1.016 6.897e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1093 0.09671 0.1841 0.1982 0.9873 0.9919 0.1094 0.7415 0.8625 0.3053 ] Network output: [ -0.003541 0.01657 1.005 9.896e-06 -4.443e-06 0.986 7.458e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09329 0.09135 0.165 0.1961 0.9852 0.9911 0.0933 0.6655 0.838 0.2482 ] Network output: [ 9.724e-05 1 -6.666e-05 1.303e-06 -5.849e-07 0.9998 9.82e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002254 Epoch 9198 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009366 0.9966 0.992 -2.153e-07 9.666e-08 -0.007328 -1.623e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003477 -0.003308 -0.007002 0.0056 0.9699 0.9743 0.006743 0.8271 0.8211 0.01674 ] Network output: [ 0.9999 0.0002058 0.0004712 -4.801e-06 2.155e-06 -0.0004648 -3.618e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2055 -0.03511 -0.1616 0.1845 0.9834 0.9932 0.2305 0.432 0.8689 0.7108 ] Network output: [ -0.009312 1.003 1.008 -2.66e-07 1.194e-07 0.007723 -2.004e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006623 0.000589 0.004411 0.003295 0.9889 0.9919 0.006751 0.8546 0.8928 0.01199 ] Network output: [ -0.0002714 0.001782 1.001 -1.505e-05 6.755e-06 0.9981 -1.134e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2188 0.1032 0.3466 0.1429 0.9849 0.9939 0.2195 0.436 0.8756 0.7047 ] Network output: [ 0.003776 -0.01784 0.9942 9.141e-06 -4.104e-06 1.016 6.889e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1093 0.09671 0.1841 0.1982 0.9873 0.9919 0.1094 0.7415 0.8625 0.3053 ] Network output: [ -0.00354 0.01656 1.005 9.885e-06 -4.438e-06 0.986 7.45e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09329 0.09135 0.165 0.1961 0.9852 0.9911 0.09331 0.6655 0.838 0.2482 ] Network output: [ 9.721e-05 1 -6.66e-05 1.301e-06 -5.843e-07 0.9998 9.808e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002252 Epoch 9199 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009365 0.9966 0.992 -2.153e-07 9.665e-08 -0.007328 -1.622e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003477 -0.003308 -0.007002 0.005599 0.9699 0.9743 0.006743 0.8271 0.8211 0.01674 ] Network output: [ 0.9999 0.0002055 0.000471 -4.795e-06 2.153e-06 -0.0004644 -3.614e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2055 -0.03511 -0.1616 0.1845 0.9834 0.9932 0.2305 0.432 0.8689 0.7108 ] Network output: [ -0.009311 1.003 1.008 -2.658e-07 1.193e-07 0.007722 -2.003e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006623 0.0005891 0.004411 0.003295 0.9889 0.9919 0.006752 0.8545 0.8928 0.01199 ] Network output: [ -0.0002713 0.001781 1.001 -1.503e-05 6.747e-06 0.9981 -1.133e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2188 0.1032 0.3466 0.1429 0.9849 0.9939 0.2195 0.436 0.8756 0.7047 ] Network output: [ 0.003775 -0.01783 0.9942 9.131e-06 -4.099e-06 1.016 6.881e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1093 0.09672 0.1841 0.1982 0.9873 0.9919 0.1094 0.7415 0.8625 0.3053 ] Network output: [ -0.003538 0.01656 1.005 9.874e-06 -4.433e-06 0.986 7.441e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09329 0.09136 0.165 0.1961 0.9852 0.9911 0.09331 0.6655 0.838 0.2482 ] Network output: [ 9.718e-05 1 -6.654e-05 1.3e-06 -5.836e-07 0.9998 9.797e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002251 Epoch 9200 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009364 0.9966 0.992 -2.153e-07 9.663e-08 -0.007327 -1.622e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003477 -0.003308 -0.007001 0.005599 0.9699 0.9743 0.006744 0.8271 0.8211 0.01674 ] Network output: [ 0.9999 0.0002053 0.0004708 -4.79e-06 2.15e-06 -0.0004641 -3.61e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2056 -0.03511 -0.1616 0.1845 0.9834 0.9932 0.2305 0.4319 0.8689 0.7108 ] Network output: [ -0.00931 1.003 1.008 -2.657e-07 1.193e-07 0.007721 -2.002e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006624 0.0005892 0.004411 0.003295 0.9889 0.9919 0.006752 0.8545 0.8928 0.01199 ] Network output: [ -0.0002711 0.00178 1.001 -1.501e-05 6.739e-06 0.9981 -1.131e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2188 0.1032 0.3466 0.1429 0.9849 0.9939 0.2195 0.436 0.8756 0.7046 ] Network output: [ 0.003773 -0.01782 0.9942 9.12e-06 -4.094e-06 1.016 6.873e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1093 0.09672 0.1841 0.1981 0.9873 0.9919 0.1094 0.7415 0.8625 0.3053 ] Network output: [ -0.003537 0.01655 1.005 9.863e-06 -4.428e-06 0.986 7.433e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0933 0.09136 0.165 0.1961 0.9852 0.9911 0.09331 0.6655 0.8379 0.2482 ] Network output: [ 9.714e-05 1 -6.648e-05 1.299e-06 -5.829e-07 0.9998 9.786e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000225 Epoch 9201 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009363 0.9966 0.992 -2.152e-07 9.662e-08 -0.007327 -1.622e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003477 -0.003308 -0.007 0.005598 0.9699 0.9743 0.006744 0.8271 0.8211 0.01674 ] Network output: [ 0.9999 0.0002051 0.0004705 -4.784e-06 2.148e-06 -0.0004638 -3.606e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2056 -0.03512 -0.1616 0.1845 0.9834 0.9932 0.2305 0.4319 0.8689 0.7107 ] Network output: [ -0.009309 1.003 1.008 -2.656e-07 1.192e-07 0.00772 -2.002e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006624 0.0005893 0.004411 0.003294 0.9889 0.9919 0.006752 0.8545 0.8928 0.01199 ] Network output: [ -0.0002709 0.00178 1.001 -1.499e-05 6.731e-06 0.9981 -1.13e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2188 0.1032 0.3466 0.1429 0.9849 0.9939 0.2195 0.436 0.8756 0.7046 ] Network output: [ 0.003772 -0.01782 0.9942 9.11e-06 -4.09e-06 1.016 6.865e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1093 0.09672 0.1841 0.1981 0.9873 0.9919 0.1094 0.7415 0.8625 0.3053 ] Network output: [ -0.003536 0.01654 1.005 9.852e-06 -4.423e-06 0.986 7.425e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0933 0.09136 0.165 0.1961 0.9852 0.9911 0.09331 0.6655 0.8379 0.2482 ] Network output: [ 9.711e-05 1 -6.642e-05 1.297e-06 -5.823e-07 0.9998 9.775e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002249 Epoch 9202 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009362 0.9966 0.992 -2.152e-07 9.66e-08 -0.007326 -1.622e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003477 -0.003308 -0.007 0.005598 0.9699 0.9743 0.006744 0.8271 0.8211 0.01674 ] Network output: [ 0.9999 0.0002049 0.0004703 -4.779e-06 2.145e-06 -0.0004634 -3.601e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2056 -0.03512 -0.1616 0.1845 0.9834 0.9932 0.2305 0.4319 0.8689 0.7107 ] Network output: [ -0.009308 1.003 1.008 -2.655e-07 1.192e-07 0.00772 -2.001e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006625 0.0005894 0.004411 0.003294 0.9889 0.9919 0.006753 0.8545 0.8928 0.01199 ] Network output: [ -0.0002707 0.001779 1.001 -1.498e-05 6.724e-06 0.9981 -1.129e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2188 0.1032 0.3466 0.1429 0.9849 0.9939 0.2195 0.436 0.8756 0.7046 ] Network output: [ 0.00377 -0.01781 0.9942 9.099e-06 -4.085e-06 1.016 6.858e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1093 0.09673 0.1841 0.1981 0.9873 0.9919 0.1094 0.7415 0.8625 0.3053 ] Network output: [ -0.003534 0.01654 1.005 9.841e-06 -4.418e-06 0.986 7.416e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0933 0.09136 0.165 0.1961 0.9852 0.9911 0.09331 0.6654 0.8379 0.2482 ] Network output: [ 9.707e-05 1 -6.637e-05 1.296e-06 -5.816e-07 0.9998 9.764e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002248 Epoch 9203 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009361 0.9966 0.992 -2.151e-07 9.659e-08 -0.007326 -1.621e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003477 -0.003309 -0.006999 0.005597 0.9699 0.9743 0.006744 0.8271 0.8211 0.01673 ] Network output: [ 0.9999 0.0002047 0.0004701 -4.773e-06 2.143e-06 -0.0004631 -3.597e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2056 -0.03512 -0.1616 0.1845 0.9834 0.9932 0.2305 0.4319 0.8689 0.7107 ] Network output: [ -0.009307 1.003 1.008 -2.653e-07 1.191e-07 0.007719 -2e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006625 0.0005895 0.00441 0.003294 0.9889 0.9919 0.006753 0.8545 0.8928 0.01199 ] Network output: [ -0.0002705 0.001778 1.001 -1.496e-05 6.716e-06 0.9981 -1.127e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2188 0.1032 0.3466 0.1429 0.9849 0.9939 0.2196 0.436 0.8756 0.7046 ] Network output: [ 0.003769 -0.0178 0.9942 9.089e-06 -4.08e-06 1.016 6.85e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1093 0.09673 0.1841 0.1981 0.9873 0.9919 0.1094 0.7415 0.8625 0.3053 ] Network output: [ -0.003533 0.01653 1.005 9.829e-06 -4.413e-06 0.986 7.408e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0933 0.09137 0.165 0.1961 0.9852 0.9911 0.09332 0.6654 0.8379 0.2482 ] Network output: [ 9.704e-05 1 -6.631e-05 1.294e-06 -5.81e-07 0.9998 9.752e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002247 Epoch 9204 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00936 0.9966 0.992 -2.151e-07 9.657e-08 -0.007326 -1.621e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003477 -0.003309 -0.006998 0.005597 0.9699 0.9743 0.006744 0.8271 0.8211 0.01673 ] Network output: [ 0.9999 0.0002045 0.0004699 -4.768e-06 2.14e-06 -0.0004627 -3.593e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2056 -0.03512 -0.1615 0.1845 0.9834 0.9932 0.2305 0.4319 0.8689 0.7107 ] Network output: [ -0.009306 1.003 1.008 -2.652e-07 1.191e-07 0.007718 -1.999e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006626 0.0005896 0.00441 0.003294 0.9889 0.9919 0.006754 0.8545 0.8928 0.01199 ] Network output: [ -0.0002703 0.001777 1.001 -1.494e-05 6.708e-06 0.9981 -1.126e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2188 0.1032 0.3466 0.1429 0.9849 0.9939 0.2196 0.4359 0.8756 0.7046 ] Network output: [ 0.003767 -0.0178 0.9942 9.078e-06 -4.076e-06 1.016 6.842e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1093 0.09674 0.1841 0.1981 0.9873 0.9919 0.1094 0.7414 0.8625 0.3053 ] Network output: [ -0.003531 0.01652 1.005 9.818e-06 -4.408e-06 0.986 7.399e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09331 0.09137 0.165 0.1961 0.9852 0.9911 0.09332 0.6654 0.8379 0.2482 ] Network output: [ 9.7e-05 1 -6.625e-05 1.293e-06 -5.803e-07 0.9998 9.741e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002245 Epoch 9205 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009358 0.9966 0.992 -2.151e-07 9.656e-08 -0.007325 -1.621e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003477 -0.003309 -0.006998 0.005596 0.9699 0.9743 0.006745 0.8271 0.8211 0.01673 ] Network output: [ 0.9999 0.0002043 0.0004696 -4.762e-06 2.138e-06 -0.0004624 -3.589e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2056 -0.03512 -0.1615 0.1844 0.9834 0.9932 0.2305 0.4319 0.8689 0.7107 ] Network output: [ -0.009305 1.003 1.008 -2.651e-07 1.19e-07 0.007717 -1.998e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006626 0.0005896 0.00441 0.003293 0.9889 0.9919 0.006754 0.8545 0.8927 0.01199 ] Network output: [ -0.0002701 0.001777 1.001 -1.492e-05 6.7e-06 0.9981 -1.125e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2188 0.1032 0.3466 0.1429 0.9849 0.9939 0.2196 0.4359 0.8756 0.7046 ] Network output: [ 0.003766 -0.01779 0.9942 9.068e-06 -4.071e-06 1.016 6.834e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1093 0.09674 0.1841 0.1981 0.9873 0.9919 0.1094 0.7414 0.8625 0.3053 ] Network output: [ -0.00353 0.01652 1.005 9.807e-06 -4.403e-06 0.9861 7.391e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09331 0.09137 0.165 0.1961 0.9852 0.9911 0.09332 0.6654 0.8379 0.2482 ] Network output: [ 9.697e-05 1 -6.62e-05 1.291e-06 -5.796e-07 0.9998 9.73e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002244 Epoch 9206 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009357 0.9966 0.992 -2.15e-07 9.654e-08 -0.007325 -1.621e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003477 -0.003309 -0.006997 0.005596 0.9699 0.9743 0.006745 0.8271 0.821 0.01673 ] Network output: [ 0.9999 0.0002041 0.0004694 -4.757e-06 2.135e-06 -0.0004621 -3.585e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2056 -0.03512 -0.1615 0.1844 0.9834 0.9932 0.2305 0.4319 0.8689 0.7107 ] Network output: [ -0.009304 1.003 1.008 -2.649e-07 1.189e-07 0.007716 -1.997e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006627 0.0005897 0.00441 0.003293 0.9889 0.9919 0.006755 0.8545 0.8927 0.01199 ] Network output: [ -0.0002699 0.001776 1.001 -1.491e-05 6.692e-06 0.9981 -1.123e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2188 0.1032 0.3466 0.1429 0.9849 0.9939 0.2196 0.4359 0.8756 0.7046 ] Network output: [ 0.003764 -0.01778 0.9942 9.058e-06 -4.066e-06 1.016 6.826e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1093 0.09675 0.1841 0.1981 0.9873 0.9919 0.1094 0.7414 0.8625 0.3053 ] Network output: [ -0.003528 0.01651 1.005 9.796e-06 -4.398e-06 0.9861 7.383e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09331 0.09137 0.165 0.1961 0.9852 0.9911 0.09332 0.6654 0.8379 0.2482 ] Network output: [ 9.694e-05 1 -6.614e-05 1.29e-06 -5.79e-07 0.9998 9.719e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002243 Epoch 9207 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009356 0.9966 0.992 -2.15e-07 9.652e-08 -0.007324 -1.62e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003478 -0.003309 -0.006996 0.005596 0.9699 0.9743 0.006745 0.827 0.821 0.01673 ] Network output: [ 0.9999 0.0002039 0.0004692 -4.751e-06 2.133e-06 -0.0004617 -3.581e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2056 -0.03512 -0.1615 0.1844 0.9834 0.9932 0.2306 0.4319 0.8689 0.7107 ] Network output: [ -0.009304 1.003 1.008 -2.648e-07 1.189e-07 0.007715 -1.996e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006627 0.0005898 0.00441 0.003293 0.9889 0.9919 0.006755 0.8545 0.8927 0.01199 ] Network output: [ -0.0002697 0.001775 1.001 -1.489e-05 6.685e-06 0.9981 -1.122e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2188 0.1032 0.3466 0.1429 0.9849 0.9939 0.2196 0.4359 0.8756 0.7046 ] Network output: [ 0.003763 -0.01777 0.9942 9.047e-06 -4.062e-06 1.016 6.818e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1093 0.09675 0.1841 0.1981 0.9873 0.9919 0.1094 0.7414 0.8624 0.3053 ] Network output: [ -0.003527 0.0165 1.005 9.785e-06 -4.393e-06 0.9861 7.374e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09331 0.09137 0.165 0.1961 0.9852 0.9911 0.09333 0.6654 0.8379 0.2482 ] Network output: [ 9.69e-05 1 -6.608e-05 1.288e-06 -5.783e-07 0.9998 9.708e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002242 Epoch 9208 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009355 0.9966 0.992 -2.15e-07 9.651e-08 -0.007324 -1.62e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003478 -0.003309 -0.006996 0.005595 0.9699 0.9743 0.006745 0.827 0.821 0.01673 ] Network output: [ 0.9999 0.0002036 0.000469 -4.745e-06 2.13e-06 -0.0004614 -3.576e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2056 -0.03512 -0.1615 0.1844 0.9834 0.9932 0.2306 0.4319 0.8689 0.7107 ] Network output: [ -0.009303 1.003 1.008 -2.647e-07 1.188e-07 0.007715 -1.995e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006627 0.0005899 0.00441 0.003293 0.9889 0.9919 0.006756 0.8545 0.8927 0.01199 ] Network output: [ -0.0002696 0.001774 1.001 -1.487e-05 6.677e-06 0.9981 -1.121e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2189 0.1033 0.3466 0.1429 0.9849 0.9939 0.2196 0.4359 0.8756 0.7046 ] Network output: [ 0.003761 -0.01777 0.9942 9.037e-06 -4.057e-06 1.016 6.81e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1093 0.09676 0.1841 0.1981 0.9873 0.9919 0.1094 0.7414 0.8624 0.3053 ] Network output: [ -0.003526 0.0165 1.005 9.774e-06 -4.388e-06 0.9861 7.366e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09331 0.09138 0.165 0.1961 0.9852 0.9911 0.09333 0.6654 0.8379 0.2482 ] Network output: [ 9.687e-05 1 -6.603e-05 1.287e-06 -5.776e-07 0.9998 9.697e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002241 Epoch 9209 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009354 0.9966 0.992 -2.149e-07 9.649e-08 -0.007323 -1.62e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003478 -0.003309 -0.006995 0.005595 0.9699 0.9743 0.006746 0.827 0.821 0.01673 ] Network output: [ 0.9999 0.0002034 0.0004687 -4.74e-06 2.128e-06 -0.000461 -3.572e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2056 -0.03512 -0.1615 0.1844 0.9834 0.9932 0.2306 0.4319 0.8689 0.7107 ] Network output: [ -0.009302 1.003 1.008 -2.645e-07 1.188e-07 0.007714 -1.994e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006628 0.00059 0.00441 0.003292 0.9889 0.9919 0.006756 0.8545 0.8927 0.01199 ] Network output: [ -0.0002694 0.001774 1.001 -1.486e-05 6.669e-06 0.9981 -1.12e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2189 0.1033 0.3466 0.1428 0.9849 0.9939 0.2196 0.4359 0.8756 0.7046 ] Network output: [ 0.00376 -0.01776 0.9942 9.026e-06 -4.052e-06 1.016 6.803e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1093 0.09676 0.1841 0.1981 0.9873 0.9919 0.1094 0.7414 0.8624 0.3053 ] Network output: [ -0.003524 0.01649 1.005 9.763e-06 -4.383e-06 0.9861 7.358e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09332 0.09138 0.165 0.1961 0.9852 0.9911 0.09333 0.6653 0.8379 0.2482 ] Network output: [ 9.683e-05 1 -6.597e-05 1.285e-06 -5.77e-07 0.9998 9.686e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000224 Epoch 9210 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009353 0.9966 0.992 -2.149e-07 9.647e-08 -0.007323 -1.62e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003478 -0.003309 -0.006994 0.005594 0.9699 0.9743 0.006746 0.827 0.821 0.01673 ] Network output: [ 0.9999 0.0002032 0.0004685 -4.734e-06 2.125e-06 -0.0004607 -3.568e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2056 -0.03512 -0.1615 0.1844 0.9834 0.9932 0.2306 0.4319 0.8689 0.7107 ] Network output: [ -0.009301 1.003 1.008 -2.644e-07 1.187e-07 0.007713 -1.993e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006628 0.0005901 0.00441 0.003292 0.9889 0.9919 0.006757 0.8545 0.8927 0.01199 ] Network output: [ -0.0002692 0.001773 1.001 -1.484e-05 6.662e-06 0.9981 -1.118e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2189 0.1033 0.3466 0.1428 0.9849 0.9939 0.2196 0.4359 0.8756 0.7046 ] Network output: [ 0.003758 -0.01775 0.9942 9.016e-06 -4.048e-06 1.016 6.795e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.09676 0.1841 0.1981 0.9873 0.9919 0.1094 0.7414 0.8624 0.3053 ] Network output: [ -0.003523 0.01648 1.005 9.752e-06 -4.378e-06 0.9861 7.349e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09332 0.09138 0.165 0.1961 0.9852 0.9911 0.09333 0.6653 0.8379 0.2482 ] Network output: [ 9.68e-05 1 -6.591e-05 1.284e-06 -5.763e-07 0.9998 9.675e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002238 Epoch 9211 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009352 0.9966 0.992 -2.149e-07 9.646e-08 -0.007322 -1.619e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003478 -0.003309 -0.006994 0.005594 0.9699 0.9743 0.006746 0.827 0.821 0.01673 ] Network output: [ 0.9999 0.000203 0.0004683 -4.729e-06 2.123e-06 -0.0004604 -3.564e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2056 -0.03513 -0.1615 0.1844 0.9834 0.9932 0.2306 0.4319 0.8689 0.7107 ] Network output: [ -0.0093 1.003 1.008 -2.643e-07 1.186e-07 0.007712 -1.992e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006629 0.0005902 0.00441 0.003292 0.9889 0.9919 0.006757 0.8545 0.8927 0.01198 ] Network output: [ -0.000269 0.001772 1.001 -1.482e-05 6.654e-06 0.9981 -1.117e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2189 0.1033 0.3466 0.1428 0.9849 0.9939 0.2196 0.4359 0.8756 0.7046 ] Network output: [ 0.003757 -0.01775 0.9942 9.006e-06 -4.043e-06 1.016 6.787e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.09677 0.1841 0.1981 0.9873 0.9919 0.1094 0.7413 0.8624 0.3053 ] Network output: [ -0.003521 0.01648 1.005 9.741e-06 -4.373e-06 0.9861 7.341e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09332 0.09138 0.165 0.1961 0.9852 0.9911 0.09333 0.6653 0.8379 0.2482 ] Network output: [ 9.677e-05 1 -6.586e-05 1.282e-06 -5.757e-07 0.9998 9.664e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002237 Epoch 9212 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009351 0.9966 0.992 -2.148e-07 9.644e-08 -0.007322 -1.619e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003478 -0.00331 -0.006993 0.005593 0.9699 0.9743 0.006746 0.827 0.821 0.01673 ] Network output: [ 0.9999 0.0002028 0.0004681 -4.723e-06 2.121e-06 -0.00046 -3.56e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2056 -0.03513 -0.1615 0.1844 0.9834 0.9932 0.2306 0.4319 0.8688 0.7107 ] Network output: [ -0.009299 1.003 1.008 -2.641e-07 1.186e-07 0.007711 -1.991e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006629 0.0005902 0.00441 0.003292 0.9889 0.9919 0.006758 0.8545 0.8927 0.01198 ] Network output: [ -0.0002688 0.001772 1.001 -1.48e-05 6.646e-06 0.9981 -1.116e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2189 0.1033 0.3467 0.1428 0.9849 0.9939 0.2196 0.4359 0.8756 0.7046 ] Network output: [ 0.003755 -0.01774 0.9942 8.995e-06 -4.038e-06 1.016 6.779e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.09677 0.1841 0.1981 0.9873 0.9919 0.1094 0.7413 0.8624 0.3053 ] Network output: [ -0.00352 0.01647 1.005 9.73e-06 -4.368e-06 0.9861 7.333e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09332 0.09139 0.165 0.1961 0.9852 0.9911 0.09334 0.6653 0.8379 0.2482 ] Network output: [ 9.673e-05 1 -6.58e-05 1.281e-06 -5.75e-07 0.9998 9.653e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002236 Epoch 9213 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00935 0.9966 0.992 -2.148e-07 9.642e-08 -0.007322 -1.619e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003478 -0.00331 -0.006992 0.005593 0.9699 0.9743 0.006746 0.827 0.821 0.01672 ] Network output: [ 0.9999 0.0002026 0.0004679 -4.718e-06 2.118e-06 -0.0004597 -3.556e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2056 -0.03513 -0.1614 0.1844 0.9834 0.9932 0.2306 0.4319 0.8688 0.7107 ] Network output: [ -0.009298 1.003 1.008 -2.64e-07 1.185e-07 0.007711 -1.99e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00663 0.0005903 0.00441 0.003291 0.9889 0.9919 0.006758 0.8545 0.8927 0.01198 ] Network output: [ -0.0002686 0.001771 1.001 -1.479e-05 6.638e-06 0.9981 -1.114e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2189 0.1033 0.3467 0.1428 0.9849 0.9939 0.2196 0.4359 0.8756 0.7046 ] Network output: [ 0.003754 -0.01773 0.9942 8.985e-06 -4.034e-06 1.016 6.771e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.09678 0.1841 0.1981 0.9873 0.9919 0.1094 0.7413 0.8624 0.3053 ] Network output: [ -0.003519 0.01646 1.005 9.719e-06 -4.363e-06 0.9861 7.325e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09333 0.09139 0.165 0.1961 0.9852 0.9911 0.09334 0.6653 0.8379 0.2482 ] Network output: [ 9.67e-05 1 -6.574e-05 1.279e-06 -5.744e-07 0.9998 9.642e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002235 Epoch 9214 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009349 0.9966 0.992 -2.147e-07 9.641e-08 -0.007321 -1.618e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003478 -0.00331 -0.006992 0.005592 0.9699 0.9743 0.006747 0.827 0.821 0.01672 ] Network output: [ 0.9999 0.0002024 0.0004676 -4.713e-06 2.116e-06 -0.0004593 -3.552e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2056 -0.03513 -0.1614 0.1844 0.9834 0.9932 0.2306 0.4319 0.8688 0.7107 ] Network output: [ -0.009297 1.003 1.008 -2.639e-07 1.185e-07 0.00771 -1.989e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00663 0.0005904 0.00441 0.003291 0.9889 0.9919 0.006758 0.8545 0.8927 0.01198 ] Network output: [ -0.0002684 0.00177 1.001 -1.477e-05 6.631e-06 0.9981 -1.113e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2189 0.1033 0.3467 0.1428 0.9849 0.9939 0.2196 0.4359 0.8756 0.7046 ] Network output: [ 0.003752 -0.01773 0.9942 8.975e-06 -4.029e-06 1.016 6.764e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.09678 0.1841 0.1981 0.9873 0.9919 0.1094 0.7413 0.8624 0.3053 ] Network output: [ -0.003517 0.01645 1.005 9.708e-06 -4.358e-06 0.9861 7.316e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09333 0.09139 0.165 0.1961 0.9852 0.9911 0.09334 0.6653 0.8379 0.2482 ] Network output: [ 9.666e-05 1 -6.569e-05 1.278e-06 -5.737e-07 0.9998 9.631e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002234 Epoch 9215 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009348 0.9966 0.992 -2.147e-07 9.639e-08 -0.007321 -1.618e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003478 -0.00331 -0.006991 0.005592 0.9699 0.9743 0.006747 0.827 0.821 0.01672 ] Network output: [ 0.9999 0.0002022 0.0004674 -4.707e-06 2.113e-06 -0.000459 -3.547e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2057 -0.03513 -0.1614 0.1844 0.9834 0.9932 0.2306 0.4319 0.8688 0.7107 ] Network output: [ -0.009296 1.003 1.008 -2.637e-07 1.184e-07 0.007709 -1.988e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006631 0.0005905 0.00441 0.003291 0.9889 0.9919 0.006759 0.8545 0.8927 0.01198 ] Network output: [ -0.0002682 0.001769 1.001 -1.475e-05 6.623e-06 0.9981 -1.112e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2189 0.1033 0.3467 0.1428 0.9849 0.9939 0.2197 0.4359 0.8756 0.7046 ] Network output: [ 0.003751 -0.01772 0.9942 8.964e-06 -4.024e-06 1.016 6.756e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.09679 0.1841 0.1981 0.9873 0.9919 0.1095 0.7413 0.8624 0.3053 ] Network output: [ -0.003516 0.01645 1.005 9.697e-06 -4.353e-06 0.9861 7.308e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09333 0.09139 0.165 0.1961 0.9852 0.9911 0.09334 0.6653 0.8379 0.2482 ] Network output: [ 9.663e-05 1 -6.563e-05 1.276e-06 -5.73e-07 0.9998 9.62e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002233 Epoch 9216 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009347 0.9966 0.992 -2.147e-07 9.637e-08 -0.00732 -1.618e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003478 -0.00331 -0.00699 0.005592 0.9699 0.9743 0.006747 0.827 0.821 0.01672 ] Network output: [ 0.9999 0.000202 0.0004672 -4.702e-06 2.111e-06 -0.0004587 -3.543e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2057 -0.03513 -0.1614 0.1844 0.9834 0.9932 0.2306 0.4318 0.8688 0.7107 ] Network output: [ -0.009295 1.003 1.008 -2.636e-07 1.183e-07 0.007708 -1.987e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006631 0.0005906 0.00441 0.00329 0.9889 0.9919 0.006759 0.8544 0.8927 0.01198 ] Network output: [ -0.0002681 0.001769 1.001 -1.474e-05 6.615e-06 0.9981 -1.111e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2189 0.1033 0.3467 0.1428 0.9849 0.9939 0.2197 0.4359 0.8756 0.7046 ] Network output: [ 0.003749 -0.01771 0.9942 8.954e-06 -4.02e-06 1.016 6.748e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.09679 0.1841 0.1981 0.9873 0.9919 0.1095 0.7413 0.8624 0.3053 ] Network output: [ -0.003514 0.01644 1.005 9.686e-06 -4.348e-06 0.9861 7.3e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09333 0.09139 0.165 0.1961 0.9852 0.9911 0.09335 0.6652 0.8379 0.2482 ] Network output: [ 9.66e-05 1 -6.558e-05 1.275e-06 -5.724e-07 0.9998 9.609e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002231 Epoch 9217 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009346 0.9966 0.992 -2.146e-07 9.636e-08 -0.00732 -1.618e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003478 -0.00331 -0.00699 0.005591 0.9699 0.9743 0.006747 0.827 0.821 0.01672 ] Network output: [ 0.9999 0.0002018 0.000467 -4.696e-06 2.108e-06 -0.0004583 -3.539e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2057 -0.03513 -0.1614 0.1844 0.9834 0.9932 0.2306 0.4318 0.8688 0.7107 ] Network output: [ -0.009295 1.003 1.008 -2.635e-07 1.183e-07 0.007707 -1.986e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006631 0.0005907 0.004409 0.00329 0.9889 0.9919 0.00676 0.8544 0.8927 0.01198 ] Network output: [ -0.0002679 0.001768 1.001 -1.472e-05 6.608e-06 0.9981 -1.109e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2189 0.1033 0.3467 0.1428 0.9849 0.9939 0.2197 0.4359 0.8756 0.7046 ] Network output: [ 0.003748 -0.0177 0.9942 8.944e-06 -4.015e-06 1.016 6.74e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.0968 0.1841 0.1981 0.9873 0.9919 0.1095 0.7413 0.8624 0.3053 ] Network output: [ -0.003513 0.01643 1.005 9.675e-06 -4.344e-06 0.9861 7.292e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09333 0.0914 0.165 0.1961 0.9852 0.9911 0.09335 0.6652 0.8379 0.2483 ] Network output: [ 9.656e-05 1 -6.552e-05 1.274e-06 -5.717e-07 0.9998 9.598e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000223 Epoch 9218 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009345 0.9966 0.992 -2.146e-07 9.634e-08 -0.007319 -1.617e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003478 -0.00331 -0.006989 0.005591 0.9699 0.9743 0.006748 0.827 0.821 0.01672 ] Network output: [ 0.9999 0.0002015 0.0004667 -4.691e-06 2.106e-06 -0.000458 -3.535e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2057 -0.03513 -0.1614 0.1844 0.9834 0.9932 0.2306 0.4318 0.8688 0.7107 ] Network output: [ -0.009294 1.003 1.008 -2.633e-07 1.182e-07 0.007706 -1.985e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006632 0.0005908 0.004409 0.00329 0.9889 0.9919 0.00676 0.8544 0.8927 0.01198 ] Network output: [ -0.0002677 0.001767 1.001 -1.47e-05 6.6e-06 0.9981 -1.108e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2189 0.1033 0.3467 0.1428 0.9849 0.9939 0.2197 0.4359 0.8756 0.7046 ] Network output: [ 0.003746 -0.0177 0.9942 8.933e-06 -4.011e-06 1.016 6.733e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.0968 0.1841 0.1981 0.9873 0.9919 0.1095 0.7412 0.8624 0.3053 ] Network output: [ -0.003511 0.01643 1.005 9.664e-06 -4.339e-06 0.9861 7.283e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09334 0.0914 0.165 0.1961 0.9852 0.9911 0.09335 0.6652 0.8379 0.2483 ] Network output: [ 9.653e-05 1 -6.546e-05 1.272e-06 -5.711e-07 0.9998 9.587e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002229 Epoch 9219 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009344 0.9966 0.992 -2.146e-07 9.632e-08 -0.007319 -1.617e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003479 -0.00331 -0.006988 0.00559 0.9699 0.9743 0.006748 0.827 0.821 0.01672 ] Network output: [ 0.9999 0.0002013 0.0004665 -4.685e-06 2.103e-06 -0.0004577 -3.531e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2057 -0.03513 -0.1614 0.1844 0.9834 0.9932 0.2306 0.4318 0.8688 0.7107 ] Network output: [ -0.009293 1.003 1.008 -2.632e-07 1.182e-07 0.007706 -1.984e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006632 0.0005908 0.004409 0.00329 0.9889 0.9919 0.006761 0.8544 0.8927 0.01198 ] Network output: [ -0.0002675 0.001766 1.001 -1.468e-05 6.592e-06 0.9981 -1.107e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2189 0.1033 0.3467 0.1428 0.9849 0.9939 0.2197 0.4358 0.8756 0.7046 ] Network output: [ 0.003745 -0.01769 0.9942 8.923e-06 -4.006e-06 1.016 6.725e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.0968 0.1841 0.1981 0.9873 0.9919 0.1095 0.7412 0.8624 0.3053 ] Network output: [ -0.00351 0.01642 1.005 9.653e-06 -4.334e-06 0.9861 7.275e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09334 0.0914 0.165 0.1961 0.9852 0.9911 0.09335 0.6652 0.8379 0.2483 ] Network output: [ 9.65e-05 1 -6.541e-05 1.271e-06 -5.704e-07 0.9998 9.576e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002228 Epoch 9220 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009343 0.9966 0.992 -2.145e-07 9.63e-08 -0.007318 -1.617e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003479 -0.00331 -0.006987 0.00559 0.9699 0.9743 0.006748 0.827 0.821 0.01672 ] Network output: [ 0.9999 0.0002011 0.0004663 -4.68e-06 2.101e-06 -0.0004573 -3.527e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2057 -0.03513 -0.1614 0.1844 0.9834 0.9932 0.2307 0.4318 0.8688 0.7107 ] Network output: [ -0.009292 1.003 1.008 -2.631e-07 1.181e-07 0.007705 -1.983e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006633 0.0005909 0.004409 0.003289 0.9889 0.9919 0.006761 0.8544 0.8927 0.01198 ] Network output: [ -0.0002673 0.001766 1.001 -1.467e-05 6.585e-06 0.9981 -1.105e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.219 0.1033 0.3467 0.1428 0.9849 0.9939 0.2197 0.4358 0.8756 0.7045 ] Network output: [ 0.003743 -0.01768 0.9942 8.913e-06 -4.001e-06 1.016 6.717e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.09681 0.1841 0.1981 0.9873 0.9919 0.1095 0.7412 0.8624 0.3053 ] Network output: [ -0.003509 0.01641 1.005 9.642e-06 -4.329e-06 0.9861 7.267e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09334 0.0914 0.165 0.1961 0.9852 0.9911 0.09336 0.6652 0.8379 0.2483 ] Network output: [ 9.646e-05 1 -6.535e-05 1.269e-06 -5.698e-07 0.9998 9.565e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002227 Epoch 9221 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009342 0.9966 0.992 -2.145e-07 9.628e-08 -0.007318 -1.616e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003479 -0.00331 -0.006987 0.005589 0.9699 0.9743 0.006748 0.827 0.821 0.01672 ] Network output: [ 0.9999 0.0002009 0.0004661 -4.674e-06 2.098e-06 -0.000457 -3.523e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2057 -0.03514 -0.1614 0.1844 0.9834 0.9932 0.2307 0.4318 0.8688 0.7107 ] Network output: [ -0.009291 1.003 1.008 -2.629e-07 1.18e-07 0.007704 -1.982e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006633 0.000591 0.004409 0.003289 0.9889 0.9919 0.006762 0.8544 0.8927 0.01198 ] Network output: [ -0.0002671 0.001765 1.001 -1.465e-05 6.577e-06 0.9981 -1.104e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.219 0.1033 0.3467 0.1428 0.9849 0.9939 0.2197 0.4358 0.8756 0.7045 ] Network output: [ 0.003742 -0.01768 0.9942 8.903e-06 -3.997e-06 1.016 6.709e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.09681 0.1841 0.1981 0.9873 0.9919 0.1095 0.7412 0.8624 0.3053 ] Network output: [ -0.003507 0.01641 1.005 9.632e-06 -4.324e-06 0.9861 7.259e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09334 0.09141 0.165 0.1961 0.9852 0.9911 0.09336 0.6652 0.8379 0.2483 ] Network output: [ 9.643e-05 1 -6.53e-05 1.268e-06 -5.691e-07 0.9998 9.554e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002226 Epoch 9222 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009341 0.9966 0.9921 -2.144e-07 9.627e-08 -0.007318 -1.616e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003479 -0.003311 -0.006986 0.005589 0.9699 0.9743 0.006748 0.827 0.821 0.01671 ] Network output: [ 0.9999 0.0002007 0.0004659 -4.669e-06 2.096e-06 -0.0004567 -3.519e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2057 -0.03514 -0.1614 0.1844 0.9834 0.9932 0.2307 0.4318 0.8688 0.7106 ] Network output: [ -0.00929 1.003 1.008 -2.628e-07 1.18e-07 0.007703 -1.981e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006634 0.0005911 0.004409 0.003289 0.9889 0.9919 0.006762 0.8544 0.8927 0.01198 ] Network output: [ -0.0002669 0.001764 1.001 -1.463e-05 6.57e-06 0.9981 -1.103e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.219 0.1033 0.3467 0.1428 0.9849 0.9939 0.2197 0.4358 0.8756 0.7045 ] Network output: [ 0.00374 -0.01767 0.9942 8.892e-06 -3.992e-06 1.016 6.702e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.09682 0.1841 0.1981 0.9873 0.9919 0.1095 0.7412 0.8624 0.3053 ] Network output: [ -0.003506 0.0164 1.005 9.621e-06 -4.319e-06 0.9861 7.25e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09335 0.09141 0.165 0.1961 0.9852 0.9911 0.09336 0.6652 0.8379 0.2483 ] Network output: [ 9.639e-05 1 -6.524e-05 1.266e-06 -5.685e-07 0.9998 9.543e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002224 Epoch 9223 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00934 0.9966 0.9921 -2.144e-07 9.625e-08 -0.007317 -1.616e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003479 -0.003311 -0.006985 0.005588 0.9699 0.9743 0.006749 0.827 0.821 0.01671 ] Network output: [ 0.9999 0.0002005 0.0004656 -4.663e-06 2.094e-06 -0.0004563 -3.515e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2057 -0.03514 -0.1613 0.1844 0.9834 0.9932 0.2307 0.4318 0.8688 0.7106 ] Network output: [ -0.009289 1.003 1.008 -2.627e-07 1.179e-07 0.007702 -1.98e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006634 0.0005912 0.004409 0.003289 0.9889 0.9919 0.006763 0.8544 0.8927 0.01197 ] Network output: [ -0.0002667 0.001764 1.001 -1.462e-05 6.562e-06 0.9981 -1.102e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.219 0.1033 0.3467 0.1428 0.9849 0.9939 0.2197 0.4358 0.8756 0.7045 ] Network output: [ 0.003739 -0.01766 0.9942 8.882e-06 -3.988e-06 1.016 6.694e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.09682 0.1841 0.1981 0.9873 0.9919 0.1095 0.7412 0.8624 0.3053 ] Network output: [ -0.003504 0.01639 1.005 9.61e-06 -4.314e-06 0.9861 7.242e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09335 0.09141 0.165 0.1961 0.9852 0.9911 0.09336 0.6652 0.8379 0.2483 ] Network output: [ 9.636e-05 1 -6.519e-05 1.265e-06 -5.678e-07 0.9998 9.532e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002223 Epoch 9224 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009339 0.9966 0.9921 -2.144e-07 9.623e-08 -0.007317 -1.615e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003479 -0.003311 -0.006985 0.005588 0.9699 0.9743 0.006749 0.827 0.821 0.01671 ] Network output: [ 0.9999 0.0002003 0.0004654 -4.658e-06 2.091e-06 -0.000456 -3.51e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2057 -0.03514 -0.1613 0.1844 0.9834 0.9932 0.2307 0.4318 0.8688 0.7106 ] Network output: [ -0.009288 1.003 1.008 -2.625e-07 1.179e-07 0.007702 -1.979e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006635 0.0005913 0.004409 0.003288 0.9889 0.9919 0.006763 0.8544 0.8927 0.01197 ] Network output: [ -0.0002666 0.001763 1.001 -1.46e-05 6.554e-06 0.9981 -1.1e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.219 0.1033 0.3467 0.1428 0.9849 0.9939 0.2197 0.4358 0.8755 0.7045 ] Network output: [ 0.003737 -0.01766 0.9942 8.872e-06 -3.983e-06 1.016 6.686e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.09683 0.1841 0.1981 0.9873 0.9919 0.1095 0.7412 0.8624 0.3053 ] Network output: [ -0.003503 0.01639 1.005 9.599e-06 -4.309e-06 0.9861 7.234e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09335 0.09141 0.165 0.1961 0.9852 0.9911 0.09336 0.6651 0.8379 0.2483 ] Network output: [ 9.633e-05 1 -6.513e-05 1.263e-06 -5.672e-07 0.9998 9.521e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002222 Epoch 9225 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009338 0.9966 0.9921 -2.143e-07 9.621e-08 -0.007316 -1.615e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003479 -0.003311 -0.006984 0.005588 0.9699 0.9743 0.006749 0.827 0.821 0.01671 ] Network output: [ 0.9999 0.0002001 0.0004652 -4.653e-06 2.089e-06 -0.0004557 -3.506e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2057 -0.03514 -0.1613 0.1844 0.9834 0.9932 0.2307 0.4318 0.8688 0.7106 ] Network output: [ -0.009287 1.003 1.008 -2.624e-07 1.178e-07 0.007701 -1.978e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006635 0.0005914 0.004409 0.003288 0.9889 0.9919 0.006763 0.8544 0.8927 0.01197 ] Network output: [ -0.0002664 0.001762 1.001 -1.458e-05 6.547e-06 0.9981 -1.099e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.219 0.1033 0.3467 0.1428 0.9849 0.9939 0.2197 0.4358 0.8755 0.7045 ] Network output: [ 0.003736 -0.01765 0.9942 8.862e-06 -3.978e-06 1.016 6.679e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.09683 0.1841 0.1981 0.9873 0.9919 0.1095 0.7412 0.8624 0.3053 ] Network output: [ -0.003501 0.01638 1.005 9.588e-06 -4.304e-06 0.9861 7.226e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09335 0.09142 0.165 0.1961 0.9852 0.9911 0.09337 0.6651 0.8379 0.2483 ] Network output: [ 9.629e-05 1 -6.508e-05 1.262e-06 -5.665e-07 0.9998 9.51e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002221 Epoch 9226 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009337 0.9966 0.9921 -2.143e-07 9.619e-08 -0.007316 -1.615e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003479 -0.003311 -0.006983 0.005587 0.9699 0.9743 0.006749 0.8269 0.821 0.01671 ] Network output: [ 0.9999 0.0001999 0.000465 -4.647e-06 2.086e-06 -0.0004553 -3.502e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2057 -0.03514 -0.1613 0.1844 0.9834 0.9932 0.2307 0.4318 0.8688 0.7106 ] Network output: [ -0.009286 1.003 1.008 -2.623e-07 1.177e-07 0.0077 -1.977e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006635 0.0005914 0.004409 0.003288 0.9889 0.9919 0.006764 0.8544 0.8927 0.01197 ] Network output: [ -0.0002662 0.001761 1.001 -1.457e-05 6.539e-06 0.9981 -1.098e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.219 0.1033 0.3467 0.1428 0.9849 0.9939 0.2197 0.4358 0.8755 0.7045 ] Network output: [ 0.003734 -0.01764 0.9942 8.852e-06 -3.974e-06 1.016 6.671e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.09683 0.1841 0.1981 0.9873 0.9919 0.1095 0.7411 0.8624 0.3053 ] Network output: [ -0.0035 0.01637 1.005 9.577e-06 -4.3e-06 0.9861 7.218e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09335 0.09142 0.165 0.1961 0.9852 0.9911 0.09337 0.6651 0.8379 0.2483 ] Network output: [ 9.626e-05 1 -6.502e-05 1.26e-06 -5.659e-07 0.9998 9.499e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000222 Epoch 9227 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009336 0.9966 0.9921 -2.142e-07 9.617e-08 -0.007315 -1.614e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003479 -0.003311 -0.006983 0.005587 0.9699 0.9743 0.00675 0.8269 0.821 0.01671 ] Network output: [ 0.9999 0.0001997 0.0004647 -4.642e-06 2.084e-06 -0.000455 -3.498e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2057 -0.03514 -0.1613 0.1844 0.9834 0.9932 0.2307 0.4318 0.8688 0.7106 ] Network output: [ -0.009286 1.003 1.008 -2.621e-07 1.177e-07 0.007699 -1.976e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006636 0.0005915 0.004409 0.003288 0.9889 0.9919 0.006764 0.8544 0.8927 0.01197 ] Network output: [ -0.000266 0.001761 1.001 -1.455e-05 6.532e-06 0.9981 -1.096e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.219 0.1033 0.3467 0.1428 0.9849 0.9939 0.2197 0.4358 0.8755 0.7045 ] Network output: [ 0.003733 -0.01764 0.9942 8.841e-06 -3.969e-06 1.016 6.663e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.09684 0.1841 0.1981 0.9873 0.9919 0.1095 0.7411 0.8624 0.3053 ] Network output: [ -0.003499 0.01637 1.005 9.566e-06 -4.295e-06 0.9861 7.21e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09336 0.09142 0.165 0.1961 0.9852 0.9911 0.09337 0.6651 0.8378 0.2483 ] Network output: [ 9.623e-05 1 -6.497e-05 1.259e-06 -5.652e-07 0.9998 9.488e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002219 Epoch 9228 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009335 0.9966 0.9921 -2.142e-07 9.616e-08 -0.007315 -1.614e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003479 -0.003311 -0.006982 0.005586 0.9699 0.9743 0.00675 0.8269 0.821 0.01671 ] Network output: [ 0.9999 0.0001995 0.0004645 -4.636e-06 2.081e-06 -0.0004547 -3.494e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2057 -0.03514 -0.1613 0.1844 0.9834 0.9932 0.2307 0.4318 0.8688 0.7106 ] Network output: [ -0.009285 1.003 1.008 -2.62e-07 1.176e-07 0.007698 -1.975e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006636 0.0005916 0.004409 0.003287 0.9889 0.9919 0.006765 0.8544 0.8927 0.01197 ] Network output: [ -0.0002658 0.00176 1.001 -1.453e-05 6.524e-06 0.9981 -1.095e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.219 0.1034 0.3467 0.1428 0.9849 0.9939 0.2198 0.4358 0.8755 0.7045 ] Network output: [ 0.003731 -0.01763 0.9942 8.831e-06 -3.965e-06 1.016 6.655e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.09684 0.1841 0.1981 0.9873 0.9919 0.1095 0.7411 0.8624 0.3053 ] Network output: [ -0.003497 0.01636 1.005 9.556e-06 -4.29e-06 0.9861 7.201e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09336 0.09142 0.165 0.1961 0.9852 0.9911 0.09337 0.6651 0.8378 0.2483 ] Network output: [ 9.619e-05 1 -6.491e-05 1.258e-06 -5.646e-07 0.9998 9.478e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002218 Epoch 9229 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009334 0.9966 0.9921 -2.141e-07 9.614e-08 -0.007314 -1.614e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003479 -0.003311 -0.006981 0.005586 0.9699 0.9743 0.00675 0.8269 0.821 0.01671 ] Network output: [ 0.9999 0.0001992 0.0004643 -4.631e-06 2.079e-06 -0.0004543 -3.49e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2058 -0.03514 -0.1613 0.1844 0.9834 0.9932 0.2307 0.4318 0.8688 0.7106 ] Network output: [ -0.009284 1.003 1.008 -2.619e-07 1.176e-07 0.007698 -1.974e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006637 0.0005917 0.004409 0.003287 0.9889 0.9919 0.006765 0.8544 0.8927 0.01197 ] Network output: [ -0.0002656 0.001759 1.001 -1.452e-05 6.516e-06 0.9981 -1.094e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.219 0.1034 0.3467 0.1428 0.9849 0.9939 0.2198 0.4358 0.8755 0.7045 ] Network output: [ 0.00373 -0.01762 0.9942 8.821e-06 -3.96e-06 1.016 6.648e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.09685 0.1841 0.1981 0.9873 0.9919 0.1095 0.7411 0.8624 0.3053 ] Network output: [ -0.003496 0.01635 1.005 9.545e-06 -4.285e-06 0.9861 7.193e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09336 0.09142 0.165 0.1961 0.9852 0.9911 0.09338 0.6651 0.8378 0.2483 ] Network output: [ 9.616e-05 1 -6.486e-05 1.256e-06 -5.639e-07 0.9998 9.467e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002216 Epoch 9230 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009333 0.9966 0.9921 -2.141e-07 9.612e-08 -0.007314 -1.614e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003479 -0.003311 -0.006981 0.005585 0.9699 0.9743 0.00675 0.8269 0.821 0.01671 ] Network output: [ 0.9999 0.000199 0.0004641 -4.626e-06 2.077e-06 -0.000454 -3.486e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2058 -0.03514 -0.1613 0.1843 0.9834 0.9932 0.2307 0.4318 0.8688 0.7106 ] Network output: [ -0.009283 1.003 1.008 -2.617e-07 1.175e-07 0.007697 -1.973e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006637 0.0005918 0.004409 0.003287 0.9889 0.9919 0.006766 0.8544 0.8927 0.01197 ] Network output: [ -0.0002654 0.001758 1.001 -1.45e-05 6.509e-06 0.9981 -1.093e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.219 0.1034 0.3468 0.1428 0.9849 0.9939 0.2198 0.4358 0.8755 0.7045 ] Network output: [ 0.003728 -0.01761 0.9942 8.811e-06 -3.956e-06 1.016 6.64e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1094 0.09685 0.1841 0.1981 0.9873 0.9919 0.1095 0.7411 0.8624 0.3053 ] Network output: [ -0.003494 0.01635 1.005 9.534e-06 -4.28e-06 0.9861 7.185e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09336 0.09143 0.165 0.1961 0.9852 0.9911 0.09338 0.6651 0.8378 0.2483 ] Network output: [ 9.613e-05 1 -6.481e-05 1.255e-06 -5.633e-07 0.9998 9.456e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002215 Epoch 9231 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009332 0.9966 0.9921 -2.141e-07 9.61e-08 -0.007314 -1.613e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003479 -0.003311 -0.00698 0.005585 0.9699 0.9743 0.00675 0.8269 0.821 0.01671 ] Network output: [ 0.9999 0.0001988 0.0004639 -4.62e-06 2.074e-06 -0.0004537 -3.482e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2058 -0.03515 -0.1613 0.1843 0.9834 0.9932 0.2307 0.4317 0.8688 0.7106 ] Network output: [ -0.009282 1.003 1.008 -2.616e-07 1.174e-07 0.007696 -1.972e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006638 0.0005919 0.004408 0.003287 0.9889 0.9919 0.006766 0.8544 0.8927 0.01197 ] Network output: [ -0.0002652 0.001758 1.001 -1.448e-05 6.501e-06 0.9981 -1.091e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.219 0.1034 0.3468 0.1428 0.9849 0.9939 0.2198 0.4358 0.8755 0.7045 ] Network output: [ 0.003727 -0.01761 0.9942 8.801e-06 -3.951e-06 1.016 6.633e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.09686 0.1841 0.1981 0.9873 0.9919 0.1095 0.7411 0.8624 0.3053 ] Network output: [ -0.003493 0.01634 1.005 9.523e-06 -4.275e-06 0.9861 7.177e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09337 0.09143 0.165 0.1961 0.9852 0.9911 0.09338 0.665 0.8378 0.2483 ] Network output: [ 9.609e-05 1 -6.475e-05 1.253e-06 -5.626e-07 0.9998 9.445e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002214 Epoch 9232 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009331 0.9966 0.9921 -2.14e-07 9.608e-08 -0.007313 -1.613e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00348 -0.003312 -0.006979 0.005584 0.9699 0.9743 0.006751 0.8269 0.821 0.0167 ] Network output: [ 0.9999 0.0001986 0.0004636 -4.615e-06 2.072e-06 -0.0004533 -3.478e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2058 -0.03515 -0.1613 0.1843 0.9834 0.9932 0.2308 0.4317 0.8688 0.7106 ] Network output: [ -0.009281 1.003 1.008 -2.615e-07 1.174e-07 0.007695 -1.971e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006638 0.000592 0.004408 0.003286 0.9889 0.9919 0.006767 0.8544 0.8927 0.01197 ] Network output: [ -0.0002651 0.001757 1.001 -1.446e-05 6.494e-06 0.9981 -1.09e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.219 0.1034 0.3468 0.1428 0.9849 0.9939 0.2198 0.4358 0.8755 0.7045 ] Network output: [ 0.003725 -0.0176 0.9942 8.791e-06 -3.946e-06 1.016 6.625e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.09686 0.1841 0.1981 0.9873 0.9919 0.1095 0.7411 0.8624 0.3053 ] Network output: [ -0.003491 0.01633 1.005 9.512e-06 -4.27e-06 0.9861 7.169e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09337 0.09143 0.165 0.1961 0.9852 0.9911 0.09338 0.665 0.8378 0.2483 ] Network output: [ 9.606e-05 1 -6.47e-05 1.252e-06 -5.62e-07 0.9998 9.434e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002213 Epoch 9233 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00933 0.9966 0.9921 -2.14e-07 9.606e-08 -0.007313 -1.613e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00348 -0.003312 -0.006979 0.005584 0.9699 0.9743 0.006751 0.8269 0.821 0.0167 ] Network output: [ 0.9999 0.0001984 0.0004634 -4.61e-06 2.069e-06 -0.000453 -3.474e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2058 -0.03515 -0.1612 0.1843 0.9834 0.9932 0.2308 0.4317 0.8688 0.7106 ] Network output: [ -0.00928 1.003 1.008 -2.613e-07 1.173e-07 0.007694 -1.97e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006639 0.000592 0.004408 0.003286 0.9889 0.9919 0.006767 0.8543 0.8927 0.01197 ] Network output: [ -0.0002649 0.001756 1.001 -1.445e-05 6.486e-06 0.9981 -1.089e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2191 0.1034 0.3468 0.1428 0.9849 0.9939 0.2198 0.4358 0.8755 0.7045 ] Network output: [ 0.003724 -0.01759 0.9942 8.78e-06 -3.942e-06 1.016 6.617e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.09686 0.1842 0.1981 0.9873 0.9919 0.1095 0.741 0.8624 0.3053 ] Network output: [ -0.00349 0.01633 1.005 9.502e-06 -4.266e-06 0.9861 7.161e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09337 0.09143 0.165 0.1961 0.9852 0.9911 0.09338 0.665 0.8378 0.2483 ] Network output: [ 9.603e-05 1 -6.464e-05 1.25e-06 -5.614e-07 0.9998 9.424e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002212 Epoch 9234 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009329 0.9966 0.9921 -2.139e-07 9.604e-08 -0.007312 -1.612e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00348 -0.003312 -0.006978 0.005584 0.9699 0.9743 0.006751 0.8269 0.821 0.0167 ] Network output: [ 0.9999 0.0001982 0.0004632 -4.604e-06 2.067e-06 -0.0004527 -3.47e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2058 -0.03515 -0.1612 0.1843 0.9834 0.9932 0.2308 0.4317 0.8688 0.7106 ] Network output: [ -0.009279 1.003 1.008 -2.612e-07 1.173e-07 0.007693 -1.969e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006639 0.0005921 0.004408 0.003286 0.9889 0.9919 0.006768 0.8543 0.8927 0.01197 ] Network output: [ -0.0002647 0.001756 1.001 -1.443e-05 6.479e-06 0.9981 -1.088e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2191 0.1034 0.3468 0.1428 0.9849 0.9939 0.2198 0.4358 0.8755 0.7045 ] Network output: [ 0.003722 -0.01759 0.9942 8.77e-06 -3.937e-06 1.016 6.61e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.09687 0.1842 0.1981 0.9873 0.9919 0.1095 0.741 0.8624 0.3053 ] Network output: [ -0.003489 0.01632 1.005 9.491e-06 -4.261e-06 0.9861 7.153e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09337 0.09144 0.165 0.1961 0.9852 0.9911 0.09339 0.665 0.8378 0.2483 ] Network output: [ 9.599e-05 1 -6.459e-05 1.249e-06 -5.607e-07 0.9998 9.413e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002211 Epoch 9235 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009328 0.9966 0.9921 -2.139e-07 9.602e-08 -0.007312 -1.612e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00348 -0.003312 -0.006977 0.005583 0.9699 0.9743 0.006751 0.8269 0.821 0.0167 ] Network output: [ 0.9999 0.000198 0.000463 -4.599e-06 2.065e-06 -0.0004523 -3.466e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2058 -0.03515 -0.1612 0.1843 0.9834 0.9932 0.2308 0.4317 0.8688 0.7106 ] Network output: [ -0.009278 1.003 1.008 -2.611e-07 1.172e-07 0.007693 -1.968e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006639 0.0005922 0.004408 0.003285 0.9889 0.9919 0.006768 0.8543 0.8927 0.01196 ] Network output: [ -0.0002645 0.001755 1.001 -1.441e-05 6.471e-06 0.9981 -1.086e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2191 0.1034 0.3468 0.1428 0.9849 0.9939 0.2198 0.4357 0.8755 0.7045 ] Network output: [ 0.003721 -0.01758 0.9942 8.76e-06 -3.933e-06 1.016 6.602e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.09687 0.1842 0.1981 0.9873 0.9919 0.1095 0.741 0.8624 0.3053 ] Network output: [ -0.003487 0.01631 1.005 9.48e-06 -4.256e-06 0.9862 7.145e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09338 0.09144 0.165 0.1962 0.9852 0.9911 0.09339 0.665 0.8378 0.2483 ] Network output: [ 9.596e-05 1 -6.454e-05 1.248e-06 -5.601e-07 0.9998 9.402e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002209 Epoch 9236 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009327 0.9966 0.9921 -2.138e-07 9.6e-08 -0.007311 -1.612e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00348 -0.003312 -0.006977 0.005583 0.9699 0.9743 0.006751 0.8269 0.821 0.0167 ] Network output: [ 0.9999 0.0001978 0.0004628 -4.593e-06 2.062e-06 -0.000452 -3.462e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2058 -0.03515 -0.1612 0.1843 0.9834 0.9932 0.2308 0.4317 0.8688 0.7106 ] Network output: [ -0.009278 1.003 1.008 -2.609e-07 1.171e-07 0.007692 -1.967e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00664 0.0005923 0.004408 0.003285 0.9889 0.9919 0.006768 0.8543 0.8927 0.01196 ] Network output: [ -0.0002643 0.001754 1.001 -1.44e-05 6.464e-06 0.9981 -1.085e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2191 0.1034 0.3468 0.1428 0.9849 0.9939 0.2198 0.4357 0.8755 0.7045 ] Network output: [ 0.003719 -0.01757 0.9942 8.75e-06 -3.928e-06 1.016 6.594e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.09688 0.1842 0.1981 0.9873 0.9919 0.1095 0.741 0.8624 0.3053 ] Network output: [ -0.003486 0.01631 1.005 9.469e-06 -4.251e-06 0.9862 7.137e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09338 0.09144 0.165 0.1962 0.9852 0.9911 0.09339 0.665 0.8378 0.2483 ] Network output: [ 9.593e-05 1 -6.448e-05 1.246e-06 -5.594e-07 0.9998 9.391e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002208 Epoch 9237 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009326 0.9966 0.9921 -2.138e-07 9.598e-08 -0.007311 -1.611e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00348 -0.003312 -0.006976 0.005582 0.9699 0.9743 0.006752 0.8269 0.821 0.0167 ] Network output: [ 0.9999 0.0001976 0.0004625 -4.588e-06 2.06e-06 -0.0004517 -3.458e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2058 -0.03515 -0.1612 0.1843 0.9834 0.9932 0.2308 0.4317 0.8688 0.7106 ] Network output: [ -0.009277 1.003 1.008 -2.608e-07 1.171e-07 0.007691 -1.966e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00664 0.0005924 0.004408 0.003285 0.9889 0.9919 0.006769 0.8543 0.8927 0.01196 ] Network output: [ -0.0002641 0.001753 1.001 -1.438e-05 6.456e-06 0.9981 -1.084e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2191 0.1034 0.3468 0.1428 0.9849 0.9939 0.2198 0.4357 0.8755 0.7045 ] Network output: [ 0.003718 -0.01757 0.9942 8.74e-06 -3.924e-06 1.016 6.587e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.09688 0.1842 0.1981 0.9873 0.9919 0.1096 0.741 0.8624 0.3053 ] Network output: [ -0.003484 0.0163 1.005 9.459e-06 -4.246e-06 0.9862 7.128e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09338 0.09144 0.165 0.1962 0.9852 0.9911 0.09339 0.665 0.8378 0.2483 ] Network output: [ 9.589e-05 1 -6.443e-05 1.245e-06 -5.588e-07 0.9998 9.38e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002207 Epoch 9238 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009325 0.9966 0.9921 -2.138e-07 9.596e-08 -0.00731 -1.611e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00348 -0.003312 -0.006975 0.005582 0.9699 0.9743 0.006752 0.8269 0.821 0.0167 ] Network output: [ 0.9999 0.0001974 0.0004623 -4.583e-06 2.057e-06 -0.0004513 -3.454e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2058 -0.03515 -0.1612 0.1843 0.9834 0.9932 0.2308 0.4317 0.8688 0.7106 ] Network output: [ -0.009276 1.003 1.008 -2.607e-07 1.17e-07 0.00769 -1.965e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006641 0.0005925 0.004408 0.003285 0.9889 0.9919 0.006769 0.8543 0.8927 0.01196 ] Network output: [ -0.0002639 0.001753 1.001 -1.436e-05 6.449e-06 0.9981 -1.083e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2191 0.1034 0.3468 0.1428 0.9849 0.9939 0.2198 0.4357 0.8755 0.7045 ] Network output: [ 0.003716 -0.01756 0.9942 8.73e-06 -3.919e-06 1.016 6.579e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.09689 0.1842 0.1981 0.9873 0.9919 0.1096 0.741 0.8624 0.3053 ] Network output: [ -0.003483 0.01629 1.005 9.448e-06 -4.242e-06 0.9862 7.12e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09338 0.09144 0.165 0.1962 0.9852 0.9911 0.0934 0.6649 0.8378 0.2483 ] Network output: [ 9.586e-05 1 -6.438e-05 1.243e-06 -5.581e-07 0.9998 9.37e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002206 Epoch 9239 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009324 0.9966 0.9921 -2.137e-07 9.594e-08 -0.00731 -1.611e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00348 -0.003312 -0.006975 0.005581 0.9699 0.9743 0.006752 0.8269 0.821 0.0167 ] Network output: [ 0.9999 0.0001972 0.0004621 -4.577e-06 2.055e-06 -0.000451 -3.45e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2058 -0.03515 -0.1612 0.1843 0.9834 0.9932 0.2308 0.4317 0.8688 0.7106 ] Network output: [ -0.009275 1.003 1.008 -2.605e-07 1.17e-07 0.007689 -1.963e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006641 0.0005926 0.004408 0.003284 0.9889 0.9919 0.00677 0.8543 0.8927 0.01196 ] Network output: [ -0.0002638 0.001752 1.001 -1.435e-05 6.441e-06 0.9981 -1.081e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2191 0.1034 0.3468 0.1428 0.9849 0.9939 0.2198 0.4357 0.8755 0.7044 ] Network output: [ 0.003715 -0.01755 0.9942 8.72e-06 -3.915e-06 1.016 6.572e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.09689 0.1842 0.1981 0.9873 0.9919 0.1096 0.741 0.8624 0.3053 ] Network output: [ -0.003482 0.01629 1.005 9.437e-06 -4.237e-06 0.9862 7.112e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09338 0.09145 0.165 0.1962 0.9852 0.9911 0.0934 0.6649 0.8378 0.2483 ] Network output: [ 9.583e-05 1 -6.432e-05 1.242e-06 -5.575e-07 0.9998 9.359e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002205 Epoch 9240 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009322 0.9966 0.9921 -2.137e-07 9.592e-08 -0.007309 -1.61e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00348 -0.003312 -0.006974 0.005581 0.9699 0.9743 0.006752 0.8269 0.821 0.0167 ] Network output: [ 0.9999 0.000197 0.0004619 -4.572e-06 2.053e-06 -0.0004507 -3.446e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2058 -0.03515 -0.1612 0.1843 0.9834 0.9932 0.2308 0.4317 0.8688 0.7106 ] Network output: [ -0.009274 1.003 1.008 -2.604e-07 1.169e-07 0.007689 -1.962e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006642 0.0005926 0.004408 0.003284 0.9889 0.9919 0.00677 0.8543 0.8927 0.01196 ] Network output: [ -0.0002636 0.001751 1.001 -1.433e-05 6.434e-06 0.9981 -1.08e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2191 0.1034 0.3468 0.1428 0.9849 0.9939 0.2198 0.4357 0.8755 0.7044 ] Network output: [ 0.003713 -0.01754 0.9942 8.71e-06 -3.91e-06 1.016 6.564e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.0969 0.1842 0.1981 0.9873 0.9919 0.1096 0.741 0.8624 0.3053 ] Network output: [ -0.00348 0.01628 1.005 9.427e-06 -4.232e-06 0.9862 7.104e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09339 0.09145 0.165 0.1962 0.9852 0.9911 0.0934 0.6649 0.8378 0.2483 ] Network output: [ 9.579e-05 1 -6.427e-05 1.24e-06 -5.569e-07 0.9998 9.348e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002204 Epoch 9241 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009321 0.9966 0.9921 -2.136e-07 9.59e-08 -0.007309 -1.61e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00348 -0.003313 -0.006973 0.005581 0.9699 0.9743 0.006753 0.8269 0.821 0.0167 ] Network output: [ 0.9999 0.0001967 0.0004617 -4.567e-06 2.05e-06 -0.0004503 -3.442e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2058 -0.03516 -0.1612 0.1843 0.9834 0.9932 0.2308 0.4317 0.8688 0.7106 ] Network output: [ -0.009273 1.003 1.008 -2.603e-07 1.168e-07 0.007688 -1.961e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006642 0.0005927 0.004408 0.003284 0.9889 0.9919 0.006771 0.8543 0.8927 0.01196 ] Network output: [ -0.0002634 0.001751 1.001 -1.431e-05 6.426e-06 0.9981 -1.079e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2191 0.1034 0.3468 0.1428 0.9849 0.9939 0.2199 0.4357 0.8755 0.7044 ] Network output: [ 0.003712 -0.01754 0.9942 8.7e-06 -3.906e-06 1.016 6.556e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.0969 0.1842 0.1981 0.9873 0.9919 0.1096 0.7409 0.8623 0.3053 ] Network output: [ -0.003479 0.01627 1.005 9.416e-06 -4.227e-06 0.9862 7.096e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09339 0.09145 0.165 0.1962 0.9852 0.9911 0.0934 0.6649 0.8378 0.2483 ] Network output: [ 9.576e-05 1 -6.422e-05 1.239e-06 -5.562e-07 0.9998 9.338e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002203 Epoch 9242 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00932 0.9966 0.9921 -2.136e-07 9.588e-08 -0.007309 -1.61e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00348 -0.003313 -0.006973 0.00558 0.9699 0.9743 0.006753 0.8269 0.821 0.01669 ] Network output: [ 0.9999 0.0001965 0.0004614 -4.562e-06 2.048e-06 -0.00045 -3.438e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2058 -0.03516 -0.1612 0.1843 0.9834 0.9932 0.2308 0.4317 0.8688 0.7106 ] Network output: [ -0.009272 1.003 1.008 -2.601e-07 1.168e-07 0.007687 -1.96e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006643 0.0005928 0.004408 0.003284 0.9889 0.9919 0.006771 0.8543 0.8927 0.01196 ] Network output: [ -0.0002632 0.00175 1.001 -1.43e-05 6.419e-06 0.9981 -1.078e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2191 0.1034 0.3468 0.1428 0.9849 0.9939 0.2199 0.4357 0.8755 0.7044 ] Network output: [ 0.00371 -0.01753 0.9942 8.69e-06 -3.901e-06 1.016 6.549e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.0969 0.1842 0.1981 0.9873 0.9919 0.1096 0.7409 0.8623 0.3053 ] Network output: [ -0.003477 0.01627 1.005 9.405e-06 -4.222e-06 0.9862 7.088e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09339 0.09145 0.165 0.1962 0.9852 0.9911 0.0934 0.6649 0.8378 0.2483 ] Network output: [ 9.573e-05 1 -6.416e-05 1.238e-06 -5.556e-07 0.9998 9.327e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002201 Epoch 9243 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009319 0.9966 0.9921 -2.135e-07 9.586e-08 -0.007308 -1.609e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00348 -0.003313 -0.006972 0.00558 0.9699 0.9743 0.006753 0.8269 0.8209 0.01669 ] Network output: [ 0.9999 0.0001963 0.0004612 -4.556e-06 2.045e-06 -0.0004497 -3.434e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2058 -0.03516 -0.1611 0.1843 0.9834 0.9932 0.2308 0.4317 0.8688 0.7105 ] Network output: [ -0.009271 1.003 1.008 -2.6e-07 1.167e-07 0.007686 -1.959e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006643 0.0005929 0.004408 0.003283 0.9889 0.9919 0.006772 0.8543 0.8927 0.01196 ] Network output: [ -0.000263 0.001749 1.001 -1.428e-05 6.411e-06 0.9981 -1.076e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2191 0.1034 0.3468 0.1428 0.9849 0.9939 0.2199 0.4357 0.8755 0.7044 ] Network output: [ 0.003709 -0.01752 0.9942 8.68e-06 -3.897e-06 1.016 6.541e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.09691 0.1842 0.1981 0.9873 0.9919 0.1096 0.7409 0.8623 0.3053 ] Network output: [ -0.003476 0.01626 1.005 9.395e-06 -4.218e-06 0.9862 7.08e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09339 0.09146 0.165 0.1962 0.9852 0.9911 0.09341 0.6649 0.8378 0.2483 ] Network output: [ 9.569e-05 1 -6.411e-05 1.236e-06 -5.55e-07 0.9998 9.316e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00022 Epoch 9244 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009318 0.9966 0.9921 -2.135e-07 9.584e-08 -0.007308 -1.609e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003481 -0.003313 -0.006971 0.005579 0.9699 0.9743 0.006753 0.8269 0.8209 0.01669 ] Network output: [ 0.9999 0.0001961 0.000461 -4.551e-06 2.043e-06 -0.0004494 -3.43e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2059 -0.03516 -0.1611 0.1843 0.9834 0.9932 0.2309 0.4317 0.8688 0.7105 ] Network output: [ -0.00927 1.003 1.008 -2.599e-07 1.167e-07 0.007685 -1.958e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006643 0.000593 0.004407 0.003283 0.9889 0.9919 0.006772 0.8543 0.8927 0.01196 ] Network output: [ -0.0002628 0.001748 1.001 -1.426e-05 6.404e-06 0.9981 -1.075e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2191 0.1034 0.3468 0.1428 0.9849 0.9939 0.2199 0.4357 0.8755 0.7044 ] Network output: [ 0.003707 -0.01752 0.9942 8.67e-06 -3.892e-06 1.016 6.534e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.09691 0.1842 0.1981 0.9873 0.9919 0.1096 0.7409 0.8623 0.3053 ] Network output: [ -0.003474 0.01625 1.005 9.384e-06 -4.213e-06 0.9862 7.072e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0934 0.09146 0.165 0.1962 0.9852 0.9911 0.09341 0.6649 0.8378 0.2483 ] Network output: [ 9.566e-05 1 -6.406e-05 1.235e-06 -5.543e-07 0.9998 9.305e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002199 Epoch 9245 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009317 0.9966 0.9921 -2.134e-07 9.582e-08 -0.007307 -1.608e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003481 -0.003313 -0.006971 0.005579 0.9699 0.9743 0.006753 0.8269 0.8209 0.01669 ] Network output: [ 0.9999 0.0001959 0.0004608 -4.546e-06 2.041e-06 -0.000449 -3.426e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2059 -0.03516 -0.1611 0.1843 0.9834 0.9932 0.2309 0.4317 0.8688 0.7105 ] Network output: [ -0.009269 1.003 1.008 -2.597e-07 1.166e-07 0.007685 -1.957e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006644 0.0005931 0.004407 0.003283 0.9889 0.9919 0.006773 0.8543 0.8927 0.01196 ] Network output: [ -0.0002626 0.001748 1.001 -1.425e-05 6.397e-06 0.9981 -1.074e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2191 0.1034 0.3468 0.1428 0.9849 0.9939 0.2199 0.4357 0.8755 0.7044 ] Network output: [ 0.003706 -0.01751 0.9942 8.66e-06 -3.888e-06 1.016 6.526e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.09692 0.1842 0.1981 0.9873 0.9919 0.1096 0.7409 0.8623 0.3053 ] Network output: [ -0.003473 0.01625 1.005 9.373e-06 -4.208e-06 0.9862 7.064e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0934 0.09146 0.165 0.1962 0.9852 0.9911 0.09341 0.6649 0.8378 0.2483 ] Network output: [ 9.563e-05 1 -6.4e-05 1.233e-06 -5.537e-07 0.9998 9.295e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002198 Epoch 9246 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009316 0.9966 0.9921 -2.134e-07 9.58e-08 -0.007307 -1.608e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003481 -0.003313 -0.00697 0.005578 0.9699 0.9743 0.006754 0.8268 0.8209 0.01669 ] Network output: [ 0.9999 0.0001957 0.0004606 -4.54e-06 2.038e-06 -0.0004487 -3.422e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2059 -0.03516 -0.1611 0.1843 0.9834 0.9932 0.2309 0.4317 0.8688 0.7105 ] Network output: [ -0.009269 1.003 1.008 -2.596e-07 1.165e-07 0.007684 -1.956e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006644 0.0005932 0.004407 0.003283 0.9889 0.9919 0.006773 0.8543 0.8927 0.01196 ] Network output: [ -0.0002625 0.001747 1.001 -1.423e-05 6.389e-06 0.9981 -1.073e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2192 0.1034 0.3468 0.1428 0.9849 0.9939 0.2199 0.4357 0.8755 0.7044 ] Network output: [ 0.003704 -0.0175 0.9942 8.65e-06 -3.883e-06 1.016 6.519e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.09692 0.1842 0.1981 0.9873 0.9919 0.1096 0.7409 0.8623 0.3053 ] Network output: [ -0.003472 0.01624 1.005 9.363e-06 -4.203e-06 0.9862 7.056e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0934 0.09146 0.165 0.1962 0.9852 0.9911 0.09341 0.6648 0.8378 0.2483 ] Network output: [ 9.559e-05 1 -6.395e-05 1.232e-06 -5.531e-07 0.9998 9.284e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002197 Epoch 9247 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009315 0.9966 0.9921 -2.133e-07 9.578e-08 -0.007306 -1.608e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003481 -0.003313 -0.006969 0.005578 0.9699 0.9743 0.006754 0.8268 0.8209 0.01669 ] Network output: [ 0.9999 0.0001955 0.0004604 -4.535e-06 2.036e-06 -0.0004484 -3.418e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2059 -0.03516 -0.1611 0.1843 0.9834 0.9932 0.2309 0.4317 0.8688 0.7105 ] Network output: [ -0.009268 1.003 1.008 -2.595e-07 1.165e-07 0.007683 -1.955e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006645 0.0005932 0.004407 0.003282 0.9889 0.9919 0.006773 0.8543 0.8927 0.01196 ] Network output: [ -0.0002623 0.001746 1.001 -1.422e-05 6.382e-06 0.9981 -1.071e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2192 0.1034 0.3468 0.1428 0.9849 0.9939 0.2199 0.4357 0.8755 0.7044 ] Network output: [ 0.003703 -0.0175 0.9942 8.64e-06 -3.879e-06 1.016 6.511e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.09693 0.1842 0.1981 0.9873 0.9919 0.1096 0.7409 0.8623 0.3053 ] Network output: [ -0.00347 0.01623 1.005 9.352e-06 -4.199e-06 0.9862 7.048e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0934 0.09146 0.165 0.1962 0.9852 0.9911 0.09342 0.6648 0.8378 0.2483 ] Network output: [ 9.556e-05 1 -6.39e-05 1.231e-06 -5.524e-07 0.9998 9.274e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002196 Epoch 9248 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009314 0.9966 0.9921 -2.133e-07 9.575e-08 -0.007306 -1.607e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003481 -0.003313 -0.006969 0.005577 0.9699 0.9743 0.006754 0.8268 0.8209 0.01669 ] Network output: [ 0.9999 0.0001953 0.0004601 -4.53e-06 2.034e-06 -0.0004481 -3.414e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2059 -0.03516 -0.1611 0.1843 0.9834 0.9932 0.2309 0.4316 0.8688 0.7105 ] Network output: [ -0.009267 1.003 1.008 -2.593e-07 1.164e-07 0.007682 -1.954e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006645 0.0005933 0.004407 0.003282 0.9889 0.9919 0.006774 0.8543 0.8927 0.01195 ] Network output: [ -0.0002621 0.001745 1.001 -1.42e-05 6.374e-06 0.9981 -1.07e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2192 0.1035 0.3468 0.1428 0.9849 0.9939 0.2199 0.4357 0.8755 0.7044 ] Network output: [ 0.003701 -0.01749 0.9942 8.63e-06 -3.874e-06 1.016 6.504e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.09693 0.1842 0.1981 0.9873 0.9919 0.1096 0.7408 0.8623 0.3053 ] Network output: [ -0.003469 0.01623 1.005 9.342e-06 -4.194e-06 0.9862 7.04e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0934 0.09147 0.165 0.1962 0.9852 0.9911 0.09342 0.6648 0.8378 0.2483 ] Network output: [ 9.553e-05 1 -6.385e-05 1.229e-06 -5.518e-07 0.9998 9.263e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002195 Epoch 9249 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009313 0.9966 0.9921 -2.132e-07 9.573e-08 -0.007305 -1.607e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003481 -0.003313 -0.006968 0.005577 0.9699 0.9743 0.006754 0.8268 0.8209 0.01669 ] Network output: [ 0.9999 0.0001951 0.0004599 -4.524e-06 2.031e-06 -0.0004477 -3.41e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2059 -0.03516 -0.1611 0.1843 0.9834 0.9932 0.2309 0.4316 0.8688 0.7105 ] Network output: [ -0.009266 1.003 1.008 -2.592e-07 1.164e-07 0.007681 -1.953e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006646 0.0005934 0.004407 0.003282 0.9889 0.9919 0.006774 0.8543 0.8927 0.01195 ] Network output: [ -0.0002619 0.001745 1.001 -1.418e-05 6.367e-06 0.9981 -1.069e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2192 0.1035 0.3469 0.1428 0.9849 0.9939 0.2199 0.4357 0.8755 0.7044 ] Network output: [ 0.0037 -0.01748 0.9942 8.62e-06 -3.87e-06 1.016 6.496e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.09693 0.1842 0.1981 0.9873 0.9919 0.1096 0.7408 0.8623 0.3053 ] Network output: [ -0.003467 0.01622 1.005 9.331e-06 -4.189e-06 0.9862 7.032e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09341 0.09147 0.165 0.1962 0.9852 0.9911 0.09342 0.6648 0.8378 0.2483 ] Network output: [ 9.55e-05 1 -6.38e-05 1.228e-06 -5.512e-07 0.9998 9.252e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002193 Epoch 9250 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009312 0.9966 0.9921 -2.132e-07 9.571e-08 -0.007305 -1.607e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003481 -0.003313 -0.006967 0.005577 0.9699 0.9743 0.006754 0.8268 0.8209 0.01669 ] Network output: [ 0.9999 0.0001949 0.0004597 -4.519e-06 2.029e-06 -0.0004474 -3.406e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2059 -0.03516 -0.1611 0.1843 0.9834 0.9932 0.2309 0.4316 0.8688 0.7105 ] Network output: [ -0.009265 1.003 1.008 -2.59e-07 1.163e-07 0.007681 -1.952e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006646 0.0005935 0.004407 0.003282 0.9889 0.9919 0.006775 0.8543 0.8927 0.01195 ] Network output: [ -0.0002617 0.001744 1.001 -1.417e-05 6.36e-06 0.9981 -1.068e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2192 0.1035 0.3469 0.1428 0.9849 0.9939 0.2199 0.4357 0.8755 0.7044 ] Network output: [ 0.003698 -0.01747 0.9942 8.61e-06 -3.865e-06 1.016 6.489e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.09694 0.1842 0.1981 0.9873 0.9919 0.1096 0.7408 0.8623 0.3053 ] Network output: [ -0.003466 0.01621 1.005 9.32e-06 -4.184e-06 0.9862 7.024e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09341 0.09147 0.165 0.1962 0.9852 0.9911 0.09342 0.6648 0.8378 0.2483 ] Network output: [ 9.546e-05 1 -6.374e-05 1.226e-06 -5.505e-07 0.9998 9.242e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002192 Epoch 9251 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009311 0.9966 0.9921 -2.131e-07 9.569e-08 -0.007304 -1.606e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003481 -0.003314 -0.006967 0.005576 0.9699 0.9743 0.006755 0.8268 0.8209 0.01669 ] Network output: [ 0.9999 0.0001947 0.0004595 -4.514e-06 2.026e-06 -0.0004471 -3.402e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2059 -0.03517 -0.1611 0.1843 0.9834 0.9932 0.2309 0.4316 0.8688 0.7105 ] Network output: [ -0.009264 1.003 1.008 -2.589e-07 1.162e-07 0.00768 -1.951e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006646 0.0005936 0.004407 0.003281 0.9889 0.9919 0.006775 0.8542 0.8927 0.01195 ] Network output: [ -0.0002615 0.001743 1.001 -1.415e-05 6.352e-06 0.9981 -1.066e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2192 0.1035 0.3469 0.1428 0.9849 0.9939 0.2199 0.4356 0.8755 0.7044 ] Network output: [ 0.003697 -0.01747 0.9942 8.6e-06 -3.861e-06 1.016 6.481e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.09694 0.1842 0.1981 0.9873 0.9919 0.1096 0.7408 0.8623 0.3053 ] Network output: [ -0.003464 0.01621 1.005 9.31e-06 -4.18e-06 0.9862 7.016e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09341 0.09147 0.165 0.1962 0.9852 0.9911 0.09343 0.6648 0.8378 0.2483 ] Network output: [ 9.543e-05 1 -6.369e-05 1.225e-06 -5.499e-07 0.9998 9.231e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002191 Epoch 9252 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00931 0.9966 0.9921 -2.131e-07 9.567e-08 -0.007304 -1.606e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003481 -0.003314 -0.006966 0.005576 0.9699 0.9743 0.006755 0.8268 0.8209 0.01668 ] Network output: [ 0.9999 0.0001945 0.0004593 -4.509e-06 2.024e-06 -0.0004467 -3.398e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2059 -0.03517 -0.1611 0.1843 0.9834 0.9932 0.2309 0.4316 0.8688 0.7105 ] Network output: [ -0.009263 1.003 1.008 -2.588e-07 1.162e-07 0.007679 -1.95e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006647 0.0005937 0.004407 0.003281 0.9889 0.9919 0.006776 0.8542 0.8927 0.01195 ] Network output: [ -0.0002613 0.001743 1.001 -1.413e-05 6.345e-06 0.9981 -1.065e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2192 0.1035 0.3469 0.1428 0.9849 0.9939 0.2199 0.4356 0.8755 0.7044 ] Network output: [ 0.003695 -0.01746 0.9942 8.59e-06 -3.856e-06 1.016 6.474e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1095 0.09695 0.1842 0.1981 0.9873 0.9919 0.1096 0.7408 0.8623 0.3053 ] Network output: [ -0.003463 0.0162 1.005 9.299e-06 -4.175e-06 0.9862 7.008e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09341 0.09148 0.165 0.1962 0.9852 0.9911 0.09343 0.6648 0.8378 0.2483 ] Network output: [ 9.54e-05 1 -6.364e-05 1.223e-06 -5.493e-07 0.9998 9.22e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000219 Epoch 9253 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009309 0.9966 0.9921 -2.131e-07 9.565e-08 -0.007303 -1.606e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003481 -0.003314 -0.006965 0.005575 0.9699 0.9743 0.006755 0.8268 0.8209 0.01668 ] Network output: [ 0.9999 0.0001943 0.0004591 -4.503e-06 2.022e-06 -0.0004464 -3.394e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2059 -0.03517 -0.161 0.1843 0.9834 0.9932 0.2309 0.4316 0.8688 0.7105 ] Network output: [ -0.009262 1.003 1.008 -2.586e-07 1.161e-07 0.007678 -1.949e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006647 0.0005937 0.004407 0.003281 0.9889 0.9919 0.006776 0.8542 0.8927 0.01195 ] Network output: [ -0.0002612 0.001742 1.001 -1.412e-05 6.337e-06 0.9981 -1.064e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2192 0.1035 0.3469 0.1428 0.9849 0.9939 0.22 0.4356 0.8755 0.7044 ] Network output: [ 0.003694 -0.01745 0.9942 8.58e-06 -3.852e-06 1.016 6.466e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.09695 0.1842 0.1981 0.9873 0.9919 0.1096 0.7408 0.8623 0.3053 ] Network output: [ -0.003462 0.01619 1.005 9.289e-06 -4.17e-06 0.9862 7e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09342 0.09148 0.165 0.1962 0.9852 0.9911 0.09343 0.6647 0.8378 0.2483 ] Network output: [ 9.536e-05 1 -6.359e-05 1.222e-06 -5.486e-07 0.9998 9.21e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002189 Epoch 9254 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009308 0.9966 0.9921 -2.13e-07 9.563e-08 -0.007303 -1.605e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003481 -0.003314 -0.006965 0.005575 0.9699 0.9743 0.006755 0.8268 0.8209 0.01668 ] Network output: [ 0.9999 0.0001941 0.0004588 -4.498e-06 2.019e-06 -0.0004461 -3.39e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2059 -0.03517 -0.161 0.1843 0.9834 0.9932 0.2309 0.4316 0.8688 0.7105 ] Network output: [ -0.009261 1.003 1.008 -2.585e-07 1.16e-07 0.007677 -1.948e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006648 0.0005938 0.004407 0.003281 0.9889 0.9919 0.006777 0.8542 0.8927 0.01195 ] Network output: [ -0.000261 0.001741 1.001 -1.41e-05 6.33e-06 0.9981 -1.063e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2192 0.1035 0.3469 0.1428 0.9849 0.9939 0.22 0.4356 0.8755 0.7044 ] Network output: [ 0.003692 -0.01745 0.9942 8.57e-06 -3.848e-06 1.016 6.459e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.09696 0.1842 0.1981 0.9873 0.9919 0.1096 0.7408 0.8623 0.3053 ] Network output: [ -0.00346 0.01618 1.005 9.278e-06 -4.165e-06 0.9862 6.992e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09342 0.09148 0.165 0.1962 0.9852 0.9911 0.09343 0.6647 0.8378 0.2483 ] Network output: [ 9.533e-05 1 -6.354e-05 1.221e-06 -5.48e-07 0.9998 9.199e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002188 Epoch 9255 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009307 0.9966 0.9921 -2.13e-07 9.56e-08 -0.007303 -1.605e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003481 -0.003314 -0.006964 0.005574 0.9699 0.9743 0.006756 0.8268 0.8209 0.01668 ] Network output: [ 0.9999 0.0001939 0.0004586 -4.493e-06 2.017e-06 -0.0004458 -3.386e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2059 -0.03517 -0.161 0.1842 0.9834 0.9932 0.2309 0.4316 0.8688 0.7105 ] Network output: [ -0.009261 1.003 1.008 -2.584e-07 1.16e-07 0.007677 -1.947e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006648 0.0005939 0.004407 0.00328 0.9889 0.9919 0.006777 0.8542 0.8927 0.01195 ] Network output: [ -0.0002608 0.00174 1.001 -1.408e-05 6.323e-06 0.9981 -1.061e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2192 0.1035 0.3469 0.1428 0.9849 0.9939 0.22 0.4356 0.8755 0.7044 ] Network output: [ 0.003691 -0.01744 0.9942 8.56e-06 -3.843e-06 1.016 6.451e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.09696 0.1842 0.1981 0.9873 0.9919 0.1096 0.7408 0.8623 0.3053 ] Network output: [ -0.003459 0.01618 1.005 9.268e-06 -4.161e-06 0.9862 6.984e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09342 0.09148 0.165 0.1962 0.9852 0.9911 0.09343 0.6647 0.8377 0.2483 ] Network output: [ 9.53e-05 1 -6.348e-05 1.219e-06 -5.474e-07 0.9998 9.189e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002187 Epoch 9256 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009306 0.9966 0.9921 -2.129e-07 9.558e-08 -0.007302 -1.605e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003481 -0.003314 -0.006963 0.005574 0.9699 0.9743 0.006756 0.8268 0.8209 0.01668 ] Network output: [ 0.9999 0.0001937 0.0004584 -4.488e-06 2.015e-06 -0.0004454 -3.382e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2059 -0.03517 -0.161 0.1842 0.9834 0.9932 0.2309 0.4316 0.8688 0.7105 ] Network output: [ -0.00926 1.003 1.008 -2.582e-07 1.159e-07 0.007676 -1.946e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006649 0.000594 0.004407 0.00328 0.9889 0.9919 0.006778 0.8542 0.8927 0.01195 ] Network output: [ -0.0002606 0.00174 1.001 -1.407e-05 6.315e-06 0.9981 -1.06e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2192 0.1035 0.3469 0.1428 0.9849 0.9939 0.22 0.4356 0.8755 0.7044 ] Network output: [ 0.003689 -0.01743 0.9942 8.551e-06 -3.839e-06 1.016 6.444e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.09696 0.1842 0.1981 0.9873 0.9919 0.1096 0.7407 0.8623 0.3053 ] Network output: [ -0.003457 0.01617 1.005 9.257e-06 -4.156e-06 0.9862 6.977e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09342 0.09149 0.165 0.1962 0.9852 0.9911 0.09344 0.6647 0.8377 0.2483 ] Network output: [ 9.527e-05 1 -6.343e-05 1.218e-06 -5.467e-07 0.9998 9.178e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002185 Epoch 9257 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009305 0.9966 0.9921 -2.129e-07 9.556e-08 -0.007302 -1.604e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003482 -0.003314 -0.006962 0.005574 0.9699 0.9743 0.006756 0.8268 0.8209 0.01668 ] Network output: [ 0.9999 0.0001934 0.0004582 -4.482e-06 2.012e-06 -0.0004451 -3.378e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2059 -0.03517 -0.161 0.1842 0.9834 0.9932 0.231 0.4316 0.8688 0.7105 ] Network output: [ -0.009259 1.003 1.008 -2.581e-07 1.159e-07 0.007675 -1.945e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006649 0.0005941 0.004407 0.00328 0.9889 0.9919 0.006778 0.8542 0.8927 0.01195 ] Network output: [ -0.0002604 0.001739 1.001 -1.405e-05 6.308e-06 0.9981 -1.059e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2192 0.1035 0.3469 0.1428 0.9849 0.9939 0.22 0.4356 0.8755 0.7044 ] Network output: [ 0.003688 -0.01743 0.9942 8.541e-06 -3.834e-06 1.016 6.437e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.09697 0.1842 0.1981 0.9873 0.9919 0.1096 0.7407 0.8623 0.3053 ] Network output: [ -0.003456 0.01616 1.005 9.247e-06 -4.151e-06 0.9862 6.969e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09342 0.09149 0.165 0.1962 0.9852 0.9911 0.09344 0.6647 0.8377 0.2483 ] Network output: [ 9.523e-05 1 -6.338e-05 1.216e-06 -5.461e-07 0.9998 9.168e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002184 Epoch 9258 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009304 0.9966 0.9921 -2.128e-07 9.554e-08 -0.007301 -1.604e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003482 -0.003314 -0.006962 0.005573 0.9699 0.9743 0.006756 0.8268 0.8209 0.01668 ] Network output: [ 0.9999 0.0001932 0.000458 -4.477e-06 2.01e-06 -0.0004448 -3.374e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2059 -0.03517 -0.161 0.1842 0.9834 0.9932 0.231 0.4316 0.8688 0.7105 ] Network output: [ -0.009258 1.003 1.008 -2.58e-07 1.158e-07 0.007674 -1.944e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00665 0.0005942 0.004406 0.00328 0.9889 0.9919 0.006778 0.8542 0.8927 0.01195 ] Network output: [ -0.0002602 0.001738 1.001 -1.403e-05 6.301e-06 0.9981 -1.058e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2192 0.1035 0.3469 0.1428 0.9849 0.9939 0.22 0.4356 0.8755 0.7044 ] Network output: [ 0.003686 -0.01742 0.9942 8.531e-06 -3.83e-06 1.016 6.429e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.09697 0.1842 0.198 0.9873 0.9919 0.1096 0.7407 0.8623 0.3053 ] Network output: [ -0.003455 0.01616 1.005 9.236e-06 -4.146e-06 0.9862 6.961e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09343 0.09149 0.165 0.1962 0.9852 0.9911 0.09344 0.6647 0.8377 0.2483 ] Network output: [ 9.52e-05 1 -6.333e-05 1.215e-06 -5.455e-07 0.9998 9.157e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002183 Epoch 9259 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009303 0.9966 0.9921 -2.128e-07 9.551e-08 -0.007301 -1.603e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003482 -0.003314 -0.006961 0.005573 0.9699 0.9743 0.006756 0.8268 0.8209 0.01668 ] Network output: [ 0.9999 0.000193 0.0004578 -4.472e-06 2.008e-06 -0.0004445 -3.37e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.206 -0.03517 -0.161 0.1842 0.9834 0.9932 0.231 0.4316 0.8688 0.7105 ] Network output: [ -0.009257 1.003 1.008 -2.578e-07 1.157e-07 0.007673 -1.943e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00665 0.0005942 0.004406 0.003279 0.9889 0.9919 0.006779 0.8542 0.8927 0.01195 ] Network output: [ -0.0002601 0.001737 1.001 -1.402e-05 6.293e-06 0.9981 -1.056e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2193 0.1035 0.3469 0.1428 0.9849 0.9939 0.22 0.4356 0.8755 0.7043 ] Network output: [ 0.003685 -0.01741 0.9942 8.521e-06 -3.825e-06 1.016 6.422e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.09698 0.1842 0.198 0.9873 0.9919 0.1097 0.7407 0.8623 0.3053 ] Network output: [ -0.003453 0.01615 1.005 9.226e-06 -4.142e-06 0.9862 6.953e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09343 0.09149 0.165 0.1962 0.9852 0.9911 0.09344 0.6647 0.8377 0.2484 ] Network output: [ 9.517e-05 1 -6.328e-05 1.214e-06 -5.449e-07 0.9998 9.147e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002182 Epoch 9260 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009302 0.9966 0.9921 -2.127e-07 9.549e-08 -0.0073 -1.603e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003482 -0.003314 -0.00696 0.005572 0.9699 0.9743 0.006757 0.8268 0.8209 0.01668 ] Network output: [ 0.9999 0.0001928 0.0004575 -4.467e-06 2.005e-06 -0.0004442 -3.366e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.206 -0.03517 -0.161 0.1842 0.9834 0.9932 0.231 0.4316 0.8688 0.7105 ] Network output: [ -0.009256 1.003 1.008 -2.577e-07 1.157e-07 0.007673 -1.942e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00665 0.0005943 0.004406 0.003279 0.9889 0.9919 0.006779 0.8542 0.8927 0.01194 ] Network output: [ -0.0002599 0.001737 1.001 -1.4e-05 6.286e-06 0.9981 -1.055e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2193 0.1035 0.3469 0.1428 0.9849 0.9939 0.22 0.4356 0.8755 0.7043 ] Network output: [ 0.003683 -0.01741 0.9942 8.511e-06 -3.821e-06 1.016 6.414e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.09698 0.1842 0.198 0.9873 0.9919 0.1097 0.7407 0.8623 0.3053 ] Network output: [ -0.003452 0.01614 1.005 9.215e-06 -4.137e-06 0.9862 6.945e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09343 0.09149 0.165 0.1962 0.9852 0.9911 0.09345 0.6646 0.8377 0.2484 ] Network output: [ 9.513e-05 1 -6.323e-05 1.212e-06 -5.442e-07 0.9998 9.136e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002181 Epoch 9261 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009301 0.9966 0.9921 -2.127e-07 9.547e-08 -0.0073 -1.603e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003482 -0.003315 -0.00696 0.005572 0.9699 0.9743 0.006757 0.8268 0.8209 0.01668 ] Network output: [ 0.9999 0.0001926 0.0004573 -4.462e-06 2.003e-06 -0.0004438 -3.362e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.206 -0.03518 -0.161 0.1842 0.9834 0.9932 0.231 0.4316 0.8688 0.7105 ] Network output: [ -0.009255 1.003 1.008 -2.575e-07 1.156e-07 0.007672 -1.941e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006651 0.0005944 0.004406 0.003279 0.9889 0.9919 0.00678 0.8542 0.8926 0.01194 ] Network output: [ -0.0002597 0.001736 1.001 -1.399e-05 6.279e-06 0.9981 -1.054e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2193 0.1035 0.3469 0.1427 0.9849 0.9939 0.22 0.4356 0.8755 0.7043 ] Network output: [ 0.003682 -0.0174 0.9942 8.501e-06 -3.817e-06 1.016 6.407e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.09699 0.1842 0.198 0.9873 0.9919 0.1097 0.7407 0.8623 0.3053 ] Network output: [ -0.00345 0.01614 1.005 9.205e-06 -4.132e-06 0.9862 6.937e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09343 0.0915 0.165 0.1962 0.9852 0.9911 0.09345 0.6646 0.8377 0.2484 ] Network output: [ 9.51e-05 1 -6.318e-05 1.211e-06 -5.436e-07 0.9998 9.126e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000218 Epoch 9262 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0093 0.9966 0.9921 -2.126e-07 9.545e-08 -0.007299 -1.602e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003482 -0.003315 -0.006959 0.005571 0.9699 0.9743 0.006757 0.8268 0.8209 0.01667 ] Network output: [ 0.9999 0.0001924 0.0004571 -4.456e-06 2.001e-06 -0.0004435 -3.358e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.206 -0.03518 -0.161 0.1842 0.9834 0.9932 0.231 0.4316 0.8688 0.7105 ] Network output: [ -0.009254 1.003 1.008 -2.574e-07 1.156e-07 0.007671 -1.94e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006651 0.0005945 0.004406 0.003278 0.9889 0.9919 0.00678 0.8542 0.8926 0.01194 ] Network output: [ -0.0002595 0.001735 1.001 -1.397e-05 6.271e-06 0.9982 -1.053e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2193 0.1035 0.3469 0.1427 0.9849 0.9939 0.22 0.4356 0.8755 0.7043 ] Network output: [ 0.00368 -0.01739 0.9942 8.491e-06 -3.812e-06 1.016 6.399e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.09699 0.1842 0.198 0.9873 0.9919 0.1097 0.7407 0.8623 0.3053 ] Network output: [ -0.003449 0.01613 1.005 9.194e-06 -4.128e-06 0.9862 6.929e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09344 0.0915 0.165 0.1962 0.9852 0.9911 0.09345 0.6646 0.8377 0.2484 ] Network output: [ 9.507e-05 1 -6.313e-05 1.21e-06 -5.43e-07 0.9998 9.115e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002179 Epoch 9263 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009299 0.9966 0.9921 -2.126e-07 9.542e-08 -0.007299 -1.602e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003482 -0.003315 -0.006958 0.005571 0.9699 0.9743 0.006757 0.8268 0.8209 0.01667 ] Network output: [ 0.9999 0.0001922 0.0004569 -4.451e-06 1.998e-06 -0.0004432 -3.355e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.206 -0.03518 -0.1609 0.1842 0.9834 0.9932 0.231 0.4316 0.8688 0.7105 ] Network output: [ -0.009253 1.003 1.008 -2.573e-07 1.155e-07 0.00767 -1.939e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006652 0.0005946 0.004406 0.003278 0.9889 0.9919 0.006781 0.8542 0.8926 0.01194 ] Network output: [ -0.0002593 0.001735 1.001 -1.395e-05 6.264e-06 0.9982 -1.052e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2193 0.1035 0.3469 0.1427 0.9849 0.9939 0.22 0.4356 0.8755 0.7043 ] Network output: [ 0.003679 -0.01738 0.9942 8.482e-06 -3.808e-06 1.016 6.392e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.09699 0.1842 0.198 0.9873 0.9919 0.1097 0.7406 0.8623 0.3053 ] Network output: [ -0.003447 0.01612 1.005 9.184e-06 -4.123e-06 0.9862 6.921e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09344 0.0915 0.165 0.1962 0.9852 0.9911 0.09345 0.6646 0.8377 0.2484 ] Network output: [ 9.504e-05 1 -6.308e-05 1.208e-06 -5.424e-07 0.9998 9.105e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002178 Epoch 9264 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009298 0.9966 0.9921 -2.125e-07 9.54e-08 -0.007298 -1.601e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003482 -0.003315 -0.006958 0.00557 0.9699 0.9743 0.006757 0.8268 0.8209 0.01667 ] Network output: [ 0.9999 0.000192 0.0004567 -4.446e-06 1.996e-06 -0.0004429 -3.351e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.206 -0.03518 -0.1609 0.1842 0.9834 0.9932 0.231 0.4315 0.8688 0.7105 ] Network output: [ -0.009253 1.003 1.008 -2.571e-07 1.154e-07 0.007669 -1.938e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006652 0.0005947 0.004406 0.003278 0.9889 0.9919 0.006781 0.8542 0.8926 0.01194 ] Network output: [ -0.0002591 0.001734 1.001 -1.394e-05 6.257e-06 0.9982 -1.05e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2193 0.1035 0.3469 0.1427 0.9849 0.9939 0.22 0.4356 0.8755 0.7043 ] Network output: [ 0.003677 -0.01738 0.9942 8.472e-06 -3.803e-06 1.016 6.385e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.097 0.1842 0.198 0.9873 0.9919 0.1097 0.7406 0.8623 0.3053 ] Network output: [ -0.003446 0.01612 1.005 9.174e-06 -4.118e-06 0.9862 6.913e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09344 0.0915 0.165 0.1962 0.9852 0.9911 0.09345 0.6646 0.8377 0.2484 ] Network output: [ 9.5e-05 1 -6.303e-05 1.207e-06 -5.417e-07 0.9998 9.094e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002176 Epoch 9265 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009297 0.9966 0.9921 -2.125e-07 9.538e-08 -0.007298 -1.601e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003482 -0.003315 -0.006957 0.00557 0.9699 0.9743 0.006758 0.8267 0.8209 0.01667 ] Network output: [ 0.9999 0.0001918 0.0004565 -4.441e-06 1.994e-06 -0.0004425 -3.347e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.206 -0.03518 -0.1609 0.1842 0.9834 0.9932 0.231 0.4315 0.8688 0.7104 ] Network output: [ -0.009252 1.003 1.008 -2.57e-07 1.154e-07 0.007669 -1.937e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006653 0.0005948 0.004406 0.003278 0.9889 0.9919 0.006782 0.8542 0.8926 0.01194 ] Network output: [ -0.000259 0.001733 1.001 -1.392e-05 6.25e-06 0.9982 -1.049e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2193 0.1035 0.3469 0.1427 0.9849 0.9939 0.22 0.4356 0.8755 0.7043 ] Network output: [ 0.003676 -0.01737 0.9942 8.462e-06 -3.799e-06 1.016 6.377e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.097 0.1842 0.198 0.9873 0.9919 0.1097 0.7406 0.8623 0.3053 ] Network output: [ -0.003445 0.01611 1.005 9.163e-06 -4.114e-06 0.9863 6.906e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09344 0.09151 0.165 0.1962 0.9852 0.9911 0.09346 0.6646 0.8377 0.2484 ] Network output: [ 9.497e-05 1 -6.298e-05 1.205e-06 -5.411e-07 0.9998 9.084e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002175 Epoch 9266 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009296 0.9966 0.9921 -2.124e-07 9.535e-08 -0.007297 -1.601e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003482 -0.003315 -0.006956 0.00557 0.9699 0.9743 0.006758 0.8267 0.8209 0.01667 ] Network output: [ 0.9999 0.0001916 0.0004562 -4.436e-06 1.991e-06 -0.0004422 -3.343e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.206 -0.03518 -0.1609 0.1842 0.9834 0.9932 0.231 0.4315 0.8688 0.7104 ] Network output: [ -0.009251 1.003 1.008 -2.569e-07 1.153e-07 0.007668 -1.936e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006653 0.0005948 0.004406 0.003277 0.9889 0.9919 0.006782 0.8542 0.8926 0.01194 ] Network output: [ -0.0002588 0.001732 1.001 -1.39e-05 6.242e-06 0.9982 -1.048e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2193 0.1035 0.3469 0.1427 0.9849 0.9939 0.2201 0.4355 0.8755 0.7043 ] Network output: [ 0.003674 -0.01736 0.9942 8.452e-06 -3.795e-06 1.016 6.37e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.09701 0.1842 0.198 0.9873 0.9919 0.1097 0.7406 0.8623 0.3053 ] Network output: [ -0.003443 0.0161 1.005 9.153e-06 -4.109e-06 0.9863 6.898e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09345 0.09151 0.165 0.1962 0.9852 0.9911 0.09346 0.6646 0.8377 0.2484 ] Network output: [ 9.494e-05 1 -6.292e-05 1.204e-06 -5.405e-07 0.9998 9.073e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002174 Epoch 9267 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009295 0.9966 0.9921 -2.123e-07 9.533e-08 -0.007297 -1.6e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003482 -0.003315 -0.006956 0.005569 0.9699 0.9743 0.006758 0.8267 0.8209 0.01667 ] Network output: [ 0.9999 0.0001914 0.000456 -4.43e-06 1.989e-06 -0.0004419 -3.339e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.206 -0.03518 -0.1609 0.1842 0.9834 0.9932 0.231 0.4315 0.8688 0.7104 ] Network output: [ -0.00925 1.003 1.008 -2.567e-07 1.152e-07 0.007667 -1.935e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006653 0.0005949 0.004406 0.003277 0.9889 0.9919 0.006782 0.8542 0.8926 0.01194 ] Network output: [ -0.0002586 0.001732 1.001 -1.389e-05 6.235e-06 0.9982 -1.047e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2193 0.1035 0.347 0.1427 0.9849 0.9939 0.2201 0.4355 0.8755 0.7043 ] Network output: [ 0.003673 -0.01736 0.9942 8.443e-06 -3.79e-06 1.016 6.363e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.09701 0.1842 0.198 0.9873 0.9919 0.1097 0.7406 0.8623 0.3053 ] Network output: [ -0.003442 0.0161 1.005 9.142e-06 -4.104e-06 0.9863 6.89e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09345 0.09151 0.165 0.1962 0.9852 0.9911 0.09346 0.6646 0.8377 0.2484 ] Network output: [ 9.491e-05 1 -6.287e-05 1.203e-06 -5.399e-07 0.9998 9.063e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002173 Epoch 9268 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009294 0.9966 0.9921 -2.123e-07 9.531e-08 -0.007297 -1.6e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003482 -0.003315 -0.006955 0.005569 0.9699 0.9743 0.006758 0.8267 0.8209 0.01667 ] Network output: [ 0.9999 0.0001912 0.0004558 -4.425e-06 1.987e-06 -0.0004416 -3.335e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.206 -0.03518 -0.1609 0.1842 0.9834 0.9932 0.231 0.4315 0.8688 0.7104 ] Network output: [ -0.009249 1.003 1.008 -2.566e-07 1.152e-07 0.007666 -1.934e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006654 0.000595 0.004406 0.003277 0.9889 0.9919 0.006783 0.8542 0.8926 0.01194 ] Network output: [ -0.0002584 0.001731 1.001 -1.387e-05 6.228e-06 0.9982 -1.045e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2193 0.1036 0.347 0.1427 0.9849 0.9939 0.2201 0.4355 0.8755 0.7043 ] Network output: [ 0.003671 -0.01735 0.9942 8.433e-06 -3.786e-06 1.016 6.355e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.09702 0.1842 0.198 0.9873 0.9919 0.1097 0.7406 0.8623 0.3053 ] Network output: [ -0.00344 0.01609 1.005 9.132e-06 -4.1e-06 0.9863 6.882e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09345 0.09151 0.165 0.1962 0.9852 0.9911 0.09346 0.6645 0.8377 0.2484 ] Network output: [ 9.487e-05 1 -6.282e-05 1.201e-06 -5.393e-07 0.9998 9.053e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002172 Epoch 9269 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009293 0.9966 0.9921 -2.122e-07 9.528e-08 -0.007296 -1.6e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003482 -0.003315 -0.006954 0.005568 0.9699 0.9743 0.006759 0.8267 0.8209 0.01667 ] Network output: [ 0.9999 0.000191 0.0004556 -4.42e-06 1.984e-06 -0.0004413 -3.331e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.206 -0.03518 -0.1609 0.1842 0.9834 0.9932 0.2311 0.4315 0.8688 0.7104 ] Network output: [ -0.009248 1.003 1.008 -2.564e-07 1.151e-07 0.007665 -1.933e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006654 0.0005951 0.004406 0.003277 0.9889 0.9919 0.006783 0.8541 0.8926 0.01194 ] Network output: [ -0.0002582 0.00173 1.001 -1.386e-05 6.221e-06 0.9982 -1.044e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2193 0.1036 0.347 0.1427 0.9849 0.9939 0.2201 0.4355 0.8755 0.7043 ] Network output: [ 0.00367 -0.01734 0.9942 8.423e-06 -3.781e-06 1.016 6.348e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.09702 0.1842 0.198 0.9873 0.9919 0.1097 0.7406 0.8623 0.3053 ] Network output: [ -0.003439 0.01608 1.005 9.122e-06 -4.095e-06 0.9863 6.874e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09345 0.09151 0.165 0.1962 0.9852 0.9911 0.09347 0.6645 0.8377 0.2484 ] Network output: [ 9.484e-05 1 -6.277e-05 1.2e-06 -5.386e-07 0.9998 9.042e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002171 Epoch 9270 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009292 0.9966 0.9921 -2.122e-07 9.526e-08 -0.007296 -1.599e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003483 -0.003315 -0.006954 0.005568 0.9699 0.9743 0.006759 0.8267 0.8209 0.01667 ] Network output: [ 0.9999 0.0001908 0.0004554 -4.415e-06 1.982e-06 -0.0004409 -3.327e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.206 -0.03518 -0.1609 0.1842 0.9834 0.9932 0.2311 0.4315 0.8688 0.7104 ] Network output: [ -0.009247 1.003 1.008 -2.563e-07 1.151e-07 0.007665 -1.932e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006655 0.0005952 0.004406 0.003276 0.9889 0.9919 0.006784 0.8541 0.8926 0.01194 ] Network output: [ -0.000258 0.00173 1.001 -1.384e-05 6.213e-06 0.9982 -1.043e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2193 0.1036 0.347 0.1427 0.9849 0.9939 0.2201 0.4355 0.8755 0.7043 ] Network output: [ 0.003668 -0.01734 0.9942 8.413e-06 -3.777e-06 1.016 6.341e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.09702 0.1842 0.198 0.9873 0.9919 0.1097 0.7406 0.8623 0.3053 ] Network output: [ -0.003438 0.01608 1.005 9.111e-06 -4.09e-06 0.9863 6.867e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09345 0.09152 0.165 0.1962 0.9852 0.9911 0.09347 0.6645 0.8377 0.2484 ] Network output: [ 9.481e-05 1 -6.272e-05 1.198e-06 -5.38e-07 0.9998 9.032e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000217 Epoch 9271 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009291 0.9966 0.9921 -2.121e-07 9.524e-08 -0.007295 -1.599e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003483 -0.003316 -0.006953 0.005567 0.9699 0.9743 0.006759 0.8267 0.8209 0.01667 ] Network output: [ 0.9999 0.0001906 0.0004552 -4.41e-06 1.98e-06 -0.0004406 -3.323e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.206 -0.03518 -0.1609 0.1842 0.9834 0.9932 0.2311 0.4315 0.8688 0.7104 ] Network output: [ -0.009246 1.003 1.008 -2.562e-07 1.15e-07 0.007664 -1.931e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006655 0.0005953 0.004405 0.003276 0.9889 0.9919 0.006784 0.8541 0.8926 0.01194 ] Network output: [ -0.0002579 0.001729 1.001 -1.382e-05 6.206e-06 0.9982 -1.042e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2194 0.1036 0.347 0.1427 0.9849 0.9939 0.2201 0.4355 0.8755 0.7043 ] Network output: [ 0.003667 -0.01733 0.9942 8.404e-06 -3.773e-06 1.016 6.333e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.09703 0.1842 0.198 0.9873 0.9919 0.1097 0.7405 0.8623 0.3053 ] Network output: [ -0.003436 0.01607 1.005 9.101e-06 -4.086e-06 0.9863 6.859e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09346 0.09152 0.165 0.1962 0.9852 0.9911 0.09347 0.6645 0.8377 0.2484 ] Network output: [ 9.478e-05 1 -6.267e-05 1.197e-06 -5.374e-07 0.9998 9.021e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002168 Epoch 9272 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00929 0.9966 0.9921 -2.121e-07 9.521e-08 -0.007295 -1.598e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003483 -0.003316 -0.006952 0.005567 0.9699 0.9743 0.006759 0.8267 0.8209 0.01666 ] Network output: [ 0.9999 0.0001904 0.000455 -4.405e-06 1.977e-06 -0.0004403 -3.319e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.206 -0.03519 -0.1608 0.1842 0.9834 0.9932 0.2311 0.4315 0.8688 0.7104 ] Network output: [ -0.009245 1.003 1.008 -2.56e-07 1.149e-07 0.007663 -1.929e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006656 0.0005953 0.004405 0.003276 0.9889 0.9919 0.006785 0.8541 0.8926 0.01194 ] Network output: [ -0.0002577 0.001728 1.001 -1.381e-05 6.199e-06 0.9982 -1.041e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2194 0.1036 0.347 0.1427 0.9849 0.9939 0.2201 0.4355 0.8755 0.7043 ] Network output: [ 0.003665 -0.01732 0.9942 8.394e-06 -3.768e-06 1.016 6.326e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.09703 0.1842 0.198 0.9873 0.9919 0.1097 0.7405 0.8623 0.3053 ] Network output: [ -0.003435 0.01606 1.005 9.091e-06 -4.081e-06 0.9863 6.851e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09346 0.09152 0.165 0.1962 0.9852 0.9911 0.09347 0.6645 0.8377 0.2484 ] Network output: [ 9.475e-05 1 -6.263e-05 1.196e-06 -5.368e-07 0.9998 9.011e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002167 Epoch 9273 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009289 0.9966 0.9921 -2.12e-07 9.519e-08 -0.007294 -1.598e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003483 -0.003316 -0.006952 0.005567 0.9699 0.9743 0.006759 0.8267 0.8209 0.01666 ] Network output: [ 0.9999 0.0001902 0.0004547 -4.4e-06 1.975e-06 -0.00044 -3.316e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.206 -0.03519 -0.1608 0.1842 0.9834 0.9932 0.2311 0.4315 0.8688 0.7104 ] Network output: [ -0.009245 1.003 1.008 -2.559e-07 1.149e-07 0.007662 -1.928e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006656 0.0005954 0.004405 0.003276 0.9889 0.9919 0.006785 0.8541 0.8926 0.01193 ] Network output: [ -0.0002575 0.001727 1.001 -1.379e-05 6.192e-06 0.9982 -1.039e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2194 0.1036 0.347 0.1427 0.9849 0.9939 0.2201 0.4355 0.8755 0.7043 ] Network output: [ 0.003664 -0.01732 0.9942 8.384e-06 -3.764e-06 1.016 6.319e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.09704 0.1842 0.198 0.9873 0.9919 0.1097 0.7405 0.8623 0.3053 ] Network output: [ -0.003433 0.01606 1.005 9.08e-06 -4.076e-06 0.9863 6.843e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09346 0.09152 0.165 0.1962 0.9852 0.9911 0.09347 0.6645 0.8377 0.2484 ] Network output: [ 9.471e-05 1 -6.258e-05 1.194e-06 -5.362e-07 0.9998 9.001e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002166 Epoch 9274 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009288 0.9966 0.9921 -2.12e-07 9.516e-08 -0.007294 -1.598e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003483 -0.003316 -0.006951 0.005566 0.9699 0.9743 0.00676 0.8267 0.8209 0.01666 ] Network output: [ 0.9999 0.00019 0.0004545 -4.394e-06 1.973e-06 -0.0004397 -3.312e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2061 -0.03519 -0.1608 0.1842 0.9834 0.9932 0.2311 0.4315 0.8688 0.7104 ] Network output: [ -0.009244 1.003 1.008 -2.557e-07 1.148e-07 0.007662 -1.927e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006657 0.0005955 0.004405 0.003275 0.9889 0.9919 0.006786 0.8541 0.8926 0.01193 ] Network output: [ -0.0002573 0.001727 1.001 -1.378e-05 6.184e-06 0.9982 -1.038e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2194 0.1036 0.347 0.1427 0.9849 0.9939 0.2201 0.4355 0.8755 0.7043 ] Network output: [ 0.003662 -0.01731 0.9942 8.374e-06 -3.76e-06 1.016 6.311e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1096 0.09704 0.1842 0.198 0.9873 0.9919 0.1097 0.7405 0.8623 0.3053 ] Network output: [ -0.003432 0.01605 1.005 9.07e-06 -4.072e-06 0.9863 6.835e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09346 0.09153 0.165 0.1962 0.9852 0.9911 0.09348 0.6645 0.8377 0.2484 ] Network output: [ 9.468e-05 1 -6.253e-05 1.193e-06 -5.356e-07 0.9998 8.99e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002165 Epoch 9275 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009287 0.9966 0.9921 -2.119e-07 9.514e-08 -0.007293 -1.597e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003483 -0.003316 -0.00695 0.005566 0.9699 0.9743 0.00676 0.8267 0.8209 0.01666 ] Network output: [ 0.9999 0.0001898 0.0004543 -4.389e-06 1.97e-06 -0.0004393 -3.308e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2061 -0.03519 -0.1608 0.1842 0.9834 0.9932 0.2311 0.4315 0.8688 0.7104 ] Network output: [ -0.009243 1.003 1.008 -2.556e-07 1.148e-07 0.007661 -1.926e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006657 0.0005956 0.004405 0.003275 0.9889 0.9919 0.006786 0.8541 0.8926 0.01193 ] Network output: [ -0.0002571 0.001726 1.001 -1.376e-05 6.177e-06 0.9982 -1.037e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2194 0.1036 0.347 0.1427 0.9849 0.9939 0.2201 0.4355 0.8755 0.7043 ] Network output: [ 0.003661 -0.0173 0.9942 8.365e-06 -3.755e-06 1.016 6.304e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.09704 0.1842 0.198 0.9873 0.9919 0.1097 0.7405 0.8623 0.3053 ] Network output: [ -0.003431 0.01604 1.005 9.06e-06 -4.067e-06 0.9863 6.828e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09347 0.09153 0.165 0.1962 0.9852 0.9911 0.09348 0.6644 0.8377 0.2484 ] Network output: [ 9.465e-05 1 -6.248e-05 1.192e-06 -5.349e-07 0.9998 8.98e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002164 Epoch 9276 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009286 0.9966 0.9921 -2.119e-07 9.512e-08 -0.007293 -1.597e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003483 -0.003316 -0.00695 0.005565 0.9699 0.9743 0.00676 0.8267 0.8209 0.01666 ] Network output: [ 0.9999 0.0001896 0.0004541 -4.384e-06 1.968e-06 -0.000439 -3.304e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2061 -0.03519 -0.1608 0.1842 0.9834 0.9932 0.2311 0.4315 0.8688 0.7104 ] Network output: [ -0.009242 1.003 1.008 -2.555e-07 1.147e-07 0.00766 -1.925e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006657 0.0005957 0.004405 0.003275 0.9889 0.9919 0.006786 0.8541 0.8926 0.01193 ] Network output: [ -0.0002569 0.001725 1.001 -1.374e-05 6.17e-06 0.9982 -1.036e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2194 0.1036 0.347 0.1427 0.9849 0.9939 0.2201 0.4355 0.8755 0.7043 ] Network output: [ 0.003659 -0.01729 0.9942 8.355e-06 -3.751e-06 1.016 6.297e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.09705 0.1842 0.198 0.9873 0.9919 0.1097 0.7405 0.8622 0.3053 ] Network output: [ -0.003429 0.01604 1.005 9.049e-06 -4.063e-06 0.9863 6.82e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09347 0.09153 0.165 0.1962 0.9852 0.9911 0.09348 0.6644 0.8377 0.2484 ] Network output: [ 9.462e-05 1 -6.243e-05 1.19e-06 -5.343e-07 0.9998 8.97e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002163 Epoch 9277 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009285 0.9966 0.9921 -2.118e-07 9.509e-08 -0.007292 -1.596e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003483 -0.003316 -0.006949 0.005565 0.9699 0.9743 0.00676 0.8267 0.8209 0.01666 ] Network output: [ 0.9999 0.0001894 0.0004539 -4.379e-06 1.966e-06 -0.0004387 -3.3e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2061 -0.03519 -0.1608 0.1842 0.9834 0.9932 0.2311 0.4315 0.8687 0.7104 ] Network output: [ -0.009241 1.003 1.008 -2.553e-07 1.146e-07 0.007659 -1.924e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006658 0.0005958 0.004405 0.003275 0.9889 0.9919 0.006787 0.8541 0.8926 0.01193 ] Network output: [ -0.0002568 0.001724 1.001 -1.373e-05 6.163e-06 0.9982 -1.035e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2194 0.1036 0.347 0.1427 0.9849 0.9939 0.2201 0.4355 0.8755 0.7043 ] Network output: [ 0.003658 -0.01729 0.9942 8.345e-06 -3.747e-06 1.016 6.289e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.09705 0.1842 0.198 0.9873 0.9919 0.1097 0.7405 0.8622 0.3053 ] Network output: [ -0.003428 0.01603 1.005 9.039e-06 -4.058e-06 0.9863 6.812e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09347 0.09153 0.165 0.1962 0.9852 0.9911 0.09348 0.6644 0.8377 0.2484 ] Network output: [ 9.458e-05 1 -6.238e-05 1.189e-06 -5.337e-07 0.9998 8.959e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002162 Epoch 9278 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009284 0.9966 0.9921 -2.118e-07 9.507e-08 -0.007292 -1.596e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003483 -0.003316 -0.006948 0.005564 0.9699 0.9743 0.00676 0.8267 0.8209 0.01666 ] Network output: [ 0.9999 0.0001892 0.0004537 -4.374e-06 1.964e-06 -0.0004384 -3.296e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2061 -0.03519 -0.1608 0.1842 0.9834 0.9932 0.2311 0.4315 0.8687 0.7104 ] Network output: [ -0.00924 1.003 1.008 -2.552e-07 1.146e-07 0.007658 -1.923e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006658 0.0005958 0.004405 0.003274 0.9889 0.9919 0.006787 0.8541 0.8926 0.01193 ] Network output: [ -0.0002566 0.001724 1.001 -1.371e-05 6.156e-06 0.9982 -1.033e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2194 0.1036 0.347 0.1427 0.9849 0.9939 0.2201 0.4355 0.8755 0.7043 ] Network output: [ 0.003656 -0.01728 0.9942 8.336e-06 -3.742e-06 1.016 6.282e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.09706 0.1842 0.198 0.9873 0.9919 0.1097 0.7405 0.8622 0.3053 ] Network output: [ -0.003426 0.01602 1.005 9.029e-06 -4.053e-06 0.9863 6.804e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09347 0.09153 0.165 0.1962 0.9852 0.9911 0.09349 0.6644 0.8377 0.2484 ] Network output: [ 9.455e-05 1 -6.233e-05 1.187e-06 -5.331e-07 0.9998 8.949e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002161 Epoch 9279 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009283 0.9966 0.9921 -2.117e-07 9.504e-08 -0.007291 -1.595e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003483 -0.003316 -0.006948 0.005564 0.9699 0.9743 0.006761 0.8267 0.8209 0.01666 ] Network output: [ 0.9999 0.000189 0.0004535 -4.369e-06 1.961e-06 -0.0004381 -3.292e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2061 -0.03519 -0.1608 0.1842 0.9834 0.9932 0.2311 0.4315 0.8687 0.7104 ] Network output: [ -0.009239 1.003 1.008 -2.551e-07 1.145e-07 0.007658 -1.922e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006659 0.0005959 0.004405 0.003274 0.9889 0.9919 0.006788 0.8541 0.8926 0.01193 ] Network output: [ -0.0002564 0.001723 1.001 -1.37e-05 6.149e-06 0.9982 -1.032e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2194 0.1036 0.347 0.1427 0.9849 0.9939 0.2202 0.4355 0.8755 0.7042 ] Network output: [ 0.003655 -0.01727 0.9942 8.326e-06 -3.738e-06 1.016 6.275e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.09706 0.1842 0.198 0.9873 0.9919 0.1097 0.7404 0.8622 0.3053 ] Network output: [ -0.003425 0.01602 1.005 9.018e-06 -4.049e-06 0.9863 6.797e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09347 0.09154 0.165 0.1962 0.9852 0.9911 0.09349 0.6644 0.8377 0.2484 ] Network output: [ 9.452e-05 1 -6.228e-05 1.186e-06 -5.325e-07 0.9998 8.939e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002159 Epoch 9280 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009282 0.9966 0.9921 -2.117e-07 9.502e-08 -0.007291 -1.595e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003483 -0.003316 -0.006947 0.005563 0.9699 0.9743 0.006761 0.8267 0.8208 0.01666 ] Network output: [ 0.9999 0.0001887 0.0004532 -4.364e-06 1.959e-06 -0.0004378 -3.289e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2061 -0.03519 -0.1608 0.1841 0.9834 0.9932 0.2311 0.4314 0.8687 0.7104 ] Network output: [ -0.009238 1.003 1.008 -2.549e-07 1.144e-07 0.007657 -1.921e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006659 0.000596 0.004405 0.003274 0.9889 0.9919 0.006788 0.8541 0.8926 0.01193 ] Network output: [ -0.0002562 0.001722 1.001 -1.368e-05 6.141e-06 0.9982 -1.031e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2194 0.1036 0.347 0.1427 0.9849 0.9939 0.2202 0.4355 0.8755 0.7042 ] Network output: [ 0.003653 -0.01727 0.9942 8.317e-06 -3.734e-06 1.016 6.268e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.09707 0.1842 0.198 0.9873 0.9919 0.1097 0.7404 0.8622 0.3053 ] Network output: [ -0.003423 0.01601 1.005 9.008e-06 -4.044e-06 0.9863 6.789e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09348 0.09154 0.165 0.1962 0.9852 0.9911 0.09349 0.6644 0.8377 0.2484 ] Network output: [ 9.449e-05 1 -6.223e-05 1.185e-06 -5.319e-07 0.9998 8.929e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002158 Epoch 9281 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009281 0.9966 0.9921 -2.116e-07 9.499e-08 -0.00729 -1.595e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003483 -0.003317 -0.006946 0.005563 0.9699 0.9743 0.006761 0.8267 0.8208 0.01666 ] Network output: [ 0.9999 0.0001885 0.000453 -4.359e-06 1.957e-06 -0.0004374 -3.285e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2061 -0.03519 -0.1608 0.1841 0.9834 0.9932 0.2311 0.4314 0.8687 0.7104 ] Network output: [ -0.009237 1.003 1.008 -2.548e-07 1.144e-07 0.007656 -1.92e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00666 0.0005961 0.004405 0.003274 0.9889 0.9919 0.006789 0.8541 0.8926 0.01193 ] Network output: [ -0.000256 0.001722 1.001 -1.366e-05 6.134e-06 0.9982 -1.03e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2194 0.1036 0.347 0.1427 0.9849 0.9939 0.2202 0.4355 0.8755 0.7042 ] Network output: [ 0.003652 -0.01726 0.9942 8.307e-06 -3.729e-06 1.016 6.26e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.09707 0.1842 0.198 0.9873 0.9919 0.1098 0.7404 0.8622 0.3053 ] Network output: [ -0.003422 0.016 1.005 8.998e-06 -4.04e-06 0.9863 6.781e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09348 0.09154 0.165 0.1962 0.9852 0.9911 0.09349 0.6644 0.8377 0.2484 ] Network output: [ 9.446e-05 1 -6.218e-05 1.183e-06 -5.313e-07 0.9998 8.918e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002157 Epoch 9282 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00928 0.9966 0.9921 -2.115e-07 9.497e-08 -0.00729 -1.594e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003483 -0.003317 -0.006946 0.005563 0.9699 0.9743 0.006761 0.8267 0.8208 0.01665 ] Network output: [ 0.9999 0.0001883 0.0004528 -4.353e-06 1.954e-06 -0.0004371 -3.281e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2061 -0.0352 -0.1607 0.1841 0.9834 0.9932 0.2312 0.4314 0.8687 0.7104 ] Network output: [ -0.009237 1.003 1.008 -2.546e-07 1.143e-07 0.007655 -1.919e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00666 0.0005962 0.004405 0.003273 0.9889 0.9919 0.006789 0.8541 0.8926 0.01193 ] Network output: [ -0.0002558 0.001721 1.001 -1.365e-05 6.127e-06 0.9982 -1.029e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2194 0.1036 0.347 0.1427 0.9849 0.9939 0.2202 0.4354 0.8755 0.7042 ] Network output: [ 0.00365 -0.01725 0.9942 8.297e-06 -3.725e-06 1.016 6.253e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.09707 0.1842 0.198 0.9873 0.9919 0.1098 0.7404 0.8622 0.3053 ] Network output: [ -0.003421 0.016 1.005 8.988e-06 -4.035e-06 0.9863 6.773e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09348 0.09154 0.165 0.1962 0.9852 0.9911 0.09349 0.6644 0.8377 0.2484 ] Network output: [ 9.442e-05 1 -6.213e-05 1.182e-06 -5.307e-07 0.9998 8.908e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002156 Epoch 9283 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009279 0.9966 0.9921 -2.115e-07 9.494e-08 -0.007289 -1.594e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003484 -0.003317 -0.006945 0.005562 0.9699 0.9743 0.006761 0.8267 0.8208 0.01665 ] Network output: [ 0.9999 0.0001881 0.0004526 -4.348e-06 1.952e-06 -0.0004368 -3.277e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2061 -0.0352 -0.1607 0.1841 0.9834 0.9932 0.2312 0.4314 0.8687 0.7104 ] Network output: [ -0.009236 1.003 1.008 -2.545e-07 1.143e-07 0.007655 -1.918e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00666 0.0005963 0.004405 0.003273 0.9889 0.9919 0.00679 0.8541 0.8926 0.01193 ] Network output: [ -0.0002557 0.00172 1.001 -1.363e-05 6.12e-06 0.9982 -1.027e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2194 0.1036 0.347 0.1427 0.9849 0.9939 0.2202 0.4354 0.8755 0.7042 ] Network output: [ 0.003649 -0.01725 0.9942 8.288e-06 -3.721e-06 1.016 6.246e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.09708 0.1842 0.198 0.9873 0.9919 0.1098 0.7404 0.8622 0.3053 ] Network output: [ -0.003419 0.01599 1.005 8.978e-06 -4.03e-06 0.9863 6.766e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09348 0.09155 0.165 0.1962 0.9852 0.9911 0.0935 0.6643 0.8377 0.2484 ] Network output: [ 9.439e-05 1 -6.209e-05 1.181e-06 -5.3e-07 0.9998 8.898e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002155 Epoch 9284 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009278 0.9966 0.9921 -2.114e-07 9.492e-08 -0.007289 -1.593e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003484 -0.003317 -0.006944 0.005562 0.9699 0.9743 0.006762 0.8267 0.8208 0.01665 ] Network output: [ 0.9999 0.0001879 0.0004524 -4.343e-06 1.95e-06 -0.0004365 -3.273e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2061 -0.0352 -0.1607 0.1841 0.9834 0.9932 0.2312 0.4314 0.8687 0.7104 ] Network output: [ -0.009235 1.003 1.008 -2.544e-07 1.142e-07 0.007654 -1.917e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006661 0.0005963 0.004404 0.003273 0.9889 0.9919 0.00679 0.8541 0.8926 0.01193 ] Network output: [ -0.0002555 0.001719 1.001 -1.362e-05 6.113e-06 0.9982 -1.026e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2195 0.1036 0.347 0.1427 0.9849 0.9939 0.2202 0.4354 0.8755 0.7042 ] Network output: [ 0.003647 -0.01724 0.9942 8.278e-06 -3.716e-06 1.016 6.239e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.09708 0.1842 0.198 0.9873 0.9919 0.1098 0.7404 0.8622 0.3053 ] Network output: [ -0.003418 0.01598 1.005 8.967e-06 -4.026e-06 0.9863 6.758e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09349 0.09155 0.165 0.1962 0.9852 0.9911 0.0935 0.6643 0.8376 0.2484 ] Network output: [ 9.436e-05 1 -6.204e-05 1.179e-06 -5.294e-07 0.9998 8.888e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002154 Epoch 9285 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009277 0.9966 0.9921 -2.114e-07 9.489e-08 -0.007289 -1.593e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003484 -0.003317 -0.006944 0.005561 0.9699 0.9743 0.006762 0.8266 0.8208 0.01665 ] Network output: [ 0.9999 0.0001877 0.0004522 -4.338e-06 1.948e-06 -0.0004362 -3.269e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2061 -0.0352 -0.1607 0.1841 0.9834 0.9932 0.2312 0.4314 0.8687 0.7104 ] Network output: [ -0.009234 1.003 1.008 -2.542e-07 1.141e-07 0.007653 -1.916e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006661 0.0005964 0.004404 0.003273 0.9889 0.9919 0.006791 0.8541 0.8926 0.01192 ] Network output: [ -0.0002553 0.001719 1.001 -1.36e-05 6.106e-06 0.9982 -1.025e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2195 0.1036 0.347 0.1427 0.9849 0.9939 0.2202 0.4354 0.8755 0.7042 ] Network output: [ 0.003646 -0.01723 0.9942 8.269e-06 -3.712e-06 1.016 6.231e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.09709 0.1842 0.198 0.9873 0.9919 0.1098 0.7404 0.8622 0.3053 ] Network output: [ -0.003416 0.01598 1.005 8.957e-06 -4.021e-06 0.9863 6.75e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09349 0.09155 0.165 0.1962 0.9852 0.9911 0.0935 0.6643 0.8376 0.2484 ] Network output: [ 9.433e-05 1 -6.199e-05 1.178e-06 -5.288e-07 0.9998 8.877e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002153 Epoch 9286 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009276 0.9966 0.9921 -2.113e-07 9.487e-08 -0.007288 -1.593e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003484 -0.003317 -0.006943 0.005561 0.9699 0.9743 0.006762 0.8266 0.8208 0.01665 ] Network output: [ 0.9999 0.0001875 0.000452 -4.333e-06 1.945e-06 -0.0004359 -3.266e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2061 -0.0352 -0.1607 0.1841 0.9834 0.9932 0.2312 0.4314 0.8687 0.7103 ] Network output: [ -0.009233 1.003 1.008 -2.541e-07 1.141e-07 0.007652 -1.915e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006662 0.0005965 0.004404 0.003272 0.9889 0.9919 0.006791 0.854 0.8926 0.01192 ] Network output: [ -0.0002551 0.001718 1.001 -1.358e-05 6.099e-06 0.9982 -1.024e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2195 0.1036 0.3471 0.1427 0.9849 0.9939 0.2202 0.4354 0.8755 0.7042 ] Network output: [ 0.003644 -0.01723 0.9942 8.259e-06 -3.708e-06 1.016 6.224e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.09709 0.1842 0.198 0.9873 0.9919 0.1098 0.7403 0.8622 0.3053 ] Network output: [ -0.003415 0.01597 1.005 8.947e-06 -4.017e-06 0.9863 6.743e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09349 0.09155 0.165 0.1962 0.9852 0.9911 0.0935 0.6643 0.8376 0.2484 ] Network output: [ 9.43e-05 1 -6.194e-05 1.177e-06 -5.282e-07 0.9998 8.867e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002152 Epoch 9287 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009275 0.9966 0.9921 -2.113e-07 9.484e-08 -0.007288 -1.592e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003484 -0.003317 -0.006942 0.00556 0.9699 0.9743 0.006762 0.8266 0.8208 0.01665 ] Network output: [ 0.9999 0.0001873 0.0004518 -4.328e-06 1.943e-06 -0.0004355 -3.262e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2061 -0.0352 -0.1607 0.1841 0.9834 0.9932 0.2312 0.4314 0.8687 0.7103 ] Network output: [ -0.009232 1.003 1.008 -2.539e-07 1.14e-07 0.007651 -1.914e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006662 0.0005966 0.004404 0.003272 0.9889 0.9919 0.006791 0.854 0.8926 0.01192 ] Network output: [ -0.0002549 0.001717 1.001 -1.357e-05 6.091e-06 0.9982 -1.023e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2195 0.1036 0.3471 0.1427 0.9849 0.9939 0.2202 0.4354 0.8755 0.7042 ] Network output: [ 0.003643 -0.01722 0.9942 8.249e-06 -3.703e-06 1.016 6.217e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.0971 0.1842 0.198 0.9873 0.9919 0.1098 0.7403 0.8622 0.3053 ] Network output: [ -0.003414 0.01596 1.005 8.937e-06 -4.012e-06 0.9863 6.735e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09349 0.09155 0.165 0.1962 0.9852 0.9911 0.09351 0.6643 0.8376 0.2484 ] Network output: [ 9.426e-05 1 -6.189e-05 1.175e-06 -5.276e-07 0.9998 8.857e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000215 Epoch 9288 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009274 0.9966 0.9921 -2.112e-07 9.482e-08 -0.007287 -1.592e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003484 -0.003317 -0.006942 0.00556 0.9699 0.9743 0.006763 0.8266 0.8208 0.01665 ] Network output: [ 0.9999 0.0001871 0.0004515 -4.323e-06 1.941e-06 -0.0004352 -3.258e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2061 -0.0352 -0.1607 0.1841 0.9834 0.9932 0.2312 0.4314 0.8687 0.7103 ] Network output: [ -0.009231 1.003 1.008 -2.538e-07 1.139e-07 0.007651 -1.913e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006663 0.0005967 0.004404 0.003272 0.9889 0.9919 0.006792 0.854 0.8926 0.01192 ] Network output: [ -0.0002547 0.001717 1.001 -1.355e-05 6.084e-06 0.9982 -1.021e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2195 0.1037 0.3471 0.1427 0.9849 0.9939 0.2202 0.4354 0.8755 0.7042 ] Network output: [ 0.003641 -0.01721 0.9942 8.24e-06 -3.699e-06 1.016 6.21e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.0971 0.1842 0.198 0.9873 0.9919 0.1098 0.7403 0.8622 0.3053 ] Network output: [ -0.003412 0.01596 1.005 8.927e-06 -4.007e-06 0.9863 6.727e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09349 0.09156 0.165 0.1962 0.9852 0.9911 0.09351 0.6643 0.8376 0.2484 ] Network output: [ 9.423e-05 1 -6.184e-05 1.174e-06 -5.27e-07 0.9998 8.847e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002149 Epoch 9289 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009273 0.9966 0.9921 -2.111e-07 9.479e-08 -0.007287 -1.591e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003484 -0.003317 -0.006941 0.00556 0.9699 0.9743 0.006763 0.8266 0.8208 0.01665 ] Network output: [ 0.9999 0.0001869 0.0004513 -4.318e-06 1.939e-06 -0.0004349 -3.254e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2062 -0.0352 -0.1607 0.1841 0.9834 0.9932 0.2312 0.4314 0.8687 0.7103 ] Network output: [ -0.00923 1.003 1.008 -2.537e-07 1.139e-07 0.00765 -1.912e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006663 0.0005968 0.004404 0.003272 0.9889 0.9919 0.006792 0.854 0.8926 0.01192 ] Network output: [ -0.0002546 0.001716 1.001 -1.354e-05 6.077e-06 0.9982 -1.02e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2195 0.1037 0.3471 0.1427 0.9849 0.9939 0.2202 0.4354 0.8755 0.7042 ] Network output: [ 0.00364 -0.0172 0.9942 8.23e-06 -3.695e-06 1.016 6.203e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.0971 0.1842 0.198 0.9873 0.9919 0.1098 0.7403 0.8622 0.3053 ] Network output: [ -0.003411 0.01595 1.005 8.916e-06 -4.003e-06 0.9863 6.72e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0935 0.09156 0.165 0.1962 0.9852 0.9911 0.09351 0.6643 0.8376 0.2484 ] Network output: [ 9.42e-05 1 -6.18e-05 1.173e-06 -5.264e-07 0.9998 8.837e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002148 Epoch 9290 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009272 0.9966 0.9921 -2.111e-07 9.477e-08 -0.007286 -1.591e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003484 -0.003317 -0.00694 0.005559 0.9699 0.9743 0.006763 0.8266 0.8208 0.01665 ] Network output: [ 0.9999 0.0001867 0.0004511 -4.313e-06 1.936e-06 -0.0004346 -3.25e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2062 -0.0352 -0.1607 0.1841 0.9834 0.9932 0.2312 0.4314 0.8687 0.7103 ] Network output: [ -0.009229 1.003 1.008 -2.535e-07 1.138e-07 0.007649 -1.911e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006663 0.0005968 0.004404 0.003271 0.9889 0.9919 0.006793 0.854 0.8926 0.01192 ] Network output: [ -0.0002544 0.001715 1.001 -1.352e-05 6.07e-06 0.9982 -1.019e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2195 0.1037 0.3471 0.1427 0.9849 0.9939 0.2202 0.4354 0.8755 0.7042 ] Network output: [ 0.003638 -0.0172 0.9942 8.221e-06 -3.691e-06 1.016 6.195e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.09711 0.1842 0.198 0.9873 0.9919 0.1098 0.7403 0.8622 0.3053 ] Network output: [ -0.003409 0.01594 1.005 8.906e-06 -3.998e-06 0.9863 6.712e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0935 0.09156 0.165 0.1962 0.9852 0.9911 0.09351 0.6642 0.8376 0.2484 ] Network output: [ 9.417e-05 1 -6.175e-05 1.171e-06 -5.258e-07 0.9998 8.826e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002147 Epoch 9291 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009271 0.9966 0.9921 -2.11e-07 9.474e-08 -0.007286 -1.59e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003484 -0.003318 -0.00694 0.005559 0.9699 0.9743 0.006763 0.8266 0.8208 0.01665 ] Network output: [ 0.9999 0.0001865 0.0004509 -4.308e-06 1.934e-06 -0.0004343 -3.247e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2062 -0.0352 -0.1607 0.1841 0.9834 0.9932 0.2312 0.4314 0.8687 0.7103 ] Network output: [ -0.009229 1.003 1.008 -2.534e-07 1.137e-07 0.007648 -1.91e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006664 0.0005969 0.004404 0.003271 0.9889 0.9919 0.006793 0.854 0.8926 0.01192 ] Network output: [ -0.0002542 0.001714 1.001 -1.351e-05 6.063e-06 0.9982 -1.018e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2195 0.1037 0.3471 0.1427 0.9849 0.9939 0.2202 0.4354 0.8755 0.7042 ] Network output: [ 0.003637 -0.01719 0.9942 8.211e-06 -3.686e-06 1.016 6.188e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.09711 0.1842 0.198 0.9873 0.9919 0.1098 0.7403 0.8622 0.3053 ] Network output: [ -0.003408 0.01594 1.005 8.896e-06 -3.994e-06 0.9863 6.704e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0935 0.09156 0.165 0.1962 0.9852 0.9911 0.09351 0.6642 0.8376 0.2484 ] Network output: [ 9.414e-05 1 -6.17e-05 1.17e-06 -5.252e-07 0.9998 8.816e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002146 Epoch 9292 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00927 0.9966 0.9921 -2.11e-07 9.471e-08 -0.007285 -1.59e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003484 -0.003318 -0.006939 0.005558 0.9699 0.9743 0.006763 0.8266 0.8208 0.01664 ] Network output: [ 0.9999 0.0001863 0.0004507 -4.303e-06 1.932e-06 -0.000434 -3.243e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2062 -0.03521 -0.1606 0.1841 0.9834 0.9932 0.2312 0.4314 0.8687 0.7103 ] Network output: [ -0.009228 1.003 1.008 -2.532e-07 1.137e-07 0.007647 -1.908e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006664 0.000597 0.004404 0.003271 0.9889 0.9919 0.006794 0.854 0.8926 0.01192 ] Network output: [ -0.000254 0.001714 1.001 -1.349e-05 6.056e-06 0.9982 -1.017e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2195 0.1037 0.3471 0.1427 0.9849 0.9939 0.2203 0.4354 0.8755 0.7042 ] Network output: [ 0.003635 -0.01718 0.9942 8.202e-06 -3.682e-06 1.016 6.181e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.09712 0.1842 0.198 0.9873 0.9919 0.1098 0.7403 0.8622 0.3053 ] Network output: [ -0.003407 0.01593 1.005 8.886e-06 -3.989e-06 0.9863 6.697e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0935 0.09157 0.165 0.1962 0.9852 0.9911 0.09352 0.6642 0.8376 0.2484 ] Network output: [ 9.41e-05 1 -6.165e-05 1.168e-06 -5.246e-07 0.9998 8.806e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002145 Epoch 9293 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009269 0.9966 0.9921 -2.109e-07 9.469e-08 -0.007285 -1.59e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003484 -0.003318 -0.006938 0.005558 0.9699 0.9743 0.006764 0.8266 0.8208 0.01664 ] Network output: [ 0.9999 0.0001861 0.0004505 -4.298e-06 1.929e-06 -0.0004337 -3.239e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2062 -0.03521 -0.1606 0.1841 0.9834 0.9932 0.2312 0.4314 0.8687 0.7103 ] Network output: [ -0.009227 1.003 1.008 -2.531e-07 1.136e-07 0.007647 -1.907e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006665 0.0005971 0.004404 0.00327 0.9889 0.9919 0.006794 0.854 0.8926 0.01192 ] Network output: [ -0.0002538 0.001713 1.001 -1.347e-05 6.049e-06 0.9982 -1.015e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2195 0.1037 0.3471 0.1427 0.9849 0.9939 0.2203 0.4354 0.8755 0.7042 ] Network output: [ 0.003634 -0.01718 0.9942 8.192e-06 -3.678e-06 1.016 6.174e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.09712 0.1842 0.198 0.9873 0.9919 0.1098 0.7403 0.8622 0.3053 ] Network output: [ -0.003405 0.01592 1.005 8.876e-06 -3.985e-06 0.9863 6.689e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09351 0.09157 0.165 0.1962 0.9852 0.9911 0.09352 0.6642 0.8376 0.2484 ] Network output: [ 9.407e-05 1 -6.161e-05 1.167e-06 -5.24e-07 0.9998 8.796e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002144 Epoch 9294 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009268 0.9966 0.9921 -2.109e-07 9.466e-08 -0.007284 -1.589e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003484 -0.003318 -0.006938 0.005557 0.9699 0.9743 0.006764 0.8266 0.8208 0.01664 ] Network output: [ 0.9999 0.0001859 0.0004503 -4.293e-06 1.927e-06 -0.0004333 -3.235e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2062 -0.03521 -0.1606 0.1841 0.9834 0.9932 0.2313 0.4314 0.8687 0.7103 ] Network output: [ -0.009226 1.003 1.008 -2.53e-07 1.136e-07 0.007646 -1.906e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006665 0.0005972 0.004404 0.00327 0.9889 0.9919 0.006795 0.854 0.8926 0.01192 ] Network output: [ -0.0002537 0.001712 1.001 -1.346e-05 6.042e-06 0.9982 -1.014e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2195 0.1037 0.3471 0.1427 0.9849 0.9939 0.2203 0.4354 0.8754 0.7042 ] Network output: [ 0.003632 -0.01717 0.9942 8.183e-06 -3.674e-06 1.016 6.167e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.09713 0.1842 0.198 0.9873 0.9919 0.1098 0.7402 0.8622 0.3053 ] Network output: [ -0.003404 0.01592 1.005 8.866e-06 -3.98e-06 0.9863 6.682e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09351 0.09157 0.165 0.1962 0.9852 0.9911 0.09352 0.6642 0.8376 0.2484 ] Network output: [ 9.404e-05 1 -6.156e-05 1.166e-06 -5.234e-07 0.9998 8.786e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002143 Epoch 9295 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009267 0.9966 0.9921 -2.108e-07 9.464e-08 -0.007284 -1.589e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003484 -0.003318 -0.006937 0.005557 0.9699 0.9743 0.006764 0.8266 0.8208 0.01664 ] Network output: [ 0.9999 0.0001857 0.0004501 -4.288e-06 1.925e-06 -0.000433 -3.231e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2062 -0.03521 -0.1606 0.1841 0.9834 0.9932 0.2313 0.4314 0.8687 0.7103 ] Network output: [ -0.009225 1.003 1.008 -2.528e-07 1.135e-07 0.007645 -1.905e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006666 0.0005973 0.004404 0.00327 0.9889 0.9919 0.006795 0.854 0.8926 0.01192 ] Network output: [ -0.0002535 0.001711 1.001 -1.344e-05 6.035e-06 0.9982 -1.013e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2195 0.1037 0.3471 0.1427 0.9849 0.9939 0.2203 0.4354 0.8754 0.7042 ] Network output: [ 0.003631 -0.01716 0.9942 8.173e-06 -3.669e-06 1.016 6.16e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.09713 0.1842 0.198 0.9873 0.9919 0.1098 0.7402 0.8622 0.3053 ] Network output: [ -0.003402 0.01591 1.005 8.856e-06 -3.976e-06 0.9864 6.674e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09351 0.09157 0.165 0.1962 0.9852 0.9911 0.09352 0.6642 0.8376 0.2484 ] Network output: [ 9.401e-05 1 -6.151e-05 1.164e-06 -5.228e-07 0.9998 8.776e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002142 Epoch 9296 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009266 0.9966 0.9921 -2.107e-07 9.461e-08 -0.007283 -1.588e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003485 -0.003318 -0.006936 0.005557 0.9699 0.9743 0.006764 0.8266 0.8208 0.01664 ] Network output: [ 0.9999 0.0001855 0.0004498 -4.283e-06 1.923e-06 -0.0004327 -3.228e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2062 -0.03521 -0.1606 0.1841 0.9834 0.9932 0.2313 0.4314 0.8687 0.7103 ] Network output: [ -0.009224 1.003 1.008 -2.527e-07 1.134e-07 0.007644 -1.904e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006666 0.0005973 0.004404 0.00327 0.9889 0.9919 0.006795 0.854 0.8926 0.01192 ] Network output: [ -0.0002533 0.001711 1.001 -1.343e-05 6.028e-06 0.9982 -1.012e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2195 0.1037 0.3471 0.1427 0.9849 0.9939 0.2203 0.4354 0.8754 0.7042 ] Network output: [ 0.003629 -0.01716 0.9942 8.164e-06 -3.665e-06 1.016 6.153e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.09713 0.1842 0.198 0.9873 0.9919 0.1098 0.7402 0.8622 0.3053 ] Network output: [ -0.003401 0.0159 1.005 8.846e-06 -3.971e-06 0.9864 6.666e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09351 0.09157 0.165 0.1962 0.9852 0.9911 0.09353 0.6642 0.8376 0.2484 ] Network output: [ 9.398e-05 1 -6.146e-05 1.163e-06 -5.222e-07 0.9998 8.766e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000214 Epoch 9297 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009265 0.9966 0.9921 -2.107e-07 9.458e-08 -0.007283 -1.588e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003485 -0.003318 -0.006936 0.005556 0.9699 0.9743 0.006764 0.8266 0.8208 0.01664 ] Network output: [ 0.9999 0.0001853 0.0004496 -4.278e-06 1.92e-06 -0.0004324 -3.224e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2062 -0.03521 -0.1606 0.1841 0.9834 0.9932 0.2313 0.4313 0.8687 0.7103 ] Network output: [ -0.009223 1.003 1.008 -2.525e-07 1.134e-07 0.007644 -1.903e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006667 0.0005974 0.004403 0.003269 0.9889 0.9919 0.006796 0.854 0.8926 0.01192 ] Network output: [ -0.0002531 0.00171 1.001 -1.341e-05 6.021e-06 0.9982 -1.011e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2196 0.1037 0.3471 0.1427 0.9849 0.9939 0.2203 0.4354 0.8754 0.7042 ] Network output: [ 0.003628 -0.01715 0.9942 8.154e-06 -3.661e-06 1.016 6.145e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1097 0.09714 0.1843 0.198 0.9873 0.9919 0.1098 0.7402 0.8622 0.3053 ] Network output: [ -0.003399 0.0159 1.005 8.836e-06 -3.967e-06 0.9864 6.659e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09351 0.09158 0.165 0.1962 0.9852 0.9911 0.09353 0.6642 0.8376 0.2484 ] Network output: [ 9.395e-05 1 -6.142e-05 1.162e-06 -5.216e-07 0.9998 8.756e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002139 Epoch 9298 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009264 0.9966 0.9921 -2.106e-07 9.456e-08 -0.007282 -1.587e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003485 -0.003318 -0.006935 0.005556 0.9699 0.9743 0.006765 0.8266 0.8208 0.01664 ] Network output: [ 0.9999 0.0001851 0.0004494 -4.273e-06 1.918e-06 -0.0004321 -3.22e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2062 -0.03521 -0.1606 0.1841 0.9834 0.9932 0.2313 0.4313 0.8687 0.7103 ] Network output: [ -0.009222 1.003 1.008 -2.524e-07 1.133e-07 0.007643 -1.902e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006667 0.0005975 0.004403 0.003269 0.9889 0.9919 0.006796 0.854 0.8926 0.01191 ] Network output: [ -0.0002529 0.001709 1.001 -1.34e-05 6.014e-06 0.9982 -1.01e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2196 0.1037 0.3471 0.1427 0.9849 0.9939 0.2203 0.4354 0.8754 0.7042 ] Network output: [ 0.003626 -0.01714 0.9942 8.145e-06 -3.657e-06 1.016 6.138e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.09714 0.1843 0.198 0.9873 0.9919 0.1098 0.7402 0.8622 0.3053 ] Network output: [ -0.003398 0.01589 1.005 8.826e-06 -3.962e-06 0.9864 6.651e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09352 0.09158 0.165 0.1962 0.9852 0.9911 0.09353 0.6641 0.8376 0.2484 ] Network output: [ 9.391e-05 1 -6.137e-05 1.16e-06 -5.21e-07 0.9998 8.746e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002138 Epoch 9299 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009263 0.9966 0.9921 -2.106e-07 9.453e-08 -0.007282 -1.587e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003485 -0.003318 -0.006934 0.005555 0.9699 0.9743 0.006765 0.8266 0.8208 0.01664 ] Network output: [ 0.9999 0.0001849 0.0004492 -4.268e-06 1.916e-06 -0.0004318 -3.216e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2062 -0.03521 -0.1606 0.1841 0.9834 0.9932 0.2313 0.4313 0.8687 0.7103 ] Network output: [ -0.009221 1.003 1.008 -2.522e-07 1.132e-07 0.007642 -1.901e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006667 0.0005976 0.004403 0.003269 0.9889 0.9919 0.006797 0.854 0.8926 0.01191 ] Network output: [ -0.0002528 0.001709 1.001 -1.338e-05 6.007e-06 0.9982 -1.008e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2196 0.1037 0.3471 0.1427 0.9849 0.9939 0.2203 0.4353 0.8754 0.7041 ] Network output: [ 0.003625 -0.01714 0.9942 8.135e-06 -3.652e-06 1.016 6.131e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.09715 0.1843 0.198 0.9873 0.9919 0.1098 0.7402 0.8622 0.3053 ] Network output: [ -0.003397 0.01588 1.005 8.815e-06 -3.958e-06 0.9864 6.644e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09352 0.09158 0.165 0.1962 0.9852 0.9911 0.09353 0.6641 0.8376 0.2484 ] Network output: [ 9.388e-05 1 -6.132e-05 1.159e-06 -5.204e-07 0.9998 8.735e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002137 Epoch 9300 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009262 0.9966 0.9921 -2.105e-07 9.45e-08 -0.007281 -1.586e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003485 -0.003318 -0.006934 0.005555 0.9699 0.9743 0.006765 0.8266 0.8208 0.01664 ] Network output: [ 0.9999 0.0001847 0.000449 -4.263e-06 1.914e-06 -0.0004315 -3.213e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2062 -0.03521 -0.1606 0.1841 0.9834 0.9932 0.2313 0.4313 0.8687 0.7103 ] Network output: [ -0.009221 1.003 1.008 -2.521e-07 1.132e-07 0.007641 -1.9e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006668 0.0005977 0.004403 0.003269 0.9889 0.9919 0.006797 0.854 0.8926 0.01191 ] Network output: [ -0.0002526 0.001708 1.001 -1.336e-05 6e-06 0.9982 -1.007e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2196 0.1037 0.3471 0.1427 0.9849 0.9939 0.2203 0.4353 0.8754 0.7041 ] Network output: [ 0.003623 -0.01713 0.9942 8.126e-06 -3.648e-06 1.016 6.124e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.09715 0.1843 0.198 0.9873 0.9919 0.1098 0.7402 0.8622 0.3053 ] Network output: [ -0.003395 0.01588 1.005 8.805e-06 -3.953e-06 0.9864 6.636e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09352 0.09158 0.165 0.1962 0.9852 0.9911 0.09353 0.6641 0.8376 0.2484 ] Network output: [ 9.385e-05 1 -6.128e-05 1.158e-06 -5.198e-07 0.9998 8.725e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002136 Epoch 9301 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009261 0.9966 0.9921 -2.104e-07 9.448e-08 -0.007281 -1.586e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003485 -0.003319 -0.006933 0.005554 0.9699 0.9743 0.006765 0.8266 0.8208 0.01664 ] Network output: [ 0.9999 0.0001845 0.0004488 -4.258e-06 1.911e-06 -0.0004312 -3.209e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2062 -0.03521 -0.1606 0.1841 0.9834 0.9932 0.2313 0.4313 0.8687 0.7103 ] Network output: [ -0.00922 1.003 1.008 -2.52e-07 1.131e-07 0.007641 -1.899e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006668 0.0005978 0.004403 0.003268 0.9889 0.9919 0.006798 0.854 0.8926 0.01191 ] Network output: [ -0.0002524 0.001707 1.001 -1.335e-05 5.993e-06 0.9982 -1.006e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2196 0.1037 0.3471 0.1427 0.9849 0.9939 0.2203 0.4353 0.8754 0.7041 ] Network output: [ 0.003622 -0.01712 0.9942 8.117e-06 -3.644e-06 1.016 6.117e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.09715 0.1843 0.198 0.9873 0.9919 0.1098 0.7402 0.8622 0.3053 ] Network output: [ -0.003394 0.01587 1.005 8.795e-06 -3.949e-06 0.9864 6.628e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09352 0.09159 0.165 0.1962 0.9852 0.9911 0.09354 0.6641 0.8376 0.2485 ] Network output: [ 9.382e-05 1 -6.123e-05 1.156e-06 -5.192e-07 0.9998 8.715e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002135 Epoch 9302 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00926 0.9966 0.9921 -2.104e-07 9.445e-08 -0.00728 -1.586e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003485 -0.003319 -0.006932 0.005554 0.9699 0.9743 0.006765 0.8266 0.8208 0.01663 ] Network output: [ 0.9999 0.0001843 0.0004486 -4.253e-06 1.909e-06 -0.0004309 -3.205e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2062 -0.03522 -0.1605 0.1841 0.9834 0.9932 0.2313 0.4313 0.8687 0.7103 ] Network output: [ -0.009219 1.003 1.008 -2.518e-07 1.131e-07 0.00764 -1.898e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006669 0.0005978 0.004403 0.003268 0.9889 0.9919 0.006798 0.854 0.8926 0.01191 ] Network output: [ -0.0002522 0.001706 1.001 -1.333e-05 5.986e-06 0.9982 -1.005e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2196 0.1037 0.3471 0.1427 0.9849 0.9939 0.2203 0.4353 0.8754 0.7041 ] Network output: [ 0.00362 -0.01712 0.9942 8.107e-06 -3.64e-06 1.016 6.11e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.09716 0.1843 0.198 0.9873 0.9919 0.1098 0.7401 0.8622 0.3053 ] Network output: [ -0.003392 0.01586 1.005 8.785e-06 -3.944e-06 0.9864 6.621e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09353 0.09159 0.165 0.1962 0.9852 0.9911 0.09354 0.6641 0.8376 0.2485 ] Network output: [ 9.379e-05 1 -6.118e-05 1.155e-06 -5.186e-07 0.9998 8.705e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002134 Epoch 9303 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009259 0.9966 0.9921 -2.103e-07 9.442e-08 -0.00728 -1.585e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003485 -0.003319 -0.006932 0.005554 0.9699 0.9743 0.006766 0.8266 0.8208 0.01663 ] Network output: [ 0.9999 0.0001841 0.0004484 -4.248e-06 1.907e-06 -0.0004305 -3.201e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2062 -0.03522 -0.1605 0.1841 0.9834 0.9932 0.2313 0.4313 0.8687 0.7103 ] Network output: [ -0.009218 1.003 1.008 -2.517e-07 1.13e-07 0.007639 -1.897e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006669 0.0005979 0.004403 0.003268 0.9889 0.9919 0.006798 0.854 0.8926 0.01191 ] Network output: [ -0.000252 0.001706 1.001 -1.332e-05 5.979e-06 0.9982 -1.004e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2196 0.1037 0.3471 0.1427 0.9849 0.9939 0.2203 0.4353 0.8754 0.7041 ] Network output: [ 0.003619 -0.01711 0.9942 8.098e-06 -3.635e-06 1.016 6.103e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.09716 0.1843 0.198 0.9873 0.9919 0.1099 0.7401 0.8622 0.3053 ] Network output: [ -0.003391 0.01586 1.005 8.775e-06 -3.94e-06 0.9864 6.613e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09353 0.09159 0.165 0.1962 0.9852 0.9911 0.09354 0.6641 0.8376 0.2485 ] Network output: [ 9.376e-05 1 -6.114e-05 1.154e-06 -5.18e-07 0.9998 8.695e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002133 Epoch 9304 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009258 0.9966 0.9921 -2.103e-07 9.44e-08 -0.007279 -1.585e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003485 -0.003319 -0.006931 0.005553 0.9699 0.9743 0.006766 0.8265 0.8208 0.01663 ] Network output: [ 0.9999 0.0001839 0.0004482 -4.243e-06 1.905e-06 -0.0004302 -3.198e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2063 -0.03522 -0.1605 0.1841 0.9834 0.9932 0.2313 0.4313 0.8687 0.7103 ] Network output: [ -0.009217 1.003 1.008 -2.515e-07 1.129e-07 0.007638 -1.896e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00667 0.000598 0.004403 0.003268 0.9889 0.9919 0.006799 0.8539 0.8926 0.01191 ] Network output: [ -0.0002519 0.001705 1.001 -1.33e-05 5.972e-06 0.9982 -1.002e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2196 0.1037 0.3471 0.1427 0.9849 0.9939 0.2204 0.4353 0.8754 0.7041 ] Network output: [ 0.003617 -0.0171 0.9942 8.088e-06 -3.631e-06 1.016 6.096e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.09717 0.1843 0.198 0.9873 0.9919 0.1099 0.7401 0.8622 0.3053 ] Network output: [ -0.00339 0.01585 1.005 8.765e-06 -3.935e-06 0.9864 6.606e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09353 0.09159 0.165 0.1962 0.9852 0.9911 0.09354 0.6641 0.8376 0.2485 ] Network output: [ 9.373e-05 1 -6.109e-05 1.152e-06 -5.174e-07 0.9998 8.685e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002131 Epoch 9305 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009257 0.9966 0.9921 -2.102e-07 9.437e-08 -0.007279 -1.584e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003485 -0.003319 -0.006931 0.005553 0.9699 0.9743 0.006766 0.8265 0.8208 0.01663 ] Network output: [ 0.9999 0.0001837 0.000448 -4.238e-06 1.903e-06 -0.0004299 -3.194e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2063 -0.03522 -0.1605 0.184 0.9834 0.9932 0.2313 0.4313 0.8687 0.7103 ] Network output: [ -0.009216 1.003 1.008 -2.514e-07 1.129e-07 0.007637 -1.895e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00667 0.0005981 0.004403 0.003267 0.9889 0.9919 0.006799 0.8539 0.8926 0.01191 ] Network output: [ -0.0002517 0.001704 1.001 -1.329e-05 5.965e-06 0.9982 -1.001e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2196 0.1037 0.3472 0.1427 0.9849 0.9939 0.2204 0.4353 0.8754 0.7041 ] Network output: [ 0.003616 -0.01709 0.9942 8.079e-06 -3.627e-06 1.016 6.089e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.09717 0.1843 0.198 0.9873 0.9919 0.1099 0.7401 0.8622 0.3053 ] Network output: [ -0.003388 0.01585 1.005 8.755e-06 -3.931e-06 0.9864 6.598e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09353 0.09159 0.165 0.1962 0.9852 0.9911 0.09355 0.664 0.8376 0.2485 ] Network output: [ 9.369e-05 1 -6.105e-05 1.151e-06 -5.168e-07 0.9998 8.675e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000213 Epoch 9306 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009256 0.9966 0.9921 -2.101e-07 9.434e-08 -0.007279 -1.584e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003485 -0.003319 -0.00693 0.005552 0.9699 0.9743 0.006766 0.8265 0.8208 0.01663 ] Network output: [ 0.9999 0.0001835 0.0004477 -4.233e-06 1.9e-06 -0.0004296 -3.19e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2063 -0.03522 -0.1605 0.184 0.9834 0.9932 0.2313 0.4313 0.8687 0.7103 ] Network output: [ -0.009215 1.003 1.008 -2.513e-07 1.128e-07 0.007637 -1.894e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00667 0.0005982 0.004403 0.003267 0.9889 0.9919 0.0068 0.8539 0.8926 0.01191 ] Network output: [ -0.0002515 0.001704 1.001 -1.327e-05 5.958e-06 0.9982 -1e-05 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2196 0.1037 0.3472 0.1427 0.9849 0.9939 0.2204 0.4353 0.8754 0.7041 ] Network output: [ 0.003614 -0.01709 0.9942 8.07e-06 -3.623e-06 1.016 6.082e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.09718 0.1843 0.198 0.9873 0.9919 0.1099 0.7401 0.8622 0.3053 ] Network output: [ -0.003387 0.01584 1.005 8.745e-06 -3.926e-06 0.9864 6.591e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09353 0.0916 0.165 0.1962 0.9852 0.9911 0.09355 0.664 0.8376 0.2485 ] Network output: [ 9.366e-05 1 -6.1e-05 1.15e-06 -5.162e-07 0.9998 8.665e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002129 Epoch 9307 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009255 0.9966 0.9921 -2.101e-07 9.431e-08 -0.007278 -1.583e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003485 -0.003319 -0.006929 0.005552 0.9699 0.9743 0.006767 0.8265 0.8208 0.01663 ] Network output: [ 0.9999 0.0001833 0.0004475 -4.228e-06 1.898e-06 -0.0004293 -3.186e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2063 -0.03522 -0.1605 0.184 0.9834 0.9932 0.2314 0.4313 0.8687 0.7103 ] Network output: [ -0.009214 1.003 1.008 -2.511e-07 1.127e-07 0.007636 -1.893e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006671 0.0005983 0.004403 0.003267 0.9889 0.9919 0.0068 0.8539 0.8926 0.01191 ] Network output: [ -0.0002513 0.001703 1.001 -1.326e-05 5.951e-06 0.9982 -9.99e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2196 0.1037 0.3472 0.1427 0.9849 0.9939 0.2204 0.4353 0.8754 0.7041 ] Network output: [ 0.003613 -0.01708 0.9942 8.06e-06 -3.619e-06 1.016 6.075e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.09718 0.1843 0.198 0.9873 0.9919 0.1099 0.7401 0.8622 0.3053 ] Network output: [ -0.003385 0.01583 1.005 8.735e-06 -3.922e-06 0.9864 6.583e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09354 0.0916 0.165 0.1962 0.9852 0.9911 0.09355 0.664 0.8376 0.2485 ] Network output: [ 9.363e-05 1 -6.095e-05 1.148e-06 -5.156e-07 0.9998 8.655e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002128 Epoch 9308 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009254 0.9966 0.9921 -2.1e-07 9.429e-08 -0.007278 -1.583e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003485 -0.003319 -0.006929 0.005551 0.9699 0.9743 0.006767 0.8265 0.8208 0.01663 ] Network output: [ 0.9999 0.0001831 0.0004473 -4.223e-06 1.896e-06 -0.000429 -3.183e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2063 -0.03522 -0.1605 0.184 0.9834 0.9932 0.2314 0.4313 0.8687 0.7102 ] Network output: [ -0.009214 1.003 1.008 -2.51e-07 1.127e-07 0.007635 -1.891e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006671 0.0005983 0.004403 0.003267 0.9889 0.9919 0.006801 0.8539 0.8926 0.01191 ] Network output: [ -0.0002511 0.001702 1.001 -1.324e-05 5.944e-06 0.9982 -9.978e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2196 0.1038 0.3472 0.1427 0.9849 0.9939 0.2204 0.4353 0.8754 0.7041 ] Network output: [ 0.003611 -0.01707 0.9942 8.051e-06 -3.614e-06 1.016 6.067e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.09718 0.1843 0.198 0.9873 0.9919 0.1099 0.7401 0.8622 0.3053 ] Network output: [ -0.003384 0.01583 1.005 8.725e-06 -3.917e-06 0.9864 6.576e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09354 0.0916 0.165 0.1962 0.9852 0.9911 0.09355 0.664 0.8376 0.2485 ] Network output: [ 9.36e-05 1 -6.091e-05 1.147e-06 -5.15e-07 0.9998 8.645e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002127 Epoch 9309 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009253 0.9966 0.9921 -2.1e-07 9.426e-08 -0.007277 -1.582e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003486 -0.003319 -0.006928 0.005551 0.9699 0.9743 0.006767 0.8265 0.8208 0.01663 ] Network output: [ 0.9999 0.0001829 0.0004471 -4.218e-06 1.894e-06 -0.0004287 -3.179e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2063 -0.03522 -0.1605 0.184 0.9834 0.9932 0.2314 0.4313 0.8687 0.7102 ] Network output: [ -0.009213 1.003 1.008 -2.508e-07 1.126e-07 0.007634 -1.89e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006672 0.0005984 0.004403 0.003266 0.9889 0.9919 0.006801 0.8539 0.8926 0.01191 ] Network output: [ -0.000251 0.001701 1.001 -1.322e-05 5.937e-06 0.9982 -9.967e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2196 0.1038 0.3472 0.1427 0.9849 0.9939 0.2204 0.4353 0.8754 0.7041 ] Network output: [ 0.00361 -0.01707 0.9942 8.042e-06 -3.61e-06 1.016 6.06e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.09719 0.1843 0.198 0.9873 0.9919 0.1099 0.74 0.8622 0.3053 ] Network output: [ -0.003383 0.01582 1.005 8.716e-06 -3.913e-06 0.9864 6.568e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09354 0.0916 0.165 0.1962 0.9852 0.9911 0.09355 0.664 0.8376 0.2485 ] Network output: [ 9.357e-05 1 -6.086e-05 1.146e-06 -5.144e-07 0.9998 8.635e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002126 Epoch 9310 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009252 0.9966 0.9921 -2.099e-07 9.423e-08 -0.007277 -1.582e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003486 -0.003319 -0.006927 0.005551 0.9699 0.9743 0.006767 0.8265 0.8208 0.01663 ] Network output: [ 0.9999 0.0001827 0.0004469 -4.213e-06 1.891e-06 -0.0004284 -3.175e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2063 -0.03522 -0.1605 0.184 0.9834 0.9932 0.2314 0.4313 0.8687 0.7102 ] Network output: [ -0.009212 1.003 1.008 -2.507e-07 1.125e-07 0.007634 -1.889e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006672 0.0005985 0.004402 0.003266 0.9889 0.9919 0.006802 0.8539 0.8926 0.01191 ] Network output: [ -0.0002508 0.001701 1.001 -1.321e-05 5.93e-06 0.9982 -9.955e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2197 0.1038 0.3472 0.1427 0.9849 0.9939 0.2204 0.4353 0.8754 0.7041 ] Network output: [ 0.003608 -0.01706 0.9942 8.032e-06 -3.606e-06 1.016 6.053e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.09719 0.1843 0.198 0.9873 0.9919 0.1099 0.74 0.8622 0.3053 ] Network output: [ -0.003381 0.01581 1.005 8.706e-06 -3.908e-06 0.9864 6.561e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09354 0.09161 0.165 0.1962 0.9852 0.9911 0.09356 0.664 0.8376 0.2485 ] Network output: [ 9.354e-05 1 -6.082e-05 1.145e-06 -5.138e-07 0.9998 8.625e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002125 Epoch 9311 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009251 0.9966 0.9921 -2.098e-07 9.42e-08 -0.007276 -1.581e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003486 -0.00332 -0.006927 0.00555 0.9699 0.9743 0.006767 0.8265 0.8208 0.01663 ] Network output: [ 0.9999 0.0001825 0.0004467 -4.208e-06 1.889e-06 -0.0004281 -3.171e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2063 -0.03522 -0.1605 0.184 0.9834 0.9932 0.2314 0.4313 0.8687 0.7102 ] Network output: [ -0.009211 1.003 1.008 -2.506e-07 1.125e-07 0.007633 -1.888e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006673 0.0005986 0.004402 0.003266 0.9889 0.9919 0.006802 0.8539 0.8926 0.0119 ] Network output: [ -0.0002506 0.0017 1.001 -1.319e-05 5.923e-06 0.9982 -9.943e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2197 0.1038 0.3472 0.1427 0.9849 0.9939 0.2204 0.4353 0.8754 0.7041 ] Network output: [ 0.003607 -0.01705 0.9942 8.023e-06 -3.602e-06 1.016 6.046e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.0972 0.1843 0.198 0.9873 0.9919 0.1099 0.74 0.8621 0.3053 ] Network output: [ -0.00338 0.01581 1.005 8.696e-06 -3.904e-06 0.9864 6.553e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09355 0.09161 0.165 0.1962 0.9852 0.9911 0.09356 0.664 0.8376 0.2485 ] Network output: [ 9.351e-05 1 -6.077e-05 1.143e-06 -5.132e-07 0.9998 8.615e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002124 Epoch 9312 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00925 0.9966 0.9921 -2.098e-07 9.417e-08 -0.007276 -1.581e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003486 -0.00332 -0.006926 0.00555 0.9699 0.9743 0.006768 0.8265 0.8208 0.01662 ] Network output: [ 0.9999 0.0001823 0.0004465 -4.203e-06 1.887e-06 -0.0004278 -3.168e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2063 -0.03522 -0.1604 0.184 0.9834 0.9932 0.2314 0.4313 0.8687 0.7102 ] Network output: [ -0.00921 1.003 1.008 -2.504e-07 1.124e-07 0.007632 -1.887e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006673 0.0005987 0.004402 0.003266 0.9889 0.9919 0.006802 0.8539 0.8926 0.0119 ] Network output: [ -0.0002504 0.001699 1.001 -1.318e-05 5.916e-06 0.9982 -9.932e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2197 0.1038 0.3472 0.1427 0.9849 0.9939 0.2204 0.4353 0.8754 0.7041 ] Network output: [ 0.003605 -0.01705 0.9942 8.014e-06 -3.598e-06 1.016 6.039e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.0972 0.1843 0.198 0.9873 0.9919 0.1099 0.74 0.8621 0.3053 ] Network output: [ -0.003378 0.0158 1.005 8.686e-06 -3.899e-06 0.9864 6.546e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09355 0.09161 0.165 0.1962 0.9852 0.9911 0.09356 0.664 0.8376 0.2485 ] Network output: [ 9.347e-05 1 -6.072e-05 1.142e-06 -5.126e-07 0.9998 8.606e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002123 Epoch 9313 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009249 0.9966 0.9921 -2.097e-07 9.415e-08 -0.007275 -1.58e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003486 -0.00332 -0.006925 0.005549 0.9699 0.9743 0.006768 0.8265 0.8208 0.01662 ] Network output: [ 0.9999 0.0001821 0.0004463 -4.198e-06 1.885e-06 -0.0004275 -3.164e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2063 -0.03523 -0.1604 0.184 0.9834 0.9932 0.2314 0.4313 0.8687 0.7102 ] Network output: [ -0.009209 1.003 1.008 -2.503e-07 1.124e-07 0.007631 -1.886e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006673 0.0005988 0.004402 0.003265 0.9889 0.9919 0.006803 0.8539 0.8926 0.0119 ] Network output: [ -0.0002502 0.001699 1.001 -1.316e-05 5.909e-06 0.9982 -9.92e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2197 0.1038 0.3472 0.1427 0.9849 0.9939 0.2204 0.4353 0.8754 0.7041 ] Network output: [ 0.003604 -0.01704 0.9942 8.004e-06 -3.593e-06 1.016 6.032e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.0972 0.1843 0.198 0.9873 0.9919 0.1099 0.74 0.8621 0.3053 ] Network output: [ -0.003377 0.01579 1.005 8.676e-06 -3.895e-06 0.9864 6.538e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09355 0.09161 0.165 0.1962 0.9852 0.9911 0.09356 0.6639 0.8375 0.2485 ] Network output: [ 9.344e-05 1 -6.068e-05 1.141e-06 -5.12e-07 0.9998 8.596e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002121 Epoch 9314 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009248 0.9966 0.9921 -2.096e-07 9.412e-08 -0.007275 -1.58e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003486 -0.00332 -0.006925 0.005549 0.9699 0.9743 0.006768 0.8265 0.8208 0.01662 ] Network output: [ 0.9999 0.0001819 0.0004461 -4.193e-06 1.883e-06 -0.0004272 -3.16e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2063 -0.03523 -0.1604 0.184 0.9834 0.9932 0.2314 0.4312 0.8687 0.7102 ] Network output: [ -0.009208 1.003 1.008 -2.501e-07 1.123e-07 0.007631 -1.885e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006674 0.0005988 0.004402 0.003265 0.9889 0.9919 0.006803 0.8539 0.8926 0.0119 ] Network output: [ -0.0002501 0.001698 1.001 -1.315e-05 5.902e-06 0.9982 -9.909e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2197 0.1038 0.3472 0.1427 0.9849 0.9939 0.2204 0.4353 0.8754 0.7041 ] Network output: [ 0.003602 -0.01703 0.9942 7.995e-06 -3.589e-06 1.016 6.025e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.09721 0.1843 0.198 0.9873 0.9919 0.1099 0.74 0.8621 0.3053 ] Network output: [ -0.003376 0.01579 1.005 8.666e-06 -3.89e-06 0.9864 6.531e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09355 0.09161 0.165 0.1962 0.9852 0.9911 0.09357 0.6639 0.8375 0.2485 ] Network output: [ 9.341e-05 1 -6.063e-05 1.139e-06 -5.114e-07 0.9998 8.586e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000212 Epoch 9315 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009247 0.9966 0.9922 -2.096e-07 9.409e-08 -0.007274 -1.58e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003486 -0.00332 -0.006924 0.005548 0.9699 0.9743 0.006768 0.8265 0.8208 0.01662 ] Network output: [ 0.9999 0.0001817 0.0004459 -4.189e-06 1.88e-06 -0.0004268 -3.157e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2063 -0.03523 -0.1604 0.184 0.9834 0.9932 0.2314 0.4312 0.8687 0.7102 ] Network output: [ -0.009207 1.003 1.008 -2.5e-07 1.122e-07 0.00763 -1.884e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006674 0.0005989 0.004402 0.003265 0.9889 0.9919 0.006804 0.8539 0.8926 0.0119 ] Network output: [ -0.0002499 0.001697 1.001 -1.313e-05 5.896e-06 0.9982 -9.897e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2197 0.1038 0.3472 0.1426 0.9849 0.9939 0.2204 0.4352 0.8754 0.7041 ] Network output: [ 0.003601 -0.01703 0.9942 7.986e-06 -3.585e-06 1.016 6.018e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.09721 0.1843 0.198 0.9873 0.9919 0.1099 0.74 0.8621 0.3053 ] Network output: [ -0.003374 0.01578 1.005 8.656e-06 -3.886e-06 0.9864 6.523e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09355 0.09162 0.165 0.1962 0.9852 0.9911 0.09357 0.6639 0.8375 0.2485 ] Network output: [ 9.338e-05 1 -6.059e-05 1.138e-06 -5.109e-07 0.9998 8.576e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002119 Epoch 9316 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009246 0.9966 0.9922 -2.095e-07 9.406e-08 -0.007274 -1.579e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003486 -0.00332 -0.006923 0.005548 0.9699 0.9743 0.006768 0.8265 0.8208 0.01662 ] Network output: [ 0.9999 0.0001815 0.0004456 -4.184e-06 1.878e-06 -0.0004265 -3.153e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2063 -0.03523 -0.1604 0.184 0.9834 0.9932 0.2314 0.4312 0.8687 0.7102 ] Network output: [ -0.009206 1.003 1.008 -2.498e-07 1.122e-07 0.007629 -1.883e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006675 0.000599 0.004402 0.003265 0.9889 0.9919 0.006804 0.8539 0.8926 0.0119 ] Network output: [ -0.0002497 0.001696 1.001 -1.312e-05 5.889e-06 0.9982 -9.885e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2197 0.1038 0.3472 0.1426 0.9849 0.9939 0.2204 0.4352 0.8754 0.7041 ] Network output: [ 0.003599 -0.01702 0.9942 7.977e-06 -3.581e-06 1.016 6.011e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.09722 0.1843 0.198 0.9873 0.9919 0.1099 0.74 0.8621 0.3053 ] Network output: [ -0.003373 0.01577 1.005 8.646e-06 -3.882e-06 0.9864 6.516e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09356 0.09162 0.165 0.1962 0.9852 0.9911 0.09357 0.6639 0.8375 0.2485 ] Network output: [ 9.335e-05 1 -6.054e-05 1.137e-06 -5.103e-07 0.9998 8.566e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002118 Epoch 9317 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009245 0.9966 0.9922 -2.095e-07 9.403e-08 -0.007273 -1.579e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003486 -0.00332 -0.006923 0.005548 0.9699 0.9743 0.006769 0.8265 0.8208 0.01662 ] Network output: [ 0.9999 0.0001813 0.0004454 -4.179e-06 1.876e-06 -0.0004262 -3.149e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2063 -0.03523 -0.1604 0.184 0.9834 0.9932 0.2314 0.4312 0.8687 0.7102 ] Network output: [ -0.009206 1.003 1.008 -2.497e-07 1.121e-07 0.007628 -1.882e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006675 0.0005991 0.004402 0.003264 0.9889 0.9919 0.006805 0.8539 0.8926 0.0119 ] Network output: [ -0.0002495 0.001696 1.001 -1.31e-05 5.882e-06 0.9982 -9.874e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2197 0.1038 0.3472 0.1426 0.9849 0.9939 0.2205 0.4352 0.8754 0.7041 ] Network output: [ 0.003598 -0.01701 0.9942 7.967e-06 -3.577e-06 1.016 6.004e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.09722 0.1843 0.1979 0.9873 0.9919 0.1099 0.7399 0.8621 0.3053 ] Network output: [ -0.003371 0.01577 1.005 8.636e-06 -3.877e-06 0.9864 6.509e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09356 0.09162 0.165 0.1962 0.9852 0.9911 0.09357 0.6639 0.8375 0.2485 ] Network output: [ 9.332e-05 1 -6.05e-05 1.135e-06 -5.097e-07 0.9998 8.556e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002117 Epoch 9318 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009244 0.9966 0.9922 -2.094e-07 9.401e-08 -0.007273 -1.578e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003486 -0.00332 -0.006922 0.005547 0.9699 0.9743 0.006769 0.8265 0.8207 0.01662 ] Network output: [ 0.9999 0.0001811 0.0004452 -4.174e-06 1.874e-06 -0.0004259 -3.146e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2063 -0.03523 -0.1604 0.184 0.9834 0.9932 0.2314 0.4312 0.8687 0.7102 ] Network output: [ -0.009205 1.003 1.008 -2.496e-07 1.12e-07 0.007627 -1.881e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006676 0.0005992 0.004402 0.003264 0.9889 0.9919 0.006805 0.8539 0.8926 0.0119 ] Network output: [ -0.0002493 0.001695 1.001 -1.309e-05 5.875e-06 0.9982 -9.862e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2197 0.1038 0.3472 0.1426 0.9849 0.9939 0.2205 0.4352 0.8754 0.7041 ] Network output: [ 0.003596 -0.01701 0.9942 7.958e-06 -3.573e-06 1.016 5.997e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.09723 0.1843 0.1979 0.9873 0.9919 0.1099 0.7399 0.8621 0.3053 ] Network output: [ -0.00337 0.01576 1.005 8.626e-06 -3.873e-06 0.9864 6.501e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09356 0.09162 0.165 0.1962 0.9852 0.9911 0.09357 0.6639 0.8375 0.2485 ] Network output: [ 9.329e-05 1 -6.045e-05 1.134e-06 -5.091e-07 0.9998 8.546e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002116 Epoch 9319 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009243 0.9966 0.9922 -2.093e-07 9.398e-08 -0.007272 -1.578e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003486 -0.00332 -0.006921 0.005547 0.9699 0.9743 0.006769 0.8265 0.8207 0.01662 ] Network output: [ 0.9999 0.0001809 0.000445 -4.169e-06 1.872e-06 -0.0004256 -3.142e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2064 -0.03523 -0.1604 0.184 0.9834 0.9932 0.2314 0.4312 0.8687 0.7102 ] Network output: [ -0.009204 1.003 1.008 -2.494e-07 1.12e-07 0.007627 -1.88e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006676 0.0005992 0.004402 0.003264 0.9889 0.9919 0.006806 0.8539 0.8925 0.0119 ] Network output: [ -0.0002492 0.001694 1.001 -1.307e-05 5.868e-06 0.9982 -9.851e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2197 0.1038 0.3472 0.1426 0.9849 0.9939 0.2205 0.4352 0.8754 0.7041 ] Network output: [ 0.003595 -0.017 0.9942 7.949e-06 -3.569e-06 1.016 5.99e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1098 0.09723 0.1843 0.1979 0.9873 0.9919 0.1099 0.7399 0.8621 0.3053 ] Network output: [ -0.003369 0.01575 1.005 8.617e-06 -3.868e-06 0.9864 6.494e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09356 0.09163 0.165 0.1962 0.9852 0.9911 0.09358 0.6639 0.8375 0.2485 ] Network output: [ 9.326e-05 1 -6.041e-05 1.133e-06 -5.085e-07 0.9998 8.536e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002115 Epoch 9320 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009242 0.9966 0.9922 -2.093e-07 9.395e-08 -0.007272 -1.577e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003486 -0.00332 -0.006921 0.005546 0.9699 0.9743 0.006769 0.8265 0.8207 0.01662 ] Network output: [ 0.9999 0.0001807 0.0004448 -4.164e-06 1.869e-06 -0.0004253 -3.138e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2064 -0.03523 -0.1604 0.184 0.9834 0.9932 0.2315 0.4312 0.8687 0.7102 ] Network output: [ -0.009203 1.003 1.008 -2.493e-07 1.119e-07 0.007626 -1.879e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006676 0.0005993 0.004402 0.003264 0.9889 0.9919 0.006806 0.8539 0.8925 0.0119 ] Network output: [ -0.000249 0.001693 1.001 -1.306e-05 5.861e-06 0.9982 -9.839e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2197 0.1038 0.3472 0.1426 0.9849 0.9939 0.2205 0.4352 0.8754 0.704 ] Network output: [ 0.003593 -0.01699 0.9942 7.94e-06 -3.564e-06 1.016 5.984e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.09723 0.1843 0.1979 0.9873 0.9919 0.1099 0.7399 0.8621 0.3053 ] Network output: [ -0.003367 0.01575 1.005 8.607e-06 -3.864e-06 0.9864 6.486e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09356 0.09163 0.165 0.1962 0.9852 0.9911 0.09358 0.6638 0.8375 0.2485 ] Network output: [ 9.323e-05 1 -6.036e-05 1.131e-06 -5.079e-07 0.9998 8.526e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002114 Epoch 9321 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009241 0.9966 0.9922 -2.092e-07 9.392e-08 -0.007271 -1.577e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003486 -0.003321 -0.00692 0.005546 0.9699 0.9743 0.006769 0.8265 0.8207 0.01662 ] Network output: [ 0.9999 0.0001805 0.0004446 -4.159e-06 1.867e-06 -0.000425 -3.134e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2064 -0.03523 -0.1604 0.184 0.9834 0.9932 0.2315 0.4312 0.8687 0.7102 ] Network output: [ -0.009202 1.003 1.008 -2.491e-07 1.118e-07 0.007625 -1.878e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006677 0.0005994 0.004402 0.003263 0.9889 0.9919 0.006806 0.8539 0.8925 0.0119 ] Network output: [ -0.0002488 0.001693 1.001 -1.304e-05 5.854e-06 0.9982 -9.828e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2197 0.1038 0.3472 0.1426 0.9849 0.9939 0.2205 0.4352 0.8754 0.704 ] Network output: [ 0.003592 -0.01698 0.9942 7.93e-06 -3.56e-06 1.016 5.977e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.09724 0.1843 0.1979 0.9873 0.9919 0.1099 0.7399 0.8621 0.3052 ] Network output: [ -0.003366 0.01574 1.005 8.597e-06 -3.859e-06 0.9864 6.479e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09357 0.09163 0.165 0.1962 0.9852 0.9911 0.09358 0.6638 0.8375 0.2485 ] Network output: [ 9.319e-05 1 -6.032e-05 1.13e-06 -5.073e-07 0.9998 8.517e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002113 Epoch 9322 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00924 0.9966 0.9922 -2.091e-07 9.389e-08 -0.007271 -1.576e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003487 -0.003321 -0.006919 0.005545 0.9699 0.9743 0.00677 0.8265 0.8207 0.01661 ] Network output: [ 0.9999 0.0001803 0.0004444 -4.154e-06 1.865e-06 -0.0004247 -3.131e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2064 -0.03523 -0.1604 0.184 0.9834 0.9932 0.2315 0.4312 0.8687 0.7102 ] Network output: [ -0.009201 1.003 1.008 -2.49e-07 1.118e-07 0.007624 -1.876e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006677 0.0005995 0.004402 0.003263 0.9889 0.9919 0.006807 0.8538 0.8925 0.0119 ] Network output: [ -0.0002486 0.001692 1.001 -1.303e-05 5.848e-06 0.9982 -9.816e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2197 0.1038 0.3472 0.1426 0.9849 0.9939 0.2205 0.4352 0.8754 0.704 ] Network output: [ 0.00359 -0.01698 0.9942 7.921e-06 -3.556e-06 1.016 5.97e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.09724 0.1843 0.1979 0.9873 0.9919 0.1099 0.7399 0.8621 0.3052 ] Network output: [ -0.003364 0.01573 1.005 8.587e-06 -3.855e-06 0.9864 6.472e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09357 0.09163 0.165 0.1962 0.9852 0.9911 0.09358 0.6638 0.8375 0.2485 ] Network output: [ 9.316e-05 1 -6.028e-05 1.129e-06 -5.067e-07 0.9998 8.507e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002112 Epoch 9323 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009239 0.9966 0.9922 -2.091e-07 9.386e-08 -0.00727 -1.576e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003487 -0.003321 -0.006919 0.005545 0.9699 0.9743 0.00677 0.8265 0.8207 0.01661 ] Network output: [ 0.9999 0.0001801 0.0004442 -4.149e-06 1.863e-06 -0.0004244 -3.127e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2064 -0.03524 -0.1603 0.184 0.9834 0.9932 0.2315 0.4312 0.8687 0.7102 ] Network output: [ -0.0092 1.003 1.008 -2.488e-07 1.117e-07 0.007624 -1.875e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006678 0.0005996 0.004401 0.003263 0.9889 0.9919 0.006807 0.8538 0.8925 0.01189 ] Network output: [ -0.0002484 0.001691 1.001 -1.301e-05 5.841e-06 0.9982 -9.805e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2198 0.1038 0.3472 0.1426 0.9849 0.9939 0.2205 0.4352 0.8754 0.704 ] Network output: [ 0.003589 -0.01697 0.9942 7.912e-06 -3.552e-06 1.016 5.963e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.09725 0.1843 0.1979 0.9873 0.9919 0.1099 0.7399 0.8621 0.3052 ] Network output: [ -0.003363 0.01573 1.005 8.577e-06 -3.851e-06 0.9864 6.464e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09357 0.09163 0.165 0.1962 0.9852 0.9911 0.09359 0.6638 0.8375 0.2485 ] Network output: [ 9.313e-05 1 -6.023e-05 1.127e-06 -5.062e-07 0.9998 8.497e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000211 Epoch 9324 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009238 0.9966 0.9922 -2.09e-07 9.383e-08 -0.00727 -1.575e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003487 -0.003321 -0.006918 0.005545 0.9699 0.9743 0.00677 0.8264 0.8207 0.01661 ] Network output: [ 0.9999 0.0001799 0.000444 -4.145e-06 1.861e-06 -0.0004241 -3.123e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2064 -0.03524 -0.1603 0.184 0.9834 0.9932 0.2315 0.4312 0.8687 0.7102 ] Network output: [ -0.009199 1.003 1.008 -2.487e-07 1.117e-07 0.007623 -1.874e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006678 0.0005997 0.004401 0.003263 0.9889 0.9919 0.006808 0.8538 0.8925 0.01189 ] Network output: [ -0.0002483 0.001691 1.001 -1.299e-05 5.834e-06 0.9982 -9.793e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2198 0.1038 0.3473 0.1426 0.9849 0.9939 0.2205 0.4352 0.8754 0.704 ] Network output: [ 0.003587 -0.01696 0.9942 7.903e-06 -3.548e-06 1.016 5.956e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.09725 0.1843 0.1979 0.9873 0.9919 0.1099 0.7399 0.8621 0.3052 ] Network output: [ -0.003362 0.01572 1.005 8.568e-06 -3.846e-06 0.9864 6.457e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09357 0.09164 0.165 0.1962 0.9852 0.9911 0.09359 0.6638 0.8375 0.2485 ] Network output: [ 9.31e-05 1 -6.019e-05 1.126e-06 -5.056e-07 0.9998 8.487e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002109 Epoch 9325 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009237 0.9966 0.9922 -2.089e-07 9.38e-08 -0.007269 -1.575e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003487 -0.003321 -0.006917 0.005544 0.9699 0.9743 0.00677 0.8264 0.8207 0.01661 ] Network output: [ 0.9999 0.0001797 0.0004438 -4.14e-06 1.858e-06 -0.0004238 -3.12e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2064 -0.03524 -0.1603 0.184 0.9834 0.9932 0.2315 0.4312 0.8687 0.7102 ] Network output: [ -0.009199 1.003 1.008 -2.486e-07 1.116e-07 0.007622 -1.873e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006679 0.0005997 0.004401 0.003262 0.9889 0.9919 0.006808 0.8538 0.8925 0.01189 ] Network output: [ -0.0002481 0.00169 1.001 -1.298e-05 5.827e-06 0.9982 -9.782e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2198 0.1038 0.3473 0.1426 0.9849 0.9939 0.2205 0.4352 0.8754 0.704 ] Network output: [ 0.003586 -0.01696 0.9942 7.894e-06 -3.544e-06 1.016 5.949e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.09725 0.1843 0.1979 0.9873 0.9919 0.1099 0.7398 0.8621 0.3052 ] Network output: [ -0.00336 0.01571 1.005 8.558e-06 -3.842e-06 0.9865 6.449e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09358 0.09164 0.165 0.1962 0.9852 0.9911 0.09359 0.6638 0.8375 0.2485 ] Network output: [ 9.307e-05 1 -6.014e-05 1.125e-06 -5.05e-07 0.9998 8.477e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002108 Epoch 9326 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009236 0.9966 0.9922 -2.089e-07 9.377e-08 -0.007269 -1.574e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003487 -0.003321 -0.006917 0.005544 0.9699 0.9743 0.00677 0.8264 0.8207 0.01661 ] Network output: [ 0.9999 0.0001795 0.0004436 -4.135e-06 1.856e-06 -0.0004235 -3.116e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2064 -0.03524 -0.1603 0.184 0.9834 0.9932 0.2315 0.4312 0.8687 0.7102 ] Network output: [ -0.009198 1.003 1.008 -2.484e-07 1.115e-07 0.007621 -1.872e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006679 0.0005998 0.004401 0.003262 0.9889 0.9919 0.006809 0.8538 0.8925 0.01189 ] Network output: [ -0.0002479 0.001689 1.001 -1.296e-05 5.82e-06 0.9982 -9.77e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2198 0.1038 0.3473 0.1426 0.9849 0.9939 0.2205 0.4352 0.8754 0.704 ] Network output: [ 0.003584 -0.01695 0.9942 7.884e-06 -3.54e-06 1.016 5.942e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.09726 0.1843 0.1979 0.9873 0.9919 0.11 0.7398 0.8621 0.3052 ] Network output: [ -0.003359 0.01571 1.005 8.548e-06 -3.838e-06 0.9865 6.442e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09358 0.09164 0.165 0.1962 0.9852 0.9911 0.09359 0.6638 0.8375 0.2485 ] Network output: [ 9.304e-05 1 -6.01e-05 1.124e-06 -5.044e-07 0.9998 8.468e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002107 Epoch 9327 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009235 0.9966 0.9922 -2.088e-07 9.375e-08 -0.007268 -1.574e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003487 -0.003321 -0.006916 0.005543 0.9699 0.9743 0.006771 0.8264 0.8207 0.01661 ] Network output: [ 0.9999 0.0001793 0.0004434 -4.13e-06 1.854e-06 -0.0004232 -3.112e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2064 -0.03524 -0.1603 0.184 0.9834 0.9932 0.2315 0.4312 0.8687 0.7102 ] Network output: [ -0.009197 1.003 1.008 -2.483e-07 1.115e-07 0.007621 -1.871e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006679 0.0005999 0.004401 0.003262 0.9889 0.9919 0.006809 0.8538 0.8925 0.01189 ] Network output: [ -0.0002477 0.001688 1.001 -1.295e-05 5.813e-06 0.9982 -9.759e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2198 0.1038 0.3473 0.1426 0.9849 0.9939 0.2205 0.4352 0.8754 0.704 ] Network output: [ 0.003583 -0.01694 0.9942 7.875e-06 -3.535e-06 1.016 5.935e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.09726 0.1843 0.1979 0.9873 0.9919 0.11 0.7398 0.8621 0.3052 ] Network output: [ -0.003357 0.0157 1.005 8.538e-06 -3.833e-06 0.9865 6.435e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09358 0.09164 0.165 0.1962 0.9852 0.9911 0.09359 0.6638 0.8375 0.2485 ] Network output: [ 9.301e-05 1 -6.005e-05 1.122e-06 -5.038e-07 0.9998 8.458e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002106 Epoch 9328 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009234 0.9966 0.9922 -2.088e-07 9.372e-08 -0.007268 -1.573e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003487 -0.003321 -0.006915 0.005543 0.9699 0.9743 0.006771 0.8264 0.8207 0.01661 ] Network output: [ 0.9999 0.0001791 0.0004432 -4.125e-06 1.852e-06 -0.0004229 -3.109e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2064 -0.03524 -0.1603 0.184 0.9834 0.9932 0.2315 0.4312 0.8687 0.7102 ] Network output: [ -0.009196 1.003 1.008 -2.481e-07 1.114e-07 0.00762 -1.87e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00668 0.0006 0.004401 0.003262 0.9889 0.9919 0.00681 0.8538 0.8925 0.01189 ] Network output: [ -0.0002475 0.001688 1.001 -1.293e-05 5.807e-06 0.9982 -9.748e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2198 0.1039 0.3473 0.1426 0.9849 0.9939 0.2205 0.4352 0.8754 0.704 ] Network output: [ 0.003581 -0.01694 0.9942 7.866e-06 -3.531e-06 1.016 5.928e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.09727 0.1843 0.1979 0.9873 0.9919 0.11 0.7398 0.8621 0.3052 ] Network output: [ -0.003356 0.01569 1.005 8.529e-06 -3.829e-06 0.9865 6.427e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09358 0.09164 0.165 0.1962 0.9852 0.9911 0.0936 0.6637 0.8375 0.2485 ] Network output: [ 9.298e-05 1 -6.001e-05 1.121e-06 -5.032e-07 0.9998 8.448e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002105 Epoch 9329 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009233 0.9966 0.9922 -2.087e-07 9.369e-08 -0.007267 -1.573e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003487 -0.003321 -0.006915 0.005542 0.9699 0.9743 0.006771 0.8264 0.8207 0.01661 ] Network output: [ 0.9999 0.0001789 0.000443 -4.12e-06 1.85e-06 -0.0004226 -3.105e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2064 -0.03524 -0.1603 0.184 0.9834 0.9932 0.2315 0.4312 0.8687 0.7102 ] Network output: [ -0.009195 1.003 1.008 -2.48e-07 1.113e-07 0.007619 -1.869e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00668 0.0006001 0.004401 0.003261 0.9889 0.9919 0.00681 0.8538 0.8925 0.01189 ] Network output: [ -0.0002474 0.001687 1.001 -1.292e-05 5.8e-06 0.9982 -9.736e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2198 0.1039 0.3473 0.1426 0.9849 0.9939 0.2205 0.4352 0.8754 0.704 ] Network output: [ 0.00358 -0.01693 0.9942 7.857e-06 -3.527e-06 1.016 5.921e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.09727 0.1843 0.1979 0.9873 0.9919 0.11 0.7398 0.8621 0.3052 ] Network output: [ -0.003355 0.01569 1.005 8.519e-06 -3.824e-06 0.9865 6.42e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09358 0.09165 0.165 0.1962 0.9852 0.9911 0.0936 0.6637 0.8375 0.2485 ] Network output: [ 9.295e-05 1 -5.997e-05 1.12e-06 -5.027e-07 0.9998 8.438e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002104 Epoch 9330 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009232 0.9966 0.9922 -2.086e-07 9.366e-08 -0.007267 -1.572e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003487 -0.003321 -0.006914 0.005542 0.9699 0.9743 0.006771 0.8264 0.8207 0.01661 ] Network output: [ 0.9999 0.0001787 0.0004427 -4.115e-06 1.848e-06 -0.0004223 -3.102e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2064 -0.03524 -0.1603 0.1839 0.9834 0.9932 0.2315 0.4311 0.8687 0.7101 ] Network output: [ -0.009194 1.003 1.008 -2.478e-07 1.113e-07 0.007618 -1.868e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006681 0.0006002 0.004401 0.003261 0.9889 0.9919 0.00681 0.8538 0.8925 0.01189 ] Network output: [ -0.0002472 0.001686 1.001 -1.29e-05 5.793e-06 0.9982 -9.725e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2198 0.1039 0.3473 0.1426 0.9849 0.9939 0.2206 0.4352 0.8754 0.704 ] Network output: [ 0.003578 -0.01692 0.9942 7.848e-06 -3.523e-06 1.016 5.914e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.09727 0.1843 0.1979 0.9873 0.9919 0.11 0.7398 0.8621 0.3052 ] Network output: [ -0.003353 0.01568 1.005 8.509e-06 -3.82e-06 0.9865 6.413e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09359 0.09165 0.165 0.1962 0.9852 0.9911 0.0936 0.6637 0.8375 0.2485 ] Network output: [ 9.292e-05 1 -5.992e-05 1.118e-06 -5.021e-07 0.9998 8.429e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002103 Epoch 9331 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009231 0.9966 0.9922 -2.086e-07 9.363e-08 -0.007266 -1.572e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003487 -0.003322 -0.006913 0.005542 0.9699 0.9743 0.006772 0.8264 0.8207 0.01661 ] Network output: [ 0.9999 0.0001785 0.0004425 -4.111e-06 1.845e-06 -0.000422 -3.098e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2064 -0.03524 -0.1603 0.1839 0.9834 0.9932 0.2315 0.4311 0.8687 0.7101 ] Network output: [ -0.009193 1.003 1.008 -2.477e-07 1.112e-07 0.007618 -1.867e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006681 0.0006002 0.004401 0.003261 0.9889 0.9919 0.006811 0.8538 0.8925 0.01189 ] Network output: [ -0.000247 0.001686 1.001 -1.289e-05 5.786e-06 0.9982 -9.713e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2198 0.1039 0.3473 0.1426 0.9849 0.9939 0.2206 0.4352 0.8754 0.704 ] Network output: [ 0.003577 -0.01692 0.9942 7.839e-06 -3.519e-06 1.016 5.908e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.09728 0.1843 0.1979 0.9873 0.9919 0.11 0.7398 0.8621 0.3052 ] Network output: [ -0.003352 0.01567 1.005 8.499e-06 -3.816e-06 0.9865 6.405e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09359 0.09165 0.165 0.1962 0.9852 0.9911 0.0936 0.6637 0.8375 0.2485 ] Network output: [ 9.289e-05 1 -5.988e-05 1.117e-06 -5.015e-07 0.9998 8.419e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002102 Epoch 9332 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00923 0.9966 0.9922 -2.085e-07 9.36e-08 -0.007266 -1.571e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003487 -0.003322 -0.006913 0.005541 0.9699 0.9743 0.006772 0.8264 0.8207 0.0166 ] Network output: [ 0.9999 0.0001783 0.0004423 -4.106e-06 1.843e-06 -0.0004217 -3.094e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2064 -0.03524 -0.1603 0.1839 0.9834 0.9932 0.2316 0.4311 0.8687 0.7101 ] Network output: [ -0.009192 1.003 1.008 -2.476e-07 1.111e-07 0.007617 -1.866e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006682 0.0006003 0.004401 0.003261 0.9889 0.9919 0.006811 0.8538 0.8925 0.01189 ] Network output: [ -0.0002468 0.001685 1.001 -1.287e-05 5.78e-06 0.9982 -9.702e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2198 0.1039 0.3473 0.1426 0.9849 0.9939 0.2206 0.4351 0.8754 0.704 ] Network output: [ 0.003575 -0.01691 0.9942 7.83e-06 -3.515e-06 1.016 5.901e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.09728 0.1843 0.1979 0.9873 0.9919 0.11 0.7398 0.8621 0.3052 ] Network output: [ -0.00335 0.01567 1.005 8.49e-06 -3.811e-06 0.9865 6.398e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09359 0.09165 0.165 0.1962 0.9852 0.9911 0.09361 0.6637 0.8375 0.2485 ] Network output: [ 9.285e-05 1 -5.984e-05 1.116e-06 -5.009e-07 0.9998 8.409e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002101 Epoch 9333 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009229 0.9966 0.9922 -2.084e-07 9.357e-08 -0.007265 -1.571e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003487 -0.003322 -0.006912 0.005541 0.9699 0.9743 0.006772 0.8264 0.8207 0.0166 ] Network output: [ 0.9999 0.0001781 0.0004421 -4.101e-06 1.841e-06 -0.0004214 -3.091e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2064 -0.03525 -0.1602 0.1839 0.9834 0.9932 0.2316 0.4311 0.8687 0.7101 ] Network output: [ -0.009191 1.003 1.008 -2.474e-07 1.111e-07 0.007616 -1.865e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006682 0.0006004 0.004401 0.00326 0.9889 0.9919 0.006812 0.8538 0.8925 0.01189 ] Network output: [ -0.0002467 0.001684 1.001 -1.286e-05 5.773e-06 0.9982 -9.691e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2198 0.1039 0.3473 0.1426 0.9849 0.9939 0.2206 0.4351 0.8754 0.704 ] Network output: [ 0.003574 -0.0169 0.9942 7.82e-06 -3.511e-06 1.016 5.894e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.09729 0.1843 0.1979 0.9873 0.9919 0.11 0.7397 0.8621 0.3052 ] Network output: [ -0.003349 0.01566 1.005 8.48e-06 -3.807e-06 0.9865 6.391e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09359 0.09166 0.165 0.1962 0.9852 0.9911 0.09361 0.6637 0.8375 0.2485 ] Network output: [ 9.282e-05 1 -5.979e-05 1.115e-06 -5.003e-07 0.9998 8.399e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002099 Epoch 9334 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009228 0.9966 0.9922 -2.084e-07 9.354e-08 -0.007265 -1.57e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003487 -0.003322 -0.006911 0.00554 0.9699 0.9743 0.006772 0.8264 0.8207 0.0166 ] Network output: [ 0.9999 0.0001779 0.0004419 -4.096e-06 1.839e-06 -0.0004211 -3.087e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2065 -0.03525 -0.1602 0.1839 0.9834 0.9932 0.2316 0.4311 0.8687 0.7101 ] Network output: [ -0.009191 1.003 1.008 -2.473e-07 1.11e-07 0.007615 -1.863e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006682 0.0006005 0.004401 0.00326 0.9889 0.9919 0.006812 0.8538 0.8925 0.01189 ] Network output: [ -0.0002465 0.001683 1.001 -1.284e-05 5.766e-06 0.9982 -9.679e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2198 0.1039 0.3473 0.1426 0.9849 0.9939 0.2206 0.4351 0.8754 0.704 ] Network output: [ 0.003572 -0.0169 0.9942 7.811e-06 -3.507e-06 1.016 5.887e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.09729 0.1843 0.1979 0.9873 0.9919 0.11 0.7397 0.8621 0.3052 ] Network output: [ -0.003348 0.01565 1.005 8.47e-06 -3.803e-06 0.9865 6.383e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0936 0.09166 0.165 0.1962 0.9852 0.9911 0.09361 0.6637 0.8375 0.2485 ] Network output: [ 9.279e-05 1 -5.975e-05 1.113e-06 -4.998e-07 0.9998 8.39e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002098 Epoch 9335 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009227 0.9966 0.9922 -2.083e-07 9.351e-08 -0.007264 -1.57e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003488 -0.003322 -0.006911 0.00554 0.9699 0.9743 0.006772 0.8264 0.8207 0.0166 ] Network output: [ 0.9999 0.0001777 0.0004417 -4.091e-06 1.837e-06 -0.0004208 -3.083e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2065 -0.03525 -0.1602 0.1839 0.9834 0.9932 0.2316 0.4311 0.8687 0.7101 ] Network output: [ -0.00919 1.003 1.008 -2.471e-07 1.109e-07 0.007615 -1.862e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006683 0.0006006 0.0044 0.00326 0.9889 0.9919 0.006813 0.8538 0.8925 0.01189 ] Network output: [ -0.0002463 0.001683 1.001 -1.283e-05 5.759e-06 0.9982 -9.668e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2198 0.1039 0.3473 0.1426 0.9849 0.9939 0.2206 0.4351 0.8754 0.704 ] Network output: [ 0.003571 -0.01689 0.9942 7.802e-06 -3.503e-06 1.016 5.88e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.0973 0.1843 0.1979 0.9873 0.9919 0.11 0.7397 0.8621 0.3052 ] Network output: [ -0.003346 0.01565 1.005 8.461e-06 -3.798e-06 0.9865 6.376e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0936 0.09166 0.165 0.1962 0.9852 0.9911 0.09361 0.6637 0.8375 0.2485 ] Network output: [ 9.276e-05 1 -5.971e-05 1.112e-06 -4.992e-07 0.9998 8.38e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002097 Epoch 9336 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009226 0.9966 0.9922 -2.082e-07 9.348e-08 -0.007264 -1.569e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003488 -0.003322 -0.00691 0.005539 0.9699 0.9743 0.006773 0.8264 0.8207 0.0166 ] Network output: [ 0.9999 0.0001775 0.0004415 -4.087e-06 1.835e-06 -0.0004205 -3.08e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2065 -0.03525 -0.1602 0.1839 0.9834 0.9932 0.2316 0.4311 0.8687 0.7101 ] Network output: [ -0.009189 1.003 1.008 -2.47e-07 1.109e-07 0.007614 -1.861e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006683 0.0006006 0.0044 0.00326 0.9889 0.9919 0.006813 0.8538 0.8925 0.01188 ] Network output: [ -0.0002461 0.001682 1.001 -1.281e-05 5.753e-06 0.9982 -9.657e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2199 0.1039 0.3473 0.1426 0.9849 0.9939 0.2206 0.4351 0.8754 0.704 ] Network output: [ 0.003569 -0.01688 0.9942 7.793e-06 -3.499e-06 1.016 5.873e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.0973 0.1843 0.1979 0.9873 0.9919 0.11 0.7397 0.8621 0.3052 ] Network output: [ -0.003345 0.01564 1.005 8.451e-06 -3.794e-06 0.9865 6.369e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0936 0.09166 0.165 0.1962 0.9852 0.9911 0.09361 0.6636 0.8375 0.2485 ] Network output: [ 9.273e-05 1 -5.966e-05 1.111e-06 -4.986e-07 0.9998 8.37e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002096 Epoch 9337 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009225 0.9966 0.9922 -2.082e-07 9.345e-08 -0.007263 -1.569e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003488 -0.003322 -0.006909 0.005539 0.9699 0.9743 0.006773 0.8264 0.8207 0.0166 ] Network output: [ 0.9999 0.0001773 0.0004413 -4.082e-06 1.832e-06 -0.0004202 -3.076e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2065 -0.03525 -0.1602 0.1839 0.9834 0.9932 0.2316 0.4311 0.8687 0.7101 ] Network output: [ -0.009188 1.003 1.008 -2.468e-07 1.108e-07 0.007613 -1.86e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006684 0.0006007 0.0044 0.003259 0.9889 0.9919 0.006813 0.8538 0.8925 0.01188 ] Network output: [ -0.0002459 0.001681 1.001 -1.28e-05 5.746e-06 0.9982 -9.645e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2199 0.1039 0.3473 0.1426 0.9849 0.9939 0.2206 0.4351 0.8754 0.704 ] Network output: [ 0.003568 -0.01687 0.9942 7.784e-06 -3.495e-06 1.016 5.866e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.0973 0.1843 0.1979 0.9873 0.9919 0.11 0.7397 0.8621 0.3052 ] Network output: [ -0.003343 0.01563 1.005 8.441e-06 -3.79e-06 0.9865 6.362e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0936 0.09166 0.165 0.1962 0.9852 0.9911 0.09362 0.6636 0.8375 0.2485 ] Network output: [ 9.27e-05 1 -5.962e-05 1.109e-06 -4.98e-07 0.9998 8.361e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002095 Epoch 9338 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009224 0.9966 0.9922 -2.081e-07 9.342e-08 -0.007263 -1.568e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003488 -0.003322 -0.006909 0.005539 0.9699 0.9743 0.006773 0.8264 0.8207 0.0166 ] Network output: [ 0.9999 0.0001771 0.0004411 -4.077e-06 1.83e-06 -0.0004199 -3.073e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2065 -0.03525 -0.1602 0.1839 0.9834 0.9932 0.2316 0.4311 0.8687 0.7101 ] Network output: [ -0.009187 1.003 1.008 -2.467e-07 1.107e-07 0.007612 -1.859e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006684 0.0006008 0.0044 0.003259 0.9889 0.9919 0.006814 0.8538 0.8925 0.01188 ] Network output: [ -0.0002458 0.001681 1.001 -1.278e-05 5.739e-06 0.9982 -9.634e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2199 0.1039 0.3473 0.1426 0.9849 0.9939 0.2206 0.4351 0.8754 0.704 ] Network output: [ 0.003566 -0.01687 0.9942 7.775e-06 -3.491e-06 1.016 5.86e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.09731 0.1843 0.1979 0.9873 0.9919 0.11 0.7397 0.8621 0.3052 ] Network output: [ -0.003342 0.01563 1.005 8.432e-06 -3.785e-06 0.9865 6.354e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0936 0.09167 0.165 0.1962 0.9852 0.9911 0.09362 0.6636 0.8375 0.2485 ] Network output: [ 9.267e-05 1 -5.958e-05 1.108e-06 -4.975e-07 0.9998 8.351e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002094 Epoch 9339 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009223 0.9966 0.9922 -2.08e-07 9.339e-08 -0.007262 -1.568e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003488 -0.003322 -0.006908 0.005538 0.9699 0.9743 0.006773 0.8264 0.8207 0.0166 ] Network output: [ 0.9999 0.000177 0.0004409 -4.072e-06 1.828e-06 -0.0004196 -3.069e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2065 -0.03525 -0.1602 0.1839 0.9834 0.9932 0.2316 0.4311 0.8687 0.7101 ] Network output: [ -0.009186 1.003 1.008 -2.465e-07 1.107e-07 0.007612 -1.858e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006685 0.0006009 0.0044 0.003259 0.9889 0.9919 0.006814 0.8538 0.8925 0.01188 ] Network output: [ -0.0002456 0.00168 1.001 -1.277e-05 5.732e-06 0.9982 -9.623e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2199 0.1039 0.3473 0.1426 0.9849 0.9939 0.2206 0.4351 0.8754 0.704 ] Network output: [ 0.003565 -0.01686 0.9942 7.766e-06 -3.486e-06 1.016 5.853e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.09731 0.1843 0.1979 0.9873 0.9919 0.11 0.7397 0.8621 0.3052 ] Network output: [ -0.003341 0.01562 1.005 8.422e-06 -3.781e-06 0.9865 6.347e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09361 0.09167 0.165 0.1962 0.9852 0.9911 0.09362 0.6636 0.8375 0.2485 ] Network output: [ 9.264e-05 1 -5.954e-05 1.107e-06 -4.969e-07 0.9998 8.341e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002093 Epoch 9340 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009222 0.9966 0.9922 -2.08e-07 9.336e-08 -0.007262 -1.567e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003488 -0.003322 -0.006907 0.005538 0.9699 0.9743 0.006773 0.8264 0.8207 0.0166 ] Network output: [ 0.9999 0.0001768 0.0004407 -4.067e-06 1.826e-06 -0.0004193 -3.065e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2065 -0.03525 -0.1602 0.1839 0.9834 0.9932 0.2316 0.4311 0.8687 0.7101 ] Network output: [ -0.009185 1.003 1.008 -2.464e-07 1.106e-07 0.007611 -1.857e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006685 0.000601 0.0044 0.003259 0.9889 0.9919 0.006815 0.8538 0.8925 0.01188 ] Network output: [ -0.0002454 0.001679 1.001 -1.275e-05 5.726e-06 0.9982 -9.612e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2199 0.1039 0.3473 0.1426 0.9849 0.9939 0.2206 0.4351 0.8754 0.7039 ] Network output: [ 0.003563 -0.01685 0.9942 7.757e-06 -3.482e-06 1.016 5.846e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.09732 0.1843 0.1979 0.9873 0.9919 0.11 0.7397 0.8621 0.3052 ] Network output: [ -0.003339 0.01561 1.005 8.412e-06 -3.777e-06 0.9865 6.34e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09361 0.09167 0.165 0.1962 0.9852 0.9911 0.09362 0.6636 0.8375 0.2485 ] Network output: [ 9.261e-05 1 -5.949e-05 1.106e-06 -4.963e-07 0.9998 8.332e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002092 Epoch 9341 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009221 0.9966 0.9922 -2.079e-07 9.333e-08 -0.007261 -1.567e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003488 -0.003323 -0.006907 0.005537 0.9699 0.9743 0.006774 0.8264 0.8207 0.0166 ] Network output: [ 0.9999 0.0001766 0.0004405 -4.063e-06 1.824e-06 -0.000419 -3.062e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2065 -0.03525 -0.1602 0.1839 0.9834 0.9932 0.2316 0.4311 0.8687 0.7101 ] Network output: [ -0.009184 1.003 1.008 -2.463e-07 1.106e-07 0.00761 -1.856e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006685 0.000601 0.0044 0.003258 0.9889 0.9919 0.006815 0.8537 0.8925 0.01188 ] Network output: [ -0.0002452 0.001678 1.001 -1.274e-05 5.719e-06 0.9982 -9.6e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2199 0.1039 0.3473 0.1426 0.9849 0.9939 0.2206 0.4351 0.8754 0.7039 ] Network output: [ 0.003562 -0.01685 0.9942 7.748e-06 -3.478e-06 1.016 5.839e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.09732 0.1843 0.1979 0.9873 0.9919 0.11 0.7396 0.8621 0.3052 ] Network output: [ -0.003338 0.01561 1.005 8.403e-06 -3.772e-06 0.9865 6.333e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09361 0.09167 0.165 0.1962 0.9852 0.9911 0.09362 0.6636 0.8375 0.2485 ] Network output: [ 9.258e-05 1 -5.945e-05 1.104e-06 -4.957e-07 0.9998 8.322e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002091 Epoch 9342 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00922 0.9966 0.9922 -2.078e-07 9.33e-08 -0.007261 -1.566e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003488 -0.003323 -0.006906 0.005537 0.9699 0.9743 0.006774 0.8264 0.8207 0.01659 ] Network output: [ 0.9999 0.0001764 0.0004403 -4.058e-06 1.822e-06 -0.0004187 -3.058e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2065 -0.03525 -0.1602 0.1839 0.9834 0.9932 0.2316 0.4311 0.8687 0.7101 ] Network output: [ -0.009184 1.003 1.008 -2.461e-07 1.105e-07 0.007609 -1.855e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006686 0.0006011 0.0044 0.003258 0.9889 0.9919 0.006816 0.8537 0.8925 0.01188 ] Network output: [ -0.0002451 0.001678 1.001 -1.272e-05 5.712e-06 0.9982 -9.589e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2199 0.1039 0.3473 0.1426 0.9849 0.9939 0.2206 0.4351 0.8754 0.7039 ] Network output: [ 0.00356 -0.01684 0.9942 7.739e-06 -3.474e-06 1.016 5.832e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1099 0.09732 0.1843 0.1979 0.9873 0.9919 0.11 0.7396 0.8621 0.3052 ] Network output: [ -0.003336 0.0156 1.005 8.393e-06 -3.768e-06 0.9865 6.325e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09361 0.09168 0.165 0.1962 0.9852 0.9911 0.09363 0.6636 0.8374 0.2485 ] Network output: [ 9.255e-05 1 -5.941e-05 1.103e-06 -4.952e-07 0.9998 8.312e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000209 Epoch 9343 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009219 0.9966 0.9922 -2.077e-07 9.327e-08 -0.00726 -1.566e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003488 -0.003323 -0.006906 0.005536 0.9699 0.9743 0.006774 0.8264 0.8207 0.01659 ] Network output: [ 0.9999 0.0001762 0.0004401 -4.053e-06 1.82e-06 -0.0004184 -3.055e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2065 -0.03526 -0.1601 0.1839 0.9834 0.9932 0.2316 0.4311 0.8687 0.7101 ] Network output: [ -0.009183 1.003 1.008 -2.46e-07 1.104e-07 0.007609 -1.854e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006686 0.0006012 0.0044 0.003258 0.9889 0.9919 0.006816 0.8537 0.8925 0.01188 ] Network output: [ -0.0002449 0.001677 1.001 -1.271e-05 5.706e-06 0.9982 -9.578e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2199 0.1039 0.3474 0.1426 0.9849 0.9939 0.2207 0.4351 0.8754 0.7039 ] Network output: [ 0.003559 -0.01683 0.9942 7.73e-06 -3.47e-06 1.016 5.826e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.09733 0.1843 0.1979 0.9873 0.9919 0.11 0.7396 0.8621 0.3052 ] Network output: [ -0.003335 0.01559 1.005 8.383e-06 -3.764e-06 0.9865 6.318e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09362 0.09168 0.165 0.1962 0.9852 0.9911 0.09363 0.6635 0.8374 0.2485 ] Network output: [ 9.252e-05 1 -5.937e-05 1.102e-06 -4.946e-07 0.9998 8.303e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002089 Epoch 9344 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009218 0.9966 0.9922 -2.077e-07 9.323e-08 -0.00726 -1.565e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003488 -0.003323 -0.006905 0.005536 0.9699 0.9743 0.006774 0.8263 0.8207 0.01659 ] Network output: [ 0.9999 0.000176 0.0004399 -4.048e-06 1.817e-06 -0.0004181 -3.051e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2065 -0.03526 -0.1601 0.1839 0.9834 0.9932 0.2316 0.4311 0.8687 0.7101 ] Network output: [ -0.009182 1.003 1.008 -2.458e-07 1.104e-07 0.007608 -1.853e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006687 0.0006013 0.0044 0.003257 0.9889 0.9919 0.006817 0.8537 0.8925 0.01188 ] Network output: [ -0.0002447 0.001676 1.001 -1.269e-05 5.699e-06 0.9982 -9.567e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2199 0.1039 0.3474 0.1426 0.9849 0.9939 0.2207 0.4351 0.8754 0.7039 ] Network output: [ 0.003557 -0.01683 0.9942 7.721e-06 -3.466e-06 1.016 5.819e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.09733 0.1843 0.1979 0.9873 0.9919 0.11 0.7396 0.8621 0.3052 ] Network output: [ -0.003334 0.01559 1.005 8.374e-06 -3.759e-06 0.9865 6.311e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09362 0.09168 0.165 0.1962 0.9852 0.9911 0.09363 0.6635 0.8374 0.2486 ] Network output: [ 9.249e-05 1 -5.932e-05 1.1e-06 -4.94e-07 0.9998 8.293e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002087 Epoch 9345 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009217 0.9966 0.9922 -2.076e-07 9.32e-08 -0.00726 -1.565e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003488 -0.003323 -0.006904 0.005536 0.9699 0.9743 0.006774 0.8263 0.8207 0.01659 ] Network output: [ 0.9999 0.0001758 0.0004397 -4.044e-06 1.815e-06 -0.0004178 -3.047e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2065 -0.03526 -0.1601 0.1839 0.9834 0.9932 0.2317 0.4311 0.8687 0.7101 ] Network output: [ -0.009181 1.003 1.008 -2.457e-07 1.103e-07 0.007607 -1.852e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006687 0.0006014 0.0044 0.003257 0.9889 0.9919 0.006817 0.8537 0.8925 0.01188 ] Network output: [ -0.0002445 0.001676 1.001 -1.268e-05 5.692e-06 0.9982 -9.556e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2199 0.1039 0.3474 0.1426 0.9849 0.9939 0.2207 0.4351 0.8754 0.7039 ] Network output: [ 0.003556 -0.01682 0.9942 7.712e-06 -3.462e-06 1.016 5.812e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.09734 0.1843 0.1979 0.9873 0.9919 0.11 0.7396 0.8621 0.3052 ] Network output: [ -0.003332 0.01558 1.005 8.364e-06 -3.755e-06 0.9865 6.304e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09362 0.09168 0.165 0.1962 0.9852 0.9911 0.09363 0.6635 0.8374 0.2486 ] Network output: [ 9.246e-05 1 -5.928e-05 1.099e-06 -4.935e-07 0.9998 8.284e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002086 Epoch 9346 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009216 0.9966 0.9922 -2.075e-07 9.317e-08 -0.007259 -1.564e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003488 -0.003323 -0.006904 0.005535 0.9699 0.9743 0.006775 0.8263 0.8207 0.01659 ] Network output: [ 0.9999 0.0001756 0.0004395 -4.039e-06 1.813e-06 -0.0004175 -3.044e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2065 -0.03526 -0.1601 0.1839 0.9834 0.9932 0.2317 0.4311 0.8686 0.7101 ] Network output: [ -0.00918 1.003 1.008 -2.455e-07 1.102e-07 0.007606 -1.85e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006688 0.0006015 0.0044 0.003257 0.9889 0.9919 0.006817 0.8537 0.8925 0.01188 ] Network output: [ -0.0002443 0.001675 1.001 -1.266e-05 5.686e-06 0.9982 -9.544e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2199 0.1039 0.3474 0.1426 0.9849 0.9939 0.2207 0.4351 0.8754 0.7039 ] Network output: [ 0.003554 -0.01681 0.9942 7.703e-06 -3.458e-06 1.016 5.805e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.09734 0.1843 0.1979 0.9873 0.9919 0.11 0.7396 0.8621 0.3052 ] Network output: [ -0.003331 0.01558 1.005 8.355e-06 -3.751e-06 0.9865 6.296e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09362 0.09168 0.165 0.1962 0.9852 0.9911 0.09364 0.6635 0.8374 0.2486 ] Network output: [ 9.243e-05 1 -5.924e-05 1.098e-06 -4.929e-07 0.9998 8.274e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002085 Epoch 9347 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009215 0.9966 0.9922 -2.075e-07 9.314e-08 -0.007259 -1.564e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003488 -0.003323 -0.006903 0.005535 0.9699 0.9743 0.006775 0.8263 0.8207 0.01659 ] Network output: [ 0.9999 0.0001754 0.0004392 -4.034e-06 1.811e-06 -0.0004172 -3.04e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2065 -0.03526 -0.1601 0.1839 0.9834 0.9932 0.2317 0.4311 0.8686 0.7101 ] Network output: [ -0.009179 1.003 1.008 -2.454e-07 1.102e-07 0.007606 -1.849e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006688 0.0006015 0.0044 0.003257 0.9889 0.9919 0.006818 0.8537 0.8925 0.01188 ] Network output: [ -0.0002442 0.001674 1.001 -1.265e-05 5.679e-06 0.9982 -9.533e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2199 0.1039 0.3474 0.1426 0.9849 0.9939 0.2207 0.4351 0.8754 0.7039 ] Network output: [ 0.003553 -0.01681 0.9942 7.694e-06 -3.454e-06 1.016 5.799e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.09734 0.1843 0.1979 0.9873 0.9919 0.11 0.7396 0.8621 0.3052 ] Network output: [ -0.003329 0.01557 1.005 8.345e-06 -3.746e-06 0.9865 6.289e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09362 0.09169 0.165 0.1962 0.9852 0.9911 0.09364 0.6635 0.8374 0.2486 ] Network output: [ 9.24e-05 1 -5.92e-05 1.097e-06 -4.923e-07 0.9998 8.265e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002084 Epoch 9348 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009214 0.9966 0.9922 -2.074e-07 9.311e-08 -0.007258 -1.563e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003488 -0.003323 -0.006902 0.005534 0.9699 0.9743 0.006775 0.8263 0.8207 0.01659 ] Network output: [ 0.9999 0.0001752 0.000439 -4.029e-06 1.809e-06 -0.0004169 -3.037e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2065 -0.03526 -0.1601 0.1839 0.9834 0.9932 0.2317 0.431 0.8686 0.7101 ] Network output: [ -0.009178 1.003 1.008 -2.452e-07 1.101e-07 0.007605 -1.848e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006688 0.0006016 0.004399 0.003256 0.9889 0.9919 0.006818 0.8537 0.8925 0.01188 ] Network output: [ -0.000244 0.001673 1.001 -1.263e-05 5.672e-06 0.9982 -9.522e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2199 0.1039 0.3474 0.1426 0.9849 0.9939 0.2207 0.435 0.8754 0.7039 ] Network output: [ 0.003551 -0.0168 0.9942 7.685e-06 -3.45e-06 1.016 5.792e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.09735 0.1843 0.1979 0.9873 0.9919 0.11 0.7395 0.862 0.3052 ] Network output: [ -0.003328 0.01556 1.005 8.336e-06 -3.742e-06 0.9865 6.282e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09363 0.09169 0.165 0.1963 0.9852 0.9911 0.09364 0.6635 0.8374 0.2486 ] Network output: [ 9.237e-05 1 -5.916e-05 1.095e-06 -4.917e-07 0.9998 8.255e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002083 Epoch 9349 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009213 0.9966 0.9922 -2.073e-07 9.308e-08 -0.007258 -1.563e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003489 -0.003323 -0.006902 0.005534 0.9699 0.9743 0.006775 0.8263 0.8207 0.01659 ] Network output: [ 0.9999 0.000175 0.0004388 -4.025e-06 1.807e-06 -0.0004166 -3.033e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2066 -0.03526 -0.1601 0.1839 0.9834 0.9932 0.2317 0.431 0.8686 0.7101 ] Network output: [ -0.009177 1.003 1.008 -2.451e-07 1.1e-07 0.007604 -1.847e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006689 0.0006017 0.004399 0.003256 0.9889 0.9919 0.006819 0.8537 0.8925 0.01187 ] Network output: [ -0.0002438 0.001673 1.001 -1.262e-05 5.666e-06 0.9982 -9.511e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.22 0.104 0.3474 0.1426 0.9849 0.9939 0.2207 0.435 0.8754 0.7039 ] Network output: [ 0.00355 -0.01679 0.9942 7.676e-06 -3.446e-06 1.016 5.785e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.09735 0.1843 0.1979 0.9873 0.9919 0.1101 0.7395 0.862 0.3052 ] Network output: [ -0.003327 0.01556 1.005 8.326e-06 -3.738e-06 0.9865 6.275e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09363 0.09169 0.165 0.1963 0.9852 0.9911 0.09364 0.6635 0.8374 0.2486 ] Network output: [ 9.234e-05 1 -5.911e-05 1.094e-06 -4.912e-07 0.9998 8.245e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002082 Epoch 9350 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009212 0.9966 0.9922 -2.073e-07 9.305e-08 -0.007257 -1.562e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003489 -0.003323 -0.006901 0.005533 0.9699 0.9743 0.006775 0.8263 0.8207 0.01659 ] Network output: [ 0.9999 0.0001748 0.0004386 -4.02e-06 1.805e-06 -0.0004163 -3.029e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2066 -0.03526 -0.1601 0.1839 0.9834 0.9932 0.2317 0.431 0.8686 0.7101 ] Network output: [ -0.009177 1.003 1.008 -2.45e-07 1.1e-07 0.007603 -1.846e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006689 0.0006018 0.004399 0.003256 0.9889 0.9919 0.006819 0.8537 0.8925 0.01187 ] Network output: [ -0.0002436 0.001672 1.001 -1.261e-05 5.659e-06 0.9982 -9.5e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.22 0.104 0.3474 0.1426 0.9849 0.9939 0.2207 0.435 0.8754 0.7039 ] Network output: [ 0.003548 -0.01679 0.9942 7.667e-06 -3.442e-06 1.016 5.778e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.09736 0.1843 0.1979 0.9873 0.9919 0.1101 0.7395 0.862 0.3052 ] Network output: [ -0.003325 0.01555 1.005 8.317e-06 -3.734e-06 0.9865 6.268e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09363 0.09169 0.165 0.1963 0.9852 0.9911 0.09364 0.6635 0.8374 0.2486 ] Network output: [ 9.231e-05 1 -5.907e-05 1.093e-06 -4.906e-07 0.9998 8.236e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002081 Epoch 9351 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009211 0.9966 0.9922 -2.072e-07 9.302e-08 -0.007257 -1.561e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003489 -0.003324 -0.0069 0.005533 0.9699 0.9743 0.006776 0.8263 0.8207 0.01659 ] Network output: [ 0.9999 0.0001746 0.0004384 -4.015e-06 1.803e-06 -0.000416 -3.026e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2066 -0.03526 -0.1601 0.1839 0.9834 0.9932 0.2317 0.431 0.8686 0.7101 ] Network output: [ -0.009176 1.003 1.008 -2.448e-07 1.099e-07 0.007603 -1.845e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00669 0.0006019 0.004399 0.003256 0.9889 0.9919 0.00682 0.8537 0.8925 0.01187 ] Network output: [ -0.0002435 0.001671 1.001 -1.259e-05 5.652e-06 0.9982 -9.489e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.22 0.104 0.3474 0.1426 0.9849 0.9939 0.2207 0.435 0.8754 0.7039 ] Network output: [ 0.003547 -0.01678 0.9942 7.658e-06 -3.438e-06 1.016 5.772e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.09736 0.1843 0.1979 0.9873 0.9919 0.1101 0.7395 0.862 0.3052 ] Network output: [ -0.003324 0.01554 1.005 8.307e-06 -3.729e-06 0.9865 6.26e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09363 0.0917 0.165 0.1963 0.9852 0.9911 0.09365 0.6634 0.8374 0.2486 ] Network output: [ 9.228e-05 1 -5.903e-05 1.092e-06 -4.9e-07 0.9998 8.226e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000208 Epoch 9352 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00921 0.9966 0.9922 -2.071e-07 9.299e-08 -0.007256 -1.561e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003489 -0.003324 -0.0069 0.005533 0.9699 0.9743 0.006776 0.8263 0.8207 0.01659 ] Network output: [ 0.9999 0.0001744 0.0004382 -4.01e-06 1.8e-06 -0.0004157 -3.022e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2066 -0.03526 -0.1601 0.1839 0.9834 0.9932 0.2317 0.431 0.8686 0.71 ] Network output: [ -0.009175 1.003 1.008 -2.447e-07 1.098e-07 0.007602 -1.844e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00669 0.0006019 0.004399 0.003255 0.9889 0.9919 0.00682 0.8537 0.8925 0.01187 ] Network output: [ -0.0002433 0.001671 1.001 -1.258e-05 5.646e-06 0.9982 -9.477e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.22 0.104 0.3474 0.1426 0.9849 0.9939 0.2207 0.435 0.8754 0.7039 ] Network output: [ 0.003545 -0.01677 0.9942 7.649e-06 -3.434e-06 1.016 5.765e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.09736 0.1843 0.1979 0.9873 0.9919 0.1101 0.7395 0.862 0.3052 ] Network output: [ -0.003322 0.01554 1.005 8.298e-06 -3.725e-06 0.9865 6.253e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09364 0.0917 0.165 0.1963 0.9852 0.9911 0.09365 0.6634 0.8374 0.2486 ] Network output: [ 9.225e-05 1 -5.899e-05 1.09e-06 -4.895e-07 0.9998 8.217e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002079 Epoch 9353 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009209 0.9966 0.9922 -2.071e-07 9.295e-08 -0.007256 -1.56e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003489 -0.003324 -0.006899 0.005532 0.9699 0.9743 0.006776 0.8263 0.8207 0.01658 ] Network output: [ 0.9999 0.0001742 0.000438 -4.006e-06 1.798e-06 -0.0004154 -3.019e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2066 -0.03526 -0.16 0.1839 0.9834 0.9932 0.2317 0.431 0.8686 0.71 ] Network output: [ -0.009174 1.003 1.008 -2.445e-07 1.098e-07 0.007601 -1.843e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00669 0.000602 0.004399 0.003255 0.9889 0.9919 0.00682 0.8537 0.8925 0.01187 ] Network output: [ -0.0002431 0.00167 1.001 -1.256e-05 5.639e-06 0.9982 -9.466e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.22 0.104 0.3474 0.1426 0.9849 0.9939 0.2207 0.435 0.8754 0.7039 ] Network output: [ 0.003544 -0.01677 0.9942 7.641e-06 -3.43e-06 1.016 5.758e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.09737 0.1843 0.1979 0.9873 0.9919 0.1101 0.7395 0.862 0.3052 ] Network output: [ -0.003321 0.01553 1.005 8.288e-06 -3.721e-06 0.9865 6.246e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09364 0.0917 0.165 0.1963 0.9852 0.9911 0.09365 0.6634 0.8374 0.2486 ] Network output: [ 9.221e-05 1 -5.895e-05 1.089e-06 -4.889e-07 0.9998 8.207e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002078 Epoch 9354 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009208 0.9966 0.9922 -2.07e-07 9.292e-08 -0.007255 -1.56e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003489 -0.003324 -0.006898 0.005532 0.9699 0.9743 0.006776 0.8263 0.8207 0.01658 ] Network output: [ 0.9999 0.000174 0.0004378 -4.001e-06 1.796e-06 -0.0004151 -3.015e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2066 -0.03527 -0.16 0.1839 0.9834 0.9932 0.2317 0.431 0.8686 0.71 ] Network output: [ -0.009173 1.003 1.008 -2.444e-07 1.097e-07 0.0076 -1.842e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006691 0.0006021 0.004399 0.003255 0.9889 0.9919 0.006821 0.8537 0.8925 0.01187 ] Network output: [ -0.0002429 0.001669 1.001 -1.255e-05 5.632e-06 0.9982 -9.455e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.22 0.104 0.3474 0.1426 0.9849 0.9939 0.2207 0.435 0.8754 0.7039 ] Network output: [ 0.003542 -0.01676 0.9942 7.632e-06 -3.426e-06 1.016 5.751e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.09737 0.1843 0.1979 0.9873 0.9919 0.1101 0.7395 0.862 0.3052 ] Network output: [ -0.00332 0.01552 1.005 8.279e-06 -3.717e-06 0.9865 6.239e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09364 0.0917 0.165 0.1963 0.9852 0.9911 0.09365 0.6634 0.8374 0.2486 ] Network output: [ 9.218e-05 1 -5.891e-05 1.088e-06 -4.883e-07 0.9998 8.198e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002077 Epoch 9355 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009207 0.9966 0.9922 -2.069e-07 9.289e-08 -0.007255 -1.559e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003489 -0.003324 -0.006898 0.005531 0.9699 0.9743 0.006776 0.8263 0.8207 0.01658 ] Network output: [ 0.9999 0.0001738 0.0004376 -3.996e-06 1.794e-06 -0.0004148 -3.012e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2066 -0.03527 -0.16 0.1839 0.9834 0.9932 0.2317 0.431 0.8686 0.71 ] Network output: [ -0.009172 1.003 1.008 -2.442e-07 1.096e-07 0.0076 -1.841e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006691 0.0006022 0.004399 0.003255 0.9889 0.9919 0.006821 0.8537 0.8925 0.01187 ] Network output: [ -0.0002428 0.001668 1.001 -1.253e-05 5.626e-06 0.9982 -9.444e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.22 0.104 0.3474 0.1426 0.9849 0.9939 0.2207 0.435 0.8754 0.7039 ] Network output: [ 0.003541 -0.01675 0.9942 7.623e-06 -3.422e-06 1.016 5.745e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.09738 0.1843 0.1979 0.9873 0.9919 0.1101 0.7395 0.862 0.3052 ] Network output: [ -0.003318 0.01552 1.005 8.269e-06 -3.712e-06 0.9866 6.232e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09364 0.0917 0.165 0.1963 0.9852 0.9911 0.09366 0.6634 0.8374 0.2486 ] Network output: [ 9.215e-05 1 -5.887e-05 1.087e-06 -4.878e-07 0.9998 8.188e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002075 Epoch 9356 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009206 0.9966 0.9922 -2.068e-07 9.286e-08 -0.007254 -1.559e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003489 -0.003324 -0.006897 0.005531 0.9699 0.9743 0.006777 0.8263 0.8206 0.01658 ] Network output: [ 0.9999 0.0001736 0.0004374 -3.992e-06 1.792e-06 -0.0004145 -3.008e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2066 -0.03527 -0.16 0.1838 0.9834 0.9932 0.2317 0.431 0.8686 0.71 ] Network output: [ -0.009171 1.003 1.008 -2.441e-07 1.096e-07 0.007599 -1.84e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006692 0.0006023 0.004399 0.003254 0.9889 0.9919 0.006822 0.8537 0.8925 0.01187 ] Network output: [ -0.0002426 0.001668 1.001 -1.252e-05 5.619e-06 0.9982 -9.433e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.22 0.104 0.3474 0.1426 0.9849 0.9939 0.2208 0.435 0.8754 0.7039 ] Network output: [ 0.003539 -0.01674 0.9942 7.614e-06 -3.418e-06 1.016 5.738e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.09738 0.1843 0.1979 0.9873 0.9919 0.1101 0.7394 0.862 0.3052 ] Network output: [ -0.003317 0.01551 1.005 8.26e-06 -3.708e-06 0.9866 6.225e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09364 0.09171 0.165 0.1963 0.9852 0.9911 0.09366 0.6634 0.8374 0.2486 ] Network output: [ 9.212e-05 1 -5.883e-05 1.085e-06 -4.872e-07 0.9998 8.179e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002074 Epoch 9357 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009205 0.9966 0.9922 -2.068e-07 9.283e-08 -0.007254 -1.558e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003489 -0.003324 -0.006896 0.00553 0.9699 0.9743 0.006777 0.8263 0.8206 0.01658 ] Network output: [ 0.9999 0.0001734 0.0004372 -3.987e-06 1.79e-06 -0.0004142 -3.005e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2066 -0.03527 -0.16 0.1838 0.9834 0.9932 0.2317 0.431 0.8686 0.71 ] Network output: [ -0.00917 1.003 1.008 -2.439e-07 1.095e-07 0.007598 -1.838e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006692 0.0006023 0.004399 0.003254 0.9889 0.9919 0.006822 0.8537 0.8925 0.01187 ] Network output: [ -0.0002424 0.001667 1.001 -1.25e-05 5.613e-06 0.9982 -9.422e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.22 0.104 0.3474 0.1426 0.9849 0.9939 0.2208 0.435 0.8754 0.7039 ] Network output: [ 0.003538 -0.01674 0.9942 7.605e-06 -3.414e-06 1.016 5.731e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.09738 0.1843 0.1979 0.9873 0.9919 0.1101 0.7394 0.862 0.3052 ] Network output: [ -0.003315 0.0155 1.005 8.25e-06 -3.704e-06 0.9866 6.218e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09365 0.09171 0.165 0.1963 0.9852 0.9911 0.09366 0.6634 0.8374 0.2486 ] Network output: [ 9.209e-05 1 -5.878e-05 1.084e-06 -4.866e-07 0.9998 8.169e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002073 Epoch 9358 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009204 0.9966 0.9922 -2.067e-07 9.279e-08 -0.007253 -1.558e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003489 -0.003324 -0.006896 0.00553 0.9699 0.9743 0.006777 0.8263 0.8206 0.01658 ] Network output: [ 0.9999 0.0001732 0.000437 -3.982e-06 1.788e-06 -0.0004139 -3.001e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2066 -0.03527 -0.16 0.1838 0.9834 0.9932 0.2318 0.431 0.8686 0.71 ] Network output: [ -0.00917 1.003 1.008 -2.438e-07 1.095e-07 0.007597 -1.837e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006693 0.0006024 0.004399 0.003254 0.9889 0.9919 0.006823 0.8537 0.8925 0.01187 ] Network output: [ -0.0002422 0.001666 1.001 -1.249e-05 5.606e-06 0.9982 -9.411e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.22 0.104 0.3474 0.1426 0.9849 0.9939 0.2208 0.435 0.8754 0.7039 ] Network output: [ 0.003536 -0.01673 0.9942 7.596e-06 -3.41e-06 1.016 5.725e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.09739 0.1843 0.1979 0.9873 0.9919 0.1101 0.7394 0.862 0.3052 ] Network output: [ -0.003314 0.0155 1.005 8.241e-06 -3.7e-06 0.9866 6.21e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09365 0.09171 0.165 0.1963 0.9852 0.9911 0.09366 0.6634 0.8374 0.2486 ] Network output: [ 9.206e-05 1 -5.874e-05 1.083e-06 -4.861e-07 0.9998 8.16e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002072 Epoch 9359 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009203 0.9967 0.9922 -2.066e-07 9.276e-08 -0.007253 -1.557e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003489 -0.003324 -0.006895 0.00553 0.9699 0.9743 0.006777 0.8263 0.8206 0.01658 ] Network output: [ 0.9999 0.000173 0.0004368 -3.978e-06 1.786e-06 -0.0004136 -2.998e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2066 -0.03527 -0.16 0.1838 0.9834 0.9932 0.2318 0.431 0.8686 0.71 ] Network output: [ -0.009169 1.003 1.008 -2.437e-07 1.094e-07 0.007597 -1.836e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006693 0.0006025 0.004399 0.003254 0.9889 0.9919 0.006823 0.8536 0.8925 0.01187 ] Network output: [ -0.000242 0.001666 1.001 -1.247e-05 5.6e-06 0.9982 -9.4e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.22 0.104 0.3474 0.1426 0.9849 0.9939 0.2208 0.435 0.8754 0.7039 ] Network output: [ 0.003535 -0.01672 0.9942 7.587e-06 -3.406e-06 1.016 5.718e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.09739 0.1843 0.1979 0.9873 0.9919 0.1101 0.7394 0.862 0.3052 ] Network output: [ -0.003313 0.01549 1.005 8.231e-06 -3.695e-06 0.9866 6.203e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09365 0.09171 0.165 0.1963 0.9852 0.9911 0.09366 0.6633 0.8374 0.2486 ] Network output: [ 9.203e-05 1 -5.87e-05 1.081e-06 -4.855e-07 0.9998 8.15e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002071 Epoch 9360 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009202 0.9967 0.9922 -2.066e-07 9.273e-08 -0.007252 -1.557e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003489 -0.003324 -0.006894 0.005529 0.9699 0.9743 0.006777 0.8263 0.8206 0.01658 ] Network output: [ 0.9999 0.0001728 0.0004366 -3.973e-06 1.784e-06 -0.0004133 -2.994e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2066 -0.03527 -0.16 0.1838 0.9834 0.9932 0.2318 0.431 0.8686 0.71 ] Network output: [ -0.009168 1.003 1.008 -2.435e-07 1.093e-07 0.007596 -1.835e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006693 0.0006026 0.004399 0.003253 0.9889 0.9919 0.006823 0.8536 0.8925 0.01187 ] Network output: [ -0.0002419 0.001665 1.001 -1.246e-05 5.593e-06 0.9982 -9.389e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.22 0.104 0.3474 0.1426 0.9849 0.9939 0.2208 0.435 0.8754 0.7039 ] Network output: [ 0.003534 -0.01672 0.9942 7.578e-06 -3.402e-06 1.016 5.711e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.0974 0.1843 0.1979 0.9873 0.9919 0.1101 0.7394 0.862 0.3052 ] Network output: [ -0.003311 0.01548 1.005 8.222e-06 -3.691e-06 0.9866 6.196e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09365 0.09171 0.165 0.1963 0.9852 0.9911 0.09367 0.6633 0.8374 0.2486 ] Network output: [ 9.2e-05 1 -5.866e-05 1.08e-06 -4.85e-07 0.9998 8.141e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000207 Epoch 9361 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009201 0.9967 0.9922 -2.065e-07 9.27e-08 -0.007252 -1.556e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003489 -0.003324 -0.006894 0.005529 0.9699 0.9743 0.006778 0.8263 0.8206 0.01658 ] Network output: [ 0.9999 0.0001726 0.0004364 -3.968e-06 1.781e-06 -0.000413 -2.991e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2066 -0.03527 -0.16 0.1838 0.9834 0.9932 0.2318 0.431 0.8686 0.71 ] Network output: [ -0.009167 1.003 1.008 -2.434e-07 1.093e-07 0.007595 -1.834e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006694 0.0006027 0.004398 0.003253 0.9889 0.9919 0.006824 0.8536 0.8925 0.01186 ] Network output: [ -0.0002417 0.001664 1.001 -1.244e-05 5.586e-06 0.9982 -9.378e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.22 0.104 0.3474 0.1426 0.9849 0.9939 0.2208 0.435 0.8754 0.7038 ] Network output: [ 0.003532 -0.01671 0.9942 7.57e-06 -3.398e-06 1.016 5.705e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.0974 0.1843 0.1979 0.9873 0.9919 0.1101 0.7394 0.862 0.3052 ] Network output: [ -0.00331 0.01548 1.005 8.212e-06 -3.687e-06 0.9866 6.189e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09365 0.09172 0.165 0.1963 0.9852 0.9911 0.09367 0.6633 0.8374 0.2486 ] Network output: [ 9.197e-05 1 -5.862e-05 1.079e-06 -4.844e-07 0.9998 8.132e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002069 Epoch 9362 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0092 0.9967 0.9922 -2.064e-07 9.267e-08 -0.007251 -1.556e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003489 -0.003325 -0.006893 0.005528 0.9699 0.9743 0.006778 0.8263 0.8206 0.01658 ] Network output: [ 0.9999 0.0001724 0.0004362 -3.964e-06 1.779e-06 -0.0004128 -2.987e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2066 -0.03527 -0.16 0.1838 0.9834 0.9932 0.2318 0.431 0.8686 0.71 ] Network output: [ -0.009166 1.003 1.008 -2.432e-07 1.092e-07 0.007594 -1.833e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006694 0.0006028 0.004398 0.003253 0.9889 0.9919 0.006824 0.8536 0.8925 0.01186 ] Network output: [ -0.0002415 0.001663 1.001 -1.243e-05 5.58e-06 0.9982 -9.367e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.22 0.104 0.3475 0.1426 0.9849 0.9939 0.2208 0.435 0.8754 0.7038 ] Network output: [ 0.003531 -0.0167 0.9942 7.561e-06 -3.394e-06 1.015 5.698e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.0974 0.1843 0.1979 0.9873 0.9919 0.1101 0.7394 0.862 0.3052 ] Network output: [ -0.003308 0.01547 1.005 8.203e-06 -3.683e-06 0.9866 6.182e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09366 0.09172 0.165 0.1963 0.9852 0.9911 0.09367 0.6633 0.8374 0.2486 ] Network output: [ 9.194e-05 1 -5.858e-05 1.078e-06 -4.838e-07 0.9998 8.122e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002068 Epoch 9363 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009199 0.9967 0.9922 -2.063e-07 9.263e-08 -0.007251 -1.555e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00349 -0.003325 -0.006893 0.005528 0.9699 0.9743 0.006778 0.8263 0.8206 0.01657 ] Network output: [ 0.9999 0.0001723 0.000436 -3.959e-06 1.777e-06 -0.0004125 -2.984e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2066 -0.03527 -0.1599 0.1838 0.9834 0.9932 0.2318 0.431 0.8686 0.71 ] Network output: [ -0.009165 1.003 1.008 -2.431e-07 1.091e-07 0.007594 -1.832e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006695 0.0006028 0.004398 0.003253 0.9889 0.9919 0.006825 0.8536 0.8925 0.01186 ] Network output: [ -0.0002413 0.001663 1.001 -1.241e-05 5.573e-06 0.9982 -9.356e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2201 0.104 0.3475 0.1426 0.9849 0.9939 0.2208 0.435 0.8754 0.7038 ] Network output: [ 0.003529 -0.0167 0.9942 7.552e-06 -3.39e-06 1.015 5.691e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.09741 0.1843 0.1979 0.9873 0.9919 0.1101 0.7394 0.862 0.3052 ] Network output: [ -0.003307 0.01546 1.005 8.194e-06 -3.678e-06 0.9866 6.175e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09366 0.09172 0.165 0.1963 0.9852 0.9911 0.09367 0.6633 0.8374 0.2486 ] Network output: [ 9.191e-05 1 -5.854e-05 1.076e-06 -4.833e-07 0.9998 8.113e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002067 Epoch 9364 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009198 0.9967 0.9922 -2.063e-07 9.26e-08 -0.00725 -1.555e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00349 -0.003325 -0.006892 0.005527 0.9699 0.9743 0.006778 0.8262 0.8206 0.01657 ] Network output: [ 0.9999 0.0001721 0.0004358 -3.954e-06 1.775e-06 -0.0004122 -2.98e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2066 -0.03528 -0.1599 0.1838 0.9834 0.9932 0.2318 0.431 0.8686 0.71 ] Network output: [ -0.009164 1.003 1.008 -2.429e-07 1.091e-07 0.007593 -1.831e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006695 0.0006029 0.004398 0.003252 0.9889 0.9919 0.006825 0.8536 0.8925 0.01186 ] Network output: [ -0.0002412 0.001662 1.001 -1.24e-05 5.567e-06 0.9982 -9.345e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2201 0.104 0.3475 0.1426 0.9849 0.9939 0.2208 0.435 0.8754 0.7038 ] Network output: [ 0.003528 -0.01669 0.9942 7.543e-06 -3.386e-06 1.015 5.685e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.09741 0.1844 0.1979 0.9873 0.9919 0.1101 0.7393 0.862 0.3052 ] Network output: [ -0.003306 0.01546 1.005 8.184e-06 -3.674e-06 0.9866 6.168e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09366 0.09172 0.165 0.1963 0.9852 0.9911 0.09368 0.6633 0.8374 0.2486 ] Network output: [ 9.188e-05 1 -5.85e-05 1.075e-06 -4.827e-07 0.9998 8.103e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002066 Epoch 9365 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009197 0.9967 0.9922 -2.062e-07 9.257e-08 -0.00725 -1.554e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00349 -0.003325 -0.006891 0.005527 0.9699 0.9743 0.006778 0.8262 0.8206 0.01657 ] Network output: [ 0.9999 0.0001719 0.0004356 -3.95e-06 1.773e-06 -0.0004119 -2.976e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2067 -0.03528 -0.1599 0.1838 0.9834 0.9932 0.2318 0.4309 0.8686 0.71 ] Network output: [ -0.009163 1.003 1.008 -2.428e-07 1.09e-07 0.007592 -1.83e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006696 0.000603 0.004398 0.003252 0.9889 0.9919 0.006826 0.8536 0.8925 0.01186 ] Network output: [ -0.000241 0.001661 1.001 -1.239e-05 5.56e-06 0.9982 -9.334e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2201 0.104 0.3475 0.1426 0.9849 0.9939 0.2208 0.4349 0.8754 0.7038 ] Network output: [ 0.003526 -0.01668 0.9942 7.534e-06 -3.382e-06 1.015 5.678e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.11 0.09742 0.1844 0.1979 0.9873 0.9919 0.1101 0.7393 0.862 0.3052 ] Network output: [ -0.003304 0.01545 1.005 8.175e-06 -3.67e-06 0.9866 6.161e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09366 0.09173 0.165 0.1963 0.9852 0.9911 0.09368 0.6633 0.8374 0.2486 ] Network output: [ 9.185e-05 1 -5.846e-05 1.074e-06 -4.822e-07 0.9998 8.094e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002065 Epoch 9366 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009196 0.9967 0.9922 -2.061e-07 9.254e-08 -0.007249 -1.553e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00349 -0.003325 -0.006891 0.005527 0.9699 0.9743 0.006779 0.8262 0.8206 0.01657 ] Network output: [ 0.9999 0.0001717 0.0004354 -3.945e-06 1.771e-06 -0.0004116 -2.973e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2067 -0.03528 -0.1599 0.1838 0.9834 0.9932 0.2318 0.4309 0.8686 0.71 ] Network output: [ -0.009163 1.003 1.008 -2.426e-07 1.089e-07 0.007591 -1.829e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006696 0.0006031 0.004398 0.003252 0.9889 0.9919 0.006826 0.8536 0.8925 0.01186 ] Network output: [ -0.0002408 0.001661 1.001 -1.237e-05 5.554e-06 0.9982 -9.323e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2201 0.104 0.3475 0.1426 0.9849 0.9939 0.2208 0.4349 0.8754 0.7038 ] Network output: [ 0.003525 -0.01668 0.9942 7.526e-06 -3.379e-06 1.015 5.672e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.09742 0.1844 0.1979 0.9873 0.9919 0.1101 0.7393 0.862 0.3052 ] Network output: [ -0.003303 0.01544 1.005 8.165e-06 -3.666e-06 0.9866 6.154e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09367 0.09173 0.165 0.1963 0.9852 0.9911 0.09368 0.6633 0.8374 0.2486 ] Network output: [ 9.182e-05 1 -5.842e-05 1.073e-06 -4.816e-07 0.9998 8.085e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002064 Epoch 9367 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009195 0.9967 0.9922 -2.061e-07 9.25e-08 -0.007249 -1.553e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00349 -0.003325 -0.00689 0.005526 0.9699 0.9743 0.006779 0.8262 0.8206 0.01657 ] Network output: [ 0.9999 0.0001715 0.0004352 -3.94e-06 1.769e-06 -0.0004113 -2.969e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2067 -0.03528 -0.1599 0.1838 0.9834 0.9932 0.2318 0.4309 0.8686 0.71 ] Network output: [ -0.009162 1.003 1.008 -2.425e-07 1.089e-07 0.007591 -1.827e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006696 0.0006032 0.004398 0.003252 0.9889 0.9919 0.006827 0.8536 0.8925 0.01186 ] Network output: [ -0.0002406 0.00166 1.001 -1.236e-05 5.547e-06 0.9982 -9.312e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2201 0.104 0.3475 0.1426 0.9849 0.9939 0.2208 0.4349 0.8754 0.7038 ] Network output: [ 0.003523 -0.01667 0.9942 7.517e-06 -3.375e-06 1.015 5.665e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.09743 0.1844 0.1979 0.9873 0.9919 0.1101 0.7393 0.862 0.3052 ] Network output: [ -0.003301 0.01544 1.005 8.156e-06 -3.662e-06 0.9866 6.147e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09367 0.09173 0.165 0.1963 0.9852 0.9911 0.09368 0.6632 0.8374 0.2486 ] Network output: [ 9.179e-05 1 -5.838e-05 1.072e-06 -4.81e-07 0.9998 8.075e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002062 Epoch 9368 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009194 0.9967 0.9922 -2.06e-07 9.247e-08 -0.007248 -1.552e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00349 -0.003325 -0.006889 0.005526 0.9699 0.9743 0.006779 0.8262 0.8206 0.01657 ] Network output: [ 0.9999 0.0001713 0.000435 -3.936e-06 1.767e-06 -0.000411 -2.966e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2067 -0.03528 -0.1599 0.1838 0.9834 0.9932 0.2318 0.4309 0.8686 0.71 ] Network output: [ -0.009161 1.003 1.008 -2.423e-07 1.088e-07 0.00759 -1.826e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006697 0.0006032 0.004398 0.003251 0.9889 0.9919 0.006827 0.8536 0.8925 0.01186 ] Network output: [ -0.0002405 0.001659 1.001 -1.234e-05 5.541e-06 0.9982 -9.301e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2201 0.104 0.3475 0.1426 0.9849 0.9939 0.2208 0.4349 0.8753 0.7038 ] Network output: [ 0.003522 -0.01666 0.9942 7.508e-06 -3.371e-06 1.015 5.658e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.09743 0.1844 0.1979 0.9873 0.9919 0.1101 0.7393 0.862 0.3052 ] Network output: [ -0.0033 0.01543 1.005 8.147e-06 -3.657e-06 0.9866 6.14e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09367 0.09173 0.165 0.1963 0.9852 0.9911 0.09368 0.6632 0.8374 0.2486 ] Network output: [ 9.176e-05 1 -5.834e-05 1.07e-06 -4.805e-07 0.9998 8.066e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002061 Epoch 9369 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009193 0.9967 0.9922 -2.059e-07 9.244e-08 -0.007248 -1.552e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00349 -0.003325 -0.006889 0.005525 0.9699 0.9743 0.006779 0.8262 0.8206 0.01657 ] Network output: [ 0.9999 0.0001711 0.0004348 -3.931e-06 1.765e-06 -0.0004107 -2.963e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2067 -0.03528 -0.1599 0.1838 0.9834 0.9932 0.2318 0.4309 0.8686 0.71 ] Network output: [ -0.00916 1.003 1.008 -2.422e-07 1.087e-07 0.007589 -1.825e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006697 0.0006033 0.004398 0.003251 0.9889 0.9919 0.006827 0.8536 0.8925 0.01186 ] Network output: [ -0.0002403 0.001658 1.001 -1.233e-05 5.534e-06 0.9982 -9.29e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2201 0.1041 0.3475 0.1426 0.9849 0.9939 0.2208 0.4349 0.8753 0.7038 ] Network output: [ 0.00352 -0.01666 0.9942 7.499e-06 -3.367e-06 1.015 5.652e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.09743 0.1844 0.1979 0.9873 0.9919 0.1101 0.7393 0.862 0.3052 ] Network output: [ -0.003299 0.01543 1.005 8.137e-06 -3.653e-06 0.9866 6.133e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09367 0.09173 0.165 0.1963 0.9852 0.9911 0.09369 0.6632 0.8374 0.2486 ] Network output: [ 9.174e-05 1 -5.83e-05 1.069e-06 -4.799e-07 0.9998 8.057e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000206 Epoch 9370 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009192 0.9967 0.9922 -2.058e-07 9.241e-08 -0.007247 -1.551e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00349 -0.003325 -0.006888 0.005525 0.9699 0.9743 0.006779 0.8262 0.8206 0.01657 ] Network output: [ 0.9999 0.0001709 0.0004346 -3.926e-06 1.763e-06 -0.0004104 -2.959e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2067 -0.03528 -0.1599 0.1838 0.9834 0.9932 0.2318 0.4309 0.8686 0.71 ] Network output: [ -0.009159 1.003 1.008 -2.421e-07 1.087e-07 0.007588 -1.824e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006698 0.0006034 0.004398 0.003251 0.9889 0.9919 0.006828 0.8536 0.8925 0.01186 ] Network output: [ -0.0002401 0.001658 1.001 -1.231e-05 5.528e-06 0.9982 -9.279e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2201 0.1041 0.3475 0.1426 0.9849 0.9939 0.2209 0.4349 0.8753 0.7038 ] Network output: [ 0.003519 -0.01665 0.9942 7.491e-06 -3.363e-06 1.015 5.645e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.09744 0.1844 0.1979 0.9873 0.9919 0.1101 0.7393 0.862 0.3052 ] Network output: [ -0.003297 0.01542 1.005 8.128e-06 -3.649e-06 0.9866 6.126e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09367 0.09174 0.165 0.1963 0.9852 0.9911 0.09369 0.6632 0.8374 0.2486 ] Network output: [ 9.171e-05 1 -5.826e-05 1.068e-06 -4.794e-07 0.9998 8.047e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002059 Epoch 9371 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009191 0.9967 0.9922 -2.058e-07 9.237e-08 -0.007247 -1.551e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00349 -0.003325 -0.006887 0.005524 0.9699 0.9743 0.00678 0.8262 0.8206 0.01657 ] Network output: [ 0.9999 0.0001707 0.0004344 -3.922e-06 1.761e-06 -0.0004101 -2.956e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2067 -0.03528 -0.1599 0.1838 0.9834 0.9932 0.2319 0.4309 0.8686 0.71 ] Network output: [ -0.009158 1.003 1.008 -2.419e-07 1.086e-07 0.007588 -1.823e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006698 0.0006035 0.004398 0.003251 0.9889 0.9919 0.006828 0.8536 0.8925 0.01186 ] Network output: [ -0.0002399 0.001657 1.001 -1.23e-05 5.521e-06 0.9982 -9.268e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2201 0.1041 0.3475 0.1425 0.9849 0.9939 0.2209 0.4349 0.8753 0.7038 ] Network output: [ 0.003517 -0.01664 0.9942 7.482e-06 -3.359e-06 1.015 5.639e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.09744 0.1844 0.1979 0.9873 0.9919 0.1101 0.7393 0.862 0.3052 ] Network output: [ -0.003296 0.01541 1.005 8.119e-06 -3.645e-06 0.9866 6.119e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09368 0.09174 0.165 0.1963 0.9852 0.9911 0.09369 0.6632 0.8374 0.2486 ] Network output: [ 9.168e-05 1 -5.822e-05 1.067e-06 -4.788e-07 0.9998 8.038e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002058 Epoch 9372 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00919 0.9967 0.9922 -2.057e-07 9.234e-08 -0.007246 -1.55e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00349 -0.003326 -0.006887 0.005524 0.9699 0.9743 0.00678 0.8262 0.8206 0.01657 ] Network output: [ 0.9999 0.0001705 0.0004342 -3.917e-06 1.759e-06 -0.0004098 -2.952e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2067 -0.03528 -0.1599 0.1838 0.9834 0.9932 0.2319 0.4309 0.8686 0.71 ] Network output: [ -0.009157 1.003 1.008 -2.418e-07 1.085e-07 0.007587 -1.822e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006699 0.0006036 0.004398 0.00325 0.9889 0.9919 0.006829 0.8536 0.8925 0.01186 ] Network output: [ -0.0002398 0.001656 1.001 -1.228e-05 5.515e-06 0.9982 -9.258e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2201 0.1041 0.3475 0.1425 0.9849 0.9939 0.2209 0.4349 0.8753 0.7038 ] Network output: [ 0.003516 -0.01664 0.9942 7.473e-06 -3.355e-06 1.015 5.632e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.09745 0.1844 0.1979 0.9873 0.9919 0.1102 0.7392 0.862 0.3052 ] Network output: [ -0.003294 0.01541 1.005 8.109e-06 -3.641e-06 0.9866 6.112e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09368 0.09174 0.165 0.1963 0.9852 0.9911 0.09369 0.6632 0.8373 0.2486 ] Network output: [ 9.165e-05 1 -5.818e-05 1.065e-06 -4.783e-07 0.9998 8.029e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002057 Epoch 9373 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009189 0.9967 0.9922 -2.056e-07 9.231e-08 -0.007246 -1.55e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00349 -0.003326 -0.006886 0.005524 0.9699 0.9743 0.00678 0.8262 0.8206 0.01656 ] Network output: [ 0.9999 0.0001703 0.000434 -3.912e-06 1.756e-06 -0.0004095 -2.949e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2067 -0.03528 -0.1598 0.1838 0.9834 0.9932 0.2319 0.4309 0.8686 0.71 ] Network output: [ -0.009156 1.003 1.008 -2.416e-07 1.085e-07 0.007586 -1.821e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006699 0.0006036 0.004397 0.00325 0.9889 0.9919 0.006829 0.8536 0.8925 0.01186 ] Network output: [ -0.0002396 0.001655 1.001 -1.227e-05 5.508e-06 0.9982 -9.247e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2201 0.1041 0.3475 0.1425 0.9849 0.9939 0.2209 0.4349 0.8753 0.7038 ] Network output: [ 0.003514 -0.01663 0.9942 7.464e-06 -3.351e-06 1.015 5.625e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.09745 0.1844 0.1979 0.9873 0.9919 0.1102 0.7392 0.862 0.3052 ] Network output: [ -0.003293 0.0154 1.005 8.1e-06 -3.636e-06 0.9866 6.105e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09368 0.09174 0.165 0.1963 0.9852 0.9911 0.09369 0.6632 0.8373 0.2486 ] Network output: [ 9.162e-05 1 -5.814e-05 1.064e-06 -4.777e-07 0.9998 8.019e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002056 Epoch 9374 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009188 0.9967 0.9922 -2.055e-07 9.227e-08 -0.007245 -1.549e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00349 -0.003326 -0.006885 0.005523 0.9699 0.9743 0.00678 0.8262 0.8206 0.01656 ] Network output: [ 0.9999 0.0001701 0.0004338 -3.908e-06 1.754e-06 -0.0004093 -2.945e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2067 -0.03529 -0.1598 0.1838 0.9834 0.9932 0.2319 0.4309 0.8686 0.7099 ] Network output: [ -0.009156 1.003 1.008 -2.415e-07 1.084e-07 0.007586 -1.82e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006699 0.0006037 0.004397 0.00325 0.9889 0.9919 0.00683 0.8536 0.8925 0.01185 ] Network output: [ -0.0002394 0.001655 1.001 -1.226e-05 5.502e-06 0.9982 -9.236e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2201 0.1041 0.3475 0.1425 0.9849 0.9939 0.2209 0.4349 0.8753 0.7038 ] Network output: [ 0.003513 -0.01662 0.9942 7.456e-06 -3.347e-06 1.015 5.619e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.09745 0.1844 0.1979 0.9873 0.9919 0.1102 0.7392 0.862 0.3052 ] Network output: [ -0.003292 0.01539 1.005 8.091e-06 -3.632e-06 0.9866 6.098e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09368 0.09175 0.165 0.1963 0.9852 0.9911 0.0937 0.6631 0.8373 0.2486 ] Network output: [ 9.159e-05 1 -5.811e-05 1.063e-06 -4.772e-07 0.9998 8.01e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002055 Epoch 9375 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009187 0.9967 0.9922 -2.055e-07 9.224e-08 -0.007245 -1.548e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00349 -0.003326 -0.006885 0.005523 0.9699 0.9743 0.00678 0.8262 0.8206 0.01656 ] Network output: [ 0.9999 0.0001699 0.0004336 -3.903e-06 1.752e-06 -0.000409 -2.942e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2067 -0.03529 -0.1598 0.1838 0.9834 0.9932 0.2319 0.4309 0.8686 0.7099 ] Network output: [ -0.009155 1.003 1.008 -2.413e-07 1.083e-07 0.007585 -1.819e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0067 0.0006038 0.004397 0.00325 0.9889 0.9919 0.00683 0.8536 0.8925 0.01185 ] Network output: [ -0.0002392 0.001654 1.001 -1.224e-05 5.495e-06 0.9982 -9.225e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2201 0.1041 0.3475 0.1425 0.9849 0.9939 0.2209 0.4349 0.8753 0.7038 ] Network output: [ 0.003511 -0.01662 0.9942 7.447e-06 -3.343e-06 1.015 5.612e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.09746 0.1844 0.1979 0.9873 0.9919 0.1102 0.7392 0.862 0.3052 ] Network output: [ -0.00329 0.01539 1.005 8.082e-06 -3.628e-06 0.9866 6.091e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09368 0.09175 0.165 0.1963 0.9852 0.9911 0.0937 0.6631 0.8373 0.2486 ] Network output: [ 9.156e-05 1 -5.807e-05 1.062e-06 -4.766e-07 0.9998 8.001e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002054 Epoch 9376 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009186 0.9967 0.9922 -2.054e-07 9.221e-08 -0.007244 -1.548e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003491 -0.003326 -0.006884 0.005522 0.9699 0.9743 0.006781 0.8262 0.8206 0.01656 ] Network output: [ 0.9999 0.0001697 0.0004334 -3.899e-06 1.75e-06 -0.0004087 -2.938e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2067 -0.03529 -0.1598 0.1838 0.9834 0.9932 0.2319 0.4309 0.8686 0.7099 ] Network output: [ -0.009154 1.003 1.008 -2.412e-07 1.083e-07 0.007584 -1.818e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0067 0.0006039 0.004397 0.003249 0.9889 0.9919 0.00683 0.8536 0.8925 0.01185 ] Network output: [ -0.0002391 0.001653 1.001 -1.223e-05 5.489e-06 0.9982 -9.214e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2202 0.1041 0.3475 0.1425 0.9849 0.9939 0.2209 0.4349 0.8753 0.7038 ] Network output: [ 0.00351 -0.01661 0.9942 7.438e-06 -3.339e-06 1.015 5.606e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.09746 0.1844 0.1979 0.9873 0.9919 0.1102 0.7392 0.862 0.3052 ] Network output: [ -0.003289 0.01538 1.005 8.072e-06 -3.624e-06 0.9866 6.084e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09369 0.09175 0.165 0.1963 0.9852 0.9911 0.0937 0.6631 0.8373 0.2486 ] Network output: [ 9.153e-05 1 -5.803e-05 1.06e-06 -4.761e-07 0.9998 7.991e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002053 Epoch 9377 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009185 0.9967 0.9922 -2.053e-07 9.217e-08 -0.007244 -1.547e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003491 -0.003326 -0.006883 0.005522 0.9699 0.9743 0.006781 0.8262 0.8206 0.01656 ] Network output: [ 0.9999 0.0001695 0.0004332 -3.894e-06 1.748e-06 -0.0004084 -2.935e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2067 -0.03529 -0.1598 0.1838 0.9834 0.9932 0.2319 0.4309 0.8686 0.7099 ] Network output: [ -0.009153 1.003 1.008 -2.41e-07 1.082e-07 0.007583 -1.816e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006701 0.000604 0.004397 0.003249 0.9889 0.9919 0.006831 0.8535 0.8925 0.01185 ] Network output: [ -0.0002389 0.001653 1.001 -1.221e-05 5.482e-06 0.9982 -9.203e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2202 0.1041 0.3475 0.1425 0.9849 0.9939 0.2209 0.4349 0.8753 0.7038 ] Network output: [ 0.003508 -0.0166 0.9942 7.43e-06 -3.335e-06 1.015 5.599e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.09747 0.1844 0.1978 0.9873 0.9919 0.1102 0.7392 0.862 0.3052 ] Network output: [ -0.003287 0.01537 1.005 8.063e-06 -3.62e-06 0.9866 6.077e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09369 0.09175 0.165 0.1963 0.9852 0.9911 0.0937 0.6631 0.8373 0.2486 ] Network output: [ 9.15e-05 1 -5.799e-05 1.059e-06 -4.755e-07 0.9998 7.982e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002052 Epoch 9378 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009184 0.9967 0.9922 -2.052e-07 9.214e-08 -0.007243 -1.547e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003491 -0.003326 -0.006883 0.005522 0.9699 0.9743 0.006781 0.8262 0.8206 0.01656 ] Network output: [ 0.9999 0.0001693 0.000433 -3.889e-06 1.746e-06 -0.0004081 -2.931e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2067 -0.03529 -0.1598 0.1838 0.9834 0.9932 0.2319 0.4309 0.8686 0.7099 ] Network output: [ -0.009152 1.003 1.008 -2.409e-07 1.081e-07 0.007583 -1.815e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006701 0.000604 0.004397 0.003249 0.9889 0.9919 0.006831 0.8535 0.8925 0.01185 ] Network output: [ -0.0002387 0.001652 1.001 -1.22e-05 5.476e-06 0.9982 -9.193e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2202 0.1041 0.3475 0.1425 0.9849 0.9939 0.2209 0.4349 0.8753 0.7038 ] Network output: [ 0.003507 -0.01659 0.9942 7.421e-06 -3.332e-06 1.015 5.593e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.09747 0.1844 0.1978 0.9873 0.9919 0.1102 0.7392 0.862 0.3052 ] Network output: [ -0.003286 0.01537 1.005 8.054e-06 -3.616e-06 0.9866 6.07e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09369 0.09175 0.165 0.1963 0.9852 0.9911 0.09371 0.6631 0.8373 0.2486 ] Network output: [ 9.147e-05 1 -5.795e-05 1.058e-06 -4.749e-07 0.9998 7.973e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002051 Epoch 9379 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009183 0.9967 0.9922 -2.052e-07 9.211e-08 -0.007243 -1.546e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003491 -0.003326 -0.006882 0.005521 0.9699 0.9743 0.006781 0.8262 0.8206 0.01656 ] Network output: [ 0.9999 0.0001692 0.0004328 -3.885e-06 1.744e-06 -0.0004078 -2.928e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2067 -0.03529 -0.1598 0.1838 0.9834 0.9932 0.2319 0.4309 0.8686 0.7099 ] Network output: [ -0.009151 1.003 1.008 -2.407e-07 1.081e-07 0.007582 -1.814e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006701 0.0006041 0.004397 0.003249 0.9889 0.9919 0.006832 0.8535 0.8924 0.01185 ] Network output: [ -0.0002385 0.001651 1.001 -1.218e-05 5.47e-06 0.9982 -9.182e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2202 0.1041 0.3475 0.1425 0.9849 0.9939 0.2209 0.4349 0.8753 0.7038 ] Network output: [ 0.003505 -0.01659 0.9942 7.412e-06 -3.328e-06 1.015 5.586e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.09747 0.1844 0.1978 0.9873 0.9919 0.1102 0.7392 0.862 0.3052 ] Network output: [ -0.003285 0.01536 1.005 8.045e-06 -3.611e-06 0.9866 6.063e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09369 0.09176 0.165 0.1963 0.9852 0.9911 0.09371 0.6631 0.8373 0.2486 ] Network output: [ 9.144e-05 1 -5.791e-05 1.057e-06 -4.744e-07 0.9998 7.964e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000205 Epoch 9380 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009182 0.9967 0.9922 -2.051e-07 9.207e-08 -0.007242 -1.546e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003491 -0.003326 -0.006881 0.005521 0.9699 0.9743 0.006782 0.8262 0.8206 0.01656 ] Network output: [ 0.9999 0.000169 0.0004326 -3.88e-06 1.742e-06 -0.0004075 -2.924e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2068 -0.03529 -0.1598 0.1838 0.9834 0.9932 0.2319 0.4309 0.8686 0.7099 ] Network output: [ -0.00915 1.003 1.008 -2.406e-07 1.08e-07 0.007581 -1.813e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006702 0.0006042 0.004397 0.003248 0.9889 0.9919 0.006832 0.8535 0.8924 0.01185 ] Network output: [ -0.0002384 0.00165 1.001 -1.217e-05 5.463e-06 0.9982 -9.171e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2202 0.1041 0.3475 0.1425 0.9849 0.9939 0.2209 0.4349 0.8753 0.7038 ] Network output: [ 0.003504 -0.01658 0.9942 7.404e-06 -3.324e-06 1.015 5.58e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.09748 0.1844 0.1978 0.9873 0.9919 0.1102 0.7391 0.862 0.3052 ] Network output: [ -0.003283 0.01535 1.005 8.035e-06 -3.607e-06 0.9866 6.056e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0937 0.09176 0.165 0.1963 0.9852 0.9911 0.09371 0.6631 0.8373 0.2486 ] Network output: [ 9.141e-05 1 -5.787e-05 1.055e-06 -4.738e-07 0.9998 7.954e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002048 Epoch 9381 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009181 0.9967 0.9922 -2.05e-07 9.204e-08 -0.007242 -1.545e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003491 -0.003326 -0.006881 0.00552 0.9699 0.9743 0.006782 0.8262 0.8206 0.01656 ] Network output: [ 0.9999 0.0001688 0.0004324 -3.876e-06 1.74e-06 -0.0004072 -2.921e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2068 -0.03529 -0.1598 0.1837 0.9834 0.9932 0.2319 0.4309 0.8686 0.7099 ] Network output: [ -0.009149 1.003 1.008 -2.404e-07 1.079e-07 0.00758 -1.812e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006702 0.0006043 0.004397 0.003248 0.9889 0.9919 0.006833 0.8535 0.8924 0.01185 ] Network output: [ -0.0002382 0.00165 1.001 -1.215e-05 5.457e-06 0.9982 -9.16e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2202 0.1041 0.3475 0.1425 0.9849 0.9939 0.2209 0.4349 0.8753 0.7037 ] Network output: [ 0.003502 -0.01657 0.9942 7.395e-06 -3.32e-06 1.015 5.573e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.09748 0.1844 0.1978 0.9873 0.9919 0.1102 0.7391 0.862 0.3052 ] Network output: [ -0.003282 0.01535 1.005 8.026e-06 -3.603e-06 0.9866 6.049e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0937 0.09176 0.165 0.1963 0.9852 0.9911 0.09371 0.6631 0.8373 0.2486 ] Network output: [ 9.138e-05 1 -5.783e-05 1.054e-06 -4.733e-07 0.9998 7.945e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002047 Epoch 9382 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00918 0.9967 0.9922 -2.049e-07 9.2e-08 -0.007241 -1.544e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003491 -0.003327 -0.00688 0.00552 0.9699 0.9743 0.006782 0.8262 0.8206 0.01656 ] Network output: [ 0.9999 0.0001686 0.0004322 -3.871e-06 1.738e-06 -0.0004069 -2.917e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2068 -0.03529 -0.1598 0.1837 0.9834 0.9932 0.2319 0.4308 0.8686 0.7099 ] Network output: [ -0.009149 1.003 1.008 -2.403e-07 1.079e-07 0.00758 -1.811e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006703 0.0006044 0.004397 0.003248 0.9889 0.9919 0.006833 0.8535 0.8924 0.01185 ] Network output: [ -0.000238 0.001649 1.001 -1.214e-05 5.45e-06 0.9982 -9.149e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2202 0.1041 0.3476 0.1425 0.9849 0.9939 0.2209 0.4348 0.8753 0.7037 ] Network output: [ 0.003501 -0.01657 0.9942 7.386e-06 -3.316e-06 1.015 5.567e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.09749 0.1844 0.1978 0.9873 0.9919 0.1102 0.7391 0.862 0.3052 ] Network output: [ -0.00328 0.01534 1.005 8.017e-06 -3.599e-06 0.9866 6.042e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0937 0.09176 0.165 0.1963 0.9852 0.9911 0.09371 0.663 0.8373 0.2486 ] Network output: [ 9.135e-05 1 -5.78e-05 1.053e-06 -4.727e-07 0.9998 7.936e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002046 Epoch 9383 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009179 0.9967 0.9922 -2.049e-07 9.197e-08 -0.00724 -1.544e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003491 -0.003327 -0.00688 0.005519 0.9699 0.9743 0.006782 0.8262 0.8206 0.01655 ] Network output: [ 0.9999 0.0001684 0.000432 -3.867e-06 1.736e-06 -0.0004067 -2.914e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2068 -0.03529 -0.1598 0.1837 0.9834 0.9932 0.2319 0.4308 0.8686 0.7099 ] Network output: [ -0.009148 1.003 1.008 -2.402e-07 1.078e-07 0.007579 -1.81e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006703 0.0006044 0.004397 0.003248 0.9889 0.9919 0.006833 0.8535 0.8924 0.01185 ] Network output: [ -0.0002378 0.001648 1.001 -1.213e-05 5.444e-06 0.9982 -9.139e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2202 0.1041 0.3476 0.1425 0.9849 0.9939 0.221 0.4348 0.8753 0.7037 ] Network output: [ 0.003499 -0.01656 0.9942 7.378e-06 -3.312e-06 1.015 5.56e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.09749 0.1844 0.1978 0.9873 0.9919 0.1102 0.7391 0.862 0.3052 ] Network output: [ -0.003279 0.01533 1.005 8.008e-06 -3.595e-06 0.9866 6.035e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0937 0.09176 0.165 0.1963 0.9852 0.9911 0.09372 0.663 0.8373 0.2486 ] Network output: [ 9.132e-05 1 -5.776e-05 1.052e-06 -4.722e-07 0.9998 7.927e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002045 Epoch 9384 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009178 0.9967 0.9922 -2.048e-07 9.194e-08 -0.00724 -1.543e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003491 -0.003327 -0.006879 0.005519 0.9699 0.9743 0.006782 0.8262 0.8206 0.01655 ] Network output: [ 0.9999 0.0001682 0.0004318 -3.862e-06 1.734e-06 -0.0004064 -2.911e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2068 -0.03529 -0.1597 0.1837 0.9834 0.9932 0.232 0.4308 0.8686 0.7099 ] Network output: [ -0.009147 1.003 1.008 -2.4e-07 1.077e-07 0.007578 -1.809e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006704 0.0006045 0.004397 0.003247 0.9889 0.9919 0.006834 0.8535 0.8924 0.01185 ] Network output: [ -0.0002377 0.001648 1.001 -1.211e-05 5.438e-06 0.9982 -9.128e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2202 0.1041 0.3476 0.1425 0.9849 0.9939 0.221 0.4348 0.8753 0.7037 ] Network output: [ 0.003498 -0.01655 0.9942 7.369e-06 -3.308e-06 1.015 5.554e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.09749 0.1844 0.1978 0.9873 0.9919 0.1102 0.7391 0.862 0.3052 ] Network output: [ -0.003278 0.01533 1.005 7.998e-06 -3.591e-06 0.9866 6.028e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0937 0.09177 0.165 0.1963 0.9852 0.9911 0.09372 0.663 0.8373 0.2486 ] Network output: [ 9.129e-05 1 -5.772e-05 1.051e-06 -4.717e-07 0.9998 7.918e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002044 Epoch 9385 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009177 0.9967 0.9922 -2.047e-07 9.19e-08 -0.007239 -1.543e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003491 -0.003327 -0.006878 0.005519 0.9699 0.9743 0.006783 0.8261 0.8206 0.01655 ] Network output: [ 0.9999 0.000168 0.0004316 -3.858e-06 1.732e-06 -0.0004061 -2.907e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2068 -0.0353 -0.1597 0.1837 0.9834 0.9932 0.232 0.4308 0.8686 0.7099 ] Network output: [ -0.009146 1.003 1.008 -2.399e-07 1.077e-07 0.007577 -1.808e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006704 0.0006046 0.004396 0.003247 0.9889 0.9919 0.006834 0.8535 0.8924 0.01185 ] Network output: [ -0.0002375 0.001647 1.001 -1.21e-05 5.431e-06 0.9982 -9.117e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2202 0.1041 0.3476 0.1425 0.9849 0.9939 0.221 0.4348 0.8753 0.7037 ] Network output: [ 0.003496 -0.01655 0.9942 7.36e-06 -3.304e-06 1.015 5.547e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.0975 0.1844 0.1978 0.9873 0.9919 0.1102 0.7391 0.8619 0.3052 ] Network output: [ -0.003276 0.01532 1.005 7.989e-06 -3.587e-06 0.9867 6.021e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09371 0.09177 0.165 0.1963 0.9852 0.9911 0.09372 0.663 0.8373 0.2486 ] Network output: [ 9.126e-05 1 -5.768e-05 1.049e-06 -4.711e-07 0.9998 7.908e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002043 Epoch 9386 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009176 0.9967 0.9922 -2.046e-07 9.187e-08 -0.007239 -1.542e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003491 -0.003327 -0.006878 0.005518 0.9699 0.9743 0.006783 0.8261 0.8206 0.01655 ] Network output: [ 0.9999 0.0001678 0.0004314 -3.853e-06 1.73e-06 -0.0004058 -2.904e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2068 -0.0353 -0.1597 0.1837 0.9834 0.9932 0.232 0.4308 0.8686 0.7099 ] Network output: [ -0.009145 1.003 1.008 -2.397e-07 1.076e-07 0.007577 -1.807e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006704 0.0006047 0.004396 0.003247 0.9889 0.9919 0.006835 0.8535 0.8924 0.01185 ] Network output: [ -0.0002373 0.001646 1.001 -1.208e-05 5.425e-06 0.9982 -9.107e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2202 0.1041 0.3476 0.1425 0.9849 0.9939 0.221 0.4348 0.8753 0.7037 ] Network output: [ 0.003495 -0.01654 0.9942 7.352e-06 -3.301e-06 1.015 5.541e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.0975 0.1844 0.1978 0.9873 0.9919 0.1102 0.7391 0.8619 0.3052 ] Network output: [ -0.003275 0.01531 1.005 7.98e-06 -3.583e-06 0.9867 6.014e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09371 0.09177 0.165 0.1963 0.9852 0.9911 0.09372 0.663 0.8373 0.2486 ] Network output: [ 9.123e-05 1 -5.764e-05 1.048e-06 -4.706e-07 0.9998 7.899e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002042 Epoch 9387 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009175 0.9967 0.9922 -2.046e-07 9.183e-08 -0.007238 -1.542e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003491 -0.003327 -0.006877 0.005518 0.9699 0.9743 0.006783 0.8261 0.8206 0.01655 ] Network output: [ 0.9999 0.0001676 0.0004312 -3.848e-06 1.728e-06 -0.0004055 -2.9e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2068 -0.0353 -0.1597 0.1837 0.9834 0.9932 0.232 0.4308 0.8686 0.7099 ] Network output: [ -0.009144 1.003 1.008 -2.396e-07 1.076e-07 0.007576 -1.805e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006705 0.0006048 0.004396 0.003247 0.9889 0.9919 0.006835 0.8535 0.8924 0.01184 ] Network output: [ -0.0002371 0.001645 1.001 -1.207e-05 5.418e-06 0.9982 -9.096e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2202 0.1041 0.3476 0.1425 0.9849 0.9939 0.221 0.4348 0.8753 0.7037 ] Network output: [ 0.003493 -0.01653 0.9942 7.343e-06 -3.297e-06 1.015 5.534e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.09751 0.1844 0.1978 0.9873 0.9919 0.1102 0.7391 0.8619 0.3052 ] Network output: [ -0.003274 0.01531 1.005 7.971e-06 -3.578e-06 0.9867 6.007e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09371 0.09177 0.165 0.1963 0.9852 0.9911 0.09372 0.663 0.8373 0.2487 ] Network output: [ 9.12e-05 1 -5.76e-05 1.047e-06 -4.7e-07 0.9998 7.89e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002041 Epoch 9388 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009174 0.9967 0.9922 -2.045e-07 9.18e-08 -0.007238 -1.541e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003491 -0.003327 -0.006876 0.005517 0.9699 0.9743 0.006783 0.8261 0.8206 0.01655 ] Network output: [ 0.9999 0.0001674 0.000431 -3.844e-06 1.726e-06 -0.0004052 -2.897e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2068 -0.0353 -0.1597 0.1837 0.9834 0.9932 0.232 0.4308 0.8686 0.7099 ] Network output: [ -0.009143 1.003 1.008 -2.394e-07 1.075e-07 0.007575 -1.804e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006705 0.0006048 0.004396 0.003246 0.9889 0.9919 0.006836 0.8535 0.8924 0.01184 ] Network output: [ -0.000237 0.001645 1.001 -1.206e-05 5.412e-06 0.9982 -9.085e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2202 0.1041 0.3476 0.1425 0.9849 0.9939 0.221 0.4348 0.8753 0.7037 ] Network output: [ 0.003492 -0.01653 0.9942 7.335e-06 -3.293e-06 1.015 5.528e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.09751 0.1844 0.1978 0.9873 0.9919 0.1102 0.739 0.8619 0.3052 ] Network output: [ -0.003272 0.0153 1.005 7.962e-06 -3.574e-06 0.9867 6e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09371 0.09178 0.165 0.1963 0.9852 0.9911 0.09373 0.663 0.8373 0.2487 ] Network output: [ 9.117e-05 1 -5.757e-05 1.046e-06 -4.695e-07 0.9998 7.881e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000204 Epoch 9389 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009173 0.9967 0.9922 -2.044e-07 9.177e-08 -0.007237 -1.54e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003491 -0.003327 -0.006876 0.005517 0.9699 0.9743 0.006783 0.8261 0.8206 0.01655 ] Network output: [ 0.9999 0.0001672 0.0004308 -3.839e-06 1.724e-06 -0.0004049 -2.893e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2068 -0.0353 -0.1597 0.1837 0.9834 0.9932 0.232 0.4308 0.8686 0.7099 ] Network output: [ -0.009142 1.003 1.008 -2.393e-07 1.074e-07 0.007575 -1.803e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006706 0.0006049 0.004396 0.003246 0.9889 0.9919 0.006836 0.8535 0.8924 0.01184 ] Network output: [ -0.0002368 0.001644 1.001 -1.204e-05 5.406e-06 0.9982 -9.074e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2203 0.1041 0.3476 0.1425 0.9849 0.9939 0.221 0.4348 0.8753 0.7037 ] Network output: [ 0.00349 -0.01652 0.9942 7.326e-06 -3.289e-06 1.015 5.521e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1101 0.09751 0.1844 0.1978 0.9873 0.9919 0.1102 0.739 0.8619 0.3052 ] Network output: [ -0.003271 0.0153 1.005 7.953e-06 -3.57e-06 0.9867 5.993e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09372 0.09178 0.165 0.1963 0.9852 0.9911 0.09373 0.663 0.8373 0.2487 ] Network output: [ 9.114e-05 1 -5.753e-05 1.045e-06 -4.689e-07 0.9998 7.872e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002039 Epoch 9390 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009172 0.9967 0.9922 -2.043e-07 9.173e-08 -0.007237 -1.54e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003492 -0.003327 -0.006875 0.005516 0.9699 0.9743 0.006784 0.8261 0.8206 0.01655 ] Network output: [ 0.9999 0.000167 0.0004306 -3.835e-06 1.722e-06 -0.0004047 -2.89e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2068 -0.0353 -0.1597 0.1837 0.9834 0.9932 0.232 0.4308 0.8686 0.7099 ] Network output: [ -0.009142 1.003 1.008 -2.391e-07 1.074e-07 0.007574 -1.802e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006706 0.000605 0.004396 0.003246 0.9889 0.9919 0.006836 0.8535 0.8924 0.01184 ] Network output: [ -0.0002366 0.001643 1.001 -1.203e-05 5.399e-06 0.9982 -9.064e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2203 0.1042 0.3476 0.1425 0.9849 0.9939 0.221 0.4348 0.8753 0.7037 ] Network output: [ 0.003489 -0.01651 0.9942 7.318e-06 -3.285e-06 1.015 5.515e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.09752 0.1844 0.1978 0.9873 0.9919 0.1102 0.739 0.8619 0.3052 ] Network output: [ -0.003269 0.01529 1.005 7.944e-06 -3.566e-06 0.9867 5.986e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09372 0.09178 0.165 0.1963 0.9852 0.9911 0.09373 0.6629 0.8373 0.2487 ] Network output: [ 9.112e-05 1 -5.749e-05 1.043e-06 -4.684e-07 0.9998 7.863e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002038 Epoch 9391 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009171 0.9967 0.9922 -2.043e-07 9.17e-08 -0.007236 -1.539e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003492 -0.003327 -0.006874 0.005516 0.9699 0.9743 0.006784 0.8261 0.8206 0.01655 ] Network output: [ 0.9999 0.0001668 0.0004304 -3.83e-06 1.72e-06 -0.0004044 -2.887e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2068 -0.0353 -0.1597 0.1837 0.9834 0.9932 0.232 0.4308 0.8686 0.7099 ] Network output: [ -0.009141 1.003 1.008 -2.39e-07 1.073e-07 0.007573 -1.801e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006706 0.0006051 0.004396 0.003246 0.9889 0.9919 0.006837 0.8535 0.8924 0.01184 ] Network output: [ -0.0002364 0.001643 1.001 -1.201e-05 5.393e-06 0.9982 -9.053e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2203 0.1042 0.3476 0.1425 0.9849 0.9939 0.221 0.4348 0.8753 0.7037 ] Network output: [ 0.003487 -0.01651 0.9942 7.309e-06 -3.281e-06 1.015 5.508e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.09752 0.1844 0.1978 0.9873 0.9919 0.1102 0.739 0.8619 0.3052 ] Network output: [ -0.003268 0.01528 1.005 7.934e-06 -3.562e-06 0.9867 5.98e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09372 0.09178 0.165 0.1963 0.9852 0.9911 0.09373 0.6629 0.8373 0.2487 ] Network output: [ 9.109e-05 1 -5.745e-05 1.042e-06 -4.678e-07 0.9998 7.854e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002037 Epoch 9392 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00917 0.9967 0.9922 -2.042e-07 9.166e-08 -0.007236 -1.539e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003492 -0.003327 -0.006874 0.005516 0.9699 0.9743 0.006784 0.8261 0.8206 0.01655 ] Network output: [ 0.9999 0.0001667 0.0004302 -3.826e-06 1.718e-06 -0.0004041 -2.883e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2068 -0.0353 -0.1597 0.1837 0.9834 0.9932 0.232 0.4308 0.8686 0.7099 ] Network output: [ -0.00914 1.003 1.008 -2.388e-07 1.072e-07 0.007572 -1.8e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006707 0.0006052 0.004396 0.003245 0.9889 0.9919 0.006837 0.8535 0.8924 0.01184 ] Network output: [ -0.0002363 0.001642 1.001 -1.2e-05 5.387e-06 0.9982 -9.042e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2203 0.1042 0.3476 0.1425 0.9849 0.9939 0.221 0.4348 0.8753 0.7037 ] Network output: [ 0.003486 -0.0165 0.9942 7.3e-06 -3.277e-06 1.015 5.502e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.09752 0.1844 0.1978 0.9873 0.9919 0.1102 0.739 0.8619 0.3052 ] Network output: [ -0.003267 0.01528 1.005 7.925e-06 -3.558e-06 0.9867 5.973e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09372 0.09178 0.165 0.1963 0.9852 0.9911 0.09374 0.6629 0.8373 0.2487 ] Network output: [ 9.106e-05 1 -5.742e-05 1.041e-06 -4.673e-07 0.9998 7.844e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002036 Epoch 9393 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009169 0.9967 0.9922 -2.041e-07 9.163e-08 -0.007235 -1.538e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003492 -0.003328 -0.006873 0.005515 0.9699 0.9743 0.006784 0.8261 0.8206 0.01655 ] Network output: [ 0.9999 0.0001665 0.00043 -3.821e-06 1.716e-06 -0.0004038 -2.88e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2068 -0.0353 -0.1597 0.1837 0.9834 0.9932 0.232 0.4308 0.8686 0.7099 ] Network output: [ -0.009139 1.003 1.008 -2.387e-07 1.072e-07 0.007572 -1.799e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006707 0.0006052 0.004396 0.003245 0.9889 0.9919 0.006838 0.8535 0.8924 0.01184 ] Network output: [ -0.0002361 0.001641 1.001 -1.198e-05 5.38e-06 0.9982 -9.032e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2203 0.1042 0.3476 0.1425 0.9849 0.9939 0.221 0.4348 0.8753 0.7037 ] Network output: [ 0.003484 -0.01649 0.9942 7.292e-06 -3.274e-06 1.015 5.495e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.09753 0.1844 0.1978 0.9873 0.9919 0.1102 0.739 0.8619 0.3052 ] Network output: [ -0.003265 0.01527 1.005 7.916e-06 -3.554e-06 0.9867 5.966e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09372 0.09179 0.165 0.1963 0.9852 0.9911 0.09374 0.6629 0.8373 0.2487 ] Network output: [ 9.103e-05 1 -5.738e-05 1.04e-06 -4.667e-07 0.9998 7.835e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002035 Epoch 9394 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009168 0.9967 0.9922 -2.04e-07 9.159e-08 -0.007235 -1.538e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003492 -0.003328 -0.006872 0.005515 0.9699 0.9743 0.006784 0.8261 0.8206 0.01654 ] Network output: [ 0.9999 0.0001663 0.0004298 -3.817e-06 1.713e-06 -0.0004035 -2.876e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2068 -0.0353 -0.1596 0.1837 0.9834 0.9932 0.232 0.4308 0.8686 0.7099 ] Network output: [ -0.009138 1.003 1.008 -2.385e-07 1.071e-07 0.007571 -1.798e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006708 0.0006053 0.004396 0.003245 0.9889 0.9919 0.006838 0.8535 0.8924 0.01184 ] Network output: [ -0.0002359 0.00164 1.001 -1.197e-05 5.374e-06 0.9982 -9.021e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2203 0.1042 0.3476 0.1425 0.9849 0.9939 0.221 0.4348 0.8753 0.7037 ] Network output: [ 0.003483 -0.01649 0.9942 7.283e-06 -3.27e-06 1.015 5.489e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.09753 0.1844 0.1978 0.9873 0.9919 0.1102 0.739 0.8619 0.3052 ] Network output: [ -0.003264 0.01526 1.005 7.907e-06 -3.55e-06 0.9867 5.959e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09373 0.09179 0.165 0.1963 0.9852 0.9911 0.09374 0.6629 0.8373 0.2487 ] Network output: [ 9.1e-05 1 -5.734e-05 1.038e-06 -4.662e-07 0.9998 7.826e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002033 Epoch 9395 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009167 0.9967 0.9922 -2.039e-07 9.156e-08 -0.007234 -1.537e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003492 -0.003328 -0.006872 0.005514 0.9699 0.9743 0.006785 0.8261 0.8205 0.01654 ] Network output: [ 0.9999 0.0001661 0.0004296 -3.812e-06 1.711e-06 -0.0004032 -2.873e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2068 -0.03531 -0.1596 0.1837 0.9834 0.9932 0.232 0.4308 0.8686 0.7099 ] Network output: [ -0.009137 1.003 1.008 -2.384e-07 1.07e-07 0.00757 -1.797e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006708 0.0006054 0.004396 0.003245 0.9889 0.9919 0.006839 0.8535 0.8924 0.01184 ] Network output: [ -0.0002358 0.00164 1.001 -1.196e-05 5.368e-06 0.9982 -9.011e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2203 0.1042 0.3476 0.1425 0.9849 0.9939 0.221 0.4348 0.8753 0.7037 ] Network output: [ 0.003481 -0.01648 0.9942 7.275e-06 -3.266e-06 1.015 5.483e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.09754 0.1844 0.1978 0.9873 0.9919 0.1103 0.739 0.8619 0.3052 ] Network output: [ -0.003262 0.01526 1.005 7.898e-06 -3.546e-06 0.9867 5.952e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09373 0.09179 0.165 0.1963 0.9852 0.9911 0.09374 0.6629 0.8373 0.2487 ] Network output: [ 9.097e-05 1 -5.731e-05 1.037e-06 -4.657e-07 0.9998 7.817e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002032 Epoch 9396 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009166 0.9967 0.9922 -2.039e-07 9.152e-08 -0.007234 -1.536e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003492 -0.003328 -0.006871 0.005514 0.9699 0.9743 0.006785 0.8261 0.8205 0.01654 ] Network output: [ 0.9999 0.0001659 0.0004294 -3.808e-06 1.709e-06 -0.0004029 -2.87e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2069 -0.03531 -0.1596 0.1837 0.9834 0.9932 0.232 0.4308 0.8686 0.7099 ] Network output: [ -0.009136 1.003 1.008 -2.382e-07 1.07e-07 0.007569 -1.796e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006709 0.0006055 0.004396 0.003244 0.9889 0.9919 0.006839 0.8534 0.8924 0.01184 ] Network output: [ -0.0002356 0.001639 1.001 -1.194e-05 5.361e-06 0.9982 -9e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2203 0.1042 0.3476 0.1425 0.9849 0.9939 0.2211 0.4348 0.8753 0.7037 ] Network output: [ 0.00348 -0.01647 0.9942 7.266e-06 -3.262e-06 1.015 5.476e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.09754 0.1844 0.1978 0.9873 0.9919 0.1103 0.7389 0.8619 0.3052 ] Network output: [ -0.003261 0.01525 1.005 7.889e-06 -3.542e-06 0.9867 5.945e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09373 0.09179 0.165 0.1963 0.9852 0.9911 0.09374 0.6629 0.8373 0.2487 ] Network output: [ 9.094e-05 1 -5.727e-05 1.036e-06 -4.651e-07 0.9998 7.808e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002031 Epoch 9397 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009165 0.9967 0.9922 -2.038e-07 9.149e-08 -0.007233 -1.536e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003492 -0.003328 -0.006871 0.005514 0.9699 0.9743 0.006785 0.8261 0.8205 0.01654 ] Network output: [ 0.9999 0.0001657 0.0004292 -3.803e-06 1.707e-06 -0.0004027 -2.866e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2069 -0.03531 -0.1596 0.1837 0.9834 0.9932 0.2321 0.4308 0.8686 0.7098 ] Network output: [ -0.009136 1.003 1.008 -2.381e-07 1.069e-07 0.007569 -1.794e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006709 0.0006055 0.004395 0.003244 0.9889 0.9919 0.006839 0.8534 0.8924 0.01184 ] Network output: [ -0.0002354 0.001638 1.001 -1.193e-05 5.355e-06 0.9982 -8.989e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2203 0.1042 0.3476 0.1425 0.9849 0.9939 0.2211 0.4348 0.8753 0.7037 ] Network output: [ 0.003478 -0.01647 0.9942 7.258e-06 -3.258e-06 1.015 5.47e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.09754 0.1844 0.1978 0.9873 0.9919 0.1103 0.7389 0.8619 0.3052 ] Network output: [ -0.00326 0.01524 1.005 7.88e-06 -3.538e-06 0.9867 5.938e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09373 0.09179 0.165 0.1963 0.9852 0.9911 0.09375 0.6629 0.8373 0.2487 ] Network output: [ 9.091e-05 1 -5.723e-05 1.035e-06 -4.646e-07 0.9998 7.799e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000203 Epoch 9398 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009164 0.9967 0.9922 -2.037e-07 9.145e-08 -0.007233 -1.535e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003492 -0.003328 -0.00687 0.005513 0.9699 0.9743 0.006785 0.8261 0.8205 0.01654 ] Network output: [ 0.9999 0.0001655 0.000429 -3.799e-06 1.705e-06 -0.0004024 -2.863e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2069 -0.03531 -0.1596 0.1837 0.9834 0.9932 0.2321 0.4308 0.8686 0.7098 ] Network output: [ -0.009135 1.003 1.008 -2.38e-07 1.068e-07 0.007568 -1.793e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006709 0.0006056 0.004395 0.003244 0.9889 0.9919 0.00684 0.8534 0.8924 0.01184 ] Network output: [ -0.0002352 0.001638 1.001 -1.191e-05 5.349e-06 0.9982 -8.979e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2203 0.1042 0.3476 0.1425 0.9849 0.9939 0.2211 0.4348 0.8753 0.7037 ] Network output: [ 0.003477 -0.01646 0.9942 7.249e-06 -3.255e-06 1.015 5.463e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.09755 0.1844 0.1978 0.9873 0.9919 0.1103 0.7389 0.8619 0.3052 ] Network output: [ -0.003258 0.01524 1.005 7.871e-06 -3.533e-06 0.9867 5.932e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09373 0.0918 0.165 0.1963 0.9852 0.9911 0.09375 0.6628 0.8373 0.2487 ] Network output: [ 9.088e-05 1 -5.719e-05 1.034e-06 -4.64e-07 0.9998 7.79e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002029 Epoch 9399 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009163 0.9967 0.9922 -2.036e-07 9.142e-08 -0.007232 -1.535e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003492 -0.003328 -0.006869 0.005513 0.9699 0.9743 0.006785 0.8261 0.8205 0.01654 ] Network output: [ 0.9999 0.0001653 0.0004288 -3.794e-06 1.703e-06 -0.0004021 -2.86e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2069 -0.03531 -0.1596 0.1837 0.9834 0.9932 0.2321 0.4308 0.8686 0.7098 ] Network output: [ -0.009134 1.003 1.008 -2.378e-07 1.068e-07 0.007567 -1.792e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00671 0.0006057 0.004395 0.003244 0.9889 0.9919 0.00684 0.8534 0.8924 0.01184 ] Network output: [ -0.0002351 0.001637 1.001 -1.19e-05 5.342e-06 0.9982 -8.968e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2203 0.1042 0.3476 0.1425 0.9849 0.9939 0.2211 0.4348 0.8753 0.7037 ] Network output: [ 0.003475 -0.01645 0.9942 7.241e-06 -3.251e-06 1.015 5.457e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.09755 0.1844 0.1978 0.9873 0.9919 0.1103 0.7389 0.8619 0.3052 ] Network output: [ -0.003257 0.01523 1.005 7.862e-06 -3.529e-06 0.9867 5.925e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09374 0.0918 0.165 0.1963 0.9852 0.9911 0.09375 0.6628 0.8373 0.2487 ] Network output: [ 9.085e-05 1 -5.716e-05 1.032e-06 -4.635e-07 0.9998 7.781e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002028 Epoch 9400 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009162 0.9967 0.9922 -2.036e-07 9.138e-08 -0.007232 -1.534e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003492 -0.003328 -0.006869 0.005512 0.9699 0.9743 0.006786 0.8261 0.8205 0.01654 ] Network output: [ 0.9999 0.0001651 0.0004286 -3.79e-06 1.701e-06 -0.0004018 -2.856e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2069 -0.03531 -0.1596 0.1837 0.9834 0.9932 0.2321 0.4307 0.8686 0.7098 ] Network output: [ -0.009133 1.003 1.008 -2.377e-07 1.067e-07 0.007567 -1.791e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00671 0.0006058 0.004395 0.003243 0.9889 0.9919 0.006841 0.8534 0.8924 0.01183 ] Network output: [ -0.0002349 0.001636 1.001 -1.189e-05 5.336e-06 0.9982 -8.958e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2203 0.1042 0.3476 0.1425 0.9849 0.9939 0.2211 0.4347 0.8753 0.7037 ] Network output: [ 0.003474 -0.01644 0.9942 7.232e-06 -3.247e-06 1.015 5.451e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.09756 0.1844 0.1978 0.9873 0.9919 0.1103 0.7389 0.8619 0.3052 ] Network output: [ -0.003255 0.01522 1.005 7.853e-06 -3.525e-06 0.9867 5.918e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09374 0.0918 0.165 0.1963 0.9852 0.9911 0.09375 0.6628 0.8373 0.2487 ] Network output: [ 9.082e-05 1 -5.712e-05 1.031e-06 -4.63e-07 0.9998 7.772e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002027 Epoch 9401 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009161 0.9967 0.9922 -2.035e-07 9.135e-08 -0.007231 -1.533e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003492 -0.003328 -0.006868 0.005512 0.9699 0.9743 0.006786 0.8261 0.8205 0.01654 ] Network output: [ 0.9999 0.0001649 0.0004284 -3.785e-06 1.699e-06 -0.0004015 -2.853e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2069 -0.03531 -0.1596 0.1837 0.9834 0.9932 0.2321 0.4307 0.8686 0.7098 ] Network output: [ -0.009132 1.003 1.008 -2.375e-07 1.066e-07 0.007566 -1.79e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006711 0.0006059 0.004395 0.003243 0.9889 0.9919 0.006841 0.8534 0.8924 0.01183 ] Network output: [ -0.0002347 0.001635 1.001 -1.187e-05 5.33e-06 0.9982 -8.947e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2203 0.1042 0.3477 0.1425 0.9849 0.9939 0.2211 0.4347 0.8753 0.7037 ] Network output: [ 0.003473 -0.01644 0.9942 7.224e-06 -3.243e-06 1.015 5.444e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.09756 0.1844 0.1978 0.9873 0.9919 0.1103 0.7389 0.8619 0.3052 ] Network output: [ -0.003254 0.01522 1.005 7.844e-06 -3.521e-06 0.9867 5.911e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09374 0.0918 0.165 0.1963 0.9852 0.9911 0.09375 0.6628 0.8373 0.2487 ] Network output: [ 9.079e-05 1 -5.708e-05 1.03e-06 -4.624e-07 0.9998 7.763e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002026 Epoch 9402 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00916 0.9967 0.9922 -2.034e-07 9.131e-08 -0.007231 -1.533e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003492 -0.003328 -0.006867 0.005511 0.9699 0.9743 0.006786 0.8261 0.8205 0.01654 ] Network output: [ 0.9999 0.0001647 0.0004282 -3.781e-06 1.697e-06 -0.0004013 -2.849e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2069 -0.03531 -0.1596 0.1837 0.9834 0.9932 0.2321 0.4307 0.8686 0.7098 ] Network output: [ -0.009131 1.003 1.008 -2.374e-07 1.066e-07 0.007565 -1.789e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006711 0.0006059 0.004395 0.003243 0.9889 0.9919 0.006842 0.8534 0.8924 0.01183 ] Network output: [ -0.0002345 0.001635 1.001 -1.186e-05 5.324e-06 0.9982 -8.937e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2204 0.1042 0.3477 0.1425 0.9849 0.9939 0.2211 0.4347 0.8753 0.7036 ] Network output: [ 0.003471 -0.01643 0.9942 7.216e-06 -3.239e-06 1.015 5.438e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.09756 0.1844 0.1978 0.9873 0.9919 0.1103 0.7389 0.8619 0.3052 ] Network output: [ -0.003253 0.01521 1.005 7.835e-06 -3.517e-06 0.9867 5.904e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09374 0.09181 0.165 0.1963 0.9852 0.9911 0.09376 0.6628 0.8373 0.2487 ] Network output: [ 9.077e-05 1 -5.705e-05 1.029e-06 -4.619e-07 0.9998 7.754e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002025 Epoch 9403 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009159 0.9967 0.9922 -2.033e-07 9.128e-08 -0.00723 -1.532e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003492 -0.003329 -0.006867 0.005511 0.9699 0.9743 0.006786 0.8261 0.8205 0.01654 ] Network output: [ 0.9999 0.0001646 0.000428 -3.776e-06 1.695e-06 -0.000401 -2.846e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2069 -0.03531 -0.1596 0.1837 0.9834 0.9932 0.2321 0.4307 0.8686 0.7098 ] Network output: [ -0.00913 1.003 1.008 -2.372e-07 1.065e-07 0.007564 -1.788e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006711 0.000606 0.004395 0.003243 0.9889 0.9919 0.006842 0.8534 0.8924 0.01183 ] Network output: [ -0.0002344 0.001634 1.001 -1.184e-05 5.317e-06 0.9982 -8.926e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2204 0.1042 0.3477 0.1425 0.9849 0.9939 0.2211 0.4347 0.8753 0.7036 ] Network output: [ 0.00347 -0.01642 0.9942 7.207e-06 -3.236e-06 1.015 5.432e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.09757 0.1844 0.1978 0.9873 0.9919 0.1103 0.7389 0.8619 0.3052 ] Network output: [ -0.003251 0.0152 1.005 7.826e-06 -3.513e-06 0.9867 5.898e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09375 0.09181 0.165 0.1963 0.9852 0.9911 0.09376 0.6628 0.8372 0.2487 ] Network output: [ 9.074e-05 1 -5.701e-05 1.028e-06 -4.614e-07 0.9998 7.745e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002024 Epoch 9404 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009158 0.9967 0.9922 -2.032e-07 9.124e-08 -0.00723 -1.532e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003493 -0.003329 -0.006866 0.005511 0.9699 0.9743 0.006786 0.8261 0.8205 0.01653 ] Network output: [ 0.9999 0.0001644 0.0004278 -3.772e-06 1.693e-06 -0.0004007 -2.843e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2069 -0.03531 -0.1595 0.1837 0.9834 0.9932 0.2321 0.4307 0.8686 0.7098 ] Network output: [ -0.009129 1.003 1.008 -2.371e-07 1.064e-07 0.007564 -1.787e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006712 0.0006061 0.004395 0.003242 0.9889 0.9919 0.006842 0.8534 0.8924 0.01183 ] Network output: [ -0.0002342 0.001633 1.001 -1.183e-05 5.311e-06 0.9982 -8.916e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2204 0.1042 0.3477 0.1425 0.9849 0.9939 0.2211 0.4347 0.8753 0.7036 ] Network output: [ 0.003468 -0.01642 0.9942 7.199e-06 -3.232e-06 1.015 5.425e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.09757 0.1844 0.1978 0.9873 0.9919 0.1103 0.7388 0.8619 0.3052 ] Network output: [ -0.00325 0.0152 1.005 7.817e-06 -3.509e-06 0.9867 5.891e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09375 0.09181 0.165 0.1963 0.9852 0.9911 0.09376 0.6628 0.8372 0.2487 ] Network output: [ 9.071e-05 1 -5.698e-05 1.026e-06 -4.608e-07 0.9998 7.736e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002023 Epoch 9405 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009157 0.9967 0.9922 -2.032e-07 9.121e-08 -0.007229 -1.531e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003493 -0.003329 -0.006865 0.00551 0.9699 0.9743 0.006787 0.826 0.8205 0.01653 ] Network output: [ 0.9999 0.0001642 0.0004276 -3.768e-06 1.691e-06 -0.0004004 -2.839e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2069 -0.03531 -0.1595 0.1837 0.9834 0.9932 0.2321 0.4307 0.8686 0.7098 ] Network output: [ -0.009129 1.003 1.008 -2.369e-07 1.064e-07 0.007563 -1.786e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006712 0.0006062 0.004395 0.003242 0.9889 0.9919 0.006843 0.8534 0.8924 0.01183 ] Network output: [ -0.000234 0.001633 1.001 -1.182e-05 5.305e-06 0.9982 -8.905e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2204 0.1042 0.3477 0.1425 0.9849 0.9939 0.2211 0.4347 0.8753 0.7036 ] Network output: [ 0.003467 -0.01641 0.9942 7.19e-06 -3.228e-06 1.015 5.419e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.09758 0.1844 0.1978 0.9873 0.9919 0.1103 0.7388 0.8619 0.3052 ] Network output: [ -0.003249 0.01519 1.005 7.808e-06 -3.505e-06 0.9867 5.884e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09375 0.09181 0.165 0.1963 0.9852 0.9911 0.09376 0.6628 0.8372 0.2487 ] Network output: [ 9.068e-05 1 -5.694e-05 1.025e-06 -4.603e-07 0.9998 7.727e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002022 Epoch 9406 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009156 0.9967 0.9922 -2.031e-07 9.117e-08 -0.007229 -1.53e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003493 -0.003329 -0.006865 0.00551 0.9699 0.9743 0.006787 0.826 0.8205 0.01653 ] Network output: [ 0.9999 0.000164 0.0004274 -3.763e-06 1.689e-06 -0.0004001 -2.836e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2069 -0.03532 -0.1595 0.1837 0.9834 0.9932 0.2321 0.4307 0.8686 0.7098 ] Network output: [ -0.009128 1.003 1.008 -2.368e-07 1.063e-07 0.007562 -1.784e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006713 0.0006063 0.004395 0.003242 0.9889 0.9919 0.006843 0.8534 0.8924 0.01183 ] Network output: [ -0.0002338 0.001632 1.001 -1.18e-05 5.299e-06 0.9982 -8.895e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2204 0.1042 0.3477 0.1425 0.9849 0.9939 0.2211 0.4347 0.8753 0.7036 ] Network output: [ 0.003465 -0.0164 0.9942 7.182e-06 -3.224e-06 1.015 5.413e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.09758 0.1844 0.1978 0.9873 0.9919 0.1103 0.7388 0.8619 0.3052 ] Network output: [ -0.003247 0.01519 1.005 7.799e-06 -3.501e-06 0.9867 5.877e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09375 0.09181 0.165 0.1963 0.9852 0.9911 0.09377 0.6627 0.8372 0.2487 ] Network output: [ 9.065e-05 1 -5.69e-05 1.024e-06 -4.597e-07 0.9998 7.718e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002021 Epoch 9407 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009155 0.9967 0.9922 -2.03e-07 9.114e-08 -0.007228 -1.53e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003493 -0.003329 -0.006864 0.005509 0.9699 0.9743 0.006787 0.826 0.8205 0.01653 ] Network output: [ 0.9999 0.0001638 0.0004272 -3.759e-06 1.687e-06 -0.0003998 -2.833e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2069 -0.03532 -0.1595 0.1837 0.9834 0.9932 0.2321 0.4307 0.8686 0.7098 ] Network output: [ -0.009127 1.003 1.008 -2.366e-07 1.062e-07 0.007562 -1.783e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006713 0.0006063 0.004395 0.003242 0.9889 0.9919 0.006844 0.8534 0.8924 0.01183 ] Network output: [ -0.0002337 0.001631 1.001 -1.179e-05 5.292e-06 0.9982 -8.884e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2204 0.1042 0.3477 0.1425 0.9849 0.9939 0.2211 0.4347 0.8753 0.7036 ] Network output: [ 0.003464 -0.0164 0.9942 7.173e-06 -3.22e-06 1.015 5.406e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.09758 0.1844 0.1978 0.9873 0.9919 0.1103 0.7388 0.8619 0.3052 ] Network output: [ -0.003246 0.01518 1.005 7.79e-06 -3.497e-06 0.9867 5.871e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09375 0.09182 0.165 0.1963 0.9852 0.9911 0.09377 0.6627 0.8372 0.2487 ] Network output: [ 9.062e-05 1 -5.687e-05 1.023e-06 -4.592e-07 0.9998 7.709e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000202 Epoch 9408 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009154 0.9967 0.9922 -2.029e-07 9.11e-08 -0.007228 -1.529e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003493 -0.003329 -0.006863 0.005509 0.9699 0.9743 0.006787 0.826 0.8205 0.01653 ] Network output: [ 0.9999 0.0001636 0.000427 -3.754e-06 1.685e-06 -0.0003996 -2.829e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2069 -0.03532 -0.1595 0.1836 0.9834 0.9932 0.2321 0.4307 0.8686 0.7098 ] Network output: [ -0.009126 1.003 1.008 -2.365e-07 1.062e-07 0.007561 -1.782e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006714 0.0006064 0.004395 0.003241 0.9889 0.9919 0.006844 0.8534 0.8924 0.01183 ] Network output: [ -0.0002335 0.00163 1.001 -1.177e-05 5.286e-06 0.9983 -8.874e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2204 0.1042 0.3477 0.1425 0.9849 0.9939 0.2211 0.4347 0.8753 0.7036 ] Network output: [ 0.003462 -0.01639 0.9942 7.165e-06 -3.217e-06 1.015 5.4e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.09759 0.1844 0.1978 0.9873 0.9919 0.1103 0.7388 0.8619 0.3052 ] Network output: [ -0.003244 0.01517 1.005 7.781e-06 -3.493e-06 0.9867 5.864e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09376 0.09182 0.165 0.1963 0.9852 0.9911 0.09377 0.6627 0.8372 0.2487 ] Network output: [ 9.059e-05 1 -5.683e-05 1.022e-06 -4.587e-07 0.9998 7.7e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002019 Epoch 9409 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009154 0.9967 0.9922 -2.028e-07 9.106e-08 -0.007227 -1.529e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003493 -0.003329 -0.006863 0.005508 0.9699 0.9743 0.006787 0.826 0.8205 0.01653 ] Network output: [ 0.9999 0.0001634 0.0004268 -3.75e-06 1.683e-06 -0.0003993 -2.826e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2069 -0.03532 -0.1595 0.1836 0.9834 0.9932 0.2321 0.4307 0.8686 0.7098 ] Network output: [ -0.009125 1.003 1.008 -2.363e-07 1.061e-07 0.00756 -1.781e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006714 0.0006065 0.004394 0.003241 0.9889 0.9919 0.006845 0.8534 0.8924 0.01183 ] Network output: [ -0.0002333 0.00163 1.001 -1.176e-05 5.28e-06 0.9983 -8.863e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2204 0.1042 0.3477 0.1425 0.9849 0.9939 0.2212 0.4347 0.8753 0.7036 ] Network output: [ 0.003461 -0.01638 0.9942 7.157e-06 -3.213e-06 1.015 5.394e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.09759 0.1844 0.1978 0.9873 0.9919 0.1103 0.7388 0.8619 0.3052 ] Network output: [ -0.003243 0.01517 1.005 7.772e-06 -3.489e-06 0.9867 5.857e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09376 0.09182 0.165 0.1963 0.9852 0.9911 0.09377 0.6627 0.8372 0.2487 ] Network output: [ 9.056e-05 1 -5.68e-05 1.021e-06 -4.581e-07 0.9998 7.691e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002018 Epoch 9410 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009153 0.9967 0.9922 -2.028e-07 9.103e-08 -0.007227 -1.528e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003493 -0.003329 -0.006862 0.005508 0.9699 0.9743 0.006788 0.826 0.8205 0.01653 ] Network output: [ 0.9999 0.0001632 0.0004266 -3.745e-06 1.681e-06 -0.000399 -2.823e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2069 -0.03532 -0.1595 0.1836 0.9834 0.9932 0.2322 0.4307 0.8686 0.7098 ] Network output: [ -0.009124 1.003 1.008 -2.362e-07 1.06e-07 0.007559 -1.78e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006714 0.0006066 0.004394 0.003241 0.9889 0.9919 0.006845 0.8534 0.8924 0.01183 ] Network output: [ -0.0002332 0.001629 1.001 -1.175e-05 5.274e-06 0.9983 -8.853e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2204 0.1042 0.3477 0.1425 0.9849 0.9939 0.2212 0.4347 0.8753 0.7036 ] Network output: [ 0.003459 -0.01638 0.9942 7.148e-06 -3.209e-06 1.015 5.387e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.0976 0.1844 0.1978 0.9873 0.9919 0.1103 0.7388 0.8619 0.3052 ] Network output: [ -0.003242 0.01516 1.005 7.763e-06 -3.485e-06 0.9867 5.85e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09376 0.09182 0.165 0.1963 0.9852 0.9911 0.09377 0.6627 0.8372 0.2487 ] Network output: [ 9.053e-05 1 -5.676e-05 1.019e-06 -4.576e-07 0.9998 7.682e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002016 Epoch 9411 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009152 0.9967 0.9922 -2.027e-07 9.099e-08 -0.007226 -1.527e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003493 -0.003329 -0.006862 0.005508 0.9699 0.9743 0.006788 0.826 0.8205 0.01653 ] Network output: [ 0.9999 0.000163 0.0004264 -3.741e-06 1.679e-06 -0.0003987 -2.819e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.207 -0.03532 -0.1595 0.1836 0.9834 0.9932 0.2322 0.4307 0.8686 0.7098 ] Network output: [ -0.009123 1.003 1.008 -2.36e-07 1.06e-07 0.007559 -1.779e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006715 0.0006067 0.004394 0.003241 0.9889 0.9919 0.006845 0.8534 0.8924 0.01183 ] Network output: [ -0.000233 0.001628 1.001 -1.173e-05 5.267e-06 0.9983 -8.842e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2204 0.1043 0.3477 0.1425 0.9849 0.9939 0.2212 0.4347 0.8753 0.7036 ] Network output: [ 0.003458 -0.01637 0.9942 7.14e-06 -3.205e-06 1.015 5.381e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.0976 0.1844 0.1978 0.9873 0.9919 0.1103 0.7388 0.8619 0.3052 ] Network output: [ -0.00324 0.01515 1.005 7.754e-06 -3.481e-06 0.9867 5.844e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09376 0.09182 0.165 0.1963 0.9852 0.9911 0.09378 0.6627 0.8372 0.2487 ] Network output: [ 9.051e-05 1 -5.672e-05 1.018e-06 -4.571e-07 0.9998 7.673e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002015 Epoch 9412 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009151 0.9967 0.9923 -2.026e-07 9.096e-08 -0.007226 -1.527e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003493 -0.003329 -0.006861 0.005507 0.9699 0.9743 0.006788 0.826 0.8205 0.01653 ] Network output: [ 0.9999 0.0001628 0.0004262 -3.736e-06 1.677e-06 -0.0003984 -2.816e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.207 -0.03532 -0.1595 0.1836 0.9834 0.9932 0.2322 0.4307 0.8686 0.7098 ] Network output: [ -0.009123 1.003 1.008 -2.359e-07 1.059e-07 0.007558 -1.778e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006715 0.0006067 0.004394 0.00324 0.9889 0.9919 0.006846 0.8534 0.8924 0.01182 ] Network output: [ -0.0002328 0.001628 1.001 -1.172e-05 5.261e-06 0.9983 -8.832e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2204 0.1043 0.3477 0.1425 0.9849 0.9939 0.2212 0.4347 0.8753 0.7036 ] Network output: [ 0.003456 -0.01636 0.9942 7.132e-06 -3.202e-06 1.015 5.375e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.0976 0.1844 0.1978 0.9873 0.9919 0.1103 0.7387 0.8619 0.3052 ] Network output: [ -0.003239 0.01515 1.005 7.745e-06 -3.477e-06 0.9867 5.837e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09376 0.09183 0.165 0.1963 0.9852 0.9911 0.09378 0.6627 0.8372 0.2487 ] Network output: [ 9.048e-05 1 -5.669e-05 1.017e-06 -4.566e-07 0.9998 7.664e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002014 Epoch 9413 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00915 0.9967 0.9923 -2.025e-07 9.092e-08 -0.007225 -1.526e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003493 -0.003329 -0.00686 0.005507 0.9699 0.9743 0.006788 0.826 0.8205 0.01653 ] Network output: [ 0.9999 0.0001626 0.000426 -3.732e-06 1.675e-06 -0.0003982 -2.813e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.207 -0.03532 -0.1595 0.1836 0.9834 0.9932 0.2322 0.4307 0.8686 0.7098 ] Network output: [ -0.009122 1.003 1.008 -2.357e-07 1.058e-07 0.007557 -1.777e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006716 0.0006068 0.004394 0.00324 0.9889 0.9919 0.006846 0.8534 0.8924 0.01182 ] Network output: [ -0.0002326 0.001627 1.001 -1.171e-05 5.255e-06 0.9983 -8.822e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2204 0.1043 0.3477 0.1425 0.9849 0.9939 0.2212 0.4347 0.8753 0.7036 ] Network output: [ 0.003455 -0.01636 0.9942 7.123e-06 -3.198e-06 1.015 5.368e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1102 0.09761 0.1844 0.1978 0.9873 0.9919 0.1103 0.7387 0.8619 0.3052 ] Network output: [ -0.003237 0.01514 1.005 7.736e-06 -3.473e-06 0.9867 5.83e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09377 0.09183 0.165 0.1963 0.9852 0.9911 0.09378 0.6627 0.8372 0.2487 ] Network output: [ 9.045e-05 1 -5.665e-05 1.016e-06 -4.56e-07 0.9998 7.655e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002013 Epoch 9414 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009149 0.9967 0.9923 -2.024e-07 9.088e-08 -0.007225 -1.526e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003493 -0.00333 -0.00686 0.005506 0.9699 0.9743 0.006788 0.826 0.8205 0.01652 ] Network output: [ 0.9999 0.0001625 0.0004258 -3.728e-06 1.673e-06 -0.0003979 -2.809e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.207 -0.03532 -0.1595 0.1836 0.9834 0.9932 0.2322 0.4307 0.8686 0.7098 ] Network output: [ -0.009121 1.003 1.008 -2.356e-07 1.058e-07 0.007557 -1.776e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006716 0.0006069 0.004394 0.00324 0.9889 0.9919 0.006847 0.8534 0.8924 0.01182 ] Network output: [ -0.0002325 0.001626 1.001 -1.169e-05 5.249e-06 0.9983 -8.811e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2204 0.1043 0.3477 0.1425 0.9849 0.9939 0.2212 0.4347 0.8753 0.7036 ] Network output: [ 0.003453 -0.01635 0.9942 7.115e-06 -3.194e-06 1.015 5.362e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.09761 0.1844 0.1978 0.9873 0.9919 0.1103 0.7387 0.8619 0.3052 ] Network output: [ -0.003236 0.01513 1.005 7.727e-06 -3.469e-06 0.9867 5.823e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09377 0.09183 0.165 0.1963 0.9852 0.9911 0.09378 0.6626 0.8372 0.2487 ] Network output: [ 9.042e-05 1 -5.662e-05 1.015e-06 -4.555e-07 0.9998 7.646e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002012 Epoch 9415 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009148 0.9967 0.9923 -2.024e-07 9.085e-08 -0.007224 -1.525e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003493 -0.00333 -0.006859 0.005506 0.9699 0.9743 0.006789 0.826 0.8205 0.01652 ] Network output: [ 0.9999 0.0001623 0.0004256 -3.723e-06 1.671e-06 -0.0003976 -2.806e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.207 -0.03532 -0.1594 0.1836 0.9834 0.9932 0.2322 0.4307 0.8686 0.7098 ] Network output: [ -0.00912 1.003 1.008 -2.354e-07 1.057e-07 0.007556 -1.774e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006716 0.000607 0.004394 0.00324 0.9889 0.9919 0.006847 0.8533 0.8924 0.01182 ] Network output: [ -0.0002323 0.001625 1.001 -1.168e-05 5.243e-06 0.9983 -8.801e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2204 0.1043 0.3477 0.1425 0.9849 0.9939 0.2212 0.4347 0.8753 0.7036 ] Network output: [ 0.003452 -0.01634 0.9942 7.107e-06 -3.19e-06 1.015 5.356e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.09762 0.1844 0.1978 0.9873 0.9919 0.1103 0.7387 0.8619 0.3052 ] Network output: [ -0.003235 0.01513 1.005 7.718e-06 -3.465e-06 0.9868 5.817e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09377 0.09183 0.165 0.1963 0.9852 0.9911 0.09378 0.6626 0.8372 0.2487 ] Network output: [ 9.039e-05 1 -5.658e-05 1.013e-06 -4.55e-07 0.9998 7.637e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002011 Epoch 9416 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009147 0.9967 0.9923 -2.023e-07 9.081e-08 -0.007224 -1.524e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003493 -0.00333 -0.006858 0.005506 0.9699 0.9743 0.006789 0.826 0.8205 0.01652 ] Network output: [ 0.9999 0.0001621 0.0004254 -3.719e-06 1.67e-06 -0.0003973 -2.803e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.207 -0.03533 -0.1594 0.1836 0.9834 0.9932 0.2322 0.4307 0.8686 0.7098 ] Network output: [ -0.009119 1.003 1.008 -2.353e-07 1.056e-07 0.007555 -1.773e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006717 0.000607 0.004394 0.003239 0.9889 0.9919 0.006847 0.8533 0.8924 0.01182 ] Network output: [ -0.0002321 0.001625 1.001 -1.166e-05 5.236e-06 0.9983 -8.79e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2205 0.1043 0.3477 0.1425 0.9849 0.9939 0.2212 0.4347 0.8753 0.7036 ] Network output: [ 0.00345 -0.01634 0.9942 7.098e-06 -3.187e-06 1.015 5.349e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.09762 0.1844 0.1978 0.9873 0.9919 0.1103 0.7387 0.8619 0.3052 ] Network output: [ -0.003233 0.01512 1.005 7.709e-06 -3.461e-06 0.9868 5.81e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09377 0.09183 0.165 0.1963 0.9852 0.9911 0.09379 0.6626 0.8372 0.2487 ] Network output: [ 9.036e-05 1 -5.655e-05 1.012e-06 -4.544e-07 0.9998 7.629e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000201 Epoch 9417 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009146 0.9967 0.9923 -2.022e-07 9.078e-08 -0.007223 -1.524e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003493 -0.00333 -0.006858 0.005505 0.9699 0.9743 0.006789 0.826 0.8205 0.01652 ] Network output: [ 0.9999 0.0001619 0.0004252 -3.714e-06 1.668e-06 -0.0003971 -2.799e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.207 -0.03533 -0.1594 0.1836 0.9834 0.9932 0.2322 0.4307 0.8685 0.7098 ] Network output: [ -0.009118 1.003 1.008 -2.351e-07 1.056e-07 0.007554 -1.772e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006717 0.0006071 0.004394 0.003239 0.9889 0.9919 0.006848 0.8533 0.8924 0.01182 ] Network output: [ -0.000232 0.001624 1.001 -1.165e-05 5.23e-06 0.9983 -8.78e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2205 0.1043 0.3477 0.1425 0.9849 0.9939 0.2212 0.4346 0.8753 0.7036 ] Network output: [ 0.003449 -0.01633 0.9942 7.09e-06 -3.183e-06 1.015 5.343e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.09762 0.1844 0.1978 0.9873 0.9919 0.1103 0.7387 0.8619 0.3052 ] Network output: [ -0.003232 0.01511 1.005 7.7e-06 -3.457e-06 0.9868 5.803e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09377 0.09184 0.165 0.1963 0.9852 0.9911 0.09379 0.6626 0.8372 0.2487 ] Network output: [ 9.033e-05 1 -5.651e-05 1.011e-06 -4.539e-07 0.9998 7.62e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002009 Epoch 9418 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009145 0.9967 0.9923 -2.021e-07 9.074e-08 -0.007223 -1.523e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003493 -0.00333 -0.006857 0.005505 0.9699 0.9743 0.006789 0.826 0.8205 0.01652 ] Network output: [ 0.9999 0.0001617 0.0004251 -3.71e-06 1.666e-06 -0.0003968 -2.796e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.207 -0.03533 -0.1594 0.1836 0.9834 0.9932 0.2322 0.4306 0.8685 0.7098 ] Network output: [ -0.009117 1.003 1.008 -2.35e-07 1.055e-07 0.007554 -1.771e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006718 0.0006072 0.004394 0.003239 0.9889 0.9919 0.006848 0.8533 0.8924 0.01182 ] Network output: [ -0.0002318 0.001623 1.001 -1.164e-05 5.224e-06 0.9983 -8.77e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2205 0.1043 0.3477 0.1425 0.9849 0.9939 0.2212 0.4346 0.8753 0.7036 ] Network output: [ 0.003447 -0.01632 0.9942 7.082e-06 -3.179e-06 1.015 5.337e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.09763 0.1844 0.1978 0.9873 0.9919 0.1103 0.7387 0.8619 0.3052 ] Network output: [ -0.00323 0.01511 1.005 7.692e-06 -3.453e-06 0.9868 5.797e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09378 0.09184 0.165 0.1963 0.9852 0.9911 0.09379 0.6626 0.8372 0.2487 ] Network output: [ 9.031e-05 1 -5.648e-05 1.01e-06 -4.534e-07 0.9998 7.611e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002008 Epoch 9419 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009144 0.9967 0.9923 -2.02e-07 9.07e-08 -0.007222 -1.523e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003494 -0.00333 -0.006856 0.005504 0.9699 0.9743 0.006789 0.826 0.8205 0.01652 ] Network output: [ 0.9999 0.0001615 0.0004249 -3.706e-06 1.664e-06 -0.0003965 -2.793e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.207 -0.03533 -0.1594 0.1836 0.9834 0.9932 0.2322 0.4306 0.8685 0.7097 ] Network output: [ -0.009116 1.003 1.008 -2.349e-07 1.054e-07 0.007553 -1.77e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006718 0.0006073 0.004394 0.003239 0.9889 0.9919 0.006849 0.8533 0.8924 0.01182 ] Network output: [ -0.0002316 0.001623 1.001 -1.162e-05 5.218e-06 0.9983 -8.759e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2205 0.1043 0.3477 0.1425 0.9849 0.9939 0.2212 0.4346 0.8753 0.7036 ] Network output: [ 0.003446 -0.01632 0.9942 7.073e-06 -3.175e-06 1.015 5.331e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.09763 0.1844 0.1978 0.9873 0.9919 0.1104 0.7387 0.8619 0.3052 ] Network output: [ -0.003229 0.0151 1.005 7.683e-06 -3.449e-06 0.9868 5.79e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09378 0.09184 0.165 0.1963 0.9852 0.9911 0.09379 0.6626 0.8372 0.2487 ] Network output: [ 9.028e-05 1 -5.644e-05 1.009e-06 -4.528e-07 0.9998 7.602e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002007 Epoch 9420 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009143 0.9967 0.9923 -2.02e-07 9.067e-08 -0.007222 -1.522e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003494 -0.00333 -0.006856 0.005504 0.9699 0.9743 0.006789 0.826 0.8205 0.01652 ] Network output: [ 0.9999 0.0001613 0.0004247 -3.701e-06 1.662e-06 -0.0003962 -2.789e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.207 -0.03533 -0.1594 0.1836 0.9834 0.9932 0.2322 0.4306 0.8685 0.7097 ] Network output: [ -0.009116 1.003 1.008 -2.347e-07 1.054e-07 0.007552 -1.769e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006718 0.0006074 0.004394 0.003238 0.9889 0.9919 0.006849 0.8533 0.8924 0.01182 ] Network output: [ -0.0002314 0.001622 1.001 -1.161e-05 5.212e-06 0.9983 -8.749e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2205 0.1043 0.3477 0.1425 0.9849 0.9939 0.2212 0.4346 0.8753 0.7036 ] Network output: [ 0.003444 -0.01631 0.9942 7.065e-06 -3.172e-06 1.015 5.324e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.09763 0.1844 0.1978 0.9873 0.9919 0.1104 0.7387 0.8619 0.3052 ] Network output: [ -0.003228 0.0151 1.005 7.674e-06 -3.445e-06 0.9868 5.783e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09378 0.09184 0.165 0.1963 0.9852 0.9911 0.0938 0.6626 0.8372 0.2487 ] Network output: [ 9.025e-05 1 -5.641e-05 1.008e-06 -4.523e-07 0.9998 7.593e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002006 Epoch 9421 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009142 0.9967 0.9923 -2.019e-07 9.063e-08 -0.007221 -1.521e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003494 -0.00333 -0.006855 0.005503 0.9699 0.9743 0.00679 0.826 0.8205 0.01652 ] Network output: [ 0.9999 0.0001611 0.0004245 -3.697e-06 1.66e-06 -0.0003959 -2.786e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.207 -0.03533 -0.1594 0.1836 0.9834 0.9932 0.2322 0.4306 0.8685 0.7097 ] Network output: [ -0.009115 1.003 1.008 -2.346e-07 1.053e-07 0.007552 -1.768e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006719 0.0006074 0.004393 0.003238 0.9889 0.9919 0.00685 0.8533 0.8924 0.01182 ] Network output: [ -0.0002313 0.001621 1.001 -1.16e-05 5.206e-06 0.9983 -8.739e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2205 0.1043 0.3478 0.1425 0.9849 0.9939 0.2212 0.4346 0.8753 0.7036 ] Network output: [ 0.003443 -0.0163 0.9942 7.057e-06 -3.168e-06 1.015 5.318e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.09764 0.1844 0.1978 0.9873 0.9919 0.1104 0.7386 0.8619 0.3052 ] Network output: [ -0.003226 0.01509 1.005 7.665e-06 -3.441e-06 0.9868 5.777e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09378 0.09185 0.165 0.1963 0.9852 0.9911 0.0938 0.6626 0.8372 0.2487 ] Network output: [ 9.022e-05 1 -5.637e-05 1.006e-06 -4.518e-07 0.9998 7.584e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002005 Epoch 9422 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009141 0.9967 0.9923 -2.018e-07 9.059e-08 -0.00722 -1.521e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003494 -0.00333 -0.006855 0.005503 0.9699 0.9743 0.00679 0.826 0.8205 0.01652 ] Network output: [ 0.9999 0.0001609 0.0004243 -3.693e-06 1.658e-06 -0.0003957 -2.783e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.207 -0.03533 -0.1594 0.1836 0.9834 0.9932 0.2322 0.4306 0.8685 0.7097 ] Network output: [ -0.009114 1.003 1.008 -2.344e-07 1.052e-07 0.007551 -1.767e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006719 0.0006075 0.004393 0.003238 0.9889 0.9919 0.00685 0.8533 0.8924 0.01182 ] Network output: [ -0.0002311 0.001621 1.001 -1.158e-05 5.2e-06 0.9983 -8.729e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2205 0.1043 0.3478 0.1425 0.9849 0.9939 0.2212 0.4346 0.8753 0.7036 ] Network output: [ 0.003441 -0.0163 0.9942 7.049e-06 -3.164e-06 1.015 5.312e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.09764 0.1844 0.1978 0.9873 0.9919 0.1104 0.7386 0.8619 0.3052 ] Network output: [ -0.003225 0.01508 1.005 7.656e-06 -3.437e-06 0.9868 5.77e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09379 0.09185 0.165 0.1963 0.9852 0.9911 0.0938 0.6625 0.8372 0.2487 ] Network output: [ 9.019e-05 1 -5.634e-05 1.005e-06 -4.513e-07 0.9998 7.575e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002004 Epoch 9423 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00914 0.9967 0.9923 -2.017e-07 9.056e-08 -0.00722 -1.52e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003494 -0.00333 -0.006854 0.005503 0.9699 0.9743 0.00679 0.826 0.8205 0.01652 ] Network output: [ 0.9999 0.0001608 0.0004241 -3.688e-06 1.656e-06 -0.0003954 -2.78e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.207 -0.03533 -0.1594 0.1836 0.9834 0.9932 0.2323 0.4306 0.8685 0.7097 ] Network output: [ -0.009113 1.003 1.008 -2.343e-07 1.052e-07 0.00755 -1.765e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00672 0.0006076 0.004393 0.003238 0.9889 0.9919 0.00685 0.8533 0.8924 0.01182 ] Network output: [ -0.0002309 0.00162 1.001 -1.157e-05 5.193e-06 0.9983 -8.718e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2205 0.1043 0.3478 0.1425 0.9849 0.9939 0.2213 0.4346 0.8753 0.7036 ] Network output: [ 0.00344 -0.01629 0.9942 7.04e-06 -3.161e-06 1.015 5.306e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.09765 0.1844 0.1978 0.9873 0.9919 0.1104 0.7386 0.8619 0.3052 ] Network output: [ -0.003224 0.01508 1.005 7.647e-06 -3.433e-06 0.9868 5.763e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09379 0.09185 0.165 0.1963 0.9852 0.9911 0.0938 0.6625 0.8372 0.2487 ] Network output: [ 9.016e-05 1 -5.631e-05 1.004e-06 -4.507e-07 0.9998 7.567e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002003 Epoch 9424 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009139 0.9967 0.9923 -2.016e-07 9.052e-08 -0.007219 -1.52e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003494 -0.003331 -0.006853 0.005502 0.9699 0.9743 0.00679 0.826 0.8205 0.01651 ] Network output: [ 0.9999 0.0001606 0.0004239 -3.684e-06 1.654e-06 -0.0003951 -2.776e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.207 -0.03533 -0.1594 0.1836 0.9834 0.9932 0.2323 0.4306 0.8685 0.7097 ] Network output: [ -0.009112 1.003 1.008 -2.341e-07 1.051e-07 0.007549 -1.764e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00672 0.0006077 0.004393 0.003237 0.9889 0.9919 0.006851 0.8533 0.8924 0.01182 ] Network output: [ -0.0002308 0.001619 1.001 -1.155e-05 5.187e-06 0.9983 -8.708e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2205 0.1043 0.3478 0.1425 0.9849 0.9939 0.2213 0.4346 0.8753 0.7035 ] Network output: [ 0.003438 -0.01628 0.9942 7.032e-06 -3.157e-06 1.015 5.3e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.09765 0.1844 0.1978 0.9873 0.9919 0.1104 0.7386 0.8618 0.3052 ] Network output: [ -0.003222 0.01507 1.005 7.639e-06 -3.429e-06 0.9868 5.757e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09379 0.09185 0.165 0.1963 0.9852 0.9911 0.0938 0.6625 0.8372 0.2487 ] Network output: [ 9.013e-05 1 -5.627e-05 1.003e-06 -4.502e-07 0.9998 7.558e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002002 Epoch 9425 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009138 0.9967 0.9923 -2.015e-07 9.048e-08 -0.007219 -1.519e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003494 -0.003331 -0.006853 0.005502 0.9699 0.9743 0.00679 0.826 0.8205 0.01651 ] Network output: [ 0.9999 0.0001604 0.0004237 -3.679e-06 1.652e-06 -0.0003948 -2.773e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.207 -0.03533 -0.1593 0.1836 0.9834 0.9932 0.2323 0.4306 0.8685 0.7097 ] Network output: [ -0.009111 1.003 1.008 -2.34e-07 1.05e-07 0.007549 -1.763e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006721 0.0006078 0.004393 0.003237 0.9889 0.9919 0.006851 0.8533 0.8924 0.01181 ] Network output: [ -0.0002306 0.001618 1.001 -1.154e-05 5.181e-06 0.9983 -8.698e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2205 0.1043 0.3478 0.1425 0.9849 0.9939 0.2213 0.4346 0.8753 0.7035 ] Network output: [ 0.003437 -0.01628 0.9942 7.024e-06 -3.153e-06 1.015 5.293e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.09765 0.1844 0.1978 0.9873 0.9919 0.1104 0.7386 0.8618 0.3052 ] Network output: [ -0.003221 0.01506 1.005 7.63e-06 -3.425e-06 0.9868 5.75e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09379 0.09185 0.165 0.1963 0.9852 0.9911 0.09381 0.6625 0.8372 0.2487 ] Network output: [ 9.011e-05 1 -5.624e-05 1.002e-06 -4.497e-07 0.9998 7.549e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002001 Epoch 9426 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009137 0.9967 0.9923 -2.015e-07 9.045e-08 -0.007218 -1.518e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003494 -0.003331 -0.006852 0.005501 0.9699 0.9743 0.006791 0.8259 0.8205 0.01651 ] Network output: [ 0.9999 0.0001602 0.0004235 -3.675e-06 1.65e-06 -0.0003946 -2.77e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.207 -0.03534 -0.1593 0.1836 0.9834 0.9932 0.2323 0.4306 0.8685 0.7097 ] Network output: [ -0.00911 1.003 1.008 -2.338e-07 1.05e-07 0.007548 -1.762e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006721 0.0006078 0.004393 0.003237 0.9889 0.9919 0.006852 0.8533 0.8924 0.01181 ] Network output: [ -0.0002304 0.001618 1.001 -1.153e-05 5.175e-06 0.9983 -8.687e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2205 0.1043 0.3478 0.1425 0.9849 0.9939 0.2213 0.4346 0.8753 0.7035 ] Network output: [ 0.003435 -0.01627 0.9942 7.016e-06 -3.15e-06 1.015 5.287e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.09766 0.1844 0.1978 0.9873 0.9919 0.1104 0.7386 0.8618 0.3052 ] Network output: [ -0.003219 0.01506 1.005 7.621e-06 -3.421e-06 0.9868 5.743e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09379 0.09186 0.165 0.1963 0.9852 0.9911 0.09381 0.6625 0.8372 0.2487 ] Network output: [ 9.008e-05 1 -5.62e-05 1.001e-06 -4.492e-07 0.9998 7.54e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0002 Epoch 9427 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009136 0.9967 0.9923 -2.014e-07 9.041e-08 -0.007218 -1.518e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003494 -0.003331 -0.006851 0.005501 0.9699 0.9743 0.006791 0.8259 0.8205 0.01651 ] Network output: [ 0.9999 0.00016 0.0004233 -3.671e-06 1.648e-06 -0.0003943 -2.766e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2071 -0.03534 -0.1593 0.1836 0.9834 0.9932 0.2323 0.4306 0.8685 0.7097 ] Network output: [ -0.00911 1.003 1.008 -2.337e-07 1.049e-07 0.007547 -1.761e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006721 0.0006079 0.004393 0.003237 0.9889 0.9919 0.006852 0.8533 0.8924 0.01181 ] Network output: [ -0.0002302 0.001617 1.001 -1.151e-05 5.169e-06 0.9983 -8.677e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2205 0.1043 0.3478 0.1424 0.9849 0.9939 0.2213 0.4346 0.8753 0.7035 ] Network output: [ 0.003434 -0.01626 0.9942 7.007e-06 -3.146e-06 1.015 5.281e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.09766 0.1844 0.1978 0.9873 0.9919 0.1104 0.7386 0.8618 0.3052 ] Network output: [ -0.003218 0.01505 1.005 7.612e-06 -3.417e-06 0.9868 5.737e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0938 0.09186 0.165 0.1963 0.9852 0.9911 0.09381 0.6625 0.8372 0.2487 ] Network output: [ 9.005e-05 1 -5.617e-05 9.994e-07 -4.486e-07 0.9998 7.531e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001999 Epoch 9428 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009135 0.9967 0.9923 -2.013e-07 9.037e-08 -0.007217 -1.517e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003494 -0.003331 -0.006851 0.005501 0.9699 0.9743 0.006791 0.8259 0.8205 0.01651 ] Network output: [ 0.9999 0.0001598 0.0004231 -3.666e-06 1.646e-06 -0.000394 -2.763e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2071 -0.03534 -0.1593 0.1836 0.9834 0.9932 0.2323 0.4306 0.8685 0.7097 ] Network output: [ -0.009109 1.003 1.008 -2.335e-07 1.048e-07 0.007547 -1.76e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006722 0.000608 0.004393 0.003236 0.9889 0.9919 0.006853 0.8533 0.8924 0.01181 ] Network output: [ -0.0002301 0.001616 1.001 -1.15e-05 5.163e-06 0.9983 -8.667e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2205 0.1043 0.3478 0.1424 0.9849 0.9939 0.2213 0.4346 0.8753 0.7035 ] Network output: [ 0.003432 -0.01626 0.9942 6.999e-06 -3.142e-06 1.015 5.275e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.09767 0.1844 0.1978 0.9873 0.9919 0.1104 0.7386 0.8618 0.3052 ] Network output: [ -0.003217 0.01504 1.005 7.603e-06 -3.413e-06 0.9868 5.73e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0938 0.09186 0.165 0.1963 0.9852 0.9911 0.09381 0.6625 0.8372 0.2487 ] Network output: [ 9.002e-05 1 -5.613e-05 9.982e-07 -4.481e-07 0.9998 7.523e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001997 Epoch 9429 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009134 0.9967 0.9923 -2.012e-07 9.033e-08 -0.007217 -1.516e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003494 -0.003331 -0.00685 0.0055 0.9699 0.9743 0.006791 0.8259 0.8205 0.01651 ] Network output: [ 0.9999 0.0001596 0.0004229 -3.662e-06 1.644e-06 -0.0003937 -2.76e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2071 -0.03534 -0.1593 0.1836 0.9834 0.9932 0.2323 0.4306 0.8685 0.7097 ] Network output: [ -0.009108 1.003 1.008 -2.334e-07 1.048e-07 0.007546 -1.759e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006722 0.0006081 0.004393 0.003236 0.9889 0.9919 0.006853 0.8533 0.8924 0.01181 ] Network output: [ -0.0002299 0.001616 1.001 -1.149e-05 5.157e-06 0.9983 -8.657e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2206 0.1043 0.3478 0.1424 0.9849 0.9939 0.2213 0.4346 0.8753 0.7035 ] Network output: [ 0.003431 -0.01625 0.9942 6.991e-06 -3.138e-06 1.015 5.269e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.09767 0.1844 0.1978 0.9873 0.9919 0.1104 0.7385 0.8618 0.3052 ] Network output: [ -0.003215 0.01504 1.005 7.595e-06 -3.41e-06 0.9868 5.724e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0938 0.09186 0.165 0.1963 0.9852 0.9911 0.09381 0.6625 0.8372 0.2487 ] Network output: [ 8.999e-05 1 -5.61e-05 9.97e-07 -4.476e-07 0.9998 7.514e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001996 Epoch 9430 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009133 0.9967 0.9923 -2.011e-07 9.03e-08 -0.007216 -1.516e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003494 -0.003331 -0.006849 0.0055 0.9699 0.9743 0.006791 0.8259 0.8205 0.01651 ] Network output: [ 0.9999 0.0001594 0.0004227 -3.658e-06 1.642e-06 -0.0003935 -2.757e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2071 -0.03534 -0.1593 0.1836 0.9834 0.9932 0.2323 0.4306 0.8685 0.7097 ] Network output: [ -0.009107 1.003 1.008 -2.332e-07 1.047e-07 0.007545 -1.758e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006723 0.0006081 0.004393 0.003236 0.9889 0.9919 0.006853 0.8533 0.8924 0.01181 ] Network output: [ -0.0002297 0.001615 1.001 -1.147e-05 5.151e-06 0.9983 -8.647e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2206 0.1043 0.3478 0.1424 0.9849 0.9939 0.2213 0.4346 0.8753 0.7035 ] Network output: [ 0.003429 -0.01624 0.9942 6.983e-06 -3.135e-06 1.015 5.262e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.09767 0.1844 0.1978 0.9873 0.9919 0.1104 0.7385 0.8618 0.3052 ] Network output: [ -0.003214 0.01503 1.005 7.586e-06 -3.406e-06 0.9868 5.717e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0938 0.09186 0.165 0.1963 0.9852 0.9911 0.09382 0.6624 0.8372 0.2487 ] Network output: [ 8.996e-05 1 -5.607e-05 9.959e-07 -4.471e-07 0.9998 7.505e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001995 Epoch 9431 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009132 0.9967 0.9923 -2.011e-07 9.026e-08 -0.007216 -1.515e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003494 -0.003331 -0.006849 0.005499 0.9699 0.9743 0.006792 0.8259 0.8205 0.01651 ] Network output: [ 0.9999 0.0001593 0.0004225 -3.653e-06 1.64e-06 -0.0003932 -2.753e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2071 -0.03534 -0.1593 0.1836 0.9834 0.9932 0.2323 0.4306 0.8685 0.7097 ] Network output: [ -0.009106 1.003 1.008 -2.331e-07 1.046e-07 0.007544 -1.757e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006723 0.0006082 0.004393 0.003236 0.9889 0.9919 0.006854 0.8533 0.8924 0.01181 ] Network output: [ -0.0002296 0.001614 1.001 -1.146e-05 5.145e-06 0.9983 -8.636e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2206 0.1043 0.3478 0.1424 0.9849 0.9939 0.2213 0.4346 0.8753 0.7035 ] Network output: [ 0.003428 -0.01623 0.9942 6.975e-06 -3.131e-06 1.015 5.256e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.09768 0.1844 0.1978 0.9873 0.9919 0.1104 0.7385 0.8618 0.3052 ] Network output: [ -0.003212 0.01502 1.005 7.577e-06 -3.402e-06 0.9868 5.71e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0938 0.09187 0.165 0.1963 0.9852 0.9911 0.09382 0.6624 0.8372 0.2488 ] Network output: [ 8.994e-05 1 -5.603e-05 9.947e-07 -4.466e-07 0.9998 7.496e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001994 Epoch 9432 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009131 0.9967 0.9923 -2.01e-07 9.022e-08 -0.007215 -1.515e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003494 -0.003331 -0.006848 0.005499 0.9699 0.9743 0.006792 0.8259 0.8205 0.01651 ] Network output: [ 0.9999 0.0001591 0.0004223 -3.649e-06 1.638e-06 -0.0003929 -2.75e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2071 -0.03534 -0.1593 0.1836 0.9834 0.9932 0.2323 0.4306 0.8685 0.7097 ] Network output: [ -0.009105 1.003 1.008 -2.329e-07 1.046e-07 0.007544 -1.755e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006723 0.0006083 0.004393 0.003235 0.9889 0.9919 0.006854 0.8533 0.8924 0.01181 ] Network output: [ -0.0002294 0.001613 1.001 -1.145e-05 5.139e-06 0.9983 -8.626e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2206 0.1044 0.3478 0.1424 0.9849 0.9939 0.2213 0.4346 0.8753 0.7035 ] Network output: [ 0.003426 -0.01623 0.9942 6.966e-06 -3.127e-06 1.015 5.25e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.09768 0.1844 0.1978 0.9873 0.9919 0.1104 0.7385 0.8618 0.3052 ] Network output: [ -0.003211 0.01502 1.005 7.568e-06 -3.398e-06 0.9868 5.704e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09381 0.09187 0.165 0.1963 0.9852 0.9911 0.09382 0.6624 0.8372 0.2488 ] Network output: [ 8.991e-05 1 -5.6e-05 9.936e-07 -4.46e-07 0.9998 7.488e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001993 Epoch 9433 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00913 0.9967 0.9923 -2.009e-07 9.019e-08 -0.007215 -1.514e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003495 -0.003331 -0.006848 0.005498 0.9699 0.9743 0.006792 0.8259 0.8205 0.01651 ] Network output: [ 0.9999 0.0001589 0.0004221 -3.645e-06 1.636e-06 -0.0003926 -2.747e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2071 -0.03534 -0.1593 0.1836 0.9834 0.9932 0.2323 0.4306 0.8685 0.7097 ] Network output: [ -0.009104 1.003 1.008 -2.328e-07 1.045e-07 0.007543 -1.754e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006724 0.0006084 0.004392 0.003235 0.9889 0.9919 0.006855 0.8533 0.8924 0.01181 ] Network output: [ -0.0002292 0.001613 1.001 -1.143e-05 5.133e-06 0.9983 -8.616e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2206 0.1044 0.3478 0.1424 0.9849 0.9939 0.2213 0.4346 0.8753 0.7035 ] Network output: [ 0.003425 -0.01622 0.9942 6.958e-06 -3.124e-06 1.015 5.244e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.09769 0.1844 0.1978 0.9873 0.9919 0.1104 0.7385 0.8618 0.3052 ] Network output: [ -0.00321 0.01501 1.005 7.56e-06 -3.394e-06 0.9868 5.697e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09381 0.09187 0.165 0.1963 0.9852 0.9911 0.09382 0.6624 0.8372 0.2488 ] Network output: [ 8.988e-05 1 -5.597e-05 9.924e-07 -4.455e-07 0.9998 7.479e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001992 Epoch 9434 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009129 0.9967 0.9923 -2.008e-07 9.015e-08 -0.007214 -1.513e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003495 -0.003331 -0.006847 0.005498 0.9699 0.9743 0.006792 0.8259 0.8205 0.01651 ] Network output: [ 0.9999 0.0001587 0.0004219 -3.64e-06 1.634e-06 -0.0003924 -2.744e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2071 -0.03534 -0.1593 0.1835 0.9834 0.9932 0.2323 0.4306 0.8685 0.7097 ] Network output: [ -0.009103 1.003 1.008 -2.326e-07 1.044e-07 0.007542 -1.753e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006724 0.0006085 0.004392 0.003235 0.9889 0.9919 0.006855 0.8532 0.8924 0.01181 ] Network output: [ -0.000229 0.001612 1.001 -1.142e-05 5.126e-06 0.9983 -8.606e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2206 0.1044 0.3478 0.1424 0.9849 0.9939 0.2213 0.4346 0.8753 0.7035 ] Network output: [ 0.003424 -0.01621 0.9942 6.95e-06 -3.12e-06 1.015 5.238e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.09769 0.1844 0.1978 0.9873 0.9919 0.1104 0.7385 0.8618 0.3052 ] Network output: [ -0.003208 0.01501 1.005 7.551e-06 -3.39e-06 0.9868 5.691e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09381 0.09187 0.165 0.1963 0.9852 0.9911 0.09383 0.6624 0.8371 0.2488 ] Network output: [ 8.985e-05 1 -5.593e-05 9.912e-07 -4.45e-07 0.9998 7.47e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001991 Epoch 9435 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009128 0.9967 0.9923 -2.007e-07 9.011e-08 -0.007214 -1.513e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003495 -0.003332 -0.006846 0.005498 0.9699 0.9743 0.006792 0.8259 0.8204 0.0165 ] Network output: [ 0.9999 0.0001585 0.0004217 -3.636e-06 1.632e-06 -0.0003921 -2.74e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2071 -0.03534 -0.1593 0.1835 0.9834 0.9932 0.2323 0.4306 0.8685 0.7097 ] Network output: [ -0.009103 1.003 1.008 -2.325e-07 1.044e-07 0.007542 -1.752e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006725 0.0006085 0.004392 0.003235 0.9889 0.9919 0.006856 0.8532 0.8924 0.01181 ] Network output: [ -0.0002289 0.001611 1.001 -1.141e-05 5.12e-06 0.9983 -8.596e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2206 0.1044 0.3478 0.1424 0.9849 0.9939 0.2213 0.4345 0.8753 0.7035 ] Network output: [ 0.003422 -0.01621 0.9942 6.942e-06 -3.116e-06 1.015 5.232e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.09769 0.1845 0.1978 0.9873 0.9919 0.1104 0.7385 0.8618 0.3052 ] Network output: [ -0.003207 0.015 1.005 7.542e-06 -3.386e-06 0.9868 5.684e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09381 0.09188 0.165 0.1963 0.9852 0.9911 0.09383 0.6624 0.8371 0.2488 ] Network output: [ 8.982e-05 1 -5.59e-05 9.901e-07 -4.445e-07 0.9998 7.462e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000199 Epoch 9436 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009127 0.9967 0.9923 -2.006e-07 9.007e-08 -0.007213 -1.512e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003495 -0.003332 -0.006846 0.005497 0.9699 0.9743 0.006793 0.8259 0.8204 0.0165 ] Network output: [ 0.9999 0.0001583 0.0004216 -3.632e-06 1.63e-06 -0.0003918 -2.737e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2071 -0.03534 -0.1592 0.1835 0.9834 0.9932 0.2324 0.4305 0.8685 0.7097 ] Network output: [ -0.009102 1.003 1.008 -2.323e-07 1.043e-07 0.007541 -1.751e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006725 0.0006086 0.004392 0.003234 0.9889 0.9919 0.006856 0.8532 0.8924 0.01181 ] Network output: [ -0.0002287 0.001611 1.001 -1.139e-05 5.114e-06 0.9983 -8.585e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2206 0.1044 0.3478 0.1424 0.9849 0.9939 0.2214 0.4345 0.8753 0.7035 ] Network output: [ 0.003421 -0.0162 0.9942 6.934e-06 -3.113e-06 1.015 5.225e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.0977 0.1845 0.1978 0.9873 0.9919 0.1104 0.7385 0.8618 0.3052 ] Network output: [ -0.003206 0.01499 1.005 7.534e-06 -3.382e-06 0.9868 5.678e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09382 0.09188 0.165 0.1963 0.9852 0.9911 0.09383 0.6624 0.8371 0.2488 ] Network output: [ 8.979e-05 1 -5.587e-05 9.889e-07 -4.44e-07 0.9998 7.453e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001989 Epoch 9437 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009126 0.9967 0.9923 -2.006e-07 9.004e-08 -0.007213 -1.511e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003495 -0.003332 -0.006845 0.005497 0.9699 0.9743 0.006793 0.8259 0.8204 0.0165 ] Network output: [ 0.9999 0.0001581 0.0004214 -3.628e-06 1.629e-06 -0.0003915 -2.734e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2071 -0.03535 -0.1592 0.1835 0.9834 0.9932 0.2324 0.4305 0.8685 0.7097 ] Network output: [ -0.009101 1.003 1.008 -2.322e-07 1.042e-07 0.00754 -1.75e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006725 0.0006087 0.004392 0.003234 0.9889 0.9919 0.006856 0.8532 0.8924 0.01181 ] Network output: [ -0.0002285 0.00161 1.001 -1.138e-05 5.108e-06 0.9983 -8.575e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2206 0.1044 0.3478 0.1424 0.9849 0.9939 0.2214 0.4345 0.8753 0.7035 ] Network output: [ 0.003419 -0.01619 0.9942 6.926e-06 -3.109e-06 1.015 5.219e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1103 0.0977 0.1845 0.1978 0.9873 0.9919 0.1104 0.7384 0.8618 0.3052 ] Network output: [ -0.003204 0.01499 1.005 7.525e-06 -3.378e-06 0.9868 5.671e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09382 0.09188 0.165 0.1963 0.9852 0.9911 0.09383 0.6624 0.8371 0.2488 ] Network output: [ 8.977e-05 1 -5.583e-05 9.878e-07 -4.435e-07 0.9998 7.444e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001988 Epoch 9438 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009125 0.9967 0.9923 -2.005e-07 9e-08 -0.007212 -1.511e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003495 -0.003332 -0.006844 0.005496 0.9699 0.9743 0.006793 0.8259 0.8204 0.0165 ] Network output: [ 0.9999 0.0001579 0.0004212 -3.623e-06 1.627e-06 -0.0003913 -2.731e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2071 -0.03535 -0.1592 0.1835 0.9834 0.9932 0.2324 0.4305 0.8685 0.7097 ] Network output: [ -0.0091 1.003 1.008 -2.32e-07 1.042e-07 0.007539 -1.749e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006726 0.0006088 0.004392 0.003234 0.9889 0.9919 0.006857 0.8532 0.8924 0.0118 ] Network output: [ -0.0002284 0.001609 1.001 -1.137e-05 5.102e-06 0.9983 -8.565e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2206 0.1044 0.3478 0.1424 0.9849 0.9939 0.2214 0.4345 0.8753 0.7035 ] Network output: [ 0.003418 -0.01619 0.9942 6.917e-06 -3.106e-06 1.015 5.213e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.0977 0.1845 0.1977 0.9873 0.9919 0.1104 0.7384 0.8618 0.3052 ] Network output: [ -0.003203 0.01498 1.005 7.516e-06 -3.374e-06 0.9868 5.664e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09382 0.09188 0.165 0.1963 0.9852 0.9911 0.09383 0.6623 0.8371 0.2488 ] Network output: [ 8.974e-05 1 -5.58e-05 9.866e-07 -4.429e-07 0.9998 7.436e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001987 Epoch 9439 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009124 0.9967 0.9923 -2.004e-07 8.996e-08 -0.007212 -1.51e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003495 -0.003332 -0.006844 0.005496 0.9699 0.9743 0.006793 0.8259 0.8204 0.0165 ] Network output: [ 0.9999 0.0001578 0.000421 -3.619e-06 1.625e-06 -0.000391 -2.727e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2071 -0.03535 -0.1592 0.1835 0.9834 0.9932 0.2324 0.4305 0.8685 0.7097 ] Network output: [ -0.009099 1.003 1.008 -2.319e-07 1.041e-07 0.007539 -1.748e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006726 0.0006088 0.004392 0.003234 0.9889 0.9919 0.006857 0.8532 0.8924 0.0118 ] Network output: [ -0.0002282 0.001608 1.001 -1.135e-05 5.096e-06 0.9983 -8.555e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2206 0.1044 0.3478 0.1424 0.9849 0.9939 0.2214 0.4345 0.8753 0.7035 ] Network output: [ 0.003416 -0.01618 0.9942 6.909e-06 -3.102e-06 1.015 5.207e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09771 0.1845 0.1977 0.9873 0.9919 0.1104 0.7384 0.8618 0.3052 ] Network output: [ -0.003201 0.01497 1.005 7.507e-06 -3.37e-06 0.9868 5.658e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09382 0.09188 0.165 0.1963 0.9852 0.9911 0.09384 0.6623 0.8371 0.2488 ] Network output: [ 8.971e-05 1 -5.577e-05 9.855e-07 -4.424e-07 0.9998 7.427e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001986 Epoch 9440 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009123 0.9967 0.9923 -2.003e-07 8.992e-08 -0.007211 -1.51e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003495 -0.003332 -0.006843 0.005496 0.9699 0.9743 0.006793 0.8259 0.8204 0.0165 ] Network output: [ 0.9999 0.0001576 0.0004208 -3.615e-06 1.623e-06 -0.0003907 -2.724e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2071 -0.03535 -0.1592 0.1835 0.9834 0.9932 0.2324 0.4305 0.8685 0.7097 ] Network output: [ -0.009098 1.003 1.008 -2.317e-07 1.04e-07 0.007538 -1.746e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006727 0.0006089 0.004392 0.003234 0.9889 0.9919 0.006858 0.8532 0.8924 0.0118 ] Network output: [ -0.000228 0.001608 1.001 -1.134e-05 5.09e-06 0.9983 -8.545e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2206 0.1044 0.3479 0.1424 0.9849 0.9939 0.2214 0.4345 0.8753 0.7035 ] Network output: [ 0.003415 -0.01617 0.9942 6.901e-06 -3.098e-06 1.015 5.201e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09771 0.1845 0.1977 0.9873 0.9919 0.1104 0.7384 0.8618 0.3052 ] Network output: [ -0.0032 0.01497 1.005 7.499e-06 -3.366e-06 0.9868 5.651e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09382 0.09189 0.165 0.1963 0.9852 0.9911 0.09384 0.6623 0.8371 0.2488 ] Network output: [ 8.968e-05 1 -5.573e-05 9.843e-07 -4.419e-07 0.9998 7.418e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001985 Epoch 9441 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009122 0.9967 0.9923 -2.002e-07 8.988e-08 -0.007211 -1.509e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003495 -0.003332 -0.006842 0.005495 0.9699 0.9743 0.006794 0.8259 0.8204 0.0165 ] Network output: [ 0.9999 0.0001574 0.0004206 -3.61e-06 1.621e-06 -0.0003905 -2.721e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2071 -0.03535 -0.1592 0.1835 0.9834 0.9932 0.2324 0.4305 0.8685 0.7097 ] Network output: [ -0.009097 1.003 1.008 -2.316e-07 1.04e-07 0.007537 -1.745e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006727 0.000609 0.004392 0.003233 0.9889 0.9919 0.006858 0.8532 0.8923 0.0118 ] Network output: [ -0.0002278 0.001607 1.001 -1.133e-05 5.084e-06 0.9983 -8.535e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2206 0.1044 0.3479 0.1424 0.9849 0.9939 0.2214 0.4345 0.8753 0.7035 ] Network output: [ 0.003413 -0.01617 0.9942 6.893e-06 -3.095e-06 1.015 5.195e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09772 0.1845 0.1977 0.9873 0.9919 0.1104 0.7384 0.8618 0.3052 ] Network output: [ -0.003199 0.01496 1.005 7.49e-06 -3.363e-06 0.9868 5.645e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09383 0.09189 0.165 0.1963 0.9852 0.9911 0.09384 0.6623 0.8371 0.2488 ] Network output: [ 8.965e-05 1 -5.57e-05 9.832e-07 -4.414e-07 0.9998 7.41e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001984 Epoch 9442 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009121 0.9967 0.9923 -2.001e-07 8.985e-08 -0.00721 -1.508e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003495 -0.003332 -0.006842 0.005495 0.9699 0.9743 0.006794 0.8259 0.8204 0.0165 ] Network output: [ 0.9999 0.0001572 0.0004204 -3.606e-06 1.619e-06 -0.0003902 -2.718e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2071 -0.03535 -0.1592 0.1835 0.9834 0.9932 0.2324 0.4305 0.8685 0.7096 ] Network output: [ -0.009097 1.003 1.008 -2.314e-07 1.039e-07 0.007537 -1.744e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006728 0.0006091 0.004392 0.003233 0.9889 0.9919 0.006858 0.8532 0.8923 0.0118 ] Network output: [ -0.0002277 0.001606 1.001 -1.131e-05 5.078e-06 0.9983 -8.525e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2206 0.1044 0.3479 0.1424 0.9849 0.9939 0.2214 0.4345 0.8753 0.7035 ] Network output: [ 0.003412 -0.01616 0.9942 6.885e-06 -3.091e-06 1.015 5.189e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09772 0.1845 0.1977 0.9873 0.9919 0.1104 0.7384 0.8618 0.3052 ] Network output: [ -0.003197 0.01495 1.005 7.482e-06 -3.359e-06 0.9868 5.638e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09383 0.09189 0.165 0.1963 0.9852 0.9911 0.09384 0.6623 0.8371 0.2488 ] Network output: [ 8.963e-05 1 -5.567e-05 9.82e-07 -4.409e-07 0.9998 7.401e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001983 Epoch 9443 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00912 0.9967 0.9923 -2e-07 8.981e-08 -0.007209 -1.508e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003495 -0.003332 -0.006841 0.005494 0.9699 0.9743 0.006794 0.8259 0.8204 0.0165 ] Network output: [ 0.9999 0.000157 0.0004202 -3.602e-06 1.617e-06 -0.0003899 -2.714e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2072 -0.03535 -0.1592 0.1835 0.9834 0.9932 0.2324 0.4305 0.8685 0.7096 ] Network output: [ -0.009096 1.003 1.008 -2.313e-07 1.038e-07 0.007536 -1.743e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006728 0.0006091 0.004392 0.003233 0.9889 0.9919 0.006859 0.8532 0.8923 0.0118 ] Network output: [ -0.0002275 0.001606 1.001 -1.13e-05 5.072e-06 0.9983 -8.515e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2207 0.1044 0.3479 0.1424 0.9849 0.9939 0.2214 0.4345 0.8753 0.7035 ] Network output: [ 0.00341 -0.01615 0.9942 6.877e-06 -3.087e-06 1.015 5.183e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09772 0.1845 0.1977 0.9873 0.9919 0.1104 0.7384 0.8618 0.3052 ] Network output: [ -0.003196 0.01495 1.005 7.473e-06 -3.355e-06 0.9868 5.632e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09383 0.09189 0.165 0.1963 0.9852 0.9911 0.09384 0.6623 0.8371 0.2488 ] Network output: [ 8.96e-05 1 -5.564e-05 9.809e-07 -4.404e-07 0.9998 7.392e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001982 Epoch 9444 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009119 0.9967 0.9923 -2e-07 8.977e-08 -0.007209 -1.507e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003495 -0.003332 -0.006841 0.005494 0.9699 0.9743 0.006794 0.8259 0.8204 0.0165 ] Network output: [ 0.9999 0.0001568 0.00042 -3.598e-06 1.615e-06 -0.0003896 -2.711e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2072 -0.03535 -0.1592 0.1835 0.9834 0.9932 0.2324 0.4305 0.8685 0.7096 ] Network output: [ -0.009095 1.003 1.008 -2.311e-07 1.038e-07 0.007535 -1.742e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006728 0.0006092 0.004392 0.003233 0.9889 0.9919 0.006859 0.8532 0.8923 0.0118 ] Network output: [ -0.0002273 0.001605 1.001 -1.129e-05 5.066e-06 0.9983 -8.505e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2207 0.1044 0.3479 0.1424 0.9849 0.9939 0.2214 0.4345 0.8753 0.7035 ] Network output: [ 0.003409 -0.01615 0.9942 6.869e-06 -3.084e-06 1.015 5.177e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09773 0.1845 0.1977 0.9873 0.9919 0.1105 0.7384 0.8618 0.3052 ] Network output: [ -0.003195 0.01494 1.005 7.464e-06 -3.351e-06 0.9868 5.625e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09383 0.09189 0.165 0.1963 0.9852 0.9911 0.09385 0.6623 0.8371 0.2488 ] Network output: [ 8.957e-05 1 -5.56e-05 9.797e-07 -4.398e-07 0.9998 7.384e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001981 Epoch 9445 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009119 0.9967 0.9923 -1.999e-07 8.973e-08 -0.007208 -1.506e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003495 -0.003332 -0.00684 0.005494 0.9699 0.9743 0.006794 0.8259 0.8204 0.01649 ] Network output: [ 0.9999 0.0001566 0.0004198 -3.593e-06 1.613e-06 -0.0003894 -2.708e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2072 -0.03535 -0.1592 0.1835 0.9834 0.9932 0.2324 0.4305 0.8685 0.7096 ] Network output: [ -0.009094 1.003 1.008 -2.31e-07 1.037e-07 0.007535 -1.741e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006729 0.0006093 0.004391 0.003232 0.9889 0.9919 0.00686 0.8532 0.8923 0.0118 ] Network output: [ -0.0002272 0.001604 1.001 -1.127e-05 5.06e-06 0.9983 -8.495e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2207 0.1044 0.3479 0.1424 0.9849 0.9939 0.2214 0.4345 0.8752 0.7034 ] Network output: [ 0.003407 -0.01614 0.9942 6.861e-06 -3.08e-06 1.015 5.171e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09773 0.1845 0.1977 0.9873 0.9919 0.1105 0.7383 0.8618 0.3052 ] Network output: [ -0.003193 0.01494 1.005 7.456e-06 -3.347e-06 0.9868 5.619e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09383 0.0919 0.165 0.1963 0.9852 0.9911 0.09385 0.6623 0.8371 0.2488 ] Network output: [ 8.954e-05 1 -5.557e-05 9.786e-07 -4.393e-07 0.9998 7.375e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000198 Epoch 9446 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009118 0.9967 0.9923 -1.998e-07 8.969e-08 -0.007208 -1.506e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003495 -0.003333 -0.006839 0.005493 0.9699 0.9743 0.006795 0.8258 0.8204 0.01649 ] Network output: [ 0.9999 0.0001564 0.0004196 -3.589e-06 1.611e-06 -0.0003891 -2.705e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2072 -0.03535 -0.1591 0.1835 0.9834 0.9932 0.2324 0.4305 0.8685 0.7096 ] Network output: [ -0.009093 1.003 1.008 -2.308e-07 1.036e-07 0.007534 -1.74e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006729 0.0006094 0.004391 0.003232 0.9889 0.9919 0.00686 0.8532 0.8923 0.0118 ] Network output: [ -0.000227 0.001603 1.001 -1.126e-05 5.054e-06 0.9983 -8.485e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2207 0.1044 0.3479 0.1424 0.9849 0.9939 0.2214 0.4345 0.8752 0.7034 ] Network output: [ 0.003406 -0.01613 0.9942 6.853e-06 -3.076e-06 1.015 5.164e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09774 0.1845 0.1977 0.9873 0.9919 0.1105 0.7383 0.8618 0.3052 ] Network output: [ -0.003192 0.01493 1.005 7.447e-06 -3.343e-06 0.9869 5.612e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09384 0.0919 0.165 0.1963 0.9852 0.9911 0.09385 0.6622 0.8371 0.2488 ] Network output: [ 8.951e-05 1 -5.554e-05 9.775e-07 -4.388e-07 0.9998 7.366e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001979 Epoch 9447 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009117 0.9967 0.9923 -1.997e-07 8.966e-08 -0.007207 -1.505e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003495 -0.003333 -0.006839 0.005493 0.9699 0.9743 0.006795 0.8258 0.8204 0.01649 ] Network output: [ 0.9999 0.0001563 0.0004194 -3.585e-06 1.609e-06 -0.0003888 -2.702e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2072 -0.03536 -0.1591 0.1835 0.9834 0.9932 0.2324 0.4305 0.8685 0.7096 ] Network output: [ -0.009092 1.003 1.008 -2.307e-07 1.036e-07 0.007533 -1.739e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00673 0.0006095 0.004391 0.003232 0.9889 0.9919 0.006861 0.8532 0.8923 0.0118 ] Network output: [ -0.0002268 0.001603 1.001 -1.125e-05 5.048e-06 0.9983 -8.475e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2207 0.1044 0.3479 0.1424 0.9849 0.9939 0.2214 0.4345 0.8752 0.7034 ] Network output: [ 0.003404 -0.01613 0.9942 6.845e-06 -3.073e-06 1.015 5.158e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09774 0.1845 0.1977 0.9873 0.9919 0.1105 0.7383 0.8618 0.3052 ] Network output: [ -0.00319 0.01492 1.005 7.438e-06 -3.339e-06 0.9869 5.606e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09384 0.0919 0.165 0.1963 0.9852 0.9911 0.09385 0.6622 0.8371 0.2488 ] Network output: [ 8.949e-05 1 -5.551e-05 9.763e-07 -4.383e-07 0.9998 7.358e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001978 Epoch 9448 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009116 0.9967 0.9923 -1.996e-07 8.962e-08 -0.007207 -1.504e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003496 -0.003333 -0.006838 0.005492 0.9699 0.9743 0.006795 0.8258 0.8204 0.01649 ] Network output: [ 0.9999 0.0001561 0.0004192 -3.581e-06 1.607e-06 -0.0003886 -2.698e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2072 -0.03536 -0.1591 0.1835 0.9834 0.9932 0.2324 0.4305 0.8685 0.7096 ] Network output: [ -0.009091 1.003 1.008 -2.305e-07 1.035e-07 0.007532 -1.737e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00673 0.0006095 0.004391 0.003232 0.9889 0.9919 0.006861 0.8532 0.8923 0.0118 ] Network output: [ -0.0002267 0.001602 1.001 -1.123e-05 5.042e-06 0.9983 -8.465e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2207 0.1044 0.3479 0.1424 0.9849 0.9939 0.2214 0.4345 0.8752 0.7034 ] Network output: [ 0.003403 -0.01612 0.9942 6.837e-06 -3.069e-06 1.015 5.152e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09774 0.1845 0.1977 0.9873 0.9919 0.1105 0.7383 0.8618 0.3052 ] Network output: [ -0.003189 0.01492 1.005 7.43e-06 -3.336e-06 0.9869 5.599e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09384 0.0919 0.165 0.1963 0.9852 0.9911 0.09385 0.6622 0.8371 0.2488 ] Network output: [ 8.946e-05 1 -5.548e-05 9.752e-07 -4.378e-07 0.9998 7.349e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001977 Epoch 9449 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009115 0.9967 0.9923 -1.995e-07 8.958e-08 -0.007206 -1.504e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003496 -0.003333 -0.006837 0.005492 0.9699 0.9743 0.006795 0.8258 0.8204 0.01649 ] Network output: [ 0.9999 0.0001559 0.000419 -3.576e-06 1.606e-06 -0.0003883 -2.695e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2072 -0.03536 -0.1591 0.1835 0.9834 0.9932 0.2325 0.4305 0.8685 0.7096 ] Network output: [ -0.009091 1.003 1.008 -2.304e-07 1.034e-07 0.007532 -1.736e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00673 0.0006096 0.004391 0.003231 0.9889 0.9919 0.006861 0.8532 0.8923 0.0118 ] Network output: [ -0.0002265 0.001601 1.001 -1.122e-05 5.036e-06 0.9983 -8.455e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2207 0.1044 0.3479 0.1424 0.9849 0.9939 0.2214 0.4345 0.8752 0.7034 ] Network output: [ 0.003401 -0.01611 0.9942 6.829e-06 -3.066e-06 1.015 5.146e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09775 0.1845 0.1977 0.9873 0.9919 0.1105 0.7383 0.8618 0.3052 ] Network output: [ -0.003188 0.01491 1.005 7.421e-06 -3.332e-06 0.9869 5.593e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09384 0.0919 0.165 0.1963 0.9852 0.9911 0.09386 0.6622 0.8371 0.2488 ] Network output: [ 8.943e-05 1 -5.544e-05 9.74e-07 -4.373e-07 0.9998 7.341e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001975 Epoch 9450 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009114 0.9967 0.9923 -1.995e-07 8.954e-08 -0.007206 -1.503e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003496 -0.003333 -0.006837 0.005491 0.9699 0.9743 0.006795 0.8258 0.8204 0.01649 ] Network output: [ 0.9999 0.0001557 0.0004189 -3.572e-06 1.604e-06 -0.000388 -2.692e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2072 -0.03536 -0.1591 0.1835 0.9834 0.9932 0.2325 0.4305 0.8685 0.7096 ] Network output: [ -0.00909 1.003 1.008 -2.303e-07 1.034e-07 0.007531 -1.735e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006731 0.0006097 0.004391 0.003231 0.9889 0.9919 0.006862 0.8532 0.8923 0.0118 ] Network output: [ -0.0002263 0.001601 1.001 -1.121e-05 5.03e-06 0.9983 -8.445e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2207 0.1044 0.3479 0.1424 0.9849 0.9939 0.2215 0.4345 0.8752 0.7034 ] Network output: [ 0.0034 -0.01611 0.9942 6.821e-06 -3.062e-06 1.015 5.14e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09775 0.1845 0.1977 0.9873 0.9919 0.1105 0.7383 0.8618 0.3052 ] Network output: [ -0.003186 0.0149 1.005 7.413e-06 -3.328e-06 0.9869 5.586e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09384 0.09191 0.165 0.1963 0.9852 0.9911 0.09386 0.6622 0.8371 0.2488 ] Network output: [ 8.94e-05 1 -5.541e-05 9.729e-07 -4.368e-07 0.9998 7.332e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001974 Epoch 9451 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009113 0.9967 0.9923 -1.994e-07 8.95e-08 -0.007205 -1.502e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003496 -0.003333 -0.006836 0.005491 0.9699 0.9743 0.006796 0.8258 0.8204 0.01649 ] Network output: [ 0.9999 0.0001555 0.0004187 -3.568e-06 1.602e-06 -0.0003877 -2.689e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2072 -0.03536 -0.1591 0.1835 0.9834 0.9932 0.2325 0.4305 0.8685 0.7096 ] Network output: [ -0.009089 1.003 1.008 -2.301e-07 1.033e-07 0.00753 -1.734e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006731 0.0006098 0.004391 0.003231 0.9889 0.9919 0.006862 0.8532 0.8923 0.01179 ] Network output: [ -0.0002262 0.0016 1.001 -1.119e-05 5.025e-06 0.9983 -8.435e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2207 0.1044 0.3479 0.1424 0.9849 0.9939 0.2215 0.4345 0.8752 0.7034 ] Network output: [ 0.003398 -0.0161 0.9942 6.813e-06 -3.058e-06 1.015 5.134e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09775 0.1845 0.1977 0.9873 0.9919 0.1105 0.7383 0.8618 0.3052 ] Network output: [ -0.003185 0.0149 1.005 7.404e-06 -3.324e-06 0.9869 5.58e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09385 0.09191 0.165 0.1963 0.9852 0.9911 0.09386 0.6622 0.8371 0.2488 ] Network output: [ 8.938e-05 1 -5.538e-05 9.718e-07 -4.363e-07 0.9998 7.324e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001973 Epoch 9452 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009112 0.9967 0.9923 -1.993e-07 8.946e-08 -0.007205 -1.502e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003496 -0.003333 -0.006835 0.005491 0.9699 0.9743 0.006796 0.8258 0.8204 0.01649 ] Network output: [ 0.9999 0.0001553 0.0004185 -3.564e-06 1.6e-06 -0.0003875 -2.686e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2072 -0.03536 -0.1591 0.1835 0.9834 0.9932 0.2325 0.4305 0.8685 0.7096 ] Network output: [ -0.009088 1.003 1.008 -2.3e-07 1.032e-07 0.00753 -1.733e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006732 0.0006098 0.004391 0.003231 0.9889 0.9919 0.006863 0.8532 0.8923 0.01179 ] Network output: [ -0.000226 0.001599 1.001 -1.118e-05 5.019e-06 0.9983 -8.425e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2207 0.1044 0.3479 0.1424 0.9849 0.9939 0.2215 0.4345 0.8752 0.7034 ] Network output: [ 0.003397 -0.01609 0.9942 6.805e-06 -3.055e-06 1.015 5.128e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09776 0.1845 0.1977 0.9873 0.9919 0.1105 0.7383 0.8618 0.3052 ] Network output: [ -0.003183 0.01489 1.005 7.396e-06 -3.32e-06 0.9869 5.574e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09385 0.09191 0.165 0.1963 0.9852 0.9911 0.09386 0.6622 0.8371 0.2488 ] Network output: [ 8.935e-05 1 -5.535e-05 9.706e-07 -4.358e-07 0.9998 7.315e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001972 Epoch 9453 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009111 0.9967 0.9923 -1.992e-07 8.943e-08 -0.007204 -1.501e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003496 -0.003333 -0.006835 0.00549 0.9699 0.9743 0.006796 0.8258 0.8204 0.01649 ] Network output: [ 0.9999 0.0001551 0.0004183 -3.559e-06 1.598e-06 -0.0003872 -2.682e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2072 -0.03536 -0.1591 0.1835 0.9834 0.9932 0.2325 0.4305 0.8685 0.7096 ] Network output: [ -0.009087 1.003 1.008 -2.298e-07 1.032e-07 0.007529 -1.732e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006732 0.0006099 0.004391 0.00323 0.9889 0.9919 0.006863 0.8531 0.8923 0.01179 ] Network output: [ -0.0002258 0.001598 1.001 -1.117e-05 5.013e-06 0.9983 -8.415e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2207 0.1044 0.3479 0.1424 0.9849 0.9939 0.2215 0.4344 0.8752 0.7034 ] Network output: [ 0.003395 -0.01609 0.9942 6.797e-06 -3.051e-06 1.015 5.122e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09776 0.1845 0.1977 0.9873 0.9919 0.1105 0.7382 0.8618 0.3052 ] Network output: [ -0.003182 0.01488 1.005 7.387e-06 -3.316e-06 0.9869 5.567e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09385 0.09191 0.165 0.1963 0.9852 0.9911 0.09387 0.6622 0.8371 0.2488 ] Network output: [ 8.932e-05 1 -5.532e-05 9.695e-07 -4.352e-07 0.9998 7.307e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001971 Epoch 9454 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00911 0.9967 0.9923 -1.991e-07 8.939e-08 -0.007204 -1.501e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003496 -0.003333 -0.006834 0.00549 0.9699 0.9743 0.006796 0.8258 0.8204 0.01649 ] Network output: [ 0.9999 0.000155 0.0004181 -3.555e-06 1.596e-06 -0.0003869 -2.679e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2072 -0.03536 -0.1591 0.1835 0.9834 0.9932 0.2325 0.4305 0.8685 0.7096 ] Network output: [ -0.009086 1.003 1.008 -2.297e-07 1.031e-07 0.007528 -1.731e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006732 0.00061 0.004391 0.00323 0.9889 0.9919 0.006863 0.8531 0.8923 0.01179 ] Network output: [ -0.0002256 0.001598 1.001 -1.115e-05 5.007e-06 0.9983 -8.405e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2207 0.1045 0.3479 0.1424 0.9849 0.9939 0.2215 0.4344 0.8752 0.7034 ] Network output: [ 0.003394 -0.01608 0.9942 6.789e-06 -3.048e-06 1.015 5.116e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09777 0.1845 0.1977 0.9873 0.9919 0.1105 0.7382 0.8618 0.3052 ] Network output: [ -0.003181 0.01488 1.005 7.378e-06 -3.312e-06 0.9869 5.561e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09385 0.09192 0.165 0.1963 0.9852 0.9911 0.09387 0.6621 0.8371 0.2488 ] Network output: [ 8.929e-05 1 -5.529e-05 9.684e-07 -4.347e-07 0.9998 7.298e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000197 Epoch 9455 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009109 0.9967 0.9923 -1.99e-07 8.935e-08 -0.007203 -1.5e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003496 -0.003333 -0.006834 0.005489 0.9699 0.9743 0.006796 0.8258 0.8204 0.01649 ] Network output: [ 0.9999 0.0001548 0.0004179 -3.551e-06 1.594e-06 -0.0003867 -2.676e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2072 -0.03536 -0.1591 0.1835 0.9834 0.9932 0.2325 0.4304 0.8685 0.7096 ] Network output: [ -0.009085 1.003 1.008 -2.295e-07 1.03e-07 0.007528 -1.73e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006733 0.0006101 0.004391 0.00323 0.9889 0.9919 0.006864 0.8531 0.8923 0.01179 ] Network output: [ -0.0002255 0.001597 1.001 -1.114e-05 5.001e-06 0.9983 -8.395e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2207 0.1045 0.3479 0.1424 0.9849 0.9939 0.2215 0.4344 0.8752 0.7034 ] Network output: [ 0.003392 -0.01607 0.9942 6.781e-06 -3.044e-06 1.015 5.11e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09777 0.1845 0.1977 0.9873 0.9919 0.1105 0.7382 0.8618 0.3052 ] Network output: [ -0.003179 0.01487 1.005 7.37e-06 -3.309e-06 0.9869 5.554e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09386 0.09192 0.165 0.1963 0.9852 0.9911 0.09387 0.6621 0.8371 0.2488 ] Network output: [ 8.926e-05 1 -5.525e-05 9.673e-07 -4.342e-07 0.9998 7.29e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001969 Epoch 9456 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009108 0.9967 0.9923 -1.989e-07 8.931e-08 -0.007203 -1.499e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003496 -0.003334 -0.006833 0.005489 0.9699 0.9743 0.006797 0.8258 0.8204 0.01648 ] Network output: [ 0.9999 0.0001546 0.0004177 -3.547e-06 1.592e-06 -0.0003864 -2.673e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2072 -0.03536 -0.1591 0.1835 0.9834 0.9932 0.2325 0.4304 0.8685 0.7096 ] Network output: [ -0.009084 1.003 1.008 -2.294e-07 1.03e-07 0.007527 -1.729e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006733 0.0006102 0.004391 0.00323 0.9889 0.9919 0.006864 0.8531 0.8923 0.01179 ] Network output: [ -0.0002253 0.001596 1.001 -1.113e-05 4.995e-06 0.9983 -8.385e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2208 0.1045 0.3479 0.1424 0.9849 0.9939 0.2215 0.4344 0.8752 0.7034 ] Network output: [ 0.003391 -0.01607 0.9942 6.773e-06 -3.041e-06 1.015 5.104e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09777 0.1845 0.1977 0.9873 0.9919 0.1105 0.7382 0.8618 0.3052 ] Network output: [ -0.003178 0.01487 1.005 7.361e-06 -3.305e-06 0.9869 5.548e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09386 0.09192 0.165 0.1963 0.9852 0.9911 0.09387 0.6621 0.8371 0.2488 ] Network output: [ 8.924e-05 1 -5.522e-05 9.661e-07 -4.337e-07 0.9998 7.281e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001968 Epoch 9457 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009107 0.9967 0.9923 -1.988e-07 8.927e-08 -0.007202 -1.499e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003496 -0.003334 -0.006832 0.005489 0.9699 0.9743 0.006797 0.8258 0.8204 0.01648 ] Network output: [ 0.9999 0.0001544 0.0004175 -3.543e-06 1.59e-06 -0.0003861 -2.67e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2072 -0.03536 -0.159 0.1835 0.9834 0.9932 0.2325 0.4304 0.8685 0.7096 ] Network output: [ -0.009084 1.003 1.008 -2.292e-07 1.029e-07 0.007526 -1.727e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006734 0.0006102 0.00439 0.003229 0.9889 0.9919 0.006865 0.8531 0.8923 0.01179 ] Network output: [ -0.0002251 0.001596 1.001 -1.111e-05 4.989e-06 0.9983 -8.375e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2208 0.1045 0.3479 0.1424 0.9849 0.9939 0.2215 0.4344 0.8752 0.7034 ] Network output: [ 0.003389 -0.01606 0.9942 6.765e-06 -3.037e-06 1.015 5.098e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09778 0.1845 0.1977 0.9873 0.9919 0.1105 0.7382 0.8618 0.3052 ] Network output: [ -0.003177 0.01486 1.005 7.353e-06 -3.301e-06 0.9869 5.541e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09386 0.09192 0.165 0.1963 0.9852 0.9911 0.09387 0.6621 0.8371 0.2488 ] Network output: [ 8.921e-05 1 -5.519e-05 9.65e-07 -4.332e-07 0.9998 7.273e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001967 Epoch 9458 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009106 0.9967 0.9923 -1.988e-07 8.923e-08 -0.007202 -1.498e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003496 -0.003334 -0.006832 0.005488 0.9699 0.9743 0.006797 0.8258 0.8204 0.01648 ] Network output: [ 0.9999 0.0001542 0.0004173 -3.538e-06 1.588e-06 -0.0003859 -2.667e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2072 -0.03537 -0.159 0.1835 0.9834 0.9932 0.2325 0.4304 0.8685 0.7096 ] Network output: [ -0.009083 1.003 1.008 -2.291e-07 1.028e-07 0.007525 -1.726e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006734 0.0006103 0.00439 0.003229 0.9889 0.9919 0.006865 0.8531 0.8923 0.01179 ] Network output: [ -0.000225 0.001595 1.001 -1.11e-05 4.983e-06 0.9983 -8.365e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2208 0.1045 0.3479 0.1424 0.9849 0.9939 0.2215 0.4344 0.8752 0.7034 ] Network output: [ 0.003388 -0.01605 0.9942 6.757e-06 -3.033e-06 1.015 5.092e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09778 0.1845 0.1977 0.9873 0.9919 0.1105 0.7382 0.8618 0.3052 ] Network output: [ -0.003175 0.01485 1.005 7.344e-06 -3.297e-06 0.9869 5.535e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09386 0.09192 0.165 0.1963 0.9852 0.9911 0.09388 0.6621 0.8371 0.2488 ] Network output: [ 8.918e-05 1 -5.516e-05 9.639e-07 -4.327e-07 0.9998 7.264e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001966 Epoch 9459 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009105 0.9967 0.9923 -1.987e-07 8.919e-08 -0.007201 -1.497e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003496 -0.003334 -0.006831 0.005488 0.9699 0.9743 0.006797 0.8258 0.8204 0.01648 ] Network output: [ 0.9999 0.000154 0.0004171 -3.534e-06 1.587e-06 -0.0003856 -2.663e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2073 -0.03537 -0.159 0.1835 0.9834 0.9932 0.2325 0.4304 0.8685 0.7096 ] Network output: [ -0.009082 1.003 1.008 -2.289e-07 1.028e-07 0.007525 -1.725e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006734 0.0006104 0.00439 0.003229 0.9889 0.9919 0.006866 0.8531 0.8923 0.01179 ] Network output: [ -0.0002248 0.001594 1.001 -1.109e-05 4.977e-06 0.9983 -8.355e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2208 0.1045 0.3479 0.1424 0.9849 0.9939 0.2215 0.4344 0.8752 0.7034 ] Network output: [ 0.003387 -0.01605 0.9942 6.749e-06 -3.03e-06 1.015 5.086e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09778 0.1845 0.1977 0.9873 0.9919 0.1105 0.7382 0.8618 0.3052 ] Network output: [ -0.003174 0.01485 1.005 7.336e-06 -3.293e-06 0.9869 5.529e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09386 0.09193 0.165 0.1963 0.9852 0.9911 0.09388 0.6621 0.8371 0.2488 ] Network output: [ 8.915e-05 1 -5.513e-05 9.627e-07 -4.322e-07 0.9998 7.256e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001965 Epoch 9460 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009104 0.9967 0.9923 -1.986e-07 8.915e-08 -0.0072 -1.497e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003496 -0.003334 -0.00683 0.005487 0.9699 0.9743 0.006797 0.8258 0.8204 0.01648 ] Network output: [ 0.9999 0.0001538 0.0004169 -3.53e-06 1.585e-06 -0.0003853 -2.66e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2073 -0.03537 -0.159 0.1834 0.9834 0.9932 0.2325 0.4304 0.8685 0.7096 ] Network output: [ -0.009081 1.003 1.008 -2.288e-07 1.027e-07 0.007524 -1.724e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006735 0.0006105 0.00439 0.003229 0.9889 0.9919 0.006866 0.8531 0.8923 0.01179 ] Network output: [ -0.0002246 0.001593 1.001 -1.107e-05 4.971e-06 0.9983 -8.345e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2208 0.1045 0.348 0.1424 0.9849 0.9939 0.2215 0.4344 0.8752 0.7034 ] Network output: [ 0.003385 -0.01604 0.9942 6.741e-06 -3.026e-06 1.015 5.08e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09779 0.1845 0.1977 0.9873 0.9919 0.1105 0.7382 0.8618 0.3052 ] Network output: [ -0.003172 0.01484 1.005 7.327e-06 -3.29e-06 0.9869 5.522e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09387 0.09193 0.165 0.1963 0.9852 0.9911 0.09388 0.6621 0.8371 0.2488 ] Network output: [ 8.913e-05 1 -5.51e-05 9.616e-07 -4.317e-07 0.9998 7.247e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001964 Epoch 9461 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009103 0.9967 0.9923 -1.985e-07 8.911e-08 -0.0072 -1.496e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003496 -0.003334 -0.00683 0.005487 0.9699 0.9743 0.006798 0.8258 0.8204 0.01648 ] Network output: [ 0.9999 0.0001537 0.0004167 -3.526e-06 1.583e-06 -0.0003851 -2.657e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2073 -0.03537 -0.159 0.1834 0.9834 0.9932 0.2325 0.4304 0.8685 0.7096 ] Network output: [ -0.00908 1.003 1.008 -2.286e-07 1.026e-07 0.007523 -1.723e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006735 0.0006105 0.00439 0.003228 0.9889 0.9919 0.006866 0.8531 0.8923 0.01179 ] Network output: [ -0.0002245 0.001593 1.001 -1.106e-05 4.965e-06 0.9983 -8.335e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2208 0.1045 0.348 0.1424 0.9849 0.9939 0.2215 0.4344 0.8752 0.7034 ] Network output: [ 0.003384 -0.01603 0.9942 6.733e-06 -3.023e-06 1.015 5.074e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.09779 0.1845 0.1977 0.9873 0.9919 0.1105 0.7382 0.8618 0.3052 ] Network output: [ -0.003171 0.01483 1.005 7.319e-06 -3.286e-06 0.9869 5.516e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09387 0.09193 0.165 0.1963 0.9852 0.9911 0.09388 0.6621 0.8371 0.2488 ] Network output: [ 8.91e-05 1 -5.507e-05 9.605e-07 -4.312e-07 0.9998 7.239e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001963 Epoch 9462 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009102 0.9967 0.9923 -1.984e-07 8.908e-08 -0.007199 -1.495e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003497 -0.003334 -0.006829 0.005486 0.9699 0.9743 0.006798 0.8258 0.8204 0.01648 ] Network output: [ 0.9999 0.0001535 0.0004166 -3.522e-06 1.581e-06 -0.0003848 -2.654e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2073 -0.03537 -0.159 0.1834 0.9834 0.9932 0.2326 0.4304 0.8685 0.7096 ] Network output: [ -0.009079 1.003 1.008 -2.285e-07 1.026e-07 0.007523 -1.722e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006736 0.0006106 0.00439 0.003228 0.9889 0.9919 0.006867 0.8531 0.8923 0.01179 ] Network output: [ -0.0002243 0.001592 1.001 -1.105e-05 4.96e-06 0.9983 -8.326e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2208 0.1045 0.348 0.1424 0.9849 0.9939 0.2215 0.4344 0.8752 0.7034 ] Network output: [ 0.003382 -0.01603 0.9942 6.725e-06 -3.019e-06 1.015 5.068e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1104 0.0978 0.1845 0.1977 0.9873 0.9919 0.1105 0.7381 0.8618 0.3052 ] Network output: [ -0.00317 0.01483 1.005 7.311e-06 -3.282e-06 0.9869 5.509e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09387 0.09193 0.165 0.1964 0.9852 0.9911 0.09388 0.662 0.8371 0.2488 ] Network output: [ 8.907e-05 1 -5.504e-05 9.594e-07 -4.307e-07 0.9998 7.23e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001962 Epoch 9463 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009101 0.9967 0.9923 -1.983e-07 8.904e-08 -0.007199 -1.495e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003497 -0.003334 -0.006829 0.005486 0.9699 0.9743 0.006798 0.8258 0.8204 0.01648 ] Network output: [ 0.9999 0.0001533 0.0004164 -3.517e-06 1.579e-06 -0.0003845 -2.651e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2073 -0.03537 -0.159 0.1834 0.9834 0.9932 0.2326 0.4304 0.8685 0.7096 ] Network output: [ -0.009078 1.003 1.008 -2.283e-07 1.025e-07 0.007522 -1.721e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006736 0.0006107 0.00439 0.003228 0.9889 0.9919 0.006867 0.8531 0.8923 0.01179 ] Network output: [ -0.0002241 0.001591 1.001 -1.103e-05 4.954e-06 0.9983 -8.316e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2208 0.1045 0.348 0.1424 0.9849 0.9939 0.2216 0.4344 0.8752 0.7034 ] Network output: [ 0.003381 -0.01602 0.9942 6.717e-06 -3.016e-06 1.015 5.062e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.0978 0.1845 0.1977 0.9873 0.9919 0.1105 0.7381 0.8618 0.3052 ] Network output: [ -0.003168 0.01482 1.005 7.302e-06 -3.278e-06 0.9869 5.503e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09387 0.09193 0.165 0.1964 0.9852 0.9911 0.09389 0.662 0.8371 0.2488 ] Network output: [ 8.904e-05 1 -5.501e-05 9.583e-07 -4.302e-07 0.9998 7.222e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001961 Epoch 9464 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0091 0.9967 0.9923 -1.982e-07 8.9e-08 -0.007198 -1.494e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003497 -0.003334 -0.006828 0.005486 0.9699 0.9743 0.006798 0.8258 0.8204 0.01648 ] Network output: [ 0.9999 0.0001531 0.0004162 -3.513e-06 1.577e-06 -0.0003843 -2.648e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2073 -0.03537 -0.159 0.1834 0.9834 0.9932 0.2326 0.4304 0.8685 0.7096 ] Network output: [ -0.009078 1.003 1.008 -2.282e-07 1.024e-07 0.007521 -1.72e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006736 0.0006108 0.00439 0.003228 0.9889 0.9919 0.006868 0.8531 0.8923 0.01178 ] Network output: [ -0.000224 0.001591 1.001 -1.102e-05 4.948e-06 0.9983 -8.306e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2208 0.1045 0.348 0.1424 0.9849 0.9939 0.2216 0.4344 0.8752 0.7034 ] Network output: [ 0.003379 -0.01601 0.9942 6.709e-06 -3.012e-06 1.015 5.056e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.0978 0.1845 0.1977 0.9873 0.9919 0.1105 0.7381 0.8617 0.3052 ] Network output: [ -0.003167 0.01481 1.005 7.294e-06 -3.274e-06 0.9869 5.497e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09387 0.09194 0.165 0.1964 0.9852 0.9911 0.09389 0.662 0.8371 0.2488 ] Network output: [ 8.902e-05 1 -5.498e-05 9.571e-07 -4.297e-07 0.9998 7.213e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000196 Epoch 9465 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009099 0.9967 0.9923 -1.982e-07 8.896e-08 -0.007198 -1.493e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003497 -0.003334 -0.006827 0.005485 0.9699 0.9743 0.006798 0.8258 0.8204 0.01648 ] Network output: [ 0.9999 0.0001529 0.000416 -3.509e-06 1.575e-06 -0.000384 -2.645e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2073 -0.03537 -0.159 0.1834 0.9834 0.9932 0.2326 0.4304 0.8685 0.7096 ] Network output: [ -0.009077 1.003 1.008 -2.28e-07 1.024e-07 0.007521 -1.718e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006737 0.0006108 0.00439 0.003227 0.9889 0.9919 0.006868 0.8531 0.8923 0.01178 ] Network output: [ -0.0002238 0.00159 1.001 -1.101e-05 4.942e-06 0.9983 -8.296e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2208 0.1045 0.348 0.1424 0.9849 0.9939 0.2216 0.4344 0.8752 0.7034 ] Network output: [ 0.003378 -0.01601 0.9942 6.701e-06 -3.009e-06 1.015 5.05e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09781 0.1845 0.1977 0.9873 0.9919 0.1105 0.7381 0.8617 0.3052 ] Network output: [ -0.003166 0.01481 1.005 7.285e-06 -3.271e-06 0.9869 5.49e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09388 0.09194 0.165 0.1964 0.9852 0.9911 0.09389 0.662 0.8371 0.2488 ] Network output: [ 8.899e-05 1 -5.495e-05 9.56e-07 -4.292e-07 0.9998 7.205e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001959 Epoch 9466 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009098 0.9967 0.9923 -1.981e-07 8.892e-08 -0.007197 -1.493e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003497 -0.003334 -0.006827 0.005485 0.9699 0.9743 0.006798 0.8258 0.8204 0.01647 ] Network output: [ 0.9999 0.0001527 0.0004158 -3.505e-06 1.573e-06 -0.0003837 -2.641e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2073 -0.03537 -0.159 0.1834 0.9834 0.9932 0.2326 0.4304 0.8685 0.7095 ] Network output: [ -0.009076 1.003 1.008 -2.279e-07 1.023e-07 0.00752 -1.717e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006737 0.0006109 0.00439 0.003227 0.9889 0.9919 0.006868 0.8531 0.8923 0.01178 ] Network output: [ -0.0002236 0.001589 1.001 -1.1e-05 4.936e-06 0.9983 -8.286e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2208 0.1045 0.348 0.1424 0.9849 0.9939 0.2216 0.4344 0.8752 0.7033 ] Network output: [ 0.003376 -0.016 0.9942 6.694e-06 -3.005e-06 1.015 5.044e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09781 0.1845 0.1977 0.9873 0.9919 0.1105 0.7381 0.8617 0.3052 ] Network output: [ -0.003164 0.0148 1.005 7.277e-06 -3.267e-06 0.9869 5.484e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09388 0.09194 0.165 0.1964 0.9852 0.9911 0.09389 0.662 0.837 0.2488 ] Network output: [ 8.896e-05 1 -5.491e-05 9.549e-07 -4.287e-07 0.9998 7.196e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001958 Epoch 9467 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009097 0.9967 0.9923 -1.98e-07 8.888e-08 -0.007197 -1.492e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003497 -0.003335 -0.006826 0.005484 0.9699 0.9743 0.006799 0.8257 0.8204 0.01647 ] Network output: [ 0.9999 0.0001525 0.0004156 -3.501e-06 1.572e-06 -0.0003835 -2.638e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2073 -0.03537 -0.1589 0.1834 0.9834 0.9932 0.2326 0.4304 0.8685 0.7095 ] Network output: [ -0.009075 1.003 1.008 -2.277e-07 1.022e-07 0.007519 -1.716e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006738 0.000611 0.00439 0.003227 0.9889 0.9919 0.006869 0.8531 0.8923 0.01178 ] Network output: [ -0.0002234 0.001588 1.001 -1.098e-05 4.93e-06 0.9983 -8.277e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2208 0.1045 0.348 0.1424 0.9849 0.9939 0.2216 0.4344 0.8752 0.7033 ] Network output: [ 0.003375 -0.01599 0.9942 6.686e-06 -3.001e-06 1.015 5.039e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09782 0.1845 0.1977 0.9873 0.9919 0.1105 0.7381 0.8617 0.3052 ] Network output: [ -0.003163 0.0148 1.005 7.268e-06 -3.263e-06 0.9869 5.478e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09388 0.09194 0.165 0.1964 0.9852 0.9911 0.09389 0.662 0.837 0.2488 ] Network output: [ 8.893e-05 1 -5.488e-05 9.538e-07 -4.282e-07 0.9998 7.188e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001957 Epoch 9468 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009096 0.9967 0.9923 -1.979e-07 8.884e-08 -0.007196 -1.491e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003497 -0.003335 -0.006825 0.005484 0.9699 0.9743 0.006799 0.8257 0.8204 0.01647 ] Network output: [ 0.9999 0.0001524 0.0004154 -3.497e-06 1.57e-06 -0.0003832 -2.635e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2073 -0.03538 -0.1589 0.1834 0.9834 0.9932 0.2326 0.4304 0.8685 0.7095 ] Network output: [ -0.009074 1.003 1.008 -2.276e-07 1.022e-07 0.007519 -1.715e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006738 0.0006111 0.00439 0.003227 0.9889 0.9919 0.006869 0.8531 0.8923 0.01178 ] Network output: [ -0.0002233 0.001588 1.001 -1.097e-05 4.924e-06 0.9983 -8.267e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2208 0.1045 0.348 0.1424 0.9849 0.9939 0.2216 0.4344 0.8752 0.7033 ] Network output: [ 0.003373 -0.01599 0.9942 6.678e-06 -2.998e-06 1.015 5.033e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09782 0.1845 0.1977 0.9873 0.9919 0.1106 0.7381 0.8617 0.3052 ] Network output: [ -0.003161 0.01479 1.005 7.26e-06 -3.259e-06 0.9869 5.471e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09388 0.09194 0.165 0.1964 0.9852 0.9911 0.0939 0.662 0.837 0.2488 ] Network output: [ 8.891e-05 1 -5.485e-05 9.527e-07 -4.277e-07 0.9998 7.18e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001956 Epoch 9469 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009095 0.9967 0.9923 -1.978e-07 8.88e-08 -0.007196 -1.491e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003497 -0.003335 -0.006825 0.005484 0.9699 0.9743 0.006799 0.8257 0.8204 0.01647 ] Network output: [ 0.9999 0.0001522 0.0004152 -3.492e-06 1.568e-06 -0.0003829 -2.632e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2073 -0.03538 -0.1589 0.1834 0.9834 0.9932 0.2326 0.4304 0.8685 0.7095 ] Network output: [ -0.009073 1.003 1.008 -2.274e-07 1.021e-07 0.007518 -1.714e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006739 0.0006111 0.004389 0.003226 0.9889 0.9919 0.00687 0.8531 0.8923 0.01178 ] Network output: [ -0.0002231 0.001587 1.001 -1.096e-05 4.919e-06 0.9983 -8.257e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2208 0.1045 0.348 0.1424 0.9849 0.9939 0.2216 0.4344 0.8752 0.7033 ] Network output: [ 0.003372 -0.01598 0.9942 6.67e-06 -2.994e-06 1.015 5.027e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09782 0.1845 0.1977 0.9873 0.9919 0.1106 0.7381 0.8617 0.3052 ] Network output: [ -0.00316 0.01478 1.005 7.252e-06 -3.255e-06 0.9869 5.465e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09388 0.09195 0.165 0.1964 0.9852 0.9911 0.0939 0.662 0.837 0.2488 ] Network output: [ 8.888e-05 1 -5.482e-05 9.516e-07 -4.272e-07 0.9998 7.171e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001955 Epoch 9470 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009094 0.9967 0.9923 -1.977e-07 8.876e-08 -0.007195 -1.49e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003497 -0.003335 -0.006824 0.005483 0.9699 0.9743 0.006799 0.8257 0.8204 0.01647 ] Network output: [ 0.9999 0.000152 0.000415 -3.488e-06 1.566e-06 -0.0003827 -2.629e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2073 -0.03538 -0.1589 0.1834 0.9834 0.9932 0.2326 0.4304 0.8685 0.7095 ] Network output: [ -0.009072 1.003 1.008 -2.273e-07 1.02e-07 0.007517 -1.713e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006739 0.0006112 0.004389 0.003226 0.9889 0.9919 0.00687 0.8531 0.8923 0.01178 ] Network output: [ -0.0002229 0.001586 1.001 -1.094e-05 4.913e-06 0.9983 -8.247e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2209 0.1045 0.348 0.1424 0.9849 0.9939 0.2216 0.4344 0.8752 0.7033 ] Network output: [ 0.00337 -0.01597 0.9942 6.662e-06 -2.991e-06 1.015 5.021e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09783 0.1845 0.1977 0.9873 0.9919 0.1106 0.738 0.8617 0.3052 ] Network output: [ -0.003159 0.01478 1.005 7.243e-06 -3.252e-06 0.9869 5.459e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09389 0.09195 0.165 0.1964 0.9852 0.9911 0.0939 0.6619 0.837 0.2488 ] Network output: [ 8.885e-05 1 -5.479e-05 9.504e-07 -4.267e-07 0.9998 7.163e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001954 Epoch 9471 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009093 0.9967 0.9923 -1.976e-07 8.872e-08 -0.007195 -1.489e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003497 -0.003335 -0.006823 0.005483 0.9699 0.9743 0.006799 0.8257 0.8204 0.01647 ] Network output: [ 0.9999 0.0001518 0.0004148 -3.484e-06 1.564e-06 -0.0003824 -2.626e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2073 -0.03538 -0.1589 0.1834 0.9834 0.9932 0.2326 0.4304 0.8685 0.7095 ] Network output: [ -0.009072 1.003 1.008 -2.271e-07 1.02e-07 0.007516 -1.712e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006739 0.0006113 0.004389 0.003226 0.9889 0.9919 0.006871 0.8531 0.8923 0.01178 ] Network output: [ -0.0002228 0.001586 1.001 -1.093e-05 4.907e-06 0.9983 -8.237e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2209 0.1045 0.348 0.1424 0.9849 0.9939 0.2216 0.4343 0.8752 0.7033 ] Network output: [ 0.003369 -0.01597 0.9942 6.654e-06 -2.987e-06 1.015 5.015e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09783 0.1845 0.1977 0.9873 0.9919 0.1106 0.738 0.8617 0.3052 ] Network output: [ -0.003157 0.01477 1.005 7.235e-06 -3.248e-06 0.9869 5.452e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09389 0.09195 0.165 0.1964 0.9852 0.9911 0.0939 0.6619 0.837 0.2488 ] Network output: [ 8.882e-05 1 -5.476e-05 9.493e-07 -4.262e-07 0.9998 7.155e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001953 Epoch 9472 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009092 0.9967 0.9923 -1.975e-07 8.868e-08 -0.007194 -1.489e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003497 -0.003335 -0.006823 0.005482 0.9699 0.9743 0.0068 0.8257 0.8204 0.01647 ] Network output: [ 0.9999 0.0001516 0.0004147 -3.48e-06 1.562e-06 -0.0003822 -2.623e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2073 -0.03538 -0.1589 0.1834 0.9834 0.9932 0.2326 0.4304 0.8685 0.7095 ] Network output: [ -0.009071 1.003 1.008 -2.27e-07 1.019e-07 0.007516 -1.711e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00674 0.0006114 0.004389 0.003226 0.9889 0.9919 0.006871 0.853 0.8923 0.01178 ] Network output: [ -0.0002226 0.001585 1.001 -1.092e-05 4.901e-06 0.9983 -8.228e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2209 0.1045 0.348 0.1424 0.9849 0.9939 0.2216 0.4343 0.8752 0.7033 ] Network output: [ 0.003367 -0.01596 0.9942 6.646e-06 -2.984e-06 1.015 5.009e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09783 0.1845 0.1977 0.9873 0.9919 0.1106 0.738 0.8617 0.3052 ] Network output: [ -0.003156 0.01476 1.005 7.226e-06 -3.244e-06 0.9869 5.446e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09389 0.09195 0.165 0.1964 0.9852 0.9911 0.0939 0.6619 0.837 0.2488 ] Network output: [ 8.88e-05 1 -5.473e-05 9.482e-07 -4.257e-07 0.9998 7.146e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001952 Epoch 9473 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009092 0.9967 0.9923 -1.974e-07 8.864e-08 -0.007194 -1.488e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003497 -0.003335 -0.006822 0.005482 0.9699 0.9743 0.0068 0.8257 0.8204 0.01647 ] Network output: [ 0.9999 0.0001514 0.0004145 -3.476e-06 1.56e-06 -0.0003819 -2.62e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2073 -0.03538 -0.1589 0.1834 0.9834 0.9932 0.2326 0.4303 0.8685 0.7095 ] Network output: [ -0.00907 1.003 1.008 -2.268e-07 1.018e-07 0.007515 -1.709e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00674 0.0006114 0.004389 0.003225 0.9889 0.9919 0.006871 0.853 0.8923 0.01178 ] Network output: [ -0.0002224 0.001584 1.001 -1.09e-05 4.895e-06 0.9983 -8.218e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2209 0.1045 0.348 0.1424 0.9849 0.9939 0.2216 0.4343 0.8752 0.7033 ] Network output: [ 0.003366 -0.01595 0.9942 6.639e-06 -2.98e-06 1.015 5.003e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09784 0.1845 0.1977 0.9873 0.9919 0.1106 0.738 0.8617 0.3052 ] Network output: [ -0.003155 0.01476 1.005 7.218e-06 -3.24e-06 0.9869 5.44e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09389 0.09195 0.165 0.1964 0.9852 0.9911 0.09391 0.6619 0.837 0.2488 ] Network output: [ 8.877e-05 1 -5.47e-05 9.471e-07 -4.252e-07 0.9998 7.138e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001951 Epoch 9474 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009091 0.9967 0.9923 -1.974e-07 8.86e-08 -0.007193 -1.487e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003497 -0.003335 -0.006822 0.005482 0.9699 0.9743 0.0068 0.8257 0.8204 0.01647 ] Network output: [ 0.9999 0.0001513 0.0004143 -3.472e-06 1.559e-06 -0.0003816 -2.616e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2073 -0.03538 -0.1589 0.1834 0.9834 0.9932 0.2326 0.4303 0.8685 0.7095 ] Network output: [ -0.009069 1.003 1.008 -2.267e-07 1.018e-07 0.007514 -1.708e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006741 0.0006115 0.004389 0.003225 0.9889 0.9919 0.006872 0.853 0.8923 0.01178 ] Network output: [ -0.0002223 0.001584 1.001 -1.089e-05 4.89e-06 0.9983 -8.208e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2209 0.1045 0.348 0.1424 0.9849 0.9939 0.2216 0.4343 0.8752 0.7033 ] Network output: [ 0.003364 -0.01595 0.9942 6.631e-06 -2.977e-06 1.015 4.997e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09784 0.1845 0.1977 0.9873 0.9919 0.1106 0.738 0.8617 0.3052 ] Network output: [ -0.003153 0.01475 1.005 7.21e-06 -3.237e-06 0.9869 5.433e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09389 0.09196 0.165 0.1964 0.9852 0.9911 0.09391 0.6619 0.837 0.2488 ] Network output: [ 8.874e-05 1 -5.468e-05 9.46e-07 -4.247e-07 0.9998 7.129e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000195 Epoch 9475 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00909 0.9967 0.9923 -1.973e-07 8.856e-08 -0.007192 -1.487e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003497 -0.003335 -0.006821 0.005481 0.9699 0.9743 0.0068 0.8257 0.8203 0.01647 ] Network output: [ 0.9999 0.0001511 0.0004141 -3.468e-06 1.557e-06 -0.0003814 -2.613e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2074 -0.03538 -0.1589 0.1834 0.9834 0.9932 0.2326 0.4303 0.8685 0.7095 ] Network output: [ -0.009068 1.003 1.008 -2.265e-07 1.017e-07 0.007514 -1.707e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006741 0.0006116 0.004389 0.003225 0.9889 0.9919 0.006872 0.853 0.8923 0.01178 ] Network output: [ -0.0002221 0.001583 1.001 -1.088e-05 4.884e-06 0.9983 -8.198e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2209 0.1046 0.348 0.1424 0.9849 0.9939 0.2216 0.4343 0.8752 0.7033 ] Network output: [ 0.003363 -0.01594 0.9942 6.623e-06 -2.973e-06 1.015 4.991e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09785 0.1845 0.1977 0.9873 0.9919 0.1106 0.738 0.8617 0.3052 ] Network output: [ -0.003152 0.01474 1.005 7.201e-06 -3.233e-06 0.9869 5.427e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0939 0.09196 0.165 0.1964 0.9852 0.9911 0.09391 0.6619 0.837 0.2488 ] Network output: [ 8.872e-05 1 -5.465e-05 9.449e-07 -4.242e-07 0.9998 7.121e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001949 Epoch 9476 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009089 0.9967 0.9923 -1.972e-07 8.852e-08 -0.007192 -1.486e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003497 -0.003335 -0.00682 0.005481 0.9699 0.9743 0.0068 0.8257 0.8203 0.01646 ] Network output: [ 0.9999 0.0001509 0.0004139 -3.463e-06 1.555e-06 -0.0003811 -2.61e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2074 -0.03538 -0.1589 0.1834 0.9834 0.9932 0.2327 0.4303 0.8685 0.7095 ] Network output: [ -0.009067 1.003 1.008 -2.264e-07 1.016e-07 0.007513 -1.706e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006741 0.0006117 0.004389 0.003225 0.9889 0.9919 0.006873 0.853 0.8923 0.01178 ] Network output: [ -0.0002219 0.001582 1.001 -1.087e-05 4.878e-06 0.9983 -8.189e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2209 0.1046 0.348 0.1424 0.9849 0.9939 0.2216 0.4343 0.8752 0.7033 ] Network output: [ 0.003361 -0.01593 0.9942 6.615e-06 -2.97e-06 1.015 4.985e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09785 0.1845 0.1977 0.9873 0.9919 0.1106 0.738 0.8617 0.3052 ] Network output: [ -0.00315 0.01474 1.005 7.193e-06 -3.229e-06 0.987 5.421e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0939 0.09196 0.165 0.1964 0.9852 0.9911 0.09391 0.6619 0.837 0.2489 ] Network output: [ 8.869e-05 1 -5.462e-05 9.438e-07 -4.237e-07 0.9998 7.113e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001948 Epoch 9477 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009088 0.9967 0.9923 -1.971e-07 8.848e-08 -0.007191 -1.485e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003498 -0.003335 -0.00682 0.00548 0.9699 0.9743 0.006801 0.8257 0.8203 0.01646 ] Network output: [ 0.9999 0.0001507 0.0004137 -3.459e-06 1.553e-06 -0.0003808 -2.607e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2074 -0.03538 -0.1589 0.1834 0.9834 0.9932 0.2327 0.4303 0.8685 0.7095 ] Network output: [ -0.009066 1.003 1.008 -2.262e-07 1.016e-07 0.007512 -1.705e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006742 0.0006118 0.004389 0.003224 0.9889 0.9919 0.006873 0.853 0.8923 0.01177 ] Network output: [ -0.0002218 0.001581 1.001 -1.085e-05 4.872e-06 0.9983 -8.179e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2209 0.1046 0.348 0.1424 0.9849 0.9939 0.2217 0.4343 0.8752 0.7033 ] Network output: [ 0.00336 -0.01592 0.9942 6.607e-06 -2.966e-06 1.015 4.98e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09785 0.1845 0.1977 0.9873 0.9919 0.1106 0.738 0.8617 0.3052 ] Network output: [ -0.003149 0.01473 1.005 7.185e-06 -3.225e-06 0.987 5.415e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0939 0.09196 0.165 0.1964 0.9852 0.9911 0.09392 0.6619 0.837 0.2489 ] Network output: [ 8.866e-05 1 -5.459e-05 9.427e-07 -4.232e-07 0.9998 7.104e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001947 Epoch 9478 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009087 0.9967 0.9923 -1.97e-07 8.844e-08 -0.007191 -1.485e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003498 -0.003336 -0.006819 0.00548 0.9699 0.9743 0.006801 0.8257 0.8203 0.01646 ] Network output: [ 0.9999 0.0001505 0.0004135 -3.455e-06 1.551e-06 -0.0003806 -2.604e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2074 -0.03538 -0.1588 0.1834 0.9834 0.9932 0.2327 0.4303 0.8685 0.7095 ] Network output: [ -0.009066 1.003 1.008 -2.261e-07 1.015e-07 0.007512 -1.704e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006742 0.0006118 0.004389 0.003224 0.9889 0.9919 0.006873 0.853 0.8923 0.01177 ] Network output: [ -0.0002216 0.001581 1.001 -1.084e-05 4.866e-06 0.9983 -8.169e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2209 0.1046 0.348 0.1424 0.9849 0.9939 0.2217 0.4343 0.8752 0.7033 ] Network output: [ 0.003358 -0.01592 0.9942 6.6e-06 -2.963e-06 1.015 4.974e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09786 0.1845 0.1977 0.9873 0.9919 0.1106 0.7379 0.8617 0.3052 ] Network output: [ -0.003148 0.01473 1.005 7.176e-06 -3.222e-06 0.987 5.408e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0939 0.09197 0.165 0.1964 0.9852 0.9911 0.09392 0.6619 0.837 0.2489 ] Network output: [ 8.863e-05 1 -5.456e-05 9.416e-07 -4.227e-07 0.9998 7.096e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001946 Epoch 9479 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009086 0.9967 0.9923 -1.969e-07 8.84e-08 -0.00719 -1.484e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003498 -0.003336 -0.006818 0.005479 0.9699 0.9743 0.006801 0.8257 0.8203 0.01646 ] Network output: [ 0.9999 0.0001503 0.0004133 -3.451e-06 1.549e-06 -0.0003803 -2.601e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2074 -0.03539 -0.1588 0.1834 0.9834 0.9932 0.2327 0.4303 0.8685 0.7095 ] Network output: [ -0.009065 1.003 1.008 -2.259e-07 1.014e-07 0.007511 -1.703e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006743 0.0006119 0.004389 0.003224 0.9889 0.9919 0.006874 0.853 0.8923 0.01177 ] Network output: [ -0.0002214 0.00158 1.001 -1.083e-05 4.861e-06 0.9983 -8.16e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2209 0.1046 0.348 0.1424 0.9849 0.9939 0.2217 0.4343 0.8752 0.7033 ] Network output: [ 0.003357 -0.01591 0.9942 6.592e-06 -2.959e-06 1.015 4.968e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09786 0.1845 0.1977 0.9873 0.9919 0.1106 0.7379 0.8617 0.3052 ] Network output: [ -0.003146 0.01472 1.005 7.168e-06 -3.218e-06 0.987 5.402e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09391 0.09197 0.165 0.1964 0.9852 0.9911 0.09392 0.6618 0.837 0.2489 ] Network output: [ 8.861e-05 1 -5.453e-05 9.405e-07 -4.222e-07 0.9998 7.088e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001944 Epoch 9480 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009085 0.9967 0.9923 -1.968e-07 8.836e-08 -0.00719 -1.483e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003498 -0.003336 -0.006818 0.005479 0.9699 0.9743 0.006801 0.8257 0.8203 0.01646 ] Network output: [ 0.9999 0.0001502 0.0004131 -3.447e-06 1.548e-06 -0.00038 -2.598e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2074 -0.03539 -0.1588 0.1834 0.9834 0.9932 0.2327 0.4303 0.8685 0.7095 ] Network output: [ -0.009064 1.003 1.008 -2.258e-07 1.014e-07 0.00751 -1.702e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006743 0.000612 0.004388 0.003224 0.9889 0.9919 0.006874 0.853 0.8923 0.01177 ] Network output: [ -0.0002213 0.001579 1.001 -1.081e-05 4.855e-06 0.9983 -8.15e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2209 0.1046 0.3481 0.1424 0.9849 0.9939 0.2217 0.4343 0.8752 0.7033 ] Network output: [ 0.003355 -0.0159 0.9942 6.584e-06 -2.956e-06 1.015 4.962e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09786 0.1845 0.1977 0.9873 0.9919 0.1106 0.7379 0.8617 0.3052 ] Network output: [ -0.003145 0.01471 1.005 7.16e-06 -3.214e-06 0.987 5.396e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09391 0.09197 0.165 0.1964 0.9852 0.9911 0.09392 0.6618 0.837 0.2489 ] Network output: [ 8.858e-05 1 -5.45e-05 9.394e-07 -4.217e-07 0.9998 7.08e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001943 Epoch 9481 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009084 0.9967 0.9923 -1.967e-07 8.832e-08 -0.007189 -1.483e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003498 -0.003336 -0.006817 0.005479 0.9699 0.9743 0.006801 0.8257 0.8203 0.01646 ] Network output: [ 0.9999 0.00015 0.000413 -3.443e-06 1.546e-06 -0.0003798 -2.595e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2074 -0.03539 -0.1588 0.1834 0.9834 0.9932 0.2327 0.4303 0.8685 0.7095 ] Network output: [ -0.009063 1.003 1.008 -2.256e-07 1.013e-07 0.00751 -1.7e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006743 0.0006121 0.004388 0.003223 0.9889 0.9919 0.006875 0.853 0.8923 0.01177 ] Network output: [ -0.0002211 0.001579 1.001 -1.08e-05 4.849e-06 0.9983 -8.14e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2209 0.1046 0.3481 0.1424 0.9849 0.9939 0.2217 0.4343 0.8752 0.7033 ] Network output: [ 0.003354 -0.0159 0.9942 6.576e-06 -2.952e-06 1.015 4.956e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09787 0.1845 0.1977 0.9873 0.9919 0.1106 0.7379 0.8617 0.3052 ] Network output: [ -0.003144 0.01471 1.005 7.151e-06 -3.211e-06 0.987 5.39e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09391 0.09197 0.165 0.1964 0.9852 0.9911 0.09392 0.6618 0.837 0.2489 ] Network output: [ 8.855e-05 1 -5.447e-05 9.383e-07 -4.212e-07 0.9998 7.071e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001942 Epoch 9482 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009083 0.9967 0.9923 -1.966e-07 8.828e-08 -0.007189 -1.482e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003498 -0.003336 -0.006817 0.005478 0.9699 0.9743 0.006802 0.8257 0.8203 0.01646 ] Network output: [ 0.9999 0.0001498 0.0004128 -3.439e-06 1.544e-06 -0.0003795 -2.592e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2074 -0.03539 -0.1588 0.1834 0.9834 0.9932 0.2327 0.4303 0.8685 0.7095 ] Network output: [ -0.009062 1.003 1.008 -2.255e-07 1.012e-07 0.007509 -1.699e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006744 0.0006121 0.004388 0.003223 0.9889 0.9919 0.006875 0.853 0.8923 0.01177 ] Network output: [ -0.0002209 0.001578 1.001 -1.079e-05 4.843e-06 0.9983 -8.131e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2209 0.1046 0.3481 0.1424 0.9849 0.9939 0.2217 0.4343 0.8752 0.7033 ] Network output: [ 0.003352 -0.01589 0.9942 6.569e-06 -2.949e-06 1.015 4.95e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09787 0.1845 0.1977 0.9873 0.9919 0.1106 0.7379 0.8617 0.3052 ] Network output: [ -0.003142 0.0147 1.005 7.143e-06 -3.207e-06 0.987 5.383e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09391 0.09197 0.165 0.1964 0.9852 0.9911 0.09393 0.6618 0.837 0.2489 ] Network output: [ 8.853e-05 1 -5.444e-05 9.372e-07 -4.207e-07 0.9998 7.063e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001941 Epoch 9483 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009082 0.9967 0.9923 -1.966e-07 8.824e-08 -0.007188 -1.481e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003498 -0.003336 -0.006816 0.005478 0.9699 0.9743 0.006802 0.8257 0.8203 0.01646 ] Network output: [ 0.9999 0.0001496 0.0004126 -3.435e-06 1.542e-06 -0.0003793 -2.589e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2074 -0.03539 -0.1588 0.1834 0.9834 0.9932 0.2327 0.4303 0.8685 0.7095 ] Network output: [ -0.009061 1.003 1.008 -2.253e-07 1.012e-07 0.007508 -1.698e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006744 0.0006122 0.004388 0.003223 0.9889 0.9919 0.006876 0.853 0.8923 0.01177 ] Network output: [ -0.0002208 0.001577 1.001 -1.078e-05 4.838e-06 0.9983 -8.121e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2209 0.1046 0.3481 0.1424 0.9849 0.9939 0.2217 0.4343 0.8752 0.7033 ] Network output: [ 0.003351 -0.01588 0.9942 6.561e-06 -2.945e-06 1.015 4.944e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09788 0.1845 0.1977 0.9873 0.9919 0.1106 0.7379 0.8617 0.3052 ] Network output: [ -0.003141 0.01469 1.005 7.135e-06 -3.203e-06 0.987 5.377e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09391 0.09198 0.165 0.1964 0.9852 0.9911 0.09393 0.6618 0.837 0.2489 ] Network output: [ 8.85e-05 1 -5.441e-05 9.361e-07 -4.203e-07 0.9998 7.055e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000194 Epoch 9484 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009081 0.9967 0.9923 -1.965e-07 8.82e-08 -0.007188 -1.481e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003498 -0.003336 -0.006815 0.005477 0.9699 0.9743 0.006802 0.8257 0.8203 0.01646 ] Network output: [ 0.9999 0.0001494 0.0004124 -3.431e-06 1.54e-06 -0.000379 -2.585e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2074 -0.03539 -0.1588 0.1834 0.9834 0.9932 0.2327 0.4303 0.8685 0.7095 ] Network output: [ -0.00906 1.003 1.008 -2.252e-07 1.011e-07 0.007508 -1.697e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006745 0.0006123 0.004388 0.003223 0.9889 0.9919 0.006876 0.853 0.8923 0.01177 ] Network output: [ -0.0002206 0.001576 1.001 -1.076e-05 4.832e-06 0.9983 -8.111e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.221 0.1046 0.3481 0.1423 0.9849 0.9939 0.2217 0.4343 0.8752 0.7033 ] Network output: [ 0.00335 -0.01588 0.9942 6.553e-06 -2.942e-06 1.015 4.939e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09788 0.1845 0.1977 0.9873 0.9919 0.1106 0.7379 0.8617 0.3052 ] Network output: [ -0.003139 0.01469 1.005 7.127e-06 -3.199e-06 0.987 5.371e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09392 0.09198 0.165 0.1964 0.9852 0.9911 0.09393 0.6618 0.837 0.2489 ] Network output: [ 8.847e-05 1 -5.438e-05 9.35e-07 -4.198e-07 0.9998 7.047e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001939 Epoch 9485 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00908 0.9967 0.9923 -1.964e-07 8.816e-08 -0.007187 -1.48e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003498 -0.003336 -0.006815 0.005477 0.9699 0.9743 0.006802 0.8257 0.8203 0.01646 ] Network output: [ 0.9999 0.0001492 0.0004122 -3.427e-06 1.538e-06 -0.0003787 -2.582e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2074 -0.03539 -0.1588 0.1834 0.9834 0.9932 0.2327 0.4303 0.8685 0.7095 ] Network output: [ -0.00906 1.003 1.008 -2.25e-07 1.01e-07 0.007507 -1.696e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006745 0.0006124 0.004388 0.003222 0.9889 0.9919 0.006876 0.853 0.8923 0.01177 ] Network output: [ -0.0002204 0.001576 1.001 -1.075e-05 4.826e-06 0.9983 -8.102e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.221 0.1046 0.3481 0.1423 0.9849 0.9939 0.2217 0.4343 0.8752 0.7033 ] Network output: [ 0.003348 -0.01587 0.9942 6.545e-06 -2.938e-06 1.015 4.933e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09788 0.1845 0.1977 0.9873 0.9919 0.1106 0.7379 0.8617 0.3052 ] Network output: [ -0.003138 0.01468 1.005 7.118e-06 -3.196e-06 0.987 5.365e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09392 0.09198 0.165 0.1964 0.9852 0.9911 0.09393 0.6618 0.837 0.2489 ] Network output: [ 8.844e-05 1 -5.436e-05 9.339e-07 -4.193e-07 0.9998 7.038e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001938 Epoch 9486 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009079 0.9967 0.9923 -1.963e-07 8.812e-08 -0.007187 -1.479e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003498 -0.003336 -0.006814 0.005477 0.9699 0.9743 0.006802 0.8257 0.8203 0.01646 ] Network output: [ 0.9999 0.0001491 0.000412 -3.423e-06 1.537e-06 -0.0003785 -2.579e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2074 -0.03539 -0.1588 0.1834 0.9834 0.9932 0.2327 0.4303 0.8685 0.7095 ] Network output: [ -0.009059 1.003 1.008 -2.249e-07 1.01e-07 0.007506 -1.695e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006745 0.0006124 0.004388 0.003222 0.9889 0.9919 0.006877 0.853 0.8923 0.01177 ] Network output: [ -0.0002203 0.001575 1.001 -1.074e-05 4.821e-06 0.9983 -8.092e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.221 0.1046 0.3481 0.1423 0.9849 0.9939 0.2217 0.4343 0.8752 0.7033 ] Network output: [ 0.003347 -0.01586 0.9942 6.538e-06 -2.935e-06 1.015 4.927e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09789 0.1845 0.1977 0.9873 0.9919 0.1106 0.7379 0.8617 0.3052 ] Network output: [ -0.003137 0.01468 1.005 7.11e-06 -3.192e-06 0.987 5.358e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09392 0.09198 0.165 0.1964 0.9852 0.9911 0.09393 0.6618 0.837 0.2489 ] Network output: [ 8.842e-05 1 -5.433e-05 9.328e-07 -4.188e-07 0.9998 7.03e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001937 Epoch 9487 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009078 0.9967 0.9923 -1.962e-07 8.808e-08 -0.007186 -1.479e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003498 -0.003336 -0.006813 0.005476 0.9699 0.9743 0.006803 0.8257 0.8203 0.01645 ] Network output: [ 0.9999 0.0001489 0.0004118 -3.418e-06 1.535e-06 -0.0003782 -2.576e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2074 -0.03539 -0.1588 0.1833 0.9834 0.9932 0.2327 0.4303 0.8685 0.7095 ] Network output: [ -0.009058 1.003 1.008 -2.247e-07 1.009e-07 0.007506 -1.694e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006746 0.0006125 0.004388 0.003222 0.9889 0.9919 0.006877 0.853 0.8923 0.01177 ] Network output: [ -0.0002201 0.001574 1.001 -1.072e-05 4.815e-06 0.9983 -8.083e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.221 0.1046 0.3481 0.1423 0.9849 0.9939 0.2217 0.4343 0.8752 0.7033 ] Network output: [ 0.003345 -0.01586 0.9942 6.53e-06 -2.932e-06 1.015 4.921e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1105 0.09789 0.1845 0.1977 0.9873 0.9919 0.1106 0.7378 0.8617 0.3052 ] Network output: [ -0.003135 0.01467 1.005 7.102e-06 -3.188e-06 0.987 5.352e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09392 0.09198 0.165 0.1964 0.9852 0.9911 0.09394 0.6617 0.837 0.2489 ] Network output: [ 8.839e-05 1 -5.43e-05 9.317e-07 -4.183e-07 0.9998 7.022e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001936 Epoch 9488 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009077 0.9967 0.9923 -1.961e-07 8.804e-08 -0.007185 -1.478e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003498 -0.003336 -0.006813 0.005476 0.9699 0.9743 0.006803 0.8256 0.8203 0.01645 ] Network output: [ 0.9999 0.0001487 0.0004116 -3.414e-06 1.533e-06 -0.000378 -2.573e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2074 -0.03539 -0.1588 0.1833 0.9834 0.9932 0.2327 0.4303 0.8685 0.7095 ] Network output: [ -0.009057 1.003 1.008 -2.246e-07 1.008e-07 0.007505 -1.692e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006746 0.0006126 0.004388 0.003222 0.9889 0.9919 0.006878 0.853 0.8923 0.01177 ] Network output: [ -0.0002199 0.001574 1.001 -1.071e-05 4.809e-06 0.9983 -8.073e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.221 0.1046 0.3481 0.1423 0.9849 0.9939 0.2217 0.4343 0.8752 0.7032 ] Network output: [ 0.003344 -0.01585 0.9942 6.522e-06 -2.928e-06 1.015 4.915e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09789 0.1845 0.1977 0.9873 0.9919 0.1106 0.7378 0.8617 0.3052 ] Network output: [ -0.003134 0.01466 1.005 7.094e-06 -3.185e-06 0.987 5.346e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09392 0.09199 0.165 0.1964 0.9852 0.9911 0.09394 0.6617 0.837 0.2489 ] Network output: [ 8.836e-05 1 -5.427e-05 9.306e-07 -4.178e-07 0.9998 7.014e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001935 Epoch 9489 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009076 0.9967 0.9923 -1.96e-07 8.8e-08 -0.007185 -1.477e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003498 -0.003337 -0.006812 0.005475 0.9699 0.9743 0.006803 0.8256 0.8203 0.01645 ] Network output: [ 0.9999 0.0001485 0.0004115 -3.41e-06 1.531e-06 -0.0003777 -2.57e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2074 -0.0354 -0.1587 0.1833 0.9834 0.9932 0.2328 0.4303 0.8685 0.7094 ] Network output: [ -0.009056 1.003 1.008 -2.244e-07 1.008e-07 0.007504 -1.691e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006747 0.0006127 0.004388 0.003221 0.9889 0.9919 0.006878 0.853 0.8923 0.01177 ] Network output: [ -0.0002198 0.001573 1.001 -1.07e-05 4.803e-06 0.9983 -8.064e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.221 0.1046 0.3481 0.1423 0.9849 0.9939 0.2217 0.4342 0.8752 0.7032 ] Network output: [ 0.003342 -0.01584 0.9942 6.515e-06 -2.925e-06 1.015 4.91e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.0979 0.1845 0.1977 0.9873 0.9919 0.1106 0.7378 0.8617 0.3052 ] Network output: [ -0.003133 0.01466 1.005 7.085e-06 -3.181e-06 0.987 5.34e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09393 0.09199 0.165 0.1964 0.9852 0.9911 0.09394 0.6617 0.837 0.2489 ] Network output: [ 8.834e-05 1 -5.424e-05 9.296e-07 -4.173e-07 0.9998 7.005e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001934 Epoch 9490 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009075 0.9967 0.9923 -1.959e-07 8.796e-08 -0.007184 -1.477e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003498 -0.003337 -0.006812 0.005475 0.9699 0.9743 0.006803 0.8256 0.8203 0.01645 ] Network output: [ 0.9999 0.0001483 0.0004113 -3.406e-06 1.529e-06 -0.0003774 -2.567e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2074 -0.0354 -0.1587 0.1833 0.9834 0.9932 0.2328 0.4303 0.8685 0.7094 ] Network output: [ -0.009055 1.003 1.008 -2.243e-07 1.007e-07 0.007503 -1.69e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006747 0.0006127 0.004388 0.003221 0.9889 0.9919 0.006878 0.853 0.8923 0.01176 ] Network output: [ -0.0002196 0.001572 1.001 -1.069e-05 4.798e-06 0.9983 -8.054e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.221 0.1046 0.3481 0.1423 0.9849 0.9939 0.2218 0.4342 0.8752 0.7032 ] Network output: [ 0.003341 -0.01584 0.9942 6.507e-06 -2.921e-06 1.015 4.904e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.0979 0.1845 0.1977 0.9873 0.9919 0.1106 0.7378 0.8617 0.3052 ] Network output: [ -0.003131 0.01465 1.005 7.077e-06 -3.177e-06 0.987 5.334e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09393 0.09199 0.165 0.1964 0.9852 0.9911 0.09394 0.6617 0.837 0.2489 ] Network output: [ 8.831e-05 1 -5.421e-05 9.285e-07 -4.168e-07 0.9998 6.997e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001933 Epoch 9491 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009074 0.9967 0.9923 -1.958e-07 8.792e-08 -0.007184 -1.476e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003498 -0.003337 -0.006811 0.005475 0.9699 0.9743 0.006803 0.8256 0.8203 0.01645 ] Network output: [ 0.9999 0.0001481 0.0004111 -3.402e-06 1.527e-06 -0.0003772 -2.564e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2075 -0.0354 -0.1587 0.1833 0.9834 0.9932 0.2328 0.4303 0.8685 0.7094 ] Network output: [ -0.009054 1.003 1.008 -2.241e-07 1.006e-07 0.007503 -1.689e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006747 0.0006128 0.004388 0.003221 0.9889 0.9919 0.006879 0.8529 0.8923 0.01176 ] Network output: [ -0.0002194 0.001571 1.001 -1.067e-05 4.792e-06 0.9983 -8.044e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.221 0.1046 0.3481 0.1423 0.9849 0.9939 0.2218 0.4342 0.8752 0.7032 ] Network output: [ 0.003339 -0.01583 0.9942 6.499e-06 -2.918e-06 1.015 4.898e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09791 0.1845 0.1977 0.9873 0.9919 0.1106 0.7378 0.8617 0.3052 ] Network output: [ -0.00313 0.01464 1.005 7.069e-06 -3.174e-06 0.987 5.327e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09393 0.09199 0.165 0.1964 0.9852 0.9911 0.09394 0.6617 0.837 0.2489 ] Network output: [ 8.828e-05 1 -5.419e-05 9.274e-07 -4.163e-07 0.9998 6.989e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001932 Epoch 9492 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009073 0.9967 0.9923 -1.957e-07 8.788e-08 -0.007183 -1.475e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003498 -0.003337 -0.00681 0.005474 0.9699 0.9743 0.006803 0.8256 0.8203 0.01645 ] Network output: [ 0.9999 0.000148 0.0004109 -3.398e-06 1.526e-06 -0.0003769 -2.561e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2075 -0.0354 -0.1587 0.1833 0.9834 0.9932 0.2328 0.4302 0.8685 0.7094 ] Network output: [ -0.009054 1.003 1.008 -2.24e-07 1.006e-07 0.007502 -1.688e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006748 0.0006129 0.004387 0.003221 0.9889 0.9919 0.006879 0.8529 0.8923 0.01176 ] Network output: [ -0.0002193 0.001571 1.001 -1.066e-05 4.786e-06 0.9983 -8.035e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.221 0.1046 0.3481 0.1423 0.9849 0.9939 0.2218 0.4342 0.8752 0.7032 ] Network output: [ 0.003338 -0.01582 0.9942 6.492e-06 -2.914e-06 1.015 4.892e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09791 0.1845 0.1977 0.9873 0.9919 0.1106 0.7378 0.8617 0.3052 ] Network output: [ -0.003128 0.01464 1.005 7.061e-06 -3.17e-06 0.987 5.321e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09393 0.09199 0.165 0.1964 0.9852 0.9911 0.09395 0.6617 0.837 0.2489 ] Network output: [ 8.826e-05 1 -5.416e-05 9.263e-07 -4.158e-07 0.9998 6.981e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001931 Epoch 9493 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009072 0.9967 0.9923 -1.957e-07 8.784e-08 -0.007183 -1.475e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003499 -0.003337 -0.00681 0.005474 0.9699 0.9743 0.006804 0.8256 0.8203 0.01645 ] Network output: [ 0.9999 0.0001478 0.0004107 -3.394e-06 1.524e-06 -0.0003767 -2.558e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2075 -0.0354 -0.1587 0.1833 0.9834 0.9932 0.2328 0.4302 0.8684 0.7094 ] Network output: [ -0.009053 1.003 1.008 -2.238e-07 1.005e-07 0.007501 -1.687e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006748 0.000613 0.004387 0.003221 0.9889 0.9919 0.00688 0.8529 0.8923 0.01176 ] Network output: [ -0.0002191 0.00157 1.001 -1.065e-05 4.781e-06 0.9983 -8.025e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.221 0.1046 0.3481 0.1423 0.9849 0.9939 0.2218 0.4342 0.8752 0.7032 ] Network output: [ 0.003336 -0.01582 0.9942 6.484e-06 -2.911e-06 1.015 4.887e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09791 0.1845 0.1977 0.9873 0.9919 0.1107 0.7378 0.8617 0.3052 ] Network output: [ -0.003127 0.01463 1.005 7.053e-06 -3.166e-06 0.987 5.315e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09393 0.092 0.165 0.1964 0.9852 0.9911 0.09395 0.6617 0.837 0.2489 ] Network output: [ 8.823e-05 1 -5.413e-05 9.252e-07 -4.154e-07 0.9998 6.973e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000193 Epoch 9494 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009071 0.9967 0.9923 -1.956e-07 8.78e-08 -0.007182 -1.474e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003499 -0.003337 -0.006809 0.005473 0.9699 0.9743 0.006804 0.8256 0.8203 0.01645 ] Network output: [ 0.9999 0.0001476 0.0004105 -3.39e-06 1.522e-06 -0.0003764 -2.555e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2075 -0.0354 -0.1587 0.1833 0.9834 0.9932 0.2328 0.4302 0.8684 0.7094 ] Network output: [ -0.009052 1.003 1.008 -2.237e-07 1.004e-07 0.007501 -1.686e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006749 0.000613 0.004387 0.00322 0.9889 0.9919 0.00688 0.8529 0.8923 0.01176 ] Network output: [ -0.0002189 0.001569 1.001 -1.064e-05 4.775e-06 0.9983 -8.016e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.221 0.1046 0.3481 0.1423 0.9849 0.9939 0.2218 0.4342 0.8752 0.7032 ] Network output: [ 0.003335 -0.01581 0.9942 6.476e-06 -2.907e-06 1.015 4.881e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09792 0.1845 0.1977 0.9873 0.9919 0.1107 0.7378 0.8617 0.3052 ] Network output: [ -0.003126 0.01462 1.005 7.044e-06 -3.162e-06 0.987 5.309e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09394 0.092 0.165 0.1964 0.9852 0.9911 0.09395 0.6617 0.837 0.2489 ] Network output: [ 8.82e-05 1 -5.41e-05 9.241e-07 -4.149e-07 0.9998 6.965e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001929 Epoch 9495 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00907 0.9967 0.9923 -1.955e-07 8.776e-08 -0.007182 -1.473e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003499 -0.003337 -0.006808 0.005473 0.9699 0.9743 0.006804 0.8256 0.8203 0.01645 ] Network output: [ 0.9999 0.0001474 0.0004103 -3.386e-06 1.52e-06 -0.0003762 -2.552e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2075 -0.0354 -0.1587 0.1833 0.9834 0.9932 0.2328 0.4302 0.8684 0.7094 ] Network output: [ -0.009051 1.003 1.008 -2.235e-07 1.004e-07 0.0075 -1.685e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006749 0.0006131 0.004387 0.00322 0.9889 0.9919 0.00688 0.8529 0.8923 0.01176 ] Network output: [ -0.0002188 0.001569 1.001 -1.062e-05 4.769e-06 0.9983 -8.006e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.221 0.1046 0.3481 0.1423 0.9849 0.9939 0.2218 0.4342 0.8752 0.7032 ] Network output: [ 0.003333 -0.0158 0.9942 6.469e-06 -2.904e-06 1.015 4.875e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09792 0.1845 0.1977 0.9873 0.9919 0.1107 0.7377 0.8617 0.3052 ] Network output: [ -0.003124 0.01462 1.005 7.036e-06 -3.159e-06 0.987 5.303e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09394 0.092 0.165 0.1964 0.9852 0.9911 0.09395 0.6616 0.837 0.2489 ] Network output: [ 8.818e-05 1 -5.407e-05 9.23e-07 -4.144e-07 0.9998 6.956e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001928 Epoch 9496 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009069 0.9967 0.9923 -1.954e-07 8.771e-08 -0.007181 -1.472e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003499 -0.003337 -0.006808 0.005473 0.9699 0.9743 0.006804 0.8256 0.8203 0.01645 ] Network output: [ 0.9999 0.0001472 0.0004101 -3.382e-06 1.518e-06 -0.0003759 -2.549e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2075 -0.0354 -0.1587 0.1833 0.9834 0.9932 0.2328 0.4302 0.8684 0.7094 ] Network output: [ -0.00905 1.003 1.008 -2.234e-07 1.003e-07 0.007499 -1.683e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006749 0.0006132 0.004387 0.00322 0.9889 0.9919 0.006881 0.8529 0.8923 0.01176 ] Network output: [ -0.0002186 0.001568 1.001 -1.061e-05 4.764e-06 0.9983 -7.997e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.221 0.1046 0.3481 0.1423 0.9849 0.9939 0.2218 0.4342 0.8752 0.7032 ] Network output: [ 0.003332 -0.0158 0.9942 6.461e-06 -2.901e-06 1.015 4.869e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09792 0.1845 0.1977 0.9873 0.9919 0.1107 0.7377 0.8617 0.3052 ] Network output: [ -0.003123 0.01461 1.005 7.028e-06 -3.155e-06 0.987 5.297e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09394 0.092 0.165 0.1964 0.9852 0.9911 0.09395 0.6616 0.837 0.2489 ] Network output: [ 8.815e-05 1 -5.405e-05 9.22e-07 -4.139e-07 0.9998 6.948e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001927 Epoch 9497 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009069 0.9967 0.9923 -1.953e-07 8.767e-08 -0.007181 -1.472e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003499 -0.003337 -0.006807 0.005472 0.9699 0.9743 0.006804 0.8256 0.8203 0.01644 ] Network output: [ 0.9999 0.0001471 0.00041 -3.378e-06 1.517e-06 -0.0003756 -2.546e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2075 -0.0354 -0.1587 0.1833 0.9834 0.9932 0.2328 0.4302 0.8684 0.7094 ] Network output: [ -0.009049 1.003 1.008 -2.232e-07 1.002e-07 0.007499 -1.682e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00675 0.0006133 0.004387 0.00322 0.9889 0.9919 0.006881 0.8529 0.8923 0.01176 ] Network output: [ -0.0002184 0.001567 1.001 -1.06e-05 4.758e-06 0.9983 -7.987e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.221 0.1047 0.3481 0.1423 0.9849 0.9939 0.2218 0.4342 0.8752 0.7032 ] Network output: [ 0.00333 -0.01579 0.9942 6.453e-06 -2.897e-06 1.015 4.864e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09793 0.1845 0.1977 0.9873 0.9919 0.1107 0.7377 0.8617 0.3052 ] Network output: [ -0.003122 0.01461 1.005 7.02e-06 -3.151e-06 0.987 5.29e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09394 0.092 0.165 0.1964 0.9852 0.9911 0.09396 0.6616 0.837 0.2489 ] Network output: [ 8.812e-05 1 -5.402e-05 9.209e-07 -4.134e-07 0.9998 6.94e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001926 Epoch 9498 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009068 0.9967 0.9923 -1.952e-07 8.763e-08 -0.00718 -1.471e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003499 -0.003337 -0.006807 0.005472 0.9699 0.9743 0.006805 0.8256 0.8203 0.01644 ] Network output: [ 0.9999 0.0001469 0.0004098 -3.374e-06 1.515e-06 -0.0003754 -2.543e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2075 -0.0354 -0.1587 0.1833 0.9834 0.9932 0.2328 0.4302 0.8684 0.7094 ] Network output: [ -0.009048 1.003 1.008 -2.231e-07 1.001e-07 0.007498 -1.681e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00675 0.0006133 0.004387 0.003219 0.9889 0.9919 0.006882 0.8529 0.8923 0.01176 ] Network output: [ -0.0002183 0.001567 1.001 -1.059e-05 4.752e-06 0.9983 -7.978e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2211 0.1047 0.3481 0.1423 0.9849 0.9939 0.2218 0.4342 0.8752 0.7032 ] Network output: [ 0.003329 -0.01578 0.9942 6.446e-06 -2.894e-06 1.015 4.858e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09793 0.1845 0.1977 0.9873 0.9919 0.1107 0.7377 0.8617 0.3052 ] Network output: [ -0.00312 0.0146 1.005 7.012e-06 -3.148e-06 0.987 5.284e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09394 0.09201 0.165 0.1964 0.9852 0.9911 0.09396 0.6616 0.8369 0.2489 ] Network output: [ 8.81e-05 1 -5.399e-05 9.198e-07 -4.129e-07 0.9998 6.932e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001925 Epoch 9499 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009067 0.9967 0.9923 -1.951e-07 8.759e-08 -0.00718 -1.47e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003499 -0.003337 -0.006806 0.005471 0.9699 0.9743 0.006805 0.8256 0.8203 0.01644 ] Network output: [ 0.9999 0.0001467 0.0004096 -3.37e-06 1.513e-06 -0.0003751 -2.54e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2075 -0.0354 -0.1586 0.1833 0.9834 0.9932 0.2328 0.4302 0.8684 0.7094 ] Network output: [ -0.009048 1.003 1.008 -2.229e-07 1.001e-07 0.007497 -1.68e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006751 0.0006134 0.004387 0.003219 0.9889 0.9919 0.006882 0.8529 0.8923 0.01176 ] Network output: [ -0.0002181 0.001566 1.001 -1.057e-05 4.747e-06 0.9983 -7.968e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2211 0.1047 0.3481 0.1423 0.9849 0.9939 0.2218 0.4342 0.8752 0.7032 ] Network output: [ 0.003327 -0.01578 0.9942 6.438e-06 -2.89e-06 1.015 4.852e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09793 0.1845 0.1976 0.9873 0.9919 0.1107 0.7377 0.8617 0.3052 ] Network output: [ -0.003119 0.01459 1.005 7.004e-06 -3.144e-06 0.987 5.278e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09395 0.09201 0.165 0.1964 0.9852 0.9911 0.09396 0.6616 0.8369 0.2489 ] Network output: [ 8.807e-05 1 -5.396e-05 9.187e-07 -4.125e-07 0.9998 6.924e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001924 Epoch 9500 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009066 0.9967 0.9923 -1.95e-07 8.755e-08 -0.007179 -1.47e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003499 -0.003338 -0.006805 0.005471 0.9699 0.9743 0.006805 0.8256 0.8203 0.01644 ] Network output: [ 0.9999 0.0001465 0.0004094 -3.366e-06 1.511e-06 -0.0003749 -2.537e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2075 -0.03541 -0.1586 0.1833 0.9834 0.9932 0.2328 0.4302 0.8684 0.7094 ] Network output: [ -0.009047 1.003 1.008 -2.228e-07 1e-07 0.007497 -1.679e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006751 0.0006135 0.004387 0.003219 0.9889 0.9919 0.006883 0.8529 0.8923 0.01176 ] Network output: [ -0.0002179 0.001565 1.001 -1.056e-05 4.741e-06 0.9983 -7.959e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2211 0.1047 0.3482 0.1423 0.9849 0.9939 0.2218 0.4342 0.8752 0.7032 ] Network output: [ 0.003326 -0.01577 0.9942 6.431e-06 -2.887e-06 1.015 4.846e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09794 0.1845 0.1976 0.9873 0.9919 0.1107 0.7377 0.8617 0.3052 ] Network output: [ -0.003117 0.01459 1.005 6.996e-06 -3.141e-06 0.987 5.272e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09395 0.09201 0.165 0.1964 0.9852 0.9911 0.09396 0.6616 0.8369 0.2489 ] Network output: [ 8.804e-05 1 -5.394e-05 9.177e-07 -4.12e-07 0.9998 6.916e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001923 Epoch 9501 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009065 0.9967 0.9923 -1.949e-07 8.751e-08 -0.007178 -1.469e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003499 -0.003338 -0.006805 0.00547 0.9699 0.9743 0.006805 0.8256 0.8203 0.01644 ] Network output: [ 0.9999 0.0001463 0.0004092 -3.362e-06 1.509e-06 -0.0003746 -2.534e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2075 -0.03541 -0.1586 0.1833 0.9834 0.9932 0.2328 0.4302 0.8684 0.7094 ] Network output: [ -0.009046 1.003 1.008 -2.226e-07 9.995e-08 0.007496 -1.678e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006751 0.0006136 0.004387 0.003219 0.9889 0.9919 0.006883 0.8529 0.8923 0.01176 ] Network output: [ -0.0002178 0.001564 1.001 -1.055e-05 4.736e-06 0.9983 -7.95e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2211 0.1047 0.3482 0.1423 0.9849 0.9939 0.2218 0.4342 0.8752 0.7032 ] Network output: [ 0.003324 -0.01576 0.9942 6.423e-06 -2.884e-06 1.015 4.841e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09794 0.1845 0.1976 0.9873 0.9919 0.1107 0.7377 0.8617 0.3052 ] Network output: [ -0.003116 0.01458 1.005 6.987e-06 -3.137e-06 0.987 5.266e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09395 0.09201 0.165 0.1964 0.9852 0.9911 0.09396 0.6616 0.8369 0.2489 ] Network output: [ 8.802e-05 1 -5.391e-05 9.166e-07 -4.115e-07 0.9998 6.908e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001922 Epoch 9502 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009064 0.9967 0.9923 -1.948e-07 8.747e-08 -0.007178 -1.468e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003499 -0.003338 -0.006804 0.00547 0.9699 0.9743 0.006805 0.8256 0.8203 0.01644 ] Network output: [ 0.9999 0.0001461 0.000409 -3.358e-06 1.508e-06 -0.0003744 -2.531e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2075 -0.03541 -0.1586 0.1833 0.9834 0.9932 0.2328 0.4302 0.8684 0.7094 ] Network output: [ -0.009045 1.003 1.008 -2.225e-07 9.988e-08 0.007495 -1.677e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006752 0.0006136 0.004387 0.003218 0.9889 0.9919 0.006883 0.8529 0.8923 0.01176 ] Network output: [ -0.0002176 0.001564 1.001 -1.054e-05 4.73e-06 0.9983 -7.94e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2211 0.1047 0.3482 0.1423 0.9849 0.9939 0.2218 0.4342 0.8752 0.7032 ] Network output: [ 0.003323 -0.01576 0.9942 6.415e-06 -2.88e-06 1.015 4.835e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09795 0.1845 0.1976 0.9873 0.9919 0.1107 0.7377 0.8617 0.3052 ] Network output: [ -0.003115 0.01457 1.005 6.979e-06 -3.133e-06 0.987 5.26e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09395 0.09201 0.165 0.1964 0.9852 0.9911 0.09397 0.6616 0.8369 0.2489 ] Network output: [ 8.799e-05 1 -5.388e-05 9.155e-07 -4.11e-07 0.9998 6.9e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001921 Epoch 9503 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009063 0.9967 0.9923 -1.947e-07 8.743e-08 -0.007177 -1.468e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003499 -0.003338 -0.006803 0.00547 0.9699 0.9743 0.006806 0.8256 0.8203 0.01644 ] Network output: [ 0.9999 0.000146 0.0004088 -3.354e-06 1.506e-06 -0.0003741 -2.528e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2075 -0.03541 -0.1586 0.1833 0.9834 0.9932 0.2329 0.4302 0.8684 0.7094 ] Network output: [ -0.009044 1.003 1.008 -2.223e-07 9.981e-08 0.007495 -1.676e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006752 0.0006137 0.004386 0.003218 0.9889 0.9919 0.006884 0.8529 0.8923 0.01175 ] Network output: [ -0.0002174 0.001563 1.001 -1.052e-05 4.724e-06 0.9983 -7.931e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2211 0.1047 0.3482 0.1423 0.9849 0.9939 0.2218 0.4342 0.8752 0.7032 ] Network output: [ 0.003321 -0.01575 0.9942 6.408e-06 -2.877e-06 1.015 4.829e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09795 0.1845 0.1976 0.9873 0.9919 0.1107 0.7377 0.8617 0.3052 ] Network output: [ -0.003113 0.01457 1.005 6.971e-06 -3.13e-06 0.987 5.254e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09395 0.09202 0.165 0.1964 0.9852 0.9911 0.09397 0.6615 0.8369 0.2489 ] Network output: [ 8.796e-05 1 -5.386e-05 9.144e-07 -4.105e-07 0.9998 6.891e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000192 Epoch 9504 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009062 0.9967 0.9923 -1.947e-07 8.739e-08 -0.007177 -1.467e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003499 -0.003338 -0.006803 0.005469 0.9699 0.9743 0.006806 0.8256 0.8203 0.01644 ] Network output: [ 0.9999 0.0001458 0.0004087 -3.35e-06 1.504e-06 -0.0003738 -2.525e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2075 -0.03541 -0.1586 0.1833 0.9834 0.9932 0.2329 0.4302 0.8684 0.7094 ] Network output: [ -0.009043 1.003 1.008 -2.222e-07 9.975e-08 0.007494 -1.674e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006753 0.0006138 0.004386 0.003218 0.9889 0.9919 0.006884 0.8529 0.8923 0.01175 ] Network output: [ -0.0002173 0.001562 1.001 -1.051e-05 4.719e-06 0.9983 -7.921e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2211 0.1047 0.3482 0.1423 0.9849 0.9939 0.2219 0.4342 0.8752 0.7032 ] Network output: [ 0.00332 -0.01574 0.9942 6.4e-06 -2.873e-06 1.015 4.823e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09795 0.1845 0.1976 0.9873 0.9919 0.1107 0.7376 0.8616 0.3052 ] Network output: [ -0.003112 0.01456 1.005 6.963e-06 -3.126e-06 0.987 5.248e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09396 0.09202 0.165 0.1964 0.9852 0.9911 0.09397 0.6615 0.8369 0.2489 ] Network output: [ 8.794e-05 1 -5.383e-05 9.134e-07 -4.1e-07 0.9998 6.883e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001919 Epoch 9505 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009061 0.9967 0.9923 -1.946e-07 8.734e-08 -0.007176 -1.466e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003499 -0.003338 -0.006802 0.005469 0.9699 0.9743 0.006806 0.8256 0.8203 0.01644 ] Network output: [ 0.9999 0.0001456 0.0004085 -3.346e-06 1.502e-06 -0.0003736 -2.522e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2075 -0.03541 -0.1586 0.1833 0.9834 0.9932 0.2329 0.4302 0.8684 0.7094 ] Network output: [ -0.009043 1.003 1.008 -2.22e-07 9.968e-08 0.007493 -1.673e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006753 0.0006139 0.004386 0.003218 0.9889 0.9919 0.006885 0.8529 0.8923 0.01175 ] Network output: [ -0.0002171 0.001562 1.001 -1.05e-05 4.713e-06 0.9983 -7.912e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2211 0.1047 0.3482 0.1423 0.9849 0.9939 0.2219 0.4342 0.8752 0.7032 ] Network output: [ 0.003319 -0.01574 0.9942 6.393e-06 -2.87e-06 1.015 4.818e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09796 0.1845 0.1976 0.9873 0.9919 0.1107 0.7376 0.8616 0.3052 ] Network output: [ -0.003111 0.01456 1.005 6.955e-06 -3.122e-06 0.987 5.242e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09396 0.09202 0.165 0.1964 0.9852 0.9911 0.09397 0.6615 0.8369 0.2489 ] Network output: [ 8.791e-05 1 -5.38e-05 9.123e-07 -4.096e-07 0.9998 6.875e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001918 Epoch 9506 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00906 0.9967 0.9923 -1.945e-07 8.73e-08 -0.007176 -1.466e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003499 -0.003338 -0.006802 0.005468 0.9699 0.9743 0.006806 0.8256 0.8203 0.01644 ] Network output: [ 0.9999 0.0001454 0.0004083 -3.342e-06 1.5e-06 -0.0003733 -2.519e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2075 -0.03541 -0.1586 0.1833 0.9834 0.9932 0.2329 0.4302 0.8684 0.7094 ] Network output: [ -0.009042 1.003 1.008 -2.219e-07 9.961e-08 0.007493 -1.672e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006753 0.0006139 0.004386 0.003217 0.9889 0.9919 0.006885 0.8529 0.8922 0.01175 ] Network output: [ -0.0002169 0.001561 1.001 -1.049e-05 4.707e-06 0.9983 -7.902e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2211 0.1047 0.3482 0.1423 0.9849 0.9939 0.2219 0.4342 0.8752 0.7032 ] Network output: [ 0.003317 -0.01573 0.9942 6.385e-06 -2.867e-06 1.015 4.812e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09796 0.1845 0.1976 0.9873 0.9919 0.1107 0.7376 0.8616 0.3052 ] Network output: [ -0.003109 0.01455 1.005 6.947e-06 -3.119e-06 0.987 5.235e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09396 0.09202 0.165 0.1964 0.9852 0.9911 0.09397 0.6615 0.8369 0.2489 ] Network output: [ 8.788e-05 1 -5.377e-05 9.112e-07 -4.091e-07 0.9998 6.867e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001917 Epoch 9507 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009059 0.9967 0.9923 -1.944e-07 8.726e-08 -0.007175 -1.465e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003499 -0.003338 -0.006801 0.005468 0.9699 0.9743 0.006806 0.8256 0.8203 0.01644 ] Network output: [ 0.9999 0.0001452 0.0004081 -3.338e-06 1.499e-06 -0.0003731 -2.516e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2076 -0.03541 -0.1586 0.1833 0.9834 0.9932 0.2329 0.4302 0.8684 0.7094 ] Network output: [ -0.009041 1.003 1.008 -2.217e-07 9.954e-08 0.007492 -1.671e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006754 0.000614 0.004386 0.003217 0.9889 0.9919 0.006885 0.8529 0.8922 0.01175 ] Network output: [ -0.0002168 0.00156 1.001 -1.047e-05 4.702e-06 0.9983 -7.893e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2211 0.1047 0.3482 0.1423 0.9849 0.9939 0.2219 0.4342 0.8752 0.7032 ] Network output: [ 0.003316 -0.01572 0.9942 6.378e-06 -2.863e-06 1.015 4.806e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09796 0.1845 0.1976 0.9873 0.9919 0.1107 0.7376 0.8616 0.3052 ] Network output: [ -0.003108 0.01454 1.005 6.939e-06 -3.115e-06 0.9871 5.229e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09396 0.09202 0.165 0.1964 0.9852 0.9911 0.09398 0.6615 0.8369 0.2489 ] Network output: [ 8.786e-05 1 -5.375e-05 9.102e-07 -4.086e-07 0.9998 6.859e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001916 Epoch 9508 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009058 0.9967 0.9923 -1.943e-07 8.722e-08 -0.007175 -1.464e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0035 -0.003338 -0.0068 0.005468 0.9699 0.9743 0.006806 0.8256 0.8203 0.01643 ] Network output: [ 0.9999 0.0001451 0.0004079 -3.334e-06 1.497e-06 -0.0003728 -2.513e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2076 -0.03541 -0.1586 0.1833 0.9834 0.9932 0.2329 0.4302 0.8684 0.7094 ] Network output: [ -0.00904 1.003 1.008 -2.216e-07 9.948e-08 0.007491 -1.67e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006754 0.0006141 0.004386 0.003217 0.9889 0.9919 0.006886 0.8529 0.8922 0.01175 ] Network output: [ -0.0002166 0.001559 1.001 -1.046e-05 4.696e-06 0.9983 -7.884e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2211 0.1047 0.3482 0.1423 0.9849 0.9939 0.2219 0.4341 0.8752 0.7032 ] Network output: [ 0.003314 -0.01572 0.9942 6.37e-06 -2.86e-06 1.015 4.801e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09797 0.1845 0.1976 0.9873 0.9919 0.1107 0.7376 0.8616 0.3052 ] Network output: [ -0.003106 0.01454 1.005 6.931e-06 -3.111e-06 0.9871 5.223e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09396 0.09203 0.165 0.1964 0.9852 0.9911 0.09398 0.6615 0.8369 0.2489 ] Network output: [ 8.783e-05 1 -5.372e-05 9.091e-07 -4.081e-07 0.9998 6.851e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001915 Epoch 9509 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009057 0.9967 0.9923 -1.942e-07 8.718e-08 -0.007174 -1.463e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0035 -0.003338 -0.0068 0.005467 0.9699 0.9743 0.006807 0.8256 0.8203 0.01643 ] Network output: [ 0.9999 0.0001449 0.0004077 -3.33e-06 1.495e-06 -0.0003726 -2.51e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2076 -0.03541 -0.1586 0.1833 0.9834 0.9932 0.2329 0.4302 0.8684 0.7094 ] Network output: [ -0.009039 1.003 1.008 -2.214e-07 9.941e-08 0.007491 -1.669e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006755 0.0006142 0.004386 0.003217 0.9889 0.9919 0.006886 0.8529 0.8922 0.01175 ] Network output: [ -0.0002164 0.001559 1.001 -1.045e-05 4.691e-06 0.9983 -7.874e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2211 0.1047 0.3482 0.1423 0.9849 0.9939 0.2219 0.4341 0.8752 0.7032 ] Network output: [ 0.003313 -0.01571 0.9942 6.363e-06 -2.856e-06 1.015 4.795e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09797 0.1845 0.1976 0.9873 0.9919 0.1107 0.7376 0.8616 0.3052 ] Network output: [ -0.003105 0.01453 1.005 6.923e-06 -3.108e-06 0.9871 5.217e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09397 0.09203 0.165 0.1964 0.9852 0.9911 0.09398 0.6615 0.8369 0.2489 ] Network output: [ 8.78e-05 1 -5.369e-05 9.08e-07 -4.076e-07 0.9998 6.843e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001914 Epoch 9510 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009056 0.9967 0.9923 -1.941e-07 8.714e-08 -0.007174 -1.463e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0035 -0.003338 -0.006799 0.005467 0.9699 0.9743 0.006807 0.8255 0.8203 0.01643 ] Network output: [ 0.9999 0.0001447 0.0004075 -3.326e-06 1.493e-06 -0.0003723 -2.507e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2076 -0.03542 -0.1585 0.1833 0.9834 0.9932 0.2329 0.4302 0.8684 0.7094 ] Network output: [ -0.009038 1.003 1.008 -2.213e-07 9.934e-08 0.00749 -1.668e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006755 0.0006142 0.004386 0.003216 0.9889 0.9919 0.006887 0.8529 0.8922 0.01175 ] Network output: [ -0.0002163 0.001558 1.001 -1.044e-05 4.685e-06 0.9983 -7.865e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2211 0.1047 0.3482 0.1423 0.9849 0.9939 0.2219 0.4341 0.8752 0.7031 ] Network output: [ 0.003311 -0.0157 0.9942 6.355e-06 -2.853e-06 1.015 4.789e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09798 0.1845 0.1976 0.9873 0.9919 0.1107 0.7376 0.8616 0.3052 ] Network output: [ -0.003104 0.01452 1.005 6.915e-06 -3.104e-06 0.9871 5.211e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09397 0.09203 0.165 0.1964 0.9852 0.9911 0.09398 0.6615 0.8369 0.2489 ] Network output: [ 8.778e-05 1 -5.367e-05 9.07e-07 -4.072e-07 0.9998 6.835e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001913 Epoch 9511 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009055 0.9967 0.9923 -1.94e-07 8.71e-08 -0.007173 -1.462e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0035 -0.003339 -0.006798 0.005466 0.9699 0.9743 0.006807 0.8255 0.8203 0.01643 ] Network output: [ 0.9999 0.0001445 0.0004074 -3.322e-06 1.491e-06 -0.0003721 -2.504e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2076 -0.03542 -0.1585 0.1833 0.9834 0.9932 0.2329 0.4301 0.8684 0.7094 ] Network output: [ -0.009037 1.003 1.008 -2.211e-07 9.928e-08 0.007489 -1.667e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006755 0.0006143 0.004386 0.003216 0.9889 0.9919 0.006887 0.8528 0.8922 0.01175 ] Network output: [ -0.0002161 0.001557 1.001 -1.042e-05 4.68e-06 0.9983 -7.856e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2212 0.1047 0.3482 0.1423 0.9849 0.9939 0.2219 0.4341 0.8752 0.7031 ] Network output: [ 0.00331 -0.0157 0.9942 6.348e-06 -2.85e-06 1.015 4.784e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09798 0.1845 0.1976 0.9873 0.9919 0.1107 0.7376 0.8616 0.3052 ] Network output: [ -0.003102 0.01452 1.005 6.907e-06 -3.101e-06 0.9871 5.205e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09397 0.09203 0.165 0.1964 0.9852 0.9911 0.09399 0.6615 0.8369 0.2489 ] Network output: [ 8.775e-05 1 -5.364e-05 9.059e-07 -4.067e-07 0.9998 6.827e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001912 Epoch 9512 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009054 0.9967 0.9923 -1.939e-07 8.705e-08 -0.007172 -1.461e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0035 -0.003339 -0.006798 0.005466 0.9699 0.9743 0.006807 0.8255 0.8203 0.01643 ] Network output: [ 0.9999 0.0001443 0.0004072 -3.318e-06 1.49e-06 -0.0003718 -2.501e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2076 -0.03542 -0.1585 0.1833 0.9834 0.9932 0.2329 0.4301 0.8684 0.7094 ] Network output: [ -0.009037 1.003 1.008 -2.21e-07 9.921e-08 0.007489 -1.665e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006756 0.0006144 0.004386 0.003216 0.9889 0.9919 0.006887 0.8528 0.8922 0.01175 ] Network output: [ -0.0002159 0.001557 1.001 -1.041e-05 4.674e-06 0.9983 -7.846e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2212 0.1047 0.3482 0.1423 0.9849 0.9939 0.2219 0.4341 0.8752 0.7031 ] Network output: [ 0.003308 -0.01569 0.9942 6.34e-06 -2.846e-06 1.015 4.778e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1106 0.09798 0.1846 0.1976 0.9873 0.9919 0.1107 0.7375 0.8616 0.3052 ] Network output: [ -0.003101 0.01451 1.005 6.899e-06 -3.097e-06 0.9871 5.199e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09397 0.09203 0.165 0.1964 0.9852 0.9911 0.09399 0.6614 0.8369 0.2489 ] Network output: [ 8.772e-05 1 -5.362e-05 9.048e-07 -4.062e-07 0.9998 6.819e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001911 Epoch 9513 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009053 0.9967 0.9924 -1.938e-07 8.701e-08 -0.007172 -1.461e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0035 -0.003339 -0.006797 0.005466 0.9699 0.9743 0.006807 0.8255 0.8203 0.01643 ] Network output: [ 0.9999 0.0001442 0.000407 -3.314e-06 1.488e-06 -0.0003715 -2.498e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2076 -0.03542 -0.1585 0.1833 0.9834 0.9932 0.2329 0.4301 0.8684 0.7093 ] Network output: [ -0.009036 1.003 1.008 -2.208e-07 9.914e-08 0.007488 -1.664e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006756 0.0006145 0.004386 0.003216 0.9889 0.9919 0.006888 0.8528 0.8922 0.01175 ] Network output: [ -0.0002158 0.001556 1.001 -1.04e-05 4.668e-06 0.9983 -7.837e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2212 0.1047 0.3482 0.1423 0.9849 0.9939 0.2219 0.4341 0.8752 0.7031 ] Network output: [ 0.003307 -0.01568 0.9942 6.333e-06 -2.843e-06 1.015 4.772e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09799 0.1846 0.1976 0.9873 0.9919 0.1107 0.7375 0.8616 0.3052 ] Network output: [ -0.0031 0.0145 1.005 6.891e-06 -3.093e-06 0.9871 5.193e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09398 0.09204 0.165 0.1964 0.9852 0.9911 0.09399 0.6614 0.8369 0.2489 ] Network output: [ 8.77e-05 1 -5.359e-05 9.038e-07 -4.057e-07 0.9998 6.811e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000191 Epoch 9514 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009052 0.9967 0.9924 -1.937e-07 8.697e-08 -0.007171 -1.46e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0035 -0.003339 -0.006797 0.005465 0.9699 0.9743 0.006808 0.8255 0.8203 0.01643 ] Network output: [ 0.9999 0.000144 0.0004068 -3.31e-06 1.486e-06 -0.0003713 -2.495e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2076 -0.03542 -0.1585 0.1832 0.9834 0.9932 0.2329 0.4301 0.8684 0.7093 ] Network output: [ -0.009035 1.003 1.008 -2.207e-07 9.907e-08 0.007487 -1.663e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006757 0.0006145 0.004385 0.003215 0.9889 0.9919 0.006888 0.8528 0.8922 0.01175 ] Network output: [ -0.0002156 0.001555 1.001 -1.039e-05 4.663e-06 0.9983 -7.828e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2212 0.1047 0.3482 0.1423 0.9849 0.9939 0.2219 0.4341 0.8752 0.7031 ] Network output: [ 0.003305 -0.01568 0.9942 6.325e-06 -2.84e-06 1.015 4.767e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09799 0.1846 0.1976 0.9873 0.9919 0.1107 0.7375 0.8616 0.3052 ] Network output: [ -0.003098 0.0145 1.005 6.883e-06 -3.09e-06 0.9871 5.187e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09398 0.09204 0.165 0.1964 0.9852 0.9911 0.09399 0.6614 0.8369 0.2489 ] Network output: [ 8.767e-05 1 -5.356e-05 9.027e-07 -4.053e-07 0.9998 6.803e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001909 Epoch 9515 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009051 0.9967 0.9924 -1.936e-07 8.693e-08 -0.007171 -1.459e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0035 -0.003339 -0.006796 0.005465 0.9699 0.9743 0.006808 0.8255 0.8203 0.01643 ] Network output: [ 0.9999 0.0001438 0.0004066 -3.306e-06 1.484e-06 -0.000371 -2.492e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2076 -0.03542 -0.1585 0.1832 0.9834 0.9932 0.2329 0.4301 0.8684 0.7093 ] Network output: [ -0.009034 1.003 1.008 -2.205e-07 9.901e-08 0.007487 -1.662e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006757 0.0006146 0.004385 0.003215 0.9889 0.9919 0.006889 0.8528 0.8922 0.01175 ] Network output: [ -0.0002154 0.001554 1.001 -1.037e-05 4.657e-06 0.9983 -7.818e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2212 0.1047 0.3482 0.1423 0.9849 0.9939 0.2219 0.4341 0.8752 0.7031 ] Network output: [ 0.003304 -0.01567 0.9942 6.318e-06 -2.836e-06 1.015 4.761e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09799 0.1846 0.1976 0.9873 0.9919 0.1107 0.7375 0.8616 0.3052 ] Network output: [ -0.003097 0.01449 1.005 6.875e-06 -3.086e-06 0.9871 5.181e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09398 0.09204 0.165 0.1964 0.9852 0.9911 0.09399 0.6614 0.8369 0.2489 ] Network output: [ 8.765e-05 1 -5.354e-05 9.017e-07 -4.048e-07 0.9998 6.795e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001908 Epoch 9516 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00905 0.9967 0.9924 -1.935e-07 8.689e-08 -0.00717 -1.459e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0035 -0.003339 -0.006795 0.005464 0.9699 0.9743 0.006808 0.8255 0.8202 0.01643 ] Network output: [ 0.9999 0.0001436 0.0004064 -3.302e-06 1.483e-06 -0.0003708 -2.489e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2076 -0.03542 -0.1585 0.1832 0.9834 0.9932 0.233 0.4301 0.8684 0.7093 ] Network output: [ -0.009033 1.003 1.008 -2.204e-07 9.894e-08 0.007486 -1.661e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006757 0.0006147 0.004385 0.003215 0.9889 0.9919 0.006889 0.8528 0.8922 0.01175 ] Network output: [ -0.0002153 0.001554 1.001 -1.036e-05 4.652e-06 0.9983 -7.809e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2212 0.1047 0.3482 0.1423 0.9849 0.9939 0.2219 0.4341 0.8752 0.7031 ] Network output: [ 0.003302 -0.01566 0.9942 6.31e-06 -2.833e-06 1.015 4.756e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.098 0.1846 0.1976 0.9873 0.9919 0.1107 0.7375 0.8616 0.3052 ] Network output: [ -0.003096 0.01449 1.005 6.867e-06 -3.083e-06 0.9871 5.175e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09398 0.09204 0.165 0.1964 0.9852 0.9911 0.094 0.6614 0.8369 0.2489 ] Network output: [ 8.762e-05 1 -5.351e-05 9.006e-07 -4.043e-07 0.9998 6.787e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001907 Epoch 9517 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00905 0.9967 0.9924 -1.934e-07 8.685e-08 -0.00717 -1.458e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0035 -0.003339 -0.006795 0.005464 0.9699 0.9743 0.006808 0.8255 0.8202 0.01643 ] Network output: [ 0.9999 0.0001434 0.0004063 -3.298e-06 1.481e-06 -0.0003705 -2.486e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2076 -0.03542 -0.1585 0.1832 0.9834 0.9932 0.233 0.4301 0.8684 0.7093 ] Network output: [ -0.009032 1.003 1.008 -2.202e-07 9.887e-08 0.007485 -1.66e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006758 0.0006148 0.004385 0.003215 0.9889 0.9919 0.006889 0.8528 0.8922 0.01174 ] Network output: [ -0.0002151 0.001553 1.001 -1.035e-05 4.646e-06 0.9983 -7.8e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2212 0.1047 0.3482 0.1423 0.9849 0.9939 0.2219 0.4341 0.8752 0.7031 ] Network output: [ 0.003301 -0.01566 0.9942 6.303e-06 -2.83e-06 1.015 4.75e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.098 0.1846 0.1976 0.9873 0.9919 0.1107 0.7375 0.8616 0.3052 ] Network output: [ -0.003094 0.01448 1.005 6.859e-06 -3.079e-06 0.9871 5.169e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09398 0.09205 0.165 0.1964 0.9852 0.9911 0.094 0.6614 0.8369 0.2489 ] Network output: [ 8.759e-05 1 -5.349e-05 8.995e-07 -4.038e-07 0.9998 6.779e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001906 Epoch 9518 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009049 0.9967 0.9924 -1.934e-07 8.68e-08 -0.007169 -1.457e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0035 -0.003339 -0.006794 0.005464 0.9699 0.9743 0.006808 0.8255 0.8202 0.01642 ] Network output: [ 0.9999 0.0001433 0.0004061 -3.295e-06 1.479e-06 -0.0003703 -2.483e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2076 -0.03542 -0.1585 0.1832 0.9834 0.9932 0.233 0.4301 0.8684 0.7093 ] Network output: [ -0.009031 1.003 1.008 -2.201e-07 9.881e-08 0.007485 -1.659e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006758 0.0006148 0.004385 0.003214 0.9889 0.9919 0.00689 0.8528 0.8922 0.01174 ] Network output: [ -0.000215 0.001552 1.001 -1.034e-05 4.641e-06 0.9983 -7.791e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2212 0.1047 0.3482 0.1423 0.9849 0.9939 0.222 0.4341 0.8752 0.7031 ] Network output: [ 0.003299 -0.01565 0.9942 6.295e-06 -2.826e-06 1.015 4.744e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.098 0.1846 0.1976 0.9873 0.9919 0.1107 0.7375 0.8616 0.3052 ] Network output: [ -0.003093 0.01447 1.005 6.851e-06 -3.076e-06 0.9871 5.163e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09399 0.09205 0.165 0.1964 0.9852 0.9911 0.094 0.6614 0.8369 0.2489 ] Network output: [ 8.757e-05 1 -5.346e-05 8.985e-07 -4.034e-07 0.9998 6.771e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001905 Epoch 9519 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009048 0.9967 0.9924 -1.933e-07 8.676e-08 -0.007169 -1.456e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0035 -0.003339 -0.006794 0.005463 0.9699 0.9743 0.006809 0.8255 0.8202 0.01642 ] Network output: [ 0.9999 0.0001431 0.0004059 -3.291e-06 1.477e-06 -0.00037 -2.48e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2076 -0.03542 -0.1585 0.1832 0.9834 0.9932 0.233 0.4301 0.8684 0.7093 ] Network output: [ -0.009031 1.003 1.008 -2.199e-07 9.874e-08 0.007484 -1.658e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006759 0.0006149 0.004385 0.003214 0.9889 0.9919 0.00689 0.8528 0.8922 0.01174 ] Network output: [ -0.0002148 0.001552 1.001 -1.033e-05 4.635e-06 0.9983 -7.781e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2212 0.1048 0.3482 0.1423 0.9849 0.9939 0.222 0.4341 0.8752 0.7031 ] Network output: [ 0.003298 -0.01564 0.9942 6.288e-06 -2.823e-06 1.015 4.739e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09801 0.1846 0.1976 0.9873 0.9919 0.1108 0.7375 0.8616 0.3052 ] Network output: [ -0.003091 0.01447 1.005 6.843e-06 -3.072e-06 0.9871 5.157e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09399 0.09205 0.165 0.1964 0.9852 0.9911 0.094 0.6614 0.8369 0.2489 ] Network output: [ 8.754e-05 1 -5.343e-05 8.974e-07 -4.029e-07 0.9998 6.763e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001904 Epoch 9520 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009047 0.9967 0.9924 -1.932e-07 8.672e-08 -0.007168 -1.456e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0035 -0.003339 -0.006793 0.005463 0.9699 0.9743 0.006809 0.8255 0.8202 0.01642 ] Network output: [ 0.9999 0.0001429 0.0004057 -3.287e-06 1.476e-06 -0.0003698 -2.477e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2076 -0.03542 -0.1585 0.1832 0.9834 0.9932 0.233 0.4301 0.8684 0.7093 ] Network output: [ -0.00903 1.003 1.008 -2.198e-07 9.867e-08 0.007483 -1.656e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006759 0.000615 0.004385 0.003214 0.9889 0.9919 0.006891 0.8528 0.8922 0.01174 ] Network output: [ -0.0002146 0.001551 1.001 -1.031e-05 4.63e-06 0.9983 -7.772e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2212 0.1048 0.3483 0.1423 0.9849 0.9939 0.222 0.4341 0.8752 0.7031 ] Network output: [ 0.003296 -0.01564 0.9942 6.28e-06 -2.82e-06 1.015 4.733e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09801 0.1846 0.1976 0.9873 0.9919 0.1108 0.7375 0.8616 0.3052 ] Network output: [ -0.00309 0.01446 1.005 6.835e-06 -3.068e-06 0.9871 5.151e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09399 0.09205 0.165 0.1964 0.9852 0.9911 0.094 0.6613 0.8369 0.2489 ] Network output: [ 8.751e-05 1 -5.341e-05 8.964e-07 -4.024e-07 0.9998 6.755e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001903 Epoch 9521 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009046 0.9967 0.9924 -1.931e-07 8.668e-08 -0.007168 -1.455e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0035 -0.003339 -0.006792 0.005462 0.9699 0.9743 0.006809 0.8255 0.8202 0.01642 ] Network output: [ 0.9999 0.0001427 0.0004055 -3.283e-06 1.474e-06 -0.0003695 -2.474e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2076 -0.03543 -0.1584 0.1832 0.9834 0.9932 0.233 0.4301 0.8684 0.7093 ] Network output: [ -0.009029 1.003 1.008 -2.196e-07 9.86e-08 0.007483 -1.655e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006759 0.000615 0.004385 0.003214 0.9889 0.9919 0.006891 0.8528 0.8922 0.01174 ] Network output: [ -0.0002145 0.00155 1.001 -1.03e-05 4.624e-06 0.9983 -7.763e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2212 0.1048 0.3483 0.1423 0.9849 0.9939 0.222 0.4341 0.8752 0.7031 ] Network output: [ 0.003295 -0.01563 0.9942 6.273e-06 -2.816e-06 1.015 4.728e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09802 0.1846 0.1976 0.9873 0.9919 0.1108 0.7374 0.8616 0.3052 ] Network output: [ -0.003089 0.01445 1.005 6.827e-06 -3.065e-06 0.9871 5.145e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09399 0.09205 0.165 0.1964 0.9852 0.9911 0.09401 0.6613 0.8369 0.249 ] Network output: [ 8.749e-05 1 -5.338e-05 8.953e-07 -4.019e-07 0.9998 6.747e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001902 Epoch 9522 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009045 0.9967 0.9924 -1.93e-07 8.664e-08 -0.007167 -1.454e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.0035 -0.00334 -0.006792 0.005462 0.9699 0.9743 0.006809 0.8255 0.8202 0.01642 ] Network output: [ 0.9999 0.0001425 0.0004053 -3.279e-06 1.472e-06 -0.0003693 -2.471e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2076 -0.03543 -0.1584 0.1832 0.9834 0.9932 0.233 0.4301 0.8684 0.7093 ] Network output: [ -0.009028 1.003 1.008 -2.195e-07 9.854e-08 0.007482 -1.654e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00676 0.0006151 0.004385 0.003213 0.9889 0.9919 0.006892 0.8528 0.8922 0.01174 ] Network output: [ -0.0002143 0.00155 1.001 -1.029e-05 4.619e-06 0.9983 -7.754e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2212 0.1048 0.3483 0.1423 0.9849 0.9939 0.222 0.4341 0.8752 0.7031 ] Network output: [ 0.003293 -0.01562 0.9942 6.266e-06 -2.813e-06 1.015 4.722e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09802 0.1846 0.1976 0.9873 0.9919 0.1108 0.7374 0.8616 0.3052 ] Network output: [ -0.003087 0.01445 1.005 6.819e-06 -3.061e-06 0.9871 5.139e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09399 0.09206 0.165 0.1964 0.9852 0.9911 0.09401 0.6613 0.8369 0.249 ] Network output: [ 8.746e-05 1 -5.336e-05 8.943e-07 -4.015e-07 0.9998 6.74e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001901 Epoch 9523 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009044 0.9967 0.9924 -1.929e-07 8.659e-08 -0.007166 -1.454e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003501 -0.00334 -0.006791 0.005462 0.9699 0.9743 0.006809 0.8255 0.8202 0.01642 ] Network output: [ 0.9999 0.0001424 0.0004051 -3.275e-06 1.47e-06 -0.000369 -2.468e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2077 -0.03543 -0.1584 0.1832 0.9834 0.9932 0.233 0.4301 0.8684 0.7093 ] Network output: [ -0.009027 1.003 1.008 -2.193e-07 9.847e-08 0.007481 -1.653e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00676 0.0006152 0.004385 0.003213 0.9889 0.9919 0.006892 0.8528 0.8922 0.01174 ] Network output: [ -0.0002141 0.001549 1.001 -1.028e-05 4.613e-06 0.9983 -7.744e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2212 0.1048 0.3483 0.1423 0.9849 0.9939 0.222 0.4341 0.8752 0.7031 ] Network output: [ 0.003292 -0.01562 0.9942 6.258e-06 -2.81e-06 1.015 4.716e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09802 0.1846 0.1976 0.9873 0.9919 0.1108 0.7374 0.8616 0.3052 ] Network output: [ -0.003086 0.01444 1.005 6.811e-06 -3.058e-06 0.9871 5.133e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.094 0.09206 0.165 0.1964 0.9852 0.9911 0.09401 0.6613 0.8369 0.249 ] Network output: [ 8.744e-05 1 -5.333e-05 8.932e-07 -4.01e-07 0.9998 6.732e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00019 Epoch 9524 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009043 0.9967 0.9924 -1.928e-07 8.655e-08 -0.007166 -1.453e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003501 -0.00334 -0.00679 0.005461 0.9699 0.9743 0.006809 0.8255 0.8202 0.01642 ] Network output: [ 0.9999 0.0001422 0.000405 -3.271e-06 1.468e-06 -0.0003688 -2.465e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2077 -0.03543 -0.1584 0.1832 0.9834 0.9932 0.233 0.4301 0.8684 0.7093 ] Network output: [ -0.009026 1.003 1.008 -2.192e-07 9.84e-08 0.007481 -1.652e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00676 0.0006153 0.004385 0.003213 0.9889 0.9919 0.006892 0.8528 0.8922 0.01174 ] Network output: [ -0.000214 0.001548 1.001 -1.026e-05 4.608e-06 0.9983 -7.735e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2212 0.1048 0.3483 0.1423 0.9849 0.9939 0.222 0.4341 0.8752 0.7031 ] Network output: [ 0.003291 -0.01561 0.9942 6.251e-06 -2.806e-06 1.015 4.711e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09803 0.1846 0.1976 0.9873 0.9919 0.1108 0.7374 0.8616 0.3052 ] Network output: [ -0.003085 0.01444 1.005 6.803e-06 -3.054e-06 0.9871 5.127e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.094 0.09206 0.165 0.1964 0.9852 0.9911 0.09401 0.6613 0.8369 0.249 ] Network output: [ 8.741e-05 1 -5.331e-05 8.922e-07 -4.005e-07 0.9998 6.724e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001899 Epoch 9525 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009042 0.9967 0.9924 -1.927e-07 8.651e-08 -0.007165 -1.452e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003501 -0.00334 -0.00679 0.005461 0.9699 0.9743 0.00681 0.8255 0.8202 0.01642 ] Network output: [ 0.9999 0.000142 0.0004048 -3.267e-06 1.467e-06 -0.0003685 -2.462e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2077 -0.03543 -0.1584 0.1832 0.9834 0.9932 0.233 0.4301 0.8684 0.7093 ] Network output: [ -0.009025 1.003 1.008 -2.19e-07 9.833e-08 0.00748 -1.651e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006761 0.0006153 0.004385 0.003213 0.9889 0.9919 0.006893 0.8528 0.8922 0.01174 ] Network output: [ -0.0002138 0.001547 1.001 -1.025e-05 4.602e-06 0.9983 -7.726e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2213 0.1048 0.3483 0.1423 0.9849 0.9939 0.222 0.4341 0.8752 0.7031 ] Network output: [ 0.003289 -0.0156 0.9942 6.243e-06 -2.803e-06 1.015 4.705e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09803 0.1846 0.1976 0.9873 0.9919 0.1108 0.7374 0.8616 0.3052 ] Network output: [ -0.003083 0.01443 1.005 6.795e-06 -3.051e-06 0.9871 5.121e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.094 0.09206 0.165 0.1964 0.9852 0.9911 0.09401 0.6613 0.8369 0.249 ] Network output: [ 8.738e-05 1 -5.328e-05 8.911e-07 -4.001e-07 0.9998 6.716e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001898 Epoch 9526 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009041 0.9967 0.9924 -1.926e-07 8.647e-08 -0.007165 -1.452e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003501 -0.00334 -0.006789 0.00546 0.9699 0.9743 0.00681 0.8255 0.8202 0.01642 ] Network output: [ 0.9999 0.0001418 0.0004046 -3.263e-06 1.465e-06 -0.0003683 -2.459e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2077 -0.03543 -0.1584 0.1832 0.9834 0.9932 0.233 0.4301 0.8684 0.7093 ] Network output: [ -0.009025 1.003 1.008 -2.189e-07 9.827e-08 0.007479 -1.65e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006761 0.0006154 0.004384 0.003212 0.9889 0.9919 0.006893 0.8528 0.8922 0.01174 ] Network output: [ -0.0002136 0.001547 1.001 -1.024e-05 4.597e-06 0.9983 -7.717e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2213 0.1048 0.3483 0.1423 0.9849 0.9939 0.222 0.4341 0.8752 0.7031 ] Network output: [ 0.003288 -0.0156 0.9942 6.236e-06 -2.8e-06 1.015 4.7e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09803 0.1846 0.1976 0.9873 0.9919 0.1108 0.7374 0.8616 0.3052 ] Network output: [ -0.003082 0.01442 1.005 6.787e-06 -3.047e-06 0.9871 5.115e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.094 0.09206 0.165 0.1964 0.9852 0.9911 0.09402 0.6613 0.8369 0.249 ] Network output: [ 8.736e-05 1 -5.326e-05 8.901e-07 -3.996e-07 0.9998 6.708e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001897 Epoch 9527 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00904 0.9967 0.9924 -1.925e-07 8.642e-08 -0.007164 -1.451e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003501 -0.00334 -0.006789 0.00546 0.9699 0.9743 0.00681 0.8255 0.8202 0.01642 ] Network output: [ 0.9999 0.0001416 0.0004044 -3.259e-06 1.463e-06 -0.000368 -2.456e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2077 -0.03543 -0.1584 0.1832 0.9834 0.9932 0.233 0.4301 0.8684 0.7093 ] Network output: [ -0.009024 1.003 1.008 -2.187e-07 9.82e-08 0.007479 -1.648e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006762 0.0006155 0.004384 0.003212 0.9889 0.9919 0.006894 0.8528 0.8922 0.01174 ] Network output: [ -0.0002135 0.001546 1.001 -1.023e-05 4.591e-06 0.9983 -7.708e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2213 0.1048 0.3483 0.1423 0.9849 0.9939 0.222 0.434 0.8752 0.7031 ] Network output: [ 0.003286 -0.01559 0.9942 6.229e-06 -2.796e-06 1.015 4.694e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09804 0.1846 0.1976 0.9873 0.9919 0.1108 0.7374 0.8616 0.3052 ] Network output: [ -0.00308 0.01442 1.005 6.779e-06 -3.043e-06 0.9871 5.109e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.094 0.09207 0.165 0.1964 0.9852 0.9911 0.09402 0.6613 0.8369 0.249 ] Network output: [ 8.733e-05 1 -5.323e-05 8.89e-07 -3.991e-07 0.9998 6.7e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001896 Epoch 9528 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009039 0.9967 0.9924 -1.924e-07 8.638e-08 -0.007164 -1.45e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003501 -0.00334 -0.006788 0.005459 0.9699 0.9743 0.00681 0.8255 0.8202 0.01642 ] Network output: [ 0.9999 0.0001415 0.0004042 -3.255e-06 1.462e-06 -0.0003678 -2.453e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2077 -0.03543 -0.1584 0.1832 0.9834 0.9932 0.233 0.4301 0.8684 0.7093 ] Network output: [ -0.009023 1.003 1.008 -2.186e-07 9.813e-08 0.007478 -1.647e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006762 0.0006156 0.004384 0.003212 0.9889 0.9919 0.006894 0.8528 0.8922 0.01174 ] Network output: [ -0.0002133 0.001545 1.001 -1.022e-05 4.586e-06 0.9983 -7.698e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2213 0.1048 0.3483 0.1423 0.9849 0.9939 0.222 0.434 0.8751 0.7031 ] Network output: [ 0.003285 -0.01558 0.9942 6.221e-06 -2.793e-06 1.015 4.689e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09804 0.1846 0.1976 0.9873 0.9919 0.1108 0.7374 0.8616 0.3052 ] Network output: [ -0.003079 0.01441 1.005 6.771e-06 -3.04e-06 0.9871 5.103e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09401 0.09207 0.165 0.1964 0.9852 0.9911 0.09402 0.6612 0.8369 0.249 ] Network output: [ 8.731e-05 1 -5.321e-05 8.88e-07 -3.987e-07 0.9998 6.692e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001895 Epoch 9529 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009038 0.9967 0.9924 -1.923e-07 8.634e-08 -0.007163 -1.449e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003501 -0.00334 -0.006787 0.005459 0.9699 0.9743 0.00681 0.8255 0.8202 0.01641 ] Network output: [ 0.9999 0.0001413 0.000404 -3.252e-06 1.46e-06 -0.0003675 -2.451e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2077 -0.03543 -0.1584 0.1832 0.9834 0.9932 0.233 0.4301 0.8684 0.7093 ] Network output: [ -0.009022 1.003 1.008 -2.184e-07 9.807e-08 0.007477 -1.646e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006762 0.0006156 0.004384 0.003212 0.9889 0.9919 0.006894 0.8528 0.8922 0.01174 ] Network output: [ -0.0002131 0.001545 1.001 -1.02e-05 4.581e-06 0.9983 -7.689e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2213 0.1048 0.3483 0.1423 0.9849 0.9939 0.222 0.434 0.8751 0.7031 ] Network output: [ 0.003283 -0.01558 0.9942 6.214e-06 -2.79e-06 1.015 4.683e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09804 0.1846 0.1976 0.9873 0.9919 0.1108 0.7373 0.8616 0.3052 ] Network output: [ -0.003078 0.0144 1.005 6.764e-06 -3.036e-06 0.9871 5.097e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09401 0.09207 0.165 0.1964 0.9852 0.9911 0.09402 0.6612 0.8369 0.249 ] Network output: [ 8.728e-05 1 -5.318e-05 8.87e-07 -3.982e-07 0.9998 6.684e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001894 Epoch 9530 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009037 0.9967 0.9924 -1.922e-07 8.63e-08 -0.007163 -1.449e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003501 -0.00334 -0.006787 0.005459 0.9699 0.9743 0.006811 0.8255 0.8202 0.01641 ] Network output: [ 0.9999 0.0001411 0.0004039 -3.248e-06 1.458e-06 -0.0003673 -2.448e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2077 -0.03543 -0.1584 0.1832 0.9834 0.9932 0.2331 0.43 0.8684 0.7093 ] Network output: [ -0.009021 1.003 1.008 -2.183e-07 9.8e-08 0.007477 -1.645e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006763 0.0006157 0.004384 0.003212 0.9889 0.9919 0.006895 0.8527 0.8922 0.01173 ] Network output: [ -0.000213 0.001544 1.001 -1.019e-05 4.575e-06 0.9983 -7.68e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2213 0.1048 0.3483 0.1423 0.9849 0.9939 0.222 0.434 0.8751 0.7031 ] Network output: [ 0.003282 -0.01557 0.9942 6.207e-06 -2.786e-06 1.015 4.678e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09805 0.1846 0.1976 0.9873 0.9919 0.1108 0.7373 0.8616 0.3052 ] Network output: [ -0.003076 0.0144 1.005 6.756e-06 -3.033e-06 0.9871 5.091e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09401 0.09207 0.165 0.1964 0.9852 0.9911 0.09402 0.6612 0.8369 0.249 ] Network output: [ 8.725e-05 1 -5.316e-05 8.859e-07 -3.977e-07 0.9998 6.677e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001893 Epoch 9531 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009036 0.9967 0.9924 -1.921e-07 8.625e-08 -0.007162 -1.448e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003501 -0.00334 -0.006786 0.005458 0.9699 0.9743 0.006811 0.8254 0.8202 0.01641 ] Network output: [ 0.9999 0.0001409 0.0004037 -3.244e-06 1.456e-06 -0.000367 -2.445e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2077 -0.03544 -0.1584 0.1832 0.9834 0.9932 0.2331 0.43 0.8684 0.7093 ] Network output: [ -0.00902 1.003 1.008 -2.181e-07 9.793e-08 0.007476 -1.644e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006763 0.0006158 0.004384 0.003211 0.9889 0.9919 0.006895 0.8527 0.8922 0.01173 ] Network output: [ -0.0002128 0.001543 1.001 -1.018e-05 4.57e-06 0.9983 -7.671e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2213 0.1048 0.3483 0.1423 0.9849 0.9939 0.222 0.434 0.8751 0.7031 ] Network output: [ 0.00328 -0.01556 0.9942 6.199e-06 -2.783e-06 1.015 4.672e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09805 0.1846 0.1976 0.9873 0.9919 0.1108 0.7373 0.8616 0.3052 ] Network output: [ -0.003075 0.01439 1.005 6.748e-06 -3.029e-06 0.9871 5.085e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09401 0.09207 0.165 0.1964 0.9852 0.9911 0.09403 0.6612 0.8368 0.249 ] Network output: [ 8.723e-05 1 -5.313e-05 8.849e-07 -3.973e-07 0.9998 6.669e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001892 Epoch 9532 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009035 0.9967 0.9924 -1.92e-07 8.621e-08 -0.007161 -1.447e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003501 -0.00334 -0.006785 0.005458 0.9699 0.9743 0.006811 0.8254 0.8202 0.01641 ] Network output: [ 0.9999 0.0001407 0.0004035 -3.24e-06 1.455e-06 -0.0003668 -2.442e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2077 -0.03544 -0.1583 0.1832 0.9834 0.9932 0.2331 0.43 0.8684 0.7093 ] Network output: [ -0.00902 1.003 1.008 -2.18e-07 9.786e-08 0.007475 -1.643e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006764 0.0006159 0.004384 0.003211 0.9889 0.9919 0.006896 0.8527 0.8922 0.01173 ] Network output: [ -0.0002127 0.001542 1.001 -1.017e-05 4.564e-06 0.9983 -7.662e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2213 0.1048 0.3483 0.1423 0.9849 0.9939 0.2221 0.434 0.8751 0.7031 ] Network output: [ 0.003279 -0.01556 0.9942 6.192e-06 -2.78e-06 1.015 4.666e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09806 0.1846 0.1976 0.9873 0.9919 0.1108 0.7373 0.8616 0.3052 ] Network output: [ -0.003074 0.01439 1.005 6.74e-06 -3.026e-06 0.9871 5.079e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09401 0.09208 0.165 0.1964 0.9852 0.9911 0.09403 0.6612 0.8368 0.249 ] Network output: [ 8.72e-05 1 -5.311e-05 8.838e-07 -3.968e-07 0.9998 6.661e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001891 Epoch 9533 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009034 0.9967 0.9924 -1.919e-07 8.617e-08 -0.007161 -1.447e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003501 -0.003341 -0.006785 0.005457 0.9699 0.9743 0.006811 0.8254 0.8202 0.01641 ] Network output: [ 0.9999 0.0001406 0.0004033 -3.236e-06 1.453e-06 -0.0003665 -2.439e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2077 -0.03544 -0.1583 0.1832 0.9834 0.9932 0.2331 0.43 0.8684 0.7093 ] Network output: [ -0.009019 1.003 1.008 -2.178e-07 9.78e-08 0.007475 -1.642e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006764 0.0006159 0.004384 0.003211 0.9889 0.9919 0.006896 0.8527 0.8922 0.01173 ] Network output: [ -0.0002125 0.001542 1.001 -1.015e-05 4.559e-06 0.9983 -7.653e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2213 0.1048 0.3483 0.1423 0.9849 0.9939 0.2221 0.434 0.8751 0.703 ] Network output: [ 0.003277 -0.01555 0.9942 6.185e-06 -2.777e-06 1.015 4.661e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09806 0.1846 0.1976 0.9873 0.9919 0.1108 0.7373 0.8616 0.3052 ] Network output: [ -0.003072 0.01438 1.005 6.732e-06 -3.022e-06 0.9871 5.074e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09402 0.09208 0.165 0.1964 0.9852 0.9911 0.09403 0.6612 0.8368 0.249 ] Network output: [ 8.718e-05 1 -5.309e-05 8.828e-07 -3.963e-07 0.9998 6.653e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000189 Epoch 9534 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009033 0.9967 0.9924 -1.918e-07 8.613e-08 -0.00716 -1.446e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003501 -0.003341 -0.006784 0.005457 0.9699 0.9743 0.006811 0.8254 0.8202 0.01641 ] Network output: [ 0.9999 0.0001404 0.0004031 -3.232e-06 1.451e-06 -0.0003663 -2.436e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2077 -0.03544 -0.1583 0.1832 0.9834 0.9932 0.2331 0.43 0.8684 0.7093 ] Network output: [ -0.009018 1.003 1.008 -2.177e-07 9.773e-08 0.007474 -1.641e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006764 0.000616 0.004384 0.003211 0.9889 0.9919 0.006896 0.8527 0.8922 0.01173 ] Network output: [ -0.0002123 0.001541 1.001 -1.014e-05 4.553e-06 0.9983 -7.644e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2213 0.1048 0.3483 0.1423 0.9849 0.9939 0.2221 0.434 0.8751 0.703 ] Network output: [ 0.003276 -0.01554 0.9942 6.177e-06 -2.773e-06 1.015 4.655e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09806 0.1846 0.1976 0.9873 0.9919 0.1108 0.7373 0.8616 0.3052 ] Network output: [ -0.003071 0.01437 1.005 6.724e-06 -3.019e-06 0.9871 5.068e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09402 0.09208 0.165 0.1964 0.9852 0.9911 0.09403 0.6612 0.8368 0.249 ] Network output: [ 8.715e-05 1 -5.306e-05 8.818e-07 -3.959e-07 0.9998 6.645e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001889 Epoch 9535 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009032 0.9967 0.9924 -1.918e-07 8.608e-08 -0.00716 -1.445e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003501 -0.003341 -0.006784 0.005457 0.9699 0.9743 0.006812 0.8254 0.8202 0.01641 ] Network output: [ 0.9999 0.0001402 0.0004029 -3.228e-06 1.449e-06 -0.000366 -2.433e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2077 -0.03544 -0.1583 0.1832 0.9834 0.9932 0.2331 0.43 0.8684 0.7093 ] Network output: [ -0.009017 1.003 1.008 -2.175e-07 9.766e-08 0.007473 -1.639e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006765 0.0006161 0.004384 0.00321 0.9889 0.9919 0.006897 0.8527 0.8922 0.01173 ] Network output: [ -0.0002122 0.00154 1.001 -1.013e-05 4.548e-06 0.9983 -7.635e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2213 0.1048 0.3483 0.1423 0.9849 0.9939 0.2221 0.434 0.8751 0.703 ] Network output: [ 0.003274 -0.01554 0.9942 6.17e-06 -2.77e-06 1.015 4.65e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09807 0.1846 0.1976 0.9873 0.9919 0.1108 0.7373 0.8616 0.3052 ] Network output: [ -0.00307 0.01437 1.005 6.716e-06 -3.015e-06 0.9871 5.062e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09402 0.09208 0.165 0.1964 0.9852 0.9911 0.09403 0.6612 0.8368 0.249 ] Network output: [ 8.712e-05 1 -5.304e-05 8.807e-07 -3.954e-07 0.9998 6.637e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001888 Epoch 9536 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009032 0.9967 0.9924 -1.917e-07 8.604e-08 -0.007159 -1.444e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003501 -0.003341 -0.006783 0.005456 0.9699 0.9743 0.006812 0.8254 0.8202 0.01641 ] Network output: [ 0.9999 0.00014 0.0004028 -3.225e-06 1.448e-06 -0.0003658 -2.43e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2077 -0.03544 -0.1583 0.1832 0.9834 0.9932 0.2331 0.43 0.8684 0.7093 ] Network output: [ -0.009016 1.003 1.008 -2.174e-07 9.759e-08 0.007473 -1.638e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006765 0.0006161 0.004384 0.00321 0.9889 0.9919 0.006897 0.8527 0.8922 0.01173 ] Network output: [ -0.000212 0.00154 1.001 -1.012e-05 4.543e-06 0.9983 -7.626e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2213 0.1048 0.3483 0.1423 0.9849 0.9939 0.2221 0.434 0.8751 0.703 ] Network output: [ 0.003273 -0.01553 0.9942 6.163e-06 -2.767e-06 1.015 4.644e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09807 0.1846 0.1976 0.9873 0.9919 0.1108 0.7373 0.8616 0.3052 ] Network output: [ -0.003068 0.01436 1.005 6.709e-06 -3.012e-06 0.9871 5.056e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09402 0.09208 0.165 0.1964 0.9852 0.9911 0.09404 0.6612 0.8368 0.249 ] Network output: [ 8.71e-05 1 -5.301e-05 8.797e-07 -3.949e-07 0.9998 6.63e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001887 Epoch 9537 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009031 0.9967 0.9924 -1.916e-07 8.6e-08 -0.007159 -1.444e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003501 -0.003341 -0.006782 0.005456 0.9699 0.9743 0.006812 0.8254 0.8202 0.01641 ] Network output: [ 0.9999 0.0001398 0.0004026 -3.221e-06 1.446e-06 -0.0003655 -2.427e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2077 -0.03544 -0.1583 0.1832 0.9834 0.9932 0.2331 0.43 0.8684 0.7092 ] Network output: [ -0.009015 1.003 1.008 -2.172e-07 9.753e-08 0.007472 -1.637e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006766 0.0006162 0.004383 0.00321 0.9889 0.9919 0.006898 0.8527 0.8922 0.01173 ] Network output: [ -0.0002118 0.001539 1.001 -1.011e-05 4.537e-06 0.9983 -7.616e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2213 0.1048 0.3483 0.1423 0.9849 0.9939 0.2221 0.434 0.8751 0.703 ] Network output: [ 0.003271 -0.01552 0.9942 6.155e-06 -2.763e-06 1.015 4.639e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09807 0.1846 0.1976 0.9873 0.9919 0.1108 0.7373 0.8616 0.3052 ] Network output: [ -0.003067 0.01435 1.005 6.701e-06 -3.008e-06 0.9872 5.05e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09402 0.09209 0.165 0.1964 0.9852 0.9911 0.09404 0.6611 0.8368 0.249 ] Network output: [ 8.707e-05 1 -5.299e-05 8.787e-07 -3.945e-07 0.9998 6.622e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001886 Epoch 9538 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00903 0.9967 0.9924 -1.915e-07 8.596e-08 -0.007158 -1.443e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003501 -0.003341 -0.006782 0.005455 0.9699 0.9743 0.006812 0.8254 0.8202 0.01641 ] Network output: [ 0.9999 0.0001397 0.0004024 -3.217e-06 1.444e-06 -0.0003653 -2.424e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2077 -0.03544 -0.1583 0.1832 0.9834 0.9932 0.2331 0.43 0.8684 0.7092 ] Network output: [ -0.009014 1.003 1.008 -2.171e-07 9.746e-08 0.007471 -1.636e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006766 0.0006163 0.004383 0.00321 0.9889 0.9919 0.006898 0.8527 0.8922 0.01173 ] Network output: [ -0.0002117 0.001538 1.001 -1.009e-05 4.532e-06 0.9983 -7.607e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2213 0.1048 0.3483 0.1423 0.9849 0.9939 0.2221 0.434 0.8751 0.703 ] Network output: [ 0.00327 -0.01552 0.9942 6.148e-06 -2.76e-06 1.015 4.633e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1107 0.09808 0.1846 0.1976 0.9873 0.9919 0.1108 0.7372 0.8616 0.3052 ] Network output: [ -0.003065 0.01435 1.005 6.693e-06 -3.005e-06 0.9872 5.044e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09403 0.09209 0.165 0.1964 0.9852 0.9911 0.09404 0.6611 0.8368 0.249 ] Network output: [ 8.705e-05 1 -5.297e-05 8.776e-07 -3.94e-07 0.9998 6.614e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001885 Epoch 9539 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009029 0.9967 0.9924 -1.914e-07 8.591e-08 -0.007158 -1.442e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003502 -0.003341 -0.006781 0.005455 0.9699 0.9743 0.006812 0.8254 0.8202 0.0164 ] Network output: [ 0.9999 0.0001395 0.0004022 -3.213e-06 1.442e-06 -0.000365 -2.421e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2077 -0.03544 -0.1583 0.1832 0.9834 0.9932 0.2331 0.43 0.8684 0.7092 ] Network output: [ -0.009014 1.003 1.008 -2.169e-07 9.739e-08 0.007471 -1.635e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006766 0.0006164 0.004383 0.003209 0.9889 0.9919 0.006898 0.8527 0.8922 0.01173 ] Network output: [ -0.0002115 0.001537 1.001 -1.008e-05 4.526e-06 0.9983 -7.598e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2214 0.1048 0.3483 0.1423 0.9849 0.9939 0.2221 0.434 0.8751 0.703 ] Network output: [ 0.003268 -0.01551 0.9942 6.141e-06 -2.757e-06 1.015 4.628e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09808 0.1846 0.1976 0.9873 0.9919 0.1108 0.7372 0.8616 0.3052 ] Network output: [ -0.003064 0.01434 1.005 6.685e-06 -3.001e-06 0.9872 5.038e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09403 0.09209 0.165 0.1964 0.9852 0.9911 0.09404 0.6611 0.8368 0.249 ] Network output: [ 8.702e-05 1 -5.294e-05 8.766e-07 -3.935e-07 0.9998 6.606e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001884 Epoch 9540 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009028 0.9967 0.9924 -1.913e-07 8.587e-08 -0.007157 -1.442e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003502 -0.003341 -0.006781 0.005455 0.9699 0.9743 0.006812 0.8254 0.8202 0.0164 ] Network output: [ 0.9999 0.0001393 0.000402 -3.209e-06 1.441e-06 -0.0003648 -2.419e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2078 -0.03544 -0.1583 0.1832 0.9834 0.9932 0.2331 0.43 0.8684 0.7092 ] Network output: [ -0.009013 1.003 1.008 -2.168e-07 9.733e-08 0.00747 -1.634e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006767 0.0006164 0.004383 0.003209 0.9889 0.9919 0.006899 0.8527 0.8922 0.01173 ] Network output: [ -0.0002113 0.001537 1.001 -1.007e-05 4.521e-06 0.9983 -7.589e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2214 0.1048 0.3484 0.1423 0.9849 0.9939 0.2221 0.434 0.8751 0.703 ] Network output: [ 0.003267 -0.0155 0.9942 6.134e-06 -2.754e-06 1.015 4.622e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09808 0.1846 0.1976 0.9873 0.9919 0.1108 0.7372 0.8616 0.3052 ] Network output: [ -0.003063 0.01434 1.005 6.677e-06 -2.998e-06 0.9872 5.032e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09403 0.09209 0.165 0.1964 0.9852 0.9911 0.09404 0.6611 0.8368 0.249 ] Network output: [ 8.7e-05 1 -5.292e-05 8.756e-07 -3.931e-07 0.9998 6.598e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001883 Epoch 9541 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009027 0.9967 0.9924 -1.912e-07 8.583e-08 -0.007156 -1.441e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003502 -0.003341 -0.00678 0.005454 0.9699 0.9743 0.006813 0.8254 0.8202 0.0164 ] Network output: [ 0.9999 0.0001391 0.0004019 -3.205e-06 1.439e-06 -0.0003645 -2.416e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2078 -0.03545 -0.1583 0.1831 0.9834 0.9932 0.2331 0.43 0.8684 0.7092 ] Network output: [ -0.009012 1.003 1.008 -2.166e-07 9.726e-08 0.007469 -1.633e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006767 0.0006165 0.004383 0.003209 0.9889 0.9919 0.006899 0.8527 0.8922 0.01173 ] Network output: [ -0.0002112 0.001536 1.001 -1.006e-05 4.516e-06 0.9983 -7.58e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2214 0.1049 0.3484 0.1423 0.9849 0.9939 0.2221 0.434 0.8751 0.703 ] Network output: [ 0.003265 -0.0155 0.9942 6.126e-06 -2.75e-06 1.015 4.617e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09809 0.1846 0.1976 0.9873 0.9919 0.1108 0.7372 0.8616 0.3052 ] Network output: [ -0.003061 0.01433 1.005 6.67e-06 -2.994e-06 0.9872 5.027e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09403 0.09209 0.165 0.1964 0.9852 0.9911 0.09405 0.6611 0.8368 0.249 ] Network output: [ 8.697e-05 1 -5.289e-05 8.745e-07 -3.926e-07 0.9998 6.591e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001882 Epoch 9542 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009026 0.9967 0.9924 -1.911e-07 8.578e-08 -0.007156 -1.44e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003502 -0.003341 -0.006779 0.005454 0.9699 0.9743 0.006813 0.8254 0.8202 0.0164 ] Network output: [ 0.9999 0.000139 0.0004017 -3.202e-06 1.437e-06 -0.0003643 -2.413e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2078 -0.03545 -0.1583 0.1831 0.9834 0.9932 0.2331 0.43 0.8684 0.7092 ] Network output: [ -0.009011 1.003 1.008 -2.165e-07 9.719e-08 0.007469 -1.632e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006768 0.0006166 0.004383 0.003209 0.9889 0.9919 0.0069 0.8527 0.8922 0.01173 ] Network output: [ -0.000211 0.001535 1.001 -1.005e-05 4.51e-06 0.9983 -7.571e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2214 0.1049 0.3484 0.1422 0.9849 0.9939 0.2221 0.434 0.8751 0.703 ] Network output: [ 0.003264 -0.01549 0.9942 6.119e-06 -2.747e-06 1.015 4.612e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09809 0.1846 0.1976 0.9873 0.9919 0.1108 0.7372 0.8616 0.3052 ] Network output: [ -0.00306 0.01432 1.005 6.662e-06 -2.991e-06 0.9872 5.021e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09403 0.0921 0.165 0.1964 0.9852 0.9911 0.09405 0.6611 0.8368 0.249 ] Network output: [ 8.694e-05 1 -5.287e-05 8.735e-07 -3.921e-07 0.9998 6.583e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001881 Epoch 9543 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009025 0.9967 0.9924 -1.91e-07 8.574e-08 -0.007155 -1.439e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003502 -0.003341 -0.006779 0.005453 0.9699 0.9743 0.006813 0.8254 0.8202 0.0164 ] Network output: [ 0.9999 0.0001388 0.0004015 -3.198e-06 1.436e-06 -0.000364 -2.41e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2078 -0.03545 -0.1582 0.1831 0.9834 0.9932 0.2332 0.43 0.8684 0.7092 ] Network output: [ -0.00901 1.003 1.008 -2.163e-07 9.712e-08 0.007468 -1.63e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006768 0.0006167 0.004383 0.003208 0.9889 0.9919 0.0069 0.8527 0.8922 0.01172 ] Network output: [ -0.0002109 0.001535 1.001 -1.003e-05 4.505e-06 0.9983 -7.562e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2214 0.1049 0.3484 0.1422 0.9849 0.9939 0.2221 0.434 0.8751 0.703 ] Network output: [ 0.003263 -0.01548 0.9942 6.112e-06 -2.744e-06 1.015 4.606e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.0981 0.1846 0.1976 0.9873 0.9919 0.1108 0.7372 0.8616 0.3052 ] Network output: [ -0.003059 0.01432 1.005 6.654e-06 -2.987e-06 0.9872 5.015e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09404 0.0921 0.165 0.1964 0.9852 0.9911 0.09405 0.6611 0.8368 0.249 ] Network output: [ 8.692e-05 1 -5.285e-05 8.725e-07 -3.917e-07 0.9998 6.575e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000188 Epoch 9544 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009024 0.9967 0.9924 -1.909e-07 8.57e-08 -0.007155 -1.439e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003502 -0.003342 -0.006778 0.005453 0.9699 0.9743 0.006813 0.8254 0.8202 0.0164 ] Network output: [ 0.9999 0.0001386 0.0004013 -3.194e-06 1.434e-06 -0.0003638 -2.407e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2078 -0.03545 -0.1582 0.1831 0.9834 0.9932 0.2332 0.43 0.8684 0.7092 ] Network output: [ -0.009009 1.003 1.008 -2.162e-07 9.706e-08 0.007467 -1.629e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006768 0.0006167 0.004383 0.003208 0.9889 0.9919 0.0069 0.8527 0.8922 0.01172 ] Network output: [ -0.0002107 0.001534 1.001 -1.002e-05 4.499e-06 0.9983 -7.553e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2214 0.1049 0.3484 0.1422 0.9849 0.9939 0.2221 0.434 0.8751 0.703 ] Network output: [ 0.003261 -0.01548 0.9942 6.105e-06 -2.741e-06 1.015 4.601e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.0981 0.1846 0.1976 0.9873 0.9919 0.1108 0.7372 0.8616 0.3052 ] Network output: [ -0.003057 0.01431 1.005 6.646e-06 -2.984e-06 0.9872 5.009e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09404 0.0921 0.165 0.1964 0.9852 0.9911 0.09405 0.6611 0.8368 0.249 ] Network output: [ 8.689e-05 1 -5.282e-05 8.715e-07 -3.912e-07 0.9998 6.568e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001879 Epoch 9545 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009023 0.9967 0.9924 -1.908e-07 8.566e-08 -0.007154 -1.438e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003502 -0.003342 -0.006777 0.005453 0.9699 0.9743 0.006813 0.8254 0.8202 0.0164 ] Network output: [ 0.9999 0.0001384 0.0004011 -3.19e-06 1.432e-06 -0.0003636 -2.404e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2078 -0.03545 -0.1582 0.1831 0.9834 0.9932 0.2332 0.43 0.8684 0.7092 ] Network output: [ -0.009008 1.003 1.008 -2.16e-07 9.699e-08 0.007467 -1.628e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006769 0.0006168 0.004383 0.003208 0.9889 0.9919 0.006901 0.8527 0.8922 0.01172 ] Network output: [ -0.0002105 0.001533 1.001 -1.001e-05 4.494e-06 0.9983 -7.544e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2214 0.1049 0.3484 0.1422 0.9849 0.9939 0.2221 0.434 0.8751 0.703 ] Network output: [ 0.00326 -0.01547 0.9942 6.097e-06 -2.737e-06 1.015 4.595e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.0981 0.1846 0.1976 0.9873 0.9919 0.1109 0.7372 0.8616 0.3052 ] Network output: [ -0.003056 0.0143 1.005 6.639e-06 -2.98e-06 0.9872 5.003e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09404 0.0921 0.165 0.1964 0.9852 0.9911 0.09405 0.661 0.8368 0.249 ] Network output: [ 8.687e-05 1 -5.28e-05 8.704e-07 -3.908e-07 0.9998 6.56e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001878 Epoch 9546 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009022 0.9967 0.9924 -1.907e-07 8.561e-08 -0.007154 -1.437e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003502 -0.003342 -0.006777 0.005452 0.9699 0.9743 0.006814 0.8254 0.8202 0.0164 ] Network output: [ 0.9999 0.0001382 0.0004009 -3.186e-06 1.43e-06 -0.0003633 -2.401e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2078 -0.03545 -0.1582 0.1831 0.9834 0.9932 0.2332 0.43 0.8684 0.7092 ] Network output: [ -0.009008 1.003 1.008 -2.159e-07 9.692e-08 0.007466 -1.627e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006769 0.0006169 0.004383 0.003208 0.9889 0.9919 0.006901 0.8527 0.8922 0.01172 ] Network output: [ -0.0002104 0.001533 1.001 -9.999e-06 4.489e-06 0.9983 -7.535e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2214 0.1049 0.3484 0.1422 0.9849 0.9939 0.2222 0.4339 0.8751 0.703 ] Network output: [ 0.003258 -0.01546 0.9942 6.09e-06 -2.734e-06 1.015 4.59e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09811 0.1846 0.1976 0.9873 0.9919 0.1109 0.7372 0.8615 0.3052 ] Network output: [ -0.003055 0.0143 1.005 6.631e-06 -2.977e-06 0.9872 4.997e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09404 0.0921 0.165 0.1964 0.9852 0.9911 0.09406 0.661 0.8368 0.249 ] Network output: [ 8.684e-05 1 -5.278e-05 8.694e-07 -3.903e-07 0.9998 6.552e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001877 Epoch 9547 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009021 0.9967 0.9924 -1.906e-07 8.557e-08 -0.007153 -1.436e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003502 -0.003342 -0.006776 0.005452 0.9699 0.9743 0.006814 0.8254 0.8202 0.0164 ] Network output: [ 0.9999 0.0001381 0.0004008 -3.182e-06 1.429e-06 -0.0003631 -2.398e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2078 -0.03545 -0.1582 0.1831 0.9834 0.9932 0.2332 0.43 0.8684 0.7092 ] Network output: [ -0.009007 1.003 1.008 -2.157e-07 9.685e-08 0.007465 -1.626e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00677 0.000617 0.004383 0.003207 0.9889 0.9919 0.006902 0.8527 0.8922 0.01172 ] Network output: [ -0.0002102 0.001532 1.001 -9.987e-06 4.483e-06 0.9983 -7.526e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2214 0.1049 0.3484 0.1422 0.9849 0.9939 0.2222 0.4339 0.8751 0.703 ] Network output: [ 0.003257 -0.01546 0.9942 6.083e-06 -2.731e-06 1.015 4.584e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09811 0.1846 0.1976 0.9873 0.9919 0.1109 0.7371 0.8615 0.3052 ] Network output: [ -0.003053 0.01429 1.005 6.623e-06 -2.973e-06 0.9872 4.991e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09404 0.09211 0.165 0.1964 0.9852 0.9911 0.09406 0.661 0.8368 0.249 ] Network output: [ 8.682e-05 1 -5.275e-05 8.684e-07 -3.898e-07 0.9998 6.544e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001876 Epoch 9548 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00902 0.9967 0.9924 -1.905e-07 8.553e-08 -0.007153 -1.436e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003502 -0.003342 -0.006776 0.005451 0.9699 0.9743 0.006814 0.8254 0.8202 0.0164 ] Network output: [ 0.9999 0.0001379 0.0004006 -3.179e-06 1.427e-06 -0.0003628 -2.396e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2078 -0.03545 -0.1582 0.1831 0.9834 0.9932 0.2332 0.43 0.8684 0.7092 ] Network output: [ -0.009006 1.003 1.008 -2.156e-07 9.679e-08 0.007465 -1.625e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00677 0.000617 0.004382 0.003207 0.9889 0.9919 0.006902 0.8527 0.8922 0.01172 ] Network output: [ -0.00021 0.001531 1.001 -9.975e-06 4.478e-06 0.9983 -7.517e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2214 0.1049 0.3484 0.1422 0.9849 0.9939 0.2222 0.4339 0.8751 0.703 ] Network output: [ 0.003255 -0.01545 0.9942 6.076e-06 -2.728e-06 1.015 4.579e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09811 0.1846 0.1976 0.9873 0.9919 0.1109 0.7371 0.8615 0.3052 ] Network output: [ -0.003052 0.01429 1.005 6.616e-06 -2.97e-06 0.9872 4.986e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09405 0.09211 0.165 0.1964 0.9852 0.9911 0.09406 0.661 0.8368 0.249 ] Network output: [ 8.679e-05 1 -5.273e-05 8.674e-07 -3.894e-07 0.9998 6.537e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001875 Epoch 9549 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009019 0.9967 0.9924 -1.904e-07 8.548e-08 -0.007152 -1.435e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003502 -0.003342 -0.006775 0.005451 0.9699 0.9743 0.006814 0.8254 0.8202 0.0164 ] Network output: [ 0.9999 0.0001377 0.0004004 -3.175e-06 1.425e-06 -0.0003626 -2.393e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2078 -0.03545 -0.1582 0.1831 0.9834 0.9932 0.2332 0.43 0.8684 0.7092 ] Network output: [ -0.009005 1.003 1.008 -2.154e-07 9.672e-08 0.007464 -1.624e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00677 0.0006171 0.004382 0.003207 0.9889 0.9919 0.006902 0.8527 0.8922 0.01172 ] Network output: [ -0.0002099 0.00153 1.001 -9.963e-06 4.473e-06 0.9983 -7.508e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2214 0.1049 0.3484 0.1422 0.9849 0.9939 0.2222 0.4339 0.8751 0.703 ] Network output: [ 0.003254 -0.01544 0.9942 6.069e-06 -2.724e-06 1.015 4.573e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09812 0.1846 0.1976 0.9873 0.9919 0.1109 0.7371 0.8615 0.3052 ] Network output: [ -0.00305 0.01428 1.005 6.608e-06 -2.966e-06 0.9872 4.98e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09405 0.09211 0.165 0.1964 0.9852 0.9911 0.09406 0.661 0.8368 0.249 ] Network output: [ 8.676e-05 1 -5.271e-05 8.663e-07 -3.889e-07 0.9998 6.529e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001874 Epoch 9550 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009018 0.9967 0.9924 -1.903e-07 8.544e-08 -0.007151 -1.434e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003502 -0.003342 -0.006774 0.005451 0.9699 0.9743 0.006814 0.8254 0.8202 0.01639 ] Network output: [ 0.9999 0.0001375 0.0004002 -3.171e-06 1.424e-06 -0.0003623 -2.39e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2078 -0.03545 -0.1582 0.1831 0.9834 0.9932 0.2332 0.4299 0.8684 0.7092 ] Network output: [ -0.009004 1.003 1.008 -2.153e-07 9.665e-08 0.007463 -1.623e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006771 0.0006172 0.004382 0.003207 0.9889 0.9919 0.006903 0.8526 0.8922 0.01172 ] Network output: [ -0.0002097 0.00153 1.001 -9.951e-06 4.467e-06 0.9983 -7.499e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2214 0.1049 0.3484 0.1422 0.9849 0.9939 0.2222 0.4339 0.8751 0.703 ] Network output: [ 0.003252 -0.01544 0.9942 6.061e-06 -2.721e-06 1.015 4.568e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09812 0.1846 0.1976 0.9873 0.9919 0.1109 0.7371 0.8615 0.3052 ] Network output: [ -0.003049 0.01427 1.005 6.6e-06 -2.963e-06 0.9872 4.974e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09405 0.09211 0.165 0.1964 0.9852 0.9911 0.09406 0.661 0.8368 0.249 ] Network output: [ 8.674e-05 1 -5.269e-05 8.653e-07 -3.885e-07 0.9998 6.521e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001873 Epoch 9551 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009017 0.9967 0.9924 -1.902e-07 8.54e-08 -0.007151 -1.434e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003502 -0.003342 -0.006774 0.00545 0.9699 0.9743 0.006815 0.8254 0.8202 0.01639 ] Network output: [ 0.9999 0.0001374 0.0004 -3.167e-06 1.422e-06 -0.0003621 -2.387e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2078 -0.03545 -0.1582 0.1831 0.9834 0.9932 0.2332 0.4299 0.8684 0.7092 ] Network output: [ -0.009003 1.003 1.008 -2.151e-07 9.659e-08 0.007463 -1.621e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006771 0.0006172 0.004382 0.003206 0.9889 0.9919 0.006903 0.8526 0.8922 0.01172 ] Network output: [ -0.0002095 0.001529 1.001 -9.939e-06 4.462e-06 0.9983 -7.49e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2214 0.1049 0.3484 0.1422 0.9849 0.9939 0.2222 0.4339 0.8751 0.703 ] Network output: [ 0.003251 -0.01543 0.9942 6.054e-06 -2.718e-06 1.015 4.563e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09812 0.1846 0.1976 0.9873 0.9919 0.1109 0.7371 0.8615 0.3052 ] Network output: [ -0.003048 0.01427 1.005 6.592e-06 -2.96e-06 0.9872 4.968e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09405 0.09211 0.165 0.1964 0.9852 0.9911 0.09407 0.661 0.8368 0.249 ] Network output: [ 8.671e-05 1 -5.266e-05 8.643e-07 -3.88e-07 0.9998 6.514e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001872 Epoch 9552 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009016 0.9967 0.9924 -1.901e-07 8.535e-08 -0.00715 -1.433e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003502 -0.003342 -0.006773 0.00545 0.9699 0.9743 0.006815 0.8254 0.8202 0.01639 ] Network output: [ 0.9999 0.0001372 0.0003999 -3.163e-06 1.42e-06 -0.0003618 -2.384e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2078 -0.03546 -0.1582 0.1831 0.9834 0.9932 0.2332 0.4299 0.8684 0.7092 ] Network output: [ -0.009003 1.003 1.008 -2.15e-07 9.652e-08 0.007462 -1.62e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006772 0.0006173 0.004382 0.003206 0.9889 0.9919 0.006904 0.8526 0.8922 0.01172 ] Network output: [ -0.0002094 0.001528 1.001 -9.927e-06 4.457e-06 0.9983 -7.482e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2214 0.1049 0.3484 0.1422 0.9849 0.9939 0.2222 0.4339 0.8751 0.703 ] Network output: [ 0.003249 -0.01542 0.9942 6.047e-06 -2.715e-06 1.015 4.557e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09813 0.1846 0.1976 0.9873 0.9919 0.1109 0.7371 0.8615 0.3052 ] Network output: [ -0.003046 0.01426 1.005 6.585e-06 -2.956e-06 0.9872 4.962e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09405 0.09212 0.165 0.1964 0.9852 0.9911 0.09407 0.661 0.8368 0.249 ] Network output: [ 8.669e-05 1 -5.264e-05 8.633e-07 -3.876e-07 0.9998 6.506e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001871 Epoch 9553 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009015 0.9967 0.9924 -1.9e-07 8.531e-08 -0.00715 -1.432e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003502 -0.003342 -0.006773 0.005449 0.9699 0.9743 0.006815 0.8253 0.8202 0.01639 ] Network output: [ 0.9999 0.000137 0.0003997 -3.16e-06 1.418e-06 -0.0003616 -2.381e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2078 -0.03546 -0.1582 0.1831 0.9834 0.9932 0.2332 0.4299 0.8684 0.7092 ] Network output: [ -0.009002 1.003 1.008 -2.148e-07 9.645e-08 0.007461 -1.619e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006772 0.0006174 0.004382 0.003206 0.9889 0.9919 0.006904 0.8526 0.8922 0.01172 ] Network output: [ -0.0002092 0.001528 1.001 -9.916e-06 4.451e-06 0.9983 -7.473e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2215 0.1049 0.3484 0.1422 0.9849 0.9939 0.2222 0.4339 0.8751 0.703 ] Network output: [ 0.003248 -0.01542 0.9942 6.04e-06 -2.712e-06 1.015 4.552e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09813 0.1846 0.1976 0.9873 0.9919 0.1109 0.7371 0.8615 0.3052 ] Network output: [ -0.003045 0.01425 1.005 6.577e-06 -2.953e-06 0.9872 4.957e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09406 0.09212 0.165 0.1964 0.9852 0.9911 0.09407 0.661 0.8368 0.249 ] Network output: [ 8.666e-05 1 -5.262e-05 8.623e-07 -3.871e-07 0.9998 6.498e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000187 Epoch 9554 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009015 0.9967 0.9924 -1.899e-07 8.527e-08 -0.007149 -1.431e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003502 -0.003342 -0.006772 0.005449 0.9699 0.9743 0.006815 0.8253 0.8202 0.01639 ] Network output: [ 0.9999 0.0001368 0.0003995 -3.156e-06 1.417e-06 -0.0003613 -2.378e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2078 -0.03546 -0.1581 0.1831 0.9834 0.9932 0.2332 0.4299 0.8684 0.7092 ] Network output: [ -0.009001 1.003 1.008 -2.147e-07 9.638e-08 0.007461 -1.618e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006772 0.0006175 0.004382 0.003206 0.9889 0.9919 0.006904 0.8526 0.8922 0.01172 ] Network output: [ -0.0002091 0.001527 1.001 -9.904e-06 4.446e-06 0.9983 -7.464e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2215 0.1049 0.3484 0.1422 0.9849 0.9939 0.2222 0.4339 0.8751 0.703 ] Network output: [ 0.003246 -0.01541 0.9942 6.033e-06 -2.708e-06 1.015 4.546e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09813 0.1846 0.1976 0.9873 0.9919 0.1109 0.7371 0.8615 0.3052 ] Network output: [ -0.003044 0.01425 1.005 6.569e-06 -2.949e-06 0.9872 4.951e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09406 0.09212 0.165 0.1964 0.9852 0.9911 0.09407 0.6609 0.8368 0.249 ] Network output: [ 8.664e-05 1 -5.259e-05 8.613e-07 -3.866e-07 0.9998 6.491e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001869 Epoch 9555 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009014 0.9967 0.9924 -1.898e-07 8.522e-08 -0.007149 -1.431e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003503 -0.003342 -0.006771 0.005449 0.9699 0.9743 0.006815 0.8253 0.8202 0.01639 ] Network output: [ 0.9999 0.0001367 0.0003993 -3.152e-06 1.415e-06 -0.0003611 -2.376e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2078 -0.03546 -0.1581 0.1831 0.9834 0.9932 0.2332 0.4299 0.8684 0.7092 ] Network output: [ -0.009 1.003 1.008 -2.145e-07 9.632e-08 0.00746 -1.617e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006773 0.0006175 0.004382 0.003206 0.9889 0.9919 0.006905 0.8526 0.8922 0.01172 ] Network output: [ -0.0002089 0.001526 1.001 -9.892e-06 4.441e-06 0.9983 -7.455e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2215 0.1049 0.3484 0.1422 0.9849 0.9939 0.2222 0.4339 0.8751 0.7029 ] Network output: [ 0.003245 -0.0154 0.9942 6.026e-06 -2.705e-06 1.015 4.541e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09814 0.1846 0.1976 0.9873 0.9919 0.1109 0.737 0.8615 0.3052 ] Network output: [ -0.003042 0.01424 1.005 6.562e-06 -2.946e-06 0.9872 4.945e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09406 0.09212 0.165 0.1964 0.9852 0.9911 0.09407 0.6609 0.8368 0.249 ] Network output: [ 8.661e-05 1 -5.257e-05 8.602e-07 -3.862e-07 0.9998 6.483e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001868 Epoch 9556 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009013 0.9967 0.9924 -1.897e-07 8.518e-08 -0.007148 -1.43e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003503 -0.003343 -0.006771 0.005448 0.9699 0.9743 0.006815 0.8253 0.8202 0.01639 ] Network output: [ 0.9999 0.0001365 0.0003991 -3.148e-06 1.413e-06 -0.0003609 -2.373e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2079 -0.03546 -0.1581 0.1831 0.9834 0.9932 0.2332 0.4299 0.8684 0.7092 ] Network output: [ -0.008999 1.003 1.008 -2.144e-07 9.625e-08 0.00746 -1.616e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006773 0.0006176 0.004382 0.003205 0.9889 0.9919 0.006905 0.8526 0.8922 0.01171 ] Network output: [ -0.0002087 0.001525 1.001 -9.88e-06 4.436e-06 0.9983 -7.446e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2215 0.1049 0.3484 0.1422 0.9849 0.9939 0.2222 0.4339 0.8751 0.7029 ] Network output: [ 0.003243 -0.0154 0.9942 6.018e-06 -2.702e-06 1.015 4.536e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09814 0.1846 0.1976 0.9873 0.9919 0.1109 0.737 0.8615 0.3052 ] Network output: [ -0.003041 0.01424 1.005 6.554e-06 -2.942e-06 0.9872 4.939e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09406 0.09212 0.165 0.1964 0.9852 0.9911 0.09408 0.6609 0.8368 0.249 ] Network output: [ 8.659e-05 1 -5.255e-05 8.592e-07 -3.857e-07 0.9998 6.475e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001867 Epoch 9557 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009012 0.9967 0.9924 -1.896e-07 8.514e-08 -0.007148 -1.429e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003503 -0.003343 -0.00677 0.005448 0.9699 0.9743 0.006816 0.8253 0.8202 0.01639 ] Network output: [ 0.9999 0.0001363 0.000399 -3.145e-06 1.412e-06 -0.0003606 -2.37e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2079 -0.03546 -0.1581 0.1831 0.9834 0.9932 0.2333 0.4299 0.8684 0.7092 ] Network output: [ -0.008998 1.003 1.008 -2.142e-07 9.618e-08 0.007459 -1.615e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006773 0.0006177 0.004382 0.003205 0.9889 0.9919 0.006906 0.8526 0.8922 0.01171 ] Network output: [ -0.0002086 0.001525 1.001 -9.868e-06 4.43e-06 0.9983 -7.437e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2215 0.1049 0.3484 0.1422 0.9849 0.9939 0.2222 0.4339 0.8751 0.7029 ] Network output: [ 0.003242 -0.01539 0.9942 6.011e-06 -2.699e-06 1.015 4.53e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09815 0.1846 0.1976 0.9873 0.9919 0.1109 0.737 0.8615 0.3052 ] Network output: [ -0.00304 0.01423 1.005 6.546e-06 -2.939e-06 0.9872 4.934e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09406 0.09213 0.165 0.1964 0.9852 0.9911 0.09408 0.6609 0.8368 0.249 ] Network output: [ 8.656e-05 1 -5.253e-05 8.582e-07 -3.853e-07 0.9998 6.468e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001866 Epoch 9558 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009011 0.9967 0.9924 -1.895e-07 8.509e-08 -0.007147 -1.428e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003503 -0.003343 -0.00677 0.005447 0.9699 0.9743 0.006816 0.8253 0.8201 0.01639 ] Network output: [ 0.9999 0.0001361 0.0003988 -3.141e-06 1.41e-06 -0.0003604 -2.367e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2079 -0.03546 -0.1581 0.1831 0.9834 0.9932 0.2333 0.4299 0.8684 0.7092 ] Network output: [ -0.008997 1.003 1.008 -2.141e-07 9.611e-08 0.007458 -1.613e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006774 0.0006177 0.004382 0.003205 0.9889 0.9919 0.006906 0.8526 0.8922 0.01171 ] Network output: [ -0.0002084 0.001524 1.001 -9.857e-06 4.425e-06 0.9983 -7.428e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2215 0.1049 0.3484 0.1422 0.9849 0.9939 0.2222 0.4339 0.8751 0.7029 ] Network output: [ 0.00324 -0.01538 0.9942 6.004e-06 -2.695e-06 1.015 4.525e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09815 0.1846 0.1976 0.9873 0.9919 0.1109 0.737 0.8615 0.3052 ] Network output: [ -0.003038 0.01422 1.005 6.539e-06 -2.935e-06 0.9872 4.928e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09407 0.09213 0.165 0.1964 0.9852 0.9911 0.09408 0.6609 0.8368 0.249 ] Network output: [ 8.654e-05 1 -5.251e-05 8.572e-07 -3.848e-07 0.9998 6.46e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001865 Epoch 9559 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00901 0.9967 0.9924 -1.894e-07 8.505e-08 -0.007146 -1.428e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003503 -0.003343 -0.006769 0.005447 0.9699 0.9743 0.006816 0.8253 0.8201 0.01639 ] Network output: [ 0.9999 0.0001359 0.0003986 -3.137e-06 1.408e-06 -0.0003601 -2.364e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2079 -0.03546 -0.1581 0.1831 0.9834 0.9932 0.2333 0.4299 0.8684 0.7092 ] Network output: [ -0.008997 1.003 1.008 -2.139e-07 9.605e-08 0.007458 -1.612e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006774 0.0006178 0.004381 0.003205 0.9889 0.9919 0.006906 0.8526 0.8922 0.01171 ] Network output: [ -0.0002082 0.001523 1.001 -9.845e-06 4.42e-06 0.9983 -7.419e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2215 0.1049 0.3484 0.1422 0.9849 0.9939 0.2222 0.4339 0.8751 0.7029 ] Network output: [ 0.003239 -0.01538 0.9942 5.997e-06 -2.692e-06 1.015 4.52e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09815 0.1846 0.1976 0.9873 0.9919 0.1109 0.737 0.8615 0.3052 ] Network output: [ -0.003037 0.01422 1.005 6.531e-06 -2.932e-06 0.9872 4.922e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09407 0.09213 0.165 0.1964 0.9852 0.9911 0.09408 0.6609 0.8368 0.249 ] Network output: [ 8.651e-05 1 -5.248e-05 8.562e-07 -3.844e-07 0.9998 6.453e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001864 Epoch 9560 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009009 0.9967 0.9924 -1.893e-07 8.5e-08 -0.007146 -1.427e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003503 -0.003343 -0.006768 0.005447 0.9699 0.9743 0.006816 0.8253 0.8201 0.01639 ] Network output: [ 0.9999 0.0001358 0.0003984 -3.133e-06 1.407e-06 -0.0003599 -2.361e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2079 -0.03546 -0.1581 0.1831 0.9834 0.9932 0.2333 0.4299 0.8684 0.7092 ] Network output: [ -0.008996 1.003 1.008 -2.138e-07 9.598e-08 0.007457 -1.611e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006775 0.0006179 0.004381 0.003204 0.9889 0.9919 0.006907 0.8526 0.8922 0.01171 ] Network output: [ -0.0002081 0.001523 1.001 -9.833e-06 4.414e-06 0.9983 -7.411e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2215 0.1049 0.3485 0.1422 0.9849 0.9939 0.2223 0.4339 0.8751 0.7029 ] Network output: [ 0.003238 -0.01537 0.9942 5.99e-06 -2.689e-06 1.015 4.514e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09816 0.1846 0.1976 0.9873 0.9919 0.1109 0.737 0.8615 0.3051 ] Network output: [ -0.003035 0.01421 1.005 6.524e-06 -2.929e-06 0.9872 4.916e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09407 0.09213 0.165 0.1964 0.9852 0.9911 0.09408 0.6609 0.8368 0.249 ] Network output: [ 8.649e-05 1 -5.246e-05 8.552e-07 -3.839e-07 0.9998 6.445e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001863 Epoch 9561 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009008 0.9967 0.9924 -1.892e-07 8.496e-08 -0.007145 -1.426e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003503 -0.003343 -0.006768 0.005446 0.9699 0.9743 0.006816 0.8253 0.8201 0.01638 ] Network output: [ 0.9999 0.0001356 0.0003982 -3.13e-06 1.405e-06 -0.0003596 -2.359e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2079 -0.03546 -0.1581 0.1831 0.9834 0.9932 0.2333 0.4299 0.8684 0.7092 ] Network output: [ -0.008995 1.003 1.008 -2.136e-07 9.591e-08 0.007456 -1.61e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006775 0.000618 0.004381 0.003204 0.9889 0.9919 0.006907 0.8526 0.8922 0.01171 ] Network output: [ -0.0002079 0.001522 1.001 -9.821e-06 4.409e-06 0.9983 -7.402e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2215 0.1049 0.3485 0.1422 0.9849 0.9939 0.2223 0.4339 0.8751 0.7029 ] Network output: [ 0.003236 -0.01536 0.9942 5.983e-06 -2.686e-06 1.015 4.509e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09816 0.1846 0.1975 0.9873 0.9919 0.1109 0.737 0.8615 0.3051 ] Network output: [ -0.003034 0.01421 1.005 6.516e-06 -2.925e-06 0.9872 4.911e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09407 0.09213 0.165 0.1964 0.9852 0.9911 0.09409 0.6609 0.8368 0.249 ] Network output: [ 8.646e-05 1 -5.244e-05 8.542e-07 -3.835e-07 0.9998 6.437e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001862 Epoch 9562 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009007 0.9967 0.9924 -1.892e-07 8.492e-08 -0.007145 -1.426e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003503 -0.003343 -0.006767 0.005446 0.9699 0.9743 0.006817 0.8253 0.8201 0.01638 ] Network output: [ 0.9999 0.0001354 0.0003981 -3.126e-06 1.403e-06 -0.0003594 -2.356e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2079 -0.03547 -0.1581 0.1831 0.9834 0.9932 0.2333 0.4299 0.8684 0.7091 ] Network output: [ -0.008994 1.003 1.008 -2.135e-07 9.585e-08 0.007456 -1.609e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006775 0.000618 0.004381 0.003204 0.9889 0.9919 0.006908 0.8526 0.8922 0.01171 ] Network output: [ -0.0002078 0.001521 1.001 -9.81e-06 4.404e-06 0.9983 -7.393e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2215 0.1049 0.3485 0.1422 0.9849 0.9939 0.2223 0.4339 0.8751 0.7029 ] Network output: [ 0.003235 -0.01536 0.9942 5.976e-06 -2.683e-06 1.015 4.504e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09816 0.1846 0.1975 0.9873 0.9919 0.1109 0.737 0.8615 0.3051 ] Network output: [ -0.003033 0.0142 1.005 6.508e-06 -2.922e-06 0.9872 4.905e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09407 0.09214 0.165 0.1964 0.9852 0.9911 0.09409 0.6608 0.8368 0.249 ] Network output: [ 8.643e-05 1 -5.242e-05 8.532e-07 -3.83e-07 0.9998 6.43e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001861 Epoch 9563 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009006 0.9967 0.9924 -1.891e-07 8.487e-08 -0.007144 -1.425e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003503 -0.003343 -0.006766 0.005445 0.9699 0.9743 0.006817 0.8253 0.8201 0.01638 ] Network output: [ 0.9999 0.0001352 0.0003979 -3.122e-06 1.402e-06 -0.0003592 -2.353e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2079 -0.03547 -0.1581 0.1831 0.9834 0.9932 0.2333 0.4299 0.8684 0.7091 ] Network output: [ -0.008993 1.003 1.008 -2.133e-07 9.578e-08 0.007455 -1.608e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006776 0.0006181 0.004381 0.003204 0.9889 0.9919 0.006908 0.8526 0.8922 0.01171 ] Network output: [ -0.0002076 0.001521 1.001 -9.798e-06 4.399e-06 0.9984 -7.384e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2215 0.1049 0.3485 0.1422 0.9849 0.9939 0.2223 0.4339 0.8751 0.7029 ] Network output: [ 0.003233 -0.01535 0.9942 5.969e-06 -2.68e-06 1.015 4.498e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09817 0.1846 0.1975 0.9873 0.9919 0.1109 0.737 0.8615 0.3051 ] Network output: [ -0.003031 0.01419 1.005 6.501e-06 -2.918e-06 0.9872 4.899e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09408 0.09214 0.165 0.1964 0.9852 0.9911 0.09409 0.6608 0.8368 0.249 ] Network output: [ 8.641e-05 1 -5.24e-05 8.522e-07 -3.826e-07 0.9998 6.422e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000186 Epoch 9564 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009005 0.9967 0.9924 -1.89e-07 8.483e-08 -0.007144 -1.424e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003503 -0.003343 -0.006766 0.005445 0.9699 0.9743 0.006817 0.8253 0.8201 0.01638 ] Network output: [ 0.9999 0.0001351 0.0003977 -3.118e-06 1.4e-06 -0.0003589 -2.35e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2079 -0.03547 -0.1581 0.1831 0.9834 0.9932 0.2333 0.4299 0.8684 0.7091 ] Network output: [ -0.008992 1.003 1.008 -2.132e-07 9.571e-08 0.007454 -1.607e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006776 0.0006182 0.004381 0.003203 0.9889 0.9919 0.006908 0.8526 0.8922 0.01171 ] Network output: [ -0.0002074 0.00152 1.001 -9.786e-06 4.393e-06 0.9984 -7.375e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2215 0.105 0.3485 0.1422 0.9849 0.9939 0.2223 0.4339 0.8751 0.7029 ] Network output: [ 0.003232 -0.01534 0.9942 5.962e-06 -2.676e-06 1.015 4.493e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1108 0.09817 0.1846 0.1975 0.9873 0.9919 0.1109 0.7369 0.8615 0.3051 ] Network output: [ -0.00303 0.01419 1.005 6.493e-06 -2.915e-06 0.9872 4.893e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09408 0.09214 0.165 0.1964 0.9852 0.9911 0.09409 0.6608 0.8368 0.249 ] Network output: [ 8.638e-05 1 -5.237e-05 8.512e-07 -3.821e-07 0.9998 6.415e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001859 Epoch 9565 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009004 0.9967 0.9924 -1.889e-07 8.479e-08 -0.007143 -1.423e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003503 -0.003343 -0.006765 0.005445 0.9699 0.9743 0.006817 0.8253 0.8201 0.01638 ] Network output: [ 0.9999 0.0001349 0.0003975 -3.115e-06 1.398e-06 -0.0003587 -2.347e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2079 -0.03547 -0.158 0.1831 0.9834 0.9932 0.2333 0.4299 0.8684 0.7091 ] Network output: [ -0.008992 1.003 1.008 -2.13e-07 9.564e-08 0.007454 -1.606e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006777 0.0006183 0.004381 0.003203 0.9889 0.9919 0.006909 0.8526 0.8922 0.01171 ] Network output: [ -0.0002073 0.001519 1.001 -9.775e-06 4.388e-06 0.9984 -7.366e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2215 0.105 0.3485 0.1422 0.9849 0.9939 0.2223 0.4338 0.8751 0.7029 ] Network output: [ 0.00323 -0.01534 0.9942 5.955e-06 -2.673e-06 1.015 4.488e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09817 0.1846 0.1975 0.9873 0.9919 0.1109 0.7369 0.8615 0.3051 ] Network output: [ -0.003029 0.01418 1.005 6.486e-06 -2.912e-06 0.9872 4.888e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09408 0.09214 0.165 0.1964 0.9852 0.9911 0.09409 0.6608 0.8367 0.249 ] Network output: [ 8.636e-05 1 -5.235e-05 8.502e-07 -3.817e-07 0.9998 6.407e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001858 Epoch 9566 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009003 0.9967 0.9924 -1.888e-07 8.474e-08 -0.007142 -1.423e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003503 -0.003343 -0.006765 0.005444 0.9699 0.9743 0.006817 0.8253 0.8201 0.01638 ] Network output: [ 0.9999 0.0001347 0.0003973 -3.111e-06 1.397e-06 -0.0003584 -2.344e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2079 -0.03547 -0.158 0.1831 0.9834 0.9932 0.2333 0.4299 0.8684 0.7091 ] Network output: [ -0.008991 1.003 1.008 -2.129e-07 9.558e-08 0.007453 -1.604e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006777 0.0006183 0.004381 0.003203 0.9889 0.9919 0.006909 0.8526 0.8922 0.01171 ] Network output: [ -0.0002071 0.001518 1.001 -9.763e-06 4.383e-06 0.9984 -7.358e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2215 0.105 0.3485 0.1422 0.9849 0.9939 0.2223 0.4338 0.8751 0.7029 ] Network output: [ 0.003229 -0.01533 0.9942 5.947e-06 -2.67e-06 1.015 4.482e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09818 0.1846 0.1975 0.9873 0.9919 0.1109 0.7369 0.8615 0.3051 ] Network output: [ -0.003027 0.01417 1.005 6.478e-06 -2.908e-06 0.9872 4.882e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09408 0.09214 0.165 0.1964 0.9852 0.9911 0.0941 0.6608 0.8367 0.249 ] Network output: [ 8.633e-05 1 -5.233e-05 8.492e-07 -3.812e-07 0.9998 6.4e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001857 Epoch 9567 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009002 0.9967 0.9924 -1.887e-07 8.47e-08 -0.007142 -1.422e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003503 -0.003344 -0.006764 0.005444 0.9699 0.9743 0.006817 0.8253 0.8201 0.01638 ] Network output: [ 0.9999 0.0001345 0.0003972 -3.107e-06 1.395e-06 -0.0003582 -2.342e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2079 -0.03547 -0.158 0.1831 0.9834 0.9932 0.2333 0.4299 0.8684 0.7091 ] Network output: [ -0.00899 1.003 1.008 -2.127e-07 9.551e-08 0.007452 -1.603e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006777 0.0006184 0.004381 0.003203 0.9889 0.9919 0.00691 0.8526 0.8922 0.01171 ] Network output: [ -0.000207 0.001518 1.001 -9.751e-06 4.378e-06 0.9984 -7.349e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2216 0.105 0.3485 0.1422 0.9849 0.9939 0.2223 0.4338 0.8751 0.7029 ] Network output: [ 0.003227 -0.01532 0.9942 5.94e-06 -2.667e-06 1.015 4.477e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09818 0.1846 0.1975 0.9873 0.9919 0.1109 0.7369 0.8615 0.3051 ] Network output: [ -0.003026 0.01417 1.005 6.47e-06 -2.905e-06 0.9872 4.876e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09408 0.09215 0.165 0.1964 0.9852 0.9911 0.0941 0.6608 0.8367 0.2491 ] Network output: [ 8.631e-05 1 -5.231e-05 8.482e-07 -3.808e-07 0.9998 6.392e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001856 Epoch 9568 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009001 0.9967 0.9924 -1.886e-07 8.465e-08 -0.007141 -1.421e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003503 -0.003344 -0.006763 0.005443 0.9699 0.9743 0.006818 0.8253 0.8201 0.01638 ] Network output: [ 0.9999 0.0001344 0.000397 -3.103e-06 1.393e-06 -0.000358 -2.339e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2079 -0.03547 -0.158 0.183 0.9834 0.9932 0.2333 0.4299 0.8684 0.7091 ] Network output: [ -0.008989 1.003 1.008 -2.126e-07 9.544e-08 0.007452 -1.602e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006778 0.0006185 0.004381 0.003202 0.9889 0.9919 0.00691 0.8526 0.8922 0.01171 ] Network output: [ -0.0002068 0.001517 1.001 -9.74e-06 4.372e-06 0.9984 -7.34e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2216 0.105 0.3485 0.1422 0.9849 0.9939 0.2223 0.4338 0.8751 0.7029 ] Network output: [ 0.003226 -0.01532 0.9942 5.933e-06 -2.664e-06 1.015 4.472e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09818 0.1846 0.1975 0.9873 0.9919 0.1109 0.7369 0.8615 0.3051 ] Network output: [ -0.003025 0.01416 1.005 6.463e-06 -2.901e-06 0.9873 4.871e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09409 0.09215 0.165 0.1964 0.9852 0.9911 0.0941 0.6608 0.8367 0.2491 ] Network output: [ 8.628e-05 1 -5.229e-05 8.472e-07 -3.803e-07 0.9998 6.385e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001855 Epoch 9569 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009 0.9967 0.9924 -1.885e-07 8.461e-08 -0.007141 -1.42e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003503 -0.003344 -0.006763 0.005443 0.9699 0.9743 0.006818 0.8253 0.8201 0.01638 ] Network output: [ 0.9999 0.0001342 0.0003968 -3.1e-06 1.392e-06 -0.0003577 -2.336e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2079 -0.03547 -0.158 0.183 0.9834 0.9932 0.2333 0.4299 0.8684 0.7091 ] Network output: [ -0.008988 1.003 1.008 -2.124e-07 9.538e-08 0.007451 -1.601e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006778 0.0006185 0.004381 0.003202 0.9889 0.9919 0.00691 0.8526 0.8922 0.0117 ] Network output: [ -0.0002066 0.001516 1.001 -9.728e-06 4.367e-06 0.9984 -7.331e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2216 0.105 0.3485 0.1422 0.9849 0.9939 0.2223 0.4338 0.8751 0.7029 ] Network output: [ 0.003224 -0.01531 0.9942 5.926e-06 -2.661e-06 1.015 4.466e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09819 0.1846 0.1975 0.9873 0.9919 0.1109 0.7369 0.8615 0.3051 ] Network output: [ -0.003023 0.01416 1.005 6.455e-06 -2.898e-06 0.9873 4.865e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09409 0.09215 0.165 0.1964 0.9852 0.9911 0.0941 0.6608 0.8367 0.2491 ] Network output: [ 8.626e-05 1 -5.227e-05 8.462e-07 -3.799e-07 0.9998 6.377e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001854 Epoch 9570 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.009 0.9967 0.9924 -1.884e-07 8.457e-08 -0.00714 -1.42e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003503 -0.003344 -0.006762 0.005443 0.9699 0.9743 0.006818 0.8253 0.8201 0.01638 ] Network output: [ 0.9999 0.000134 0.0003966 -3.096e-06 1.39e-06 -0.0003575 -2.333e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2079 -0.03547 -0.158 0.183 0.9834 0.9932 0.2333 0.4298 0.8684 0.7091 ] Network output: [ -0.008987 1.003 1.008 -2.123e-07 9.531e-08 0.00745 -1.6e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006779 0.0006186 0.00438 0.003202 0.9889 0.9919 0.006911 0.8525 0.8922 0.0117 ] Network output: [ -0.0002065 0.001516 1.001 -9.716e-06 4.362e-06 0.9984 -7.323e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2216 0.105 0.3485 0.1422 0.9849 0.9939 0.2223 0.4338 0.8751 0.7029 ] Network output: [ 0.003223 -0.0153 0.9942 5.919e-06 -2.657e-06 1.015 4.461e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09819 0.1846 0.1975 0.9873 0.9919 0.1109 0.7369 0.8615 0.3051 ] Network output: [ -0.003022 0.01415 1.005 6.448e-06 -2.895e-06 0.9873 4.859e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09409 0.09215 0.165 0.1964 0.9852 0.9911 0.0941 0.6608 0.8367 0.2491 ] Network output: [ 8.623e-05 1 -5.225e-05 8.452e-07 -3.794e-07 0.9998 6.37e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001853 Epoch 9571 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008999 0.9967 0.9924 -1.883e-07 8.452e-08 -0.00714 -1.419e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003504 -0.003344 -0.006762 0.005442 0.9699 0.9743 0.006818 0.8253 0.8201 0.01637 ] Network output: [ 0.9999 0.0001338 0.0003964 -3.092e-06 1.388e-06 -0.0003572 -2.33e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2079 -0.03547 -0.158 0.183 0.9834 0.9932 0.2334 0.4298 0.8684 0.7091 ] Network output: [ -0.008987 1.003 1.008 -2.121e-07 9.524e-08 0.00745 -1.599e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006779 0.0006187 0.00438 0.003202 0.9889 0.9919 0.006911 0.8525 0.8922 0.0117 ] Network output: [ -0.0002063 0.001515 1.001 -9.705e-06 4.357e-06 0.9984 -7.314e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2216 0.105 0.3485 0.1422 0.9849 0.9939 0.2223 0.4338 0.8751 0.7029 ] Network output: [ 0.003221 -0.0153 0.9942 5.912e-06 -2.654e-06 1.015 4.456e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09819 0.1846 0.1975 0.9873 0.9919 0.111 0.7369 0.8615 0.3051 ] Network output: [ -0.003021 0.01414 1.005 6.44e-06 -2.891e-06 0.9873 4.854e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09409 0.09215 0.165 0.1964 0.9852 0.9911 0.09411 0.6607 0.8367 0.2491 ] Network output: [ 8.621e-05 1 -5.222e-05 8.442e-07 -3.79e-07 0.9998 6.362e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001852 Epoch 9572 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008998 0.9967 0.9924 -1.882e-07 8.448e-08 -0.007139 -1.418e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003504 -0.003344 -0.006761 0.005442 0.9699 0.9743 0.006818 0.8253 0.8201 0.01637 ] Network output: [ 0.9999 0.0001337 0.0003963 -3.089e-06 1.387e-06 -0.000357 -2.328e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2079 -0.03547 -0.158 0.183 0.9834 0.9932 0.2334 0.4298 0.8684 0.7091 ] Network output: [ -0.008986 1.003 1.008 -2.12e-07 9.517e-08 0.007449 -1.598e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006779 0.0006188 0.00438 0.003201 0.9889 0.9919 0.006912 0.8525 0.8922 0.0117 ] Network output: [ -0.0002061 0.001514 1.001 -9.693e-06 4.352e-06 0.9984 -7.305e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2216 0.105 0.3485 0.1422 0.9849 0.9939 0.2223 0.4338 0.8751 0.7029 ] Network output: [ 0.00322 -0.01529 0.9942 5.905e-06 -2.651e-06 1.015 4.45e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.0982 0.1846 0.1975 0.9873 0.9919 0.111 0.7369 0.8615 0.3051 ] Network output: [ -0.003019 0.01414 1.005 6.433e-06 -2.888e-06 0.9873 4.848e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09409 0.09216 0.165 0.1964 0.9852 0.9911 0.09411 0.6607 0.8367 0.2491 ] Network output: [ 8.618e-05 1 -5.22e-05 8.432e-07 -3.785e-07 0.9998 6.355e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001851 Epoch 9573 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008997 0.9967 0.9924 -1.881e-07 8.443e-08 -0.007139 -1.417e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003504 -0.003344 -0.00676 0.005441 0.9699 0.9743 0.006819 0.8253 0.8201 0.01637 ] Network output: [ 0.9999 0.0001335 0.0003961 -3.085e-06 1.385e-06 -0.0003568 -2.325e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.208 -0.03548 -0.158 0.183 0.9834 0.9932 0.2334 0.4298 0.8683 0.7091 ] Network output: [ -0.008985 1.003 1.008 -2.118e-07 9.511e-08 0.007448 -1.597e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00678 0.0006188 0.00438 0.003201 0.9889 0.9919 0.006912 0.8525 0.8921 0.0117 ] Network output: [ -0.000206 0.001513 1.001 -9.682e-06 4.346e-06 0.9984 -7.296e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2216 0.105 0.3485 0.1422 0.9849 0.9939 0.2223 0.4338 0.8751 0.7029 ] Network output: [ 0.003218 -0.01528 0.9942 5.898e-06 -2.648e-06 1.015 4.445e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.0982 0.1846 0.1975 0.9873 0.9919 0.111 0.7368 0.8615 0.3051 ] Network output: [ -0.003018 0.01413 1.005 6.425e-06 -2.885e-06 0.9873 4.842e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0941 0.09216 0.165 0.1964 0.9852 0.9911 0.09411 0.6607 0.8367 0.2491 ] Network output: [ 8.616e-05 1 -5.218e-05 8.422e-07 -3.781e-07 0.9998 6.347e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000185 Epoch 9574 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008996 0.9967 0.9924 -1.88e-07 8.439e-08 -0.007138 -1.417e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003504 -0.003344 -0.00676 0.005441 0.9699 0.9743 0.006819 0.8252 0.8201 0.01637 ] Network output: [ 0.9999 0.0001333 0.0003959 -3.081e-06 1.383e-06 -0.0003565 -2.322e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.208 -0.03548 -0.158 0.183 0.9834 0.9932 0.2334 0.4298 0.8683 0.7091 ] Network output: [ -0.008984 1.003 1.008 -2.117e-07 9.504e-08 0.007448 -1.595e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00678 0.0006189 0.00438 0.003201 0.9889 0.9919 0.006912 0.8525 0.8921 0.0117 ] Network output: [ -0.0002058 0.001513 1.001 -9.67e-06 4.341e-06 0.9984 -7.288e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2216 0.105 0.3485 0.1422 0.9849 0.9939 0.2224 0.4338 0.8751 0.7029 ] Network output: [ 0.003217 -0.01528 0.9942 5.891e-06 -2.645e-06 1.015 4.44e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09821 0.1846 0.1975 0.9873 0.9919 0.111 0.7368 0.8615 0.3051 ] Network output: [ -0.003016 0.01412 1.005 6.418e-06 -2.881e-06 0.9873 4.837e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0941 0.09216 0.165 0.1964 0.9852 0.9911 0.09411 0.6607 0.8367 0.2491 ] Network output: [ 8.613e-05 1 -5.216e-05 8.412e-07 -3.776e-07 0.9998 6.34e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001849 Epoch 9575 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008995 0.9967 0.9924 -1.879e-07 8.435e-08 -0.007137 -1.416e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003504 -0.003344 -0.006759 0.005441 0.9699 0.9743 0.006819 0.8252 0.8201 0.01637 ] Network output: [ 0.9999 0.0001331 0.0003957 -3.078e-06 1.382e-06 -0.0003563 -2.319e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.208 -0.03548 -0.158 0.183 0.9834 0.9932 0.2334 0.4298 0.8683 0.7091 ] Network output: [ -0.008983 1.003 1.008 -2.116e-07 9.497e-08 0.007447 -1.594e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00678 0.000619 0.00438 0.003201 0.9889 0.9919 0.006913 0.8525 0.8921 0.0117 ] Network output: [ -0.0002057 0.001512 1.001 -9.659e-06 4.336e-06 0.9984 -7.279e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2216 0.105 0.3485 0.1422 0.9849 0.9939 0.2224 0.4338 0.8751 0.7029 ] Network output: [ 0.003215 -0.01527 0.9942 5.884e-06 -2.642e-06 1.015 4.435e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09821 0.1846 0.1975 0.9873 0.9919 0.111 0.7368 0.8615 0.3051 ] Network output: [ -0.003015 0.01412 1.005 6.41e-06 -2.878e-06 0.9873 4.831e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0941 0.09216 0.165 0.1964 0.9852 0.9911 0.09411 0.6607 0.8367 0.2491 ] Network output: [ 8.611e-05 1 -5.214e-05 8.402e-07 -3.772e-07 0.9998 6.332e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001848 Epoch 9576 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008994 0.9967 0.9924 -1.878e-07 8.43e-08 -0.007137 -1.415e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003504 -0.003344 -0.006759 0.00544 0.9699 0.9743 0.006819 0.8252 0.8201 0.01637 ] Network output: [ 0.9999 0.000133 0.0003956 -3.074e-06 1.38e-06 -0.000356 -2.317e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.208 -0.03548 -0.1579 0.183 0.9834 0.9932 0.2334 0.4298 0.8683 0.7091 ] Network output: [ -0.008982 1.003 1.008 -2.114e-07 9.491e-08 0.007447 -1.593e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006781 0.000619 0.00438 0.0032 0.9889 0.9919 0.006913 0.8525 0.8921 0.0117 ] Network output: [ -0.0002055 0.001511 1.001 -9.647e-06 4.331e-06 0.9984 -7.27e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2216 0.105 0.3485 0.1422 0.9849 0.9939 0.2224 0.4338 0.8751 0.7029 ] Network output: [ 0.003214 -0.01526 0.9942 5.877e-06 -2.639e-06 1.015 4.429e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09821 0.1846 0.1975 0.9873 0.9919 0.111 0.7368 0.8615 0.3051 ] Network output: [ -0.003014 0.01411 1.005 6.403e-06 -2.874e-06 0.9873 4.825e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0941 0.09216 0.165 0.1964 0.9852 0.9911 0.09412 0.6607 0.8367 0.2491 ] Network output: [ 8.608e-05 1 -5.212e-05 8.392e-07 -3.768e-07 0.9998 6.325e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001847 Epoch 9577 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008993 0.9967 0.9924 -1.877e-07 8.426e-08 -0.007136 -1.414e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003504 -0.003344 -0.006758 0.00544 0.9699 0.9743 0.006819 0.8252 0.8201 0.01637 ] Network output: [ 0.9999 0.0001328 0.0003954 -3.07e-06 1.378e-06 -0.0003558 -2.314e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.208 -0.03548 -0.1579 0.183 0.9834 0.9932 0.2334 0.4298 0.8683 0.7091 ] Network output: [ -0.008981 1.003 1.008 -2.113e-07 9.484e-08 0.007446 -1.592e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006781 0.0006191 0.00438 0.0032 0.9889 0.9919 0.006914 0.8525 0.8921 0.0117 ] Network output: [ -0.0002053 0.001511 1.001 -9.636e-06 4.326e-06 0.9984 -7.262e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2216 0.105 0.3485 0.1422 0.9849 0.9939 0.2224 0.4338 0.8751 0.7029 ] Network output: [ 0.003213 -0.01526 0.9942 5.87e-06 -2.635e-06 1.015 4.424e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09822 0.1846 0.1975 0.9873 0.9919 0.111 0.7368 0.8615 0.3051 ] Network output: [ -0.003012 0.01411 1.005 6.395e-06 -2.871e-06 0.9873 4.82e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0941 0.09217 0.165 0.1965 0.9852 0.9911 0.09412 0.6607 0.8367 0.2491 ] Network output: [ 8.606e-05 1 -5.21e-05 8.382e-07 -3.763e-07 0.9998 6.317e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001846 Epoch 9578 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008992 0.9967 0.9924 -1.876e-07 8.421e-08 -0.007136 -1.414e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003504 -0.003345 -0.006757 0.005439 0.9699 0.9743 0.006819 0.8252 0.8201 0.01637 ] Network output: [ 0.9999 0.0001326 0.0003952 -3.067e-06 1.377e-06 -0.0003556 -2.311e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.208 -0.03548 -0.1579 0.183 0.9834 0.9932 0.2334 0.4298 0.8683 0.7091 ] Network output: [ -0.008981 1.003 1.008 -2.111e-07 9.477e-08 0.007445 -1.591e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006782 0.0006192 0.00438 0.0032 0.9889 0.9919 0.006914 0.8525 0.8921 0.0117 ] Network output: [ -0.0002052 0.00151 1.001 -9.624e-06 4.321e-06 0.9984 -7.253e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2216 0.105 0.3485 0.1422 0.9849 0.9939 0.2224 0.4338 0.8751 0.7028 ] Network output: [ 0.003211 -0.01525 0.9942 5.863e-06 -2.632e-06 1.015 4.419e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09822 0.1846 0.1975 0.9873 0.9919 0.111 0.7368 0.8615 0.3051 ] Network output: [ -0.003011 0.0141 1.005 6.388e-06 -2.868e-06 0.9873 4.814e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09411 0.09217 0.165 0.1965 0.9852 0.9911 0.09412 0.6607 0.8367 0.2491 ] Network output: [ 8.603e-05 1 -5.208e-05 8.372e-07 -3.759e-07 0.9998 6.31e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001845 Epoch 9579 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008991 0.9967 0.9924 -1.875e-07 8.417e-08 -0.007135 -1.413e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003504 -0.003345 -0.006757 0.005439 0.9699 0.9743 0.00682 0.8252 0.8201 0.01637 ] Network output: [ 0.9999 0.0001324 0.000395 -3.063e-06 1.375e-06 -0.0003553 -2.308e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.208 -0.03548 -0.1579 0.183 0.9834 0.9932 0.2334 0.4298 0.8683 0.7091 ] Network output: [ -0.00898 1.003 1.008 -2.11e-07 9.47e-08 0.007445 -1.59e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006782 0.0006193 0.00438 0.0032 0.9889 0.9919 0.006914 0.8525 0.8921 0.0117 ] Network output: [ -0.000205 0.001509 1.001 -9.613e-06 4.315e-06 0.9984 -7.244e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2216 0.105 0.3485 0.1422 0.9849 0.9939 0.2224 0.4338 0.8751 0.7028 ] Network output: [ 0.00321 -0.01524 0.9942 5.856e-06 -2.629e-06 1.015 4.414e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09822 0.1846 0.1975 0.9873 0.9919 0.111 0.7368 0.8615 0.3051 ] Network output: [ -0.00301 0.01409 1.005 6.38e-06 -2.864e-06 0.9873 4.808e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09411 0.09217 0.165 0.1965 0.9852 0.9911 0.09412 0.6607 0.8367 0.2491 ] Network output: [ 8.601e-05 1 -5.206e-05 8.363e-07 -3.754e-07 0.9998 6.302e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001844 Epoch 9580 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00899 0.9967 0.9924 -1.874e-07 8.412e-08 -0.007135 -1.412e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003504 -0.003345 -0.006756 0.005439 0.9699 0.9743 0.00682 0.8252 0.8201 0.01637 ] Network output: [ 0.9999 0.0001323 0.0003948 -3.059e-06 1.373e-06 -0.0003551 -2.306e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.208 -0.03548 -0.1579 0.183 0.9834 0.9932 0.2334 0.4298 0.8683 0.7091 ] Network output: [ -0.008979 1.003 1.008 -2.108e-07 9.464e-08 0.007444 -1.589e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006782 0.0006193 0.00438 0.0032 0.9889 0.9919 0.006915 0.8525 0.8921 0.0117 ] Network output: [ -0.0002049 0.001509 1.001 -9.601e-06 4.31e-06 0.9984 -7.236e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2216 0.105 0.3485 0.1422 0.9849 0.9939 0.2224 0.4338 0.8751 0.7028 ] Network output: [ 0.003208 -0.01524 0.9942 5.849e-06 -2.626e-06 1.015 4.408e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09823 0.1846 0.1975 0.9873 0.9919 0.111 0.7368 0.8615 0.3051 ] Network output: [ -0.003008 0.01409 1.005 6.373e-06 -2.861e-06 0.9873 4.803e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09411 0.09217 0.165 0.1965 0.9852 0.9911 0.09412 0.6606 0.8367 0.2491 ] Network output: [ 8.598e-05 1 -5.204e-05 8.353e-07 -3.75e-07 0.9998 6.295e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001843 Epoch 9581 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008989 0.9967 0.9924 -1.873e-07 8.408e-08 -0.007134 -1.411e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003504 -0.003345 -0.006755 0.005438 0.9699 0.9743 0.00682 0.8252 0.8201 0.01637 ] Network output: [ 0.9999 0.0001321 0.0003947 -3.056e-06 1.372e-06 -0.0003548 -2.303e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.208 -0.03548 -0.1579 0.183 0.9834 0.9932 0.2334 0.4298 0.8683 0.7091 ] Network output: [ -0.008978 1.003 1.008 -2.107e-07 9.457e-08 0.007443 -1.588e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006783 0.0006194 0.004379 0.003199 0.9889 0.9919 0.006915 0.8525 0.8921 0.0117 ] Network output: [ -0.0002047 0.001508 1.001 -9.59e-06 4.305e-06 0.9984 -7.227e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2217 0.105 0.3486 0.1422 0.9849 0.9939 0.2224 0.4338 0.8751 0.7028 ] Network output: [ 0.003207 -0.01523 0.9942 5.842e-06 -2.623e-06 1.015 4.403e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09823 0.1846 0.1975 0.9873 0.9919 0.111 0.7368 0.8615 0.3051 ] Network output: [ -0.003007 0.01408 1.005 6.365e-06 -2.858e-06 0.9873 4.797e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09411 0.09217 0.165 0.1965 0.9852 0.9911 0.09413 0.6606 0.8367 0.2491 ] Network output: [ 8.596e-05 1 -5.202e-05 8.343e-07 -3.745e-07 0.9998 6.287e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001842 Epoch 9582 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008988 0.9967 0.9924 -1.872e-07 8.404e-08 -0.007133 -1.411e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003504 -0.003345 -0.006755 0.005438 0.9699 0.9743 0.00682 0.8252 0.8201 0.01636 ] Network output: [ 0.9999 0.0001319 0.0003945 -3.052e-06 1.37e-06 -0.0003546 -2.3e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.208 -0.03548 -0.1579 0.183 0.9834 0.9932 0.2334 0.4298 0.8683 0.7091 ] Network output: [ -0.008977 1.003 1.008 -2.105e-07 9.45e-08 0.007443 -1.586e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006783 0.0006195 0.004379 0.003199 0.9889 0.9919 0.006916 0.8525 0.8921 0.0117 ] Network output: [ -0.0002045 0.001507 1.001 -9.578e-06 4.3e-06 0.9984 -7.218e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2217 0.105 0.3486 0.1422 0.9849 0.9939 0.2224 0.4338 0.8751 0.7028 ] Network output: [ 0.003205 -0.01522 0.9942 5.836e-06 -2.62e-06 1.015 4.398e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09823 0.1846 0.1975 0.9873 0.9919 0.111 0.7367 0.8615 0.3051 ] Network output: [ -0.003006 0.01407 1.005 6.358e-06 -2.854e-06 0.9873 4.792e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09411 0.09218 0.165 0.1965 0.9852 0.9911 0.09413 0.6606 0.8367 0.2491 ] Network output: [ 8.593e-05 1 -5.2e-05 8.333e-07 -3.741e-07 0.9998 6.28e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001841 Epoch 9583 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008987 0.9967 0.9924 -1.871e-07 8.399e-08 -0.007133 -1.41e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003504 -0.003345 -0.006754 0.005437 0.9699 0.9743 0.00682 0.8252 0.8201 0.01636 ] Network output: [ 0.9999 0.0001317 0.0003943 -3.048e-06 1.368e-06 -0.0003544 -2.297e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.208 -0.03549 -0.1579 0.183 0.9834 0.9932 0.2334 0.4298 0.8683 0.7091 ] Network output: [ -0.008976 1.003 1.008 -2.104e-07 9.444e-08 0.007442 -1.585e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006784 0.0006195 0.004379 0.003199 0.9889 0.9919 0.006916 0.8525 0.8921 0.01169 ] Network output: [ -0.0002044 0.001506 1.001 -9.567e-06 4.295e-06 0.9984 -7.21e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2217 0.105 0.3486 0.1422 0.9849 0.9939 0.2224 0.4338 0.8751 0.7028 ] Network output: [ 0.003204 -0.01522 0.9942 5.829e-06 -2.617e-06 1.015 4.393e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09824 0.1846 0.1975 0.9873 0.9919 0.111 0.7367 0.8615 0.3051 ] Network output: [ -0.003004 0.01407 1.005 6.351e-06 -2.851e-06 0.9873 4.786e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09412 0.09218 0.165 0.1965 0.9852 0.9911 0.09413 0.6606 0.8367 0.2491 ] Network output: [ 8.591e-05 1 -5.198e-05 8.323e-07 -3.737e-07 0.9998 6.273e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000184 Epoch 9584 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008986 0.9967 0.9924 -1.87e-07 8.395e-08 -0.007132 -1.409e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003504 -0.003345 -0.006754 0.005437 0.9699 0.9743 0.006821 0.8252 0.8201 0.01636 ] Network output: [ 0.9999 0.0001316 0.0003941 -3.045e-06 1.367e-06 -0.0003541 -2.294e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.208 -0.03549 -0.1579 0.183 0.9834 0.9932 0.2335 0.4298 0.8683 0.7091 ] Network output: [ -0.008976 1.003 1.008 -2.102e-07 9.437e-08 0.007441 -1.584e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006784 0.0006196 0.004379 0.003199 0.9889 0.9919 0.006916 0.8525 0.8921 0.01169 ] Network output: [ -0.0002042 0.001506 1.001 -9.555e-06 4.29e-06 0.9984 -7.201e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2217 0.105 0.3486 0.1422 0.9849 0.9939 0.2224 0.4338 0.8751 0.7028 ] Network output: [ 0.003202 -0.01521 0.9942 5.822e-06 -2.614e-06 1.015 4.387e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09824 0.1846 0.1975 0.9873 0.9919 0.111 0.7367 0.8615 0.3051 ] Network output: [ -0.003003 0.01406 1.005 6.343e-06 -2.848e-06 0.9873 4.78e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09412 0.09218 0.165 0.1965 0.9852 0.9911 0.09413 0.6606 0.8367 0.2491 ] Network output: [ 8.588e-05 1 -5.196e-05 8.313e-07 -3.732e-07 0.9998 6.265e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001839 Epoch 9585 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008986 0.9967 0.9924 -1.869e-07 8.39e-08 -0.007132 -1.408e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003504 -0.003345 -0.006753 0.005437 0.9699 0.9743 0.006821 0.8252 0.8201 0.01636 ] Network output: [ 0.9999 0.0001314 0.000394 -3.041e-06 1.365e-06 -0.0003539 -2.292e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.208 -0.03549 -0.1579 0.183 0.9834 0.9932 0.2335 0.4298 0.8683 0.7091 ] Network output: [ -0.008975 1.003 1.008 -2.101e-07 9.43e-08 0.007441 -1.583e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006784 0.0006197 0.004379 0.003198 0.9889 0.9919 0.006917 0.8525 0.8921 0.01169 ] Network output: [ -0.0002041 0.001505 1.001 -9.544e-06 4.285e-06 0.9984 -7.193e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2217 0.105 0.3486 0.1422 0.9849 0.9939 0.2224 0.4337 0.8751 0.7028 ] Network output: [ 0.003201 -0.0152 0.9942 5.815e-06 -2.61e-06 1.015 4.382e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09824 0.1846 0.1975 0.9873 0.9919 0.111 0.7367 0.8615 0.3051 ] Network output: [ -0.003002 0.01406 1.005 6.336e-06 -2.844e-06 0.9873 4.775e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09412 0.09218 0.165 0.1965 0.9852 0.9911 0.09413 0.6606 0.8367 0.2491 ] Network output: [ 8.586e-05 1 -5.194e-05 8.304e-07 -3.728e-07 0.9998 6.258e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001838 Epoch 9586 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008985 0.9967 0.9924 -1.868e-07 8.386e-08 -0.007131 -1.408e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003504 -0.003345 -0.006752 0.005436 0.9699 0.9743 0.006821 0.8252 0.8201 0.01636 ] Network output: [ 0.9999 0.0001312 0.0003938 -3.037e-06 1.364e-06 -0.0003537 -2.289e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.208 -0.03549 -0.1579 0.183 0.9834 0.9932 0.2335 0.4298 0.8683 0.709 ] Network output: [ -0.008974 1.003 1.008 -2.099e-07 9.423e-08 0.00744 -1.582e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006785 0.0006198 0.004379 0.003198 0.9889 0.9919 0.006917 0.8525 0.8921 0.01169 ] Network output: [ -0.0002039 0.001504 1.001 -9.532e-06 4.279e-06 0.9984 -7.184e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2217 0.1051 0.3486 0.1422 0.9849 0.9939 0.2224 0.4337 0.8751 0.7028 ] Network output: [ 0.003199 -0.0152 0.9942 5.808e-06 -2.607e-06 1.015 4.377e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09825 0.1846 0.1975 0.9873 0.9919 0.111 0.7367 0.8615 0.3051 ] Network output: [ -0.003 0.01405 1.005 6.328e-06 -2.841e-06 0.9873 4.769e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09412 0.09218 0.165 0.1965 0.9852 0.9911 0.09414 0.6606 0.8367 0.2491 ] Network output: [ 8.584e-05 1 -5.192e-05 8.294e-07 -3.723e-07 0.9998 6.25e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001837 Epoch 9587 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008984 0.9967 0.9924 -1.867e-07 8.381e-08 -0.007131 -1.407e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003504 -0.003345 -0.006752 0.005436 0.9699 0.9743 0.006821 0.8252 0.8201 0.01636 ] Network output: [ 0.9999 0.000131 0.0003936 -3.034e-06 1.362e-06 -0.0003534 -2.286e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.208 -0.03549 -0.1578 0.183 0.9834 0.9932 0.2335 0.4298 0.8683 0.709 ] Network output: [ -0.008973 1.003 1.008 -2.098e-07 9.417e-08 0.00744 -1.581e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006785 0.0006198 0.004379 0.003198 0.9889 0.9919 0.006918 0.8525 0.8921 0.01169 ] Network output: [ -0.0002037 0.001504 1 -9.521e-06 4.274e-06 0.9984 -7.175e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2217 0.1051 0.3486 0.1422 0.9849 0.9939 0.2224 0.4337 0.8751 0.7028 ] Network output: [ 0.003198 -0.01519 0.9942 5.801e-06 -2.604e-06 1.015 4.372e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09825 0.1846 0.1975 0.9873 0.9919 0.111 0.7367 0.8615 0.3051 ] Network output: [ -0.002999 0.01404 1.005 6.321e-06 -2.838e-06 0.9873 4.764e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09412 0.09219 0.165 0.1965 0.9852 0.9911 0.09414 0.6606 0.8367 0.2491 ] Network output: [ 8.581e-05 1 -5.19e-05 8.284e-07 -3.719e-07 0.9998 6.243e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001836 Epoch 9588 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008983 0.9967 0.9924 -1.866e-07 8.377e-08 -0.00713 -1.406e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003505 -0.003345 -0.006751 0.005435 0.9699 0.9743 0.006821 0.8252 0.8201 0.01636 ] Network output: [ 0.9999 0.0001309 0.0003934 -3.03e-06 1.36e-06 -0.0003532 -2.283e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.208 -0.03549 -0.1578 0.183 0.9834 0.9932 0.2335 0.4298 0.8683 0.709 ] Network output: [ -0.008972 1.003 1.008 -2.096e-07 9.41e-08 0.007439 -1.58e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006785 0.0006199 0.004379 0.003198 0.9889 0.9919 0.006918 0.8525 0.8921 0.01169 ] Network output: [ -0.0002036 0.001503 1 -9.51e-06 4.269e-06 0.9984 -7.167e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2217 0.1051 0.3486 0.1422 0.9849 0.9939 0.2225 0.4337 0.8751 0.7028 ] Network output: [ 0.003196 -0.01518 0.9942 5.794e-06 -2.601e-06 1.015 4.367e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09825 0.1846 0.1975 0.9873 0.9919 0.111 0.7367 0.8615 0.3051 ] Network output: [ -0.002997 0.01404 1.005 6.314e-06 -2.834e-06 0.9873 4.758e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09413 0.09219 0.165 0.1965 0.9852 0.9911 0.09414 0.6605 0.8367 0.2491 ] Network output: [ 8.579e-05 1 -5.188e-05 8.274e-07 -3.715e-07 0.9998 6.236e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001835 Epoch 9589 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008982 0.9967 0.9924 -1.865e-07 8.372e-08 -0.007129 -1.405e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003505 -0.003345 -0.006751 0.005435 0.9699 0.9743 0.006821 0.8252 0.8201 0.01636 ] Network output: [ 0.9999 0.0001307 0.0003932 -3.026e-06 1.359e-06 -0.000353 -2.281e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2081 -0.03549 -0.1578 0.183 0.9834 0.9932 0.2335 0.4298 0.8683 0.709 ] Network output: [ -0.008971 1.003 1.008 -2.095e-07 9.403e-08 0.007438 -1.579e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006786 0.00062 0.004379 0.003197 0.9889 0.9919 0.006918 0.8525 0.8921 0.01169 ] Network output: [ -0.0002034 0.001502 1 -9.498e-06 4.264e-06 0.9984 -7.158e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2217 0.1051 0.3486 0.1422 0.9849 0.9939 0.2225 0.4337 0.8751 0.7028 ] Network output: [ 0.003195 -0.01518 0.9942 5.787e-06 -2.598e-06 1.015 4.361e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09826 0.1846 0.1975 0.9873 0.9919 0.111 0.7367 0.8615 0.3051 ] Network output: [ -0.002996 0.01403 1.005 6.306e-06 -2.831e-06 0.9873 4.753e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09413 0.09219 0.165 0.1965 0.9852 0.9911 0.09414 0.6605 0.8367 0.2491 ] Network output: [ 8.576e-05 1 -5.186e-05 8.264e-07 -3.71e-07 0.9998 6.228e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001834 Epoch 9590 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008981 0.9967 0.9924 -1.864e-07 8.368e-08 -0.007129 -1.405e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003505 -0.003346 -0.00675 0.005435 0.9699 0.9743 0.006822 0.8252 0.8201 0.01636 ] Network output: [ 0.9999 0.0001305 0.0003931 -3.023e-06 1.357e-06 -0.0003527 -2.278e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2081 -0.03549 -0.1578 0.183 0.9834 0.9932 0.2335 0.4297 0.8683 0.709 ] Network output: [ -0.008971 1.003 1.008 -2.093e-07 9.397e-08 0.007438 -1.577e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006786 0.00062 0.004379 0.003197 0.9889 0.9919 0.006919 0.8525 0.8921 0.01169 ] Network output: [ -0.0002032 0.001502 1 -9.487e-06 4.259e-06 0.9984 -7.15e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2217 0.1051 0.3486 0.1422 0.9849 0.9939 0.2225 0.4337 0.8751 0.7028 ] Network output: [ 0.003193 -0.01517 0.9942 5.78e-06 -2.595e-06 1.015 4.356e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09826 0.1846 0.1975 0.9873 0.9919 0.111 0.7367 0.8614 0.3051 ] Network output: [ -0.002995 0.01403 1.005 6.299e-06 -2.828e-06 0.9873 4.747e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09413 0.09219 0.165 0.1965 0.9852 0.9911 0.09414 0.6605 0.8367 0.2491 ] Network output: [ 8.574e-05 1 -5.184e-05 8.255e-07 -3.706e-07 0.9998 6.221e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001833 Epoch 9591 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00898 0.9967 0.9924 -1.863e-07 8.363e-08 -0.007128 -1.404e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003505 -0.003346 -0.006749 0.005434 0.9699 0.9743 0.006822 0.8252 0.8201 0.01636 ] Network output: [ 0.9999 0.0001304 0.0003929 -3.019e-06 1.355e-06 -0.0003525 -2.275e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2081 -0.03549 -0.1578 0.183 0.9834 0.9932 0.2335 0.4297 0.8683 0.709 ] Network output: [ -0.00897 1.003 1.008 -2.092e-07 9.39e-08 0.007437 -1.576e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006787 0.0006201 0.004379 0.003197 0.9889 0.9919 0.006919 0.8524 0.8921 0.01169 ] Network output: [ -0.0002031 0.001501 1 -9.476e-06 4.254e-06 0.9984 -7.141e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2217 0.1051 0.3486 0.1422 0.9849 0.9939 0.2225 0.4337 0.8751 0.7028 ] Network output: [ 0.003192 -0.01516 0.9942 5.773e-06 -2.592e-06 1.015 4.351e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1109 0.09826 0.1846 0.1975 0.9873 0.9919 0.111 0.7366 0.8614 0.3051 ] Network output: [ -0.002993 0.01402 1.005 6.291e-06 -2.824e-06 0.9873 4.741e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09413 0.09219 0.165 0.1965 0.9852 0.9911 0.09415 0.6605 0.8367 0.2491 ] Network output: [ 8.571e-05 1 -5.182e-05 8.245e-07 -3.701e-07 0.9998 6.214e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001832 Epoch 9592 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008979 0.9967 0.9924 -1.862e-07 8.359e-08 -0.007128 -1.403e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003505 -0.003346 -0.006749 0.005434 0.9699 0.9743 0.006822 0.8252 0.8201 0.01636 ] Network output: [ 0.9999 0.0001302 0.0003927 -3.015e-06 1.354e-06 -0.0003522 -2.273e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2081 -0.03549 -0.1578 0.183 0.9834 0.9932 0.2335 0.4297 0.8683 0.709 ] Network output: [ -0.008969 1.003 1.008 -2.09e-07 9.383e-08 0.007436 -1.575e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006787 0.0006202 0.004378 0.003197 0.9889 0.9919 0.00692 0.8524 0.8921 0.01169 ] Network output: [ -0.0002029 0.0015 1 -9.464e-06 4.249e-06 0.9984 -7.133e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2217 0.1051 0.3486 0.1422 0.9849 0.9939 0.2225 0.4337 0.8751 0.7028 ] Network output: [ 0.003191 -0.01516 0.9942 5.767e-06 -2.589e-06 1.015 4.346e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09827 0.1846 0.1975 0.9873 0.9919 0.111 0.7366 0.8614 0.3051 ] Network output: [ -0.002992 0.01401 1.005 6.284e-06 -2.821e-06 0.9873 4.736e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09413 0.09219 0.165 0.1965 0.9852 0.9911 0.09415 0.6605 0.8367 0.2491 ] Network output: [ 8.569e-05 1 -5.18e-05 8.235e-07 -3.697e-07 0.9998 6.206e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001831 Epoch 9593 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008978 0.9967 0.9924 -1.861e-07 8.354e-08 -0.007127 -1.402e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003505 -0.003346 -0.006748 0.005433 0.9699 0.9743 0.006822 0.8252 0.8201 0.01635 ] Network output: [ 0.9999 0.00013 0.0003925 -3.012e-06 1.352e-06 -0.000352 -2.27e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2081 -0.03549 -0.1578 0.183 0.9834 0.9932 0.2335 0.4297 0.8683 0.709 ] Network output: [ -0.008968 1.003 1.008 -2.089e-07 9.376e-08 0.007436 -1.574e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006787 0.0006202 0.004378 0.003196 0.9889 0.9919 0.00692 0.8524 0.8921 0.01169 ] Network output: [ -0.0002028 0.001499 1 -9.453e-06 4.244e-06 0.9984 -7.124e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2217 0.1051 0.3486 0.1422 0.9849 0.9939 0.2225 0.4337 0.8751 0.7028 ] Network output: [ 0.003189 -0.01515 0.9942 5.76e-06 -2.586e-06 1.015 4.341e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09827 0.1846 0.1975 0.9873 0.9919 0.111 0.7366 0.8614 0.3051 ] Network output: [ -0.002991 0.01401 1.005 6.277e-06 -2.818e-06 0.9873 4.73e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09414 0.0922 0.165 0.1965 0.9852 0.9911 0.09415 0.6605 0.8367 0.2491 ] Network output: [ 8.566e-05 1 -5.178e-05 8.226e-07 -3.693e-07 0.9998 6.199e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000183 Epoch 9594 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008977 0.9967 0.9924 -1.86e-07 8.35e-08 -0.007127 -1.402e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003505 -0.003346 -0.006748 0.005433 0.9699 0.9743 0.006822 0.8252 0.8201 0.01635 ] Network output: [ 0.9999 0.0001298 0.0003924 -3.008e-06 1.351e-06 -0.0003518 -2.267e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2081 -0.0355 -0.1578 0.183 0.9834 0.9932 0.2335 0.4297 0.8683 0.709 ] Network output: [ -0.008967 1.003 1.008 -2.087e-07 9.37e-08 0.007435 -1.573e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006788 0.0006203 0.004378 0.003196 0.9889 0.9919 0.00692 0.8524 0.8921 0.01169 ] Network output: [ -0.0002026 0.001499 1 -9.442e-06 4.239e-06 0.9984 -7.116e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2217 0.1051 0.3486 0.1422 0.9849 0.9939 0.2225 0.4337 0.8751 0.7028 ] Network output: [ 0.003188 -0.01514 0.9942 5.753e-06 -2.583e-06 1.015 4.336e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09827 0.1846 0.1975 0.9873 0.9919 0.111 0.7366 0.8614 0.3051 ] Network output: [ -0.002989 0.014 1.005 6.269e-06 -2.815e-06 0.9873 4.725e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09414 0.0922 0.165 0.1965 0.9852 0.9911 0.09415 0.6605 0.8367 0.2491 ] Network output: [ 8.564e-05 1 -5.176e-05 8.216e-07 -3.688e-07 0.9998 6.192e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001829 Epoch 9595 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008976 0.9967 0.9924 -1.859e-07 8.346e-08 -0.007126 -1.401e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003505 -0.003346 -0.006747 0.005433 0.9699 0.9743 0.006823 0.8252 0.8201 0.01635 ] Network output: [ 0.9999 0.0001297 0.0003922 -3.005e-06 1.349e-06 -0.0003515 -2.264e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2081 -0.0355 -0.1578 0.183 0.9834 0.9932 0.2335 0.4297 0.8683 0.709 ] Network output: [ -0.008966 1.003 1.008 -2.086e-07 9.363e-08 0.007434 -1.572e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006788 0.0006204 0.004378 0.003196 0.9889 0.9919 0.006921 0.8524 0.8921 0.01169 ] Network output: [ -0.0002024 0.001498 1 -9.43e-06 4.234e-06 0.9984 -7.107e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2217 0.1051 0.3486 0.1422 0.9849 0.9939 0.2225 0.4337 0.8751 0.7028 ] Network output: [ 0.003186 -0.01514 0.9942 5.746e-06 -2.58e-06 1.015 4.33e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09828 0.1846 0.1975 0.9873 0.9919 0.111 0.7366 0.8614 0.3051 ] Network output: [ -0.002988 0.01399 1.005 6.262e-06 -2.811e-06 0.9873 4.719e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09414 0.0922 0.165 0.1965 0.9852 0.9911 0.09415 0.6605 0.8367 0.2491 ] Network output: [ 8.561e-05 1 -5.174e-05 8.206e-07 -3.684e-07 0.9998 6.184e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001828 Epoch 9596 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008975 0.9967 0.9924 -1.858e-07 8.341e-08 -0.007126 -1.4e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003505 -0.003346 -0.006746 0.005432 0.9699 0.9743 0.006823 0.8251 0.8201 0.01635 ] Network output: [ 0.9999 0.0001295 0.000392 -3.001e-06 1.347e-06 -0.0003513 -2.262e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2081 -0.0355 -0.1578 0.1829 0.9834 0.9932 0.2335 0.4297 0.8683 0.709 ] Network output: [ -0.008965 1.003 1.008 -2.084e-07 9.356e-08 0.007434 -1.571e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006789 0.0006205 0.004378 0.003196 0.9889 0.9919 0.006921 0.8524 0.8921 0.01168 ] Network output: [ -0.0002023 0.001497 1 -9.419e-06 4.229e-06 0.9984 -7.099e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2218 0.1051 0.3486 0.1422 0.9849 0.9939 0.2225 0.4337 0.8751 0.7028 ] Network output: [ 0.003185 -0.01513 0.9942 5.739e-06 -2.577e-06 1.015 4.325e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09828 0.1847 0.1975 0.9873 0.9919 0.111 0.7366 0.8614 0.3051 ] Network output: [ -0.002987 0.01399 1.005 6.255e-06 -2.808e-06 0.9873 4.714e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09414 0.0922 0.165 0.1965 0.9852 0.9911 0.09416 0.6605 0.8367 0.2491 ] Network output: [ 8.559e-05 1 -5.172e-05 8.196e-07 -3.68e-07 0.9998 6.177e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001827 Epoch 9597 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008974 0.9967 0.9924 -1.857e-07 8.337e-08 -0.007125 -1.399e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003505 -0.003346 -0.006746 0.005432 0.9699 0.9743 0.006823 0.8251 0.8201 0.01635 ] Network output: [ 0.9999 0.0001293 0.0003918 -2.997e-06 1.346e-06 -0.0003511 -2.259e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2081 -0.0355 -0.1578 0.1829 0.9834 0.9932 0.2335 0.4297 0.8683 0.709 ] Network output: [ -0.008965 1.003 1.008 -2.083e-07 9.35e-08 0.007433 -1.57e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006789 0.0006205 0.004378 0.003195 0.9889 0.9919 0.006922 0.8524 0.8921 0.01168 ] Network output: [ -0.0002021 0.001497 1 -9.408e-06 4.224e-06 0.9984 -7.09e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2218 0.1051 0.3486 0.1422 0.9849 0.9939 0.2225 0.4337 0.8751 0.7028 ] Network output: [ 0.003183 -0.01512 0.9942 5.732e-06 -2.573e-06 1.015 4.32e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09829 0.1847 0.1975 0.9873 0.9919 0.111 0.7366 0.8614 0.3051 ] Network output: [ -0.002985 0.01398 1.005 6.247e-06 -2.805e-06 0.9873 4.708e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09414 0.0922 0.165 0.1965 0.9852 0.9911 0.09416 0.6604 0.8367 0.2491 ] Network output: [ 8.556e-05 1 -5.171e-05 8.187e-07 -3.675e-07 0.9998 6.17e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001826 Epoch 9598 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008973 0.9967 0.9924 -1.856e-07 8.332e-08 -0.007124 -1.399e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003505 -0.003346 -0.006745 0.005431 0.9699 0.9743 0.006823 0.8251 0.8201 0.01635 ] Network output: [ 0.9999 0.0001291 0.0003917 -2.994e-06 1.344e-06 -0.0003508 -2.256e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2081 -0.0355 -0.1577 0.1829 0.9834 0.9932 0.2336 0.4297 0.8683 0.709 ] Network output: [ -0.008964 1.003 1.008 -2.081e-07 9.343e-08 0.007433 -1.568e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006789 0.0006206 0.004378 0.003195 0.9889 0.9919 0.006922 0.8524 0.8921 0.01168 ] Network output: [ -0.000202 0.001496 1 -9.397e-06 4.219e-06 0.9984 -7.082e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2218 0.1051 0.3486 0.1422 0.9849 0.9939 0.2225 0.4337 0.8751 0.7028 ] Network output: [ 0.003182 -0.01512 0.9942 5.726e-06 -2.57e-06 1.015 4.315e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09829 0.1847 0.1975 0.9873 0.9919 0.1111 0.7366 0.8614 0.3051 ] Network output: [ -0.002984 0.01398 1.005 6.24e-06 -2.801e-06 0.9873 4.703e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09415 0.09221 0.165 0.1965 0.9852 0.9911 0.09416 0.6604 0.8367 0.2491 ] Network output: [ 8.554e-05 1 -5.169e-05 8.177e-07 -3.671e-07 0.9998 6.163e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001825 Epoch 9599 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008972 0.9967 0.9924 -1.855e-07 8.328e-08 -0.007124 -1.398e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003505 -0.003346 -0.006745 0.005431 0.9699 0.9743 0.006823 0.8251 0.8201 0.01635 ] Network output: [ 0.9999 0.000129 0.0003915 -2.99e-06 1.342e-06 -0.0003506 -2.254e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2081 -0.0355 -0.1577 0.1829 0.9834 0.9932 0.2336 0.4297 0.8683 0.709 ] Network output: [ -0.008963 1.003 1.008 -2.08e-07 9.336e-08 0.007432 -1.567e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00679 0.0006207 0.004378 0.003195 0.9889 0.9919 0.006922 0.8524 0.8921 0.01168 ] Network output: [ -0.0002018 0.001495 1 -9.385e-06 4.213e-06 0.9984 -7.073e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2218 0.1051 0.3486 0.1422 0.9849 0.9939 0.2225 0.4337 0.8751 0.7028 ] Network output: [ 0.00318 -0.01511 0.9942 5.719e-06 -2.567e-06 1.015 4.31e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09829 0.1847 0.1975 0.9873 0.9919 0.1111 0.7366 0.8614 0.3051 ] Network output: [ -0.002983 0.01397 1.005 6.233e-06 -2.798e-06 0.9874 4.697e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09415 0.09221 0.165 0.1965 0.9852 0.9911 0.09416 0.6604 0.8366 0.2491 ] Network output: [ 8.552e-05 1 -5.167e-05 8.167e-07 -3.667e-07 0.9998 6.155e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001824 Epoch 9600 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008972 0.9967 0.9924 -1.854e-07 8.323e-08 -0.007123 -1.397e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003505 -0.003346 -0.006744 0.005431 0.9699 0.9743 0.006823 0.8251 0.82 0.01635 ] Network output: [ 0.9999 0.0001288 0.0003913 -2.987e-06 1.341e-06 -0.0003504 -2.251e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2081 -0.0355 -0.1577 0.1829 0.9834 0.9932 0.2336 0.4297 0.8683 0.709 ] Network output: [ -0.008962 1.003 1.008 -2.078e-07 9.33e-08 0.007431 -1.566e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00679 0.0006207 0.004378 0.003195 0.9889 0.9919 0.006923 0.8524 0.8921 0.01168 ] Network output: [ -0.0002016 0.001494 1 -9.374e-06 4.208e-06 0.9984 -7.065e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2218 0.1051 0.3486 0.1422 0.9849 0.9939 0.2225 0.4337 0.8751 0.7028 ] Network output: [ 0.003179 -0.0151 0.9942 5.712e-06 -2.564e-06 1.015 4.305e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.0983 0.1847 0.1975 0.9873 0.9919 0.1111 0.7365 0.8614 0.3051 ] Network output: [ -0.002981 0.01396 1.005 6.225e-06 -2.795e-06 0.9874 4.692e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09415 0.09221 0.165 0.1965 0.9852 0.9911 0.09416 0.6604 0.8366 0.2491 ] Network output: [ 8.549e-05 1 -5.165e-05 8.158e-07 -3.662e-07 0.9998 6.148e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001823 Epoch 9601 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008971 0.9967 0.9924 -1.853e-07 8.319e-08 -0.007123 -1.396e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003505 -0.003347 -0.006743 0.00543 0.9699 0.9743 0.006824 0.8251 0.82 0.01635 ] Network output: [ 0.9999 0.0001286 0.0003911 -2.983e-06 1.339e-06 -0.0003501 -2.248e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2081 -0.0355 -0.1577 0.1829 0.9834 0.9932 0.2336 0.4297 0.8683 0.709 ] Network output: [ -0.008961 1.003 1.008 -2.077e-07 9.323e-08 0.007431 -1.565e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00679 0.0006208 0.004378 0.003195 0.9889 0.9919 0.006923 0.8524 0.8921 0.01168 ] Network output: [ -0.0002015 0.001494 1 -9.363e-06 4.203e-06 0.9984 -7.056e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2218 0.1051 0.3487 0.1421 0.9849 0.9939 0.2225 0.4337 0.8751 0.7027 ] Network output: [ 0.003177 -0.0151 0.9942 5.705e-06 -2.561e-06 1.015 4.3e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.0983 0.1847 0.1975 0.9873 0.9919 0.1111 0.7365 0.8614 0.3051 ] Network output: [ -0.00298 0.01396 1.005 6.218e-06 -2.792e-06 0.9874 4.686e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09415 0.09221 0.165 0.1965 0.9852 0.9911 0.09417 0.6604 0.8366 0.2491 ] Network output: [ 8.547e-05 1 -5.163e-05 8.148e-07 -3.658e-07 0.9998 6.141e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001822 Epoch 9602 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00897 0.9967 0.9924 -1.852e-07 8.314e-08 -0.007122 -1.396e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003505 -0.003347 -0.006743 0.00543 0.9699 0.9743 0.006824 0.8251 0.82 0.01635 ] Network output: [ 0.9999 0.0001284 0.000391 -2.98e-06 1.338e-06 -0.0003499 -2.245e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2081 -0.0355 -0.1577 0.1829 0.9834 0.9932 0.2336 0.4297 0.8683 0.709 ] Network output: [ -0.00896 1.003 1.008 -2.075e-07 9.316e-08 0.00743 -1.564e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006791 0.0006209 0.004378 0.003194 0.9889 0.9919 0.006923 0.8524 0.8921 0.01168 ] Network output: [ -0.0002013 0.001493 1 -9.352e-06 4.198e-06 0.9984 -7.048e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2218 0.1051 0.3487 0.1421 0.9849 0.9939 0.2226 0.4337 0.8751 0.7027 ] Network output: [ 0.003176 -0.01509 0.9942 5.698e-06 -2.558e-06 1.015 4.294e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.0983 0.1847 0.1975 0.9873 0.9919 0.1111 0.7365 0.8614 0.3051 ] Network output: [ -0.002978 0.01395 1.005 6.211e-06 -2.788e-06 0.9874 4.681e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09415 0.09221 0.165 0.1965 0.9852 0.9911 0.09417 0.6604 0.8366 0.2491 ] Network output: [ 8.544e-05 1 -5.161e-05 8.139e-07 -3.654e-07 0.9998 6.133e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001821 Epoch 9603 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008969 0.9967 0.9924 -1.851e-07 8.31e-08 -0.007122 -1.395e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003505 -0.003347 -0.006742 0.005429 0.9699 0.9743 0.006824 0.8251 0.82 0.01634 ] Network output: [ 0.9999 0.0001283 0.0003908 -2.976e-06 1.336e-06 -0.0003497 -2.243e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2081 -0.0355 -0.1577 0.1829 0.9834 0.9932 0.2336 0.4297 0.8683 0.709 ] Network output: [ -0.00896 1.003 1.008 -2.074e-07 9.309e-08 0.007429 -1.563e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006791 0.0006209 0.004377 0.003194 0.9889 0.9919 0.006924 0.8524 0.8921 0.01168 ] Network output: [ -0.0002012 0.001492 1 -9.341e-06 4.193e-06 0.9984 -7.039e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2218 0.1051 0.3487 0.1421 0.9849 0.9939 0.2226 0.4337 0.8751 0.7027 ] Network output: [ 0.003174 -0.01508 0.9942 5.692e-06 -2.555e-06 1.015 4.289e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09831 0.1847 0.1975 0.9873 0.9919 0.1111 0.7365 0.8614 0.3051 ] Network output: [ -0.002977 0.01395 1.005 6.204e-06 -2.785e-06 0.9874 4.675e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09416 0.09222 0.165 0.1965 0.9852 0.9911 0.09417 0.6604 0.8366 0.2491 ] Network output: [ 8.542e-05 1 -5.159e-05 8.129e-07 -3.649e-07 0.9998 6.126e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001821 Epoch 9604 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008968 0.9967 0.9924 -1.85e-07 8.305e-08 -0.007121 -1.394e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003506 -0.003347 -0.006742 0.005429 0.9699 0.9743 0.006824 0.8251 0.82 0.01634 ] Network output: [ 0.9999 0.0001281 0.0003906 -2.972e-06 1.334e-06 -0.0003494 -2.24e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2081 -0.03551 -0.1577 0.1829 0.9834 0.9932 0.2336 0.4297 0.8683 0.709 ] Network output: [ -0.008959 1.003 1.008 -2.072e-07 9.303e-08 0.007429 -1.562e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006792 0.000621 0.004377 0.003194 0.9889 0.9919 0.006924 0.8524 0.8921 0.01168 ] Network output: [ -0.000201 0.001492 1 -9.329e-06 4.188e-06 0.9984 -7.031e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2218 0.1051 0.3487 0.1421 0.9849 0.9939 0.2226 0.4337 0.8751 0.7027 ] Network output: [ 0.003173 -0.01508 0.9942 5.685e-06 -2.552e-06 1.015 4.284e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09831 0.1847 0.1975 0.9873 0.9919 0.1111 0.7365 0.8614 0.3051 ] Network output: [ -0.002976 0.01394 1.005 6.196e-06 -2.782e-06 0.9874 4.67e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09416 0.09222 0.165 0.1965 0.9852 0.9911 0.09417 0.6604 0.8366 0.2491 ] Network output: [ 8.539e-05 1 -5.158e-05 8.119e-07 -3.645e-07 0.9998 6.119e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000182 Epoch 9605 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008967 0.9967 0.9924 -1.849e-07 8.301e-08 -0.00712 -1.393e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003506 -0.003347 -0.006741 0.005429 0.9699 0.9743 0.006824 0.8251 0.82 0.01634 ] Network output: [ 0.9999 0.0001279 0.0003904 -2.969e-06 1.333e-06 -0.0003492 -2.237e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2081 -0.03551 -0.1577 0.1829 0.9834 0.9932 0.2336 0.4297 0.8683 0.709 ] Network output: [ -0.008958 1.003 1.008 -2.071e-07 9.296e-08 0.007428 -1.561e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006792 0.0006211 0.004377 0.003194 0.9889 0.9919 0.006925 0.8524 0.8921 0.01168 ] Network output: [ -0.0002009 0.001491 1 -9.318e-06 4.183e-06 0.9984 -7.023e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2218 0.1051 0.3487 0.1421 0.9849 0.9939 0.2226 0.4336 0.8751 0.7027 ] Network output: [ 0.003171 -0.01507 0.9942 5.678e-06 -2.549e-06 1.015 4.279e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09831 0.1847 0.1975 0.9873 0.9919 0.1111 0.7365 0.8614 0.3051 ] Network output: [ -0.002974 0.01393 1.005 6.189e-06 -2.779e-06 0.9874 4.664e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09416 0.09222 0.165 0.1965 0.9852 0.9911 0.09417 0.6604 0.8366 0.2491 ] Network output: [ 8.537e-05 1 -5.156e-05 8.11e-07 -3.641e-07 0.9998 6.112e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001819 Epoch 9606 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008966 0.9967 0.9924 -1.848e-07 8.296e-08 -0.00712 -1.393e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003506 -0.003347 -0.00674 0.005428 0.9699 0.9743 0.006824 0.8251 0.82 0.01634 ] Network output: [ 0.9999 0.0001278 0.0003903 -2.965e-06 1.331e-06 -0.000349 -2.235e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2082 -0.03551 -0.1577 0.1829 0.9834 0.9932 0.2336 0.4297 0.8683 0.709 ] Network output: [ -0.008957 1.003 1.008 -2.069e-07 9.289e-08 0.007427 -1.559e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006792 0.0006212 0.004377 0.003193 0.9889 0.9919 0.006925 0.8524 0.8921 0.01168 ] Network output: [ -0.0002007 0.00149 1 -9.307e-06 4.178e-06 0.9984 -7.014e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2218 0.1051 0.3487 0.1421 0.9849 0.9939 0.2226 0.4336 0.8751 0.7027 ] Network output: [ 0.00317 -0.01506 0.9942 5.671e-06 -2.546e-06 1.015 4.274e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09832 0.1847 0.1975 0.9873 0.9919 0.1111 0.7365 0.8614 0.3051 ] Network output: [ -0.002973 0.01393 1.005 6.182e-06 -2.775e-06 0.9874 4.659e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09416 0.09222 0.165 0.1965 0.9852 0.9911 0.09418 0.6603 0.8366 0.2491 ] Network output: [ 8.535e-05 1 -5.154e-05 8.1e-07 -3.636e-07 0.9998 6.105e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001818 Epoch 9607 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008965 0.9967 0.9924 -1.847e-07 8.291e-08 -0.007119 -1.392e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003506 -0.003347 -0.00674 0.005428 0.9699 0.9743 0.006825 0.8251 0.82 0.01634 ] Network output: [ 0.9999 0.0001276 0.0003901 -2.962e-06 1.33e-06 -0.0003487 -2.232e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2082 -0.03551 -0.1577 0.1829 0.9834 0.9932 0.2336 0.4297 0.8683 0.709 ] Network output: [ -0.008956 1.003 1.008 -2.068e-07 9.283e-08 0.007427 -1.558e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006793 0.0006212 0.004377 0.003193 0.9889 0.9919 0.006925 0.8524 0.8921 0.01168 ] Network output: [ -0.0002005 0.00149 1 -9.296e-06 4.173e-06 0.9984 -7.006e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2218 0.1051 0.3487 0.1421 0.9849 0.9939 0.2226 0.4336 0.8751 0.7027 ] Network output: [ 0.003169 -0.01506 0.9942 5.665e-06 -2.543e-06 1.014 4.269e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09832 0.1847 0.1975 0.9873 0.9919 0.1111 0.7365 0.8614 0.3051 ] Network output: [ -0.002972 0.01392 1.005 6.175e-06 -2.772e-06 0.9874 4.653e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09416 0.09222 0.165 0.1965 0.9852 0.9911 0.09418 0.6603 0.8366 0.2491 ] Network output: [ 8.532e-05 1 -5.152e-05 8.091e-07 -3.632e-07 0.9998 6.097e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001817 Epoch 9608 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008964 0.9967 0.9924 -1.846e-07 8.287e-08 -0.007119 -1.391e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003506 -0.003347 -0.006739 0.005427 0.9699 0.9743 0.006825 0.8251 0.82 0.01634 ] Network output: [ 0.9999 0.0001274 0.0003899 -2.958e-06 1.328e-06 -0.0003485 -2.229e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2082 -0.03551 -0.1577 0.1829 0.9834 0.9932 0.2336 0.4297 0.8683 0.709 ] Network output: [ -0.008955 1.003 1.008 -2.066e-07 9.276e-08 0.007426 -1.557e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006793 0.0006213 0.004377 0.003193 0.9889 0.9919 0.006926 0.8524 0.8921 0.01168 ] Network output: [ -0.0002004 0.001489 1 -9.285e-06 4.168e-06 0.9984 -6.997e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2218 0.1051 0.3487 0.1421 0.9849 0.9939 0.2226 0.4336 0.8751 0.7027 ] Network output: [ 0.003167 -0.01505 0.9942 5.658e-06 -2.54e-06 1.014 4.264e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09832 0.1847 0.1975 0.9873 0.9919 0.1111 0.7365 0.8614 0.3051 ] Network output: [ -0.00297 0.01391 1.005 6.167e-06 -2.769e-06 0.9874 4.648e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09416 0.09223 0.165 0.1965 0.9852 0.9911 0.09418 0.6603 0.8366 0.2491 ] Network output: [ 8.53e-05 1 -5.15e-05 8.081e-07 -3.628e-07 0.9998 6.09e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001816 Epoch 9609 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008963 0.9967 0.9924 -1.845e-07 8.282e-08 -0.007118 -1.39e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003506 -0.003347 -0.006739 0.005427 0.9699 0.9743 0.006825 0.8251 0.82 0.01634 ] Network output: [ 0.9999 0.0001272 0.0003897 -2.955e-06 1.326e-06 -0.0003483 -2.227e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2082 -0.03551 -0.1576 0.1829 0.9834 0.9932 0.2336 0.4297 0.8683 0.709 ] Network output: [ -0.008955 1.003 1.008 -2.065e-07 9.269e-08 0.007426 -1.556e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006794 0.0006214 0.004377 0.003193 0.9889 0.9919 0.006926 0.8524 0.8921 0.01167 ] Network output: [ -0.0002002 0.001488 1 -9.274e-06 4.163e-06 0.9984 -6.989e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2218 0.1052 0.3487 0.1421 0.9849 0.9939 0.2226 0.4336 0.8751 0.7027 ] Network output: [ 0.003166 -0.01505 0.9942 5.651e-06 -2.537e-06 1.014 4.259e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09833 0.1847 0.1975 0.9873 0.9919 0.1111 0.7364 0.8614 0.3051 ] Network output: [ -0.002969 0.01391 1.005 6.16e-06 -2.766e-06 0.9874 4.642e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09417 0.09223 0.165 0.1965 0.9852 0.9911 0.09418 0.6603 0.8366 0.2491 ] Network output: [ 8.527e-05 1 -5.149e-05 8.072e-07 -3.624e-07 0.9998 6.083e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001815 Epoch 9610 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008962 0.9967 0.9924 -1.844e-07 8.278e-08 -0.007117 -1.39e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003506 -0.003347 -0.006738 0.005427 0.9699 0.9743 0.006825 0.8251 0.82 0.01634 ] Network output: [ 0.9999 0.0001271 0.0003896 -2.951e-06 1.325e-06 -0.0003481 -2.224e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2082 -0.03551 -0.1576 0.1829 0.9834 0.9932 0.2336 0.4296 0.8683 0.709 ] Network output: [ -0.008954 1.003 1.008 -2.063e-07 9.263e-08 0.007425 -1.555e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006794 0.0006214 0.004377 0.003192 0.9889 0.9919 0.006927 0.8524 0.8921 0.01167 ] Network output: [ -0.0002001 0.001487 1 -9.263e-06 4.158e-06 0.9984 -6.981e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2219 0.1052 0.3487 0.1421 0.9849 0.9939 0.2226 0.4336 0.8751 0.7027 ] Network output: [ 0.003164 -0.01504 0.9942 5.644e-06 -2.534e-06 1.014 4.254e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09833 0.1847 0.1975 0.9873 0.9919 0.1111 0.7364 0.8614 0.3051 ] Network output: [ -0.002968 0.0139 1.005 6.153e-06 -2.762e-06 0.9874 4.637e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09417 0.09223 0.165 0.1965 0.9852 0.9911 0.09418 0.6603 0.8366 0.2491 ] Network output: [ 8.525e-05 1 -5.147e-05 8.062e-07 -3.619e-07 0.9998 6.076e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001814 Epoch 9611 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008961 0.9967 0.9924 -1.843e-07 8.273e-08 -0.007117 -1.389e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003506 -0.003347 -0.006737 0.005426 0.9699 0.9743 0.006825 0.8251 0.82 0.01634 ] Network output: [ 0.9999 0.0001269 0.0003894 -2.947e-06 1.323e-06 -0.0003478 -2.221e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2082 -0.03551 -0.1576 0.1829 0.9834 0.9932 0.2336 0.4296 0.8683 0.7089 ] Network output: [ -0.008953 1.003 1.008 -2.062e-07 9.256e-08 0.007424 -1.554e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006794 0.0006215 0.004377 0.003192 0.9889 0.9919 0.006927 0.8523 0.8921 0.01167 ] Network output: [ -0.0001999 0.001487 1 -9.252e-06 4.153e-06 0.9984 -6.972e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2219 0.1052 0.3487 0.1421 0.9849 0.9939 0.2226 0.4336 0.8751 0.7027 ] Network output: [ 0.003163 -0.01503 0.9942 5.638e-06 -2.531e-06 1.014 4.249e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09833 0.1847 0.1975 0.9873 0.9919 0.1111 0.7364 0.8614 0.3051 ] Network output: [ -0.002966 0.0139 1.005 6.146e-06 -2.759e-06 0.9874 4.632e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09417 0.09223 0.165 0.1965 0.9852 0.9911 0.09418 0.6603 0.8366 0.2491 ] Network output: [ 8.522e-05 1 -5.145e-05 8.052e-07 -3.615e-07 0.9998 6.069e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001813 Epoch 9612 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00896 0.9967 0.9924 -1.842e-07 8.269e-08 -0.007116 -1.388e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003506 -0.003347 -0.006737 0.005426 0.9699 0.9743 0.006826 0.8251 0.82 0.01634 ] Network output: [ 0.9999 0.0001267 0.0003892 -2.944e-06 1.322e-06 -0.0003476 -2.219e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2082 -0.03551 -0.1576 0.1829 0.9834 0.9932 0.2337 0.4296 0.8683 0.7089 ] Network output: [ -0.008952 1.003 1.008 -2.06e-07 9.249e-08 0.007424 -1.553e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006795 0.0006216 0.004377 0.003192 0.9889 0.9919 0.006927 0.8523 0.8921 0.01167 ] Network output: [ -0.0001997 0.001486 1 -9.24e-06 4.148e-06 0.9984 -6.964e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2219 0.1052 0.3487 0.1421 0.9849 0.9939 0.2226 0.4336 0.8751 0.7027 ] Network output: [ 0.003161 -0.01503 0.9942 5.631e-06 -2.528e-06 1.014 4.244e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09834 0.1847 0.1975 0.9873 0.9919 0.1111 0.7364 0.8614 0.3051 ] Network output: [ -0.002965 0.01389 1.005 6.138e-06 -2.756e-06 0.9874 4.626e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09417 0.09223 0.165 0.1965 0.9852 0.9911 0.09419 0.6603 0.8366 0.2491 ] Network output: [ 8.52e-05 1 -5.143e-05 8.043e-07 -3.611e-07 0.9998 6.061e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001812 Epoch 9613 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008959 0.9967 0.9924 -1.841e-07 8.264e-08 -0.007116 -1.387e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003506 -0.003348 -0.006736 0.005425 0.9699 0.9743 0.006826 0.8251 0.82 0.01634 ] Network output: [ 0.9999 0.0001266 0.000389 -2.94e-06 1.32e-06 -0.0003474 -2.216e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2082 -0.03551 -0.1576 0.1829 0.9834 0.9932 0.2337 0.4296 0.8683 0.7089 ] Network output: [ -0.008951 1.003 1.008 -2.059e-07 9.243e-08 0.007423 -1.552e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006795 0.0006216 0.004377 0.003192 0.9889 0.9919 0.006928 0.8523 0.8921 0.01167 ] Network output: [ -0.0001996 0.001485 1 -9.229e-06 4.143e-06 0.9984 -6.956e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2219 0.1052 0.3487 0.1421 0.9849 0.9939 0.2226 0.4336 0.8751 0.7027 ] Network output: [ 0.00316 -0.01502 0.9942 5.624e-06 -2.525e-06 1.014 4.239e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09834 0.1847 0.1975 0.9873 0.9919 0.1111 0.7364 0.8614 0.3051 ] Network output: [ -0.002964 0.01388 1.005 6.131e-06 -2.753e-06 0.9874 4.621e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09417 0.09224 0.165 0.1965 0.9852 0.9911 0.09419 0.6603 0.8366 0.2492 ] Network output: [ 8.518e-05 1 -5.142e-05 8.033e-07 -3.607e-07 0.9998 6.054e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001811 Epoch 9614 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008959 0.9967 0.9924 -1.84e-07 8.26e-08 -0.007115 -1.387e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003506 -0.003348 -0.006735 0.005425 0.9699 0.9743 0.006826 0.8251 0.82 0.01633 ] Network output: [ 0.9999 0.0001264 0.0003889 -2.937e-06 1.318e-06 -0.0003471 -2.213e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2082 -0.03551 -0.1576 0.1829 0.9834 0.9932 0.2337 0.4296 0.8683 0.7089 ] Network output: [ -0.00895 1.003 1.008 -2.057e-07 9.236e-08 0.007422 -1.55e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006795 0.0006217 0.004376 0.003191 0.9889 0.9919 0.006928 0.8523 0.8921 0.01167 ] Network output: [ -0.0001994 0.001485 1 -9.218e-06 4.138e-06 0.9984 -6.947e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2219 0.1052 0.3487 0.1421 0.9849 0.9939 0.2226 0.4336 0.8751 0.7027 ] Network output: [ 0.003158 -0.01501 0.9942 5.618e-06 -2.522e-06 1.014 4.234e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09834 0.1847 0.1975 0.9873 0.9919 0.1111 0.7364 0.8614 0.3051 ] Network output: [ -0.002962 0.01388 1.005 6.124e-06 -2.749e-06 0.9874 4.615e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09418 0.09224 0.165 0.1965 0.9852 0.9911 0.09419 0.6602 0.8366 0.2492 ] Network output: [ 8.515e-05 1 -5.14e-05 8.024e-07 -3.602e-07 0.9998 6.047e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000181 Epoch 9615 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008958 0.9968 0.9924 -1.839e-07 8.255e-08 -0.007115 -1.386e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003506 -0.003348 -0.006735 0.005425 0.9699 0.9743 0.006826 0.8251 0.82 0.01633 ] Network output: [ 0.9999 0.0001262 0.0003887 -2.933e-06 1.317e-06 -0.0003469 -2.211e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2082 -0.03552 -0.1576 0.1829 0.9834 0.9932 0.2337 0.4296 0.8683 0.7089 ] Network output: [ -0.00895 1.003 1.008 -2.056e-07 9.229e-08 0.007422 -1.549e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006796 0.0006218 0.004376 0.003191 0.9889 0.9919 0.006929 0.8523 0.8921 0.01167 ] Network output: [ -0.0001993 0.001484 1 -9.207e-06 4.134e-06 0.9984 -6.939e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2219 0.1052 0.3487 0.1421 0.9849 0.9939 0.2226 0.4336 0.8751 0.7027 ] Network output: [ 0.003157 -0.01501 0.9942 5.611e-06 -2.519e-06 1.014 4.229e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09835 0.1847 0.1975 0.9873 0.9919 0.1111 0.7364 0.8614 0.3051 ] Network output: [ -0.002961 0.01387 1.005 6.117e-06 -2.746e-06 0.9874 4.61e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09418 0.09224 0.165 0.1965 0.9852 0.9911 0.09419 0.6602 0.8366 0.2492 ] Network output: [ 8.513e-05 1 -5.138e-05 8.014e-07 -3.598e-07 0.9998 6.04e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001809 Epoch 9616 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008957 0.9968 0.9924 -1.838e-07 8.251e-08 -0.007114 -1.385e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003506 -0.003348 -0.006734 0.005424 0.9699 0.9743 0.006826 0.8251 0.82 0.01633 ] Network output: [ 0.9999 0.000126 0.0003885 -2.93e-06 1.315e-06 -0.0003467 -2.208e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2082 -0.03552 -0.1576 0.1829 0.9834 0.9932 0.2337 0.4296 0.8683 0.7089 ] Network output: [ -0.008949 1.003 1.008 -2.054e-07 9.222e-08 0.007421 -1.548e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006796 0.0006219 0.004376 0.003191 0.9889 0.9919 0.006929 0.8523 0.8921 0.01167 ] Network output: [ -0.0001991 0.001483 1 -9.196e-06 4.129e-06 0.9984 -6.931e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2219 0.1052 0.3487 0.1421 0.9849 0.9939 0.2227 0.4336 0.875 0.7027 ] Network output: [ 0.003155 -0.015 0.9942 5.604e-06 -2.516e-06 1.014 4.223e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09835 0.1847 0.1975 0.9873 0.9919 0.1111 0.7364 0.8614 0.3051 ] Network output: [ -0.00296 0.01387 1.005 6.11e-06 -2.743e-06 0.9874 4.605e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09418 0.09224 0.165 0.1965 0.9852 0.9911 0.09419 0.6602 0.8366 0.2492 ] Network output: [ 8.51e-05 1 -5.136e-05 8.005e-07 -3.594e-07 0.9998 6.033e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001808 Epoch 9617 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008956 0.9968 0.9925 -1.837e-07 8.246e-08 -0.007113 -1.384e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003506 -0.003348 -0.006734 0.005424 0.9699 0.9743 0.006826 0.8251 0.82 0.01633 ] Network output: [ 0.9999 0.0001259 0.0003883 -2.926e-06 1.314e-06 -0.0003464 -2.205e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2082 -0.03552 -0.1576 0.1829 0.9834 0.9932 0.2337 0.4296 0.8683 0.7089 ] Network output: [ -0.008948 1.003 1.008 -2.053e-07 9.216e-08 0.007421 -1.547e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006797 0.0006219 0.004376 0.003191 0.9889 0.9919 0.006929 0.8523 0.8921 0.01167 ] Network output: [ -0.0001989 0.001483 1 -9.185e-06 4.124e-06 0.9984 -6.922e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2219 0.1052 0.3487 0.1421 0.9849 0.9939 0.2227 0.4336 0.875 0.7027 ] Network output: [ 0.003154 -0.01499 0.9942 5.598e-06 -2.513e-06 1.014 4.218e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09835 0.1847 0.1975 0.9873 0.9919 0.1111 0.7364 0.8614 0.3051 ] Network output: [ -0.002958 0.01386 1.005 6.103e-06 -2.74e-06 0.9874 4.599e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09418 0.09224 0.165 0.1965 0.9852 0.9911 0.0942 0.6602 0.8366 0.2492 ] Network output: [ 8.508e-05 1 -5.135e-05 7.996e-07 -3.589e-07 0.9998 6.026e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001807 Epoch 9618 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008955 0.9968 0.9925 -1.836e-07 8.242e-08 -0.007113 -1.384e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003506 -0.003348 -0.006733 0.005423 0.9699 0.9743 0.006827 0.825 0.82 0.01633 ] Network output: [ 0.9999 0.0001257 0.0003882 -2.923e-06 1.312e-06 -0.0003462 -2.203e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2082 -0.03552 -0.1576 0.1829 0.9834 0.9932 0.2337 0.4296 0.8683 0.7089 ] Network output: [ -0.008947 1.003 1.008 -2.051e-07 9.209e-08 0.00742 -1.546e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006797 0.000622 0.004376 0.00319 0.9889 0.9919 0.00693 0.8523 0.8921 0.01167 ] Network output: [ -0.0001988 0.001482 1 -9.174e-06 4.119e-06 0.9984 -6.914e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2219 0.1052 0.3487 0.1421 0.9849 0.9939 0.2227 0.4336 0.875 0.7027 ] Network output: [ 0.003152 -0.01499 0.9942 5.591e-06 -2.51e-06 1.014 4.213e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09836 0.1847 0.1975 0.9873 0.9919 0.1111 0.7363 0.8614 0.3051 ] Network output: [ -0.002957 0.01385 1.005 6.095e-06 -2.736e-06 0.9874 4.594e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09418 0.09225 0.165 0.1965 0.9852 0.9911 0.0942 0.6602 0.8366 0.2492 ] Network output: [ 8.506e-05 1 -5.133e-05 7.986e-07 -3.585e-07 0.9998 6.019e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001806 Epoch 9619 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008954 0.9968 0.9925 -1.835e-07 8.237e-08 -0.007112 -1.383e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003506 -0.003348 -0.006732 0.005423 0.9699 0.9743 0.006827 0.825 0.82 0.01633 ] Network output: [ 0.9999 0.0001255 0.000388 -2.919e-06 1.311e-06 -0.000346 -2.2e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2082 -0.03552 -0.1576 0.1829 0.9834 0.9932 0.2337 0.4296 0.8683 0.7089 ] Network output: [ -0.008946 1.003 1.008 -2.05e-07 9.202e-08 0.007419 -1.545e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006797 0.0006221 0.004376 0.00319 0.9889 0.9919 0.00693 0.8523 0.8921 0.01167 ] Network output: [ -0.0001986 0.001481 1 -9.163e-06 4.114e-06 0.9984 -6.906e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2219 0.1052 0.3487 0.1421 0.9849 0.9939 0.2227 0.4336 0.875 0.7027 ] Network output: [ 0.003151 -0.01498 0.9943 5.584e-06 -2.507e-06 1.014 4.208e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.111 0.09836 0.1847 0.1975 0.9873 0.9919 0.1111 0.7363 0.8614 0.3051 ] Network output: [ -0.002955 0.01385 1.005 6.088e-06 -2.733e-06 0.9874 4.588e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09419 0.09225 0.165 0.1965 0.9852 0.9911 0.0942 0.6602 0.8366 0.2492 ] Network output: [ 8.503e-05 1 -5.131e-05 7.977e-07 -3.581e-07 0.9998 6.011e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001805 Epoch 9620 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008953 0.9968 0.9925 -1.834e-07 8.232e-08 -0.007112 -1.382e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003506 -0.003348 -0.006732 0.005423 0.9699 0.9743 0.006827 0.825 0.82 0.01633 ] Network output: [ 0.9999 0.0001254 0.0003878 -2.916e-06 1.309e-06 -0.0003458 -2.197e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2082 -0.03552 -0.1575 0.1829 0.9834 0.9932 0.2337 0.4296 0.8683 0.7089 ] Network output: [ -0.008945 1.003 1.008 -2.048e-07 9.196e-08 0.007419 -1.544e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006798 0.0006221 0.004376 0.00319 0.9889 0.9919 0.006931 0.8523 0.8921 0.01167 ] Network output: [ -0.0001985 0.00148 1 -9.152e-06 4.109e-06 0.9984 -6.897e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2219 0.1052 0.3487 0.1421 0.9849 0.9939 0.2227 0.4336 0.875 0.7027 ] Network output: [ 0.003149 -0.01497 0.9943 5.578e-06 -2.504e-06 1.014 4.203e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09836 0.1847 0.1975 0.9873 0.9919 0.1111 0.7363 0.8614 0.3051 ] Network output: [ -0.002954 0.01384 1.005 6.081e-06 -2.73e-06 0.9874 4.583e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09419 0.09225 0.165 0.1965 0.9852 0.9911 0.0942 0.6602 0.8366 0.2492 ] Network output: [ 8.501e-05 1 -5.129e-05 7.967e-07 -3.577e-07 0.9998 6.004e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001804 Epoch 9621 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008952 0.9968 0.9925 -1.833e-07 8.228e-08 -0.007111 -1.381e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003507 -0.003348 -0.006731 0.005422 0.9699 0.9743 0.006827 0.825 0.82 0.01633 ] Network output: [ 0.9999 0.0001252 0.0003876 -2.912e-06 1.307e-06 -0.0003455 -2.195e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2082 -0.03552 -0.1575 0.1829 0.9834 0.9932 0.2337 0.4296 0.8683 0.7089 ] Network output: [ -0.008944 1.003 1.008 -2.047e-07 9.189e-08 0.007418 -1.543e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006798 0.0006222 0.004376 0.00319 0.9889 0.9919 0.006931 0.8523 0.8921 0.01167 ] Network output: [ -0.0001983 0.00148 1 -9.141e-06 4.104e-06 0.9984 -6.889e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2219 0.1052 0.3487 0.1421 0.9849 0.9939 0.2227 0.4336 0.875 0.7027 ] Network output: [ 0.003148 -0.01497 0.9943 5.571e-06 -2.501e-06 1.014 4.198e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09837 0.1847 0.1975 0.9873 0.9919 0.1111 0.7363 0.8614 0.3051 ] Network output: [ -0.002953 0.01383 1.005 6.074e-06 -2.727e-06 0.9874 4.578e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09419 0.09225 0.165 0.1965 0.9852 0.9911 0.0942 0.6602 0.8366 0.2492 ] Network output: [ 8.498e-05 1 -5.128e-05 7.958e-07 -3.573e-07 0.9998 5.997e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001803 Epoch 9622 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008951 0.9968 0.9925 -1.832e-07 8.223e-08 -0.007111 -1.38e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003507 -0.003348 -0.006731 0.005422 0.9699 0.9743 0.006827 0.825 0.82 0.01633 ] Network output: [ 0.9999 0.000125 0.0003875 -2.909e-06 1.306e-06 -0.0003453 -2.192e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2082 -0.03552 -0.1575 0.1829 0.9834 0.9932 0.2337 0.4296 0.8683 0.7089 ] Network output: [ -0.008944 1.003 1.008 -2.045e-07 9.182e-08 0.007417 -1.541e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006798 0.0006223 0.004376 0.00319 0.9889 0.9919 0.006931 0.8523 0.8921 0.01167 ] Network output: [ -0.0001982 0.001479 1 -9.13e-06 4.099e-06 0.9984 -6.881e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2219 0.1052 0.3488 0.1421 0.9849 0.9939 0.2227 0.4336 0.875 0.7027 ] Network output: [ 0.003147 -0.01496 0.9943 5.564e-06 -2.498e-06 1.014 4.193e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09837 0.1847 0.1975 0.9873 0.9919 0.1111 0.7363 0.8614 0.3051 ] Network output: [ -0.002951 0.01383 1.005 6.067e-06 -2.724e-06 0.9874 4.572e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09419 0.09225 0.165 0.1965 0.9852 0.9911 0.09421 0.6602 0.8366 0.2492 ] Network output: [ 8.496e-05 1 -5.126e-05 7.948e-07 -3.568e-07 0.9998 5.99e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001802 Epoch 9623 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00895 0.9968 0.9925 -1.831e-07 8.219e-08 -0.00711 -1.38e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003507 -0.003348 -0.00673 0.005421 0.9699 0.9743 0.006828 0.825 0.82 0.01633 ] Network output: [ 0.9999 0.0001248 0.0003873 -2.905e-06 1.304e-06 -0.0003451 -2.19e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2083 -0.03552 -0.1575 0.1829 0.9834 0.9932 0.2337 0.4296 0.8683 0.7089 ] Network output: [ -0.008943 1.003 1.008 -2.044e-07 9.176e-08 0.007417 -1.54e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006799 0.0006223 0.004376 0.003189 0.9889 0.9919 0.006932 0.8523 0.8921 0.01166 ] Network output: [ -0.000198 0.001478 1 -9.119e-06 4.094e-06 0.9984 -6.873e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2219 0.1052 0.3488 0.1421 0.9849 0.9939 0.2227 0.4336 0.875 0.7027 ] Network output: [ 0.003145 -0.01495 0.9943 5.558e-06 -2.495e-06 1.014 4.188e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09837 0.1847 0.1974 0.9873 0.9919 0.1111 0.7363 0.8614 0.3051 ] Network output: [ -0.00295 0.01382 1.005 6.06e-06 -2.72e-06 0.9874 4.567e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09419 0.09226 0.165 0.1965 0.9852 0.9911 0.09421 0.6601 0.8366 0.2492 ] Network output: [ 8.494e-05 1 -5.124e-05 7.939e-07 -3.564e-07 0.9998 5.983e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001801 Epoch 9624 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008949 0.9968 0.9925 -1.83e-07 8.214e-08 -0.007109 -1.379e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003507 -0.003348 -0.006729 0.005421 0.9699 0.9743 0.006828 0.825 0.82 0.01633 ] Network output: [ 0.9999 0.0001247 0.0003871 -2.902e-06 1.303e-06 -0.0003448 -2.187e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2083 -0.03552 -0.1575 0.1828 0.9834 0.9932 0.2337 0.4296 0.8683 0.7089 ] Network output: [ -0.008942 1.003 1.008 -2.042e-07 9.169e-08 0.007416 -1.539e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006799 0.0006224 0.004375 0.003189 0.9889 0.9919 0.006932 0.8523 0.8921 0.01166 ] Network output: [ -0.0001978 0.001478 1 -9.109e-06 4.089e-06 0.9984 -6.865e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.222 0.1052 0.3488 0.1421 0.9849 0.9939 0.2227 0.4336 0.875 0.7026 ] Network output: [ 0.003144 -0.01495 0.9943 5.551e-06 -2.492e-06 1.014 4.183e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09838 0.1847 0.1974 0.9873 0.9919 0.1111 0.7363 0.8614 0.3051 ] Network output: [ -0.002949 0.01382 1.005 6.053e-06 -2.717e-06 0.9874 4.562e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0942 0.09226 0.165 0.1965 0.9852 0.9911 0.09421 0.6601 0.8366 0.2492 ] Network output: [ 8.491e-05 1 -5.123e-05 7.93e-07 -3.56e-07 0.9998 5.976e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00018 Epoch 9625 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008948 0.9968 0.9925 -1.829e-07 8.21e-08 -0.007109 -1.378e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003507 -0.003349 -0.006729 0.005421 0.9699 0.9743 0.006828 0.825 0.82 0.01632 ] Network output: [ 0.9999 0.0001245 0.0003869 -2.898e-06 1.301e-06 -0.0003446 -2.184e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2083 -0.03553 -0.1575 0.1828 0.9834 0.9932 0.2337 0.4296 0.8683 0.7089 ] Network output: [ -0.008941 1.003 1.008 -2.041e-07 9.162e-08 0.007416 -1.538e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0068 0.0006225 0.004375 0.003189 0.9889 0.9919 0.006932 0.8523 0.8921 0.01166 ] Network output: [ -0.0001977 0.001477 1 -9.098e-06 4.084e-06 0.9984 -6.856e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.222 0.1052 0.3488 0.1421 0.9849 0.9939 0.2227 0.4335 0.875 0.7026 ] Network output: [ 0.003142 -0.01494 0.9943 5.544e-06 -2.489e-06 1.014 4.178e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09838 0.1847 0.1974 0.9873 0.9919 0.1111 0.7363 0.8614 0.3051 ] Network output: [ -0.002947 0.01381 1.005 6.046e-06 -2.714e-06 0.9874 4.556e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0942 0.09226 0.165 0.1965 0.9852 0.9911 0.09421 0.6601 0.8366 0.2492 ] Network output: [ 8.489e-05 1 -5.121e-05 7.92e-07 -3.556e-07 0.9998 5.969e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001799 Epoch 9626 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008947 0.9968 0.9925 -1.828e-07 8.205e-08 -0.007108 -1.377e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003507 -0.003349 -0.006728 0.00542 0.9699 0.9743 0.006828 0.825 0.82 0.01632 ] Network output: [ 0.9999 0.0001243 0.0003868 -2.895e-06 1.3e-06 -0.0003444 -2.182e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2083 -0.03553 -0.1575 0.1828 0.9834 0.9932 0.2338 0.4296 0.8683 0.7089 ] Network output: [ -0.00894 1.003 1.008 -2.039e-07 9.156e-08 0.007415 -1.537e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0068 0.0006225 0.004375 0.003189 0.9889 0.9919 0.006933 0.8523 0.8921 0.01166 ] Network output: [ -0.0001975 0.001476 1 -9.087e-06 4.079e-06 0.9984 -6.848e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.222 0.1052 0.3488 0.1421 0.9849 0.9939 0.2227 0.4335 0.875 0.7026 ] Network output: [ 0.003141 -0.01493 0.9943 5.538e-06 -2.486e-06 1.014 4.173e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09838 0.1847 0.1974 0.9873 0.9919 0.1112 0.7363 0.8614 0.3051 ] Network output: [ -0.002946 0.0138 1.005 6.038e-06 -2.711e-06 0.9874 4.551e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0942 0.09226 0.165 0.1965 0.9852 0.9911 0.09421 0.6601 0.8366 0.2492 ] Network output: [ 8.486e-05 1 -5.119e-05 7.911e-07 -3.551e-07 0.9998 5.962e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001798 Epoch 9627 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008947 0.9968 0.9925 -1.827e-07 8.201e-08 -0.007108 -1.377e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003507 -0.003349 -0.006728 0.00542 0.9699 0.9743 0.006828 0.825 0.82 0.01632 ] Network output: [ 0.9999 0.0001242 0.0003866 -2.891e-06 1.298e-06 -0.0003442 -2.179e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2083 -0.03553 -0.1575 0.1828 0.9834 0.9932 0.2338 0.4296 0.8683 0.7089 ] Network output: [ -0.008939 1.003 1.008 -2.038e-07 9.149e-08 0.007414 -1.536e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0068 0.0006226 0.004375 0.003188 0.9889 0.9919 0.006933 0.8523 0.8921 0.01166 ] Network output: [ -0.0001974 0.001475 1 -9.076e-06 4.074e-06 0.9984 -6.84e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.222 0.1052 0.3488 0.1421 0.9849 0.9939 0.2227 0.4335 0.875 0.7026 ] Network output: [ 0.003139 -0.01493 0.9943 5.531e-06 -2.483e-06 1.014 4.168e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09839 0.1847 0.1974 0.9873 0.9919 0.1112 0.7362 0.8614 0.3051 ] Network output: [ -0.002945 0.0138 1.005 6.031e-06 -2.708e-06 0.9874 4.545e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0942 0.09226 0.165 0.1965 0.9852 0.9911 0.09422 0.6601 0.8366 0.2492 ] Network output: [ 8.484e-05 1 -5.118e-05 7.901e-07 -3.547e-07 0.9998 5.955e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001797 Epoch 9628 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008946 0.9968 0.9925 -1.826e-07 8.196e-08 -0.007107 -1.376e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003507 -0.003349 -0.006727 0.00542 0.9699 0.9743 0.006828 0.825 0.82 0.01632 ] Network output: [ 0.9999 0.000124 0.0003864 -2.888e-06 1.296e-06 -0.0003439 -2.176e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2083 -0.03553 -0.1575 0.1828 0.9834 0.9932 0.2338 0.4296 0.8683 0.7089 ] Network output: [ -0.008939 1.003 1.008 -2.036e-07 9.142e-08 0.007414 -1.535e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006801 0.0006227 0.004375 0.003188 0.9889 0.9919 0.006934 0.8523 0.8921 0.01166 ] Network output: [ -0.0001972 0.001475 1 -9.065e-06 4.07e-06 0.9984 -6.832e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.222 0.1052 0.3488 0.1421 0.9849 0.9939 0.2227 0.4335 0.875 0.7026 ] Network output: [ 0.003138 -0.01492 0.9943 5.525e-06 -2.48e-06 1.014 4.164e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09839 0.1847 0.1974 0.9873 0.9919 0.1112 0.7362 0.8614 0.3051 ] Network output: [ -0.002943 0.01379 1.005 6.024e-06 -2.705e-06 0.9874 4.54e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0942 0.09226 0.165 0.1965 0.9852 0.9911 0.09422 0.6601 0.8366 0.2492 ] Network output: [ 8.482e-05 1 -5.116e-05 7.892e-07 -3.543e-07 0.9998 5.948e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001796 Epoch 9629 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008945 0.9968 0.9925 -1.825e-07 8.191e-08 -0.007107 -1.375e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003507 -0.003349 -0.006726 0.005419 0.9699 0.9743 0.006829 0.825 0.82 0.01632 ] Network output: [ 0.9999 0.0001238 0.0003863 -2.884e-06 1.295e-06 -0.0003437 -2.174e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2083 -0.03553 -0.1575 0.1828 0.9834 0.9932 0.2338 0.4296 0.8683 0.7089 ] Network output: [ -0.008938 1.003 1.008 -2.035e-07 9.136e-08 0.007413 -1.534e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006801 0.0006228 0.004375 0.003188 0.9889 0.9919 0.006934 0.8523 0.8921 0.01166 ] Network output: [ -0.000197 0.001474 1 -9.054e-06 4.065e-06 0.9984 -6.823e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.222 0.1052 0.3488 0.1421 0.9849 0.9939 0.2227 0.4335 0.875 0.7026 ] Network output: [ 0.003136 -0.01491 0.9943 5.518e-06 -2.477e-06 1.014 4.159e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09839 0.1847 0.1974 0.9873 0.9919 0.1112 0.7362 0.8614 0.3051 ] Network output: [ -0.002942 0.01379 1.005 6.017e-06 -2.701e-06 0.9875 4.535e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09421 0.09227 0.165 0.1965 0.9852 0.9911 0.09422 0.6601 0.8366 0.2492 ] Network output: [ 8.479e-05 1 -5.115e-05 7.883e-07 -3.539e-07 0.9998 5.941e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001795 Epoch 9630 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008944 0.9968 0.9925 -1.824e-07 8.187e-08 -0.007106 -1.374e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003507 -0.003349 -0.006726 0.005419 0.9699 0.9743 0.006829 0.825 0.82 0.01632 ] Network output: [ 0.9999 0.0001236 0.0003861 -2.881e-06 1.293e-06 -0.0003435 -2.171e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2083 -0.03553 -0.1575 0.1828 0.9834 0.9932 0.2338 0.4296 0.8683 0.7089 ] Network output: [ -0.008937 1.003 1.008 -2.033e-07 9.129e-08 0.007412 -1.532e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006801 0.0006228 0.004375 0.003188 0.9889 0.9919 0.006934 0.8523 0.8921 0.01166 ] Network output: [ -0.0001969 0.001473 1 -9.043e-06 4.06e-06 0.9984 -6.815e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.222 0.1052 0.3488 0.1421 0.9849 0.9939 0.2228 0.4335 0.875 0.7026 ] Network output: [ 0.003135 -0.01491 0.9943 5.511e-06 -2.474e-06 1.014 4.154e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.0984 0.1847 0.1974 0.9873 0.9919 0.1112 0.7362 0.8614 0.3051 ] Network output: [ -0.002941 0.01378 1.005 6.01e-06 -2.698e-06 0.9875 4.529e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09421 0.09227 0.165 0.1965 0.9852 0.9911 0.09422 0.6601 0.8366 0.2492 ] Network output: [ 8.477e-05 1 -5.113e-05 7.873e-07 -3.535e-07 0.9998 5.934e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001794 Epoch 9631 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008943 0.9968 0.9925 -1.823e-07 8.182e-08 -0.007105 -1.374e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003507 -0.003349 -0.006725 0.005418 0.9699 0.9743 0.006829 0.825 0.82 0.01632 ] Network output: [ 0.9999 0.0001235 0.0003859 -2.877e-06 1.292e-06 -0.0003432 -2.169e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2083 -0.03553 -0.1574 0.1828 0.9834 0.9932 0.2338 0.4295 0.8683 0.7089 ] Network output: [ -0.008936 1.003 1.008 -2.032e-07 9.122e-08 0.007412 -1.531e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006802 0.0006229 0.004375 0.003187 0.9889 0.9919 0.006935 0.8523 0.8921 0.01166 ] Network output: [ -0.0001967 0.001473 1 -9.032e-06 4.055e-06 0.9984 -6.807e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.222 0.1052 0.3488 0.1421 0.9849 0.9939 0.2228 0.4335 0.875 0.7026 ] Network output: [ 0.003133 -0.0149 0.9943 5.505e-06 -2.471e-06 1.014 4.149e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.0984 0.1847 0.1974 0.9873 0.9919 0.1112 0.7362 0.8614 0.3051 ] Network output: [ -0.002939 0.01377 1.005 6.003e-06 -2.695e-06 0.9875 4.524e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09421 0.09227 0.165 0.1965 0.9852 0.9911 0.09422 0.6601 0.8366 0.2492 ] Network output: [ 8.475e-05 1 -5.111e-05 7.864e-07 -3.53e-07 0.9998 5.927e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001793 Epoch 9632 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008942 0.9968 0.9925 -1.822e-07 8.178e-08 -0.007105 -1.373e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003507 -0.003349 -0.006725 0.005418 0.9699 0.9743 0.006829 0.825 0.82 0.01632 ] Network output: [ 0.9999 0.0001233 0.0003857 -2.874e-06 1.29e-06 -0.000343 -2.166e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2083 -0.03553 -0.1574 0.1828 0.9834 0.9932 0.2338 0.4295 0.8683 0.7089 ] Network output: [ -0.008935 1.003 1.008 -2.03e-07 9.116e-08 0.007411 -1.53e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006802 0.000623 0.004375 0.003187 0.9889 0.9919 0.006935 0.8522 0.8921 0.01166 ] Network output: [ -0.0001966 0.001472 1 -9.022e-06 4.05e-06 0.9984 -6.799e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.222 0.1052 0.3488 0.1421 0.9849 0.9939 0.2228 0.4335 0.875 0.7026 ] Network output: [ 0.003132 -0.01489 0.9943 5.498e-06 -2.468e-06 1.014 4.144e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.0984 0.1847 0.1974 0.9873 0.9919 0.1112 0.7362 0.8614 0.3051 ] Network output: [ -0.002938 0.01377 1.005 5.996e-06 -2.692e-06 0.9875 4.519e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09421 0.09227 0.165 0.1965 0.9852 0.9911 0.09423 0.66 0.8366 0.2492 ] Network output: [ 8.472e-05 1 -5.11e-05 7.855e-07 -3.526e-07 0.9998 5.92e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001792 Epoch 9633 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008941 0.9968 0.9925 -1.821e-07 8.173e-08 -0.007104 -1.372e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003507 -0.003349 -0.006724 0.005418 0.9699 0.9743 0.006829 0.825 0.82 0.01632 ] Network output: [ 0.9999 0.0001231 0.0003856 -2.871e-06 1.289e-06 -0.0003428 -2.163e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2083 -0.03553 -0.1574 0.1828 0.9834 0.9932 0.2338 0.4295 0.8683 0.7089 ] Network output: [ -0.008934 1.003 1.008 -2.029e-07 9.109e-08 0.007411 -1.529e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006803 0.000623 0.004375 0.003187 0.9889 0.9919 0.006936 0.8522 0.8921 0.01166 ] Network output: [ -0.0001964 0.001471 1 -9.011e-06 4.045e-06 0.9984 -6.791e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.222 0.1053 0.3488 0.1421 0.9849 0.9939 0.2228 0.4335 0.875 0.7026 ] Network output: [ 0.00313 -0.01489 0.9943 5.492e-06 -2.465e-06 1.014 4.139e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09841 0.1847 0.1974 0.9873 0.9919 0.1112 0.7362 0.8614 0.3051 ] Network output: [ -0.002937 0.01376 1.005 5.989e-06 -2.689e-06 0.9875 4.514e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09421 0.09227 0.165 0.1965 0.9852 0.9911 0.09423 0.66 0.8366 0.2492 ] Network output: [ 8.47e-05 1 -5.108e-05 7.846e-07 -3.522e-07 0.9998 5.913e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001792 Epoch 9634 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00894 0.9968 0.9925 -1.819e-07 8.168e-08 -0.007104 -1.371e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003507 -0.003349 -0.006723 0.005417 0.9699 0.9743 0.006829 0.825 0.82 0.01632 ] Network output: [ 0.9999 0.000123 0.0003854 -2.867e-06 1.287e-06 -0.0003426 -2.161e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2083 -0.03553 -0.1574 0.1828 0.9834 0.9932 0.2338 0.4295 0.8683 0.7089 ] Network output: [ -0.008934 1.003 1.008 -2.028e-07 9.102e-08 0.00741 -1.528e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006803 0.0006231 0.004375 0.003187 0.9889 0.9919 0.006936 0.8522 0.8921 0.01166 ] Network output: [ -0.0001963 0.001471 1 -9e-06 4.04e-06 0.9984 -6.783e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.222 0.1053 0.3488 0.1421 0.9849 0.9939 0.2228 0.4335 0.875 0.7026 ] Network output: [ 0.003129 -0.01488 0.9943 5.485e-06 -2.463e-06 1.014 4.134e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09841 0.1847 0.1974 0.9873 0.9919 0.1112 0.7362 0.8613 0.3051 ] Network output: [ -0.002935 0.01376 1.005 5.982e-06 -2.686e-06 0.9875 4.508e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09422 0.09228 0.165 0.1965 0.9852 0.9911 0.09423 0.66 0.8365 0.2492 ] Network output: [ 8.467e-05 1 -5.106e-05 7.836e-07 -3.518e-07 0.9998 5.906e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001791 Epoch 9635 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008939 0.9968 0.9925 -1.818e-07 8.164e-08 -0.007103 -1.37e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003507 -0.003349 -0.006723 0.005417 0.9699 0.9743 0.00683 0.825 0.82 0.01632 ] Network output: [ 0.9999 0.0001228 0.0003852 -2.864e-06 1.286e-06 -0.0003423 -2.158e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2083 -0.03553 -0.1574 0.1828 0.9834 0.9932 0.2338 0.4295 0.8683 0.7089 ] Network output: [ -0.008933 1.003 1.008 -2.026e-07 9.096e-08 0.007409 -1.527e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006803 0.0006232 0.004374 0.003186 0.9889 0.9919 0.006936 0.8522 0.8921 0.01166 ] Network output: [ -0.0001961 0.00147 1 -8.989e-06 4.036e-06 0.9984 -6.775e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.222 0.1053 0.3488 0.1421 0.9849 0.9939 0.2228 0.4335 0.875 0.7026 ] Network output: [ 0.003128 -0.01487 0.9943 5.479e-06 -2.46e-06 1.014 4.129e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09841 0.1847 0.1974 0.9873 0.9919 0.1112 0.7362 0.8613 0.3051 ] Network output: [ -0.002934 0.01375 1.005 5.975e-06 -2.682e-06 0.9875 4.503e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09422 0.09228 0.165 0.1965 0.9852 0.9911 0.09423 0.66 0.8365 0.2492 ] Network output: [ 8.465e-05 1 -5.105e-05 7.827e-07 -3.514e-07 0.9998 5.899e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000179 Epoch 9636 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008938 0.9968 0.9925 -1.817e-07 8.159e-08 -0.007102 -1.37e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003507 -0.00335 -0.006722 0.005416 0.9699 0.9743 0.00683 0.825 0.82 0.01631 ] Network output: [ 0.9999 0.0001226 0.000385 -2.86e-06 1.284e-06 -0.0003421 -2.156e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2083 -0.03554 -0.1574 0.1828 0.9834 0.9932 0.2338 0.4295 0.8683 0.7089 ] Network output: [ -0.008932 1.003 1.008 -2.025e-07 9.089e-08 0.007409 -1.526e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006804 0.0006232 0.004374 0.003186 0.9889 0.9919 0.006937 0.8522 0.8921 0.01165 ] Network output: [ -0.0001959 0.001469 1 -8.978e-06 4.031e-06 0.9984 -6.766e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.222 0.1053 0.3488 0.1421 0.9849 0.9939 0.2228 0.4335 0.875 0.7026 ] Network output: [ 0.003126 -0.01487 0.9943 5.472e-06 -2.457e-06 1.014 4.124e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09842 0.1847 0.1974 0.9873 0.9919 0.1112 0.7361 0.8613 0.3051 ] Network output: [ -0.002932 0.01374 1.005 5.968e-06 -2.679e-06 0.9875 4.498e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09422 0.09228 0.165 0.1965 0.9852 0.9911 0.09423 0.66 0.8365 0.2492 ] Network output: [ 8.463e-05 1 -5.103e-05 7.818e-07 -3.51e-07 0.9998 5.892e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001789 Epoch 9637 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008937 0.9968 0.9925 -1.816e-07 8.155e-08 -0.007102 -1.369e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003507 -0.00335 -0.006722 0.005416 0.9699 0.9743 0.00683 0.825 0.82 0.01631 ] Network output: [ 0.9999 0.0001225 0.0003849 -2.857e-06 1.283e-06 -0.0003419 -2.153e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2083 -0.03554 -0.1574 0.1828 0.9834 0.9932 0.2338 0.4295 0.8683 0.7088 ] Network output: [ -0.008931 1.003 1.008 -2.023e-07 9.082e-08 0.007408 -1.525e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006804 0.0006233 0.004374 0.003186 0.9889 0.9919 0.006937 0.8522 0.8921 0.01165 ] Network output: [ -0.0001958 0.001468 1 -8.968e-06 4.026e-06 0.9984 -6.758e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.222 0.1053 0.3488 0.1421 0.9849 0.9939 0.2228 0.4335 0.875 0.7026 ] Network output: [ 0.003125 -0.01486 0.9943 5.466e-06 -2.454e-06 1.014 4.119e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09842 0.1847 0.1974 0.9873 0.9919 0.1112 0.7361 0.8613 0.3051 ] Network output: [ -0.002931 0.01374 1.005 5.961e-06 -2.676e-06 0.9875 4.492e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09422 0.09228 0.165 0.1965 0.9852 0.9911 0.09424 0.66 0.8365 0.2492 ] Network output: [ 8.46e-05 1 -5.102e-05 7.808e-07 -3.505e-07 0.9998 5.885e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001788 Epoch 9638 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008936 0.9968 0.9925 -1.815e-07 8.15e-08 -0.007101 -1.368e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003508 -0.00335 -0.006721 0.005416 0.9699 0.9743 0.00683 0.825 0.82 0.01631 ] Network output: [ 0.9999 0.0001223 0.0003847 -2.853e-06 1.281e-06 -0.0003417 -2.15e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2083 -0.03554 -0.1574 0.1828 0.9834 0.9932 0.2338 0.4295 0.8683 0.7088 ] Network output: [ -0.00893 1.003 1.008 -2.022e-07 9.076e-08 0.007407 -1.524e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006805 0.0006234 0.004374 0.003186 0.9889 0.9919 0.006938 0.8522 0.8921 0.01165 ] Network output: [ -0.0001956 0.001468 1 -8.957e-06 4.021e-06 0.9984 -6.75e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.222 0.1053 0.3488 0.1421 0.9849 0.9939 0.2228 0.4335 0.875 0.7026 ] Network output: [ 0.003123 -0.01485 0.9943 5.459e-06 -2.451e-06 1.014 4.114e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09842 0.1847 0.1974 0.9873 0.9919 0.1112 0.7361 0.8613 0.3051 ] Network output: [ -0.00293 0.01373 1.005 5.954e-06 -2.673e-06 0.9875 4.487e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09422 0.09228 0.165 0.1965 0.9852 0.9911 0.09424 0.66 0.8365 0.2492 ] Network output: [ 8.458e-05 1 -5.1e-05 7.799e-07 -3.501e-07 0.9998 5.878e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001787 Epoch 9639 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008935 0.9968 0.9925 -1.814e-07 8.145e-08 -0.007101 -1.367e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003508 -0.00335 -0.00672 0.005415 0.9699 0.9743 0.00683 0.825 0.82 0.01631 ] Network output: [ 0.9999 0.0001221 0.0003845 -2.85e-06 1.279e-06 -0.0003414 -2.148e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2083 -0.03554 -0.1574 0.1828 0.9834 0.9932 0.2338 0.4295 0.8683 0.7088 ] Network output: [ -0.008929 1.003 1.008 -2.02e-07 9.069e-08 0.007407 -1.522e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006805 0.0006234 0.004374 0.003186 0.9889 0.9919 0.006938 0.8522 0.8921 0.01165 ] Network output: [ -0.0001955 0.001467 1 -8.946e-06 4.016e-06 0.9984 -6.742e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2221 0.1053 0.3488 0.1421 0.9849 0.9939 0.2228 0.4335 0.875 0.7026 ] Network output: [ 0.003122 -0.01485 0.9943 5.453e-06 -2.448e-06 1.014 4.109e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09843 0.1847 0.1974 0.9873 0.9919 0.1112 0.7361 0.8613 0.3051 ] Network output: [ -0.002928 0.01372 1.005 5.947e-06 -2.67e-06 0.9875 4.482e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09422 0.09229 0.165 0.1965 0.9852 0.9911 0.09424 0.66 0.8365 0.2492 ] Network output: [ 8.456e-05 1 -5.099e-05 7.79e-07 -3.497e-07 0.9998 5.871e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001786 Epoch 9640 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008935 0.9968 0.9925 -1.813e-07 8.141e-08 -0.0071 -1.367e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003508 -0.00335 -0.00672 0.005415 0.9699 0.9743 0.006831 0.825 0.82 0.01631 ] Network output: [ 0.9999 0.0001219 0.0003844 -2.846e-06 1.278e-06 -0.0003412 -2.145e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2084 -0.03554 -0.1574 0.1828 0.9834 0.9932 0.2339 0.4295 0.8683 0.7088 ] Network output: [ -0.008929 1.003 1.008 -2.019e-07 9.062e-08 0.007406 -1.521e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006805 0.0006235 0.004374 0.003185 0.9889 0.9919 0.006938 0.8522 0.8921 0.01165 ] Network output: [ -0.0001953 0.001466 1 -8.935e-06 4.011e-06 0.9984 -6.734e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2221 0.1053 0.3488 0.1421 0.9849 0.9939 0.2228 0.4335 0.875 0.7026 ] Network output: [ 0.00312 -0.01484 0.9943 5.446e-06 -2.445e-06 1.014 4.104e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09843 0.1847 0.1974 0.9873 0.9919 0.1112 0.7361 0.8613 0.3051 ] Network output: [ -0.002927 0.01372 1.005 5.94e-06 -2.667e-06 0.9875 4.477e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09423 0.09229 0.165 0.1965 0.9852 0.9911 0.09424 0.66 0.8365 0.2492 ] Network output: [ 8.453e-05 1 -5.097e-05 7.781e-07 -3.493e-07 0.9998 5.864e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001785 Epoch 9641 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008934 0.9968 0.9925 -1.812e-07 8.136e-08 -0.0071 -1.366e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003508 -0.00335 -0.006719 0.005414 0.9699 0.9743 0.006831 0.8249 0.82 0.01631 ] Network output: [ 0.9999 0.0001218 0.0003842 -2.843e-06 1.276e-06 -0.000341 -2.143e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2084 -0.03554 -0.1574 0.1828 0.9834 0.9932 0.2339 0.4295 0.8683 0.7088 ] Network output: [ -0.008928 1.003 1.008 -2.017e-07 9.056e-08 0.007406 -1.52e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006806 0.0006236 0.004374 0.003185 0.9889 0.9919 0.006939 0.8522 0.8921 0.01165 ] Network output: [ -0.0001952 0.001466 1 -8.925e-06 4.007e-06 0.9984 -6.726e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2221 0.1053 0.3488 0.1421 0.9849 0.9939 0.2228 0.4335 0.875 0.7026 ] Network output: [ 0.003119 -0.01483 0.9943 5.44e-06 -2.442e-06 1.014 4.099e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09843 0.1847 0.1974 0.9873 0.9919 0.1112 0.7361 0.8613 0.3051 ] Network output: [ -0.002926 0.01371 1.005 5.933e-06 -2.664e-06 0.9875 4.471e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09423 0.09229 0.165 0.1965 0.9852 0.9911 0.09424 0.6599 0.8365 0.2492 ] Network output: [ 8.451e-05 1 -5.096e-05 7.771e-07 -3.489e-07 0.9998 5.857e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001784 Epoch 9642 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008933 0.9968 0.9925 -1.811e-07 8.132e-08 -0.007099 -1.365e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003508 -0.00335 -0.006719 0.005414 0.9699 0.9743 0.006831 0.8249 0.82 0.01631 ] Network output: [ 0.9999 0.0001216 0.000384 -2.84e-06 1.275e-06 -0.0003408 -2.14e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2084 -0.03554 -0.1573 0.1828 0.9834 0.9932 0.2339 0.4295 0.8683 0.7088 ] Network output: [ -0.008927 1.003 1.008 -2.016e-07 9.049e-08 0.007405 -1.519e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006806 0.0006236 0.004374 0.003185 0.9889 0.9919 0.006939 0.8522 0.892 0.01165 ] Network output: [ -0.000195 0.001465 1 -8.914e-06 4.002e-06 0.9984 -6.718e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2221 0.1053 0.3489 0.1421 0.9849 0.9939 0.2228 0.4335 0.875 0.7026 ] Network output: [ 0.003117 -0.01483 0.9943 5.433e-06 -2.439e-06 1.014 4.095e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09844 0.1847 0.1974 0.9873 0.9919 0.1112 0.7361 0.8613 0.3051 ] Network output: [ -0.002924 0.01371 1.005 5.926e-06 -2.66e-06 0.9875 4.466e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09423 0.09229 0.165 0.1965 0.9852 0.9911 0.09424 0.6599 0.8365 0.2492 ] Network output: [ 8.449e-05 1 -5.094e-05 7.762e-07 -3.485e-07 0.9998 5.85e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001783 Epoch 9643 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008932 0.9968 0.9925 -1.81e-07 8.127e-08 -0.007098 -1.364e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003508 -0.00335 -0.006718 0.005414 0.9699 0.9743 0.006831 0.8249 0.8199 0.01631 ] Network output: [ 0.9999 0.0001214 0.0003838 -2.836e-06 1.273e-06 -0.0003405 -2.137e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2084 -0.03554 -0.1573 0.1828 0.9834 0.9932 0.2339 0.4295 0.8683 0.7088 ] Network output: [ -0.008926 1.003 1.008 -2.014e-07 9.042e-08 0.007404 -1.518e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006806 0.0006237 0.004374 0.003185 0.9889 0.9919 0.006939 0.8522 0.892 0.01165 ] Network output: [ -0.0001948 0.001464 1 -8.903e-06 3.997e-06 0.9984 -6.71e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2221 0.1053 0.3489 0.1421 0.9849 0.9939 0.2228 0.4335 0.875 0.7026 ] Network output: [ 0.003116 -0.01482 0.9943 5.427e-06 -2.436e-06 1.014 4.09e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09844 0.1847 0.1974 0.9873 0.9919 0.1112 0.7361 0.8613 0.3051 ] Network output: [ -0.002923 0.0137 1.005 5.919e-06 -2.657e-06 0.9875 4.461e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09423 0.09229 0.165 0.1965 0.9852 0.9911 0.09425 0.6599 0.8365 0.2492 ] Network output: [ 8.446e-05 1 -5.093e-05 7.753e-07 -3.481e-07 0.9998 5.843e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001782 Epoch 9644 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008931 0.9968 0.9925 -1.809e-07 8.122e-08 -0.007098 -1.364e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003508 -0.00335 -0.006717 0.005413 0.9699 0.9743 0.006831 0.8249 0.8199 0.01631 ] Network output: [ 0.9999 0.0001213 0.0003837 -2.833e-06 1.272e-06 -0.0003403 -2.135e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2084 -0.03554 -0.1573 0.1828 0.9834 0.9932 0.2339 0.4295 0.8683 0.7088 ] Network output: [ -0.008925 1.003 1.008 -2.013e-07 9.036e-08 0.007404 -1.517e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006807 0.0006238 0.004374 0.003184 0.9889 0.9919 0.00694 0.8522 0.892 0.01165 ] Network output: [ -0.0001947 0.001464 1 -8.893e-06 3.992e-06 0.9984 -6.702e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2221 0.1053 0.3489 0.1421 0.9849 0.9939 0.2228 0.4335 0.875 0.7026 ] Network output: [ 0.003114 -0.01481 0.9943 5.42e-06 -2.433e-06 1.014 4.085e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09844 0.1847 0.1974 0.9873 0.9919 0.1112 0.7361 0.8613 0.3051 ] Network output: [ -0.002922 0.01369 1.005 5.912e-06 -2.654e-06 0.9875 4.456e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09423 0.0923 0.165 0.1965 0.9852 0.9911 0.09425 0.6599 0.8365 0.2492 ] Network output: [ 8.444e-05 1 -5.091e-05 7.744e-07 -3.477e-07 0.9998 5.836e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001781 Epoch 9645 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00893 0.9968 0.9925 -1.808e-07 8.118e-08 -0.007097 -1.363e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003508 -0.00335 -0.006717 0.005413 0.9699 0.9743 0.006831 0.8249 0.8199 0.01631 ] Network output: [ 0.9999 0.0001211 0.0003835 -2.829e-06 1.27e-06 -0.0003401 -2.132e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2084 -0.03554 -0.1573 0.1828 0.9834 0.9932 0.2339 0.4295 0.8683 0.7088 ] Network output: [ -0.008924 1.003 1.008 -2.011e-07 9.029e-08 0.007403 -1.516e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006807 0.0006238 0.004374 0.003184 0.9889 0.9919 0.00694 0.8522 0.892 0.01165 ] Network output: [ -0.0001945 0.001463 1 -8.882e-06 3.987e-06 0.9984 -6.694e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2221 0.1053 0.3489 0.1421 0.9849 0.9939 0.2229 0.4335 0.875 0.7026 ] Network output: [ 0.003113 -0.01481 0.9943 5.414e-06 -2.43e-06 1.014 4.08e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09845 0.1847 0.1974 0.9873 0.9919 0.1112 0.736 0.8613 0.3051 ] Network output: [ -0.00292 0.01369 1.005 5.905e-06 -2.651e-06 0.9875 4.45e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09424 0.0923 0.165 0.1965 0.9852 0.9911 0.09425 0.6599 0.8365 0.2492 ] Network output: [ 8.441e-05 1 -5.09e-05 7.735e-07 -3.472e-07 0.9998 5.829e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000178 Epoch 9646 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008929 0.9968 0.9925 -1.807e-07 8.113e-08 -0.007097 -1.362e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003508 -0.00335 -0.006716 0.005412 0.9699 0.9743 0.006832 0.8249 0.8199 0.01631 ] Network output: [ 0.9999 0.0001209 0.0003833 -2.826e-06 1.269e-06 -0.0003399 -2.13e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2084 -0.03555 -0.1573 0.1828 0.9834 0.9932 0.2339 0.4295 0.8683 0.7088 ] Network output: [ -0.008924 1.003 1.008 -2.01e-07 9.022e-08 0.007402 -1.515e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006808 0.0006239 0.004373 0.003184 0.9889 0.9919 0.006941 0.8522 0.892 0.01165 ] Network output: [ -0.0001944 0.001462 1 -8.871e-06 3.983e-06 0.9984 -6.686e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2221 0.1053 0.3489 0.1421 0.9849 0.9939 0.2229 0.4334 0.875 0.7026 ] Network output: [ 0.003111 -0.0148 0.9943 5.407e-06 -2.427e-06 1.014 4.075e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09845 0.1847 0.1974 0.9873 0.9919 0.1112 0.736 0.8613 0.3051 ] Network output: [ -0.002919 0.01368 1.005 5.898e-06 -2.648e-06 0.9875 4.445e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09424 0.0923 0.165 0.1965 0.9852 0.9911 0.09425 0.6599 0.8365 0.2492 ] Network output: [ 8.439e-05 1 -5.088e-05 7.726e-07 -3.468e-07 0.9998 5.822e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001779 Epoch 9647 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008928 0.9968 0.9925 -1.806e-07 8.108e-08 -0.007096 -1.361e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003508 -0.00335 -0.006716 0.005412 0.9699 0.9743 0.006832 0.8249 0.8199 0.0163 ] Network output: [ 0.9999 0.0001208 0.0003832 -2.823e-06 1.267e-06 -0.0003397 -2.127e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2084 -0.03555 -0.1573 0.1828 0.9834 0.9932 0.2339 0.4295 0.8683 0.7088 ] Network output: [ -0.008923 1.003 1.008 -2.008e-07 9.016e-08 0.007402 -1.513e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006808 0.000624 0.004373 0.003184 0.9889 0.9919 0.006941 0.8522 0.892 0.01165 ] Network output: [ -0.0001942 0.001461 1 -8.861e-06 3.978e-06 0.9984 -6.678e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2221 0.1053 0.3489 0.1421 0.9849 0.9939 0.2229 0.4334 0.875 0.7026 ] Network output: [ 0.00311 -0.01479 0.9943 5.401e-06 -2.425e-06 1.014 4.07e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1111 0.09845 0.1847 0.1974 0.9873 0.9919 0.1112 0.736 0.8613 0.3051 ] Network output: [ -0.002918 0.01368 1.005 5.891e-06 -2.645e-06 0.9875 4.44e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09424 0.0923 0.165 0.1965 0.9852 0.9911 0.09425 0.6599 0.8365 0.2492 ] Network output: [ 8.437e-05 1 -5.087e-05 7.716e-07 -3.464e-07 0.9998 5.815e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001778 Epoch 9648 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008927 0.9968 0.9925 -1.805e-07 8.104e-08 -0.007095 -1.36e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003508 -0.003351 -0.006715 0.005412 0.9699 0.9743 0.006832 0.8249 0.8199 0.0163 ] Network output: [ 0.9999 0.0001206 0.000383 -2.819e-06 1.266e-06 -0.0003394 -2.125e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2084 -0.03555 -0.1573 0.1828 0.9834 0.9932 0.2339 0.4295 0.8683 0.7088 ] Network output: [ -0.008922 1.003 1.008 -2.007e-07 9.009e-08 0.007401 -1.512e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006808 0.0006241 0.004373 0.003183 0.9889 0.9919 0.006941 0.8522 0.892 0.01165 ] Network output: [ -0.0001941 0.001461 1 -8.85e-06 3.973e-06 0.9984 -6.67e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2221 0.1053 0.3489 0.1421 0.9849 0.9939 0.2229 0.4334 0.875 0.7025 ] Network output: [ 0.003108 -0.01479 0.9943 5.394e-06 -2.422e-06 1.014 4.065e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09846 0.1847 0.1974 0.9873 0.9919 0.1112 0.736 0.8613 0.3051 ] Network output: [ -0.002916 0.01367 1.005 5.884e-06 -2.642e-06 0.9875 4.435e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09424 0.0923 0.165 0.1965 0.9852 0.9911 0.09426 0.6599 0.8365 0.2492 ] Network output: [ 8.434e-05 1 -5.085e-05 7.707e-07 -3.46e-07 0.9998 5.808e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001777 Epoch 9649 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008926 0.9968 0.9925 -1.804e-07 8.099e-08 -0.007095 -1.36e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003508 -0.003351 -0.006715 0.005411 0.9699 0.9743 0.006832 0.8249 0.8199 0.0163 ] Network output: [ 0.9999 0.0001204 0.0003828 -2.816e-06 1.264e-06 -0.0003392 -2.122e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2084 -0.03555 -0.1573 0.1828 0.9834 0.9932 0.2339 0.4295 0.8683 0.7088 ] Network output: [ -0.008921 1.003 1.008 -2.005e-07 9.002e-08 0.007401 -1.511e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006809 0.0006241 0.004373 0.003183 0.9889 0.9919 0.006942 0.8522 0.892 0.01164 ] Network output: [ -0.0001939 0.00146 1 -8.839e-06 3.968e-06 0.9984 -6.662e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2221 0.1053 0.3489 0.1421 0.9849 0.9939 0.2229 0.4334 0.875 0.7025 ] Network output: [ 0.003107 -0.01478 0.9943 5.388e-06 -2.419e-06 1.014 4.06e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09846 0.1847 0.1974 0.9873 0.9919 0.1112 0.736 0.8613 0.3051 ] Network output: [ -0.002915 0.01366 1.005 5.877e-06 -2.639e-06 0.9875 4.429e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09424 0.09231 0.165 0.1965 0.9852 0.9911 0.09426 0.6599 0.8365 0.2492 ] Network output: [ 8.432e-05 1 -5.084e-05 7.698e-07 -3.456e-07 0.9998 5.802e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001776 Epoch 9650 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008925 0.9968 0.9925 -1.803e-07 8.095e-08 -0.007094 -1.359e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003508 -0.003351 -0.006714 0.005411 0.9699 0.9743 0.006832 0.8249 0.8199 0.0163 ] Network output: [ 0.9999 0.0001203 0.0003826 -2.812e-06 1.263e-06 -0.000339 -2.12e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2084 -0.03555 -0.1573 0.1828 0.9834 0.9932 0.2339 0.4295 0.8683 0.7088 ] Network output: [ -0.00892 1.003 1.008 -2.004e-07 8.996e-08 0.0074 -1.51e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006809 0.0006242 0.004373 0.003183 0.9889 0.9919 0.006942 0.8522 0.892 0.01164 ] Network output: [ -0.0001937 0.001459 1 -8.829e-06 3.964e-06 0.9984 -6.654e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2221 0.1053 0.3489 0.1421 0.9849 0.9939 0.2229 0.4334 0.875 0.7025 ] Network output: [ 0.003106 -0.01477 0.9943 5.381e-06 -2.416e-06 1.014 4.056e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09846 0.1847 0.1974 0.9873 0.9919 0.1112 0.736 0.8613 0.3051 ] Network output: [ -0.002914 0.01366 1.005 5.871e-06 -2.636e-06 0.9875 4.424e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09425 0.09231 0.165 0.1965 0.9852 0.9911 0.09426 0.6598 0.8365 0.2492 ] Network output: [ 8.43e-05 1 -5.082e-05 7.689e-07 -3.452e-07 0.9998 5.795e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001775 Epoch 9651 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008924 0.9968 0.9925 -1.802e-07 8.09e-08 -0.007094 -1.358e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003508 -0.003351 -0.006713 0.00541 0.9699 0.9743 0.006832 0.8249 0.8199 0.0163 ] Network output: [ 0.9999 0.0001201 0.0003825 -2.809e-06 1.261e-06 -0.0003388 -2.117e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2084 -0.03555 -0.1573 0.1828 0.9834 0.9932 0.2339 0.4295 0.8683 0.7088 ] Network output: [ -0.008919 1.003 1.008 -2.002e-07 8.989e-08 0.007399 -1.509e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006809 0.0006243 0.004373 0.003183 0.9889 0.9919 0.006943 0.8522 0.892 0.01164 ] Network output: [ -0.0001936 0.001459 1 -8.818e-06 3.959e-06 0.9984 -6.646e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2221 0.1053 0.3489 0.1421 0.9849 0.9939 0.2229 0.4334 0.875 0.7025 ] Network output: [ 0.003104 -0.01477 0.9943 5.375e-06 -2.413e-06 1.014 4.051e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09847 0.1847 0.1974 0.9873 0.9919 0.1112 0.736 0.8613 0.3051 ] Network output: [ -0.002912 0.01365 1.005 5.864e-06 -2.632e-06 0.9875 4.419e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09425 0.09231 0.165 0.1965 0.9852 0.9911 0.09426 0.6598 0.8365 0.2492 ] Network output: [ 8.427e-05 1 -5.081e-05 7.68e-07 -3.448e-07 0.9998 5.788e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001774 Epoch 9652 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008924 0.9968 0.9925 -1.801e-07 8.085e-08 -0.007093 -1.357e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003508 -0.003351 -0.006713 0.00541 0.9699 0.9743 0.006833 0.8249 0.8199 0.0163 ] Network output: [ 0.9999 0.0001199 0.0003823 -2.806e-06 1.26e-06 -0.0003385 -2.114e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2084 -0.03555 -0.1573 0.1827 0.9834 0.9932 0.2339 0.4294 0.8683 0.7088 ] Network output: [ -0.008919 1.003 1.008 -2.001e-07 8.982e-08 0.007399 -1.508e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00681 0.0006243 0.004373 0.003182 0.9889 0.9919 0.006943 0.8521 0.892 0.01164 ] Network output: [ -0.0001934 0.001458 1 -8.808e-06 3.954e-06 0.9984 -6.638e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2221 0.1053 0.3489 0.1421 0.9849 0.9939 0.2229 0.4334 0.875 0.7025 ] Network output: [ 0.003103 -0.01476 0.9943 5.369e-06 -2.41e-06 1.014 4.046e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09847 0.1847 0.1974 0.9873 0.9919 0.1112 0.736 0.8613 0.3051 ] Network output: [ -0.002911 0.01365 1.005 5.857e-06 -2.629e-06 0.9875 4.414e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09425 0.09231 0.165 0.1965 0.9852 0.9911 0.09426 0.6598 0.8365 0.2492 ] Network output: [ 8.425e-05 1 -5.079e-05 7.671e-07 -3.444e-07 0.9998 5.781e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001773 Epoch 9653 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008923 0.9968 0.9925 -1.8e-07 8.081e-08 -0.007093 -1.357e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003508 -0.003351 -0.006712 0.00541 0.9699 0.9743 0.006833 0.8249 0.8199 0.0163 ] Network output: [ 0.9999 0.0001198 0.0003821 -2.802e-06 1.258e-06 -0.0003383 -2.112e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2084 -0.03555 -0.1573 0.1827 0.9834 0.9932 0.2339 0.4294 0.8683 0.7088 ] Network output: [ -0.008918 1.003 1.008 -1.999e-07 8.976e-08 0.007398 -1.507e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00681 0.0006244 0.004373 0.003182 0.9889 0.9919 0.006943 0.8521 0.892 0.01164 ] Network output: [ -0.0001933 0.001457 1 -8.797e-06 3.949e-06 0.9984 -6.63e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2222 0.1053 0.3489 0.1421 0.9849 0.9939 0.2229 0.4334 0.875 0.7025 ] Network output: [ 0.003101 -0.01475 0.9943 5.362e-06 -2.407e-06 1.014 4.041e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09847 0.1847 0.1974 0.9873 0.9919 0.1112 0.736 0.8613 0.3051 ] Network output: [ -0.00291 0.01364 1.005 5.85e-06 -2.626e-06 0.9875 4.409e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09425 0.09231 0.165 0.1965 0.9852 0.9911 0.09427 0.6598 0.8365 0.2492 ] Network output: [ 8.423e-05 1 -5.078e-05 7.662e-07 -3.44e-07 0.9998 5.774e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001772 Epoch 9654 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008922 0.9968 0.9925 -1.799e-07 8.076e-08 -0.007092 -1.356e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003508 -0.003351 -0.006712 0.005409 0.9699 0.9743 0.006833 0.8249 0.8199 0.0163 ] Network output: [ 0.9999 0.0001196 0.000382 -2.799e-06 1.257e-06 -0.0003381 -2.109e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2084 -0.03555 -0.1572 0.1827 0.9834 0.9932 0.234 0.4294 0.8683 0.7088 ] Network output: [ -0.008917 1.003 1.008 -1.998e-07 8.969e-08 0.007398 -1.506e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006811 0.0006245 0.004373 0.003182 0.9889 0.9919 0.006944 0.8521 0.892 0.01164 ] Network output: [ -0.0001931 0.001457 1 -8.786e-06 3.945e-06 0.9984 -6.622e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2222 0.1053 0.3489 0.1421 0.9849 0.9939 0.2229 0.4334 0.875 0.7025 ] Network output: [ 0.0031 -0.01475 0.9943 5.356e-06 -2.404e-06 1.014 4.036e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09848 0.1847 0.1974 0.9873 0.9919 0.1113 0.7359 0.8613 0.3051 ] Network output: [ -0.002908 0.01363 1.005 5.843e-06 -2.623e-06 0.9875 4.403e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09425 0.09231 0.165 0.1965 0.9852 0.9911 0.09427 0.6598 0.8365 0.2492 ] Network output: [ 8.42e-05 1 -5.076e-05 7.653e-07 -3.436e-07 0.9998 5.767e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001771 Epoch 9655 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008921 0.9968 0.9925 -1.798e-07 8.071e-08 -0.007091 -1.355e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003508 -0.003351 -0.006711 0.005409 0.9699 0.9743 0.006833 0.8249 0.8199 0.0163 ] Network output: [ 0.9999 0.0001194 0.0003818 -2.795e-06 1.255e-06 -0.0003379 -2.107e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2084 -0.03555 -0.1572 0.1827 0.9834 0.9932 0.234 0.4294 0.8683 0.7088 ] Network output: [ -0.008916 1.003 1.008 -1.996e-07 8.962e-08 0.007397 -1.505e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006811 0.0006245 0.004373 0.003182 0.9889 0.9919 0.006944 0.8521 0.892 0.01164 ] Network output: [ -0.000193 0.001456 1 -8.776e-06 3.94e-06 0.9984 -6.614e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2222 0.1053 0.3489 0.1421 0.9849 0.9939 0.2229 0.4334 0.875 0.7025 ] Network output: [ 0.003098 -0.01474 0.9943 5.349e-06 -2.402e-06 1.014 4.031e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09848 0.1847 0.1974 0.9873 0.9919 0.1113 0.7359 0.8613 0.3051 ] Network output: [ -0.002907 0.01363 1.005 5.836e-06 -2.62e-06 0.9875 4.398e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09426 0.09232 0.165 0.1965 0.9852 0.9911 0.09427 0.6598 0.8365 0.2492 ] Network output: [ 8.418e-05 1 -5.075e-05 7.644e-07 -3.431e-07 0.9998 5.76e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000177 Epoch 9656 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00892 0.9968 0.9925 -1.797e-07 8.067e-08 -0.007091 -1.354e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003509 -0.003351 -0.00671 0.005409 0.9699 0.9743 0.006833 0.8249 0.8199 0.0163 ] Network output: [ 0.9999 0.0001192 0.0003816 -2.792e-06 1.253e-06 -0.0003377 -2.104e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2084 -0.03555 -0.1572 0.1827 0.9834 0.9932 0.234 0.4294 0.8683 0.7088 ] Network output: [ -0.008915 1.003 1.008 -1.995e-07 8.956e-08 0.007396 -1.503e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006811 0.0006246 0.004372 0.003182 0.9889 0.9919 0.006944 0.8521 0.892 0.01164 ] Network output: [ -0.0001928 0.001455 1 -8.765e-06 3.935e-06 0.9984 -6.606e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2222 0.1054 0.3489 0.1421 0.9849 0.9939 0.2229 0.4334 0.875 0.7025 ] Network output: [ 0.003097 -0.01474 0.9943 5.343e-06 -2.399e-06 1.014 4.027e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09848 0.1847 0.1974 0.9873 0.9919 0.1113 0.7359 0.8613 0.3051 ] Network output: [ -0.002906 0.01362 1.005 5.829e-06 -2.617e-06 0.9875 4.393e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09426 0.09232 0.165 0.1965 0.9852 0.9911 0.09427 0.6598 0.8365 0.2492 ] Network output: [ 8.416e-05 1 -5.074e-05 7.634e-07 -3.427e-07 0.9998 5.754e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000177 Epoch 9657 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008919 0.9968 0.9925 -1.796e-07 8.062e-08 -0.00709 -1.353e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003509 -0.003351 -0.00671 0.005408 0.9699 0.9743 0.006833 0.8249 0.8199 0.01629 ] Network output: [ 0.9999 0.0001191 0.0003814 -2.789e-06 1.252e-06 -0.0003374 -2.102e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2085 -0.03556 -0.1572 0.1827 0.9834 0.9932 0.234 0.4294 0.8683 0.7088 ] Network output: [ -0.008914 1.003 1.008 -1.993e-07 8.949e-08 0.007396 -1.502e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006812 0.0006247 0.004372 0.003181 0.9889 0.9919 0.006945 0.8521 0.892 0.01164 ] Network output: [ -0.0001926 0.001454 1 -8.755e-06 3.93e-06 0.9984 -6.598e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2222 0.1054 0.3489 0.1421 0.9849 0.9939 0.2229 0.4334 0.875 0.7025 ] Network output: [ 0.003095 -0.01473 0.9943 5.337e-06 -2.396e-06 1.014 4.022e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09849 0.1847 0.1974 0.9873 0.9919 0.1113 0.7359 0.8613 0.3051 ] Network output: [ -0.002904 0.01361 1.005 5.822e-06 -2.614e-06 0.9875 4.388e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09426 0.09232 0.165 0.1965 0.9852 0.9911 0.09427 0.6598 0.8365 0.2492 ] Network output: [ 8.413e-05 1 -5.072e-05 7.625e-07 -3.423e-07 0.9998 5.747e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001769 Epoch 9658 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008918 0.9968 0.9925 -1.795e-07 8.057e-08 -0.00709 -1.353e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003509 -0.003351 -0.006709 0.005408 0.9699 0.9743 0.006834 0.8249 0.8199 0.01629 ] Network output: [ 0.9999 0.0001189 0.0003813 -2.785e-06 1.25e-06 -0.0003372 -2.099e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2085 -0.03556 -0.1572 0.1827 0.9834 0.9932 0.234 0.4294 0.8682 0.7088 ] Network output: [ -0.008914 1.003 1.008 -1.992e-07 8.943e-08 0.007395 -1.501e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006812 0.0006247 0.004372 0.003181 0.9889 0.9919 0.006945 0.8521 0.892 0.01164 ] Network output: [ -0.0001925 0.001454 1 -8.744e-06 3.926e-06 0.9984 -6.59e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2222 0.1054 0.3489 0.1421 0.9849 0.9939 0.2229 0.4334 0.875 0.7025 ] Network output: [ 0.003094 -0.01472 0.9943 5.33e-06 -2.393e-06 1.014 4.017e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09849 0.1847 0.1974 0.9873 0.9919 0.1113 0.7359 0.8613 0.3051 ] Network output: [ -0.002903 0.01361 1.005 5.816e-06 -2.611e-06 0.9875 4.383e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09426 0.09232 0.165 0.1965 0.9852 0.9911 0.09428 0.6598 0.8365 0.2492 ] Network output: [ 8.411e-05 1 -5.071e-05 7.616e-07 -3.419e-07 0.9998 5.74e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001768 Epoch 9659 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008917 0.9968 0.9925 -1.794e-07 8.053e-08 -0.007089 -1.352e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003509 -0.003351 -0.006709 0.005407 0.9699 0.9743 0.006834 0.8249 0.8199 0.01629 ] Network output: [ 0.9999 0.0001187 0.0003811 -2.782e-06 1.249e-06 -0.000337 -2.097e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2085 -0.03556 -0.1572 0.1827 0.9834 0.9932 0.234 0.4294 0.8682 0.7088 ] Network output: [ -0.008913 1.003 1.008 -1.99e-07 8.936e-08 0.007395 -1.5e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006812 0.0006248 0.004372 0.003181 0.9889 0.9919 0.006946 0.8521 0.892 0.01164 ] Network output: [ -0.0001923 0.001453 1 -8.734e-06 3.921e-06 0.9984 -6.582e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2222 0.1054 0.3489 0.1421 0.9849 0.9939 0.223 0.4334 0.875 0.7025 ] Network output: [ 0.003092 -0.01472 0.9943 5.324e-06 -2.39e-06 1.014 4.012e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09849 0.1847 0.1974 0.9873 0.9919 0.1113 0.7359 0.8613 0.3051 ] Network output: [ -0.002902 0.0136 1.005 5.809e-06 -2.608e-06 0.9875 4.378e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09426 0.09232 0.165 0.1965 0.9852 0.9911 0.09428 0.6597 0.8365 0.2492 ] Network output: [ 8.409e-05 1 -5.069e-05 7.607e-07 -3.415e-07 0.9998 5.733e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001767 Epoch 9660 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008916 0.9968 0.9925 -1.793e-07 8.048e-08 -0.007088 -1.351e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003509 -0.003352 -0.006708 0.005407 0.9699 0.9743 0.006834 0.8249 0.8199 0.01629 ] Network output: [ 0.9999 0.0001186 0.0003809 -2.779e-06 1.247e-06 -0.0003368 -2.094e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2085 -0.03556 -0.1572 0.1827 0.9834 0.9932 0.234 0.4294 0.8682 0.7088 ] Network output: [ -0.008912 1.003 1.008 -1.989e-07 8.929e-08 0.007394 -1.499e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006813 0.0006249 0.004372 0.003181 0.9889 0.9919 0.006946 0.8521 0.892 0.01164 ] Network output: [ -0.0001922 0.001452 1 -8.723e-06 3.916e-06 0.9984 -6.574e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2222 0.1054 0.3489 0.1421 0.9849 0.9939 0.223 0.4334 0.875 0.7025 ] Network output: [ 0.003091 -0.01471 0.9943 5.317e-06 -2.387e-06 1.014 4.007e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.0985 0.1847 0.1974 0.9873 0.9919 0.1113 0.7359 0.8613 0.3051 ] Network output: [ -0.0029 0.0136 1.005 5.802e-06 -2.605e-06 0.9876 4.372e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09427 0.09233 0.165 0.1965 0.9852 0.9911 0.09428 0.6597 0.8365 0.2492 ] Network output: [ 8.407e-05 1 -5.068e-05 7.598e-07 -3.411e-07 0.9998 5.726e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001766 Epoch 9661 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008915 0.9968 0.9925 -1.792e-07 8.043e-08 -0.007088 -1.35e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003509 -0.003352 -0.006707 0.005407 0.9699 0.9743 0.006834 0.8249 0.8199 0.01629 ] Network output: [ 0.9999 0.0001184 0.0003808 -2.775e-06 1.246e-06 -0.0003366 -2.092e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2085 -0.03556 -0.1572 0.1827 0.9834 0.9932 0.234 0.4294 0.8682 0.7088 ] Network output: [ -0.008911 1.003 1.008 -1.988e-07 8.923e-08 0.007393 -1.498e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006813 0.0006249 0.004372 0.00318 0.9889 0.9919 0.006946 0.8521 0.892 0.01164 ] Network output: [ -0.000192 0.001452 1 -8.713e-06 3.911e-06 0.9984 -6.566e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2222 0.1054 0.3489 0.142 0.9849 0.9939 0.223 0.4334 0.875 0.7025 ] Network output: [ 0.003089 -0.0147 0.9943 5.311e-06 -2.384e-06 1.014 4.003e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.0985 0.1847 0.1974 0.9873 0.9919 0.1113 0.7359 0.8613 0.3051 ] Network output: [ -0.002899 0.01359 1.005 5.795e-06 -2.602e-06 0.9876 4.367e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09427 0.09233 0.165 0.1965 0.9852 0.9911 0.09428 0.6597 0.8365 0.2493 ] Network output: [ 8.404e-05 1 -5.067e-05 7.589e-07 -3.407e-07 0.9998 5.72e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001765 Epoch 9662 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008914 0.9968 0.9925 -1.791e-07 8.039e-08 -0.007087 -1.349e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003509 -0.003352 -0.006707 0.005406 0.9699 0.9743 0.006834 0.8249 0.8199 0.01629 ] Network output: [ 0.9999 0.0001182 0.0003806 -2.772e-06 1.244e-06 -0.0003363 -2.089e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2085 -0.03556 -0.1572 0.1827 0.9834 0.9932 0.234 0.4294 0.8682 0.7088 ] Network output: [ -0.00891 1.003 1.008 -1.986e-07 8.916e-08 0.007393 -1.497e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006813 0.000625 0.004372 0.00318 0.9889 0.9919 0.006947 0.8521 0.892 0.01164 ] Network output: [ -0.0001919 0.001451 1 -8.702e-06 3.907e-06 0.9984 -6.558e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2222 0.1054 0.3489 0.142 0.9849 0.9939 0.223 0.4334 0.875 0.7025 ] Network output: [ 0.003088 -0.0147 0.9943 5.305e-06 -2.382e-06 1.014 3.998e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.0985 0.1847 0.1974 0.9873 0.9919 0.1113 0.7359 0.8613 0.3051 ] Network output: [ -0.002897 0.01358 1.005 5.788e-06 -2.599e-06 0.9876 4.362e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09427 0.09233 0.165 0.1965 0.9852 0.9911 0.09428 0.6597 0.8365 0.2493 ] Network output: [ 8.402e-05 1 -5.065e-05 7.58e-07 -3.403e-07 0.9998 5.713e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001764 Epoch 9663 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008913 0.9968 0.9925 -1.79e-07 8.034e-08 -0.007087 -1.349e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003509 -0.003352 -0.006706 0.005406 0.9699 0.9743 0.006835 0.8248 0.8199 0.01629 ] Network output: [ 0.9999 0.0001181 0.0003804 -2.769e-06 1.243e-06 -0.0003361 -2.087e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2085 -0.03556 -0.1572 0.1827 0.9834 0.9932 0.234 0.4294 0.8682 0.7087 ] Network output: [ -0.008909 1.003 1.008 -1.985e-07 8.909e-08 0.007392 -1.496e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006814 0.0006251 0.004372 0.00318 0.9889 0.9919 0.006947 0.8521 0.892 0.01163 ] Network output: [ -0.0001917 0.00145 1 -8.692e-06 3.902e-06 0.9984 -6.55e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2222 0.1054 0.349 0.142 0.9849 0.9939 0.223 0.4334 0.875 0.7025 ] Network output: [ 0.003087 -0.01469 0.9943 5.298e-06 -2.379e-06 1.014 3.993e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09851 0.1847 0.1974 0.9873 0.9919 0.1113 0.7358 0.8613 0.3051 ] Network output: [ -0.002896 0.01358 1.005 5.781e-06 -2.595e-06 0.9876 4.357e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09427 0.09233 0.165 0.1965 0.9852 0.9911 0.09428 0.6597 0.8365 0.2493 ] Network output: [ 8.4e-05 1 -5.064e-05 7.571e-07 -3.399e-07 0.9998 5.706e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001763 Epoch 9664 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008913 0.9968 0.9925 -1.789e-07 8.03e-08 -0.007086 -1.348e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003509 -0.003352 -0.006706 0.005405 0.9699 0.9743 0.006835 0.8248 0.8199 0.01629 ] Network output: [ 0.9999 0.0001179 0.0003803 -2.765e-06 1.241e-06 -0.0003359 -2.084e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2085 -0.03556 -0.1572 0.1827 0.9834 0.9932 0.234 0.4294 0.8682 0.7087 ] Network output: [ -0.008909 1.003 1.008 -1.983e-07 8.903e-08 0.007391 -1.495e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006814 0.0006251 0.004372 0.00318 0.9889 0.9919 0.006948 0.8521 0.892 0.01163 ] Network output: [ -0.0001916 0.00145 1 -8.681e-06 3.897e-06 0.9984 -6.543e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2222 0.1054 0.349 0.142 0.9849 0.9939 0.223 0.4334 0.875 0.7025 ] Network output: [ 0.003085 -0.01468 0.9943 5.292e-06 -2.376e-06 1.014 3.988e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09851 0.1847 0.1974 0.9873 0.9919 0.1113 0.7358 0.8613 0.3051 ] Network output: [ -0.002895 0.01357 1.005 5.775e-06 -2.592e-06 0.9876 4.352e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09427 0.09233 0.165 0.1965 0.9852 0.9911 0.09429 0.6597 0.8365 0.2493 ] Network output: [ 8.397e-05 1 -5.063e-05 7.562e-07 -3.395e-07 0.9998 5.699e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001762 Epoch 9665 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008912 0.9968 0.9925 -1.788e-07 8.025e-08 -0.007086 -1.347e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003509 -0.003352 -0.006705 0.005405 0.9699 0.9743 0.006835 0.8248 0.8199 0.01629 ] Network output: [ 0.9999 0.0001177 0.0003801 -2.762e-06 1.24e-06 -0.0003357 -2.082e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2085 -0.03556 -0.1571 0.1827 0.9834 0.9932 0.234 0.4294 0.8682 0.7087 ] Network output: [ -0.008908 1.003 1.008 -1.982e-07 8.896e-08 0.007391 -1.493e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006815 0.0006252 0.004372 0.003179 0.9889 0.9919 0.006948 0.8521 0.892 0.01163 ] Network output: [ -0.0001914 0.001449 1 -8.671e-06 3.893e-06 0.9984 -6.535e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2222 0.1054 0.349 0.142 0.9849 0.9939 0.223 0.4334 0.875 0.7025 ] Network output: [ 0.003084 -0.01468 0.9943 5.286e-06 -2.373e-06 1.014 3.984e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09851 0.1847 0.1974 0.9873 0.9919 0.1113 0.7358 0.8613 0.3051 ] Network output: [ -0.002893 0.01357 1.005 5.768e-06 -2.589e-06 0.9876 4.347e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09427 0.09234 0.165 0.1965 0.9852 0.9911 0.09429 0.6597 0.8365 0.2493 ] Network output: [ 8.395e-05 1 -5.061e-05 7.553e-07 -3.391e-07 0.9998 5.692e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001761 Epoch 9666 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008911 0.9968 0.9925 -1.786e-07 8.02e-08 -0.007085 -1.346e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003509 -0.003352 -0.006704 0.005405 0.9699 0.9743 0.006835 0.8248 0.8199 0.01629 ] Network output: [ 0.9999 0.0001176 0.0003799 -2.759e-06 1.238e-06 -0.0003355 -2.079e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2085 -0.03556 -0.1571 0.1827 0.9834 0.9932 0.234 0.4294 0.8682 0.7087 ] Network output: [ -0.008907 1.003 1.008 -1.98e-07 8.89e-08 0.00739 -1.492e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006815 0.0006253 0.004372 0.003179 0.9889 0.9919 0.006948 0.8521 0.892 0.01163 ] Network output: [ -0.0001912 0.001448 1 -8.661e-06 3.888e-06 0.9984 -6.527e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2222 0.1054 0.349 0.142 0.9849 0.9939 0.223 0.4334 0.875 0.7025 ] Network output: [ 0.003082 -0.01467 0.9943 5.279e-06 -2.37e-06 1.014 3.979e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09852 0.1847 0.1974 0.9873 0.9919 0.1113 0.7358 0.8613 0.3051 ] Network output: [ -0.002892 0.01356 1.005 5.761e-06 -2.586e-06 0.9876 4.342e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09428 0.09234 0.165 0.1965 0.9852 0.9911 0.09429 0.6597 0.8365 0.2493 ] Network output: [ 8.393e-05 1 -5.06e-05 7.544e-07 -3.387e-07 0.9998 5.686e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000176 Epoch 9667 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00891 0.9968 0.9925 -1.785e-07 8.016e-08 -0.007084 -1.346e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003509 -0.003352 -0.006704 0.005404 0.9699 0.9743 0.006835 0.8248 0.8199 0.01629 ] Network output: [ 0.9999 0.0001174 0.0003797 -2.755e-06 1.237e-06 -0.0003352 -2.077e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2085 -0.03557 -0.1571 0.1827 0.9834 0.9932 0.234 0.4294 0.8682 0.7087 ] Network output: [ -0.008906 1.003 1.008 -1.979e-07 8.883e-08 0.00739 -1.491e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006815 0.0006253 0.004371 0.003179 0.9889 0.9919 0.006949 0.8521 0.892 0.01163 ] Network output: [ -0.0001911 0.001447 1 -8.65e-06 3.883e-06 0.9984 -6.519e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2222 0.1054 0.349 0.142 0.9849 0.9939 0.223 0.4333 0.875 0.7025 ] Network output: [ 0.003081 -0.01466 0.9943 5.273e-06 -2.367e-06 1.014 3.974e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09852 0.1847 0.1974 0.9873 0.9919 0.1113 0.7358 0.8613 0.3051 ] Network output: [ -0.002891 0.01355 1.005 5.754e-06 -2.583e-06 0.9876 4.337e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09428 0.09234 0.165 0.1965 0.9852 0.9911 0.09429 0.6597 0.8365 0.2493 ] Network output: [ 8.39e-05 1 -5.059e-05 7.535e-07 -3.383e-07 0.9998 5.679e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001759 Epoch 9668 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008909 0.9968 0.9925 -1.784e-07 8.011e-08 -0.007084 -1.345e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003509 -0.003352 -0.006703 0.005404 0.9699 0.9743 0.006835 0.8248 0.8199 0.01628 ] Network output: [ 0.9999 0.0001172 0.0003796 -2.752e-06 1.235e-06 -0.000335 -2.074e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2085 -0.03557 -0.1571 0.1827 0.9834 0.9932 0.2341 0.4294 0.8682 0.7087 ] Network output: [ -0.008905 1.003 1.008 -1.977e-07 8.876e-08 0.007389 -1.49e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006816 0.0006254 0.004371 0.003179 0.9889 0.9919 0.006949 0.8521 0.892 0.01163 ] Network output: [ -0.0001909 0.001447 1 -8.64e-06 3.879e-06 0.9984 -6.511e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2223 0.1054 0.349 0.142 0.9849 0.9939 0.223 0.4333 0.875 0.7025 ] Network output: [ 0.003079 -0.01466 0.9943 5.267e-06 -2.364e-06 1.014 3.969e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09852 0.1847 0.1974 0.9873 0.9919 0.1113 0.7358 0.8613 0.3051 ] Network output: [ -0.002889 0.01355 1.005 5.747e-06 -2.58e-06 0.9876 4.331e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09428 0.09234 0.165 0.1965 0.9852 0.9911 0.09429 0.6596 0.8365 0.2493 ] Network output: [ 8.388e-05 1 -5.057e-05 7.526e-07 -3.379e-07 0.9998 5.672e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001758 Epoch 9669 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008908 0.9968 0.9925 -1.783e-07 8.006e-08 -0.007083 -1.344e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003509 -0.003352 -0.006703 0.005403 0.9699 0.9743 0.006836 0.8248 0.8199 0.01628 ] Network output: [ 0.9999 0.0001171 0.0003794 -2.749e-06 1.234e-06 -0.0003348 -2.071e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2085 -0.03557 -0.1571 0.1827 0.9834 0.9932 0.2341 0.4294 0.8682 0.7087 ] Network output: [ -0.008904 1.003 1.008 -1.976e-07 8.87e-08 0.007388 -1.489e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006816 0.0006255 0.004371 0.003178 0.9889 0.9919 0.006949 0.8521 0.892 0.01163 ] Network output: [ -0.0001908 0.001446 1 -8.629e-06 3.874e-06 0.9984 -6.503e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2223 0.1054 0.349 0.142 0.9849 0.9939 0.223 0.4333 0.875 0.7025 ] Network output: [ 0.003078 -0.01465 0.9943 5.261e-06 -2.362e-06 1.014 3.965e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09853 0.1847 0.1974 0.9873 0.9919 0.1113 0.7358 0.8613 0.3051 ] Network output: [ -0.002888 0.01354 1.005 5.741e-06 -2.577e-06 0.9876 4.326e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09428 0.09234 0.165 0.1965 0.9852 0.9911 0.0943 0.6596 0.8365 0.2493 ] Network output: [ 8.386e-05 1 -5.056e-05 7.518e-07 -3.375e-07 0.9998 5.665e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001757 Epoch 9670 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008907 0.9968 0.9925 -1.782e-07 8.001e-08 -0.007083 -1.343e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003509 -0.003352 -0.006702 0.005403 0.9699 0.9743 0.006836 0.8248 0.8199 0.01628 ] Network output: [ 0.9999 0.0001169 0.0003792 -2.745e-06 1.232e-06 -0.0003346 -2.069e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2085 -0.03557 -0.1571 0.1827 0.9834 0.9932 0.2341 0.4294 0.8682 0.7087 ] Network output: [ -0.008904 1.003 1.008 -1.974e-07 8.863e-08 0.007388 -1.488e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006816 0.0006255 0.004371 0.003178 0.9889 0.9919 0.00695 0.8521 0.892 0.01163 ] Network output: [ -0.0001906 0.001445 1 -8.619e-06 3.869e-06 0.9984 -6.496e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2223 0.1054 0.349 0.142 0.9849 0.9939 0.223 0.4333 0.875 0.7025 ] Network output: [ 0.003076 -0.01464 0.9943 5.254e-06 -2.359e-06 1.014 3.96e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09853 0.1847 0.1974 0.9873 0.9919 0.1113 0.7358 0.8613 0.3051 ] Network output: [ -0.002887 0.01354 1.005 5.734e-06 -2.574e-06 0.9876 4.321e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09428 0.09234 0.165 0.1965 0.9852 0.9911 0.0943 0.6596 0.8364 0.2493 ] Network output: [ 8.384e-05 1 -5.055e-05 7.509e-07 -3.371e-07 0.9998 5.659e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001756 Epoch 9671 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008906 0.9968 0.9925 -1.781e-07 7.997e-08 -0.007082 -1.342e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003509 -0.003352 -0.006701 0.005403 0.9699 0.9743 0.006836 0.8248 0.8199 0.01628 ] Network output: [ 0.9999 0.0001167 0.0003791 -2.742e-06 1.231e-06 -0.0003344 -2.067e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2085 -0.03557 -0.1571 0.1827 0.9834 0.9932 0.2341 0.4294 0.8682 0.7087 ] Network output: [ -0.008903 1.003 1.008 -1.973e-07 8.856e-08 0.007387 -1.487e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006817 0.0006256 0.004371 0.003178 0.9889 0.9919 0.00695 0.8521 0.892 0.01163 ] Network output: [ -0.0001905 0.001445 1 -8.609e-06 3.865e-06 0.9984 -6.488e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2223 0.1054 0.349 0.142 0.9849 0.9939 0.223 0.4333 0.875 0.7025 ] Network output: [ 0.003075 -0.01464 0.9943 5.248e-06 -2.356e-06 1.014 3.955e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09853 0.1847 0.1974 0.9873 0.9919 0.1113 0.7358 0.8613 0.3051 ] Network output: [ -0.002885 0.01353 1.005 5.727e-06 -2.571e-06 0.9876 4.316e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09429 0.09235 0.165 0.1965 0.9852 0.9911 0.0943 0.6596 0.8364 0.2493 ] Network output: [ 8.381e-05 1 -5.053e-05 7.5e-07 -3.367e-07 0.9998 5.652e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001755 Epoch 9672 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008905 0.9968 0.9925 -1.78e-07 7.992e-08 -0.007081 -1.342e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003509 -0.003353 -0.006701 0.005402 0.9699 0.9743 0.006836 0.8248 0.8199 0.01628 ] Network output: [ 0.9999 0.0001166 0.0003789 -2.739e-06 1.23e-06 -0.0003341 -2.064e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2085 -0.03557 -0.1571 0.1827 0.9834 0.9932 0.2341 0.4294 0.8682 0.7087 ] Network output: [ -0.008902 1.003 1.008 -1.971e-07 8.85e-08 0.007387 -1.486e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006817 0.0006257 0.004371 0.003178 0.9889 0.9919 0.006951 0.8521 0.892 0.01163 ] Network output: [ -0.0001903 0.001444 1 -8.598e-06 3.86e-06 0.9984 -6.48e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2223 0.1054 0.349 0.142 0.9849 0.9939 0.223 0.4333 0.875 0.7024 ] Network output: [ 0.003073 -0.01463 0.9943 5.242e-06 -2.353e-06 1.014 3.95e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09854 0.1847 0.1974 0.9873 0.9919 0.1113 0.7357 0.8613 0.3051 ] Network output: [ -0.002884 0.01352 1.005 5.72e-06 -2.568e-06 0.9876 4.311e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09429 0.09235 0.165 0.1965 0.9852 0.9911 0.0943 0.6596 0.8364 0.2493 ] Network output: [ 8.379e-05 1 -5.052e-05 7.491e-07 -3.363e-07 0.9998 5.645e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001754 Epoch 9673 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008904 0.9968 0.9925 -1.779e-07 7.987e-08 -0.007081 -1.341e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003509 -0.003353 -0.0067 0.005402 0.9699 0.9743 0.006836 0.8248 0.8199 0.01628 ] Network output: [ 0.9999 0.0001164 0.0003787 -2.735e-06 1.228e-06 -0.0003339 -2.062e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2085 -0.03557 -0.1571 0.1827 0.9834 0.9932 0.2341 0.4294 0.8682 0.7087 ] Network output: [ -0.008901 1.003 1.008 -1.97e-07 8.843e-08 0.007386 -1.485e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006818 0.0006257 0.004371 0.003178 0.9889 0.9919 0.006951 0.852 0.892 0.01163 ] Network output: [ -0.0001902 0.001443 1 -8.588e-06 3.855e-06 0.9984 -6.472e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2223 0.1054 0.349 0.142 0.9849 0.9939 0.223 0.4333 0.875 0.7024 ] Network output: [ 0.003072 -0.01462 0.9943 5.235e-06 -2.35e-06 1.014 3.946e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09854 0.1847 0.1974 0.9873 0.9919 0.1113 0.7357 0.8613 0.3051 ] Network output: [ -0.002883 0.01352 1.005 5.714e-06 -2.565e-06 0.9876 4.306e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09429 0.09235 0.165 0.1965 0.9852 0.9911 0.0943 0.6596 0.8364 0.2493 ] Network output: [ 8.377e-05 1 -5.051e-05 7.482e-07 -3.359e-07 0.9998 5.639e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001753 Epoch 9674 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008903 0.9968 0.9925 -1.778e-07 7.983e-08 -0.00708 -1.34e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00351 -0.003353 -0.0067 0.005401 0.9699 0.9743 0.006836 0.8248 0.8199 0.01628 ] Network output: [ 0.9999 0.0001162 0.0003786 -2.732e-06 1.227e-06 -0.0003337 -2.059e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2086 -0.03557 -0.1571 0.1827 0.9834 0.9932 0.2341 0.4293 0.8682 0.7087 ] Network output: [ -0.0089 1.003 1.008 -1.968e-07 8.837e-08 0.007385 -1.483e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006818 0.0006258 0.004371 0.003177 0.9889 0.9919 0.006951 0.852 0.892 0.01163 ] Network output: [ -0.00019 0.001442 1 -8.578e-06 3.851e-06 0.9984 -6.464e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2223 0.1054 0.349 0.142 0.9849 0.9939 0.2231 0.4333 0.875 0.7024 ] Network output: [ 0.00307 -0.01462 0.9943 5.229e-06 -2.348e-06 1.014 3.941e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09854 0.1847 0.1974 0.9873 0.9919 0.1113 0.7357 0.8613 0.3051 ] Network output: [ -0.002881 0.01351 1.005 5.707e-06 -2.562e-06 0.9876 4.301e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09429 0.09235 0.165 0.1965 0.9852 0.9911 0.09431 0.6596 0.8364 0.2493 ] Network output: [ 8.374e-05 1 -5.05e-05 7.473e-07 -3.355e-07 0.9998 5.632e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001752 Epoch 9675 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008903 0.9968 0.9925 -1.777e-07 7.978e-08 -0.00708 -1.339e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00351 -0.003353 -0.006699 0.005401 0.9699 0.9743 0.006837 0.8248 0.8199 0.01628 ] Network output: [ 0.9999 0.0001161 0.0003784 -2.729e-06 1.225e-06 -0.0003335 -2.057e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2086 -0.03557 -0.1571 0.1827 0.9834 0.9932 0.2341 0.4293 0.8682 0.7087 ] Network output: [ -0.0089 1.003 1.008 -1.967e-07 8.83e-08 0.007385 -1.482e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006818 0.0006259 0.004371 0.003177 0.9889 0.9919 0.006952 0.852 0.892 0.01163 ] Network output: [ -0.0001898 0.001442 1 -8.567e-06 3.846e-06 0.9984 -6.457e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2223 0.1054 0.349 0.142 0.9849 0.9939 0.2231 0.4333 0.875 0.7024 ] Network output: [ 0.003069 -0.01461 0.9943 5.223e-06 -2.345e-06 1.014 3.936e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1112 0.09855 0.1847 0.1974 0.9873 0.9919 0.1113 0.7357 0.8613 0.3051 ] Network output: [ -0.00288 0.01351 1.005 5.7e-06 -2.559e-06 0.9876 4.296e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09429 0.09235 0.165 0.1965 0.9852 0.9911 0.09431 0.6596 0.8364 0.2493 ] Network output: [ 8.372e-05 1 -5.048e-05 7.464e-07 -3.351e-07 0.9998 5.625e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001752 Epoch 9676 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008902 0.9968 0.9925 -1.776e-07 7.973e-08 -0.007079 -1.338e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00351 -0.003353 -0.006698 0.005401 0.9699 0.9743 0.006837 0.8248 0.8199 0.01628 ] Network output: [ 0.9999 0.0001159 0.0003782 -2.726e-06 1.224e-06 -0.0003333 -2.054e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2086 -0.03557 -0.157 0.1827 0.9834 0.9932 0.2341 0.4293 0.8682 0.7087 ] Network output: [ -0.008899 1.003 1.008 -1.965e-07 8.823e-08 0.007384 -1.481e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006819 0.0006259 0.004371 0.003177 0.9889 0.9919 0.006952 0.852 0.892 0.01162 ] Network output: [ -0.0001897 0.001441 1 -8.557e-06 3.842e-06 0.9984 -6.449e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2223 0.1054 0.349 0.142 0.9849 0.9939 0.2231 0.4333 0.875 0.7024 ] Network output: [ 0.003068 -0.0146 0.9943 5.217e-06 -2.342e-06 1.014 3.931e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09855 0.1847 0.1974 0.9873 0.9919 0.1113 0.7357 0.8613 0.3051 ] Network output: [ -0.002879 0.0135 1.005 5.694e-06 -2.556e-06 0.9876 4.291e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0943 0.09236 0.165 0.1965 0.9852 0.9911 0.09431 0.6596 0.8364 0.2493 ] Network output: [ 8.37e-05 1 -5.047e-05 7.455e-07 -3.347e-07 0.9998 5.619e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001751 Epoch 9677 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008901 0.9968 0.9925 -1.775e-07 7.969e-08 -0.007078 -1.338e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00351 -0.003353 -0.006698 0.0054 0.9699 0.9743 0.006837 0.8248 0.8199 0.01628 ] Network output: [ 0.9999 0.0001157 0.0003781 -2.722e-06 1.222e-06 -0.0003331 -2.052e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2086 -0.03557 -0.157 0.1827 0.9834 0.9932 0.2341 0.4293 0.8682 0.7087 ] Network output: [ -0.008898 1.003 1.008 -1.964e-07 8.817e-08 0.007384 -1.48e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006819 0.000626 0.00437 0.003177 0.9889 0.9919 0.006953 0.852 0.892 0.01162 ] Network output: [ -0.0001895 0.00144 1 -8.547e-06 3.837e-06 0.9984 -6.441e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2223 0.1054 0.349 0.142 0.9849 0.9939 0.2231 0.4333 0.875 0.7024 ] Network output: [ 0.003066 -0.0146 0.9943 5.21e-06 -2.339e-06 1.014 3.927e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09855 0.1847 0.1974 0.9873 0.9919 0.1113 0.7357 0.8613 0.3051 ] Network output: [ -0.002877 0.01349 1.005 5.687e-06 -2.553e-06 0.9876 4.286e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0943 0.09236 0.165 0.1965 0.9852 0.9911 0.09431 0.6595 0.8364 0.2493 ] Network output: [ 8.368e-05 1 -5.046e-05 7.446e-07 -3.343e-07 0.9998 5.612e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000175 Epoch 9678 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0089 0.9968 0.9925 -1.774e-07 7.964e-08 -0.007078 -1.337e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00351 -0.003353 -0.006697 0.0054 0.9699 0.9743 0.006837 0.8248 0.8199 0.01628 ] Network output: [ 0.9999 0.0001156 0.0003779 -2.719e-06 1.221e-06 -0.0003328 -2.049e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2086 -0.03558 -0.157 0.1827 0.9834 0.9932 0.2341 0.4293 0.8682 0.7087 ] Network output: [ -0.008897 1.003 1.008 -1.962e-07 8.81e-08 0.007383 -1.479e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006819 0.0006261 0.00437 0.003176 0.9889 0.9919 0.006953 0.852 0.892 0.01162 ] Network output: [ -0.0001894 0.00144 1 -8.536e-06 3.832e-06 0.9984 -6.433e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2223 0.1054 0.349 0.142 0.9849 0.9939 0.2231 0.4333 0.875 0.7024 ] Network output: [ 0.003065 -0.01459 0.9943 5.204e-06 -2.336e-06 1.014 3.922e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09856 0.1847 0.1974 0.9873 0.9919 0.1113 0.7357 0.8613 0.3051 ] Network output: [ -0.002876 0.01349 1.005 5.68e-06 -2.55e-06 0.9876 4.281e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0943 0.09236 0.165 0.1965 0.9852 0.9911 0.09431 0.6595 0.8364 0.2493 ] Network output: [ 8.365e-05 1 -5.045e-05 7.438e-07 -3.339e-07 0.9998 5.605e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001749 Epoch 9679 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008899 0.9968 0.9925 -1.773e-07 7.959e-08 -0.007077 -1.336e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00351 -0.003353 -0.006697 0.0054 0.9699 0.9743 0.006837 0.8248 0.8199 0.01627 ] Network output: [ 0.9999 0.0001154 0.0003777 -2.716e-06 1.219e-06 -0.0003326 -2.047e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2086 -0.03558 -0.157 0.1827 0.9834 0.9932 0.2341 0.4293 0.8682 0.7087 ] Network output: [ -0.008896 1.003 1.008 -1.961e-07 8.803e-08 0.007382 -1.478e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00682 0.0006261 0.00437 0.003176 0.9889 0.9919 0.006953 0.852 0.892 0.01162 ] Network output: [ -0.0001892 0.001439 1 -8.526e-06 3.828e-06 0.9984 -6.426e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2223 0.1054 0.349 0.142 0.9849 0.9939 0.2231 0.4333 0.875 0.7024 ] Network output: [ 0.003063 -0.01458 0.9943 5.198e-06 -2.334e-06 1.014 3.917e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09856 0.1847 0.1974 0.9873 0.9919 0.1113 0.7357 0.8613 0.3051 ] Network output: [ -0.002875 0.01348 1.005 5.673e-06 -2.547e-06 0.9876 4.276e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0943 0.09236 0.165 0.1965 0.9852 0.9911 0.09432 0.6595 0.8364 0.2493 ] Network output: [ 8.363e-05 1 -5.043e-05 7.429e-07 -3.335e-07 0.9998 5.599e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001748 Epoch 9680 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008898 0.9968 0.9925 -1.772e-07 7.955e-08 -0.007077 -1.335e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00351 -0.003353 -0.006696 0.005399 0.9699 0.9743 0.006837 0.8248 0.8199 0.01627 ] Network output: [ 0.9999 0.0001152 0.0003776 -2.712e-06 1.218e-06 -0.0003324 -2.044e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2086 -0.03558 -0.157 0.1826 0.9834 0.9932 0.2341 0.4293 0.8682 0.7087 ] Network output: [ -0.008895 1.003 1.008 -1.959e-07 8.797e-08 0.007382 -1.477e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00682 0.0006262 0.00437 0.003176 0.9889 0.9919 0.006954 0.852 0.892 0.01162 ] Network output: [ -0.0001891 0.001438 1 -8.516e-06 3.823e-06 0.9984 -6.418e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2223 0.1055 0.349 0.142 0.9849 0.9939 0.2231 0.4333 0.875 0.7024 ] Network output: [ 0.003062 -0.01458 0.9943 5.192e-06 -2.331e-06 1.014 3.913e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09856 0.1847 0.1974 0.9873 0.9919 0.1113 0.7357 0.8613 0.3051 ] Network output: [ -0.002873 0.01348 1.005 5.667e-06 -2.544e-06 0.9876 4.271e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0943 0.09236 0.165 0.1965 0.9852 0.9911 0.09432 0.6595 0.8364 0.2493 ] Network output: [ 8.361e-05 1 -5.042e-05 7.42e-07 -3.331e-07 0.9998 5.592e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001747 Epoch 9681 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008897 0.9968 0.9925 -1.771e-07 7.95e-08 -0.007076 -1.335e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00351 -0.003353 -0.006696 0.005399 0.9699 0.9743 0.006838 0.8248 0.8199 0.01627 ] Network output: [ 0.9999 0.0001151 0.0003774 -2.709e-06 1.216e-06 -0.0003322 -2.042e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2086 -0.03558 -0.157 0.1826 0.9834 0.9932 0.2341 0.4293 0.8682 0.7087 ] Network output: [ -0.008895 1.003 1.008 -1.958e-07 8.79e-08 0.007381 -1.476e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006821 0.0006263 0.00437 0.003176 0.9889 0.9919 0.006954 0.852 0.892 0.01162 ] Network output: [ -0.0001889 0.001438 1 -8.506e-06 3.818e-06 0.9984 -6.41e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2223 0.1055 0.349 0.142 0.9849 0.9939 0.2231 0.4333 0.875 0.7024 ] Network output: [ 0.00306 -0.01457 0.9943 5.186e-06 -2.328e-06 1.014 3.908e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09857 0.1847 0.1974 0.9873 0.9919 0.1113 0.7357 0.8612 0.3051 ] Network output: [ -0.002872 0.01347 1.005 5.66e-06 -2.541e-06 0.9876 4.266e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0943 0.09237 0.165 0.1965 0.9852 0.9911 0.09432 0.6595 0.8364 0.2493 ] Network output: [ 8.358e-05 1 -5.041e-05 7.411e-07 -3.327e-07 0.9998 5.585e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001746 Epoch 9682 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008896 0.9968 0.9925 -1.77e-07 7.945e-08 -0.007076 -1.334e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00351 -0.003353 -0.006695 0.005398 0.9699 0.9743 0.006838 0.8248 0.8199 0.01627 ] Network output: [ 0.9999 0.0001149 0.0003772 -2.706e-06 1.215e-06 -0.000332 -2.039e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2086 -0.03558 -0.157 0.1826 0.9834 0.9932 0.2341 0.4293 0.8682 0.7087 ] Network output: [ -0.008894 1.003 1.008 -1.957e-07 8.784e-08 0.007381 -1.475e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006821 0.0006263 0.00437 0.003175 0.9889 0.9919 0.006954 0.852 0.892 0.01162 ] Network output: [ -0.0001888 0.001437 1 -8.495e-06 3.814e-06 0.9984 -6.402e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2224 0.1055 0.349 0.142 0.9849 0.9939 0.2231 0.4333 0.875 0.7024 ] Network output: [ 0.003059 -0.01456 0.9943 5.179e-06 -2.325e-06 1.014 3.903e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09857 0.1847 0.1974 0.9873 0.9919 0.1113 0.7356 0.8612 0.3051 ] Network output: [ -0.002871 0.01346 1.005 5.653e-06 -2.538e-06 0.9876 4.261e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09431 0.09237 0.165 0.1965 0.9852 0.9911 0.09432 0.6595 0.8364 0.2493 ] Network output: [ 8.356e-05 1 -5.04e-05 7.402e-07 -3.323e-07 0.9998 5.579e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001745 Epoch 9683 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008895 0.9968 0.9925 -1.769e-07 7.941e-08 -0.007075 -1.333e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00351 -0.003353 -0.006694 0.005398 0.9699 0.9743 0.006838 0.8248 0.8199 0.01627 ] Network output: [ 0.9999 0.0001147 0.0003771 -2.703e-06 1.213e-06 -0.0003318 -2.037e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2086 -0.03558 -0.157 0.1826 0.9834 0.9932 0.2342 0.4293 0.8682 0.7087 ] Network output: [ -0.008893 1.003 1.008 -1.955e-07 8.777e-08 0.00738 -1.473e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006821 0.0006264 0.00437 0.003175 0.9889 0.9919 0.006955 0.852 0.892 0.01162 ] Network output: [ -0.0001886 0.001436 1 -8.485e-06 3.809e-06 0.9984 -6.395e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2224 0.1055 0.349 0.142 0.9849 0.9939 0.2231 0.4333 0.875 0.7024 ] Network output: [ 0.003057 -0.01456 0.9943 5.173e-06 -2.322e-06 1.014 3.899e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09857 0.1847 0.1974 0.9873 0.9919 0.1114 0.7356 0.8612 0.3051 ] Network output: [ -0.002869 0.01346 1.005 5.647e-06 -2.535e-06 0.9876 4.256e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09431 0.09237 0.165 0.1965 0.9852 0.9911 0.09432 0.6595 0.8364 0.2493 ] Network output: [ 8.354e-05 1 -5.039e-05 7.394e-07 -3.319e-07 0.9998 5.572e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001744 Epoch 9684 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008894 0.9968 0.9925 -1.768e-07 7.936e-08 -0.007074 -1.332e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00351 -0.003354 -0.006694 0.005398 0.9699 0.9743 0.006838 0.8248 0.8199 0.01627 ] Network output: [ 0.9999 0.0001146 0.0003769 -2.699e-06 1.212e-06 -0.0003315 -2.034e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2086 -0.03558 -0.157 0.1826 0.9834 0.9932 0.2342 0.4293 0.8682 0.7087 ] Network output: [ -0.008892 1.003 1.008 -1.954e-07 8.77e-08 0.007379 -1.472e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006822 0.0006265 0.00437 0.003175 0.9889 0.9919 0.006955 0.852 0.892 0.01162 ] Network output: [ -0.0001884 0.001435 1 -8.475e-06 3.805e-06 0.9984 -6.387e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2224 0.1055 0.3491 0.142 0.9849 0.9939 0.2231 0.4333 0.875 0.7024 ] Network output: [ 0.003056 -0.01455 0.9943 5.167e-06 -2.32e-06 1.014 3.894e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09858 0.1847 0.1974 0.9873 0.9919 0.1114 0.7356 0.8612 0.3051 ] Network output: [ -0.002868 0.01345 1.005 5.64e-06 -2.532e-06 0.9876 4.251e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09431 0.09237 0.165 0.1965 0.9852 0.9911 0.09432 0.6595 0.8364 0.2493 ] Network output: [ 8.352e-05 1 -5.037e-05 7.385e-07 -3.315e-07 0.9998 5.565e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001743 Epoch 9685 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008893 0.9968 0.9925 -1.767e-07 7.931e-08 -0.007074 -1.331e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00351 -0.003354 -0.006693 0.005397 0.9699 0.9743 0.006838 0.8248 0.8199 0.01627 ] Network output: [ 0.9999 0.0001144 0.0003767 -2.696e-06 1.21e-06 -0.0003313 -2.032e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2086 -0.03558 -0.157 0.1826 0.9834 0.9932 0.2342 0.4293 0.8682 0.7087 ] Network output: [ -0.008891 1.003 1.008 -1.952e-07 8.764e-08 0.007379 -1.471e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006822 0.0006265 0.00437 0.003175 0.9889 0.9919 0.006956 0.852 0.892 0.01162 ] Network output: [ -0.0001883 0.001435 1 -8.465e-06 3.8e-06 0.9984 -6.379e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2224 0.1055 0.3491 0.142 0.9849 0.9939 0.2231 0.4333 0.875 0.7024 ] Network output: [ 0.003054 -0.01454 0.9943 5.161e-06 -2.317e-06 1.014 3.889e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09858 0.1847 0.1974 0.9873 0.9919 0.1114 0.7356 0.8612 0.3051 ] Network output: [ -0.002867 0.01345 1.005 5.633e-06 -2.529e-06 0.9876 4.246e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09431 0.09237 0.165 0.1965 0.9852 0.9911 0.09433 0.6595 0.8364 0.2493 ] Network output: [ 8.349e-05 1 -5.036e-05 7.376e-07 -3.311e-07 0.9998 5.559e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001742 Epoch 9686 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008893 0.9968 0.9925 -1.766e-07 7.926e-08 -0.007073 -1.331e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00351 -0.003354 -0.006693 0.005397 0.9699 0.9743 0.006838 0.8247 0.8199 0.01627 ] Network output: [ 0.9999 0.0001142 0.0003765 -2.693e-06 1.209e-06 -0.0003311 -2.029e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2086 -0.03558 -0.157 0.1826 0.9834 0.9932 0.2342 0.4293 0.8682 0.7087 ] Network output: [ -0.00889 1.003 1.008 -1.951e-07 8.757e-08 0.007378 -1.47e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006822 0.0006266 0.00437 0.003174 0.9889 0.9919 0.006956 0.852 0.892 0.01162 ] Network output: [ -0.0001881 0.001434 1 -8.454e-06 3.796e-06 0.9984 -6.372e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2224 0.1055 0.3491 0.142 0.9849 0.9939 0.2231 0.4333 0.875 0.7024 ] Network output: [ 0.003053 -0.01454 0.9943 5.155e-06 -2.314e-06 1.014 3.885e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09858 0.1847 0.1973 0.9873 0.9919 0.1114 0.7356 0.8612 0.3051 ] Network output: [ -0.002865 0.01344 1.005 5.627e-06 -2.526e-06 0.9876 4.241e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09431 0.09237 0.165 0.1965 0.9852 0.9911 0.09433 0.6594 0.8364 0.2493 ] Network output: [ 8.347e-05 1 -5.035e-05 7.367e-07 -3.307e-07 0.9998 5.552e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001741 Epoch 9687 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008892 0.9968 0.9925 -1.765e-07 7.922e-08 -0.007073 -1.33e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00351 -0.003354 -0.006692 0.005396 0.9699 0.9743 0.006839 0.8247 0.8198 0.01627 ] Network output: [ 0.9999 0.0001141 0.0003764 -2.69e-06 1.207e-06 -0.0003309 -2.027e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2086 -0.03558 -0.157 0.1826 0.9834 0.9932 0.2342 0.4293 0.8682 0.7087 ] Network output: [ -0.00889 1.003 1.008 -1.949e-07 8.751e-08 0.007378 -1.469e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006823 0.0006267 0.00437 0.003174 0.9889 0.9919 0.006956 0.852 0.892 0.01162 ] Network output: [ -0.000188 0.001433 1 -8.444e-06 3.791e-06 0.9984 -6.364e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2224 0.1055 0.3491 0.142 0.9849 0.9939 0.2231 0.4333 0.875 0.7024 ] Network output: [ 0.003052 -0.01453 0.9943 5.148e-06 -2.311e-06 1.014 3.88e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09859 0.1847 0.1973 0.9873 0.9919 0.1114 0.7356 0.8612 0.3051 ] Network output: [ -0.002864 0.01343 1.005 5.62e-06 -2.523e-06 0.9876 4.236e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09432 0.09238 0.165 0.1965 0.9852 0.9911 0.09433 0.6594 0.8364 0.2493 ] Network output: [ 8.345e-05 1 -5.034e-05 7.358e-07 -3.303e-07 0.9998 5.546e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000174 Epoch 9688 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008891 0.9968 0.9925 -1.764e-07 7.917e-08 -0.007072 -1.329e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00351 -0.003354 -0.006691 0.005396 0.9699 0.9743 0.006839 0.8247 0.8198 0.01627 ] Network output: [ 0.9999 0.0001139 0.0003762 -2.686e-06 1.206e-06 -0.0003307 -2.025e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2086 -0.03559 -0.1569 0.1826 0.9834 0.9932 0.2342 0.4293 0.8682 0.7087 ] Network output: [ -0.008889 1.003 1.008 -1.948e-07 8.744e-08 0.007377 -1.468e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006823 0.0006267 0.004369 0.003174 0.9889 0.9919 0.006957 0.852 0.892 0.01162 ] Network output: [ -0.0001878 0.001433 1 -8.434e-06 3.786e-06 0.9984 -6.356e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2224 0.1055 0.3491 0.142 0.9849 0.9939 0.2232 0.4332 0.875 0.7024 ] Network output: [ 0.00305 -0.01452 0.9943 5.142e-06 -2.309e-06 1.014 3.875e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09859 0.1847 0.1973 0.9873 0.9919 0.1114 0.7356 0.8612 0.3051 ] Network output: [ -0.002863 0.01343 1.005 5.614e-06 -2.52e-06 0.9876 4.231e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09432 0.09238 0.165 0.1965 0.9852 0.9911 0.09433 0.6594 0.8364 0.2493 ] Network output: [ 8.343e-05 1 -5.033e-05 7.35e-07 -3.3e-07 0.9998 5.539e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001739 Epoch 9689 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00889 0.9968 0.9925 -1.762e-07 7.912e-08 -0.007071 -1.328e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00351 -0.003354 -0.006691 0.005396 0.9699 0.9743 0.006839 0.8247 0.8198 0.01627 ] Network output: [ 0.9999 0.0001137 0.000376 -2.683e-06 1.205e-06 -0.0003305 -2.022e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2086 -0.03559 -0.1569 0.1826 0.9834 0.9932 0.2342 0.4293 0.8682 0.7086 ] Network output: [ -0.008888 1.003 1.008 -1.946e-07 8.737e-08 0.007376 -1.467e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006823 0.0006268 0.004369 0.003174 0.9889 0.9919 0.006957 0.852 0.892 0.01162 ] Network output: [ -0.0001877 0.001432 1 -8.424e-06 3.782e-06 0.9984 -6.349e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2224 0.1055 0.3491 0.142 0.9849 0.9939 0.2232 0.4332 0.875 0.7024 ] Network output: [ 0.003049 -0.01452 0.9943 5.136e-06 -2.306e-06 1.014 3.871e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09859 0.1848 0.1973 0.9873 0.9919 0.1114 0.7356 0.8612 0.3051 ] Network output: [ -0.002861 0.01342 1.005 5.607e-06 -2.517e-06 0.9876 4.226e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09432 0.09238 0.165 0.1965 0.9852 0.9911 0.09433 0.6594 0.8364 0.2493 ] Network output: [ 8.34e-05 1 -5.032e-05 7.341e-07 -3.296e-07 0.9998 5.532e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001738 Epoch 9690 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008889 0.9968 0.9925 -1.761e-07 7.908e-08 -0.007071 -1.327e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00351 -0.003354 -0.00669 0.005395 0.9699 0.9743 0.006839 0.8247 0.8198 0.01626 ] Network output: [ 0.9999 0.0001136 0.0003759 -2.68e-06 1.203e-06 -0.0003303 -2.02e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2086 -0.03559 -0.1569 0.1826 0.9834 0.9932 0.2342 0.4293 0.8682 0.7086 ] Network output: [ -0.008887 1.003 1.008 -1.945e-07 8.731e-08 0.007376 -1.466e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006824 0.0006269 0.004369 0.003174 0.9889 0.9919 0.006957 0.852 0.892 0.01161 ] Network output: [ -0.0001875 0.001431 1 -8.414e-06 3.777e-06 0.9984 -6.341e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2224 0.1055 0.3491 0.142 0.9849 0.9939 0.2232 0.4332 0.875 0.7024 ] Network output: [ 0.003047 -0.01451 0.9943 5.13e-06 -2.303e-06 1.014 3.866e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.0986 0.1848 0.1973 0.9873 0.9919 0.1114 0.7356 0.8612 0.3051 ] Network output: [ -0.00286 0.01341 1.005 5.6e-06 -2.514e-06 0.9877 4.221e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09432 0.09238 0.165 0.1965 0.9852 0.9911 0.09434 0.6594 0.8364 0.2493 ] Network output: [ 8.338e-05 1 -5.03e-05 7.332e-07 -3.292e-07 0.9998 5.526e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001737 Epoch 9691 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008888 0.9968 0.9925 -1.76e-07 7.903e-08 -0.00707 -1.327e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00351 -0.003354 -0.00669 0.005395 0.9699 0.9743 0.006839 0.8247 0.8198 0.01626 ] Network output: [ 0.9999 0.0001134 0.0003757 -2.677e-06 1.202e-06 -0.00033 -2.017e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2087 -0.03559 -0.1569 0.1826 0.9834 0.9932 0.2342 0.4293 0.8682 0.7086 ] Network output: [ -0.008886 1.003 1.008 -1.943e-07 8.724e-08 0.007375 -1.465e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006824 0.0006269 0.004369 0.003173 0.9889 0.9919 0.006958 0.852 0.892 0.01161 ] Network output: [ -0.0001874 0.001431 1 -8.404e-06 3.773e-06 0.9984 -6.333e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2224 0.1055 0.3491 0.142 0.9849 0.9939 0.2232 0.4332 0.875 0.7024 ] Network output: [ 0.003046 -0.01451 0.9943 5.124e-06 -2.3e-06 1.014 3.861e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.0986 0.1848 0.1973 0.9873 0.9919 0.1114 0.7355 0.8612 0.3051 ] Network output: [ -0.002859 0.01341 1.005 5.594e-06 -2.511e-06 0.9877 4.216e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09432 0.09238 0.165 0.1965 0.9852 0.9911 0.09434 0.6594 0.8364 0.2493 ] Network output: [ 8.336e-05 1 -5.029e-05 7.324e-07 -3.288e-07 0.9998 5.519e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001736 Epoch 9692 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008887 0.9968 0.9925 -1.759e-07 7.898e-08 -0.00707 -1.326e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003511 -0.003354 -0.006689 0.005394 0.9699 0.9743 0.006839 0.8247 0.8198 0.01626 ] Network output: [ 0.9999 0.0001133 0.0003755 -2.673e-06 1.2e-06 -0.0003298 -2.015e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2087 -0.03559 -0.1569 0.1826 0.9834 0.9932 0.2342 0.4293 0.8682 0.7086 ] Network output: [ -0.008885 1.003 1.008 -1.942e-07 8.718e-08 0.007375 -1.463e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006825 0.000627 0.004369 0.003173 0.9889 0.9919 0.006958 0.852 0.892 0.01161 ] Network output: [ -0.0001872 0.00143 1 -8.394e-06 3.768e-06 0.9984 -6.326e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2224 0.1055 0.3491 0.142 0.9849 0.9939 0.2232 0.4332 0.875 0.7024 ] Network output: [ 0.003044 -0.0145 0.9943 5.118e-06 -2.297e-06 1.014 3.857e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.0986 0.1848 0.1973 0.9873 0.9919 0.1114 0.7355 0.8612 0.3051 ] Network output: [ -0.002857 0.0134 1.005 5.587e-06 -2.508e-06 0.9877 4.211e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09433 0.09239 0.165 0.1965 0.9852 0.9911 0.09434 0.6594 0.8364 0.2493 ] Network output: [ 8.334e-05 1 -5.028e-05 7.315e-07 -3.284e-07 0.9998 5.513e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001736 Epoch 9693 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008886 0.9968 0.9925 -1.758e-07 7.893e-08 -0.007069 -1.325e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003511 -0.003354 -0.006688 0.005394 0.9699 0.9743 0.00684 0.8247 0.8198 0.01626 ] Network output: [ 0.9999 0.0001131 0.0003754 -2.67e-06 1.199e-06 -0.0003296 -2.012e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2087 -0.03559 -0.1569 0.1826 0.9834 0.9932 0.2342 0.4293 0.8682 0.7086 ] Network output: [ -0.008885 1.003 1.008 -1.94e-07 8.711e-08 0.007374 -1.462e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006825 0.0006271 0.004369 0.003173 0.9889 0.9919 0.006959 0.852 0.892 0.01161 ] Network output: [ -0.0001871 0.001429 1 -8.383e-06 3.764e-06 0.9984 -6.318e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2224 0.1055 0.3491 0.142 0.9849 0.9939 0.2232 0.4332 0.875 0.7024 ] Network output: [ 0.003043 -0.01449 0.9943 5.111e-06 -2.295e-06 1.014 3.852e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.0986 0.1848 0.1973 0.9873 0.9919 0.1114 0.7355 0.8612 0.3051 ] Network output: [ -0.002856 0.0134 1.005 5.58e-06 -2.505e-06 0.9877 4.206e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09433 0.09239 0.165 0.1965 0.9852 0.9911 0.09434 0.6594 0.8364 0.2493 ] Network output: [ 8.331e-05 1 -5.027e-05 7.306e-07 -3.28e-07 0.9998 5.506e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001735 Epoch 9694 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008885 0.9968 0.9925 -1.757e-07 7.889e-08 -0.007068 -1.324e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003511 -0.003354 -0.006688 0.005394 0.9699 0.9743 0.00684 0.8247 0.8198 0.01626 ] Network output: [ 0.9999 0.0001129 0.0003752 -2.667e-06 1.197e-06 -0.0003294 -2.01e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2087 -0.03559 -0.1569 0.1826 0.9834 0.9932 0.2342 0.4293 0.8682 0.7086 ] Network output: [ -0.008884 1.003 1.008 -1.939e-07 8.704e-08 0.007373 -1.461e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006825 0.0006271 0.004369 0.003173 0.9889 0.9919 0.006959 0.8519 0.892 0.01161 ] Network output: [ -0.0001869 0.001428 1 -8.373e-06 3.759e-06 0.9984 -6.31e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2224 0.1055 0.3491 0.142 0.9849 0.9939 0.2232 0.4332 0.875 0.7024 ] Network output: [ 0.003041 -0.01449 0.9943 5.105e-06 -2.292e-06 1.014 3.848e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09861 0.1848 0.1973 0.9873 0.9919 0.1114 0.7355 0.8612 0.3051 ] Network output: [ -0.002855 0.01339 1.005 5.574e-06 -2.502e-06 0.9877 4.201e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09433 0.09239 0.165 0.1965 0.9852 0.9911 0.09434 0.6594 0.8364 0.2493 ] Network output: [ 8.329e-05 1 -5.026e-05 7.297e-07 -3.276e-07 0.9998 5.5e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001734 Epoch 9695 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008884 0.9968 0.9925 -1.756e-07 7.884e-08 -0.007068 -1.323e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003511 -0.003354 -0.006687 0.005393 0.9699 0.9743 0.00684 0.8247 0.8198 0.01626 ] Network output: [ 0.9999 0.0001128 0.000375 -2.664e-06 1.196e-06 -0.0003292 -2.007e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2087 -0.03559 -0.1569 0.1826 0.9834 0.9932 0.2342 0.4293 0.8682 0.7086 ] Network output: [ -0.008883 1.003 1.008 -1.937e-07 8.698e-08 0.007373 -1.46e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006826 0.0006272 0.004369 0.003172 0.9889 0.9919 0.006959 0.8519 0.892 0.01161 ] Network output: [ -0.0001868 0.001428 1 -8.363e-06 3.755e-06 0.9984 -6.303e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2224 0.1055 0.3491 0.142 0.9849 0.9939 0.2232 0.4332 0.875 0.7024 ] Network output: [ 0.00304 -0.01448 0.9943 5.099e-06 -2.289e-06 1.014 3.843e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09861 0.1848 0.1973 0.9873 0.9919 0.1114 0.7355 0.8612 0.3051 ] Network output: [ -0.002853 0.01338 1.005 5.567e-06 -2.499e-06 0.9877 4.196e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09433 0.09239 0.165 0.1966 0.9852 0.9911 0.09435 0.6593 0.8364 0.2493 ] Network output: [ 8.327e-05 1 -5.025e-05 7.289e-07 -3.272e-07 0.9998 5.493e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001733 Epoch 9696 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008883 0.9968 0.9925 -1.755e-07 7.879e-08 -0.007067 -1.323e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003511 -0.003355 -0.006687 0.005393 0.9699 0.9743 0.00684 0.8247 0.8198 0.01626 ] Network output: [ 0.9999 0.0001126 0.0003749 -2.66e-06 1.194e-06 -0.000329 -2.005e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2087 -0.03559 -0.1569 0.1826 0.9834 0.9932 0.2342 0.4292 0.8682 0.7086 ] Network output: [ -0.008882 1.003 1.008 -1.936e-07 8.691e-08 0.007372 -1.459e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006826 0.0006272 0.004369 0.003172 0.9889 0.9919 0.00696 0.8519 0.892 0.01161 ] Network output: [ -0.0001866 0.001427 1 -8.353e-06 3.75e-06 0.9984 -6.295e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2224 0.1055 0.3491 0.142 0.9849 0.9939 0.2232 0.4332 0.875 0.7023 ] Network output: [ 0.003038 -0.01447 0.9943 5.093e-06 -2.286e-06 1.014 3.838e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09861 0.1848 0.1973 0.9873 0.9919 0.1114 0.7355 0.8612 0.3051 ] Network output: [ -0.002852 0.01338 1.005 5.561e-06 -2.496e-06 0.9877 4.191e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09433 0.09239 0.165 0.1966 0.9852 0.9911 0.09435 0.6593 0.8364 0.2493 ] Network output: [ 8.325e-05 1 -5.024e-05 7.28e-07 -3.268e-07 0.9998 5.486e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001732 Epoch 9697 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008883 0.9968 0.9925 -1.754e-07 7.875e-08 -0.007067 -1.322e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003511 -0.003355 -0.006686 0.005393 0.9699 0.9743 0.00684 0.8247 0.8198 0.01626 ] Network output: [ 0.9999 0.0001124 0.0003747 -2.657e-06 1.193e-06 -0.0003288 -2.003e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2087 -0.03559 -0.1569 0.1826 0.9834 0.9932 0.2343 0.4292 0.8682 0.7086 ] Network output: [ -0.008881 1.003 1.008 -1.935e-07 8.685e-08 0.007372 -1.458e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006826 0.0006273 0.004369 0.003172 0.9889 0.9919 0.00696 0.8519 0.892 0.01161 ] Network output: [ -0.0001864 0.001426 1 -8.343e-06 3.746e-06 0.9984 -6.288e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2225 0.1055 0.3491 0.142 0.9849 0.9939 0.2232 0.4332 0.875 0.7023 ] Network output: [ 0.003037 -0.01447 0.9943 5.087e-06 -2.284e-06 1.014 3.834e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09862 0.1848 0.1973 0.9873 0.9919 0.1114 0.7355 0.8612 0.3051 ] Network output: [ -0.002851 0.01337 1.005 5.554e-06 -2.493e-06 0.9877 4.186e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09433 0.0924 0.165 0.1966 0.9852 0.9911 0.09435 0.6593 0.8364 0.2493 ] Network output: [ 8.322e-05 1 -5.023e-05 7.271e-07 -3.264e-07 0.9998 5.48e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001731 Epoch 9698 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008882 0.9968 0.9925 -1.753e-07 7.87e-08 -0.007066 -1.321e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003511 -0.003355 -0.006685 0.005392 0.9699 0.9743 0.006841 0.8247 0.8198 0.01626 ] Network output: [ 0.9999 0.0001123 0.0003745 -2.654e-06 1.192e-06 -0.0003286 -2e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2087 -0.0356 -0.1569 0.1826 0.9834 0.9932 0.2343 0.4292 0.8682 0.7086 ] Network output: [ -0.00888 1.003 1.008 -1.933e-07 8.678e-08 0.007371 -1.457e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006827 0.0006274 0.004368 0.003172 0.9889 0.9919 0.00696 0.8519 0.892 0.01161 ] Network output: [ -0.0001863 0.001426 1 -8.333e-06 3.741e-06 0.9984 -6.28e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2225 0.1055 0.3491 0.142 0.9849 0.9939 0.2232 0.4332 0.875 0.7023 ] Network output: [ 0.003035 -0.01446 0.9943 5.081e-06 -2.281e-06 1.014 3.829e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09862 0.1848 0.1973 0.9873 0.9919 0.1114 0.7355 0.8612 0.3051 ] Network output: [ -0.002849 0.01337 1.005 5.548e-06 -2.491e-06 0.9877 4.181e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09434 0.0924 0.165 0.1966 0.9852 0.9911 0.09435 0.6593 0.8364 0.2493 ] Network output: [ 8.32e-05 1 -5.022e-05 7.263e-07 -3.261e-07 0.9998 5.473e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000173 Epoch 9699 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008881 0.9968 0.9925 -1.752e-07 7.865e-08 -0.007065 -1.32e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003511 -0.003355 -0.006685 0.005392 0.9699 0.9743 0.006841 0.8247 0.8198 0.01626 ] Network output: [ 0.9999 0.0001121 0.0003744 -2.651e-06 1.19e-06 -0.0003283 -1.998e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2087 -0.0356 -0.1568 0.1826 0.9834 0.9932 0.2343 0.4292 0.8682 0.7086 ] Network output: [ -0.00888 1.003 1.008 -1.932e-07 8.672e-08 0.00737 -1.456e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006827 0.0006274 0.004368 0.003171 0.9889 0.9919 0.006961 0.8519 0.892 0.01161 ] Network output: [ -0.0001861 0.001425 1 -8.323e-06 3.737e-06 0.9984 -6.273e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2225 0.1055 0.3491 0.142 0.9849 0.9939 0.2232 0.4332 0.875 0.7023 ] Network output: [ 0.003034 -0.01445 0.9943 5.075e-06 -2.278e-06 1.014 3.825e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09862 0.1848 0.1973 0.9873 0.9919 0.1114 0.7355 0.8612 0.3051 ] Network output: [ -0.002848 0.01336 1.005 5.541e-06 -2.488e-06 0.9877 4.176e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09434 0.0924 0.165 0.1966 0.9852 0.9911 0.09435 0.6593 0.8364 0.2493 ] Network output: [ 8.318e-05 1 -5.02e-05 7.254e-07 -3.257e-07 0.9998 5.467e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001729 Epoch 9700 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00888 0.9968 0.9925 -1.751e-07 7.86e-08 -0.007065 -1.32e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003511 -0.003355 -0.006684 0.005391 0.9699 0.9743 0.006841 0.8247 0.8198 0.01626 ] Network output: [ 0.9999 0.0001119 0.0003742 -2.648e-06 1.189e-06 -0.0003281 -1.995e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2087 -0.0356 -0.1568 0.1826 0.9834 0.9932 0.2343 0.4292 0.8682 0.7086 ] Network output: [ -0.008879 1.003 1.008 -1.93e-07 8.665e-08 0.00737 -1.455e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006828 0.0006275 0.004368 0.003171 0.9889 0.9919 0.006961 0.8519 0.892 0.01161 ] Network output: [ -0.000186 0.001424 1 -8.313e-06 3.732e-06 0.9984 -6.265e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2225 0.1055 0.3491 0.142 0.9849 0.9939 0.2232 0.4332 0.875 0.7023 ] Network output: [ 0.003033 -0.01445 0.9943 5.069e-06 -2.276e-06 1.014 3.82e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09863 0.1848 0.1973 0.9873 0.9919 0.1114 0.7355 0.8612 0.3051 ] Network output: [ -0.002846 0.01335 1.005 5.535e-06 -2.485e-06 0.9877 4.171e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09434 0.0924 0.165 0.1966 0.9852 0.9911 0.09435 0.6593 0.8364 0.2493 ] Network output: [ 8.316e-05 1 -5.019e-05 7.245e-07 -3.253e-07 0.9998 5.46e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001728 Epoch 9701 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008879 0.9968 0.9925 -1.75e-07 7.856e-08 -0.007064 -1.319e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003511 -0.003355 -0.006684 0.005391 0.9699 0.9743 0.006841 0.8247 0.8198 0.01625 ] Network output: [ 0.9999 0.0001118 0.000374 -2.644e-06 1.187e-06 -0.0003279 -1.993e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2087 -0.0356 -0.1568 0.1826 0.9834 0.9932 0.2343 0.4292 0.8682 0.7086 ] Network output: [ -0.008878 1.003 1.008 -1.929e-07 8.658e-08 0.007369 -1.453e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006828 0.0006276 0.004368 0.003171 0.9889 0.9919 0.006962 0.8519 0.892 0.01161 ] Network output: [ -0.0001858 0.001424 1 -8.303e-06 3.728e-06 0.9984 -6.257e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2225 0.1055 0.3491 0.142 0.9849 0.9939 0.2232 0.4332 0.875 0.7023 ] Network output: [ 0.003031 -0.01444 0.9943 5.063e-06 -2.273e-06 1.014 3.815e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09863 0.1848 0.1973 0.9873 0.9919 0.1114 0.7354 0.8612 0.3051 ] Network output: [ -0.002845 0.01335 1.005 5.528e-06 -2.482e-06 0.9877 4.166e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09434 0.0924 0.165 0.1966 0.9852 0.9911 0.09436 0.6593 0.8364 0.2493 ] Network output: [ 8.313e-05 1 -5.018e-05 7.237e-07 -3.249e-07 0.9998 5.454e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001727 Epoch 9702 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008878 0.9968 0.9925 -1.749e-07 7.851e-08 -0.007064 -1.318e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003511 -0.003355 -0.006683 0.005391 0.9699 0.9743 0.006841 0.8247 0.8198 0.01625 ] Network output: [ 0.9999 0.0001116 0.0003739 -2.641e-06 1.186e-06 -0.0003277 -1.991e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2087 -0.0356 -0.1568 0.1826 0.9834 0.9932 0.2343 0.4292 0.8682 0.7086 ] Network output: [ -0.008877 1.003 1.008 -1.927e-07 8.652e-08 0.007369 -1.452e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006828 0.0006276 0.004368 0.003171 0.9889 0.9919 0.006962 0.8519 0.892 0.01161 ] Network output: [ -0.0001857 0.001423 1 -8.293e-06 3.723e-06 0.9984 -6.25e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2225 0.1055 0.3491 0.142 0.9849 0.9939 0.2232 0.4332 0.875 0.7023 ] Network output: [ 0.00303 -0.01443 0.9943 5.057e-06 -2.27e-06 1.014 3.811e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09863 0.1848 0.1973 0.9873 0.9919 0.1114 0.7354 0.8612 0.3051 ] Network output: [ -0.002844 0.01334 1.005 5.521e-06 -2.479e-06 0.9877 4.161e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09434 0.0924 0.165 0.1966 0.9852 0.9911 0.09436 0.6593 0.8364 0.2493 ] Network output: [ 8.311e-05 1 -5.017e-05 7.228e-07 -3.245e-07 0.9998 5.447e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001726 Epoch 9703 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008877 0.9968 0.9925 -1.748e-07 7.846e-08 -0.007063 -1.317e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003511 -0.003355 -0.006683 0.00539 0.9699 0.9743 0.006841 0.8247 0.8198 0.01625 ] Network output: [ 0.9999 0.0001114 0.0003737 -2.638e-06 1.184e-06 -0.0003275 -1.988e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2087 -0.0356 -0.1568 0.1826 0.9834 0.9932 0.2343 0.4292 0.8682 0.7086 ] Network output: [ -0.008876 1.003 1.008 -1.926e-07 8.645e-08 0.007368 -1.451e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006829 0.0006277 0.004368 0.003171 0.9889 0.9919 0.006962 0.8519 0.892 0.0116 ] Network output: [ -0.0001855 0.001422 1 -8.283e-06 3.719e-06 0.9984 -6.242e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2225 0.1055 0.3491 0.142 0.9849 0.9939 0.2233 0.4332 0.875 0.7023 ] Network output: [ 0.003028 -0.01443 0.9943 5.051e-06 -2.267e-06 1.014 3.806e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09864 0.1848 0.1973 0.9873 0.9919 0.1114 0.7354 0.8612 0.3051 ] Network output: [ -0.002842 0.01334 1.005 5.515e-06 -2.476e-06 0.9877 4.156e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09435 0.09241 0.165 0.1966 0.9852 0.9911 0.09436 0.6593 0.8364 0.2493 ] Network output: [ 8.309e-05 1 -5.016e-05 7.22e-07 -3.241e-07 0.9998 5.441e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001725 Epoch 9704 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008876 0.9968 0.9925 -1.747e-07 7.841e-08 -0.007062 -1.316e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003511 -0.003355 -0.006682 0.00539 0.9699 0.9743 0.006842 0.8247 0.8198 0.01625 ] Network output: [ 0.9999 0.0001113 0.0003735 -2.635e-06 1.183e-06 -0.0003273 -1.986e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2087 -0.0356 -0.1568 0.1826 0.9834 0.9932 0.2343 0.4292 0.8682 0.7086 ] Network output: [ -0.008876 1.003 1.008 -1.924e-07 8.639e-08 0.007367 -1.45e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006829 0.0006278 0.004368 0.00317 0.9889 0.9919 0.006963 0.8519 0.892 0.0116 ] Network output: [ -0.0001854 0.001421 1 -8.273e-06 3.714e-06 0.9984 -6.235e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2225 0.1056 0.3491 0.142 0.9849 0.9939 0.2233 0.4332 0.875 0.7023 ] Network output: [ 0.003027 -0.01442 0.9943 5.044e-06 -2.265e-06 1.014 3.802e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09864 0.1848 0.1973 0.9873 0.9919 0.1114 0.7354 0.8612 0.3051 ] Network output: [ -0.002841 0.01333 1.005 5.508e-06 -2.473e-06 0.9877 4.151e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09435 0.09241 0.165 0.1966 0.9852 0.9911 0.09436 0.6592 0.8364 0.2493 ] Network output: [ 8.307e-05 1 -5.015e-05 7.211e-07 -3.237e-07 0.9998 5.434e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001724 Epoch 9705 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008875 0.9968 0.9925 -1.746e-07 7.837e-08 -0.007062 -1.316e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003511 -0.003355 -0.006681 0.005389 0.9699 0.9743 0.006842 0.8247 0.8198 0.01625 ] Network output: [ 0.9999 0.0001111 0.0003734 -2.632e-06 1.181e-06 -0.0003271 -1.983e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2087 -0.0356 -0.1568 0.1826 0.9834 0.9932 0.2343 0.4292 0.8682 0.7086 ] Network output: [ -0.008875 1.003 1.008 -1.923e-07 8.632e-08 0.007367 -1.449e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006829 0.0006278 0.004368 0.00317 0.9889 0.9919 0.006963 0.8519 0.892 0.0116 ] Network output: [ -0.0001852 0.001421 1 -8.263e-06 3.71e-06 0.9984 -6.227e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2225 0.1056 0.3492 0.142 0.9849 0.9939 0.2233 0.4332 0.875 0.7023 ] Network output: [ 0.003025 -0.01441 0.9943 5.038e-06 -2.262e-06 1.014 3.797e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1113 0.09864 0.1848 0.1973 0.9873 0.9919 0.1114 0.7354 0.8612 0.3051 ] Network output: [ -0.00284 0.01332 1.005 5.502e-06 -2.47e-06 0.9877 4.146e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09435 0.09241 0.165 0.1966 0.9852 0.9911 0.09436 0.6592 0.8364 0.2493 ] Network output: [ 8.304e-05 1 -5.014e-05 7.202e-07 -3.233e-07 0.9998 5.428e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001723 Epoch 9706 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008874 0.9968 0.9925 -1.745e-07 7.832e-08 -0.007061 -1.315e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003511 -0.003355 -0.006681 0.005389 0.9699 0.9743 0.006842 0.8247 0.8198 0.01625 ] Network output: [ 0.9999 0.0001109 0.0003732 -2.629e-06 1.18e-06 -0.0003269 -1.981e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2087 -0.0356 -0.1568 0.1826 0.9834 0.9932 0.2343 0.4292 0.8682 0.7086 ] Network output: [ -0.008874 1.003 1.008 -1.921e-07 8.626e-08 0.007366 -1.448e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00683 0.0006279 0.004368 0.00317 0.9889 0.9919 0.006963 0.8519 0.892 0.0116 ] Network output: [ -0.0001851 0.00142 1 -8.253e-06 3.705e-06 0.9984 -6.22e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2225 0.1056 0.3492 0.142 0.9849 0.9939 0.2233 0.4332 0.875 0.7023 ] Network output: [ 0.003024 -0.01441 0.9943 5.032e-06 -2.259e-06 1.014 3.793e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09865 0.1848 0.1973 0.9873 0.9919 0.1114 0.7354 0.8612 0.3051 ] Network output: [ -0.002838 0.01332 1.005 5.495e-06 -2.467e-06 0.9877 4.142e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09435 0.09241 0.165 0.1966 0.9852 0.9911 0.09437 0.6592 0.8364 0.2493 ] Network output: [ 8.302e-05 1 -5.013e-05 7.194e-07 -3.23e-07 0.9998 5.422e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001722 Epoch 9707 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008873 0.9968 0.9925 -1.744e-07 7.827e-08 -0.007061 -1.314e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003511 -0.003355 -0.00668 0.005389 0.9699 0.9743 0.006842 0.8247 0.8198 0.01625 ] Network output: [ 0.9999 0.0001108 0.000373 -2.625e-06 1.179e-06 -0.0003267 -1.979e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2087 -0.0356 -0.1568 0.1826 0.9834 0.9932 0.2343 0.4292 0.8682 0.7086 ] Network output: [ -0.008873 1.003 1.008 -1.92e-07 8.619e-08 0.007366 -1.447e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00683 0.000628 0.004368 0.00317 0.9889 0.9919 0.006964 0.8519 0.892 0.0116 ] Network output: [ -0.0001849 0.001419 1 -8.243e-06 3.701e-06 0.9984 -6.212e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2225 0.1056 0.3492 0.142 0.9849 0.9939 0.2233 0.4332 0.875 0.7023 ] Network output: [ 0.003022 -0.0144 0.9943 5.026e-06 -2.257e-06 1.014 3.788e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09865 0.1848 0.1973 0.9873 0.9919 0.1114 0.7354 0.8612 0.3051 ] Network output: [ -0.002837 0.01331 1.005 5.489e-06 -2.464e-06 0.9877 4.137e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09435 0.09241 0.165 0.1966 0.9852 0.9911 0.09437 0.6592 0.8363 0.2493 ] Network output: [ 8.3e-05 1 -5.012e-05 7.185e-07 -3.226e-07 0.9998 5.415e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001722 Epoch 9708 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008873 0.9968 0.9925 -1.742e-07 7.822e-08 -0.00706 -1.313e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003511 -0.003356 -0.00668 0.005388 0.9699 0.9743 0.006842 0.8247 0.8198 0.01625 ] Network output: [ 0.9999 0.0001106 0.0003729 -2.622e-06 1.177e-06 -0.0003264 -1.976e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2088 -0.0356 -0.1568 0.1826 0.9834 0.9932 0.2343 0.4292 0.8682 0.7086 ] Network output: [ -0.008872 1.003 1.008 -1.918e-07 8.612e-08 0.007365 -1.446e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00683 0.000628 0.004367 0.003169 0.9889 0.9919 0.006964 0.8519 0.892 0.0116 ] Network output: [ -0.0001848 0.001419 1 -8.233e-06 3.696e-06 0.9984 -6.205e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2225 0.1056 0.3492 0.142 0.9849 0.9939 0.2233 0.4332 0.875 0.7023 ] Network output: [ 0.003021 -0.01439 0.9943 5.02e-06 -2.254e-06 1.014 3.783e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09865 0.1848 0.1973 0.9873 0.9919 0.1114 0.7354 0.8612 0.3051 ] Network output: [ -0.002836 0.01331 1.005 5.482e-06 -2.461e-06 0.9877 4.132e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09435 0.09242 0.165 0.1966 0.9852 0.9911 0.09437 0.6592 0.8363 0.2493 ] Network output: [ 8.298e-05 1 -5.011e-05 7.177e-07 -3.222e-07 0.9998 5.409e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001721 Epoch 9709 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008872 0.9968 0.9925 -1.741e-07 7.818e-08 -0.007059 -1.312e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003511 -0.003356 -0.006679 0.005388 0.9699 0.9743 0.006842 0.8246 0.8198 0.01625 ] Network output: [ 0.9999 0.0001105 0.0003727 -2.619e-06 1.176e-06 -0.0003262 -1.974e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2088 -0.03561 -0.1568 0.1825 0.9834 0.9932 0.2343 0.4292 0.8682 0.7086 ] Network output: [ -0.008871 1.003 1.008 -1.917e-07 8.606e-08 0.007364 -1.445e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006831 0.0006281 0.004367 0.003169 0.9889 0.9919 0.006965 0.8519 0.892 0.0116 ] Network output: [ -0.0001846 0.001418 1 -8.223e-06 3.692e-06 0.9984 -6.197e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2225 0.1056 0.3492 0.142 0.9849 0.9939 0.2233 0.4331 0.875 0.7023 ] Network output: [ 0.003019 -0.01439 0.9943 5.014e-06 -2.251e-06 1.014 3.779e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09866 0.1848 0.1973 0.9873 0.9919 0.1114 0.7354 0.8612 0.3051 ] Network output: [ -0.002834 0.0133 1.005 5.476e-06 -2.458e-06 0.9877 4.127e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09436 0.09242 0.165 0.1966 0.9852 0.9911 0.09437 0.6592 0.8363 0.2494 ] Network output: [ 8.296e-05 1 -5.01e-05 7.168e-07 -3.218e-07 0.9998 5.402e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000172 Epoch 9710 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008871 0.9968 0.9925 -1.74e-07 7.813e-08 -0.007059 -1.312e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003512 -0.003356 -0.006678 0.005388 0.9699 0.9743 0.006843 0.8246 0.8198 0.01625 ] Network output: [ 0.9999 0.0001103 0.0003726 -2.616e-06 1.174e-06 -0.000326 -1.971e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2088 -0.03561 -0.1568 0.1825 0.9834 0.9932 0.2343 0.4292 0.8682 0.7086 ] Network output: [ -0.008871 1.003 1.008 -1.915e-07 8.599e-08 0.007364 -1.444e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006831 0.0006282 0.004367 0.003169 0.9889 0.9919 0.006965 0.8519 0.892 0.0116 ] Network output: [ -0.0001844 0.001417 1 -8.213e-06 3.687e-06 0.9984 -6.19e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2225 0.1056 0.3492 0.142 0.9849 0.9939 0.2233 0.4331 0.875 0.7023 ] Network output: [ 0.003018 -0.01438 0.9943 5.008e-06 -2.248e-06 1.014 3.774e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09866 0.1848 0.1973 0.9873 0.9919 0.1114 0.7353 0.8612 0.3051 ] Network output: [ -0.002833 0.01329 1.005 5.47e-06 -2.455e-06 0.9877 4.122e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09436 0.09242 0.165 0.1966 0.9852 0.9911 0.09437 0.6592 0.8363 0.2494 ] Network output: [ 8.293e-05 1 -5.009e-05 7.16e-07 -3.214e-07 0.9998 5.396e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001719 Epoch 9711 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00887 0.9968 0.9925 -1.739e-07 7.808e-08 -0.007058 -1.311e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003512 -0.003356 -0.006678 0.005387 0.9699 0.9743 0.006843 0.8246 0.8198 0.01625 ] Network output: [ 0.9999 0.0001101 0.0003724 -2.613e-06 1.173e-06 -0.0003258 -1.969e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2088 -0.03561 -0.1567 0.1825 0.9834 0.9932 0.2344 0.4292 0.8682 0.7086 ] Network output: [ -0.00887 1.003 1.008 -1.914e-07 8.593e-08 0.007363 -1.442e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006832 0.0006282 0.004367 0.003169 0.9889 0.9919 0.006965 0.8519 0.892 0.0116 ] Network output: [ -0.0001843 0.001417 1 -8.203e-06 3.683e-06 0.9984 -6.182e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2225 0.1056 0.3492 0.142 0.9849 0.9939 0.2233 0.4331 0.8749 0.7023 ] Network output: [ 0.003017 -0.01437 0.9943 5.002e-06 -2.246e-06 1.014 3.77e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09866 0.1848 0.1973 0.9873 0.9919 0.1114 0.7353 0.8612 0.3051 ] Network output: [ -0.002832 0.01329 1.005 5.463e-06 -2.453e-06 0.9877 4.117e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09436 0.09242 0.165 0.1966 0.9852 0.9911 0.09437 0.6592 0.8363 0.2494 ] Network output: [ 8.291e-05 1 -5.008e-05 7.151e-07 -3.21e-07 0.9998 5.389e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001718 Epoch 9712 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008869 0.9968 0.9925 -1.738e-07 7.804e-08 -0.007058 -1.31e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003512 -0.003356 -0.006677 0.005387 0.9699 0.9743 0.006843 0.8246 0.8198 0.01624 ] Network output: [ 0.9999 0.00011 0.0003722 -2.609e-06 1.171e-06 -0.0003256 -1.967e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2088 -0.03561 -0.1567 0.1825 0.9834 0.9932 0.2344 0.4292 0.8682 0.7086 ] Network output: [ -0.008869 1.003 1.008 -1.913e-07 8.586e-08 0.007363 -1.441e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006832 0.0006283 0.004367 0.003168 0.9889 0.9919 0.006966 0.8519 0.892 0.0116 ] Network output: [ -0.0001841 0.001416 1 -8.194e-06 3.678e-06 0.9984 -6.175e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2226 0.1056 0.3492 0.142 0.9849 0.9939 0.2233 0.4331 0.8749 0.7023 ] Network output: [ 0.003015 -0.01437 0.9943 4.996e-06 -2.243e-06 1.014 3.765e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09867 0.1848 0.1973 0.9873 0.9919 0.1115 0.7353 0.8612 0.3051 ] Network output: [ -0.00283 0.01328 1.005 5.457e-06 -2.45e-06 0.9877 4.112e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09436 0.09242 0.165 0.1966 0.9852 0.9911 0.09438 0.6592 0.8363 0.2494 ] Network output: [ 8.289e-05 1 -5.007e-05 7.143e-07 -3.207e-07 0.9998 5.383e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001717 Epoch 9713 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008868 0.9968 0.9925 -1.737e-07 7.799e-08 -0.007057 -1.309e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003512 -0.003356 -0.006677 0.005386 0.9699 0.9743 0.006843 0.8246 0.8198 0.01624 ] Network output: [ 0.9999 0.0001098 0.0003721 -2.606e-06 1.17e-06 -0.0003254 -1.964e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2088 -0.03561 -0.1567 0.1825 0.9834 0.9932 0.2344 0.4292 0.8682 0.7086 ] Network output: [ -0.008868 1.003 1.008 -1.911e-07 8.58e-08 0.007362 -1.44e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006832 0.0006284 0.004367 0.003168 0.9889 0.9919 0.006966 0.8519 0.892 0.0116 ] Network output: [ -0.000184 0.001415 1 -8.184e-06 3.674e-06 0.9984 -6.167e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2226 0.1056 0.3492 0.142 0.9849 0.9939 0.2233 0.4331 0.8749 0.7023 ] Network output: [ 0.003014 -0.01436 0.9943 4.99e-06 -2.24e-06 1.014 3.761e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09867 0.1848 0.1973 0.9873 0.9919 0.1115 0.7353 0.8612 0.3051 ] Network output: [ -0.002829 0.01328 1.005 5.45e-06 -2.447e-06 0.9877 4.107e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09436 0.09242 0.165 0.1966 0.9852 0.9911 0.09438 0.6592 0.8363 0.2494 ] Network output: [ 8.287e-05 1 -5.006e-05 7.134e-07 -3.203e-07 0.9998 5.377e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001716 Epoch 9714 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008867 0.9968 0.9925 -1.736e-07 7.794e-08 -0.007056 -1.308e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003512 -0.003356 -0.006676 0.005386 0.9699 0.9743 0.006843 0.8246 0.8198 0.01624 ] Network output: [ 0.9999 0.0001096 0.0003719 -2.603e-06 1.169e-06 -0.0003252 -1.962e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2088 -0.03561 -0.1567 0.1825 0.9834 0.9932 0.2344 0.4292 0.8682 0.7086 ] Network output: [ -0.008867 1.003 1.008 -1.91e-07 8.573e-08 0.007362 -1.439e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006833 0.0006284 0.004367 0.003168 0.9889 0.9919 0.006966 0.8519 0.892 0.0116 ] Network output: [ -0.0001838 0.001414 1 -8.174e-06 3.669e-06 0.9984 -6.16e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2226 0.1056 0.3492 0.142 0.9849 0.9939 0.2233 0.4331 0.8749 0.7023 ] Network output: [ 0.003012 -0.01435 0.9943 4.984e-06 -2.238e-06 1.014 3.756e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09867 0.1848 0.1973 0.9873 0.9919 0.1115 0.7353 0.8612 0.3051 ] Network output: [ -0.002828 0.01327 1.005 5.444e-06 -2.444e-06 0.9877 4.103e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09437 0.09243 0.165 0.1966 0.9852 0.9911 0.09438 0.6591 0.8363 0.2494 ] Network output: [ 8.285e-05 1 -5.005e-05 7.126e-07 -3.199e-07 0.9998 5.37e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001715 Epoch 9715 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008866 0.9968 0.9925 -1.735e-07 7.789e-08 -0.007056 -1.308e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003512 -0.003356 -0.006676 0.005386 0.9699 0.9743 0.006843 0.8246 0.8198 0.01624 ] Network output: [ 0.9999 0.0001095 0.0003717 -2.6e-06 1.167e-06 -0.000325 -1.959e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2088 -0.03561 -0.1567 0.1825 0.9834 0.9932 0.2344 0.4292 0.8682 0.7086 ] Network output: [ -0.008866 1.003 1.008 -1.908e-07 8.567e-08 0.007361 -1.438e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006833 0.0006285 0.004367 0.003168 0.9889 0.9919 0.006967 0.8519 0.8919 0.0116 ] Network output: [ -0.0001837 0.001414 1 -8.164e-06 3.665e-06 0.9984 -6.153e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2226 0.1056 0.3492 0.142 0.9849 0.9939 0.2233 0.4331 0.8749 0.7023 ] Network output: [ 0.003011 -0.01435 0.9943 4.978e-06 -2.235e-06 1.014 3.752e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09867 0.1848 0.1973 0.9873 0.9919 0.1115 0.7353 0.8612 0.3051 ] Network output: [ -0.002826 0.01326 1.005 5.437e-06 -2.441e-06 0.9877 4.098e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09437 0.09243 0.165 0.1966 0.9852 0.9911 0.09438 0.6591 0.8363 0.2494 ] Network output: [ 8.282e-05 1 -5.004e-05 7.117e-07 -3.195e-07 0.9998 5.364e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001714 Epoch 9716 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008865 0.9968 0.9925 -1.734e-07 7.785e-08 -0.007055 -1.307e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003512 -0.003356 -0.006675 0.005385 0.9699 0.9743 0.006844 0.8246 0.8198 0.01624 ] Network output: [ 0.9999 0.0001093 0.0003716 -2.597e-06 1.166e-06 -0.0003248 -1.957e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2088 -0.03561 -0.1567 0.1825 0.9834 0.9932 0.2344 0.4292 0.8682 0.7085 ] Network output: [ -0.008866 1.003 1.008 -1.907e-07 8.56e-08 0.00736 -1.437e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006833 0.0006285 0.004367 0.003168 0.9889 0.9919 0.006967 0.8518 0.8919 0.0116 ] Network output: [ -0.0001835 0.001413 1 -8.154e-06 3.661e-06 0.9984 -6.145e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2226 0.1056 0.3492 0.142 0.9849 0.9939 0.2233 0.4331 0.8749 0.7023 ] Network output: [ 0.003009 -0.01434 0.9943 4.972e-06 -2.232e-06 1.014 3.747e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09868 0.1848 0.1973 0.9873 0.9919 0.1115 0.7353 0.8612 0.3051 ] Network output: [ -0.002825 0.01326 1.005 5.431e-06 -2.438e-06 0.9877 4.093e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09437 0.09243 0.165 0.1966 0.9852 0.9911 0.09438 0.6591 0.8363 0.2494 ] Network output: [ 8.28e-05 1 -5.003e-05 7.109e-07 -3.191e-07 0.9998 5.357e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001713 Epoch 9717 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008864 0.9968 0.9925 -1.733e-07 7.78e-08 -0.007055 -1.306e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003512 -0.003356 -0.006674 0.005385 0.9699 0.9743 0.006844 0.8246 0.8198 0.01624 ] Network output: [ 0.9999 0.0001091 0.0003714 -2.594e-06 1.164e-06 -0.0003246 -1.955e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2088 -0.03561 -0.1567 0.1825 0.9834 0.9932 0.2344 0.4292 0.8682 0.7085 ] Network output: [ -0.008865 1.003 1.008 -1.905e-07 8.553e-08 0.00736 -1.436e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006834 0.0006286 0.004367 0.003167 0.9889 0.9919 0.006968 0.8518 0.8919 0.01159 ] Network output: [ -0.0001834 0.001412 1 -8.144e-06 3.656e-06 0.9984 -6.138e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2226 0.1056 0.3492 0.142 0.9849 0.9939 0.2234 0.4331 0.8749 0.7023 ] Network output: [ 0.003008 -0.01434 0.9943 4.966e-06 -2.23e-06 1.014 3.743e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09868 0.1848 0.1973 0.9873 0.9919 0.1115 0.7353 0.8612 0.3051 ] Network output: [ -0.002824 0.01325 1.005 5.424e-06 -2.435e-06 0.9877 4.088e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09437 0.09243 0.165 0.1966 0.9852 0.9911 0.09439 0.6591 0.8363 0.2494 ] Network output: [ 8.278e-05 1 -5.002e-05 7.1e-07 -3.188e-07 0.9998 5.351e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001712 Epoch 9718 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008864 0.9968 0.9925 -1.732e-07 7.775e-08 -0.007054 -1.305e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003512 -0.003356 -0.006674 0.005384 0.9699 0.9743 0.006844 0.8246 0.8198 0.01624 ] Network output: [ 0.9999 0.000109 0.0003712 -2.591e-06 1.163e-06 -0.0003244 -1.952e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2088 -0.03561 -0.1567 0.1825 0.9834 0.9932 0.2344 0.4291 0.8682 0.7085 ] Network output: [ -0.008864 1.003 1.008 -1.904e-07 8.547e-08 0.007359 -1.435e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006834 0.0006287 0.004367 0.003167 0.9889 0.9919 0.006968 0.8518 0.8919 0.01159 ] Network output: [ -0.0001832 0.001412 1 -8.134e-06 3.652e-06 0.9984 -6.13e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2226 0.1056 0.3492 0.142 0.9849 0.9939 0.2234 0.4331 0.8749 0.7023 ] Network output: [ 0.003006 -0.01433 0.9943 4.96e-06 -2.227e-06 1.014 3.738e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09868 0.1848 0.1973 0.9873 0.9919 0.1115 0.7353 0.8612 0.3051 ] Network output: [ -0.002822 0.01325 1.005 5.418e-06 -2.432e-06 0.9877 4.083e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09437 0.09243 0.165 0.1966 0.9852 0.9911 0.09439 0.6591 0.8363 0.2494 ] Network output: [ 8.276e-05 1 -5.001e-05 7.092e-07 -3.184e-07 0.9998 5.345e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001711 Epoch 9719 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008863 0.9968 0.9925 -1.731e-07 7.77e-08 -0.007053 -1.304e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003512 -0.003356 -0.006673 0.005384 0.9699 0.9743 0.006844 0.8246 0.8198 0.01624 ] Network output: [ 0.9999 0.0001088 0.0003711 -2.587e-06 1.162e-06 -0.0003241 -1.95e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2088 -0.03562 -0.1567 0.1825 0.9834 0.9932 0.2344 0.4291 0.8682 0.7085 ] Network output: [ -0.008863 1.003 1.008 -1.902e-07 8.54e-08 0.007359 -1.434e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006834 0.0006287 0.004366 0.003167 0.9889 0.9919 0.006968 0.8518 0.8919 0.01159 ] Network output: [ -0.0001831 0.001411 1 -8.125e-06 3.647e-06 0.9984 -6.123e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2226 0.1056 0.3492 0.142 0.9849 0.9939 0.2234 0.4331 0.8749 0.7023 ] Network output: [ 0.003005 -0.01432 0.9943 4.954e-06 -2.224e-06 1.014 3.734e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09869 0.1848 0.1973 0.9873 0.9919 0.1115 0.7353 0.8612 0.3051 ] Network output: [ -0.002821 0.01324 1.005 5.412e-06 -2.429e-06 0.9877 4.078e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09438 0.09244 0.165 0.1966 0.9852 0.9911 0.09439 0.6591 0.8363 0.2494 ] Network output: [ 8.274e-05 1 -5.001e-05 7.083e-07 -3.18e-07 0.9998 5.338e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000171 Epoch 9720 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008862 0.9968 0.9925 -1.73e-07 7.765e-08 -0.007053 -1.304e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003512 -0.003356 -0.006673 0.005384 0.9699 0.9743 0.006844 0.8246 0.8198 0.01624 ] Network output: [ 0.9999 0.0001087 0.0003709 -2.584e-06 1.16e-06 -0.0003239 -1.948e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2088 -0.03562 -0.1567 0.1825 0.9834 0.9932 0.2344 0.4291 0.8682 0.7085 ] Network output: [ -0.008862 1.003 1.008 -1.901e-07 8.534e-08 0.007358 -1.433e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006835 0.0006288 0.004366 0.003167 0.9889 0.9919 0.006969 0.8518 0.8919 0.01159 ] Network output: [ -0.0001829 0.00141 1 -8.115e-06 3.643e-06 0.9984 -6.116e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2226 0.1056 0.3492 0.142 0.9849 0.9939 0.2234 0.4331 0.8749 0.7023 ] Network output: [ 0.003003 -0.01432 0.9943 4.949e-06 -2.222e-06 1.014 3.729e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09869 0.1848 0.1973 0.9873 0.9919 0.1115 0.7352 0.8612 0.3051 ] Network output: [ -0.00282 0.01323 1.005 5.405e-06 -2.427e-06 0.9877 4.074e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09438 0.09244 0.165 0.1966 0.9852 0.9911 0.09439 0.6591 0.8363 0.2494 ] Network output: [ 8.271e-05 1 -5e-05 7.075e-07 -3.176e-07 0.9998 5.332e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001709 Epoch 9721 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008861 0.9968 0.9925 -1.729e-07 7.761e-08 -0.007052 -1.303e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003512 -0.003357 -0.006672 0.005383 0.9699 0.9743 0.006844 0.8246 0.8198 0.01624 ] Network output: [ 0.9999 0.0001085 0.0003707 -2.581e-06 1.159e-06 -0.0003237 -1.945e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2088 -0.03562 -0.1567 0.1825 0.9834 0.9932 0.2344 0.4291 0.8682 0.7085 ] Network output: [ -0.008862 1.003 1.008 -1.899e-07 8.527e-08 0.007357 -1.431e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006835 0.0006289 0.004366 0.003166 0.9889 0.9919 0.006969 0.8518 0.8919 0.01159 ] Network output: [ -0.0001828 0.00141 1 -8.105e-06 3.639e-06 0.9984 -6.108e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2226 0.1056 0.3492 0.1419 0.9849 0.9939 0.2234 0.4331 0.8749 0.7022 ] Network output: [ 0.003002 -0.01431 0.9943 4.943e-06 -2.219e-06 1.014 3.725e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09869 0.1848 0.1973 0.9873 0.9919 0.1115 0.7352 0.8612 0.3051 ] Network output: [ -0.002818 0.01323 1.005 5.399e-06 -2.424e-06 0.9878 4.069e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09438 0.09244 0.165 0.1966 0.9852 0.9911 0.09439 0.6591 0.8363 0.2494 ] Network output: [ 8.269e-05 1 -4.999e-05 7.066e-07 -3.172e-07 0.9998 5.325e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001709 Epoch 9722 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00886 0.9968 0.9925 -1.728e-07 7.756e-08 -0.007052 -1.302e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003512 -0.003357 -0.006671 0.005383 0.9699 0.9743 0.006845 0.8246 0.8198 0.01624 ] Network output: [ 0.9999 0.0001083 0.0003706 -2.578e-06 1.157e-06 -0.0003235 -1.943e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2088 -0.03562 -0.1566 0.1825 0.9834 0.9932 0.2344 0.4291 0.8682 0.7085 ] Network output: [ -0.008861 1.003 1.008 -1.898e-07 8.521e-08 0.007357 -1.43e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006836 0.0006289 0.004366 0.003166 0.9889 0.9919 0.006969 0.8518 0.8919 0.01159 ] Network output: [ -0.0001826 0.001409 1 -8.095e-06 3.634e-06 0.9984 -6.101e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2226 0.1056 0.3492 0.1419 0.9849 0.9939 0.2234 0.4331 0.8749 0.7022 ] Network output: [ 0.003001 -0.0143 0.9943 4.937e-06 -2.216e-06 1.014 3.72e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.0987 0.1848 0.1973 0.9873 0.9919 0.1115 0.7352 0.8612 0.3051 ] Network output: [ -0.002817 0.01322 1.005 5.392e-06 -2.421e-06 0.9878 4.064e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09438 0.09244 0.165 0.1966 0.9852 0.9911 0.09439 0.6591 0.8363 0.2494 ] Network output: [ 8.267e-05 1 -4.998e-05 7.058e-07 -3.169e-07 0.9998 5.319e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001708 Epoch 9723 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008859 0.9968 0.9925 -1.727e-07 7.751e-08 -0.007051 -1.301e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003512 -0.003357 -0.006671 0.005382 0.9699 0.9743 0.006845 0.8246 0.8198 0.01623 ] Network output: [ 0.9999 0.0001082 0.0003704 -2.575e-06 1.156e-06 -0.0003233 -1.941e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2088 -0.03562 -0.1566 0.1825 0.9834 0.9932 0.2344 0.4291 0.8682 0.7085 ] Network output: [ -0.00886 1.003 1.008 -1.897e-07 8.514e-08 0.007356 -1.429e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006836 0.000629 0.004366 0.003166 0.9889 0.9919 0.00697 0.8518 0.8919 0.01159 ] Network output: [ -0.0001825 0.001408 1 -8.085e-06 3.63e-06 0.9984 -6.093e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2226 0.1056 0.3492 0.1419 0.9849 0.9939 0.2234 0.4331 0.8749 0.7022 ] Network output: [ 0.002999 -0.0143 0.9943 4.931e-06 -2.214e-06 1.014 3.716e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.0987 0.1848 0.1973 0.9873 0.9919 0.1115 0.7352 0.8612 0.3051 ] Network output: [ -0.002816 0.01322 1.005 5.386e-06 -2.418e-06 0.9878 4.059e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09438 0.09244 0.165 0.1966 0.9852 0.9911 0.0944 0.659 0.8363 0.2494 ] Network output: [ 8.265e-05 1 -4.997e-05 7.05e-07 -3.165e-07 0.9998 5.313e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001707 Epoch 9724 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008858 0.9968 0.9925 -1.726e-07 7.746e-08 -0.00705 -1.3e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003512 -0.003357 -0.00667 0.005382 0.9699 0.9743 0.006845 0.8246 0.8198 0.01623 ] Network output: [ 0.9999 0.000108 0.0003702 -2.572e-06 1.155e-06 -0.0003231 -1.938e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2088 -0.03562 -0.1566 0.1825 0.9834 0.9932 0.2344 0.4291 0.8682 0.7085 ] Network output: [ -0.008859 1.003 1.008 -1.895e-07 8.508e-08 0.007356 -1.428e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006836 0.0006291 0.004366 0.003166 0.9889 0.9919 0.00697 0.8518 0.8919 0.01159 ] Network output: [ -0.0001823 0.001407 1 -8.076e-06 3.625e-06 0.9984 -6.086e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2226 0.1056 0.3492 0.1419 0.9849 0.9939 0.2234 0.4331 0.8749 0.7022 ] Network output: [ 0.002998 -0.01429 0.9943 4.925e-06 -2.211e-06 1.014 3.711e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.0987 0.1848 0.1973 0.9873 0.9919 0.1115 0.7352 0.8612 0.3051 ] Network output: [ -0.002814 0.01321 1.005 5.38e-06 -2.415e-06 0.9878 4.054e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09438 0.09244 0.165 0.1966 0.9852 0.9911 0.0944 0.659 0.8363 0.2494 ] Network output: [ 8.263e-05 1 -4.996e-05 7.041e-07 -3.161e-07 0.9998 5.306e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001706 Epoch 9725 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008857 0.9968 0.9925 -1.724e-07 7.742e-08 -0.00705 -1.3e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003512 -0.003357 -0.00667 0.005382 0.9699 0.9743 0.006845 0.8246 0.8198 0.01623 ] Network output: [ 0.9999 0.0001078 0.0003701 -2.569e-06 1.153e-06 -0.0003229 -1.936e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2088 -0.03562 -0.1566 0.1825 0.9834 0.9932 0.2344 0.4291 0.8682 0.7085 ] Network output: [ -0.008858 1.003 1.008 -1.894e-07 8.501e-08 0.007355 -1.427e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006837 0.0006291 0.004366 0.003165 0.9889 0.9919 0.006971 0.8518 0.8919 0.01159 ] Network output: [ -0.0001822 0.001407 1 -8.066e-06 3.621e-06 0.9984 -6.079e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2226 0.1056 0.3492 0.1419 0.9849 0.9939 0.2234 0.4331 0.8749 0.7022 ] Network output: [ 0.002996 -0.01428 0.9943 4.919e-06 -2.208e-06 1.014 3.707e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09871 0.1848 0.1973 0.9873 0.9919 0.1115 0.7352 0.8612 0.3051 ] Network output: [ -0.002813 0.0132 1.005 5.373e-06 -2.412e-06 0.9878 4.05e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09439 0.09245 0.165 0.1966 0.9852 0.9911 0.0944 0.659 0.8363 0.2494 ] Network output: [ 8.26e-05 1 -4.995e-05 7.033e-07 -3.157e-07 0.9998 5.3e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001705 Epoch 9726 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008856 0.9968 0.9926 -1.723e-07 7.737e-08 -0.007049 -1.299e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003512 -0.003357 -0.006669 0.005381 0.9699 0.9743 0.006845 0.8246 0.8198 0.01623 ] Network output: [ 0.9999 0.0001077 0.0003699 -2.566e-06 1.152e-06 -0.0003227 -1.934e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2089 -0.03562 -0.1566 0.1825 0.9834 0.9932 0.2345 0.4291 0.8682 0.7085 ] Network output: [ -0.008857 1.003 1.008 -1.892e-07 8.495e-08 0.007354 -1.426e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006837 0.0006292 0.004366 0.003165 0.9889 0.9919 0.006971 0.8518 0.8919 0.01159 ] Network output: [ -0.000182 0.001406 1 -8.056e-06 3.617e-06 0.9984 -6.071e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2226 0.1056 0.3493 0.1419 0.9849 0.9939 0.2234 0.4331 0.8749 0.7022 ] Network output: [ 0.002995 -0.01428 0.9943 4.913e-06 -2.206e-06 1.014 3.703e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09871 0.1848 0.1973 0.9873 0.9919 0.1115 0.7352 0.8612 0.3051 ] Network output: [ -0.002812 0.0132 1.005 5.367e-06 -2.409e-06 0.9878 4.045e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09439 0.09245 0.165 0.1966 0.9852 0.9911 0.0944 0.659 0.8363 0.2494 ] Network output: [ 8.258e-05 1 -4.994e-05 7.024e-07 -3.154e-07 0.9998 5.294e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001704 Epoch 9727 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008855 0.9968 0.9926 -1.722e-07 7.732e-08 -0.007049 -1.298e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003512 -0.003357 -0.006669 0.005381 0.9699 0.9743 0.006845 0.8246 0.8198 0.01623 ] Network output: [ 0.9999 0.0001075 0.0003698 -2.563e-06 1.15e-06 -0.0003225 -1.931e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2089 -0.03562 -0.1566 0.1825 0.9834 0.9932 0.2345 0.4291 0.8682 0.7085 ] Network output: [ -0.008857 1.003 1.008 -1.891e-07 8.488e-08 0.007354 -1.425e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006837 0.0006293 0.004366 0.003165 0.9889 0.9919 0.006971 0.8518 0.8919 0.01159 ] Network output: [ -0.0001819 0.001405 1 -8.046e-06 3.612e-06 0.9984 -6.064e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2227 0.1056 0.3493 0.1419 0.9849 0.9939 0.2234 0.4331 0.8749 0.7022 ] Network output: [ 0.002993 -0.01427 0.9943 4.907e-06 -2.203e-06 1.014 3.698e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09871 0.1848 0.1973 0.9873 0.9919 0.1115 0.7352 0.8612 0.3051 ] Network output: [ -0.00281 0.01319 1.005 5.361e-06 -2.407e-06 0.9878 4.04e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09439 0.09245 0.165 0.1966 0.9852 0.9911 0.0944 0.659 0.8363 0.2494 ] Network output: [ 8.256e-05 1 -4.993e-05 7.016e-07 -3.15e-07 0.9998 5.288e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001703 Epoch 9728 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008855 0.9968 0.9926 -1.721e-07 7.727e-08 -0.007048 -1.297e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003512 -0.003357 -0.006668 0.005381 0.9699 0.9743 0.006846 0.8246 0.8198 0.01623 ] Network output: [ 0.9999 0.0001074 0.0003696 -2.559e-06 1.149e-06 -0.0003223 -1.929e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2089 -0.03562 -0.1566 0.1825 0.9834 0.9932 0.2345 0.4291 0.8682 0.7085 ] Network output: [ -0.008856 1.003 1.008 -1.889e-07 8.481e-08 0.007353 -1.424e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006838 0.0006293 0.004366 0.003165 0.9889 0.9919 0.006972 0.8518 0.8919 0.01159 ] Network output: [ -0.0001817 0.001405 1 -8.037e-06 3.608e-06 0.9985 -6.057e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2227 0.1057 0.3493 0.1419 0.9849 0.9939 0.2234 0.4331 0.8749 0.7022 ] Network output: [ 0.002992 -0.01426 0.9943 4.901e-06 -2.2e-06 1.014 3.694e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09872 0.1848 0.1973 0.9873 0.9919 0.1115 0.7352 0.8612 0.3051 ] Network output: [ -0.002809 0.01319 1.005 5.354e-06 -2.404e-06 0.9878 4.035e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09439 0.09245 0.165 0.1966 0.9852 0.9911 0.09441 0.659 0.8363 0.2494 ] Network output: [ 8.254e-05 1 -4.993e-05 7.008e-07 -3.146e-07 0.9998 5.281e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001702 Epoch 9729 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008854 0.9968 0.9926 -1.72e-07 7.723e-08 -0.007047 -1.296e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003513 -0.003357 -0.006667 0.00538 0.9699 0.9743 0.006846 0.8246 0.8198 0.01623 ] Network output: [ 0.9999 0.0001072 0.0003694 -2.556e-06 1.148e-06 -0.0003221 -1.927e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2089 -0.03562 -0.1566 0.1825 0.9834 0.9932 0.2345 0.4291 0.8682 0.7085 ] Network output: [ -0.008855 1.003 1.008 -1.888e-07 8.475e-08 0.007353 -1.423e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006838 0.0006294 0.004365 0.003165 0.9889 0.9919 0.006972 0.8518 0.8919 0.01159 ] Network output: [ -0.0001815 0.001404 1 -8.027e-06 3.604e-06 0.9985 -6.049e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2227 0.1057 0.3493 0.1419 0.9849 0.9939 0.2234 0.4331 0.8749 0.7022 ] Network output: [ 0.00299 -0.01426 0.9943 4.895e-06 -2.198e-06 1.014 3.689e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09872 0.1848 0.1973 0.9873 0.9919 0.1115 0.7351 0.8611 0.3051 ] Network output: [ -0.002808 0.01318 1.005 5.348e-06 -2.401e-06 0.9878 4.03e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09439 0.09245 0.165 0.1966 0.9852 0.9911 0.09441 0.659 0.8363 0.2494 ] Network output: [ 8.252e-05 1 -4.992e-05 6.999e-07 -3.142e-07 0.9998 5.275e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001701 Epoch 9730 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008853 0.9968 0.9926 -1.719e-07 7.718e-08 -0.007047 -1.296e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003513 -0.003357 -0.006667 0.00538 0.9699 0.9743 0.006846 0.8246 0.8198 0.01623 ] Network output: [ 0.9999 0.000107 0.0003693 -2.553e-06 1.146e-06 -0.0003219 -1.924e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2089 -0.03563 -0.1566 0.1825 0.9834 0.9932 0.2345 0.4291 0.8682 0.7085 ] Network output: [ -0.008854 1.003 1.008 -1.886e-07 8.468e-08 0.007352 -1.422e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006838 0.0006294 0.004365 0.003164 0.9889 0.9919 0.006972 0.8518 0.8919 0.01159 ] Network output: [ -0.0001814 0.001403 1 -8.017e-06 3.599e-06 0.9985 -6.042e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2227 0.1057 0.3493 0.1419 0.9849 0.9939 0.2234 0.4331 0.8749 0.7022 ] Network output: [ 0.002989 -0.01425 0.9943 4.889e-06 -2.195e-06 1.014 3.685e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09872 0.1848 0.1973 0.9873 0.9919 0.1115 0.7351 0.8611 0.305 ] Network output: [ -0.002806 0.01317 1.005 5.342e-06 -2.398e-06 0.9878 4.026e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0944 0.09246 0.165 0.1966 0.9852 0.9911 0.09441 0.659 0.8363 0.2494 ] Network output: [ 8.25e-05 1 -4.991e-05 6.991e-07 -3.139e-07 0.9998 5.269e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00017 Epoch 9731 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008852 0.9968 0.9926 -1.718e-07 7.713e-08 -0.007046 -1.295e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003513 -0.003357 -0.006666 0.005379 0.9699 0.9743 0.006846 0.8246 0.8198 0.01623 ] Network output: [ 0.9999 0.0001069 0.0003691 -2.55e-06 1.145e-06 -0.0003217 -1.922e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2089 -0.03563 -0.1566 0.1825 0.9834 0.9932 0.2345 0.4291 0.8682 0.7085 ] Network output: [ -0.008853 1.003 1.008 -1.885e-07 8.462e-08 0.007352 -1.421e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006839 0.0006295 0.004365 0.003164 0.9889 0.9919 0.006973 0.8518 0.8919 0.01158 ] Network output: [ -0.0001812 0.001403 1 -8.008e-06 3.595e-06 0.9985 -6.035e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2227 0.1057 0.3493 0.1419 0.9849 0.9939 0.2234 0.433 0.8749 0.7022 ] Network output: [ 0.002987 -0.01424 0.9943 4.884e-06 -2.192e-06 1.014 3.68e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09872 0.1848 0.1973 0.9873 0.9919 0.1115 0.7351 0.8611 0.305 ] Network output: [ -0.002805 0.01317 1.005 5.335e-06 -2.395e-06 0.9878 4.021e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0944 0.09246 0.165 0.1966 0.9852 0.9911 0.09441 0.659 0.8363 0.2494 ] Network output: [ 8.247e-05 1 -4.99e-05 6.983e-07 -3.135e-07 0.9998 5.262e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001699 Epoch 9732 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008851 0.9968 0.9926 -1.717e-07 7.708e-08 -0.007046 -1.294e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003513 -0.003357 -0.006666 0.005379 0.9699 0.9743 0.006846 0.8245 0.8197 0.01623 ] Network output: [ 0.9999 0.0001067 0.0003689 -2.547e-06 1.143e-06 -0.0003215 -1.92e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2089 -0.03563 -0.1566 0.1825 0.9834 0.9932 0.2345 0.4291 0.8682 0.7085 ] Network output: [ -0.008852 1.003 1.008 -1.883e-07 8.455e-08 0.007351 -1.419e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006839 0.0006296 0.004365 0.003164 0.9889 0.9919 0.006973 0.8518 0.8919 0.01158 ] Network output: [ -0.0001811 0.001402 1 -7.998e-06 3.591e-06 0.9985 -6.027e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2227 0.1057 0.3493 0.1419 0.9849 0.9939 0.2235 0.433 0.8749 0.7022 ] Network output: [ 0.002986 -0.01424 0.9943 4.878e-06 -2.19e-06 1.014 3.676e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09873 0.1848 0.1973 0.9873 0.9919 0.1115 0.7351 0.8611 0.305 ] Network output: [ -0.002804 0.01316 1.005 5.329e-06 -2.392e-06 0.9878 4.016e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0944 0.09246 0.165 0.1966 0.9852 0.9911 0.09441 0.6589 0.8363 0.2494 ] Network output: [ 8.245e-05 1 -4.989e-05 6.974e-07 -3.131e-07 0.9998 5.256e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001698 Epoch 9733 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00885 0.9968 0.9926 -1.716e-07 7.703e-08 -0.007045 -1.293e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003513 -0.003358 -0.006665 0.005379 0.9699 0.9743 0.006846 0.8245 0.8197 0.01623 ] Network output: [ 0.9999 0.0001065 0.0003688 -2.544e-06 1.142e-06 -0.0003213 -1.917e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2089 -0.03563 -0.1566 0.1825 0.9834 0.9932 0.2345 0.4291 0.8682 0.7085 ] Network output: [ -0.008852 1.003 1.008 -1.882e-07 8.449e-08 0.00735 -1.418e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00684 0.0006296 0.004365 0.003164 0.9889 0.9919 0.006974 0.8518 0.8919 0.01158 ] Network output: [ -0.0001809 0.001401 1 -7.988e-06 3.586e-06 0.9985 -6.02e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2227 0.1057 0.3493 0.1419 0.9849 0.9939 0.2235 0.433 0.8749 0.7022 ] Network output: [ 0.002985 -0.01423 0.9943 4.872e-06 -2.187e-06 1.014 3.672e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09873 0.1848 0.1973 0.9873 0.9919 0.1115 0.7351 0.8611 0.305 ] Network output: [ -0.002802 0.01316 1.005 5.323e-06 -2.39e-06 0.9878 4.011e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0944 0.09246 0.165 0.1966 0.9852 0.9911 0.09442 0.6589 0.8363 0.2494 ] Network output: [ 8.243e-05 1 -4.988e-05 6.966e-07 -3.127e-07 0.9998 5.25e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001697 Epoch 9734 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008849 0.9968 0.9926 -1.715e-07 7.699e-08 -0.007044 -1.292e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003513 -0.003358 -0.006664 0.005378 0.9699 0.9743 0.006847 0.8245 0.8197 0.01622 ] Network output: [ 0.9999 0.0001064 0.0003686 -2.541e-06 1.141e-06 -0.000321 -1.915e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2089 -0.03563 -0.1565 0.1825 0.9834 0.9932 0.2345 0.4291 0.8682 0.7085 ] Network output: [ -0.008851 1.003 1.008 -1.881e-07 8.442e-08 0.00735 -1.417e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00684 0.0006297 0.004365 0.003163 0.9889 0.9919 0.006974 0.8518 0.8919 0.01158 ] Network output: [ -0.0001808 0.001401 1 -7.979e-06 3.582e-06 0.9985 -6.013e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2227 0.1057 0.3493 0.1419 0.9849 0.9939 0.2235 0.433 0.8749 0.7022 ] Network output: [ 0.002983 -0.01422 0.9943 4.866e-06 -2.184e-06 1.014 3.667e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09873 0.1848 0.1973 0.9873 0.9919 0.1115 0.7351 0.8611 0.305 ] Network output: [ -0.002801 0.01315 1.005 5.316e-06 -2.387e-06 0.9878 4.007e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0944 0.09246 0.165 0.1966 0.9852 0.9911 0.09442 0.6589 0.8363 0.2494 ] Network output: [ 8.241e-05 1 -4.988e-05 6.958e-07 -3.124e-07 0.9998 5.244e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001697 Epoch 9735 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008848 0.9968 0.9926 -1.714e-07 7.694e-08 -0.007044 -1.292e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003513 -0.003358 -0.006664 0.005378 0.9699 0.9743 0.006847 0.8245 0.8197 0.01622 ] Network output: [ 0.9999 0.0001062 0.0003684 -2.538e-06 1.139e-06 -0.0003208 -1.913e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2089 -0.03563 -0.1565 0.1825 0.9834 0.9932 0.2345 0.4291 0.8682 0.7085 ] Network output: [ -0.00885 1.003 1.008 -1.879e-07 8.436e-08 0.007349 -1.416e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00684 0.0006298 0.004365 0.003163 0.9889 0.9919 0.006974 0.8518 0.8919 0.01158 ] Network output: [ -0.0001806 0.0014 1 -7.969e-06 3.578e-06 0.9985 -6.006e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2227 0.1057 0.3493 0.1419 0.9849 0.9939 0.2235 0.433 0.8749 0.7022 ] Network output: [ 0.002982 -0.01422 0.9943 4.86e-06 -2.182e-06 1.014 3.663e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1114 0.09874 0.1848 0.1973 0.9873 0.9919 0.1115 0.7351 0.8611 0.305 ] Network output: [ -0.0028 0.01314 1.005 5.31e-06 -2.384e-06 0.9878 4.002e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0944 0.09246 0.165 0.1966 0.9852 0.9911 0.09442 0.6589 0.8363 0.2494 ] Network output: [ 8.239e-05 1 -4.987e-05 6.949e-07 -3.12e-07 0.9998 5.237e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001696 Epoch 9736 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008847 0.9968 0.9926 -1.713e-07 7.689e-08 -0.007043 -1.291e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003513 -0.003358 -0.006663 0.005378 0.9699 0.9743 0.006847 0.8245 0.8197 0.01622 ] Network output: [ 0.9999 0.0001061 0.0003683 -2.535e-06 1.138e-06 -0.0003206 -1.91e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2089 -0.03563 -0.1565 0.1825 0.9834 0.9932 0.2345 0.4291 0.8682 0.7085 ] Network output: [ -0.008849 1.003 1.008 -1.878e-07 8.429e-08 0.007349 -1.415e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006841 0.0006298 0.004365 0.003163 0.9889 0.9919 0.006975 0.8518 0.8919 0.01158 ] Network output: [ -0.0001805 0.001399 1 -7.959e-06 3.573e-06 0.9985 -5.998e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2227 0.1057 0.3493 0.1419 0.9849 0.9939 0.2235 0.433 0.8749 0.7022 ] Network output: [ 0.00298 -0.01421 0.9943 4.854e-06 -2.179e-06 1.014 3.658e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09874 0.1848 0.1973 0.9873 0.9919 0.1115 0.7351 0.8611 0.305 ] Network output: [ -0.002798 0.01314 1.005 5.304e-06 -2.381e-06 0.9878 3.997e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09441 0.09247 0.165 0.1966 0.9852 0.9911 0.09442 0.6589 0.8363 0.2494 ] Network output: [ 8.237e-05 1 -4.986e-05 6.941e-07 -3.116e-07 0.9998 5.231e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001695 Epoch 9737 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008846 0.9968 0.9926 -1.712e-07 7.684e-08 -0.007043 -1.29e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003513 -0.003358 -0.006663 0.005377 0.9699 0.9743 0.006847 0.8245 0.8197 0.01622 ] Network output: [ 0.9999 0.0001059 0.0003681 -2.532e-06 1.137e-06 -0.0003204 -1.908e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2089 -0.03563 -0.1565 0.1824 0.9834 0.9932 0.2345 0.4291 0.8682 0.7085 ] Network output: [ -0.008848 1.003 1.008 -1.876e-07 8.423e-08 0.007348 -1.414e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006841 0.0006299 0.004365 0.003163 0.9889 0.9919 0.006975 0.8517 0.8919 0.01158 ] Network output: [ -0.0001803 0.001398 1 -7.95e-06 3.569e-06 0.9985 -5.991e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2227 0.1057 0.3493 0.1419 0.9849 0.9939 0.2235 0.433 0.8749 0.7022 ] Network output: [ 0.002979 -0.0142 0.9943 4.848e-06 -2.177e-06 1.014 3.654e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09874 0.1848 0.1973 0.9873 0.9919 0.1115 0.7351 0.8611 0.305 ] Network output: [ -0.002797 0.01313 1.005 5.297e-06 -2.378e-06 0.9878 3.992e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09441 0.09247 0.165 0.1966 0.9852 0.9911 0.09442 0.6589 0.8363 0.2494 ] Network output: [ 8.234e-05 1 -4.985e-05 6.933e-07 -3.112e-07 0.9998 5.225e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001694 Epoch 9738 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008846 0.9968 0.9926 -1.711e-07 7.68e-08 -0.007042 -1.289e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003513 -0.003358 -0.006662 0.005377 0.9699 0.9743 0.006847 0.8245 0.8197 0.01622 ] Network output: [ 0.9999 0.0001057 0.000368 -2.529e-06 1.135e-06 -0.0003202 -1.906e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2089 -0.03563 -0.1565 0.1824 0.9834 0.9932 0.2345 0.4291 0.8682 0.7085 ] Network output: [ -0.008848 1.003 1.008 -1.875e-07 8.416e-08 0.007347 -1.413e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006841 0.00063 0.004365 0.003162 0.9889 0.9919 0.006975 0.8517 0.8919 0.01158 ] Network output: [ -0.0001802 0.001398 1 -7.94e-06 3.565e-06 0.9985 -5.984e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2227 0.1057 0.3493 0.1419 0.9849 0.9939 0.2235 0.433 0.8749 0.7022 ] Network output: [ 0.002977 -0.0142 0.9943 4.843e-06 -2.174e-06 1.014 3.65e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09875 0.1848 0.1973 0.9873 0.9919 0.1115 0.7351 0.8611 0.305 ] Network output: [ -0.002796 0.01313 1.005 5.291e-06 -2.375e-06 0.9878 3.988e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09441 0.09247 0.165 0.1966 0.9852 0.9911 0.09442 0.6589 0.8363 0.2494 ] Network output: [ 8.232e-05 1 -4.984e-05 6.925e-07 -3.109e-07 0.9998 5.219e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001693 Epoch 9739 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008845 0.9968 0.9926 -1.71e-07 7.675e-08 -0.007041 -1.288e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003513 -0.003358 -0.006662 0.005376 0.9699 0.9743 0.006847 0.8245 0.8197 0.01622 ] Network output: [ 0.9999 0.0001056 0.0003678 -2.525e-06 1.134e-06 -0.00032 -1.903e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2089 -0.03563 -0.1565 0.1824 0.9834 0.9932 0.2345 0.4291 0.8682 0.7085 ] Network output: [ -0.008847 1.003 1.008 -1.873e-07 8.41e-08 0.007347 -1.412e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006842 0.00063 0.004364 0.003162 0.9889 0.9919 0.006976 0.8517 0.8919 0.01158 ] Network output: [ -0.00018 0.001397 1 -7.93e-06 3.56e-06 0.9985 -5.977e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2227 0.1057 0.3493 0.1419 0.9849 0.9939 0.2235 0.433 0.8749 0.7022 ] Network output: [ 0.002976 -0.01419 0.9943 4.837e-06 -2.171e-06 1.014 3.645e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09875 0.1848 0.1973 0.9873 0.9919 0.1115 0.735 0.8611 0.305 ] Network output: [ -0.002794 0.01312 1.005 5.285e-06 -2.373e-06 0.9878 3.983e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09441 0.09247 0.165 0.1966 0.9852 0.9911 0.09443 0.6589 0.8363 0.2494 ] Network output: [ 8.23e-05 1 -4.984e-05 6.916e-07 -3.105e-07 0.9998 5.212e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001692 Epoch 9740 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008844 0.9968 0.9926 -1.708e-07 7.67e-08 -0.007041 -1.288e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003513 -0.003358 -0.006661 0.005376 0.9699 0.9743 0.006848 0.8245 0.8197 0.01622 ] Network output: [ 0.9999 0.0001054 0.0003676 -2.522e-06 1.132e-06 -0.0003198 -1.901e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2089 -0.03564 -0.1565 0.1824 0.9834 0.9932 0.2346 0.429 0.8682 0.7085 ] Network output: [ -0.008846 1.003 1.008 -1.872e-07 8.403e-08 0.007346 -1.411e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006842 0.0006301 0.004364 0.003162 0.9889 0.9919 0.006976 0.8517 0.8919 0.01158 ] Network output: [ -0.0001799 0.001396 1 -7.921e-06 3.556e-06 0.9985 -5.969e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2227 0.1057 0.3493 0.1419 0.9849 0.9939 0.2235 0.433 0.8749 0.7022 ] Network output: [ 0.002974 -0.01418 0.9943 4.831e-06 -2.169e-06 1.014 3.641e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09875 0.1848 0.1973 0.9873 0.9919 0.1115 0.735 0.8611 0.305 ] Network output: [ -0.002793 0.01311 1.005 5.279e-06 -2.37e-06 0.9878 3.978e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09441 0.09247 0.165 0.1966 0.9852 0.9911 0.09443 0.6589 0.8363 0.2494 ] Network output: [ 8.228e-05 1 -4.983e-05 6.908e-07 -3.101e-07 0.9998 5.206e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001691 Epoch 9741 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008843 0.9968 0.9926 -1.707e-07 7.665e-08 -0.00704 -1.287e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003513 -0.003358 -0.00666 0.005376 0.9699 0.9743 0.006848 0.8245 0.8197 0.01622 ] Network output: [ 0.9999 0.0001053 0.0003675 -2.519e-06 1.131e-06 -0.0003196 -1.899e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2089 -0.03564 -0.1565 0.1824 0.9834 0.9932 0.2346 0.429 0.8682 0.7085 ] Network output: [ -0.008845 1.003 1.008 -1.87e-07 8.397e-08 0.007346 -1.41e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006842 0.0006301 0.004364 0.003162 0.9889 0.9919 0.006976 0.8517 0.8919 0.01158 ] Network output: [ -0.0001797 0.001396 1 -7.911e-06 3.552e-06 0.9985 -5.962e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2227 0.1057 0.3493 0.1419 0.9849 0.9939 0.2235 0.433 0.8749 0.7022 ] Network output: [ 0.002973 -0.01418 0.9943 4.825e-06 -2.166e-06 1.014 3.636e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09876 0.1848 0.1973 0.9873 0.9919 0.1115 0.735 0.8611 0.305 ] Network output: [ -0.002792 0.01311 1.005 5.272e-06 -2.367e-06 0.9878 3.973e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09442 0.09248 0.165 0.1966 0.9852 0.9911 0.09443 0.6589 0.8363 0.2494 ] Network output: [ 8.226e-05 1 -4.982e-05 6.9e-07 -3.098e-07 0.9998 5.2e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000169 Epoch 9742 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008842 0.9968 0.9926 -1.706e-07 7.66e-08 -0.00704 -1.286e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003513 -0.003358 -0.00666 0.005375 0.9699 0.9743 0.006848 0.8245 0.8197 0.01622 ] Network output: [ 0.9999 0.0001051 0.0003673 -2.516e-06 1.13e-06 -0.0003194 -1.896e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2089 -0.03564 -0.1565 0.1824 0.9834 0.9932 0.2346 0.429 0.8682 0.7085 ] Network output: [ -0.008844 1.003 1.008 -1.869e-07 8.39e-08 0.007345 -1.408e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006843 0.0006302 0.004364 0.003161 0.9889 0.9919 0.006977 0.8517 0.8919 0.01158 ] Network output: [ -0.0001796 0.001395 1 -7.902e-06 3.547e-06 0.9985 -5.955e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2228 0.1057 0.3493 0.1419 0.9849 0.9939 0.2235 0.433 0.8749 0.7022 ] Network output: [ 0.002971 -0.01417 0.9943 4.819e-06 -2.164e-06 1.014 3.632e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09876 0.1848 0.1973 0.9873 0.9919 0.1116 0.735 0.8611 0.305 ] Network output: [ -0.00279 0.0131 1.005 5.266e-06 -2.364e-06 0.9878 3.969e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09442 0.09248 0.165 0.1966 0.9852 0.9911 0.09443 0.6588 0.8363 0.2494 ] Network output: [ 8.224e-05 1 -4.981e-05 6.892e-07 -3.094e-07 0.9998 5.194e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001689 Epoch 9743 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008841 0.9968 0.9926 -1.705e-07 7.656e-08 -0.007039 -1.285e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003513 -0.003358 -0.006659 0.005375 0.9699 0.9743 0.006848 0.8245 0.8197 0.01622 ] Network output: [ 0.9999 0.0001049 0.0003671 -2.513e-06 1.128e-06 -0.0003192 -1.894e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.209 -0.03564 -0.1565 0.1824 0.9834 0.9932 0.2346 0.429 0.8682 0.7084 ] Network output: [ -0.008843 1.003 1.008 -1.867e-07 8.384e-08 0.007345 -1.407e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006843 0.0006303 0.004364 0.003161 0.9889 0.9919 0.006977 0.8517 0.8919 0.01158 ] Network output: [ -0.0001794 0.001394 1 -7.892e-06 3.543e-06 0.9985 -5.948e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2228 0.1057 0.3493 0.1419 0.9849 0.9939 0.2235 0.433 0.8749 0.7022 ] Network output: [ 0.00297 -0.01417 0.9943 4.814e-06 -2.161e-06 1.014 3.628e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09876 0.1848 0.1973 0.9873 0.9919 0.1116 0.735 0.8611 0.305 ] Network output: [ -0.002789 0.0131 1.005 5.26e-06 -2.361e-06 0.9878 3.964e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09442 0.09248 0.165 0.1966 0.9852 0.9911 0.09443 0.6588 0.8363 0.2494 ] Network output: [ 8.222e-05 1 -4.981e-05 6.883e-07 -3.09e-07 0.9998 5.187e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001688 Epoch 9744 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00884 0.9968 0.9926 -1.704e-07 7.651e-08 -0.007038 -1.284e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003513 -0.003358 -0.006659 0.005374 0.9699 0.9743 0.006848 0.8245 0.8197 0.01622 ] Network output: [ 0.9999 0.0001048 0.000367 -2.51e-06 1.127e-06 -0.000319 -1.892e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.209 -0.03564 -0.1565 0.1824 0.9834 0.9932 0.2346 0.429 0.8682 0.7084 ] Network output: [ -0.008843 1.003 1.008 -1.866e-07 8.377e-08 0.007344 -1.406e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006843 0.0006303 0.004364 0.003161 0.9889 0.9919 0.006978 0.8517 0.8919 0.01157 ] Network output: [ -0.0001793 0.001394 1 -7.883e-06 3.539e-06 0.9985 -5.941e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2228 0.1057 0.3493 0.1419 0.9849 0.9939 0.2235 0.433 0.8749 0.7022 ] Network output: [ 0.002969 -0.01416 0.9943 4.808e-06 -2.158e-06 1.014 3.623e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09876 0.1848 0.1973 0.9873 0.9919 0.1116 0.735 0.8611 0.305 ] Network output: [ -0.002788 0.01309 1.005 5.254e-06 -2.359e-06 0.9878 3.959e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09442 0.09248 0.165 0.1966 0.9852 0.9911 0.09443 0.6588 0.8363 0.2494 ] Network output: [ 8.219e-05 1 -4.98e-05 6.875e-07 -3.086e-07 0.9998 5.181e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001687 Epoch 9745 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008839 0.9968 0.9926 -1.703e-07 7.646e-08 -0.007038 -1.284e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003513 -0.003358 -0.006658 0.005374 0.9699 0.9743 0.006848 0.8245 0.8197 0.01621 ] Network output: [ 0.9999 0.0001046 0.0003668 -2.507e-06 1.126e-06 -0.0003188 -1.889e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.209 -0.03564 -0.1565 0.1824 0.9834 0.9932 0.2346 0.429 0.8682 0.7084 ] Network output: [ -0.008842 1.003 1.008 -1.865e-07 8.371e-08 0.007343 -1.405e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006844 0.0006304 0.004364 0.003161 0.9889 0.9919 0.006978 0.8517 0.8919 0.01157 ] Network output: [ -0.0001791 0.001393 1 -7.873e-06 3.535e-06 0.9985 -5.933e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2228 0.1057 0.3493 0.1419 0.9849 0.9939 0.2235 0.433 0.8749 0.7022 ] Network output: [ 0.002967 -0.01415 0.9943 4.802e-06 -2.156e-06 1.014 3.619e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09877 0.1848 0.1973 0.9873 0.9919 0.1116 0.735 0.8611 0.305 ] Network output: [ -0.002786 0.01308 1.005 5.247e-06 -2.356e-06 0.9878 3.955e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09442 0.09248 0.165 0.1966 0.9852 0.9911 0.09444 0.6588 0.8362 0.2494 ] Network output: [ 8.217e-05 1 -4.979e-05 6.867e-07 -3.083e-07 0.9998 5.175e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001686 Epoch 9746 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008838 0.9968 0.9926 -1.702e-07 7.641e-08 -0.007037 -1.283e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003513 -0.003359 -0.006657 0.005374 0.9699 0.9743 0.006849 0.8245 0.8197 0.01621 ] Network output: [ 0.9999 0.0001045 0.0003667 -2.504e-06 1.124e-06 -0.0003186 -1.887e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.209 -0.03564 -0.1564 0.1824 0.9834 0.9932 0.2346 0.429 0.8682 0.7084 ] Network output: [ -0.008841 1.003 1.008 -1.863e-07 8.364e-08 0.007343 -1.404e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006844 0.0006305 0.004364 0.003161 0.9889 0.9919 0.006978 0.8517 0.8919 0.01157 ] Network output: [ -0.000179 0.001392 1 -7.864e-06 3.53e-06 0.9985 -5.926e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2228 0.1057 0.3493 0.1419 0.9849 0.9939 0.2235 0.433 0.8749 0.7021 ] Network output: [ 0.002966 -0.01415 0.9943 4.796e-06 -2.153e-06 1.014 3.615e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09877 0.1848 0.1973 0.9873 0.9919 0.1116 0.735 0.8611 0.305 ] Network output: [ -0.002785 0.01308 1.005 5.241e-06 -2.353e-06 0.9878 3.95e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09442 0.09248 0.165 0.1966 0.9852 0.9911 0.09444 0.6588 0.8362 0.2494 ] Network output: [ 8.215e-05 1 -4.979e-05 6.859e-07 -3.079e-07 0.9998 5.169e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001686 Epoch 9747 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008838 0.9968 0.9926 -1.701e-07 7.637e-08 -0.007037 -1.282e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003513 -0.003359 -0.006657 0.005373 0.9699 0.9743 0.006849 0.8245 0.8197 0.01621 ] Network output: [ 0.9999 0.0001043 0.0003665 -2.501e-06 1.123e-06 -0.0003184 -1.885e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.209 -0.03564 -0.1564 0.1824 0.9834 0.9932 0.2346 0.429 0.8682 0.7084 ] Network output: [ -0.00884 1.003 1.008 -1.862e-07 8.358e-08 0.007342 -1.403e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006845 0.0006305 0.004364 0.00316 0.9889 0.9919 0.006979 0.8517 0.8919 0.01157 ] Network output: [ -0.0001788 0.001391 1 -7.854e-06 3.526e-06 0.9985 -5.919e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2228 0.1057 0.3494 0.1419 0.9849 0.9939 0.2236 0.433 0.8749 0.7021 ] Network output: [ 0.002964 -0.01414 0.9943 4.79e-06 -2.151e-06 1.014 3.61e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09877 0.1848 0.1973 0.9873 0.9919 0.1116 0.735 0.8611 0.305 ] Network output: [ -0.002784 0.01307 1.005 5.235e-06 -2.35e-06 0.9878 3.945e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09443 0.09249 0.165 0.1966 0.9852 0.9911 0.09444 0.6588 0.8362 0.2494 ] Network output: [ 8.213e-05 1 -4.978e-05 6.85e-07 -3.075e-07 0.9998 5.163e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001685 Epoch 9748 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008837 0.9968 0.9926 -1.7e-07 7.632e-08 -0.007036 -1.281e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003514 -0.003359 -0.006656 0.005373 0.9699 0.9743 0.006849 0.8245 0.8197 0.01621 ] Network output: [ 0.9999 0.0001041 0.0003663 -2.498e-06 1.121e-06 -0.0003182 -1.883e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.209 -0.03564 -0.1564 0.1824 0.9834 0.9932 0.2346 0.429 0.8682 0.7084 ] Network output: [ -0.008839 1.003 1.008 -1.86e-07 8.351e-08 0.007342 -1.402e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006845 0.0006306 0.004364 0.00316 0.9889 0.9919 0.006979 0.8517 0.8919 0.01157 ] Network output: [ -0.0001787 0.001391 1 -7.845e-06 3.522e-06 0.9985 -5.912e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2228 0.1057 0.3494 0.1419 0.9849 0.9939 0.2236 0.433 0.8749 0.7021 ] Network output: [ 0.002963 -0.01413 0.9943 4.785e-06 -2.148e-06 1.014 3.606e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09878 0.1848 0.1972 0.9873 0.9919 0.1116 0.7349 0.8611 0.305 ] Network output: [ -0.002782 0.01307 1.005 5.229e-06 -2.347e-06 0.9878 3.941e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09443 0.09249 0.165 0.1966 0.9852 0.9911 0.09444 0.6588 0.8362 0.2494 ] Network output: [ 8.211e-05 1 -4.977e-05 6.842e-07 -3.072e-07 0.9998 5.157e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001684 Epoch 9749 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008836 0.9968 0.9926 -1.699e-07 7.627e-08 -0.007035 -1.28e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003514 -0.003359 -0.006656 0.005373 0.9699 0.9743 0.006849 0.8245 0.8197 0.01621 ] Network output: [ 0.9999 0.000104 0.0003662 -2.495e-06 1.12e-06 -0.000318 -1.88e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.209 -0.03564 -0.1564 0.1824 0.9834 0.9932 0.2346 0.429 0.8681 0.7084 ] Network output: [ -0.008839 1.003 1.008 -1.859e-07 8.345e-08 0.007341 -1.401e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006845 0.0006306 0.004363 0.00316 0.9889 0.9919 0.006979 0.8517 0.8919 0.01157 ] Network output: [ -0.0001785 0.00139 1 -7.835e-06 3.517e-06 0.9985 -5.905e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2228 0.1057 0.3494 0.1419 0.9849 0.9939 0.2236 0.433 0.8749 0.7021 ] Network output: [ 0.002961 -0.01413 0.9943 4.779e-06 -2.145e-06 1.014 3.602e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09878 0.1848 0.1972 0.9873 0.9919 0.1116 0.7349 0.8611 0.305 ] Network output: [ -0.002781 0.01306 1.005 5.223e-06 -2.345e-06 0.9878 3.936e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09443 0.09249 0.165 0.1966 0.9852 0.9911 0.09444 0.6588 0.8362 0.2494 ] Network output: [ 8.209e-05 1 -4.976e-05 6.834e-07 -3.068e-07 0.9998 5.15e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001683 Epoch 9750 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008835 0.9968 0.9926 -1.698e-07 7.622e-08 -0.007035 -1.28e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003514 -0.003359 -0.006655 0.005372 0.9699 0.9743 0.006849 0.8245 0.8197 0.01621 ] Network output: [ 0.9999 0.0001038 0.000366 -2.492e-06 1.119e-06 -0.0003178 -1.878e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.209 -0.03565 -0.1564 0.1824 0.9834 0.9932 0.2346 0.429 0.8681 0.7084 ] Network output: [ -0.008838 1.003 1.008 -1.857e-07 8.338e-08 0.007341 -1.4e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006846 0.0006307 0.004363 0.00316 0.9889 0.9919 0.00698 0.8517 0.8919 0.01157 ] Network output: [ -0.0001784 0.001389 1 -7.826e-06 3.513e-06 0.9985 -5.898e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2228 0.1057 0.3494 0.1419 0.9849 0.9939 0.2236 0.433 0.8749 0.7021 ] Network output: [ 0.00296 -0.01412 0.9943 4.773e-06 -2.143e-06 1.014 3.597e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09878 0.1848 0.1972 0.9873 0.9919 0.1116 0.7349 0.8611 0.305 ] Network output: [ -0.00278 0.01305 1.005 5.216e-06 -2.342e-06 0.9878 3.931e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09443 0.09249 0.165 0.1966 0.9852 0.9911 0.09445 0.6588 0.8362 0.2494 ] Network output: [ 8.207e-05 1 -4.976e-05 6.826e-07 -3.064e-07 0.9998 5.144e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001682 Epoch 9751 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008834 0.9968 0.9926 -1.697e-07 7.617e-08 -0.007034 -1.279e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003514 -0.003359 -0.006655 0.005372 0.9699 0.9743 0.006849 0.8245 0.8197 0.01621 ] Network output: [ 0.9999 0.0001036 0.0003658 -2.489e-06 1.117e-06 -0.0003176 -1.876e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.209 -0.03565 -0.1564 0.1824 0.9834 0.9932 0.2346 0.429 0.8681 0.7084 ] Network output: [ -0.008837 1.003 1.008 -1.856e-07 8.332e-08 0.00734 -1.399e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006846 0.0006308 0.004363 0.003159 0.9889 0.9919 0.00698 0.8517 0.8919 0.01157 ] Network output: [ -0.0001782 0.001389 1 -7.816e-06 3.509e-06 0.9985 -5.891e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2228 0.1057 0.3494 0.1419 0.9849 0.9939 0.2236 0.433 0.8749 0.7021 ] Network output: [ 0.002958 -0.01411 0.9943 4.767e-06 -2.14e-06 1.014 3.593e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09879 0.1848 0.1972 0.9873 0.9919 0.1116 0.7349 0.8611 0.305 ] Network output: [ -0.002778 0.01305 1.005 5.21e-06 -2.339e-06 0.9878 3.927e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09443 0.09249 0.165 0.1966 0.9852 0.9911 0.09445 0.6587 0.8362 0.2494 ] Network output: [ 8.204e-05 1 -4.975e-05 6.818e-07 -3.061e-07 0.9998 5.138e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001681 Epoch 9752 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008833 0.9968 0.9926 -1.696e-07 7.613e-08 -0.007033 -1.278e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003514 -0.003359 -0.006654 0.005371 0.9699 0.9743 0.00685 0.8245 0.8197 0.01621 ] Network output: [ 0.9999 0.0001035 0.0003657 -2.486e-06 1.116e-06 -0.0003174 -1.874e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.209 -0.03565 -0.1564 0.1824 0.9834 0.9932 0.2346 0.429 0.8681 0.7084 ] Network output: [ -0.008836 1.003 1.008 -1.854e-07 8.325e-08 0.007339 -1.398e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006846 0.0006308 0.004363 0.003159 0.9889 0.9919 0.006981 0.8517 0.8919 0.01157 ] Network output: [ -0.0001781 0.001388 1 -7.807e-06 3.505e-06 0.9985 -5.883e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2228 0.1057 0.3494 0.1419 0.9849 0.9939 0.2236 0.433 0.8749 0.7021 ] Network output: [ 0.002957 -0.01411 0.9943 4.762e-06 -2.138e-06 1.014 3.589e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09879 0.1848 0.1972 0.9873 0.9919 0.1116 0.7349 0.8611 0.305 ] Network output: [ -0.002777 0.01304 1.005 5.204e-06 -2.336e-06 0.9879 3.922e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09444 0.0925 0.165 0.1966 0.9852 0.9911 0.09445 0.6587 0.8362 0.2494 ] Network output: [ 8.202e-05 1 -4.974e-05 6.81e-07 -3.057e-07 0.9998 5.132e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000168 Epoch 9753 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008832 0.9968 0.9926 -1.695e-07 7.608e-08 -0.007033 -1.277e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003514 -0.003359 -0.006653 0.005371 0.9699 0.9743 0.00685 0.8245 0.8197 0.01621 ] Network output: [ 0.9999 0.0001033 0.0003655 -2.483e-06 1.115e-06 -0.0003172 -1.871e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.209 -0.03565 -0.1564 0.1824 0.9834 0.9932 0.2346 0.429 0.8681 0.7084 ] Network output: [ -0.008835 1.003 1.008 -1.853e-07 8.319e-08 0.007339 -1.396e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006847 0.0006309 0.004363 0.003159 0.9889 0.9919 0.006981 0.8517 0.8919 0.01157 ] Network output: [ -0.0001779 0.001387 1 -7.797e-06 3.5e-06 0.9985 -5.876e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2228 0.1058 0.3494 0.1419 0.9849 0.9939 0.2236 0.433 0.8749 0.7021 ] Network output: [ 0.002955 -0.0141 0.9943 4.756e-06 -2.135e-06 1.014 3.584e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09879 0.1848 0.1972 0.9873 0.9919 0.1116 0.7349 0.8611 0.305 ] Network output: [ -0.002776 0.01304 1.005 5.198e-06 -2.334e-06 0.9879 3.917e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09444 0.0925 0.165 0.1966 0.9852 0.9911 0.09445 0.6587 0.8362 0.2494 ] Network output: [ 8.2e-05 1 -4.974e-05 6.802e-07 -3.053e-07 0.9998 5.126e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001679 Epoch 9754 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008831 0.9968 0.9926 -1.694e-07 7.603e-08 -0.007032 -1.276e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003514 -0.003359 -0.006653 0.005371 0.9699 0.9743 0.00685 0.8245 0.8197 0.01621 ] Network output: [ 0.9999 0.0001032 0.0003654 -2.48e-06 1.113e-06 -0.000317 -1.869e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.209 -0.03565 -0.1564 0.1824 0.9834 0.9932 0.2346 0.429 0.8681 0.7084 ] Network output: [ -0.008834 1.003 1.008 -1.852e-07 8.312e-08 0.007338 -1.395e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006847 0.000631 0.004363 0.003159 0.9889 0.9919 0.006981 0.8517 0.8919 0.01157 ] Network output: [ -0.0001778 0.001387 1 -7.788e-06 3.496e-06 0.9985 -5.869e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2228 0.1058 0.3494 0.1419 0.9849 0.9939 0.2236 0.4329 0.8749 0.7021 ] Network output: [ 0.002954 -0.01409 0.9943 4.75e-06 -2.133e-06 1.014 3.58e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.0988 0.1848 0.1972 0.9873 0.9919 0.1116 0.7349 0.8611 0.305 ] Network output: [ -0.002774 0.01303 1.005 5.192e-06 -2.331e-06 0.9879 3.913e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09444 0.0925 0.165 0.1966 0.9852 0.9911 0.09445 0.6587 0.8362 0.2494 ] Network output: [ 8.198e-05 1 -4.973e-05 6.793e-07 -3.05e-07 0.9998 5.12e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001678 Epoch 9755 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00883 0.9968 0.9926 -1.692e-07 7.598e-08 -0.007032 -1.276e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003514 -0.003359 -0.006652 0.00537 0.9699 0.9743 0.00685 0.8244 0.8197 0.01621 ] Network output: [ 0.9999 0.000103 0.0003652 -2.477e-06 1.112e-06 -0.0003168 -1.867e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.209 -0.03565 -0.1564 0.1824 0.9834 0.9932 0.2347 0.429 0.8681 0.7084 ] Network output: [ -0.008834 1.003 1.008 -1.85e-07 8.306e-08 0.007338 -1.394e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006847 0.000631 0.004363 0.003159 0.9889 0.9919 0.006982 0.8517 0.8919 0.01157 ] Network output: [ -0.0001776 0.001386 1 -7.778e-06 3.492e-06 0.9985 -5.862e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2228 0.1058 0.3494 0.1419 0.9849 0.9939 0.2236 0.4329 0.8749 0.7021 ] Network output: [ 0.002953 -0.01409 0.9943 4.744e-06 -2.13e-06 1.014 3.576e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.0988 0.1848 0.1972 0.9873 0.9919 0.1116 0.7349 0.8611 0.305 ] Network output: [ -0.002773 0.01302 1.005 5.186e-06 -2.328e-06 0.9879 3.908e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09444 0.0925 0.165 0.1966 0.9852 0.9911 0.09445 0.6587 0.8362 0.2494 ] Network output: [ 8.196e-05 1 -4.972e-05 6.785e-07 -3.046e-07 0.9998 5.114e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001677 Epoch 9756 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008829 0.9968 0.9926 -1.691e-07 7.593e-08 -0.007031 -1.275e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003514 -0.003359 -0.006652 0.00537 0.9699 0.9743 0.00685 0.8244 0.8197 0.0162 ] Network output: [ 0.9999 0.0001028 0.000365 -2.474e-06 1.111e-06 -0.0003166 -1.864e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.209 -0.03565 -0.1564 0.1824 0.9834 0.9932 0.2347 0.429 0.8681 0.7084 ] Network output: [ -0.008833 1.003 1.008 -1.849e-07 8.299e-08 0.007337 -1.393e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006848 0.0006311 0.004363 0.003158 0.9889 0.9919 0.006982 0.8517 0.8919 0.01157 ] Network output: [ -0.0001775 0.001385 1 -7.769e-06 3.488e-06 0.9985 -5.855e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2228 0.1058 0.3494 0.1419 0.9849 0.9939 0.2236 0.4329 0.8749 0.7021 ] Network output: [ 0.002951 -0.01408 0.9943 4.739e-06 -2.127e-06 1.014 3.571e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.0988 0.1848 0.1972 0.9873 0.9919 0.1116 0.7349 0.8611 0.305 ] Network output: [ -0.002772 0.01302 1.005 5.179e-06 -2.325e-06 0.9879 3.903e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09444 0.0925 0.165 0.1966 0.9852 0.9911 0.09446 0.6587 0.8362 0.2494 ] Network output: [ 8.194e-05 1 -4.972e-05 6.777e-07 -3.043e-07 0.9998 5.108e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001676 Epoch 9757 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008829 0.9968 0.9926 -1.69e-07 7.589e-08 -0.00703 -1.274e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003514 -0.003359 -0.006651 0.005369 0.9699 0.9743 0.00685 0.8244 0.8197 0.0162 ] Network output: [ 0.9999 0.0001027 0.0003649 -2.471e-06 1.109e-06 -0.0003164 -1.862e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.209 -0.03565 -0.1563 0.1824 0.9834 0.9932 0.2347 0.429 0.8681 0.7084 ] Network output: [ -0.008832 1.003 1.008 -1.847e-07 8.293e-08 0.007336 -1.392e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006848 0.0006311 0.004363 0.003158 0.9889 0.9919 0.006982 0.8517 0.8919 0.01157 ] Network output: [ -0.0001773 0.001384 1 -7.76e-06 3.484e-06 0.9985 -5.848e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2229 0.1058 0.3494 0.1419 0.9849 0.9939 0.2236 0.4329 0.8749 0.7021 ] Network output: [ 0.00295 -0.01407 0.9943 4.733e-06 -2.125e-06 1.014 3.567e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.0988 0.1848 0.1972 0.9873 0.9919 0.1116 0.7349 0.8611 0.305 ] Network output: [ -0.00277 0.01301 1.005 5.173e-06 -2.322e-06 0.9879 3.899e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09444 0.0925 0.165 0.1966 0.9852 0.9911 0.09446 0.6587 0.8362 0.2494 ] Network output: [ 8.192e-05 1 -4.971e-05 6.769e-07 -3.039e-07 0.9998 5.101e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001675 Epoch 9758 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008828 0.9968 0.9926 -1.689e-07 7.584e-08 -0.00703 -1.273e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003514 -0.00336 -0.006651 0.005369 0.9699 0.9743 0.006851 0.8244 0.8197 0.0162 ] Network output: [ 0.9999 0.0001025 0.0003647 -2.468e-06 1.108e-06 -0.0003162 -1.86e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.209 -0.03565 -0.1563 0.1824 0.9834 0.9932 0.2347 0.429 0.8681 0.7084 ] Network output: [ -0.008831 1.003 1.008 -1.846e-07 8.286e-08 0.007336 -1.391e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006848 0.0006312 0.004363 0.003158 0.9889 0.9919 0.006983 0.8517 0.8919 0.01156 ] Network output: [ -0.0001772 0.001384 1 -7.75e-06 3.479e-06 0.9985 -5.841e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2229 0.1058 0.3494 0.1419 0.9849 0.9939 0.2236 0.4329 0.8749 0.7021 ] Network output: [ 0.002948 -0.01407 0.9943 4.727e-06 -2.122e-06 1.014 3.563e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09881 0.1848 0.1972 0.9873 0.9919 0.1116 0.7348 0.8611 0.305 ] Network output: [ -0.002769 0.01301 1.005 5.167e-06 -2.32e-06 0.9879 3.894e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09445 0.09251 0.165 0.1966 0.9852 0.9911 0.09446 0.6587 0.8362 0.2494 ] Network output: [ 8.19e-05 1 -4.97e-05 6.761e-07 -3.035e-07 0.9998 5.095e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001675 Epoch 9759 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008827 0.9968 0.9926 -1.688e-07 7.579e-08 -0.007029 -1.272e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003514 -0.00336 -0.00665 0.005369 0.9699 0.9743 0.006851 0.8244 0.8197 0.0162 ] Network output: [ 0.9999 0.0001024 0.0003645 -2.465e-06 1.107e-06 -0.000316 -1.858e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.209 -0.03565 -0.1563 0.1824 0.9834 0.9932 0.2347 0.429 0.8681 0.7084 ] Network output: [ -0.00883 1.003 1.008 -1.844e-07 8.28e-08 0.007335 -1.39e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006849 0.0006313 0.004362 0.003158 0.9889 0.9919 0.006983 0.8516 0.8919 0.01156 ] Network output: [ -0.000177 0.001383 1 -7.741e-06 3.475e-06 0.9985 -5.834e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2229 0.1058 0.3494 0.1419 0.9849 0.9939 0.2236 0.4329 0.8749 0.7021 ] Network output: [ 0.002947 -0.01406 0.9943 4.722e-06 -2.12e-06 1.014 3.558e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09881 0.1848 0.1972 0.9873 0.9919 0.1116 0.7348 0.8611 0.305 ] Network output: [ -0.002768 0.013 1.005 5.161e-06 -2.317e-06 0.9879 3.89e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09445 0.09251 0.165 0.1966 0.9852 0.9911 0.09446 0.6587 0.8362 0.2495 ] Network output: [ 8.187e-05 1 -4.97e-05 6.753e-07 -3.032e-07 0.9998 5.089e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001674 Epoch 9760 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008826 0.9968 0.9926 -1.687e-07 7.574e-08 -0.007029 -1.271e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003514 -0.00336 -0.006649 0.005368 0.9699 0.9743 0.006851 0.8244 0.8197 0.0162 ] Network output: [ 0.9999 0.0001022 0.0003644 -2.462e-06 1.105e-06 -0.0003158 -1.855e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.209 -0.03565 -0.1563 0.1824 0.9834 0.9932 0.2347 0.429 0.8681 0.7084 ] Network output: [ -0.00883 1.003 1.008 -1.843e-07 8.274e-08 0.007335 -1.389e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006849 0.0006313 0.004362 0.003157 0.9889 0.9919 0.006983 0.8516 0.8919 0.01156 ] Network output: [ -0.0001769 0.001382 1 -7.731e-06 3.471e-06 0.9985 -5.827e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2229 0.1058 0.3494 0.1419 0.9849 0.9939 0.2236 0.4329 0.8749 0.7021 ] Network output: [ 0.002945 -0.01405 0.9943 4.716e-06 -2.117e-06 1.014 3.554e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09881 0.1848 0.1972 0.9873 0.9919 0.1116 0.7348 0.8611 0.305 ] Network output: [ -0.002766 0.01299 1.005 5.155e-06 -2.314e-06 0.9879 3.885e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09445 0.09251 0.165 0.1966 0.9852 0.9911 0.09446 0.6586 0.8362 0.2495 ] Network output: [ 8.185e-05 1 -4.969e-05 6.745e-07 -3.028e-07 0.9998 5.083e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001673 Epoch 9761 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008825 0.9968 0.9926 -1.686e-07 7.569e-08 -0.007028 -1.271e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003514 -0.00336 -0.006649 0.005368 0.9699 0.9743 0.006851 0.8244 0.8197 0.0162 ] Network output: [ 0.9999 0.000102 0.0003642 -2.459e-06 1.104e-06 -0.0003156 -1.853e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2091 -0.03566 -0.1563 0.1824 0.9834 0.9932 0.2347 0.429 0.8681 0.7084 ] Network output: [ -0.008829 1.003 1.008 -1.841e-07 8.267e-08 0.007334 -1.388e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00685 0.0006314 0.004362 0.003157 0.9889 0.9919 0.006984 0.8516 0.8919 0.01156 ] Network output: [ -0.0001767 0.001382 1 -7.722e-06 3.467e-06 0.9985 -5.82e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2229 0.1058 0.3494 0.1419 0.9849 0.9939 0.2236 0.4329 0.8749 0.7021 ] Network output: [ 0.002944 -0.01405 0.9943 4.71e-06 -2.115e-06 1.014 3.55e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09882 0.1848 0.1972 0.9873 0.9919 0.1116 0.7348 0.8611 0.305 ] Network output: [ -0.002765 0.01299 1.005 5.149e-06 -2.311e-06 0.9879 3.88e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09445 0.09251 0.165 0.1966 0.9852 0.9911 0.09447 0.6586 0.8362 0.2495 ] Network output: [ 8.183e-05 1 -4.969e-05 6.737e-07 -3.024e-07 0.9998 5.077e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001672 Epoch 9762 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008824 0.9968 0.9926 -1.685e-07 7.565e-08 -0.007027 -1.27e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003514 -0.00336 -0.006648 0.005368 0.9699 0.9743 0.006851 0.8244 0.8197 0.0162 ] Network output: [ 0.9999 0.0001019 0.0003641 -2.456e-06 1.103e-06 -0.0003154 -1.851e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2091 -0.03566 -0.1563 0.1824 0.9834 0.9932 0.2347 0.429 0.8681 0.7084 ] Network output: [ -0.008828 1.003 1.008 -1.84e-07 8.261e-08 0.007334 -1.387e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00685 0.0006315 0.004362 0.003157 0.9889 0.9919 0.006984 0.8516 0.8919 0.01156 ] Network output: [ -0.0001766 0.001381 1 -7.713e-06 3.463e-06 0.9985 -5.813e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2229 0.1058 0.3494 0.1419 0.9849 0.9939 0.2237 0.4329 0.8749 0.7021 ] Network output: [ 0.002942 -0.01404 0.9943 4.705e-06 -2.112e-06 1.014 3.546e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09882 0.1848 0.1972 0.9873 0.9919 0.1116 0.7348 0.8611 0.305 ] Network output: [ -0.002764 0.01298 1.005 5.143e-06 -2.309e-06 0.9879 3.876e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09445 0.09251 0.165 0.1966 0.9852 0.9911 0.09447 0.6586 0.8362 0.2495 ] Network output: [ 8.181e-05 1 -4.968e-05 6.729e-07 -3.021e-07 0.9998 5.071e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001671 Epoch 9763 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008823 0.9968 0.9926 -1.684e-07 7.56e-08 -0.007027 -1.269e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003514 -0.00336 -0.006648 0.005367 0.9699 0.9743 0.006851 0.8244 0.8197 0.0162 ] Network output: [ 0.9999 0.0001017 0.0003639 -2.453e-06 1.101e-06 -0.0003152 -1.849e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2091 -0.03566 -0.1563 0.1824 0.9834 0.9932 0.2347 0.4289 0.8681 0.7084 ] Network output: [ -0.008827 1.003 1.008 -1.839e-07 8.254e-08 0.007333 -1.386e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00685 0.0006315 0.004362 0.003157 0.9889 0.9919 0.006985 0.8516 0.8919 0.01156 ] Network output: [ -0.0001764 0.00138 1 -7.703e-06 3.458e-06 0.9985 -5.805e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2229 0.1058 0.3494 0.1419 0.9849 0.9939 0.2237 0.4329 0.8749 0.7021 ] Network output: [ 0.002941 -0.01404 0.9943 4.699e-06 -2.11e-06 1.014 3.541e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09882 0.1848 0.1972 0.9873 0.9919 0.1116 0.7348 0.8611 0.305 ] Network output: [ -0.002762 0.01298 1.005 5.137e-06 -2.306e-06 0.9879 3.871e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09445 0.09252 0.165 0.1966 0.9852 0.9911 0.09447 0.6586 0.8362 0.2495 ] Network output: [ 8.179e-05 1 -4.967e-05 6.721e-07 -3.017e-07 0.9998 5.065e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000167 Epoch 9764 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008822 0.9968 0.9926 -1.683e-07 7.555e-08 -0.007026 -1.268e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003514 -0.00336 -0.006647 0.005367 0.9699 0.9743 0.006851 0.8244 0.8197 0.0162 ] Network output: [ 0.9999 0.0001016 0.0003637 -2.45e-06 1.1e-06 -0.000315 -1.846e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2091 -0.03566 -0.1563 0.1824 0.9834 0.9932 0.2347 0.4289 0.8681 0.7084 ] Network output: [ -0.008826 1.003 1.008 -1.837e-07 8.248e-08 0.007332 -1.385e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006851 0.0006316 0.004362 0.003156 0.9889 0.9919 0.006985 0.8516 0.8919 0.01156 ] Network output: [ -0.0001763 0.00138 1 -7.694e-06 3.454e-06 0.9985 -5.798e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2229 0.1058 0.3494 0.1419 0.9849 0.9939 0.2237 0.4329 0.8749 0.7021 ] Network output: [ 0.002939 -0.01403 0.9943 4.693e-06 -2.107e-06 1.014 3.537e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09883 0.1848 0.1972 0.9873 0.9919 0.1116 0.7348 0.8611 0.305 ] Network output: [ -0.002761 0.01297 1.005 5.131e-06 -2.303e-06 0.9879 3.867e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09446 0.09252 0.165 0.1966 0.9852 0.9911 0.09447 0.6586 0.8362 0.2495 ] Network output: [ 8.177e-05 1 -4.967e-05 6.713e-07 -3.014e-07 0.9998 5.059e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001669 Epoch 9765 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008821 0.9968 0.9926 -1.682e-07 7.55e-08 -0.007026 -1.267e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003514 -0.00336 -0.006646 0.005366 0.9699 0.9743 0.006852 0.8244 0.8197 0.0162 ] Network output: [ 0.9999 0.0001014 0.0003636 -2.447e-06 1.099e-06 -0.0003148 -1.844e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2091 -0.03566 -0.1563 0.1824 0.9834 0.9932 0.2347 0.4289 0.8681 0.7084 ] Network output: [ -0.008825 1.003 1.008 -1.836e-07 8.241e-08 0.007332 -1.383e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006851 0.0006316 0.004362 0.003156 0.9889 0.9919 0.006985 0.8516 0.8919 0.01156 ] Network output: [ -0.0001761 0.001379 1 -7.685e-06 3.45e-06 0.9985 -5.791e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2229 0.1058 0.3494 0.1419 0.9849 0.9939 0.2237 0.4329 0.8749 0.7021 ] Network output: [ 0.002938 -0.01402 0.9943 4.688e-06 -2.104e-06 1.014 3.533e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1115 0.09883 0.1848 0.1972 0.9873 0.9919 0.1116 0.7348 0.8611 0.305 ] Network output: [ -0.00276 0.01297 1.005 5.124e-06 -2.301e-06 0.9879 3.862e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09446 0.09252 0.165 0.1966 0.9852 0.9911 0.09447 0.6586 0.8362 0.2495 ] Network output: [ 8.175e-05 1 -4.966e-05 6.705e-07 -3.01e-07 0.9998 5.053e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001668 Epoch 9766 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008821 0.9968 0.9926 -1.681e-07 7.545e-08 -0.007025 -1.267e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003514 -0.00336 -0.006646 0.005366 0.9699 0.9743 0.006852 0.8244 0.8197 0.0162 ] Network output: [ 0.9999 0.0001012 0.0003634 -2.444e-06 1.097e-06 -0.0003146 -1.842e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2091 -0.03566 -0.1563 0.1823 0.9834 0.9932 0.2347 0.4289 0.8681 0.7084 ] Network output: [ -0.008825 1.003 1.008 -1.834e-07 8.235e-08 0.007331 -1.382e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006851 0.0006317 0.004362 0.003156 0.9889 0.9919 0.006986 0.8516 0.8919 0.01156 ] Network output: [ -0.000176 0.001378 1 -7.675e-06 3.446e-06 0.9985 -5.784e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2229 0.1058 0.3494 0.1419 0.9849 0.9939 0.2237 0.4329 0.8749 0.7021 ] Network output: [ 0.002937 -0.01402 0.9943 4.682e-06 -2.102e-06 1.014 3.529e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09883 0.1848 0.1972 0.9873 0.9919 0.1116 0.7348 0.8611 0.305 ] Network output: [ -0.002758 0.01296 1.005 5.118e-06 -2.298e-06 0.9879 3.857e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09446 0.09252 0.165 0.1966 0.9852 0.9911 0.09447 0.6586 0.8362 0.2495 ] Network output: [ 8.173e-05 1 -4.966e-05 6.697e-07 -3.006e-07 0.9998 5.047e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001667 Epoch 9767 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00882 0.9968 0.9926 -1.68e-07 7.54e-08 -0.007024 -1.266e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003514 -0.00336 -0.006645 0.005366 0.9699 0.9743 0.006852 0.8244 0.8197 0.01619 ] Network output: [ 0.9999 0.0001011 0.0003633 -2.441e-06 1.096e-06 -0.0003144 -1.84e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2091 -0.03566 -0.1563 0.1823 0.9834 0.9932 0.2347 0.4289 0.8681 0.7084 ] Network output: [ -0.008824 1.003 1.008 -1.833e-07 8.228e-08 0.007331 -1.381e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006852 0.0006318 0.004362 0.003156 0.9889 0.9919 0.006986 0.8516 0.8919 0.01156 ] Network output: [ -0.0001758 0.001377 1 -7.666e-06 3.442e-06 0.9985 -5.777e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2229 0.1058 0.3494 0.1419 0.9849 0.9939 0.2237 0.4329 0.8749 0.7021 ] Network output: [ 0.002935 -0.01401 0.9943 4.676e-06 -2.099e-06 1.014 3.524e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09883 0.1848 0.1972 0.9873 0.9919 0.1116 0.7348 0.8611 0.305 ] Network output: [ -0.002757 0.01295 1.005 5.112e-06 -2.295e-06 0.9879 3.853e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09446 0.09252 0.165 0.1966 0.9852 0.9911 0.09448 0.6586 0.8362 0.2495 ] Network output: [ 8.171e-05 1 -4.965e-05 6.689e-07 -3.003e-07 0.9998 5.041e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001666 Epoch 9768 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008819 0.9968 0.9926 -1.679e-07 7.536e-08 -0.007024 -1.265e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003515 -0.00336 -0.006645 0.005365 0.9699 0.9743 0.006852 0.8244 0.8197 0.01619 ] Network output: [ 0.9999 0.0001009 0.0003631 -2.438e-06 1.095e-06 -0.0003142 -1.837e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2091 -0.03566 -0.1563 0.1823 0.9834 0.9932 0.2347 0.4289 0.8681 0.7084 ] Network output: [ -0.008823 1.003 1.008 -1.831e-07 8.222e-08 0.00733 -1.38e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006852 0.0006318 0.004362 0.003156 0.9889 0.9919 0.006986 0.8516 0.8919 0.01156 ] Network output: [ -0.0001757 0.001377 1 -7.657e-06 3.437e-06 0.9985 -5.77e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2229 0.1058 0.3495 0.1419 0.9849 0.9939 0.2237 0.4329 0.8749 0.7021 ] Network output: [ 0.002934 -0.014 0.9943 4.671e-06 -2.097e-06 1.014 3.52e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09884 0.1848 0.1972 0.9873 0.9919 0.1116 0.7347 0.8611 0.305 ] Network output: [ -0.002756 0.01295 1.005 5.106e-06 -2.292e-06 0.9879 3.848e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09446 0.09252 0.165 0.1966 0.9852 0.9911 0.09448 0.6586 0.8362 0.2495 ] Network output: [ 8.168e-05 1 -4.965e-05 6.681e-07 -2.999e-07 0.9998 5.035e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001665 Epoch 9769 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008818 0.9968 0.9926 -1.677e-07 7.531e-08 -0.007023 -1.264e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003515 -0.00336 -0.006644 0.005365 0.9699 0.9743 0.006852 0.8244 0.8197 0.01619 ] Network output: [ 0.9999 0.0001008 0.0003629 -2.435e-06 1.093e-06 -0.000314 -1.835e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2091 -0.03566 -0.1562 0.1823 0.9834 0.9932 0.2347 0.4289 0.8681 0.7084 ] Network output: [ -0.008822 1.003 1.008 -1.83e-07 8.215e-08 0.00733 -1.379e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006852 0.0006319 0.004361 0.003155 0.9889 0.9919 0.006987 0.8516 0.8919 0.01156 ] Network output: [ -0.0001755 0.001376 1 -7.648e-06 3.433e-06 0.9985 -5.763e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2229 0.1058 0.3495 0.1419 0.9849 0.9939 0.2237 0.4329 0.8749 0.7021 ] Network output: [ 0.002932 -0.014 0.9943 4.665e-06 -2.094e-06 1.014 3.516e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09884 0.1848 0.1972 0.9873 0.9919 0.1116 0.7347 0.8611 0.305 ] Network output: [ -0.002754 0.01294 1.005 5.1e-06 -2.29e-06 0.9879 3.844e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09447 0.09253 0.165 0.1966 0.9852 0.9911 0.09448 0.6586 0.8362 0.2495 ] Network output: [ 8.166e-05 1 -4.964e-05 6.673e-07 -2.996e-07 0.9998 5.029e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001665 Epoch 9770 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008817 0.9968 0.9926 -1.676e-07 7.526e-08 -0.007022 -1.263e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003515 -0.00336 -0.006644 0.005365 0.9699 0.9743 0.006852 0.8244 0.8197 0.01619 ] Network output: [ 0.9999 0.0001006 0.0003628 -2.432e-06 1.092e-06 -0.0003138 -1.833e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2091 -0.03566 -0.1562 0.1823 0.9834 0.9932 0.2348 0.4289 0.8681 0.7083 ] Network output: [ -0.008821 1.003 1.008 -1.829e-07 8.209e-08 0.007329 -1.378e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006853 0.0006319 0.004361 0.003155 0.9889 0.9919 0.006987 0.8516 0.8919 0.01156 ] Network output: [ -0.0001754 0.001375 1 -7.638e-06 3.429e-06 0.9985 -5.756e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2229 0.1058 0.3495 0.1419 0.9849 0.9939 0.2237 0.4329 0.8749 0.7021 ] Network output: [ 0.002931 -0.01399 0.9943 4.659e-06 -2.092e-06 1.014 3.512e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09884 0.1848 0.1972 0.9873 0.9919 0.1116 0.7347 0.8611 0.305 ] Network output: [ -0.002753 0.01294 1.005 5.094e-06 -2.287e-06 0.9879 3.839e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09447 0.09253 0.165 0.1966 0.9852 0.9911 0.09448 0.6585 0.8362 0.2495 ] Network output: [ 8.164e-05 1 -4.963e-05 6.665e-07 -2.992e-07 0.9998 5.023e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001664 Epoch 9771 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008816 0.9968 0.9926 -1.675e-07 7.521e-08 -0.007022 -1.263e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003515 -0.003361 -0.006643 0.005364 0.9699 0.9743 0.006853 0.8244 0.8197 0.01619 ] Network output: [ 0.9999 0.0001005 0.0003626 -2.429e-06 1.091e-06 -0.0003136 -1.831e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2091 -0.03567 -0.1562 0.1823 0.9834 0.9932 0.2348 0.4289 0.8681 0.7083 ] Network output: [ -0.008821 1.003 1.008 -1.827e-07 8.203e-08 0.007328 -1.377e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006853 0.000632 0.004361 0.003155 0.9889 0.9919 0.006987 0.8516 0.8919 0.01156 ] Network output: [ -0.0001752 0.001375 1 -7.629e-06 3.425e-06 0.9985 -5.749e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2229 0.1058 0.3495 0.1419 0.9849 0.9939 0.2237 0.4329 0.8749 0.702 ] Network output: [ 0.002929 -0.01398 0.9943 4.654e-06 -2.089e-06 1.014 3.507e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09885 0.1848 0.1972 0.9873 0.9919 0.1116 0.7347 0.8611 0.305 ] Network output: [ -0.002752 0.01293 1.005 5.088e-06 -2.284e-06 0.9879 3.834e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09447 0.09253 0.165 0.1966 0.9852 0.9911 0.09448 0.6585 0.8362 0.2495 ] Network output: [ 8.162e-05 1 -4.963e-05 6.657e-07 -2.988e-07 0.9998 5.017e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001663 Epoch 9772 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008815 0.9968 0.9926 -1.674e-07 7.516e-08 -0.007021 -1.262e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003515 -0.003361 -0.006642 0.005364 0.9699 0.9743 0.006853 0.8244 0.8197 0.01619 ] Network output: [ 0.9999 0.0001003 0.0003625 -2.426e-06 1.089e-06 -0.0003134 -1.829e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2091 -0.03567 -0.1562 0.1823 0.9834 0.9932 0.2348 0.4289 0.8681 0.7083 ] Network output: [ -0.00882 1.003 1.008 -1.826e-07 8.196e-08 0.007328 -1.376e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006853 0.0006321 0.004361 0.003155 0.9889 0.9919 0.006988 0.8516 0.8919 0.01155 ] Network output: [ -0.0001751 0.001374 1 -7.62e-06 3.421e-06 0.9985 -5.742e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.223 0.1058 0.3495 0.1419 0.9849 0.9939 0.2237 0.4329 0.8749 0.702 ] Network output: [ 0.002928 -0.01398 0.9943 4.648e-06 -2.087e-06 1.014 3.503e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09885 0.1848 0.1972 0.9873 0.9919 0.1116 0.7347 0.8611 0.305 ] Network output: [ -0.00275 0.01292 1.005 5.082e-06 -2.281e-06 0.9879 3.83e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09447 0.09253 0.165 0.1966 0.9852 0.9911 0.09449 0.6585 0.8362 0.2495 ] Network output: [ 8.16e-05 1 -4.962e-05 6.649e-07 -2.985e-07 0.9998 5.011e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001662 Epoch 9773 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008814 0.9968 0.9926 -1.673e-07 7.512e-08 -0.007021 -1.261e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003515 -0.003361 -0.006642 0.005363 0.9699 0.9743 0.006853 0.8244 0.8197 0.01619 ] Network output: [ 0.9999 0.0001001 0.0003623 -2.423e-06 1.088e-06 -0.0003132 -1.826e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2091 -0.03567 -0.1562 0.1823 0.9834 0.9932 0.2348 0.4289 0.8681 0.7083 ] Network output: [ -0.008819 1.003 1.008 -1.824e-07 8.19e-08 0.007327 -1.375e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006854 0.0006321 0.004361 0.003154 0.9889 0.9919 0.006988 0.8516 0.8919 0.01155 ] Network output: [ -0.0001749 0.001373 1 -7.611e-06 3.417e-06 0.9985 -5.736e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.223 0.1058 0.3495 0.1419 0.9849 0.9939 0.2237 0.4329 0.8749 0.702 ] Network output: [ 0.002926 -0.01397 0.9943 4.643e-06 -2.084e-06 1.014 3.499e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09885 0.1848 0.1972 0.9873 0.9919 0.1117 0.7347 0.8611 0.305 ] Network output: [ -0.002749 0.01292 1.005 5.076e-06 -2.279e-06 0.9879 3.825e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09447 0.09253 0.165 0.1966 0.9852 0.9911 0.09449 0.6585 0.8362 0.2495 ] Network output: [ 8.158e-05 1 -4.962e-05 6.641e-07 -2.981e-07 0.9998 5.005e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001661 Epoch 9774 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008813 0.9968 0.9926 -1.672e-07 7.507e-08 -0.00702 -1.26e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003515 -0.003361 -0.006641 0.005363 0.9699 0.9743 0.006853 0.8244 0.8197 0.01619 ] Network output: [ 0.9999 9.998e-05 0.0003621 -2.42e-06 1.087e-06 -0.000313 -1.824e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2091 -0.03567 -0.1562 0.1823 0.9834 0.9932 0.2348 0.4289 0.8681 0.7083 ] Network output: [ -0.008818 1.003 1.008 -1.823e-07 8.183e-08 0.007327 -1.374e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006854 0.0006322 0.004361 0.003154 0.9889 0.9919 0.006989 0.8516 0.8919 0.01155 ] Network output: [ -0.0001748 0.001373 1 -7.601e-06 3.413e-06 0.9985 -5.729e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.223 0.1058 0.3495 0.1419 0.9849 0.9939 0.2237 0.4329 0.8749 0.702 ] Network output: [ 0.002925 -0.01396 0.9943 4.637e-06 -2.082e-06 1.014 3.495e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09886 0.1848 0.1972 0.9873 0.9919 0.1117 0.7347 0.8611 0.305 ] Network output: [ -0.002748 0.01291 1.005 5.07e-06 -2.276e-06 0.9879 3.821e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09447 0.09253 0.165 0.1966 0.9852 0.9911 0.09449 0.6585 0.8362 0.2495 ] Network output: [ 8.156e-05 1 -4.961e-05 6.633e-07 -2.978e-07 0.9998 4.999e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000166 Epoch 9775 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008813 0.9968 0.9926 -1.671e-07 7.502e-08 -0.007019 -1.259e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003515 -0.003361 -0.006641 0.005363 0.9699 0.9743 0.006853 0.8244 0.8197 0.01619 ] Network output: [ 0.9999 9.982e-05 0.000362 -2.417e-06 1.085e-06 -0.0003128 -1.822e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2091 -0.03567 -0.1562 0.1823 0.9834 0.9932 0.2348 0.4289 0.8681 0.7083 ] Network output: [ -0.008817 1.003 1.008 -1.821e-07 8.177e-08 0.007326 -1.373e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006855 0.0006323 0.004361 0.003154 0.9889 0.9919 0.006989 0.8516 0.8919 0.01155 ] Network output: [ -0.0001746 0.001372 1 -7.592e-06 3.408e-06 0.9985 -5.722e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.223 0.1058 0.3495 0.1419 0.9849 0.9939 0.2237 0.4329 0.8749 0.702 ] Network output: [ 0.002923 -0.01396 0.9943 4.631e-06 -2.079e-06 1.014 3.49e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09886 0.1848 0.1972 0.9873 0.9919 0.1117 0.7347 0.8611 0.305 ] Network output: [ -0.002746 0.01291 1.005 5.064e-06 -2.273e-06 0.9879 3.816e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09448 0.09254 0.165 0.1966 0.9852 0.9911 0.09449 0.6585 0.8362 0.2495 ] Network output: [ 8.154e-05 1 -4.961e-05 6.625e-07 -2.974e-07 0.9998 4.993e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001659 Epoch 9776 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008812 0.9968 0.9926 -1.67e-07 7.497e-08 -0.007019 -1.259e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003515 -0.003361 -0.00664 0.005362 0.9699 0.9743 0.006853 0.8244 0.8197 0.01619 ] Network output: [ 0.9999 9.966e-05 0.0003618 -2.415e-06 1.084e-06 -0.0003126 -1.82e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2091 -0.03567 -0.1562 0.1823 0.9834 0.9932 0.2348 0.4289 0.8681 0.7083 ] Network output: [ -0.008816 1.003 1.008 -1.82e-07 8.17e-08 0.007326 -1.372e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006855 0.0006323 0.004361 0.003154 0.9889 0.9919 0.006989 0.8516 0.8919 0.01155 ] Network output: [ -0.0001745 0.001371 1 -7.583e-06 3.404e-06 0.9985 -5.715e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.223 0.1058 0.3495 0.1419 0.9849 0.9939 0.2237 0.4328 0.8749 0.702 ] Network output: [ 0.002922 -0.01395 0.9943 4.626e-06 -2.077e-06 1.014 3.486e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09886 0.1848 0.1972 0.9873 0.9919 0.1117 0.7347 0.8611 0.305 ] Network output: [ -0.002745 0.0129 1.005 5.058e-06 -2.271e-06 0.9879 3.812e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09448 0.09254 0.165 0.1966 0.9852 0.9911 0.09449 0.6585 0.8362 0.2495 ] Network output: [ 8.152e-05 1 -4.96e-05 6.617e-07 -2.971e-07 0.9998 4.987e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001658 Epoch 9777 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008811 0.9968 0.9926 -1.669e-07 7.492e-08 -0.007018 -1.258e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003515 -0.003361 -0.00664 0.005362 0.9699 0.9743 0.006854 0.8244 0.8197 0.01619 ] Network output: [ 0.9999 9.95e-05 0.0003617 -2.412e-06 1.083e-06 -0.0003124 -1.817e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2091 -0.03567 -0.1562 0.1823 0.9834 0.9932 0.2348 0.4289 0.8681 0.7083 ] Network output: [ -0.008816 1.003 1.008 -1.819e-07 8.164e-08 0.007325 -1.37e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006855 0.0006324 0.004361 0.003153 0.9889 0.9919 0.00699 0.8516 0.8919 0.01155 ] Network output: [ -0.0001743 0.00137 1 -7.574e-06 3.4e-06 0.9985 -5.708e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.223 0.1058 0.3495 0.1419 0.9849 0.9939 0.2238 0.4328 0.8749 0.702 ] Network output: [ 0.002921 -0.01394 0.9943 4.62e-06 -2.074e-06 1.014 3.482e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09886 0.1848 0.1972 0.9873 0.9919 0.1117 0.7347 0.8611 0.305 ] Network output: [ -0.002744 0.01289 1.005 5.052e-06 -2.268e-06 0.9879 3.807e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09448 0.09254 0.165 0.1966 0.9852 0.9911 0.09449 0.6585 0.8362 0.2495 ] Network output: [ 8.15e-05 1 -4.96e-05 6.609e-07 -2.967e-07 0.9998 4.981e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001657 Epoch 9778 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00881 0.9968 0.9926 -1.668e-07 7.488e-08 -0.007018 -1.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003515 -0.003361 -0.006639 0.005361 0.9699 0.9743 0.006854 0.8244 0.8196 0.01618 ] Network output: [ 0.9999 9.934e-05 0.0003615 -2.409e-06 1.081e-06 -0.0003122 -1.815e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2091 -0.03567 -0.1562 0.1823 0.9834 0.9932 0.2348 0.4289 0.8681 0.7083 ] Network output: [ -0.008815 1.003 1.008 -1.817e-07 8.158e-08 0.007325 -1.369e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006856 0.0006324 0.004361 0.003153 0.9889 0.9919 0.00699 0.8516 0.8919 0.01155 ] Network output: [ -0.0001742 0.00137 1 -7.565e-06 3.396e-06 0.9985 -5.701e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.223 0.1059 0.3495 0.1419 0.9849 0.9939 0.2238 0.4328 0.8749 0.702 ] Network output: [ 0.002919 -0.01394 0.9943 4.615e-06 -2.072e-06 1.014 3.478e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09887 0.1848 0.1972 0.9873 0.9919 0.1117 0.7346 0.8611 0.305 ] Network output: [ -0.002743 0.01289 1.005 5.046e-06 -2.265e-06 0.9879 3.803e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09448 0.09254 0.165 0.1966 0.9852 0.9911 0.0945 0.6585 0.8362 0.2495 ] Network output: [ 8.148e-05 1 -4.959e-05 6.601e-07 -2.963e-07 0.9998 4.975e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001656 Epoch 9779 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008809 0.9968 0.9926 -1.667e-07 7.483e-08 -0.007017 -1.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003515 -0.003361 -0.006638 0.005361 0.9699 0.9743 0.006854 0.8243 0.8196 0.01618 ] Network output: [ 0.9999 9.919e-05 0.0003613 -2.406e-06 1.08e-06 -0.000312 -1.813e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2092 -0.03567 -0.1562 0.1823 0.9834 0.9932 0.2348 0.4289 0.8681 0.7083 ] Network output: [ -0.008814 1.003 1.008 -1.816e-07 8.151e-08 0.007324 -1.368e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006856 0.0006325 0.004361 0.003153 0.9889 0.9919 0.00699 0.8516 0.8919 0.01155 ] Network output: [ -0.000174 0.001369 1 -7.555e-06 3.392e-06 0.9985 -5.694e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.223 0.1059 0.3495 0.1419 0.9849 0.9939 0.2238 0.4328 0.8749 0.702 ] Network output: [ 0.002918 -0.01393 0.9943 4.609e-06 -2.069e-06 1.014 3.474e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09887 0.1848 0.1972 0.9873 0.9919 0.1117 0.7346 0.861 0.305 ] Network output: [ -0.002741 0.01288 1.005 5.04e-06 -2.263e-06 0.9879 3.798e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09448 0.09254 0.165 0.1966 0.9852 0.9911 0.0945 0.6585 0.8362 0.2495 ] Network output: [ 8.145e-05 1 -4.959e-05 6.593e-07 -2.96e-07 0.9998 4.969e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001656 Epoch 9780 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008808 0.9968 0.9926 -1.666e-07 7.478e-08 -0.007016 -1.255e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003515 -0.003361 -0.006638 0.005361 0.9699 0.9743 0.006854 0.8243 0.8196 0.01618 ] Network output: [ 0.9999 9.903e-05 0.0003612 -2.403e-06 1.079e-06 -0.0003118 -1.811e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2092 -0.03567 -0.1562 0.1823 0.9834 0.9932 0.2348 0.4289 0.8681 0.7083 ] Network output: [ -0.008813 1.003 1.008 -1.814e-07 8.145e-08 0.007323 -1.367e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006856 0.0006326 0.00436 0.003153 0.9889 0.9919 0.006991 0.8516 0.8919 0.01155 ] Network output: [ -0.0001739 0.001368 1 -7.546e-06 3.388e-06 0.9985 -5.687e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.223 0.1059 0.3495 0.1419 0.9849 0.9939 0.2238 0.4328 0.8749 0.702 ] Network output: [ 0.002916 -0.01392 0.9943 4.604e-06 -2.067e-06 1.014 3.469e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09887 0.1848 0.1972 0.9873 0.9919 0.1117 0.7346 0.861 0.305 ] Network output: [ -0.00274 0.01288 1.005 5.034e-06 -2.26e-06 0.9879 3.794e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09448 0.09255 0.165 0.1966 0.9852 0.9911 0.0945 0.6584 0.8362 0.2495 ] Network output: [ 8.143e-05 1 -4.958e-05 6.585e-07 -2.956e-07 0.9998 4.963e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001655 Epoch 9781 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008807 0.9968 0.9926 -1.665e-07 7.473e-08 -0.007016 -1.255e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003515 -0.003361 -0.006637 0.00536 0.9699 0.9743 0.006854 0.8243 0.8196 0.01618 ] Network output: [ 0.9999 9.887e-05 0.000361 -2.4e-06 1.077e-06 -0.0003116 -1.809e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2092 -0.03567 -0.1561 0.1823 0.9834 0.9932 0.2348 0.4289 0.8681 0.7083 ] Network output: [ -0.008812 1.003 1.008 -1.813e-07 8.138e-08 0.007323 -1.366e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006857 0.0006326 0.00436 0.003153 0.9889 0.9919 0.006991 0.8515 0.8919 0.01155 ] Network output: [ -0.0001737 0.001368 1 -7.537e-06 3.384e-06 0.9985 -5.68e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.223 0.1059 0.3495 0.1418 0.9849 0.9939 0.2238 0.4328 0.8749 0.702 ] Network output: [ 0.002915 -0.01392 0.9943 4.598e-06 -2.064e-06 1.014 3.465e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09888 0.1848 0.1972 0.9873 0.9919 0.1117 0.7346 0.861 0.305 ] Network output: [ -0.002739 0.01287 1.005 5.028e-06 -2.257e-06 0.9879 3.789e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09449 0.09255 0.165 0.1966 0.9852 0.9911 0.0945 0.6584 0.8362 0.2495 ] Network output: [ 8.141e-05 1 -4.958e-05 6.577e-07 -2.953e-07 0.9998 4.957e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001654 Epoch 9782 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008806 0.9968 0.9926 -1.664e-07 7.468e-08 -0.007015 -1.254e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003515 -0.003361 -0.006637 0.00536 0.9699 0.9743 0.006854 0.8243 0.8196 0.01618 ] Network output: [ 0.9999 9.871e-05 0.0003609 -2.397e-06 1.076e-06 -0.0003114 -1.806e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2092 -0.03568 -0.1561 0.1823 0.9834 0.9932 0.2348 0.4289 0.8681 0.7083 ] Network output: [ -0.008812 1.003 1.008 -1.811e-07 8.132e-08 0.007322 -1.365e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006857 0.0006327 0.00436 0.003152 0.9889 0.9919 0.006991 0.8515 0.8919 0.01155 ] Network output: [ -0.0001736 0.001367 1 -7.528e-06 3.38e-06 0.9985 -5.673e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.223 0.1059 0.3495 0.1418 0.9849 0.9939 0.2238 0.4328 0.8749 0.702 ] Network output: [ 0.002913 -0.01391 0.9943 4.593e-06 -2.062e-06 1.014 3.461e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09888 0.1848 0.1972 0.9873 0.9919 0.1117 0.7346 0.861 0.305 ] Network output: [ -0.002737 0.01286 1.005 5.022e-06 -2.255e-06 0.988 3.785e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09449 0.09255 0.165 0.1966 0.9852 0.9911 0.0945 0.6584 0.8362 0.2495 ] Network output: [ 8.139e-05 1 -4.957e-05 6.57e-07 -2.949e-07 0.9998 4.951e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001653 Epoch 9783 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008805 0.9968 0.9926 -1.662e-07 7.463e-08 -0.007015 -1.253e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003515 -0.003362 -0.006636 0.00536 0.9699 0.9743 0.006855 0.8243 0.8196 0.01618 ] Network output: [ 0.9999 9.855e-05 0.0003607 -2.394e-06 1.075e-06 -0.0003112 -1.804e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2092 -0.03568 -0.1561 0.1823 0.9834 0.9932 0.2348 0.4289 0.8681 0.7083 ] Network output: [ -0.008811 1.003 1.008 -1.81e-07 8.125e-08 0.007322 -1.364e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006857 0.0006327 0.00436 0.003152 0.9889 0.9919 0.006992 0.8515 0.8919 0.01155 ] Network output: [ -0.0001734 0.001366 1 -7.519e-06 3.375e-06 0.9985 -5.666e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.223 0.1059 0.3495 0.1418 0.9849 0.9939 0.2238 0.4328 0.8749 0.702 ] Network output: [ 0.002912 -0.01391 0.9943 4.587e-06 -2.059e-06 1.014 3.457e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09888 0.1848 0.1972 0.9873 0.9919 0.1117 0.7346 0.861 0.305 ] Network output: [ -0.002736 0.01286 1.005 5.016e-06 -2.252e-06 0.988 3.78e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09449 0.09255 0.165 0.1966 0.9852 0.9911 0.0945 0.6584 0.8361 0.2495 ] Network output: [ 8.137e-05 1 -4.957e-05 6.562e-07 -2.946e-07 0.9998 4.945e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001652 Epoch 9784 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008805 0.9968 0.9926 -1.661e-07 7.459e-08 -0.007014 -1.252e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003515 -0.003362 -0.006636 0.005359 0.9699 0.9743 0.006855 0.8243 0.8196 0.01618 ] Network output: [ 0.9999 9.84e-05 0.0003605 -2.391e-06 1.073e-06 -0.000311 -1.802e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2092 -0.03568 -0.1561 0.1823 0.9834 0.9932 0.2349 0.4289 0.8681 0.7083 ] Network output: [ -0.00881 1.003 1.008 -1.808e-07 8.119e-08 0.007321 -1.363e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006858 0.0006328 0.00436 0.003152 0.9889 0.9919 0.006992 0.8515 0.8919 0.01155 ] Network output: [ -0.0001733 0.001366 1 -7.51e-06 3.371e-06 0.9985 -5.659e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.223 0.1059 0.3495 0.1418 0.9849 0.9939 0.2238 0.4328 0.8749 0.702 ] Network output: [ 0.00291 -0.0139 0.9943 4.581e-06 -2.057e-06 1.014 3.453e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09889 0.1848 0.1972 0.9873 0.9919 0.1117 0.7346 0.861 0.305 ] Network output: [ -0.002735 0.01285 1.005 5.01e-06 -2.249e-06 0.988 3.776e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09449 0.09255 0.165 0.1966 0.9852 0.9911 0.09451 0.6584 0.8361 0.2495 ] Network output: [ 8.135e-05 1 -4.957e-05 6.554e-07 -2.942e-07 0.9998 4.939e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001651 Epoch 9785 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008804 0.9968 0.9926 -1.66e-07 7.454e-08 -0.007013 -1.251e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003515 -0.003362 -0.006635 0.005359 0.9699 0.9743 0.006855 0.8243 0.8196 0.01618 ] Network output: [ 0.9999 9.824e-05 0.0003604 -2.388e-06 1.072e-06 -0.0003108 -1.8e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2092 -0.03568 -0.1561 0.1823 0.9834 0.9932 0.2349 0.4289 0.8681 0.7083 ] Network output: [ -0.008809 1.003 1.008 -1.807e-07 8.113e-08 0.007321 -1.362e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006858 0.0006329 0.00436 0.003152 0.9889 0.9919 0.006993 0.8515 0.8919 0.01154 ] Network output: [ -0.0001731 0.001365 1 -7.501e-06 3.367e-06 0.9985 -5.653e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.223 0.1059 0.3495 0.1418 0.9849 0.9939 0.2238 0.4328 0.8749 0.702 ] Network output: [ 0.002909 -0.01389 0.9943 4.576e-06 -2.054e-06 1.014 3.449e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09889 0.1848 0.1972 0.9873 0.9919 0.1117 0.7346 0.861 0.305 ] Network output: [ -0.002733 0.01285 1.005 5.004e-06 -2.246e-06 0.988 3.771e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09449 0.09255 0.165 0.1966 0.9852 0.9911 0.09451 0.6584 0.8361 0.2495 ] Network output: [ 8.133e-05 1 -4.956e-05 6.546e-07 -2.939e-07 0.9998 4.933e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000165 Epoch 9786 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008803 0.9968 0.9926 -1.659e-07 7.449e-08 -0.007013 -1.25e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003515 -0.003362 -0.006634 0.005358 0.9699 0.9743 0.006855 0.8243 0.8196 0.01618 ] Network output: [ 0.9999 9.808e-05 0.0003602 -2.385e-06 1.071e-06 -0.0003107 -1.798e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2092 -0.03568 -0.1561 0.1823 0.9834 0.9932 0.2349 0.4289 0.8681 0.7083 ] Network output: [ -0.008808 1.003 1.008 -1.806e-07 8.106e-08 0.00732 -1.361e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006858 0.0006329 0.00436 0.003151 0.9889 0.9919 0.006993 0.8515 0.8919 0.01154 ] Network output: [ -0.000173 0.001364 1 -7.491e-06 3.363e-06 0.9985 -5.646e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.223 0.1059 0.3495 0.1418 0.9849 0.9939 0.2238 0.4328 0.8749 0.702 ] Network output: [ 0.002908 -0.01389 0.9943 4.57e-06 -2.052e-06 1.014 3.444e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09889 0.1848 0.1972 0.9873 0.9919 0.1117 0.7346 0.861 0.305 ] Network output: [ -0.002732 0.01284 1.005 4.998e-06 -2.244e-06 0.988 3.767e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0945 0.09256 0.165 0.1966 0.9852 0.9911 0.09451 0.6584 0.8361 0.2495 ] Network output: [ 8.131e-05 1 -4.956e-05 6.538e-07 -2.935e-07 0.9998 4.927e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001649 Epoch 9787 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008802 0.9968 0.9926 -1.658e-07 7.444e-08 -0.007012 -1.25e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003515 -0.003362 -0.006634 0.005358 0.9699 0.9743 0.006855 0.8243 0.8196 0.01618 ] Network output: [ 0.9999 9.792e-05 0.0003601 -2.382e-06 1.07e-06 -0.0003105 -1.795e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2092 -0.03568 -0.1561 0.1823 0.9834 0.9932 0.2349 0.4288 0.8681 0.7083 ] Network output: [ -0.008808 1.003 1.008 -1.804e-07 8.1e-08 0.007319 -1.36e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006859 0.000633 0.00436 0.003151 0.9889 0.9919 0.006993 0.8515 0.8919 0.01154 ] Network output: [ -0.0001728 0.001364 1 -7.482e-06 3.359e-06 0.9985 -5.639e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2231 0.1059 0.3495 0.1418 0.9849 0.9939 0.2238 0.4328 0.8749 0.702 ] Network output: [ 0.002906 -0.01388 0.9943 4.565e-06 -2.049e-06 1.014 3.44e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09889 0.1848 0.1972 0.9873 0.9919 0.1117 0.7346 0.861 0.305 ] Network output: [ -0.002731 0.01283 1.005 4.992e-06 -2.241e-06 0.988 3.762e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0945 0.09256 0.165 0.1966 0.9852 0.9911 0.09451 0.6584 0.8361 0.2495 ] Network output: [ 8.129e-05 1 -4.955e-05 6.53e-07 -2.932e-07 0.9998 4.921e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001648 Epoch 9788 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008801 0.9968 0.9926 -1.657e-07 7.439e-08 -0.007011 -1.249e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003516 -0.003362 -0.006633 0.005358 0.9699 0.9743 0.006855 0.8243 0.8196 0.01618 ] Network output: [ 0.9999 9.776e-05 0.0003599 -2.38e-06 1.068e-06 -0.0003103 -1.793e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2092 -0.03568 -0.1561 0.1823 0.9834 0.9932 0.2349 0.4288 0.8681 0.7083 ] Network output: [ -0.008807 1.003 1.008 -1.803e-07 8.093e-08 0.007319 -1.359e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006859 0.000633 0.00436 0.003151 0.9889 0.9919 0.006994 0.8515 0.8919 0.01154 ] Network output: [ -0.0001727 0.001363 1 -7.473e-06 3.355e-06 0.9985 -5.632e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2231 0.1059 0.3495 0.1418 0.9849 0.9939 0.2238 0.4328 0.8749 0.702 ] Network output: [ 0.002905 -0.01387 0.9943 4.559e-06 -2.047e-06 1.014 3.436e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.0989 0.1848 0.1972 0.9873 0.9919 0.1117 0.7345 0.861 0.305 ] Network output: [ -0.002729 0.01283 1.005 4.986e-06 -2.238e-06 0.988 3.758e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0945 0.09256 0.165 0.1966 0.9852 0.9911 0.09451 0.6584 0.8361 0.2495 ] Network output: [ 8.127e-05 1 -4.955e-05 6.522e-07 -2.928e-07 0.9998 4.916e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001647 Epoch 9789 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0088 0.9968 0.9926 -1.656e-07 7.434e-08 -0.007011 -1.248e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003516 -0.003362 -0.006633 0.005357 0.9699 0.9743 0.006856 0.8243 0.8196 0.01617 ] Network output: [ 0.9999 9.761e-05 0.0003598 -2.377e-06 1.067e-06 -0.0003101 -1.791e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2092 -0.03568 -0.1561 0.1823 0.9834 0.9932 0.2349 0.4288 0.8681 0.7083 ] Network output: [ -0.008806 1.003 1.008 -1.801e-07 8.087e-08 0.007318 -1.358e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006859 0.0006331 0.004359 0.003151 0.9889 0.9919 0.006994 0.8515 0.8919 0.01154 ] Network output: [ -0.0001725 0.001362 1 -7.464e-06 3.351e-06 0.9985 -5.625e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2231 0.1059 0.3496 0.1418 0.9849 0.9939 0.2238 0.4328 0.8749 0.702 ] Network output: [ 0.002903 -0.01387 0.9943 4.554e-06 -2.044e-06 1.014 3.432e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.0989 0.1848 0.1972 0.9873 0.9919 0.1117 0.7345 0.861 0.305 ] Network output: [ -0.002728 0.01282 1.005 4.98e-06 -2.236e-06 0.988 3.753e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0945 0.09256 0.165 0.1966 0.9852 0.9911 0.09452 0.6583 0.8361 0.2495 ] Network output: [ 8.125e-05 1 -4.954e-05 6.515e-07 -2.925e-07 0.9998 4.91e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001646 Epoch 9790 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008799 0.9968 0.9926 -1.655e-07 7.43e-08 -0.00701 -1.247e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003516 -0.003362 -0.006632 0.005357 0.9699 0.9743 0.006856 0.8243 0.8196 0.01617 ] Network output: [ 0.9999 9.745e-05 0.0003596 -2.374e-06 1.066e-06 -0.0003099 -1.789e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2092 -0.03568 -0.1561 0.1823 0.9834 0.9932 0.2349 0.4288 0.8681 0.7083 ] Network output: [ -0.008805 1.003 1.008 -1.8e-07 8.081e-08 0.007318 -1.356e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00686 0.0006332 0.004359 0.00315 0.9889 0.9919 0.006994 0.8515 0.8918 0.01154 ] Network output: [ -0.0001724 0.001361 1 -7.455e-06 3.347e-06 0.9985 -5.618e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2231 0.1059 0.3496 0.1418 0.9849 0.9939 0.2238 0.4328 0.8749 0.702 ] Network output: [ 0.002902 -0.01386 0.9943 4.548e-06 -2.042e-06 1.014 3.428e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.0989 0.1848 0.1972 0.9873 0.9919 0.1117 0.7345 0.861 0.305 ] Network output: [ -0.002727 0.01282 1.005 4.974e-06 -2.233e-06 0.988 3.749e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0945 0.09256 0.165 0.1966 0.9852 0.9911 0.09452 0.6583 0.8361 0.2495 ] Network output: [ 8.123e-05 1 -4.954e-05 6.507e-07 -2.921e-07 0.9998 4.904e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001646 Epoch 9791 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008798 0.9968 0.9926 -1.654e-07 7.425e-08 -0.00701 -1.246e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003516 -0.003362 -0.006632 0.005357 0.9699 0.9743 0.006856 0.8243 0.8196 0.01617 ] Network output: [ 0.9999 9.729e-05 0.0003594 -2.371e-06 1.064e-06 -0.0003097 -1.787e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2092 -0.03568 -0.1561 0.1823 0.9834 0.9932 0.2349 0.4288 0.8681 0.7083 ] Network output: [ -0.008804 1.003 1.008 -1.799e-07 8.074e-08 0.007317 -1.355e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00686 0.0006332 0.004359 0.00315 0.9889 0.9919 0.006995 0.8515 0.8918 0.01154 ] Network output: [ -0.0001722 0.001361 1 -7.446e-06 3.343e-06 0.9985 -5.612e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2231 0.1059 0.3496 0.1418 0.9849 0.9939 0.2238 0.4328 0.8749 0.702 ] Network output: [ 0.0029 -0.01385 0.9943 4.543e-06 -2.039e-06 1.014 3.424e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09891 0.1848 0.1972 0.9873 0.9919 0.1117 0.7345 0.861 0.305 ] Network output: [ -0.002725 0.01281 1.005 4.968e-06 -2.231e-06 0.988 3.744e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0945 0.09256 0.165 0.1966 0.9852 0.9911 0.09452 0.6583 0.8361 0.2495 ] Network output: [ 8.121e-05 1 -4.954e-05 6.499e-07 -2.918e-07 0.9998 4.898e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001645 Epoch 9792 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008797 0.9968 0.9926 -1.653e-07 7.42e-08 -0.007009 -1.246e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003516 -0.003362 -0.006631 0.005356 0.9699 0.9743 0.006856 0.8243 0.8196 0.01617 ] Network output: [ 0.9999 9.713e-05 0.0003593 -2.368e-06 1.063e-06 -0.0003095 -1.785e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2092 -0.03569 -0.1561 0.1823 0.9834 0.9932 0.2349 0.4288 0.8681 0.7083 ] Network output: [ -0.008803 1.003 1.008 -1.797e-07 8.068e-08 0.007317 -1.354e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006861 0.0006333 0.004359 0.00315 0.9889 0.9919 0.006995 0.8515 0.8918 0.01154 ] Network output: [ -0.0001721 0.00136 1 -7.437e-06 3.339e-06 0.9985 -5.605e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2231 0.1059 0.3496 0.1418 0.9849 0.9939 0.2239 0.4328 0.8749 0.702 ] Network output: [ 0.002899 -0.01385 0.9943 4.537e-06 -2.037e-06 1.014 3.42e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09891 0.1848 0.1972 0.9873 0.9919 0.1117 0.7345 0.861 0.305 ] Network output: [ -0.002724 0.0128 1.005 4.963e-06 -2.228e-06 0.988 3.74e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09451 0.09257 0.165 0.1966 0.9852 0.9911 0.09452 0.6583 0.8361 0.2495 ] Network output: [ 8.119e-05 1 -4.953e-05 6.491e-07 -2.914e-07 0.9998 4.892e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001644 Epoch 9793 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008797 0.9968 0.9926 -1.652e-07 7.415e-08 -0.007008 -1.245e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003516 -0.003362 -0.00663 0.005356 0.9699 0.9743 0.006856 0.8243 0.8196 0.01617 ] Network output: [ 0.9999 9.698e-05 0.0003591 -2.365e-06 1.062e-06 -0.0003093 -1.782e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2092 -0.03569 -0.156 0.1823 0.9834 0.9932 0.2349 0.4288 0.8681 0.7083 ] Network output: [ -0.008803 1.003 1.008 -1.796e-07 8.061e-08 0.007316 -1.353e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006861 0.0006333 0.004359 0.00315 0.9889 0.9919 0.006995 0.8515 0.8918 0.01154 ] Network output: [ -0.0001719 0.001359 1 -7.428e-06 3.335e-06 0.9985 -5.598e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2231 0.1059 0.3496 0.1418 0.9849 0.9939 0.2239 0.4328 0.8749 0.702 ] Network output: [ 0.002897 -0.01384 0.9943 4.532e-06 -2.035e-06 1.014 3.415e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09891 0.1848 0.1972 0.9873 0.9919 0.1117 0.7345 0.861 0.305 ] Network output: [ -0.002723 0.0128 1.005 4.957e-06 -2.225e-06 0.988 3.735e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09451 0.09257 0.165 0.1966 0.9852 0.9911 0.09452 0.6583 0.8361 0.2495 ] Network output: [ 8.117e-05 1 -4.953e-05 6.484e-07 -2.911e-07 0.9998 4.886e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001643 Epoch 9794 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008796 0.9968 0.9926 -1.651e-07 7.41e-08 -0.007008 -1.244e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003516 -0.003362 -0.00663 0.005355 0.9699 0.9743 0.006856 0.8243 0.8196 0.01617 ] Network output: [ 0.9999 9.682e-05 0.000359 -2.362e-06 1.06e-06 -0.0003091 -1.78e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2092 -0.03569 -0.156 0.1823 0.9834 0.9932 0.2349 0.4288 0.8681 0.7083 ] Network output: [ -0.008802 1.003 1.008 -1.794e-07 8.055e-08 0.007315 -1.352e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006861 0.0006334 0.004359 0.00315 0.9889 0.9919 0.006996 0.8515 0.8918 0.01154 ] Network output: [ -0.0001718 0.001359 1 -7.419e-06 3.331e-06 0.9985 -5.591e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2231 0.1059 0.3496 0.1418 0.9849 0.9939 0.2239 0.4328 0.8749 0.702 ] Network output: [ 0.002896 -0.01383 0.9943 4.526e-06 -2.032e-06 1.014 3.411e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09891 0.1848 0.1972 0.9873 0.9919 0.1117 0.7345 0.861 0.305 ] Network output: [ -0.002721 0.01279 1.005 4.951e-06 -2.223e-06 0.988 3.731e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09451 0.09257 0.165 0.1966 0.9852 0.9911 0.09452 0.6583 0.8361 0.2495 ] Network output: [ 8.115e-05 1 -4.953e-05 6.476e-07 -2.907e-07 0.9998 4.88e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001642 Epoch 9795 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008795 0.9968 0.9926 -1.65e-07 7.406e-08 -0.007007 -1.243e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003516 -0.003362 -0.006629 0.005355 0.9699 0.9743 0.006856 0.8243 0.8196 0.01617 ] Network output: [ 0.9999 9.666e-05 0.0003588 -2.359e-06 1.059e-06 -0.0003089 -1.778e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2092 -0.03569 -0.156 0.1823 0.9834 0.9932 0.2349 0.4288 0.8681 0.7083 ] Network output: [ -0.008801 1.003 1.008 -1.793e-07 8.049e-08 0.007315 -1.351e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006862 0.0006335 0.004359 0.003149 0.9889 0.9919 0.006996 0.8515 0.8918 0.01154 ] Network output: [ -0.0001716 0.001358 1 -7.41e-06 3.327e-06 0.9985 -5.584e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2231 0.1059 0.3496 0.1418 0.9849 0.9939 0.2239 0.4328 0.8749 0.702 ] Network output: [ 0.002894 -0.01383 0.9943 4.521e-06 -2.03e-06 1.014 3.407e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09892 0.1849 0.1972 0.9873 0.9919 0.1117 0.7345 0.861 0.305 ] Network output: [ -0.00272 0.01279 1.005 4.945e-06 -2.22e-06 0.988 3.727e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09451 0.09257 0.165 0.1966 0.9852 0.9911 0.09453 0.6583 0.8361 0.2495 ] Network output: [ 8.113e-05 1 -4.952e-05 6.468e-07 -2.904e-07 0.9998 4.874e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001641 Epoch 9796 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008794 0.9968 0.9926 -1.648e-07 7.401e-08 -0.007006 -1.242e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003516 -0.003363 -0.006629 0.005355 0.9699 0.9743 0.006857 0.8243 0.8196 0.01617 ] Network output: [ 0.9999 9.651e-05 0.0003586 -2.356e-06 1.058e-06 -0.0003087 -1.776e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2092 -0.03569 -0.156 0.1822 0.9834 0.9932 0.2349 0.4288 0.8681 0.7083 ] Network output: [ -0.0088 1.003 1.008 -1.791e-07 8.042e-08 0.007314 -1.35e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006862 0.0006335 0.004359 0.003149 0.9889 0.9919 0.006997 0.8515 0.8918 0.01154 ] Network output: [ -0.0001715 0.001357 1 -7.401e-06 3.323e-06 0.9985 -5.578e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2231 0.1059 0.3496 0.1418 0.9849 0.9939 0.2239 0.4328 0.8749 0.702 ] Network output: [ 0.002893 -0.01382 0.9943 4.516e-06 -2.027e-06 1.014 3.403e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09892 0.1849 0.1972 0.9873 0.9919 0.1117 0.7345 0.861 0.305 ] Network output: [ -0.002719 0.01278 1.005 4.939e-06 -2.217e-06 0.988 3.722e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09451 0.09257 0.165 0.1966 0.9852 0.9911 0.09453 0.6583 0.8361 0.2495 ] Network output: [ 8.11e-05 1 -4.952e-05 6.46e-07 -2.9e-07 0.9998 4.869e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000164 Epoch 9797 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008793 0.9968 0.9926 -1.647e-07 7.396e-08 -0.007006 -1.242e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003516 -0.003363 -0.006628 0.005354 0.9699 0.9743 0.006857 0.8243 0.8196 0.01617 ] Network output: [ 0.9999 9.635e-05 0.0003585 -2.354e-06 1.057e-06 -0.0003085 -1.774e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2093 -0.03569 -0.156 0.1822 0.9834 0.9932 0.2349 0.4288 0.8681 0.7083 ] Network output: [ -0.008799 1.003 1.008 -1.79e-07 8.036e-08 0.007314 -1.349e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006862 0.0006336 0.004359 0.003149 0.9889 0.9919 0.006997 0.8515 0.8918 0.01154 ] Network output: [ -0.0001713 0.001357 1 -7.392e-06 3.319e-06 0.9985 -5.571e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2231 0.1059 0.3496 0.1418 0.9849 0.9939 0.2239 0.4328 0.8749 0.7019 ] Network output: [ 0.002892 -0.01381 0.9943 4.51e-06 -2.025e-06 1.014 3.399e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1116 0.09892 0.1849 0.1972 0.9873 0.9919 0.1117 0.7344 0.861 0.305 ] Network output: [ -0.002717 0.01278 1.005 4.933e-06 -2.215e-06 0.988 3.718e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09451 0.09258 0.165 0.1966 0.9852 0.9911 0.09453 0.6583 0.8361 0.2495 ] Network output: [ 8.108e-05 1 -4.951e-05 6.452e-07 -2.897e-07 0.9998 4.863e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001639 Epoch 9798 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008792 0.9968 0.9926 -1.646e-07 7.391e-08 -0.007005 -1.241e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003516 -0.003363 -0.006628 0.005354 0.9699 0.9743 0.006857 0.8243 0.8196 0.01617 ] Network output: [ 0.9999 9.619e-05 0.0003583 -2.351e-06 1.055e-06 -0.0003083 -1.772e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2093 -0.03569 -0.156 0.1822 0.9834 0.9932 0.2349 0.4288 0.8681 0.7082 ] Network output: [ -0.008799 1.003 1.008 -1.789e-07 8.029e-08 0.007313 -1.348e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006863 0.0006336 0.004359 0.003149 0.9889 0.9919 0.006997 0.8515 0.8918 0.01154 ] Network output: [ -0.0001712 0.001356 1 -7.383e-06 3.315e-06 0.9985 -5.564e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2231 0.1059 0.3496 0.1418 0.9849 0.9939 0.2239 0.4328 0.8749 0.7019 ] Network output: [ 0.00289 -0.01381 0.9943 4.505e-06 -2.022e-06 1.014 3.395e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09893 0.1849 0.1972 0.9873 0.9919 0.1117 0.7344 0.861 0.305 ] Network output: [ -0.002716 0.01277 1.005 4.927e-06 -2.212e-06 0.988 3.713e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09452 0.09258 0.165 0.1966 0.9852 0.9911 0.09453 0.6583 0.8361 0.2495 ] Network output: [ 8.106e-05 1 -4.951e-05 6.445e-07 -2.893e-07 0.9998 4.857e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001638 Epoch 9799 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008791 0.9968 0.9926 -1.645e-07 7.386e-08 -0.007005 -1.24e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003516 -0.003363 -0.006627 0.005354 0.9699 0.9743 0.006857 0.8243 0.8196 0.01617 ] Network output: [ 0.9999 9.604e-05 0.0003582 -2.348e-06 1.054e-06 -0.0003081 -1.769e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2093 -0.03569 -0.156 0.1822 0.9834 0.9932 0.235 0.4288 0.8681 0.7082 ] Network output: [ -0.008798 1.003 1.008 -1.787e-07 8.023e-08 0.007313 -1.347e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006863 0.0006337 0.004358 0.003148 0.9889 0.9919 0.006998 0.8515 0.8918 0.01153 ] Network output: [ -0.000171 0.001355 1 -7.374e-06 3.31e-06 0.9985 -5.557e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2231 0.1059 0.3496 0.1418 0.9849 0.9939 0.2239 0.4328 0.8749 0.7019 ] Network output: [ 0.002889 -0.0138 0.9943 4.499e-06 -2.02e-06 1.014 3.391e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09893 0.1849 0.1972 0.9873 0.9919 0.1117 0.7344 0.861 0.305 ] Network output: [ -0.002715 0.01276 1.005 4.921e-06 -2.209e-06 0.988 3.709e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09452 0.09258 0.165 0.1966 0.9852 0.9911 0.09453 0.6582 0.8361 0.2495 ] Network output: [ 8.104e-05 1 -4.951e-05 6.437e-07 -2.89e-07 0.9998 4.851e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001638 Epoch 9800 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00879 0.9968 0.9926 -1.644e-07 7.381e-08 -0.007004 -1.239e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003516 -0.003363 -0.006626 0.005353 0.9699 0.9743 0.006857 0.8243 0.8196 0.01617 ] Network output: [ 0.9999 9.588e-05 0.000358 -2.345e-06 1.053e-06 -0.0003079 -1.767e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2093 -0.03569 -0.156 0.1822 0.9834 0.9932 0.235 0.4288 0.8681 0.7082 ] Network output: [ -0.008797 1.003 1.008 -1.786e-07 8.017e-08 0.007312 -1.346e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006863 0.0006338 0.004358 0.003148 0.9889 0.9919 0.006998 0.8515 0.8918 0.01153 ] Network output: [ -0.0001709 0.001354 1 -7.365e-06 3.306e-06 0.9985 -5.551e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2231 0.1059 0.3496 0.1418 0.9849 0.9939 0.2239 0.4327 0.8749 0.7019 ] Network output: [ 0.002887 -0.01379 0.9943 4.494e-06 -2.017e-06 1.014 3.387e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09893 0.1849 0.1972 0.9873 0.9919 0.1117 0.7344 0.861 0.305 ] Network output: [ -0.002713 0.01276 1.005 4.915e-06 -2.207e-06 0.988 3.704e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09452 0.09258 0.165 0.1966 0.9852 0.9911 0.09453 0.6582 0.8361 0.2495 ] Network output: [ 8.102e-05 1 -4.95e-05 6.429e-07 -2.886e-07 0.9998 4.845e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001637 Epoch 9801 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008789 0.9968 0.9926 -1.643e-07 7.377e-08 -0.007003 -1.238e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003516 -0.003363 -0.006626 0.005353 0.9699 0.9743 0.006857 0.8243 0.8196 0.01616 ] Network output: [ 0.9999 9.572e-05 0.0003579 -2.342e-06 1.051e-06 -0.0003077 -1.765e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2093 -0.03569 -0.156 0.1822 0.9834 0.9932 0.235 0.4288 0.8681 0.7082 ] Network output: [ -0.008796 1.003 1.008 -1.784e-07 8.01e-08 0.007312 -1.345e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006864 0.0006338 0.004358 0.003148 0.9889 0.9919 0.006998 0.8515 0.8918 0.01153 ] Network output: [ -0.0001707 0.001354 1 -7.356e-06 3.302e-06 0.9985 -5.544e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2231 0.1059 0.3496 0.1418 0.9849 0.9939 0.2239 0.4327 0.8749 0.7019 ] Network output: [ 0.002886 -0.01379 0.9943 4.488e-06 -2.015e-06 1.014 3.383e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09894 0.1849 0.1972 0.9873 0.9919 0.1117 0.7344 0.861 0.305 ] Network output: [ -0.002712 0.01275 1.005 4.91e-06 -2.204e-06 0.988 3.7e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09452 0.09258 0.165 0.1966 0.9852 0.9911 0.09454 0.6582 0.8361 0.2495 ] Network output: [ 8.1e-05 1 -4.95e-05 6.422e-07 -2.883e-07 0.9998 4.84e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001636 Epoch 9802 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008789 0.9968 0.9926 -1.642e-07 7.372e-08 -0.007003 -1.237e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003516 -0.003363 -0.006625 0.005352 0.9699 0.9743 0.006858 0.8243 0.8196 0.01616 ] Network output: [ 0.9999 9.557e-05 0.0003577 -2.339e-06 1.05e-06 -0.0003076 -1.763e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2093 -0.0357 -0.156 0.1822 0.9834 0.9932 0.235 0.4288 0.8681 0.7082 ] Network output: [ -0.008795 1.003 1.008 -1.783e-07 8.004e-08 0.007311 -1.344e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006864 0.0006339 0.004358 0.003148 0.9889 0.9919 0.006999 0.8515 0.8918 0.01153 ] Network output: [ -0.0001706 0.001353 1 -7.347e-06 3.298e-06 0.9985 -5.537e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2232 0.1059 0.3496 0.1418 0.9849 0.9939 0.2239 0.4327 0.8749 0.7019 ] Network output: [ 0.002884 -0.01378 0.9943 4.483e-06 -2.013e-06 1.014 3.378e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09894 0.1849 0.1972 0.9873 0.9919 0.1117 0.7344 0.861 0.305 ] Network output: [ -0.002711 0.01275 1.005 4.904e-06 -2.201e-06 0.988 3.696e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09452 0.09258 0.165 0.1966 0.9852 0.9911 0.09454 0.6582 0.8361 0.2495 ] Network output: [ 8.098e-05 1 -4.95e-05 6.414e-07 -2.879e-07 0.9998 4.834e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001635 Epoch 9803 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008788 0.9968 0.9926 -1.641e-07 7.367e-08 -0.007002 -1.237e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003516 -0.003363 -0.006625 0.005352 0.9699 0.9743 0.006858 0.8242 0.8196 0.01616 ] Network output: [ 0.9999 9.541e-05 0.0003575 -2.336e-06 1.049e-06 -0.0003074 -1.761e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2093 -0.0357 -0.156 0.1822 0.9834 0.9932 0.235 0.4288 0.8681 0.7082 ] Network output: [ -0.008794 1.003 1.008 -1.781e-07 7.997e-08 0.00731 -1.343e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006864 0.0006339 0.004358 0.003148 0.9889 0.9919 0.006999 0.8515 0.8918 0.01153 ] Network output: [ -0.0001704 0.001352 1 -7.338e-06 3.294e-06 0.9985 -5.53e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2232 0.106 0.3496 0.1418 0.9849 0.9939 0.2239 0.4327 0.8749 0.7019 ] Network output: [ 0.002883 -0.01378 0.9943 4.477e-06 -2.01e-06 1.014 3.374e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09894 0.1849 0.1972 0.9873 0.9919 0.1117 0.7344 0.861 0.305 ] Network output: [ -0.002709 0.01274 1.005 4.898e-06 -2.199e-06 0.988 3.691e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09453 0.09259 0.165 0.1966 0.9852 0.9911 0.09454 0.6582 0.8361 0.2495 ] Network output: [ 8.096e-05 1 -4.949e-05 6.406e-07 -2.876e-07 0.9998 4.828e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001634 Epoch 9804 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008787 0.9968 0.9926 -1.64e-07 7.362e-08 -0.007002 -1.236e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003516 -0.003363 -0.006624 0.005352 0.9699 0.9743 0.006858 0.8242 0.8196 0.01616 ] Network output: [ 0.9999 9.525e-05 0.0003574 -2.334e-06 1.048e-06 -0.0003072 -1.759e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2093 -0.0357 -0.156 0.1822 0.9834 0.9932 0.235 0.4288 0.8681 0.7082 ] Network output: [ -0.008794 1.003 1.008 -1.78e-07 7.991e-08 0.00731 -1.341e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006865 0.000634 0.004358 0.003147 0.9889 0.9919 0.006999 0.8514 0.8918 0.01153 ] Network output: [ -0.0001703 0.001352 1 -7.329e-06 3.29e-06 0.9985 -5.524e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2232 0.106 0.3496 0.1418 0.9849 0.9939 0.2239 0.4327 0.8749 0.7019 ] Network output: [ 0.002881 -0.01377 0.9943 4.472e-06 -2.008e-06 1.014 3.37e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09894 0.1849 0.1972 0.9873 0.9919 0.1117 0.7344 0.861 0.305 ] Network output: [ -0.002708 0.01273 1.005 4.892e-06 -2.196e-06 0.988 3.687e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09453 0.09259 0.165 0.1966 0.9852 0.9911 0.09454 0.6582 0.8361 0.2495 ] Network output: [ 8.094e-05 1 -4.949e-05 6.399e-07 -2.873e-07 0.9998 4.822e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001633 Epoch 9805 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008786 0.9968 0.9926 -1.639e-07 7.357e-08 -0.007001 -1.235e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003516 -0.003363 -0.006624 0.005351 0.9699 0.9743 0.006858 0.8242 0.8196 0.01616 ] Network output: [ 0.9999 9.51e-05 0.0003572 -2.331e-06 1.046e-06 -0.000307 -1.757e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2093 -0.0357 -0.1559 0.1822 0.9834 0.9932 0.235 0.4288 0.8681 0.7082 ] Network output: [ -0.008793 1.003 1.008 -1.779e-07 7.985e-08 0.007309 -1.34e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006865 0.0006341 0.004358 0.003147 0.9889 0.9919 0.007 0.8514 0.8918 0.01153 ] Network output: [ -0.0001701 0.001351 1 -7.32e-06 3.286e-06 0.9985 -5.517e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2232 0.106 0.3496 0.1418 0.9849 0.9939 0.2239 0.4327 0.8749 0.7019 ] Network output: [ 0.00288 -0.01376 0.9943 4.467e-06 -2.005e-06 1.014 3.366e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09895 0.1849 0.1972 0.9873 0.9919 0.1118 0.7344 0.861 0.305 ] Network output: [ -0.002707 0.01273 1.005 4.886e-06 -2.194e-06 0.988 3.682e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09453 0.09259 0.165 0.1966 0.9852 0.9911 0.09454 0.6582 0.8361 0.2495 ] Network output: [ 8.092e-05 1 -4.949e-05 6.391e-07 -2.869e-07 0.9998 4.816e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001632 Epoch 9806 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008785 0.9968 0.9926 -1.638e-07 7.352e-08 -0.007 -1.234e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003516 -0.003363 -0.006623 0.005351 0.9699 0.9743 0.006858 0.8242 0.8196 0.01616 ] Network output: [ 0.9999 9.494e-05 0.0003571 -2.328e-06 1.045e-06 -0.0003068 -1.754e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2093 -0.0357 -0.1559 0.1822 0.9834 0.9932 0.235 0.4288 0.8681 0.7082 ] Network output: [ -0.008792 1.003 1.008 -1.777e-07 7.978e-08 0.007309 -1.339e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006865 0.0006341 0.004358 0.003147 0.9889 0.9919 0.007 0.8514 0.8918 0.01153 ] Network output: [ -0.00017 0.00135 1 -7.312e-06 3.282e-06 0.9985 -5.51e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2232 0.106 0.3496 0.1418 0.9849 0.9939 0.2239 0.4327 0.8749 0.7019 ] Network output: [ 0.002879 -0.01376 0.9943 4.461e-06 -2.003e-06 1.014 3.362e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09895 0.1849 0.1972 0.9873 0.9919 0.1118 0.7344 0.861 0.305 ] Network output: [ -0.002705 0.01272 1.005 4.88e-06 -2.191e-06 0.988 3.678e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09453 0.09259 0.165 0.1966 0.9852 0.9911 0.09454 0.6582 0.8361 0.2495 ] Network output: [ 8.09e-05 1 -4.949e-05 6.383e-07 -2.866e-07 0.9998 4.811e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001631 Epoch 9807 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008784 0.9968 0.9926 -1.637e-07 7.348e-08 -0.007 -1.233e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003516 -0.003363 -0.006622 0.005351 0.9699 0.9743 0.006858 0.8242 0.8196 0.01616 ] Network output: [ 0.9999 9.478e-05 0.0003569 -2.325e-06 1.044e-06 -0.0003066 -1.752e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2093 -0.0357 -0.1559 0.1822 0.9834 0.9932 0.235 0.4288 0.8681 0.7082 ] Network output: [ -0.008791 1.003 1.008 -1.776e-07 7.972e-08 0.007308 -1.338e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006866 0.0006342 0.004358 0.003147 0.9889 0.9919 0.007001 0.8514 0.8918 0.01153 ] Network output: [ -0.0001698 0.00135 1 -7.303e-06 3.278e-06 0.9985 -5.504e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2232 0.106 0.3496 0.1418 0.9849 0.9939 0.224 0.4327 0.8749 0.7019 ] Network output: [ 0.002877 -0.01375 0.9943 4.456e-06 -2e-06 1.014 3.358e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09895 0.1849 0.1972 0.9873 0.9919 0.1118 0.7343 0.861 0.305 ] Network output: [ -0.002704 0.01272 1.005 4.875e-06 -2.188e-06 0.988 3.674e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09453 0.09259 0.165 0.1966 0.9852 0.9911 0.09455 0.6582 0.8361 0.2495 ] Network output: [ 8.088e-05 1 -4.948e-05 6.376e-07 -2.862e-07 0.9998 4.805e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000163 Epoch 9808 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008783 0.9968 0.9926 -1.636e-07 7.343e-08 -0.006999 -1.233e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003517 -0.003363 -0.006622 0.00535 0.9699 0.9743 0.006859 0.8242 0.8196 0.01616 ] Network output: [ 0.9999 9.463e-05 0.0003568 -2.322e-06 1.043e-06 -0.0003064 -1.75e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2093 -0.0357 -0.1559 0.1822 0.9834 0.9932 0.235 0.4288 0.8681 0.7082 ] Network output: [ -0.00879 1.003 1.008 -1.774e-07 7.966e-08 0.007308 -1.337e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006866 0.0006342 0.004358 0.003146 0.9889 0.9919 0.007001 0.8514 0.8918 0.01153 ] Network output: [ -0.0001697 0.001349 1 -7.294e-06 3.274e-06 0.9985 -5.497e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2232 0.106 0.3496 0.1418 0.9849 0.9939 0.224 0.4327 0.8749 0.7019 ] Network output: [ 0.002876 -0.01374 0.9943 4.45e-06 -1.998e-06 1.014 3.354e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09896 0.1849 0.1972 0.9873 0.9919 0.1118 0.7343 0.861 0.305 ] Network output: [ -0.002703 0.01271 1.005 4.869e-06 -2.186e-06 0.988 3.669e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09453 0.09259 0.165 0.1966 0.9852 0.9911 0.09455 0.6582 0.8361 0.2495 ] Network output: [ 8.086e-05 1 -4.948e-05 6.368e-07 -2.859e-07 0.9998 4.799e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001629 Epoch 9809 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008782 0.9968 0.9926 -1.634e-07 7.338e-08 -0.006998 -1.232e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003517 -0.003364 -0.006621 0.00535 0.9699 0.9743 0.006859 0.8242 0.8196 0.01616 ] Network output: [ 0.9999 9.447e-05 0.0003566 -2.319e-06 1.041e-06 -0.0003062 -1.748e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2093 -0.0357 -0.1559 0.1822 0.9834 0.9932 0.235 0.4288 0.8681 0.7082 ] Network output: [ -0.00879 1.003 1.008 -1.773e-07 7.959e-08 0.007307 -1.336e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006866 0.0006343 0.004357 0.003146 0.9889 0.9919 0.007001 0.8514 0.8918 0.01153 ] Network output: [ -0.0001695 0.001348 1 -7.285e-06 3.27e-06 0.9985 -5.49e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2232 0.106 0.3496 0.1418 0.9849 0.9939 0.224 0.4327 0.8749 0.7019 ] Network output: [ 0.002874 -0.01374 0.9943 4.445e-06 -1.996e-06 1.014 3.35e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09896 0.1849 0.1972 0.9873 0.9919 0.1118 0.7343 0.861 0.305 ] Network output: [ -0.002701 0.0127 1.005 4.863e-06 -2.183e-06 0.988 3.665e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09454 0.0926 0.165 0.1966 0.9852 0.9911 0.09455 0.6581 0.8361 0.2496 ] Network output: [ 8.084e-05 1 -4.948e-05 6.36e-07 -2.855e-07 0.9998 4.793e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001629 Epoch 9810 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008782 0.9968 0.9926 -1.633e-07 7.333e-08 -0.006998 -1.231e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003517 -0.003364 -0.006621 0.005349 0.9699 0.9743 0.006859 0.8242 0.8196 0.01616 ] Network output: [ 0.9999 9.432e-05 0.0003564 -2.317e-06 1.04e-06 -0.000306 -1.746e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2093 -0.0357 -0.1559 0.1822 0.9834 0.9932 0.235 0.4288 0.8681 0.7082 ] Network output: [ -0.008789 1.003 1.008 -1.772e-07 7.953e-08 0.007307 -1.335e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006867 0.0006344 0.004357 0.003146 0.9889 0.9919 0.007002 0.8514 0.8918 0.01153 ] Network output: [ -0.0001694 0.001347 1 -7.276e-06 3.267e-06 0.9985 -5.483e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2232 0.106 0.3497 0.1418 0.9849 0.9939 0.224 0.4327 0.8749 0.7019 ] Network output: [ 0.002873 -0.01373 0.9943 4.44e-06 -1.993e-06 1.014 3.346e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09896 0.1849 0.1971 0.9873 0.9919 0.1118 0.7343 0.861 0.305 ] Network output: [ -0.0027 0.0127 1.005 4.857e-06 -2.181e-06 0.988 3.661e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09454 0.0926 0.165 0.1966 0.9852 0.9911 0.09455 0.6581 0.8361 0.2496 ] Network output: [ 8.082e-05 1 -4.948e-05 6.353e-07 -2.852e-07 0.9998 4.788e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001628 Epoch 9811 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008781 0.9968 0.9926 -1.632e-07 7.328e-08 -0.006997 -1.23e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003517 -0.003364 -0.00662 0.005349 0.9699 0.9743 0.006859 0.8242 0.8196 0.01616 ] Network output: [ 0.9999 9.416e-05 0.0003563 -2.314e-06 1.039e-06 -0.0003058 -1.744e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2093 -0.0357 -0.1559 0.1822 0.9834 0.9932 0.235 0.4287 0.8681 0.7082 ] Network output: [ -0.008788 1.003 1.008 -1.77e-07 7.947e-08 0.007306 -1.334e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006867 0.0006344 0.004357 0.003146 0.9889 0.9919 0.007002 0.8514 0.8918 0.01153 ] Network output: [ -0.0001693 0.001347 1 -7.267e-06 3.263e-06 0.9985 -5.477e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2232 0.106 0.3497 0.1418 0.9849 0.9939 0.224 0.4327 0.8749 0.7019 ] Network output: [ 0.002871 -0.01372 0.9943 4.434e-06 -1.991e-06 1.014 3.342e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09896 0.1849 0.1971 0.9873 0.9919 0.1118 0.7343 0.861 0.305 ] Network output: [ -0.002699 0.01269 1.005 4.851e-06 -2.178e-06 0.988 3.656e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09454 0.0926 0.165 0.1966 0.9852 0.9911 0.09455 0.6581 0.8361 0.2496 ] Network output: [ 8.08e-05 1 -4.947e-05 6.345e-07 -2.848e-07 0.9998 4.782e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001627 Epoch 9812 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00878 0.9968 0.9926 -1.631e-07 7.323e-08 -0.006997 -1.229e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003517 -0.003364 -0.00662 0.005349 0.9699 0.9743 0.006859 0.8242 0.8196 0.01615 ] Network output: [ 0.9999 9.401e-05 0.0003561 -2.311e-06 1.037e-06 -0.0003056 -1.742e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2093 -0.03571 -0.1559 0.1822 0.9834 0.9932 0.235 0.4287 0.8681 0.7082 ] Network output: [ -0.008787 1.003 1.008 -1.769e-07 7.94e-08 0.007305 -1.333e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006867 0.0006345 0.004357 0.003145 0.9889 0.9919 0.007002 0.8514 0.8918 0.01153 ] Network output: [ -0.0001691 0.001346 1 -7.258e-06 3.259e-06 0.9985 -5.47e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2232 0.106 0.3497 0.1418 0.9849 0.9939 0.224 0.4327 0.8749 0.7019 ] Network output: [ 0.00287 -0.01372 0.9943 4.429e-06 -1.988e-06 1.014 3.338e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09897 0.1849 0.1971 0.9873 0.9919 0.1118 0.7343 0.861 0.305 ] Network output: [ -0.002697 0.01269 1.005 4.846e-06 -2.175e-06 0.988 3.652e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09454 0.0926 0.165 0.1966 0.9852 0.9911 0.09456 0.6581 0.8361 0.2496 ] Network output: [ 8.078e-05 1 -4.947e-05 6.337e-07 -2.845e-07 0.9998 4.776e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001626 Epoch 9813 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008779 0.9968 0.9926 -1.63e-07 7.319e-08 -0.006996 -1.229e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003517 -0.003364 -0.006619 0.005348 0.9699 0.9743 0.006859 0.8242 0.8196 0.01615 ] Network output: [ 0.9999 9.385e-05 0.000356 -2.308e-06 1.036e-06 -0.0003055 -1.739e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2093 -0.03571 -0.1559 0.1822 0.9834 0.9932 0.235 0.4287 0.8681 0.7082 ] Network output: [ -0.008786 1.003 1.008 -1.767e-07 7.934e-08 0.007305 -1.332e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006868 0.0006345 0.004357 0.003145 0.9889 0.9919 0.007003 0.8514 0.8918 0.01152 ] Network output: [ -0.000169 0.001345 1 -7.25e-06 3.255e-06 0.9985 -5.464e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2232 0.106 0.3497 0.1418 0.9849 0.9939 0.224 0.4327 0.8749 0.7019 ] Network output: [ 0.002868 -0.01371 0.9943 4.424e-06 -1.986e-06 1.014 3.334e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09897 0.1849 0.1971 0.9873 0.9919 0.1118 0.7343 0.861 0.305 ] Network output: [ -0.002696 0.01268 1.005 4.84e-06 -2.173e-06 0.9881 3.648e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09454 0.0926 0.165 0.1966 0.9852 0.9911 0.09456 0.6581 0.8361 0.2496 ] Network output: [ 8.076e-05 1 -4.947e-05 6.33e-07 -2.842e-07 0.9998 4.77e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001625 Epoch 9814 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008778 0.9968 0.9926 -1.629e-07 7.314e-08 -0.006995 -1.228e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003517 -0.003364 -0.006618 0.005348 0.9699 0.9743 0.006859 0.8242 0.8196 0.01615 ] Network output: [ 0.9999 9.369e-05 0.0003558 -2.305e-06 1.035e-06 -0.0003053 -1.737e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2093 -0.03571 -0.1559 0.1822 0.9834 0.9932 0.2351 0.4287 0.8681 0.7082 ] Network output: [ -0.008786 1.003 1.008 -1.766e-07 7.928e-08 0.007304 -1.331e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006868 0.0006346 0.004357 0.003145 0.9889 0.9919 0.007003 0.8514 0.8918 0.01152 ] Network output: [ -0.0001688 0.001345 1 -7.241e-06 3.251e-06 0.9985 -5.457e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2232 0.106 0.3497 0.1418 0.9849 0.9939 0.224 0.4327 0.8748 0.7019 ] Network output: [ 0.002867 -0.0137 0.9943 4.418e-06 -1.984e-06 1.014 3.33e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09897 0.1849 0.1971 0.9873 0.9919 0.1118 0.7343 0.861 0.305 ] Network output: [ -0.002695 0.01267 1.005 4.834e-06 -2.17e-06 0.9881 3.643e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09454 0.0926 0.165 0.1966 0.9852 0.9911 0.09456 0.6581 0.8361 0.2496 ] Network output: [ 8.074e-05 1 -4.947e-05 6.322e-07 -2.838e-07 0.9998 4.765e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001624 Epoch 9815 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008777 0.9968 0.9926 -1.628e-07 7.309e-08 -0.006995 -1.227e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003517 -0.003364 -0.006618 0.005347 0.9699 0.9743 0.00686 0.8242 0.8196 0.01615 ] Network output: [ 0.9999 9.354e-05 0.0003557 -2.303e-06 1.034e-06 -0.0003051 -1.735e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2094 -0.03571 -0.1559 0.1822 0.9834 0.9932 0.2351 0.4287 0.8681 0.7082 ] Network output: [ -0.008785 1.003 1.008 -1.764e-07 7.921e-08 0.007304 -1.33e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006869 0.0006347 0.004357 0.003145 0.9889 0.9919 0.007003 0.8514 0.8918 0.01152 ] Network output: [ -0.0001687 0.001344 1 -7.232e-06 3.247e-06 0.9985 -5.45e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2232 0.106 0.3497 0.1418 0.9849 0.9939 0.224 0.4327 0.8748 0.7019 ] Network output: [ 0.002866 -0.0137 0.9943 4.413e-06 -1.981e-06 1.014 3.326e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09898 0.1849 0.1971 0.9873 0.9919 0.1118 0.7343 0.861 0.305 ] Network output: [ -0.002694 0.01267 1.005 4.828e-06 -2.168e-06 0.9881 3.639e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09455 0.09261 0.165 0.1966 0.9852 0.9911 0.09456 0.6581 0.8361 0.2496 ] Network output: [ 8.072e-05 1 -4.946e-05 6.315e-07 -2.835e-07 0.9998 4.759e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001623 Epoch 9816 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008776 0.9968 0.9926 -1.627e-07 7.304e-08 -0.006994 -1.226e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003517 -0.003364 -0.006617 0.005347 0.9699 0.9743 0.00686 0.8242 0.8196 0.01615 ] Network output: [ 0.9999 9.338e-05 0.0003555 -2.3e-06 1.032e-06 -0.0003049 -1.733e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2094 -0.03571 -0.1559 0.1822 0.9834 0.9932 0.2351 0.4287 0.8681 0.7082 ] Network output: [ -0.008784 1.003 1.008 -1.763e-07 7.915e-08 0.007303 -1.329e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006869 0.0006347 0.004357 0.003145 0.9889 0.9919 0.007004 0.8514 0.8918 0.01152 ] Network output: [ -0.0001685 0.001343 1 -7.223e-06 3.243e-06 0.9985 -5.444e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2232 0.106 0.3497 0.1418 0.9849 0.9939 0.224 0.4327 0.8748 0.7019 ] Network output: [ 0.002864 -0.01369 0.9943 4.408e-06 -1.979e-06 1.014 3.322e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09898 0.1849 0.1971 0.9873 0.9919 0.1118 0.7343 0.861 0.305 ] Network output: [ -0.002692 0.01266 1.005 4.823e-06 -2.165e-06 0.9881 3.634e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09455 0.09261 0.165 0.1966 0.9852 0.9911 0.09456 0.6581 0.8361 0.2496 ] Network output: [ 8.07e-05 1 -4.946e-05 6.307e-07 -2.831e-07 0.9998 4.753e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001622 Epoch 9817 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008775 0.9968 0.9926 -1.626e-07 7.299e-08 -0.006993 -1.225e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003517 -0.003364 -0.006617 0.005347 0.9699 0.9743 0.00686 0.8242 0.8196 0.01615 ] Network output: [ 0.9999 9.323e-05 0.0003553 -2.297e-06 1.031e-06 -0.0003047 -1.731e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2094 -0.03571 -0.1558 0.1822 0.9834 0.9932 0.2351 0.4287 0.8681 0.7082 ] Network output: [ -0.008783 1.003 1.008 -1.762e-07 7.909e-08 0.007303 -1.328e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006869 0.0006348 0.004357 0.003144 0.9889 0.9919 0.007004 0.8514 0.8918 0.01152 ] Network output: [ -0.0001684 0.001343 1 -7.214e-06 3.239e-06 0.9985 -5.437e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2233 0.106 0.3497 0.1418 0.9849 0.9939 0.224 0.4327 0.8748 0.7019 ] Network output: [ 0.002863 -0.01368 0.9943 4.402e-06 -1.976e-06 1.014 3.318e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09898 0.1849 0.1971 0.9873 0.9919 0.1118 0.7342 0.861 0.305 ] Network output: [ -0.002691 0.01266 1.005 4.817e-06 -2.162e-06 0.9881 3.63e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09455 0.09261 0.165 0.1966 0.9852 0.9911 0.09456 0.6581 0.8361 0.2496 ] Network output: [ 8.068e-05 1 -4.946e-05 6.299e-07 -2.828e-07 0.9998 4.747e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001621 Epoch 9818 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008775 0.9968 0.9926 -1.625e-07 7.294e-08 -0.006993 -1.225e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003517 -0.003364 -0.006616 0.005346 0.9699 0.9743 0.00686 0.8242 0.8196 0.01615 ] Network output: [ 0.9999 9.307e-05 0.0003552 -2.294e-06 1.03e-06 -0.0003045 -1.729e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2094 -0.03571 -0.1558 0.1822 0.9834 0.9932 0.2351 0.4287 0.8681 0.7082 ] Network output: [ -0.008782 1.003 1.008 -1.76e-07 7.902e-08 0.007302 -1.327e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00687 0.0006348 0.004357 0.003144 0.9889 0.9919 0.007004 0.8514 0.8918 0.01152 ] Network output: [ -0.0001682 0.001342 1 -7.206e-06 3.235e-06 0.9985 -5.43e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2233 0.106 0.3497 0.1418 0.9849 0.9939 0.224 0.4327 0.8748 0.7019 ] Network output: [ 0.002861 -0.01368 0.9943 4.397e-06 -1.974e-06 1.014 3.314e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09898 0.1849 0.1971 0.9873 0.9919 0.1118 0.7342 0.861 0.305 ] Network output: [ -0.00269 0.01265 1.005 4.811e-06 -2.16e-06 0.9881 3.626e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09455 0.09261 0.165 0.1967 0.9852 0.9911 0.09457 0.658 0.8361 0.2496 ] Network output: [ 8.066e-05 1 -4.946e-05 6.292e-07 -2.825e-07 0.9998 4.742e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001621 Epoch 9819 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008774 0.9968 0.9926 -1.624e-07 7.289e-08 -0.006992 -1.224e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003517 -0.003364 -0.006616 0.005346 0.9699 0.9743 0.00686 0.8242 0.8196 0.01615 ] Network output: [ 0.9999 9.292e-05 0.000355 -2.291e-06 1.029e-06 -0.0003043 -1.727e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2094 -0.03571 -0.1558 0.1822 0.9834 0.9932 0.2351 0.4287 0.8681 0.7082 ] Network output: [ -0.008782 1.003 1.008 -1.759e-07 7.896e-08 0.007302 -1.325e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00687 0.0006349 0.004356 0.003144 0.9889 0.9919 0.007005 0.8514 0.8918 0.01152 ] Network output: [ -0.0001681 0.001341 1 -7.197e-06 3.231e-06 0.9985 -5.424e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2233 0.106 0.3497 0.1418 0.9849 0.9939 0.224 0.4327 0.8748 0.7019 ] Network output: [ 0.00286 -0.01367 0.9943 4.392e-06 -1.972e-06 1.014 3.31e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09899 0.1849 0.1971 0.9873 0.9919 0.1118 0.7342 0.861 0.305 ] Network output: [ -0.002688 0.01265 1.005 4.805e-06 -2.157e-06 0.9881 3.622e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09455 0.09261 0.165 0.1967 0.9852 0.9911 0.09457 0.658 0.8361 0.2496 ] Network output: [ 8.064e-05 1 -4.946e-05 6.284e-07 -2.821e-07 0.9998 4.736e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000162 Epoch 9820 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008773 0.9968 0.9926 -1.623e-07 7.285e-08 -0.006992 -1.223e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003517 -0.003364 -0.006615 0.005346 0.9699 0.9743 0.00686 0.8242 0.8196 0.01615 ] Network output: [ 0.9999 9.276e-05 0.0003549 -2.289e-06 1.027e-06 -0.0003041 -1.725e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2094 -0.03571 -0.1558 0.1822 0.9834 0.9932 0.2351 0.4287 0.8681 0.7082 ] Network output: [ -0.008781 1.003 1.008 -1.757e-07 7.89e-08 0.007301 -1.324e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00687 0.000635 0.004356 0.003144 0.9889 0.9919 0.007005 0.8514 0.8918 0.01152 ] Network output: [ -0.0001679 0.001341 1 -7.188e-06 3.227e-06 0.9985 -5.417e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2233 0.106 0.3497 0.1418 0.9849 0.9939 0.224 0.4327 0.8748 0.7019 ] Network output: [ 0.002858 -0.01367 0.9943 4.386e-06 -1.969e-06 1.014 3.306e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09899 0.1849 0.1971 0.9873 0.9919 0.1118 0.7342 0.861 0.305 ] Network output: [ -0.002687 0.01264 1.005 4.8e-06 -2.155e-06 0.9881 3.617e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09455 0.09261 0.165 0.1967 0.9852 0.9911 0.09457 0.658 0.8361 0.2496 ] Network output: [ 8.062e-05 1 -4.945e-05 6.277e-07 -2.818e-07 0.9998 4.73e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001619 Epoch 9821 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008772 0.9968 0.9926 -1.622e-07 7.28e-08 -0.006991 -1.222e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003517 -0.003364 -0.006614 0.005345 0.9699 0.9743 0.006861 0.8242 0.8196 0.01615 ] Network output: [ 0.9999 9.261e-05 0.0003547 -2.286e-06 1.026e-06 -0.0003039 -1.723e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2094 -0.03571 -0.1558 0.1822 0.9834 0.9932 0.2351 0.4287 0.8681 0.7082 ] Network output: [ -0.00878 1.003 1.008 -1.756e-07 7.883e-08 0.0073 -1.323e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006871 0.000635 0.004356 0.003143 0.9889 0.9919 0.007005 0.8514 0.8918 0.01152 ] Network output: [ -0.0001678 0.00134 1 -7.179e-06 3.223e-06 0.9985 -5.411e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2233 0.106 0.3497 0.1418 0.9849 0.9939 0.224 0.4327 0.8748 0.7019 ] Network output: [ 0.002857 -0.01366 0.9943 4.381e-06 -1.967e-06 1.014 3.302e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09899 0.1849 0.1971 0.9873 0.9919 0.1118 0.7342 0.861 0.305 ] Network output: [ -0.002686 0.01263 1.005 4.794e-06 -2.152e-06 0.9881 3.613e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09456 0.09262 0.165 0.1967 0.9852 0.9911 0.09457 0.658 0.8361 0.2496 ] Network output: [ 8.06e-05 1 -4.945e-05 6.269e-07 -2.814e-07 0.9998 4.725e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001618 Epoch 9822 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008771 0.9968 0.9926 -1.62e-07 7.275e-08 -0.00699 -1.221e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003517 -0.003365 -0.006614 0.005345 0.9699 0.9743 0.006861 0.8242 0.8196 0.01615 ] Network output: [ 0.9999 9.245e-05 0.0003546 -2.283e-06 1.025e-06 -0.0003038 -1.72e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2094 -0.03571 -0.1558 0.1822 0.9834 0.9932 0.2351 0.4287 0.8681 0.7082 ] Network output: [ -0.008779 1.003 1.008 -1.755e-07 7.877e-08 0.0073 -1.322e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006871 0.0006351 0.004356 0.003143 0.9889 0.9919 0.007006 0.8514 0.8918 0.01152 ] Network output: [ -0.0001676 0.001339 1 -7.171e-06 3.219e-06 0.9985 -5.404e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2233 0.106 0.3497 0.1418 0.9849 0.9939 0.224 0.4327 0.8748 0.7019 ] Network output: [ 0.002855 -0.01365 0.9944 4.376e-06 -1.964e-06 1.014 3.298e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.099 0.1849 0.1971 0.9873 0.9919 0.1118 0.7342 0.861 0.305 ] Network output: [ -0.002684 0.01263 1.005 4.788e-06 -2.15e-06 0.9881 3.609e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09456 0.09262 0.165 0.1967 0.9852 0.9911 0.09457 0.658 0.8361 0.2496 ] Network output: [ 8.058e-05 1 -4.945e-05 6.262e-07 -2.811e-07 0.9998 4.719e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001617 Epoch 9823 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00877 0.9968 0.9926 -1.619e-07 7.27e-08 -0.00699 -1.22e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003517 -0.003365 -0.006613 0.005344 0.9699 0.9743 0.006861 0.8242 0.8196 0.01614 ] Network output: [ 0.9999 9.23e-05 0.0003544 -2.28e-06 1.024e-06 -0.0003036 -1.718e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2094 -0.03572 -0.1558 0.1822 0.9834 0.9932 0.2351 0.4287 0.8681 0.7082 ] Network output: [ -0.008778 1.003 1.008 -1.753e-07 7.871e-08 0.007299 -1.321e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006871 0.0006351 0.004356 0.003143 0.9889 0.9919 0.007006 0.8514 0.8918 0.01152 ] Network output: [ -0.0001675 0.001338 1 -7.162e-06 3.215e-06 0.9985 -5.397e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2233 0.106 0.3497 0.1418 0.9849 0.9939 0.2241 0.4326 0.8748 0.7018 ] Network output: [ 0.002854 -0.01365 0.9944 4.37e-06 -1.962e-06 1.014 3.294e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.099 0.1849 0.1971 0.9873 0.9919 0.1118 0.7342 0.861 0.305 ] Network output: [ -0.002683 0.01262 1.005 4.783e-06 -2.147e-06 0.9881 3.604e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09456 0.09262 0.165 0.1967 0.9852 0.9911 0.09457 0.658 0.836 0.2496 ] Network output: [ 8.056e-05 1 -4.945e-05 6.254e-07 -2.808e-07 0.9998 4.713e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001616 Epoch 9824 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008769 0.9968 0.9926 -1.618e-07 7.265e-08 -0.006989 -1.22e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003517 -0.003365 -0.006613 0.005344 0.9699 0.9743 0.006861 0.8242 0.8195 0.01614 ] Network output: [ 0.9999 9.214e-05 0.0003542 -2.277e-06 1.022e-06 -0.0003034 -1.716e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2094 -0.03572 -0.1558 0.1822 0.9834 0.9932 0.2351 0.4287 0.8681 0.7082 ] Network output: [ -0.008777 1.003 1.008 -1.752e-07 7.864e-08 0.007299 -1.32e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006872 0.0006352 0.004356 0.003143 0.9889 0.9919 0.007007 0.8514 0.8918 0.01152 ] Network output: [ -0.0001673 0.001338 1 -7.153e-06 3.211e-06 0.9985 -5.391e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2233 0.106 0.3497 0.1418 0.9849 0.9939 0.2241 0.4326 0.8748 0.7018 ] Network output: [ 0.002852 -0.01364 0.9944 4.365e-06 -1.96e-06 1.014 3.29e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.099 0.1849 0.1971 0.9873 0.9919 0.1118 0.7342 0.861 0.305 ] Network output: [ -0.002682 0.01262 1.005 4.777e-06 -2.144e-06 0.9881 3.6e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09456 0.09262 0.165 0.1967 0.9852 0.9911 0.09458 0.658 0.836 0.2496 ] Network output: [ 8.054e-05 1 -4.945e-05 6.247e-07 -2.804e-07 0.9998 4.708e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001615 Epoch 9825 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008768 0.9968 0.9926 -1.617e-07 7.26e-08 -0.006988 -1.219e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003517 -0.003365 -0.006612 0.005344 0.9699 0.9743 0.006861 0.8242 0.8195 0.01614 ] Network output: [ 0.9999 9.199e-05 0.0003541 -2.275e-06 1.021e-06 -0.0003032 -1.714e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2094 -0.03572 -0.1558 0.1821 0.9834 0.9932 0.2351 0.4287 0.8681 0.7082 ] Network output: [ -0.008777 1.003 1.008 -1.75e-07 7.858e-08 0.007298 -1.319e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006872 0.0006352 0.004356 0.003142 0.9889 0.9919 0.007007 0.8514 0.8918 0.01152 ] Network output: [ -0.0001672 0.001337 1 -7.144e-06 3.207e-06 0.9985 -5.384e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2233 0.106 0.3497 0.1418 0.9849 0.9939 0.2241 0.4326 0.8748 0.7018 ] Network output: [ 0.002851 -0.01363 0.9944 4.36e-06 -1.957e-06 1.014 3.286e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.099 0.1849 0.1971 0.9873 0.9919 0.1118 0.7342 0.861 0.305 ] Network output: [ -0.00268 0.01261 1.005 4.771e-06 -2.142e-06 0.9881 3.596e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09456 0.09262 0.165 0.1967 0.9852 0.9911 0.09458 0.658 0.836 0.2496 ] Network output: [ 8.052e-05 1 -4.944e-05 6.239e-07 -2.801e-07 0.9998 4.702e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001614 Epoch 9826 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008767 0.9968 0.9926 -1.616e-07 7.256e-08 -0.006988 -1.218e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003517 -0.003365 -0.006612 0.005343 0.9699 0.9743 0.006861 0.8242 0.8195 0.01614 ] Network output: [ 0.9999 9.183e-05 0.0003539 -2.272e-06 1.02e-06 -0.000303 -1.712e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2094 -0.03572 -0.1558 0.1821 0.9834 0.9932 0.2351 0.4287 0.8681 0.7082 ] Network output: [ -0.008776 1.003 1.008 -1.749e-07 7.852e-08 0.007298 -1.318e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006872 0.0006353 0.004356 0.003142 0.9889 0.9919 0.007007 0.8513 0.8918 0.01152 ] Network output: [ -0.000167 0.001336 1 -7.136e-06 3.204e-06 0.9985 -5.378e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2233 0.106 0.3497 0.1418 0.9849 0.9939 0.2241 0.4326 0.8748 0.7018 ] Network output: [ 0.00285 -0.01363 0.9944 4.355e-06 -1.955e-06 1.014 3.282e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09901 0.1849 0.1971 0.9873 0.9919 0.1118 0.7342 0.861 0.305 ] Network output: [ -0.002679 0.0126 1.005 4.765e-06 -2.139e-06 0.9881 3.591e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09457 0.09263 0.165 0.1967 0.9852 0.9911 0.09458 0.658 0.836 0.2496 ] Network output: [ 8.05e-05 1 -4.944e-05 6.232e-07 -2.798e-07 0.9998 4.696e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001613 Epoch 9827 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008767 0.9968 0.9926 -1.615e-07 7.251e-08 -0.006987 -1.217e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003517 -0.003365 -0.006611 0.005343 0.9699 0.9743 0.006862 0.8241 0.8195 0.01614 ] Network output: [ 0.9999 9.168e-05 0.0003538 -2.269e-06 1.019e-06 -0.0003028 -1.71e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2094 -0.03572 -0.1558 0.1821 0.9834 0.9932 0.2351 0.4287 0.8681 0.7081 ] Network output: [ -0.008775 1.003 1.008 -1.748e-07 7.845e-08 0.007297 -1.317e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006873 0.0006354 0.004356 0.003142 0.9889 0.9919 0.007008 0.8513 0.8918 0.01151 ] Network output: [ -0.0001669 0.001336 1 -7.127e-06 3.2e-06 0.9985 -5.371e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2233 0.106 0.3497 0.1418 0.9849 0.9939 0.2241 0.4326 0.8748 0.7018 ] Network output: [ 0.002848 -0.01362 0.9944 4.349e-06 -1.953e-06 1.014 3.278e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09901 0.1849 0.1971 0.9873 0.9919 0.1118 0.7342 0.861 0.305 ] Network output: [ -0.002678 0.0126 1.005 4.76e-06 -2.137e-06 0.9881 3.587e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09457 0.09263 0.165 0.1967 0.9852 0.9911 0.09458 0.658 0.836 0.2496 ] Network output: [ 8.048e-05 1 -4.944e-05 6.224e-07 -2.794e-07 0.9998 4.691e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001613 Epoch 9828 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008766 0.9968 0.9926 -1.614e-07 7.246e-08 -0.006987 -1.216e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003517 -0.003365 -0.00661 0.005343 0.9699 0.9743 0.006862 0.8241 0.8195 0.01614 ] Network output: [ 0.9999 9.152e-05 0.0003536 -2.266e-06 1.017e-06 -0.0003026 -1.708e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2094 -0.03572 -0.1558 0.1821 0.9834 0.9932 0.2351 0.4287 0.8681 0.7081 ] Network output: [ -0.008774 1.003 1.008 -1.746e-07 7.839e-08 0.007297 -1.316e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006873 0.0006354 0.004356 0.003142 0.9889 0.9919 0.007008 0.8513 0.8918 0.01151 ] Network output: [ -0.0001667 0.001335 1 -7.118e-06 3.196e-06 0.9985 -5.365e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2233 0.106 0.3497 0.1418 0.9849 0.9939 0.2241 0.4326 0.8748 0.7018 ] Network output: [ 0.002847 -0.01361 0.9944 4.344e-06 -1.95e-06 1.014 3.274e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09901 0.1849 0.1971 0.9873 0.9919 0.1118 0.7341 0.861 0.305 ] Network output: [ -0.002676 0.01259 1.005 4.754e-06 -2.134e-06 0.9881 3.583e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09457 0.09263 0.165 0.1967 0.9852 0.9911 0.09458 0.6579 0.836 0.2496 ] Network output: [ 8.046e-05 1 -4.944e-05 6.217e-07 -2.791e-07 0.9998 4.685e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001612 Epoch 9829 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008765 0.9968 0.9926 -1.613e-07 7.241e-08 -0.006986 -1.216e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003518 -0.003365 -0.00661 0.005342 0.9699 0.9743 0.006862 0.8241 0.8195 0.01614 ] Network output: [ 0.9999 9.137e-05 0.0003535 -2.264e-06 1.016e-06 -0.0003024 -1.706e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2094 -0.03572 -0.1557 0.1821 0.9834 0.9932 0.2352 0.4287 0.8681 0.7081 ] Network output: [ -0.008773 1.003 1.008 -1.745e-07 7.833e-08 0.007296 -1.315e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006873 0.0006355 0.004355 0.003142 0.9889 0.9919 0.007008 0.8513 0.8918 0.01151 ] Network output: [ -0.0001666 0.001334 1 -7.11e-06 3.192e-06 0.9985 -5.358e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2233 0.1061 0.3497 0.1418 0.9849 0.9939 0.2241 0.4326 0.8748 0.7018 ] Network output: [ 0.002845 -0.01361 0.9944 4.339e-06 -1.948e-06 1.014 3.27e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09902 0.1849 0.1971 0.9873 0.9919 0.1118 0.7341 0.861 0.305 ] Network output: [ -0.002675 0.01259 1.005 4.748e-06 -2.132e-06 0.9881 3.579e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09457 0.09263 0.165 0.1967 0.9852 0.9911 0.09458 0.6579 0.836 0.2496 ] Network output: [ 8.044e-05 1 -4.944e-05 6.209e-07 -2.788e-07 0.9998 4.679e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001611 Epoch 9830 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008764 0.9968 0.9926 -1.612e-07 7.236e-08 -0.006985 -1.215e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003518 -0.003365 -0.006609 0.005342 0.9699 0.9743 0.006862 0.8241 0.8195 0.01614 ] Network output: [ 0.9999 9.121e-05 0.0003533 -2.261e-06 1.015e-06 -0.0003023 -1.704e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2094 -0.03572 -0.1557 0.1821 0.9834 0.9932 0.2352 0.4287 0.8681 0.7081 ] Network output: [ -0.008773 1.003 1.008 -1.743e-07 7.826e-08 0.007295 -1.314e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006874 0.0006355 0.004355 0.003141 0.9889 0.9919 0.007009 0.8513 0.8918 0.01151 ] Network output: [ -0.0001665 0.001334 1 -7.101e-06 3.188e-06 0.9985 -5.352e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2233 0.1061 0.3497 0.1418 0.9849 0.9939 0.2241 0.4326 0.8748 0.7018 ] Network output: [ 0.002844 -0.0136 0.9944 4.334e-06 -1.945e-06 1.014 3.266e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1117 0.09902 0.1849 0.1971 0.9873 0.9919 0.1118 0.7341 0.861 0.305 ] Network output: [ -0.002674 0.01258 1.005 4.743e-06 -2.129e-06 0.9881 3.574e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09457 0.09263 0.165 0.1967 0.9852 0.9911 0.09459 0.6579 0.836 0.2496 ] Network output: [ 8.042e-05 1 -4.944e-05 6.202e-07 -2.784e-07 0.9998 4.674e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000161 Epoch 9831 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008763 0.9968 0.9926 -1.611e-07 7.231e-08 -0.006985 -1.214e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003518 -0.003365 -0.006609 0.005341 0.9699 0.9743 0.006862 0.8241 0.8195 0.01614 ] Network output: [ 0.9999 9.106e-05 0.0003532 -2.258e-06 1.014e-06 -0.0003021 -1.702e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2094 -0.03572 -0.1557 0.1821 0.9834 0.9932 0.2352 0.4287 0.8681 0.7081 ] Network output: [ -0.008772 1.003 1.008 -1.742e-07 7.82e-08 0.007295 -1.313e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006874 0.0006356 0.004355 0.003141 0.9889 0.9919 0.007009 0.8513 0.8918 0.01151 ] Network output: [ -0.0001663 0.001333 1 -7.092e-06 3.184e-06 0.9985 -5.345e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2233 0.1061 0.3497 0.1418 0.9849 0.9939 0.2241 0.4326 0.8748 0.7018 ] Network output: [ 0.002842 -0.01359 0.9944 4.328e-06 -1.943e-06 1.014 3.262e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09902 0.1849 0.1971 0.9873 0.9919 0.1118 0.7341 0.8609 0.305 ] Network output: [ -0.002672 0.01257 1.005 4.737e-06 -2.127e-06 0.9881 3.57e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09457 0.09263 0.165 0.1967 0.9852 0.9911 0.09459 0.6579 0.836 0.2496 ] Network output: [ 8.04e-05 1 -4.944e-05 6.194e-07 -2.781e-07 0.9998 4.668e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001609 Epoch 9832 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008762 0.9968 0.9926 -1.61e-07 7.227e-08 -0.006984 -1.213e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003518 -0.003365 -0.006608 0.005341 0.9699 0.9743 0.006862 0.8241 0.8195 0.01614 ] Network output: [ 0.9999 9.091e-05 0.000353 -2.255e-06 1.012e-06 -0.0003019 -1.7e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2094 -0.03572 -0.1557 0.1821 0.9834 0.9932 0.2352 0.4287 0.8681 0.7081 ] Network output: [ -0.008771 1.003 1.008 -1.74e-07 7.814e-08 0.007294 -1.312e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006874 0.0006357 0.004355 0.003141 0.9889 0.9919 0.007009 0.8513 0.8918 0.01151 ] Network output: [ -0.0001662 0.001332 1 -7.084e-06 3.18e-06 0.9985 -5.339e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2233 0.1061 0.3498 0.1418 0.9849 0.9939 0.2241 0.4326 0.8748 0.7018 ] Network output: [ 0.002841 -0.01359 0.9944 4.323e-06 -1.941e-06 1.014 3.258e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09902 0.1849 0.1971 0.9873 0.9919 0.1118 0.7341 0.8609 0.305 ] Network output: [ -0.002671 0.01257 1.005 4.731e-06 -2.124e-06 0.9881 3.566e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09458 0.09264 0.165 0.1967 0.9852 0.9911 0.09459 0.6579 0.836 0.2496 ] Network output: [ 8.038e-05 1 -4.944e-05 6.187e-07 -2.778e-07 0.9998 4.663e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001608 Epoch 9833 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008761 0.9968 0.9926 -1.609e-07 7.222e-08 -0.006983 -1.212e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003518 -0.003365 -0.006608 0.005341 0.9699 0.9743 0.006862 0.8241 0.8195 0.01614 ] Network output: [ 0.9999 9.075e-05 0.0003528 -2.252e-06 1.011e-06 -0.0003017 -1.698e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2095 -0.03573 -0.1557 0.1821 0.9834 0.9932 0.2352 0.4287 0.8681 0.7081 ] Network output: [ -0.00877 1.003 1.008 -1.739e-07 7.807e-08 0.007294 -1.311e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006875 0.0006357 0.004355 0.003141 0.9889 0.9919 0.00701 0.8513 0.8918 0.01151 ] Network output: [ -0.000166 0.001331 1 -7.075e-06 3.176e-06 0.9985 -5.332e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2234 0.1061 0.3498 0.1418 0.9849 0.9939 0.2241 0.4326 0.8748 0.7018 ] Network output: [ 0.002839 -0.01358 0.9944 4.318e-06 -1.938e-06 1.014 3.254e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09903 0.1849 0.1971 0.9873 0.9919 0.1118 0.7341 0.8609 0.305 ] Network output: [ -0.00267 0.01256 1.005 4.726e-06 -2.122e-06 0.9881 3.561e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09458 0.09264 0.165 0.1967 0.9852 0.9911 0.09459 0.6579 0.836 0.2496 ] Network output: [ 8.036e-05 1 -4.943e-05 6.179e-07 -2.774e-07 0.9998 4.657e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001607 Epoch 9834 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00876 0.9968 0.9926 -1.608e-07 7.217e-08 -0.006983 -1.212e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003518 -0.003365 -0.006607 0.00534 0.9699 0.9743 0.006863 0.8241 0.8195 0.01613 ] Network output: [ 0.9999 9.06e-05 0.0003527 -2.25e-06 1.01e-06 -0.0003015 -1.695e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2095 -0.03573 -0.1557 0.1821 0.9834 0.9932 0.2352 0.4287 0.8681 0.7081 ] Network output: [ -0.008769 1.003 1.008 -1.738e-07 7.801e-08 0.007293 -1.31e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006875 0.0006358 0.004355 0.00314 0.9889 0.9919 0.00701 0.8513 0.8918 0.01151 ] Network output: [ -0.0001659 0.001331 1 -7.067e-06 3.172e-06 0.9985 -5.326e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2234 0.1061 0.3498 0.1418 0.9849 0.9939 0.2241 0.4326 0.8748 0.7018 ] Network output: [ 0.002838 -0.01357 0.9944 4.313e-06 -1.936e-06 1.014 3.25e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09903 0.1849 0.1971 0.9873 0.9919 0.1118 0.7341 0.8609 0.305 ] Network output: [ -0.002668 0.01256 1.005 4.72e-06 -2.119e-06 0.9881 3.557e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09458 0.09264 0.165 0.1967 0.9852 0.9911 0.09459 0.6579 0.836 0.2496 ] Network output: [ 8.034e-05 1 -4.943e-05 6.172e-07 -2.771e-07 0.9998 4.651e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001606 Epoch 9835 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00876 0.9968 0.9926 -1.606e-07 7.212e-08 -0.006982 -1.211e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003518 -0.003366 -0.006607 0.00534 0.9699 0.9743 0.006863 0.8241 0.8195 0.01613 ] Network output: [ 0.9999 9.044e-05 0.0003525 -2.247e-06 1.009e-06 -0.0003013 -1.693e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2095 -0.03573 -0.1557 0.1821 0.9834 0.9932 0.2352 0.4286 0.8681 0.7081 ] Network output: [ -0.008769 1.003 1.008 -1.736e-07 7.795e-08 0.007293 -1.309e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006875 0.0006358 0.004355 0.00314 0.9889 0.9919 0.00701 0.8513 0.8918 0.01151 ] Network output: [ -0.0001657 0.00133 1 -7.058e-06 3.169e-06 0.9985 -5.319e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2234 0.1061 0.3498 0.1418 0.9849 0.9939 0.2241 0.4326 0.8748 0.7018 ] Network output: [ 0.002837 -0.01357 0.9944 4.307e-06 -1.934e-06 1.014 3.246e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09903 0.1849 0.1971 0.9873 0.9919 0.1118 0.7341 0.8609 0.305 ] Network output: [ -0.002667 0.01255 1.005 4.714e-06 -2.117e-06 0.9881 3.553e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09458 0.09264 0.165 0.1967 0.9852 0.9911 0.09459 0.6579 0.836 0.2496 ] Network output: [ 8.032e-05 1 -4.943e-05 6.165e-07 -2.768e-07 0.9998 4.646e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001606 Epoch 9836 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008759 0.9968 0.9926 -1.605e-07 7.207e-08 -0.006982 -1.21e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003518 -0.003366 -0.006606 0.00534 0.9699 0.9743 0.006863 0.8241 0.8195 0.01613 ] Network output: [ 0.9999 9.029e-05 0.0003524 -2.244e-06 1.008e-06 -0.0003011 -1.691e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2095 -0.03573 -0.1557 0.1821 0.9834 0.9932 0.2352 0.4286 0.8681 0.7081 ] Network output: [ -0.008768 1.003 1.008 -1.735e-07 7.789e-08 0.007292 -1.307e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006876 0.0006359 0.004355 0.00314 0.9889 0.9919 0.007011 0.8513 0.8918 0.01151 ] Network output: [ -0.0001656 0.001329 1 -7.049e-06 3.165e-06 0.9985 -5.313e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2234 0.1061 0.3498 0.1418 0.9849 0.9939 0.2241 0.4326 0.8748 0.7018 ] Network output: [ 0.002835 -0.01356 0.9944 4.302e-06 -1.931e-06 1.014 3.242e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09904 0.1849 0.1971 0.9873 0.9919 0.1118 0.7341 0.8609 0.305 ] Network output: [ -0.002666 0.01255 1.005 4.709e-06 -2.114e-06 0.9881 3.549e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09458 0.09264 0.165 0.1967 0.9852 0.9911 0.0946 0.6579 0.836 0.2496 ] Network output: [ 8.03e-05 1 -4.943e-05 6.157e-07 -2.764e-07 0.9998 4.64e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001605 Epoch 9837 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008758 0.9968 0.9926 -1.604e-07 7.202e-08 -0.006981 -1.209e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003518 -0.003366 -0.006605 0.005339 0.9699 0.9743 0.006863 0.8241 0.8195 0.01613 ] Network output: [ 0.9999 9.014e-05 0.0003522 -2.242e-06 1.006e-06 -0.000301 -1.689e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2095 -0.03573 -0.1557 0.1821 0.9834 0.9932 0.2352 0.4286 0.8681 0.7081 ] Network output: [ -0.008767 1.003 1.008 -1.733e-07 7.782e-08 0.007292 -1.306e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006876 0.0006359 0.004355 0.00314 0.9889 0.9919 0.007011 0.8513 0.8918 0.01151 ] Network output: [ -0.0001654 0.001329 1 -7.041e-06 3.161e-06 0.9985 -5.306e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2234 0.1061 0.3498 0.1418 0.9849 0.9939 0.2241 0.4326 0.8748 0.7018 ] Network output: [ 0.002834 -0.01356 0.9944 4.297e-06 -1.929e-06 1.014 3.238e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09904 0.1849 0.1971 0.9873 0.9919 0.1119 0.7341 0.8609 0.305 ] Network output: [ -0.002664 0.01254 1.005 4.703e-06 -2.111e-06 0.9881 3.545e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09458 0.09264 0.165 0.1967 0.9852 0.9911 0.0946 0.6579 0.836 0.2496 ] Network output: [ 8.028e-05 1 -4.943e-05 6.15e-07 -2.761e-07 0.9998 4.635e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001604 Epoch 9838 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008757 0.9968 0.9926 -1.603e-07 7.198e-08 -0.00698 -1.208e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003518 -0.003366 -0.006605 0.005339 0.9699 0.9743 0.006863 0.8241 0.8195 0.01613 ] Network output: [ 0.9999 8.998e-05 0.0003521 -2.239e-06 1.005e-06 -0.0003008 -1.687e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2095 -0.03573 -0.1557 0.1821 0.9834 0.9932 0.2352 0.4286 0.8681 0.7081 ] Network output: [ -0.008766 1.003 1.008 -1.732e-07 7.776e-08 0.007291 -1.305e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006876 0.000636 0.004355 0.00314 0.9889 0.9919 0.007012 0.8513 0.8918 0.01151 ] Network output: [ -0.0001653 0.001328 1 -7.032e-06 3.157e-06 0.9985 -5.3e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2234 0.1061 0.3498 0.1418 0.9849 0.9939 0.2242 0.4326 0.8748 0.7018 ] Network output: [ 0.002832 -0.01355 0.9944 4.292e-06 -1.927e-06 1.014 3.234e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09904 0.1849 0.1971 0.9873 0.9919 0.1119 0.734 0.8609 0.305 ] Network output: [ -0.002663 0.01253 1.005 4.698e-06 -2.109e-06 0.9881 3.54e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09459 0.09265 0.165 0.1967 0.9852 0.9911 0.0946 0.6578 0.836 0.2496 ] Network output: [ 8.026e-05 1 -4.943e-05 6.142e-07 -2.758e-07 0.9998 4.629e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001603 Epoch 9839 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008756 0.9968 0.9926 -1.602e-07 7.193e-08 -0.00698 -1.207e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003518 -0.003366 -0.006604 0.005338 0.9699 0.9743 0.006863 0.8241 0.8195 0.01613 ] Network output: [ 0.9999 8.983e-05 0.0003519 -2.236e-06 1.004e-06 -0.0003006 -1.685e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2095 -0.03573 -0.1557 0.1821 0.9834 0.9932 0.2352 0.4286 0.8681 0.7081 ] Network output: [ -0.008765 1.003 1.008 -1.731e-07 7.77e-08 0.007291 -1.304e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006877 0.0006361 0.004354 0.003139 0.9889 0.9919 0.007012 0.8513 0.8918 0.01151 ] Network output: [ -0.0001651 0.001327 1 -7.024e-06 3.153e-06 0.9985 -5.293e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2234 0.1061 0.3498 0.1418 0.9849 0.9939 0.2242 0.4326 0.8748 0.7018 ] Network output: [ 0.002831 -0.01354 0.9944 4.287e-06 -1.924e-06 1.014 3.23e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09904 0.1849 0.1971 0.9873 0.9919 0.1119 0.734 0.8609 0.305 ] Network output: [ -0.002662 0.01253 1.005 4.692e-06 -2.106e-06 0.9881 3.536e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09459 0.09265 0.165 0.1967 0.9852 0.9911 0.0946 0.6578 0.836 0.2496 ] Network output: [ 8.024e-05 1 -4.943e-05 6.135e-07 -2.754e-07 0.9998 4.624e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001602 Epoch 9840 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008755 0.9968 0.9927 -1.601e-07 7.188e-08 -0.006979 -1.207e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003518 -0.003366 -0.006604 0.005338 0.9699 0.9743 0.006864 0.8241 0.8195 0.01613 ] Network output: [ 0.9999 8.967e-05 0.0003518 -2.233e-06 1.003e-06 -0.0003004 -1.683e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2095 -0.03573 -0.1557 0.1821 0.9834 0.9932 0.2352 0.4286 0.8681 0.7081 ] Network output: [ -0.008765 1.003 1.008 -1.729e-07 7.763e-08 0.00729 -1.303e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006877 0.0006361 0.004354 0.003139 0.9889 0.9919 0.007012 0.8513 0.8918 0.01151 ] Network output: [ -0.000165 0.001327 1 -7.015e-06 3.149e-06 0.9985 -5.287e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2234 0.1061 0.3498 0.1418 0.9849 0.9939 0.2242 0.4326 0.8748 0.7018 ] Network output: [ 0.002829 -0.01354 0.9944 4.281e-06 -1.922e-06 1.014 3.227e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09905 0.1849 0.1971 0.9873 0.9919 0.1119 0.734 0.8609 0.305 ] Network output: [ -0.002661 0.01252 1.005 4.686e-06 -2.104e-06 0.9881 3.532e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09459 0.09265 0.165 0.1967 0.9852 0.9911 0.0946 0.6578 0.836 0.2496 ] Network output: [ 8.022e-05 1 -4.943e-05 6.128e-07 -2.751e-07 0.9998 4.618e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001601 Epoch 9841 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008754 0.9968 0.9927 -1.6e-07 7.183e-08 -0.006978 -1.206e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003518 -0.003366 -0.006603 0.005338 0.9699 0.9743 0.006864 0.8241 0.8195 0.01613 ] Network output: [ 0.9999 8.952e-05 0.0003516 -2.231e-06 1.001e-06 -0.0003002 -1.681e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2095 -0.03573 -0.1556 0.1821 0.9834 0.9932 0.2352 0.4286 0.8681 0.7081 ] Network output: [ -0.008764 1.003 1.008 -1.728e-07 7.757e-08 0.007289 -1.302e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006877 0.0006362 0.004354 0.003139 0.9889 0.9919 0.007013 0.8513 0.8918 0.0115 ] Network output: [ -0.0001648 0.001326 1 -7.007e-06 3.146e-06 0.9985 -5.28e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2234 0.1061 0.3498 0.1417 0.9849 0.9939 0.2242 0.4326 0.8748 0.7018 ] Network output: [ 0.002828 -0.01353 0.9944 4.276e-06 -1.92e-06 1.014 3.223e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09905 0.1849 0.1971 0.9873 0.9919 0.1119 0.734 0.8609 0.305 ] Network output: [ -0.002659 0.01252 1.005 4.681e-06 -2.101e-06 0.9881 3.528e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09459 0.09265 0.165 0.1967 0.9852 0.9911 0.09461 0.6578 0.836 0.2496 ] Network output: [ 8.02e-05 1 -4.943e-05 6.12e-07 -2.748e-07 0.9998 4.612e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00016 Epoch 9842 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008753 0.9968 0.9927 -1.599e-07 7.178e-08 -0.006978 -1.205e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003518 -0.003366 -0.006603 0.005337 0.9699 0.9743 0.006864 0.8241 0.8195 0.01613 ] Network output: [ 0.9999 8.937e-05 0.0003515 -2.228e-06 1e-06 -0.0003 -1.679e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2095 -0.03573 -0.1556 0.1821 0.9834 0.9932 0.2352 0.4286 0.8681 0.7081 ] Network output: [ -0.008763 1.003 1.008 -1.726e-07 7.751e-08 0.007289 -1.301e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006878 0.0006362 0.004354 0.003139 0.9889 0.9919 0.007013 0.8513 0.8918 0.0115 ] Network output: [ -0.0001647 0.001325 1 -6.998e-06 3.142e-06 0.9985 -5.274e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2234 0.1061 0.3498 0.1417 0.9849 0.9939 0.2242 0.4326 0.8748 0.7018 ] Network output: [ 0.002826 -0.01352 0.9944 4.271e-06 -1.917e-06 1.014 3.219e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09905 0.1849 0.1971 0.9873 0.9919 0.1119 0.734 0.8609 0.305 ] Network output: [ -0.002658 0.01251 1.005 4.675e-06 -2.099e-06 0.9881 3.523e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09459 0.09265 0.165 0.1967 0.9852 0.9911 0.09461 0.6578 0.836 0.2496 ] Network output: [ 8.018e-05 1 -4.943e-05 6.113e-07 -2.744e-07 0.9998 4.607e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001599 Epoch 9843 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008753 0.9968 0.9927 -1.598e-07 7.173e-08 -0.006977 -1.204e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003518 -0.003366 -0.006602 0.005337 0.9699 0.9743 0.006864 0.8241 0.8195 0.01613 ] Network output: [ 0.9999 8.921e-05 0.0003513 -2.225e-06 9.989e-07 -0.0002998 -1.677e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2095 -0.03574 -0.1556 0.1821 0.9834 0.9932 0.2352 0.4286 0.8681 0.7081 ] Network output: [ -0.008762 1.003 1.008 -1.725e-07 7.745e-08 0.007288 -1.3e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006878 0.0006363 0.004354 0.003138 0.9889 0.9919 0.007013 0.8513 0.8918 0.0115 ] Network output: [ -0.0001645 0.001325 1 -6.989e-06 3.138e-06 0.9985 -5.268e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2234 0.1061 0.3498 0.1417 0.9849 0.9939 0.2242 0.4326 0.8748 0.7018 ] Network output: [ 0.002825 -0.01352 0.9944 4.266e-06 -1.915e-06 1.014 3.215e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09906 0.1849 0.1971 0.9873 0.9919 0.1119 0.734 0.8609 0.305 ] Network output: [ -0.002657 0.0125 1.005 4.67e-06 -2.096e-06 0.9882 3.519e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09459 0.09265 0.165 0.1967 0.9852 0.9911 0.09461 0.6578 0.836 0.2496 ] Network output: [ 8.016e-05 1 -4.943e-05 6.106e-07 -2.741e-07 0.9998 4.601e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001598 Epoch 9844 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008752 0.9968 0.9927 -1.597e-07 7.168e-08 -0.006977 -1.203e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003518 -0.003366 -0.006601 0.005337 0.9699 0.9743 0.006864 0.8241 0.8195 0.01613 ] Network output: [ 0.9999 8.906e-05 0.0003511 -2.222e-06 9.977e-07 -0.0002997 -1.675e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2095 -0.03574 -0.1556 0.1821 0.9834 0.9932 0.2353 0.4286 0.8681 0.7081 ] Network output: [ -0.008761 1.003 1.008 -1.724e-07 7.738e-08 0.007288 -1.299e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006879 0.0006363 0.004354 0.003138 0.9889 0.9919 0.007014 0.8513 0.8918 0.0115 ] Network output: [ -0.0001644 0.001324 1 -6.981e-06 3.134e-06 0.9985 -5.261e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2234 0.1061 0.3498 0.1417 0.9849 0.9939 0.2242 0.4326 0.8748 0.7018 ] Network output: [ 0.002824 -0.01351 0.9944 4.261e-06 -1.913e-06 1.014 3.211e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09906 0.1849 0.1971 0.9873 0.9919 0.1119 0.734 0.8609 0.305 ] Network output: [ -0.002655 0.0125 1.005 4.664e-06 -2.094e-06 0.9882 3.515e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0946 0.09266 0.165 0.1967 0.9852 0.9911 0.09461 0.6578 0.836 0.2496 ] Network output: [ 8.014e-05 1 -4.943e-05 6.098e-07 -2.738e-07 0.9998 4.596e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001598 Epoch 9845 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008751 0.9968 0.9927 -1.596e-07 7.164e-08 -0.006976 -1.203e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003518 -0.003366 -0.006601 0.005336 0.9699 0.9743 0.006864 0.8241 0.8195 0.01612 ] Network output: [ 0.9999 8.891e-05 0.000351 -2.22e-06 9.965e-07 -0.0002995 -1.673e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2095 -0.03574 -0.1556 0.1821 0.9834 0.9932 0.2353 0.4286 0.8681 0.7081 ] Network output: [ -0.008761 1.003 1.008 -1.722e-07 7.732e-08 0.007287 -1.298e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006879 0.0006364 0.004354 0.003138 0.9889 0.9919 0.007014 0.8513 0.8918 0.0115 ] Network output: [ -0.0001643 0.001323 1 -6.972e-06 3.13e-06 0.9985 -5.255e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2234 0.1061 0.3498 0.1417 0.9849 0.9939 0.2242 0.4326 0.8748 0.7018 ] Network output: [ 0.002822 -0.0135 0.9944 4.255e-06 -1.91e-06 1.014 3.207e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09906 0.1849 0.1971 0.9873 0.9919 0.1119 0.734 0.8609 0.305 ] Network output: [ -0.002654 0.01249 1.005 4.658e-06 -2.091e-06 0.9882 3.511e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0946 0.09266 0.165 0.1967 0.9852 0.9911 0.09461 0.6578 0.836 0.2496 ] Network output: [ 8.013e-05 1 -4.943e-05 6.091e-07 -2.734e-07 0.9998 4.59e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001597 Epoch 9846 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00875 0.9968 0.9927 -1.595e-07 7.159e-08 -0.006975 -1.202e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003518 -0.003366 -0.0066 0.005336 0.9699 0.9743 0.006864 0.8241 0.8195 0.01612 ] Network output: [ 0.9999 8.875e-05 0.0003508 -2.217e-06 9.953e-07 -0.0002993 -1.671e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2095 -0.03574 -0.1556 0.1821 0.9834 0.9932 0.2353 0.4286 0.868 0.7081 ] Network output: [ -0.00876 1.003 1.008 -1.721e-07 7.726e-08 0.007287 -1.297e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006879 0.0006365 0.004354 0.003138 0.9889 0.9919 0.007014 0.8513 0.8918 0.0115 ] Network output: [ -0.0001641 0.001322 1 -6.964e-06 3.126e-06 0.9985 -5.248e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2234 0.1061 0.3498 0.1417 0.9849 0.9939 0.2242 0.4326 0.8748 0.7018 ] Network output: [ 0.002821 -0.0135 0.9944 4.25e-06 -1.908e-06 1.014 3.203e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09906 0.1849 0.1971 0.9873 0.9919 0.1119 0.734 0.8609 0.305 ] Network output: [ -0.002653 0.01249 1.005 4.653e-06 -2.089e-06 0.9882 3.507e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0946 0.09266 0.165 0.1967 0.9852 0.9911 0.09461 0.6578 0.836 0.2496 ] Network output: [ 8.011e-05 1 -4.943e-05 6.084e-07 -2.731e-07 0.9998 4.585e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001596 Epoch 9847 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008749 0.9968 0.9927 -1.594e-07 7.154e-08 -0.006975 -1.201e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003518 -0.003366 -0.0066 0.005336 0.9699 0.9743 0.006865 0.8241 0.8195 0.01612 ] Network output: [ 0.9999 8.86e-05 0.0003507 -2.214e-06 9.941e-07 -0.0002991 -1.669e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2095 -0.03574 -0.1556 0.1821 0.9834 0.9932 0.2353 0.4286 0.868 0.7081 ] Network output: [ -0.008759 1.003 1.008 -1.719e-07 7.719e-08 0.007286 -1.296e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00688 0.0006365 0.004354 0.003138 0.9889 0.9919 0.007015 0.8513 0.8918 0.0115 ] Network output: [ -0.000164 0.001322 1 -6.955e-06 3.123e-06 0.9985 -5.242e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2234 0.1061 0.3498 0.1417 0.9849 0.9939 0.2242 0.4325 0.8748 0.7018 ] Network output: [ 0.002819 -0.01349 0.9944 4.245e-06 -1.906e-06 1.014 3.199e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09907 0.1849 0.1971 0.9873 0.9919 0.1119 0.734 0.8609 0.305 ] Network output: [ -0.002651 0.01248 1.005 4.647e-06 -2.086e-06 0.9882 3.502e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0946 0.09266 0.1649 0.1967 0.9852 0.9911 0.09462 0.6578 0.836 0.2496 ] Network output: [ 8.009e-05 1 -4.943e-05 6.076e-07 -2.728e-07 0.9998 4.579e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001595 Epoch 9848 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008748 0.9968 0.9927 -1.592e-07 7.149e-08 -0.006974 -1.2e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003518 -0.003367 -0.006599 0.005335 0.9699 0.9743 0.006865 0.8241 0.8195 0.01612 ] Network output: [ 0.9999 8.845e-05 0.0003505 -2.212e-06 9.929e-07 -0.0002989 -1.667e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2095 -0.03574 -0.1556 0.1821 0.9834 0.9932 0.2353 0.4286 0.868 0.7081 ] Network output: [ -0.008758 1.003 1.008 -1.718e-07 7.713e-08 0.007286 -1.295e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00688 0.0006366 0.004354 0.003137 0.9889 0.9919 0.007015 0.8513 0.8918 0.0115 ] Network output: [ -0.0001638 0.001321 1 -6.947e-06 3.119e-06 0.9985 -5.235e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2235 0.1061 0.3498 0.1417 0.9849 0.9939 0.2242 0.4325 0.8748 0.7018 ] Network output: [ 0.002818 -0.01348 0.9944 4.24e-06 -1.903e-06 1.014 3.195e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09907 0.1849 0.1971 0.9873 0.9919 0.1119 0.7339 0.8609 0.305 ] Network output: [ -0.00265 0.01248 1.005 4.642e-06 -2.084e-06 0.9882 3.498e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0946 0.09266 0.1649 0.1967 0.9852 0.9911 0.09462 0.6577 0.836 0.2496 ] Network output: [ 8.007e-05 1 -4.943e-05 6.069e-07 -2.725e-07 0.9998 4.574e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001594 Epoch 9849 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008747 0.9968 0.9927 -1.591e-07 7.144e-08 -0.006973 -1.199e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003518 -0.003367 -0.006599 0.005335 0.9699 0.9743 0.006865 0.8241 0.8195 0.01612 ] Network output: [ 0.9999 8.83e-05 0.0003504 -2.209e-06 9.916e-07 -0.0002987 -1.665e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2095 -0.03574 -0.1556 0.1821 0.9834 0.9932 0.2353 0.4286 0.868 0.7081 ] Network output: [ -0.008757 1.003 1.008 -1.717e-07 7.707e-08 0.007285 -1.294e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00688 0.0006366 0.004353 0.003137 0.9889 0.9919 0.007015 0.8512 0.8918 0.0115 ] Network output: [ -0.0001637 0.00132 1 -6.939e-06 3.115e-06 0.9985 -5.229e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2235 0.1061 0.3498 0.1417 0.9849 0.9939 0.2242 0.4325 0.8748 0.7017 ] Network output: [ 0.002816 -0.01348 0.9944 4.235e-06 -1.901e-06 1.013 3.192e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09907 0.1849 0.1971 0.9873 0.9919 0.1119 0.7339 0.8609 0.305 ] Network output: [ -0.002649 0.01247 1.005 4.636e-06 -2.081e-06 0.9882 3.494e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0946 0.09266 0.1649 0.1967 0.9852 0.9911 0.09462 0.6577 0.836 0.2496 ] Network output: [ 8.005e-05 1 -4.943e-05 6.062e-07 -2.721e-07 0.9998 4.568e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001593 Epoch 9850 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008746 0.9968 0.9927 -1.59e-07 7.139e-08 -0.006973 -1.198e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003518 -0.003367 -0.006598 0.005334 0.9699 0.9743 0.006865 0.8241 0.8195 0.01612 ] Network output: [ 0.9999 8.814e-05 0.0003502 -2.206e-06 9.904e-07 -0.0002986 -1.663e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2095 -0.03574 -0.1556 0.1821 0.9834 0.9932 0.2353 0.4286 0.868 0.7081 ] Network output: [ -0.008756 1.003 1.008 -1.715e-07 7.701e-08 0.007285 -1.293e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006881 0.0006367 0.004353 0.003137 0.9889 0.9919 0.007016 0.8512 0.8918 0.0115 ] Network output: [ -0.0001635 0.00132 1 -6.93e-06 3.111e-06 0.9985 -5.223e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2235 0.1061 0.3498 0.1417 0.9849 0.9939 0.2242 0.4325 0.8748 0.7017 ] Network output: [ 0.002815 -0.01347 0.9944 4.23e-06 -1.899e-06 1.013 3.188e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09908 0.1849 0.1971 0.9873 0.9919 0.1119 0.7339 0.8609 0.305 ] Network output: [ -0.002647 0.01246 1.005 4.631e-06 -2.079e-06 0.9882 3.49e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09461 0.09267 0.1649 0.1967 0.9852 0.9911 0.09462 0.6577 0.836 0.2496 ] Network output: [ 8.003e-05 1 -4.943e-05 6.054e-07 -2.718e-07 0.9998 4.563e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001592 Epoch 9851 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008746 0.9968 0.9927 -1.589e-07 7.135e-08 -0.006972 -1.198e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003519 -0.003367 -0.006597 0.005334 0.9699 0.9743 0.006865 0.824 0.8195 0.01612 ] Network output: [ 0.9999 8.799e-05 0.0003501 -2.203e-06 9.892e-07 -0.0002984 -1.661e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2096 -0.03574 -0.1556 0.1821 0.9834 0.9932 0.2353 0.4286 0.868 0.7081 ] Network output: [ -0.008756 1.003 1.008 -1.714e-07 7.694e-08 0.007284 -1.292e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006881 0.0006368 0.004353 0.003137 0.9889 0.9919 0.007016 0.8512 0.8918 0.0115 ] Network output: [ -0.0001634 0.001319 1 -6.922e-06 3.107e-06 0.9985 -5.216e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2235 0.1061 0.3498 0.1417 0.9849 0.9939 0.2242 0.4325 0.8748 0.7017 ] Network output: [ 0.002813 -0.01346 0.9944 4.225e-06 -1.897e-06 1.013 3.184e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09908 0.1849 0.1971 0.9873 0.9919 0.1119 0.7339 0.8609 0.305 ] Network output: [ -0.002646 0.01246 1.005 4.625e-06 -2.076e-06 0.9882 3.486e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09461 0.09267 0.1649 0.1967 0.9852 0.9911 0.09462 0.6577 0.836 0.2496 ] Network output: [ 8.001e-05 1 -4.943e-05 6.047e-07 -2.715e-07 0.9998 4.557e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001591 Epoch 9852 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008745 0.9968 0.9927 -1.588e-07 7.13e-08 -0.006972 -1.197e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003519 -0.003367 -0.006597 0.005334 0.9699 0.9743 0.006865 0.824 0.8195 0.01612 ] Network output: [ 0.9999 8.784e-05 0.0003499 -2.201e-06 9.88e-07 -0.0002982 -1.659e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2096 -0.03574 -0.1556 0.1821 0.9834 0.9932 0.2353 0.4286 0.868 0.7081 ] Network output: [ -0.008755 1.003 1.008 -1.713e-07 7.688e-08 0.007283 -1.291e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006881 0.0006368 0.004353 0.003136 0.9889 0.9919 0.007016 0.8512 0.8918 0.0115 ] Network output: [ -0.0001632 0.001318 1 -6.913e-06 3.104e-06 0.9985 -5.21e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2235 0.1061 0.3498 0.1417 0.9849 0.9939 0.2242 0.4325 0.8748 0.7017 ] Network output: [ 0.002812 -0.01346 0.9944 4.219e-06 -1.894e-06 1.013 3.18e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09908 0.1849 0.1971 0.9873 0.9919 0.1119 0.7339 0.8609 0.305 ] Network output: [ -0.002645 0.01245 1.005 4.62e-06 -2.074e-06 0.9882 3.482e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09461 0.09267 0.1649 0.1967 0.9852 0.9911 0.09462 0.6577 0.836 0.2496 ] Network output: [ 7.999e-05 1 -4.943e-05 6.04e-07 -2.711e-07 0.9998 4.552e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001591 Epoch 9853 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008744 0.9968 0.9927 -1.587e-07 7.125e-08 -0.006971 -1.196e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003519 -0.003367 -0.006596 0.005333 0.9699 0.9743 0.006866 0.824 0.8195 0.01612 ] Network output: [ 0.9999 8.768e-05 0.0003498 -2.198e-06 9.868e-07 -0.000298 -1.657e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2096 -0.03574 -0.1555 0.1821 0.9834 0.9932 0.2353 0.4286 0.868 0.7081 ] Network output: [ -0.008754 1.003 1.008 -1.711e-07 7.682e-08 0.007283 -1.29e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006882 0.0006369 0.004353 0.003136 0.9889 0.9919 0.007017 0.8512 0.8918 0.0115 ] Network output: [ -0.0001631 0.001318 1 -6.905e-06 3.1e-06 0.9985 -5.204e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2235 0.1061 0.3499 0.1417 0.9849 0.9939 0.2243 0.4325 0.8748 0.7017 ] Network output: [ 0.002811 -0.01345 0.9944 4.214e-06 -1.892e-06 1.013 3.176e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09908 0.1849 0.1971 0.9873 0.9919 0.1119 0.7339 0.8609 0.305 ] Network output: [ -0.002643 0.01245 1.005 4.614e-06 -2.071e-06 0.9882 3.477e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09461 0.09267 0.1649 0.1967 0.9852 0.9911 0.09463 0.6577 0.836 0.2496 ] Network output: [ 7.997e-05 1 -4.943e-05 6.032e-07 -2.708e-07 0.9998 4.546e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000159 Epoch 9854 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008743 0.9968 0.9927 -1.586e-07 7.12e-08 -0.00697 -1.195e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003519 -0.003367 -0.006596 0.005333 0.9699 0.9743 0.006866 0.824 0.8195 0.01612 ] Network output: [ 0.9999 8.753e-05 0.0003496 -2.195e-06 9.856e-07 -0.0002978 -1.655e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2096 -0.03575 -0.1555 0.1821 0.9834 0.9932 0.2353 0.4286 0.868 0.7081 ] Network output: [ -0.008753 1.003 1.008 -1.71e-07 7.676e-08 0.007282 -1.289e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006882 0.0006369 0.004353 0.003136 0.9889 0.9919 0.007017 0.8512 0.8918 0.0115 ] Network output: [ -0.0001629 0.001317 1 -6.896e-06 3.096e-06 0.9985 -5.197e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2235 0.1061 0.3499 0.1417 0.9849 0.9939 0.2243 0.4325 0.8748 0.7017 ] Network output: [ 0.002809 -0.01345 0.9944 4.209e-06 -1.89e-06 1.013 3.172e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09909 0.1849 0.1971 0.9873 0.9919 0.1119 0.7339 0.8609 0.305 ] Network output: [ -0.002642 0.01244 1.005 4.609e-06 -2.069e-06 0.9882 3.473e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09461 0.09267 0.1649 0.1967 0.9852 0.9911 0.09463 0.6577 0.836 0.2496 ] Network output: [ 7.995e-05 1 -4.943e-05 6.025e-07 -2.705e-07 0.9998 4.541e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001589 Epoch 9855 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008742 0.9968 0.9927 -1.585e-07 7.115e-08 -0.00697 -1.194e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003519 -0.003367 -0.006595 0.005333 0.9699 0.9743 0.006866 0.824 0.8195 0.01612 ] Network output: [ 0.9999 8.738e-05 0.0003495 -2.193e-06 9.844e-07 -0.0002976 -1.653e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2096 -0.03575 -0.1555 0.182 0.9834 0.9932 0.2353 0.4286 0.868 0.7081 ] Network output: [ -0.008752 1.003 1.008 -1.708e-07 7.669e-08 0.007282 -1.287e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006882 0.000637 0.004353 0.003136 0.9889 0.9919 0.007017 0.8512 0.8918 0.01149 ] Network output: [ -0.0001628 0.001316 1 -6.888e-06 3.092e-06 0.9985 -5.191e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2235 0.1062 0.3499 0.1417 0.9849 0.9939 0.2243 0.4325 0.8748 0.7017 ] Network output: [ 0.002808 -0.01344 0.9944 4.204e-06 -1.887e-06 1.013 3.168e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09909 0.1849 0.1971 0.9873 0.9919 0.1119 0.7339 0.8609 0.305 ] Network output: [ -0.002641 0.01243 1.005 4.603e-06 -2.067e-06 0.9882 3.469e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09461 0.09267 0.1649 0.1967 0.9852 0.9911 0.09463 0.6577 0.836 0.2496 ] Network output: [ 7.993e-05 1 -4.943e-05 6.018e-07 -2.702e-07 0.9998 4.535e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001588 Epoch 9856 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008741 0.9968 0.9927 -1.584e-07 7.11e-08 -0.006969 -1.194e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003519 -0.003367 -0.006595 0.005332 0.9699 0.9743 0.006866 0.824 0.8195 0.01612 ] Network output: [ 0.9999 8.723e-05 0.0003493 -2.19e-06 9.832e-07 -0.0002975 -1.65e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2096 -0.03575 -0.1555 0.182 0.9834 0.9932 0.2353 0.4286 0.868 0.708 ] Network output: [ -0.008752 1.003 1.008 -1.707e-07 7.663e-08 0.007281 -1.286e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006883 0.000637 0.004353 0.003135 0.9889 0.9919 0.007018 0.8512 0.8918 0.01149 ] Network output: [ -0.0001627 0.001316 1 -6.88e-06 3.088e-06 0.9985 -5.185e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2235 0.1062 0.3499 0.1417 0.9849 0.9939 0.2243 0.4325 0.8748 0.7017 ] Network output: [ 0.002806 -0.01343 0.9944 4.199e-06 -1.885e-06 1.013 3.165e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09909 0.1849 0.1971 0.9873 0.9919 0.1119 0.7339 0.8609 0.305 ] Network output: [ -0.002639 0.01243 1.005 4.598e-06 -2.064e-06 0.9882 3.465e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09462 0.09268 0.1649 0.1967 0.9852 0.9911 0.09463 0.6577 0.836 0.2496 ] Network output: [ 7.991e-05 1 -4.943e-05 6.011e-07 -2.698e-07 0.9998 4.53e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001587 Epoch 9857 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00874 0.9968 0.9927 -1.583e-07 7.105e-08 -0.006968 -1.193e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003519 -0.003367 -0.006594 0.005332 0.9699 0.9743 0.006866 0.824 0.8195 0.01611 ] Network output: [ 0.9999 8.707e-05 0.0003491 -2.187e-06 9.82e-07 -0.0002973 -1.648e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2096 -0.03575 -0.1555 0.182 0.9834 0.9932 0.2353 0.4286 0.868 0.708 ] Network output: [ -0.008751 1.003 1.008 -1.706e-07 7.657e-08 0.007281 -1.285e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006883 0.0006371 0.004353 0.003135 0.9889 0.9919 0.007018 0.8512 0.8918 0.01149 ] Network output: [ -0.0001625 0.001315 1 -6.871e-06 3.085e-06 0.9985 -5.178e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2235 0.1062 0.3499 0.1417 0.9849 0.9939 0.2243 0.4325 0.8748 0.7017 ] Network output: [ 0.002805 -0.01343 0.9944 4.194e-06 -1.883e-06 1.013 3.161e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09909 0.1849 0.1971 0.9873 0.9919 0.1119 0.7339 0.8609 0.305 ] Network output: [ -0.002638 0.01242 1.005 4.592e-06 -2.062e-06 0.9882 3.461e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09462 0.09268 0.1649 0.1967 0.9852 0.9911 0.09463 0.6577 0.836 0.2496 ] Network output: [ 7.989e-05 1 -4.943e-05 6.003e-07 -2.695e-07 0.9998 4.524e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001586 Epoch 9858 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00874 0.9968 0.9927 -1.582e-07 7.101e-08 -0.006968 -1.192e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003519 -0.003367 -0.006594 0.005331 0.9699 0.9743 0.006866 0.824 0.8195 0.01611 ] Network output: [ 0.9999 8.692e-05 0.000349 -2.185e-06 9.808e-07 -0.0002971 -1.646e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2096 -0.03575 -0.1555 0.182 0.9834 0.9932 0.2353 0.4286 0.868 0.708 ] Network output: [ -0.00875 1.003 1.008 -1.704e-07 7.651e-08 0.00728 -1.284e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006883 0.0006371 0.004352 0.003135 0.9889 0.9919 0.007019 0.8512 0.8918 0.01149 ] Network output: [ -0.0001624 0.001314 1 -6.863e-06 3.081e-06 0.9985 -5.172e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2235 0.1062 0.3499 0.1417 0.9849 0.9939 0.2243 0.4325 0.8748 0.7017 ] Network output: [ 0.002803 -0.01342 0.9944 4.189e-06 -1.881e-06 1.013 3.157e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.0991 0.1849 0.1971 0.9873 0.9919 0.1119 0.7338 0.8609 0.305 ] Network output: [ -0.002637 0.01242 1.005 4.587e-06 -2.059e-06 0.9882 3.457e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09462 0.09268 0.1649 0.1967 0.9852 0.9911 0.09463 0.6576 0.836 0.2496 ] Network output: [ 7.987e-05 1 -4.943e-05 5.996e-07 -2.692e-07 0.9998 4.519e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001585 Epoch 9859 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008739 0.9968 0.9927 -1.581e-07 7.096e-08 -0.006967 -1.191e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003519 -0.003367 -0.006593 0.005331 0.9699 0.9743 0.006866 0.824 0.8195 0.01611 ] Network output: [ 0.9999 8.677e-05 0.0003488 -2.182e-06 9.796e-07 -0.0002969 -1.644e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2096 -0.03575 -0.1555 0.182 0.9834 0.9932 0.2354 0.4286 0.868 0.708 ] Network output: [ -0.008749 1.003 1.008 -1.703e-07 7.644e-08 0.00728 -1.283e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006884 0.0006372 0.004352 0.003135 0.9889 0.9919 0.007019 0.8512 0.8918 0.01149 ] Network output: [ -0.0001622 0.001313 1 -6.854e-06 3.077e-06 0.9985 -5.166e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2235 0.1062 0.3499 0.1417 0.9849 0.9939 0.2243 0.4325 0.8748 0.7017 ] Network output: [ 0.002802 -0.01341 0.9944 4.184e-06 -1.878e-06 1.013 3.153e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.0991 0.1849 0.1971 0.9873 0.9919 0.1119 0.7338 0.8609 0.305 ] Network output: [ -0.002636 0.01241 1.005 4.581e-06 -2.057e-06 0.9882 3.452e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09462 0.09268 0.1649 0.1967 0.9852 0.9911 0.09464 0.6576 0.836 0.2496 ] Network output: [ 7.985e-05 1 -4.943e-05 5.989e-07 -2.689e-07 0.9998 4.514e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001584 Epoch 9860 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008738 0.9968 0.9927 -1.579e-07 7.091e-08 -0.006966 -1.19e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003519 -0.003367 -0.006592 0.005331 0.9699 0.9743 0.006867 0.824 0.8195 0.01611 ] Network output: [ 0.9999 8.662e-05 0.0003487 -2.179e-06 9.784e-07 -0.0002967 -1.642e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2096 -0.03575 -0.1555 0.182 0.9834 0.9932 0.2354 0.4285 0.868 0.708 ] Network output: [ -0.008748 1.003 1.008 -1.701e-07 7.638e-08 0.007279 -1.282e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006884 0.0006373 0.004352 0.003135 0.9889 0.9919 0.007019 0.8512 0.8918 0.01149 ] Network output: [ -0.0001621 0.001313 1 -6.846e-06 3.073e-06 0.9985 -5.159e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2235 0.1062 0.3499 0.1417 0.9849 0.9939 0.2243 0.4325 0.8748 0.7017 ] Network output: [ 0.0028 -0.01341 0.9944 4.179e-06 -1.876e-06 1.013 3.149e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.0991 0.1849 0.1971 0.9873 0.9919 0.1119 0.7338 0.8609 0.305 ] Network output: [ -0.002634 0.0124 1.005 4.576e-06 -2.054e-06 0.9882 3.448e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09462 0.09268 0.1649 0.1967 0.9852 0.9911 0.09464 0.6576 0.836 0.2496 ] Network output: [ 7.983e-05 1 -4.943e-05 5.982e-07 -2.685e-07 0.9998 4.508e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001584 Epoch 9861 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008737 0.9968 0.9927 -1.578e-07 7.086e-08 -0.006966 -1.19e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003519 -0.003367 -0.006592 0.00533 0.9699 0.9743 0.006867 0.824 0.8195 0.01611 ] Network output: [ 0.9999 8.647e-05 0.0003485 -2.177e-06 9.772e-07 -0.0002966 -1.64e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2096 -0.03575 -0.1555 0.182 0.9834 0.9932 0.2354 0.4285 0.868 0.708 ] Network output: [ -0.008748 1.003 1.008 -1.7e-07 7.632e-08 0.007279 -1.281e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006884 0.0006373 0.004352 0.003134 0.9889 0.9919 0.00702 0.8512 0.8918 0.01149 ] Network output: [ -0.0001619 0.001312 1 -6.838e-06 3.07e-06 0.9985 -5.153e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2235 0.1062 0.3499 0.1417 0.9849 0.9939 0.2243 0.4325 0.8748 0.7017 ] Network output: [ 0.002799 -0.0134 0.9944 4.174e-06 -1.874e-06 1.013 3.145e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09911 0.1849 0.1971 0.9873 0.9919 0.1119 0.7338 0.8609 0.305 ] Network output: [ -0.002633 0.0124 1.005 4.57e-06 -2.052e-06 0.9882 3.444e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09462 0.09268 0.1649 0.1967 0.9852 0.9911 0.09464 0.6576 0.836 0.2497 ] Network output: [ 7.982e-05 1 -4.943e-05 5.975e-07 -2.682e-07 0.9998 4.503e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001583 Epoch 9862 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008736 0.9968 0.9927 -1.577e-07 7.081e-08 -0.006965 -1.189e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003519 -0.003368 -0.006591 0.00533 0.9699 0.9743 0.006867 0.824 0.8195 0.01611 ] Network output: [ 0.9999 8.631e-05 0.0003484 -2.174e-06 9.76e-07 -0.0002964 -1.638e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2096 -0.03575 -0.1555 0.182 0.9834 0.9932 0.2354 0.4285 0.868 0.708 ] Network output: [ -0.008747 1.003 1.008 -1.699e-07 7.626e-08 0.007278 -1.28e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006885 0.0006374 0.004352 0.003134 0.9889 0.9919 0.00702 0.8512 0.8918 0.01149 ] Network output: [ -0.0001618 0.001311 1 -6.829e-06 3.066e-06 0.9985 -5.147e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2235 0.1062 0.3499 0.1417 0.9849 0.9939 0.2243 0.4325 0.8748 0.7017 ] Network output: [ 0.002798 -0.01339 0.9944 4.169e-06 -1.871e-06 1.013 3.142e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09911 0.1849 0.1971 0.9873 0.9919 0.1119 0.7338 0.8609 0.305 ] Network output: [ -0.002632 0.01239 1.005 4.565e-06 -2.049e-06 0.9882 3.44e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09463 0.09269 0.1649 0.1967 0.9852 0.9911 0.09464 0.6576 0.836 0.2497 ] Network output: [ 7.98e-05 1 -4.944e-05 5.967e-07 -2.679e-07 0.9998 4.497e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001582 Epoch 9863 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008735 0.9968 0.9927 -1.576e-07 7.076e-08 -0.006965 -1.188e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003519 -0.003368 -0.006591 0.00533 0.9699 0.9743 0.006867 0.824 0.8195 0.01611 ] Network output: [ 0.9999 8.616e-05 0.0003482 -2.171e-06 9.748e-07 -0.0002962 -1.636e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2096 -0.03575 -0.1555 0.182 0.9834 0.9932 0.2354 0.4285 0.868 0.708 ] Network output: [ -0.008746 1.003 1.008 -1.697e-07 7.62e-08 0.007278 -1.279e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006885 0.0006374 0.004352 0.003134 0.9889 0.9919 0.00702 0.8512 0.8918 0.01149 ] Network output: [ -0.0001616 0.001311 1 -6.821e-06 3.062e-06 0.9985 -5.141e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2235 0.1062 0.3499 0.1417 0.9849 0.9939 0.2243 0.4325 0.8748 0.7017 ] Network output: [ 0.002796 -0.01339 0.9944 4.163e-06 -1.869e-06 1.013 3.138e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1118 0.09911 0.1849 0.1971 0.9873 0.9919 0.1119 0.7338 0.8609 0.305 ] Network output: [ -0.00263 0.01239 1.005 4.559e-06 -2.047e-06 0.9882 3.436e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09463 0.09269 0.1649 0.1967 0.9852 0.9911 0.09464 0.6576 0.836 0.2497 ] Network output: [ 7.978e-05 1 -4.944e-05 5.96e-07 -2.676e-07 0.9998 4.492e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001581 Epoch 9864 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008734 0.9968 0.9927 -1.575e-07 7.072e-08 -0.006964 -1.187e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003519 -0.003368 -0.00659 0.005329 0.9699 0.9743 0.006867 0.824 0.8195 0.01611 ] Network output: [ 0.9999 8.601e-05 0.0003481 -2.169e-06 9.736e-07 -0.000296 -1.634e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2096 -0.03576 -0.1555 0.182 0.9834 0.9932 0.2354 0.4285 0.868 0.708 ] Network output: [ -0.008745 1.003 1.008 -1.696e-07 7.613e-08 0.007277 -1.278e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006885 0.0006375 0.004352 0.003134 0.9889 0.9919 0.007021 0.8512 0.8918 0.01149 ] Network output: [ -0.0001615 0.00131 1 -6.813e-06 3.058e-06 0.9985 -5.134e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2236 0.1062 0.3499 0.1417 0.9849 0.9939 0.2243 0.4325 0.8748 0.7017 ] Network output: [ 0.002795 -0.01338 0.9944 4.158e-06 -1.867e-06 1.013 3.134e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09911 0.1849 0.1971 0.9873 0.9919 0.1119 0.7338 0.8609 0.305 ] Network output: [ -0.002629 0.01238 1.005 4.554e-06 -2.044e-06 0.9882 3.432e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09463 0.09269 0.1649 0.1967 0.9852 0.9911 0.09464 0.6576 0.8359 0.2497 ] Network output: [ 7.976e-05 1 -4.944e-05 5.953e-07 -2.673e-07 0.9998 4.486e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000158 Epoch 9865 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008733 0.9968 0.9927 -1.574e-07 7.067e-08 -0.006963 -1.186e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003519 -0.003368 -0.00659 0.005329 0.9699 0.9743 0.006867 0.824 0.8195 0.01611 ] Network output: [ 0.9999 8.586e-05 0.0003479 -2.166e-06 9.724e-07 -0.0002958 -1.632e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2096 -0.03576 -0.1554 0.182 0.9834 0.9932 0.2354 0.4285 0.868 0.708 ] Network output: [ -0.008744 1.003 1.008 -1.694e-07 7.607e-08 0.007276 -1.277e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006886 0.0006375 0.004352 0.003133 0.9889 0.9919 0.007021 0.8512 0.8918 0.01149 ] Network output: [ -0.0001613 0.001309 1 -6.804e-06 3.055e-06 0.9985 -5.128e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2236 0.1062 0.3499 0.1417 0.9849 0.9939 0.2243 0.4325 0.8748 0.7017 ] Network output: [ 0.002793 -0.01337 0.9944 4.153e-06 -1.865e-06 1.013 3.13e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09912 0.1849 0.1971 0.9873 0.9919 0.1119 0.7338 0.8609 0.3049 ] Network output: [ -0.002628 0.01238 1.005 4.548e-06 -2.042e-06 0.9882 3.428e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09463 0.09269 0.1649 0.1967 0.9852 0.9911 0.09465 0.6576 0.8359 0.2497 ] Network output: [ 7.974e-05 1 -4.944e-05 5.946e-07 -2.669e-07 0.9998 4.481e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001579 Epoch 9866 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008733 0.9968 0.9927 -1.573e-07 7.062e-08 -0.006963 -1.185e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003519 -0.003368 -0.006589 0.005328 0.9699 0.9743 0.006868 0.824 0.8195 0.01611 ] Network output: [ 0.9999 8.571e-05 0.0003478 -2.163e-06 9.712e-07 -0.0002957 -1.63e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2096 -0.03576 -0.1554 0.182 0.9834 0.9932 0.2354 0.4285 0.868 0.708 ] Network output: [ -0.008744 1.003 1.008 -1.693e-07 7.601e-08 0.007276 -1.276e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006886 0.0006376 0.004352 0.003133 0.9889 0.9919 0.007021 0.8512 0.8918 0.01149 ] Network output: [ -0.0001612 0.001309 1 -6.796e-06 3.051e-06 0.9985 -5.122e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2236 0.1062 0.3499 0.1417 0.9849 0.9939 0.2243 0.4325 0.8748 0.7017 ] Network output: [ 0.002792 -0.01337 0.9944 4.148e-06 -1.862e-06 1.013 3.126e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09912 0.1849 0.1971 0.9873 0.9919 0.1119 0.7338 0.8609 0.3049 ] Network output: [ -0.002626 0.01237 1.005 4.543e-06 -2.039e-06 0.9882 3.424e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09463 0.09269 0.1649 0.1967 0.9852 0.9911 0.09465 0.6576 0.8359 0.2497 ] Network output: [ 7.972e-05 1 -4.944e-05 5.939e-07 -2.666e-07 0.9998 4.476e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001578 Epoch 9867 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008732 0.9968 0.9927 -1.572e-07 7.057e-08 -0.006962 -1.185e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003519 -0.003368 -0.006589 0.005328 0.9699 0.9743 0.006868 0.824 0.8195 0.01611 ] Network output: [ 0.9999 8.556e-05 0.0003476 -2.161e-06 9.7e-07 -0.0002955 -1.628e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2096 -0.03576 -0.1554 0.182 0.9834 0.9932 0.2354 0.4285 0.868 0.708 ] Network output: [ -0.008743 1.003 1.008 -1.692e-07 7.595e-08 0.007275 -1.275e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006886 0.0006377 0.004352 0.003133 0.9889 0.9919 0.007022 0.8512 0.8918 0.01149 ] Network output: [ -0.0001611 0.001308 1 -6.788e-06 3.047e-06 0.9985 -5.115e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2236 0.1062 0.3499 0.1417 0.9849 0.9939 0.2243 0.4325 0.8748 0.7017 ] Network output: [ 0.00279 -0.01336 0.9944 4.143e-06 -1.86e-06 1.013 3.123e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09912 0.1849 0.1971 0.9873 0.9919 0.1119 0.7338 0.8609 0.3049 ] Network output: [ -0.002625 0.01236 1.005 4.537e-06 -2.037e-06 0.9882 3.42e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09463 0.09269 0.1649 0.1967 0.9852 0.9911 0.09465 0.6576 0.8359 0.2497 ] Network output: [ 7.97e-05 1 -4.944e-05 5.932e-07 -2.663e-07 0.9998 4.47e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001577 Epoch 9868 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008731 0.9968 0.9927 -1.571e-07 7.052e-08 -0.006961 -1.184e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003519 -0.003368 -0.006588 0.005328 0.9699 0.9743 0.006868 0.824 0.8195 0.0161 ] Network output: [ 0.9999 8.54e-05 0.0003475 -2.158e-06 9.689e-07 -0.0002953 -1.626e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2096 -0.03576 -0.1554 0.182 0.9834 0.9932 0.2354 0.4285 0.868 0.708 ] Network output: [ -0.008742 1.003 1.008 -1.69e-07 7.588e-08 0.007275 -1.274e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006887 0.0006377 0.004351 0.003133 0.9889 0.9919 0.007022 0.8512 0.8918 0.01149 ] Network output: [ -0.0001609 0.001307 1 -6.779e-06 3.044e-06 0.9985 -5.109e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2236 0.1062 0.3499 0.1417 0.9849 0.9939 0.2243 0.4325 0.8748 0.7017 ] Network output: [ 0.002789 -0.01336 0.9944 4.138e-06 -1.858e-06 1.013 3.119e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09912 0.1849 0.1971 0.9873 0.9919 0.1119 0.7338 0.8609 0.3049 ] Network output: [ -0.002624 0.01236 1.005 4.532e-06 -2.035e-06 0.9882 3.415e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09464 0.0927 0.1649 0.1967 0.9852 0.9911 0.09465 0.6575 0.8359 0.2497 ] Network output: [ 7.968e-05 1 -4.944e-05 5.924e-07 -2.66e-07 0.9998 4.465e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001577 Epoch 9869 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00873 0.9968 0.9927 -1.57e-07 7.047e-08 -0.006961 -1.183e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003519 -0.003368 -0.006587 0.005327 0.9699 0.9743 0.006868 0.824 0.8195 0.0161 ] Network output: [ 0.9999 8.525e-05 0.0003473 -2.155e-06 9.677e-07 -0.0002951 -1.624e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2096 -0.03576 -0.1554 0.182 0.9834 0.9932 0.2354 0.4285 0.868 0.708 ] Network output: [ -0.008741 1.003 1.008 -1.689e-07 7.582e-08 0.007274 -1.273e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006887 0.0006378 0.004351 0.003133 0.9889 0.9919 0.007022 0.8512 0.8918 0.01148 ] Network output: [ -0.0001608 0.001306 1 -6.771e-06 3.04e-06 0.9985 -5.103e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2236 0.1062 0.3499 0.1417 0.9849 0.9939 0.2244 0.4325 0.8748 0.7017 ] Network output: [ 0.002787 -0.01335 0.9944 4.133e-06 -1.856e-06 1.013 3.115e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09913 0.1849 0.1971 0.9873 0.9919 0.1119 0.7337 0.8609 0.3049 ] Network output: [ -0.002622 0.01235 1.005 4.527e-06 -2.032e-06 0.9882 3.411e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09464 0.0927 0.1649 0.1967 0.9852 0.9911 0.09465 0.6575 0.8359 0.2497 ] Network output: [ 7.966e-05 1 -4.944e-05 5.917e-07 -2.656e-07 0.9998 4.459e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001576 Epoch 9870 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008729 0.9968 0.9927 -1.569e-07 7.043e-08 -0.00696 -1.182e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003519 -0.003368 -0.006587 0.005327 0.9699 0.9743 0.006868 0.824 0.8195 0.0161 ] Network output: [ 0.9999 8.51e-05 0.0003472 -2.153e-06 9.665e-07 -0.0002949 -1.622e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2097 -0.03576 -0.1554 0.182 0.9834 0.9932 0.2354 0.4285 0.868 0.708 ] Network output: [ -0.00874 1.003 1.008 -1.688e-07 7.576e-08 0.007274 -1.272e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006887 0.0006378 0.004351 0.003132 0.9889 0.9919 0.007023 0.8512 0.8917 0.01148 ] Network output: [ -0.0001606 0.001306 1 -6.763e-06 3.036e-06 0.9985 -5.097e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2236 0.1062 0.3499 0.1417 0.9849 0.9939 0.2244 0.4325 0.8748 0.7017 ] Network output: [ 0.002786 -0.01334 0.9944 4.128e-06 -1.853e-06 1.013 3.111e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09913 0.1849 0.1971 0.9873 0.9919 0.1119 0.7337 0.8609 0.3049 ] Network output: [ -0.002621 0.01235 1.005 4.521e-06 -2.03e-06 0.9882 3.407e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09464 0.0927 0.1649 0.1967 0.9852 0.9911 0.09465 0.6575 0.8359 0.2497 ] Network output: [ 7.964e-05 1 -4.945e-05 5.91e-07 -2.653e-07 0.9998 4.454e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001575 Epoch 9871 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008728 0.9968 0.9927 -1.568e-07 7.038e-08 -0.00696 -1.181e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003519 -0.003368 -0.006586 0.005327 0.9699 0.9743 0.006868 0.824 0.8194 0.0161 ] Network output: [ 0.9999 8.495e-05 0.000347 -2.15e-06 9.653e-07 -0.0002948 -1.62e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2097 -0.03576 -0.1554 0.182 0.9834 0.9932 0.2354 0.4285 0.868 0.708 ] Network output: [ -0.00874 1.003 1.008 -1.686e-07 7.57e-08 0.007273 -1.271e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006888 0.0006379 0.004351 0.003132 0.9889 0.9919 0.007023 0.8512 0.8917 0.01148 ] Network output: [ -0.0001605 0.001305 1 -6.755e-06 3.032e-06 0.9985 -5.091e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2236 0.1062 0.3499 0.1417 0.9849 0.9939 0.2244 0.4325 0.8748 0.7017 ] Network output: [ 0.002785 -0.01334 0.9944 4.123e-06 -1.851e-06 1.013 3.107e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09913 0.1849 0.1971 0.9873 0.9919 0.112 0.7337 0.8609 0.3049 ] Network output: [ -0.00262 0.01234 1.005 4.516e-06 -2.027e-06 0.9882 3.403e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09464 0.0927 0.1649 0.1967 0.9852 0.9911 0.09466 0.6575 0.8359 0.2497 ] Network output: [ 7.962e-05 1 -4.945e-05 5.903e-07 -2.65e-07 0.9998 4.449e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001574 Epoch 9872 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008727 0.9968 0.9927 -1.567e-07 7.033e-08 -0.006959 -1.181e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003519 -0.003368 -0.006586 0.005326 0.9699 0.9743 0.006868 0.824 0.8194 0.0161 ] Network output: [ 0.9999 8.48e-05 0.0003469 -2.148e-06 9.641e-07 -0.0002946 -1.618e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2097 -0.03576 -0.1554 0.182 0.9834 0.9932 0.2354 0.4285 0.868 0.708 ] Network output: [ -0.008739 1.003 1.008 -1.685e-07 7.564e-08 0.007273 -1.27e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006888 0.0006379 0.004351 0.003132 0.9889 0.9919 0.007023 0.8511 0.8917 0.01148 ] Network output: [ -0.0001603 0.001304 1 -6.746e-06 3.029e-06 0.9985 -5.084e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2236 0.1062 0.3499 0.1417 0.9849 0.9939 0.2244 0.4324 0.8748 0.7017 ] Network output: [ 0.002783 -0.01333 0.9944 4.118e-06 -1.849e-06 1.013 3.104e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09914 0.1849 0.197 0.9873 0.9919 0.112 0.7337 0.8609 0.3049 ] Network output: [ -0.002618 0.01233 1.005 4.51e-06 -2.025e-06 0.9882 3.399e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09464 0.0927 0.1649 0.1967 0.9852 0.9911 0.09466 0.6575 0.8359 0.2497 ] Network output: [ 7.96e-05 1 -4.945e-05 5.896e-07 -2.647e-07 0.9998 4.443e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001573 Epoch 9873 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008726 0.9968 0.9927 -1.565e-07 7.028e-08 -0.006958 -1.18e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00352 -0.003368 -0.006585 0.005326 0.9699 0.9743 0.006869 0.824 0.8194 0.0161 ] Network output: [ 0.9999 8.465e-05 0.0003467 -2.145e-06 9.629e-07 -0.0002944 -1.617e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2097 -0.03576 -0.1554 0.182 0.9834 0.9932 0.2354 0.4285 0.868 0.708 ] Network output: [ -0.008738 1.003 1.008 -1.683e-07 7.557e-08 0.007272 -1.269e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006888 0.000638 0.004351 0.003132 0.9889 0.9919 0.007024 0.8511 0.8917 0.01148 ] Network output: [ -0.0001602 0.001304 1 -6.738e-06 3.025e-06 0.9985 -5.078e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2236 0.1062 0.3499 0.1417 0.9849 0.9939 0.2244 0.4324 0.8748 0.7017 ] Network output: [ 0.002782 -0.01332 0.9944 4.113e-06 -1.847e-06 1.013 3.1e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09914 0.1849 0.197 0.9873 0.9919 0.112 0.7337 0.8609 0.3049 ] Network output: [ -0.002617 0.01233 1.005 4.505e-06 -2.022e-06 0.9882 3.395e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09464 0.0927 0.1649 0.1967 0.9852 0.9911 0.09466 0.6575 0.8359 0.2497 ] Network output: [ 7.959e-05 1 -4.945e-05 5.889e-07 -2.644e-07 0.9998 4.438e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001572 Epoch 9874 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008726 0.9968 0.9927 -1.564e-07 7.023e-08 -0.006958 -1.179e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00352 -0.003368 -0.006585 0.005325 0.9699 0.9743 0.006869 0.824 0.8194 0.0161 ] Network output: [ 0.9999 8.45e-05 0.0003466 -2.142e-06 9.618e-07 -0.0002942 -1.615e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2097 -0.03577 -0.1554 0.182 0.9834 0.9932 0.2355 0.4285 0.868 0.708 ] Network output: [ -0.008737 1.003 1.008 -1.682e-07 7.551e-08 0.007272 -1.268e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006889 0.000638 0.004351 0.003131 0.9889 0.9919 0.007024 0.8511 0.8917 0.01148 ] Network output: [ -0.00016 0.001303 1 -6.73e-06 3.021e-06 0.9985 -5.072e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2236 0.1062 0.3499 0.1417 0.9849 0.9939 0.2244 0.4324 0.8748 0.7017 ] Network output: [ 0.00278 -0.01332 0.9944 4.108e-06 -1.844e-06 1.013 3.096e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09914 0.1849 0.197 0.9873 0.9919 0.112 0.7337 0.8609 0.3049 ] Network output: [ -0.002616 0.01232 1.005 4.5e-06 -2.02e-06 0.9883 3.391e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09465 0.09271 0.1649 0.1967 0.9852 0.9911 0.09466 0.6575 0.8359 0.2497 ] Network output: [ 7.957e-05 1 -4.945e-05 5.882e-07 -2.641e-07 0.9998 4.433e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001571 Epoch 9875 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008725 0.9968 0.9927 -1.563e-07 7.018e-08 -0.006957 -1.178e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00352 -0.003369 -0.006584 0.005325 0.9699 0.9743 0.006869 0.824 0.8194 0.0161 ] Network output: [ 0.9999 8.435e-05 0.0003464 -2.14e-06 9.606e-07 -0.000294 -1.613e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2097 -0.03577 -0.1554 0.182 0.9834 0.9932 0.2355 0.4285 0.868 0.708 ] Network output: [ -0.008736 1.003 1.008 -1.681e-07 7.545e-08 0.007271 -1.267e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006889 0.0006381 0.004351 0.003131 0.9889 0.9919 0.007024 0.8511 0.8917 0.01148 ] Network output: [ -0.0001599 0.001302 1 -6.722e-06 3.018e-06 0.9985 -5.066e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2236 0.1062 0.35 0.1417 0.9849 0.9939 0.2244 0.4324 0.8748 0.7017 ] Network output: [ 0.002779 -0.01331 0.9944 4.103e-06 -1.842e-06 1.013 3.092e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09914 0.1849 0.197 0.9873 0.9919 0.112 0.7337 0.8609 0.3049 ] Network output: [ -0.002614 0.01232 1.005 4.494e-06 -2.018e-06 0.9883 3.387e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09465 0.09271 0.1649 0.1967 0.9852 0.9911 0.09466 0.6575 0.8359 0.2497 ] Network output: [ 7.955e-05 1 -4.945e-05 5.875e-07 -2.637e-07 0.9998 4.427e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001571 Epoch 9876 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008724 0.9968 0.9927 -1.562e-07 7.013e-08 -0.006956 -1.177e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00352 -0.003369 -0.006583 0.005325 0.9699 0.9743 0.006869 0.8239 0.8194 0.0161 ] Network output: [ 0.9999 8.419e-05 0.0003463 -2.137e-06 9.594e-07 -0.0002939 -1.611e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2097 -0.03577 -0.1554 0.182 0.9834 0.9932 0.2355 0.4285 0.868 0.708 ] Network output: [ -0.008736 1.003 1.008 -1.679e-07 7.539e-08 0.007271 -1.266e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006889 0.0006382 0.004351 0.003131 0.9889 0.9919 0.007025 0.8511 0.8917 0.01148 ] Network output: [ -0.0001598 0.001302 1 -6.714e-06 3.014e-06 0.9985 -5.06e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2236 0.1062 0.35 0.1417 0.9849 0.9939 0.2244 0.4324 0.8748 0.7016 ] Network output: [ 0.002777 -0.0133 0.9944 4.098e-06 -1.84e-06 1.013 3.089e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09915 0.1849 0.197 0.9873 0.9919 0.112 0.7337 0.8609 0.3049 ] Network output: [ -0.002613 0.01231 1.005 4.489e-06 -2.015e-06 0.9883 3.383e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09465 0.09271 0.1649 0.1967 0.9852 0.9911 0.09466 0.6575 0.8359 0.2497 ] Network output: [ 7.953e-05 1 -4.946e-05 5.868e-07 -2.634e-07 0.9998 4.422e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000157 Epoch 9877 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008723 0.9968 0.9927 -1.561e-07 7.009e-08 -0.006956 -1.177e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00352 -0.003369 -0.006583 0.005324 0.9699 0.9743 0.006869 0.8239 0.8194 0.0161 ] Network output: [ 0.9999 8.404e-05 0.0003461 -2.134e-06 9.582e-07 -0.0002937 -1.609e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2097 -0.03577 -0.1553 0.182 0.9834 0.9932 0.2355 0.4285 0.868 0.708 ] Network output: [ -0.008735 1.003 1.008 -1.678e-07 7.533e-08 0.00727 -1.265e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00689 0.0006382 0.004351 0.003131 0.9889 0.9919 0.007025 0.8511 0.8917 0.01148 ] Network output: [ -0.0001596 0.001301 1 -6.705e-06 3.01e-06 0.9985 -5.053e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2236 0.1062 0.35 0.1417 0.9849 0.9939 0.2244 0.4324 0.8748 0.7016 ] Network output: [ 0.002776 -0.0133 0.9944 4.093e-06 -1.838e-06 1.013 3.085e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09915 0.1849 0.197 0.9873 0.9919 0.112 0.7337 0.8609 0.3049 ] Network output: [ -0.002612 0.01231 1.005 4.483e-06 -2.013e-06 0.9883 3.379e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09465 0.09271 0.1649 0.1967 0.9852 0.9911 0.09467 0.6575 0.8359 0.2497 ] Network output: [ 7.951e-05 1 -4.946e-05 5.86e-07 -2.631e-07 0.9998 4.417e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001569 Epoch 9878 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008722 0.9968 0.9927 -1.56e-07 7.004e-08 -0.006955 -1.176e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00352 -0.003369 -0.006582 0.005324 0.9699 0.9743 0.006869 0.8239 0.8194 0.0161 ] Network output: [ 0.9999 8.389e-05 0.0003459 -2.132e-06 9.571e-07 -0.0002935 -1.607e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2097 -0.03577 -0.1553 0.182 0.9834 0.9932 0.2355 0.4285 0.868 0.708 ] Network output: [ -0.008734 1.003 1.008 -1.677e-07 7.526e-08 0.00727 -1.263e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00689 0.0006383 0.00435 0.003131 0.9889 0.9919 0.007025 0.8511 0.8917 0.01148 ] Network output: [ -0.0001595 0.0013 1 -6.697e-06 3.007e-06 0.9985 -5.047e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2236 0.1062 0.35 0.1417 0.9849 0.9939 0.2244 0.4324 0.8748 0.7016 ] Network output: [ 0.002774 -0.01329 0.9944 4.088e-06 -1.835e-06 1.013 3.081e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09915 0.1849 0.197 0.9873 0.9919 0.112 0.7337 0.8609 0.3049 ] Network output: [ -0.002611 0.0123 1.005 4.478e-06 -2.01e-06 0.9883 3.375e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09465 0.09271 0.1649 0.1967 0.9852 0.9911 0.09467 0.6574 0.8359 0.2497 ] Network output: [ 7.949e-05 1 -4.946e-05 5.853e-07 -2.628e-07 0.9998 4.411e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001568 Epoch 9879 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008721 0.9968 0.9927 -1.559e-07 6.999e-08 -0.006954 -1.175e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00352 -0.003369 -0.006582 0.005324 0.9699 0.9743 0.00687 0.8239 0.8194 0.01609 ] Network output: [ 0.9999 8.374e-05 0.0003458 -2.129e-06 9.559e-07 -0.0002933 -1.605e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2097 -0.03577 -0.1553 0.182 0.9834 0.9932 0.2355 0.4285 0.868 0.708 ] Network output: [ -0.008733 1.003 1.008 -1.675e-07 7.52e-08 0.007269 -1.262e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00689 0.0006383 0.00435 0.00313 0.9889 0.9919 0.007026 0.8511 0.8917 0.01148 ] Network output: [ -0.0001593 0.0013 1 -6.689e-06 3.003e-06 0.9985 -5.041e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2237 0.1062 0.35 0.1417 0.9849 0.9939 0.2244 0.4324 0.8748 0.7016 ] Network output: [ 0.002773 -0.01328 0.9944 4.083e-06 -1.833e-06 1.013 3.077e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09915 0.1849 0.197 0.9873 0.9919 0.112 0.7336 0.8609 0.3049 ] Network output: [ -0.002609 0.01229 1.005 4.473e-06 -2.008e-06 0.9883 3.371e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09465 0.09271 0.1649 0.1967 0.9852 0.9911 0.09467 0.6574 0.8359 0.2497 ] Network output: [ 7.947e-05 1 -4.946e-05 5.846e-07 -2.625e-07 0.9998 4.406e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001567 Epoch 9880 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00872 0.9968 0.9927 -1.558e-07 6.994e-08 -0.006954 -1.174e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00352 -0.003369 -0.006581 0.005323 0.9699 0.9743 0.00687 0.8239 0.8194 0.01609 ] Network output: [ 0.9999 8.359e-05 0.0003456 -2.127e-06 9.547e-07 -0.0002931 -1.603e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2097 -0.03577 -0.1553 0.182 0.9834 0.9932 0.2355 0.4285 0.868 0.708 ] Network output: [ -0.008732 1.003 1.008 -1.674e-07 7.514e-08 0.007268 -1.261e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006891 0.0006384 0.00435 0.00313 0.9889 0.9919 0.007026 0.8511 0.8917 0.01148 ] Network output: [ -0.0001592 0.001299 1 -6.681e-06 2.999e-06 0.9985 -5.035e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2237 0.1062 0.35 0.1417 0.9849 0.9939 0.2244 0.4324 0.8748 0.7016 ] Network output: [ 0.002772 -0.01328 0.9944 4.078e-06 -1.831e-06 1.013 3.074e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09916 0.1849 0.197 0.9873 0.9919 0.112 0.7336 0.8609 0.3049 ] Network output: [ -0.002608 0.01229 1.005 4.467e-06 -2.006e-06 0.9883 3.367e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09466 0.09272 0.1649 0.1967 0.9852 0.9911 0.09467 0.6574 0.8359 0.2497 ] Network output: [ 7.945e-05 1 -4.946e-05 5.839e-07 -2.621e-07 0.9998 4.401e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001566 Epoch 9881 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00872 0.9968 0.9927 -1.557e-07 6.989e-08 -0.006953 -1.173e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00352 -0.003369 -0.006581 0.005323 0.9699 0.9743 0.00687 0.8239 0.8194 0.01609 ] Network output: [ 0.9999 8.344e-05 0.0003455 -2.124e-06 9.536e-07 -0.000293 -1.601e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2097 -0.03577 -0.1553 0.182 0.9834 0.9932 0.2355 0.4285 0.868 0.708 ] Network output: [ -0.008732 1.003 1.008 -1.672e-07 7.508e-08 0.007268 -1.26e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006891 0.0006384 0.00435 0.00313 0.9889 0.9919 0.007027 0.8511 0.8917 0.01148 ] Network output: [ -0.000159 0.001298 1 -6.673e-06 2.996e-06 0.9985 -5.029e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2237 0.1063 0.35 0.1417 0.9849 0.9939 0.2244 0.4324 0.8748 0.7016 ] Network output: [ 0.00277 -0.01327 0.9944 4.073e-06 -1.829e-06 1.013 3.07e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09916 0.1849 0.197 0.9873 0.9919 0.112 0.7336 0.8609 0.3049 ] Network output: [ -0.002607 0.01228 1.005 4.462e-06 -2.003e-06 0.9883 3.363e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09466 0.09272 0.1649 0.1967 0.9852 0.9911 0.09467 0.6574 0.8359 0.2497 ] Network output: [ 7.943e-05 1 -4.947e-05 5.832e-07 -2.618e-07 0.9998 4.395e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001565 Epoch 9882 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008719 0.9968 0.9927 -1.556e-07 6.984e-08 -0.006953 -1.172e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00352 -0.003369 -0.00658 0.005323 0.9699 0.9743 0.00687 0.8239 0.8194 0.01609 ] Network output: [ 0.9999 8.329e-05 0.0003453 -2.121e-06 9.524e-07 -0.0002928 -1.599e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2097 -0.03577 -0.1553 0.182 0.9834 0.9932 0.2355 0.4285 0.868 0.708 ] Network output: [ -0.008731 1.003 1.008 -1.671e-07 7.502e-08 0.007267 -1.259e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006891 0.0006385 0.00435 0.00313 0.9889 0.9919 0.007027 0.8511 0.8917 0.01148 ] Network output: [ -0.0001589 0.001297 1 -6.664e-06 2.992e-06 0.9985 -5.023e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2237 0.1063 0.35 0.1417 0.9849 0.9939 0.2244 0.4324 0.8748 0.7016 ] Network output: [ 0.002769 -0.01327 0.9944 4.068e-06 -1.826e-06 1.013 3.066e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09916 0.1849 0.197 0.9873 0.9919 0.112 0.7336 0.8609 0.3049 ] Network output: [ -0.002605 0.01228 1.005 4.457e-06 -2.001e-06 0.9883 3.359e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09466 0.09272 0.1649 0.1967 0.9852 0.9911 0.09467 0.6574 0.8359 0.2497 ] Network output: [ 7.942e-05 1 -4.947e-05 5.825e-07 -2.615e-07 0.9998 4.39e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001564 Epoch 9883 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008718 0.9968 0.9927 -1.555e-07 6.98e-08 -0.006952 -1.172e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00352 -0.003369 -0.00658 0.005322 0.9699 0.9743 0.00687 0.8239 0.8194 0.01609 ] Network output: [ 0.9999 8.314e-05 0.0003452 -2.119e-06 9.512e-07 -0.0002926 -1.597e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2097 -0.03577 -0.1553 0.182 0.9834 0.9932 0.2355 0.4285 0.868 0.708 ] Network output: [ -0.00873 1.003 1.008 -1.67e-07 7.496e-08 0.007267 -1.258e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006892 0.0006385 0.00435 0.003129 0.9889 0.9919 0.007027 0.8511 0.8917 0.01147 ] Network output: [ -0.0001587 0.001297 1 -6.656e-06 2.988e-06 0.9985 -5.016e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2237 0.1063 0.35 0.1417 0.9849 0.9939 0.2244 0.4324 0.8748 0.7016 ] Network output: [ 0.002767 -0.01326 0.9944 4.064e-06 -1.824e-06 1.013 3.062e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09917 0.1849 0.197 0.9873 0.9919 0.112 0.7336 0.8609 0.3049 ] Network output: [ -0.002604 0.01227 1.005 4.451e-06 -1.998e-06 0.9883 3.355e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09466 0.09272 0.1649 0.1967 0.9852 0.9911 0.09468 0.6574 0.8359 0.2497 ] Network output: [ 7.94e-05 1 -4.947e-05 5.818e-07 -2.612e-07 0.9998 4.385e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001564 Epoch 9884 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008717 0.9968 0.9927 -1.554e-07 6.975e-08 -0.006951 -1.171e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00352 -0.003369 -0.006579 0.005322 0.9699 0.9743 0.00687 0.8239 0.8194 0.01609 ] Network output: [ 0.9999 8.299e-05 0.000345 -2.116e-06 9.501e-07 -0.0002924 -1.595e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2097 -0.03578 -0.1553 0.182 0.9834 0.9932 0.2355 0.4285 0.868 0.708 ] Network output: [ -0.008729 1.003 1.008 -1.668e-07 7.489e-08 0.007266 -1.257e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006892 0.0006386 0.00435 0.003129 0.9889 0.9919 0.007028 0.8511 0.8917 0.01147 ] Network output: [ -0.0001586 0.001296 1 -6.648e-06 2.985e-06 0.9985 -5.01e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2237 0.1063 0.35 0.1417 0.9849 0.9939 0.2245 0.4324 0.8748 0.7016 ] Network output: [ 0.002766 -0.01325 0.9944 4.059e-06 -1.822e-06 1.013 3.059e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09917 0.1849 0.197 0.9873 0.9919 0.112 0.7336 0.8609 0.3049 ] Network output: [ -0.002603 0.01226 1.005 4.446e-06 -1.996e-06 0.9883 3.351e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09466 0.09272 0.1649 0.1967 0.9852 0.9911 0.09468 0.6574 0.8359 0.2497 ] Network output: [ 7.938e-05 1 -4.947e-05 5.811e-07 -2.609e-07 0.9998 4.379e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001563 Epoch 9885 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008716 0.9968 0.9927 -1.553e-07 6.97e-08 -0.006951 -1.17e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00352 -0.003369 -0.006578 0.005321 0.9699 0.9743 0.00687 0.8239 0.8194 0.01609 ] Network output: [ 0.9999 8.284e-05 0.0003449 -2.114e-06 9.489e-07 -0.0002923 -1.593e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2097 -0.03578 -0.1553 0.1819 0.9834 0.9932 0.2355 0.4284 0.868 0.7079 ] Network output: [ -0.008728 1.003 1.008 -1.667e-07 7.483e-08 0.007266 -1.256e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006892 0.0006387 0.00435 0.003129 0.9889 0.9919 0.007028 0.8511 0.8917 0.01147 ] Network output: [ -0.0001585 0.001295 1 -6.64e-06 2.981e-06 0.9985 -5.004e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2237 0.1063 0.35 0.1417 0.9849 0.9939 0.2245 0.4324 0.8748 0.7016 ] Network output: [ 0.002764 -0.01325 0.9944 4.054e-06 -1.82e-06 1.013 3.055e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09917 0.1849 0.197 0.9873 0.9919 0.112 0.7336 0.8608 0.3049 ] Network output: [ -0.002601 0.01226 1.005 4.441e-06 -1.994e-06 0.9883 3.347e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09466 0.09272 0.1649 0.1967 0.9852 0.9911 0.09468 0.6574 0.8359 0.2497 ] Network output: [ 7.936e-05 1 -4.948e-05 5.804e-07 -2.606e-07 0.9998 4.374e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001562 Epoch 9886 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008715 0.9968 0.9927 -1.551e-07 6.965e-08 -0.00695 -1.169e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00352 -0.003369 -0.006578 0.005321 0.9699 0.9743 0.006871 0.8239 0.8194 0.01609 ] Network output: [ 0.9999 8.269e-05 0.0003447 -2.111e-06 9.477e-07 -0.0002921 -1.591e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2097 -0.03578 -0.1553 0.1819 0.9834 0.9932 0.2355 0.4284 0.868 0.7079 ] Network output: [ -0.008728 1.003 1.007 -1.666e-07 7.477e-08 0.007265 -1.255e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006893 0.0006387 0.00435 0.003129 0.9889 0.9919 0.007028 0.8511 0.8917 0.01147 ] Network output: [ -0.0001583 0.001295 1 -6.632e-06 2.977e-06 0.9985 -4.998e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2237 0.1063 0.35 0.1417 0.9849 0.9939 0.2245 0.4324 0.8748 0.7016 ] Network output: [ 0.002763 -0.01324 0.9944 4.049e-06 -1.818e-06 1.013 3.051e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09917 0.1849 0.197 0.9873 0.9919 0.112 0.7336 0.8608 0.3049 ] Network output: [ -0.0026 0.01225 1.005 4.435e-06 -1.991e-06 0.9883 3.343e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09467 0.09273 0.1649 0.1967 0.9852 0.9911 0.09468 0.6574 0.8359 0.2497 ] Network output: [ 7.934e-05 1 -4.948e-05 5.797e-07 -2.603e-07 0.9998 4.369e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001561 Epoch 9887 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008714 0.9968 0.9927 -1.55e-07 6.96e-08 -0.006949 -1.168e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00352 -0.003369 -0.006577 0.005321 0.9699 0.9743 0.006871 0.8239 0.8194 0.01609 ] Network output: [ 0.9999 8.254e-05 0.0003446 -2.108e-06 9.466e-07 -0.0002919 -1.589e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2097 -0.03578 -0.1553 0.1819 0.9834 0.9932 0.2355 0.4284 0.868 0.7079 ] Network output: [ -0.008727 1.003 1.007 -1.664e-07 7.471e-08 0.007265 -1.254e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006893 0.0006388 0.004349 0.003128 0.9889 0.9919 0.007029 0.8511 0.8917 0.01147 ] Network output: [ -0.0001582 0.001294 1 -6.624e-06 2.974e-06 0.9985 -4.992e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2237 0.1063 0.35 0.1417 0.9849 0.9939 0.2245 0.4324 0.8748 0.7016 ] Network output: [ 0.002762 -0.01323 0.9944 4.044e-06 -1.815e-06 1.013 3.048e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09918 0.1849 0.197 0.9873 0.9919 0.112 0.7336 0.8608 0.3049 ] Network output: [ -0.002599 0.01225 1.005 4.43e-06 -1.989e-06 0.9883 3.339e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09467 0.09273 0.1649 0.1967 0.9852 0.9911 0.09468 0.6574 0.8359 0.2497 ] Network output: [ 7.932e-05 1 -4.948e-05 5.79e-07 -2.599e-07 0.9998 4.364e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000156 Epoch 9888 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008713 0.9968 0.9927 -1.549e-07 6.955e-08 -0.006949 -1.168e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00352 -0.003369 -0.006577 0.00532 0.9699 0.9743 0.006871 0.8239 0.8194 0.01609 ] Network output: [ 0.9999 8.239e-05 0.0003444 -2.106e-06 9.454e-07 -0.0002917 -1.587e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2098 -0.03578 -0.1553 0.1819 0.9834 0.9932 0.2355 0.4284 0.868 0.7079 ] Network output: [ -0.008726 1.003 1.007 -1.663e-07 7.465e-08 0.007264 -1.253e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006893 0.0006388 0.004349 0.003128 0.9889 0.9919 0.007029 0.8511 0.8917 0.01147 ] Network output: [ -0.000158 0.001293 1 -6.616e-06 2.97e-06 0.9985 -4.986e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2237 0.1063 0.35 0.1417 0.9849 0.9939 0.2245 0.4324 0.8748 0.7016 ] Network output: [ 0.00276 -0.01323 0.9944 4.039e-06 -1.813e-06 1.013 3.044e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09918 0.1849 0.197 0.9873 0.9919 0.112 0.7336 0.8608 0.3049 ] Network output: [ -0.002597 0.01224 1.005 4.425e-06 -1.986e-06 0.9883 3.335e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09467 0.09273 0.1649 0.1967 0.9852 0.9911 0.09468 0.6573 0.8359 0.2497 ] Network output: [ 7.93e-05 1 -4.948e-05 5.783e-07 -2.596e-07 0.9998 4.358e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001559 Epoch 9889 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008713 0.9968 0.9927 -1.548e-07 6.951e-08 -0.006948 -1.167e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00352 -0.00337 -0.006576 0.00532 0.9699 0.9743 0.006871 0.8239 0.8194 0.01609 ] Network output: [ 0.9999 8.224e-05 0.0003443 -2.103e-06 9.443e-07 -0.0002916 -1.585e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2098 -0.03578 -0.1552 0.1819 0.9834 0.9932 0.2355 0.4284 0.868 0.7079 ] Network output: [ -0.008725 1.003 1.007 -1.661e-07 7.459e-08 0.007264 -1.252e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006894 0.0006389 0.004349 0.003128 0.9889 0.9919 0.007029 0.8511 0.8917 0.01147 ] Network output: [ -0.0001579 0.001293 1 -6.608e-06 2.966e-06 0.9985 -4.98e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2237 0.1063 0.35 0.1417 0.9849 0.9939 0.2245 0.4324 0.8748 0.7016 ] Network output: [ 0.002759 -0.01322 0.9944 4.034e-06 -1.811e-06 1.013 3.04e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09918 0.1849 0.197 0.9873 0.9919 0.112 0.7336 0.8608 0.3049 ] Network output: [ -0.002596 0.01224 1.005 4.419e-06 -1.984e-06 0.9883 3.331e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09467 0.09273 0.1649 0.1967 0.9852 0.9911 0.09469 0.6573 0.8359 0.2497 ] Network output: [ 7.928e-05 1 -4.949e-05 5.776e-07 -2.593e-07 0.9998 4.353e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001558 Epoch 9890 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008712 0.9968 0.9927 -1.547e-07 6.946e-08 -0.006947 -1.166e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00352 -0.00337 -0.006576 0.00532 0.9699 0.9743 0.006871 0.8239 0.8194 0.01609 ] Network output: [ 0.9999 8.209e-05 0.0003441 -2.101e-06 9.431e-07 -0.0002914 -1.583e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2098 -0.03578 -0.1552 0.1819 0.9834 0.9932 0.2356 0.4284 0.868 0.7079 ] Network output: [ -0.008724 1.003 1.007 -1.66e-07 7.452e-08 0.007263 -1.251e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006894 0.0006389 0.004349 0.003128 0.9889 0.9919 0.00703 0.8511 0.8917 0.01147 ] Network output: [ -0.0001577 0.001292 1 -6.6e-06 2.963e-06 0.9985 -4.974e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2237 0.1063 0.35 0.1417 0.9849 0.9939 0.2245 0.4324 0.8748 0.7016 ] Network output: [ 0.002757 -0.01321 0.9944 4.029e-06 -1.809e-06 1.013 3.036e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09918 0.1849 0.197 0.9873 0.9919 0.112 0.7335 0.8608 0.3049 ] Network output: [ -0.002595 0.01223 1.005 4.414e-06 -1.982e-06 0.9883 3.327e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09467 0.09273 0.1649 0.1967 0.9852 0.9911 0.09469 0.6573 0.8359 0.2497 ] Network output: [ 7.926e-05 1 -4.949e-05 5.769e-07 -2.59e-07 0.9998 4.348e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001558 Epoch 9891 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008711 0.9968 0.9927 -1.546e-07 6.941e-08 -0.006947 -1.165e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00352 -0.00337 -0.006575 0.005319 0.9699 0.9743 0.006871 0.8239 0.8194 0.01608 ] Network output: [ 0.9999 8.194e-05 0.000344 -2.098e-06 9.419e-07 -0.0002912 -1.581e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2098 -0.03578 -0.1552 0.1819 0.9834 0.9932 0.2356 0.4284 0.868 0.7079 ] Network output: [ -0.008724 1.003 1.007 -1.659e-07 7.446e-08 0.007263 -1.25e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006894 0.000639 0.004349 0.003128 0.9889 0.9919 0.00703 0.8511 0.8917 0.01147 ] Network output: [ -0.0001576 0.001291 1 -6.591e-06 2.959e-06 0.9985 -4.968e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2237 0.1063 0.35 0.1417 0.9849 0.9939 0.2245 0.4324 0.8748 0.7016 ] Network output: [ 0.002756 -0.01321 0.9944 4.024e-06 -1.807e-06 1.013 3.033e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09919 0.1849 0.197 0.9873 0.9919 0.112 0.7335 0.8608 0.3049 ] Network output: [ -0.002594 0.01222 1.005 4.409e-06 -1.979e-06 0.9883 3.323e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09467 0.09273 0.1649 0.1967 0.9852 0.9911 0.09469 0.6573 0.8359 0.2497 ] Network output: [ 7.925e-05 1 -4.949e-05 5.762e-07 -2.587e-07 0.9998 4.343e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001557 Epoch 9892 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00871 0.9968 0.9927 -1.545e-07 6.936e-08 -0.006946 -1.164e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00352 -0.00337 -0.006575 0.005319 0.9699 0.9743 0.006871 0.8239 0.8194 0.01608 ] Network output: [ 0.9999 8.179e-05 0.0003438 -2.096e-06 9.408e-07 -0.000291 -1.579e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2098 -0.03578 -0.1552 0.1819 0.9834 0.9932 0.2356 0.4284 0.868 0.7079 ] Network output: [ -0.008723 1.003 1.007 -1.657e-07 7.44e-08 0.007262 -1.249e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006895 0.000639 0.004349 0.003127 0.9889 0.9919 0.00703 0.8511 0.8917 0.01147 ] Network output: [ -0.0001574 0.001291 1 -6.583e-06 2.956e-06 0.9985 -4.961e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2237 0.1063 0.35 0.1417 0.9849 0.9939 0.2245 0.4324 0.8748 0.7016 ] Network output: [ 0.002754 -0.0132 0.9944 4.019e-06 -1.804e-06 1.013 3.029e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09919 0.1849 0.197 0.9873 0.9919 0.112 0.7335 0.8608 0.3049 ] Network output: [ -0.002592 0.01222 1.005 4.404e-06 -1.977e-06 0.9883 3.319e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09468 0.09274 0.1649 0.1967 0.9852 0.9911 0.09469 0.6573 0.8359 0.2497 ] Network output: [ 7.923e-05 1 -4.95e-05 5.755e-07 -2.584e-07 0.9998 4.337e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001556 Epoch 9893 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008709 0.9968 0.9927 -1.544e-07 6.931e-08 -0.006946 -1.164e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00352 -0.00337 -0.006574 0.005318 0.9699 0.9743 0.006872 0.8239 0.8194 0.01608 ] Network output: [ 0.9999 8.164e-05 0.0003437 -2.093e-06 9.396e-07 -0.0002908 -1.577e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2098 -0.03578 -0.1552 0.1819 0.9834 0.9932 0.2356 0.4284 0.868 0.7079 ] Network output: [ -0.008722 1.003 1.007 -1.656e-07 7.434e-08 0.007262 -1.248e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006895 0.0006391 0.004349 0.003127 0.9889 0.9919 0.007031 0.8511 0.8917 0.01147 ] Network output: [ -0.0001573 0.00129 1 -6.575e-06 2.952e-06 0.9985 -4.955e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2237 0.1063 0.35 0.1417 0.9849 0.9939 0.2245 0.4324 0.8748 0.7016 ] Network output: [ 0.002753 -0.01319 0.9944 4.014e-06 -1.802e-06 1.013 3.025e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09919 0.1849 0.197 0.9873 0.9919 0.112 0.7335 0.8608 0.3049 ] Network output: [ -0.002591 0.01221 1.005 4.398e-06 -1.975e-06 0.9883 3.315e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09468 0.09274 0.1649 0.1967 0.9852 0.9911 0.09469 0.6573 0.8359 0.2497 ] Network output: [ 7.921e-05 1 -4.95e-05 5.748e-07 -2.581e-07 0.9998 4.332e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001555 Epoch 9894 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008708 0.9968 0.9927 -1.543e-07 6.926e-08 -0.006945 -1.163e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00352 -0.00337 -0.006573 0.005318 0.9699 0.9743 0.006872 0.8239 0.8194 0.01608 ] Network output: [ 0.9999 8.149e-05 0.0003435 -2.09e-06 9.385e-07 -0.0002907 -1.575e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2098 -0.03579 -0.1552 0.1819 0.9834 0.9932 0.2356 0.4284 0.868 0.7079 ] Network output: [ -0.008721 1.003 1.007 -1.655e-07 7.428e-08 0.007261 -1.247e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006895 0.0006392 0.004349 0.003127 0.9889 0.9919 0.007031 0.8511 0.8917 0.01147 ] Network output: [ -0.0001572 0.001289 1 -6.567e-06 2.948e-06 0.9985 -4.949e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2237 0.1063 0.35 0.1417 0.9849 0.9939 0.2245 0.4324 0.8748 0.7016 ] Network output: [ 0.002751 -0.01319 0.9944 4.009e-06 -1.8e-06 1.013 3.022e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.0992 0.1849 0.197 0.9873 0.9919 0.112 0.7335 0.8608 0.3049 ] Network output: [ -0.00259 0.01221 1.005 4.393e-06 -1.972e-06 0.9883 3.311e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09468 0.09274 0.1649 0.1967 0.9852 0.9911 0.09469 0.6573 0.8359 0.2497 ] Network output: [ 7.919e-05 1 -4.95e-05 5.741e-07 -2.578e-07 0.9998 4.327e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001554 Epoch 9895 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008707 0.9968 0.9927 -1.542e-07 6.922e-08 -0.006944 -1.162e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.00337 -0.006573 0.005318 0.9699 0.9743 0.006872 0.8239 0.8194 0.01608 ] Network output: [ 0.9999 8.134e-05 0.0003434 -2.088e-06 9.373e-07 -0.0002905 -1.573e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2098 -0.03579 -0.1552 0.1819 0.9834 0.9932 0.2356 0.4284 0.868 0.7079 ] Network output: [ -0.00872 1.003 1.007 -1.653e-07 7.422e-08 0.00726 -1.246e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006896 0.0006392 0.004349 0.003127 0.9889 0.9919 0.007031 0.8511 0.8917 0.01147 ] Network output: [ -0.000157 0.001288 1 -6.559e-06 2.945e-06 0.9985 -4.943e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2238 0.1063 0.35 0.1417 0.9849 0.9939 0.2245 0.4324 0.8748 0.7016 ] Network output: [ 0.00275 -0.01318 0.9944 4.005e-06 -1.798e-06 1.013 3.018e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.0992 0.1849 0.197 0.9873 0.9919 0.112 0.7335 0.8608 0.3049 ] Network output: [ -0.002588 0.0122 1.005 4.388e-06 -1.97e-06 0.9883 3.307e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09468 0.09274 0.1649 0.1967 0.9852 0.9911 0.09469 0.6573 0.8359 0.2497 ] Network output: [ 7.917e-05 1 -4.95e-05 5.734e-07 -2.574e-07 0.9998 4.322e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001553 Epoch 9896 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008707 0.9968 0.9927 -1.541e-07 6.917e-08 -0.006944 -1.161e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.00337 -0.006572 0.005317 0.9699 0.9743 0.006872 0.8239 0.8194 0.01608 ] Network output: [ 0.9999 8.119e-05 0.0003432 -2.085e-06 9.362e-07 -0.0002903 -1.572e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2098 -0.03579 -0.1552 0.1819 0.9834 0.9932 0.2356 0.4284 0.868 0.7079 ] Network output: [ -0.00872 1.003 1.007 -1.652e-07 7.416e-08 0.00726 -1.245e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006896 0.0006393 0.004349 0.003126 0.9889 0.9919 0.007032 0.851 0.8917 0.01147 ] Network output: [ -0.0001569 0.001288 1 -6.551e-06 2.941e-06 0.9985 -4.937e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2238 0.1063 0.3501 0.1417 0.9849 0.9939 0.2245 0.4324 0.8748 0.7016 ] Network output: [ 0.002749 -0.01318 0.9944 4e-06 -1.796e-06 1.013 3.014e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.0992 0.1849 0.197 0.9873 0.9919 0.112 0.7335 0.8608 0.3049 ] Network output: [ -0.002587 0.0122 1.005 4.382e-06 -1.967e-06 0.9883 3.303e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09468 0.09274 0.1649 0.1967 0.9852 0.9911 0.0947 0.6573 0.8359 0.2497 ] Network output: [ 7.915e-05 1 -4.951e-05 5.728e-07 -2.571e-07 0.9998 4.316e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001552 Epoch 9897 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008706 0.9968 0.9927 -1.54e-07 6.912e-08 -0.006943 -1.16e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.00337 -0.006572 0.005317 0.9699 0.9743 0.006872 0.8239 0.8194 0.01608 ] Network output: [ 0.9999 8.104e-05 0.0003431 -2.083e-06 9.35e-07 -0.0002901 -1.57e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2098 -0.03579 -0.1552 0.1819 0.9834 0.9932 0.2356 0.4284 0.868 0.7079 ] Network output: [ -0.008719 1.003 1.007 -1.65e-07 7.409e-08 0.007259 -1.244e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006896 0.0006393 0.004348 0.003126 0.9889 0.9919 0.007032 0.851 0.8917 0.01146 ] Network output: [ -0.0001567 0.001287 1 -6.543e-06 2.938e-06 0.9985 -4.931e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2238 0.1063 0.3501 0.1417 0.9849 0.9939 0.2245 0.4323 0.8748 0.7016 ] Network output: [ 0.002747 -0.01317 0.9944 3.995e-06 -1.793e-06 1.013 3.011e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.0992 0.1849 0.197 0.9873 0.9919 0.112 0.7335 0.8608 0.3049 ] Network output: [ -0.002586 0.01219 1.005 4.377e-06 -1.965e-06 0.9883 3.299e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09468 0.09274 0.1649 0.1967 0.9852 0.9911 0.0947 0.6573 0.8359 0.2497 ] Network output: [ 7.913e-05 1 -4.951e-05 5.721e-07 -2.568e-07 0.9998 4.311e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001552 Epoch 9898 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008705 0.9968 0.9927 -1.539e-07 6.907e-08 -0.006942 -1.159e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.00337 -0.006571 0.005317 0.9699 0.9743 0.006872 0.8239 0.8194 0.01608 ] Network output: [ 0.9999 8.089e-05 0.0003429 -2.08e-06 9.339e-07 -0.00029 -1.568e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2098 -0.03579 -0.1552 0.1819 0.9834 0.9932 0.2356 0.4284 0.868 0.7079 ] Network output: [ -0.008718 1.003 1.007 -1.649e-07 7.403e-08 0.007259 -1.243e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006897 0.0006394 0.004348 0.003126 0.9889 0.9919 0.007032 0.851 0.8917 0.01146 ] Network output: [ -0.0001566 0.001286 1 -6.535e-06 2.934e-06 0.9985 -4.925e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2238 0.1063 0.3501 0.1417 0.9849 0.9939 0.2245 0.4323 0.8748 0.7016 ] Network output: [ 0.002746 -0.01316 0.9944 3.99e-06 -1.791e-06 1.013 3.007e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1119 0.09921 0.1849 0.197 0.9873 0.9919 0.112 0.7335 0.8608 0.3049 ] Network output: [ -0.002584 0.01218 1.005 4.372e-06 -1.963e-06 0.9883 3.295e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09469 0.09274 0.1649 0.1967 0.9852 0.9911 0.0947 0.6572 0.8359 0.2497 ] Network output: [ 7.912e-05 1 -4.951e-05 5.714e-07 -2.565e-07 0.9998 4.306e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001551 Epoch 9899 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008704 0.9968 0.9927 -1.537e-07 6.902e-08 -0.006942 -1.159e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.00337 -0.006571 0.005316 0.9699 0.9743 0.006873 0.8239 0.8194 0.01608 ] Network output: [ 0.9999 8.074e-05 0.0003428 -2.078e-06 9.327e-07 -0.0002898 -1.566e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2098 -0.03579 -0.1552 0.1819 0.9834 0.9932 0.2356 0.4284 0.868 0.7079 ] Network output: [ -0.008717 1.003 1.007 -1.648e-07 7.397e-08 0.007258 -1.242e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006897 0.0006394 0.004348 0.003126 0.9889 0.9919 0.007033 0.851 0.8917 0.01146 ] Network output: [ -0.0001564 0.001286 1 -6.527e-06 2.93e-06 0.9985 -4.919e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2238 0.1063 0.3501 0.1417 0.9849 0.9939 0.2245 0.4323 0.8748 0.7016 ] Network output: [ 0.002744 -0.01316 0.9944 3.985e-06 -1.789e-06 1.013 3.003e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09921 0.1849 0.197 0.9873 0.9919 0.112 0.7335 0.8608 0.3049 ] Network output: [ -0.002583 0.01218 1.005 4.367e-06 -1.96e-06 0.9883 3.291e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09469 0.09275 0.1649 0.1967 0.9852 0.9911 0.0947 0.6572 0.8359 0.2497 ] Network output: [ 7.91e-05 1 -4.952e-05 5.707e-07 -2.562e-07 0.9998 4.301e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000155 Epoch 9900 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008703 0.9968 0.9927 -1.536e-07 6.897e-08 -0.006941 -1.158e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.00337 -0.00657 0.005316 0.9699 0.9743 0.006873 0.8238 0.8194 0.01608 ] Network output: [ 0.9999 8.059e-05 0.0003426 -2.075e-06 9.316e-07 -0.0002896 -1.564e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2098 -0.03579 -0.1552 0.1819 0.9834 0.9932 0.2356 0.4284 0.868 0.7079 ] Network output: [ -0.008716 1.003 1.007 -1.646e-07 7.391e-08 0.007258 -1.241e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006897 0.0006395 0.004348 0.003126 0.9889 0.9919 0.007033 0.851 0.8917 0.01146 ] Network output: [ -0.0001563 0.001285 1 -6.519e-06 2.927e-06 0.9985 -4.913e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2238 0.1063 0.3501 0.1416 0.9849 0.9939 0.2246 0.4323 0.8748 0.7016 ] Network output: [ 0.002743 -0.01315 0.9944 3.98e-06 -1.787e-06 1.013 3e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09921 0.1849 0.197 0.9873 0.9919 0.112 0.7334 0.8608 0.3049 ] Network output: [ -0.002582 0.01217 1.005 4.361e-06 -1.958e-06 0.9883 3.287e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09469 0.09275 0.1649 0.1967 0.9852 0.9911 0.0947 0.6572 0.8359 0.2497 ] Network output: [ 7.908e-05 1 -4.952e-05 5.7e-07 -2.559e-07 0.9998 4.296e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001549 Epoch 9901 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008702 0.9968 0.9927 -1.535e-07 6.893e-08 -0.00694 -1.157e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.00337 -0.00657 0.005315 0.9699 0.9743 0.006873 0.8238 0.8194 0.01608 ] Network output: [ 0.9999 8.044e-05 0.0003425 -2.073e-06 9.305e-07 -0.0002895 -1.562e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2098 -0.03579 -0.1552 0.1819 0.9834 0.9932 0.2356 0.4284 0.868 0.7079 ] Network output: [ -0.008716 1.003 1.007 -1.645e-07 7.385e-08 0.007257 -1.24e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006898 0.0006395 0.004348 0.003125 0.9889 0.9919 0.007033 0.851 0.8917 0.01146 ] Network output: [ -0.0001562 0.001284 1 -6.511e-06 2.923e-06 0.9986 -4.907e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2238 0.1063 0.3501 0.1416 0.9849 0.9939 0.2246 0.4323 0.8748 0.7016 ] Network output: [ 0.002741 -0.01314 0.9944 3.975e-06 -1.785e-06 1.013 2.996e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09921 0.1849 0.197 0.9873 0.9919 0.112 0.7334 0.8608 0.3049 ] Network output: [ -0.00258 0.01217 1.005 4.356e-06 -1.956e-06 0.9883 3.283e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09469 0.09275 0.1649 0.1967 0.9852 0.9911 0.0947 0.6572 0.8359 0.2497 ] Network output: [ 7.906e-05 1 -4.953e-05 5.693e-07 -2.556e-07 0.9998 4.29e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001548 Epoch 9902 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008701 0.9968 0.9927 -1.534e-07 6.888e-08 -0.00694 -1.156e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.003371 -0.006569 0.005315 0.9699 0.9743 0.006873 0.8238 0.8194 0.01607 ] Network output: [ 0.9999 8.03e-05 0.0003423 -2.07e-06 9.293e-07 -0.0002893 -1.56e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2098 -0.03579 -0.1551 0.1819 0.9834 0.9932 0.2356 0.4284 0.868 0.7079 ] Network output: [ -0.008715 1.003 1.007 -1.644e-07 7.379e-08 0.007257 -1.239e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006898 0.0006396 0.004348 0.003125 0.9889 0.9919 0.007034 0.851 0.8917 0.01146 ] Network output: [ -0.000156 0.001284 1 -6.503e-06 2.92e-06 0.9986 -4.901e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2238 0.1063 0.3501 0.1416 0.9849 0.9939 0.2246 0.4323 0.8748 0.7016 ] Network output: [ 0.00274 -0.01314 0.9944 3.971e-06 -1.783e-06 1.013 2.992e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09922 0.1849 0.197 0.9873 0.9919 0.112 0.7334 0.8608 0.3049 ] Network output: [ -0.002579 0.01216 1.005 4.351e-06 -1.953e-06 0.9883 3.279e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09469 0.09275 0.1649 0.1967 0.9852 0.9911 0.09471 0.6572 0.8359 0.2497 ] Network output: [ 7.904e-05 1 -4.953e-05 5.686e-07 -2.553e-07 0.9998 4.285e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001547 Epoch 9903 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008701 0.9968 0.9927 -1.533e-07 6.883e-08 -0.006939 -1.155e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.003371 -0.006568 0.005315 0.9699 0.9743 0.006873 0.8238 0.8194 0.01607 ] Network output: [ 0.9999 8.015e-05 0.0003422 -2.067e-06 9.282e-07 -0.0002891 -1.558e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2098 -0.03579 -0.1551 0.1819 0.9834 0.9932 0.2356 0.4284 0.868 0.7079 ] Network output: [ -0.008714 1.003 1.007 -1.642e-07 7.373e-08 0.007256 -1.238e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006898 0.0006396 0.004348 0.003125 0.9889 0.9919 0.007034 0.851 0.8917 0.01146 ] Network output: [ -0.0001559 0.001283 1 -6.495e-06 2.916e-06 0.9986 -4.895e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2238 0.1063 0.3501 0.1416 0.9849 0.9939 0.2246 0.4323 0.8748 0.7016 ] Network output: [ 0.002738 -0.01313 0.9944 3.966e-06 -1.78e-06 1.013 2.989e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09922 0.1849 0.197 0.9873 0.9919 0.112 0.7334 0.8608 0.3049 ] Network output: [ -0.002578 0.01215 1.005 4.346e-06 -1.951e-06 0.9883 3.275e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09469 0.09275 0.1649 0.1967 0.9852 0.9911 0.09471 0.6572 0.8359 0.2497 ] Network output: [ 7.902e-05 1 -4.953e-05 5.679e-07 -2.55e-07 0.9998 4.28e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001546 Epoch 9904 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0087 0.9968 0.9927 -1.532e-07 6.878e-08 -0.006938 -1.155e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.003371 -0.006568 0.005314 0.9699 0.9743 0.006873 0.8238 0.8194 0.01607 ] Network output: [ 0.9999 8e-05 0.000342 -2.065e-06 9.27e-07 -0.0002889 -1.556e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2098 -0.03579 -0.1551 0.1819 0.9834 0.9932 0.2356 0.4284 0.868 0.7079 ] Network output: [ -0.008713 1.003 1.007 -1.641e-07 7.367e-08 0.007256 -1.237e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006899 0.0006397 0.004348 0.003125 0.9889 0.9919 0.007034 0.851 0.8917 0.01146 ] Network output: [ -0.0001557 0.001282 1 -6.487e-06 2.912e-06 0.9986 -4.889e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2238 0.1063 0.3501 0.1416 0.9849 0.9939 0.2246 0.4323 0.8748 0.7015 ] Network output: [ 0.002737 -0.01312 0.9944 3.961e-06 -1.778e-06 1.013 2.985e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09922 0.1849 0.197 0.9873 0.9919 0.112 0.7334 0.8608 0.3049 ] Network output: [ -0.002576 0.01215 1.005 4.341e-06 -1.949e-06 0.9883 3.271e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0947 0.09275 0.1649 0.1967 0.9852 0.9911 0.09471 0.6572 0.8359 0.2497 ] Network output: [ 7.9e-05 1 -4.954e-05 5.672e-07 -2.547e-07 0.9998 4.275e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001546 Epoch 9905 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008699 0.9968 0.9927 -1.531e-07 6.873e-08 -0.006938 -1.154e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.003371 -0.006567 0.005314 0.9699 0.9743 0.006873 0.8238 0.8194 0.01607 ] Network output: [ 0.9999 7.985e-05 0.0003419 -2.062e-06 9.259e-07 -0.0002888 -1.554e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2098 -0.0358 -0.1551 0.1819 0.9834 0.9932 0.2357 0.4284 0.868 0.7079 ] Network output: [ -0.008712 1.003 1.007 -1.64e-07 7.36e-08 0.007255 -1.236e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006899 0.0006398 0.004348 0.003124 0.9889 0.9919 0.007035 0.851 0.8917 0.01146 ] Network output: [ -0.0001556 0.001282 1 -6.479e-06 2.909e-06 0.9986 -4.883e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2238 0.1063 0.3501 0.1416 0.9849 0.9939 0.2246 0.4323 0.8748 0.7015 ] Network output: [ 0.002736 -0.01312 0.9944 3.956e-06 -1.776e-06 1.013 2.981e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09922 0.1849 0.197 0.9873 0.9919 0.112 0.7334 0.8608 0.3049 ] Network output: [ -0.002575 0.01214 1.005 4.335e-06 -1.946e-06 0.9884 3.267e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0947 0.09276 0.1649 0.1967 0.9852 0.9911 0.09471 0.6572 0.8358 0.2497 ] Network output: [ 7.899e-05 1 -4.954e-05 5.666e-07 -2.544e-07 0.9998 4.27e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001545 Epoch 9906 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008698 0.9968 0.9927 -1.53e-07 6.868e-08 -0.006937 -1.153e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.003371 -0.006567 0.005314 0.9699 0.9743 0.006874 0.8238 0.8194 0.01607 ] Network output: [ 0.9999 7.97e-05 0.0003417 -2.06e-06 9.248e-07 -0.0002886 -1.552e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2098 -0.0358 -0.1551 0.1819 0.9834 0.9932 0.2357 0.4284 0.868 0.7079 ] Network output: [ -0.008712 1.003 1.007 -1.638e-07 7.354e-08 0.007255 -1.235e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006899 0.0006398 0.004347 0.003124 0.9889 0.9919 0.007035 0.851 0.8917 0.01146 ] Network output: [ -0.0001554 0.001281 1 -6.472e-06 2.905e-06 0.9986 -4.877e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2238 0.1063 0.3501 0.1416 0.9849 0.9939 0.2246 0.4323 0.8748 0.7015 ] Network output: [ 0.002734 -0.01311 0.9944 3.951e-06 -1.774e-06 1.013 2.978e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09923 0.1849 0.197 0.9873 0.9919 0.1121 0.7334 0.8608 0.3049 ] Network output: [ -0.002574 0.01214 1.005 4.33e-06 -1.944e-06 0.9884 3.263e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0947 0.09276 0.1649 0.1967 0.9852 0.9911 0.09471 0.6572 0.8358 0.2497 ] Network output: [ 7.897e-05 1 -4.954e-05 5.659e-07 -2.54e-07 0.9998 4.265e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001544 Epoch 9907 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008697 0.9968 0.9927 -1.529e-07 6.864e-08 -0.006937 -1.152e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.003371 -0.006566 0.005313 0.9699 0.9743 0.006874 0.8238 0.8194 0.01607 ] Network output: [ 0.9999 7.955e-05 0.0003416 -2.057e-06 9.236e-07 -0.0002884 -1.55e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2099 -0.0358 -0.1551 0.1819 0.9834 0.9932 0.2357 0.4284 0.868 0.7079 ] Network output: [ -0.008711 1.003 1.007 -1.637e-07 7.348e-08 0.007254 -1.234e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0069 0.0006399 0.004347 0.003124 0.9889 0.9919 0.007035 0.851 0.8917 0.01146 ] Network output: [ -0.0001553 0.00128 1 -6.464e-06 2.902e-06 0.9986 -4.871e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2238 0.1063 0.3501 0.1416 0.9849 0.9939 0.2246 0.4323 0.8748 0.7015 ] Network output: [ 0.002733 -0.0131 0.9944 3.946e-06 -1.772e-06 1.013 2.974e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09923 0.1849 0.197 0.9873 0.9919 0.1121 0.7334 0.8608 0.3049 ] Network output: [ -0.002573 0.01213 1.005 4.325e-06 -1.942e-06 0.9884 3.259e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0947 0.09276 0.1649 0.1967 0.9852 0.9911 0.09471 0.6572 0.8358 0.2497 ] Network output: [ 7.895e-05 1 -4.955e-05 5.652e-07 -2.537e-07 0.9998 4.259e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001543 Epoch 9908 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008696 0.9968 0.9927 -1.528e-07 6.859e-08 -0.006936 -1.151e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.003371 -0.006566 0.005313 0.9699 0.9743 0.006874 0.8238 0.8194 0.01607 ] Network output: [ 0.9999 7.94e-05 0.0003414 -2.055e-06 9.225e-07 -0.0002882 -1.549e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2099 -0.0358 -0.1551 0.1819 0.9834 0.9932 0.2357 0.4284 0.868 0.7079 ] Network output: [ -0.00871 1.003 1.007 -1.635e-07 7.342e-08 0.007254 -1.233e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0069 0.0006399 0.004347 0.003124 0.9889 0.9919 0.007036 0.851 0.8917 0.01146 ] Network output: [ -0.0001552 0.001279 1 -6.456e-06 2.898e-06 0.9986 -4.865e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2238 0.1064 0.3501 0.1416 0.9849 0.9939 0.2246 0.4323 0.8748 0.7015 ] Network output: [ 0.002731 -0.0131 0.9944 3.942e-06 -1.77e-06 1.013 2.971e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09923 0.1849 0.197 0.9873 0.9919 0.1121 0.7334 0.8608 0.3049 ] Network output: [ -0.002571 0.01213 1.005 4.32e-06 -1.939e-06 0.9884 3.255e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0947 0.09276 0.1649 0.1967 0.9852 0.9911 0.09472 0.6572 0.8358 0.2497 ] Network output: [ 7.893e-05 1 -4.955e-05 5.645e-07 -2.534e-07 0.9998 4.254e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001542 Epoch 9909 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008695 0.9968 0.9927 -1.527e-07 6.854e-08 -0.006935 -1.151e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.003371 -0.006565 0.005313 0.9699 0.9743 0.006874 0.8238 0.8194 0.01607 ] Network output: [ 0.9999 7.925e-05 0.0003413 -2.052e-06 9.214e-07 -0.0002881 -1.547e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2099 -0.0358 -0.1551 0.1819 0.9834 0.9932 0.2357 0.4284 0.868 0.7079 ] Network output: [ -0.008709 1.003 1.007 -1.634e-07 7.336e-08 0.007253 -1.231e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.0069 0.00064 0.004347 0.003124 0.9889 0.9919 0.007036 0.851 0.8917 0.01146 ] Network output: [ -0.000155 0.001279 1 -6.448e-06 2.895e-06 0.9986 -4.859e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2238 0.1064 0.3501 0.1416 0.9849 0.9939 0.2246 0.4323 0.8748 0.7015 ] Network output: [ 0.00273 -0.01309 0.9944 3.937e-06 -1.767e-06 1.013 2.967e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09924 0.1849 0.197 0.9873 0.9919 0.1121 0.7334 0.8608 0.3049 ] Network output: [ -0.00257 0.01212 1.005 4.315e-06 -1.937e-06 0.9884 3.252e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0947 0.09276 0.1649 0.1967 0.9852 0.9911 0.09472 0.6571 0.8358 0.2497 ] Network output: [ 7.891e-05 1 -4.956e-05 5.638e-07 -2.531e-07 0.9998 4.249e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001541 Epoch 9910 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008695 0.9968 0.9927 -1.526e-07 6.849e-08 -0.006935 -1.15e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.003371 -0.006565 0.005312 0.9699 0.9743 0.006874 0.8238 0.8194 0.01607 ] Network output: [ 0.9999 7.911e-05 0.0003411 -2.05e-06 9.202e-07 -0.0002879 -1.545e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2099 -0.0358 -0.1551 0.1819 0.9834 0.9932 0.2357 0.4284 0.868 0.7079 ] Network output: [ -0.008708 1.003 1.007 -1.633e-07 7.33e-08 0.007253 -1.23e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006901 0.00064 0.004347 0.003123 0.9889 0.9919 0.007036 0.851 0.8917 0.01146 ] Network output: [ -0.0001549 0.001278 1 -6.44e-06 2.891e-06 0.9986 -4.853e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2238 0.1064 0.3501 0.1416 0.9849 0.9939 0.2246 0.4323 0.8748 0.7015 ] Network output: [ 0.002728 -0.01309 0.9944 3.932e-06 -1.765e-06 1.013 2.963e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09924 0.1849 0.197 0.9873 0.9919 0.1121 0.7334 0.8608 0.3049 ] Network output: [ -0.002569 0.01211 1.005 4.309e-06 -1.935e-06 0.9884 3.248e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0947 0.09276 0.1649 0.1967 0.9852 0.9911 0.09472 0.6571 0.8358 0.2497 ] Network output: [ 7.889e-05 1 -4.956e-05 5.631e-07 -2.528e-07 0.9998 4.244e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000154 Epoch 9911 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008694 0.9968 0.9927 -1.525e-07 6.844e-08 -0.006934 -1.149e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.003371 -0.006564 0.005312 0.9699 0.9743 0.006874 0.8238 0.8194 0.01607 ] Network output: [ 0.9999 7.896e-05 0.000341 -2.047e-06 9.191e-07 -0.0002877 -1.543e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2099 -0.0358 -0.1551 0.1819 0.9834 0.9932 0.2357 0.4283 0.868 0.7079 ] Network output: [ -0.008708 1.003 1.007 -1.631e-07 7.324e-08 0.007252 -1.229e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006901 0.0006401 0.004347 0.003123 0.9889 0.9919 0.007037 0.851 0.8917 0.01145 ] Network output: [ -0.0001547 0.001277 1 -6.432e-06 2.888e-06 0.9986 -4.847e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2239 0.1064 0.3501 0.1416 0.9849 0.9939 0.2246 0.4323 0.8748 0.7015 ] Network output: [ 0.002727 -0.01308 0.9944 3.927e-06 -1.763e-06 1.013 2.96e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09924 0.1849 0.197 0.9873 0.9919 0.1121 0.7333 0.8608 0.3049 ] Network output: [ -0.002567 0.01211 1.005 4.304e-06 -1.932e-06 0.9884 3.244e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09471 0.09277 0.1649 0.1967 0.9852 0.9911 0.09472 0.6571 0.8358 0.2497 ] Network output: [ 7.888e-05 1 -4.956e-05 5.625e-07 -2.525e-07 0.9998 4.239e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000154 Epoch 9912 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008693 0.9968 0.9927 -1.523e-07 6.839e-08 -0.006933 -1.148e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.003371 -0.006563 0.005311 0.9699 0.9743 0.006874 0.8238 0.8194 0.01607 ] Network output: [ 0.9999 7.881e-05 0.0003408 -2.045e-06 9.18e-07 -0.0002875 -1.541e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2099 -0.0358 -0.1551 0.1819 0.9834 0.9932 0.2357 0.4283 0.868 0.7079 ] Network output: [ -0.008707 1.003 1.007 -1.63e-07 7.318e-08 0.007252 -1.228e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006901 0.0006401 0.004347 0.003123 0.9889 0.9919 0.007037 0.851 0.8917 0.01145 ] Network output: [ -0.0001546 0.001277 1 -6.424e-06 2.884e-06 0.9986 -4.841e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2239 0.1064 0.3501 0.1416 0.9849 0.9939 0.2246 0.4323 0.8748 0.7015 ] Network output: [ 0.002726 -0.01307 0.9944 3.923e-06 -1.761e-06 1.013 2.956e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09924 0.1849 0.197 0.9873 0.9919 0.1121 0.7333 0.8608 0.3049 ] Network output: [ -0.002566 0.0121 1.005 4.299e-06 -1.93e-06 0.9884 3.24e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09471 0.09277 0.1649 0.1967 0.9852 0.9911 0.09472 0.6571 0.8358 0.2497 ] Network output: [ 7.886e-05 1 -4.957e-05 5.618e-07 -2.522e-07 0.9998 4.234e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001539 Epoch 9913 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008692 0.9968 0.9927 -1.522e-07 6.835e-08 -0.006933 -1.147e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.003371 -0.006563 0.005311 0.9699 0.9743 0.006875 0.8238 0.8194 0.01607 ] Network output: [ 0.9999 7.866e-05 0.0003407 -2.042e-06 9.168e-07 -0.0002874 -1.539e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2099 -0.0358 -0.1551 0.1819 0.9834 0.9932 0.2357 0.4283 0.868 0.7079 ] Network output: [ -0.008706 1.003 1.007 -1.629e-07 7.312e-08 0.007251 -1.227e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006902 0.0006402 0.004347 0.003123 0.9889 0.9919 0.007037 0.851 0.8917 0.01145 ] Network output: [ -0.0001544 0.001276 1 -6.416e-06 2.881e-06 0.9986 -4.836e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2239 0.1064 0.3501 0.1416 0.9849 0.9939 0.2246 0.4323 0.8748 0.7015 ] Network output: [ 0.002724 -0.01307 0.9944 3.918e-06 -1.759e-06 1.013 2.953e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09925 0.1849 0.197 0.9873 0.9919 0.1121 0.7333 0.8608 0.3049 ] Network output: [ -0.002565 0.0121 1.005 4.294e-06 -1.928e-06 0.9884 3.236e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09471 0.09277 0.1649 0.1967 0.9852 0.9911 0.09472 0.6571 0.8358 0.2497 ] Network output: [ 7.884e-05 1 -4.957e-05 5.611e-07 -2.519e-07 0.9998 4.229e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001538 Epoch 9914 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008691 0.9968 0.9927 -1.521e-07 6.83e-08 -0.006932 -1.147e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.003371 -0.006562 0.005311 0.9699 0.9743 0.006875 0.8238 0.8194 0.01606 ] Network output: [ 0.9999 7.851e-05 0.0003405 -2.04e-06 9.157e-07 -0.0002872 -1.537e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2099 -0.0358 -0.155 0.1819 0.9834 0.9932 0.2357 0.4283 0.868 0.7079 ] Network output: [ -0.008705 1.003 1.007 -1.627e-07 7.306e-08 0.007251 -1.226e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006902 0.0006402 0.004347 0.003122 0.9889 0.9919 0.007038 0.851 0.8917 0.01145 ] Network output: [ -0.0001543 0.001275 1 -6.408e-06 2.877e-06 0.9986 -4.83e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2239 0.1064 0.3501 0.1416 0.9849 0.9939 0.2246 0.4323 0.8748 0.7015 ] Network output: [ 0.002723 -0.01306 0.9944 3.913e-06 -1.757e-06 1.013 2.949e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09925 0.1849 0.197 0.9873 0.9919 0.1121 0.7333 0.8608 0.3049 ] Network output: [ -0.002563 0.01209 1.005 4.289e-06 -1.925e-06 0.9884 3.232e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09471 0.09277 0.1649 0.1967 0.9852 0.9911 0.09473 0.6571 0.8358 0.2498 ] Network output: [ 7.882e-05 1 -4.958e-05 5.604e-07 -2.516e-07 0.9998 4.224e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001537 Epoch 9915 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00869 0.9968 0.9927 -1.52e-07 6.825e-08 -0.006931 -1.146e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.003371 -0.006562 0.00531 0.9699 0.9743 0.006875 0.8238 0.8194 0.01606 ] Network output: [ 0.9999 7.837e-05 0.0003404 -2.037e-06 9.146e-07 -0.000287 -1.535e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2099 -0.03581 -0.155 0.1818 0.9834 0.9932 0.2357 0.4283 0.868 0.7079 ] Network output: [ -0.008704 1.003 1.007 -1.626e-07 7.299e-08 0.00725 -1.225e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006902 0.0006403 0.004347 0.003122 0.9889 0.9919 0.007038 0.851 0.8917 0.01145 ] Network output: [ -0.0001542 0.001275 1 -6.401e-06 2.873e-06 0.9986 -4.824e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2239 0.1064 0.3501 0.1416 0.9849 0.9939 0.2246 0.4323 0.8748 0.7015 ] Network output: [ 0.002721 -0.01305 0.9944 3.908e-06 -1.755e-06 1.013 2.945e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09925 0.1849 0.197 0.9873 0.9919 0.1121 0.7333 0.8608 0.3049 ] Network output: [ -0.002562 0.01208 1.005 4.284e-06 -1.923e-06 0.9884 3.228e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09471 0.09277 0.1649 0.1967 0.9852 0.9911 0.09473 0.6571 0.8358 0.2498 ] Network output: [ 7.88e-05 1 -4.958e-05 5.598e-07 -2.513e-07 0.9998 4.218e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001536 Epoch 9916 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008689 0.9968 0.9927 -1.519e-07 6.82e-08 -0.006931 -1.145e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.003372 -0.006561 0.00531 0.9699 0.9743 0.006875 0.8238 0.8194 0.01606 ] Network output: [ 0.9999 7.822e-05 0.0003402 -2.035e-06 9.135e-07 -0.0002869 -1.533e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2099 -0.03581 -0.155 0.1818 0.9834 0.9932 0.2357 0.4283 0.868 0.7078 ] Network output: [ -0.008704 1.003 1.007 -1.625e-07 7.293e-08 0.007249 -1.224e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006903 0.0006403 0.004346 0.003122 0.9889 0.9919 0.007039 0.851 0.8917 0.01145 ] Network output: [ -0.000154 0.001274 1 -6.393e-06 2.87e-06 0.9986 -4.818e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2239 0.1064 0.3501 0.1416 0.9849 0.9939 0.2247 0.4323 0.8748 0.7015 ] Network output: [ 0.00272 -0.01305 0.9944 3.903e-06 -1.752e-06 1.013 2.942e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09925 0.1849 0.197 0.9873 0.9919 0.1121 0.7333 0.8608 0.3049 ] Network output: [ -0.002561 0.01208 1.005 4.278e-06 -1.921e-06 0.9884 3.224e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09471 0.09277 0.1649 0.1967 0.9852 0.9911 0.09473 0.6571 0.8358 0.2498 ] Network output: [ 7.878e-05 1 -4.959e-05 5.591e-07 -2.51e-07 0.9998 4.213e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001535 Epoch 9917 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008689 0.9968 0.9927 -1.518e-07 6.815e-08 -0.00693 -1.144e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.003372 -0.006561 0.00531 0.9699 0.9743 0.006875 0.8238 0.8194 0.01606 ] Network output: [ 0.9999 7.807e-05 0.0003401 -2.032e-06 9.124e-07 -0.0002867 -1.532e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2099 -0.03581 -0.155 0.1818 0.9834 0.9932 0.2357 0.4283 0.868 0.7078 ] Network output: [ -0.008703 1.003 1.007 -1.623e-07 7.287e-08 0.007249 -1.223e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006903 0.0006404 0.004346 0.003122 0.9889 0.9919 0.007039 0.851 0.8917 0.01145 ] Network output: [ -0.0001539 0.001273 1 -6.385e-06 2.866e-06 0.9986 -4.812e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2239 0.1064 0.3501 0.1416 0.9849 0.9939 0.2247 0.4323 0.8748 0.7015 ] Network output: [ 0.002718 -0.01304 0.9944 3.899e-06 -1.75e-06 1.013 2.938e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09926 0.1849 0.197 0.9873 0.9919 0.1121 0.7333 0.8608 0.3049 ] Network output: [ -0.00256 0.01207 1.005 4.273e-06 -1.918e-06 0.9884 3.22e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09472 0.09278 0.1649 0.1967 0.9852 0.9911 0.09473 0.6571 0.8358 0.2498 ] Network output: [ 7.877e-05 1 -4.959e-05 5.584e-07 -2.507e-07 0.9998 4.208e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001534 Epoch 9918 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008688 0.9968 0.9927 -1.517e-07 6.81e-08 -0.00693 -1.143e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003521 -0.003372 -0.00656 0.005309 0.9699 0.9743 0.006875 0.8238 0.8194 0.01606 ] Network output: [ 0.9999 7.792e-05 0.0003399 -2.03e-06 9.112e-07 -0.0002865 -1.53e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2099 -0.03581 -0.155 0.1818 0.9834 0.9932 0.2357 0.4283 0.868 0.7078 ] Network output: [ -0.008702 1.003 1.007 -1.622e-07 7.281e-08 0.007248 -1.222e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006903 0.0006404 0.004346 0.003122 0.9889 0.9919 0.007039 0.851 0.8917 0.01145 ] Network output: [ -0.0001537 0.001273 1 -6.377e-06 2.863e-06 0.9986 -4.806e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2239 0.1064 0.3502 0.1416 0.9849 0.9939 0.2247 0.4323 0.8748 0.7015 ] Network output: [ 0.002717 -0.01303 0.9944 3.894e-06 -1.748e-06 1.013 2.935e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09926 0.1849 0.197 0.9873 0.9919 0.1121 0.7333 0.8608 0.3049 ] Network output: [ -0.002558 0.01207 1.005 4.268e-06 -1.916e-06 0.9884 3.217e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09472 0.09278 0.1649 0.1967 0.9852 0.9911 0.09473 0.6571 0.8358 0.2498 ] Network output: [ 7.875e-05 1 -4.96e-05 5.577e-07 -2.504e-07 0.9998 4.203e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001534 Epoch 9919 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008687 0.9968 0.9927 -1.516e-07 6.806e-08 -0.006929 -1.142e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003372 -0.00656 0.005309 0.9699 0.9743 0.006875 0.8238 0.8194 0.01606 ] Network output: [ 0.9999 7.777e-05 0.0003398 -2.027e-06 9.101e-07 -0.0002863 -1.528e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2099 -0.03581 -0.155 0.1818 0.9834 0.9932 0.2357 0.4283 0.868 0.7078 ] Network output: [ -0.008701 1.003 1.007 -1.621e-07 7.275e-08 0.007248 -1.221e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006904 0.0006405 0.004346 0.003121 0.9889 0.9919 0.00704 0.8509 0.8917 0.01145 ] Network output: [ -0.0001536 0.001272 1 -6.369e-06 2.859e-06 0.9986 -4.8e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2239 0.1064 0.3502 0.1416 0.9849 0.9939 0.2247 0.4323 0.8748 0.7015 ] Network output: [ 0.002715 -0.01303 0.9944 3.889e-06 -1.746e-06 1.013 2.931e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09926 0.1849 0.197 0.9873 0.9919 0.1121 0.7333 0.8608 0.3049 ] Network output: [ -0.002557 0.01206 1.005 4.263e-06 -1.914e-06 0.9884 3.213e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09472 0.09278 0.1649 0.1967 0.9852 0.9911 0.09473 0.657 0.8358 0.2498 ] Network output: [ 7.873e-05 1 -4.96e-05 5.57e-07 -2.501e-07 0.9998 4.198e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001533 Epoch 9920 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008686 0.9968 0.9927 -1.515e-07 6.801e-08 -0.006928 -1.142e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003372 -0.006559 0.005309 0.9699 0.9743 0.006876 0.8238 0.8193 0.01606 ] Network output: [ 0.9999 7.763e-05 0.0003396 -2.025e-06 9.09e-07 -0.0002862 -1.526e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2099 -0.03581 -0.155 0.1818 0.9834 0.9932 0.2358 0.4283 0.868 0.7078 ] Network output: [ -0.0087 1.003 1.007 -1.619e-07 7.269e-08 0.007247 -1.22e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006904 0.0006406 0.004346 0.003121 0.9889 0.9919 0.00704 0.8509 0.8917 0.01145 ] Network output: [ -0.0001534 0.001271 1 -6.361e-06 2.856e-06 0.9986 -4.794e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2239 0.1064 0.3502 0.1416 0.9849 0.9939 0.2247 0.4323 0.8748 0.7015 ] Network output: [ 0.002714 -0.01302 0.9944 3.884e-06 -1.744e-06 1.013 2.927e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09926 0.1849 0.197 0.9873 0.9919 0.1121 0.7333 0.8608 0.3049 ] Network output: [ -0.002556 0.01206 1.005 4.258e-06 -1.912e-06 0.9884 3.209e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09472 0.09278 0.1649 0.1967 0.9852 0.9911 0.09474 0.657 0.8358 0.2498 ] Network output: [ 7.871e-05 1 -4.96e-05 5.564e-07 -2.498e-07 0.9998 4.193e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001532 Epoch 9921 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008685 0.9968 0.9927 -1.514e-07 6.796e-08 -0.006928 -1.141e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003372 -0.006558 0.005308 0.9699 0.9743 0.006876 0.8238 0.8193 0.01606 ] Network output: [ 0.9999 7.748e-05 0.0003395 -2.022e-06 9.079e-07 -0.000286 -1.524e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2099 -0.03581 -0.155 0.1818 0.9834 0.9932 0.2358 0.4283 0.868 0.7078 ] Network output: [ -0.0087 1.003 1.007 -1.618e-07 7.263e-08 0.007247 -1.219e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006904 0.0006406 0.004346 0.003121 0.9889 0.9919 0.00704 0.8509 0.8917 0.01145 ] Network output: [ -0.0001533 0.00127 1 -6.354e-06 2.852e-06 0.9986 -4.788e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2239 0.1064 0.3502 0.1416 0.9849 0.9939 0.2247 0.4323 0.8748 0.7015 ] Network output: [ 0.002713 -0.01301 0.9944 3.88e-06 -1.742e-06 1.013 2.924e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09927 0.1849 0.197 0.9873 0.9919 0.1121 0.7332 0.8608 0.3049 ] Network output: [ -0.002554 0.01205 1.005 4.253e-06 -1.909e-06 0.9884 3.205e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09472 0.09278 0.1649 0.1967 0.9852 0.9911 0.09474 0.657 0.8358 0.2498 ] Network output: [ 7.869e-05 1 -4.961e-05 5.557e-07 -2.495e-07 0.9998 4.188e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001531 Epoch 9922 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008684 0.9968 0.9927 -1.513e-07 6.791e-08 -0.006927 -1.14e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003372 -0.006558 0.005308 0.9699 0.9743 0.006876 0.8238 0.8193 0.01606 ] Network output: [ 0.9999 7.733e-05 0.0003394 -2.02e-06 9.068e-07 -0.0002858 -1.522e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2099 -0.03581 -0.155 0.1818 0.9834 0.9932 0.2358 0.4283 0.868 0.7078 ] Network output: [ -0.008699 1.003 1.007 -1.616e-07 7.257e-08 0.007246 -1.218e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006905 0.0006407 0.004346 0.003121 0.9889 0.9919 0.007041 0.8509 0.8917 0.01145 ] Network output: [ -0.0001532 0.00127 1 -6.346e-06 2.849e-06 0.9986 -4.782e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2239 0.1064 0.3502 0.1416 0.9849 0.9939 0.2247 0.4323 0.8748 0.7015 ] Network output: [ 0.002711 -0.01301 0.9944 3.875e-06 -1.74e-06 1.013 2.92e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09927 0.185 0.197 0.9873 0.9919 0.1121 0.7332 0.8608 0.3049 ] Network output: [ -0.002553 0.01204 1.005 4.248e-06 -1.907e-06 0.9884 3.201e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09472 0.09278 0.1649 0.1967 0.9852 0.9911 0.09474 0.657 0.8358 0.2498 ] Network output: [ 7.867e-05 1 -4.961e-05 5.55e-07 -2.492e-07 0.9998 4.183e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000153 Epoch 9923 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008683 0.9968 0.9927 -1.512e-07 6.786e-08 -0.006926 -1.139e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003372 -0.006557 0.005307 0.9699 0.9743 0.006876 0.8238 0.8193 0.01606 ] Network output: [ 0.9999 7.718e-05 0.0003392 -2.017e-06 9.057e-07 -0.0002857 -1.52e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2099 -0.03581 -0.155 0.1818 0.9834 0.9932 0.2358 0.4283 0.868 0.7078 ] Network output: [ -0.008698 1.003 1.007 -1.615e-07 7.251e-08 0.007246 -1.217e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006905 0.0006407 0.004346 0.00312 0.9889 0.9919 0.007041 0.8509 0.8917 0.01145 ] Network output: [ -0.000153 0.001269 1 -6.338e-06 2.845e-06 0.9986 -4.777e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2239 0.1064 0.3502 0.1416 0.9849 0.9939 0.2247 0.4322 0.8748 0.7015 ] Network output: [ 0.00271 -0.013 0.9944 3.87e-06 -1.738e-06 1.013 2.917e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09927 0.185 0.197 0.9873 0.9919 0.1121 0.7332 0.8608 0.3049 ] Network output: [ -0.002552 0.01204 1.005 4.243e-06 -1.905e-06 0.9884 3.197e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09473 0.09278 0.1649 0.1967 0.9852 0.9911 0.09474 0.657 0.8358 0.2498 ] Network output: [ 7.866e-05 1 -4.962e-05 5.544e-07 -2.489e-07 0.9998 4.178e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001529 Epoch 9924 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008683 0.9968 0.9927 -1.511e-07 6.781e-08 -0.006926 -1.138e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003372 -0.006557 0.005307 0.9699 0.9743 0.006876 0.8238 0.8193 0.01606 ] Network output: [ 0.9999 7.704e-05 0.0003391 -2.015e-06 9.045e-07 -0.0002855 -1.518e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2099 -0.03581 -0.155 0.1818 0.9834 0.9932 0.2358 0.4283 0.868 0.7078 ] Network output: [ -0.008697 1.003 1.007 -1.614e-07 7.245e-08 0.007245 -1.216e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006905 0.0006408 0.004346 0.00312 0.9889 0.9919 0.007041 0.8509 0.8917 0.01145 ] Network output: [ -0.0001529 0.001268 1 -6.33e-06 2.842e-06 0.9986 -4.771e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2239 0.1064 0.3502 0.1416 0.9849 0.9939 0.2247 0.4322 0.8748 0.7015 ] Network output: [ 0.002708 -0.013 0.9944 3.866e-06 -1.735e-06 1.013 2.913e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09927 0.185 0.197 0.9873 0.9919 0.1121 0.7332 0.8608 0.3049 ] Network output: [ -0.00255 0.01203 1.005 4.237e-06 -1.902e-06 0.9884 3.193e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09473 0.09279 0.1649 0.1967 0.9852 0.9911 0.09474 0.657 0.8358 0.2498 ] Network output: [ 7.864e-05 1 -4.962e-05 5.537e-07 -2.486e-07 0.9998 4.173e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001528 Epoch 9925 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008682 0.9968 0.9927 -1.509e-07 6.777e-08 -0.006925 -1.138e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003372 -0.006556 0.005307 0.9699 0.9743 0.006876 0.8238 0.8193 0.01605 ] Network output: [ 0.9999 7.689e-05 0.0003389 -2.012e-06 9.034e-07 -0.0002853 -1.517e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2099 -0.03582 -0.155 0.1818 0.9834 0.9932 0.2358 0.4283 0.868 0.7078 ] Network output: [ -0.008696 1.003 1.007 -1.612e-07 7.239e-08 0.007245 -1.215e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006906 0.0006408 0.004345 0.00312 0.9889 0.9919 0.007042 0.8509 0.8917 0.01144 ] Network output: [ -0.0001527 0.001268 1 -6.323e-06 2.838e-06 0.9986 -4.765e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2239 0.1064 0.3502 0.1416 0.9849 0.9939 0.2247 0.4322 0.8747 0.7015 ] Network output: [ 0.002707 -0.01299 0.9944 3.861e-06 -1.733e-06 1.013 2.91e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09928 0.185 0.197 0.9873 0.9919 0.1121 0.7332 0.8608 0.3049 ] Network output: [ -0.002549 0.01203 1.005 4.232e-06 -1.9e-06 0.9884 3.19e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09473 0.09279 0.1649 0.1967 0.9852 0.9911 0.09474 0.657 0.8358 0.2498 ] Network output: [ 7.862e-05 1 -4.963e-05 5.53e-07 -2.483e-07 0.9998 4.168e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001528 Epoch 9926 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008681 0.9968 0.9927 -1.508e-07 6.772e-08 -0.006924 -1.137e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003372 -0.006556 0.005306 0.9699 0.9743 0.006877 0.8237 0.8193 0.01605 ] Network output: [ 0.9999 7.674e-05 0.0003388 -2.01e-06 9.023e-07 -0.0002852 -1.515e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.21 -0.03582 -0.1549 0.1818 0.9834 0.9932 0.2358 0.4283 0.868 0.7078 ] Network output: [ -0.008696 1.003 1.007 -1.611e-07 7.233e-08 0.007244 -1.214e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006906 0.0006409 0.004345 0.00312 0.9889 0.9919 0.007042 0.8509 0.8917 0.01144 ] Network output: [ -0.0001526 0.001267 1 -6.315e-06 2.835e-06 0.9986 -4.759e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2239 0.1064 0.3502 0.1416 0.9849 0.9939 0.2247 0.4322 0.8747 0.7015 ] Network output: [ 0.002705 -0.01298 0.9944 3.856e-06 -1.731e-06 1.013 2.906e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09928 0.185 0.197 0.9873 0.9919 0.1121 0.7332 0.8608 0.3049 ] Network output: [ -0.002548 0.01202 1.005 4.227e-06 -1.898e-06 0.9884 3.186e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09473 0.09279 0.1649 0.1967 0.9852 0.9911 0.09474 0.657 0.8358 0.2498 ] Network output: [ 7.86e-05 1 -4.963e-05 5.523e-07 -2.48e-07 0.9998 4.163e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001527 Epoch 9927 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00868 0.9968 0.9927 -1.507e-07 6.767e-08 -0.006924 -1.136e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003372 -0.006555 0.005306 0.9699 0.9743 0.006877 0.8237 0.8193 0.01605 ] Network output: [ 0.9999 7.659e-05 0.0003386 -2.007e-06 9.012e-07 -0.000285 -1.513e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.21 -0.03582 -0.1549 0.1818 0.9834 0.9932 0.2358 0.4283 0.868 0.7078 ] Network output: [ -0.008695 1.003 1.007 -1.61e-07 7.227e-08 0.007244 -1.213e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006906 0.0006409 0.004345 0.00312 0.9889 0.9919 0.007042 0.8509 0.8917 0.01144 ] Network output: [ -0.0001524 0.001266 1 -6.307e-06 2.831e-06 0.9986 -4.753e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.224 0.1064 0.3502 0.1416 0.9849 0.9939 0.2247 0.4322 0.8747 0.7015 ] Network output: [ 0.002704 -0.01298 0.9944 3.851e-06 -1.729e-06 1.013 2.903e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09928 0.185 0.197 0.9873 0.9919 0.1121 0.7332 0.8608 0.3049 ] Network output: [ -0.002546 0.01202 1.005 4.222e-06 -1.895e-06 0.9884 3.182e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09473 0.09279 0.1649 0.1967 0.9852 0.9911 0.09475 0.657 0.8358 0.2498 ] Network output: [ 7.858e-05 1 -4.964e-05 5.517e-07 -2.477e-07 0.9998 4.158e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001526 Epoch 9928 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008679 0.9968 0.9927 -1.506e-07 6.762e-08 -0.006923 -1.135e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003372 -0.006555 0.005306 0.9699 0.9743 0.006877 0.8237 0.8193 0.01605 ] Network output: [ 0.9999 7.645e-05 0.0003385 -2.005e-06 9.001e-07 -0.0002848 -1.511e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.21 -0.03582 -0.1549 0.1818 0.9834 0.9932 0.2358 0.4283 0.868 0.7078 ] Network output: [ -0.008694 1.003 1.007 -1.608e-07 7.221e-08 0.007243 -1.212e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006907 0.000641 0.004345 0.003119 0.9889 0.9919 0.007043 0.8509 0.8917 0.01144 ] Network output: [ -0.0001523 0.001266 1 -6.299e-06 2.828e-06 0.9986 -4.747e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.224 0.1064 0.3502 0.1416 0.9849 0.9939 0.2247 0.4322 0.8747 0.7015 ] Network output: [ 0.002702 -0.01297 0.9944 3.847e-06 -1.727e-06 1.013 2.899e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09928 0.185 0.197 0.9873 0.9919 0.1121 0.7332 0.8608 0.3049 ] Network output: [ -0.002545 0.01201 1.005 4.217e-06 -1.893e-06 0.9884 3.178e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09473 0.09279 0.1649 0.1967 0.9852 0.9911 0.09475 0.657 0.8358 0.2498 ] Network output: [ 7.857e-05 1 -4.964e-05 5.51e-07 -2.474e-07 0.9998 4.153e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001525 Epoch 9929 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008678 0.9968 0.9927 -1.505e-07 6.757e-08 -0.006922 -1.134e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003372 -0.006554 0.005305 0.9699 0.9743 0.006877 0.8237 0.8193 0.01605 ] Network output: [ 0.9999 7.63e-05 0.0003383 -2.003e-06 8.99e-07 -0.0002846 -1.509e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.21 -0.03582 -0.1549 0.1818 0.9834 0.9932 0.2358 0.4283 0.868 0.7078 ] Network output: [ -0.008693 1.003 1.007 -1.607e-07 7.215e-08 0.007243 -1.211e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006907 0.000641 0.004345 0.003119 0.9889 0.9919 0.007043 0.8509 0.8917 0.01144 ] Network output: [ -0.0001522 0.001265 1 -6.292e-06 2.825e-06 0.9986 -4.742e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.224 0.1064 0.3502 0.1416 0.9849 0.9939 0.2247 0.4322 0.8747 0.7015 ] Network output: [ 0.002701 -0.01296 0.9944 3.842e-06 -1.725e-06 1.013 2.895e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09929 0.185 0.197 0.9873 0.9919 0.1121 0.7332 0.8608 0.3049 ] Network output: [ -0.002544 0.012 1.005 4.212e-06 -1.891e-06 0.9884 3.174e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09474 0.09279 0.1649 0.1967 0.9852 0.9911 0.09475 0.6569 0.8358 0.2498 ] Network output: [ 7.855e-05 1 -4.965e-05 5.503e-07 -2.471e-07 0.9998 4.148e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001524 Epoch 9930 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008677 0.9968 0.9927 -1.504e-07 6.753e-08 -0.006922 -1.134e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003373 -0.006554 0.005305 0.9699 0.9743 0.006877 0.8237 0.8193 0.01605 ] Network output: [ 0.9999 7.615e-05 0.0003382 -2e-06 8.979e-07 -0.0002845 -1.507e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.21 -0.03582 -0.1549 0.1818 0.9834 0.9932 0.2358 0.4283 0.868 0.7078 ] Network output: [ -0.008692 1.003 1.007 -1.606e-07 7.209e-08 0.007242 -1.21e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006907 0.0006411 0.004345 0.003119 0.9889 0.9919 0.007043 0.8509 0.8917 0.01144 ] Network output: [ -0.000152 0.001264 1 -6.284e-06 2.821e-06 0.9986 -4.736e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.224 0.1064 0.3502 0.1416 0.9849 0.9939 0.2247 0.4322 0.8747 0.7015 ] Network output: [ 0.0027 -0.01296 0.9944 3.837e-06 -1.723e-06 1.013 2.892e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09929 0.185 0.197 0.9873 0.9919 0.1121 0.7332 0.8608 0.3049 ] Network output: [ -0.002543 0.012 1.005 4.207e-06 -1.889e-06 0.9884 3.171e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09474 0.0928 0.1649 0.1967 0.9852 0.9911 0.09475 0.6569 0.8358 0.2498 ] Network output: [ 7.853e-05 1 -4.966e-05 5.497e-07 -2.468e-07 0.9998 4.143e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001523 Epoch 9931 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008677 0.9968 0.9927 -1.503e-07 6.748e-08 -0.006921 -1.133e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003373 -0.006553 0.005304 0.9699 0.9743 0.006877 0.8237 0.8193 0.01605 ] Network output: [ 0.9999 7.601e-05 0.000338 -1.998e-06 8.968e-07 -0.0002843 -1.505e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.21 -0.03582 -0.1549 0.1818 0.9834 0.9932 0.2358 0.4283 0.868 0.7078 ] Network output: [ -0.008692 1.003 1.007 -1.604e-07 7.202e-08 0.007242 -1.209e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006908 0.0006411 0.004345 0.003119 0.9889 0.9919 0.007044 0.8509 0.8917 0.01144 ] Network output: [ -0.0001519 0.001263 1 -6.276e-06 2.818e-06 0.9986 -4.73e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.224 0.1064 0.3502 0.1416 0.9849 0.9939 0.2248 0.4322 0.8747 0.7015 ] Network output: [ 0.002698 -0.01295 0.9944 3.833e-06 -1.721e-06 1.013 2.888e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09929 0.185 0.197 0.9873 0.9919 0.1121 0.7332 0.8608 0.3049 ] Network output: [ -0.002541 0.01199 1.005 4.202e-06 -1.886e-06 0.9884 3.167e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09474 0.0928 0.1649 0.1967 0.9852 0.9911 0.09475 0.6569 0.8358 0.2498 ] Network output: [ 7.851e-05 1 -4.966e-05 5.49e-07 -2.465e-07 0.9998 4.138e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001523 Epoch 9932 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008676 0.9968 0.9927 -1.502e-07 6.743e-08 -0.00692 -1.132e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003373 -0.006552 0.005304 0.9699 0.9743 0.006877 0.8237 0.8193 0.01605 ] Network output: [ 0.9999 7.586e-05 0.0003379 -1.995e-06 8.957e-07 -0.0002841 -1.504e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.21 -0.03582 -0.1549 0.1818 0.9834 0.9932 0.2358 0.4283 0.868 0.7078 ] Network output: [ -0.008691 1.003 1.007 -1.603e-07 7.196e-08 0.007241 -1.208e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006908 0.0006412 0.004345 0.003118 0.9889 0.9919 0.007044 0.8509 0.8917 0.01144 ] Network output: [ -0.0001517 0.001263 1 -6.268e-06 2.814e-06 0.9986 -4.724e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.224 0.1064 0.3502 0.1416 0.9849 0.9939 0.2248 0.4322 0.8747 0.7014 ] Network output: [ 0.002697 -0.01294 0.9944 3.828e-06 -1.719e-06 1.013 2.885e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.09929 0.185 0.197 0.9873 0.9919 0.1121 0.7331 0.8608 0.3049 ] Network output: [ -0.00254 0.01199 1.005 4.197e-06 -1.884e-06 0.9884 3.163e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09474 0.0928 0.1649 0.1967 0.9852 0.9911 0.09475 0.6569 0.8358 0.2498 ] Network output: [ 7.849e-05 1 -4.967e-05 5.483e-07 -2.462e-07 0.9998 4.133e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001522 Epoch 9933 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008675 0.9968 0.9927 -1.501e-07 6.738e-08 -0.00692 -1.131e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003373 -0.006552 0.005304 0.9699 0.9743 0.006878 0.8237 0.8193 0.01605 ] Network output: [ 0.9999 7.571e-05 0.0003377 -1.993e-06 8.946e-07 -0.000284 -1.502e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.21 -0.03582 -0.1549 0.1818 0.9834 0.9932 0.2358 0.4283 0.868 0.7078 ] Network output: [ -0.00869 1.003 1.007 -1.602e-07 7.19e-08 0.007241 -1.207e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006908 0.0006412 0.004345 0.003118 0.9889 0.9919 0.007044 0.8509 0.8917 0.01144 ] Network output: [ -0.0001516 0.001262 1 -6.261e-06 2.811e-06 0.9986 -4.718e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.224 0.1064 0.3502 0.1416 0.9849 0.9939 0.2248 0.4322 0.8747 0.7014 ] Network output: [ 0.002695 -0.01294 0.9944 3.823e-06 -1.716e-06 1.013 2.881e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.0993 0.185 0.197 0.9873 0.9919 0.1121 0.7331 0.8608 0.3049 ] Network output: [ -0.002539 0.01198 1.005 4.192e-06 -1.882e-06 0.9884 3.159e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09474 0.0928 0.1649 0.1967 0.9852 0.9911 0.09476 0.6569 0.8358 0.2498 ] Network output: [ 7.848e-05 1 -4.967e-05 5.477e-07 -2.459e-07 0.9998 4.128e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001521 Epoch 9934 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008674 0.9968 0.9927 -1.5e-07 6.733e-08 -0.006919 -1.13e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003373 -0.006551 0.005303 0.9699 0.9743 0.006878 0.8237 0.8193 0.01605 ] Network output: [ 0.9999 7.557e-05 0.0003376 -1.99e-06 8.935e-07 -0.0002838 -1.5e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.21 -0.03582 -0.1549 0.1818 0.9834 0.9932 0.2358 0.4283 0.868 0.7078 ] Network output: [ -0.008689 1.003 1.007 -1.6e-07 7.184e-08 0.00724 -1.206e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006909 0.0006413 0.004345 0.003118 0.9889 0.9919 0.007045 0.8509 0.8917 0.01144 ] Network output: [ -0.0001515 0.001261 1 -6.253e-06 2.807e-06 0.9986 -4.713e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.224 0.1064 0.3502 0.1416 0.9849 0.9939 0.2248 0.4322 0.8747 0.7014 ] Network output: [ 0.002694 -0.01293 0.9944 3.819e-06 -1.714e-06 1.013 2.878e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.112 0.0993 0.185 0.1969 0.9873 0.9919 0.1121 0.7331 0.8608 0.3049 ] Network output: [ -0.002537 0.01198 1.005 4.187e-06 -1.88e-06 0.9884 3.155e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09474 0.0928 0.1649 0.1967 0.9852 0.9911 0.09476 0.6569 0.8358 0.2498 ] Network output: [ 7.846e-05 1 -4.968e-05 5.47e-07 -2.456e-07 0.9998 4.123e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000152 Epoch 9935 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008673 0.9968 0.9927 -1.499e-07 6.728e-08 -0.006919 -1.13e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003373 -0.006551 0.005303 0.9699 0.9743 0.006878 0.8237 0.8193 0.01605 ] Network output: [ 0.9999 7.542e-05 0.0003374 -1.988e-06 8.924e-07 -0.0002836 -1.498e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.21 -0.03583 -0.1549 0.1818 0.9834 0.9932 0.2358 0.4283 0.868 0.7078 ] Network output: [ -0.008688 1.003 1.007 -1.599e-07 7.178e-08 0.00724 -1.205e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006909 0.0006413 0.004344 0.003118 0.9889 0.9919 0.007045 0.8509 0.8917 0.01144 ] Network output: [ -0.0001513 0.001261 1 -6.245e-06 2.804e-06 0.9986 -4.707e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.224 0.1064 0.3502 0.1416 0.9849 0.9939 0.2248 0.4322 0.8747 0.7014 ] Network output: [ 0.002692 -0.01292 0.9944 3.814e-06 -1.712e-06 1.013 2.874e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.0993 0.185 0.1969 0.9873 0.9919 0.1121 0.7331 0.8608 0.3049 ] Network output: [ -0.002536 0.01197 1.005 4.182e-06 -1.877e-06 0.9885 3.151e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09474 0.0928 0.1649 0.1967 0.9852 0.9911 0.09476 0.6569 0.8358 0.2498 ] Network output: [ 7.844e-05 1 -4.968e-05 5.464e-07 -2.453e-07 0.9998 4.118e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001519 Epoch 9936 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008672 0.9968 0.9927 -1.498e-07 6.724e-08 -0.006918 -1.129e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003373 -0.00655 0.005303 0.9699 0.9743 0.006878 0.8237 0.8193 0.01604 ] Network output: [ 0.9999 7.527e-05 0.0003373 -1.985e-06 8.913e-07 -0.0002835 -1.496e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.21 -0.03583 -0.1549 0.1818 0.9834 0.9932 0.2359 0.4283 0.868 0.7078 ] Network output: [ -0.008688 1.003 1.007 -1.598e-07 7.172e-08 0.007239 -1.204e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006909 0.0006414 0.004344 0.003118 0.9889 0.9919 0.007045 0.8509 0.8917 0.01144 ] Network output: [ -0.0001512 0.00126 1 -6.238e-06 2.8e-06 0.9986 -4.701e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.224 0.1065 0.3502 0.1416 0.9849 0.9939 0.2248 0.4322 0.8747 0.7014 ] Network output: [ 0.002691 -0.01292 0.9944 3.809e-06 -1.71e-06 1.013 2.871e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09931 0.185 0.1969 0.9873 0.9919 0.1121 0.7331 0.8608 0.3049 ] Network output: [ -0.002535 0.01196 1.005 4.177e-06 -1.875e-06 0.9885 3.148e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09475 0.09281 0.1649 0.1967 0.9852 0.9911 0.09476 0.6569 0.8358 0.2498 ] Network output: [ 7.842e-05 1 -4.969e-05 5.457e-07 -2.45e-07 0.9998 4.113e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001518 Epoch 9937 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008671 0.9968 0.9927 -1.497e-07 6.719e-08 -0.006917 -1.128e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003373 -0.00655 0.005302 0.9699 0.9743 0.006878 0.8237 0.8193 0.01604 ] Network output: [ 0.9999 7.513e-05 0.0003371 -1.983e-06 8.902e-07 -0.0002833 -1.494e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.21 -0.03583 -0.1549 0.1818 0.9834 0.9932 0.2359 0.4283 0.868 0.7078 ] Network output: [ -0.008687 1.003 1.007 -1.596e-07 7.166e-08 0.007239 -1.203e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00691 0.0006415 0.004344 0.003117 0.9889 0.9919 0.007046 0.8509 0.8917 0.01144 ] Network output: [ -0.000151 0.001259 1 -6.23e-06 2.797e-06 0.9986 -4.695e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.224 0.1065 0.3502 0.1416 0.9849 0.9939 0.2248 0.4322 0.8747 0.7014 ] Network output: [ 0.00269 -0.01291 0.9944 3.805e-06 -1.708e-06 1.013 2.867e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09931 0.185 0.1969 0.9873 0.9919 0.1121 0.7331 0.8608 0.3049 ] Network output: [ -0.002533 0.01196 1.005 4.172e-06 -1.873e-06 0.9885 3.144e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09475 0.09281 0.1649 0.1967 0.9852 0.9911 0.09476 0.6569 0.8358 0.2498 ] Network output: [ 7.84e-05 1 -4.97e-05 5.45e-07 -2.447e-07 0.9998 4.108e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001517 Epoch 9938 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008671 0.9968 0.9927 -1.496e-07 6.714e-08 -0.006917 -1.127e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003373 -0.006549 0.005302 0.9699 0.9743 0.006878 0.8237 0.8193 0.01604 ] Network output: [ 0.9999 7.498e-05 0.000337 -1.98e-06 8.891e-07 -0.0002831 -1.493e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.21 -0.03583 -0.1549 0.1818 0.9834 0.9932 0.2359 0.4282 0.868 0.7078 ] Network output: [ -0.008686 1.003 1.007 -1.595e-07 7.16e-08 0.007238 -1.202e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00691 0.0006415 0.004344 0.003117 0.9889 0.9919 0.007046 0.8509 0.8917 0.01144 ] Network output: [ -0.0001509 0.001259 1 -6.222e-06 2.794e-06 0.9986 -4.689e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.224 0.1065 0.3502 0.1416 0.9849 0.9939 0.2248 0.4322 0.8747 0.7014 ] Network output: [ 0.002688 -0.01291 0.9944 3.8e-06 -1.706e-06 1.013 2.864e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09931 0.185 0.1969 0.9873 0.9919 0.1121 0.7331 0.8608 0.3049 ] Network output: [ -0.002532 0.01195 1.005 4.167e-06 -1.871e-06 0.9885 3.14e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09475 0.09281 0.1649 0.1967 0.9852 0.9911 0.09476 0.6569 0.8358 0.2498 ] Network output: [ 7.839e-05 1 -4.97e-05 5.444e-07 -2.444e-07 0.9998 4.103e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001517 Epoch 9939 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00867 0.9968 0.9927 -1.494e-07 6.709e-08 -0.006916 -1.126e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003373 -0.006549 0.005302 0.9699 0.9743 0.006878 0.8237 0.8193 0.01604 ] Network output: [ 0.9999 7.483e-05 0.0003368 -1.978e-06 8.88e-07 -0.000283 -1.491e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.21 -0.03583 -0.1548 0.1818 0.9834 0.9932 0.2359 0.4282 0.868 0.7078 ] Network output: [ -0.008685 1.003 1.007 -1.594e-07 7.154e-08 0.007238 -1.201e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00691 0.0006416 0.004344 0.003117 0.9889 0.9919 0.007046 0.8509 0.8917 0.01143 ] Network output: [ -0.0001507 0.001258 1 -6.215e-06 2.79e-06 0.9986 -4.684e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.224 0.1065 0.3503 0.1416 0.9849 0.9939 0.2248 0.4322 0.8747 0.7014 ] Network output: [ 0.002687 -0.0129 0.9944 3.795e-06 -1.704e-06 1.013 2.86e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09931 0.185 0.1969 0.9873 0.9919 0.1121 0.7331 0.8608 0.3049 ] Network output: [ -0.002531 0.01195 1.005 4.162e-06 -1.868e-06 0.9885 3.136e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09475 0.09281 0.1649 0.1967 0.9852 0.9911 0.09477 0.6569 0.8358 0.2498 ] Network output: [ 7.837e-05 1 -4.971e-05 5.437e-07 -2.441e-07 0.9998 4.098e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001516 Epoch 9940 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008669 0.9968 0.9927 -1.493e-07 6.704e-08 -0.006915 -1.125e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003373 -0.006548 0.005301 0.9699 0.9743 0.006879 0.8237 0.8193 0.01604 ] Network output: [ 0.9999 7.469e-05 0.0003367 -1.976e-06 8.869e-07 -0.0002828 -1.489e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.21 -0.03583 -0.1548 0.1818 0.9834 0.9932 0.2359 0.4282 0.868 0.7078 ] Network output: [ -0.008684 1.003 1.007 -1.592e-07 7.148e-08 0.007237 -1.2e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006911 0.0006416 0.004344 0.003117 0.9889 0.9919 0.007047 0.8509 0.8917 0.01143 ] Network output: [ -0.0001506 0.001257 1 -6.207e-06 2.787e-06 0.9986 -4.678e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.224 0.1065 0.3503 0.1416 0.9849 0.9939 0.2248 0.4322 0.8747 0.7014 ] Network output: [ 0.002685 -0.01289 0.9944 3.791e-06 -1.702e-06 1.013 2.857e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09932 0.185 0.1969 0.9873 0.9919 0.1121 0.7331 0.8608 0.3049 ] Network output: [ -0.00253 0.01194 1.005 4.157e-06 -1.866e-06 0.9885 3.133e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09475 0.09281 0.1649 0.1967 0.9852 0.9911 0.09477 0.6568 0.8358 0.2498 ] Network output: [ 7.835e-05 1 -4.971e-05 5.431e-07 -2.438e-07 0.9998 4.093e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001515 Epoch 9941 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008668 0.9968 0.9927 -1.492e-07 6.7e-08 -0.006915 -1.125e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003373 -0.006547 0.005301 0.9699 0.9743 0.006879 0.8237 0.8193 0.01604 ] Network output: [ 0.9999 7.454e-05 0.0003365 -1.973e-06 8.858e-07 -0.0002826 -1.487e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.21 -0.03583 -0.1548 0.1818 0.9834 0.9932 0.2359 0.4282 0.868 0.7078 ] Network output: [ -0.008684 1.003 1.007 -1.591e-07 7.142e-08 0.007237 -1.199e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006911 0.0006417 0.004344 0.003116 0.9889 0.9919 0.007047 0.8509 0.8917 0.01143 ] Network output: [ -0.0001505 0.001257 1 -6.2e-06 2.783e-06 0.9986 -4.672e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.224 0.1065 0.3503 0.1416 0.9849 0.9939 0.2248 0.4322 0.8747 0.7014 ] Network output: [ 0.002684 -0.01289 0.9944 3.786e-06 -1.7e-06 1.013 2.853e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09932 0.185 0.1969 0.9873 0.9919 0.1121 0.7331 0.8608 0.3049 ] Network output: [ -0.002528 0.01193 1.005 4.152e-06 -1.864e-06 0.9885 3.129e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09475 0.09281 0.1649 0.1967 0.9852 0.9911 0.09477 0.6568 0.8358 0.2498 ] Network output: [ 7.833e-05 1 -4.972e-05 5.424e-07 -2.435e-07 0.9998 4.088e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001514 Epoch 9942 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008667 0.9968 0.9927 -1.491e-07 6.695e-08 -0.006914 -1.124e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003522 -0.003373 -0.006547 0.0053 0.9699 0.9743 0.006879 0.8237 0.8193 0.01604 ] Network output: [ 0.9999 7.44e-05 0.0003364 -1.971e-06 8.847e-07 -0.0002825 -1.485e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.21 -0.03583 -0.1548 0.1818 0.9834 0.9932 0.2359 0.4282 0.868 0.7078 ] Network output: [ -0.008683 1.003 1.007 -1.59e-07 7.136e-08 0.007236 -1.198e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006911 0.0006417 0.004344 0.003116 0.9889 0.9919 0.007047 0.8509 0.8917 0.01143 ] Network output: [ -0.0001503 0.001256 1 -6.192e-06 2.78e-06 0.9986 -4.666e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.224 0.1065 0.3503 0.1416 0.9849 0.9939 0.2248 0.4322 0.8747 0.7014 ] Network output: [ 0.002682 -0.01288 0.9944 3.782e-06 -1.698e-06 1.013 2.85e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09932 0.185 0.1969 0.9873 0.9919 0.1122 0.7331 0.8607 0.3049 ] Network output: [ -0.002527 0.01193 1.005 4.147e-06 -1.862e-06 0.9885 3.125e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09476 0.09281 0.1649 0.1967 0.9852 0.9911 0.09477 0.6568 0.8358 0.2498 ] Network output: [ 7.831e-05 1 -4.973e-05 5.417e-07 -2.432e-07 0.9998 4.083e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001513 Epoch 9943 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008666 0.9968 0.9927 -1.49e-07 6.69e-08 -0.006913 -1.123e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003373 -0.006546 0.0053 0.9699 0.9743 0.006879 0.8237 0.8193 0.01604 ] Network output: [ 0.9999 7.425e-05 0.0003363 -1.968e-06 8.837e-07 -0.0002823 -1.483e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.21 -0.03583 -0.1548 0.1818 0.9834 0.9932 0.2359 0.4282 0.868 0.7078 ] Network output: [ -0.008682 1.003 1.007 -1.588e-07 7.13e-08 0.007235 -1.197e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006912 0.0006418 0.004344 0.003116 0.9889 0.9919 0.007048 0.8508 0.8917 0.01143 ] Network output: [ -0.0001502 0.001255 1 -6.184e-06 2.776e-06 0.9986 -4.661e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2241 0.1065 0.3503 0.1416 0.9849 0.9939 0.2248 0.4322 0.8747 0.7014 ] Network output: [ 0.002681 -0.01287 0.9944 3.777e-06 -1.696e-06 1.013 2.846e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09932 0.185 0.1969 0.9873 0.9919 0.1122 0.733 0.8607 0.3049 ] Network output: [ -0.002526 0.01192 1.005 4.142e-06 -1.859e-06 0.9885 3.121e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09476 0.09282 0.1649 0.1967 0.9852 0.9911 0.09477 0.6568 0.8358 0.2498 ] Network output: [ 7.83e-05 1 -4.973e-05 5.411e-07 -2.429e-07 0.9998 4.078e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001512 Epoch 9944 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008665 0.9968 0.9927 -1.489e-07 6.685e-08 -0.006913 -1.122e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003374 -0.006546 0.0053 0.9699 0.9743 0.006879 0.8237 0.8193 0.01604 ] Network output: [ 0.9999 7.41e-05 0.0003361 -1.966e-06 8.826e-07 -0.0002821 -1.482e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.21 -0.03583 -0.1548 0.1818 0.9834 0.9932 0.2359 0.4282 0.868 0.7078 ] Network output: [ -0.008681 1.003 1.007 -1.587e-07 7.124e-08 0.007235 -1.196e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006912 0.0006418 0.004343 0.003116 0.9889 0.9919 0.007048 0.8508 0.8917 0.01143 ] Network output: [ -0.00015 0.001255 1 -6.177e-06 2.773e-06 0.9986 -4.655e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2241 0.1065 0.3503 0.1416 0.9849 0.9939 0.2248 0.4322 0.8747 0.7014 ] Network output: [ 0.00268 -0.01287 0.9944 3.772e-06 -1.694e-06 1.013 2.843e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09933 0.185 0.1969 0.9873 0.9919 0.1122 0.733 0.8607 0.3049 ] Network output: [ -0.002524 0.01192 1.005 4.137e-06 -1.857e-06 0.9885 3.118e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09476 0.09282 0.1649 0.1967 0.9852 0.9911 0.09477 0.6568 0.8358 0.2498 ] Network output: [ 7.828e-05 1 -4.974e-05 5.404e-07 -2.426e-07 0.9998 4.073e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001512 Epoch 9945 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008665 0.9968 0.9927 -1.488e-07 6.68e-08 -0.006912 -1.121e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003374 -0.006545 0.005299 0.9699 0.9743 0.006879 0.8237 0.8193 0.01604 ] Network output: [ 0.9999 7.396e-05 0.000336 -1.963e-06 8.815e-07 -0.000282 -1.48e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2101 -0.03584 -0.1548 0.1818 0.9834 0.9932 0.2359 0.4282 0.868 0.7078 ] Network output: [ -0.00868 1.003 1.007 -1.586e-07 7.118e-08 0.007234 -1.195e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006912 0.0006419 0.004343 0.003116 0.9889 0.9919 0.007048 0.8508 0.8917 0.01143 ] Network output: [ -0.0001499 0.001254 1 -6.169e-06 2.77e-06 0.9986 -4.649e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2241 0.1065 0.3503 0.1416 0.9849 0.9939 0.2248 0.4322 0.8747 0.7014 ] Network output: [ 0.002678 -0.01286 0.9944 3.768e-06 -1.691e-06 1.013 2.839e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09933 0.185 0.1969 0.9873 0.9919 0.1122 0.733 0.8607 0.3049 ] Network output: [ -0.002523 0.01191 1.005 4.132e-06 -1.855e-06 0.9885 3.114e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09476 0.09282 0.1649 0.1967 0.9852 0.9911 0.09478 0.6568 0.8358 0.2498 ] Network output: [ 7.826e-05 1 -4.974e-05 5.398e-07 -2.423e-07 0.9998 4.068e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001511 Epoch 9946 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008664 0.9968 0.9927 -1.487e-07 6.675e-08 -0.006911 -1.121e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003374 -0.006545 0.005299 0.9699 0.9743 0.006879 0.8237 0.8193 0.01604 ] Network output: [ 0.9999 7.381e-05 0.0003358 -1.961e-06 8.804e-07 -0.0002818 -1.478e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2101 -0.03584 -0.1548 0.1817 0.9834 0.9932 0.2359 0.4282 0.868 0.7078 ] Network output: [ -0.00868 1.003 1.007 -1.584e-07 7.112e-08 0.007234 -1.194e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006913 0.0006419 0.004343 0.003115 0.9889 0.9919 0.007049 0.8508 0.8917 0.01143 ] Network output: [ -0.0001498 0.001253 1 -6.162e-06 2.766e-06 0.9986 -4.644e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2241 0.1065 0.3503 0.1416 0.9849 0.9939 0.2248 0.4322 0.8747 0.7014 ] Network output: [ 0.002677 -0.01285 0.9944 3.763e-06 -1.689e-06 1.013 2.836e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09933 0.185 0.1969 0.9873 0.9919 0.1122 0.733 0.8607 0.3049 ] Network output: [ -0.002522 0.01191 1.005 4.127e-06 -1.853e-06 0.9885 3.11e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09476 0.09282 0.1649 0.1968 0.9852 0.9911 0.09478 0.6568 0.8358 0.2498 ] Network output: [ 7.824e-05 1 -4.975e-05 5.391e-07 -2.42e-07 0.9998 4.063e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000151 Epoch 9947 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008663 0.9968 0.9927 -1.486e-07 6.671e-08 -0.006911 -1.12e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003374 -0.006544 0.005299 0.9699 0.9743 0.00688 0.8237 0.8193 0.01604 ] Network output: [ 0.9999 7.367e-05 0.0003357 -1.959e-06 8.793e-07 -0.0002816 -1.476e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2101 -0.03584 -0.1548 0.1817 0.9834 0.9932 0.2359 0.4282 0.868 0.7077 ] Network output: [ -0.008679 1.003 1.007 -1.583e-07 7.106e-08 0.007233 -1.193e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006913 0.000642 0.004343 0.003115 0.9889 0.9919 0.007049 0.8508 0.8917 0.01143 ] Network output: [ -0.0001496 0.001252 1 -6.154e-06 2.763e-06 0.9986 -4.638e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2241 0.1065 0.3503 0.1416 0.9849 0.9939 0.2249 0.4322 0.8747 0.7014 ] Network output: [ 0.002675 -0.01285 0.9944 3.758e-06 -1.687e-06 1.013 2.833e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09933 0.185 0.1969 0.9873 0.9919 0.1122 0.733 0.8607 0.3049 ] Network output: [ -0.00252 0.0119 1.005 4.122e-06 -1.85e-06 0.9885 3.106e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09476 0.09282 0.1649 0.1968 0.9852 0.9911 0.09478 0.6568 0.8358 0.2498 ] Network output: [ 7.823e-05 1 -4.976e-05 5.385e-07 -2.417e-07 0.9998 4.058e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001509 Epoch 9948 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008662 0.9968 0.9927 -1.485e-07 6.666e-08 -0.00691 -1.119e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003374 -0.006544 0.005298 0.9699 0.9743 0.00688 0.8237 0.8193 0.01603 ] Network output: [ 0.9999 7.352e-05 0.0003355 -1.956e-06 8.782e-07 -0.0002815 -1.474e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2101 -0.03584 -0.1548 0.1817 0.9834 0.9932 0.2359 0.4282 0.868 0.7077 ] Network output: [ -0.008678 1.003 1.007 -1.582e-07 7.1e-08 0.007233 -1.192e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006913 0.000642 0.004343 0.003115 0.9889 0.9919 0.007049 0.8508 0.8917 0.01143 ] Network output: [ -0.0001495 0.001252 1 -6.147e-06 2.759e-06 0.9986 -4.632e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2241 0.1065 0.3503 0.1416 0.9849 0.9939 0.2249 0.4322 0.8747 0.7014 ] Network output: [ 0.002674 -0.01284 0.9944 3.754e-06 -1.685e-06 1.013 2.829e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09934 0.185 0.1969 0.9873 0.9919 0.1122 0.733 0.8607 0.3049 ] Network output: [ -0.002519 0.01189 1.005 4.117e-06 -1.848e-06 0.9885 3.103e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09477 0.09282 0.1649 0.1968 0.9852 0.9911 0.09478 0.6568 0.8357 0.2498 ] Network output: [ 7.821e-05 1 -4.976e-05 5.378e-07 -2.414e-07 0.9998 4.053e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001508 Epoch 9949 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008661 0.9968 0.9927 -1.484e-07 6.661e-08 -0.006909 -1.118e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003374 -0.006543 0.005298 0.9699 0.9743 0.00688 0.8237 0.8193 0.01603 ] Network output: [ 0.9999 7.337e-05 0.0003354 -1.954e-06 8.771e-07 -0.0002813 -1.472e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2101 -0.03584 -0.1548 0.1817 0.9834 0.9932 0.2359 0.4282 0.868 0.7077 ] Network output: [ -0.008677 1.003 1.007 -1.58e-07 7.094e-08 0.007232 -1.191e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006913 0.0006421 0.004343 0.003115 0.9889 0.9919 0.00705 0.8508 0.8917 0.01143 ] Network output: [ -0.0001493 0.001251 1 -6.139e-06 2.756e-06 0.9986 -4.627e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2241 0.1065 0.3503 0.1416 0.9849 0.9939 0.2249 0.4321 0.8747 0.7014 ] Network output: [ 0.002672 -0.01284 0.9944 3.749e-06 -1.683e-06 1.013 2.826e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09934 0.185 0.1969 0.9873 0.9919 0.1122 0.733 0.8607 0.3049 ] Network output: [ -0.002518 0.01189 1.005 4.112e-06 -1.846e-06 0.9885 3.099e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09477 0.09283 0.1649 0.1968 0.9852 0.9911 0.09478 0.6568 0.8357 0.2498 ] Network output: [ 7.819e-05 1 -4.977e-05 5.372e-07 -2.412e-07 0.9998 4.048e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001507 Epoch 9950 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00866 0.9968 0.9927 -1.483e-07 6.656e-08 -0.006909 -1.117e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003374 -0.006543 0.005298 0.9699 0.9743 0.00688 0.8237 0.8193 0.01603 ] Network output: [ 0.9999 7.323e-05 0.0003352 -1.951e-06 8.761e-07 -0.0002811 -1.471e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2101 -0.03584 -0.1548 0.1817 0.9834 0.9932 0.2359 0.4282 0.868 0.7077 ] Network output: [ -0.008677 1.003 1.007 -1.579e-07 7.088e-08 0.007232 -1.19e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006914 0.0006421 0.004343 0.003114 0.9889 0.9919 0.00705 0.8508 0.8917 0.01143 ] Network output: [ -0.0001492 0.00125 1 -6.131e-06 2.753e-06 0.9986 -4.621e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2241 0.1065 0.3503 0.1416 0.9849 0.9939 0.2249 0.4321 0.8747 0.7014 ] Network output: [ 0.002671 -0.01283 0.9944 3.745e-06 -1.681e-06 1.013 2.822e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09934 0.185 0.1969 0.9873 0.9919 0.1122 0.733 0.8607 0.3049 ] Network output: [ -0.002516 0.01188 1.005 4.107e-06 -1.844e-06 0.9885 3.095e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09477 0.09283 0.1649 0.1968 0.9852 0.9911 0.09478 0.6567 0.8357 0.2498 ] Network output: [ 7.817e-05 1 -4.978e-05 5.365e-07 -2.409e-07 0.9998 4.043e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001506 Epoch 9951 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008659 0.9968 0.9927 -1.482e-07 6.651e-08 -0.006908 -1.117e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003374 -0.006542 0.005297 0.9699 0.9743 0.00688 0.8236 0.8193 0.01603 ] Network output: [ 0.9999 7.308e-05 0.0003351 -1.949e-06 8.75e-07 -0.000281 -1.469e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2101 -0.03584 -0.1547 0.1817 0.9834 0.9932 0.236 0.4282 0.8679 0.7077 ] Network output: [ -0.008676 1.003 1.007 -1.578e-07 7.082e-08 0.007231 -1.189e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006914 0.0006422 0.004343 0.003114 0.9889 0.9919 0.00705 0.8508 0.8917 0.01143 ] Network output: [ -0.000149 0.00125 1 -6.124e-06 2.749e-06 0.9986 -4.615e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2241 0.1065 0.3503 0.1416 0.9849 0.9939 0.2249 0.4321 0.8747 0.7014 ] Network output: [ 0.002669 -0.01282 0.9944 3.74e-06 -1.679e-06 1.013 2.819e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09934 0.185 0.1969 0.9873 0.9919 0.1122 0.733 0.8607 0.3049 ] Network output: [ -0.002515 0.01188 1.005 4.102e-06 -1.841e-06 0.9885 3.091e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09477 0.09283 0.1649 0.1968 0.9852 0.9911 0.09478 0.6567 0.8357 0.2498 ] Network output: [ 7.816e-05 1 -4.978e-05 5.359e-07 -2.406e-07 0.9998 4.038e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001506 Epoch 9952 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008659 0.9968 0.9927 -1.481e-07 6.647e-08 -0.006908 -1.116e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003374 -0.006541 0.005297 0.9699 0.9743 0.00688 0.8236 0.8193 0.01603 ] Network output: [ 0.9999 7.294e-05 0.0003349 -1.947e-06 8.739e-07 -0.0002808 -1.467e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2101 -0.03584 -0.1547 0.1817 0.9834 0.9932 0.236 0.4282 0.8679 0.7077 ] Network output: [ -0.008675 1.003 1.007 -1.576e-07 7.076e-08 0.007231 -1.188e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006914 0.0006422 0.004343 0.003114 0.9889 0.9919 0.007051 0.8508 0.8917 0.01143 ] Network output: [ -0.0001489 0.001249 1 -6.116e-06 2.746e-06 0.9986 -4.61e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2241 0.1065 0.3503 0.1416 0.9849 0.9939 0.2249 0.4321 0.8747 0.7014 ] Network output: [ 0.002668 -0.01282 0.9944 3.736e-06 -1.677e-06 1.013 2.815e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09935 0.185 0.1969 0.9873 0.9919 0.1122 0.733 0.8607 0.3049 ] Network output: [ -0.002514 0.01187 1.005 4.097e-06 -1.839e-06 0.9885 3.088e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09477 0.09283 0.1649 0.1968 0.9852 0.9911 0.09479 0.6567 0.8357 0.2498 ] Network output: [ 7.814e-05 1 -4.979e-05 5.352e-07 -2.403e-07 0.9998 4.034e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001505 Epoch 9953 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008658 0.9968 0.9927 -1.479e-07 6.642e-08 -0.006907 -1.115e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003374 -0.006541 0.005296 0.9699 0.9743 0.00688 0.8236 0.8193 0.01603 ] Network output: [ 0.9999 7.279e-05 0.0003348 -1.944e-06 8.728e-07 -0.0002806 -1.465e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2101 -0.03584 -0.1547 0.1817 0.9834 0.9932 0.236 0.4282 0.8679 0.7077 ] Network output: [ -0.008674 1.003 1.007 -1.575e-07 7.07e-08 0.00723 -1.187e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006915 0.0006423 0.004342 0.003114 0.9889 0.9919 0.007051 0.8508 0.8916 0.01142 ] Network output: [ -0.0001488 0.001248 1 -6.109e-06 2.742e-06 0.9986 -4.604e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2241 0.1065 0.3503 0.1416 0.9849 0.9939 0.2249 0.4321 0.8747 0.7014 ] Network output: [ 0.002667 -0.01281 0.9944 3.731e-06 -1.675e-06 1.013 2.812e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09935 0.185 0.1969 0.9873 0.9919 0.1122 0.733 0.8607 0.3049 ] Network output: [ -0.002513 0.01187 1.005 4.092e-06 -1.837e-06 0.9885 3.084e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09477 0.09283 0.1649 0.1968 0.9852 0.9911 0.09479 0.6567 0.8357 0.2498 ] Network output: [ 7.812e-05 1 -4.98e-05 5.346e-07 -2.4e-07 0.9998 4.029e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001504 Epoch 9954 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008657 0.9968 0.9927 -1.478e-07 6.637e-08 -0.006906 -1.114e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003374 -0.00654 0.005296 0.9699 0.9743 0.006881 0.8236 0.8193 0.01603 ] Network output: [ 0.9999 7.265e-05 0.0003346 -1.942e-06 8.718e-07 -0.0002805 -1.463e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2101 -0.03584 -0.1547 0.1817 0.9834 0.9932 0.236 0.4282 0.8679 0.7077 ] Network output: [ -0.008673 1.003 1.007 -1.574e-07 7.064e-08 0.00723 -1.186e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006915 0.0006423 0.004342 0.003114 0.9889 0.9919 0.007051 0.8508 0.8916 0.01142 ] Network output: [ -0.0001486 0.001248 1 -6.101e-06 2.739e-06 0.9986 -4.598e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2241 0.1065 0.3503 0.1416 0.9849 0.9939 0.2249 0.4321 0.8747 0.7014 ] Network output: [ 0.002665 -0.0128 0.9944 3.726e-06 -1.673e-06 1.013 2.808e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09935 0.185 0.1969 0.9873 0.9919 0.1122 0.7329 0.8607 0.3049 ] Network output: [ -0.002511 0.01186 1.005 4.087e-06 -1.835e-06 0.9885 3.08e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09477 0.09283 0.1649 0.1968 0.9852 0.9911 0.09479 0.6567 0.8357 0.2498 ] Network output: [ 7.81e-05 1 -4.98e-05 5.339e-07 -2.397e-07 0.9998 4.024e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001503 Epoch 9955 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008656 0.9968 0.9927 -1.477e-07 6.632e-08 -0.006906 -1.113e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003374 -0.00654 0.005296 0.9699 0.9743 0.006881 0.8236 0.8193 0.01603 ] Network output: [ 0.9999 7.25e-05 0.0003345 -1.939e-06 8.707e-07 -0.0002803 -1.462e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2101 -0.03585 -0.1547 0.1817 0.9834 0.9932 0.236 0.4282 0.8679 0.7077 ] Network output: [ -0.008673 1.003 1.007 -1.572e-07 7.058e-08 0.007229 -1.185e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006915 0.0006424 0.004342 0.003113 0.9889 0.9919 0.007052 0.8508 0.8916 0.01142 ] Network output: [ -0.0001485 0.001247 1 -6.094e-06 2.736e-06 0.9986 -4.593e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2241 0.1065 0.3503 0.1416 0.9849 0.9939 0.2249 0.4321 0.8747 0.7014 ] Network output: [ 0.002664 -0.0128 0.9944 3.722e-06 -1.671e-06 1.013 2.805e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09935 0.185 0.1969 0.9873 0.9919 0.1122 0.7329 0.8607 0.3049 ] Network output: [ -0.00251 0.01185 1.005 4.082e-06 -1.833e-06 0.9885 3.076e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09478 0.09284 0.1649 0.1968 0.9852 0.9911 0.09479 0.6567 0.8357 0.2498 ] Network output: [ 7.808e-05 1 -4.981e-05 5.333e-07 -2.394e-07 0.9998 4.019e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001502 Epoch 9956 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008655 0.9968 0.9927 -1.476e-07 6.627e-08 -0.006905 -1.113e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003374 -0.006539 0.005295 0.9699 0.9743 0.006881 0.8236 0.8193 0.01603 ] Network output: [ 0.9999 7.236e-05 0.0003344 -1.937e-06 8.696e-07 -0.0002801 -1.46e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2101 -0.03585 -0.1547 0.1817 0.9834 0.9932 0.236 0.4282 0.8679 0.7077 ] Network output: [ -0.008672 1.003 1.007 -1.571e-07 7.052e-08 0.007229 -1.184e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006916 0.0006424 0.004342 0.003113 0.9889 0.9919 0.007052 0.8508 0.8916 0.01142 ] Network output: [ -0.0001483 0.001246 1 -6.086e-06 2.732e-06 0.9986 -4.587e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2241 0.1065 0.3503 0.1416 0.9849 0.9939 0.2249 0.4321 0.8747 0.7014 ] Network output: [ 0.002662 -0.01279 0.9944 3.717e-06 -1.669e-06 1.013 2.802e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09936 0.185 0.1969 0.9873 0.9919 0.1122 0.7329 0.8607 0.3049 ] Network output: [ -0.002509 0.01185 1.005 4.077e-06 -1.83e-06 0.9885 3.073e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09478 0.09284 0.1649 0.1968 0.9852 0.9911 0.09479 0.6567 0.8357 0.2498 ] Network output: [ 7.807e-05 1 -4.982e-05 5.326e-07 -2.391e-07 0.9998 4.014e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001501 Epoch 9957 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008654 0.9968 0.9927 -1.475e-07 6.623e-08 -0.006904 -1.112e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003374 -0.006539 0.005295 0.9699 0.9743 0.006881 0.8236 0.8193 0.01603 ] Network output: [ 0.9999 7.221e-05 0.0003342 -1.935e-06 8.686e-07 -0.00028 -1.458e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2101 -0.03585 -0.1547 0.1817 0.9834 0.9932 0.236 0.4282 0.8679 0.7077 ] Network output: [ -0.008671 1.003 1.007 -1.57e-07 7.046e-08 0.007228 -1.183e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006916 0.0006425 0.004342 0.003113 0.9889 0.9919 0.007052 0.8508 0.8916 0.01142 ] Network output: [ -0.0001482 0.001246 1 -6.079e-06 2.729e-06 0.9986 -4.581e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2241 0.1065 0.3503 0.1416 0.9849 0.9939 0.2249 0.4321 0.8747 0.7014 ] Network output: [ 0.002661 -0.01278 0.9944 3.713e-06 -1.667e-06 1.013 2.798e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09936 0.185 0.1969 0.9873 0.9919 0.1122 0.7329 0.8607 0.3049 ] Network output: [ -0.002507 0.01184 1.005 4.072e-06 -1.828e-06 0.9885 3.069e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09478 0.09284 0.1649 0.1968 0.9852 0.9911 0.09479 0.6567 0.8357 0.2498 ] Network output: [ 7.805e-05 1 -4.983e-05 5.32e-07 -2.388e-07 0.9998 4.009e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001501 Epoch 9958 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008653 0.9968 0.9928 -1.474e-07 6.618e-08 -0.006904 -1.111e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003375 -0.006538 0.005295 0.9699 0.9743 0.006881 0.8236 0.8193 0.01603 ] Network output: [ 0.9999 7.207e-05 0.0003341 -1.932e-06 8.675e-07 -0.0002798 -1.456e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2101 -0.03585 -0.1547 0.1817 0.9834 0.9932 0.236 0.4282 0.8679 0.7077 ] Network output: [ -0.00867 1.003 1.007 -1.568e-07 7.041e-08 0.007228 -1.182e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006916 0.0006425 0.004342 0.003113 0.9889 0.9919 0.007053 0.8508 0.8916 0.01142 ] Network output: [ -0.0001481 0.001245 1 -6.071e-06 2.726e-06 0.9986 -4.576e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2241 0.1065 0.3503 0.1416 0.9849 0.9939 0.2249 0.4321 0.8747 0.7014 ] Network output: [ 0.002659 -0.01278 0.9944 3.708e-06 -1.665e-06 1.013 2.795e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09936 0.185 0.1969 0.9873 0.9919 0.1122 0.7329 0.8607 0.3049 ] Network output: [ -0.002506 0.01184 1.005 4.067e-06 -1.826e-06 0.9885 3.065e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09478 0.09284 0.1649 0.1968 0.9852 0.9911 0.0948 0.6567 0.8357 0.2498 ] Network output: [ 7.803e-05 1 -4.983e-05 5.313e-07 -2.385e-07 0.9998 4.004e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00015 Epoch 9959 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008653 0.9968 0.9928 -1.473e-07 6.613e-08 -0.006903 -1.11e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003375 -0.006538 0.005294 0.9699 0.9743 0.006881 0.8236 0.8193 0.01602 ] Network output: [ 0.9999 7.192e-05 0.0003339 -1.93e-06 8.664e-07 -0.0002796 -1.454e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2101 -0.03585 -0.1547 0.1817 0.9834 0.9932 0.236 0.4282 0.8679 0.7077 ] Network output: [ -0.008669 1.003 1.007 -1.567e-07 7.035e-08 0.007227 -1.181e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006917 0.0006426 0.004342 0.003112 0.9889 0.9919 0.007053 0.8508 0.8916 0.01142 ] Network output: [ -0.0001479 0.001244 1 -6.064e-06 2.722e-06 0.9986 -4.57e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2242 0.1065 0.3503 0.1416 0.9849 0.9939 0.2249 0.4321 0.8747 0.7014 ] Network output: [ 0.002658 -0.01277 0.9944 3.704e-06 -1.663e-06 1.013 2.791e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09936 0.185 0.1969 0.9873 0.9919 0.1122 0.7329 0.8607 0.3049 ] Network output: [ -0.002505 0.01183 1.005 4.063e-06 -1.824e-06 0.9885 3.062e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09478 0.09284 0.1649 0.1968 0.9852 0.9911 0.0948 0.6567 0.8357 0.2498 ] Network output: [ 7.801e-05 1 -4.984e-05 5.307e-07 -2.382e-07 0.9998 3.999e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001499 Epoch 9960 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008652 0.9968 0.9928 -1.472e-07 6.608e-08 -0.006902 -1.109e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003375 -0.006537 0.005294 0.9699 0.9743 0.006881 0.8236 0.8193 0.01602 ] Network output: [ 0.9999 7.178e-05 0.0003338 -1.928e-06 8.653e-07 -0.0002795 -1.453e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2101 -0.03585 -0.1547 0.1817 0.9834 0.9932 0.236 0.4282 0.8679 0.7077 ] Network output: [ -0.008669 1.003 1.007 -1.566e-07 7.029e-08 0.007227 -1.18e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006917 0.0006426 0.004342 0.003112 0.9889 0.9919 0.007053 0.8508 0.8916 0.01142 ] Network output: [ -0.0001478 0.001243 1 -6.057e-06 2.719e-06 0.9986 -4.564e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2242 0.1065 0.3503 0.1415 0.9849 0.9939 0.2249 0.4321 0.8747 0.7014 ] Network output: [ 0.002657 -0.01277 0.9944 3.699e-06 -1.661e-06 1.013 2.788e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09937 0.185 0.1969 0.9873 0.9919 0.1122 0.7329 0.8607 0.3049 ] Network output: [ -0.002503 0.01183 1.005 4.058e-06 -1.822e-06 0.9885 3.058e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09478 0.09284 0.1649 0.1968 0.9852 0.9911 0.0948 0.6567 0.8357 0.2498 ] Network output: [ 7.8e-05 1 -4.985e-05 5.3e-07 -2.38e-07 0.9998 3.995e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001498 Epoch 9961 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008651 0.9968 0.9928 -1.471e-07 6.603e-08 -0.006902 -1.109e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003375 -0.006537 0.005294 0.9699 0.9743 0.006882 0.8236 0.8193 0.01602 ] Network output: [ 0.9999 7.163e-05 0.0003336 -1.925e-06 8.643e-07 -0.0002793 -1.451e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2101 -0.03585 -0.1547 0.1817 0.9834 0.9932 0.236 0.4282 0.8679 0.7077 ] Network output: [ -0.008668 1.003 1.007 -1.564e-07 7.023e-08 0.007226 -1.179e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006917 0.0006427 0.004342 0.003112 0.9889 0.9919 0.007054 0.8508 0.8916 0.01142 ] Network output: [ -0.0001476 0.001243 1 -6.049e-06 2.716e-06 0.9986 -4.559e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2242 0.1065 0.3504 0.1415 0.9849 0.9939 0.2249 0.4321 0.8747 0.7013 ] Network output: [ 0.002655 -0.01276 0.9944 3.695e-06 -1.659e-06 1.013 2.784e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09937 0.185 0.1969 0.9873 0.9919 0.1122 0.7329 0.8607 0.3049 ] Network output: [ -0.002502 0.01182 1.005 4.053e-06 -1.819e-06 0.9885 3.054e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09479 0.09284 0.1649 0.1968 0.9852 0.9911 0.0948 0.6566 0.8357 0.2498 ] Network output: [ 7.798e-05 1 -4.985e-05 5.294e-07 -2.377e-07 0.9998 3.99e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001497 Epoch 9962 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00865 0.9968 0.9928 -1.47e-07 6.599e-08 -0.006901 -1.108e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003375 -0.006536 0.005293 0.9699 0.9743 0.006882 0.8236 0.8193 0.01602 ] Network output: [ 0.9999 7.149e-05 0.0003335 -1.923e-06 8.632e-07 -0.0002791 -1.449e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2101 -0.03585 -0.1547 0.1817 0.9834 0.9932 0.236 0.4282 0.8679 0.7077 ] Network output: [ -0.008667 1.003 1.007 -1.563e-07 7.017e-08 0.007226 -1.178e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006918 0.0006427 0.004342 0.003112 0.9889 0.9919 0.007054 0.8508 0.8916 0.01142 ] Network output: [ -0.0001475 0.001242 1 -6.042e-06 2.712e-06 0.9986 -4.553e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2242 0.1065 0.3504 0.1415 0.9849 0.9939 0.2249 0.4321 0.8747 0.7013 ] Network output: [ 0.002654 -0.01275 0.9944 3.69e-06 -1.657e-06 1.013 2.781e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09937 0.185 0.1969 0.9873 0.9919 0.1122 0.7329 0.8607 0.3049 ] Network output: [ -0.002501 0.01181 1.005 4.048e-06 -1.817e-06 0.9885 3.051e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09479 0.09285 0.1649 0.1968 0.9852 0.9911 0.0948 0.6566 0.8357 0.2498 ] Network output: [ 7.796e-05 1 -4.986e-05 5.288e-07 -2.374e-07 0.9998 3.985e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001496 Epoch 9963 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008649 0.9968 0.9928 -1.469e-07 6.594e-08 -0.0069 -1.107e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003375 -0.006535 0.005293 0.9699 0.9743 0.006882 0.8236 0.8193 0.01602 ] Network output: [ 0.9999 7.134e-05 0.0003333 -1.92e-06 8.622e-07 -0.000279 -1.447e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2101 -0.03585 -0.1547 0.1817 0.9834 0.9932 0.236 0.4282 0.8679 0.7077 ] Network output: [ -0.008666 1.003 1.007 -1.562e-07 7.011e-08 0.007225 -1.177e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006918 0.0006428 0.004341 0.003112 0.9889 0.9919 0.007054 0.8508 0.8916 0.01142 ] Network output: [ -0.0001474 0.001241 1 -6.034e-06 2.709e-06 0.9986 -4.548e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2242 0.1065 0.3504 0.1415 0.9849 0.9939 0.225 0.4321 0.8747 0.7013 ] Network output: [ 0.002652 -0.01275 0.9944 3.686e-06 -1.655e-06 1.013 2.778e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09937 0.185 0.1969 0.9873 0.9919 0.1122 0.7329 0.8607 0.3049 ] Network output: [ -0.0025 0.01181 1.005 4.043e-06 -1.815e-06 0.9885 3.047e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09479 0.09285 0.1649 0.1968 0.9852 0.9911 0.0948 0.6566 0.8357 0.2498 ] Network output: [ 7.794e-05 1 -4.987e-05 5.281e-07 -2.371e-07 0.9998 3.98e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001496 Epoch 9964 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008648 0.9968 0.9928 -1.468e-07 6.589e-08 -0.0069 -1.106e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003375 -0.006535 0.005292 0.9699 0.9743 0.006882 0.8236 0.8193 0.01602 ] Network output: [ 0.9999 7.12e-05 0.0003332 -1.918e-06 8.611e-07 -0.0002788 -1.446e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2102 -0.03585 -0.1546 0.1817 0.9834 0.9932 0.236 0.4282 0.8679 0.7077 ] Network output: [ -0.008665 1.003 1.007 -1.56e-07 7.005e-08 0.007225 -1.176e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006918 0.0006428 0.004341 0.003111 0.9889 0.9919 0.007055 0.8508 0.8916 0.01142 ] Network output: [ -0.0001472 0.001241 1 -6.027e-06 2.706e-06 0.9986 -4.542e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2242 0.1066 0.3504 0.1415 0.9849 0.9939 0.225 0.4321 0.8747 0.7013 ] Network output: [ 0.002651 -0.01274 0.9944 3.681e-06 -1.653e-06 1.013 2.774e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09938 0.185 0.1969 0.9873 0.9919 0.1122 0.7329 0.8607 0.3049 ] Network output: [ -0.002498 0.0118 1.005 4.038e-06 -1.813e-06 0.9885 3.043e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09479 0.09285 0.1649 0.1968 0.9852 0.9911 0.0948 0.6566 0.8357 0.2498 ] Network output: [ 7.793e-05 1 -4.988e-05 5.275e-07 -2.368e-07 0.9998 3.975e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001495 Epoch 9965 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008648 0.9968 0.9928 -1.467e-07 6.584e-08 -0.006899 -1.105e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003375 -0.006534 0.005292 0.9699 0.9743 0.006882 0.8236 0.8193 0.01602 ] Network output: [ 0.9999 7.105e-05 0.000333 -1.916e-06 8.6e-07 -0.0002787 -1.444e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2102 -0.03586 -0.1546 0.1817 0.9834 0.9932 0.236 0.4281 0.8679 0.7077 ] Network output: [ -0.008665 1.003 1.007 -1.559e-07 6.999e-08 0.007224 -1.175e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006919 0.0006429 0.004341 0.003111 0.9889 0.9919 0.007055 0.8508 0.8916 0.01142 ] Network output: [ -0.0001471 0.00124 1 -6.019e-06 2.702e-06 0.9986 -4.536e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2242 0.1066 0.3504 0.1415 0.9849 0.9939 0.225 0.4321 0.8747 0.7013 ] Network output: [ 0.002649 -0.01273 0.9944 3.677e-06 -1.651e-06 1.013 2.771e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09938 0.185 0.1969 0.9873 0.9919 0.1122 0.7328 0.8607 0.3049 ] Network output: [ -0.002497 0.0118 1.005 4.033e-06 -1.811e-06 0.9885 3.04e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09479 0.09285 0.1649 0.1968 0.9852 0.9911 0.09481 0.6566 0.8357 0.2498 ] Network output: [ 7.791e-05 1 -4.988e-05 5.268e-07 -2.365e-07 0.9998 3.97e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001494 Epoch 9966 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008647 0.9968 0.9928 -1.466e-07 6.579e-08 -0.006898 -1.104e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003375 -0.006534 0.005292 0.9699 0.9743 0.006882 0.8236 0.8193 0.01602 ] Network output: [ 0.9999 7.091e-05 0.0003329 -1.913e-06 8.59e-07 -0.0002785 -1.442e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2102 -0.03586 -0.1546 0.1817 0.9834 0.9932 0.236 0.4281 0.8679 0.7077 ] Network output: [ -0.008664 1.003 1.007 -1.558e-07 6.993e-08 0.007224 -1.174e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006919 0.0006429 0.004341 0.003111 0.9889 0.9919 0.007055 0.8508 0.8916 0.01142 ] Network output: [ -0.0001469 0.001239 1 -6.012e-06 2.699e-06 0.9986 -4.531e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2242 0.1066 0.3504 0.1415 0.9849 0.9939 0.225 0.4321 0.8747 0.7013 ] Network output: [ 0.002648 -0.01273 0.9944 3.672e-06 -1.649e-06 1.013 2.767e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09938 0.185 0.1969 0.9873 0.9919 0.1122 0.7328 0.8607 0.3049 ] Network output: [ -0.002496 0.01179 1.005 4.028e-06 -1.808e-06 0.9886 3.036e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09479 0.09285 0.1649 0.1968 0.9852 0.9911 0.09481 0.6566 0.8357 0.2498 ] Network output: [ 7.789e-05 1 -4.989e-05 5.262e-07 -2.362e-07 0.9998 3.966e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001493 Epoch 9967 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008646 0.9968 0.9928 -1.464e-07 6.575e-08 -0.006898 -1.104e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003523 -0.003375 -0.006533 0.005291 0.9699 0.9743 0.006882 0.8236 0.8193 0.01602 ] Network output: [ 0.9999 7.077e-05 0.0003328 -1.911e-06 8.579e-07 -0.0002783 -1.44e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2102 -0.03586 -0.1546 0.1817 0.9834 0.9932 0.2361 0.4281 0.8679 0.7077 ] Network output: [ -0.008663 1.003 1.007 -1.556e-07 6.987e-08 0.007223 -1.173e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006919 0.000643 0.004341 0.003111 0.9889 0.9919 0.007056 0.8507 0.8916 0.01141 ] Network output: [ -0.0001468 0.001239 1 -6.005e-06 2.696e-06 0.9986 -4.525e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2242 0.1066 0.3504 0.1415 0.9849 0.9939 0.225 0.4321 0.8747 0.7013 ] Network output: [ 0.002646 -0.01272 0.9944 3.668e-06 -1.647e-06 1.013 2.764e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09938 0.185 0.1969 0.9873 0.9919 0.1122 0.7328 0.8607 0.3049 ] Network output: [ -0.002494 0.01179 1.005 4.024e-06 -1.806e-06 0.9886 3.032e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09479 0.09285 0.1649 0.1968 0.9852 0.9911 0.09481 0.6566 0.8357 0.2498 ] Network output: [ 7.787e-05 1 -4.99e-05 5.256e-07 -2.359e-07 0.9998 3.961e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001492 Epoch 9968 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008645 0.9968 0.9928 -1.463e-07 6.57e-08 -0.006897 -1.103e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003375 -0.006533 0.005291 0.9699 0.9743 0.006883 0.8236 0.8193 0.01602 ] Network output: [ 0.9999 7.062e-05 0.0003326 -1.909e-06 8.569e-07 -0.0002782 -1.438e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2102 -0.03586 -0.1546 0.1817 0.9834 0.9932 0.2361 0.4281 0.8679 0.7077 ] Network output: [ -0.008662 1.003 1.007 -1.555e-07 6.981e-08 0.007223 -1.172e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00692 0.000643 0.004341 0.00311 0.9889 0.9919 0.007056 0.8507 0.8916 0.01141 ] Network output: [ -0.0001467 0.001238 1 -5.997e-06 2.692e-06 0.9986 -4.52e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2242 0.1066 0.3504 0.1415 0.9849 0.9939 0.225 0.4321 0.8747 0.7013 ] Network output: [ 0.002645 -0.01271 0.9944 3.663e-06 -1.645e-06 1.013 2.761e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09939 0.185 0.1969 0.9873 0.9919 0.1122 0.7328 0.8607 0.3049 ] Network output: [ -0.002493 0.01178 1.005 4.019e-06 -1.804e-06 0.9886 3.029e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0948 0.09286 0.1649 0.1968 0.9852 0.9911 0.09481 0.6566 0.8357 0.2499 ] Network output: [ 7.786e-05 1 -4.991e-05 5.249e-07 -2.357e-07 0.9998 3.956e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001491 Epoch 9969 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008644 0.9968 0.9928 -1.462e-07 6.565e-08 -0.006896 -1.102e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003375 -0.006532 0.005291 0.9699 0.9743 0.006883 0.8236 0.8192 0.01602 ] Network output: [ 0.9999 7.048e-05 0.0003325 -1.906e-06 8.558e-07 -0.000278 -1.437e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2102 -0.03586 -0.1546 0.1817 0.9834 0.9932 0.2361 0.4281 0.8679 0.7077 ] Network output: [ -0.008661 1.003 1.007 -1.554e-07 6.975e-08 0.007222 -1.171e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00692 0.0006431 0.004341 0.00311 0.9889 0.9919 0.007056 0.8507 0.8916 0.01141 ] Network output: [ -0.0001465 0.001237 1 -5.99e-06 2.689e-06 0.9986 -4.514e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2242 0.1066 0.3504 0.1415 0.9849 0.9939 0.225 0.4321 0.8747 0.7013 ] Network output: [ 0.002644 -0.01271 0.9944 3.659e-06 -1.643e-06 1.013 2.757e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09939 0.185 0.1969 0.9873 0.9919 0.1122 0.7328 0.8607 0.3049 ] Network output: [ -0.002492 0.01177 1.005 4.014e-06 -1.802e-06 0.9886 3.025e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0948 0.09286 0.1649 0.1968 0.9852 0.9911 0.09481 0.6566 0.8357 0.2499 ] Network output: [ 7.784e-05 1 -4.992e-05 5.243e-07 -2.354e-07 0.9998 3.951e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001491 Epoch 9970 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008643 0.9968 0.9928 -1.461e-07 6.56e-08 -0.006896 -1.101e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003375 -0.006532 0.00529 0.9699 0.9743 0.006883 0.8236 0.8192 0.01602 ] Network output: [ 0.9999 7.033e-05 0.0003323 -1.904e-06 8.547e-07 -0.0002778 -1.435e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2102 -0.03586 -0.1546 0.1817 0.9834 0.9932 0.2361 0.4281 0.8679 0.7077 ] Network output: [ -0.008661 1.003 1.007 -1.552e-07 6.969e-08 0.007222 -1.17e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00692 0.0006431 0.004341 0.00311 0.9889 0.9919 0.007057 0.8507 0.8916 0.01141 ] Network output: [ -0.0001464 0.001237 1 -5.982e-06 2.686e-06 0.9986 -4.509e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2242 0.1066 0.3504 0.1415 0.9849 0.9939 0.225 0.4321 0.8747 0.7013 ] Network output: [ 0.002642 -0.0127 0.9944 3.654e-06 -1.641e-06 1.013 2.754e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09939 0.185 0.1969 0.9873 0.9919 0.1122 0.7328 0.8607 0.3049 ] Network output: [ -0.00249 0.01177 1.005 4.009e-06 -1.8e-06 0.9886 3.021e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0948 0.09286 0.1649 0.1968 0.9852 0.9911 0.09481 0.6566 0.8357 0.2499 ] Network output: [ 7.782e-05 1 -4.992e-05 5.236e-07 -2.351e-07 0.9998 3.946e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000149 Epoch 9971 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008642 0.9968 0.9928 -1.46e-07 6.555e-08 -0.006895 -1.1e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003375 -0.006531 0.00529 0.9699 0.9743 0.006883 0.8236 0.8192 0.01601 ] Network output: [ 0.9999 7.019e-05 0.0003322 -1.902e-06 8.537e-07 -0.0002777 -1.433e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2102 -0.03586 -0.1546 0.1817 0.9834 0.9932 0.2361 0.4281 0.8679 0.7077 ] Network output: [ -0.00866 1.003 1.007 -1.551e-07 6.963e-08 0.007221 -1.169e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006921 0.0006432 0.004341 0.00311 0.9889 0.9919 0.007057 0.8507 0.8916 0.01141 ] Network output: [ -0.0001462 0.001236 1 -5.975e-06 2.682e-06 0.9986 -4.503e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2242 0.1066 0.3504 0.1415 0.9849 0.9939 0.225 0.4321 0.8747 0.7013 ] Network output: [ 0.002641 -0.01269 0.9944 3.65e-06 -1.639e-06 1.013 2.751e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1121 0.09939 0.185 0.1969 0.9873 0.9919 0.1122 0.7328 0.8607 0.3049 ] Network output: [ -0.002489 0.01176 1.005 4.004e-06 -1.798e-06 0.9886 3.018e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0948 0.09286 0.1649 0.1968 0.9852 0.9911 0.09482 0.6566 0.8357 0.2499 ] Network output: [ 7.781e-05 1 -4.993e-05 5.23e-07 -2.348e-07 0.9998 3.942e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001489 Epoch 9972 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008642 0.9968 0.9928 -1.459e-07 6.551e-08 -0.006895 -1.1e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003376 -0.006531 0.00529 0.9699 0.9743 0.006883 0.8236 0.8192 0.01601 ] Network output: [ 0.9999 7.005e-05 0.000332 -1.899e-06 8.526e-07 -0.0002775 -1.431e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2102 -0.03586 -0.1546 0.1817 0.9834 0.9932 0.2361 0.4281 0.8679 0.7077 ] Network output: [ -0.008659 1.003 1.007 -1.55e-07 6.957e-08 0.007221 -1.168e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006921 0.0006432 0.00434 0.00311 0.9889 0.9919 0.007057 0.8507 0.8916 0.01141 ] Network output: [ -0.0001461 0.001235 1 -5.968e-06 2.679e-06 0.9986 -4.497e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2242 0.1066 0.3504 0.1415 0.9849 0.9939 0.225 0.4321 0.8747 0.7013 ] Network output: [ 0.002639 -0.01269 0.9944 3.645e-06 -1.637e-06 1.013 2.747e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.0994 0.185 0.1969 0.9873 0.9919 0.1122 0.7328 0.8607 0.3049 ] Network output: [ -0.002488 0.01176 1.005 3.999e-06 -1.795e-06 0.9886 3.014e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0948 0.09286 0.1649 0.1968 0.9852 0.9911 0.09482 0.6565 0.8357 0.2499 ] Network output: [ 7.779e-05 1 -4.994e-05 5.224e-07 -2.345e-07 0.9998 3.937e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001488 Epoch 9973 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008641 0.9968 0.9928 -1.458e-07 6.546e-08 -0.006894 -1.099e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003376 -0.00653 0.005289 0.9699 0.9743 0.006883 0.8236 0.8192 0.01601 ] Network output: [ 0.9999 6.99e-05 0.0003319 -1.897e-06 8.516e-07 -0.0002773 -1.43e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2102 -0.03586 -0.1546 0.1817 0.9834 0.9932 0.2361 0.4281 0.8679 0.7077 ] Network output: [ -0.008658 1.003 1.007 -1.548e-07 6.951e-08 0.00722 -1.167e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006921 0.0006433 0.00434 0.003109 0.9889 0.9919 0.007058 0.8507 0.8916 0.01141 ] Network output: [ -0.000146 0.001234 1 -5.96e-06 2.676e-06 0.9986 -4.492e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2242 0.1066 0.3504 0.1415 0.9849 0.9939 0.225 0.4321 0.8747 0.7013 ] Network output: [ 0.002638 -0.01268 0.9944 3.641e-06 -1.635e-06 1.013 2.744e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.0994 0.185 0.1969 0.9873 0.9919 0.1122 0.7328 0.8607 0.3049 ] Network output: [ -0.002487 0.01175 1.005 3.994e-06 -1.793e-06 0.9886 3.01e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0948 0.09286 0.1649 0.1968 0.9852 0.9911 0.09482 0.6565 0.8357 0.2499 ] Network output: [ 7.777e-05 1 -4.995e-05 5.217e-07 -2.342e-07 0.9998 3.932e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001487 Epoch 9974 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00864 0.9968 0.9928 -1.457e-07 6.541e-08 -0.006893 -1.098e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003376 -0.006529 0.005289 0.9699 0.9743 0.006883 0.8236 0.8192 0.01601 ] Network output: [ 0.9999 6.976e-05 0.0003317 -1.895e-06 8.505e-07 -0.0002772 -1.428e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2102 -0.03586 -0.1546 0.1817 0.9834 0.9932 0.2361 0.4281 0.8679 0.7077 ] Network output: [ -0.008658 1.003 1.007 -1.547e-07 6.946e-08 0.00722 -1.166e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006922 0.0006433 0.00434 0.003109 0.9889 0.9919 0.007058 0.8507 0.8916 0.01141 ] Network output: [ -0.0001458 0.001234 1 -5.953e-06 2.673e-06 0.9986 -4.486e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2242 0.1066 0.3504 0.1415 0.9849 0.9939 0.225 0.4321 0.8747 0.7013 ] Network output: [ 0.002636 -0.01268 0.9944 3.636e-06 -1.633e-06 1.013 2.74e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.0994 0.185 0.1969 0.9873 0.9919 0.1122 0.7328 0.8607 0.3049 ] Network output: [ -0.002485 0.01175 1.005 3.99e-06 -1.791e-06 0.9886 3.007e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09481 0.09286 0.1649 0.1968 0.9852 0.9911 0.09482 0.6565 0.8357 0.2499 ] Network output: [ 7.775e-05 1 -4.996e-05 5.211e-07 -2.339e-07 0.9998 3.927e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001486 Epoch 9975 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008639 0.9968 0.9928 -1.456e-07 6.536e-08 -0.006893 -1.097e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003376 -0.006529 0.005288 0.9699 0.9743 0.006884 0.8236 0.8192 0.01601 ] Network output: [ 0.9999 6.961e-05 0.0003316 -1.892e-06 8.495e-07 -0.000277 -1.426e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2102 -0.03587 -0.1546 0.1817 0.9834 0.9932 0.2361 0.4281 0.8679 0.7077 ] Network output: [ -0.008657 1.003 1.007 -1.546e-07 6.94e-08 0.007219 -1.165e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006922 0.0006434 0.00434 0.003109 0.9889 0.9919 0.007058 0.8507 0.8916 0.01141 ] Network output: [ -0.0001457 0.001233 1 -5.946e-06 2.669e-06 0.9986 -4.481e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2243 0.1066 0.3504 0.1415 0.9849 0.9939 0.225 0.4321 0.8747 0.7013 ] Network output: [ 0.002635 -0.01267 0.9944 3.632e-06 -1.631e-06 1.013 2.737e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.0994 0.185 0.1969 0.9873 0.9919 0.1122 0.7328 0.8607 0.3049 ] Network output: [ -0.002484 0.01174 1.005 3.985e-06 -1.789e-06 0.9886 3.003e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09481 0.09287 0.1649 0.1968 0.9852 0.9911 0.09482 0.6565 0.8357 0.2499 ] Network output: [ 7.774e-05 1 -4.996e-05 5.205e-07 -2.337e-07 0.9998 3.922e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001486 Epoch 9976 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008638 0.9968 0.9928 -1.455e-07 6.532e-08 -0.006892 -1.096e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003376 -0.006528 0.005288 0.9699 0.9743 0.006884 0.8236 0.8192 0.01601 ] Network output: [ 0.9999 6.947e-05 0.0003314 -1.89e-06 8.484e-07 -0.0002769 -1.424e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2102 -0.03587 -0.1546 0.1817 0.9834 0.9932 0.2361 0.4281 0.8679 0.7077 ] Network output: [ -0.008656 1.003 1.007 -1.544e-07 6.934e-08 0.007219 -1.164e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006922 0.0006434 0.00434 0.003109 0.9889 0.9919 0.007059 0.8507 0.8916 0.01141 ] Network output: [ -0.0001455 0.001232 1 -5.938e-06 2.666e-06 0.9986 -4.475e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2243 0.1066 0.3504 0.1415 0.9849 0.9939 0.225 0.432 0.8747 0.7013 ] Network output: [ 0.002634 -0.01266 0.9944 3.627e-06 -1.629e-06 1.013 2.734e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09941 0.185 0.1969 0.9873 0.9919 0.1122 0.7327 0.8607 0.3049 ] Network output: [ -0.002483 0.01173 1.005 3.98e-06 -1.787e-06 0.9886 2.999e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09481 0.09287 0.1649 0.1968 0.9852 0.9911 0.09482 0.6565 0.8357 0.2499 ] Network output: [ 7.772e-05 1 -4.997e-05 5.198e-07 -2.334e-07 0.9998 3.918e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001485 Epoch 9977 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008637 0.9969 0.9928 -1.454e-07 6.527e-08 -0.006891 -1.096e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003376 -0.006528 0.005288 0.9699 0.9743 0.006884 0.8235 0.8192 0.01601 ] Network output: [ 0.9999 6.933e-05 0.0003313 -1.888e-06 8.474e-07 -0.0002767 -1.423e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2102 -0.03587 -0.1545 0.1816 0.9834 0.9932 0.2361 0.4281 0.8679 0.7077 ] Network output: [ -0.008655 1.003 1.007 -1.543e-07 6.928e-08 0.007218 -1.163e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006922 0.0006435 0.00434 0.003108 0.9889 0.9919 0.007059 0.8507 0.8916 0.01141 ] Network output: [ -0.0001454 0.001232 1 -5.931e-06 2.663e-06 0.9986 -4.47e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2243 0.1066 0.3504 0.1415 0.9849 0.9939 0.225 0.432 0.8747 0.7013 ] Network output: [ 0.002632 -0.01266 0.9945 3.623e-06 -1.627e-06 1.013 2.73e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09941 0.185 0.1969 0.9873 0.9919 0.1122 0.7327 0.8607 0.3049 ] Network output: [ -0.002481 0.01173 1.005 3.975e-06 -1.785e-06 0.9886 2.996e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09481 0.09287 0.1649 0.1968 0.9852 0.9911 0.09482 0.6565 0.8357 0.2499 ] Network output: [ 7.77e-05 1 -4.998e-05 5.192e-07 -2.331e-07 0.9998 3.913e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001484 Epoch 9978 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008637 0.9969 0.9928 -1.453e-07 6.522e-08 -0.006891 -1.095e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003376 -0.006527 0.005287 0.9699 0.9743 0.006884 0.8235 0.8192 0.01601 ] Network output: [ 0.9999 6.918e-05 0.0003312 -1.885e-06 8.464e-07 -0.0002765 -1.421e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2102 -0.03587 -0.1545 0.1816 0.9834 0.9932 0.2361 0.4281 0.8679 0.7077 ] Network output: [ -0.008654 1.003 1.007 -1.542e-07 6.922e-08 0.007218 -1.162e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006923 0.0006435 0.00434 0.003108 0.9889 0.9919 0.007059 0.8507 0.8916 0.01141 ] Network output: [ -0.0001453 0.001231 1 -5.924e-06 2.659e-06 0.9986 -4.464e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2243 0.1066 0.3504 0.1415 0.9849 0.9939 0.225 0.432 0.8747 0.7013 ] Network output: [ 0.002631 -0.01265 0.9945 3.619e-06 -1.625e-06 1.013 2.727e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09941 0.185 0.1969 0.9873 0.9919 0.1122 0.7327 0.8607 0.3049 ] Network output: [ -0.00248 0.01172 1.005 3.97e-06 -1.782e-06 0.9886 2.992e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09481 0.09287 0.1649 0.1968 0.9852 0.9911 0.09483 0.6565 0.8357 0.2499 ] Network output: [ 7.768e-05 1 -4.999e-05 5.186e-07 -2.328e-07 0.9998 3.908e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001483 Epoch 9979 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008636 0.9969 0.9928 -1.452e-07 6.517e-08 -0.00689 -1.094e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003376 -0.006527 0.005287 0.9699 0.9743 0.006884 0.8235 0.8192 0.01601 ] Network output: [ 0.9999 6.904e-05 0.000331 -1.883e-06 8.453e-07 -0.0002764 -1.419e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2102 -0.03587 -0.1545 0.1816 0.9834 0.9932 0.2361 0.4281 0.8679 0.7076 ] Network output: [ -0.008654 1.003 1.007 -1.541e-07 6.916e-08 0.007217 -1.161e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006923 0.0006436 0.00434 0.003108 0.9889 0.9919 0.00706 0.8507 0.8916 0.01141 ] Network output: [ -0.0001451 0.00123 1 -5.917e-06 2.656e-06 0.9986 -4.459e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2243 0.1066 0.3504 0.1415 0.9849 0.9939 0.2251 0.432 0.8747 0.7013 ] Network output: [ 0.002629 -0.01264 0.9945 3.614e-06 -1.623e-06 1.013 2.724e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09941 0.185 0.1969 0.9873 0.9919 0.1122 0.7327 0.8607 0.3048 ] Network output: [ -0.002479 0.01172 1.005 3.966e-06 -1.78e-06 0.9886 2.989e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09481 0.09287 0.1649 0.1968 0.9852 0.9911 0.09483 0.6565 0.8357 0.2499 ] Network output: [ 7.767e-05 1 -5e-05 5.18e-07 -2.325e-07 0.9998 3.903e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001482 Epoch 9980 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008635 0.9969 0.9928 -1.451e-07 6.512e-08 -0.006889 -1.093e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003376 -0.006526 0.005287 0.9699 0.9743 0.006884 0.8235 0.8192 0.01601 ] Network output: [ 0.9999 6.89e-05 0.0003309 -1.881e-06 8.443e-07 -0.0002762 -1.417e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2102 -0.03587 -0.1545 0.1816 0.9834 0.9932 0.2361 0.4281 0.8679 0.7076 ] Network output: [ -0.008653 1.003 1.007 -1.539e-07 6.91e-08 0.007217 -1.16e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006923 0.0006436 0.00434 0.003108 0.9889 0.9919 0.00706 0.8507 0.8916 0.01141 ] Network output: [ -0.000145 0.00123 1 -5.909e-06 2.653e-06 0.9986 -4.453e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2243 0.1066 0.3504 0.1415 0.9849 0.9939 0.2251 0.432 0.8747 0.7013 ] Network output: [ 0.002628 -0.01264 0.9945 3.61e-06 -1.621e-06 1.013 2.72e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09941 0.185 0.1969 0.9873 0.9919 0.1123 0.7327 0.8607 0.3048 ] Network output: [ -0.002478 0.01171 1.005 3.961e-06 -1.778e-06 0.9886 2.985e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09481 0.09287 0.1649 0.1968 0.9852 0.9911 0.09483 0.6565 0.8357 0.2499 ] Network output: [ 7.765e-05 1 -5.001e-05 5.173e-07 -2.322e-07 0.9998 3.899e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001481 Epoch 9981 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008634 0.9969 0.9928 -1.45e-07 6.508e-08 -0.006889 -1.092e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003376 -0.006526 0.005286 0.9699 0.9743 0.006884 0.8235 0.8192 0.01601 ] Network output: [ 0.9999 6.875e-05 0.0003307 -1.878e-06 8.432e-07 -0.0002761 -1.416e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2102 -0.03587 -0.1545 0.1816 0.9834 0.9932 0.2361 0.4281 0.8679 0.7076 ] Network output: [ -0.008652 1.003 1.007 -1.538e-07 6.904e-08 0.007216 -1.159e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006924 0.0006437 0.004339 0.003108 0.9889 0.9919 0.00706 0.8507 0.8916 0.0114 ] Network output: [ -0.0001448 0.001229 1 -5.902e-06 2.65e-06 0.9986 -4.448e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2243 0.1066 0.3504 0.1415 0.9849 0.9939 0.2251 0.432 0.8747 0.7013 ] Network output: [ 0.002626 -0.01263 0.9945 3.605e-06 -1.619e-06 1.013 2.717e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09942 0.185 0.1969 0.9873 0.9919 0.1123 0.7327 0.8607 0.3048 ] Network output: [ -0.002476 0.01171 1.005 3.956e-06 -1.776e-06 0.9886 2.981e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09482 0.09288 0.1649 0.1968 0.9852 0.9911 0.09483 0.6565 0.8357 0.2499 ] Network output: [ 7.763e-05 1 -5.001e-05 5.167e-07 -2.32e-07 0.9998 3.894e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001481 Epoch 9982 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008633 0.9969 0.9928 -1.449e-07 6.503e-08 -0.006888 -1.092e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003376 -0.006525 0.005286 0.9699 0.9743 0.006885 0.8235 0.8192 0.016 ] Network output: [ 0.9999 6.861e-05 0.0003306 -1.876e-06 8.422e-07 -0.0002759 -1.414e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2102 -0.03587 -0.1545 0.1816 0.9834 0.9932 0.2361 0.4281 0.8679 0.7076 ] Network output: [ -0.008651 1.003 1.007 -1.537e-07 6.898e-08 0.007216 -1.158e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006924 0.0006437 0.004339 0.003107 0.9889 0.9919 0.007061 0.8507 0.8916 0.0114 ] Network output: [ -0.0001447 0.001228 1 -5.895e-06 2.646e-06 0.9986 -4.442e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2243 0.1066 0.3504 0.1415 0.9849 0.9939 0.2251 0.432 0.8747 0.7013 ] Network output: [ 0.002625 -0.01262 0.9945 3.601e-06 -1.617e-06 1.013 2.714e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09942 0.185 0.1969 0.9873 0.9919 0.1123 0.7327 0.8607 0.3048 ] Network output: [ -0.002475 0.0117 1.005 3.951e-06 -1.774e-06 0.9886 2.978e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09482 0.09288 0.1649 0.1968 0.9852 0.9911 0.09483 0.6564 0.8357 0.2499 ] Network output: [ 7.762e-05 1 -5.002e-05 5.161e-07 -2.317e-07 0.9998 3.889e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000148 Epoch 9983 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008632 0.9969 0.9928 -1.447e-07 6.498e-08 -0.006887 -1.091e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003376 -0.006525 0.005286 0.9699 0.9743 0.006885 0.8235 0.8192 0.016 ] Network output: [ 0.9999 6.847e-05 0.0003304 -1.874e-06 8.411e-07 -0.0002757 -1.412e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2103 -0.03587 -0.1545 0.1816 0.9834 0.9932 0.2362 0.4281 0.8679 0.7076 ] Network output: [ -0.00865 1.003 1.007 -1.535e-07 6.892e-08 0.007215 -1.157e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006924 0.0006438 0.004339 0.003107 0.9889 0.9919 0.007061 0.8507 0.8916 0.0114 ] Network output: [ -0.0001446 0.001228 1 -5.887e-06 2.643e-06 0.9986 -4.437e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2243 0.1066 0.3505 0.1415 0.9849 0.9939 0.2251 0.432 0.8747 0.7013 ] Network output: [ 0.002624 -0.01262 0.9945 3.597e-06 -1.615e-06 1.013 2.71e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09942 0.185 0.1969 0.9873 0.9919 0.1123 0.7327 0.8607 0.3048 ] Network output: [ -0.002474 0.01169 1.005 3.947e-06 -1.772e-06 0.9886 2.974e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09482 0.09288 0.1649 0.1968 0.9852 0.9911 0.09483 0.6564 0.8357 0.2499 ] Network output: [ 7.76e-05 1 -5.003e-05 5.154e-07 -2.314e-07 0.9998 3.885e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001479 Epoch 9984 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008631 0.9969 0.9928 -1.446e-07 6.493e-08 -0.006887 -1.09e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003376 -0.006524 0.005285 0.9699 0.9743 0.006885 0.8235 0.8192 0.016 ] Network output: [ 0.9999 6.832e-05 0.0003303 -1.871e-06 8.401e-07 -0.0002756 -1.41e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2103 -0.03587 -0.1545 0.1816 0.9834 0.9932 0.2362 0.4281 0.8679 0.7076 ] Network output: [ -0.00865 1.003 1.007 -1.534e-07 6.887e-08 0.007215 -1.156e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006925 0.0006438 0.004339 0.003107 0.9889 0.9919 0.007061 0.8507 0.8916 0.0114 ] Network output: [ -0.0001444 0.001227 1 -5.88e-06 2.64e-06 0.9986 -4.432e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2243 0.1066 0.3505 0.1415 0.9849 0.9939 0.2251 0.432 0.8747 0.7013 ] Network output: [ 0.002622 -0.01261 0.9945 3.592e-06 -1.613e-06 1.013 2.707e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09942 0.185 0.1969 0.9873 0.9919 0.1123 0.7327 0.8607 0.3048 ] Network output: [ -0.002472 0.01169 1.005 3.942e-06 -1.77e-06 0.9886 2.971e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09482 0.09288 0.1649 0.1968 0.9852 0.9911 0.09484 0.6564 0.8357 0.2499 ] Network output: [ 7.758e-05 1 -5.004e-05 5.148e-07 -2.311e-07 0.9998 3.88e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001478 Epoch 9985 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008631 0.9969 0.9928 -1.445e-07 6.489e-08 -0.006886 -1.089e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003376 -0.006523 0.005285 0.9699 0.9743 0.006885 0.8235 0.8192 0.016 ] Network output: [ 0.9999 6.818e-05 0.0003302 -1.869e-06 8.391e-07 -0.0002754 -1.409e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2103 -0.03588 -0.1545 0.1816 0.9834 0.9932 0.2362 0.4281 0.8679 0.7076 ] Network output: [ -0.008649 1.003 1.007 -1.533e-07 6.881e-08 0.007214 -1.155e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006925 0.0006439 0.004339 0.003107 0.9889 0.9919 0.007062 0.8507 0.8916 0.0114 ] Network output: [ -0.0001443 0.001226 1 -5.873e-06 2.637e-06 0.9986 -4.426e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2243 0.1066 0.3505 0.1415 0.9849 0.9939 0.2251 0.432 0.8747 0.7013 ] Network output: [ 0.002621 -0.01261 0.9945 3.588e-06 -1.611e-06 1.013 2.704e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09943 0.185 0.1969 0.9873 0.9919 0.1123 0.7327 0.8607 0.3048 ] Network output: [ -0.002471 0.01168 1.005 3.937e-06 -1.767e-06 0.9886 2.967e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09482 0.09288 0.1649 0.1968 0.9852 0.9911 0.09484 0.6564 0.8357 0.2499 ] Network output: [ 7.756e-05 1 -5.005e-05 5.142e-07 -2.308e-07 0.9998 3.875e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001477 Epoch 9986 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00863 0.9969 0.9928 -1.444e-07 6.484e-08 -0.006885 -1.088e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003377 -0.006523 0.005285 0.9699 0.9743 0.006885 0.8235 0.8192 0.016 ] Network output: [ 0.9999 6.804e-05 0.00033 -1.867e-06 8.38e-07 -0.0002753 -1.407e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2103 -0.03588 -0.1545 0.1816 0.9834 0.9932 0.2362 0.4281 0.8679 0.7076 ] Network output: [ -0.008648 1.003 1.007 -1.531e-07 6.875e-08 0.007214 -1.154e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006925 0.0006439 0.004339 0.003106 0.9889 0.9919 0.007062 0.8507 0.8916 0.0114 ] Network output: [ -0.0001441 0.001225 1 -5.866e-06 2.633e-06 0.9986 -4.421e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2243 0.1066 0.3505 0.1415 0.9849 0.9939 0.2251 0.432 0.8747 0.7013 ] Network output: [ 0.002619 -0.0126 0.9945 3.583e-06 -1.609e-06 1.013 2.701e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09943 0.185 0.1969 0.9873 0.9919 0.1123 0.7327 0.8607 0.3048 ] Network output: [ -0.00247 0.01168 1.005 3.932e-06 -1.765e-06 0.9886 2.963e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09482 0.09288 0.1649 0.1968 0.9852 0.9911 0.09484 0.6564 0.8357 0.2499 ] Network output: [ 7.755e-05 1 -5.006e-05 5.136e-07 -2.306e-07 0.9998 3.87e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001476 Epoch 9987 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008629 0.9969 0.9928 -1.443e-07 6.479e-08 -0.006885 -1.088e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003377 -0.006522 0.005284 0.9699 0.9743 0.006885 0.8235 0.8192 0.016 ] Network output: [ 0.9999 6.789e-05 0.0003299 -1.864e-06 8.37e-07 -0.0002751 -1.405e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2103 -0.03588 -0.1545 0.1816 0.9834 0.9932 0.2362 0.4281 0.8679 0.7076 ] Network output: [ -0.008647 1.003 1.007 -1.53e-07 6.869e-08 0.007213 -1.153e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006926 0.000644 0.004339 0.003106 0.9889 0.9919 0.007062 0.8507 0.8916 0.0114 ] Network output: [ -0.000144 0.001225 1 -5.859e-06 2.63e-06 0.9986 -4.415e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2243 0.1066 0.3505 0.1415 0.9849 0.9939 0.2251 0.432 0.8747 0.7013 ] Network output: [ 0.002618 -0.01259 0.9945 3.579e-06 -1.607e-06 1.013 2.697e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09943 0.185 0.1969 0.9873 0.9919 0.1123 0.7326 0.8607 0.3048 ] Network output: [ -0.002468 0.01167 1.005 3.927e-06 -1.763e-06 0.9886 2.96e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09483 0.09288 0.1649 0.1968 0.9852 0.9911 0.09484 0.6564 0.8357 0.2499 ] Network output: [ 7.753e-05 1 -5.007e-05 5.129e-07 -2.303e-07 0.9998 3.866e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001476 Epoch 9988 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008628 0.9969 0.9928 -1.442e-07 6.474e-08 -0.006884 -1.087e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003377 -0.006522 0.005284 0.9699 0.9743 0.006885 0.8235 0.8192 0.016 ] Network output: [ 0.9999 6.775e-05 0.0003297 -1.862e-06 8.36e-07 -0.0002749 -1.403e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2103 -0.03588 -0.1545 0.1816 0.9834 0.9932 0.2362 0.4281 0.8679 0.7076 ] Network output: [ -0.008646 1.003 1.007 -1.529e-07 6.863e-08 0.007213 -1.152e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006926 0.000644 0.004339 0.003106 0.9889 0.9919 0.007062 0.8507 0.8916 0.0114 ] Network output: [ -0.0001439 0.001224 1 -5.851e-06 2.627e-06 0.9986 -4.41e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2243 0.1066 0.3505 0.1415 0.9849 0.9939 0.2251 0.432 0.8747 0.7013 ] Network output: [ 0.002616 -0.01259 0.9945 3.575e-06 -1.605e-06 1.013 2.694e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09943 0.185 0.1969 0.9873 0.9919 0.1123 0.7326 0.8607 0.3048 ] Network output: [ -0.002467 0.01167 1.005 3.923e-06 -1.761e-06 0.9886 2.956e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09483 0.09289 0.1649 0.1968 0.9852 0.9911 0.09484 0.6564 0.8357 0.2499 ] Network output: [ 7.751e-05 1 -5.008e-05 5.123e-07 -2.3e-07 0.9998 3.861e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001475 Epoch 9989 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008627 0.9969 0.9928 -1.441e-07 6.469e-08 -0.006883 -1.086e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003377 -0.006521 0.005283 0.9699 0.9743 0.006886 0.8235 0.8192 0.016 ] Network output: [ 0.9999 6.761e-05 0.0003296 -1.86e-06 8.349e-07 -0.0002748 -1.402e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2103 -0.03588 -0.1544 0.1816 0.9834 0.9932 0.2362 0.4281 0.8679 0.7076 ] Network output: [ -0.008646 1.003 1.007 -1.527e-07 6.857e-08 0.007212 -1.151e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006926 0.0006441 0.004339 0.003106 0.9889 0.9919 0.007063 0.8507 0.8916 0.0114 ] Network output: [ -0.0001437 0.001223 1 -5.844e-06 2.624e-06 0.9986 -4.404e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2243 0.1066 0.3505 0.1415 0.9849 0.9939 0.2251 0.432 0.8747 0.7013 ] Network output: [ 0.002615 -0.01258 0.9945 3.57e-06 -1.603e-06 1.013 2.691e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09944 0.185 0.1969 0.9873 0.9919 0.1123 0.7326 0.8607 0.3048 ] Network output: [ -0.002466 0.01166 1.005 3.918e-06 -1.759e-06 0.9886 2.953e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09483 0.09289 0.1649 0.1968 0.9852 0.9911 0.09484 0.6564 0.8357 0.2499 ] Network output: [ 7.75e-05 1 -5.008e-05 5.117e-07 -2.297e-07 0.9998 3.856e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001474 Epoch 9990 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008626 0.9969 0.9928 -1.44e-07 6.465e-08 -0.006883 -1.085e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003377 -0.006521 0.005283 0.9699 0.9743 0.006886 0.8235 0.8192 0.016 ] Network output: [ 0.9999 6.747e-05 0.0003294 -1.858e-06 8.339e-07 -0.0002746 -1.4e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2103 -0.03588 -0.1544 0.1816 0.9834 0.9932 0.2362 0.4281 0.8679 0.7076 ] Network output: [ -0.008645 1.003 1.007 -1.526e-07 6.851e-08 0.007212 -1.15e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006927 0.0006441 0.004339 0.003106 0.9889 0.9919 0.007063 0.8507 0.8916 0.0114 ] Network output: [ -0.0001436 0.001223 1 -5.837e-06 2.62e-06 0.9986 -4.399e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2243 0.1066 0.3505 0.1415 0.9849 0.9939 0.2251 0.432 0.8747 0.7012 ] Network output: [ 0.002614 -0.01257 0.9945 3.566e-06 -1.601e-06 1.013 2.687e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09944 0.185 0.1969 0.9873 0.9919 0.1123 0.7326 0.8607 0.3048 ] Network output: [ -0.002465 0.01165 1.005 3.913e-06 -1.757e-06 0.9886 2.949e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09483 0.09289 0.1649 0.1968 0.9852 0.9911 0.09484 0.6564 0.8357 0.2499 ] Network output: [ 7.748e-05 1 -5.009e-05 5.111e-07 -2.294e-07 0.9998 3.852e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001473 Epoch 9991 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008626 0.9969 0.9928 -1.439e-07 6.46e-08 -0.006882 -1.084e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003377 -0.00652 0.005283 0.9699 0.9743 0.006886 0.8235 0.8192 0.016 ] Network output: [ 0.9999 6.732e-05 0.0003293 -1.855e-06 8.329e-07 -0.0002745 -1.398e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2103 -0.03588 -0.1544 0.1816 0.9834 0.9932 0.2362 0.4281 0.8679 0.7076 ] Network output: [ -0.008644 1.003 1.007 -1.525e-07 6.845e-08 0.007211 -1.149e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006927 0.0006442 0.004338 0.003105 0.9889 0.9919 0.007063 0.8507 0.8916 0.0114 ] Network output: [ -0.0001434 0.001222 1 -5.83e-06 2.617e-06 0.9986 -4.393e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2244 0.1066 0.3505 0.1415 0.9849 0.9939 0.2251 0.432 0.8747 0.7012 ] Network output: [ 0.002612 -0.01257 0.9945 3.561e-06 -1.599e-06 1.013 2.684e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09944 0.185 0.1969 0.9873 0.9919 0.1123 0.7326 0.8607 0.3048 ] Network output: [ -0.002463 0.01165 1.005 3.909e-06 -1.755e-06 0.9886 2.946e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09483 0.09289 0.1649 0.1968 0.9852 0.9911 0.09485 0.6564 0.8357 0.2499 ] Network output: [ 7.746e-05 1 -5.01e-05 5.104e-07 -2.292e-07 0.9998 3.847e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001472 Epoch 9992 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008625 0.9969 0.9928 -1.438e-07 6.455e-08 -0.006881 -1.084e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003524 -0.003377 -0.00652 0.005282 0.9699 0.9743 0.006886 0.8235 0.8192 0.016 ] Network output: [ 0.9999 6.718e-05 0.0003291 -1.853e-06 8.319e-07 -0.0002743 -1.396e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2103 -0.03588 -0.1544 0.1816 0.9834 0.9932 0.2362 0.4281 0.8679 0.7076 ] Network output: [ -0.008643 1.003 1.007 -1.524e-07 6.84e-08 0.007211 -1.148e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006927 0.0006442 0.004338 0.003105 0.9889 0.9919 0.007064 0.8506 0.8916 0.0114 ] Network output: [ -0.0001433 0.001221 1 -5.823e-06 2.614e-06 0.9986 -4.388e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2244 0.1067 0.3505 0.1415 0.9849 0.9939 0.2251 0.432 0.8747 0.7012 ] Network output: [ 0.002611 -0.01256 0.9945 3.557e-06 -1.597e-06 1.013 2.681e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09944 0.185 0.1969 0.9873 0.9919 0.1123 0.7326 0.8607 0.3048 ] Network output: [ -0.002462 0.01164 1.005 3.904e-06 -1.753e-06 0.9886 2.942e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09483 0.09289 0.1649 0.1968 0.9852 0.9911 0.09485 0.6564 0.8356 0.2499 ] Network output: [ 7.745e-05 1 -5.011e-05 5.098e-07 -2.289e-07 0.9998 3.842e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001472 Epoch 9993 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008624 0.9969 0.9928 -1.437e-07 6.45e-08 -0.006881 -1.083e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003377 -0.006519 0.005282 0.9699 0.9743 0.006886 0.8235 0.8192 0.016 ] Network output: [ 0.9999 6.704e-05 0.000329 -1.851e-06 8.308e-07 -0.0002741 -1.395e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2103 -0.03588 -0.1544 0.1816 0.9834 0.9932 0.2362 0.428 0.8679 0.7076 ] Network output: [ -0.008643 1.003 1.007 -1.522e-07 6.834e-08 0.00721 -1.147e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006928 0.0006443 0.004338 0.003105 0.9889 0.9919 0.007064 0.8506 0.8916 0.0114 ] Network output: [ -0.0001432 0.001221 1 -5.815e-06 2.611e-06 0.9986 -4.383e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2244 0.1067 0.3505 0.1415 0.9849 0.9939 0.2251 0.432 0.8747 0.7012 ] Network output: [ 0.002609 -0.01255 0.9945 3.553e-06 -1.595e-06 1.013 2.677e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09945 0.185 0.1969 0.9873 0.9919 0.1123 0.7326 0.8607 0.3048 ] Network output: [ -0.002461 0.01164 1.005 3.899e-06 -1.75e-06 0.9886 2.938e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09483 0.09289 0.1649 0.1968 0.9852 0.9911 0.09485 0.6563 0.8356 0.2499 ] Network output: [ 7.743e-05 1 -5.012e-05 5.092e-07 -2.286e-07 0.9998 3.838e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001471 Epoch 9994 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008623 0.9969 0.9928 -1.436e-07 6.446e-08 -0.00688 -1.082e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003377 -0.006519 0.005282 0.9699 0.9743 0.006886 0.8235 0.8192 0.01599 ] Network output: [ 0.9999 6.69e-05 0.0003289 -1.848e-06 8.298e-07 -0.000274 -1.393e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2103 -0.03588 -0.1544 0.1816 0.9834 0.9932 0.2362 0.428 0.8679 0.7076 ] Network output: [ -0.008642 1.003 1.007 -1.521e-07 6.828e-08 0.00721 -1.146e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006928 0.0006443 0.004338 0.003105 0.9889 0.9919 0.007064 0.8506 0.8916 0.0114 ] Network output: [ -0.000143 0.00122 1 -5.808e-06 2.608e-06 0.9986 -4.377e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2244 0.1067 0.3505 0.1415 0.9849 0.9939 0.2251 0.432 0.8747 0.7012 ] Network output: [ 0.002608 -0.01255 0.9945 3.548e-06 -1.593e-06 1.013 2.674e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09945 0.185 0.1969 0.9873 0.9919 0.1123 0.7326 0.8607 0.3048 ] Network output: [ -0.002459 0.01163 1.005 3.894e-06 -1.748e-06 0.9886 2.935e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09484 0.09289 0.1649 0.1968 0.9852 0.9911 0.09485 0.6563 0.8356 0.2499 ] Network output: [ 7.741e-05 1 -5.013e-05 5.086e-07 -2.283e-07 0.9998 3.833e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000147 Epoch 9995 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008622 0.9969 0.9928 -1.435e-07 6.441e-08 -0.006879 -1.081e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003377 -0.006518 0.005281 0.9699 0.9743 0.006886 0.8235 0.8192 0.01599 ] Network output: [ 0.9999 6.675e-05 0.0003287 -1.846e-06 8.288e-07 -0.0002738 -1.391e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2103 -0.03589 -0.1544 0.1816 0.9834 0.9932 0.2362 0.428 0.8679 0.7076 ] Network output: [ -0.008641 1.003 1.007 -1.52e-07 6.822e-08 0.007209 -1.145e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006928 0.0006444 0.004338 0.003104 0.9889 0.9919 0.007065 0.8506 0.8916 0.0114 ] Network output: [ -0.0001429 0.001219 1 -5.801e-06 2.604e-06 0.9986 -4.372e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2244 0.1067 0.3505 0.1415 0.9849 0.9939 0.2251 0.432 0.8747 0.7012 ] Network output: [ 0.002606 -0.01254 0.9945 3.544e-06 -1.591e-06 1.013 2.671e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09945 0.185 0.1968 0.9873 0.9919 0.1123 0.7326 0.8607 0.3048 ] Network output: [ -0.002458 0.01163 1.005 3.89e-06 -1.746e-06 0.9886 2.931e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09484 0.0929 0.1649 0.1968 0.9852 0.9911 0.09485 0.6563 0.8356 0.2499 ] Network output: [ 7.739e-05 1 -5.014e-05 5.08e-07 -2.28e-07 0.9998 3.828e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001469 Epoch 9996 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008621 0.9969 0.9928 -1.434e-07 6.436e-08 -0.006879 -1.08e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003377 -0.006518 0.005281 0.9699 0.9743 0.006887 0.8235 0.8192 0.01599 ] Network output: [ 0.9999 6.661e-05 0.0003286 -1.844e-06 8.278e-07 -0.0002737 -1.39e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2103 -0.03589 -0.1544 0.1816 0.9834 0.9932 0.2362 0.428 0.8679 0.7076 ] Network output: [ -0.00864 1.003 1.007 -1.518e-07 6.816e-08 0.007209 -1.144e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006929 0.0006444 0.004338 0.003104 0.9889 0.9919 0.007065 0.8506 0.8916 0.01139 ] Network output: [ -0.0001428 0.001219 1 -5.794e-06 2.601e-06 0.9986 -4.366e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2244 0.1067 0.3505 0.1415 0.9849 0.9939 0.2252 0.432 0.8747 0.7012 ] Network output: [ 0.002605 -0.01254 0.9945 3.54e-06 -1.589e-06 1.013 2.668e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09945 0.185 0.1968 0.9873 0.9919 0.1123 0.7326 0.8607 0.3048 ] Network output: [ -0.002457 0.01162 1.005 3.885e-06 -1.744e-06 0.9887 2.928e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09484 0.0929 0.1649 0.1968 0.9852 0.9911 0.09485 0.6563 0.8356 0.2499 ] Network output: [ 7.738e-05 1 -5.015e-05 5.074e-07 -2.278e-07 0.9998 3.824e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001468 Epoch 9997 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008621 0.9969 0.9928 -1.433e-07 6.431e-08 -0.006878 -1.08e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003377 -0.006517 0.005281 0.9699 0.9743 0.006887 0.8235 0.8192 0.01599 ] Network output: [ 0.9999 6.647e-05 0.0003284 -1.842e-06 8.267e-07 -0.0002735 -1.388e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2103 -0.03589 -0.1544 0.1816 0.9834 0.9932 0.2362 0.428 0.8679 0.7076 ] Network output: [ -0.008639 1.003 1.007 -1.517e-07 6.81e-08 0.007208 -1.143e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006929 0.0006445 0.004338 0.003104 0.9889 0.9919 0.007065 0.8506 0.8916 0.01139 ] Network output: [ -0.0001426 0.001218 1 -5.787e-06 2.598e-06 0.9986 -4.361e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2244 0.1067 0.3505 0.1415 0.9849 0.9939 0.2252 0.432 0.8747 0.7012 ] Network output: [ 0.002604 -0.01253 0.9945 3.535e-06 -1.587e-06 1.013 2.664e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09946 0.185 0.1968 0.9873 0.9919 0.1123 0.7326 0.8607 0.3048 ] Network output: [ -0.002455 0.01161 1.005 3.88e-06 -1.742e-06 0.9887 2.924e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09484 0.0929 0.1649 0.1968 0.9852 0.9911 0.09486 0.6563 0.8356 0.2499 ] Network output: [ 7.736e-05 1 -5.016e-05 5.067e-07 -2.275e-07 0.9998 3.819e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001467 Epoch 9998 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00862 0.9969 0.9928 -1.432e-07 6.427e-08 -0.006877 -1.079e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003377 -0.006516 0.00528 0.9699 0.9743 0.006887 0.8235 0.8192 0.01599 ] Network output: [ 0.9999 6.633e-05 0.0003283 -1.839e-06 8.257e-07 -0.0002733 -1.386e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2103 -0.03589 -0.1544 0.1816 0.9834 0.9932 0.2362 0.428 0.8679 0.7076 ] Network output: [ -0.008639 1.003 1.007 -1.516e-07 6.805e-08 0.007208 -1.142e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006929 0.0006445 0.004338 0.003104 0.9889 0.9919 0.007066 0.8506 0.8916 0.01139 ] Network output: [ -0.0001425 0.001217 1 -5.78e-06 2.595e-06 0.9986 -4.356e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2244 0.1067 0.3505 0.1415 0.9849 0.9939 0.2252 0.432 0.8747 0.7012 ] Network output: [ 0.002602 -0.01252 0.9945 3.531e-06 -1.585e-06 1.013 2.661e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09946 0.185 0.1968 0.9873 0.9919 0.1123 0.7325 0.8607 0.3048 ] Network output: [ -0.002454 0.01161 1.005 3.876e-06 -1.74e-06 0.9887 2.921e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09484 0.0929 0.1649 0.1968 0.9852 0.9911 0.09486 0.6563 0.8356 0.2499 ] Network output: [ 7.734e-05 1 -5.017e-05 5.061e-07 -2.272e-07 0.9998 3.814e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001467 Epoch 9999 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008619 0.9969 0.9928 -1.43e-07 6.422e-08 -0.006877 -1.078e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003377 -0.006516 0.00528 0.9699 0.9743 0.006887 0.8235 0.8192 0.01599 ] Network output: [ 0.9999 6.619e-05 0.0003281 -1.837e-06 8.247e-07 -0.0002732 -1.384e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2103 -0.03589 -0.1544 0.1816 0.9834 0.9932 0.2363 0.428 0.8679 0.7076 ] Network output: [ -0.008638 1.003 1.007 -1.514e-07 6.799e-08 0.007207 -1.141e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006929 0.0006446 0.004338 0.003104 0.9889 0.9919 0.007066 0.8506 0.8916 0.01139 ] Network output: [ -0.0001423 0.001216 1 -5.772e-06 2.591e-06 0.9986 -4.35e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2244 0.1067 0.3505 0.1415 0.9849 0.9939 0.2252 0.432 0.8747 0.7012 ] Network output: [ 0.002601 -0.01252 0.9945 3.527e-06 -1.583e-06 1.013 2.658e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09946 0.185 0.1968 0.9873 0.9919 0.1123 0.7325 0.8607 0.3048 ] Network output: [ -0.002453 0.0116 1.005 3.871e-06 -1.738e-06 0.9887 2.917e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09484 0.0929 0.1649 0.1968 0.9852 0.9911 0.09486 0.6563 0.8356 0.2499 ] Network output: [ 7.733e-05 1 -5.018e-05 5.055e-07 -2.269e-07 0.9998 3.81e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001466 Epoch 10000 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008618 0.9969 0.9928 -1.429e-07 6.417e-08 -0.006876 -1.077e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003377 -0.006515 0.005279 0.9699 0.9743 0.006887 0.8235 0.8192 0.01599 ] Network output: [ 0.9999 6.604e-05 0.000328 -1.835e-06 8.237e-07 -0.000273 -1.383e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2103 -0.03589 -0.1544 0.1816 0.9834 0.9932 0.2363 0.428 0.8679 0.7076 ] Network output: [ -0.008637 1.003 1.007 -1.513e-07 6.793e-08 0.007207 -1.14e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00693 0.0006446 0.004337 0.003103 0.9889 0.9919 0.007066 0.8506 0.8916 0.01139 ] Network output: [ -0.0001422 0.001216 1 -5.765e-06 2.588e-06 0.9986 -4.345e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2244 0.1067 0.3505 0.1415 0.9849 0.9939 0.2252 0.432 0.8747 0.7012 ] Network output: [ 0.002599 -0.01251 0.9945 3.522e-06 -1.581e-06 1.013 2.655e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09946 0.185 0.1968 0.9873 0.9919 0.1123 0.7325 0.8607 0.3048 ] Network output: [ -0.002452 0.0116 1.005 3.866e-06 -1.736e-06 0.9887 2.914e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09485 0.0929 0.1649 0.1968 0.9852 0.9911 0.09486 0.6563 0.8356 0.2499 ] Network output: [ 7.731e-05 1 -5.019e-05 5.049e-07 -2.267e-07 0.9998 3.805e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001465 Epoch 10001 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008617 0.9969 0.9928 -1.428e-07 6.412e-08 -0.006876 -1.076e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003378 -0.006515 0.005279 0.9699 0.9743 0.006887 0.8235 0.8192 0.01599 ] Network output: [ 0.9999 6.59e-05 0.0003279 -1.832e-06 8.227e-07 -0.0002729 -1.381e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2103 -0.03589 -0.1544 0.1816 0.9834 0.9932 0.2363 0.428 0.8679 0.7076 ] Network output: [ -0.008636 1.003 1.007 -1.512e-07 6.787e-08 0.007206 -1.139e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00693 0.0006447 0.004337 0.003103 0.9889 0.9919 0.007067 0.8506 0.8916 0.01139 ] Network output: [ -0.0001421 0.001215 1 -5.758e-06 2.585e-06 0.9986 -4.34e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2244 0.1067 0.3505 0.1415 0.9849 0.9939 0.2252 0.432 0.8747 0.7012 ] Network output: [ 0.002598 -0.0125 0.9945 3.518e-06 -1.579e-06 1.013 2.651e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09947 0.185 0.1968 0.9873 0.9919 0.1123 0.7325 0.8607 0.3048 ] Network output: [ -0.00245 0.01159 1.005 3.862e-06 -1.734e-06 0.9887 2.91e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09485 0.09291 0.1649 0.1968 0.9852 0.9911 0.09486 0.6563 0.8356 0.2499 ] Network output: [ 7.729e-05 1 -5.02e-05 5.043e-07 -2.264e-07 0.9998 3.8e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001464 Epoch 10002 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008616 0.9969 0.9928 -1.427e-07 6.407e-08 -0.006875 -1.076e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003378 -0.006514 0.005279 0.9699 0.9743 0.006887 0.8235 0.8192 0.01599 ] Network output: [ 0.9999 6.576e-05 0.0003277 -1.83e-06 8.216e-07 -0.0002727 -1.379e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2104 -0.03589 -0.1543 0.1816 0.9834 0.9932 0.2363 0.428 0.8679 0.7076 ] Network output: [ -0.008635 1.003 1.007 -1.51e-07 6.781e-08 0.007206 -1.138e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00693 0.0006447 0.004337 0.003103 0.9889 0.9919 0.007067 0.8506 0.8916 0.01139 ] Network output: [ -0.0001419 0.001214 1 -5.751e-06 2.582e-06 0.9986 -4.334e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2244 0.1067 0.3505 0.1415 0.9849 0.9939 0.2252 0.432 0.8747 0.7012 ] Network output: [ 0.002596 -0.0125 0.9945 3.514e-06 -1.577e-06 1.013 2.648e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09947 0.185 0.1968 0.9873 0.9919 0.1123 0.7325 0.8606 0.3048 ] Network output: [ -0.002449 0.01159 1.005 3.857e-06 -1.731e-06 0.9887 2.907e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09485 0.09291 0.1649 0.1968 0.9852 0.9911 0.09486 0.6563 0.8356 0.2499 ] Network output: [ 7.728e-05 1 -5.021e-05 5.037e-07 -2.261e-07 0.9998 3.796e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001463 Epoch 10003 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008615 0.9969 0.9928 -1.426e-07 6.403e-08 -0.006874 -1.075e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003378 -0.006514 0.005278 0.9699 0.9743 0.006887 0.8234 0.8192 0.01599 ] Network output: [ 0.9999 6.562e-05 0.0003276 -1.828e-06 8.206e-07 -0.0002726 -1.378e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2104 -0.03589 -0.1543 0.1816 0.9834 0.9932 0.2363 0.428 0.8679 0.7076 ] Network output: [ -0.008635 1.003 1.007 -1.509e-07 6.775e-08 0.007205 -1.137e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006931 0.0006448 0.004337 0.003103 0.9889 0.9919 0.007067 0.8506 0.8916 0.01139 ] Network output: [ -0.0001418 0.001214 1 -5.744e-06 2.579e-06 0.9986 -4.329e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2244 0.1067 0.3505 0.1415 0.9849 0.9939 0.2252 0.432 0.8747 0.7012 ] Network output: [ 0.002595 -0.01249 0.9945 3.509e-06 -1.575e-06 1.013 2.645e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09947 0.185 0.1968 0.9873 0.9919 0.1123 0.7325 0.8606 0.3048 ] Network output: [ -0.002448 0.01158 1.005 3.852e-06 -1.729e-06 0.9887 2.903e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09485 0.09291 0.1649 0.1968 0.9852 0.9911 0.09486 0.6563 0.8356 0.2499 ] Network output: [ 7.726e-05 1 -5.022e-05 5.03e-07 -2.258e-07 0.9998 3.791e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001462 Epoch 10004 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008615 0.9969 0.9928 -1.425e-07 6.398e-08 -0.006874 -1.074e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003378 -0.006513 0.005278 0.9699 0.9743 0.006888 0.8234 0.8192 0.01599 ] Network output: [ 0.9999 6.548e-05 0.0003274 -1.826e-06 8.196e-07 -0.0002724 -1.376e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2104 -0.03589 -0.1543 0.1816 0.9834 0.9932 0.2363 0.428 0.8679 0.7076 ] Network output: [ -0.008634 1.003 1.007 -1.508e-07 6.77e-08 0.007205 -1.136e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006931 0.0006448 0.004337 0.003102 0.9889 0.9919 0.007068 0.8506 0.8916 0.01139 ] Network output: [ -0.0001416 0.001213 1 -5.737e-06 2.576e-06 0.9986 -4.324e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2244 0.1067 0.3505 0.1415 0.9849 0.9939 0.2252 0.4319 0.8747 0.7012 ] Network output: [ 0.002594 -0.01248 0.9945 3.505e-06 -1.574e-06 1.013 2.642e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09947 0.185 0.1968 0.9873 0.9919 0.1123 0.7325 0.8606 0.3048 ] Network output: [ -0.002446 0.01157 1.005 3.848e-06 -1.727e-06 0.9887 2.9e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09485 0.09291 0.1649 0.1968 0.9852 0.9911 0.09487 0.6562 0.8356 0.2499 ] Network output: [ 7.724e-05 1 -5.023e-05 5.024e-07 -2.256e-07 0.9998 3.787e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001462 Epoch 10005 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008614 0.9969 0.9928 -1.424e-07 6.393e-08 -0.006873 -1.073e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003378 -0.006513 0.005278 0.9699 0.9743 0.006888 0.8234 0.8192 0.01599 ] Network output: [ 0.9999 6.533e-05 0.0003273 -1.823e-06 8.186e-07 -0.0002722 -1.374e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2104 -0.0359 -0.1543 0.1816 0.9834 0.9932 0.2363 0.428 0.8679 0.7076 ] Network output: [ -0.008633 1.003 1.007 -1.507e-07 6.764e-08 0.007204 -1.135e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006931 0.0006449 0.004337 0.003102 0.9889 0.9919 0.007068 0.8506 0.8916 0.01139 ] Network output: [ -0.0001415 0.001212 1 -5.73e-06 2.572e-06 0.9986 -4.318e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2244 0.1067 0.3506 0.1415 0.9849 0.9939 0.2252 0.4319 0.8747 0.7012 ] Network output: [ 0.002592 -0.01248 0.9945 3.501e-06 -1.572e-06 1.013 2.638e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09947 0.185 0.1968 0.9873 0.9919 0.1123 0.7325 0.8606 0.3048 ] Network output: [ -0.002445 0.01157 1.005 3.843e-06 -1.725e-06 0.9887 2.896e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09485 0.09291 0.1649 0.1968 0.9852 0.9911 0.09487 0.6562 0.8356 0.2499 ] Network output: [ 7.723e-05 1 -5.024e-05 5.018e-07 -2.253e-07 0.9998 3.782e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001461 Epoch 10006 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008613 0.9969 0.9928 -1.423e-07 6.388e-08 -0.006872 -1.072e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003378 -0.006512 0.005277 0.9699 0.9743 0.006888 0.8234 0.8192 0.01598 ] Network output: [ 0.9999 6.519e-05 0.0003271 -1.821e-06 8.176e-07 -0.0002721 -1.372e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2104 -0.0359 -0.1543 0.1816 0.9834 0.9932 0.2363 0.428 0.8679 0.7076 ] Network output: [ -0.008632 1.003 1.007 -1.505e-07 6.758e-08 0.007204 -1.134e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006932 0.0006449 0.004337 0.003102 0.9889 0.9919 0.007068 0.8506 0.8916 0.01139 ] Network output: [ -0.0001414 0.001212 1 -5.723e-06 2.569e-06 0.9986 -4.313e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2244 0.1067 0.3506 0.1415 0.9849 0.9939 0.2252 0.4319 0.8747 0.7012 ] Network output: [ 0.002591 -0.01247 0.9945 3.497e-06 -1.57e-06 1.013 2.635e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09948 0.185 0.1968 0.9873 0.9919 0.1123 0.7325 0.8606 0.3048 ] Network output: [ -0.002444 0.01156 1.005 3.838e-06 -1.723e-06 0.9887 2.893e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09485 0.09291 0.1649 0.1968 0.9852 0.9911 0.09487 0.6562 0.8356 0.2499 ] Network output: [ 7.721e-05 1 -5.025e-05 5.012e-07 -2.25e-07 0.9998 3.777e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000146 Epoch 10007 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008612 0.9969 0.9928 -1.422e-07 6.384e-08 -0.006872 -1.072e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003378 -0.006512 0.005277 0.9699 0.9743 0.006888 0.8234 0.8192 0.01598 ] Network output: [ 0.9999 6.505e-05 0.000327 -1.819e-06 8.166e-07 -0.0002719 -1.371e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2104 -0.0359 -0.1543 0.1816 0.9834 0.9932 0.2363 0.428 0.8679 0.7076 ] Network output: [ -0.008632 1.003 1.007 -1.504e-07 6.752e-08 0.007203 -1.133e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006932 0.000645 0.004337 0.003102 0.9889 0.9919 0.007069 0.8506 0.8916 0.01139 ] Network output: [ -0.0001412 0.001211 1 -5.716e-06 2.566e-06 0.9986 -4.308e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2244 0.1067 0.3506 0.1415 0.9849 0.9939 0.2252 0.4319 0.8747 0.7012 ] Network output: [ 0.002589 -0.01247 0.9945 3.492e-06 -1.568e-06 1.013 2.632e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09948 0.185 0.1968 0.9873 0.9919 0.1123 0.7325 0.8606 0.3048 ] Network output: [ -0.002443 0.01156 1.005 3.834e-06 -1.721e-06 0.9887 2.889e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09486 0.09291 0.1649 0.1968 0.9852 0.9911 0.09487 0.6562 0.8356 0.2499 ] Network output: [ 7.719e-05 1 -5.026e-05 5.006e-07 -2.247e-07 0.9998 3.773e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001459 Epoch 10008 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008611 0.9969 0.9928 -1.421e-07 6.379e-08 -0.006871 -1.071e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003378 -0.006511 0.005277 0.9699 0.9743 0.006888 0.8234 0.8192 0.01598 ] Network output: [ 0.9999 6.491e-05 0.0003269 -1.817e-06 8.156e-07 -0.0002718 -1.369e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2104 -0.0359 -0.1543 0.1815 0.9834 0.9932 0.2363 0.428 0.8679 0.7076 ] Network output: [ -0.008631 1.003 1.007 -1.503e-07 6.746e-08 0.007203 -1.132e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006932 0.000645 0.004337 0.003102 0.9889 0.9919 0.007069 0.8506 0.8916 0.01139 ] Network output: [ -0.0001411 0.00121 1 -5.709e-06 2.563e-06 0.9986 -4.302e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2245 0.1067 0.3506 0.1415 0.9849 0.9939 0.2252 0.4319 0.8747 0.7012 ] Network output: [ 0.002588 -0.01246 0.9945 3.488e-06 -1.566e-06 1.013 2.629e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09948 0.185 0.1968 0.9873 0.9919 0.1123 0.7325 0.8606 0.3048 ] Network output: [ -0.002441 0.01155 1.005 3.829e-06 -1.719e-06 0.9887 2.886e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09486 0.09292 0.1649 0.1968 0.9852 0.9911 0.09487 0.6562 0.8356 0.2499 ] Network output: [ 7.718e-05 1 -5.027e-05 5e-07 -2.245e-07 0.9998 3.768e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001458 Epoch 10009 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00861 0.9969 0.9928 -1.42e-07 6.374e-08 -0.00687 -1.07e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003378 -0.006511 0.005276 0.9699 0.9743 0.006888 0.8234 0.8192 0.01598 ] Network output: [ 0.9999 6.477e-05 0.0003267 -1.814e-06 8.146e-07 -0.0002716 -1.367e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2104 -0.0359 -0.1543 0.1815 0.9834 0.9932 0.2363 0.428 0.8679 0.7076 ] Network output: [ -0.00863 1.003 1.007 -1.501e-07 6.74e-08 0.007202 -1.132e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006933 0.0006451 0.004336 0.003101 0.9889 0.9919 0.007069 0.8506 0.8916 0.01139 ] Network output: [ -0.000141 0.00121 1 -5.702e-06 2.56e-06 0.9986 -4.297e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2245 0.1067 0.3506 0.1415 0.9849 0.9939 0.2252 0.4319 0.8747 0.7012 ] Network output: [ 0.002586 -0.01245 0.9945 3.484e-06 -1.564e-06 1.013 2.625e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09948 0.185 0.1968 0.9873 0.9919 0.1123 0.7325 0.8606 0.3048 ] Network output: [ -0.00244 0.01155 1.005 3.824e-06 -1.717e-06 0.9887 2.882e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09486 0.09292 0.1649 0.1968 0.9852 0.9911 0.09487 0.6562 0.8356 0.2499 ] Network output: [ 7.716e-05 1 -5.028e-05 4.994e-07 -2.242e-07 0.9998 3.764e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001458 Epoch 10010 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00861 0.9969 0.9928 -1.419e-07 6.369e-08 -0.00687 -1.069e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003378 -0.00651 0.005276 0.9699 0.9743 0.006888 0.8234 0.8192 0.01598 ] Network output: [ 0.9999 6.463e-05 0.0003266 -1.812e-06 8.136e-07 -0.0002715 -1.366e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2104 -0.0359 -0.1543 0.1815 0.9834 0.9932 0.2363 0.428 0.8679 0.7076 ] Network output: [ -0.008629 1.003 1.007 -1.5e-07 6.735e-08 0.007202 -1.131e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006933 0.0006451 0.004336 0.003101 0.9889 0.9919 0.00707 0.8506 0.8916 0.01138 ] Network output: [ -0.0001408 0.001209 1 -5.695e-06 2.557e-06 0.9986 -4.292e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2245 0.1067 0.3506 0.1415 0.9849 0.9939 0.2252 0.4319 0.8747 0.7012 ] Network output: [ 0.002585 -0.01245 0.9945 3.479e-06 -1.562e-06 1.013 2.622e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1122 0.09949 0.185 0.1968 0.9873 0.9919 0.1123 0.7324 0.8606 0.3048 ] Network output: [ -0.002439 0.01154 1.005 3.82e-06 -1.715e-06 0.9887 2.879e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09486 0.09292 0.1649 0.1968 0.9852 0.9911 0.09487 0.6562 0.8356 0.2499 ] Network output: [ 7.714e-05 1 -5.029e-05 4.988e-07 -2.239e-07 0.9998 3.759e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001457 Epoch 10011 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008609 0.9969 0.9928 -1.418e-07 6.365e-08 -0.006869 -1.068e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003378 -0.006509 0.005276 0.9699 0.9743 0.006889 0.8234 0.8192 0.01598 ] Network output: [ 0.9999 6.449e-05 0.0003264 -1.81e-06 8.126e-07 -0.0002713 -1.364e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2104 -0.0359 -0.1543 0.1815 0.9834 0.9932 0.2363 0.428 0.8679 0.7075 ] Network output: [ -0.008628 1.003 1.007 -1.499e-07 6.729e-08 0.007201 -1.13e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006933 0.0006452 0.004336 0.003101 0.9889 0.9919 0.00707 0.8506 0.8916 0.01138 ] Network output: [ -0.0001407 0.001208 1 -5.688e-06 2.553e-06 0.9986 -4.286e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2245 0.1067 0.3506 0.1415 0.9849 0.9939 0.2252 0.4319 0.8747 0.7012 ] Network output: [ 0.002583 -0.01244 0.9945 3.475e-06 -1.56e-06 1.013 2.619e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09949 0.185 0.1968 0.9873 0.9919 0.1123 0.7324 0.8606 0.3048 ] Network output: [ -0.002437 0.01153 1.005 3.815e-06 -1.713e-06 0.9887 2.875e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09486 0.09292 0.1649 0.1968 0.9852 0.9911 0.09488 0.6562 0.8356 0.2499 ] Network output: [ 7.713e-05 1 -5.03e-05 4.982e-07 -2.236e-07 0.9998 3.754e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001456 Epoch 10012 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008608 0.9969 0.9928 -1.417e-07 6.36e-08 -0.006868 -1.068e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003378 -0.006509 0.005275 0.9699 0.9743 0.006889 0.8234 0.8192 0.01598 ] Network output: [ 0.9999 6.435e-05 0.0003263 -1.808e-06 8.116e-07 -0.0002711 -1.362e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2104 -0.0359 -0.1543 0.1815 0.9834 0.9932 0.2363 0.428 0.8679 0.7075 ] Network output: [ -0.008628 1.003 1.007 -1.498e-07 6.723e-08 0.007201 -1.129e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006934 0.0006452 0.004336 0.003101 0.9889 0.9919 0.00707 0.8506 0.8916 0.01138 ] Network output: [ -0.0001405 0.001208 1 -5.681e-06 2.55e-06 0.9986 -4.281e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2245 0.1067 0.3506 0.1415 0.9849 0.9939 0.2253 0.4319 0.8747 0.7012 ] Network output: [ 0.002582 -0.01243 0.9945 3.471e-06 -1.558e-06 1.013 2.616e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09949 0.185 0.1968 0.9873 0.9919 0.1123 0.7324 0.8606 0.3048 ] Network output: [ -0.002436 0.01153 1.005 3.81e-06 -1.711e-06 0.9887 2.872e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09486 0.09292 0.1649 0.1968 0.9852 0.9911 0.09488 0.6562 0.8356 0.2499 ] Network output: [ 7.711e-05 1 -5.031e-05 4.976e-07 -2.234e-07 0.9998 3.75e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001455 Epoch 10013 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008607 0.9969 0.9928 -1.416e-07 6.355e-08 -0.006868 -1.067e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003378 -0.006508 0.005275 0.9699 0.9743 0.006889 0.8234 0.8192 0.01598 ] Network output: [ 0.9999 6.42e-05 0.0003262 -1.805e-06 8.105e-07 -0.000271 -1.361e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2104 -0.0359 -0.1543 0.1815 0.9834 0.9932 0.2363 0.428 0.8679 0.7075 ] Network output: [ -0.008627 1.003 1.007 -1.496e-07 6.717e-08 0.0072 -1.128e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006934 0.0006453 0.004336 0.003101 0.9889 0.9919 0.007071 0.8506 0.8916 0.01138 ] Network output: [ -0.0001404 0.001207 1 -5.674e-06 2.547e-06 0.9986 -4.276e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2245 0.1067 0.3506 0.1415 0.9849 0.9939 0.2253 0.4319 0.8747 0.7012 ] Network output: [ 0.002581 -0.01243 0.9945 3.467e-06 -1.556e-06 1.013 2.613e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09949 0.185 0.1968 0.9873 0.9919 0.1123 0.7324 0.8606 0.3048 ] Network output: [ -0.002435 0.01152 1.005 3.806e-06 -1.709e-06 0.9887 2.868e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09486 0.09292 0.1649 0.1968 0.9852 0.9911 0.09488 0.6562 0.8356 0.2499 ] Network output: [ 7.709e-05 1 -5.032e-05 4.97e-07 -2.231e-07 0.9998 3.745e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001454 Epoch 10014 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008606 0.9969 0.9928 -1.415e-07 6.35e-08 -0.006867 -1.066e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003378 -0.006508 0.005274 0.9699 0.9743 0.006889 0.8234 0.8192 0.01598 ] Network output: [ 0.9999 6.406e-05 0.000326 -1.803e-06 8.095e-07 -0.0002708 -1.359e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2104 -0.0359 -0.1543 0.1815 0.9834 0.9932 0.2363 0.428 0.8679 0.7075 ] Network output: [ -0.008626 1.003 1.007 -1.495e-07 6.711e-08 0.0072 -1.127e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006934 0.0006453 0.004336 0.0031 0.9889 0.9919 0.007071 0.8506 0.8916 0.01138 ] Network output: [ -0.0001403 0.001206 1 -5.667e-06 2.544e-06 0.9986 -4.271e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2245 0.1067 0.3506 0.1415 0.9849 0.9939 0.2253 0.4319 0.8747 0.7012 ] Network output: [ 0.002579 -0.01242 0.9945 3.462e-06 -1.554e-06 1.013 2.609e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.0995 0.185 0.1968 0.9873 0.9919 0.1123 0.7324 0.8606 0.3048 ] Network output: [ -0.002434 0.01152 1.005 3.801e-06 -1.707e-06 0.9887 2.865e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09487 0.09292 0.1649 0.1968 0.9852 0.9911 0.09488 0.6562 0.8356 0.2499 ] Network output: [ 7.708e-05 1 -5.033e-05 4.964e-07 -2.228e-07 0.9998 3.741e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001453 Epoch 10015 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008605 0.9969 0.9928 -1.413e-07 6.346e-08 -0.006866 -1.065e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003378 -0.006507 0.005274 0.9699 0.9743 0.006889 0.8234 0.8192 0.01598 ] Network output: [ 0.9999 6.392e-05 0.0003259 -1.801e-06 8.085e-07 -0.0002707 -1.357e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2104 -0.03591 -0.1542 0.1815 0.9834 0.9932 0.2364 0.428 0.8679 0.7075 ] Network output: [ -0.008625 1.003 1.007 -1.494e-07 6.706e-08 0.007199 -1.126e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006934 0.0006453 0.004336 0.0031 0.9889 0.9919 0.007071 0.8506 0.8916 0.01138 ] Network output: [ -0.0001401 0.001205 1 -5.66e-06 2.541e-06 0.9986 -4.265e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2245 0.1067 0.3506 0.1415 0.9849 0.9939 0.2253 0.4319 0.8747 0.7012 ] Network output: [ 0.002578 -0.01241 0.9945 3.458e-06 -1.552e-06 1.013 2.606e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.0995 0.185 0.1968 0.9873 0.9919 0.1123 0.7324 0.8606 0.3048 ] Network output: [ -0.002432 0.01151 1.005 3.797e-06 -1.704e-06 0.9887 2.861e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09487 0.09293 0.1649 0.1968 0.9852 0.9911 0.09488 0.6561 0.8356 0.2499 ] Network output: [ 7.706e-05 1 -5.034e-05 4.957e-07 -2.226e-07 0.9998 3.736e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001453 Epoch 10016 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008605 0.9969 0.9928 -1.412e-07 6.341e-08 -0.006866 -1.064e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003379 -0.006507 0.005274 0.9699 0.9743 0.006889 0.8234 0.8192 0.01598 ] Network output: [ 0.9999 6.378e-05 0.0003257 -1.799e-06 8.075e-07 -0.0002705 -1.356e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2104 -0.03591 -0.1542 0.1815 0.9834 0.9932 0.2364 0.428 0.8679 0.7075 ] Network output: [ -0.008624 1.003 1.007 -1.492e-07 6.7e-08 0.007199 -1.125e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006935 0.0006454 0.004336 0.0031 0.9889 0.9919 0.007072 0.8506 0.8916 0.01138 ] Network output: [ -0.00014 0.001205 1 -5.653e-06 2.538e-06 0.9986 -4.26e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2245 0.1067 0.3506 0.1415 0.9849 0.9939 0.2253 0.4319 0.8747 0.7012 ] Network output: [ 0.002576 -0.01241 0.9945 3.454e-06 -1.551e-06 1.013 2.603e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.0995 0.185 0.1968 0.9873 0.9919 0.1123 0.7324 0.8606 0.3048 ] Network output: [ -0.002431 0.01151 1.005 3.792e-06 -1.702e-06 0.9887 2.858e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09487 0.09293 0.1649 0.1968 0.9852 0.9911 0.09488 0.6561 0.8356 0.2499 ] Network output: [ 7.704e-05 1 -5.035e-05 4.951e-07 -2.223e-07 0.9998 3.732e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001452 Epoch 10017 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008604 0.9969 0.9928 -1.411e-07 6.336e-08 -0.006865 -1.064e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003379 -0.006506 0.005273 0.9699 0.9743 0.006889 0.8234 0.8192 0.01597 ] Network output: [ 0.9999 6.364e-05 0.0003256 -1.797e-06 8.065e-07 -0.0002704 -1.354e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2104 -0.03591 -0.1542 0.1815 0.9834 0.9932 0.2364 0.428 0.8679 0.7075 ] Network output: [ -0.008624 1.003 1.007 -1.491e-07 6.694e-08 0.007198 -1.124e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006935 0.0006454 0.004336 0.0031 0.9889 0.9919 0.007072 0.8505 0.8916 0.01138 ] Network output: [ -0.0001398 0.001204 1 -5.646e-06 2.535e-06 0.9986 -4.255e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2245 0.1067 0.3506 0.1415 0.9849 0.9939 0.2253 0.4319 0.8747 0.7012 ] Network output: [ 0.002575 -0.0124 0.9945 3.45e-06 -1.549e-06 1.013 2.6e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.0995 0.185 0.1968 0.9873 0.9919 0.1123 0.7324 0.8606 0.3048 ] Network output: [ -0.00243 0.0115 1.005 3.787e-06 -1.7e-06 0.9887 2.854e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09487 0.09293 0.1649 0.1968 0.9852 0.9911 0.09488 0.6561 0.8356 0.2499 ] Network output: [ 7.703e-05 1 -5.036e-05 4.945e-07 -2.22e-07 0.9998 3.727e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001451 Epoch 10018 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008603 0.9969 0.9928 -1.41e-07 6.331e-08 -0.006864 -1.063e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003379 -0.006506 0.005273 0.9699 0.9743 0.00689 0.8234 0.8192 0.01597 ] Network output: [ 0.9999 6.35e-05 0.0003254 -1.794e-06 8.056e-07 -0.0002702 -1.352e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2104 -0.03591 -0.1542 0.1815 0.9834 0.9932 0.2364 0.428 0.8679 0.7075 ] Network output: [ -0.008623 1.003 1.007 -1.49e-07 6.688e-08 0.007198 -1.123e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006935 0.0006455 0.004335 0.003099 0.9889 0.9919 0.007072 0.8505 0.8916 0.01138 ] Network output: [ -0.0001397 0.001203 1 -5.639e-06 2.531e-06 0.9986 -4.25e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2245 0.1067 0.3506 0.1415 0.9849 0.9939 0.2253 0.4319 0.8747 0.7012 ] Network output: [ 0.002573 -0.0124 0.9945 3.445e-06 -1.547e-06 1.013 2.597e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09951 0.185 0.1968 0.9873 0.9919 0.1123 0.7324 0.8606 0.3048 ] Network output: [ -0.002428 0.01149 1.005 3.783e-06 -1.698e-06 0.9887 2.851e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09487 0.09293 0.1649 0.1968 0.9852 0.9911 0.09489 0.6561 0.8356 0.2499 ] Network output: [ 7.701e-05 1 -5.037e-05 4.939e-07 -2.217e-07 0.9998 3.722e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000145 Epoch 10019 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008602 0.9969 0.9928 -1.409e-07 6.327e-08 -0.006864 -1.062e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003525 -0.003379 -0.006505 0.005273 0.9699 0.9743 0.00689 0.8234 0.8191 0.01597 ] Network output: [ 0.9999 6.336e-05 0.0003253 -1.792e-06 8.046e-07 -0.0002701 -1.351e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2104 -0.03591 -0.1542 0.1815 0.9834 0.9932 0.2364 0.428 0.8679 0.7075 ] Network output: [ -0.008622 1.003 1.007 -1.489e-07 6.683e-08 0.007197 -1.122e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006936 0.0006455 0.004335 0.003099 0.9889 0.9919 0.007073 0.8505 0.8916 0.01138 ] Network output: [ -0.0001396 0.001203 1 -5.632e-06 2.528e-06 0.9986 -4.244e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2245 0.1067 0.3506 0.1414 0.9849 0.9939 0.2253 0.4319 0.8747 0.7012 ] Network output: [ 0.002572 -0.01239 0.9945 3.441e-06 -1.545e-06 1.013 2.593e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09951 0.185 0.1968 0.9873 0.9919 0.1124 0.7324 0.8606 0.3048 ] Network output: [ -0.002427 0.01149 1.005 3.778e-06 -1.696e-06 0.9887 2.847e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09487 0.09293 0.1649 0.1968 0.9852 0.9911 0.09489 0.6561 0.8356 0.2499 ] Network output: [ 7.699e-05 1 -5.038e-05 4.933e-07 -2.215e-07 0.9998 3.718e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001449 Epoch 10020 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008601 0.9969 0.9928 -1.408e-07 6.322e-08 -0.006863 -1.061e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.003379 -0.006505 0.005272 0.9699 0.9743 0.00689 0.8234 0.8191 0.01597 ] Network output: [ 0.9999 6.322e-05 0.0003252 -1.79e-06 8.036e-07 -0.0002699 -1.349e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2104 -0.03591 -0.1542 0.1815 0.9834 0.9932 0.2364 0.428 0.8679 0.7075 ] Network output: [ -0.008621 1.003 1.007 -1.487e-07 6.677e-08 0.007197 -1.121e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006936 0.0006456 0.004335 0.003099 0.9889 0.9919 0.007073 0.8505 0.8916 0.01138 ] Network output: [ -0.0001394 0.001202 1 -5.625e-06 2.525e-06 0.9986 -4.239e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2245 0.1067 0.3506 0.1414 0.9849 0.9939 0.2253 0.4319 0.8747 0.7011 ] Network output: [ 0.002571 -0.01238 0.9945 3.437e-06 -1.543e-06 1.013 2.59e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09951 0.185 0.1968 0.9873 0.9919 0.1124 0.7324 0.8606 0.3048 ] Network output: [ -0.002426 0.01148 1.005 3.774e-06 -1.694e-06 0.9887 2.844e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09487 0.09293 0.1649 0.1968 0.9852 0.9911 0.09489 0.6561 0.8356 0.2499 ] Network output: [ 7.698e-05 1 -5.039e-05 4.927e-07 -2.212e-07 0.9998 3.713e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001449 Epoch 10021 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0086 0.9969 0.9928 -1.407e-07 6.317e-08 -0.006862 -1.06e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.003379 -0.006504 0.005272 0.9699 0.9743 0.00689 0.8234 0.8191 0.01597 ] Network output: [ 0.9999 6.308e-05 0.000325 -1.788e-06 8.026e-07 -0.0002697 -1.347e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2104 -0.03591 -0.1542 0.1815 0.9834 0.9932 0.2364 0.428 0.8679 0.7075 ] Network output: [ -0.008621 1.003 1.007 -1.486e-07 6.671e-08 0.007196 -1.12e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006936 0.0006456 0.004335 0.003099 0.9889 0.9919 0.007073 0.8505 0.8916 0.01138 ] Network output: [ -0.0001393 0.001201 1 -5.618e-06 2.522e-06 0.9986 -4.234e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2245 0.1068 0.3506 0.1414 0.9849 0.9939 0.2253 0.4319 0.8747 0.7011 ] Network output: [ 0.002569 -0.01238 0.9945 3.433e-06 -1.541e-06 1.013 2.587e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09951 0.185 0.1968 0.9873 0.9919 0.1124 0.7323 0.8606 0.3048 ] Network output: [ -0.002424 0.01148 1.005 3.769e-06 -1.692e-06 0.9887 2.841e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09488 0.09293 0.1649 0.1968 0.9852 0.9911 0.09489 0.6561 0.8356 0.2499 ] Network output: [ 7.696e-05 1 -5.041e-05 4.921e-07 -2.209e-07 0.9998 3.709e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001448 Epoch 10022 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0086 0.9969 0.9928 -1.406e-07 6.313e-08 -0.006862 -1.06e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.003379 -0.006504 0.005272 0.9699 0.9743 0.00689 0.8234 0.8191 0.01597 ] Network output: [ 0.9999 6.294e-05 0.0003249 -1.785e-06 8.016e-07 -0.0002696 -1.346e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2105 -0.03591 -0.1542 0.1815 0.9834 0.9932 0.2364 0.4279 0.8679 0.7075 ] Network output: [ -0.00862 1.003 1.007 -1.485e-07 6.665e-08 0.007196 -1.119e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006937 0.0006457 0.004335 0.003099 0.9889 0.9919 0.007074 0.8505 0.8916 0.01138 ] Network output: [ -0.0001392 0.001201 1 -5.611e-06 2.519e-06 0.9986 -4.229e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2245 0.1068 0.3506 0.1414 0.9849 0.9939 0.2253 0.4319 0.8747 0.7011 ] Network output: [ 0.002568 -0.01237 0.9945 3.428e-06 -1.539e-06 1.013 2.584e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09951 0.185 0.1968 0.9873 0.9919 0.1124 0.7323 0.8606 0.3048 ] Network output: [ -0.002423 0.01147 1.005 3.765e-06 -1.69e-06 0.9887 2.837e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09488 0.09294 0.1649 0.1968 0.9852 0.9911 0.09489 0.6561 0.8356 0.2499 ] Network output: [ 7.694e-05 1 -5.042e-05 4.915e-07 -2.207e-07 0.9998 3.704e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001447 Epoch 10023 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008599 0.9969 0.9928 -1.405e-07 6.308e-08 -0.006861 -1.059e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.003379 -0.006503 0.005271 0.9699 0.9743 0.00689 0.8234 0.8191 0.01597 ] Network output: [ 0.9999 6.28e-05 0.0003247 -1.783e-06 8.006e-07 -0.0002694 -1.344e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2105 -0.03591 -0.1542 0.1815 0.9834 0.9932 0.2364 0.4279 0.8679 0.7075 ] Network output: [ -0.008619 1.003 1.007 -1.483e-07 6.659e-08 0.007195 -1.118e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006937 0.0006457 0.004335 0.003098 0.9889 0.9919 0.007074 0.8505 0.8916 0.01138 ] Network output: [ -0.000139 0.0012 1 -5.604e-06 2.516e-06 0.9986 -4.223e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2245 0.1068 0.3506 0.1414 0.9849 0.9939 0.2253 0.4319 0.8747 0.7011 ] Network output: [ 0.002566 -0.01236 0.9945 3.424e-06 -1.537e-06 1.013 2.581e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09952 0.185 0.1968 0.9873 0.9919 0.1124 0.7323 0.8606 0.3048 ] Network output: [ -0.002422 0.01147 1.005 3.76e-06 -1.688e-06 0.9887 2.834e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09488 0.09294 0.1649 0.1968 0.9852 0.9911 0.09489 0.6561 0.8356 0.2499 ] Network output: [ 7.693e-05 1 -5.043e-05 4.909e-07 -2.204e-07 0.9998 3.7e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001446 Epoch 10024 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008598 0.9969 0.9928 -1.404e-07 6.303e-08 -0.00686 -1.058e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.003379 -0.006502 0.005271 0.9699 0.9743 0.00689 0.8234 0.8191 0.01597 ] Network output: [ 0.9999 6.266e-05 0.0003246 -1.781e-06 7.996e-07 -0.0002693 -1.342e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2105 -0.03591 -0.1542 0.1815 0.9834 0.9932 0.2364 0.4279 0.8679 0.7075 ] Network output: [ -0.008618 1.003 1.007 -1.482e-07 6.654e-08 0.007195 -1.117e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006937 0.0006458 0.004335 0.003098 0.9889 0.9919 0.007074 0.8505 0.8916 0.01137 ] Network output: [ -0.0001389 0.001199 1 -5.597e-06 2.513e-06 0.9986 -4.218e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2246 0.1068 0.3506 0.1414 0.9849 0.9939 0.2253 0.4319 0.8747 0.7011 ] Network output: [ 0.002565 -0.01236 0.9945 3.42e-06 -1.535e-06 1.013 2.577e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09952 0.185 0.1968 0.9873 0.9919 0.1124 0.7323 0.8606 0.3048 ] Network output: [ -0.002421 0.01146 1.005 3.755e-06 -1.686e-06 0.9887 2.83e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09488 0.09294 0.1649 0.1968 0.9852 0.9911 0.0949 0.6561 0.8356 0.25 ] Network output: [ 7.691e-05 1 -5.044e-05 4.903e-07 -2.201e-07 0.9998 3.695e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001445 Epoch 10025 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008597 0.9969 0.9928 -1.403e-07 6.298e-08 -0.00686 -1.057e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.003379 -0.006502 0.005271 0.9699 0.9743 0.006891 0.8234 0.8191 0.01597 ] Network output: [ 0.9999 6.252e-05 0.0003245 -1.779e-06 7.986e-07 -0.0002691 -1.341e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2105 -0.03592 -0.1542 0.1815 0.9834 0.9932 0.2364 0.4279 0.8679 0.7075 ] Network output: [ -0.008617 1.003 1.007 -1.481e-07 6.648e-08 0.007194 -1.116e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006938 0.0006458 0.004335 0.003098 0.9889 0.9919 0.007074 0.8505 0.8916 0.01137 ] Network output: [ -0.0001387 0.001199 1 -5.59e-06 2.51e-06 0.9986 -4.213e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2246 0.1068 0.3506 0.1414 0.9849 0.9939 0.2253 0.4319 0.8747 0.7011 ] Network output: [ 0.002563 -0.01235 0.9945 3.416e-06 -1.534e-06 1.013 2.574e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09952 0.185 0.1968 0.9873 0.9919 0.1124 0.7323 0.8606 0.3048 ] Network output: [ -0.002419 0.01145 1.005 3.751e-06 -1.684e-06 0.9887 2.827e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09488 0.09294 0.1649 0.1968 0.9852 0.9911 0.0949 0.6561 0.8356 0.25 ] Network output: [ 7.689e-05 1 -5.045e-05 4.897e-07 -2.199e-07 0.9998 3.691e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001444 Epoch 10026 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008596 0.9969 0.9928 -1.402e-07 6.294e-08 -0.006859 -1.057e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.003379 -0.006501 0.00527 0.9699 0.9743 0.006891 0.8234 0.8191 0.01597 ] Network output: [ 0.9999 6.238e-05 0.0003243 -1.777e-06 7.976e-07 -0.000269 -1.339e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2105 -0.03592 -0.1542 0.1815 0.9834 0.9932 0.2364 0.4279 0.8679 0.7075 ] Network output: [ -0.008617 1.003 1.007 -1.48e-07 6.642e-08 0.007194 -1.115e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006938 0.0006459 0.004335 0.003098 0.9889 0.9919 0.007075 0.8505 0.8916 0.01137 ] Network output: [ -0.0001386 0.001198 1 -5.583e-06 2.507e-06 0.9986 -4.208e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2246 0.1068 0.3506 0.1414 0.9849 0.9939 0.2253 0.4319 0.8747 0.7011 ] Network output: [ 0.002562 -0.01234 0.9945 3.412e-06 -1.532e-06 1.013 2.571e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09952 0.185 0.1968 0.9873 0.9919 0.1124 0.7323 0.8606 0.3048 ] Network output: [ -0.002418 0.01145 1.005 3.746e-06 -1.682e-06 0.9888 2.823e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09488 0.09294 0.1649 0.1968 0.9852 0.9911 0.0949 0.656 0.8356 0.25 ] Network output: [ 7.688e-05 1 -5.046e-05 4.891e-07 -2.196e-07 0.9998 3.686e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001444 Epoch 10027 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008595 0.9969 0.9928 -1.401e-07 6.289e-08 -0.006858 -1.056e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.003379 -0.006501 0.00527 0.9699 0.9743 0.006891 0.8234 0.8191 0.01597 ] Network output: [ 0.9999 6.224e-05 0.0003242 -1.774e-06 7.966e-07 -0.0002688 -1.337e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2105 -0.03592 -0.1542 0.1815 0.9834 0.9932 0.2364 0.4279 0.8679 0.7075 ] Network output: [ -0.008616 1.003 1.007 -1.478e-07 6.636e-08 0.007193 -1.114e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006938 0.0006459 0.004334 0.003097 0.9889 0.9919 0.007075 0.8505 0.8916 0.01137 ] Network output: [ -0.0001385 0.001197 1 -5.576e-06 2.503e-06 0.9986 -4.203e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2246 0.1068 0.3507 0.1414 0.9849 0.9939 0.2253 0.4319 0.8747 0.7011 ] Network output: [ 0.002561 -0.01234 0.9945 3.407e-06 -1.53e-06 1.013 2.568e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09953 0.185 0.1968 0.9873 0.9919 0.1124 0.7323 0.8606 0.3048 ] Network output: [ -0.002417 0.01144 1.005 3.742e-06 -1.68e-06 0.9888 2.82e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09489 0.09294 0.1649 0.1968 0.9852 0.9911 0.0949 0.656 0.8356 0.25 ] Network output: [ 7.686e-05 1 -5.047e-05 4.886e-07 -2.193e-07 0.9998 3.682e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001443 Epoch 10028 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008595 0.9969 0.9928 -1.4e-07 6.284e-08 -0.006858 -1.055e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.003379 -0.0065 0.005269 0.9699 0.9743 0.006891 0.8234 0.8191 0.01597 ] Network output: [ 0.9999 6.21e-05 0.000324 -1.772e-06 7.956e-07 -0.0002687 -1.336e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2105 -0.03592 -0.1541 0.1815 0.9834 0.9932 0.2364 0.4279 0.8679 0.7075 ] Network output: [ -0.008615 1.003 1.007 -1.477e-07 6.631e-08 0.007193 -1.113e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006939 0.000646 0.004334 0.003097 0.9889 0.9919 0.007075 0.8505 0.8916 0.01137 ] Network output: [ -0.0001383 0.001196 1 -5.569e-06 2.5e-06 0.9986 -4.197e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2246 0.1068 0.3507 0.1414 0.9849 0.9939 0.2254 0.4319 0.8747 0.7011 ] Network output: [ 0.002559 -0.01233 0.9945 3.403e-06 -1.528e-06 1.013 2.565e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09953 0.185 0.1968 0.9873 0.9919 0.1124 0.7323 0.8606 0.3048 ] Network output: [ -0.002415 0.01144 1.005 3.737e-06 -1.678e-06 0.9888 2.817e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09489 0.09294 0.1649 0.1968 0.9852 0.9911 0.0949 0.656 0.8356 0.25 ] Network output: [ 7.684e-05 1 -5.048e-05 4.88e-07 -2.191e-07 0.9998 3.677e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001442 Epoch 10029 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008594 0.9969 0.9928 -1.399e-07 6.279e-08 -0.006857 -1.054e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.003379 -0.0065 0.005269 0.9699 0.9743 0.006891 0.8233 0.8191 0.01596 ] Network output: [ 0.9999 6.196e-05 0.0003239 -1.77e-06 7.947e-07 -0.0002685 -1.334e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2105 -0.03592 -0.1541 0.1815 0.9834 0.9932 0.2364 0.4279 0.8679 0.7075 ] Network output: [ -0.008614 1.003 1.007 -1.476e-07 6.625e-08 0.007192 -1.112e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006939 0.000646 0.004334 0.003097 0.9889 0.9919 0.007076 0.8505 0.8916 0.01137 ] Network output: [ -0.0001382 0.001196 1 -5.563e-06 2.497e-06 0.9986 -4.192e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2246 0.1068 0.3507 0.1414 0.9849 0.9939 0.2254 0.4319 0.8747 0.7011 ] Network output: [ 0.002558 -0.01233 0.9945 3.399e-06 -1.526e-06 1.013 2.562e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09953 0.185 0.1968 0.9873 0.9919 0.1124 0.7323 0.8606 0.3048 ] Network output: [ -0.002414 0.01143 1.005 3.733e-06 -1.676e-06 0.9888 2.813e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09489 0.09295 0.1649 0.1968 0.9852 0.9911 0.0949 0.656 0.8356 0.25 ] Network output: [ 7.683e-05 1 -5.049e-05 4.874e-07 -2.188e-07 0.9998 3.673e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001441 Epoch 10030 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008593 0.9969 0.9928 -1.398e-07 6.275e-08 -0.006856 -1.053e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.00338 -0.006499 0.005269 0.9699 0.9743 0.006891 0.8233 0.8191 0.01596 ] Network output: [ 0.9999 6.182e-05 0.0003238 -1.768e-06 7.937e-07 -0.0002684 -1.332e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2105 -0.03592 -0.1541 0.1815 0.9834 0.9932 0.2364 0.4279 0.8679 0.7075 ] Network output: [ -0.008613 1.003 1.007 -1.474e-07 6.619e-08 0.007192 -1.111e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006939 0.0006461 0.004334 0.003097 0.9889 0.9919 0.007076 0.8505 0.8916 0.01137 ] Network output: [ -0.0001381 0.001195 1 -5.556e-06 2.494e-06 0.9986 -4.187e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2246 0.1068 0.3507 0.1414 0.9849 0.9939 0.2254 0.4319 0.8747 0.7011 ] Network output: [ 0.002556 -0.01232 0.9945 3.395e-06 -1.524e-06 1.013 2.559e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09953 0.185 0.1968 0.9873 0.9919 0.1124 0.7323 0.8606 0.3048 ] Network output: [ -0.002413 0.01143 1.005 3.728e-06 -1.674e-06 0.9888 2.81e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09489 0.09295 0.1649 0.1968 0.9852 0.9911 0.0949 0.656 0.8356 0.25 ] Network output: [ 7.681e-05 1 -5.051e-05 4.868e-07 -2.185e-07 0.9998 3.668e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000144 Epoch 10031 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008592 0.9969 0.9928 -1.397e-07 6.27e-08 -0.006856 -1.053e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.00338 -0.006499 0.005268 0.9699 0.9743 0.006891 0.8233 0.8191 0.01596 ] Network output: [ 0.9999 6.168e-05 0.0003236 -1.766e-06 7.927e-07 -0.0002682 -1.331e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2105 -0.03592 -0.1541 0.1815 0.9834 0.9932 0.2365 0.4279 0.8679 0.7075 ] Network output: [ -0.008613 1.003 1.007 -1.473e-07 6.613e-08 0.007191 -1.11e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006939 0.0006461 0.004334 0.003097 0.9889 0.9919 0.007076 0.8505 0.8916 0.01137 ] Network output: [ -0.0001379 0.001194 1 -5.549e-06 2.491e-06 0.9986 -4.182e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2246 0.1068 0.3507 0.1414 0.9849 0.9939 0.2254 0.4319 0.8747 0.7011 ] Network output: [ 0.002555 -0.01231 0.9945 3.391e-06 -1.522e-06 1.013 2.555e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09954 0.185 0.1968 0.9873 0.9919 0.1124 0.7323 0.8606 0.3048 ] Network output: [ -0.002412 0.01142 1.005 3.724e-06 -1.672e-06 0.9888 2.806e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09489 0.09295 0.1649 0.1968 0.9852 0.9911 0.09491 0.656 0.8356 0.25 ] Network output: [ 7.68e-05 1 -5.052e-05 4.862e-07 -2.183e-07 0.9998 3.664e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000144 Epoch 10032 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008591 0.9969 0.9928 -1.396e-07 6.265e-08 -0.006855 -1.052e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.00338 -0.006498 0.005268 0.9699 0.9743 0.006891 0.8233 0.8191 0.01596 ] Network output: [ 0.9999 6.154e-05 0.0003235 -1.764e-06 7.917e-07 -0.0002681 -1.329e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2105 -0.03592 -0.1541 0.1815 0.9834 0.9932 0.2365 0.4279 0.8679 0.7075 ] Network output: [ -0.008612 1.003 1.007 -1.472e-07 6.608e-08 0.007191 -1.109e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00694 0.0006461 0.004334 0.003096 0.9889 0.9919 0.007077 0.8505 0.8916 0.01137 ] Network output: [ -0.0001378 0.001194 1 -5.542e-06 2.488e-06 0.9986 -4.177e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2246 0.1068 0.3507 0.1414 0.9849 0.9939 0.2254 0.4318 0.8747 0.7011 ] Network output: [ 0.002553 -0.01231 0.9945 3.387e-06 -1.52e-06 1.013 2.552e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09954 0.185 0.1968 0.9873 0.9919 0.1124 0.7323 0.8606 0.3048 ] Network output: [ -0.00241 0.01142 1.005 3.719e-06 -1.67e-06 0.9888 2.803e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09489 0.09295 0.1649 0.1968 0.9852 0.9911 0.09491 0.656 0.8356 0.25 ] Network output: [ 7.678e-05 1 -5.053e-05 4.856e-07 -2.18e-07 0.9998 3.659e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001439 Epoch 10033 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00859 0.9969 0.9928 -1.395e-07 6.261e-08 -0.006854 -1.051e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.00338 -0.006498 0.005268 0.9699 0.9743 0.006892 0.8233 0.8191 0.01596 ] Network output: [ 0.9999 6.14e-05 0.0003233 -1.761e-06 7.907e-07 -0.0002679 -1.327e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2105 -0.03592 -0.1541 0.1815 0.9834 0.9932 0.2365 0.4279 0.8679 0.7075 ] Network output: [ -0.008611 1.003 1.007 -1.471e-07 6.602e-08 0.00719 -1.108e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00694 0.0006462 0.004334 0.003096 0.9889 0.9919 0.007077 0.8505 0.8916 0.01137 ] Network output: [ -0.0001376 0.001193 1 -5.535e-06 2.485e-06 0.9986 -4.171e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2246 0.1068 0.3507 0.1414 0.9849 0.9939 0.2254 0.4318 0.8747 0.7011 ] Network output: [ 0.002552 -0.0123 0.9945 3.382e-06 -1.518e-06 1.013 2.549e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09954 0.185 0.1968 0.9873 0.9919 0.1124 0.7322 0.8606 0.3048 ] Network output: [ -0.002409 0.01141 1.005 3.715e-06 -1.668e-06 0.9888 2.8e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09489 0.09295 0.1649 0.1968 0.9852 0.9911 0.09491 0.656 0.8356 0.25 ] Network output: [ 7.676e-05 1 -5.054e-05 4.85e-07 -2.177e-07 0.9998 3.655e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001438 Epoch 10034 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00859 0.9969 0.9928 -1.393e-07 6.256e-08 -0.006854 -1.05e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.00338 -0.006497 0.005267 0.9699 0.9743 0.006892 0.8233 0.8191 0.01596 ] Network output: [ 0.9999 6.126e-05 0.0003232 -1.759e-06 7.898e-07 -0.0002677 -1.326e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2105 -0.03592 -0.1541 0.1815 0.9834 0.9932 0.2365 0.4279 0.8679 0.7075 ] Network output: [ -0.00861 1.003 1.007 -1.469e-07 6.596e-08 0.00719 -1.107e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00694 0.0006462 0.004334 0.003096 0.9889 0.9919 0.007077 0.8505 0.8916 0.01137 ] Network output: [ -0.0001375 0.001192 1 -5.528e-06 2.482e-06 0.9986 -4.166e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2246 0.1068 0.3507 0.1414 0.9849 0.9939 0.2254 0.4318 0.8747 0.7011 ] Network output: [ 0.002551 -0.01229 0.9945 3.378e-06 -1.517e-06 1.013 2.546e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09954 0.185 0.1968 0.9873 0.9919 0.1124 0.7322 0.8606 0.3048 ] Network output: [ -0.002408 0.0114 1.005 3.71e-06 -1.666e-06 0.9888 2.796e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0949 0.09295 0.1649 0.1968 0.9852 0.9911 0.09491 0.656 0.8356 0.25 ] Network output: [ 7.675e-05 1 -5.055e-05 4.844e-07 -2.175e-07 0.9998 3.651e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001437 Epoch 10035 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008589 0.9969 0.9928 -1.392e-07 6.251e-08 -0.006853 -1.049e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.00338 -0.006497 0.005267 0.9699 0.9743 0.006892 0.8233 0.8191 0.01596 ] Network output: [ 0.9999 6.112e-05 0.000323 -1.757e-06 7.888e-07 -0.0002676 -1.324e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2105 -0.03593 -0.1541 0.1815 0.9834 0.9932 0.2365 0.4279 0.8679 0.7075 ] Network output: [ -0.00861 1.003 1.007 -1.468e-07 6.591e-08 0.00719 -1.106e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006941 0.0006463 0.004334 0.003096 0.9889 0.9919 0.007078 0.8505 0.8916 0.01137 ] Network output: [ -0.0001374 0.001192 1 -5.521e-06 2.479e-06 0.9986 -4.161e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2246 0.1068 0.3507 0.1414 0.9849 0.9939 0.2254 0.4318 0.8747 0.7011 ] Network output: [ 0.002549 -0.01229 0.9945 3.374e-06 -1.515e-06 1.013 2.543e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09954 0.185 0.1968 0.9873 0.9919 0.1124 0.7322 0.8606 0.3048 ] Network output: [ -0.002406 0.0114 1.005 3.706e-06 -1.664e-06 0.9888 2.793e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0949 0.09296 0.1649 0.1968 0.9852 0.9911 0.09491 0.656 0.8356 0.25 ] Network output: [ 7.673e-05 1 -5.056e-05 4.838e-07 -2.172e-07 0.9998 3.646e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001436 Epoch 10036 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008588 0.9969 0.9928 -1.391e-07 6.246e-08 -0.006852 -1.049e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.00338 -0.006496 0.005267 0.9699 0.9743 0.006892 0.8233 0.8191 0.01596 ] Network output: [ 0.9999 6.098e-05 0.0003229 -1.755e-06 7.878e-07 -0.0002674 -1.322e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2105 -0.03593 -0.1541 0.1815 0.9834 0.9932 0.2365 0.4279 0.8679 0.7075 ] Network output: [ -0.008609 1.003 1.007 -1.467e-07 6.585e-08 0.007189 -1.105e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006941 0.0006463 0.004333 0.003095 0.9889 0.9919 0.007078 0.8505 0.8916 0.01137 ] Network output: [ -0.0001372 0.001191 1 -5.515e-06 2.476e-06 0.9986 -4.156e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2246 0.1068 0.3507 0.1414 0.9849 0.9939 0.2254 0.4318 0.8747 0.7011 ] Network output: [ 0.002548 -0.01228 0.9945 3.37e-06 -1.513e-06 1.013 2.54e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09955 0.185 0.1968 0.9873 0.9919 0.1124 0.7322 0.8606 0.3048 ] Network output: [ -0.002405 0.01139 1.005 3.701e-06 -1.662e-06 0.9888 2.789e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0949 0.09296 0.1649 0.1968 0.9852 0.9911 0.09491 0.656 0.8356 0.25 ] Network output: [ 7.671e-05 1 -5.058e-05 4.832e-07 -2.169e-07 0.9998 3.642e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001436 Epoch 10037 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008587 0.9969 0.9928 -1.39e-07 6.242e-08 -0.006852 -1.048e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.00338 -0.006496 0.005266 0.9699 0.9743 0.006892 0.8233 0.8191 0.01596 ] Network output: [ 0.9999 6.084e-05 0.0003228 -1.753e-06 7.868e-07 -0.0002673 -1.321e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2105 -0.03593 -0.1541 0.1815 0.9834 0.9932 0.2365 0.4279 0.8679 0.7075 ] Network output: [ -0.008608 1.003 1.007 -1.465e-07 6.579e-08 0.007189 -1.104e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006941 0.0006464 0.004333 0.003095 0.9889 0.9919 0.007078 0.8505 0.8916 0.01137 ] Network output: [ -0.0001371 0.00119 1 -5.508e-06 2.473e-06 0.9986 -4.151e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2246 0.1068 0.3507 0.1414 0.9849 0.9939 0.2254 0.4318 0.8747 0.7011 ] Network output: [ 0.002546 -0.01227 0.9945 3.366e-06 -1.511e-06 1.013 2.537e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09955 0.185 0.1968 0.9873 0.9919 0.1124 0.7322 0.8606 0.3048 ] Network output: [ -0.002404 0.01139 1.005 3.697e-06 -1.66e-06 0.9888 2.786e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0949 0.09296 0.1649 0.1968 0.9852 0.9911 0.09491 0.6559 0.8356 0.25 ] Network output: [ 7.67e-05 1 -5.059e-05 4.826e-07 -2.167e-07 0.9998 3.637e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001435 Epoch 10038 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008586 0.9969 0.9928 -1.389e-07 6.237e-08 -0.006851 -1.047e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.00338 -0.006495 0.005266 0.9699 0.9743 0.006892 0.8233 0.8191 0.01596 ] Network output: [ 0.9999 6.07e-05 0.0003226 -1.75e-06 7.859e-07 -0.0002671 -1.319e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2105 -0.03593 -0.1541 0.1815 0.9834 0.9932 0.2365 0.4279 0.8679 0.7075 ] Network output: [ -0.008607 1.003 1.007 -1.464e-07 6.573e-08 0.007188 -1.103e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006942 0.0006464 0.004333 0.003095 0.9889 0.9919 0.007079 0.8505 0.8916 0.01136 ] Network output: [ -0.000137 0.00119 1 -5.501e-06 2.47e-06 0.9986 -4.146e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2246 0.1068 0.3507 0.1414 0.9849 0.9939 0.2254 0.4318 0.8747 0.7011 ] Network output: [ 0.002545 -0.01227 0.9945 3.362e-06 -1.509e-06 1.013 2.533e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09955 0.185 0.1968 0.9873 0.9919 0.1124 0.7322 0.8606 0.3048 ] Network output: [ -0.002403 0.01138 1.005 3.692e-06 -1.658e-06 0.9888 2.783e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0949 0.09296 0.1649 0.1968 0.9852 0.9911 0.09492 0.6559 0.8355 0.25 ] Network output: [ 7.668e-05 1 -5.06e-05 4.82e-07 -2.164e-07 0.9998 3.633e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001434 Epoch 10039 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008585 0.9969 0.9928 -1.388e-07 6.232e-08 -0.00685 -1.046e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.00338 -0.006494 0.005266 0.9699 0.9743 0.006892 0.8233 0.8191 0.01596 ] Network output: [ 0.9999 6.056e-05 0.0003225 -1.748e-06 7.849e-07 -0.000267 -1.318e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2105 -0.03593 -0.1541 0.1814 0.9834 0.9932 0.2365 0.4279 0.8679 0.7075 ] Network output: [ -0.008606 1.003 1.007 -1.463e-07 6.568e-08 0.007188 -1.103e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006942 0.0006465 0.004333 0.003095 0.9889 0.9919 0.007079 0.8505 0.8916 0.01136 ] Network output: [ -0.0001368 0.001189 1 -5.494e-06 2.467e-06 0.9986 -4.141e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2246 0.1068 0.3507 0.1414 0.9849 0.9939 0.2254 0.4318 0.8747 0.7011 ] Network output: [ 0.002543 -0.01226 0.9945 3.358e-06 -1.507e-06 1.013 2.53e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09955 0.185 0.1968 0.9873 0.9919 0.1124 0.7322 0.8606 0.3048 ] Network output: [ -0.002401 0.01138 1.005 3.688e-06 -1.656e-06 0.9888 2.779e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0949 0.09296 0.1649 0.1968 0.9852 0.9911 0.09492 0.6559 0.8355 0.25 ] Network output: [ 7.667e-05 1 -5.061e-05 4.814e-07 -2.161e-07 0.9998 3.628e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001433 Epoch 10040 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008585 0.9969 0.9928 -1.387e-07 6.227e-08 -0.00685 -1.045e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.00338 -0.006494 0.005265 0.9699 0.9743 0.006893 0.8233 0.8191 0.01596 ] Network output: [ 0.9999 6.042e-05 0.0003223 -1.746e-06 7.839e-07 -0.0002668 -1.316e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2105 -0.03593 -0.1541 0.1814 0.9834 0.9932 0.2365 0.4279 0.8679 0.7075 ] Network output: [ -0.008606 1.003 1.007 -1.462e-07 6.562e-08 0.007187 -1.102e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006942 0.0006465 0.004333 0.003095 0.9889 0.9919 0.007079 0.8505 0.8916 0.01136 ] Network output: [ -0.0001367 0.001188 1 -5.487e-06 2.464e-06 0.9986 -4.136e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2246 0.1068 0.3507 0.1414 0.9849 0.9939 0.2254 0.4318 0.8747 0.7011 ] Network output: [ 0.002542 -0.01226 0.9945 3.353e-06 -1.505e-06 1.013 2.527e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09956 0.185 0.1968 0.9873 0.9919 0.1124 0.7322 0.8606 0.3048 ] Network output: [ -0.0024 0.01137 1.005 3.683e-06 -1.654e-06 0.9888 2.776e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0949 0.09296 0.1649 0.1968 0.9852 0.9911 0.09492 0.6559 0.8355 0.25 ] Network output: [ 7.665e-05 1 -5.062e-05 4.809e-07 -2.159e-07 0.9998 3.624e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001432 Epoch 10041 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008584 0.9969 0.9928 -1.386e-07 6.223e-08 -0.006849 -1.045e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.00338 -0.006493 0.005265 0.9699 0.9743 0.006893 0.8233 0.8191 0.01595 ] Network output: [ 0.9999 6.028e-05 0.0003222 -1.744e-06 7.829e-07 -0.0002667 -1.314e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2106 -0.03593 -0.154 0.1814 0.9834 0.9932 0.2365 0.4279 0.8679 0.7075 ] Network output: [ -0.008605 1.003 1.007 -1.46e-07 6.556e-08 0.007187 -1.101e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006943 0.0006466 0.004333 0.003094 0.9889 0.9919 0.00708 0.8505 0.8915 0.01136 ] Network output: [ -0.0001365 0.001188 1 -5.481e-06 2.46e-06 0.9986 -4.13e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2247 0.1068 0.3507 0.1414 0.9849 0.9939 0.2254 0.4318 0.8747 0.7011 ] Network output: [ 0.002541 -0.01225 0.9945 3.349e-06 -1.504e-06 1.013 2.524e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09956 0.185 0.1968 0.9873 0.9919 0.1124 0.7322 0.8606 0.3048 ] Network output: [ -0.002399 0.01136 1.005 3.679e-06 -1.652e-06 0.9888 2.772e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09491 0.09296 0.1649 0.1968 0.9852 0.9911 0.09492 0.6559 0.8355 0.25 ] Network output: [ 7.663e-05 1 -5.064e-05 4.803e-07 -2.156e-07 0.9998 3.62e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001431 Epoch 10042 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008583 0.9969 0.9928 -1.385e-07 6.218e-08 -0.006848 -1.044e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.00338 -0.006493 0.005264 0.9699 0.9743 0.006893 0.8233 0.8191 0.01595 ] Network output: [ 0.9999 6.014e-05 0.0003221 -1.742e-06 7.82e-07 -0.0002665 -1.313e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2106 -0.03593 -0.154 0.1814 0.9834 0.9932 0.2365 0.4279 0.8679 0.7075 ] Network output: [ -0.008604 1.003 1.007 -1.459e-07 6.551e-08 0.007186 -1.1e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006943 0.0006466 0.004333 0.003094 0.9889 0.9919 0.00708 0.8504 0.8915 0.01136 ] Network output: [ -0.0001364 0.001187 1 -5.474e-06 2.457e-06 0.9986 -4.125e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2247 0.1068 0.3507 0.1414 0.9849 0.9939 0.2254 0.4318 0.8747 0.7011 ] Network output: [ 0.002539 -0.01224 0.9945 3.345e-06 -1.502e-06 1.013 2.521e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09956 0.185 0.1968 0.9873 0.9919 0.1124 0.7322 0.8606 0.3048 ] Network output: [ -0.002397 0.01136 1.005 3.674e-06 -1.65e-06 0.9888 2.769e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09491 0.09297 0.1649 0.1968 0.9852 0.9911 0.09492 0.6559 0.8355 0.25 ] Network output: [ 7.662e-05 1 -5.065e-05 4.797e-07 -2.154e-07 0.9998 3.615e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001431 Epoch 10043 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008582 0.9969 0.9928 -1.384e-07 6.213e-08 -0.006848 -1.043e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.00338 -0.006492 0.005264 0.9699 0.9743 0.006893 0.8233 0.8191 0.01595 ] Network output: [ 0.9999 6.001e-05 0.0003219 -1.74e-06 7.81e-07 -0.0002664 -1.311e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2106 -0.03593 -0.154 0.1814 0.9834 0.9932 0.2365 0.4279 0.8679 0.7075 ] Network output: [ -0.008603 1.003 1.007 -1.458e-07 6.545e-08 0.007186 -1.099e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006943 0.0006467 0.004333 0.003094 0.9889 0.9919 0.00708 0.8504 0.8915 0.01136 ] Network output: [ -0.0001363 0.001186 1 -5.467e-06 2.454e-06 0.9986 -4.12e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2247 0.1068 0.3507 0.1414 0.9849 0.9939 0.2254 0.4318 0.8747 0.7011 ] Network output: [ 0.002538 -0.01224 0.9945 3.341e-06 -1.5e-06 1.013 2.518e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09956 0.185 0.1968 0.9873 0.9919 0.1124 0.7322 0.8606 0.3048 ] Network output: [ -0.002396 0.01135 1.005 3.67e-06 -1.648e-06 0.9888 2.766e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09491 0.09297 0.1649 0.1968 0.9852 0.9911 0.09492 0.6559 0.8355 0.25 ] Network output: [ 7.66e-05 1 -5.066e-05 4.791e-07 -2.151e-07 0.9998 3.611e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000143 Epoch 10044 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008581 0.9969 0.9928 -1.383e-07 6.209e-08 -0.006847 -1.042e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.00338 -0.006492 0.005264 0.9699 0.9743 0.006893 0.8233 0.8191 0.01595 ] Network output: [ 0.9999 5.987e-05 0.0003218 -1.738e-06 7.8e-07 -0.0002662 -1.309e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2106 -0.03593 -0.154 0.1814 0.9834 0.9932 0.2365 0.4279 0.8679 0.7074 ] Network output: [ -0.008603 1.003 1.007 -1.457e-07 6.539e-08 0.007185 -1.098e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006944 0.0006467 0.004333 0.003094 0.9889 0.9919 0.007081 0.8504 0.8915 0.01136 ] Network output: [ -0.0001361 0.001185 1 -5.46e-06 2.451e-06 0.9986 -4.115e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2247 0.1068 0.3507 0.1414 0.9849 0.9939 0.2254 0.4318 0.8747 0.7011 ] Network output: [ 0.002536 -0.01223 0.9945 3.337e-06 -1.498e-06 1.013 2.515e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09957 0.185 0.1968 0.9873 0.9919 0.1124 0.7321 0.8606 0.3048 ] Network output: [ -0.002395 0.01135 1.005 3.665e-06 -1.646e-06 0.9888 2.762e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09491 0.09297 0.1649 0.1968 0.9852 0.9911 0.09492 0.6559 0.8355 0.25 ] Network output: [ 7.658e-05 1 -5.067e-05 4.785e-07 -2.148e-07 0.9998 3.606e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001429 Epoch 10045 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00858 0.9969 0.9928 -1.382e-07 6.204e-08 -0.006846 -1.041e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.003381 -0.006491 0.005263 0.9699 0.9743 0.006893 0.8233 0.8191 0.01595 ] Network output: [ 0.9999 5.973e-05 0.0003216 -1.735e-06 7.791e-07 -0.0002661 -1.308e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2106 -0.03594 -0.154 0.1814 0.9834 0.9932 0.2365 0.4279 0.8679 0.7074 ] Network output: [ -0.008602 1.003 1.007 -1.455e-07 6.533e-08 0.007185 -1.097e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006944 0.0006467 0.004333 0.003093 0.9889 0.9919 0.007081 0.8504 0.8915 0.01136 ] Network output: [ -0.000136 0.001185 1 -5.454e-06 2.448e-06 0.9986 -4.11e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2247 0.1068 0.3507 0.1414 0.9849 0.9939 0.2255 0.4318 0.8747 0.7011 ] Network output: [ 0.002535 -0.01222 0.9945 3.333e-06 -1.496e-06 1.013 2.512e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09957 0.185 0.1968 0.9873 0.9919 0.1124 0.7321 0.8606 0.3048 ] Network output: [ -0.002394 0.01134 1.005 3.661e-06 -1.644e-06 0.9888 2.759e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09491 0.09297 0.1649 0.1968 0.9852 0.9911 0.09493 0.6559 0.8355 0.25 ] Network output: [ 7.657e-05 1 -5.068e-05 4.779e-07 -2.146e-07 0.9998 3.602e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001428 Epoch 10046 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00858 0.9969 0.9928 -1.381e-07 6.199e-08 -0.006846 -1.041e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.003381 -0.006491 0.005263 0.9699 0.9743 0.006893 0.8233 0.8191 0.01595 ] Network output: [ 0.9999 5.959e-05 0.0003215 -1.733e-06 7.781e-07 -0.0002659 -1.306e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2106 -0.03594 -0.154 0.1814 0.9834 0.9932 0.2365 0.4279 0.8679 0.7074 ] Network output: [ -0.008601 1.003 1.007 -1.454e-07 6.528e-08 0.007184 -1.096e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006944 0.0006468 0.004332 0.003093 0.9889 0.9919 0.007081 0.8504 0.8915 0.01136 ] Network output: [ -0.0001359 0.001184 1 -5.447e-06 2.445e-06 0.9986 -4.105e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2247 0.1068 0.3507 0.1414 0.9849 0.9939 0.2255 0.4318 0.8747 0.7011 ] Network output: [ 0.002534 -0.01222 0.9945 3.329e-06 -1.494e-06 1.013 2.509e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09957 0.185 0.1968 0.9873 0.9919 0.1124 0.7321 0.8606 0.3048 ] Network output: [ -0.002392 0.01134 1.005 3.657e-06 -1.642e-06 0.9888 2.756e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09491 0.09297 0.1649 0.1968 0.9852 0.9911 0.09493 0.6559 0.8355 0.25 ] Network output: [ 7.655e-05 1 -5.07e-05 4.774e-07 -2.143e-07 0.9998 3.597e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001427 Epoch 10047 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008579 0.9969 0.9928 -1.38e-07 6.194e-08 -0.006845 -1.04e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003526 -0.003381 -0.00649 0.005263 0.9699 0.9743 0.006893 0.8233 0.8191 0.01595 ] Network output: [ 0.9999 5.945e-05 0.0003214 -1.731e-06 7.771e-07 -0.0002658 -1.305e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2106 -0.03594 -0.154 0.1814 0.9834 0.9932 0.2366 0.4279 0.8679 0.7074 ] Network output: [ -0.0086 1.003 1.007 -1.453e-07 6.522e-08 0.007184 -1.095e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006944 0.0006468 0.004332 0.003093 0.9889 0.9919 0.007081 0.8504 0.8915 0.01136 ] Network output: [ -0.0001357 0.001183 1 -5.44e-06 2.442e-06 0.9986 -4.1e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2247 0.1068 0.3507 0.1414 0.9849 0.9939 0.2255 0.4318 0.8747 0.7011 ] Network output: [ 0.002532 -0.01221 0.9945 3.325e-06 -1.493e-06 1.013 2.506e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09957 0.185 0.1968 0.9873 0.9919 0.1124 0.7321 0.8606 0.3048 ] Network output: [ -0.002391 0.01133 1.005 3.652e-06 -1.64e-06 0.9888 2.752e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09491 0.09297 0.1649 0.1968 0.9852 0.9911 0.09493 0.6559 0.8355 0.25 ] Network output: [ 7.654e-05 1 -5.071e-05 4.768e-07 -2.14e-07 0.9998 3.593e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001427 Epoch 10048 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008578 0.9969 0.9928 -1.379e-07 6.19e-08 -0.006844 -1.039e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003381 -0.00649 0.005262 0.9699 0.9743 0.006894 0.8233 0.8191 0.01595 ] Network output: [ 0.9999 5.931e-05 0.0003212 -1.729e-06 7.762e-07 -0.0002656 -1.303e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2106 -0.03594 -0.154 0.1814 0.9834 0.9932 0.2366 0.4279 0.8679 0.7074 ] Network output: [ -0.008599 1.003 1.007 -1.452e-07 6.516e-08 0.007183 -1.094e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006945 0.0006469 0.004332 0.003093 0.9889 0.9919 0.007082 0.8504 0.8915 0.01136 ] Network output: [ -0.0001356 0.001183 1 -5.433e-06 2.439e-06 0.9986 -4.095e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2247 0.1068 0.3507 0.1414 0.9849 0.9939 0.2255 0.4318 0.8747 0.7011 ] Network output: [ 0.002531 -0.0122 0.9945 3.321e-06 -1.491e-06 1.013 2.502e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09957 0.185 0.1968 0.9873 0.9919 0.1124 0.7321 0.8606 0.3048 ] Network output: [ -0.00239 0.01132 1.005 3.648e-06 -1.638e-06 0.9888 2.749e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09492 0.09297 0.1649 0.1968 0.9852 0.9911 0.09493 0.6558 0.8355 0.25 ] Network output: [ 7.652e-05 1 -5.072e-05 4.762e-07 -2.138e-07 0.9998 3.589e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001426 Epoch 10049 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008577 0.9969 0.9928 -1.378e-07 6.185e-08 -0.006844 -1.038e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003381 -0.006489 0.005262 0.9699 0.9743 0.006894 0.8233 0.8191 0.01595 ] Network output: [ 0.9999 5.917e-05 0.0003211 -1.727e-06 7.752e-07 -0.0002655 -1.301e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2106 -0.03594 -0.154 0.1814 0.9834 0.9932 0.2366 0.4279 0.8679 0.7074 ] Network output: [ -0.008599 1.003 1.007 -1.45e-07 6.511e-08 0.007183 -1.093e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006945 0.0006469 0.004332 0.003093 0.9889 0.9919 0.007082 0.8504 0.8915 0.01136 ] Network output: [ -0.0001355 0.001182 1 -5.427e-06 2.436e-06 0.9986 -4.09e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2247 0.1068 0.3508 0.1414 0.9849 0.9939 0.2255 0.4318 0.8746 0.7011 ] Network output: [ 0.002529 -0.0122 0.9945 3.316e-06 -1.489e-06 1.013 2.499e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09958 0.185 0.1968 0.9873 0.9919 0.1124 0.7321 0.8606 0.3048 ] Network output: [ -0.002388 0.01132 1.005 3.643e-06 -1.636e-06 0.9888 2.746e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09492 0.09298 0.1649 0.1968 0.9852 0.9911 0.09493 0.6558 0.8355 0.25 ] Network output: [ 7.65e-05 1 -5.073e-05 4.756e-07 -2.135e-07 0.9998 3.584e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001425 Epoch 10050 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008576 0.9969 0.9928 -1.377e-07 6.18e-08 -0.006843 -1.038e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003381 -0.006489 0.005262 0.9699 0.9743 0.006894 0.8233 0.8191 0.01595 ] Network output: [ 0.9999 5.903e-05 0.0003209 -1.725e-06 7.743e-07 -0.0002653 -1.3e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2106 -0.03594 -0.154 0.1814 0.9834 0.9932 0.2366 0.4279 0.8679 0.7074 ] Network output: [ -0.008598 1.003 1.007 -1.449e-07 6.505e-08 0.007182 -1.092e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006945 0.000647 0.004332 0.003092 0.9889 0.9919 0.007082 0.8504 0.8915 0.01136 ] Network output: [ -0.0001353 0.001181 1 -5.42e-06 2.433e-06 0.9986 -4.085e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2247 0.1069 0.3508 0.1414 0.9849 0.9939 0.2255 0.4318 0.8746 0.701 ] Network output: [ 0.002528 -0.01219 0.9945 3.312e-06 -1.487e-06 1.013 2.496e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1123 0.09958 0.185 0.1968 0.9873 0.9919 0.1124 0.7321 0.8606 0.3048 ] Network output: [ -0.002387 0.01131 1.005 3.639e-06 -1.634e-06 0.9888 2.742e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09492 0.09298 0.1649 0.1968 0.9852 0.9911 0.09493 0.6558 0.8355 0.25 ] Network output: [ 7.649e-05 1 -5.075e-05 4.75e-07 -2.133e-07 0.9998 3.58e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001424 Epoch 10051 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008575 0.9969 0.9928 -1.376e-07 6.176e-08 -0.006842 -1.037e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003381 -0.006488 0.005261 0.9699 0.9743 0.006894 0.8233 0.8191 0.01595 ] Network output: [ 0.9999 5.89e-05 0.0003208 -1.723e-06 7.733e-07 -0.0002652 -1.298e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2106 -0.03594 -0.154 0.1814 0.9834 0.9932 0.2366 0.4278 0.8679 0.7074 ] Network output: [ -0.008597 1.003 1.007 -1.448e-07 6.499e-08 0.007182 -1.091e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006946 0.000647 0.004332 0.003092 0.9889 0.9919 0.007083 0.8504 0.8915 0.01136 ] Network output: [ -0.0001352 0.001181 1 -5.413e-06 2.43e-06 0.9986 -4.08e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2247 0.1069 0.3508 0.1414 0.9849 0.9939 0.2255 0.4318 0.8746 0.701 ] Network output: [ 0.002526 -0.01219 0.9945 3.308e-06 -1.485e-06 1.013 2.493e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09958 0.185 0.1968 0.9873 0.9919 0.1124 0.7321 0.8606 0.3048 ] Network output: [ -0.002386 0.01131 1.005 3.634e-06 -1.632e-06 0.9888 2.739e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09492 0.09298 0.1649 0.1968 0.9852 0.9911 0.09493 0.6558 0.8355 0.25 ] Network output: [ 7.647e-05 1 -5.076e-05 4.744e-07 -2.13e-07 0.9998 3.576e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001423 Epoch 10052 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008575 0.9969 0.9928 -1.375e-07 6.171e-08 -0.006842 -1.036e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003381 -0.006488 0.005261 0.9699 0.9743 0.006894 0.8233 0.8191 0.01594 ] Network output: [ 0.9999 5.876e-05 0.0003207 -1.72e-06 7.723e-07 -0.000265 -1.297e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2106 -0.03594 -0.154 0.1814 0.9834 0.9932 0.2366 0.4278 0.8679 0.7074 ] Network output: [ -0.008596 1.003 1.007 -1.446e-07 6.494e-08 0.007181 -1.09e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006946 0.0006471 0.004332 0.003092 0.9889 0.9919 0.007083 0.8504 0.8915 0.01136 ] Network output: [ -0.000135 0.00118 1 -5.407e-06 2.427e-06 0.9986 -4.075e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2247 0.1069 0.3508 0.1414 0.9849 0.9939 0.2255 0.4318 0.8746 0.701 ] Network output: [ 0.002525 -0.01218 0.9945 3.304e-06 -1.483e-06 1.013 2.49e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09958 0.185 0.1968 0.9873 0.9919 0.1124 0.7321 0.8606 0.3048 ] Network output: [ -0.002385 0.0113 1.005 3.63e-06 -1.63e-06 0.9888 2.736e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09492 0.09298 0.1649 0.1968 0.9852 0.9911 0.09494 0.6558 0.8355 0.25 ] Network output: [ 7.646e-05 1 -5.077e-05 4.739e-07 -2.127e-07 0.9998 3.571e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001423 Epoch 10053 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008574 0.9969 0.9928 -1.374e-07 6.166e-08 -0.006841 -1.035e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003381 -0.006487 0.005261 0.9699 0.9743 0.006894 0.8233 0.8191 0.01594 ] Network output: [ 0.9999 5.862e-05 0.0003205 -1.718e-06 7.714e-07 -0.0002649 -1.295e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2106 -0.03594 -0.154 0.1814 0.9834 0.9932 0.2366 0.4278 0.8679 0.7074 ] Network output: [ -0.008595 1.003 1.007 -1.445e-07 6.488e-08 0.007181 -1.089e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006946 0.0006471 0.004332 0.003092 0.9889 0.9919 0.007083 0.8504 0.8915 0.01135 ] Network output: [ -0.0001349 0.001179 1 -5.4e-06 2.424e-06 0.9986 -4.07e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2247 0.1069 0.3508 0.1414 0.9849 0.9939 0.2255 0.4318 0.8746 0.701 ] Network output: [ 0.002524 -0.01217 0.9945 3.3e-06 -1.482e-06 1.013 2.487e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09959 0.185 0.1968 0.9873 0.9919 0.1124 0.7321 0.8606 0.3048 ] Network output: [ -0.002383 0.0113 1.005 3.626e-06 -1.628e-06 0.9888 2.732e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09492 0.09298 0.1649 0.1968 0.9852 0.9911 0.09494 0.6558 0.8355 0.25 ] Network output: [ 7.644e-05 1 -5.079e-05 4.733e-07 -2.125e-07 0.9998 3.567e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001422 Epoch 10054 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008573 0.9969 0.9928 -1.372e-07 6.162e-08 -0.00684 -1.034e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003381 -0.006486 0.00526 0.9699 0.9743 0.006894 0.8233 0.8191 0.01594 ] Network output: [ 0.9999 5.848e-05 0.0003204 -1.716e-06 7.704e-07 -0.0002647 -1.293e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2106 -0.03594 -0.1539 0.1814 0.9834 0.9932 0.2366 0.4278 0.8679 0.7074 ] Network output: [ -0.008595 1.003 1.007 -1.444e-07 6.482e-08 0.00718 -1.088e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006947 0.0006472 0.004332 0.003092 0.9889 0.9919 0.007084 0.8504 0.8915 0.01135 ] Network output: [ -0.0001348 0.001179 1 -5.393e-06 2.421e-06 0.9986 -4.064e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2247 0.1069 0.3508 0.1414 0.9849 0.9939 0.2255 0.4318 0.8746 0.701 ] Network output: [ 0.002522 -0.01217 0.9945 3.296e-06 -1.48e-06 1.013 2.484e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09959 0.185 0.1968 0.9873 0.9919 0.1124 0.7321 0.8606 0.3048 ] Network output: [ -0.002382 0.01129 1.005 3.621e-06 -1.626e-06 0.9888 2.729e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09492 0.09298 0.1649 0.1968 0.9852 0.9911 0.09494 0.6558 0.8355 0.25 ] Network output: [ 7.642e-05 1 -5.08e-05 4.727e-07 -2.122e-07 0.9998 3.562e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001421 Epoch 10055 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008572 0.9969 0.9928 -1.371e-07 6.157e-08 -0.00684 -1.034e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003381 -0.006486 0.00526 0.9699 0.9743 0.006895 0.8233 0.8191 0.01594 ] Network output: [ 0.9999 5.834e-05 0.0003203 -1.714e-06 7.695e-07 -0.0002646 -1.292e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2106 -0.03595 -0.1539 0.1814 0.9834 0.9932 0.2366 0.4278 0.8679 0.7074 ] Network output: [ -0.008594 1.003 1.007 -1.443e-07 6.477e-08 0.00718 -1.087e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006947 0.0006472 0.004331 0.003091 0.9889 0.9919 0.007084 0.8504 0.8915 0.01135 ] Network output: [ -0.0001346 0.001178 1 -5.387e-06 2.418e-06 0.9986 -4.059e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2247 0.1069 0.3508 0.1414 0.9849 0.9939 0.2255 0.4318 0.8746 0.701 ] Network output: [ 0.002521 -0.01216 0.9945 3.292e-06 -1.478e-06 1.013 2.481e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09959 0.185 0.1968 0.9873 0.9919 0.1124 0.7321 0.8606 0.3048 ] Network output: [ -0.002381 0.01128 1.005 3.617e-06 -1.624e-06 0.9888 2.726e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09493 0.09298 0.1649 0.1968 0.9852 0.9911 0.09494 0.6558 0.8355 0.25 ] Network output: [ 7.641e-05 1 -5.081e-05 4.721e-07 -2.12e-07 0.9998 3.558e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000142 Epoch 10056 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008571 0.9969 0.9928 -1.37e-07 6.152e-08 -0.006839 -1.033e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003381 -0.006485 0.005259 0.9699 0.9743 0.006895 0.8232 0.8191 0.01594 ] Network output: [ 0.9999 5.821e-05 0.0003201 -1.712e-06 7.685e-07 -0.0002644 -1.29e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2106 -0.03595 -0.1539 0.1814 0.9834 0.9932 0.2366 0.4278 0.8679 0.7074 ] Network output: [ -0.008593 1.003 1.007 -1.441e-07 6.471e-08 0.007179 -1.086e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006947 0.0006472 0.004331 0.003091 0.9889 0.9919 0.007084 0.8504 0.8915 0.01135 ] Network output: [ -0.0001345 0.001177 1 -5.38e-06 2.415e-06 0.9986 -4.054e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2247 0.1069 0.3508 0.1414 0.9849 0.9939 0.2255 0.4318 0.8746 0.701 ] Network output: [ 0.002519 -0.01215 0.9945 3.288e-06 -1.476e-06 1.013 2.478e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09959 0.185 0.1967 0.9873 0.9919 0.1124 0.732 0.8606 0.3048 ] Network output: [ -0.002379 0.01128 1.005 3.612e-06 -1.622e-06 0.9888 2.722e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09493 0.09299 0.1649 0.1968 0.9852 0.9911 0.09494 0.6558 0.8355 0.25 ] Network output: [ 7.639e-05 1 -5.082e-05 4.716e-07 -2.117e-07 0.9998 3.554e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001419 Epoch 10057 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00857 0.9969 0.9928 -1.369e-07 6.148e-08 -0.006838 -1.032e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003381 -0.006485 0.005259 0.9699 0.9743 0.006895 0.8232 0.8191 0.01594 ] Network output: [ 0.9999 5.807e-05 0.00032 -1.71e-06 7.676e-07 -0.0002643 -1.289e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2106 -0.03595 -0.1539 0.1814 0.9834 0.9932 0.2366 0.4278 0.8679 0.7074 ] Network output: [ -0.008592 1.003 1.007 -1.44e-07 6.465e-08 0.007179 -1.085e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006947 0.0006473 0.004331 0.003091 0.9889 0.9919 0.007085 0.8504 0.8915 0.01135 ] Network output: [ -0.0001344 0.001176 1 -5.373e-06 2.412e-06 0.9986 -4.049e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2248 0.1069 0.3508 0.1414 0.9849 0.9939 0.2255 0.4318 0.8746 0.701 ] Network output: [ 0.002518 -0.01215 0.9945 3.284e-06 -1.474e-06 1.013 2.475e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09959 0.185 0.1967 0.9873 0.9919 0.1124 0.732 0.8606 0.3048 ] Network output: [ -0.002378 0.01127 1.005 3.608e-06 -1.62e-06 0.9889 2.719e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09493 0.09299 0.1649 0.1968 0.9852 0.9911 0.09494 0.6558 0.8355 0.25 ] Network output: [ 7.638e-05 1 -5.084e-05 4.71e-07 -2.114e-07 0.9998 3.549e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001419 Epoch 10058 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00857 0.9969 0.9928 -1.368e-07 6.143e-08 -0.006838 -1.031e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003381 -0.006484 0.005259 0.9699 0.9743 0.006895 0.8232 0.8191 0.01594 ] Network output: [ 0.9999 5.793e-05 0.0003198 -1.708e-06 7.666e-07 -0.0002641 -1.287e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2106 -0.03595 -0.1539 0.1814 0.9834 0.9932 0.2366 0.4278 0.8679 0.7074 ] Network output: [ -0.008592 1.003 1.007 -1.439e-07 6.46e-08 0.007178 -1.084e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006948 0.0006473 0.004331 0.003091 0.9889 0.9919 0.007085 0.8504 0.8915 0.01135 ] Network output: [ -0.0001342 0.001176 1 -5.367e-06 2.409e-06 0.9986 -4.044e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2248 0.1069 0.3508 0.1414 0.9849 0.9939 0.2255 0.4318 0.8746 0.701 ] Network output: [ 0.002516 -0.01214 0.9945 3.28e-06 -1.472e-06 1.013 2.472e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.0996 0.185 0.1967 0.9873 0.9919 0.1124 0.732 0.8606 0.3048 ] Network output: [ -0.002377 0.01127 1.005 3.604e-06 -1.618e-06 0.9889 2.716e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09493 0.09299 0.1649 0.1968 0.9852 0.9911 0.09494 0.6558 0.8355 0.25 ] Network output: [ 7.636e-05 1 -5.085e-05 4.704e-07 -2.112e-07 0.9998 3.545e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001418 Epoch 10059 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008569 0.9969 0.9928 -1.367e-07 6.138e-08 -0.006837 -1.03e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003381 -0.006484 0.005258 0.9699 0.9743 0.006895 0.8232 0.8191 0.01594 ] Network output: [ 0.9999 5.779e-05 0.0003197 -1.706e-06 7.657e-07 -0.000264 -1.285e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2106 -0.03595 -0.1539 0.1814 0.9834 0.9932 0.2366 0.4278 0.8679 0.7074 ] Network output: [ -0.008591 1.003 1.007 -1.438e-07 6.454e-08 0.007178 -1.083e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006948 0.0006474 0.004331 0.00309 0.9889 0.9919 0.007085 0.8504 0.8915 0.01135 ] Network output: [ -0.0001341 0.001175 1 -5.36e-06 2.406e-06 0.9986 -4.039e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2248 0.1069 0.3508 0.1414 0.9849 0.9939 0.2255 0.4318 0.8746 0.701 ] Network output: [ 0.002515 -0.01214 0.9945 3.276e-06 -1.471e-06 1.013 2.469e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.0996 0.185 0.1967 0.9873 0.9919 0.1124 0.732 0.8606 0.3048 ] Network output: [ -0.002376 0.01126 1.005 3.599e-06 -1.616e-06 0.9889 2.713e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09493 0.09299 0.1649 0.1968 0.9852 0.9911 0.09495 0.6557 0.8355 0.25 ] Network output: [ 7.634e-05 1 -5.086e-05 4.698e-07 -2.109e-07 0.9998 3.541e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001417 Epoch 10060 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008568 0.9969 0.9928 -1.366e-07 6.133e-08 -0.006836 -1.03e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003382 -0.006483 0.005258 0.9699 0.9743 0.006895 0.8232 0.8191 0.01594 ] Network output: [ 0.9999 5.765e-05 0.0003196 -1.703e-06 7.647e-07 -0.0002638 -1.284e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2106 -0.03595 -0.1539 0.1814 0.9834 0.9932 0.2366 0.4278 0.8679 0.7074 ] Network output: [ -0.00859 1.003 1.007 -1.436e-07 6.448e-08 0.007177 -1.083e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006948 0.0006474 0.004331 0.00309 0.9889 0.9919 0.007086 0.8504 0.8915 0.01135 ] Network output: [ -0.000134 0.001174 1 -5.353e-06 2.403e-06 0.9986 -4.034e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2248 0.1069 0.3508 0.1414 0.9849 0.9939 0.2255 0.4318 0.8746 0.701 ] Network output: [ 0.002514 -0.01213 0.9945 3.272e-06 -1.469e-06 1.013 2.466e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.0996 0.185 0.1967 0.9873 0.9919 0.1125 0.732 0.8606 0.3048 ] Network output: [ -0.002374 0.01126 1.005 3.595e-06 -1.614e-06 0.9889 2.709e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09493 0.09299 0.1649 0.1968 0.9852 0.9911 0.09495 0.6557 0.8355 0.25 ] Network output: [ 7.633e-05 1 -5.088e-05 4.693e-07 -2.107e-07 0.9998 3.536e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001416 Epoch 10061 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008567 0.9969 0.9928 -1.365e-07 6.129e-08 -0.006836 -1.029e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003382 -0.006483 0.005258 0.9699 0.9743 0.006895 0.8232 0.8191 0.01594 ] Network output: [ 0.9999 5.752e-05 0.0003194 -1.701e-06 7.638e-07 -0.0002637 -1.282e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2107 -0.03595 -0.1539 0.1814 0.9834 0.9932 0.2366 0.4278 0.8679 0.7074 ] Network output: [ -0.008589 1.003 1.007 -1.435e-07 6.443e-08 0.007177 -1.082e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006949 0.0006475 0.004331 0.00309 0.9889 0.9919 0.007086 0.8504 0.8915 0.01135 ] Network output: [ -0.0001338 0.001174 1 -5.347e-06 2.4e-06 0.9986 -4.029e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2248 0.1069 0.3508 0.1414 0.9849 0.9939 0.2256 0.4317 0.8746 0.701 ] Network output: [ 0.002512 -0.01212 0.9945 3.268e-06 -1.467e-06 1.013 2.463e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.0996 0.185 0.1967 0.9873 0.9919 0.1125 0.732 0.8606 0.3048 ] Network output: [ -0.002373 0.01125 1.005 3.591e-06 -1.612e-06 0.9889 2.706e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09493 0.09299 0.1649 0.1968 0.9852 0.9911 0.09495 0.6557 0.8355 0.25 ] Network output: [ 7.631e-05 1 -5.089e-05 4.687e-07 -2.104e-07 0.9998 3.532e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001415 Epoch 10062 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008566 0.9969 0.9928 -1.364e-07 6.124e-08 -0.006835 -1.028e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003382 -0.006482 0.005257 0.9699 0.9743 0.006895 0.8232 0.8191 0.01594 ] Network output: [ 0.9999 5.738e-05 0.0003193 -1.699e-06 7.628e-07 -0.0002635 -1.281e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2107 -0.03595 -0.1539 0.1814 0.9834 0.9932 0.2366 0.4278 0.8679 0.7074 ] Network output: [ -0.008588 1.003 1.007 -1.434e-07 6.437e-08 0.007176 -1.081e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006949 0.0006475 0.004331 0.00309 0.9889 0.9919 0.007086 0.8504 0.8915 0.01135 ] Network output: [ -0.0001337 0.001173 1 -5.34e-06 2.397e-06 0.9986 -4.024e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2248 0.1069 0.3508 0.1414 0.9849 0.9939 0.2256 0.4317 0.8746 0.701 ] Network output: [ 0.002511 -0.01212 0.9945 3.264e-06 -1.465e-06 1.013 2.46e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09961 0.185 0.1967 0.9873 0.9919 0.1125 0.732 0.8606 0.3048 ] Network output: [ -0.002372 0.01125 1.005 3.586e-06 -1.61e-06 0.9889 2.703e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09494 0.09299 0.1649 0.1968 0.9852 0.9911 0.09495 0.6557 0.8355 0.25 ] Network output: [ 7.63e-05 1 -5.09e-05 4.681e-07 -2.102e-07 0.9998 3.528e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001415 Epoch 10063 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008565 0.9969 0.9928 -1.363e-07 6.119e-08 -0.006834 -1.027e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003382 -0.006482 0.005257 0.9699 0.9743 0.006896 0.8232 0.8191 0.01594 ] Network output: [ 0.9999 5.724e-05 0.0003191 -1.697e-06 7.619e-07 -0.0002634 -1.279e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2107 -0.03595 -0.1539 0.1814 0.9834 0.9932 0.2367 0.4278 0.8679 0.7074 ] Network output: [ -0.008588 1.003 1.007 -1.433e-07 6.432e-08 0.007176 -1.08e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006949 0.0006476 0.004331 0.00309 0.9889 0.9919 0.007087 0.8504 0.8915 0.01135 ] Network output: [ -0.0001335 0.001172 1 -5.333e-06 2.394e-06 0.9986 -4.019e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2248 0.1069 0.3508 0.1414 0.9849 0.9939 0.2256 0.4317 0.8746 0.701 ] Network output: [ 0.002509 -0.01211 0.9945 3.26e-06 -1.463e-06 1.013 2.457e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09961 0.185 0.1967 0.9873 0.9919 0.1125 0.732 0.8606 0.3048 ] Network output: [ -0.00237 0.01124 1.005 3.582e-06 -1.608e-06 0.9889 2.699e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09494 0.093 0.1649 0.1968 0.9852 0.9911 0.09495 0.6557 0.8355 0.25 ] Network output: [ 7.628e-05 1 -5.092e-05 4.675e-07 -2.099e-07 0.9998 3.524e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001414 Epoch 10064 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008565 0.9969 0.9928 -1.362e-07 6.115e-08 -0.006834 -1.026e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003382 -0.006481 0.005257 0.9699 0.9743 0.006896 0.8232 0.8191 0.01593 ] Network output: [ 0.9999 5.71e-05 0.000319 -1.695e-06 7.609e-07 -0.0002632 -1.277e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2107 -0.03595 -0.1539 0.1814 0.9834 0.9932 0.2367 0.4278 0.8679 0.7074 ] Network output: [ -0.008587 1.003 1.007 -1.431e-07 6.426e-08 0.007176 -1.079e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00695 0.0006476 0.00433 0.003089 0.9889 0.9919 0.007087 0.8504 0.8915 0.01135 ] Network output: [ -0.0001334 0.001172 1 -5.327e-06 2.391e-06 0.9986 -4.014e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2248 0.1069 0.3508 0.1414 0.9849 0.9939 0.2256 0.4317 0.8746 0.701 ] Network output: [ 0.002508 -0.0121 0.9945 3.256e-06 -1.462e-06 1.013 2.454e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09961 0.185 0.1967 0.9873 0.9919 0.1125 0.732 0.8606 0.3048 ] Network output: [ -0.002369 0.01123 1.005 3.577e-06 -1.606e-06 0.9889 2.696e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09494 0.093 0.1649 0.1968 0.9852 0.9911 0.09495 0.6557 0.8355 0.25 ] Network output: [ 7.626e-05 1 -5.093e-05 4.67e-07 -2.096e-07 0.9998 3.519e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001413 Epoch 10065 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008564 0.9969 0.9928 -1.361e-07 6.11e-08 -0.006833 -1.026e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003382 -0.006481 0.005256 0.9699 0.9743 0.006896 0.8232 0.8191 0.01593 ] Network output: [ 0.9999 5.697e-05 0.0003189 -1.693e-06 7.6e-07 -0.0002631 -1.276e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2107 -0.03596 -0.1539 0.1814 0.9834 0.9932 0.2367 0.4278 0.8679 0.7074 ] Network output: [ -0.008586 1.003 1.007 -1.43e-07 6.42e-08 0.007175 -1.078e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00695 0.0006477 0.00433 0.003089 0.9889 0.9919 0.007087 0.8504 0.8915 0.01135 ] Network output: [ -0.0001333 0.001171 1 -5.32e-06 2.388e-06 0.9986 -4.009e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2248 0.1069 0.3508 0.1414 0.9849 0.9939 0.2256 0.4317 0.8746 0.701 ] Network output: [ 0.002506 -0.0121 0.9945 3.252e-06 -1.46e-06 1.013 2.451e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09961 0.185 0.1967 0.9873 0.9919 0.1125 0.732 0.8605 0.3048 ] Network output: [ -0.002368 0.01123 1.005 3.573e-06 -1.604e-06 0.9889 2.693e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09494 0.093 0.1649 0.1968 0.9852 0.9911 0.09495 0.6557 0.8355 0.25 ] Network output: [ 7.625e-05 1 -5.094e-05 4.664e-07 -2.094e-07 0.9998 3.515e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001412 Epoch 10066 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008563 0.9969 0.9928 -1.36e-07 6.105e-08 -0.006832 -1.025e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003382 -0.00648 0.005256 0.9699 0.9743 0.006896 0.8232 0.8191 0.01593 ] Network output: [ 0.9999 5.683e-05 0.0003187 -1.691e-06 7.59e-07 -0.0002629 -1.274e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2107 -0.03596 -0.1539 0.1814 0.9834 0.9932 0.2367 0.4278 0.8678 0.7074 ] Network output: [ -0.008585 1.003 1.007 -1.429e-07 6.415e-08 0.007175 -1.077e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00695 0.0006477 0.00433 0.003089 0.9889 0.9919 0.007087 0.8504 0.8915 0.01135 ] Network output: [ -0.0001331 0.00117 1 -5.314e-06 2.385e-06 0.9986 -4.005e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2248 0.1069 0.3508 0.1414 0.9849 0.9939 0.2256 0.4317 0.8746 0.701 ] Network output: [ 0.002505 -0.01209 0.9945 3.248e-06 -1.458e-06 1.013 2.448e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09961 0.185 0.1967 0.9873 0.9919 0.1125 0.732 0.8605 0.3048 ] Network output: [ -0.002367 0.01122 1.005 3.569e-06 -1.602e-06 0.9889 2.69e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09494 0.093 0.1649 0.1968 0.9852 0.9911 0.09496 0.6557 0.8355 0.25 ] Network output: [ 7.623e-05 1 -5.096e-05 4.658e-07 -2.091e-07 0.9998 3.511e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001411 Epoch 10067 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008562 0.9969 0.9928 -1.359e-07 6.101e-08 -0.006832 -1.024e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003382 -0.00648 0.005256 0.9699 0.9743 0.006896 0.8232 0.8191 0.01593 ] Network output: [ 0.9999 5.669e-05 0.0003186 -1.689e-06 7.581e-07 -0.0002628 -1.273e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2107 -0.03596 -0.1538 0.1814 0.9834 0.9932 0.2367 0.4278 0.8678 0.7074 ] Network output: [ -0.008585 1.003 1.007 -1.428e-07 6.409e-08 0.007174 -1.076e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006951 0.0006477 0.00433 0.003089 0.9889 0.9919 0.007088 0.8504 0.8915 0.01134 ] Network output: [ -0.000133 0.00117 1 -5.307e-06 2.383e-06 0.9986 -4e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2248 0.1069 0.3508 0.1414 0.9849 0.9939 0.2256 0.4317 0.8746 0.701 ] Network output: [ 0.002504 -0.01208 0.9945 3.244e-06 -1.456e-06 1.013 2.445e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09962 0.185 0.1967 0.9873 0.9919 0.1125 0.732 0.8605 0.3048 ] Network output: [ -0.002365 0.01122 1.005 3.564e-06 -1.6e-06 0.9889 2.686e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09494 0.093 0.1649 0.1968 0.9852 0.9911 0.09496 0.6557 0.8355 0.25 ] Network output: [ 7.622e-05 1 -5.097e-05 4.653e-07 -2.089e-07 0.9998 3.506e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001411 Epoch 10068 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008561 0.9969 0.9928 -1.358e-07 6.096e-08 -0.006831 -1.023e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003382 -0.006479 0.005255 0.9699 0.9743 0.006896 0.8232 0.8191 0.01593 ] Network output: [ 0.9999 5.655e-05 0.0003185 -1.687e-06 7.572e-07 -0.0002626 -1.271e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2107 -0.03596 -0.1538 0.1814 0.9834 0.9932 0.2367 0.4278 0.8678 0.7074 ] Network output: [ -0.008584 1.003 1.007 -1.426e-07 6.403e-08 0.007174 -1.075e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006951 0.0006478 0.00433 0.003088 0.9889 0.9919 0.007088 0.8503 0.8915 0.01134 ] Network output: [ -0.0001329 0.001169 1 -5.3e-06 2.38e-06 0.9986 -3.995e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2248 0.1069 0.3508 0.1414 0.9849 0.9939 0.2256 0.4317 0.8746 0.701 ] Network output: [ 0.002502 -0.01208 0.9945 3.24e-06 -1.454e-06 1.013 2.442e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09962 0.185 0.1967 0.9873 0.9919 0.1125 0.7319 0.8605 0.3048 ] Network output: [ -0.002364 0.01121 1.005 3.56e-06 -1.598e-06 0.9889 2.683e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09494 0.093 0.1649 0.1968 0.9852 0.9911 0.09496 0.6557 0.8355 0.25 ] Network output: [ 7.62e-05 1 -5.098e-05 4.647e-07 -2.086e-07 0.9998 3.502e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000141 Epoch 10069 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00856 0.9969 0.9928 -1.357e-07 6.091e-08 -0.00683 -1.023e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003382 -0.006479 0.005255 0.9699 0.9743 0.006896 0.8232 0.8191 0.01593 ] Network output: [ 0.9999 5.642e-05 0.0003183 -1.684e-06 7.562e-07 -0.0002625 -1.269e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2107 -0.03596 -0.1538 0.1814 0.9834 0.9932 0.2367 0.4278 0.8678 0.7074 ] Network output: [ -0.008583 1.003 1.007 -1.425e-07 6.398e-08 0.007173 -1.074e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006951 0.0006478 0.00433 0.003088 0.9889 0.9919 0.007088 0.8503 0.8915 0.01134 ] Network output: [ -0.0001327 0.001168 1 -5.294e-06 2.377e-06 0.9986 -3.99e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2248 0.1069 0.3508 0.1414 0.9849 0.9939 0.2256 0.4317 0.8746 0.701 ] Network output: [ 0.002501 -0.01207 0.9945 3.236e-06 -1.453e-06 1.013 2.439e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09962 0.185 0.1967 0.9873 0.9919 0.1125 0.7319 0.8605 0.3048 ] Network output: [ -0.002363 0.01121 1.005 3.556e-06 -1.596e-06 0.9889 2.68e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09495 0.093 0.1649 0.1968 0.9852 0.9911 0.09496 0.6557 0.8355 0.25 ] Network output: [ 7.619e-05 1 -5.1e-05 4.641e-07 -2.084e-07 0.9998 3.498e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001409 Epoch 10070 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00856 0.9969 0.9928 -1.356e-07 6.087e-08 -0.00683 -1.022e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003382 -0.006478 0.005255 0.9699 0.9743 0.006897 0.8232 0.8191 0.01593 ] Network output: [ 0.9999 5.628e-05 0.0003182 -1.682e-06 7.553e-07 -0.0002624 -1.268e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2107 -0.03596 -0.1538 0.1813 0.9834 0.9932 0.2367 0.4278 0.8678 0.7074 ] Network output: [ -0.008582 1.003 1.007 -1.424e-07 6.392e-08 0.007173 -1.073e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006951 0.0006479 0.00433 0.003088 0.9889 0.9919 0.007089 0.8503 0.8915 0.01134 ] Network output: [ -0.0001326 0.001168 1 -5.287e-06 2.374e-06 0.9986 -3.985e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2248 0.1069 0.3508 0.1414 0.9849 0.9939 0.2256 0.4317 0.8746 0.701 ] Network output: [ 0.002499 -0.01207 0.9945 3.232e-06 -1.451e-06 1.013 2.436e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09962 0.185 0.1967 0.9873 0.9919 0.1125 0.7319 0.8605 0.3048 ] Network output: [ -0.002361 0.0112 1.005 3.551e-06 -1.594e-06 0.9889 2.676e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09495 0.093 0.1649 0.1968 0.9852 0.9911 0.09496 0.6556 0.8355 0.25 ] Network output: [ 7.617e-05 1 -5.101e-05 4.636e-07 -2.081e-07 0.9998 3.493e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001408 Epoch 10071 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008559 0.9969 0.9928 -1.355e-07 6.082e-08 -0.006829 -1.021e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003382 -0.006477 0.005254 0.9699 0.9743 0.006897 0.8232 0.819 0.01593 ] Network output: [ 0.9999 5.614e-05 0.000318 -1.68e-06 7.543e-07 -0.0002622 -1.266e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2107 -0.03596 -0.1538 0.1813 0.9834 0.9932 0.2367 0.4278 0.8678 0.7074 ] Network output: [ -0.008581 1.003 1.007 -1.423e-07 6.387e-08 0.007172 -1.072e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006952 0.0006479 0.00433 0.003088 0.9889 0.9919 0.007089 0.8503 0.8915 0.01134 ] Network output: [ -0.0001325 0.001167 1 -5.281e-06 2.371e-06 0.9986 -3.98e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2248 0.1069 0.3509 0.1414 0.9849 0.9939 0.2256 0.4317 0.8746 0.701 ] Network output: [ 0.002498 -0.01206 0.9945 3.228e-06 -1.449e-06 1.013 2.433e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09963 0.185 0.1967 0.9873 0.9919 0.1125 0.7319 0.8605 0.3048 ] Network output: [ -0.00236 0.01119 1.005 3.547e-06 -1.592e-06 0.9889 2.673e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09495 0.09301 0.1649 0.1968 0.9852 0.9911 0.09496 0.6556 0.8355 0.25 ] Network output: [ 7.615e-05 1 -5.103e-05 4.63e-07 -2.079e-07 0.9998 3.489e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001407 Epoch 10072 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008558 0.9969 0.9928 -1.354e-07 6.077e-08 -0.006828 -1.02e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003382 -0.006477 0.005254 0.9699 0.9743 0.006897 0.8232 0.819 0.01593 ] Network output: [ 0.9999 5.601e-05 0.0003179 -1.678e-06 7.534e-07 -0.0002621 -1.265e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2107 -0.03596 -0.1538 0.1813 0.9834 0.9932 0.2367 0.4278 0.8678 0.7074 ] Network output: [ -0.008581 1.003 1.007 -1.421e-07 6.381e-08 0.007172 -1.071e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006952 0.000648 0.00433 0.003088 0.9889 0.9919 0.007089 0.8503 0.8915 0.01134 ] Network output: [ -0.0001323 0.001166 1 -5.274e-06 2.368e-06 0.9986 -3.975e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2248 0.1069 0.3509 0.1414 0.9849 0.9939 0.2256 0.4317 0.8746 0.701 ] Network output: [ 0.002496 -0.01205 0.9945 3.224e-06 -1.447e-06 1.013 2.43e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09963 0.185 0.1967 0.9873 0.9919 0.1125 0.7319 0.8605 0.3048 ] Network output: [ -0.002359 0.01119 1.005 3.543e-06 -1.59e-06 0.9889 2.67e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09495 0.09301 0.1649 0.1968 0.9852 0.9911 0.09496 0.6556 0.8355 0.25 ] Network output: [ 7.614e-05 1 -5.104e-05 4.624e-07 -2.076e-07 0.9998 3.485e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001407 Epoch 10073 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008557 0.9969 0.9928 -1.353e-07 6.073e-08 -0.006828 -1.019e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003382 -0.006476 0.005253 0.9699 0.9743 0.006897 0.8232 0.819 0.01593 ] Network output: [ 0.9999 5.587e-05 0.0003178 -1.676e-06 7.525e-07 -0.0002619 -1.263e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2107 -0.03596 -0.1538 0.1813 0.9834 0.9932 0.2367 0.4278 0.8678 0.7074 ] Network output: [ -0.00858 1.003 1.007 -1.42e-07 6.375e-08 0.007171 -1.07e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006952 0.000648 0.004329 0.003087 0.9889 0.9919 0.00709 0.8503 0.8915 0.01134 ] Network output: [ -0.0001322 0.001165 1 -5.268e-06 2.365e-06 0.9986 -3.97e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2248 0.1069 0.3509 0.1414 0.9849 0.9939 0.2256 0.4317 0.8746 0.701 ] Network output: [ 0.002495 -0.01205 0.9945 3.22e-06 -1.446e-06 1.013 2.427e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09963 0.185 0.1967 0.9873 0.9919 0.1125 0.7319 0.8605 0.3048 ] Network output: [ -0.002358 0.01118 1.005 3.538e-06 -1.589e-06 0.9889 2.667e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09495 0.09301 0.1649 0.1968 0.9852 0.9911 0.09497 0.6556 0.8355 0.25 ] Network output: [ 7.612e-05 1 -5.105e-05 4.619e-07 -2.073e-07 0.9998 3.481e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001406 Epoch 10074 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008556 0.9969 0.9928 -1.352e-07 6.068e-08 -0.006827 -1.019e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003382 -0.006476 0.005253 0.9699 0.9743 0.006897 0.8232 0.819 0.01593 ] Network output: [ 0.9999 5.573e-05 0.0003176 -1.674e-06 7.515e-07 -0.0002618 -1.262e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2107 -0.03596 -0.1538 0.1813 0.9834 0.9932 0.2367 0.4278 0.8678 0.7074 ] Network output: [ -0.008579 1.003 1.007 -1.419e-07 6.37e-08 0.007171 -1.069e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006953 0.0006481 0.004329 0.003087 0.9889 0.9919 0.00709 0.8503 0.8915 0.01134 ] Network output: [ -0.0001321 0.001165 1 -5.261e-06 2.362e-06 0.9986 -3.965e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2249 0.1069 0.3509 0.1414 0.9849 0.9939 0.2256 0.4317 0.8746 0.701 ] Network output: [ 0.002494 -0.01204 0.9945 3.216e-06 -1.444e-06 1.013 2.424e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09963 0.185 0.1967 0.9873 0.9919 0.1125 0.7319 0.8605 0.3048 ] Network output: [ -0.002356 0.01118 1.005 3.534e-06 -1.587e-06 0.9889 2.663e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09495 0.09301 0.1649 0.1968 0.9852 0.9911 0.09497 0.6556 0.8355 0.25 ] Network output: [ 7.611e-05 1 -5.107e-05 4.613e-07 -2.071e-07 0.9998 3.476e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001405 Epoch 10075 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008555 0.9969 0.9928 -1.351e-07 6.063e-08 -0.006826 -1.018e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003383 -0.006475 0.005253 0.9699 0.9743 0.006897 0.8232 0.819 0.01593 ] Network output: [ 0.9999 5.56e-05 0.0003175 -1.672e-06 7.506e-07 -0.0002616 -1.26e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2107 -0.03597 -0.1538 0.1813 0.9834 0.9932 0.2367 0.4278 0.8678 0.7074 ] Network output: [ -0.008578 1.003 1.007 -1.418e-07 6.364e-08 0.00717 -1.068e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006953 0.0006481 0.004329 0.003087 0.9889 0.9919 0.00709 0.8503 0.8915 0.01134 ] Network output: [ -0.0001319 0.001164 1 -5.255e-06 2.359e-06 0.9986 -3.96e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2249 0.1069 0.3509 0.1414 0.9849 0.9939 0.2256 0.4317 0.8746 0.701 ] Network output: [ 0.002492 -0.01203 0.9945 3.212e-06 -1.442e-06 1.013 2.421e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09963 0.185 0.1967 0.9873 0.9919 0.1125 0.7319 0.8605 0.3048 ] Network output: [ -0.002355 0.01117 1.005 3.53e-06 -1.585e-06 0.9889 2.66e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09495 0.09301 0.1649 0.1968 0.9852 0.9911 0.09497 0.6556 0.8355 0.25 ] Network output: [ 7.609e-05 1 -5.108e-05 4.607e-07 -2.068e-07 0.9998 3.472e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001404 Epoch 10076 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008555 0.9969 0.9928 -1.35e-07 6.059e-08 -0.006826 -1.017e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003527 -0.003383 -0.006475 0.005252 0.9699 0.9743 0.006897 0.8232 0.819 0.01592 ] Network output: [ 0.9999 5.546e-05 0.0003173 -1.67e-06 7.497e-07 -0.0002615 -1.258e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2107 -0.03597 -0.1538 0.1813 0.9834 0.9932 0.2367 0.4278 0.8678 0.7074 ] Network output: [ -0.008578 1.003 1.007 -1.416e-07 6.359e-08 0.00717 -1.067e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006953 0.0006481 0.004329 0.003087 0.9889 0.9919 0.007091 0.8503 0.8915 0.01134 ] Network output: [ -0.0001318 0.001163 1 -5.248e-06 2.356e-06 0.9986 -3.955e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2249 0.1069 0.3509 0.1414 0.9849 0.9939 0.2256 0.4317 0.8746 0.701 ] Network output: [ 0.002491 -0.01203 0.9945 3.208e-06 -1.44e-06 1.013 2.418e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09964 0.185 0.1967 0.9873 0.9919 0.1125 0.7319 0.8605 0.3048 ] Network output: [ -0.002354 0.01117 1.005 3.526e-06 -1.583e-06 0.9889 2.657e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09496 0.09301 0.1649 0.1968 0.9852 0.9911 0.09497 0.6556 0.8355 0.25 ] Network output: [ 7.608e-05 1 -5.109e-05 4.602e-07 -2.066e-07 0.9998 3.468e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001403 Epoch 10077 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008554 0.9969 0.9928 -1.349e-07 6.054e-08 -0.006825 -1.016e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003383 -0.006474 0.005252 0.9699 0.9743 0.006897 0.8232 0.819 0.01592 ] Network output: [ 0.9999 5.532e-05 0.0003172 -1.668e-06 7.487e-07 -0.0002613 -1.257e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2107 -0.03597 -0.1538 0.1813 0.9834 0.9932 0.2367 0.4278 0.8678 0.7074 ] Network output: [ -0.008577 1.003 1.007 -1.415e-07 6.353e-08 0.007169 -1.066e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006954 0.0006482 0.004329 0.003087 0.9889 0.9919 0.007091 0.8503 0.8915 0.01134 ] Network output: [ -0.0001316 0.001163 1 -5.242e-06 2.353e-06 0.9986 -3.95e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2249 0.1069 0.3509 0.1413 0.9849 0.9939 0.2256 0.4317 0.8746 0.701 ] Network output: [ 0.002489 -0.01202 0.9945 3.204e-06 -1.438e-06 1.013 2.415e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09964 0.185 0.1967 0.9873 0.9919 0.1125 0.7319 0.8605 0.3048 ] Network output: [ -0.002352 0.01116 1.005 3.521e-06 -1.581e-06 0.9889 2.654e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09496 0.09301 0.1649 0.1968 0.9852 0.9911 0.09497 0.6556 0.8355 0.25 ] Network output: [ 7.606e-05 1 -5.111e-05 4.596e-07 -2.063e-07 0.9998 3.464e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001403 Epoch 10078 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008553 0.9969 0.9928 -1.347e-07 6.049e-08 -0.006824 -1.015e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003383 -0.006474 0.005252 0.9699 0.9743 0.006898 0.8232 0.819 0.01592 ] Network output: [ 0.9999 5.519e-05 0.0003171 -1.666e-06 7.478e-07 -0.0002612 -1.255e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2107 -0.03597 -0.1538 0.1813 0.9834 0.9932 0.2367 0.4278 0.8678 0.7074 ] Network output: [ -0.008576 1.003 1.007 -1.414e-07 6.347e-08 0.007169 -1.066e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006954 0.0006482 0.004329 0.003086 0.9889 0.9919 0.007091 0.8503 0.8915 0.01134 ] Network output: [ -0.0001315 0.001162 1 -5.235e-06 2.35e-06 0.9986 -3.945e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2249 0.1069 0.3509 0.1413 0.9849 0.9939 0.2257 0.4317 0.8746 0.701 ] Network output: [ 0.002488 -0.01201 0.9945 3.2e-06 -1.437e-06 1.013 2.412e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09964 0.185 0.1967 0.9873 0.9919 0.1125 0.7319 0.8605 0.3047 ] Network output: [ -0.002351 0.01116 1.005 3.517e-06 -1.579e-06 0.9889 2.651e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09496 0.09302 0.1649 0.1968 0.9852 0.9911 0.09497 0.6556 0.8355 0.25 ] Network output: [ 7.604e-05 1 -5.112e-05 4.59e-07 -2.061e-07 0.9998 3.459e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001402 Epoch 10079 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008552 0.9969 0.9928 -1.346e-07 6.045e-08 -0.006824 -1.015e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003383 -0.006473 0.005251 0.9699 0.9743 0.006898 0.8232 0.819 0.01592 ] Network output: [ 0.9999 5.505e-05 0.0003169 -1.664e-06 7.469e-07 -0.000261 -1.254e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2107 -0.03597 -0.1538 0.1813 0.9834 0.9932 0.2368 0.4278 0.8678 0.7073 ] Network output: [ -0.008575 1.003 1.007 -1.413e-07 6.342e-08 0.007168 -1.065e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006954 0.0006483 0.004329 0.003086 0.9889 0.9919 0.007092 0.8503 0.8915 0.01134 ] Network output: [ -0.0001314 0.001161 1 -5.229e-06 2.347e-06 0.9986 -3.94e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2249 0.1069 0.3509 0.1413 0.9849 0.9939 0.2257 0.4317 0.8746 0.701 ] Network output: [ 0.002486 -0.01201 0.9945 3.196e-06 -1.435e-06 1.013 2.409e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09964 0.185 0.1967 0.9873 0.9919 0.1125 0.7319 0.8605 0.3047 ] Network output: [ -0.00235 0.01115 1.005 3.513e-06 -1.577e-06 0.9889 2.647e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09496 0.09302 0.1649 0.1968 0.9852 0.9911 0.09497 0.6556 0.8355 0.25 ] Network output: [ 7.603e-05 1 -5.114e-05 4.585e-07 -2.058e-07 0.9998 3.455e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001401 Epoch 10080 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008551 0.9969 0.9928 -1.345e-07 6.04e-08 -0.006823 -1.014e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003383 -0.006473 0.005251 0.9699 0.9743 0.006898 0.8232 0.819 0.01592 ] Network output: [ 0.9999 5.491e-05 0.0003168 -1.662e-06 7.46e-07 -0.0002609 -1.252e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2107 -0.03597 -0.1537 0.1813 0.9834 0.9932 0.2368 0.4278 0.8678 0.7073 ] Network output: [ -0.008574 1.003 1.007 -1.411e-07 6.336e-08 0.007168 -1.064e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006954 0.0006483 0.004329 0.003086 0.9889 0.9919 0.007092 0.8503 0.8915 0.01134 ] Network output: [ -0.0001312 0.001161 1 -5.222e-06 2.344e-06 0.9986 -3.936e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2249 0.1069 0.3509 0.1413 0.9849 0.9939 0.2257 0.4317 0.8746 0.701 ] Network output: [ 0.002485 -0.012 0.9945 3.192e-06 -1.433e-06 1.013 2.406e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09964 0.185 0.1967 0.9873 0.9919 0.1125 0.7318 0.8605 0.3047 ] Network output: [ -0.002349 0.01114 1.005 3.508e-06 -1.575e-06 0.9889 2.644e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09496 0.09302 0.1649 0.1968 0.9852 0.9911 0.09498 0.6556 0.8355 0.25 ] Network output: [ 7.601e-05 1 -5.115e-05 4.579e-07 -2.056e-07 0.9998 3.451e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00014 Epoch 10081 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008551 0.9969 0.9928 -1.344e-07 6.035e-08 -0.006822 -1.013e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003383 -0.006472 0.005251 0.9699 0.9743 0.006898 0.8232 0.819 0.01592 ] Network output: [ 0.9999 5.478e-05 0.0003167 -1.66e-06 7.45e-07 -0.0002607 -1.251e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2108 -0.03597 -0.1537 0.1813 0.9834 0.9932 0.2368 0.4278 0.8678 0.7073 ] Network output: [ -0.008574 1.003 1.007 -1.41e-07 6.331e-08 0.007167 -1.063e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006955 0.0006484 0.004329 0.003086 0.9889 0.9919 0.007092 0.8503 0.8915 0.01134 ] Network output: [ -0.0001311 0.00116 1 -5.216e-06 2.342e-06 0.9986 -3.931e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2249 0.107 0.3509 0.1413 0.9849 0.9939 0.2257 0.4317 0.8746 0.7009 ] Network output: [ 0.002484 -0.012 0.9945 3.188e-06 -1.431e-06 1.013 2.403e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09965 0.185 0.1967 0.9873 0.9919 0.1125 0.7318 0.8605 0.3047 ] Network output: [ -0.002347 0.01114 1.005 3.504e-06 -1.573e-06 0.9889 2.641e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09496 0.09302 0.1649 0.1968 0.9852 0.9911 0.09498 0.6556 0.8355 0.2501 ] Network output: [ 7.6e-05 1 -5.117e-05 4.574e-07 -2.053e-07 0.9998 3.447e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001399 Epoch 10082 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00855 0.9969 0.9929 -1.343e-07 6.031e-08 -0.006822 -1.012e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003383 -0.006472 0.00525 0.9699 0.9743 0.006898 0.8232 0.819 0.01592 ] Network output: [ 0.9999 5.464e-05 0.0003165 -1.658e-06 7.441e-07 -0.0002606 -1.249e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2108 -0.03597 -0.1537 0.1813 0.9834 0.9932 0.2368 0.4277 0.8678 0.7073 ] Network output: [ -0.008573 1.003 1.007 -1.409e-07 6.325e-08 0.007167 -1.062e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006955 0.0006484 0.004328 0.003085 0.9889 0.9919 0.007092 0.8503 0.8915 0.01133 ] Network output: [ -0.000131 0.001159 1 -5.209e-06 2.339e-06 0.9986 -3.926e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2249 0.107 0.3509 0.1413 0.9849 0.9939 0.2257 0.4317 0.8746 0.7009 ] Network output: [ 0.002482 -0.01199 0.9945 3.184e-06 -1.43e-06 1.013 2.4e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09965 0.185 0.1967 0.9873 0.9919 0.1125 0.7318 0.8605 0.3047 ] Network output: [ -0.002346 0.01113 1.005 3.5e-06 -1.571e-06 0.9889 2.638e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09496 0.09302 0.1649 0.1968 0.9852 0.9911 0.09498 0.6555 0.8355 0.2501 ] Network output: [ 7.598e-05 1 -5.118e-05 4.568e-07 -2.051e-07 0.9998 3.443e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001399 Epoch 10083 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008549 0.9969 0.9929 -1.342e-07 6.026e-08 -0.006821 -1.012e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003383 -0.006471 0.00525 0.9699 0.9743 0.006898 0.8231 0.819 0.01592 ] Network output: [ 0.9999 5.45e-05 0.0003164 -1.655e-06 7.432e-07 -0.0002605 -1.248e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2108 -0.03597 -0.1537 0.1813 0.9834 0.9932 0.2368 0.4277 0.8678 0.7073 ] Network output: [ -0.008572 1.003 1.007 -1.408e-07 6.319e-08 0.007167 -1.061e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006955 0.0006484 0.004328 0.003085 0.9889 0.9919 0.007093 0.8503 0.8915 0.01133 ] Network output: [ -0.0001308 0.001159 1 -5.203e-06 2.336e-06 0.9987 -3.921e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2249 0.107 0.3509 0.1413 0.9849 0.9939 0.2257 0.4317 0.8746 0.7009 ] Network output: [ 0.002481 -0.01198 0.9945 3.18e-06 -1.428e-06 1.013 2.397e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09965 0.185 0.1967 0.9873 0.9919 0.1125 0.7318 0.8605 0.3047 ] Network output: [ -0.002345 0.01113 1.005 3.496e-06 -1.569e-06 0.9889 2.634e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09497 0.09302 0.1649 0.1968 0.9852 0.9911 0.09498 0.6555 0.8355 0.2501 ] Network output: [ 7.597e-05 1 -5.119e-05 4.562e-07 -2.048e-07 0.9998 3.438e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001398 Epoch 10084 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008548 0.9969 0.9929 -1.341e-07 6.021e-08 -0.00682 -1.011e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003383 -0.006471 0.00525 0.9699 0.9743 0.006898 0.8231 0.819 0.01592 ] Network output: [ 0.9999 5.437e-05 0.0003162 -1.653e-06 7.423e-07 -0.0002603 -1.246e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2108 -0.03597 -0.1537 0.1813 0.9834 0.9932 0.2368 0.4277 0.8678 0.7073 ] Network output: [ -0.008571 1.003 1.007 -1.406e-07 6.314e-08 0.007166 -1.06e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006956 0.0006485 0.004328 0.003085 0.9889 0.9919 0.007093 0.8503 0.8915 0.01133 ] Network output: [ -0.0001307 0.001158 1 -5.196e-06 2.333e-06 0.9987 -3.916e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2249 0.107 0.3509 0.1413 0.9849 0.9939 0.2257 0.4317 0.8746 0.7009 ] Network output: [ 0.002479 -0.01198 0.9945 3.176e-06 -1.426e-06 1.012 2.394e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09965 0.185 0.1967 0.9873 0.9919 0.1125 0.7318 0.8605 0.3047 ] Network output: [ -0.002343 0.01112 1.005 3.491e-06 -1.567e-06 0.9889 2.631e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09497 0.09302 0.1649 0.1969 0.9852 0.9911 0.09498 0.6555 0.8354 0.2501 ] Network output: [ 7.595e-05 1 -5.121e-05 4.557e-07 -2.046e-07 0.9998 3.434e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001397 Epoch 10085 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008547 0.9969 0.9929 -1.34e-07 6.017e-08 -0.00682 -1.01e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003383 -0.00647 0.005249 0.9699 0.9743 0.006898 0.8231 0.819 0.01592 ] Network output: [ 0.9999 5.423e-05 0.0003161 -1.651e-06 7.413e-07 -0.0002602 -1.245e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2108 -0.03598 -0.1537 0.1813 0.9834 0.9932 0.2368 0.4277 0.8678 0.7073 ] Network output: [ -0.008571 1.003 1.007 -1.405e-07 6.308e-08 0.007166 -1.059e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006956 0.0006485 0.004328 0.003085 0.9889 0.9919 0.007093 0.8503 0.8915 0.01133 ] Network output: [ -0.0001306 0.001157 1 -5.19e-06 2.33e-06 0.9987 -3.911e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2249 0.107 0.3509 0.1413 0.9849 0.9939 0.2257 0.4317 0.8746 0.7009 ] Network output: [ 0.002478 -0.01197 0.9945 3.173e-06 -1.424e-06 1.012 2.391e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09966 0.185 0.1967 0.9873 0.9919 0.1125 0.7318 0.8605 0.3047 ] Network output: [ -0.002342 0.01112 1.005 3.487e-06 -1.566e-06 0.9889 2.628e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09497 0.09303 0.1649 0.1969 0.9852 0.9911 0.09498 0.6555 0.8354 0.2501 ] Network output: [ 7.594e-05 1 -5.122e-05 4.551e-07 -2.043e-07 0.9998 3.43e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001396 Epoch 10086 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008546 0.9969 0.9929 -1.339e-07 6.012e-08 -0.006819 -1.009e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003383 -0.00647 0.005249 0.9699 0.9743 0.006899 0.8231 0.819 0.01592 ] Network output: [ 0.9999 5.41e-05 0.000316 -1.649e-06 7.404e-07 -0.00026 -1.243e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2108 -0.03598 -0.1537 0.1813 0.9834 0.9932 0.2368 0.4277 0.8678 0.7073 ] Network output: [ -0.00857 1.003 1.007 -1.404e-07 6.303e-08 0.007165 -1.058e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006956 0.0006486 0.004328 0.003085 0.9889 0.9919 0.007094 0.8503 0.8915 0.01133 ] Network output: [ -0.0001304 0.001157 1 -5.183e-06 2.327e-06 0.9987 -3.906e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2249 0.107 0.3509 0.1413 0.9849 0.9939 0.2257 0.4317 0.8746 0.7009 ] Network output: [ 0.002477 -0.01196 0.9945 3.169e-06 -1.423e-06 1.012 2.388e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09966 0.185 0.1967 0.9873 0.9919 0.1125 0.7318 0.8605 0.3047 ] Network output: [ -0.002341 0.01111 1.005 3.483e-06 -1.564e-06 0.9889 2.625e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09497 0.09303 0.1649 0.1969 0.9852 0.9911 0.09498 0.6555 0.8354 0.2501 ] Network output: [ 7.592e-05 1 -5.124e-05 4.546e-07 -2.041e-07 0.9998 3.426e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001395 Epoch 10087 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008546 0.9969 0.9929 -1.338e-07 6.007e-08 -0.006818 -1.008e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003383 -0.006469 0.005249 0.9699 0.9743 0.006899 0.8231 0.819 0.01592 ] Network output: [ 0.9999 5.396e-05 0.0003158 -1.647e-06 7.395e-07 -0.0002599 -1.241e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2108 -0.03598 -0.1537 0.1813 0.9834 0.9932 0.2368 0.4277 0.8678 0.7073 ] Network output: [ -0.008569 1.003 1.007 -1.403e-07 6.297e-08 0.007165 -1.057e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006957 0.0006486 0.004328 0.003084 0.9889 0.9919 0.007094 0.8503 0.8915 0.01133 ] Network output: [ -0.0001303 0.001156 1 -5.177e-06 2.324e-06 0.9987 -3.902e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2249 0.107 0.3509 0.1413 0.9849 0.9939 0.2257 0.4317 0.8746 0.7009 ] Network output: [ 0.002475 -0.01196 0.9945 3.165e-06 -1.421e-06 1.012 2.385e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09966 0.185 0.1967 0.9873 0.9919 0.1125 0.7318 0.8605 0.3047 ] Network output: [ -0.00234 0.0111 1.005 3.479e-06 -1.562e-06 0.989 2.622e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09497 0.09303 0.1649 0.1969 0.9852 0.9911 0.09499 0.6555 0.8354 0.2501 ] Network output: [ 7.591e-05 1 -5.125e-05 4.54e-07 -2.038e-07 0.9998 3.422e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001395 Epoch 10088 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008545 0.9969 0.9929 -1.337e-07 6.003e-08 -0.006818 -1.008e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003383 -0.006468 0.005248 0.9699 0.9743 0.006899 0.8231 0.819 0.01591 ] Network output: [ 0.9999 5.382e-05 0.0003157 -1.645e-06 7.386e-07 -0.0002597 -1.24e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2108 -0.03598 -0.1537 0.1813 0.9834 0.9932 0.2368 0.4277 0.8678 0.7073 ] Network output: [ -0.008568 1.003 1.007 -1.401e-07 6.292e-08 0.007164 -1.056e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006957 0.0006487 0.004328 0.003084 0.9889 0.9919 0.007094 0.8503 0.8915 0.01133 ] Network output: [ -0.0001302 0.001155 1 -5.171e-06 2.321e-06 0.9987 -3.897e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2249 0.107 0.3509 0.1413 0.9849 0.9939 0.2257 0.4317 0.8746 0.7009 ] Network output: [ 0.002474 -0.01195 0.9945 3.161e-06 -1.419e-06 1.012 2.382e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09966 0.185 0.1967 0.9873 0.9919 0.1125 0.7318 0.8605 0.3047 ] Network output: [ -0.002338 0.0111 1.005 3.474e-06 -1.56e-06 0.989 2.618e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09497 0.09303 0.1649 0.1969 0.9852 0.9911 0.09499 0.6555 0.8354 0.2501 ] Network output: [ 7.589e-05 1 -5.127e-05 4.534e-07 -2.036e-07 0.9998 3.417e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001394 Epoch 10089 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008544 0.9969 0.9929 -1.336e-07 5.998e-08 -0.006817 -1.007e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003383 -0.006468 0.005248 0.9699 0.9743 0.006899 0.8231 0.819 0.01591 ] Network output: [ 0.9999 5.369e-05 0.0003156 -1.643e-06 7.377e-07 -0.0002596 -1.238e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2108 -0.03598 -0.1537 0.1813 0.9834 0.9932 0.2368 0.4277 0.8678 0.7073 ] Network output: [ -0.008567 1.003 1.007 -1.4e-07 6.286e-08 0.007164 -1.055e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006957 0.0006487 0.004328 0.003084 0.9889 0.9919 0.007095 0.8503 0.8915 0.01133 ] Network output: [ -0.00013 0.001154 1 -5.164e-06 2.318e-06 0.9987 -3.892e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2249 0.107 0.3509 0.1413 0.9849 0.9939 0.2257 0.4317 0.8746 0.7009 ] Network output: [ 0.002472 -0.01195 0.9945 3.157e-06 -1.417e-06 1.012 2.379e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09966 0.185 0.1967 0.9873 0.9919 0.1125 0.7318 0.8605 0.3047 ] Network output: [ -0.002337 0.01109 1.005 3.47e-06 -1.558e-06 0.989 2.615e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09497 0.09303 0.1649 0.1969 0.9852 0.9911 0.09499 0.6555 0.8354 0.2501 ] Network output: [ 7.587e-05 1 -5.128e-05 4.529e-07 -2.033e-07 0.9998 3.413e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001393 Epoch 10090 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008543 0.9969 0.9929 -1.335e-07 5.993e-08 -0.006816 -1.006e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003383 -0.006467 0.005247 0.9699 0.9743 0.006899 0.8231 0.819 0.01591 ] Network output: [ 0.9999 5.355e-05 0.0003154 -1.641e-06 7.368e-07 -0.0002594 -1.237e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2108 -0.03598 -0.1537 0.1813 0.9834 0.9932 0.2368 0.4277 0.8678 0.7073 ] Network output: [ -0.008567 1.003 1.007 -1.399e-07 6.281e-08 0.007163 -1.054e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006957 0.0006488 0.004328 0.003084 0.9889 0.9919 0.007095 0.8503 0.8915 0.01133 ] Network output: [ -0.0001299 0.001154 1 -5.158e-06 2.316e-06 0.9987 -3.887e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2249 0.107 0.3509 0.1413 0.9849 0.9939 0.2257 0.4317 0.8746 0.7009 ] Network output: [ 0.002471 -0.01194 0.9945 3.153e-06 -1.416e-06 1.012 2.376e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09967 0.185 0.1967 0.9873 0.9919 0.1125 0.7318 0.8605 0.3047 ] Network output: [ -0.002336 0.01109 1.005 3.466e-06 -1.556e-06 0.989 2.612e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09498 0.09303 0.1649 0.1969 0.9852 0.9911 0.09499 0.6555 0.8354 0.2501 ] Network output: [ 7.586e-05 1 -5.13e-05 4.523e-07 -2.031e-07 0.9998 3.409e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001392 Epoch 10091 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008542 0.9969 0.9929 -1.334e-07 5.989e-08 -0.006816 -1.005e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003384 -0.006467 0.005247 0.9699 0.9743 0.006899 0.8231 0.819 0.01591 ] Network output: [ 0.9999 5.342e-05 0.0003153 -1.639e-06 7.358e-07 -0.0002593 -1.235e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2108 -0.03598 -0.1537 0.1813 0.9834 0.9932 0.2368 0.4277 0.8678 0.7073 ] Network output: [ -0.008566 1.003 1.007 -1.398e-07 6.275e-08 0.007163 -1.053e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006958 0.0006488 0.004327 0.003083 0.9889 0.9919 0.007095 0.8503 0.8915 0.01133 ] Network output: [ -0.0001298 0.001153 1 -5.151e-06 2.313e-06 0.9987 -3.882e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.225 0.107 0.3509 0.1413 0.9849 0.9939 0.2257 0.4317 0.8746 0.7009 ] Network output: [ 0.002469 -0.01193 0.9945 3.149e-06 -1.414e-06 1.012 2.373e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09967 0.1851 0.1967 0.9873 0.9919 0.1125 0.7318 0.8605 0.3047 ] Network output: [ -0.002334 0.01108 1.005 3.462e-06 -1.554e-06 0.989 2.609e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09498 0.09303 0.1649 0.1969 0.9852 0.9911 0.09499 0.6555 0.8354 0.2501 ] Network output: [ 7.584e-05 1 -5.131e-05 4.518e-07 -2.028e-07 0.9998 3.405e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001391 Epoch 10092 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008541 0.9969 0.9929 -1.333e-07 5.984e-08 -0.006815 -1.005e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003384 -0.006466 0.005247 0.9699 0.9743 0.006899 0.8231 0.819 0.01591 ] Network output: [ 0.9999 5.328e-05 0.0003152 -1.637e-06 7.349e-07 -0.0002592 -1.234e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2108 -0.03598 -0.1537 0.1813 0.9834 0.9932 0.2368 0.4277 0.8678 0.7073 ] Network output: [ -0.008565 1.003 1.007 -1.397e-07 6.269e-08 0.007162 -1.052e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006958 0.0006488 0.004327 0.003083 0.9889 0.9919 0.007096 0.8503 0.8915 0.01133 ] Network output: [ -0.0001296 0.001152 1 -5.145e-06 2.31e-06 0.9987 -3.877e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.225 0.107 0.3509 0.1413 0.9849 0.9939 0.2257 0.4316 0.8746 0.7009 ] Network output: [ 0.002468 -0.01193 0.9945 3.145e-06 -1.412e-06 1.012 2.37e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09967 0.1851 0.1967 0.9873 0.9919 0.1125 0.7317 0.8605 0.3047 ] Network output: [ -0.002333 0.01108 1.005 3.458e-06 -1.552e-06 0.989 2.606e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09498 0.09304 0.1649 0.1969 0.9852 0.9911 0.09499 0.6555 0.8354 0.2501 ] Network output: [ 7.583e-05 1 -5.133e-05 4.512e-07 -2.026e-07 0.9998 3.401e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001391 Epoch 10093 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008541 0.9969 0.9929 -1.332e-07 5.979e-08 -0.006814 -1.004e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003384 -0.006466 0.005246 0.9699 0.9743 0.0069 0.8231 0.819 0.01591 ] Network output: [ 0.9999 5.315e-05 0.000315 -1.635e-06 7.34e-07 -0.000259 -1.232e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2108 -0.03598 -0.1536 0.1813 0.9834 0.9932 0.2368 0.4277 0.8678 0.7073 ] Network output: [ -0.008564 1.003 1.007 -1.395e-07 6.264e-08 0.007162 -1.052e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006958 0.0006489 0.004327 0.003083 0.9889 0.9919 0.007096 0.8503 0.8915 0.01133 ] Network output: [ -0.0001295 0.001152 1 -5.139e-06 2.307e-06 0.9987 -3.873e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.225 0.107 0.351 0.1413 0.9849 0.9939 0.2257 0.4316 0.8746 0.7009 ] Network output: [ 0.002467 -0.01192 0.9945 3.141e-06 -1.41e-06 1.012 2.367e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1124 0.09967 0.1851 0.1967 0.9873 0.9919 0.1125 0.7317 0.8605 0.3047 ] Network output: [ -0.002332 0.01107 1.005 3.453e-06 -1.55e-06 0.989 2.603e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09498 0.09304 0.1649 0.1969 0.9852 0.9911 0.09499 0.6554 0.8354 0.2501 ] Network output: [ 7.581e-05 1 -5.134e-05 4.507e-07 -2.023e-07 0.9998 3.396e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000139 Epoch 10094 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00854 0.9969 0.9929 -1.331e-07 5.975e-08 -0.006814 -1.003e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003384 -0.006465 0.005246 0.9699 0.9743 0.0069 0.8231 0.819 0.01591 ] Network output: [ 0.9999 5.301e-05 0.0003149 -1.633e-06 7.331e-07 -0.0002589 -1.231e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2108 -0.03599 -0.1536 0.1813 0.9834 0.9932 0.2368 0.4277 0.8678 0.7073 ] Network output: [ -0.008564 1.003 1.007 -1.394e-07 6.258e-08 0.007161 -1.051e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006959 0.0006489 0.004327 0.003083 0.9889 0.9919 0.007096 0.8502 0.8915 0.01133 ] Network output: [ -0.0001294 0.001151 1 -5.132e-06 2.304e-06 0.9987 -3.868e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.225 0.107 0.351 0.1413 0.9849 0.9939 0.2257 0.4316 0.8746 0.7009 ] Network output: [ 0.002465 -0.01191 0.9945 3.137e-06 -1.409e-06 1.012 2.365e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09968 0.1851 0.1967 0.9873 0.9919 0.1125 0.7317 0.8605 0.3047 ] Network output: [ -0.002331 0.01107 1.005 3.449e-06 -1.548e-06 0.989 2.599e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09498 0.09304 0.1649 0.1969 0.9852 0.9911 0.095 0.6554 0.8354 0.2501 ] Network output: [ 7.58e-05 1 -5.136e-05 4.501e-07 -2.021e-07 0.9998 3.392e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001389 Epoch 10095 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008539 0.9969 0.9929 -1.33e-07 5.97e-08 -0.006813 -1.002e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003384 -0.006465 0.005246 0.9699 0.9743 0.0069 0.8231 0.819 0.01591 ] Network output: [ 0.9999 5.288e-05 0.0003147 -1.631e-06 7.322e-07 -0.0002587 -1.229e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2108 -0.03599 -0.1536 0.1813 0.9834 0.9932 0.2368 0.4277 0.8678 0.7073 ] Network output: [ -0.008563 1.003 1.007 -1.393e-07 6.253e-08 0.007161 -1.05e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006959 0.000649 0.004327 0.003083 0.9889 0.9919 0.007096 0.8502 0.8915 0.01133 ] Network output: [ -0.0001292 0.00115 1 -5.126e-06 2.301e-06 0.9987 -3.863e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.225 0.107 0.351 0.1413 0.9849 0.9939 0.2258 0.4316 0.8746 0.7009 ] Network output: [ 0.002464 -0.01191 0.9945 3.134e-06 -1.407e-06 1.012 2.362e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09968 0.1851 0.1967 0.9873 0.9919 0.1125 0.7317 0.8605 0.3047 ] Network output: [ -0.002329 0.01106 1.005 3.445e-06 -1.547e-06 0.989 2.596e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09498 0.09304 0.1649 0.1969 0.9852 0.9911 0.095 0.6554 0.8354 0.2501 ] Network output: [ 7.578e-05 1 -5.137e-05 4.496e-07 -2.018e-07 0.9998 3.388e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001388 Epoch 10096 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008538 0.9969 0.9929 -1.329e-07 5.966e-08 -0.006812 -1.001e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003384 -0.006464 0.005245 0.9699 0.9743 0.0069 0.8231 0.819 0.01591 ] Network output: [ 0.9999 5.274e-05 0.0003146 -1.629e-06 7.313e-07 -0.0002586 -1.228e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2108 -0.03599 -0.1536 0.1813 0.9834 0.9932 0.2369 0.4277 0.8678 0.7073 ] Network output: [ -0.008562 1.003 1.007 -1.392e-07 6.247e-08 0.00716 -1.049e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006959 0.000649 0.004327 0.003082 0.9889 0.9919 0.007097 0.8502 0.8915 0.01132 ] Network output: [ -0.0001291 0.00115 1 -5.119e-06 2.298e-06 0.9987 -3.858e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.225 0.107 0.351 0.1413 0.9849 0.9939 0.2258 0.4316 0.8746 0.7009 ] Network output: [ 0.002462 -0.0119 0.9945 3.13e-06 -1.405e-06 1.012 2.359e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09968 0.1851 0.1967 0.9873 0.9919 0.1125 0.7317 0.8605 0.3047 ] Network output: [ -0.002328 0.01105 1.005 3.441e-06 -1.545e-06 0.989 2.593e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09498 0.09304 0.1649 0.1969 0.9852 0.9911 0.095 0.6554 0.8354 0.2501 ] Network output: [ 7.577e-05 1 -5.139e-05 4.49e-07 -2.016e-07 0.9998 3.384e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001388 Epoch 10097 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008537 0.9969 0.9929 -1.328e-07 5.961e-08 -0.006812 -1.001e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003384 -0.006464 0.005245 0.9699 0.9743 0.0069 0.8231 0.819 0.01591 ] Network output: [ 0.9999 5.26e-05 0.0003145 -1.627e-06 7.304e-07 -0.0002584 -1.226e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2108 -0.03599 -0.1536 0.1813 0.9834 0.9932 0.2369 0.4277 0.8678 0.7073 ] Network output: [ -0.008561 1.003 1.007 -1.39e-07 6.242e-08 0.00716 -1.048e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00696 0.0006491 0.004327 0.003082 0.9889 0.9919 0.007097 0.8502 0.8915 0.01132 ] Network output: [ -0.000129 0.001149 1 -5.113e-06 2.295e-06 0.9987 -3.853e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.225 0.107 0.351 0.1413 0.9849 0.9939 0.2258 0.4316 0.8746 0.7009 ] Network output: [ 0.002461 -0.01189 0.9945 3.126e-06 -1.403e-06 1.012 2.356e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09968 0.1851 0.1967 0.9873 0.9919 0.1125 0.7317 0.8605 0.3047 ] Network output: [ -0.002327 0.01105 1.005 3.437e-06 -1.543e-06 0.989 2.59e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09498 0.09304 0.1649 0.1969 0.9852 0.9911 0.095 0.6554 0.8354 0.2501 ] Network output: [ 7.575e-05 1 -5.14e-05 4.485e-07 -2.013e-07 0.9998 3.38e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001387 Epoch 10098 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008537 0.9969 0.9929 -1.327e-07 5.956e-08 -0.006811 -9.999e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003384 -0.006463 0.005245 0.9699 0.9743 0.0069 0.8231 0.819 0.01591 ] Network output: [ 0.9999 5.247e-05 0.0003143 -1.625e-06 7.295e-07 -0.0002583 -1.225e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2108 -0.03599 -0.1536 0.1813 0.9834 0.9932 0.2369 0.4277 0.8678 0.7073 ] Network output: [ -0.00856 1.003 1.007 -1.389e-07 6.236e-08 0.007159 -1.047e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00696 0.0006491 0.004327 0.003082 0.9889 0.9919 0.007097 0.8502 0.8915 0.01132 ] Network output: [ -0.0001288 0.001148 1 -5.107e-06 2.293e-06 0.9987 -3.849e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.225 0.107 0.351 0.1413 0.9849 0.9939 0.2258 0.4316 0.8746 0.7009 ] Network output: [ 0.002459 -0.01189 0.9945 3.122e-06 -1.402e-06 1.012 2.353e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09968 0.1851 0.1967 0.9873 0.9919 0.1125 0.7317 0.8605 0.3047 ] Network output: [ -0.002326 0.01104 1.005 3.432e-06 -1.541e-06 0.989 2.587e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09499 0.09304 0.1649 0.1969 0.9852 0.9911 0.095 0.6554 0.8354 0.2501 ] Network output: [ 7.574e-05 1 -5.142e-05 4.479e-07 -2.011e-07 0.9998 3.376e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001386 Epoch 10099 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008536 0.9969 0.9929 -1.326e-07 5.952e-08 -0.00681 -9.991e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003384 -0.006463 0.005244 0.9699 0.9743 0.0069 0.8231 0.819 0.0159 ] Network output: [ 0.9999 5.233e-05 0.0003142 -1.623e-06 7.286e-07 -0.0002581 -1.223e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2108 -0.03599 -0.1536 0.1813 0.9834 0.9932 0.2369 0.4277 0.8678 0.7073 ] Network output: [ -0.00856 1.003 1.007 -1.388e-07 6.231e-08 0.007159 -1.046e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00696 0.0006491 0.004327 0.003082 0.9889 0.9919 0.007098 0.8502 0.8915 0.01132 ] Network output: [ -0.0001287 0.001148 1 -5.1e-06 2.29e-06 0.9987 -3.844e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.225 0.107 0.351 0.1413 0.9849 0.9939 0.2258 0.4316 0.8746 0.7009 ] Network output: [ 0.002458 -0.01188 0.9945 3.118e-06 -1.4e-06 1.012 2.35e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09969 0.1851 0.1967 0.9873 0.9919 0.1125 0.7317 0.8605 0.3047 ] Network output: [ -0.002324 0.01104 1.005 3.428e-06 -1.539e-06 0.989 2.584e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09499 0.09305 0.1649 0.1969 0.9852 0.9911 0.095 0.6554 0.8354 0.2501 ] Network output: [ 7.572e-05 1 -5.143e-05 4.474e-07 -2.008e-07 0.9998 3.372e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001385 Epoch 10100 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008535 0.9969 0.9929 -1.325e-07 5.947e-08 -0.00681 -9.983e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003384 -0.006462 0.005244 0.9699 0.9743 0.0069 0.8231 0.819 0.0159 ] Network output: [ 0.9999 5.22e-05 0.0003141 -1.621e-06 7.276e-07 -0.000258 -1.222e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2108 -0.03599 -0.1536 0.1813 0.9834 0.9932 0.2369 0.4277 0.8678 0.7073 ] Network output: [ -0.008559 1.003 1.007 -1.387e-07 6.225e-08 0.007158 -1.045e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00696 0.0006492 0.004326 0.003082 0.9889 0.9919 0.007098 0.8502 0.8915 0.01132 ] Network output: [ -0.0001285 0.001147 1 -5.094e-06 2.287e-06 0.9987 -3.839e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.225 0.107 0.351 0.1413 0.9849 0.9939 0.2258 0.4316 0.8746 0.7009 ] Network output: [ 0.002457 -0.01188 0.9945 3.114e-06 -1.398e-06 1.012 2.347e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09969 0.1851 0.1967 0.9873 0.9919 0.1125 0.7317 0.8605 0.3047 ] Network output: [ -0.002323 0.01103 1.005 3.424e-06 -1.537e-06 0.989 2.58e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09499 0.09305 0.1649 0.1969 0.9852 0.9911 0.095 0.6554 0.8354 0.2501 ] Network output: [ 7.571e-05 1 -5.145e-05 4.468e-07 -2.006e-07 0.9998 3.367e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001384 Epoch 10101 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008534 0.9969 0.9929 -1.324e-07 5.942e-08 -0.006809 -9.976e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003384 -0.006462 0.005244 0.9699 0.9743 0.006901 0.8231 0.819 0.0159 ] Network output: [ 0.9999 5.206e-05 0.0003139 -1.619e-06 7.267e-07 -0.0002579 -1.22e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2109 -0.03599 -0.1536 0.1813 0.9834 0.9932 0.2369 0.4277 0.8678 0.7073 ] Network output: [ -0.008558 1.003 1.007 -1.385e-07 6.22e-08 0.007158 -1.044e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006961 0.0006492 0.004326 0.003081 0.9889 0.9919 0.007098 0.8502 0.8915 0.01132 ] Network output: [ -0.0001284 0.001146 1 -5.088e-06 2.284e-06 0.9987 -3.834e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.225 0.107 0.351 0.1413 0.9849 0.9939 0.2258 0.4316 0.8746 0.7009 ] Network output: [ 0.002455 -0.01187 0.9945 3.11e-06 -1.396e-06 1.012 2.344e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09969 0.1851 0.1967 0.9873 0.9919 0.1125 0.7317 0.8605 0.3047 ] Network output: [ -0.002322 0.01103 1.005 3.42e-06 -1.535e-06 0.989 2.577e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09499 0.09305 0.1649 0.1969 0.9852 0.9911 0.095 0.6554 0.8354 0.2501 ] Network output: [ 7.569e-05 1 -5.146e-05 4.463e-07 -2.004e-07 0.9998 3.363e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001384 Epoch 10102 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008533 0.9969 0.9929 -1.323e-07 5.938e-08 -0.006808 -9.968e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003384 -0.006461 0.005243 0.9699 0.9743 0.006901 0.8231 0.819 0.0159 ] Network output: [ 0.9999 5.193e-05 0.0003138 -1.617e-06 7.258e-07 -0.0002577 -1.218e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2109 -0.03599 -0.1536 0.1812 0.9834 0.9932 0.2369 0.4277 0.8678 0.7073 ] Network output: [ -0.008557 1.003 1.007 -1.384e-07 6.214e-08 0.007158 -1.043e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006961 0.0006493 0.004326 0.003081 0.9889 0.9919 0.007099 0.8502 0.8915 0.01132 ] Network output: [ -0.0001283 0.001146 1 -5.081e-06 2.281e-06 0.9987 -3.83e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.225 0.107 0.351 0.1413 0.9849 0.9939 0.2258 0.4316 0.8746 0.7009 ] Network output: [ 0.002454 -0.01186 0.9945 3.107e-06 -1.395e-06 1.012 2.341e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09969 0.1851 0.1967 0.9873 0.9919 0.1125 0.7317 0.8605 0.3047 ] Network output: [ -0.00232 0.01102 1.005 3.416e-06 -1.533e-06 0.989 2.574e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09499 0.09305 0.1649 0.1969 0.9852 0.9911 0.09501 0.6554 0.8354 0.2501 ] Network output: [ 7.567e-05 1 -5.148e-05 4.457e-07 -2.001e-07 0.9998 3.359e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001383 Epoch 10103 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008532 0.9969 0.9929 -1.322e-07 5.933e-08 -0.006808 -9.96e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003384 -0.006461 0.005243 0.9699 0.9743 0.006901 0.8231 0.819 0.0159 ] Network output: [ 0.9999 5.179e-05 0.0003137 -1.615e-06 7.249e-07 -0.0002576 -1.217e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2109 -0.03599 -0.1536 0.1812 0.9834 0.9932 0.2369 0.4277 0.8678 0.7073 ] Network output: [ -0.008557 1.003 1.007 -1.383e-07 6.209e-08 0.007157 -1.042e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006961 0.0006493 0.004326 0.003081 0.9889 0.9919 0.007099 0.8502 0.8915 0.01132 ] Network output: [ -0.0001281 0.001145 1 -5.075e-06 2.278e-06 0.9987 -3.825e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.225 0.107 0.351 0.1413 0.9849 0.9939 0.2258 0.4316 0.8746 0.7009 ] Network output: [ 0.002452 -0.01186 0.9945 3.103e-06 -1.393e-06 1.012 2.338e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09969 0.1851 0.1967 0.9873 0.9919 0.1126 0.7317 0.8605 0.3047 ] Network output: [ -0.002319 0.01102 1.005 3.412e-06 -1.532e-06 0.989 2.571e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09499 0.09305 0.1649 0.1969 0.9852 0.9911 0.09501 0.6554 0.8354 0.2501 ] Network output: [ 7.566e-05 1 -5.15e-05 4.452e-07 -1.999e-07 0.9998 3.355e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001382 Epoch 10104 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008532 0.9969 0.9929 -1.321e-07 5.929e-08 -0.006807 -9.952e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003384 -0.00646 0.005243 0.9699 0.9743 0.006901 0.8231 0.819 0.0159 ] Network output: [ 0.9999 5.166e-05 0.0003135 -1.613e-06 7.24e-07 -0.0002574 -1.215e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2109 -0.036 -0.1536 0.1812 0.9834 0.9932 0.2369 0.4277 0.8678 0.7073 ] Network output: [ -0.008556 1.003 1.007 -1.382e-07 6.203e-08 0.007157 -1.041e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006962 0.0006493 0.004326 0.003081 0.9889 0.9919 0.007099 0.8502 0.8915 0.01132 ] Network output: [ -0.000128 0.001144 1 -5.069e-06 2.276e-06 0.9987 -3.82e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.225 0.107 0.351 0.1413 0.9849 0.9939 0.2258 0.4316 0.8746 0.7009 ] Network output: [ 0.002451 -0.01185 0.9945 3.099e-06 -1.391e-06 1.012 2.335e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.0997 0.1851 0.1967 0.9873 0.9919 0.1126 0.7316 0.8605 0.3047 ] Network output: [ -0.002318 0.01101 1.005 3.407e-06 -1.53e-06 0.989 2.568e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09499 0.09305 0.1649 0.1969 0.9852 0.9911 0.09501 0.6554 0.8354 0.2501 ] Network output: [ 7.564e-05 1 -5.151e-05 4.446e-07 -1.996e-07 0.9998 3.351e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001381 Epoch 10105 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008531 0.9969 0.9929 -1.32e-07 5.924e-08 -0.006806 -9.944e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003384 -0.00646 0.005242 0.9699 0.9743 0.006901 0.8231 0.819 0.0159 ] Network output: [ 0.9999 5.153e-05 0.0003134 -1.611e-06 7.231e-07 -0.0002573 -1.214e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2109 -0.036 -0.1536 0.1812 0.9834 0.9932 0.2369 0.4277 0.8678 0.7073 ] Network output: [ -0.008555 1.003 1.007 -1.381e-07 6.198e-08 0.007156 -1.04e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006962 0.0006494 0.004326 0.00308 0.9889 0.9919 0.0071 0.8502 0.8915 0.01132 ] Network output: [ -0.0001279 0.001143 1 -5.063e-06 2.273e-06 0.9987 -3.815e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.225 0.107 0.351 0.1413 0.9849 0.9939 0.2258 0.4316 0.8746 0.7009 ] Network output: [ 0.00245 -0.01184 0.9945 3.095e-06 -1.39e-06 1.012 2.333e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.0997 0.1851 0.1967 0.9873 0.9919 0.1126 0.7316 0.8605 0.3047 ] Network output: [ -0.002317 0.011 1.005 3.403e-06 -1.528e-06 0.989 2.565e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.095 0.09305 0.1649 0.1969 0.9852 0.9911 0.09501 0.6553 0.8354 0.2501 ] Network output: [ 7.563e-05 1 -5.153e-05 4.441e-07 -1.994e-07 0.9998 3.347e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000138 Epoch 10106 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00853 0.9969 0.9929 -1.319e-07 5.919e-08 -0.006806 -9.937e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003385 -0.006459 0.005242 0.9699 0.9743 0.006901 0.8231 0.819 0.0159 ] Network output: [ 0.9999 5.139e-05 0.0003132 -1.609e-06 7.222e-07 -0.0002572 -1.212e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2109 -0.036 -0.1535 0.1812 0.9834 0.9932 0.2369 0.4277 0.8678 0.7073 ] Network output: [ -0.008554 1.003 1.007 -1.379e-07 6.192e-08 0.007156 -1.039e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006962 0.0006494 0.004326 0.00308 0.9889 0.9919 0.0071 0.8502 0.8915 0.01132 ] Network output: [ -0.0001277 0.001143 1 -5.056e-06 2.27e-06 0.9987 -3.811e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.225 0.107 0.351 0.1413 0.9849 0.9939 0.2258 0.4316 0.8746 0.7009 ] Network output: [ 0.002448 -0.01184 0.9945 3.091e-06 -1.388e-06 1.012 2.33e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.0997 0.1851 0.1967 0.9873 0.9919 0.1126 0.7316 0.8605 0.3047 ] Network output: [ -0.002315 0.011 1.005 3.399e-06 -1.526e-06 0.989 2.562e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.095 0.09305 0.1649 0.1969 0.9852 0.9911 0.09501 0.6553 0.8354 0.2501 ] Network output: [ 7.561e-05 1 -5.154e-05 4.436e-07 -1.991e-07 0.9998 3.343e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000138 Epoch 10107 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008529 0.9969 0.9929 -1.317e-07 5.915e-08 -0.006805 -9.929e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003528 -0.003385 -0.006459 0.005242 0.9699 0.9743 0.006901 0.8231 0.819 0.0159 ] Network output: [ 0.9999 5.126e-05 0.0003131 -1.607e-06 7.213e-07 -0.000257 -1.211e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2109 -0.036 -0.1535 0.1812 0.9834 0.9932 0.2369 0.4277 0.8678 0.7073 ] Network output: [ -0.008554 1.003 1.007 -1.378e-07 6.187e-08 0.007155 -1.039e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006963 0.0006495 0.004326 0.00308 0.9889 0.9919 0.0071 0.8502 0.8915 0.01132 ] Network output: [ -0.0001276 0.001142 1 -5.05e-06 2.267e-06 0.9987 -3.806e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.225 0.107 0.351 0.1413 0.9849 0.9939 0.2258 0.4316 0.8746 0.7009 ] Network output: [ 0.002447 -0.01183 0.9945 3.087e-06 -1.386e-06 1.012 2.327e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.0997 0.1851 0.1967 0.9873 0.9919 0.1126 0.7316 0.8605 0.3047 ] Network output: [ -0.002314 0.01099 1.005 3.395e-06 -1.524e-06 0.989 2.559e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.095 0.09306 0.1649 0.1969 0.9852 0.9911 0.09501 0.6553 0.8354 0.2501 ] Network output: [ 7.56e-05 1 -5.156e-05 4.43e-07 -1.989e-07 0.9998 3.339e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001379 Epoch 10108 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008528 0.9969 0.9929 -1.316e-07 5.91e-08 -0.006804 -9.921e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003385 -0.006458 0.005241 0.9699 0.9743 0.006901 0.8231 0.819 0.0159 ] Network output: [ 0.9999 5.112e-05 0.000313 -1.605e-06 7.204e-07 -0.0002569 -1.209e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2109 -0.036 -0.1535 0.1812 0.9834 0.9932 0.2369 0.4277 0.8678 0.7073 ] Network output: [ -0.008553 1.003 1.007 -1.377e-07 6.181e-08 0.007155 -1.038e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006963 0.0006495 0.004325 0.00308 0.9889 0.9919 0.0071 0.8502 0.8915 0.01132 ] Network output: [ -0.0001275 0.001141 1 -5.044e-06 2.264e-06 0.9987 -3.801e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2251 0.107 0.351 0.1413 0.9849 0.9939 0.2258 0.4316 0.8746 0.7009 ] Network output: [ 0.002445 -0.01183 0.9946 3.084e-06 -1.384e-06 1.012 2.324e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.0997 0.1851 0.1967 0.9873 0.9919 0.1126 0.7316 0.8605 0.3047 ] Network output: [ -0.002313 0.01099 1.005 3.391e-06 -1.522e-06 0.989 2.555e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.095 0.09306 0.1649 0.1969 0.9852 0.9911 0.09501 0.6553 0.8354 0.2501 ] Network output: [ 7.558e-05 1 -5.157e-05 4.425e-07 -1.986e-07 0.9998 3.335e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001378 Epoch 10109 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008527 0.9969 0.9929 -1.315e-07 5.905e-08 -0.006803 -9.913e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003385 -0.006457 0.005241 0.9699 0.9743 0.006902 0.8231 0.819 0.0159 ] Network output: [ 0.9999 5.099e-05 0.0003128 -1.603e-06 7.195e-07 -0.0002567 -1.208e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2109 -0.036 -0.1535 0.1812 0.9834 0.9932 0.2369 0.4277 0.8678 0.7073 ] Network output: [ -0.008552 1.003 1.007 -1.376e-07 6.176e-08 0.007154 -1.037e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006963 0.0006496 0.004325 0.00308 0.9889 0.9919 0.007101 0.8502 0.8915 0.01132 ] Network output: [ -0.0001273 0.001141 1 -5.037e-06 2.261e-06 0.9987 -3.796e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2251 0.107 0.351 0.1413 0.9849 0.9939 0.2258 0.4316 0.8746 0.7009 ] Network output: [ 0.002444 -0.01182 0.9946 3.08e-06 -1.383e-06 1.012 2.321e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09971 0.1851 0.1967 0.9873 0.9919 0.1126 0.7316 0.8605 0.3047 ] Network output: [ -0.002311 0.01098 1.005 3.387e-06 -1.52e-06 0.989 2.552e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.095 0.09306 0.1649 0.1969 0.9852 0.9911 0.09502 0.6553 0.8354 0.2501 ] Network output: [ 7.557e-05 1 -5.159e-05 4.419e-07 -1.984e-07 0.9998 3.33e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001377 Epoch 10110 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008527 0.9969 0.9929 -1.314e-07 5.901e-08 -0.006803 -9.906e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003385 -0.006457 0.00524 0.9699 0.9743 0.006902 0.823 0.819 0.0159 ] Network output: [ 0.9999 5.085e-05 0.0003127 -1.601e-06 7.187e-07 -0.0002566 -1.206e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2109 -0.036 -0.1535 0.1812 0.9834 0.9932 0.2369 0.4277 0.8678 0.7073 ] Network output: [ -0.008551 1.003 1.007 -1.374e-07 6.17e-08 0.007154 -1.036e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006963 0.0006496 0.004325 0.003079 0.9889 0.9919 0.007101 0.8502 0.8915 0.01132 ] Network output: [ -0.0001272 0.00114 1 -5.031e-06 2.259e-06 0.9987 -3.792e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2251 0.107 0.351 0.1413 0.9849 0.9939 0.2258 0.4316 0.8746 0.7009 ] Network output: [ 0.002442 -0.01181 0.9946 3.076e-06 -1.381e-06 1.012 2.318e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09971 0.1851 0.1967 0.9873 0.9919 0.1126 0.7316 0.8605 0.3047 ] Network output: [ -0.00231 0.01098 1.005 3.383e-06 -1.519e-06 0.989 2.549e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.095 0.09306 0.1649 0.1969 0.9852 0.9911 0.09502 0.6553 0.8354 0.2501 ] Network output: [ 7.555e-05 1 -5.161e-05 4.414e-07 -1.982e-07 0.9998 3.326e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001376 Epoch 10111 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008526 0.9969 0.9929 -1.313e-07 5.896e-08 -0.006802 -9.898e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003385 -0.006456 0.00524 0.9699 0.9743 0.006902 0.823 0.819 0.01589 ] Network output: [ 0.9999 5.072e-05 0.0003126 -1.599e-06 7.178e-07 -0.0002564 -1.205e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2109 -0.036 -0.1535 0.1812 0.9834 0.9932 0.2369 0.4277 0.8678 0.7073 ] Network output: [ -0.00855 1.003 1.007 -1.373e-07 6.165e-08 0.007153 -1.035e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006964 0.0006496 0.004325 0.003079 0.9889 0.9919 0.007101 0.8502 0.8915 0.01131 ] Network output: [ -0.0001271 0.001139 1 -5.025e-06 2.256e-06 0.9987 -3.787e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2251 0.107 0.351 0.1413 0.9849 0.9939 0.2258 0.4316 0.8746 0.7009 ] Network output: [ 0.002441 -0.01181 0.9946 3.072e-06 -1.379e-06 1.012 2.315e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09971 0.1851 0.1967 0.9873 0.9919 0.1126 0.7316 0.8605 0.3047 ] Network output: [ -0.002309 0.01097 1.005 3.378e-06 -1.517e-06 0.989 2.546e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.095 0.09306 0.1649 0.1969 0.9852 0.9911 0.09502 0.6553 0.8354 0.2501 ] Network output: [ 7.554e-05 1 -5.162e-05 4.408e-07 -1.979e-07 0.9998 3.322e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001376 Epoch 10112 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008525 0.9969 0.9929 -1.312e-07 5.892e-08 -0.006801 -9.89e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003385 -0.006456 0.00524 0.9699 0.9743 0.006902 0.823 0.819 0.01589 ] Network output: [ 0.9999 5.058e-05 0.0003124 -1.597e-06 7.169e-07 -0.0002563 -1.203e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2109 -0.036 -0.1535 0.1812 0.9834 0.9932 0.237 0.4277 0.8678 0.7073 ] Network output: [ -0.00855 1.003 1.007 -1.372e-07 6.159e-08 0.007153 -1.034e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006964 0.0006497 0.004325 0.003079 0.9889 0.9919 0.007102 0.8502 0.8915 0.01131 ] Network output: [ -0.0001269 0.001139 1 -5.019e-06 2.253e-06 0.9987 -3.782e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2251 0.1071 0.351 0.1413 0.9849 0.9939 0.2259 0.4316 0.8746 0.7009 ] Network output: [ 0.00244 -0.0118 0.9946 3.068e-06 -1.378e-06 1.012 2.312e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09971 0.1851 0.1967 0.9873 0.9919 0.1126 0.7316 0.8605 0.3047 ] Network output: [ -0.002308 0.01096 1.005 3.374e-06 -1.515e-06 0.989 2.543e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09501 0.09306 0.1649 0.1969 0.9852 0.9911 0.09502 0.6553 0.8354 0.2501 ] Network output: [ 7.552e-05 1 -5.164e-05 4.403e-07 -1.977e-07 0.9998 3.318e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001375 Epoch 10113 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008524 0.9969 0.9929 -1.311e-07 5.887e-08 -0.006801 -9.882e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003385 -0.006455 0.005239 0.9699 0.9743 0.006902 0.823 0.819 0.01589 ] Network output: [ 0.9999 5.045e-05 0.0003123 -1.595e-06 7.16e-07 -0.0002562 -1.202e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2109 -0.036 -0.1535 0.1812 0.9834 0.9932 0.237 0.4276 0.8678 0.7073 ] Network output: [ -0.008549 1.003 1.007 -1.371e-07 6.154e-08 0.007152 -1.033e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006964 0.0006497 0.004325 0.003079 0.9889 0.9919 0.007102 0.8502 0.8915 0.01131 ] Network output: [ -0.0001268 0.001138 1 -5.012e-06 2.25e-06 0.9987 -3.778e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2251 0.1071 0.351 0.1413 0.9849 0.9939 0.2259 0.4316 0.8746 0.7008 ] Network output: [ 0.002438 -0.01179 0.9946 3.065e-06 -1.376e-06 1.012 2.31e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09971 0.1851 0.1967 0.9873 0.9919 0.1126 0.7316 0.8605 0.3047 ] Network output: [ -0.002306 0.01096 1.005 3.37e-06 -1.513e-06 0.989 2.54e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09501 0.09306 0.1649 0.1969 0.9852 0.9911 0.09502 0.6553 0.8354 0.2501 ] Network output: [ 7.551e-05 1 -5.165e-05 4.398e-07 -1.974e-07 0.9998 3.314e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001374 Epoch 10114 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008523 0.9969 0.9929 -1.31e-07 5.882e-08 -0.0068 -9.875e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003385 -0.006455 0.005239 0.9699 0.9743 0.006902 0.823 0.819 0.01589 ] Network output: [ 0.9999 5.032e-05 0.0003122 -1.593e-06 7.151e-07 -0.000256 -1.2e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2109 -0.03601 -0.1535 0.1812 0.9834 0.9932 0.237 0.4276 0.8678 0.7072 ] Network output: [ -0.008548 1.003 1.007 -1.37e-07 6.148e-08 0.007152 -1.032e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006965 0.0006498 0.004325 0.003078 0.9889 0.9919 0.007102 0.8502 0.8915 0.01131 ] Network output: [ -0.0001267 0.001137 1 -5.006e-06 2.247e-06 0.9987 -3.773e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2251 0.1071 0.351 0.1413 0.9849 0.9939 0.2259 0.4316 0.8746 0.7008 ] Network output: [ 0.002437 -0.01179 0.9946 3.061e-06 -1.374e-06 1.012 2.307e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09972 0.1851 0.1967 0.9873 0.9919 0.1126 0.7316 0.8605 0.3047 ] Network output: [ -0.002305 0.01095 1.005 3.366e-06 -1.511e-06 0.989 2.537e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09501 0.09307 0.1649 0.1969 0.9852 0.9911 0.09502 0.6553 0.8354 0.2501 ] Network output: [ 7.549e-05 1 -5.167e-05 4.392e-07 -1.972e-07 0.9998 3.31e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001373 Epoch 10115 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008523 0.9969 0.9929 -1.309e-07 5.878e-08 -0.006799 -9.867e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003385 -0.006454 0.005239 0.9699 0.9743 0.006902 0.823 0.819 0.01589 ] Network output: [ 0.9999 5.018e-05 0.000312 -1.591e-06 7.142e-07 -0.0002559 -1.199e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2109 -0.03601 -0.1535 0.1812 0.9834 0.9932 0.237 0.4276 0.8678 0.7072 ] Network output: [ -0.008547 1.003 1.007 -1.368e-07 6.143e-08 0.007152 -1.031e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006965 0.0006498 0.004325 0.003078 0.9889 0.9919 0.007103 0.8502 0.8915 0.01131 ] Network output: [ -0.0001265 0.001137 1 -5e-06 2.245e-06 0.9987 -3.768e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2251 0.1071 0.3511 0.1413 0.9849 0.9939 0.2259 0.4316 0.8746 0.7008 ] Network output: [ 0.002435 -0.01178 0.9946 3.057e-06 -1.372e-06 1.012 2.304e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09972 0.1851 0.1967 0.9873 0.9919 0.1126 0.7316 0.8605 0.3047 ] Network output: [ -0.002304 0.01095 1.005 3.362e-06 -1.509e-06 0.989 2.534e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09501 0.09307 0.1649 0.1969 0.9852 0.9911 0.09502 0.6553 0.8354 0.2501 ] Network output: [ 7.548e-05 1 -5.169e-05 4.387e-07 -1.969e-07 0.9998 3.306e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001373 Epoch 10116 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008522 0.9969 0.9929 -1.308e-07 5.873e-08 -0.006799 -9.859e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003385 -0.006454 0.005238 0.9699 0.9743 0.006902 0.823 0.819 0.01589 ] Network output: [ 0.9999 5.005e-05 0.0003119 -1.589e-06 7.133e-07 -0.0002557 -1.197e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2109 -0.03601 -0.1535 0.1812 0.9834 0.9932 0.237 0.4276 0.8678 0.7072 ] Network output: [ -0.008547 1.003 1.007 -1.367e-07 6.137e-08 0.007151 -1.03e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006965 0.0006499 0.004325 0.003078 0.9889 0.9919 0.007103 0.8502 0.8915 0.01131 ] Network output: [ -0.0001264 0.001136 1 -4.994e-06 2.242e-06 0.9987 -3.763e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2251 0.1071 0.3511 0.1413 0.9849 0.9939 0.2259 0.4316 0.8746 0.7008 ] Network output: [ 0.002434 -0.01177 0.9946 3.053e-06 -1.371e-06 1.012 2.301e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09972 0.1851 0.1966 0.9873 0.9919 0.1126 0.7315 0.8605 0.3047 ] Network output: [ -0.002303 0.01094 1.005 3.358e-06 -1.507e-06 0.989 2.531e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09501 0.09307 0.1649 0.1969 0.9852 0.9911 0.09503 0.6552 0.8354 0.2501 ] Network output: [ 7.546e-05 1 -5.17e-05 4.381e-07 -1.967e-07 0.9998 3.302e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001372 Epoch 10117 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008521 0.9969 0.9929 -1.307e-07 5.869e-08 -0.006798 -9.852e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003385 -0.006453 0.005238 0.9699 0.9743 0.006903 0.823 0.819 0.01589 ] Network output: [ 0.9999 4.991e-05 0.0003118 -1.587e-06 7.124e-07 -0.0002556 -1.196e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2109 -0.03601 -0.1535 0.1812 0.9834 0.9932 0.237 0.4276 0.8678 0.7072 ] Network output: [ -0.008546 1.003 1.007 -1.366e-07 6.132e-08 0.007151 -1.029e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006966 0.0006499 0.004324 0.003078 0.9889 0.9919 0.007103 0.8502 0.8915 0.01131 ] Network output: [ -0.0001263 0.001135 1 -4.988e-06 2.239e-06 0.9987 -3.759e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2251 0.1071 0.3511 0.1413 0.9849 0.9939 0.2259 0.4316 0.8746 0.7008 ] Network output: [ 0.002432 -0.01177 0.9946 3.05e-06 -1.369e-06 1.012 2.298e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09972 0.1851 0.1966 0.9873 0.9919 0.1126 0.7315 0.8605 0.3047 ] Network output: [ -0.002301 0.01094 1.005 3.354e-06 -1.506e-06 0.9891 2.527e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09501 0.09307 0.1649 0.1969 0.9852 0.9911 0.09503 0.6552 0.8354 0.2501 ] Network output: [ 7.545e-05 1 -5.172e-05 4.376e-07 -1.965e-07 0.9998 3.298e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001371 Epoch 10118 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00852 0.9969 0.9929 -1.306e-07 5.864e-08 -0.006797 -9.844e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003385 -0.006453 0.005238 0.9699 0.9743 0.006903 0.823 0.819 0.01589 ] Network output: [ 0.9999 4.978e-05 0.0003116 -1.585e-06 7.115e-07 -0.0002555 -1.194e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2109 -0.03601 -0.1535 0.1812 0.9834 0.9932 0.237 0.4276 0.8678 0.7072 ] Network output: [ -0.008545 1.003 1.007 -1.365e-07 6.126e-08 0.00715 -1.028e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006966 0.0006499 0.004324 0.003078 0.9889 0.9919 0.007104 0.8502 0.8915 0.01131 ] Network output: [ -0.0001261 0.001135 1 -4.981e-06 2.236e-06 0.9987 -3.754e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2251 0.1071 0.3511 0.1413 0.9849 0.9939 0.2259 0.4316 0.8746 0.7008 ] Network output: [ 0.002431 -0.01176 0.9946 3.046e-06 -1.367e-06 1.012 2.295e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09973 0.1851 0.1966 0.9873 0.9919 0.1126 0.7315 0.8605 0.3047 ] Network output: [ -0.0023 0.01093 1.005 3.35e-06 -1.504e-06 0.9891 2.524e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09501 0.09307 0.1649 0.1969 0.9852 0.9911 0.09503 0.6552 0.8354 0.2501 ] Network output: [ 7.543e-05 1 -5.174e-05 4.371e-07 -1.962e-07 0.9998 3.294e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000137 Epoch 10119 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008519 0.9969 0.9929 -1.305e-07 5.859e-08 -0.006797 -9.836e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003385 -0.006452 0.005237 0.9699 0.9743 0.006903 0.823 0.819 0.01589 ] Network output: [ 0.9999 4.965e-05 0.0003115 -1.583e-06 7.106e-07 -0.0002553 -1.193e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2109 -0.03601 -0.1534 0.1812 0.9834 0.9932 0.237 0.4276 0.8678 0.7072 ] Network output: [ -0.008544 1.003 1.007 -1.363e-07 6.121e-08 0.00715 -1.028e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006966 0.00065 0.004324 0.003077 0.9889 0.9919 0.007104 0.8502 0.8915 0.01131 ] Network output: [ -0.000126 0.001134 1 -4.975e-06 2.234e-06 0.9987 -3.749e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2251 0.1071 0.3511 0.1413 0.9849 0.9939 0.2259 0.4316 0.8746 0.7008 ] Network output: [ 0.00243 -0.01176 0.9946 3.042e-06 -1.366e-06 1.012 2.293e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09973 0.1851 0.1966 0.9873 0.9919 0.1126 0.7315 0.8605 0.3047 ] Network output: [ -0.002299 0.01093 1.005 3.346e-06 -1.502e-06 0.9891 2.521e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09501 0.09307 0.1649 0.1969 0.9852 0.9911 0.09503 0.6552 0.8354 0.2501 ] Network output: [ 7.542e-05 1 -5.175e-05 4.365e-07 -1.96e-07 0.9998 3.29e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001369 Epoch 10120 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008518 0.9969 0.9929 -1.304e-07 5.855e-08 -0.006796 -9.828e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003385 -0.006452 0.005237 0.9699 0.9743 0.006903 0.823 0.819 0.01589 ] Network output: [ 0.9999 4.951e-05 0.0003114 -1.581e-06 7.098e-07 -0.0002552 -1.191e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2109 -0.03601 -0.1534 0.1812 0.9834 0.9932 0.237 0.4276 0.8678 0.7072 ] Network output: [ -0.008543 1.003 1.007 -1.362e-07 6.116e-08 0.007149 -1.027e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006966 0.00065 0.004324 0.003077 0.9889 0.9919 0.007104 0.8501 0.8915 0.01131 ] Network output: [ -0.0001259 0.001133 1 -4.969e-06 2.231e-06 0.9987 -3.745e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2251 0.1071 0.3511 0.1413 0.9849 0.9939 0.2259 0.4316 0.8746 0.7008 ] Network output: [ 0.002428 -0.01175 0.9946 3.038e-06 -1.364e-06 1.012 2.29e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09973 0.1851 0.1966 0.9873 0.9919 0.1126 0.7315 0.8605 0.3047 ] Network output: [ -0.002297 0.01092 1.005 3.341e-06 -1.5e-06 0.9891 2.518e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09502 0.09307 0.1649 0.1969 0.9852 0.9911 0.09503 0.6552 0.8354 0.2501 ] Network output: [ 7.54e-05 1 -5.177e-05 4.36e-07 -1.957e-07 0.9998 3.286e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001369 Epoch 10121 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008518 0.9969 0.9929 -1.303e-07 5.85e-08 -0.006795 -9.821e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003385 -0.006451 0.005237 0.9699 0.9743 0.006903 0.823 0.819 0.01589 ] Network output: [ 0.9999 4.938e-05 0.0003112 -1.579e-06 7.089e-07 -0.000255 -1.19e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.03601 -0.1534 0.1812 0.9834 0.9932 0.237 0.4276 0.8678 0.7072 ] Network output: [ -0.008543 1.003 1.007 -1.361e-07 6.11e-08 0.007149 -1.026e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006967 0.0006501 0.004324 0.003077 0.9889 0.9919 0.007104 0.8501 0.8915 0.01131 ] Network output: [ -0.0001257 0.001132 1 -4.963e-06 2.228e-06 0.9987 -3.74e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2251 0.1071 0.3511 0.1413 0.9849 0.9939 0.2259 0.4316 0.8746 0.7008 ] Network output: [ 0.002427 -0.01174 0.9946 3.034e-06 -1.362e-06 1.012 2.287e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09973 0.1851 0.1966 0.9873 0.9919 0.1126 0.7315 0.8605 0.3047 ] Network output: [ -0.002296 0.01091 1.005 3.337e-06 -1.498e-06 0.9891 2.515e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09502 0.09307 0.1649 0.1969 0.9852 0.9911 0.09503 0.6552 0.8354 0.2501 ] Network output: [ 7.539e-05 1 -5.178e-05 4.355e-07 -1.955e-07 0.9998 3.282e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001368 Epoch 10122 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008517 0.9969 0.9929 -1.302e-07 5.846e-08 -0.006795 -9.813e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003386 -0.006451 0.005236 0.9699 0.9743 0.006903 0.823 0.819 0.01589 ] Network output: [ 0.9999 4.925e-05 0.0003111 -1.577e-06 7.08e-07 -0.0002549 -1.189e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.03601 -0.1534 0.1812 0.9834 0.9932 0.237 0.4276 0.8678 0.7072 ] Network output: [ -0.008542 1.003 1.007 -1.36e-07 6.105e-08 0.007148 -1.025e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006967 0.0006501 0.004324 0.003077 0.9889 0.9919 0.007105 0.8501 0.8915 0.01131 ] Network output: [ -0.0001256 0.001132 1 -4.957e-06 2.225e-06 0.9987 -3.735e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2251 0.1071 0.3511 0.1413 0.9849 0.9939 0.2259 0.4316 0.8746 0.7008 ] Network output: [ 0.002425 -0.01174 0.9946 3.031e-06 -1.361e-06 1.012 2.284e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09973 0.1851 0.1966 0.9873 0.9919 0.1126 0.7315 0.8605 0.3047 ] Network output: [ -0.002295 0.01091 1.005 3.333e-06 -1.496e-06 0.9891 2.512e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09502 0.09308 0.1649 0.1969 0.9852 0.9911 0.09503 0.6552 0.8354 0.2501 ] Network output: [ 7.537e-05 1 -5.18e-05 4.349e-07 -1.953e-07 0.9998 3.278e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001367 Epoch 10123 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008516 0.9969 0.9929 -1.301e-07 5.841e-08 -0.006794 -9.805e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003386 -0.00645 0.005236 0.9699 0.9743 0.006903 0.823 0.8189 0.01588 ] Network output: [ 0.9999 4.911e-05 0.0003109 -1.575e-06 7.071e-07 -0.0002548 -1.187e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.03601 -0.1534 0.1812 0.9834 0.9932 0.237 0.4276 0.8678 0.7072 ] Network output: [ -0.008541 1.003 1.007 -1.359e-07 6.099e-08 0.007148 -1.024e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006967 0.0006501 0.004324 0.003077 0.9889 0.9919 0.007105 0.8501 0.8915 0.01131 ] Network output: [ -0.0001255 0.001131 1 -4.95e-06 2.222e-06 0.9987 -3.731e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2251 0.1071 0.3511 0.1413 0.9849 0.9939 0.2259 0.4315 0.8746 0.7008 ] Network output: [ 0.002424 -0.01173 0.9946 3.027e-06 -1.359e-06 1.012 2.281e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09974 0.1851 0.1966 0.9873 0.9919 0.1126 0.7315 0.8605 0.3047 ] Network output: [ -0.002294 0.0109 1.005 3.329e-06 -1.495e-06 0.9891 2.509e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09502 0.09308 0.1649 0.1969 0.9852 0.9911 0.09503 0.6552 0.8354 0.2501 ] Network output: [ 7.536e-05 1 -5.182e-05 4.344e-07 -1.95e-07 0.9998 3.274e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001366 Epoch 10124 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008515 0.9969 0.9929 -1.3e-07 5.836e-08 -0.006793 -9.798e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003386 -0.00645 0.005236 0.9699 0.9743 0.006903 0.823 0.8189 0.01588 ] Network output: [ 0.9999 4.898e-05 0.0003108 -1.573e-06 7.062e-07 -0.0002546 -1.186e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.03602 -0.1534 0.1812 0.9834 0.9932 0.237 0.4276 0.8678 0.7072 ] Network output: [ -0.00854 1.003 1.007 -1.357e-07 6.094e-08 0.007147 -1.023e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006968 0.0006502 0.004324 0.003076 0.9889 0.9919 0.007105 0.8501 0.8915 0.01131 ] Network output: [ -0.0001253 0.00113 1 -4.944e-06 2.22e-06 0.9987 -3.726e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2251 0.1071 0.3511 0.1413 0.9849 0.9939 0.2259 0.4315 0.8746 0.7008 ] Network output: [ 0.002423 -0.01172 0.9946 3.023e-06 -1.357e-06 1.012 2.278e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09974 0.1851 0.1966 0.9873 0.9919 0.1126 0.7315 0.8605 0.3047 ] Network output: [ -0.002292 0.0109 1.005 3.325e-06 -1.493e-06 0.9891 2.506e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09502 0.09308 0.1649 0.1969 0.9852 0.9911 0.09504 0.6552 0.8354 0.2501 ] Network output: [ 7.534e-05 1 -5.183e-05 4.339e-07 -1.948e-07 0.9998 3.27e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001366 Epoch 10125 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008514 0.9969 0.9929 -1.299e-07 5.832e-08 -0.006793 -9.79e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003386 -0.006449 0.005235 0.9699 0.9743 0.006904 0.823 0.8189 0.01588 ] Network output: [ 0.9999 4.885e-05 0.0003107 -1.571e-06 7.054e-07 -0.0002545 -1.184e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.03602 -0.1534 0.1812 0.9834 0.9932 0.237 0.4276 0.8678 0.7072 ] Network output: [ -0.00854 1.003 1.007 -1.356e-07 6.088e-08 0.007147 -1.022e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006968 0.0006502 0.004324 0.003076 0.9889 0.9919 0.007106 0.8501 0.8915 0.0113 ] Network output: [ -0.0001252 0.00113 1 -4.938e-06 2.217e-06 0.9987 -3.722e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2252 0.1071 0.3511 0.1413 0.9849 0.9939 0.2259 0.4315 0.8746 0.7008 ] Network output: [ 0.002421 -0.01172 0.9946 3.02e-06 -1.356e-06 1.012 2.276e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09974 0.1851 0.1966 0.9873 0.9919 0.1126 0.7315 0.8605 0.3047 ] Network output: [ -0.002291 0.01089 1.005 3.321e-06 -1.491e-06 0.9891 2.503e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09502 0.09308 0.1649 0.1969 0.9852 0.9911 0.09504 0.6552 0.8354 0.2501 ] Network output: [ 7.533e-05 1 -5.185e-05 4.333e-07 -1.945e-07 0.9998 3.266e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001365 Epoch 10126 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008514 0.9969 0.9929 -1.298e-07 5.827e-08 -0.006792 -9.782e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003386 -0.006449 0.005235 0.9699 0.9743 0.006904 0.823 0.8189 0.01588 ] Network output: [ 0.9999 4.871e-05 0.0003105 -1.569e-06 7.045e-07 -0.0002543 -1.183e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.03602 -0.1534 0.1812 0.9834 0.9932 0.237 0.4276 0.8678 0.7072 ] Network output: [ -0.008539 1.003 1.007 -1.355e-07 6.083e-08 0.007146 -1.021e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006968 0.0006503 0.004323 0.003076 0.9889 0.9919 0.007106 0.8501 0.8915 0.0113 ] Network output: [ -0.0001251 0.001129 1 -4.932e-06 2.214e-06 0.9987 -3.717e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2252 0.1071 0.3511 0.1413 0.9849 0.9939 0.2259 0.4315 0.8746 0.7008 ] Network output: [ 0.00242 -0.01171 0.9946 3.016e-06 -1.354e-06 1.012 2.273e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09974 0.1851 0.1966 0.9873 0.9919 0.1126 0.7315 0.8605 0.3047 ] Network output: [ -0.00229 0.01089 1.005 3.317e-06 -1.489e-06 0.9891 2.5e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09502 0.09308 0.1649 0.1969 0.9852 0.9911 0.09504 0.6552 0.8354 0.2501 ] Network output: [ 7.531e-05 1 -5.187e-05 4.328e-07 -1.943e-07 0.9998 3.262e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001364 Epoch 10127 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008513 0.9969 0.9929 -1.297e-07 5.823e-08 -0.006791 -9.774e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003386 -0.006448 0.005235 0.9699 0.9743 0.006904 0.823 0.8189 0.01588 ] Network output: [ 0.9999 4.858e-05 0.0003104 -1.567e-06 7.036e-07 -0.0002542 -1.181e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.03602 -0.1534 0.1812 0.9834 0.9932 0.237 0.4276 0.8678 0.7072 ] Network output: [ -0.008538 1.003 1.007 -1.354e-07 6.077e-08 0.007146 -1.02e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006968 0.0006503 0.004323 0.003076 0.9889 0.9919 0.007106 0.8501 0.8915 0.0113 ] Network output: [ -0.0001249 0.001128 1 -4.926e-06 2.211e-06 0.9987 -3.712e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2252 0.1071 0.3511 0.1413 0.9849 0.9939 0.2259 0.4315 0.8746 0.7008 ] Network output: [ 0.002418 -0.01171 0.9946 3.012e-06 -1.352e-06 1.012 2.27e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09974 0.1851 0.1966 0.9873 0.9919 0.1126 0.7315 0.8605 0.3047 ] Network output: [ -0.002288 0.01088 1.005 3.313e-06 -1.487e-06 0.9891 2.497e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09503 0.09308 0.1649 0.1969 0.9852 0.9911 0.09504 0.6552 0.8354 0.2501 ] Network output: [ 7.53e-05 1 -5.188e-05 4.323e-07 -1.941e-07 0.9998 3.258e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001363 Epoch 10128 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008512 0.9969 0.9929 -1.296e-07 5.818e-08 -0.006791 -9.767e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003386 -0.006448 0.005234 0.9699 0.9743 0.006904 0.823 0.8189 0.01588 ] Network output: [ 0.9999 4.845e-05 0.0003103 -1.565e-06 7.027e-07 -0.0002541 -1.18e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.03602 -0.1534 0.1812 0.9834 0.9932 0.237 0.4276 0.8678 0.7072 ] Network output: [ -0.008537 1.003 1.007 -1.353e-07 6.072e-08 0.007145 -1.019e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006969 0.0006503 0.004323 0.003075 0.9889 0.9919 0.007107 0.8501 0.8915 0.0113 ] Network output: [ -0.0001248 0.001128 1 -4.92e-06 2.209e-06 0.9987 -3.708e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2252 0.1071 0.3511 0.1413 0.9849 0.9939 0.2259 0.4315 0.8746 0.7008 ] Network output: [ 0.002417 -0.0117 0.9946 3.008e-06 -1.351e-06 1.012 2.267e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09975 0.1851 0.1966 0.9873 0.9919 0.1126 0.7314 0.8605 0.3047 ] Network output: [ -0.002287 0.01088 1.005 3.309e-06 -1.486e-06 0.9891 2.494e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09503 0.09308 0.1649 0.1969 0.9852 0.9911 0.09504 0.6551 0.8354 0.2501 ] Network output: [ 7.528e-05 1 -5.19e-05 4.317e-07 -1.938e-07 0.9998 3.254e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001362 Epoch 10129 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008511 0.9969 0.9929 -1.295e-07 5.813e-08 -0.00679 -9.759e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003386 -0.006447 0.005234 0.9699 0.9743 0.006904 0.823 0.8189 0.01588 ] Network output: [ 0.9999 4.831e-05 0.0003101 -1.563e-06 7.018e-07 -0.0002539 -1.178e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.03602 -0.1534 0.1812 0.9834 0.9932 0.2371 0.4276 0.8678 0.7072 ] Network output: [ -0.008537 1.003 1.007 -1.351e-07 6.067e-08 0.007145 -1.018e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006969 0.0006504 0.004323 0.003075 0.9889 0.9919 0.007107 0.8501 0.8915 0.0113 ] Network output: [ -0.0001247 0.001127 1 -4.914e-06 2.206e-06 0.9987 -3.703e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2252 0.1071 0.3511 0.1413 0.9849 0.9939 0.226 0.4315 0.8746 0.7008 ] Network output: [ 0.002415 -0.01169 0.9946 3.005e-06 -1.349e-06 1.012 2.264e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09975 0.1851 0.1966 0.9873 0.9919 0.1126 0.7314 0.8605 0.3047 ] Network output: [ -0.002286 0.01087 1.005 3.305e-06 -1.484e-06 0.9891 2.491e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09503 0.09309 0.1649 0.1969 0.9852 0.9911 0.09504 0.6551 0.8354 0.2501 ] Network output: [ 7.527e-05 1 -5.192e-05 4.312e-07 -1.936e-07 0.9998 3.25e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001362 Epoch 10130 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00851 0.9969 0.9929 -1.294e-07 5.809e-08 -0.006789 -9.751e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003386 -0.006446 0.005233 0.9699 0.9743 0.006904 0.823 0.8189 0.01588 ] Network output: [ 0.9999 4.818e-05 0.00031 -1.561e-06 7.01e-07 -0.0002538 -1.177e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.03602 -0.1534 0.1812 0.9834 0.9932 0.2371 0.4276 0.8678 0.7072 ] Network output: [ -0.008536 1.003 1.007 -1.35e-07 6.061e-08 0.007145 -1.017e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006969 0.0006504 0.004323 0.003075 0.9889 0.9919 0.007107 0.8501 0.8915 0.0113 ] Network output: [ -0.0001245 0.001126 1 -4.908e-06 2.203e-06 0.9987 -3.698e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2252 0.1071 0.3511 0.1413 0.9849 0.9939 0.226 0.4315 0.8746 0.7008 ] Network output: [ 0.002414 -0.01169 0.9946 3.001e-06 -1.347e-06 1.012 2.262e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09975 0.1851 0.1966 0.9873 0.9919 0.1126 0.7314 0.8605 0.3047 ] Network output: [ -0.002285 0.01086 1.005 3.301e-06 -1.482e-06 0.9891 2.488e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09503 0.09309 0.1649 0.1969 0.9852 0.9911 0.09504 0.6551 0.8354 0.2501 ] Network output: [ 7.525e-05 1 -5.194e-05 4.307e-07 -1.933e-07 0.9998 3.246e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001361 Epoch 10131 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00851 0.9969 0.9929 -1.293e-07 5.804e-08 -0.006789 -9.744e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003386 -0.006446 0.005233 0.9699 0.9743 0.006904 0.823 0.8189 0.01588 ] Network output: [ 0.9999 4.805e-05 0.0003099 -1.559e-06 7.001e-07 -0.0002536 -1.175e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.03602 -0.1534 0.1812 0.9834 0.9932 0.2371 0.4276 0.8678 0.7072 ] Network output: [ -0.008535 1.003 1.007 -1.349e-07 6.056e-08 0.007144 -1.017e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00697 0.0006505 0.004323 0.003075 0.9889 0.9919 0.007107 0.8501 0.8915 0.0113 ] Network output: [ -0.0001244 0.001126 1 -4.901e-06 2.2e-06 0.9987 -3.694e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2252 0.1071 0.3511 0.1413 0.9849 0.9939 0.226 0.4315 0.8746 0.7008 ] Network output: [ 0.002413 -0.01168 0.9946 2.997e-06 -1.346e-06 1.012 2.259e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09975 0.1851 0.1966 0.9873 0.9919 0.1126 0.7314 0.8605 0.3047 ] Network output: [ -0.002283 0.01086 1.005 3.297e-06 -1.48e-06 0.9891 2.485e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09503 0.09309 0.1649 0.1969 0.9852 0.9911 0.09505 0.6551 0.8354 0.2501 ] Network output: [ 7.524e-05 1 -5.195e-05 4.301e-07 -1.931e-07 0.9998 3.242e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000136 Epoch 10132 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008509 0.9969 0.9929 -1.292e-07 5.8e-08 -0.006788 -9.736e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003386 -0.006445 0.005233 0.9699 0.9743 0.006905 0.823 0.8189 0.01588 ] Network output: [ 0.9999 4.791e-05 0.0003097 -1.558e-06 6.992e-07 -0.0002535 -1.174e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.03602 -0.1534 0.1812 0.9834 0.9932 0.2371 0.4276 0.8678 0.7072 ] Network output: [ -0.008534 1.003 1.007 -1.348e-07 6.05e-08 0.007144 -1.016e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00697 0.0006505 0.004323 0.003075 0.9889 0.9919 0.007108 0.8501 0.8915 0.0113 ] Network output: [ -0.0001243 0.001125 1 -4.895e-06 2.198e-06 0.9987 -3.689e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2252 0.1071 0.3511 0.1413 0.9849 0.9939 0.226 0.4315 0.8746 0.7008 ] Network output: [ 0.002411 -0.01167 0.9946 2.993e-06 -1.344e-06 1.012 2.256e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09975 0.1851 0.1966 0.9873 0.9919 0.1126 0.7314 0.8605 0.3047 ] Network output: [ -0.002282 0.01085 1.005 3.293e-06 -1.478e-06 0.9891 2.482e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09503 0.09309 0.1649 0.1969 0.9852 0.9911 0.09505 0.6551 0.8354 0.2501 ] Network output: [ 7.522e-05 1 -5.197e-05 4.296e-07 -1.929e-07 0.9998 3.238e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001359 Epoch 10133 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008508 0.9969 0.9929 -1.291e-07 5.795e-08 -0.006787 -9.728e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003386 -0.006445 0.005232 0.9699 0.9743 0.006905 0.823 0.8189 0.01588 ] Network output: [ 0.9999 4.778e-05 0.0003096 -1.556e-06 6.984e-07 -0.0002534 -1.172e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.03603 -0.1533 0.1812 0.9834 0.9932 0.2371 0.4276 0.8678 0.7072 ] Network output: [ -0.008533 1.003 1.007 -1.346e-07 6.045e-08 0.007143 -1.015e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00697 0.0006505 0.004323 0.003074 0.9889 0.9919 0.007108 0.8501 0.8914 0.0113 ] Network output: [ -0.0001241 0.001124 1 -4.889e-06 2.195e-06 0.9987 -3.685e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2252 0.1071 0.3511 0.1413 0.9849 0.9939 0.226 0.4315 0.8746 0.7008 ] Network output: [ 0.00241 -0.01167 0.9946 2.99e-06 -1.342e-06 1.012 2.253e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09976 0.1851 0.1966 0.9873 0.9919 0.1126 0.7314 0.8604 0.3047 ] Network output: [ -0.002281 0.01085 1.005 3.289e-06 -1.476e-06 0.9891 2.479e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09503 0.09309 0.1649 0.1969 0.9852 0.9911 0.09505 0.6551 0.8353 0.2501 ] Network output: [ 7.521e-05 1 -5.199e-05 4.291e-07 -1.926e-07 0.9998 3.234e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001359 Epoch 10134 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008507 0.9969 0.9929 -1.29e-07 5.791e-08 -0.006787 -9.721e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003386 -0.006444 0.005232 0.9699 0.9743 0.006905 0.823 0.8189 0.01588 ] Network output: [ 0.9999 4.765e-05 0.0003095 -1.554e-06 6.975e-07 -0.0002532 -1.171e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.03603 -0.1533 0.1811 0.9834 0.9932 0.2371 0.4276 0.8678 0.7072 ] Network output: [ -0.008533 1.003 1.007 -1.345e-07 6.04e-08 0.007143 -1.014e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006971 0.0006506 0.004323 0.003074 0.9889 0.9919 0.007108 0.8501 0.8914 0.0113 ] Network output: [ -0.000124 0.001124 1 -4.883e-06 2.192e-06 0.9987 -3.68e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2252 0.1071 0.3511 0.1413 0.9849 0.9939 0.226 0.4315 0.8746 0.7008 ] Network output: [ 0.002408 -0.01166 0.9946 2.986e-06 -1.341e-06 1.012 2.25e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09976 0.1851 0.1966 0.9873 0.9919 0.1126 0.7314 0.8604 0.3047 ] Network output: [ -0.00228 0.01084 1.005 3.285e-06 -1.475e-06 0.9891 2.476e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09503 0.09309 0.1649 0.1969 0.9852 0.9911 0.09505 0.6551 0.8353 0.2501 ] Network output: [ 7.52e-05 1 -5.2e-05 4.286e-07 -1.924e-07 0.9998 3.23e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001358 Epoch 10135 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008506 0.9969 0.9929 -1.289e-07 5.786e-08 -0.006786 -9.713e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003386 -0.006444 0.005232 0.9699 0.9743 0.006905 0.823 0.8189 0.01587 ] Network output: [ 0.9999 4.752e-05 0.0003093 -1.552e-06 6.966e-07 -0.0002531 -1.169e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.03603 -0.1533 0.1811 0.9834 0.9932 0.2371 0.4276 0.8678 0.7072 ] Network output: [ -0.008532 1.003 1.007 -1.344e-07 6.034e-08 0.007142 -1.013e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006971 0.0006506 0.004322 0.003074 0.9889 0.9919 0.007109 0.8501 0.8914 0.0113 ] Network output: [ -0.0001239 0.001123 1 -4.877e-06 2.189e-06 0.9987 -3.676e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2252 0.1071 0.3511 0.1412 0.9849 0.9939 0.226 0.4315 0.8746 0.7008 ] Network output: [ 0.002407 -0.01165 0.9946 2.982e-06 -1.339e-06 1.012 2.248e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09976 0.1851 0.1966 0.9873 0.9919 0.1126 0.7314 0.8604 0.3047 ] Network output: [ -0.002278 0.01084 1.005 3.281e-06 -1.473e-06 0.9891 2.473e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09504 0.09309 0.1649 0.1969 0.9852 0.9911 0.09505 0.6551 0.8353 0.2501 ] Network output: [ 7.518e-05 1 -5.202e-05 4.28e-07 -1.922e-07 0.9998 3.226e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001357 Epoch 10136 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008505 0.9969 0.9929 -1.288e-07 5.781e-08 -0.006785 -9.705e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003386 -0.006443 0.005231 0.9699 0.9743 0.006905 0.823 0.8189 0.01587 ] Network output: [ 0.9999 4.738e-05 0.0003092 -1.55e-06 6.958e-07 -0.000253 -1.168e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.03603 -0.1533 0.1811 0.9834 0.9932 0.2371 0.4276 0.8678 0.7072 ] Network output: [ -0.008531 1.003 1.007 -1.343e-07 6.029e-08 0.007142 -1.012e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006971 0.0006507 0.004322 0.003074 0.9889 0.9919 0.007109 0.8501 0.8914 0.0113 ] Network output: [ -0.0001237 0.001122 1 -4.871e-06 2.187e-06 0.9987 -3.671e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2252 0.1071 0.3511 0.1412 0.9849 0.9939 0.226 0.4315 0.8746 0.7008 ] Network output: [ 0.002406 -0.01165 0.9946 2.979e-06 -1.337e-06 1.012 2.245e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09976 0.1851 0.1966 0.9873 0.9919 0.1126 0.7314 0.8604 0.3047 ] Network output: [ -0.002277 0.01083 1.005 3.277e-06 -1.471e-06 0.9891 2.469e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09504 0.09309 0.1649 0.1969 0.9852 0.9911 0.09505 0.6551 0.8353 0.2501 ] Network output: [ 7.517e-05 1 -5.204e-05 4.275e-07 -1.919e-07 0.9998 3.222e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001356 Epoch 10137 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008505 0.9969 0.9929 -1.287e-07 5.777e-08 -0.006784 -9.698e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003386 -0.006443 0.005231 0.9699 0.9743 0.006905 0.823 0.8189 0.01587 ] Network output: [ 0.9999 4.725e-05 0.0003091 -1.548e-06 6.949e-07 -0.0002528 -1.167e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.03603 -0.1533 0.1811 0.9834 0.9932 0.2371 0.4276 0.8678 0.7072 ] Network output: [ -0.00853 1.003 1.007 -1.342e-07 6.023e-08 0.007141 -1.011e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006971 0.0006507 0.004322 0.003074 0.9889 0.9919 0.007109 0.8501 0.8914 0.0113 ] Network output: [ -0.0001236 0.001122 1 -4.865e-06 2.184e-06 0.9987 -3.666e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2252 0.1071 0.3512 0.1412 0.9849 0.9939 0.226 0.4315 0.8746 0.7008 ] Network output: [ 0.002404 -0.01164 0.9946 2.975e-06 -1.336e-06 1.012 2.242e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09976 0.1851 0.1966 0.9873 0.9919 0.1126 0.7314 0.8604 0.3047 ] Network output: [ -0.002276 0.01083 1.005 3.273e-06 -1.469e-06 0.9891 2.466e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09504 0.0931 0.1649 0.1969 0.9852 0.9911 0.09505 0.6551 0.8353 0.2501 ] Network output: [ 7.515e-05 1 -5.206e-05 4.27e-07 -1.917e-07 0.9998 3.218e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001355 Epoch 10138 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008504 0.9969 0.9929 -1.286e-07 5.772e-08 -0.006784 -9.69e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003387 -0.006442 0.005231 0.9699 0.9743 0.006905 0.8229 0.8189 0.01587 ] Network output: [ 0.9999 4.712e-05 0.0003089 -1.546e-06 6.94e-07 -0.0002527 -1.165e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.03603 -0.1533 0.1811 0.9834 0.9932 0.2371 0.4276 0.8678 0.7072 ] Network output: [ -0.00853 1.003 1.007 -1.34e-07 6.018e-08 0.007141 -1.01e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006972 0.0006507 0.004322 0.003073 0.9889 0.9919 0.00711 0.8501 0.8914 0.0113 ] Network output: [ -0.0001235 0.001121 1 -4.859e-06 2.181e-06 0.9987 -3.662e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2252 0.1071 0.3512 0.1412 0.9849 0.9939 0.226 0.4315 0.8746 0.7008 ] Network output: [ 0.002403 -0.01164 0.9946 2.971e-06 -1.334e-06 1.012 2.239e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1125 0.09977 0.1851 0.1966 0.9873 0.9919 0.1126 0.7314 0.8604 0.3047 ] Network output: [ -0.002274 0.01082 1.005 3.269e-06 -1.467e-06 0.9891 2.463e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09504 0.0931 0.1649 0.1969 0.9852 0.9911 0.09505 0.6551 0.8353 0.2501 ] Network output: [ 7.514e-05 1 -5.207e-05 4.265e-07 -1.915e-07 0.9998 3.214e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001355 Epoch 10139 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008503 0.9969 0.9929 -1.285e-07 5.768e-08 -0.006783 -9.682e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003529 -0.003387 -0.006442 0.00523 0.9699 0.9743 0.006905 0.8229 0.8189 0.01587 ] Network output: [ 0.9999 4.699e-05 0.0003088 -1.544e-06 6.932e-07 -0.0002525 -1.164e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.03603 -0.1533 0.1811 0.9834 0.9932 0.2371 0.4276 0.8678 0.7072 ] Network output: [ -0.008529 1.003 1.007 -1.339e-07 6.013e-08 0.00714 -1.009e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006972 0.0006508 0.004322 0.003073 0.9889 0.9919 0.00711 0.8501 0.8914 0.0113 ] Network output: [ -0.0001233 0.00112 1 -4.853e-06 2.179e-06 0.9987 -3.657e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2252 0.1071 0.3512 0.1412 0.9849 0.9939 0.226 0.4315 0.8746 0.7008 ] Network output: [ 0.002401 -0.01163 0.9946 2.968e-06 -1.332e-06 1.012 2.236e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09977 0.1851 0.1966 0.9873 0.9919 0.1126 0.7314 0.8604 0.3047 ] Network output: [ -0.002273 0.01081 1.005 3.265e-06 -1.466e-06 0.9891 2.46e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09504 0.0931 0.1649 0.1969 0.9852 0.9911 0.09506 0.6551 0.8353 0.2501 ] Network output: [ 7.512e-05 1 -5.209e-05 4.259e-07 -1.912e-07 0.9998 3.21e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001354 Epoch 10140 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008502 0.9969 0.9929 -1.284e-07 5.763e-08 -0.006782 -9.675e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003387 -0.006441 0.00523 0.9699 0.9743 0.006906 0.8229 0.8189 0.01587 ] Network output: [ 0.9999 4.685e-05 0.0003087 -1.542e-06 6.923e-07 -0.0002524 -1.162e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.03603 -0.1533 0.1811 0.9834 0.9932 0.2371 0.4276 0.8678 0.7072 ] Network output: [ -0.008528 1.003 1.007 -1.338e-07 6.007e-08 0.00714 -1.008e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006972 0.0006508 0.004322 0.003073 0.9889 0.9919 0.00711 0.8501 0.8914 0.01129 ] Network output: [ -0.0001232 0.001119 1 -4.847e-06 2.176e-06 0.9987 -3.653e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2252 0.1071 0.3512 0.1412 0.9849 0.9939 0.226 0.4315 0.8746 0.7008 ] Network output: [ 0.0024 -0.01162 0.9946 2.964e-06 -1.331e-06 1.012 2.234e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09977 0.1851 0.1966 0.9873 0.9919 0.1126 0.7313 0.8604 0.3047 ] Network output: [ -0.002272 0.01081 1.005 3.261e-06 -1.464e-06 0.9891 2.457e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09504 0.0931 0.1649 0.1969 0.9852 0.9911 0.09506 0.655 0.8353 0.2501 ] Network output: [ 7.511e-05 1 -5.211e-05 4.254e-07 -1.91e-07 0.9998 3.206e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001353 Epoch 10141 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008501 0.9969 0.9929 -1.283e-07 5.759e-08 -0.006782 -9.667e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003387 -0.006441 0.00523 0.9699 0.9743 0.006906 0.8229 0.8189 0.01587 ] Network output: [ 0.9999 4.672e-05 0.0003085 -1.54e-06 6.914e-07 -0.0002523 -1.161e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.211 -0.03603 -0.1533 0.1811 0.9834 0.9932 0.2371 0.4276 0.8678 0.7072 ] Network output: [ -0.008527 1.003 1.007 -1.337e-07 6.002e-08 0.00714 -1.008e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006973 0.0006509 0.004322 0.003073 0.9889 0.9919 0.00711 0.8501 0.8914 0.01129 ] Network output: [ -0.0001231 0.001119 1 -4.841e-06 2.173e-06 0.9987 -3.648e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2252 0.1071 0.3512 0.1412 0.9849 0.9939 0.226 0.4315 0.8746 0.7008 ] Network output: [ 0.002398 -0.01162 0.9946 2.96e-06 -1.329e-06 1.012 2.231e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09977 0.1851 0.1966 0.9873 0.9919 0.1126 0.7313 0.8604 0.3047 ] Network output: [ -0.002271 0.0108 1.005 3.257e-06 -1.462e-06 0.9891 2.454e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09504 0.0931 0.1649 0.1969 0.9852 0.9911 0.09506 0.655 0.8353 0.2502 ] Network output: [ 7.509e-05 1 -5.213e-05 4.249e-07 -1.908e-07 0.9998 3.202e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001352 Epoch 10142 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008501 0.9969 0.9929 -1.282e-07 5.754e-08 -0.006781 -9.659e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003387 -0.00644 0.005229 0.9699 0.9743 0.006906 0.8229 0.8189 0.01587 ] Network output: [ 0.9999 4.659e-05 0.0003084 -1.538e-06 6.906e-07 -0.0002521 -1.159e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2111 -0.03603 -0.1533 0.1811 0.9834 0.9932 0.2371 0.4276 0.8678 0.7072 ] Network output: [ -0.008527 1.003 1.007 -1.336e-07 5.996e-08 0.007139 -1.007e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006973 0.0006509 0.004322 0.003072 0.9889 0.9919 0.007111 0.8501 0.8914 0.01129 ] Network output: [ -0.0001229 0.001118 1 -4.835e-06 2.17e-06 0.9987 -3.644e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2253 0.1071 0.3512 0.1412 0.9849 0.9939 0.226 0.4315 0.8746 0.7008 ] Network output: [ 0.002397 -0.01161 0.9946 2.957e-06 -1.327e-06 1.012 2.228e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09977 0.1851 0.1966 0.9873 0.9919 0.1126 0.7313 0.8604 0.3047 ] Network output: [ -0.002269 0.0108 1.005 3.253e-06 -1.46e-06 0.9891 2.451e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09505 0.0931 0.1649 0.1969 0.9852 0.9911 0.09506 0.655 0.8353 0.2502 ] Network output: [ 7.508e-05 1 -5.214e-05 4.244e-07 -1.905e-07 0.9998 3.198e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001352 Epoch 10143 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0085 0.9969 0.9929 -1.281e-07 5.749e-08 -0.00678 -9.652e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003387 -0.00644 0.005229 0.9699 0.9743 0.006906 0.8229 0.8189 0.01587 ] Network output: [ 0.9999 4.646e-05 0.0003083 -1.536e-06 6.897e-07 -0.000252 -1.158e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2111 -0.03604 -0.1533 0.1811 0.9834 0.9932 0.2371 0.4276 0.8678 0.7072 ] Network output: [ -0.008526 1.003 1.007 -1.334e-07 5.991e-08 0.007139 -1.006e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006973 0.0006509 0.004322 0.003072 0.9889 0.9919 0.007111 0.8501 0.8914 0.01129 ] Network output: [ -0.0001228 0.001117 1 -4.829e-06 2.168e-06 0.9987 -3.639e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2253 0.1072 0.3512 0.1412 0.9849 0.9939 0.226 0.4315 0.8746 0.7008 ] Network output: [ 0.002396 -0.0116 0.9946 2.953e-06 -1.326e-06 1.012 2.225e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09978 0.1851 0.1966 0.9873 0.9919 0.1126 0.7313 0.8604 0.3047 ] Network output: [ -0.002268 0.01079 1.005 3.249e-06 -1.459e-06 0.9891 2.448e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09505 0.0931 0.1649 0.1969 0.9852 0.9911 0.09506 0.655 0.8353 0.2502 ] Network output: [ 7.506e-05 1 -5.216e-05 4.239e-07 -1.903e-07 0.9998 3.194e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001351 Epoch 10144 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008499 0.9969 0.9929 -1.28e-07 5.745e-08 -0.00678 -9.644e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003387 -0.006439 0.005229 0.9699 0.9743 0.006906 0.8229 0.8189 0.01587 ] Network output: [ 0.9999 4.632e-05 0.0003081 -1.534e-06 6.888e-07 -0.0002519 -1.156e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2111 -0.03604 -0.1533 0.1811 0.9834 0.9932 0.2371 0.4276 0.8678 0.7072 ] Network output: [ -0.008525 1.003 1.007 -1.333e-07 5.986e-08 0.007138 -1.005e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006973 0.000651 0.004321 0.003072 0.9889 0.9919 0.007111 0.8501 0.8914 0.01129 ] Network output: [ -0.0001227 0.001117 1 -4.823e-06 2.165e-06 0.9987 -3.635e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2253 0.1072 0.3512 0.1412 0.9849 0.9939 0.226 0.4315 0.8746 0.7008 ] Network output: [ 0.002394 -0.0116 0.9946 2.949e-06 -1.324e-06 1.012 2.223e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09978 0.1851 0.1966 0.9873 0.9919 0.1126 0.7313 0.8604 0.3047 ] Network output: [ -0.002267 0.01079 1.005 3.245e-06 -1.457e-06 0.9891 2.445e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09505 0.09311 0.1649 0.1969 0.9852 0.9911 0.09506 0.655 0.8353 0.2502 ] Network output: [ 7.505e-05 1 -5.218e-05 4.233e-07 -1.9e-07 0.9998 3.19e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000135 Epoch 10145 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008498 0.9969 0.9929 -1.279e-07 5.74e-08 -0.006779 -9.636e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003387 -0.006439 0.005228 0.9699 0.9743 0.006906 0.8229 0.8189 0.01587 ] Network output: [ 0.9999 4.619e-05 0.000308 -1.532e-06 6.88e-07 -0.0002517 -1.155e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2111 -0.03604 -0.1533 0.1811 0.9834 0.9932 0.2371 0.4275 0.8678 0.7072 ] Network output: [ -0.008524 1.003 1.007 -1.332e-07 5.98e-08 0.007138 -1.004e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006974 0.000651 0.004321 0.003072 0.9889 0.9919 0.007112 0.8501 0.8914 0.01129 ] Network output: [ -0.0001225 0.001116 1 -4.817e-06 2.162e-06 0.9987 -3.63e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2253 0.1072 0.3512 0.1412 0.9849 0.9939 0.226 0.4315 0.8746 0.7008 ] Network output: [ 0.002393 -0.01159 0.9946 2.946e-06 -1.322e-06 1.012 2.22e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09978 0.1851 0.1966 0.9873 0.9919 0.1126 0.7313 0.8604 0.3047 ] Network output: [ -0.002266 0.01078 1.005 3.241e-06 -1.455e-06 0.9891 2.442e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09505 0.09311 0.1649 0.1969 0.9852 0.9911 0.09506 0.655 0.8353 0.2502 ] Network output: [ 7.503e-05 1 -5.22e-05 4.228e-07 -1.898e-07 0.9998 3.186e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001349 Epoch 10146 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008497 0.9969 0.9929 -1.278e-07 5.736e-08 -0.006778 -9.629e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003387 -0.006438 0.005228 0.9699 0.9743 0.006906 0.8229 0.8189 0.01587 ] Network output: [ 0.9999 4.606e-05 0.0003079 -1.531e-06 6.871e-07 -0.0002516 -1.153e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2111 -0.03604 -0.1532 0.1811 0.9834 0.9932 0.2372 0.4275 0.8678 0.7072 ] Network output: [ -0.008523 1.003 1.007 -1.331e-07 5.975e-08 0.007137 -1.003e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006974 0.0006511 0.004321 0.003072 0.9889 0.9919 0.007112 0.8501 0.8914 0.01129 ] Network output: [ -0.0001224 0.001115 1 -4.811e-06 2.16e-06 0.9987 -3.625e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2253 0.1072 0.3512 0.1412 0.9849 0.9939 0.2261 0.4315 0.8746 0.7007 ] Network output: [ 0.002391 -0.01159 0.9946 2.942e-06 -1.321e-06 1.012 2.217e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09978 0.1851 0.1966 0.9873 0.9919 0.1126 0.7313 0.8604 0.3047 ] Network output: [ -0.002264 0.01078 1.005 3.237e-06 -1.453e-06 0.9891 2.439e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09505 0.09311 0.1649 0.1969 0.9852 0.9911 0.09507 0.655 0.8353 0.2502 ] Network output: [ 7.502e-05 1 -5.221e-05 4.223e-07 -1.896e-07 0.9998 3.183e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001348 Epoch 10147 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008496 0.9969 0.9929 -1.277e-07 5.731e-08 -0.006778 -9.621e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003387 -0.006438 0.005228 0.9699 0.9743 0.006906 0.8229 0.8189 0.01586 ] Network output: [ 0.9999 4.593e-05 0.0003077 -1.529e-06 6.863e-07 -0.0002515 -1.152e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2111 -0.03604 -0.1532 0.1811 0.9834 0.9932 0.2372 0.4275 0.8678 0.7072 ] Network output: [ -0.008523 1.003 1.007 -1.33e-07 5.97e-08 0.007137 -1.002e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006974 0.0006511 0.004321 0.003071 0.9889 0.9919 0.007112 0.85 0.8914 0.01129 ] Network output: [ -0.0001223 0.001115 1 -4.805e-06 2.157e-06 0.9987 -3.621e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2253 0.1072 0.3512 0.1412 0.9849 0.9939 0.2261 0.4315 0.8746 0.7007 ] Network output: [ 0.00239 -0.01158 0.9946 2.938e-06 -1.319e-06 1.012 2.214e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09978 0.1851 0.1966 0.9873 0.9919 0.1126 0.7313 0.8604 0.3047 ] Network output: [ -0.002263 0.01077 1.005 3.233e-06 -1.451e-06 0.9892 2.436e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09505 0.09311 0.1649 0.1969 0.9852 0.9911 0.09507 0.655 0.8353 0.2502 ] Network output: [ 7.5e-05 1 -5.223e-05 4.218e-07 -1.893e-07 0.9998 3.179e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001348 Epoch 10148 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008496 0.9969 0.9929 -1.276e-07 5.727e-08 -0.006777 -9.613e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003387 -0.006437 0.005227 0.9699 0.9743 0.006906 0.8229 0.8189 0.01586 ] Network output: [ 0.9999 4.58e-05 0.0003076 -1.527e-06 6.854e-07 -0.0002513 -1.151e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2111 -0.03604 -0.1532 0.1811 0.9834 0.9932 0.2372 0.4275 0.8678 0.7072 ] Network output: [ -0.008522 1.003 1.007 -1.329e-07 5.964e-08 0.007136 -1.001e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006975 0.0006511 0.004321 0.003071 0.9889 0.9919 0.007113 0.85 0.8914 0.01129 ] Network output: [ -0.0001221 0.001114 1 -4.799e-06 2.154e-06 0.9987 -3.616e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2253 0.1072 0.3512 0.1412 0.9849 0.9939 0.2261 0.4315 0.8746 0.7007 ] Network output: [ 0.002388 -0.01157 0.9946 2.935e-06 -1.317e-06 1.012 2.212e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09979 0.1851 0.1966 0.9873 0.9919 0.1127 0.7313 0.8604 0.3047 ] Network output: [ -0.002262 0.01076 1.005 3.229e-06 -1.45e-06 0.9892 2.434e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09505 0.09311 0.1649 0.1969 0.9852 0.9911 0.09507 0.655 0.8353 0.2502 ] Network output: [ 7.499e-05 1 -5.225e-05 4.213e-07 -1.891e-07 0.9998 3.175e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001347 Epoch 10149 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008495 0.9969 0.9929 -1.275e-07 5.722e-08 -0.006776 -9.606e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003387 -0.006437 0.005227 0.9699 0.9743 0.006907 0.8229 0.8189 0.01586 ] Network output: [ 0.9999 4.567e-05 0.0003075 -1.525e-06 6.846e-07 -0.0002512 -1.149e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2111 -0.03604 -0.1532 0.1811 0.9834 0.9932 0.2372 0.4275 0.8678 0.7072 ] Network output: [ -0.008521 1.003 1.007 -1.327e-07 5.959e-08 0.007136 -1e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006975 0.0006512 0.004321 0.003071 0.9889 0.9919 0.007113 0.85 0.8914 0.01129 ] Network output: [ -0.000122 0.001113 1 -4.793e-06 2.152e-06 0.9987 -3.612e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2253 0.1072 0.3512 0.1412 0.9849 0.9939 0.2261 0.4315 0.8746 0.7007 ] Network output: [ 0.002387 -0.01157 0.9946 2.931e-06 -1.316e-06 1.012 2.209e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09979 0.1851 0.1966 0.9873 0.9919 0.1127 0.7313 0.8604 0.3047 ] Network output: [ -0.00226 0.01076 1.005 3.225e-06 -1.448e-06 0.9892 2.431e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09505 0.09311 0.1649 0.1969 0.9852 0.9911 0.09507 0.655 0.8353 0.2502 ] Network output: [ 7.498e-05 1 -5.227e-05 4.207e-07 -1.889e-07 0.9998 3.171e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001346 Epoch 10150 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008494 0.9969 0.9929 -1.274e-07 5.718e-08 -0.006776 -9.598e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003387 -0.006436 0.005227 0.9699 0.9743 0.006907 0.8229 0.8189 0.01586 ] Network output: [ 0.9999 4.553e-05 0.0003073 -1.523e-06 6.837e-07 -0.000251 -1.148e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2111 -0.03604 -0.1532 0.1811 0.9834 0.9932 0.2372 0.4275 0.8678 0.7071 ] Network output: [ -0.00852 1.003 1.007 -1.326e-07 5.953e-08 0.007135 -9.994e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006975 0.0006512 0.004321 0.003071 0.9889 0.9919 0.007113 0.85 0.8914 0.01129 ] Network output: [ -0.0001219 0.001113 1 -4.787e-06 2.149e-06 0.9987 -3.607e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2253 0.1072 0.3512 0.1412 0.9849 0.9939 0.2261 0.4315 0.8746 0.7007 ] Network output: [ 0.002386 -0.01156 0.9946 2.927e-06 -1.314e-06 1.012 2.206e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09979 0.1851 0.1966 0.9873 0.9919 0.1127 0.7313 0.8604 0.3047 ] Network output: [ -0.002259 0.01075 1.005 3.221e-06 -1.446e-06 0.9892 2.428e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09506 0.09311 0.1649 0.1969 0.9852 0.9911 0.09507 0.655 0.8353 0.2502 ] Network output: [ 7.496e-05 1 -5.229e-05 4.202e-07 -1.887e-07 0.9998 3.167e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001345 Epoch 10151 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008493 0.9969 0.9929 -1.273e-07 5.713e-08 -0.006775 -9.59e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003387 -0.006436 0.005226 0.9699 0.9743 0.006907 0.8229 0.8189 0.01586 ] Network output: [ 0.9999 4.54e-05 0.0003072 -1.521e-06 6.829e-07 -0.0002509 -1.146e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2111 -0.03604 -0.1532 0.1811 0.9834 0.9932 0.2372 0.4275 0.8678 0.7071 ] Network output: [ -0.00852 1.003 1.007 -1.325e-07 5.948e-08 0.007135 -9.985e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006975 0.0006513 0.004321 0.003071 0.9889 0.9919 0.007113 0.85 0.8914 0.01129 ] Network output: [ -0.0001217 0.001112 1 -4.781e-06 2.146e-06 0.9987 -3.603e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2253 0.1072 0.3512 0.1412 0.9849 0.9939 0.2261 0.4315 0.8746 0.7007 ] Network output: [ 0.002384 -0.01155 0.9946 2.924e-06 -1.313e-06 1.012 2.203e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09979 0.1851 0.1966 0.9873 0.9919 0.1127 0.7313 0.8604 0.3047 ] Network output: [ -0.002258 0.01075 1.005 3.217e-06 -1.444e-06 0.9892 2.425e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09506 0.09311 0.1649 0.1969 0.9852 0.9911 0.09507 0.655 0.8353 0.2502 ] Network output: [ 7.495e-05 1 -5.23e-05 4.197e-07 -1.884e-07 0.9998 3.163e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001345 Epoch 10152 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008492 0.9969 0.9929 -1.272e-07 5.708e-08 -0.006774 -9.583e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003387 -0.006435 0.005226 0.9699 0.9743 0.006907 0.8229 0.8189 0.01586 ] Network output: [ 0.9999 4.527e-05 0.0003071 -1.519e-06 6.82e-07 -0.0002508 -1.145e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2111 -0.03604 -0.1532 0.1811 0.9834 0.9932 0.2372 0.4275 0.8678 0.7071 ] Network output: [ -0.008519 1.003 1.007 -1.324e-07 5.943e-08 0.007134 -9.976e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006976 0.0006513 0.004321 0.00307 0.9889 0.9919 0.007114 0.85 0.8914 0.01129 ] Network output: [ -0.0001216 0.001111 1 -4.775e-06 2.144e-06 0.9987 -3.598e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2253 0.1072 0.3512 0.1412 0.9849 0.9939 0.2261 0.4315 0.8746 0.7007 ] Network output: [ 0.002383 -0.01155 0.9946 2.92e-06 -1.311e-06 1.012 2.201e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09979 0.1851 0.1966 0.9873 0.9919 0.1127 0.7313 0.8604 0.3047 ] Network output: [ -0.002257 0.01074 1.005 3.213e-06 -1.443e-06 0.9892 2.422e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09506 0.09312 0.1649 0.1969 0.9852 0.9911 0.09507 0.6549 0.8353 0.2502 ] Network output: [ 7.493e-05 1 -5.232e-05 4.192e-07 -1.882e-07 0.9998 3.159e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001344 Epoch 10153 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008492 0.9969 0.9929 -1.271e-07 5.704e-08 -0.006774 -9.575e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003387 -0.006435 0.005225 0.9699 0.9743 0.006907 0.8229 0.8189 0.01586 ] Network output: [ 0.9999 4.514e-05 0.0003069 -1.517e-06 6.812e-07 -0.0002506 -1.143e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2111 -0.03605 -0.1532 0.1811 0.9834 0.9932 0.2372 0.4275 0.8678 0.7071 ] Network output: [ -0.008518 1.003 1.007 -1.323e-07 5.937e-08 0.007134 -9.967e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006976 0.0006513 0.00432 0.00307 0.9889 0.9919 0.007114 0.85 0.8914 0.01129 ] Network output: [ -0.0001215 0.001111 1 -4.769e-06 2.141e-06 0.9987 -3.594e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2253 0.1072 0.3512 0.1412 0.9849 0.9939 0.2261 0.4315 0.8746 0.7007 ] Network output: [ 0.002381 -0.01154 0.9946 2.917e-06 -1.309e-06 1.012 2.198e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.0998 0.1851 0.1966 0.9873 0.9919 0.1127 0.7312 0.8604 0.3047 ] Network output: [ -0.002255 0.01074 1.005 3.209e-06 -1.441e-06 0.9892 2.419e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09506 0.09312 0.1649 0.1969 0.9852 0.9911 0.09507 0.6549 0.8353 0.2502 ] Network output: [ 7.492e-05 1 -5.234e-05 4.187e-07 -1.88e-07 0.9998 3.155e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001343 Epoch 10154 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008491 0.9969 0.9929 -1.27e-07 5.699e-08 -0.006773 -9.568e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003388 -0.006434 0.005225 0.9699 0.9743 0.006907 0.8229 0.8189 0.01586 ] Network output: [ 0.9999 4.501e-05 0.0003068 -1.515e-06 6.803e-07 -0.0002505 -1.142e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2111 -0.03605 -0.1532 0.1811 0.9834 0.9932 0.2372 0.4275 0.8678 0.7071 ] Network output: [ -0.008517 1.003 1.007 -1.321e-07 5.932e-08 0.007134 -9.958e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006976 0.0006514 0.00432 0.00307 0.9889 0.9919 0.007114 0.85 0.8914 0.01128 ] Network output: [ -0.0001214 0.00111 1 -4.763e-06 2.138e-06 0.9987 -3.59e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2253 0.1072 0.3512 0.1412 0.9849 0.9939 0.2261 0.4315 0.8746 0.7007 ] Network output: [ 0.00238 -0.01154 0.9946 2.913e-06 -1.308e-06 1.012 2.195e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.0998 0.1851 0.1966 0.9873 0.9919 0.1127 0.7312 0.8604 0.3047 ] Network output: [ -0.002254 0.01073 1.005 3.205e-06 -1.439e-06 0.9892 2.416e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09506 0.09312 0.1649 0.1969 0.9852 0.9911 0.09508 0.6549 0.8353 0.2502 ] Network output: [ 7.49e-05 1 -5.236e-05 4.182e-07 -1.877e-07 0.9998 3.151e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001342 Epoch 10155 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00849 0.9969 0.9929 -1.269e-07 5.695e-08 -0.006772 -9.56e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003388 -0.006434 0.005225 0.9699 0.9743 0.006907 0.8229 0.8189 0.01586 ] Network output: [ 0.9999 4.488e-05 0.0003067 -1.513e-06 6.795e-07 -0.0002504 -1.141e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2111 -0.03605 -0.1532 0.1811 0.9834 0.9932 0.2372 0.4275 0.8678 0.7071 ] Network output: [ -0.008517 1.003 1.007 -1.32e-07 5.927e-08 0.007133 -9.949e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006977 0.0006514 0.00432 0.00307 0.9889 0.9919 0.007115 0.85 0.8914 0.01128 ] Network output: [ -0.0001212 0.001109 1 -4.757e-06 2.136e-06 0.9987 -3.585e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2253 0.1072 0.3512 0.1412 0.9849 0.9939 0.2261 0.4314 0.8746 0.7007 ] Network output: [ 0.002379 -0.01153 0.9946 2.909e-06 -1.306e-06 1.012 2.193e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.0998 0.1851 0.1966 0.9873 0.9919 0.1127 0.7312 0.8604 0.3047 ] Network output: [ -0.002253 0.01073 1.005 3.202e-06 -1.437e-06 0.9892 2.413e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09506 0.09312 0.1649 0.1969 0.9852 0.9911 0.09508 0.6549 0.8353 0.2502 ] Network output: [ 7.489e-05 1 -5.238e-05 4.176e-07 -1.875e-07 0.9998 3.147e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001342 Epoch 10156 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008489 0.9969 0.9929 -1.267e-07 5.69e-08 -0.006772 -9.552e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003388 -0.006433 0.005224 0.9699 0.9743 0.006907 0.8229 0.8189 0.01586 ] Network output: [ 0.9999 4.475e-05 0.0003065 -1.512e-06 6.786e-07 -0.0002502 -1.139e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2111 -0.03605 -0.1532 0.1811 0.9834 0.9932 0.2372 0.4275 0.8678 0.7071 ] Network output: [ -0.008516 1.003 1.007 -1.319e-07 5.921e-08 0.007133 -9.94e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006977 0.0006515 0.00432 0.003069 0.9889 0.9919 0.007115 0.85 0.8914 0.01128 ] Network output: [ -0.0001211 0.001108 1 -4.751e-06 2.133e-06 0.9987 -3.581e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2253 0.1072 0.3512 0.1412 0.9849 0.9939 0.2261 0.4314 0.8746 0.7007 ] Network output: [ 0.002377 -0.01152 0.9946 2.906e-06 -1.304e-06 1.012 2.19e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.0998 0.1851 0.1966 0.9873 0.9919 0.1127 0.7312 0.8604 0.3047 ] Network output: [ -0.002252 0.01072 1.005 3.198e-06 -1.436e-06 0.9892 2.41e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09506 0.09312 0.1649 0.1969 0.9852 0.9911 0.09508 0.6549 0.8353 0.2502 ] Network output: [ 7.487e-05 1 -5.24e-05 4.171e-07 -1.873e-07 0.9998 3.144e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001341 Epoch 10157 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008488 0.9969 0.9929 -1.266e-07 5.686e-08 -0.006771 -9.545e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003388 -0.006432 0.005224 0.9699 0.9743 0.006908 0.8229 0.8189 0.01586 ] Network output: [ 0.9999 4.461e-05 0.0003064 -1.51e-06 6.778e-07 -0.0002501 -1.138e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2111 -0.03605 -0.1532 0.1811 0.9834 0.9932 0.2372 0.4275 0.8678 0.7071 ] Network output: [ -0.008515 1.003 1.007 -1.318e-07 5.916e-08 0.007132 -9.931e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006977 0.0006515 0.00432 0.003069 0.9889 0.9919 0.007115 0.85 0.8914 0.01128 ] Network output: [ -0.000121 0.001108 1 -4.745e-06 2.13e-06 0.9987 -3.576e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2253 0.1072 0.3512 0.1412 0.9849 0.9939 0.2261 0.4314 0.8746 0.7007 ] Network output: [ 0.002376 -0.01152 0.9946 2.902e-06 -1.303e-06 1.012 2.187e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.0998 0.1851 0.1966 0.9873 0.9919 0.1127 0.7312 0.8604 0.3047 ] Network output: [ -0.00225 0.01071 1.005 3.194e-06 -1.434e-06 0.9892 2.407e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09507 0.09312 0.1649 0.1969 0.9852 0.9911 0.09508 0.6549 0.8353 0.2502 ] Network output: [ 7.486e-05 1 -5.241e-05 4.166e-07 -1.87e-07 0.9998 3.14e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000134 Epoch 10158 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008488 0.9969 0.9929 -1.265e-07 5.681e-08 -0.00677 -9.537e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003388 -0.006432 0.005224 0.9699 0.9743 0.006908 0.8229 0.8189 0.01586 ] Network output: [ 0.9999 4.448e-05 0.0003063 -1.508e-06 6.769e-07 -0.00025 -1.136e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2111 -0.03605 -0.1532 0.1811 0.9834 0.9932 0.2372 0.4275 0.8678 0.7071 ] Network output: [ -0.008514 1.003 1.007 -1.317e-07 5.911e-08 0.007132 -9.922e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006978 0.0006515 0.00432 0.003069 0.9889 0.9919 0.007116 0.85 0.8914 0.01128 ] Network output: [ -0.0001208 0.001107 1 -4.739e-06 2.128e-06 0.9987 -3.572e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2253 0.1072 0.3512 0.1412 0.9849 0.9939 0.2261 0.4314 0.8746 0.7007 ] Network output: [ 0.002374 -0.01151 0.9946 2.899e-06 -1.301e-06 1.012 2.184e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09981 0.1851 0.1966 0.9873 0.9919 0.1127 0.7312 0.8604 0.3047 ] Network output: [ -0.002249 0.01071 1.005 3.19e-06 -1.432e-06 0.9892 2.404e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09507 0.09312 0.1649 0.1969 0.9852 0.9911 0.09508 0.6549 0.8353 0.2502 ] Network output: [ 7.485e-05 1 -5.243e-05 4.161e-07 -1.868e-07 0.9998 3.136e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001339 Epoch 10159 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008487 0.9969 0.9929 -1.264e-07 5.677e-08 -0.00677 -9.529e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003388 -0.006431 0.005223 0.9699 0.9743 0.006908 0.8229 0.8189 0.01585 ] Network output: [ 0.9999 4.435e-05 0.0003061 -1.506e-06 6.761e-07 -0.0002498 -1.135e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2111 -0.03605 -0.1532 0.1811 0.9834 0.9932 0.2372 0.4275 0.8678 0.7071 ] Network output: [ -0.008513 1.003 1.007 -1.315e-07 5.905e-08 0.007131 -9.913e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006978 0.0006516 0.00432 0.003069 0.9889 0.9919 0.007116 0.85 0.8914 0.01128 ] Network output: [ -0.0001207 0.001106 1 -4.733e-06 2.125e-06 0.9987 -3.567e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2254 0.1072 0.3513 0.1412 0.9849 0.9939 0.2261 0.4314 0.8746 0.7007 ] Network output: [ 0.002373 -0.0115 0.9946 2.895e-06 -1.3e-06 1.012 2.182e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09981 0.1851 0.1966 0.9873 0.9919 0.1127 0.7312 0.8604 0.3047 ] Network output: [ -0.002248 0.0107 1.005 3.186e-06 -1.43e-06 0.9892 2.401e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09507 0.09312 0.1649 0.1969 0.9852 0.9911 0.09508 0.6549 0.8353 0.2502 ] Network output: [ 7.483e-05 1 -5.245e-05 4.156e-07 -1.866e-07 0.9998 3.132e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001338 Epoch 10160 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008486 0.9969 0.9929 -1.263e-07 5.672e-08 -0.006769 -9.522e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003388 -0.006431 0.005223 0.9699 0.9743 0.006908 0.8229 0.8189 0.01585 ] Network output: [ 0.9999 4.422e-05 0.000306 -1.504e-06 6.752e-07 -0.0002497 -1.134e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2111 -0.03605 -0.1531 0.1811 0.9834 0.9932 0.2372 0.4275 0.8678 0.7071 ] Network output: [ -0.008513 1.003 1.007 -1.314e-07 5.9e-08 0.007131 -9.905e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006978 0.0006516 0.00432 0.003069 0.9889 0.9919 0.007116 0.85 0.8914 0.01128 ] Network output: [ -0.0001206 0.001106 1 -4.727e-06 2.122e-06 0.9987 -3.563e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2254 0.1072 0.3513 0.1412 0.9849 0.9939 0.2261 0.4314 0.8746 0.7007 ] Network output: [ 0.002372 -0.0115 0.9946 2.891e-06 -1.298e-06 1.012 2.179e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09981 0.1851 0.1966 0.9873 0.9919 0.1127 0.7312 0.8604 0.3047 ] Network output: [ -0.002246 0.0107 1.005 3.182e-06 -1.428e-06 0.9892 2.398e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09507 0.09313 0.1649 0.1969 0.9852 0.9911 0.09508 0.6549 0.8353 0.2502 ] Network output: [ 7.482e-05 1 -5.247e-05 4.151e-07 -1.863e-07 0.9998 3.128e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001338 Epoch 10161 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008485 0.9969 0.9929 -1.262e-07 5.668e-08 -0.006768 -9.514e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003388 -0.00643 0.005223 0.9699 0.9743 0.006908 0.8229 0.8189 0.01585 ] Network output: [ 0.9999 4.409e-05 0.0003059 -1.502e-06 6.744e-07 -0.0002496 -1.132e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2111 -0.03605 -0.1531 0.1811 0.9834 0.9932 0.2372 0.4275 0.8678 0.7071 ] Network output: [ -0.008512 1.003 1.007 -1.313e-07 5.895e-08 0.00713 -9.896e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006978 0.0006517 0.004319 0.003068 0.9889 0.9919 0.007116 0.85 0.8914 0.01128 ] Network output: [ -0.0001204 0.001105 1 -4.722e-06 2.12e-06 0.9987 -3.558e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2254 0.1072 0.3513 0.1412 0.9849 0.9939 0.2261 0.4314 0.8746 0.7007 ] Network output: [ 0.00237 -0.01149 0.9946 2.888e-06 -1.296e-06 1.012 2.176e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09981 0.1851 0.1966 0.9873 0.9919 0.1127 0.7312 0.8604 0.3047 ] Network output: [ -0.002245 0.01069 1.005 3.178e-06 -1.427e-06 0.9892 2.395e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09507 0.09313 0.1649 0.1969 0.9852 0.9911 0.09508 0.6549 0.8353 0.2502 ] Network output: [ 7.48e-05 1 -5.249e-05 4.146e-07 -1.861e-07 0.9998 3.124e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001337 Epoch 10162 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008484 0.9969 0.9929 -1.261e-07 5.663e-08 -0.006767 -9.507e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003388 -0.00643 0.005222 0.9699 0.9743 0.006908 0.8229 0.8189 0.01585 ] Network output: [ 0.9999 4.396e-05 0.0003057 -1.5e-06 6.735e-07 -0.0002494 -1.131e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2112 -0.03605 -0.1531 0.1811 0.9834 0.9932 0.2373 0.4275 0.8678 0.7071 ] Network output: [ -0.008511 1.003 1.007 -1.312e-07 5.889e-08 0.00713 -9.887e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006979 0.0006517 0.004319 0.003068 0.9889 0.9919 0.007117 0.85 0.8914 0.01128 ] Network output: [ -0.0001203 0.001104 1 -4.716e-06 2.117e-06 0.9987 -3.554e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2254 0.1072 0.3513 0.1412 0.9849 0.9939 0.2261 0.4314 0.8746 0.7007 ] Network output: [ 0.002369 -0.01149 0.9946 2.884e-06 -1.295e-06 1.012 2.174e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09981 0.1851 0.1966 0.9873 0.9919 0.1127 0.7312 0.8604 0.3047 ] Network output: [ -0.002244 0.01069 1.005 3.174e-06 -1.425e-06 0.9892 2.392e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09507 0.09313 0.1649 0.1969 0.9852 0.9911 0.09509 0.6549 0.8353 0.2502 ] Network output: [ 7.479e-05 1 -5.251e-05 4.141e-07 -1.859e-07 0.9998 3.12e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001336 Epoch 10163 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008484 0.9969 0.9929 -1.26e-07 5.659e-08 -0.006767 -9.499e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003388 -0.006429 0.005222 0.9699 0.9743 0.006908 0.8229 0.8189 0.01585 ] Network output: [ 0.9999 4.383e-05 0.0003056 -1.498e-06 6.727e-07 -0.0002493 -1.129e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2112 -0.03606 -0.1531 0.1811 0.9834 0.9932 0.2373 0.4275 0.8678 0.7071 ] Network output: [ -0.00851 1.003 1.007 -1.311e-07 5.884e-08 0.00713 -9.878e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006979 0.0006517 0.004319 0.003068 0.9889 0.9919 0.007117 0.85 0.8914 0.01128 ] Network output: [ -0.0001202 0.001104 1 -4.71e-06 2.114e-06 0.9987 -3.549e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2254 0.1072 0.3513 0.1412 0.9849 0.9939 0.2262 0.4314 0.8746 0.7007 ] Network output: [ 0.002367 -0.01148 0.9946 2.881e-06 -1.293e-06 1.012 2.171e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09982 0.1851 0.1966 0.9873 0.9919 0.1127 0.7312 0.8604 0.3047 ] Network output: [ -0.002243 0.01068 1.005 3.17e-06 -1.423e-06 0.9892 2.389e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09507 0.09313 0.1649 0.1969 0.9852 0.9911 0.09509 0.6549 0.8353 0.2502 ] Network output: [ 7.477e-05 1 -5.253e-05 4.135e-07 -1.857e-07 0.9998 3.117e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001335 Epoch 10164 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008483 0.9969 0.9929 -1.259e-07 5.654e-08 -0.006766 -9.491e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003388 -0.006429 0.005222 0.9699 0.9743 0.006908 0.8229 0.8189 0.01585 ] Network output: [ 0.9999 4.37e-05 0.0003055 -1.497e-06 6.719e-07 -0.0002492 -1.128e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2112 -0.03606 -0.1531 0.1811 0.9834 0.9932 0.2373 0.4275 0.8678 0.7071 ] Network output: [ -0.00851 1.003 1.007 -1.31e-07 5.879e-08 0.007129 -9.869e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006979 0.0006518 0.004319 0.003068 0.9889 0.9919 0.007117 0.85 0.8914 0.01128 ] Network output: [ -0.00012 0.001103 1 -4.704e-06 2.112e-06 0.9987 -3.545e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2254 0.1072 0.3513 0.1412 0.9849 0.9939 0.2262 0.4314 0.8746 0.7007 ] Network output: [ 0.002366 -0.01147 0.9946 2.877e-06 -1.292e-06 1.012 2.168e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09982 0.1851 0.1966 0.9873 0.9919 0.1127 0.7312 0.8604 0.3047 ] Network output: [ -0.002241 0.01068 1.005 3.166e-06 -1.422e-06 0.9892 2.386e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09507 0.09313 0.1649 0.1969 0.9852 0.9911 0.09509 0.6548 0.8353 0.2502 ] Network output: [ 7.476e-05 1 -5.254e-05 4.13e-07 -1.854e-07 0.9998 3.113e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001335 Epoch 10165 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008482 0.9969 0.9929 -1.258e-07 5.649e-08 -0.006765 -9.484e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003388 -0.006428 0.005221 0.9699 0.9743 0.006909 0.8229 0.8189 0.01585 ] Network output: [ 0.9999 4.357e-05 0.0003053 -1.495e-06 6.71e-07 -0.000249 -1.126e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2112 -0.03606 -0.1531 0.1811 0.9834 0.9932 0.2373 0.4275 0.8678 0.7071 ] Network output: [ -0.008509 1.003 1.007 -1.308e-07 5.874e-08 0.007129 -9.86e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00698 0.0006518 0.004319 0.003068 0.9889 0.9919 0.007118 0.85 0.8914 0.01128 ] Network output: [ -0.0001199 0.001102 1 -4.698e-06 2.109e-06 0.9987 -3.541e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2254 0.1072 0.3513 0.1412 0.9849 0.9939 0.2262 0.4314 0.8746 0.7007 ] Network output: [ 0.002364 -0.01147 0.9946 2.873e-06 -1.29e-06 1.012 2.166e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09982 0.1851 0.1966 0.9873 0.9919 0.1127 0.7312 0.8604 0.3047 ] Network output: [ -0.00224 0.01067 1.005 3.163e-06 -1.42e-06 0.9892 2.383e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09508 0.09313 0.1649 0.1969 0.9852 0.9911 0.09509 0.6548 0.8353 0.2502 ] Network output: [ 7.474e-05 1 -5.256e-05 4.125e-07 -1.852e-07 0.9998 3.109e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001334 Epoch 10166 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008481 0.9969 0.9929 -1.257e-07 5.645e-08 -0.006765 -9.476e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003388 -0.006428 0.005221 0.9699 0.9743 0.006909 0.8228 0.8189 0.01585 ] Network output: [ 0.9999 4.344e-05 0.0003052 -1.493e-06 6.702e-07 -0.0002489 -1.125e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2112 -0.03606 -0.1531 0.1811 0.9834 0.9932 0.2373 0.4275 0.8678 0.7071 ] Network output: [ -0.008508 1.003 1.007 -1.307e-07 5.868e-08 0.007128 -9.851e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00698 0.0006519 0.004319 0.003067 0.9889 0.9919 0.007118 0.85 0.8914 0.01128 ] Network output: [ -0.0001198 0.001102 1 -4.692e-06 2.106e-06 0.9987 -3.536e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2254 0.1072 0.3513 0.1412 0.9849 0.9939 0.2262 0.4314 0.8746 0.7007 ] Network output: [ 0.002363 -0.01146 0.9946 2.87e-06 -1.288e-06 1.012 2.163e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09982 0.1851 0.1966 0.9873 0.9919 0.1127 0.7311 0.8604 0.3047 ] Network output: [ -0.002239 0.01066 1.005 3.159e-06 -1.418e-06 0.9892 2.38e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09508 0.09313 0.1649 0.1969 0.9852 0.9911 0.09509 0.6548 0.8353 0.2502 ] Network output: [ 7.473e-05 1 -5.258e-05 4.12e-07 -1.85e-07 0.9998 3.105e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001333 Epoch 10167 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00848 0.9969 0.9929 -1.256e-07 5.64e-08 -0.006764 -9.469e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003388 -0.006427 0.005221 0.9699 0.9743 0.006909 0.8228 0.8189 0.01585 ] Network output: [ 0.9999 4.331e-05 0.0003051 -1.491e-06 6.693e-07 -0.0002488 -1.124e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2112 -0.03606 -0.1531 0.181 0.9834 0.9932 0.2373 0.4275 0.8678 0.7071 ] Network output: [ -0.008507 1.003 1.007 -1.306e-07 5.863e-08 0.007128 -9.842e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00698 0.0006519 0.004319 0.003067 0.9889 0.9919 0.007118 0.85 0.8914 0.01128 ] Network output: [ -0.0001196 0.001101 1 -4.686e-06 2.104e-06 0.9987 -3.532e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2254 0.1072 0.3513 0.1412 0.9849 0.9939 0.2262 0.4314 0.8746 0.7007 ] Network output: [ 0.002362 -0.01145 0.9946 2.866e-06 -1.287e-06 1.012 2.16e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09982 0.1851 0.1966 0.9873 0.9919 0.1127 0.7311 0.8604 0.3046 ] Network output: [ -0.002238 0.01066 1.005 3.155e-06 -1.416e-06 0.9892 2.378e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09508 0.09313 0.1649 0.1969 0.9852 0.9911 0.09509 0.6548 0.8353 0.2502 ] Network output: [ 7.472e-05 1 -5.26e-05 4.115e-07 -1.847e-07 0.9998 3.101e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001332 Epoch 10168 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008479 0.9969 0.9929 -1.255e-07 5.636e-08 -0.006763 -9.461e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003388 -0.006427 0.00522 0.9699 0.9743 0.006909 0.8228 0.8189 0.01585 ] Network output: [ 0.9999 4.318e-05 0.0003049 -1.489e-06 6.685e-07 -0.0002486 -1.122e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2112 -0.03606 -0.1531 0.181 0.9834 0.9932 0.2373 0.4275 0.8678 0.7071 ] Network output: [ -0.008507 1.003 1.007 -1.305e-07 5.858e-08 0.007127 -9.833e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00698 0.0006519 0.004319 0.003067 0.9889 0.9919 0.007119 0.85 0.8914 0.01128 ] Network output: [ -0.0001195 0.0011 1 -4.68e-06 2.101e-06 0.9987 -3.527e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2254 0.1072 0.3513 0.1412 0.9849 0.9939 0.2262 0.4314 0.8746 0.7007 ] Network output: [ 0.00236 -0.01145 0.9946 2.863e-06 -1.285e-06 1.012 2.157e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09982 0.1851 0.1966 0.9873 0.9919 0.1127 0.7311 0.8604 0.3046 ] Network output: [ -0.002236 0.01065 1.005 3.151e-06 -1.415e-06 0.9892 2.375e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09508 0.09314 0.1649 0.1969 0.9852 0.9911 0.09509 0.6548 0.8353 0.2502 ] Network output: [ 7.47e-05 1 -5.262e-05 4.11e-07 -1.845e-07 0.9998 3.097e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001332 Epoch 10169 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008479 0.9969 0.9929 -1.254e-07 5.631e-08 -0.006763 -9.453e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003388 -0.006426 0.00522 0.9699 0.9743 0.006909 0.8228 0.8189 0.01585 ] Network output: [ 0.9999 4.305e-05 0.0003048 -1.487e-06 6.677e-07 -0.0002485 -1.121e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2112 -0.03606 -0.1531 0.181 0.9834 0.9932 0.2373 0.4275 0.8678 0.7071 ] Network output: [ -0.008506 1.003 1.007 -1.304e-07 5.852e-08 0.007127 -9.824e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006981 0.000652 0.004319 0.003067 0.9889 0.9919 0.007119 0.85 0.8914 0.01127 ] Network output: [ -0.0001194 0.0011 1 -4.675e-06 2.099e-06 0.9987 -3.523e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2254 0.1072 0.3513 0.1412 0.9849 0.9939 0.2262 0.4314 0.8746 0.7007 ] Network output: [ 0.002359 -0.01144 0.9946 2.859e-06 -1.284e-06 1.012 2.155e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09983 0.1851 0.1966 0.9873 0.9919 0.1127 0.7311 0.8604 0.3046 ] Network output: [ -0.002235 0.01065 1.005 3.147e-06 -1.413e-06 0.9892 2.372e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09508 0.09314 0.1649 0.1969 0.9852 0.9911 0.0951 0.6548 0.8353 0.2502 ] Network output: [ 7.469e-05 1 -5.264e-05 4.105e-07 -1.843e-07 0.9998 3.094e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001331 Epoch 10170 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008478 0.9969 0.9929 -1.253e-07 5.627e-08 -0.006762 -9.446e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003389 -0.006426 0.00522 0.9699 0.9743 0.006909 0.8228 0.8189 0.01585 ] Network output: [ 0.9999 4.291e-05 0.0003047 -1.485e-06 6.668e-07 -0.0002484 -1.119e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2112 -0.03606 -0.1531 0.181 0.9834 0.9932 0.2373 0.4275 0.8678 0.7071 ] Network output: [ -0.008505 1.003 1.007 -1.302e-07 5.847e-08 0.007126 -9.816e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006981 0.000652 0.004318 0.003066 0.9889 0.9919 0.007119 0.85 0.8914 0.01127 ] Network output: [ -0.0001192 0.001099 1 -4.669e-06 2.096e-06 0.9987 -3.519e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2254 0.1072 0.3513 0.1412 0.9849 0.9939 0.2262 0.4314 0.8746 0.7007 ] Network output: [ 0.002357 -0.01143 0.9946 2.856e-06 -1.282e-06 1.012 2.152e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09983 0.1851 0.1966 0.9873 0.9919 0.1127 0.7311 0.8604 0.3046 ] Network output: [ -0.002234 0.01064 1.005 3.143e-06 -1.411e-06 0.9892 2.369e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09508 0.09314 0.1649 0.1969 0.9852 0.9911 0.0951 0.6548 0.8353 0.2502 ] Network output: [ 7.467e-05 1 -5.266e-05 4.1e-07 -1.841e-07 0.9998 3.09e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000133 Epoch 10171 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008477 0.9969 0.9929 -1.252e-07 5.622e-08 -0.006761 -9.438e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003389 -0.006425 0.005219 0.9699 0.9743 0.006909 0.8228 0.8189 0.01584 ] Network output: [ 0.9999 4.278e-05 0.0003046 -1.484e-06 6.66e-07 -0.0002482 -1.118e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2112 -0.03606 -0.1531 0.181 0.9834 0.9932 0.2373 0.4275 0.8678 0.7071 ] Network output: [ -0.008504 1.003 1.007 -1.301e-07 5.842e-08 0.007126 -9.807e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006981 0.000652 0.004318 0.003066 0.9889 0.9919 0.007119 0.85 0.8914 0.01127 ] Network output: [ -0.0001191 0.001098 1 -4.663e-06 2.093e-06 0.9987 -3.514e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2254 0.1072 0.3513 0.1412 0.9849 0.9939 0.2262 0.4314 0.8746 0.7007 ] Network output: [ 0.002356 -0.01143 0.9946 2.852e-06 -1.28e-06 1.012 2.149e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09983 0.1851 0.1966 0.9873 0.9919 0.1127 0.7311 0.8604 0.3046 ] Network output: [ -0.002233 0.01064 1.005 3.139e-06 -1.409e-06 0.9892 2.366e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09508 0.09314 0.1649 0.1969 0.9852 0.9911 0.0951 0.6548 0.8353 0.2502 ] Network output: [ 7.466e-05 1 -5.268e-05 4.095e-07 -1.838e-07 0.9998 3.086e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001329 Epoch 10172 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008476 0.9969 0.9929 -1.251e-07 5.618e-08 -0.006761 -9.431e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003389 -0.006425 0.005219 0.9699 0.9743 0.006909 0.8228 0.8189 0.01584 ] Network output: [ 0.9999 4.265e-05 0.0003044 -1.482e-06 6.652e-07 -0.0002481 -1.117e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2112 -0.03607 -0.1531 0.181 0.9834 0.9932 0.2373 0.4275 0.8678 0.7071 ] Network output: [ -0.008503 1.003 1.007 -1.3e-07 5.837e-08 0.007125 -9.798e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006982 0.0006521 0.004318 0.003066 0.9889 0.9919 0.00712 0.85 0.8914 0.01127 ] Network output: [ -0.000119 0.001098 1 -4.657e-06 2.091e-06 0.9987 -3.51e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2254 0.1072 0.3513 0.1412 0.9849 0.9939 0.2262 0.4314 0.8746 0.7007 ] Network output: [ 0.002355 -0.01142 0.9946 2.849e-06 -1.279e-06 1.012 2.147e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09983 0.1851 0.1966 0.9873 0.9919 0.1127 0.7311 0.8604 0.3046 ] Network output: [ -0.002231 0.01063 1.005 3.136e-06 -1.408e-06 0.9892 2.363e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09508 0.09314 0.1649 0.1969 0.9852 0.9911 0.0951 0.6548 0.8353 0.2502 ] Network output: [ 7.465e-05 1 -5.27e-05 4.09e-07 -1.836e-07 0.9998 3.082e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001328 Epoch 10173 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008475 0.9969 0.9929 -1.25e-07 5.613e-08 -0.00676 -9.423e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003389 -0.006424 0.005219 0.9699 0.9743 0.00691 0.8228 0.8189 0.01584 ] Network output: [ 0.9999 4.252e-05 0.0003043 -1.48e-06 6.643e-07 -0.000248 -1.115e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2112 -0.03607 -0.153 0.181 0.9834 0.9932 0.2373 0.4275 0.8678 0.7071 ] Network output: [ -0.008503 1.003 1.007 -1.299e-07 5.831e-08 0.007125 -9.789e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006982 0.0006521 0.004318 0.003066 0.9889 0.9919 0.00712 0.85 0.8914 0.01127 ] Network output: [ -0.0001188 0.001097 1 -4.651e-06 2.088e-06 0.9987 -3.505e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2254 0.1072 0.3513 0.1412 0.9849 0.9939 0.2262 0.4314 0.8746 0.7007 ] Network output: [ 0.002353 -0.01142 0.9946 2.845e-06 -1.277e-06 1.012 2.144e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09983 0.1851 0.1966 0.9873 0.9919 0.1127 0.7311 0.8604 0.3046 ] Network output: [ -0.00223 0.01063 1.005 3.132e-06 -1.406e-06 0.9892 2.36e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09509 0.09314 0.1649 0.1969 0.9852 0.9911 0.0951 0.6548 0.8353 0.2502 ] Network output: [ 7.463e-05 1 -5.272e-05 4.085e-07 -1.834e-07 0.9998 3.078e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001328 Epoch 10174 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008475 0.9969 0.9929 -1.249e-07 5.609e-08 -0.006759 -9.416e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.00353 -0.003389 -0.006424 0.005218 0.9699 0.9743 0.00691 0.8228 0.8189 0.01584 ] Network output: [ 0.9999 4.239e-05 0.0003042 -1.478e-06 6.635e-07 -0.0002478 -1.114e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2112 -0.03607 -0.153 0.181 0.9834 0.9932 0.2373 0.4275 0.8678 0.7071 ] Network output: [ -0.008502 1.003 1.007 -1.298e-07 5.826e-08 0.007125 -9.78e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006982 0.0006522 0.004318 0.003066 0.9889 0.9919 0.00712 0.85 0.8914 0.01127 ] Network output: [ -0.0001187 0.001096 1 -4.646e-06 2.086e-06 0.9987 -3.501e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2254 0.1072 0.3513 0.1412 0.9849 0.9939 0.2262 0.4314 0.8746 0.7007 ] Network output: [ 0.002352 -0.01141 0.9946 2.842e-06 -1.276e-06 1.012 2.141e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09984 0.1851 0.1966 0.9873 0.9919 0.1127 0.7311 0.8604 0.3046 ] Network output: [ -0.002229 0.01062 1.005 3.128e-06 -1.404e-06 0.9892 2.357e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09509 0.09314 0.1649 0.1969 0.9852 0.9911 0.0951 0.6548 0.8353 0.2502 ] Network output: [ 7.462e-05 1 -5.273e-05 4.08e-07 -1.832e-07 0.9998 3.075e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001327 Epoch 10175 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008474 0.9969 0.9929 -1.248e-07 5.604e-08 -0.006759 -9.408e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.003389 -0.006423 0.005218 0.9699 0.9743 0.00691 0.8228 0.8189 0.01584 ] Network output: [ 0.9999 4.226e-05 0.000304 -1.476e-06 6.627e-07 -0.0002477 -1.112e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2112 -0.03607 -0.153 0.181 0.9834 0.9932 0.2373 0.4275 0.8678 0.7071 ] Network output: [ -0.008501 1.003 1.007 -1.297e-07 5.821e-08 0.007124 -9.771e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006982 0.0006522 0.004318 0.003065 0.9889 0.9919 0.007121 0.8499 0.8914 0.01127 ] Network output: [ -0.0001186 0.001095 1 -4.64e-06 2.083e-06 0.9987 -3.497e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2254 0.1073 0.3513 0.1412 0.9849 0.9939 0.2262 0.4314 0.8746 0.7007 ] Network output: [ 0.00235 -0.0114 0.9946 2.838e-06 -1.274e-06 1.012 2.139e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09984 0.1851 0.1966 0.9873 0.9919 0.1127 0.7311 0.8604 0.3046 ] Network output: [ -0.002227 0.01061 1.005 3.124e-06 -1.402e-06 0.9892 2.354e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09509 0.09314 0.1649 0.1969 0.9852 0.9911 0.0951 0.6548 0.8353 0.2502 ] Network output: [ 7.46e-05 1 -5.275e-05 4.075e-07 -1.829e-07 0.9998 3.071e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001326 Epoch 10176 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008473 0.9969 0.9929 -1.247e-07 5.6e-08 -0.006758 -9.401e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.003389 -0.006423 0.005218 0.9699 0.9743 0.00691 0.8228 0.8189 0.01584 ] Network output: [ 0.9999 4.213e-05 0.0003039 -1.474e-06 6.619e-07 -0.0002476 -1.111e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2112 -0.03607 -0.153 0.181 0.9834 0.9932 0.2373 0.4275 0.8678 0.7071 ] Network output: [ -0.0085 1.003 1.007 -1.295e-07 5.815e-08 0.007124 -9.762e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006983 0.0006522 0.004318 0.003065 0.9889 0.9919 0.007121 0.8499 0.8914 0.01127 ] Network output: [ -0.0001185 0.001095 1 -4.634e-06 2.08e-06 0.9987 -3.492e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2254 0.1073 0.3513 0.1412 0.9849 0.9939 0.2262 0.4314 0.8746 0.7007 ] Network output: [ 0.002349 -0.0114 0.9946 2.834e-06 -1.273e-06 1.012 2.136e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09984 0.1851 0.1965 0.9873 0.9919 0.1127 0.7311 0.8604 0.3046 ] Network output: [ -0.002226 0.01061 1.005 3.12e-06 -1.401e-06 0.9892 2.351e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09509 0.09315 0.1649 0.1969 0.9852 0.9911 0.0951 0.6547 0.8353 0.2502 ] Network output: [ 7.459e-05 1 -5.277e-05 4.07e-07 -1.827e-07 0.9998 3.067e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001325 Epoch 10177 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008472 0.9969 0.9929 -1.246e-07 5.595e-08 -0.006757 -9.393e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.003389 -0.006422 0.005217 0.9699 0.9743 0.00691 0.8228 0.8188 0.01584 ] Network output: [ 0.9999 4.2e-05 0.0003038 -1.472e-06 6.61e-07 -0.0002474 -1.11e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2112 -0.03607 -0.153 0.181 0.9834 0.9932 0.2373 0.4275 0.8678 0.7071 ] Network output: [ -0.0085 1.003 1.007 -1.294e-07 5.81e-08 0.007123 -9.754e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006983 0.0006523 0.004318 0.003065 0.9889 0.9919 0.007121 0.8499 0.8914 0.01127 ] Network output: [ -0.0001183 0.001094 1 -4.628e-06 2.078e-06 0.9987 -3.488e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2255 0.1073 0.3513 0.1412 0.9849 0.9939 0.2262 0.4314 0.8746 0.7007 ] Network output: [ 0.002347 -0.01139 0.9946 2.831e-06 -1.271e-06 1.012 2.134e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09984 0.1851 0.1965 0.9873 0.9919 0.1127 0.7311 0.8604 0.3046 ] Network output: [ -0.002225 0.0106 1.005 3.116e-06 -1.399e-06 0.9892 2.349e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09509 0.09315 0.1649 0.1969 0.9852 0.9911 0.09511 0.6547 0.8353 0.2502 ] Network output: [ 7.457e-05 1 -5.279e-05 4.065e-07 -1.825e-07 0.9998 3.063e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001325 Epoch 10178 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008471 0.9969 0.9929 -1.245e-07 5.591e-08 -0.006756 -9.385e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.003389 -0.006422 0.005217 0.9699 0.9743 0.00691 0.8228 0.8188 0.01584 ] Network output: [ 0.9999 4.187e-05 0.0003036 -1.471e-06 6.602e-07 -0.0002473 -1.108e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2112 -0.03607 -0.153 0.181 0.9834 0.9932 0.2373 0.4275 0.8678 0.7071 ] Network output: [ -0.008499 1.003 1.007 -1.293e-07 5.805e-08 0.007123 -9.745e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006983 0.0006523 0.004318 0.003065 0.9889 0.9919 0.007121 0.8499 0.8914 0.01127 ] Network output: [ -0.0001182 0.001093 1 -4.622e-06 2.075e-06 0.9987 -3.484e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2255 0.1073 0.3513 0.1412 0.9849 0.9939 0.2262 0.4314 0.8746 0.7007 ] Network output: [ 0.002346 -0.01138 0.9946 2.827e-06 -1.269e-06 1.012 2.131e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09984 0.1851 0.1965 0.9873 0.9919 0.1127 0.731 0.8604 0.3046 ] Network output: [ -0.002224 0.0106 1.005 3.113e-06 -1.397e-06 0.9893 2.346e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09509 0.09315 0.1649 0.1969 0.9852 0.9911 0.09511 0.6547 0.8353 0.2502 ] Network output: [ 7.456e-05 1 -5.281e-05 4.06e-07 -1.823e-07 0.9998 3.06e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001324 Epoch 10179 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008471 0.9969 0.9929 -1.244e-07 5.586e-08 -0.006756 -9.378e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.003389 -0.006421 0.005216 0.9699 0.9743 0.00691 0.8228 0.8188 0.01584 ] Network output: [ 0.9999 4.174e-05 0.0003035 -1.469e-06 6.594e-07 -0.0002472 -1.107e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2112 -0.03607 -0.153 0.181 0.9834 0.9932 0.2374 0.4274 0.8678 0.7071 ] Network output: [ -0.008498 1.003 1.007 -1.292e-07 5.8e-08 0.007122 -9.736e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006984 0.0006523 0.004317 0.003065 0.9889 0.9919 0.007122 0.8499 0.8914 0.01127 ] Network output: [ -0.0001181 0.001093 1 -4.617e-06 2.073e-06 0.9987 -3.479e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2255 0.1073 0.3513 0.1412 0.9849 0.9939 0.2262 0.4314 0.8746 0.7006 ] Network output: [ 0.002345 -0.01138 0.9946 2.824e-06 -1.268e-06 1.012 2.128e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09985 0.1851 0.1965 0.9873 0.9919 0.1127 0.731 0.8604 0.3046 ] Network output: [ -0.002222 0.01059 1.005 3.109e-06 -1.396e-06 0.9893 2.343e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09509 0.09315 0.1648 0.1969 0.9852 0.9911 0.09511 0.6547 0.8353 0.2502 ] Network output: [ 7.455e-05 1 -5.283e-05 4.055e-07 -1.82e-07 0.9998 3.056e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001323 Epoch 10180 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00847 0.9969 0.9929 -1.243e-07 5.582e-08 -0.006755 -9.37e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.003389 -0.006421 0.005216 0.9699 0.9743 0.00691 0.8228 0.8188 0.01584 ] Network output: [ 0.9999 4.162e-05 0.0003034 -1.467e-06 6.586e-07 -0.000247 -1.106e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2112 -0.03607 -0.153 0.181 0.9834 0.9932 0.2374 0.4274 0.8678 0.7071 ] Network output: [ -0.008497 1.003 1.007 -1.291e-07 5.794e-08 0.007122 -9.727e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006984 0.0006524 0.004317 0.003064 0.9889 0.9919 0.007122 0.8499 0.8914 0.01127 ] Network output: [ -0.0001179 0.001092 1 -4.611e-06 2.07e-06 0.9987 -3.475e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2255 0.1073 0.3513 0.1412 0.9849 0.9939 0.2263 0.4314 0.8746 0.7006 ] Network output: [ 0.002343 -0.01137 0.9946 2.82e-06 -1.266e-06 1.012 2.126e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09985 0.1851 0.1965 0.9873 0.9919 0.1127 0.731 0.8604 0.3046 ] Network output: [ -0.002221 0.01059 1.005 3.105e-06 -1.394e-06 0.9893 2.34e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09509 0.09315 0.1648 0.1969 0.9852 0.9911 0.09511 0.6547 0.8353 0.2502 ] Network output: [ 7.453e-05 1 -5.285e-05 4.05e-07 -1.818e-07 0.9998 3.052e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001322 Epoch 10181 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008469 0.9969 0.9929 -1.242e-07 5.577e-08 -0.006754 -9.363e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.003389 -0.00642 0.005216 0.9699 0.9743 0.006911 0.8228 0.8188 0.01584 ] Network output: [ 0.9999 4.149e-05 0.0003032 -1.465e-06 6.577e-07 -0.0002469 -1.104e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2112 -0.03607 -0.153 0.181 0.9834 0.9932 0.2374 0.4274 0.8678 0.7071 ] Network output: [ -0.008497 1.003 1.007 -1.29e-07 5.789e-08 0.007121 -9.718e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006984 0.0006524 0.004317 0.003064 0.9889 0.9919 0.007122 0.8499 0.8914 0.01127 ] Network output: [ -0.0001178 0.001091 1 -4.605e-06 2.067e-06 0.9987 -3.471e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2255 0.1073 0.3514 0.1412 0.9849 0.9939 0.2263 0.4314 0.8746 0.7006 ] Network output: [ 0.002342 -0.01137 0.9946 2.817e-06 -1.265e-06 1.012 2.123e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09985 0.1851 0.1965 0.9873 0.9919 0.1127 0.731 0.8604 0.3046 ] Network output: [ -0.00222 0.01058 1.005 3.101e-06 -1.392e-06 0.9893 2.337e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0951 0.09315 0.1648 0.1969 0.9852 0.9911 0.09511 0.6547 0.8353 0.2502 ] Network output: [ 7.452e-05 1 -5.287e-05 4.045e-07 -1.816e-07 0.9998 3.048e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001322 Epoch 10182 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008468 0.9969 0.9929 -1.241e-07 5.573e-08 -0.006754 -9.355e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.003389 -0.00642 0.005215 0.9699 0.9743 0.006911 0.8228 0.8188 0.01584 ] Network output: [ 0.9999 4.136e-05 0.0003031 -1.463e-06 6.569e-07 -0.0002468 -1.103e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2112 -0.03608 -0.153 0.181 0.9834 0.9932 0.2374 0.4274 0.8678 0.7071 ] Network output: [ -0.008496 1.003 1.007 -1.288e-07 5.784e-08 0.007121 -9.709e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006984 0.0006525 0.004317 0.003064 0.9889 0.9919 0.007123 0.8499 0.8914 0.01127 ] Network output: [ -0.0001177 0.001091 1 -4.599e-06 2.065e-06 0.9987 -3.466e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2255 0.1073 0.3514 0.1412 0.9849 0.9939 0.2263 0.4314 0.8746 0.7006 ] Network output: [ 0.00234 -0.01136 0.9946 2.813e-06 -1.263e-06 1.012 2.12e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09985 0.1851 0.1965 0.9873 0.9919 0.1127 0.731 0.8604 0.3046 ] Network output: [ -0.002219 0.01058 1.005 3.097e-06 -1.39e-06 0.9893 2.334e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0951 0.09315 0.1648 0.1969 0.9852 0.9911 0.09511 0.6547 0.8353 0.2502 ] Network output: [ 7.45e-05 1 -5.289e-05 4.04e-07 -1.814e-07 0.9998 3.045e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001321 Epoch 10183 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008467 0.9969 0.9929 -1.24e-07 5.568e-08 -0.006753 -9.348e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.003389 -0.006419 0.005215 0.9699 0.9743 0.006911 0.8228 0.8188 0.01583 ] Network output: [ 0.9999 4.123e-05 0.000303 -1.461e-06 6.561e-07 -0.0002466 -1.101e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2113 -0.03608 -0.153 0.181 0.9834 0.9932 0.2374 0.4274 0.8678 0.7071 ] Network output: [ -0.008495 1.003 1.007 -1.287e-07 5.779e-08 0.007121 -9.701e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006985 0.0006525 0.004317 0.003064 0.9889 0.9919 0.007123 0.8499 0.8914 0.01127 ] Network output: [ -0.0001175 0.00109 1 -4.594e-06 2.062e-06 0.9987 -3.462e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2255 0.1073 0.3514 0.1412 0.9849 0.9939 0.2263 0.4314 0.8746 0.7006 ] Network output: [ 0.002339 -0.01135 0.9946 2.81e-06 -1.261e-06 1.012 2.118e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09985 0.1851 0.1965 0.9873 0.9919 0.1127 0.731 0.8604 0.3046 ] Network output: [ -0.002217 0.01057 1.005 3.094e-06 -1.389e-06 0.9893 2.331e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0951 0.09316 0.1648 0.1969 0.9852 0.9911 0.09511 0.6547 0.8352 0.2502 ] Network output: [ 7.449e-05 1 -5.291e-05 4.035e-07 -1.811e-07 0.9998 3.041e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000132 Epoch 10184 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008467 0.9969 0.9929 -1.239e-07 5.564e-08 -0.006752 -9.34e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.003389 -0.006419 0.005215 0.9699 0.9743 0.006911 0.8228 0.8188 0.01583 ] Network output: [ 0.9999 4.11e-05 0.0003028 -1.46e-06 6.553e-07 -0.0002465 -1.1e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2113 -0.03608 -0.153 0.181 0.9834 0.9932 0.2374 0.4274 0.8678 0.7071 ] Network output: [ -0.008494 1.003 1.007 -1.286e-07 5.773e-08 0.00712 -9.692e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006985 0.0006525 0.004317 0.003063 0.9889 0.9919 0.007123 0.8499 0.8914 0.01126 ] Network output: [ -0.0001174 0.001089 1 -4.588e-06 2.06e-06 0.9987 -3.458e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2255 0.1073 0.3514 0.1412 0.9849 0.9939 0.2263 0.4314 0.8746 0.7006 ] Network output: [ 0.002338 -0.01135 0.9946 2.806e-06 -1.26e-06 1.012 2.115e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09986 0.1851 0.1965 0.9873 0.9919 0.1127 0.731 0.8604 0.3046 ] Network output: [ -0.002216 0.01056 1.005 3.09e-06 -1.387e-06 0.9893 2.329e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0951 0.09316 0.1648 0.1969 0.9852 0.9911 0.09511 0.6547 0.8352 0.2502 ] Network output: [ 7.448e-05 1 -5.293e-05 4.03e-07 -1.809e-07 0.9998 3.037e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001319 Epoch 10185 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008466 0.9969 0.9929 -1.238e-07 5.559e-08 -0.006752 -9.333e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.003389 -0.006418 0.005214 0.9699 0.9743 0.006911 0.8228 0.8188 0.01583 ] Network output: [ 0.9999 4.097e-05 0.0003027 -1.458e-06 6.545e-07 -0.0002464 -1.099e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2113 -0.03608 -0.153 0.181 0.9834 0.9932 0.2374 0.4274 0.8678 0.7071 ] Network output: [ -0.008494 1.003 1.007 -1.285e-07 5.768e-08 0.00712 -9.683e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006985 0.0006526 0.004317 0.003063 0.9889 0.9919 0.007124 0.8499 0.8914 0.01126 ] Network output: [ -0.0001173 0.001089 1 -4.582e-06 2.057e-06 0.9987 -3.453e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2255 0.1073 0.3514 0.1412 0.9849 0.9939 0.2263 0.4314 0.8746 0.7006 ] Network output: [ 0.002336 -0.01134 0.9946 2.803e-06 -1.258e-06 1.012 2.112e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09986 0.1851 0.1965 0.9873 0.9919 0.1127 0.731 0.8604 0.3046 ] Network output: [ -0.002215 0.01056 1.005 3.086e-06 -1.385e-06 0.9893 2.326e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0951 0.09316 0.1648 0.1969 0.9852 0.9911 0.09512 0.6547 0.8352 0.2502 ] Network output: [ 7.446e-05 1 -5.295e-05 4.025e-07 -1.807e-07 0.9998 3.033e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001319 Epoch 10186 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008465 0.9969 0.9929 -1.237e-07 5.555e-08 -0.006751 -9.325e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.00339 -0.006418 0.005214 0.9699 0.9743 0.006911 0.8228 0.8188 0.01583 ] Network output: [ 0.9999 4.084e-05 0.0003026 -1.456e-06 6.536e-07 -0.0002462 -1.097e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2113 -0.03608 -0.153 0.181 0.9834 0.9932 0.2374 0.4274 0.8678 0.7071 ] Network output: [ -0.008493 1.003 1.007 -1.284e-07 5.763e-08 0.007119 -9.674e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006986 0.0006526 0.004317 0.003063 0.9889 0.9919 0.007124 0.8499 0.8914 0.01126 ] Network output: [ -0.0001171 0.001088 1 -4.576e-06 2.055e-06 0.9987 -3.449e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2255 0.1073 0.3514 0.1412 0.9849 0.9939 0.2263 0.4314 0.8746 0.7006 ] Network output: [ 0.002335 -0.01133 0.9946 2.799e-06 -1.257e-06 1.012 2.11e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1126 0.09986 0.1851 0.1965 0.9873 0.9919 0.1127 0.731 0.8604 0.3046 ] Network output: [ -0.002214 0.01055 1.005 3.082e-06 -1.384e-06 0.9893 2.323e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0951 0.09316 0.1648 0.1969 0.9852 0.9911 0.09512 0.6547 0.8352 0.2502 ] Network output: [ 7.445e-05 1 -5.297e-05 4.02e-07 -1.805e-07 0.9998 3.03e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001318 Epoch 10187 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008464 0.9969 0.9929 -1.236e-07 5.55e-08 -0.00675 -9.318e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.00339 -0.006417 0.005214 0.9699 0.9743 0.006911 0.8228 0.8188 0.01583 ] Network output: [ 0.9999 4.071e-05 0.0003025 -1.454e-06 6.528e-07 -0.0002461 -1.096e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2113 -0.03608 -0.1529 0.181 0.9834 0.9932 0.2374 0.4274 0.8678 0.7071 ] Network output: [ -0.008492 1.003 1.007 -1.283e-07 5.758e-08 0.007119 -9.665e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006986 0.0006526 0.004317 0.003063 0.9889 0.9919 0.007124 0.8499 0.8914 0.01126 ] Network output: [ -0.000117 0.001087 1 -4.571e-06 2.052e-06 0.9987 -3.445e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2255 0.1073 0.3514 0.1412 0.9849 0.9939 0.2263 0.4314 0.8746 0.7006 ] Network output: [ 0.002333 -0.01133 0.9946 2.796e-06 -1.255e-06 1.012 2.107e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09986 0.1851 0.1965 0.9873 0.9919 0.1127 0.731 0.8604 0.3046 ] Network output: [ -0.002212 0.01055 1.005 3.078e-06 -1.382e-06 0.9893 2.32e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0951 0.09316 0.1648 0.1969 0.9852 0.9911 0.09512 0.6547 0.8352 0.2502 ] Network output: [ 7.443e-05 1 -5.299e-05 4.015e-07 -1.802e-07 0.9998 3.026e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001317 Epoch 10188 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008463 0.9969 0.9929 -1.235e-07 5.546e-08 -0.00675 -9.31e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.00339 -0.006417 0.005213 0.9699 0.9743 0.006911 0.8228 0.8188 0.01583 ] Network output: [ 0.9999 4.058e-05 0.0003023 -1.452e-06 6.52e-07 -0.000246 -1.095e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2113 -0.03608 -0.1529 0.181 0.9834 0.9932 0.2374 0.4274 0.8678 0.707 ] Network output: [ -0.008491 1.003 1.007 -1.281e-07 5.752e-08 0.007118 -9.657e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006986 0.0006527 0.004316 0.003063 0.9889 0.9919 0.007124 0.8499 0.8914 0.01126 ] Network output: [ -0.0001169 0.001087 1 -4.565e-06 2.049e-06 0.9987 -3.44e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2255 0.1073 0.3514 0.1412 0.9849 0.9939 0.2263 0.4314 0.8745 0.7006 ] Network output: [ 0.002332 -0.01132 0.9946 2.793e-06 -1.254e-06 1.012 2.105e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09986 0.1851 0.1965 0.9873 0.9919 0.1127 0.731 0.8604 0.3046 ] Network output: [ -0.002211 0.01054 1.005 3.075e-06 -1.38e-06 0.9893 2.317e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0951 0.09316 0.1648 0.1969 0.9852 0.9911 0.09512 0.6546 0.8352 0.2502 ] Network output: [ 7.442e-05 1 -5.301e-05 4.01e-07 -1.8e-07 0.9998 3.022e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001316 Epoch 10189 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008463 0.9969 0.9929 -1.234e-07 5.541e-08 -0.006749 -9.303e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.00339 -0.006416 0.005213 0.9699 0.9743 0.006911 0.8228 0.8188 0.01583 ] Network output: [ 0.9999 4.045e-05 0.0003022 -1.451e-06 6.512e-07 -0.0002459 -1.093e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2113 -0.03608 -0.1529 0.181 0.9834 0.9932 0.2374 0.4274 0.8678 0.707 ] Network output: [ -0.00849 1.003 1.007 -1.28e-07 5.747e-08 0.007118 -9.648e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006986 0.0006527 0.004316 0.003062 0.9889 0.9919 0.007125 0.8499 0.8914 0.01126 ] Network output: [ -0.0001168 0.001086 1 -4.559e-06 2.047e-06 0.9987 -3.436e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2255 0.1073 0.3514 0.1412 0.9849 0.9939 0.2263 0.4313 0.8745 0.7006 ] Network output: [ 0.002331 -0.01132 0.9946 2.789e-06 -1.252e-06 1.012 2.102e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09986 0.1851 0.1965 0.9873 0.9919 0.1127 0.731 0.8604 0.3046 ] Network output: [ -0.00221 0.01054 1.005 3.071e-06 -1.379e-06 0.9893 2.314e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09511 0.09316 0.1648 0.1969 0.9852 0.9911 0.09512 0.6546 0.8352 0.2502 ] Network output: [ 7.441e-05 1 -5.303e-05 4.005e-07 -1.798e-07 0.9998 3.018e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001316 Epoch 10190 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008462 0.9969 0.9929 -1.233e-07 5.537e-08 -0.006748 -9.295e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.00339 -0.006416 0.005213 0.9699 0.9743 0.006912 0.8228 0.8188 0.01583 ] Network output: [ 0.9999 4.032e-05 0.0003021 -1.449e-06 6.504e-07 -0.0002457 -1.092e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2113 -0.03608 -0.1529 0.181 0.9834 0.9932 0.2374 0.4274 0.8678 0.707 ] Network output: [ -0.00849 1.003 1.007 -1.279e-07 5.742e-08 0.007117 -9.639e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006987 0.0006528 0.004316 0.003062 0.9889 0.9919 0.007125 0.8499 0.8914 0.01126 ] Network output: [ -0.0001166 0.001085 1 -4.554e-06 2.044e-06 0.9987 -3.432e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2255 0.1073 0.3514 0.1412 0.9849 0.9939 0.2263 0.4313 0.8745 0.7006 ] Network output: [ 0.002329 -0.01131 0.9946 2.786e-06 -1.251e-06 1.012 2.099e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09987 0.1851 0.1965 0.9873 0.9919 0.1127 0.731 0.8604 0.3046 ] Network output: [ -0.002208 0.01053 1.005 3.067e-06 -1.377e-06 0.9893 2.311e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09511 0.09316 0.1648 0.1969 0.9852 0.9911 0.09512 0.6546 0.8352 0.2502 ] Network output: [ 7.439e-05 1 -5.305e-05 4e-07 -1.796e-07 0.9998 3.015e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001315 Epoch 10191 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008461 0.9969 0.9929 -1.232e-07 5.533e-08 -0.006748 -9.287e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.00339 -0.006415 0.005212 0.9699 0.9743 0.006912 0.8228 0.8188 0.01583 ] Network output: [ 0.9999 4.019e-05 0.0003019 -1.447e-06 6.496e-07 -0.0002456 -1.09e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2113 -0.03609 -0.1529 0.181 0.9834 0.9932 0.2374 0.4274 0.8678 0.707 ] Network output: [ -0.008489 1.003 1.007 -1.278e-07 5.737e-08 0.007117 -9.63e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006987 0.0006528 0.004316 0.003062 0.9889 0.9919 0.007125 0.8499 0.8914 0.01126 ] Network output: [ -0.0001165 0.001084 1 -4.548e-06 2.042e-06 0.9987 -3.427e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2255 0.1073 0.3514 0.1412 0.9849 0.9939 0.2263 0.4313 0.8745 0.7006 ] Network output: [ 0.002328 -0.0113 0.9946 2.782e-06 -1.249e-06 1.012 2.097e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09987 0.1851 0.1965 0.9873 0.9919 0.1127 0.7309 0.8604 0.3046 ] Network output: [ -0.002207 0.01053 1.005 3.063e-06 -1.375e-06 0.9893 2.309e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09511 0.09316 0.1648 0.1969 0.9852 0.9911 0.09512 0.6546 0.8352 0.2502 ] Network output: [ 7.438e-05 1 -5.307e-05 3.995e-07 -1.794e-07 0.9998 3.011e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001314 Epoch 10192 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00846 0.9969 0.9929 -1.231e-07 5.528e-08 -0.006747 -9.28e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.00339 -0.006415 0.005212 0.9699 0.9743 0.006912 0.8228 0.8188 0.01583 ] Network output: [ 0.9999 4.006e-05 0.0003018 -1.445e-06 6.488e-07 -0.0002455 -1.089e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2113 -0.03609 -0.1529 0.181 0.9834 0.9932 0.2374 0.4274 0.8678 0.707 ] Network output: [ -0.008488 1.003 1.007 -1.277e-07 5.732e-08 0.007116 -9.622e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006987 0.0006528 0.004316 0.003062 0.9889 0.9919 0.007126 0.8499 0.8914 0.01126 ] Network output: [ -0.0001164 0.001084 1 -4.542e-06 2.039e-06 0.9987 -3.423e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2255 0.1073 0.3514 0.1411 0.9849 0.9939 0.2263 0.4313 0.8745 0.7006 ] Network output: [ 0.002326 -0.0113 0.9946 2.779e-06 -1.247e-06 1.012 2.094e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09987 0.1851 0.1965 0.9873 0.9919 0.1127 0.7309 0.8604 0.3046 ] Network output: [ -0.002206 0.01052 1.005 3.06e-06 -1.374e-06 0.9893 2.306e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09511 0.09317 0.1648 0.1969 0.9852 0.9911 0.09512 0.6546 0.8352 0.2502 ] Network output: [ 7.436e-05 1 -5.309e-05 3.99e-07 -1.791e-07 0.9998 3.007e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001313 Epoch 10193 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008459 0.9969 0.9929 -1.23e-07 5.524e-08 -0.006746 -9.272e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.00339 -0.006414 0.005212 0.9699 0.9743 0.006912 0.8228 0.8188 0.01583 ] Network output: [ 0.9999 3.994e-05 0.0003017 -1.443e-06 6.479e-07 -0.0002453 -1.088e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2113 -0.03609 -0.1529 0.181 0.9834 0.9932 0.2374 0.4274 0.8677 0.707 ] Network output: [ -0.008487 1.003 1.007 -1.276e-07 5.726e-08 0.007116 -9.613e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006988 0.0006529 0.004316 0.003062 0.9889 0.9919 0.007126 0.8499 0.8914 0.01126 ] Network output: [ -0.0001162 0.001083 1 -4.537e-06 2.037e-06 0.9987 -3.419e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2255 0.1073 0.3514 0.1411 0.9849 0.9939 0.2263 0.4313 0.8745 0.7006 ] Network output: [ 0.002325 -0.01129 0.9946 2.775e-06 -1.246e-06 1.012 2.091e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09987 0.1851 0.1965 0.9873 0.9919 0.1127 0.7309 0.8604 0.3046 ] Network output: [ -0.002205 0.01051 1.005 3.056e-06 -1.372e-06 0.9893 2.303e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09511 0.09317 0.1648 0.1969 0.9852 0.9911 0.09513 0.6546 0.8352 0.2502 ] Network output: [ 7.435e-05 1 -5.311e-05 3.985e-07 -1.789e-07 0.9998 3.003e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001312 Epoch 10194 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008459 0.9969 0.9929 -1.229e-07 5.519e-08 -0.006746 -9.265e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.00339 -0.006413 0.005211 0.9699 0.9743 0.006912 0.8227 0.8188 0.01583 ] Network output: [ 0.9999 3.981e-05 0.0003015 -1.441e-06 6.471e-07 -0.0002452 -1.086e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2113 -0.03609 -0.1529 0.181 0.9834 0.9932 0.2374 0.4274 0.8677 0.707 ] Network output: [ -0.008487 1.003 1.007 -1.274e-07 5.721e-08 0.007116 -9.604e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006988 0.0006529 0.004316 0.003061 0.9889 0.9919 0.007126 0.8499 0.8914 0.01126 ] Network output: [ -0.0001161 0.001082 1 -4.531e-06 2.034e-06 0.9987 -3.415e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2256 0.1073 0.3514 0.1411 0.9849 0.9939 0.2263 0.4313 0.8745 0.7006 ] Network output: [ 0.002323 -0.01128 0.9946 2.772e-06 -1.244e-06 1.012 2.089e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09987 0.1851 0.1965 0.9873 0.9919 0.1127 0.7309 0.8604 0.3046 ] Network output: [ -0.002203 0.01051 1.005 3.052e-06 -1.37e-06 0.9893 2.3e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09511 0.09317 0.1648 0.1969 0.9852 0.9911 0.09513 0.6546 0.8352 0.2502 ] Network output: [ 7.434e-05 1 -5.313e-05 3.98e-07 -1.787e-07 0.9998 3e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001312 Epoch 10195 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008458 0.9969 0.9929 -1.228e-07 5.515e-08 -0.006745 -9.257e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.00339 -0.006413 0.005211 0.9699 0.9743 0.006912 0.8227 0.8188 0.01582 ] Network output: [ 0.9999 3.968e-05 0.0003014 -1.44e-06 6.463e-07 -0.0002451 -1.085e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2113 -0.03609 -0.1529 0.181 0.9834 0.9932 0.2374 0.4274 0.8677 0.707 ] Network output: [ -0.008486 1.003 1.007 -1.273e-07 5.716e-08 0.007115 -9.595e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006988 0.0006529 0.004316 0.003061 0.9889 0.9919 0.007126 0.8499 0.8914 0.01126 ] Network output: [ -0.000116 0.001082 1 -4.525e-06 2.032e-06 0.9987 -3.41e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2256 0.1073 0.3514 0.1411 0.9849 0.9939 0.2263 0.4313 0.8745 0.7006 ] Network output: [ 0.002322 -0.01128 0.9946 2.768e-06 -1.243e-06 1.012 2.086e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09988 0.1851 0.1965 0.9873 0.9919 0.1127 0.7309 0.8604 0.3046 ] Network output: [ -0.002202 0.0105 1.005 3.048e-06 -1.368e-06 0.9893 2.297e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09511 0.09317 0.1648 0.1969 0.9852 0.9911 0.09513 0.6546 0.8352 0.2502 ] Network output: [ 7.432e-05 1 -5.315e-05 3.976e-07 -1.785e-07 0.9998 2.996e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001311 Epoch 10196 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008457 0.9969 0.9929 -1.227e-07 5.51e-08 -0.006744 -9.25e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.00339 -0.006412 0.005211 0.9699 0.9743 0.006912 0.8227 0.8188 0.01582 ] Network output: [ 0.9999 3.955e-05 0.0003013 -1.438e-06 6.455e-07 -0.0002449 -1.084e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2113 -0.03609 -0.1529 0.181 0.9834 0.9932 0.2375 0.4274 0.8677 0.707 ] Network output: [ -0.008485 1.003 1.007 -1.272e-07 5.711e-08 0.007115 -9.587e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006988 0.000653 0.004315 0.003061 0.9889 0.9919 0.007127 0.8499 0.8914 0.01126 ] Network output: [ -0.0001158 0.001081 1 -4.52e-06 2.029e-06 0.9987 -3.406e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2256 0.1073 0.3514 0.1411 0.9849 0.9939 0.2263 0.4313 0.8745 0.7006 ] Network output: [ 0.002321 -0.01127 0.9946 2.765e-06 -1.241e-06 1.012 2.084e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09988 0.1851 0.1965 0.9873 0.9919 0.1127 0.7309 0.8604 0.3046 ] Network output: [ -0.002201 0.0105 1.005 3.045e-06 -1.367e-06 0.9893 2.294e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09511 0.09317 0.1648 0.1969 0.9852 0.9911 0.09513 0.6546 0.8352 0.2502 ] Network output: [ 7.431e-05 1 -5.317e-05 3.971e-07 -1.783e-07 0.9998 2.992e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000131 Epoch 10197 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008456 0.9969 0.9929 -1.226e-07 5.506e-08 -0.006743 -9.242e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.00339 -0.006412 0.00521 0.9699 0.9743 0.006912 0.8227 0.8188 0.01582 ] Network output: [ 0.9999 3.942e-05 0.0003011 -1.436e-06 6.447e-07 -0.0002448 -1.082e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2113 -0.03609 -0.1529 0.181 0.9834 0.9932 0.2375 0.4274 0.8677 0.707 ] Network output: [ -0.008484 1.003 1.007 -1.271e-07 5.706e-08 0.007114 -9.578e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006989 0.000653 0.004315 0.003061 0.9889 0.9919 0.007127 0.8499 0.8914 0.01126 ] Network output: [ -0.0001157 0.00108 1 -4.514e-06 2.026e-06 0.9987 -3.402e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2256 0.1073 0.3514 0.1411 0.9849 0.9939 0.2264 0.4313 0.8745 0.7006 ] Network output: [ 0.002319 -0.01127 0.9946 2.761e-06 -1.24e-06 1.012 2.081e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09988 0.1851 0.1965 0.9873 0.9919 0.1128 0.7309 0.8604 0.3046 ] Network output: [ -0.0022 0.01049 1.005 3.041e-06 -1.365e-06 0.9893 2.292e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09512 0.09317 0.1648 0.1969 0.9852 0.9911 0.09513 0.6546 0.8352 0.2502 ] Network output: [ 7.429e-05 1 -5.319e-05 3.966e-07 -1.78e-07 0.9998 2.989e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001309 Epoch 10198 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008455 0.9969 0.9929 -1.225e-07 5.501e-08 -0.006743 -9.235e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.00339 -0.006411 0.00521 0.9699 0.9743 0.006913 0.8227 0.8188 0.01582 ] Network output: [ 0.9999 3.929e-05 0.000301 -1.434e-06 6.439e-07 -0.0002447 -1.081e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2113 -0.03609 -0.1529 0.181 0.9834 0.9932 0.2375 0.4274 0.8677 0.707 ] Network output: [ -0.008484 1.003 1.007 -1.27e-07 5.7e-08 0.007114 -9.569e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006989 0.000653 0.004315 0.00306 0.9889 0.9919 0.007127 0.8499 0.8914 0.01125 ] Network output: [ -0.0001156 0.00108 1 -4.508e-06 2.024e-06 0.9987 -3.398e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2256 0.1073 0.3514 0.1411 0.9849 0.9939 0.2264 0.4313 0.8745 0.7006 ] Network output: [ 0.002318 -0.01126 0.9946 2.758e-06 -1.238e-06 1.012 2.079e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09988 0.1851 0.1965 0.9873 0.9919 0.1128 0.7309 0.8604 0.3046 ] Network output: [ -0.002198 0.01049 1.005 3.037e-06 -1.363e-06 0.9893 2.289e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09512 0.09317 0.1648 0.1969 0.9852 0.9911 0.09513 0.6546 0.8352 0.2502 ] Network output: [ 7.428e-05 1 -5.321e-05 3.961e-07 -1.778e-07 0.9998 2.985e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001309 Epoch 10199 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008455 0.9969 0.9929 -1.224e-07 5.497e-08 -0.006742 -9.228e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.00339 -0.006411 0.00521 0.9699 0.9743 0.006913 0.8227 0.8188 0.01582 ] Network output: [ 0.9999 3.916e-05 0.0003009 -1.432e-06 6.431e-07 -0.0002446 -1.08e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2113 -0.03609 -0.1529 0.1809 0.9834 0.9932 0.2375 0.4274 0.8677 0.707 ] Network output: [ -0.008483 1.003 1.007 -1.269e-07 5.695e-08 0.007113 -9.561e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006989 0.0006531 0.004315 0.00306 0.9889 0.9919 0.007128 0.8499 0.8914 0.01125 ] Network output: [ -0.0001154 0.001079 1 -4.503e-06 2.021e-06 0.9987 -3.393e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2256 0.1073 0.3514 0.1411 0.9849 0.9939 0.2264 0.4313 0.8745 0.7006 ] Network output: [ 0.002316 -0.01125 0.9946 2.755e-06 -1.237e-06 1.012 2.076e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09988 0.1851 0.1965 0.9873 0.9919 0.1128 0.7309 0.8604 0.3046 ] Network output: [ -0.002197 0.01048 1.005 3.033e-06 -1.362e-06 0.9893 2.286e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09512 0.09317 0.1648 0.1969 0.9852 0.9911 0.09513 0.6546 0.8352 0.2502 ] Network output: [ 7.427e-05 1 -5.323e-05 3.956e-07 -1.776e-07 0.9998 2.981e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001308 Epoch 10200 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008454 0.9969 0.9929 -1.223e-07 5.492e-08 -0.006741 -9.22e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.00339 -0.00641 0.005209 0.9699 0.9743 0.006913 0.8227 0.8188 0.01582 ] Network output: [ 0.9999 3.904e-05 0.0003008 -1.431e-06 6.423e-07 -0.0002444 -1.078e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2113 -0.03609 -0.1528 0.1809 0.9834 0.9932 0.2375 0.4274 0.8677 0.707 ] Network output: [ -0.008482 1.003 1.007 -1.267e-07 5.69e-08 0.007113 -9.552e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006989 0.0006531 0.004315 0.00306 0.9889 0.9919 0.007128 0.8499 0.8914 0.01125 ] Network output: [ -0.0001153 0.001078 1 -4.497e-06 2.019e-06 0.9987 -3.389e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2256 0.1073 0.3514 0.1411 0.9849 0.9939 0.2264 0.4313 0.8745 0.7006 ] Network output: [ 0.002315 -0.01125 0.9946 2.751e-06 -1.235e-06 1.012 2.073e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09989 0.1851 0.1965 0.9873 0.9919 0.1128 0.7309 0.8604 0.3046 ] Network output: [ -0.002196 0.01048 1.005 3.03e-06 -1.36e-06 0.9893 2.283e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09512 0.09318 0.1648 0.1969 0.9852 0.9911 0.09513 0.6545 0.8352 0.2502 ] Network output: [ 7.425e-05 1 -5.325e-05 3.951e-07 -1.774e-07 0.9998 2.978e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001307 Epoch 10201 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008453 0.9969 0.9929 -1.222e-07 5.488e-08 -0.006741 -9.213e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.00339 -0.00641 0.005209 0.9699 0.9743 0.006913 0.8227 0.8188 0.01582 ] Network output: [ 0.9999 3.891e-05 0.0003006 -1.429e-06 6.415e-07 -0.0002443 -1.077e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2113 -0.0361 -0.1528 0.1809 0.9834 0.9932 0.2375 0.4274 0.8677 0.707 ] Network output: [ -0.008481 1.003 1.007 -1.266e-07 5.685e-08 0.007113 -9.543e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00699 0.0006532 0.004315 0.00306 0.9889 0.9919 0.007128 0.8499 0.8914 0.01125 ] Network output: [ -0.0001152 0.001078 1 -4.491e-06 2.016e-06 0.9987 -3.385e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2256 0.1073 0.3514 0.1411 0.9849 0.9939 0.2264 0.4313 0.8745 0.7006 ] Network output: [ 0.002314 -0.01124 0.9946 2.748e-06 -1.234e-06 1.012 2.071e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09989 0.1851 0.1965 0.9873 0.9919 0.1128 0.7309 0.8604 0.3046 ] Network output: [ -0.002195 0.01047 1.005 3.026e-06 -1.358e-06 0.9893 2.28e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09512 0.09318 0.1648 0.1969 0.9852 0.9911 0.09514 0.6545 0.8352 0.2502 ] Network output: [ 7.424e-05 1 -5.327e-05 3.946e-07 -1.772e-07 0.9998 2.974e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001306 Epoch 10202 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008452 0.9969 0.9929 -1.221e-07 5.483e-08 -0.00674 -9.205e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.00339 -0.006409 0.005209 0.9699 0.9743 0.006913 0.8227 0.8188 0.01582 ] Network output: [ 0.9999 3.878e-05 0.0003005 -1.427e-06 6.407e-07 -0.0002442 -1.076e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2113 -0.0361 -0.1528 0.1809 0.9834 0.9932 0.2375 0.4274 0.8677 0.707 ] Network output: [ -0.008481 1.003 1.007 -1.265e-07 5.68e-08 0.007112 -9.534e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00699 0.0006532 0.004315 0.00306 0.9889 0.9919 0.007128 0.8498 0.8914 0.01125 ] Network output: [ -0.0001151 0.001077 1 -4.486e-06 2.014e-06 0.9987 -3.381e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2256 0.1073 0.3514 0.1411 0.9849 0.9939 0.2264 0.4313 0.8745 0.7006 ] Network output: [ 0.002312 -0.01123 0.9946 2.744e-06 -1.232e-06 1.012 2.068e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09989 0.1851 0.1965 0.9873 0.9919 0.1128 0.7309 0.8604 0.3046 ] Network output: [ -0.002193 0.01047 1.005 3.022e-06 -1.357e-06 0.9893 2.278e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09512 0.09318 0.1648 0.1969 0.9852 0.9911 0.09514 0.6545 0.8352 0.2502 ] Network output: [ 7.423e-05 1 -5.329e-05 3.941e-07 -1.769e-07 0.9998 2.97e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001306 Epoch 10203 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008451 0.9969 0.9929 -1.22e-07 5.479e-08 -0.006739 -9.198e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.003391 -0.006409 0.005208 0.9699 0.9743 0.006913 0.8227 0.8188 0.01582 ] Network output: [ 0.9999 3.865e-05 0.0003004 -1.425e-06 6.399e-07 -0.000244 -1.074e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2113 -0.0361 -0.1528 0.1809 0.9834 0.9932 0.2375 0.4274 0.8677 0.707 ] Network output: [ -0.00848 1.003 1.007 -1.264e-07 5.674e-08 0.007112 -9.526e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00699 0.0006532 0.004315 0.003059 0.9889 0.9919 0.007129 0.8498 0.8914 0.01125 ] Network output: [ -0.0001149 0.001076 1 -4.48e-06 2.011e-06 0.9987 -3.376e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2256 0.1073 0.3514 0.1411 0.9849 0.9939 0.2264 0.4313 0.8745 0.7006 ] Network output: [ 0.002311 -0.01123 0.9946 2.741e-06 -1.23e-06 1.012 2.066e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09989 0.1851 0.1965 0.9873 0.9919 0.1128 0.7309 0.8604 0.3046 ] Network output: [ -0.002192 0.01046 1.005 3.018e-06 -1.355e-06 0.9893 2.275e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09512 0.09318 0.1648 0.1969 0.9852 0.9911 0.09514 0.6545 0.8352 0.2503 ] Network output: [ 7.421e-05 1 -5.332e-05 3.936e-07 -1.767e-07 0.9998 2.967e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001305 Epoch 10204 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008451 0.9969 0.9929 -1.219e-07 5.475e-08 -0.006739 -9.19e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.003391 -0.006408 0.005208 0.9699 0.9743 0.006913 0.8227 0.8188 0.01582 ] Network output: [ 0.9999 3.852e-05 0.0003002 -1.424e-06 6.391e-07 -0.0002439 -1.073e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2114 -0.0361 -0.1528 0.1809 0.9834 0.9932 0.2375 0.4274 0.8677 0.707 ] Network output: [ -0.008479 1.003 1.007 -1.263e-07 5.669e-08 0.007111 -9.517e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006991 0.0006533 0.004315 0.003059 0.9889 0.9919 0.007129 0.8498 0.8914 0.01125 ] Network output: [ -0.0001148 0.001076 1 -4.475e-06 2.009e-06 0.9987 -3.372e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2256 0.1073 0.3515 0.1411 0.9849 0.9939 0.2264 0.4313 0.8745 0.7006 ] Network output: [ 0.002309 -0.01122 0.9946 2.738e-06 -1.229e-06 1.012 2.063e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09989 0.1851 0.1965 0.9873 0.9919 0.1128 0.7308 0.8604 0.3046 ] Network output: [ -0.002191 0.01045 1.005 3.015e-06 -1.353e-06 0.9893 2.272e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09512 0.09318 0.1648 0.1969 0.9852 0.9911 0.09514 0.6545 0.8352 0.2503 ] Network output: [ 7.42e-05 1 -5.334e-05 3.932e-07 -1.765e-07 0.9998 2.963e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001304 Epoch 10205 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00845 0.9969 0.9929 -1.218e-07 5.47e-08 -0.006738 -9.183e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.003391 -0.006408 0.005208 0.9699 0.9743 0.006913 0.8227 0.8188 0.01582 ] Network output: [ 0.9999 3.839e-05 0.0003001 -1.422e-06 6.383e-07 -0.0002438 -1.071e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2114 -0.0361 -0.1528 0.1809 0.9834 0.9932 0.2375 0.4274 0.8677 0.707 ] Network output: [ -0.008478 1.003 1.007 -1.262e-07 5.664e-08 0.007111 -9.508e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006991 0.0006533 0.004314 0.003059 0.9889 0.9919 0.007129 0.8498 0.8914 0.01125 ] Network output: [ -0.0001147 0.001075 1 -4.469e-06 2.006e-06 0.9987 -3.368e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2256 0.1073 0.3515 0.1411 0.9849 0.9939 0.2264 0.4313 0.8745 0.7006 ] Network output: [ 0.002308 -0.01122 0.9946 2.734e-06 -1.227e-06 1.012 2.061e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09989 0.1851 0.1965 0.9873 0.9919 0.1128 0.7308 0.8603 0.3046 ] Network output: [ -0.00219 0.01045 1.005 3.011e-06 -1.352e-06 0.9893 2.269e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09513 0.09318 0.1648 0.1969 0.9852 0.9911 0.09514 0.6545 0.8352 0.2503 ] Network output: [ 7.418e-05 1 -5.336e-05 3.927e-07 -1.763e-07 0.9998 2.959e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001303 Epoch 10206 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008449 0.9969 0.9929 -1.217e-07 5.466e-08 -0.006737 -9.175e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.003391 -0.006407 0.005207 0.9699 0.9743 0.006914 0.8227 0.8188 0.01582 ] Network output: [ 0.9999 3.827e-05 0.0003 -1.42e-06 6.375e-07 -0.0002437 -1.07e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2114 -0.0361 -0.1528 0.1809 0.9834 0.9932 0.2375 0.4274 0.8677 0.707 ] Network output: [ -0.008477 1.003 1.007 -1.261e-07 5.659e-08 0.00711 -9.5e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006991 0.0006533 0.004314 0.003059 0.9889 0.9919 0.00713 0.8498 0.8914 0.01125 ] Network output: [ -0.0001145 0.001074 1 -4.463e-06 2.004e-06 0.9987 -3.364e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2256 0.1073 0.3515 0.1411 0.9849 0.9939 0.2264 0.4313 0.8745 0.7006 ] Network output: [ 0.002307 -0.01121 0.9946 2.731e-06 -1.226e-06 1.012 2.058e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.0999 0.1851 0.1965 0.9873 0.9919 0.1128 0.7308 0.8603 0.3046 ] Network output: [ -0.002188 0.01044 1.005 3.007e-06 -1.35e-06 0.9893 2.266e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09513 0.09318 0.1648 0.1969 0.9852 0.9911 0.09514 0.6545 0.8352 0.2503 ] Network output: [ 7.417e-05 1 -5.338e-05 3.922e-07 -1.761e-07 0.9998 2.956e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001303 Epoch 10207 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008448 0.9969 0.9929 -1.216e-07 5.461e-08 -0.006737 -9.168e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.003391 -0.006407 0.005207 0.9699 0.9743 0.006914 0.8227 0.8188 0.01581 ] Network output: [ 0.9999 3.814e-05 0.0002998 -1.418e-06 6.367e-07 -0.0002435 -1.069e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2114 -0.0361 -0.1528 0.1809 0.9834 0.9932 0.2375 0.4274 0.8677 0.707 ] Network output: [ -0.008477 1.003 1.007 -1.259e-07 5.654e-08 0.00711 -9.491e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006991 0.0006534 0.004314 0.003059 0.9889 0.9919 0.00713 0.8498 0.8914 0.01125 ] Network output: [ -0.0001144 0.001074 1 -4.458e-06 2.001e-06 0.9987 -3.36e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2256 0.1073 0.3515 0.1411 0.9849 0.9939 0.2264 0.4313 0.8745 0.7006 ] Network output: [ 0.002305 -0.0112 0.9946 2.727e-06 -1.224e-06 1.012 2.055e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.0999 0.1851 0.1965 0.9873 0.9919 0.1128 0.7308 0.8603 0.3046 ] Network output: [ -0.002187 0.01044 1.005 3.004e-06 -1.348e-06 0.9893 2.264e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09513 0.09318 0.1648 0.1969 0.9852 0.9911 0.09514 0.6545 0.8352 0.2503 ] Network output: [ 7.416e-05 1 -5.34e-05 3.917e-07 -1.759e-07 0.9998 2.952e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001302 Epoch 10208 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008447 0.9969 0.9929 -1.215e-07 5.457e-08 -0.006736 -9.16e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.003391 -0.006406 0.005207 0.9699 0.9743 0.006914 0.8227 0.8188 0.01581 ] Network output: [ 0.9999 3.801e-05 0.0002997 -1.416e-06 6.359e-07 -0.0002434 -1.067e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2114 -0.0361 -0.1528 0.1809 0.9834 0.9932 0.2375 0.4274 0.8677 0.707 ] Network output: [ -0.008476 1.003 1.007 -1.258e-07 5.649e-08 0.007109 -9.482e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006992 0.0006534 0.004314 0.003058 0.9889 0.9919 0.00713 0.8498 0.8914 0.01125 ] Network output: [ -0.0001143 0.001073 1 -4.452e-06 1.999e-06 0.9987 -3.355e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2256 0.1073 0.3515 0.1411 0.9849 0.9939 0.2264 0.4313 0.8745 0.7006 ] Network output: [ 0.002304 -0.0112 0.9946 2.724e-06 -1.223e-06 1.012 2.053e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.0999 0.1851 0.1965 0.9873 0.9919 0.1128 0.7308 0.8603 0.3046 ] Network output: [ -0.002186 0.01043 1.005 3e-06 -1.347e-06 0.9894 2.261e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09513 0.09319 0.1648 0.1969 0.9852 0.9911 0.09514 0.6545 0.8352 0.2503 ] Network output: [ 7.414e-05 1 -5.342e-05 3.912e-07 -1.756e-07 0.9998 2.948e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001301 Epoch 10209 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008447 0.9969 0.9929 -1.214e-07 5.452e-08 -0.006735 -9.153e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.003391 -0.006406 0.005206 0.9699 0.9743 0.006914 0.8227 0.8188 0.01581 ] Network output: [ 1 3.788e-05 0.0002996 -1.415e-06 6.351e-07 -0.0002433 -1.066e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2114 -0.0361 -0.1528 0.1809 0.9834 0.9932 0.2375 0.4274 0.8677 0.707 ] Network output: [ -0.008475 1.003 1.007 -1.257e-07 5.643e-08 0.007109 -9.474e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006992 0.0006534 0.004314 0.003058 0.9889 0.9919 0.007131 0.8498 0.8914 0.01125 ] Network output: [ -0.0001141 0.001072 1 -4.447e-06 1.996e-06 0.9987 -3.351e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2256 0.1074 0.3515 0.1411 0.9849 0.9939 0.2264 0.4313 0.8745 0.7006 ] Network output: [ 0.002302 -0.01119 0.9946 2.721e-06 -1.221e-06 1.012 2.05e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.0999 0.1851 0.1965 0.9873 0.9919 0.1128 0.7308 0.8603 0.3046 ] Network output: [ -0.002184 0.01043 1.005 2.996e-06 -1.345e-06 0.9894 2.258e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09513 0.09319 0.1648 0.1969 0.9852 0.9911 0.09515 0.6545 0.8352 0.2503 ] Network output: [ 7.413e-05 1 -5.344e-05 3.907e-07 -1.754e-07 0.9998 2.945e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00013 Epoch 10210 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008446 0.9969 0.9929 -1.214e-07 5.448e-08 -0.006734 -9.145e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003531 -0.003391 -0.006405 0.005206 0.9699 0.9743 0.006914 0.8227 0.8188 0.01581 ] Network output: [ 1 3.776e-05 0.0002995 -1.413e-06 6.343e-07 -0.0002431 -1.065e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2114 -0.0361 -0.1528 0.1809 0.9834 0.9932 0.2375 0.4274 0.8677 0.707 ] Network output: [ -0.008474 1.003 1.007 -1.256e-07 5.638e-08 0.007109 -9.465e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006992 0.0006535 0.004314 0.003058 0.9889 0.9919 0.007131 0.8498 0.8914 0.01125 ] Network output: [ -0.000114 0.001071 1 -4.441e-06 1.994e-06 0.9987 -3.347e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2256 0.1074 0.3515 0.1411 0.9849 0.9939 0.2264 0.4313 0.8745 0.7006 ] Network output: [ 0.002301 -0.01118 0.9946 2.717e-06 -1.22e-06 1.012 2.048e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.0999 0.1851 0.1965 0.9873 0.9919 0.1128 0.7308 0.8603 0.3046 ] Network output: [ -0.002183 0.01042 1.005 2.993e-06 -1.343e-06 0.9894 2.255e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09513 0.09319 0.1648 0.1969 0.9852 0.9911 0.09515 0.6545 0.8352 0.2503 ] Network output: [ 7.412e-05 1 -5.346e-05 3.903e-07 -1.752e-07 0.9998 2.941e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00013 Epoch 10211 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008445 0.9969 0.993 -1.213e-07 5.443e-08 -0.006734 -9.138e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003391 -0.006405 0.005206 0.9699 0.9743 0.006914 0.8227 0.8188 0.01581 ] Network output: [ 1 3.763e-05 0.0002993 -1.411e-06 6.335e-07 -0.000243 -1.063e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2114 -0.03611 -0.1528 0.1809 0.9834 0.9932 0.2375 0.4274 0.8677 0.707 ] Network output: [ -0.008474 1.003 1.007 -1.255e-07 5.633e-08 0.007108 -9.456e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006993 0.0006535 0.004314 0.003058 0.9889 0.9919 0.007131 0.8498 0.8914 0.01125 ] Network output: [ -0.0001139 0.001071 1 -4.435e-06 1.991e-06 0.9987 -3.343e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2256 0.1074 0.3515 0.1411 0.9849 0.9939 0.2264 0.4313 0.8745 0.7006 ] Network output: [ 0.002299 -0.01118 0.9946 2.714e-06 -1.218e-06 1.012 2.045e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09991 0.1851 0.1965 0.9873 0.9919 0.1128 0.7308 0.8603 0.3046 ] Network output: [ -0.002182 0.01042 1.005 2.989e-06 -1.342e-06 0.9894 2.253e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09513 0.09319 0.1648 0.1969 0.9852 0.9911 0.09515 0.6545 0.8352 0.2503 ] Network output: [ 7.41e-05 1 -5.348e-05 3.898e-07 -1.75e-07 0.9998 2.937e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001299 Epoch 10212 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008444 0.9969 0.993 -1.212e-07 5.439e-08 -0.006733 -9.13e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003391 -0.006404 0.005205 0.9699 0.9743 0.006914 0.8227 0.8188 0.01581 ] Network output: [ 1 3.75e-05 0.0002992 -1.409e-06 6.327e-07 -0.0002429 -1.062e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2114 -0.03611 -0.1528 0.1809 0.9834 0.9932 0.2375 0.4274 0.8677 0.707 ] Network output: [ -0.008473 1.003 1.007 -1.254e-07 5.628e-08 0.007108 -9.448e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006993 0.0006536 0.004314 0.003057 0.9889 0.9919 0.007131 0.8498 0.8914 0.01125 ] Network output: [ -0.0001138 0.00107 1 -4.43e-06 1.989e-06 0.9987 -3.339e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2257 0.1074 0.3515 0.1411 0.9849 0.9939 0.2264 0.4313 0.8745 0.7006 ] Network output: [ 0.002298 -0.01117 0.9946 2.71e-06 -1.217e-06 1.012 2.043e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09991 0.1851 0.1965 0.9873 0.9919 0.1128 0.7308 0.8603 0.3046 ] Network output: [ -0.002181 0.01041 1.005 2.985e-06 -1.34e-06 0.9894 2.25e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09513 0.09319 0.1648 0.1969 0.9852 0.9911 0.09515 0.6545 0.8352 0.2503 ] Network output: [ 7.409e-05 1 -5.35e-05 3.893e-07 -1.748e-07 0.9998 2.934e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001298 Epoch 10213 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008443 0.9969 0.993 -1.211e-07 5.435e-08 -0.006732 -9.123e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003391 -0.006404 0.005205 0.9699 0.9743 0.006914 0.8227 0.8188 0.01581 ] Network output: [ 1 3.737e-05 0.0002991 -1.408e-06 6.319e-07 -0.0002428 -1.061e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2114 -0.03611 -0.1528 0.1809 0.9834 0.9932 0.2376 0.4274 0.8677 0.707 ] Network output: [ -0.008472 1.003 1.007 -1.252e-07 5.623e-08 0.007107 -9.439e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006993 0.0006536 0.004314 0.003057 0.9889 0.9919 0.007132 0.8498 0.8914 0.01124 ] Network output: [ -0.0001136 0.001069 1 -4.424e-06 1.986e-06 0.9987 -3.334e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2257 0.1074 0.3515 0.1411 0.9849 0.9939 0.2264 0.4313 0.8745 0.7006 ] Network output: [ 0.002297 -0.01117 0.9946 2.707e-06 -1.215e-06 1.012 2.04e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09991 0.1851 0.1965 0.9873 0.9919 0.1128 0.7308 0.8603 0.3046 ] Network output: [ -0.002179 0.0104 1.005 2.982e-06 -1.339e-06 0.9894 2.247e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09514 0.09319 0.1648 0.1969 0.9852 0.9911 0.09515 0.6544 0.8352 0.2503 ] Network output: [ 7.408e-05 1 -5.352e-05 3.888e-07 -1.746e-07 0.9998 2.93e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001297 Epoch 10214 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008443 0.9969 0.993 -1.21e-07 5.43e-08 -0.006732 -9.116e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003391 -0.006403 0.005204 0.9699 0.9743 0.006914 0.8227 0.8188 0.01581 ] Network output: [ 1 3.725e-05 0.0002989 -1.406e-06 6.311e-07 -0.0002426 -1.059e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2114 -0.03611 -0.1527 0.1809 0.9834 0.9932 0.2376 0.4273 0.8677 0.707 ] Network output: [ -0.008471 1.003 1.007 -1.251e-07 5.618e-08 0.007107 -9.431e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006993 0.0006536 0.004313 0.003057 0.9889 0.9919 0.007132 0.8498 0.8914 0.01124 ] Network output: [ -0.0001135 0.001069 1 -4.419e-06 1.984e-06 0.9987 -3.33e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2257 0.1074 0.3515 0.1411 0.9849 0.9939 0.2264 0.4313 0.8745 0.7005 ] Network output: [ 0.002295 -0.01116 0.9946 2.704e-06 -1.214e-06 1.012 2.038e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09991 0.1851 0.1965 0.9873 0.9919 0.1128 0.7308 0.8603 0.3046 ] Network output: [ -0.002178 0.0104 1.005 2.978e-06 -1.337e-06 0.9894 2.244e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09514 0.09319 0.1648 0.1969 0.9852 0.9911 0.09515 0.6544 0.8352 0.2503 ] Network output: [ 7.406e-05 1 -5.355e-05 3.883e-07 -1.743e-07 0.9998 2.927e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001297 Epoch 10215 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008442 0.9969 0.993 -1.209e-07 5.426e-08 -0.006731 -9.108e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003391 -0.006403 0.005204 0.9699 0.9743 0.006915 0.8227 0.8188 0.01581 ] Network output: [ 1 3.712e-05 0.0002988 -1.404e-06 6.303e-07 -0.0002425 -1.058e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2114 -0.03611 -0.1527 0.1809 0.9834 0.9932 0.2376 0.4273 0.8677 0.707 ] Network output: [ -0.008471 1.003 1.007 -1.25e-07 5.613e-08 0.007106 -9.422e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006994 0.0006537 0.004313 0.003057 0.9889 0.9919 0.007132 0.8498 0.8914 0.01124 ] Network output: [ -0.0001134 0.001068 1 -4.413e-06 1.981e-06 0.9987 -3.326e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2257 0.1074 0.3515 0.1411 0.9849 0.9939 0.2265 0.4313 0.8745 0.7005 ] Network output: [ 0.002294 -0.01115 0.9946 2.7e-06 -1.212e-06 1.012 2.035e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09991 0.1851 0.1965 0.9873 0.9919 0.1128 0.7308 0.8603 0.3046 ] Network output: [ -0.002177 0.01039 1.005 2.974e-06 -1.335e-06 0.9894 2.242e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09514 0.09319 0.1648 0.1969 0.9852 0.9911 0.09515 0.6544 0.8352 0.2503 ] Network output: [ 7.405e-05 1 -5.357e-05 3.879e-07 -1.741e-07 0.9998 2.923e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001296 Epoch 10216 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008441 0.9969 0.993 -1.208e-07 5.421e-08 -0.00673 -9.101e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003391 -0.006402 0.005204 0.9699 0.9743 0.006915 0.8227 0.8188 0.01581 ] Network output: [ 1 3.699e-05 0.0002987 -1.402e-06 6.295e-07 -0.0002424 -1.057e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2114 -0.03611 -0.1527 0.1809 0.9834 0.9932 0.2376 0.4273 0.8677 0.707 ] Network output: [ -0.00847 1.003 1.007 -1.249e-07 5.607e-08 0.007106 -9.413e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006994 0.0006537 0.004313 0.003057 0.9889 0.9919 0.007133 0.8498 0.8914 0.01124 ] Network output: [ -0.0001132 0.001067 1 -4.408e-06 1.979e-06 0.9987 -3.322e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2257 0.1074 0.3515 0.1411 0.9849 0.9939 0.2265 0.4313 0.8745 0.7005 ] Network output: [ 0.002292 -0.01115 0.9946 2.697e-06 -1.211e-06 1.012 2.032e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09991 0.1851 0.1965 0.9873 0.9919 0.1128 0.7308 0.8603 0.3046 ] Network output: [ -0.002176 0.01039 1.005 2.971e-06 -1.334e-06 0.9894 2.239e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09514 0.0932 0.1648 0.1969 0.9852 0.9911 0.09515 0.6544 0.8352 0.2503 ] Network output: [ 7.403e-05 1 -5.359e-05 3.874e-07 -1.739e-07 0.9998 2.919e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001295 Epoch 10217 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00844 0.9969 0.993 -1.207e-07 5.417e-08 -0.00673 -9.093e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003391 -0.006402 0.005203 0.9699 0.9743 0.006915 0.8227 0.8188 0.01581 ] Network output: [ 1 3.686e-05 0.0002986 -1.401e-06 6.288e-07 -0.0002423 -1.055e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2114 -0.03611 -0.1527 0.1809 0.9834 0.9932 0.2376 0.4273 0.8677 0.707 ] Network output: [ -0.008469 1.003 1.007 -1.248e-07 5.602e-08 0.007105 -9.405e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006994 0.0006537 0.004313 0.003056 0.9889 0.9919 0.007133 0.8498 0.8914 0.01124 ] Network output: [ -0.0001131 0.001067 1 -4.402e-06 1.976e-06 0.9987 -3.318e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2257 0.1074 0.3515 0.1411 0.9849 0.9939 0.2265 0.4313 0.8745 0.7005 ] Network output: [ 0.002291 -0.01114 0.9946 2.694e-06 -1.209e-06 1.012 2.03e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09992 0.1851 0.1965 0.9873 0.9919 0.1128 0.7307 0.8603 0.3046 ] Network output: [ -0.002174 0.01038 1.005 2.967e-06 -1.332e-06 0.9894 2.236e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09514 0.0932 0.1648 0.1969 0.9852 0.9911 0.09516 0.6544 0.8352 0.2503 ] Network output: [ 7.402e-05 1 -5.361e-05 3.869e-07 -1.737e-07 0.9998 2.916e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001294 Epoch 10218 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008439 0.9969 0.993 -1.206e-07 5.412e-08 -0.006729 -9.086e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003391 -0.006401 0.005203 0.9699 0.9743 0.006915 0.8227 0.8188 0.01581 ] Network output: [ 1 3.674e-05 0.0002984 -1.399e-06 6.28e-07 -0.0002421 -1.054e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2114 -0.03611 -0.1527 0.1809 0.9834 0.9932 0.2376 0.4273 0.8677 0.707 ] Network output: [ -0.008468 1.003 1.007 -1.247e-07 5.597e-08 0.007105 -9.396e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006995 0.0006538 0.004313 0.003056 0.9889 0.9919 0.007133 0.8498 0.8914 0.01124 ] Network output: [ -0.000113 0.001066 1 -4.397e-06 1.974e-06 0.9987 -3.314e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2257 0.1074 0.3515 0.1411 0.9849 0.9939 0.2265 0.4313 0.8745 0.7005 ] Network output: [ 0.00229 -0.01113 0.9946 2.69e-06 -1.208e-06 1.012 2.027e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09992 0.1851 0.1965 0.9873 0.9919 0.1128 0.7307 0.8603 0.3046 ] Network output: [ -0.002173 0.01038 1.005 2.963e-06 -1.33e-06 0.9894 2.233e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09514 0.0932 0.1648 0.1969 0.9852 0.9911 0.09516 0.6544 0.8352 0.2503 ] Network output: [ 7.401e-05 1 -5.363e-05 3.864e-07 -1.735e-07 0.9998 2.912e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001294 Epoch 10219 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008439 0.9969 0.993 -1.205e-07 5.408e-08 -0.006728 -9.078e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003392 -0.006401 0.005203 0.9699 0.9743 0.006915 0.8227 0.8188 0.0158 ] Network output: [ 1 3.661e-05 0.0002983 -1.397e-06 6.272e-07 -0.000242 -1.053e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2114 -0.03611 -0.1527 0.1809 0.9834 0.9932 0.2376 0.4273 0.8677 0.707 ] Network output: [ -0.008468 1.003 1.007 -1.246e-07 5.592e-08 0.007105 -9.387e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006995 0.0006538 0.004313 0.003056 0.9889 0.9919 0.007133 0.8498 0.8914 0.01124 ] Network output: [ -0.0001128 0.001065 1 -4.391e-06 1.971e-06 0.9987 -3.309e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2257 0.1074 0.3515 0.1411 0.9849 0.9939 0.2265 0.4313 0.8745 0.7005 ] Network output: [ 0.002288 -0.01113 0.9946 2.687e-06 -1.206e-06 1.012 2.025e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09992 0.1851 0.1965 0.9873 0.9919 0.1128 0.7307 0.8603 0.3046 ] Network output: [ -0.002172 0.01037 1.005 2.96e-06 -1.329e-06 0.9894 2.231e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09514 0.0932 0.1648 0.1969 0.9852 0.9911 0.09516 0.6544 0.8352 0.2503 ] Network output: [ 7.399e-05 1 -5.365e-05 3.859e-07 -1.733e-07 0.9998 2.909e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001293 Epoch 10220 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008438 0.9969 0.993 -1.204e-07 5.404e-08 -0.006728 -9.071e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003392 -0.0064 0.005202 0.9699 0.9743 0.006915 0.8227 0.8188 0.0158 ] Network output: [ 1 3.648e-05 0.0002982 -1.395e-06 6.264e-07 -0.0002419 -1.052e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2114 -0.03612 -0.1527 0.1809 0.9834 0.9932 0.2376 0.4273 0.8677 0.707 ] Network output: [ -0.008467 1.003 1.007 -1.244e-07 5.587e-08 0.007104 -9.379e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006995 0.0006538 0.004313 0.003056 0.9889 0.9919 0.007134 0.8498 0.8914 0.01124 ] Network output: [ -0.0001127 0.001065 1 -4.386e-06 1.969e-06 0.9987 -3.305e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2257 0.1074 0.3515 0.1411 0.9849 0.9939 0.2265 0.4313 0.8745 0.7005 ] Network output: [ 0.002287 -0.01112 0.9946 2.683e-06 -1.205e-06 1.012 2.022e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09992 0.1851 0.1965 0.9873 0.9919 0.1128 0.7307 0.8603 0.3046 ] Network output: [ -0.002171 0.01037 1.005 2.956e-06 -1.327e-06 0.9894 2.228e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09514 0.0932 0.1648 0.1969 0.9852 0.9911 0.09516 0.6544 0.8352 0.2503 ] Network output: [ 7.398e-05 1 -5.367e-05 3.855e-07 -1.73e-07 0.9998 2.905e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001292 Epoch 10221 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008437 0.9969 0.993 -1.203e-07 5.399e-08 -0.006727 -9.064e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003392 -0.0064 0.005202 0.9699 0.9743 0.006915 0.8227 0.8188 0.0158 ] Network output: [ 1 3.636e-05 0.000298 -1.394e-06 6.256e-07 -0.0002418 -1.05e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2114 -0.03612 -0.1527 0.1809 0.9834 0.9932 0.2376 0.4273 0.8677 0.707 ] Network output: [ -0.008466 1.003 1.007 -1.243e-07 5.582e-08 0.007104 -9.37e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006995 0.0006539 0.004313 0.003056 0.9889 0.9919 0.007134 0.8498 0.8914 0.01124 ] Network output: [ -0.0001126 0.001064 1 -4.38e-06 1.966e-06 0.9987 -3.301e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2257 0.1074 0.3515 0.1411 0.9849 0.9939 0.2265 0.4313 0.8745 0.7005 ] Network output: [ 0.002285 -0.01112 0.9946 2.68e-06 -1.203e-06 1.012 2.02e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09992 0.1851 0.1965 0.9873 0.9919 0.1128 0.7307 0.8603 0.3046 ] Network output: [ -0.002169 0.01036 1.005 2.952e-06 -1.325e-06 0.9894 2.225e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09515 0.0932 0.1648 0.1969 0.9852 0.9911 0.09516 0.6544 0.8352 0.2503 ] Network output: [ 7.397e-05 1 -5.37e-05 3.85e-07 -1.728e-07 0.9998 2.901e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001291 Epoch 10222 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008436 0.9969 0.993 -1.202e-07 5.395e-08 -0.006726 -9.056e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003392 -0.006399 0.005202 0.9699 0.9743 0.006915 0.8227 0.8188 0.0158 ] Network output: [ 1 3.623e-05 0.0002979 -1.392e-06 6.248e-07 -0.0002416 -1.049e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2114 -0.03612 -0.1527 0.1809 0.9834 0.9932 0.2376 0.4273 0.8677 0.707 ] Network output: [ -0.008465 1.003 1.007 -1.242e-07 5.577e-08 0.007103 -9.362e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006996 0.0006539 0.004312 0.003055 0.9889 0.9919 0.007134 0.8498 0.8914 0.01124 ] Network output: [ -0.0001125 0.001063 1 -4.375e-06 1.964e-06 0.9987 -3.297e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2257 0.1074 0.3515 0.1411 0.9849 0.9939 0.2265 0.4313 0.8745 0.7005 ] Network output: [ 0.002284 -0.01111 0.9946 2.677e-06 -1.202e-06 1.012 2.017e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09992 0.1851 0.1965 0.9873 0.9919 0.1128 0.7307 0.8603 0.3046 ] Network output: [ -0.002168 0.01036 1.005 2.949e-06 -1.324e-06 0.9894 2.222e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09515 0.0932 0.1648 0.1969 0.9852 0.9911 0.09516 0.6544 0.8352 0.2503 ] Network output: [ 7.395e-05 1 -5.372e-05 3.845e-07 -1.726e-07 0.9998 2.898e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001291 Epoch 10223 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008435 0.9969 0.993 -1.201e-07 5.39e-08 -0.006725 -9.049e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003392 -0.006399 0.005201 0.9699 0.9743 0.006915 0.8226 0.8188 0.0158 ] Network output: [ 1 3.61e-05 0.0002978 -1.39e-06 6.24e-07 -0.0002415 -1.048e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2114 -0.03612 -0.1527 0.1809 0.9834 0.9932 0.2376 0.4273 0.8677 0.707 ] Network output: [ -0.008465 1.003 1.007 -1.241e-07 5.572e-08 0.007103 -9.353e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006996 0.0006539 0.004312 0.003055 0.9889 0.9919 0.007135 0.8498 0.8914 0.01124 ] Network output: [ -0.0001123 0.001063 1 -4.369e-06 1.962e-06 0.9987 -3.293e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2257 0.1074 0.3515 0.1411 0.9849 0.9939 0.2265 0.4312 0.8745 0.7005 ] Network output: [ 0.002283 -0.0111 0.9946 2.673e-06 -1.2e-06 1.012 2.015e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09993 0.1851 0.1965 0.9873 0.9919 0.1128 0.7307 0.8603 0.3046 ] Network output: [ -0.002167 0.01035 1.005 2.945e-06 -1.322e-06 0.9894 2.22e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09515 0.0932 0.1648 0.1969 0.9852 0.9911 0.09516 0.6544 0.8352 0.2503 ] Network output: [ 7.394e-05 1 -5.374e-05 3.84e-07 -1.724e-07 0.9998 2.894e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000129 Epoch 10224 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008435 0.9969 0.993 -1.2e-07 5.386e-08 -0.006725 -9.041e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003392 -0.006398 0.005201 0.9699 0.9743 0.006916 0.8226 0.8188 0.0158 ] Network output: [ 1 3.598e-05 0.0002977 -1.388e-06 6.233e-07 -0.0002414 -1.046e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2114 -0.03612 -0.1527 0.1809 0.9834 0.9932 0.2376 0.4273 0.8677 0.707 ] Network output: [ -0.008464 1.003 1.007 -1.24e-07 5.567e-08 0.007102 -9.345e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006996 0.000654 0.004312 0.003055 0.9889 0.9919 0.007135 0.8498 0.8914 0.01124 ] Network output: [ -0.0001122 0.001062 1 -4.364e-06 1.959e-06 0.9987 -3.289e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2257 0.1074 0.3515 0.1411 0.9849 0.9939 0.2265 0.4312 0.8745 0.7005 ] Network output: [ 0.002281 -0.0111 0.9946 2.67e-06 -1.199e-06 1.012 2.012e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09993 0.1851 0.1965 0.9873 0.9919 0.1128 0.7307 0.8603 0.3046 ] Network output: [ -0.002166 0.01034 1.005 2.942e-06 -1.321e-06 0.9894 2.217e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09515 0.09321 0.1648 0.1969 0.9852 0.9911 0.09516 0.6544 0.8352 0.2503 ] Network output: [ 7.393e-05 1 -5.376e-05 3.836e-07 -1.722e-07 0.9998 2.891e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001289 Epoch 10225 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008434 0.9969 0.993 -1.199e-07 5.381e-08 -0.006724 -9.034e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003392 -0.006398 0.005201 0.9699 0.9743 0.006916 0.8226 0.8188 0.0158 ] Network output: [ 1 3.585e-05 0.0002975 -1.387e-06 6.225e-07 -0.0002412 -1.045e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.03612 -0.1527 0.1809 0.9834 0.9932 0.2376 0.4273 0.8677 0.707 ] Network output: [ -0.008463 1.003 1.007 -1.239e-07 5.561e-08 0.007102 -9.336e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006996 0.000654 0.004312 0.003055 0.9889 0.9919 0.007135 0.8498 0.8914 0.01124 ] Network output: [ -0.0001121 0.001061 1 -4.358e-06 1.957e-06 0.9987 -3.285e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2257 0.1074 0.3515 0.1411 0.9849 0.9939 0.2265 0.4312 0.8745 0.7005 ] Network output: [ 0.00228 -0.01109 0.9947 2.667e-06 -1.197e-06 1.012 2.01e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09993 0.1851 0.1965 0.9873 0.9919 0.1128 0.7307 0.8603 0.3046 ] Network output: [ -0.002164 0.01034 1.005 2.938e-06 -1.319e-06 0.9894 2.214e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09515 0.09321 0.1648 0.1969 0.9852 0.9911 0.09516 0.6543 0.8352 0.2503 ] Network output: [ 7.391e-05 1 -5.378e-05 3.831e-07 -1.72e-07 0.9998 2.887e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001288 Epoch 10226 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008433 0.9969 0.993 -1.198e-07 5.377e-08 -0.006723 -9.027e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003392 -0.006397 0.0052 0.9699 0.9743 0.006916 0.8226 0.8188 0.0158 ] Network output: [ 1 3.572e-05 0.0002974 -1.385e-06 6.217e-07 -0.0002411 -1.044e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.03612 -0.1527 0.1809 0.9834 0.9932 0.2376 0.4273 0.8677 0.707 ] Network output: [ -0.008462 1.003 1.007 -1.238e-07 5.556e-08 0.007102 -9.327e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006997 0.000654 0.004312 0.003054 0.9889 0.9919 0.007135 0.8498 0.8914 0.01124 ] Network output: [ -0.0001119 0.001061 1 -4.353e-06 1.954e-06 0.9987 -3.28e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2257 0.1074 0.3516 0.1411 0.9849 0.9939 0.2265 0.4312 0.8745 0.7005 ] Network output: [ 0.002278 -0.01108 0.9947 2.664e-06 -1.196e-06 1.012 2.007e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09993 0.1851 0.1965 0.9873 0.9919 0.1128 0.7307 0.8603 0.3046 ] Network output: [ -0.002163 0.01033 1.005 2.934e-06 -1.317e-06 0.9894 2.211e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09515 0.09321 0.1648 0.1969 0.9852 0.9911 0.09517 0.6543 0.8352 0.2503 ] Network output: [ 7.39e-05 1 -5.38e-05 3.826e-07 -1.718e-07 0.9998 2.883e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001288 Epoch 10227 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008432 0.9969 0.993 -1.197e-07 5.373e-08 -0.006723 -9.019e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003392 -0.006397 0.0052 0.9699 0.9743 0.006916 0.8226 0.8188 0.0158 ] Network output: [ 1 3.56e-05 0.0002973 -1.383e-06 6.209e-07 -0.000241 -1.042e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.03612 -0.1527 0.1809 0.9834 0.9932 0.2376 0.4273 0.8677 0.7069 ] Network output: [ -0.008461 1.003 1.007 -1.237e-07 5.551e-08 0.007101 -9.319e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006997 0.0006541 0.004312 0.003054 0.9889 0.9919 0.007136 0.8498 0.8914 0.01124 ] Network output: [ -0.0001118 0.00106 1 -4.347e-06 1.952e-06 0.9987 -3.276e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2257 0.1074 0.3516 0.1411 0.9849 0.9939 0.2265 0.4312 0.8745 0.7005 ] Network output: [ 0.002277 -0.01108 0.9947 2.66e-06 -1.194e-06 1.012 2.005e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09993 0.1851 0.1965 0.9873 0.9919 0.1128 0.7307 0.8603 0.3046 ] Network output: [ -0.002162 0.01033 1.005 2.931e-06 -1.316e-06 0.9894 2.209e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09515 0.09321 0.1648 0.1969 0.9852 0.9911 0.09517 0.6543 0.8352 0.2503 ] Network output: [ 7.389e-05 1 -5.383e-05 3.821e-07 -1.716e-07 0.9998 2.88e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001287 Epoch 10228 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008431 0.9969 0.993 -1.196e-07 5.368e-08 -0.006722 -9.012e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003392 -0.006396 0.0052 0.9699 0.9743 0.006916 0.8226 0.8188 0.0158 ] Network output: [ 1 3.547e-05 0.0002971 -1.381e-06 6.201e-07 -0.0002409 -1.041e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.03612 -0.1526 0.1809 0.9834 0.9932 0.2376 0.4273 0.8677 0.7069 ] Network output: [ -0.008461 1.003 1.007 -1.235e-07 5.546e-08 0.007101 -9.31e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006997 0.0006541 0.004312 0.003054 0.9889 0.9919 0.007136 0.8498 0.8914 0.01123 ] Network output: [ -0.0001117 0.001059 1 -4.342e-06 1.949e-06 0.9987 -3.272e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2257 0.1074 0.3516 0.1411 0.9849 0.9939 0.2265 0.4312 0.8745 0.7005 ] Network output: [ 0.002276 -0.01107 0.9947 2.657e-06 -1.193e-06 1.012 2.002e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09994 0.1851 0.1965 0.9873 0.9919 0.1128 0.7307 0.8603 0.3046 ] Network output: [ -0.00216 0.01032 1.005 2.927e-06 -1.314e-06 0.9894 2.206e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09515 0.09321 0.1648 0.1969 0.9852 0.9911 0.09517 0.6543 0.8352 0.2503 ] Network output: [ 7.387e-05 1 -5.385e-05 3.817e-07 -1.713e-07 0.9998 2.876e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001286 Epoch 10229 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008431 0.9969 0.993 -1.195e-07 5.364e-08 -0.006721 -9.004e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003392 -0.006396 0.005199 0.9699 0.9743 0.006916 0.8226 0.8188 0.0158 ] Network output: [ 1 3.534e-05 0.000297 -1.38e-06 6.194e-07 -0.0002407 -1.04e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.03612 -0.1526 0.1809 0.9834 0.9932 0.2376 0.4273 0.8677 0.7069 ] Network output: [ -0.00846 1.003 1.007 -1.234e-07 5.541e-08 0.0071 -9.302e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006998 0.0006541 0.004312 0.003054 0.9889 0.9919 0.007136 0.8498 0.8914 0.01123 ] Network output: [ -0.0001116 0.001058 1 -4.337e-06 1.947e-06 0.9987 -3.268e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2257 0.1074 0.3516 0.1411 0.9849 0.9939 0.2265 0.4312 0.8745 0.7005 ] Network output: [ 0.002274 -0.01107 0.9947 2.654e-06 -1.191e-06 1.012 2e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09994 0.1851 0.1965 0.9873 0.9919 0.1128 0.7307 0.8603 0.3046 ] Network output: [ -0.002159 0.01032 1.005 2.923e-06 -1.312e-06 0.9894 2.203e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09516 0.09321 0.1648 0.1969 0.9852 0.9911 0.09517 0.6543 0.8352 0.2503 ] Network output: [ 7.386e-05 1 -5.387e-05 3.812e-07 -1.711e-07 0.9998 2.873e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001285 Epoch 10230 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00843 0.9969 0.993 -1.194e-07 5.359e-08 -0.006721 -8.997e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003392 -0.006395 0.005199 0.9699 0.9743 0.006916 0.8226 0.8188 0.0158 ] Network output: [ 1 3.522e-05 0.0002969 -1.378e-06 6.186e-07 -0.0002406 -1.038e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.03613 -0.1526 0.1809 0.9834 0.9932 0.2376 0.4273 0.8677 0.7069 ] Network output: [ -0.008459 1.003 1.007 -1.233e-07 5.536e-08 0.0071 -9.293e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006998 0.0006542 0.004312 0.003054 0.9889 0.9919 0.007137 0.8498 0.8914 0.01123 ] Network output: [ -0.0001114 0.001058 1 -4.331e-06 1.944e-06 0.9987 -3.264e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2258 0.1074 0.3516 0.1411 0.9849 0.9939 0.2265 0.4312 0.8745 0.7005 ] Network output: [ 0.002273 -0.01106 0.9947 2.65e-06 -1.19e-06 1.012 1.997e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09994 0.1851 0.1965 0.9873 0.9919 0.1128 0.7306 0.8603 0.3046 ] Network output: [ -0.002158 0.01031 1.005 2.92e-06 -1.311e-06 0.9894 2.201e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09516 0.09321 0.1648 0.1969 0.9852 0.9911 0.09517 0.6543 0.8352 0.2503 ] Network output: [ 7.385e-05 1 -5.389e-05 3.807e-07 -1.709e-07 0.9998 2.869e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001285 Epoch 10231 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008429 0.9969 0.993 -1.193e-07 5.355e-08 -0.00672 -8.99e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003392 -0.006395 0.005199 0.9699 0.9743 0.006916 0.8226 0.8188 0.01579 ] Network output: [ 1 3.509e-05 0.0002968 -1.376e-06 6.178e-07 -0.0002405 -1.037e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.03613 -0.1526 0.1809 0.9834 0.9932 0.2377 0.4273 0.8677 0.7069 ] Network output: [ -0.008458 1.003 1.007 -1.232e-07 5.531e-08 0.007099 -9.285e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006998 0.0006542 0.004311 0.003053 0.9889 0.9919 0.007137 0.8497 0.8914 0.01123 ] Network output: [ -0.0001113 0.001057 1 -4.326e-06 1.942e-06 0.9987 -3.26e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2258 0.1074 0.3516 0.1411 0.9849 0.9939 0.2265 0.4312 0.8745 0.7005 ] Network output: [ 0.002271 -0.01105 0.9947 2.647e-06 -1.188e-06 1.012 1.995e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09994 0.1851 0.1965 0.9873 0.9919 0.1128 0.7306 0.8603 0.3046 ] Network output: [ -0.002157 0.01031 1.005 2.916e-06 -1.309e-06 0.9894 2.198e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09516 0.09321 0.1648 0.1969 0.9852 0.9911 0.09517 0.6543 0.8352 0.2503 ] Network output: [ 7.383e-05 1 -5.391e-05 3.803e-07 -1.707e-07 0.9998 2.866e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001284 Epoch 10232 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008428 0.9969 0.993 -1.192e-07 5.351e-08 -0.006719 -8.982e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003392 -0.006394 0.005198 0.9699 0.9743 0.006917 0.8226 0.8187 0.01579 ] Network output: [ 1 3.496e-05 0.0002966 -1.374e-06 6.17e-07 -0.0002404 -1.036e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.03613 -0.1526 0.1808 0.9834 0.9932 0.2377 0.4273 0.8677 0.7069 ] Network output: [ -0.008458 1.003 1.007 -1.231e-07 5.526e-08 0.007099 -9.276e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006998 0.0006543 0.004311 0.003053 0.9889 0.9919 0.007137 0.8497 0.8913 0.01123 ] Network output: [ -0.0001112 0.001056 1 -4.32e-06 1.94e-06 0.9987 -3.256e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2258 0.1074 0.3516 0.1411 0.9849 0.9939 0.2265 0.4312 0.8745 0.7005 ] Network output: [ 0.00227 -0.01105 0.9947 2.644e-06 -1.187e-06 1.012 1.992e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09994 0.1851 0.1965 0.9873 0.9919 0.1128 0.7306 0.8603 0.3046 ] Network output: [ -0.002155 0.0103 1.005 2.913e-06 -1.308e-06 0.9894 2.195e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09516 0.09321 0.1648 0.1969 0.9852 0.9911 0.09517 0.6543 0.8352 0.2503 ] Network output: [ 7.382e-05 1 -5.394e-05 3.798e-07 -1.705e-07 0.9998 2.862e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001283 Epoch 10233 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008427 0.9969 0.993 -1.191e-07 5.346e-08 -0.006719 -8.975e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003392 -0.006394 0.005198 0.9699 0.9743 0.006917 0.8226 0.8187 0.01579 ] Network output: [ 1 3.484e-05 0.0002965 -1.373e-06 6.163e-07 -0.0002402 -1.035e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.03613 -0.1526 0.1808 0.9834 0.9932 0.2377 0.4273 0.8677 0.7069 ] Network output: [ -0.008457 1.003 1.007 -1.23e-07 5.521e-08 0.007098 -9.268e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006999 0.0006543 0.004311 0.003053 0.9889 0.9919 0.007137 0.8497 0.8913 0.01123 ] Network output: [ -0.000111 0.001056 1 -4.315e-06 1.937e-06 0.9987 -3.252e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2258 0.1074 0.3516 0.1411 0.9849 0.9939 0.2266 0.4312 0.8745 0.7005 ] Network output: [ 0.002268 -0.01104 0.9947 2.64e-06 -1.185e-06 1.012 1.99e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09994 0.1851 0.1965 0.9873 0.9919 0.1128 0.7306 0.8603 0.3046 ] Network output: [ -0.002154 0.01029 1.005 2.909e-06 -1.306e-06 0.9894 2.192e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09516 0.09322 0.1648 0.1969 0.9852 0.9911 0.09517 0.6543 0.8352 0.2503 ] Network output: [ 7.381e-05 1 -5.396e-05 3.793e-07 -1.703e-07 0.9998 2.859e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001282 Epoch 10234 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008427 0.9969 0.993 -1.19e-07 5.342e-08 -0.006718 -8.967e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003392 -0.006393 0.005198 0.9699 0.9743 0.006917 0.8226 0.8187 0.01579 ] Network output: [ 1 3.471e-05 0.0002964 -1.371e-06 6.155e-07 -0.0002401 -1.033e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.03613 -0.1526 0.1808 0.9834 0.9932 0.2377 0.4273 0.8677 0.7069 ] Network output: [ -0.008456 1.003 1.007 -1.229e-07 5.516e-08 0.007098 -9.259e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006999 0.0006543 0.004311 0.003053 0.9889 0.9919 0.007138 0.8497 0.8913 0.01123 ] Network output: [ -0.0001109 0.001055 1 -4.309e-06 1.935e-06 0.9987 -3.248e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2258 0.1074 0.3516 0.1411 0.9849 0.9939 0.2266 0.4312 0.8745 0.7005 ] Network output: [ 0.002267 -0.01103 0.9947 2.637e-06 -1.184e-06 1.012 1.987e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09995 0.1851 0.1965 0.9873 0.9919 0.1128 0.7306 0.8603 0.3046 ] Network output: [ -0.002153 0.01029 1.005 2.906e-06 -1.304e-06 0.9894 2.19e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09516 0.09322 0.1648 0.197 0.9852 0.9911 0.09518 0.6543 0.8351 0.2503 ] Network output: [ 7.379e-05 1 -5.398e-05 3.788e-07 -1.701e-07 0.9998 2.855e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001282 Epoch 10235 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008426 0.9969 0.993 -1.189e-07 5.337e-08 -0.006717 -8.96e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003392 -0.006393 0.005197 0.9699 0.9743 0.006917 0.8226 0.8187 0.01579 ] Network output: [ 1 3.458e-05 0.0002962 -1.369e-06 6.147e-07 -0.00024 -1.032e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.03613 -0.1526 0.1808 0.9834 0.9932 0.2377 0.4273 0.8677 0.7069 ] Network output: [ -0.008455 1.003 1.007 -1.227e-07 5.511e-08 0.007098 -9.251e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.006999 0.0006544 0.004311 0.003053 0.9889 0.9919 0.007138 0.8497 0.8913 0.01123 ] Network output: [ -0.0001108 0.001054 1 -4.304e-06 1.932e-06 0.9987 -3.244e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2258 0.1074 0.3516 0.1411 0.9849 0.9939 0.2266 0.4312 0.8745 0.7005 ] Network output: [ 0.002266 -0.01103 0.9947 2.634e-06 -1.182e-06 1.012 1.985e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09995 0.1851 0.1964 0.9873 0.9919 0.1128 0.7306 0.8603 0.3046 ] Network output: [ -0.002152 0.01028 1.005 2.902e-06 -1.303e-06 0.9894 2.187e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09516 0.09322 0.1648 0.197 0.9852 0.991 0.09518 0.6543 0.8351 0.2503 ] Network output: [ 7.378e-05 1 -5.4e-05 3.784e-07 -1.699e-07 0.9998 2.852e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001281 Epoch 10236 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008425 0.9969 0.993 -1.188e-07 5.333e-08 -0.006716 -8.953e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003393 -0.006392 0.005197 0.9699 0.9743 0.006917 0.8226 0.8187 0.01579 ] Network output: [ 1 3.446e-05 0.0002961 -1.368e-06 6.139e-07 -0.0002399 -1.031e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.03613 -0.1526 0.1808 0.9834 0.9932 0.2377 0.4273 0.8677 0.7069 ] Network output: [ -0.008455 1.003 1.007 -1.226e-07 5.505e-08 0.007097 -9.242e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007 0.0006544 0.004311 0.003052 0.9889 0.9919 0.007138 0.8497 0.8913 0.01123 ] Network output: [ -0.0001106 0.001054 1 -4.299e-06 1.93e-06 0.9987 -3.24e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2258 0.1074 0.3516 0.1411 0.9849 0.9939 0.2266 0.4312 0.8745 0.7005 ] Network output: [ 0.002264 -0.01102 0.9947 2.631e-06 -1.181e-06 1.012 1.982e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09995 0.1851 0.1964 0.9873 0.9919 0.1128 0.7306 0.8603 0.3046 ] Network output: [ -0.00215 0.01028 1.005 2.898e-06 -1.301e-06 0.9894 2.184e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09516 0.09322 0.1648 0.197 0.9852 0.991 0.09518 0.6543 0.8351 0.2503 ] Network output: [ 7.377e-05 1 -5.403e-05 3.779e-07 -1.697e-07 0.9998 2.848e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000128 Epoch 10237 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008424 0.9969 0.993 -1.187e-07 5.329e-08 -0.006716 -8.945e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003393 -0.006392 0.005197 0.9699 0.9743 0.006917 0.8226 0.8187 0.01579 ] Network output: [ 1 3.433e-05 0.000296 -1.366e-06 6.132e-07 -0.0002397 -1.029e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.03613 -0.1526 0.1808 0.9834 0.9932 0.2377 0.4273 0.8677 0.7069 ] Network output: [ -0.008454 1.003 1.007 -1.225e-07 5.5e-08 0.007097 -9.234e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007 0.0006544 0.004311 0.003052 0.9889 0.9919 0.007139 0.8497 0.8913 0.01123 ] Network output: [ -0.0001105 0.001053 1 -4.293e-06 1.927e-06 0.9987 -3.236e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2258 0.1074 0.3516 0.1411 0.9849 0.9939 0.2266 0.4312 0.8745 0.7005 ] Network output: [ 0.002263 -0.01102 0.9947 2.627e-06 -1.179e-06 1.012 1.98e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1127 0.09995 0.1851 0.1964 0.9873 0.9919 0.1128 0.7306 0.8603 0.3046 ] Network output: [ -0.002149 0.01027 1.005 2.895e-06 -1.3e-06 0.9894 2.182e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09516 0.09322 0.1648 0.197 0.9852 0.991 0.09518 0.6543 0.8351 0.2503 ] Network output: [ 7.375e-05 1 -5.405e-05 3.774e-07 -1.694e-07 0.9998 2.845e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001279 Epoch 10238 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008423 0.9969 0.993 -1.186e-07 5.324e-08 -0.006715 -8.938e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003393 -0.006391 0.005196 0.9699 0.9743 0.006917 0.8226 0.8187 0.01579 ] Network output: [ 1 3.421e-05 0.0002959 -1.364e-06 6.124e-07 -0.0002396 -1.028e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.03613 -0.1526 0.1808 0.9834 0.9932 0.2377 0.4273 0.8677 0.7069 ] Network output: [ -0.008453 1.003 1.007 -1.224e-07 5.495e-08 0.007096 -9.225e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007 0.0006545 0.004311 0.003052 0.9889 0.9919 0.007139 0.8497 0.8913 0.01123 ] Network output: [ -0.0001104 0.001052 1 -4.288e-06 1.925e-06 0.9987 -3.231e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2258 0.1074 0.3516 0.1411 0.9849 0.9939 0.2266 0.4312 0.8745 0.7005 ] Network output: [ 0.002261 -0.01101 0.9947 2.624e-06 -1.178e-06 1.012 1.977e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09995 0.1851 0.1964 0.9873 0.9919 0.1128 0.7306 0.8603 0.3046 ] Network output: [ -0.002148 0.01027 1.005 2.891e-06 -1.298e-06 0.9895 2.179e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09517 0.09322 0.1648 0.197 0.9852 0.991 0.09518 0.6542 0.8351 0.2503 ] Network output: [ 7.374e-05 1 -5.407e-05 3.77e-07 -1.692e-07 0.9998 2.841e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001279 Epoch 10239 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008423 0.9969 0.993 -1.185e-07 5.32e-08 -0.006714 -8.931e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003393 -0.006391 0.005196 0.9699 0.9743 0.006917 0.8226 0.8187 0.01579 ] Network output: [ 1 3.408e-05 0.0002957 -1.362e-06 6.116e-07 -0.0002395 -1.027e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.03614 -0.1526 0.1808 0.9834 0.9932 0.2377 0.4273 0.8677 0.7069 ] Network output: [ -0.008452 1.003 1.007 -1.223e-07 5.49e-08 0.007096 -9.217e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007 0.0006545 0.00431 0.003052 0.9889 0.9919 0.007139 0.8497 0.8913 0.01123 ] Network output: [ -0.0001103 0.001052 1 -4.283e-06 1.923e-06 0.9987 -3.227e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2258 0.1074 0.3516 0.1411 0.9849 0.9939 0.2266 0.4312 0.8745 0.7005 ] Network output: [ 0.00226 -0.011 0.9947 2.621e-06 -1.177e-06 1.012 1.975e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09995 0.1851 0.1964 0.9873 0.9919 0.1128 0.7306 0.8603 0.3046 ] Network output: [ -0.002147 0.01026 1.005 2.888e-06 -1.296e-06 0.9895 2.176e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09517 0.09322 0.1648 0.197 0.9852 0.991 0.09518 0.6542 0.8351 0.2503 ] Network output: [ 7.373e-05 1 -5.409e-05 3.765e-07 -1.69e-07 0.9998 2.837e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001278 Epoch 10240 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008422 0.9969 0.993 -1.184e-07 5.316e-08 -0.006714 -8.923e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003393 -0.00639 0.005196 0.9699 0.9743 0.006917 0.8226 0.8187 0.01579 ] Network output: [ 1 3.396e-05 0.0002956 -1.361e-06 6.109e-07 -0.0002394 -1.025e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.03614 -0.1526 0.1808 0.9834 0.9932 0.2377 0.4273 0.8677 0.7069 ] Network output: [ -0.008452 1.003 1.007 -1.222e-07 5.485e-08 0.007095 -9.208e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007001 0.0006545 0.00431 0.003051 0.9889 0.9919 0.007139 0.8497 0.8913 0.01123 ] Network output: [ -0.0001101 0.001051 1 -4.277e-06 1.92e-06 0.9987 -3.223e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2258 0.1074 0.3516 0.1411 0.9849 0.9939 0.2266 0.4312 0.8745 0.7005 ] Network output: [ 0.002259 -0.011 0.9947 2.617e-06 -1.175e-06 1.012 1.973e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09996 0.1851 0.1964 0.9873 0.9919 0.1128 0.7306 0.8603 0.3046 ] Network output: [ -0.002145 0.01026 1.005 2.884e-06 -1.295e-06 0.9895 2.174e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09517 0.09322 0.1648 0.197 0.9852 0.991 0.09518 0.6542 0.8351 0.2503 ] Network output: [ 7.371e-05 1 -5.412e-05 3.76e-07 -1.688e-07 0.9998 2.834e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001277 Epoch 10241 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008421 0.9969 0.993 -1.183e-07 5.311e-08 -0.006713 -8.916e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003393 -0.00639 0.005195 0.9699 0.9743 0.006918 0.8226 0.8187 0.01579 ] Network output: [ 1 3.383e-05 0.0002955 -1.359e-06 6.101e-07 -0.0002393 -1.024e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.03614 -0.1526 0.1808 0.9834 0.9932 0.2377 0.4273 0.8677 0.7069 ] Network output: [ -0.008451 1.003 1.007 -1.221e-07 5.48e-08 0.007095 -9.2e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007001 0.0006546 0.00431 0.003051 0.9889 0.9919 0.00714 0.8497 0.8913 0.01123 ] Network output: [ -0.00011 0.00105 1 -4.272e-06 1.918e-06 0.9987 -3.219e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2258 0.1074 0.3516 0.1411 0.9849 0.9939 0.2266 0.4312 0.8745 0.7005 ] Network output: [ 0.002257 -0.01099 0.9947 2.614e-06 -1.174e-06 1.012 1.97e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09996 0.1851 0.1964 0.9873 0.9919 0.1128 0.7306 0.8603 0.3046 ] Network output: [ -0.002144 0.01025 1.005 2.881e-06 -1.293e-06 0.9895 2.171e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09517 0.09323 0.1648 0.197 0.9852 0.991 0.09518 0.6542 0.8351 0.2503 ] Network output: [ 7.37e-05 1 -5.414e-05 3.756e-07 -1.686e-07 0.9998 2.83e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001277 Epoch 10242 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00842 0.9969 0.993 -1.182e-07 5.307e-08 -0.006712 -8.909e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003393 -0.006389 0.005195 0.9699 0.9743 0.006918 0.8226 0.8187 0.01579 ] Network output: [ 1 3.37e-05 0.0002953 -1.357e-06 6.093e-07 -0.0002391 -1.023e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.03614 -0.1525 0.1808 0.9834 0.9932 0.2377 0.4273 0.8677 0.7069 ] Network output: [ -0.00845 1.003 1.007 -1.22e-07 5.475e-08 0.007095 -9.191e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007001 0.0006546 0.00431 0.003051 0.9889 0.9919 0.00714 0.8497 0.8913 0.01122 ] Network output: [ -0.0001099 0.00105 1 -4.266e-06 1.915e-06 0.9987 -3.215e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2258 0.1074 0.3516 0.1411 0.9849 0.9939 0.2266 0.4312 0.8745 0.7005 ] Network output: [ 0.002256 -0.01098 0.9947 2.611e-06 -1.172e-06 1.012 1.968e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09996 0.1851 0.1964 0.9873 0.9919 0.1128 0.7306 0.8603 0.3046 ] Network output: [ -0.002143 0.01025 1.005 2.877e-06 -1.292e-06 0.9895 2.168e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09517 0.09323 0.1648 0.197 0.9852 0.991 0.09519 0.6542 0.8351 0.2503 ] Network output: [ 7.369e-05 1 -5.416e-05 3.751e-07 -1.684e-07 0.9998 2.827e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001276 Epoch 10243 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008419 0.9969 0.993 -1.181e-07 5.302e-08 -0.006712 -8.901e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003393 -0.006389 0.005195 0.9699 0.9743 0.006918 0.8226 0.8187 0.01578 ] Network output: [ 1 3.358e-05 0.0002952 -1.356e-06 6.086e-07 -0.000239 -1.022e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.03614 -0.1525 0.1808 0.9834 0.9932 0.2377 0.4273 0.8677 0.7069 ] Network output: [ -0.008449 1.003 1.007 -1.218e-07 5.47e-08 0.007094 -9.183e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007001 0.0006546 0.00431 0.003051 0.9889 0.9919 0.00714 0.8497 0.8913 0.01122 ] Network output: [ -0.0001097 0.001049 1 -4.261e-06 1.913e-06 0.9987 -3.211e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2258 0.1075 0.3516 0.1411 0.9849 0.9939 0.2266 0.4312 0.8745 0.7005 ] Network output: [ 0.002254 -0.01098 0.9947 2.608e-06 -1.171e-06 1.012 1.965e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09996 0.1851 0.1964 0.9873 0.9919 0.1128 0.7306 0.8603 0.3046 ] Network output: [ -0.002142 0.01024 1.005 2.874e-06 -1.29e-06 0.9895 2.166e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09517 0.09323 0.1648 0.197 0.9852 0.991 0.09519 0.6542 0.8351 0.2503 ] Network output: [ 7.367e-05 1 -5.418e-05 3.746e-07 -1.682e-07 0.9998 2.823e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001275 Epoch 10244 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008419 0.9969 0.993 -1.18e-07 5.298e-08 -0.006711 -8.894e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003393 -0.006388 0.005194 0.9699 0.9743 0.006918 0.8226 0.8187 0.01578 ] Network output: [ 1 3.345e-05 0.0002951 -1.354e-06 6.078e-07 -0.0002389 -1.02e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.03614 -0.1525 0.1808 0.9834 0.9932 0.2377 0.4273 0.8677 0.7069 ] Network output: [ -0.008449 1.003 1.007 -1.217e-07 5.465e-08 0.007094 -9.174e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007002 0.0006547 0.00431 0.003051 0.9889 0.9919 0.007141 0.8497 0.8913 0.01122 ] Network output: [ -0.0001096 0.001048 1 -4.256e-06 1.911e-06 0.9987 -3.207e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2258 0.1075 0.3516 0.1411 0.9849 0.9939 0.2266 0.4312 0.8745 0.7005 ] Network output: [ 0.002253 -0.01097 0.9947 2.604e-06 -1.169e-06 1.012 1.963e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09996 0.1851 0.1964 0.9873 0.9919 0.1128 0.7305 0.8603 0.3046 ] Network output: [ -0.00214 0.01023 1.005 2.87e-06 -1.288e-06 0.9895 2.163e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09517 0.09323 0.1648 0.197 0.9852 0.991 0.09519 0.6542 0.8351 0.2503 ] Network output: [ 7.366e-05 1 -5.421e-05 3.742e-07 -1.68e-07 0.9998 2.82e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001274 Epoch 10245 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008418 0.9969 0.993 -1.179e-07 5.294e-08 -0.00671 -8.887e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003393 -0.006388 0.005194 0.9699 0.9743 0.006918 0.8226 0.8187 0.01578 ] Network output: [ 1 3.333e-05 0.000295 -1.352e-06 6.071e-07 -0.0002388 -1.019e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.03614 -0.1525 0.1808 0.9834 0.9932 0.2377 0.4273 0.8677 0.7069 ] Network output: [ -0.008448 1.003 1.007 -1.216e-07 5.46e-08 0.007093 -9.166e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007002 0.0006547 0.00431 0.00305 0.9889 0.9919 0.007141 0.8497 0.8913 0.01122 ] Network output: [ -0.0001095 0.001048 1 -4.25e-06 1.908e-06 0.9987 -3.203e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2258 0.1075 0.3516 0.1411 0.9849 0.9939 0.2266 0.4312 0.8745 0.7005 ] Network output: [ 0.002252 -0.01097 0.9947 2.601e-06 -1.168e-06 1.012 1.96e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09996 0.1851 0.1964 0.9873 0.9919 0.1128 0.7305 0.8603 0.3046 ] Network output: [ -0.002139 0.01023 1.005 2.866e-06 -1.287e-06 0.9895 2.16e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09517 0.09323 0.1648 0.197 0.9852 0.991 0.09519 0.6542 0.8351 0.2503 ] Network output: [ 7.365e-05 1 -5.423e-05 3.737e-07 -1.678e-07 0.9998 2.816e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001274 Epoch 10246 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008417 0.9969 0.993 -1.178e-07 5.289e-08 -0.00671 -8.879e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003393 -0.006387 0.005194 0.9699 0.9743 0.006918 0.8226 0.8187 0.01578 ] Network output: [ 1 3.32e-05 0.0002948 -1.351e-06 6.063e-07 -0.0002386 -1.018e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2115 -0.03614 -0.1525 0.1808 0.9834 0.9932 0.2377 0.4273 0.8677 0.7069 ] Network output: [ -0.008447 1.003 1.007 -1.215e-07 5.455e-08 0.007093 -9.157e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007002 0.0006547 0.00431 0.00305 0.9889 0.9919 0.007141 0.8497 0.8913 0.01122 ] Network output: [ -0.0001094 0.001047 1 -4.245e-06 1.906e-06 0.9987 -3.199e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2258 0.1075 0.3516 0.1411 0.9849 0.9939 0.2266 0.4312 0.8745 0.7005 ] Network output: [ 0.00225 -0.01096 0.9947 2.598e-06 -1.166e-06 1.012 1.958e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09997 0.1851 0.1964 0.9873 0.9919 0.1128 0.7305 0.8603 0.3046 ] Network output: [ -0.002138 0.01022 1.005 2.863e-06 -1.285e-06 0.9895 2.158e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09518 0.09323 0.1648 0.197 0.9852 0.991 0.09519 0.6542 0.8351 0.2503 ] Network output: [ 7.363e-05 1 -5.425e-05 3.733e-07 -1.676e-07 0.9998 2.813e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001273 Epoch 10247 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008416 0.9969 0.993 -1.177e-07 5.285e-08 -0.006709 -8.872e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003393 -0.006387 0.005193 0.9699 0.9743 0.006918 0.8226 0.8187 0.01578 ] Network output: [ 1 3.308e-05 0.0002947 -1.349e-06 6.055e-07 -0.0002385 -1.017e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.03614 -0.1525 0.1808 0.9834 0.9932 0.2377 0.4273 0.8677 0.7069 ] Network output: [ -0.008446 1.003 1.007 -1.214e-07 5.45e-08 0.007092 -9.149e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007003 0.0006548 0.00431 0.00305 0.9889 0.9919 0.007141 0.8497 0.8913 0.01122 ] Network output: [ -0.0001092 0.001046 1 -4.24e-06 1.903e-06 0.9987 -3.195e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2258 0.1075 0.3516 0.1411 0.9849 0.9939 0.2266 0.4312 0.8745 0.7005 ] Network output: [ 0.002249 -0.01095 0.9947 2.595e-06 -1.165e-06 1.012 1.955e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09997 0.1851 0.1964 0.9873 0.9919 0.1128 0.7305 0.8603 0.3046 ] Network output: [ -0.002137 0.01022 1.005 2.859e-06 -1.284e-06 0.9895 2.155e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09518 0.09323 0.1648 0.197 0.9852 0.991 0.09519 0.6542 0.8351 0.2503 ] Network output: [ 7.362e-05 1 -5.427e-05 3.728e-07 -1.674e-07 0.9998 2.81e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001272 Epoch 10248 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008415 0.9969 0.993 -1.176e-07 5.281e-08 -0.006708 -8.865e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003393 -0.006386 0.005193 0.9699 0.9743 0.006918 0.8226 0.8187 0.01578 ] Network output: [ 1 3.295e-05 0.0002946 -1.347e-06 6.048e-07 -0.0002384 -1.015e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.03614 -0.1525 0.1808 0.9834 0.9932 0.2378 0.4273 0.8677 0.7069 ] Network output: [ -0.008446 1.003 1.007 -1.213e-07 5.445e-08 0.007092 -9.14e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007003 0.0006548 0.004309 0.00305 0.9889 0.9919 0.007142 0.8497 0.8913 0.01122 ] Network output: [ -0.0001091 0.001046 1 -4.234e-06 1.901e-06 0.9987 -3.191e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2259 0.1075 0.3517 0.141 0.9849 0.9939 0.2266 0.4312 0.8745 0.7005 ] Network output: [ 0.002247 -0.01095 0.9947 2.591e-06 -1.163e-06 1.012 1.953e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09997 0.1851 0.1964 0.9873 0.9919 0.1128 0.7305 0.8603 0.3045 ] Network output: [ -0.002135 0.01021 1.005 2.856e-06 -1.282e-06 0.9895 2.152e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09518 0.09323 0.1648 0.197 0.9852 0.991 0.09519 0.6542 0.8351 0.2503 ] Network output: [ 7.361e-05 1 -5.43e-05 3.723e-07 -1.672e-07 0.9998 2.806e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001271 Epoch 10249 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008415 0.9969 0.993 -1.175e-07 5.276e-08 -0.006707 -8.857e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003393 -0.006386 0.005193 0.9699 0.9743 0.006918 0.8226 0.8187 0.01578 ] Network output: [ 1 3.283e-05 0.0002945 -1.345e-06 6.04e-07 -0.0002383 -1.014e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.03615 -0.1525 0.1808 0.9834 0.9932 0.2378 0.4273 0.8677 0.7069 ] Network output: [ -0.008445 1.003 1.007 -1.212e-07 5.44e-08 0.007092 -9.132e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007003 0.0006548 0.004309 0.00305 0.9889 0.9919 0.007142 0.8497 0.8913 0.01122 ] Network output: [ -0.000109 0.001045 1 -4.229e-06 1.899e-06 0.9987 -3.187e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2259 0.1075 0.3517 0.141 0.9849 0.9939 0.2266 0.4312 0.8745 0.7005 ] Network output: [ 0.002246 -0.01094 0.9947 2.588e-06 -1.162e-06 1.012 1.951e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09997 0.1851 0.1964 0.9873 0.9919 0.1129 0.7305 0.8603 0.3045 ] Network output: [ -0.002134 0.01021 1.005 2.852e-06 -1.281e-06 0.9895 2.15e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09518 0.09323 0.1648 0.197 0.9852 0.991 0.09519 0.6542 0.8351 0.2503 ] Network output: [ 7.359e-05 1 -5.432e-05 3.719e-07 -1.669e-07 0.9998 2.803e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001271 Epoch 10250 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008414 0.9969 0.993 -1.174e-07 5.272e-08 -0.006707 -8.85e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003532 -0.003393 -0.006385 0.005192 0.9699 0.9743 0.006919 0.8226 0.8187 0.01578 ] Network output: [ 1 3.27e-05 0.0002943 -1.344e-06 6.033e-07 -0.0002381 -1.013e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.03615 -0.1525 0.1808 0.9834 0.9932 0.2378 0.4273 0.8677 0.7069 ] Network output: [ -0.008444 1.003 1.007 -1.211e-07 5.435e-08 0.007091 -9.124e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007003 0.0006549 0.004309 0.003049 0.9889 0.9919 0.007142 0.8497 0.8913 0.01122 ] Network output: [ -0.0001088 0.001044 1 -4.224e-06 1.896e-06 0.9987 -3.183e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2259 0.1075 0.3517 0.141 0.9849 0.9939 0.2267 0.4312 0.8745 0.7004 ] Network output: [ 0.002245 -0.01093 0.9947 2.585e-06 -1.16e-06 1.012 1.948e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09997 0.1851 0.1964 0.9873 0.9919 0.1129 0.7305 0.8603 0.3045 ] Network output: [ -0.002133 0.0102 1.005 2.849e-06 -1.279e-06 0.9895 2.147e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09518 0.09324 0.1648 0.197 0.9852 0.991 0.09519 0.6541 0.8351 0.2503 ] Network output: [ 7.358e-05 1 -5.434e-05 3.714e-07 -1.667e-07 0.9998 2.799e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000127 Epoch 10251 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008413 0.9969 0.993 -1.173e-07 5.268e-08 -0.006706 -8.843e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003393 -0.006385 0.005192 0.9699 0.9743 0.006919 0.8226 0.8187 0.01578 ] Network output: [ 1 3.258e-05 0.0002942 -1.342e-06 6.025e-07 -0.000238 -1.011e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.03615 -0.1525 0.1808 0.9834 0.9932 0.2378 0.4272 0.8677 0.7069 ] Network output: [ -0.008443 1.003 1.007 -1.21e-07 5.43e-08 0.007091 -9.115e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007004 0.0006549 0.004309 0.003049 0.9889 0.9919 0.007142 0.8497 0.8913 0.01122 ] Network output: [ -0.0001087 0.001043 1 -4.218e-06 1.894e-06 0.9987 -3.179e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2259 0.1075 0.3517 0.141 0.9849 0.9939 0.2267 0.4312 0.8745 0.7004 ] Network output: [ 0.002243 -0.01093 0.9947 2.582e-06 -1.159e-06 1.012 1.946e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09998 0.1851 0.1964 0.9873 0.9919 0.1129 0.7305 0.8603 0.3045 ] Network output: [ -0.002132 0.0102 1.005 2.845e-06 -1.277e-06 0.9895 2.144e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09518 0.09324 0.1648 0.197 0.9852 0.991 0.0952 0.6541 0.8351 0.2503 ] Network output: [ 7.357e-05 1 -5.437e-05 3.71e-07 -1.665e-07 0.9998 2.796e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001269 Epoch 10252 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008412 0.9969 0.993 -1.172e-07 5.263e-08 -0.006705 -8.835e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003393 -0.006384 0.005192 0.9699 0.9743 0.006919 0.8225 0.8187 0.01578 ] Network output: [ 1 3.245e-05 0.0002941 -1.34e-06 6.017e-07 -0.0002379 -1.01e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.03615 -0.1525 0.1808 0.9834 0.9932 0.2378 0.4272 0.8677 0.7069 ] Network output: [ -0.008443 1.003 1.007 -1.208e-07 5.425e-08 0.00709 -9.107e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007004 0.0006549 0.004309 0.003049 0.9889 0.9919 0.007143 0.8497 0.8913 0.01122 ] Network output: [ -0.0001086 0.001043 1 -4.213e-06 1.891e-06 0.9987 -3.175e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2259 0.1075 0.3517 0.141 0.9849 0.9939 0.2267 0.4312 0.8745 0.7004 ] Network output: [ 0.002242 -0.01092 0.9947 2.579e-06 -1.158e-06 1.012 1.943e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09998 0.1851 0.1964 0.9873 0.9919 0.1129 0.7305 0.8603 0.3045 ] Network output: [ -0.00213 0.01019 1.005 2.842e-06 -1.276e-06 0.9895 2.142e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09518 0.09324 0.1648 0.197 0.9852 0.991 0.0952 0.6541 0.8351 0.2503 ] Network output: [ 7.355e-05 1 -5.439e-05 3.705e-07 -1.663e-07 0.9998 2.792e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001268 Epoch 10253 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008411 0.9969 0.993 -1.171e-07 5.259e-08 -0.006705 -8.828e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003393 -0.006384 0.005191 0.9699 0.9743 0.006919 0.8225 0.8187 0.01578 ] Network output: [ 1 3.233e-05 0.0002939 -1.339e-06 6.01e-07 -0.0002378 -1.009e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.03615 -0.1525 0.1808 0.9834 0.9932 0.2378 0.4272 0.8677 0.7069 ] Network output: [ -0.008442 1.003 1.007 -1.207e-07 5.42e-08 0.00709 -9.098e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007004 0.000655 0.004309 0.003049 0.9889 0.9919 0.007143 0.8497 0.8913 0.01122 ] Network output: [ -0.0001085 0.001042 1 -4.208e-06 1.889e-06 0.9987 -3.171e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2259 0.1075 0.3517 0.141 0.9849 0.9939 0.2267 0.4312 0.8745 0.7004 ] Network output: [ 0.00224 -0.01092 0.9947 2.575e-06 -1.156e-06 1.012 1.941e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09998 0.1851 0.1964 0.9873 0.9919 0.1129 0.7305 0.8603 0.3045 ] Network output: [ -0.002129 0.01019 1.005 2.838e-06 -1.274e-06 0.9895 2.139e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09518 0.09324 0.1648 0.197 0.9852 0.991 0.0952 0.6541 0.8351 0.2503 ] Network output: [ 7.354e-05 1 -5.441e-05 3.7e-07 -1.661e-07 0.9998 2.789e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001268 Epoch 10254 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008411 0.9969 0.993 -1.17e-07 5.254e-08 -0.006704 -8.821e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003394 -0.006383 0.005191 0.9699 0.9743 0.006919 0.8225 0.8187 0.01578 ] Network output: [ 1 3.22e-05 0.0002938 -1.337e-06 6.002e-07 -0.0002377 -1.008e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.03615 -0.1525 0.1808 0.9834 0.9932 0.2378 0.4272 0.8677 0.7069 ] Network output: [ -0.008441 1.003 1.007 -1.206e-07 5.415e-08 0.007089 -9.09e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007004 0.000655 0.004309 0.003049 0.9889 0.9919 0.007143 0.8497 0.8913 0.01122 ] Network output: [ -0.0001083 0.001041 1 -4.203e-06 1.887e-06 0.9987 -3.167e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2259 0.1075 0.3517 0.141 0.9849 0.9939 0.2267 0.4312 0.8745 0.7004 ] Network output: [ 0.002239 -0.01091 0.9947 2.572e-06 -1.155e-06 1.012 1.938e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09998 0.1851 0.1964 0.9873 0.9919 0.1129 0.7305 0.8603 0.3045 ] Network output: [ -0.002128 0.01018 1.005 2.835e-06 -1.273e-06 0.9895 2.136e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09518 0.09324 0.1648 0.197 0.9852 0.991 0.0952 0.6541 0.8351 0.2503 ] Network output: [ 7.353e-05 1 -5.444e-05 3.696e-07 -1.659e-07 0.9998 2.785e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001267 Epoch 10255 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00841 0.9969 0.993 -1.169e-07 5.25e-08 -0.006703 -8.813e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003394 -0.006383 0.005191 0.9699 0.9743 0.006919 0.8225 0.8187 0.01577 ] Network output: [ 1 3.208e-05 0.0002937 -1.335e-06 5.995e-07 -0.0002375 -1.006e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.03615 -0.1525 0.1808 0.9834 0.9932 0.2378 0.4272 0.8677 0.7069 ] Network output: [ -0.00844 1.003 1.007 -1.205e-07 5.41e-08 0.007089 -9.082e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007005 0.000655 0.004309 0.003048 0.9889 0.9919 0.007144 0.8497 0.8913 0.01122 ] Network output: [ -0.0001082 0.001041 1 -4.197e-06 1.884e-06 0.9987 -3.163e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2259 0.1075 0.3517 0.141 0.9849 0.9939 0.2267 0.4312 0.8745 0.7004 ] Network output: [ 0.002238 -0.0109 0.9947 2.569e-06 -1.153e-06 1.012 1.936e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09998 0.1851 0.1964 0.9873 0.9919 0.1129 0.7305 0.8603 0.3045 ] Network output: [ -0.002127 0.01017 1.005 2.831e-06 -1.271e-06 0.9895 2.134e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09519 0.09324 0.1648 0.197 0.9852 0.991 0.0952 0.6541 0.8351 0.2503 ] Network output: [ 7.352e-05 1 -5.446e-05 3.691e-07 -1.657e-07 0.9998 2.782e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001266 Epoch 10256 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008409 0.9969 0.993 -1.168e-07 5.246e-08 -0.006703 -8.806e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003394 -0.006382 0.00519 0.9699 0.9743 0.006919 0.8225 0.8187 0.01577 ] Network output: [ 1 3.195e-05 0.0002936 -1.334e-06 5.987e-07 -0.0002374 -1.005e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.03615 -0.1524 0.1808 0.9834 0.9932 0.2378 0.4272 0.8677 0.7069 ] Network output: [ -0.00844 1.003 1.007 -1.204e-07 5.405e-08 0.007089 -9.073e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007005 0.0006551 0.004309 0.003048 0.9889 0.9919 0.007144 0.8497 0.8913 0.01122 ] Network output: [ -0.0001081 0.00104 1 -4.192e-06 1.882e-06 0.9987 -3.159e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2259 0.1075 0.3517 0.141 0.9849 0.9939 0.2267 0.4312 0.8745 0.7004 ] Network output: [ 0.002236 -0.0109 0.9947 2.566e-06 -1.152e-06 1.012 1.934e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09998 0.1851 0.1964 0.9873 0.9919 0.1129 0.7305 0.8603 0.3045 ] Network output: [ -0.002125 0.01017 1.005 2.828e-06 -1.27e-06 0.9895 2.131e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09519 0.09324 0.1648 0.197 0.9852 0.991 0.0952 0.6541 0.8351 0.2503 ] Network output: [ 7.35e-05 1 -5.448e-05 3.687e-07 -1.655e-07 0.9998 2.778e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001265 Epoch 10257 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008408 0.9969 0.993 -1.168e-07 5.241e-08 -0.006702 -8.799e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003394 -0.006382 0.00519 0.9699 0.9743 0.006919 0.8225 0.8187 0.01577 ] Network output: [ 1 3.183e-05 0.0002934 -1.332e-06 5.98e-07 -0.0002373 -1.004e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.03615 -0.1524 0.1808 0.9834 0.9932 0.2378 0.4272 0.8677 0.7069 ] Network output: [ -0.008439 1.003 1.007 -1.203e-07 5.4e-08 0.007088 -9.065e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007005 0.0006551 0.004308 0.003048 0.9889 0.9919 0.007144 0.8497 0.8913 0.01121 ] Network output: [ -0.0001079 0.001039 1 -4.187e-06 1.88e-06 0.9987 -3.155e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2259 0.1075 0.3517 0.141 0.9849 0.9939 0.2267 0.4312 0.8745 0.7004 ] Network output: [ 0.002235 -0.01089 0.9947 2.562e-06 -1.15e-06 1.012 1.931e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09999 0.1851 0.1964 0.9873 0.9919 0.1129 0.7304 0.8603 0.3045 ] Network output: [ -0.002124 0.01016 1.005 2.824e-06 -1.268e-06 0.9895 2.129e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09519 0.09324 0.1648 0.197 0.9852 0.991 0.0952 0.6541 0.8351 0.2503 ] Network output: [ 7.349e-05 1 -5.451e-05 3.682e-07 -1.653e-07 0.9998 2.775e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001265 Epoch 10258 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008408 0.9969 0.993 -1.167e-07 5.237e-08 -0.006701 -8.791e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003394 -0.006381 0.00519 0.9699 0.9743 0.00692 0.8225 0.8187 0.01577 ] Network output: [ 1 3.17e-05 0.0002933 -1.33e-06 5.972e-07 -0.0002372 -1.003e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.03616 -0.1524 0.1808 0.9834 0.9932 0.2378 0.4272 0.8677 0.7069 ] Network output: [ -0.008438 1.003 1.007 -1.202e-07 5.395e-08 0.007088 -9.056e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007006 0.0006551 0.004308 0.003048 0.9889 0.9919 0.007144 0.8497 0.8913 0.01121 ] Network output: [ -0.0001078 0.001039 1 -4.182e-06 1.877e-06 0.9987 -3.151e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2259 0.1075 0.3517 0.141 0.9849 0.9939 0.2267 0.4312 0.8745 0.7004 ] Network output: [ 0.002233 -0.01088 0.9947 2.559e-06 -1.149e-06 1.012 1.929e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09999 0.1851 0.1964 0.9873 0.9919 0.1129 0.7304 0.8603 0.3045 ] Network output: [ -0.002123 0.01016 1.005 2.821e-06 -1.266e-06 0.9895 2.126e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09519 0.09325 0.1648 0.197 0.9852 0.991 0.0952 0.6541 0.8351 0.2503 ] Network output: [ 7.348e-05 1 -5.453e-05 3.677e-07 -1.651e-07 0.9998 2.771e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001264 Epoch 10259 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008407 0.9969 0.993 -1.166e-07 5.233e-08 -0.0067 -8.784e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003394 -0.006381 0.005189 0.9699 0.9743 0.00692 0.8225 0.8187 0.01577 ] Network output: [ 1 3.158e-05 0.0002932 -1.329e-06 5.965e-07 -0.0002371 -1.001e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.03616 -0.1524 0.1808 0.9834 0.9932 0.2378 0.4272 0.8677 0.7069 ] Network output: [ -0.008437 1.003 1.007 -1.201e-07 5.39e-08 0.007087 -9.048e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007006 0.0006552 0.004308 0.003047 0.9889 0.9919 0.007145 0.8497 0.8913 0.01121 ] Network output: [ -0.0001077 0.001038 1 -4.176e-06 1.875e-06 0.9987 -3.147e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2259 0.1075 0.3517 0.141 0.9849 0.9939 0.2267 0.4312 0.8745 0.7004 ] Network output: [ 0.002232 -0.01088 0.9947 2.556e-06 -1.148e-06 1.012 1.926e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09999 0.1851 0.1964 0.9873 0.9919 0.1129 0.7304 0.8603 0.3045 ] Network output: [ -0.002122 0.01015 1.005 2.817e-06 -1.265e-06 0.9895 2.123e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09519 0.09325 0.1648 0.197 0.9852 0.991 0.09521 0.6541 0.8351 0.2503 ] Network output: [ 7.346e-05 1 -5.455e-05 3.673e-07 -1.649e-07 0.9998 2.768e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001263 Epoch 10260 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008406 0.9969 0.993 -1.165e-07 5.228e-08 -0.0067 -8.777e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003394 -0.00638 0.005189 0.9699 0.9743 0.00692 0.8225 0.8187 0.01577 ] Network output: [ 1 3.145e-05 0.0002931 -1.327e-06 5.957e-07 -0.0002369 -1e-06 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.03616 -0.1524 0.1808 0.9834 0.9932 0.2378 0.4272 0.8677 0.7069 ] Network output: [ -0.008436 1.003 1.007 -1.199e-07 5.385e-08 0.007087 -9.04e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007006 0.0006552 0.004308 0.003047 0.9889 0.9919 0.007145 0.8496 0.8913 0.01121 ] Network output: [ -0.0001076 0.001037 1 -4.171e-06 1.873e-06 0.9987 -3.143e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2259 0.1075 0.3517 0.141 0.9849 0.9939 0.2267 0.4311 0.8745 0.7004 ] Network output: [ 0.00223 -0.01087 0.9947 2.553e-06 -1.146e-06 1.012 1.924e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09999 0.1851 0.1964 0.9873 0.9919 0.1129 0.7304 0.8603 0.3045 ] Network output: [ -0.00212 0.01015 1.005 2.814e-06 -1.263e-06 0.9895 2.121e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09519 0.09325 0.1648 0.197 0.9852 0.991 0.09521 0.6541 0.8351 0.2503 ] Network output: [ 7.345e-05 1 -5.458e-05 3.668e-07 -1.647e-07 0.9998 2.765e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001263 Epoch 10261 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008405 0.9969 0.993 -1.164e-07 5.224e-08 -0.006699 -8.77e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003394 -0.00638 0.005189 0.9699 0.9743 0.00692 0.8225 0.8187 0.01577 ] Network output: [ 1 3.133e-05 0.0002929 -1.325e-06 5.95e-07 -0.0002368 -9.988e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.03616 -0.1524 0.1808 0.9834 0.9932 0.2378 0.4272 0.8677 0.7069 ] Network output: [ -0.008436 1.003 1.007 -1.198e-07 5.38e-08 0.007086 -9.031e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007006 0.0006552 0.004308 0.003047 0.9889 0.9919 0.007145 0.8496 0.8913 0.01121 ] Network output: [ -0.0001074 0.001037 1 -4.166e-06 1.87e-06 0.9987 -3.14e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2259 0.1075 0.3517 0.141 0.9849 0.9939 0.2267 0.4311 0.8745 0.7004 ] Network output: [ 0.002229 -0.01087 0.9947 2.55e-06 -1.145e-06 1.012 1.922e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09999 0.1851 0.1964 0.9873 0.9919 0.1129 0.7304 0.8603 0.3045 ] Network output: [ -0.002119 0.01014 1.005 2.81e-06 -1.262e-06 0.9895 2.118e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09519 0.09325 0.1648 0.197 0.9852 0.991 0.09521 0.6541 0.8351 0.2503 ] Network output: [ 7.344e-05 1 -5.46e-05 3.664e-07 -1.645e-07 0.9998 2.761e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001262 Epoch 10262 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008404 0.9969 0.993 -1.163e-07 5.22e-08 -0.006698 -8.762e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003394 -0.006379 0.005188 0.9699 0.9743 0.00692 0.8225 0.8187 0.01577 ] Network output: [ 1 3.121e-05 0.0002928 -1.324e-06 5.942e-07 -0.0002367 -9.975e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.03616 -0.1524 0.1808 0.9834 0.9932 0.2378 0.4272 0.8677 0.7069 ] Network output: [ -0.008435 1.003 1.007 -1.197e-07 5.375e-08 0.007086 -9.023e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007007 0.0006553 0.004308 0.003047 0.9889 0.9919 0.007146 0.8496 0.8913 0.01121 ] Network output: [ -0.0001073 0.001036 1 -4.161e-06 1.868e-06 0.9987 -3.136e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2259 0.1075 0.3517 0.141 0.9849 0.9939 0.2267 0.4311 0.8745 0.7004 ] Network output: [ 0.002228 -0.01086 0.9947 2.546e-06 -1.143e-06 1.012 1.919e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.09999 0.1851 0.1964 0.9873 0.9919 0.1129 0.7304 0.8603 0.3045 ] Network output: [ -0.002118 0.01014 1.005 2.807e-06 -1.26e-06 0.9895 2.115e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09519 0.09325 0.1648 0.197 0.9852 0.991 0.09521 0.6541 0.8351 0.2503 ] Network output: [ 7.342e-05 1 -5.462e-05 3.659e-07 -1.643e-07 0.9998 2.758e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001261 Epoch 10263 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008404 0.9969 0.993 -1.162e-07 5.215e-08 -0.006698 -8.755e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003394 -0.006379 0.005188 0.9699 0.9743 0.00692 0.8225 0.8187 0.01577 ] Network output: [ 1 3.108e-05 0.0002927 -1.322e-06 5.935e-07 -0.0002366 -9.963e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.03616 -0.1524 0.1808 0.9834 0.9932 0.2378 0.4272 0.8677 0.7069 ] Network output: [ -0.008434 1.003 1.007 -1.196e-07 5.37e-08 0.007086 -9.015e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007007 0.0006553 0.004308 0.003047 0.9889 0.9919 0.007146 0.8496 0.8913 0.01121 ] Network output: [ -0.0001072 0.001035 1 -4.155e-06 1.866e-06 0.9987 -3.132e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2259 0.1075 0.3517 0.141 0.9849 0.9939 0.2267 0.4311 0.8745 0.7004 ] Network output: [ 0.002226 -0.01085 0.9947 2.543e-06 -1.142e-06 1.012 1.917e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7304 0.8603 0.3045 ] Network output: [ -0.002117 0.01013 1.005 2.804e-06 -1.259e-06 0.9895 2.113e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0952 0.09325 0.1648 0.197 0.9852 0.991 0.09521 0.654 0.8351 0.2503 ] Network output: [ 7.341e-05 1 -5.465e-05 3.655e-07 -1.641e-07 0.9998 2.754e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000126 Epoch 10264 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008403 0.9969 0.993 -1.161e-07 5.211e-08 -0.006697 -8.748e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003394 -0.006378 0.005187 0.9699 0.9743 0.00692 0.8225 0.8187 0.01577 ] Network output: [ 1 3.096e-05 0.0002926 -1.32e-06 5.927e-07 -0.0002364 -9.95e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.03616 -0.1524 0.1808 0.9834 0.9932 0.2378 0.4272 0.8677 0.7069 ] Network output: [ -0.008433 1.003 1.007 -1.195e-07 5.365e-08 0.007085 -9.006e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007007 0.0006553 0.004308 0.003046 0.9889 0.9919 0.007146 0.8496 0.8913 0.01121 ] Network output: [ -0.0001071 0.001035 1 -4.15e-06 1.863e-06 0.9987 -3.128e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2259 0.1075 0.3517 0.141 0.9849 0.9939 0.2267 0.4311 0.8745 0.7004 ] Network output: [ 0.002225 -0.01085 0.9947 2.54e-06 -1.14e-06 1.012 1.914e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7304 0.8603 0.3045 ] Network output: [ -0.002115 0.01013 1.005 2.8e-06 -1.257e-06 0.9895 2.11e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0952 0.09325 0.1648 0.197 0.9852 0.991 0.09521 0.654 0.8351 0.2503 ] Network output: [ 7.34e-05 1 -5.467e-05 3.65e-07 -1.639e-07 0.9998 2.751e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000126 Epoch 10265 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008402 0.9969 0.993 -1.16e-07 5.207e-08 -0.006696 -8.741e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003394 -0.006378 0.005187 0.9699 0.9743 0.00692 0.8225 0.8187 0.01577 ] Network output: [ 1 3.083e-05 0.0002924 -1.319e-06 5.92e-07 -0.0002363 -9.938e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.03616 -0.1524 0.1808 0.9834 0.9932 0.2379 0.4272 0.8677 0.7069 ] Network output: [ -0.008433 1.003 1.007 -1.194e-07 5.36e-08 0.007085 -8.998e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007007 0.0006553 0.004307 0.003046 0.9889 0.9919 0.007146 0.8496 0.8913 0.01121 ] Network output: [ -0.0001069 0.001034 1 -4.145e-06 1.861e-06 0.9987 -3.124e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2259 0.1075 0.3517 0.141 0.9849 0.9939 0.2267 0.4311 0.8745 0.7004 ] Network output: [ 0.002223 -0.01084 0.9947 2.537e-06 -1.139e-06 1.012 1.912e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7304 0.8603 0.3045 ] Network output: [ -0.002114 0.01012 1.005 2.797e-06 -1.255e-06 0.9895 2.108e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0952 0.09325 0.1648 0.197 0.9852 0.991 0.09521 0.654 0.8351 0.2503 ] Network output: [ 7.339e-05 1 -5.469e-05 3.646e-07 -1.637e-07 0.9998 2.748e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001259 Epoch 10266 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008401 0.9969 0.993 -1.159e-07 5.202e-08 -0.006696 -8.733e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003394 -0.006377 0.005187 0.9699 0.9743 0.00692 0.8225 0.8187 0.01577 ] Network output: [ 1 3.071e-05 0.0002923 -1.317e-06 5.912e-07 -0.0002362 -9.925e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.03616 -0.1524 0.1807 0.9834 0.9932 0.2379 0.4272 0.8677 0.7069 ] Network output: [ -0.008432 1.003 1.007 -1.193e-07 5.355e-08 0.007084 -8.99e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007008 0.0006554 0.004307 0.003046 0.9889 0.9919 0.007147 0.8496 0.8913 0.01121 ] Network output: [ -0.0001068 0.001033 1 -4.14e-06 1.858e-06 0.9987 -3.12e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.226 0.1075 0.3517 0.141 0.9849 0.9939 0.2267 0.4311 0.8745 0.7004 ] Network output: [ 0.002222 -0.01083 0.9947 2.534e-06 -1.138e-06 1.012 1.91e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7304 0.8603 0.3045 ] Network output: [ -0.002113 0.01011 1.005 2.793e-06 -1.254e-06 0.9895 2.105e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0952 0.09325 0.1648 0.197 0.9852 0.991 0.09521 0.654 0.8351 0.2503 ] Network output: [ 7.337e-05 1 -5.472e-05 3.641e-07 -1.635e-07 0.9998 2.744e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001258 Epoch 10267 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0084 0.9969 0.993 -1.158e-07 5.198e-08 -0.006695 -8.726e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003394 -0.006377 0.005186 0.9699 0.9743 0.006921 0.8225 0.8187 0.01576 ] Network output: [ 1 3.059e-05 0.0002922 -1.315e-06 5.905e-07 -0.0002361 -9.913e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2116 -0.03617 -0.1524 0.1807 0.9834 0.9932 0.2379 0.4272 0.8677 0.7068 ] Network output: [ -0.008431 1.003 1.007 -1.192e-07 5.35e-08 0.007084 -8.981e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007008 0.0006554 0.004307 0.003046 0.9889 0.9919 0.007147 0.8496 0.8913 0.01121 ] Network output: [ -0.0001067 0.001033 1 -4.135e-06 1.856e-06 0.9987 -3.116e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.226 0.1075 0.3517 0.141 0.9849 0.9939 0.2267 0.4311 0.8745 0.7004 ] Network output: [ 0.002221 -0.01083 0.9947 2.531e-06 -1.136e-06 1.012 1.907e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7304 0.8603 0.3045 ] Network output: [ -0.002111 0.01011 1.005 2.79e-06 -1.252e-06 0.9895 2.102e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0952 0.09326 0.1648 0.197 0.9852 0.991 0.09521 0.654 0.8351 0.2503 ] Network output: [ 7.336e-05 1 -5.474e-05 3.637e-07 -1.633e-07 0.9998 2.741e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001257 Epoch 10268 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0084 0.9969 0.993 -1.157e-07 5.194e-08 -0.006694 -8.719e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003394 -0.006376 0.005186 0.9699 0.9743 0.006921 0.8225 0.8187 0.01576 ] Network output: [ 1 3.046e-05 0.000292 -1.314e-06 5.898e-07 -0.000236 -9.9e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2117 -0.03617 -0.1524 0.1807 0.9834 0.9932 0.2379 0.4272 0.8677 0.7068 ] Network output: [ -0.00843 1.003 1.007 -1.191e-07 5.345e-08 0.007083 -8.973e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007008 0.0006554 0.004307 0.003046 0.9889 0.9919 0.007147 0.8496 0.8913 0.01121 ] Network output: [ -0.0001065 0.001032 1 -4.129e-06 1.854e-06 0.9987 -3.112e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.226 0.1075 0.3517 0.141 0.9849 0.9939 0.2268 0.4311 0.8745 0.7004 ] Network output: [ 0.002219 -0.01082 0.9947 2.527e-06 -1.135e-06 1.012 1.905e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7304 0.8603 0.3045 ] Network output: [ -0.00211 0.0101 1.005 2.786e-06 -1.251e-06 0.9896 2.1e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0952 0.09326 0.1648 0.197 0.9852 0.991 0.09522 0.654 0.8351 0.2504 ] Network output: [ 7.335e-05 1 -5.476e-05 3.632e-07 -1.631e-07 0.9998 2.737e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001257 Epoch 10269 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008399 0.9969 0.993 -1.156e-07 5.189e-08 -0.006694 -8.711e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003394 -0.006376 0.005186 0.9699 0.9743 0.006921 0.8225 0.8187 0.01576 ] Network output: [ 1 3.034e-05 0.0002919 -1.312e-06 5.89e-07 -0.0002358 -9.888e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2117 -0.03617 -0.1524 0.1807 0.9834 0.9932 0.2379 0.4272 0.8677 0.7068 ] Network output: [ -0.00843 1.003 1.007 -1.19e-07 5.34e-08 0.007083 -8.965e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007008 0.0006555 0.004307 0.003045 0.9889 0.9919 0.007148 0.8496 0.8913 0.01121 ] Network output: [ -0.0001064 0.001031 1 -4.124e-06 1.851e-06 0.9987 -3.108e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.226 0.1075 0.3517 0.141 0.9849 0.9939 0.2268 0.4311 0.8745 0.7004 ] Network output: [ 0.002218 -0.01082 0.9947 2.524e-06 -1.133e-06 1.012 1.902e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7304 0.8603 0.3045 ] Network output: [ -0.002109 0.0101 1.005 2.783e-06 -1.249e-06 0.9896 2.097e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0952 0.09326 0.1648 0.197 0.9852 0.991 0.09522 0.654 0.8351 0.2504 ] Network output: [ 7.333e-05 1 -5.479e-05 3.628e-07 -1.629e-07 0.9998 2.734e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001256 Epoch 10270 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008398 0.9969 0.993 -1.155e-07 5.185e-08 -0.006693 -8.704e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003394 -0.006375 0.005185 0.9699 0.9743 0.006921 0.8225 0.8187 0.01576 ] Network output: [ 1 3.021e-05 0.0002918 -1.31e-06 5.883e-07 -0.0002357 -9.875e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2117 -0.03617 -0.1523 0.1807 0.9834 0.9932 0.2379 0.4272 0.8677 0.7068 ] Network output: [ -0.008429 1.003 1.007 -1.188e-07 5.335e-08 0.007083 -8.956e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007009 0.0006555 0.004307 0.003045 0.9889 0.9919 0.007148 0.8496 0.8913 0.01121 ] Network output: [ -0.0001063 0.001031 1 -4.119e-06 1.849e-06 0.9987 -3.104e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.226 0.1075 0.3517 0.141 0.9849 0.9939 0.2268 0.4311 0.8745 0.7004 ] Network output: [ 0.002216 -0.01081 0.9947 2.521e-06 -1.132e-06 1.012 1.9e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7304 0.8603 0.3045 ] Network output: [ -0.002108 0.01009 1.005 2.779e-06 -1.248e-06 0.9896 2.095e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0952 0.09326 0.1648 0.197 0.9852 0.991 0.09522 0.654 0.8351 0.2504 ] Network output: [ 7.332e-05 1 -5.481e-05 3.623e-07 -1.627e-07 0.9998 2.731e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001255 Epoch 10271 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008397 0.9969 0.993 -1.154e-07 5.181e-08 -0.006692 -8.697e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003395 -0.006375 0.005185 0.9699 0.9743 0.006921 0.8225 0.8187 0.01576 ] Network output: [ 1 3.009e-05 0.0002917 -1.309e-06 5.875e-07 -0.0002356 -9.863e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2117 -0.03617 -0.1523 0.1807 0.9834 0.9932 0.2379 0.4272 0.8677 0.7068 ] Network output: [ -0.008428 1.003 1.007 -1.187e-07 5.33e-08 0.007082 -8.948e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007009 0.0006555 0.004307 0.003045 0.9889 0.9919 0.007148 0.8496 0.8913 0.01121 ] Network output: [ -0.0001062 0.00103 1 -4.114e-06 1.847e-06 0.9988 -3.1e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.226 0.1075 0.3518 0.141 0.9849 0.9939 0.2268 0.4311 0.8745 0.7004 ] Network output: [ 0.002215 -0.0108 0.9947 2.518e-06 -1.13e-06 1.012 1.898e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7303 0.8603 0.3045 ] Network output: [ -0.002106 0.01009 1.005 2.776e-06 -1.246e-06 0.9896 2.092e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0952 0.09326 0.1648 0.197 0.9852 0.991 0.09522 0.654 0.8351 0.2504 ] Network output: [ 7.331e-05 1 -5.484e-05 3.619e-07 -1.625e-07 0.9998 2.727e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001254 Epoch 10272 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008396 0.9969 0.993 -1.153e-07 5.176e-08 -0.006691 -8.69e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003395 -0.006374 0.005185 0.9699 0.9743 0.006921 0.8225 0.8187 0.01576 ] Network output: [ 1 2.997e-05 0.0002915 -1.307e-06 5.868e-07 -0.0002355 -9.851e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2117 -0.03617 -0.1523 0.1807 0.9834 0.9932 0.2379 0.4272 0.8677 0.7068 ] Network output: [ -0.008427 1.003 1.007 -1.186e-07 5.325e-08 0.007082 -8.94e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007009 0.0006556 0.004307 0.003045 0.9889 0.9919 0.007148 0.8496 0.8913 0.0112 ] Network output: [ -0.000106 0.001029 1 -4.109e-06 1.845e-06 0.9988 -3.096e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.226 0.1075 0.3518 0.141 0.9849 0.9939 0.2268 0.4311 0.8745 0.7004 ] Network output: [ 0.002214 -0.0108 0.9947 2.515e-06 -1.129e-06 1.012 1.895e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7303 0.8603 0.3045 ] Network output: [ -0.002105 0.01008 1.005 2.773e-06 -1.245e-06 0.9896 2.089e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09521 0.09326 0.1648 0.197 0.9852 0.991 0.09522 0.654 0.8351 0.2504 ] Network output: [ 7.33e-05 1 -5.486e-05 3.614e-07 -1.623e-07 0.9998 2.724e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001254 Epoch 10273 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008396 0.9969 0.993 -1.152e-07 5.172e-08 -0.006691 -8.682e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003395 -0.006374 0.005184 0.9699 0.9743 0.006921 0.8225 0.8187 0.01576 ] Network output: [ 1 2.984e-05 0.0002914 -1.305e-06 5.861e-07 -0.0002354 -9.838e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2117 -0.03617 -0.1523 0.1807 0.9834 0.9932 0.2379 0.4272 0.8677 0.7068 ] Network output: [ -0.008427 1.003 1.007 -1.185e-07 5.32e-08 0.007081 -8.931e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00701 0.0006556 0.004307 0.003044 0.9889 0.9919 0.007149 0.8496 0.8913 0.0112 ] Network output: [ -0.0001059 0.001028 1 -4.103e-06 1.842e-06 0.9988 -3.093e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.226 0.1075 0.3518 0.141 0.9849 0.9939 0.2268 0.4311 0.8745 0.7004 ] Network output: [ 0.002212 -0.01079 0.9947 2.512e-06 -1.128e-06 1.012 1.893e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7303 0.8603 0.3045 ] Network output: [ -0.002104 0.01008 1.005 2.769e-06 -1.243e-06 0.9896 2.087e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09521 0.09326 0.1648 0.197 0.9852 0.991 0.09522 0.654 0.8351 0.2504 ] Network output: [ 7.328e-05 1 -5.488e-05 3.61e-07 -1.621e-07 0.9998 2.72e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001253 Epoch 10274 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008395 0.9969 0.993 -1.151e-07 5.168e-08 -0.00669 -8.675e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003395 -0.006373 0.005184 0.9699 0.9743 0.006921 0.8225 0.8187 0.01576 ] Network output: [ 1 2.972e-05 0.0002913 -1.304e-06 5.853e-07 -0.0002352 -9.826e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2117 -0.03617 -0.1523 0.1807 0.9834 0.9932 0.2379 0.4272 0.8677 0.7068 ] Network output: [ -0.008426 1.003 1.007 -1.184e-07 5.315e-08 0.007081 -8.923e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00701 0.0006556 0.004306 0.003044 0.9889 0.9919 0.007149 0.8496 0.8913 0.0112 ] Network output: [ -0.0001058 0.001028 1 -4.098e-06 1.84e-06 0.9988 -3.089e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.226 0.1075 0.3518 0.141 0.9849 0.9939 0.2268 0.4311 0.8745 0.7004 ] Network output: [ 0.002211 -0.01078 0.9947 2.509e-06 -1.126e-06 1.012 1.891e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7303 0.8603 0.3045 ] Network output: [ -0.002103 0.01007 1.005 2.766e-06 -1.242e-06 0.9896 2.084e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09521 0.09326 0.1648 0.197 0.9852 0.991 0.09522 0.654 0.8351 0.2504 ] Network output: [ 7.327e-05 1 -5.491e-05 3.605e-07 -1.619e-07 0.9998 2.717e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001252 Epoch 10275 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008394 0.9969 0.993 -1.15e-07 5.164e-08 -0.006689 -8.668e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003395 -0.006373 0.005184 0.9699 0.9743 0.006921 0.8225 0.8187 0.01576 ] Network output: [ 1 2.96e-05 0.0002912 -1.302e-06 5.846e-07 -0.0002351 -9.814e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2117 -0.03617 -0.1523 0.1807 0.9834 0.9932 0.2379 0.4272 0.8677 0.7068 ] Network output: [ -0.008425 1.003 1.007 -1.183e-07 5.311e-08 0.00708 -8.915e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00701 0.0006557 0.004306 0.003044 0.9889 0.9919 0.007149 0.8496 0.8913 0.0112 ] Network output: [ -0.0001056 0.001027 1 -4.093e-06 1.838e-06 0.9988 -3.085e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.226 0.1075 0.3518 0.141 0.9849 0.9939 0.2268 0.4311 0.8745 0.7004 ] Network output: [ 0.002209 -0.01078 0.9947 2.505e-06 -1.125e-06 1.012 1.888e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7303 0.8603 0.3045 ] Network output: [ -0.002101 0.01007 1.005 2.762e-06 -1.24e-06 0.9896 2.082e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09521 0.09326 0.1648 0.197 0.9852 0.991 0.09522 0.654 0.8351 0.2504 ] Network output: [ 7.326e-05 1 -5.493e-05 3.601e-07 -1.617e-07 0.9998 2.714e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001252 Epoch 10276 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008393 0.9969 0.993 -1.149e-07 5.159e-08 -0.006689 -8.661e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003395 -0.006372 0.005183 0.9699 0.9743 0.006922 0.8225 0.8187 0.01576 ] Network output: [ 1 2.947e-05 0.000291 -1.301e-06 5.839e-07 -0.000235 -9.801e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2117 -0.03617 -0.1523 0.1807 0.9834 0.9932 0.2379 0.4272 0.8677 0.7068 ] Network output: [ -0.008424 1.003 1.007 -1.182e-07 5.306e-08 0.00708 -8.907e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00701 0.0006557 0.004306 0.003044 0.9889 0.9919 0.007149 0.8496 0.8913 0.0112 ] Network output: [ -0.0001055 0.001026 1 -4.088e-06 1.835e-06 0.9988 -3.081e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.226 0.1075 0.3518 0.141 0.9849 0.9939 0.2268 0.4311 0.8745 0.7004 ] Network output: [ 0.002208 -0.01077 0.9947 2.502e-06 -1.123e-06 1.012 1.886e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7303 0.8603 0.3045 ] Network output: [ -0.0021 0.01006 1.005 2.759e-06 -1.239e-06 0.9896 2.079e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09521 0.09327 0.1648 0.197 0.9852 0.991 0.09522 0.6539 0.8351 0.2504 ] Network output: [ 7.324e-05 1 -5.496e-05 3.596e-07 -1.615e-07 0.9998 2.71e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001251 Epoch 10277 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008393 0.9969 0.993 -1.148e-07 5.155e-08 -0.006688 -8.654e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003395 -0.006372 0.005183 0.9699 0.9743 0.006922 0.8225 0.8187 0.01576 ] Network output: [ 1 2.935e-05 0.0002909 -1.299e-06 5.831e-07 -0.0002349 -9.789e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2117 -0.03618 -0.1523 0.1807 0.9834 0.9932 0.2379 0.4272 0.8677 0.7068 ] Network output: [ -0.008424 1.003 1.007 -1.181e-07 5.301e-08 0.00708 -8.898e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007011 0.0006557 0.004306 0.003044 0.9889 0.9919 0.00715 0.8496 0.8913 0.0112 ] Network output: [ -0.0001054 0.001026 1 -4.083e-06 1.833e-06 0.9988 -3.077e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.226 0.1075 0.3518 0.141 0.9849 0.9939 0.2268 0.4311 0.8745 0.7004 ] Network output: [ 0.002207 -0.01077 0.9947 2.499e-06 -1.122e-06 1.012 1.883e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7303 0.8603 0.3045 ] Network output: [ -0.002099 0.01005 1.005 2.755e-06 -1.237e-06 0.9896 2.077e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09521 0.09327 0.1648 0.197 0.9852 0.991 0.09523 0.6539 0.8351 0.2504 ] Network output: [ 7.323e-05 1 -5.498e-05 3.592e-07 -1.613e-07 0.9998 2.707e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000125 Epoch 10278 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008392 0.9969 0.993 -1.147e-07 5.151e-08 -0.006687 -8.646e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003395 -0.006371 0.005183 0.9699 0.9743 0.006922 0.8225 0.8187 0.01576 ] Network output: [ 1 2.923e-05 0.0002908 -1.297e-06 5.824e-07 -0.0002348 -9.777e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2117 -0.03618 -0.1523 0.1807 0.9834 0.9932 0.2379 0.4272 0.8677 0.7068 ] Network output: [ -0.008423 1.003 1.007 -1.18e-07 5.296e-08 0.007079 -8.89e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007011 0.0006558 0.004306 0.003043 0.9889 0.9919 0.00715 0.8496 0.8913 0.0112 ] Network output: [ -0.0001053 0.001025 1 -4.078e-06 1.831e-06 0.9988 -3.073e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.226 0.1076 0.3518 0.141 0.9849 0.9939 0.2268 0.4311 0.8745 0.7004 ] Network output: [ 0.002205 -0.01076 0.9947 2.496e-06 -1.121e-06 1.012 1.881e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7303 0.8603 0.3045 ] Network output: [ -0.002098 0.01005 1.005 2.752e-06 -1.236e-06 0.9896 2.074e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09521 0.09327 0.1648 0.197 0.9852 0.991 0.09523 0.6539 0.8351 0.2504 ] Network output: [ 7.322e-05 1 -5.501e-05 3.587e-07 -1.611e-07 0.9998 2.704e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001249 Epoch 10279 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008391 0.9969 0.993 -1.146e-07 5.146e-08 -0.006687 -8.639e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003395 -0.006371 0.005182 0.9699 0.9743 0.006922 0.8225 0.8187 0.01575 ] Network output: [ 1 2.91e-05 0.0002907 -1.296e-06 5.817e-07 -0.0002346 -9.764e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2117 -0.03618 -0.1523 0.1807 0.9834 0.9932 0.2379 0.4272 0.8677 0.7068 ] Network output: [ -0.008422 1.003 1.007 -1.179e-07 5.291e-08 0.007079 -8.882e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007011 0.0006558 0.004306 0.003043 0.9889 0.9919 0.00715 0.8496 0.8913 0.0112 ] Network output: [ -0.0001051 0.001024 1 -4.073e-06 1.828e-06 0.9988 -3.069e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.226 0.1076 0.3518 0.141 0.9849 0.9939 0.2268 0.4311 0.8745 0.7004 ] Network output: [ 0.002204 -0.01075 0.9947 2.493e-06 -1.119e-06 1.012 1.879e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7303 0.8603 0.3045 ] Network output: [ -0.002096 0.01004 1.005 2.749e-06 -1.234e-06 0.9896 2.071e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09521 0.09327 0.1648 0.197 0.9852 0.991 0.09523 0.6539 0.8351 0.2504 ] Network output: [ 7.321e-05 1 -5.503e-05 3.583e-07 -1.609e-07 0.9998 2.7e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001249 Epoch 10280 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00839 0.9969 0.993 -1.145e-07 5.142e-08 -0.006686 -8.632e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003395 -0.00637 0.005182 0.9699 0.9743 0.006922 0.8225 0.8187 0.01575 ] Network output: [ 1 2.898e-05 0.0002905 -1.294e-06 5.809e-07 -0.0002345 -9.752e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2117 -0.03618 -0.1523 0.1807 0.9834 0.9932 0.2379 0.4272 0.8677 0.7068 ] Network output: [ -0.008421 1.003 1.007 -1.177e-07 5.286e-08 0.007078 -8.873e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007011 0.0006558 0.004306 0.003043 0.9889 0.9919 0.007151 0.8496 0.8913 0.0112 ] Network output: [ -0.000105 0.001024 1 -4.067e-06 1.826e-06 0.9988 -3.065e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.226 0.1076 0.3518 0.141 0.9849 0.9939 0.2268 0.4311 0.8745 0.7004 ] Network output: [ 0.002202 -0.01075 0.9947 2.49e-06 -1.118e-06 1.012 1.876e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7303 0.8603 0.3045 ] Network output: [ -0.002095 0.01004 1.005 2.745e-06 -1.232e-06 0.9896 2.069e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09521 0.09327 0.1648 0.197 0.9852 0.991 0.09523 0.6539 0.8351 0.2504 ] Network output: [ 7.319e-05 1 -5.505e-05 3.578e-07 -1.607e-07 0.9998 2.697e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001248 Epoch 10281 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008389 0.9969 0.993 -1.144e-07 5.138e-08 -0.006685 -8.625e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003395 -0.00637 0.005182 0.9699 0.9743 0.006922 0.8225 0.8187 0.01575 ] Network output: [ 1 2.886e-05 0.0002904 -1.292e-06 5.802e-07 -0.0002344 -9.74e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2117 -0.03618 -0.1523 0.1807 0.9834 0.9932 0.2379 0.4272 0.8677 0.7068 ] Network output: [ -0.008421 1.003 1.007 -1.176e-07 5.281e-08 0.007078 -8.865e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007012 0.0006559 0.004306 0.003043 0.9889 0.9919 0.007151 0.8496 0.8913 0.0112 ] Network output: [ -0.0001049 0.001023 1 -4.062e-06 1.824e-06 0.9988 -3.062e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.226 0.1076 0.3518 0.141 0.9849 0.9939 0.2268 0.4311 0.8745 0.7004 ] Network output: [ 0.002201 -0.01074 0.9947 2.487e-06 -1.116e-06 1.012 1.874e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7303 0.8603 0.3045 ] Network output: [ -0.002094 0.01003 1.005 2.742e-06 -1.231e-06 0.9896 2.066e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09522 0.09327 0.1648 0.197 0.9852 0.991 0.09523 0.6539 0.8351 0.2504 ] Network output: [ 7.318e-05 1 -5.508e-05 3.574e-07 -1.605e-07 0.9998 2.694e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001247 Epoch 10282 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008389 0.9969 0.993 -1.143e-07 5.133e-08 -0.006684 -8.617e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003395 -0.006369 0.005181 0.9699 0.9743 0.006922 0.8224 0.8187 0.01575 ] Network output: [ 1 2.873e-05 0.0002903 -1.291e-06 5.795e-07 -0.0002343 -9.728e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2117 -0.03618 -0.1523 0.1807 0.9834 0.9932 0.2379 0.4272 0.8677 0.7068 ] Network output: [ -0.00842 1.003 1.007 -1.175e-07 5.276e-08 0.007077 -8.857e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007012 0.0006559 0.004305 0.003043 0.9889 0.9919 0.007151 0.8496 0.8913 0.0112 ] Network output: [ -0.0001048 0.001022 1 -4.057e-06 1.821e-06 0.9988 -3.058e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.226 0.1076 0.3518 0.141 0.9849 0.9939 0.2268 0.4311 0.8745 0.7004 ] Network output: [ 0.0022 -0.01073 0.9947 2.484e-06 -1.115e-06 1.012 1.872e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7303 0.8603 0.3045 ] Network output: [ -0.002093 0.01003 1.005 2.739e-06 -1.229e-06 0.9896 2.064e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09522 0.09327 0.1648 0.197 0.9852 0.991 0.09523 0.6539 0.8351 0.2504 ] Network output: [ 7.317e-05 1 -5.51e-05 3.57e-07 -1.603e-07 0.9998 2.69e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001246 Epoch 10283 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008388 0.9969 0.993 -1.143e-07 5.129e-08 -0.006684 -8.61e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003395 -0.006369 0.005181 0.9699 0.9743 0.006922 0.8224 0.8187 0.01575 ] Network output: [ 1 2.861e-05 0.0002902 -1.289e-06 5.787e-07 -0.0002342 -9.715e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2117 -0.03618 -0.1523 0.1807 0.9834 0.9932 0.238 0.4272 0.8677 0.7068 ] Network output: [ -0.008419 1.003 1.007 -1.174e-07 5.271e-08 0.007077 -8.849e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007012 0.0006559 0.004305 0.003042 0.9889 0.9919 0.007151 0.8496 0.8913 0.0112 ] Network output: [ -0.0001046 0.001022 1 -4.052e-06 1.819e-06 0.9988 -3.054e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.226 0.1076 0.3518 0.141 0.9849 0.9939 0.2268 0.4311 0.8745 0.7004 ] Network output: [ 0.002198 -0.01073 0.9947 2.48e-06 -1.114e-06 1.012 1.869e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7303 0.8602 0.3045 ] Network output: [ -0.002091 0.01002 1.005 2.735e-06 -1.228e-06 0.9896 2.061e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09522 0.09327 0.1648 0.197 0.9852 0.991 0.09523 0.6539 0.8351 0.2504 ] Network output: [ 7.316e-05 1 -5.513e-05 3.565e-07 -1.601e-07 0.9998 2.687e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001246 Epoch 10284 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008387 0.9969 0.993 -1.142e-07 5.125e-08 -0.006683 -8.603e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003395 -0.006368 0.005181 0.9699 0.9743 0.006922 0.8224 0.8187 0.01575 ] Network output: [ 1 2.849e-05 0.00029 -1.288e-06 5.78e-07 -0.0002341 -9.703e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2117 -0.03618 -0.1522 0.1807 0.9834 0.9932 0.238 0.4272 0.8677 0.7068 ] Network output: [ -0.008418 1.003 1.007 -1.173e-07 5.266e-08 0.007077 -8.84e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007013 0.000656 0.004305 0.003042 0.9889 0.9919 0.007152 0.8496 0.8913 0.0112 ] Network output: [ -0.0001045 0.001021 1 -4.047e-06 1.817e-06 0.9988 -3.05e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2261 0.1076 0.3518 0.141 0.9849 0.9939 0.2268 0.4311 0.8745 0.7004 ] Network output: [ 0.002197 -0.01072 0.9947 2.477e-06 -1.112e-06 1.012 1.867e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7303 0.8602 0.3045 ] Network output: [ -0.00209 0.01002 1.005 2.732e-06 -1.226e-06 0.9896 2.059e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09522 0.09327 0.1648 0.197 0.9852 0.991 0.09523 0.6539 0.8351 0.2504 ] Network output: [ 7.314e-05 1 -5.515e-05 3.561e-07 -1.599e-07 0.9998 2.684e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001245 Epoch 10285 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008386 0.9969 0.993 -1.141e-07 5.121e-08 -0.006682 -8.596e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003395 -0.006368 0.00518 0.9699 0.9743 0.006923 0.8224 0.8187 0.01575 ] Network output: [ 1 2.836e-05 0.0002899 -1.286e-06 5.773e-07 -0.0002339 -9.691e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2117 -0.03618 -0.1522 0.1807 0.9834 0.9932 0.238 0.4272 0.8677 0.7068 ] Network output: [ -0.008418 1.003 1.007 -1.172e-07 5.261e-08 0.007076 -8.832e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007013 0.000656 0.004305 0.003042 0.9889 0.9919 0.007152 0.8496 0.8913 0.0112 ] Network output: [ -0.0001044 0.00102 1 -4.042e-06 1.815e-06 0.9988 -3.046e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2261 0.1076 0.3518 0.141 0.9849 0.9939 0.2268 0.4311 0.8745 0.7004 ] Network output: [ 0.002195 -0.01072 0.9947 2.474e-06 -1.111e-06 1.012 1.865e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7302 0.8602 0.3045 ] Network output: [ -0.002089 0.01001 1.005 2.728e-06 -1.225e-06 0.9896 2.056e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09522 0.09328 0.1648 0.197 0.9852 0.991 0.09523 0.6539 0.8351 0.2504 ] Network output: [ 7.313e-05 1 -5.518e-05 3.556e-07 -1.597e-07 0.9998 2.68e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001244 Epoch 10286 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008385 0.9969 0.993 -1.14e-07 5.116e-08 -0.006682 -8.589e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003395 -0.006367 0.00518 0.9699 0.9743 0.006923 0.8224 0.8187 0.01575 ] Network output: [ 1 2.824e-05 0.0002898 -1.284e-06 5.766e-07 -0.0002338 -9.679e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2117 -0.03619 -0.1522 0.1807 0.9834 0.9932 0.238 0.4272 0.8677 0.7068 ] Network output: [ -0.008417 1.003 1.007 -1.171e-07 5.256e-08 0.007076 -8.824e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007013 0.000656 0.004305 0.003042 0.9889 0.9919 0.007152 0.8496 0.8913 0.0112 ] Network output: [ -0.0001042 0.00102 1 -4.037e-06 1.812e-06 0.9988 -3.042e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2261 0.1076 0.3518 0.141 0.9849 0.9939 0.2269 0.4311 0.8745 0.7003 ] Network output: [ 0.002194 -0.01071 0.9947 2.471e-06 -1.109e-06 1.012 1.862e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7302 0.8602 0.3045 ] Network output: [ -0.002088 0.01001 1.005 2.725e-06 -1.223e-06 0.9896 2.054e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09522 0.09328 0.1648 0.197 0.9852 0.991 0.09524 0.6539 0.8351 0.2504 ] Network output: [ 7.312e-05 1 -5.52e-05 3.552e-07 -1.595e-07 0.9998 2.677e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001244 Epoch 10287 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008385 0.9969 0.993 -1.139e-07 5.112e-08 -0.006681 -8.581e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003395 -0.006367 0.00518 0.9699 0.9743 0.006923 0.8224 0.8187 0.01575 ] Network output: [ 1 2.812e-05 0.0002897 -1.283e-06 5.758e-07 -0.0002337 -9.666e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2117 -0.03619 -0.1522 0.1807 0.9834 0.9932 0.238 0.4272 0.8677 0.7068 ] Network output: [ -0.008416 1.003 1.007 -1.17e-07 5.252e-08 0.007075 -8.816e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007013 0.000656 0.004305 0.003042 0.9889 0.9919 0.007152 0.8496 0.8913 0.01119 ] Network output: [ -0.0001041 0.001019 1 -4.032e-06 1.81e-06 0.9988 -3.039e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2261 0.1076 0.3518 0.141 0.9849 0.9939 0.2269 0.4311 0.8745 0.7003 ] Network output: [ 0.002193 -0.0107 0.9947 2.468e-06 -1.108e-06 1.012 1.86e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7302 0.8602 0.3045 ] Network output: [ -0.002086 0.01 1.005 2.722e-06 -1.222e-06 0.9896 2.051e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09522 0.09328 0.1648 0.197 0.9852 0.991 0.09524 0.6539 0.8351 0.2504 ] Network output: [ 7.311e-05 1 -5.523e-05 3.548e-07 -1.593e-07 0.9998 2.674e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001243 Epoch 10288 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008384 0.9969 0.993 -1.138e-07 5.108e-08 -0.00668 -8.574e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003395 -0.006366 0.005179 0.9699 0.9743 0.006923 0.8224 0.8187 0.01575 ] Network output: [ 1 2.8e-05 0.0002895 -1.281e-06 5.751e-07 -0.0002336 -9.654e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2117 -0.03619 -0.1522 0.1807 0.9834 0.9932 0.238 0.4272 0.8677 0.7068 ] Network output: [ -0.008415 1.003 1.007 -1.169e-07 5.247e-08 0.007075 -8.808e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007014 0.0006561 0.004305 0.003041 0.9889 0.9919 0.007153 0.8496 0.8913 0.01119 ] Network output: [ -0.000104 0.001018 1 -4.027e-06 1.808e-06 0.9988 -3.035e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2261 0.1076 0.3518 0.141 0.9849 0.9939 0.2269 0.4311 0.8745 0.7003 ] Network output: [ 0.002191 -0.0107 0.9947 2.465e-06 -1.107e-06 1.012 1.858e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7302 0.8602 0.3045 ] Network output: [ -0.002085 0.009995 1.005 2.718e-06 -1.22e-06 0.9896 2.049e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09522 0.09328 0.1648 0.197 0.9852 0.991 0.09524 0.6539 0.835 0.2504 ] Network output: [ 7.309e-05 1 -5.525e-05 3.543e-07 -1.591e-07 0.9998 2.67e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001242 Epoch 10289 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008383 0.9969 0.993 -1.137e-07 5.103e-08 -0.00668 -8.567e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003396 -0.006366 0.005179 0.9699 0.9743 0.006923 0.8224 0.8186 0.01575 ] Network output: [ 1 2.787e-05 0.0002894 -1.279e-06 5.744e-07 -0.0002335 -9.642e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2117 -0.03619 -0.1522 0.1807 0.9834 0.9932 0.238 0.4271 0.8677 0.7068 ] Network output: [ -0.008415 1.003 1.007 -1.168e-07 5.242e-08 0.007074 -8.799e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007014 0.0006561 0.004305 0.003041 0.9889 0.9919 0.007153 0.8495 0.8913 0.01119 ] Network output: [ -0.0001039 0.001018 1 -4.022e-06 1.805e-06 0.9988 -3.031e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2261 0.1076 0.3518 0.141 0.9849 0.9939 0.2269 0.4311 0.8745 0.7003 ] Network output: [ 0.00219 -0.01069 0.9947 2.462e-06 -1.105e-06 1.012 1.855e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7302 0.8602 0.3045 ] Network output: [ -0.002084 0.009989 1.005 2.715e-06 -1.219e-06 0.9896 2.046e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09522 0.09328 0.1648 0.197 0.9852 0.991 0.09524 0.6538 0.835 0.2504 ] Network output: [ 7.308e-05 1 -5.527e-05 3.539e-07 -1.589e-07 0.9998 2.667e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001241 Epoch 10290 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008382 0.9969 0.993 -1.136e-07 5.099e-08 -0.006679 -8.56e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003396 -0.006365 0.005179 0.9699 0.9743 0.006923 0.8224 0.8186 0.01575 ] Network output: [ 1 2.775e-05 0.0002893 -1.278e-06 5.737e-07 -0.0002334 -9.63e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.03619 -0.1522 0.1807 0.9834 0.9932 0.238 0.4271 0.8677 0.7068 ] Network output: [ -0.008414 1.003 1.007 -1.167e-07 5.237e-08 0.007074 -8.791e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007014 0.0006561 0.004305 0.003041 0.9889 0.9919 0.007153 0.8495 0.8913 0.01119 ] Network output: [ -0.0001037 0.001017 1 -4.017e-06 1.803e-06 0.9988 -3.027e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2261 0.1076 0.3518 0.141 0.9849 0.9939 0.2269 0.4311 0.8745 0.7003 ] Network output: [ 0.002188 -0.01068 0.9947 2.459e-06 -1.104e-06 1.012 1.853e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7302 0.8602 0.3045 ] Network output: [ -0.002083 0.009984 1.005 2.712e-06 -1.217e-06 0.9896 2.044e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09523 0.09328 0.1648 0.197 0.9852 0.991 0.09524 0.6538 0.835 0.2504 ] Network output: [ 7.307e-05 1 -5.53e-05 3.534e-07 -1.587e-07 0.9998 2.664e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001241 Epoch 10291 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008382 0.9969 0.993 -1.135e-07 5.095e-08 -0.006678 -8.553e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003396 -0.006365 0.005178 0.9699 0.9743 0.006923 0.8224 0.8186 0.01575 ] Network output: [ 1 2.763e-05 0.0002892 -1.276e-06 5.729e-07 -0.0002332 -9.618e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.03619 -0.1522 0.1807 0.9834 0.9932 0.238 0.4271 0.8677 0.7068 ] Network output: [ -0.008413 1.003 1.007 -1.165e-07 5.232e-08 0.007074 -8.783e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007014 0.0006562 0.004304 0.003041 0.9889 0.9919 0.007154 0.8495 0.8913 0.01119 ] Network output: [ -0.0001036 0.001016 1 -4.012e-06 1.801e-06 0.9988 -3.023e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2261 0.1076 0.3518 0.141 0.9849 0.9939 0.2269 0.4311 0.8745 0.7003 ] Network output: [ 0.002187 -0.01068 0.9947 2.456e-06 -1.102e-06 1.012 1.851e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7302 0.8602 0.3045 ] Network output: [ -0.002081 0.009978 1.005 2.708e-06 -1.216e-06 0.9896 2.041e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09523 0.09328 0.1648 0.197 0.9852 0.991 0.09524 0.6538 0.835 0.2504 ] Network output: [ 7.306e-05 1 -5.532e-05 3.53e-07 -1.585e-07 0.9998 2.66e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000124 Epoch 10292 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008381 0.9969 0.993 -1.134e-07 5.091e-08 -0.006677 -8.546e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003396 -0.006364 0.005178 0.9699 0.9743 0.006923 0.8224 0.8186 0.01574 ] Network output: [ 1 2.751e-05 0.000289 -1.275e-06 5.722e-07 -0.0002331 -9.606e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.03619 -0.1522 0.1807 0.9834 0.9932 0.238 0.4271 0.8677 0.7068 ] Network output: [ -0.008412 1.003 1.007 -1.164e-07 5.227e-08 0.007073 -8.775e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007015 0.0006562 0.004304 0.00304 0.9889 0.9919 0.007154 0.8495 0.8913 0.01119 ] Network output: [ -0.0001035 0.001016 1 -4.007e-06 1.799e-06 0.9988 -3.019e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2261 0.1076 0.3518 0.141 0.9849 0.9939 0.2269 0.4311 0.8745 0.7003 ] Network output: [ 0.002186 -0.01067 0.9947 2.453e-06 -1.101e-06 1.012 1.848e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1964 0.9873 0.9919 0.1129 0.7302 0.8602 0.3045 ] Network output: [ -0.00208 0.009973 1.005 2.705e-06 -1.214e-06 0.9896 2.038e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09523 0.09328 0.1648 0.197 0.9852 0.991 0.09524 0.6538 0.835 0.2504 ] Network output: [ 7.304e-05 1 -5.535e-05 3.526e-07 -1.583e-07 0.9998 2.657e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001239 Epoch 10293 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00838 0.9969 0.993 -1.133e-07 5.086e-08 -0.006677 -8.538e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003396 -0.006364 0.005178 0.9699 0.9743 0.006923 0.8224 0.8186 0.01574 ] Network output: [ 1 2.738e-05 0.0002889 -1.273e-06 5.715e-07 -0.000233 -9.594e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.03619 -0.1522 0.1807 0.9834 0.9932 0.238 0.4271 0.8677 0.7068 ] Network output: [ -0.008412 1.003 1.007 -1.163e-07 5.222e-08 0.007073 -8.767e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007015 0.0006562 0.004304 0.00304 0.9889 0.9919 0.007154 0.8495 0.8913 0.01119 ] Network output: [ -0.0001034 0.001015 1 -4.001e-06 1.796e-06 0.9988 -3.016e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2261 0.1076 0.3519 0.141 0.9849 0.9939 0.2269 0.4311 0.8745 0.7003 ] Network output: [ 0.002184 -0.01067 0.9947 2.45e-06 -1.1e-06 1.012 1.846e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1128 0.1 0.1851 0.1963 0.9873 0.9919 0.1129 0.7302 0.8602 0.3045 ] Network output: [ -0.002079 0.009968 1.005 2.702e-06 -1.213e-06 0.9896 2.036e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09523 0.09328 0.1648 0.197 0.9852 0.991 0.09524 0.6538 0.835 0.2504 ] Network output: [ 7.303e-05 1 -5.537e-05 3.521e-07 -1.581e-07 0.9998 2.654e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001238 Epoch 10294 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008379 0.9969 0.993 -1.132e-07 5.082e-08 -0.006676 -8.531e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003533 -0.003396 -0.006363 0.005177 0.9699 0.9743 0.006924 0.8224 0.8186 0.01574 ] Network output: [ 1 2.726e-05 0.0002888 -1.271e-06 5.708e-07 -0.0002329 -9.582e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.03619 -0.1522 0.1807 0.9834 0.9932 0.238 0.4271 0.8677 0.7068 ] Network output: [ -0.008411 1.003 1.007 -1.162e-07 5.217e-08 0.007072 -8.758e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007015 0.0006563 0.004304 0.00304 0.9889 0.9919 0.007154 0.8495 0.8913 0.01119 ] Network output: [ -0.0001032 0.001014 1 -3.996e-06 1.794e-06 0.9988 -3.012e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2261 0.1076 0.3519 0.141 0.9849 0.9939 0.2269 0.4311 0.8745 0.7003 ] Network output: [ 0.002183 -0.01066 0.9947 2.447e-06 -1.098e-06 1.012 1.844e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1 0.1851 0.1963 0.9873 0.9919 0.1129 0.7302 0.8602 0.3045 ] Network output: [ -0.002078 0.009962 1.005 2.698e-06 -1.211e-06 0.9896 2.033e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09523 0.09329 0.1648 0.197 0.9852 0.991 0.09525 0.6538 0.835 0.2504 ] Network output: [ 7.302e-05 1 -5.54e-05 3.517e-07 -1.579e-07 0.9998 2.65e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001238 Epoch 10295 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008378 0.9969 0.993 -1.131e-07 5.078e-08 -0.006675 -8.524e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003396 -0.006363 0.005177 0.9699 0.9743 0.006924 0.8224 0.8186 0.01574 ] Network output: [ 1 2.714e-05 0.0002887 -1.27e-06 5.701e-07 -0.0002328 -9.57e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.03619 -0.1522 0.1807 0.9834 0.9932 0.238 0.4271 0.8677 0.7068 ] Network output: [ -0.00841 1.003 1.007 -1.161e-07 5.213e-08 0.007072 -8.75e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007015 0.0006563 0.004304 0.00304 0.9889 0.9919 0.007155 0.8495 0.8913 0.01119 ] Network output: [ -0.0001031 0.001013 1 -3.991e-06 1.792e-06 0.9988 -3.008e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2261 0.1076 0.3519 0.141 0.9849 0.9939 0.2269 0.4311 0.8745 0.7003 ] Network output: [ 0.002181 -0.01065 0.9947 2.444e-06 -1.097e-06 1.012 1.842e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1 0.1851 0.1963 0.9873 0.9919 0.1129 0.7302 0.8602 0.3045 ] Network output: [ -0.002076 0.009957 1.005 2.695e-06 -1.21e-06 0.9896 2.031e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09523 0.09329 0.1648 0.197 0.9852 0.991 0.09525 0.6538 0.835 0.2504 ] Network output: [ 7.301e-05 1 -5.542e-05 3.512e-07 -1.577e-07 0.9998 2.647e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001237 Epoch 10296 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008378 0.9969 0.993 -1.13e-07 5.074e-08 -0.006675 -8.517e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003396 -0.006362 0.005177 0.9699 0.9743 0.006924 0.8224 0.8186 0.01574 ] Network output: [ 1 2.702e-05 0.0002885 -1.268e-06 5.693e-07 -0.0002326 -9.558e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.0362 -0.1522 0.1807 0.9834 0.9932 0.238 0.4271 0.8677 0.7068 ] Network output: [ -0.008409 1.003 1.007 -1.16e-07 5.208e-08 0.007072 -8.742e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007016 0.0006563 0.004304 0.00304 0.9889 0.9919 0.007155 0.8495 0.8913 0.01119 ] Network output: [ -0.000103 0.001013 1 -3.986e-06 1.79e-06 0.9988 -3.004e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2261 0.1076 0.3519 0.141 0.9849 0.9939 0.2269 0.4311 0.8745 0.7003 ] Network output: [ 0.00218 -0.01065 0.9947 2.44e-06 -1.096e-06 1.012 1.839e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1 0.1851 0.1963 0.9873 0.9919 0.1129 0.7302 0.8602 0.3045 ] Network output: [ -0.002075 0.009951 1.005 2.692e-06 -1.208e-06 0.9896 2.028e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09523 0.09329 0.1648 0.197 0.9852 0.991 0.09525 0.6538 0.835 0.2504 ] Network output: [ 7.299e-05 1 -5.545e-05 3.508e-07 -1.575e-07 0.9998 2.644e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001236 Epoch 10297 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008377 0.9969 0.993 -1.129e-07 5.069e-08 -0.006674 -8.51e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003396 -0.006362 0.005176 0.9699 0.9743 0.006924 0.8224 0.8186 0.01574 ] Network output: [ 1 2.69e-05 0.0002884 -1.267e-06 5.686e-07 -0.0002325 -9.546e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.0362 -0.1522 0.1807 0.9834 0.9932 0.238 0.4271 0.8677 0.7068 ] Network output: [ -0.008409 1.003 1.007 -1.159e-07 5.203e-08 0.007071 -8.734e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007016 0.0006563 0.004304 0.003039 0.9889 0.9919 0.007155 0.8495 0.8913 0.01119 ] Network output: [ -0.0001029 0.001012 1 -3.981e-06 1.787e-06 0.9988 -3e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2261 0.1076 0.3519 0.141 0.9849 0.9939 0.2269 0.4311 0.8745 0.7003 ] Network output: [ 0.002179 -0.01064 0.9947 2.437e-06 -1.094e-06 1.012 1.837e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.1129 0.7302 0.8602 0.3045 ] Network output: [ -0.002074 0.009946 1.005 2.688e-06 -1.207e-06 0.9896 2.026e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09523 0.09329 0.1648 0.197 0.9852 0.991 0.09525 0.6538 0.835 0.2504 ] Network output: [ 7.298e-05 1 -5.547e-05 3.504e-07 -1.573e-07 0.9998 2.641e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001236 Epoch 10298 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008376 0.9969 0.993 -1.128e-07 5.065e-08 -0.006673 -8.503e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003396 -0.006361 0.005176 0.9699 0.9743 0.006924 0.8224 0.8186 0.01574 ] Network output: [ 1 2.677e-05 0.0002883 -1.265e-06 5.679e-07 -0.0002324 -9.533e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.0362 -0.1521 0.1807 0.9834 0.9932 0.238 0.4271 0.8677 0.7068 ] Network output: [ -0.008408 1.003 1.007 -1.158e-07 5.198e-08 0.007071 -8.726e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007016 0.0006564 0.004304 0.003039 0.9889 0.9919 0.007156 0.8495 0.8913 0.01119 ] Network output: [ -0.0001027 0.001011 1 -3.976e-06 1.785e-06 0.9988 -2.997e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2261 0.1076 0.3519 0.141 0.9849 0.9939 0.2269 0.431 0.8745 0.7003 ] Network output: [ 0.002177 -0.01063 0.9947 2.434e-06 -1.093e-06 1.012 1.835e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.1129 0.7302 0.8602 0.3045 ] Network output: [ -0.002073 0.00994 1.005 2.685e-06 -1.205e-06 0.9897 2.023e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09523 0.09329 0.1648 0.197 0.9852 0.991 0.09525 0.6538 0.835 0.2504 ] Network output: [ 7.297e-05 1 -5.55e-05 3.499e-07 -1.571e-07 0.9998 2.637e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001235 Epoch 10299 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008375 0.9969 0.993 -1.127e-07 5.061e-08 -0.006673 -8.495e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003396 -0.006361 0.005176 0.9699 0.9743 0.006924 0.8224 0.8186 0.01574 ] Network output: [ 1 2.665e-05 0.0002882 -1.263e-06 5.672e-07 -0.0002323 -9.521e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.0362 -0.1521 0.1806 0.9834 0.9932 0.238 0.4271 0.8677 0.7068 ] Network output: [ -0.008407 1.003 1.007 -1.157e-07 5.193e-08 0.00707 -8.718e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007017 0.0006564 0.004303 0.003039 0.9889 0.9919 0.007156 0.8495 0.8913 0.01119 ] Network output: [ -0.0001026 0.001011 1 -3.971e-06 1.783e-06 0.9988 -2.993e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2261 0.1076 0.3519 0.141 0.9849 0.9939 0.2269 0.431 0.8745 0.7003 ] Network output: [ 0.002176 -0.01063 0.9947 2.431e-06 -1.092e-06 1.012 1.832e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.1129 0.7301 0.8602 0.3045 ] Network output: [ -0.002071 0.009935 1.005 2.682e-06 -1.204e-06 0.9897 2.021e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09524 0.09329 0.1648 0.197 0.9852 0.991 0.09525 0.6538 0.835 0.2504 ] Network output: [ 7.296e-05 1 -5.552e-05 3.495e-07 -1.569e-07 0.9998 2.634e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001234 Epoch 10300 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008374 0.9969 0.993 -1.126e-07 5.056e-08 -0.006672 -8.488e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003396 -0.00636 0.005175 0.9699 0.9743 0.006924 0.8224 0.8186 0.01574 ] Network output: [ 1 2.653e-05 0.000288 -1.262e-06 5.665e-07 -0.0002322 -9.509e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.0362 -0.1521 0.1806 0.9834 0.9932 0.238 0.4271 0.8677 0.7068 ] Network output: [ -0.008406 1.003 1.007 -1.156e-07 5.188e-08 0.00707 -8.71e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007017 0.0006564 0.004303 0.003039 0.9889 0.9919 0.007156 0.8495 0.8913 0.01119 ] Network output: [ -0.0001025 0.00101 1 -3.966e-06 1.781e-06 0.9988 -2.989e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2261 0.1076 0.3519 0.141 0.9849 0.9939 0.2269 0.431 0.8745 0.7003 ] Network output: [ 0.002174 -0.01062 0.9947 2.428e-06 -1.09e-06 1.012 1.83e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.1129 0.7301 0.8602 0.3045 ] Network output: [ -0.00207 0.00993 1.005 2.678e-06 -1.202e-06 0.9897 2.018e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09524 0.09329 0.1648 0.197 0.9852 0.991 0.09525 0.6538 0.835 0.2504 ] Network output: [ 7.294e-05 1 -5.555e-05 3.491e-07 -1.567e-07 0.9998 2.631e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001233 Epoch 10301 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008374 0.9969 0.993 -1.125e-07 5.052e-08 -0.006671 -8.481e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003396 -0.00636 0.005175 0.9699 0.9743 0.006924 0.8224 0.8186 0.01574 ] Network output: [ 1 2.641e-05 0.0002879 -1.26e-06 5.658e-07 -0.0002321 -9.498e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.0362 -0.1521 0.1806 0.9834 0.9932 0.2381 0.4271 0.8677 0.7068 ] Network output: [ -0.008406 1.003 1.007 -1.155e-07 5.183e-08 0.007069 -8.701e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007017 0.0006565 0.004303 0.003039 0.9889 0.9919 0.007156 0.8495 0.8913 0.01119 ] Network output: [ -0.0001023 0.001009 1 -3.961e-06 1.778e-06 0.9988 -2.985e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2261 0.1076 0.3519 0.141 0.9849 0.9939 0.2269 0.431 0.8745 0.7003 ] Network output: [ 0.002173 -0.01062 0.9947 2.425e-06 -1.089e-06 1.012 1.828e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.1129 0.7301 0.8602 0.3045 ] Network output: [ -0.002069 0.009924 1.005 2.675e-06 -1.201e-06 0.9897 2.016e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09524 0.09329 0.1648 0.197 0.9852 0.991 0.09525 0.6538 0.835 0.2504 ] Network output: [ 7.293e-05 1 -5.558e-05 3.486e-07 -1.565e-07 0.9998 2.627e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001233 Epoch 10302 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008373 0.9969 0.993 -1.124e-07 5.048e-08 -0.00667 -8.474e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003396 -0.006359 0.005175 0.9699 0.9743 0.006924 0.8224 0.8186 0.01574 ] Network output: [ 1 2.629e-05 0.0002878 -1.259e-06 5.651e-07 -0.000232 -9.486e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.0362 -0.1521 0.1806 0.9834 0.9932 0.2381 0.4271 0.8677 0.7068 ] Network output: [ -0.008405 1.003 1.007 -1.154e-07 5.179e-08 0.007069 -8.693e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007017 0.0006565 0.004303 0.003038 0.9889 0.9919 0.007157 0.8495 0.8913 0.01118 ] Network output: [ -0.0001022 0.001009 1 -3.956e-06 1.776e-06 0.9988 -2.982e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2262 0.1076 0.3519 0.141 0.9849 0.9939 0.2269 0.431 0.8745 0.7003 ] Network output: [ 0.002172 -0.01061 0.9947 2.422e-06 -1.087e-06 1.012 1.825e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.1129 0.7301 0.8602 0.3045 ] Network output: [ -0.002068 0.009919 1.005 2.672e-06 -1.199e-06 0.9897 2.013e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09524 0.09329 0.1648 0.197 0.9852 0.991 0.09525 0.6537 0.835 0.2504 ] Network output: [ 7.292e-05 1 -5.56e-05 3.482e-07 -1.563e-07 0.9998 2.624e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001232 Epoch 10303 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008372 0.9969 0.993 -1.123e-07 5.044e-08 -0.00667 -8.467e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003396 -0.006359 0.005174 0.9699 0.9743 0.006924 0.8224 0.8186 0.01574 ] Network output: [ 1 2.616e-05 0.0002877 -1.257e-06 5.643e-07 -0.0002318 -9.474e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.0362 -0.1521 0.1806 0.9834 0.9932 0.2381 0.4271 0.8677 0.7068 ] Network output: [ -0.008404 1.003 1.007 -1.152e-07 5.174e-08 0.007069 -8.685e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007018 0.0006565 0.004303 0.003038 0.9889 0.9919 0.007157 0.8495 0.8913 0.01118 ] Network output: [ -0.0001021 0.001008 1 -3.951e-06 1.774e-06 0.9988 -2.978e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2262 0.1076 0.3519 0.141 0.9849 0.9939 0.2269 0.431 0.8745 0.7003 ] Network output: [ 0.00217 -0.0106 0.9947 2.419e-06 -1.086e-06 1.012 1.823e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.1129 0.7301 0.8602 0.3045 ] Network output: [ -0.002066 0.009913 1.005 2.668e-06 -1.198e-06 0.9897 2.011e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09524 0.0933 0.1648 0.197 0.9852 0.991 0.09525 0.6537 0.835 0.2504 ] Network output: [ 7.291e-05 1 -5.563e-05 3.478e-07 -1.561e-07 0.9998 2.621e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001231 Epoch 10304 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008371 0.9969 0.993 -1.123e-07 5.039e-08 -0.006669 -8.46e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003396 -0.006358 0.005174 0.9699 0.9743 0.006925 0.8224 0.8186 0.01573 ] Network output: [ 1 2.604e-05 0.0002875 -1.255e-06 5.636e-07 -0.0002317 -9.462e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.0362 -0.1521 0.1806 0.9834 0.9932 0.2381 0.4271 0.8677 0.7068 ] Network output: [ -0.008403 1.003 1.007 -1.151e-07 5.169e-08 0.007068 -8.677e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007018 0.0006566 0.004303 0.003038 0.9889 0.9919 0.007157 0.8495 0.8913 0.01118 ] Network output: [ -0.000102 0.001007 1 -3.946e-06 1.772e-06 0.9988 -2.974e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2262 0.1076 0.3519 0.1409 0.9849 0.9939 0.227 0.431 0.8745 0.7003 ] Network output: [ 0.002169 -0.0106 0.9947 2.416e-06 -1.085e-06 1.012 1.821e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.1129 0.7301 0.8602 0.3045 ] Network output: [ -0.002065 0.009908 1.005 2.665e-06 -1.196e-06 0.9897 2.008e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09524 0.0933 0.1648 0.197 0.9852 0.991 0.09526 0.6537 0.835 0.2504 ] Network output: [ 7.289e-05 1 -5.565e-05 3.473e-07 -1.559e-07 0.9998 2.618e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000123 Epoch 10305 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008371 0.9969 0.993 -1.122e-07 5.035e-08 -0.006668 -8.453e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003396 -0.006358 0.005174 0.9699 0.9743 0.006925 0.8224 0.8186 0.01573 ] Network output: [ 1 2.592e-05 0.0002874 -1.254e-06 5.629e-07 -0.0002316 -9.45e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.03621 -0.1521 0.1806 0.9834 0.9932 0.2381 0.4271 0.8677 0.7068 ] Network output: [ -0.008403 1.003 1.007 -1.15e-07 5.164e-08 0.007068 -8.669e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007018 0.0006566 0.004303 0.003038 0.9889 0.9919 0.007157 0.8495 0.8913 0.01118 ] Network output: [ -0.0001018 0.001007 1 -3.941e-06 1.769e-06 0.9988 -2.97e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2262 0.1076 0.3519 0.1409 0.9849 0.9939 0.227 0.431 0.8745 0.7003 ] Network output: [ 0.002167 -0.01059 0.9947 2.413e-06 -1.083e-06 1.012 1.819e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7301 0.8602 0.3045 ] Network output: [ -0.002064 0.009902 1.005 2.662e-06 -1.195e-06 0.9897 2.006e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09524 0.0933 0.1648 0.197 0.9852 0.991 0.09526 0.6537 0.835 0.2504 ] Network output: [ 7.288e-05 1 -5.568e-05 3.469e-07 -1.557e-07 0.9998 2.614e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000123 Epoch 10306 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00837 0.9969 0.993 -1.121e-07 5.031e-08 -0.006668 -8.446e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003397 -0.006357 0.005173 0.9699 0.9743 0.006925 0.8224 0.8186 0.01573 ] Network output: [ 1 2.58e-05 0.0002873 -1.252e-06 5.622e-07 -0.0002315 -9.438e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.03621 -0.1521 0.1806 0.9834 0.9932 0.2381 0.4271 0.8677 0.7068 ] Network output: [ -0.008402 1.003 1.007 -1.149e-07 5.159e-08 0.007067 -8.661e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007018 0.0006566 0.004303 0.003038 0.9889 0.9919 0.007158 0.8495 0.8913 0.01118 ] Network output: [ -0.0001017 0.001006 1 -3.936e-06 1.767e-06 0.9988 -2.967e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2262 0.1076 0.3519 0.1409 0.9849 0.9939 0.227 0.431 0.8745 0.7003 ] Network output: [ 0.002166 -0.01059 0.9947 2.41e-06 -1.082e-06 1.012 1.816e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7301 0.8602 0.3045 ] Network output: [ -0.002063 0.009897 1.005 2.658e-06 -1.193e-06 0.9897 2.003e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09524 0.0933 0.1648 0.197 0.9852 0.991 0.09526 0.6537 0.835 0.2504 ] Network output: [ 7.287e-05 1 -5.57e-05 3.465e-07 -1.555e-07 0.9998 2.611e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001229 Epoch 10307 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008369 0.9969 0.993 -1.12e-07 5.027e-08 -0.006667 -8.438e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003397 -0.006357 0.005173 0.9699 0.9743 0.006925 0.8224 0.8186 0.01573 ] Network output: [ 1 2.568e-05 0.0002872 -1.251e-06 5.615e-07 -0.0002314 -9.426e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.03621 -0.1521 0.1806 0.9834 0.9932 0.2381 0.4271 0.8677 0.7068 ] Network output: [ -0.008401 1.003 1.007 -1.148e-07 5.154e-08 0.007067 -8.653e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007019 0.0006566 0.004302 0.003037 0.9889 0.9919 0.007158 0.8495 0.8913 0.01118 ] Network output: [ -0.0001016 0.001005 1 -3.932e-06 1.765e-06 0.9988 -2.963e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2262 0.1076 0.3519 0.1409 0.9849 0.9939 0.227 0.431 0.8745 0.7003 ] Network output: [ 0.002165 -0.01058 0.9947 2.407e-06 -1.081e-06 1.012 1.814e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7301 0.8602 0.3045 ] Network output: [ -0.002061 0.009892 1.005 2.655e-06 -1.192e-06 0.9897 2.001e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09524 0.0933 0.1648 0.197 0.9852 0.991 0.09526 0.6537 0.835 0.2504 ] Network output: [ 7.286e-05 1 -5.573e-05 3.46e-07 -1.554e-07 0.9998 2.608e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001228 Epoch 10308 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008368 0.9969 0.993 -1.119e-07 5.022e-08 -0.006666 -8.431e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003397 -0.006356 0.005173 0.9699 0.9743 0.006925 0.8224 0.8186 0.01573 ] Network output: [ 1 2.556e-05 0.000287 -1.249e-06 5.608e-07 -0.0002313 -9.414e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.03621 -0.1521 0.1806 0.9834 0.9932 0.2381 0.4271 0.8677 0.7068 ] Network output: [ -0.0084 1.003 1.007 -1.147e-07 5.15e-08 0.007067 -8.645e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007019 0.0006567 0.004302 0.003037 0.9889 0.9919 0.007158 0.8495 0.8913 0.01118 ] Network output: [ -0.0001015 0.001005 1 -3.927e-06 1.763e-06 0.9988 -2.959e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2262 0.1076 0.3519 0.1409 0.9849 0.9939 0.227 0.431 0.8745 0.7003 ] Network output: [ 0.002163 -0.01057 0.9947 2.404e-06 -1.079e-06 1.012 1.812e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7301 0.8602 0.3045 ] Network output: [ -0.00206 0.009886 1.005 2.652e-06 -1.191e-06 0.9897 1.999e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09525 0.0933 0.1648 0.197 0.9852 0.991 0.09526 0.6537 0.835 0.2504 ] Network output: [ 7.284e-05 1 -5.575e-05 3.456e-07 -1.552e-07 0.9998 2.605e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001228 Epoch 10309 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008367 0.9969 0.993 -1.118e-07 5.018e-08 -0.006666 -8.424e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003397 -0.006356 0.005172 0.9699 0.9743 0.006925 0.8224 0.8186 0.01573 ] Network output: [ 1 2.544e-05 0.0002869 -1.248e-06 5.601e-07 -0.0002311 -9.402e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.03621 -0.1521 0.1806 0.9834 0.9932 0.2381 0.4271 0.8677 0.7067 ] Network output: [ -0.0084 1.003 1.007 -1.146e-07 5.145e-08 0.007066 -8.636e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007019 0.0006567 0.004302 0.003037 0.9889 0.9919 0.007159 0.8495 0.8913 0.01118 ] Network output: [ -0.0001013 0.001004 1 -3.922e-06 1.761e-06 0.9988 -2.955e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2262 0.1076 0.3519 0.1409 0.9849 0.9939 0.227 0.431 0.8745 0.7003 ] Network output: [ 0.002162 -0.01057 0.9947 2.401e-06 -1.078e-06 1.012 1.809e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7301 0.8602 0.3045 ] Network output: [ -0.002059 0.009881 1.005 2.649e-06 -1.189e-06 0.9897 1.996e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09525 0.0933 0.1648 0.197 0.9852 0.991 0.09526 0.6537 0.835 0.2504 ] Network output: [ 7.283e-05 1 -5.578e-05 3.452e-07 -1.55e-07 0.9998 2.601e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001227 Epoch 10310 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008367 0.9969 0.993 -1.117e-07 5.014e-08 -0.006665 -8.417e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003397 -0.006355 0.005172 0.9699 0.9743 0.006925 0.8224 0.8186 0.01573 ] Network output: [ 1 2.531e-05 0.0002868 -1.246e-06 5.594e-07 -0.000231 -9.39e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.03621 -0.1521 0.1806 0.9834 0.9932 0.2381 0.4271 0.8677 0.7067 ] Network output: [ -0.008399 1.003 1.007 -1.145e-07 5.14e-08 0.007066 -8.628e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007019 0.0006567 0.004302 0.003037 0.9889 0.9919 0.007159 0.8495 0.8913 0.01118 ] Network output: [ -0.0001012 0.001003 1 -3.917e-06 1.758e-06 0.9988 -2.952e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2262 0.1076 0.3519 0.1409 0.9849 0.9939 0.227 0.431 0.8745 0.7003 ] Network output: [ 0.00216 -0.01056 0.9947 2.398e-06 -1.077e-06 1.012 1.807e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7301 0.8602 0.3045 ] Network output: [ -0.002058 0.009875 1.005 2.645e-06 -1.188e-06 0.9897 1.994e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09525 0.0933 0.1648 0.197 0.9852 0.991 0.09526 0.6537 0.835 0.2504 ] Network output: [ 7.282e-05 1 -5.581e-05 3.448e-07 -1.548e-07 0.9998 2.598e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001226 Epoch 10311 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008366 0.9969 0.993 -1.116e-07 5.01e-08 -0.006664 -8.41e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003397 -0.006355 0.005172 0.9699 0.9743 0.006925 0.8224 0.8186 0.01573 ] Network output: [ 1 2.519e-05 0.0002867 -1.244e-06 5.587e-07 -0.0002309 -9.379e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2118 -0.03621 -0.1521 0.1806 0.9834 0.9932 0.2381 0.4271 0.8677 0.7067 ] Network output: [ -0.008398 1.003 1.007 -1.144e-07 5.135e-08 0.007065 -8.62e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00702 0.0006568 0.004302 0.003036 0.9889 0.9919 0.007159 0.8495 0.8913 0.01118 ] Network output: [ -0.0001011 0.001003 1 -3.912e-06 1.756e-06 0.9988 -2.948e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2262 0.1076 0.3519 0.1409 0.9849 0.9939 0.227 0.431 0.8745 0.7003 ] Network output: [ 0.002159 -0.01055 0.9947 2.395e-06 -1.075e-06 1.012 1.805e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7301 0.8602 0.3045 ] Network output: [ -0.002056 0.00987 1.005 2.642e-06 -1.186e-06 0.9897 1.991e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09525 0.0933 0.1648 0.197 0.9852 0.991 0.09526 0.6537 0.835 0.2504 ] Network output: [ 7.281e-05 1 -5.583e-05 3.443e-07 -1.546e-07 0.9998 2.595e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001225 Epoch 10312 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008365 0.9969 0.993 -1.115e-07 5.006e-08 -0.006663 -8.403e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003397 -0.006354 0.005171 0.9699 0.9743 0.006925 0.8224 0.8186 0.01573 ] Network output: [ 1 2.507e-05 0.0002866 -1.243e-06 5.58e-07 -0.0002308 -9.367e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.03621 -0.152 0.1806 0.9834 0.9932 0.2381 0.4271 0.8677 0.7067 ] Network output: [ -0.008397 1.003 1.007 -1.143e-07 5.13e-08 0.007065 -8.612e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00702 0.0006568 0.004302 0.003036 0.9889 0.9919 0.007159 0.8495 0.8913 0.01118 ] Network output: [ -0.000101 0.001002 1 -3.907e-06 1.754e-06 0.9988 -2.944e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2262 0.1076 0.3519 0.1409 0.9849 0.9939 0.227 0.431 0.8745 0.7003 ] Network output: [ 0.002158 -0.01055 0.9947 2.392e-06 -1.074e-06 1.012 1.803e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7301 0.8602 0.3045 ] Network output: [ -0.002055 0.009865 1.005 2.639e-06 -1.185e-06 0.9897 1.989e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09525 0.09331 0.1648 0.197 0.9852 0.991 0.09526 0.6537 0.835 0.2504 ] Network output: [ 7.279e-05 1 -5.586e-05 3.439e-07 -1.544e-07 0.9998 2.592e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001225 Epoch 10313 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008364 0.9969 0.993 -1.114e-07 5.001e-08 -0.006663 -8.396e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003397 -0.006354 0.005171 0.9699 0.9743 0.006926 0.8223 0.8186 0.01573 ] Network output: [ 1 2.495e-05 0.0002864 -1.241e-06 5.573e-07 -0.0002307 -9.355e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.03621 -0.152 0.1806 0.9834 0.9932 0.2381 0.4271 0.8677 0.7067 ] Network output: [ -0.008397 1.003 1.007 -1.142e-07 5.125e-08 0.007064 -8.604e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00702 0.0006568 0.004302 0.003036 0.9889 0.9919 0.00716 0.8495 0.8913 0.01118 ] Network output: [ -0.0001008 0.001001 1 -3.902e-06 1.752e-06 0.9988 -2.941e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2262 0.1076 0.3519 0.1409 0.9849 0.9939 0.227 0.431 0.8745 0.7003 ] Network output: [ 0.002156 -0.01054 0.9947 2.389e-06 -1.073e-06 1.012 1.8e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.73 0.8602 0.3045 ] Network output: [ -0.002054 0.009859 1.005 2.635e-06 -1.183e-06 0.9897 1.986e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09525 0.09331 0.1648 0.197 0.9852 0.991 0.09527 0.6537 0.835 0.2504 ] Network output: [ 7.278e-05 1 -5.588e-05 3.435e-07 -1.542e-07 0.9998 2.588e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001224 Epoch 10314 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008364 0.9969 0.993 -1.113e-07 4.997e-08 -0.006662 -8.389e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003397 -0.006353 0.005171 0.9699 0.9743 0.006926 0.8223 0.8186 0.01573 ] Network output: [ 1 2.483e-05 0.0002863 -1.24e-06 5.566e-07 -0.0002306 -9.343e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.03622 -0.152 0.1806 0.9834 0.9932 0.2381 0.4271 0.8677 0.7067 ] Network output: [ -0.008396 1.003 1.007 -1.141e-07 5.121e-08 0.007064 -8.596e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00702 0.0006569 0.004302 0.003036 0.9889 0.9919 0.00716 0.8495 0.8913 0.01118 ] Network output: [ -0.0001007 0.001001 1 -3.897e-06 1.749e-06 0.9988 -2.937e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2262 0.1077 0.3519 0.1409 0.9849 0.9939 0.227 0.431 0.8745 0.7003 ] Network output: [ 0.002155 -0.01054 0.9947 2.386e-06 -1.071e-06 1.012 1.798e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.73 0.8602 0.3045 ] Network output: [ -0.002053 0.009854 1.005 2.632e-06 -1.182e-06 0.9897 1.984e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09525 0.09331 0.1648 0.197 0.9852 0.991 0.09527 0.6537 0.835 0.2504 ] Network output: [ 7.277e-05 1 -5.591e-05 3.43e-07 -1.54e-07 0.9998 2.585e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001223 Epoch 10315 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008363 0.9969 0.993 -1.112e-07 4.993e-08 -0.006661 -8.382e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003397 -0.006353 0.00517 0.9699 0.9743 0.006926 0.8223 0.8186 0.01573 ] Network output: [ 1 2.471e-05 0.0002862 -1.238e-06 5.559e-07 -0.0002305 -9.331e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.03622 -0.152 0.1806 0.9834 0.9932 0.2381 0.4271 0.8677 0.7067 ] Network output: [ -0.008395 1.003 1.007 -1.14e-07 5.116e-08 0.007064 -8.588e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007021 0.0006569 0.004302 0.003036 0.9889 0.9919 0.00716 0.8495 0.8913 0.01118 ] Network output: [ -0.0001006 0.0009998 1 -3.892e-06 1.747e-06 0.9988 -2.933e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2262 0.1077 0.352 0.1409 0.9849 0.9939 0.227 0.431 0.8745 0.7003 ] Network output: [ 0.002153 -0.01053 0.9947 2.383e-06 -1.07e-06 1.011 1.796e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.73 0.8602 0.3045 ] Network output: [ -0.002051 0.009848 1.005 2.629e-06 -1.18e-06 0.9897 1.981e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09525 0.09331 0.1648 0.197 0.9852 0.991 0.09527 0.6536 0.835 0.2504 ] Network output: [ 7.276e-05 1 -5.593e-05 3.426e-07 -1.538e-07 0.9998 2.582e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001223 Epoch 10316 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008362 0.9969 0.993 -1.111e-07 4.989e-08 -0.006661 -8.374e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003397 -0.006352 0.00517 0.9699 0.9743 0.006926 0.8223 0.8186 0.01572 ] Network output: [ 1 2.459e-05 0.0002861 -1.237e-06 5.552e-07 -0.0002303 -9.32e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.03622 -0.152 0.1806 0.9834 0.9932 0.2381 0.4271 0.8677 0.7067 ] Network output: [ -0.008394 1.003 1.007 -1.138e-07 5.111e-08 0.007063 -8.58e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007021 0.0006569 0.004301 0.003035 0.9889 0.9919 0.00716 0.8495 0.8913 0.01118 ] Network output: [ -0.0001005 0.0009992 1 -3.887e-06 1.745e-06 0.9988 -2.929e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2262 0.1077 0.352 0.1409 0.9849 0.9939 0.227 0.431 0.8745 0.7003 ] Network output: [ 0.002152 -0.01052 0.9947 2.38e-06 -1.068e-06 1.011 1.794e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.73 0.8602 0.3045 ] Network output: [ -0.00205 0.009843 1.005 2.626e-06 -1.179e-06 0.9897 1.979e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09525 0.09331 0.1648 0.197 0.9852 0.991 0.09527 0.6536 0.835 0.2504 ] Network output: [ 7.275e-05 1 -5.596e-05 3.422e-07 -1.536e-07 0.9998 2.579e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001222 Epoch 10317 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008361 0.9969 0.993 -1.11e-07 4.984e-08 -0.00666 -8.367e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003397 -0.006352 0.00517 0.9699 0.9743 0.006926 0.8223 0.8186 0.01572 ] Network output: [ 1 2.447e-05 0.0002859 -1.235e-06 5.545e-07 -0.0002302 -9.308e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.03622 -0.152 0.1806 0.9834 0.9932 0.2381 0.4271 0.8677 0.7067 ] Network output: [ -0.008394 1.003 1.007 -1.137e-07 5.106e-08 0.007063 -8.572e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007021 0.0006569 0.004301 0.003035 0.9889 0.9919 0.007161 0.8495 0.8913 0.01117 ] Network output: [ -0.0001003 0.0009985 1 -3.882e-06 1.743e-06 0.9988 -2.926e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2262 0.1077 0.352 0.1409 0.9849 0.9939 0.227 0.431 0.8745 0.7003 ] Network output: [ 0.002151 -0.01052 0.9947 2.377e-06 -1.067e-06 1.011 1.791e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.73 0.8602 0.3045 ] Network output: [ -0.002049 0.009837 1.005 2.622e-06 -1.177e-06 0.9897 1.976e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09526 0.09331 0.1648 0.197 0.9852 0.991 0.09527 0.6536 0.835 0.2504 ] Network output: [ 7.273e-05 1 -5.599e-05 3.418e-07 -1.534e-07 0.9998 2.576e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001221 Epoch 10318 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00836 0.9969 0.993 -1.109e-07 4.98e-08 -0.006659 -8.36e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003397 -0.006351 0.005169 0.9699 0.9743 0.006926 0.8223 0.8186 0.01572 ] Network output: [ 1 2.435e-05 0.0002858 -1.234e-06 5.538e-07 -0.0002301 -9.296e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.03622 -0.152 0.1806 0.9834 0.9932 0.2382 0.4271 0.8677 0.7067 ] Network output: [ -0.008393 1.003 1.007 -1.136e-07 5.101e-08 0.007062 -8.564e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007022 0.000657 0.004301 0.003035 0.9889 0.9919 0.007161 0.8495 0.8913 0.01117 ] Network output: [ -0.0001002 0.0009978 1 -3.877e-06 1.741e-06 0.9988 -2.922e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2262 0.1077 0.352 0.1409 0.9849 0.9939 0.227 0.431 0.8745 0.7003 ] Network output: [ 0.002149 -0.01051 0.9947 2.374e-06 -1.066e-06 1.011 1.789e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.73 0.8602 0.3045 ] Network output: [ -0.002048 0.009832 1.005 2.619e-06 -1.176e-06 0.9897 1.974e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09526 0.09331 0.1648 0.197 0.9852 0.991 0.09527 0.6536 0.835 0.2504 ] Network output: [ 7.272e-05 1 -5.601e-05 3.413e-07 -1.532e-07 0.9998 2.572e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000122 Epoch 10319 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00836 0.9969 0.993 -1.108e-07 4.976e-08 -0.006658 -8.353e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003397 -0.006351 0.005169 0.9699 0.9743 0.006926 0.8223 0.8186 0.01572 ] Network output: [ 1 2.423e-05 0.0002857 -1.232e-06 5.531e-07 -0.00023 -9.284e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.03622 -0.152 0.1806 0.9834 0.9932 0.2382 0.4271 0.8677 0.7067 ] Network output: [ -0.008392 1.003 1.007 -1.135e-07 5.097e-08 0.007062 -8.556e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007022 0.000657 0.004301 0.003035 0.9889 0.9919 0.007161 0.8494 0.8913 0.01117 ] Network output: [ -0.0001001 0.0009971 1 -3.872e-06 1.738e-06 0.9988 -2.918e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2262 0.1077 0.352 0.1409 0.9849 0.9939 0.227 0.431 0.8745 0.7003 ] Network output: [ 0.002148 -0.0105 0.9947 2.371e-06 -1.064e-06 1.011 1.787e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.73 0.8602 0.3045 ] Network output: [ -0.002047 0.009827 1.005 2.616e-06 -1.174e-06 0.9897 1.971e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09526 0.09331 0.1648 0.197 0.9852 0.991 0.09527 0.6536 0.835 0.2504 ] Network output: [ 7.271e-05 1 -5.604e-05 3.409e-07 -1.53e-07 0.9998 2.569e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000122 Epoch 10320 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008359 0.9969 0.993 -1.107e-07 4.972e-08 -0.006658 -8.346e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003397 -0.00635 0.005169 0.9699 0.9743 0.006926 0.8223 0.8186 0.01572 ] Network output: [ 1 2.411e-05 0.0002856 -1.23e-06 5.524e-07 -0.0002299 -9.273e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.03622 -0.152 0.1806 0.9834 0.9932 0.2382 0.4271 0.8677 0.7067 ] Network output: [ -0.008391 1.003 1.007 -1.134e-07 5.092e-08 0.007062 -8.548e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007022 0.000657 0.004301 0.003035 0.9889 0.9919 0.007161 0.8494 0.8913 0.01117 ] Network output: [ -9.995e-05 0.0009964 1 -3.868e-06 1.736e-06 0.9988 -2.915e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2263 0.1077 0.352 0.1409 0.9849 0.9939 0.227 0.431 0.8745 0.7003 ] Network output: [ 0.002146 -0.0105 0.9947 2.368e-06 -1.063e-06 1.011 1.785e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.73 0.8602 0.3045 ] Network output: [ -0.002045 0.009821 1.005 2.613e-06 -1.173e-06 0.9897 1.969e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09526 0.09331 0.1648 0.197 0.9852 0.991 0.09527 0.6536 0.835 0.2504 ] Network output: [ 7.27e-05 1 -5.606e-05 3.405e-07 -1.529e-07 0.9998 2.566e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001219 Epoch 10321 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008358 0.9969 0.993 -1.107e-07 4.968e-08 -0.006657 -8.339e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003397 -0.00635 0.005168 0.9699 0.9743 0.006926 0.8223 0.8186 0.01572 ] Network output: [ 1 2.399e-05 0.0002854 -1.229e-06 5.517e-07 -0.0002298 -9.261e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.03622 -0.152 0.1806 0.9834 0.9932 0.2382 0.4271 0.8677 0.7067 ] Network output: [ -0.008391 1.003 1.007 -1.133e-07 5.087e-08 0.007061 -8.54e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007022 0.0006571 0.004301 0.003034 0.9889 0.9919 0.007162 0.8494 0.8913 0.01117 ] Network output: [ -9.982e-05 0.0009958 1 -3.863e-06 1.734e-06 0.9988 -2.911e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2263 0.1077 0.352 0.1409 0.9849 0.9939 0.227 0.431 0.8745 0.7003 ] Network output: [ 0.002145 -0.01049 0.9947 2.365e-06 -1.062e-06 1.011 1.782e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.73 0.8602 0.3045 ] Network output: [ -0.002044 0.009816 1.005 2.609e-06 -1.171e-06 0.9897 1.967e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09526 0.09331 0.1648 0.197 0.9852 0.991 0.09527 0.6536 0.835 0.2504 ] Network output: [ 7.268e-05 1 -5.609e-05 3.401e-07 -1.527e-07 0.9998 2.563e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001218 Epoch 10322 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008357 0.9969 0.993 -1.106e-07 4.963e-08 -0.006656 -8.332e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003397 -0.006349 0.005168 0.9699 0.9743 0.006927 0.8223 0.8186 0.01572 ] Network output: [ 1 2.387e-05 0.0002853 -1.227e-06 5.51e-07 -0.0002297 -9.249e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.03622 -0.152 0.1806 0.9834 0.9932 0.2382 0.4271 0.8677 0.7067 ] Network output: [ -0.00839 1.003 1.007 -1.132e-07 5.082e-08 0.007061 -8.532e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007023 0.0006571 0.004301 0.003034 0.9889 0.9919 0.007162 0.8494 0.8913 0.01117 ] Network output: [ -9.97e-05 0.0009951 1 -3.858e-06 1.732e-06 0.9988 -2.907e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2263 0.1077 0.352 0.1409 0.9849 0.9939 0.227 0.431 0.8745 0.7003 ] Network output: [ 0.002144 -0.01049 0.9947 2.362e-06 -1.06e-06 1.011 1.78e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.73 0.8602 0.3044 ] Network output: [ -0.002043 0.00981 1.005 2.606e-06 -1.17e-06 0.9897 1.964e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09526 0.09332 0.1648 0.197 0.9852 0.991 0.09528 0.6536 0.835 0.2504 ] Network output: [ 7.267e-05 1 -5.612e-05 3.396e-07 -1.525e-07 0.9998 2.56e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001218 Epoch 10323 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008356 0.9969 0.993 -1.105e-07 4.959e-08 -0.006656 -8.325e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003397 -0.006349 0.005168 0.9699 0.9743 0.006927 0.8223 0.8186 0.01572 ] Network output: [ 1 2.374e-05 0.0002852 -1.226e-06 5.503e-07 -0.0002295 -9.238e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.03623 -0.152 0.1806 0.9834 0.9932 0.2382 0.4271 0.8677 0.7067 ] Network output: [ -0.008389 1.003 1.007 -1.131e-07 5.078e-08 0.00706 -8.524e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007023 0.0006571 0.004301 0.003034 0.9889 0.9919 0.007162 0.8494 0.8913 0.01117 ] Network output: [ -9.957e-05 0.0009944 1 -3.853e-06 1.73e-06 0.9988 -2.904e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2263 0.1077 0.352 0.1409 0.9849 0.9939 0.2271 0.431 0.8745 0.7003 ] Network output: [ 0.002142 -0.01048 0.9947 2.359e-06 -1.059e-06 1.011 1.778e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.73 0.8602 0.3044 ] Network output: [ -0.002042 0.009805 1.005 2.603e-06 -1.169e-06 0.9897 1.962e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09526 0.09332 0.1648 0.197 0.9852 0.991 0.09528 0.6536 0.835 0.2504 ] Network output: [ 7.266e-05 1 -5.614e-05 3.392e-07 -1.523e-07 0.9998 2.556e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001217 Epoch 10324 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008356 0.9969 0.993 -1.104e-07 4.955e-08 -0.006655 -8.318e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003397 -0.006348 0.005167 0.9699 0.9743 0.006927 0.8223 0.8186 0.01572 ] Network output: [ 1 2.362e-05 0.0002851 -1.224e-06 5.496e-07 -0.0002294 -9.226e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.03623 -0.152 0.1806 0.9834 0.9932 0.2382 0.4271 0.8677 0.7067 ] Network output: [ -0.008388 1.003 1.007 -1.13e-07 5.073e-08 0.00706 -8.516e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007023 0.0006571 0.0043 0.003034 0.9889 0.9919 0.007163 0.8494 0.8913 0.01117 ] Network output: [ -9.945e-05 0.0009937 1 -3.848e-06 1.728e-06 0.9988 -2.9e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2263 0.1077 0.352 0.1409 0.9849 0.9939 0.2271 0.431 0.8745 0.7003 ] Network output: [ 0.002141 -0.01047 0.9947 2.356e-06 -1.058e-06 1.011 1.776e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.73 0.8602 0.3044 ] Network output: [ -0.00204 0.0098 1.005 2.6e-06 -1.167e-06 0.9897 1.959e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09526 0.09332 0.1648 0.197 0.9852 0.991 0.09528 0.6536 0.835 0.2504 ] Network output: [ 7.265e-05 1 -5.617e-05 3.388e-07 -1.521e-07 0.9998 2.553e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001216 Epoch 10325 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008355 0.9969 0.993 -1.103e-07 4.951e-08 -0.006654 -8.311e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003398 -0.006348 0.005167 0.9699 0.9743 0.006927 0.8223 0.8186 0.01572 ] Network output: [ 1 2.35e-05 0.0002849 -1.223e-06 5.489e-07 -0.0002293 -9.214e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.03623 -0.152 0.1806 0.9834 0.9932 0.2382 0.4271 0.8677 0.7067 ] Network output: [ -0.008388 1.003 1.007 -1.129e-07 5.068e-08 0.007059 -8.508e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007023 0.0006572 0.0043 0.003034 0.9889 0.9919 0.007163 0.8494 0.8913 0.01117 ] Network output: [ -9.932e-05 0.000993 1 -3.843e-06 1.725e-06 0.9988 -2.896e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2263 0.1077 0.352 0.1409 0.9849 0.9939 0.2271 0.431 0.8745 0.7002 ] Network output: [ 0.002139 -0.01047 0.9947 2.353e-06 -1.056e-06 1.011 1.774e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.73 0.8602 0.3044 ] Network output: [ -0.002039 0.009794 1.005 2.597e-06 -1.166e-06 0.9897 1.957e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09526 0.09332 0.1648 0.197 0.9852 0.991 0.09528 0.6536 0.835 0.2504 ] Network output: [ 7.264e-05 1 -5.62e-05 3.384e-07 -1.519e-07 0.9998 2.55e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001215 Epoch 10326 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008354 0.9969 0.993 -1.102e-07 4.947e-08 -0.006654 -8.304e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003398 -0.006347 0.005167 0.9699 0.9743 0.006927 0.8223 0.8186 0.01572 ] Network output: [ 1 2.338e-05 0.0002848 -1.221e-06 5.482e-07 -0.0002292 -9.203e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.03623 -0.152 0.1806 0.9834 0.9932 0.2382 0.4271 0.8677 0.7067 ] Network output: [ -0.008387 1.003 1.007 -1.128e-07 5.063e-08 0.007059 -8.5e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007024 0.0006572 0.0043 0.003033 0.9889 0.9919 0.007163 0.8494 0.8913 0.01117 ] Network output: [ -9.92e-05 0.0009923 1 -3.838e-06 1.723e-06 0.9988 -2.893e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2263 0.1077 0.352 0.1409 0.9849 0.9939 0.2271 0.431 0.8745 0.7002 ] Network output: [ 0.002138 -0.01046 0.9947 2.35e-06 -1.055e-06 1.011 1.771e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.73 0.8602 0.3044 ] Network output: [ -0.002038 0.009789 1.005 2.593e-06 -1.164e-06 0.9897 1.954e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09527 0.09332 0.1648 0.197 0.9852 0.991 0.09528 0.6536 0.835 0.2504 ] Network output: [ 7.262e-05 1 -5.622e-05 3.38e-07 -1.517e-07 0.9998 2.547e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001215 Epoch 10327 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008353 0.9969 0.993 -1.101e-07 4.942e-08 -0.006653 -8.297e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003398 -0.006347 0.005166 0.9699 0.9743 0.006927 0.8223 0.8186 0.01572 ] Network output: [ 1 2.326e-05 0.0002847 -1.22e-06 5.475e-07 -0.0002291 -9.191e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.03623 -0.1519 0.1806 0.9834 0.9932 0.2382 0.4271 0.8677 0.7067 ] Network output: [ -0.008386 1.003 1.007 -1.127e-07 5.059e-08 0.007059 -8.492e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007024 0.0006572 0.0043 0.003033 0.9889 0.9919 0.007163 0.8494 0.8913 0.01117 ] Network output: [ -9.907e-05 0.0009917 1 -3.834e-06 1.721e-06 0.9988 -2.889e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2263 0.1077 0.352 0.1409 0.9849 0.9939 0.2271 0.431 0.8745 0.7002 ] Network output: [ 0.002137 -0.01045 0.9947 2.347e-06 -1.054e-06 1.011 1.769e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7299 0.8602 0.3044 ] Network output: [ -0.002037 0.009783 1.005 2.59e-06 -1.163e-06 0.9897 1.952e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09527 0.09332 0.1648 0.197 0.9852 0.991 0.09528 0.6536 0.835 0.2504 ] Network output: [ 7.261e-05 1 -5.625e-05 3.375e-07 -1.515e-07 0.9998 2.544e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001214 Epoch 10328 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008353 0.9969 0.993 -1.1e-07 4.938e-08 -0.006652 -8.29e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003398 -0.006346 0.005166 0.9699 0.9743 0.006927 0.8223 0.8186 0.01571 ] Network output: [ 1 2.314e-05 0.0002846 -1.218e-06 5.468e-07 -0.000229 -9.18e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.03623 -0.1519 0.1806 0.9834 0.9932 0.2382 0.4271 0.8677 0.7067 ] Network output: [ -0.008385 1.003 1.007 -1.126e-07 5.054e-08 0.007058 -8.484e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007024 0.0006573 0.0043 0.003033 0.9889 0.9919 0.007164 0.8494 0.8913 0.01117 ] Network output: [ -9.895e-05 0.000991 1 -3.829e-06 1.719e-06 0.9988 -2.885e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2263 0.1077 0.352 0.1409 0.9849 0.9939 0.2271 0.431 0.8745 0.7002 ] Network output: [ 0.002135 -0.01045 0.9947 2.344e-06 -1.053e-06 1.011 1.767e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7299 0.8602 0.3044 ] Network output: [ -0.002035 0.009778 1.005 2.587e-06 -1.161e-06 0.9898 1.95e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09527 0.09332 0.1648 0.197 0.9852 0.991 0.09528 0.6536 0.835 0.2504 ] Network output: [ 7.26e-05 1 -5.628e-05 3.371e-07 -1.513e-07 0.9998 2.541e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001213 Epoch 10329 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008352 0.9969 0.993 -1.099e-07 4.934e-08 -0.006651 -8.283e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003398 -0.006346 0.005166 0.9699 0.9743 0.006927 0.8223 0.8186 0.01571 ] Network output: [ 1 2.302e-05 0.0002845 -1.217e-06 5.461e-07 -0.0002289 -9.168e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.03623 -0.1519 0.1806 0.9834 0.9932 0.2382 0.427 0.8677 0.7067 ] Network output: [ -0.008385 1.003 1.007 -1.125e-07 5.049e-08 0.007058 -8.476e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007024 0.0006573 0.0043 0.003033 0.9889 0.9919 0.007164 0.8494 0.8913 0.01117 ] Network output: [ -9.882e-05 0.0009903 1 -3.824e-06 1.717e-06 0.9988 -2.882e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2263 0.1077 0.352 0.1409 0.9849 0.9939 0.2271 0.431 0.8745 0.7002 ] Network output: [ 0.002134 -0.01044 0.9947 2.342e-06 -1.051e-06 1.011 1.765e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7299 0.8602 0.3044 ] Network output: [ -0.002034 0.009773 1.005 2.584e-06 -1.16e-06 0.9898 1.947e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09527 0.09332 0.1648 0.197 0.9852 0.991 0.09528 0.6535 0.835 0.2504 ] Network output: [ 7.259e-05 1 -5.63e-05 3.367e-07 -1.512e-07 0.9998 2.537e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001213 Epoch 10330 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008351 0.9969 0.993 -1.098e-07 4.93e-08 -0.006651 -8.276e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003398 -0.006345 0.005165 0.9699 0.9743 0.006927 0.8223 0.8186 0.01571 ] Network output: [ 1 2.29e-05 0.0002843 -1.215e-06 5.454e-07 -0.0002288 -9.156e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.03623 -0.1519 0.1806 0.9834 0.9932 0.2382 0.427 0.8677 0.7067 ] Network output: [ -0.008384 1.003 1.007 -1.124e-07 5.044e-08 0.007057 -8.468e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007025 0.0006573 0.0043 0.003032 0.9889 0.9919 0.007164 0.8494 0.8913 0.01117 ] Network output: [ -9.869e-05 0.0009896 1 -3.819e-06 1.715e-06 0.9988 -2.878e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2263 0.1077 0.352 0.1409 0.9849 0.9939 0.2271 0.431 0.8745 0.7002 ] Network output: [ 0.002132 -0.01044 0.9947 2.339e-06 -1.05e-06 1.011 1.762e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7299 0.8602 0.3044 ] Network output: [ -0.002033 0.009767 1.005 2.581e-06 -1.158e-06 0.9898 1.945e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09527 0.09332 0.1648 0.197 0.9852 0.991 0.09528 0.6535 0.835 0.2504 ] Network output: [ 7.258e-05 1 -5.633e-05 3.363e-07 -1.51e-07 0.9998 2.534e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001212 Epoch 10331 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00835 0.9969 0.993 -1.097e-07 4.926e-08 -0.00665 -8.269e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003398 -0.006345 0.005165 0.9699 0.9743 0.006927 0.8223 0.8186 0.01571 ] Network output: [ 1 2.278e-05 0.0002842 -1.213e-06 5.448e-07 -0.0002286 -9.145e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.03623 -0.1519 0.1806 0.9834 0.9932 0.2382 0.427 0.8677 0.7067 ] Network output: [ -0.008383 1.003 1.007 -1.123e-07 5.04e-08 0.007057 -8.46e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007025 0.0006573 0.0043 0.003032 0.9889 0.9919 0.007164 0.8494 0.8913 0.01117 ] Network output: [ -9.857e-05 0.0009889 1 -3.814e-06 1.712e-06 0.9988 -2.875e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2263 0.1077 0.352 0.1409 0.9849 0.9939 0.2271 0.431 0.8745 0.7002 ] Network output: [ 0.002131 -0.01043 0.9947 2.336e-06 -1.049e-06 1.011 1.76e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7299 0.8602 0.3044 ] Network output: [ -0.002032 0.009762 1.005 2.577e-06 -1.157e-06 0.9898 1.942e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09527 0.09333 0.1648 0.197 0.9852 0.991 0.09529 0.6535 0.835 0.2504 ] Network output: [ 7.256e-05 1 -5.635e-05 3.359e-07 -1.508e-07 0.9998 2.531e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001211 Epoch 10332 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008349 0.9969 0.993 -1.096e-07 4.921e-08 -0.006649 -8.262e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003398 -0.006344 0.005165 0.9699 0.9743 0.006928 0.8223 0.8186 0.01571 ] Network output: [ 1 2.266e-05 0.0002841 -1.212e-06 5.441e-07 -0.0002285 -9.133e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.03623 -0.1519 0.1806 0.9834 0.9932 0.2382 0.427 0.8677 0.7067 ] Network output: [ -0.008382 1.003 1.007 -1.121e-07 5.035e-08 0.007057 -8.452e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007025 0.0006574 0.0043 0.003032 0.9889 0.9919 0.007165 0.8494 0.8913 0.01116 ] Network output: [ -9.844e-05 0.0009883 1 -3.809e-06 1.71e-06 0.9988 -2.871e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2263 0.1077 0.352 0.1409 0.9849 0.9939 0.2271 0.431 0.8745 0.7002 ] Network output: [ 0.00213 -0.01042 0.9948 2.333e-06 -1.047e-06 1.011 1.758e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7299 0.8602 0.3044 ] Network output: [ -0.00203 0.009756 1.005 2.574e-06 -1.156e-06 0.9898 1.94e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09527 0.09333 0.1648 0.197 0.9852 0.991 0.09529 0.6535 0.835 0.2504 ] Network output: [ 7.255e-05 1 -5.638e-05 3.354e-07 -1.506e-07 0.9998 2.528e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000121 Epoch 10333 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008349 0.9969 0.993 -1.095e-07 4.917e-08 -0.006649 -8.255e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003398 -0.006344 0.005164 0.9699 0.9743 0.006928 0.8223 0.8186 0.01571 ] Network output: [ 1 2.254e-05 0.000284 -1.21e-06 5.434e-07 -0.0002284 -9.122e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2119 -0.03624 -0.1519 0.1805 0.9834 0.9932 0.2382 0.427 0.8677 0.7067 ] Network output: [ -0.008382 1.003 1.007 -1.12e-07 5.03e-08 0.007056 -8.444e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007025 0.0006574 0.004299 0.003032 0.9889 0.9919 0.007165 0.8494 0.8913 0.01116 ] Network output: [ -9.832e-05 0.0009876 1 -3.805e-06 1.708e-06 0.9988 -2.867e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2263 0.1077 0.352 0.1409 0.9849 0.9939 0.2271 0.431 0.8745 0.7002 ] Network output: [ 0.002128 -0.01042 0.9948 2.33e-06 -1.046e-06 1.011 1.756e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7299 0.8602 0.3044 ] Network output: [ -0.002029 0.009751 1.005 2.571e-06 -1.154e-06 0.9898 1.938e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09527 0.09333 0.1648 0.197 0.9852 0.991 0.09529 0.6535 0.835 0.2504 ] Network output: [ 7.254e-05 1 -5.641e-05 3.35e-07 -1.504e-07 0.9998 2.525e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000121 Epoch 10334 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008348 0.9969 0.993 -1.094e-07 4.913e-08 -0.006648 -8.248e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003398 -0.006343 0.005164 0.9699 0.9743 0.006928 0.8223 0.8186 0.01571 ] Network output: [ 1 2.242e-05 0.0002838 -1.209e-06 5.427e-07 -0.0002283 -9.11e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.212 -0.03624 -0.1519 0.1805 0.9834 0.9932 0.2382 0.427 0.8677 0.7067 ] Network output: [ -0.008381 1.003 1.007 -1.119e-07 5.025e-08 0.007056 -8.436e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007026 0.0006574 0.004299 0.003032 0.9889 0.9919 0.007165 0.8494 0.8913 0.01116 ] Network output: [ -9.819e-05 0.0009869 1 -3.8e-06 1.706e-06 0.9988 -2.864e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2263 0.1077 0.352 0.1409 0.9849 0.9939 0.2271 0.431 0.8745 0.7002 ] Network output: [ 0.002127 -0.01041 0.9948 2.327e-06 -1.045e-06 1.011 1.754e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7299 0.8602 0.3044 ] Network output: [ -0.002028 0.009746 1.005 2.568e-06 -1.153e-06 0.9898 1.935e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09527 0.09333 0.1648 0.197 0.9852 0.991 0.09529 0.6535 0.835 0.2504 ] Network output: [ 7.253e-05 1 -5.643e-05 3.346e-07 -1.502e-07 0.9998 2.522e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001209 Epoch 10335 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008347 0.9969 0.993 -1.093e-07 4.909e-08 -0.006647 -8.24e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003398 -0.006343 0.005164 0.9699 0.9743 0.006928 0.8223 0.8186 0.01571 ] Network output: [ 1 2.23e-05 0.0002837 -1.207e-06 5.42e-07 -0.0002282 -9.099e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.212 -0.03624 -0.1519 0.1805 0.9834 0.9932 0.2382 0.427 0.8677 0.7067 ] Network output: [ -0.00838 1.003 1.007 -1.118e-07 5.021e-08 0.007055 -8.428e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007026 0.0006574 0.004299 0.003031 0.9889 0.9919 0.007166 0.8494 0.8913 0.01116 ] Network output: [ -9.807e-05 0.0009862 1 -3.795e-06 1.704e-06 0.9988 -2.86e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2263 0.1077 0.352 0.1409 0.9849 0.9939 0.2271 0.431 0.8745 0.7002 ] Network output: [ 0.002125 -0.01041 0.9948 2.324e-06 -1.043e-06 1.011 1.751e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7299 0.8602 0.3044 ] Network output: [ -0.002027 0.00974 1.005 2.565e-06 -1.151e-06 0.9898 1.933e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09527 0.09333 0.1648 0.197 0.9852 0.991 0.09529 0.6535 0.835 0.2505 ] Network output: [ 7.252e-05 1 -5.646e-05 3.342e-07 -1.5e-07 0.9998 2.519e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001208 Epoch 10336 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008346 0.9969 0.993 -1.093e-07 4.905e-08 -0.006646 -8.233e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003398 -0.006342 0.005163 0.9699 0.9743 0.006928 0.8223 0.8186 0.01571 ] Network output: [ 1 2.219e-05 0.0002836 -1.206e-06 5.413e-07 -0.0002281 -9.087e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.212 -0.03624 -0.1519 0.1805 0.9834 0.9932 0.2383 0.427 0.8676 0.7067 ] Network output: [ -0.008379 1.003 1.007 -1.117e-07 5.016e-08 0.007055 -8.42e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007026 0.0006575 0.004299 0.003031 0.9889 0.9919 0.007166 0.8494 0.8913 0.01116 ] Network output: [ -9.794e-05 0.0009855 1 -3.79e-06 1.702e-06 0.9988 -2.856e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2263 0.1077 0.352 0.1409 0.9849 0.9939 0.2271 0.431 0.8745 0.7002 ] Network output: [ 0.002124 -0.0104 0.9948 2.321e-06 -1.042e-06 1.011 1.749e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7299 0.8602 0.3044 ] Network output: [ -0.002025 0.009735 1.005 2.561e-06 -1.15e-06 0.9898 1.93e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09528 0.09333 0.1648 0.197 0.9852 0.991 0.09529 0.6535 0.835 0.2505 ] Network output: [ 7.25e-05 1 -5.649e-05 3.338e-07 -1.498e-07 0.9998 2.515e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001208 Epoch 10337 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008346 0.9969 0.993 -1.092e-07 4.9e-08 -0.006646 -8.226e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003398 -0.006342 0.005163 0.9699 0.9743 0.006928 0.8223 0.8186 0.01571 ] Network output: [ 1 2.207e-05 0.0002835 -1.204e-06 5.406e-07 -0.000228 -9.076e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.212 -0.03624 -0.1519 0.1805 0.9834 0.9932 0.2383 0.427 0.8676 0.7067 ] Network output: [ -0.008379 1.003 1.007 -1.116e-07 5.011e-08 0.007055 -8.412e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007026 0.0006575 0.004299 0.003031 0.9889 0.9919 0.007166 0.8494 0.8912 0.01116 ] Network output: [ -9.782e-05 0.0009849 1 -3.785e-06 1.699e-06 0.9988 -2.853e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2263 0.1077 0.352 0.1409 0.9849 0.9939 0.2271 0.431 0.8745 0.7002 ] Network output: [ 0.002123 -0.01039 0.9948 2.318e-06 -1.041e-06 1.011 1.747e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7299 0.8602 0.3044 ] Network output: [ -0.002024 0.009729 1.005 2.558e-06 -1.148e-06 0.9898 1.928e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09528 0.09333 0.1648 0.197 0.9852 0.991 0.09529 0.6535 0.835 0.2505 ] Network output: [ 7.249e-05 1 -5.652e-05 3.334e-07 -1.497e-07 0.9998 2.512e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001207 Epoch 10338 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008345 0.9969 0.993 -1.091e-07 4.896e-08 -0.006645 -8.219e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003398 -0.006341 0.005163 0.9699 0.9743 0.006928 0.8223 0.8186 0.01571 ] Network output: [ 1 2.195e-05 0.0002834 -1.203e-06 5.4e-07 -0.0002279 -9.064e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.212 -0.03624 -0.1519 0.1805 0.9834 0.9932 0.2383 0.427 0.8676 0.7067 ] Network output: [ -0.008378 1.003 1.007 -1.115e-07 5.006e-08 0.007054 -8.404e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007027 0.0006575 0.004299 0.003031 0.9889 0.9919 0.007166 0.8494 0.8912 0.01116 ] Network output: [ -9.769e-05 0.0009842 1 -3.781e-06 1.697e-06 0.9988 -2.849e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2263 0.1077 0.3521 0.1409 0.9849 0.9939 0.2271 0.4309 0.8745 0.7002 ] Network output: [ 0.002121 -0.01039 0.9948 2.315e-06 -1.039e-06 1.011 1.745e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7299 0.8602 0.3044 ] Network output: [ -0.002023 0.009724 1.005 2.555e-06 -1.147e-06 0.9898 1.926e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09528 0.09333 0.1648 0.197 0.9852 0.991 0.09529 0.6535 0.835 0.2505 ] Network output: [ 7.248e-05 1 -5.654e-05 3.329e-07 -1.495e-07 0.9998 2.509e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001206 Epoch 10339 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008344 0.9969 0.993 -1.09e-07 4.892e-08 -0.006644 -8.212e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003398 -0.006341 0.005162 0.9699 0.9743 0.006928 0.8223 0.8186 0.01571 ] Network output: [ 1 2.183e-05 0.0002832 -1.201e-06 5.393e-07 -0.0002277 -9.053e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.212 -0.03624 -0.1519 0.1805 0.9834 0.9932 0.2383 0.427 0.8676 0.7067 ] Network output: [ -0.008377 1.003 1.007 -1.114e-07 5.002e-08 0.007054 -8.396e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007027 0.0006576 0.004299 0.003031 0.9889 0.9919 0.007167 0.8494 0.8912 0.01116 ] Network output: [ -9.757e-05 0.0009835 1 -3.776e-06 1.695e-06 0.9988 -2.846e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2264 0.1077 0.3521 0.1409 0.9849 0.9939 0.2271 0.4309 0.8745 0.7002 ] Network output: [ 0.00212 -0.01038 0.9948 2.312e-06 -1.038e-06 1.011 1.743e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7299 0.8602 0.3044 ] Network output: [ -0.002022 0.009719 1.005 2.552e-06 -1.146e-06 0.9898 1.923e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09528 0.09333 0.1648 0.197 0.9852 0.991 0.09529 0.6535 0.835 0.2505 ] Network output: [ 7.247e-05 1 -5.657e-05 3.325e-07 -1.493e-07 0.9998 2.506e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001205 Epoch 10340 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008343 0.9969 0.993 -1.089e-07 4.888e-08 -0.006644 -8.205e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003398 -0.00634 0.005162 0.9699 0.9743 0.006928 0.8223 0.8186 0.01571 ] Network output: [ 1 2.171e-05 0.0002831 -1.2e-06 5.386e-07 -0.0002276 -9.042e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.212 -0.03624 -0.1519 0.1805 0.9834 0.9932 0.2383 0.427 0.8676 0.7067 ] Network output: [ -0.008376 1.003 1.007 -1.113e-07 4.997e-08 0.007053 -8.388e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007027 0.0006576 0.004299 0.00303 0.9889 0.9919 0.007167 0.8494 0.8912 0.01116 ] Network output: [ -9.744e-05 0.0009828 1 -3.771e-06 1.693e-06 0.9988 -2.842e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2264 0.1077 0.3521 0.1409 0.9849 0.9939 0.2271 0.4309 0.8745 0.7002 ] Network output: [ 0.002118 -0.01037 0.9948 2.309e-06 -1.037e-06 1.011 1.74e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7299 0.8602 0.3044 ] Network output: [ -0.00202 0.009713 1.005 2.549e-06 -1.144e-06 0.9898 1.921e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09528 0.09333 0.1648 0.197 0.9852 0.991 0.09529 0.6535 0.835 0.2505 ] Network output: [ 7.246e-05 1 -5.66e-05 3.321e-07 -1.491e-07 0.9998 2.503e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001205 Epoch 10341 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008342 0.9969 0.993 -1.088e-07 4.884e-08 -0.006643 -8.198e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003398 -0.00634 0.005162 0.9699 0.9743 0.006929 0.8223 0.8186 0.0157 ] Network output: [ 1 2.159e-05 0.000283 -1.198e-06 5.379e-07 -0.0002275 -9.03e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.212 -0.03624 -0.1518 0.1805 0.9834 0.9932 0.2383 0.427 0.8676 0.7067 ] Network output: [ -0.008376 1.003 1.007 -1.112e-07 4.992e-08 0.007053 -8.381e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007027 0.0006576 0.004298 0.00303 0.9889 0.9919 0.007167 0.8494 0.8912 0.01116 ] Network output: [ -9.732e-05 0.0009821 1 -3.766e-06 1.691e-06 0.9988 -2.838e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2264 0.1077 0.3521 0.1409 0.9849 0.9939 0.2272 0.4309 0.8745 0.7002 ] Network output: [ 0.002117 -0.01037 0.9948 2.307e-06 -1.035e-06 1.011 1.738e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7299 0.8602 0.3044 ] Network output: [ -0.002019 0.009708 1.005 2.546e-06 -1.143e-06 0.9898 1.918e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09528 0.09334 0.1648 0.197 0.9852 0.991 0.0953 0.6535 0.835 0.2505 ] Network output: [ 7.244e-05 1 -5.662e-05 3.317e-07 -1.489e-07 0.9998 2.5e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001204 Epoch 10342 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008342 0.9969 0.993 -1.087e-07 4.88e-08 -0.006642 -8.191e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003398 -0.006339 0.005161 0.9699 0.9743 0.006929 0.8223 0.8186 0.0157 ] Network output: [ 1 2.147e-05 0.0002829 -1.197e-06 5.372e-07 -0.0002274 -9.019e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.212 -0.03625 -0.1518 0.1805 0.9834 0.9932 0.2383 0.427 0.8676 0.7067 ] Network output: [ -0.008375 1.003 1.007 -1.111e-07 4.988e-08 0.007052 -8.373e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007028 0.0006576 0.004298 0.00303 0.9889 0.9919 0.007167 0.8494 0.8912 0.01116 ] Network output: [ -9.719e-05 0.0009815 1 -3.762e-06 1.689e-06 0.9988 -2.835e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2264 0.1077 0.3521 0.1409 0.9849 0.9939 0.2272 0.4309 0.8745 0.7002 ] Network output: [ 0.002116 -0.01036 0.9948 2.304e-06 -1.034e-06 1.011 1.736e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7298 0.8602 0.3044 ] Network output: [ -0.002018 0.009703 1.005 2.542e-06 -1.141e-06 0.9898 1.916e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09528 0.09334 0.1648 0.197 0.9852 0.991 0.0953 0.6534 0.835 0.2505 ] Network output: [ 7.243e-05 1 -5.665e-05 3.313e-07 -1.487e-07 0.9998 2.497e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001203 Epoch 10343 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008341 0.9969 0.993 -1.086e-07 4.875e-08 -0.006642 -8.184e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003534 -0.003399 -0.006339 0.005161 0.9699 0.9743 0.006929 0.8222 0.8186 0.0157 ] Network output: [ 1 2.135e-05 0.0002827 -1.195e-06 5.366e-07 -0.0002273 -9.007e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.212 -0.03625 -0.1518 0.1805 0.9834 0.9932 0.2383 0.427 0.8676 0.7067 ] Network output: [ -0.008374 1.003 1.007 -1.11e-07 4.983e-08 0.007052 -8.365e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007028 0.0006577 0.004298 0.00303 0.9889 0.9919 0.007168 0.8494 0.8912 0.01116 ] Network output: [ -9.707e-05 0.0009808 1 -3.757e-06 1.687e-06 0.9988 -2.831e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2264 0.1077 0.3521 0.1409 0.9849 0.9939 0.2272 0.4309 0.8745 0.7002 ] Network output: [ 0.002114 -0.01036 0.9948 2.301e-06 -1.033e-06 1.011 1.734e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7298 0.8602 0.3044 ] Network output: [ -0.002017 0.009697 1.005 2.539e-06 -1.14e-06 0.9898 1.914e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09528 0.09334 0.1648 0.197 0.9852 0.991 0.0953 0.6534 0.835 0.2505 ] Network output: [ 7.242e-05 1 -5.668e-05 3.309e-07 -1.485e-07 0.9998 2.494e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001203 Epoch 10344 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00834 0.9969 0.993 -1.085e-07 4.871e-08 -0.006641 -8.177e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003399 -0.006338 0.005161 0.9699 0.9743 0.006929 0.8222 0.8186 0.0157 ] Network output: [ 1 2.123e-05 0.0002826 -1.194e-06 5.359e-07 -0.0002272 -8.996e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.212 -0.03625 -0.1518 0.1805 0.9834 0.9932 0.2383 0.427 0.8676 0.7067 ] Network output: [ -0.008373 1.003 1.007 -1.109e-07 4.978e-08 0.007052 -8.357e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007028 0.0006577 0.004298 0.00303 0.9889 0.9919 0.007168 0.8494 0.8912 0.01116 ] Network output: [ -9.694e-05 0.0009801 1 -3.752e-06 1.684e-06 0.9988 -2.828e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2264 0.1077 0.3521 0.1409 0.9849 0.9939 0.2272 0.4309 0.8745 0.7002 ] Network output: [ 0.002113 -0.01035 0.9948 2.298e-06 -1.032e-06 1.011 1.732e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7298 0.8602 0.3044 ] Network output: [ -0.002015 0.009692 1.005 2.536e-06 -1.139e-06 0.9898 1.911e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09528 0.09334 0.1648 0.197 0.9852 0.991 0.0953 0.6534 0.8349 0.2505 ] Network output: [ 7.241e-05 1 -5.67e-05 3.305e-07 -1.484e-07 0.9998 2.49e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001202 Epoch 10345 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008339 0.9969 0.993 -1.084e-07 4.867e-08 -0.00664 -8.171e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003399 -0.006338 0.00516 0.9699 0.9743 0.006929 0.8222 0.8186 0.0157 ] Network output: [ 1 2.111e-05 0.0002825 -1.192e-06 5.352e-07 -0.0002271 -8.985e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.212 -0.03625 -0.1518 0.1805 0.9834 0.9932 0.2383 0.427 0.8676 0.7067 ] Network output: [ -0.008373 1.003 1.007 -1.108e-07 4.973e-08 0.007051 -8.349e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007029 0.0006577 0.004298 0.003029 0.9889 0.9919 0.007168 0.8494 0.8912 0.01116 ] Network output: [ -9.682e-05 0.0009794 1 -3.747e-06 1.682e-06 0.9988 -2.824e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2264 0.1077 0.3521 0.1409 0.9849 0.9939 0.2272 0.4309 0.8745 0.7002 ] Network output: [ 0.002111 -0.01034 0.9948 2.295e-06 -1.03e-06 1.011 1.73e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7298 0.8602 0.3044 ] Network output: [ -0.002014 0.009686 1.005 2.533e-06 -1.137e-06 0.9898 1.909e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09529 0.09334 0.1648 0.197 0.9852 0.991 0.0953 0.6534 0.8349 0.2505 ] Network output: [ 7.24e-05 1 -5.673e-05 3.3e-07 -1.482e-07 0.9998 2.487e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001201 Epoch 10346 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008339 0.9969 0.993 -1.083e-07 4.863e-08 -0.006639 -8.164e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003399 -0.006337 0.00516 0.9699 0.9743 0.006929 0.8222 0.8186 0.0157 ] Network output: [ 1 2.099e-05 0.0002824 -1.191e-06 5.345e-07 -0.000227 -8.973e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.212 -0.03625 -0.1518 0.1805 0.9834 0.9932 0.2383 0.427 0.8676 0.7067 ] Network output: [ -0.008372 1.003 1.007 -1.107e-07 4.969e-08 0.007051 -8.341e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007029 0.0006578 0.004298 0.003029 0.9889 0.9919 0.007168 0.8494 0.8912 0.01116 ] Network output: [ -9.669e-05 0.0009787 1 -3.743e-06 1.68e-06 0.9988 -2.821e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2264 0.1077 0.3521 0.1409 0.9849 0.9939 0.2272 0.4309 0.8745 0.7002 ] Network output: [ 0.00211 -0.01034 0.9948 2.292e-06 -1.029e-06 1.011 1.727e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7298 0.8602 0.3044 ] Network output: [ -0.002013 0.009681 1.005 2.53e-06 -1.136e-06 0.9898 1.907e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09529 0.09334 0.1648 0.197 0.9852 0.991 0.0953 0.6534 0.8349 0.2505 ] Network output: [ 7.238e-05 1 -5.676e-05 3.296e-07 -1.48e-07 0.9998 2.484e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001201 Epoch 10347 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008338 0.9969 0.9931 -1.082e-07 4.859e-08 -0.006639 -8.157e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003399 -0.006337 0.00516 0.9699 0.9743 0.006929 0.8222 0.8185 0.0157 ] Network output: [ 1 2.087e-05 0.0002823 -1.189e-06 5.339e-07 -0.0002269 -8.962e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.212 -0.03625 -0.1518 0.1805 0.9834 0.9932 0.2383 0.427 0.8676 0.7067 ] Network output: [ -0.008371 1.003 1.007 -1.106e-07 4.964e-08 0.00705 -8.333e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007029 0.0006578 0.004298 0.003029 0.9889 0.9919 0.007169 0.8494 0.8912 0.01115 ] Network output: [ -9.657e-05 0.0009781 1 -3.738e-06 1.678e-06 0.9988 -2.817e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2264 0.1077 0.3521 0.1409 0.9849 0.9939 0.2272 0.4309 0.8744 0.7002 ] Network output: [ 0.002109 -0.01033 0.9948 2.289e-06 -1.028e-06 1.011 1.725e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7298 0.8602 0.3044 ] Network output: [ -0.002012 0.009676 1.005 2.527e-06 -1.134e-06 0.9898 1.904e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09529 0.09334 0.1648 0.197 0.9852 0.991 0.0953 0.6534 0.8349 0.2505 ] Network output: [ 7.237e-05 1 -5.679e-05 3.292e-07 -1.478e-07 0.9998 2.481e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00012 Epoch 10348 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008337 0.9969 0.9931 -1.081e-07 4.855e-08 -0.006638 -8.15e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003399 -0.006336 0.005159 0.9699 0.9743 0.006929 0.8222 0.8185 0.0157 ] Network output: [ 1 2.075e-05 0.0002821 -1.188e-06 5.332e-07 -0.0002267 -8.951e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.212 -0.03625 -0.1518 0.1805 0.9834 0.9932 0.2383 0.427 0.8676 0.7067 ] Network output: [ -0.00837 1.003 1.007 -1.105e-07 4.959e-08 0.00705 -8.325e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007029 0.0006578 0.004298 0.003029 0.9889 0.9919 0.007169 0.8494 0.8912 0.01115 ] Network output: [ -9.644e-05 0.0009774 1 -3.733e-06 1.676e-06 0.9988 -2.813e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2264 0.1077 0.3521 0.1409 0.9849 0.9939 0.2272 0.4309 0.8744 0.7002 ] Network output: [ 0.002107 -0.01032 0.9948 2.286e-06 -1.026e-06 1.011 1.723e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7298 0.8602 0.3044 ] Network output: [ -0.00201 0.00967 1.005 2.524e-06 -1.133e-06 0.9898 1.902e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09529 0.09334 0.1648 0.197 0.9852 0.991 0.0953 0.6534 0.8349 0.2505 ] Network output: [ 7.236e-05 1 -5.681e-05 3.288e-07 -1.476e-07 0.9998 2.478e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001199 Epoch 10349 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008336 0.9969 0.9931 -1.08e-07 4.851e-08 -0.006637 -8.143e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003399 -0.006336 0.005159 0.9699 0.9743 0.006929 0.8222 0.8185 0.0157 ] Network output: [ 1 2.064e-05 0.000282 -1.186e-06 5.325e-07 -0.0002266 -8.939e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.212 -0.03625 -0.1518 0.1805 0.9834 0.9932 0.2383 0.427 0.8676 0.7067 ] Network output: [ -0.00837 1.003 1.007 -1.104e-07 4.955e-08 0.00705 -8.317e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00703 0.0006578 0.004297 0.003029 0.9889 0.9919 0.007169 0.8494 0.8912 0.01115 ] Network output: [ -9.632e-05 0.0009767 1 -3.728e-06 1.674e-06 0.9988 -2.81e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2264 0.1077 0.3521 0.1409 0.9849 0.9939 0.2272 0.4309 0.8744 0.7002 ] Network output: [ 0.002106 -0.01032 0.9948 2.283e-06 -1.025e-06 1.011 1.721e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7298 0.8602 0.3044 ] Network output: [ -0.002009 0.009665 1.005 2.52e-06 -1.131e-06 0.9898 1.899e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09529 0.09334 0.1648 0.197 0.9852 0.991 0.0953 0.6534 0.8349 0.2505 ] Network output: [ 7.235e-05 1 -5.684e-05 3.284e-07 -1.474e-07 0.9998 2.475e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001198 Epoch 10350 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008336 0.9969 0.9931 -1.08e-07 4.846e-08 -0.006637 -8.136e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003399 -0.006335 0.005159 0.9699 0.9743 0.006929 0.8222 0.8185 0.0157 ] Network output: [ 1 2.052e-05 0.0002819 -1.185e-06 5.318e-07 -0.0002265 -8.928e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.212 -0.03625 -0.1518 0.1805 0.9834 0.9932 0.2383 0.427 0.8676 0.7067 ] Network output: [ -0.008369 1.003 1.007 -1.103e-07 4.95e-08 0.007049 -8.31e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00703 0.0006579 0.004297 0.003028 0.9889 0.9919 0.007169 0.8493 0.8912 0.01115 ] Network output: [ -9.619e-05 0.000976 1 -3.724e-06 1.672e-06 0.9988 -2.806e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2264 0.1077 0.3521 0.1409 0.9849 0.9939 0.2272 0.4309 0.8744 0.7002 ] Network output: [ 0.002105 -0.01031 0.9948 2.281e-06 -1.024e-06 1.011 1.719e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1963 0.9873 0.9919 0.113 0.7298 0.8602 0.3044 ] Network output: [ -0.002008 0.009659 1.005 2.517e-06 -1.13e-06 0.9898 1.897e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09529 0.09334 0.1648 0.197 0.9852 0.991 0.09531 0.6534 0.8349 0.2505 ] Network output: [ 7.234e-05 1 -5.687e-05 3.28e-07 -1.472e-07 0.9998 2.472e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001198 Epoch 10351 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008335 0.9969 0.9931 -1.079e-07 4.842e-08 -0.006636 -8.129e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003399 -0.006335 0.005158 0.9699 0.9743 0.00693 0.8222 0.8185 0.0157 ] Network output: [ 1 2.04e-05 0.0002818 -1.183e-06 5.312e-07 -0.0002264 -8.917e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.212 -0.03626 -0.1518 0.1805 0.9834 0.9932 0.2383 0.427 0.8676 0.7067 ] Network output: [ -0.008368 1.003 1.007 -1.102e-07 4.945e-08 0.007049 -8.302e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00703 0.0006579 0.004297 0.003028 0.9889 0.9919 0.00717 0.8493 0.8912 0.01115 ] Network output: [ -9.607e-05 0.0009754 1 -3.719e-06 1.67e-06 0.9988 -2.803e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2264 0.1078 0.3521 0.1409 0.9849 0.9939 0.2272 0.4309 0.8744 0.7002 ] Network output: [ 0.002103 -0.01031 0.9948 2.278e-06 -1.023e-06 1.011 1.717e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1962 0.9873 0.9919 0.113 0.7298 0.8602 0.3044 ] Network output: [ -0.002007 0.009654 1.005 2.514e-06 -1.129e-06 0.9898 1.895e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09529 0.09335 0.1648 0.197 0.9852 0.991 0.09531 0.6534 0.8349 0.2505 ] Network output: [ 7.233e-05 1 -5.69e-05 3.276e-07 -1.471e-07 0.9998 2.469e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001197 Epoch 10352 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008334 0.9969 0.9931 -1.078e-07 4.838e-08 -0.006635 -8.122e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003399 -0.006334 0.005158 0.9699 0.9743 0.00693 0.8222 0.8185 0.0157 ] Network output: [ 1 2.028e-05 0.0002816 -1.182e-06 5.305e-07 -0.0002263 -8.905e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.212 -0.03626 -0.1518 0.1805 0.9834 0.9932 0.2383 0.427 0.8676 0.7067 ] Network output: [ -0.008367 1.003 1.007 -1.101e-07 4.941e-08 0.007048 -8.294e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00703 0.0006579 0.004297 0.003028 0.9889 0.9919 0.00717 0.8493 0.8912 0.01115 ] Network output: [ -9.594e-05 0.0009747 1 -3.714e-06 1.668e-06 0.9988 -2.799e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2264 0.1078 0.3521 0.1409 0.9849 0.9939 0.2272 0.4309 0.8744 0.7002 ] Network output: [ 0.002102 -0.0103 0.9948 2.275e-06 -1.021e-06 1.011 1.714e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1962 0.9873 0.9919 0.113 0.7298 0.8602 0.3044 ] Network output: [ -0.002006 0.009649 1.005 2.511e-06 -1.127e-06 0.9898 1.892e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09529 0.09335 0.1648 0.197 0.9852 0.991 0.09531 0.6534 0.8349 0.2505 ] Network output: [ 7.231e-05 1 -5.692e-05 3.272e-07 -1.469e-07 0.9998 2.466e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001196 Epoch 10353 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008333 0.9969 0.9931 -1.077e-07 4.834e-08 -0.006634 -8.115e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003399 -0.006334 0.005158 0.9699 0.9743 0.00693 0.8222 0.8185 0.01569 ] Network output: [ 1 2.016e-05 0.0002815 -1.18e-06 5.298e-07 -0.0002262 -8.894e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.212 -0.03626 -0.1518 0.1805 0.9834 0.9932 0.2383 0.427 0.8676 0.7066 ] Network output: [ -0.008367 1.003 1.007 -1.099e-07 4.936e-08 0.007048 -8.286e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007031 0.0006579 0.004297 0.003028 0.9889 0.9919 0.00717 0.8493 0.8912 0.01115 ] Network output: [ -9.582e-05 0.000974 1 -3.71e-06 1.665e-06 0.9988 -2.796e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2264 0.1078 0.3521 0.1409 0.9849 0.9939 0.2272 0.4309 0.8744 0.7002 ] Network output: [ 0.0021 -0.01029 0.9948 2.272e-06 -1.02e-06 1.011 1.712e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1962 0.9873 0.9919 0.113 0.7298 0.8602 0.3044 ] Network output: [ -0.002004 0.009643 1.005 2.508e-06 -1.126e-06 0.9898 1.89e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09529 0.09335 0.1648 0.197 0.9852 0.991 0.09531 0.6534 0.8349 0.2505 ] Network output: [ 7.23e-05 1 -5.695e-05 3.268e-07 -1.467e-07 0.9998 2.463e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001196 Epoch 10354 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008332 0.9969 0.9931 -1.076e-07 4.83e-08 -0.006634 -8.108e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003399 -0.006333 0.005157 0.9699 0.9743 0.00693 0.8222 0.8185 0.01569 ] Network output: [ 1 2.004e-05 0.0002814 -1.179e-06 5.292e-07 -0.0002261 -8.883e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.212 -0.03626 -0.1518 0.1805 0.9834 0.9932 0.2384 0.427 0.8676 0.7066 ] Network output: [ -0.008366 1.003 1.007 -1.098e-07 4.931e-08 0.007048 -8.278e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007031 0.000658 0.004297 0.003027 0.9889 0.9919 0.007171 0.8493 0.8912 0.01115 ] Network output: [ -9.569e-05 0.0009733 1 -3.705e-06 1.663e-06 0.9988 -2.792e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2264 0.1078 0.3521 0.1409 0.9849 0.9939 0.2272 0.4309 0.8744 0.7002 ] Network output: [ 0.002099 -0.01029 0.9948 2.269e-06 -1.019e-06 1.011 1.71e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1129 0.1001 0.1851 0.1962 0.9873 0.9919 0.113 0.7298 0.8602 0.3044 ] Network output: [ -0.002003 0.009638 1.005 2.505e-06 -1.124e-06 0.9898 1.888e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09529 0.09335 0.1648 0.197 0.9852 0.991 0.09531 0.6534 0.8349 0.2505 ] Network output: [ 7.229e-05 1 -5.698e-05 3.264e-07 -1.465e-07 0.9998 2.46e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001195 Epoch 10355 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008332 0.9969 0.9931 -1.075e-07 4.826e-08 -0.006633 -8.101e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003399 -0.006333 0.005157 0.9699 0.9743 0.00693 0.8222 0.8185 0.01569 ] Network output: [ 1 1.992e-05 0.0002813 -1.177e-06 5.285e-07 -0.000226 -8.872e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.212 -0.03626 -0.1517 0.1805 0.9834 0.9932 0.2384 0.427 0.8676 0.7066 ] Network output: [ -0.008365 1.003 1.007 -1.097e-07 4.927e-08 0.007047 -8.27e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007031 0.000658 0.004297 0.003027 0.9889 0.9919 0.007171 0.8493 0.8912 0.01115 ] Network output: [ -9.557e-05 0.0009726 1 -3.7e-06 1.661e-06 0.9988 -2.789e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2264 0.1078 0.3521 0.1409 0.9849 0.9939 0.2272 0.4309 0.8744 0.7002 ] Network output: [ 0.002098 -0.01028 0.9948 2.266e-06 -1.017e-06 1.011 1.708e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1001 0.1851 0.1962 0.9873 0.9919 0.113 0.7298 0.8602 0.3044 ] Network output: [ -0.002002 0.009633 1.005 2.502e-06 -1.123e-06 0.9898 1.885e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0953 0.09335 0.1648 0.197 0.9852 0.991 0.09531 0.6534 0.8349 0.2505 ] Network output: [ 7.228e-05 1 -5.701e-05 3.26e-07 -1.463e-07 0.9998 2.457e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001194 Epoch 10356 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008331 0.9969 0.9931 -1.074e-07 4.822e-08 -0.006632 -8.094e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003399 -0.006332 0.005157 0.9699 0.9743 0.00693 0.8222 0.8185 0.01569 ] Network output: [ 1 1.981e-05 0.0002812 -1.176e-06 5.278e-07 -0.0002259 -8.861e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03626 -0.1517 0.1805 0.9834 0.9932 0.2384 0.427 0.8676 0.7066 ] Network output: [ -0.008364 1.003 1.007 -1.096e-07 4.922e-08 0.007047 -8.262e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007031 0.000658 0.004297 0.003027 0.9889 0.9919 0.007171 0.8493 0.8912 0.01115 ] Network output: [ -9.545e-05 0.000972 1 -3.696e-06 1.659e-06 0.9988 -2.785e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2264 0.1078 0.3521 0.1409 0.9849 0.9939 0.2272 0.4309 0.8744 0.7002 ] Network output: [ 0.002096 -0.01028 0.9948 2.263e-06 -1.016e-06 1.011 1.706e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1001 0.1851 0.1962 0.9873 0.9919 0.113 0.7297 0.8602 0.3044 ] Network output: [ -0.002001 0.009627 1.005 2.499e-06 -1.122e-06 0.9898 1.883e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0953 0.09335 0.1648 0.197 0.9852 0.991 0.09531 0.6533 0.8349 0.2505 ] Network output: [ 7.227e-05 1 -5.703e-05 3.255e-07 -1.462e-07 0.9998 2.453e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001193 Epoch 10357 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00833 0.9969 0.9931 -1.073e-07 4.817e-08 -0.006632 -8.087e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003399 -0.006332 0.005156 0.9699 0.9743 0.00693 0.8222 0.8185 0.01569 ] Network output: [ 1 1.969e-05 0.000281 -1.174e-06 5.272e-07 -0.0002258 -8.849e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03626 -0.1517 0.1805 0.9834 0.9932 0.2384 0.427 0.8676 0.7066 ] Network output: [ -0.008364 1.003 1.007 -1.095e-07 4.917e-08 0.007046 -8.255e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007032 0.000658 0.004297 0.003027 0.9889 0.9919 0.007171 0.8493 0.8912 0.01115 ] Network output: [ -9.532e-05 0.0009713 1 -3.691e-06 1.657e-06 0.9988 -2.782e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2265 0.1078 0.3521 0.1409 0.9849 0.9939 0.2272 0.4309 0.8744 0.7002 ] Network output: [ 0.002095 -0.01027 0.9948 2.261e-06 -1.015e-06 1.011 1.704e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1001 0.1851 0.1962 0.9873 0.9919 0.113 0.7297 0.8602 0.3044 ] Network output: [ -0.001999 0.009622 1.005 2.495e-06 -1.12e-06 0.9898 1.881e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0953 0.09335 0.1648 0.197 0.9852 0.991 0.09531 0.6533 0.8349 0.2505 ] Network output: [ 7.225e-05 1 -5.706e-05 3.251e-07 -1.46e-07 0.9998 2.45e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001193 Epoch 10358 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008329 0.9969 0.9931 -1.072e-07 4.813e-08 -0.006631 -8.08e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003399 -0.006331 0.005156 0.9699 0.9743 0.00693 0.8222 0.8185 0.01569 ] Network output: [ 1 1.957e-05 0.0002809 -1.173e-06 5.265e-07 -0.0002256 -8.838e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03626 -0.1517 0.1805 0.9834 0.9932 0.2384 0.427 0.8676 0.7066 ] Network output: [ -0.008363 1.003 1.007 -1.094e-07 4.913e-08 0.007046 -8.247e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007032 0.0006581 0.004296 0.003027 0.9889 0.9919 0.007172 0.8493 0.8912 0.01115 ] Network output: [ -9.52e-05 0.0009706 1 -3.686e-06 1.655e-06 0.9988 -2.778e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2265 0.1078 0.3521 0.1408 0.9849 0.9939 0.2272 0.4309 0.8744 0.7002 ] Network output: [ 0.002093 -0.01026 0.9948 2.258e-06 -1.014e-06 1.011 1.702e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1001 0.1851 0.1962 0.9873 0.9919 0.113 0.7297 0.8602 0.3044 ] Network output: [ -0.001998 0.009616 1.005 2.492e-06 -1.119e-06 0.9899 1.878e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0953 0.09335 0.1648 0.197 0.9852 0.991 0.09531 0.6533 0.8349 0.2505 ] Network output: [ 7.224e-05 1 -5.709e-05 3.247e-07 -1.458e-07 0.9998 2.447e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001192 Epoch 10359 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008329 0.9969 0.9931 -1.071e-07 4.809e-08 -0.00663 -8.073e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003399 -0.006331 0.005156 0.9699 0.9743 0.00693 0.8222 0.8185 0.01569 ] Network output: [ 1 1.945e-05 0.0002808 -1.171e-06 5.258e-07 -0.0002255 -8.827e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03626 -0.1517 0.1805 0.9834 0.9932 0.2384 0.427 0.8676 0.7066 ] Network output: [ -0.008362 1.003 1.007 -1.093e-07 4.908e-08 0.007046 -8.239e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007032 0.0006581 0.004296 0.003026 0.9889 0.9919 0.007172 0.8493 0.8912 0.01115 ] Network output: [ -9.507e-05 0.0009699 1 -3.682e-06 1.653e-06 0.9988 -2.775e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2265 0.1078 0.3521 0.1408 0.9849 0.9939 0.2273 0.4309 0.8744 0.7002 ] Network output: [ 0.002092 -0.01026 0.9948 2.255e-06 -1.012e-06 1.011 1.699e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1001 0.1851 0.1962 0.9873 0.9919 0.113 0.7297 0.8602 0.3044 ] Network output: [ -0.001997 0.009611 1.005 2.489e-06 -1.118e-06 0.9899 1.876e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0953 0.09335 0.1648 0.197 0.9852 0.991 0.09531 0.6533 0.8349 0.2505 ] Network output: [ 7.223e-05 1 -5.712e-05 3.243e-07 -1.456e-07 0.9998 2.444e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001191 Epoch 10360 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008328 0.9969 0.9931 -1.07e-07 4.805e-08 -0.006629 -8.066e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003399 -0.006331 0.005155 0.9699 0.9743 0.00693 0.8222 0.8185 0.01569 ] Network output: [ 1 1.933e-05 0.0002807 -1.17e-06 5.252e-07 -0.0002254 -8.816e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03627 -0.1517 0.1805 0.9834 0.9932 0.2384 0.427 0.8676 0.7066 ] Network output: [ -0.008361 1.003 1.007 -1.092e-07 4.903e-08 0.007045 -8.231e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007032 0.0006581 0.004296 0.003026 0.9889 0.9919 0.007172 0.8493 0.8912 0.01115 ] Network output: [ -9.495e-05 0.0009692 1 -3.677e-06 1.651e-06 0.9988 -2.771e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2265 0.1078 0.3522 0.1408 0.9849 0.9939 0.2273 0.4309 0.8744 0.7002 ] Network output: [ 0.002091 -0.01025 0.9948 2.252e-06 -1.011e-06 1.011 1.697e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1001 0.1851 0.1962 0.9873 0.9919 0.113 0.7297 0.8602 0.3044 ] Network output: [ -0.001996 0.009606 1.005 2.486e-06 -1.116e-06 0.9899 1.874e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0953 0.09336 0.1648 0.197 0.9852 0.991 0.09532 0.6533 0.8349 0.2505 ] Network output: [ 7.222e-05 1 -5.714e-05 3.239e-07 -1.454e-07 0.9998 2.441e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001191 Epoch 10361 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008327 0.9969 0.9931 -1.069e-07 4.801e-08 -0.006629 -8.059e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.0034 -0.00633 0.005155 0.9699 0.9743 0.006931 0.8222 0.8185 0.01569 ] Network output: [ 1 1.922e-05 0.0002806 -1.168e-06 5.245e-07 -0.0002253 -8.805e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03627 -0.1517 0.1805 0.9834 0.9932 0.2384 0.427 0.8676 0.7066 ] Network output: [ -0.008361 1.003 1.007 -1.091e-07 4.899e-08 0.007045 -8.223e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007033 0.0006581 0.004296 0.003026 0.9889 0.9919 0.007172 0.8493 0.8912 0.01115 ] Network output: [ -9.482e-05 0.0009686 1 -3.672e-06 1.649e-06 0.9988 -2.768e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2265 0.1078 0.3522 0.1408 0.9849 0.9939 0.2273 0.4309 0.8744 0.7002 ] Network output: [ 0.002089 -0.01024 0.9948 2.249e-06 -1.01e-06 1.011 1.695e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1001 0.1851 0.1962 0.9873 0.9919 0.113 0.7297 0.8602 0.3044 ] Network output: [ -0.001994 0.0096 1.005 2.483e-06 -1.115e-06 0.9899 1.871e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0953 0.09336 0.1648 0.197 0.9852 0.991 0.09532 0.6533 0.8349 0.2505 ] Network output: [ 7.221e-05 1 -5.717e-05 3.235e-07 -1.452e-07 0.9998 2.438e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000119 Epoch 10362 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008326 0.9969 0.9931 -1.068e-07 4.797e-08 -0.006628 -8.052e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.0034 -0.00633 0.005155 0.9699 0.9743 0.006931 0.8222 0.8185 0.01569 ] Network output: [ 1 1.91e-05 0.0002804 -1.167e-06 5.238e-07 -0.0002252 -8.794e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03627 -0.1517 0.1805 0.9834 0.9932 0.2384 0.427 0.8676 0.7066 ] Network output: [ -0.00836 1.003 1.007 -1.09e-07 4.894e-08 0.007044 -8.216e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007033 0.0006582 0.004296 0.003026 0.9889 0.9919 0.007173 0.8493 0.8912 0.01114 ] Network output: [ -9.47e-05 0.0009679 1 -3.668e-06 1.647e-06 0.9988 -2.764e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2265 0.1078 0.3522 0.1408 0.9849 0.9939 0.2273 0.4309 0.8744 0.7002 ] Network output: [ 0.002088 -0.01024 0.9948 2.246e-06 -1.009e-06 1.011 1.693e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1001 0.1851 0.1962 0.9873 0.9919 0.113 0.7297 0.8602 0.3044 ] Network output: [ -0.001993 0.009595 1.005 2.48e-06 -1.113e-06 0.9899 1.869e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0953 0.09336 0.1648 0.197 0.9852 0.991 0.09532 0.6533 0.8349 0.2505 ] Network output: [ 7.22e-05 1 -5.72e-05 3.231e-07 -1.451e-07 0.9998 2.435e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001189 Epoch 10363 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008325 0.9969 0.9931 -1.068e-07 4.793e-08 -0.006627 -8.045e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.0034 -0.006329 0.005154 0.9699 0.9743 0.006931 0.8222 0.8185 0.01569 ] Network output: [ 1 1.898e-05 0.0002803 -1.165e-06 5.232e-07 -0.0002251 -8.782e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03627 -0.1517 0.1805 0.9834 0.9932 0.2384 0.427 0.8676 0.7066 ] Network output: [ -0.008359 1.003 1.007 -1.089e-07 4.889e-08 0.007044 -8.208e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007033 0.0006582 0.004296 0.003026 0.9889 0.9919 0.007173 0.8493 0.8912 0.01114 ] Network output: [ -9.458e-05 0.0009672 1 -3.663e-06 1.644e-06 0.9988 -2.761e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2265 0.1078 0.3522 0.1408 0.9849 0.9939 0.2273 0.4309 0.8744 0.7002 ] Network output: [ 0.002086 -0.01023 0.9948 2.244e-06 -1.007e-06 1.011 1.691e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1001 0.1851 0.1962 0.9873 0.9919 0.113 0.7297 0.8602 0.3044 ] Network output: [ -0.001992 0.00959 1.005 2.477e-06 -1.112e-06 0.9899 1.867e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0953 0.09336 0.1648 0.197 0.9852 0.991 0.09532 0.6533 0.8349 0.2505 ] Network output: [ 7.218e-05 1 -5.723e-05 3.227e-07 -1.449e-07 0.9998 2.432e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001189 Epoch 10364 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008325 0.9969 0.9931 -1.067e-07 4.789e-08 -0.006627 -8.039e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.0034 -0.006329 0.005154 0.9699 0.9743 0.006931 0.8222 0.8185 0.01569 ] Network output: [ 1 1.886e-05 0.0002802 -1.164e-06 5.225e-07 -0.000225 -8.771e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03627 -0.1517 0.1805 0.9834 0.9932 0.2384 0.427 0.8676 0.7066 ] Network output: [ -0.008358 1.003 1.007 -1.088e-07 4.885e-08 0.007043 -8.2e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007033 0.0006582 0.004296 0.003025 0.9889 0.9919 0.007173 0.8493 0.8912 0.01114 ] Network output: [ -9.445e-05 0.0009665 1 -3.658e-06 1.642e-06 0.9988 -2.757e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2265 0.1078 0.3522 0.1408 0.9849 0.9939 0.2273 0.4309 0.8744 0.7001 ] Network output: [ 0.002085 -0.01023 0.9948 2.241e-06 -1.006e-06 1.011 1.689e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1001 0.1851 0.1962 0.9873 0.9919 0.113 0.7297 0.8602 0.3044 ] Network output: [ -0.001991 0.009584 1.005 2.474e-06 -1.111e-06 0.9899 1.864e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0953 0.09336 0.1648 0.197 0.9852 0.991 0.09532 0.6533 0.8349 0.2505 ] Network output: [ 7.217e-05 1 -5.726e-05 3.223e-07 -1.447e-07 0.9998 2.429e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001188 Epoch 10365 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008324 0.9969 0.9931 -1.066e-07 4.784e-08 -0.006626 -8.032e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.0034 -0.006328 0.005154 0.9699 0.9743 0.006931 0.8222 0.8185 0.01568 ] Network output: [ 1 1.874e-05 0.0002801 -1.162e-06 5.218e-07 -0.0002249 -8.76e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03627 -0.1517 0.1805 0.9834 0.9932 0.2384 0.427 0.8676 0.7066 ] Network output: [ -0.008358 1.003 1.007 -1.087e-07 4.88e-08 0.007043 -8.192e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007034 0.0006583 0.004296 0.003025 0.9889 0.9919 0.007173 0.8493 0.8912 0.01114 ] Network output: [ -9.433e-05 0.0009658 1 -3.654e-06 1.64e-06 0.9988 -2.754e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2265 0.1078 0.3522 0.1408 0.9849 0.9939 0.2273 0.4309 0.8744 0.7001 ] Network output: [ 0.002084 -0.01022 0.9948 2.238e-06 -1.005e-06 1.011 1.687e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.113 0.7297 0.8602 0.3044 ] Network output: [ -0.001989 0.009579 1.005 2.471e-06 -1.109e-06 0.9899 1.862e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09531 0.09336 0.1648 0.197 0.9852 0.991 0.09532 0.6533 0.8349 0.2505 ] Network output: [ 7.216e-05 1 -5.728e-05 3.219e-07 -1.445e-07 0.9998 2.426e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001187 Epoch 10366 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008323 0.9969 0.9931 -1.065e-07 4.78e-08 -0.006625 -8.025e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.0034 -0.006328 0.005153 0.9699 0.9743 0.006931 0.8222 0.8185 0.01568 ] Network output: [ 1 1.863e-05 0.00028 -1.161e-06 5.212e-07 -0.0002248 -8.749e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03627 -0.1517 0.1805 0.9834 0.9932 0.2384 0.427 0.8676 0.7066 ] Network output: [ -0.008357 1.003 1.007 -1.086e-07 4.876e-08 0.007043 -8.185e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007034 0.0006583 0.004295 0.003025 0.9889 0.9919 0.007174 0.8493 0.8912 0.01114 ] Network output: [ -9.42e-05 0.0009652 1 -3.649e-06 1.638e-06 0.9988 -2.75e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2265 0.1078 0.3522 0.1408 0.9849 0.9939 0.2273 0.4309 0.8744 0.7001 ] Network output: [ 0.002082 -0.01021 0.9948 2.235e-06 -1.003e-06 1.011 1.684e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.113 0.7297 0.8602 0.3044 ] Network output: [ -0.001988 0.009573 1.005 2.468e-06 -1.108e-06 0.9899 1.86e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09531 0.09336 0.1648 0.197 0.9852 0.991 0.09532 0.6533 0.8349 0.2505 ] Network output: [ 7.215e-05 1 -5.731e-05 3.215e-07 -1.443e-07 0.9998 2.423e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001186 Epoch 10367 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008322 0.9969 0.9931 -1.064e-07 4.776e-08 -0.006625 -8.018e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.0034 -0.006327 0.005153 0.9699 0.9743 0.006931 0.8222 0.8185 0.01568 ] Network output: [ 1 1.851e-05 0.0002798 -1.159e-06 5.205e-07 -0.0002247 -8.738e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03627 -0.1517 0.1804 0.9834 0.9932 0.2384 0.427 0.8676 0.7066 ] Network output: [ -0.008356 1.003 1.007 -1.085e-07 4.871e-08 0.007042 -8.177e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007034 0.0006583 0.004295 0.003025 0.9889 0.9919 0.007174 0.8493 0.8912 0.01114 ] Network output: [ -9.408e-05 0.0009645 1 -3.645e-06 1.636e-06 0.9988 -2.747e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2265 0.1078 0.3522 0.1408 0.9849 0.9939 0.2273 0.4309 0.8744 0.7001 ] Network output: [ 0.002081 -0.01021 0.9948 2.232e-06 -1.002e-06 1.011 1.682e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.113 0.7297 0.8602 0.3044 ] Network output: [ -0.001987 0.009568 1.005 2.465e-06 -1.106e-06 0.9899 1.857e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09531 0.09336 0.1648 0.197 0.9852 0.991 0.09532 0.6533 0.8349 0.2505 ] Network output: [ 7.214e-05 1 -5.734e-05 3.211e-07 -1.442e-07 0.9998 2.42e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001186 Epoch 10368 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008322 0.9969 0.9931 -1.063e-07 4.772e-08 -0.006624 -8.011e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.0034 -0.006327 0.005153 0.9699 0.9743 0.006931 0.8222 0.8185 0.01568 ] Network output: [ 1 1.839e-05 0.0002797 -1.158e-06 5.199e-07 -0.0002246 -8.727e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03627 -0.1517 0.1804 0.9834 0.9932 0.2384 0.427 0.8676 0.7066 ] Network output: [ -0.008355 1.003 1.007 -1.084e-07 4.866e-08 0.007042 -8.169e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007034 0.0006583 0.004295 0.003025 0.9889 0.9919 0.007174 0.8493 0.8912 0.01114 ] Network output: [ -9.395e-05 0.0009638 1 -3.64e-06 1.634e-06 0.9988 -2.743e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2265 0.1078 0.3522 0.1408 0.9849 0.9939 0.2273 0.4309 0.8744 0.7001 ] Network output: [ 0.002079 -0.0102 0.9948 2.23e-06 -1.001e-06 1.011 1.68e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7297 0.8601 0.3044 ] Network output: [ -0.001986 0.009563 1.005 2.462e-06 -1.105e-06 0.9899 1.855e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09531 0.09336 0.1648 0.197 0.9852 0.991 0.09532 0.6533 0.8349 0.2505 ] Network output: [ 7.213e-05 1 -5.737e-05 3.207e-07 -1.44e-07 0.9998 2.417e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001185 Epoch 10369 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008321 0.9969 0.9931 -1.062e-07 4.768e-08 -0.006623 -8.004e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.0034 -0.006326 0.005152 0.9699 0.9743 0.006931 0.8222 0.8185 0.01568 ] Network output: [ 1 1.827e-05 0.0002796 -1.157e-06 5.192e-07 -0.0002245 -8.716e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03627 -0.1517 0.1804 0.9834 0.9932 0.2384 0.427 0.8676 0.7066 ] Network output: [ -0.008355 1.003 1.007 -1.083e-07 4.862e-08 0.007041 -8.161e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007035 0.0006584 0.004295 0.003024 0.9889 0.9919 0.007174 0.8493 0.8912 0.01114 ] Network output: [ -9.383e-05 0.0009631 1 -3.635e-06 1.632e-06 0.9988 -2.74e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2265 0.1078 0.3522 0.1408 0.9849 0.9939 0.2273 0.4309 0.8744 0.7001 ] Network output: [ 0.002078 -0.01019 0.9948 2.227e-06 -9.997e-07 1.011 1.678e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7297 0.8601 0.3044 ] Network output: [ -0.001984 0.009557 1.005 2.459e-06 -1.104e-06 0.9899 1.853e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09531 0.09336 0.1648 0.197 0.9852 0.991 0.09532 0.6533 0.8349 0.2505 ] Network output: [ 7.212e-05 1 -5.74e-05 3.203e-07 -1.438e-07 0.9998 2.414e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001184 Epoch 10370 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00832 0.9969 0.9931 -1.061e-07 4.764e-08 -0.006622 -7.997e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.0034 -0.006326 0.005152 0.9699 0.9743 0.006932 0.8222 0.8185 0.01568 ] Network output: [ 1 1.816e-05 0.0002795 -1.155e-06 5.186e-07 -0.0002243 -8.705e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03628 -0.1516 0.1804 0.9834 0.9932 0.2384 0.427 0.8676 0.7066 ] Network output: [ -0.008354 1.003 1.007 -1.082e-07 4.857e-08 0.007041 -8.154e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007035 0.0006584 0.004295 0.003024 0.9889 0.9919 0.007175 0.8493 0.8912 0.01114 ] Network output: [ -9.371e-05 0.0009624 1 -3.631e-06 1.63e-06 0.9988 -2.736e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2265 0.1078 0.3522 0.1408 0.9849 0.9939 0.2273 0.4309 0.8744 0.7001 ] Network output: [ 0.002077 -0.01019 0.9948 2.224e-06 -9.984e-07 1.011 1.676e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7297 0.8601 0.3044 ] Network output: [ -0.001983 0.009552 1.005 2.455e-06 -1.102e-06 0.9899 1.851e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09531 0.09337 0.1648 0.197 0.9852 0.991 0.09533 0.6532 0.8349 0.2505 ] Network output: [ 7.21e-05 1 -5.743e-05 3.199e-07 -1.436e-07 0.9998 2.411e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001184 Epoch 10371 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008319 0.9969 0.9931 -1.06e-07 4.76e-08 -0.006622 -7.99e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.0034 -0.006325 0.005152 0.9699 0.9743 0.006932 0.8222 0.8185 0.01568 ] Network output: [ 1 1.804e-05 0.0002793 -1.154e-06 5.179e-07 -0.0002242 -8.694e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03628 -0.1516 0.1804 0.9834 0.9932 0.2384 0.4269 0.8676 0.7066 ] Network output: [ -0.008353 1.003 1.007 -1.081e-07 4.852e-08 0.007041 -8.146e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007035 0.0006584 0.004295 0.003024 0.9889 0.9919 0.007175 0.8493 0.8912 0.01114 ] Network output: [ -9.358e-05 0.0009618 1 -3.626e-06 1.628e-06 0.9988 -2.733e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2265 0.1078 0.3522 0.1408 0.9849 0.9939 0.2273 0.4309 0.8744 0.7001 ] Network output: [ 0.002075 -0.01018 0.9948 2.221e-06 -9.971e-07 1.011 1.674e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7296 0.8601 0.3044 ] Network output: [ -0.001982 0.009547 1.005 2.452e-06 -1.101e-06 0.9899 1.848e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09531 0.09337 0.1648 0.197 0.9852 0.991 0.09533 0.6532 0.8349 0.2505 ] Network output: [ 7.209e-05 1 -5.745e-05 3.195e-07 -1.434e-07 0.9998 2.408e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001183 Epoch 10372 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008319 0.9969 0.9931 -1.059e-07 4.756e-08 -0.006621 -7.983e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.0034 -0.006325 0.005151 0.9699 0.9743 0.006932 0.8222 0.8185 0.01568 ] Network output: [ 1 1.792e-05 0.0002792 -1.152e-06 5.172e-07 -0.0002241 -8.683e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03628 -0.1516 0.1804 0.9834 0.9932 0.2385 0.4269 0.8676 0.7066 ] Network output: [ -0.008353 1.003 1.007 -1.08e-07 4.848e-08 0.00704 -8.138e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007035 0.0006584 0.004295 0.003024 0.9889 0.9919 0.007175 0.8493 0.8912 0.01114 ] Network output: [ -9.346e-05 0.0009611 1 -3.622e-06 1.626e-06 0.9988 -2.729e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2265 0.1078 0.3522 0.1408 0.9849 0.9939 0.2273 0.4309 0.8744 0.7001 ] Network output: [ 0.002074 -0.01018 0.9948 2.218e-06 -9.959e-07 1.011 1.672e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7296 0.8601 0.3044 ] Network output: [ -0.001981 0.009541 1.005 2.449e-06 -1.1e-06 0.9899 1.846e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09531 0.09337 0.1648 0.197 0.9852 0.991 0.09533 0.6532 0.8349 0.2505 ] Network output: [ 7.208e-05 1 -5.748e-05 3.191e-07 -1.433e-07 0.9998 2.405e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001182 Epoch 10373 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008318 0.9969 0.9931 -1.058e-07 4.752e-08 -0.00662 -7.977e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.0034 -0.006324 0.005151 0.9699 0.9743 0.006932 0.8222 0.8185 0.01568 ] Network output: [ 1 1.78e-05 0.0002791 -1.151e-06 5.166e-07 -0.000224 -8.672e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03628 -0.1516 0.1804 0.9834 0.9932 0.2385 0.4269 0.8676 0.7066 ] Network output: [ -0.008352 1.003 1.007 -1.079e-07 4.843e-08 0.00704 -8.13e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007036 0.0006585 0.004295 0.003023 0.9889 0.9919 0.007176 0.8493 0.8912 0.01114 ] Network output: [ -9.333e-05 0.0009604 1 -3.617e-06 1.624e-06 0.9988 -2.726e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2265 0.1078 0.3522 0.1408 0.9849 0.9939 0.2273 0.4309 0.8744 0.7001 ] Network output: [ 0.002072 -0.01017 0.9948 2.216e-06 -9.946e-07 1.011 1.67e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7296 0.8601 0.3044 ] Network output: [ -0.001979 0.009536 1.005 2.446e-06 -1.098e-06 0.9899 1.844e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09531 0.09337 0.1648 0.197 0.9852 0.991 0.09533 0.6532 0.8349 0.2505 ] Network output: [ 7.207e-05 1 -5.751e-05 3.187e-07 -1.431e-07 0.9998 2.402e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001182 Epoch 10374 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008317 0.9969 0.9931 -1.057e-07 4.748e-08 -0.00662 -7.97e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.0034 -0.006324 0.005151 0.9699 0.9743 0.006932 0.8222 0.8185 0.01568 ] Network output: [ 1 1.769e-05 0.000279 -1.149e-06 5.159e-07 -0.0002239 -8.661e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03628 -0.1516 0.1804 0.9834 0.9932 0.2385 0.4269 0.8676 0.7066 ] Network output: [ -0.008351 1.003 1.007 -1.078e-07 4.839e-08 0.007039 -8.123e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007036 0.0006585 0.004294 0.003023 0.9889 0.9919 0.007176 0.8493 0.8912 0.01114 ] Network output: [ -9.321e-05 0.0009597 1 -3.612e-06 1.622e-06 0.9988 -2.722e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2265 0.1078 0.3522 0.1408 0.9849 0.9939 0.2273 0.4309 0.8744 0.7001 ] Network output: [ 0.002071 -0.01016 0.9948 2.213e-06 -9.934e-07 1.011 1.668e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7296 0.8601 0.3044 ] Network output: [ -0.001978 0.009531 1.005 2.443e-06 -1.097e-06 0.9899 1.841e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09531 0.09337 0.1648 0.197 0.9852 0.991 0.09533 0.6532 0.8349 0.2505 ] Network output: [ 7.206e-05 1 -5.754e-05 3.183e-07 -1.429e-07 0.9998 2.399e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001181 Epoch 10375 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008316 0.9969 0.9931 -1.057e-07 4.743e-08 -0.006619 -7.963e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.0034 -0.006323 0.00515 0.9699 0.9743 0.006932 0.8221 0.8185 0.01568 ] Network output: [ 1 1.757e-05 0.0002789 -1.148e-06 5.153e-07 -0.0002238 -8.65e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03628 -0.1516 0.1804 0.9834 0.9932 0.2385 0.4269 0.8676 0.7066 ] Network output: [ -0.00835 1.003 1.007 -1.077e-07 4.834e-08 0.007039 -8.115e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007036 0.0006585 0.004294 0.003023 0.9889 0.9919 0.007176 0.8493 0.8912 0.01114 ] Network output: [ -9.309e-05 0.0009591 1 -3.608e-06 1.62e-06 0.9988 -2.719e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2265 0.1078 0.3522 0.1408 0.9849 0.9939 0.2273 0.4309 0.8744 0.7001 ] Network output: [ 0.00207 -0.01016 0.9948 2.21e-06 -9.921e-07 1.011 1.666e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7296 0.8601 0.3044 ] Network output: [ -0.001977 0.009525 1.005 2.44e-06 -1.096e-06 0.9899 1.839e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09532 0.09337 0.1648 0.197 0.9852 0.991 0.09533 0.6532 0.8349 0.2505 ] Network output: [ 7.205e-05 1 -5.757e-05 3.179e-07 -1.427e-07 0.9998 2.396e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000118 Epoch 10376 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008315 0.9969 0.9931 -1.056e-07 4.739e-08 -0.006618 -7.956e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.0034 -0.006323 0.00515 0.9699 0.9743 0.006932 0.8221 0.8185 0.01568 ] Network output: [ 1 1.745e-05 0.0002787 -1.146e-06 5.146e-07 -0.0002237 -8.639e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03628 -0.1516 0.1804 0.9834 0.9932 0.2385 0.4269 0.8676 0.7066 ] Network output: [ -0.00835 1.003 1.007 -1.076e-07 4.829e-08 0.007039 -8.107e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007036 0.0006585 0.004294 0.003023 0.9889 0.9919 0.007176 0.8493 0.8912 0.01114 ] Network output: [ -9.296e-05 0.0009584 1 -3.603e-06 1.618e-06 0.9988 -2.716e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2266 0.1078 0.3522 0.1408 0.9849 0.9939 0.2273 0.4309 0.8744 0.7001 ] Network output: [ 0.002068 -0.01015 0.9948 2.207e-06 -9.909e-07 1.011 1.663e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7296 0.8601 0.3044 ] Network output: [ -0.001976 0.00952 1.005 2.437e-06 -1.094e-06 0.9899 1.837e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09532 0.09337 0.1648 0.197 0.9852 0.991 0.09533 0.6532 0.8349 0.2505 ] Network output: [ 7.203e-05 1 -5.76e-05 3.175e-07 -1.425e-07 0.9998 2.393e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001179 Epoch 10377 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008315 0.9969 0.9931 -1.055e-07 4.735e-08 -0.006617 -7.949e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.0034 -0.006322 0.00515 0.9699 0.9743 0.006932 0.8221 0.8185 0.01568 ] Network output: [ 1 1.734e-05 0.0002786 -1.145e-06 5.14e-07 -0.0002236 -8.628e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03628 -0.1516 0.1804 0.9834 0.9932 0.2385 0.4269 0.8676 0.7066 ] Network output: [ -0.008349 1.003 1.007 -1.075e-07 4.825e-08 0.007038 -8.099e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007037 0.0006586 0.004294 0.003023 0.9889 0.9919 0.007177 0.8493 0.8912 0.01113 ] Network output: [ -9.284e-05 0.0009577 1 -3.599e-06 1.616e-06 0.9988 -2.712e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2266 0.1078 0.3522 0.1408 0.9849 0.9939 0.2273 0.4309 0.8744 0.7001 ] Network output: [ 0.002067 -0.01015 0.9948 2.204e-06 -9.896e-07 1.011 1.661e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7296 0.8601 0.3044 ] Network output: [ -0.001975 0.009514 1.005 2.434e-06 -1.093e-06 0.9899 1.834e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09532 0.09337 0.1648 0.197 0.9852 0.991 0.09533 0.6532 0.8349 0.2505 ] Network output: [ 7.202e-05 1 -5.762e-05 3.171e-07 -1.424e-07 0.9998 2.39e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001179 Epoch 10378 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008314 0.9969 0.9931 -1.054e-07 4.731e-08 -0.006617 -7.942e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.0034 -0.006322 0.005149 0.9699 0.9743 0.006932 0.8221 0.8185 0.01567 ] Network output: [ 1 1.722e-05 0.0002785 -1.143e-06 5.133e-07 -0.0002235 -8.617e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2121 -0.03628 -0.1516 0.1804 0.9834 0.9932 0.2385 0.4269 0.8676 0.7066 ] Network output: [ -0.008348 1.003 1.007 -1.074e-07 4.82e-08 0.007038 -8.092e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007037 0.0006586 0.004294 0.003022 0.9889 0.9919 0.007177 0.8493 0.8912 0.01113 ] Network output: [ -9.272e-05 0.000957 1 -3.594e-06 1.614e-06 0.9988 -2.709e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2266 0.1078 0.3522 0.1408 0.9849 0.9939 0.2274 0.4309 0.8744 0.7001 ] Network output: [ 0.002065 -0.01014 0.9948 2.202e-06 -9.884e-07 1.011 1.659e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7296 0.8601 0.3044 ] Network output: [ -0.001973 0.009509 1.005 2.431e-06 -1.091e-06 0.9899 1.832e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09532 0.09337 0.1648 0.197 0.9852 0.991 0.09533 0.6532 0.8349 0.2505 ] Network output: [ 7.201e-05 1 -5.765e-05 3.167e-07 -1.422e-07 0.9998 2.387e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001178 Epoch 10379 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008313 0.9969 0.9931 -1.053e-07 4.727e-08 -0.006616 -7.935e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.0034 -0.006321 0.005149 0.9699 0.9743 0.006932 0.8221 0.8185 0.01567 ] Network output: [ 1 1.71e-05 0.0002784 -1.142e-06 5.127e-07 -0.0002234 -8.606e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2122 -0.03629 -0.1516 0.1804 0.9834 0.9932 0.2385 0.4269 0.8676 0.7066 ] Network output: [ -0.008347 1.003 1.007 -1.073e-07 4.816e-08 0.007037 -8.084e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007037 0.0006586 0.004294 0.003022 0.9889 0.9919 0.007177 0.8493 0.8912 0.01113 ] Network output: [ -9.259e-05 0.0009563 1 -3.59e-06 1.611e-06 0.9988 -2.705e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2266 0.1078 0.3522 0.1408 0.9849 0.9939 0.2274 0.4309 0.8744 0.7001 ] Network output: [ 0.002064 -0.01013 0.9948 2.199e-06 -9.872e-07 1.011 1.657e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7296 0.8601 0.3044 ] Network output: [ -0.001972 0.009504 1.005 2.428e-06 -1.09e-06 0.9899 1.83e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09532 0.09337 0.1648 0.197 0.9852 0.991 0.09533 0.6532 0.8349 0.2505 ] Network output: [ 7.2e-05 1 -5.768e-05 3.163e-07 -1.42e-07 0.9998 2.384e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001177 Epoch 10380 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008312 0.9969 0.9931 -1.052e-07 4.723e-08 -0.006615 -7.928e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003401 -0.006321 0.005149 0.9699 0.9743 0.006933 0.8221 0.8185 0.01567 ] Network output: [ 1 1.699e-05 0.0002783 -1.141e-06 5.12e-07 -0.0002233 -8.595e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2122 -0.03629 -0.1516 0.1804 0.9834 0.9932 0.2385 0.4269 0.8676 0.7066 ] Network output: [ -0.008347 1.003 1.007 -1.072e-07 4.811e-08 0.007037 -8.076e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007037 0.0006586 0.004294 0.003022 0.9889 0.9919 0.007177 0.8493 0.8912 0.01113 ] Network output: [ -9.247e-05 0.0009557 1 -3.585e-06 1.609e-06 0.9988 -2.702e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2266 0.1078 0.3522 0.1408 0.9849 0.9939 0.2274 0.4308 0.8744 0.7001 ] Network output: [ 0.002063 -0.01013 0.9948 2.196e-06 -9.859e-07 1.011 1.655e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7296 0.8601 0.3044 ] Network output: [ -0.001971 0.009498 1.005 2.425e-06 -1.089e-06 0.9899 1.828e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09532 0.09337 0.1648 0.197 0.9852 0.991 0.09534 0.6532 0.8349 0.2505 ] Network output: [ 7.199e-05 1 -5.771e-05 3.159e-07 -1.418e-07 0.9998 2.381e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001177 Epoch 10381 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008312 0.9969 0.9931 -1.051e-07 4.719e-08 -0.006615 -7.922e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003401 -0.00632 0.005148 0.9699 0.9743 0.006933 0.8221 0.8185 0.01567 ] Network output: [ 1 1.687e-05 0.0002781 -1.139e-06 5.114e-07 -0.0002232 -8.585e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2122 -0.03629 -0.1516 0.1804 0.9834 0.9932 0.2385 0.4269 0.8676 0.7066 ] Network output: [ -0.008346 1.003 1.007 -1.071e-07 4.806e-08 0.007037 -8.069e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007038 0.0006587 0.004294 0.003022 0.9889 0.9919 0.007178 0.8492 0.8912 0.01113 ] Network output: [ -9.235e-05 0.000955 1 -3.58e-06 1.607e-06 0.9988 -2.698e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2266 0.1078 0.3522 0.1408 0.9849 0.9939 0.2274 0.4308 0.8744 0.7001 ] Network output: [ 0.002061 -0.01012 0.9948 2.193e-06 -9.847e-07 1.011 1.653e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7296 0.8601 0.3044 ] Network output: [ -0.00197 0.009493 1.005 2.422e-06 -1.087e-06 0.9899 1.825e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09532 0.09338 0.1648 0.197 0.9852 0.991 0.09534 0.6532 0.8349 0.2505 ] Network output: [ 7.198e-05 1 -5.774e-05 3.155e-07 -1.417e-07 0.9998 2.378e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001176 Epoch 10382 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008311 0.9969 0.9931 -1.05e-07 4.715e-08 -0.006614 -7.915e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003401 -0.00632 0.005148 0.9699 0.9743 0.006933 0.8221 0.8185 0.01567 ] Network output: [ 1 1.675e-05 0.000278 -1.138e-06 5.107e-07 -0.0002231 -8.574e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2122 -0.03629 -0.1516 0.1804 0.9834 0.9932 0.2385 0.4269 0.8676 0.7066 ] Network output: [ -0.008345 1.003 1.007 -1.07e-07 4.802e-08 0.007036 -8.061e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007038 0.0006587 0.004293 0.003022 0.9889 0.9919 0.007178 0.8492 0.8912 0.01113 ] Network output: [ -9.222e-05 0.0009543 1 -3.576e-06 1.605e-06 0.9988 -2.695e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2266 0.1078 0.3522 0.1408 0.9849 0.9939 0.2274 0.4308 0.8744 0.7001 ] Network output: [ 0.00206 -0.01011 0.9948 2.191e-06 -9.834e-07 1.011 1.651e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7296 0.8601 0.3044 ] Network output: [ -0.001968 0.009488 1.005 2.419e-06 -1.086e-06 0.9899 1.823e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09532 0.09338 0.1648 0.197 0.9852 0.991 0.09534 0.6532 0.8349 0.2505 ] Network output: [ 7.197e-05 1 -5.777e-05 3.151e-07 -1.415e-07 0.9998 2.375e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001175 Epoch 10383 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00831 0.9969 0.9931 -1.049e-07 4.711e-08 -0.006613 -7.908e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003401 -0.006319 0.005148 0.9699 0.9743 0.006933 0.8221 0.8185 0.01567 ] Network output: [ 1 1.664e-05 0.0002779 -1.136e-06 5.101e-07 -0.000223 -8.563e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2122 -0.03629 -0.1516 0.1804 0.9834 0.9932 0.2385 0.4269 0.8676 0.7066 ] Network output: [ -0.008344 1.003 1.007 -1.069e-07 4.797e-08 0.007036 -8.053e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007038 0.0006587 0.004293 0.003021 0.9889 0.9919 0.007178 0.8492 0.8912 0.01113 ] Network output: [ -9.21e-05 0.0009536 1 -3.571e-06 1.603e-06 0.9988 -2.692e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2266 0.1078 0.3523 0.1408 0.9849 0.9939 0.2274 0.4308 0.8744 0.7001 ] Network output: [ 0.002059 -0.01011 0.9948 2.188e-06 -9.822e-07 1.011 1.649e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7296 0.8601 0.3044 ] Network output: [ -0.001967 0.009482 1.005 2.416e-06 -1.085e-06 0.9899 1.821e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09532 0.09338 0.1648 0.197 0.9852 0.991 0.09534 0.6532 0.8349 0.2505 ] Network output: [ 7.195e-05 1 -5.78e-05 3.148e-07 -1.413e-07 0.9998 2.372e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001175 Epoch 10384 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008309 0.9969 0.9931 -1.048e-07 4.707e-08 -0.006612 -7.901e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003401 -0.006319 0.005148 0.9699 0.9743 0.006933 0.8221 0.8185 0.01567 ] Network output: [ 1 1.652e-05 0.0002778 -1.135e-06 5.094e-07 -0.0002228 -8.552e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2122 -0.03629 -0.1516 0.1804 0.9834 0.9932 0.2385 0.4269 0.8676 0.7066 ] Network output: [ -0.008344 1.003 1.007 -1.068e-07 4.793e-08 0.007035 -8.046e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007038 0.0006587 0.004293 0.003021 0.9889 0.9919 0.007178 0.8492 0.8912 0.01113 ] Network output: [ -9.197e-05 0.0009529 1 -3.567e-06 1.601e-06 0.9988 -2.688e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2266 0.1078 0.3523 0.1408 0.9849 0.9939 0.2274 0.4308 0.8744 0.7001 ] Network output: [ 0.002057 -0.0101 0.9948 2.185e-06 -9.81e-07 1.011 1.647e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7296 0.8601 0.3044 ] Network output: [ -0.001966 0.009477 1.005 2.413e-06 -1.083e-06 0.9899 1.819e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09532 0.09338 0.1648 0.197 0.9852 0.991 0.09534 0.6531 0.8349 0.2505 ] Network output: [ 7.194e-05 1 -5.783e-05 3.144e-07 -1.411e-07 0.9998 2.369e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001174 Epoch 10385 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008309 0.9969 0.9931 -1.047e-07 4.703e-08 -0.006612 -7.894e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003401 -0.006318 0.005147 0.9699 0.9743 0.006933 0.8221 0.8185 0.01567 ] Network output: [ 1 1.64e-05 0.0002777 -1.133e-06 5.088e-07 -0.0002227 -8.541e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2122 -0.03629 -0.1515 0.1804 0.9834 0.9932 0.2385 0.4269 0.8676 0.7066 ] Network output: [ -0.008343 1.003 1.007 -1.067e-07 4.788e-08 0.007035 -8.038e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007039 0.0006588 0.004293 0.003021 0.9889 0.9919 0.007179 0.8492 0.8912 0.01113 ] Network output: [ -9.185e-05 0.0009523 1 -3.562e-06 1.599e-06 0.9988 -2.685e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2266 0.1078 0.3523 0.1408 0.9849 0.9939 0.2274 0.4308 0.8744 0.7001 ] Network output: [ 0.002056 -0.0101 0.9948 2.182e-06 -9.797e-07 1.011 1.645e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7296 0.8601 0.3044 ] Network output: [ -0.001965 0.009472 1.005 2.41e-06 -1.082e-06 0.9899 1.816e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09533 0.09338 0.1648 0.197 0.9852 0.991 0.09534 0.6531 0.8349 0.2505 ] Network output: [ 7.193e-05 1 -5.785e-05 3.14e-07 -1.41e-07 0.9998 2.366e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001173 Epoch 10386 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008308 0.9969 0.9931 -1.047e-07 4.699e-08 -0.006611 -7.887e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003401 -0.006318 0.005147 0.9699 0.9743 0.006933 0.8221 0.8185 0.01567 ] Network output: [ 1 1.629e-05 0.0002775 -1.132e-06 5.082e-07 -0.0002226 -8.53e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2122 -0.03629 -0.1515 0.1804 0.9834 0.9932 0.2385 0.4269 0.8676 0.7066 ] Network output: [ -0.008342 1.003 1.007 -1.066e-07 4.784e-08 0.007035 -8.03e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007039 0.0006588 0.004293 0.003021 0.9889 0.9919 0.007179 0.8492 0.8912 0.01113 ] Network output: [ -9.173e-05 0.0009516 1 -3.558e-06 1.597e-06 0.9988 -2.681e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2266 0.1078 0.3523 0.1408 0.9849 0.9939 0.2274 0.4308 0.8744 0.7001 ] Network output: [ 0.002054 -0.01009 0.9948 2.18e-06 -9.785e-07 1.011 1.643e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7295 0.8601 0.3044 ] Network output: [ -0.001963 0.009466 1.005 2.407e-06 -1.081e-06 0.9899 1.814e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09533 0.09338 0.1648 0.197 0.9852 0.991 0.09534 0.6531 0.8349 0.2505 ] Network output: [ 7.192e-05 1 -5.788e-05 3.136e-07 -1.408e-07 0.9998 2.363e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001172 Epoch 10387 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008307 0.9969 0.9931 -1.046e-07 4.694e-08 -0.00661 -7.881e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003401 -0.006317 0.005147 0.9699 0.9743 0.006933 0.8221 0.8185 0.01567 ] Network output: [ 1 1.617e-05 0.0002774 -1.13e-06 5.075e-07 -0.0002225 -8.52e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2122 -0.03629 -0.1515 0.1804 0.9834 0.9932 0.2385 0.4269 0.8676 0.7066 ] Network output: [ -0.008341 1.003 1.007 -1.065e-07 4.779e-08 0.007034 -8.023e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007039 0.0006588 0.004293 0.003021 0.9889 0.9919 0.007179 0.8492 0.8912 0.01113 ] Network output: [ -9.16e-05 0.0009509 1 -3.553e-06 1.595e-06 0.9988 -2.678e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2266 0.1078 0.3523 0.1408 0.9849 0.9939 0.2274 0.4308 0.8744 0.7001 ] Network output: [ 0.002053 -0.01008 0.9948 2.177e-06 -9.773e-07 1.011 1.641e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7295 0.8601 0.3044 ] Network output: [ -0.001962 0.009461 1.005 2.404e-06 -1.079e-06 0.99 1.812e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09533 0.09338 0.1648 0.197 0.9852 0.991 0.09534 0.6531 0.8349 0.2505 ] Network output: [ 7.191e-05 1 -5.791e-05 3.132e-07 -1.406e-07 0.9998 2.36e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001172 Epoch 10388 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008306 0.9969 0.9931 -1.045e-07 4.69e-08 -0.00661 -7.874e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003401 -0.006317 0.005146 0.9699 0.9743 0.006933 0.8221 0.8185 0.01567 ] Network output: [ 1 1.605e-05 0.0002773 -1.129e-06 5.069e-07 -0.0002224 -8.509e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2122 -0.0363 -0.1515 0.1804 0.9834 0.9932 0.2385 0.4269 0.8676 0.7066 ] Network output: [ -0.008341 1.003 1.007 -1.064e-07 4.775e-08 0.007034 -8.015e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007039 0.0006588 0.004293 0.00302 0.9889 0.9919 0.007179 0.8492 0.8912 0.01113 ] Network output: [ -9.148e-05 0.0009502 1 -3.549e-06 1.593e-06 0.9988 -2.675e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2266 0.1078 0.3523 0.1408 0.9849 0.9939 0.2274 0.4308 0.8744 0.7001 ] Network output: [ 0.002052 -0.01008 0.9948 2.174e-06 -9.76e-07 1.011 1.638e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7295 0.8601 0.3044 ] Network output: [ -0.001961 0.009456 1.005 2.401e-06 -1.078e-06 0.99 1.81e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09533 0.09338 0.1648 0.197 0.9852 0.991 0.09534 0.6531 0.8349 0.2505 ] Network output: [ 7.19e-05 1 -5.794e-05 3.128e-07 -1.404e-07 0.9998 2.357e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001171 Epoch 10389 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008305 0.9969 0.9931 -1.044e-07 4.686e-08 -0.006609 -7.867e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003401 -0.006316 0.005146 0.9699 0.9743 0.006933 0.8221 0.8185 0.01567 ] Network output: [ 1 1.594e-05 0.0002772 -1.128e-06 5.062e-07 -0.0002223 -8.498e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2122 -0.0363 -0.1515 0.1804 0.9834 0.9932 0.2385 0.4269 0.8676 0.7066 ] Network output: [ -0.00834 1.003 1.007 -1.062e-07 4.77e-08 0.007033 -8.007e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00704 0.0006589 0.004293 0.00302 0.9889 0.9919 0.00718 0.8492 0.8912 0.01113 ] Network output: [ -9.136e-05 0.0009496 1 -3.544e-06 1.591e-06 0.9988 -2.671e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2266 0.1078 0.3523 0.1408 0.9849 0.9939 0.2274 0.4308 0.8744 0.7001 ] Network output: [ 0.00205 -0.01007 0.9948 2.171e-06 -9.748e-07 1.011 1.636e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7295 0.8601 0.3044 ] Network output: [ -0.00196 0.00945 1.005 2.398e-06 -1.077e-06 0.99 1.807e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09533 0.09338 0.1648 0.197 0.9852 0.991 0.09534 0.6531 0.8349 0.2505 ] Network output: [ 7.189e-05 1 -5.797e-05 3.124e-07 -1.402e-07 0.9998 2.354e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000117 Epoch 10390 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008305 0.9969 0.9931 -1.043e-07 4.682e-08 -0.006608 -7.86e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003401 -0.006316 0.005146 0.9699 0.9743 0.006934 0.8221 0.8185 0.01566 ] Network output: [ 1 1.582e-05 0.0002771 -1.126e-06 5.056e-07 -0.0002222 -8.487e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2122 -0.0363 -0.1515 0.1804 0.9834 0.9932 0.2385 0.4269 0.8676 0.7066 ] Network output: [ -0.008339 1.003 1.007 -1.061e-07 4.765e-08 0.007033 -8e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00704 0.0006589 0.004293 0.00302 0.9889 0.9919 0.00718 0.8492 0.8912 0.01113 ] Network output: [ -9.123e-05 0.0009489 1 -3.54e-06 1.589e-06 0.9988 -2.668e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2266 0.1079 0.3523 0.1408 0.9849 0.9939 0.2274 0.4308 0.8744 0.7001 ] Network output: [ 0.002049 -0.01007 0.9948 2.169e-06 -9.736e-07 1.011 1.634e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7295 0.8601 0.3044 ] Network output: [ -0.001958 0.009445 1.005 2.395e-06 -1.075e-06 0.99 1.805e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09533 0.09338 0.1648 0.197 0.9852 0.991 0.09535 0.6531 0.8349 0.2505 ] Network output: [ 7.188e-05 1 -5.8e-05 3.12e-07 -1.401e-07 0.9998 2.351e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000117 Epoch 10391 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008304 0.9969 0.9931 -1.042e-07 4.678e-08 -0.006607 -7.853e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003401 -0.006315 0.005145 0.9699 0.9743 0.006934 0.8221 0.8185 0.01566 ] Network output: [ 1 1.57e-05 0.0002769 -1.125e-06 5.049e-07 -0.0002221 -8.477e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2122 -0.0363 -0.1515 0.1804 0.9834 0.9932 0.2386 0.4269 0.8676 0.7066 ] Network output: [ -0.008338 1.003 1.007 -1.06e-07 4.761e-08 0.007033 -7.992e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00704 0.0006589 0.004292 0.00302 0.9889 0.9919 0.00718 0.8492 0.8912 0.01113 ] Network output: [ -9.111e-05 0.0009482 1 -3.535e-06 1.587e-06 0.9988 -2.664e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2266 0.1079 0.3523 0.1408 0.9849 0.9939 0.2274 0.4308 0.8744 0.7001 ] Network output: [ 0.002047 -0.01006 0.9948 2.166e-06 -9.723e-07 1.011 1.632e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7295 0.8601 0.3044 ] Network output: [ -0.001957 0.00944 1.005 2.392e-06 -1.074e-06 0.99 1.803e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09533 0.09339 0.1648 0.197 0.9852 0.991 0.09535 0.6531 0.8349 0.2505 ] Network output: [ 7.186e-05 1 -5.803e-05 3.116e-07 -1.399e-07 0.9998 2.348e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001169 Epoch 10392 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008303 0.9969 0.9931 -1.041e-07 4.674e-08 -0.006607 -7.847e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003401 -0.006315 0.005145 0.9699 0.9743 0.006934 0.8221 0.8185 0.01566 ] Network output: [ 1 1.559e-05 0.0002768 -1.123e-06 5.043e-07 -0.000222 -8.466e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2122 -0.0363 -0.1515 0.1804 0.9834 0.9932 0.2386 0.4269 0.8676 0.7066 ] Network output: [ -0.008338 1.003 1.007 -1.059e-07 4.756e-08 0.007032 -7.984e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00704 0.0006589 0.004292 0.00302 0.9889 0.9919 0.00718 0.8492 0.8912 0.01112 ] Network output: [ -9.099e-05 0.0009475 1 -3.531e-06 1.585e-06 0.9988 -2.661e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2266 0.1079 0.3523 0.1408 0.9849 0.9939 0.2274 0.4308 0.8744 0.7001 ] Network output: [ 0.002046 -0.01005 0.9948 2.163e-06 -9.711e-07 1.011 1.63e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7295 0.8601 0.3043 ] Network output: [ -0.001956 0.009434 1.005 2.389e-06 -1.073e-06 0.99 1.801e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09533 0.09339 0.1648 0.197 0.9852 0.991 0.09535 0.6531 0.8349 0.2505 ] Network output: [ 7.185e-05 1 -5.806e-05 3.112e-07 -1.397e-07 0.9998 2.346e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001168 Epoch 10393 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008302 0.9969 0.9931 -1.04e-07 4.67e-08 -0.006606 -7.84e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003401 -0.006314 0.005145 0.9699 0.9743 0.006934 0.8221 0.8185 0.01566 ] Network output: [ 1 1.547e-05 0.0002767 -1.122e-06 5.037e-07 -0.0002219 -8.455e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2122 -0.0363 -0.1515 0.1804 0.9834 0.9932 0.2386 0.4269 0.8676 0.7066 ] Network output: [ -0.008337 1.003 1.007 -1.058e-07 4.752e-08 0.007032 -7.977e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007041 0.000659 0.004292 0.003019 0.9889 0.9919 0.007181 0.8492 0.8912 0.01112 ] Network output: [ -9.086e-05 0.0009468 1 -3.526e-06 1.583e-06 0.9988 -2.658e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2266 0.1079 0.3523 0.1408 0.9849 0.9939 0.2274 0.4308 0.8744 0.7001 ] Network output: [ 0.002045 -0.01005 0.9948 2.16e-06 -9.699e-07 1.011 1.628e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7295 0.8601 0.3043 ] Network output: [ -0.001955 0.009429 1.005 2.386e-06 -1.071e-06 0.99 1.798e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09533 0.09339 0.1648 0.197 0.9852 0.991 0.09535 0.6531 0.8349 0.2505 ] Network output: [ 7.184e-05 1 -5.809e-05 3.108e-07 -1.395e-07 0.9998 2.343e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001168 Epoch 10394 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008302 0.9969 0.9931 -1.039e-07 4.666e-08 -0.006605 -7.833e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003401 -0.006314 0.005144 0.9699 0.9743 0.006934 0.8221 0.8185 0.01566 ] Network output: [ 1 1.536e-05 0.0002766 -1.12e-06 5.03e-07 -0.0002218 -8.444e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2122 -0.0363 -0.1515 0.1804 0.9834 0.9932 0.2386 0.4269 0.8676 0.7066 ] Network output: [ -0.008336 1.003 1.007 -1.057e-07 4.747e-08 0.007031 -7.969e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007041 0.000659 0.004292 0.003019 0.9889 0.9919 0.007181 0.8492 0.8912 0.01112 ] Network output: [ -9.074e-05 0.0009462 1 -3.522e-06 1.581e-06 0.9988 -2.654e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2266 0.1079 0.3523 0.1408 0.9849 0.9939 0.2274 0.4308 0.8744 0.7001 ] Network output: [ 0.002043 -0.01004 0.9948 2.158e-06 -9.687e-07 1.011 1.626e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7295 0.8601 0.3043 ] Network output: [ -0.001954 0.009424 1.005 2.383e-06 -1.07e-06 0.99 1.796e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09533 0.09339 0.1648 0.197 0.9852 0.991 0.09535 0.6531 0.8349 0.2505 ] Network output: [ 7.183e-05 1 -5.812e-05 3.105e-07 -1.394e-07 0.9998 2.34e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001167 Epoch 10395 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008301 0.9969 0.9931 -1.038e-07 4.662e-08 -0.006605 -7.826e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003401 -0.006314 0.005144 0.9699 0.9743 0.006934 0.8221 0.8185 0.01566 ] Network output: [ 1 1.524e-05 0.0002765 -1.119e-06 5.024e-07 -0.0002217 -8.434e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2122 -0.0363 -0.1515 0.1804 0.9834 0.9932 0.2386 0.4269 0.8676 0.7066 ] Network output: [ -0.008335 1.003 1.007 -1.056e-07 4.743e-08 0.007031 -7.962e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007041 0.000659 0.004292 0.003019 0.9889 0.9919 0.007181 0.8492 0.8912 0.01112 ] Network output: [ -9.062e-05 0.0009455 1 -3.517e-06 1.579e-06 0.9988 -2.651e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2267 0.1079 0.3523 0.1408 0.9849 0.9939 0.2274 0.4308 0.8744 0.7001 ] Network output: [ 0.002042 -0.01003 0.9948 2.155e-06 -9.674e-07 1.011 1.624e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7295 0.8601 0.3043 ] Network output: [ -0.001952 0.009418 1.005 2.38e-06 -1.069e-06 0.99 1.794e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09534 0.09339 0.1648 0.197 0.9852 0.991 0.09535 0.6531 0.8349 0.2505 ] Network output: [ 7.182e-05 1 -5.815e-05 3.101e-07 -1.392e-07 0.9998 2.337e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001166 Epoch 10396 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0083 0.9969 0.9931 -1.038e-07 4.658e-08 -0.006604 -7.819e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003401 -0.006313 0.005144 0.9699 0.9743 0.006934 0.8221 0.8185 0.01566 ] Network output: [ 1 1.512e-05 0.0002764 -1.118e-06 5.018e-07 -0.0002216 -8.423e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2122 -0.0363 -0.1515 0.1804 0.9834 0.9932 0.2386 0.4269 0.8676 0.7066 ] Network output: [ -0.008335 1.003 1.007 -1.055e-07 4.738e-08 0.007031 -7.954e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007041 0.000659 0.004292 0.003019 0.9889 0.9919 0.007181 0.8492 0.8912 0.01112 ] Network output: [ -9.05e-05 0.0009448 1 -3.513e-06 1.577e-06 0.9988 -2.648e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2267 0.1079 0.3523 0.1408 0.9849 0.9939 0.2274 0.4308 0.8744 0.7001 ] Network output: [ 0.00204 -0.01003 0.9948 2.152e-06 -9.662e-07 1.011 1.622e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7295 0.8601 0.3043 ] Network output: [ -0.001951 0.009413 1.005 2.377e-06 -1.067e-06 0.99 1.792e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09534 0.09339 0.1648 0.197 0.9852 0.991 0.09535 0.6531 0.8349 0.2505 ] Network output: [ 7.181e-05 1 -5.817e-05 3.097e-07 -1.39e-07 0.9998 2.334e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001166 Epoch 10397 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008299 0.9969 0.9931 -1.037e-07 4.654e-08 -0.006603 -7.813e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003401 -0.006313 0.005143 0.9699 0.9743 0.006934 0.8221 0.8185 0.01566 ] Network output: [ 1 1.501e-05 0.0002762 -1.116e-06 5.011e-07 -0.0002215 -8.412e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2122 -0.03631 -0.1515 0.1804 0.9834 0.9932 0.2386 0.4269 0.8676 0.7066 ] Network output: [ -0.008334 1.003 1.007 -1.054e-07 4.734e-08 0.00703 -7.946e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007042 0.000659 0.004292 0.003018 0.9889 0.9919 0.007182 0.8492 0.8912 0.01112 ] Network output: [ -9.037e-05 0.0009441 1 -3.509e-06 1.575e-06 0.9988 -2.644e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2267 0.1079 0.3523 0.1408 0.9849 0.9939 0.2275 0.4308 0.8744 0.7001 ] Network output: [ 0.002039 -0.01002 0.9948 2.15e-06 -9.65e-07 1.011 1.62e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7295 0.8601 0.3043 ] Network output: [ -0.00195 0.009408 1.005 2.374e-06 -1.066e-06 0.99 1.789e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09534 0.09339 0.1648 0.197 0.9852 0.991 0.09535 0.6531 0.8349 0.2505 ] Network output: [ 7.18e-05 1 -5.82e-05 3.093e-07 -1.389e-07 0.9998 2.331e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001165 Epoch 10398 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008299 0.9969 0.9931 -1.036e-07 4.65e-08 -0.006602 -7.806e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003401 -0.006312 0.005143 0.9699 0.9743 0.006934 0.8221 0.8185 0.01566 ] Network output: [ 1 1.489e-05 0.0002761 -1.115e-06 5.005e-07 -0.0002214 -8.402e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2122 -0.03631 -0.1515 0.1804 0.9834 0.9932 0.2386 0.4269 0.8676 0.7066 ] Network output: [ -0.008333 1.003 1.007 -1.053e-07 4.729e-08 0.00703 -7.939e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007042 0.0006591 0.004292 0.003018 0.9889 0.9919 0.007182 0.8492 0.8912 0.01112 ] Network output: [ -9.025e-05 0.0009435 1 -3.504e-06 1.573e-06 0.9988 -2.641e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2267 0.1079 0.3523 0.1408 0.9849 0.9939 0.2275 0.4308 0.8744 0.7001 ] Network output: [ 0.002038 -0.01002 0.9948 2.147e-06 -9.638e-07 1.011 1.618e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7295 0.8601 0.3043 ] Network output: [ -0.001949 0.009402 1.005 2.371e-06 -1.065e-06 0.99 1.787e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09534 0.09339 0.1648 0.197 0.9852 0.991 0.09535 0.653 0.8349 0.2505 ] Network output: [ 7.179e-05 1 -5.823e-05 3.089e-07 -1.387e-07 0.9998 2.328e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001164 Epoch 10399 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008298 0.9969 0.9931 -1.035e-07 4.646e-08 -0.006602 -7.799e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003401 -0.006312 0.005143 0.9699 0.9743 0.006934 0.8221 0.8185 0.01566 ] Network output: [ 1 1.478e-05 0.000276 -1.113e-06 4.998e-07 -0.0002213 -8.391e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2122 -0.03631 -0.1514 0.1804 0.9834 0.9932 0.2386 0.4269 0.8676 0.7065 ] Network output: [ -0.008332 1.003 1.007 -1.052e-07 4.725e-08 0.007029 -7.931e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007042 0.0006591 0.004291 0.003018 0.9889 0.9919 0.007182 0.8492 0.8912 0.01112 ] Network output: [ -9.013e-05 0.0009428 1 -3.5e-06 1.571e-06 0.9988 -2.637e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2267 0.1079 0.3523 0.1408 0.9849 0.9939 0.2275 0.4308 0.8744 0.7001 ] Network output: [ 0.002036 -0.01001 0.9948 2.144e-06 -9.626e-07 1.011 1.616e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7295 0.8601 0.3043 ] Network output: [ -0.001947 0.009397 1.005 2.368e-06 -1.063e-06 0.99 1.785e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09534 0.09339 0.1648 0.197 0.9852 0.991 0.09535 0.653 0.8349 0.2505 ] Network output: [ 7.177e-05 1 -5.826e-05 3.085e-07 -1.385e-07 0.9998 2.325e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001163 Epoch 10400 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008297 0.9969 0.9931 -1.034e-07 4.642e-08 -0.006601 -7.792e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003535 -0.003402 -0.006311 0.005142 0.9699 0.9743 0.006935 0.8221 0.8185 0.01566 ] Network output: [ 1 1.466e-05 0.0002759 -1.112e-06 4.992e-07 -0.0002212 -8.38e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2122 -0.03631 -0.1514 0.1804 0.9834 0.9932 0.2386 0.4269 0.8676 0.7065 ] Network output: [ -0.008332 1.003 1.007 -1.051e-07 4.72e-08 0.007029 -7.924e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007042 0.0006591 0.004291 0.003018 0.9889 0.9919 0.007182 0.8492 0.8912 0.01112 ] Network output: [ -9e-05 0.0009421 1 -3.495e-06 1.569e-06 0.9988 -2.634e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2267 0.1079 0.3523 0.1408 0.9849 0.9939 0.2275 0.4308 0.8744 0.7001 ] Network output: [ 0.002035 -0.01 0.9948 2.141e-06 -9.614e-07 1.011 1.614e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7295 0.8601 0.3043 ] Network output: [ -0.001946 0.009391 1.005 2.365e-06 -1.062e-06 0.99 1.783e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09534 0.09339 0.1648 0.197 0.9852 0.991 0.09536 0.653 0.8349 0.2505 ] Network output: [ 7.176e-05 1 -5.829e-05 3.081e-07 -1.383e-07 0.9998 2.322e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001163 Epoch 10401 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008296 0.9969 0.9931 -1.033e-07 4.638e-08 -0.0066 -7.786e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003402 -0.006311 0.005142 0.9699 0.9743 0.006935 0.8221 0.8185 0.01566 ] Network output: [ 1 1.455e-05 0.0002758 -1.111e-06 4.986e-07 -0.0002211 -8.37e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03631 -0.1514 0.1803 0.9834 0.9932 0.2386 0.4269 0.8676 0.7065 ] Network output: [ -0.008331 1.003 1.007 -1.05e-07 4.716e-08 0.007029 -7.916e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007043 0.0006591 0.004291 0.003018 0.9889 0.9919 0.007183 0.8492 0.8912 0.01112 ] Network output: [ -8.988e-05 0.0009414 1 -3.491e-06 1.567e-06 0.9988 -2.631e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2267 0.1079 0.3523 0.1408 0.9849 0.9939 0.2275 0.4308 0.8744 0.7001 ] Network output: [ 0.002034 -0.009997 0.9948 2.139e-06 -9.602e-07 1.011 1.612e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7294 0.8601 0.3043 ] Network output: [ -0.001945 0.009386 1.005 2.363e-06 -1.061e-06 0.99 1.78e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09534 0.0934 0.1648 0.197 0.9852 0.991 0.09536 0.653 0.8349 0.2505 ] Network output: [ 7.175e-05 1 -5.832e-05 3.077e-07 -1.382e-07 0.9998 2.319e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001162 Epoch 10402 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008295 0.9969 0.9931 -1.032e-07 4.634e-08 -0.0066 -7.779e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003402 -0.00631 0.005142 0.9699 0.9743 0.006935 0.8221 0.8185 0.01566 ] Network output: [ 1 1.443e-05 0.0002756 -1.109e-06 4.98e-07 -0.000221 -8.359e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03631 -0.1514 0.1803 0.9834 0.9932 0.2386 0.4269 0.8676 0.7065 ] Network output: [ -0.00833 1.003 1.007 -1.049e-07 4.711e-08 0.007028 -7.909e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007043 0.0006592 0.004291 0.003017 0.9889 0.9919 0.007183 0.8492 0.8912 0.01112 ] Network output: [ -8.976e-05 0.0009407 1 -3.486e-06 1.565e-06 0.9988 -2.627e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2267 0.1079 0.3523 0.1408 0.9849 0.9939 0.2275 0.4308 0.8744 0.7001 ] Network output: [ 0.002032 -0.009991 0.9948 2.136e-06 -9.589e-07 1.011 1.61e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7294 0.8601 0.3043 ] Network output: [ -0.001944 0.009381 1.005 2.36e-06 -1.059e-06 0.99 1.778e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09534 0.0934 0.1648 0.197 0.9852 0.991 0.09536 0.653 0.8348 0.2505 ] Network output: [ 7.174e-05 1 -5.835e-05 3.074e-07 -1.38e-07 0.9998 2.316e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001161 Epoch 10403 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008295 0.9969 0.9931 -1.031e-07 4.63e-08 -0.006599 -7.772e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003402 -0.00631 0.005141 0.9699 0.9743 0.006935 0.8221 0.8185 0.01565 ] Network output: [ 1 1.432e-05 0.0002755 -1.108e-06 4.973e-07 -0.0002209 -8.349e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03631 -0.1514 0.1803 0.9834 0.9932 0.2386 0.4269 0.8676 0.7065 ] Network output: [ -0.00833 1.003 1.007 -1.048e-07 4.707e-08 0.007028 -7.901e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007043 0.0006592 0.004291 0.003017 0.9889 0.9919 0.007183 0.8492 0.8912 0.01112 ] Network output: [ -8.963e-05 0.0009401 1 -3.482e-06 1.563e-06 0.9988 -2.624e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2267 0.1079 0.3523 0.1408 0.9849 0.9939 0.2275 0.4308 0.8744 0.7001 ] Network output: [ 0.002031 -0.009985 0.9948 2.133e-06 -9.577e-07 1.011 1.608e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7294 0.8601 0.3043 ] Network output: [ -0.001942 0.009375 1.005 2.357e-06 -1.058e-06 0.99 1.776e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09534 0.0934 0.1648 0.197 0.9852 0.991 0.09536 0.653 0.8348 0.2505 ] Network output: [ 7.173e-05 1 -5.838e-05 3.07e-07 -1.378e-07 0.9998 2.313e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001161 Epoch 10404 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008294 0.9969 0.9931 -1.03e-07 4.626e-08 -0.006598 -7.765e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003402 -0.006309 0.005141 0.9699 0.9743 0.006935 0.8221 0.8185 0.01565 ] Network output: [ 1 1.42e-05 0.0002754 -1.106e-06 4.967e-07 -0.0002207 -8.338e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03631 -0.1514 0.1803 0.9834 0.9932 0.2386 0.4269 0.8676 0.7065 ] Network output: [ -0.008329 1.003 1.007 -1.047e-07 4.702e-08 0.007027 -7.893e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007043 0.0006592 0.004291 0.003017 0.9889 0.9919 0.007184 0.8492 0.8912 0.01112 ] Network output: [ -8.951e-05 0.0009394 1 -3.478e-06 1.561e-06 0.9988 -2.621e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2267 0.1079 0.3523 0.1408 0.9849 0.9939 0.2275 0.4308 0.8744 0.7001 ] Network output: [ 0.002029 -0.009979 0.9948 2.131e-06 -9.565e-07 1.011 1.606e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7294 0.8601 0.3043 ] Network output: [ -0.001941 0.00937 1.005 2.354e-06 -1.057e-06 0.99 1.774e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09534 0.0934 0.1648 0.1971 0.9852 0.991 0.09536 0.653 0.8348 0.2505 ] Network output: [ 7.172e-05 1 -5.841e-05 3.066e-07 -1.376e-07 0.9998 2.311e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000116 Epoch 10405 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008293 0.9969 0.9931 -1.029e-07 4.622e-08 -0.006597 -7.758e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003402 -0.006309 0.005141 0.9699 0.9743 0.006935 0.8221 0.8185 0.01565 ] Network output: [ 1 1.408e-05 0.0002753 -1.105e-06 4.961e-07 -0.0002206 -8.327e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03631 -0.1514 0.1803 0.9834 0.9932 0.2386 0.4269 0.8676 0.7065 ] Network output: [ -0.008328 1.003 1.007 -1.046e-07 4.698e-08 0.007027 -7.886e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007044 0.0006592 0.004291 0.003017 0.9889 0.9919 0.007184 0.8492 0.8912 0.01112 ] Network output: [ -8.939e-05 0.0009387 1 -3.473e-06 1.559e-06 0.9988 -2.617e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2267 0.1079 0.3524 0.1408 0.9849 0.9939 0.2275 0.4308 0.8744 0.7 ] Network output: [ 0.002028 -0.009973 0.9948 2.128e-06 -9.553e-07 1.011 1.604e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7294 0.8601 0.3043 ] Network output: [ -0.00194 0.009365 1.005 2.351e-06 -1.055e-06 0.99 1.772e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09535 0.0934 0.1648 0.1971 0.9852 0.991 0.09536 0.653 0.8348 0.2505 ] Network output: [ 7.171e-05 1 -5.844e-05 3.062e-07 -1.375e-07 0.9998 2.308e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001159 Epoch 10406 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008292 0.9969 0.9931 -1.029e-07 4.618e-08 -0.006597 -7.752e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003402 -0.006308 0.00514 0.9699 0.9743 0.006935 0.8221 0.8184 0.01565 ] Network output: [ 1 1.397e-05 0.0002752 -1.104e-06 4.954e-07 -0.0002205 -8.317e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03632 -0.1514 0.1803 0.9834 0.9932 0.2386 0.4269 0.8676 0.7065 ] Network output: [ -0.008327 1.003 1.007 -1.045e-07 4.693e-08 0.007027 -7.878e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007044 0.0006593 0.004291 0.003017 0.9889 0.9919 0.007184 0.8492 0.8912 0.01112 ] Network output: [ -8.927e-05 0.000938 1 -3.469e-06 1.557e-06 0.9988 -2.614e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2267 0.1079 0.3524 0.1408 0.9849 0.9939 0.2275 0.4308 0.8744 0.7 ] Network output: [ 0.002027 -0.009966 0.9948 2.125e-06 -9.541e-07 1.011 1.602e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7294 0.8601 0.3043 ] Network output: [ -0.001939 0.009359 1.005 2.348e-06 -1.054e-06 0.99 1.769e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09535 0.0934 0.1648 0.1971 0.9852 0.991 0.09536 0.653 0.8348 0.2506 ] Network output: [ 7.17e-05 1 -5.847e-05 3.058e-07 -1.373e-07 0.9998 2.305e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001159 Epoch 10407 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008292 0.9969 0.9931 -1.028e-07 4.614e-08 -0.006596 -7.745e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003402 -0.006308 0.00514 0.9699 0.9743 0.006935 0.822 0.8184 0.01565 ] Network output: [ 1 1.385e-05 0.000275 -1.102e-06 4.948e-07 -0.0002204 -8.306e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03632 -0.1514 0.1803 0.9834 0.9932 0.2386 0.4269 0.8676 0.7065 ] Network output: [ -0.008327 1.003 1.007 -1.044e-07 4.689e-08 0.007026 -7.871e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007044 0.0006593 0.00429 0.003016 0.9889 0.9919 0.007184 0.8492 0.8912 0.01111 ] Network output: [ -8.914e-05 0.0009374 1 -3.464e-06 1.555e-06 0.9988 -2.611e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2267 0.1079 0.3524 0.1408 0.9849 0.9939 0.2275 0.4308 0.8744 0.7 ] Network output: [ 0.002025 -0.00996 0.9948 2.123e-06 -9.529e-07 1.011 1.6e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1962 0.9873 0.9919 0.1131 0.7294 0.8601 0.3043 ] Network output: [ -0.001938 0.009354 1.005 2.345e-06 -1.053e-06 0.99 1.767e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09535 0.0934 0.1648 0.1971 0.9852 0.991 0.09536 0.653 0.8348 0.2506 ] Network output: [ 7.169e-05 1 -5.85e-05 3.054e-07 -1.371e-07 0.9998 2.302e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001158 Epoch 10408 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008291 0.9969 0.9931 -1.027e-07 4.61e-08 -0.006595 -7.738e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003402 -0.006307 0.00514 0.9699 0.9743 0.006935 0.822 0.8184 0.01565 ] Network output: [ 1 1.374e-05 0.0002749 -1.101e-06 4.942e-07 -0.0002203 -8.296e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03632 -0.1514 0.1803 0.9834 0.9932 0.2386 0.4269 0.8676 0.7065 ] Network output: [ -0.008326 1.003 1.007 -1.043e-07 4.684e-08 0.007026 -7.863e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007044 0.0006593 0.00429 0.003016 0.9889 0.9919 0.007185 0.8492 0.8912 0.01111 ] Network output: [ -8.902e-05 0.0009367 1 -3.46e-06 1.553e-06 0.9988 -2.608e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2267 0.1079 0.3524 0.1408 0.9849 0.9939 0.2275 0.4308 0.8744 0.7 ] Network output: [ 0.002024 -0.009954 0.9948 2.12e-06 -9.517e-07 1.011 1.598e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7294 0.8601 0.3043 ] Network output: [ -0.001936 0.009349 1.005 2.342e-06 -1.051e-06 0.99 1.765e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09535 0.0934 0.1648 0.1971 0.9852 0.991 0.09536 0.653 0.8348 0.2506 ] Network output: [ 7.167e-05 1 -5.853e-05 3.051e-07 -1.37e-07 0.9998 2.299e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001157 Epoch 10409 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00829 0.9969 0.9931 -1.026e-07 4.606e-08 -0.006595 -7.731e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003402 -0.006307 0.005139 0.9699 0.9743 0.006935 0.822 0.8184 0.01565 ] Network output: [ 1 1.362e-05 0.0002748 -1.099e-06 4.935e-07 -0.0002202 -8.285e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03632 -0.1514 0.1803 0.9834 0.9932 0.2387 0.4269 0.8676 0.7065 ] Network output: [ -0.008325 1.003 1.007 -1.042e-07 4.68e-08 0.007025 -7.856e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007045 0.0006593 0.00429 0.003016 0.9889 0.9919 0.007185 0.8492 0.8912 0.01111 ] Network output: [ -8.89e-05 0.000936 1 -3.456e-06 1.551e-06 0.9988 -2.604e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2267 0.1079 0.3524 0.1408 0.9849 0.9939 0.2275 0.4308 0.8744 0.7 ] Network output: [ 0.002022 -0.009948 0.9948 2.117e-06 -9.505e-07 1.011 1.596e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7294 0.8601 0.3043 ] Network output: [ -0.001935 0.009343 1.005 2.339e-06 -1.05e-06 0.99 1.763e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09535 0.0934 0.1648 0.1971 0.9852 0.991 0.09536 0.653 0.8348 0.2506 ] Network output: [ 7.166e-05 1 -5.856e-05 3.047e-07 -1.368e-07 0.9999 2.296e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001157 Epoch 10410 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008289 0.9969 0.9931 -1.025e-07 4.602e-08 -0.006594 -7.725e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003402 -0.006306 0.005139 0.9699 0.9743 0.006936 0.822 0.8184 0.01565 ] Network output: [ 1 1.351e-05 0.0002747 -1.098e-06 4.929e-07 -0.0002201 -8.275e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03632 -0.1514 0.1803 0.9834 0.9932 0.2387 0.4269 0.8676 0.7065 ] Network output: [ -0.008324 1.003 1.007 -1.041e-07 4.675e-08 0.007025 -7.848e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007045 0.0006593 0.00429 0.003016 0.9889 0.9919 0.007185 0.8492 0.8912 0.01111 ] Network output: [ -8.878e-05 0.0009353 1 -3.451e-06 1.549e-06 0.9988 -2.601e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2267 0.1079 0.3524 0.1408 0.9849 0.9939 0.2275 0.4308 0.8744 0.7 ] Network output: [ 0.002021 -0.009942 0.9948 2.115e-06 -9.493e-07 1.011 1.594e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7294 0.8601 0.3043 ] Network output: [ -0.001934 0.009338 1.005 2.336e-06 -1.049e-06 0.99 1.761e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09535 0.0934 0.1648 0.1971 0.9852 0.991 0.09536 0.653 0.8348 0.2506 ] Network output: [ 7.165e-05 1 -5.859e-05 3.043e-07 -1.366e-07 0.9999 2.293e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001156 Epoch 10411 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008289 0.9969 0.9931 -1.024e-07 4.598e-08 -0.006593 -7.718e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003402 -0.006306 0.005139 0.9699 0.9743 0.006936 0.822 0.8184 0.01565 ] Network output: [ 1 1.339e-05 0.0002746 -1.097e-06 4.923e-07 -0.00022 -8.264e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03632 -0.1514 0.1803 0.9834 0.9932 0.2387 0.4269 0.8676 0.7065 ] Network output: [ -0.008324 1.003 1.007 -1.04e-07 4.671e-08 0.007025 -7.841e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007045 0.0006594 0.00429 0.003016 0.9889 0.9919 0.007185 0.8492 0.8912 0.01111 ] Network output: [ -8.865e-05 0.0009346 1 -3.447e-06 1.547e-06 0.9988 -2.598e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2267 0.1079 0.3524 0.1408 0.9849 0.9939 0.2275 0.4308 0.8744 0.7 ] Network output: [ 0.00202 -0.009936 0.9948 2.112e-06 -9.481e-07 1.011 1.592e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7294 0.8601 0.3043 ] Network output: [ -0.001933 0.009333 1.005 2.333e-06 -1.047e-06 0.99 1.758e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09535 0.0934 0.1648 0.1971 0.9852 0.991 0.09537 0.653 0.8348 0.2506 ] Network output: [ 7.164e-05 1 -5.862e-05 3.039e-07 -1.364e-07 0.9999 2.29e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001155 Epoch 10412 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008288 0.9969 0.9931 -1.023e-07 4.594e-08 -0.006592 -7.711e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003402 -0.006305 0.005138 0.9699 0.9743 0.006936 0.822 0.8184 0.01565 ] Network output: [ 1 1.328e-05 0.0002745 -1.095e-06 4.917e-07 -0.0002199 -8.254e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03632 -0.1514 0.1803 0.9834 0.9932 0.2387 0.4269 0.8676 0.7065 ] Network output: [ -0.008323 1.003 1.007 -1.039e-07 4.666e-08 0.007024 -7.833e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007045 0.0006594 0.00429 0.003015 0.9889 0.9919 0.007186 0.8492 0.8912 0.01111 ] Network output: [ -8.853e-05 0.000934 1 -3.442e-06 1.545e-06 0.9988 -2.594e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2267 0.1079 0.3524 0.1407 0.9849 0.9939 0.2275 0.4308 0.8744 0.7 ] Network output: [ 0.002018 -0.009929 0.9948 2.109e-06 -9.469e-07 1.011 1.59e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7294 0.8601 0.3043 ] Network output: [ -0.001931 0.009327 1.005 2.33e-06 -1.046e-06 0.99 1.756e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09535 0.09341 0.1648 0.1971 0.9852 0.991 0.09537 0.6529 0.8348 0.2506 ] Network output: [ 7.163e-05 1 -5.865e-05 3.035e-07 -1.363e-07 0.9999 2.288e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001155 Epoch 10413 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008287 0.9969 0.9931 -1.022e-07 4.59e-08 -0.006592 -7.705e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003402 -0.006305 0.005138 0.9699 0.9743 0.006936 0.822 0.8184 0.01565 ] Network output: [ 1 1.316e-05 0.0002743 -1.094e-06 4.911e-07 -0.0002198 -8.243e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03632 -0.1514 0.1803 0.9834 0.9932 0.2387 0.4269 0.8676 0.7065 ] Network output: [ -0.008322 1.003 1.007 -1.038e-07 4.662e-08 0.007024 -7.826e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007046 0.0006594 0.00429 0.003015 0.9889 0.9919 0.007186 0.8491 0.8912 0.01111 ] Network output: [ -8.841e-05 0.0009333 1 -3.438e-06 1.543e-06 0.9988 -2.591e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2267 0.1079 0.3524 0.1407 0.9849 0.9939 0.2275 0.4308 0.8744 0.7 ] Network output: [ 0.002017 -0.009923 0.9948 2.107e-06 -9.457e-07 1.011 1.588e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7294 0.8601 0.3043 ] Network output: [ -0.00193 0.009322 1.005 2.327e-06 -1.045e-06 0.99 1.754e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09535 0.09341 0.1648 0.1971 0.9852 0.991 0.09537 0.6529 0.8348 0.2506 ] Network output: [ 7.162e-05 1 -5.868e-05 3.032e-07 -1.361e-07 0.9999 2.285e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001154 Epoch 10414 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008286 0.9969 0.9931 -1.021e-07 4.586e-08 -0.006591 -7.698e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003402 -0.006304 0.005138 0.9699 0.9743 0.006936 0.822 0.8184 0.01565 ] Network output: [ 1 1.305e-05 0.0002742 -1.092e-06 4.904e-07 -0.0002197 -8.233e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03632 -0.1513 0.1803 0.9834 0.9932 0.2387 0.4269 0.8676 0.7065 ] Network output: [ -0.008321 1.003 1.007 -1.037e-07 4.657e-08 0.007024 -7.818e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007046 0.0006594 0.00429 0.003015 0.9889 0.9919 0.007186 0.8491 0.8912 0.01111 ] Network output: [ -8.829e-05 0.0009326 1 -3.434e-06 1.542e-06 0.9988 -2.588e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2268 0.1079 0.3524 0.1407 0.9849 0.9939 0.2275 0.4308 0.8744 0.7 ] Network output: [ 0.002015 -0.009917 0.9948 2.104e-06 -9.445e-07 1.011 1.586e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7294 0.8601 0.3043 ] Network output: [ -0.001929 0.009317 1.005 2.325e-06 -1.044e-06 0.99 1.752e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09535 0.09341 0.1648 0.1971 0.9851 0.991 0.09537 0.6529 0.8348 0.2506 ] Network output: [ 7.161e-05 1 -5.871e-05 3.028e-07 -1.359e-07 0.9999 2.282e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001153 Epoch 10415 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008286 0.9969 0.9931 -1.021e-07 4.582e-08 -0.00659 -7.691e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003402 -0.006304 0.005137 0.9699 0.9743 0.006936 0.822 0.8184 0.01564 ] Network output: [ 1 1.294e-05 0.0002741 -1.091e-06 4.898e-07 -0.0002196 -8.222e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03633 -0.1513 0.1803 0.9834 0.9932 0.2387 0.4269 0.8676 0.7065 ] Network output: [ -0.008321 1.003 1.007 -1.036e-07 4.653e-08 0.007023 -7.811e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007046 0.0006595 0.004289 0.003015 0.9889 0.9919 0.007186 0.8491 0.8912 0.01111 ] Network output: [ -8.816e-05 0.0009319 1 -3.429e-06 1.54e-06 0.9988 -2.584e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2268 0.1079 0.3524 0.1407 0.9849 0.9939 0.2275 0.4308 0.8744 0.7 ] Network output: [ 0.002014 -0.009911 0.9948 2.101e-06 -9.433e-07 1.011 1.584e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7294 0.8601 0.3043 ] Network output: [ -0.001928 0.009312 1.005 2.322e-06 -1.042e-06 0.99 1.75e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09535 0.09341 0.1648 0.1971 0.9851 0.991 0.09537 0.6529 0.8348 0.2506 ] Network output: [ 7.16e-05 1 -5.874e-05 3.024e-07 -1.358e-07 0.9999 2.279e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001152 Epoch 10416 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008285 0.9969 0.9931 -1.02e-07 4.578e-08 -0.00659 -7.684e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003402 -0.006303 0.005137 0.9699 0.9743 0.006936 0.822 0.8184 0.01564 ] Network output: [ 1 1.282e-05 0.000274 -1.09e-06 4.892e-07 -0.0002195 -8.212e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03633 -0.1513 0.1803 0.9834 0.9932 0.2387 0.4268 0.8676 0.7065 ] Network output: [ -0.00832 1.003 1.007 -1.035e-07 4.648e-08 0.007023 -7.803e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007046 0.0006595 0.004289 0.003015 0.9889 0.9919 0.007187 0.8491 0.8912 0.01111 ] Network output: [ -8.804e-05 0.0009313 1 -3.425e-06 1.538e-06 0.9988 -2.581e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2268 0.1079 0.3524 0.1407 0.9849 0.9939 0.2276 0.4308 0.8744 0.7 ] Network output: [ 0.002013 -0.009905 0.9948 2.099e-06 -9.421e-07 1.011 1.582e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7294 0.8601 0.3043 ] Network output: [ -0.001926 0.009306 1.005 2.319e-06 -1.041e-06 0.99 1.747e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09536 0.09341 0.1648 0.1971 0.9851 0.991 0.09537 0.6529 0.8348 0.2506 ] Network output: [ 7.159e-05 1 -5.877e-05 3.02e-07 -1.356e-07 0.9999 2.276e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001152 Epoch 10417 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008284 0.9969 0.9931 -1.019e-07 4.574e-08 -0.006589 -7.678e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003402 -0.006303 0.005137 0.9699 0.9743 0.006936 0.822 0.8184 0.01564 ] Network output: [ 1 1.271e-05 0.0002739 -1.088e-06 4.886e-07 -0.0002194 -8.202e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03633 -0.1513 0.1803 0.9834 0.9932 0.2387 0.4268 0.8676 0.7065 ] Network output: [ -0.008319 1.003 1.007 -1.034e-07 4.644e-08 0.007022 -7.796e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007047 0.0006595 0.004289 0.003014 0.9889 0.9919 0.007187 0.8491 0.8912 0.01111 ] Network output: [ -8.792e-05 0.0009306 1 -3.421e-06 1.536e-06 0.9988 -2.578e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2268 0.1079 0.3524 0.1407 0.9849 0.9939 0.2276 0.4308 0.8744 0.7 ] Network output: [ 0.002011 -0.009899 0.9948 2.096e-06 -9.41e-07 1.011 1.58e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7293 0.8601 0.3043 ] Network output: [ -0.001925 0.009301 1.005 2.316e-06 -1.04e-06 0.9901 1.745e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09536 0.09341 0.1648 0.1971 0.9851 0.991 0.09537 0.6529 0.8348 0.2506 ] Network output: [ 7.157e-05 1 -5.88e-05 3.016e-07 -1.354e-07 0.9999 2.273e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001151 Epoch 10418 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008283 0.9969 0.9931 -1.018e-07 4.57e-08 -0.006588 -7.671e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003402 -0.006302 0.005136 0.9699 0.9743 0.006936 0.822 0.8184 0.01564 ] Network output: [ 1 1.259e-05 0.0002737 -1.087e-06 4.879e-07 -0.0002193 -8.191e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03633 -0.1513 0.1803 0.9834 0.9932 0.2387 0.4268 0.8676 0.7065 ] Network output: [ -0.008318 1.003 1.007 -1.033e-07 4.639e-08 0.007022 -7.788e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007047 0.0006595 0.004289 0.003014 0.9889 0.9919 0.007187 0.8491 0.8912 0.01111 ] Network output: [ -8.78e-05 0.0009299 1 -3.416e-06 1.534e-06 0.9988 -2.575e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2268 0.1079 0.3524 0.1407 0.9849 0.9939 0.2276 0.4308 0.8744 0.7 ] Network output: [ 0.00201 -0.009893 0.9948 2.093e-06 -9.398e-07 1.011 1.578e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7293 0.8601 0.3043 ] Network output: [ -0.001924 0.009296 1.005 2.313e-06 -1.038e-06 0.9901 1.743e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09536 0.09341 0.1648 0.1971 0.9851 0.991 0.09537 0.6529 0.8348 0.2506 ] Network output: [ 7.156e-05 1 -5.883e-05 3.013e-07 -1.352e-07 0.9999 2.27e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000115 Epoch 10419 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008283 0.9969 0.9931 -1.017e-07 4.566e-08 -0.006587 -7.664e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003403 -0.006302 0.005136 0.9699 0.9743 0.006936 0.822 0.8184 0.01564 ] Network output: [ 1 1.248e-05 0.0002736 -1.086e-06 4.873e-07 -0.0002192 -8.181e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03633 -0.1513 0.1803 0.9834 0.9932 0.2387 0.4268 0.8676 0.7065 ] Network output: [ -0.008318 1.003 1.007 -1.032e-07 4.635e-08 0.007022 -7.781e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007047 0.0006596 0.004289 0.003014 0.9889 0.9919 0.007187 0.8491 0.8912 0.01111 ] Network output: [ -8.767e-05 0.0009292 1 -3.412e-06 1.532e-06 0.9988 -2.571e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2268 0.1079 0.3524 0.1407 0.9849 0.9939 0.2276 0.4308 0.8744 0.7 ] Network output: [ 0.002009 -0.009886 0.9948 2.091e-06 -9.386e-07 1.011 1.576e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7293 0.8601 0.3043 ] Network output: [ -0.001923 0.00929 1.005 2.31e-06 -1.037e-06 0.9901 1.741e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09536 0.09341 0.1648 0.1971 0.9851 0.991 0.09537 0.6529 0.8348 0.2506 ] Network output: [ 7.155e-05 1 -5.886e-05 3.009e-07 -1.351e-07 0.9999 2.268e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000115 Epoch 10420 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008282 0.9969 0.9931 -1.016e-07 4.562e-08 -0.006587 -7.658e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003403 -0.006301 0.005136 0.9699 0.9743 0.006936 0.822 0.8184 0.01564 ] Network output: [ 1 1.236e-05 0.0002735 -1.084e-06 4.867e-07 -0.0002191 -8.17e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03633 -0.1513 0.1803 0.9834 0.9932 0.2387 0.4268 0.8676 0.7065 ] Network output: [ -0.008317 1.003 1.007 -1.031e-07 4.63e-08 0.007021 -7.773e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007047 0.0006596 0.004289 0.003014 0.9889 0.9919 0.007188 0.8491 0.8912 0.01111 ] Network output: [ -8.755e-05 0.0009285 1 -3.408e-06 1.53e-06 0.9988 -2.568e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2268 0.1079 0.3524 0.1407 0.9849 0.9939 0.2276 0.4308 0.8744 0.7 ] Network output: [ 0.002007 -0.00988 0.9948 2.088e-06 -9.374e-07 1.011 1.574e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7293 0.8601 0.3043 ] Network output: [ -0.001922 0.009285 1.005 2.307e-06 -1.036e-06 0.9901 1.739e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09536 0.09341 0.1648 0.1971 0.9851 0.991 0.09537 0.6529 0.8348 0.2506 ] Network output: [ 7.154e-05 1 -5.889e-05 3.005e-07 -1.349e-07 0.9999 2.265e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001149 Epoch 10421 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008281 0.9969 0.9931 -1.015e-07 4.558e-08 -0.006586 -7.651e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003403 -0.006301 0.005135 0.9699 0.9743 0.006937 0.822 0.8184 0.01564 ] Network output: [ 1 1.225e-05 0.0002734 -1.083e-06 4.861e-07 -0.000219 -8.16e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03633 -0.1513 0.1803 0.9834 0.9932 0.2387 0.4268 0.8676 0.7065 ] Network output: [ -0.008316 1.003 1.007 -1.03e-07 4.626e-08 0.007021 -7.766e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007048 0.0006596 0.004289 0.003013 0.9889 0.9919 0.007188 0.8491 0.8912 0.01111 ] Network output: [ -8.743e-05 0.0009279 1 -3.403e-06 1.528e-06 0.9988 -2.565e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2268 0.1079 0.3524 0.1407 0.9849 0.9939 0.2276 0.4308 0.8744 0.7 ] Network output: [ 0.002006 -0.009874 0.9948 2.085e-06 -9.362e-07 1.011 1.572e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7293 0.8601 0.3043 ] Network output: [ -0.00192 0.00928 1.005 2.304e-06 -1.034e-06 0.9901 1.737e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09536 0.09341 0.1648 0.1971 0.9851 0.991 0.09538 0.6529 0.8348 0.2506 ] Network output: [ 7.153e-05 1 -5.892e-05 3.001e-07 -1.347e-07 0.9999 2.262e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001148 Epoch 10422 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00828 0.9969 0.9931 -1.014e-07 4.554e-08 -0.006585 -7.644e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003403 -0.0063 0.005135 0.9699 0.9743 0.006937 0.822 0.8184 0.01564 ] Network output: [ 1 1.213e-05 0.0002733 -1.081e-06 4.855e-07 -0.0002189 -8.15e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03633 -0.1513 0.1803 0.9834 0.9932 0.2387 0.4268 0.8676 0.7065 ] Network output: [ -0.008315 1.003 1.007 -1.029e-07 4.622e-08 0.00702 -7.758e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007048 0.0006596 0.004289 0.003013 0.9889 0.9919 0.007188 0.8491 0.8912 0.0111 ] Network output: [ -8.731e-05 0.0009272 1 -3.399e-06 1.526e-06 0.9988 -2.562e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2268 0.1079 0.3524 0.1407 0.9849 0.9939 0.2276 0.4308 0.8744 0.7 ] Network output: [ 0.002004 -0.009868 0.9948 2.083e-06 -9.35e-07 1.011 1.57e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7293 0.8601 0.3043 ] Network output: [ -0.001919 0.009274 1.005 2.301e-06 -1.033e-06 0.9901 1.734e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09536 0.09341 0.1648 0.1971 0.9851 0.991 0.09538 0.6529 0.8348 0.2506 ] Network output: [ 7.152e-05 1 -5.895e-05 2.998e-07 -1.346e-07 0.9999 2.259e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001148 Epoch 10423 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008279 0.9969 0.9931 -1.013e-07 4.55e-08 -0.006585 -7.638e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003403 -0.0063 0.005135 0.9699 0.9743 0.006937 0.822 0.8184 0.01564 ] Network output: [ 1 1.202e-05 0.0002732 -1.08e-06 4.849e-07 -0.0002188 -8.139e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2123 -0.03633 -0.1513 0.1803 0.9834 0.9932 0.2387 0.4268 0.8676 0.7065 ] Network output: [ -0.008315 1.003 1.007 -1.028e-07 4.617e-08 0.00702 -7.751e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007048 0.0006596 0.004289 0.003013 0.9889 0.9919 0.007188 0.8491 0.8912 0.0111 ] Network output: [ -8.719e-05 0.0009265 1 -3.395e-06 1.524e-06 0.9988 -2.558e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2268 0.1079 0.3524 0.1407 0.9849 0.9939 0.2276 0.4308 0.8744 0.7 ] Network output: [ 0.002003 -0.009862 0.9948 2.08e-06 -9.339e-07 1.011 1.568e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7293 0.8601 0.3043 ] Network output: [ -0.001918 0.009269 1.005 2.299e-06 -1.032e-06 0.9901 1.732e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09536 0.09342 0.1647 0.1971 0.9851 0.991 0.09538 0.6529 0.8348 0.2506 ] Network output: [ 7.151e-05 1 -5.898e-05 2.994e-07 -1.344e-07 0.9999 2.256e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001147 Epoch 10424 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008279 0.9969 0.9931 -1.013e-07 4.546e-08 -0.006584 -7.631e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003403 -0.0063 0.005134 0.9699 0.9743 0.006937 0.822 0.8184 0.01564 ] Network output: [ 1 1.191e-05 0.000273 -1.079e-06 4.842e-07 -0.0002187 -8.129e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03634 -0.1513 0.1803 0.9834 0.9932 0.2387 0.4268 0.8676 0.7065 ] Network output: [ -0.008314 1.003 1.007 -1.027e-07 4.613e-08 0.00702 -7.743e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007048 0.0006597 0.004288 0.003013 0.9889 0.9919 0.007189 0.8491 0.8912 0.0111 ] Network output: [ -8.706e-05 0.0009258 1 -3.39e-06 1.522e-06 0.9988 -2.555e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2268 0.1079 0.3524 0.1407 0.9849 0.9939 0.2276 0.4308 0.8744 0.7 ] Network output: [ 0.002002 -0.009856 0.9948 2.078e-06 -9.327e-07 1.011 1.566e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.113 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7293 0.8601 0.3043 ] Network output: [ -0.001917 0.009264 1.005 2.296e-06 -1.031e-06 0.9901 1.73e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09536 0.09342 0.1647 0.1971 0.9851 0.991 0.09538 0.6529 0.8348 0.2506 ] Network output: [ 7.15e-05 1 -5.901e-05 2.99e-07 -1.342e-07 0.9999 2.253e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001146 Epoch 10425 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008278 0.9969 0.9931 -1.012e-07 4.542e-08 -0.006583 -7.624e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003403 -0.006299 0.005134 0.9699 0.9743 0.006937 0.822 0.8184 0.01564 ] Network output: [ 1 1.179e-05 0.0002729 -1.077e-06 4.836e-07 -0.0002186 -8.119e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03634 -0.1513 0.1803 0.9834 0.9932 0.2387 0.4268 0.8676 0.7065 ] Network output: [ -0.008313 1.003 1.007 -1.026e-07 4.608e-08 0.007019 -7.736e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007049 0.0006597 0.004288 0.003013 0.9889 0.9919 0.007189 0.8491 0.8912 0.0111 ] Network output: [ -8.694e-05 0.0009252 1 -3.386e-06 1.52e-06 0.9988 -2.552e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2268 0.1079 0.3524 0.1407 0.9849 0.9939 0.2276 0.4307 0.8744 0.7 ] Network output: [ 0.002 -0.009849 0.9948 2.075e-06 -9.315e-07 1.011 1.564e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7293 0.8601 0.3043 ] Network output: [ -0.001915 0.009258 1.005 2.293e-06 -1.029e-06 0.9901 1.728e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09536 0.09342 0.1647 0.1971 0.9851 0.991 0.09538 0.6529 0.8348 0.2506 ] Network output: [ 7.149e-05 1 -5.904e-05 2.986e-07 -1.341e-07 0.9999 2.251e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001146 Epoch 10426 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008277 0.9969 0.9931 -1.011e-07 4.538e-08 -0.006582 -7.617e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003403 -0.006299 0.005134 0.9699 0.9743 0.006937 0.822 0.8184 0.01564 ] Network output: [ 1 1.168e-05 0.0002728 -1.076e-06 4.83e-07 -0.0002185 -8.108e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03634 -0.1513 0.1803 0.9834 0.9932 0.2387 0.4268 0.8676 0.7065 ] Network output: [ -0.008313 1.003 1.007 -1.026e-07 4.604e-08 0.007019 -7.729e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007049 0.0006597 0.004288 0.003012 0.9889 0.9919 0.007189 0.8491 0.8912 0.0111 ] Network output: [ -8.682e-05 0.0009245 1 -3.382e-06 1.518e-06 0.9988 -2.549e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2268 0.1079 0.3524 0.1407 0.9849 0.9939 0.2276 0.4307 0.8744 0.7 ] Network output: [ 0.001999 -0.009843 0.9948 2.072e-06 -9.303e-07 1.011 1.562e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7293 0.8601 0.3043 ] Network output: [ -0.001914 0.009253 1.005 2.29e-06 -1.028e-06 0.9901 1.726e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09536 0.09342 0.1647 0.1971 0.9851 0.991 0.09538 0.6528 0.8348 0.2506 ] Network output: [ 7.148e-05 1 -5.907e-05 2.983e-07 -1.339e-07 0.9999 2.248e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001145 Epoch 10427 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008276 0.9969 0.9931 -1.01e-07 4.534e-08 -0.006582 -7.611e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003403 -0.006298 0.005133 0.9699 0.9743 0.006937 0.822 0.8184 0.01564 ] Network output: [ 1 1.156e-05 0.0002727 -1.075e-06 4.824e-07 -0.0002184 -8.098e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03634 -0.1513 0.1803 0.9834 0.9932 0.2387 0.4268 0.8676 0.7065 ] Network output: [ -0.008312 1.003 1.007 -1.025e-07 4.599e-08 0.007018 -7.721e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007049 0.0006597 0.004288 0.003012 0.9889 0.9919 0.007189 0.8491 0.8912 0.0111 ] Network output: [ -8.67e-05 0.0009238 1 -3.377e-06 1.516e-06 0.9988 -2.545e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2268 0.1079 0.3525 0.1407 0.9849 0.9939 0.2276 0.4307 0.8744 0.7 ] Network output: [ 0.001997 -0.009837 0.9948 2.07e-06 -9.291e-07 1.011 1.56e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7293 0.8601 0.3043 ] Network output: [ -0.001913 0.009248 1.005 2.287e-06 -1.027e-06 0.9901 1.724e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09537 0.09342 0.1647 0.1971 0.9851 0.991 0.09538 0.6528 0.8348 0.2506 ] Network output: [ 7.147e-05 1 -5.91e-05 2.979e-07 -1.337e-07 0.9999 2.245e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001144 Epoch 10428 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008276 0.9969 0.9931 -1.009e-07 4.53e-08 -0.006581 -7.604e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003403 -0.006298 0.005133 0.9699 0.9743 0.006937 0.822 0.8184 0.01563 ] Network output: [ 1 1.145e-05 0.0002726 -1.073e-06 4.818e-07 -0.0002183 -8.088e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03634 -0.1513 0.1803 0.9834 0.9932 0.2388 0.4268 0.8676 0.7065 ] Network output: [ -0.008311 1.003 1.007 -1.024e-07 4.595e-08 0.007018 -7.714e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007049 0.0006598 0.004288 0.003012 0.9889 0.9919 0.00719 0.8491 0.8912 0.0111 ] Network output: [ -8.658e-05 0.0009231 1 -3.373e-06 1.514e-06 0.9988 -2.542e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2268 0.1079 0.3525 0.1407 0.9849 0.9939 0.2276 0.4307 0.8744 0.7 ] Network output: [ 0.001996 -0.009831 0.9948 2.067e-06 -9.28e-07 1.011 1.558e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7293 0.8601 0.3043 ] Network output: [ -0.001912 0.009242 1.005 2.284e-06 -1.025e-06 0.9901 1.721e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09537 0.09342 0.1647 0.1971 0.9851 0.991 0.09538 0.6528 0.8348 0.2506 ] Network output: [ 7.145e-05 1 -5.913e-05 2.975e-07 -1.336e-07 0.9999 2.242e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001144 Epoch 10429 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008275 0.9969 0.9931 -1.008e-07 4.526e-08 -0.00658 -7.598e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003403 -0.006297 0.005133 0.9699 0.9743 0.006937 0.822 0.8184 0.01563 ] Network output: [ 1 1.134e-05 0.0002724 -1.072e-06 4.812e-07 -0.0002182 -8.078e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03634 -0.1512 0.1803 0.9834 0.9932 0.2388 0.4268 0.8676 0.7065 ] Network output: [ -0.00831 1.003 1.007 -1.023e-07 4.591e-08 0.007018 -7.706e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007049 0.0006598 0.004288 0.003012 0.9889 0.9919 0.00719 0.8491 0.8912 0.0111 ] Network output: [ -8.645e-05 0.0009225 1 -3.369e-06 1.512e-06 0.9988 -2.539e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2268 0.1079 0.3525 0.1407 0.9849 0.9939 0.2276 0.4307 0.8744 0.7 ] Network output: [ 0.001995 -0.009825 0.9948 2.064e-06 -9.268e-07 1.011 1.556e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7293 0.8601 0.3043 ] Network output: [ -0.00191 0.009237 1.005 2.281e-06 -1.024e-06 0.9901 1.719e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09537 0.09342 0.1647 0.1971 0.9851 0.991 0.09538 0.6528 0.8348 0.2506 ] Network output: [ 7.144e-05 1 -5.916e-05 2.971e-07 -1.334e-07 0.9999 2.239e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001143 Epoch 10430 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008274 0.9969 0.9931 -1.007e-07 4.522e-08 -0.00658 -7.591e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003403 -0.006297 0.005132 0.9699 0.9743 0.006937 0.822 0.8184 0.01563 ] Network output: [ 1 1.122e-05 0.0002723 -1.07e-06 4.806e-07 -0.0002181 -8.067e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03634 -0.1512 0.1803 0.9834 0.9932 0.2388 0.4268 0.8676 0.7065 ] Network output: [ -0.00831 1.003 1.007 -1.022e-07 4.586e-08 0.007017 -7.699e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00705 0.0006598 0.004288 0.003012 0.9889 0.9919 0.00719 0.8491 0.8912 0.0111 ] Network output: [ -8.633e-05 0.0009218 1 -3.365e-06 1.51e-06 0.9988 -2.536e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2268 0.108 0.3525 0.1407 0.9849 0.9939 0.2276 0.4307 0.8744 0.7 ] Network output: [ 0.001993 -0.009819 0.9948 2.062e-06 -9.256e-07 1.011 1.554e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7293 0.8601 0.3043 ] Network output: [ -0.001909 0.009232 1.005 2.279e-06 -1.023e-06 0.9901 1.717e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09537 0.09342 0.1647 0.1971 0.9851 0.991 0.09538 0.6528 0.8348 0.2506 ] Network output: [ 7.143e-05 1 -5.919e-05 2.968e-07 -1.332e-07 0.9999 2.236e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001142 Epoch 10431 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008273 0.9969 0.9931 -1.006e-07 4.518e-08 -0.006579 -7.584e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003403 -0.006296 0.005132 0.9699 0.9743 0.006938 0.822 0.8184 0.01563 ] Network output: [ 1 1.111e-05 0.0002722 -1.069e-06 4.8e-07 -0.000218 -8.057e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03634 -0.1512 0.1803 0.9834 0.9932 0.2388 0.4268 0.8676 0.7065 ] Network output: [ -0.008309 1.003 1.007 -1.021e-07 4.582e-08 0.007017 -7.691e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00705 0.0006598 0.004288 0.003011 0.9889 0.9919 0.00719 0.8491 0.8912 0.0111 ] Network output: [ -8.621e-05 0.0009211 1 -3.36e-06 1.509e-06 0.9988 -2.532e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2268 0.108 0.3525 0.1407 0.9849 0.9939 0.2276 0.4307 0.8744 0.7 ] Network output: [ 0.001992 -0.009813 0.9949 2.059e-06 -9.245e-07 1.011 1.552e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7293 0.8601 0.3043 ] Network output: [ -0.001908 0.009226 1.005 2.276e-06 -1.022e-06 0.9901 1.715e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09537 0.09342 0.1647 0.1971 0.9851 0.991 0.09538 0.6528 0.8348 0.2506 ] Network output: [ 7.142e-05 1 -5.923e-05 2.964e-07 -1.331e-07 0.9999 2.234e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001142 Epoch 10432 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008273 0.9969 0.9931 -1.005e-07 4.514e-08 -0.006578 -7.578e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003403 -0.006296 0.005132 0.9699 0.9743 0.006938 0.822 0.8184 0.01563 ] Network output: [ 1 1.1e-05 0.0002721 -1.068e-06 4.793e-07 -0.0002179 -8.047e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03634 -0.1512 0.1803 0.9834 0.9932 0.2388 0.4268 0.8676 0.7065 ] Network output: [ -0.008308 1.003 1.007 -1.02e-07 4.577e-08 0.007016 -7.684e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00705 0.0006598 0.004287 0.003011 0.9889 0.9919 0.007191 0.8491 0.8912 0.0111 ] Network output: [ -8.609e-05 0.0009204 1 -3.356e-06 1.507e-06 0.9988 -2.529e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2268 0.108 0.3525 0.1407 0.9849 0.9939 0.2276 0.4307 0.8744 0.7 ] Network output: [ 0.001991 -0.009806 0.9949 2.057e-06 -9.233e-07 1.011 1.55e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7292 0.8601 0.3043 ] Network output: [ -0.001907 0.009221 1.004 2.273e-06 -1.02e-06 0.9901 1.713e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09537 0.09342 0.1647 0.1971 0.9851 0.991 0.09539 0.6528 0.8348 0.2506 ] Network output: [ 7.141e-05 1 -5.926e-05 2.96e-07 -1.329e-07 0.9999 2.231e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001141 Epoch 10433 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008272 0.9969 0.9931 -1.005e-07 4.51e-08 -0.006577 -7.571e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003403 -0.006295 0.005131 0.9699 0.9743 0.006938 0.822 0.8184 0.01563 ] Network output: [ 1 1.088e-05 0.000272 -1.066e-06 4.787e-07 -0.0002178 -8.037e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03635 -0.1512 0.1803 0.9834 0.9932 0.2388 0.4268 0.8676 0.7065 ] Network output: [ -0.008307 1.003 1.007 -1.019e-07 4.573e-08 0.007016 -7.677e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00705 0.0006599 0.004287 0.003011 0.9889 0.9919 0.007191 0.8491 0.8912 0.0111 ] Network output: [ -8.597e-05 0.0009197 1 -3.352e-06 1.505e-06 0.9988 -2.526e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2269 0.108 0.3525 0.1407 0.9849 0.9939 0.2276 0.4307 0.8744 0.7 ] Network output: [ 0.001989 -0.0098 0.9949 2.054e-06 -9.221e-07 1.011 1.548e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7292 0.8601 0.3043 ] Network output: [ -0.001906 0.009216 1.004 2.27e-06 -1.019e-06 0.9901 1.711e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09537 0.09342 0.1647 0.1971 0.9851 0.991 0.09539 0.6528 0.8348 0.2506 ] Network output: [ 7.14e-05 1 -5.929e-05 2.956e-07 -1.327e-07 0.9999 2.228e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000114 Epoch 10434 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008271 0.9969 0.9931 -1.004e-07 4.506e-08 -0.006577 -7.564e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003403 -0.006295 0.005131 0.9699 0.9743 0.006938 0.822 0.8184 0.01563 ] Network output: [ 1 1.077e-05 0.0002719 -1.065e-06 4.781e-07 -0.0002177 -8.026e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03635 -0.1512 0.1803 0.9834 0.9932 0.2388 0.4268 0.8676 0.7065 ] Network output: [ -0.008307 1.003 1.007 -1.018e-07 4.569e-08 0.007016 -7.669e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007051 0.0006599 0.004287 0.003011 0.9889 0.9919 0.007191 0.8491 0.8912 0.0111 ] Network output: [ -8.584e-05 0.0009191 1 -3.348e-06 1.503e-06 0.9988 -2.523e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2269 0.108 0.3525 0.1407 0.9849 0.9939 0.2276 0.4307 0.8744 0.7 ] Network output: [ 0.001988 -0.009794 0.9949 2.051e-06 -9.21e-07 1.011 1.546e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7292 0.8601 0.3043 ] Network output: [ -0.001904 0.00921 1.004 2.267e-06 -1.018e-06 0.9901 1.709e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09537 0.09343 0.1647 0.1971 0.9851 0.991 0.09539 0.6528 0.8348 0.2506 ] Network output: [ 7.139e-05 1 -5.932e-05 2.953e-07 -1.326e-07 0.9999 2.225e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000114 Epoch 10435 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00827 0.9969 0.9931 -1.003e-07 4.502e-08 -0.006576 -7.558e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003403 -0.006294 0.005131 0.9699 0.9743 0.006938 0.822 0.8184 0.01563 ] Network output: [ 1 1.066e-05 0.0002717 -1.064e-06 4.775e-07 -0.0002176 -8.016e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03635 -0.1512 0.1803 0.9834 0.9932 0.2388 0.4268 0.8676 0.7065 ] Network output: [ -0.008306 1.003 1.007 -1.017e-07 4.564e-08 0.007015 -7.662e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007051 0.0006599 0.004287 0.003011 0.9889 0.9919 0.007191 0.8491 0.8912 0.0111 ] Network output: [ -8.572e-05 0.0009184 1 -3.343e-06 1.501e-06 0.9988 -2.52e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2269 0.108 0.3525 0.1407 0.9849 0.9939 0.2277 0.4307 0.8744 0.7 ] Network output: [ 0.001986 -0.009788 0.9949 2.049e-06 -9.198e-07 1.011 1.544e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7292 0.8601 0.3043 ] Network output: [ -0.001903 0.009205 1.004 2.264e-06 -1.017e-06 0.9901 1.706e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09537 0.09343 0.1647 0.1971 0.9851 0.991 0.09539 0.6528 0.8348 0.2506 ] Network output: [ 7.138e-05 1 -5.935e-05 2.949e-07 -1.324e-07 0.9999 2.223e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001139 Epoch 10436 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00827 0.9969 0.9931 -1.002e-07 4.498e-08 -0.006575 -7.551e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003403 -0.006294 0.005131 0.9699 0.9743 0.006938 0.822 0.8184 0.01563 ] Network output: [ 1 1.054e-05 0.0002716 -1.062e-06 4.769e-07 -0.0002175 -8.006e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03635 -0.1512 0.1802 0.9834 0.9932 0.2388 0.4268 0.8676 0.7065 ] Network output: [ -0.008305 1.003 1.007 -1.016e-07 4.56e-08 0.007015 -7.654e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007051 0.0006599 0.004287 0.00301 0.9889 0.9919 0.007192 0.8491 0.8912 0.0111 ] Network output: [ -8.56e-05 0.0009177 1 -3.339e-06 1.499e-06 0.9988 -2.516e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2269 0.108 0.3525 0.1407 0.9849 0.9939 0.2277 0.4307 0.8744 0.7 ] Network output: [ 0.001985 -0.009782 0.9949 2.046e-06 -9.186e-07 1.011 1.542e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7292 0.8601 0.3043 ] Network output: [ -0.001902 0.0092 1.004 2.262e-06 -1.015e-06 0.9901 1.704e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09537 0.09343 0.1647 0.1971 0.9851 0.991 0.09539 0.6528 0.8348 0.2506 ] Network output: [ 7.137e-05 1 -5.938e-05 2.945e-07 -1.322e-07 0.9999 2.22e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001138 Epoch 10437 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008269 0.9969 0.9931 -1.001e-07 4.494e-08 -0.006575 -7.544e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003403 -0.006293 0.00513 0.9699 0.9743 0.006938 0.822 0.8184 0.01563 ] Network output: [ 1 1.043e-05 0.0002715 -1.061e-06 4.763e-07 -0.0002174 -7.996e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03635 -0.1512 0.1802 0.9834 0.9932 0.2388 0.4268 0.8676 0.7065 ] Network output: [ -0.008304 1.003 1.007 -1.015e-07 4.555e-08 0.007014 -7.647e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007051 0.0006599 0.004287 0.00301 0.9889 0.9919 0.007192 0.8491 0.8912 0.01109 ] Network output: [ -8.548e-05 0.000917 1 -3.335e-06 1.497e-06 0.9988 -2.513e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2269 0.108 0.3525 0.1407 0.9849 0.9939 0.2277 0.4307 0.8744 0.7 ] Network output: [ 0.001984 -0.009776 0.9949 2.044e-06 -9.175e-07 1.011 1.54e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1002 0.1851 0.1961 0.9873 0.9919 0.1131 0.7292 0.8601 0.3043 ] Network output: [ -0.001901 0.009195 1.004 2.259e-06 -1.014e-06 0.9901 1.702e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09538 0.09343 0.1647 0.1971 0.9851 0.991 0.09539 0.6528 0.8348 0.2506 ] Network output: [ 7.136e-05 1 -5.941e-05 2.942e-07 -1.321e-07 0.9999 2.217e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001138 Epoch 10438 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008268 0.9969 0.9931 -1e-07 4.49e-08 -0.006574 -7.538e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003403 -0.006293 0.00513 0.9699 0.9743 0.006938 0.822 0.8184 0.01563 ] Network output: [ 1 1.032e-05 0.0002714 -1.06e-06 4.757e-07 -0.0002173 -7.986e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03635 -0.1512 0.1802 0.9834 0.9932 0.2388 0.4268 0.8676 0.7065 ] Network output: [ -0.008304 1.003 1.007 -1.014e-07 4.551e-08 0.007014 -7.64e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007052 0.00066 0.004287 0.00301 0.9889 0.9919 0.007192 0.8491 0.8912 0.01109 ] Network output: [ -8.536e-05 0.0009164 1 -3.331e-06 1.495e-06 0.9988 -2.51e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2269 0.108 0.3525 0.1407 0.9849 0.9939 0.2277 0.4307 0.8744 0.7 ] Network output: [ 0.001982 -0.00977 0.9949 2.041e-06 -9.163e-07 1.011 1.538e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1002 0.1851 0.1961 0.9873 0.9919 0.1132 0.7292 0.8601 0.3043 ] Network output: [ -0.001899 0.009189 1.004 2.256e-06 -1.013e-06 0.9901 1.7e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09538 0.09343 0.1647 0.1971 0.9851 0.991 0.09539 0.6528 0.8348 0.2506 ] Network output: [ 7.135e-05 1 -5.944e-05 2.938e-07 -1.319e-07 0.9999 2.214e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001137 Epoch 10439 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008267 0.9969 0.9931 -9.993e-08 4.486e-08 -0.006573 -7.531e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003404 -0.006292 0.00513 0.9699 0.9743 0.006938 0.8219 0.8184 0.01563 ] Network output: [ 1 1.02e-05 0.0002713 -1.058e-06 4.751e-07 -0.0002172 -7.976e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03635 -0.1512 0.1802 0.9834 0.9932 0.2388 0.4268 0.8676 0.7065 ] Network output: [ -0.008303 1.003 1.007 -1.013e-07 4.547e-08 0.007014 -7.632e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007052 0.00066 0.004287 0.00301 0.9889 0.9919 0.007192 0.8491 0.8912 0.01109 ] Network output: [ -8.524e-05 0.0009157 1 -3.326e-06 1.493e-06 0.9988 -2.507e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2269 0.108 0.3525 0.1407 0.9849 0.9939 0.2277 0.4307 0.8744 0.7 ] Network output: [ 0.001981 -0.009763 0.9949 2.039e-06 -9.152e-07 1.011 1.536e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1002 0.1851 0.1961 0.9873 0.9919 0.1132 0.7292 0.8601 0.3043 ] Network output: [ -0.001898 0.009184 1.004 2.253e-06 -1.011e-06 0.9901 1.698e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09538 0.09343 0.1647 0.1971 0.9851 0.991 0.09539 0.6528 0.8348 0.2506 ] Network output: [ 7.134e-05 1 -5.947e-05 2.934e-07 -1.317e-07 0.9999 2.211e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001136 Epoch 10440 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008267 0.9969 0.9931 -9.984e-08 4.482e-08 -0.006572 -7.524e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003404 -0.006292 0.005129 0.9699 0.9743 0.006938 0.8219 0.8184 0.01562 ] Network output: [ 1 1.009e-05 0.0002712 -1.057e-06 4.745e-07 -0.0002171 -7.965e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03635 -0.1512 0.1802 0.9834 0.9932 0.2388 0.4268 0.8676 0.7065 ] Network output: [ -0.008302 1.003 1.007 -1.012e-07 4.542e-08 0.007013 -7.625e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007052 0.00066 0.004286 0.00301 0.9889 0.9919 0.007193 0.8491 0.8912 0.01109 ] Network output: [ -8.511e-05 0.000915 1 -3.322e-06 1.491e-06 0.9988 -2.504e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2269 0.108 0.3525 0.1407 0.9849 0.9939 0.2277 0.4307 0.8744 0.7 ] Network output: [ 0.001979 -0.009757 0.9949 2.036e-06 -9.14e-07 1.011 1.534e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1002 0.1851 0.1961 0.9873 0.9919 0.1132 0.7292 0.8601 0.3043 ] Network output: [ -0.001897 0.009179 1.004 2.25e-06 -1.01e-06 0.9901 1.696e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09538 0.09343 0.1647 0.1971 0.9851 0.991 0.09539 0.6528 0.8348 0.2506 ] Network output: [ 7.133e-05 1 -5.95e-05 2.931e-07 -1.316e-07 0.9999 2.209e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001135 Epoch 10441 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008266 0.9969 0.9931 -9.975e-08 4.478e-08 -0.006572 -7.518e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003404 -0.006291 0.005129 0.9699 0.9743 0.006938 0.8219 0.8184 0.01562 ] Network output: [ 1 9.976e-06 0.000271 -1.056e-06 4.739e-07 -0.000217 -7.955e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03635 -0.1512 0.1802 0.9834 0.9932 0.2388 0.4268 0.8676 0.7065 ] Network output: [ -0.008301 1.003 1.007 -1.011e-07 4.538e-08 0.007013 -7.618e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007052 0.00066 0.004286 0.003009 0.9889 0.9919 0.007193 0.8491 0.8912 0.01109 ] Network output: [ -8.499e-05 0.0009143 1 -3.318e-06 1.489e-06 0.9988 -2.5e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2269 0.108 0.3525 0.1407 0.9849 0.9939 0.2277 0.4307 0.8744 0.7 ] Network output: [ 0.001978 -0.009751 0.9949 2.033e-06 -9.129e-07 1.011 1.532e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1002 0.1851 0.1961 0.9873 0.9919 0.1132 0.7292 0.8601 0.3043 ] Network output: [ -0.001896 0.009173 1.004 2.247e-06 -1.009e-06 0.9901 1.694e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09538 0.09343 0.1647 0.1971 0.9851 0.991 0.09539 0.6527 0.8348 0.2506 ] Network output: [ 7.131e-05 1 -5.953e-05 2.927e-07 -1.314e-07 0.9999 2.206e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001135 Epoch 10442 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008265 0.9969 0.9931 -9.967e-08 4.474e-08 -0.006571 -7.511e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003404 -0.006291 0.005129 0.9699 0.9743 0.006939 0.8219 0.8184 0.01562 ] Network output: [ 1 9.863e-06 0.0002709 -1.054e-06 4.733e-07 -0.0002169 -7.945e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03636 -0.1512 0.1802 0.9834 0.9932 0.2388 0.4268 0.8676 0.7065 ] Network output: [ -0.008301 1.003 1.007 -1.01e-07 4.533e-08 0.007013 -7.61e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007053 0.00066 0.004286 0.003009 0.9889 0.9919 0.007193 0.8491 0.8912 0.01109 ] Network output: [ -8.487e-05 0.0009137 1 -3.314e-06 1.488e-06 0.9988 -2.497e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2269 0.108 0.3525 0.1407 0.9849 0.9939 0.2277 0.4307 0.8744 0.7 ] Network output: [ 0.001977 -0.009745 0.9949 2.031e-06 -9.117e-07 1.011 1.53e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1002 0.1851 0.1961 0.9873 0.9919 0.1132 0.7292 0.8601 0.3043 ] Network output: [ -0.001895 0.009168 1.004 2.245e-06 -1.008e-06 0.9901 1.692e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09538 0.09343 0.1647 0.1971 0.9851 0.991 0.09539 0.6527 0.8348 0.2506 ] Network output: [ 7.13e-05 1 -5.957e-05 2.923e-07 -1.312e-07 0.9999 2.203e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001134 Epoch 10443 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008264 0.9969 0.9931 -9.958e-08 4.47e-08 -0.00657 -7.505e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003404 -0.00629 0.005128 0.9699 0.9743 0.006939 0.8219 0.8184 0.01562 ] Network output: [ 1 9.75e-06 0.0002708 -1.053e-06 4.727e-07 -0.0002168 -7.935e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03636 -0.1512 0.1802 0.9834 0.9932 0.2388 0.4268 0.8676 0.7065 ] Network output: [ -0.0083 1.003 1.007 -1.009e-07 4.529e-08 0.007012 -7.603e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007053 0.0006601 0.004286 0.003009 0.9889 0.9919 0.007193 0.8491 0.8912 0.01109 ] Network output: [ -8.475e-05 0.000913 1 -3.309e-06 1.486e-06 0.9988 -2.494e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2269 0.108 0.3525 0.1407 0.9849 0.9939 0.2277 0.4307 0.8744 0.7 ] Network output: [ 0.001975 -0.009739 0.9949 2.028e-06 -9.105e-07 1.011 1.529e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.1961 0.9873 0.9919 0.1132 0.7292 0.8601 0.3043 ] Network output: [ -0.001893 0.009163 1.004 2.242e-06 -1.006e-06 0.9901 1.69e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09538 0.09343 0.1647 0.1971 0.9851 0.991 0.0954 0.6527 0.8348 0.2506 ] Network output: [ 7.129e-05 1 -5.96e-05 2.92e-07 -1.311e-07 0.9999 2.2e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001133 Epoch 10444 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008264 0.9969 0.9931 -9.949e-08 4.467e-08 -0.00657 -7.498e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003404 -0.00629 0.005128 0.9699 0.9743 0.006939 0.8219 0.8184 0.01562 ] Network output: [ 1 9.637e-06 0.0002707 -1.052e-06 4.721e-07 -0.0002167 -7.925e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03636 -0.1511 0.1802 0.9834 0.9932 0.2388 0.4268 0.8676 0.7065 ] Network output: [ -0.008299 1.003 1.007 -1.008e-07 4.525e-08 0.007012 -7.596e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007053 0.0006601 0.004286 0.003009 0.9889 0.9919 0.007194 0.8491 0.8912 0.01109 ] Network output: [ -8.463e-05 0.0009123 1 -3.305e-06 1.484e-06 0.9988 -2.491e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2269 0.108 0.3525 0.1407 0.9849 0.9939 0.2277 0.4307 0.8744 0.7 ] Network output: [ 0.001974 -0.009733 0.9949 2.026e-06 -9.094e-07 1.011 1.527e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.1961 0.9873 0.9919 0.1132 0.7292 0.8601 0.3043 ] Network output: [ -0.001892 0.009157 1.004 2.239e-06 -1.005e-06 0.9901 1.687e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09538 0.09343 0.1647 0.1971 0.9851 0.991 0.0954 0.6527 0.8348 0.2506 ] Network output: [ 7.128e-05 1 -5.963e-05 2.916e-07 -1.309e-07 0.9999 2.198e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001133 Epoch 10445 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008263 0.9969 0.9931 -9.94e-08 4.463e-08 -0.006569 -7.491e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003404 -0.006289 0.005128 0.9699 0.9743 0.006939 0.8219 0.8184 0.01562 ] Network output: [ 1 9.524e-06 0.0002706 -1.05e-06 4.715e-07 -0.0002166 -7.915e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03636 -0.1511 0.1802 0.9834 0.9932 0.2388 0.4268 0.8676 0.7065 ] Network output: [ -0.008299 1.003 1.007 -1.007e-07 4.52e-08 0.007011 -7.588e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007053 0.0006601 0.004286 0.003009 0.9889 0.9919 0.007194 0.8491 0.8912 0.01109 ] Network output: [ -8.451e-05 0.0009116 1 -3.301e-06 1.482e-06 0.9988 -2.488e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2269 0.108 0.3525 0.1407 0.9849 0.9939 0.2277 0.4307 0.8744 0.7 ] Network output: [ 0.001973 -0.009726 0.9949 2.023e-06 -9.082e-07 1.011 1.525e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.1961 0.9873 0.9919 0.1132 0.7292 0.8601 0.3043 ] Network output: [ -0.001891 0.009152 1.004 2.236e-06 -1.004e-06 0.9901 1.685e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09538 0.09344 0.1647 0.1971 0.9851 0.991 0.0954 0.6527 0.8348 0.2506 ] Network output: [ 7.127e-05 1 -5.966e-05 2.912e-07 -1.307e-07 0.9999 2.195e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001132 Epoch 10446 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008262 0.9969 0.9931 -9.932e-08 4.459e-08 -0.006568 -7.485e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003404 -0.006289 0.005127 0.9699 0.9743 0.006939 0.8219 0.8184 0.01562 ] Network output: [ 1 9.411e-06 0.0002705 -1.049e-06 4.709e-07 -0.0002165 -7.905e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2124 -0.03636 -0.1511 0.1802 0.9834 0.9932 0.2389 0.4268 0.8676 0.7065 ] Network output: [ -0.008298 1.003 1.007 -1.006e-07 4.516e-08 0.007011 -7.581e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007054 0.0006601 0.004286 0.003008 0.9889 0.9919 0.007194 0.849 0.8912 0.01109 ] Network output: [ -8.439e-05 0.000911 1 -3.297e-06 1.48e-06 0.9988 -2.485e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2269 0.108 0.3525 0.1407 0.9849 0.9939 0.2277 0.4307 0.8744 0.7 ] Network output: [ 0.001971 -0.00972 0.9949 2.021e-06 -9.071e-07 1.011 1.523e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.1961 0.9873 0.9919 0.1132 0.7292 0.8601 0.3043 ] Network output: [ -0.00189 0.009147 1.004 2.233e-06 -1.003e-06 0.9901 1.683e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09538 0.09344 0.1647 0.1971 0.9851 0.991 0.0954 0.6527 0.8348 0.2506 ] Network output: [ 7.126e-05 1 -5.969e-05 2.909e-07 -1.306e-07 0.9999 2.192e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001131 Epoch 10447 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008261 0.9969 0.9931 -9.923e-08 4.455e-08 -0.006567 -7.478e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003404 -0.006289 0.005127 0.9699 0.9743 0.006939 0.8219 0.8184 0.01562 ] Network output: [ 1 9.299e-06 0.0002703 -1.048e-06 4.703e-07 -0.0002164 -7.895e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03636 -0.1511 0.1802 0.9834 0.9932 0.2389 0.4268 0.8676 0.7065 ] Network output: [ -0.008297 1.003 1.007 -1.005e-07 4.512e-08 0.007011 -7.574e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007054 0.0006602 0.004286 0.003008 0.9889 0.9919 0.007194 0.849 0.8912 0.01109 ] Network output: [ -8.426e-05 0.0009103 1 -3.293e-06 1.478e-06 0.9988 -2.481e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2269 0.108 0.3525 0.1407 0.9849 0.9939 0.2277 0.4307 0.8744 0.7 ] Network output: [ 0.00197 -0.009714 0.9949 2.018e-06 -9.059e-07 1.011 1.521e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.1961 0.9873 0.9919 0.1132 0.7292 0.8601 0.3043 ] Network output: [ -0.001888 0.009142 1.004 2.231e-06 -1.001e-06 0.9902 1.681e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09538 0.09344 0.1647 0.1971 0.9851 0.991 0.0954 0.6527 0.8348 0.2506 ] Network output: [ 7.125e-05 1 -5.972e-05 2.905e-07 -1.304e-07 0.9999 2.189e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001131 Epoch 10448 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00826 0.9969 0.9931 -9.914e-08 4.451e-08 -0.006567 -7.472e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003404 -0.006288 0.005127 0.9699 0.9743 0.006939 0.8219 0.8184 0.01562 ] Network output: [ 1 9.186e-06 0.0002702 -1.046e-06 4.697e-07 -0.0002163 -7.885e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03636 -0.1511 0.1802 0.9834 0.9932 0.2389 0.4268 0.8676 0.7064 ] Network output: [ -0.008296 1.003 1.007 -1.004e-07 4.507e-08 0.00701 -7.566e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007054 0.0006602 0.004285 0.003008 0.9889 0.9919 0.007195 0.849 0.8912 0.01109 ] Network output: [ -8.414e-05 0.0009096 1 -3.288e-06 1.476e-06 0.9988 -2.478e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2269 0.108 0.3525 0.1407 0.9849 0.9939 0.2277 0.4307 0.8744 0.6999 ] Network output: [ 0.001968 -0.009708 0.9949 2.015e-06 -9.048e-07 1.011 1.519e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.1961 0.9873 0.9919 0.1132 0.7291 0.8601 0.3043 ] Network output: [ -0.001887 0.009136 1.004 2.228e-06 -1e-06 0.9902 1.679e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09539 0.09344 0.1647 0.1971 0.9851 0.991 0.0954 0.6527 0.8348 0.2506 ] Network output: [ 7.124e-05 1 -5.975e-05 2.901e-07 -1.303e-07 0.9999 2.187e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000113 Epoch 10449 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00826 0.9969 0.9931 -9.905e-08 4.447e-08 -0.006566 -7.465e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003404 -0.006288 0.005126 0.9699 0.9743 0.006939 0.8219 0.8184 0.01562 ] Network output: [ 1 9.073e-06 0.0002701 -1.045e-06 4.691e-07 -0.0002162 -7.875e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03636 -0.1511 0.1802 0.9834 0.9932 0.2389 0.4268 0.8676 0.7064 ] Network output: [ -0.008296 1.003 1.007 -1.003e-07 4.503e-08 0.00701 -7.559e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007054 0.0006602 0.004285 0.003008 0.9889 0.9919 0.007195 0.849 0.8912 0.01109 ] Network output: [ -8.402e-05 0.0009089 1 -3.284e-06 1.474e-06 0.9988 -2.475e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2269 0.108 0.3525 0.1407 0.9849 0.9939 0.2277 0.4307 0.8744 0.6999 ] Network output: [ 0.001967 -0.009702 0.9949 2.013e-06 -9.037e-07 1.011 1.517e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.1961 0.9873 0.9919 0.1132 0.7291 0.8601 0.3043 ] Network output: [ -0.001886 0.009131 1.004 2.225e-06 -9.989e-07 0.9902 1.677e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09539 0.09344 0.1647 0.1971 0.9851 0.991 0.0954 0.6527 0.8348 0.2506 ] Network output: [ 7.123e-05 1 -5.978e-05 2.898e-07 -1.301e-07 0.9999 2.184e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001129 Epoch 10450 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008259 0.9969 0.9931 -9.897e-08 4.443e-08 -0.006565 -7.458e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003404 -0.006287 0.005126 0.9699 0.9743 0.006939 0.8219 0.8184 0.01562 ] Network output: [ 1 8.961e-06 0.00027 -1.044e-06 4.685e-07 -0.0002161 -7.865e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03636 -0.1511 0.1802 0.9834 0.9932 0.2389 0.4268 0.8676 0.7064 ] Network output: [ -0.008295 1.003 1.007 -1.002e-07 4.499e-08 0.007009 -7.552e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007055 0.0006602 0.004285 0.003007 0.9889 0.9919 0.007195 0.849 0.8912 0.01109 ] Network output: [ -8.39e-05 0.0009083 1 -3.28e-06 1.473e-06 0.9988 -2.472e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2269 0.108 0.3526 0.1407 0.9849 0.9939 0.2277 0.4307 0.8744 0.6999 ] Network output: [ 0.001966 -0.009696 0.9949 2.01e-06 -9.025e-07 1.011 1.515e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.1961 0.9873 0.9919 0.1132 0.7291 0.8601 0.3043 ] Network output: [ -0.001885 0.009126 1.004 2.222e-06 -9.977e-07 0.9902 1.675e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09539 0.09344 0.1647 0.1971 0.9851 0.991 0.0954 0.6527 0.8348 0.2506 ] Network output: [ 7.122e-05 1 -5.982e-05 2.894e-07 -1.299e-07 0.9999 2.181e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001129 Epoch 10451 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008258 0.9969 0.9931 -9.888e-08 4.439e-08 -0.006565 -7.452e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003404 -0.006287 0.005126 0.9699 0.9743 0.006939 0.8219 0.8184 0.01562 ] Network output: [ 1 8.848e-06 0.0002699 -1.042e-06 4.679e-07 -0.000216 -7.855e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03637 -0.1511 0.1802 0.9834 0.9932 0.2389 0.4268 0.8676 0.7064 ] Network output: [ -0.008294 1.003 1.007 -1.001e-07 4.494e-08 0.007009 -7.544e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007055 0.0006602 0.004285 0.003007 0.9889 0.9919 0.007195 0.849 0.8911 0.01109 ] Network output: [ -8.378e-05 0.0009076 1 -3.276e-06 1.471e-06 0.9988 -2.469e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2269 0.108 0.3526 0.1407 0.9849 0.9939 0.2277 0.4307 0.8744 0.6999 ] Network output: [ 0.001964 -0.00969 0.9949 2.008e-06 -9.014e-07 1.011 1.513e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.1961 0.9873 0.9919 0.1132 0.7291 0.8601 0.3043 ] Network output: [ -0.001884 0.00912 1.004 2.22e-06 -9.964e-07 0.9902 1.673e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09539 0.09344 0.1647 0.1971 0.9851 0.991 0.0954 0.6527 0.8348 0.2506 ] Network output: [ 7.121e-05 1 -5.985e-05 2.89e-07 -1.298e-07 0.9999 2.178e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001128 Epoch 10452 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008257 0.9969 0.9931 -9.879e-08 4.435e-08 -0.006564 -7.445e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003404 -0.006286 0.005125 0.9699 0.9743 0.00694 0.8219 0.8184 0.01562 ] Network output: [ 1 8.736e-06 0.0002698 -1.041e-06 4.673e-07 -0.0002159 -7.845e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03637 -0.1511 0.1802 0.9834 0.9932 0.2389 0.4268 0.8676 0.7064 ] Network output: [ -0.008293 1.003 1.007 -1e-07 4.49e-08 0.007009 -7.537e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007055 0.0006603 0.004285 0.003007 0.9889 0.9919 0.007196 0.849 0.8911 0.01109 ] Network output: [ -8.366e-05 0.0009069 1 -3.272e-06 1.469e-06 0.9988 -2.466e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.227 0.108 0.3526 0.1407 0.9849 0.9939 0.2277 0.4307 0.8744 0.6999 ] Network output: [ 0.001963 -0.009683 0.9949 2.005e-06 -9.002e-07 1.011 1.511e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.1961 0.9873 0.9919 0.1132 0.7291 0.8601 0.3043 ] Network output: [ -0.001882 0.009115 1.004 2.217e-06 -9.952e-07 0.9902 1.671e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09539 0.09344 0.1647 0.1971 0.9851 0.991 0.0954 0.6527 0.8348 0.2506 ] Network output: [ 7.12e-05 1 -5.988e-05 2.887e-07 -1.296e-07 0.9999 2.176e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001127 Epoch 10453 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008257 0.9969 0.9931 -9.871e-08 4.431e-08 -0.006563 -7.439e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003404 -0.006286 0.005125 0.9699 0.9743 0.00694 0.8219 0.8184 0.01561 ] Network output: [ 1 8.624e-06 0.0002696 -1.04e-06 4.667e-07 -0.0002158 -7.835e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03637 -0.1511 0.1802 0.9834 0.9932 0.2389 0.4268 0.8676 0.7064 ] Network output: [ -0.008293 1.003 1.007 -9.991e-08 4.485e-08 0.007008 -7.53e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007055 0.0006603 0.004285 0.003007 0.9889 0.9919 0.007196 0.849 0.8911 0.01108 ] Network output: [ -8.354e-05 0.0009062 1 -3.268e-06 1.467e-06 0.9988 -2.463e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.227 0.108 0.3526 0.1407 0.9849 0.9939 0.2277 0.4307 0.8744 0.6999 ] Network output: [ 0.001961 -0.009677 0.9949 2.003e-06 -8.991e-07 1.011 1.509e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.1961 0.9873 0.9919 0.1132 0.7291 0.8601 0.3043 ] Network output: [ -0.001881 0.00911 1.004 2.214e-06 -9.939e-07 0.9902 1.669e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09539 0.09344 0.1647 0.1971 0.9851 0.991 0.0954 0.6527 0.8348 0.2506 ] Network output: [ 7.119e-05 1 -5.991e-05 2.883e-07 -1.294e-07 0.9999 2.173e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001127 Epoch 10454 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008256 0.9969 0.9931 -9.862e-08 4.427e-08 -0.006562 -7.432e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003404 -0.006285 0.005125 0.9699 0.9743 0.00694 0.8219 0.8184 0.01561 ] Network output: [ 1 8.511e-06 0.0002695 -1.038e-06 4.661e-07 -0.0002157 -7.825e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03637 -0.1511 0.1802 0.9834 0.9932 0.2389 0.4268 0.8676 0.7064 ] Network output: [ -0.008292 1.003 1.007 -9.982e-08 4.481e-08 0.007008 -7.523e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007056 0.0006603 0.004285 0.003007 0.9889 0.9919 0.007196 0.849 0.8911 0.01108 ] Network output: [ -8.342e-05 0.0009055 1 -3.263e-06 1.465e-06 0.9988 -2.459e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.227 0.108 0.3526 0.1407 0.9849 0.9939 0.2278 0.4307 0.8744 0.6999 ] Network output: [ 0.00196 -0.009671 0.9949 2e-06 -8.98e-07 1.011 1.507e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.1961 0.9873 0.9919 0.1132 0.7291 0.8601 0.3043 ] Network output: [ -0.00188 0.009104 1.004 2.211e-06 -9.927e-07 0.9902 1.666e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09539 0.09344 0.1647 0.1971 0.9851 0.991 0.09541 0.6527 0.8348 0.2506 ] Network output: [ 7.118e-05 1 -5.994e-05 2.88e-07 -1.293e-07 0.9999 2.17e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001126 Epoch 10455 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008255 0.9969 0.9931 -9.853e-08 4.423e-08 -0.006562 -7.426e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003404 -0.006285 0.005124 0.9699 0.9743 0.00694 0.8219 0.8184 0.01561 ] Network output: [ 1 8.399e-06 0.0002694 -1.037e-06 4.655e-07 -0.0002156 -7.815e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03637 -0.1511 0.1802 0.9834 0.9932 0.2389 0.4268 0.8676 0.7064 ] Network output: [ -0.008291 1.003 1.007 -9.972e-08 4.477e-08 0.007008 -7.515e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007056 0.0006603 0.004285 0.003006 0.9889 0.9919 0.007196 0.849 0.8911 0.01108 ] Network output: [ -8.33e-05 0.0009049 1 -3.259e-06 1.463e-06 0.9988 -2.456e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.227 0.108 0.3526 0.1407 0.9849 0.9939 0.2278 0.4307 0.8744 0.6999 ] Network output: [ 0.001959 -0.009665 0.9949 1.998e-06 -8.968e-07 1.011 1.506e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.1961 0.9873 0.9919 0.1132 0.7291 0.8601 0.3043 ] Network output: [ -0.001879 0.009099 1.004 2.208e-06 -9.915e-07 0.9902 1.664e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09539 0.09344 0.1647 0.1971 0.9851 0.991 0.09541 0.6526 0.8348 0.2506 ] Network output: [ 7.117e-05 1 -5.997e-05 2.876e-07 -1.291e-07 0.9999 2.167e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001125 Epoch 10456 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008254 0.9969 0.9931 -9.844e-08 4.42e-08 -0.006561 -7.419e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003404 -0.006284 0.005124 0.9699 0.9743 0.00694 0.8219 0.8184 0.01561 ] Network output: [ 1 8.287e-06 0.0002693 -1.036e-06 4.649e-07 -0.0002155 -7.805e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03637 -0.1511 0.1802 0.9834 0.9932 0.2389 0.4268 0.8676 0.7064 ] Network output: [ -0.00829 1.003 1.007 -9.962e-08 4.472e-08 0.007007 -7.508e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007056 0.0006603 0.004284 0.003006 0.9889 0.9919 0.007197 0.849 0.8911 0.01108 ] Network output: [ -8.317e-05 0.0009042 1 -3.255e-06 1.461e-06 0.9988 -2.453e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.227 0.108 0.3526 0.1407 0.9849 0.9939 0.2278 0.4307 0.8744 0.6999 ] Network output: [ 0.001957 -0.009659 0.9949 1.995e-06 -8.957e-07 1.011 1.504e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.1961 0.9873 0.9919 0.1132 0.7291 0.8601 0.3043 ] Network output: [ -0.001877 0.009094 1.004 2.206e-06 -9.902e-07 0.9902 1.662e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09539 0.09345 0.1647 0.1971 0.9851 0.991 0.09541 0.6526 0.8348 0.2506 ] Network output: [ 7.116e-05 1 -6e-05 2.872e-07 -1.289e-07 0.9999 2.165e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001125 Epoch 10457 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008254 0.9969 0.9931 -9.836e-08 4.416e-08 -0.00656 -7.413e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003404 -0.006284 0.005124 0.9699 0.9743 0.00694 0.8219 0.8184 0.01561 ] Network output: [ 1 8.175e-06 0.0002692 -1.034e-06 4.643e-07 -0.0002154 -7.795e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03637 -0.1511 0.1802 0.9834 0.9932 0.2389 0.4268 0.8676 0.7064 ] Network output: [ -0.00829 1.003 1.007 -9.953e-08 4.468e-08 0.007007 -7.501e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007056 0.0006604 0.004284 0.003006 0.9889 0.9919 0.007197 0.849 0.8911 0.01108 ] Network output: [ -8.305e-05 0.0009035 1 -3.251e-06 1.459e-06 0.9988 -2.45e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.227 0.108 0.3526 0.1407 0.9849 0.9939 0.2278 0.4307 0.8744 0.6999 ] Network output: [ 0.001956 -0.009653 0.9949 1.993e-06 -8.946e-07 1.011 1.502e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.1961 0.9873 0.9919 0.1132 0.7291 0.8601 0.3042 ] Network output: [ -0.001876 0.009089 1.004 2.203e-06 -9.89e-07 0.9902 1.66e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09539 0.09345 0.1647 0.1971 0.9851 0.991 0.09541 0.6526 0.8348 0.2506 ] Network output: [ 7.115e-05 1 -6.004e-05 2.869e-07 -1.288e-07 0.9999 2.162e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001124 Epoch 10458 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008253 0.9969 0.9931 -9.827e-08 4.412e-08 -0.00656 -7.406e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003404 -0.006283 0.005123 0.9699 0.9743 0.00694 0.8219 0.8184 0.01561 ] Network output: [ 1 8.063e-06 0.0002691 -1.033e-06 4.638e-07 -0.0002153 -7.785e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03637 -0.1511 0.1802 0.9833 0.9932 0.2389 0.4268 0.8676 0.7064 ] Network output: [ -0.008289 1.003 1.007 -9.943e-08 4.464e-08 0.007006 -7.493e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007056 0.0006604 0.004284 0.003006 0.9889 0.9919 0.007197 0.849 0.8911 0.01108 ] Network output: [ -8.293e-05 0.0009028 1 -3.247e-06 1.458e-06 0.9988 -2.447e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.227 0.108 0.3526 0.1407 0.9849 0.9939 0.2278 0.4307 0.8744 0.6999 ] Network output: [ 0.001955 -0.009647 0.9949 1.99e-06 -8.934e-07 1.011 1.5e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.1961 0.9873 0.9919 0.1132 0.7291 0.8601 0.3042 ] Network output: [ -0.001875 0.009083 1.004 2.2e-06 -9.877e-07 0.9902 1.658e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09539 0.09345 0.1647 0.1971 0.9851 0.991 0.09541 0.6526 0.8348 0.2506 ] Network output: [ 7.113e-05 1 -6.007e-05 2.865e-07 -1.286e-07 0.9999 2.159e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001123 Epoch 10459 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008252 0.9969 0.9931 -9.818e-08 4.408e-08 -0.006559 -7.399e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003405 -0.006283 0.005123 0.9699 0.9743 0.00694 0.8219 0.8184 0.01561 ] Network output: [ 1 7.951e-06 0.0002689 -1.032e-06 4.632e-07 -0.0002152 -7.775e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03637 -0.151 0.1802 0.9833 0.9931 0.2389 0.4268 0.8676 0.7064 ] Network output: [ -0.008288 1.003 1.007 -9.934e-08 4.46e-08 0.007006 -7.486e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007057 0.0006604 0.004284 0.003006 0.9889 0.9919 0.007197 0.849 0.8911 0.01108 ] Network output: [ -8.281e-05 0.0009022 1 -3.243e-06 1.456e-06 0.9988 -2.444e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.227 0.108 0.3526 0.1407 0.9849 0.9939 0.2278 0.4307 0.8744 0.6999 ] Network output: [ 0.001953 -0.009641 0.9949 1.988e-06 -8.923e-07 1.011 1.498e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.1961 0.9873 0.9919 0.1132 0.7291 0.8601 0.3042 ] Network output: [ -0.001874 0.009078 1.004 2.197e-06 -9.865e-07 0.9902 1.656e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09539 0.09345 0.1647 0.1971 0.9851 0.991 0.09541 0.6526 0.8348 0.2506 ] Network output: [ 7.112e-05 1 -6.01e-05 2.862e-07 -1.285e-07 0.9999 2.157e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001123 Epoch 10460 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008251 0.9969 0.9931 -9.81e-08 4.404e-08 -0.006558 -7.393e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003405 -0.006282 0.005123 0.9699 0.9743 0.00694 0.8219 0.8184 0.01561 ] Network output: [ 1 7.839e-06 0.0002688 -1.03e-06 4.626e-07 -0.0002151 -7.765e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03638 -0.151 0.1802 0.9833 0.9931 0.2389 0.4268 0.8676 0.7064 ] Network output: [ -0.008287 1.003 1.007 -9.924e-08 4.455e-08 0.007006 -7.479e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007057 0.0006604 0.004284 0.003005 0.9889 0.9919 0.007198 0.849 0.8911 0.01108 ] Network output: [ -8.269e-05 0.0009015 1 -3.239e-06 1.454e-06 0.9988 -2.441e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.227 0.108 0.3526 0.1407 0.9849 0.9939 0.2278 0.4307 0.8744 0.6999 ] Network output: [ 0.001952 -0.009634 0.9949 1.985e-06 -8.912e-07 1.011 1.496e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.1961 0.9873 0.9919 0.1132 0.7291 0.8601 0.3042 ] Network output: [ -0.001872 0.009073 1.004 2.195e-06 -9.853e-07 0.9902 1.654e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0954 0.09345 0.1647 0.1971 0.9851 0.991 0.09541 0.6526 0.8348 0.2506 ] Network output: [ 7.111e-05 1 -6.013e-05 2.858e-07 -1.283e-07 0.9999 2.154e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001122 Epoch 10461 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008251 0.9969 0.9931 -9.801e-08 4.4e-08 -0.006557 -7.386e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003405 -0.006282 0.005122 0.9699 0.9743 0.00694 0.8219 0.8184 0.01561 ] Network output: [ 1 7.727e-06 0.0002687 -1.029e-06 4.62e-07 -0.000215 -7.755e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03638 -0.151 0.1802 0.9833 0.9931 0.2389 0.4268 0.8676 0.7064 ] Network output: [ -0.008287 1.003 1.007 -9.914e-08 4.451e-08 0.007005 -7.472e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007057 0.0006604 0.004284 0.003005 0.9889 0.9919 0.007198 0.849 0.8911 0.01108 ] Network output: [ -8.257e-05 0.0009008 1 -3.234e-06 1.452e-06 0.9988 -2.438e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.227 0.108 0.3526 0.1407 0.9849 0.9939 0.2278 0.4307 0.8744 0.6999 ] Network output: [ 0.00195 -0.009628 0.9949 1.983e-06 -8.9e-07 1.011 1.494e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.1961 0.9873 0.9919 0.1132 0.7291 0.8601 0.3042 ] Network output: [ -0.001871 0.009067 1.004 2.192e-06 -9.84e-07 0.9902 1.652e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0954 0.09345 0.1647 0.1971 0.9851 0.991 0.09541 0.6526 0.8348 0.2506 ] Network output: [ 7.11e-05 1 -6.016e-05 2.854e-07 -1.281e-07 0.9999 2.151e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001121 Epoch 10462 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00825 0.9969 0.9931 -9.792e-08 4.396e-08 -0.006557 -7.38e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003405 -0.006281 0.005122 0.9699 0.9743 0.00694 0.8219 0.8184 0.01561 ] Network output: [ 1 7.615e-06 0.0002686 -1.028e-06 4.614e-07 -0.0002149 -7.746e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03638 -0.151 0.1802 0.9833 0.9931 0.2389 0.4268 0.8676 0.7064 ] Network output: [ -0.008286 1.003 1.007 -9.905e-08 4.447e-08 0.007005 -7.465e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007057 0.0006605 0.004284 0.003005 0.9889 0.9919 0.007198 0.849 0.8911 0.01108 ] Network output: [ -8.245e-05 0.0009001 1 -3.23e-06 1.45e-06 0.9988 -2.434e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.227 0.108 0.3526 0.1407 0.9849 0.9939 0.2278 0.4307 0.8744 0.6999 ] Network output: [ 0.001949 -0.009622 0.9949 1.98e-06 -8.889e-07 1.011 1.492e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.1961 0.9873 0.9919 0.1132 0.7291 0.8601 0.3042 ] Network output: [ -0.00187 0.009062 1.004 2.189e-06 -9.828e-07 0.9902 1.65e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0954 0.09345 0.1647 0.1971 0.9851 0.991 0.09541 0.6526 0.8348 0.2506 ] Network output: [ 7.109e-05 1 -6.02e-05 2.851e-07 -1.28e-07 0.9999 2.148e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001121 Epoch 10463 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008249 0.9969 0.9931 -9.784e-08 4.392e-08 -0.006556 -7.373e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003405 -0.006281 0.005122 0.9699 0.9743 0.006941 0.8219 0.8184 0.01561 ] Network output: [ 1 7.504e-06 0.0002685 -1.026e-06 4.608e-07 -0.0002148 -7.736e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03638 -0.151 0.1802 0.9833 0.9931 0.2389 0.4268 0.8676 0.7064 ] Network output: [ -0.008285 1.003 1.007 -9.895e-08 4.442e-08 0.007004 -7.457e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007058 0.0006605 0.004284 0.003005 0.9889 0.9919 0.007198 0.849 0.8911 0.01108 ] Network output: [ -8.233e-05 0.0008995 1 -3.226e-06 1.448e-06 0.9988 -2.431e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.227 0.108 0.3526 0.1407 0.9849 0.9939 0.2278 0.4307 0.8744 0.6999 ] Network output: [ 0.001948 -0.009616 0.9949 1.978e-06 -8.878e-07 1.011 1.49e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.1961 0.9873 0.9919 0.1132 0.7291 0.86 0.3042 ] Network output: [ -0.001869 0.009057 1.004 2.186e-06 -9.816e-07 0.9902 1.648e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0954 0.09345 0.1647 0.1971 0.9851 0.991 0.09541 0.6526 0.8347 0.2506 ] Network output: [ 7.108e-05 1 -6.023e-05 2.847e-07 -1.278e-07 0.9999 2.146e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000112 Epoch 10464 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008248 0.9969 0.9931 -9.775e-08 4.388e-08 -0.006555 -7.367e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003405 -0.00628 0.005121 0.9699 0.9743 0.006941 0.8219 0.8184 0.01561 ] Network output: [ 1 7.392e-06 0.0002684 -1.025e-06 4.602e-07 -0.0002147 -7.726e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03638 -0.151 0.1802 0.9833 0.9931 0.2389 0.4267 0.8676 0.7064 ] Network output: [ -0.008285 1.003 1.007 -9.886e-08 4.438e-08 0.007004 -7.45e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007058 0.0006605 0.004283 0.003005 0.9889 0.9919 0.007199 0.849 0.8911 0.01108 ] Network output: [ -8.221e-05 0.0008988 1 -3.222e-06 1.447e-06 0.9988 -2.428e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.227 0.108 0.3526 0.1406 0.9849 0.9939 0.2278 0.4307 0.8744 0.6999 ] Network output: [ 0.001946 -0.00961 0.9949 1.975e-06 -8.867e-07 1.011 1.488e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.729 0.86 0.3042 ] Network output: [ -0.001868 0.009052 1.004 2.184e-06 -9.803e-07 0.9902 1.646e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0954 0.09345 0.1647 0.1971 0.9851 0.991 0.09541 0.6526 0.8347 0.2506 ] Network output: [ 7.107e-05 1 -6.026e-05 2.844e-07 -1.277e-07 0.9999 2.143e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001119 Epoch 10465 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008248 0.9969 0.9931 -9.766e-08 4.384e-08 -0.006554 -7.36e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003405 -0.00628 0.005121 0.9699 0.9743 0.006941 0.8219 0.8184 0.0156 ] Network output: [ 1 7.28e-06 0.0002682 -1.024e-06 4.596e-07 -0.0002146 -7.716e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03638 -0.151 0.1802 0.9833 0.9931 0.239 0.4267 0.8676 0.7064 ] Network output: [ -0.008284 1.003 1.007 -9.876e-08 4.434e-08 0.007004 -7.443e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007058 0.0006605 0.004283 0.003004 0.9889 0.9919 0.007199 0.849 0.8911 0.01108 ] Network output: [ -8.209e-05 0.0008981 1 -3.218e-06 1.445e-06 0.9989 -2.425e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.227 0.108 0.3526 0.1406 0.9849 0.9939 0.2278 0.4307 0.8744 0.6999 ] Network output: [ 0.001945 -0.009604 0.9949 1.973e-06 -8.855e-07 1.011 1.487e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.729 0.86 0.3042 ] Network output: [ -0.001866 0.009046 1.004 2.181e-06 -9.791e-07 0.9902 1.644e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0954 0.09345 0.1647 0.1971 0.9851 0.991 0.09542 0.6526 0.8347 0.2506 ] Network output: [ 7.106e-05 1 -6.029e-05 2.84e-07 -1.275e-07 0.9999 2.14e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001119 Epoch 10466 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008247 0.9969 0.9931 -9.758e-08 4.381e-08 -0.006554 -7.354e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003405 -0.006279 0.005121 0.9699 0.9743 0.006941 0.8219 0.8184 0.0156 ] Network output: [ 1 7.169e-06 0.0002681 -1.023e-06 4.591e-07 -0.0002145 -7.706e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03638 -0.151 0.1802 0.9833 0.9931 0.239 0.4267 0.8676 0.7064 ] Network output: [ -0.008283 1.003 1.007 -9.866e-08 4.429e-08 0.007003 -7.436e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007058 0.0006605 0.004283 0.003004 0.9889 0.9919 0.007199 0.849 0.8911 0.01108 ] Network output: [ -8.197e-05 0.0008974 1 -3.214e-06 1.443e-06 0.9989 -2.422e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.227 0.108 0.3526 0.1406 0.9849 0.9939 0.2278 0.4307 0.8744 0.6999 ] Network output: [ 0.001944 -0.009598 0.9949 1.97e-06 -8.844e-07 1.011 1.485e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.729 0.86 0.3042 ] Network output: [ -0.001865 0.009041 1.004 2.178e-06 -9.779e-07 0.9902 1.642e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0954 0.09345 0.1647 0.1971 0.9851 0.991 0.09542 0.6526 0.8347 0.2506 ] Network output: [ 7.105e-05 1 -6.032e-05 2.836e-07 -1.273e-07 0.9999 2.138e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001118 Epoch 10467 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008246 0.9969 0.9931 -9.749e-08 4.377e-08 -0.006553 -7.347e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003405 -0.006279 0.00512 0.9699 0.9743 0.006941 0.8219 0.8183 0.0156 ] Network output: [ 1 7.057e-06 0.000268 -1.021e-06 4.585e-07 -0.0002144 -7.696e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03638 -0.151 0.1802 0.9833 0.9931 0.239 0.4267 0.8676 0.7064 ] Network output: [ -0.008282 1.003 1.007 -9.857e-08 4.425e-08 0.007003 -7.428e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007059 0.0006606 0.004283 0.003004 0.9889 0.9919 0.007199 0.849 0.8911 0.01108 ] Network output: [ -8.185e-05 0.0008968 1 -3.21e-06 1.441e-06 0.9989 -2.419e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.227 0.108 0.3526 0.1406 0.9849 0.9939 0.2278 0.4307 0.8744 0.6999 ] Network output: [ 0.001942 -0.009591 0.9949 1.968e-06 -8.833e-07 1.011 1.483e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.729 0.86 0.3042 ] Network output: [ -0.001864 0.009036 1.004 2.176e-06 -9.767e-07 0.9902 1.64e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0954 0.09345 0.1647 0.1971 0.9851 0.991 0.09542 0.6526 0.8347 0.2506 ] Network output: [ 7.104e-05 1 -6.035e-05 2.833e-07 -1.272e-07 0.9999 2.135e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001117 Epoch 10468 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008245 0.9969 0.9931 -9.74e-08 4.373e-08 -0.006552 -7.341e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003405 -0.006279 0.00512 0.9699 0.9743 0.006941 0.8219 0.8183 0.0156 ] Network output: [ 1 6.946e-06 0.0002679 -1.02e-06 4.579e-07 -0.0002143 -7.687e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03638 -0.151 0.1802 0.9833 0.9931 0.239 0.4267 0.8676 0.7064 ] Network output: [ -0.008282 1.003 1.007 -9.847e-08 4.421e-08 0.007003 -7.421e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007059 0.0006606 0.004283 0.003004 0.9889 0.9919 0.007199 0.849 0.8911 0.01107 ] Network output: [ -8.172e-05 0.0008961 1 -3.206e-06 1.439e-06 0.9989 -2.416e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.227 0.108 0.3526 0.1406 0.9849 0.9939 0.2278 0.4307 0.8744 0.6999 ] Network output: [ 0.001941 -0.009585 0.9949 1.965e-06 -8.822e-07 1.011 1.481e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.729 0.86 0.3042 ] Network output: [ -0.001863 0.00903 1.004 2.173e-06 -9.754e-07 0.9902 1.637e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0954 0.09346 0.1647 0.1971 0.9851 0.991 0.09542 0.6526 0.8347 0.2506 ] Network output: [ 7.103e-05 1 -6.039e-05 2.829e-07 -1.27e-07 0.9999 2.132e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001117 Epoch 10469 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008245 0.9969 0.9931 -9.732e-08 4.369e-08 -0.006552 -7.334e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003405 -0.006278 0.00512 0.9699 0.9743 0.006941 0.8219 0.8183 0.0156 ] Network output: [ 1 6.835e-06 0.0002678 -1.019e-06 4.573e-07 -0.0002142 -7.677e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03639 -0.151 0.1802 0.9833 0.9931 0.239 0.4267 0.8676 0.7064 ] Network output: [ -0.008281 1.003 1.007 -9.838e-08 4.416e-08 0.007002 -7.414e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007059 0.0006606 0.004283 0.003004 0.9889 0.9919 0.0072 0.849 0.8911 0.01107 ] Network output: [ -8.16e-05 0.0008954 1 -3.202e-06 1.437e-06 0.9989 -2.413e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.227 0.108 0.3526 0.1406 0.9849 0.9939 0.2278 0.4307 0.8744 0.6999 ] Network output: [ 0.001939 -0.009579 0.9949 1.963e-06 -8.811e-07 1.011 1.479e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.729 0.86 0.3042 ] Network output: [ -0.001861 0.009025 1.004 2.17e-06 -9.742e-07 0.9902 1.635e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0954 0.09346 0.1647 0.1971 0.9851 0.991 0.09542 0.6526 0.8347 0.2506 ] Network output: [ 7.102e-05 1 -6.042e-05 2.826e-07 -1.269e-07 0.9999 2.13e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001116 Epoch 10470 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008244 0.9969 0.9931 -9.723e-08 4.365e-08 -0.006551 -7.328e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003405 -0.006278 0.005119 0.9699 0.9743 0.006941 0.8219 0.8183 0.0156 ] Network output: [ 1 6.723e-06 0.0002677 -1.017e-06 4.567e-07 -0.0002141 -7.667e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2125 -0.03639 -0.151 0.1802 0.9833 0.9931 0.239 0.4267 0.8676 0.7064 ] Network output: [ -0.00828 1.003 1.007 -9.828e-08 4.412e-08 0.007002 -7.407e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007059 0.0006606 0.004283 0.003003 0.9889 0.9919 0.0072 0.849 0.8911 0.01107 ] Network output: [ -8.148e-05 0.0008947 1 -3.198e-06 1.435e-06 0.9989 -2.41e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.227 0.108 0.3526 0.1406 0.9849 0.9939 0.2278 0.4307 0.8744 0.6999 ] Network output: [ 0.001938 -0.009573 0.9949 1.96e-06 -8.799e-07 1.011 1.477e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.729 0.86 0.3042 ] Network output: [ -0.00186 0.00902 1.004 2.167e-06 -9.73e-07 0.9902 1.633e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0954 0.09346 0.1647 0.1971 0.9851 0.991 0.09542 0.6525 0.8347 0.2506 ] Network output: [ 7.101e-05 1 -6.045e-05 2.822e-07 -1.267e-07 0.9999 2.127e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001115 Epoch 10471 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008243 0.9969 0.9931 -9.715e-08 4.361e-08 -0.00655 -7.321e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003536 -0.003405 -0.006277 0.005119 0.9699 0.9743 0.006941 0.8219 0.8183 0.0156 ] Network output: [ 1 6.612e-06 0.0002676 -1.016e-06 4.561e-07 -0.000214 -7.657e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.03639 -0.151 0.1801 0.9833 0.9931 0.239 0.4267 0.8676 0.7064 ] Network output: [ -0.008279 1.003 1.007 -9.819e-08 4.408e-08 0.007001 -7.4e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00706 0.0006606 0.004283 0.003003 0.9889 0.9919 0.0072 0.849 0.8911 0.01107 ] Network output: [ -8.136e-05 0.0008941 1 -3.193e-06 1.434e-06 0.9989 -2.407e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.227 0.108 0.3526 0.1406 0.9849 0.9939 0.2278 0.4307 0.8744 0.6999 ] Network output: [ 0.001937 -0.009567 0.9949 1.958e-06 -8.788e-07 1.011 1.475e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.729 0.86 0.3042 ] Network output: [ -0.001859 0.009015 1.004 2.165e-06 -9.718e-07 0.9902 1.631e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09541 0.09346 0.1647 0.1971 0.9851 0.991 0.09542 0.6525 0.8347 0.2506 ] Network output: [ 7.1e-05 1 -6.048e-05 2.819e-07 -1.265e-07 0.9999 2.124e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001115 Epoch 10472 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008242 0.9969 0.9931 -9.706e-08 4.357e-08 -0.006549 -7.315e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003405 -0.006277 0.005119 0.9699 0.9743 0.006941 0.8218 0.8183 0.0156 ] Network output: [ 1 6.501e-06 0.0002674 -1.015e-06 4.556e-07 -0.0002139 -7.648e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.03639 -0.151 0.1801 0.9833 0.9931 0.239 0.4267 0.8676 0.7064 ] Network output: [ -0.008279 1.003 1.007 -9.809e-08 4.404e-08 0.007001 -7.392e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00706 0.0006606 0.004282 0.003003 0.9889 0.9919 0.0072 0.849 0.8911 0.01107 ] Network output: [ -8.124e-05 0.0008934 1 -3.189e-06 1.432e-06 0.9989 -2.404e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2271 0.1081 0.3527 0.1406 0.9849 0.9939 0.2278 0.4307 0.8744 0.6999 ] Network output: [ 0.001935 -0.009561 0.9949 1.955e-06 -8.777e-07 1.011 1.473e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.729 0.86 0.3042 ] Network output: [ -0.001858 0.009009 1.004 2.162e-06 -9.706e-07 0.9902 1.629e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09541 0.09346 0.1647 0.1971 0.9851 0.991 0.09542 0.6525 0.8347 0.2506 ] Network output: [ 7.099e-05 1 -6.052e-05 2.815e-07 -1.264e-07 0.9999 2.122e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001114 Epoch 10473 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008242 0.9969 0.9931 -9.697e-08 4.353e-08 -0.006549 -7.308e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003405 -0.006276 0.005119 0.9699 0.9743 0.006941 0.8218 0.8183 0.0156 ] Network output: [ 1 6.39e-06 0.0002673 -1.013e-06 4.55e-07 -0.0002138 -7.638e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.03639 -0.151 0.1801 0.9833 0.9931 0.239 0.4267 0.8676 0.7064 ] Network output: [ -0.008278 1.003 1.007 -9.8e-08 4.399e-08 0.007001 -7.385e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00706 0.0006607 0.004282 0.003003 0.9889 0.9919 0.007201 0.849 0.8911 0.01107 ] Network output: [ -8.112e-05 0.0008927 1 -3.185e-06 1.43e-06 0.9989 -2.401e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2271 0.1081 0.3527 0.1406 0.9849 0.9939 0.2279 0.4306 0.8744 0.6999 ] Network output: [ 0.001934 -0.009555 0.9949 1.953e-06 -8.766e-07 1.011 1.472e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.729 0.86 0.3042 ] Network output: [ -0.001857 0.009004 1.004 2.159e-06 -9.694e-07 0.9902 1.627e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09541 0.09346 0.1647 0.1971 0.9851 0.991 0.09542 0.6525 0.8347 0.2506 ] Network output: [ 7.098e-05 1 -6.055e-05 2.812e-07 -1.262e-07 0.9999 2.119e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001113 Epoch 10474 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008241 0.9969 0.9931 -9.689e-08 4.35e-08 -0.006548 -7.302e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003405 -0.006276 0.005118 0.9699 0.9743 0.006942 0.8218 0.8183 0.0156 ] Network output: [ 1 6.279e-06 0.0002672 -1.012e-06 4.544e-07 -0.0002137 -7.628e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.03639 -0.1509 0.1801 0.9833 0.9931 0.239 0.4267 0.8676 0.7064 ] Network output: [ -0.008277 1.003 1.007 -9.79e-08 4.395e-08 0.007 -7.378e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00706 0.0006607 0.004282 0.003003 0.9889 0.9919 0.007201 0.849 0.8911 0.01107 ] Network output: [ -8.1e-05 0.000892 1 -3.181e-06 1.428e-06 0.9989 -2.397e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2271 0.1081 0.3527 0.1406 0.9849 0.9939 0.2279 0.4306 0.8744 0.6999 ] Network output: [ 0.001932 -0.009549 0.9949 1.95e-06 -8.755e-07 1.011 1.47e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.729 0.86 0.3042 ] Network output: [ -0.001855 0.008999 1.004 2.157e-06 -9.681e-07 0.9902 1.625e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09541 0.09346 0.1647 0.1971 0.9851 0.991 0.09542 0.6525 0.8347 0.2506 ] Network output: [ 7.097e-05 1 -6.058e-05 2.808e-07 -1.261e-07 0.9999 2.116e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001113 Epoch 10475 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00824 0.9969 0.9931 -9.68e-08 4.346e-08 -0.006547 -7.295e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003405 -0.006275 0.005118 0.9699 0.9743 0.006942 0.8218 0.8183 0.0156 ] Network output: [ 1 6.168e-06 0.0002671 -1.011e-06 4.538e-07 -0.0002136 -7.618e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.03639 -0.1509 0.1801 0.9833 0.9931 0.239 0.4267 0.8676 0.7064 ] Network output: [ -0.008277 1.003 1.007 -9.781e-08 4.391e-08 0.007 -7.371e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00706 0.0006607 0.004282 0.003002 0.9889 0.9919 0.007201 0.849 0.8911 0.01107 ] Network output: [ -8.088e-05 0.0008914 1 -3.177e-06 1.426e-06 0.9989 -2.394e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2271 0.1081 0.3527 0.1406 0.9849 0.9939 0.2279 0.4306 0.8744 0.6999 ] Network output: [ 0.001931 -0.009542 0.9949 1.948e-06 -8.744e-07 1.011 1.468e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.729 0.86 0.3042 ] Network output: [ -0.001854 0.008993 1.004 2.154e-06 -9.669e-07 0.9902 1.623e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09541 0.09346 0.1647 0.1971 0.9851 0.991 0.09542 0.6525 0.8347 0.2506 ] Network output: [ 7.096e-05 1 -6.061e-05 2.805e-07 -1.259e-07 0.9999 2.114e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001112 Epoch 10476 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008239 0.9969 0.9931 -9.672e-08 4.342e-08 -0.006547 -7.289e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003405 -0.006275 0.005118 0.9699 0.9743 0.006942 0.8218 0.8183 0.0156 ] Network output: [ 1 6.057e-06 0.000267 -1.01e-06 4.532e-07 -0.0002136 -7.609e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.03639 -0.1509 0.1801 0.9833 0.9931 0.239 0.4267 0.8676 0.7064 ] Network output: [ -0.008276 1.003 1.007 -9.771e-08 4.387e-08 0.006999 -7.364e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007061 0.0006607 0.004282 0.003002 0.9889 0.9919 0.007201 0.849 0.8911 0.01107 ] Network output: [ -8.076e-05 0.0008907 1 -3.173e-06 1.425e-06 0.9989 -2.391e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2271 0.1081 0.3527 0.1406 0.9849 0.9939 0.2279 0.4306 0.8744 0.6999 ] Network output: [ 0.00193 -0.009536 0.9949 1.945e-06 -8.733e-07 1.011 1.466e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.729 0.86 0.3042 ] Network output: [ -0.001853 0.008988 1.004 2.151e-06 -9.657e-07 0.9903 1.621e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09541 0.09346 0.1647 0.1971 0.9851 0.991 0.09542 0.6525 0.8347 0.2506 ] Network output: [ 7.095e-05 1 -6.064e-05 2.801e-07 -1.257e-07 0.9999 2.111e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001111 Epoch 10477 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008239 0.9969 0.9931 -9.663e-08 4.338e-08 -0.006546 -7.282e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003405 -0.006274 0.005117 0.9699 0.9743 0.006942 0.8218 0.8183 0.0156 ] Network output: [ 1 5.946e-06 0.0002669 -1.008e-06 4.527e-07 -0.0002135 -7.599e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.03639 -0.1509 0.1801 0.9833 0.9931 0.239 0.4267 0.8676 0.7064 ] Network output: [ -0.008275 1.003 1.007 -9.762e-08 4.382e-08 0.006999 -7.357e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007061 0.0006607 0.004282 0.003002 0.9889 0.9919 0.007202 0.849 0.8911 0.01107 ] Network output: [ -8.064e-05 0.00089 1 -3.169e-06 1.423e-06 0.9989 -2.388e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2271 0.1081 0.3527 0.1406 0.9849 0.9939 0.2279 0.4306 0.8744 0.6999 ] Network output: [ 0.001928 -0.00953 0.9949 1.943e-06 -8.722e-07 1.011 1.464e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.729 0.86 0.3042 ] Network output: [ -0.001852 0.008983 1.004 2.148e-06 -9.645e-07 0.9903 1.619e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09541 0.09346 0.1647 0.1971 0.9851 0.991 0.09543 0.6525 0.8347 0.2506 ] Network output: [ 7.094e-05 1 -6.068e-05 2.797e-07 -1.256e-07 0.9999 2.108e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001111 Epoch 10478 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008238 0.9969 0.9931 -9.654e-08 4.334e-08 -0.006545 -7.276e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003405 -0.006274 0.005117 0.9699 0.9743 0.006942 0.8218 0.8183 0.01559 ] Network output: [ 1 5.835e-06 0.0002667 -1.007e-06 4.521e-07 -0.0002134 -7.589e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.0364 -0.1509 0.1801 0.9833 0.9931 0.239 0.4267 0.8676 0.7064 ] Network output: [ -0.008274 1.003 1.007 -9.752e-08 4.378e-08 0.006999 -7.349e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007061 0.0006608 0.004282 0.003002 0.9889 0.9919 0.007202 0.849 0.8911 0.01107 ] Network output: [ -8.052e-05 0.0008893 1 -3.165e-06 1.421e-06 0.9989 -2.385e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2271 0.1081 0.3527 0.1406 0.9849 0.9939 0.2279 0.4306 0.8744 0.6999 ] Network output: [ 0.001927 -0.009524 0.9949 1.94e-06 -8.711e-07 1.011 1.462e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.729 0.86 0.3042 ] Network output: [ -0.001851 0.008978 1.004 2.146e-06 -9.633e-07 0.9903 1.617e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09541 0.09346 0.1647 0.1971 0.9851 0.991 0.09543 0.6525 0.8347 0.2506 ] Network output: [ 7.093e-05 1 -6.071e-05 2.794e-07 -1.254e-07 0.9999 2.106e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000111 Epoch 10479 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008237 0.9969 0.9931 -9.646e-08 4.33e-08 -0.006544 -7.269e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003406 -0.006273 0.005117 0.9699 0.9743 0.006942 0.8218 0.8183 0.01559 ] Network output: [ 1 5.724e-06 0.0002666 -1.006e-06 4.515e-07 -0.0002133 -7.58e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.0364 -0.1509 0.1801 0.9833 0.9931 0.239 0.4267 0.8676 0.7064 ] Network output: [ -0.008274 1.003 1.007 -9.743e-08 4.374e-08 0.006998 -7.342e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007061 0.0006608 0.004282 0.003002 0.9889 0.9919 0.007202 0.8489 0.8911 0.01107 ] Network output: [ -8.04e-05 0.0008887 1 -3.161e-06 1.419e-06 0.9989 -2.382e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2271 0.1081 0.3527 0.1406 0.9849 0.9939 0.2279 0.4306 0.8744 0.6999 ] Network output: [ 0.001926 -0.009518 0.9949 1.938e-06 -8.7e-07 1.011 1.46e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.729 0.86 0.3042 ] Network output: [ -0.001849 0.008972 1.004 2.143e-06 -9.621e-07 0.9903 1.615e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09541 0.09346 0.1647 0.1971 0.9851 0.991 0.09543 0.6525 0.8347 0.2506 ] Network output: [ 7.092e-05 1 -6.074e-05 2.79e-07 -1.253e-07 0.9999 2.103e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001109 Epoch 10480 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008236 0.9969 0.9931 -9.637e-08 4.326e-08 -0.006544 -7.263e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003406 -0.006273 0.005116 0.9699 0.9743 0.006942 0.8218 0.8183 0.01559 ] Network output: [ 1 5.613e-06 0.0002665 -1.004e-06 4.509e-07 -0.0002132 -7.57e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.0364 -0.1509 0.1801 0.9833 0.9931 0.239 0.4267 0.8676 0.7064 ] Network output: [ -0.008273 1.003 1.007 -9.733e-08 4.37e-08 0.006998 -7.335e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007062 0.0006608 0.004282 0.003001 0.9889 0.9919 0.007202 0.8489 0.8911 0.01107 ] Network output: [ -8.028e-05 0.000888 1 -3.157e-06 1.417e-06 0.9989 -2.379e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2271 0.1081 0.3527 0.1406 0.9849 0.9939 0.2279 0.4306 0.8744 0.6999 ] Network output: [ 0.001924 -0.009512 0.9949 1.935e-06 -8.689e-07 1.011 1.459e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.729 0.86 0.3042 ] Network output: [ -0.001848 0.008967 1.004 2.14e-06 -9.609e-07 0.9903 1.613e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09541 0.09347 0.1647 0.1971 0.9851 0.991 0.09543 0.6525 0.8347 0.2506 ] Network output: [ 7.091e-05 1 -6.077e-05 2.787e-07 -1.251e-07 0.9999 2.1e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001109 Epoch 10481 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008236 0.9969 0.9931 -9.629e-08 4.323e-08 -0.006543 -7.256e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003406 -0.006272 0.005116 0.9699 0.9743 0.006942 0.8218 0.8183 0.01559 ] Network output: [ 1 5.503e-06 0.0002664 -1.003e-06 4.504e-07 -0.0002131 -7.56e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.0364 -0.1509 0.1801 0.9833 0.9931 0.239 0.4267 0.8676 0.7064 ] Network output: [ -0.008272 1.003 1.007 -9.724e-08 4.365e-08 0.006998 -7.328e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007062 0.0006608 0.004281 0.003001 0.9889 0.9919 0.007203 0.8489 0.8911 0.01107 ] Network output: [ -8.016e-05 0.0008873 1 -3.153e-06 1.415e-06 0.9989 -2.376e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2271 0.1081 0.3527 0.1406 0.9849 0.9939 0.2279 0.4306 0.8744 0.6999 ] Network output: [ 0.001923 -0.009506 0.9949 1.933e-06 -8.678e-07 1.011 1.457e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7289 0.86 0.3042 ] Network output: [ -0.001847 0.008962 1.004 2.138e-06 -9.597e-07 0.9903 1.611e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09541 0.09347 0.1647 0.1971 0.9851 0.991 0.09543 0.6525 0.8347 0.2506 ] Network output: [ 7.09e-05 1 -6.081e-05 2.783e-07 -1.25e-07 0.9999 2.098e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001108 Epoch 10482 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008235 0.9969 0.9931 -9.62e-08 4.319e-08 -0.006542 -7.25e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003406 -0.006272 0.005116 0.9699 0.9743 0.006942 0.8218 0.8183 0.01559 ] Network output: [ 1 5.392e-06 0.0002663 -1.002e-06 4.498e-07 -0.000213 -7.551e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.0364 -0.1509 0.1801 0.9833 0.9931 0.239 0.4267 0.8676 0.7064 ] Network output: [ -0.008271 1.003 1.007 -9.714e-08 4.361e-08 0.006997 -7.321e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007062 0.0006608 0.004281 0.003001 0.9889 0.9919 0.007203 0.8489 0.8911 0.01107 ] Network output: [ -8.004e-05 0.0008866 1 -3.149e-06 1.414e-06 0.9989 -2.373e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2271 0.1081 0.3527 0.1406 0.9849 0.9939 0.2279 0.4306 0.8744 0.6999 ] Network output: [ 0.001921 -0.0095 0.9949 1.93e-06 -8.667e-07 1.011 1.455e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7289 0.86 0.3042 ] Network output: [ -0.001846 0.008957 1.004 2.135e-06 -9.585e-07 0.9903 1.609e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09541 0.09347 0.1647 0.1971 0.9851 0.991 0.09543 0.6525 0.8347 0.2507 ] Network output: [ 7.089e-05 1 -6.084e-05 2.78e-07 -1.248e-07 0.9999 2.095e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001107 Epoch 10483 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008234 0.9969 0.9931 -9.611e-08 4.315e-08 -0.006541 -7.244e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003406 -0.006271 0.005115 0.9699 0.9743 0.006942 0.8218 0.8183 0.01559 ] Network output: [ 1 5.282e-06 0.0002662 -1.001e-06 4.492e-07 -0.0002129 -7.541e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.0364 -0.1509 0.1801 0.9833 0.9931 0.239 0.4267 0.8676 0.7064 ] Network output: [ -0.008271 1.003 1.007 -9.705e-08 4.357e-08 0.006997 -7.314e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007062 0.0006608 0.004281 0.003001 0.9889 0.9919 0.007203 0.8489 0.8911 0.01106 ] Network output: [ -7.992e-05 0.000886 1 -3.145e-06 1.412e-06 0.9989 -2.37e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2271 0.1081 0.3527 0.1406 0.9849 0.9939 0.2279 0.4306 0.8744 0.6999 ] Network output: [ 0.00192 -0.009493 0.9949 1.928e-06 -8.656e-07 1.011 1.453e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7289 0.86 0.3042 ] Network output: [ -0.001844 0.008951 1.004 2.132e-06 -9.573e-07 0.9903 1.607e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09542 0.09347 0.1647 0.1971 0.9851 0.991 0.09543 0.6525 0.8347 0.2507 ] Network output: [ 7.088e-05 1 -6.087e-05 2.776e-07 -1.246e-07 0.9999 2.092e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001107 Epoch 10484 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008233 0.9969 0.9931 -9.603e-08 4.311e-08 -0.006541 -7.237e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003406 -0.006271 0.005115 0.9699 0.9743 0.006942 0.8218 0.8183 0.01559 ] Network output: [ 1 5.171e-06 0.0002661 -9.994e-07 4.487e-07 -0.0002128 -7.532e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.0364 -0.1509 0.1801 0.9833 0.9931 0.2391 0.4267 0.8676 0.7064 ] Network output: [ -0.00827 1.003 1.007 -9.695e-08 4.353e-08 0.006996 -7.307e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007063 0.0006609 0.004281 0.003 0.9889 0.9919 0.007203 0.8489 0.8911 0.01106 ] Network output: [ -7.98e-05 0.0008853 1 -3.141e-06 1.41e-06 0.9989 -2.367e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2271 0.1081 0.3527 0.1406 0.9849 0.9939 0.2279 0.4306 0.8744 0.6999 ] Network output: [ 0.001919 -0.009487 0.9949 1.926e-06 -8.645e-07 1.011 1.451e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7289 0.86 0.3042 ] Network output: [ -0.001843 0.008946 1.004 2.13e-06 -9.561e-07 0.9903 1.605e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09542 0.09347 0.1647 0.1971 0.9851 0.991 0.09543 0.6525 0.8347 0.2507 ] Network output: [ 7.087e-05 1 -6.091e-05 2.773e-07 -1.245e-07 0.9999 2.09e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001106 Epoch 10485 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008233 0.9969 0.9931 -9.594e-08 4.307e-08 -0.00654 -7.231e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003406 -0.00627 0.005115 0.9699 0.9743 0.006943 0.8218 0.8183 0.01559 ] Network output: [ 1 5.061e-06 0.0002659 -9.981e-07 4.481e-07 -0.0002127 -7.522e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.0364 -0.1509 0.1801 0.9833 0.9931 0.2391 0.4267 0.8676 0.7064 ] Network output: [ -0.008269 1.003 1.007 -9.686e-08 4.348e-08 0.006996 -7.3e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007063 0.0006609 0.004281 0.003 0.9889 0.9919 0.007204 0.8489 0.8911 0.01106 ] Network output: [ -7.968e-05 0.0008846 1 -3.137e-06 1.408e-06 0.9989 -2.364e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2271 0.1081 0.3527 0.1406 0.9849 0.9939 0.2279 0.4306 0.8744 0.6999 ] Network output: [ 0.001917 -0.009481 0.9949 1.923e-06 -8.634e-07 1.011 1.449e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7289 0.86 0.3042 ] Network output: [ -0.001842 0.008941 1.004 2.127e-06 -9.549e-07 0.9903 1.603e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09542 0.09347 0.1647 0.1971 0.9851 0.991 0.09543 0.6524 0.8347 0.2507 ] Network output: [ 7.086e-05 1 -6.094e-05 2.769e-07 -1.243e-07 0.9999 2.087e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001105 Epoch 10486 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008232 0.9969 0.9931 -9.586e-08 4.303e-08 -0.006539 -7.224e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003406 -0.00627 0.005114 0.9699 0.9743 0.006943 0.8218 0.8183 0.01559 ] Network output: [ 1 4.951e-06 0.0002658 -9.968e-07 4.475e-07 -0.0002126 -7.512e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.0364 -0.1509 0.1801 0.9833 0.9931 0.2391 0.4267 0.8676 0.7064 ] Network output: [ -0.008268 1.003 1.007 -9.676e-08 4.344e-08 0.006996 -7.292e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007063 0.0006609 0.004281 0.003 0.9889 0.9919 0.007204 0.8489 0.8911 0.01106 ] Network output: [ -7.956e-05 0.0008839 1 -3.133e-06 1.406e-06 0.9989 -2.361e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2271 0.1081 0.3527 0.1406 0.9849 0.9939 0.2279 0.4306 0.8744 0.6999 ] Network output: [ 0.001916 -0.009475 0.9949 1.921e-06 -8.623e-07 1.011 1.448e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7289 0.86 0.3042 ] Network output: [ -0.001841 0.008935 1.004 2.124e-06 -9.537e-07 0.9903 1.601e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09542 0.09347 0.1647 0.1971 0.9851 0.991 0.09543 0.6524 0.8347 0.2507 ] Network output: [ 7.085e-05 1 -6.097e-05 2.766e-07 -1.242e-07 0.9999 2.085e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001105 Epoch 10487 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008231 0.9969 0.9931 -9.577e-08 4.3e-08 -0.006539 -7.218e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003406 -0.00627 0.005114 0.9699 0.9743 0.006943 0.8218 0.8183 0.01559 ] Network output: [ 1 4.84e-06 0.0002657 -9.955e-07 4.469e-07 -0.0002125 -7.503e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.03641 -0.1509 0.1801 0.9833 0.9931 0.2391 0.4267 0.8676 0.7064 ] Network output: [ -0.008268 1.003 1.007 -9.667e-08 4.34e-08 0.006995 -7.285e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007063 0.0006609 0.004281 0.003 0.9889 0.9919 0.007204 0.8489 0.8911 0.01106 ] Network output: [ -7.944e-05 0.0008833 1 -3.129e-06 1.405e-06 0.9989 -2.358e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2271 0.1081 0.3527 0.1406 0.9849 0.9939 0.2279 0.4306 0.8744 0.6999 ] Network output: [ 0.001915 -0.009469 0.9949 1.918e-06 -8.612e-07 1.011 1.446e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7289 0.86 0.3042 ] Network output: [ -0.00184 0.00893 1.004 2.122e-06 -9.525e-07 0.9903 1.599e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09542 0.09347 0.1647 0.1971 0.9851 0.991 0.09543 0.6524 0.8347 0.2507 ] Network output: [ 7.084e-05 1 -6.1e-05 2.762e-07 -1.24e-07 0.9999 2.082e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001104 Epoch 10488 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00823 0.9969 0.9932 -9.569e-08 4.296e-08 -0.006538 -7.211e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003406 -0.006269 0.005114 0.9699 0.9743 0.006943 0.8218 0.8183 0.01559 ] Network output: [ 1 4.73e-06 0.0002656 -9.943e-07 4.464e-07 -0.0002124 -7.493e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.03641 -0.1509 0.1801 0.9833 0.9931 0.2391 0.4267 0.8676 0.7064 ] Network output: [ -0.008267 1.003 1.007 -9.658e-08 4.336e-08 0.006995 -7.278e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007064 0.0006609 0.004281 0.003 0.9889 0.9919 0.007204 0.8489 0.8911 0.01106 ] Network output: [ -7.932e-05 0.0008826 1 -3.125e-06 1.403e-06 0.9989 -2.355e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2271 0.1081 0.3527 0.1406 0.9849 0.9939 0.2279 0.4306 0.8744 0.6999 ] Network output: [ 0.001913 -0.009463 0.9949 1.916e-06 -8.601e-07 1.011 1.444e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7289 0.86 0.3042 ] Network output: [ -0.001838 0.008925 1.004 2.119e-06 -9.513e-07 0.9903 1.597e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09542 0.09347 0.1647 0.1971 0.9851 0.991 0.09543 0.6524 0.8347 0.2507 ] Network output: [ 7.082e-05 1 -6.104e-05 2.759e-07 -1.239e-07 0.9999 2.079e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001103 Epoch 10489 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00823 0.9969 0.9932 -9.56e-08 4.292e-08 -0.006537 -7.205e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003406 -0.006269 0.005113 0.9699 0.9743 0.006943 0.8218 0.8183 0.01559 ] Network output: [ 1 4.62e-06 0.0002655 -9.93e-07 4.458e-07 -0.0002123 -7.484e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.03641 -0.1508 0.1801 0.9833 0.9931 0.2391 0.4267 0.8676 0.7064 ] Network output: [ -0.008266 1.003 1.007 -9.648e-08 4.331e-08 0.006994 -7.271e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007064 0.000661 0.00428 0.002999 0.9889 0.9919 0.007205 0.8489 0.8911 0.01106 ] Network output: [ -7.92e-05 0.0008819 1 -3.121e-06 1.401e-06 0.9989 -2.352e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2271 0.1081 0.3527 0.1406 0.9849 0.9939 0.2279 0.4306 0.8744 0.6999 ] Network output: [ 0.001912 -0.009457 0.9949 1.913e-06 -8.59e-07 1.011 1.442e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7289 0.86 0.3042 ] Network output: [ -0.001837 0.00892 1.004 2.116e-06 -9.501e-07 0.9903 1.595e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09542 0.09347 0.1647 0.1971 0.9851 0.991 0.09544 0.6524 0.8347 0.2507 ] Network output: [ 7.081e-05 1 -6.107e-05 2.756e-07 -1.237e-07 0.9999 2.077e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001103 Epoch 10490 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008229 0.9969 0.9932 -9.552e-08 4.288e-08 -0.006536 -7.198e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003406 -0.006268 0.005113 0.9699 0.9743 0.006943 0.8218 0.8183 0.01558 ] Network output: [ 1 4.51e-06 0.0002654 -9.917e-07 4.452e-07 -0.0002122 -7.474e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.03641 -0.1508 0.1801 0.9833 0.9931 0.2391 0.4267 0.8676 0.7064 ] Network output: [ -0.008266 1.003 1.007 -9.639e-08 4.327e-08 0.006994 -7.264e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007064 0.000661 0.00428 0.002999 0.9889 0.9919 0.007205 0.8489 0.8911 0.01106 ] Network output: [ -7.908e-05 0.0008812 1 -3.117e-06 1.399e-06 0.9989 -2.349e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2271 0.1081 0.3527 0.1406 0.9849 0.9939 0.2279 0.4306 0.8744 0.6999 ] Network output: [ 0.00191 -0.009451 0.9949 1.911e-06 -8.579e-07 1.011 1.44e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7289 0.86 0.3042 ] Network output: [ -0.001836 0.008914 1.004 2.114e-06 -9.489e-07 0.9903 1.593e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09542 0.09347 0.1647 0.1971 0.9851 0.991 0.09544 0.6524 0.8347 0.2507 ] Network output: [ 7.08e-05 1 -6.11e-05 2.752e-07 -1.235e-07 0.9999 2.074e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001102 Epoch 10491 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008228 0.9969 0.9932 -9.543e-08 4.284e-08 -0.006536 -7.192e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003406 -0.006268 0.005113 0.9699 0.9743 0.006943 0.8218 0.8183 0.01558 ] Network output: [ 1 4.4e-06 0.0002653 -9.905e-07 4.447e-07 -0.0002121 -7.465e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.03641 -0.1508 0.1801 0.9833 0.9931 0.2391 0.4267 0.8676 0.7064 ] Network output: [ -0.008265 1.003 1.007 -9.629e-08 4.323e-08 0.006994 -7.257e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007064 0.000661 0.00428 0.002999 0.9889 0.9919 0.007205 0.8489 0.8911 0.01106 ] Network output: [ -7.896e-05 0.0008806 1 -3.113e-06 1.398e-06 0.9989 -2.346e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2271 0.1081 0.3527 0.1406 0.9849 0.9939 0.2279 0.4306 0.8744 0.6999 ] Network output: [ 0.001909 -0.009444 0.9949 1.909e-06 -8.568e-07 1.011 1.438e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7289 0.86 0.3042 ] Network output: [ -0.001835 0.008909 1.004 2.111e-06 -9.477e-07 0.9903 1.591e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09542 0.09347 0.1647 0.1971 0.9851 0.991 0.09544 0.6524 0.8347 0.2507 ] Network output: [ 7.079e-05 1 -6.113e-05 2.749e-07 -1.234e-07 0.9999 2.071e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001101 Epoch 10492 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008227 0.9969 0.9932 -9.535e-08 4.28e-08 -0.006535 -7.186e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003406 -0.006267 0.005112 0.9699 0.9743 0.006943 0.8218 0.8183 0.01558 ] Network output: [ 1 4.29e-06 0.0002651 -9.892e-07 4.441e-07 -0.000212 -7.455e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.03641 -0.1508 0.1801 0.9833 0.9931 0.2391 0.4267 0.8676 0.7064 ] Network output: [ -0.008264 1.003 1.007 -9.62e-08 4.319e-08 0.006993 -7.25e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007064 0.000661 0.00428 0.002999 0.9889 0.9919 0.007205 0.8489 0.8911 0.01106 ] Network output: [ -7.884e-05 0.0008799 1 -3.109e-06 1.396e-06 0.9989 -2.343e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2272 0.1081 0.3527 0.1406 0.9849 0.9939 0.2279 0.4306 0.8744 0.6999 ] Network output: [ 0.001908 -0.009438 0.9949 1.906e-06 -8.557e-07 1.011 1.437e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7289 0.86 0.3042 ] Network output: [ -0.001833 0.008904 1.004 2.108e-06 -9.465e-07 0.9903 1.589e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09542 0.09348 0.1647 0.1971 0.9851 0.991 0.09544 0.6524 0.8347 0.2507 ] Network output: [ 7.078e-05 1 -6.117e-05 2.745e-07 -1.232e-07 0.9999 2.069e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001101 Epoch 10493 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008227 0.9969 0.9932 -9.526e-08 4.277e-08 -0.006534 -7.179e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003406 -0.006267 0.005112 0.9699 0.9743 0.006943 0.8218 0.8183 0.01558 ] Network output: [ 1 4.18e-06 0.000265 -9.88e-07 4.435e-07 -0.0002119 -7.446e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2126 -0.03641 -0.1508 0.1801 0.9833 0.9931 0.2391 0.4267 0.8676 0.7064 ] Network output: [ -0.008263 1.003 1.007 -9.611e-08 4.315e-08 0.006993 -7.243e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007065 0.000661 0.00428 0.002999 0.9889 0.9919 0.007206 0.8489 0.8911 0.01106 ] Network output: [ -7.872e-05 0.0008792 1 -3.105e-06 1.394e-06 0.9989 -2.34e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2272 0.1081 0.3527 0.1406 0.9849 0.9939 0.228 0.4306 0.8744 0.6998 ] Network output: [ 0.001906 -0.009432 0.9949 1.904e-06 -8.547e-07 1.011 1.435e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7289 0.86 0.3042 ] Network output: [ -0.001832 0.008899 1.004 2.106e-06 -9.454e-07 0.9903 1.587e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09542 0.09348 0.1647 0.1971 0.9851 0.991 0.09544 0.6524 0.8347 0.2507 ] Network output: [ 7.077e-05 1 -6.12e-05 2.742e-07 -1.231e-07 0.9999 2.066e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.00011 Epoch 10494 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008226 0.9969 0.9932 -9.518e-08 4.273e-08 -0.006534 -7.173e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003406 -0.006266 0.005112 0.9699 0.9743 0.006943 0.8218 0.8183 0.01558 ] Network output: [ 1 4.07e-06 0.0002649 -9.867e-07 4.43e-07 -0.0002118 -7.436e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03641 -0.1508 0.1801 0.9833 0.9931 0.2391 0.4267 0.8676 0.7064 ] Network output: [ -0.008263 1.003 1.007 -9.601e-08 4.31e-08 0.006993 -7.236e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007065 0.000661 0.00428 0.002998 0.9889 0.9919 0.007206 0.8489 0.8911 0.01106 ] Network output: [ -7.86e-05 0.0008785 1 -3.101e-06 1.392e-06 0.9989 -2.337e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2272 0.1081 0.3527 0.1406 0.9849 0.9939 0.228 0.4306 0.8744 0.6998 ] Network output: [ 0.001905 -0.009426 0.9949 1.901e-06 -8.536e-07 1.011 1.433e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7289 0.86 0.3042 ] Network output: [ -0.001831 0.008893 1.004 2.103e-06 -9.442e-07 0.9903 1.585e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09542 0.09348 0.1647 0.1971 0.9851 0.991 0.09544 0.6524 0.8347 0.2507 ] Network output: [ 7.076e-05 1 -6.123e-05 2.738e-07 -1.229e-07 0.9999 2.064e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001099 Epoch 10495 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008225 0.9969 0.9932 -9.509e-08 4.269e-08 -0.006533 -7.166e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003406 -0.006266 0.005111 0.9699 0.9743 0.006943 0.8218 0.8183 0.01558 ] Network output: [ 1 3.96e-06 0.0002648 -9.854e-07 4.424e-07 -0.0002117 -7.427e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03641 -0.1508 0.1801 0.9833 0.9931 0.2391 0.4267 0.8676 0.7064 ] Network output: [ -0.008262 1.003 1.007 -9.592e-08 4.306e-08 0.006992 -7.229e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007065 0.0006611 0.00428 0.002998 0.9889 0.9919 0.007206 0.8489 0.8911 0.01106 ] Network output: [ -7.848e-05 0.0008779 1 -3.097e-06 1.39e-06 0.9989 -2.334e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2272 0.1081 0.3528 0.1406 0.9849 0.9939 0.228 0.4306 0.8744 0.6998 ] Network output: [ 0.001904 -0.00942 0.9949 1.899e-06 -8.525e-07 1.011 1.431e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7289 0.86 0.3042 ] Network output: [ -0.00183 0.008888 1.004 2.1e-06 -9.43e-07 0.9903 1.583e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09543 0.09348 0.1647 0.1971 0.9851 0.991 0.09544 0.6524 0.8347 0.2507 ] Network output: [ 7.075e-05 1 -6.127e-05 2.735e-07 -1.228e-07 0.9999 2.061e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001099 Epoch 10496 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008224 0.9969 0.9932 -9.501e-08 4.265e-08 -0.006532 -7.16e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003406 -0.006265 0.005111 0.9699 0.9743 0.006944 0.8218 0.8183 0.01558 ] Network output: [ 1 3.851e-06 0.0002647 -9.842e-07 4.418e-07 -0.0002117 -7.417e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03642 -0.1508 0.1801 0.9833 0.9931 0.2391 0.4267 0.8676 0.7064 ] Network output: [ -0.008261 1.003 1.007 -9.583e-08 4.302e-08 0.006992 -7.222e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007065 0.0006611 0.00428 0.002998 0.9889 0.9919 0.007206 0.8489 0.8911 0.01106 ] Network output: [ -7.836e-05 0.0008772 1 -3.093e-06 1.389e-06 0.9989 -2.331e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2272 0.1081 0.3528 0.1406 0.9849 0.9939 0.228 0.4306 0.8744 0.6998 ] Network output: [ 0.001902 -0.009414 0.9949 1.896e-06 -8.514e-07 1.011 1.429e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7289 0.86 0.3042 ] Network output: [ -0.001829 0.008883 1.004 2.098e-06 -9.418e-07 0.9903 1.581e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09543 0.09348 0.1647 0.1971 0.9851 0.991 0.09544 0.6524 0.8347 0.2507 ] Network output: [ 7.074e-05 1 -6.13e-05 2.731e-07 -1.226e-07 0.9999 2.058e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001098 Epoch 10497 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008224 0.9969 0.9932 -9.492e-08 4.261e-08 -0.006531 -7.154e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003406 -0.006265 0.005111 0.9699 0.9743 0.006944 0.8218 0.8183 0.01558 ] Network output: [ 1 3.741e-06 0.0002646 -9.829e-07 4.413e-07 -0.0002116 -7.408e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03642 -0.1508 0.1801 0.9833 0.9931 0.2391 0.4267 0.8676 0.7064 ] Network output: [ -0.00826 1.003 1.007 -9.573e-08 4.298e-08 0.006991 -7.215e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007066 0.0006611 0.004279 0.002998 0.9889 0.9919 0.007206 0.8489 0.8911 0.01106 ] Network output: [ -7.824e-05 0.0008765 1 -3.089e-06 1.387e-06 0.9989 -2.328e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2272 0.1081 0.3528 0.1406 0.9849 0.9939 0.228 0.4306 0.8744 0.6998 ] Network output: [ 0.001901 -0.009408 0.9949 1.894e-06 -8.503e-07 1.011 1.427e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7288 0.86 0.3042 ] Network output: [ -0.001827 0.008878 1.004 2.095e-06 -9.406e-07 0.9903 1.579e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09543 0.09348 0.1647 0.1971 0.9851 0.991 0.09544 0.6524 0.8347 0.2507 ] Network output: [ 7.073e-05 1 -6.133e-05 2.728e-07 -1.225e-07 0.9999 2.056e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001097 Epoch 10498 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008223 0.9969 0.9932 -9.484e-08 4.258e-08 -0.006531 -7.147e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003406 -0.006264 0.00511 0.9699 0.9743 0.006944 0.8218 0.8183 0.01558 ] Network output: [ 1 3.631e-06 0.0002645 -9.817e-07 4.407e-07 -0.0002115 -7.398e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03642 -0.1508 0.1801 0.9833 0.9931 0.2391 0.4267 0.8676 0.7064 ] Network output: [ -0.00826 1.003 1.007 -9.564e-08 4.294e-08 0.006991 -7.208e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007066 0.0006611 0.004279 0.002998 0.9889 0.9919 0.007207 0.8489 0.8911 0.01106 ] Network output: [ -7.812e-05 0.0008758 1 -3.085e-06 1.385e-06 0.9989 -2.325e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2272 0.1081 0.3528 0.1406 0.9849 0.9939 0.228 0.4306 0.8744 0.6998 ] Network output: [ 0.001899 -0.009402 0.9949 1.892e-06 -8.492e-07 1.011 1.426e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7288 0.86 0.3042 ] Network output: [ -0.001826 0.008872 1.004 2.093e-06 -9.394e-07 0.9903 1.577e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09543 0.09348 0.1647 0.1971 0.9851 0.991 0.09544 0.6524 0.8347 0.2507 ] Network output: [ 7.072e-05 1 -6.137e-05 2.724e-07 -1.223e-07 0.9999 2.053e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001097 Epoch 10499 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008222 0.9969 0.9932 -9.475e-08 4.254e-08 -0.00653 -7.141e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003406 -0.006264 0.00511 0.9699 0.9743 0.006944 0.8218 0.8183 0.01558 ] Network output: [ 1 3.522e-06 0.0002643 -9.804e-07 4.402e-07 -0.0002114 -7.389e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03642 -0.1508 0.1801 0.9833 0.9931 0.2391 0.4267 0.8676 0.7064 ] Network output: [ -0.008259 1.003 1.007 -9.555e-08 4.289e-08 0.006991 -7.201e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007066 0.0006611 0.004279 0.002997 0.9889 0.9919 0.007207 0.8489 0.8911 0.01105 ] Network output: [ -7.8e-05 0.0008752 1 -3.081e-06 1.383e-06 0.9989 -2.322e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2272 0.1081 0.3528 0.1406 0.9849 0.9939 0.228 0.4306 0.8744 0.6998 ] Network output: [ 0.001898 -0.009395 0.9949 1.889e-06 -8.482e-07 1.011 1.424e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7288 0.86 0.3042 ] Network output: [ -0.001825 0.008867 1.004 2.09e-06 -9.383e-07 0.9903 1.575e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09543 0.09348 0.1647 0.1971 0.9851 0.991 0.09544 0.6524 0.8347 0.2507 ] Network output: [ 7.071e-05 1 -6.14e-05 2.721e-07 -1.222e-07 0.9999 2.051e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001096 Epoch 10500 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008221 0.9969 0.9932 -9.467e-08 4.25e-08 -0.006529 -7.134e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003407 -0.006263 0.00511 0.9699 0.9743 0.006944 0.8218 0.8183 0.01558 ] Network output: [ 1 3.412e-06 0.0002642 -9.792e-07 4.396e-07 -0.0002113 -7.379e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03642 -0.1508 0.1801 0.9833 0.9931 0.2391 0.4267 0.8675 0.7063 ] Network output: [ -0.008258 1.003 1.007 -9.545e-08 4.285e-08 0.00699 -7.194e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007066 0.0006611 0.004279 0.002997 0.9889 0.9919 0.007207 0.8489 0.8911 0.01105 ] Network output: [ -7.788e-05 0.0008745 1 -3.077e-06 1.382e-06 0.9989 -2.319e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2272 0.1081 0.3528 0.1406 0.9849 0.9939 0.228 0.4306 0.8744 0.6998 ] Network output: [ 0.001897 -0.009389 0.9949 1.887e-06 -8.471e-07 1.011 1.422e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7288 0.86 0.3042 ] Network output: [ -0.001824 0.008862 1.004 2.087e-06 -9.371e-07 0.9903 1.573e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09543 0.09348 0.1647 0.1971 0.9851 0.991 0.09544 0.6523 0.8347 0.2507 ] Network output: [ 7.07e-05 1 -6.143e-05 2.718e-07 -1.22e-07 0.9999 2.048e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001095 Epoch 10501 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008221 0.9969 0.9932 -9.458e-08 4.246e-08 -0.006528 -7.128e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003407 -0.006263 0.00511 0.9699 0.9743 0.006944 0.8218 0.8183 0.01558 ] Network output: [ 1 3.303e-06 0.0002641 -9.779e-07 4.39e-07 -0.0002112 -7.37e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03642 -0.1508 0.1801 0.9833 0.9931 0.2391 0.4267 0.8675 0.7063 ] Network output: [ -0.008258 1.003 1.007 -9.536e-08 4.281e-08 0.00699 -7.187e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007067 0.0006612 0.004279 0.002997 0.9889 0.9919 0.007207 0.8489 0.8911 0.01105 ] Network output: [ -7.776e-05 0.0008738 1 -3.073e-06 1.38e-06 0.9989 -2.316e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2272 0.1081 0.3528 0.1406 0.9849 0.9939 0.228 0.4306 0.8744 0.6998 ] Network output: [ 0.001895 -0.009383 0.9949 1.884e-06 -8.46e-07 1.011 1.42e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7288 0.86 0.3042 ] Network output: [ -0.001822 0.008857 1.004 2.085e-06 -9.359e-07 0.9903 1.571e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09543 0.09348 0.1647 0.1971 0.9851 0.991 0.09545 0.6523 0.8347 0.2507 ] Network output: [ 7.069e-05 1 -6.147e-05 2.714e-07 -1.218e-07 0.9999 2.045e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001095 Epoch 10502 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00822 0.9969 0.9932 -9.45e-08 4.242e-08 -0.006528 -7.122e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003407 -0.006262 0.005109 0.9699 0.9743 0.006944 0.8218 0.8183 0.01558 ] Network output: [ 1 3.193e-06 0.000264 -9.767e-07 4.385e-07 -0.0002111 -7.361e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03642 -0.1508 0.1801 0.9833 0.9931 0.2391 0.4267 0.8675 0.7063 ] Network output: [ -0.008257 1.003 1.007 -9.527e-08 4.277e-08 0.00699 -7.18e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007067 0.0006612 0.004279 0.002997 0.9889 0.9919 0.007208 0.8489 0.8911 0.01105 ] Network output: [ -7.765e-05 0.0008731 1 -3.07e-06 1.378e-06 0.9989 -2.313e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2272 0.1081 0.3528 0.1406 0.9849 0.9939 0.228 0.4306 0.8744 0.6998 ] Network output: [ 0.001894 -0.009377 0.9949 1.882e-06 -8.449e-07 1.011 1.418e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7288 0.86 0.3042 ] Network output: [ -0.001821 0.008851 1.004 2.082e-06 -9.347e-07 0.9903 1.569e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09543 0.09348 0.1647 0.1971 0.9851 0.991 0.09545 0.6523 0.8347 0.2507 ] Network output: [ 7.068e-05 1 -6.15e-05 2.711e-07 -1.217e-07 0.9999 2.043e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001094 Epoch 10503 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008219 0.9969 0.9932 -9.441e-08 4.239e-08 -0.006527 -7.115e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003407 -0.006262 0.005109 0.9699 0.9743 0.006944 0.8218 0.8183 0.01557 ] Network output: [ 1 3.084e-06 0.0002639 -9.754e-07 4.379e-07 -0.000211 -7.351e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03642 -0.1508 0.1801 0.9833 0.9931 0.2392 0.4267 0.8675 0.7063 ] Network output: [ -0.008256 1.003 1.007 -9.517e-08 4.273e-08 0.006989 -7.173e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007067 0.0006612 0.004279 0.002997 0.9889 0.9919 0.007208 0.8489 0.8911 0.01105 ] Network output: [ -7.753e-05 0.0008725 1 -3.066e-06 1.376e-06 0.9989 -2.31e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2272 0.1081 0.3528 0.1406 0.9849 0.9939 0.228 0.4306 0.8744 0.6998 ] Network output: [ 0.001892 -0.009371 0.9949 1.88e-06 -8.439e-07 1.011 1.417e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7288 0.86 0.3042 ] Network output: [ -0.00182 0.008846 1.004 2.079e-06 -9.336e-07 0.9903 1.567e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09543 0.09348 0.1647 0.1971 0.9851 0.991 0.09545 0.6523 0.8347 0.2507 ] Network output: [ 7.067e-05 1 -6.153e-05 2.707e-07 -1.215e-07 0.9999 2.04e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001093 Epoch 10504 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008218 0.9969 0.9932 -9.433e-08 4.235e-08 -0.006526 -7.109e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003407 -0.006262 0.005109 0.9699 0.9743 0.006944 0.8218 0.8183 0.01557 ] Network output: [ 1 2.975e-06 0.0002638 -9.742e-07 4.374e-07 -0.0002109 -7.342e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03642 -0.1508 0.1801 0.9833 0.9931 0.2392 0.4267 0.8675 0.7063 ] Network output: [ -0.008255 1.003 1.007 -9.508e-08 4.268e-08 0.006989 -7.166e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007067 0.0006612 0.004279 0.002996 0.9889 0.9919 0.007208 0.8489 0.8911 0.01105 ] Network output: [ -7.741e-05 0.0008718 1 -3.062e-06 1.375e-06 0.9989 -2.307e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2272 0.1081 0.3528 0.1406 0.9849 0.9939 0.228 0.4306 0.8744 0.6998 ] Network output: [ 0.001891 -0.009365 0.9949 1.877e-06 -8.428e-07 1.011 1.415e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7288 0.86 0.3042 ] Network output: [ -0.001819 0.008841 1.004 2.077e-06 -9.324e-07 0.9903 1.565e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09543 0.09349 0.1647 0.1971 0.9851 0.991 0.09545 0.6523 0.8347 0.2507 ] Network output: [ 7.066e-05 1 -6.157e-05 2.704e-07 -1.214e-07 0.9999 2.038e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001093 Epoch 10505 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008218 0.9969 0.9932 -9.425e-08 4.231e-08 -0.006526 -7.103e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003407 -0.006261 0.005108 0.9699 0.9743 0.006944 0.8218 0.8183 0.01557 ] Network output: [ 1 2.866e-06 0.0002637 -9.73e-07 4.368e-07 -0.0002108 -7.332e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03643 -0.1507 0.1801 0.9833 0.9931 0.2392 0.4267 0.8675 0.7063 ] Network output: [ -0.008255 1.003 1.007 -9.499e-08 4.264e-08 0.006988 -7.159e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007067 0.0006612 0.004278 0.002996 0.9889 0.9919 0.007208 0.8489 0.8911 0.01105 ] Network output: [ -7.729e-05 0.0008711 1 -3.058e-06 1.373e-06 0.9989 -2.304e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2272 0.1081 0.3528 0.1406 0.9849 0.9939 0.228 0.4306 0.8744 0.6998 ] Network output: [ 0.00189 -0.009359 0.9949 1.875e-06 -8.417e-07 1.011 1.413e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1131 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7288 0.86 0.3042 ] Network output: [ -0.001818 0.008836 1.004 2.074e-06 -9.312e-07 0.9903 1.563e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09543 0.09349 0.1647 0.1971 0.9851 0.991 0.09545 0.6523 0.8347 0.2507 ] Network output: [ 7.065e-05 1 -6.16e-05 2.701e-07 -1.212e-07 0.9999 2.035e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001092 Epoch 10506 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008217 0.9969 0.9932 -9.416e-08 4.227e-08 -0.006525 -7.096e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003407 -0.006261 0.005108 0.9699 0.9743 0.006944 0.8217 0.8183 0.01557 ] Network output: [ 1 2.756e-06 0.0002635 -9.717e-07 4.362e-07 -0.0002107 -7.323e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03643 -0.1507 0.18 0.9833 0.9931 0.2392 0.4267 0.8675 0.7063 ] Network output: [ -0.008254 1.003 1.007 -9.489e-08 4.26e-08 0.006988 -7.152e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007068 0.0006612 0.004278 0.002996 0.9889 0.9919 0.007209 0.8489 0.8911 0.01105 ] Network output: [ -7.717e-05 0.0008705 1 -3.054e-06 1.371e-06 0.9989 -2.302e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2272 0.1081 0.3528 0.1406 0.9849 0.9939 0.228 0.4306 0.8744 0.6998 ] Network output: [ 0.001888 -0.009353 0.9949 1.873e-06 -8.407e-07 1.011 1.411e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7288 0.86 0.3042 ] Network output: [ -0.001816 0.00883 1.004 2.072e-06 -9.301e-07 0.9904 1.561e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09543 0.09349 0.1647 0.1971 0.9851 0.991 0.09545 0.6523 0.8347 0.2507 ] Network output: [ 7.064e-05 1 -6.163e-05 2.697e-07 -1.211e-07 0.9999 2.033e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001092 Epoch 10507 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008216 0.9969 0.9932 -9.408e-08 4.223e-08 -0.006524 -7.09e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003407 -0.00626 0.005108 0.9699 0.9743 0.006945 0.8217 0.8183 0.01557 ] Network output: [ 1 2.647e-06 0.0002634 -9.705e-07 4.357e-07 -0.0002106 -7.314e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03643 -0.1507 0.18 0.9833 0.9931 0.2392 0.4267 0.8675 0.7063 ] Network output: [ -0.008253 1.003 1.007 -9.48e-08 4.256e-08 0.006988 -7.145e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007068 0.0006613 0.004278 0.002996 0.9889 0.9919 0.007209 0.8489 0.8911 0.01105 ] Network output: [ -7.705e-05 0.0008698 1 -3.05e-06 1.369e-06 0.9989 -2.299e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2272 0.1081 0.3528 0.1406 0.9849 0.9939 0.228 0.4306 0.8744 0.6998 ] Network output: [ 0.001887 -0.009347 0.9949 1.87e-06 -8.396e-07 1.011 1.409e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7288 0.86 0.3042 ] Network output: [ -0.001815 0.008825 1.004 2.069e-06 -9.289e-07 0.9904 1.559e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09544 0.09349 0.1647 0.1971 0.9851 0.991 0.09545 0.6523 0.8347 0.2507 ] Network output: [ 7.063e-05 1 -6.167e-05 2.694e-07 -1.209e-07 0.9999 2.03e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001091 Epoch 10508 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008215 0.9969 0.9932 -9.399e-08 4.22e-08 -0.006523 -7.084e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003407 -0.00626 0.005107 0.9699 0.9743 0.006945 0.8217 0.8183 0.01557 ] Network output: [ 1 2.538e-06 0.0002633 -9.692e-07 4.351e-07 -0.0002105 -7.304e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03643 -0.1507 0.18 0.9833 0.9931 0.2392 0.4267 0.8675 0.7063 ] Network output: [ -0.008252 1.003 1.007 -9.471e-08 4.252e-08 0.006987 -7.138e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007068 0.0006613 0.004278 0.002996 0.9889 0.9919 0.007209 0.8489 0.8911 0.01105 ] Network output: [ -7.693e-05 0.0008691 1 -3.046e-06 1.368e-06 0.9989 -2.296e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2272 0.1081 0.3528 0.1406 0.9849 0.9939 0.228 0.4306 0.8744 0.6998 ] Network output: [ 0.001886 -0.00934 0.9949 1.868e-06 -8.385e-07 1.011 1.408e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7288 0.86 0.3042 ] Network output: [ -0.001814 0.00882 1.004 2.066e-06 -9.277e-07 0.9904 1.557e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09544 0.09349 0.1647 0.1971 0.9851 0.991 0.09545 0.6523 0.8347 0.2507 ] Network output: [ 7.062e-05 1 -6.17e-05 2.69e-07 -1.208e-07 0.9999 2.028e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000109 Epoch 10509 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008215 0.9969 0.9932 -9.391e-08 4.216e-08 -0.006523 -7.077e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003407 -0.006259 0.005107 0.9699 0.9743 0.006945 0.8217 0.8183 0.01557 ] Network output: [ 1 2.429e-06 0.0002632 -9.68e-07 4.346e-07 -0.0002104 -7.295e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03643 -0.1507 0.18 0.9833 0.9931 0.2392 0.4267 0.8675 0.7063 ] Network output: [ -0.008252 1.003 1.007 -9.462e-08 4.248e-08 0.006987 -7.131e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007068 0.0006613 0.004278 0.002995 0.9889 0.9919 0.007209 0.8489 0.8911 0.01105 ] Network output: [ -7.681e-05 0.0008684 1 -3.042e-06 1.366e-06 0.9989 -2.293e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2272 0.1081 0.3528 0.1406 0.9849 0.9939 0.228 0.4306 0.8744 0.6998 ] Network output: [ 0.001884 -0.009334 0.9949 1.865e-06 -8.375e-07 1.011 1.406e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7288 0.86 0.3042 ] Network output: [ -0.001813 0.008815 1.004 2.064e-06 -9.266e-07 0.9904 1.555e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09544 0.09349 0.1647 0.1971 0.9851 0.991 0.09545 0.6523 0.8347 0.2507 ] Network output: [ 7.061e-05 1 -6.173e-05 2.687e-07 -1.206e-07 0.9999 2.025e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000109 Epoch 10510 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008214 0.9969 0.9932 -9.382e-08 4.212e-08 -0.006522 -7.071e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003407 -0.006259 0.005107 0.9699 0.9743 0.006945 0.8217 0.8183 0.01557 ] Network output: [ 1 2.321e-06 0.0002631 -9.668e-07 4.34e-07 -0.0002103 -7.286e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03643 -0.1507 0.18 0.9833 0.9931 0.2392 0.4267 0.8675 0.7063 ] Network output: [ -0.008251 1.003 1.007 -9.452e-08 4.243e-08 0.006987 -7.124e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007069 0.0006613 0.004278 0.002995 0.9889 0.9919 0.00721 0.8489 0.8911 0.01105 ] Network output: [ -7.669e-05 0.0008678 1 -3.038e-06 1.364e-06 0.9989 -2.29e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2272 0.1081 0.3528 0.1406 0.9849 0.9939 0.228 0.4306 0.8744 0.6998 ] Network output: [ 0.001883 -0.009328 0.9949 1.863e-06 -8.364e-07 1.011 1.404e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7288 0.86 0.3042 ] Network output: [ -0.001812 0.008809 1.004 2.061e-06 -9.254e-07 0.9904 1.553e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09544 0.09349 0.1647 0.1971 0.9851 0.991 0.09545 0.6523 0.8347 0.2507 ] Network output: [ 7.06e-05 1 -6.177e-05 2.684e-07 -1.205e-07 0.9999 2.022e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001089 Epoch 10511 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008213 0.9969 0.9932 -9.374e-08 4.208e-08 -0.006521 -7.065e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003407 -0.006258 0.005106 0.9699 0.9743 0.006945 0.8217 0.8183 0.01557 ] Network output: [ 1 2.212e-06 0.000263 -9.655e-07 4.335e-07 -0.0002103 -7.277e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03643 -0.1507 0.18 0.9833 0.9931 0.2392 0.4267 0.8675 0.7063 ] Network output: [ -0.00825 1.003 1.007 -9.443e-08 4.239e-08 0.006986 -7.117e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007069 0.0006613 0.004278 0.002995 0.9889 0.9919 0.00721 0.8489 0.8911 0.01105 ] Network output: [ -7.657e-05 0.0008671 1 -3.034e-06 1.362e-06 0.9989 -2.287e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2273 0.1081 0.3528 0.1406 0.9849 0.9939 0.228 0.4306 0.8744 0.6998 ] Network output: [ 0.001881 -0.009322 0.9949 1.861e-06 -8.353e-07 1.011 1.402e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7288 0.86 0.3042 ] Network output: [ -0.00181 0.008804 1.004 2.059e-06 -9.242e-07 0.9904 1.552e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09544 0.09349 0.1647 0.1971 0.9851 0.991 0.09545 0.6523 0.8347 0.2507 ] Network output: [ 7.059e-05 1 -6.18e-05 2.68e-07 -1.203e-07 0.9999 2.02e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001088 Epoch 10512 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008212 0.9969 0.9932 -9.366e-08 4.205e-08 -0.006521 -7.058e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003407 -0.006258 0.005106 0.9699 0.9743 0.006945 0.8217 0.8183 0.01557 ] Network output: [ 1 2.103e-06 0.0002629 -9.643e-07 4.329e-07 -0.0002102 -7.267e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03643 -0.1507 0.18 0.9833 0.9931 0.2392 0.4267 0.8675 0.7063 ] Network output: [ -0.008249 1.003 1.007 -9.434e-08 4.235e-08 0.006986 -7.11e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007069 0.0006613 0.004278 0.002995 0.9889 0.9919 0.00721 0.8489 0.8911 0.01105 ] Network output: [ -7.645e-05 0.0008664 1 -3.031e-06 1.361e-06 0.9989 -2.284e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2273 0.1081 0.3528 0.1406 0.9849 0.9939 0.2281 0.4306 0.8744 0.6998 ] Network output: [ 0.00188 -0.009316 0.9949 1.858e-06 -8.343e-07 1.011 1.401e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7288 0.86 0.3042 ] Network output: [ -0.001809 0.008799 1.004 2.056e-06 -9.231e-07 0.9904 1.55e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09544 0.09349 0.1647 0.1971 0.9851 0.991 0.09545 0.6523 0.8347 0.2507 ] Network output: [ 7.058e-05 1 -6.183e-05 2.677e-07 -1.202e-07 0.9999 2.017e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001088 Epoch 10513 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008212 0.9969 0.9932 -9.357e-08 4.201e-08 -0.00652 -7.052e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003407 -0.006257 0.005106 0.9699 0.9743 0.006945 0.8217 0.8183 0.01557 ] Network output: [ 1 1.994e-06 0.0002627 -9.631e-07 4.324e-07 -0.0002101 -7.258e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03644 -0.1507 0.18 0.9833 0.9931 0.2392 0.4267 0.8675 0.7063 ] Network output: [ -0.008249 1.003 1.007 -9.425e-08 4.231e-08 0.006985 -7.103e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007069 0.0006614 0.004277 0.002995 0.9889 0.9919 0.00721 0.8489 0.8911 0.01105 ] Network output: [ -7.633e-05 0.0008657 1 -3.027e-06 1.359e-06 0.9989 -2.281e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2273 0.1081 0.3528 0.1406 0.9849 0.9939 0.2281 0.4306 0.8744 0.6998 ] Network output: [ 0.001879 -0.00931 0.9949 1.856e-06 -8.332e-07 1.011 1.399e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7288 0.86 0.3042 ] Network output: [ -0.001808 0.008794 1.004 2.054e-06 -9.219e-07 0.9904 1.548e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09544 0.09349 0.1647 0.1971 0.9851 0.991 0.09546 0.6523 0.8347 0.2507 ] Network output: [ 7.057e-05 1 -6.187e-05 2.673e-07 -1.2e-07 0.9999 2.015e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001087 Epoch 10514 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008211 0.9969 0.9932 -9.349e-08 4.197e-08 -0.006519 -7.046e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003407 -0.006257 0.005105 0.9699 0.9743 0.006945 0.8217 0.8183 0.01557 ] Network output: [ 1 1.886e-06 0.0002626 -9.618e-07 4.318e-07 -0.00021 -7.249e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03644 -0.1507 0.18 0.9833 0.9931 0.2392 0.4267 0.8675 0.7063 ] Network output: [ -0.008248 1.003 1.007 -9.415e-08 4.227e-08 0.006985 -7.096e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00707 0.0006614 0.004277 0.002994 0.9889 0.9919 0.007211 0.8488 0.8911 0.01104 ] Network output: [ -7.621e-05 0.0008651 1 -3.023e-06 1.357e-06 0.9989 -2.278e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2273 0.1081 0.3528 0.1406 0.9849 0.9939 0.2281 0.4306 0.8744 0.6998 ] Network output: [ 0.001877 -0.009304 0.9949 1.854e-06 -8.322e-07 1.011 1.397e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7287 0.86 0.3042 ] Network output: [ -0.001807 0.008788 1.004 2.051e-06 -9.208e-07 0.9904 1.546e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09544 0.09349 0.1647 0.1971 0.9851 0.991 0.09546 0.6523 0.8347 0.2507 ] Network output: [ 7.056e-05 1 -6.19e-05 2.67e-07 -1.199e-07 0.9999 2.012e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001086 Epoch 10515 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00821 0.9969 0.9932 -9.34e-08 4.193e-08 -0.006518 -7.039e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003407 -0.006256 0.005105 0.9699 0.9743 0.006945 0.8217 0.8183 0.01557 ] Network output: [ 1 1.777e-06 0.0002625 -9.606e-07 4.313e-07 -0.0002099 -7.239e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03644 -0.1507 0.18 0.9833 0.9931 0.2392 0.4267 0.8675 0.7063 ] Network output: [ -0.008247 1.003 1.007 -9.406e-08 4.223e-08 0.006985 -7.089e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00707 0.0006614 0.004277 0.002994 0.9889 0.9919 0.007211 0.8488 0.8911 0.01104 ] Network output: [ -7.61e-05 0.0008644 1 -3.019e-06 1.355e-06 0.9989 -2.275e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2273 0.1081 0.3528 0.1406 0.9849 0.9939 0.2281 0.4306 0.8744 0.6998 ] Network output: [ 0.001876 -0.009298 0.9949 1.851e-06 -8.311e-07 1.011 1.395e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7287 0.86 0.3042 ] Network output: [ -0.001805 0.008783 1.004 2.048e-06 -9.196e-07 0.9904 1.544e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09544 0.09349 0.1647 0.1971 0.9851 0.991 0.09546 0.6523 0.8347 0.2507 ] Network output: [ 7.055e-05 1 -6.194e-05 2.667e-07 -1.197e-07 0.9999 2.01e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001086 Epoch 10516 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008209 0.9969 0.9932 -9.332e-08 4.189e-08 -0.006518 -7.033e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003407 -0.006256 0.005105 0.9699 0.9743 0.006945 0.8217 0.8183 0.01556 ] Network output: [ 1 1.668e-06 0.0002624 -9.594e-07 4.307e-07 -0.0002098 -7.23e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03644 -0.1507 0.18 0.9833 0.9931 0.2392 0.4267 0.8675 0.7063 ] Network output: [ -0.008247 1.003 1.007 -9.397e-08 4.219e-08 0.006984 -7.082e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00707 0.0006614 0.004277 0.002994 0.9889 0.9919 0.007211 0.8488 0.8911 0.01104 ] Network output: [ -7.598e-05 0.0008637 1 -3.015e-06 1.354e-06 0.9989 -2.272e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2273 0.1082 0.3528 0.1405 0.9849 0.9939 0.2281 0.4306 0.8744 0.6998 ] Network output: [ 0.001875 -0.009292 0.9949 1.849e-06 -8.3e-07 1.011 1.393e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7287 0.86 0.3042 ] Network output: [ -0.001804 0.008778 1.004 2.046e-06 -9.184e-07 0.9904 1.542e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09544 0.09349 0.1647 0.1971 0.9851 0.991 0.09546 0.6522 0.8347 0.2507 ] Network output: [ 7.055e-05 1 -6.197e-05 2.663e-07 -1.196e-07 0.9999 2.007e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001085 Epoch 10517 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008209 0.9969 0.9932 -9.324e-08 4.186e-08 -0.006517 -7.027e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003407 -0.006255 0.005104 0.9699 0.9743 0.006945 0.8217 0.8183 0.01556 ] Network output: [ 1 1.56e-06 0.0002623 -9.582e-07 4.302e-07 -0.0002097 -7.221e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2127 -0.03644 -0.1507 0.18 0.9833 0.9931 0.2392 0.4266 0.8675 0.7063 ] Network output: [ -0.008246 1.003 1.007 -9.388e-08 4.215e-08 0.006984 -7.075e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00707 0.0006614 0.004277 0.002994 0.9889 0.9919 0.007211 0.8488 0.8911 0.01104 ] Network output: [ -7.586e-05 0.000863 1 -3.011e-06 1.352e-06 0.9989 -2.269e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2273 0.1082 0.3529 0.1405 0.9849 0.9939 0.2281 0.4306 0.8744 0.6998 ] Network output: [ 0.001873 -0.009285 0.9949 1.847e-06 -8.29e-07 1.011 1.392e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7287 0.86 0.3042 ] Network output: [ -0.001803 0.008773 1.004 2.043e-06 -9.173e-07 0.9904 1.54e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09544 0.0935 0.1647 0.1971 0.9851 0.991 0.09546 0.6522 0.8347 0.2507 ] Network output: [ 7.054e-05 1 -6.2e-05 2.66e-07 -1.194e-07 0.9999 2.005e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001084 Epoch 10518 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008208 0.9969 0.9932 -9.315e-08 4.182e-08 -0.006516 -7.02e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003407 -0.006255 0.005104 0.9699 0.9743 0.006945 0.8217 0.8183 0.01556 ] Network output: [ 1 1.452e-06 0.0002622 -9.569e-07 4.296e-07 -0.0002096 -7.212e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03644 -0.1507 0.18 0.9833 0.9931 0.2392 0.4266 0.8675 0.7063 ] Network output: [ -0.008245 1.003 1.007 -9.379e-08 4.21e-08 0.006984 -7.068e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00707 0.0006614 0.004277 0.002993 0.9889 0.9919 0.007211 0.8488 0.8911 0.01104 ] Network output: [ -7.574e-05 0.0008624 1 -3.007e-06 1.35e-06 0.9989 -2.266e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2273 0.1082 0.3529 0.1405 0.9849 0.9939 0.2281 0.4306 0.8744 0.6998 ] Network output: [ 0.001872 -0.009279 0.9949 1.844e-06 -8.279e-07 1.011 1.39e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7287 0.86 0.3042 ] Network output: [ -0.001802 0.008767 1.004 2.041e-06 -9.161e-07 0.9904 1.538e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09544 0.0935 0.1647 0.1971 0.9851 0.991 0.09546 0.6522 0.8347 0.2507 ] Network output: [ 7.053e-05 1 -6.204e-05 2.657e-07 -1.193e-07 0.9999 2.002e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001084 Epoch 10519 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008207 0.9969 0.9932 -9.307e-08 4.178e-08 -0.006515 -7.014e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003407 -0.006255 0.005104 0.9699 0.9743 0.006946 0.8217 0.8183 0.01556 ] Network output: [ 1 1.343e-06 0.0002621 -9.557e-07 4.291e-07 -0.0002095 -7.203e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03644 -0.1507 0.18 0.9833 0.9931 0.2392 0.4266 0.8675 0.7063 ] Network output: [ -0.008244 1.003 1.007 -9.369e-08 4.206e-08 0.006983 -7.061e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007071 0.0006615 0.004277 0.002993 0.9889 0.9919 0.007212 0.8488 0.8911 0.01104 ] Network output: [ -7.562e-05 0.0008617 1 -3.004e-06 1.348e-06 0.9989 -2.264e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2273 0.1082 0.3529 0.1405 0.9849 0.9939 0.2281 0.4306 0.8744 0.6998 ] Network output: [ 0.00187 -0.009273 0.9949 1.842e-06 -8.269e-07 1.011 1.388e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.196 0.9873 0.9919 0.1132 0.7287 0.86 0.3041 ] Network output: [ -0.001801 0.008762 1.004 2.038e-06 -9.15e-07 0.9904 1.536e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09545 0.0935 0.1647 0.1971 0.9851 0.991 0.09546 0.6522 0.8347 0.2507 ] Network output: [ 7.052e-05 1 -6.207e-05 2.653e-07 -1.191e-07 0.9999 2e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001083 Epoch 10520 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008206 0.9969 0.9932 -9.298e-08 4.174e-08 -0.006515 -7.008e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003407 -0.006254 0.005103 0.9699 0.9743 0.006946 0.8217 0.8183 0.01556 ] Network output: [ 1 1.235e-06 0.000262 -9.545e-07 4.285e-07 -0.0002094 -7.193e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03644 -0.1506 0.18 0.9833 0.9931 0.2392 0.4266 0.8675 0.7063 ] Network output: [ -0.008244 1.003 1.007 -9.36e-08 4.202e-08 0.006983 -7.054e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007071 0.0006615 0.004277 0.002993 0.9889 0.9919 0.007212 0.8488 0.8911 0.01104 ] Network output: [ -7.55e-05 0.000861 1 -3e-06 1.347e-06 0.9989 -2.261e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2273 0.1082 0.3529 0.1405 0.9849 0.9939 0.2281 0.4306 0.8744 0.6998 ] Network output: [ 0.001869 -0.009267 0.9949 1.84e-06 -8.258e-07 1.011 1.386e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.1959 0.9873 0.9919 0.1132 0.7287 0.86 0.3041 ] Network output: [ -0.001799 0.008757 1.004 2.036e-06 -9.138e-07 0.9904 1.534e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09545 0.0935 0.1647 0.1971 0.9851 0.991 0.09546 0.6522 0.8347 0.2507 ] Network output: [ 7.051e-05 1 -6.21e-05 2.65e-07 -1.19e-07 0.9999 1.997e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001082 Epoch 10521 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008206 0.9969 0.9932 -9.29e-08 4.171e-08 -0.006514 -7.001e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003408 -0.006254 0.005103 0.9699 0.9743 0.006946 0.8217 0.8183 0.01556 ] Network output: [ 1 1.127e-06 0.0002618 -9.533e-07 4.28e-07 -0.0002093 -7.184e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03644 -0.1506 0.18 0.9833 0.9931 0.2392 0.4266 0.8675 0.7063 ] Network output: [ -0.008243 1.003 1.007 -9.351e-08 4.198e-08 0.006982 -7.047e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007071 0.0006615 0.004276 0.002993 0.9889 0.9919 0.007212 0.8488 0.8911 0.01104 ] Network output: [ -7.538e-05 0.0008603 1 -2.996e-06 1.345e-06 0.9989 -2.258e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2273 0.1082 0.3529 0.1405 0.9849 0.9939 0.2281 0.4306 0.8744 0.6998 ] Network output: [ 0.001868 -0.009261 0.9949 1.837e-06 -8.248e-07 1.011 1.385e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.1959 0.9873 0.9919 0.1132 0.7287 0.86 0.3041 ] Network output: [ -0.001798 0.008752 1.004 2.033e-06 -9.127e-07 0.9904 1.532e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09545 0.0935 0.1647 0.1971 0.9851 0.991 0.09546 0.6522 0.8347 0.2507 ] Network output: [ 7.05e-05 1 -6.214e-05 2.647e-07 -1.188e-07 0.9999 1.995e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001082 Epoch 10522 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008205 0.9969 0.9932 -9.282e-08 4.167e-08 -0.006513 -6.995e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003408 -0.006253 0.005103 0.9699 0.9743 0.006946 0.8217 0.8183 0.01556 ] Network output: [ 1 1.018e-06 0.0002617 -9.521e-07 4.274e-07 -0.0002092 -7.175e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03645 -0.1506 0.18 0.9833 0.9931 0.2393 0.4266 0.8675 0.7063 ] Network output: [ -0.008242 1.003 1.007 -9.342e-08 4.194e-08 0.006982 -7.04e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007071 0.0006615 0.004276 0.002993 0.9889 0.9919 0.007212 0.8488 0.8911 0.01104 ] Network output: [ -7.526e-05 0.0008597 1 -2.992e-06 1.343e-06 0.9989 -2.255e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2273 0.1082 0.3529 0.1405 0.9849 0.9939 0.2281 0.4306 0.8744 0.6998 ] Network output: [ 0.001866 -0.009255 0.9949 1.835e-06 -8.237e-07 1.011 1.383e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.1959 0.9873 0.9919 0.1133 0.7287 0.86 0.3041 ] Network output: [ -0.001797 0.008746 1.004 2.03e-06 -9.115e-07 0.9904 1.53e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09545 0.0935 0.1647 0.1971 0.9851 0.991 0.09546 0.6522 0.8347 0.2507 ] Network output: [ 7.049e-05 1 -6.217e-05 2.643e-07 -1.187e-07 0.9999 1.992e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001081 Epoch 10523 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008204 0.9969 0.9932 -9.273e-08 4.163e-08 -0.006513 -6.989e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003408 -0.006253 0.005103 0.9699 0.9743 0.006946 0.8217 0.8183 0.01556 ] Network output: [ 1 9.104e-07 0.0002616 -9.508e-07 4.269e-07 -0.0002092 -7.166e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03645 -0.1506 0.18 0.9833 0.9931 0.2393 0.4266 0.8675 0.7063 ] Network output: [ -0.008241 1.003 1.007 -9.333e-08 4.19e-08 0.006982 -7.033e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007072 0.0006615 0.004276 0.002992 0.9889 0.9919 0.007213 0.8488 0.8911 0.01104 ] Network output: [ -7.514e-05 0.000859 1 -2.988e-06 1.342e-06 0.9989 -2.252e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2273 0.1082 0.3529 0.1405 0.9849 0.9939 0.2281 0.4306 0.8744 0.6998 ] Network output: [ 0.001865 -0.009249 0.9949 1.833e-06 -8.227e-07 1.011 1.381e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.1959 0.9873 0.9919 0.1133 0.7287 0.86 0.3041 ] Network output: [ -0.001796 0.008741 1.004 2.028e-06 -9.104e-07 0.9904 1.528e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09545 0.0935 0.1647 0.1971 0.9851 0.991 0.09546 0.6522 0.8347 0.2507 ] Network output: [ 7.048e-05 1 -6.221e-05 2.64e-07 -1.185e-07 0.9999 1.99e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000108 Epoch 10524 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008203 0.9969 0.9932 -9.265e-08 4.159e-08 -0.006512 -6.982e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003408 -0.006252 0.005102 0.9699 0.9743 0.006946 0.8217 0.8183 0.01556 ] Network output: [ 1 8.023e-07 0.0002615 -9.496e-07 4.263e-07 -0.0002091 -7.157e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03645 -0.1506 0.18 0.9833 0.9931 0.2393 0.4266 0.8675 0.7063 ] Network output: [ -0.008241 1.003 1.007 -9.324e-08 4.186e-08 0.006981 -7.027e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007072 0.0006615 0.004276 0.002992 0.9889 0.9919 0.007213 0.8488 0.8911 0.01104 ] Network output: [ -7.503e-05 0.0008583 1 -2.984e-06 1.34e-06 0.9989 -2.249e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2273 0.1082 0.3529 0.1405 0.9849 0.9939 0.2281 0.4306 0.8744 0.6998 ] Network output: [ 0.001864 -0.009243 0.995 1.83e-06 -8.216e-07 1.011 1.379e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.1959 0.9873 0.9919 0.1133 0.7287 0.86 0.3041 ] Network output: [ -0.001795 0.008736 1.004 2.025e-06 -9.093e-07 0.9904 1.526e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09545 0.0935 0.1647 0.1971 0.9851 0.991 0.09546 0.6522 0.8347 0.2507 ] Network output: [ 7.047e-05 1 -6.224e-05 2.637e-07 -1.184e-07 0.9999 1.987e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000108 Epoch 10525 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008203 0.9969 0.9932 -9.257e-08 4.156e-08 -0.006511 -6.976e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003408 -0.006252 0.005102 0.9699 0.9743 0.006946 0.8217 0.8183 0.01556 ] Network output: [ 1 6.943e-07 0.0002614 -9.484e-07 4.258e-07 -0.000209 -7.148e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03645 -0.1506 0.18 0.9833 0.9931 0.2393 0.4266 0.8675 0.7063 ] Network output: [ -0.00824 1.003 1.007 -9.314e-08 4.182e-08 0.006981 -7.02e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007072 0.0006616 0.004276 0.002992 0.9889 0.9919 0.007213 0.8488 0.8911 0.01104 ] Network output: [ -7.491e-05 0.0008577 1 -2.981e-06 1.338e-06 0.9989 -2.246e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2273 0.1082 0.3529 0.1405 0.9849 0.9939 0.2281 0.4306 0.8744 0.6998 ] Network output: [ 0.001862 -0.009237 0.995 1.828e-06 -8.206e-07 1.011 1.378e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.1959 0.9873 0.9919 0.1133 0.7287 0.86 0.3041 ] Network output: [ -0.001793 0.008731 1.004 2.023e-06 -9.081e-07 0.9904 1.524e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09545 0.0935 0.1647 0.1971 0.9851 0.991 0.09546 0.6522 0.8347 0.2507 ] Network output: [ 7.046e-05 1 -6.227e-05 2.633e-07 -1.182e-07 0.9999 1.984e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001079 Epoch 10526 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008202 0.9969 0.9932 -9.248e-08 4.152e-08 -0.00651 -6.97e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003408 -0.006251 0.005102 0.9699 0.9743 0.006946 0.8217 0.8183 0.01556 ] Network output: [ 1 5.864e-07 0.0002613 -9.472e-07 4.252e-07 -0.0002089 -7.138e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03645 -0.1506 0.18 0.9833 0.9931 0.2393 0.4266 0.8675 0.7063 ] Network output: [ -0.008239 1.003 1.007 -9.305e-08 4.177e-08 0.006981 -7.013e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007072 0.0006616 0.004276 0.002992 0.9889 0.9919 0.007213 0.8488 0.8911 0.01104 ] Network output: [ -7.479e-05 0.000857 1 -2.977e-06 1.336e-06 0.9989 -2.243e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2273 0.1082 0.3529 0.1405 0.9849 0.9939 0.2281 0.4305 0.8744 0.6998 ] Network output: [ 0.001861 -0.009231 0.995 1.826e-06 -8.196e-07 1.011 1.376e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.1959 0.9873 0.9919 0.1133 0.7287 0.86 0.3041 ] Network output: [ -0.001792 0.008725 1.004 2.02e-06 -9.07e-07 0.9904 1.523e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09545 0.0935 0.1647 0.1971 0.9851 0.991 0.09547 0.6522 0.8347 0.2507 ] Network output: [ 7.045e-05 1 -6.231e-05 2.63e-07 -1.181e-07 0.9999 1.982e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001078 Epoch 10527 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008201 0.9969 0.9932 -9.24e-08 4.148e-08 -0.00651 -6.964e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003408 -0.006251 0.005101 0.9699 0.9743 0.006946 0.8217 0.8183 0.01556 ] Network output: [ 1 4.785e-07 0.0002612 -9.46e-07 4.247e-07 -0.0002088 -7.129e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03645 -0.1506 0.18 0.9833 0.9931 0.2393 0.4266 0.8675 0.7063 ] Network output: [ -0.008239 1.003 1.007 -9.296e-08 4.173e-08 0.00698 -7.006e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007072 0.0006616 0.004276 0.002992 0.9889 0.9919 0.007214 0.8488 0.8911 0.01104 ] Network output: [ -7.467e-05 0.0008563 1 -2.973e-06 1.335e-06 0.9989 -2.241e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2273 0.1082 0.3529 0.1405 0.9849 0.9939 0.2281 0.4305 0.8744 0.6998 ] Network output: [ 0.00186 -0.009224 0.995 1.823e-06 -8.185e-07 1.011 1.374e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.1959 0.9873 0.9919 0.1133 0.7287 0.86 0.3041 ] Network output: [ -0.001791 0.00872 1.004 2.018e-06 -9.058e-07 0.9904 1.521e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09545 0.0935 0.1647 0.1971 0.9851 0.991 0.09547 0.6522 0.8346 0.2507 ] Network output: [ 7.044e-05 1 -6.234e-05 2.627e-07 -1.179e-07 0.9999 1.979e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001078 Epoch 10528 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008201 0.9969 0.9932 -9.232e-08 4.144e-08 -0.006509 -6.957e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003408 -0.00625 0.005101 0.9699 0.9743 0.006946 0.8217 0.8183 0.01555 ] Network output: [ 1 3.707e-07 0.0002611 -9.448e-07 4.241e-07 -0.0002087 -7.12e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03645 -0.1506 0.18 0.9833 0.9931 0.2393 0.4266 0.8675 0.7063 ] Network output: [ -0.008238 1.003 1.007 -9.287e-08 4.169e-08 0.00698 -6.999e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007073 0.0006616 0.004276 0.002991 0.9889 0.9919 0.007214 0.8488 0.8911 0.01104 ] Network output: [ -7.455e-05 0.0008556 1 -2.969e-06 1.333e-06 0.9989 -2.238e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2273 0.1082 0.3529 0.1405 0.9849 0.9939 0.2281 0.4305 0.8744 0.6998 ] Network output: [ 0.001858 -0.009218 0.995 1.821e-06 -8.175e-07 1.011 1.372e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.1959 0.9873 0.9919 0.1133 0.7287 0.86 0.3041 ] Network output: [ -0.00179 0.008715 1.004 2.015e-06 -9.047e-07 0.9904 1.519e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09545 0.0935 0.1647 0.1971 0.9851 0.991 0.09547 0.6522 0.8346 0.2507 ] Network output: [ 7.043e-05 1 -6.238e-05 2.623e-07 -1.178e-07 0.9999 1.977e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001077 Epoch 10529 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.0082 0.9969 0.9932 -9.223e-08 4.141e-08 -0.006508 -6.951e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003408 -0.00625 0.005101 0.9699 0.9743 0.006946 0.8217 0.8183 0.01555 ] Network output: [ 1 2.629e-07 0.0002609 -9.436e-07 4.236e-07 -0.0002086 -7.111e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03645 -0.1506 0.18 0.9833 0.9931 0.2393 0.4266 0.8675 0.7063 ] Network output: [ -0.008237 1.003 1.007 -9.278e-08 4.165e-08 0.006979 -6.992e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007073 0.0006616 0.004275 0.002991 0.9889 0.9919 0.007214 0.8488 0.8911 0.01103 ] Network output: [ -7.443e-05 0.000855 1 -2.965e-06 1.331e-06 0.9989 -2.235e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2273 0.1082 0.3529 0.1405 0.9849 0.9939 0.2281 0.4305 0.8744 0.6998 ] Network output: [ 0.001857 -0.009212 0.995 1.819e-06 -8.164e-07 1.011 1.371e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.1959 0.9873 0.9919 0.1133 0.7287 0.86 0.3041 ] Network output: [ -0.001789 0.00871 1.004 2.013e-06 -9.036e-07 0.9904 1.517e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09545 0.09351 0.1647 0.1971 0.9851 0.991 0.09547 0.6522 0.8346 0.2507 ] Network output: [ 7.042e-05 1 -6.241e-05 2.62e-07 -1.176e-07 0.9999 1.974e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001077 Epoch 10530 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008199 0.9969 0.9932 -9.215e-08 4.137e-08 -0.006507 -6.945e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003408 -0.006249 0.0051 0.9699 0.9743 0.006947 0.8217 0.8182 0.01555 ] Network output: [ 1 1.552e-07 0.0002608 -9.424e-07 4.231e-07 -0.0002085 -7.102e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03645 -0.1506 0.18 0.9833 0.9931 0.2393 0.4266 0.8675 0.7063 ] Network output: [ -0.008236 1.003 1.007 -9.269e-08 4.161e-08 0.006979 -6.985e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007073 0.0006616 0.004275 0.002991 0.9889 0.9919 0.007214 0.8488 0.8911 0.01103 ] Network output: [ -7.431e-05 0.0008543 1 -2.962e-06 1.33e-06 0.9989 -2.232e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2273 0.1082 0.3529 0.1405 0.9849 0.9939 0.2281 0.4305 0.8744 0.6998 ] Network output: [ 0.001855 -0.009206 0.995 1.816e-06 -8.154e-07 1.011 1.369e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.1959 0.9873 0.9919 0.1133 0.7287 0.86 0.3041 ] Network output: [ -0.001787 0.008705 1.004 2.01e-06 -9.024e-07 0.9904 1.515e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09545 0.09351 0.1647 0.1971 0.9851 0.991 0.09547 0.6522 0.8346 0.2507 ] Network output: [ 7.041e-05 1 -6.245e-05 2.617e-07 -1.175e-07 0.9999 1.972e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001076 Epoch 10531 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008198 0.9969 0.9932 -9.207e-08 4.133e-08 -0.006507 -6.939e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003408 -0.006249 0.0051 0.9699 0.9743 0.006947 0.8217 0.8182 0.01555 ] Network output: [ 1 4.756e-08 0.0002607 -9.412e-07 4.225e-07 -0.0002084 -7.093e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03646 -0.1506 0.18 0.9833 0.9931 0.2393 0.4266 0.8675 0.7063 ] Network output: [ -0.008236 1.003 1.007 -9.26e-08 4.157e-08 0.006979 -6.978e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007073 0.0006616 0.004275 0.002991 0.9889 0.9919 0.007215 0.8488 0.8911 0.01103 ] Network output: [ -7.42e-05 0.0008536 1 -2.958e-06 1.328e-06 0.9989 -2.229e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2274 0.1082 0.3529 0.1405 0.9849 0.9939 0.2281 0.4305 0.8744 0.6998 ] Network output: [ 0.001854 -0.0092 0.995 1.814e-06 -8.144e-07 1.011 1.367e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.1959 0.9873 0.9919 0.1133 0.7286 0.86 0.3041 ] Network output: [ -0.001786 0.008699 1.004 2.008e-06 -9.013e-07 0.9904 1.513e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09545 0.09351 0.1647 0.1971 0.9851 0.991 0.09547 0.6521 0.8346 0.2507 ] Network output: [ 7.04e-05 1 -6.248e-05 2.613e-07 -1.173e-07 0.9999 1.97e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001075 Epoch 10532 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008198 0.9969 0.9932 -9.199e-08 4.13e-08 -0.006506 -6.932e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003408 -0.006248 0.0051 0.9699 0.9743 0.006947 0.8217 0.8182 0.01555 ] Network output: [ 1 -6.002e-08 0.0002606 -9.4e-07 4.22e-07 -0.0002083 -7.084e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03646 -0.1506 0.18 0.9833 0.9931 0.2393 0.4266 0.8675 0.7063 ] Network output: [ -0.008235 1.003 1.007 -9.251e-08 4.153e-08 0.006978 -6.972e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007074 0.0006617 0.004275 0.002991 0.9889 0.9919 0.007215 0.8488 0.8911 0.01103 ] Network output: [ -7.408e-05 0.0008529 1 -2.954e-06 1.326e-06 0.9989 -2.226e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2274 0.1082 0.3529 0.1405 0.9849 0.9939 0.2282 0.4305 0.8744 0.6998 ] Network output: [ 0.001853 -0.009194 0.995 1.812e-06 -8.133e-07 1.011 1.365e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.1959 0.9873 0.9919 0.1133 0.7286 0.86 0.3041 ] Network output: [ -0.001785 0.008694 1.004 2.005e-06 -9.002e-07 0.9904 1.511e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09546 0.09351 0.1647 0.1971 0.9851 0.991 0.09547 0.6521 0.8346 0.2507 ] Network output: [ 7.039e-05 1 -6.251e-05 2.61e-07 -1.172e-07 0.9999 1.967e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001075 Epoch 10533 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008197 0.9969 0.9932 -9.19e-08 4.126e-08 -0.006505 -6.926e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003408 -0.006248 0.005099 0.9699 0.9743 0.006947 0.8217 0.8182 0.01555 ] Network output: [ 1 -1.675e-07 0.0002605 -9.388e-07 4.214e-07 -0.0002082 -7.075e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03646 -0.1506 0.18 0.9833 0.9931 0.2393 0.4266 0.8675 0.7063 ] Network output: [ -0.008234 1.003 1.007 -9.241e-08 4.149e-08 0.006978 -6.965e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007074 0.0006617 0.004275 0.00299 0.9889 0.9919 0.007215 0.8488 0.8911 0.01103 ] Network output: [ -7.396e-05 0.0008523 1 -2.95e-06 1.324e-06 0.9989 -2.223e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2274 0.1082 0.3529 0.1405 0.9849 0.9939 0.2282 0.4305 0.8744 0.6998 ] Network output: [ 0.001851 -0.009188 0.995 1.809e-06 -8.123e-07 1.011 1.364e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.1959 0.9873 0.9919 0.1133 0.7286 0.86 0.3041 ] Network output: [ -0.001784 0.008689 1.004 2.003e-06 -8.99e-07 0.9904 1.509e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09546 0.09351 0.1647 0.1971 0.9851 0.991 0.09547 0.6521 0.8346 0.2507 ] Network output: [ 7.038e-05 1 -6.255e-05 2.607e-07 -1.17e-07 0.9999 1.965e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001074 Epoch 10534 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008196 0.9969 0.9932 -9.182e-08 4.122e-08 -0.006505 -6.92e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003408 -0.006248 0.005099 0.9699 0.9743 0.006947 0.8217 0.8182 0.01555 ] Network output: [ 1 -2.75e-07 0.0002604 -9.376e-07 4.209e-07 -0.0002082 -7.066e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03646 -0.1506 0.18 0.9833 0.9931 0.2393 0.4266 0.8675 0.7063 ] Network output: [ -0.008233 1.003 1.007 -9.232e-08 4.145e-08 0.006978 -6.958e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007074 0.0006617 0.004275 0.00299 0.9889 0.9919 0.007215 0.8488 0.8911 0.01103 ] Network output: [ -7.384e-05 0.0008516 1 -2.946e-06 1.323e-06 0.9989 -2.221e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2274 0.1082 0.3529 0.1405 0.9849 0.9939 0.2282 0.4305 0.8744 0.6998 ] Network output: [ 0.00185 -0.009182 0.995 1.807e-06 -8.113e-07 1.011 1.362e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.1959 0.9873 0.9919 0.1133 0.7286 0.86 0.3041 ] Network output: [ -0.001782 0.008684 1.004 2e-06 -8.979e-07 0.9904 1.507e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09546 0.09351 0.1647 0.1971 0.9851 0.991 0.09547 0.6521 0.8346 0.2507 ] Network output: [ 7.037e-05 1 -6.258e-05 2.603e-07 -1.169e-07 0.9999 1.962e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001073 Epoch 10535 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008195 0.9969 0.9932 -9.174e-08 4.118e-08 -0.006504 -6.914e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003408 -0.006247 0.005099 0.9699 0.9743 0.006947 0.8217 0.8182 0.01555 ] Network output: [ 1 -3.824e-07 0.0002603 -9.364e-07 4.204e-07 -0.0002081 -7.057e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03646 -0.1505 0.18 0.9833 0.9931 0.2393 0.4266 0.8675 0.7063 ] Network output: [ -0.008233 1.003 1.007 -9.223e-08 4.141e-08 0.006977 -6.951e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007074 0.0006617 0.004275 0.00299 0.9889 0.9919 0.007215 0.8488 0.8911 0.01103 ] Network output: [ -7.372e-05 0.0008509 1 -2.943e-06 1.321e-06 0.9989 -2.218e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2274 0.1082 0.3529 0.1405 0.9849 0.9939 0.2282 0.4305 0.8744 0.6998 ] Network output: [ 0.001849 -0.009176 0.995 1.805e-06 -8.102e-07 1.011 1.36e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.1959 0.9873 0.9919 0.1133 0.7286 0.86 0.3041 ] Network output: [ -0.001781 0.008678 1.004 1.998e-06 -8.968e-07 0.9905 1.505e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09546 0.09351 0.1647 0.1971 0.9851 0.991 0.09547 0.6521 0.8346 0.2507 ] Network output: [ 7.036e-05 1 -6.262e-05 2.6e-07 -1.167e-07 0.9999 1.96e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001073 Epoch 10536 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008195 0.9969 0.9932 -9.165e-08 4.115e-08 -0.006503 -6.907e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003408 -0.006247 0.005098 0.9699 0.9743 0.006947 0.8217 0.8182 0.01555 ] Network output: [ 1 -4.897e-07 0.0002602 -9.352e-07 4.198e-07 -0.000208 -7.048e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03646 -0.1505 0.18 0.9833 0.9931 0.2393 0.4266 0.8675 0.7063 ] Network output: [ -0.008232 1.003 1.007 -9.214e-08 4.137e-08 0.006977 -6.944e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007075 0.0006617 0.004275 0.00299 0.9889 0.9919 0.007216 0.8488 0.8911 0.01103 ] Network output: [ -7.36e-05 0.0008503 1 -2.939e-06 1.319e-06 0.9989 -2.215e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2274 0.1082 0.3529 0.1405 0.9849 0.9939 0.2282 0.4305 0.8744 0.6998 ] Network output: [ 0.001847 -0.00917 0.995 1.802e-06 -8.092e-07 1.011 1.358e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.1959 0.9873 0.9919 0.1133 0.7286 0.86 0.3041 ] Network output: [ -0.00178 0.008673 1.004 1.995e-06 -8.956e-07 0.9905 1.504e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09546 0.09351 0.1647 0.1971 0.9851 0.991 0.09547 0.6521 0.8346 0.2507 ] Network output: [ 7.035e-05 1 -6.265e-05 2.597e-07 -1.166e-07 0.9999 1.957e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001072 Epoch 10537 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008194 0.9969 0.9932 -9.157e-08 4.111e-08 -0.006502 -6.901e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003408 -0.006246 0.005098 0.9699 0.9743 0.006947 0.8217 0.8182 0.01555 ] Network output: [ 1 -5.97e-07 0.00026 -9.34e-07 4.193e-07 -0.0002079 -7.039e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03646 -0.1505 0.18 0.9833 0.9931 0.2393 0.4266 0.8675 0.7063 ] Network output: [ -0.008231 1.003 1.007 -9.205e-08 4.133e-08 0.006976 -6.937e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007075 0.0006617 0.004274 0.00299 0.9889 0.9919 0.007216 0.8488 0.8911 0.01103 ] Network output: [ -7.349e-05 0.0008496 1 -2.935e-06 1.318e-06 0.9989 -2.212e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2274 0.1082 0.3529 0.1405 0.9849 0.9939 0.2282 0.4305 0.8744 0.6998 ] Network output: [ 0.001846 -0.009163 0.995 1.8e-06 -8.082e-07 1.011 1.357e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.1959 0.9873 0.9919 0.1133 0.7286 0.86 0.3041 ] Network output: [ -0.001779 0.008668 1.004 1.992e-06 -8.945e-07 0.9905 1.502e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09546 0.09351 0.1647 0.1971 0.9851 0.991 0.09547 0.6521 0.8346 0.2507 ] Network output: [ 7.034e-05 1 -6.269e-05 2.594e-07 -1.164e-07 0.9999 1.955e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001071 Epoch 10538 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008193 0.9969 0.9932 -9.149e-08 4.107e-08 -0.006502 -6.895e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003408 -0.006246 0.005098 0.9699 0.9743 0.006947 0.8217 0.8182 0.01555 ] Network output: [ 1 -7.043e-07 0.0002599 -9.328e-07 4.188e-07 -0.0002078 -7.03e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03646 -0.1505 0.18 0.9833 0.9931 0.2393 0.4266 0.8675 0.7063 ] Network output: [ -0.008231 1.003 1.007 -9.196e-08 4.128e-08 0.006976 -6.931e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007075 0.0006617 0.004274 0.002989 0.9889 0.9919 0.007216 0.8488 0.8911 0.01103 ] Network output: [ -7.337e-05 0.0008489 1 -2.931e-06 1.316e-06 0.9989 -2.209e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2274 0.1082 0.3529 0.1405 0.9849 0.9939 0.2282 0.4305 0.8743 0.6998 ] Network output: [ 0.001844 -0.009157 0.995 1.798e-06 -8.071e-07 1.011 1.355e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1003 0.1851 0.1959 0.9873 0.9919 0.1133 0.7286 0.86 0.3041 ] Network output: [ -0.001778 0.008663 1.004 1.99e-06 -8.934e-07 0.9905 1.5e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09546 0.09351 0.1647 0.1971 0.9851 0.991 0.09548 0.6521 0.8346 0.2507 ] Network output: [ 7.033e-05 1 -6.272e-05 2.59e-07 -1.163e-07 0.9999 1.952e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001071 Epoch 10539 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008192 0.9969 0.9932 -9.141e-08 4.104e-08 -0.006501 -6.889e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003408 -0.006245 0.005097 0.9699 0.9743 0.006947 0.8217 0.8182 0.01555 ] Network output: [ 1 -8.114e-07 0.0002598 -9.316e-07 4.182e-07 -0.0002077 -7.021e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03646 -0.1505 0.18 0.9833 0.9931 0.2393 0.4266 0.8675 0.7063 ] Network output: [ -0.00823 1.003 1.007 -9.187e-08 4.124e-08 0.006976 -6.924e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007075 0.0006618 0.004274 0.002989 0.9889 0.9919 0.007216 0.8488 0.8911 0.01103 ] Network output: [ -7.325e-05 0.0008482 1 -2.928e-06 1.314e-06 0.9989 -2.206e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2274 0.1082 0.3529 0.1405 0.9849 0.9939 0.2282 0.4305 0.8743 0.6998 ] Network output: [ 0.001843 -0.009151 0.995 1.796e-06 -8.061e-07 1.011 1.353e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7286 0.86 0.3041 ] Network output: [ -0.001776 0.008658 1.004 1.987e-06 -8.923e-07 0.9905 1.498e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09546 0.09351 0.1647 0.1971 0.9851 0.991 0.09548 0.6521 0.8346 0.2507 ] Network output: [ 7.032e-05 1 -6.276e-05 2.587e-07 -1.161e-07 0.9999 1.95e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000107 Epoch 10540 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008192 0.9969 0.9932 -9.132e-08 4.1e-08 -0.0065 -6.882e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003408 -0.006245 0.005097 0.9699 0.9743 0.006947 0.8217 0.8182 0.01555 ] Network output: [ 1 -9.185e-07 0.0002597 -9.304e-07 4.177e-07 -0.0002076 -7.012e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03647 -0.1505 0.18 0.9833 0.9931 0.2393 0.4266 0.8675 0.7063 ] Network output: [ -0.008229 1.003 1.007 -9.178e-08 4.12e-08 0.006975 -6.917e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007075 0.0006618 0.004274 0.002989 0.9889 0.9919 0.007217 0.8488 0.8911 0.01103 ] Network output: [ -7.313e-05 0.0008476 1 -2.924e-06 1.313e-06 0.9989 -2.204e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2274 0.1082 0.353 0.1405 0.9849 0.9939 0.2282 0.4305 0.8743 0.6998 ] Network output: [ 0.001842 -0.009145 0.995 1.793e-06 -8.051e-07 1.011 1.352e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7286 0.86 0.3041 ] Network output: [ -0.001775 0.008652 1.004 1.985e-06 -8.911e-07 0.9905 1.496e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09546 0.09351 0.1647 0.1971 0.9851 0.991 0.09548 0.6521 0.8346 0.2507 ] Network output: [ 7.031e-05 1 -6.279e-05 2.584e-07 -1.16e-07 0.9999 1.947e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001069 Epoch 10541 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008191 0.9969 0.9932 -9.124e-08 4.096e-08 -0.006499 -6.876e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003408 -0.006244 0.005097 0.9699 0.9743 0.006947 0.8216 0.8182 0.01554 ] Network output: [ 1 -1.026e-06 0.0002596 -9.292e-07 4.172e-07 -0.0002075 -7.003e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03647 -0.1505 0.18 0.9833 0.9931 0.2393 0.4266 0.8675 0.7063 ] Network output: [ -0.008228 1.003 1.007 -9.169e-08 4.116e-08 0.006975 -6.91e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007076 0.0006618 0.004274 0.002989 0.9889 0.9919 0.007217 0.8488 0.8911 0.01103 ] Network output: [ -7.301e-05 0.0008469 1 -2.92e-06 1.311e-06 0.9989 -2.201e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2274 0.1082 0.353 0.1405 0.9849 0.9939 0.2282 0.4305 0.8743 0.6997 ] Network output: [ 0.00184 -0.009139 0.995 1.791e-06 -8.041e-07 1.01 1.35e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7286 0.86 0.3041 ] Network output: [ -0.001774 0.008647 1.004 1.982e-06 -8.9e-07 0.9905 1.494e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09546 0.09351 0.1647 0.1971 0.9851 0.991 0.09548 0.6521 0.8346 0.2507 ] Network output: [ 7.03e-05 1 -6.282e-05 2.581e-07 -1.159e-07 0.9999 1.945e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001069 Epoch 10542 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00819 0.9969 0.9932 -9.116e-08 4.092e-08 -0.006499 -6.87e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003408 -0.006244 0.005096 0.9699 0.9743 0.006948 0.8216 0.8182 0.01554 ] Network output: [ 1 -1.133e-06 0.0002595 -9.28e-07 4.166e-07 -0.0002074 -6.994e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2128 -0.03647 -0.1505 0.1799 0.9833 0.9931 0.2394 0.4266 0.8675 0.7063 ] Network output: [ -0.008228 1.003 1.007 -9.16e-08 4.112e-08 0.006975 -6.903e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007076 0.0006618 0.004274 0.002989 0.9889 0.9919 0.007217 0.8488 0.8911 0.01103 ] Network output: [ -7.289e-05 0.0008462 1 -2.916e-06 1.309e-06 0.9989 -2.198e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2274 0.1082 0.353 0.1405 0.9849 0.9939 0.2282 0.4305 0.8743 0.6997 ] Network output: [ 0.001839 -0.009133 0.995 1.789e-06 -8.03e-07 1.01 1.348e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7286 0.86 0.3041 ] Network output: [ -0.001773 0.008642 1.004 1.98e-06 -8.889e-07 0.9905 1.492e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09546 0.09351 0.1647 0.1971 0.9851 0.991 0.09548 0.6521 0.8346 0.2507 ] Network output: [ 7.029e-05 1 -6.286e-05 2.577e-07 -1.157e-07 0.9999 1.942e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001068 Epoch 10543 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008189 0.9969 0.9932 -9.108e-08 4.089e-08 -0.006498 -6.864e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003409 -0.006243 0.005096 0.9699 0.9743 0.006948 0.8216 0.8182 0.01554 ] Network output: [ 1 -1.239e-06 0.0002594 -9.268e-07 4.161e-07 -0.0002074 -6.985e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03647 -0.1505 0.1799 0.9833 0.9931 0.2394 0.4266 0.8675 0.7063 ] Network output: [ -0.008227 1.003 1.007 -9.151e-08 4.108e-08 0.006974 -6.896e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007076 0.0006618 0.004274 0.002988 0.9889 0.9919 0.007217 0.8488 0.8911 0.01103 ] Network output: [ -7.278e-05 0.0008455 1 -2.913e-06 1.308e-06 0.9989 -2.195e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2274 0.1082 0.353 0.1405 0.9849 0.9939 0.2282 0.4305 0.8743 0.6997 ] Network output: [ 0.001838 -0.009127 0.995 1.786e-06 -8.02e-07 1.01 1.346e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7286 0.86 0.3041 ] Network output: [ -0.001772 0.008637 1.004 1.978e-06 -8.878e-07 0.9905 1.49e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09546 0.09352 0.1647 0.1971 0.9851 0.991 0.09548 0.6521 0.8346 0.2507 ] Network output: [ 7.028e-05 1 -6.289e-05 2.574e-07 -1.156e-07 0.9999 1.94e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001067 Epoch 10544 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008189 0.9969 0.9932 -9.099e-08 4.085e-08 -0.006497 -6.858e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003409 -0.006243 0.005096 0.9699 0.9743 0.006948 0.8216 0.8182 0.01554 ] Network output: [ 1 -1.346e-06 0.0002593 -9.257e-07 4.156e-07 -0.0002073 -6.976e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03647 -0.1505 0.1799 0.9833 0.9931 0.2394 0.4266 0.8675 0.7063 ] Network output: [ -0.008226 1.003 1.007 -9.142e-08 4.104e-08 0.006974 -6.89e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007076 0.0006618 0.004274 0.002988 0.9889 0.9919 0.007218 0.8488 0.8911 0.01103 ] Network output: [ -7.266e-05 0.0008449 1 -2.909e-06 1.306e-06 0.9989 -2.192e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2274 0.1082 0.353 0.1405 0.9849 0.9939 0.2282 0.4305 0.8743 0.6997 ] Network output: [ 0.001836 -0.009121 0.995 1.784e-06 -8.01e-07 1.01 1.345e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7286 0.86 0.3041 ] Network output: [ -0.00177 0.008631 1.004 1.975e-06 -8.867e-07 0.9905 1.488e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09546 0.09352 0.1647 0.1971 0.9851 0.991 0.09548 0.6521 0.8346 0.2507 ] Network output: [ 7.027e-05 1 -6.293e-05 2.571e-07 -1.154e-07 0.9999 1.937e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001067 Epoch 10545 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008188 0.9969 0.9932 -9.091e-08 4.081e-08 -0.006497 -6.851e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003409 -0.006242 0.005096 0.9699 0.9743 0.006948 0.8216 0.8182 0.01554 ] Network output: [ 1 -1.453e-06 0.0002591 -9.245e-07 4.15e-07 -0.0002072 -6.967e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03647 -0.1505 0.1799 0.9833 0.9931 0.2394 0.4266 0.8675 0.7063 ] Network output: [ -0.008226 1.003 1.007 -9.133e-08 4.1e-08 0.006974 -6.883e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007077 0.0006618 0.004273 0.002988 0.9889 0.9919 0.007218 0.8488 0.8911 0.01102 ] Network output: [ -7.254e-05 0.0008442 1 -2.905e-06 1.304e-06 0.9989 -2.189e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2274 0.1082 0.353 0.1405 0.9849 0.9939 0.2282 0.4305 0.8743 0.6997 ] Network output: [ 0.001835 -0.009115 0.995 1.782e-06 -8e-07 1.01 1.343e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7286 0.86 0.3041 ] Network output: [ -0.001769 0.008626 1.004 1.973e-06 -8.855e-07 0.9905 1.487e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09547 0.09352 0.1647 0.1971 0.9851 0.991 0.09548 0.6521 0.8346 0.2507 ] Network output: [ 7.026e-05 1 -6.296e-05 2.568e-07 -1.153e-07 0.9999 1.935e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001066 Epoch 10546 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008187 0.9969 0.9932 -9.083e-08 4.078e-08 -0.006496 -6.845e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003409 -0.006242 0.005095 0.9699 0.9743 0.006948 0.8216 0.8182 0.01554 ] Network output: [ 1 -1.56e-06 0.000259 -9.233e-07 4.145e-07 -0.0002071 -6.958e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03647 -0.1505 0.1799 0.9833 0.9931 0.2394 0.4266 0.8675 0.7063 ] Network output: [ -0.008225 1.003 1.007 -9.124e-08 4.096e-08 0.006973 -6.876e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007077 0.0006619 0.004273 0.002988 0.9889 0.9919 0.007218 0.8488 0.8911 0.01102 ] Network output: [ -7.242e-05 0.0008435 1 -2.902e-06 1.303e-06 0.9989 -2.187e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2274 0.1082 0.353 0.1405 0.9849 0.9939 0.2282 0.4305 0.8743 0.6997 ] Network output: [ 0.001833 -0.009109 0.995 1.78e-06 -7.99e-07 1.01 1.341e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7286 0.86 0.3041 ] Network output: [ -0.001768 0.008621 1.004 1.97e-06 -8.844e-07 0.9905 1.485e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09547 0.09352 0.1647 0.1971 0.9851 0.991 0.09548 0.6521 0.8346 0.2507 ] Network output: [ 7.025e-05 1 -6.3e-05 2.564e-07 -1.151e-07 0.9999 1.933e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001066 Epoch 10547 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008186 0.9969 0.9932 -9.075e-08 4.074e-08 -0.006495 -6.839e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003409 -0.006241 0.005095 0.9699 0.9743 0.006948 0.8216 0.8182 0.01554 ] Network output: [ 1 -1.667e-06 0.0002589 -9.221e-07 4.14e-07 -0.000207 -6.949e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03647 -0.1505 0.1799 0.9833 0.9931 0.2394 0.4266 0.8675 0.7063 ] Network output: [ -0.008224 1.003 1.007 -9.115e-08 4.092e-08 0.006973 -6.869e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007077 0.0006619 0.004273 0.002988 0.9889 0.9919 0.007218 0.8488 0.8911 0.01102 ] Network output: [ -7.23e-05 0.0008429 1 -2.898e-06 1.301e-06 0.9989 -2.184e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2274 0.1082 0.353 0.1405 0.9849 0.9939 0.2282 0.4305 0.8743 0.6997 ] Network output: [ 0.001832 -0.009103 0.995 1.777e-06 -7.979e-07 1.01 1.34e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7286 0.86 0.3041 ] Network output: [ -0.001767 0.008616 1.004 1.968e-06 -8.833e-07 0.9905 1.483e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09547 0.09352 0.1647 0.1971 0.9851 0.991 0.09548 0.652 0.8346 0.2507 ] Network output: [ 7.025e-05 1 -6.303e-05 2.561e-07 -1.15e-07 0.9999 1.93e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001065 Epoch 10548 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008186 0.9969 0.9932 -9.066e-08 4.07e-08 -0.006494 -6.833e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003409 -0.006241 0.005095 0.9699 0.9743 0.006948 0.8216 0.8182 0.01554 ] Network output: [ 1 -1.773e-06 0.0002588 -9.209e-07 4.134e-07 -0.0002069 -6.94e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03648 -0.1505 0.1799 0.9833 0.9931 0.2394 0.4266 0.8675 0.7063 ] Network output: [ -0.008223 1.003 1.007 -9.106e-08 4.088e-08 0.006972 -6.863e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007077 0.0006619 0.004273 0.002987 0.9889 0.9919 0.007218 0.8488 0.8911 0.01102 ] Network output: [ -7.219e-05 0.0008422 1 -2.894e-06 1.299e-06 0.9989 -2.181e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2274 0.1082 0.353 0.1405 0.9849 0.9939 0.2282 0.4305 0.8743 0.6997 ] Network output: [ 0.001831 -0.009096 0.995 1.775e-06 -7.969e-07 1.01 1.338e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7286 0.86 0.3041 ] Network output: [ -0.001766 0.008611 1.004 1.965e-06 -8.822e-07 0.9905 1.481e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09547 0.09352 0.1647 0.1971 0.9851 0.991 0.09548 0.652 0.8346 0.2507 ] Network output: [ 7.024e-05 1 -6.307e-05 2.558e-07 -1.148e-07 0.9999 1.928e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001064 Epoch 10549 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008185 0.9969 0.9932 -9.058e-08 4.067e-08 -0.006494 -6.827e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003409 -0.006241 0.005094 0.9699 0.9743 0.006948 0.8216 0.8182 0.01554 ] Network output: [ 1 -1.88e-06 0.0002587 -9.197e-07 4.129e-07 -0.0002068 -6.932e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03648 -0.1505 0.1799 0.9833 0.9931 0.2394 0.4266 0.8675 0.7063 ] Network output: [ -0.008223 1.003 1.007 -9.097e-08 4.084e-08 0.006972 -6.856e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007077 0.0006619 0.004273 0.002987 0.9889 0.9919 0.007219 0.8487 0.8911 0.01102 ] Network output: [ -7.207e-05 0.0008415 1 -2.89e-06 1.298e-06 0.9989 -2.178e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2274 0.1082 0.353 0.1405 0.9849 0.9939 0.2282 0.4305 0.8743 0.6997 ] Network output: [ 0.001829 -0.00909 0.995 1.773e-06 -7.959e-07 1.01 1.336e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7285 0.86 0.3041 ] Network output: [ -0.001764 0.008605 1.004 1.963e-06 -8.811e-07 0.9905 1.479e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09547 0.09352 0.1647 0.1971 0.9851 0.991 0.09548 0.652 0.8346 0.2507 ] Network output: [ 7.023e-05 1 -6.31e-05 2.555e-07 -1.147e-07 0.9999 1.925e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001064 Epoch 10550 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008184 0.9969 0.9932 -9.05e-08 4.063e-08 -0.006493 -6.82e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003409 -0.00624 0.005094 0.9699 0.9743 0.006948 0.8216 0.8182 0.01554 ] Network output: [ 1 -1.986e-06 0.0002586 -9.186e-07 4.124e-07 -0.0002067 -6.923e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03648 -0.1505 0.1799 0.9833 0.9931 0.2394 0.4266 0.8675 0.7063 ] Network output: [ -0.008222 1.003 1.007 -9.088e-08 4.08e-08 0.006972 -6.849e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007078 0.0006619 0.004273 0.002987 0.9889 0.9919 0.007219 0.8487 0.8911 0.01102 ] Network output: [ -7.195e-05 0.0008408 1 -2.887e-06 1.296e-06 0.9989 -2.175e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2274 0.1082 0.353 0.1405 0.9849 0.9939 0.2282 0.4305 0.8743 0.6997 ] Network output: [ 0.001828 -0.009084 0.995 1.771e-06 -7.949e-07 1.01 1.334e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7285 0.86 0.3041 ] Network output: [ -0.001763 0.0086 1.004 1.96e-06 -8.8e-07 0.9905 1.477e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09547 0.09352 0.1647 0.1971 0.9851 0.991 0.09548 0.652 0.8346 0.2507 ] Network output: [ 7.022e-05 1 -6.314e-05 2.551e-07 -1.145e-07 0.9999 1.923e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001063 Epoch 10551 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008183 0.9969 0.9932 -9.042e-08 4.059e-08 -0.006492 -6.814e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003409 -0.00624 0.005094 0.9699 0.9743 0.006948 0.8216 0.8182 0.01554 ] Network output: [ 1 -2.093e-06 0.0002585 -9.174e-07 4.119e-07 -0.0002066 -6.914e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03648 -0.1504 0.1799 0.9833 0.9931 0.2394 0.4266 0.8675 0.7063 ] Network output: [ -0.008221 1.003 1.007 -9.079e-08 4.076e-08 0.006971 -6.842e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007078 0.0006619 0.004273 0.002987 0.9889 0.9919 0.007219 0.8487 0.8911 0.01102 ] Network output: [ -7.183e-05 0.0008402 1 -2.883e-06 1.294e-06 0.9989 -2.173e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2275 0.1082 0.353 0.1405 0.9849 0.9939 0.2282 0.4305 0.8743 0.6997 ] Network output: [ 0.001827 -0.009078 0.995 1.768e-06 -7.939e-07 1.01 1.333e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7285 0.86 0.3041 ] Network output: [ -0.001762 0.008595 1.004 1.958e-06 -8.789e-07 0.9905 1.475e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09547 0.09352 0.1647 0.1971 0.9851 0.991 0.09548 0.652 0.8346 0.2507 ] Network output: [ 7.021e-05 1 -6.317e-05 2.548e-07 -1.144e-07 0.9999 1.92e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001062 Epoch 10552 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008183 0.9969 0.9932 -9.034e-08 4.056e-08 -0.006491 -6.808e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003409 -0.006239 0.005093 0.9699 0.9743 0.006948 0.8216 0.8182 0.01554 ] Network output: [ 1 -2.199e-06 0.0002584 -9.162e-07 4.113e-07 -0.0002066 -6.905e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03648 -0.1504 0.1799 0.9833 0.9931 0.2394 0.4266 0.8675 0.7063 ] Network output: [ -0.00822 1.003 1.007 -9.07e-08 4.072e-08 0.006971 -6.836e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007078 0.0006619 0.004273 0.002987 0.9889 0.9919 0.007219 0.8487 0.8911 0.01102 ] Network output: [ -7.172e-05 0.0008395 1 -2.879e-06 1.293e-06 0.9989 -2.17e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2275 0.1082 0.353 0.1405 0.9849 0.9939 0.2283 0.4305 0.8743 0.6997 ] Network output: [ 0.001825 -0.009072 0.995 1.766e-06 -7.929e-07 1.01 1.331e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7285 0.86 0.3041 ] Network output: [ -0.001761 0.00859 1.004 1.955e-06 -8.778e-07 0.9905 1.474e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09547 0.09352 0.1647 0.1971 0.9851 0.991 0.09549 0.652 0.8346 0.2507 ] Network output: [ 7.02e-05 1 -6.321e-05 2.545e-07 -1.143e-07 0.9999 1.918e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001062 Epoch 10553 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008182 0.9969 0.9932 -9.025e-08 4.052e-08 -0.006491 -6.802e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003409 -0.006239 0.005093 0.9699 0.9743 0.006948 0.8216 0.8182 0.01554 ] Network output: [ 1 -2.305e-06 0.0002583 -9.151e-07 4.108e-07 -0.0002065 -6.896e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03648 -0.1504 0.1799 0.9833 0.9931 0.2394 0.4266 0.8675 0.7063 ] Network output: [ -0.00822 1.003 1.007 -9.061e-08 4.068e-08 0.006971 -6.829e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007078 0.000662 0.004272 0.002986 0.9889 0.9919 0.00722 0.8487 0.8911 0.01102 ] Network output: [ -7.16e-05 0.0008388 1 -2.876e-06 1.291e-06 0.9989 -2.167e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2275 0.1082 0.353 0.1405 0.9849 0.9939 0.2283 0.4305 0.8743 0.6997 ] Network output: [ 0.001824 -0.009066 0.995 1.764e-06 -7.919e-07 1.01 1.329e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7285 0.86 0.3041 ] Network output: [ -0.001759 0.008584 1.004 1.953e-06 -8.767e-07 0.9905 1.472e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09547 0.09352 0.1647 0.1971 0.9851 0.991 0.09549 0.652 0.8346 0.2507 ] Network output: [ 7.019e-05 1 -6.324e-05 2.542e-07 -1.141e-07 0.9999 1.915e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001061 Epoch 10554 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008181 0.9969 0.9932 -9.017e-08 4.048e-08 -0.00649 -6.796e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003409 -0.006238 0.005093 0.9699 0.9743 0.006949 0.8216 0.8182 0.01553 ] Network output: [ 1 -2.412e-06 0.0002581 -9.139e-07 4.103e-07 -0.0002064 -6.887e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03648 -0.1504 0.1799 0.9833 0.9931 0.2394 0.4266 0.8675 0.7063 ] Network output: [ -0.008219 1.003 1.007 -9.052e-08 4.064e-08 0.00697 -6.822e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007079 0.000662 0.004272 0.002986 0.9889 0.9919 0.00722 0.8487 0.8911 0.01102 ] Network output: [ -7.148e-05 0.0008382 1 -2.872e-06 1.289e-06 0.9989 -2.164e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2275 0.1082 0.353 0.1405 0.9849 0.9939 0.2283 0.4305 0.8743 0.6997 ] Network output: [ 0.001822 -0.00906 0.995 1.762e-06 -7.909e-07 1.01 1.328e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7285 0.86 0.3041 ] Network output: [ -0.001758 0.008579 1.004 1.95e-06 -8.756e-07 0.9905 1.47e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09547 0.09352 0.1647 0.1971 0.9851 0.991 0.09549 0.652 0.8346 0.2507 ] Network output: [ 7.018e-05 1 -6.328e-05 2.538e-07 -1.14e-07 0.9999 1.913e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000106 Epoch 10555 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008181 0.9969 0.9932 -9.009e-08 4.044e-08 -0.006489 -6.79e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003409 -0.006238 0.005092 0.9699 0.9743 0.006949 0.8216 0.8182 0.01553 ] Network output: [ 1 -2.518e-06 0.000258 -9.127e-07 4.097e-07 -0.0002063 -6.878e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03648 -0.1504 0.1799 0.9833 0.9931 0.2394 0.4266 0.8675 0.7062 ] Network output: [ -0.008218 1.003 1.007 -9.043e-08 4.06e-08 0.00697 -6.815e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007079 0.000662 0.004272 0.002986 0.9889 0.9919 0.00722 0.8487 0.8911 0.01102 ] Network output: [ -7.136e-05 0.0008375 1 -2.868e-06 1.288e-06 0.9989 -2.162e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2275 0.1082 0.353 0.1405 0.9849 0.9939 0.2283 0.4305 0.8743 0.6997 ] Network output: [ 0.001821 -0.009054 0.995 1.759e-06 -7.899e-07 1.01 1.326e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7285 0.86 0.3041 ] Network output: [ -0.001757 0.008574 1.004 1.948e-06 -8.745e-07 0.9905 1.468e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09547 0.09352 0.1647 0.1971 0.9851 0.991 0.09549 0.652 0.8346 0.2507 ] Network output: [ 7.017e-05 1 -6.331e-05 2.535e-07 -1.138e-07 0.9999 1.911e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000106 Epoch 10556 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00818 0.9969 0.9932 -9.001e-08 4.041e-08 -0.006489 -6.783e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003409 -0.006237 0.005092 0.9699 0.9743 0.006949 0.8216 0.8182 0.01553 ] Network output: [ 1 -2.624e-06 0.0002579 -9.115e-07 4.092e-07 -0.0002062 -6.87e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03648 -0.1504 0.1799 0.9833 0.9931 0.2394 0.4266 0.8675 0.7062 ] Network output: [ -0.008218 1.003 1.007 -9.034e-08 4.056e-08 0.006969 -6.809e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007079 0.000662 0.004272 0.002986 0.9889 0.9919 0.00722 0.8487 0.8911 0.01102 ] Network output: [ -7.124e-05 0.0008368 1 -2.865e-06 1.286e-06 0.9989 -2.159e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2275 0.1082 0.353 0.1405 0.9849 0.9939 0.2283 0.4305 0.8743 0.6997 ] Network output: [ 0.00182 -0.009048 0.995 1.757e-06 -7.888e-07 1.01 1.324e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7285 0.86 0.3041 ] Network output: [ -0.001756 0.008569 1.004 1.945e-06 -8.734e-07 0.9905 1.466e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09547 0.09353 0.1647 0.1971 0.9851 0.991 0.09549 0.652 0.8346 0.2507 ] Network output: [ 7.016e-05 1 -6.335e-05 2.532e-07 -1.137e-07 0.9999 1.908e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001059 Epoch 10557 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008179 0.9969 0.9932 -8.993e-08 4.037e-08 -0.006488 -6.777e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003409 -0.006237 0.005092 0.9699 0.9743 0.006949 0.8216 0.8182 0.01553 ] Network output: [ 1 -2.73e-06 0.0002578 -9.104e-07 4.087e-07 -0.0002061 -6.861e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03649 -0.1504 0.1799 0.9833 0.9931 0.2394 0.4266 0.8675 0.7062 ] Network output: [ -0.008217 1.003 1.007 -9.025e-08 4.052e-08 0.006969 -6.802e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007079 0.000662 0.004272 0.002986 0.9889 0.9919 0.007221 0.8487 0.8911 0.01102 ] Network output: [ -7.113e-05 0.0008361 1 -2.861e-06 1.284e-06 0.9989 -2.156e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2275 0.1082 0.353 0.1405 0.9849 0.9939 0.2283 0.4305 0.8743 0.6997 ] Network output: [ 0.001818 -0.009042 0.995 1.755e-06 -7.878e-07 1.01 1.323e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7285 0.86 0.3041 ] Network output: [ -0.001755 0.008564 1.004 1.943e-06 -8.723e-07 0.9905 1.464e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09547 0.09353 0.1647 0.1971 0.9851 0.991 0.09549 0.652 0.8346 0.2507 ] Network output: [ 7.015e-05 1 -6.338e-05 2.529e-07 -1.135e-07 0.9999 1.906e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001059 Epoch 10558 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008178 0.9969 0.9932 -8.985e-08 4.033e-08 -0.006487 -6.771e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003409 -0.006236 0.005091 0.9699 0.9743 0.006949 0.8216 0.8182 0.01553 ] Network output: [ 1 -2.836e-06 0.0002577 -9.092e-07 4.082e-07 -0.000206 -6.852e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03649 -0.1504 0.1799 0.9833 0.9931 0.2394 0.4266 0.8675 0.7062 ] Network output: [ -0.008216 1.003 1.007 -9.017e-08 4.048e-08 0.006969 -6.795e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007079 0.000662 0.004272 0.002985 0.9889 0.9919 0.007221 0.8487 0.8911 0.01102 ] Network output: [ -7.101e-05 0.0008355 1 -2.857e-06 1.283e-06 0.9989 -2.153e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2275 0.1082 0.353 0.1405 0.9849 0.9939 0.2283 0.4305 0.8743 0.6997 ] Network output: [ 0.001817 -0.009036 0.995 1.753e-06 -7.868e-07 1.01 1.321e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7285 0.86 0.3041 ] Network output: [ -0.001753 0.008558 1.004 1.94e-06 -8.712e-07 0.9905 1.462e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09548 0.09353 0.1647 0.1971 0.9851 0.991 0.09549 0.652 0.8346 0.2507 ] Network output: [ 7.014e-05 1 -6.342e-05 2.526e-07 -1.134e-07 0.9999 1.903e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001058 Epoch 10559 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008178 0.9969 0.9932 -8.976e-08 4.03e-08 -0.006486 -6.765e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003409 -0.006236 0.005091 0.9699 0.9743 0.006949 0.8216 0.8182 0.01553 ] Network output: [ 1 -2.942e-06 0.0002576 -9.08e-07 4.077e-07 -0.0002059 -6.843e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03649 -0.1504 0.1799 0.9833 0.9931 0.2394 0.4266 0.8675 0.7062 ] Network output: [ -0.008215 1.003 1.007 -9.008e-08 4.044e-08 0.006968 -6.788e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00708 0.000662 0.004272 0.002985 0.9889 0.9919 0.007221 0.8487 0.8911 0.01102 ] Network output: [ -7.089e-05 0.0008348 1 -2.854e-06 1.281e-06 0.9989 -2.151e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2275 0.1082 0.353 0.1405 0.9849 0.9939 0.2283 0.4305 0.8743 0.6997 ] Network output: [ 0.001816 -0.009029 0.995 1.75e-06 -7.858e-07 1.01 1.319e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7285 0.86 0.3041 ] Network output: [ -0.001752 0.008553 1.004 1.938e-06 -8.701e-07 0.9905 1.461e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09548 0.09353 0.1647 0.1971 0.9851 0.991 0.09549 0.652 0.8346 0.2507 ] Network output: [ 7.013e-05 1 -6.345e-05 2.522e-07 -1.132e-07 0.9999 1.901e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001057 Epoch 10560 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008177 0.9969 0.9932 -8.968e-08 4.026e-08 -0.006486 -6.759e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003409 -0.006235 0.005091 0.9699 0.9743 0.006949 0.8216 0.8182 0.01553 ] Network output: [ 1 -3.048e-06 0.0002575 -9.069e-07 4.071e-07 -0.0002059 -6.835e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03649 -0.1504 0.1799 0.9833 0.9931 0.2394 0.4266 0.8675 0.7062 ] Network output: [ -0.008215 1.003 1.007 -8.999e-08 4.04e-08 0.006968 -6.782e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00708 0.0006621 0.004272 0.002985 0.9889 0.9919 0.007221 0.8487 0.8911 0.01101 ] Network output: [ -7.077e-05 0.0008341 1 -2.85e-06 1.279e-06 0.9989 -2.148e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2275 0.1082 0.353 0.1405 0.9849 0.9939 0.2283 0.4305 0.8743 0.6997 ] Network output: [ 0.001814 -0.009023 0.995 1.748e-06 -7.848e-07 1.01 1.318e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7285 0.86 0.3041 ] Network output: [ -0.001751 0.008548 1.004 1.936e-06 -8.69e-07 0.9905 1.459e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09548 0.09353 0.1647 0.1971 0.9851 0.991 0.09549 0.652 0.8346 0.2507 ] Network output: [ 7.012e-05 1 -6.349e-05 2.519e-07 -1.131e-07 0.9999 1.899e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001057 Epoch 10561 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008176 0.9969 0.9932 -8.96e-08 4.023e-08 -0.006485 -6.753e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003409 -0.006235 0.00509 0.9699 0.9743 0.006949 0.8216 0.8182 0.01553 ] Network output: [ 1 -3.154e-06 0.0002574 -9.057e-07 4.066e-07 -0.0002058 -6.826e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03649 -0.1504 0.1799 0.9833 0.9931 0.2395 0.4266 0.8675 0.7062 ] Network output: [ -0.008214 1.003 1.007 -8.99e-08 4.036e-08 0.006968 -6.775e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00708 0.0006621 0.004271 0.002985 0.9889 0.9919 0.007221 0.8487 0.8911 0.01101 ] Network output: [ -7.066e-05 0.0008335 1 -2.846e-06 1.278e-06 0.9989 -2.145e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2275 0.1082 0.353 0.1405 0.9849 0.9939 0.2283 0.4305 0.8743 0.6997 ] Network output: [ 0.001813 -0.009017 0.995 1.746e-06 -7.838e-07 1.01 1.316e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7285 0.86 0.3041 ] Network output: [ -0.00175 0.008543 1.004 1.933e-06 -8.679e-07 0.9905 1.457e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09548 0.09353 0.1647 0.1971 0.9851 0.991 0.09549 0.652 0.8346 0.2507 ] Network output: [ 7.011e-05 1 -6.352e-05 2.516e-07 -1.13e-07 0.9999 1.896e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001056 Epoch 10562 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008175 0.9969 0.9932 -8.952e-08 4.019e-08 -0.006484 -6.746e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003409 -0.006235 0.00509 0.9699 0.9743 0.006949 0.8216 0.8182 0.01553 ] Network output: [ 1 -3.26e-06 0.0002573 -9.046e-07 4.061e-07 -0.0002057 -6.817e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03649 -0.1504 0.1799 0.9833 0.9931 0.2395 0.4266 0.8675 0.7062 ] Network output: [ -0.008213 1.003 1.007 -8.981e-08 4.032e-08 0.006967 -6.768e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00708 0.0006621 0.004271 0.002984 0.9889 0.9919 0.007222 0.8487 0.8911 0.01101 ] Network output: [ -7.054e-05 0.0008328 1 -2.843e-06 1.276e-06 0.9989 -2.142e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2275 0.1082 0.3531 0.1405 0.9849 0.9939 0.2283 0.4305 0.8743 0.6997 ] Network output: [ 0.001812 -0.009011 0.995 1.744e-06 -7.828e-07 1.01 1.314e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7285 0.86 0.3041 ] Network output: [ -0.001749 0.008538 1.004 1.931e-06 -8.668e-07 0.9905 1.455e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09548 0.09353 0.1647 0.1971 0.9851 0.991 0.09549 0.652 0.8346 0.2507 ] Network output: [ 7.01e-05 1 -6.356e-05 2.513e-07 -1.128e-07 0.9999 1.894e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001055 Epoch 10563 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008175 0.9969 0.9932 -8.944e-08 4.015e-08 -0.006483 -6.74e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003409 -0.006234 0.00509 0.9699 0.9743 0.006949 0.8216 0.8182 0.01553 ] Network output: [ 1 -3.366e-06 0.0002571 -9.034e-07 4.056e-07 -0.0002056 -6.808e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03649 -0.1504 0.1799 0.9833 0.9931 0.2395 0.4266 0.8675 0.7062 ] Network output: [ -0.008212 1.003 1.007 -8.972e-08 4.028e-08 0.006967 -6.762e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007081 0.0006621 0.004271 0.002984 0.9889 0.9919 0.007222 0.8487 0.8911 0.01101 ] Network output: [ -7.042e-05 0.0008321 1 -2.839e-06 1.275e-06 0.9989 -2.14e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2275 0.1083 0.3531 0.1405 0.9849 0.9939 0.2283 0.4305 0.8743 0.6997 ] Network output: [ 0.00181 -0.009005 0.995 1.742e-06 -7.818e-07 1.01 1.312e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7285 0.86 0.3041 ] Network output: [ -0.001747 0.008532 1.004 1.928e-06 -8.657e-07 0.9905 1.453e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09548 0.09353 0.1647 0.1971 0.9851 0.991 0.09549 0.6519 0.8346 0.2508 ] Network output: [ 7.009e-05 1 -6.36e-05 2.51e-07 -1.127e-07 0.9999 1.891e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001055 Epoch 10564 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008174 0.9969 0.9932 -8.936e-08 4.012e-08 -0.006483 -6.734e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.003409 -0.006234 0.005089 0.9699 0.9743 0.006949 0.8216 0.8182 0.01553 ] Network output: [ 1 -3.471e-06 0.000257 -9.023e-07 4.051e-07 -0.0002055 -6.8e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03649 -0.1504 0.1799 0.9833 0.9931 0.2395 0.4266 0.8675 0.7062 ] Network output: [ -0.008212 1.003 1.007 -8.963e-08 4.024e-08 0.006967 -6.755e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007081 0.0006621 0.004271 0.002984 0.9889 0.9919 0.007222 0.8487 0.8911 0.01101 ] Network output: [ -7.03e-05 0.0008314 1 -2.835e-06 1.273e-06 0.9989 -2.137e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2275 0.1083 0.3531 0.1405 0.9849 0.9939 0.2283 0.4305 0.8743 0.6997 ] Network output: [ 0.001809 -0.008999 0.995 1.739e-06 -7.808e-07 1.01 1.311e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7285 0.86 0.3041 ] Network output: [ -0.001746 0.008527 1.004 1.926e-06 -8.646e-07 0.9905 1.451e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09548 0.09353 0.1647 0.1971 0.9851 0.991 0.09549 0.6519 0.8346 0.2508 ] Network output: [ 7.008e-05 1 -6.363e-05 2.507e-07 -1.125e-07 0.9999 1.889e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001054 Epoch 10565 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008173 0.9969 0.9932 -8.928e-08 4.008e-08 -0.006482 -6.728e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.00341 -0.006233 0.005089 0.9699 0.9743 0.006949 0.8216 0.8182 0.01553 ] Network output: [ 1 -3.577e-06 0.0002569 -9.011e-07 4.045e-07 -0.0002054 -6.791e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.03649 -0.1504 0.1799 0.9833 0.9931 0.2395 0.4266 0.8675 0.7062 ] Network output: [ -0.008211 1.003 1.007 -8.954e-08 4.02e-08 0.006966 -6.748e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007081 0.0006621 0.004271 0.002984 0.9889 0.9919 0.007222 0.8487 0.8911 0.01101 ] Network output: [ -7.019e-05 0.0008308 1 -2.832e-06 1.271e-06 0.9989 -2.134e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2275 0.1083 0.3531 0.1405 0.9849 0.9939 0.2283 0.4305 0.8743 0.6997 ] Network output: [ 0.001807 -0.008993 0.995 1.737e-06 -7.798e-07 1.01 1.309e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7285 0.86 0.3041 ] Network output: [ -0.001745 0.008522 1.004 1.923e-06 -8.635e-07 0.9906 1.45e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09548 0.09353 0.1647 0.1971 0.9851 0.991 0.0955 0.6519 0.8346 0.2508 ] Network output: [ 7.008e-05 1 -6.367e-05 2.503e-07 -1.124e-07 0.9999 1.887e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001053 Epoch 10566 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008172 0.9969 0.9932 -8.919e-08 4.004e-08 -0.006481 -6.722e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.00341 -0.006233 0.005089 0.9699 0.9743 0.00695 0.8216 0.8182 0.01553 ] Network output: [ 1 -3.682e-06 0.0002568 -8.999e-07 4.04e-07 -0.0002053 -6.782e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2129 -0.0365 -0.1504 0.1799 0.9833 0.9931 0.2395 0.4266 0.8675 0.7062 ] Network output: [ -0.00821 1.003 1.007 -8.946e-08 4.016e-08 0.006966 -6.742e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007081 0.0006621 0.004271 0.002984 0.9889 0.9919 0.007223 0.8487 0.8911 0.01101 ] Network output: [ -7.007e-05 0.0008301 1 -2.828e-06 1.27e-06 0.9989 -2.131e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2275 0.1083 0.3531 0.1405 0.9849 0.9939 0.2283 0.4305 0.8743 0.6997 ] Network output: [ 0.001806 -0.008987 0.995 1.735e-06 -7.789e-07 1.01 1.307e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7285 0.86 0.3041 ] Network output: [ -0.001744 0.008517 1.004 1.921e-06 -8.624e-07 0.9906 1.448e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09548 0.09353 0.1647 0.1971 0.9851 0.991 0.0955 0.6519 0.8346 0.2508 ] Network output: [ 7.007e-05 1 -6.37e-05 2.5e-07 -1.122e-07 0.9999 1.884e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001053 Epoch 10567 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008172 0.9969 0.9932 -8.911e-08 4.001e-08 -0.006481 -6.716e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.00341 -0.006232 0.005089 0.9699 0.9743 0.00695 0.8216 0.8182 0.01552 ] Network output: [ 1 -3.788e-06 0.0002567 -8.988e-07 4.035e-07 -0.0002052 -6.774e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.0365 -0.1503 0.1799 0.9833 0.9931 0.2395 0.4266 0.8675 0.7062 ] Network output: [ -0.00821 1.003 1.007 -8.937e-08 4.012e-08 0.006965 -6.735e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007081 0.0006621 0.004271 0.002983 0.9889 0.9919 0.007223 0.8487 0.8911 0.01101 ] Network output: [ -6.995e-05 0.0008294 1 -2.824e-06 1.268e-06 0.9989 -2.129e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2275 0.1083 0.3531 0.1404 0.9849 0.9939 0.2283 0.4305 0.8743 0.6997 ] Network output: [ 0.001805 -0.008981 0.995 1.733e-06 -7.779e-07 1.01 1.306e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7284 0.86 0.3041 ] Network output: [ -0.001743 0.008512 1.004 1.919e-06 -8.613e-07 0.9906 1.446e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09548 0.09353 0.1647 0.1971 0.9851 0.991 0.0955 0.6519 0.8346 0.2508 ] Network output: [ 7.006e-05 1 -6.374e-05 2.497e-07 -1.121e-07 0.9999 1.882e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001052 Epoch 10568 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008171 0.9969 0.9932 -8.903e-08 3.997e-08 -0.00648 -6.71e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.00341 -0.006232 0.005088 0.9699 0.9743 0.00695 0.8216 0.8182 0.01552 ] Network output: [ 1 -3.893e-06 0.0002566 -8.976e-07 4.03e-07 -0.0002052 -6.765e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.0365 -0.1503 0.1799 0.9833 0.9931 0.2395 0.4266 0.8675 0.7062 ] Network output: [ -0.008209 1.003 1.007 -8.928e-08 4.008e-08 0.006965 -6.728e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007082 0.0006622 0.004271 0.002983 0.9889 0.9919 0.007223 0.8487 0.8911 0.01101 ] Network output: [ -6.984e-05 0.0008288 1 -2.821e-06 1.266e-06 0.9989 -2.126e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2275 0.1083 0.3531 0.1404 0.9849 0.9939 0.2283 0.4305 0.8743 0.6997 ] Network output: [ 0.001803 -0.008975 0.995 1.73e-06 -7.769e-07 1.01 1.304e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7284 0.86 0.3041 ] Network output: [ -0.001741 0.008506 1.004 1.916e-06 -8.602e-07 0.9906 1.444e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09548 0.09353 0.1647 0.1971 0.9851 0.991 0.0955 0.6519 0.8346 0.2508 ] Network output: [ 7.005e-05 1 -6.377e-05 2.494e-07 -1.12e-07 0.9999 1.879e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001052 Epoch 10569 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00817 0.9969 0.9932 -8.895e-08 3.993e-08 -0.006479 -6.704e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.00341 -0.006231 0.005088 0.9699 0.9743 0.00695 0.8216 0.8182 0.01552 ] Network output: [ 1 -3.999e-06 0.0002565 -8.965e-07 4.025e-07 -0.0002051 -6.756e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.0365 -0.1503 0.1799 0.9833 0.9931 0.2395 0.4266 0.8675 0.7062 ] Network output: [ -0.008208 1.003 1.007 -8.919e-08 4.004e-08 0.006965 -6.722e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007082 0.0006622 0.00427 0.002983 0.9889 0.9919 0.007223 0.8487 0.8911 0.01101 ] Network output: [ -6.972e-05 0.0008281 1 -2.817e-06 1.265e-06 0.9989 -2.123e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2275 0.1083 0.3531 0.1404 0.9849 0.9939 0.2283 0.4305 0.8743 0.6997 ] Network output: [ 0.001802 -0.008969 0.995 1.728e-06 -7.759e-07 1.01 1.302e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7284 0.86 0.3041 ] Network output: [ -0.00174 0.008501 1.004 1.914e-06 -8.592e-07 0.9906 1.442e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09548 0.09353 0.1647 0.1971 0.9851 0.991 0.0955 0.6519 0.8346 0.2508 ] Network output: [ 7.004e-05 1 -6.381e-05 2.491e-07 -1.118e-07 0.9999 1.877e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001051 Epoch 10570 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008169 0.9969 0.9932 -8.887e-08 3.99e-08 -0.006478 -6.698e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.00341 -0.006231 0.005088 0.9699 0.9743 0.00695 0.8216 0.8182 0.01552 ] Network output: [ 1 -4.104e-06 0.0002564 -8.953e-07 4.02e-07 -0.000205 -6.748e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.0365 -0.1503 0.1799 0.9833 0.9931 0.2395 0.4266 0.8675 0.7062 ] Network output: [ -0.008207 1.003 1.007 -8.91e-08 4e-08 0.006964 -6.715e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007082 0.0006622 0.00427 0.002983 0.9889 0.9919 0.007223 0.8487 0.8911 0.01101 ] Network output: [ -6.96e-05 0.0008274 1 -2.814e-06 1.263e-06 0.9989 -2.12e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2275 0.1083 0.3531 0.1404 0.9849 0.9939 0.2283 0.4305 0.8743 0.6997 ] Network output: [ 0.001801 -0.008963 0.995 1.726e-06 -7.749e-07 1.01 1.301e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7284 0.86 0.3041 ] Network output: [ -0.001739 0.008496 1.004 1.911e-06 -8.581e-07 0.9906 1.44e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09548 0.09354 0.1647 0.1971 0.9851 0.991 0.0955 0.6519 0.8346 0.2508 ] Network output: [ 7.003e-05 1 -6.384e-05 2.488e-07 -1.117e-07 0.9999 1.875e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000105 Epoch 10571 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008169 0.9969 0.9932 -8.879e-08 3.986e-08 -0.006478 -6.691e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.00341 -0.00623 0.005087 0.9699 0.9743 0.00695 0.8216 0.8182 0.01552 ] Network output: [ 1 -4.209e-06 0.0002563 -8.942e-07 4.014e-07 -0.0002049 -6.739e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.0365 -0.1503 0.1799 0.9833 0.9931 0.2395 0.4266 0.8675 0.7062 ] Network output: [ -0.008207 1.003 1.007 -8.901e-08 3.996e-08 0.006964 -6.708e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007082 0.0006622 0.00427 0.002983 0.9889 0.9919 0.007224 0.8487 0.8911 0.01101 ] Network output: [ -6.948e-05 0.0008267 1 -2.81e-06 1.261e-06 0.9989 -2.118e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2275 0.1083 0.3531 0.1404 0.9849 0.9939 0.2283 0.4305 0.8743 0.6997 ] Network output: [ 0.001799 -0.008956 0.995 1.724e-06 -7.739e-07 1.01 1.299e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7284 0.86 0.3041 ] Network output: [ -0.001738 0.008491 1.004 1.909e-06 -8.57e-07 0.9906 1.439e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09548 0.09354 0.1647 0.1971 0.9851 0.991 0.0955 0.6519 0.8346 0.2508 ] Network output: [ 7.002e-05 1 -6.388e-05 2.484e-07 -1.115e-07 0.9999 1.872e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000105 Epoch 10572 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008168 0.9969 0.9932 -8.871e-08 3.982e-08 -0.006477 -6.685e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.00341 -0.00623 0.005087 0.9699 0.9743 0.00695 0.8216 0.8182 0.01552 ] Network output: [ 1 -4.315e-06 0.0002561 -8.93e-07 4.009e-07 -0.0002048 -6.73e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.0365 -0.1503 0.1799 0.9833 0.9931 0.2395 0.4266 0.8675 0.7062 ] Network output: [ -0.008206 1.003 1.007 -8.893e-08 3.992e-08 0.006964 -6.702e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007082 0.0006622 0.00427 0.002982 0.9889 0.9919 0.007224 0.8487 0.8911 0.01101 ] Network output: [ -6.937e-05 0.0008261 1 -2.806e-06 1.26e-06 0.9989 -2.115e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2276 0.1083 0.3531 0.1404 0.9849 0.9939 0.2284 0.4305 0.8743 0.6997 ] Network output: [ 0.001798 -0.00895 0.995 1.722e-06 -7.729e-07 1.01 1.298e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7284 0.8599 0.3041 ] Network output: [ -0.001737 0.008486 1.004 1.907e-06 -8.559e-07 0.9906 1.437e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09549 0.09354 0.1647 0.1971 0.9851 0.991 0.0955 0.6519 0.8346 0.2508 ] Network output: [ 7.001e-05 1 -6.392e-05 2.481e-07 -1.114e-07 0.9999 1.87e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001049 Epoch 10573 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008167 0.9969 0.9932 -8.863e-08 3.979e-08 -0.006476 -6.679e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.00341 -0.006229 0.005087 0.9699 0.9743 0.00695 0.8216 0.8182 0.01552 ] Network output: [ 1 -4.42e-06 0.000256 -8.919e-07 4.004e-07 -0.0002047 -6.722e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.0365 -0.1503 0.1799 0.9833 0.9931 0.2395 0.4266 0.8675 0.7062 ] Network output: [ -0.008205 1.003 1.007 -8.884e-08 3.988e-08 0.006963 -6.695e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007083 0.0006622 0.00427 0.002982 0.9889 0.9919 0.007224 0.8487 0.8911 0.01101 ] Network output: [ -6.925e-05 0.0008254 1 -2.803e-06 1.258e-06 0.9989 -2.112e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2276 0.1083 0.3531 0.1404 0.9849 0.9939 0.2284 0.4305 0.8743 0.6997 ] Network output: [ 0.001796 -0.008944 0.995 1.719e-06 -7.719e-07 1.01 1.296e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1959 0.9873 0.9919 0.1133 0.7284 0.8599 0.3041 ] Network output: [ -0.001735 0.00848 1.004 1.904e-06 -8.548e-07 0.9906 1.435e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09549 0.09354 0.1647 0.1971 0.9851 0.991 0.0955 0.6519 0.8346 0.2508 ] Network output: [ 7e-05 1 -6.395e-05 2.478e-07 -1.113e-07 0.9999 1.868e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001048 Epoch 10574 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008167 0.9969 0.9932 -8.855e-08 3.975e-08 -0.006475 -6.673e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.00341 -0.006229 0.005086 0.9699 0.9743 0.00695 0.8216 0.8182 0.01552 ] Network output: [ 1 -4.525e-06 0.0002559 -8.908e-07 3.999e-07 -0.0002046 -6.713e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.0365 -0.1503 0.1799 0.9833 0.9931 0.2395 0.4265 0.8675 0.7062 ] Network output: [ -0.008205 1.003 1.007 -8.875e-08 3.984e-08 0.006963 -6.688e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007083 0.0006622 0.00427 0.002982 0.9889 0.9919 0.007224 0.8487 0.8911 0.01101 ] Network output: [ -6.913e-05 0.0008247 1 -2.799e-06 1.257e-06 0.9989 -2.11e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2276 0.1083 0.3531 0.1404 0.9849 0.9939 0.2284 0.4305 0.8743 0.6997 ] Network output: [ 0.001795 -0.008938 0.995 1.717e-06 -7.709e-07 1.01 1.294e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7284 0.8599 0.3041 ] Network output: [ -0.001734 0.008475 1.004 1.902e-06 -8.538e-07 0.9906 1.433e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09549 0.09354 0.1647 0.1971 0.9851 0.991 0.0955 0.6519 0.8346 0.2508 ] Network output: [ 6.999e-05 1 -6.399e-05 2.475e-07 -1.111e-07 0.9999 1.865e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001048 Epoch 10575 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008166 0.9969 0.9932 -8.847e-08 3.972e-08 -0.006475 -6.667e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.00341 -0.006229 0.005086 0.9699 0.9743 0.00695 0.8216 0.8182 0.01552 ] Network output: [ 1 -4.63e-06 0.0002558 -8.896e-07 3.994e-07 -0.0002046 -6.704e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.03651 -0.1503 0.1799 0.9833 0.9931 0.2395 0.4265 0.8675 0.7062 ] Network output: [ -0.008204 1.003 1.007 -8.866e-08 3.98e-08 0.006963 -6.682e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007083 0.0006622 0.00427 0.002982 0.9889 0.9919 0.007225 0.8487 0.891 0.01101 ] Network output: [ -6.902e-05 0.0008241 1 -2.796e-06 1.255e-06 0.9989 -2.107e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2276 0.1083 0.3531 0.1404 0.9849 0.9939 0.2284 0.4305 0.8743 0.6997 ] Network output: [ 0.001794 -0.008932 0.995 1.715e-06 -7.7e-07 1.01 1.293e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7284 0.8599 0.3041 ] Network output: [ -0.001733 0.00847 1.004 1.899e-06 -8.527e-07 0.9906 1.431e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09549 0.09354 0.1647 0.1971 0.9851 0.991 0.0955 0.6519 0.8346 0.2508 ] Network output: [ 6.998e-05 1 -6.402e-05 2.472e-07 -1.11e-07 0.9999 1.863e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001047 Epoch 10576 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008165 0.9969 0.9932 -8.839e-08 3.968e-08 -0.006474 -6.661e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003537 -0.00341 -0.006228 0.005086 0.9699 0.9743 0.00695 0.8215 0.8182 0.01552 ] Network output: [ 1 -4.735e-06 0.0002557 -8.885e-07 3.989e-07 -0.0002045 -6.696e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.03651 -0.1503 0.1799 0.9833 0.9931 0.2395 0.4265 0.8675 0.7062 ] Network output: [ -0.008203 1.003 1.007 -8.857e-08 3.976e-08 0.006962 -6.675e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007083 0.0006623 0.004269 0.002982 0.9889 0.9919 0.007225 0.8487 0.891 0.011 ] Network output: [ -6.89e-05 0.0008234 1 -2.792e-06 1.253e-06 0.9989 -2.104e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2276 0.1083 0.3531 0.1404 0.9849 0.9939 0.2284 0.4305 0.8743 0.6997 ] Network output: [ 0.001792 -0.008926 0.995 1.713e-06 -7.69e-07 1.01 1.291e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7284 0.8599 0.3041 ] Network output: [ -0.001732 0.008465 1.004 1.897e-06 -8.516e-07 0.9906 1.43e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09549 0.09354 0.1647 0.1971 0.9851 0.991 0.0955 0.6519 0.8346 0.2508 ] Network output: [ 6.997e-05 1 -6.406e-05 2.469e-07 -1.108e-07 0.9999 1.861e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001046 Epoch 10577 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008164 0.9969 0.9932 -8.83e-08 3.964e-08 -0.006473 -6.655e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.00341 -0.006228 0.005085 0.9699 0.9743 0.00695 0.8215 0.8182 0.01552 ] Network output: [ 1 -4.84e-06 0.0002556 -8.873e-07 3.984e-07 -0.0002044 -6.687e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.03651 -0.1503 0.1799 0.9833 0.9931 0.2395 0.4265 0.8675 0.7062 ] Network output: [ -0.008202 1.003 1.007 -8.849e-08 3.972e-08 0.006962 -6.669e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007084 0.0006623 0.004269 0.002981 0.9889 0.9919 0.007225 0.8487 0.891 0.011 ] Network output: [ -6.878e-05 0.0008227 1 -2.788e-06 1.252e-06 0.9989 -2.101e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2276 0.1083 0.3531 0.1404 0.9849 0.9939 0.2284 0.4305 0.8743 0.6997 ] Network output: [ 0.001791 -0.00892 0.995 1.711e-06 -7.68e-07 1.01 1.289e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7284 0.8599 0.304 ] Network output: [ -0.001731 0.00846 1.004 1.895e-06 -8.505e-07 0.9906 1.428e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09549 0.09354 0.1647 0.1971 0.9851 0.991 0.0955 0.6519 0.8346 0.2508 ] Network output: [ 6.996e-05 1 -6.409e-05 2.466e-07 -1.107e-07 0.9999 1.858e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001046 Epoch 10578 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008164 0.9969 0.9932 -8.822e-08 3.961e-08 -0.006473 -6.649e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.00341 -0.006227 0.005085 0.9699 0.9743 0.006951 0.8215 0.8182 0.01552 ] Network output: [ 1 -4.945e-06 0.0002555 -8.862e-07 3.979e-07 -0.0002043 -6.679e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.03651 -0.1503 0.1798 0.9833 0.9931 0.2395 0.4265 0.8675 0.7062 ] Network output: [ -0.008202 1.003 1.007 -8.84e-08 3.969e-08 0.006961 -6.662e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007084 0.0006623 0.004269 0.002981 0.9889 0.9919 0.007225 0.8487 0.891 0.011 ] Network output: [ -6.866e-05 0.000822 1 -2.785e-06 1.25e-06 0.9989 -2.099e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2276 0.1083 0.3531 0.1404 0.9849 0.9939 0.2284 0.4305 0.8743 0.6997 ] Network output: [ 0.00179 -0.008914 0.995 1.709e-06 -7.67e-07 1.01 1.288e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7284 0.8599 0.304 ] Network output: [ -0.001729 0.008454 1.004 1.892e-06 -8.495e-07 0.9906 1.426e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09549 0.09354 0.1647 0.1971 0.9851 0.991 0.0955 0.6519 0.8346 0.2508 ] Network output: [ 6.996e-05 1 -6.413e-05 2.463e-07 -1.106e-07 0.9999 1.856e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001045 Epoch 10579 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008163 0.9969 0.9932 -8.814e-08 3.957e-08 -0.006472 -6.643e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.00341 -0.006227 0.005085 0.9699 0.9743 0.006951 0.8215 0.8182 0.01552 ] Network output: [ 1 -5.049e-06 0.0002554 -8.851e-07 3.973e-07 -0.0002042 -6.67e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.03651 -0.1503 0.1798 0.9833 0.9931 0.2395 0.4265 0.8675 0.7062 ] Network output: [ -0.008201 1.003 1.007 -8.831e-08 3.965e-08 0.006961 -6.655e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007084 0.0006623 0.004269 0.002981 0.9889 0.9919 0.007226 0.8487 0.891 0.011 ] Network output: [ -6.855e-05 0.0008214 1 -2.781e-06 1.249e-06 0.9989 -2.096e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2276 0.1083 0.3531 0.1404 0.9849 0.9939 0.2284 0.4305 0.8743 0.6997 ] Network output: [ 0.001788 -0.008908 0.995 1.706e-06 -7.66e-07 1.01 1.286e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7284 0.8599 0.304 ] Network output: [ -0.001728 0.008449 1.004 1.89e-06 -8.484e-07 0.9906 1.424e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09549 0.09354 0.1647 0.1971 0.9851 0.991 0.09551 0.6519 0.8346 0.2508 ] Network output: [ 6.995e-05 1 -6.417e-05 2.459e-07 -1.104e-07 0.9999 1.853e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001045 Epoch 10580 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008162 0.9969 0.9932 -8.806e-08 3.953e-08 -0.006471 -6.637e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.00341 -0.006226 0.005084 0.9699 0.9743 0.006951 0.8215 0.8182 0.01551 ] Network output: [ 1 -5.154e-06 0.0002553 -8.839e-07 3.968e-07 -0.0002041 -6.662e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.03651 -0.1503 0.1798 0.9833 0.9931 0.2395 0.4265 0.8675 0.7062 ] Network output: [ -0.0082 1.003 1.007 -8.822e-08 3.961e-08 0.006961 -6.649e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007084 0.0006623 0.004269 0.002981 0.9889 0.9919 0.007226 0.8487 0.891 0.011 ] Network output: [ -6.843e-05 0.0008207 1 -2.778e-06 1.247e-06 0.9989 -2.093e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2276 0.1083 0.3531 0.1404 0.9849 0.9939 0.2284 0.4305 0.8743 0.6997 ] Network output: [ 0.001787 -0.008902 0.995 1.704e-06 -7.651e-07 1.01 1.284e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7284 0.8599 0.304 ] Network output: [ -0.001727 0.008444 1.004 1.887e-06 -8.473e-07 0.9906 1.422e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09549 0.09354 0.1647 0.1971 0.9851 0.991 0.09551 0.6518 0.8346 0.2508 ] Network output: [ 6.994e-05 1 -6.42e-05 2.456e-07 -1.103e-07 0.9999 1.851e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001044 Epoch 10581 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008161 0.9969 0.9932 -8.798e-08 3.95e-08 -0.00647 -6.631e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.00341 -0.006226 0.005084 0.9699 0.9743 0.006951 0.8215 0.8182 0.01551 ] Network output: [ 1 -5.259e-06 0.0002552 -8.828e-07 3.963e-07 -0.000204 -6.653e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.03651 -0.1503 0.1798 0.9833 0.9931 0.2396 0.4265 0.8675 0.7062 ] Network output: [ -0.008199 1.003 1.007 -8.814e-08 3.957e-08 0.00696 -6.642e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007084 0.0006623 0.004269 0.002981 0.9889 0.9919 0.007226 0.8487 0.891 0.011 ] Network output: [ -6.831e-05 0.00082 1 -2.774e-06 1.245e-06 0.9989 -2.091e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2276 0.1083 0.3531 0.1404 0.9849 0.9939 0.2284 0.4305 0.8743 0.6997 ] Network output: [ 0.001786 -0.008896 0.995 1.702e-06 -7.641e-07 1.01 1.283e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7284 0.8599 0.304 ] Network output: [ -0.001726 0.008439 1.004 1.885e-06 -8.462e-07 0.9906 1.421e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09549 0.09354 0.1647 0.1971 0.9851 0.991 0.09551 0.6518 0.8346 0.2508 ] Network output: [ 6.993e-05 1 -6.424e-05 2.453e-07 -1.101e-07 0.9999 1.849e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001043 Epoch 10582 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008161 0.9969 0.9932 -8.79e-08 3.946e-08 -0.00647 -6.625e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.00341 -0.006225 0.005084 0.9699 0.9743 0.006951 0.8215 0.8182 0.01551 ] Network output: [ 1 -5.364e-06 0.000255 -8.817e-07 3.958e-07 -0.000204 -6.645e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.03651 -0.1502 0.1798 0.9833 0.9931 0.2396 0.4265 0.8675 0.7062 ] Network output: [ -0.008199 1.003 1.007 -8.805e-08 3.953e-08 0.00696 -6.636e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007085 0.0006623 0.004269 0.00298 0.9889 0.9919 0.007226 0.8487 0.891 0.011 ] Network output: [ -6.82e-05 0.0008194 1 -2.771e-06 1.244e-06 0.9989 -2.088e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2276 0.1083 0.3531 0.1404 0.9849 0.9939 0.2284 0.4305 0.8743 0.6997 ] Network output: [ 0.001784 -0.00889 0.995 1.7e-06 -7.631e-07 1.01 1.281e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7284 0.8599 0.304 ] Network output: [ -0.001725 0.008434 1.004 1.883e-06 -8.452e-07 0.9906 1.419e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09549 0.09354 0.1647 0.1971 0.9851 0.991 0.09551 0.6518 0.8346 0.2508 ] Network output: [ 6.992e-05 1 -6.427e-05 2.45e-07 -1.1e-07 0.9999 1.846e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001043 Epoch 10583 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00816 0.9969 0.9932 -8.782e-08 3.943e-08 -0.006469 -6.619e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.00341 -0.006225 0.005084 0.9699 0.9743 0.006951 0.8215 0.8182 0.01551 ] Network output: [ 1 -5.468e-06 0.0002549 -8.805e-07 3.953e-07 -0.0002039 -6.636e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.03652 -0.1502 0.1798 0.9833 0.9931 0.2396 0.4265 0.8675 0.7062 ] Network output: [ -0.008198 1.003 1.007 -8.796e-08 3.949e-08 0.00696 -6.629e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007085 0.0006623 0.004269 0.00298 0.9889 0.9919 0.007226 0.8487 0.891 0.011 ] Network output: [ -6.808e-05 0.0008187 1 -2.767e-06 1.242e-06 0.9989 -2.085e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2276 0.1083 0.3531 0.1404 0.9849 0.9939 0.2284 0.4305 0.8743 0.6997 ] Network output: [ 0.001783 -0.008884 0.995 1.698e-06 -7.621e-07 1.01 1.279e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7284 0.8599 0.304 ] Network output: [ -0.001723 0.008428 1.004 1.88e-06 -8.441e-07 0.9906 1.417e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09549 0.09354 0.1647 0.1971 0.9851 0.991 0.09551 0.6518 0.8346 0.2508 ] Network output: [ 6.991e-05 1 -6.431e-05 2.447e-07 -1.099e-07 0.9999 1.844e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001042 Epoch 10584 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008159 0.9969 0.9932 -8.774e-08 3.939e-08 -0.006468 -6.612e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.00341 -0.006224 0.005083 0.9699 0.9743 0.006951 0.8215 0.8182 0.01551 ] Network output: [ 1 -5.573e-06 0.0002548 -8.794e-07 3.948e-07 -0.0002038 -6.628e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.03652 -0.1502 0.1798 0.9833 0.9931 0.2396 0.4265 0.8675 0.7062 ] Network output: [ -0.008197 1.003 1.007 -8.787e-08 3.945e-08 0.006959 -6.622e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007085 0.0006623 0.004268 0.00298 0.9889 0.9919 0.007227 0.8487 0.891 0.011 ] Network output: [ -6.796e-05 0.000818 1 -2.763e-06 1.241e-06 0.9989 -2.083e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2276 0.1083 0.3531 0.1404 0.9849 0.9939 0.2284 0.4304 0.8743 0.6997 ] Network output: [ 0.001781 -0.008878 0.995 1.695e-06 -7.612e-07 1.01 1.278e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7284 0.8599 0.304 ] Network output: [ -0.001722 0.008423 1.004 1.878e-06 -8.43e-07 0.9906 1.415e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09549 0.09355 0.1647 0.1971 0.9851 0.991 0.09551 0.6518 0.8346 0.2508 ] Network output: [ 6.99e-05 1 -6.435e-05 2.444e-07 -1.097e-07 0.9999 1.842e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001041 Epoch 10585 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008158 0.9969 0.9932 -8.766e-08 3.935e-08 -0.006467 -6.606e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.00341 -0.006224 0.005083 0.9699 0.9743 0.006951 0.8215 0.8182 0.01551 ] Network output: [ 1 -5.677e-06 0.0002547 -8.783e-07 3.943e-07 -0.0002037 -6.619e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.03652 -0.1502 0.1798 0.9833 0.9931 0.2396 0.4265 0.8675 0.7062 ] Network output: [ -0.008197 1.003 1.007 -8.779e-08 3.941e-08 0.006959 -6.616e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007085 0.0006624 0.004268 0.00298 0.9889 0.9919 0.007227 0.8487 0.891 0.011 ] Network output: [ -6.785e-05 0.0008173 1 -2.76e-06 1.239e-06 0.9989 -2.08e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2276 0.1083 0.3532 0.1404 0.9849 0.9939 0.2284 0.4304 0.8743 0.6997 ] Network output: [ 0.00178 -0.008871 0.995 1.693e-06 -7.602e-07 1.01 1.276e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7283 0.8599 0.304 ] Network output: [ -0.001721 0.008418 1.004 1.876e-06 -8.42e-07 0.9906 1.413e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09549 0.09355 0.1647 0.1971 0.9851 0.991 0.09551 0.6518 0.8346 0.2508 ] Network output: [ 6.989e-05 1 -6.438e-05 2.441e-07 -1.096e-07 0.9999 1.839e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001041 Epoch 10586 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008158 0.9969 0.9932 -8.758e-08 3.932e-08 -0.006467 -6.6e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.00341 -0.006223 0.005083 0.9699 0.9743 0.006951 0.8215 0.8182 0.01551 ] Network output: [ 1 -5.781e-06 0.0002546 -8.772e-07 3.938e-07 -0.0002036 -6.611e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.03652 -0.1502 0.1798 0.9833 0.9931 0.2396 0.4265 0.8675 0.7062 ] Network output: [ -0.008196 1.003 1.007 -8.77e-08 3.937e-08 0.006959 -6.609e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007086 0.0006624 0.004268 0.00298 0.9889 0.9919 0.007227 0.8486 0.891 0.011 ] Network output: [ -6.773e-05 0.0008167 1 -2.756e-06 1.237e-06 0.9989 -2.077e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2276 0.1083 0.3532 0.1404 0.9849 0.9939 0.2284 0.4304 0.8743 0.6997 ] Network output: [ 0.001779 -0.008865 0.995 1.691e-06 -7.592e-07 1.01 1.275e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7283 0.8599 0.304 ] Network output: [ -0.00172 0.008413 1.004 1.873e-06 -8.409e-07 0.9906 1.412e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0955 0.09355 0.1647 0.1971 0.9851 0.991 0.09551 0.6518 0.8346 0.2508 ] Network output: [ 6.988e-05 1 -6.442e-05 2.438e-07 -1.094e-07 0.9999 1.837e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000104 Epoch 10587 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008157 0.9969 0.9932 -8.75e-08 3.928e-08 -0.006466 -6.594e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.00341 -0.006223 0.005082 0.9699 0.9743 0.006951 0.8215 0.8182 0.01551 ] Network output: [ 1 -5.886e-06 0.0002545 -8.76e-07 3.933e-07 -0.0002035 -6.602e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.03652 -0.1502 0.1798 0.9833 0.9931 0.2396 0.4265 0.8675 0.7062 ] Network output: [ -0.008195 1.003 1.007 -8.761e-08 3.933e-08 0.006958 -6.603e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007086 0.0006624 0.004268 0.002979 0.9889 0.9919 0.007227 0.8486 0.891 0.011 ] Network output: [ -6.761e-05 0.000816 1 -2.753e-06 1.236e-06 0.9989 -2.075e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2276 0.1083 0.3532 0.1404 0.9849 0.9939 0.2284 0.4304 0.8743 0.6997 ] Network output: [ 0.001777 -0.008859 0.995 1.689e-06 -7.583e-07 1.01 1.273e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7283 0.8599 0.304 ] Network output: [ -0.001719 0.008408 1.004 1.871e-06 -8.399e-07 0.9906 1.41e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0955 0.09355 0.1647 0.1971 0.9851 0.991 0.09551 0.6518 0.8346 0.2508 ] Network output: [ 6.987e-05 1 -6.445e-05 2.435e-07 -1.093e-07 0.9999 1.835e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001039 Epoch 10588 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008156 0.9969 0.9932 -8.742e-08 3.925e-08 -0.006465 -6.588e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.006223 0.005082 0.9699 0.9743 0.006951 0.8215 0.8182 0.01551 ] Network output: [ 1 -5.99e-06 0.0002544 -8.749e-07 3.928e-07 -0.0002034 -6.594e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.03652 -0.1502 0.1798 0.9833 0.9931 0.2396 0.4265 0.8675 0.7062 ] Network output: [ -0.008194 1.003 1.007 -8.753e-08 3.929e-08 0.006958 -6.596e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007086 0.0006624 0.004268 0.002979 0.9889 0.9919 0.007228 0.8486 0.891 0.011 ] Network output: [ -6.75e-05 0.0008153 1 -2.749e-06 1.234e-06 0.9989 -2.072e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2276 0.1083 0.3532 0.1404 0.9849 0.9939 0.2284 0.4304 0.8743 0.6997 ] Network output: [ 0.001776 -0.008853 0.995 1.687e-06 -7.573e-07 1.01 1.271e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7283 0.8599 0.304 ] Network output: [ -0.001717 0.008403 1.004 1.868e-06 -8.388e-07 0.9906 1.408e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0955 0.09355 0.1647 0.1971 0.9851 0.991 0.09551 0.6518 0.8346 0.2508 ] Network output: [ 6.986e-05 1 -6.449e-05 2.431e-07 -1.092e-07 0.9999 1.832e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001039 Epoch 10589 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008155 0.9969 0.9932 -8.734e-08 3.921e-08 -0.006464 -6.582e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.006222 0.005082 0.9699 0.9743 0.006951 0.8215 0.8182 0.01551 ] Network output: [ 1 -6.094e-06 0.0002543 -8.738e-07 3.923e-07 -0.0002034 -6.585e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.03652 -0.1502 0.1798 0.9833 0.9931 0.2396 0.4265 0.8675 0.7062 ] Network output: [ -0.008194 1.003 1.007 -8.744e-08 3.925e-08 0.006957 -6.59e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007086 0.0006624 0.004268 0.002979 0.9889 0.9919 0.007228 0.8486 0.891 0.011 ] Network output: [ -6.738e-05 0.0008147 1 -2.746e-06 1.233e-06 0.9989 -2.069e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2276 0.1083 0.3532 0.1404 0.9849 0.9939 0.2284 0.4304 0.8743 0.6997 ] Network output: [ 0.001775 -0.008847 0.995 1.685e-06 -7.563e-07 1.01 1.27e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7283 0.8599 0.304 ] Network output: [ -0.001716 0.008397 1.004 1.866e-06 -8.377e-07 0.9906 1.406e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0955 0.09355 0.1647 0.1971 0.9851 0.991 0.09551 0.6518 0.8346 0.2508 ] Network output: [ 6.985e-05 1 -6.453e-05 2.428e-07 -1.09e-07 0.9999 1.83e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001038 Epoch 10590 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008155 0.9969 0.9932 -8.726e-08 3.917e-08 -0.006464 -6.576e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.006222 0.005081 0.9699 0.9743 0.006952 0.8215 0.8182 0.01551 ] Network output: [ 1 -6.198e-06 0.0002542 -8.727e-07 3.918e-07 -0.0002033 -6.577e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.03652 -0.1502 0.1798 0.9833 0.9931 0.2396 0.4265 0.8675 0.7062 ] Network output: [ -0.008193 1.003 1.007 -8.735e-08 3.922e-08 0.006957 -6.583e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007086 0.0006624 0.004268 0.002979 0.9889 0.9919 0.007228 0.8486 0.891 0.011 ] Network output: [ -6.726e-05 0.000814 1 -2.742e-06 1.231e-06 0.9989 -2.067e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2276 0.1083 0.3532 0.1404 0.9849 0.9939 0.2284 0.4304 0.8743 0.6997 ] Network output: [ 0.001773 -0.008841 0.995 1.683e-06 -7.554e-07 1.01 1.268e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7283 0.8599 0.304 ] Network output: [ -0.001715 0.008392 1.004 1.864e-06 -8.367e-07 0.9906 1.405e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0955 0.09355 0.1647 0.1971 0.9851 0.991 0.09551 0.6518 0.8346 0.2508 ] Network output: [ 6.985e-05 1 -6.456e-05 2.425e-07 -1.089e-07 0.9999 1.828e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001038 Epoch 10591 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008154 0.9969 0.9932 -8.718e-08 3.914e-08 -0.006463 -6.57e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.006221 0.005081 0.9699 0.9743 0.006952 0.8215 0.8182 0.01551 ] Network output: [ 1 -6.302e-06 0.0002541 -8.716e-07 3.913e-07 -0.0002032 -6.568e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.213 -0.03652 -0.1502 0.1798 0.9833 0.9931 0.2396 0.4265 0.8675 0.7062 ] Network output: [ -0.008192 1.003 1.007 -8.726e-08 3.918e-08 0.006957 -6.577e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007087 0.0006624 0.004268 0.002979 0.9889 0.9919 0.007228 0.8486 0.891 0.01099 ] Network output: [ -6.715e-05 0.0008133 1 -2.739e-06 1.229e-06 0.9989 -2.064e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2276 0.1083 0.3532 0.1404 0.9849 0.9939 0.2284 0.4304 0.8743 0.6996 ] Network output: [ 0.001772 -0.008835 0.995 1.68e-06 -7.544e-07 1.01 1.266e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7283 0.8599 0.304 ] Network output: [ -0.001714 0.008387 1.004 1.861e-06 -8.356e-07 0.9906 1.403e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0955 0.09355 0.1647 0.1971 0.9851 0.991 0.09551 0.6518 0.8346 0.2508 ] Network output: [ 6.984e-05 1 -6.46e-05 2.422e-07 -1.087e-07 0.9999 1.825e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001037 Epoch 10592 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008153 0.9969 0.9932 -8.71e-08 3.91e-08 -0.006462 -6.564e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.006221 0.005081 0.9699 0.9743 0.006952 0.8215 0.8182 0.0155 ] Network output: [ 1 -6.407e-06 0.0002539 -8.704e-07 3.908e-07 -0.0002031 -6.56e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03653 -0.1502 0.1798 0.9833 0.9931 0.2396 0.4265 0.8675 0.7062 ] Network output: [ -0.008192 1.003 1.007 -8.718e-08 3.914e-08 0.006956 -6.57e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007087 0.0006624 0.004267 0.002978 0.9889 0.9919 0.007228 0.8486 0.891 0.01099 ] Network output: [ -6.703e-05 0.0008127 1 -2.735e-06 1.228e-06 0.9989 -2.061e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2277 0.1083 0.3532 0.1404 0.9849 0.9939 0.2284 0.4304 0.8743 0.6996 ] Network output: [ 0.001771 -0.008829 0.995 1.678e-06 -7.534e-07 1.01 1.265e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7283 0.8599 0.304 ] Network output: [ -0.001713 0.008382 1.004 1.859e-06 -8.346e-07 0.9906 1.401e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0955 0.09355 0.1647 0.1971 0.9851 0.991 0.09551 0.6518 0.8346 0.2508 ] Network output: [ 6.983e-05 1 -6.464e-05 2.419e-07 -1.086e-07 0.9999 1.823e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001036 Epoch 10593 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008153 0.9969 0.9932 -8.702e-08 3.907e-08 -0.006462 -6.558e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.00622 0.00508 0.9699 0.9743 0.006952 0.8215 0.8182 0.0155 ] Network output: [ 1 -6.511e-06 0.0002538 -8.693e-07 3.903e-07 -0.000203 -6.551e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03653 -0.1502 0.1798 0.9833 0.9931 0.2396 0.4265 0.8675 0.7062 ] Network output: [ -0.008191 1.003 1.007 -8.709e-08 3.91e-08 0.006956 -6.563e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007087 0.0006624 0.004267 0.002978 0.9889 0.9919 0.007229 0.8486 0.891 0.01099 ] Network output: [ -6.691e-05 0.000812 1 -2.732e-06 1.226e-06 0.9989 -2.059e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2277 0.1083 0.3532 0.1404 0.9849 0.9939 0.2285 0.4304 0.8743 0.6996 ] Network output: [ 0.001769 -0.008823 0.995 1.676e-06 -7.525e-07 1.01 1.263e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7283 0.8599 0.304 ] Network output: [ -0.001711 0.008377 1.004 1.857e-06 -8.335e-07 0.9906 1.399e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0955 0.09355 0.1647 0.1971 0.9851 0.991 0.09552 0.6518 0.8346 0.2508 ] Network output: [ 6.982e-05 1 -6.467e-05 2.416e-07 -1.085e-07 0.9999 1.821e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001036 Epoch 10594 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008152 0.9969 0.9932 -8.694e-08 3.903e-08 -0.006461 -6.552e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.00622 0.00508 0.9699 0.9743 0.006952 0.8215 0.8182 0.0155 ] Network output: [ 1 -6.614e-06 0.0002537 -8.682e-07 3.898e-07 -0.0002029 -6.543e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03653 -0.1502 0.1798 0.9833 0.9931 0.2396 0.4265 0.8675 0.7062 ] Network output: [ -0.00819 1.003 1.007 -8.7e-08 3.906e-08 0.006956 -6.557e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007087 0.0006625 0.004267 0.002978 0.9889 0.9919 0.007229 0.8486 0.891 0.01099 ] Network output: [ -6.68e-05 0.0008113 1 -2.728e-06 1.225e-06 0.9989 -2.056e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2277 0.1083 0.3532 0.1404 0.9849 0.9939 0.2285 0.4304 0.8743 0.6996 ] Network output: [ 0.001768 -0.008817 0.995 1.674e-06 -7.515e-07 1.01 1.262e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7283 0.8599 0.304 ] Network output: [ -0.00171 0.008371 1.004 1.854e-06 -8.325e-07 0.9907 1.397e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0955 0.09355 0.1647 0.1971 0.9851 0.991 0.09552 0.6518 0.8345 0.2508 ] Network output: [ 6.981e-05 1 -6.471e-05 2.413e-07 -1.083e-07 0.9999 1.819e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001035 Epoch 10595 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008151 0.9969 0.9932 -8.686e-08 3.9e-08 -0.00646 -6.546e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.006219 0.00508 0.9699 0.9743 0.006952 0.8215 0.8181 0.0155 ] Network output: [ 1 -6.718e-06 0.0002536 -8.671e-07 3.893e-07 -0.0002029 -6.535e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03653 -0.1502 0.1798 0.9833 0.9931 0.2396 0.4265 0.8675 0.7062 ] Network output: [ -0.008189 1.003 1.007 -8.692e-08 3.902e-08 0.006955 -6.55e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007087 0.0006625 0.004267 0.002978 0.9889 0.9919 0.007229 0.8486 0.891 0.01099 ] Network output: [ -6.668e-05 0.0008106 1 -2.725e-06 1.223e-06 0.9989 -2.053e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2277 0.1083 0.3532 0.1404 0.9849 0.9939 0.2285 0.4304 0.8743 0.6996 ] Network output: [ 0.001766 -0.008811 0.995 1.672e-06 -7.506e-07 1.01 1.26e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7283 0.8599 0.304 ] Network output: [ -0.001709 0.008366 1.004 1.852e-06 -8.314e-07 0.9907 1.396e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0955 0.09355 0.1647 0.1971 0.9851 0.991 0.09552 0.6518 0.8345 0.2508 ] Network output: [ 6.98e-05 1 -6.474e-05 2.41e-07 -1.082e-07 0.9999 1.816e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001034 Epoch 10596 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00815 0.9969 0.9932 -8.678e-08 3.896e-08 -0.006459 -6.54e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.006219 0.005079 0.9699 0.9743 0.006952 0.8215 0.8181 0.0155 ] Network output: [ 1 -6.822e-06 0.0002535 -8.66e-07 3.888e-07 -0.0002028 -6.526e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03653 -0.1502 0.1798 0.9833 0.9931 0.2396 0.4265 0.8675 0.7062 ] Network output: [ -0.008189 1.003 1.007 -8.683e-08 3.898e-08 0.006955 -6.544e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007088 0.0006625 0.004267 0.002978 0.9889 0.9919 0.007229 0.8486 0.891 0.01099 ] Network output: [ -6.657e-05 0.00081 1 -2.721e-06 1.222e-06 0.9989 -2.051e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2277 0.1083 0.3532 0.1404 0.9849 0.9939 0.2285 0.4304 0.8743 0.6996 ] Network output: [ 0.001765 -0.008805 0.995 1.67e-06 -7.496e-07 1.01 1.258e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7283 0.8599 0.304 ] Network output: [ -0.001708 0.008361 1.004 1.85e-06 -8.304e-07 0.9907 1.394e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0955 0.09355 0.1647 0.1971 0.9851 0.991 0.09552 0.6517 0.8345 0.2508 ] Network output: [ 6.979e-05 1 -6.478e-05 2.407e-07 -1.081e-07 0.9999 1.814e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001034 Epoch 10597 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00815 0.9969 0.9932 -8.67e-08 3.892e-08 -0.006459 -6.534e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.006218 0.005079 0.9699 0.9743 0.006952 0.8215 0.8181 0.0155 ] Network output: [ 1 -6.926e-06 0.0002534 -8.649e-07 3.883e-07 -0.0002027 -6.518e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03653 -0.1502 0.1798 0.9833 0.9931 0.2396 0.4265 0.8675 0.7062 ] Network output: [ -0.008188 1.003 1.007 -8.675e-08 3.894e-08 0.006955 -6.537e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007088 0.0006625 0.004267 0.002977 0.9889 0.9919 0.00723 0.8486 0.891 0.01099 ] Network output: [ -6.645e-05 0.0008093 1 -2.718e-06 1.22e-06 0.9989 -2.048e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2277 0.1083 0.3532 0.1404 0.9849 0.9939 0.2285 0.4304 0.8743 0.6996 ] Network output: [ 0.001764 -0.008799 0.995 1.668e-06 -7.486e-07 1.01 1.257e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7283 0.8599 0.304 ] Network output: [ -0.001707 0.008356 1.004 1.847e-06 -8.293e-07 0.9907 1.392e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0955 0.09355 0.1647 0.1971 0.9851 0.991 0.09552 0.6517 0.8345 0.2508 ] Network output: [ 6.978e-05 1 -6.482e-05 2.404e-07 -1.079e-07 0.9999 1.812e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001033 Epoch 10598 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008149 0.9969 0.9932 -8.662e-08 3.889e-08 -0.006458 -6.528e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.006218 0.005079 0.9699 0.9743 0.006952 0.8215 0.8181 0.0155 ] Network output: [ 1 -7.03e-06 0.0002533 -8.638e-07 3.878e-07 -0.0002026 -6.51e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03653 -0.1501 0.1798 0.9833 0.9931 0.2396 0.4265 0.8675 0.7062 ] Network output: [ -0.008187 1.003 1.007 -8.666e-08 3.89e-08 0.006954 -6.531e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007088 0.0006625 0.004267 0.002977 0.9889 0.9919 0.00723 0.8486 0.891 0.01099 ] Network output: [ -6.633e-05 0.0008086 1 -2.714e-06 1.218e-06 0.9989 -2.045e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2277 0.1083 0.3532 0.1404 0.9849 0.9939 0.2285 0.4304 0.8743 0.6996 ] Network output: [ 0.001762 -0.008793 0.995 1.665e-06 -7.477e-07 1.01 1.255e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7283 0.8599 0.304 ] Network output: [ -0.001705 0.008351 1.004 1.845e-06 -8.283e-07 0.9907 1.39e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0955 0.09355 0.1647 0.1971 0.9851 0.991 0.09552 0.6517 0.8345 0.2508 ] Network output: [ 6.977e-05 1 -6.485e-05 2.401e-07 -1.078e-07 0.9999 1.809e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001033 Epoch 10599 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008148 0.9969 0.9932 -8.654e-08 3.885e-08 -0.006457 -6.522e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.006217 0.005078 0.9699 0.9743 0.006952 0.8215 0.8181 0.0155 ] Network output: [ 1 -7.133e-06 0.0002532 -8.627e-07 3.873e-07 -0.0002025 -6.501e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03653 -0.1501 0.1798 0.9833 0.9931 0.2396 0.4265 0.8675 0.7062 ] Network output: [ -0.008187 1.003 1.007 -8.657e-08 3.887e-08 0.006954 -6.524e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007088 0.0006625 0.004267 0.002977 0.9889 0.9919 0.00723 0.8486 0.891 0.01099 ] Network output: [ -6.622e-05 0.000808 1 -2.711e-06 1.217e-06 0.9989 -2.043e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2277 0.1083 0.3532 0.1404 0.9849 0.9939 0.2285 0.4304 0.8743 0.6996 ] Network output: [ 0.001761 -0.008786 0.995 1.663e-06 -7.467e-07 1.01 1.254e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7283 0.8599 0.304 ] Network output: [ -0.001704 0.008346 1.004 1.843e-06 -8.272e-07 0.9907 1.389e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.0955 0.09356 0.1647 0.1971 0.9851 0.991 0.09552 0.6517 0.8345 0.2508 ] Network output: [ 6.976e-05 1 -6.489e-05 2.398e-07 -1.076e-07 0.9999 1.807e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001032 Epoch 10600 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008147 0.9969 0.9932 -8.646e-08 3.882e-08 -0.006456 -6.516e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.006217 0.005078 0.9699 0.9743 0.006952 0.8215 0.8181 0.0155 ] Network output: [ 1 -7.237e-06 0.0002531 -8.615e-07 3.868e-07 -0.0002024 -6.493e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03654 -0.1501 0.1798 0.9833 0.9931 0.2396 0.4265 0.8675 0.7062 ] Network output: [ -0.008186 1.003 1.007 -8.649e-08 3.883e-08 0.006954 -6.518e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007089 0.0006625 0.004266 0.002977 0.9889 0.9919 0.00723 0.8486 0.891 0.01099 ] Network output: [ -6.61e-05 0.0008073 1 -2.707e-06 1.215e-06 0.9989 -2.04e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2277 0.1083 0.3532 0.1404 0.9849 0.9939 0.2285 0.4304 0.8743 0.6996 ] Network output: [ 0.00176 -0.00878 0.995 1.661e-06 -7.458e-07 1.01 1.252e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7283 0.8599 0.304 ] Network output: [ -0.001703 0.00834 1.004 1.84e-06 -8.262e-07 0.9907 1.387e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09551 0.09356 0.1647 0.1971 0.9851 0.991 0.09552 0.6517 0.8345 0.2508 ] Network output: [ 6.976e-05 1 -6.493e-05 2.395e-07 -1.075e-07 0.9999 1.805e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001031 Epoch 10601 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008147 0.9969 0.9932 -8.639e-08 3.878e-08 -0.006456 -6.51e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.006217 0.005078 0.9699 0.9743 0.006952 0.8215 0.8181 0.0155 ] Network output: [ 1 -7.34e-06 0.000253 -8.604e-07 3.863e-07 -0.0002024 -6.485e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03654 -0.1501 0.1798 0.9833 0.9931 0.2397 0.4265 0.8675 0.7062 ] Network output: [ -0.008185 1.003 1.007 -8.64e-08 3.879e-08 0.006953 -6.511e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007089 0.0006625 0.004266 0.002977 0.9889 0.9919 0.00723 0.8486 0.891 0.01099 ] Network output: [ -6.598e-05 0.0008066 1 -2.704e-06 1.214e-06 0.9989 -2.038e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2277 0.1083 0.3532 0.1404 0.9849 0.9939 0.2285 0.4304 0.8743 0.6996 ] Network output: [ 0.001758 -0.008774 0.995 1.659e-06 -7.448e-07 1.01 1.25e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7283 0.8599 0.304 ] Network output: [ -0.001702 0.008335 1.004 1.838e-06 -8.251e-07 0.9907 1.385e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09551 0.09356 0.1647 0.1971 0.9851 0.991 0.09552 0.6517 0.8345 0.2508 ] Network output: [ 6.975e-05 1 -6.496e-05 2.392e-07 -1.074e-07 0.9999 1.802e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001031 Epoch 10602 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008146 0.9969 0.9932 -8.631e-08 3.875e-08 -0.006455 -6.504e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.006216 0.005078 0.9699 0.9743 0.006952 0.8215 0.8181 0.0155 ] Network output: [ 1 -7.444e-06 0.0002528 -8.593e-07 3.858e-07 -0.0002023 -6.476e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03654 -0.1501 0.1798 0.9833 0.9931 0.2397 0.4265 0.8675 0.7062 ] Network output: [ -0.008184 1.003 1.007 -8.631e-08 3.875e-08 0.006953 -6.505e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007089 0.0006625 0.004266 0.002976 0.9889 0.9919 0.007231 0.8486 0.891 0.01099 ] Network output: [ -6.587e-05 0.0008059 1 -2.7e-06 1.212e-06 0.9989 -2.035e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2277 0.1083 0.3532 0.1404 0.9849 0.9939 0.2285 0.4304 0.8743 0.6996 ] Network output: [ 0.001757 -0.008768 0.995 1.657e-06 -7.439e-07 1.01 1.249e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7283 0.8599 0.304 ] Network output: [ -0.001701 0.00833 1.004 1.836e-06 -8.241e-07 0.9907 1.383e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09551 0.09356 0.1647 0.1971 0.9851 0.991 0.09552 0.6517 0.8345 0.2508 ] Network output: [ 6.974e-05 1 -6.5e-05 2.389e-07 -1.072e-07 0.9999 1.8e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000103 Epoch 10603 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008145 0.9969 0.9932 -8.623e-08 3.871e-08 -0.006454 -6.498e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.006216 0.005077 0.9699 0.9743 0.006953 0.8215 0.8181 0.0155 ] Network output: [ 1 -7.547e-06 0.0002527 -8.582e-07 3.853e-07 -0.0002022 -6.468e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03654 -0.1501 0.1798 0.9833 0.9931 0.2397 0.4265 0.8675 0.7062 ] Network output: [ -0.008184 1.003 1.007 -8.623e-08 3.871e-08 0.006952 -6.498e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007089 0.0006626 0.004266 0.002976 0.9889 0.9919 0.007231 0.8486 0.891 0.01099 ] Network output: [ -6.575e-05 0.0008053 1 -2.697e-06 1.211e-06 0.9989 -2.032e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2277 0.1083 0.3532 0.1404 0.9849 0.9939 0.2285 0.4304 0.8743 0.6996 ] Network output: [ 0.001756 -0.008762 0.995 1.655e-06 -7.429e-07 1.01 1.247e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7282 0.8599 0.304 ] Network output: [ -0.001699 0.008325 1.004 1.833e-06 -8.231e-07 0.9907 1.382e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09551 0.09356 0.1647 0.1971 0.9851 0.991 0.09552 0.6517 0.8345 0.2508 ] Network output: [ 6.973e-05 1 -6.504e-05 2.386e-07 -1.071e-07 0.9999 1.798e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001029 Epoch 10604 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008145 0.9969 0.9932 -8.615e-08 3.867e-08 -0.006454 -6.492e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.006215 0.005077 0.9699 0.9743 0.006953 0.8215 0.8181 0.0155 ] Network output: [ 1 -7.65e-06 0.0002526 -8.571e-07 3.848e-07 -0.0002021 -6.46e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03654 -0.1501 0.1798 0.9833 0.9931 0.2397 0.4265 0.8675 0.7062 ] Network output: [ -0.008183 1.003 1.007 -8.614e-08 3.867e-08 0.006952 -6.492e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007089 0.0006626 0.004266 0.002976 0.9889 0.9919 0.007231 0.8486 0.891 0.01099 ] Network output: [ -6.564e-05 0.0008046 1 -2.693e-06 1.209e-06 0.9989 -2.03e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2277 0.1083 0.3532 0.1404 0.9849 0.9939 0.2285 0.4304 0.8743 0.6996 ] Network output: [ 0.001754 -0.008756 0.995 1.653e-06 -7.42e-07 1.01 1.246e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7282 0.8599 0.304 ] Network output: [ -0.001698 0.00832 1.004 1.831e-06 -8.22e-07 0.9907 1.38e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09551 0.09356 0.1647 0.1971 0.9851 0.991 0.09552 0.6517 0.8345 0.2508 ] Network output: [ 6.972e-05 1 -6.507e-05 2.383e-07 -1.07e-07 0.9999 1.796e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001029 Epoch 10605 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008144 0.9969 0.9932 -8.607e-08 3.864e-08 -0.006453 -6.486e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.006215 0.005077 0.9699 0.9743 0.006953 0.8215 0.8181 0.01549 ] Network output: [ 1 -7.754e-06 0.0002525 -8.56e-07 3.843e-07 -0.000202 -6.451e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03654 -0.1501 0.1798 0.9833 0.9931 0.2397 0.4265 0.8675 0.7062 ] Network output: [ -0.008182 1.003 1.007 -8.606e-08 3.863e-08 0.006952 -6.486e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00709 0.0006626 0.004266 0.002976 0.9889 0.9919 0.007231 0.8486 0.891 0.01099 ] Network output: [ -6.552e-05 0.0008039 1 -2.69e-06 1.208e-06 0.9989 -2.027e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2277 0.1083 0.3532 0.1404 0.9849 0.9939 0.2285 0.4304 0.8743 0.6996 ] Network output: [ 0.001753 -0.00875 0.995 1.651e-06 -7.41e-07 1.01 1.244e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7282 0.8599 0.304 ] Network output: [ -0.001697 0.008315 1.004 1.829e-06 -8.21e-07 0.9907 1.378e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09551 0.09356 0.1647 0.1971 0.9851 0.991 0.09552 0.6517 0.8345 0.2508 ] Network output: [ 6.971e-05 1 -6.511e-05 2.38e-07 -1.068e-07 0.9999 1.793e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001028 Epoch 10606 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008143 0.9969 0.9932 -8.599e-08 3.86e-08 -0.006452 -6.48e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.006214 0.005076 0.9699 0.9743 0.006953 0.8215 0.8181 0.01549 ] Network output: [ 1 -7.857e-06 0.0002524 -8.549e-07 3.838e-07 -0.0002019 -6.443e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03654 -0.1501 0.1798 0.9833 0.9931 0.2397 0.4265 0.8675 0.7062 ] Network output: [ -0.008181 1.003 1.007 -8.597e-08 3.86e-08 0.006951 -6.479e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00709 0.0006626 0.004266 0.002976 0.9889 0.9919 0.007232 0.8486 0.891 0.01099 ] Network output: [ -6.54e-05 0.0008033 1 -2.686e-06 1.206e-06 0.9989 -2.025e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2277 0.1083 0.3532 0.1404 0.9849 0.9939 0.2285 0.4304 0.8743 0.6996 ] Network output: [ 0.001751 -0.008744 0.995 1.649e-06 -7.401e-07 1.01 1.242e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7282 0.8599 0.304 ] Network output: [ -0.001696 0.008309 1.004 1.826e-06 -8.199e-07 0.9907 1.376e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09551 0.09356 0.1647 0.1971 0.9851 0.991 0.09552 0.6517 0.8345 0.2508 ] Network output: [ 6.97e-05 1 -6.515e-05 2.377e-07 -1.067e-07 0.9999 1.791e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001028 Epoch 10607 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008142 0.9969 0.9932 -8.591e-08 3.857e-08 -0.006451 -6.475e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.006214 0.005076 0.9699 0.9743 0.006953 0.8215 0.8181 0.01549 ] Network output: [ 1 -7.96e-06 0.0002523 -8.538e-07 3.833e-07 -0.0002019 -6.435e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03654 -0.1501 0.1798 0.9833 0.9931 0.2397 0.4265 0.8675 0.7062 ] Network output: [ -0.008181 1.003 1.007 -8.589e-08 3.856e-08 0.006951 -6.473e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00709 0.0006626 0.004266 0.002975 0.9889 0.9919 0.007232 0.8486 0.891 0.01098 ] Network output: [ -6.529e-05 0.0008026 1 -2.683e-06 1.204e-06 0.9989 -2.022e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2277 0.1083 0.3533 0.1404 0.9849 0.9939 0.2285 0.4304 0.8743 0.6996 ] Network output: [ 0.00175 -0.008738 0.995 1.646e-06 -7.391e-07 1.01 1.241e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1132 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7282 0.8599 0.304 ] Network output: [ -0.001695 0.008304 1.004 1.824e-06 -8.189e-07 0.9907 1.375e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09551 0.09356 0.1647 0.1971 0.9851 0.991 0.09552 0.6517 0.8345 0.2508 ] Network output: [ 6.969e-05 1 -6.518e-05 2.374e-07 -1.066e-07 0.9999 1.789e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001027 Epoch 10608 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008142 0.9969 0.9932 -8.583e-08 3.853e-08 -0.006451 -6.469e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.006213 0.005076 0.9699 0.9743 0.006953 0.8215 0.8181 0.01549 ] Network output: [ 1 -8.063e-06 0.0002522 -8.527e-07 3.828e-07 -0.0002018 -6.427e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03654 -0.1501 0.1798 0.9833 0.9931 0.2397 0.4265 0.8675 0.7062 ] Network output: [ -0.00818 1.003 1.007 -8.58e-08 3.852e-08 0.006951 -6.466e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00709 0.0006626 0.004265 0.002975 0.9889 0.9919 0.007232 0.8486 0.891 0.01098 ] Network output: [ -6.517e-05 0.0008019 1 -2.679e-06 1.203e-06 0.9989 -2.019e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2277 0.1083 0.3533 0.1404 0.9849 0.9939 0.2285 0.4304 0.8743 0.6996 ] Network output: [ 0.001749 -0.008732 0.995 1.644e-06 -7.382e-07 1.01 1.239e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7282 0.8599 0.304 ] Network output: [ -0.001693 0.008299 1.004 1.822e-06 -8.179e-07 0.9907 1.373e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09551 0.09356 0.1647 0.1971 0.9851 0.991 0.09553 0.6517 0.8345 0.2508 ] Network output: [ 6.968e-05 1 -6.522e-05 2.371e-07 -1.064e-07 0.9999 1.787e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001026 Epoch 10609 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008141 0.9969 0.9932 -8.575e-08 3.85e-08 -0.00645 -6.463e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.006213 0.005075 0.9699 0.9743 0.006953 0.8215 0.8181 0.01549 ] Network output: [ 1 -8.166e-06 0.0002521 -8.516e-07 3.823e-07 -0.0002017 -6.418e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03655 -0.1501 0.1798 0.9833 0.9931 0.2397 0.4265 0.8675 0.7062 ] Network output: [ -0.008179 1.003 1.007 -8.571e-08 3.848e-08 0.00695 -6.46e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.00709 0.0006626 0.004265 0.002975 0.9889 0.9919 0.007232 0.8486 0.891 0.01098 ] Network output: [ -6.506e-05 0.0008013 1 -2.676e-06 1.201e-06 0.9989 -2.017e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2277 0.1083 0.3533 0.1404 0.9849 0.9939 0.2285 0.4304 0.8743 0.6996 ] Network output: [ 0.001747 -0.008726 0.995 1.642e-06 -7.372e-07 1.01 1.238e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7282 0.8599 0.304 ] Network output: [ -0.001692 0.008294 1.004 1.819e-06 -8.168e-07 0.9907 1.371e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09551 0.09356 0.1647 0.1971 0.9851 0.991 0.09553 0.6517 0.8345 0.2508 ] Network output: [ 6.968e-05 1 -6.526e-05 2.368e-07 -1.063e-07 0.9999 1.784e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001026 Epoch 10610 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00814 0.9969 0.9932 -8.567e-08 3.846e-08 -0.006449 -6.457e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003411 -0.006212 0.005075 0.9699 0.9743 0.006953 0.8215 0.8181 0.01549 ] Network output: [ 1 -8.269e-06 0.000252 -8.506e-07 3.818e-07 -0.0002016 -6.41e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03655 -0.1501 0.1798 0.9833 0.9931 0.2397 0.4265 0.8675 0.7062 ] Network output: [ -0.008179 1.003 1.007 -8.563e-08 3.844e-08 0.00695 -6.453e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007091 0.0006626 0.004265 0.002975 0.9889 0.9919 0.007232 0.8486 0.891 0.01098 ] Network output: [ -6.494e-05 0.0008006 1 -2.673e-06 1.2e-06 0.9989 -2.014e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2277 0.1083 0.3533 0.1404 0.9849 0.9939 0.2285 0.4304 0.8743 0.6996 ] Network output: [ 0.001746 -0.00872 0.995 1.64e-06 -7.363e-07 1.01 1.236e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7282 0.8599 0.304 ] Network output: [ -0.001691 0.008289 1.004 1.817e-06 -8.158e-07 0.9907 1.369e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09551 0.09356 0.1647 0.1971 0.9851 0.991 0.09553 0.6517 0.8345 0.2508 ] Network output: [ 6.967e-05 1 -6.529e-05 2.365e-07 -1.062e-07 0.9999 1.782e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001025 Epoch 10611 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008139 0.9969 0.9932 -8.56e-08 3.843e-08 -0.006448 -6.451e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.006212 0.005075 0.9699 0.9743 0.006953 0.8215 0.8181 0.01549 ] Network output: [ 1 -8.372e-06 0.0002519 -8.495e-07 3.814e-07 -0.0002015 -6.402e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03655 -0.1501 0.1798 0.9833 0.9931 0.2397 0.4265 0.8675 0.7062 ] Network output: [ -0.008178 1.003 1.007 -8.554e-08 3.84e-08 0.00695 -6.447e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007091 0.0006626 0.004265 0.002975 0.9889 0.9919 0.007233 0.8486 0.891 0.01098 ] Network output: [ -6.482e-05 0.0007999 1 -2.669e-06 1.198e-06 0.9989 -2.012e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2277 0.1084 0.3533 0.1404 0.9849 0.9939 0.2285 0.4304 0.8743 0.6996 ] Network output: [ 0.001745 -0.008714 0.995 1.638e-06 -7.354e-07 1.01 1.234e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7282 0.8599 0.304 ] Network output: [ -0.00169 0.008284 1.004 1.815e-06 -8.148e-07 0.9907 1.368e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09551 0.09356 0.1647 0.1972 0.9851 0.991 0.09553 0.6517 0.8345 0.2508 ] Network output: [ 6.966e-05 1 -6.533e-05 2.362e-07 -1.06e-07 0.9999 1.78e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001024 Epoch 10612 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008139 0.9969 0.9932 -8.552e-08 3.839e-08 -0.006448 -6.445e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.006212 0.005074 0.9699 0.9743 0.006953 0.8214 0.8181 0.01549 ] Network output: [ 1 -8.475e-06 0.0002518 -8.484e-07 3.809e-07 -0.0002014 -6.394e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03655 -0.1501 0.1798 0.9833 0.9931 0.2397 0.4265 0.8675 0.7062 ] Network output: [ -0.008177 1.003 1.007 -8.546e-08 3.837e-08 0.006949 -6.44e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007091 0.0006626 0.004265 0.002974 0.9889 0.9919 0.007233 0.8486 0.891 0.01098 ] Network output: [ -6.471e-05 0.0007993 1 -2.666e-06 1.197e-06 0.9989 -2.009e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2277 0.1084 0.3533 0.1404 0.9849 0.9939 0.2285 0.4304 0.8743 0.6996 ] Network output: [ 0.001743 -0.008708 0.9951 1.636e-06 -7.344e-07 1.01 1.233e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7282 0.8599 0.304 ] Network output: [ -0.001689 0.008278 1.004 1.813e-06 -8.137e-07 0.9907 1.366e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09551 0.09356 0.1647 0.1972 0.9851 0.991 0.09553 0.6517 0.8345 0.2508 ] Network output: [ 6.965e-05 1 -6.537e-05 2.359e-07 -1.059e-07 0.9999 1.777e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001024 Epoch 10613 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008138 0.9969 0.9932 -8.544e-08 3.836e-08 -0.006447 -6.439e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.006211 0.005074 0.9699 0.9743 0.006953 0.8214 0.8181 0.01549 ] Network output: [ 1 -8.578e-06 0.0002517 -8.473e-07 3.804e-07 -0.0002014 -6.385e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03655 -0.1501 0.1798 0.9833 0.9931 0.2397 0.4265 0.8675 0.7062 ] Network output: [ -0.008176 1.003 1.007 -8.537e-08 3.833e-08 0.006949 -6.434e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007091 0.0006627 0.004265 0.002974 0.9889 0.9919 0.007233 0.8486 0.891 0.01098 ] Network output: [ -6.459e-05 0.0007986 1 -2.662e-06 1.195e-06 0.9989 -2.006e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2278 0.1084 0.3533 0.1404 0.9849 0.9939 0.2286 0.4304 0.8743 0.6996 ] Network output: [ 0.001742 -0.008702 0.9951 1.634e-06 -7.335e-07 1.01 1.231e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7282 0.8599 0.304 ] Network output: [ -0.001687 0.008273 1.004 1.81e-06 -8.127e-07 0.9907 1.364e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09551 0.09356 0.1647 0.1972 0.9851 0.991 0.09553 0.6516 0.8345 0.2508 ] Network output: [ 6.964e-05 1 -6.54e-05 2.356e-07 -1.058e-07 0.9999 1.775e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001023 Epoch 10614 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008137 0.9969 0.9932 -8.536e-08 3.832e-08 -0.006446 -6.433e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.006211 0.005074 0.9699 0.9743 0.006953 0.8214 0.8181 0.01549 ] Network output: [ 1 -8.681e-06 0.0002515 -8.462e-07 3.799e-07 -0.0002013 -6.377e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03655 -0.15 0.1797 0.9833 0.9931 0.2397 0.4265 0.8675 0.7062 ] Network output: [ -0.008176 1.003 1.007 -8.529e-08 3.829e-08 0.006949 -6.428e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007092 0.0006627 0.004265 0.002974 0.9889 0.9919 0.007233 0.8486 0.891 0.01098 ] Network output: [ -6.448e-05 0.0007979 1 -2.659e-06 1.194e-06 0.9989 -2.004e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2278 0.1084 0.3533 0.1404 0.9849 0.9939 0.2286 0.4304 0.8743 0.6996 ] Network output: [ 0.001741 -0.008696 0.9951 1.632e-06 -7.325e-07 1.01 1.23e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7282 0.8599 0.304 ] Network output: [ -0.001686 0.008268 1.004 1.808e-06 -8.117e-07 0.9907 1.363e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09551 0.09357 0.1647 0.1972 0.9851 0.991 0.09553 0.6516 0.8345 0.2508 ] Network output: [ 6.963e-05 1 -6.544e-05 2.353e-07 -1.056e-07 0.9999 1.773e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001023 Epoch 10615 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008137 0.9969 0.9932 -8.528e-08 3.829e-08 -0.006445 -6.427e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.00621 0.005073 0.9699 0.9743 0.006953 0.8214 0.8181 0.01549 ] Network output: [ 1 -8.783e-06 0.0002514 -8.451e-07 3.794e-07 -0.0002012 -6.369e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03655 -0.15 0.1797 0.9833 0.9931 0.2397 0.4265 0.8675 0.7061 ] Network output: [ -0.008175 1.003 1.007 -8.52e-08 3.825e-08 0.006948 -6.421e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007092 0.0006627 0.004265 0.002974 0.9889 0.9919 0.007234 0.8486 0.891 0.01098 ] Network output: [ -6.436e-05 0.0007972 1 -2.655e-06 1.192e-06 0.9989 -2.001e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2278 0.1084 0.3533 0.1404 0.9849 0.9939 0.2286 0.4304 0.8743 0.6996 ] Network output: [ 0.001739 -0.00869 0.9951 1.63e-06 -7.316e-07 1.01 1.228e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7282 0.8599 0.304 ] Network output: [ -0.001685 0.008263 1.004 1.806e-06 -8.107e-07 0.9907 1.361e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09552 0.09357 0.1647 0.1972 0.9851 0.991 0.09553 0.6516 0.8345 0.2508 ] Network output: [ 6.962e-05 1 -6.548e-05 2.35e-07 -1.055e-07 0.9999 1.771e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001022 Epoch 10616 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008136 0.9969 0.9932 -8.52e-08 3.825e-08 -0.006445 -6.421e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.00621 0.005073 0.9699 0.9743 0.006954 0.8214 0.8181 0.01549 ] Network output: [ 1 -8.886e-06 0.0002513 -8.44e-07 3.789e-07 -0.0002011 -6.361e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2131 -0.03655 -0.15 0.1797 0.9833 0.9931 0.2397 0.4265 0.8675 0.7061 ] Network output: [ -0.008174 1.003 1.007 -8.512e-08 3.821e-08 0.006948 -6.415e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007092 0.0006627 0.004264 0.002974 0.9889 0.9919 0.007234 0.8486 0.891 0.01098 ] Network output: [ -6.424e-05 0.0007966 1 -2.652e-06 1.191e-06 0.9989 -1.999e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2278 0.1084 0.3533 0.1403 0.9849 0.9939 0.2286 0.4304 0.8743 0.6996 ] Network output: [ 0.001738 -0.008683 0.9951 1.628e-06 -7.307e-07 1.01 1.227e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7282 0.8599 0.304 ] Network output: [ -0.001684 0.008258 1.004 1.803e-06 -8.096e-07 0.9907 1.359e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09552 0.09357 0.1647 0.1972 0.9851 0.991 0.09553 0.6516 0.8345 0.2508 ] Network output: [ 6.961e-05 1 -6.552e-05 2.347e-07 -1.053e-07 0.9999 1.768e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001021 Epoch 10617 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008135 0.9969 0.9932 -8.512e-08 3.821e-08 -0.006444 -6.415e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.006209 0.005073 0.9699 0.9743 0.006954 0.8214 0.8181 0.01549 ] Network output: [ 1 -8.989e-06 0.0002512 -8.429e-07 3.784e-07 -0.000201 -6.353e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03655 -0.15 0.1797 0.9833 0.9931 0.2397 0.4265 0.8675 0.7061 ] Network output: [ -0.008174 1.003 1.007 -8.503e-08 3.817e-08 0.006947 -6.408e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007092 0.0006627 0.004264 0.002973 0.9889 0.9919 0.007234 0.8486 0.891 0.01098 ] Network output: [ -6.413e-05 0.0007959 1 -2.649e-06 1.189e-06 0.9989 -1.996e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2278 0.1084 0.3533 0.1403 0.9849 0.9939 0.2286 0.4304 0.8743 0.6996 ] Network output: [ 0.001736 -0.008677 0.9951 1.625e-06 -7.297e-07 1.01 1.225e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7282 0.8599 0.304 ] Network output: [ -0.001683 0.008253 1.004 1.801e-06 -8.086e-07 0.9907 1.357e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09552 0.09357 0.1647 0.1972 0.9851 0.991 0.09553 0.6516 0.8345 0.2508 ] Network output: [ 6.96e-05 1 -6.555e-05 2.344e-07 -1.052e-07 0.9999 1.766e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001021 Epoch 10618 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008134 0.9969 0.9932 -8.504e-08 3.818e-08 -0.006443 -6.409e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.006209 0.005073 0.9699 0.9743 0.006954 0.8214 0.8181 0.01548 ] Network output: [ 1 -9.091e-06 0.0002511 -8.419e-07 3.779e-07 -0.0002009 -6.344e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03656 -0.15 0.1797 0.9833 0.9931 0.2397 0.4265 0.8675 0.7061 ] Network output: [ -0.008173 1.003 1.007 -8.495e-08 3.814e-08 0.006947 -6.402e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007092 0.0006627 0.004264 0.002973 0.9889 0.9919 0.007234 0.8486 0.891 0.01098 ] Network output: [ -6.401e-05 0.0007952 1 -2.645e-06 1.188e-06 0.9989 -1.993e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2278 0.1084 0.3533 0.1403 0.9849 0.9939 0.2286 0.4304 0.8743 0.6996 ] Network output: [ 0.001735 -0.008671 0.9951 1.623e-06 -7.288e-07 1.01 1.223e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7282 0.8599 0.304 ] Network output: [ -0.001681 0.008247 1.004 1.799e-06 -8.076e-07 0.9907 1.356e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09552 0.09357 0.1647 0.1972 0.9851 0.991 0.09553 0.6516 0.8345 0.2508 ] Network output: [ 6.96e-05 1 -6.559e-05 2.341e-07 -1.051e-07 0.9999 1.764e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000102 Epoch 10619 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008134 0.9969 0.9932 -8.497e-08 3.814e-08 -0.006443 -6.403e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.006208 0.005072 0.9699 0.9743 0.006954 0.8214 0.8181 0.01548 ] Network output: [ 1 -9.194e-06 0.000251 -8.408e-07 3.775e-07 -0.0002009 -6.336e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03656 -0.15 0.1797 0.9833 0.9931 0.2397 0.4265 0.8675 0.7061 ] Network output: [ -0.008172 1.003 1.007 -8.486e-08 3.81e-08 0.006947 -6.396e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007093 0.0006627 0.004264 0.002973 0.9889 0.9919 0.007234 0.8486 0.891 0.01098 ] Network output: [ -6.39e-05 0.0007946 1 -2.642e-06 1.186e-06 0.9989 -1.991e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2278 0.1084 0.3533 0.1403 0.9849 0.9939 0.2286 0.4304 0.8743 0.6996 ] Network output: [ 0.001734 -0.008665 0.9951 1.621e-06 -7.279e-07 1.01 1.222e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7282 0.8599 0.304 ] Network output: [ -0.00168 0.008242 1.004 1.797e-06 -8.066e-07 0.9907 1.354e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09552 0.09357 0.1647 0.1972 0.9851 0.991 0.09553 0.6516 0.8345 0.2508 ] Network output: [ 6.959e-05 1 -6.563e-05 2.338e-07 -1.049e-07 0.9999 1.762e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001019 Epoch 10620 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008133 0.9969 0.9932 -8.489e-08 3.811e-08 -0.006442 -6.397e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.006208 0.005072 0.9699 0.9743 0.006954 0.8214 0.8181 0.01548 ] Network output: [ 1 -9.296e-06 0.0002509 -8.397e-07 3.77e-07 -0.0002008 -6.328e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03656 -0.15 0.1797 0.9833 0.9931 0.2397 0.4265 0.8675 0.7061 ] Network output: [ -0.008171 1.003 1.007 -8.478e-08 3.806e-08 0.006946 -6.389e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007093 0.0006627 0.004264 0.002973 0.9889 0.9919 0.007235 0.8486 0.891 0.01098 ] Network output: [ -6.378e-05 0.0007939 1 -2.638e-06 1.184e-06 0.9989 -1.988e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2278 0.1084 0.3533 0.1403 0.9849 0.9939 0.2286 0.4304 0.8743 0.6996 ] Network output: [ 0.001732 -0.008659 0.9951 1.619e-06 -7.269e-07 1.01 1.22e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7282 0.8599 0.304 ] Network output: [ -0.001679 0.008237 1.004 1.794e-06 -8.055e-07 0.9907 1.352e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09552 0.09357 0.1647 0.1972 0.9851 0.991 0.09553 0.6516 0.8345 0.2508 ] Network output: [ 6.958e-05 1 -6.566e-05 2.335e-07 -1.048e-07 0.9999 1.76e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001019 Epoch 10621 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008132 0.9969 0.9932 -8.481e-08 3.807e-08 -0.006441 -6.392e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.006207 0.005072 0.9699 0.9743 0.006954 0.8214 0.8181 0.01548 ] Network output: [ 1 -9.398e-06 0.0002508 -8.386e-07 3.765e-07 -0.0002007 -6.32e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03656 -0.15 0.1797 0.9833 0.9931 0.2398 0.4265 0.8675 0.7061 ] Network output: [ -0.008171 1.003 1.007 -8.469e-08 3.802e-08 0.006946 -6.383e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007093 0.0006627 0.004264 0.002972 0.9889 0.9919 0.007235 0.8486 0.891 0.01098 ] Network output: [ -6.367e-05 0.0007932 1 -2.635e-06 1.183e-06 0.9989 -1.986e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2278 0.1084 0.3533 0.1403 0.9849 0.9939 0.2286 0.4304 0.8743 0.6996 ] Network output: [ 0.001731 -0.008653 0.9951 1.617e-06 -7.26e-07 1.01 1.219e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7282 0.8599 0.304 ] Network output: [ -0.001678 0.008232 1.004 1.792e-06 -8.045e-07 0.9907 1.351e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09552 0.09357 0.1647 0.1972 0.9851 0.991 0.09553 0.6516 0.8345 0.2508 ] Network output: [ 6.957e-05 1 -6.57e-05 2.332e-07 -1.047e-07 0.9999 1.757e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001018 Epoch 10622 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008131 0.9969 0.9932 -8.473e-08 3.804e-08 -0.00644 -6.386e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.006207 0.005071 0.9699 0.9743 0.006954 0.8214 0.8181 0.01548 ] Network output: [ 1 -9.501e-06 0.0002507 -8.375e-07 3.76e-07 -0.0002006 -6.312e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03656 -0.15 0.1797 0.9833 0.9931 0.2398 0.4265 0.8675 0.7061 ] Network output: [ -0.00817 1.003 1.007 -8.461e-08 3.798e-08 0.006946 -6.376e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007093 0.0006627 0.004264 0.002972 0.9889 0.9919 0.007235 0.8486 0.891 0.01097 ] Network output: [ -6.355e-05 0.0007926 1 -2.632e-06 1.181e-06 0.9989 -1.983e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2278 0.1084 0.3533 0.1403 0.9849 0.9939 0.2286 0.4304 0.8743 0.6996 ] Network output: [ 0.00173 -0.008647 0.9951 1.615e-06 -7.251e-07 1.01 1.217e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7281 0.8599 0.304 ] Network output: [ -0.001677 0.008227 1.004 1.79e-06 -8.035e-07 0.9907 1.349e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09552 0.09357 0.1647 0.1972 0.9851 0.991 0.09553 0.6516 0.8345 0.2508 ] Network output: [ 6.956e-05 1 -6.574e-05 2.329e-07 -1.045e-07 0.9999 1.755e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001018 Epoch 10623 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008131 0.9969 0.9932 -8.465e-08 3.8e-08 -0.00644 -6.38e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.006207 0.005071 0.9699 0.9743 0.006954 0.8214 0.8181 0.01548 ] Network output: [ 1 -9.603e-06 0.0002506 -8.365e-07 3.755e-07 -0.0002005 -6.304e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03656 -0.15 0.1797 0.9833 0.9931 0.2398 0.4265 0.8675 0.7061 ] Network output: [ -0.008169 1.003 1.007 -8.452e-08 3.795e-08 0.006945 -6.37e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007093 0.0006627 0.004264 0.002972 0.9889 0.9919 0.007235 0.8485 0.891 0.01097 ] Network output: [ -6.344e-05 0.0007919 1 -2.628e-06 1.18e-06 0.9989 -1.981e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2278 0.1084 0.3533 0.1403 0.9849 0.9939 0.2286 0.4304 0.8743 0.6996 ] Network output: [ 0.001728 -0.008641 0.9951 1.613e-06 -7.242e-07 1.01 1.216e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7281 0.8599 0.304 ] Network output: [ -0.001675 0.008222 1.004 1.788e-06 -8.025e-07 0.9907 1.347e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09552 0.09357 0.1647 0.1972 0.9851 0.991 0.09554 0.6516 0.8345 0.2508 ] Network output: [ 6.955e-05 1 -6.578e-05 2.326e-07 -1.044e-07 0.9999 1.753e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001017 Epoch 10624 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00813 0.9969 0.9932 -8.458e-08 3.797e-08 -0.006439 -6.374e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.006206 0.005071 0.9699 0.9743 0.006954 0.8214 0.8181 0.01548 ] Network output: [ 1 -9.705e-06 0.0002505 -8.354e-07 3.75e-07 -0.0002005 -6.296e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03656 -0.15 0.1797 0.9833 0.9931 0.2398 0.4265 0.8675 0.7061 ] Network output: [ -0.008169 1.003 1.007 -8.444e-08 3.791e-08 0.006945 -6.364e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007094 0.0006628 0.004263 0.002972 0.9889 0.9919 0.007235 0.8485 0.891 0.01097 ] Network output: [ -6.332e-05 0.0007912 1 -2.625e-06 1.178e-06 0.9989 -1.978e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2278 0.1084 0.3533 0.1403 0.9849 0.9939 0.2286 0.4304 0.8743 0.6996 ] Network output: [ 0.001727 -0.008635 0.9951 1.611e-06 -7.232e-07 1.01 1.214e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7281 0.8599 0.304 ] Network output: [ -0.001674 0.008216 1.004 1.785e-06 -8.015e-07 0.9908 1.345e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09552 0.09357 0.1646 0.1972 0.9851 0.991 0.09554 0.6516 0.8345 0.2508 ] Network output: [ 6.954e-05 1 -6.581e-05 2.323e-07 -1.043e-07 0.9999 1.751e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001016 Epoch 10625 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008129 0.9969 0.9932 -8.45e-08 3.793e-08 -0.006438 -6.368e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.006206 0.00507 0.9699 0.9743 0.006954 0.8214 0.8181 0.01548 ] Network output: [ 1 -9.807e-06 0.0002504 -8.343e-07 3.746e-07 -0.0002004 -6.288e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03656 -0.15 0.1797 0.9833 0.9931 0.2398 0.4265 0.8675 0.7061 ] Network output: [ -0.008168 1.003 1.007 -8.436e-08 3.787e-08 0.006945 -6.357e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007094 0.0006628 0.004263 0.002972 0.9889 0.9919 0.007236 0.8485 0.891 0.01097 ] Network output: [ -6.32e-05 0.0007906 1 -2.621e-06 1.177e-06 0.9989 -1.976e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2278 0.1084 0.3533 0.1403 0.9849 0.9939 0.2286 0.4304 0.8743 0.6996 ] Network output: [ 0.001726 -0.008629 0.9951 1.609e-06 -7.223e-07 1.01 1.213e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7281 0.8599 0.304 ] Network output: [ -0.001673 0.008211 1.004 1.783e-06 -8.005e-07 0.9908 1.344e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09552 0.09357 0.1646 0.1972 0.9851 0.991 0.09554 0.6516 0.8345 0.2508 ] Network output: [ 6.953e-05 1 -6.585e-05 2.32e-07 -1.041e-07 0.9999 1.748e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001016 Epoch 10626 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008129 0.9969 0.9932 -8.442e-08 3.79e-08 -0.006437 -6.362e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.006205 0.00507 0.9699 0.9743 0.006954 0.8214 0.8181 0.01548 ] Network output: [ 1 -9.909e-06 0.0002502 -8.332e-07 3.741e-07 -0.0002003 -6.28e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03657 -0.15 0.1797 0.9833 0.9931 0.2398 0.4265 0.8675 0.7061 ] Network output: [ -0.008167 1.003 1.007 -8.427e-08 3.783e-08 0.006944 -6.351e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007094 0.0006628 0.004263 0.002971 0.9889 0.9919 0.007236 0.8485 0.891 0.01097 ] Network output: [ -6.309e-05 0.0007899 1 -2.618e-06 1.175e-06 0.9989 -1.973e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2278 0.1084 0.3533 0.1403 0.9849 0.9939 0.2286 0.4304 0.8743 0.6996 ] Network output: [ 0.001724 -0.008623 0.9951 1.607e-06 -7.214e-07 1.01 1.211e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7281 0.8599 0.304 ] Network output: [ -0.001672 0.008206 1.004 1.781e-06 -7.994e-07 0.9908 1.342e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09552 0.09357 0.1646 0.1972 0.9851 0.991 0.09554 0.6516 0.8345 0.2508 ] Network output: [ 6.953e-05 1 -6.589e-05 2.317e-07 -1.04e-07 0.9999 1.746e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001015 Epoch 10627 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008128 0.9969 0.9932 -8.434e-08 3.786e-08 -0.006437 -6.356e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.006205 0.00507 0.9699 0.9743 0.006954 0.8214 0.8181 0.01548 ] Network output: [ 1 -1.001e-05 0.0002501 -8.322e-07 3.736e-07 -0.0002002 -6.271e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03657 -0.15 0.1797 0.9833 0.9931 0.2398 0.4265 0.8675 0.7061 ] Network output: [ -0.008166 1.003 1.007 -8.419e-08 3.779e-08 0.006944 -6.345e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007094 0.0006628 0.004263 0.002971 0.9889 0.9919 0.007236 0.8485 0.891 0.01097 ] Network output: [ -6.297e-05 0.0007892 1 -2.615e-06 1.174e-06 0.9989 -1.97e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2278 0.1084 0.3533 0.1403 0.9849 0.9939 0.2286 0.4304 0.8743 0.6996 ] Network output: [ 0.001723 -0.008617 0.9951 1.605e-06 -7.205e-07 1.01 1.209e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1958 0.9873 0.9919 0.1133 0.7281 0.8599 0.304 ] Network output: [ -0.001671 0.008201 1.004 1.778e-06 -7.984e-07 0.9908 1.34e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09552 0.09357 0.1646 0.1972 0.9851 0.991 0.09554 0.6516 0.8345 0.2508 ] Network output: [ 6.952e-05 1 -6.593e-05 2.314e-07 -1.039e-07 0.9999 1.744e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001015 Epoch 10628 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008127 0.9969 0.9932 -8.426e-08 3.783e-08 -0.006436 -6.35e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.006204 0.005069 0.9699 0.9743 0.006954 0.8214 0.8181 0.01548 ] Network output: [ 1 -1.011e-05 0.00025 -8.311e-07 3.731e-07 -0.0002001 -6.263e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03657 -0.15 0.1797 0.9833 0.9931 0.2398 0.4265 0.8675 0.7061 ] Network output: [ -0.008166 1.003 1.007 -8.41e-08 3.776e-08 0.006944 -6.338e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007094 0.0006628 0.004263 0.002971 0.9889 0.9919 0.007236 0.8485 0.891 0.01097 ] Network output: [ -6.286e-05 0.0007885 1 -2.611e-06 1.172e-06 0.9989 -1.968e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2278 0.1084 0.3533 0.1403 0.9849 0.9939 0.2286 0.4304 0.8743 0.6996 ] Network output: [ 0.001722 -0.008611 0.9951 1.603e-06 -7.195e-07 1.01 1.208e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1133 0.7281 0.8599 0.304 ] Network output: [ -0.001669 0.008196 1.004 1.776e-06 -7.974e-07 0.9908 1.339e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09552 0.09357 0.1646 0.1972 0.9851 0.991 0.09554 0.6516 0.8345 0.2508 ] Network output: [ 6.951e-05 1 -6.596e-05 2.311e-07 -1.038e-07 0.9999 1.742e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001014 Epoch 10629 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008126 0.9969 0.9932 -8.419e-08 3.779e-08 -0.006435 -6.344e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.006204 0.005069 0.9699 0.9743 0.006955 0.8214 0.8181 0.01548 ] Network output: [ 1 -1.021e-05 0.0002499 -8.3e-07 3.726e-07 -0.0002001 -6.255e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03657 -0.15 0.1797 0.9833 0.9931 0.2398 0.4265 0.8675 0.7061 ] Network output: [ -0.008165 1.003 1.007 -8.402e-08 3.772e-08 0.006943 -6.332e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007095 0.0006628 0.004263 0.002971 0.9889 0.9919 0.007237 0.8485 0.891 0.01097 ] Network output: [ -6.274e-05 0.0007879 1 -2.608e-06 1.171e-06 0.9989 -1.965e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2278 0.1084 0.3533 0.1403 0.9849 0.9939 0.2286 0.4304 0.8743 0.6996 ] Network output: [ 0.00172 -0.008605 0.9951 1.601e-06 -7.186e-07 1.01 1.206e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1133 0.7281 0.8599 0.304 ] Network output: [ -0.001668 0.008191 1.004 1.774e-06 -7.964e-07 0.9908 1.337e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09552 0.09357 0.1646 0.1972 0.9851 0.991 0.09554 0.6516 0.8345 0.2508 ] Network output: [ 6.95e-05 1 -6.6e-05 2.308e-07 -1.036e-07 0.9999 1.74e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001013 Epoch 10630 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008126 0.9969 0.9932 -8.411e-08 3.776e-08 -0.006434 -6.339e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.006203 0.005069 0.9699 0.9743 0.006955 0.8214 0.8181 0.01548 ] Network output: [ 1 -1.032e-05 0.0002498 -8.29e-07 3.722e-07 -0.0002 -6.247e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03657 -0.1499 0.1797 0.9833 0.9931 0.2398 0.4265 0.8675 0.7061 ] Network output: [ -0.008164 1.003 1.007 -8.393e-08 3.768e-08 0.006943 -6.326e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007095 0.0006628 0.004263 0.002971 0.9889 0.9919 0.007237 0.8485 0.891 0.01097 ] Network output: [ -6.263e-05 0.0007872 1 -2.605e-06 1.169e-06 0.9989 -1.963e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2278 0.1084 0.3534 0.1403 0.9849 0.9939 0.2286 0.4304 0.8743 0.6996 ] Network output: [ 0.001719 -0.008599 0.9951 1.599e-06 -7.177e-07 1.01 1.205e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1134 0.7281 0.8599 0.304 ] Network output: [ -0.001667 0.008186 1.004 1.772e-06 -7.954e-07 0.9908 1.335e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09552 0.09358 0.1646 0.1972 0.9851 0.991 0.09554 0.6515 0.8345 0.2508 ] Network output: [ 6.949e-05 1 -6.604e-05 2.305e-07 -1.035e-07 0.9999 1.737e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001013 Epoch 10631 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008125 0.9969 0.9932 -8.403e-08 3.772e-08 -0.006434 -6.333e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.006203 0.005068 0.9699 0.9743 0.006955 0.8214 0.8181 0.01547 ] Network output: [ 1 -1.042e-05 0.0002497 -8.279e-07 3.717e-07 -0.0001999 -6.239e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03657 -0.1499 0.1797 0.9833 0.9931 0.2398 0.4265 0.8675 0.7061 ] Network output: [ -0.008164 1.003 1.007 -8.385e-08 3.764e-08 0.006942 -6.319e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007095 0.0006628 0.004263 0.00297 0.9889 0.9919 0.007237 0.8485 0.891 0.01097 ] Network output: [ -6.251e-05 0.0007865 1 -2.601e-06 1.168e-06 0.9989 -1.96e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2278 0.1084 0.3534 0.1403 0.9849 0.9939 0.2286 0.4304 0.8743 0.6996 ] Network output: [ 0.001717 -0.008593 0.9951 1.597e-06 -7.168e-07 1.01 1.203e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1134 0.7281 0.8599 0.304 ] Network output: [ -0.001666 0.00818 1.004 1.77e-06 -7.944e-07 0.9908 1.334e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09553 0.09358 0.1646 0.1972 0.9851 0.991 0.09554 0.6515 0.8345 0.2508 ] Network output: [ 6.948e-05 1 -6.608e-05 2.302e-07 -1.034e-07 0.9999 1.735e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001012 Epoch 10632 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008124 0.9969 0.9932 -8.395e-08 3.769e-08 -0.006433 -6.327e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.006202 0.005068 0.9699 0.9743 0.006955 0.8214 0.8181 0.01547 ] Network output: [ 1 -1.052e-05 0.0002496 -8.268e-07 3.712e-07 -0.0001998 -6.231e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03657 -0.1499 0.1797 0.9833 0.9931 0.2398 0.4265 0.8675 0.7061 ] Network output: [ -0.008163 1.003 1.007 -8.377e-08 3.761e-08 0.006942 -6.313e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007095 0.0006628 0.004262 0.00297 0.9889 0.9919 0.007237 0.8485 0.891 0.01097 ] Network output: [ -6.24e-05 0.0007859 1 -2.598e-06 1.166e-06 0.9989 -1.958e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2278 0.1084 0.3534 0.1403 0.9849 0.9939 0.2286 0.4304 0.8743 0.6996 ] Network output: [ 0.001716 -0.008587 0.9951 1.595e-06 -7.159e-07 1.01 1.202e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1134 0.7281 0.8599 0.304 ] Network output: [ -0.001665 0.008175 1.004 1.767e-06 -7.934e-07 0.9908 1.332e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09553 0.09358 0.1646 0.1972 0.9851 0.991 0.09554 0.6515 0.8345 0.2508 ] Network output: [ 6.947e-05 1 -6.611e-05 2.299e-07 -1.032e-07 0.9999 1.733e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001011 Epoch 10633 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008123 0.9969 0.9932 -8.387e-08 3.765e-08 -0.006432 -6.321e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003412 -0.006202 0.005068 0.9699 0.9743 0.006955 0.8214 0.8181 0.01547 ] Network output: [ 1 -1.062e-05 0.0002495 -8.258e-07 3.707e-07 -0.0001997 -6.223e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03657 -0.1499 0.1797 0.9833 0.9931 0.2398 0.4265 0.8675 0.7061 ] Network output: [ -0.008162 1.003 1.007 -8.368e-08 3.757e-08 0.006942 -6.307e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007096 0.0006628 0.004262 0.00297 0.9889 0.9919 0.007237 0.8485 0.891 0.01097 ] Network output: [ -6.228e-05 0.0007852 1 -2.595e-06 1.165e-06 0.9989 -1.955e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2279 0.1084 0.3534 0.1403 0.9849 0.9939 0.2286 0.4304 0.8743 0.6996 ] Network output: [ 0.001715 -0.008581 0.9951 1.593e-06 -7.149e-07 1.01 1.2e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1134 0.7281 0.8599 0.3039 ] Network output: [ -0.001663 0.00817 1.004 1.765e-06 -7.924e-07 0.9908 1.33e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09553 0.09358 0.1646 0.1972 0.9851 0.991 0.09554 0.6515 0.8345 0.2508 ] Network output: [ 6.947e-05 1 -6.615e-05 2.296e-07 -1.031e-07 0.9999 1.731e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001011 Epoch 10634 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008123 0.9969 0.9932 -8.38e-08 3.762e-08 -0.006432 -6.315e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003413 -0.006202 0.005068 0.9699 0.9743 0.006955 0.8214 0.8181 0.01547 ] Network output: [ 1 -1.072e-05 0.0002494 -8.247e-07 3.702e-07 -0.0001997 -6.215e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03657 -0.1499 0.1797 0.9833 0.9931 0.2398 0.4265 0.8675 0.7061 ] Network output: [ -0.008161 1.003 1.007 -8.36e-08 3.753e-08 0.006941 -6.3e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007096 0.0006628 0.004262 0.00297 0.9889 0.9919 0.007238 0.8485 0.891 0.01097 ] Network output: [ -6.217e-05 0.0007845 1 -2.591e-06 1.163e-06 0.9989 -1.953e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2279 0.1084 0.3534 0.1403 0.9849 0.9939 0.2287 0.4304 0.8743 0.6996 ] Network output: [ 0.001713 -0.008575 0.9951 1.59e-06 -7.14e-07 1.01 1.199e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1134 0.7281 0.8599 0.3039 ] Network output: [ -0.001662 0.008165 1.004 1.763e-06 -7.914e-07 0.9908 1.328e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09553 0.09358 0.1646 0.1972 0.9851 0.991 0.09554 0.6515 0.8345 0.2508 ] Network output: [ 6.946e-05 1 -6.619e-05 2.294e-07 -1.03e-07 0.9999 1.728e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000101 Epoch 10635 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008122 0.9969 0.9932 -8.372e-08 3.758e-08 -0.006431 -6.309e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003413 -0.006201 0.005067 0.9699 0.9743 0.006955 0.8214 0.8181 0.01547 ] Network output: [ 1 -1.083e-05 0.0002493 -8.236e-07 3.698e-07 -0.0001996 -6.207e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03658 -0.1499 0.1797 0.9833 0.9931 0.2398 0.4265 0.8675 0.7061 ] Network output: [ -0.008161 1.003 1.007 -8.351e-08 3.749e-08 0.006941 -6.294e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007096 0.0006629 0.004262 0.00297 0.9889 0.9919 0.007238 0.8485 0.891 0.01097 ] Network output: [ -6.205e-05 0.0007839 1 -2.588e-06 1.162e-06 0.9989 -1.95e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2279 0.1084 0.3534 0.1403 0.9849 0.9939 0.2287 0.4304 0.8743 0.6996 ] Network output: [ 0.001712 -0.008569 0.9951 1.588e-06 -7.131e-07 1.01 1.197e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1134 0.7281 0.8599 0.3039 ] Network output: [ -0.001661 0.00816 1.004 1.761e-06 -7.904e-07 0.9908 1.327e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09553 0.09358 0.1646 0.1972 0.9851 0.991 0.09554 0.6515 0.8345 0.2508 ] Network output: [ 6.945e-05 1 -6.623e-05 2.291e-07 -1.028e-07 0.9999 1.726e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.000101 Epoch 10636 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008121 0.9969 0.9932 -8.364e-08 3.755e-08 -0.00643 -6.304e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003413 -0.006201 0.005067 0.9699 0.9743 0.006955 0.8214 0.8181 0.01547 ] Network output: [ 1 -1.093e-05 0.0002492 -8.226e-07 3.693e-07 -0.0001995 -6.199e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03658 -0.1499 0.1797 0.9833 0.9931 0.2398 0.4265 0.8675 0.7061 ] Network output: [ -0.00816 1.003 1.007 -8.343e-08 3.746e-08 0.006941 -6.288e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007096 0.0006629 0.004262 0.002969 0.9889 0.9919 0.007238 0.8485 0.891 0.01097 ] Network output: [ -6.194e-05 0.0007832 1 -2.585e-06 1.16e-06 0.9989 -1.948e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2279 0.1084 0.3534 0.1403 0.9849 0.9939 0.2287 0.4304 0.8743 0.6996 ] Network output: [ 0.001711 -0.008562 0.9951 1.586e-06 -7.122e-07 1.01 1.196e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1134 0.7281 0.8599 0.3039 ] Network output: [ -0.00166 0.008155 1.004 1.758e-06 -7.894e-07 0.9908 1.325e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09553 0.09358 0.1646 0.1972 0.9851 0.991 0.09554 0.6515 0.8345 0.2508 ] Network output: [ 6.944e-05 1 -6.626e-05 2.288e-07 -1.027e-07 0.9999 1.724e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001009 Epoch 10637 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008121 0.9969 0.9933 -8.356e-08 3.752e-08 -0.006429 -6.298e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003413 -0.0062 0.005067 0.9699 0.9743 0.006955 0.8214 0.8181 0.01547 ] Network output: [ 1 -1.103e-05 0.0002491 -8.215e-07 3.688e-07 -0.0001994 -6.191e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03658 -0.1499 0.1797 0.9833 0.9931 0.2398 0.4265 0.8675 0.7061 ] Network output: [ -0.008159 1.003 1.007 -8.335e-08 3.742e-08 0.00694 -6.281e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007096 0.0006629 0.004262 0.002969 0.9889 0.9919 0.007238 0.8485 0.891 0.01097 ] Network output: [ -6.182e-05 0.0007825 1 -2.581e-06 1.159e-06 0.9989 -1.945e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2279 0.1084 0.3534 0.1403 0.9849 0.9939 0.2287 0.4304 0.8743 0.6996 ] Network output: [ 0.001709 -0.008556 0.9951 1.584e-06 -7.113e-07 1.01 1.194e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1134 0.7281 0.8599 0.3039 ] Network output: [ -0.001659 0.00815 1.004 1.756e-06 -7.884e-07 0.9908 1.323e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09553 0.09358 0.1646 0.1972 0.9851 0.991 0.09554 0.6515 0.8345 0.2508 ] Network output: [ 6.943e-05 1 -6.63e-05 2.285e-07 -1.026e-07 0.9999 1.722e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001008 Epoch 10638 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00812 0.9969 0.9933 -8.349e-08 3.748e-08 -0.006429 -6.292e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003413 -0.0062 0.005066 0.9699 0.9743 0.006955 0.8214 0.8181 0.01547 ] Network output: [ 1 -1.113e-05 0.000249 -8.205e-07 3.683e-07 -0.0001993 -6.183e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03658 -0.1499 0.1797 0.9833 0.9931 0.2398 0.4265 0.8675 0.7061 ] Network output: [ -0.008159 1.003 1.007 -8.326e-08 3.738e-08 0.00694 -6.275e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007097 0.0006629 0.004262 0.002969 0.9889 0.9919 0.007239 0.8485 0.891 0.01096 ] Network output: [ -6.171e-05 0.0007819 1 -2.578e-06 1.157e-06 0.9989 -1.943e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2279 0.1084 0.3534 0.1403 0.9849 0.9939 0.2287 0.4304 0.8743 0.6996 ] Network output: [ 0.001708 -0.00855 0.9951 1.582e-06 -7.104e-07 1.01 1.193e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1134 0.7281 0.8599 0.3039 ] Network output: [ -0.001657 0.008144 1.004 1.754e-06 -7.874e-07 0.9908 1.322e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09553 0.09358 0.1646 0.1972 0.9851 0.991 0.09554 0.6515 0.8345 0.2508 ] Network output: [ 6.942e-05 1 -6.634e-05 2.282e-07 -1.024e-07 0.9999 1.72e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001008 Epoch 10639 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008119 0.9969 0.9933 -8.341e-08 3.745e-08 -0.006428 -6.286e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003413 -0.006199 0.005066 0.9699 0.9743 0.006955 0.8214 0.8181 0.01547 ] Network output: [ 1 -1.123e-05 0.0002488 -8.194e-07 3.679e-07 -0.0001993 -6.175e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03658 -0.1499 0.1797 0.9833 0.9931 0.2398 0.4264 0.8675 0.7061 ] Network output: [ -0.008158 1.003 1.007 -8.318e-08 3.734e-08 0.00694 -6.269e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007097 0.0006629 0.004261 0.002969 0.9889 0.9919 0.007239 0.8485 0.891 0.01096 ] Network output: [ -6.159e-05 0.0007812 1 -2.575e-06 1.156e-06 0.9989 -1.94e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2279 0.1084 0.3534 0.1403 0.9849 0.9939 0.2287 0.4304 0.8743 0.6996 ] Network output: [ 0.001707 -0.008544 0.9951 1.58e-06 -7.095e-07 1.01 1.191e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1134 0.7281 0.8599 0.3039 ] Network output: [ -0.001656 0.008139 1.004 1.752e-06 -7.864e-07 0.9908 1.32e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09553 0.09358 0.1646 0.1972 0.9851 0.991 0.09555 0.6515 0.8345 0.2508 ] Network output: [ 6.941e-05 1 -6.638e-05 2.279e-07 -1.023e-07 0.9999 1.718e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001007 Epoch 10640 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008118 0.9969 0.9933 -8.333e-08 3.741e-08 -0.006427 -6.28e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003413 -0.006199 0.005066 0.9699 0.9743 0.006955 0.8214 0.8181 0.01547 ] Network output: [ 1 -1.133e-05 0.0002487 -8.184e-07 3.674e-07 -0.0001992 -6.167e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03658 -0.1499 0.1797 0.9833 0.9931 0.2398 0.4264 0.8675 0.7061 ] Network output: [ -0.008157 1.003 1.007 -8.31e-08 3.731e-08 0.006939 -6.262e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007097 0.0006629 0.004261 0.002969 0.9889 0.9919 0.007239 0.8485 0.891 0.01096 ] Network output: [ -6.148e-05 0.0007805 1 -2.571e-06 1.154e-06 0.9989 -1.938e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2279 0.1084 0.3534 0.1403 0.9849 0.9939 0.2287 0.4304 0.8743 0.6996 ] Network output: [ 0.001705 -0.008538 0.9951 1.578e-06 -7.086e-07 1.01 1.189e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1134 0.7281 0.8599 0.3039 ] Network output: [ -0.001655 0.008134 1.004 1.749e-06 -7.854e-07 0.9908 1.318e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09553 0.09358 0.1646 0.1972 0.9851 0.991 0.09555 0.6515 0.8345 0.2508 ] Network output: [ 6.941e-05 1 -6.641e-05 2.276e-07 -1.022e-07 0.9999 1.715e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001007 Epoch 10641 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008118 0.9969 0.9933 -8.326e-08 3.738e-08 -0.006426 -6.274e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003413 -0.006198 0.005065 0.9699 0.9743 0.006955 0.8214 0.8181 0.01547 ] Network output: [ 1 -1.143e-05 0.0002486 -8.173e-07 3.669e-07 -0.0001991 -6.16e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2132 -0.03658 -0.1499 0.1797 0.9833 0.9931 0.2399 0.4264 0.8675 0.7061 ] Network output: [ -0.008156 1.003 1.007 -8.301e-08 3.727e-08 0.006939 -6.256e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007097 0.0006629 0.004261 0.002968 0.9889 0.9919 0.007239 0.8485 0.891 0.01096 ] Network output: [ -6.136e-05 0.0007799 1 -2.568e-06 1.153e-06 0.9989 -1.935e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2279 0.1084 0.3534 0.1403 0.9849 0.9939 0.2287 0.4304 0.8743 0.6996 ] Network output: [ 0.001704 -0.008532 0.9951 1.576e-06 -7.076e-07 1.01 1.188e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1134 0.728 0.8599 0.3039 ] Network output: [ -0.001654 0.008129 1.004 1.747e-06 -7.844e-07 0.9908 1.317e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09553 0.09358 0.1646 0.1972 0.9851 0.991 0.09555 0.6515 0.8345 0.2508 ] Network output: [ 6.94e-05 1 -6.645e-05 2.273e-07 -1.021e-07 0.9999 1.713e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001006 Epoch 10642 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008117 0.9969 0.9933 -8.318e-08 3.734e-08 -0.006426 -6.269e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003413 -0.006198 0.005065 0.9699 0.9743 0.006956 0.8214 0.8181 0.01547 ] Network output: [ 1 -1.153e-05 0.0002485 -8.163e-07 3.665e-07 -0.000199 -6.152e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2133 -0.03658 -0.1499 0.1797 0.9833 0.9931 0.2399 0.4264 0.8675 0.7061 ] Network output: [ -0.008156 1.003 1.007 -8.293e-08 3.723e-08 0.006939 -6.25e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007097 0.0006629 0.004261 0.002968 0.9889 0.9919 0.007239 0.8485 0.891 0.01096 ] Network output: [ -6.125e-05 0.0007792 1 -2.565e-06 1.151e-06 0.9989 -1.933e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2279 0.1084 0.3534 0.1403 0.9849 0.9939 0.2287 0.4304 0.8743 0.6996 ] Network output: [ 0.001702 -0.008526 0.9951 1.574e-06 -7.067e-07 1.01 1.186e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1134 0.728 0.8599 0.3039 ] Network output: [ -0.001653 0.008124 1.004 1.745e-06 -7.834e-07 0.9908 1.315e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09553 0.09358 0.1646 0.1972 0.9851 0.991 0.09555 0.6515 0.8345 0.2508 ] Network output: [ 6.939e-05 1 -6.649e-05 2.27e-07 -1.019e-07 0.9999 1.711e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001005 Epoch 10643 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008116 0.9969 0.9933 -8.31e-08 3.731e-08 -0.006425 -6.263e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003413 -0.006197 0.005065 0.9699 0.9743 0.006956 0.8214 0.8181 0.01547 ] Network output: [ 1 -1.164e-05 0.0002484 -8.152e-07 3.66e-07 -0.0001989 -6.144e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2133 -0.03659 -0.1499 0.1797 0.9833 0.9931 0.2399 0.4264 0.8675 0.7061 ] Network output: [ -0.008155 1.003 1.007 -8.285e-08 3.719e-08 0.006938 -6.244e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007098 0.0006629 0.004261 0.002968 0.9889 0.9919 0.00724 0.8485 0.891 0.01096 ] Network output: [ -6.113e-05 0.0007785 1 -2.561e-06 1.15e-06 0.9989 -1.93e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2279 0.1084 0.3534 0.1403 0.9849 0.9939 0.2287 0.4304 0.8743 0.6996 ] Network output: [ 0.001701 -0.00852 0.9951 1.572e-06 -7.058e-07 1.01 1.185e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1134 0.728 0.8599 0.3039 ] Network output: [ -0.001651 0.008119 1.004 1.743e-06 -7.824e-07 0.9908 1.313e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09553 0.09358 0.1646 0.1972 0.9851 0.991 0.09555 0.6515 0.8345 0.2508 ] Network output: [ 6.938e-05 1 -6.653e-05 2.267e-07 -1.018e-07 0.9999 1.709e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001005 Epoch 10644 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008115 0.9969 0.9933 -8.302e-08 3.727e-08 -0.006424 -6.257e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003413 -0.006197 0.005064 0.9699 0.9743 0.006956 0.8214 0.8181 0.01546 ] Network output: [ 1 -1.174e-05 0.0002483 -8.142e-07 3.655e-07 -0.0001989 -6.136e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2133 -0.03659 -0.1499 0.1797 0.9833 0.9931 0.2399 0.4264 0.8675 0.7061 ] Network output: [ -0.008154 1.003 1.007 -8.276e-08 3.716e-08 0.006938 -6.237e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007098 0.0006629 0.004261 0.002968 0.9889 0.9919 0.00724 0.8485 0.891 0.01096 ] Network output: [ -6.102e-05 0.0007779 1 -2.558e-06 1.148e-06 0.9989 -1.928e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2279 0.1084 0.3534 0.1403 0.9849 0.9939 0.2287 0.4304 0.8743 0.6995 ] Network output: [ 0.0017 -0.008514 0.9951 1.57e-06 -7.049e-07 1.01 1.183e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1134 0.728 0.8599 0.3039 ] Network output: [ -0.00165 0.008114 1.004 1.741e-06 -7.814e-07 0.9908 1.312e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09553 0.09358 0.1646 0.1972 0.9851 0.991 0.09555 0.6515 0.8345 0.2508 ] Network output: [ 6.937e-05 1 -6.657e-05 2.265e-07 -1.017e-07 0.9999 1.707e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001004 Epoch 10645 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008115 0.9969 0.9933 -8.295e-08 3.724e-08 -0.006423 -6.251e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003413 -0.006197 0.005064 0.9699 0.9743 0.006956 0.8214 0.8181 0.01546 ] Network output: [ 1 -1.184e-05 0.0002482 -8.131e-07 3.65e-07 -0.0001988 -6.128e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2133 -0.03659 -0.1499 0.1797 0.9833 0.9931 0.2399 0.4264 0.8675 0.7061 ] Network output: [ -0.008154 1.003 1.007 -8.268e-08 3.712e-08 0.006938 -6.231e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007098 0.0006629 0.004261 0.002968 0.9889 0.9919 0.00724 0.8485 0.891 0.01096 ] Network output: [ -6.09e-05 0.0007772 1 -2.555e-06 1.147e-06 0.9989 -1.925e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2279 0.1084 0.3534 0.1403 0.9849 0.9939 0.2287 0.4304 0.8743 0.6995 ] Network output: [ 0.001698 -0.008508 0.9951 1.568e-06 -7.04e-07 1.01 1.182e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1134 0.728 0.8599 0.3039 ] Network output: [ -0.001649 0.008108 1.004 1.738e-06 -7.804e-07 0.9908 1.31e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09553 0.09358 0.1646 0.1972 0.9851 0.991 0.09555 0.6515 0.8345 0.2508 ] Network output: [ 6.936e-05 1 -6.66e-05 2.262e-07 -1.015e-07 0.9999 1.704e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001003 Epoch 10646 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008114 0.9969 0.9933 -8.287e-08 3.72e-08 -0.006423 -6.245e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003413 -0.006196 0.005064 0.9699 0.9743 0.006956 0.8214 0.8181 0.01546 ] Network output: [ 1 -1.194e-05 0.0002481 -8.121e-07 3.646e-07 -0.0001987 -6.12e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2133 -0.03659 -0.1498 0.1797 0.9833 0.9931 0.2399 0.4264 0.8675 0.7061 ] Network output: [ -0.008153 1.003 1.007 -8.26e-08 3.708e-08 0.006937 -6.225e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007098 0.0006629 0.004261 0.002967 0.9889 0.9919 0.00724 0.8485 0.891 0.01096 ] Network output: [ -6.079e-05 0.0007765 1 -2.551e-06 1.145e-06 0.9989 -1.923e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2279 0.1084 0.3534 0.1403 0.9849 0.9939 0.2287 0.4304 0.8743 0.6995 ] Network output: [ 0.001697 -0.008502 0.9951 1.566e-06 -7.031e-07 1.01 1.18e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1134 0.728 0.8599 0.3039 ] Network output: [ -0.001648 0.008103 1.004 1.736e-06 -7.794e-07 0.9908 1.308e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09553 0.09358 0.1646 0.1972 0.9851 0.991 0.09555 0.6515 0.8345 0.2508 ] Network output: [ 6.935e-05 1 -6.664e-05 2.259e-07 -1.014e-07 0.9999 1.702e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001003 Epoch 10647 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008113 0.9969 0.9933 -8.279e-08 3.717e-08 -0.006422 -6.24e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003413 -0.006196 0.005064 0.9699 0.9743 0.006956 0.8214 0.8181 0.01546 ] Network output: [ 1 -1.204e-05 0.000248 -8.11e-07 3.641e-07 -0.0001986 -6.112e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2133 -0.03659 -0.1498 0.1797 0.9833 0.9931 0.2399 0.4264 0.8675 0.7061 ] Network output: [ -0.008152 1.003 1.007 -8.252e-08 3.704e-08 0.006937 -6.219e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007098 0.0006629 0.00426 0.002967 0.9889 0.9919 0.00724 0.8485 0.891 0.01096 ] Network output: [ -6.067e-05 0.0007758 1 -2.548e-06 1.144e-06 0.9989 -1.92e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2279 0.1084 0.3534 0.1403 0.9849 0.9939 0.2287 0.4304 0.8743 0.6995 ] Network output: [ 0.001696 -0.008496 0.9951 1.564e-06 -7.022e-07 1.01 1.179e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1134 0.728 0.8599 0.3039 ] Network output: [ -0.001647 0.008098 1.004 1.734e-06 -7.784e-07 0.9908 1.307e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09554 0.09359 0.1646 0.1972 0.9851 0.991 0.09555 0.6514 0.8345 0.2508 ] Network output: [ 6.935e-05 1 -6.668e-05 2.256e-07 -1.013e-07 0.9999 1.7e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001002 Epoch 10648 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008113 0.9969 0.9933 -8.272e-08 3.713e-08 -0.006421 -6.234e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003413 -0.006195 0.005063 0.9699 0.9743 0.006956 0.8214 0.8181 0.01546 ] Network output: [ 1 -1.214e-05 0.0002479 -8.1e-07 3.636e-07 -0.0001985 -6.104e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2133 -0.03659 -0.1498 0.1797 0.9833 0.9931 0.2399 0.4264 0.8675 0.7061 ] Network output: [ -0.008151 1.003 1.007 -8.243e-08 3.701e-08 0.006936 -6.212e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007099 0.000663 0.00426 0.002967 0.9889 0.9919 0.007241 0.8485 0.891 0.01096 ] Network output: [ -6.056e-05 0.0007752 1 -2.545e-06 1.142e-06 0.9989 -1.918e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2279 0.1084 0.3534 0.1403 0.9849 0.9939 0.2287 0.4304 0.8743 0.6995 ] Network output: [ 0.001694 -0.00849 0.9951 1.562e-06 -7.013e-07 1.01 1.177e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1134 0.728 0.8599 0.3039 ] Network output: [ -0.001646 0.008093 1.004 1.732e-06 -7.775e-07 0.9908 1.305e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09554 0.09359 0.1646 0.1972 0.9851 0.991 0.09555 0.6514 0.8345 0.2508 ] Network output: [ 6.934e-05 1 -6.672e-05 2.253e-07 -1.011e-07 0.9999 1.698e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001002 Epoch 10649 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008112 0.9969 0.9933 -8.264e-08 3.71e-08 -0.006421 -6.228e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003413 -0.006195 0.005063 0.9699 0.9743 0.006956 0.8213 0.8181 0.01546 ] Network output: [ 1 -1.224e-05 0.0002478 -8.089e-07 3.632e-07 -0.0001985 -6.096e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2133 -0.03659 -0.1498 0.1797 0.9833 0.9931 0.2399 0.4264 0.8675 0.7061 ] Network output: [ -0.008151 1.003 1.007 -8.235e-08 3.697e-08 0.006936 -6.206e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007099 0.000663 0.00426 0.002967 0.9889 0.9919 0.007241 0.8485 0.891 0.01096 ] Network output: [ -6.044e-05 0.0007745 1 -2.542e-06 1.141e-06 0.9989 -1.915e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2279 0.1084 0.3534 0.1403 0.9849 0.9939 0.2287 0.4303 0.8743 0.6995 ] Network output: [ 0.001693 -0.008484 0.9951 1.56e-06 -7.004e-07 1.01 1.176e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1134 0.728 0.8599 0.3039 ] Network output: [ -0.001644 0.008088 1.004 1.73e-06 -7.765e-07 0.9908 1.303e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09554 0.09359 0.1646 0.1972 0.9851 0.991 0.09555 0.6514 0.8345 0.2508 ] Network output: [ 6.933e-05 1 -6.676e-05 2.25e-07 -1.01e-07 0.9999 1.696e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001001 Epoch 10650 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.008111 0.9969 0.9933 -8.256e-08 3.707e-08 -0.00642 -6.222e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003413 -0.006194 0.005063 0.9699 0.9743 0.006956 0.8213 0.8181 0.01546 ] Network output: [ 1 -1.234e-05 0.0002477 -8.079e-07 3.627e-07 -0.0001984 -6.089e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2133 -0.03659 -0.1498 0.1796 0.9833 0.9931 0.2399 0.4264 0.8675 0.7061 ] Network output: [ -0.00815 1.003 1.007 -8.227e-08 3.693e-08 0.006936 -6.2e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007099 0.000663 0.00426 0.002967 0.9889 0.9919 0.007241 0.8485 0.891 0.01096 ] Network output: [ -6.033e-05 0.0007738 1 -2.538e-06 1.14e-06 0.9989 -1.913e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2279 0.1084 0.3534 0.1403 0.9849 0.9939 0.2287 0.4303 0.8743 0.6995 ] Network output: [ 0.001692 -0.008478 0.9951 1.558e-06 -6.995e-07 1.01 1.174e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1134 0.728 0.8599 0.3039 ] Network output: [ -0.001643 0.008083 1.004 1.727e-06 -7.755e-07 0.9908 1.302e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09554 0.09359 0.1646 0.1972 0.9851 0.991 0.09555 0.6514 0.8345 0.2508 ] Network output: [ 6.932e-05 1 -6.679e-05 2.247e-07 -1.009e-07 0.9999 1.694e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 0.0001 Epoch 10651 - Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00811 0.9969 0.9933 -8.249e-08 3.703e-08 -0.006419 -6.216e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003413 -0.006194 0.005062 0.9699 0.9743 0.006956 0.8213 0.8181 0.01546 ] Network output: [ 1 -1.244e-05 0.0002476 -8.069e-07 3.622e-07 -0.0001983 -6.081e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2133 -0.03659 -0.1498 0.1796 0.9833 0.9931 0.2399 0.4264 0.8675 0.7061 ] Network output: [ -0.008149 1.003 1.007 -8.218e-08 3.69e-08 0.006935 -6.194e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 1 0 0 0 0 0 0.007099 0.000663 0.00426 0.002966 0.9889 0.9919 0.007241 0.8485 0.891 0.01096 ] Network output: [ -6.021e-05 0.0007732 1 -2.535e-06 1.138e-06 0.9989 -1.91e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 4: Input: [ 0 0 1 0 0 0 0 0.2279 0.1084 0.3534 0.1403 0.9849 0.9939 0.2287 0.4303 0.8743 0.6995 ] Network output: [ 0.00169 -0.008472 0.9951 1.556e-06 -6.986e-07 1.01 1.173e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 5: Input: [ 0 0 1 0 0 0 0 0.1133 0.1004 0.1851 0.1957 0.9873 0.9919 0.1134 0.728 0.8599 0.3039 ] Network output: [ -0.001642 0.008078 1.004 1.725e-06 -7.745e-07 0.9908 1.3e-06 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.09554 0.09359 0.1646 0.1972 0.9851 0.991 0.09555 0.6514 0.8345 0.2509 ] Network output: [ 6.931e-05 1 -6.683e-05 2.244e-07 -1.008e-07 0.9999 1.691e-07 ] Targets: [0 1 0 0 0 1 0 ] Training Mean Square Error: 9.998e-05 Network achieved classification in 10652 epochs. Time Step 0: Input: [ 1 0 0 0 0 0 0 0 0 0 0 0.5 0.5 0.5 0.5 0.5 0.5 ] Network output: [ 0.00811 0.9969 0.9933 -8.241e-08 3.7e-08 -0.006418 -6.211e-08 ] Targets: [0 1 1 0 0 0 0 ] Time Step 1: Input: [ 0 1 0 0 0 0 0 0.003538 -0.003413 -0.006193 0.005062 0.9699 0.9743 0.006956 0.8213 0.8181 0.01546 ] Network output: [ 1.001 -0.0003102 -0.0004966 -8.143e-07 3.656e-07 -0.0009064 -6.137e-07 ] Targets: [1 0 0 0 0 0 0 ] Time Step 2: Input: [ 1 0 0 0 0 0 0 0.2133 -0.0366 -0.1498 0.1796 0.9833 0.9931 0.2399 0.4264 0.8675 0.7061 ] Network output: [ -0.007338 1.002 1.006 -1.71e-07 7.679e-08 0.006271 -1.289e-07 ] Targets: [0 1 1 0 0 0 0 ] Time Step 3: Input: [ 0 0 1 0 0 0 0 0.0071 0.000663 0.004259 0.002966 0.9889 0.9919 0.007242 0.8485 0.891 0.01096 ] Network output: [ 0.06862 0.5842 0.89 3.415e-07 -1.533e-07 0.3885 2.573e-07 ] Targets: [0 0 0 1 1 0 0 ] Time Step 4: Input: [ 0 0 0 0 1 0 0 0.0882 0.06463 0.06911 0.08741 0.9822 0.9895 0.08859 0.5599 0.7962 0.1953 ] Network output: [ 0.005265 0.8211 0.7824 1.404e-08 -6.302e-09 0.386 1.058e-08 ] Targets: [0 0 0 1 1 0 0 ] Time Step 5: Input: [ 0 0 0 0 1 0 0 0.2044 0.1922 0.1341 0.06 0.9766 0.9851 0.2046 0.6503 0.8295 0.2703 ] Network output: [ -0.1199 0.9411 0.922 -6.17e-07 2.77e-07 0.3768 -4.65e-07 ] Targets: [0 0 1 0 0 1 0 ] Time Step 6: Input: [ 0 0 0 0 0 1 0 0.1895 0.1874 0.1795 0.02723 0.9812 0.988 0.1896 0.6994 0.8535 0.2626 ] Network output: [ -0.3725 3.006 0.2132 -5.143e-06 2.309e-06 -0.4743 -3.876e-06 ] Targets: [0 1 0 0 0 1 0 ] Testing Mean Square Error: 2.057